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Sandy4321/CS784-Project
8,650,064,138,633
128f5f989d1a5771578e6507748f69172a119233
74dcbb1d3fe56c57cf0d9c0846b69fe949119758
/information_extraction.py
fdce77cf8915ecb62c2f4b5e5dceef3e36354306
[]
no_license
https://github.com/Sandy4321/CS784-Project
1e0a25525c3d9976c1ec313225ea249fe215ce71
9cc1be0a8f47ff6f3042961f893728f8441ff8ee
refs/heads/master
2021-01-13T04:09:03.662740
2016-05-11T22:58:41
2016-05-11T22:58:41
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__author__ = 'wintere' import csv import lxml import datrie import re import string import sys from collections import defaultdict from bs4 import BeautifulSoup import pickle class InformationExtractor: def __init__(self): #initialize brand trie brand_trie = datrie.Trie(string.printable) syn_dict = dict() with open('big_dict.csv', 'r', encoding='latin-1') as brand_dict_csv_file: brand_dict_reader = csv.reader(brand_dict_csv_file, delimiter=',', quotechar='"') for brand in brand_dict_reader: b_name = brand[0] b_name = b_name.title() # Add permutations of the brand name to our trie object. # These will help us catch things like different character casing, spacing, etc. smushed = ''.join(b_name.split(' ')) dehyphen = ''.join(b_name.split('-')) permutations = [smushed.upper(),dehyphen.upper()] for permutation in permutations: brand_trie[permutation] = int(brand[1]) #add a key, value pair that links transformed brand name to the original syn_dict[permutation] = b_name # Also record the frequency of each brand name to our trie brand_trie[b_name] = int(brand[1]) self.brand_trie = brand_trie self.syn_dict = syn_dict #color dict self.colors = [] with open('colors.txt','r') as c_file: for c in c_file.readlines(): self.colors.append(c.strip('\n')) self.longd_tfidf = pickle.load(open("tfidf_longd.p", "rb")) self.pname_tfidf = pickle.load(open("tfidf_pname.p", "rb")) def text_from_html(self, description): if len(description) < 5: return description.lower() try: html = BeautifulSoup(description, "lxml") # html = BeautifulSoup(description) text = html.getText(' ') if text is None: return description.lower() else: text = re.sub(r'[^\x00-\x7F]+',' ', text) return text.lower() except UserWarning: return description.lower() def standardizer(self,string): brand_dict = {'cables to go': 'c2g', 'startech.com': 'startech', 'pny technologies': 'pny', 'everki usa inc':'everki','rubbermaid home': 'rubbermaid', 'tripp-lite':'tripp lite', 'hewlett packard':'hp', 'buffalo technology':'buffalo', 'officemate international corp':'officemate', 'phillips monitors':'phillips', 'pyle audio':'pyle'} for key, value in brand_dict.items(): if key in string: string = string.replace(key, value) return string # a cheap haaaack def brand_adjuster(self, d, ld=False): if ld: for entry in ['brand', 'product name', 'manufacturer', 'product short description', 'product long description', 'brand name']: if entry in d: val = d[entry] val = self.standardizer(val) d[entry] = val else: for entry in ['brand', 'product name', 'manufacturer', 'product short description', 'product long description', 'brand name']: if entry in d: val = d[entry][0] val = self.standardizer(val) d[entry] = [val] return d def color_from_name(self, product_name): colors = [] product_name = product_name.lower() product_name_list = product_name.split() for i in product_name_list: if i in self.colors: if i not in colors: colors.append(i) return colors def brand_from_string(self, product_name): product_name = product_name.upper() product_name = re.sub(r'\|\(\)',' ', product_name) substrings = product_name.split(' ') s_array = [] # Because prefix only recognize substrings at the beginning of a string, divide string into substrings # to recognize brand names anywhere in the string for i in range(len(substrings) - 1): s_array.append(' '.join(substrings[i:])) # Identify which strings are candidates for brand name cands = [] for substring in s_array: # Get candidates from prefix tree cand = self.brand_trie.prefixes(substring) final_cands = set() for c in cand: sub = False for st in substrings: if (c in st) and (len(c) < len(st)): sub = True # Remove candidates that are less than 1 word (ie 'Sm' for 'Smart Technologies' if (c == st): break if sub == False: # Return regularized versions of 'synonyms', ie. Cooler Master for Cooler Master if c in self.syn_dict: final_cands.add(self.syn_dict[c]) else: final_cands.add(c) if len(cand) > 0: #Add acceptable candidates to final list cands.extend(final_cands) # Select the longest candidate at the earliest index of all candidates chosen = "" candindex = defaultdict(list) lower_name = product_name.lower() for candidate in cands: index = lower_name.find(candidate.lower()) candindex[index].append(candidate) if len(candindex) > 0: min_index = min(candindex) chosen = candindex[min_index][0] for candidate in candindex[min_index]: if len(candidate) > len(chosen): chosen = candidate return chosen.lower() #moved from feature_operations for better modularity def unitsFromString(self, tokens): measurement_units = ['khz', 'mhz', 'ghz', 'watt', 'nm', 'um', 'mm', 'cm', 'm', 'km', 'ft', 'in', 's', 'ms', 'mb', 'gb', 'tb', 'gb/s', 'mb/s', 'mbps', 'awg', 'a', 'w', 'g', 'lb', 'dba', 'cfm', 'rpm', 'amp', 'mah', 'watts', 'vac','nits','volts','inches','pounds','ounces','lbs'] units = [] for index in range(0, len(tokens)): token = tokens[index].lower() # Look for units split across multiple tokens if re.match("^[0-9\.]+$", token): if index < len(tokens) - 1: nextToken = str(tokens[index + 1]).lower().replace(".", "") if nextToken in measurement_units: unit_value = re.sub(r'\.[0]+', "", token) # Remove any trailing decimal points + 0s # print("Token=" + str(token) + ", unit value=" + str(unit_value)) units.append(str(unit_value + " " + nextToken)) # Also look for units compacted into a single token elif re.match("^[0-9\.]+(\s)*[a-z\./]+$", token): unit_data = re.match("^([0-9\.]+)[\s]*([a-z\./]+)$", token) if str(unit_data.groups(0)[1]) in measurement_units: unit_value = re.sub(r'\.[0]+', "", unit_data.groups(0)[0]) # Remove any trailing decimal points + 0s # print("Token=" + str(token) + ", unit value=" + str(unit_value)) units.append(str(unit_value) + " " + str(unit_data.groups(0)[1])) return units
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JaJasiok/akai-rekrutacja
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a4f12f6a375eb7cda44aaa5aa988fbe7c7b5863d
/python/tasker/src/json/Exporter.py
4efd2b93715bb2f02ffcb5917092828491d9a0c0
[]
no_license
https://github.com/JaJasiok/akai-rekrutacja
3429486ab4a0e976856d579897653fdb4d95c4d1
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2021-11-06T20:47:20
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import json class Exporter: def __init__(self): pass def save_tasks(self, tasks): # TODO zapisz taski do pliku tutaj with open("taski.json", "w") as f: json.dump(tasks, f, indent = 4) f.close()
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wbrs-codestellation-2018/audio-recognizer
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f1bf06136897041f43a24a5e46a7e02b45b895c9
/setup.py
dbb7d24dc675c8b62a907c9952f2d3434abf8f47
[]
no_license
https://github.com/wbrs-codestellation-2018/audio-recognizer
36e2d09047aef079ec9911c1ddda5c2ad3b6937a
55e3d0feb9e171d5dcd156fcb069595623bdaf6f
refs/heads/master
2020-04-05T16:27:57.967779
2018-11-10T20:54:06
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from setuptools import setup setup(name='audio_recognizer', version='0.1', description='Gets genre info from files', url='https://github.com/wbrs-codestellation-2018/audio-recognizer', author='Sam Stern', author_email='sternj@brandeis.edu', license='MIT', packages=['audio_recognizer'], dependency_links=['https://github.com/acrcloud/acrcloud_sdk_python/tarball/master'], zip_safe=False)
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Kunstenpunt/havelovewilltravel
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/hlwtadmin/migrations/0043_auto_20210126_0833.py
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permissive
https://github.com/Kunstenpunt/havelovewilltravel
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refs/heads/master
2022-12-10T10:00:53.351251
2022-05-17T08:20:10
2022-05-17T08:20:10
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Apache-2.0
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2020-02-03T12:45:54
2022-02-18T20:31:45
2022-12-08T03:32:59
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# Generated by Django 3.0.7 on 2021-01-26 08:33 from django.db import migrations, models class Migration(migrations.Migration): dependencies = [ ('hlwtadmin', '0042_auto_20201203_1116'), ] operations = [ migrations.AlterField( model_name='concert', name='date', field=models.DateField(blank=True, db_index=True, null=True), ), migrations.AlterField( model_name='historicalconcert', name='date', field=models.DateField(blank=True, db_index=True, null=True), ), ]
UTF-8
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py
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flyhigher139/geektime_ebook_maker
12,034,498,398,048
3b5e5492272bfbc140b90896bc75fc35daee12dd
1df13f93dbedaefc1cdfd0caf6085d6027120094
/geektime_dl/data_client/__init__.py
adf802368201b12dddd909e8724fe6db0b5c5261
[]
no_license
https://github.com/flyhigher139/geektime_ebook_maker
da88a0f4422fcb52bdb54a8c554628cf2f40ba5b
9d92e6b55411a6bc291ff1aa9c32fdf846001fca
refs/heads/master
2021-06-11T09:04:32.665398
2021-06-02T10:49:14
2021-06-02T10:49:14
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null
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2021-06-02T10:49:15
2018-10-12T08:32:19
2021-04-22T02:01:02
2021-06-02T10:49:14
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# coding=utf8 import json import os import functools import threading import time import atexit from tinydb import TinyDB, Query from tinydb.storages import JSONStorage from tqdm import tqdm from geektime_dl.data_client.gk_apis import GkApiClient from geektime_dl.utils import synchronized def _local_storage(table: str): """ 存取本地 课程/章节 内容 """ def decorator(func): @functools.wraps(func) def wrap(self: 'DataClient', *args, **kwargs): nonlocal table force = kwargs.get('force') _id = kwargs.get('{}_id'.format(table)) or args[0] collection = Query() data = None if not force: res = self.db.table(table).search(collection.id == _id) if res: data = res[0] if data is None: data = func(self, *args, **kwargs) self.db.table(table).upsert(data, collection.id == _id) return data return wrap return decorator class DataClient: def __init__(self, gk: GkApiClient, db: TinyDB): self._gk = gk self.db = db self._lock = threading.Lock() # tinydb 线程不安全 @property def gk(self): return self._gk def get_course_list(self, **kwargs) -> dict: """ 获取课程列表 """ return self._gk.get_course_list() @synchronized() @_local_storage('course') def get_course_intro(self, course_id: int, **kwargs) -> dict: """ 获取 course 简介 """ data = self._gk.get_course_intro(course_id) return data @synchronized() @_local_storage('post') def get_post_content(self, post_id: int, **kwargs) -> dict: """ 获取 post 的所有内容,包括评论 """ data = self._gk.get_post_content(post_id) data['comments'] = self._get_post_comments(post_id) return data def _get_post_comments(self, post_id: int) -> list: """ 获取 post 的评论 """ data = self._gk.get_post_comments(post_id) for c in data: c['replies'] = json.dumps(c.get('replies', [])) return data def get_course_content(self, course_id: int, force: bool = False, pbar=True, pbar_desc='') -> list: """ 获取课程ID为 course_id 的所有章节内容 """ posts = [] post_ids = self._gk.get_post_list_of(course_id) if pbar: post_ids = tqdm(post_ids) post_ids.set_description(pbar_desc) for post in post_ids: post_detail = self.get_post_content(post['id'], force=force) posts.append(post_detail) return posts def get_video_collection_list(self, **kwargs) -> list: """ 获取每日一课合辑列表 """ return self._gk.get_video_collection_list() @synchronized() @_local_storage('video-collection') def get_video_collection_intro(self, collection_id: int, **kwargs) -> dict: """ 获取每日一课合辑简介 """ data = self._gk.get_video_collection_intro(collection_id) return data @synchronized() @_local_storage('daily') def get_daily_content(self, video_id: int, **kwargs) -> dict: """ 获取每日一课内容 """ data = self._gk.get_post_content(video_id) return data def get_video_collection_content(self, collection_id: int, force: bool = False, pbar=True, pbar_desc='') -> list: """ 获取每日一课合辑ID 为 collection_id 的所有视频内容 """ data = [] v_ids = self._gk.get_video_list_of(collection_id) if pbar: v_ids = tqdm(v_ids) v_ids.set_description(pbar_desc) for v_id in v_ids: v = self.get_daily_content(v_id['article_id'], force=force) data.append(v) return data class _JSONStorage(JSONStorage): """ Store the data in a JSON file. 重写 JSONStorage,优化性能 1、read 不读文件 2、write 10s 刷一次盘 3、退出时刷盘保存数据以免数据丢失 """ SAVE_DELTA = 10 def __init__(self, path, create_dirs=False, encoding=None, **kwargs): super().__init__(path, create_dirs, encoding, **kwargs) self._data = super().read() self._last_save = time.time() atexit.register(self._register_exit) def _register_exit(self): super().write(self._data) super().close() def read(self) -> dict: return self._data def write(self, data) -> None: self._data = data now = time.time() if now - self._last_save > self.SAVE_DELTA: super().write(data) self._last_save = now def close(self): pass dc_global = None def get_data_client(cfg: dict) -> DataClient: global dc_global if dc_global is not None: return dc_global gk = GkApiClient( account=cfg['account'], password=cfg['password'], area=cfg['area'], no_login=cfg['no_login'] ) f = os.path.expanduser( os.path.join(cfg['output_folder'], 'geektime-localstorage.json')) db = TinyDB(f, storage=_JSONStorage) dc = DataClient(gk, db) dc_global = dc return dc
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danbok/city-places
5,385,889,001,188
048c01127381fa87a0d527201341312b32b5d10e
ce6337c0faef6f2b3a6635759a21ccf7ec3c2a87
/custom_auth_system/views.py
fa68572a6b0ebb73aec17e9c88ee6a7c3da089a4
[]
no_license
https://github.com/danbok/city-places
1e86c2c720c0f2a1b4d6a7580fe342e6bcec333a
4a545789dd511014c71622c77c10322e808d6752
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2016-09-22T22:02:10.326239
2016-06-16T06:12:23
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from django.contrib.auth import logout from django.shortcuts import render def logout_user(request): logout(request) return render(request, template_name='static_pages/index.html')
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jgera/Semantic-WoT-Environment-Simulation
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63503dd158fb12a60a0528a4e2ccf8c086129caf
491b7696660f1f39f9cbcfcf6e4d1cfec9066630
/test/netuse/test_devices.py
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refs/heads/master
2020-12-31T02:32:12.780345
2013-12-13T16:18:04
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# -*- coding: utf-8 -*- ''' Copyright (C) 2012 onwards University of Deusto All rights reserved. This software is licensed as described in the file COPYING, which you should have received as part of this distribution. This software consists of contributions made by many individuals, listed below: @author: Aitor Gómez Goiri <aitor.gomez@deusto.es> ''' import unittest from mock import Mock, patch from netuse.devices import XBee def side_effect(*args): return args[0] + args[1]/2 # just to check how to configure different returns rndMock = Mock() rndMock.normalvariate.side_effect = side_effect #rndMock.normalvariate.return_value = 0 class TestDeviceType(unittest.TestCase): #def setUp(self): def getMockedDevice(self, device): resources = Mock() resources.capacity = 10 resources.n = 0 device._DeviceType__resources = resources return device @patch('netuse.results.G.Rnd', rndMock) # new global unrandomized variable def test_get_time_needed_to_answer(self): dev = self.getMockedDevice(XBee()) #self.assertEquals(779.0, dev.getTimeNeededToAnswer()) self.assertTimeNeeded(dev,1,0,(77,1)) # 1 resources being used (me!) self.assertTimeNeeded(dev,5,0,(392,8)) # 5 resources being used self.assertTimeNeeded(dev,7,2,(392,8)) # 5 resources being used self.assertTimeNeeded(dev,10,0,(775.0,8.0)) # 10 resources being used self.assertTimeNeeded(dev,20,10,(775.0,8.0)) # 10 resources being used self.assertTimeNeeded(dev,2,0,(155.75,2.75)) # =(392-77)/4*1 +77 self.assertTimeNeeded(dev,3,0,(234.5,4.5)) # =(392-77)/4*2 +77 self.assertTimeNeeded(dev,4,0,(313.25,6.25)) # =(392-77)/4*3 +77 self.assertTimeNeeded(dev,6,0,(468.6,8.0)) # =(775-392)/4*1 +392 self.assertTimeNeeded(dev,7,0,(545.2,8.0)) self.assertTimeNeeded(dev,8,0,(621.8,8.0)) self.assertTimeNeeded(dev,9,0,(698.4,8.0)) def assertTimeNeeded(self, device, capacity, free_resources, expectParameters): device.get_time_needed_to_answer(capacity - free_resources) rndMock.normalvariate.assert_called_with(expectParameters[0], expectParameters[1]) # for 10 concurrent requests if __name__ == '__main__': unittest.main()
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nurshahjalal/PyAPI
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/src/utilities/requestsUtilities.py
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[]
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https://github.com/nurshahjalal/PyAPI
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import requests import os from requests_oauthlib import OAuth1 from src.configs.host_config import API_HOST from src.utilities.credentialUtility import CredentialUtility import logging as logger class RequestUtility(object): def __init__(self): # get the ENV variable from environment if ENV is not set then default is test self.env = os.environ.get("ENV", "test") self.baseURL = API_HOST[self.env] # As get_api_keys is static method the class does not need to instantiate and no need self api_creds = CredentialUtility.get_api_keys() self.auth = OAuth1(api_creds["CLIENT_KEY"], api_creds["CLIENT_SECRET"]) def get(self): pass def check_stataus_code(self, res_status_code, expected_status_code): assert res_status_code == expected_status_code, \ f"Expected status code {expected_status_code} but actual {res_status_code}" def post(self, endpoint, payload=None, headers=None, expected_status_code=200): post_url = self.baseURL + endpoint if not headers: headers = { "Content-Type": "Application/json" } post_response = requests.post(url=post_url, data=payload, headers=headers, auth=self.auth) res_status_code = post_response.status_code self.check_stataus_code(res_status_code, expected_status_code) rs_jason = post_response.json() logger.debug(f"Response Json : \n {rs_jason} \n ########") logger.debug(f'Response Status Code {res_status_code} ') # assert post_response.status_code == int(expected_status_code), \ # f"Expected status code {expected_status_code} but actual {post_response.status_code}" return rs_jason
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tingxin/SeleniumProjectTemplate
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/cases/workflow.py
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[]
no_license
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from common.testcases import TestCases from selenium.webdriver.common.keys import Keys import time class WorkflowTestCases(TestCases): def test_search_many_times(self): print("Test searching in DevNet") driver = self.driver test_keys = ["deviot", "cmx", "aci", "sdn"] for i in range(0, len(test_keys)): driver.get("https://developer.cisco.com/site/devnet/home/index.gsp") self.assertIn("Cisco Devnet", driver.title) elem = driver.find_element_by_name("q") elem.send_keys(test_keys[i]) elem.send_keys(Keys.RETURN) assert "Cisco DevNet: DevNetCreations - DevIoT" not in driver.page_source time.sleep(1)
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/socket_client_tcp.py
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import socket sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) sock.connect(('127.0.0.1', 8888)) msg = input("Message: ") msg = msg.encode(encoding="utf-8") # msg = bytearray(msg, 'UTF-8') sock.send(msg) sock.close()
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/translator/backend/serializers.py
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no_license
https://github.com/Guin-/translator
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refs/heads/master
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from rest_framework import serializers from .models import Translation class TranslationSerializer(serializers.ModelSerializer): language = serializers.CharField(read_only=True) translation = serializers.CharField(read_only=True) class Meta: model = Translation fields = ('id', 'input_text', 'language', 'translation', 'timestamp')
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gabriellaec/desoft-analise-exercicios
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/backup/user_301/ch14_2019_03_14_22_52_38_250667.py
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[]
no_license
https://github.com/gabriellaec/desoft-analise-exercicios
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refs/heads/main
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def calcula_volume_da_esfera(R): y=(4*math.pi*R**3)/4 return y
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lionheartStark/GCN-graduate-design
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/Elliptic_dataset_GCN.py
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#!/usr/bin/env python # coding: utf-8 # <a href="https://colab.research.google.com/github/JungWoo-Chae/GCN_Elliptic_dataset/blob/main/Elliptic_dataset_GCN.ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a> # # Bitcoin Fraud Detection System with GCN # ## Pytorch Geometric Environment Setting # In[ ]: # Install required packages. # !pip install -q torch-scatter==latest+cu102 -f https://pytorch-geometric.com/whl/torch-1.7.0.html # !pip install -q torch-sparse==latest+cu102 -f https://pytorch-geometric.com/whl/torch-1.7.0.html # !pip install -q git+https://github.com/rusty1s/pytorch_geometric.git # ## Library Import # In[1]: import numpy as np import networkx as nx import os import pandas as pd from sklearn.metrics import f1_score, precision_score, recall_score from sklearn.manifold import TSNE import matplotlib.pyplot as plt import torch import torch.nn as nn import torch.nn.functional as F from torch.nn import Embedding from torch.nn import Parameter from torch_geometric.data import Data, DataLoader from torch_geometric.nn import GCNConv,GATConv from torch_geometric.utils.convert import to_networkx from torch_geometric.utils import to_undirected # In[2]: device = torch.device('cuda' if torch.cuda.is_available() else 'cpu') # # **Please insert Kaggle username and kaggle key** # In[3]: # os.environ['KAGGLE_USERNAME'] = "@@@@@@@@@" # username from the json file # os.environ['KAGGLE_KEY'] = "####################" # key from the json file # !kaggle datasets download -d ellipticco/elliptic-data-set # !unzip elliptic-data-set.zip # !mkdir elliptic_bitcoin_dataset_cont # ## Data Preparation # In[4]: # Load Dataframe df_edge = pd.read_csv('elliptic_bitcoin_dataset/elliptic_txs_edgelist.csv') df_class = pd.read_csv('elliptic_bitcoin_dataset/elliptic_txs_classes.csv') df_features = pd.read_csv('elliptic_bitcoin_dataset/elliptic_txs_features.csv', header=None) # Setting Column name df_features.columns = ['id', 'time step'] + [f'trans_feat_{i}' for i in range(93)] + [f'agg_feat_{i}' for i in range(72)] print('Number of edges: {}'.format(len(df_edge))) # ## Get Node Index # In[5]: all_nodes = list( set(df_edge['txId1']).union(set(df_edge['txId2'])).union(set(df_class['txId'])).union(set(df_features['id']))) nodes_df = pd.DataFrame(all_nodes, columns=['id']).reset_index() print('Number of nodes: {}'.format(len(nodes_df))) # ## Fix id index # In[6]: df_edge = df_edge.join(nodes_df.rename(columns={'id': 'txId1'}).set_index('txId1'), on='txId1', how='inner').join( nodes_df.rename(columns={'id': 'txId2'}).set_index('txId2'), on='txId2', how='inner', rsuffix='2').drop( columns=['txId1', 'txId2']).rename(columns={'index': 'txId1', 'index2': 'txId2'}) df_edge.head() # In[7]: df_class = df_class.join(nodes_df.rename(columns={'id': 'txId'}).set_index('txId'), on='txId', how='inner').drop( columns=['txId']).rename(columns={'index': 'txId'})[['txId', 'class']] df_class.head() # In[8]: df_features = df_features.join(nodes_df.set_index('id'), on='id', how='inner').drop(columns=['id']).rename( columns={'index': 'id'}) df_features = df_features[['id'] + list(df_features.drop(columns=['id']).columns)] df_features.head() # In[9]: df_edge_time = df_edge.join(df_features[['id', 'time step']].rename(columns={'id': 'txId1'}).set_index('txId1'), on='txId1', how='left', rsuffix='1').join( df_features[['id', 'time step']].rename(columns={'id': 'txId2'}).set_index('txId2'), on='txId2', how='left', rsuffix='2') df_edge_time['is_time_same'] = df_edge_time['time step'] == df_edge_time['time step2'] df_edge_time_fin = df_edge_time[['txId1', 'txId2', 'time step']].rename( columns={'txId1': 'source', 'txId2': 'target', 'time step': 'time'}) # ## Create csv from Dataframe # In[10]: df_features.drop(columns=['time step']).to_csv('elliptic_bitcoin_dataset_cont/elliptic_txs_features.csv', index=False, header=None) df_class.rename(columns={'txId': 'nid', 'class': 'label'})[['nid', 'label']].sort_values(by='nid').to_csv( 'elliptic_bitcoin_dataset_cont/elliptic_txs_classes.csv', index=False, header=None) df_features[['id', 'time step']].rename(columns={'id': 'nid', 'time step': 'time'})[['nid', 'time']].sort_values( by='nid').to_csv('elliptic_bitcoin_dataset_cont/elliptic_txs_nodetime.csv', index=False, header=None) df_edge_time_fin[['source', 'target', 'time']].to_csv('elliptic_bitcoin_dataset_cont/elliptic_txs_edgelist_timed.csv', index=False, header=None) # ## Graph Preprocessing # In[11]: node_label = df_class.rename(columns={'txId': 'nid', 'class': 'label'})[['nid', 'label']].sort_values(by='nid').merge( df_features[['id', 'time step']].rename(columns={'id': 'nid', 'time step': 'time'}), on='nid', how='left') node_label['label'] = node_label['label'].apply(lambda x: '3' if x == 'unknown' else x).astype(int) - 1 node_label.head() # In[12]: merged_nodes_df = node_label.merge( df_features.rename(columns={'id': 'nid', 'time step': 'time'}).drop(columns=['time']), on='nid', how='left') merged_nodes_df.head() # In[13]: train_dataset = [] test_dataset = [] for i in range(49): nodes_df_tmp = merged_nodes_df[merged_nodes_df['time'] == i + 1].reset_index() nodes_df_tmp['index'] = nodes_df_tmp.index df_edge_tmp = df_edge_time_fin.join( nodes_df_tmp.rename(columns={'nid': 'source'})[['source', 'index']].set_index('source'), on='source', how='inner').join(nodes_df_tmp.rename(columns={'nid': 'target'})[['target', 'index']].set_index('target'), on='target', how='inner', rsuffix='2').drop(columns=['source', 'target']).rename( columns={'index': 'source', 'index2': 'target'}) x = torch.tensor(np.array(nodes_df_tmp.sort_values(by='index').drop(columns=['index', 'nid', 'label'])), dtype=torch.float) edge_index = torch.tensor(np.array(df_edge_tmp[['source', 'target']]).T, dtype=torch.long) edge_index = to_undirected(edge_index) mask = nodes_df_tmp['label'] != 2 y = torch.tensor(np.array(nodes_df_tmp['label'])) if i + 1 < 35: data = Data(x=x, edge_index=edge_index, train_mask=mask, y=y) train_dataset.append(data) else: data = Data(x=x, edge_index=edge_index, test_mask=mask, y=y) test_dataset.append(data) train_loader = DataLoader(train_dataset, batch_size=1, shuffle=True) test_loader = DataLoader(test_dataset, batch_size=1, shuffle=False) # ## Model # In[ ]: class GCN(torch.nn.Module): def __init__(self, num_node_features, hidden_channels, use_skip=False, conv1=GCNConv, conv2=GCNConv): super(GCN, self).__init__() self.conv1 = conv1(num_node_features, hidden_channels[0]) self.conv2 = conv2(hidden_channels[0], 2) self.use_skip = use_skip if self.use_skip: self.weight = nn.init.xavier_normal_(Parameter(torch.Tensor(num_node_features, 2))) def forward(self, data): x = self.conv1(data.x, data.edge_index) x = x.relu() x = F.dropout(x, p=0.2, training=self.training) x = self.conv2(x, data.edge_index) if self.use_skip: x = F.softmax(x + torch.matmul(data.x, self.weight), dim=-1) else: x = F.softmax(x, dim=-1) return x def embed(self, data): x = self.conv1(data.x, data.edge_index) return x # In[ ]: model = GCN(num_node_features=data.num_node_features, hidden_channels=[100]) model.to(device) # ## Train # #### Hyperparameter # In[ ]: patience = 50 lr = 0.001 epoches = 1000 # In[ ]: optimizer = torch.optim.Adam(model.parameters(), lr=lr) criterion = torch.nn.CrossEntropyLoss(weight=torch.tensor([0.7, 0.3]).to(device)) train_losses = [] val_losses = [] accuracies = [] if1 = [] precisions = [] recalls = [] iterations = [] for epoch in range(epoches): model.train() train_loss = 0 for data in train_loader: data = data.to(device) optimizer.zero_grad() out = model(data) loss = criterion(out[data.train_mask], data.y[data.train_mask].long()) _, pred = out[data.train_mask].max(dim=1) loss.backward() train_loss += loss.item() * data.num_graphs optimizer.step() train_loss /= len(train_loader.dataset) if (epoch + 1) % patience == 0: model.eval() ys, preds = [], [] val_loss = 0 for data in test_loader: data = data.to(device) out = model(data) loss = criterion(out[data.test_mask], data.y[data.test_mask].long()) val_loss += loss.item() * data.num_graphs _, pred = out[data.test_mask].max(dim=1) ys.append(data.y[data.test_mask].cpu()) preds.append(pred.cpu()) y, pred = torch.cat(ys, dim=0).numpy(), torch.cat(preds, dim=0).numpy() val_loss /= len(test_loader.dataset) f1 = f1_score(y, pred, average=None) mf1 = f1_score(y, pred, average='micro') precision = precision_score(y, pred, average=None) recall = recall_score(y, pred, average=None) iterations.append(epoch + 1) train_losses.append(train_loss) val_losses.append(val_loss) if1.append(f1[0]) accuracies.append(mf1) precisions.append(precision[0]) recalls.append(recall[0]) print( 'Epoch: {:02d}, Train_Loss: {:.4f}, Val_Loss: {:.4f}, Precision: {:.4f}, Recall: {:.4f}, Illicit f1: {:.4f}, F1: {:.4f}'.format( epoch + 1, train_loss, val_loss, precision[0], recall[0], f1[0], mf1)) # In[ ]: a, b, c, d = train_losses, val_losses, if1, accuracies import pickle g = [a, b, c, d] pickle.dump(g, open('res_' + f'{epoches}', 'wb')) with open('res_' + f'{epoches}', "rb") as f: g = pickle.load(f) a, b, c, d = g ep = [i for i in range(patience, epoches + 1, patience)] plt.figure() plt.plot(np.array(ep), np.array(a), 'r', label='Train loss') plt.plot(np.array(ep), np.array(b), 'g', label='Valid loss') plt.plot(np.array(ep), np.array(c), 'black', label='Illicit F1') plt.plot(np.array(ep), np.array(d), 'orange', label='F1') plt.legend(['Train loss', 'Valid loss', 'Illicit F1', 'F1']) plt.ylim([0, 1.0]) plt.xlim([patience, epoches]) plt.savefig("filename.png") plt.show() # In[ ]:
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Raghuvar/edyst_assignment
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# FILENAME: robot.py # BY: Andrew Holbrook # DATE: 9/24/2015 from OpenGL import GL from . import GLWindow, Vector4, Matrix4 class Joint(object): """Base class for all joint types (prismatic, revolute, etc). """ def __init__(self, partA, partB, axis=(0,1,0), offset=(0,0,0)): """Creates a joint linking parts A and B. The axis of motion can be specified, along with the offset between parts A and B. """ self.partA = partA self.partB = partB self.value = 0.0 self.velocity = 0.0 self.axis = axis self.offset = offset self.valueMin = 0.0 self.valueMax = 0.0 self.dfunc = self.increaseValue self.offsetMatrix = Matrix4.getTranslation(*offset) def increaseValue(self, dtime): """Increase the value (angle or distance) of the joint with respect to the elapsed time (dtime)--the maximum joint value is observed. """ self.value = min(self.valueMax, self.value + self.velocity * dtime) if self.value == self.valueMax: self.dfunc = self.decreaseValue def decreaseValue(self, dtime): """Decrease the value (angle or distance) of the joint with respect to the elapsed time (dtime)--the minimum joint value is observed. """ self.value = max(self.valueMin, self.value - self.velocity * dtime) if self.value == self.valueMin: self.dfunc = self.increaseValue def setLimits(self, min, max): """Sets the minimum/maximum joint limits. """ self.valueMin = min self.valueMax = max class RevoluteJoint(Joint): """Class for representing a rotating joint with one degree of freedom. """ def __init__(self, partA, partB, axis=(0,1,0), offset=(0,0,0)): """See Joint class. """ super().__init__(partA, partB, axis, offset) def getTransformation(self): """Return the transformation matrix representing partB relative to partA. """ angleList = [a * self.value for a in self.axis] return self.offsetMatrix * Matrix4.getRotation(*angleList) class PrismaticJoint(Joint): def __init__(self, partA, partB, axis=(0,1,0), offset=(0,0,0)): """See Joint class. """ super().__init__(partA, partB, axis, offset) def getTransformation(self): """Return the transformation matrix representing partB relative to partA. """ dList = [a * self.value for a in self.axis] return self.offsetMatrix * Matrix4.getTranslation(*dList) class Robot(object): def __init__(self, model): self.model = model self.joints = [] self.position = Vector4() self.orientation = Vector4() renderDelegate = GLWindow.getInstance().renderDelegate self.modelview_loc = renderDelegate.modelview_loc def addJoint(self, joint): self.joints.append(joint) def cleanup(self): self.model.cleanup() def update(self, dtime): for j in self.joints: j.dfunc(dtime) def render(self): rotMatrix_ow = Matrix4.getRotation(*self.orientation.getXYZ()) tranMatrix_ow = Matrix4.getTranslation(*self.position.getXYZ()) # object to world matrix matrix_ow = tranMatrix_ow * rotMatrix_ow GL.glUniformMatrix4fv(self.modelview_loc, 1, False, matrix_ow.getCType()) self.model.renderPartByName(self.joints[0].partA) for j in self.joints: matrix_ow *= j.getTransformation() GL.glUniformMatrix4fv(self.modelview_loc, 1, False, matrix_ow.getCType()) self.model.renderPartByName(j.partB) class Scara(Robot): def __init__(self, model): super().__init__(model) self.addJoint(RevoluteJoint("L0", "L1")) self.addJoint(RevoluteJoint("L1", "L2", offset=(-0.325,0.0))) self.addJoint(PrismaticJoint("L2", "d3")) self.joints[0].velocity = 386.0 / 1000.0 self.joints[1].velocity = 720.0 / 2000.0 self.joints[2].velocity = 1.1 / 1000.0 self.joints[0].setLimits(-105, 105) self.joints[1].setLimits(-150, 150) self.joints[2].setLimits(-0.21, 0.21) class Viper(Robot): def __init__(self, model): super().__init__(model) self.addJoint(RevoluteJoint('L0', 'L1')) self.addJoint(RevoluteJoint('L1', 'L2', (0, 0, 1), (-0.075, 0.335, 0.0))) self.addJoint(RevoluteJoint('L2', 'L3', (0, 0, 1), (-0.365, 0, 0))) self.addJoint(RevoluteJoint('L3', 'L4', (0, 1, 0), (0.09, 0, 0))) self.addJoint(RevoluteJoint('L4', 'L5', (0, 0, 1), (0, 0.4, 0))) self.joints[0].velocity = 328.0 / 1000.0 self.joints[1].velocity = 300.0 / 1000.0 self.joints[2].velocity = 375.0 / 1000.0 self.joints[3].velocity = 375.0 / 1000.0 self.joints[4].velocity = 375.0 / 1000.0 self.joints[0].setLimits(-170, 170) self.joints[1].setLimits(-190, 45) self.joints[2].setLimits(-29, 256) self.joints[3].setLimits(-190, 190) self.joints[4].setLimits(-120, 120)
UTF-8
Python
false
false
5,268
py
11
robot.py
7
0.585801
0.540623
0
145
35.275862
85
abrarShariar/Algorithmic-problem-solving
11,441,792,924,870
bab14b3f89a7f90971f353ab5b461c5197ce17b4
d6d65c502c3fa3e6570355530db61b32527ff1b0
