diff --git "a/1453.jsonl" "b/1453.jsonl" new file mode 100644--- /dev/null +++ "b/1453.jsonl" @@ -0,0 +1,421 @@ +{"seq_id": "352735469", "text": "import time\nfrom urllib.request import urlopen\nfrom multiprocessing import Process\n\n\ndef get_urls_to_crawl():\n urls_list = list()\n urls_list.append('http://www.cnn.com/')\n urls_list.append('https://www.foxnews.com/')\n urls_list.append('https://www.bbc.com/')\n urls_list.append('https://www.cnbc.com')\n urls_list.append('https://www.dawn.com')\n return urls_list\n\n\ndef crawl_one_url(url):\n html = urlopen(url)\n txt = html.read()\n\n\nif __name__ == \"__main__\":\n urls_to_crawl = get_urls_to_crawl()\n start = time.time()\n\n processes = []\n for url in urls_to_crawl:\n processes.append(Process(target=crawl_one_url, args=(url,)))\n\n for process in processes:\n process.start()\n\n for process in processes:\n process.join()\n\n elapsed = time.time() - start\n print(f\"\\nURLs downloaded in {elapsed:.2f}s\")\n", "sub_path": "asyncio_module/web_crawler_example_multiprocess.py", "file_name": "web_crawler_example_multiprocess.py", "file_ext": "py", "file_size_in_byte": 860, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "urllib.request.urlopen", "line_number": 17, "usage_type": "call"}, {"api_name": "time.time", "line_number": 23, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 27, "usage_type": "call"}, {"api_name": "time.time", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "226788604", "text": "import math\n\nfrom rx import Observable\nfrom rx.subjects import Subject\n\n# import needed for to_backpressure operator\nimport rxbackpressure\n\n# time value samples recorded by some device\ntime_value_record = Subject()\n\n# separate time and value\ntime = time_value_record.map(lambda pair: pair[0])\nsignal = time_value_record.map(lambda pair: pair[1])\n\n# timebase synchronization\nsync1 = Subject()\nsync2 = Subject()\n\n# synchronize time samples to two timebases\ntime_sync2_bp = time.to_backpressure().zip(sync2.repeat_first(), lambda t, sync_time: t + sync_time)\ntime_sync1_bp = time.to_backpressure().zip(sync1.repeat_first(), lambda t, sync_time: t + sync_time)\n\n# pairing time value observables\ntime_sync1_bp.to_observable().zip(signal, lambda t, v: (t, v)).unsafe_subscribe()\ntime_sync2_bp.to_observable().zip(signal, lambda t, v: (t, v)).unsafe_subscribe(print, on_completed=lambda: print('completed'))\n\n# emulating hot observable\nObservable.range(0,100) \\\n .map(lambda v: (float(v)+3.2)/1000) \\\n .map(lambda t: (t, math.sin(t/0.05*2*math.pi))) \\\n .unsafe_subscribe(time_value_record)\nObservable.just(-3.2/1000).unsafe_subscribe(sync1)\nObservable.just(2/1000).unsafe_subscribe(sync2)\n", "sub_path": "examples/timevaluepairs.py", "file_name": "timevaluepairs.py", "file_ext": "py", "file_size_in_byte": 1191, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "rx.subjects.Subject", "line_number": 10, "usage_type": "call"}, {"api_name": "rx.subjects.Subject", "line_number": 17, "usage_type": "call"}, {"api_name": "rx.subjects.Subject", "line_number": 18, "usage_type": "call"}, {"api_name": "rx.Observable.range", "line_number": 29, "usage_type": "call"}, {"api_name": "rx.Observable", "line_number": 29, "usage_type": "name"}, {"api_name": "math.sin", "line_number": 31, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 31, "usage_type": "attribute"}, {"api_name": "rx.Observable.just", "line_number": 33, "usage_type": "call"}, {"api_name": "rx.Observable", "line_number": 33, "usage_type": "name"}, {"api_name": "rx.Observable.just", "line_number": 34, "usage_type": "call"}, {"api_name": "rx.Observable", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "192740277", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# This file is part of Python Challenge Solutions\n# https://github.com/scorphus/PythonChallengeSolutions\n\n# Licensed under the BSD-3-Clause license:\n# https://opensource.org/licenses/BSD-3-Clause\n# Copyright (c) 2018, Pablo S. Blum de Aguiar \n\n# http://www.pythonchallenge.com/pc/return/mozart.html\n\nfrom PIL import Image # pip install pillow\nfrom base64 import encodebytes\nfrom urllib.request import Request, urlopen\n\nurl = 'http://www.pythonchallenge.com/pc/return/mozart.gif'\nauth = encodebytes(b'huge:file').decode().rstrip()\nheaders = {'Authorization': f'Basic {auth}'}\n\nimage = Image.open(urlopen(Request(url=url, headers=headers))).convert('RGB')\nnew_image = Image.new(image.mode, (2 * image.width, image.height))\n\nfor y in range(image.height):\n X = iter(range(image.width))\n row = list()\n for x in X:\n row.append(image.getpixel((x, y)))\n if row[-1][0] == row[-1][2] == 255 and row[-1][1] == 0:\n threshold = image.width - x\n break\n for x, pixel in enumerate(row):\n new_image.putpixel((threshold + x, y), pixel)\n for x in X:\n new_image.putpixel((threshold + x, y), image.getpixel((x, y)))\n\nnew_image.save('16-mozart.png', 'PNG')\nprint('Open 16-mozart.png')\n", "sub_path": "16-mozart.py", "file_name": "16-mozart.py", "file_ext": "py", "file_size_in_byte": 1296, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "base64.encodebytes", "line_number": 18, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 21, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 21, "usage_type": "name"}, {"api_name": "urllib.request.urlopen", "line_number": 21, "usage_type": "call"}, {"api_name": "urllib.request.Request", "line_number": 21, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 22, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 22, "usage_type": "name"}]} +{"seq_id": "451198497", "text": "# coding: utf-8\nfrom __future__ import unicode_literals\n\nimport re\n\nfrom .common import InfoExtractor\nfrom ..utils import int_or_none\n\n\nclass TelemadridIE(InfoExtractor):\n _VALID_URL = r'https?://(?:www\\.)?telemadrid\\.es/[^/]+/[^/]+/(?P.+?)--\\d+.html'\n _TESTS = [\n {\n 'url': 'http://www.telemadrid.es/programas/telenoticias-fin-de-semana/cerca-lejos-nuevo-Klapisch-2-2169403070--20191020083747.html',\n 'info_dict': {\n 'id': '6096258464001',\n 'ext': 'mp4',\n 'title': ''Tan cerca, tan lejos', lo nuevo de Klapisch',\n 'description': 'md5:10ac0514bdbdeeea9de495ae0720c6ff',\n 'thumbnail': r're:^https?://images.telemadrid.es/2019/10/20/programas/telenoticias-fin-de-semana/cerca-lejos-nuevo-Klapisch_2169403070_7342970_1300x813.png$',\n 'timestamp': 1571596692,\n 'upload_date': '20191020'\n }\n },\n {\n 'url': 'http://www.telemadrid.es/programas/telenoticias-fin-de-semana/Doce-detenidos-altercados-registrados-Madrid-2-2169403042--20191020100828.html',\n 'info_dict': {\n 'id': '6096226698001',\n 'ext': 'mp4',\n 'title': 'Doce detenidos en los altercados registrados en el centro de Madrid',\n 'description': 'md5:77a37a10cfe8b8cd595d11bf762e90da',\n 'thumbnail': r're:^https?://images.telemadrid.es/2019/10/20/programas/telenoticias-fin-de-semana/Doce-detenidos-altercados-registrados-Madrid_2169403042_7342376_4000x2666.jpg$',\n 'timestamp': 1571573534,\n 'upload_date': '20191020'\n }\n },\n {\n 'url': 'http://www.telemadrid.es/programas/120-minutos/minutos-Parte-uno-2-2165803426--20191008033109.html',\n 'info_dict': {\n 'id': '6093135605001',\n 'ext': 'mp4',\n 'title': '120 minutos 08.10.2019 (Parte 1)',\n 'description': 'md5:eea20844c4aef07638b53d8f40fe8e23',\n 'thumbnail': r're:^https?://images.telemadrid.es/2019/10/08/programas/120-minutos/minutos-Parte-uno_2165803426_7312298_1920x1080.jpg$',\n 'timestamp': 1570541701,\n 'upload_date': '20191008'\n }\n }\n ]\n\n _VIDEO_BASE = 'http://c.brightcove.com/services/mobile/streaming/index/master.m3u8?videoId='\n\n def _real_extract(self, url):\n display_id = re.match(self._VALID_URL, url).groups()\n webpage = self._download_webpage(url, display_id)\n\n video_figure = self._search_regex(r']+class=\\\"media-video\\\"[^>]+itemtype=\\\"http://schema\\.org/VideoObject\\\"*>(.*?)', webpage, 'video_figure', flags=re.DOTALL)\n video_id = self._search_regex(r'', video_figure, 'video_id', flags=re.DOTALL)\n name = self._search_regex(r'', video_figure, 'name', flags=re.DOTALL)\n description = self._html_search_regex(r'', video_figure, 'description', flags=re.DOTALL)\n thumbnail = self._search_regex(r'', video_figure, 'thumbnail', flags=re.DOTALL)\n timestamp = self._search_regex(r'', video_figure, 'timestamp', flags=re.DOTALL)\n\n formats = self._extract_m3u8_formats(self._VIDEO_BASE + video_id, video_id, 'mp4', 'm3u8_native', m3u8_id='hls', fatal=False)\n\n return {\n 'id': video_id,\n 'display_id': display_id,\n 'title': name,\n 'description': description,\n 'thumbnail': thumbnail,\n 'timestamp': int_or_none(timestamp),\n 'formats': formats\n }\n", "sub_path": "youtube_dl/extractor/telemadrid.py", "file_name": "telemadrid.py", "file_ext": "py", "file_size_in_byte": 3850, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "common.InfoExtractor", "line_number": 10, "usage_type": "name"}, {"api_name": "re.match", "line_number": 54, "usage_type": "call"}, {"api_name": "re.DOTALL", "line_number": 57, "usage_type": "attribute"}, {"api_name": "re.DOTALL", "line_number": 58, "usage_type": "attribute"}, {"api_name": "re.DOTALL", "line_number": 59, "usage_type": "attribute"}, {"api_name": "re.DOTALL", "line_number": 60, "usage_type": "attribute"}, {"api_name": "re.DOTALL", "line_number": 61, "usage_type": "attribute"}, {"api_name": "re.DOTALL", "line_number": 62, "usage_type": "attribute"}, {"api_name": "utils.int_or_none", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "300548269", "text": "import sys\nimport time\nimport logging\n\nfrom server import repository, scrape\n\ndef _parse_sysargv():\n \"\"\"Naively parse command line args into dict.\"\"\"\n return dict(arg.split('=') for arg in sys.argv[1:])\n\ndef _config_logging(opts):\n lvl = getattr(logging, opts.get('--loglevel', 'warn').upper()) \n filename = opts.get('--logfile', 'scrape.log')\n logging.basicConfig(filename=filename, level=lvl)\n\n__opts = _parse_sysargv()\n_config_logging(__opts)\n\n\ndef full_scrape():\n \"\"\"Full scrape of top currencies and their historical prices.\"\"\"\n logging.info('============ Starting full scrape ============')\n scrape.scrape_top_currency_prices()\n time.sleep(5)\n # Now for all the fetched currencies, get their historical prices\n for currency in repository.get_all_currency_ids():\n logging.info('Fetching historical prices for: %s', currency['symbol'])\n scrape.scrape_historical_prices(currency)\n time.sleep(5)\n logging.info('============ Full scrape complete! ============')\n\ndef daily_scrape():\n \"\"\"Partial scrape of top currencies and their most recent prices.\"\"\"\n logging.info('============ Starting daily scrape ============')\n scrape.scrape_top_currency_prices()\n logging.info('============ Daily scrape complete! ============')\n\n\nif __name__ == '__main__':\n scrape_type = __opts.get('--scrape', 'daily') \n if scrape_type == 'full':\n full_scrape()\n else:\n daily_scrape()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1401, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.argv", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 14, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 22, "usage_type": "call"}, {"api_name": "server.scrape.scrape_top_currency_prices", "line_number": 23, "usage_type": "call"}, {"api_name": "server.scrape", "line_number": 23, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 24, "usage_type": "call"}, {"api_name": "server.repository.get_all_currency_ids", "line_number": 26, "usage_type": "call"}, {"api_name": "server.repository", "line_number": 26, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 27, "usage_type": "call"}, {"api_name": "server.scrape.scrape_historical_prices", "line_number": 28, "usage_type": "call"}, {"api_name": "server.scrape", "line_number": 28, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 29, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 34, "usage_type": "call"}, {"api_name": "server.scrape.scrape_top_currency_prices", "line_number": 35, "usage_type": "call"}, {"api_name": "server.scrape", "line_number": 35, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "178213342", "text": "#“United States”と”U.S.”のコサイン類似度を計算せよ．\n\nfrom gensim.models import KeyedVectors\n\nmodel = KeyedVectors.load_word2vec_format(\n \"GoogleNews-vectors-negative300.bin\", binary=True\n)\nprint(model.similarity('United_States','U.S.'))\n\n#0.73107743", "sub_path": "Mana/chapter07/knock61.py", "file_name": "knock61.py", "file_ext": "py", "file_size_in_byte": 276, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "gensim.models.KeyedVectors.load_word2vec_format", "line_number": 5, "usage_type": "call"}, {"api_name": "gensim.models.KeyedVectors", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "104234887", "text": "#!/usr/bin/env python\n\nimport rospy\nimport cv2\nimport os\nfrom custom_msgs.srv import EnableModel\nfrom sensor_msgs.msg import Image\nfrom cv_bridge import CvBridge\n\n\n# Mock the camera by publishing the same image to a topic\nclass DummyImagePublisher:\n\n NODE_NAME = 'test_images'\n CAMERA = 'left'\n IMAGE_TOPIC = '/camera/{}/image_raw'.format(CAMERA)\n\n # Read in the dummy image and other misc. setup work\n def __init__(self):\n self.image_publisher = rospy.Publisher(self.IMAGE_TOPIC, Image, queue_size=10)\n\n path = os.path.dirname(__file__)\n image = cv2.imread(os.path.join(path, '../assets/buoy.jpg'), cv2.IMREAD_COLOR)\n bridge = CvBridge()\n\n self.image_msg = bridge.cv2_to_imgmsg(image, 'bgr8')\n\n # Publish dummy image to topic every few seconds\n def run(self):\n rospy.init_node(self.NODE_NAME)\n\n # Testing enable_model service\n service_name = 'enable_model_{}'.format(self.CAMERA)\n rospy.wait_for_service(service_name)\n enable_model = rospy.ServiceProxy(service_name, EnableModel)\n\n loop_rate = rospy.Rate(1)\n model_enabled = True\n\n count = 0\n while not rospy.is_shutdown():\n self.image_publisher.publish(self.image_msg)\n\n # Testing enable\n if count % 30 == 0:\n enable_model('buoy', model_enabled)\n model_enabled = not model_enabled\n\n count += 1\n loop_rate.sleep()\n\n\nif __name__ == '__main__':\n DummyImagePublisher().run()\n", "sub_path": "onboard/catkin_ws/src/cv/scripts/test_images.py", "file_name": "test_images.py", "file_ext": "py", "file_size_in_byte": 1530, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "rospy.Publisher", "line_number": 20, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.Image", "line_number": 20, "usage_type": "argument"}, {"api_name": "os.path.dirname", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 23, "usage_type": "attribute"}, {"api_name": "cv_bridge.CvBridge", "line_number": 24, "usage_type": "call"}, {"api_name": "rospy.init_node", "line_number": 30, "usage_type": "call"}, {"api_name": "rospy.wait_for_service", "line_number": 34, "usage_type": "call"}, {"api_name": "rospy.ServiceProxy", "line_number": 35, "usage_type": "call"}, {"api_name": "custom_msgs.srv.EnableModel", "line_number": 35, "usage_type": "argument"}, {"api_name": "rospy.Rate", "line_number": 37, "usage_type": "call"}, {"api_name": "rospy.is_shutdown", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "395939058", "text": "from django.conf.urls import url, include\nfrom . import views\nfrom django.urls import path\n\napp_name = 'home'\n\nurlpatterns = [\n path('quiz2', views.quiz, name = 'quiz'),\n path('scenario', views.scenario, name = 'scenario'),\n path('quiz', views.quiz2, name='quiz2'),\n path('thankyou', views.resultsPage, name='resultsPage'),\n path('moderator', views.moderator, name='moderator'),\n path('info', views.info, name='info'),\n path('feedback', views.feedback, name='feedback'),\n path('reattempt', views.reattempt, name='reattempt'),\n path('revisit', views.revisit, name='revisit'),\n path('preresults', views.preresults, name='preresults'),\n path('comeback/', views.comeback, name='comeback'),\n path('view_feedback', views.view_feedback, name='view_feedback'),\n path('', views.home, name='home')\n]\n", "sub_path": "pls-website/home/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 833, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 16, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 17, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 18, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "31206942", "text": "#!/usr/bin/python3\n# -*-:coding=utf-8-*-\n\nfrom __future__ import unicode_literals\n\nimport logging\nimport os\nimport re\nimport time\n\ntry:\n from urllib.parse import urlparse\nexcept:\n from urlparse import urlparse\n\nimport pdfkit\nimport requests\nfrom bs4 import BeautifulSoup\n\nhtml_template = \"\"\"\n\n\n\n \n\n\n{content}\n\n\n\"\"\"\n\nclass Crawler(object):\n name = None\n\n def __init__(self, name, url):\n self.name = name\n self.url = url\n self.domain = '{uri.scheme}://{uri.netloc}'.format(uri=urlparse(self.url))\n\n def crawl(self, url):\n print(url)\n response = requests.get(url)\n return response\n\n def parse_menu(self, response):\n raise NotImplementedError\n\n def parse_body(self, response):\n raise NotImplementedError\n\n def run(self):\n start = time.time()\n options = {\n 'page-size' : 'A4',\n 'margin-top': '0,75in',\n 'margin-right': '0.5in',\n 'margin-bottom': '0.75in',\n 'margin-left': '0.5in',\n 'encoding': 'utf-8',\n 'custom-header': [\n \t('Acce[pt-Encoding', 'gzip')\n ],\n 'outline-depth': 10,\n 'cookie' : [\n \t('cookie-name1', 'cookie-value1'),\n \t('cookie-name2', 'cookie-value2')\n ]\n }\n\n htmls = []\n for index, sub_url in enumerate(self.parse_menu(self.crawl(self.url))) :\n html = self.parse_body(self.crawl(sub_url))\n f_name = '.'.join([str(index), \"html\"])\n with open(f_name, 'wb') as f:\n f.write(html)\n htmls.append(f_name)\n pdfkit.from_file(htmls, self.name + '.pdf', options = options)\n for html in htmls:\n os.remove(html)\n total_time = time.time() - start\n print(u'耗时：%f 秒' % total_time)\n\nclass MyCrawler(Crawler):\n def parse_menu(self, response):\n soup = BeautifulSoup(response.content, 'html.parser')\n menu_tag = soup.find_all(class_ = \"uk-nav uk-nav-side\")[1]\n for li in menu_tag.find_all('li'):\n url = li.a.get('href')\n if not url.startswith(\"http\"):\n url = \"\".join([self.domain, url])\n yield url\n\n def parse_body(self, response):\n try:\n soup = BeautifulSoup(response.content, 'html.parser')\n body = soup.find_all(class_ = 'x-wiki-content')[0]\n\n title = soup.find('h4').get_text()\n center_tag = soup.new_tag(\"center\")\n title_tag = soup.new_tag(\"h1\")\n title_tag.string = title\n center_tag.insert(1, title_tag)\n body.insert(1, center_tag)\n\n html = str(body)\n pattern = \"( 200:\n return\n\ndef dequantize_state_dict(state_dict):\n new_dict = {}\n drop_keys = {\n 'weight_fake_quant',\n 'qconfig',\n 'activation_post_process',\n }\n for key, value in state_dict.items():\n keep = True\n for drop in drop_keys:\n if drop in key:\n keep = False\n break\n if keep:\n new_dict[key] = value\n return new_dict\n\nstate_dict = torch.load('./friday_net.pth', map_location='cpu')\n\n#qat_net = QATNet()\n#qat_net.load_state_dict(state_dict)\n\nnet = Net()\nnet.load_state_dict(dequantize_state_dict(state_dict))\nnet.eval()\n#net=torch.quantization.convert(qat_net.eval(), inplace=False)\n\nim = Image.open('./test.jpg')\ndata = val_transform(im).reshape((1, 3, 224, 224))\ntflite_input = to_tflite_input(data)\nprint(tflite_input, tflite_input.mean(), tflite_input.std())\ntorch_output = net(data)\n#torch_quantize_output = qat_net(data)\n\ndummy_input = torch.randn(1, 3, 224, 224)\ninput_names = ['image_array']\noutput_names = ['category']\n\nprint('onnx export')\nonnx_model_path = 'model.onnx'\ntorch.onnx.export(net, dummy_input, onnx_model_path, input_names=input_names, output_names=output_names)\n\nprint('keras export')\nsaved_model_dir = 'saved_model'\npytorch2savedmodel(onnx_model_path, saved_model_dir)\n\nprint('tflite export')\ntflite_model_path = 'model.tflite'\ntflite_model = savedmodel2tflite(saved_model_dir, tflite_model_path, quantize=False)\n\n\nprint('tflite quantized export')\ntflite_quantized_model_path = 'model_quantized.tflite'\ntflite_quantized_model = savedmodel2tflite(saved_model_dir,\n tflite_quantized_model_path, quantize=True,\n representative_dataset=representative_dataset_gen)\n\nprint('edgetpu_compiler')\nos.system(f\"edgetpu_compiler {tflite_quantized_model_path}\")\n\ntflite_output = get_tflite_outputs(tflite_input, tflite_model).reshape(-1, )\ntflite_quantized_output = get_tflite_outputs(tflite_input, tflite_quantized_model).reshape(-1, )\nprint('torch', torch_output)\n#print('torch_quantized', torch_output)\nprint('tflite', tflite_output)\nprint('tflite_quantized', tflite_quantized_output)\n", "sub_path": "convert.py", "file_name": "convert.py", "file_ext": "py", "file_size_in_byte": 2724, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torchvision.datasets.ImageFolder", "line_number": 11, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 11, "usage_type": "attribute"}, {"api_name": "model.val_transform", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 12, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 40, "usage_type": "call"}, {"api_name": "model.Net", "line_number": 45, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 50, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 50, "usage_type": "name"}, {"api_name": "model.val_transform", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.onnx.export", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.onnx", "line_number": 63, "usage_type": "attribute"}, {"api_name": "converters.pytorch2savedmodel", "line_number": 67, "usage_type": "call"}, {"api_name": "converters.savedmodel2tflite", "line_number": 71, "usage_type": "call"}, {"api_name": "converters.savedmodel2tflite", "line_number": 76, "usage_type": "call"}, {"api_name": "os.system", "line_number": 81, "usage_type": "call"}, {"api_name": "tflite.get_tflite_outputs", "line_number": 83, "usage_type": "call"}, {"api_name": "tflite.get_tflite_outputs", "line_number": 84, "usage_type": "call"}]} +{"seq_id": "214564267", "text": "import numpy as np \nimport matplotlib.pyplot as plt \nimport pandas as pd\n\ndata = []\nwith open('facebook.txt', 'r') as f:\n\tfor line in f:\n\t\tdata.append(line.replace('\\n', '').split(','))\n\nplt.xlabel('age')\nplt.ylabel('count')\nplt.title('facebook user age distribution')\ndel data[1][0]\nclasses = ['18-22', '23-27', '28+']\nbar1 = plt.bar(np.arange(0, 3, 1)-.2, [78,49,21], color='blue', label='yes', width=.3)\nbar2 = plt.bar(np.arange(0, 3, 1)+.1, [4,21,46], color='red', label='no', width=.3, align='center')\nplt.xticks(np.arange(0, 3, 1), ['18-22', '23-27', '28+'])\nplt.legend(loc='upper right', title='facebook user?')\nplt.show()\n\n# d = [1, 2, 3, 4, 5]\n# print(np.std(d, ddof=0))\n\n", "sub_path": "facebook_graph_visualization/facebook.py", "file_name": "facebook.py", "file_ext": "py", "file_size_in_byte": 681, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.xlabel", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 11, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 12, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.bar", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}]} +{"seq_id": "336636836", "text": "#!/usr/bin/python\n\nimport argparse, csv\n\nfrom classes import modify_otu\n\ndef main():\n\tparser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\tparser.add_argument('-i','--input-table-fp',help='file that is tabtab', required=True)\n\tparser.add_argument('-o','--output-fp',help='output base file path',required=True)\n\tparser.add_argument('-c',help='class to split on',required=True)\n\targs = parser.parse_args()\n\tholder = modify_otu.OtuTable(args.input_table_fp, args.c, args.output_fp)\n\tholder.write_all()\n\nif __name__ == '__main__':\n\tmain()\n", "sub_path": "split_on_state.py", "file_name": "split_on_state.py", "file_ext": "py", "file_size_in_byte": 596, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "argparse.ArgumentDefaultsHelpFormatter", "line_number": 8, "usage_type": "attribute"}, {"api_name": "classes.modify_otu.OtuTable", "line_number": 13, "usage_type": "call"}, {"api_name": "classes.modify_otu", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "233152231", "text": "import uuid\nimport threading\nfrom datetime import datetime\nfrom .execution import RunExecution\n\n\nclass RunBackend(object):\n def __init__(self, workspace):\n self.workspace = workspace\n self.db = workspace.db\n self.storage = workspace.storage\n\n def create_run(self, specification):\n run_id = str(uuid.uuid4())\n\n run = {\n \"run_id\": run_id,\n \"status\": \"created\",\n \"created\": datetime.utcnow(),\n \"specification\": specification,\n }\n\n self.db.create_run(run)\n\n run_execution = RunExecution(self.workspace, run_id)\n run_execution_thread = threading.Thread(\n target=run_execution.run, name=f\"RunExecution {run_id}\"\n )\n run_execution_thread.start()\n\n return run\n\n def terminate_run(self, run_id):\n run = self.db.get_run(run_id)\n\n if run[\"status\"] == \"terminated\" or run[\"status\"] == \"run finished\":\n return\n\n run[\"status\"] = \"terminated\"\n run[\"terminated\"] = datetime.now()\n self.db.update_run(run)\n\n def delete_run(self, run_id):\n self.terminate_run(run_id)\n self.db.delete_run(run_id)\n self.storage.delete_logs(run_id)\n self.storage.delete_code(run_id)\n\n def get_run(self, run_id):\n return self.db.get_run(run_id)\n\n def get_run_ids(self):\n return self.db.get_run_ids()\n\n def get_all_runs(self):\n return self.db.get_all_runs()\n", "sub_path": "src/datalaunch_server/backend/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 1473, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "uuid.uuid4", "line_number": 14, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 19, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 19, "usage_type": "name"}, {"api_name": "execution.RunExecution", "line_number": 25, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 26, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "362156527", "text": "from unittest import TestCase\nfrom settings.settings import PROJECT_ROOT\n\nfrom utils.helpers import get_json_config\nfrom hamcrest import assert_that, raises, calling, contains\n\n\nclass JsonFileTester(TestCase):\n\n def test_successfully_read_config_file(self):\n actual_json = get_json_config(\n file_path='{}/app/tests/fixtures/test_config.json'.format(\n PROJECT_ROOT\n )\n )\n for _ in actual_json:\n assert_that(\n contains('repo', 'config_path', 'tokenize')\n )\n\n def test_raises_io_error_if_fail(self):\n assert_that(\n calling(get_json_config).with_args(file_name='askldfj'),\n raises(TypeError)\n )", "sub_path": "app/tests/json_file_tests.py", "file_name": "json_file_tests.py", "file_ext": "py", "file_size_in_byte": 727, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 8, "usage_type": "name"}, {"api_name": "utils.helpers.get_json_config", "line_number": 11, "usage_type": "call"}, {"api_name": "settings.settings.PROJECT_ROOT", "line_number": 13, "usage_type": "argument"}, {"api_name": "hamcrest.assert_that", "line_number": 17, "usage_type": "call"}, {"api_name": "hamcrest.contains", "line_number": 18, "usage_type": "call"}, {"api_name": "hamcrest.assert_that", "line_number": 22, "usage_type": "call"}, {"api_name": "hamcrest.calling", "line_number": 23, "usage_type": "call"}, {"api_name": "utils.helpers.get_json_config", "line_number": 23, "usage_type": "argument"}, {"api_name": "hamcrest.raises", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "89258259", "text": "import pandas as pd\r\nimport numpy as np\r\nimport os\r\nfrom sklearn.ensemble import GradientBoostingClassifier\r\nfrom collections import defaultdict\r\n\r\n# 预测结果文件：src/step1/ground_truth/test_prediction.csv\r\nnum = []\r\ndef getPrediction():\r\n data_train = os.path.join('input', 'train.csv')\r\n data_test = os.path.join( 'input', 'test.csv')\r\n\r\n converters = defaultdict(int)\r\n ads = pd.read_csv(data_train, converters=converters)\r\n\r\n X = ads.drop('TARGET', axis=1).values\r\n y = ads[\"TARGET\"].values\r\n ads_test = pd.read_csv(data_test)\r\n X_TEST = ads_test.values\r\n y_ID = ads_test['ID'].values\r\n\r\n clf = GradientBoostingClassifier(learning_rate=0.01, n_estimators=600, max_features = 3, subsample = 0.9)\r\n clf.fit(X, y)\r\n y_predictor = clf.predict_proba(X_TEST)\r\n\r\n sub_name = os.path.join('input', 'test_prediction.csv')\r\n with open(sub_name, 'w') as file:\r\n file.write('ID,TARGET\\n')\r\n k = len(y_ID)\r\n for i in range(int(k)):\r\n line = str(y_ID[i]) + ',' + str(y_predictor[i][1])\r\n file.write(line + '\\n')\r\n\r\n\r\n\r\ngetPrediction()", "sub_path": "manyidu.py", "file_name": "manyidu.py", "file_ext": "py", "file_size_in_byte": 1117, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "attribute"}, {"api_name": "collections.defaultdict", "line_number": 13, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 18, "usage_type": "call"}, {"api_name": "sklearn.ensemble.GradientBoostingClassifier", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}]} +{"seq_id": "27302585", "text": "import asyncio\r\n\r\nimport asyncpg\r\nimport os\r\nimport uvicorn\r\nfrom starlette.applications import Starlette\r\nfrom starlette.middleware import Middleware\r\nfrom starlette.middleware.cors import CORSMiddleware\r\nfrom starlette.requests import Request\r\nfrom starlette.routing import Mount\r\nfrom starlette.templating import Jinja2Templates\r\nfrom starlette.staticfiles import StaticFiles\r\nfrom starlette.responses import JSONResponse\r\n\r\nmiddleware = [\r\n Middleware(CORSMiddleware, allow_origins=['*'])\r\n]\r\n\r\nroutes = [\r\n Mount('/static', StaticFiles(directory='app/static'), name='static')\r\n]\r\n\r\ntemplates = Jinja2Templates(directory='app/templates')\r\n\r\n\r\nasync def startup():\r\n global conn\r\n print(\"connect\")\r\n conn = await asyncpg.connect(\r\n \"postgres://mxqkomkc:w2w1BetfK154mVvEMfJpuNGAYyqlzVyo@john.db.elephantsql.com:5432/mxqkomkc\")\r\n\r\napp = Starlette(middleware=middleware, on_startup=[startup], routes=routes, debug=True)\r\n\r\n\r\n\r\n@app.route(\"/\")\r\nasync def proc_hom(request: Request):\r\n return templates.TemplateResponse('index.html',\r\n {'request': request})\r\n\r\n@app.route(\"/villa\", methods=['GET'])\r\nasync def proc_sen(request: Request):\r\n sentence: str = request.query_params['id']\r\n if not sentence:\r\n return JSONResponse({\"error\": \"no id provided\"}, status_code=400)\r\n else:\r\n try:\r\n id = int(sentence)\r\n except ValueError:\r\n return JSONResponse({\"error\": \"id is not a number\"},\r\n status_code=400)\r\n out = await conn.fetch(\"SELECT * FROM villas WHERE villa_number=$1\", id)\r\n base = []\r\n for val in out:\r\n sd = dict()\r\n for key, v in val.items():\r\n sd[key] = v\r\n base.append(sd)\r\n return templates.TemplateResponse('answer.html',{'request': request, 'data': base, 'results': len(base), 'id': id})\r\n@app.route(\"/name\", methods=['GET'])\r\nasync def proc_sen(request: Request):\r\n sentence: str = request.query_params['id']\r\n if not sentence:\r\n return JSONResponse({\"error\": \"no id provided\"}, status_code=400)\r\n else:\r\n try:\r\n id = str(sentence)\r\n except ValueError:\r\n return JSONResponse({\"error\": \"id is not a name\"},\r\n status_code=400)\r\n out = await conn.fetch(\"SELECT * FROM villas WHERE member_name=$1\", id)\r\n base = []\r\n for val in out:\r\n sd = dict()\r\n for key, v in val.items():\r\n sd[key] = v\r\n base.append(sd)\r\n return templates.TemplateResponse('answer.html',{'request': request, 'data': base, 'results': len(base), 'id': id})\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n uvicorn.run(app, host='0.0.0.0', port=os.getenv(\"PORT\", 5000))\r\n", "sub_path": "app/__main__.py", "file_name": "__main__.py", "file_ext": "py", "file_size_in_byte": 2815, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "starlette.middleware.Middleware", "line_number": 16, "usage_type": "call"}, {"api_name": "starlette.middleware.cors.CORSMiddleware", "line_number": 16, "usage_type": "argument"}, {"api_name": "starlette.routing.Mount", "line_number": 20, "usage_type": "call"}, {"api_name": "starlette.staticfiles.StaticFiles", "line_number": 20, "usage_type": "call"}, {"api_name": "starlette.templating.Jinja2Templates", "line_number": 23, "usage_type": "call"}, {"api_name": "asyncpg.connect", "line_number": 29, "usage_type": "call"}, {"api_name": "starlette.applications.Starlette", "line_number": 32, "usage_type": "call"}, {"api_name": "starlette.requests.Request", "line_number": 37, "usage_type": "name"}, {"api_name": "starlette.requests.Request", "line_number": 42, "usage_type": "name"}, {"api_name": "starlette.responses.JSONResponse", "line_number": 45, "usage_type": "call"}, {"api_name": "starlette.responses.JSONResponse", "line_number": 50, "usage_type": "call"}, {"api_name": "starlette.requests.Request", "line_number": 61, "usage_type": "name"}, {"api_name": "starlette.responses.JSONResponse", "line_number": 64, "usage_type": "call"}, {"api_name": "starlette.responses.JSONResponse", "line_number": 69, "usage_type": "call"}, {"api_name": "uvicorn.run", "line_number": 83, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "334599157", "text": "#coding=utf-8\nimport sys\nreload(sys)\nsys.setdefaultencoding('utf8')\n# Copyright 2008 Google Inc.\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\nimport logging\nimport re\nimport wsgiref.handlers\nfrom google.appengine.api import xmpp\nfrom google.appengine.api.labs import taskqueue\nfrom google.appengine.ext import webapp\nfrom google.appengine.ext.webapp import xmpp_handlers\nfrom django.utils import simplejson\nfrom models import Channel\nfrom models import Log\nfrom models import Person\nfrom models import Room\n\n\nclass XmppController(xmpp_handlers.CommandHandler):\n \"\"\"Handler class for all XMPP activity.\"\"\"\n\n _CHANNEL_SIZE_LIMIT = 100 # Max people in a channel.\n _LIST_LIMIT = 20 # Max channels to show for /list command.\n _NAME_LIMIT = 20 # Max names to show for /name command.\n\n def Broadcast(self, channel, message, system=False, exclude_self=True):\n \"\"\"Queues a broadcast message.\n\n Args:\n channel: The target channel.\n message: The message to broadcast.\n system: Whether this is a system message (i.e. not a chat).\n exclude_self: Whether to exclude self.person from the broadcast.\n \"\"\"\n if system:\n lines = ['* ' + l for l in message.split('\\n')]\n message = '\\n'.join(lines)\n params = {\n 'channel': channel.name,\n 'message': message,\n }\n if exclude_self:\n params['skip'] = self.person.jid()\n taskqueue.Task(url='/task/broadcast', params=params).add('chats')\n self.Log(channel, message)\n\n def Log(self, channel, body, person=None, system=False):\n \"\"\"Log a message.\n\n Args:\n channel: The channel object the message was sent to.\n body: The body of the message.\n person: The person who sent the message. Defaults to self.person.\n system: Whether this is a system log (i.e. not a chat).\n\n You should not use person and system at the same time.\n \"\"\"\n if person and system:\n raise RuntimeError('You can\\'t use person and system here')\n if not system:\n if not person: person = self.person\n log = Log(channel=channel.name,\n user=person.user.email(),\n body=body)\n else:\n log = Log(channel=channel.name,\n system=True,\n body=body)\n log.put()\n\n def help_command(self, msg):\n channel_rx = '/#' + Channel.CHANNEL_NAME_REGEX + '/'\n lines = [\n '* 支援的命令如下:',\n '* /help * 取得本說明文件',\n '* /newroom * 建立新房間物件',\n '* /join # * 加入頻道，若無該頻道則創建一個新的',\n '* /look [who] * 看週遭或是人',\n '* /gossip YOUR MESSAGE * 送訊息到頻道',\n '* /nickname [NICKNAME] * 取得或是設定暱稱',\n '* /leave * 離開頻道',\n '* /list * 列出所有頻道名稱',\n '* /who [#] * 看同頻道內有誰，有點像 /look',\n '* /me * 顯示我的資訊',\n '* ',\n ('* 頻道名稱大抵是用英文，語法要符合 %s; 別忘了前面要加 #' %\n channel_rx),\n ]\n msg.reply(u'\\n'.join(lines))\n\n def newroom_command(self, msg):\n json_decoder = simplejson.decoder.JSONDecoder()\n msg.reply(u'%s 創建了新房間 %s' % msg.arg)\n #room_json = json_decoder.decode(msg.arg)\n #msg.reply(room_json)\n\n def look_command(self, msg):\n channel = self.person.channel\n if not channel:\n msg.reply(u'* 您應該先進頻道內再用此一語法查詢.')\n return\n if not msg.arg:\n q = Person.all().filter('channel =', channel)\n else:\n q = Person.all().filter('channel =', channel).filter('name =', msg.arg)\n people = q.fetch(self._NAME_LIMIT + 1)\n lines = []\n for p in people:\n lines.append(u'*** %s email 是 %s' % (p.name, p.user.email()))\n msg.reply(u'\\n'.join(lines))\n\n def join_command(self, msg):\n m = re.match(r'^#(?P' + Channel.CHANNEL_NAME_REGEX + ')$',\n msg.arg)\n if not m:\n msg.reply(u'* /join 語法錯誤')\n return\n name = m.group('channel')\n if self.person.channel and (self.person.channel.name == name):\n msg.reply(u'* 你已經在頻道 #%s 中!' % name)\n return\n\n # Leave the existing channel, and tell them about it.\n if self.person.channel:\n old = self.person.channel\n message = '%s has left %s' % (self.person, old)\n self.Broadcast(old, message, system=True)\n self.Log(old, message, system=True)\n self.person.channel = None\n taskqueue.Task(url='/task/update-channel-stats',\n params={'channel': old.name}).add('stats')\n\n channel = Channel.ChannelByName(name, create=True)\n if channel.num_members >= self._CHANNEL_SIZE_LIMIT:\n msg.reply(u'* 抱歉，頻道 %s 內已經遠到容量上限(%d/%d)人' %\n (channel, channel.num_members, self._CHANNEL_SIZE_LIMIT))\n return\n self.person.channel = channel\n self.person.put()\n msg.reply(u'* 你已經加入 %s' % channel)\n message = (u'%s 已經加入 %s' % (self.person, channel))\n self.Broadcast(channel, message, system=True)\n self.Log(channel, message, system=True)\n taskqueue.Task(url='/task/update-channel-stats',\n params={'channel': channel.name}).add('stats')\n\n def leave_command(self, msg):\n if not self.person.channel:\n msg.reply(u'* 你並不在頻道內!')\n else:\n message = (u'%s 已經離開 %s' % (self.person, self.person.channel))\n self.Broadcast(self.person.channel, message, system=True)\n self.Log(self.person.channel, message, system=True)\n\n name = self.person.channel.name\n self.person.channel = None\n self.person.put()\n msg.reply(u'* 你已經離開 #%s' % name)\n taskqueue.Task(url='/task/update-channel-stats',\n params={'channel': name}).add('stats')\n\n def list_command(self, msg):\n \"\"\"Handle /list commands.\"\"\"\n lines = []\n q = Channel.all().order('-num_members').filter('num_members >', 0)\n channels = q.fetch(self._LIST_LIMIT + 1)\n if not len(channels):\n msg.reply(u'* 沒有任何頻道!')\n return\n if len(channels) <= self._LIST_LIMIT:\n # Show all, sorted by channel name.\n channels.sort(key=lambda c: c.name)\n lines.append('* 所有頻道清單如下:')\n else:\n # Show the top N channels, sorted by num_members.\n channels.pop()\n lines.append('* 頻道數超過 %d; 底下是最受歡迎的清單:' %\n self._LIST_LIMIT)\n for c in channels:\n if c.num_members == 1:\n count = '1 個人'\n else:\n count = '%d 個人' % c.num_members\n s = '* - %s (%s)' % (c, count)\n lines.append(s)\n msg.reply(u'\\n'.join(lines))\n\n def who_command(self, msg):\n m = re.match(r'^(#(?P' + Channel.CHANNEL_NAME_REGEX + '))?$',\n msg.arg)\n if not m:\n msg.reply(u'* /who 後面的頻道名稱似乎有誤')\n return\n if m.group('channel'):\n channel = Channel.ChannelByName(m.group('channel'), create=False)\n if not channel:\n msg.reply(u'* 沒有您要查的頻道: #%s' % m.group('channel'))\n return\n else:\n channel = self.person.channel\n if not channel:\n msg.reply(u'* 您應該先進頻道內再用此一語法查詢.')\n return\n q = Person.all().filter('channel =', channel)\n people = q.fetch(self._NAME_LIMIT + 1)\n if len(people) <= self._NAME_LIMIT:\n people = people[0:self._NAME_LIMIT]\n names = sorted([p.name for p in people])\n msg.reply(u'* 在 %s 頻道內的人有: %s' % (channel, ', '.join(names)))\n else:\n msg.reply(u'* 在頻道 %s 的人數超過 %d' % (channel, self._NAME_LIMIT))\n\n def me_command(self, msg):\n msg.reply(u'*** 您的暱稱是 %s, email 是 %s' % (self.person.name, self.person.user.email()))\n\n def nickname_command(self, msg):\n if msg.arg:\n self.person.setName(msg.arg)\n msg.reply(u'* 您設定新暱稱為 %s' % msg.arg)\n else:\n msg.reply(u'* 您的暱稱是 %s' % self.person.name)\n\n def gossip_command(self, msg):\n # gossip to your channel, but only if you're in a channel.\n channel = self.person.channel\n if channel:\n self.Broadcast(channel, u'%s gossip: %s' % (self.person, msg.arg))\n else:\n msg.reply(u'* 您需要先加入(/join)頻道才能送訊息.')\n\n def text_message(self, msg):\n \"\"\"Handle plain messages.\"\"\"\n # Chat, but only if you're in a channel.\n channel = self.person.channel\n if channel:\n self.Broadcast(channel, u'%s 說: %s' % (self.person.name, msg.body))\n else:\n msg.reply(u'* 你必須在某頻道內說話才有意義.')\n\n def message_received(self, msg):\n \"\"\"Handle all messages; overrides CommandHandlerMixin.\"\"\"\n logging.debug('%s sent \"%s\"', msg.sender, msg.body)\n\n match = re.match(r'^([^/]+)(/.*)?$', msg.sender)\n if not match:\n msg.reply(u'* 您正在用奇怪的帳號!')\n return\n self.person = Person.PersonByEmail(match.group(1))\n if not self.person:\n msg.reply(u'* 抱歉，您是誰?')\n return\n\n super(XmppController, self).message_received(msg)\n\n\ndef main():\n app = webapp.WSGIApplication([\n ('/_ah/xmpp/message/chat/', XmppController),\n ], debug=True)\n wsgiref.handlers.CGIHandler().run(app)\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "controllers/xmpp.py", "file_name": "xmpp.py", "file_ext": "py", "file_size_in_byte": 9855, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.setdefaultencoding", "line_number": 4, "usage_type": "call"}, {"api_name": "google.appengine.ext.webapp.xmpp_handlers.CommandHandler", "line_number": 33, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.webapp.xmpp_handlers", "line_number": 33, "usage_type": "name"}, {"api_name": "google.appengine.api.labs.taskqueue.Task", "line_number": 58, "usage_type": "call"}, {"api_name": "google.appengine.api.labs.taskqueue", "line_number": 58, "usage_type": "name"}, {"api_name": "models.Log", "line_number": 76, "usage_type": "call"}, {"api_name": "models.Log", "line_number": 80, "usage_type": "call"}, {"api_name": "models.Channel.CHANNEL_NAME_REGEX", "line_number": 86, "usage_type": "attribute"}, {"api_name": "models.Channel", "line_number": 86, "usage_type": "name"}, {"api_name": "django.utils.simplejson.decoder.JSONDecoder", "line_number": 106, "usage_type": "call"}, {"api_name": "django.utils.simplejson.decoder", "line_number": 106, "usage_type": "attribute"}, {"api_name": "django.utils.simplejson", "line_number": 106, "usage_type": "name"}, {"api_name": "models.Person.all", "line_number": 117, "usage_type": "call"}, {"api_name": "models.Person", "line_number": 117, "usage_type": "name"}, {"api_name": "models.Person.all", "line_number": 119, "usage_type": "call"}, {"api_name": "models.Person", "line_number": 119, "usage_type": "name"}, {"api_name": "re.match", "line_number": 127, "usage_type": "call"}, {"api_name": "models.Channel.CHANNEL_NAME_REGEX", "line_number": 127, "usage_type": "attribute"}, {"api_name": "models.Channel", "line_number": 127, "usage_type": "name"}, {"api_name": "google.appengine.api.labs.taskqueue.Task", "line_number": 144, "usage_type": "call"}, {"api_name": "google.appengine.api.labs.taskqueue", "line_number": 144, "usage_type": "name"}, {"api_name": "models.Channel.ChannelByName", "line_number": 147, "usage_type": "call"}, {"api_name": "models.Channel", "line_number": 147, "usage_type": "name"}, {"api_name": "google.appengine.api.labs.taskqueue.Task", "line_number": 158, "usage_type": "call"}, {"api_name": "google.appengine.api.labs.taskqueue", "line_number": 158, "usage_type": "name"}, {"api_name": "google.appengine.api.labs.taskqueue.Task", "line_number": 173, "usage_type": "call"}, {"api_name": "google.appengine.api.labs.taskqueue", "line_number": 173, "usage_type": "name"}, {"api_name": "models.Channel.all", "line_number": 179, "usage_type": "call"}, {"api_name": "models.Channel", "line_number": 179, "usage_type": "name"}, {"api_name": "re.match", "line_number": 203, "usage_type": "call"}, {"api_name": "models.Channel.CHANNEL_NAME_REGEX", "line_number": 203, "usage_type": "attribute"}, {"api_name": "models.Channel", "line_number": 203, "usage_type": "name"}, {"api_name": "models.Channel.ChannelByName", "line_number": 209, "usage_type": "call"}, {"api_name": "models.Channel", "line_number": 209, "usage_type": "name"}, {"api_name": "models.Person.all", "line_number": 218, "usage_type": "call"}, {"api_name": "models.Person", "line_number": 218, "usage_type": "name"}, {"api_name": "logging.debug", "line_number": 256, "usage_type": "call"}, {"api_name": "re.match", "line_number": 258, "usage_type": "call"}, {"api_name": "models.Person.PersonByEmail", "line_number": 262, "usage_type": "call"}, {"api_name": "models.Person", "line_number": 262, "usage_type": "name"}, {"api_name": "google.appengine.ext.webapp.WSGIApplication", "line_number": 271, "usage_type": "call"}, {"api_name": "google.appengine.ext.webapp", "line_number": 271, "usage_type": "name"}, {"api_name": "wsgiref.handlers.handlers.CGIHandler", "line_number": 274, "usage_type": "call"}, {"api_name": "wsgiref.handlers.handlers", "line_number": 274, "usage_type": "attribute"}, {"api_name": "wsgiref.handlers", "line_number": 274, "usage_type": "name"}]} +{"seq_id": "536488335", "text": "#!/usr/bin/evn python\n# -*- coding:utf-8 -*-\n\n# FileName Config.py\n# Author: HeyNiu\n# Created Time: 2016/8/22\n\"\"\"\nhttp接口测试框架配置信息解析器\n\"\"\"\nimport configparser\nimport os\n\nimport utils.GlobalList\n\n\nclass Config(object):\n def __init__(self, api_type):\n self.config = configparser.ConfigParser()\n self.conf_path = os.path.join(os.getcwd()[::-1].split('\\\\', 1)[-1][::-1], 'conf', 'config.conf')\n if not os.path.exists(self.conf_path):\n # 持续集成时配置文件目录有改变，需要兼容\n self.conf_path = os.path.join(os.path.dirname(\n os.path.abspath(__file__)[::-1].split('\\\\', 1)[-1][::-1]), 'conf', 'config.conf')\n if not os.path.exists(self.conf_path):\n raise FileNotFoundError(\"请确保配置文件存在！\")\n self.config.read(self.conf_path, encoding='utf-8')\n self.type = api_type\n self.conf = {\n 'tester': '',\n 'project': '',\n 'versionName': '',\n 'versionCode': '',\n 'AppBuild': '',\n 'host': '',\n 'systemType': '2',\n 'DeviceId': 'ffffffff-b3f1-87ad-90ef-ebeb00000000',\n 'Model': 'MI+4LTE',\n 'DeviceOS': '23',\n 'Release': '6.0.1',\n 'getTokenHost': '',\n 'loginHost': '',\n 'loginInfo': '',\n 'SessionsPath': '',\n 'ApiURL': '',\n 'SpecialSessions': '',\n 'SessionsPair': '',\n 'DuplicateSwitch': False\n }\n\n self.__get_conf()\n\n def __get_conf(self):\n print('读取配置文件中...')\n self.conf['tester'] = self.config.get(self.config.sections()[self.type], 'tester')\n self.conf['project'] = self.config.get(self.config.sections()[self.type], 'project')\n self.conf['versionName'] = self.config.get(self.config.sections()[self.type], 'versionName')\n self.conf['versionCode'] = self.config.get(self.config.sections()[self.type], 'versionCode')\n self.conf['AppBuild'] = self.conf['versionCode']\n self.conf['host'] = self.config.get(self.config.sections()[self.type], 'host')\n utils.GlobalList.HOST = self.conf['host']\n self.conf['getTokenHost'] = self.config.get(self.config.sections()[self.type], 'getTokenHost')\n self.conf['loginHost'] = self.config.get(self.config.sections()[self.type], 'loginHost')\n self.conf['loginInfo'] = self.config.get(self.config.sections()[self.type], 'loginInfo')\n self.conf['SessionsPath'] = self.config.get(self.config.sections()[self.type], 'SessionsPath')\n utils.GlobalList.SESSIONS_PATH = self.conf['SessionsPath']\n self.conf['ApiURL'] = self.config.get(self.config.sections()[self.type], 'ApiURL')\n utils.GlobalList.API_URL = self.conf['ApiURL']\n self.conf['SpecialSessions'] = self.config.get(self.config.sections()[self.type], 'SpecialSessions')\n utils.GlobalList.SPECIAL_SESSIONS = self.conf['SpecialSessions']\n self.conf['SessionsPair'] = self.config.get(self.config.sections()[self.type], 'SessionsPair')\n utils.GlobalList.SESSIONS_PAIR = self.conf['SessionsPair']\n self.conf['DuplicateSwitch'] = self.config.getboolean(self.config.sections()[self.type], 'DuplicateSwitch')\n utils.GlobalList.DUPLICATE_SWITCH = self.conf['DuplicateSwitch']\n self.__init_data()\n utils.GlobalList.CONF = self.conf\n\n def __init_data(self):\n \"\"\"\n 初始化接口对，提取出创建数据接口与删除数据接口\n :return:\n \"\"\"\n for i in eval(self.conf['SessionsPair']):\n session_create_name = i.split(':')[0]\n session_create_parameter = i.split(':')[1].split('|')[0]\n session_delete_name = i.split('|')[-1].split(':')[0]\n session_delete_parameter = i.split(':')[-1]\n utils.GlobalList.CREATE_DICT[session_create_name] = session_create_parameter\n utils.GlobalList.DELETE_DICT[session_delete_name] = session_delete_parameter\n utils.GlobalList.MAPPING_DICT[session_delete_name] = session_create_name\n\n\nif __name__ == '__main__':\n Config(0)\n", "sub_path": "conf/Config.py", "file_name": "Config.py", "file_ext": "py", "file_size_in_byte": 4201, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "configparser.ConfigParser", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "utils.GlobalList.GlobalList", "line_number": 60, "usage_type": "attribute"}, {"api_name": "utils.GlobalList", "line_number": 60, "usage_type": "name"}, {"api_name": "utils.GlobalList.GlobalList", "line_number": 65, "usage_type": "attribute"}, {"api_name": "utils.GlobalList", "line_number": 65, "usage_type": "name"}, {"api_name": "utils.GlobalList.GlobalList", "line_number": 67, "usage_type": "attribute"}, {"api_name": "utils.GlobalList", "line_number": 67, "usage_type": "name"}, {"api_name": "utils.GlobalList.GlobalList", "line_number": 69, "usage_type": "attribute"}, {"api_name": "utils.GlobalList", "line_number": 69, "usage_type": "name"}, {"api_name": "utils.GlobalList.GlobalList", "line_number": 71, "usage_type": "attribute"}, {"api_name": "utils.GlobalList", "line_number": 71, "usage_type": "name"}, {"api_name": "utils.GlobalList.GlobalList", "line_number": 73, "usage_type": "attribute"}, {"api_name": "utils.GlobalList", "line_number": 73, "usage_type": "name"}, {"api_name": "utils.GlobalList.GlobalList", "line_number": 75, "usage_type": "attribute"}, {"api_name": "utils.GlobalList", "line_number": 75, "usage_type": "name"}, {"api_name": "utils.GlobalList.GlobalList", "line_number": 87, "usage_type": "attribute"}, {"api_name": "utils.GlobalList", "line_number": 87, "usage_type": "name"}, {"api_name": "utils.GlobalList.GlobalList", "line_number": 88, "usage_type": "attribute"}, {"api_name": "utils.GlobalList", "line_number": 88, "usage_type": "name"}, {"api_name": "utils.GlobalList.GlobalList", "line_number": 89, "usage_type": "attribute"}, {"api_name": "utils.GlobalList", "line_number": 89, "usage_type": "name"}]} +{"seq_id": "93242589", "text": "#!/usr/bin/python3\n# -*- coding:utf-8 -*-\nimport os\nimport utils\n#utils.execShellCommand(\"rm -rf *.txt\")\nalgs = ['LLF','SJF','WRR','OUR']\nsyss = ['sys0','sys1','sys2']\nforms = ['fct_tcp_out_1']\nprojects = ['P0', 'P1', 'P2', 'P3', 'P4', 'P5', 'P6', 'P7']\ndebug = False\noffline_syss = ['S0','S1','S2']\noffline_time = 60\noffline_when = 30\nutils.execShellCommand(\"mkdir fct_offline_analysis\")\nfor form in forms:\n for alg in algs:\n outpath=\"fct_offline_analysis/fct_offline_analysis_\"+alg+\".txt\"\n fout = open(outpath,\"w+\")\n for offline_sys in offline_syss:\n for project in projects:\n fct_collect = []\n # 拿到三个系统的同一个用户的所有数据\n project_iperf3_file_list = []\n for sys in syss:\n path = \"./\"+alg+\"/\"+sys+\"/\"+form+\"/out/dat/1\"\n # print(path)\n\n raw_file_list = os.listdir(path) # 得到文件夹下的所有文件名称\n # print(raw_file_list)\n\n for raw_file in raw_file_list: # 遍历文件夹\n if not os.path.isdir(raw_file): # 判断是否是文件夹\n if project in raw_file:\n project_iperf3_file_list.append(\n path+\"/\"+raw_file)\n #print(project)\n #print(raw_file)\n\n #print(project_iperf3_file_list)\n for project_iperf3_file in project_iperf3_file_list :\n raw_dat_list =utils.execShellCommand(\"cat \"+project_iperf3_file+\" | grep receiver\").split(\"\\n\")[:-1]\n #print(raw_dat_list)\n dat_list = []\n for raw_dat in raw_dat_list:\n dat_list.append(raw_dat.split(\" \")[2].strip())\n\n #print(dat_list)\n\n time_list = []\n for dat in dat_list:\n time_list.append(float(dat.split(\"-\")[1].strip()))\n #print(time_list)\n \n fct = []\n fct_temp = 0\n offline_flag = False\n for time in time_list:\n if offline_sys in project_iperf3_file and fct_temp > offline_when and offline_flag == False :\n fct_temp += offline_time\n offline_flag = True\n fct_temp += time\n fct.append(fct_temp)\n fct_collect.append(fct_temp)\n\n fout.write(\"=========================================================\\n\")\n fout.write(project_iperf3_file+\"\\n\"+ str(str(fct))+\"\\n\")\n \n fct_collect.sort()\n if len(fct_collect)<50 :\n print(\"ERROR\")\n exit()\n fout.write(\"=========================================================\\n\")\n fout.write(str(fct_collect))\n fout.write(\"\\n-- analysis || \"+\" alg: \"+alg+\" offline_sys: \"+str(offline_sys)+\" offline_when: \"+str(offline_when)+\" project \"+project+\" fct : \"+str(format(fct_collect[-10],'.2f'))+\" , \" +str(format(fct_collect[-9],'.2f'))+\" , \"+str(format(fct_collect[-8],'.2f'))+\" , \"+str(format(fct_collect[-7],'.2f'))+\" , \"+str(format(fct_collect[-6],'.2f'))+\" , \"+str(format(fct_collect[-5],'.2f'))+\" , \"+str(format(fct_collect[-4],'.2f'))+\" , \"+str(format(fct_collect[-3],'.2f'))+\" , \"+str(format(fct_collect[-2],'.2f'))+\" , \"+str(format(fct_collect[-1],'.2f'))+\"\\n\\n\")\n \n if debug:\n exit()\n", "sub_path": "testbed/fct_offline.py", "file_name": "fct_offline.py", "file_ext": "py", "file_size_in_byte": 3747, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "utils.execShellCommand", "line_number": 14, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}, {"api_name": "utils.execShellCommand", "line_number": 41, "usage_type": "call"}]} +{"seq_id": "82190344", "text": "__author__ = 'Jason Ragsdale'\n\nimport time\nimport logging\n\nfrom JOBS_LCD import JOBS_LCD\nfrom JOBS_Temperature import JOBS_Temperature\nfrom JOBS_Keypad import JOBS_Keypad\n\n\nclass JOBS():\n _version = \"0.01\"\n\n bus_number = -1\n address = 0x20\n\n def __init__(self):\n logging.info(\"Starting JOBSv\" + self._version)\n\n self.keypad = JOBS_Keypad()\n\n self.temperature = JOBS_Temperature()\n\n self.lcd = JOBS_LCD(self.address, self.bus_number)\n self.lcd.init()\n self.lcd.setCursor(0, 0)\n self.lcd.message(\" JOBS v\" + self._version)\n time.sleep(5)\n self.menu()\n\n def main(self):\n while True:\n self.lcd.setCursor(0, 1)\n self.lcd.message(time.strftime(\"%Y/%m/%d %H:%M:%S\"))\n temp_c, temp_f = self.temperature.read_temp()\n self.lcd.setCursor(0, 2)\n self.lcd.message(\"Temp C: \" + \"{0:.4g}\".format(temp_c))\n self.lcd.setCursor(0, 3)\n self.lcd.message(\"Temp F: \" + \"{0:.4g}\".format(temp_f))\n time.sleep(.5)\n\n def menu(self):\n self.lcd.clear()\n self.lcd.setCursor(0, 0)\n self.lcd.message(\"1.) Set Timer\")\n self.lcd.setCursor(0, 1)\n self.lcd.message(\"2.) Set Temperature\")\n self.lcd.setCursor(0, 2)\n self.lcd.message(\"4.) Start\")\n\n while True:\n if self.keypad.buttonPressed(1):\n self.setTimer()\n if self.keypad.buttonPressed(2):\n self.setTemperature()\n if self.keypad.buttonPressed(3):\n self.start()\n\n def setTimer(self):\n self.lcd.clear()\n self.lcd.setCursor(0, 0)\n self.lcd.message(\"Enter Time Duration:\")\n self.lcd.setCursor(0, 1)\n temp = None\n while True:\n key = self.keypad.getKey()\n if key is not None:\n temp = temp + key\n\n self.lcd.message(str(temp))\n\n def setTemperature(self):\n pass\n\n def start(self):\n pass\n\n\nif __name__ == '__main__':\n logging.basicConfig(filename='JOBS.log', format='%(asctime)s %(message)s', level=logging.INFO)\n jobs = JOBS()\n", "sub_path": "python/JOBS/JOBS.py", "file_name": "JOBS.py", "file_ext": "py", "file_size_in_byte": 2170, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.info", "line_number": 18, "usage_type": "call"}, {"api_name": "JOBS_Keypad.JOBS_Keypad", "line_number": 20, "usage_type": "call"}, {"api_name": "JOBS_Temperature.JOBS_Temperature", "line_number": 22, "usage_type": "call"}, {"api_name": "JOBS_LCD.JOBS_LCD", "line_number": 24, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 28, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 34, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 40, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 80, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 80, "usage_type": "attribute"}]} +{"seq_id": "376714057", "text": "# Copyright (c) 2021 Mira Geoscience Ltd.\n#\n# This file is part of geoapps.\n#\n# geoapps is distributed under the terms and conditions of the MIT License\n# (see LICENSE file at the root of this source code package).\n\nfrom uuid import UUID\n\nfrom geoh5py.workspace import Workspace\n\nrequired_parameters = []\ndefaults = {}\n\ndefault_ui_json = {\n \"title\": \"Octree Mesh Creator\",\n \"geoh5\": \"../../assets/FlinFlon.geoh5\",\n \"objects\": {\n \"enabled\": True,\n \"group\": \"1- Core\",\n \"label\": \"Core hull extent\",\n \"main\": True,\n \"meshType\": [\n \"{202C5DB1-A56D-4004-9CAD-BAAFD8899406}\",\n \"{6A057FDC-B355-11E3-95BE-FD84A7FFCB88}\",\n \"{F26FEBA3-ADED-494B-B9E9-B2BBCBE298E1}\",\n ],\n \"value\": \"{656acd40-25de-4865-814c-cb700f6ee51a}\",\n },\n \"u_cell_size\": {\n \"enabled\": True,\n \"group\": \"2- Core cell size\",\n \"label\": \"Easting (m)\",\n \"main\": True,\n \"value\": 25,\n },\n \"v_cell_size\": {\n \"enabled\": True,\n \"group\": \"2- Core cell size\",\n \"label\": \"Northing (m)\",\n \"main\": True,\n \"value\": 25,\n },\n \"w_cell_size\": {\n \"enabled\": True,\n \"group\": \"2- Core cell size\",\n \"label\": \"Vertical (m)\",\n \"main\": True,\n \"value\": 25,\n },\n \"horizontal_padding\": {\n \"enabled\": True,\n \"group\": \"3- Padding distance\",\n \"label\": \"Horizontal (m)\",\n \"main\": True,\n \"value\": 1000.0,\n },\n \"vertical_padding\": {\n \"enabled\": True,\n \"group\": \"3- Padding distance\",\n \"label\": \"Vertical (m)\",\n \"main\": True,\n \"value\": 1000.0,\n },\n \"depth_core\": {\n \"enabled\": True,\n \"group\": \"1- Core\",\n \"label\": \"Minimum Depth (m)\",\n \"main\": True,\n \"value\": 500.0,\n },\n \"ga_group_name\": {\n \"enabled\": True,\n \"group\": \"\",\n \"label\": \"Name:\",\n \"value\": \"Octree_Mesh\",\n },\n \"Refinement A Object\": {\n \"enabled\": True,\n \"group\": \"Refinement A\",\n \"label\": \"Object\",\n \"meshType\": [\n \"{202C5DB1-A56D-4004-9CAD-BAAFD8899406}\",\n \"{6A057FDC-B355-11E3-95BE-FD84A7FFCB88}\",\n \"{F26FEBA3-ADED-494B-B9E9-B2BBCBE298E1}\",\n ],\n \"value\": \"{656acd40-25de-4865-814c-cb700f6ee51a}\",\n },\n \"Refinement A Levels\": {\n \"enabled\": True,\n \"group\": \"Refinement A\",\n \"label\": \"Levels\",\n \"value\": \"4,4,4\",\n },\n \"Refinement A Type\": {\n \"choiceList\": [\"surface\", \"radial\"],\n \"enabled\": True,\n \"group\": \"Refinement A\",\n \"label\": \"Type\",\n \"value\": \"radial\",\n },\n \"Refinement A Distance\": {\n \"enabled\": True,\n \"group\": \"Refinement A\",\n \"label\": \"Distance\",\n \"value\": 1000.0,\n },\n \"Refinement B Object\": {\n \"enabled\": True,\n \"group\": \"Refinement B\",\n \"label\": \"Object\",\n \"meshType\": [\n \"{202C5DB1-A56D-4004-9CAD-BAAFD8899406}\",\n \"{6A057FDC-B355-11E3-95BE-FD84A7FFCB88}\",\n \"{F26FEBA3-ADED-494B-B9E9-B2BBCBE298E1}\",\n ],\n \"value\": \"\",\n },\n \"Refinement B Levels\": {\n \"enabled\": True,\n \"group\": \"Refinement B\",\n \"label\": \"Levels\",\n \"value\": \"0,0,2\",\n },\n \"Refinement B Type\": {\n \"choiceList\": [\"surface\", \"radial\"],\n \"enabled\": True,\n \"group\": \"Refinement B\",\n \"label\": \"Type\",\n \"value\": \"surface\",\n },\n \"Refinement B Distance\": {\n \"enabled\": True,\n \"group\": \"Refinement B\",\n \"label\": \"Distance\",\n \"value\": 1000.0,\n },\n \"run_command\": (\"geoapps.create.octree_mesh\"),\n \"monitoring_directory\": \"\",\n \"conda_environment\": \"geoapps\",\n}\n\nrequired_parameters = []\n\nvalidations = {\n \"title\": {\n \"types\": [str],\n },\n \"geoh5\": {\n \"types\": [str, Workspace],\n },\n \"objects\": {\n \"types\": [str, UUID],\n \"uuid\": [],\n },\n \"u_cell_size\": {\n \"types\": [int, float],\n },\n \"v_cell_size\": {\n \"types\": [int, float],\n },\n \"w_cell_size\": {\n \"types\": [int, float],\n },\n \"horizontal_padding\": {\n \"types\": [int, float],\n },\n \"vertical_padding\": {\n \"types\": [int, float],\n },\n \"depth_core\": {\n \"types\": [int, float],\n },\n \"refinement_object\": {\n \"types\": [str, UUID],\n \"uuid\": [],\n },\n \"refinement_levels\": {\n \"types\": [int, float],\n },\n \"refinement_type\": {\n \"types\": [str],\n \"values\": [\"surface\", \"radial\"],\n },\n \"refinement_distance\": {\n \"types\": [int, float],\n },\n \"ga_group_name\": {\n \"types\": [str],\n },\n \"monitoring_directory\": {\n \"types\": [str],\n },\n \"workspace_geoh5\": {\n \"types\": [str, Workspace],\n },\n \"run_command\": {\n \"types\": [str],\n },\n \"run_command_boolean\": {\n \"types\": [bool],\n },\n \"conda_environment\": {\n \"types\": [str],\n },\n \"conda_environment_boolean\": {\n \"types\": [bool],\n },\n \"workspace\": {\n \"types\": [str, Workspace],\n },\n}\n", "sub_path": "geoapps/io/Octree/constants.py", "file_name": "constants.py", "file_ext": "py", "file_size_in_byte": 5159, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "geoh5py.workspace.Workspace", "line_number": 150, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 153, "usage_type": "name"}, {"api_name": "uuid.UUID", "line_number": 175, "usage_type": "name"}, {"api_name": "geoh5py.workspace.Workspace", "line_number": 195, "usage_type": "name"}, {"api_name": "geoh5py.workspace.Workspace", "line_number": 210, "usage_type": "name"}]} +{"seq_id": "385780923", "text": "from unittest import TestCase\nfrom hamcrest import assert_that, is_\n\nfrom media_platform.metadata.image.image_features import ImageFeatures\n\n\nclass TestImageFeatures(TestCase):\n\n def test_serialize_deserialize_with_explicit_content(self):\n data = {\n 'labels': [\n {'name': 'one', 'score': 0.2323},\n {'name': 'two', 'score': 0.9}\n ],\n 'faces': [\n {'x': 383, 'y': 393, 'width': 155, 'height': 180},\n {'x': 460, 'y': 385, 'width': 145, 'height': 173}\n ],\n 'colors': [\n {'r': 138, 'g': 218, 'b': 244, 'pixelFraction': 0.38548386, 'score': 0.688166},\n ],\n 'explicitContent': [\n {\n 'name': 'adult',\n 'likelihood': 'VERY_UNLIKELY'\n }\n ]\n }\n\n image_features = ImageFeatures.deserialize(data)\n\n assert_that(len(image_features.labels), is_(2))\n assert_that(len(image_features.faces), is_(2))\n assert_that(len(image_features.colors), is_(1))\n assert_that(len(image_features.explicit_content), is_(1))\n", "sub_path": "tests/metadata/image/test_image_features.py", "file_name": "test_image_features.py", "file_ext": "py", "file_size_in_byte": 1172, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 7, "usage_type": "name"}, {"api_name": "media_platform.metadata.image.image_features.ImageFeatures.deserialize", "line_number": 30, "usage_type": "call"}, {"api_name": "media_platform.metadata.image.image_features.ImageFeatures", "line_number": 30, "usage_type": "name"}, {"api_name": "hamcrest.assert_that", "line_number": 32, "usage_type": "call"}, {"api_name": "hamcrest.is_", "line_number": 32, "usage_type": "call"}, {"api_name": "hamcrest.assert_that", "line_number": 33, "usage_type": "call"}, {"api_name": "hamcrest.is_", "line_number": 33, "usage_type": "call"}, {"api_name": "hamcrest.assert_that", "line_number": 34, "usage_type": "call"}, {"api_name": "hamcrest.is_", "line_number": 34, "usage_type": "call"}, {"api_name": "hamcrest.assert_that", "line_number": 35, "usage_type": "call"}, {"api_name": "hamcrest.is_", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "599181545", "text": "# USAGE\n# python long_exposure.py --video videos/river_02.mov --output river_02.png --time 5\n#new --> python3 longExposure.py --fileName file --time 5\n\n# import the necessary packages\nimport argparse\nimport imutils\nimport time\nimport cv2\n\n# construct the argument parse and parse the arguments\nap = argparse.ArgumentParser()\n#ap.add_argument(\"-v\", \"--video\", required=True,\n#\thelp=\"path to input video file\")\n#ap.add_argument(\"-o\", \"--output\", required=True,\n#\thelp=\"path to output 'long exposure'\")\nap.add_argument(\"-f\", \"--fileName\", required=True,\n\thelp=\"filename\")\nap.add_argument(\"-t\", \"--time\",required = True,\n\thelp=\"time to record video'\")\nargs = vars(ap.parse_args())\n\n\n# Create a VideoCapture object\ncap = cv2.VideoCapture(0)\n\n# Check if camera opened successfully\nif (cap.isOpened() == False): \n print(\"Unable to read camera feed\")\n\n# Default resolutions of the frame are obtained.The default resolutions are system dependent.\n# We convert the resolutions from float to integer.\nframe_width = int(cap.get(3))\nframe_height = int(cap.get(4))\n\n# Define the codec and create VideoWriter object.The output is stored in 'outpy.avi' file.\nout = cv2.VideoWriter(\"/home/pi/Videos/\"+args[\"fileName\"]+\".mp4\",cv2.VideoWriter_fourcc('M','J','P','G'), 10, (frame_width,frame_height))\nstartTime = time.time()\nwhile(int(time.time())-startTime < int(args[\"time\"])):\n ret, frame = cap.read()\n\n if ret == True: \n \n # Write the frame into the file 'output.avi'\n #frame = cv2.flip(frame, flipCode = -1)\n out.write(frame)\n\n # Display the resulting frame \n cv2.imshow('frame',frame)\n\n # Press Q on keyboard to stop recording\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n\n # Break the loop\n else:\n break \n\n# When everything done, release the video capture and video write objects\ncap.release()\nout.release()\n\n# Closes all the frames\ncv2.destroyAllWindows() \ntime.sleep(5)\n\n# initialize the Red, Green, and Blue channel averages, along with\n# the total number of frames read from the file\n(rAvg, gAvg, bAvg) = (None, None, None)\ntotal = 0\n\n# open a pointer to the video file\nprint(\"[INFO] opening video file pointer...\")\nstream = cv2.VideoCapture(\"/home/pi/Videos/\"+args[\"fileName\"]+\".mp4\")\nprint(\"[INFO] computing frame averages (this will take awhile)...\")\n\n# loop over frames from the video file stream\nwhile True:\n\t# grab the frame from the file stream\n\t(grabbed, frame) = stream.read()\n\n\t# if the frame was not grabbed, then we have reached the end of\n\t# the sfile\n\tif not grabbed:\n\t\tbreak\n\n\t# otherwise, split the frmae into its respective channels\n\t(B, G, R) = cv2.split(frame.astype(\"float\"))\n\n\t# if the frame averages are None, initialize them\n\tif rAvg is None:\n\t\trAvg = R\n\t\tbAvg = B\n\t\tgAvg = G\n\n\t# otherwise, compute the weighted average between the history of\n\t# frames and the current frames\n\telse:\n\t\trAvg = ((total * rAvg) + (1 * R)) / (total + 1.0)\n\t\tgAvg = ((total * gAvg) + (1 * G)) / (total + 1.0)\n\t\tbAvg = ((total * bAvg) + (1 * B)) / (total + 1.0)\n\n\t# increment the total number of frames read thus far\n\ttotal += 1\n\n# merge the RGB averages together and write the output image to disk\navg = cv2.merge([bAvg, gAvg, rAvg]).astype(\"uint8\")\ncv2.imwrite(\"/home/pi/Pictures/\"+args[\"fileName\"]+\".png\", avg)\n\n# do a bit of cleanup on the file pointer\nstream.release()\n", "sub_path": "long_exposure/rec_longExposure.py", "file_name": "rec_longExposure.py", "file_ext": "py", "file_size_in_byte": 3307, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 12, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 25, "usage_type": "call"}, {"api_name": "cv2.VideoWriter", "line_number": 37, "usage_type": "call"}, {"api_name": "cv2.VideoWriter_fourcc", "line_number": 37, "usage_type": "call"}, {"api_name": "time.time", "line_number": 38, "usage_type": "call"}, {"api_name": "time.time", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 64, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 65, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 74, "usage_type": "call"}, {"api_name": "cv2.split", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.merge", "line_number": 107, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "238494", "text": "#coding=gbk\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport jieba\nfrom wordcloud import WordCloud,ImageColorGenerator\nfrom PIL import Image\n#打开歌词并进行分词\nwith open(r\"C:\\Users\\john\\Desktop\\jay.txt\",\"r\",encoding=\"utf-8\") as f:\n text=f.read()\ncut_text=jieba.cut(text)\nresult=\" \".join(cut_text)\n#选用背景图片\nimage=np.array(Image.open(r\"C:\\Users\\john\\Desktop\\jay.jpg\"))\n#设置参数\nwc=WordCloud(font_path=r\"C:\\Windows\\Fonts\\STZHONGS.TTF\",\n background_color=\"white\",\n width=500,\n height=350,\n max_font_size=50,\n min_font_size=10,\n mask=image\n )\nwc.generate(result)\n#设置背景颜色随图片颜色改变\nimage_colors=ImageColorGenerator(image)\nplt.show(wc.recolor(color_func=image_colors))\n#展示图片\nplt.imshow(wc)\nplt.axis(\"off\")\nplt.show()\n#保存图片\nwc.to_file(r\"C:\\Users\\john\\Desktop\\jay1.png\")\n\n\n", "sub_path": "worldColoud.py", "file_name": "worldColoud.py", "file_ext": "py", "file_size_in_byte": 925, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "jieba.cut", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 13, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 13, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 13, "usage_type": "name"}, {"api_name": "wordcloud.WordCloud", "line_number": 15, "usage_type": "call"}, {"api_name": "wordcloud.ImageColorGenerator", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}]} +{"seq_id": "246785396", "text": "# import requests\n# r = requests.post('https://httpbin.org/post?page=\"2\" &count=\"25\"')\n# # print(help(r))\n#\n# # with open('comic.png' , 'wb') as f:\n# # f.write(r.content)\n#\n# #print(r.status_code)\n#\n# print(r.text)\n\nposts = [\n {\n 'author': 'Corey Schafer',\n 'title': 'Blog Post 1',\n 'content': 'First post content',\n 'date_posted': 'April 20,2018'\n },\n {\n 'author': 'Muhammad Waqas',\n 'title': 'Blog Post 2',\n 'content': 'Second post content',\n 'date_posted': 'April 21,2018'\n },\n {\n 'author': 'Hamid Khan',\n 'title': 'Blog Post 3',\n 'content': 'Third post content',\n 'date_posted': 'April 29,2018'\n }\n]\nfrom flask import Flask, jsonify,request\n\napp = Flask(__name__)\n\n@app.route('/', methods = ['GET', 'POST'])\ndef index():\n if(request.method == 'POST'):\n some_json = request.get_json()\n return jsonify({'you sent': some_json})\n else:\n return jsonify(posts)\n\n\nif __name__ == '__main__':\n app.run(debug=True)\n", "sub_path": "probation period/python_Requests/rdemo.py", "file_name": "rdemo.py", "file_ext": "py", "file_size_in_byte": 1045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 34, "usage_type": "call"}, {"api_name": "flask.request.method", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.get_json", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "7405854", "text": "#\n# ICRAR - International Centre for Radio Astronomy Research\n# (c) UWA - The University of Western Australia, 2014\n# Copyright by UWA (in the framework of the ICRAR)\n# All rights reserved\n#\n# This library is free software; you can redistribute it and/or\n# modify it under the terms of the GNU Lesser General Public\n# License as published by the Free Software Foundation; either\n# version 2.1 of the License, or (at your option) any later version.\n#\n# This library 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 GNU\n# Lesser General Public License for more details.\n#\n# You should have received a copy of the GNU Lesser General Public\n# License along with this library; if not, write to the Free Software\n# Foundation, Inc., 59 Temple Place, Suite 330, Boston,\n# MA 02111-1307 USA\n#\n\"\"\"\nModule containing the NodeManager, which directly manages DROP instances, and\nthus represents the bottom of the DROP management hierarchy.\n\"\"\"\n\nimport importlib\nimport inspect\nimport logging\nimport os\nimport sys\n\nfrom dfms import droputils\nfrom dfms.lifecycle.dlm import DataLifecycleManager\nfrom dfms.manager import repository\nfrom dfms.manager.drop_manager import DROPManager\nfrom dfms.manager.session import Session\n\n\nlogger = logging.getLogger(__name__)\n\ndef _functionAsTemplate(f):\n args, _, _, defaults = inspect.getargspec(f)\n\n # 'defaults' might be shorter than 'args' if some of the arguments\n # are not optional. In the general case anyway the optional\n # arguments go at the end of the method declaration, and therefore\n # a reverse iteration should yield the correct match between\n # arguments and their defaults\n defaults = list(defaults) if defaults else []\n defaults.reverse()\n argsList = []\n for i, arg in enumerate(reversed(args)):\n if i >= len(defaults):\n # mandatory argument\n argsList.append({'name':arg})\n else:\n # optional with default value\n argsList.append({'name':arg, 'default':defaults[i]})\n\n return {'name': inspect.getmodule(f).__name__ + \".\" + f.__name__, 'args': argsList}\n\nclass NodeManager(DROPManager):\n \"\"\"\n A DROPManager that creates and holds references to DROPs.\n\n A NodeManager is the ultimate responsible of handling DROPs. It does so not\n directly, but via Sessions, which represent and encapsulate separate,\n independent DROP graph executions. All DROPs created by the\n different Sessions are also given to a common DataLifecycleManager, which\n takes care of expiring them when needed and replicating them.\n\n Since a NodeManager can handle more than one session, in principle only one\n NodeManager is needed for each computing node, thus its name.\n \"\"\"\n\n def __init__(self, useDLM=True, dfmsPath=None, host=None, error_listener=None,\n enable_luigi=False):\n self._dlm = DataLifecycleManager() if useDLM else None\n self._sessions = {}\n self._host = host\n\n # dfmsPath contains code added by the user with possible\n # DROP applications\n if dfmsPath:\n dfmsPath = os.path.expanduser(dfmsPath)\n if os.path.isdir(dfmsPath):\n if logger.isEnabledFor(logging.INFO):\n logger.info(\"Adding %s to the system path\" % (dfmsPath))\n sys.path.append(dfmsPath)\n\n # Error listener used by users to deal with errors coming from specific\n # Drops in whatever way they want\n if error_listener:\n if isinstance(error_listener, basestring):\n try:\n parts = error_listener.split('.')\n module = importlib.import_module('.'.join(parts[:-1]))\n except:\n logger.exception('Creating the error listener')\n raise\n error_listener = getattr(module, parts[-1])()\n if not hasattr(error_listener, 'on_error'):\n raise ValueError(\"error_listener doesn't contain an on_error method\")\n self._error_listener = error_listener\n\n self._enable_luigi = enable_luigi\n\n def createSession(self, sessionId):\n if sessionId in self._sessions:\n raise Exception('A session already exists for sessionId %s' % (str(sessionId)))\n self._sessions[sessionId] = Session(sessionId, self._host, self._error_listener, self._enable_luigi)\n if logger.isEnabledFor(logging.INFO):\n logger.info('Created session %s' % (sessionId))\n\n def getSessionStatus(self, sessionId):\n return self._sessions[sessionId].status\n\n def quickDeploy(self, sessionId, graphSpec):\n self.createSession(sessionId)\n self.addGraphSpec(sessionId, graphSpec)\n return self.deploySession(sessionId)\n\n def linkGraphParts(self, sessionId, lhOID, rhOID, linkType):\n self._sessions[sessionId].linkGraphParts(lhOID, rhOID, linkType)\n\n def addGraphSpec(self, sessionId, graphSpec):\n self._sessions[sessionId].addGraphSpec(graphSpec)\n\n def getGraphStatus(self, sessionId):\n return self._sessions[sessionId].getGraphStatus()\n\n def getGraph(self, sessionId):\n return self._sessions[sessionId].getGraph()\n\n def deploySession(self, sessionId, completedDrops=[]):\n session = self._sessions[sessionId]\n session.deploy(completedDrops=completedDrops)\n roots = session.roots\n\n # We register the new DROPs with the DLM if there is one\n if self._dlm:\n if logger.isEnabledFor(logging.DEBUG):\n logger.debug('Registering new DROPs with the DataLifecycleManager')\n droputils.breadFirstTraverse(roots, lambda drop: self._dlm.addDrop(drop))\n\n # Finally, we also collect the Pyro URIs of our DROPs and return them\n uris = {}\n droputils.breadFirstTraverse(roots, lambda drop: uris.__setitem__(drop.uid, drop.uri))\n return uris\n\n def destroySession(self, sessionId):\n session = self._sessions.pop(sessionId)\n session.destroy()\n\n def getSessionIds(self):\n return self._sessions.keys()\n\n def getGraphSize(self, sessionId):\n session = self._sessions[sessionId]\n return len(session._graph)\n\n def getTemplates(self):\n\n # TODO: we currently have a hardcoded list of functions, but we should\n # load these repositories in a different way, like in this\n # commented code\n #tplDir = os.path.expanduser(\"~/.dfms/templates\")\n #if not os.path.isdir(tplDir):\n # logger.warning('%s directory not found, no templates available' % (tplDir))\n # return []\n #\n #templates = []\n #for fname in os.listdir(tplDir):\n # if not os.path.isfile(fname): continue\n # if fname[-3:] != '.py': continue\n #\n # with open(fname) as f:\n # m = imp.load_module(fname[-3:], f, fname)\n # functions = m.list_templates()\n # for f in functions:\n # templates.append(_functionAsTemplate(f))\n\n templates = []\n for f in repository.complex_graph, repository.pip_cont_img_pg, repository.archiving_app:\n templates.append(_functionAsTemplate(f))\n return templates\n\n def materializeTemplate(self, tpl, sessionId, **tplParams):\n # tpl currently has the form \n parts = tpl.split('.')\n module = importlib.import_module('.'.join(parts[:-1]))\n tplFunction = getattr(module, parts[-1])\n\n # invoke the template function with the given parameters\n # and add the new graph spec to the session\n graphSpec = tplFunction(**tplParams)\n self.addGraphSpec(sessionId, graphSpec)\n\n if logger.isEnabledFor(logging.INFO):\n logger.info('Added graph from template %s to session %s with params: %s' % (tpl, sessionId, tplParams))\n", "sub_path": "dfms/manager/node_manager.py", "file_name": "node_manager.py", "file_ext": "py", "file_size_in_byte": 8077, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 40, "usage_type": "call"}, {"api_name": "inspect.getargspec", "line_number": 43, "usage_type": "call"}, {"api_name": "inspect.getmodule", "line_number": 61, "usage_type": "call"}, {"api_name": "dfms.manager.drop_manager.DROPManager", "line_number": 63, "usage_type": "name"}, {"api_name": "dfms.lifecycle.dlm.DataLifecycleManager", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path.expanduser", "line_number": 86, "usage_type": "call"}, {"api_name": "os.path", "line_number": 86, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 88, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 90, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 90, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 98, "usage_type": "call"}, {"api_name": "dfms.manager.session.Session", "line_number": 112, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 113, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 143, "usage_type": "attribute"}, {"api_name": "dfms.droputils.breadFirstTraverse", "line_number": 145, "usage_type": "call"}, {"api_name": "dfms.droputils", "line_number": 145, "usage_type": "name"}, {"api_name": "dfms.droputils.breadFirstTraverse", "line_number": 149, "usage_type": "call"}, {"api_name": "dfms.droputils", "line_number": 149, "usage_type": "name"}, {"api_name": "dfms.manager.repository.complex_graph", "line_number": 185, "usage_type": "attribute"}, {"api_name": "dfms.manager.repository", "line_number": 185, "usage_type": "name"}, {"api_name": "dfms.manager.repository.pip_cont_img_pg", "line_number": 185, "usage_type": "attribute"}, {"api_name": "dfms.manager.repository.archiving_app", "line_number": 185, "usage_type": "attribute"}, {"api_name": "importlib.import_module", "line_number": 192, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 200, "usage_type": "attribute"}]} +{"seq_id": "85612502", "text": "import pytest\nfrom src.auth import auth_register_v2\nfrom src.error import AccessError, InputError\nfrom src.dm import dm_details_v1, dm_create_v1\nimport jwt\nfrom src.other import clear_v1\n\n@pytest.fixture\ndef num_members():\n return 5\n\n@pytest.fixture\ndef users(num_members):\n\n u_ids = []\n tokens = []\n for i in range(num_members):\n email = f\"test{i}email@gmail.com\"\n password = f\"TestTest{i}\"\n firstname = f\"firstname{i}\"\n lastname = f\"lastname{i}\"\n user = auth_register_v2(email,password,firstname, lastname)\n u_ids.append(user['auth_user_id'])\n tokens.append(user['token'])\n return {'tokens' : tokens, 'u_ids': u_ids}\n\n \n@pytest.fixture\ndef clear():\n clear_v1()\n\ndef test_invalid_token(clear):\n with pytest.raises(AccessError):\n dm_details_v1(jwt.encode({'test' : 'token'}, 'testSecret', algorithm='HS256'), 5)\n\ndef test_user_not_in_dm(clear, users):\n dm = dm_create_v1(users['tokens'][1], users['u_ids'][2:])\n with pytest.raises(AccessError):\n dm_details_v1(users['tokens'][0], dm['dm_id'])\n\ndef test_invalid_dm_id(clear, users):\n with pytest.raises(InputError):\n dm_details_v1(users['tokens'][0], 'test_dm_id')\n\ndef test_user_in_dm(clear, users, num_members):\n dm = dm_create_v1(users['tokens'][0], users['u_ids'][1:])\n details = dm_details_v1(users['tokens'][1], dm['dm_id'])\n assert len(details) == 2\n assert len(details['members']) == num_members\n\ndef test_valid_dict_keys(clear, users):\n dm = dm_create_v1(users['tokens'][0], users['u_ids'])\n details = dm_details_v1(users['tokens'][1], dm['dm_id'])\n assert 'names' and 'members' in details \n assert 'user_id' in details['members'][0] \n assert 'email' in details['members'][0] \n assert 'name_first' in details['members'][0] \n assert 'name_last' in details['members'][0]\n assert 'handle_str' in details['members'][0] \n\n", "sub_path": "tests/dm_details_v1_test.py", "file_name": "dm_details_v1_test.py", "file_ext": "py", "file_size_in_byte": 1914, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pytest.fixture", "line_number": 8, "usage_type": "attribute"}, {"api_name": "src.auth.auth_register_v2", "line_number": 22, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 12, "usage_type": "attribute"}, {"api_name": "src.other.clear_v1", "line_number": 30, "usage_type": "call"}, {"api_name": "pytest.fixture", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 33, "usage_type": "call"}, {"api_name": "src.error.AccessError", "line_number": 33, "usage_type": "argument"}, {"api_name": "src.dm.dm_details_v1", "line_number": 34, "usage_type": "call"}, {"api_name": "jwt.encode", "line_number": 34, "usage_type": "call"}, {"api_name": "src.dm.dm_create_v1", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 38, "usage_type": "call"}, {"api_name": "src.error.AccessError", "line_number": 38, "usage_type": "argument"}, {"api_name": "src.dm.dm_details_v1", "line_number": 39, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 42, "usage_type": "call"}, {"api_name": "src.error.InputError", "line_number": 42, "usage_type": "argument"}, {"api_name": "src.dm.dm_details_v1", "line_number": 43, "usage_type": "call"}, {"api_name": "src.dm.dm_create_v1", "line_number": 46, "usage_type": "call"}, {"api_name": "src.dm.dm_details_v1", "line_number": 47, "usage_type": "call"}, {"api_name": "src.dm.dm_create_v1", "line_number": 52, "usage_type": "call"}, {"api_name": "src.dm.dm_details_v1", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "24396499", "text": "#!/usr/bin/env python\n\n# tile-generator\n#\n# Copyright (c) 2015-Present Pivotal Software, 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\nfrom __future__ import absolute_import, division, print_function\nfrom setuptools import setup\nimport os\nimport sys\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\ndef read_readme():\n\ttry:\n\t\timport pypandoc\n\t\treturn pypandoc.convert('README.md', 'rst')\n\texcept ImportError:\n\t\twith open(os.path.join(here, 'README.md')) as f:\n\t\t\treturn f.read()\n\ndef get_version():\n\tversion_file = os.path.join(here, 'version.txt')\n\ttry:\n\t\twith open(version_file) as f:\n\t\t\treturn f.read()\n\texcept:\n\t\treturn '0.0.0'\n\nsetup(\n\tname = \"tile-generator\",\n\tversion = get_version(),\n\tdescription = 'Tools supporting development of Pivotal Cloud Foundry services and add-ons.',\n\tlong_description = read_readme(),\n\turl = 'https://github.com/cf-platform-eng/tile-generator',\n\tauthor = 'Pivotal Cloud Foundry Platform Engineering',\n\tlicense = 'Apache 2.0',\n\tclassifiers = [\n\t\t'Development Status :: 4 - Beta',\n\t\t'Environment :: Console',\n\t\t'Intended Audience :: Developers',\n\t\t'License :: OSI Approved :: Apache Software License',\n\t\t'Programming Language :: Python :: 2 :: Only',\n\t\t'Topic :: Software Development',\n\t\t'Topic :: Software Development :: Code Generators',\n\t],\n\tkeywords = [\n\t\t'pivotal cloud foundry',\n\t\t'tile',\n\t\t'generator'\n\t],\n\tpackages = [ 'tile_generator' ],\n\tinstall_requires = [\n\t\t'Cerberus>=1.1',\n\t\t'click>=6.2',\n\t\t'Jinja2>=2.8',\n\t\t'PyYAML>=3.1',\n\t\t'docker-py>=1.6.0',\n\t\t'requests>=2.9.1,<2.11',\n\t\t'requests-toolbelt',\n\t\t'mock>=2.0.0',\n\t\t'pexpect>=4.2.1'\n\t],\n\tinclude_package_data = True,\n\tentry_points = {\n\t\t'console_scripts': [\n\t\t\t'tile = tile_generator.tile:cli',\n\t\t\t'pcf = tile_generator.pcf:main',\n\t\t]\n\t}\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 2266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.abspath", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path", "line_number": 24, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 24, "usage_type": "call"}, {"api_name": "pypandoc.convert", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "setuptools.setup", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "305440534", "text": "# from django.conf.urls.defaults import patterns, include, url\n# from minesite.views import hello, current_datetime, hours_ahead\n# from minesite import books\n# from minesite.contact.views import contact\nfrom django.conf.urls.defaults import *\n# from minesite import views\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n (r'^hello/$', 'minesite.views.hello'),\n (r'^time/$', 'minesite.views.current_datetime'),\n (r'^time/plus/(\\d{1,2})/$', 'minesite.views.hours_ahead'),\n# (r'^search-form/$', views.search_form),\n (r'^search/$', 'minesite.books.views.search'),\n (r'^contact/$', 'minesite.contact.contact'),\n # Examples:\n # url(r'^$', 'mysite.views.home', name='home'),\n # url(r'^minesite/', include('mysite.foo.urls')),\n\n # Uncomment the admin/doc line below to enable admin documentation:\n # url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n # Uncomment the next line to enable the admin:\n url(r'^admin/', include(admin.site.urls)),\n)\n", "sub_path": "urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1191, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.contrib.admin.autodiscover", "line_number": 10, "usage_type": "call"}, {"api_name": "django.contrib.admin", "line_number": 10, "usage_type": "name"}, {"api_name": "django.contrib.admin.site", "line_number": 27, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "627635890", "text": "import pytesseract\nimport numpy as np\nimport cv2\n\nimg = cv2.imread('../../Util/Imagens/saida.jpg')\nrgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n\n#tesseract --help-extra (executar o comando no cmd)\nconfig_tesseract = \"--psm 8\"\ntexto = pytesseract.image_to_string(img, lang=\"por\", config=config_tesseract)\n\nprint(texto)\ncv2.imshow(\"Image\",rgb)\ncv2.waitKey(0)\n\n", "sub_path": "pythonCodes/step1/OCR5.py", "file_name": "OCR5.py", "file_ext": "py", "file_size_in_byte": 356, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pytesseract.image_to_string", "line_number": 10, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 13, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "577158245", "text": "from pygame.math import Vector2 as vec\n\n#screen settings\nWIDTH, HEIGHT = 610, 670\nTP_BUFFER = 50\n\nMAZE_WIDTH, MAZE_HEIGHT = WIDTH - TP_BUFFER, HEIGHT - TP_BUFFER\nfps = 60\n\n\n#colour settings\nBLACK = (0,0,0)\nWHITE = (255,255,255)\nBLUE = (170,130,60)\nYELLOW = (30,130,160)\nRED = (255,160,25)\nGRAY = (110,110,110)\n\n#font settings\nSTART_TEXT_SIZE = 16\nSTART_FONT = 'arial black'\n\n#player\nPLAYER_START_POS = vec(1,1)\n\n\n# bot settings", "sub_path": "settings.py", "file_name": "settings.py", "file_ext": "py", "file_size_in_byte": 427, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pygame.math.Vector2", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "339747495", "text": "import tensorflow as tf\nimport numpy as npy\nimport pickle\nimport datetime\n\nfrom tensorflow.examples.tutorials.mnist import input_data\n\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Activation, Flatten, Reshape\nfrom keras.layers import Conv2D, Conv2DTranspose, UpSampling2D,MaxPooling2D\nfrom keras.layers import LeakyReLU, Dropout\nfrom keras.layers import BatchNormalization\nfrom keras.optimizers import Adam, RMSprop, SGD\n\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\nfrom PIL import Image\n\nclass DCGAN(object):\n\n def __init__(self,img_rows=32, img_cols=32, img_chanl=3):\n self.image_rows = img_rows\n self.image_columns = img_cols\n self.image_channels = img_chanl\n self.Discriminator = None\n self.Generator = None\n self.Discriminator_Model = None\n self.Adversarial_Model = None\n self.Compressor_Model = None\n\n def generator(self):\n if self.Generator:\n return self.Generator\n self.Generator = Sequential()\n dropout = 0.4\n dimen = 8\n depth = 96\n self.Generator.add(Dense(dimen*dimen*depth,input_dim=3072))\n self.Generator.add(LeakyReLU(alpha=0.05))\n # self.Generator.add(Activation('relu'))\n self.Generator.add(Reshape((dimen, dimen, depth)))\n self.Generator.add(Dropout(dropout))\n\n self.Generator.add(UpSampling2D())\n self.Generator.add(Conv2DTranspose(int(depth/2), 5, padding='same'))\n self.Generator.add(LeakyReLU(alpha=0.05))\n # self.Generator.add(Activation('relu'))\n\n self.Generator.add(UpSampling2D())\n self.Generator.add(Conv2DTranspose(int(depth/4), 5, padding='same'))\n self.Generator.add(LeakyReLU(alpha=0.05))\n # self.Generator.add(Activation('relu'))\n\n self.Generator.add(Conv2DTranspose(int(depth/8), 5, padding='same'))\n self.Generator.add(LeakyReLU(alpha=0.05))\n \n self.Generator.add(Conv2DTranspose(int(depth/16), 5, padding='same'))\n self.Generator.add(LeakyReLU(alpha=0.05))\n # self.Generator.add(Activation('relu'))\n\n self.Generator.add(Conv2DTranspose(int(depth/32), 5, padding='same'))\n# self.Generator.add(LeakyReLU(alpha=0.05))\n \n self.Generator.add(Activation('sigmoid'))\n print(\"Generator Summary\")\n self.Generator.summary()\n return self.Generator\n\n\n def discriminator(self):\n if self.Discriminator:\n return self.Discriminator\n self.Discriminator = Sequential()\n depth = 64\n dropout = 0.4\n input_shape = (self.image_rows, self.image_columns, self.image_channels)\n\n self.Discriminator.add(Conv2D(6,5,strides=(2,2),padding='same',input_shape=input_shape))\n self.Discriminator.add(LeakyReLU(alpha=0.05))\n self.Discriminator.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n self.Discriminator.add(Dropout(0.25))\n self.Discriminator.add(Conv2D(6,5,strides=(2,2),padding='same'))\n self.Discriminator.add(LeakyReLU(alpha=0.05))\n self.Discriminator.add(MaxPooling2D(pool_size=(2,2),strides=(2,2)))\n self.Discriminator.add(Flatten())\n self.Discriminator.add(Dense(120))\n self.Discriminator.add(LeakyReLU(alpha=0.05))\n self.Discriminator.add(Dense(84))\n self.Discriminator.add(LeakyReLU(alpha=0.05))\n self.Discriminator.add(Dense(1))\n self.Discriminator.add(Activation('sigmoid'))\n print(\"Discriminator Summary\")\n self.Discriminator.summary()\n return self.Discriminator\n# \n\n def discriminator_model(self):\n if self.Discriminator_Model:\n return self.Discriminator_Model\n optimizer = RMSprop(lr=0.0002, decay=6e-8) \n self.Discriminator_Model = Sequential()\n self.Discriminator_Model.add(self.discriminator())\n self.Discriminator_Model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n return self.Discriminator_Model\n\n def adversarial_model(self):\n if self.Adversarial_Model:\n return self.Adversarial_Model\n optimizer = RMSprop(lr=0.0001, decay=3e-8) \n self.Adversarial_Model = Sequential()\n self.Adversarial_Model.add(self.generator())\n self.Adversarial_Model.add(self.discriminator())\n self.Adversarial_Model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy'])\n return self.Adversarial_Model\n\n\nclass CIFAR(object):\n\n def __init__(self):\n self.img_rows = 32\n self.img_cols = 32\n self.img_chanl = 3\n\n# self.x_train = input_data.read_data_sets(\"mnist\", one_hot=True).train.images\n# self.x_train = self.x_train.reshape(-1, self.img_rows, self.img_cols, 1).astype(npy.float32)\n airfile = open('./Automobile/automobile_train.pickle', 'rb') \n self.x_train = pickle.load(airfile)\n airfile.close()\n self.x_train = self.x_train.astype(npy.float32)\n\n self.DCGAN = DCGAN()\n self.generator = self.DCGAN.generator()\n self.discriminator = self.DCGAN.discriminator_model()\n self.adversary = self.DCGAN.adversarial_model()\n\n def train(self, train_steps=5000, batch_size=256):\n\n # noise_input = npy.random.uniform(-1,1,size=[16, 784])\n for i in range(train_steps):\n images_train = self.x_train[npy.random.randint(0,self.x_train.shape[0], size=batch_size), :, :, :]\n# print(\"Images train\",npy.shape(images_train))\n if(i==0):\n print(npy.shape(images_train))\n img = images_train[0]/255\n# image = npy.reshape(images_train[0], [self.img_rows, self.img_cols,self.img_chanl])\n# print(images_train[0])\n plt.imshow(img)\n plt.show()\n noise = npy.random.uniform(-1.0, 1.0, size=[batch_size, 3072]) #changed\n images_gen = self.generator.predict(noise)\n# print(\"Images Gen\",npy.shape(images_gen))\n x = npy.concatenate((images_train, images_gen))\n y = npy.ones([2*batch_size, 1])\n y[batch_size:, :] = 0\n d_loss = self.discriminator.train_on_batch(x,y)\n\n y = npy.ones([batch_size, 1])\n noise = npy.random.uniform(-1.0, 1.0, size=[batch_size, 3072]) #changed\n a_loss = self.adversary.train_on_batch(noise, y)\n\n log_mesg = \"%d: [D loss: %f, acc: %f]\" % (i, d_loss[0], d_loss[1])\n log_mesg = \"%s [A loss: %f, acc: %f]\" % (log_mesg, a_loss[0], a_loss[1])\n print(log_mesg)\n\n if (i%100==0):\n noisy = npy.random.uniform(-1.0, 1.0, size=[1, 3072]) #changed\n images = self.generator.predict(noisy)\n image = npy.reshape(images, [self.img_rows, self.img_cols,self.img_chanl])\n plt.imshow(image)\n plt.axis('off')\n plt.imsave('/content/drive/My Drive/smai_cifar/autos_gen_cifar.png',image)\n plt.tight_layout()\n plt.show()\n self.generator.save_weights('/content/drive/My Drive/smai_cifar/autos_generator_weights.h5')\n self.discriminator.save_weights('/content/drive/My Drive/smai_cifar/autos_discriminator_weights.h5')\n print('Weights last saved at :')\n print(datetime.datetime.now())\n \n \n \n \n #Saving weights of model\n self.generator.save_weights('/content/drive/My Drive/smai_cifar/autos_generator_weights.h5')\n self.discriminator.save_weights('/content/drive/My Drive/smai_cifar/autos_discriminator_weights.h5')\n\n\nif __name__ == \"__main__\":\n print('Program started at:')\n print(datetime.datetime.now())\n cifar_dcgan = CIFAR()\n cifar_dcgan.train(train_steps=30001, batch_size=256)\n print('Program ended at :')\n print(datetime.datetime.now())", "sub_path": "Automobile_model.py", "file_name": "Automobile_model.py", "file_ext": "py", "file_size_in_byte": 7950, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "keras.models.Sequential", "line_number": 34, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 39, "usage_type": "call"}, {"api_name": "keras.layers.Reshape", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.UpSampling2D", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.UpSampling2D", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 83, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 86, "usage_type": "call"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 87, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.layers.LeakyReLU", "line_number": 89, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.optimizers.RMSprop", "line_number": 100, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.optimizers.RMSprop", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 110, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 129, "usage_type": "attribute"}, {"api_name": "numpy.random.randint", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 140, "usage_type": "attribute"}, {"api_name": "numpy.shape", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "numpy.random.uniform", "line_number": 149, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 149, "usage_type": "attribute"}, {"api_name": "numpy.concatenate", "line_number": 152, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.random.uniform", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 158, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 166, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 166, "usage_type": "attribute"}, {"api_name": "numpy.reshape", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axis", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tight_layout", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 177, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 177, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 189, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 189, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 193, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 193, "usage_type": "attribute"}]} +{"seq_id": "573686937", "text": "# Unless explicitly stated otherwise all files in this repository are licensed under the Apache-2.0 License.\n# This product includes software developed at Datadog (https://www.datadoghq.com/).\n# Copyright 2019-Present Datadog, Inc.\nfrom __future__ import annotations\n\nfrom typing import Union\n\nfrom datadog_api_client.model_utils import (\n ModelNormal,\n cached_property,\n none_type,\n unset,\n UnsetType,\n)\n\n\nclass SearchSLOResponseLinks(ModelNormal):\n @cached_property\n def openapi_types(_):\n return {\n \"first\": (str,),\n \"last\": (str, none_type),\n \"next\": (str,),\n \"prev\": (str, none_type),\n \"self\": (str,),\n }\n\n attribute_map = {\n \"first\": \"first\",\n \"last\": \"last\",\n \"next\": \"next\",\n \"prev\": \"prev\",\n \"self\": \"self\",\n }\n\n def __init__(\n self_,\n first: Union[str, UnsetType] = unset,\n last: Union[str, none_type, UnsetType] = unset,\n next: Union[str, UnsetType] = unset,\n prev: Union[str, none_type, UnsetType] = unset,\n self: Union[str, UnsetType] = unset,\n **kwargs,\n ):\n \"\"\"\n Pagination links.\n\n :param first: Link to last page.\n :type first: str, optional\n\n :param last: Link to first page.\n :type last: str, none_type, optional\n\n :param next: Link to the next page.\n :type next: str, optional\n\n :param prev: Link to previous page.\n :type prev: str, none_type, optional\n\n :param self: Link to current page.\n :type self: str, optional\n \"\"\"\n if first is not unset:\n kwargs[\"first\"] = first\n if last is not unset:\n kwargs[\"last\"] = last\n if next is not unset:\n kwargs[\"next\"] = next\n if prev is not unset:\n kwargs[\"prev\"] = prev\n if self is not unset:\n kwargs[\"self\"] = self\n super().__init__(kwargs)\n", "sub_path": "src/datadog_api_client/v1/model/search_slo_response_links.py", "file_name": "search_slo_response_links.py", "file_ext": "py", "file_size_in_byte": 1975, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datadog_api_client.model_utils.ModelNormal", "line_number": 17, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.none_type", "line_number": 22, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.none_type", "line_number": 24, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.cached_property", "line_number": 18, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 38, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.UnsetType", "line_number": 38, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 39, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.none_type", "line_number": 39, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.UnsetType", "line_number": 39, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 40, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.UnsetType", "line_number": 40, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 41, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.none_type", "line_number": 41, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.UnsetType", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 42, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.UnsetType", "line_number": 42, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 38, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 39, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 40, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 41, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 42, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 63, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 65, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 67, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 69, "usage_type": "name"}, {"api_name": "datadog_api_client.model_utils.unset", "line_number": 71, "usage_type": "name"}]} +{"seq_id": "343351930", "text": "from flask import Flask, jsonify, abort, make_response, request\nfrom flask.ext.httpauth import HTTPBasicAuth\napp = Flask(__name__)\nauth = HTTPBasicAuth()\n\nusers = [\n\t{\n\t\t\"nome\": \"Lucas\",\n\t\t\"id\": 1,\n\t\t\"idade\": 15,\n\t\t\"email\": \"example@example.com\"\n\t},\n\t{\n\t\t\"nome\": \"Dim\",\n\t\t\"id\": 2,\n\t\t\"idade\": 16,\n\t\t\"email\": \"example2@example.com\"\n\t}\n]\n@app.route('/user/', methods=['GET'])\n@auth.login_required\ndef get_user(id_usuario):\n\tuser = [user for user in users if user[\"id\"] == id_usuario]\n\tif len(user) == 0:\n\t\tabort(404)\n\treturn jsonify({\"user\":user[0]})\n@app.errorhandler(404)\ndef not_found(error):\n\treturn make_response(jsonify({'Error': 'Not found'}), 404)\n\n@app.route('/user', methods=['POST'])\n@auth.login_required\ndef create_user():\n\tuser = [\n\t\t{\n\t\t\t\"nome\": request.json.get(\"nome\", \"\"),\n\t\t\t\"id\": users[-1]['id'] + 1,\n\t\t\t\"idade\": request.json.get(\"idade\"),\n\t\t\t\"email\": request.json.get(\"email\", \"\")\n\t\t}\n\t]\n\t\n\tusers.append(user)\n\treturn jsonify({\"user\": user}), 201\n\t\n@app.route('/remover/user/', methods=['DELETE'])\n@auth.login_required\ndef delete_user(id_usuario):\n\tuser = [user for user in users if user[\"id\"] == id_usuario]\n\tif len(user) == 0:\n\t\tabort(404)\n\tusers.remove(user[0])\n\treturn jsonify({\"result\": True})\n\n@app.route('/atualizar/user/', methods=['PUT'])\n@auth.login_required\ndef update_user(id_usuario):\n\n\tuser = [user for user in users if user[\"id\"] == id_usuario]\n \n\tif (len(user) == 0):\n\t\tabort(404)\n\t\n\tif (not request.json):\n\t\tabort(400)\n \n\t\n\t\n\tuser[0]['nome'] = request.json.get('nome', user[0]['nome'])\n\tuser[0]['email'] = request.json.get('email', user[0]['email'])\n\tuser[0]['idade'] = request.json.get('idade', user[0]['idade'])\n\t\n\treturn jsonify({'user': user[0]})\n\t\n@auth.get_password\ndef get_password(username):\n if username == 'Example':\n return '12345'\n return None\n\n@auth.error_handler\ndef unauthorized():\n return make_response(jsonify({'error': 'Unauthorized access'}), 401)\n\t\nif __name__ == '__main__':\n\tapp.run(debug=True)\n", "sub_path": "cadastro2.py", "file_name": "cadastro2.py", "file_ext": "py", "file_size_in_byte": 2021, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 3, "usage_type": "call"}, {"api_name": "flask.ext.httpauth.HTTPBasicAuth", "line_number": 4, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request.json.get", "line_number": 36, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.request.json.get", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 38, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.request.json.get", "line_number": 39, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 39, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 44, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 51, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 64, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.request.json.get", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 69, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 69, "usage_type": "name"}, {"api_name": "flask.request.json.get", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 70, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 70, "usage_type": "name"}, {"api_name": "flask.request.json.get", "line_number": 71, "usage_type": "call"}, {"api_name": "flask.request.json", "line_number": 71, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 71, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.make_response", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 83, "usage_type": "call"}]} +{"seq_id": "337832304", "text": "import os\nfrom pathlib import Path\nimport uuid\nimport json\n\nimport aiofiles\nimport aiohttp_jinja2\nimport aioredis\nimport jinja2\nfrom aiohttp import web\nfrom aiohttp_session import setup, get_session\nfrom celery_workers import worker\nfrom aiohttp_session import session_middleware\nfrom aiohttp_session.redis_storage import RedisStorage\n\nimport settings\n\nroutes = web.RouteTableDef()\n\n\n@routes.get('/analyse/start')\n@aiohttp_jinja2.template('analyse_start.html')\nasync def upload_form(request):\n return {'title': 'Welcome Page'}\n\n\n@routes.post('/analyse/start')\n@aiohttp_jinja2.template('analyse_succeed_created.html')\nasync def upload_process(request):\n async for obj in (await request.multipart()):\n if obj.filename is not None:\n\n file_path = os.path.join(settings.MEDIA_ROOT, obj.filename)\n f = await aiofiles.open(file_path, 'wb')\n await f.write(await obj.read())\n await f.close()\n await worker.create_task(file_path)\n session = await get_session(request)\n file = [\n {\n 'file_name': obj.filename,\n 'task_id': str(uuid.uuid4())\n }\n ]\n if 'files' in session:\n session['files'] = session['files'] + file\n else:\n session['files'] = file\n\n\n@routes.get(r'/analyse/list')\n@aiohttp_jinja2.template('analyse_list.html')\nasync def get_analyse_list(request):\n session = await get_session(request)\n return {'analysed_list': session.get('files')}\n\n\n@routes.get(r'/analyse/result/{task_id}')\n@aiohttp_jinja2.template('analyse_result.html')\nasync def get_result_view(request):\n task_id = request.match_info['task_id']\n df = await worker.get_result(task_id)\n return {\n 'describe': df['describe'].to_html(),\n 'info': df['info'],\n }\n\n\nasync def init():\n redis = await aioredis.create_pool(('localhost', 6379))\n app = web.Application(middlewares=[session_middleware(RedisStorage(redis))])\n app.add_routes(routes)\n aiohttp_jinja2.setup(app, loader=jinja2.FileSystemLoader(settings.TEMPLATE_ROOT))\n Path(settings.MEDIA_ROOT).mkdir(parents=True, exist_ok=True)\n return app\n\nweb.run_app(init())\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 2246, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "aiohttp.web.RouteTableDef", "line_number": 18, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 18, "usage_type": "name"}, {"api_name": "aiohttp_jinja2.template", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "attribute"}, {"api_name": "settings.MEDIA_ROOT", "line_number": 33, "usage_type": "attribute"}, {"api_name": "aiofiles.open", "line_number": 34, "usage_type": "call"}, {"api_name": "celery_workers.worker.create_task", "line_number": 37, "usage_type": "call"}, {"api_name": "celery_workers.worker", "line_number": 37, "usage_type": "name"}, {"api_name": "aiohttp_session.get_session", "line_number": 38, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 42, "usage_type": "call"}, {"api_name": "aiohttp_jinja2.template", "line_number": 28, "usage_type": "call"}, {"api_name": "aiohttp_session.get_session", "line_number": 54, "usage_type": "call"}, {"api_name": "aiohttp_jinja2.template", "line_number": 52, "usage_type": "call"}, {"api_name": "celery_workers.worker.get_result", "line_number": 62, "usage_type": "call"}, {"api_name": "celery_workers.worker", "line_number": 62, "usage_type": "name"}, {"api_name": "aiohttp_jinja2.template", "line_number": 59, "usage_type": "call"}, {"api_name": "aioredis.create_pool", "line_number": 70, "usage_type": "call"}, {"api_name": "aiohttp.web.Application", "line_number": 71, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 71, "usage_type": "name"}, {"api_name": "aiohttp_session.session_middleware", "line_number": 71, "usage_type": "call"}, {"api_name": "aiohttp_session.redis_storage.RedisStorage", "line_number": 71, "usage_type": "call"}, {"api_name": "aiohttp_jinja2.setup", "line_number": 73, "usage_type": "call"}, {"api_name": "jinja2.FileSystemLoader", "line_number": 73, "usage_type": "call"}, {"api_name": "settings.TEMPLATE_ROOT", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 74, "usage_type": "call"}, {"api_name": "settings.MEDIA_ROOT", "line_number": 74, "usage_type": "attribute"}, {"api_name": "aiohttp.web.run_app", "line_number": 77, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "131818787", "text": "\"\"\"\r\n主函数\r\n\"\"\"\r\n\r\nfrom multiprocessing import Process\r\nfrom ProxyValidSchedule import run as ValidRun\r\nfrom ProxyRefreshSchedule import run as RefreshRun\r\n\r\ndef run():\r\n p_list = list()\r\n p3 = Process(target=RefreshRun, name=\"RefreshRun\")\r\n p_list.append(p3)\r\n p2 = Process(target=ValidRun, name=\"ValidRun\")\r\n p_list.append(p2)\r\n\r\n\r\n for p in p_list:\r\n p.start()\r\n for p in p_list:\r\n p.join()\r\n\r\nif __name__ == '__main__':\r\n run()", "sub_path": "proxyIpSpider/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 473, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "multiprocessing.Process", "line_number": 11, "usage_type": "call"}, {"api_name": "ProxyRefreshSchedule.run", "line_number": 11, "usage_type": "name"}, {"api_name": "multiprocessing.Process", "line_number": 