/python-linkedin/Ex_Files_python_dpatterns/Exercise Files/Ch02/02_06/singleton.py
ad582f0a09a77065fc8ae901acdb024d69fa6b2e
[]
no_license
https://github.com/abrarShariar/Algorithmic-problem-solving
f6b129e85e7120ec46faad9c3293b37db056211f
5649fa35352a1c468c2a935135f46202280575c0
refs/heads/master
2023-04-11T00:54:39.280958
2021-12-16T13:50:06
2021-12-16T13:50:06
49,055,600
0
0
null
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class Borg: """The Borg design pattern""" # attribute dictionary _shared_state = {} def __init__(self): self.__dict__ = self._shared_state class Singleton(Borg): """The singleton class"""
UTF-8
Python
false
false
230
py
657
singleton.py
622
0.556522
0.556522
0
13
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muzammilafsar/Trailzz
8,486,855,383,549
f433413bc077775ed7d7764545fcfc6c3212a989
b7b495abf20c37506ff1a86839fb74a2a5cf75b6
/movie/views.py
aee23e8ee6968278c478635fe6db1140e1aff97d
[]
no_license
https://github.com/muzammilafsar/Trailzz
6e7eda05627b8cf04ca5c9773ab4cf3a417057b7
1435103a22a2d1f7ebbbe01766295be5d5147e21
refs/heads/master
2020-07-20T18:56:51.557444
2016-09-11T18:06:56
2016-09-11T18:06:56
67,941,788
0
0
null
null
null
null
null
null
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null
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from django.views import generic from .models import Movie_Details # Create your views here. class Home(generic.ListView): template_name = 'movie/home.html' context_object_name = 'object_list' def get_queryset(self): return Movie_Details.objects.all()[::-1] class Detail(generic.DetailView): model = Movie_Details template_name = 'movie/detail.html'
UTF-8
Python
false
false
379
py
22
views.py
13
0.712401
0.709763
0
13
28.230769
48
ulisesmx/exercises
2,456,721,304,768
daed40a235a56a96a1090d28f10f617822e75341
994333bed095004c9aac822ec19974f402aa4ea5
/legacy_code/bit_manipulation/flipping_bits.py
54b164451c939b5580fd0fc3a02652c01a4faadd
[]
no_license
https://github.com/ulisesmx/exercises
84c37526ceede9399a43b5cd9baa9c3e7b7643bc
fb2430e508ab7f9da79c3dae5ed98413c28a2536
refs/heads/master
2020-04-03T22:05:51.345706
2019-01-22T06:49:33
2019-01-22T06:49:33
56,024,774
1
0
null
false
2016-05-05T16:18:30
2016-04-12T02:37:58
2016-04-12T02:43:24
2016-05-05T16:18:30
19
0
0
0
Python
null
null
#!/bin/python t = int(raw_input()) sum_val = 2**32 for _ in xrange(t): print (~int(raw_input()) + sum_val)
UTF-8
Python
false
false
113
py
486
flipping_bits.py
486
0.566372
0.539823
0
7
15.142857
39
Bekbolot888/Hoome_Worke
17,214,228,947,997
aaaf8af8321efb0a02b516fdd2ab344dd6d597da
d1813b0217264057c867c23e70a6262586bbe2f3
/Warmup-1/monkey_trouble.py
7a088b96367d71903b657b650f7fa5ed9e0f2f26
[]
no_license
https://github.com/Bekbolot888/Hoome_Worke
7642dc769522cc9d5610e145f12b3144546a11c5
168f206101dc309f4157b89d4eb4751fe86951b3
refs/heads/master
2022-12-19T08:21:19.744052
2020-09-27T13:28:32
2020-09-27T13:28:32
297,949,833
0
0
null
null
null
null
null
null
null
null
null
null
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null
null
a_monkey = bool (False) b_monkey = bool (True) print(a_monkey, b_monkey) if (a_monkey == True) and (b_monkey == True) or (a_monkey == False) and (b_monkey == False): print("we are in trouble") else: print("we are not in trouble")
UTF-8
Python
false
false
243
py
17
monkey_trouble.py
17
0.621399
0.621399
0
7
33
92
Gordey007/parserSuper
17,403,207,506,313
40377614df657c059a17dddd81f3e84110108fc2
aa247390a6500e4fcdb3686d8d27008cbcd3cdd5
/ParserSuperJob.py
a93d18ba35cbd5d4fb77501e84d93a56273945ec
[]
no_license
https://github.com/Gordey007/parserSuper
fa42aa4cdf282c1fa1ad0450fca1fd265b9ce348
4a20be401055196634962e8524da2b5d9b681672
refs/heads/master
2023-06-18T21:34:08.139164
2021-07-10T14:52:52
2021-07-10T14:52:52
305,690,272
1
0
null
null
null
null
null
null
null
null
null
null
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null
# use python ParserSuperJob.py from urllib.request import urlopen from urllib.parse import urljoin from urllib.parse import quote from lxml.html import fromstring import xlsxwriter ITEM_PATH = '._2CsQi ._2g1F- ._34bJi' ITEM_PATH2 = '._2CsQi ._2g1F- .YYC5F' PAGE = '._1BOkc' def parser_vacancies(): f = urlopen(url) list_html = f.read().decode('utf-8') list_doc = fromstring(list_html) dates = [] for elem in list_doc.cssselect(ITEM_PATH): span = elem.cssselect('span')[0] dates.append(span.text) urls = [] for elem in list_doc.cssselect(ITEM_PATH2): a = elem.cssselect('a')[0] urls.append(urljoin(url2, a.get('href'))) vacancies = [] i = 0 for item in dates: vacancy = {'date': item, 'url': urls[i]} vacancies.append(vacancy) i += 1 return vacancies def export_excel(filename, vacancies): workbook = xlsxwriter.Workbook(filename) worksheet = workbook.add_worksheet() bold = workbook.add_format({'bold': True}) field_names = ('Дата', 'URL') for i, field in enumerate(field_names): worksheet.write(0, i, field, bold) fields = ('date', 'url') for row, vacancy in enumerate(vacancies, start=1): for col, field in enumerate(fields): worksheet.write(row, col, vacancy[field]) workbook.close() print('Ввидете, что искать') search = input('> ') print('Ввидете номер города или страны\nКомсомольск-на-амуре - 0\nХабаровск - 1\nrussia - 2') sity_array = ['komsomolsk-na-amure', 'habarovsk', 'russia'] sity = sity_array[int(input('> '))] url = 'https://' + sity + '.superjob.ru/resume/search_resume.html?keywords%5B0%5D%5Bkeys%5D=' + quote(search)\ + '&keywords%5B0%5D%5Bskwc%5D=and&keywords%5B0%5D%5Bsrws%5D=7&sbmit=1' url2 = 'https://' + sity + '.superjob.ru' f = urlopen(url) list_html = f.read().decode('utf-8') list_doc = fromstring(list_html) export_excel('Вакансии ' + search + ' ' + sity + '.xlsx', parser_vacancies())
UTF-8
Python
false
false
2,083
py
1
ParserSuperJob.py
1
0.637681
0.616692
0
71
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110
nameusea/pyGreat
7,696,581,412,514
c8907b1c04991a72ad375be9859448da8f1155b6
dd3f5a712dbab0d3c4f4526c64c08ba710f78b81
/use/ReinforceLearning/gymTest/tframe.py
d69e67c3f600c08c9e50517f4b6d817027adf9e6
[]
no_license
https://github.com/nameusea/pyGreat
3988ebcce3f80a7e458a20f9b2e3ccba368efcf8
dde8b6a1348620ffd3b2d65db3d5b4331e5c78be
refs/heads/master
2023-04-25T09:02:32.831423
2021-05-17T11:31:22
2021-05-17T11:31:22
null
0
0
null
null
null
null
null
null
null
null
null
null
null
null
null
# gym环境类子类框架解读 import gym class TestEnv(gym.Env): # 元数据,用于支持可视化的一些设定,改变渲染环境时的参数,如果不想改变设置,可以无 metadata = { 'render.modes': ['human', 'rgb_array'], 'video.frames_per_second': 2 } # __init__():将会初始化动作空间与状态空间等环境所需的参量,便于强化学习算法在给定的状态空间中搜索合适的动作 def __init__(self): self.action_space = None self.observation_space = None pass # step():用于编写智能体与环境交互的逻辑 # 它接受一个动作(action)的输入 # 根据action给出下一时刻的状态(state)、当前动作的回报(reward)、探索是否结束(done)及调试帮助信息信息。 def step(self, action): reward = None done = False info = {} return self.state, reward, done, info # reset():用于在每轮开始之前重置智能体的状态 def reset(self): return self.observation_space.sample() # render():用来绘制画面可视化 def render(self, mode='human'): return None # close():用来在程序结束时清理画布 def close(self): return None if __name__ == '__main__': pass
UTF-8
Python
false
false
1,344
py
414
tframe.py
317
0.603448
0.602371
0
39
22.820513
69
yimengliu0216/OctConv_DCGAN
17,231,408,827,575
1362394eafcd587c88fff0aacf23b1d31a5b6dc1
ae9db1e04077a24a2ca0f1ad7163d76cc2dd1419
/plot_time.py
f54e4d14e62bb08a529103927f42420fb8bacaa3
[]
no_license
https://github.com/yimengliu0216/OctConv_DCGAN
6ab47ec98331b60f7ed716c46066cdc8cb02566d
15da491b298db16c34af27dd110dacadebfb6117
refs/heads/master
2020-06-11T13:33:42.265583
2019-06-26T21:58:29
2019-06-26T21:58:29
193,982,994
1
0
null
null
null
null
null
null
null
null
null
null
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#!/usr/bin/env python3 # -*- coding: utf-8 -*- import matplotlib.pyplot as plt fig_time, ax_time = plt.subplots() num_epoch = 200 dcgan_time = [] dcgan_g_time = [] dcgan_d_time = [] dcgan_gd_time = [] dcgan_time_avg = 0.0 dcgan_g_time_avg = 0.0 dcgan_d_time_avg = 0.0 dcgan_gd_time_avg = 0.0 with open('time/dcgan_time.txt', 'r') as f: dcgan_time_str = f.read().split('\n') for i in range(num_epoch): dcgan_time.append(float(dcgan_time_str[i])) dcgan_time_avg += float(dcgan_time_str[i]) dcgan_time_avg /= num_epoch with open('time/dcgan_time_g.txt', 'r') as f: dcgan_g_time_str = f.read().split('\n') for i in range(num_epoch): dcgan_g_time.append(float(dcgan_g_time_str[i])) dcgan_g_time_avg += float(dcgan_g_time_str[i]) dcgan_g_time_avg /= num_epoch with open('time/dcgan_time_d.txt', 'r') as f: dcgan_d_time_str = f.read().split('\n') for i in range(num_epoch): dcgan_d_time.append(float(dcgan_d_time_str[i])) dcgan_d_time_avg += float(dcgan_d_time_str[i]) dcgan_d_time_avg /= num_epoch with open('time/dcgan_time_gd.txt', 'r') as f: dcgan_gd_time_str = f.read().split('\n') for i in range(num_epoch): dcgan_gd_time.append(float(dcgan_gd_time_str[i])) dcgan_gd_time_avg += float(dcgan_gd_time_str[i]) dcgan_gd_time_avg /= num_epoch ax_time.plot(dcgan_time, label="DCGAN") ax_time.plot(dcgan_g_time, label="DCGAN_G") ax_time.plot(dcgan_d_time, label="DCGAN_D") ax_time.plot(dcgan_gd_time, label="DCGAN_GD") ax_time.set(xlabel='Epoch', ylabel='Time (s)', title='Training Time') ax_time.legend() fig_time.savefig("time/train_time.png") dcgans_time_avg = [dcgan_time_avg, dcgan_g_time_avg, dcgan_d_time_avg, dcgan_gd_time_avg] with open('time/dcgans_time_avg.txt', 'a') as f: f.write(str(dcgans_time_avg))
UTF-8
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false
1,853
py
1
plot_time.py
1
0.626552
0.619536
0
65
27.507692
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macbre/wbc.macbre.net
7,095,286,003,588
0da77df582a02a4942169d686d50cd3dd3165d77
50c4b11cf0482c404505d1134a3d3f6744da1b6e
/app/wbc/models/__init__.py
56812e2bc91369854cefcff467368a82ed0d5dfe
[ "MIT" ]
permissive
https://github.com/macbre/wbc.macbre.net
1c647e18707340850f36c0c64a13bfc04103c6ec
68ccf6cd437f0dacf3cea6f0c02175efbd992237
refs/heads/master
2023-04-07T21:42:44.527275
2022-10-19T14:52:36
2022-10-19T14:52:36
63,534,115
0
0
MIT
false
2023-09-08T02:16:21
2016-07-17T13:48:25
2021-12-06T11:49:56
2023-09-08T02:16:18
505
0
0
6
Python
false
false
from .model import Model from .documents import DocumentModel from .issues import IssuesModel
UTF-8
Python
false
false
94
py
48
__init__.py
31
0.840426
0.840426
0
3
30.333333
36
WanliXue/BF_implemation
2,525,440,806,598
8682d05fdb4e92b4f069b8195e0d154dbf657278
a98c455a318ab2d47b10ef1aa195b7dfd1b5449c
/venv/lib/python2.7/site-packages/cvxpy/constraints/attributes.py
1def7c027982859b02aa1b7b0a9a1888a19f24c5
[]
no_license
https://github.com/WanliXue/BF_implemation
ddd463ed906e1f4ee0de492da48bc6de3574bfd0
211aa963f3be755858daf03fca5690d3c9532053
refs/heads/main
2022-12-26T07:04:05.280651
2020-10-13T02:08:55
2020-10-13T02:08:55
303,561,823
1
0
null
null
null
null
null
null
null
null
null
null
null
null
null
""" Copyright 2017 Robin Verschueren This file is part of CVXPY. CVXPY is free software: you can redistribute it and/or modify it under the terms of the GNU General Public License as published by the Free Software Foundation, either version 3 of the License, or (at your option) any later version. CVXPY is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU General Public License for more details. You should have received a copy of the GNU General Public License along with CVXPY. If not, see <http://www.gnu.org/licenses/>. """ import inspect import sys from cvxpy.constraints import Zero, NonPos, SOC, ExpCone, PSD def attributes(): """Return all attributes, i.e. all functions in this module except this function""" this_module_name = __name__ return [obj for name, obj in inspect.getmembers(sys.modules[this_module_name]) if (inspect.isfunction(obj) and name != 'attributes')] def is_qp_constraint(constraint): if type(constraint) in {Zero, NonPos}: return True return False def is_cone_constraint(constraint): if type(constraint) in {Zero, NonPos, SOC, ExpCone, PSD}: return True return False def is_ecos_constraint(constraint): if type(constraint) in {Zero, NonPos, SOC, ExpCone}: return True return False def are_arguments_affine(constraint): return all(arg.is_affine for arg in constraint.args)
UTF-8
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1,535
py
108
attributes.py
94
0.72443
0.721173
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52
28.519231
87
claumariut/pythonCrashCourse_exercises
10,342,281,293,337
3ba18050b2256f5afe052d5e0ba94cab3a6d98e3
ed583e633544e0113413c606f6bc7ca5ad734c45
/Chapter 4. Working with Lists/foods.py
cc0a5ec38f0d1a5f6953ff6e8946aeaddcea6841
[]
no_license
https://github.com/claumariut/pythonCrashCourse_exercises
8b13b82ce9bda81d8435ad12a67b690fee366cba
1574c4a360591ff19c0da54cfcbad706a296625b
refs/heads/master
2022-12-22T03:10:52.134345
2020-10-01T20:52:27
2020-10-01T20:52:27
294,251,797
0
0
null
null
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my_foods = ['pizza', 'falafel', 'carrot cake'] friend_foods = my_foods[:] # To copy an entire list. We have to use slicing # always. If not, the list is applied to # both variables. print('My favorite foods are:') for food in my_foods: print(food) print("\n My friend's favorite foods are:") for food in friend_foods: print(food) my_foods.append('cannoli') friend_foods.append('ice cream') print('My favorite foods are:') print(my_foods) print("\n My friend's favorite foods are:") print(friend_foods)
UTF-8
Python
false
false
531
py
94
foods.py
91
0.677966
0.677966
0
17
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76
yjyszzr/spider
1,640,677,539,254
0644c7f399abfdc51a66f2daecc61e422f3426e4
176c85e338cabd9a02ad21c11eff321609e3efce
/t_spider/dl_asia/dl_asia.py
8fd7beaabf60597a774f9b04b9ec3c198f23474c
[]
no_license
https://github.com/yjyszzr/spider
3ebb9d92d87b17cc353b018c1630e5c4541b5f6d
71d1a94d0f76884974fe5f153f55aef9fbb00bf2
refs/heads/master
2020-09-15T18:41:06.910701
2019-09-24T10:00:11
2019-09-24T10:00:11
223,529,689
1
0
null
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null
null
import time import os while True: try: print("爬虫马上启动....") os.system('scrapy crawl real_ya --nolog') print("爬虫已完毕,休眠5秒钟") time.sleep(5) except: print("异常错误稍后五秒重试!") time.sleep(5)
UTF-8
Python
false
false
244
py
117
dl_asia.py
111
0.654639
0.639175
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Aleum/MiniGo
16,458,314,709,672
56a909080c4a9e38ebc5fc232963fdbd02313c95
3e75b15d582c801a168d3037f813a198da7b6152
/play_DCL/feature/utility.py
ab85ba6f6a42d62ab3d24432904fe747062d0478
[]
no_license
https://github.com/Aleum/MiniGo
db270ec485c4202a584a0a6dd6f27ab934614d7d
fe8d780c573e38e787ca935ffa9557a4ec5b32ac
refs/heads/master
2020-12-25T11:05:35.723433
2016-07-27T06:38:32
2016-07-27T06:38:32
61,085,318
3
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# -*- coding: utf-8 -*- """ utility.py """ import os import os.path from SGFGame import SGFGame def filenames_in(path, extensions): for entryname in os.listdir(path): entrypath = os.path.abspath(os.path.join(path, entryname)) entry_ext = os.path.splitext(entrypath)[1][1:] if os.path.isfile(entrypath) and (entry_ext in [ext.replace(".", "") for ext in extensions.split(";")]): yield entrypath def print_features(feature_map): for row_index in range(feature_map.rows): row = "".join([(value or ".") for value in feature_map.board[row_index]]) row += "\t" row += "\t".join(["".join(["{0}".format(value or ".") for value in feature[row_index]]) for feature in feature_map.features]) print(row) def print_board(board): for row_index in range(board.rows): print("".join([(value or ".") for value in board[row_index]])) def print_feature(feature): for row_index in range(len(feature)): print("".join(["{0}".format(value or ".") for value in feature[row_index]])) def print_int_feature(board, feature): for row_index in range(board.rows): row = "".join([(value or ".") for value in board[row_index]]) row += "\t" row += " ".join(["{0:3}".format(value or "...") for value in feature[row_index]]) print(row)
UTF-8
Python
false
false
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py
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utility.py
80
0.588832
0.583756
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kmulrey/particle_bias_test
1,803,886,273,706
8ed2c80a32e08b2b54e76af8bc3c2fd80529f609
41a936256682758d3583edecbbc6b1418912986d
/run_bias.py
c7a29f7df3987d2f30868bb1dc81d2cf320f1fce
[]
no_license
https://github.com/kmulrey/particle_bias_test
77fd3dfcf36223e5c4bbc54995051d506b30ba72
b8152db3e5b3b2b9fbe99cb965e80b57aa121fb9
refs/heads/master
2020-09-22T14:01:46.422078
2019-12-01T23:48:28
2019-12-01T23:48:28
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import check_bias_new19nov as bias import numpy as np import trigger path='/vol/astro3/lofar/sim/pipeline/events/' ''' for example: event number: 196034009 zenith=90-63.97 azimuth=307.92 core x=-105.58 core y=-46.23 ''' coreas_path='/vol/astro3/lofar/sim/pipeline/events/196034009/0/coreas/' zenith=26.0*np.pi/180 azimuth=307.9*np.pi/180 xcore=-105.58 ycore=-46.23 ## selection of events with different trigger criteria #event=196034009 event=93990550 #event=95749948 #event=87892283 #event=272981612 #event=174634099 working_detectors, global_trigger_condition, local_trigger_condition, trigger_type=trigger.find_trigger(event) # working detectors= array of 0/1 for each scintillator based on daily lora counts # trigger type = 'd' for detector (#/20) or 's' for station (#/5) # global trigger condition= #/20 detectors or #/5 stations # local trigger contition = lora station condition. normally 3/4, except some instances where it was changed to 2/4 if one detector was broken print '_____________________________' print trigger_type print working_detectors print global_trigger_condition print local_trigger_condition print '_____________________________' ntrials=500 min_percentage_trigger=95.0 (bias_passed, min_chance_of_hit, chance_of_hit_all_sims) = bias.find_bias(coreas_path, zenith, azimuth, xcore, ycore, ntrials, working_detectors,trigger_type,local_trigger_condition=local_trigger_condition,min_percentage_trigger=min_percentage_trigger, trigger_condition_nof_detectors=global_trigger_condition) print bias_passed print min_chance_of_hit print chance_of_hit_all_sims
UTF-8
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py
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run_bias.py
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0.749844
0.666041
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JanMalte/secondhandshop_server
4,621,384,832,681
23f25268d4b9bd8606da2595557ccb09cea06acb
178512c82b4b9513e44a8a692eca2fc50cee9b7d
/src/pos/tests.py
dbb2fd490556e7fa8d65cb0348a40c4a99e58fd1
[ "MIT" ]
permissive
https://github.com/JanMalte/secondhandshop_server
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refs/heads/master
2020-04-05T22:47:05.344271
2019-03-04T08:46:41
2019-03-04T08:46:41
42,958,798
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from django.contrib.auth import get_user_model from django.core.urlresolvers import reverse from django_dynamic_fixture import G from django_webtest import WebTest from events.models import Event from .models import Cart class CartAdminTest(WebTest): def setUp(self): self.event = G(Event, is_active=True) self.admin = G(get_user_model(), is_superuser=True, is_staff=True) def test_article_list(self): url = reverse("admin:pos_cart_changelist") G(Cart) response = self.app.get(url, user=self.admin) # FIXME test list display fields
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py
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tests.py
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0.703204
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28.65
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13g10n/django.handbook
5,961,414,611,550
b9b00ea4977e995243c2b9b00c2a89740f78d23d
370d4f6e8d41a439f0afd99e406f4523b20afedf
/config/views.py
fe5de02b787595a26ccd25618ba6e663e07d63e0
[]
no_license
https://github.com/13g10n/django.handbook
15f39e1213d025a72682dbbd8c4c48a201b5bea0
e6e37704c0c58b17cea978a35ad5d969624ac07d
refs/heads/master
2021-08-11T08:41:22.402526
2017-11-13T12:28:11
2017-11-13T12:28:11
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from django.contrib.auth import get_user_model from django.views.generic import TemplateView class TestView(TemplateView): template_name = "test.html" def get_context_data(self, **kwargs): context = super(TestView, self).get_context_data()
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py
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views.py
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0.724138
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Ma-Jun-a/my_funny_exploration
6,914,897,361,339
c480364094f8de99c3627c38b6fba554047055f3
0a8dfb6e1b021a132bd1f256a03dfd3c50746a4a
/selenuim_.py/runner_test_.py
5c4c7e4c3861ba1d917ab62b503c8c7aa872e19b
[]
no_license
https://github.com/Ma-Jun-a/my_funny_exploration
54c71a9602a0f6cee6fbc2536d1e872ad766a543
e220e2dd18d843d21c77a2554335fbc011fe6a9d
refs/heads/master
2023-05-12T14:09:45.912442
2021-03-12T03:40:26
2021-03-12T03:40:26
207,444,731
0
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null
false
2023-05-01T20:48:14
2019-09-10T02:10:17
2021-03-12T03:40:46
2023-05-01T20:48:13
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0
0
1
Python
false
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import unittest from utils.HTMLTestRunner import HTMLTestRunner class MytestCase(unittest.TestCase): def test0(self): print('当然') def test1(self): print('test1') suit = unittest.TestSuite() # suit1 = unittest.defaultTestLoader(uuuu,) suit.addTest(unittest.makeSuite(MytestCase)) with open('./reports/登陆测试','wb') as f: runner = HTMLTestRunner(stream=f,tittle='***',description='***') runner.run(suit)
UTF-8
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false
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465
py
78
runner_test_.py
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0.668874
0.660044
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23.277778
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FifiTheBulldog/shortcuts-permissions
2,860,448,237,236
ac2a01645b933af16759d8b0c7da344314e3d4b7
c2719506930b056f2c9bafaf9e4672919b02f0d6
/scan_shortcut.py
a8224f8d2cc811856c18a7032be009c73d26904f
[ "MIT" ]
permissive
https://github.com/FifiTheBulldog/shortcuts-permissions
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refs/heads/main
2023-05-08T12:16:06.658936
2021-06-03T04:29:36
2021-06-03T04:29:36
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# scan_shortcut.py # # Exports scan_shortcut, a function that returns an array of permissions # for a shortcut. The function accepts a .shortcut file as a bytes object. import json import plistlib ACTIONS_PATH = "./actions.json" with open(ACTIONS_PATH) as file: action_list = json.load(file) def scan_shortcut(plist): '''Accepts a bytes object containing a shortcut, and returns a list of the shortcut's required permissions.''' actions = plistlib.loads(plist)["WFWorkflowActions"] perms = [] for action in actions: action_id = action["WFWorkflowActionIdentifier"] if action_id in action_list: for perm in action_list[action_id]: if not perm in perms: perms.append(perm) perms.sort() return perms
UTF-8
Python
false
false
804
py
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scan_shortcut.py
9
0.664179
0.664179
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26.758621
114
dr-dos-ok/Code_Jam_Webscraper
7,232,724,949,447
92e12d7ceb45ad8822386de166e5bc295b7ac848
15f321878face2af9317363c5f6de1e5ddd9b749
/solutions_python/Problem_207/683.py
1ce2365b57e9ac08be18246a000f65b2373998f4
[]
no_license
https://github.com/dr-dos-ok/Code_Jam_Webscraper
c06fd59870842664cd79c41eb460a09553e1c80a
26a35bf114a3aa30fc4c677ef069d95f41665cc0
refs/heads/master
2020-04-06T08:17:40.938460
2018-10-14T10:12:47
2018-10-14T10:12:47
null
0
0
null
null
null
null
null
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#!/usr/bin/env python import numpy as np inFile = open('in.txt', 'r') outFile = open('out.txt', 'w') t = int(inFile.readline()) for test in range(1, t+1): N, R, O, Y, G, B, V = map(int, inFile.readline().split(' ')) ans = ['']*N available = {'R': R, 'Y': Y, 'B': B, 'G': G, 'O': O, 'V': V} keys = dict((v, k) for k, v in available.iteritems()) fill = max(available.values()) toUse = keys[fill] # possible = True remaining = ['R', 'Y', 'B'] if(fill > N/2): outFile.write("Case #{}: IMPOSSIBLE\n".format(test)) # print 'IMPOSSIBLE' continue positions = [2*x for x in range(fill)] last = positions[-1] + 2 # print toUse # print positions for x in positions: ans[x] = toUse # print ans remaining.remove(toUse) toUse = remaining[0] fill = available[toUse] nextPos = last positions = [] for i in range(fill): if(nextPos > N-1): nextPos = 1 positions.append(nextPos) nextPos += 2 # print toUse # print positions for x in positions: ans[x] = toUse # print ans toUse = remaining[-1] # prev = -5 for i in range(len(ans)): # cur = 0 if(not ans[i]): # cur = i ans[i] = toUse outFile.write("Case #{}: {}\n".format(test, ''.join(ans)))
UTF-8
Python
false
false
1,441
py
60,747
683.py
60,742
0.489244
0.480222
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55
25.2
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Imrager/Family-Guy-Episode-Finder
14,027,363,205,332
e7f8da8a097e9b18b3e22f4af019b01465b92077
2519cf1e81b59deaaf17c6f830f39a8afa279ed1
/tunr_app/serializers.py
ab651f2daadc2fde1eee71e7a584f70b06d855d0
[]
no_license
https://github.com/Imrager/Family-Guy-Episode-Finder
9471c6806141eb5183a47dc49958f6d2310008dc
8f9d9451e5c413f6d3544e9b9f9d1971a8abe248
refs/heads/master
2023-01-10T13:22:20.268413
2019-06-18T17:05:04
2019-06-18T17:05:04
190,751,296
0
1
null
false
2023-01-03T23:38:07
2019-06-07T13:50:59
2019-06-18T17:05:13
2023-01-03T23:38:07
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0
1
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JavaScript
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false
from rest_framework import serializers from .models import User, Review, Comment # class SongSerializer(serializers.ModelSerializer): # class Meta: # model = Song # fields = ('id', 'title', 'album', 'preview_url', 'artist') # class ArtistSerializer(serializers.ModelSerializer): # songs = SongSerializer(many=True, read_only=True) # class Meta: # model = Artist # fields = ('id', 'name', 'photo_url', 'nationality', 'songs') class CommentSerializer(serializers.ModelSerializer): class Meta: model = Comment fields = ('id', 'reply', 'review') class ReviewSerializer(serializers.ModelSerializer): comments = CommentSerializer(many=True, read_only=True) class Meta: model = Review fields = ('id', 'review', 'user', 'comments', "episode") class UserSerializer(serializers.ModelSerializer): reviews = ReviewSerializer(many=True, read_only=True) class Meta: model = User fields = ('id', 'name', 'password', 'image', 'reviews')
UTF-8
Python
false
false
1,041
py
20
serializers.py
13
0.650336
0.650336
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32
31.5
70
DanielBetancourt1/Reverse-Engineering-applied-to-a-MicroScribe-device-and-development-of-a-GUI
8,349,416,466,008
534ebc04f468b1479ccf17f39ed42f4bec963500
da3e06bc7862d301eec29ab9b928be96c0a5bfda
/Python project/Protocolo.py
a9471c250bc3c99a7f9450b085b495abb43b648a
[]
no_license
https://github.com/DanielBetancourt1/Reverse-Engineering-applied-to-a-MicroScribe-device-and-development-of-a-GUI
89bd4367f67e15560041af6f6f705b1fbc7671dc
ba0176c6b0046b054d0b59cea0d4e1efa36bc4bb
refs/heads/master
2020-05-29T19:25:02.652832
2019-05-30T21:02:36
2019-05-30T21:02:36
189,329,590
1
0
null
null
null
null
null
null
null
null
null
null
null
null
null
# - This script function start communication with the MS by means of special commands defined for the MS, # some of this commands returns relevant information about the MS like physical parameters. import serial, time, sys import Maxes as Mx import PhysicalParameters as PhP try: # Link between Host and MicroScribe (Writing commands and reading data). def protocolo(TxRx): TxRx.flushInput() # flush input buffer, discarding all its contents TxRx.flushOutput() # flush output buffer, aborting current output TxRx.write(b'BEGIN') # - Start linking D_ID = Comm(TxRx) D_ID = D_ID.decode("utf-8") # print("Device ID: ", D_ID, '\n') # MSCR TxRx.write(bytes.fromhex('C8')) # Get product name Pnm = str(Comm(TxRx))[6:-1] # print("Product Name: ", Pnm, '\n') # ÈMicroScribe3D. TxRx.write(bytes.fromhex('C9')) # Get Product ID P_ID2 = Comm(TxRx) # print("Product ID2: ", P_ID2, '\n') # ÉMSCR. TxRx.write(bytes.fromhex('CA')) # Get Model Name MN = str(Comm(TxRx))[6:-1] # print("Model Name: ", MN, '\n') # ÊDX. TxRx.write(bytes.fromhex('CB')) # Get Serial Number SN = str(Comm(TxRx))[6:-1] # print("Serial Number: ", SN, '\n') # Ë40937. TxRx.write(bytes.fromhex('CC')) # Get Comment string CS = str(Comm(TxRx))[6:-1] # print("Comments: ", CS, '\n') # ÌStandard+Beta. TxRx.write(bytes.fromhex('CD')) # Get parameter format PF = str(Comm(TxRx))[6:-1] # print("Parameter format: Denavit-Hartenberg form 0.5: ", PF, '\n') # ÍFormat DH0.5. TxRx.write(bytes.fromhex('CE')) # Get version FV = str(Comm(TxRx))[6:-1] # print("Firmware Version: ", FV, '\n') # ÎHCI 2.0. TxRx.write(bytes.fromhex('C6')) # Get pulses/ rev values for each encoder. ME = Comm(TxRx) print("Pulses per revolution of the encoders: ") Encoderfactor = Mx.PulseRev(ME) # Pulses/Rev print('#------------------------------#') TxRx.write(bytes.fromhex('C0')) # Request extra parameters to compute # all positions and orientations (Needed cause the comment is Standard+Beta) EP = Comm(TxRx) print("Physical Parameters: \n") TxRx.write(bytes.fromhex('D3')) # Get Extra Extended Physical Parameters. EEP = Comm(TxRx) print("Extended Physical Parameters: ", '\n') [cA, sA, A, D, cB, sB] = PhP.PhParameters(EP, EEP) # TxRx.write(bytes.fromhex('D1')) # set home ref # EB = Comm(TxRx) # print("Encoder bits: ", EB) # REPORT_MOTION 0xCF # SET_HOME_REF 0xD0 # RESTORE_FACTORY 0xD1 # INSERT_MARKER 0xD2 # GET_EXT_PARAMS 0xD3 return Encoderfactor, cA, sA, A, D, cB, sB, D_ID, Pnm, MN, SN, CS, PF, FV # ---------------------------------------------------------- # # - This short function ask for the quantity of data in the buffer and read it. def Comm(TxRx): TxRx.inWaiting() time.sleep(0.10) Rm = TxRx.read(TxRx.inWaiting()) # Read response data from MicroScribe. return Rm except KeyboardInterrupt: TxRx.write(b'END') TxRx.flushInput() # flush input buffer, discarding all its contents TxRx.flushOutput() # flush output buffer, aborting current output TxRx.flush() TxRx.close() print('Communication was interrupted manually', '\n') except serial.portNotOpenError: print('You are trying to use a port that is not open', '\n')
UTF-8
Python
false
false
3,701
py
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Protocolo.py
12
0.568219
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98
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db7777/python-practice
781,684,050,722
2fb9b4630c1aa4068932d652be02b8a0c3d74dda
f8410cc79645b7a7ef67132b92bbb945e7b0c43c
/csvread/cvsread.py
ac2ea34618c268aeb8a4b26db3f5d2eed5d80a91
[]
no_license
https://github.com/db7777/python-practice
85aab118b71b80c89f641d8429ecc29b0f6f7d17
acbbe7cbb05e18b9b566f4763083c7b6f9c73841
refs/heads/master
2016-08-07T02:00:17.388509
2015-08-30T15:15:20
2015-08-30T15:15:20
41,615,722
0
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#! python3 __author__ = 'DB' import csv def readToList(): exampleFile = open('example.csv') exampleReader = csv.reader(exampleFile) exampleData = list(exampleReader) # the easies way to load into array print(exampleData) def readToIterate(): exampleFile = open('example.csv') exampleReader = csv.reader(exampleFile) for row in exampleReader: print('Row #' + str(exampleReader.line_num) + ' ' + str(row)) readToList() readToIterate()
UTF-8
Python
false
false
477
py
2
cvsread.py
2
0.672956
0.67086
0
20
22.9
73
GRSEB9S/linconfig
5,772,436,066,136
1cb33417b2e8ee38ebf7081c1268423d85e0d938
79424b68bf129c5a5171494120f7e315a76180b3
/qgis/qgis2/python/plugins/profiletool/ui/ui_ptdockwidget.py
5f41812aacee07b58430411362ece027d2765d0c
[]
no_license
https://github.com/GRSEB9S/linconfig
a016937412662db878b0c923e5147f91875604b7
84bc9117bb8e6109ef7fd10bf73354d4cf148c36
refs/heads/master
2021-09-08T03:17:02.648452
2018-03-06T16:32:18
2018-03-06T16:32:18
null
0
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null
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null
null
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null
# -*- coding: utf-8 -*- #----------------------------------------------------------- # # Profile # Copyright (C) 2012 Patrice Verchere #----------------------------------------------------------- # # licensed under the terms of GNU GPL 2 # # This program is free software; you can redistribute it and/or modify # it under the terms of the GNU General Public License as published by # the Free Software Foundation; either version 2 of the License, or # (at your option) any later version. # # This program is distributed in the hope that it will be useful, # but WITHOUT ANY WARRANTY; without even the implied warranty of # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the # GNU General Public License for more details. # # You should have received a copy of the GNU General Public License along # with this program; if not, print to the Free Software Foundation, Inc., # 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA. # #--------------------------------------------------------------------- from PyQt4.QtCore import * from PyQt4.QtGui import * from qgis.core import * from qgis.gui import * from profiletool import Ui_ProfileTool from ..tools.plottingtool import * #from ..profileplugin import ProfilePlugin try: from PyQt4.Qwt5 import * Qwt5_loaded = True except ImportError: Qwt5_loaded = False try: from matplotlib import * import matplotlib matplotlib_loaded = True except ImportError: matplotlib_loaded = False import platform class Ui_PTDockWidget(QDockWidget,Ui_ProfileTool): TITLE = "MirrorMap" def __init__(self, parent, iface1, mdl1): QDockWidget.__init__(self, parent) self.setAttribute(Qt.WA_DeleteOnClose) #self.mainWidget = MirrorMap(self, iface) self.location = Qt.RightDockWidgetArea self.iface = iface1 self.setupUi(self) #self.connect(self, SIGNAL("dockLocationChanged(Qt::DockWidgetArea)"), self.setLocation) self.mdl = mdl1 #self.showed = False QObject.connect(self.butSaveAs, SIGNAL("clicked()"), self.saveAs) def showIt(self): #self.setLocation( Qt.BottomDockWidgetArea ) self.location = Qt.BottomDockWidgetArea minsize = self.minimumSize() maxsize = self.maximumSize() self.setMinimumSize(minsize) self.setMaximumSize(maxsize) self.iface.mapCanvas().setRenderFlag(False) #TableWiew thing self.tableView.setModel(self.mdl) self.tableView.setColumnWidth(0, 20) self.tableView.setColumnWidth(1, 20) self.tableView.setColumnWidth(2, 150) hh = self.tableView.horizontalHeader() hh.setStretchLastSection(True) self.tableView.setColumnHidden(4 , True) self.mdl.setHorizontalHeaderLabels(["","","Layer","Band"]) #self.checkBox.setEnabled(False) #The ploting area self.plotWdg = None #Draw the widget self.iface.addDockWidget(self.location, self) self.iface.mapCanvas().setRenderFlag(True) def addOptionComboboxItems(self): #self.comboBox.addItem("Temporary polyline") #self.comboBox.addItem("Selected polyline") if Qwt5_loaded: self.comboBox_2.addItem("Qwt5") if matplotlib_loaded: self.comboBox_2.addItem("Matplotlib") def closeEvent(self, event): self.emit( SIGNAL( "closed(PyQt_PyObject)" ), self ) QObject.disconnect(self.butSaveAs, SIGNAL("clicked()"), self.saveAs) return QDockWidget.closeEvent(self, event) def addPlotWidget(self, library): layout = self.frame_for_plot.layout() while layout.count(): child = layout.takeAt(0) child.widget().deleteLater() if library == "Qwt5": self.stackedWidget.setCurrentIndex(0) widget1 = self.stackedWidget.widget(1) if widget1: self.stackedWidget.removeWidget( widget1 ) widget1 = None #self.widget_save_buttons.setVisible( True ) self.plotWdg = PlottingTool().changePlotWidget("Qwt5", self.frame_for_plot) layout.addWidget(self.plotWdg) if QT_VERSION < 0X040100: idx = self.cbxSaveAs.model().index(0, 0) self.cbxSaveAs.model().setData(idx, QVariant(0), Qt.UserRole - 1) self.cbxSaveAs.setCurrentIndex(1) if QT_VERSION < 0X040300: idx = self.cbxSaveAs.model().index(1, 0) self.cbxSaveAs.model().setData(idx, QVariant(0), Qt.UserRole - 1) self.cbxSaveAs.setCurrentIndex(2) elif library == "Matplotlib": self.stackedWidget.setCurrentIndex(0) #self.widget_save_buttons.setVisible( False ) self.plotWdg = PlottingTool().changePlotWidget("Matplotlib", self.frame_for_plot) layout.addWidget(self.plotWdg) mpltoolbar = matplotlib.backends.backend_qt4agg.NavigationToolbar2QTAgg(self.plotWdg, self.frame_for_plot) #layout.addWidget( mpltoolbar ) self.stackedWidget.insertWidget(1, mpltoolbar) self.stackedWidget.setCurrentIndex(1) lstActions = mpltoolbar.actions() mpltoolbar.removeAction( lstActions[ 7 ] ) mpltoolbar.removeAction( lstActions[ 8 ] ) # generic save as button def saveAs(self): idx = self.cbxSaveAs.currentIndex() if idx == 0: self.outPDF() elif idx == 1: self.outPNG() elif idx == 2: self.outSVG() elif idx == 3: self.outPrint() else: print('plottingtool: invalid index '+str(idx)) def outPrint(self): # Postscript file rendering doesn't work properly yet. PlottingTool().outPrint(self.iface, self, self.mdl, self.comboBox_2.currentText ()) def outPDF(self): PlottingTool().outPDF(self.iface, self, self.mdl, self.comboBox_2.currentText ()) def outSVG(self): PlottingTool().outSVG(self.iface, self, self.mdl, self.comboBox_2.currentText ()) def outPNG(self): PlottingTool().outPNG(self.iface, self, self.mdl, self.comboBox_2.currentText ())
UTF-8