13, "usage_type": "call"}, {"api_name": "ProxyValidSchedule.run", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "613451493", "text": "#Script for performing topic modelling on patchnotes\n#Author: Tim Jonathan Rupp\n#DISC applied project\n\n#import needed modules\nimport nltk\nnltk.download('stopwords')\nfrom nltk.corpus import stopwords\n\nimport re\n\nimport pandas as pd\n\nfrom pprint import pprint\n\nimport spacy\n\nimport gensim\nimport gensim.corpora as corpora\nfrom gensim.utils import simple_preprocess\nfrom gensim.models import CoherenceModel\n\nimport pyLDAvis.gensim_models\n\nimport matplotlib.pyplot as plt\n\nimport sqlite3 as sql\n\n#define stop words\nstop_words = stopwords.words('english')\n\n#connect to SQLite database\nconn = sql.connect('app_reviews.sqlite')\n\n#read reviews from database\nreviews = pd.read_sql('SELECT content FROM reviews;', conn)\n\n#convert to list\nreviews = reviews.values.tolist()\n\n#convert contents to string\nreviews = [str(i) for i in reviews]\n\n#save as all texts\nall_texts = reviews\n\n#define function for removing emojis\ndef remove_emojis(data):\n emoj = re.compile(\"[\"\n u\"\\U0001F600-\\U0001F64F\"\n u\"\\U0001F300-\\U0001F5FF\" \n u\"\\U0001F680-\\U0001F6FF\" \n u\"\\U0001F1E0-\\U0001F1FF\" \n u\"\\U00002500-\\U00002BEF\" \n u\"\\U00002702-\\U000027B0\"\n u\"\\U00002702-\\U000027B0\"\n u\"\\U000024C2-\\U0001F251\"\n u\"\\U0001f926-\\U0001f937\"\n u\"\\U00010000-\\U0010ffff\"\n u\"\\u2640-\\u2642\"\n u\"\\u2600-\\u2B55\"\n u\"\\u200d\"\n u\"\\u23cf\"\n u\"\\u23e9\"\n u\"\\u231a\"\n u\"\\ufe0f\" \n u\"\\u3030\"\n \"]+\", re.UNICODE)\n return re.sub(emoj, '', data)\n\n#remove emojis and save patch notes in text_list\ntext_list = []\nfor text in all_texts:\n text_list.append(remove_emojis(text))\n\n#remove line breaks\ntext_list = [re.sub('\\\\s+', ' ', sent) for sent in text_list]\n\n#define function for tokenisation, normalisation and removal of punctuation\ndef sent_to_words(sentences):\n for sentence in sentences:\n yield gensim.utils.simple_preprocess(str(sentence), deacc=True)\n\n#use on text_list\ndata_words = list(sent_to_words(text_list))\n\nprint(data_words[:4])\n\n#define bigrams (have to occur min 5 times)\nbigram = gensim.models.Phrases(data_words, min_count=5, threshold=100)\nbigram_mod = gensim.models.phrases.Phraser(bigram)\n\n#define function to remove stopwords\ndef remove_stopwords(texts):\n return [[word for word in simple_preprocess(str(doc)) if word not in stop_words] for doc in texts]\n\n#function to get bigrams\ndef make_bigrams(texts):\n return [bigram_mod[doc] for doc in texts]\n\n#function for lemmatisation and POS-tagging\n#keep only nouns, adjectives, verbs and adverbs\ndef lemmatization(texts, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV']):\n texts_out = []\n for sent in texts:\n doc = nlp(\" \".join(sent))\n texts_out.append([token.lemma_ for token in doc if token.pos_ in allowed_postags])\n return texts_out\n\n#remove stopwords\ndata_words_nostops = remove_stopwords(data_words)\n\n#calculate bigrams\ndata_words_bigrams = make_bigrams(data_words_nostops)\n\n#define lemmatiser and POS-tagger\nnlp = spacy.load('en_core_web_sm', disable=['parser', 'ner'])\n\n#lemmatise and tag words\ndata_lemmatized = lemmatization(data_words_bigrams, allowed_postags=['NOUN', 'ADJ', 'VERB', 'ADV'])\n\nprint(data_lemmatized[:1])\n\n#create dictionary\nid2word = corpora.Dictionary(data_lemmatized)\n\n#create corpus\ntexts = data_lemmatized\n\n#TF-IDF (term frequency and inverse document frequency\ncorpus = [id2word.doc2bow(text) for text in texts]\n\nprint(corpus[:1])\n\n#build model\nlda_model = gensim.models.ldamodel.LdaModel(corpus=corpus,\n id2word=id2word,\n num_topics=20,\n random_state=100,\n update_every=1,\n chunksize=100,\n passes=10,\n alpha='auto',\n per_word_topics=True)\n#print topics\npprint(lda_model.print_topics())\n\n#save model\ndoc_lda = lda_model[corpus]\n\n#Print perplexity\nprint('\\nPerplexity: ', lda_model.log_perplexity(corpus))\n\n#Print coherence\ncoherence_model_lda = CoherenceModel(model=lda_model, texts=data_lemmatized, dictionary=id2word, coherence='u_mass')\ncoherence_lda = coherence_model_lda.get_coherence()\nprint('\\nCoherence: ', coherence_lda)\n\nvis = pyLDAvis.gensim_models.prepare(lda_model, corpus, id2word)\npyLDAvis.save_html(vis, 'lda_review.html')\n\n\n#function to compute multiple LDAs with varying topic numbers\n#coherence type is u_mass\n#returns a list of models and corresponding coherence values\ndef compute_coherence_values(dictionary, corpus, texts, limit, start=2, step=3):\n coherence_values = []\n model_list = []\n for num_topics in range(start, limit, step):\n model = gensim.models.ldamodel.LdaModel(corpus=corpus, num_topics=num_topics, id2word=id2word)\n model_list.append(model)\n coherencemodel = CoherenceModel(model=model, texts=texts, dictionary=dictionary, coherence='u_mass')\n coherence_values.append(coherencemodel.get_coherence())\n\n return model_list, coherence_values\n\n#call function\nmodel_list, coherence_values = compute_coherence_values(dictionary=id2word, corpus=corpus, texts=data_lemmatized,\n start=2, limit=40, step=4)\n\n#plot distribution of coherence values for different numbers of topics\nlimit = 40;\nstart = 2;\nstep = 4;\nx = range(start, limit, step)\nplt.plot(x, coherence_values)\nplt.xlabel(\"Num Topics\")\nplt.ylabel(\"Coherence score\")\nplt.legend((\"coherence_values\"), loc='best')\nplt.show()\n\nfor m, um in zip(x, coherence_values):\n print(\"Num Topics =\", m, \" has Coherence Value of\", round(um, 4))\n\n#choose optimal model depending on coherence\noptimal_model = model_list[1]\n\n#show topics of optimal model\nmodel_topics = optimal_model.show_topics(formatted=False)\npprint(optimal_model.print_topics(num_words=10))\n\n#visualize and save as html\nvis = pyLDAvis.gensim_models.prepare(optimal_model, corpus, id2word)\npyLDAvis.save_html(vis, 'lda_reviews3.html')\n\n\n#save topwords of topics to sql\ntopics = pd.DataFrame(optimal_model.print_topics(num_words = 10))\n\ntopics.to_sql(\"LDA_reviews_topwords3\", conn)\n\n#get values for each review corresponding to each topic\nall_topics = optimal_model.get_document_topics(corpus, minimum_probability=0.0)\nall_topics_csr = gensim.matutils.corpus2csc(all_topics)\nall_topics_numpy = all_topics_csr.T.toarray()\nall_topics_df = pd.DataFrame(all_topics_numpy)\n\n#save values in sql\nall_topics_df.to_sql(\"LDA_reviews3\", conn)\n", "sub_path": "14_LDA_reviews.py", "file_name": "14_LDA_reviews.py", "file_ext": "py", "file_size_in_byte": 6885, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "nltk.download", "line_number": 7, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 30, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 30, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 33, "usage_type": "call"}, {"api_name": "pandas.read_sql", "line_number": 36, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 49, "usage_type": "call"}, {"api_name": "re.UNICODE", "line_number": 68, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 69, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 77, "usage_type": "call"}, {"api_name": "gensim.utils.simple_preprocess", "line_number": 82, "usage_type": "call"}, {"api_name": "gensim.utils", "line_number": 82, "usage_type": "attribute"}, {"api_name": "gensim.models.Phrases", "line_number": 90, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 90, "usage_type": "attribute"}, {"api_name": "gensim.models.phrases.Phraser", "line_number": 91, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 91, "usage_type": "attribute"}, {"api_name": "gensim.utils.simple_preprocess", "line_number": 95, "usage_type": "call"}, {"api_name": "spacy.load", "line_number": 117, "usage_type": "call"}, {"api_name": "gensim.corpora.Dictionary", "line_number": 125, "usage_type": "call"}, {"api_name": "gensim.corpora", "line_number": 125, "usage_type": "name"}, {"api_name": "gensim.models.ldamodel.LdaModel", "line_number": 136, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 136, "usage_type": "attribute"}, {"api_name": "pprint.pprint", "line_number": 146, "usage_type": "call"}, {"api_name": "gensim.models.CoherenceModel", "line_number": 155, "usage_type": "call"}, {"api_name": "pyLDAvis.gensim_models.gensim_models.prepare", "line_number": 159, "usage_type": "call"}, {"api_name": "pyLDAvis.gensim_models.gensim_models", "line_number": 159, "usage_type": "attribute"}, {"api_name": "pyLDAvis.gensim_models", "line_number": 159, "usage_type": "name"}, {"api_name": "pyLDAvis.gensim_models.save_html", "line_number": 160, "usage_type": "call"}, {"api_name": "pyLDAvis.gensim_models", "line_number": 160, "usage_type": "name"}, {"api_name": "gensim.models.ldamodel.LdaModel", "line_number": 170, "usage_type": "call"}, {"api_name": "gensim.models", "line_number": 170, "usage_type": "attribute"}, {"api_name": "gensim.models.CoherenceModel", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 187, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 187, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 190, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 190, "usage_type": "name"}, {"api_name": "pprint.pprint", "line_number": 200, "usage_type": "call"}, {"api_name": "pyLDAvis.gensim_models.gensim_models.prepare", "line_number": 203, "usage_type": "call"}, {"api_name": "pyLDAvis.gensim_models.gensim_models", "line_number": 203, "usage_type": "attribute"}, {"api_name": "pyLDAvis.gensim_models", "line_number": 203, "usage_type": "name"}, {"api_name": "pyLDAvis.gensim_models.save_html", "line_number": 204, "usage_type": "call"}, {"api_name": "pyLDAvis.gensim_models", "line_number": 204, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 208, "usage_type": "call"}, {"api_name": "gensim.matutils.corpus2csc", "line_number": 214, "usage_type": "call"}, {"api_name": "gensim.matutils", "line_number": 214, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 216, "usage_type": "call"}]} +{"seq_id": "318259004", "text": "import sqlite3\nimport sys\nfrom PyQt4.QtCore import *\nfrom PyQt4.QtGui import *\nfrom table_display import *\nimport time\nfrom cascade_style_sheet import *\n\nclass AssignCustomer(QDialog):\n \"\"\"this class will be used to either assign a customer that has\n made a booking to a table or assign a customer that has not made a booking\n to a table\"\"\"\n\n def __init__(self,TableNumber):\n super().__init__()\n self.setWindowTitle(\"Assign customer to table {0}\".format(TableNumber))\n self.setMinimumSize(600,600)\n self.tableNumber = TableNumber\n self.setStyleSheet(css)\n\n self.titleFont = QFont()\n self.titleFont.setPointSize(15)\n\n\n self.todays_bookings_label = QLabel(\"Todays bookings for table {0}\".format(TableNumber))\n self.todays_bookings_label.setFont(self.titleFont)\n self.todays_bookings_label.setAlignment(Qt.AlignLeft)\n self.todays_bookings_label.setFixedWidth(400)\n\n\n self.main_assign_layout = QVBoxLayout()\n self.choose_customer = QHBoxLayout()\n self.create_combo_box(TableNumber)\n self.add_customer_layout = QGridLayout()\n self.create_complete_layout = QHBoxLayout()\n \n\n self.choose_customer.addWidget(self.customer_combo_box)\n self.select_customer = QPushButton(\"Select\")\n self.choose_customer.addWidget(self.select_customer)\n self.select_customer.clicked.connect(self.select_connect) \n \n #create buttons\n self.create_complete = QPushButton(\"Create\")\n self.create_complete.clicked.connect(self.create_booking)\n \n #labels\n self.table_number_label = QLabel(\"Table Number : \")\n self.number_of_people_label = QLabel(\"Number Of People : \")\n self.time_arrived_label = QLabel(\"Time Of Arrival : \")\n self.date_arrived_label = QLabel(\"Date Of Arrival : \")\n\n self.systemtime = time.strftime(\"%H:%M\")\n self.system_time_label = QLineEdit(self.systemtime)\n self.system_time_label.setReadOnly(True)\n sizehint = self.system_time_label.sizeHint()\n self.system_time_label.setMaximumSize(sizehint)\n\n self.systemdate = time.strftime(\"%d/%m/%Y\")\n self.system_date_label = QLineEdit(self.systemdate)\n self.system_date_label.setReadOnly(True)\n self.system_date_label.setMaximumSize(sizehint)\n\n self.display_table_number = QLineEdit(\"{0}\".format(TableNumber))\n self.display_table_number.setReadOnly(True)\n self.display_table_number.setMaximumSize(sizehint)\n\n regexp = QRegExp(\"^\\\\d\\\\d?$\")\n validator = QRegExpValidator(regexp)\n self.input_number_of_people = QLineEdit()\n self.input_number_of_people.setValidator(validator)\n self.input_number_of_people.setMaximumSize(sizehint)\n\n\n displayQuery = \"\"\"SELECT\n Customers.FirstName,\n Customers.LastName,\n Bookings.NumberOfPeople,\n Bookings.Time\n FROM Customers\n INNER JOIN Bookings\n ON Customers.CustomerID = Bookings.CustomerID\n WHERE Bookings.Date = '{0}'\n AND Bookings.TableNumber = {1}\n \"\"\".format(self.systemdate,TableNumber)\n\n self.display_customers = DisplayTable()\n self.display_customers.show_results(displayQuery)\n\n\n self.add_customer_layout.addWidget(self.table_number_label,0,0)\n self.add_customer_layout.addWidget(self.display_table_number,0,1)\n self.add_customer_layout.addWidget(self.time_arrived_label,1,0)\n self.add_customer_layout.addWidget(self.date_arrived_label,2,0)\n self.add_customer_layout.addWidget(self.system_time_label,1,1)\n self.add_customer_layout.addWidget(self.system_date_label,2,1)\n self.add_customer_layout.addWidget(self.number_of_people_label,3,0)\n self.add_customer_layout.addWidget(self.input_number_of_people,3,1)\n self.add_customer_layout.addWidget(self.create_complete,4,0,2,2) \n\n self.assign_street_box = QGroupBox(\"Customer that has not booked in advance\")\n self.assign_street_box.setLayout(self.add_customer_layout)\n\n self.assign_booked_box = QGroupBox(\"Customer that has booked in advance\")\n self.assign_booked_box.setLayout(self.choose_customer)\n\n self.main_assign_layout.addWidget(self.todays_bookings_label)\n self.main_assign_layout.addWidget(self.display_customers)\n self.main_assign_layout.addWidget(self.assign_booked_box)\n self.main_assign_layout.addWidget(self.assign_street_box) \n self.setLayout(self.main_assign_layout)\n \n self.exec_()\n\n def create_booking(self):\n #create bookingID for customer that has walked in\n TableNumber = self.display_table_number.text()\n CustomerID = 1\n NumberOfPeople = self.input_number_of_people.text()\n Date = self.systemdate\n Time = self.systemtime\n \n Booking = (CustomerID,TableNumber,NumberOfPeople,Date,Time)\n\n\n\n if len(NumberOfPeople) > 0 and (int(NumberOfPeople)>0):\n\n with sqlite3.connect(\"restaurant.db\") as db:\n cursor = db.cursor()\n sql = \"insert into Bookings(CustomerID, TableNumber, NumberOfPeople, Date, Time) values (?,?,?,?,?)\"\n cursor.execute(\"PRAGMA foreign_keys = ON\")\n cursor.execute(sql,Booking)\n db.commit()\n \n with sqlite3.connect(\"restaurant.db\") as db:\n cursor = db.cursor()\n cursor.execute(\"select * from Bookings where CustomerID = {0} and TableNumber = {1} and NumberOfPeople = {2} and Date = '{3}' and Time = '{4}' \".format(CustomerID, TableNumber, NumberOfPeople, Date, Time))\n self.bookingDetails = cursor.fetchone()\n\n self.close()\n return self.bookingDetails\n else:\n print(\"Please enter a valid number.\")\n\n \n\n def select_connect(self):\n TodaysDate = time.strftime(\"%d/%m/%Y\")\n customerCurrentIndex = self.customer_combo_box.currentIndex()\n print(\"Customer : {0}\".format(customerCurrentIndex))\n CustomerID = self.CustomerList[customerCurrentIndex]\n print(\"Customer ID: {0}\".format(CustomerID))\n \n with sqlite3.connect(\"restaurant.db\") as db:\n cursor = db.cursor()\n cursor.execute(\"select * from Bookings where CustomerID = {0} and TableNumber = {1} and Date = '{2}'\".format(CustomerID, self.tableNumber, TodaysDate))\n self.bookingDetails = cursor.fetchone() \n print(self.bookingDetails)\n\n self.close()\n \n return self.bookingDetails\n\n def create_combo_box(self,TableNumber):\n self.CustomerList = []\n CustomerLastName = []\n TodaysDate = time.strftime(\"%d/%m/%Y\")\n\n ## get all customer IDs that are on table _\n with sqlite3.connect(\"restaurant.db\") as db:\n cursor = db.cursor()\n cursor.execute(\"select CustomerID from Bookings where TableNumber = {0} and Date = '{1}'\".format(TableNumber,TodaysDate))\n customers = cursor.fetchall()\n for each in customers:\n self.CustomerList.append(each[0]) \n\n ## get all last names from previouse fetchall \n for customer in self.CustomerList:\n with sqlite3.connect(\"restaurant.db\") as db:\n cursor = db.cursor()\n cursor.execute(\"select LastName from Customers where CustomerID = {0}\".format(customer))\n customer = cursor.fetchone()\n CustomerLastName.append(customer[0]) \n \n #create combo, insert all last names from fetchall\n self.customer_combo_box = QComboBox(self)\n for each in CustomerLastName:\n self.customer_combo_box.addItem(each)\n\nif __name__ == \"__main__\":\n TableNumber = 1\n application = QApplication(sys.argv)\n window = AssignCustomer(TableNumber)\n window.show()\n window.raise_()\n application.exec()\n", "sub_path": "Implementation/GUI/assign_table_customer.py", "file_name": "assign_table_customer.py", "file_ext": "py", "file_size_in_byte": 8206, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "time.strftime", "line_number": 53, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 59, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 129, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 136, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 149, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 155, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 168, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 171, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 180, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 193, "usage_type": "attribute"}]} +{"seq_id": "568535711", "text": "import telebot\nfrom telebot import types\n\nbot = telebot.TeleBot(\"250806434:AAFEmXPORxni3FlMJuTPWtwYIxc04A_3j3U\")\n\n\n@bot.message_handler(commands=['start'])\ndef handle_start(message):\n print(\"+\")\n user_markup = telebot.types.ReplyKeyboardMarkup(True, True)\n user_markup.row('Не шути больше', '21')\n user_markup.row('Переверни моё сообщение пож')\n bot.send_message(message.from_user.id, 'Хай', reply_markup=user_markup)\n\n\n@bot.message_handler(commands=['help'])\ndef handle_start(message):\n print(\"+\")\n user_markup = telebot.types.ReplyKeyboardMarkup(True, True)\n user_markup.row('Не шути больше', '21')\n user_markup.row('Переверни моё сообщение пож')\n bot.send_message(message.from_user.id, 'Используй клаву и интуицию', reply_markup=user_markup)\n\n\n@bot.message_handler(content_types=[\"text\"])\ndef handle_command(message):\n\n if message.text == \"Не шути больше\":\n bot.send_message(message.chat.id, \"Ацтань\")\n elif message.text == \"21\":\n user_markup = telebot.types.ReplyKeyboardMarkup(True, True)\n user_markup.row('Тоттенхэм', 'Осип')\n user_markup.row('Бавария')\n bot.send_message(message.from_user.id, 'За кого ты болеешь?', reply_markup=user_markup)\n elif message.text == \"Переверни моё сообщение пож\":\n bot.send_message(message.chat.id, \"Что перевернуть?\")\n elif message.text == \"Ясен\":\n bot.send_message(message.chat.id, \"Красен\")\n\n elif message.text == \"Бавария\":\n user_markup = telebot.types.ReplyKeyboardMarkup(True, True)\n user_markup.row('Не шути больше', '21')\n user_markup.row('Переверни моё сообщение пож')\n bot.send_message(message.from_user.id, 'Вань, когда тебя уже отчислят?', reply_markup=user_markup)\n elif message.text == \"Тоттенхэм\":\n user_markup = telebot.types.ReplyKeyboardMarkup(True, True)\n user_markup.row('Не шути больше', '21')\n user_markup.row('Переверни моё сообщение пож')\n bot.send_message(message.from_user.id, 'Люблю их', reply_markup=user_markup)\n elif message.text == \"Осип\":\n user_markup = telebot.types.ReplyKeyboardMarkup(True, True)\n user_markup.row('Не шути больше', '21')\n user_markup.row('Переверни моё сообщение пож')\n bot.send_message(message.from_user.id, 'Мой пидор', reply_markup=user_markup)\n else:\n newString = \"\"\n ranged = len(message.text) - 1\n while ranged != -1:\n newString += message.text[ranged]\n ranged -= 1\n bot.send_message(message.from_user.id, newString)\n\nbot.polling(none_stop=True, interval=0)", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2914, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "telebot.TeleBot", "line_number": 4, "usage_type": "call"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 10, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 10, "usage_type": "attribute"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 19, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 19, "usage_type": "attribute"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 31, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 31, "usage_type": "attribute"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 41, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 41, "usage_type": "attribute"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 46, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 46, "usage_type": "attribute"}, {"api_name": "telebot.types.ReplyKeyboardMarkup", "line_number": 51, "usage_type": "call"}, {"api_name": "telebot.types", "line_number": 51, "usage_type": "attribute"}]} +{"seq_id": "149116192", "text": "import numpy as np # linear algebra\nimport pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\nimport tensorflow as tf\nimport math\nimport csv\nimport torch\nimport torch.nn as nn\nfrom sklearn import metrics\n# Input data files are available in the \"../input/\" directory.\n# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory\n\nimport os\n\n# Any results you write to the current directory are saved as output.\ndef write_result(name, predictions):\n \"\"\"\n \"\"\"\n if predictions is None:\n raise Exception('need predictions')\n\n predictions = predictions.flatten()\n\n if not os.path.exists('./results'):\n os.makedirs('./results')\n\n path = os.path.join('./results', name)\n\n with open(path, 'wt', encoding='utf-8', newline='') as csv_target_file:\n target_writer = csv.writer(csv_target_file, lineterminator='\\n')\n\n header = [\n 'user_id',\n 'time_slot_0', 'time_slot_1', 'time_slot_2', 'time_slot_3',\n 'time_slot_4', 'time_slot_5', 'time_slot_6', 'time_slot_7',\n 'time_slot_8', 'time_slot_9', 'time_slot_10', 'time_slot_11',\n 'time_slot_12', 'time_slot_13', 'time_slot_14', 'time_slot_15',\n 'time_slot_16', 'time_slot_17', 'time_slot_18', 'time_slot_19',\n 'time_slot_20', 'time_slot_21', 'time_slot_22', 'time_slot_23',\n 'time_slot_24', 'time_slot_25', 'time_slot_26', 'time_slot_27',\n ]\n\n target_writer.writerow(header)\n\n for i in range(0, len(predictions), 28):\n # NOTE: 57159 is the offset of user ids\n userid = [57159 + i // 28]\n labels = predictions[i:i+28].tolist()\n\n target_writer.writerow(userid + labels)\n\n# NOTE: load the data from the npz\ndataset = np.load('./datasets/v0_eigens.npz')\n\n# NOTE: read features of test set\ntest_eigens = dataset['issue_eigens'][:, :-28].reshape(-1, 32, 28).astype(float)\n\n# Hyperparameters\nsequence_length = 32\ninput_size = 28\nhidden_size1 = 128\nfc_hidden_size = [28, 128, 128, 128, 28]\nnum_layers = 2\nnum_classes = 28\nbatch_size = 64\nnum_epochs = 60\nlearning_rate = 0.0001\n\nclass RNN(nn.Module):\n def __init__(self, input_size, hidden_size1, num_layers, num_classes):\n super(RNN, self).__init__()\n self.hidden_size1 = hidden_size1\n self.num_layers = num_layers\n self.fc1 = nn.Linear(input_size, fc_hidden_size[0])\n self.fc2 = nn.Linear(fc_hidden_size[0], fc_hidden_size[1])\n self.fc3 = nn.Linear(fc_hidden_size[1], fc_hidden_size[2])\n self.lstm1 = nn.LSTM(fc_hidden_size[2], hidden_size1, num_layers, batch_first = True, dropout = 0.5)\n self.fc4 = nn.Linear(hidden_size1, fc_hidden_size[3])\n self.fc5 = nn.Linear(fc_hidden_size[3], fc_hidden_size[4])\n self.fc6 = nn.Linear(fc_hidden_size[4], num_classes)\n self.drop = nn.Dropout(p=0.3)\n self.sigmoid = nn.Sigmoid()\n \n def forward(self, x):\n \n out = self.fc1(x)\n out = self.fc2(out)\n out = self.fc3(out)\n \n # Set initial hidden and cell states \n h0 = torch.zeros(self.num_layers, out.size(0), self.hidden_size1).float()\n c0 = torch.zeros(self.num_layers, out.size(0), self.hidden_size1).float()\n \n # Forward propagate LSTM\n out, _ = self.lstm1(out, (h0, c0)) # out: tensor of shape (batch_size, seq_length, hidden_size)\n\n # Decode the hidden state of the last time step\n out = self.fc4(out[:, -1, :])\n out = self.fc5(out)\n out = self.fc6(out)\n \n out = self.sigmoid(out)\n \n return out\n \nmodel = RNN(input_size, hidden_size1, num_layers, num_classes)\nmodel.load_state_dict(torch.load('model_lstm_81_0.8799_0.2104.ckpt'))\n\nwith torch.no_grad():\n test_eigens = torch.tensor(test_eigens).float()\n outputs = model.forward(test_eigens)\n one = torch.ones(len(test_eigens), 28)\n zero = torch.zeros(len(test_eigens), 28)\n\n result2 = torch.where(outputs > 0.1, one, zero)\n result3 = torch.where(outputs > 0.09, one, zero)\n write_result('best_submission.csv', outputs.detach().numpy())", "sub_path": "lstm_predict.py", "file_name": "lstm_predict.py", "file_ext": "py", "file_size_in_byte": 4174, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 24, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 26, "usage_type": "call"}, {"api_name": "os.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "csv.writer", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 68, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 68, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 73, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 73, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.nn.LSTM", "line_number": 76, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 76, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 79, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 79, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 80, "usage_type": "name"}, {"api_name": "torch.nn.Sigmoid", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 81, "usage_type": "name"}, {"api_name": "torch.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 91, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 106, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.tensor", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.ones", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.where", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.where", "line_number": 115, "usage_type": "call"}]} +{"seq_id": "575195650", "text": "#! /usr/bin/env python \n \n \n##########################################################################################\n# PostColorTrack_MCHE470_Fall2013_cv2.py\n#\n# Script to process Mini-Project 3b videos\n#\n# Requires OpenCV\n# \n# Created: 11/2/13 \n# - Joshua Vaughan \n# - joshua.vaughan@louisiana.edu\n# - http://www.ucs.louisiana.edu/~jev9637\n#\n# Modified:\n# * 11/4/13 - Joshua Vaughan - joshua.vaughan@louisiana.edu\n# - hard coded video names due to Tkinter file dialog bug\n#\n########################################################################################## \n \nimport cv2 as cv2\nimport numpy as np\nfrom time import localtime, strftime, sleep\n#from Tkinter import Tk\nimport sys\n#from matplotlib.pyplot import * # Grab MATLAB plotting functions\n\ncolor_tracker_window = \"Color Tracker\"\n\ntrial_number = 1\n\n#if not os.path.exists(folder):\n # os.makedirs(folder)\n\n# filename = strftime(\"%m_%d_%Y_%H%M%S\") #names the output file as the date and time that the program is run\n# filepath = filename + \".csv\" #gives the path of the file to be opened\n\nfilepath = 'CSV files/pantographic_arm_vibration_{}.csv'.format(trial_number)\n# vid_name = sys.argv[1]\n \nf = open(filepath, \"a+\") #opens the output file in append mode\nf.write('Time (s), X Position (pixels), Y Position (pixels)' + '\\n') \n\nshow_images = 1\nprint_images = 0\n\nclass ColorTracker:\n def __init__(self): \n# cv2.NamedWindow( color_tracker_window, 1 ) \n\n# tk = Tk()\n# tk.withdraw() # we don't want a full GUI, so keep the root window from appearing\n# video_filename = askopenfilename(parent=tk) # show an \"Open\" dialog box and return the path to the selected file\n# \n# tk.destroy()\n \n video_filename = '/Users/Matt/Desktop/pantographic_arm_vibration_recordings/pantographic_arm_vibration_{}.mov'.format(trial_number)\n # video_filename = '/Users/Matt/Desktop/pantog/pantographic_arm_vibration_{}.mov'.format(trial_number)\n\n \n self.capture = cv2.VideoCapture(video_filename)\n \n \n def run(self): \n initialTime = 0. #sets the initial time\n# num_Frames = int( cv2.GetCaptureProperty( self.capture, cv2.CV_CAP_PROP_FRAME_COUNT ) )\n num_Frames = int(self.capture.get(cv2.cv.CV_CAP_PROP_FRAME_COUNT))\n # fps = self.capture.get(cv2.cv.CV_CAP_PROP_FPS)\n fps = 30.0\n# fps = cv2.GetCaptureProperty( self.capture, cv2.CV_CAP_PROP_FPS )\n \n for ii in range(num_Frames-9):\n \n print('Frame: ' + str(ii) + ' of ' + str(num_Frames))\n # read the ii-th frame\n# img = cv2.QueryFrame( self.capture ) \n img = self.capture.read()[1]\n \n if show_images:\n cv2.imshow('Raw Frame',img)\n # raw_input(\"Press Enter to continue...\")\n \n if print_images:\n savefig('Raw_Frame.png')\n \n \n # Blur the source image to reduce color noise \n # cv2.Smooth(img, img, cv2.CV_BLUR, 10) \n img = cv2.blur(img,(10,10))\n \n if show_images:\n cv2.imshow('Blurred',img)\n# raw_input(\"Press Enter to continue...\")\n \n if print_images:\n savefig('10x10_blur.png')\n\n \n # Convert the image to hsv(Hue, Saturation, Value) so its \n # It's easier to determine the color to track(hue) \n hsv_img = cv2.cvtColor(img, cv2.COLOR_BGR2HSV) \n\n # Define min and max HSV values to threshold\n Track_MIN = np.array([0, 0, 245],np.uint8)\n Track_MAX = np.array([180, 10, 255],np.uint8)\n \n # threshold the image\n thresholded_img = cv2.inRange(hsv_img, Track_MIN, Track_MAX)\n \n if show_images:\n cv2.imshow('Thresholded Image',thresholded_img)\n# raw_input(\"Press Enter to continue...\")\n \n if print_images:\n savefig('Thresholded_Frame.png')\n \n # fill the top with black\n thresholded_img[0:75,0:720] = 0\n thresholded_img[0:480,650:720] = 0\n \n if show_images:\n# if ii > 100:\n cv2.imshow('Thresholded Image',thresholded_img)\n# raw_input(\"Press Enter to continue...\")\n \n \n #determine the objects moments and check that the area is large \n #enough to be our object \n# thresholded_img2 = cv2.GetMat(thresholded_img)\n moments = cv2.moments(thresholded_img,0) \n area = moments['m00'] \n \n \n # there can be noise in the video so ignore objects with small areas \n if(area > 1500): \n #determine the x and y coordinates of the center of the object \n #we are tracking by dividing the 1, 0 and 0, 1 moments by the area \n x = moments['m10'] / area\n y = moments['m01'] / area\n\n elapsedTime = ii/fps\n \n f.write(str(elapsedTime) + ',' + '%013.9f' % x + ',' + '%013.9f' % y + \"\\n\") #prints output to the specified output file for later use\n \n x = int(x)\n y = int(y)\n \n# #create an overlay to mark the center of the tracked object \n# overlay = cv2.CreateImage(cv2.GetSize(img), 8, 3) \n# \n# cv2.Circle(overlay, (x, y), 2, (255, 255, 255), 20) \n# cv2.Add(img, overlay, img) \n# #add the thresholded image back to the img so we can see what was \n# #left after it was applied \n# cv2.Merge(thresholded_img, None, None, None, img) \n# \n# #display the image \n# cv2.ShowImage(color_tracker_window, img) \n \n # close the data file\n f.close()\n\n \nif __name__==\"__main__\": \n color_tracker = ColorTracker() \n color_tracker.run() \n", "sub_path": "Code/Tinkering/Machine_Vision/MachineVision_VideoProcessing.py", "file_name": "MachineVision_VideoProcessing.py", "file_ext": "py", "file_size_in_byte": 6189, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.VideoCapture", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.cv", "line_number": 68, "usage_type": "attribute"}, {"api_name": "cv2.imshow", "line_number": 81, "usage_type": "call"}, {"api_name": "cv2.blur", "line_number": 90, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 93, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2HSV", "line_number": 102, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 105, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 105, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 106, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 106, "usage_type": "attribute"}, {"api_name": "cv2.inRange", "line_number": 109, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 112, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 124, "usage_type": "call"}, {"api_name": "cv2.moments", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "627825286", "text": "\"\"\"\nGraphQL definitions for the Authentication App\n\"\"\"\nimport datetime\nfrom django.db.models import Q\nfrom django.contrib.auth.models import User\nfrom graphene import AbstractType, Argument, Field, Float, Int, List, Mutation, \\\n NonNull, ObjectType, String, relay\nfrom graphene_django import DjangoObjectType\nfrom graphene_django.filter import DjangoFilterConnectionField\nfrom trading.models import TradingAccount\nfrom trading.graphql import GTradingAccount\nfrom stocks.graphql import GInvestmentBucket, GStock\nfrom stocks.models import InvestmentBucket, Stock\nfrom .models import Profile, UserBank\n\n\n# pylint: disable=too-few-public-methods\nclass GUser(DjangoObjectType):\n \"\"\"\n GraphQL representation of a User\n \"\"\"\n class Meta(object):\n \"\"\"\n Meta Model for User. We must make sure to not expose\n the whole usere object\n \"\"\"\n model = User\n only_fields = ('id', 'profile', 'username', 'userbank')\n interfaces = (relay.Node, )\n\n\nclass GProfile(DjangoObjectType):\n \"\"\"\n GraphQL representation of a Profile\n \"\"\"\n stock_find = List(\n GStock, args={'text': Argument(NonNull(String)), 'first': Argument(Int)})\n invest_suggestions = DjangoFilterConnectionField(\n GInvestmentBucket,\n )\n\n class Meta(object):\n \"\"\"\n Meta Model for Profile\n \"\"\"\n model = Profile\n only_fields = ('id', 'trading_accounts', 'stock_find')\n interfaces = (relay.Node, )\n\n @staticmethod\n def resolve_stock_find(_self, args, _context, _info):\n \"\"\"\n Finds a stock given a case insensitive name\n \"\"\"\n query = Stock.objects.filter(name__icontains=args['text'])\n if 'first' in args:\n query = query[:args['first']]\n return query\n\n @staticmethod\n def resolve_invest_suggestions(_data, _args, context, _info):\n \"\"\"\n Finds all the investment suggestions available to the user\n \"\"\"\n return InvestmentBucket.objects.filter(Q(owner=context.user.profile) | Q(public=True))\n\n\nclass DataPoint(object):\n \"\"\"\n Dummy class to represent a date / value DataPoint\n \"\"\"\n def __init__(self, date, value):\n self.date = date\n self.value = value\n\n\nclass GDataPoint(ObjectType):\n \"\"\"\n GraphQL definition of the DataPoint above\n \"\"\"\n date = String()\n value = Float()\n\n\nclass GUserBank(DjangoObjectType):\n \"\"\"\n GraphQL representation of a UserBank\n \"\"\"\n balance = Float()\n income = Float()\n name = String()\n outcome = Float()\n history = List(GDataPoint, args={'start': Argument(NonNull(String))})\n\n class Meta(object):\n \"\"\"\n Meta Model for UserBank\n \"\"\"\n model = UserBank\n only_fields = ('id', 'balance', 'income', 'outcome')\n interfaces = (relay.Node, )\n\n @staticmethod\n def resolve_history(data, args, context, _info):\n \"\"\"\n Builds the financial history for the user\n \"\"\"\n start = args['start']\n end = datetime.datetime.now().strftime(\"%Y-%m-%d\")\n response = context.plaid.Transactions.get(\n data.access_token,\n start_date=start,\n end_date=end\n )\n transactions = response['transactions']\n value = GUserBank.resolve_balance(data, {}, context, None)\n value_list = [DataPoint(end, value)]\n for transaction in transactions:\n value = value - transaction['amount']\n if not value_list[-1].date == transaction['date']:\n value_list.append(DataPoint(transaction['date'], value))\n return value_list\n\n @staticmethod\n def resolve_balance(data, _args, context, _info):\n \"\"\"\n Finds the current balance of the user\n \"\"\"\n balances = context.plaid.Accounts.balance.get(data.access_token)['accounts']\n extracted_balances = [((b['balances']['available']\n if b['balances']['available'] is not None else\n b['balances']['current']) *\n (1\n if b['subtype'] == 'credit card' else -1))\n for b in balances]\n balance = sum(extracted_balances)\n return float(balance)\n\n @staticmethod\n def resolve_name(data, _args, context, _info):\n \"\"\"\n Returns the name of the bank account\n \"\"\"\n name = context.plaid.Accounts.get(data.access_token)['accounts'][0]['name']\n return name\n\n @staticmethod\n def resolve_income(data, _args, context, _info):\n \"\"\"\n Calculates the income a user has per month\n \"\"\"\n start = (datetime.datetime.now() - datetime.timedelta(days=30)).strftime(\"%Y-%m-%d\")\n end = datetime.datetime.now().strftime(\"%Y-%m-%d\")\n response = context.plaid.Transactions.get(\n data.access_token,\n start_date=start,\n end_date=end,\n )\n transactions = response['transactions']\n plus = sum(filter(lambda x: x > 0, [tx['amount'] for tx in transactions]))\n return float(plus)\n\n @staticmethod\n def resolve_outcome(data, _args, context, _info):\n \"\"\"\n Calculates the expenses a user has\n \"\"\"\n start = (datetime.datetime.now() - datetime.timedelta(days=30)).strftime(\"%Y-%m-%d\")\n end = datetime.datetime.now().strftime(\"%Y-%m-%d\")\n response = context.plaid.Transactions.get(\n data.access_token,\n start_date=start,\n end_date=end,\n )\n transactions = response['transactions']\n plus = sum(filter(lambda x: x < 0, [tx['amount'] for tx in transactions]))\n return float(plus)\n\n\n# pylint: disable=no-init\nclass Query(AbstractType):\n \"\"\"\n Query represents the entry method for a GraphQL request\n \"\"\"\n viewer = Field(GUser, )\n\n @staticmethod\n def resolve_viewer(_self, _args, context, _info):\n \"\"\"\n The viewer represents the current logged in user\n \"\"\"\n if not context.user.is_authenticated():\n return None\n return context.user\n# pylint: enable=no-init\n\n\nclass AddTradingAccount(Mutation):\n \"\"\"\n AddTradingAccount creates a new TradingAccount for the user\n \"\"\"\n class Input(object):\n \"\"\"\n Input to create a trading account. Right now it's only a name.\n \"\"\"\n name = String()\n account = Field(lambda: GTradingAccount)\n\n @staticmethod\n def mutate(_self, args, context, _info):\n \"\"\"\n Creates a TradingAccount and saves it to the DB\n \"\"\"\n account = TradingAccount(\n profile=context.user.profile,\n account_name=args['name']\n )\n account.save()\n return AddTradingAccount(account=account)\n# pylint: enable=too-few-public-methods\n", "sub_path": "authentication/graphql.py", "file_name": "graphql.py", "file_ext": "py", "file_size_in_byte": 6857, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "graphene_django.DjangoObjectType", "line_number": 19, "usage_type": "name"}, {"api_name": "django.contrib.auth.models.User", "line_number": 28, "usage_type": "name"}, {"api_name": "graphene.relay.Node", "line_number": 30, "usage_type": "attribute"}, {"api_name": "graphene.relay", "line_number": 30, "usage_type": "name"}, {"api_name": "graphene_django.DjangoObjectType", "line_number": 33, "usage_type": "name"}, {"api_name": "graphene.List", "line_number": 37, "usage_type": "call"}, {"api_name": "stocks.graphql.GStock", "line_number": 38, "usage_type": "argument"}, {"api_name": "graphene.Argument", "line_number": 38, "usage_type": "call"}, {"api_name": "graphene.NonNull", "line_number": 38, "usage_type": "call"}, {"api_name": "graphene.String", "line_number": 38, "usage_type": "argument"}, {"api_name": "graphene.Int", "line_number": 38, "usage_type": "argument"}, {"api_name": "graphene_django.filter.DjangoFilterConnectionField", "line_number": 39, "usage_type": "call"}, {"api_name": "stocks.graphql.GInvestmentBucket", "line_number": 40, "usage_type": "argument"}, {"api_name": "models.Profile", "line_number": 47, "usage_type": "name"}, {"api_name": "graphene.relay.Node", "line_number": 49, "usage_type": "attribute"}, {"api_name": "graphene.relay", "line_number": 49, "usage_type": "name"}, {"api_name": "stocks.models.Stock.objects.filter", "line_number": 56, "usage_type": "call"}, {"api_name": "stocks.models.Stock.objects", "line_number": 56, "usage_type": "attribute"}, {"api_name": "stocks.models.Stock", "line_number": 56, "usage_type": "name"}, {"api_name": "stocks.models.InvestmentBucket.objects.filter", "line_number": 66, "usage_type": "call"}, {"api_name": "stocks.models.InvestmentBucket.objects", "line_number": 66, "usage_type": "attribute"}, {"api_name": "stocks.models.InvestmentBucket", "line_number": 66, "usage_type": "name"}, {"api_name": "django.db.models.Q", "line_number": 66, "usage_type": "call"}, {"api_name": "graphene.ObjectType", "line_number": 78, "usage_type": "name"}, {"api_name": "graphene.String", "line_number": 82, "usage_type": "call"}, {"api_name": "graphene.Float", "line_number": 83, "usage_type": "call"}, {"api_name": "graphene_django.DjangoObjectType", "line_number": 86, "usage_type": "name"}, {"api_name": "graphene.Float", "line_number": 90, "usage_type": "call"}, {"api_name": "graphene.Float", "line_number": 91, "usage_type": "call"}, {"api_name": "graphene.String", "line_number": 92, "usage_type": "call"}, {"api_name": "graphene.Float", "line_number": 93, "usage_type": "call"}, {"api_name": "graphene.List", "line_number": 94, "usage_type": "call"}, {"api_name": "graphene.Argument", "line_number": 94, "usage_type": "call"}, {"api_name": "graphene.NonNull", "line_number": 94, "usage_type": "call"}, {"api_name": "graphene.String", "line_number": 94, "usage_type": "argument"}, {"api_name": "models.UserBank", "line_number": 100, "usage_type": "name"}, {"api_name": "graphene.relay.Node", "line_number": 102, "usage_type": "attribute"}, {"api_name": "graphene.relay", "line_number": 102, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 110, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 110, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 153, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 153, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 153, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 154, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 154, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 169, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 169, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 169, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 170, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 170, "usage_type": "attribute"}, {"api_name": "graphene.AbstractType", "line_number": 182, "usage_type": "name"}, {"api_name": "graphene.Field", "line_number": 186, "usage_type": "call"}, {"api_name": "graphene.Mutation", "line_number": 199, "usage_type": "name"}, {"api_name": "graphene.String", "line_number": 207, "usage_type": "call"}, {"api_name": "graphene.Field", "line_number": 208, "usage_type": "call"}, {"api_name": "trading.graphql.GTradingAccount", "line_number": 208, "usage_type": "name"}, {"api_name": "trading.models.TradingAccount", "line_number": 215, "usage_type": "call"}]} +{"seq_id": "396208503", "text": "#!/usr/bin/python\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport os\n\ntime = np.asarray([])\ntemperature = np.asarray([])\nmass_fraction = np.asarray([])\nCH2O = np.asarray([])\nCO = np.asarray([])\nHCN = np.asarray([])\nN2O = np.asarray([])\nNO = np.asarray([])\nNO2 = np.asarray([])\nH2O = np.asarray([])\nCO2 = np.asarray([])\n\nwith open('./output_TGA/mole_fractions_gas.txt','r') as File:\n lines = File.readlines()\n \n header = lines[0].split()\n \n iCH2O = header.index('CH2O')\n iCO = header.index('CO')\n iHCN = header.index('HCN')\n iN2O = header.index('N2O')\n iNO = header.index('NO')\n iNO2 = header.index('NO2')\n iH2O = header.index('H2O')\n iCO2 = header.index('CO2')\n \n new_lines = lines[1::3]\n #temp = lines[70::100]\n \n #new_lines.extend(temp)\n\n for line in new_lines:\n time = np.append(time,float(line.split()[0]))\n temperature = np.append(temperature,float(line.split()[1])-273.0)\n mass_fraction = np.append(mass_fraction,100.0*float(line.split()[2]))\n CH2O = np.append(CH2O,float(line.split()[iCH2O]))\n CO = np.append(CO,float(line.split()[iCO]))\n HCN = np.append(HCN,float(line.split()[iHCN]))\n N2O = np.append(N2O,float(line.split()[iN2O]))\n NO = np.append(NO,float(line.split()[iNO]))\n NO2 = np.append(NO2,float(line.split()[iNO2]))\n H2O = np.append(H2O,float(line.split()[iH2O]))\n CO2 = np.append(CO2,float(line.split()[iCO2]))\n\nHMXc = np.asarray([])\nTc = np.asarray([])\nwith open('./output_TGA/mass_fractions_liquid.txt','r') as File:\n lines = File.readlines()\n \n header = lines[0].split()\n \n iHMX = header.index('HMX')\n for line in lines[1:]:\n Tc = np.append(Tc,float(line.split()[1])-273.0)\n HMXc = np.append(HMXc,float(line.split()[iHMX])*100.0)\n\n#---------------------------------------------------------------------------------------------\nTexp = np.asarray([])\nm1 = np.asarray([])\nm2 = np.asarray([])\nm3 = np.asarray([])\nm4 = np.asarray([])\nm5 = np.asarray([])\nm6 = np.asarray([])\nm7 = np.asarray([])\nm8 = np.asarray([])\nm9 = np.asarray([])\n\ndsc1 = np.asarray([])\ndsc2 = np.asarray([])\ndsc3 = np.asarray([])\ndsc4 = np.asarray([])\ndsc5 = np.asarray([])\ndsc6 = np.asarray([])\ndsc7 = np.asarray([])\ndsc8 = np.asarray([])\ndsc9 = np.asarray([])\n\nstart = 1\nstep = 1\n\npath_mass_loss = './mass_loss_data'\n\nwith open(os.path.join(path_mass_loss,'HMX_5KPM_1198ug.txt'),'r') as File:\n lines = File.readlines()\n lines = lines[start::step]\n for line in lines:\n Texp = np.append(Texp,float(line.split()[0]))\n dsc1 = np.append(dsc1,float(line.split()[2]))\n m1 = np.append(m1,float(line.split()[3])-15.0)\n\nwith open(os.path.join(path_mass_loss,'HMX_5KPM_1180ug.txt'),'r') as File:\n lines = File.readlines()\n lines = lines[start::step]\n for line in lines:\n dsc2 = np.append(dsc2,float(line.split()[2]))\n m2 = np.append(m2,float(line.split()[3])-1.0)\n \nwith open(os.path.join(path_mass_loss,'HMX_5KPM_1104ug.txt'),'r') as File:\n lines = File.readlines()\n lines = lines[start::step]\n for line in lines:\n dsc3 = np.append(dsc3,float(line.split()[2]))\n m3 = np.append(m3,float(line.split()[3])-8.0)\n\nwith open(os.path.join(path_mass_loss,'HMX_10KPM_1050ug.txt'),'r') as File:\n lines = File.readlines()\n lines = lines[start::step]\n for line in lines:\n dsc4 = np.append(dsc4,float(line.split()[2])-2.5)\n m4 = np.append(m4,float(line.split()[3]))\n\nwith open(os.path.join(path_mass_loss,'HMX_10KPM_1024ug.txt'),'r') as File:\n lines = File.readlines()\n lines = lines[start::step]\n for line in lines:\n dsc5 = np.append(dsc5,float(line.split()[2])-2.2)\n m5 = np.append(m5,float(line.split()[3])-7.0)\n \nwith open(os.path.join(path_mass_loss,'HMX_10KPM_1094ug.txt'),'r') as File:\n lines = File.readlines()\n lines = lines[start::step]\n for line in lines:\n dsc6 = np.append(dsc6,float(line.split()[2])-2.1)\n m6 = np.append(m6,float(line.split()[3])-6.0)\n \n#with open(os.path.join(path_mass_loss,'HMX_15KPM_1176ug.txt'),'r') as File:\n# lines = File.readlines()\n# lines = lines[start::step]\n# for line in lines:\n# dsc7 = np.append(dsc7,float(line.split()[2]))\n# m7 = np.append(m7,float(line.split()[3])+3.0)\n\nwith open(os.path.join(path_mass_loss,'HMX_15KPM_1158ug.txt'),'r') as File:\n lines = File.readlines()\n lines = lines[start::step]\n for line in lines:\n dsc8 = np.append(dsc8,float(line.split()[2])-7.2)\n m8 = np.append(m8,float(line.split()[3])+1.0)\n \nwith open(os.path.join(path_mass_loss,'HMX_15KPM_1142ug.txt'),'r') as File:\n lines = File.readlines()\n lines = lines[start::step]\n for line in lines:\n dsc9 = np.append(dsc9,float(line.split()[2])-7.2)\n m9 = np.append(m9,float(line.split()[3])+3.0)\n\nm5_average = np.mean([m1,m2,m3],axis=0)\nm10_average = np.mean([m4,m5,m6],axis=0)\nm15_average = np.mean([m8,m9],axis=0)\n\ndsc5_average = np.mean([dsc1,dsc2,dsc3],axis=0)\ndsc10_average = np.mean([dsc4,dsc5,dsc6],axis=0)\ndsc15_average = np.mean([dsc8,dsc9],axis=0)\n#---------------------------------------------------------------------------------------------\n\nT_start = 260.0\nT_end = 300.0\n\nfig, ax1 = plt.subplots()\nax1.plot(temperature,100.0*CH2O,label='CH2O',marker=\">\")\nax1.plot(temperature,100.0*CO,label='CO',marker=\"p\")\nax1.plot(temperature,100.0*HCN,label='HCN',marker=\"<\")\nax1.plot(temperature,100.0*N2O,label='N2O',marker=\"D\")\nax1.plot(temperature,100.0*NO,label='NO',marker=\"^\")\nax1.plot(temperature,100.0*NO2,label='NO2',marker=\"o\")\nax1.plot(temperature,100.0*H2O,label='H2O',marker=\"s\")\nax1.plot(temperature,100.0*CO2,label='CO2',marker=\"d\")\n\nax1.ticklabel_format(style='sci',axis='y',scilimits=(-1,1))\nax1.set_xlim(T_start,T_end)\nax1.set_xlabel('Temperature($^o$C)')\nax1.set_ylabel('Mole fraction (%)')\nax1.legend(loc='upper center',bbox_to_anchor=(0.5,1.15),ncol=4)\n\n#ax2 = ax1.twinx()\n#ax2.plot(temperature,mass_fraction,label='TGA',color='black')\n\n#ax2.plot(Texp,m5_average,label='TGA',marker=\"*\",color='black')\n#ax2.plot(Texp,m10_average,label='TGA',marker=\"*\",color='black')\n#ax2.plot(Texp[::4],m15_average[::4],label='TGA',marker='*',color='black')\n\n#ax2.plot(Tc,HMXc,color='magenta')\n#ax2.legend()\n#ax2.set_ylabel('Condensed-phase mass (%)')\nplt.savefig('./output_TGA/Gases.pdf')\n\nfig, ax1 = plt.subplots()\nax1.plot(time,temperature)\nax1.set_ylabel('Temperature($^o$C)')\nax1.set_xlabel('time (s)')\nplt.savefig('./output_TGA/Temperature.pdf')\n", "sub_path": "plots_TGA.py", "file_name": "plots_TGA.py", "file_ext": "py", "file_size_in_byte": 6558, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.asarray", "line_number": 6, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 7, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 14, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 38, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 59, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 70, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 80, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 95, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 109, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 111, "usage_type": "call"}, {"api_name": "os.path", "line_number": 111, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 118, "usage_type": "call"}, {"api_name": "os.path", "line_number": 118, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 123, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 125, "usage_type": "call"}, {"api_name": "os.path", "line_number": 125, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 129, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 130, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 139, "usage_type": "call"}, {"api_name": "os.path", "line_number": 139, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 146, "usage_type": "call"}, {"api_name": "os.path", "line_number": 146, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 150, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 151, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 153, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 155, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 157, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 158, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 165, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 165, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 193, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 193, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}]} +{"seq_id": "478790357", "text": "import numpy as np\nfrom Cython.Build import cythonize\nfrom setuptools import setup, Extension\n\nstan_include_dirs = ['src/stanflow/cmdstan/stan/src/',\n 'src/stanflow/cmdstan/stan/lib/stan_math',\n 'src/stanflow/cmdstan/stan/lib/stan_math/lib/eigen_3.3.3',\n 'src/stanflow/cmdstan/stan/lib/stan_math/lib/boost_1.69.0',\n 'src/stanflow/cmdstan/stan/lib/stan_math/lib/sundials_3.1.0/include']\n\nstan_macros = [\n ('BOOST_DISABLE_ASSERTS', None),\n ('BOOST_NO_DECLTYPE', None),\n ('BOOST_RESULT_OF_USE_TR1', None),\n]\n\nextra_compile_args = [\n '-Os',\n '-ftemplate-depth-256',\n '-Wno-unused-function',\n '-Wno-uninitialized',\n '-std=c++14'\n]\n\nextensions = [\n Extension('stanflow.compute_effective_sample_size',\n ['src/stanflow/compute_effective_sample_size.pyx'],\n language='c++',\n define_macros=stan_macros,\n include_dirs=stan_include_dirs + [np.get_include()],\n extra_compile_args=extra_compile_args)\n]\n\nsetup(name='stanflow',\n version='0.1',\n description='Python tools for a Stan workflow using CmdStan.',\n url='http://github.com/roualdes/stanflow',\n author='Edward A. Roualdes',\n author_email='eroualdes@csuchico.edu',\n license='BSD (3-clause)',\n install_requires=[\n 'cython>=0.22,!=0.25.1',\n 'numpy>=1.7,<2.0',\n 'scipy>=0.19.1',\n ],\n ext_modules=cythonize(extensions),\n packages=['stanflow'],\n package_dir={'': 'src'},\n include_package_data=True,\n zip_safe=False)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1637, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "setuptools.Extension", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.get_include", "line_number": 30, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 34, "usage_type": "call"}, {"api_name": "Cython.Build.cythonize", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "199767257", "text": "# Copyright (c) Facebook, Inc. and its affiliates. (http://www.facebook.com)\n# -*- coding: utf-8 -*-\n\n\"\"\"\nmapillary.models.geojson\n~~~~~~~~~~~~~~~~~~~~~~~~\n\nThis module contains the class implementation for the geojson\n\nFor more information about the API, please check out\nhttps://www.mapillary.com/developer/api-documentation/.\n\n:copyright: (c) 2021 Facebook\n:license: MIT LICENSE\n\"\"\"\n\n# Package\nimport json\n\n# Local\n\n# # Exceptions\nfrom models.exceptions import InvalidOptionError\n\n\nclass Properties:\n \"\"\"Representation for the properties in a GeoJSON\n\n :param properties: The properties as the input\n :type properties: dict\n\n '''\n :raise InvalidOptionError: Raised when the geojson passed is the invalid type - not a dict\n '''\n\n :return: A class representation of the model\n :rtype: \n \"\"\"\n\n def __init__(self, *properties, **kwargs) -> None:\n \"\"\"Initializing Properties constructor\"\"\"\n\n # Validate that the geojson passed is indeed a dictionary\n if not isinstance(properties, dict):\n\n # Raise InvalidOptionError\n InvalidOptionError(\n # The parameter that caused the exception\n param=\"Properties.