Python
false
false
6,256
py
168
ui_ptdockwidget.py
125
0.623561
0.609015
0
177
34.327684
109
wfs123456/GCANet
10,642,929,007,680
9ef8a33ca5d2645c4971c5c6ed26c6c6cf7483f6
623400be7830f222682dfb3ff4f8b20dbcf93284
/model.py
7ee7418f1ebefaa1f6c33ced22f83a29f29c323f
[]
no_license
https://github.com/wfs123456/GCANet
a30b955c01a27d1fbd2965261d1297bbd668c403
dfc9971a158100916c7a58ac88e1c7cd0e74faa7
refs/heads/main
2023-05-13T06:22:17.658150
2021-06-06T06:13:00
2021-06-06T06:13:00
373,827,845
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# _*_ coding: utf-8 _*_ # @author : 王福森 # @time : 2021/4/3 13:46 # @File : model.py # @Software : PyCharm import torch.nn as nn import torch.nn.functional as F from torchvision import models import torch import os def make_layers(cfg, in_channels, batch_norm=True): layers = [] # in_channels = 3 for v in cfg: if v == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: conv2d = nn.Conv2d(in_channels, v, kernel_size=3, padding=1) if batch_norm: layers += [conv2d, nn.BatchNorm2d(v), nn.ReLU(inplace=True)] else: layers += [conv2d, nn.ReLU(inplace=True)] in_channels = v return nn.Sequential(*layers) def conv(in_channel, out_channel, kernel_size, dilation=1, bn=True): #padding = 0 # if kernel_size % 2 == 1: # padding = int((kernel_size - 1) / 2) padding = dilation # maintain the previous size if bn: return nn.Sequential( nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding, dilation=dilation,), nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True) ) else: return nn.Sequential( nn.Conv2d(in_channel, out_channel, kernel_size, padding=padding, dilation=dilation,), # nn.BatchNorm2d(out_channel, momentum=0.005), nn.ReLU(inplace=True) ) # class Inception(nn.Module): # def __init__(self,in_channel): # super(Inception, self).__init__() # self.conv1x1 = nn.Sequential(nn.Conv2d(in_channel,64,1), nn.BatchNorm2d(64), nn.ReLU(inplace=True)) # self.conv2x1 = nn.Sequential(nn.Conv2d(in_channel,64, 1), nn.BatchNorm2d(64), nn.ReLU(inplace=True)) # self.conv3x1 = nn.Sequential(nn.Conv2d(in_channel, 128, 1), nn.BatchNorm2d(128), nn.ReLU(inplace=True)) # self.conv4x1 = nn.Sequential(nn.Conv2d(in_channel, 256, 1), nn.BatchNorm2d(256), nn.ReLU(inplace=True)) # # self.conv2x2 = nn.Sequential(nn.Conv2d(64,64,3,padding=2,dilation=2), nn.BatchNorm2d(64), nn.ReLU(inplace=True)) # self.conv3x2 = nn.Sequential(nn.Conv2d(128,128,5,padding=4,dilation=2), nn.BatchNorm2d(128), nn.ReLU(inplace=True)) # self.conv4x2 = nn.Sequential(nn.Conv2d(256,256,7,padding=6,dilation=2), nn.BatchNorm2d(256), nn.ReLU(inplace=True)) # self.init_param() # def forward(self, x): # x1 = self.conv1x1(x) # x2 = self.conv2x2(self.conv2x1(x)) # x3 = self.conv3x2(self.conv3x1(x)) # x4 = self.conv4x2(self.conv4x1(x)) # x = torch.cat((x1,x2,x3,x4),1) # return x # # def init_param(self): # for m in self.modules(): # if isinstance(m, nn.Conv2d): # nn.init.normal_(m.weight, std=0.01) # if m.bias is not None: # nn.init.constant_(m.bias, 0) # elif isinstance(m, nn.BatchNorm2d): # nn.init.constant_(m.weight, 1) # nn.init.constant_(m.bias, 0) class AttenModule(nn.Module): def __init__(self,in_channel,out_channel): super(AttenModule, self).__init__() self.attention = nn.Sequential(nn.Conv2d(in_channel, 1, 3,padding=1, bias=True), nn.Sigmoid() ) self.conv1 = nn.Sequential(nn.Conv2d(in_channel, out_channel, 3, padding=2, dilation=2, bias=False), nn.BatchNorm2d(out_channel), nn.ReLU(inplace=True), ) self.init_param() def forward(self, x): atten = self.attention(x) features = self.conv1(x) x = features * atten return x, atten def init_param(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.normal_(m.weight, std=0.01) if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) class SPPSELayer(nn.Module): def __init__(self,in_cahnnel, channel, reduction=16): super(SPPSELayer, self).__init__() self.avg_pool1 = nn.AdaptiveAvgPool2d(1) self.avg_pool2 = nn.AdaptiveAvgPool2d(2) self.avg_pool4 = nn.AdaptiveAvgPool2d(4) self.fc = nn.Sequential( nn.Linear(in_cahnnel*21, in_cahnnel // reduction, bias=False), nn.ReLU(inplace=True), nn.Linear(in_cahnnel // reduction, channel, bias=False), nn.Sigmoid() ) self.conv2 = nn.Sequential(nn.Conv2d(in_cahnnel, channel, 1), nn.BatchNorm2d(channel), nn.ReLU(inplace=True)) def forward(self, x): b, c, _, _ = x.size() # b: number; c: channel; y1 = self.avg_pool1(x).view(b, c) # like resize() in numpy y2 = self.avg_pool2(x).view(b, 4 * c) y3 = self.avg_pool4(x).view(b, 16 * c) y = torch.cat((y1, y2, y3), 1) y = self.fc(y) b,out_channel = y.size() y = y.view(b, out_channel, 1, 1) x = self.conv2(x) y = y * x return y def init_param(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.normal_(m.weight, std=0.01) if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) class Net(nn.Module): def __init__(self): super(Net,self).__init__() # cfg1 = [64, 64, 'M', 128, 128, 'M', 256, 256, 256] cfg2 = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512] # self.front_end1 = make_layers(cfg1, 3, batch_norm=True) self.front_end2 = make_layers(cfg2, 3, batch_norm=True) # self.Inception1 = Inception(512) self.attenModule1 = AttenModule(512, 256) self.attenModule2 = AttenModule(256, 128) self.attenModule3 = AttenModule(128, 64) self.SPPSEMoudule1 = SPPSELayer(512,256) self.SPPSEMoudule2 = SPPSELayer(256, 128) self.SPPSEMoudule3 = SPPSELayer(128, 64) self.ReduConv1 = conv(512, 256, 3, dilation=1) self.ReduConv2 = conv(256, 128, 3, dilation=2) self.ReduConv3 = conv(128, 64, 3, dilation=3) # self.A_conv = nn.Sequential(conv(512, 128, 3), conv(128, 64, 3)) # self.final = nn.Sequential(nn.Conv2d(64, 1, 1), nn.ReLU()) self.final = nn.Conv2d(64,1,1) self.init_param() def init_param(self): for m in self.modules(): if isinstance(m, nn.Conv2d): nn.init.normal_(m.weight, std=0.01) if m.bias is not None: nn.init.constant_(m.bias, 0) elif isinstance(m, nn.BatchNorm2d): nn.init.constant_(m.weight, 1) nn.init.constant_(m.bias, 0) print("loading pretrained vgg16_bn!") if os.path.exists("/home/liuqi/PycharmProjects/SPPSENet/weights/vgg16_bn.pth"): print("find pretrained weights!") vgg16_bn = models.vgg16_bn(pretrained=False) vgg16_weights = torch.load("/home/liuqi/PycharmProjects/SPPSENet/weights/vgg16_bn.pth") vgg16_bn.load_state_dict(vgg16_weights) else: vgg16_bn = models.vgg16_bn(pretrained=True) # the front conv block's parameter no training # for p in self.front_end1.parameters(): # p.requires_grad = False # self.front_end1.load_state_dict(vgg16_bn.features[:23].state_dict()) self.front_end2.load_state_dict(vgg16_bn.features[:33].state_dict()) def forward(self, x, vis=False): # y = self.front_end1(x) #dense block x = self.front_end2(x) x1,atten1 = self.attenModule1(x) y1 = self.SPPSEMoudule1(x) x = torch.cat((x1,y1), 1) x = self.ReduConv1(x) x2,atten2 = self.attenModule2(x) y2 = self.SPPSEMoudule2(x) x = torch.cat((x2, y2), 1) x = self.ReduConv2(x) x3, atten3 = self.attenModule3(x) y3 = self.SPPSEMoudule3(x) x = torch.cat((x3, y3), 1) x = self.ReduConv3(x) x = self.final(x) # att = F.interpolate(att, scale_factor=8, mode="nearest", align_corners=None) # x = F.interpolate(x, scale_factor=8, mode="nearest", align_corners=None) if vis: return x, atten1, atten2, atten3 return x if __name__ == "__main__": os.environ["CUDA_VISIBLE_DEVICES"] = "0" net = Net() # print(net.front_end.state_dict()) x = torch.ones((16, 3, 128, 128)) print(x.size()) y= net(x) print(y.size())
UTF-8
Python
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false
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model.py
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AndreaCossio/sem2.0
10,651,518,923,520
2b039a9536f4fbd5197c614e49e35e831729be34
8230dba89f8392cb57fd7227d78be3a20157e336
/SEM 2.0/2.0.2/RADAR 0.2.py
9461bfa0efdaa27cd684e6e765163cc51e0bbd8b
[]
no_license
https://github.com/AndreaCossio/sem2.0
febfac72715e9a4834cae2c96adc691881bd7b07
c256a52b1b7af915d29750638125329bddfc86de
refs/heads/master
2020-04-13T05:15:49.003281
2017-08-04T07:54:54
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import serial import time import pygame, sys from pygame.locals import * from pygame.color import THECOLORS import random pygame.init() pygame.display.set_caption('ARDUINO-PROJECT') screen = pygame.display.set_mode([600, 600]) screen.fill ([255, 255, 255]) class radar(): def __init__(self, x, y, color, radius, width, speed): self.position = (x, y) self.width = width self.color = color self.image = pygame.draw.circle(screen, color, (x, y), width, width) self.speed = speed self.seconds = time.time() self.state = time.time() self.radius = width self.max_radius = radius def get_enable(self): self.seconds = time.time() if int(self.seconds) != int(self.state): new_radius = int(self.seconds - self.state)*self.speed + self.radius if new_radius > self.max_radius: self.radius = new_radius self.radius = self.width self.seconds = time.time() self.state = time.time() return False else: return True else: return True def run(self): self.seconds = time.time() if int(self.seconds) != int(self.state): new_radius = int(self.seconds - self.state)*self.speed + self.radius self.radius = new_radius if self.radius > self.max_radius: self.radius = self.width self.seconds = time.time() self.state = time.time() if self.width >= self.radius: self.radius = self.width Surface = pygame.Surface((self.radius * 2, self.radius * 2)) Surface.fill ([255, 255, 255]) Surface.set_colorkey((255, 255, 255)) self.image = pygame.draw.circle(Surface, self.color, [self.radius, self.radius], int(self.radius), self.width) alpha =((self.max_radius - self.radius)/self.max_radius)**(0.5) * 255 Surface.set_alpha(alpha) screen.blit(Surface, (self.position[0] - self.radius, self.position[1] - self.radius)) class unit(): def __init__(self, x, y, color, radius, width, speed): self.rect = pygame.draw.rect(screen, [255, 255, 255] ,[x, y , 1, 1], 1) self.object = radar(x, y, color, radius, width, speed) self.position = [x, y] self.enable = False def run(self): if self.enable: self.enable = self.object.get_enable() if self.enable: self.object.run() clock = pygame.time.Clock() green_radar = radar(300, 300, [0, 204, 0], 300, 2, 0.2) click = 0 radar_list = [] unit_list = [] while True: screen.fill([255, 255, 255]) for event in pygame.event.get(): if event.type == pygame.QUIT: sys.exit () elif event.type == MOUSEBUTTONDOWN: (pos_x, pos_y) = pygame.mouse.get_pos() object = unit(pos_x, pos_y, [0, 102, 255], 50, 2, 0.05) unit_list.append(object) elif event.type == pygame.KEYDOWN and event.key == pygame.K_RETURN: position_x = random.randint (0, 600) position_y = random.randint (0, 600) position_m = (position_x + position_y)/2 width = random.randint(1, 5) radius = random.randint(5, position_m) speed = random.randint(1, 99) / 100.0 object = radar(position_x, position_y, THECOLORS[random.choice(THECOLORS.keys())], radius, width, speed) radar_list.append(object) elif event.type == pygame.KEYDOWN and event.key == pygame.K_DELETE: unit_list = [] radar_list = [] green_radar.run() for unit_object in unit_list: #print ((unit_object.position[0]-green_radar.position[0])**2 + (unit_object.position[1]-green_radar.position[1])**2) - (int(green_radar.radius)**2) if abs(((unit_object.position[0]-green_radar.position[0])**2 + (unit_object.position[1]-green_radar.position[1])**2) - (int(green_radar.radius)**2))<= green_radar.radius: #if unit_object.rect.colliderect(green_radar.image): unit_object.run() unit_object.enable = True if unit_object not in radar_list: radar_list.append(unit_object) for radar_object in radar_list: radar_object.run() clock.tick(100) pygame.display.update() click += 1
UTF-8
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py
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RADAR 0.2.py
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diegord13/programas
13,013,750,914,227
65e2813aae5475b608a85300e75e950db75b9bee
cfd41ae22d3586ac247b1f3fc4e5946a71cb13f4
/Interfaz_grafica/raiz.py
c1e5e316d0722a515bbf2f95b04c7dc5cb15a44b
[]
no_license
https://github.com/diegord13/programas
c29d7fcfd3d43e45e073d3d7290fb4f7074bfa4b
cf402879047a0b65eddbff302537bd0b700d1067
refs/heads/master
2023-08-15T15:12:17.501460
2021-10-21T22:49:01
2021-10-21T22:49:01
407,954,837
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import tkinter raiz = tkinter.Tk() raiz.title("mi programa") raiz.mainloop()
UTF-8
Python
false
false
79
py
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raiz.py
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0.721519
0.721519
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ActorExpose/prexploit
19,396,072,336,238
f6153e5ebcdd70faaca38f0f5e14b86545a50e9b
c795161c834b18b120e6c906a3618c5841e72b45
/prexploit/util/report.py
a4537dd04e344bedf8b1bcee4c36c77da4fbd82d
[ "Apache-2.0" ]
permissive
https://github.com/ActorExpose/prexploit
266151de9557d5e53eedc7c32a1cf077d0dff8c0
894a099dc4466526a1d66ae24755675b757b8bcf
refs/heads/main
2023-02-22T15:43:07.943431
2021-01-20T14:44:36
2021-01-20T14:44:36
331,435,177
1
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Apache-2.0
true
2021-01-20T21:19:49
2021-01-20T21:19:49
2021-01-20T21:19:48
2021-01-20T14:44:59
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import numpy as np from tabulate import tabulate class Report(object): def __init__(self): self.__report = {} @classmethod def from_dict(cls, dic): report = cls() report._Report__report = dic return report def __add_scores(self, cate, precision, recall, fscore): if cate not in self.__report: self.__report[cate] = [] self.__report[cate].append({ 'precision': precision, 'recall': recall, 'fscore': fscore, }) def add_train_scores(self, precision, recall, fscore): self.__add_scores('train', precision, recall, fscore) def add_valid_scores(self, precision, recall, fscore): self.__add_scores('valid', precision, recall, fscore) def add_test_scores(self, precision, recall, fscore): self.__add_scores('test', precision, recall, fscore) def as_dict(self): return self.__report def get_average_precision_recall_fscore(self): def avg_prf(xs): p = np.mean([x['precision'] for x in xs]).round(2) r = np.mean([x['recall'] for x in xs]).round(2) f = np.mean([x['fscore'] for x in xs]).round(2) return [p, r, f] return avg_prf(self.__report['train'])\ + avg_prf(self.__report['valid'])\ + avg_prf(self.__report['test'])\ def table_report(reports, names): table = [ ['', 'Train', 'Train', 'Train', 'Valid', 'Valid', 'Valid', 'Test', 'Test', 'Test'], ['', 'Precision', 'Recall', 'F Score', 'Precision', 'Recall', 'F Score', 'Precision', 'Recall', 'F Score'] ] for report, name in zip(reports, names): table.append([name] + report.get_average_precision_recall_fscore()) return tabulate(table)
UTF-8
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false
false
1,847
py
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report.py
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0.545208
0.543584
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61
29.278689
75
krishSona/testbackend
154,618,848,039
863858aec2f2de10dc685e019768510c193687f2
4b87a0de0f43de2bde41f2590faac970c18fe482
/core/models.py
6893eef8672f9b0b2c3b3328b1c8cfee0c95287d
[]
no_license
https://github.com/krishSona/testbackend
d0bc325776537d9814b9022b3538b5e8a840e6a4
d87e050d02542c58876d4f81c2ea99815ab4160e
refs/heads/master
2023-04-08T01:26:42.070058
2021-04-03T06:08:54
2021-04-03T06:08:54
354,214,243
0
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import uuid from django.contrib.postgres.fields import JSONField from django.db import models from django.db.models import Q from dynamic_validator import ModelFieldRequiredMixin import utilities import datetime import calendar import math class Industry(ModelFieldRequiredMixin, models.Model): name = models.CharField(max_length=50) REQUIRED_FIELDS = ['name'] def __str__(self): return str(self.name) class EmployeeRange(ModelFieldRequiredMixin, models.Model): number = models.CharField(max_length=10) REQUIRED_FIELDS = ['number'] def __str__(self): return str(self.number) class City(ModelFieldRequiredMixin, models.Model): name = models.CharField(max_length=50) REQUIRED_FIELDS = ['name'] def __str__(self): return str(self.name) class State(ModelFieldRequiredMixin, models.Model): name = models.CharField(max_length=50) REQUIRED_FIELDS = ['name'] def __str__(self): return str(self.name) COMPANY_CATEGORY = [ (0, 'contractor'), (1, 'principal_employer'), ] class Company(ModelFieldRequiredMixin, models.Model): rid = models.UUIDField(default=uuid.uuid4, editable=False) name = models.CharField(max_length=50) industry = models.ForeignKey( Industry, on_delete=models.PROTECT, null=True, blank=True) employee_range = models.ForeignKey( EmployeeRange, on_delete=models.PROTECT, null=True, blank=True) office_address = models.TextField(null=True, blank=True) city = models.ForeignKey( City, on_delete=models.PROTECT, null=True, blank=True) state = models.ForeignKey( State, on_delete=models.PROTECT, null=True, blank=True) pincode = models.CharField(max_length=6, null=True, blank=True) gstin = models.CharField( max_length=15, unique=True, null=True, blank=True) average_monthly_salary_payout = models.IntegerField( blank=True, null=True) monthly_salary_day = models.IntegerField(blank=True, null=True) category = models.IntegerField(default=0, choices=COMPANY_CATEGORY) code = models.CharField(max_length=6, null=True, blank=True, unique=True) domain_name = models.CharField(max_length=250, null=True, blank=True, unique=True) tie_up = models.BooleanField(default=False) subscription_amount = models.FloatField(default=0) REQUIRED_FIELDS = ['name'] def __str__(self): return str(self.name) class QrCode(models.Model, ModelFieldRequiredMixin): qr_id = models.CharField(max_length=23, unique=True) longitude = models.DecimalField(max_digits=10, decimal_places=7, null=True, blank=True) latitude = models.DecimalField(max_digits=10, decimal_places=7, null=True, blank=True) company = models.ForeignKey(Company, on_delete=models.PROTECT) REQUIRED_FIELDS = ['qr_id', 'company'] def __str__(self): return str(self.qr_id) class Department(ModelFieldRequiredMixin, models.Model): name = models.CharField(max_length=50) REQUIRED_FIELDS = ['name'] def __str__(self): return str(self.name) class Designation(ModelFieldRequiredMixin, models.Model): name = models.CharField(max_length=50) REQUIRED_FIELDS = ['name'] def __str__(self): return str(self.name) class Bank(ModelFieldRequiredMixin, models.Model): name = models.CharField(max_length=100) REQUIRED_FIELDS = ['name'] def __str__(self): return str(self.name) class Ifs(ModelFieldRequiredMixin, models.Model): code = models.CharField(max_length=11) bank = models.ForeignKey(Bank, on_delete=models.PROTECT) REQUIRED_FIELDS = ['code', 'bank'] def __str__(self): return str(self.code) class Level(ModelFieldRequiredMixin, models.Model): title = models.CharField(max_length=50) REQUIRED_FIELDS = ['title'] def __str__(self): return str(self.title) class Employer(ModelFieldRequiredMixin, models.Model): rid = models.UUIDField(default=uuid.uuid4, editable=False) name = models.CharField(max_length=50, null=True, blank=True) phone = models.CharField(max_length=10, null=True, blank=True) email = models.CharField(max_length=255) user = models.ForeignKey('authentication.User', on_delete=models.PROTECT) company = models.ForeignKey( Company, on_delete=models.PROTECT, blank=True, null=True) department = models.ForeignKey( Department, on_delete=models.PROTECT, null=True, blank=True) designation = models.ForeignKey( Designation, on_delete=models.PROTECT, null=True, blank=True) principal_companies = models.ManyToManyField(Company, related_name='employers', blank=True) REQUIRED_FIELDS = ['email', 'name', 'phone', 'company'] def __str__(self): return str(self.email) def get_default_work_days(): return {"days": ["Mon", "Tue", "Wed", "Thu", "Fri", "Sat"]} class Employee(models.Model): rid = models.UUIDField(default=uuid.uuid4, editable=False) name = models.CharField(max_length=100, null=True, blank=True) phone = models.CharField(max_length=10, null=True, blank=True) email = models.EmailField( verbose_name='email address', max_length=255, null=True, blank=True ) mail_verified = models.BooleanField(null=True, blank=True) company = models.ForeignKey( Company, on_delete=models.PROTECT, blank=True, null=True) company_verified = models.BooleanField(null=True, blank=True) net_monthly_salary = models.IntegerField() verified_salary = models.IntegerField(null=True, blank=True) due_day = models.IntegerField(null=True, blank=True) kyc = models.BooleanField(null=True, blank=True) e_nach = models.BooleanField(null=True, blank=True) is_verified = models.BooleanField(default=False) employee_id = models.CharField(max_length=50, null=True, blank=True) extra_data = JSONField(null=True, blank=True) joining_date = models.DateField(null=True, blank=True) salary_day = models.IntegerField(null=True, blank=True) created_at = models.DateField(auto_now_add=True, null=True, blank=True) permanent_address = models.TextField(null=True, blank=True) permanent_city = models.CharField(max_length=50, blank=True, null=True) permanent_state = models.CharField(max_length=50, blank=True, null=True) permanent_pincode = models.IntegerField(null=True, blank=True) current_address = models.TextField(null=True, blank=True) current_city = models.CharField(max_length=50, blank=True, null=True) current_state = models.CharField(max_length=50, blank=True, null=True) current_pincode = models.IntegerField(null=True, blank=True) service_status = models.IntegerField(default=0) credit_limit = models.FloatField(default=50) bank_account_number = models.CharField(max_length=18, null=True, blank=True) ifs = models.ForeignKey(Ifs, on_delete=models.PROTECT, null=True, blank=True) level = models.ForeignKey( Level, on_delete=models.PROTECT, null=True, blank=True) confirmed = models.BooleanField(default=False) user = models.ForeignKey('authentication.User', on_delete=models.PROTECT) salary_type = models.CharField(max_length=50, default="net") agreed_with_terms_and_conditions = models.BooleanField(default=False) daily_salary = models.FloatField(default=0) balance = models.FloatField(default=0) credited = models.FloatField(default=0) debited = models.FloatField(default=0) withdraw = models.FloatField(default=0) fees = models.FloatField(default=0) gst = models.FloatField(default=0) work_days = JSONField(default=get_default_work_days) work_timings = models.CharField(max_length=17, default="09:00 AM-06:00 PM") department = models.ForeignKey( Department, on_delete=models.PROTECT, null=True, blank=True) designation = models.ForeignKey( Designation, on_delete=models.PROTECT, null=True, blank=True) employer = models.ForeignKey( Employer, on_delete=models.PROTECT, blank=True, null=True) beneficiary_id = models.CharField(null=True, blank=True, max_length=20) is_beneficiary = models.BooleanField(default=False) check_location = models.BooleanField(default=True) wish_listing = models.BooleanField(default=False) mail_enabled = models.BooleanField(default=True, null=False) mail_bounced = models.IntegerField(default=0) mail_token = models.CharField(max_length=40, null=True, blank=True) unsubscribe_token = models.CharField(max_length=40, null=True, blank=True) deleted_at = models.DateTimeField(null=True, blank=True) digital_time_stamp = models.TextField(null=True, blank=True) active = models.BooleanField(default=True) REQUIRED_FIELDS = ['net_monthly_salary', 'user', 'company', 'employer'] def save(self, *args, **kwargs): if not self.pk: # create mail_token and unsubscribe_token of employee self.mail_token = utilities.get_random_time_based_token() self.unsubscribe_token = utilities.get_random_time_based_token() super(Employee, self).save(*args, **kwargs) def __str__(self): return str(self.phone) def get_daily_salary(self): now = datetime.datetime.now() year = now.year month = now.month num_days_in_current_month = calendar.monthrange(year, month)[1] work_days_in_current_month = [ datetime.date(year, month, day).weekday() for day in range(1, num_days_in_current_month + 1) if datetime.date(year, month, day).strftime('%a') in self.work_days.get('days') ] num_work_days_in_current_month = len(work_days_in_current_month) daily_salary = self.net_monthly_salary / num_work_days_in_current_month daily_salary = (math.floor(daily_salary / 50)) * 50 return daily_salary # TODO: optimize (make a model field) def get_available_balance(self): attendance_queryset = Attendance.objects.filter( employee=self.pk, date__month=datetime.datetime.now().month, date__year=datetime.datetime.now().year ) total_verified_salary = sum([attendance.verified_salary for attendance in attendance_queryset]) transfer_up_to = (total_verified_salary * self.credit_limit) / 100 transfer_up_to = transfer_up_to - self.withdraw return float(round(transfer_up_to)) def calculate_work_day(self, num_days): now = datetime.datetime.now() year = now.year month = now.month work_days = [ datetime.date(year, month, day).weekday() for day in range(1, num_days + 1) if datetime.date(year, month, day).strftime('%a') in self.work_days.get('days') ] return len(work_days) DURATION_CHOICES = [ ("full_day", "full_day"), ("half_day", "half_day"), ] WORK_LOCATION_CHOICES = [ ("office", "office"), ("home", "home"), ("other", "other"), ] class Attendance(ModelFieldRequiredMixin, models.Model): date = models.DateField(db_index=True, default=datetime.datetime.now) status = models.CharField( max_length=30, null=True, db_index=True) duration = models.CharField( max_length=10, null=True, blank=True, db_index=True, choices=DURATION_CHOICES) start_at = models.TimeField(null=True, blank=True) end_at = models.TimeField(null=True, blank=True) work_location = models.CharField( max_length=10, null=True, blank=True, choices=WORK_LOCATION_CHOICES) qr_code_scanned = models.BooleanField(null=True, blank=True) face_detected = models.BooleanField(null=True, blank=True) employee = models.ForeignKey(Employee, on_delete=models.PROTECT) company = models.ForeignKey(Company, on_delete=models.PROTECT) salary = models.FloatField(default=0) verified_salary = models.FloatField(default=0) description = models.CharField(max_length=255, null=True, blank=True) image = models.ImageField(upload_to='images/attendance/', null=True, blank=True) REQUIRED_FIELDS = ['status', 'employee', 'company'] def __str__(self): return str(self.date) class Meta: constraints = [ models.UniqueConstraint( fields=['date', 'employee', 'company'], name='make_unique_date_employee_company' ), ] STATEMENT_STATUS_CHOICES = [ ("initialized", "initialized"), ("pending", "pending"), ("waiting", "waiting"), ("rejected", "rejected"), ("approved", "approved"), ("cancelled", "cancelled"), ("completed", "completed"), ] class Statement(ModelFieldRequiredMixin, models.Model): rid = models.UUIDField(default=uuid.uuid4, editable=False) date = models.DateField(auto_now_add=True, db_index=True) status = models.CharField(max_length=15, choices=STATEMENT_STATUS_CHOICES, default="initialized") description = models.CharField(max_length=255) credit = models.FloatField(null=True) debit = models.FloatField(null=True) withdraw = models.FloatField(null=True, blank=True) fees = models.FloatField(null=True, blank=True) gst = models.FloatField(null=True, blank=True) balance = models.FloatField(null=True, blank=True) current_due = models.FloatField(null=True, blank=True) previous_due = models.FloatField(null=True, blank=True) interest = models.FloatField(null=True, blank=True) created_at = models.DateTimeField(auto_now_add=True, db_index=True) updated_at = models.DateTimeField(auto_now=True, db_index=True) employee = models.ForeignKey(Employee, on_delete=models.PROTECT) company = models.ForeignKey(Company, on_delete=models.PROTECT) otp = models.CharField(max_length=6, null=True, blank=True) otp_valid_till = models.DateTimeField(null=True, blank=True) digital_time_stamp = models.TextField(null=True, blank=True) REQUIRED_FIELDS = ['description', 'balance', 'employee', 'company'] def __str__(self): return str(self.date) class Meta: constraints = [ models.CheckConstraint( check=Q(debit__isnull=False) | Q(credit__isnull=False), name='not_both_null' ) ] BOOKING_STATUS_CHOICES = [ (0, 'open'), (1, 'pending'), (2, 'closed'), ] CATEGORY_CHOICE = [ (1, 'employee'), (2, 'employer'), ] class Booking(models.Model): name = models.CharField(max_length=255) company = models.CharField(max_length=255, null=True, blank=True) phone = models.CharField(max_length=10) email = models.CharField(max_length=255, null=True, blank=True) category = models.IntegerField(null=True, blank=True, choices=CATEGORY_CHOICE) status = models.IntegerField(default=0, db_index=True, choices=BOOKING_STATUS_CHOICES) created_at = models.DateTimeField(auto_now_add=True, null=True) updated_at = models.DateTimeField(auto_now=True, null=True) def __str__(self): return str(self.name) class Setting(models.Model): key = models.CharField(max_length=255, unique=True) value = models.CharField(max_length=255) def __str__(self): return str(self.key) class Pricing(models.Model): company = models.ForeignKey(Company, on_delete=models.PROTECT, null=True, blank=True) min_price = models.IntegerField(null=True, blank=True) max_price = models.IntegerField() fee = models.IntegerField() def __str__(self): return str(self.fee) @staticmethod def calculate_fees(amount, company_obj): if amount is None or amount < 0: return None if company_obj.tie_up is False and amount == 0: return None if company_obj.tie_up is True and amount == 0: price_obj = Pricing.objects.filter( Q(company=company_obj) & Q(min_price=None) & Q(max_price=0) ).first() fee = float(price_obj.fee) if price_obj else None else: price_obj = Pricing.objects.filter( Q(company=company_obj) & Q(min_price__lt=amount) & Q(max_price__gte=amount) ).first() fee = float(price_obj.fee) if price_obj else None if fee and fee > 0: gst = (fee * 18) / 100 fee = fee + gst return fee class Verifier(models.Model): employee = models.ManyToManyField(Employee) email = models.EmailField() counter = models.IntegerField(default=0) def __str__(self): return str(self.email) DOMAIN_CHOICE = [ (0, 'generic'), (1, 'company') ] class Domain(models.Model): name = models.CharField(max_length=30, unique=True, db_index=True) category = models.IntegerField(null=True, blank=True, choices=DOMAIN_CHOICE) company = models.ForeignKey(Company, on_delete=models.PROTECT, null=True, blank=True) def __str__(self): return str(self.name) class Application(models.Model): name = models.CharField(max_length=255, null=True, blank=True) phone = models.CharField(max_length=255, null=True, blank=True) company_email = models.CharField(max_length=255, null=True, blank=True) employee_id = models.CharField(max_length=255, null=True, blank=True) company_name = models.CharField(max_length=255, null=True, blank=True) net_monthly_salary = models.CharField(max_length=255, null=True, blank=True) salary_day = models.CharField(max_length=255, null=True, blank=True) bank_name = models.CharField(max_length=255, null=True, blank=True) bank_account_name = models.CharField(max_length=255, null=True, blank=True) bank_account_number1 = models.CharField(max_length=255, null=True, blank=True) bank_account_number2 = models.CharField(max_length=255, null=True, blank=True) ifsc = models.CharField(max_length=255, null=True, blank=True) utm_source = models.CharField(max_length=255, null=True, blank=True) utm_medium = models.CharField(max_length=255, null=True, blank=True) utm_campaign = models.CharField(max_length=255, null=True, blank=True) deleted_at = models.DateTimeField(null=True, blank=True) created_at = models.DateTimeField(auto_now_add=True, db_index=True, null=True, blank=True) def __str__(self): return str(self.name)
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RodenLuo/mummi-ras
1,047,972,056,379
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/mummi_ras/online/cg/baseFastSingleFrame.py