__init__.properties\",\n # The invalid value passed\n value=properties,\n # The keys that should be passed instead\n options=[\"dict\"],\n )\n\n for item in properties:\n for key in item:\n setattr(self, key, item[key])\n for key in kwargs:\n setattr(self, key, kwargs[key])\n\n def to_dict(self):\n \"\"\"Return the dictionary representation of the Properties\"\"\"\n\n attr_representation = [\n key for key in dir(self) if not key.startswith(\"__\") and key != \"to_dict\"\n ]\n\n return {key: getattr(self, key) for key in attr_representation}\n\n def __str__(self):\n \"\"\"Return the informal string representation of the Properties\"\"\"\n\n attr_representation = [\n key for key in dir(self) if not key.startswith(\"__\") and key != \"to_dict\"\n ]\n\n attr_key_value_pair = {key: getattr(self, key) for key in attr_representation}\n\n return f\"{attr_key_value_pair}\"\n\n def __repr__(self):\n \"\"\"Return the formal string representation of the Properties\"\"\"\n\n attr_representation = [\n key for key in dir(self) if not key.startswith(\"__\") and key != \"to_dict\"\n ]\n\n attr_key_value_pair = {key: getattr(self, key) for key in attr_representation}\n\n return f\"{attr_key_value_pair}\"\n\n\nclass Geometry:\n \"\"\"Representation for the geometry in a GeoJSON\n\n :param geometry: The geometry as the input\n :type geometry: dict\n\n '''\n :raise InvalidOptionError: Raised when the geometry passed is the invalid type - not a dict\n '''\n\n :return: A class representation of the model\n :rtype: \n \"\"\"\n\n def __init__(self, geometry) -> None:\n \"\"\"Initializing Geometry constructor\"\"\"\n\n # Validate that the geojson passed is indeed a dictionary\n if not isinstance(geometry, dict):\n\n # Raise InvalidOptionError\n InvalidOptionError(\n # The parameter that caused the exception\n param=\"Geometry.__init__.geometry\",\n # The invalid value passed\n value=geometry,\n # The keys that should be passed instead\n options=[\"dict\"],\n )\n\n # Setting the type of the selected geometry\n self.type: str = geometry[\"type\"]\n\n # Setting the coordinates of the geometry\n self.coordinates: list = geometry[\"coordinates\"]\n\n def to_dict(self):\n \"\"\"Return dictionary representation of the geometry\"\"\"\n\n return {\"type\": self.type, \"coordinates\": self.coordinates}\n\n def __str__(self):\n \"\"\"Return the informal string representation of the Geometry\"\"\"\n\n return f\"{{'type': {self.type}, 'coordinates': {self.coordinates}}}\"\n\n def __repr__(self):\n \"\"\"Return the formal string representation of the Geometry\"\"\"\n\n return f\"{{'type': {self.type}, 'coordinates': {self.coordinates}}}\"\n\n\nclass Feature:\n \"\"\"Representation for a feature in a feature list\n\n :param geojson: The GeoJSON as the input\n :type geojson: dict\n\n '''\n :raise InvalidOptionError: Raised when the geojson passed is the invalid type - not a dict\n '''\n\n :return: A class representation of the model\n :rtype: \n \"\"\"\n\n def __init__(self, feature: dict) -> None:\n \"\"\"Initializing Feature constructor\"\"\"\n\n # Validate that the geojson passed is indeed a dictionary\n if not isinstance(feature, dict):\n\n # If not, raise `InvalidOptionError`\n InvalidOptionError(\n # The parameter that caused the exception\n param=\"Feature.__init__.feature\",\n # The invalid value passed\n value=feature,\n # The type of value that should be passed instead\n options=[\"dict\"],\n )\n\n # Setting the type of the selected FeatureList\n self.type = \"Feature\"\n\n # Setting the `geometry` property\n self.geometry = Geometry(feature[\"geometry\"])\n\n # Setting the `properties` property\n self.properties = Properties(feature[\"properties\"])\n\n def to_dict(self) -> dict:\n \"\"\"Return the dictionary representation of the Feature\"\"\"\n\n return {\n \"type\": self.type,\n \"geometry\": self.geometry.to_dict(),\n \"properties\": self.properties.to_dict(),\n }\n\n def __str__(self) -> str:\n \"\"\"Return the informal string representation of the Feature\"\"\"\n\n return (\n f\"{{\"\n f\"'type': '{self.type}', \"\n f\"'geometry': {self.geometry}, \"\n f\"'properties': {self.properties}\"\n f\"}}\"\n )\n\n def __repr__(self) -> str:\n \"\"\"Return the formal string representation of the Feature\"\"\"\n\n return (\n f\"{{\"\n f\"'type': {self.type}, \"\n f\"'geometry': {self.geometry}, \"\n f\"'properties': {self.properties}\"\n f\"}}\"\n )\n\n\nclass GeoJSON:\n \"\"\"Representation for a geojson\n\n :param geojson: The GeoJSON as the input\n :type geojson: dict\n\n '''\n :raise InvalidOptionError: Raised when the geojson passed is the invalid type - not a dict\n '''\n\n :return: A class representation of the model\n :rtype: \n\n Usage::\n >>> import mapillary as mly\n >>> from models.geojson import GeoJSON\n >>> mly.set_access_token('MLY|XXX')\n >>> data = mly.get_image_close_to(longitude=31, latitude=31)\n >>> geojson = GeoJSON(geojson=data)\n >>> type(geojson)\n ... \n >>> type(geojson.type)\n ... \n >>> type(geojson.features)\n ... \n >>> type(geojson.features[0])\n ... \n >>> type(geojson.features[0].type)\n ... \n >>> type(geojson.features[0].geometry)\n ... \n >>> type(geojson.features[0].geometry.type)\n ... \n >>> type(geojson.features[0].geometry.coordinates)\n ... \n >>> type(geojson.features[0].properties)\n ... \n >>> type(geojson.features[0].properties.captured_at)\n ... \n >>> type(geojson.features[0].properties.is_pano)\n ... \n \"\"\"\n\n def __init__(self, geojson: dict) -> None:\n \"\"\"Initializing GeoJSON constructor\"\"\"\n\n # Validate that the geojson passed is indeed a dictionary\n if isinstance(geojson, dict):\n\n # The GeoJSON should only contain the keys of `type`, `features`, if not empty,\n # raise exception\n if [key for key in geojson.keys() if key not in [\"type\", \"features\"]] != []:\n\n # Raise InvalidOptionError\n InvalidOptionError(\n # The parameter that caused the exception\n param=\"GeoJSON.__init__.geojson\",\n # The invalid value passed\n value=geojson,\n # The keys that should be passed instead\n options=[\"type\", \"features\"],\n )\n\n # If the GeoJSON is not of type dictionary\n else:\n\n # Raise InvalidOptionError\n InvalidOptionError(\n # The parameter that caused the exception\n param=\"GeoJSON.__init__.geojson\",\n # The invalid value passed\n value=geojson,\n # The keys that should be passed instead\n options=[\"type\", \"features\"],\n )\n\n # Validate that the geojson passed is indeed a dictionary\n if not isinstance(geojson[\"features\"], list):\n\n # If not, raise InvalidOptionError\n InvalidOptionError(\n # The parameter that caused the exception\n param=\"FeatureList.__init__.geojson['features']\",\n # The invalid value passed\n value=geojson[\"features\"],\n # The type of the value that should be passed\n options=[\"list\"],\n )\n\n # Setting the type parameter\n self.type: str = geojson[\"type\"]\n\n # Setting the list of features\n self.features: list = (\n [Feature(feature=feature) for feature in geojson[\"features\"]]\n if (geojson[\"features\"] != []) or (geojson[\"features\"] is not None)\n else []\n )\n\n def append_features(self, features: list) -> None:\n\n for feature in features:\n self.append_feature(feature)\n\n def append_feature(self, feature_inputs: dict) -> None:\n\n feature = Feature(feature=feature_inputs)\n\n if feature not in self.features:\n self.features.append(feature)\n\n def encode(self):\n\n return json.dumps(self.__dict__)\n\n def to_dict(self):\n \"\"\"Return the dict format representation of the GeoJSON\"\"\"\n\n return {\n \"type\": self.type,\n \"features\": [feature.to_dict() for feature in self.features]\n if self.features != []\n else [],\n }\n\n def __str__(self):\n \"\"\"Return the informal string representation of the GeoJSON\"\"\"\n\n return f\"{{'type': '{self.type}', 'features': {self.features}}}\"\n\n def __repr__(self):\n \"\"\"Return the formal string representation of the GeoJSON\"\"\"\n\n return f\"{{'type': '{self.type}', 'features': {self.features}}}\"\n", "sub_path": "mapillary/models/geojson.py", "file_name": "geojson.py", "file_ext": "py", "file_size_in_byte": 10987, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "models.exceptions.InvalidOptionError", "line_number": 47, "usage_type": "call"}, {"api_name": "models.exceptions.InvalidOptionError", "line_number": 115, "usage_type": "call"}, {"api_name": "models.exceptions.InvalidOptionError", "line_number": 167, "usage_type": "call"}, {"api_name": "models.exceptions.InvalidOptionError", "line_number": 271, "usage_type": "call"}, {"api_name": "models.exceptions.InvalidOptionError", "line_number": 284, "usage_type": "call"}, {"api_name": "models.exceptions.InvalidOptionError", "line_number": 297, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 330, "usage_type": "call"}]} +{"seq_id": "228355387", "text": "import scrapy\nfrom SpiderNews.config import NEWS_TYPE,get_header\nfrom SpiderNews.items import NewsSpiderItem\nfrom scrapy import log\nfrom selenium import webdriver\n\n\nclass NetEaseSpider(scrapy.Spider):\n start_urls = ['http://news.baidu.com']\n name = 'baiduindex'\n allowed_domains = ['news.baidu.com']\n base_url = 'http://news.baidu.com/'\n\n def parse(self, response):\n yield scrapy.Request(self.base_url,self.parseNewsPage,headers=get_header())\n\n def parseList(self, response):\n urls = response.xpath(\"//a/@href\").extract()\n for url in urls:\n yield scrapy.Request(url, self.parseNews)\n\n def parseNewsPage(self, response):\n log.msg(type(response), level=log.WARNING)\n item = NewsSpiderItem()\n #首页热点新闻模块\n news_url = response.xpath(\"//li/a/@href\").extract()\n news_text = response.xpath(\"//li/a/text()\").extract()\n\n print(news_url)\n print(news_text)\n\n pane_news_url = response.xpath(\"//div[@id='pane-news']//li/a/@href\").extract()\n pane_news_text = response.xpath(\"//div[@id='pane-news']//li/a/text()\").extract()\n local_news_url = response.xpath(\"//div[@id='local_news']//li/a/@href\").extract()\n print(local_news_url)\n\n focusUrl = response.xpath(\"//div[@id='col_focus']//li/a/@href\").extract()\n focusText = response.xpath(\"//div[@id='col_focus']//li/a/text()\").extract()\n self.parse_instat_news(response)\n for i in range(1,len(focusUrl)+1):\n item['url'] = focusUrl[i]\n item['title'] = focusText[i]\n item['category'] = ''\n item ['secCategory'] = 'focus'\n yield item\n\n #print(focusText)\n #print (focusUrl)\n def parse_instat_news(self,response):\n attimeUrl = response.xpath(\"//div[@id='instant-news']//li/a/@href\").extract()\n attimeText = response.xpath(\"//div[@id='instant-news']//li/a/text()\").extract()\n item = NewsSpiderItem()\n for i in range(1, len(attimeUrl) + 1):\n item['url'] = attimeUrl[i]\n item['title'] = attimeText[i]\n item['category'] = ''\n item['secCategory'] = 'attime'\n yield item\n '''\n titles = response.xpath(\"//a/text()\").extract()\n url = response.xpath(\"//a/@href\").extract()\n for i in range(1,len(titles)):\n item['title'] = titles[i]\n item['url'] = url[i]\n item['category'] = 'ent'\n yield item'''\n #timee = data.xpath(\"//div[@class='post_time_source']/text()\").extract()\n #title = data.xpath(\"//h1/text()\").extract()\n #content = data.xpath(\"//div[@class='post_text']/p/text()\").extract()\n", "sub_path": "SpiderNews/spiders/BaiduNewsIndex.py", "file_name": "BaiduNewsIndex.py", "file_ext": "py", "file_size_in_byte": 2770, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "scrapy.Spider", "line_number": 8, "usage_type": "attribute"}, {"api_name": "scrapy.Request", "line_number": 15, "usage_type": "call"}, {"api_name": "SpiderNews.config.get_header", "line_number": 15, "usage_type": "call"}, {"api_name": "scrapy.Request", "line_number": 20, "usage_type": "call"}, {"api_name": "scrapy.log.msg", "line_number": 23, "usage_type": "call"}, {"api_name": "scrapy.log", "line_number": 23, "usage_type": "name"}, {"api_name": "scrapy.log.WARNING", "line_number": 23, "usage_type": "attribute"}, {"api_name": "SpiderNews.items.NewsSpiderItem", "line_number": 24, "usage_type": "call"}, {"api_name": "SpiderNews.items.NewsSpiderItem", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "456878053", "text": "from __future__ import print_function\nimport torch\nimport os\nimport numpy as np\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torchvision\nimport torchvision.transforms as transforms\nimport matplotlib.pyplot as plt\nfrom matplotlib.backends.backend_pdf import PdfPages\n\nclass CifarAlexNet(nn.Module):\n def __init__(self):\n super(CifarAlexNet, self).__init__()\n self.conv1 = nn.Conv2d(3, 32, 3, padding = 1)\n self.pool = nn.MaxPool2d(2, 2)\n self.conv2 = nn.Conv2d(32, 64, 3, padding = 1)\n self.conv3 = nn.Conv2d(64, 96, 3, padding = 1)\n self.conv4 = nn.Conv2d(96, 96, 3, padding = 1)\n self.conv5 = nn.Conv2d(96, 128, 3, padding= 1)\n self.fc1 = nn.Linear(128 * 4 * 4, 1024)\n self.fc2 = nn.Linear(1024, 1024)\n self.fc3 = nn.Linear(1024, 10)\n self.dropout = nn.Dropout(p = 0.5)\n\n\n def forward(self, x):\n x = self.pool(F.relu(self.conv1(x)))\n x = self.pool(F.relu(self.conv2(x)))\n x = F.relu(self.conv3(x))\n x = F.relu(self.conv4(x))\n # y = x # that is reconstruct_v2\n x = self.pool(F.relu(self.conv5(x)))\n y = x # that is reconstruct\n x = self.dropout(x.view(-1, 128 * 4 * 4))\n x = self.dropout(F.relu(self.fc1(x)))\n x = self.dropout(F.relu(self.fc2(x)))\n x = self.fc3(x)\n return x, y\n\n# Train the alexnet(simplified for cifar)\nif __name__ == \"__main__\":\n keepOn = False\n transform = transforms.Compose(\n\t [transforms.ToTensor(),\n\t transforms.Normalize((0.5, 0.5, 0.5),(0.5, 0.5, 0.5))])\n\t\n trainset = torchvision.datasets.CIFAR10(root = './data', train = True, transform = transform)\n trainloader = torch.utils.data.DataLoader(trainset, batch_size = 128, shuffle = True, num_workers = 0)\n\n testset = torchvision.datasets.CIFAR10(root='./data', train=False, transform=transform)\n testloader = torch.utils.data.DataLoader(testset, batch_size = 128, shuffle=False, num_workers=0)\n\n classes = ('plane', 'car', 'bird', 'cat',\n 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n device = torch.device(\"cuda:0\")\n net = CifarAlexNet()\n net.to(device)\n crit = nn.CrossEntropyLoss()\n learningRate = [0.005 for i in range(40)]\n learningRate.extend(0.0005 for i in range(20))\n learningRate.extend(0.0001 for i in range(10))\n # Record the performance\n train_loss = []\n train_accu = []\n test_loss = []\n test_accu = []\n x_axis = []\n start = 0\n if keepOn:\n res = os.listdir(\"./data/exp\")\n start = len(res)\n net = torch.load(\"./data/exp/alex\"+str(start)+\".pkl\")\n for epoch in range(start,70):\n x_axis.append(epoch + 1)\n optimizer = optim.SGD(net.parameters(), lr = learningRate[epoch], momentum = 0.9)\n correct = 0\n total = 0\n accu_loss = 0\n batchNum = 0\n\n # Train\n for i, data in enumerate(trainloader, 0):\n batchNum += 1\n inputs, labels = data\n inputs, labels = inputs.to(device), labels.to(device)\n optimizer.zero_grad()\n\n outputs, features = net(inputs)\n # Update parameters\n loss = crit(outputs, labels)\n loss.backward()\n optimizer.step()\n # Calculate the performance\n outputs, predicted = torch.max(outputs.data, 1)\n correct += (predicted == labels).sum().item()\n accu_loss += loss.item()\n total += labels.size(0)\n accuracy = correct / total\n print('[train] epoch: %2d, batch: %3d, loss: %.3f, accuracy: %.3f'\\\n % (epoch + 1, i + 1, accu_loss / (i+1), accuracy))\n train_loss.append(accu_loss / batchNum)\n train_accu.append(correct / total)\n\n # Test\n correct = 0\n total = 0\n accu_loss = 0\n batchNum = 0\n with torch.no_grad():\n for i, data in enumerate(testloader, 0):\n batchNum += 1\n inputs, labels = data\n inputs, labels = inputs.to(device), labels.to(device)\n outputs = net(inputs)\n loss = crit(outputs, labels)\n # Calculate the performance\n outputs, predicted = torch.max(outputs.data, 1)\n correct += (predicted == labels).sum().item()\n accu_loss += loss.item()\n total += labels.size(0)\n accuracy = correct / total\n print('[test] epoch: %2d, batch: %3d, loss: %.3f, accuracy: %.3f'\\\n % (epoch + 1, i + 1, accu_loss / (i+1), accuracy))\n test_loss.append(accu_loss / batchNum)\n test_accu.append(correct / total)\n\n #draw the figures\n pdf = PdfPages(\"alex_figure.pdf\")\n plt.figure(1)\n plt.subplot(121)\n plt.plot(x_axis, train_accu, x_axis, test_accu)\n plt.xlabel(\"epoch\")\n plt.ylabel(\"accuracy\")\n\n plt.subplot(122)\n plt.plot(x_axis, train_loss, x_axis, test_loss)\n plt.xlabel(\"epoch\")\n plt.ylabel(\"loss\")\n pdf.savefig()\n plt.close()\n pdf.close()\n\n # Save the net\n net_name = \"./data/exp/alex\" + str(epoch+1) + \".pkl\"\n torch.save(net, net_name)\n\n print('over')", "sub_path": "cifar_alex.py", "file_name": "cifar_alex.py", "file_ext": "py", "file_size_in_byte": 5309, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.nn.Module", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 18, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 18, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 19, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 38, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 38, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 45, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 45, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 46, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 46, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 47, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 47, "usage_type": "name"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 49, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 50, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 50, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.CIFAR10", "line_number": 52, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 53, "usage_type": "attribute"}, {"api_name": "torch.device", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.optim.SGD", "line_number": 77, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 77, "usage_type": "name"}, {"api_name": "torch.max", "line_number": 96, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 111, "usage_type": "call"}, {"api_name": "torch.max", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_pdf.PdfPages", "line_number": 130, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 131, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 131, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 132, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 132, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 135, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 135, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 137, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 139, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 139, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 140, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 140, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.close", "line_number": 142, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 142, "usage_type": "name"}, {"api_name": "torch.save", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "490184879", "text": "#!/usr/bin/env python3\nimport os\nimport sys\nimport time\nimport copy\nimport json\nimport zmqutils\nimport logging as log\nimport threading\nfrom centroidtracker import CentroidTracker\nfrom iothub_client import IoTHubClient, IoTHubTransportProvider\nfrom iothub_client import IoTHubMessage, IoTHubError\n\n\nlog.basicConfig(stream=sys.stdout, level=log.DEBUG)\n\n# Get value from env variable\nCONNECTION_STRING = os.getenv(\"AZ_CONNECTION_STRING\", None)\n# Using the MQTT protocol.\nPROTOCOL = IoTHubTransportProvider.MQTT\n# ENV\nvideo_config_path = os.getenv(\"VIDEO_CONFIG_PATH\", \"\")\ncam_id_path = os.getenv(\"MAC_ADDRESS_PATH\", \"\")\nzmq_address = os.getenv(\"ZMQ_ADDRESS\", \"tcp://localhost:5960\")\nzmq_video_address = os.getenv(\"ZMQ_VIDEO_ADDRESS_SUB\", \"tcp://*:5561\")\nsending_interval = int(os.getenv(\"IOTHUB_SENDING_INTERVAL\", \"5\"))\nmax_disappeared = os.getenv(\"MAX_DISAPPEARED\", \"50\")\nthread_sending_interval_video_address = int(\n os.getenv(\"THREAD_SENDING_INTERVAL_VIDEO_ADDRESS\", \"20\")\n)\n\n# Define global variable\ntimestamps = None\ntimestamps_is_checked = False\n\n\ndef send_confirmation_callback(message, result, user_context):\n print(\"IoT Hub responded to message with status: %s\" % (result))\n\n\ndef iothub_client_init():\n # Create an IoT Hub client\n client = IoTHubClient(CONNECTION_STRING, PROTOCOL)\n return client\n\n\ndef iothub_client_run(json_object):\n\n try:\n # Build the message with detection result values.\n message = IoTHubMessage(json_object)\n\n # Send the message.\n client.send_event_async(message, send_confirmation_callback, None)\n\n except IoTHubError as iothub_error:\n log.error(\"Unexpected error %s from IoTHub\" % iothub_error)\n return\n except KeyboardInterrupt:\n log.error(\"IoTHubClient sample stopped\")\n\n\n# ZMQ Publisher\npub, pub_ctx = zmqutils.pub(zmq_video_address)\n# ZMQ Subscriber\nsub, ctx = zmqutils.sub(zmq_address)\n\n# Init a IoT Hub client\nclient = iothub_client_init()\n\n\ndef get_video_source_from_config_file():\n data = {\"video_address\": \"\"}\n try:\n video_address_from_config_file = (\n open(video_config_path).readline().split(\"=\")[1].strip()\n )\n\n except BaseException:\n # Get defaul value\n video_address_from_config_file = \"tcp://video-manager:5562\"\n log.info(\n \"Error config file!!! Using this default value is {}\".format(\n video_address_from_config_file\n )\n )\n\n data[\"video_address\"] = video_address_from_config_file\n pub.send_json(data, flags=0)\n log.info(\n \"Just sent the video source address : {}\".format(\n video_address_from_config_file\n )\n )\n\n\ndef get_cam_id_from_config_file():\n try:\n cam_id = open(cam_id_path).readline().split(\"=\")[1].strip()\n\n except BaseException:\n # Get defaul value\n cam_id = \"00:00:00:00:00:00\"\n log.info(\n \"Error config file!!! Using this default value is {}\".format(\n cam_id\n )\n )\n return cam_id\n\n\n# Get video source at first\nget_video_source_from_config_file()\n\n# Get mac address from config file\nmac_address = get_cam_id_from_config_file()\n\n# Tracker\nct = CentroidTracker(float(max_disappeared))\n\n\ndef check_point_in_polygon(face_geometry, person_geometry):\n center_face = (\n face_geometry[0] + (face_geometry[2] / 2),\n face_geometry[1] + (face_geometry[3] / 2),\n )\n if (\n center_face[0] > person_geometry[0] and\n center_face[0] < person_geometry[2] and\n center_face[1] > person_geometry[1] and\n center_face[1] < person_geometry[3]\n ):\n return True\n else:\n return False\n\n\ndef thread_change_video_address():\n while True:\n get_video_source_from_config_file()\n time.sleep(thread_sending_interval_video_address)\n\n\ndef thread_send_message_to_hub():\n global timestamps\n global timestamps_is_checked\n global ct\n while True:\n # Define send data format\n log.info(\"Value of persons are {}\".format(ct.persons))\n data = {\"timestamp\": timestamps, \"frames\": [], \"camID\": mac_address}\n\n trackID_tmp = []\n\n for person in ct.persons.values():\n if person[\"is_sent\"] is True:\n continue\n else:\n element_box = {\n \"trackID\": None,\n \"timestamp\": None,\n \"recognition\": {},\n }\n element_box[\"trackID\"] = person[\"trackID\"]\n element_box[\"timestamp\"] = person[\"timestamp\"]\n element_box[\"recognition\"] = person[\"recognition\"]\n data[\"frames\"].append(element_box)\n\n person[\"is_sent\"] = True\n\n ct.update_persons(person[\"trackID\"], person)\n if person[\"is_sent\"] and person[\"is_exceed_threshold\"]:\n trackID_tmp.append(person[\"trackID\"])\n # Remove the trackID info that was sent to IoT Hub \\\n # and exceed threshold\n for trackID in trackID_tmp:\n ct.deregister_persons(trackID)\n\n if data[\"frames\"] is not None and len(data[\"frames\"]) > 0:\n data[\"frames\"] = sorted(data[\"frames\"], key=lambda x: x[\"trackID\"])\n timestamps_is_checked = False\n log.info(\"Just sent to Hub : {}\".format(json.dumps(data)))\n iothub_client_run(json.dumps(data))\n else:\n log.info(\"No data for sending to IoT Hub\")\n\n time.sleep(sending_interval)\n\n\n# Running a thread for sending video address\nthread_for_pub = threading.Thread(target=thread_change_video_address)\nthread_for_pub.start()\n\n# Running a thread for sending msg to hub\nthread_for_send_msg = threading.Thread(target=thread_send_message_to_hub)\nthread_for_send_msg.start()\n\nbase_box = {\n \"trackID\": 0,\n \"timestamp\": 1562241186.4627721,\n \"recognition\": {},\n \"is_sent\": False,\n \"is_fulled\": False,\n \"is_exceed_threshold\": False,\n}\n\nwhile True:\n log.info(\"Got a data from ncs2-manager!!!\")\n json_data = sub.recv_json()\n person_rect = []\n # Extract frame data\n frame = json_data[\"frame\"][0]\n timestamp = frame[\"timestamp\"]\n\n if not len(frame[\"obj_boxes\"]):\n log.info(\"Data with no person. Skip to next frame\")\n # Update track ID\n objects = ct.update(person_rect)\n continue\n\n people = [\n x for x in frame[\"obj_boxes\"] if x[\"detection\"][\"label\"] == \"person\"\n ]\n faces = [\n x for x in frame[\"obj_boxes\"] if x[\"detection\"][\"label\"] == \"face\"\n ]\n\n for person in people:\n ymin, xmin, ymax, xmax = person[\"detection\"][\"box_geometry\"]\n person_rect.append((xmin, ymin, xmax, ymax))\n\n # Update track ID\n objects = ct.update(person_rect)\n\n for person in people:\n ymin, xmin, ymax, xmax = person[\"detection\"][\"box_geometry\"]\n cX = int((xmin + xmax) / 2.0)\n cY = int((ymin + ymax) / 2.0)\n\n # Create result object\n result_object = copy.deepcopy(base_box)\n result_object[\"timestamp\"] = timestamp\n\n for face in faces:\n ymin_face, xmin_face, ymax_face, xmax_face = face[\"detection\"][\n \"box_geometry\"\n ]\n if (\n check_point_in_polygon(\n (ymin_face, xmin_face, ymax_face, xmax_face),\n (ymin, xmin, ymax, xmax),\n ) and face[\"recognition\"]\n ):\n result_object[\"recognition\"] = {\n \"age_gender\": face[\"recognition\"][\"age_gender\"]\n }\n break\n else:\n log.info(\"Do not mapping face & person !!!\")\n\n for (objectID, centroid) in objects.items():\n if (cX, cY) == (centroid[0], centroid[1]):\n result_object[\"trackID\"] = objectID\n\n if objectID not in ct.persons:\n if not timestamps_is_checked:\n timestamps = timestamp\n\n if result_object[\"recognition\"]:\n result_object[\"is_fulled\"] = True\n ct.update_persons(objectID, result_object)\n\n else:\n current_tracker = ct.persons[objectID]\n\n if (\n current_tracker[\"is_fulled\"] is not True and\n result_object[\"recognition\"]\n ):\n result_object[\"is_fulled\"] = True\n result_object[\"is_sent\"] = False\n log.info(\n \"Just update the track ID \\\n information: {}\".format(\n result_object\n )\n )\n timestamps = timestamp\n timestamps_is_checked = True\n ct.update_persons(objectID, result_object)\n", "sub_path": "src/inference-app/docker-compose/inference-engine/run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 8920, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 15, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 15, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.getenv", "line_number": 18, "usage_type": "call"}, {"api_name": "iothub_client.IoTHubTransportProvider.MQTT", "line_number": 20, "usage_type": "attribute"}, {"api_name": "iothub_client.IoTHubTransportProvider", "line_number": 20, "usage_type": "name"}, {"api_name": "os.getenv", "line_number": 22, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 23, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 24, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 25, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 26, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 27, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 29, "usage_type": "call"}, {"api_name": "iothub_client.IoTHubClient", "line_number": 43, "usage_type": "call"}, {"api_name": "iothub_client.IoTHubMessage", "line_number": 51, "usage_type": "call"}, {"api_name": "iothub_client.IoTHubError", "line_number": 56, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 57, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 60, "usage_type": "call"}, {"api_name": "zmqutils.pub", "line_number": 64, "usage_type": "call"}, {"api_name": "zmqutils.sub", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 82, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 90, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 104, "usage_type": "call"}, {"api_name": "centroidtracker.CentroidTracker", "line_number": 119, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 141, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 150, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 182, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 182, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 183, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 185, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 187, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 191, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 195, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 208, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 216, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 241, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 259, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 282, "usage_type": "call"}]} +{"seq_id": "12377349", "text": "import os\nfrom twilio.rest import Client\n\nos.environ['TWILIO_ACCOUNT_SID'] = 'ACcd99094db783d1141bf1de6a0f4c1e5a'\nos.environ['TWILIO_AUTH_TOKEN'] = 'ad848429edfbe548faaaafc94f7f86ad'\n\nclient = Client()\n\nfrom_whatsapp_number=\"whatsapp:+14155238886\"\nto_whatsapp_number=\"whatsapp:+821090292356\"\n\notp = 'asdfas'\nrequest.user.profile.otp = otp\nuser.profile.save()\n\nclient.messages.create(body='Verify your phone number by submitting the OTP: ' + otp,\n from_=from_whatsapp_number,\n to=to_whatsapp_number)\n", "sub_path": "whatsapp.py", "file_name": "whatsapp.py", "file_ext": "py", "file_size_in_byte": 543, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ", "line_number": 4, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 5, "usage_type": "attribute"}, {"api_name": "twilio.rest.Client", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "139790325", "text": "# -*- coding: utf-8 -*-\n\n# dcf\n# ---\n# A Python library for generating discounted cashflows.\n# \n# Author: sonntagsgesicht, based on a fork of Deutsche Postbank [pbrisk]\n# Version: 0.3, copyright Wednesday, 18 September 2019\n# Website: https://github.com/sonntagsgesicht/dcf\n# License: Apache License 2.0 (see LICENSE file)\n\n\nfrom .interpolation import constant, linear\nfrom .interpolationscheme import dyn_scheme\nfrom .compounding import continuous_compounding, continuous_rate\n\n\ndef act_36525(start, end):\n if hasattr(start, 'diff_in_days'):\n # duck typing businessdate.BusinessDate.diff_in_days\n d = start.diff_in_days(end)\n else:\n d = end - start\n if hasattr(d, 'days'):\n # assume datetime.date or finance.BusinessDate (else days as float)\n d = d.days\n return float(d) / 365.25\n\n\nclass Curve(object):\n _interpolation = dyn_scheme(constant, linear, constant)\n\n def __init__(self, domain=(), data=(), interpolation=None):\n r\"\"\"\n Curve object to build function\n\n :param list(float) domain: source values\n :param list(float) data: target values\n :param function interpolation: interpolation function on x_list (optional), default is taken from class member _interpolation\n\n Curve object to build function :math:`f:R \\rightarrow R, x \\mapsto y`\n from finite point vectors :math:`x` and :math:`y`\n using piecewise various interpolation functions.\n \"\"\"\n # sort data by domain values\n if not len(domain) == len(data):\n raise ValueError('%s requires equal length input for domain and data' % self.__class__.__name__)\n\n if domain:\n domain, data = map(list,zip(*sorted(zip(*(domain, data)))))\n\n if interpolation is None:\n interpolation = self.__class__._interpolation\n\n self._scheme = interpolation\n self._func = interpolation(domain, data)\n self._domain = domain\n\n @property\n def interpolation(self):\n return self._scheme\n\n @property\n def domain(self):\n return self._domain\n\n def __call__(self, x):\n if isinstance(x, (tuple, list)):\n return [self(xx) for xx in x]\n return self._func(x)\n\n def __add__(self, other):\n x_list = sorted(set(self.domain + other.domain))\n y_list = [self(x) + other(x) for x in x_list]\n return self.__class__(x_list, y_list, self.interpolation)\n\n def __sub__(self, other):\n x_list = sorted(set(self.domain + other.domain))\n y_list = [self(x) - other(x) for x in x_list]\n return self.__class__(x_list, y_list, self.interpolation)\n\n def __mul__(self, other):\n x_list = sorted(set(self.domain + other.domain))\n y_list = [self(x) * other(x) for x in x_list]\n return self.__class__(x_list, y_list, self.interpolation)\n\n def __truediv__(self, other):\n return self.__div__(other)\n\n def __div__(self, other):\n x_list = sorted(set(self.domain + other.domain))\n if any(not other(x) for x in x_list):\n raise ZeroDivisionError(\"Division with %s requires on zero values.\" % other.__class__.__name__)\n y_list = [self(x) / other(x) for x in x_list]\n return self.__class__(x_list, y_list, self.interpolation)\n\n def __str__(self):\n return str([z for z in zip(self.domain, self(self.domain))])\n\n def __repr__(self):\n return self.__class__.__name__ + '(' + self.__str__() + ')'\n\n def shifted(self, delta=0.0):\n if delta:\n x_list = [x + delta for x in self.domain]\n else:\n x_list = self.domain\n y_list = self(self.domain)\n return self.__class__(x_list, y_list, self.interpolation)\n\n\nclass DateCurve(Curve):\n\n @staticmethod\n def _default_day_count(start, end):\n if hasattr(start, 'diff_in_days'):\n # duck typing businessdate.BusinessDate.diff_in_days\n d = start.diff_in_days(end)\n else:\n d = end - start\n if hasattr(d, 'days'):\n # assume datetime.date or finance.BusinessDate (else days as float)\n d = d.days\n return float(d) / 365.25\n\n _time_shift = '1d'\n\n def __init__(self, domain=(), data=(), interpolation=None, origin=None, day_count=None):\n self._origin = domain[0] if origin is None and domain else origin\n self._day_count = self._default_day_count if day_count is None else day_count\n flt_domain = [self._day_count(self._origin, x) for x in domain]\n super(DateCurve, self).__init__(flt_domain, data, interpolation)\n self._domain = domain\n\n @property\n def domain(self):\n \"\"\" domain of curve as list of dates where curve values are given \"\"\"\n return self._domain\n\n @property\n def origin(self):\n \"\"\" date of origin (date zero) \"\"\"\n return self._origin\n\n def __call__(self, x):\n if isinstance(x, (list, tuple)):\n return [self(xx) for xx in x]\n return super(DateCurve, self).__call__(self.day_count(self.origin, x))\n\n def __add__(self, other):\n new = super(DateCurve, self).__add__(other.shifted(self.origin - other.origin))\n self.__class__(new.domain, new(new.domain), new.interpolation, self.origin, self._day_count)\n return new\n\n def __sub__(self, other):\n new = super(DateCurve, self).__sub__(other.shifted(self.origin - other.origin))\n self.__class__(new.domain, new(new.domain), new.interpolation, self.origin, self._day_count)\n return new\n\n def __mul__(self, other):\n new = super(DateCurve, self).__mul__(other.shifted(self.origin - other.origin))\n self.__class__(new.domain, new(new.domain), new.interpolation, self.origin, self._day_count)\n return new\n\n def __div__(self, other):\n new = super(DateCurve, self).__div__(other.shifted(self.origin - other.origin))\n new.origin = self.origin\n return new\n\n def day_count(self, start, end):\n return self._day_count(start, end)\n\n def to_curve(self, origin=None):\n origin = self.origin if origin is None else origin\n x_list = [self.day_count(origin, x) for x in self.domain]\n y_list = self(self.domain)\n return Curve(x_list, y_list, self.interpolation)\n\n def integrate(self, start, stop):\n \"\"\" integrates curve and returns results as annualized rates \"\"\"\n # try use result, error = scipy.integrate(self, start, stop)\n try:\n from scipy.integrate import quad\n #raise ImportError()\n s = self.day_count(self.origin, start)\n e = self.day_count(self.origin, stop)\n f = super(DateCurve, self).__call__\n value, error = quad(f, s, e)\n except ImportError:\n value = 0.0\n step = self.__class__._time_shift\n current = start\n while current + step < stop:\n value += self(current) * self.day_count(current, current + step)\n current += step\n value += self(current) * self.day_count(current, stop)\n result = value / self.day_count(start, stop)\n return result\n\n def derivative(self, start):\n # todo use scipy.misc.derivative(self, start, self.__class__._time_shift)\n try:\n from scipy.misc import derivative\n s = self.day_count(self.origin, start)\n dx = self.day_count(start, start + self.__class__._time_shift)\n f = super(DateCurve, self).__call__\n result = derivative(f, s, dx)\n except ImportError:\n stop = start + self.__class__._time_shift\n value = self(stop) - self(start)\n result = value / self.day_count(start, stop)\n return result\n\n\nclass RateCurve(DateCurve):\n _time_shift = '1D'\n _forward_tenor = '3M'\n\n @staticmethod\n def get_storage_type(curve, x):\n raise NotImplementedError\n\n def cast(self, cast_type, **kwargs):\n new = cast_type(kwargs.get('domain', self.domain),\n [cast_type.get_storage_type(self, x) for x in kwargs.get('domain', self.domain)],\n kwargs.get('interpolation', None),\n kwargs.get('origin', self.origin),\n kwargs.get('day_count', self.day_count),\n kwargs.get('forward_tenor', self.forward_tenor))\n return new\n\n def __init__(self, domain=(), data=(), interpolation=None, origin=None, day_count=None, forward_tenor=None):\n super(RateCurve, self).__init__(domain, data, interpolation, origin, day_count)\n self.forward_tenor = self.__class__._forward_tenor if forward_tenor is None else forward_tenor\n\n def __add__(self, other):\n casted = other.cast(self.__class__)\n new = super(RateCurve, self).__add__(casted)\n new.forward_tenor = self.forward_tenor\n return new\n\n def __sub__(self, other):\n casted = other.cast(self.__class__)\n new = super(RateCurve, self).__sub__(casted)\n new.forward_tenor = self.forward_tenor\n return new\n\n def __mul__(self, other):\n casted = other.cast(self.__class__)\n new = super(RateCurve, self).__mul__(casted)\n new.forward_tenor = self.forward_tenor\n return new\n\n def __div__(self, other):\n casted = other.cast(self.__class__)\n new = super(RateCurve, self).__div__(casted)\n new.forward_tenor = self.forward_tenor\n return new\n\n def _get_compounding_factor(self, start, stop):\n if start == stop:\n return 1.\n ir = self._get_compounding_rate(start, stop)\n t = self.day_count(start, stop)\n return continuous_compounding(ir, t)\n\n def _get_compounding_rate(self, start, stop):\n if start == stop:\n return self._get_compounding_rate(start, start + self.__class__._time_shift)\n df = self._get_compounding_factor(start, stop)\n t = self.day_count(start, stop)\n return continuous_rate(df, t)\n", "sub_path": "dcf/curve.py", "file_name": "curve.py", "file_ext": "py", "file_size_in_byte": 10079, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "interpolationscheme.dyn_scheme", "line_number": 31, "usage_type": "call"}, {"api_name": "interpolation.constant", "line_number": 31, "usage_type": "argument"}, {"api_name": "interpolation.linear", "line_number": 31, "usage_type": "argument"}, {"api_name": "scipy.integrate.quad", "line_number": 188, "usage_type": "call"}, {"api_name": "scipy.misc.derivative", "line_number": 207, "usage_type": "call"}, {"api_name": "compounding.continuous_compounding", "line_number": 265, "usage_type": "call"}, {"api_name": "compounding.continuous_rate", "line_number": 272, "usage_type": "call"}]} +{"seq_id": "439118152", "text": "# coding=utf-8\nfrom django.http import HttpRequest, HttpResponse\nfrom django.shortcuts import render, redirect\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.contrib.auth import authenticate, login as l_in, logout as l_out\nfrom django.http import HttpResponseRedirect\nfrom django.http.response import Http404, JsonResponse\nfrom django.contrib.auth.decorators import login_required\nfrom django.views import View\nimport json\n\nfrom ask_app.forms import *\nfrom .models import *\n\n\nclass LoadView(View):\n def get(self, request):\n start = int(request.GET.get('start'))\n recent_questions = Question.objects.recent_questions()\n questions_for_send = []\n result = recent_questions[start:start + 4]\n for questn in result:\n questions_for_send.append(\n {\n 'text': questn.text,\n 'title': questn.title,\n 'author': questn.author.username,\n 'id': questn.id,\n 'avatar': questn.author.avatar.url,\n # 'tags': list(questn.tags),\n 'number_answers': questn.number_answers,\n 'likes': questn.likes\n }\n )\n return HttpResponse(json.dumps(questions_for_send), content_type='application/json')\n\n\nclass AddAnswerView(View):\n def post(self, request):\n try:\n text = str(request.POST.get('text'))\n userid = int(request.POST.get('user'))\n questionid = int(request.POST.get('question'))\n except:\n return JsonResponse(dict(error='bad data'))\n\n if text:\n new_answer = Answer(author=UserProfile.objects.get(id=userid),\n question=Question.objects.get(id=questionid),\n text=text)\n new_answer.save()\n answer_for_send = []\n answer_for_send.append(\n {\n 'text': new_answer.text,\n 'createdate': str(new_answer.create_date.year) + \".\" + str(new_answer.create_date.month) + \".\" +\n str(new_answer.create_date.day),\n 'id': new_answer.id\n # 'text': \"super text for answer\",\n # 'createdate': \"11.23.2017\",\n # 'id': \"3\"\n }\n )\n return HttpResponse(json.dumps(answer_for_send), content_type='application/json')\n else:\n return JsonResponse(dict(error='bad length of text'))\n\n\nclass IndexView(View):\n def get(self, request):\n context = {}\n context = _get_user_context(request, context)\n\n questions = Question.objects.recent_questions()\n questions_for_render = questions[0:20]\n context['objects'] = questions_for_render\n context['enable_modal_ask'] = True\n form = AskForm()\n\n context['form'] = form\n return render(request, 'index.html', context)\n\n def post(self, request):\n context = {}\n context = _get_user_context(request, context)\n\n questions = Question.objects.recent_questions()\n questions_for_render = questions[0:20]\n context['objects'] = questions_for_render\n context['enable_modal_ask'] = True\n form = AskForm(request.POST, UserProfile.objects.get(id=request.user.id))\n if form.is_valid():\n new_question = form.save()\n return redirect('question', new_question.id)\n\n context['form'] = form\n return render(request, 'index.html', context)\n\nclass TagView(View):\n def get(self, request, name):\n context = {}\n context = _get_user_context(request, context)\n questions = Question.objects.questions_by_tag(name)\n questions_for_render = questions[0:20]\n context['objects'] = questions_for_render\n # context['enable_modal_ask'] = True\n # if request.method == 'POST':\n # form = AskForm(request.POST, UserProfile.objects.get(id=request.user.id))\n # if form.is_valid():\n # new_question = form.save()\n # return redirect('question', new_question.id)\n # else:\n # form = AskForm()\n # context['form'] = form\n return render(request, 'tag.html', context)\n\nclass HotView(View):\n def get(self, request):\n context = {}\n context = _get_user_context(request, context)\n questions = Question.objects.questions_with_high_rating()\n questions_for_render = paginate(questions, request)\n context['objects'] = questions_for_render\n return render(request, 'index.html', context)\n\n\nclass QuestionView(View):\n def get(self, request, _id):\n context = {}\n context = _get_user_context(request, context)\n\n try:\n main_question = Question.objects.get_with_tags(_id)\n except Question.DoesNotExist:\n raise Http404()\n\n answers = Answer.objects.get_with_likes(_id)\n answers_for_render = paginate(answers, request)\n form = AnswerForm()\n\n context['form'] = form\n context['question'] = main_question\n context['answers'] = answers_for_render\n return render(request, 'question.html', context)\n\n def post(self, request, _id):\n context = {}\n context = _get_user_context(request, context)\n\n try:\n main_question = Question.objects.get_with_tags(_id)\n except Question.DoesNotExist:\n raise Http404()\n answers = Answer.objects.get_with_likes(_id)\n answers_for_render = paginate(answers, request)\n form = AnswerForm(request.POST, context['user'], main_question)\n if form.is_valid():\n form.save()\n return redirect('question', _id)\n\n context['form'] = form\n context['question'] = main_question\n context['answers'] = answers_for_render\n return render(request, 'question.html', context)\n\n\nclass AskView(View):\n def get(self, request):\n context = {}\n context = _get_user_context(request, context)\n form = AskForm()\n\n context['form'] = form\n return render(request, 'ask.html', context)\n\n def post(self, request):\n context = {}\n context = _get_user_context(request, context)\n form = AskForm(request.POST, UserProfile.objects.get(id=request.user.id))\n if form.is_valid():\n new_question = form.save()\n return redirect('question', new_question.id)\n context['form'] = form\n return render(request, 'ask.html', context)\n\n\ndef paginate(objects_list, request, page_objects_num=20):\n paginator = Paginator(objects_list, page_objects_num)\n page = request.GET.get('page')\n\n try:\n objects_page = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer, deliver first page.\n objects_page = paginator.page(1)\n except EmptyPage:\n # If page is out of range (e.g. 9999), deliver last page of results.\n objects_page = paginator.page(paginator.num_pages)\n return objects_page\n\n\ndef _get_user_context(request, context):\n if request.user.is_authenticated():\n context['user_logged_in'] = True\n # context['user'] = UserProfile.objects.get_or_create(username=request.user.username)\n context['user'] = UserProfile.objects.get(username=request.user.username)\n else:\n context['user_logged_in'] = False\n\n context['enable_modal_ask'] = False\n return context\n\n\nclass LoginView(View):\n def get(self, request):\n if request.user.is_authenticated():\n context = {}\n context = _get_user_context(request, context)\n return render(request, 'index.html', context)\n\n form = LoginForm()\n return render(request, 'login.html', {\n 'form': form\n })\n\n def post(self, request):\n if request.user.is_authenticated():\n context = {}\n context = _get_user_context(request, context)\n return render(request, 'index.html', context)\n\n form = LoginForm(request.POST) # initialize the form with POST data\n if form.is_valid():\n username = form.cleaned_data[\"username\"]\n password = form.cleaned_data[\"password\"]\n user_auth = authenticate(username=username, password=password) # try auth\n if user_auth is not None: # if auth is success\n l_in(request, user_auth) # start session\n return HttpResponseRedirect(\"/success\")\n else: # else, auth gone wrong\n form.add_error(None, \"Username or password is incorrect\")\n return render(request, 'login.html', {\n 'form': form\n })\n\n\nclass RegistrationView(View):\n def get(self, request):\n if request.user.is_authenticated():\n context = {}\n context = _get_user_context(request, context)\n return render(request, 'index.html', context)\n\n register_form = RegisterForm()\n return render(request, 'registration.html', {\n 'form': register_form\n })\n\n def post(self, request):\n register_form = RegisterForm(request.POST, request.FILES)\n if register_form.is_valid():\n new_profile = register_form.save()\n l_in(request, new_profile)\n return HttpResponseRedirect(\"/success\")\n return render(request, 'registration.html', {\n 'form': register_form\n })\n\n\nclass SuccessView(View):\n def get(self, request):\n context = {}\n context = _get_user_context(request, context)\n if request.user.is_authenticated():\n context['success'] = True\n return redirect('/')\n else:\n return render(request, 'success.html', {\n 'success': False\n })\n\n\nclass LogoutView(View):\n def get(self, request):\n if request.user.is_authenticated():\n l_out(request)\n return HttpResponseRedirect('/')\n else:\n return HttpResponseRedirect('/')\n\n\nclass SettingsView(View):\n def get(self, request):\n user = request.user\n _profile = UserProfile.objects.filter(user_ptr_id=user.id).last()\n print(\"=====================\")\n print(user.id)\n\n init = {\"username\": _profile.username,\n \"email\": _profile.email,\n \"avatar\": _profile.avatar}\n form = ProfileForm(initial=init)\n context = {'form': form}\n context = _get_user_context(request, context)\n return render(request, 'settings.html', context)\n\n def post(self, request):\n user = request.user\n _profile = UserProfile.objects.filter(user_ptr_id=user.id).last()\n print(\"=====================\")\n print(user.id)\n\n form = ProfileForm(request.POST, request.FILES, _profile)\n if form.is_valid():\n _profile.username = form.cleaned_data[\"username\"]\n _profile.email = form.cleaned_data[\"email\"]\n if form.cleaned_data[\"avatar\"]:\n _profile.avatar = form.cleaned_data[\"avatar\"]\n _profile.save()\n context = {'form': form}\n context = _get_user_context(request, context)\n return render(request, 'settings.html', context)\n\n\nclass VoteView(View):\n def post(self, request):\n try:\n qid = int(request.POST.get('qid'))\n except:\n return JsonResponse(dict(error='bad question id'))\n\n _vote = request.POST.get('vote')\n question = Question.objects.get_with_tags(question_id=qid)\n likes = question.likes\n if _vote == \"inc\":\n likes += 1\n else:\n likes -= 1\n return JsonResponse(dict(ok=1, vote=_vote, likes=likes))\n\nclass AnswerView(View):\n def post(self, request):\n try:\n qid = int(request.POST.get('qid'))\n except:\n return JsonResponse(dict(error='bad question id'))\n\n _vote = request.POST.get('vote')\n question = Question.objects.get_with_tags(question_id=qid)\n likes = question.likes\n if _vote == \"inc\":\n likes += 1\n else:\n likes -= 1\n return JsonResponse(dict(ok=1, vote=_vote, likes=likes))", "sub_path": "ask_app/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 12349, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.views.View", "line_number": 16, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 35, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 35, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 38, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 45, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 64, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 64, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 66, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 69, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 81, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 94, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 97, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 99, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 115, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 117, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 124, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 127, "usage_type": "name"}, {"api_name": "django.http.response.Http404", "line_number": 135, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 144, "usage_type": "call"}, {"api_name": "django.http.response.Http404", "line_number": 153, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 159, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 164, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 167, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 174, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 182, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 184, "usage_type": "call"}, {"api_name": "django.core.paginator.Paginator", "line_number": 188, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 193, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 196, "usage_type": "name"}, {"api_name": "django.views.View", "line_number": 214, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 219, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 222, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 230, "usage_type": "call"}, {"api_name": "django.contrib.auth.authenticate", "line_number": 236, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 238, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 239, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 242, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 247, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 252, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 255, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 263, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 264, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 265, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 270, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 276, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 278, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 283, "usage_type": "name"}, {"api_name": "django.contrib.auth.logout", "line_number": 286, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 287, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 289, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 292, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 305, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 322, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 325, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 330, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 339, "usage_type": "call"}, {"api_name": "django.views.View", "line_number": 341, "usage_type": "name"}, {"api_name": "django.http.response.JsonResponse", "line_number": 346, "usage_type": "call"}, {"api_name": "django.http.response.JsonResponse", "line_number": 355, "usage_type": "call"}]} +{"seq_id": "341562206", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Feb 6 12:25:37 2019\n\n@author: yohei\n\"\"\"\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport csv\n\np = np.zeros((401, 201))\nC = []\nwith open('data3.csv', 'r') as f:\n reader = csv.reader(f)\n for row in reader:\n if row != []:\n c = row[0].split(' ')[2]\n C.append(float(c))\n for i in range(1, 401):\n for j in range(1, 201):\n p[i-1][j-1] += C[201*(i-1)+j-2]\n\nX = np.linspace(-10.0, 30.0, 401)\nY = np.linspace(-10.0, 10.0, 201)\nprint(p)\n#print(p)\n#print(len(X)*len(Y))\nk = 0\nfor i in range(len(p)):\n for j in range(len(p[i])):\n if p[i][j] < -0:\n k+=1\nprint(k)\n \nplt.pcolormesh(X, Y, p.T, cmap='jet')\nplt.colorbar()\nplt.xlabel('x')\nplt.ylabel('y')\ncont = plt.contour(X, Y, p.T)\nplt.title('Tstep=10000, time=200[s]')\nplt.savefig('object_3.png')", "sub_path": "src/visualization3.py", "file_name": "visualization3.py", "file_ext": "py", "file_size_in_byte": 892, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.zeros", "line_number": 12, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 24, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 25, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.pcolormesh", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 37, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 37, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 38, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 38, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 39, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 39, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contour", "line_number": 40, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 40, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 41, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 41, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "539615610", "text": "from flask import Blueprint\nfrom api.utils import get_zone_facts_select_columns\nfrom flask import jsonify\n\ndef construct_project_extended_blueprint(name, engine):\n '''\n Provides an endpoint that provides an extended version of the project table that has been joined to \n other tables. In particular, it joins to the zone_facts table to provide de-normalized statistics \n about nearby developments. All endpoints still return one record per project. \n '''\n\n blueprint = Blueprint(name, __name__, url_prefix='/api/project')\n\n @blueprint.route('/')\n @blueprint.route('/')\n def project_with_zone_facts(nlihc_id= None):\n\n ward_selects, cluster_selects, tract_selects = get_zone_facts_select_columns(engine)\n \n q = \"\"\"\n select\n p.*\n \"\"\"\n q += ward_selects\n q += cluster_selects\n q += tract_selects\n\n q +=\"\"\"\n from project as p\n left join zone_facts as z1 on z1.zone = p.ward\n left join zone_facts as z2 on z2.zone = p.neighborhood_cluster\n left join zone_facts as z3 on z3.zone = p.census_tract\n \"\"\"\n if nlihc_id != None:\n q+= \"WHERE nlihc_id = '{}'\".format(nlihc_id)\n\n conn = engine.connect()\n proxy = conn.execute(q)\n results = [dict(x) for x in proxy.fetchall()]\n conn.close()\n output = {'objects': results}\n return jsonify(output)\n\n return blueprint", "sub_path": "python/api/project_extended_constructor.py", "file_name": "project_extended_constructor.py", "file_ext": "py", "file_size_in_byte": 1484, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Blueprint", "line_number": 12, "usage_type": "call"}, {"api_name": "api.utils.get_zone_facts_select_columns", "line_number": 18, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "359262738", "text": "import cv2\n\"\"\"Compute depth maps for images in the input folder.\n\"\"\"\nimport os\nimport glob\nimport torch\nimport utils\nimport cv2\nimport random\nimport time\n\nfrom torchvision.transforms import Compose\nfrom models.midas_net import MidasNet\nfrom models.transforms import Resize, NormalizeImage, PrepareForNet\n\n\ndef run(model_path):\n \"\"\"Run MonoDepthNN to compute depth maps.\n\n Args:\n model_path (str): path to saved model\n \"\"\"\n print(\"initialize\")\n\n # select device\n device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n print(\"device: %s\" % device)\n\n # load network\n model = MidasNet(model_path, non_negative=True)\n\n transform = Compose(\n [\n Resize(\n 384,\n 384,\n resize_target=None,\n keep_aspect_ratio=True,\n ensure_multiple_of=32,\n resize_method=\"upper_bound\",\n image_interpolation_method=cv2.INTER_CUBIC,\n ),\n NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n PrepareForNet(),\n ]\n )\n\n model.to(device)\n model.eval()\n\n cap = cv2.VideoCapture(1)\n print(\"is camera open\", cap.isOpened())\n cap.set(3,320)\n cap.set(4,240)\n print(\"start processing\")\n\n i = 0\n while cap.isOpened():\n start = time.time()\n ret, frame = cap.read()\n print(\"new frame\", ret)\n p1 = time.time()\n print(f\"take a picture {p1 - start}\")\n if ret:\n img = utils.process_camera_img(frame)\n img_input = transform({\"image\": img})[\"image\"]\n p2 = time.time()\n print(f\"transoform image {p2 - p1}\")\n # compute\n with torch.no_grad():\n sample = torch.from_numpy(img_input).to(device).unsqueeze(0)\n p3 = time.time()\n print(f\"from numpy to cuda {p3 - p2}\")\n prediction = model.forward(sample)\n p4 = time.time()\n print(f\"prediction {p4 - p3}\")\n prediction = (\n torch.nn.functional.interpolate(\n prediction.unsqueeze(1),\n size=img.shape[:2],\n mode=\"bicubic\",\n align_corners=False,\n )\n .squeeze()\n .cpu()\n .numpy()\n )\n p5 = time.time()\n print(f\"prediction from cuda to cpu {p5 - p4}\")\n\n\n # output\n\n r = random.randint(0, 10000)\n cv2.imwrite(f\"output/input-{i}-{r}.png\", frame)\n utils.write_depth(f\"output/depth-{i}-{r}\", prediction, bits=2)\n p6 = time.time()\n print(f\"save input and write depth {p6 - p5}\")\n\n cv2.imshow('frame', frame)\n cv2.imshow('prediction', prediction)\n p7 = time.time()\n print(f\"show images {p7 - p6}\")\n i += 1\n\n if cv2.waitKey(1) & 0xFF == ord('q'):\n break\n else:\n print(\"Camera is not recording\")\n print(f\"image took {time.time() - start} s\")\n print(\"\\n-----------------------\\n\")\n\n # When everything done, release the capture\n cap.release()\n cv2.destroyAllWindows()\n\n print(\"finished\")\n\n\nif __name__ == \"__main__\":\n MODEL_PATH = \"model.pt\"\n\n # set torch options\n torch.backends.cudnn.enabled = True\n torch.backends.cudnn.benchmark = True\n\n # compute depth maps\n run(MODEL_PATH)\n", "sub_path": "run_cv.py", "file_name": "run_cv.py", "file_ext": "py", "file_size_in_byte": 3573, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.device", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 26, "usage_type": "attribute"}, {"api_name": "models.midas_net.MidasNet", "line_number": 30, "usage_type": "call"}, {"api_name": "torchvision.transforms.Compose", "line_number": 32, "usage_type": "call"}, {"api_name": "models.transforms.Resize", "line_number": 34, "usage_type": "call"}, {"api_name": "cv2.INTER_CUBIC", "line_number": 41, "usage_type": "attribute"}, {"api_name": "models.transforms.NormalizeImage", "line_number": 43, "usage_type": "call"}, {"api_name": "models.transforms.PrepareForNet", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 51, "usage_type": "call"}, {"api_name": "time.time", "line_number": 59, "usage_type": "call"}, {"api_name": "time.time", "line_number": 62, "usage_type": "call"}, {"api_name": "utils.process_camera_img", "line_number": 65, "usage_type": "call"}, {"api_name": "time.time", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 71, "usage_type": "call"}, {"api_name": "time.time", "line_number": 72, "usage_type": "call"}, {"api_name": "time.time", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "attribute"}, {"api_name": "time.time", "line_number": 88, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 94, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 95, "usage_type": "call"}, {"api_name": "utils.write_depth", "line_number": 96, "usage_type": "call"}, {"api_name": "time.time", "line_number": 97, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 101, "usage_type": "call"}, {"api_name": "time.time", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 106, "usage_type": "call"}, {"api_name": "time.time", "line_number": 110, "usage_type": "call"}, {"api_name": "cv2.destroyAllWindows", "line_number": 115, "usage_type": "call"}, {"api_name": "torch.backends", "line_number": 124, "usage_type": "attribute"}, {"api_name": "torch.backends", "line_number": 125, "usage_type": "attribute"}]} +{"seq_id": "501355172", "text": "# encoding=utf8\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# 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, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport logging\n\nfrom django.core.cache import cache\n\nfrom billing_proxy.api import ceilometer\nfrom billing_proxy.api import exceptions\nfrom billing_proxy.api import keystone\nfrom billing_proxy.api import utils\nfrom billing_proxy.api.views import APIView\nfrom billing_proxy.client import openstack_client\nfrom billing_proxy import models\nfrom billing_proxy.models import BillingResOrder\nfrom billing_proxy.models import resource_order_select_for_update_by_id\nfrom billing_proxy.worker import cache_manager\n\n\nLOG = logging.getLogger(__name__)\n\nCACHE_KEY_CONTRACT = \"contracts\"\n\nFIP_METER = \"network.floating.ip.outgoing.bytes\"\nSYSTEMSNAPSHOT_METER = \"instance.snapshot.size\"\nPROTECTED_GROUP_VOLUME_METER = \"protectiongroup.backup.volum.size\"\nPROTECTED_GROUP_NETWORK_METER = \"protectiongroup.volume.network.bytes\"\n\n\nclass Contract(APIView):\n\n @utils.argument_check([\"account\", \"contractCode\", \"isDisplayPrice\",\n \"contractType\", \"contractId\"])\n def post(self, request, *args, **kwargs):\n try:\n # add new product_alias into cache\n contract_id = kwargs[\"contractId\"]\n data = cache_manager.contract_data_adapter(kwargs)\n cache_manager.cache_contract_by_key(data,\n contract_id=contract_id)\n except Exception as e:\n LOG.exception(\"Create contract fail : {0}\".format(e))\n\n @utils.argument_check([\"account\", \"contractCode\", \"isDisplayPrice\",\n \"contractType\"])\n def put(self, request, *args, **kwargs):\n contract_id = kwargs[\"contract_id\"]\n account = kwargs[\"account\"]\n utils.check_user_is_existed(account)\n # check if contract_id is exsited\n old_contracts = cache.get(CACHE_KEY_CONTRACT, {})\n if contract_id not in old_contracts[\"index\"]:\n raise exceptions.ContractNotExisted\n data = cache_manager.contract_data_adapter(kwargs)\n cache_manager.cache_contract_by_key(data, contract_id)\n\n def delete(self, request, *args, **kwargs):\n contract_id = kwargs[\"contract_id\"]\n old_contracts = cache.get(CACHE_KEY_CONTRACT, {})\n if contract_id not in old_contracts[\"index\"]:\n raise exceptions.ContractNotExisted\n contract_res_relation = BillingResOrder.objects.filter(\n contract_id=contract_id)\n if len(contract_res_relation):\n raise exceptions.ContractResourceRelationExisted\n cache_manager.delete_cached_contract(contract_id)\n\n\nclass ContractResourceRelation(APIView):\n\n @utils.argument_check([\"ResContract\"])\n def put(self, request, *args, **kwargs):\n \"\"\"update relation between resource and contract\"\"\"\n\n res_contracts = kwargs[\"ResContract\"]\n # check if all resource id is existed,otherwise ,raise a exception\n for res_con in res_contracts:\n account = res_con[\"account\"]\n utils.check_user_is_existed(account)\n resource_id = res_con.get(\"resourceId\")\n try:\n BillingResOrder.objects.get(resource_id=resource_id)\n except Exception as e:\n LOG.exception(e)\n raise exceptions.ResourceNotFound\n\n # do update\n for res_con in res_contracts:\n resource_id = res_con.get(\"resourceId\")\n contract_id = res_con.get(\"contractId\")\n resource_order_select_for_update_by_id(resource_id=resource_id,\n contract_id=contract_id)\n\n\nclass InstanceFipView(APIView):\n\n @utils.argument_check([\"instance_ids\"])\n def post(self, request, *args, **kwargs):\n \"\"\"获取云主机下面的所有绑定的公网ip\n\n ret = {\n \"InstanceFip\":\n [\n {\n \"id\": \"project_id\",\n \"floatingips\": [\"1.2.3.4\"]\n }\n ]\n }\n \"\"\"\n instance_ids = kwargs[\"instance_ids\"]\n nova = openstack_client.get_novaclient()\n ret = {\n \"InstanceFip\": []\n }\n for instance_id in instance_ids:\n try:\n instance = nova.servers.get(instance_id)\n except Exception as e:\n LOG.error(\"Instance Not Found : {0}\".format(e))\n continue\n addresses = instance.addresses\n project_id = instance.tenant_id\n floating_ips = []\n for net_name in addresses:\n spec_network_list = addresses[net_name]\n for network in spec_network_list:\n if network['OS-EXT-IPS:type'] == 'floating':\n floating_ips.append(network['addr'])\n\n ret['InstanceFip'].append({'id': project_id,\n 'floatingips': floating_ips})\n return ret\n\n\ndef query_data(date_from,\n date_to,\n meter,\n stats_attr,\n date_options=\"other\",\n period=None,\n instance=None):\n # 格式化开始时间和结束时间，做了2件事：\n # 1.格式化为yyyy-mm-dd格式，2.把输入的结束时间往后延了23小时59分59秒，\n # 这样当你输入同一个时间的时候，默认会获取到这一整天的使用量\n date_from, date_to = ceilometer._calc_date_args(date_from,\n date_to,\n date_options)\n # 计算开始时间到结束时间的秒数，即颗粒度\n if not period:\n period = ceilometer._calc_period(date_from, date_to)\n additional_query = []\n # 组装成查询序列\n if date_from:\n additional_query += [{'field': 'timestamp',\n 'op': 'ge',\n 'value': date_from}]\n if date_to:\n additional_query += [{'field': 'timestamp',\n 'op': 'le',\n 'value': date_to}]\n\n query = []\n # 资源ID\n if instance:\n query += [{'field': 'resource_id', 'op': 'eq', 'value': instance}]\n\n if additional_query:\n if not ceilometer.is_iterable(additional_query):\n raise ValueError(\"Additional query must be list of\"\n \" conditions. See the docs for format.\")\n query = query + additional_query\n aggregates = []\n # 对所有数据求和\n aggregate_args = \"sum\"\n try:\n LOG.info(\"meter: {0}, query: {1}, period: {2}\".format(meter, query,\n period))\n aggregates.append(dict(zip(('func', 'param'),\n aggregate_args.split(\"<-\"))))\n # 调用ceilometer API查询\n statistics = ceilometer.statistic_list(meter, query=query,\n period=period,\n aggregates=aggregates)\n # 正常返回一个Statistic对象的列表，列表中只可能返回一个结果\n if statistics:\n return getattr(statistics[0], stats_attr, None)\n\n LOG.info(\"statistic data: {0}\".format(statistics))\n except Exception as e:\n LOG.exception(\"get meter data fail : {0}\".format(e))\n statistics = None\n return statistics\n\n\ndef get_floatingip_usage(date_from, date_to, resource_id, meter, stats_attr):\n \"\"\"get floating ip sum usage from start_time to end_time\n\n :param date_from:\n :param date_to:\n :param resource_id:\n :param meter:\n :return:\n \"\"\"\n statistics = query_data(date_from,\n date_to,\n meter,\n stats_attr,\n instance=resource_id)\n return statistics\n\n\ndef get_statistic_data(resource_type, resource_id,\n start_time, end_time, stats_attr):\n if resource_type == \"bandwidth\":\n # 公网ip的使用量\n LOG.info(\"get floating ip statistic data, {0}\".format(\n resource_id))\n value = get_floatingip_usage(start_time, end_time, resource_id,\n FIP_METER, stats_attr)\n if not value:\n value = 0\n return {\"resourceId\": resource_id,\n \"resourceType\": \"floatingBandwidth\",\n \"startTime\": start_time,\n \"endTime\": end_time,\n \"statistics\": [{\"name\": \"fixedBandwidth\",\n \"value\": value,\n \"unit\": \"Mbps\"}]}\n elif resource_type == \"systemSnapshot\":\n # 系统盘快照的使用量\n LOG.info(\"get systemSnapshot statistic data, {0}\".format(\n resource_id))\n value = query_data(start_time,\n end_time,\n SYSTEMSNAPSHOT_METER,\n stats_attr,\n instance=resource_id)\n\n if not value:\n value = 0\n return {\"resourceId\": resource_id,\n \"resourceType\": \"systemSnapshot\",\n \"startTime\": start_time,\n \"endTime\": end_time,\n \"statistics\": [{\"name\": \"SnapshotCapability\",\n \"value\": value,\n \"unit\": \"GBHour\"}]}\n\n elif resource_type == \"Anti-DDoS\":\n # 定义在模块中改为这里函数中定义，可以防止防护组件初始化失败时，不至于整个bp都挂掉\n cloud_guard_client = openstack_client.get_antiddos_client()\n # 云安全组件的使用量\n LOG.info(\"get Anti-DDoS statistic data, {0}\".format(\n resource_id))\n # 获取资源对应的订单id\n order_id = models.get_order_by_resource_id(resource_id)\n query_body = {}\n query_body.update({\"startdate\": start_time, \"enddate\": end_time,\n \"orderId\": order_id})\n # 获取防护套餐的统计\n statistics_data = cloud_guard_client.get_cloud_guard_statistic(\n params=query_body)\n return {\"resourceId\": resource_id,\n \"statistics\": statistics_data}\n elif resource_type == \"volumeBackup\":\n # 保护组使用量\n LOG.info(\"get protectgroup network statistic data, {0}\".format(\n resource_id))\n statistics = []\n # 获取保护组资源的使用量, stats_attr是sum\n value = query_data(start_time,\n end_time,\n PROTECTED_GROUP_NETWORK_METER,\n stats_attr,\n instance=resource_id)\n statistics.append({\"name\": \"BackupCapability\",\n \"value\": value,\n \"unit\": \"GB\"})\n LOG.info(\"get protectgroup volume statistic data, {0}\".format(\n resource_id))\n value = query_data(start_time,\n end_time,\n PROTECTED_GROUP_VOLUME_METER,\n stats_attr,\n instance=resource_id)\n statistics.append({\"name\": \"BackupFlow\",\n \"value\": value,\n \"unit\": \"GB\"})\n return {\"resourceId\": resource_id,\n \"statistics\": statistics}\n else:\n raise exceptions.StatisticDataNotReady\n\n\nclass ResourceUsageView(APIView):\n @utils.argument_check([\"usage\", \"startTime\", \"endTime\"])\n def post(self, request, *args, **kwargs):\n usage = kwargs[\"usage\"]\n startTime = kwargs[\"startTime\"]\n endTime = kwargs[\"endTime\"]\n stats_attr = \"sum\"\n ret = {\"usage\": []}\n for req_res in usage:\n resource_type = req_res[\"resourceType\"]\n resource_id = req_res[\"resourceId\"]\n LOG.info(\"getting statistic data\"\n \"resource_id: {0},\"\n \" resource_type: {1}\".format(resource_id,\n resource_type))\n statistics_data = get_statistic_data(resource_type,\n resource_id,\n startTime, endTime,\n stats_attr)\n if statistics_data:\n ret[\"usage\"].append(statistics_data)\n return ret\n\n\nclass UserUsage(APIView):\n\n def is_resource_a_flow_type(self, resource_id):\n product_type = models.get_product_type_by_resource_id(resource_id)\n if product_type == \"floatingBandwidth\":\n return True\n product_name = models.get_product_name_by_resource_id(resource_id)\n if product_name == \"systemSnapshot\":\n return True\n elif product_name == \"Anti-DDoS\":\n return True\n else:\n return False\n\n def get_flow_resource(self, user_name):\n resource_ids_types = models.get_existed_resource_attr_by_user_name(\n user_name, \"resource_id\", \"resource_type\")\n return [(resource_id, resource_type) for resource_id, resource_type in\n resource_ids_types\n if self.is_resource_a_flow_type(resource_id)]\n\n @utils.argument_check([\"account\", \"startTime\", \"endTime\"])\n def post(self, request, *args, **kwargs):\n ret = {\"usage\": []}\n startTime = kwargs[\"startTime\"]\n endTime = kwargs[\"endTime\"]\n stats_attr = \"sum\"\n users = keystone.list_user(username=kwargs['account'])\n if not users:\n raise exceptions.UserNotExists\n resource_ids_types = self.get_flow_resource(kwargs['account'])\n for resource_id, resource_type in resource_ids_types:\n product_type = models.get_product_type_by_resource_id(resource_id)\n if product_type == \"floatingBandwidth\":\n resource_type = \"bandwidth\"\n LOG.info(\"get {0},resource_id:{1} statistic data\".format(\n resource_type, resource_id))\n try:\n statistics_data = get_statistic_data(resource_type,\n resource_id,\n startTime, endTime,\n stats_attr)\n if statistics_data:\n ret[\"usage\"].append(statistics_data)\n except Exception:\n LOG.error(\"getting statistic data error,\"\n \"{0},resource_id:{1}\".format(resource_type,\n resource_id))\n return ret\n", "sub_path": "billing_proxy/api/contract/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 15266, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 30, "usage_type": "call"}, {"api_name": "billing_proxy.api.views.APIView", "line_number": 40, "usage_type": "name"}, {"api_name": "billing_proxy.worker.cache_manager.contract_data_adapter", "line_number": 48, "usage_type": "call"}, {"api_name": "billing_proxy.worker.cache_manager", "line_number": 48, "usage_type": "name"}, {"api_name": "billing_proxy.worker.cache_manager.cache_contract_by_key", "line_number": 49, "usage_type": "call"}, {"api_name": "billing_proxy.worker.cache_manager", "line_number": 49, "usage_type": "name"}, {"api_name": "billing_proxy.api.utils.argument_check", "line_number": 42, "usage_type": "call"}, {"api_name": "billing_proxy.api.utils", "line_number": 42, "usage_type": "name"}, {"api_name": "billing_proxy.api.utils.check_user_is_existed", "line_number": 59, "usage_type": "call"}, {"api_name": "billing_proxy.api.utils", "line_number": 59, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 61, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 61, "usage_type": "name"}, {"api_name": "billing_proxy.api.exceptions.ContractNotExisted", "line_number": 63, "usage_type": "attribute"}, {"api_name": "billing_proxy.api.exceptions", "line_number": 63, "usage_type": "name"}, {"api_name": "billing_proxy.worker.cache_manager.contract_data_adapter", "line_number": 64, "usage_type": "call"}, {"api_name": "billing_proxy.worker.cache_manager", "line_number": 64, "usage_type": "name"}, {"api_name": "billing_proxy.worker.cache_manager.cache_contract_by_key", "line_number": 65, "usage_type": "call"}, {"api_name": "billing_proxy.worker.cache_manager", "line_number": 65, "usage_type": "name"}, {"api_name": "billing_proxy.api.utils.argument_check", "line_number": 54, "usage_type": "call"}, {"api_name": "billing_proxy.api.utils", "line_number": 54, "usage_type": "name"}, {"api_name": "django.core.cache.cache.get", "line_number": 69, "usage_type": "call"}, {"api_name": "django.core.cache.cache", "line_number": 69, "usage_type": "name"}, {"api_name": "billing_proxy.api.exceptions.ContractNotExisted", "line_number": 71, "usage_type": "attribute"}, {"api_name": "billing_proxy.api.exceptions", "line_number": 71, "usage_type": "name"}, {"api_name": "billing_proxy.models.BillingResOrder.objects.filter", "line_number": 72, "usage_type": "call"}, {"api_name": "billing_proxy.models.BillingResOrder.objects", "line_number": 72, "usage_type": "attribute"}, {"api_name": "billing_proxy.models.BillingResOrder", "line_number": 72, "usage_type": "name"}, {"api_name": "billing_proxy.api.exceptions.ContractResourceRelationExisted", "line_number": 75, "usage_type": "attribute"}, {"api_name": "billing_proxy.api.exceptions", "line_number": 75, "usage_type": "name"}, {"api_name": "billing_proxy.worker.cache_manager.delete_cached_contract", "line_number": 76, "usage_type": "call"}, {"api_name": "billing_proxy.worker.cache_manager", "line_number": 76, "usage_type": "name"}, {"api_name": "billing_proxy.api.views.APIView", "line_number": 79, "usage_type": "name"}, {"api_name": "billing_proxy.api.utils.check_user_is_existed", "line_number": 89, "usage_type": "call"}, {"api_name": "billing_proxy.api.utils", "line_number": 89, "usage_type": "name"}, {"api_name": "billing_proxy.models.BillingResOrder.objects.get", "line_number": 92, "usage_type": "call"}, {"api_name": "billing_proxy.models.BillingResOrder.objects", "line_number": 92, "usage_type": "attribute"}, {"api_name": "billing_proxy.models.BillingResOrder", "line_number": 92, "usage_type": "name"}, {"api_name": "billing_proxy.api.exceptions.ResourceNotFound", "line_number": 95, "usage_type": "attribute"}, {"api_name": "billing_proxy.api.exceptions", "line_number": 95, "usage_type": "name"}, {"api_name": "billing_proxy.models.resource_order_select_for_update_by_id", "line_number": 101, "usage_type": "call"}, {"api_name": "billing_proxy.api.utils.argument_check", "line_number": 81, "usage_type": "call"}, {"api_name": "billing_proxy.api.utils", "line_number": 81, "usage_type": "name"}, {"api_name": "billing_proxy.api.views.APIView", "line_number": 105, "usage_type": "name"}, {"api_name": "billing_proxy.client.openstack_client.get_novaclient", "line_number": 122, "usage_type": "call"}, {"api_name": "billing_proxy.client.openstack_client", "line_number": 122, "usage_type": "name"}, {"api_name": "billing_proxy.api.utils.argument_check", "line_number": 107, "usage_type": "call"}, {"api_name": "billing_proxy.api.utils", "line_number": 107, "usage_type": "name"}, {"api_name": "billing_proxy.api.ceilometer._calc_date_args", "line_number": 156, "usage_type": "call"}, {"api_name": "billing_proxy.api.ceilometer", "line_number": 156, "usage_type": "name"}, {"api_name": "billing_proxy.api.ceilometer._calc_period", "line_number": 161, "usage_type": "call"}, {"api_name": "billing_proxy.api.ceilometer", "line_number": 161, "usage_type": "name"}, {"api_name": "billing_proxy.api.ceilometer.is_iterable", "line_number": 179, "usage_type": "call"}, {"api_name": "billing_proxy.api.ceilometer", "line_number": 179, "usage_type": "name"}, {"api_name": "billing_proxy.api.ceilometer.statistic_list", "line_number": 192, "usage_type": "call"}, {"api_name": "billing_proxy.api.ceilometer", "line_number": 192, "usage_type": "name"}, {"api_name": "billing_proxy.client.openstack_client.get_antiddos_client", "line_number": 262, "usage_type": "call"}, {"api_name": "billing_proxy.client.openstack_client", "line_number": 262, "usage_type": "name"}, {"api_name": "billing_proxy.models.get_order_by_resource_id", "line_number": 267, "usage_type": "call"}, {"api_name": "billing_proxy.models", "line_number": 267, "usage_type": "name"}, {"api_name": "billing_proxy.api.exceptions.StatisticDataNotReady", "line_number": 303, "usage_type": "attribute"}, {"api_name": "billing_proxy.api.exceptions", "line_number": 303, "usage_type": "name"}, {"api_name": "billing_proxy.api.views.APIView", "line_number": 306, "usage_type": "name"}, {"api_name": "billing_proxy.api.utils.argument_check", "line_number": 307, "usage_type": "call"}, {"api_name": "billing_proxy.api.utils", "line_number": 307, "usage_type": "name"}, {"api_name": "billing_proxy.api.views.APIView", "line_number": 330, "usage_type": "name"}, {"api_name": "billing_proxy.models.get_product_type_by_resource_id", "line_number": 333, "usage_type": "call"}, {"api_name": "billing_proxy.models", "line_number": 333, "usage_type": "name"}, {"api_name": "billing_proxy.models.get_product_name_by_resource_id", "line_number": 336, "usage_type": "call"}, {"api_name": "billing_proxy.models", "line_number": 336, "usage_type": "name"}, {"api_name": "billing_proxy.models.get_existed_resource_attr_by_user_name", "line_number": 345, "usage_type": "call"}, {"api_name": "billing_proxy.models", "line_number": 345, "usage_type": "name"}, {"api_name": "billing_proxy.api.keystone.list_user", "line_number": 357, "usage_type": "call"}, {"api_name": "billing_proxy.api.keystone", "line_number": 357, "usage_type": "name"}, {"api_name": "billing_proxy.api.exceptions.UserNotExists", "line_number": 359, "usage_type": "attribute"}, {"api_name": "billing_proxy.api.exceptions", "line_number": 359, "usage_type": "name"}, {"api_name": "billing_proxy.models.get_product_type_by_resource_id", "line_number": 362, "usage_type": "call"}, {"api_name": "billing_proxy.models", "line_number": 362, "usage_type": "name"}, {"api_name": "billing_proxy.api.utils.argument_check", "line_number": 351, "usage_type": "call"}, {"api_name": "billing_proxy.api.utils", "line_number": 351, "usage_type": "name"}]} +{"seq_id": "241825662", "text": "import math\nimport shelve\n\nimport numpy as np\n\nfrom dateutil import relativedelta\nfrom datetime import datetime\nfrom typing import Dict, List, Tuple, Union, Any\n\n\n_dir = '/Users/matthewjbelcher/PycharmProjects/Reserver'\n\n\ndef compare_dicts(dict1: Dict, dict2: Dict) -> bool:\n \"\"\" Compares two dictionaries to see if they are equal.\n\n :param dict1: first dictionary\n :param dict2: second dictionary\n :return: True if two dictionaries are equal; False otherwise\n \"\"\"\n dirty = False\n\n if dict1 is None or dict2 is None:\n dirty = True\n elif dict1.keys() != dict2.keys():\n dirty = True\n else:\n for key in dict1:\n try:\n if dict1[key] != dict2[key]:\n dirty = True\n except ValueError:\n if not ((dict1[key] == dict2[key]) | (np.isnan(dict1[key]) & np.isnan(dict2[key]))).all():\n dirty = True\n except KeyError:\n dirty = True\n\n return dirty\n\n\ndef get_app_settings(keyword) -> Any:\n\n with shelve.open(f'{_dir}/settings/app', 'r') as s:\n assert keyword in s, 'keyword: %r not found in app settings' % keyword\n return s[keyword]\n\n\ndef get_dataset_label_path(name_path: str) -> str:\n project_path = get_session_settings('project path')\n\n _dict = {key.rstrip(): value for value, key in [line.split(sep='=') for line in\n open('{0}/dataset_map.txt'.format(project_path))]}\n\n return _dict[name_path]\n\n\ndef get_dataset_name_path(label_path: str) -> str:\n project_path = get_session_settings('project path')\n\n _dict = {key: value.rstrip() for key, value in [line.split(sep='=') for line in\n open('{0}/dataset_map.txt'.format(project_path))]}\n\n return _dict[label_path]\n\n\ndef get_project_settings(keywords: Union[str, Tuple]):\n \"\"\" Gets the settings for the current project.\n\n :param keywords: tuple of key(s) providing direction to the desired property(s)\n :return: if several keywords is/are specified, settings are returned as a tuple; otherwise single property returned\n as most appropriate type\n \"\"\"\n project_path = get_session_settings('project path')\n\n dict_ = {key: value.rstrip() for key, value in [line.split(sep='=') for line in\n open('{0}/settings.txt'.format(project_path))]}\n\n for key in dict_:\n\n if dict_[key].count('/') == 2 and len(dict_[key]) == 10: # probably a date; try to convert\n try:\n dict_[key] = datetime.strptime(dict_[key], '%d/%m/%Y').date()\n except ValueError:\n pass\n else:\n try:\n dict_[key] = float(dict_[key])\n except ValueError:\n pass\n\n if type(keywords) == str:\n return dict_[keywords]\n else:\n return tuple(dict_[keyword] for keyword in keywords)\n\n\ndef get_session_settings(key: str=None) -> Union[Dict, str]:\n \"\"\" Gets the settings for the current session.\n\n :param key: optional key providing direction to the desired property\n :return: if no keyword is specified, all settings are returned as dictionary; otherwise, single property returned\n \"\"\"\n with shelve.open('{0}/settings/session'.format(_dir), flag='r') as s:\n if str:\n return s[key] # type: str\n else:\n return {key: s[key] for key in s} # type: Dict\n\n\ndef set_session_settings(key: str, value: str) -> None:\n \"\"\" Updates the session settings file.\n\n :param key: key providing direction to the property to be updated\n :param value: new value for the setting\n :return: None\n \"\"\"\n with shelve.open('{0}/settings/session'.format(_dir), flag='c') as s:\n s[key] = value\n\n\ndef get_dHeaders(dLength: int, dCount: int, _type: str, basis: str='development') -> Tuple[str, ...]:\n \"\"\" Returns a tuple of development headers for a triangle or method.\n\n :param dLength: development length of the triangle/method for which headers are to be returned\n :param dCount: number of development periods in the triangle/method\n :param _type: whether the headers being return are for a triangle or CLM (no other types currently supported)\n :param basis: whether the headers need to be on a development or calendar period basis. Only relevant for triangles\n :return: tuple containing the development headers\n \"\"\"\n assert _type in ('triangle', 'CLM'), 'type must be one of: triangle, CLM; entered %r' % type\n if _type == 'CLM':\n assert basis == 'development', 'basis must be set to development if prefix == CLM; entered %r' % basis\n\n p_start_date, p_end_date, p_dLength = get_project_settings(('start date', 'end date', 'dLength'))\n p_monthCount = (p_end_date.year - p_start_date.year) * 12 + (p_end_date.month - p_start_date.month) + 1\n p_dCount = int((p_monthCount - 1) // p_dLength + 1)\n\n if _type == 'triangle' and basis == 'development':\n labels = list(range(p_dCount, 0, -dLength))\n labels.reverse()\n\n elif _type == 'triangle' and basis == 'calendar':\n labels = [(p_end_date - relativedelta.relativedelta(months=d)).strftime('%b%y')\n for d in range(0, dCount, dLength)]\n labels.reverse()\n\n elif _type == 'CLM':\n _ = list(range(p_dCount, 0, -dLength))\n _.reverse()\n labels = ('({0})-({1})'.format(_[d], _[d + 1]) for d in range(len(_) - 1))\n\n else:\n labels = ()\n\n return tuple(labels)\n\n\ndef get_dCount(dLength: int) -> int:\n \"\"\" Get the number of development periods for a specified development length.\n\n :param dLength: development period length\n :return: number of development periods\n \"\"\"\n start, stop = get_project_settings(('start date', 'end date')) # type: datetime\n dCount_in_months = (stop.year - start.year) * 12 + (stop.month - start.month) + 1\n dCount = int(math.ceil(dCount_in_months / dLength))\n\n return dCount\n\n\ndef get_oCount(oLength: int) -> int:\n \"\"\" Get the number of origin periods for a specified origin length.\n\n :param oLength: origin period length\n :return: number of origin periods\n \"\"\"\n start, stop = get_project_settings(('start date', 'end date')) # type: datetime\n oCount_in_months = (stop.year - start.year) * 12 + (12 - start.month) + 1\n oCount = int(math.ceil(oCount_in_months / oLength))\n\n return oCount\n\n\ndef get_oHeaders(oLength: int, oCount: int) -> Tuple[str, ...]:\n \"\"\" Returns a tuple of origin headers for a triangle or method.\n\n :param oLength: origin length of the triangle/method for which headers are to be returned\n :param oCount: number of origin periods in the triangle/method\n :return: tuple containing the origin headers\n \"\"\"\n start_date = get_project_settings('start date')\n oPeriods = [start_date + relativedelta.relativedelta(months=o * oLength) for o in range(oCount)] # type: List\n\n if oLength == 1:\n labels = tuple(oPeriod.strftime('%b%y') for oPeriod in oPeriods)\n elif oLength == 3:\n labels = tuple('{0} Q{1}'. format(oPeriod.year, (oPeriod.month - 1) // 3 + 1) for oPeriod in oPeriods)\n elif oLength == 6:\n labels = tuple('{0} H{1}'.format(oPeriod.year, (oPeriod.month - 1) // 6 + 1) for oPeriod in oPeriods)\n elif oLength == 12:\n labels = tuple(oPeriod.year for oPeriod in oPeriods)\n else:\n oPeriods.append(start_date + relativedelta.relativedelta(months=oCount * oLength)) # add the end date\n labels = tuple('{0}-{1}'.format(oPeriods[o].strftime('%b%y'),\n (oPeriods[o + 1] - relativedelta.relativedelta(days=1)).strftime('%b%y'))\n for o in range(oCount))\n\n return labels\n\n\ndef get_root(directory: str) -> str:\n \"\"\" Gets the root directory from a specified sub-directory.\n\n :param directory: sub-directory from which to extract the root\n :return: path of the root directory\n \"\"\"\n\n tail = directory[directory.find('db/') + 3:].split('/', maxsplit=2)[2]\n root = directory.replace(tail, '')\n print(root)\n return root\n\n\ndef lcm(a: int, b: int) -> int:\n \"\"\" Lowest common multiple of two integers. \"\"\"\n return (a * b) // math.gcd(a, b)\n\n\nif __name__ == '__main__':\n print(get_project_settings('oLength'))\n", "sub_path": "misc.py", "file_name": "misc.py", "file_ext": "py", "file_size_in_byte": 8366, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "typing.Dict", "line_number": 14, "usage_type": "name"}, {"api_name": "numpy.isnan", "line_number": 33, "usage_type": "call"}, {"api_name": "shelve.open", "line_number": 43, "usage_type": "call"}, {"api_name": "typing.Any", "line_number": 41, "usage_type": "name"}, {"api_name": "typing.Union", "line_number": 66, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 66, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 82, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 82, "usage_type": "name"}, {"api_name": "shelve.open", "line_number": 103, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 97, "usage_type": "name"}, {"api_name": "typing.Dict", "line_number": 97, "usage_type": "name"}, {"api_name": "shelve.open", "line_number": 117, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 143, "usage_type": "call"}, {"api_name": "dateutil.relativedelta", "line_number": 143, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 121, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 166, "usage_type": "call"}, {"api_name": "math.ceil", "line_number": 179, "usage_type": "call"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 192, "usage_type": "call"}, {"api_name": "dateutil.relativedelta", "line_number": 192, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 203, "usage_type": "call"}, {"api_name": "dateutil.relativedelta", "line_number": 203, "usage_type": "name"}, {"api_name": "dateutil.relativedelta.relativedelta", "line_number": 205, "usage_type": "call"}, {"api_name": "dateutil.relativedelta", "line_number": 205, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 184, "usage_type": "name"}, {"api_name": "math.gcd", "line_number": 226, "usage_type": "call"}]} +{"seq_id": "287857609", "text": "import stapipy as st\nimport cv2\nimport numpy as np\nimport threading\nimport matplotlib.pyplot as plt\nimport pickle\nimport serial\nimport time\n\nfrom annv2c import NewPrediction\n\n#communicate with arduino to read and write data\n# port_arduino = 'COM 4'\n# toArduino1 = serial.Serial(port_arduino,9600, timeout=1)\nport_arduino2 = 'COM 5'\ntoArduino2 = serial.Serial(port_arduino2, 9600, timeout=1)\n\nDISPLAY_RESIZE_FACTOR = 0.3\n\nclass CMyCallback:\n \"\"\"\n Class that contains a callback function.\n \"\"\"\n\n def __init__(self):\n self._image = None\n self._lock = threading.Lock()\n\n @property\n def image(self):\n \"\"\"Property: return PyIStImage of the grabbed image.\"\"\"\n duplicate = None\n self._lock.acquire()\n if self._image is not None:\n duplicate = self._image.copy()\n self._lock.release()\n return duplicate\n\n def datastream_callback(self, handle=None, context=None):\n \"\"\"\n Callback to handle events from DataStream.\n\n :param handle: handle that trigger the callback.\n :param context: user data passed on during callback registration.\n \"\"\"\n st_datastream = handle.module\n if st_datastream:\n with st_datastream.retrieve_buffer() as st_buffer:\n # Check if the acquired data contains image data.\n if st_buffer.info.is_image_present:\n # Create an image object.\n st_image = st_buffer.get_image()\n\n # Check the pixelformat of the input image.\n pixel_format = st_image.pixel_format\n pixel_format_info = st.get_pixel_format_info(pixel_format)\n\n # Only mono or bayer is processed.\n if not(pixel_format_info.is_mono or pixel_format_info.is_bayer):\n return\n\n # Get raw image data.\n data = st_image.get_image_data()\n\n # Perform pixel value scaling if each pixel component is\n # larger than 8bit. Example: 10bit Bayer/Mono, 12bit, etc.\n if pixel_format_info.each_component_total_bit_count > 8:\n nparr = np.frombuffer(data, np.uint16)\n division = pow(2, pixel_format_info\n .each_component_valid_bit_count - 8)\n nparr = (nparr / division).astype('uint8')\n else:\n nparr = np.frombuffer(data, np.uint8)\n\n # Process image for display.\n nparr = nparr.reshape(st_image.height, st_image.width, 1)\n\n # Perform color conversion for Bayer.\n if pixel_format_info.is_bayer:\n bayer_type = pixel_format_info.get_pixel_color_filter()\n if bayer_type == st.EStPixelColorFilter.BayerRG:\n nparr = cv2.cvtColor(nparr, cv2.COLOR_BAYER_RG2RGB)\n elif bayer_type == st.EStPixelColorFilter.BayerGR:\n nparr = cv2.cvtColor(nparr, cv2.COLOR_BAYER_GR2RGB)\n elif bayer_type == st.EStPixelColorFilter.BayerGB:\n nparr = cv2.cvtColor(nparr, cv2.COLOR_BAYER_GB2RGB)\n elif bayer_type == st.EStPixelColorFilter.BayerBG:\n nparr = cv2.cvtColor(nparr, cv2.COLOR_BAYER_BG2RGB)\n\n # Resize image and store to self._image.\n nparr = cv2.resize(nparr, None,\n fx=DISPLAY_RESIZE_FACTOR,\n fy=DISPLAY_RESIZE_FACTOR)\n self._lock.acquire()\n self._image = nparr\n self._lock.release()\n\n\nst.initialize()\nst_system = st.create_system()\nst_device = st_system.create_first_device()\nst_datastream = st_device.create_datastream()\nst_datastream.start_acquisition()\nst_device.acquisition_start()\n\nmy_callback = CMyCallback()\ncb_func = my_callback.datastream_callback\n\n# Register callback for datastream\ncallback = st_datastream.register_callback(cb_func)\n\ncap = cv2.VideoCapture(1)\nmean = None\nfirst_frame = None\nresponses = {}\n\nstatus = True\nwhile status:\n output_image = my_callback.image\n print('output_image',output_image)\n if output_image is not None:\n cv2.imshow('image', output_image)\n key_input = cv2.waitKey(1)\n\n #motion detection. this happens, if camera detection motion of palm fruit\n ret, frame = cap.read()\n gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)\n gray = cv2.GaussianBlur(gray, (21,21), 0)\n if first_frame is None:\n time.sleep(10)\n first_frame = gray\n continue\n delta_frame = cv2.absdiff(first_frame, gray)\n\n if mean is None:\n mean = np.mean(delta_frame)\n continue\n print(np.mean(delta_frame))\n print(np.mean(delta_frame))\n if np.mean(delta_frame) > mean+10 or np.mean(delta_frame) < mean-10:\n responses['moved'] = '1'\n print('object moved')\n else:\n responses['moved'] = '0'\n print('no object moved')\n \n thresh_delta = cv2.threshold(delta_frame, 30, 255, cv2.THRESH_BINARY)[1]\n thresh_delta = cv2.dilate(thresh_delta, None, iterations=0)\n cnts, __ = cv2.findContours(thresh_delta.copy(), cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\n for contour in cnts:\n if cv2.contourArea(contour)<10000:\n continue\n (x,y,w,h) = cv2.boundingRect(contour)\n cv2.rectangle(frame, (x,y), (x+w, y+h), (0,255,0), 3)\n\n cv2.imshow('frame', frame)\n cv2.imshow('capturing', gray)\n cv2.imshow('delta', delta_frame)\n cv2.imshow('thresh', thresh_delta)\n\n capturing = None\n\n print(responses)\n if responses['moved'] == '1':\n time.sleep(1)\n # conveyor_stoped = toArduino2.write(str.encode(responses['moved']))\n\n conveyor_stoped = toArduino2.write(str.encode('0'))\n print('conveyor stop moving')\n\n status = False\n rescale_frame = cv2.resize(output_image, (1024,1088))\n h,w,l = rescale_frame.shape\n result_array = np.zeros((h,231,1))\n start_time = datetime.now()\n\n # capture frame per second\n for _ in range(50):\n ret, frame = cap.read()\n rescale_frame = cv2.resize(frame, (1024, 1088))\n crop_frame = rescale_frame[:, 370:601]\n result_array = np.append(result_array, crop_frame, axis=2)\n\n result_array = result_array[:,:,1:101]\n\n # Modify matrix of white reference and dark reference\n file_wr = 'wr.mat'\n file_blk = 'blk.mat'\n wr = sc.loadmat('wr.mat')['wr'].astype(int)\n blk = sc.loadmat('blk.mat')['blk'].astype(int)\n y = np.subtract(wr, blk)\n\n m, n = y.shape\n for s in range(m):\n for t in range(n):\n if y[s][t] < 0:\n y[s][t] = 0\n if y[s][t] == 0:\n y[s][t] = 1\n\n h1,w1,l1 = result_array.shape\n\n for i in range(l1):\n temp = np.subtract(result_array[:,:,i],blk)\n m, n = temp.shape\n for s in range(m):\n for t in range(n):\n if temp[s][t] < 0:\n temp[s][t] = 0\n result_array[:,:,i] = np.divide(temp, y)\n\n result_arrayv2 = np.zeros((100,231,1088))\n Ax, Ay, r = result_array.shape\n for i in range(Ax):\n for z in range(r):\n result_arrayv2[z,:,i] = result_array[i,:,z]\n\n print('dimension of array is {}'.format(result_arrayv2.shape))\n mean = []\n\n for i in range(1088):\n n = 1087-i\n res = result_arrayv2[:,:,n][40:55, 100:125]\n mean.append(np.mean(res))\n\n plt.plot(mean)\n end_time = datetime.now()\n time_needed = end_time - start_time\n print('the time needed is {} seconds'.format(time_needed.seconds))\n\n filename = \"parameterValue\"\n #Prediction\n prediction = NewPrediction(filename, np.mean(mean))\n result = prediction.predict()\n print(\"Result Prediction is {}\".format(result))\n\n # Sending result response to arduino to turn on conveyor\n # conveyor_moved = toArduino2.write(str.encode(mv_dtc['status_off']))\n print('conveyor moved {}'.format(conveyor_moved))\n time.sleep(3)\n\n #move arm\n time.sleep(1)\n # arm_moved = toArduino1.write( str.encode(result['index']))\n print('arm_moved'.format(arm_moved))\n \n print (\"Program done\")\n\n time.sleep(3)\n status = True\n plt.show()\n if key_input == ord('q'):\n break\n\nst_device.acquisition_stop()\nst_datastream.stop_acquisition()", "sub_path": "run.py", "file_name": "run.py", "file_ext": "py", "file_size_in_byte": 8828, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "serial.Serial", "line_number": 16, "usage_type": "call"}, {"api_name": "threading.Lock", "line_number": 27, "usage_type": "call"}, {"api_name": "stapipy.get_pixel_format_info", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.frombuffer", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.uint16", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.frombuffer", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 73, "usage_type": "attribute"}, {"api_name": "stapipy.EStPixelColorFilter", "line_number": 81, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.COLOR_BAYER_RG2RGB", "line_number": 82, "usage_type": "attribute"}, {"api_name": "stapipy.EStPixelColorFilter", "line_number": 83, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.COLOR_BAYER_GR2RGB", "line_number": 84, "usage_type": "attribute"}, {"api_name": "stapipy.EStPixelColorFilter", "line_number": 85, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 86, "usage_type": "call"}, {"api_name": "cv2.COLOR_BAYER_GB2RGB", "line_number": 86, "usage_type": "attribute"}, {"api_name": "stapipy.EStPixelColorFilter", "line_number": 87, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 88, "usage_type": "call"}, {"api_name": "cv2.COLOR_BAYER_BG2RGB", "line_number": 88, "usage_type": "attribute"}, {"api_name": "cv2.resize", "line_number": 91, "usage_type": "call"}, {"api_name": "stapipy.initialize", "line_number": 99, "usage_type": "call"}, {"api_name": "stapipy.create_system", "line_number": 100, "usage_type": "call"}, {"api_name": "cv2.VideoCapture", "line_number": 112, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 122, "usage_type": "call"}, {"api_name": "cv2.waitKey", "line_number": 123, "usage_type": "call"}, {"api_name": "cv2.cvtColor", "line_number": 127, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2GRAY", "line_number": 127, "usage_type": "attribute"}, {"api_name": "cv2.GaussianBlur", "line_number": 128, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 130, "usage_type": "call"}, {"api_name": "cv2.absdiff", "line_number": 133, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 140, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 147, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 147, "usage_type": "attribute"}, {"api_name": "cv2.dilate", "line_number": 148, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 149, "usage_type": "call"}, {"api_name": "cv2.RETR_EXTERNAL", "line_number": 149, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 149, "usage_type": "attribute"}, {"api_name": "cv2.contourArea", "line_number": 152, "usage_type": "call"}, {"api_name": "cv2.boundingRect", "line_number": 154, "usage_type": "call"}, {"api_name": "cv2.rectangle", "line_number": 155, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 157, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 158, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 159, "usage_type": "call"}, {"api_name": "cv2.imshow", "line_number": 160, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 166, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 175, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 183, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 192, "usage_type": "call"}, {"api_name": "numpy.subtract", "line_number": 205, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 211, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 227, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 227, "usage_type": "name"}, {"api_name": "annv2c.NewPrediction", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 234, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 241, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 244, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 250, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}]} +{"seq_id": "35283248", "text": "import logging\nimport os\nimport requests\nimport tempfile\nfrom contextlib import contextmanager\nfrom google.cloud import storage\nimport hail as hl\nfrom tqdm import tqdm\n\nfrom sv_pipeline.utils.common import parse_gs_path_to_bucket\n\nlogger = logging.getLogger(__name__)\n\n@contextmanager\ndef file_writer(file_path, get_existing_size=False):\n bucket = None\n size = None\n if is_gs_path(file_path):\n local_file_path = os.path.join(tempfile.gettempdir(), os.path.basename(file_path))\n bucket, file_name = parse_gs_path_to_bucket(file_path)\n if get_existing_size:\n blob = bucket.get_blob(file_name)\n size = blob and blob.size\n else:\n local_file_path = file_path\n if get_existing_size:\n size = os.path.isfile(local_file_path) and os.path.getsize(local_file_path)\n\n local_file = open(local_file_path, 'wb')\n\n yield local_file, size\n\n local_file.close()\n\n if bucket:\n blob = bucket.blob(file_name)\n blob.upload_from_filename(local_file_path)\n\n\ndef is_gs_path(path):\n return path.startswith('gs://')\n\n\ndef path_exists(path):\n is_gs = is_gs_path(path)\n return (is_gs and hl.hadoop_exists(path)) or (not is_gs and os.path.exists(path))\n\n\ndef download_file(url, to_dir=tempfile.gettempdir(), verbose=True):\n \"\"\"Download the given file and returns its local path.\n Args:\n url (string): HTTP or FTP url\n Returns:\n string: local file path\n \"\"\"\n\n if not (url and url.startswith((\"http://\", \"https://\"))):\n raise ValueError(\"Invalid url: {}\".format(url))\n remote_file_size = _get_remote_file_size(url)\n\n file_path = os.path.join(to_dir, filename)\n with file_writer(file_path, get_existing_size=True) as fw:\n f, file_size = fw\n if file_size and file_size == remote_file_size:\n logger.info(\"Re-using {} previously downloaded from {}\".format(local_file_path, url))\n return file_path\n\n is_gz = url.endswith(\".gz\")\n response = requests.get(url, stream=is_gz)\n input_iter = response if is_gz else response.iter_content()\n if verbose:\n logger.info(\"Downloading {} to {}\".format(url, local_file_path))\n input_iter = tqdm(input_iter, unit=\" data\" if is_gz else \" lines\")\n\n f.writelines(input_iter)\n input_iter.close()\n\n return file_path\n\n\ndef _get_remote_file_size(url):\n return int(requests.head(url).headers.get('Content-Length', '0'))\n", "sub_path": "sv_pipeline/genome/utils/download_utils.py", "file_name": "download_utils.py", "file_ext": "py", "file_size_in_byte": 2477, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "tempfile.gettempdir", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 19, "usage_type": "call"}, {"api_name": "sv_pipeline.utils.common.parse_gs_path_to_bucket", "line_number": 20, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 27, "usage_type": "call"}, {"api_name": "contextlib.contextmanager", "line_number": 14, "usage_type": "name"}, {"api_name": "hail.hadoop_exists", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "tempfile.gettempdir", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 61, "usage_type": "call"}, {"api_name": "os.path", "line_number": 61, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 69, "usage_type": "call"}, {"api_name": "tqdm.tqdm", "line_number": 73, "usage_type": "call"}, {"api_name": "requests.head", "line_number": 82, "usage_type": "call"}]} +{"seq_id": "433681909", "text": "from typing import Callable, Generator, Optional\nfrom testutils.trees import TreeNode, build_tree\n\n\ndef inorder_traversal(root: TreeNode) -> Generator[int, None, None]:\n stack: list[TreeNode] = []\n current: Optional[TreeNode] = root\n while True:\n if current is not None:\n stack.append(current)\n current = current.left\n\n elif stack:\n node = stack.pop()\n yield node.val\n current = node.right\n\n else:\n break\n\n\nclass Solution:\n def kthSmallest(self, root: Optional[TreeNode], k: int) -> int:\n if root is None:\n return -1\n\n counter = 1\n for value in inorder_traversal(root):\n if counter == k:\n return value\n\n counter += 1\n\n return -1\n\n\ntests = [\n (\n ([3, 1, 4, None, 2], 1,),\n 1,\n ),\n (\n ([5, 3, 6, 2, 4, None, None, 1], 3,),\n 3,\n ),\n]\n\n\ndef validator(\n kthSmallest: Callable[[Optional[TreeNode], int], int],\n inputs: tuple[list[Optional[int]], int],\n expected: int,\n) -> None:\n values, k = inputs\n tree = build_tree(values)\n output = kthSmallest(tree, k)\n assert output == expected, (output, expected)\n", "sub_path": "kth_smallest_element_in_a_bst.py", "file_name": "kth_smallest_element_in_a_bst.py", "file_ext": "py", "file_size_in_byte": 1246, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "testutils.trees.TreeNode", "line_number": 5, "usage_type": "name"}, {"api_name": "testutils.trees.TreeNode", "line_number": 6, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 7, "usage_type": "name"}, {"api_name": "testutils.trees.TreeNode", "line_number": 7, "usage_type": "name"}, {"api_name": "typing.Generator", "line_number": 5, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 23, "usage_type": "name"}, {"api_name": "testutils.trees.TreeNode", "line_number": 23, "usage_type": "name"}, {"api_name": "typing.Callable", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 50, "usage_type": "name"}, {"api_name": "testutils.trees.TreeNode", "line_number": 50, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 51, "usage_type": "name"}, {"api_name": "testutils.trees.build_tree", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "186242555", "text": "import datetime\nimport requests\nimport nltk\nfrom pycorenlp import StanfordCoreNLP\nfrom pyspark import SparkContext, SparkConf\nconf = SparkConf().setAppName(\"app\")\nsc = SparkContext(conf=conf)\n\ndef telegram_bot_sendtext(service_name, time, type, bot_message):\n bot_token = '1297258570:AAGTzLSNjMrE9gLhpJuQ2EOyL45Bb5yGwZc'\n bot_chatID = '-467351323'\n mess = type + '\\t' + service_name + '\\n' + time + '\\n' + bot_message\n mess = mess.replace('_', '-')\n send_text = 'https://api.telegram.org/bot' + bot_token + '/sendMessage?chat_id=' + bot_chatID + '&parse_mode=Markdown&text=' + mess\n response = requests.get(send_text)\n return response.json()\n\ndef format_relation(str):\n arr = [pos for pos, char in enumerate(str) if char == \" \"]\n result = \"\"\n for index, item in enumerate(str):\n if (index - 1) in arr:\n result += item.upper()\n else:\n result += item\n result = result.replace(\" \", \"\")\n return result\n\ndef extracter(sent):\n output= nlp.annotate(sent, properties={'annotators': 'tokenize, ssplit, pos, depparse, parse, openie','outputFormat': 'json'})\n triple = []\n try:\n for item in output['sentences'][0]['openie']:\n tmp = item['subject'].replace(\" \", \"_\") + \"\\t\" + format_relation(item['relation']) + \"\\t\" + item[\"object\"].replace(\" \", \"_\")\n triple.append(tmp)\n return triple\n except Exception as e:\n return repr(e)\n \n\ndef filter_trump(triple):\n return triple.lower().__contains__('trump')\n\nsite = 'news_fox'\nnow = datetime.datetime.now()\ndistTime = now - datetime.timedelta(1)\nfolder_name = distTime.__format__(\"%Y-%m-%d\")\n#folder_name = \"2020-05-27\"\nfolder_input = \"/user/hduser/processed_data/\"+site+\"/\"+folder_name+\"/*\"\nfoler_save = \"hdfs:///user/hduser/triples/\"+site+\"/\"+folder_name+\".txt\"\ntime_now = now.__format__('%Y-%m-%d %H:%M:%S')\ntry:\n telegram_bot_sendtext(\"extraction.py\", time_now, \"INFO\", \"Start extracting triples from \" + site + \", date: \" + folder_name)\n data = sc.textFile(folder_input)\n data2 = data.map(lambda x: nltk.sent_tokenize(x)[0])\n nlp = StanfordCoreNLP('http://localhost:9000')\n out = data2.flatMap(lambda x: extracter(x))\n res = out.filter(lambda x: filter_trump(x))\n res.saveAsTextFile(foler_save)\n\n time_now = datetime.datetime.now().__format__('%Y-%m-%d %H:%M:%S')\n telegram_bot_sendtext(\"extraction.py\", time_now, \"INFO\", \"Successfully extract triples from \" + site + \", date: \" + folder_name)\nexcept Exception as e:\n mess = \"ERROR when extract tripple news from \" +site+\", date: \" + folder_name + \"\\n\" + repr(e)\n time_now = datetime.datetime.now().