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# -*- Mode: python; tab-width: 4; indent-tabs-mode:nil; coding:utf-8 -*- # vim: tabstop=4 expandtab shiftwidth=4 softtabstop=4 # # MDAnalysis --- http://www.mdanalysis.org # Copyright (c) 2006-2017 The MDAnalysis Development Team and contributors # (see the file AUTHORS for the full list of names) # # Released under the GNU Public Licence, v2 or any higher version # # Please cite your use of MDAnalysis in published work: # # R. J. Gowers, M. Linke, J. Barnoud, T. J. E. Reddy, M. N. Melo, S. L. Seyler, # D. L. Dotson, J. Domanski, S. Buchoux, I. M. Kenney, and O. Beckstein. # MDAnalysis: A Python package for the rapid analysis of molecular dynamics # simulations. In S. Benthall and S. Rostrup editors, Proceedings of the 15th # Python in Science Conference, pages 102-109, Austin, TX, 2016. SciPy. # # N. Michaud-Agrawal, E. J. Denning, T. B. Woolf, and O. Beckstein. # MDAnalysis: A Toolkit for the Analysis of Molecular Dynamics Simulations. # J. Comput. Chem. 32 (2011), 2319--2327, doi:10.1002/jcc.21787 # """ Analysis building blocks --- :mod:`MDAnalysis.analysis.base` ============================================================ A collection of useful building blocks for creating Analysis classes. """ import six from six.moves import range, zip import inspect import numpy as np from MDAnalysis import coordinates from MDAnalysis.core.groups import AtomGroup class AnalysisBase(object): """Base class for defining multi frame analysis The class it is designed as a template for creating multiframe analyses. This class will automatically take care of setting up the trajectory reader for iterating, and it offers to show a progress meter. To define a new Analysis, `AnalysisBase` needs to be subclassed `_single_frame` must be defined. It is also possible to define `_prepare` and `_conclude` for pre and post processing. See the example below. .. code-block:: python class NewAnalysis(AnalysisBase): def __init__(self, atomgroup, parameter, **kwargs): super(NewAnalysis, self).__init__(atomgroup.universe.trajectory, **kwargs) self._parameter = parameter self._ag = atomgroup def _prepare(self): # OPTIONAL # Called before iteration on the trajectory has begun. # Data structures can be set up at this time self.result = [] def _single_frame(self): # REQUIRED # Called after the trajectory is moved onto each new frame. # store result of `some_function` for a single frame self.result.append(some_function(self._ag, self._parameter)) def _conclude(self): # OPTIONAL # Called once iteration on the trajectory is finished. # Apply normalisation and averaging to results here. self.result = np.asarray(self.result) / np.sum(self.result) Afterwards the new analysis can be run like this. .. code-block:: python na = NewAnalysis(u.select_atoms('name CA'), 35).run() print(na.result) """ def __init__(self, trajectory, start=None, stop=None, step=None, verbose=None, quiet=None): """ Parameters ---------- trajectory : mda.Reader A trajectory Reader start : int, optional start frame of analysis stop : int, optional stop frame of analysis step : int, optional number of frames to skip between each analysed frame verbose : bool, optional Turn on verbosity """ # @TODO - this needs to be fixed, _verbose is not supported in current MDAnalysi, also see below #self._verbose = _set_verbose(verbose, quiet, default=False) self._verbose = True self._quiet = not self._verbose self._setup_frames(trajectory, start, stop, step) def _setup_frames(self, trajectory, start=None, stop=None, step=None): """ Pass a Reader object and define the desired iteration pattern through the trajectory Parameters ---------- trajectory : mda.Reader A trajectory Reader start : int, optional start frame of analysis stop : int, optional stop frame of analysis step : int, optional number of frames to skip between each analysed frame """ self._trajectory = trajectory start, stop, step = trajectory.check_slice_indices(start, stop, step) self.start = start self.stop = stop self.step = step self.n_frames = len(list(range(start, stop, step))) interval = int(self.n_frames // 100) if interval == 0: interval = 1 # ensure _verbose is set when __init__ wasn't called, this is to not # break pre 0.16.0 API usage of AnalysisBase if not hasattr(self, '_verbose'): # @TODO - this needs to be fixed, _verbose is not supported in current MDAnalysis so here just commented out ''' if hasattr(self, '_quiet'): # Here, we are in the odd case where a children class defined # self._quiet without going through AnalysisBase.__init__. warnings.warn("The *_quiet* attribute of analyses is " "deprecated (from 0.16)use *_verbose* instead.", DeprecationWarning) self._verbose = not self._quiet else: self._verbose = True self._quiet = not self._verbose ''' #self._pm = ProgressBar(self.n_frames if self.n_frames else 1, # interval=interval, verbose=self._verbose) def _single_frame(self): """Calculate data from a single frame of trajectory Don't worry about normalising, just deal with a single frame. """ raise NotImplementedError("Only implemented in child classes") def _prepare(self): """Set things up before the analysis loop begins""" pass def _conclude(self): """Finalise the results you've gathered. Called at the end of the run() method to finish everything up. """ pass def run(self): """Perform the calculation""" # logger.info("Starting preparation") self._prepare() # for i, ts in enumerate( # self._trajectory[self.start:self.stop:self.step]): # self._frame_index = i # self._ts = ts # logger.info("--> Doing frame {} of {}".format(i+1, self.n_frames)) # self._single_frame() # self._pm.echo(self._frame_index) # logger.info("Finishing up") self._frame_index = 0 # this would work: "self._ts = self._trajectory[0]" but it takes too much time for the copy, so just use self.g1 in the method self._single_frame() #self._pm.echo(self._frame_index) self._conclude() return self class AnalysisFromFunction(AnalysisBase): """ Create an analysis from a function working on AtomGroups Attributes ---------- results : ndarray results of calculation are stored after call to ``run`` Example ------- >>> def rotation_matrix(mobile, ref): >>> return mda.analysis.align.rotation_matrix(mobile, ref)[0] >>> rot = AnalysisFromFunction(rotation_matrix, trajectory, mobile, ref).run() >>> print(rot.results) Raises ------ ValueError : if ``function`` has the same kwargs as ``BaseAnalysis`` """ def __init__(self, function, trajectory=None, *args, **kwargs): """Parameters ---------- function : callable function to evaluate at each frame trajectory : mda.coordinates.Reader (optional) trajectory to iterate over. If ``None`` the first AtomGroup found in args and kwargs is used as a source for the trajectory. *args : list arguments for ``function`` **kwargs : dict arugments for ``function`` and ``AnalysisBase`` """ if (trajectory is not None) and (not isinstance( trajectory, coordinates.base.ProtoReader)): args = args + (trajectory,) trajectory = None if trajectory is None: for arg in args: if isinstance(arg, AtomGroup): trajectory = arg.universe.trajectory # when we still didn't find anything if trajectory is None: for arg in six.itervalues(kwargs): if isinstance(arg, AtomGroup): trajectory = arg.universe.trajectory if trajectory is None: raise ValueError("Couldn't find a trajectory") self.function = function self.args = args base_kwargs, self.kwargs = _filter_baseanalysis_kwargs(self.function, kwargs) super(AnalysisFromFunction, self).__init__(trajectory, **base_kwargs) def _prepare(self): self.results = [] def _single_frame(self): self.results.append(self.function(*self.args, **self.kwargs)) def _conclude(self): self.results = np.asarray(self.results) def analysis_class(function): """ Transform a function operating on a single frame to an analysis class For an usage in a library we recommend the following style: >>> def rotation_matrix(mobile, ref): >>> return mda.analysis.align.rotation_matrix(mobile, ref)[0] >>> RotationMatrix = analysis_class(rotation_matrix) It can also be used as a decorator: >>> @analysis_class >>> def RotationMatrix(mobile, ref): >>> return mda.analysis.align.rotation_matrix(mobile, ref)[0] >>> rot = RotationMatrix(u.trajectory, mobile, ref, step=2).run() >>> print(rot.results) """ class WrapperClass(AnalysisFromFunction): def __init__(self, trajectory=None, *args, **kwargs): super(WrapperClass, self).__init__(function, trajectory, *args, **kwargs) return WrapperClass def _filter_baseanalysis_kwargs(function, kwargs): """ create two dictionaries with kwargs separated for function and AnalysisBase Parameters ---------- function : callable function to be called kwargs : dict keyword argument dictionary Returns ------- base_args : dict dictionary of AnalysisBase kwargs kwargs : dict kwargs without AnalysisBase kwargs Raises ------ ValueError : if ``function`` has the same kwargs as ``BaseAnalysis`` """ base_argspec = inspect.getargspec(AnalysisBase.__init__) n_base_defaults = len(base_argspec.defaults) base_kwargs = {name: val for name, val in zip(base_argspec.args[-n_base_defaults:], base_argspec.defaults)} argspec = inspect.getargspec(function) for base_kw in six.iterkeys(base_kwargs): if base_kw in argspec.args: raise ValueError( "argument name '{}' clashes with AnalysisBase argument." "Now allowed are: {}".format(base_kw, list(base_kwargs.keys()))) base_args = {} for argname, default in six.iteritems(base_kwargs): base_args[argname] = kwargs.pop(argname, default) return base_args, kwargs
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HaroldMills/Vesper
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https://github.com/HaroldMills/Vesper
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ec92fe5231f54336499db189a3bbc6cb08a19e61
refs/heads/master
2023-07-05T22:45:27.316498
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2023-02-14T16:09:19
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""" Script that creates CSV files from a directory of USNO tables. The script assumes that every file in the input directory is either a rise/set table or an altitude/azimuth table. It creates two CSV files, one named "USNO Rise Set Data.csv" and the other named "USNO Altitude Azimuth Data.csv". """ import csv import datetime import os import vesper.ephem.usno_table_utils as utils _DATA_DIR_PATH = r'C:\Users\Harold\Desktop\NFC\Data' _TABLES_DIR_PATH = os.path.join(_DATA_DIR_PATH, 'USNO Tables Test') _RS_CSV_FILE_PATH = os.path.join(_DATA_DIR_PATH, 'USNO Rise Set Data.csv') _AA_CSV_FILE_PATH = os.path.join( _DATA_DIR_PATH, 'USNO Altitude Azimuth Data.csv') _RS_COLUMN_NAMES = ( 'Latitude', 'Longitude', 'Local Date', 'Event', 'UTC Time') _AA_COLUMN_NAMES = ( 'Latitude', 'Longitude', 'UTC Time', 'Body', 'Altitude', 'Azimuth', 'Illumination') _RS_TABLE_TYPES = frozenset(utils.RISE_SET_TABLE_TYPES) _AA_TABLE_TYPES = frozenset(utils.ALTITUDE_AZIMUTH_TABLE_TYPES) def _main(): rs_file, rs_writer = _open_csv_file(_RS_CSV_FILE_PATH, _RS_COLUMN_NAMES) aa_file, aa_writer = _open_csv_file(_AA_CSV_FILE_PATH, _AA_COLUMN_NAMES) for (dir_path, _, file_names) in os.walk(_TABLES_DIR_PATH): for file_name in file_names: table = _create_table(dir_path, file_name) if table.type in _RS_TABLE_TYPES: _append_rs_table_data(table, rs_writer) else: _append_aa_table_data(table, aa_writer) print(file_name, table.type) rs_file.close() aa_file.close() def _open_csv_file(file_path, column_names): file_ = open(file_path, 'w', newline='') writer = csv.writer(file_) writer.writerow(column_names) return (file_, writer) def _create_table(dir_path, file_name): file_name_table_type = file_name.split('_')[0] table_type = utils.get_table_type(file_name_table_type) table_class = utils.get_table_class(table_type) file_path = os.path.join(dir_path, file_name) with open(file_path, 'r') as file_: table_text = file_.read() return table_class(table_text) _RISE_EVENTS = { 'Sunrise/Sunset': 'Sunrise', 'Moonrise/Moonset': 'Moonrise', 'Civil Twilight': 'Civil Dawn', 'Nautical Twilight': 'Nautical Dawn', 'Astronomical Twilight': 'Astronomical Dawn' } _SET_EVENTS = { 'Sunrise/Sunset': 'Sunset', 'Moonrise/Moonset': 'Moonset', 'Civil Twilight': 'Civil Dusk', 'Nautical Twilight': 'Nautical Dusk', 'Astronomical Twilight': 'Astronomical Dusk' } def _append_rs_table_data(table, writer): rows = [] lat = table.lat lon = table.lon event = _RISE_EVENTS[table.type] _append_rows(rows, lat, lon, event, table.rising_times) event = _SET_EVENTS[table.type] _append_rows(rows, lat, lon, event, table.setting_times) writer.writerows(rows) def _append_rows(rows, lat, lon, event, times): for dt in times: local_dt = _get_naive_local_time(dt, lon) date = local_dt.date() time = dt.strftime('%Y-%m-%d %H:%M') rows.append((lat, lon, date, event, time)) def _get_naive_local_time(time, lon): naive_time = time.replace(tzinfo=None) utc_offset = datetime.timedelta(hours=lon * 24. / 360.) return naive_time + utc_offset def _append_aa_table_data(table, writer): rows = [] lat = table.lat lon = table.lon body = table.body for data in table.data: if table.body == 'Sun': (time, alt, az) = data illumination = None else: (time, alt, az, illumination) = data time = time.strftime('%Y-%m-%d %H:%M') rows.append((lat, lon, time, body, alt, az, illumination)) writer.writerows(rows) if __name__ == '__main__': _main()
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FLHCoLtd/hassio-ferqo-cc
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ed152fb8573920c9ed40cfaab341c8af835c57f8
2dd568a0cf5a5aff965c2526c3488f4c148313f4
/scene.py
3b3c4fbf1f6482ffafd083c31ef9f227ffd96285
[]
no_license
https://github.com/FLHCoLtd/hassio-ferqo-cc
d44fa423a5c6a77abbdf98b92016ad2deadb4e2c
e1049be281551a503db90b0a431e585162c76a21
refs/heads/main
2023-04-09T17:11:29.885023
2021-04-14T01:40:53
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from homeassistant.helpers.entity import Entity import logging from .const import DOMAIN async def async_setup_platform(hass, config, async_add_entities, discovery_info=None): """Add the Sonoff Sensor entities""" # for device in hass.data[DOMAIN].get_devices(force_update = False): # as far as i know only 1-switch devices seem to have sensor-like capabilities async def loadingentities(): entities = [] for device in hass.data[DOMAIN].getEntitiesType("s"): CC_device = FerqoCCSensor(device, hass) entities.append(CC_device) return entities entities = await loadingentities() if len(entities): async_add_entities(entities, update_before_add=False) return True class FerqoCCSensor(Entity): """Representation of a sensor.""" def __init__(self, CC_device, hass): self.hub = hass.data[DOMAIN] self.CC_device = CC_device self.sensorType = CC_device["sensorType"] self._state = CC_device[str(CC_device["sensorType"])] self.unit = CC_device["sensorUnit"] self._name = "Ferqo." + CC_device["name"] self.node_id = CC_device["node_id"] @property def name(self): """Return the name of the device.""" return self._name @property def state(self): """Return the state of the sensor.""" return self._state @property def unit_of_measurement(self): """Return the unit of measurement.""" return self.unit async def async_update(self): """Retrieve latest state.""" List = self.hub.getEntitiesType("sensor") self.CC_List = List for i in range(len(self.CC_List)): if (self.node_id == self.CC_List[i]["node_id"]): if (self.sensorType == self.CC_List[i]["sensorType"]): self._state = self.CC_List[i][str(self.CC_List[i]["sensorType"])] self._name = "Ferqo." + self.CC_List[i]["name"]
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mcneel/compute.rhino3d
876,173,378,174
9f13c9a93c8c0459ac12b7052638689201e86c75
91d5349ef6a8259ba0a551e70e37cb29d6817652
/src/ghhops-server-py/ghhops_server/params.py
02196b3b5ef5eaa57cd0a9efab2e8e90a37d26e2
[ "MIT" ]
permissive
https://github.com/mcneel/compute.rhino3d
641dc3e88f53d892f6b75ce14924a752ff949e5b
0acf93ae9aa520fbbfa64ee97f77088a9005f3d4
refs/heads/7.x
2023-08-28T03:41:04.618336
2023-08-23T18:11:47
2023-08-23T18:11:47
119,090,587
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2018-01-26T18:53:54
2023-08-05T19:45:28
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"""Hops Component Parameter wrappers""" import json from enum import Enum import inspect from ghhops_server.base import _HopsEncoder from ghhops_server.logger import hlogger from pprint import pprint __all__ = ( "HopsParamAccess", # "HopsArc", "HopsBoolean", # "HopsBox", "HopsBrep", "HopsCircle", # "HopsColour", # "HopsComplex" # "HopsCulture", "HopsCurve", # "HopsField", # "HopsFilePath", # "HopsGeometry", "HopsInteger", # "HopsInterval", # "HopsInterval2D" "HopsLine", # "HopsMatrix", "HopsMesh", # "HopsMeshFace", "HopsNumber", "HopsPlane", "HopsPoint", # "HopsRectangle", "HopsString", # "HopsStructurePath", "HopsSubD", "HopsSurface", # "HopsTime", # "HopsTransform", "HopsVector", ) RHINO = None RHINO_FROMJSON = None RHINO_TOJSON = None RHINO_GEOM = None CONVERT_VALUE = None def _init_rhinoinside(): global RHINO global RHINO_FROMJSON global RHINO_TOJSON global RHINO_GEOM global CONVERT_VALUE # initialize with Rhino.Inside Cpython ========== import clr clr.AddReference("System.Collections") clr.AddReference("Newtonsoft.Json.Rhino") import System import Newtonsoft.Json as NJ from System.Collections.Generic import Dictionary def from_json(json_obj): """Convert to RhinoCommon from json""" data_dict = Dictionary[str, str]() for k, v in json_obj.items(): data_dict[k] = str(v) return RHINO.Runtime.CommonObject.FromJSON(data_dict) def to_json(value): """Convert RhinoCommon object to json""" return NJ.JsonConvert.SerializeObject(value) def convert_value(value): # FIXME: more value types probably need to be handled if isinstance(value, bool): return System.Boolean(value) elif isinstance(value, int): return System.Int32(value) elif isinstance(value, float): return System.Double(value) elif isinstance(value, str): return System.String(value) return value RHINO_FROMJSON = from_json RHINO_TOJSON = to_json import Rhino RHINO = Rhino RHINO_GEOM = Rhino.Geometry CONVERT_VALUE = convert_value def _init_rhino3dm(): global RHINO global RHINO_FROMJSON global RHINO_TOJSON global RHINO_GEOM global CONVERT_VALUE import rhino3dm def from_json(json_obj): """Convert to rhino3dm from json""" return rhino3dm.CommonObject.Decode(json_obj) def to_json(value): """Convert rhino3dm object to json""" return json.dumps(value, cls=_HopsEncoder) def convert_value(value): return value RHINO_FROMJSON = from_json RHINO_TOJSON = to_json RHINO_GEOM = rhino3dm CONVERT_VALUE = convert_value class HopsParamAccess(Enum): """GH Item Access""" ITEM = 0 LIST = 1 TREE = 2 # TODO: # - params can have icons too # cast methods class _GHParam: coercers = [] param_type = None result_type = None def __init__( self, name, nickname=None, desc=None, access: HopsParamAccess = HopsParamAccess.ITEM, optional=False, default=None, ): self.name = name self.nickname = nickname self.description = desc self.access: HopsParamAccess = access or HopsParamAccess.ITEM self.optional = optional self.default = default or inspect.Parameter.empty def _coerce_value(self, param_type, param_data): # get data as dict data = json.loads(param_data) # parse data if isinstance(self.coercers, dict): coercer = self.coercers.get(param_type, None) if coercer: return coercer(data) elif param_type.startswith("Rhino.Geometry."): return RHINO_FROMJSON(data) return param_data def encode(self): """Parameter serializer""" param_def = { "Name": self.name, "Nickname": self.nickname, "Description": self.description, "ParamType": self.param_type, "ResultType": self.result_type, "AtLeast": 1, } if HopsParamAccess.ITEM == self.access: param_def["AtMost"] = 1 if HopsParamAccess.LIST == self.access: param_def["AtMost"] = 2147483647 # Max 32 bit integer value if HopsParamAccess.TREE == self.access: param_def["AtLeast"] = -1 param_def["AtMost"] = -1 if self.default != inspect.Parameter.empty: param_def["Default"] = self.default return param_def def from_input(self, input_data): """Extract parameter data from serialized input""" pprint(input_data) if self.access == HopsParamAccess.TREE: paths = input_data["InnerTree"] tree = {} for k, v in paths.items(): data = [] for param_value_item in v: param_type = param_value_item["type"] param_value = param_value_item["data"] data.append(self._coerce_value(param_type, param_value)) tree[k] = data return tree data = [] for param_value_item in input_data["InnerTree"]["{0}"]: param_type = param_value_item["type"] param_value = param_value_item["data"] data.append(self._coerce_value(param_type, param_value)) if self.access == HopsParamAccess.ITEM: return data[0] return data def from_result(self, value): """Serialize parameter with given value for output""" if self.access == HopsParamAccess.TREE and isinstance(value, dict): tree = {} for key in value.keys(): branch_data = [ { "type": self.result_type, "data": RHINO_TOJSON(CONVERT_VALUE(v)), } for v in value[key] ] tree[key] = branch_data output = { "ParamName": self.name, "InnerTree": tree, } return output if not isinstance(value, tuple) and not isinstance(value, list): value = (value,) output_list = [ {"type": self.result_type, "data": RHINO_TOJSON(CONVERT_VALUE(v))} for v in value ] output = { "ParamName": self.name, "InnerTree": {"0": output_list}, } return output class HopsBoolean(_GHParam): """Wrapper for GH_Boolean""" param_type = "Boolean" result_type = "System.Boolean" coercers = {"System.Boolean": lambda b: bool(b)} class HopsBrep(_GHParam): """Wrapper for GH Brep""" param_type = "Brep" result_type = "Rhino.Geometry.Brep" class HopsCircle(_GHParam): """Wrapper for GH_Circle""" param_type = "Circle" result_type = "Rhino.Geometry.Circle" coercers = { "Rhino.Geometry.Circle": lambda d: HopsCircle._make_circle( HopsPlane._make_plane( d["Plane"]["Origin"], d["Plane"]["XAxis"], d["Plane"]["YAxis"] ), d["Radius"], ) } @staticmethod def _make_circle(p, r): circle = RHINO_GEOM.Circle(r) circle.Plane = p return circle class HopsCurve(_GHParam): """Wrapper for GH Curve""" param_type = "Curve" result_type = "Rhino.Geometry.Curve" class HopsInteger(_GHParam): """Wrapper for GH_Integer""" param_type = "Integer" result_type = "System.Int32" coercers = {"System.Int32": lambda i: int(i)} class HopsLine(_GHParam): """Wrapper for GH_Line""" param_type = "Line" result_type = "Rhino.Geometry.Line" coercers = { "Rhino.Geometry.Line": lambda l: RHINO_GEOM.Line( HopsLine._make_point(l["From"]), HopsLine._make_point(l["To"]) ) } @staticmethod def _make_point(a): return RHINO_GEOM.Point3d(a["X"], a["Y"], a["Z"]) class HopsMesh(_GHParam): """Wrapper for GH Mesh""" param_type = "Mesh" result_type = "Rhino.Geometry.Mesh" class HopsNumber(_GHParam): """Wrapper for GH Number""" param_type = "Number" result_type = "System.Double" coercers = { "System.Double": lambda d: float(d), } class HopsPlane(_GHParam): """Wrapper for GH_Plane""" param_type = "Plane" result_type = "Rhino.Geometry.Plane" coercers = { "Rhino.Geometry.Plane": lambda p: HopsPlane._make_plane( p["Origin"], p["XAxis"], p["YAxis"] ) } @staticmethod def _make_plane(o, x, y): return RHINO_GEOM.Plane( RHINO_GEOM.Point3d(o["X"], o["Y"], o["Z"]), RHINO_GEOM.Vector3d(x["X"], x["Y"], x["Z"]), RHINO_GEOM.Vector3d(y["X"], y["Y"], y["Z"]), ) class HopsPoint(_GHParam): """Wrapper for GH Point""" param_type = "Point" result_type = "Rhino.Geometry.Point3d" coercers = { "Rhino.Geometry.Point2d": lambda d: RHINO_GEOM.Point2d(d["X"], d["Y"]), "Rhino.Geometry.Point3d": lambda d: RHINO_GEOM.Point3d( d["X"], d["Y"], d["Z"] ), "Rhino.Geometry.Vector3d": lambda d: RHINO_GEOM.Vector3d( d["X"], d["Y"], d["Z"] ), } class HopsString(_GHParam): """Wrapper for GH_String""" param_type = "Text" result_type = "System.String" coercers = {"System.String": lambda s: s} class HopsSubD(_GHParam): """Wrapper for GH SubD""" param_type = "SubD" result_type = "Rhino.Geometry.SubD" class HopsSurface(_GHParam): """Wrapper for GH Surface""" param_type = "Surface" result_type = "Rhino.Geometry.Brep" class HopsVector(_GHParam): """Wrapper for GH Vector""" param_type = "Vector" result_type = "Rhino.Geometry.Vector3d" coercers = { "Rhino.Geometry.Point2d": lambda d: RHINO_GEOM.Point2d(d["X"], d["Y"]), "Rhino.Geometry.Point3d": lambda d: RHINO_GEOM.Point3d( d["X"], d["Y"], d["Z"] ), "Rhino.Geometry.Vector3d": lambda d: RHINO_GEOM.Vector3d( d["X"], d["Y"], d["Z"] ), }
UTF-8
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params.py
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0.557357
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jdariasl/prueba
2,473,901,198,981
a92bebe8d0013a6b55c9cbc992b33ddfff4d5f91
20ac3af659da6f12d359fd889fd0e80565d2219f
/_build/jupyter_execute/content/Clase 08 - Modelos de Mezclas de Gausianas.py
7983439718e61f33c238a7cb7d69c98f1914051f
[]
no_license
https://github.com/jdariasl/prueba
a1b07a64f71325bd1653d0be839f7e265451a0b6
c920bf3ca11dc4ddaebed37d4dc5fa038b614a61
refs/heads/master
2022-12-23T12:39:13.729297
2020-10-03T02:14:17
2020-10-03T02:14:17
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#!/usr/bin/env python # coding: utf-8 # # Modelos de Mezcla de Funciones Gaussianas # ### Julián D. Arias Londoño # # Profesor Asociado # Departamento de Ingeniería de Sistemas # Universidad de Antioquia, Medellín, Colombia # julian.ariasl@udea.edu.co # Hasta el momento hemos visto que existen dos posibles aproximaciones al problema de clasificación: # <li>Encontrar una función que represente la frontera de separación entre dos clases</li> # <li>Separar las muestras por clases y estimar una función de densidad de probabilidad (fdp) por cada una de ellas</li> # Los modelos pertenecientes a la primera aproximación se conocen como <b>Discriminativos</b>, debido a que para el ajuste de la frontera se utilizan las muestras de las dos clases al mismo tiempo y el criterio de ajuste del modelo está directamente relacionado con disminuir el error de clasificación. # Los modelos pertenecientes a la segunda aproximación se conocen como <b>Generativos</b> debido a que los modelos se enfocan principalmente en estimar correctamente la fdp de las muestras de cada clase (por ejemplo maximizar la verosimilitud de los datos y el modelo) y no necesariamente en minimizar el error de clasificación. Una vez se tiene un modelo de densidad de probabilidad éste se puede usar para "generar" nuevas muestras, es decir se puede muestrear la fdp y obtener muestras de la misma distribución, por esa razón recibe el nombre de generativos. # In[1]: get_ipython().run_line_magic('matplotlib', 'inline') # In[2]: import numpy as np import math import matplotlib.pyplot as plt from pylab import * # Supongamos un problema de clasificación en el cual las muestras se distribuyen de la siguiente manera: # In[3]: x1 = np.random.rand(2,50) x2 = np.random.rand(2,50) + 2 x3 = np.random.rand(2,50) + np.tile([[-1],[2]], (1, 50)) #np.tile Es equivalente a repmat en matlab x4 = np.random.rand(2,50) + np.tile([[3],[1]], (1, 50)) XC1 = np.concatenate((x1,x3),axis=1) XC2 = np.concatenate((x2,x4),axis=1) plt.title('Espacio de caracteristicas', fontsize=14) plt.xlabel('Caracteristica 1') plt.ylabel('Caracteristica 2') plt.scatter(XC1[0,:], XC1[1,:]) plt.scatter(XC2[0,:], XC2[1,:],color='red') # Si nuestro deseo es usar un clasificador basado en la fdp de cada clase, y por simplicidad decidimos usar un clasificador por función discriminante Gaussiana, es decir, ajustar una función de densidad Gausiana para cada una de las clases, el resultado obtenido sería el siguiente: # In[4]: from matplotlib.patches import Ellipse def plot_ellipse(ax, mu ,sigma): vals, vecs = np.linalg.eigh(sigma) x , y = vecs[:, 0] theta = np.degrees(np.arctan2(y,x)) w,h = 4* np.sqrt(vals) ax.tick_params(axis='both',which='major',labelsize=20) ellipse = Ellipse(mu,w,h,theta,color='k') ellipse.set_alpha(0.5) ax.add_artist(ellipse) # In[20]: fig, ax = plt.subplots(figsize=(5,5)) ax.set_title('Espacio de caracteristicas', fontsize=14) ax.set_xlabel('Caracteristica 1') ax.set_ylabel('Caracteristica 2') ax.scatter(XC1[0,:], XC1[1,:]) ax.scatter(XC2[0,:], XC2[1,:],color='red') ax.set_ylim(-1, 4) ax.set_xlim(-1.5, 5) plot_ellipse(ax, np.mean(XC1, axis=1) ,np.cov(XC1)) plot_ellipse(ax, np.mean(XC2, axis=1) ,np.cov(XC2)) # En la figura anterior, cada una de las elipses representa la fdp obtenida para cada una de las clases. El centro de la elipse corresponde a su media y la línea corresponde a dos veces la desviación estándar en cada sentido. Como podemos observar en la figura anterior los modelos se ajustan muy mal debido a que las muestras de cada clase no están agrupadas en un solo conglomerado (cluster). En realidad cada clase está a su vez dividida en varios grupos, y lo que necesitamos es un modelo que pueda representar correctamente esos diferentes grupos. # In[22]: fig, ax = plt.subplots(figsize=(5,5)) ax.set_title('Espacio de caracteristicas', fontsize=14) ax.set_xlabel('Caracteristica 1') ax.set_ylabel('Caracteristica 2') ax.scatter(XC1[0,:], XC1[1,:]) ax.scatter(XC2[0,:], XC2[1,:],color='red') ax.set_ylim(-1, 4) ax.set_xlim(-1.5, 5) plot_ellipse(ax, np.mean(x1, axis=1) ,np.cov(x1)) plot_ellipse(ax, np.mean(x2, axis=1) ,np.cov(x2)) plot_ellipse(ax, np.mean(x3, axis=1) ,np.cov(x3)) plot_ellipse(ax, np.mean(x4, axis=1) ,np.cov(x4)) # Cada conglomerado estaría entonces representado por un vector de medias ${\bf{\mu}}_{ij}$ (clase $i$, conglomerado $j$) y una matriz de covarianza $\Sigma _{ij}$. Sin embargo en este punto surgen varias preguntas que debemos responder: # <li> ¿Qué forma tendría la función de densidad de probabilidad de toda una clase? </li> # <li> ¿En cuántos conglomerados podrían estar agrupadas las muestras? </li> # <li> ¿Cómo determinar cuáles muestras usar para estimar la media y la covarianza del primer conglomerado y cuáles para el segundo? </li> # <li> ¿Cómo se determina el número de conglomerados si no se pueden visualizar las muestras porque el número de características es mayor a 3? </li> # Este tipo de modelos se conocen como <b> Modelos de Mezclas de Funciones Gaussianas</b> (en inglés <b> Gaussian Mixture Models - GMM</b>), y su forma general está dada por la siguiente función: # $$p({\bf{x}}|\Theta_i) = \sum_{j=1}^{M} \omega_{ij} \mathcal{N}({\bf{x}}|\mu_{ij},\Sigma_{ij})$$ # In[98]: def GaussProb(X,medias,covars,pesos): M = len(covars) N,d = X.shape Pprob = np.zeros(N).reshape(N,1) precision = [] for i in range(M): precision.append(np.linalg.inv(covars[i])) for j in range(N): prob = 0 for i in range(M): tem = (X[j,:]-medias[i]) tem1 = np.dot(np.dot(tem,precision[i]),tem.T) tem2 = 1/((math.pi**(d/2))*(np.linalg.det(covars[i]))**(0.5)) prob+=pesos[i]*tem2*np.exp(-0.5*tem1) Pprob[j] = prob return Pprob # donde $M$ es el número de conglomerados en los cuales se van a dividir las muestras, $\omega_{ij}$ son pesos que se le asignan a cada conglomerado, es decir, dan una idea de que tan representativo es el conglomerado dentro de la distribución completa de una clase; deben cumplir la restricción: $\sum_{j=1}^{M} \omega_{ij} = 1$, es decir que la suma de los pesos del modelo GMM para una clase debe ser igual a 1. # El número de conglomerados ($M$) en los cuales se subdivide una clase, es un hiperparámetro del modelo que debe ser ajustado. A partir de las figuras anteriores es fácil determinar que ambas clases están divididas en dos conglomerados, sin embargo, en la gran mayoría de los casos el número de características con las que se cuenta es mucho mayor a 3, razón por la cual no se puede definir el valor de $M$ de manera visual. La forma habitual es utilizar un procedimiento de validación cruzada para hayar el mejor valor de $M$, similar a como debe hacerse para encontrar el mejor valor de $K$ en el modelo de K-vécinos más cercanos. # El problema de aprendizaje en este caso, corresponde a estimar el conjunto de parámetros $\Theta$ para cada una de las clases, dada un conjunto de muestras de entrenamiento $\mathcal{D} = \left\lbrace \left( {\bf{x}}_k, y_k \right) \right\rbrace _{k=1} ^{N}$. Del total de muestras de entrenamiento, $N_i$ pertenecen a la clase $i$, es decir $\sum_{i=1}^{\mathcal{C}} N_i = N$, donde $\mathcal{C}$ es el número de clases en el conjunto de muestras de entrenamiento ($y_k$ puede tomar valores $1,2,...,\mathcal{C}$), es decir que el modelo de cada clase se ajusta únicamentente utilizando las $N_i$ muestras pertenecientes a dicha clase. # Como el entrenamiento de un modelo GMM corresponde al ajuste de una fdp, el criterio que nuevamente puede ser de utilidad es el criterio de máxima verosimilitud. Asumiendo entonces que las muestras de entrenamiento de la clase $i$ son i.i.d., podemos expresar el problema de aprendizaje como: # $$\mathop {\max }\limits_\Theta \log \prod\limits_{k = 1}^{N_i} {p\left( {{\bf{x}}_k |\Theta_i } \right)}$$ # reemplazando la forma general del modelo GMM para la clase $i$: # $$ = \mathop {\max }\limits_\Theta \log \prod\limits_{k = 1}^{N_i} {\sum\limits_{j = 1}^M {w_{ij}{\mathcal N}\left( {{\bf{x}}_k|\mu _{ij} ,\Sigma _{ij} } \right)} }$$ # $$= \mathop {\max }\limits_\Theta \sum\limits_{k = 1}^{N_i} \log {\sum\limits_{j = 1}^M {w_{ij} # {\mathcal N}\left( {{\bf{x}}_k|\mu _{ij} ,\Sigma _{ij} } \right)} }$$ # Para encontrar los parámetros que maximizan la función de verosimilitud debemos derivar con respecto a cada uno e igualar a cero. Derivando con respecto a $\mu_{il}$ tenemos: # $$ 0 = - \sum_{k=1}^{N_i} \frac{{w_{il}}\mathcal{N}({\bf{x}}_k|\mu _{il} ,\Sigma _{il})}{\sum _{j} w _{ij} \mathcal{N}({\bf{x}}_k|\mu _{ij} ,\Sigma _{ij})} \Sigma _{il}({\bf{x}}_k- \mu _{il})$$ # Si observamos con detenimiento el término # $$\gamma_{kl} = \frac{{w_{il}}\mathcal{N}({\bf{x}}_k|\mu _{il} ,\Sigma _{il})}{\sum _{j} w _{ij} \mathcal{N}({\bf{x}}_k|\mu _{ij} ,\Sigma _{ij})}$$ # Mide la probabilidad de que la muestra ${\bf{x}}_k$ sea generada por el conglomerado $l$ dentro de la clase. A $\gamma _{kl}$ también se le conoce como la responsabilidad de la componente $l$ en la "explicación" de la observación de la muestra ${\bf{x}}_k$. # Reordenando la derivada de la función de verosimilitud que obtuvimos anteriormente, se obtiene: # # $$ \hat \mu _{il} = \frac{1}{n_l} \sum_{k=1}^{N_i} \gamma _{kl} {\bf{x}}_k \;\;\;(*)$$ # donde $n_l = \sum_{k=1}^{N_i} \gamma _{kl}$ # Teniendo en cuenta que $\gamma _{kl}$ me da una idea del "peso" que tiene la componente $l$ del modelo para generar la muestra $k$, $n_l$ puede entenderse como el peso total de la componente $l$ en el modelo (es una suma para todas las muestras de entrenamiento), o el número total de puntos asignados al conglomerado $l$. # De manera similar se puede derivar con respecto a $\Sigma_{il}$ y obtener: # $$ \hat \Sigma_{il} = \frac{1}{n_l} \sum_{k=1}^{N_i} \gamma _{kl}({\bf{x}}_k - \mu _{il}) ({\bf{x}}_k - \mu _{il})^{T} \;\;\; (* *)$$ # que es equivalente a la forma de estimación de la matriz de covarianza en el caso de una sola componente, pero sopesando cada muestra con respecto a la responsabilidad del conglomerado bajo análisis. # Finalmente para la estimación de $w_{ij}$ se hace de manera similar a los dos casos anteriores, pero teniendo en cuenta que los pesos $w$ deben cumplir la restricción estocástica. La función a maximizar en este caso sería: # $$\mathop {\max }\limits_\Theta \sum\limits_{k = 1}^{N_i} \log {\sum\limits_{j = 1}^M {w_{ij} # {\mathcal N}\left( {{\bf{x}}_k|\mu _{ij} ,\Sigma _{ij} } \right)} } + \lambda \left(\sum _{j=1}^{M} w _{ij} - 1\right)$$ # donde $\lambda$ es un multiplicador de Lagrange. Derivando e igualando a cero se obtiene: # $$ 0 = \sum_{k=1}^{N_i} \frac{\mathcal{N}({\bf{x}}_k|\mu _{il} ,\Sigma _{il})}{\sum _{j} w _{ij} \mathcal{N}({\bf{x}}_k|\mu _{ij} ,\Sigma _{ij})} + \lambda$$ # Para poder encontrar el valor de $\lambda$ se puede multiplicar a ambos lados de la ecuación anterior por $w_{il}$ # $$w_{il}\lambda = -\sum_{k=1}^{N_i} \frac{w_{il} \mathcal{N}({\bf{x}}_k|\mu _{il} ,\Sigma _{il})}{\sum _{j} w _{ij} \mathcal{N}({\bf{x}}_k|\mu _{ij} ,\Sigma _{ij})}$$ # sumando a ambos lados con respecto a $l$, fácilmente obtendremos que el valor de $\lambda = -N_i$. Por consiguiente reemplazando el valor de $\lambda$ en la ecuación anterior obtendremos: # $$\hat w_{il} = \frac{n_l}{n_i} \;\; (** *) $$ # Es importante resaltar que las ecuaciones marcadas con $(*)$ no constituyen una forma cerrada para obtener los valores de los parámetros del modelo, porque todas ellas dependen del valor de $\gamma_{kl}$ que a su vez depende, de una manera compleja, del valor de cada uno de los parámetros. Sin embargo, el resultado proporciona un esquema iterativo simple para encontrar una solución al problema de máxima verosimilitud. El algoritmo que implementa esta solución es conocido como el <b>Algoritmo de Esperanza y Maximización (EM) </b>. Los pasos del algoritmo son: # <li> Dar un valor inicial a cada uno de los parámetros del modelo </li> # <li> Paso E: Calcular el valor de $\gamma_{kl}$, note que $\gamma$ es en realidad una matriz que contiene un número de filas igual al número de muestras $N_i$ y un número de columnas igual al número de conglomerados $M$. </li> # <li> Paso M: Utilizar el valor de $\gamma$ para encontrar unos nuevos valores de los parámetros del modelo usando las ecuaciones $(*)$. </li> # <li> Repetir consecutivamente los pasos E y M hasta alcanzar convergencia. </li> # El algoritmo EM no garantiza la convergencia a un máximo global, pero si garantiza que en cada iteración (Repetición de los pasos E y M), la verosimilitud del modelo crece o permanece igual, pero no decrece. # Veamos un ejemplo. A continuación se van a generar una serie de valores aletorios unidimensionales y graficaremos el histograma para darnos una idea visual de la forma que tiene la distribución de probabilidad del conjunto de datos: # In[5]: from time import sleep from numpy import * from matplotlib.pylab import * x1 = np.random.normal(0, 2, 1000) x2 = np.random.normal(20, 4, 1000) x3 = np.random.normal(-20, 6, 1000) X = np.concatenate((x1,x2,x3),axis=0) Y = np.array(X)[np.newaxis] Y = Y.T hist(X,41, (-50,50)) show() # Aplicamos el algoritmo EM al conjunto de datos anteriores y veremos el resultado del algoritmo para diferentes iteraciones. # In[7]: get_ipython().run_line_magic('matplotlib', 'notebook') import time from sklearn.mixture import GaussianMixture xplt = np.linspace(-40, 40, 200) x1plt = np.array(xplt).reshape(200,1) fig, ax = plt.subplots(1,1) gmm = GaussianMixture(n_components=3, covariance_type='full', max_iter=1, verbose=0, verbose_interval=10, means_init=np.array([0,4,10]).reshape(3,1)) gmm.fit(Y) logprob = np.exp(gmm.score_samples(x1plt)) line1, = ax.plot(xplt,logprob) ax.set_ylim(0,0.08) for i in [1,3,7,10,20,50,200,500]: gmm = GaussianMixture(n_components=3, covariance_type='full', max_iter=i, verbose=0, verbose_interval=10, means_init=np.array([0,4,10]).reshape(3,1)) gmm.fit(Y) logprob = np.exp(gmm.score_samples(x1plt)) line1.set_ydata(logprob) fig.canvas.draw() fig.canvas.flush_events() time.sleep(.300) # In[8]: gmm.means_ # In[10]: gmm.covariances_ # En el próximo ejemplo se generarán una serie de muestras en dos dimensiones, a partir de un modelo GMM para el cual los valores de los parámetros se han ajustado de manera arbitraria. Posteriormente se usael algoritmo EM para a partir del conjunto de puntos generados, estima los valores de los parámetros del modelo. Al final podremos comparar que tanto se asemejan los parámetros encontrados por el algoritmo con respecto a los parámetros reales. # In[14]: get_ipython().run_line_magic('matplotlib', 'inline') mc = [0.4, 0.4, 0.2] # Mixing coefficients centroids = [ array([0,0]), array([3,3]), array([0,4]) ] ccov = [ array([[1,0.4],[0.4,1]]), diag((1,2)), diag((0.4,0.1)) ] # Generate samples from the gaussian mixture model x1 = np.random.multivariate_normal(centroids[0], ccov[0], 200) x2 = np.random.multivariate_normal(centroids[1], ccov[1], 200) x3 = np.random.multivariate_normal(centroids[2], ccov[2], 100) X = np.concatenate((x1,x2,x3),axis=0) fig, ax = plt.subplots(figsize=(5,5)) ax.plot(X[:,0], X[:,1], '.') n_components = 3 #Expectation-Maximization of Mixture of Gaussians gmm = GaussianMixture(n_components=n_components, covariance_type='full', max_iter=100, verbose=2, verbose_interval=1) gmm.fit(X) for i in range(n_components): plot_ellipse(ax,gmm.means_[i,:],gmm.covariances_[i,:,:].reshape(2,2)) ax.set_xlim(-3, 7) ax.set_ylim(-3, 7) ax.set_xticks(np.arange(-3,8,2)) ax.set_yticks(np.arange(-3,8,2)) # Comparemos los pesos $w$ puestos de manera arbitraria con los pesos hayados por el algoritmo # In[29]: print(gmm.weights_) # Los centros o medias hayados por el algoritmo # In[12]: print(gmm.means_) # Y las matrices de covarianza hayadas por el algoritmo # In[31]: print((gmm.covariances_)) # Covarianza diagonal # In[32]: #Expectation-Maximization of Mixture of Gaussians gmm = GaussianMixture(n_components=3, covariance_type='diag', max_iter=100, verbose=2, verbose_interval=1) gmm.fit(X) fig, ax = plt.subplots(figsize=(5,5)) ax = plt.subplot(111) ax.plot(X[:,0], X[:,1], '.') for i in range(3): plot_ellipse(ax,gmm.means_[i,:],np.diag(gmm.covariances_[i,:]).reshape(2,2)) ax.set_xlim(-3, 7) ax.set_ylim(-3, 7) ax.set_xticks(np.arange(-3,8,2)) ax.set_yticks(np.arange(-3,8,2)) # Covarianza esférica # In[33]: #Expectation-Maximization of Mixture of Gaussians gmm = GaussianMixture(n_components=3, covariance_type='spherical', max_iter=100, verbose=2, verbose_interval=1) gmm.fit(X) fig, ax = plt.subplots(figsize=(5,5)) ax = plt.subplot(111) ax.plot(X[:,0], X[:,1], '.') for i in range(3): plot_ellipse(ax,gmm.means_[i,:],np.identity(2)* gmm.covariances_[i]) ax.set_xlim(-3, 7) ax.set_ylim(-3, 7) ax.set_xticks(np.arange(-3,8,2)) ax.set_yticks(np.arange(-3,8,2)) # ------------------------------------------------------------------------------------------------------------------------------- [1] Bishop, C.M. Pattern Recognition and Machine Learning. Springer, 2006. # In[ ]:
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Clase 08 - Modelos de Mezclas de Gausianas.py
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silvanaolmedo/opencv
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/src/filters/affine.py
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import cv2 import numpy as np #Lee imagen gatito.jpg img = cv2.imread('gatito.jpg') #Muestra imagen gatito cv2.imshow("Original", img) #Define una alto y largo para la imagen height, width = img.shape[:2] img_sized = cv2.resize(img, (width/2, height/2)) cv2.imshow("Resized", img_sized) translation_matrix = np.float32([[1,0,70],[0,1,100]]) img_warpAff = cv2.warpAffine(img_sized, translation_matrix, (width/2, height/2)) cv2.imshow("Translation", img_warpAff) cv2.waitKey(0) cv2.destroyAllWindows()
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DaKSK/pythonSDA
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/data_structures/tree_exercise.py
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class TreeNode: def __init__(self, name, size=0, is_directory=True): self.data = {"name": name, "size": size, "directory": is_directory} self.parent = None self.children = [] def __repr__(self): return self.data["name"] def add_child(self, child): child.parent = self self.children.append(child) def get_level(self): level = 0 seeker = self.parent while seeker: level += 1 seeker = seeker.parent return level def visualize_tree(self): prefix = " " * self.get_level() if self.data["directory"]: print(f"{prefix}|__ {self} -dir") else: print(f"{prefix}|__ {self} -file size:{self.data['size']} bytes") if self.children: for child in self.children: child.visualize_tree() def get_size(self): if not self.children: return self.data["size"] else: return sum(self.data["size"] + child.get_size() for child in self.children) def build_tree(): # Building the tree from example5.png # Creating the root node and it's 2 children nodes root = TreeNode("Home") jakub = TreeNode("jakub") var = TreeNode("var") root.add_child(jakub) root.add_child(var) # Adding children to root.children jakub.add_child(TreeNode(".bashrc", size=50, is_directory=False)) jakub.add_child(TreeNode(".vimrc", size=100, is_directory=False)) jakub.add_child(TreeNode("blob", size=1023, is_directory=False)) # Creating the grandchild of root log = TreeNode("log") var.add_child(log) # Adding a grandchild to the child of root ("var") log.add_child(TreeNode("sys.log", size=10, is_directory=False)) return root if __name__ == "__main__": dirs = build_tree() dirs.visualize_tree() print("Home folder size is", dirs.get_size(), "bytes") jakub_node_index = dirs.children.index("jakub") jakub_folder = dirs.children[jakub_node_index] print("home/jakub/ size is", jakub_folder.get_size())
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ESA-VirES/ViRES-Server
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/vires/vires/processes/util/filters/range.py
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#------------------------------------------------------------------------------- # # Data filters - scalar and vector component range filters # # Authors: Martin Paces <martin.paces@eox.at> #------------------------------------------------------------------------------- # Copyright (C) 2016 EOX IT Services GmbH # # Permission is hereby granted, free of charge, to any person obtaining a copy # of this software and associated documentation files (the "Software"), to deal # in the Software without restriction, including without limitation the rights # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell # copies of the Software, and to permit persons to whom the Software is # furnished to do so, subject to the following conditions: # # The above copyright notice and this permission notice shall be included in all # copies of this Software or works derived from this Software. # # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN # THE SOFTWARE. #------------------------------------------------------------------------------- # pylint: disable=too-many-arguments from logging import getLogger, LoggerAdapter from vires.util import between from .base import Filter from .exceptions import FilterError class BaseRangeFilter(Filter): """ Base scalar value range filter. """ # pylint: disable=abstract-method class _LoggerAdapter(LoggerAdapter): def process(self, msg, kwargs): return 'filter %s: %s' % (self.extra["variable"], msg), kwargs def __init__(self, variable, vmin, vmax, logger): self.variable = variable self.vmin = vmin self.vmax = vmax self.logger = logger @property def label(self): """ Get filter label. """ return self.variable @property def required_variables(self): return (self.variable,) def _filter(self, data): """ Low level filter. """ self.logger.debug("value range: %s %s", self.vmin, self.vmax) self.logger.debug("initial size: %d", data.shape[0]) return between(data, self.vmin, self.vmax) def __str__(self): return "%s:%.17g,%.17g" % (self.label, self.vmin, self.vmax) class ScalarRangeFilter(BaseRangeFilter): """ Simple scalar value range filter. """ def __init__(self, variable, vmin, vmax, logger=None): BaseRangeFilter.__init__( self, variable, vmin, vmax, self._LoggerAdapter( logger or getLogger(__name__), {"variable": variable} ) ) def filter(self, dataset, index=None): data = dataset[self.variable] if data.ndim != 1: raise FilterError( "An attempt to apply a scalar range filter to a non-scalar " "variable %s!" % self.variable ) if index is None: index = self._filter(data).nonzero()[0] else: index = index[self._filter(data[index])] self.logger.debug("filtered size: %d", index.size) return index class VectorComponentRangeFilter(BaseRangeFilter): """ Single vector component range filter. """ def __init__(self, variable, component, vmin, vmax, logger=None): BaseRangeFilter.__init__( self, variable, vmin, vmax, self._LoggerAdapter( logger or getLogger(__name__), { "variable": "%s[%s]" % (variable, component) } ) ) self.component = component @property def label(self): return "%s[%d]" % (self.variable, self.component) def filter(self, dataset, index=None): data = dataset[self.variable] if data.ndim != 2: raise FilterError( "An attempt to apply a vector component range filter to a " "non-vector variable %s!" % self.variable ) if index is None: index = self._filter(data[:, self.component]).nonzero()[0] else: index = index[self._filter(data[index, self.component])] self.logger.debug("filtered size: %d", index.size) return index
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vesteves33/cursoPython
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/Exercicios/ex052.py
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num = int(input('Digite um número inteiro: ')) totalContagem = 0 for contador in range(1, num+1): if num % contador == 0: totalContagem += 1 if totalContagem == 2: print('Número {} é primo'.format(num)) else: print('Número {} não é primo'.format(num))
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higornucci/classificacao-aulas
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401f084771e500f1881fb875133112fe2cb7ecec
132373547f88d59cd87d8f99b81d9df1e306c1e4
/handson/housing.py
96514948ab81f1c108b034331367d1b67e010459
[]
no_license
https://github.com/higornucci/classificacao-aulas
be117acd0907b044fed5aaccbf147c82c28cb39b
1dfdc15c6ddbfa7ba5217f63d6d43c73b3b4bb0b
refs/heads/master
2021-06-04T03:30:54.553705
2020-06-18T22:40:37
2020-06-18T22:40:37
110,111,682
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from __future__ import division, print_function, unicode_literals import os import tarfile import warnings import matplotlib.pyplot as plt import numpy as np import pandas as pd from six.moves import urllib from future_encoders import ColumnTransformer, OneHotEncoder from sklearn.linear_model import LinearRegression from sklearn.tree import DecisionTreeRegressor from sklearn.metrics import mean_squared_error from sklearn.base import BaseEstimator, TransformerMixin from sklearn.model_selection import StratifiedShuffleSplit, train_test_split, cross_val_score, GridSearchCV from sklearn.pipeline import FeatureUnion, Pipeline from sklearn.preprocessing import LabelEncoder, LabelBinarizer, StandardScaler, Imputer from sklearn.ensemble import RandomForestRegressor warnings.filterwarnings(action="ignore", message="^internal gelsd") pd.set_option('display.max_columns', 500) pd.set_option('display.width', 2000) np.random.seed(42) plt.rcParams['axes.labelsize'] = 14 plt.rcParams['xtick.labelsize'] = 12 plt.rcParams['ytick.labelsize'] = 12 rooms_ix, bedrooms_ix, population_ix, household_ix = 3, 4, 5, 6 class CombinedAttributesAdder(BaseEstimator, TransformerMixin): def __init__(self, add_bedrooms_per_room=True): # no *args or **kargs self.add_bedrooms_per_room = add_bedrooms_per_room def fit(self, X, y=None): return self # nothing else to do def transform(self, X, y=None): rooms_per_household = X[:, rooms_ix] / X[:, household_ix] population_per_household = X[:, population_ix] / X[:, household_ix] if self.add_bedrooms_per_room: bedrooms_per_room = X[:, bedrooms_ix] / X[:, rooms_ix] return np.c_[X, rooms_per_household, population_per_household, bedrooms_per_room] else: return np.c_[X, rooms_per_household, population_per_household] # class DataFrameSelector(BaseEstimator, TransformerMixin): # def __init__(self, attribute_names): # self.attribute_names = attribute_names # # def fit(self, X, y=None): # return self # # def transform(self, X): # return X[self.attribute_names].values PROJECT_ROOT_DIR = "." CHAPTER_ID = "end_to_end_project" IMAGES_PATH = os.path.join(PROJECT_ROOT_DIR, "images", CHAPTER_ID) DOWNLOAD_ROOT = "https://raw.githubusercontent.com/ageron/handson-ml/master/" HOUSING_PATH = "datasets/housing" HOUSING_URL = DOWNLOAD_ROOT + HOUSING_PATH + "/housing.tgz" def save_fig(fig_id, tight_layout=True, fig_extension="png", resolution=300): path = os.path.join(IMAGES_PATH, fig_id + "." + fig_extension) print("Saving figure", fig_id) if tight_layout: plt.tight_layout() plt.savefig(path, format=fig_extension, dpi=resolution) def fetch_housing_data(housing_url=HOUSING_URL, housing_path=HOUSING_PATH): if not os.path.isdir(housing_path): os.makedirs(housing_path) tgz_path = os.path.join(housing_path, "housing.tgz") urllib.request.urlretrieve(housing_url, tgz_path) housing_tgz = tarfile.open(tgz_path) housing_tgz.extractall(path=housing_path) housing_tgz.close() def load_housing_data(housing_path=HOUSING_PATH): csv_path = os.path.join(housing_path, "housing.csv") return pd.read_csv(csv_path) def income_cat_proportions(data): return data["income_cat"].value_counts() / len(data) def display_scores(scores): print("Scores:", scores) print("Mean:", scores.mean()) print("Standard deviation:", scores.std()) housing = load_housing_data() print(housing.head(30)) print(housing.info()) print(housing["ocean_proximity"].value_counts()) print(housing.describe()) housing["income_cat"] = np.ceil(housing["median_income"] / 1.5) housing["income_cat"].where(housing["income_cat"] < 5, 5.0, inplace=True) split = StratifiedShuffleSplit(n_splits=1, test_size=0.2, random_state=42) for train_index, test_index in split.split(housing, housing["income_cat"]): strat_train_set = housing.loc[train_index] strat_test_set = housing.loc[test_index] print(strat_test_set["income_cat"].value_counts() / len(strat_test_set)) print(housing["income_cat"].value_counts() / len(housing)) train_set, test_set = train_test_split(housing, test_size=0.2, random_state=42) compare_props = pd.DataFrame({ "Overall": income_cat_proportions(housing), "Stratified": income_cat_proportions(strat_test_set), "Random": income_cat_proportions(test_set), }).sort_index() compare_props["Rand. %error"] = 100 * compare_props["Random"] / compare_props["Overall"] - 100 compare_props["Strat. %error"] = 100 * compare_props["Stratified"] / compare_props["Overall"] - 100 print(compare_props) for set_ in (strat_train_set, strat_test_set): set_.drop(["income_cat"], axis=1, inplace=True) # visualizando os dados housing = strat_train_set.copy() # california_img = mpimg.imread(PROJECT_ROOT_DIR + '/images/end_to_end_project/california.png') # ax = housing.plot(kind="scatter", x="longitude", y="latitude", figsize=(10,7), # s=housing['population']/100, label="Population", # c="median_house_value", cmap=plt.get_cmap("jet"), # colorbar=False, alpha=0.4, # ) # plt.imshow(california_img, extent=[-124.55, -113.80, 32.45, 42.05], alpha=0.5, # cmap=plt.get_cmap("jet")) # plt.ylabel("Latitude", fontsize=14) # plt.xlabel("Longitude", fontsize=14) # # prices = housing["median_house_value"] # tick_values = np.linspace(prices.min(), prices.max(), 11) # cbar = plt.colorbar() # cbar.ax.set_yticklabels(["$%dk"%(round(v/1000)) for v in tick_values], fontsize=14) # cbar.set_label('Median House Value', fontsize=16) # # plt.legend(fontsize=16) # save_fig("california_housing_prices_plot") # plt.show() corr_matrix = housing.corr() print(corr_matrix["median_house_value"].sort_values(ascending=False)) # attributes = ["median_house_value", "median_income", "total_rooms", # "housing_median_age"] # scatter_matrix(housing[attributes], figsize=(12, 8)) # save_fig("scatter_matrix_plot") housing["rooms_per_household"] = housing["total_rooms"] / housing["households"] housing["bedrooms_per_room"] = housing["total_bedrooms"] / housing["total_rooms"] housing["population_per_household"] = housing["population"] / housing["households"] corr_matrix = housing.corr() corr_matrix["median_house_value"].sort_values(ascending=False) # preparando para machine learning housing = strat_train_set.drop("median_house_value", axis=1) housing_labels = strat_train_set["median_house_value"].copy() housing.dropna(subset=["total_bedrooms"]) # remove as linhas que contêm valores nulos housing.drop("total_bedrooms", axis=1) # remove a coluna inteira median = housing["total_bedrooms"].median() housing["total_bedrooms"].fillna(median) # substitui os valores nulos pela mediana imputer = Imputer(strategy="median") housing_num = housing.drop("ocean_proximity", axis=1) # remover atributos não numéricos imputer.fit(housing_num) # usar sklearn para completar os valores nulos com a mediana print(imputer.statistics_) X = imputer.transform(housing_num) housing_tr = pd.DataFrame(X, columns=housing_num.columns) encoder = LabelEncoder() # pŕoblema que os algoritmos de ml acham que categorias mais próximas são similares housing_cat = housing["ocean_proximity"] housing_cat_encoded = encoder.fit_transform(housing_cat) print(housing_cat_encoded) encoder = OneHotEncoder() housing_cat_1hot = encoder.fit_transform(housing_cat_encoded.reshape(-1, 1)) print(housing_cat_1hot) encoder = LabelBinarizer() housing_cat_1hot = encoder.fit_transform(housing_cat) print(housing_cat_1hot) attr_adder = CombinedAttributesAdder(add_bedrooms_per_room=False) housing_extra_attribs = attr_adder.transform(housing.values) housing_extra_attribs = pd.DataFrame( housing_extra_attribs, columns=list(housing.columns)+["rooms_per_household", "population_per_household"]) print(housing_extra_attribs.head()) num_attribs = list(housing_num) cat_attribs = ["ocean_proximity"] num_pipeline = Pipeline([ # ('selector', DataFrameSelector(num_attribs)), ('imputer', Imputer(strategy="median")), ('attribs_adder', CombinedAttributesAdder()), ('std_scaler', StandardScaler()), ]) # cat_pipeline = Pipeline([ # ('selector', DataFrameSelector(cat_attribs)), # ('cat_encoder', OneHotEncoder()), # ]) full_pipeline = ColumnTransformer([ ("num", num_pipeline, num_attribs), ("cat", OneHotEncoder(), cat_attribs), ]) housing_prepared = full_pipeline.fit_transform(housing) print(housing_prepared) print(housing_prepared.shape) # Trainando o modelo lin_reg = LinearRegression() lin_reg.fit(housing_prepared, housing_labels) some_data = housing.iloc[:5] some_labels = housing_labels.iloc[:5] some_data_prepared = full_pipeline.transform(some_data) print("Predictions:\t", lin_reg.predict(some_data_prepared)) print("Labels:\t\t", list(some_labels)) housing_predictions = lin_reg.predict(housing_prepared) lin_mse = mean_squared_error(housing_labels, housing_predictions) lin_rmse = np.sqrt(lin_mse) print(lin_rmse) tree_reg = DecisionTreeRegressor() tree_reg.fit(housing_prepared, housing_labels) housing_predictions = tree_reg.predict(housing_prepared) tree_mse = mean_squared_error(housing_labels, housing_predictions) tree_rmse = np.sqrt(tree_mse) print(tree_rmse) scores = cross_val_score(tree_reg, housing_prepared, housing_labels, scoring='neg_mean_squared_error', cv=10) rmse_scores = np.sqrt(-scores) display_scores(rmse_scores) forest_reg = RandomForestRegressor() forest_reg.fit(housing_prepared, housing_labels) housing_predictions = forest_reg.predict(housing_prepared) forest_mse = mean_squared_error(housing_labels, housing_predictions) forest_rmse = np.sqrt(forest_mse) print(forest_rmse) scores = cross_val_score(forest_reg, housing_prepared, housing_labels, scoring='neg_mean_squared_error', cv=10) rmse_scores = np.sqrt(-scores) display_scores(rmse_scores) param_grid = [ {'n_estimators': [3, 10, 30, 40, 50], 'max_features': [2, 4, 5, 6, 7, 8]}, {'bootstrap': [False], 'n_estimators': [3, 10], 'max_features': [2, 3, 4]} ] forest_reg = RandomForestRegressor() grid_search = GridSearchCV(forest_reg, param_grid, cv=5, scoring='neg_mean_squared_error') grid_search.fit(housing_prepared, housing_labels) print(grid_search.best_params_) print(grid_search.best_estimator_) cvres = grid_search.cv_results_ for mean_score, params in zip(cvres['mean_test_score'], cvres['params']): print(np.sqrt(-mean_score), params) feature_importances = grid_search.best_estimator_.feature_importances_ print(feature_importances) extra_attribs = ['rooms_per_hhold', 'pop_per_hhold', 'bedrooms_per_rooms'] cat_one_hot_attribs = list(encoder.classes_) attributes = num_attribs + extra_attribs + cat_one_hot_attribs sorted(zip(feature_importances)) final_model = grid_search.best_estimator_ X_test = strat_test_set.drop("median_house_value", axis=1) Y_test = strat_test_set["median_house_value"].copy() X_test_prepared = full_pipeline.transform(X_test) final_predictions = final_model.predict(X_test_prepared) final_mse = mean_squared_error(Y_test, final_predictions) final_rmse = np.sqrt(final_mse) display_scores(final_rmse)
UTF-8
Python
false
false
11,301
py
86
housing.py
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0.71359
0.701549
0
308
35.672078
111
Cornex-Inc/Coffee
9,826,885,178,144
d31292a1af2ea2bd80b27eb0ba0b626d36bc2742
7deda84f7a280f5a0ee69b98c6a6e7a2225dab24
/Radiation/views.py
310906cad23ae433ea4cc5a41883be3cf74c5547
[]
no_license
https://github.com/Cornex-Inc/Coffee
476e30f29412373fb847b2d518331e6c6b9fdbbf
fcd86f20152e2b0905f223ff0e40b1881db634cf
refs/heads/master
2023-01-13T01:56:52.755527
2020-06-08T02:59:18
2020-06-08T02:59:18
240,187,025
0
0
null
false
2023-01-05T23:58:52
2020-02-13T05:47:41
2023-01-05T19:41:25
2023-01-05T23:58:51
66,217
1
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Python
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from django.shortcuts import render,redirect from django.http import JsonResponse from django.contrib.auth.decorators import login_required import functools import operator from django.db.models import Q,Case,When, CharField,Count,Sum from .forms import * from Laboratory.forms import * from .models import * # Create your views here. @login_required def index(request): form = RadiationForm() search_form=PrecedureManageForm() error = False if 'save' in request.POST: form = RadiationForm(request.POST, request.FILES,) selected_radi_manage = request.POST['selected_test_manage'] if selected_radi_manage is not '': selected_img_id = request.POST['id'] precedure = PrecedureManager.objects.get(pk = selected_radi_manage) if selected_img_id is '': #new radi_manage = RadiationManage() radi_manage.progress = 'done' radi_manage.name_service = precedure.precedure.name radi_manage.manager = precedure radi_manage.save() else: radi_manage = RadiationManage.objects.get(pk = selected_img_id) if form.is_valid(): form.instance.id = radi_manage.id form.instance.manager_id= radi_manage.manager.id form.instance.progress = 'done' form.instance.date_ordered = radi_manage.date_ordered form.instance.name_service = radi_manage.name_service form.save() return redirect('/radiation/') else: error = 'select patient.' depart = Depart.objects.all() request.POST = request.POST.copy() if 'selected_test_manage' in request.POST: request.POST['selected_test_manage']='' if 'id' in request.POST: request.POST['id']='' if 'save' in request.POST: request.POST['save']='' request.FILES['image'] = None return render(request, 'Radiation/index.html', { 'form':form, 'search':search_form, 'error':error, 'depart':depart, }, ) def get_image(request): manage_id = request.POST.get('manage_id') manage = RadiationManage.objects.get(pk = manage_id) datas={ 'id':manage.id, } if manage.image: datas.update({ 'id':manage.id, 'path':manage.image.url, 'remark':manage.remark, }) return JsonResponse(datas) def zoom_in(request,img_id): try: img = RadiationManage.objects.get(pk = img_id) except RadiationManage.DoesNotExist: return return render(request, 'Radiation/zoomin.html', { 'img_url':img.image.url, }, ) def waiting_list(request): date_start = request.POST.get('start_date') date_end = request.POST.get('end_date') filter = request.POST.get('filter') input = request.POST.get('input').lower() depart_id = request.POST.get('depart_id') kwargs={} date_min = datetime.datetime.combine(datetime.datetime.strptime(date_start, "%Y-%m-%d").date(), datetime.time.min) date_max = datetime.datetime.combine(datetime.datetime.strptime(date_end, "%Y-%m-%d").date(), datetime.time.max) argument_list = [] kwargs={} if depart_id != '' : kwargs['diagnosis__reception__depart_id'] = depart_id if input !='': argument_list.append( Q(**{'diagnosis__reception__patient__name_kor__icontains':input} ) ) argument_list.append( Q(**{'diagnosis__reception__patient__name_eng__icontains':input} ) ) argument_list.append( Q(**{'diagnosis__reception__patient__id__icontains':input} ) ) radios =PrecedureManager.objects.select_related( 'diagnosis__reception__patient' ).select_related( 'precedure' ).filter( functools.reduce(operator.or_, argument_list), **kwargs, precedure__code__icontains='R', diagnosis__recorded_date__range= (date_min,date_max), ).exclude(diagnosis__reception__progress='deleted') else: radios =PrecedureManager.objects.select_related( 'diagnosis__reception__patient' ).select_related( 'precedure' ).filter( **kwargs, precedure__code__icontains='R', diagnosis__recorded_date__range= (date_min,date_max), ).exclude(diagnosis__reception__progress='deleted') datas = [] for radio in radios: data= { 'chart':radio.diagnosis.reception.patient.get_chart_no(), 'name_kor':radio.diagnosis.reception.patient.name_kor, 'name_eng':radio.diagnosis.reception.patient.name_eng, 'Depart':radio.diagnosis.reception.depart.name, 'Doctor':radio.diagnosis.reception.doctor.name_kor, 'Date_of_Birth': radio.diagnosis.reception.patient.date_of_birth.strftime('%Y-%m-%d'), 'Gender/Age':'(' + radio.diagnosis.reception.patient.get_gender_simple() + '/' + str(radio.diagnosis.reception.patient.get_age()) + ')', 'name_service':radio.precedure.name if radio.precedure.name else radio.precedure.name_vie, 'date_ordered':'' if radio.diagnosis.reception.recorded_date is None else radio.diagnosis.reception.recorded_date.strftime('%Y-%m-%d %H:%M'), 'precedure_manage_id':radio.id,#radi_manage_id } check_done = RadiationManage.objects.filter(manager_id = radio.id).count() if check_done == 0: data.update({ 'progress':'new', }) else: data.update({ 'progress':'done', }) datas.append(data) context = {'datas':datas} return JsonResponse(context) def waiting_selected(request): radi_manage_id = request.POST.get('radi_manage_id') precedure = PrecedureManager.objects.get(pk = radi_manage_id) radi_images = RadiationManage.objects.filter(manager_id = radi_manage_id) datas = {} for radi_image in radi_images: date = radi_image.date_ordered.strftime('%Y-%m-%d') if date not in datas: datas[date] = [] data = { 'path':radi_image.image.url if radi_image.image else '', 'id':radi_image.id, 'service':radi_image.name_service, 'remark':radi_image.remark, } datas[date].append(data) context = { 'datas':datas, 'chart':precedure.diagnosis.reception.patient.get_chart_no(), 'Name':precedure.diagnosis.reception.patient.name_kor + ' ' + precedure.diagnosis.reception.patient.name_eng, 'Date_of_birth':precedure.diagnosis.reception.patient.date_of_birth.strftime('%Y-%m-%d') + '(' + precedure.diagnosis.reception.patient.get_gender_simple() + '/' + str(precedure.diagnosis.reception.patient.get_age()) + ')',}; context.update({ 'Lab':precedure.precedure.name if precedure.precedure.name else precedure.precedure.name_vie, 'date_ordered':'' if precedure.diagnosis.reception.recorded_date is None else precedure.diagnosis.reception.recorded_date.strftime('%Y-%m-%d %H:%M') , 'Depart':precedure.diagnosis.reception.depart.name + ' ( ' + precedure.diagnosis.reception.doctor.name_kor + ' )', }) return JsonResponse(context) def delete_image(request): image_id = request.POST.get('image_id') RadiationManage.objects.get(pk = image_id).delete() res = 'success' return JsonResponse({'result':res})
UTF-8
Python
false
false
7,919
py
415
views.py
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0.583533
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project-rig/pynn_spinnaker
11,544,872,097,212
42d35ba0d557f1d059562fe3e3bbc8bf14c37341
c2dec05f3eb894e616acadd4e3c30ac697fd2656
/pynn_spinnaker/spinnaker/regions/__init__.py
f2b56ef349325633d6e113e4842b934c63cc046d
[]
no_license
https://github.com/project-rig/pynn_spinnaker
da1ee4c52dbf0015cec0a5f2676f4e031032848d
89e9bdba78157804f491948bd3d630101d7b9cb6
refs/heads/master
2020-04-09T16:52:06.658914
2016-12-19T14:14:37
2016-12-19T14:14:37
31,414,869
0
2
null
false
2016-12-19T14:12:17
2015-02-27T10:39:38
2016-10-28T09:48:17
2016-12-19T14:12:16
1,824
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Python
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null
from analogue_recording import AnalogueRecording from connection_builder import ConnectionBuilder from delay_buffer import DelayBuffer from extended_plastic_synaptic_matrix import ExtendedPlasticSynapticMatrix from flush import Flush from homogeneous_parameter_space import HomogeneousParameterSpace from input_buffer import InputBuffer from key_lookup_binary_search import KeyLookupBinarySearch from neuron import Neuron from output_buffer import OutputBuffer from output_weight import OutputWeight from parameter_space import ParameterSpace from plastic_synaptic_matrix import PlasticSynapticMatrix from sdram_back_prop_input import SDRAMBackPropInput from sdram_back_prop_output import SDRAMBackPropOutput from spike_recording import SpikeRecording from spike_source_array import SpikeSourceArray from spike_source_poisson import SpikeSourcePoisson from static_synaptic_matrix import StaticSynapticMatrix from synaptic_matrix import SynapticMatrix
UTF-8
Python
false
false
951
py
112
__init__.py
105
0.883281
0.883281
0
20
46.55
74
lse13/Question2-
14,199,161,895,800
fa9693f1bc1478134a4cedd9d5c2e7979b6d3bba
d644e45fcf3b1e9ab1afa71a0f2e657ee77a755c
/ex1.py
5d1cbae1b2c29255c20598dc720c472bb0eefffc
[]
no_license
https://github.com/lse13/Question2-
f3b30b2d786f4c1c86e55bb0702605469bd32e00
a38484308babd5894a147d898db339b5a80f3553
refs/heads/master
2020-08-30T04:40:39.029858
2019-10-29T11:03:24
2019-10-29T11:03:24
218,266,429
0
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# Exercise 1: Work with the person next to you to design # classes to manage the products, customers, and purchase # orders for an online book store such as amazon.com. # Outline the data attributes and useful methods for # each class. You can discuss and create the outline together. class Amazon_orders(object): def __init__(self): self.products=None self.customers=None def add_products(self,product_list): self.product.append(product_list) def get_products(self): return self.products def get_customers(self): return self.customers def set_customers(self, customers_list): self.customers = customers_list
UTF-8
Python
false
false
666
py
1
ex1.py
1
0.717718
0.716216
0
23
27.826087
63
suritechiez03/IMS_Python
1,786,706,420,686
f4c2434ccc80a8fdd6b16a7312631031aa8f3045
40fd858c4a7b08b02e1030fe33f9e6541d67b8c6
/ordermanagement/urls.py
98160b1b3b1d39468db35d0e9ad2ebed4d23e7db
[]
no_license
https://github.com/suritechiez03/IMS_Python
366ac1ae6a5309ebc2a7c2686a16320235dfdd99
1280cd49f8a1d8b3a1a815ddf53f2f18ea10b0a9
refs/heads/master
2021-05-10T08:42:02.362882
2018-09-10T09:28:41
2018-09-10T09:28:41
118,899,316
0
0
null
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null
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from django.conf.urls import url from . import views urlpatterns = [ url(r'^$', views.index, name='index'), url(r'^api/save_order$',views.save_order, name="save_order"), url(r'^api/get_order_list$', views.get_order_list, name="get_order_list"), url(r'^api/get_order_details/(?P<order_number>[0-9]+)/$', views.get_order_list_by_number, name="get_order_details"), url(r'^api/remove_product', views.remove_product, name="remove_product"), url(r'^api/get_order_by_customer$', views.get_order_by_customer, name="get_order_by_customer"), ]
UTF-8
Python
false
false
560
py
84
urls.py
62
0.671429
0.667857
0
13
42.153846
120
bopopescu/dbfinal
9,191,230,052,159
19b8672e96c94215a52120366308b079c3a01ba0
4d063e7bec6226c34cab2233d6e3e84d146ffaa3
/gtmd/models/Order.py
3e832a7839353edc748ff9aa8f4ef50804a10114
[]
no_license
https://github.com/bopopescu/dbfinal
9e3b4fd405fb6ec5b2b99d5151693b226d0799d8
02b17b3f5e492d6392077cddc2872f574c3fa833
refs/heads/master
2022-11-19T23:15:42.912480
2019-12-31T15:46:10
2019-12-31T15:46:10
281,665,426
0
0
null
true
2020-07-22T12:00:53
2020-07-22T12:00:52
2019-12-31T15:46:13
2019-12-31T15:46:11
41,294
0
0
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from gtmd.app import db import datetime class Order(db.Model): # 订单id order_id = db.Column(db.String, primary_key=True, index=True, nullable=False) # 用户id buyer_id = db.Column(db.String, index=True, nullable=False) # 商店id store_id = db.Column(db.String, index=True, nullable=False) # 创建时间 createtime = db.Column(db.DATETIME, default=datetime.datetime.now, nullable=False) # 订单状态 status = db.Column(db.String, nullable=False) orderdetail = db.relationship("Orderdetail", backref="orderdetail")
UTF-8
Python
false
false
568
py
33
Order.py
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0.694444
0.694444
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freelawproject/juriscraper
11,501,922,452,240
dcd2dc30acb601dfd4f58df80a071fe2220609be
362196f32e8248e025cb2f6cf0b88f812c9a059c
/juriscraper/opinions/united_states/federal_appellate/ca9_u.py
9b65804cd9453cb372e89d3694e4c87ee25a7184
[ "BSD-3-Clause", "LicenseRef-scancode-generic-cla", "LicenseRef-scancode-unknown-license-reference", "BSD-2-Clause" ]
permissive
https://github.com/freelawproject/juriscraper
0fea8d4bb512808cb1e036aaaf819e9cc0847a6b