__format__('%Y-%m-%d %H:%M:%S')\n telegram_bot_sendtext(\"extraction.py\", time_now, \"ERROR\", mess)\n", "sub_path": "server/extraction/news_fox/extraction_newsfox.py", "file_name": "extraction_newsfox.py", "file_ext": "py", "file_size_in_byte": 2755, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pyspark.SparkConf", "line_number": 6, "usage_type": "call"}, {"api_name": "pyspark.SparkContext", "line_number": 7, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 45, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 45, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 46, "usage_type": "call"}, {"api_name": "nltk.sent_tokenize", "line_number": 55, "usage_type": "call"}, {"api_name": "pycorenlp.StanfordCoreNLP", "line_number": 56, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 61, "usage_type": "attribute"}, {"api_name": "datetime.datetime.now", "line_number": 65, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 65, "usage_type": "attribute"}]} +{"seq_id": "43170030", "text": "import argparse\nfrom subnetwork import Discriminator\nimport torch\nimport numpy as np\n\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--gpu_number', type=int, default=0)\nparser.add_argument('--image_size', type=int, default=128)\nparser.add_argument('--z_dim', type=int, default=128)\n\nargs = parser.parse_args()\n\n\ndevice = 'cuda:{}'.format(args.gpu_number) if torch.cuda.is_available() else 'cpu'\n\n# build discriminator class\ndiscriminator = Discriminator(args=args, in_channels=3)\n\n\nreal_image = np.random.randn(10, 3, args.image_size, args.image_size)\nreal_image_tensor = torch.from_numpy(real_image).to(device).float()\nprint('input image tensor shape:', real_image_tensor.shape)\ndiscriminator(real_image_tensor)\n\n\n", "sub_path": "test_discriminator.py", "file_name": "test_discriminator.py", "file_ext": "py", "file_size_in_byte": 726, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 8, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 16, "usage_type": "attribute"}, {"api_name": "subnetwork.Discriminator", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 22, "usage_type": "attribute"}, {"api_name": "torch.from_numpy", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "432661016", "text": "from wx import wx\r\nimport wx.richtext\r\nimport sqlite3\r\n\r\nfrom StringIO import StringIO\r\n\r\n# Create Content Class\r\nclass CreateContentTab(wx.Panel):\r\n \r\n def __init__(self, parent):\r\n wx.Panel.__init__(self, parent)\r\n\r\n self.toolbar_ADDCONTENT = wx.NewId()\r\n self.toolbar_UPDATECONTENT = wx.NewId()\r\n # toolbar_STRIKETHROUGH = wx.NewId()\r\n # toolbar_SUBSCRIPT = wx.NewId()\r\n # toolbar_SUPERSCRIPT = wx.NewId()\r\n # toolbar_ORDEREDLIST = wx.NewId()\r\n # toolbar_UNORDEREDLIST = wx.NewId()\r\n richToolbar = self.MakeToolBar()\r\n self.box = wx.BoxSizer(wx.VERTICAL)\r\n self.createContentRichTextCtrl = wx.richtext.RichTextCtrl(self, -1, style=wx.VSCROLL | wx.BORDER_SUNKEN | wx.ALWAYS_SHOW_SB | wx.WANTS_CHARS)\r\n self.listBox = wx.ListBox(self, -1, style= wx.BORDER_SUNKEN | wx.LB_SINGLE | wx.LB_ALWAYS_SB | wx.LB_HSCROLL)\r\n self.box.Add(richToolbar, 0, wx.ALL | wx.ALIGN_LEFT | wx.EXPAND)\r\n self.box.Add(self.createContentRichTextCtrl, 1, wx.EXPAND)\r\n self.box.Add(self.listBox, 1, wx.EXPAND)\r\n self.SetSizer(self.box)\r\n wx.EVT_LISTBOX(self, self.listBox.GetId(), self.OnSelectedContentItem)\r\n \r\n # Create Event Function\r\n def OnSelectedContentItem(self, event):\r\n if(self.listBox.GetSelection() != -1):\r\n tmpString = self.listBox.GetStringSelection()\r\n tmpString2 = tmpString.split(\".\", 1)\r\n self.content = self.TopLevelParent.currentContentDict[int(tmpString2[0])]\r\n self.content = self.content[0].encode(\"UTF-8\")\r\n out = StringIO()\r\n handler = wx.richtext.RichTextXMLHandler()\r\n txtBuffer = self.createContentRichTextCtrl.GetBuffer()\r\n txtBuffer.AddHandler(handler)\r\n out.write(self.content)\r\n out.seek(0)\r\n handler.LoadStream(txtBuffer, out)\r\n out.close()\r\n self.createContentRichTextCtrl.Refresh()\r\n\r\n # Create RichText Toolbar\r\n def MakeToolBar(self):\r\n tb = wx.ToolBar(self, -1, style=wx.TB_FLAT | wx.NO_BORDER)\r\n self.ToolBar = tb\r\n tb.AddTool(wx.ID_CUT, wx.Bitmap(\"images/edit-cut.png\", wx.BITMAP_TYPE_PNG), isToggle=False, shortHelpString=\"Cut\")\r\n tb.AddTool(wx.ID_COPY, wx.Bitmap(\"images/edit-copy.png\", wx.BITMAP_TYPE_PNG), isToggle=False,shortHelpString=\"Copy\")\r\n tb.AddTool(wx.ID_PASTE, wx.Bitmap(\"images/edit-paste.png\", wx.BITMAP_TYPE_PNG), isToggle=False, shortHelpString=\"Paste\")\r\n tb.AddTool(wx.ID_UNDO, wx.Bitmap(\"images/edit-undo.png\", wx.BITMAP_TYPE_PNG), isToggle=False, shortHelpString=\"Undo\")\r\n tb.AddTool(wx.ID_REDO, wx.Bitmap(\"images/edit-redo.png\", wx.BITMAP_TYPE_PNG), isToggle=False, shortHelpString=\"Redo\")\r\n tb.AddSeparator()\r\n tb.AddTool(wx.ID_BOLD, wx.Bitmap(\"images/format-text-bold.png\", wx.BITMAP_TYPE_PNG), wx.Bitmap(\"images/format-text-bold-off.png\", wx.BITMAP_TYPE_PNG), isToggle=True, shortHelpString=\"Bold\")\r\n tb.AddTool(wx.ID_ITALIC, wx.Bitmap(\"images/format-text-italic.png\", wx.BITMAP_TYPE_PNG), isToggle=True, shortHelpString=\"Italic\")\r\n tb.AddTool(wx.ID_UNDERLINE, wx.Bitmap(\"images/format-text-underline.png\", wx.BITMAP_TYPE_PNG), isToggle=True, shortHelpString=\"Underline\")\r\n # tb.AddTool(toolbar_STRIKETHROUGH, wx.Bitmap(\"images/format-text-strikethrough.png\", wx.BITMAP_TYPE_PNG), isToggle=True, shortHelpString=\"Strikethrough\")\r\n # tb.AddTool(toolbar_SUBSCRIPT, wx.Bitmap(\"images/Subscript.png\", wx.BITMAP_TYPE_PNG), isToggle=True, shortHelpString=\"Subscript\")\r\n # tb.AddTool(toolbar_SUPERSCRIPT, wx.Bitmap(\"images/Superscript.png\", wx.BITMAP_TYPE_PNG), isToggle=True, shortHelpString=\"Superscript\")\r\n tb.AddSeparator()\r\n tb.AddTool(wx.ID_JUSTIFY_LEFT, wx.Bitmap(\"images/format-justify-left.png\", wx.BITMAP_TYPE_PNG), isToggle=True, shortHelpString=\"Left Align\")\r\n tb.AddTool(wx.ID_JUSTIFY_CENTER, wx.Bitmap(\"images/format-justify-center.png\", wx.BITMAP_TYPE_PNG), isToggle=True, shortHelpString=\"Align Center\")\r\n tb.AddTool(wx.ID_JUSTIFY_RIGHT, wx.Bitmap(\"images/format-justify-right.png\", wx.BITMAP_TYPE_PNG), isToggle=True, shortHelpString=\"Right Align\")\r\n tb.AddTool(wx.ID_JUSTIFY_FILL, wx.Bitmap(\"images/format-justify-fill.png\", wx.BITMAP_TYPE_PNG), isToggle=True, shortHelpString=\"Justify\")\r\n tb.AddSeparator()\r\n # tb.AddTool(toolbar_ORDEREDLIST, wx.Bitmap(\"images/NumbersList.png\", wx.BITMAP_TYPE_PNG), isToggle=True, shortHelpString=\"Ordered List\")\r\n # tb.AddTool(toolbar_UNORDEREDLIST, wx.Bitmap(\"images/BulletList.png\", wx.BITMAP_TYPE_PNG), isToggle=True, shortHelpString=\"Unordered List\")\r\n # tb.AddSeparator()\r\n # tb.AddTool(wx.ID_INDENT, wx.Bitmap(\"images/format-indent-more.png\", wx.BITMAP_TYPE_PNG), isToggle=False, shortHelpString=\"Indent\")\r\n # tb.AddTool(wx.ID_UNINDENT, wx.Bitmap(\"images/format-indent-less.png\", wx.BITMAP_TYPE_PNG), isToggle=False, shortHelpString=\"Outdent\")\r\n # tb.AddSeparator()\r\n tb.AddTool(self.toolbar_ADDCONTENT, wx.Bitmap(\"images/list-add.png\", wx.BITMAP_TYPE_PNG), isToggle=False, shortHelpString=\"Add New Content\")\r\n tb.AddTool(self.toolbar_UPDATECONTENT, wx.Bitmap(\"images/document-save.png\", wx.BITMAP_TYPE_PNG), isToggle=False, shortHelpString=\"Update Existing Content\")\r\n wx.EVT_TOOL(self, wx.ID_CUT, self.ForwardEvent)\r\n wx.EVT_TOOL(self, wx.ID_COPY, self.ForwardEvent)\r\n wx.EVT_TOOL(self, wx.ID_PASTE, self.ForwardEvent)\r\n wx.EVT_TOOL(self, wx.ID_UNDO, self.ForwardEvent)\r\n wx.EVT_TOOL(self, wx.ID_REDO, self.ForwardEvent)\r\n wx.EVT_TOOL(self, wx.ID_BOLD, self.OnBold)\r\n wx.EVT_UPDATE_UI(self, wx.ID_BOLD, self.OnUpdateBold)\r\n wx.EVT_TOOL(self, wx.ID_ITALIC, self.OnItalic)\r\n wx.EVT_UPDATE_UI(self, wx.ID_ITALIC, self.OnUpdateItalic)\r\n wx.EVT_TOOL(self, wx.ID_UNDERLINE, self.OnUnderline)\r\n wx.EVT_UPDATE_UI(self, wx.ID_UNDERLINE, self.OnUpdateUnderline)\r\n wx.EVT_TOOL(self, wx.ID_JUSTIFY_LEFT, self.OnAlignLeft)\r\n wx.EVT_UPDATE_UI(self, wx.ID_JUSTIFY_LEFT, self.OnUpdateAlignLeft)\r\n wx.EVT_TOOL(self, wx.ID_JUSTIFY_CENTER, self.OnAlignCenter)\r\n wx.EVT_UPDATE_UI(self, wx.ID_JUSTIFY_CENTER, self.OnUpdateAlignCenter)\r\n wx.EVT_TOOL(self, wx.ID_JUSTIFY_RIGHT, self.OnAlignRight)\r\n wx.EVT_UPDATE_UI(self, wx.ID_JUSTIFY_RIGHT, self.OnUpdateAlignRight)\r\n wx.EVT_TOOL(self, wx.ID_JUSTIFY_FILL, self.OnAlignJustify)\r\n wx.EVT_UPDATE_UI(self, wx.ID_JUSTIFY_FILL, self.OnUpdateAlignJustify)\r\n #wx.EVT_TOOL(self, toolbar_ORDEREDLIST, self.OnNumList)\r\n #wx.EVT_TOOL(self, toolbar_UNORDEREDLIST, self.OnBulletList)\r\n wx.EVT_TOOL(self, self.toolbar_ADDCONTENT, self.AddContent)\r\n wx.EVT_TOOL(self, self.toolbar_UPDATECONTENT, self.UpdateContent)\r\n tb.Realize()\r\n \r\n return tb\r\n\r\n def ForwardEvent(self, evt):\r\n # The RichTextCtrl can handle menu and update events for undo,\r\n # redo, cut, copy, paste, delete, and select all, so just\r\n # forward the event to it.\r\n self.createContentRichTextCtrl.ProcessEvent(evt)\r\n\r\n def OnStrikethrough(self, evt):\r\n self.TopLevelParent.DisplayDebug(\"\")\r\n #self.createContentRichTextCtrl\r\n \r\n def OnUpdateBold(self, evt):\r\n evt.Check(self.createContentRichTextCtrl.IsSelectionBold())\r\n\r\n def OnBold(self, evt):\r\n self.createContentRichTextCtrl.ApplyBoldToSelection()\r\n\r\n def OnItalic(self, evt): \r\n self.createContentRichTextCtrl.ApplyItalicToSelection()\r\n \r\n def OnUnderline(self, evt):\r\n self.createContentRichTextCtrl.ApplyUnderlineToSelection()\r\n \r\n def OnAlignLeft(self, evt):\r\n self.createContentRichTextCtrl.ApplyAlignmentToSelection(wx.richtext.TEXT_ALIGNMENT_LEFT)\r\n \r\n def OnAlignRight(self, evt):\r\n self.createContentRichTextCtrl.ApplyAlignmentToSelection(wx.richtext.TEXT_ALIGNMENT_RIGHT)\r\n \r\n def OnAlignCenter(self, evt):\r\n self.createContentRichTextCtrl.ApplyAlignmentToSelection(wx.richtext.TEXT_ALIGNMENT_CENTRE)\r\n \r\n def OnAlignJustify(self, evt):\r\n self.createContentRichTextCtrl.ApplyAlignmentToSelection(wx.richtext.TEXT_ALIGNMENT_JUSTIFIED)\r\n \r\n def OnUpdateItalic(self, evt): \r\n evt.Check(self.createContentRichTextCtrl.IsSelectionItalics())\r\n \r\n def OnUpdateUnderline(self, evt): \r\n evt.Check(self.createContentRichTextCtrl.IsSelectionUnderlined())\r\n \r\n def OnUpdateAlignLeft(self, evt):\r\n evt.Check(self.createContentRichTextCtrl.IsSelectionAligned(wx.richtext.TEXT_ALIGNMENT_LEFT))\r\n \r\n def OnUpdateAlignCenter(self, evt):\r\n evt.Check(self.createContentRichTextCtrl.IsSelectionAligned(wx.richtext.TEXT_ALIGNMENT_CENTRE))\r\n \r\n def OnUpdateAlignRight(self, evt):\r\n evt.Check(self.createContentRichTextCtrl.IsSelectionAligned(wx.richtext.TEXT_ALIGNMENT_RIGHT))\r\n \r\n def OnUpdateAlignJustify(self, evt):\r\n evt.Check(self.createContentRichTextCtrl.IsSelectionAligned(wx.richtext.TEXT_ALIGNMENT_JUSTIFIED))\r\n\r\n # Create Event Function\r\n def AddContent(self, evt):\r\n # should be disabled until someone enters content...\r\n out = StringIO()\r\n handler = wx.richtext.RichTextXMLHandler()\r\n txtBuffer = self.createContentRichTextCtrl.GetBuffer()\r\n handler.SaveStream(txtBuffer, out)\r\n out.seek(0)\r\n self.content = out.read()\r\n out.close()\r\n con = sqlite3.connect(self.TopLevelParent.database)\r\n cur = con.cursor()\r\n cur.execute(\"INSERT INTO ResourceTable(resourceData, courseID, resourceTypeID) VALUES(?, ?, 1)\", (self.content, self.TopLevelParent.currentCourseID))\r\n con.commit()\r\n con.close()\r\n self.TopLevelParent.currentContentDict = self.TopLevelParent.RefreshListBox(self.listBox, self.TopLevelParent.currentCourseID, 1, None)\r\n # put code in here to refresh available resources list box...\r\n # or we could just put code to refresh available resources list box when you select the layout tab button...\r\n # if that loads too quickly, then do it here\r\n \r\n def UpdateContent(self, evt):\r\n tmpString = self.listBox.GetStringSelection()\r\n tmpString2 = tmpString.split(\".\", 1)\r\n out = StringIO()\r\n handler = wx.richtext.RichTextXMLHandler()\r\n txtBuffer = self.createContentRichTextCtrl.GetBuffer()\r\n handler.SaveStream(txtBuffer, out)\r\n out.seek(0)\r\n self.content = out.read()\r\n out.close()\r\n con = sqlite3.connect(self.TopLevelParent.database)\r\n cur = con.cursor()\r\n cur.execute(\"UPDATE ResourceTable set resourceData = (?) where resourceID = (?)\", (self.content, int(tmpString2[0]),))\r\n con.commit()\r\n con.close()\r\n self.TopLevelParent.currentContentDict = self.TopLevelParent.RefreshListBox(self.listBox, self.TopLevelParent.currentCourseID, 1, None)\r\n # put code in here to refresh available resources list box...\r\n # or we could just put code to refresh available resources list box when you select the layout tab button...\r\n # if that loads too quickly, then do it here\r\n", "sub_path": "src/ContentTab.py", "file_name": "ContentTab.py", "file_ext": "py", "file_size_in_byte": 11304, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "wx.wx.Panel", "line_number": 8, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 8, "usage_type": "name"}, {"api_name": "wx.wx.Panel.__init__", "line_number": 11, "usage_type": "call"}, {"api_name": "wx.wx.Panel", "line_number": 11, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 11, "usage_type": "name"}, {"api_name": "wx.wx.NewId", "line_number": 13, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 13, "usage_type": "name"}, {"api_name": "wx.wx.NewId", "line_number": 14, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 14, "usage_type": "name"}, {"api_name": "wx.wx.BoxSizer", "line_number": 21, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 21, "usage_type": "name"}, {"api_name": "wx.wx.VERTICAL", "line_number": 21, "usage_type": "attribute"}, {"api_name": "wx.wx.richtext.RichTextCtrl", "line_number": 22, "usage_type": "call"}, {"api_name": "wx.wx.richtext", "line_number": 22, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 22, "usage_type": "name"}, {"api_name": "wx.wx.VSCROLL", "line_number": 22, "usage_type": "attribute"}, {"api_name": "wx.wx.BORDER_SUNKEN", "line_number": 22, "usage_type": "attribute"}, {"api_name": "wx.wx.ALWAYS_SHOW_SB", "line_number": 22, "usage_type": "attribute"}, {"api_name": "wx.wx.WANTS_CHARS", "line_number": 22, "usage_type": "attribute"}, {"api_name": "wx.wx.ListBox", "line_number": 23, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 23, "usage_type": "name"}, {"api_name": "wx.wx.BORDER_SUNKEN", "line_number": 23, "usage_type": "attribute"}, {"api_name": "wx.wx.LB_SINGLE", "line_number": 23, "usage_type": "attribute"}, {"api_name": "wx.wx.LB_ALWAYS_SB", "line_number": 23, "usage_type": "attribute"}, {"api_name": "wx.wx.LB_HSCROLL", "line_number": 23, "usage_type": "attribute"}, {"api_name": "wx.wx.ALL", "line_number": 24, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 24, "usage_type": "name"}, {"api_name": "wx.wx.ALIGN_LEFT", "line_number": 24, "usage_type": "attribute"}, {"api_name": "wx.wx.EXPAND", "line_number": 24, "usage_type": "attribute"}, {"api_name": "wx.wx.EXPAND", "line_number": 25, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 25, "usage_type": "name"}, {"api_name": "wx.wx.EXPAND", "line_number": 26, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 26, "usage_type": "name"}, {"api_name": "wx.wx.EVT_LISTBOX", "line_number": 28, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 28, "usage_type": "name"}, {"api_name": "StringIO.StringIO", "line_number": 37, "usage_type": "call"}, {"api_name": "wx.wx.richtext.RichTextXMLHandler", "line_number": 38, "usage_type": "call"}, {"api_name": "wx.wx.richtext", "line_number": 38, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 38, "usage_type": "name"}, {"api_name": "wx.wx.ToolBar", "line_number": 49, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 49, "usage_type": "name"}, {"api_name": "wx.wx.TB_FLAT", "line_number": 49, "usage_type": "attribute"}, {"api_name": "wx.wx.NO_BORDER", "line_number": 49, "usage_type": "attribute"}, {"api_name": "wx.wx.ID_CUT", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 51, "usage_type": "name"}, {"api_name": "wx.wx.Bitmap", "line_number": 51, "usage_type": "call"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 51, "usage_type": "attribute"}, {"api_name": "wx.wx.ID_COPY", "line_number": 52, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 52, "usage_type": "name"}, {"api_name": "wx.wx.Bitmap", "line_number": 52, "usage_type": "call"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 52, "usage_type": "attribute"}, {"api_name": "wx.wx.ID_PASTE", "line_number": 53, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 53, "usage_type": "name"}, {"api_name": "wx.wx.Bitmap", "line_number": 53, "usage_type": "call"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 53, "usage_type": "attribute"}, {"api_name": "wx.wx.ID_UNDO", "line_number": 54, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 54, "usage_type": "name"}, {"api_name": "wx.wx.Bitmap", "line_number": 54, "usage_type": "call"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 54, "usage_type": "attribute"}, {"api_name": "wx.wx.ID_REDO", "line_number": 55, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 55, "usage_type": "name"}, {"api_name": "wx.wx.Bitmap", "line_number": 55, "usage_type": "call"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 55, "usage_type": "attribute"}, {"api_name": "wx.wx.ID_BOLD", "line_number": 57, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 57, "usage_type": "name"}, {"api_name": "wx.wx.Bitmap", "line_number": 57, "usage_type": "call"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 57, "usage_type": "attribute"}, {"api_name": "wx.wx.ID_ITALIC", "line_number": 58, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 58, "usage_type": "name"}, {"api_name": "wx.wx.Bitmap", "line_number": 58, "usage_type": "call"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 58, "usage_type": "attribute"}, {"api_name": "wx.wx.ID_UNDERLINE", "line_number": 59, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 59, "usage_type": "name"}, {"api_name": "wx.wx.Bitmap", "line_number": 59, "usage_type": "call"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 59, "usage_type": "attribute"}, {"api_name": "wx.wx.ID_JUSTIFY_LEFT", "line_number": 64, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 64, "usage_type": "name"}, {"api_name": "wx.wx.Bitmap", "line_number": 64, "usage_type": "call"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 64, "usage_type": "attribute"}, {"api_name": "wx.wx.ID_JUSTIFY_CENTER", "line_number": 65, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 65, "usage_type": "name"}, {"api_name": "wx.wx.Bitmap", "line_number": 65, "usage_type": "call"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 65, "usage_type": "attribute"}, {"api_name": "wx.wx.ID_JUSTIFY_RIGHT", "line_number": 66, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 66, "usage_type": "name"}, {"api_name": "wx.wx.Bitmap", "line_number": 66, "usage_type": "call"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 66, "usage_type": "attribute"}, {"api_name": "wx.wx.ID_JUSTIFY_FILL", "line_number": 67, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 67, "usage_type": "name"}, {"api_name": "wx.wx.Bitmap", "line_number": 67, "usage_type": "call"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 67, "usage_type": "attribute"}, {"api_name": "wx.wx.Bitmap", "line_number": 75, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 75, "usage_type": "name"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 75, "usage_type": "attribute"}, {"api_name": "wx.wx.Bitmap", "line_number": 76, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 76, "usage_type": "name"}, {"api_name": "wx.wx.BITMAP_TYPE_PNG", "line_number": 76, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 77, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 77, "usage_type": "name"}, {"api_name": "wx.wx.ID_CUT", "line_number": 77, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 78, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 78, "usage_type": "name"}, {"api_name": "wx.wx.ID_COPY", "line_number": 78, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 79, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 79, "usage_type": "name"}, {"api_name": "wx.wx.ID_PASTE", "line_number": 79, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 80, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 80, "usage_type": "name"}, {"api_name": "wx.wx.ID_UNDO", "line_number": 80, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 81, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 81, "usage_type": "name"}, {"api_name": "wx.wx.ID_REDO", "line_number": 81, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 82, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 82, "usage_type": "name"}, {"api_name": "wx.wx.ID_BOLD", "line_number": 82, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_UPDATE_UI", "line_number": 83, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 83, "usage_type": "name"}, {"api_name": "wx.wx.ID_BOLD", "line_number": 83, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 84, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 84, "usage_type": "name"}, {"api_name": "wx.wx.ID_ITALIC", "line_number": 84, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_UPDATE_UI", "line_number": 85, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 85, "usage_type": "name"}, {"api_name": "wx.wx.ID_ITALIC", "line_number": 85, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 86, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 86, "usage_type": "name"}, {"api_name": "wx.wx.ID_UNDERLINE", "line_number": 86, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_UPDATE_UI", "line_number": 87, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 87, "usage_type": "name"}, {"api_name": "wx.wx.ID_UNDERLINE", "line_number": 87, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 88, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 88, "usage_type": "name"}, {"api_name": "wx.wx.ID_JUSTIFY_LEFT", "line_number": 88, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_UPDATE_UI", "line_number": 89, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 89, "usage_type": "name"}, {"api_name": "wx.wx.ID_JUSTIFY_LEFT", "line_number": 89, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 90, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 90, "usage_type": "name"}, {"api_name": "wx.wx.ID_JUSTIFY_CENTER", "line_number": 90, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_UPDATE_UI", "line_number": 91, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 91, "usage_type": "name"}, {"api_name": "wx.wx.ID_JUSTIFY_CENTER", "line_number": 91, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 92, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 92, "usage_type": "name"}, {"api_name": "wx.wx.ID_JUSTIFY_RIGHT", "line_number": 92, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_UPDATE_UI", "line_number": 93, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 93, "usage_type": "name"}, {"api_name": "wx.wx.ID_JUSTIFY_RIGHT", "line_number": 93, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 94, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 94, "usage_type": "name"}, {"api_name": "wx.wx.ID_JUSTIFY_FILL", "line_number": 94, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_UPDATE_UI", "line_number": 95, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 95, "usage_type": "name"}, {"api_name": "wx.wx.ID_JUSTIFY_FILL", "line_number": 95, "usage_type": "attribute"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 98, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 98, "usage_type": "name"}, {"api_name": "wx.wx.EVT_TOOL", "line_number": 99, "usage_type": "call"}, {"api_name": "wx.wx", "line_number": 99, "usage_type": "name"}, {"api_name": "wx.wx.richtext", "line_number": 127, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 127, "usage_type": "name"}, {"api_name": "wx.wx.richtext", "line_number": 130, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 130, "usage_type": "name"}, {"api_name": "wx.wx.richtext", "line_number": 133, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 133, "usage_type": "name"}, {"api_name": "wx.wx.richtext", "line_number": 136, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 136, "usage_type": "name"}, {"api_name": "wx.wx.richtext", "line_number": 145, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 145, "usage_type": "name"}, {"api_name": "wx.wx.richtext", "line_number": 148, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 148, "usage_type": "name"}, {"api_name": "wx.wx.richtext", "line_number": 151, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 151, "usage_type": "name"}, {"api_name": "wx.wx.richtext", "line_number": 154, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 154, "usage_type": "name"}, {"api_name": "StringIO.StringIO", "line_number": 159, "usage_type": "call"}, {"api_name": "wx.wx.richtext.RichTextXMLHandler", "line_number": 160, "usage_type": "call"}, {"api_name": "wx.wx.richtext", "line_number": 160, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 160, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 166, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 179, "usage_type": "call"}, {"api_name": "wx.wx.richtext.RichTextXMLHandler", "line_number": 180, "usage_type": "call"}, {"api_name": "wx.wx.richtext", "line_number": 180, "usage_type": "attribute"}, {"api_name": "wx.wx", "line_number": 180, "usage_type": "name"}, {"api_name": "sqlite3.connect", "line_number": 186, "usage_type": "call"}]} +{"seq_id": "210209193", "text": "from datetime import datetime\n\nfrom flask import Flask, abort, flash, redirect, render_template, request, url_for\n\napp = Flask(__name__)\napp.config['DEBUG'] = True\napp.config['SECRET_KEY'] = 'some_really_long_random_string_here'\n\n# local embedding_dims = std.extVar('embedding_dims');\n# local dataset = std.extVar('dataset');\n# local lang = std.extVar('lang');\n# local idf_weights = std.extVar('idf_weights');\n# local dan = std.extVar('dan');\n# local doc_projection = std.extVar('doc_projection');\n# local averaged = std.extVar('averaged');\n# local num_filters = std.extVar('num_filters');\n# local query_averaged = std.extVar('query_averaged');\n# local l2 = std.extVar('l2');\n# local lr = std.extVar('lr');\n\nvariables = {\n 'hyperparameters': [],\n 'architecture': [\n {'name': 'embedding_dims', 'title': 'Embedding Dimensions', 'type': 'text'},\n {'name': 'idf_weights', 'title': 'Use IDF Weights', 'type': 'bool'},\n {'name': 'dan', 'title': 'Use Averaging Composer', 'type': 'bool'}\n ],\n 'dataset': []\n}\n\ndef render_jsonnet():\n pass\n\n@app.route('/')\ndef configure():\n return render_template('configure.html')\n\nif __name__ == '__main__':\n app.run()\n", "sub_path": "neuclir/config/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1188, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 5, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 36, "usage_type": "call"}]} +{"seq_id": "315317358", "text": "# Copyright 2023 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nfrom __future__ import annotations\n\nfrom pants.backend.python.subsystems.python_tool_base import PythonToolBase\nfrom pants.backend.python.util_rules.pex_requirements import PexRequirements, Resolve\nfrom pants.testutil.option_util import create_subsystem\nfrom pants.util.ordered_set import FrozenOrderedSet\n\n\nclass _DummyTool(PythonToolBase):\n options_scope = \"dummy\"\n default_lockfile_resource = (\"dummy\", \"dummy\")\n\n\ndef test_install_from_resolve_default() -> None:\n tool = create_subsystem(\n _DummyTool,\n lockfile=\"dummy.lock\",\n install_from_resolve=\"dummy_resolve\",\n requirements=[\"foo\", \"bar\", \"baz\"],\n version=\"\",\n extra_requirements=[],\n )\n pex_reqs = tool.pex_requirements()\n assert isinstance(pex_reqs, PexRequirements)\n assert pex_reqs.from_superset == Resolve(\"dummy_resolve\", False)\n assert pex_reqs.req_strings_or_addrs == FrozenOrderedSet([\"bar\", \"baz\", \"foo\"])\n", "sub_path": "src/python/pants/backend/python/subsystems/python_tool_base_test.py", "file_name": "python_tool_base_test.py", "file_ext": "py", "file_size_in_byte": 1072, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pants.backend.python.subsystems.python_tool_base.PythonToolBase", "line_number": 12, "usage_type": "name"}, {"api_name": "pants.testutil.option_util.create_subsystem", "line_number": 18, "usage_type": "call"}, {"api_name": "pants.backend.python.util_rules.pex_requirements.PexRequirements", "line_number": 27, "usage_type": "argument"}, {"api_name": "pants.backend.python.util_rules.pex_requirements.Resolve", "line_number": 28, "usage_type": "call"}, {"api_name": "pants.util.ordered_set.FrozenOrderedSet", "line_number": 29, "usage_type": "call"}]} +{"seq_id": "359904485", "text": "#!/usr/bin/env python\n# Copyright 2019 Google LLC\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# https://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\"\"\"This example creates a keyword plan.\n\nKeyword plans can be reused for retrieving forecast metrics and historic\nmetrics.\n\"\"\"\n\n\nimport argparse\nimport sys\nimport uuid\n\nfrom google.ads.googleads.client import GoogleAdsClient\nfrom google.ads.googleads.errors import GoogleAdsException\n\n\n# [START add_keyword_plan]\ndef main(client, customer_id):\n \"\"\"Adds a keyword plan, campaign, ad group, etc. to the customer account.\n\n Also handles errors from the API and prints them.\n\n Args:\n client: An initialized instance of GoogleAdsClient\n customer_id: A str of the customer_id to use in requests.\n \"\"\"\n _add_keyword_plan(client, customer_id)\n\n\ndef _add_keyword_plan(client, customer_id):\n \"\"\"Adds a keyword plan, campaign, ad group, etc. to the customer account.\n\n Args:\n client: An initialized instance of GoogleAdsClient\n customer_id: A str of the customer_id to use in requests.\n\n Raises:\n GoogleAdsException: If an error is returned from the API.\n \"\"\"\n keyword_plan = _create_keyword_plan(client, customer_id)\n keyword_plan_campaign = _create_keyword_plan_campaign(\n client, customer_id, keyword_plan\n )\n keyword_plan_ad_group = _create_keyword_plan_ad_group(\n client, customer_id, keyword_plan_campaign\n )\n _create_keyword_plan_ad_group_keywords(\n client, customer_id, keyword_plan_ad_group\n )\n _create_keyword_plan_negative_campaign_keywords(\n client, customer_id, keyword_plan_campaign\n )\n\n\ndef _create_keyword_plan(client, customer_id):\n \"\"\"Adds a keyword plan to the given customer account.\n\n Args:\n client: An initialized instance of GoogleAdsClient\n customer_id: A str of the customer_id to use in requests.\n\n Returns:\n A str of the resource_name for the newly created keyword plan.\n\n Raises:\n GoogleAdsException: If an error is returned from the API.\n \"\"\"\n keyword_plan_service = client.get_service(\"KeywordPlanService\")\n operation = client.get_type(\"KeywordPlanOperation\")\n keyword_plan = operation.create\n\n keyword_plan.name = f\"Keyword plan for traffic estimate {uuid.uuid4()}\"\n\n forecast_interval = (\n client.enums.KeywordPlanForecastIntervalEnum.NEXT_QUARTER\n )\n keyword_plan.forecast_period.date_interval = forecast_interval\n\n response = keyword_plan_service.mutate_keyword_plans(\n customer_id=customer_id, operations=[operation]\n )\n resource_name = response.results[0].resource_name\n\n print(f\"Created keyword plan with resource name: {resource_name}\")\n\n return resource_name\n\n\ndef _create_keyword_plan_campaign(client, customer_id, keyword_plan):\n \"\"\"Adds a keyword plan campaign to the given keyword plan.\n\n Args:\n client: An initialized instance of GoogleAdsClient\n customer_id: A str of the customer_id to use in requests.\n keyword_plan: A str of the keyword plan resource_name this keyword plan\n campaign should be attributed to.create_keyword_plan.\n\n Returns:\n A str of the resource_name for the newly created keyword plan campaign.\n\n Raises:\n GoogleAdsException: If an error is returned from the API.\n \"\"\"\n keyword_plan_campaign_service = client.get_service(\n \"KeywordPlanCampaignService\"\n )\n operation = client.get_type(\"KeywordPlanCampaignOperation\")\n keyword_plan_campaign = operation.create\n\n keyword_plan_campaign.name = f\"Keyword plan campaign {uuid.uuid4()}\"\n keyword_plan_campaign.cpc_bid_micros = 1000000\n keyword_plan_campaign.keyword_plan = keyword_plan\n\n network = client.enums.KeywordPlanNetworkEnum.GOOGLE_SEARCH\n keyword_plan_campaign.keyword_plan_network = network\n\n geo_target = client.get_type(\"KeywordPlanGeoTarget\")\n # Constant for U.S. Other geo target constants can be referenced here:\n # https://developers.google.com/google-ads/api/reference/data/geotargets\n geo_target.geo_target_constant = \"geoTargetConstants/2840\"\n keyword_plan_campaign.geo_targets.append(geo_target)\n\n # Constant for English\n language = \"languageConstants/1000\"\n keyword_plan_campaign.language_constants.append(language)\n\n response = keyword_plan_campaign_service.mutate_keyword_plan_campaigns(\n customer_id=customer_id, operations=[operation]\n )\n\n resource_name = response.results[0].resource_name\n\n print(f\"Created keyword plan campaign with resource name: {resource_name}\")\n\n return resource_name\n\n\ndef _create_keyword_plan_ad_group(client, customer_id, keyword_plan_campaign):\n \"\"\"Adds a keyword plan ad group to the given keyword plan campaign.\n\n Args:\n client: An initialized instance of GoogleAdsClient\n customer_id: A str of the customer_id to use in requests.\n keyword_plan_campaign: A str of the keyword plan campaign resource_name\n this keyword plan ad group should be attributed to.\n\n Returns:\n A str of the resource_name for the newly created keyword plan ad group.\n\n Raises:\n GoogleAdsException: If an error is returned from the API.\n \"\"\"\n operation = client.get_type(\"KeywordPlanAdGroupOperation\")\n keyword_plan_ad_group = operation.create\n\n keyword_plan_ad_group.name = f\"Keyword plan ad group {uuid.uuid4()}\"\n keyword_plan_ad_group.cpc_bid_micros = 2500000\n keyword_plan_ad_group.keyword_plan_campaign = keyword_plan_campaign\n\n keyword_plan_ad_group_service = client.get_service(\n \"KeywordPlanAdGroupService\"\n )\n response = keyword_plan_ad_group_service.mutate_keyword_plan_ad_groups(\n customer_id=customer_id, operations=[operation]\n )\n\n resource_name = response.results[0].resource_name\n\n print(f\"Created keyword plan ad group with resource name: {resource_name}\")\n\n return resource_name\n\n\ndef _create_keyword_plan_ad_group_keywords(client, customer_id, plan_ad_group):\n \"\"\"Adds keyword plan ad group keywords to the given keyword plan ad group.\n\n Args:\n client: An initialized instance of GoogleAdsClient\n customer_id: A str of the customer_id to use in requests.\n plan_ad_group: A str of the keyword plan ad group resource_name\n these keyword plan keywords should be attributed to.\n\n Raises:\n GoogleAdsException: If an error is returned from the API.\n \"\"\"\n keyword_plan_ad_group_keyword_service = client.get_service(\n \"KeywordPlanAdGroupKeywordService\"\n )\n operation = client.get_type(\"KeywordPlanAdGroupKeywordOperation\")\n operations = []\n\n operation = client.get_type(\"KeywordPlanAdGroupKeywordOperation\")\n keyword_plan_ad_group_keyword1 = operation.create\n keyword_plan_ad_group_keyword1.text = \"mars cruise\"\n keyword_plan_ad_group_keyword1.cpc_bid_micros = 2000000\n keyword_plan_ad_group_keyword1.match_type = (\n client.enums.KeywordMatchTypeEnum.BROAD\n )\n keyword_plan_ad_group_keyword1.keyword_plan_ad_group = plan_ad_group\n operations.append(operation)\n\n operation = client.get_type(\"KeywordPlanAdGroupKeywordOperation\")\n keyword_plan_ad_group_keyword2 = operation.create\n keyword_plan_ad_group_keyword2.text = \"cheap cruise\"\n keyword_plan_ad_group_keyword2.cpc_bid_micros = 1500000\n keyword_plan_ad_group_keyword2.match_type = (\n client.enums.KeywordMatchTypeEnum.PHRASE\n )\n keyword_plan_ad_group_keyword2.keyword_plan_ad_group = plan_ad_group\n operations.append(operation)\n\n operation = client.get_type(\"KeywordPlanAdGroupKeywordOperation\")\n keyword_plan_ad_group_keyword3 = operation.create\n keyword_plan_ad_group_keyword3.text = \"jupiter cruise\"\n keyword_plan_ad_group_keyword3.cpc_bid_micros = 1990000\n keyword_plan_ad_group_keyword3.match_type = (\n client.enums.KeywordMatchTypeEnum.EXACT\n )\n keyword_plan_ad_group_keyword3.keyword_plan_ad_group = plan_ad_group\n operations.append(operation)\n\n response = keyword_plan_ad_group_keyword_service.mutate_keyword_plan_ad_group_keywords(\n customer_id=customer_id, operations=operations\n )\n\n for result in response.results:\n print(\n \"Created keyword plan ad group keyword with resource name: \"\n f\"{result.resource_name}\"\n )\n\n\ndef _create_keyword_plan_negative_campaign_keywords(\n client, customer_id, plan_campaign\n):\n \"\"\"Adds a keyword plan negative campaign keyword to the given campaign.\n\n Args:\n client: An initialized instance of GoogleAdsClient\n customer_id: A str of the customer_id to use in requests.\n plan_campaign: A str of the keyword plan campaign resource_name\n this keyword plan negative keyword should be attributed to.\n\n Raises:\n GoogleAdsException: If an error is returned from the API.\n \"\"\"\n keyword_plan_negative_keyword_service = client.get_service(\n \"KeywordPlanCampaignKeywordService\"\n )\n operation = client.get_type(\"KeywordPlanCampaignKeywordOperation\")\n\n keyword_plan_campaign_keyword = operation.create\n keyword_plan_campaign_keyword.text = \"moon walk\"\n keyword_plan_campaign_keyword.match_type = (\n client.enums.KeywordMatchTypeEnum.BROAD\n )\n keyword_plan_campaign_keyword.keyword_plan_campaign = plan_campaign\n keyword_plan_campaign_keyword.negative = True\n\n response = keyword_plan_negative_keyword_service.mutate_keyword_plan_campaign_keywords(\n customer_id=customer_id, operations=[operation]\n )\n\n print(\n \"Created keyword plan campaign keyword with resource name: \"\n f\"{response.results[0].resource_name}\"\n )\n # [END add_keyword_plan]\n\n\nif __name__ == \"__main__\":\n # GoogleAdsClient will read the google-ads.yaml configuration file in the\n # home directory if none is specified.\n googleads_client = GoogleAdsClient.load_from_storage(version=\"v10\")\n\n parser = argparse.ArgumentParser(\n description=\"Creates a keyword plan for specified customer.\"\n )\n # The following argument(s) should be provided to run the example.\n parser.add_argument(\n \"-c\",\n \"--customer_id\",\n type=str,\n required=True,\n help=\"The Google Ads customer ID.\",\n )\n args = parser.parse_args()\n\n try:\n main(googleads_client, args.customer_id)\n except GoogleAdsException as ex:\n print(\n f'Request with ID \"{ex.request_id}\" failed with status '\n f'\"{ex.error.code().name}\" and includes the following errors:'\n )\n for error in ex.failure.errors:\n print(f'\\tError with message \"{error.message}\".')\n if error.location:\n for field_path_element in error.location.field_path_elements:\n print(f\"\\t\\tOn field: {field_path_element.field_name}\")\n sys.exit(1)\n", "sub_path": "examples/planning/add_keyword_plan.py", "file_name": "add_keyword_plan.py", "file_ext": "py", "file_size_in_byte": 11329, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "uuid.uuid4", "line_number": 85, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 123, "usage_type": "call"}, {"api_name": "uuid.uuid4", "line_number": 169, "usage_type": "call"}, {"api_name": "google.ads.googleads.client.GoogleAdsClient.load_from_storage", "line_number": 287, "usage_type": "call"}, {"api_name": "google.ads.googleads.client.GoogleAdsClient", "line_number": 287, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 289, "usage_type": "call"}, {"api_name": "google.ads.googleads.errors.GoogleAdsException", "line_number": 304, "usage_type": "name"}, {"api_name": "sys.exit", "line_number": 314, "usage_type": "call"}]} +{"seq_id": "176357436", "text": "import json\n\nin_file = open(\"US_fires_9_1.json\", 'r')\n\nout_file = open('readable_eq_data.json','w')\n\neq_data = json.load(in_file)\n\njson.dump(eq_data,out_file, indent = 4)\n\n\nlist_of_eqs = eq_data\n\n\nmags,lons,lats = [],[],[]\n\nfor eq in list_of_eqs:\n if(eq[\"brightness\"] > 450):\n mags.append(eq[\"brightness\"])\n lons.append(eq[\"longitude\"])\n lats.append(eq[\"latitude\"])\n\nfrom plotly.graph_objs import Scattergeo, Layout\nfrom plotly import offline\n\ndata = [{\n 'type': 'scattergeo',\n 'lon': lons,\n 'lat': lats,\n 'marker': {\n 'size': [.03*mag for mag in mags],\n 'color': mags,\n \"colorscale\": 'Viridis',\n 'reversescale': True,\n 'colorbar': {'title': 'Magnitude'}\n },\n}]\n\nmy_layout = Layout(title = \"US Fires - 9/1/2020 through 9/13/2020\")\n\nfig = {'data':data, 'layout':my_layout}\n\noffline.plot(fig, filename = '09_1_2020_fires.html')\n", "sub_path": "1-13_data_file.py", "file_name": "1-13_data_file.py", "file_ext": "py", "file_size_in_byte": 900, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.load", "line_number": 7, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 9, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 39, "usage_type": "call"}, {"api_name": "plotly.offline.plot", "line_number": 43, "usage_type": "call"}, {"api_name": "plotly.offline", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "542998385", "text": "import json\n\ndef get_rememebered_name():\n filename='usernamev2.json'\n try:\n with open(filename) as f:\n usernamev2= json.load(f)\n except FileNotFoundError:\n return None\n else:\n return usernamev2\n\ndef greet_user():\n username = get_rememebered_name()\n if username:\n print(f\"Welcome back {username}\")\n else:\n username=input(\"What is your name: \")\n filename = 'username.json'\n with open(filename, 'w') as f:\n json.dump(username, f)\n print(f\"We'll remember you when you come back {username}\")\n\ngreet_user()", "sub_path": "Chapter_10/get_remembered_name.py", "file_name": "get_remembered_name.py", "file_ext": "py", "file_size_in_byte": 603, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.load", "line_number": 7, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 21, "usage_type": "call"}]} +{"seq_id": "299313921", "text": "\"\"\"Greets user by name if available or\n get username from user and save it to disk\"\"\"\n\nimport json\n\ndef get_stored_username():\n \"\"\"Get stored username if available.\"\"\"\n filename = 'Username.json'\n try:\n with open(filename) as file_obj:\n username = json.load(file_obj)\n except FileNotFoundError:\n return None\n else:\n return username\n\ndef get_new_username():\n \"\"\"Get new username and save it to disk\"\"\"\n username = input(\"Input your username: \")\n filename = 'Username.json'\n with open(filename, 'w') as file_obj:\n json.dump(username, file_obj)\n return username\n\ndef verify_user(username):\n verify_user = input(\"Are you \" + username + \"? (y/n)\")\n if verify_user.lower() == 'y':\n return True\n else:\n return False\n\ndef greet_user():\n \"\"\"Greet the user by name.\"\"\"\n username = get_stored_username()\n if not username:\n username = get_new_username()\n print(\"Username, \" + username + \", saved!\")\n elif not verify_user(username):\n username = get_new_username()\n print(\"Username, \" + username + \", saved!\")\n else:\n print(\"Welcome back, \" + username + \"!\")\n\n\ngreet_user()\n", "sub_path": "ch10/verify_user.py", "file_name": "verify_user.py", "file_ext": "py", "file_size_in_byte": 1204, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.load", "line_number": 11, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 22, "usage_type": "call"}]} +{"seq_id": "272600607", "text": "from datetime import datetime\nfrom bs4 import BeautifulSoup\n\nfrom aLib import logger\nimport requests\n\nclass DataGet:\n\tcurrent_date = datetime.now()\n\ttoday = (current_date.strftime('%m/%d/%Y')).replace(\"/\",\"-\")\n\tblad = \"eb\"\n\tblade = [\"eb\",\"p2\",\"p3\",\"p4\"]\n\tresponse = \"\"\n\tmyLogger = logger.Logger()\n\tdata = {}\n\n\n\tdef __init__(self, blad=None):\n\t\tif(blad != None):\n\t\t\tself.blad = blad\n\t\tself.myLogger.log(\" --- init --- on blad: \" + str(self.blad))\n\n\tdef getData(self):\n\t\treturnData = {}\n\t\tprint(\"[-------------- SCAN --------------]\")\n\t\tif self.blad == \"eb\":\n\t\t########################## EKSTRABLADET ###########################\n\t\t\tr = requests.get(\"https://ekstrabladet.dk/\")\n\t\t\tdata = r.text\n\t\t\tsoup = BeautifulSoup(data, \"html.parser\")\n\t\t\tupper = soup.findAll(\"div\", {\"class\": \"df-article-content\"})\n\t\t\tfor a in upper:\n\t\t\t\tprint(a)\n\t\t\t\ttitle = \"\"\n\t\t\t\ttry:\n\t\t\t\t\tspans = a.findAll(\"h3\")\n\t\t\t\t\tlink = a.find(\"a\")['href']\n\t\t\t\texcept TypeError as e:\n\t\t\t\t\tbreak #no link so skip\n\t\t\t\tfor s in spans:\n\t\t\t\t\ttry:\n\t\t\t\t\t\ttitle_part = (s.find(\"span\").text)\n\t\t\t\t\t\ttitle +=\" \"+ title_part\n\t\t\t\t\texcept Exception as e:\n\t\t\t\t\t\tpass\n\t\t\t\ttitle = title.strip()\n\t\t\t\tinner = requests.get(link)\n\t\t\t\tinnerData = inner.text\n\t\t\t\tinnerSoup = BeautifulSoup(innerData, \"html.parser\")\n\t\t\t\tfullContent = \"\"\n\t\t\t\tkommentareAntal = None\n\t\t\t\ttry:\n\t\t\t\t\trealTitle = (innerSoup.find(\"h1\", {\"class\": \"art-title\"}).text).strip()\n\t\t\t\t\ttimeOfArticle = (innerSoup.find((\"time\"),{\"class\": [\"eb-row-item\",\"eb-row-item--grow\",\"article-timestamp--top\"]}).text).strip()\n\t\t\t\t\tkommentareAntal = (innerSoup.find(\"span\", id=\"fnTalkCommentText\").text).split(\" \")[0]\n\t\t\t\texcept Exception as e:\n\t\t\t\t\tpass\n\n\t\t\t\t#print(timeOfArticle)\n\t\t\t\tfor part in (innerSoup.findAll(\"p\", class_=False)):\n\t\t\t\t\tpart = part.text\n\t\t\t\t\tif(\"function(apntag)\" not in part and \"eller prøv igen senere.\" not in part):\n\t\t\t\t\t\tif(\"Foto:\" in part):\n\t\t\t\t\t\t\tpart = \"[FOTO]\" + part.split(\"Foto:\")[0] + \"[FOTO]\"\n\t\t\t\t\t\tfullContent = fullContent + part\n\n\t\t\t\t#print(title + \" --- \" + realTitle)\n\t\t\t\tif \"ekstrabladet.dk\" in link:\n\t\t\t\t\tif (not innerSoup.find(\"body\", {\"class\": \"body--plus\"})) and (not innerSoup.find(\"body\", {\"class\": \"body--plus\"})):\n\t\t\t\t\t\treturnData[link] = [title,realTitle,fullContent,timeOfArticle, kommentareAntal]\n\t\t\t\telse:\n\t\t\t\t\treturnData[link] = [title,\"N/A\",fullContent,\"N/A\", 0]\n\n\t\treturn returnData\n\t\t########################## EKSTRABLADET ###########################\n\n\n\tdef parseData(self,response):\n\t\ttry:\n\t\t\treturn response\n\t\texcept:\n\t\t\tpass", "sub_path": "src/popData.py", "file_name": "popData.py", "file_ext": "py", "file_size_in_byte": 2482, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime.now", "line_number": 8, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 8, "usage_type": "name"}, {"api_name": "aLib.logger.Logger", "line_number": 13, "usage_type": "call"}, {"api_name": "aLib.logger", "line_number": 13, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 27, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 46, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 48, "usage_type": "call"}]} +{"seq_id": "419904023", "text": "from collections import OrderedDict as odict\nimport importlib\n\nfrom omnium.processes import Process\n\nclass LastFiveDayMean(Process):\n name = 'last_five_day_mean'\n out_ext = 'txt'\n\n def load_modules(self):\n self.iris = importlib.import_module('iris')\n\n def load_upstream(self):\n super(LastFiveDayMean, self).load_upstream()\n filenames = [n.filename(self.config) for n in self.node.from_nodes]\n all_timeseries = self.iris.load(filenames)\n self.data = all_timeseries\n return all_timeseries\n\n def run(self):\n super(LastFiveDayMean, self).run()\n all_timeseries = self.data\n self.processed_data = []\n for timeseries in all_timeseries:\n if self.node.name == 'surf_ts_means_large_dom':\n five_days = -144*5*3 # 20s ts. output every 10ts\n else:\n five_days = -144*5 # output every 10 min.\n\n time_in_hours = timeseries.coord('time').points[-1]\\\n - timeseries.coord('time').points[five_days]\n value = timeseries[five_days:]\\\n .collapsed('time', self.iris.analysis.MEAN)\n # print(timeseries.name(), value.data)\n self.processed_data.append('{},{},{},{}'.format(timeseries.name(),\n time_in_hours,\n value.data,\n timeseries.units))\n\n def save(self):\n super(LastFiveDayMean, self).save()\n with open(self.node.filename(self.config), 'w') as f:\n f.write('\\n'.join(self.processed_data))\n", "sub_path": "processes/last_five_day_mean.py", "file_name": "last_five_day_mean.py", "file_ext": "py", "file_size_in_byte": 1699, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "omnium.processes.Process", "line_number": 6, "usage_type": "name"}, {"api_name": "importlib.import_module", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "602192617", "text": "import numpy as np\nfrom math import ceil\nimport logging\nfrom typing import List, Tuple\n\nfrom ..core.position import Position\nfrom .route import Route\nfrom .base_router import BaseRouter\n\n\nclass LinearRouter(BaseRouter):\n \"\"\" Calculates routes as straight lines and haversine distances,\n kind of \"bee line\" distance\n\n Usage sample::\n\n >>> from simobility.routers.linear_router import LinearRouter\n >>> my_router = LinearRouter(clock=clock, speed=speed_kmph)\n \"\"\"\n\n def __init__(self, clock, speed: int = 20):\n self.clock = clock\n self.speed = speed\n\n def map_match(self, position: Position) -> Position:\n return Position(*position.coords)\n\n def calculate_route(self, origin: Position, destination: Position) -> Route:\n \"\"\"\n Calculate route between 2 points\n\n Params\n ------\n\n origin : Position\n destination : Position\n\n Returns\n -------\n\n route : Route\n \"\"\"\n\n trip_duration = self.estimate_duration(origin, destination)\n\n y = np.linspace(origin.lat, destination.lat, trip_duration + 1)\n x = np.linspace(origin.lon, destination.lon, trip_duration + 1)\n\n path = np.array([x, y]).T.tolist()\n waypoints = [Position(x_, y_) for x_, y_ in path]\n\n distance_km = origin.distance(destination)\n\n return Route(\n self.clock.now, waypoints, trip_duration, distance_km, origin, destination\n )\n\n def estimate_duration(self, origin: Position, destination: Position) -> int:\n \"\"\" Duration in clock units\n\n Params\n ------\n\n origin : Position\n destination : Position\n\n Returns\n -------\n\n duration : int\n Trip duration in clock units\n \"\"\"\n\n distance_km = origin.distance(destination)\n # convert to minutes\n travel_time = distance_km / self.speed * 60\n\n return ceil(self.clock.time_to_clock_time(travel_time, \"m\"))\n\n def calculate_distance_matrix(\n self,\n sources: List[Position],\n destinations: List[Position],\n travel_time: bool = True,\n ) -> np.array:\n \"\"\" Calculate all-to-all travel time - all source to all destinations.\n Here distance means \"distance in time\"\n\n Params\n ------\n\n sources : list\n List of Positions\n\n destinations : list\n List of Positions\n\n Returns\n -------\n\n distance_matrix : np.array\n All-to-all trip durations (distance in time) in clock units\n \"\"\"\n\n n_sources = len(sources)\n n_dest = len(destinations)\n\n matrix = np.zeros([n_sources, n_dest])\n\n for ind1, src in enumerate(sources):\n for ind2, dest in enumerate(destinations):\n\n if travel_time:\n matrix[ind1, ind2] = self.estimate_duration(src, dest)\n else:\n matrix[ind1, ind2] = src.distance(dest)\n\n return matrix\n", "sub_path": "simobility/routers/linear_router.py", "file_name": "linear_router.py", "file_ext": "py", "file_size_in_byte": 3019, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "base_router.BaseRouter", "line_number": 11, "usage_type": "name"}, {"api_name": "core.position.Position", "line_number": 25, "usage_type": "name"}, {"api_name": "core.position.Position", "line_number": 26, "usage_type": "call"}, {"api_name": "core.position.Position", "line_number": 28, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 49, "usage_type": "call"}, {"api_name": "core.position.Position", "line_number": 50, "usage_type": "call"}, {"api_name": "route.Route", "line_number": 54, "usage_type": "call"}, {"api_name": "route.Route", "line_number": 28, "usage_type": "name"}, {"api_name": "core.position.Position", "line_number": 58, "usage_type": "name"}, {"api_name": "math.ceil", "line_number": 78, "usage_type": "call"}, {"api_name": "typing.List", "line_number": 82, "usage_type": "name"}, {"api_name": "core.position.Position", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.List", "line_number": 83, "usage_type": "name"}, {"api_name": "core.position.Position", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 85, "usage_type": "attribute"}]} +{"seq_id": "209832106", "text": "from setuptools import setup\n\nwith open('README') as f:\n long_description = f.read()\n\nwith open('VERSION') as f:\n version = f.readline().strip()\n\nsetup(\n name='ase-gaming',\n description='playing w/ setup.py',\n long_description=long_description,\n license='MIT',\n author='@wolfhesse',\n author_email='wolfgang.schuessel@gmail.com',\n url='asecms.base.wolfspool.at/py-ase-gaming-pg',\n version=version,\n packages=[\n 'ase_gaming',\n ],\n # scripts=[\n # 'scripts/eins.py',\n # ],\n classifiers=[\n 'Development Status :: 4 - Beta',\n 'License :: OSI Approved :: MIT license',\n ],\n zip_safe=False,\n install_requires=['pytest', 'ase_game_py_mod'],\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 716, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "setuptools.setup", "line_number": 9, "usage_type": "call"}]} +{"seq_id": "516017164", "text": "\"\"\"\n Functions for various anti-aliasing functions and wrappers.\n\"\"\"\nfrom vsutil import get_w, get_y, split\nfrom typing import Optional\n\nimport vapoursynth as vs\n\nfrom . import util\n\ncore = vs.core\n\n\ndef nneedi3_clamp(clip: vs.VideoNode, strength: int = 1,\n mask: Optional[vs.VideoNode] = None, ret_mask: bool = False,\n show_mask: bool = False,\n opencl: bool = False) -> vs.VideoNode:\n \"\"\"\n Function written by Zastin to clamp eedi3 to nnedi3 for the purpose of reducing artifacts.\n This should fix every issue created by eedi3. For example: https://i.imgur.com/hYVhetS.jpg\n\n Dependencies:\n\n * kagefunc (optional: retinex edgemask)\n * vapoursynth-retinex (optional: retinex edgemask)\n * vapoursynth-tcanny (optional: retinex edgemask)\n * vapoursynth-eedi3\n * vapoursynth-nnedi3 or znedi3\n * vapoursynth-nnedi3cl (optional: opencl)\n * vsTAAmbk\n\n :param clip: Input clip\n :param strength: Set threshold strength (Default: 1)\n :param mask: Clip to use for custom mask (Default: None)\n :param ret_mask: Replace default mask with a retinex edgemask (Default: False)\n :param show_mask: Return mask instead of clip (Default: False)\n :param opencl: OpenCL acceleration (Default: False)\n\n :return: Antialiased clip\n \"\"\"\n try:\n from vsTAAmbk import TAAmbk\n except ModuleNotFoundError:\n raise ModuleNotFoundError(\"nnedi3_clamp: missing dependency 'vsTAAmbk'\")\n\n bits = clip.format.bits_per_sample - 8\n thr = strength * (1 >> bits)\n strong = TAAmbk(clip, aatype='Eedi3', alpha=0.25, beta=0.5, gamma=40, nrad=2, mdis=20, mtype=0,\n opencl=opencl)\n weak = TAAmbk(clip, aatype='Nnedi3', nsize=3, nns=3, qual=1, mtype=0, opencl=opencl)\n expr = 'x z - y z - * 0 < y x y {0} + min y {0} - max ?'