d2c6672696e13e33ec9981a1901b87047d8108c5
refs/heads/main
2023-08-09T13:27:21.357915
2023-07-06T22:33:01
2023-07-06T22:33:01
22,757,589
283
97
BSD-2-Clause
false
2023-09-08T22:59:36
2014-08-08T12:50:35
2023-09-08T16:49:38
2023-09-08T22:59:36
53,996
287
81
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HTML
false
false
""" History: - 2014-08-05: Updated by mlr because it was not working, however, in middle of update, site appeared to change. At first there were about five columns in the table and scraper was failing. Soon, there were seven and the scraper started working without my fixing it. Very odd. - 2023-01-13: Update to use RSS Feed """ from juriscraper.opinions.united_states.federal_appellate import ca9_p class Site(ca9_p.Site): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) self.court_id = self.__module__ self.url = "https://www.ca9.uscourts.gov/memoranda/index.xml" self.status = "Unpublished"
UTF-8
Python
false
false
683
py
4,602
ca9_u.py
327
0.666179
0.63836
0
18
36.944444
79
kirina001/musicPytest
7,602,092,136,731
77b731d71920ac37a473e39e9f22348aa67349c6
2177cad4601dff84ce9e9f6ccd59051a5c4fc5dd
/test/test01app.py
9fc9dadf03c6464f57e48bd8df8db5c5dc41687b
[]
no_license
https://github.com/kirina001/musicPytest
4eaaae63547fe7e48d5040f3202bb6a2ab2cd6ce
cb3e488be7fa7b7036a169029eb095866773eb48
refs/heads/master
2020-07-12T17:09:04.260323
2019-08-28T07:32:55
2019-08-28T07:32:55
204,869,970
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null
null
null
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# -*- coding: UTF-8 -*- conf = { "platformName": "Android", "platformVersion": "6.0.1", "deviceName": "127.0.0.1:7555", "appPackage": "com.FreshAir", "appActivity": ".activity.WelcomeActivity", "noReset": "true", "unicodeKeyboard": "true", "resetKeyboard": "true" } c = eval(str(conf)) print(type(c))
UTF-8
Python
false
false
335
py
26
test01app.py
24
0.58209
0.540299
0
15
21.333333
47
Davies-Sam/Genetris
4,569,845,230,733
6ce3b8140178ab76be6cdafbf7200460f95f2bc3
38fb7643782351fa9fab631aac6037ba63f82a59
/tetris.py
f53c7e9677b174128d6ab8ec8ff61e8f1da5bd47
[ "MIT" ]
permissive
https://github.com/Davies-Sam/Genetris
c2f2589414fc08e560d5ce3fafb7d445fb3b0203
1d46d2d975baffd7c08933170ed0cc744b8e00a2
refs/heads/master
2021-06-22T03:05:22.816896
2021-05-27T23:56:51
2021-05-27T23:56:51
222,779,924
0
0
null
null
null
null
null
null
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null
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null
null
import random import pygame, sys from copy import deepcopy import numpy CELL_SIZE = 30 COLS = 10 ROWS = 22 MAXFPS = 30 PIECELIMIT = float("inf") DROP_TIME = 60 DRAW = True tetris_shapes = [ [[1, 1, 1], [0, 1, 0]], [[0, 2, 2], [2, 2, 0]], [[3, 3, 0], [0, 3, 3]], [[4, 0, 0], [4, 4, 4]], [[0, 0, 5], [5, 5, 5]], [[6, 6, 6, 6]], [[7, 7], [7, 7]] ] colors = [ (0, 0, 0 ), (255, 85, 85), (100, 200, 115), (120, 108, 245), (255, 140, 50 ), (50, 120, 52 ), (146, 202, 73 ), (150, 161, 218 ), (35, 35, 35) # Helper color for background grid ] #rotates the pieces clockwise def rotate_clockwise(shape): return [ [ shape[y][x] for y in range(len(shape)) ] for x in range(len(shape[0]) - 1, -1, -1) ] #checks that no pieces are overlapping def check_collision(board, shape, offset): off_x, off_y = offset for cy, row in enumerate(shape): for cx, cell in enumerate(row): try: if cell and board[ cy + off_y ][ cx + off_x ]: return True except IndexError: return True return False #removes a row from the board def remove_row(board, row): del board[row] return [[0 for i in range(COLS)]] + board #adds a placed piece to the board def join_matrixes(mat1, mat2, mat2_off): mat3 = deepcopy(mat1) off_x, off_y = mat2_off for cy, row in enumerate(mat2): for cx, val in enumerate(row): mat3[cy+off_y-1][cx+off_x] += val return mat3 #create the board def new_board(): board = [ [ 0 for x in range(COLS) ] for y in range(ROWS) ] #next line not needed, just there for clarity (adds a base row to the grid) board += [[ 1 for x in range(COLS)]] return board class TetrisApp(object): def __init__(self, genetics): self.DROPEVENT = pygame.USEREVENT + 1 pygame.init() pygame.display.set_caption("Final Project") pygame.key.set_repeat(250,25) self.width = CELL_SIZE * (COLS+10) self.height = CELL_SIZE * ROWS self.rlim = CELL_SIZE * COLS self.bground_grid = [[ 8 if x%2==y%2 else 0 for x in range(COLS)] for y in range(ROWS)] self.default_font = pygame.font.Font(pygame.font.get_default_font(), 11) if DRAW: self.screen = pygame.display.set_mode((self.width, self.height)) self.next_stone = tetris_shapes[5] self.linesCleared = 0 self.gameover = False self.genetics = genetics self.ai = None self.limit = PIECELIMIT self.piecesPlayed = 0 if self.genetics.sequenceType == "fixed": self.init_game(self.genetics.seed) elif self.genetics.sequenceType == "random": self.init_game(numpy.random.random()) def new_stone(self): self.stone = self.next_stone nextStone = random.randint(0, len(tetris_shapes)-1) self.next_stone = tetris_shapes[nextStone] self.stone_x = COLS//2 - len(self.stone[0])//2 self.stone_y = 0 self.score += 1 self.piecesPlayed += 1 if check_collision(self.board, self.stone, (self.stone_x, self.stone_y)): self.gameover = True if self.genetics: #print(self.linesCleared) self.genetics.GameOver(self.linesCleared) def init_game(self,seed): random.seed(seed) self.board = new_board() self.score = 0 self.linesCleared = 0 #start every game with a flat piece self.next_stone = tetris_shapes[6] self.new_stone() pygame.time.set_timer(self.DROPEVENT, DROP_TIME) def disp_msg(self, msg, topleft): x,y = topleft for line in msg.splitlines(): self.screen.blit(self.default_font.render(line, False, (255,255,255), (0,0,0)), (x,y)) y+=14 def center_msg(self, msg): for i, line in enumerate(msg.splitlines()): msg_image = self.default_font.render(line, False, (255,255,255), (0,0,0)) msgim_center_x, msgim_center_y = msg_image.get_size() msgim_center_x //= 2 msgim_center_y //= 2 self.screen.blit(msg_image, ( self.width // 2-msgim_center_x, self.height // 2-msgim_center_y+i*22)) def draw_matrix(self, matrix, offset): off_x, off_y = offset for y, row in enumerate(matrix): for x, val in enumerate(row): if val: #corrupt board exception from https://tinyurl.com/wu7gl48 try: pygame.draw.rect(self.screen, colors[val], pygame.Rect((off_x+x)*CELL_SIZE, (off_y+y)*CELL_SIZE, CELL_SIZE, CELL_SIZE), 0) except IndexError: pass #print("Corrupted board") #self.print_board() def add_cl_lines(self, n): linescores = [0, 40, 100, 300, 1200] self.score += linescores[n] self.linesCleared += n def move_to(self, x): self.move(x - self.stone_x) def move(self, delta_x): if not self.gameover: new_x = self.stone_x + delta_x if new_x < 0: new_x = 0 if new_x > COLS - len(self.stone[0]): new_x = COLS - len(self.stone[0]) if not check_collision(self.board, self.stone, (new_x, self.stone_y)): self.stone_x = new_x def drop(self): if not self.gameover: self.stone_y += 1 if check_collision(self.board, self.stone, (self.stone_x, self.stone_y)): self.board = join_matrixes(self.board, self.stone, (self.stone_x, self.stone_y)) self.new_stone() cleared_rows = 0 for i, row in enumerate(self.board[:-1]): if 0 not in row: self.board = remove_row(self.board, i) cleared_rows += 1 self.add_cl_lines(cleared_rows) if self.ai: self.ai.update_board() return True return False def insta_drop(self): if not self.gameover: while not self.drop(): pass def rotate_stone(self): if not self.gameover: new_stone = rotate_clockwise(self.stone) if not check_collision(self.board, new_stone, (self.stone_x, self.stone_y)): self.stone = new_stone def start_game(self,seed): if self.gameover: self.init_game(seed) self.gameover = False def quit(self): self.center_msg("exiting...") pygame.display.update() """ make sure fitnesses are recorded for a in self.genetics.population: print(a.fitness) print("\n") """ sys.exit() def ai_toggle_instantPlay(self): if self.ai: self.ai.instantPlay = not self.ai.instantPlay def print_board(self): i=0 for row in self.board: print(self.board[i]) print('\n') i+=1 """for testing import heuristics print("height %s" % heuristics.TotalHeight(self.board)) print("bump %s" % heuristics.Bumpiness(self.board)) print("holes %s" % heuristics.HolesCreated(self.board)) print("linesc %s" % heuristics.LinesCleared(self.board)) print("connectedholes %s" % heuristics.ConnectedHoles(self.board)) print("blockade %s" % heuristics.Blockades(self.board)) print("altDelta %s" % heuristics.AltitudeDelta(self.board)) print("WeighteBlocks %s" % heuristics.WeightedBlocks(self.board)) print("Horiz R %s" % heuristics.HorizontalRoughness(self.board)) print("Vert R %s" % heuristics.VerticalRoughness(self.board)) print("wells %s" % heuristics.Wells(self.board)) print("max well %s" % heuristics.MaxWell(self.board)) """ def run(self): key_actions = { 'ESCAPE': self.quit, 'LEFT': lambda: self.move(-1), 'RIGHT': lambda: self.move(+1), 'DOWN': self.drop, 'UP': self.rotate_stone, 'RETURN': self.insta_drop, 'p': self.ai_toggle_instantPlay, 't' : self.print_board } clock = pygame.time.Clock() while True: if DRAW: self.screen.fill((0,0,0)) if self.gameover: self.center_msg("Game Over!\nYour score: %d\nPress space to continue" % self.score) else: pygame.draw.line(self.screen, (255,255,255), (self.rlim+1, 0), (self.rlim+1, self.height-1)) self.disp_msg("Next:", (self.rlim+CELL_SIZE, 2)) self.disp_msg("Score: %d" % self.score, (self.rlim+CELL_SIZE, CELL_SIZE*5)) if self.ai and self.genetics: chromosome = self.genetics.population[self.genetics.current_organism] self.disp_msg("Generation: %s" % self.genetics.current_generation, (self.rlim+CELL_SIZE, CELL_SIZE*5)) self.disp_msg("\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n %s: %s\n" % ( "Organism #", self.genetics.current_organism, "Name", chromosome.name, "Played", chromosome.played, "Fitness", chromosome.fitness, "Age", chromosome.age, "Height", chromosome.heuristics[0], "Bumpiness", chromosome.heuristics[1], "Holes", chromosome.heuristics[2], "Lines", chromosome.heuristics[3], "Connected Holes", chromosome.heuristics[4], "Blockades", chromosome.heuristics[5], "Altitude Delta", chromosome.heuristics[6], "Weighted Blocks", chromosome.heuristics[7], "Horizonal Roughness", chromosome.heuristics[8], "Vertical Roughness", chromosome.heuristics[9], "Wells", chromosome.heuristics[10], "Biggest Well", chromosome.heuristics[11], "Lines Cleared", self.linesCleared ), (self.rlim+CELL_SIZE, CELL_SIZE*7)) self.draw_matrix(self.bground_grid, (0,0)) self.draw_matrix(self.board, (0,0)) self.draw_matrix(self.stone, (self.stone_x, self.stone_y)) self.draw_matrix(self.next_stone, (COLS+1,2)) pygame.display.update() for event in pygame.event.get(): if event.type == self.DROPEVENT: self.drop() elif event.type == pygame.QUIT: sys.exit() elif event.type == pygame.KEYDOWN: for key in key_actions: if event.key == eval("pygame.K_" + key): key_actions[key]() if self.piecesPlayed > PIECELIMIT: self.gameover = True if self.genetics: #print(self.linesCleared) self.genetics.GameOver(self.linesCleared) clock.tick(145) if __name__ == "__main__": from agent import Agent app = TetrisApp() app.ai = Agent(app) app.ai.instantPlay = True app.run()
UTF-8
Python
false
false
9,665
py
6
tetris.py
4
0.636213
0.608898
0
332
28.111446
201
mydear33000/Person-Reid
19,610,820,701,955
3b88142fba6095908d6bac6f98dea0143537c6c8
a82fe21d1027b1a7aa9647af63e76bc80f2f575c
/scripts/attrconf.py
b449fbcb15341bf26b5fa2c668e948a8d512cc77
[ "MIT" ]
permissive
https://github.com/mydear33000/Person-Reid
4b99ae3b39aacee4361176ea6d36ac100e41c5a6
0aad210d370737ec8654972d509ad848b22f6ee6
refs/heads/master
2017-05-12T08:29:37.359631
2014-03-07T15:54:11
2014-03-07T15:54:11
null
0
0
null
null
null
null
null
null
null
null
null
null
null
null
null
#!/usr/bin/python2 # -*- coding: utf-8 -*- # datasets = ['CUHK', 'CUHKL', 'CUHKD', 'GRID', 'PRID', 'VIPeR', '3DPeS', 'SARC3D'] datasets = ['3DPeS', 'SARC3D', 'CUHK_01', 'CUHK_02', 'CUHK_03', 'CUHK_04', 'CUHK_05', 'CUHK_07'] names = [ 'genderFemale', 'genderMale', 'ageChild', 'ageYouth', 'ageMiddle', 'ageElder', 'raceAsian', 'raceBlack', 'raceWhite', 'accessoryCap', 'accessoryFaceMask', 'accessoryGlasses', 'accessoryHairBand', 'accessoryHat', 'accessoryHeadphone', 'accessoryKerchief', 'accessoryMuffler', 'accessorySunglasses', 'accessoryTie', 'accessoryOther', 'accessoryNothing', 'carryingBackpack', 'carryingHandbag', 'carryingLuggageCase', 'carryingOutwear', 'carryingShoppingBag', 'carryingShoulderBag', 'carryingUmbrella', 'carryingOther', 'carryingNothing', 'upperBodyBlack', 'upperBodyBlue', 'upperBodyBrown', 'upperBodyGreen', 'upperBodyGrey', 'upperBodyOrange', 'upperBodyPink', 'upperBodyPurple', 'upperBodyRed', 'upperBodyWhite', 'upperBodyYellow', 'upperBodyOtherColor', 'upperBodyCoat', 'upperBodyDownCoat', 'upperBodyDress', 'upperBodyJacket', 'upperBodyShirt', 'upperBodySweater', 'upperBodySuit', 'upperBodyTshirt', 'upperBodyOtherStyle', 'upperBodyNoSleeve', 'upperBodyShortSleeve', 'upperBodyLongSleeve', 'upperBodyLogo', 'upperBodyPlaid', 'upperBodyHStripe', 'upperBodyVStripe', 'upperBodyOtherTexture', 'upperBodyNoTexture', 'lowerBodyBlack', 'lowerBodyBlue', 'lowerBodyBrown', 'lowerBodyGreen', 'lowerBodyGrey', 'lowerBodyOrange', 'lowerBodyPink', 'lowerBodyPurple', 'lowerBodyRed', 'lowerBodyWhite', 'lowerBodyYellow', 'lowerBodyOtherColor', 'lowerBodyJeans', 'lowerBodyPants', 'lowerBodySports', 'lowerBodySkirt', 'lowerBodyStockings', 'lowerBodySuit', 'lowerBodyOtherStyle', 'lowerBodyShort', 'lowerBodyCapri', 'lowerBodyLong', 'lowerBodyLogo', 'lowerBodyPlaid', 'lowerBodyHStripe', 'lowerBodyVStripe', 'lowerBodyOtherTexture', 'lowerBodyNoTexture', 'hairBlack', 'hairBlue', 'hairBrown', 'hairGreen', 'hairGrey', 'hairOrange', 'hairPink', 'hairPurple', 'hairRed', 'hairWhite', 'hairYellow', 'hairOtherColor', 'hairBald', 'hairBrushCut', 'hairMidLength', 'hairLong' ] unival = [ ['genderFemale', 'genderMale'], ['ageChild', 'ageYouth', 'ageMiddle', 'ageElder'], ['raceAsian', 'raceBlack', 'raceWhite'], ['upperBodyNoSleeve', 'upperBodyShortSleeve', 'upperBodyLongSleeve'], ['upperBodyLogo', 'upperBodyPlaid', 'upperBodyHStripe', 'upperBodyVStripe', 'upperBodyOtherTexture', 'upperBodyNoTexture'], ['lowerBodyBlack', 'lowerBodyBlue', 'lowerBodyBrown', 'lowerBodyGreen', 'lowerBodyGrey', 'lowerBodyOrange', 'lowerBodyPink', 'lowerBodyPurple', 'lowerBodyRed', 'lowerBodyWhite', 'lowerBodyYellow', 'lowerBodyOtherColor'], ['lowerBodyJeans', 'lowerBodyPants', 'lowerBodySports', 'lowerBodySkirt', 'lowerBodyStockings', 'lowerBodySuit', 'lowerBodyOtherStyle'], ['lowerBodyShort', 'lowerBodyCapri', 'lowerBodyLong'], ['lowerBodyLogo', 'lowerBodyPlaid', 'lowerBodyHStripe', 'lowerBodyVStripe', 'lowerBodyOtherTexture', 'lowerBodyNoTexture'], ['hairBlack', 'hairBlue', 'hairBrown', 'hairGreen', 'hairGrey', 'hairOrange', 'hairPink', 'hairPurple', 'hairRed', 'hairWhite', 'hairYellow', 'hairOtherColor'], ['hairBald', 'hairBrushCut', 'hairMidLength', 'hairLong'] ] unival_titles = [ 'Gender', 'Age', 'Race', 'Upper Body Sleeve', 'Upper Body Texture', 'Lower Body Color', 'Lower Body Style', 'Lower Body Length', 'Lower Body Texture', 'Hair Color', 'Hair Style' ] multival = [ ['accessoryCap', 'accessoryFaceMask', 'accessoryGlasses', 'accessoryHairBand', 'accessoryHat', 'accessoryHeadphone', 'accessoryKerchief', 'accessoryMuffler', 'accessorySunglasses', 'accessoryTie', 'accessoryOther', 'accessoryNothing'], ['carryingBackpack', 'carryingHandbag', 'carryingLuggageCase', 'carryingOutwear', 'carryingShoppingBag', 'carryingShoulderBag', 'carryingUmbrella', 'carryingOther', 'carryingNothing'], ['upperBodyBlack', 'upperBodyBlue', 'upperBodyBrown', 'upperBodyGreen', 'upperBodyGrey', 'upperBodyOrange', 'upperBodyPink', 'upperBodyPurple', 'upperBodyRed', 'upperBodyWhite', 'upperBodyYellow', 'upperBodyOtherColor'], ['upperBodyCoat', 'upperBodyDownCoat', 'upperBodyDress', 'upperBodyJacket', 'upperBodyShirt', 'upperBodySweater', 'upperBodySuit', 'upperBodyTshirt', 'upperBodyOtherStyle'] ] multival_titles = [ 'Accessories', 'Carryings', 'Upper Body Colors', 'Upper Body Styles' ]
UTF-8
Python
false
false
4,844
py
54
attrconf.py
35
0.666804
0.663088
0
154
30.454545
239
demo112/1807
14,817,637,192,745
065ae040b22eb5404aaaa28adaba45cca1642008
bf397e60bba27b649084966aee686869c7df595d
/PythonNet/day09/day9/thread_server.py
149f4b89af80e3e479a6378481bf0c2461dffce5
[]
no_license
https://github.com/demo112/1807
3783e37f7dab3945a3fc857ff8f77f4690012fbe
9b921c90b3003226d919017d521a32da47e546ad
refs/heads/master
2022-12-01T10:50:24.086828
2018-12-06T09:48:14
2018-12-06T09:48:14
150,758,323
0
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null
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null
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null
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null
null
null
from socket import * import os,sys from threading import * HOST = "0.0.0.0" PORT = 8888 ADDR = (HOST,PORT) #客户端处理函数 def handler(connfd): print("Connect from",connfd.getpeername()) while True: data = connfd.recv(1024).decode() if not data: break print(data) connfd.send(b'Receive your msg') connfd.close() s = socket() s.bind(ADDR) s.listen(5) while True: try: connfd,addr = s.accept() except KeyboardInterrupt: s.close() sys.exit("服务器退出") except Exception as e: print(e) continue t = Thread(target=handler,args= (connfd,)) t.setDaemon(True) t.start()
UTF-8
Python
false
false
705
py
350
thread_server.py
263
0.584435
0.565345
0
36
17.888889
46
Phoenicians-2020/barter-2020
4,569,845,249,594
98a1e24f37e5548c6962297bfa6aff1c46da7d86
7ae1f55ad577831316d27caa6c9dd7d99521d538
/users/admin.py
fa0719033df8d39538bf2fbf0371c3e7ab99fcd2
[]
no_license
https://github.com/Phoenicians-2020/barter-2020
51ca701aedebe0ec11d30edbea9b197c25d75db3
efd235ba2f3cf3ad7fdee85f230a1c3f530aa264
refs/heads/master
2022-11-25T06:07:11.178513
2020-07-30T06:34:47
2020-07-30T06:34:47
281,867,116
0
0
null
null
null
null
null
null
null
null
null
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from django.contrib import admin from django.contrib.auth import get_user_model from users.models import ( Profile, Interests, User ) @admin.register(Profile) class ProfileAdmin(admin.ModelAdmin): list_display = ["id", "user", "gender", ] search_fields = ["user__name", "user__email"] @admin.register(Interests) class InterestsAdmin(admin.ModelAdmin): list_display = ["id", "name", "date_created", "date_updated"] search_fields = ["name"] @admin.register(User) class UserAdmin(admin.ModelAdmin): list_display = ["id", "username", "name", "is_superuser"] readonly_fields = ["password"] search_fields = ["name"]
UTF-8
Python
false
false
657
py
23
admin.py
19
0.674277
0.674277
0
27
23.333333
65
kanatnadyrbekov/Ch1Part2-Task-17
17,910,013,628,071
cd98b77f7b44f64495f7221e320b8f7967830e9b
1012238136c7fd2e2ed5e0f1271ce93b8576279b
/task17.py
42d818cecfa2dbb3341d461a5314c373249705cd
[]
no_license
https://github.com/kanatnadyrbekov/Ch1Part2-Task-17
6f380aa5024e82f4e71c2554b744d9bec1427ff6
18c398cd31a894db3b7aa7244bc14c27e790168b
refs/heads/master
2020-12-02T01:49:45.815116
2019-12-30T04:40:44
2019-12-30T04:40:44
230,848,490
0
0
null
null
null
null
null
null
null
null
null
null
null
null
null
# Write the code which will write excepted data to files below # For example given offices of Google: # 1) google_kazakstan.txt # 2) google_paris.txt # 3)google_uar.txt # 4)google_kyrgystan.txt # 5)google_san_francisco.txt # 6)google_germany.txt # 7)google_moscow.txt # 8)google_sweden.txt # When the user will say “Hello” # Your code must communicate and give a choice from listed offices. After it # has to receive a complain from user, and write it to file chosen by user. # Hint: Use construction “with open” def complains(): google_branches = {1: 'google_kazakhstan.txt', 2: 'google_paris.txt', 3: 'google_kyrgyzstan.txt', 4: 'google_san_francisco.txt', 5: 'google_germany.txt', 6: 'google_moscow.txt', 7: 'google_sweden.txt' } print("Enter a number: ") for key, value in google_branches.items(): office = value.replace('_', ' ').title() print(f"{key}:{office.replace('.Txt','')}") user_choice = int(input("Enter branch num:")) try: office = google_branches[user_choice] user_text = input("Enter your text:") with open(office, 'w') as the_file: the_file.write(user_text) print("Thanks for feedback") except KeyError: print("Choose from the list above") complains() complains()
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Python
false
false
1,457
py
1
task17.py
1
0.586611
0.576259
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45
31.222222
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LopesAbigail/intro-ciencia-computacao
3,788,161,207,111
f53a6ab7c6ae22246ab69f7b9d1faf2a941ef065
8e7f63c9c4f9da6bdd2ed23a38e912400c8e4097
/Tests/raizQuadrada.py
1542303574f2d79e5f16301e3348c2ece860208e
[]
no_license
https://github.com/LopesAbigail/intro-ciencia-computacao
3e339c6778734bded8c3830a4d6100b1110887a4
ca85640ee5c415bdb4b86af64189b5761b4d9c30
refs/heads/main
2023-03-16T09:39:12.626005
2021-03-10T14:27:26
2021-03-10T14:27:26
345,622,914
0
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import math a = int(input("Insira um valor para A: ")) b = int(input("Insira um valor para B: ")) c = int(input("Insira um valor para C: ")) delta = (b**2)-4*a*c if (delta >= 0): x1 = (-b + math.sqrt(delta)) / (2*a) x2 = (-b - math.sqrt(delta)) / (2*a) if (delta == 0): print("A equação possui apenas uma raiz real, que é",x1,"\nDelta =",delta) else: print("A equação possui duas raízes reais, que são:",x1,"e",x2,"\nDelta =",delta) else: print("A equação não possui raízes reais.\nDelta =",delta)
UTF-8
Python
false
false
548
py
67
raizQuadrada.py
66
0.58473
0.564246
0
17
30.588235
89
affinitic/fbk.policy
15,367,393,025,151
5d5da09eac746454d64be0bea33a1b2466b6a0f3
de645aaf06af4cc87e10e599434a306555e055d6
/src/fbk/policy/content/members.py
abecd5f1c37cceffcd27ff9879607e49ed513be0
[]
no_license
https://github.com/affinitic/fbk.policy
205c6bc34e5ff1a7940eaacf0fa8a96015cfaa69
8d3d9a9b4b367d2afc063b5cac1fe318148161ae
refs/heads/master
2016-09-15T21:18:15.113156
2016-03-15T20:23:56
2016-03-15T20:23:56
39,332,229
0
0
null
null
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# -*- coding: utf-8 -*- """ fbk.policy ---------- Created by mpeeters :copyright: (c) 2015 by Affinitic SPRL :license: GPL, see LICENCE.txt for more details. """ from collective.contact.core.content.directory import Directory from collective.contact.core.content.directory import IDirectory from five import grok from plone.autoform import directives as form from plone.dexterity.schema import DexteritySchemaPolicy from zope.interface import implements class IMembers(IDirectory): form.omitted('position_types', 'organization_types', 'organization_levels') class Members(Directory): implements(IMembers) class MembersSchemaPolicy(grok.GlobalUtility, DexteritySchemaPolicy): grok.name('members_schema_policy') def bases(self, schema_name, tree): return (IMembers, )
UTF-8
Python
false
false
799
py
83
members.py
46
0.758448
0.75219
0
31
24.774194
79
Amos-x/Operation
1,133,871,392,966
debb9f0cf6b6bd7838a6c7498f95ce09da8528a2
96c24d4d8b620104ac7ea4ecd31610203bb4b6f6
/apps/assets/forms/domain.py
257f5a1ee5f442514cea644af583f87757b9d47f
[]
no_license
https://github.com/Amos-x/Operation
7bd7ef0582e0e700ff9a40c7d47ab435425185bf
274ab76ad2af79f47aa01f7b35992eef76d59a29
refs/heads/master
2020-03-28T18:18:50.409056
2019-04-16T11:13:45
2019-04-16T11:13:45
148,870,418
0
0
null
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# -*- coding:utf-8 -*- # __author__ = Amos # Email = 379833553@qq.com # Create_at = 2019-03-05 14:19 # FileName = domain from django import forms from django.utils.translation import gettext_lazy as _ from assets.models import Domain, Asset, Gateway from .user import PasswordAndKeyAuthForm __all__ = ['DomainForm', 'GatewayForm'] class DomainForm(forms.ModelForm): assets = forms.ModelMultipleChoiceField( queryset=Asset.objects.all(), label=_("Asset"), required=False, widget=forms.SelectMultiple(attrs={ 'class': 'select2', 'data-placeholder': _('Select assets') }) ) class Meta: model = Domain fields = ['name', 'comment', 'assets'] def __init__(self, *args, **kwargs): if kwargs.get('instance', None): initial = kwargs.get('initial', {}) initial['assets'] = kwargs['instance'].domain_assets.all() super().__init__(*args,**kwargs) def save(self, commit=True): instance = super().save(commit=commit) assets = self.cleaned_data['assets'] instance.domain_assets.set(assets) return instance class GatewayForm(PasswordAndKeyAuthForm): class Meta: model = Gateway fields = [ 'name', 'ip', 'port', 'username', 'protocol', 'domain', 'password', 'private_key_file', 'is_active', 'comment' ] widgets = { 'name': forms.TextInput(attrs={'placeholder': _('Name')}), 'username': forms.TextInput(attrs={'placeholder': _('Username')}) } help_texts = { 'name': '* required', 'username': '* required' } def save(self, commit=True): """ 因为定义了自定义字段,所以要重写save函数 """ instance = super().save() password = self.cleaned_data.get('password') private_key, public_key = super().gen_keys() instance.set_auth(password=password, private_key=private_key) return instance
UTF-8
Python
false
false
2,036
py
103
domain.py
99
0.58
0.5685
0
65
29.769231
79
hainingpan/SPT
7,610,682,080,244
d1d6078043c9c51b18e98a4b74c8a8a53078c8d2
dbef1e401d443e17e484f7e0f87d8c9b556bb98f
/MI_LN_CI.py
326f9c2a466176e79a9ac44302bd42f91f50ae33
[]
no_license
https://github.com/hainingpan/SPT
cd67222bc513ed475974626b91cd26b089e0464f
fe1b465462fd93ca247dfd5ed56ddec416cba8e7
refs/heads/master
2023-07-30T12:38:22.760225
2021-09-24T15:27:16
2021-09-24T15:27:16
353,170,894
1
0
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from Chern_insulator import * import matplotlib.pyplot as plt import argparse import pickle import numpy as np import time from mpi4py.futures import MPIPoolExecutor from copy import copy def run(params_init,subregionA,subregionB,subregionAp,subregionBp,Bp): params=copy(params_init) params.measure_all_Born(subregionAp) if Bp: params.measure_all_Born(subregionBp) MI=params.mutual_information_m(subregionA,subregionB) LN=params.log_neg(subregionA,subregionB) return MI,LN if __name__=="__main__": # if rank==0: parser=argparse.ArgumentParser() parser.add_argument('--es',default=100,type=int) parser.add_argument('--timing',default=False,type=bool) parser.add_argument('--Lx',default=32,type=int) parser.add_argument('--Ly',default=16,type=int) parser.add_argument('--pts',default=100,type=int) parser.add_argument('--Bp',default=False,type=bool) args=parser.parse_args() if args.timing: st=time.time() eta_Born_list=[] MI_Born_list=[] LN_Born_list=[] params_init=Params(m=2,Lx=args.Lx,Ly=args.Ly) executor=MPIPoolExecutor() mutual_info_ensemble_list_pool=[] for pt in range(args.pts): MI_ensemble_list=[] LN_ensemble_list=[] inputs=[] x=sorted(np.random.choice(np.arange(1,args.Lx),3,replace=False)) x=[0]+x eta=cross_ratio(x,args.Lx) eta_Born_list.append(eta) subregionA=[np.arange(x[0],x[1]),np.arange(params_init.Ly)] subregionB=[np.arange(x[2],x[3]),np.arange(params_init.Ly)] subregionAp=[np.arange(x[1],x[2]),np.arange(params_init.Ly)] subregionBp=[np.arange(x[3],args.Lx),np.arange(params_init.Ly)] inputs=[(params_init,subregionA,subregionB,subregionAp,subregionBp,args.Bp) for _ in range(args.es)] mutual_info_ensemble_list_pool.append(executor.starmap(run,inputs)) for pt in range(args.pts): print("{:d}:".format(pt),end='') st=time.time() MI_ensemble_list=[] LN_ensemble_list=[] for result in mutual_info_ensemble_list_pool[pt]: MI,LN=result MI_ensemble_list.append(MI) LN_ensemble_list.append(LN) MI_Born_list.append(MI_ensemble_list) LN_Born_list.append(LN_ensemble_list) print("{:.1f}".format(time.time()-st)) executor.shutdown() eta_Born_list=np.array(eta_Born_list) MI_Born_list=np.array(MI_Born_list) LN_Born_list=np.array(LN_Born_list) with open('MI_LN_CI_Born_En{:d}_pts{:d}_Lx{:d}_Ly{:d}_Ap{:s}.pickle'.format(args.es,args.pts,args.Lx,args.Ly,args.Bp*'Bp'),'wb') as f: pickle.dump([eta_Born_list,MI_Born_list,LN_Born_list],f) if args.timing: print('Elapsed:{:.1f}'.format(time.time()-st))
UTF-8
Python
false
false
2,806
py
53
MI_LN_CI.py
33
0.638275
0.629366
0
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34.075
138
gehuangyi20/random_spiking
13,632,226,219,867
b3d78c87acb2af2f2bfe6e00f388db32db93fdfe
11f810f2cf7d875e2d974ebe703831b7c66822da
/RsNet/compute_adv_diff_vs_tran_cross.py
95daf237784e4c7e20ffa3241c22dd17112cc982
[ "MIT", "LicenseRef-scancode-unknown-license-reference" ]
permissive
https://github.com/gehuangyi20/random_spiking
7ca0a5ead3a617dfe3693c9e68bbcdb3cf6b0990
c98b550420ae4061b9d47ca475e86c981caf5514
refs/heads/master
2021-04-09T20:54:17.834219
2020-03-21T23:05:00
2020-03-21T23:05:00
248,879,259
1
0
null
null
null
null
null
null
null
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null
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#!/usr/bin/python3 import os import json import csv import argparse import numpy as np import re parser = argparse.ArgumentParser(description='create table for adv_diff vs transferability cross methods.') parser.add_argument('-d', '--dir', help='directory, required', type=str, default=None) parser.add_argument('-c', '--config', help='config file, default config.json', type=str, default='config.json') parser.add_argument('-o', '--output', help='output name, default summary', type=str, default='summary_bin') parser.add_argument('--suffix', help='dataset suffix', type=str, default='') args = parser.parse_args() _dir = args.dir output_file = args.output config_fp = open(os.path.join(_dir, args.config), "rb") json_str = config_fp.read() config_fp.close() config = json.loads(json_str.decode()) # mkdir if not os.path.exists(os.path.dirname(os.path.join(_dir, output_file))): os.makedirs(os.path.dirname(os.path.join(_dir, output_file))) _bin = [] att = [] for mthd in config: cur_raw_trans_fp = open(os.path.join(_dir, mthd['transfer']), "r") cur_transfer_reader = csv.DictReader(cur_raw_trans_fp, dialect='excel-tab') cur_att = { "name": mthd['name'] } cur_att_def = [] cur_data = {} cur_data_std = {} cur_pred = {} cur_pred_std = {} for transfer_row in cur_transfer_reader: tmp_def_name = args.suffix + re.sub('[^A-Za-z]+', '', transfer_row['dataset']) tmp_bin = int(transfer_row['bin']) _bin.append(tmp_bin) if tmp_def_name not in cur_att_def: cur_att_def.append(tmp_def_name) cur_data[tmp_def_name] = {} cur_data_std[tmp_def_name] = {} cur_pred[tmp_def_name] = {} cur_pred_std[tmp_def_name] = {} cur_data[tmp_def_name][tmp_bin] = float(transfer_row['trans_rate_mean'])*100 cur_data_std[tmp_def_name][tmp_bin] = float(transfer_row['trans_rate_std'])*100 cur_pred[tmp_def_name][tmp_bin] = float(transfer_row['pred_rate_mean']) * 100 cur_pred_std[tmp_def_name][tmp_bin] = float(transfer_row['pred_rate_std']) * 100 cur_att['def'] = cur_att_def cur_att['data'] = cur_data cur_att['data_std'] = cur_data_std cur_att['pred'] = cur_pred cur_att['pred_std'] = cur_pred_std att.append(cur_att) unique_bin = np.unique(_bin) for cur_bin in _bin: cur_fp = open(os.path.join(_dir, output_file + str(cur_bin) + '.csv'), "wb") cur_pred_fp = open(os.path.join(_dir, output_file + "_pred" + str(cur_bin) + '.csv'), "wb") cur_fp.write('att'.encode()) cur_pred_fp.write('att'.encode()) for cur_def_name in att[0]['def']: cur_fp.write(('|' + cur_def_name + '|' + cur_def_name + 'std').encode()) cur_pred_fp.write(('|' + cur_def_name + '|' + cur_def_name + 'std').encode()) cur_fp.write('\n'.encode()) cur_pred_fp.write('\n'.encode()) for cur_att in att: skip = False for cur_def_name in cur_att['def']: if str(cur_att['data'][cur_def_name][cur_bin]) == 'nan': skip = True break if skip: continue cur_fp.write(cur_att['name'].encode()) cur_pred_fp.write(cur_att['name'].encode()) for cur_def_name in cur_att['def']: cur_fp.write(('|' + str(cur_att['data'][cur_def_name][cur_bin]) + '|' + str(cur_att['data_std'][cur_def_name][cur_bin])).encode()) cur_pred_fp.write(('|' + str(cur_att['pred'][cur_def_name][cur_bin]) + '|' + str(cur_att['pred_std'][cur_def_name][cur_bin])).encode()) cur_fp.write('\n'.encode()) cur_pred_fp.write('\n'.encode()) cur_fp.close() cur_pred_fp.close()
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3,743
py
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compute_adv_diff_vs_tran_cross.py
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hanskamin/music-inference
13,709,535,641,081
12d676862d020b0f577e811c78fe7ab7ed8f9a3b
335cc9ec1aa397431616c6a44e815d358fe1c815
/onto_utils.py
b56bdced450b421885e2c74f8773944a6048b42c
[ "MIT" ]
permissive
https://github.com/hanskamin/music-inference
ab9fcdf4a087618cc1da39007364619835db04a9
5b7830561d8538de10a3c8fe5a140b0b7892e604
refs/heads/master
2020-05-26T04:18:02.830067
2019-06-12T01:53:37
2019-06-12T01:53:37
188,100,036
0
0
MIT
false
2019-06-07T17:24:01
2019-05-22T19:21:00
2019-06-07T00:06:46
2019-06-07T17:24:00
38,348
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Python
false
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""" This module will query the OWL Ontology based on a user's inputted genre to select a set of instruments as midi program integers """ import owlready2 as owl from music21 import instrument def load_ontology(): return owl.get_ontology("root-ontology.owl").load() def get_genre_map(ontology): genres = {} key = 0 for individual in ontology.search(type=ontology.MusicalGenre): genres.update({key: individual}) key += 1 return genres def get_instruments(genre, ontology): programs = [] if genre.label[0] == "Blues": programs.append(instrument.AcousticGuitar().midiProgram) programs.append(instrument.Harmonica().midiProgram) programs.append(instrument.TomTom().midiProgram) elif genre.label[0] == "Folk": programs.append(instrument.Banjo().midiProgram) programs.append(instrument.AcousticBass().midiProgram) programs.append(instrument.Piano().midiProgram) elif genre.label[0] == "Rock": programs.append(instrument.ElectricGuitar().midiProgram) programs.append(instrument.ElectricBass().midiProgram) programs.append(instrument.BassDrum().midiProgram) elif genre.label[0] == "Classical": programs.append(instrument.Violin().midiProgram) programs.append(instrument.Oboe().midiProgram) programs.append(instrument.Flute().midiProgram) programs.append(instrument.Viola().midiProgram) elif genre.label[0] == "Country": programs.append(instrument.AcousticGuitar().midiProgram) programs.append(instrument.Banjo().midiProgram) programs.append(instrument.TomTom().midiProgram) return programs
UTF-8
Python
false
false
1,686
py
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onto_utils.py
8
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0.691578
0
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kjhcode/2021
16,398,185,158,759
25dc35f6f4cb7415df59ec51fcb3fdd397947b9a
3eed03943877231dbbb50c98c6d27af6fa98b387
/142-한학기내신등급산출.py
8ad5040da7ea4635f5ea414b4f3f8469b4fd7ad6
[]
no_license
https://github.com/kjhcode/2021
22bbbc552274ae0bb6fd4d27b151f6b08260ae16
0a57c7c2456f2a022bd533ce0fb92992b31cef23
refs/heads/main
2023-08-28T17:12:53.336111
2021-10-05T08:25:06
2021-10-05T08:25:06
413,659,546
0
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null
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print("[내신 등급 계산 프로그램]") myGrade = 0 totUnit = 0 totGrade = 0 subjectCount = int(input("과목 수: ")) for i in range(subjectCount): print(i+1, end=" ") unit = int(input("과목 단위 수: ")) grade = int(input("석차 등급: ")) totUnit += unit #-> 단위수합=단위수합+단위수 totGrade += grade*unit #-> totGrade=등급*단위수 myGrade = totGrade/totUnit print("내신 등급은", myGrade, "입니다.") if myGrade <= 3: myLevel = "상위권" elif myGrade <= 6: myLevel = "중위권" else: myLevel = "하위권" print("수준은",myLevel,"입니다.")
UTF-8
Python
false
false
688
py
9
142-한학기내신등급산출.py
7
0.526502
0.515901
0
20
25.7
64
malhotraa/aoc2020
1,700,807,080,130
49dcd2ffdcc7c08f7c45b9b08117b71c5d1f71ef
607eb192347f05c0af64912724f29de8ae47229f
/day12/solution.py
19b35055aeb395cb7d85b68773d1e672ab3f63e9
[]
no_license
https://github.com/malhotraa/aoc2020
5ca5862d0158c4289b33a23db7fc11616d78328b
0ad03c54f273604ac9ff091e9d43a209815b9861
refs/heads/main
2023-02-04T17:12:43.709446
2020-12-25T15:31:46
2020-12-25T15:31:46
318,332,585
0
0
null
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with open('input.txt') as f: lines = f.read().split('\n') def normalize_orientation(theta): while theta <= -180: theta += 360 while theta > 180: theta -= 360 return theta def part1_manhattan(lines): dir_to_xy = { 'N': (0, -1), 'S': (0, 1), 'E': (1, 0), 'W': (-1, 0), } orientation_to_xy = { 0: (1, 0), 90: (0, -1), 180: (-1, 0), -90: (0, 1), } rotation_to_theta = { 'L': 1, 'R': -1, } x, y = 0, 0 theta = 0 for line in lines: action = line[0] value = int(line[1:]) if action == 'F': mul_x, mul_y = orientation_to_xy[theta] x += mul_x * value y += mul_y * value elif action in set(['N', 'S', 'E', 'W']): mul_x, mul_y = dir_to_xy[action] x += mul_x * value y += mul_y * value elif action in set(['L', 'R']): theta += (rotation_to_theta[action] * value) theta = normalize_orientation(theta) return abs(x) + abs(y) def rotate_xy(x, y, theta): assert -270 <= theta <= 270 if theta == 90 or theta == -270: return y, -x elif theta == 180 or theta == -180: return -x, -y elif theta == 270 or theta == -90: return -y, x def part2_manhattan(lines): dir_to_xy = { 'N': (0, 1), 'S': (0, -1), 'E': (1, 0), 'W': (-1, 0), } rotation_to_theta = { 'L': -1, 'R': 1, } x, y = 0, 0 way_x, way_y = 10, 1 for line in lines: action = line[0] value = int(line[1:]) if action == 'F': x += way_x * value y += way_y * value elif action in set(['N', 'S', 'E', 'W']): mul_x, mul_y = dir_to_xy[action] way_x += mul_x * value way_y += mul_y * value elif action in set(['L', 'R']): way_x, way_y = rotate_xy(way_x, way_y, rotation_to_theta[action] * value) return abs(x) + abs(y) print('part1 manhattan: ', part1_manhattan(lines)) print('part2 manhattan: ', part2_manhattan(lines))
UTF-8
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false
false
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py
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solution.py
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0.396504
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83
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SimonGuillot/scilang
11,287,174,065,323
af3723071a9d8777167a1fe12faf9d9f12598cbd
e25a704fd0e751369b662daf5876f3c306839afc
/S3 morpho/main.py
1d9fb27ad62a2d4cfeb536218d9a7f50c8645b78
[]
no_license
https://github.com/SimonGuillot/scilang
c25d24426c69beae50e4a7583c1d2fb71e2e3b3d
f94d81a3fc6435bcb99a6ed89964ec7543d7f85b
refs/heads/master
2020-04-24T15:12:08.770432
2019-04-14T16:39:46
2019-04-14T16:39:46
172,056,468
0
2
null
null
null
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null
null
import re import yaml def get(path): test_value=path[0] print(test_value) #print(dictionnaire[test_value]) #cette étape ne fonctionne pas, pourquoi pas comme dico """def compare(cont_end,cont_beg) intersection = cont_end.intersection(cont_beg) if intersection != null : return intersection""" def count_context(dictionnaire): n_context=len(yourdict) return n_context ################################################################## LES SPLITS ICI #string récupérée depuis clé dictionnaire, un contexte string_travail="{u't-': u'^(.*[fs][iyEe92a])t$'}" def split_trans_cont(string): transformation,contexte=string.split(":") return transformation, contexte #split char à char sur transfo et set à set sur contexte ? def split(a): liste_trans=list(transfor) for i in range (len(liste_trans)): if liste_trans[i]=='-' : pre_trans=liste_trans[0:i] post_trans=liste_trans[i+1:len(liste_trans)] print(pre_trans) print(post_trans) return pre_trans,post_trans """ def apply_trans(transformation, contexte): """ ############################################## def main() : #ouverture table yaml stream = open('ca.yaml', 'r') yaml.dump(yaml.load(stream)) dictionnaire=stream #chemin à parcourir path=[(u'ii1P', u'pP'), (u'pP', u'is1S'), (u'is1S', u'ii1S'), (u'ii1S', u'ppMP'), (u'ppMP', u'ii1P')] print(path[0]) #erreur en 'none' #begin #pathing """ get(path) if n_context == 1 : #nécessité de considérer différement les split(contexte) #cas où on a plusieurs contextes pour pouvoir les enregistrer ensemble liste_result.append(compare(cont_end,cont_beg)) elif n_context > 1 : context_step_n for i in range len(context_step_n): split(contexte) result_step_n.append(compare(cont_end,cont_beg)) liste_result.append(result_step_n) return list """ #split and compare #end ######################################## if __name__ == "__main__": win = None main() ######################################### """ #itération dictionnaire for key, value in d.items(): if isinstance(value, dict): for sub_key, sub_value in value.items(): print(key,sub_key,sub_value) else: print(key,value) with open("fichier.yaml", 'r') as stream: try: print(yaml.load(stream)) yaml.load(stream) yaml.dump(yaml.load(stream)) except yaml.YAMLError as exc: print(exc) print(mes_tuples) def searchStringInYaml(fichier,string): with open(filename, 'r') as stream: content = yaml.load(stream) if string in content: print string return 1 else: return 0 stream.close() stream = file('fichier.yaml', 'r') dict = yaml.load(stream) for key in dict: if key in dict == "ai1P": print (key), dict[key] key = 'ai1P' for key, value in yaml.load(open('fichier.yaml'))[('ai1P', 'ai1S')].iteritems(): print (key) print(value) y = yaml.load(open("fichier.yaml", "w")) print(y) #tentative de set à set x="(.*[ptkbdgfsSvzZmnJjrwHiyEe926auOoêûâô][ptkbdgfsSvzZmnJjlr])E$" lx=list(x) print(lx) print(len(lx)) liste_organisee=list() en_tete=list() liste_organisee.append(en_tete) for i in range(len(lx)) : if i == "[" : en_tete.append(i) print(en_tete) def split(): "variables" x="(.*[ptkbdgfsSvzZmnJjrwHiyEe926auOoêûâô][ptkbdgfsSvzZmnJjlr])E$" lx=list(x) en_tete=list() test1=list() "===============" for i in range(len(lx)) : if lx[i]== '(' : a1=list() split() string_travail="{u't-': u'^(.*[fs][iyEe92a])t$'}" def split_trans_cont(string): transformation, contexte=string.split(":") return transformation, contexte print(split_trans_cont(string_travail)) #print(transformation) def split_contexte(contexte): liste_travail=list(contexte) print(liste_travail) def main(): string_travail="{u't-': u'^(.*[fs][iyEe92a])t$'}" split_trans_cont(string_travail) print(split_trans_cont(string_travail)) print(transformation) split_contexte(split_trans_cont(string_travail))"""
UTF-8
Python
false
false
4,384
py
7
main.py
2
0.576932
0.569594
0
174
24.063218
114
EtavaresMixes/drappi-CMS---django
13,804,024,904,090
5d79b0c61caaf2e1f69b0cc8a5d2a3bffc90bbec
23658085c5eab02a86ff866a2127803622bd8a8d
/users/views.py
62496c5202782621839deeca81f71ab37e4e48d0
[]
no_license
https://github.com/EtavaresMixes/drappi-CMS---django
5014875d737f0925a005b05d5b1a48fbb74a416d
9c1223aa8f0d4a2cfe77423816f9630a63e0af0e
refs/heads/master
2023-03-12T09:07:55.510351
2021-03-05T12:43:17
2021-03-05T12:43:17
null
0
0
null
null
null
null
null
null
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from django.shortcuts import render, redirect from django.contrib.auth.forms import UserCreationForm from django.contrib import messages from . forms import UserRegisterForm, LoginForm, CadastraCNPJ from django.contrib.auth.decorators import login_required from django.contrib.auth.models import Group from clientes.models import Cliente from . decorators import usuario_nao_autenticado, usuario_permitido from django.views.generic import DetailView, ListView from pedidos.models import Pedido from django.contrib.auth.views import LoginView from django.contrib.auth import authenticate, login from django.core.paginator import Paginator @usuario_nao_autenticado def register(request): title = 'CADASTRO' slang = '"Registre sua conta!"' form = UserRegisterForm() cnpj_form = CadastraCNPJ() if request.method == 'POST': form = UserRegisterForm(request.POST) cnpj_form = CadastraCNPJ(request.POST) if form.is_valid() and cnpj_form.is_valid(): user = form.save() cnpj = cnpj_form.cleaned_data['cnpj'] username = form.cleaned_data.get('username') email = form.cleaned_data.get('email') first_name = form.cleaned_data.get('first_name') last_name = form.cleaned_data.get('last_name') telefone = cnpj_form.cleaned_data.get('telefone') empresa = cnpj_form.cleaned_data.get('empresa') group = Group.objects.get(name='clientes') user.groups.add(group) Cliente.objects.create(user=user, cnpj=cnpj, email=email, nome=first_name, sobrenome=last_name, telefone=telefone, empresa=empresa ) messages.success(request, f'Conta criada para {username}!') return redirect('login') else: form = UserRegisterForm() return render(request, 'users/register.html', { 'form': form, 'cnpj_form': cnpj_form, 'title': title, 'slang': slang}) @login_required(login_url='homepage') @usuario_permitido(allowed_roles=['clientes']) def profile(request): cliente = Cliente.objects.get(id=request.user.id) pedidos = cliente.pedido_set.all() total_de_pedidos = pedidos.count() title = 'Perfil' slang = '"Dados pessoais do cliente!"' paginator = Paginator(pedidos, 10) page = request.GET.get('p') pedidos = paginator.get_page(page) context = { "clientes": cliente, "pedidos": pedidos, 'title': title, 'slang': slang, "total_de_pedidos": total_de_pedidos, } return render(request, 'users/profile.html', context) def login_page(request): title = 'Login' slang = '"Insira seu dados pessoais!"' form = LoginForm(request.POST or None) context = { 'title': title, 'slang': slang, 'form': form} if form.is_valid(): username = form.cleaned_data.get("username") password = form.cleaned_data.get("password") user = authenticate(request, username=username, password=password) if user is not None: login(request, user) # Redirect to a success page. return redirect('/') # context['form'] = LoginForm() else: context['login_message'] = "Senha inválida!!" return render(request, 'users/login.html', context) return render(request, 'users/login.html', context)
UTF-8
Python
false
false
3,519
py
74
views.py
37
0.627629
0.627061
0
101
33.831683
74
jpur3846/dfe-contractors
5,523,327,969,872
32c7eba5491efe4e8fbb3e0b2c6fcea811185e3b
e3e311a7a7a86d97799ee4ff58185e155d745106
/contractors/con/apps.py
068c2b3bc2078ecc92b68cdef2d015bd07680a45
[]
no_license
https://github.com/jpur3846/dfe-contractors
d8938edc1e6b231b7fd132bdf9848b451bb8fdaf
3cf34751d588cc6f0914b504a7c1c7f3e11bc385
refs/heads/master
2022-11-24T16:37:38.297265
2020-07-30T13:36:21
2020-07-30T13:36:21
276,302,265
2
0
null
null
null
null
null
null
null
null
null
null
null
null
null
from django.apps import AppConfig class ConConfig(AppConfig): name = 'con'
UTF-8
Python
false
false
80
py
118
apps.py
85
0.7375
0.7375
0
4
19
33
tranhoangkhuongvn/algo_gym
12,489,764,926,698
87360ad7c3a6502a6025062788f9cfbd1230acfb
2ce595e4cf76dca58fce8673325418cc13696e4d
/EPI/4-2-swap-bits.py
9665f527680f31e9869f37ae23d345d4379cd0ba
[]
no_license
https://github.com/tranhoangkhuongvn/algo_gym
4ae9d9570da5b0ef78594fd799734da829e02271
049af6be6a042c0f4f7a6532078761172ec212ae
refs/heads/master
2020-09-02T15:02:50.246948
2020-01-19T00:24:50
2020-01-19T00:24:50
219,245,440
0
0
null
null
null
null
null
null
null
null
null
null
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null
#Write a function takes a 64-bit integer and swap bits at indices i and j #LSB is at rightmost bit def swap_bits(n, i, j): bit0 = (1 & (n >> i)) bit1 = (1 & (n >> j)) if bit0 != bit1: if bit0: #reset bit0 to 0, set bit1 to 1 n = n & (~(1 << i)) n = n | (1 << j) else: n = n | (1 << i) n = n & (~(1 << j)) print(bin(n)) return n print(swap_bits(73, 1, 6))
UTF-8
Python
false
false
387
py
28
4-2-swap-bits.py
27
0.503876
0.449612
0
19
19.105263
73
krishnakalyan3/spark_streaming
1,194,000,956,084
256b2b17fe3e9f80606e46dae04b2deac346dbe0
81087a55da2f8c96c53a71fc9db577269da40e44
/src/examples/03_agg.py
87ce1cec6f05c304f691f3f4e4527f3e680ffe1c
[]
no_license
https://github.com/krishnakalyan3/spark_streaming
bf5a0b6fa189658c9476638789476c2dbf2882df
57dd570981fd1ac608caa1d4496a5cc6356bf950
refs/heads/master
2020-03-14T10:46:33.037695
2018-05-07T13:30:57
2018-05-07T13:30:57
131,575,391
0
0
null
null
null
null
null
null
null
null
null
null
null
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null
from pyspark.sql import SparkSession from pyspark.sql.types import StructType from pyspark.sql.types import IntegerType, StringType, TimestampType from pyspark.sql.types import StructField from pyspark.sql import functions as F from pyspark.sql.functions import split if __name__ == "__main__": spark = SparkSession\ .builder\ .appName("StructuredNetworkWordCountWindowed")\ .getOrCreate() # Create DataFrame representing the stream of input lines from connection to host:port lines = spark\ .readStream \ .format('socket')\ .option('host', 'localhost')\ .option('port', 9999)\ .option('includeTimestamp', 'true')\ .load() data_frame = lines.select( (split(lines.value, ' ')).getItem(0).cast('integer').alias("time"), (split(lines.value, ' ')).getItem(1).cast('double').alias("moisture"), (split(lines.value, ' ')).getItem(2).cast('float').alias("temp"), (split(lines.value, ' ')).getItem(3).cast('float').alias("battery"), (split(lines.value, ' ')).getItem(3).cast('double').alias("battery"), lines.timestamp ) # Select df_select = data_frame.select(data_frame.time, data_frame.temp) # Filter df_filter = df_select.filter(df_select.temp < 0) # Use this with watermark # Group #df_group = df_select.groupBy(df_filter.time).sum() query = df_filter.writeStream \ .outputMode('append') \ .format('console') \ .start() query.awaitTermination() # $SPARK_HOME/bin/spark-submit 03_agg.py
UTF-8
Python
false
false
1,591
py
7
03_agg.py
6
0.629164
0.621622
0
50
30.82
90
kejukeji/bourjois_sub
16,947,940,969,334
37fc23b8f9af7d3fd6546108950c684467d1fa87
c24e1b0f9fc43593aaf35358be58bccb78ee67f3
/bourjois/models/__init__.py
65f21fdd7138ff3a11ec1bbd31636e43e428547d
[]
no_license
https://github.com/kejukeji/bourjois_sub
5debebd2d39e72e8a291a301aea887b83e767eb2
d8614345ae8c3ede4e4069a9d3883e06a3adff23
refs/heads/master
2016-09-03T07:15:52.021348
2014-05-16T16:05:16
2014-05-16T16:05:16
19,665,950
0
1
null
null
null
null
null
null
null
null
null
null
null
null
null
# coding: UTF-8 from .coupon import * from .times import *
UTF-8
Python
false
false
58
py
22
__init__.py
11
0.706897
0.689655
0
3
18.666667
21
sh2MAN/newsgather
9,440,338,160,984
3a230644d01dd10c07ed468739a3e4363d8f5d07
09c9723fe97a4e5b92207de47fdf5dd717710e97
/core/utils.py
be0139b04d3cd22a2678e7303aefd7290ae51785
[ "MIT" ]
permissive
https://github.com/sh2MAN/newsgather
7107e208fa6ef9255624fbc53077630dcac51edb
c6ed86bc0c4568ffa19b988165643033eff66158
refs/heads/main
2023-03-30T10:06:20.989662
2021-03-25T21:06:46
2021-03-25T21:06:46
349,650,242
0
0
null
null
null
null
null
null
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from loguru import logger from starlette.requests import Request logger.add( 'logging.log', format="{name} {message}", level="INFO", rotation="5MB" ) def get_db(request: Request): """Возвращает текущую сессию.""" return request.state.db
UTF-8
Python
false
false
275
py
15
utils.py
10
0.702381
0.698413
0
11
21.909091
74
vadim-ivlev/text-processor
16,793,322,158,589
a0657ab1e0024c6542dba17e8575dbe900242028
eb6de9d204ae12b8066cf437ec6d0bbedbdb5809
/tests/test-text-processor.py
26f2bfc0f615238cd4061fa16f7d3a622a344ccf
[]
no_license
https://github.com/vadim-ivlev/text-processor
9c4b13015c60f022dddf341f3dd59ca30983923e
f129b08560ab97c0590d06a675e202638b73705c
refs/heads/master
2023-01-20T18:44:14.062049
2020-11-26T15:03:37
2020-11-26T15:03:37
293,517,049
0
0
null
null
null
null
null
null
null
null
null
null
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sample_text = """ <p>Глава государства начал встречу сразу с проблемных вопросов: в регионе сокращается сельхозпроизводство на фоне солидного роста по стране. Голубев выразил надежду, что в этом году тенденцию удастся переломить. Президент заметил: если необходима поддержка, то нужно сформулировать просьбы. "И по стройке: сокращаются и объемы строительства. Здесь что?" - уточнил он. Собеседник рассказал о заметном сокращении индивидуального жилищного строительства. В целом строительная программа "плюсует", но жилье "минусует", признал Голубев. "Поэтому это стало нашим приоритетом", - заверил региональный лидер.</p><p>"Вы человек очень опытный, знаете, мы последнее время, последние годы большое внимание обращаем на проблемы демографии", - заметил Путин. По его словам, после определенного подъема у нас сейчас наблюдается сокращение численности постоянного населения. "Но в Ростовской области, несмотря на то, что это южный, благоприятный климатически регион, сокращение происходит даже в большем объеме, чем в среднем по стране", - сказал президент и назвал возможные причины: недостаточное количество врачей и мест в детсадах. "Это очень важный фактор для того, чтобы люди себя чувствовали уверенно и комфортно", - объяснил глава государства.</p><p>Важен и такой показатель, как уровень безработицы. "Ясно, что это сегодня одна из главных проблем в стране, это совершенно очевидно", - заметил Путин. Но Ростовская область развита и в промышленном отношении, и в отношении возможностей для сельского хозяйства. "Конечно, нужно обратить внимание на рынок труда", - указал президент.</p><p>"У нас действительно "выскочила" реально безработица - 96 с лишним тысяч человек (4,6 процента) при том, что до определенного времени уровень безработицы был не выше или на уровне среднероссийского, - признал губернатор. - Мы предпринимаем сейчас меры для того, чтобы максимально запустить те механизмы, которые позволяют людям работать". "Мы будем искать новые решения. Я думаю, что для нас это важнейшая задача, усилия будем прилагать для того, чтобы здесь ситуацию переломить", - заверил он.</p><div class="incut">В Ростовской области минимальная долговая нагрузка, низкий уровень аварийного жилья и очень хорошие перспективы с точки зрения инвестпроектов&nbsp;</div><p>Глава государства также заявил, что опережающий темп роста промышленного производства - заслуга самого Голубева и его команды. За первое полугодие, конечно, есть спад, но он меньше, чем по стране. В нормальном состоянии и региональные финансы, в области минимальная долговая нагрузка, низкий уровень аварийного жилья и очень хорошие перспективы с точки зрения инвестпроектов, оценил Путин. Президент призвал поддержать усилия бизнеса по созданию новых, хорошо оплачиваемых, качественных и современных высокотехнологичных рабочих мест. Голубев также сообщил, что селяне прекрасно сработали по уборке ранних зерновых, и президент одобрил предложение наградить их.</p><div class="Section">Между тем</div><p>Состоялся телефонный разговор Владимира Путина с президентом Республики Беларусь Александром Лукашенко, сообщили в пресс-службе Кремля. Александр Лукашенко проинформировал о предпринимаемых мерах в целях нормализации обстановки в стране. Затрагивалась также тематика двустороннего сотрудничества в вопросах противодействия коронавирусной инфекции.</p> """ # sample_text = """ # Медведев возглавит комиссию по Арктике. Медведев возглавит комиссию по Арктике. # Президент подписал указ о создании межведомственной комиссии Совбеза по вопросам обеспечения интересов РФ в Арктике. Возглавит комиссию заместитель председателя Совбеза, сейчас эту должность занимает Дмитрий Медведев. # """ # sample_text = """ # Медведев возглавит комиссию по Арктике. # """ import os, sys sys.path.insert(1, os.path.dirname(sys.path[0])) from deploy import text_processor import simplejson as json o = text_processor.process_text(sample_text) print(json.dumps(o,indent=2, ensure_ascii=False))
UTF-8
Python
false
false
6,936
py
44
test-text-processor.py
22
0.805625
0.803867
0
48
81.958333
219
tgandor/meats
2,877,628,122,579
2fbfa5ce53f95d2e45735c0c0d85dfb250a313f9
41a4887a52afe81f203d0917c5ef54ccbe2389fe
/opencv_py/deskew.py
696a5c1eab2d86a55ca5ace674b8aec53a858fff
[]
no_license
https://github.com/tgandor/meats
2efc2e144fc59b2b99aeeaec5f5419dbbb323f9b
26eb57e49752dab98722a356e80a15f26cbf5929
refs/heads/master
2023-08-30T20:35:47.949622
2023-08-25T13:26:23
2023-08-25T13:26:23
32,311,574
13
9
null
false
2022-06-22T20:44:44
2015-03-16T08:39:21
2022-01-06T00:31:18
2022-06-22T20:44:43
8,543
11
5
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Python
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#!/usr/bin/env python import math import cv2 import sys import os from fractions import Fraction def cv_size(img): return tuple(img.shape[1::-1]) def cv_center(img): w, h = cv_size(img) return w/2, h/2 def get_screen_res(): if sys.platform.startswith('linux'): import os lines = [line for line in os.popen('xrandr').read().split('\n') if line.find('*') != -1] return tuple(map(int, lines[0].split()[0].split('x'))) else: try: from win32gui import GetDesktopWindow, GetWindowRect return tuple(GetWindowRect(GetDesktopWindow())[2:]) except ImportError: pass return 800, 600 SCREEN_WIDTH, SCREEN_HEIGHT = get_screen_res() def show_fit(img, name='preview', expand=False, rotate=False): # partially deprecated: cv2.WINDOW_NORMAL flag w, h = cv_size(img) W, H = SCREEN_WIDTH, SCREEN_HEIGHT if w <= W and h <= H and not expand: to_show = img elif w > W or h > H: if rotate and min(Fraction(W, w), Fraction(H, h)) < min(Fraction(W, h), Fraction(H, w)): img = cv2.flip(cv2.transpose(img), 0) w, h = h, w if h * W > H * w: w1 = w * H / h h1 = H else: w1 = W h1 = h * W / w to_show = cv2.resize(img, (w1, h1)) else: # expand ... raise NotImplementedError('Cannot expand preview image') scale = 1.0 while True: cv2.imshow(name, to_show) key = cv2.waitKey(1500) if key == -1: break char_code = key % 256 if char_code == ord(' '): # 'pause' cv2.waitKey(0) break if char_code == ord('+'): scale *= 2 to_show = cv2.resize(img, (int(w1*scale), int(h1*scale))) continue if char_code == ord('-'): scale /= 2 to_show = cv2.resize(img, (int(w1*scale), int(h1*scale))) continue if char_code == ord('q'): exit() def process(filename): print('Processing {0}...'.format(filename)) img = cv2.imread(filename) print(cv_size(img)) gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # _, region = cv2.threshold(img, 128.0, 255.0, cv2.THRESH_BINARY_INV) edges = cv2.Canny(gray, 50, 150, apertureSize=3) lines = cv2.HoughLinesP(edges, rho=1, theta=math.pi/180.0, threshold=80, minLineLength=60, maxLineGap=10) n = len(lines[0]) for x1, y1, x2, y2 in lines[0]: cv2.line(gray, (x1, y1), (x2, y2), (0, 255, 0), 2) angles = sorted([math.atan2(y2-y1, x2-x1) for x1, y1, x2, y2 in lines[0]]) print("There's {0} lines.".format(n)) middle_angle = angles[n / 2] * 180/math.pi print('The middle angle is: {0}'.format(middle_angle)) if -5.0 < middle_angle < 5.0: if middle_angle > 0.125 or middle_angle < -0.125: img_rotated = cv2.warpAffine( img, cv2.getRotationMatrix2D(cv_center(img), middle_angle , 1.0), cv_size(img), borderMode=cv2.BORDER_CONSTANT, borderValue=(255, 255, 255) ) file_, extension = os.path.splitext(filename) cv2.imwrite('_'+file_+'_'+extension, img_rotated) else: print('The angle is too small. No action taken.') else: print('The angle is too radical. No action taken.') show_fit(edges, rotate=False) show_fit(gray, rotate=False) def main(): if len(sys.argv) < 2: print('Usage: {0} <image_with_text_file>...'.format(sys.argv[0])) map(process, sys.argv[1:]) if __name__ == '__main__': main()
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cogitare-ai/cogitare
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/tests/test_plugins/test_plot.py
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permissive
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refs/heads/master
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2019-09-24T11:52:08
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MIT
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2018-02-01T15:25:38
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import unittest import os import tempfile import pytest from cogitare.plugins import PlottingMatplotlib class TestPlottingMatplotlib(unittest.TestCase): def setUp(self): self.p = PlottingMatplotlib() def test_plot(self): self.p.add_variable('test', 'Test') with pytest.raises(KeyError) as info: self.p() self.assertIn('test', str(info.value)) self.p(test=1) self.p(test=2) self.p(test=3) self.p(test=2) self.p(test=[1, 2, 3, 4, 5]) self.p(test=0) self.p(test=-1) def test_plot_with_std(self): self.p.add_variable('test', 'Test', use_std=True) self.p(test=1) self.p(test=[1, 2, 3, 4]) self.p(test=2) self.p(test=2) def test_save_img(self): f = tempfile.NamedTemporaryFile() name = f.name + '.png' p = PlottingMatplotlib(file_name=name) p.add_variable('test', 'Test', use_std=True) p(test=1) p(test=[1, 2, 3, 4]) p(test=2) f.flush() self.assertGreater(os.path.getsize(name), 0)
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nregnault/lsst_cadence
8,890,582,337,640
b72757f00dcf38615bf9a49cef5c510fed734ad9
f74c78ba3efe9668899980b022a6d66aa1f26044
/sncadence/summary.py
9fe49e4d62dca3c7587b5397bb1895ad0d161ec8
[]
no_license
https://github.com/nregnault/lsst_cadence
cee16b73647884ea29228ff0f21f04c17e5753e7
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refs/heads/master
2021-07-25T20:59:23.238328
2021-07-06T01:16:58
2021-07-06T01:16:58
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#multiplex cross_prod group_by "analyze[-1]" import numpy as np r = get_input() print '*' * 72 to_merge = glob_parent('sim_history.npy', 'analyze') print to_merge data = [np.load(nm) for nm in to_merge] # merge the history ntuples into a big one d = np.hstack(data) # extract just the final state from the NTuples d_final_state = np.rec.fromrecords([dd[-1].tolist() for dd in data], names = d.dtype.names) # dump into the summary segment combined_sim_history_fn = get_data_fn('combined_sim_history.npy') np.save(combined_sim_history_fn, d) final_state_sim_history_fn = get_data_fn('final_state_sim_history.npy') np.save(final_state_sim_history_fn, d_final_state) # summary plots summary_plot_with_labels = get_data_fn('nsn_vs_z_labels.png') cmd = ['cadence_plots.py', '-o', summary_plot_with_labels, '--title', '$N_{SN} vs. z$ [%s]' % r[0], final_state_sim_history_fn] logged_subprocess(cmd) summary_plot_no_labels = get_data_fn('nsn_vs_z_nolabels.png') cmd = ['cadence_plots.py', '-o', summary_plot_no_labels, '--nolabels', '--title', '$N_{SN} vs. z$ [%s] % r[0]', final_state_sim_history_fn] logged_subprocess(cmd) seg_output = r
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MrCubanfrog/OHappening
16,011,638,105,224
2aa1254c17d7f1ab019fe7e03a3cf7796533a266
2be0e7024f59563dbe8aab9e5e31ead9128562cf
/ohappening/ohappening.py
da827d877dd54fbc44b1eac4bf71f80b93d6cb8d
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permissive
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refs/heads/master
2020-04-17T19:43:28.779747
2019-11-07T14:22:01
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""" Main module for starting the OHappening application. Run the start function to start the application. FEATURE LIST ------------ Started Done -Project initialization and basics [X] [X] -Working calendar [X] [X] -Clock [X]     [X] -Various timers [X] [X] -Syncing with google calendar [X] [X] -HSL widget [X] [ ] -Event timer Widget [ ] [ ] Last updated: 11.02.2019, Ilmo Salmenperä """ import sys import pkg_resources import logging from datetime import datetime from PyQt5.QtWidgets import QMainWindow, QApplication, QGridLayout, QWidget from PyQt5.QtCore import QTimer from ohappening.event import Event, EventWidget from ohappening.eventdescriptor import EventDescriptorWidget from ohappening.eventlist import EventListWidget from ohappening.pageheader import PageHeaderWidget from ohappening.hslwidget import HSLWidget from ohappening.eventtimer import EventTimerWidget from ohappening.calendarmanager import CalendarManager from ohappening.config import CALENDAR_CONFIG_JSON class OHappenWidget(QWidget): """ Class for containing all widgets for OHappening. """ def __init__(self, parent, logger): super().__init__(parent) self.logger = logger self.logger.info('Creating Grid layout') self.layout = QGridLayout(self) self.layout.setSpacing(0) self.logger.info('Creating Widgets') self.page_header_widget = PageHeaderWidget(self, self.logger) self.event_list_widget = EventListWidget(self, self.logger) self.event_descriptor_widget = EventDescriptorWidget(self, self.logger) self.hsl_widget = HSLWidget(self, self.logger) self.event_timer_widget = EventTimerWidget(self, self.logger) self.logger.info('Creating Managers') self.calendar_managers = [] for calendar_config in CALENDAR_CONFIG_JSON['calendars']: self.calendar_managers.append(CalendarManager(calendar_config, logger)) self.layout.addWidget(self.page_header_widget, 0, 0, 1, 5) self.layout.addWidget(self.event_list_widget, 1, 0, 4, 3) self.layout.addWidget(self.hsl_widget, 1, 3, 1, 2) self.layout.addWidget(self.event_descriptor_widget, 2, 3, 2, 2) self.layout.addWidget(self.event_timer_widget, 4, 3, 1, 2) self.setLayout(self.layout) self.logger.info('Creating Timer') start_clock_timer = QTimer(self) start_clock_timer.setSingleShot(True) start_clock_timer.timeout.connect(self.startMainTimer) start_clock_timer.start(1000 * (60 - datetime.now().second)) self.updateCalendarElements() def startMainTimer(self): """ Function for starting the main clock counter """ self.minute_timer = QTimer(self) self.minute_timer.timeout.connect(self.minuteChanged) self.minute_timer.start(60 * 1000) self.minuteChanged() def minuteChanged(self): """ Function that will be called every time the minute in this computer changes """ self.logger.info("Minute Change!") self.updateCalendarElements() def updateCalendarElements(self): """ Function that updates all calendar elements and fetches new elements from the calendars """ events = [] for calendar in self.calendar_managers: events.extend(calendar.fetchEvents()) self.page_header_widget.updatePageHeader() self.event_list_widget.updateEvents(events) self.event_descriptor_widget.setNewEventToDescriptor() self.hsl_widget.updateHslWidget() class OHappenWindow(QMainWindow): """ Class for containing all functionality in OHappening. The heart of it all. """ def __init__(self, screen_size, debug): super().__init__() self.initLogging(debug) self.setStyleSheet('background-color : white') self.logger.info('Initializing OHappenWindow variables') OHAPPENING_VERSION = pkg_resources.require("OHappening")[0].version self.title = "OHappening {0}".format(OHAPPENING_VERSION) self.setWindowTitle(self.title) self.logger.info('Adding OHappenWidget to MainWindow') self.setCentralWidget(OHappenWidget(self, self.logger)) self.centralWidget().layout.setContentsMargins(0, 0, 0, 0) self.logger.info('Program initialized. Showing fullscreen') self.showFullScreen() def initLogging(self, debug): """ Initialize all logging related stuff. """ self.logger = logging.Logger('OHappening Log') ch = logging.StreamHandler() if debug: self.logger.setLevel(logging.DEBUG) ch.setLevel(logging.DEBUG) else: self.logger.setLevel(logging.INFO) ch.setLevel(logging.INFO) formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s') ch.setFormatter(formatter) self.logger.addHandler(ch) self.logger.debug("Logger initialized") def start(debug = False): """ Starting function for the application. Creates the QApplication and such. Setting debug to True will enable debugging logs. """ if len(sys.argv) == 2 and sys.argv[1] == 'DEBUG': debug = True app = QApplication(sys.argv) screen_size = QApplication.desktop().screenGeometry() ex = OHappenWindow(screen_size, debug) sys.exit(app.exec_()) if __name__ == "__main__": start()
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samarthdave/nagini
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[]
no_license
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# use ScrapingClub.com > Exercise 3 # imports import requests from bs4 import BeautifulSoup base_url = 'https://scrapingclub.com/exercise/list_basic/' resp = requests.get(base_url) soup = BeautifulSoup(resp.text, 'lxml') # find all cards count = 0 items = soup.find_all('div', class_='col-lg-4 col-md-6 mb-4') # text properties: name - card-title, price - h5 tag for block in items: count += 1 itemName = block.find('h4', class_='card-title').text.strip('\n') itemPrice = block.find('h5').text print('{0}: {1:<30} - {2}'.format(count, itemName, itemPrice)) # PAGINATION # hold all urls (prev, 1, 2, ... 5, next) page_refs = soup.find('ul', class_='pagination') # append to these arrays with .../...?page=N urls = [] full_urls = [] # get all anchor tags links = page_refs.find_all('a', class_='page-link') for link in links: pageNum = int(link.text) if link.text.isdigit() else None # if not None (since None is "falsey") if pageNum: x = link.get('href') urls.append(x) full_urls.append(base_url + x) # print all hrefs print('='*20) for i in full_urls: print(i) print('='*20) # ask user if should paginate through all URLs prompt_resp = input("Explore the above URLs found in pagination (yes/no)? ") should_explore = prompt_resp.lower() == 'yes' # convert all to real urls from base url for url in full_urls: # if shouldn't make request to all paths then break if not should_explore: break resp = requests.get(url) soup = BeautifulSoup(resp.text, 'lxml') # find all cards items = soup.find_all('div', class_='col-lg-4 col-md-6 mb-4') # text properties: name - card-title, price - h5 tag for block in items: count += 1 itemName = block.find('h4', class_='card-title').text.strip('\n') itemPrice = block.find('h5').text print('{0}: {1:<30} - {2}'.format(count, itemName, itemPrice))
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club.py
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carlgval/python-challenges
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cff275ce805925e24637ce0960bd8796963c610d
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/hanoi_tower_moves.py
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[]
no_license
https://github.com/carlgval/python-challenges
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refs/heads/master
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2019-03-15T13:12:11
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#!/usr/bin/env python2 # -*- coding: utf-8 -*- """ Created on Thu Jan 10 08:21:13 2019 The Tower of Hanoi is a puzzle game with three rods and n disks, each a different size. All the disks start off on the first rod in a stack. They are ordered by size, with the largest disk on the bottom and the smallest one at the top. The goal of this puzzle is to move all the disks from the first rod to the last rod while following these rules: You can only move one disk at a time. A move consists of taking the uppermost disk from one of the stacks and placing it on top of another stack. You cannot place a larger disk on top of a smaller disk. Write a function that prints out all the steps necessary to complete the Tower of Hanoi. You should assume that the rods are numbered, with the first rod being 1, the second (auxiliary) rod being 2, and the last (goal) rod being 3. For example, with n = 3, we can do this in 7 moves: Move 1 to 3 Move 1 to 2 Move 3 to 2 Move 1 to 3 Move 2 to 1 Move 2 to 3 Move 1 to 3 @author: carlgval """ def moves_hanoi_tower(levels): if levels > 1: moves = 1 + moves_hanoi_tower(levels - 1) * 2 else: moves = 1 return moves if __name__ == '__main__': print(moves_hanoi_tower(3)) print(moves_hanoi_tower(5)) print(moves_hanoi_tower(7))
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mutaku/Stumpy
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/stumpy.fcgi
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#!/usr/bin/env python import sys, os sys.path.insert(0, "..") os.environ['PYTHON_EGG_CACHE'] = "/tmp/" os.environ['DJANGO_SETTINGS_MODULE'] = "settings" from django.core.servers.fastcgi import runfastcgi runfastcgi(["method=threaded", "daemonize=false", "debug=1"])
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stumpy.fcgi
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chaman21/sparking
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/banking/banking/migrations/0006_auto_20210911_1515.py
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[]
no_license
https://github.com/chaman21/sparking
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2021-09-12T16:38:44
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# Generated by Django 3.1.6 on 2021-09-11 09:45 from django.db import migrations class Migration(migrations.Migration): dependencies = [ ('banking', '0005_auto_20210911_1507'), ] operations = [ migrations.RemoveField( model_name='trans', name='email', ), migrations.RemoveField( model_name='trans', name='mobile', ), migrations.RemoveField( model_name='trans', name='name', ), ]
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0.522727
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IUIUN/The-Best-Time-Is-Now
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/938. Range Sum of BST.py
433c73667ea0f5e2d3115bcf55ea0855d3eee222
[]
no_license
https://github.com/IUIUN/The-Best-Time-Is-Now
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refs/heads/master
2020-09-14T12:06:24.074973