.format(thr)\n\n if clip.format.num_planes > 1:\n expr = [expr, '']\n aa = core.std.Expr([strong, weak, clip], expr)\n\n if mask:\n merged = clip.std.MaskedMerge(aa, mask, planes=0)\n elif ret_mask:\n try:\n import kagefunc as kgf\n except ModuleNotFoundError:\n raise ModuleNotFoundError(\"nnedi3_clamp: missing dependency 'kagefunc'\")\n mask = kgf.retinex_edgemask(clip, 1).std.Binarize()\n merged = clip.std.MaskedMerge(aa, mask, planes=0)\n else:\n mask = clip.std.Prewitt(planes=0).std.Binarize(planes=0).std.Maximum(planes=0).std.Convolution([1] * 9, planes=0)\n mask = get_y(mask)\n merged = clip.std.MaskedMerge(aa, mask, planes=0)\n\n if show_mask:\n return mask\n return merged if clip.format.color_family == vs.GRAY else core.std.ShufflePlanes([merged, clip], [0, 1, 2], vs.YUV)\n\n\ndef transpose_aa(clip: vs.VideoNode,\n eedi3: bool = False) -> vs.VideoNode:\n \"\"\"\n Function written by Zastin and modified by LightArrowsEXE to perform anti-aliasing\n over a clip by using Nnedi3, transposing, using Nnedi3 again, and transposing a final time.\n This results in overall stronger anti-aliasing.\n Useful for shows like Yuru Camp with bad lineart problems.\n\n Dependencies: vapoursynth-eedi3, vapoursynth-nnedi3, znedi3\n\n :param clip: Input clip\n :param eedi3: Use eedi3 for the interpolation (Default: False)\n\n :return: Antialiased clip\n \"\"\"\n clip_y = get_y(clip)\n\n if eedi3:\n def _aa(clip_y):\n clip_y = clip_y.std.Transpose()\n clip_y = clip_y.eedi3m.EEDI3(0, 1, 0, 0.5, 0.2)\n clip_y = clip_y.znedi3.nnedi3(1, 0, 0, 3, 4, 2)\n clip_y = clip_y.resize.Spline36(clip.height, clip.width, src_top=.5)\n clip_y = clip_y.std.Transpose()\n clip_y = clip_y.eedi3m.EEDI3(0, 1, 0, 0.5, 0.2)\n clip_y = clip_y.znedi3.nnedi3(1, 0, 0, 3, 4, 2)\n return clip_y.resize.Spline36(clip.width, clip.height, src_top=.5)\n else:\n def _aa(clip_y):\n clip_y = clip_y.std.Transpose()\n clip_y = clip_y.nnedi3.nnedi3(0, 1, 0, 3, 3, 2)\n clip_y = clip_y.nnedi3.nnedi3(1, 0, 0, 3, 3, 2)\n clip_y = clip_y.resize.Spline36(clip.height, clip.width, src_top=.5)\n clip_y = clip_y.std.Transpose()\n clip_y = clip_y.nnedi3.nnedi3(0, 1, 0, 3, 3, 2)\n clip_y = clip_y.nnedi3.nnedi3(1, 0, 0, 3, 3, 2)\n return clip_y.resize.Spline36(clip.width, clip.height, src_top=.5)\n\n def _csharp(flt, clip):\n blur = core.std.Convolution(flt, [1] * 9)\n return core.std.Expr([flt, clip, blur], 'x y < x x + z - x max y min x x + z - x min y max ?')\n\n aaclip = _aa(clip_y)\n aaclip = _csharp(aaclip, clip_y).rgvs.Repair(clip_y, 13)\n\n return aaclip if clip.format.color_family is vs.GRAY else core.std.ShufflePlanes([aaclip, clip], [0, 1, 2], vs.YUV)\n\n\ndef upscaled_sraa(clip: vs.VideoNode,\n rfactor: float = 1.5,\n rep: Optional[int] = None,\n h: Optional[int] = None, ar: Optional[int] = None,\n sharp_downscale: bool = False) -> vs.VideoNode:\n \"\"\"\n Another AA written by Zastin and modified by LightArrowsEXE to perform\n an upscaled single-rate AA to deal with heavy aliasing.\n Useful for Web rips, where the source quality is not good enough to descale,\n but you still want to deal with some bad aliasing and lineart.\n\n Dependencies: fmtconv, rgsf (optional: 32bit clip), vapoursynth-eedi3, vapoursynth-nnedi3\n\n :param clip: Input clip\n :param rfactor: Image enlargement factor. 1.3..2 makes it comparable in strength to vsTAAmbk.\n It is not recommended to go below 1.3 (Default: 1.5)\n :param rep: Repair mode (Default: None)\n :param h: Set custom height. Width and aspect ratio are auto-calculated (Default: None)\n :param ar: Force custom aspect ratio. Width is auto-calculated (Default: None)\n :param sharp_downscale: Use a sharper downscaling kernel (inverse gauss) (Default: False)\n\n :return: Antialiased clip\n \"\"\"\n planes = split(clip)\n\n nnargs = dict(nsize=0, nns=4, qual=2)\n eeargs = dict(alpha=0.2, beta=0.6, gamma=40, nrad=2, mdis=20) # TAAmbk defaults are 0.5, 0.2, 20, 3, 30\n\n ssw = round( clip.width * rfactor )\n ssh = round( clip.height * rfactor )\n\n while ssw % 2:\n ssw += 1\n while ssh % 2:\n ssh += 1\n\n if h:\n if not ar:\n ar = clip.width / clip.height\n w = get_w(h, aspect_ratio=ar)\n else:\n w, h = clip.width, clip.height\n\n # Nnedi3 upscale from source height to source height * rounding (Default 1.5)\n up_y = core.nnedi3.nnedi3(planes[0], 0, 1, 0, **nnargs)\n up_y = core.resize.Spline36(up_y, height=ssh, src_top=.5)\n up_y = core.std.Transpose(up_y)\n up_y = core.nnedi3.nnedi3(up_y, 0, 1, 0, **nnargs)\n up_y = core.resize.Spline36(up_y, height=ssw, src_top=.5)\n\n # Single-rate AA\n aa_y = core.eedi3m.EEDI3(up_y, 0, 0, 0, **eeargs, sclip=core.nnedi3.nnedi3(up_y, 0, 0, 0, **nnargs))\n aa_y = core.std.Transpose(aa_y)\n aa_y = core.eedi3m.EEDI3(aa_y, 0, 0, 0, **eeargs, sclip=core.nnedi3.nnedi3(aa_y, 0, 0, 0, **nnargs))\n\n # Back to source clip height or given height\n scaled = core.fmtc.resample(aa_y, w, h, kernel='gauss', invks=True, invkstaps=2, taps=1, a1=32) if sharp_downscale else core.resize.Spline36(aa_y, w, h)\n\n if rep:\n scaled = util.pick_repair(scaled)(scaled, planes[0].resize.Spline36(w, h), rep)\n return scaled if clip.format.color_family is vs.GRAY else core.std.ShufflePlanes([scaled, clip], [0, 1, 2], vs.YUV)\n", "sub_path": "lvsfunc/aa.py", "file_name": "aa.py", "file_ext": "py", "file_size_in_byte": 7723, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "vapoursynth.core", "line_number": 11, "usage_type": "attribute"}, {"api_name": "vapoursynth.VideoNode", "line_number": 14, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 15, "usage_type": "name"}, {"api_name": "vapoursynth.VideoNode", "line_number": 15, "usage_type": "attribute"}, {"api_name": "vsTAAmbk.TAAmbk", "line_number": 48, "usage_type": "call"}, {"api_name": "vsTAAmbk.TAAmbk", "line_number": 50, "usage_type": "call"}, {"api_name": "kagefunc.retinex_edgemask", "line_number": 64, "usage_type": "call"}, {"api_name": "vsutil.get_y", "line_number": 68, "usage_type": "call"}, {"api_name": "vapoursynth.GRAY", "line_number": 73, "usage_type": "attribute"}, {"api_name": "vapoursynth.YUV", "line_number": 73, "usage_type": "attribute"}, {"api_name": "vapoursynth.VideoNode", "line_number": 17, "usage_type": "attribute"}, {"api_name": "vapoursynth.VideoNode", "line_number": 76, "usage_type": "attribute"}, {"api_name": "vsutil.get_y", "line_number": 91, "usage_type": "call"}, {"api_name": "vapoursynth.GRAY", "line_number": 121, "usage_type": "attribute"}, {"api_name": "vapoursynth.YUV", "line_number": 121, "usage_type": "attribute"}, {"api_name": "vapoursynth.VideoNode", "line_number": 77, "usage_type": "attribute"}, {"api_name": "vapoursynth.VideoNode", "line_number": 124, "usage_type": "attribute"}, {"api_name": "typing.Optional", "line_number": 126, "usage_type": "name"}, {"api_name": "typing.Optional", "line_number": 127, "usage_type": "name"}, {"api_name": "vsutil.split", "line_number": 147, "usage_type": "call"}, {"api_name": "vsutil.get_w", "line_number": 163, "usage_type": "call"}, {"api_name": "vapoursynth.GRAY", "line_number": 184, "usage_type": "attribute"}, {"api_name": "vapoursynth.YUV", "line_number": 184, "usage_type": "attribute"}, {"api_name": "vapoursynth.VideoNode", "line_number": 128, "usage_type": "attribute"}]} +{"seq_id": "492884638", "text": "# Copyright (c) 2016, Daniele Venzano\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\n# implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\n\"\"\"The Discovery API endpoint.\"\"\"\n\nfrom tornado.web import RequestHandler\n\nfrom zoe_api.api_endpoint import APIEndpoint # pylint: disable=unused-import\nfrom zoe_api.rest_api.utils import catch_exceptions, manage_cors_headers\n\n\nclass DiscoveryAPI(RequestHandler):\n \"\"\"The Discovery API endpoint.\"\"\"\n\n def initialize(self, **kwargs):\n \"\"\"Initializes the request handler.\"\"\"\n self.api_endpoint = kwargs['api_endpoint'] # type: APIEndpoint\n\n def set_default_headers(self):\n \"\"\"Set up the headers for enabling CORS.\"\"\"\n manage_cors_headers(self)\n\n def options(self):\n \"\"\"Needed for CORS.\"\"\"\n self.set_status(204)\n self.finish()\n\n @catch_exceptions\n def get(self, execution_id: int, service_group: str):\n \"\"\"HTTP GET method.\"\"\"\n self.api_endpoint.execution_by_id(0, 'admin', execution_id)\n if service_group != 'all':\n services = self.api_endpoint.service_list(0, 'admin', service_group=service_group, execution_id=execution_id)\n else:\n services = self.api_endpoint.service_list(0, 'admin', execution_id=execution_id)\n ret = {\n 'service_type': service_group,\n 'execution_id': execution_id,\n 'dns_names': [s.dns_name for s in services]\n }\n\n self.write(ret)\n\n def data_received(self, chunk):\n \"\"\"Not implemented as we do not use stream uploads\"\"\"\n pass\n", "sub_path": "zoe_api/rest_api/discovery.py", "file_name": "discovery.py", "file_ext": "py", "file_size_in_byte": 2020, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tornado.web.RequestHandler", "line_number": 24, "usage_type": "name"}, {"api_name": "zoe_api.rest_api.utils.manage_cors_headers", "line_number": 33, "usage_type": "call"}, {"api_name": "zoe_api.rest_api.utils.catch_exceptions", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "572845721", "text": "import openpyxl\nimport numpy\nimport statistics\nimport math\n\n#************************** Function for Getting BIN Number for a given height ***************\ndef get_bin_num (i, Min, Max, bin_cnt=32):\n return (round ((bin_cnt-1) * ((i-Min)/(Max-Min))))\n\ndef get_pd (x, mu, sig):\n tmp1 = (1 / (math.sqrt(2 * math.pi) *sig))\n tmp2 = (-0.5* ( ((x - mu)/sig) * ((x - mu)/sig) ))\n tmp3 = math.exp (tmp2)\n prob_den = tmp1 * tmp3\n return(prob_den)\n\ndef bayesian (Nm, pm, Nf, pf):\n temp1 = (Nm*pm) /(Nm+Nf)\n temp2 = (Nf*pf)/(Nm+Nf)\n Pf = temp2 / (temp1+temp2)\n return (Pf)\n\n \n \n#************* End of Functions Definitions ***************\n\nwb = openpyxl.load_workbook('Assignment_1_Data_and_Template.xlsx')\n#print (type(wb))\n#wb.get_sheet_names()\nsheet = wb.get_sheet_by_name('Data')\nlast_row_number = sheet.max_row\nprint (\"Max row Number in XL sheet=\", last_row_number)\nprint (\"Total Number of Rows in the Training set =\", (last_row_number-1))\n\n###1. Initialize Lists and Dictionaries. \nMale =[]\nFemale =[]\nMale_And_Female = []\nBin_count = 32\nH={}\nNm = 0\nNf = 0\npm = 0\npf = 0\n\n###2.\noutput = \"Initialise Histogram Dictionary with a size of \" + str(Bin_count) + \". Index <0 thru\" + str(Bin_count-1) + \">: \"\nprint (output.center(100, \"*\"))\n \nfor i in range(0,Bin_count):\n H[i]= {'male':0, 'female':0}\n#print(H)\n\n###3.\noutput = \"Read all the Rows from XL and save the hights into 3 lists: Male, Femal, Male_And_Female :\"\nprint (output.center(100, \"*\"))\n \n#for i in (range(2, 52)):\nfor i in (range(2, (last_row_number+1) )):\n fv_h_ft = sheet[\"A\"+str(i)].value\n fv_h_in = sheet[\"B\"+str(i)].value\n fv_gender = sheet[\"C\"+str(i)].value\n \n Height = (fv_h_ft * 12 + fv_h_in)\n Male_And_Female.append(Height)\n\n if (fv_gender == \"Male\"):\n Male.append(Height)\n elif (fv_gender == \"Female\"):\n Female.append(Height)\n\n#print (\"Male List:\",Male)\n#print (\"Female List:\",Female)\n#print (\"Male and Female List:\", Male_And_Female) \n\nMale_And_Female.sort()\nMax=Male_And_Female[-1]\nMin=Male_And_Female[0]\nNm = len(Male)\nNf = len(Female)\noutput = \"Max and Minimum Height from Male_And_Female List: \"\nprint (\"Max:\", Male_And_Female[-1])\nprint (\"Min:\", Male_And_Female[0])\nprint (\"Size of Male List:\", Nm)\nprint (\"Size of FeMale List:\", Nf)\nprint (\"Sum of both the list sizes:\", (Nm+Nf))\n\nif(((len(Male)+len(Female)) == (last_row_number -1))):\n print (\"Combined length of Male and Female Lists = Number of Rows in Training Set - Check PASS\")\nelse:\n print (\"Combined length of Male and Female Lists != Number of Rows in Training Set - Check FAIL\")\n \n#print (\"Male and Female List, after sort:\", Male_And_Female)\n\noutput = \"Create Male Histogram: \"\nprint (output.center(100, \"*\"))\n\nfor i in (Male):\n bin_num = get_bin_num(i, Min, Max)\n H[bin_num]['male']=H[bin_num]['male']+1\n\noutput = \"Create Female Histogram: \"\nprint (output.center(100, \"*\"))\n \nfor i in (Female):\n bin_num = get_bin_num(i, Min, Max)\n H[bin_num]['female']=H[bin_num]['female']+1\n\noutput = \"Mean and Standrd Deviation for Male and Female Histograms: \"\nprint (output.center(100, \"*\"))\n \nMu_M = statistics.mean(Male)\nMu_F = statistics.mean(Female)\n\nsig_M = statistics.stdev(Male)\nsig_F = statistics.stdev(Female)\n\nprint (\"Mean Male:\",Mu_M)\nprint (\"Mean Female:\",Mu_F)\nprint (\"stdev Male:\",sig_M)\nprint (\"stdev Female:\",sig_F)\n\noutput2 = \"\"\nprint (output2.center(100, \"~\"))\nprint (\"Counts in each Bin:\", H);\nprint (output2.center(100, \"~\"))\n\noutput = \"Check whether the Sum of all Buckets is Equal to Number of Rows in Training Set : \"\nprint (\"Max:\", Male_And_Female[-1])\nprint (\"Min:\", Male_And_Female[0])\ncount_h = 0\nfor i in (H):\n count_h = count_h + H[i]['male']+H[i]['female']\n\n\nif ( count_h == (last_row_number -1)):\n print (\"The Sum of all Buckets is Equal to Number of Rows in Training Set - Check PASS\")\nelse:\n print (\"The Sum of all Buckets is Equal to Number of Rows in Training Set - Check FAIL\")\n\n#****************************************************************************************\n#************************************ Start of Testing **********************************\n#****************************************************************************************\n \noutput = \"Start of Testing \"\noutput2 = \"\"\nprint (output2.center(100, \"*\"))\nprint (output.center(100, \"*\"))\nprint (output2.center(100, \"*\"))\ntcs = [55,60,65,70,75,80]\n\n#***************** Using Histograms ******************************\noutput = \"Results using Histogram Method\"\nprint (output.center(100, \"~\"))\n\nfor ht_in in (tcs):\n bin_num = get_bin_num(ht_in, Min, Max)\n male_cnt = H[bin_num]['male']\n female_cnt = H[bin_num]['female']\n p_f = (female_cnt/(female_cnt+male_cnt))\n print (\"Height:\", ht_in, \"bin number:\", bin_num, \"Male Count:\", male_cnt, \"female count:\", female_cnt, \"Probability of being female: \", p_f, sep =\"\\t\")\n\n#***************** Using Gaussian Model ******************************\noutput = \"Results using Gaussian Method\"\nprint (output.center(100, \"~\"))\n\nfor ht_in in (tcs):\n pd_m = get_pd (ht_in, Mu_M, sig_M)\n pd_f = get_pd (ht_in, Mu_F, sig_F)\n Pf = bayesian(Nm, pd_m, Nf, pd_f)\n print (\"Height:\", ht_in, \"PD_Male:\", pd_m, \"PD_f:\", pd_f, \"Pf:\", Pf, sep =\"\\t\")\n", "sub_path": "Assignment_1/assignment1_v3.py", "file_name": "assignment1_v3.py", "file_ext": "py", "file_size_in_byte": 5268, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "math.sqrt", "line_number": 11, "usage_type": "call"}, {"api_name": "math.pi", "line_number": 11, "usage_type": "attribute"}, {"api_name": "math.exp", "line_number": 13, "usage_type": "call"}, {"api_name": "openpyxl.load_workbook", "line_number": 27, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 112, "usage_type": "call"}, {"api_name": "statistics.mean", "line_number": 113, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 115, "usage_type": "call"}, {"api_name": "statistics.stdev", "line_number": 116, "usage_type": "call"}]} +{"seq_id": "354812026", "text": "# Copyright (C) 2018 Google Inc.\n# Licensed under http://www.apache.org/licenses/LICENSE-2.0 \n\n\"\"\"Test Access Control Roleable mixin\"\"\"\nimport ddt\nfrom ggrc import db\nfrom ggrc.models import all_models\nfrom integration.ggrc import TestCase\nfrom integration.ggrc import api_helper\nfrom integration.ggrc.models import factories\n\n\n@ddt.ddt\nclass TestAccessControlRoleable(TestCase):\n \"\"\"TestAccessControlList\"\"\"\n\n def setUp(self):\n super(TestAccessControlRoleable, self).setUp()\n with factories.single_commit():\n self.role = factories.AccessControlRoleFactory()\n self.person = factories.PersonFactory()\n\n @ddt.data(lambda self: [{\n \"ac_role_id\": self.role.id,\n \"person\": {\n \"id\": self.person.id\n }\n }], lambda self: [{\n \"person\": self.person,\n \"ac_role\": self.role\n }])\n def test_with_dict(self, acl_list):\n \"\"\"Test access_control_list setter with a basic dict object\n This is the format the frontend uses\"\"\"\n obj = all_models.Control(\n title=\"New Control\",\n access_control_list=acl_list(self))\n self.assertIsNotNone(obj.access_control_list)\n acl = obj.access_control_list[0]\n self.assertIsNotNone(acl)\n self.assertIsInstance(acl, all_models.AccessControlList)\n self.assertEqual(acl.person.id, self.person.id)\n self.assertEqual(acl.ac_role.id, self.role.id)\n self.assertEqual(acl.object, obj)\n\n def test_with_dict_objs_multiple(self):\n \"\"\"Test access_control_list setter without ids\"\"\"\n\n def acl_query():\n return db.session.query(\n all_models.AccessControlList.person_id,\n all_models.AccessControlList.ac_role_id\n ).filter(\n all_models.AccessControlList.object_id == obj.id,\n all_models.AccessControlList.object_type == \"Control\"\n ).all()\n person_1 = all_models.Person(name=\"Frodo\", email=\"frodo@baggins.com\")\n person_2 = all_models.Person(name=\"Bilbo\", email=\"bilbo@baggins.com\")\n person_3 = factories.PersonFactory(name=\"Merry\", email=\"merry@buck.com\")\n role = all_models.AccessControlRole(name=\"Hobbit\")\n obj = all_models.Control(title=\"Test Control\", access_control_list=[{\n \"person\": person_1,\n \"ac_role\": self.role,\n }, {\n \"person\": person_2,\n \"ac_role\": role,\n }])\n db.session.commit()\n self.assertIsNotNone(obj.access_control_list)\n self.assertEqual(len(obj.access_control_list), 2)\n self.assertEqual(obj.access_control_list[0].person, person_1)\n self.assertEqual(obj.access_control_list[1].person, person_2)\n\n acls = acl_query()\n self.assertItemsEqual([\n (person_1.id, self.role.id),\n (person_2.id, role.id)\n ], acls)\n\n obj.access_control_list = [{\n \"person\": {\n \"id\": person_2.id,\n },\n \"ac_role_id\": role.id,\n }, {\n \"person\": {\n \"id\": person_3.id,\n },\n \"ac_role_id\": role.id,\n }]\n db.session.commit()\n\n acls = acl_query()\n self.assertItemsEqual([\n (person_2.id, role.id),\n (person_3.id, role.id)\n ], acls)\n\n def test_full_access_control_list(self):\n \"\"\"Test if access_control_list property filters out propagated roles\n\n Before sending the access_control_list to the frontend, propagated roles\n need to be filtered out to help prevent performance issues\"\"\"\n with factories.single_commit():\n # Create an object with one external and one propagated role\n obj = factories.ControlFactory()\n acl = factories.AccessControlList(\n object=obj,\n ac_role=self.role,\n person=self.person\n )\n factories.AccessControlList(\n object=obj,\n ac_role=self.role,\n person=self.person,\n parent=acl\n )\n # full_access_control_list should have all rows:\n self.assertEqual(len(obj.full_access_control_list), 2,\n \"full_access_control_list doesn't include all roles\")\n # access_control_list should only have non propagated ones\n self.assertEqual(len(obj.access_control_list), 1,\n \"access_control_list doesn't include all the roles\")\n obj_id, acl_id = obj.id, acl.id\n api = api_helper.Api()\n response = api.get(all_models.Control, obj_id)\n acl = response.json[\"control\"][\"access_control_list\"]\n # Check if the response filtered out the propagated access_control_role\n self.assertEqual(len(acl), 1,\n \"acl didn't filter out propagated roles correctly\")\n self.assertEqual(acl[0][\"id\"], acl_id,\n \"acl didn't filter out propagated roles correctly\")\n", "sub_path": "test/integration/ggrc/access_control/test_roleable.py", "file_name": "test_roleable.py", "file_ext": "py", "file_size_in_byte": 4607, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "integration.ggrc.TestCase", "line_number": 14, "usage_type": "name"}, {"api_name": "integration.ggrc.models.factories.single_commit", "line_number": 19, "usage_type": "call"}, {"api_name": "integration.ggrc.models.factories", "line_number": 19, "usage_type": "name"}, {"api_name": "integration.ggrc.models.factories.AccessControlRoleFactory", "line_number": 20, "usage_type": "call"}, {"api_name": "integration.ggrc.models.factories", "line_number": 20, "usage_type": "name"}, {"api_name": "integration.ggrc.models.factories.PersonFactory", "line_number": 21, "usage_type": "call"}, {"api_name": "integration.ggrc.models.factories", "line_number": 21, "usage_type": "name"}, {"api_name": "ggrc.models.all_models.Control", "line_number": 35, "usage_type": "call"}, {"api_name": "ggrc.models.all_models", "line_number": 35, "usage_type": "name"}, {"api_name": "ggrc.models.all_models.AccessControlList", "line_number": 41, "usage_type": "attribute"}, {"api_name": "ggrc.models.all_models", "line_number": 41, "usage_type": "name"}, {"api_name": "ddt.data", "line_number": 23, "usage_type": "call"}, {"api_name": "ggrc.db.session.query", "line_number": 50, "usage_type": "call"}, {"api_name": "ggrc.db.session", "line_number": 50, "usage_type": "attribute"}, {"api_name": "ggrc.db", "line_number": 50, "usage_type": "name"}, {"api_name": "ggrc.models.all_models.AccessControlList", "line_number": 51, "usage_type": "attribute"}, {"api_name": "ggrc.models.all_models", "line_number": 51, "usage_type": "name"}, {"api_name": "ggrc.models.all_models.AccessControlList", "line_number": 52, "usage_type": "attribute"}, {"api_name": "ggrc.models.all_models", "line_number": 52, "usage_type": "name"}, {"api_name": "ggrc.models.all_models.AccessControlList", "line_number": 54, "usage_type": "attribute"}, {"api_name": "ggrc.models.all_models", "line_number": 54, "usage_type": "name"}, {"api_name": "ggrc.models.all_models.AccessControlList", "line_number": 55, "usage_type": "attribute"}, {"api_name": "ggrc.models.all_models", "line_number": 55, "usage_type": "name"}, {"api_name": "ggrc.models.all_models.Person", "line_number": 57, "usage_type": "call"}, {"api_name": "ggrc.models.all_models", "line_number": 57, "usage_type": "name"}, {"api_name": "ggrc.models.all_models.Person", "line_number": 58, "usage_type": "call"}, {"api_name": "ggrc.models.all_models", "line_number": 58, "usage_type": "name"}, {"api_name": "integration.ggrc.models.factories.PersonFactory", "line_number": 59, "usage_type": "call"}, {"api_name": "integration.ggrc.models.factories", "line_number": 59, "usage_type": "name"}, {"api_name": "ggrc.models.all_models.AccessControlRole", "line_number": 60, "usage_type": "call"}, {"api_name": "ggrc.models.all_models", "line_number": 60, "usage_type": "name"}, {"api_name": "ggrc.models.all_models.Control", "line_number": 61, "usage_type": "call"}, {"api_name": "ggrc.models.all_models", "line_number": 61, "usage_type": "name"}, {"api_name": "ggrc.db.session.commit", "line_number": 68, "usage_type": "call"}, {"api_name": "ggrc.db.session", "line_number": 68, "usage_type": "attribute"}, {"api_name": "ggrc.db", "line_number": 68, "usage_type": "name"}, {"api_name": "ggrc.db.session.commit", "line_number": 91, "usage_type": "call"}, {"api_name": "ggrc.db.session", "line_number": 91, "usage_type": "attribute"}, {"api_name": "ggrc.db", "line_number": 91, "usage_type": "name"}, {"api_name": "integration.ggrc.models.factories.single_commit", "line_number": 104, "usage_type": "call"}, {"api_name": "integration.ggrc.models.factories", "line_number": 104, "usage_type": "name"}, {"api_name": "integration.ggrc.models.factories.ControlFactory", "line_number": 106, "usage_type": "call"}, {"api_name": "integration.ggrc.models.factories", "line_number": 106, "usage_type": "name"}, {"api_name": "integration.ggrc.models.factories.AccessControlList", "line_number": 107, "usage_type": "call"}, {"api_name": "integration.ggrc.models.factories", "line_number": 107, "usage_type": "name"}, {"api_name": "integration.ggrc.models.factories.AccessControlList", "line_number": 112, "usage_type": "call"}, {"api_name": "integration.ggrc.models.factories", "line_number": 112, "usage_type": "name"}, {"api_name": "integration.ggrc.api_helper.Api", "line_number": 125, "usage_type": "call"}, {"api_name": "integration.ggrc.api_helper", "line_number": 125, "usage_type": "name"}, {"api_name": "ggrc.models.all_models.Control", "line_number": 126, "usage_type": "attribute"}, {"api_name": "ggrc.models.all_models", "line_number": 126, "usage_type": "name"}, {"api_name": "ddt.ddt", "line_number": 13, "usage_type": "attribute"}]} +{"seq_id": "58175506", "text": "import twitter\n\napi = twitter.Api(consumer_key='EpswX5oXXgWwRv2VcmROHYzUX',\n consumer_secret='tThHxDg78USbY2eDLqm2IR6AxU1w8ahSPFaHucSNZBsdskh7ar',\n access_token_key='758635295084384256-5JYlLxdl8gEbTVeUlpaLQGI0KiJHmPJ',\n access_token_secret='kQsA9TTjDIYPpxIUkooYY8kVHYPKWY9yfdOrh3JOdyaQI')\n\nprint(api.VerifyCredentials())\n\nTRACK = ['#เลื่อนแม่มึงสิ']\n\nLANGUAGES = ['th']\n\nfor line in api.GetStreamFilter(track=TRACK, languages=LANGUAGES):\n print(line)\n", "sub_path": "twiter_module.py", "file_name": "twiter_module.py", "file_ext": "py", "file_size_in_byte": 533, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "twitter.Api", "line_number": 3, "usage_type": "call"}]} +{"seq_id": "598739309", "text": "#!/usr/bin/env python3.6\nfrom math import *\nfrom decimal import *\nimport sys\nimport argparse\n\nex = 5\neps = round(pow(1/10, ex), ex)\npartsize = 2\n\n\ndef main():\n stat = \"sin(x)\"\n\n if len(sys.argv) > 1:\n parser = argparse.ArgumentParser(description='Proof of Bolzano-Cauchy Theorem calculator',\n usage=\"__main__.py STATEMENT [-h] [-a EX] [-p PARTSIZE]\"\n \"[-r SEG [SEG ...]] [-s STAT]\")\n parser.add_argument('-a', action='store', default=3, type=int, dest='ex',\n help='Accuracy of numbers (<= 4).')\n parser.add_argument('-p', action='store', default=2, type=float, dest='partsize',\n help='Size of parts segment to be divided.')\n parser.add_argument('-r', action='store', default=(-32, 32), nargs='+', type=float, dest='seg',\n help='Segment to be considered.')\n parser.add_argument('-s', action='store', default=\"sin(x)\", type=str, dest='stat',\n help='The statement.')\n\n args = parser.parse_args()\n global ex\n ex = args.ex\n if ex > 4:\n print(\"\\033[91mUsing accuracy > 4 is highly not recommended!\\033[0m\")\n global partsize\n partsize = args.partsize\n\n seg = args.seg\n stat = args.stat\n\n global parts\n parts = round((seg[1] - seg[0]) / partsize)\n\n print(\"\\033[92mLooking for roots f(x) = \" + stat)\n print(\"on [\" + str(seg[0]) + \"; \" + str(seg[1]) + \"] segment\" + \"\\033[0m\")\n\n roots = find_roots(stat, seg)\n print(\"\\t\" + str(len(roots)) + \" roots found.\")\n\n i = 0\n for i in range(0, len(roots)):\n if roots[i] == 0:\n print(str(i + 1) + \". \" + str(0))\n else:\n print(str(i + 1) + \". \" + str(roots[i]))\n\n\ndef find_roots(stat, intseg, roots=[]):\n isfound = False\n segs = divide(intseg, parts)\n for seg in segs:\n left = calculate(stat, seg[0])\n right = calculate(stat, seg[1])\n if (left * right) <= 0:\n if calculate(stat, seg[1]) == 0:\n root = seg[1]\n isfound = True\n else:\n if seg[1] - seg[0] >= eps:\n root = find_roots(stat, seg, roots)\n isfound = True\n else:\n return round((seg[0] + seg[1]) / 2, ex)\n if isfound:\n if type(root) is not list and not find(roots, root):\n roots.append(root)\n isfound = False\n return roots\n\n\ndef find(list, obj):\n for el in list:\n if el == obj:\n return True\n return False\n\n\ndef calculate(stat, arg):\n stat = stat.replace('x', '(' + str(arg) + ')')\n try:\n ans = eval(stat)\n return ans\n except Exception as msg:\n print(\"\\033[91mCan't calculate \\\"f(x) = \" + stat + \"\\\"\\033[0m\")\n sys.exit()\n\n\ndef divide(seg, times):\n segs = []\n i = 0\n diff = seg[1] - seg[0]\n for i in range(0, times):\n if i == 0:\n left = seg[0]\n else:\n left = right\n right = diff * (i + 1) / times + seg[0]\n segs.append([left, right])\n return segs\n\n\nif __name__ == \"__main__\":\n main()", "sub_path": "prog.py", "file_name": "prog.py", "file_ext": "py", "file_size_in_byte": 3272, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.argv", "line_number": 15, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 16, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 93, "usage_type": "call"}]} +{"seq_id": "411470717", "text": "import sqlite3\nimport csv\nimport json\n\n# proj3_choc.py\n# You can change anything in this file you want as long as you pass the tests\n# and meet the project requirements! You will need to implement several new\n# functions.\n\n# Part 1: Read data from CSV and JSON into a new database called choc.db\nDBNAME = 'choc.db'\nBARSCSV = 'flavors_of_cacao_cleaned.csv'\nCOUNTRIESJSON = 'countries.json'\n\ndef create_bars():\n try:\n conn = sqlite3.connect(DBNAME)\n cur = conn.cursor()\n except:\n print(\"Could not connect to database.\")\n\n try:\n statement = '''\n DROP TABLE IF EXISTS 'Bars';\n '''\n cur.execute(statement)\n statement = '''CREATE TABLE 'Bars' \n ('Id' INTEGER PRIMARY KEY AUTOINCREMENT, 'Company' Text, 'SpecificBeanBarName' TEXT, 'REF' TEXT,\n 'ReviewDate' TEXT, 'CocoaPercent' REAL, 'CompanyLocationId' INT, 'Rating' REAL, 'BeanType' TEXT,\n 'BroadBeanOriginId' INT);'''\n cur.execute(statement)\n\n conn.commit()\n conn.close()\n except:\n print(\"Could not create table Bars.\")\n\ndef populate_bars():\n try:\n conn = sqlite3.connect(DBNAME)\n #From https://stackoverflow.com/questions/3425320/sqlite3-programmingerror-you-must-not-use-8-bit-bytestrings-unless-you-use-a-te\n conn.text_factory = str\n cur = conn.cursor()\n except:\n print(\"Could not connect to database.\")\n\n try:\n with open(BARSCSV) as f:\n csvReader = csv.reader(f)\n statement = '''INSERT INTO 'Bars' (Company, SpecificBeanBarName, REF, ReviewDate, CocoaPercent,\n CompanyLocationId, Rating, BeanType, BroadBeanOriginId) VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?)'''\n for row in csvReader:\n if row[0] == \"Company\":\n continue\n cocoa = ((float(row[4][:-1])) / 100)\n cur.execute(statement, (row[0], row[1], row[2], row[3], cocoa, row[5], row[6], row[7], row[8]))\n\n conn.commit()\n conn.close()\n except:\n print(\"Could not populate table Bars.\")\n\ndef create_countries():\n try:\n conn = sqlite3.connect(DBNAME)\n cur = conn.cursor()\n except:\n print(\"Could not connect to database.\")\n try:\n statement = '''\n DROP TABLE IF EXISTS 'Countries';\n '''\n cur.execute(statement)\n statement = '''CREATE TABLE 'Countries' \n ('Id' INTEGER PRIMARY KEY AUTOINCREMENT, 'Alpha2' Text, 'Alpha3' TEXT, 'EnglishName' TEXT,\n 'Region' TEXT, 'Subregion' TEXT, 'Population' INT, 'Area' REAL);'''\n cur.execute(statement)\n\n conn.commit()\n conn.close()\n except:\n print(\"Could not create table Countries.\") \n\ndef populate_countries():\n try:\n conn = sqlite3.connect(DBNAME)\n #From https://stackoverflow.com/questions/3425320/sqlite3-programmingerror-you-must-not-use-8-bit-bytestrings-unless-you-use-a-te\n conn.text_factory = str\n cur = conn.cursor()\n except:\n print(\"Could not connect to database.\")\n\n try:\n f = json.load(open('countries.json'))\n statement = '''INSERT INTO 'Countries' (Alpha2, Alpha3, EnglishName, Region, Subregion,\n Population, Area) VALUES (?, ?, ?, ?, ?, ?, ?)'''\n \n for country in f:\n cur.execute(statement, (country['alpha2Code'], country['alpha3Code'], country['name'], country['region'], country['subregion'], country['population'], country['area']))\n\n cur.execute(statement, ('UN', 'UNK', 'Unknown', 'Unknown', '', 0, 0)) \n \n conn.commit()\n conn.close()\n except:\n print(\"Could not populate table Countries.\")\n\ndef update_tables_with_foreign_keys():\n try:\n conn = sqlite3.connect(DBNAME)\n #From https://stackoverflow.com/questions/3425320/sqlite3-programmingerror-you-must-not-use-8-bit-bytestrings-unless-you-use-a-te\n conn.text_factory = str\n cur = conn.cursor()\n except:\n print(\"Could not connect to database.\")\n\n\n f = json.load(open(COUNTRIESJSON))\n\n for country in f:\n country_id = cur.execute(\"SELECT id FROM 'Countries' WHERE EnglishName=?\", (country['name'],)).fetchone()[0]\n #statement = '''UPDATE Bars SET CompanyLocationId = ? WHERE CompanyLocationId LIKE ? '''\n #cur.execute(statement,(country_id,'%'+country['name']+'%'))\n #statement = '''UPDATE Bars SET BroadBeanOriginId = ? WHERE BroadBeanOriginId LIKE ? '''\n #cur.execute(statement,(country_id,'%'+country['name']+'%'))\n statement = '''UPDATE Bars SET CompanyLocationId = ? WHERE CompanyLocationId = ?'''\n cur.execute(statement,(country_id, country['name']))\n statement = '''UPDATE Bars SET BroadBeanOriginId = ? WHERE BroadBeanOriginId = ? '''\n cur.execute(statement,(country_id, country['name']))\n statement = '''UPDATE Bars SET BroadBeanOriginId = ? WHERE BroadBeanOriginId = ? '''\n cur.execute(statement,(251, \"Unknown\"))\n\n\n conn.commit()\n conn.close()\n\ncreate_bars()\npopulate_bars()\n\ncreate_countries()\npopulate_countries()\n\nupdate_tables_with_foreign_keys()\n# Part 2: Implement logic to process user commands\ndef process_command(command):\n try:\n conn = sqlite3.connect(DBNAME)\n #From https://stackoverflow.com/questions/3425320/sqlite3-programmingerror-you-must-not-use-8-bit-bytestrings-unless-you-use-a-te\n conn.text_factory = str\n cur = conn.cursor()\n except:\n print(\"Could not connect to database.\")\n lst = []\n command_split = command.split()\n if command_split[0] == \"bars\":\n sortby = 'rating'\n number = 10\n sortby_query = \" ORDER BY rating DESC LIMIT ?\"\n country_query = \"None\"\n country = \"\"\n bottom = False\n for word in command_split:\n if \"sellcountry\" in word:\n country = word[-2:]\n country_query = \" WHERE c.Alpha2= ? AND c.EnglishName != \\\"Unknown\\\"\"\n elif \"sourcecountry\" in word:\n country = word[-2:]\n country_query = \" WHERE z.Alpha2 = ? AND z.EnglishName != \\\"Unknown\\\"\"\n elif \"sellregion\" in word:\n split_word = word.split('=')\n country = split_word[1]\n country_query = \" WHERE c.Region = ? \"\n elif \"sourceregion\" in word:\n split_word = word.split('=')\n country = split_word[1]\n country_query = \" WHERE z.Region = ? \"\n elif word == \"cocoa\":\n sortby = \"CocoaPercent\"\n elif \"bottom\" in word:\n split_word = word.split('=')\n number = int(split_word[1])\n sortby_query = \" ORDER BY \" + sortby + \" LIMIT ?\"\n bottom = True\n elif \"top\" in word:\n split_word = word.split('=')\n number = int(split_word[1]) \n sortby_query = \" ORDER BY \" + sortby + \" DESC LIMIT ?\"\n elif word == \"ratings\":\n continue \n elif word == \"bars\":\n continue\n else:\n print(\"Command not recognized: \" + command)\n return\n\n if not bottom:\n sortby_query = \" ORDER BY \" + sortby + \" DESC LIMIT ?\"\n\n base_statement = '''SELECT Bars.SpecificBeanBarName, Bars.Company, c.EnglishName, Bars.Rating, Bars.CocoaPercent, z.EnglishName\n FROM Bars\n JOIN Countries as c ON Bars.CompanyLocationId = c.id\n JOIN Countries as z ON Bars.BroadBeanOriginId = z.id'''\n if country_query != \"None\":\n statement = base_statement + country_query + sortby_query\n cur.execute(statement,(country,number))\n else:\n statement = base_statement + sortby_query\n cur.execute(statement,(number,))\n\n lst = cur.fetchall()\n\n elif command_split[0] == \"companies\":\n select_statement = '''SELECT Bars.Company, Countries.EnglishName, AVG(Bars.Rating) '''\n join_statement = '''FROM Bars JOIN Countries ON Countries.Id = Bars.CompanyLocationId'''\n number = 10\n sortby_query = ''' ORDER BY AVG(Bars.Rating) DESC LIMIT ?'''\n sortby = \"AVG(Bars.Rating)\"\n groupby_query = ''' GROUP BY Bars.Company HAVING COUNT(*) > 4'''\n country = \"\"\n country_query = \"None\"\n bottom = False\n for word in command_split:\n if \"country\" in word:\n country = word[-2:]\n country_query = \" WHERE Countries.Alpha2= ? AND Countries.EnglishName != \\\"Unknown\\\"\"\n elif \"region\" in word:\n split_word = word.split('=')\n country = split_word[1]\n country_query = \" WHERE Countries.Region = ? AND Countries.EnglishName != \\\"Unknown\\\"\"\n elif word == \"cocoa\":\n sortby = \"AVG(Bars.CocoaPercent)\"\n select_statement = '''SELECT Bars.Company, Countries.EnglishName, AVG(Bars.CocoaPercent) ''' \n elif word == \"bars_sold\":\n sortby = \"COUNT(*)\"\n select_statement = '''SELECT Bars.Company, Countries.EnglishName, COUNT(*) '''\n elif \"bottom\" in word:\n split_word = word.split('=')\n number = int(split_word[1])\n sortby_query = \" ORDER BY \" + sortby + \" LIMIT ?\"\n bottom = True\n elif \"top\" in word:\n split_word = word.split('=')\n number = int(split_word[1])\n sortby_query = \" ORDER BY \" + sortby + \" DESC LIMIT ?\" \n elif word == \"ratings\":\n continue\n elif word == \"companies\":\n continue\n else:\n print(\"Command not recognized: \" + command)\n return\n\n if not bottom:\n sortby_query = \" ORDER BY \" + sortby + \" DESC LIMIT ?\"\n\n if country_query != \"None\":\n statement = select_statement + join_statement + country_query + groupby_query + sortby_query\n cur.execute(statement,(country,number))\n else:\n statement = select_statement + join_statement + groupby_query + sortby_query\n cur.execute(statement,(number,)) \n lst = cur.fetchall() \n\n elif command_split[0] == \"countries\":\n select_statement = '''SELECT Countries.EnglishName, Countries.Region, AVG(Bars.Rating)'''\n join_statement = ''' FROM Bars JOIN Countries ON Bars.CompanyLocationId = Countries.Id WHERE Countries.EnglishName != \"Unknown\"'''\n groupby_query = ''' GROUP BY Countries.EnglishName HAVING COUNT(*) > 4'''\n sortby_query = ''' ORDER BY AVG(Bars.Rating) DESC LIMIT ?'''\n country = \"\"\n country_query = \"None\"\n sortby = \"AVG(Bars.Rating)\"\n number = 10\n bottom = False\n for word in command_split:\n if \"region\" in word:\n split_word = word.split('=')\n country = split_word[1]\n country_query = \" AND Countries.Region = ?\"\n elif word == \"sources\":\n join_statement = ''' FROM Bars JOIN Countries ON Bars.BroadBeanOriginId = Countries.Id WHERE Countries.EnglishName != \"Unknown\"'''\n elif word == \"cocoa\":\n select_statement = '''SELECT Countries.EnglishName, Countries.Region, AVG(Bars.CocoaPercent)'''\n sortby = \"AVG(Bars.CocoaPercent)\"\n elif word == \"bars_sold\":\n select_statement = '''SELECT Countries.EnglishName, Countries.Region, COUNT(SpecificBeanBarName) '''\n sortby = \"COUNT(*)\"\n elif \"bottom\" in word:\n split_word = word.split('=')\n number = int(split_word[1])\n sortby_query = \" ORDER BY \" + sortby + \" LIMIT ?\"\n bottom = True\n elif \"top\" in word:\n split_word = word.split('=')\n number = int(split_word[1])\n sortby_query = \" ORDER BY \" + sortby + \" DESC LIMIT ?\" \n elif word == \"ratings\":\n continue\n elif word == \"sellers\":\n continue\n elif word == \"countries\":\n continue\n else:\n print(\"Command not recognized: \" + command)\n return\n\n if not bottom:\n sortby_query = \" ORDER BY \" + sortby + \" DESC LIMIT ?\"\n\n if country_query != \"None\":\n statement = select_statement + join_statement + country_query + groupby_query + sortby_query\n cur.execute(statement,(country,number))\n else:\n statement = select_statement + join_statement + groupby_query + sortby_query\n cur.execute(statement,(number,)) \n lst = cur.fetchall() \n\n elif command_split[0] == \"regions\":\n select_statement = '''SELECT Countries.Region, AVG(Bars.Rating)'''\n join_statement = ''' FROM Bars JOIN Countries ON Bars.CompanyLocationId = Countries.Id WHERE Countries.EnglishName != \\\"Unknown\\\"'''\n groupby_query = ''' GROUP BY Countries.Region HAVING COUNT(*) > 4'''\n sortby_query = ''' ORDER BY AVG(Bars.Rating) DESC LIMIT ?'''\n country = \"\"\n country_query = \"None\"\n sortby = \"AVG(Bars.Rating)\"\n number = 10\n bottom = False\n for word in command_split:\n if word == \"sources\":\n join_statement = ''' FROM Bars JOIN Countries ON Bars.BroadBeanOriginId = Countries.Id WHERE Countries.EnglishName != \\\"Unknown\\\"'''\n elif word == \"cocoa\":\n select_statement = '''SELECT Countries.Region, AVG(Bars.CocoaPercent)'''\n sortby = \"AVG(Bars.CocoaPercent)\"\n elif word == \"bars_sold\":\n select_statement = '''SELECT Countries.Region, COUNT(*) '''\n sortby = \"COUNT(*)\"\n elif \"bottom\" in word:\n split_word = word.split('=')\n number = int(split_word[1])\n sortby_query = \" ORDER BY \" + sortby + \" LIMIT ?\"\n bottom = True\n elif \"top\" in word:\n split_word = word.split('=')\n number = int(split_word[1])\n sortby_query = \" ORDER BY \" + sortby + \" DESC LIMIT ?\" \n elif word == \"ratings\":\n continue\n elif word == \"sellers\":\n continue\n elif word == \"regions\":\n continue\n else:\n print(\"Command not recognized: \" + command)\n return\n\n if not bottom:\n sortby_query = \" ORDER BY \" + sortby + \" DESC LIMIT ?\"\n\n statement = select_statement + join_statement + groupby_query + sortby_query\n cur.execute(statement,(number,)) \n \n lst = cur.fetchall() \n \n else:\n print(\"Command not recognized: \" + command)\n return\n \n conn.close()\n\n return lst\n\n\ndef load_help_text():\n with open('help.txt') as f:\n return f.read()\n\n# Part 3: Implement interactive prompt. We've started for you!\ndef interactive_prompt():\n help_text = load_help_text()\n response = ''\n end = False\n while response != 'exit':\n response = input('Enter a command: ')\n if response != 'exit':\n try:\n result = process_command(response)\n except:\n print(\"Unable to process command.\")\n continue\n if type(result) == list:\n tup_len = len(result[0])\n for tup in result:\n x = 1\n for word in tup: \n if x == tup_len:\n end = True\n else:\n end = False\n if type(word) == float:\n word = round(word, 1)\n if word <= 1 and x != 4:\n word = word * 100\n word = str(word)\n word = word.split('.')\n word = word[0] + \"% \"\n if(end):\n print(word)\n else:\n print(word, end = \" \")\n else:\n word = str(word) + \" \"\n if(end):\n print(word)\n else:\n print(word, end = \" \")\n elif type(word) == str:\n #From https://stackoverflow.com/questions/2872512/python-truncate-a-long-string\n word = (word[:12] + '...') if len(word) > 12 else word\n if(end):\n print('{0: <15}'.format(word))\n else: \n print('{0: <15}'.format(word), end = \" \")\n elif(end):\n print(word)\n else:\n print(word, end = \" \")\n x += 1\n if response == 'help':\n print(help_text)\n continue\n return\n\n# Make sure nothing runs or prints out when this file is run as a module\nif __name__==\"__main__\":\n interactive_prompt()\n", "sub_path": "proj3_choc.py", "file_name": "proj3_choc.py", "file_ext": "py", "file_size_in_byte": 17492, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sqlite3.connect", "line_number": 17, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 40, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 49, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 65, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 86, "usage_type": "call"}, {"api_name": "json.load", "line_number": 94, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 110, "usage_type": "call"}, {"api_name": "json.load", "line_number": 118, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "180951037", "text": "import pandas\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport datetime\nimport jieba\n\n# 聊天记录文件路径（需在QQ消息管理器以TXT格式导出）\nchat_log_path = './chat_log.txt'\n\n# TXT文件行号\nline_num = 0\n# 聊天记录总条数\nitem_sum = 0\n# 聊天文字总和\ntotal_text = ''\n\n# 修改分词权重\njieba.suggest_freq('亲爱的', True)\njieba.suggest_freq('大可爱', True)\njieba.suggest_freq('小可爱', True)\njieba.suggest_freq('吧唧', True)\njieba.suggest_freq('亲亲', True)\njieba.suggest_freq('哼唧', True)\njieba.suggest_freq('emmm', True)\njieba.del_word('一下')\njieba.del_word('然后')\njieba.del_word('这样')\njieba.del_word('这个')\njieba.del_word('还是')\njieba.del_word('就是')\njieba.del_word('一个')\njieba.del_word('晚上')\njieba.del_word('什么')\njieba.del_word('那个')\njieba.del_word('觉得')\njieba.del_word('不是')\njieba.del_word('感觉')\njieba.del_word('可能')\njieba.del_word('没有')\njieba.del_word('有点')\njieba.del_word('怎么')\njieba.del_word('还有')\n\n# 配置单字\nsingle_word = {'嗷', '嗯', '嬲'}\n\nhours = {}\n\n\n# 处理时间\ndef countTime(str_date):\n # print(str_date)\n # date = datetime.datetime.strptime(str_date, \"%Y-%m-%d %H:%M:%S\")\n # print(date)\n try:\n hour = int(str_date[10:13])\n hours[hour] = hours.get(hour, 0) + 1\n except:\n hour = int(str_date[10:12])\n hours[hour] = hours.get(hour, 0) + 1\n return\n\n\n# 读取文本文件\nfor line in open(chat_log_path, 'r', encoding='utf-8'):\n line_num = line_num + 1\n if line_num < 9:\n continue\n if line == '':\n continue\n else:\n # 时间戳特判\n if line.startswith('2017-') or line.startswith('2018-') or line.startswith('2019-'):\n item_sum = item_sum + 1\n countTime(line[0:19])\n continue\n else:\n # 过滤聊天图片和表情\n if not (line.startswith('[图片]') or line.startswith('[表情]')):\n total_text = total_text + line\n\nprint(\"记录总条数：\" + str(item_sum))\n\nwords = jieba.cut(total_text)\ncounts = {}\n\nfor word in words:\n if len(word) == 1:\n if word in single_word:\n counts[word] = counts.get(word, 0) + 1\n else:\n counts[word] = counts.get(word, 0) + 1\n\n# 将键值对转换成列表\nitems = list(counts.items())\n# 根据词语出现的次数进行从大到小排序\nitems.sort(key=lambda x: x[1], reverse=True)\n\nfor i in range(30):\n word, count = items[i]\n print(word + ' \\t' + str(count))\n\n# 绘制分时聊天频率折线图\nhours = [(k, hours[k]) for k in sorted(hours.keys())]\nplt.plot(hours)\nplt.title('聊天记录条数24小时分布图')\nplt.ylabel('消息条数')\nplt.xlabel('时间(小时)')\nplt.show()\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 2773, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "jieba.suggest_freq", "line_number": 18, "usage_type": "call"}, {"api_name": "jieba.suggest_freq", "line_number": 19, "usage_type": "call"}, {"api_name": "jieba.suggest_freq", "line_number": 20, "usage_type": "call"}, {"api_name": "jieba.suggest_freq", "line_number": 21, "usage_type": "call"}, {"api_name": "jieba.suggest_freq", "line_number": 22, "usage_type": "call"}, {"api_name": "jieba.suggest_freq", "line_number": 23, "usage_type": "call"}, {"api_name": "jieba.suggest_freq", "line_number": 24, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 25, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 26, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 27, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 28, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 29, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 30, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 31, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 32, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 33, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 34, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 35, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 36, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 37, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 38, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 39, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 40, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 41, "usage_type": "call"}, {"api_name": "jieba.del_word", "line_number": 42, "usage_type": "call"}, {"api_name": "jieba.cut", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 105, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 105, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 106, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 106, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 107, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 107, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}]} +{"seq_id": "308341642", "text": "from .boundary_loss import BDLoss,SoftDiceLoss,DC_and_BD_loss,DC_and_HDBinary_loss,DistBinaryDiceLoss,HDDTBinaryLoss\r\nfrom .dice_loss import DC_and_CE_loss,DC_and_topk_loss,GDiceLoss,GDiceLossV2,SSLoss,IoULoss,TopKLoss,TverskyLoss,FocalTversky_loss\r\nfrom .dice_loss import AsymLoss,PenaltyGDiceLoss,ExpLog_loss\r\nfrom .focal_loss import FocalLoss\r\nfrom .lovasz_loss import LovaszSoftmax\r\nfrom .ND_Crossentropy import CrossentropyND,WeightedCrossEntropyLoss,WeightedCrossEntropyLossV2,DisPenalizedCE\r\nfrom torch import nn\r\nlosses_seg = {}\r\nlosses_seg[\"BDLoss\"] = BDLoss\r\nlosses_seg[\"SoftDiceLoss\"] = SoftDiceLoss\r\nlosses_seg[\"DC_and_BD_loss\"] = DC_and_BD_loss\r\nlosses_seg[\"DC_and_HDBinary_loss\"] = DC_and_HDBinary_loss\r\nlosses_seg[\"DistBinaryDiceLoss\"] = DistBinaryDiceLoss\r\nlosses_seg[\"HDDTBinaryLoss\"] = HDDTBinaryLoss\r\nlosses_seg[\"DC_and_CE_loss\"] = DC_and_CE_loss\r\nlosses_seg[\"DC_and_topk_loss\"] = DC_and_topk_loss\r\nlosses_seg[\"GDiceLoss\"] = GDiceLoss\r\nlosses_seg[\"GDiceLossV2\"] = GDiceLossV2\r\nlosses_seg[\"SSLoss\"] = SSLoss\r\nlosses_seg[\"IoULoss\"] = IoULoss\r\nlosses_seg[\"TopKLoss\"] = TopKLoss\r\nlosses_seg[\"TverskyLoss\"] = TverskyLoss\r\nlosses_seg[\"FocalTversky_loss\"] = FocalTversky_loss\r\nlosses_seg[\"AsymLoss\"] = AsymLoss\r\nlosses_seg[\"PenaltyGDiceLoss\"] = PenaltyGDiceLoss\r\nlosses_seg[\"ExpLog_loss\"] = ExpLog_loss\r\nlosses_seg[\"FocalLoss\"] = FocalLoss\r\nlosses_seg[\"LovaszSoftmax\"] = LovaszSoftmax\r\nlosses_seg[\"CrossentropyND\"] = CrossentropyND\r\nlosses_seg[\"CrossEntropyLoss\"] = nn.CrossEntropyLoss\r\nlosses_seg[\"BCEWithLogitsLoss\"] = nn.BCEWithLogitsLoss\r\nlosses_seg[\"WeightedCrossEntropyLoss\"] = WeightedCrossEntropyLoss\r\nlosses_seg[\"WeightedCrossEntropyLossV2\"] = WeightedCrossEntropyLossV2\r\nlosses_seg[\"DisPenalizedCE\"] = DisPenalizedCE\r\n\r\ndef get_loss_func(name):\r\n return losses_seg[name]\r\n", "sub_path": "utils/losses/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1797, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "boundary_loss.BDLoss", "line_number": 9, "usage_type": "name"}, {"api_name": "boundary_loss.SoftDiceLoss", "line_number": 10, "usage_type": "name"}, {"api_name": "boundary_loss.DC_and_BD_loss", "line_number": 11, "usage_type": "name"}, {"api_name": "boundary_loss.DC_and_HDBinary_loss", "line_number": 12, "usage_type": "name"}, {"api_name": "boundary_loss.DistBinaryDiceLoss", "line_number": 13, "usage_type": "name"}, {"api_name": "boundary_loss.HDDTBinaryLoss", "line_number": 14, "usage_type": "name"}, {"api_name": "dice_loss.DC_and_CE_loss", "line_number": 15, "usage_type": "name"}, {"api_name": "dice_loss.DC_and_topk_loss", "line_number": 16, "usage_type": "name"}, {"api_name": "dice_loss.GDiceLoss", "line_number": 17, "usage_type": "name"}, {"api_name": "dice_loss.GDiceLossV2", "line_number": 18, "usage_type": "name"}, {"api_name": "dice_loss.SSLoss", "line_number": 19, "usage_type": "name"}, {"api_name": "dice_loss.IoULoss", "line_number": 20, "usage_type": "name"}, {"api_name": "dice_loss.TopKLoss", "line_number": 21, "usage_type": "name"}, {"api_name": "dice_loss.TverskyLoss", "line_number": 22, "usage_type": "name"}, {"api_name": "dice_loss.FocalTversky_loss", "line_number": 23, "usage_type": "name"}, {"api_name": "dice_loss.AsymLoss", "line_number": 24, "usage_type": "name"}, {"api_name": "dice_loss.PenaltyGDiceLoss", "line_number": 25, "usage_type": "name"}, {"api_name": "dice_loss.ExpLog_loss", "line_number": 26, "usage_type": "name"}, {"api_name": "focal_loss.FocalLoss", "line_number": 27, "usage_type": "name"}, {"api_name": "lovasz_loss.LovaszSoftmax", "line_number": 28, "usage_type": "name"}, {"api_name": "ND_Crossentropy.CrossentropyND", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 30, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.BCEWithLogitsLoss", "line_number": 31, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "ND_Crossentropy.WeightedCrossEntropyLoss", "line_number": 32, "usage_type": "name"}, {"api_name": "ND_Crossentropy.WeightedCrossEntropyLossV2", "line_number": 33, "usage_type": "name"}, {"api_name": "ND_Crossentropy.DisPenalizedCE", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "110409477", "text": "__author__ = 'rbtying'\ntry:\n from cStringIO import StringIO, InputType, OutputType\n from StringIO import StringIO as pyStringIO\n\n def _checkIsStringIO(obj):\n return isinstance(obj, (InputType, OutputType, pyStringIO))\nexcept ImportError:\n from StringIO import StringIO\n\n def _checkIsStringIO(obj):\n return isinstance(obj, StringIO)\n\nimport pygame\n\n# ROS specific imports\nimport sensor_msgs.msg\n\n\nclass ImageConverter(object):\n \"\"\"\n Convert images/compressedimages to and from ROS\n \"\"\"\n\n _ENCODINGMAP_PY_TO_ROS = {'L': 'mono8', 'RGB': 'rgb8',\n 'RGBA': 'rgba8', 'YCbCr': 'yuv422'}\n _ENCODINGMAP_ROS_TO_PY = {'mono8': 'L', 'rgb8': 'RGB',\n 'rgba8': 'RGBA', 'yuv422': 'YCbCr'}\n _PIL_MODE_CHANNELS = {'L': 1, 'RGB': 3, 'RGBA': 4, 'YCbCr': 3}\n\n @staticmethod\n def to_ros(img):\n \"\"\"\n Convert a PIL/pygame image to a ROS compatible message (sensor_msgs.Image).\n \"\"\"\n\n # Everything ok, convert PIL.Image to ROS and return it\n if img.mode == 'P':\n img = img.convert('RGB')\n\n rosimage = sensor_msgs.msg.Image()\n rosimage.encoding = ImageConverter._ENCODINGMAP_PY_TO_ROS[img.mode]\n (rosimage.width, rosimage.height) = img.size\n rosimage.step = (ImageConverter._PIL_MODE_CHANNELS[img.mode]\n * rosimage.width)\n rosimage.data = img.tostring()\n return rosimage\n\n @classmethod\n def from_ros(cls, rosMsg):\n \"\"\"\n Converts a ROS sensor_msgs.Image or sensor_msgs.CompressedImage to a pygame Surface\n :param rosMsg: The message to convert\n :return: an alpha-converted pygame Surface\n \"\"\"\n pyimg = None\n if isinstance(rosMsg, sensor_msgs.msg.Image):\n pyimg = pygame.image.fromstring(rosMsg.data, (rosMsg.width, rosMsg.height),\n cls._ENCODINGMAP_ROS_TO_PY[rosMsg.encoding])\n elif isinstance(rosMsg, sensor_msgs.msg.CompressedImage):\n pyimg = pygame.image.load(StringIO(rosMsg.data))\n\n if not pyimg:\n raise TypeError('rosMsg is not an Image or CompressedImage!')