2020-02-15T06:55:08
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class Solution: def rangeSumBST(self, root: TreeNode, L: int, R: int) -> int: self.res = 0 self.dfs(root, L, R, self.res) return self.res def dfs(self, root, L, R, res): if not root: return if L <= root.val <= R: self.res += root.val if root.val < R: self.dfs(root.right, L, R, self.res) if root.val > L: self.dfs(root.left, L, R, self.res)
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938. Range Sum of BST.py
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Gaurangsharma/guiv
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ad04e9358184eee37931bb7fecc010292152d3d8
/codechef/goal.py
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[]
no_license
https://github.com/Gaurangsharma/guiv
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refs/heads/master
2021-06-28T19:47:12.656147
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for _ in range(int(input())): n=int(input()) #n initially goals=list(input()) win_point=(n//2)+1 count=0 k=False team_A,team_B=0,0 for i in range(len(goals)): if i%2==0: if goals[i]=='1': team_A+=1 else: if goals[i]=='1': team_B+=1 if team_A==team_B: if team_A>=win_point: print(i+2) k=True break if team_B>=win_point: print(i+1) k=True break if k==False: print(len(goals)) #n ended
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mattseddon/billy_cart
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6cd7a0aae71903c7d2dc147662bb3e0d9b482320
/app/market/model/handler.py
2f54ed136f095b6fe0855efc2183a801ec99dcb6
[]
no_license
https://github.com/mattseddon/billy_cart
d58d863037d0c9f8cfd49112ffb55a441f818fa6
ab3272dc3c184bb4f7149c155c998f5e48dc54d6
refs/heads/master
2022-01-10T21:40:16.068032
2022-01-02T04:01:37
2022-01-02T04:01:37
209,914,286
0
0
null
false
2021-06-02T02:22:50
2019-09-21T02:44:38
2021-04-06T20:48:07
2021-06-02T02:22:50
448
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Python
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from app.colleague import Colleague from infrastructure.third_party.adapter.numpy_utils import calculate_log, not_a_number from infrastructure.built_in.adapter.copy_utils import make_copy class ModelHandler(Colleague): def __init__(self, mediator, wls_model, event_country="AU"): self.__event_country = event_country self.wls_model = wls_model Colleague.__init__(self, mediator=mediator) self.__market_back_size = None def run_models(self, items): self.__market_back_size = self.__get_market_back_size(items=items) results = [] for item in items: if ( self._meets_wlr_criteria(item=item) and self._meets_wlr_threshold(item=item) and self._has_overlay( item=item, probability="compositional_sp_probability_pit" ) ): has_value = self.__standardise_result( item=item, probability="compositional_sp_probability_pit", order_type="BUY", model_id="SPMB", ex_price="ex_offered_back_price_pit", returns_price="ex_offered_back_price_mc_pit", ) results.append(has_value) continue elif ( self._meets_high_back_size_threshold(item=item) and self.__event_country == "AU" and self._has_overlay( item=item, probability="compositional_ex_average_probability_pit" ) ): has_value = self.__standardise_result( item=item, probability="compositional_ex_average_probability_pit", order_type="BUY", model_id="MBG2", ex_price="ex_offered_back_price_pit", returns_price="ex_offered_back_price_mc_pit", ) results.append(has_value) continue elif ( self._meets_low_back_size_threshold(item=item) and self.__event_country == "AU" and self._has_overlay( item=item, probability="compositional_ex_average_probability_pit" ) ): has_value = self.__standardise_result( item=item, probability="compositional_ex_average_probability_pit", order_type="BUY", model_id="MBL2", ex_price="ex_offered_back_price_pit", returns_price="ex_offered_back_price_mc_pit", ) results.append(has_value) continue return ( self._mediator.notify(event="models have results", data=results) if results else self._mediator.notify(event="finished processing", data=None) ) def __standardise_result( self, item, probability, order_type, model_id, ex_price, returns_price ): has_value = {} has_value["id"] = item.get("id") has_value["probability"] = item.get(probability) has_value["type"] = order_type has_value["model_id"] = model_id has_value["ex_price"] = item.get(ex_price) has_value["returns_price"] = item.get(returns_price) return has_value def _has_overlay(self, item, probability): return item.get(probability) > (1 / item.get("ex_offered_back_price_mc_pit")) def _meets_wlr_criteria(self, item): y = self._get_log_returns(y=item.get("compositional_sp_back_price_ts")) self.wls_model.run( y=y, x=item.get("extract_time_ts"), weights=item.get("combined_back_size_ts"), ) alpha = self.wls_model.get_alpha() Beta = self.wls_model.get_Beta() return Beta < 0 and alpha < -0.00001 def _meets_wlr_threshold(self, item): back_size = item.get("combined_back_size_pit") return ( back_size >= 5000 and self.__event_country != "GB" ) or back_size >= 30000 def _meets_high_back_size_threshold(self, item): back_size = item.get("combined_back_size_pit") return ( item.get("ex_offered_back_price_pit") > 2 and (back_size / self.__market_back_size) >= 0.6 and back_size >= 20000 ) def _meets_low_back_size_threshold(self, item): back_size = item.get("combined_back_size_pit") offered_back_price = item.get("ex_offered_back_price_pit") return ( offered_back_price <= 2 and (back_size / self.__market_back_size) >= max([0.6, (1 / offered_back_price)]) and back_size >= 10000 ) def _get_log_returns(self, y): data = make_copy(y) shifted_list = make_copy(data) shifted_list.pop() shifted_list.insert(0, not_a_number()) return [ calculate_log(point_in_time / previous_point_in_time) for point_in_time, previous_point_in_time in zip(data, shifted_list) ] def __get_market_back_size(self, items): market_back_size = 0 for item in items: market_back_size += item.get("combined_back_size_pit") return market_back_size
UTF-8
Python
false
false
5,435
py
82
handler.py
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0.531371
0.524379
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35.722973
86
WillahScott/adventofcode
11,982,958,792,593
ca137c4e715fcfc29b95e0369d74e4d43f6380d7
e109c76e1d244dd6f1c58bb585699f40b649ed7f
/day11.py
22ab48bbdad87878b555a3fcb54b0668db7e4e58
[]
no_license
https://github.com/WillahScott/adventofcode
20d386998b38e8da184552ee41ae861e19a8e358
8a6c5c9b5cbe04191a59fda419a4dfc3d997d8c1
refs/heads/master
2021-05-06T08:41:26.788335
2017-12-30T14:59:11
2017-12-30T14:59:11
114,059,155
0
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# Advent of code - DAY 11 from itertools import groupby # Read data with open('data/d11.txt') as f: new_data = f.read().split(',') # -- Problem 1 function -- def get_path_dict(steps): gpb = groupby(sorted(steps)) return { i: len(list(v)) for i, v in gpb } OPP = [('n', 's'), ('ne', 'sw'), ('nw', 'se')] DIAG = { 'n': {'sw': 'nw', 'se': 'ne'}, 'nw': {'s': 'sw', 'ne': 'n'}, 'ne': {'s': 'se', 'nw': 'n'}, 's': {'nw': 'sw', 'ne': 'se'}, 'sw': {'n': 'nw', 'se': 's'}, 'se': {'n': 'ne', 'sw': 's'}, } def reduce_path(long_path_d, verbose=False): int_path = [] # reduce opposites - sort by max to min for i, j in OPP: _steps = long_path_d[i] - long_path_d[j] int_path.append([i if _steps > 0 else j, abs(_steps)]) int_path.sort(key=lambda x: x[1], reverse=True) if verbose: print('No opps:', int_path) # reduce diagonals red_path = {} while int_path: k_red, v_red = int_path.pop(0) # to reduce pos_reds = DIAG[k_red] # possible reductions for k, v in int_path: if k in pos_reds: # reduce and remove the other with which we reduced red_path[pos_reds[k]] = red_path.get(pos_reds[k],0) + v int_path.remove([k,v]) left = v_red - v if left > 0: # if there is still left int_path.append( [k_red, left] ) int_path.sort(key=lambda x: x[1], reverse=True) break else: # the item is irreducible red_path[k_red] = red_path.get(k_red,0) + v_red if verbose: print('PATH:', red_path ) return sum(red_path.values()) # Test # > ? # Run problem 1 reduce_path( get_path_dict(new_data), verbose=True ) # > 707 ## PATH: {'n': 317, 'nw': 390} # -- Problem 2 function -- PAST_PATH = [] max_steps = 0 for d in new_data: PAST_PATH.append(d) max_steps = max( max_steps, reduce_path(get_path_dict(PAST_PATH)) ) # Run problem 2 print(max_steps) # > 1490
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2,104
py
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day11.py
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0.499525
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shwang0416/Jungle_week03
1,090,921,699,946
f30b6497d0fbbe50e22725fc90a144dbb673d518
3d02bab6338a4984f5fba18165e60820b2dc0a47
/07_1987_알파벳.py
a28f35df197343c157c51a221de1939f914629ed
[]
no_license
https://github.com/shwang0416/Jungle_week03
15fdceb2929d238c5154e5bcef9d46c5d4f5bda6
25be00f7db6c679ae07f014021f45c1394f42fd2
refs/heads/master
2023-02-03T15:54:14.896061
2020-12-28T13:57:47
2020-12-28T13:57:47
null
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# [백준] https://www.acmicpc.net/problem/1987 알파벳 # [참고] https://it-garden.tistory.com/272 # DFS, 백트래킹 import sys sys.stdin = open('test/07.txt') sys.setrecursionlimit(100000) # input Y, X = map(int, sys.stdin.readline().split()) alpha = [list(map(lambda x : ord(x)-65, sys.stdin.readline()))for i in range(Y)] # 방문한 적이 없어야하고, 갖고 있지 않은 알파벳이어야 한다. visited = [0 for _ in range(26)] ans = 1 dy = [-1,0,1,0] dx = [0,-1,0,1] def find_len(y,x,cnt): visited[alpha[y][x]] = 1 global ans ans = max(ans, cnt) # for dx,dy in [[-1,0],[1,0],[0,-1],[0,1]]: # nx = dx+x # ny = dy+y # 이거 대신에 dx dy 배열로 바꿨더니 시간초과가 안나고 통과됨!! for i in range(4): ny = y + dy[i]; nx = x + dx[i] if 0 <= nx < X and 0 <= ny < Y: if visited[alpha[ny][nx]] == 0: find_len(ny,nx,cnt+1) visited[alpha[ny][nx]] = 0 find_len(0,0,ans) sys.stdout.write(str(ans))
UTF-8
Python
false
false
1,042
py
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07_1987_알파벳.py
17
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0.487041
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neoareslinux/python
14,130,442,451,842
6106c155fade54e6a3671f2e776af6bedd9dff08
f4c13cc816445c2d3d454e67f09430c969c61813
/ser-net/backpexpect.py
dda11931aec167aeeca95a63d10751972e85c8af
[]
no_license
https://github.com/neoareslinux/python
c669818aa185808777126465aa04c221e96cb04d
383a77639c33e6135d22c6c1eecc3756eef3918c
refs/heads/master
2015-08-13T05:29:25.749984
2014-10-29T13:30:18
2014-10-29T13:30:18
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#!/usr/bin/python #coding=utf-8 import pexpect import sys import re import tempfile import getpass username = 'test' password = 'test' new = pexpect.spawn('telnet 1.1.1.2',timeout=3) index = new.expect(['login: ','Username',pexpect.TIMEOUT,"(?i)Unknown host"]) #this if could identify the switches of juniper if index == 0: new.sendline(username) new.expect('Password:') new.sendline(password) new.expect(['% Login failed!','Login incorrect']) elif index == 1: new.sendline(username) new.expect('Password:') new.sendline(password) i = new.expect(['% Login failed!','>','The initial password poses']) if i == 0: print 'wrong password' new.close() elif i == 1: sysname_info = new.before.strip() sysname = list(sysname_info) sysname = sysname[1:] switch_name = ''.join(sysname) print switch_name new.sendline('n') index2 = new.expect([' % Incomplete command','nn']) if index2 == 0: print 'device is h3c swich' new.sendline('dis interface Bridge-Aggregation') new.sendline(' '*10) new.sendline('dis link-aggregation verbose') new.sendline(' '*10) new.sendline('quit') print new.before elif i == 2: new.sendline('n') new.sendline(' ') print 'device is huawei switch' new.sendline('quit') print new.before elif index == 2: new.close() print new print 'time out' elif index == 3: print 'unkonw host' ###############this piece of code is to identify h3c,huawei and others ############################################################ new.expect(pexpect.EOF) #create temporary file tfile = tempfile.TemporaryFile() tfile.write(new.before) tfile.seek(0) number = 0 mydict = {} for line in tfile: m1 = re.search('current state',line) if m1: list1 = re.split(r'\s+',line) a = re.findall(r'Bridge-Agg.*',line)[0] a = re.split(r'\s+',a) print a x= tfile.next() x= tfile.next() x= tfile.next() m2 = re.search('speed mode',x) if m2: list2 = re.split(r'\s+',x) mydict[list1[1]] = {} mydict[list1[1]]['state'] = a[3] mydict[list1[1]]['speed'] = list2[1] m2 = re.search('Aggregation Interface',line) if m2: list3 = re.split(r'\s+',line) for subline in tfile: m3 =re.search('Oper-Key',subline) if m3: mydict[list3[2]]['interface'] = {} x = tfile.next() while True: x = tfile.next() m3 = re.search('GE',x) if m3: list4 = re.split(r'\s+',x) mydict[list3[2]]['interface'][list4[1]] = list4[2] # mydict[list[3]]['interface'] else: break break l = mydict.keys() for i in l: if mydict[i]['interface'] == {}: mydict[i]['info'] = '该聚合组没有成员端口' print mydict
UTF-8
Python
false
false
2,681
py
31
backpexpect.py
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0.58587
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KEYuni/UAS-Pemrosesan-Teks
6,708,738,925,961
60a3569fb2a96e8d008c25d01f69137157f35232
340e965d3fc9897104a33d09173d5e106017a6dc
/uas.py
97c583def2a44c06ede8251f66a72c16166f4e00
[]
no_license
https://github.com/KEYuni/UAS-Pemrosesan-Teks
bcadda8b9b07b4d00b3a41cfcd227af5d1c61ac2
93370369d9ed0e46a436345fd4fef86a16cf8a1a
refs/heads/master
2020-03-19T05:17:51.184878
2018-06-09T03:40:51
2018-06-09T03:40:51
135,917,971
0
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#Program Name : Autogenerate Keyword #Author : Yuniarti Musa'adah #Email : yuniartimusaadah@gmail.com #================================================================================================ # LIBRARY YANG DIGUNAKAN DALAM PROGRAM #================================================================================================ #import library yang dibutuhkan dalam program import numpy as np import nltk from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer from nltk.corpus import stopwords from stemming.porter2 import stem #________________________________________________________________________________________________ #================================================================================================ # PROSEDUR DAN FUNGSI DALAM PROGRAM #================================================================================================ # ==== 1. fungsi menghapus stopword def removeStopWords(sentence): dQ = vectorizer.fit_transform({sentence}) dN = vectorizer.get_feature_names() dN = [w for w in dN if not w in stop_words] senRemoved = ' '.join(w for w in dN) return senRemoved # ==== 2. prosedur sorting dari yang paling besar ke yang paling kecil def sorting(hasilAkhir, database, databaseStem): #sorting database dari yang paling mendekati query tukar = 1 while(tukar == 1): tukar = 0 for i in range(0, len(database)-1): #jika nilai TFIDF ke i lebih kecil dari nilai TFIDF selanjutnya if(hasilAkhir[i] < hasilAkhir[i+1]): #menukar posisi nilai TFIDF ke i dengan nilai TFIDF ke i+1 temp = hasilAkhir[i] hasilAkhir[i] = hasilAkhir[i+1] hasilAkhir[i+1] = temp #menukar posisi ke i dengan i+1 pada database yang sudah di stem tempTerm1 = databaseStem[i] databaseStem[i] = databaseStem[i+1] databaseStem[i+1] = tempTerm1 #menukar posisi ke i dengan ke i+1 pada database awal tempTerm = database[i] database[i] = database[i+1] database[i+1] = tempTerm tukar = 1 #________________________________________________________________________________________________ #================================================================================================ # ALUR AUTO-GENERATE KEYWORDS #================================================================================================ # deklarasi vectorizer vectorizer = CountVectorizer() # 1. MEMBACA FILE =============================================================================== # Membaca file memasukannya ke dalam list "database" # -- Memasukan data Training ke database database = open("coba.501").read().split('.') # -- Menghilangkan kalimat yang kosong / tidak mengandung kata apapun for i in range(0, len(database)): if not database[i]: database.pop(i) # 2. STOPWORD REMOVAL =========================================================================== # deklarasi stopword untuk B.Inggris stop_words = set(stopwords.words('english')) for i in range(0, len(database)): database[i] = removeStopWords(database[i]) # 3. STEMMING DATABASE ========================================================================== # Men-Stemming setiap kata dari kalimat-kalimat di database databaseStem = database databaseStem = [' '.join(stem(word) for word in sentence.split(" ")) for sentence in databaseStem] dbStem = [[stem(word) for word in sentence.split(" ")] for sentence in databaseStem] #for i in range(0, len(database)): # print(i+1, database[i]) # 5. MENENTUKAN NILAI TF-IDF DARI SETIAP KALIMAT DALAM DOKUMEN ================================== termNoun = [] # variable yang menampung term-term yang berupa noun dari kalimat newSentence = [] # untuk menampung gabungan term-term noun menjadi 1 kalimat # pos tagging setiap kata menggunakan nltk pos_tag for i in range(0, len(database)): # tokenizing kalimat tokens = nltk.word_tokenize(database[i]) # melakukan pos tagging pada setiap kata dalam kalimat tagged = nltk.pos_tag(tokens) if i == 1 : print(tokens) print(tagged) for j in range (0, len(tagged)): # mencari kata-kata yang berupa Noun (NN, NNP, NNPS, NNS) dilihat dari huruf awal log = (tagged[j][1][0] == 'N') # jika noun, maka katanya di stem dan dimasukan ke variable termNoun if log == True: hStem = stem(tagged[j][0]) termNoun.append(hStem) # kata-kata noun yang telah didapatkan digabungkan menjadi 1 kalimat newSentence.append(' '.join(w for w in termNoun)) # mengosongkan kembali variable termNoun del termNoun[:] # 4. MENENTUKAN FEATURE-FEATURE DARI DATABASE =================================================== X = vectorizer.fit_transform(databaseStem) # variable yang menampung semua nama feature dari database db_FeatureName = vectorizer.get_feature_names() # deklarasi tfidf transformer transformer = TfidfTransformer(smooth_idf=False) # menghitung nilai TFIDF dari database yang hanya berisi noun =================================== # mentransform kalimat yang hanya berisi noun kalimatNoun = vectorizer.transform(newSentence) # variable untuk menampung nilai tfidf, nilai minimal 0.5 arrayNoun = np.full(((len(database)),len(db_FeatureName)), 0.5) # memasukan nilai TFIDF for i in range(0, len(databaseStem)): for j in range(0, len(db_FeatureName)): if (kalimatNoun.toarray()[i][j] is not 0) : arrayNoun[i][j] = arrayNoun[i][j] + kalimatNoun.toarray()[i][j] tfidf_Noun = transformer.fit_transform(arrayNoun) naNoun = [] for i in range(0, len(databaseStem)) : naNoun.append(0) for j in range(0, len(db_FeatureName)): naNoun[i] = naNoun[i] + tfidf_Noun.toarray()[i][j] # menghitung nilai TFIDF dari database yang lengkap ============================================= kalimatFull = vectorizer.transform(databaseStem) arrayFull = np.full(((len(database)),len(db_FeatureName)), 0.5) for i in range(0, len(databaseStem)): for j in range(0, len(db_FeatureName)): if (kalimatFull.toarray()[i][j] is not 0) : arrayFull[i][j] = arrayFull[i][j] + kalimatFull.toarray()[i][j] tfidf_Full = transformer.fit_transform(arrayFull) naFull = [] for i in range(0, len(databaseStem)) : naFull.append(0) for j in range(0, len(db_FeatureName)): naFull[i] = naFull[i] + tfidf_Full.toarray()[i][j] # menghitung nilai akhir TFIDF dari setiap kalimat dalam dokumen # (jumlah TFIDF kata Noun dibagi TFIDF kata lengkap) hasilAkhir = [] for i in range(0, len(databaseStem)): hasilAkhir.append(naNoun[i] / naFull[i]) # mensorting kalimat dari nilai TFIDF terbesar sampai terkecil sorting(hasilAkhir, database, databaseStem) # mengcopykan kalimat 3 teratas kalimatTop = database[0:3] # stemming kalimat 3 teratas topStem = [[stem(word) for word in sentence.split(" ")] for sentence in kalimatTop] # kalimat 3 teratas dijadikan 1 kalimat kalimatTopStem = [' '.join(stem(word) for word in sentence.split(" ")) for sentence in databaseStem] gabungTop = [[(word) for word in sentence.split(" ")] for sentence in kalimatTop] #print("gabungTop : ", gabungTop) #print("=====") #print(topStem) abcd = kalimatTopStem[0:3] a10 = ' '.join(word for word in abcd) Y = vectorizer.fit_transform({a10}) b10 = vectorizer.get_feature_names() kandidatKey = [] nilaiKandidat = [] asliWord = [] for one in range(0, len(topStem)): for i in range(0, len(topStem[one])) : jumlah = 0 for j in range(0, len(dbStem)) : for k in range(0, len(dbStem[j])) : if dbStem[j][k] == topStem[one][i]: jumlah = jumlah + 1 kandidatKey.append(topStem[one][i]) nilaiKandidat.append(jumlah * hasilAkhir[one]) asliWord.append(gabungTop[one][i]) sorting(nilaiKandidat, kandidatKey, asliWord) i= 0 status = 0 while (status == 0) : status = 1 for j in range(i+1, len(kandidatKey)-1): if(kandidatKey[i] == kandidatKey[j]): status = 0 kandidatKey.pop(j) nilaiKandidat.pop(j) asliWord.pop(j) i = i + 1 #print(kandidatKey) #print(nilaiKandidat) print(asliWord[0], ", ", asliWord[1], ", ", asliWord[2], ", ", asliWord[3], ", ", asliWord[4]) #print(tfidf.toarray()) # #for i in range(0, len(hasilStemming)): # print("Stem : ", i+1, " : ", hasilStemming[i])
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uas.py
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nukui-s/mlens
10,471,130,270,689
ceb33a76a9caf6cdec83f096bf39ea74a04b5892
141166565426af8233782bd8de6f3c4a5d227cea
/mlens/parallel/tests/test_a_learner_full.py
4762b3cc7c1ce6e9df8c4db7a5661291a17eddf4
[ "MIT" ]
permissive
https://github.com/nukui-s/mlens
4fa1ee513494120c0a3e34a659d6ce701989f221
91d89c8daff6da508d5d5577349fba051b1c8eb9
refs/heads/master
2020-03-19T06:08:52.188474
2018-06-04T10:39:38
2018-06-04T10:39:38
135,994,418
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MIT
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2018-06-04T10:39:39
2018-06-04T08:28:45
2018-06-04T08:28:48
2018-06-04T10:39:38
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Python
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""""ML-ENSEMBLE Testing suite for Learner and Transformer """ from mlens.testing import Data, EstimatorContainer, get_learner, run_learner def test_predict(): """[Parallel | Learner | Full | No Proba | No Prep] test fit and predict""" args = get_learner('predict', 'full', False, False) run_learner(*args) def test_transform(): """[Parallel | Learner | Full | No Proba | No Prep] test fit and transform""" args = get_learner('transform', 'full', False, False) run_learner(*args) def test_predict_prep(): """[Parallel | Learner | Full | No Proba | Prep] test fit and predict""" args = get_learner('predict', 'full', False, True) run_learner(*args) def test_transform_prep(): """[Parallel | Learner | Full | No Proba | Prep] test fit and transform""" args = get_learner('transform', 'full', False, True) run_learner(*args) def test_predict_proba(): """[Parallel | Learner | Full | Proba | No Prep] test fit and predict""" args = get_learner('predict', 'full', True, False) run_learner(*args) def test_transform_proba(): """[Parallel | Learner | Full | Proba | No Prep] test fit and transform""" args = get_learner('transform', 'full', True, False) run_learner(*args) def test_predict_prep_proba(): """[Parallel | Learner | Full | Proba | No Prep] test predict""" args = get_learner('predict', 'full', True, True) run_learner(*args) def test_transform_prep_proba(): """[Parallel | Learner | Full | Proba | Prep] test transform""" args = get_learner('transform', 'full', True, True) run_learner(*args)
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suneric/aircraft_scanning
6,030,134,110,848
22b5fb733b19e8f9655f82936707c8377ee0cc46
5f535b35375d68f407ee2f1153b97b686c9a8365
/aircraft_scanning_control/scripts/ugv_trajectory.py
38027ad4bbcba363f8bf6dabc988f648c8a4e4a0
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https://github.com/suneric/aircraft_scanning
d32a0ba3e44a0954a1a6a4a283615ca142a4cee8
18c7deb8405eabecab643e7ebbda5f3a61e78393
refs/heads/master
2022-06-04T11:17:27.210208
2022-05-18T22:49:32
2022-05-18T22:49:32
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#!/usr/bin/env python import rospy import numpy as np import os import sys from geometry_msgs.msg import Pose from math import * import transform from transform import MMRobotTransform class trajectory_defined: def __init__(self): self.trajectory = [] self.index = 0 self.transform_util = MMRobotTransform() # self._create_trajectory() file = os.path.join(sys.path[0],'../../aircraft_scanning_plan/trajectory/ugv/viewpoints.txt'); self._load_trajectory(file) def completed(self): return self.index >= len(self.trajectory) def next_pose(self): if self.completed(): print("done.") return None else: pose = self.trajectory[self.index] self.index = self.index + 1 print("traverse to ", pose) return pose def _load_trajectory(self,file): print("load trajectory from", file) with open(file,'r') as reader: for line in reader.read().splitlines(): data = line.split(" ") idx = int(data[0]) px = float(data[1]) py = float(data[2]) pz = float(data[3]) ox = float(data[4]) oy = float(data[5]) oz = float(data[6]) ow = float(data[7]) pose = Pose(); pose.position.x = px pose.position.y = py pose.position.z = pz pose.orientation.x = ox pose.orientation.y = oy pose.orientation.z = oz pose.orientation.w = ow ugv, arm = self.transform_util.camera2mobilebase(pose) self.trajectory.append([ugv,arm]) reader.close() def _create_trajectory(self): arm_joint = [0.0,1.57,0.0,-0.3,0.0,-1.87,0.0] self.trajectory.append([(0,-28,0.5*pi),arm_joint]) self._create_updownpath([-27,-22.5],[1.0,-1.0],1.0,arm_joint) # self._create_updownpath([-20,-8],[1.0,-1.0]) # self._create_updownpath([-8,8],[1.5,0,-1.5]) # self._create_updownpath([8,25],[1.0,-1.0]) # self.trajectory.append((0,26,0.5*pi)) # self.trajectory.append((0,27,0.5*pi)) def _create_updownpath(self, y_range, x_pos, offset, arm_joint): i = 0 y = y_range[0] while y < y_range[1]: if np.mod(i,2) == 0: for x in x_pos: self.trajectory.append([(x,y,0.5*pi),arm_joint]) else: for x in reversed(x_pos): self.trajectory.append([(x,y,0.5*pi),arm_joint]) i += 1 y += offset
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jianjunyue/python-learn-ml
12,773,232,777,123
19ccc7360a84ef900ce2a5852083c6ed8127400f
eccbb87eefe632a1aa4eafb1e5581420ccf2224a
/tianchi/智能制造质量预测/Ridge_model.py
ca93a8c2908f01516b2156a760c521ec636a95a7
[]
no_license
https://github.com/jianjunyue/python-learn-ml
4191fc675d79830308fd06a62f16a23295a48d32
195df28b0b8b8b7dc78c57dd1a6a4505e48e499f
refs/heads/master
2018-11-09T15:31:50.360084
2018-08-25T07:47:20
2018-08-25T07:47:20
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import pandas as pd import numpy as np #Python sklearn数据分析中常用方法 #http://blog.csdn.net/qq_16234613/article/details/76534673 import matplotlib.pyplot as plt import seaborn as sns from sklearn.preprocessing import Imputer from sklearn.model_selection import train_test_split from sklearn.model_selection import cross_val_score from sklearn.linear_model import Ridge # train_df = pd.read_excel('/Users/jianjun.yue/PycharmGItHub/data/智能制造质量预测/训练.xlsx',header=0,encoding='utf-8') # y_train=train_df["Y"] # y_train.to_csv("/Users/jianjun.yue/PycharmGItHub/data/智能制造质量预测/训练_y_train_20171216.csv",index=False) X_train=pd.read_csv('/Users/jianjun.yue/PycharmGItHub/data/智能制造质量预测/训练_quantity_2017121622.csv',header=0) # X_train.shape y_train=pd.read_csv('/Users/jianjun.yue/PycharmGItHub/data/智能制造质量预测/训练_y_train_20171216.csv',header=0) # train_df=train_df.drop(["ID","Y"], axis=1) # quantity = [attr for attr in train_df.columns if train_df.dtypes[attr] != 'object'] # 数值变量集合 quantity = [attr for attr in X_train.columns if X_train.dtypes[attr] == 'float64'] # 数值变量集合 print(len(quantity)) print(quantity) # print(X_train[quantity].head()) # X_train=X_train.drop(["750X1452"], axis=1) print(X_train.shape) print(y_train.shape) # # print(len(X_train.columns)) # X_train=train_df[quantity] # X_train.to_csv("/Users/jianjun.yue/PycharmGItHub/data/智能制造质量预测/训练_quantity_20171216.csv",index=False) # X_train = Imputer().fit_transform(X_train) # data1=np.isnan(X_train).any() # print( anynull(data1).head()) # 检查数据中是否有缺失值 # print(type(np.isnan(X_train).any())) # 2、删除有缺失值的行 # train.dropna(inplace=True) num=0 # count=0 # for column in X_train.columns: # try: # count=count+1 # if X_train.dtypes[column] == 'float64': # print(column + "::"+str(count)+"/"+str(num)) # X_train[column] = X_train[column].astype(float) # print(X_train.dtypes[column]) # print(X_train[column][0]) # # X_train[column] = X_train[column].apply(lambda x:) # # data['year'] = data.Date.apply(lambda x: x.split('-')[0]) # except Exception as err: # print(column+":::"+err) # # X_train[column] = X_train[column].fillna(0) # # X_train = Imputer().fit_transform(train_df) # for column in X_train.columns: # try: # count=count+1 # print(column + "::"+str(count)+"/"+str(num)) # X_train[column] = X_train[column].fillna(X_train[column].median()) # except Exception as err: # print(column+":::"+err) # X_train[column] = X_train[column].fillna(0) # # X_train = Imputer().fit_transform(train_df) # X_train.to_csv("/Users/jianjun.yue/PycharmGItHub/data/智能制造质量预测/训练_quantity_2017121622.csv",index=False) #交叉验证,获取最优参数( 最终使用GridSearch获取最优参数 ) alphas=np.logspace(-3,2,50) test_scores=[] for alpha in alphas: clf=Ridge(alpha) test_score=np.sqrt(-cross_val_score(clf,X_train,y_train,cv=5,scoring="neg_mean_squared_error")) test_scores.append(np.mean(test_score)) print(test_scores) plt.plot(alphas,test_scores) plt.title("Ridge Model") plt.show()
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noironetworks/gbp-tempest-plugin
5,085,241,283,657
1b657f0216a1d5d0de3451866aa4ddec1d32dc49
c2c84be5ed5d326b24ab14c159edb7d1fee949b9
/gbp_tempest_plugin/services/gbp/v2/json/nat_pool_client.py
a08282afedfb7bcc3c03be54f52fe9de07411ad3
[]
no_license
https://github.com/noironetworks/gbp-tempest-plugin
d7e4e745d1ade2b2db34ccdc7ed7e129b57c5caf
8b7640e82aa9b12ebee49177da0f7fe7ed2f699f
refs/heads/master
2020-04-09T10:16:47.284119
2018-11-02T11:05:50
2018-11-02T11:05:50
null
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from tempest.lib.common.utils import data_utils from six.moves import http_client from tempest.lib.common import rest_client from oslo_serialization import jsonutils as json from gbp_tempest_plugin.services.gbp.v2.json import base class NATPoolClient(base.GbpClientV2Base): """API V2 Tempest REST client for GBP NAT Pool API""" resource = "/grouppolicy/nat_pools" def create_nat_pool(self, name, external_segment_id, ip_pool, **kwargs): """Create a NAT Pool""" post_body = {'nat_pool': {'name': name, 'external_segment_id': external_segment_id, 'ip_pool': ip_pool}} if kwargs.get('description'): post_body['nat_pool']['description'] = kwargs.get('description') post_body = json.dumps(post_body) resp, body = self.post(self.get_uri(self.resource), post_body) body = json.loads(body) self.expected_success(http_client.CREATED, resp.status) return rest_client.ResponseBody(resp, body) def list_nat_pools(self): """List NAT Pools""" resp, body = self.get(self.get_uri(self.resource)) body = json.loads(body) self.expected_success(http_client.OK, resp.status) return rest_client.ResponseBody(resp, body) def delete_nat_pool(self, id): """Delete a NAT Pool""" resp, body = self.delete(self.get_uri(self.resource, id)) self.expected_success(http_client.NO_CONTENT, resp.status) return rest_client.ResponseBody(resp, body) def show_nat_pool(self, id): """Show a NAT Pool""" resp, body = self.get(self.get_uri(self.resource, id)) body = json.loads(body) self.expected_success(http_client.OK, resp.status) return rest_client.ResponseBody(resp, body) def update_nat_pool(self, id, **kwargs): """Update an existing External Policy""" resp, body = self.put(self.get_uri(self.resource, id), json.dumps({'nat_pool':kwargs})) body = json.loads(body) self.expected_success(http_client.OK, resp.status) return rest_client.ResponseBody(resp, body)
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nat_pool_client.py
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tjudoubi/tbcnn_vector_js
8,074,538,538,326
761d4e11271f95600074188bad1b144c73c6540b
be42e602d56238f8d316649fd711a1430577a5ef
/datasetforTBCCD-master/sampleJava.py
4e5ae1355c64222c7655b0d8a03f02131ed23faf
[]
no_license
https://github.com/tjudoubi/tbcnn_vector_js
0749513c76ef4fd4dac9a7be1b8a2aa5571f7de3
638c4a46d7d9c83637edc5b7ff1b0cc54ab4e2c1
refs/heads/master
2022-12-18T11:39:51.282433
2020-10-05T15:56:25
2020-10-05T15:56:25
301,389,163
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import javalang from javalang.ast import Node def _name(node): return type(node).__name__ def dfsSearch_withid(children): if not isinstance(children, (str, Node, list, tuple)): return if isinstance(children, (str, Node)): if str(children) == '': return if str(children).startswith('"'): return if str(children).startswith("'"): return if str(children).startswith("/*"): return # ss = str(children) global num_nodes num_nodes += 1 listt1.append(children) return for child in children: if isinstance(child, (str, Node, list, tuple)): dfsSearch_withid(child) def _traverse_treewithid(root): global num_nodes num_nodes = 1 queue = [root] root_json = { "node": _name(root), "children": [] } queue_json = [root_json] while queue: current_node = queue.pop(0) current_node_json = queue_json.pop(0) global listt1 listt1 = [] dfsSearch_withid(current_node.children) children = listt1 for child in children: child_json = { "node": str(child), "children": [] } current_node_json['children'].append(child_json) if isinstance(child, (Node)): queue_json.append(child_json) queue.append(child) return root_json, num_nodes def _pad_nobatch(children): child_len = max([len(c) for n in children for c in n]) children = [[c + [0] * (child_len - len(c)) for c in sample] for sample in children] return children def dfsSearch_noid(children): if not isinstance(children, (Node, list, tuple)): return if isinstance(children, Node): global num_nodes num_nodes+=1 listt1.append(children) return for child in children: if isinstance(child, (Node, list, tuple)): dfsSearch_noid(child) def _traverse_tree_noid(root): global num_nodes num_nodes = 1 queue = [root] root_json = { "node": _name(root), "children": [] } queue_json = [root_json] while queue: current_node = queue.pop(0) current_node_json = queue_json.pop(0) global listt1 listt1=[] dfsSearch_noid(current_node.children) children = listt1 for child in children: child_json = { "node": str(child), "children": [] } current_node_json['children'].append(child_json) queue_json.append(child_json) queue.append(child) return root_json, num_nodes def _traverse_tree_noast(root): global num_nodes num_nodes = 1 queue = [root] root_json = { "node": _name(root), "children": [] } queue_json = [root_json] while queue: current_node = queue.pop(0) current_node_json = queue_json.pop(0) global listt1 listt1 = [] dfsSearch_withid(current_node.children) children = listt1 for child in children: if isinstance(child, (Node)): child_json = { "node": "AstNode", "children": [] } current_node_json['children'].append(child_json) queue_json.append(child_json) queue.append(child) else: child_json = { "node": str(child), "children": [] } current_node_json['children'].append(child_json) return root_json, num_nodes def getData_nofinetune(l,dictt,embeddings): nodes11 = [] children11 = [] nodes22 = [] children22 = [] label = l[2] queue1 = [(dictt[l[0]], -1)] while queue1: node1, parent_ind1 = queue1.pop(0) node_ind1 = len(nodes11) queue1.extend([(child, node_ind1) for child in node1['children']]) children11.append([]) if parent_ind1 > -1: children11[parent_ind1].append(node_ind1) nodes11.append(embeddings[node1['node']]) queue2 = [(dictt[l[1]], -1)] while queue2: node2, parent_ind2 = queue2.pop(0) node_ind2 = len(nodes22) queue2.extend([(child, node_ind2) for child in node2['children']]) children22.append([]) if parent_ind2 > -1: children22[parent_ind2].append(node_ind2) nodes22.append(embeddings[node2['node']]) children111 = [] children222 = [] children111.append(children11) children222.append(children22) children1 = _pad_nobatch(children111) children2 = _pad_nobatch(children222) return [nodes11],children1,[nodes22],children2,label def getData_finetune(l,dictt,embeddings): nodes11 = [] children11 = [] nodes22 = [] children22 = [] label = l[2] queue1 = [(dictt[l[0]], -1)] while queue1: node1, parent_ind1 = queue1.pop(0) node_ind1 = len(nodes11) queue1.extend([(child, node_ind1) for child in node1['children']]) children11.append([]) if parent_ind1 > -1: children11[parent_ind1].append(node_ind1) nodes11.append(embeddings[node1['node']]) queue2 = [(dictt[l[1]], -1)] while queue2: node2, parent_ind2 = queue2.pop(0) node_ind2 = len(nodes22) queue2.extend([(child, node_ind2) for child in node2['children']]) children22.append([]) if parent_ind2 > -1: children22[parent_ind2].append(node_ind2) nodes22.append(embeddings[node2['node']]) children111 = [] children222 = [] batch_labels = [] children111.append(children11) children222.append(children22) children1 = _pad_nobatch(children111) children2 = _pad_nobatch(children222) batch_labels.append(label) return nodes11,children1,nodes22,children2,batch_labels
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sampleJava.py
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