\n\n return pyimg.convert_alpha()\n", "sub_path": "image_coverter.py", "file_name": "image_coverter.py", "file_ext": "py", "file_size_in_byte": 2243, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cStringIO.InputType", "line_number": 7, "usage_type": "name"}, {"api_name": "cStringIO.OutputType", "line_number": 7, "usage_type": "name"}, {"api_name": "StringIO.StringIO", "line_number": 7, "usage_type": "name"}, {"api_name": "StringIO.StringIO", "line_number": 12, "usage_type": "argument"}, {"api_name": "sensor_msgs.msg.msg.Image", "line_number": 41, "usage_type": "call"}, {"api_name": "sensor_msgs.msg.msg", "line_number": 41, "usage_type": "attribute"}, {"api_name": "sensor_msgs.msg", "line_number": 41, "usage_type": "name"}, {"api_name": "sensor_msgs.msg.msg", "line_number": 57, "usage_type": "attribute"}, {"api_name": "sensor_msgs.msg", "line_number": 57, "usage_type": "name"}, {"api_name": "pygame.image.fromstring", "line_number": 58, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 58, "usage_type": "attribute"}, {"api_name": "sensor_msgs.msg.msg", "line_number": 60, "usage_type": "attribute"}, {"api_name": "sensor_msgs.msg", "line_number": 60, "usage_type": "name"}, {"api_name": "pygame.image.load", "line_number": 61, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 61, "usage_type": "attribute"}, {"api_name": "StringIO.StringIO", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "183745951", "text": "import numpy as np\r\nfrom collections import defaultdict\r\nfrom sklearn.feature_extraction.text import TfidfVectorizer\r\n\r\n\r\ndef get_glove(fname):\r\n\r\n with open(fname, \"rb\") as lines:\r\n wvec = {line.split()[0].decode(\"utf-8\"): np.array(line.split()[1:], dtype=np.float32)\r\n for line in lines}\r\n return wvec\r\n\r\n\r\n# sklearn's classifiers\r\nclass SumEmbeddingVectorizer(object):\r\n def __init__(self, word2vec):\r\n self.word2vec = word2vec\r\n if len(word2vec) > 0:\r\n self.dim = len(word2vec[next(iter(wvec))])\r\n else:\r\n self.dim = 0\r\n\r\n def fit(self, X, y):\r\n return self\r\n\r\n def transform(self, X):\r\n return np.array([\r\n np.sum([self.word2vec[w] for w in words if w in self.word2vec]\r\n or [np.zeros(self.dim)], axis=0)\r\n for words in X\r\n ])\r\n\r\n\r\nclass TfidfEmbeddingVectorizer(object):\r\n def __init__(self, word2vec):\r\n self.word2vec = word2vec\r\n self.word2weight = None\r\n if len(word2vec)>0:\r\n self.dim=len(word2vec[next(iter(wvec))])\r\n else:\r\n self.dim=0\r\n\r\n def fit(self, X, y):\r\n tfidf = TfidfVectorizer(analyzer=lambda x: x)\r\n tfidf.fit(X)\r\n # if a word was never seen - it must be at least as infrequent\r\n # as any of the known words - so the default idf is the max of\r\n # known idf's\r\n max_idf = max(tfidf.idf_)\r\n self.word2weight = defaultdict(\r\n lambda: max_idf,\r\n [(w, tfidf.idf_[i]) for w, i in tfidf.vocabulary_.items()])\r\n\r\n return self\r\n\r\n def transform(self, X):\r\n return np.array([\r\n np.sum([self.word2vec[w] * self.word2weight[w]\r\n for w in words if w in self.word2vec] or\r\n [np.zeros(self.dim)], axis=0)\r\n for words in X\r\n ])\r\n", "sub_path": "embedding_utils.py", "file_name": "embedding_utils.py", "file_ext": "py", "file_size_in_byte": 1913, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.array", "line_number": 9, "usage_type": "call"}, {"api_name": "numpy.float32", "line_number": 9, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.feature_extraction.text.TfidfVectorizer", "line_number": 44, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "526033629", "text": "import httpx\nfrom httpx import Timeout\n\n\nasync def async_get(url: str, return_json: bool = True):\n async with httpx.AsyncClient(timeout=Timeout(timeout=10.0)) as client:\n raw_response = await client.get(url)\n\n if return_json:\n return raw_response.json()\n else:\n return raw_response\n", "sub_path": "dnd_discord_bot/requests_handler/async_requests.py", "file_name": "async_requests.py", "file_ext": "py", "file_size_in_byte": 312, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "httpx.AsyncClient", "line_number": 6, "usage_type": "call"}, {"api_name": "httpx.Timeout", "line_number": 6, "usage_type": "call"}]} +{"seq_id": "110253886", "text": "from flask import redirect, url_for, request, abort, flash, session, render_template\nfrom web import app, authorize\nfrom core.ops import leagues, events, event_invites\nfrom core.errors import ValidationError\nimport datetime\n\n\n\n@authorize\n@app.route('/leagues//new/', methods=['GET'])\ndef events_new(league_slug):\n start = datetime.datetime.utcnow()\n end = start + datetime.timedelta(days=2)\n league = leagues.get(league_slug)\n\n if league and league.creator_username == session['username']:\n return render_template('events/new.html', league=leagues.get(league_slug), start=start, end=end)\n\n abort(401)\n\n\n\n@authorize\n@app.route('/leagues//', methods=['POST'])\ndef events_create(league_slug):\n name = request.form.get('name')\n start, end = __build_dates(request.form)\n max_number_of_teams = int(request.form.get('max_number_of_teams'))\n max_team_size = int(request.form.get('max_team_size'))\n bug_label = request.form.get('bug_label')\n feature_label = request.form.get('feature_label')\n\n league = leagues.get(league_slug)\n if league and league.creator_username == session['username']:\n try:\n event = events.create(name, league_slug, start, end, max_number_of_teams, max_team_size, bug_label, feature_label)\n flash(\"Event created\", 'success')\n return redirect(url_for('events_show', league_slug=league.slug, slug=event.slug))\n except ValidationError as e:\n flash(e.message, 'error')\n return redirect(url_for('events_new', league_slug=league.slug))\n\n\n\n@app.route('/leagues//events//', methods=['GET', 'DELETE'])\ndef events_show(league_slug, slug):\n if request.method == 'DELETE':\n return redirect(url_for('events_delete'))\n\n event = events.get(slug, league_slug)\n\n if event:\n return render_template('events/show.html', event=event)\n\n return abort(404)\n\n\n@authorize\n@app.route('/leagues//events//edit/', methods=['GET'])\ndef events_edit(league_slug, slug):\n event = events.get(slug, league_slug)\n league = leagues.get(league_slug)\n\n if event and event.league.creator_username == session.get('username'):\n return render_template('events/edit.html', event=event, league=league)\n\n return abort(401)\n\n\n\n@authorize\n@app.route('/leagues//events//', methods=['POST', 'PUT'])\ndef events_update(league_slug, slug):\n event = events.get(slug, league_slug)\n\n if event and event.league.creator_username == session.get('username'):\n start, end = __build_dates(request.form)\n max_number_of_teams = int(request.form.get('max_number_of_teams'))\n max_team_size = int(request.form.get('max_team_size'))\n bug_label = request.form.get('bug_label')\n feature_label = request.form.get('feature_label')\n\n try:\n events.update(event.slug, league_slug, start, end, max_number_of_teams, max_team_size, bug_label, feature_label)\n flash(\"Event updated\", 'success')\n return redirect(url_for('events_show', league_slug=league_slug, slug=slug))\n except ValidationError as e:\n flash(e.message, 'error')\n return redirect(url_for('events_edit', league_slug=league_slug, slug=slug))\n\n return abort(401)\n\n\n\n@authorize\n@app.route('/leagues//events//delete/', methods=['GET'])\ndef events_delete(league_slug, slug):\n event = events.get(league_slug, slug)\n\n if event and event.league.creator_username == session.get('username'):\n events.delete(event)\n flash(\"Event Deleted\", 'success')\n return redirect(url_for('events_show', slug=slug))\n\n return abort(401)\n\n\n@authorize\n@app.route('/leagues//events//invites/', methods=['POST'])\ndef event_invites_create(league_slug, slug):\n event = events.get(slug, league_slug)\n\n if event.league.creator_username == session.get('username'):\n try:\n event_invites.create(event.id, request.form.get('team_slug'))\n flash(\"Invite Sent\")\n except ValidationError as e:\n flash(e.message, \"error\")\n\n return redirect(url_for('events_show', league_slug=league_slug, slug=slug))\n\n return abort(401)\n\n\n@authorize\n@app.route('/leagues//events//invites//', methods=['POST'])\ndef event_invites_respond(league_slug, slug, event_invite_id):\n invite = event_invites.get_by_id(event_invite_id)\n\n if invite and invite.team.creator_username == session.get('username'):\n if request.form.get('accept'):\n try:\n event_invites.accept(invite.event_id, invite.team_slug)\n flash(\"Invite Accepted\", 'success')\n except ValidationError as e:\n flash(e.message, 'error')\n else:\n event_invites.decline(invite.event_id, invite.team_slug)\n flash(\"Invite Declined\", 'success')\n\n return redirect(url_for('teams_show', slug=invite.team_slug))\n\n return abort(401)\n\n\ndef __build_dates(form):\n start = datetime.datetime.strptime(form.get('start'),\"%Y-%m-%d %H:%M\")\n end = datetime.datetime.strptime(form.get('end'),\"%Y-%m-%d %H:%M\")\n return start, end", "sub_path": "web/views/events.py", "file_name": "events.py", "file_ext": "py", "file_size_in_byte": 5245, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.datetime.utcnow", "line_number": 12, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 12, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 13, "usage_type": "call"}, {"api_name": "core.ops.leagues.get", "line_number": 14, "usage_type": "call"}, {"api_name": "core.ops.leagues", "line_number": 14, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 16, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 17, "usage_type": "call"}, {"api_name": "core.ops.leagues.get", "line_number": 17, "usage_type": "call"}, {"api_name": "core.ops.leagues", "line_number": 17, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 19, "usage_type": "call"}, {"api_name": "web.authorize", "line_number": 9, "usage_type": "name"}, {"api_name": "web.app.route", "line_number": 10, "usage_type": "call"}, {"api_name": "web.app", "line_number": 10, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 26, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 26, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 26, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 27, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 27, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 29, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 29, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 29, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 30, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 30, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 30, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 31, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 31, "usage_type": "name"}, {"api_name": "core.ops.leagues.get", "line_number": 33, "usage_type": "call"}, {"api_name": "core.ops.leagues", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 34, "usage_type": "name"}, {"api_name": "core.ops.events.create", "line_number": 36, "usage_type": "call"}, {"api_name": "core.ops.events", "line_number": 36, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 37, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 38, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 38, "usage_type": "call"}, {"api_name": "core.errors.ValidationError", "line_number": 39, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 40, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 41, "usage_type": "call"}, {"api_name": "web.authorize", "line_number": 23, "usage_type": "name"}, {"api_name": "web.app.route", "line_number": 24, "usage_type": "call"}, {"api_name": "web.app", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.request.method", "line_number": 47, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 47, "usage_type": "name"}, {"api_name": "flask.redirect", "line_number": 48, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 48, "usage_type": "call"}, {"api_name": "core.ops.events.get", "line_number": 50, "usage_type": "call"}, {"api_name": "core.ops.events", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 55, "usage_type": "call"}, {"api_name": "web.app.route", "line_number": 45, "usage_type": "call"}, {"api_name": "web.app", "line_number": 45, "usage_type": "name"}, {"api_name": "core.ops.events.get", "line_number": 61, "usage_type": "call"}, {"api_name": "core.ops.events", "line_number": 61, "usage_type": "name"}, {"api_name": "core.ops.leagues.get", "line_number": 62, "usage_type": "call"}, {"api_name": "core.ops.leagues", "line_number": 62, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 65, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 67, "usage_type": "call"}, {"api_name": "web.authorize", "line_number": 58, "usage_type": "name"}, {"api_name": "web.app.route", "line_number": 59, "usage_type": "call"}, {"api_name": "web.app", "line_number": 59, "usage_type": "name"}, {"api_name": "core.ops.events.get", "line_number": 74, "usage_type": "call"}, {"api_name": "core.ops.events", "line_number": 74, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 76, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 78, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 78, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 78, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 79, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 79, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 79, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 80, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 80, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 80, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 81, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 81, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 81, "usage_type": "name"}, {"api_name": "core.ops.events.update", "line_number": 84, "usage_type": "call"}, {"api_name": "core.ops.events", "line_number": 84, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 85, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 86, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 86, "usage_type": "call"}, {"api_name": "core.errors.ValidationError", "line_number": 87, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 88, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 91, "usage_type": "call"}, {"api_name": "web.authorize", "line_number": 71, "usage_type": "name"}, {"api_name": "web.app.route", "line_number": 72, "usage_type": "call"}, {"api_name": "web.app", "line_number": 72, "usage_type": "name"}, {"api_name": "core.ops.events.get", "line_number": 98, "usage_type": "call"}, {"api_name": "core.ops.events", "line_number": 98, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 100, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 100, "usage_type": "name"}, {"api_name": "core.ops.events.delete", "line_number": 101, "usage_type": "call"}, {"api_name": "core.ops.events", "line_number": 101, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 102, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 103, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 105, "usage_type": "call"}, {"api_name": "web.authorize", "line_number": 95, "usage_type": "name"}, {"api_name": "web.app.route", "line_number": 96, "usage_type": "call"}, {"api_name": "web.app", "line_number": 96, "usage_type": "name"}, {"api_name": "core.ops.events.get", "line_number": 111, "usage_type": "call"}, {"api_name": "core.ops.events", "line_number": 111, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 113, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 113, "usage_type": "name"}, {"api_name": "core.ops.event_invites.create", "line_number": 115, "usage_type": "call"}, {"api_name": "core.ops.event_invites", "line_number": 115, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 115, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 115, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 115, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 116, "usage_type": "call"}, {"api_name": "core.errors.ValidationError", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 120, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 122, "usage_type": "call"}, {"api_name": "web.authorize", "line_number": 108, "usage_type": "name"}, {"api_name": "web.app.route", "line_number": 109, "usage_type": "call"}, {"api_name": "web.app", "line_number": 109, "usage_type": "name"}, {"api_name": "core.ops.event_invites.get_by_id", "line_number": 128, "usage_type": "call"}, {"api_name": "core.ops.event_invites", "line_number": 128, "usage_type": "name"}, {"api_name": "flask.session.get", "line_number": 130, "usage_type": "call"}, {"api_name": "flask.session", "line_number": 130, "usage_type": "name"}, {"api_name": "flask.request.form.get", "line_number": 131, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 131, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 131, "usage_type": "name"}, {"api_name": "core.ops.event_invites.accept", "line_number": 133, "usage_type": "call"}, {"api_name": "core.ops.event_invites", "line_number": 133, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 134, "usage_type": "call"}, {"api_name": "core.errors.ValidationError", "line_number": 135, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 136, "usage_type": "call"}, {"api_name": "core.ops.event_invites.decline", "line_number": 138, "usage_type": "call"}, {"api_name": "core.ops.event_invites", "line_number": 138, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 139, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 141, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 141, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 143, "usage_type": "call"}, {"api_name": "web.authorize", "line_number": 125, "usage_type": "name"}, {"api_name": "web.app.route", "line_number": 126, "usage_type": "call"}, {"api_name": "web.app", "line_number": 126, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 147, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 147, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 148, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 148, "usage_type": "attribute"}]} +{"seq_id": "462333106", "text": "import itertools\nimport curses\nimport curses.panel as panel\nfrom src.util import *\nfrom src.game import *\nimport src.game as Game\nimport textwrap\n\ndef color(color_name):\n \"Declaring color pairs for ncurses\"\n colors = Game.state.color_pairs\n return curses.color_pair(colors.get(color_name) or 1)\n\n\ndef UpdateUI():\n \"Called every frame to update the ui (ncurses)\"\n windows = Game.state.windows.values()\n # Render windows\n for window in windows:\n def window_render():\n window.win.move(1, 1) # move cursor to 1, 1\n window.win.clrtobot() # clear to bot\n window.render(window) # call the window's render method\n window.win.border() # redraw the border\n window.win.noutrefresh() # refresh with calling doupdate repeatedly\n \n window_render()\n\n # if there are still problems can probably fix with an occasional call to clearok\n # window.clearok(True) on a resize event and it should refresh appropriately\n curses.doupdate()\n curses.update_lines_cols()\n curses.panel.update_panels()\n\n\ndef NcursesReset():\n \"Exit the game cleanly\"\n Game.state.windows.screen.win.keypad(0)\n curses.nocbreak()\n curses.echo()\n curses.endwin()\n \n \ndef NcursesSetup():\n \"Setup ncurses tui library\"\n # curses.raw()\n curses.noecho() # don't print type back\n curses.curs_set(0) # hide cursor\n curses.cbreak() # react to keypresses instantly\n curses.start_color() # allow for colors\n curses.setupterm(\"Advenjur\") # set title\n curses.mousemask(curses.ALL_MOUSE_EVENTS | curses.REPORT_MOUSE_POSITION) # enable mouse\n curses.mouseinterval(25) # how long to wait for a mouse press\n\n\ndef ColorSetup():\n \"Setup colors to be used with ncurses\"\n # pair_number, foreground, background\n Game.state.color_pairs = adict()\n def add_color(name, colora, colorb):\n colors = Game.state.color_pairs\n pair = len(colors)+1\n a = curses.__dict__[f'color_{colora}'.upper()] # get color_blue for example from curses module\n b = curses.__dict__[f'color_{colorb}'.upper()]\n colors[name] = pair\n curses.init_pair(pair, a, b) # curses color system work in pairs\n \n add_color('black_on_blue', 'black', 'blue')\n add_color('grass', 'yellow', 'green')\n add_color('debug', 'red', 'black')\n add_color('game_log', 'magenta', 'black')\n add_color('main_text', 'cyan', 'black')\n add_color('cmd_text', 'blue', 'black')\n add_color('inventory', 'yellow', 'black')\n\n \ndef AddWindow1(name, win, render):\n height, width = win.getmaxyx()\n y, x = win.getbegyx()\n return AddWindow2(name, y, x, height, width, render, win=win)\n \n \ndef AddWindow2(name, y, x, height, width, render, win=None):\n if win is None:\n win = curses.newwin(height, width, y, x)\n \n win.idlok(True) # necessary for scrolling\n win.scrollok(1)\n \n Game.state.windows[name] = adict(name=name, \n win=win,\n panel=panel.new_panel(win),\n width=width,\n height=height,\n x=x, \n y=y,\n render=render)\n return Game.state.windows[name]\n\n\ndef UISetup():\n scr = AddWindow1('screen', curses.initscr(), MainWindowRender)\n scr.win.keypad(1) # will return a special value instead of a multibyte thing\n scr.win.timeout(1) # time to wait in milliseconds before rerendering\n scr.win.nodelay(True)\n scr.win.notimeout(True)\n NcursesSetup()\n ColorSetup()\n\n # Windows\n # Main Window on left side\n # Typing Window at bottom left\n # Interactive Window is on top right\n # Debug Window is on bottom right\n \n # Main Window\n width = 30\n cmd_window_height = 4\n main_window = AddWindow2('main_window', 1, 1,\n scr.height-cmd_window_height-2,\n scr.width-width,\n MainWindowRender)\n \n # CMD Window\n y = main_window.height + main_window.y\n cmd_window = AddWindow2('cmd_window', y, 1, \n cmd_window_height, \n scr.width-width,\n CmdWindowRender)\n\n\n height = 20\n width = width - 2\n \n # Side Display Window\n y = 1\n x = main_window.width+main_window.x\n height = 25\n sidedisplay_window = AddWindow2('sidedisplay_window', y, x, height, width,\n SideDisplayWindowRender)\n\n # Interactive Window\n y = sidedisplay_window.y + sidedisplay_window.height\n x = main_window.width+main_window.x\n interactive_window = AddWindow2('interactive_window', y, x, height, width,\n InteractiveWindowRender)\n\n # Debug Window, on bottom right.\n height = scr.height - (interactive_window.y + interactive_window.height) - 1\n y = interactive_window.y + interactive_window.height\n x = interactive_window.x\n\n debug_window = AddWindow2('debug_window', y, x, height, width, DebugWindowRender)\n\n\n # Log the size of terminal\n Game.Log(\"debug_log\", f\"Lines: {curses.LINES}\")\n Game.Log(\"debug_log\", f\"Cols: {curses.COLS}\")\n\n\n# count how many spaces starting at at\ndef spacelen(s, tabsize=2, at=0):\n spaces = 0 \n for character in s:\n if character == ' ': spaces += 1\n elif character == '\\t': spaces += tabsize\n else: break\n return spaces\n\n \ndef addstr(window, s, y, x, _color='debug'):\n \"Add string to window with text wrapping. Also whitespace at line start won't be highlighted.\"\n height, width = window.win.getmaxyx()\n tabsize = 2\n tw = textwrap.TextWrapper(expand_tabs=True, tabsize=tabsize, replace_whitespace=False,\n drop_whitespace=False, width=width-3)\n line_count = 0\n _color = color(_color)\n final_lines = []\n for split_line in s.split('\\n'):\n if split_line == '':\n final_lines.append('\\n')\n else:\n lines = tw.wrap(split_line)\n for line in lines:\n final_lines.append(line)\n \n # only show the final lines, this effectively scrolls the window\n # +2 is the border/margin size\n final_lines = final_lines[-window.height+2:]\n \n for line_count, line in enumerate(final_lines):\n spaces = spacelen(line, tabsize)\n window.win.addstr(y+line_count, x+spaces, line[spaces:], _color)\n \n # Return the final y position\n return y + len(final_lines)\n\n\n# For inventory and so forth\ndef InteractiveWindowRender(window):\n player = Game.state.player\n items = player._items\n items_str = '\\n\\n'.join(map(str, items))\n s = (\n f\"\"\"Inventory:\\n\n{items_str}\n\"\"\")\n y = addstr(window, s, 1, 1, 'inventory')\n \n# Rendering Functions for windows\ndef SideDisplayWindowRender(window):\n input_mode = Game.state.input_mode\n player = Game.state.player\n width = window.width\n \n y = addstr(window, f\"\"\"{player.name}\"\"\", 1, 1)\n window.win.hline(y+1, 0, curses.ACS_HLINE, width) \n y = addstr(window, f\"\"\"Mode: {input_mode}\n\nDirections:\\n\\tn(orth)\\n\\ts(outh)\\n\\te(ast)\\n\\tw(est)\n\nActions:\\n\\tg(et)\\n\\td(rop)\n\n😛\nああああ\n█▀█ █▄█ ▀█▀ \n\"\"\", y+2, 1)\n\n y = window.height - 3\n addstr(window, \"q(uit)\\n`(return to normal mode)\", y, 1)\n\n\ndef DebugWindowRender(window):\n def debug_display():\n return \"Debug:\\n\\t\" + \"\\n\\t\".join(Game.state.debug_log[-8:])\n addstr(window, debug_display(), 1, 1, 'debug')\n\n\n# Idea: draw the map using boxes.\n# Idea: make a window with a draggable interior. It does this by\n# using its position to offset how it renders things.\ndef MainWindowRender(window):\n player = Game.state.player\n room = player.room\n items = room._items \n # have to list out items\n # annoyingly have to map even if I make a __str__ and __repr__ method\n items_str = '\\n\\n'.join(map(str, items))\n \n# window.win.bkgdset('@')\n# TODO give every item in the room a temporary number that can be used with a g(et) command\n s = f\"\"\"\nRoom {room.name}\n{items_str}\n\"\"\"\n addstr(window, s, 1, 1, 'main_text')\n def game_log_display():\n return \"\\n\".join(Game.state.game_log[-8:])\n \n y = window.height - 10\n addstr(window, game_log_display(), y, 1, 'game_log')\n \ndef CmdWindowRender(window):\n s = Game.state.text_buffer \n # not working\n window.win.attron(curses.A_DIM | curses.A_ALTCHARSET | curses.A_UNDERLINE)\n addstr(window, s, 1, 1, 'cmd_text')\n window.win.addch(curses.ACS_PI)\n window.win.attroff(curses.A_DIM | curses.A_ALTCHARSET | curses.A_UNDERLINE)\n \n", "sub_path": "src/ui.py", "file_name": "ui.py", "file_ext": "py", "file_size_in_byte": 8758, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "src.game.state", "line_number": 11, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 11, "usage_type": "name"}, {"api_name": "curses.color_pair", "line_number": 12, "usage_type": "call"}, {"api_name": "src.game.state.windows.values", "line_number": 17, "usage_type": "call"}, {"api_name": "src.game.state", "line_number": 17, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 17, "usage_type": "name"}, {"api_name": "curses.doupdate", "line_number": 31, "usage_type": "call"}, {"api_name": "curses.update_lines_cols", "line_number": 32, "usage_type": "call"}, {"api_name": "curses.panel.update_panels", "line_number": 33, "usage_type": "call"}, {"api_name": "curses.panel", "line_number": 33, "usage_type": "attribute"}, {"api_name": "src.game.state.windows.screen.win.keypad", "line_number": 38, "usage_type": "call"}, {"api_name": "src.game.state", "line_number": 38, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 38, "usage_type": "name"}, {"api_name": "curses.nocbreak", "line_number": 39, "usage_type": "call"}, {"api_name": "curses.echo", "line_number": 40, "usage_type": "call"}, {"api_name": "curses.endwin", "line_number": 41, "usage_type": "call"}, {"api_name": "curses.noecho", "line_number": 47, "usage_type": "call"}, {"api_name": "curses.curs_set", "line_number": 48, "usage_type": "call"}, {"api_name": "curses.cbreak", "line_number": 49, "usage_type": "call"}, {"api_name": "curses.start_color", "line_number": 50, "usage_type": "call"}, {"api_name": "curses.setupterm", "line_number": 51, "usage_type": "call"}, {"api_name": "curses.mousemask", "line_number": 52, "usage_type": "call"}, {"api_name": "curses.ALL_MOUSE_EVENTS", "line_number": 52, "usage_type": "attribute"}, {"api_name": "curses.REPORT_MOUSE_POSITION", "line_number": 52, "usage_type": "attribute"}, {"api_name": "curses.mouseinterval", "line_number": 53, "usage_type": "call"}, {"api_name": "src.game.state", "line_number": 59, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 59, "usage_type": "name"}, {"api_name": "src.game.state", "line_number": 61, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 61, "usage_type": "name"}, {"api_name": "curses.__dict__", "line_number": 63, "usage_type": "attribute"}, {"api_name": "curses.__dict__", "line_number": 64, "usage_type": "attribute"}, {"api_name": "curses.init_pair", "line_number": 66, "usage_type": "call"}, {"api_name": "curses.newwin", "line_number": 85, "usage_type": "call"}, {"api_name": "src.game.state", "line_number": 90, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 90, "usage_type": "name"}, {"api_name": "curses.panel.new_panel", "line_number": 92, "usage_type": "call"}, {"api_name": "curses.panel", "line_number": 92, "usage_type": "name"}, {"api_name": "src.game.state", "line_number": 98, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 98, "usage_type": "name"}, {"api_name": "curses.initscr", "line_number": 102, "usage_type": "call"}, {"api_name": "src.game.Log", "line_number": 157, "usage_type": "call"}, {"api_name": "src.game", "line_number": 157, "usage_type": "name"}, {"api_name": "curses.LINES", "line_number": 157, "usage_type": "attribute"}, {"api_name": "src.game.Log", "line_number": 158, "usage_type": "call"}, {"api_name": "src.game", "line_number": 158, "usage_type": "name"}, {"api_name": "curses.COLS", "line_number": 158, "usage_type": "attribute"}, {"api_name": "textwrap.TextWrapper", "line_number": 175, "usage_type": "call"}, {"api_name": "src.game.state", "line_number": 202, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 202, "usage_type": "name"}, {"api_name": "src.game.state", "line_number": 213, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 213, "usage_type": "name"}, {"api_name": "src.game.state", "line_number": 214, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 214, "usage_type": "name"}, {"api_name": "curses.ACS_HLINE", "line_number": 218, "usage_type": "attribute"}, {"api_name": "src.game.state", "line_number": 236, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 236, "usage_type": "name"}, {"api_name": "src.game.state", "line_number": 244, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 244, "usage_type": "name"}, {"api_name": "src.game.state", "line_number": 259, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 259, "usage_type": "name"}, {"api_name": "src.game.state", "line_number": 265, "usage_type": "attribute"}, {"api_name": "src.game", "line_number": 265, "usage_type": "name"}, {"api_name": "curses.A_DIM", "line_number": 267, "usage_type": "attribute"}, {"api_name": "curses.A_ALTCHARSET", "line_number": 267, "usage_type": "attribute"}, {"api_name": "curses.A_UNDERLINE", "line_number": 267, "usage_type": "attribute"}, {"api_name": "curses.ACS_PI", "line_number": 269, "usage_type": "attribute"}, {"api_name": "curses.A_DIM", "line_number": 270, "usage_type": "attribute"}, {"api_name": "curses.A_ALTCHARSET", "line_number": 270, "usage_type": "attribute"}, {"api_name": "curses.A_UNDERLINE", "line_number": 270, "usage_type": "attribute"}]} +{"seq_id": "198578972", "text": "# Model copied from https://github.com/abhijeet3922/Object-recognition-CIFAR-10\n# https://github.com/keras-team/keras/blob/master/examples/cifar10_cnn.py\n\nfrom keras.datasets import cifar10\nfrom keras.preprocessing.image import ImageDataGenerator\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten\nfrom keras.layers import BatchNormalization, Conv2D, MaxPooling2D\nfrom keras.optimizers import RMSprop\nfrom keras.regularizers import l2\nfrom keras.callbacks import ModelCheckpoint, CSVLogger, LearningRateScheduler\nfrom keras.utils import to_categorical\nimport os\n\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"1\"\n\nbatch_size = 64\nnum_classes = 10\nepochs = 200\ndata_augmentation = True\n\n# The data, split between train and test sets:\n(x_train, y_train), (x_test, y_test) = cifar10.load_data()\nprint('x_train shape:', x_train.shape)\nprint(x_train.shape[0], 'train samples')\nprint(x_test.shape[0], 'test samples')\n\n\n# Normalize and OHC\n(x_train, y_train), (x_test, y_test) = cifar10.load_data()\nx_train = x_train.astype('float32')\nx_test = x_test.astype('float32')\n \nx_train = x_train / 255.0\nx_test = x_test / 255.0\n \ny_train = to_categorical(y_train, num_classes)\ny_test = to_categorical(y_test, num_classes)\n\n\nweight_decay = 1e-4\n\nmodel = Sequential()\nmodel.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=l2(weight_decay), input_shape=x_train.shape[1:]))\nmodel.add(Activation('elu'))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(32, (3,3), padding='same', kernel_regularizer=l2(weight_decay)))\nmodel.add(Activation('elu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.2))\n\nmodel.add(Conv2D(64, (3,3), padding='same', kernel_regularizer=l2(weight_decay)))\nmodel.add(Activation('elu'))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(64, (3,3), padding='same', kernel_regularizer=l2(weight_decay)))\nmodel.add(Activation('elu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.3))\n\nmodel.add(Conv2D(128, (3,3), padding='same', kernel_regularizer=l2(weight_decay)))\nmodel.add(Activation('elu'))\nmodel.add(BatchNormalization())\nmodel.add(Conv2D(128, (3,3), padding='same', kernel_regularizer=l2(weight_decay)))\nmodel.add(Activation('elu'))\nmodel.add(BatchNormalization())\nmodel.add(MaxPooling2D(pool_size=(2,2)))\nmodel.add(Dropout(0.4))\n\nmodel.add(Flatten())\nmodel.add(Dense(num_classes))\nmodel.add(Activation('softmax'))\n\n\n# initiate RMSprop optimizer\nopt = RMSprop(lr=0.001, decay=1e-6)\n\n# Let's train the model using RMSprop\nmodel.compile(loss='categorical_crossentropy',\n optimizer=opt,\n metrics=['accuracy'])\n\nprint(model.summary())\n\nmodel_type = 'vgg_like'\nsave_dir = os.path.join(os.getcwd(), 'saved_models')\nmodel_name = 'cifar10_%s_{epoch:03d}.h5' % model_type\nif not os.path.isdir(save_dir):\n os.makedirs(save_dir)\nfilepath = os.path.join(save_dir, model_name)\n\n\ncheckpoint = ModelCheckpoint(filepath=filepath,\n monitor='val_acc',\n verbose=1,\n save_best_only=True)\ncsvlog = CSVLogger('cifar10_vgglike_log.csv')\n\ndef lr_schedule(epoch):\n lrate = 0.001\n if epoch > 75:\n lrate = 0.0005\n elif epoch > 100:\n lrate = 0.0003 \n return lrate\n\nlr_sched = LearningRateScheduler(lr_schedule)\n\ncallbacks = [checkpoint, csvlog, lr_sched]\n\nif not data_augmentation:\n print('Not using data augmentation.')\n model.fit(x_train, y_train,\n batch_size=batch_size,\n epochs=epochs,\n validation_data=(x_test, y_test),\n shuffle=True)\nelse:\n print('Using real-time data augmentation.')\n # This will do preprocessing and realtime data augmentation:\n datagen = ImageDataGenerator(\n featurewise_center=False, # set input mean to 0 over the dataset\n samplewise_center=False, # set each sample mean to 0\n featurewise_std_normalization=False, # divide inputs by std of the dataset\n samplewise_std_normalization=False, # divide each input by its std\n zca_whitening=False, # apply ZCA whitening\n zca_epsilon=1e-06, # epsilon for ZCA whitening\n rotation_range=15, # randomly rotate images in the range (degrees, 0 to 180)\n # randomly shift images horizontally (fraction of total width)\n width_shift_range=0.1,\n # randomly shift images vertically (fraction of total height)\n height_shift_range=0.1,\n shear_range=0., # set range for random shear\n zoom_range=0., # set range for random zoom\n channel_shift_range=0., # set range for random channel shifts\n # set mode for filling points outside the input boundaries\n fill_mode='nearest',\n cval=0., # value used for fill_mode = \"constant\"\n horizontal_flip=True, # randomly flip images\n vertical_flip=False, # randomly flip images\n # set rescaling factor (applied before any other transformation)\n rescale=None,\n # set function that will be applied on each input\n preprocessing_function=None,\n # image data format, either \"channels_first\" or \"channels_last\"\n data_format=None,\n # fraction of images reserved for validation (strictly between 0 and 1)\n validation_split=0.0)\n\n # Compute quantities required for feature-wise normalization\n # (std, mean, and principal components if ZCA whitening is applied).\n datagen.fit(x_train)\n\n # Fit the model on the batches generated by datagen.flow().\n model.fit_generator(datagen.flow(x_train, y_train,\n batch_size=batch_size),\n epochs=epochs, callbacks=callbacks,\n validation_data=(x_test, y_test),\n workers=8)\n\n# Score trained model.\nscores = model.evaluate(x_test, y_test, verbose=1)\nprint('Test loss:', scores[0])\nprint('Test accuracy:', scores[1])\n", "sub_path": "Scripts/cifar10_scripts/cifar10_cnn_good.py", "file_name": "cifar10_cnn_good.py", "file_ext": "py", "file_size_in_byte": 5982, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "keras.datasets.cifar10.load_data", "line_number": 23, "usage_type": "call"}, {"api_name": "keras.datasets.cifar10", "line_number": 23, "usage_type": "name"}, {"api_name": "keras.datasets.cifar10.load_data", "line_number": 30, "usage_type": "call"}, {"api_name": "keras.datasets.cifar10", "line_number": 30, "usage_type": "name"}, {"api_name": "keras.utils.to_categorical", "line_number": 37, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 43, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 44, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 45, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 50, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 53, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 58, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 59, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 63, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.regularizers.l2", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.layers.BatchNormalization", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.optimizers.RMSprop", "line_number": 77, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.getcwd", "line_number": 87, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path", "line_number": 89, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 90, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.callbacks.CSVLogger", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.callbacks.LearningRateScheduler", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.preprocessing.image.ImageDataGenerator", "line_number": 122, "usage_type": "call"}]} +{"seq_id": "604739812", "text": "#!/usr/bin/python\n# coding=utf-8\nfrom lxml import etree\nfrom os.path import join,dirname\n\nDATA_DIR='xml-data'\n\nchina_regions = etree.parse(join(dirname(__file__), DATA_DIR, 'china_regions.xml'))\nprovince_list = [p.attrib for p in china_regions.xpath('/*/*/*')]\n\ndef node_of(region_code):\n regions = china_regions.xpath('//*[@region-code='+region_code+']')\n if len(regions):\n return regions[0]\n else:\n return None\n\ndef attrs_of(region_code):\n elem = node_of(region_code)\n if elem:\n return elem.attrib\n else:\n return None\n\ndef children_of(region_code):\n ret = []\n elem = node_of(region_code)\n if elem:\n for child in elem.getchildren():\n ret.append(child.attrib)\n return ret\n \ndef city_by_name(name):\n elem = china_regions.xpath('//*[@name=\"'+name+'\"]')[0]\n return elem.attrib\n\n# for p in children_of('12'):\n # print p['name'],p['id'],p['layer']\n", "sub_path": "query_json/db.py", "file_name": "db.py", "file_ext": "py", "file_size_in_byte": 935, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "lxml.etree.parse", "line_number": 8, "usage_type": "call"}, {"api_name": "lxml.etree", "line_number": 8, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "651698135", "text": "from flask import Flask, render_template\nfrom flask_socketio import SocketIO\nfrom flask_socketio import send, emit\nimport time\n\nfrom multiprocessing import Process\n\napp = Flask(__name__)\napp.config['SECRET_KEY'] = 'secret!'\nsocketio = SocketIO(app)\n\n\ndef some_function(data):\n socketio.emit('test', {'data': data})\n\n@socketio.on('test')\ndef handle_my_custom_event(json):\n emit('test', str(json)+\"中文我收到了\")\n for i in range(5):\n p = Process(target=some_function, args=(i,))\n p.start()\n #some_function(i)\n time.sleep(1)\n #socketio.sleep(0.1)\n\nif __name__ == '__main__':\n socketio.run(app,debug=True)\n", "sub_path": "others/tyone.py", "file_name": "tyone.py", "file_ext": "py", "file_size_in_byte": 655, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}, {"api_name": "flask_socketio.SocketIO", "line_number": 10, "usage_type": "call"}, {"api_name": "flask_socketio.emit", "line_number": 18, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 20, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "407347702", "text": "# -*- coding: utf-8 -*-\n\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# a copy of the License at\n#\n# https://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, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\n\nimport os\nimport sys\nimport urllib.parse\nimport requests\nimport psycopg2\nfrom argparse import ArgumentParser\n\nfrom flask import Flask, request, abort\nfrom linebot import (\n LineBotApi, WebhookHandler\n)\nfrom linebot.exceptions import (\n InvalidSignatureError\n)\nfrom linebot.models import (\n MessageEvent, TextMessage, TextSendMessage,\n)\n\napp = Flask(__name__)\n\n# get channel_secret and channel_access_token from your environment variable\nchannel_secret = os.getenv('LINE_CHANNEL_SECRET', None)\nchannel_access_token = os.getenv('LINE_CHANNEL_ACCESS_TOKEN', None)\ndb_url = os.getenv('DATABASE_URL', None)\n\nif channel_secret is None: \n print('Specify LINE_CH name in postgresql as environment variable.')\n sys.exit(1)\nif channel_access_token is None:\n print('Specify LINE_CHANNEL_ACCESS_TOKEN as environment variable.')\n sys.exit(1)\n\nline_bot_api = LineBotApi(channel_access_token)\nhandler = WebhookHandler(channel_secret)\n\ndef calculate(expr):\n expr=urllib.parse.quote(expr)\n link = \"http://api.mathjs.org/v4/?expr=\" + expr\n response = requests.get(link)\n return response\n\n@app.route(\"/callback\", methods=['POST'])\ndef callback():\n # get X-Line-Signature header value\n signature = request.headers['X-Line-Signature']\n\n # get request body as text\n body = request.get_data(as_text=True)\n sys.stdout.flush()\n app.logger.info(\"Request body: \" + body)\n # handle webhook body\n try:\n handler.handle(body, signature)\n except InvalidSignatureError:\n abort(400)\n\n return 'OK'\n\n\n@handler.add(MessageEvent, message=TextMessage)\ndef message_text(event):\n if (event.message.text==\"/history\"):\n uid = str(event.source.user_id)\n conn = psycopg2.connect(db_url, sslmode='require') \n cur = conn.cursor() \n cur.execute(\"select * from calc_history where uid = '%s';\" % (uid))\n results = cur.fetchall()\n content =\"\"\n if (len(results)>0):\n for i in range (0,len(results)):\n content += results[i][0] + results[i][1] + \"\\n\"\n else : \n content = \"No calculation before\"\n conn.commit() \n conn.close()\n else:\n content = calculate(event.message.text).text\n uid = str(event.source.user_id)\n conn = psycopg2.connect(db_url, sslmode='require') \n cur = conn.cursor() \n cur.execute(\"insert into calc_history (uid,expression,result) values ('%s','%s','%s');\" %(uid,event.message.text,content))\n conn.commit() \n conn.close()\n line_bot_api.reply_message(\n event.reply_token,\n TextSendMessage(text=content)\n )\n\n\nif __name__ == \"__main__\":\n arg_parser = ArgumentParser(\n usage='Usage: python ' + __file__ + ' [--port ] [--help]'\n )\n arg_parser.add_argument('-p', '--port', default=8000, help='port')\n arg_parser.add_argument('-d', '--debug', default=False, help='debug')\n options = arg_parser.parse_args()\n\n app.run(debug=options.debug, port=options.port)", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 3584, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 33, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 36, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 37, "usage_type": "call"}, {"api_name": "os.getenv", "line_number": 38, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 42, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 45, "usage_type": "call"}, {"api_name": "linebot.LineBotApi", "line_number": 47, "usage_type": "call"}, {"api_name": "linebot.WebhookHandler", "line_number": 48, "usage_type": "call"}, {"api_name": "urllib.parse.parse.quote", "line_number": 51, "usage_type": "call"}, {"api_name": "urllib.parse.parse", "line_number": 51, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 51, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.request.headers", "line_number": 59, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 59, "usage_type": "name"}, {"api_name": "flask.request.get_data", "line_number": 62, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 62, "usage_type": "name"}, {"api_name": "sys.stdout.flush", "line_number": 63, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 63, "usage_type": "attribute"}, {"api_name": "linebot.exceptions.InvalidSignatureError", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 69, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 78, "usage_type": "call"}, {"api_name": "psycopg2.connect", "line_number": 93, "usage_type": "call"}, {"api_name": "linebot.models.TextSendMessage", "line_number": 100, "usage_type": "call"}, {"api_name": "linebot.models.MessageEvent", "line_number": 74, "usage_type": "argument"}, {"api_name": "linebot.models.TextMessage", "line_number": 74, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 105, "usage_type": "call"}]} +{"seq_id": "505866082", "text": "#Elizabeth Doss\n#SoftDev1 pd1\n#K8 -- Lemme Flask You Sump’n\n#2019-09-18\n\n#prepares flask\nfrom flask import Flask\napp = Flask(__name__) #create instance of class Flask\n\n#normal route\n@app.route(\"/\") #assign following fxn to run when root route requested\ndef queso():\n print(__name__ + \"norm\") #prints in terminal\n return \"No hablo queso!\" #prints on webpage\n\n#route 1\n@app.route(\"/escribo\") #if added to url, opens new page\ndef food1():\n print(__name__ + \"test1\")\n return \"No escribo queso!\"\n\n#route 2\n@app.route(\"/escucho\") #if added to url, opens new page\ndef food2():\n print(__name__ + \"test2\")\n return \"No escucho queso!\"\n\n#route 3\n@app.route(\"/soy\") #if added to url, opens new page\ndef food3():\n print(__name__ + \"test3\")\n return \"No soy queso!\"\n\n#main\nif __name__ == \"__main__\":\n app.debug = True\n app.run()\n", "sub_path": "fall/08_app0/NoHabloQueso.py", "file_name": "NoHabloQueso.py", "file_ext": "py", "file_size_in_byte": 846, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 8, "usage_type": "call"}]} +{"seq_id": "280581790", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nfrom angular_fingerprintFeature_m import Angular_Fingerprint\nfrom gaussComparator import gaussComparator\nfrom krr_class_new import krr_class\n\nfrom ase import Atoms\nfrom ase.io import read, write\nfrom ase.visualize import view\nimport time\natoms = read('fromThomas/data_SnO.traj', index=':')\nNdata = len(atoms)\n\nE = np.array([a.get_potential_energy() for a in atoms])\n\nRc1 = 5\nRc2 = 5\nbinwidth1 = 0.1\nNbins2 = 30\nsigma1 = 0.4\ngamma = 3\n\n\ndef FVU_train(fingerprints, E, krr_model, Npoints, Npermutations):\n # Perform training with cross-validation\n np.random.seed(101)\n N_array = np.logspace(1, np.log10(Ndata), Npoints).astype(int)\n FVU = np.zeros((Npermutations, Npoints))\n GSkwargs = {'reg': [1e-5], 'sigma': np.logspace(0,2,10)}\n\n for k in range(Npermutations):\n print('training: {}/{}'.format(k, Npermutations))\n permutation = np.random.permutation(Ndata)\n E = E[permutation]\n fingerprints = fingerprints[permutation]\n\n for i, N in enumerate(N_array):\n Esub = E[:N]\n fingerprints_sub = fingerprints[:N]\n \n FVU_temp, params = krr.train(Esub, featureMat=fingerprints_sub, add_new_data=False, k=10, **GSkwargs)\n FVU[k, i] += FVU_temp\n FVU_mean = FVU.mean(axis=0)\n return FVU_mean[-1]\n\nNeta = 15\neta_array = np.linspace(1, 30, Neta).astype(int)\nresults = []\n\nplt.figure(1)\nfor name in ['', '_r_fcut', '_fcut']:\n for sigma2 in [0.05, 0.1, 0.2]:\n filename = 'SnO_features/SnO_radialAngFeatures_gauss{7:s}_Rc1_2_{0:d}_{1:d}_binwidth1_{2:.1f}_Nbins2_{3:d}_sigma1_2_{4:.1f}_{5:.2f}_gamma_{6:d}.txt'.format(Rc1, Rc2, binwidth1, Nbins2, sigma1, sigma2, gamma, name)\n fingerprints = np.loadtxt(filename, delimiter='\\t')\n\n print(sigma2)\n # Set up KRR-model\n featureCalculator = Angular_Fingerprint(atoms[0], Rc1=Rc1, Rc2=Rc2, binwidth1=binwidth1, Nbins2=Nbins2, sigma1=sigma1, sigma2=sigma2, gamma=gamma, use_angular=True)\n comparator = gaussComparator()\n krr = krr_class(comparator=comparator, featureCalculator=featureCalculator)\n \n MAEcurve = np.zeros(Neta)\n for i, eta in enumerate(eta_array):\n Nradial = int(Rc1/binwidth1)\n fingerprints_eta = fingerprints.copy()\n fingerprints_eta[:, 3*Nradial:] *= eta\n MAEcurve[i] = FVU_train(fingerprints_eta, E, krr, Npoints=10, Npermutations=5)\n print(MAEcurve)\n plt.plot(eta_array, MAEcurve, label='{0:s} sigmaAng={1:.2f}'.format(name, sigma2))\n results.append(MAEcurve)\n\nresults = np.array(results)\nnp.savetxt('resultsAngFing_paramCurves2.txt', results, delimiter='\\t')\n\nplt.legend()\nplt.show()\n", "sub_path": "krrThomas/AngFing_ParameterCurves.py", "file_name": "AngFing_ParameterCurves.py", "file_ext": "py", "file_size_in_byte": 2737, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "ase.io.read", "line_number": 12, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 27, "usage_type": "attribute"}, {"api_name": "numpy.logspace", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.logspace", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.random.permutation", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 34, "usage_type": "attribute"}, {"api_name": "numpy.linspace", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 51, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 51, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 55, "usage_type": "call"}, {"api_name": "angular_fingerprintFeature_m.Angular_Fingerprint", "line_number": 59, "usage_type": "call"}, {"api_name": "gaussComparator.gaussComparator", "line_number": 60, "usage_type": "call"}, {"api_name": "krr_class_new.krr_class", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 70, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 70, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.savetxt", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}]} +{"seq_id": "197773809", "text": "import xlrd\nimport datetime\nimport math\nfrom .base import TableParser\n\n\nclass WorkbookParser(TableParser):\n workbook = None\n worksheet = None\n sheet_name = 0\n start_row = None\n column_count = None\n\n def parse(self):\n self.parse_workbook()\n if self.sheet_name is None:\n self.data = [{'name': name, 'data': self.get_sheet_by_name(name)}\n for name in self.sheet_names]\n return\n\n sheet_name = self.sheet_name\n if isinstance(self.sheet_name, int):\n sheet_name = self.sheet_names[sheet_name]\n\n self.parse_worksheet(sheet_name)\n\n if self.header_row is None:\n if self.start_row is not None:\n self.header_row = self.start_row - 1\n else:\n self.column_count = 0\n\n def checkval(cell):\n if cell.value is not None and cell.value != '':\n return True\n return False\n\n for row in range(min(len(self.worksheet) - 1, 5), -1, -1):\n count = len(filter(checkval, self.worksheet[row]))\n if count >= self.column_count:\n self.column_count = count\n self.header_row = row\n\n if self.start_row is None:\n self.start_row = self.header_row + 1\n\n if self.field_names is None:\n rows = self.worksheet[self.header_row:self.start_row]\n self.field_names = [\n unicode(c.value) or u'c%s' % i for i, c in enumerate(rows[0])\n ]\n for row in rows[1:]:\n for i, c in enumerate(row):\n self.field_names[i] += \"\\n\" + unicode(c.value)\n\n seen_fields = set()\n for i, field in enumerate(self.field_names):\n if field in seen_fields:\n field += unicode(i)\n self.field_names[i] = field\n seen_fields.add(field)\n\n self.data = map(self.parse_row, self.worksheet[self.start_row:])\n if self.header_row > 0:\n for r in range(0, self.header_row):\n for c, cell in enumerate(self.worksheet[r]):\n val = self.get_value(cell)\n if val is not None and val != '':\n self.extra_data.setdefault(r, {})\n self.extra_data[r][c] = val\n\n def parse_workbook(self):\n raise NotImplementedError\n\n @property\n def sheet_names(self):\n raise NotImplementedError\n\n def get_sheet_by_name(self, name):\n raise NotImplementedError\n\n def parse_worksheet(self, name):\n raise NotImplementedError\n\n def parse_row(self, row):\n raise NotImplementedError\n\n def get_value(self, cell):\n raise NotImplementedError\n\n\nclass ExcelParser(WorkbookParser):\n def parse_workbook(self):\n self.workbook = xlrd.open_workbook(file_contents=self.file.read())\n\n @property\n def sheet_names(self):\n return self.workbook.sheet_names()\n\n def get_sheet_by_name(self, name):\n return self.workbook.sheet_by_name(name)\n\n def parse_worksheet(self, name):\n worksheet = self.get_sheet_by_name(name)\n self.worksheet = [worksheet.row(i) for i in range(worksheet.nrows)]\n\n def parse_row(self, row):\n return {name: self.get_value(row[i])\n for i, name in enumerate(self.get_field_names())\n if i < len(row)}\n\n def get_value(self, cell):\n if cell.ctype == xlrd.XL_CELL_DATE:\n time, date = math.modf(cell.value)\n tpl = xlrd.xldate_as_tuple(cell.value, self.workbook.datemode)\n if date and time:\n return datetime.datetime(*tpl)\n elif date:\n return datetime.date(*tpl[0:3])\n else:\n return datetime.time(*tpl[3:6])\n return cell.value\n", "sub_path": "parsers/xls.py", "file_name": "xls.py", "file_ext": "py", "file_size_in_byte": 3939, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "base.TableParser", "line_number": 7, "usage_type": "name"}, {"api_name": "xlrd.open_workbook", "line_number": 94, "usage_type": "call"}, {"api_name": "xlrd.XL_CELL_DATE", "line_number": 113, "usage_type": "attribute"}, {"api_name": "math.modf", "line_number": 114, "usage_type": "call"}, {"api_name": "xlrd.xldate_as_tuple", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 117, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 119, "usage_type": "call"}, {"api_name": "datetime.time", "line_number": 121, "usage_type": "call"}]} +{"seq_id": "241480361", "text": "import logging\nimport re\n\nfrom streamlink.compat import urlparse\nfrom streamlink.plugin import Plugin\nfrom streamlink.plugin.api.utils import itertags\nfrom streamlink.stream import HLSStream, HTTPStream\ntry:\n from urlparse import urljoin # Python 2\nexcept ImportError:\n from urllib.parse import urljoin # Python 3\n\nlog = logging.getLogger(__name__)\n\nclass NetondemandMt(Plugin):\n '''\n Support for live TV channel and videos on netondemand.mt\n '''\n url_re = re.compile(r'https?://(www\\.)?netondemand\\.mt')\n\n stream_re = re.compile(r'\"sourceURL\"\\s*:\\s*\"((?:http(s)?:)?//[^\"]*?)\"')\n\n @classmethod\n def can_handle_url(cls, url):\n return cls.url_re.match(url) is not None\n\n def _get_streams(self):\n res = self.session.http.get(self.url)\n\n stream_url = None\n\n if '/play/' in self.url:\n # Playing video\n log.info('Playing video')\n\n # Find video source URL\n for source in itertags(res.text, 'source'):\n if source.attributes.get('src'):\n stream_url = source.attributes.get('src')\n break\n \n stream_url = urljoin(self.url, stream_url)\n\n else:\n # Playing live TV channel\n log.info('Playing live TV channel')\n\n # Find stream URL\n stream_url_m = self.stream_re.search(res.text)\n stream_url = stream_url_m and stream_url_m.group(1)\n\n if not stream_url:\n log.error('Could not find stream URL')\n return\n\n log.debug('Found stream URL: {}', stream_url)\n\n if '.m3u8' in stream_url:\n streams = HLSStream.parse_variant_playlist(self.session, stream_url, verify=False)\n if not streams:\n log.debug('Play whole m3u8 file')\n yield 'live', HLSStream(self.session, stream_url, verify=False)\n else:\n log.debug('Play single stream')\n for s in streams.items():\n yield s\n\n else:\n yield 'video', HTTPStream(self.session, stream_url, verify=False)\n\n\n__plugin__ = NetondemandMt\n", "sub_path": "netondemand_mt.py", "file_name": "netondemand_mt.py", "file_ext": "py", "file_size_in_byte": 2157, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "streamlink.plugin.Plugin", "line_number": 15, "usage_type": "name"}, {"api_name": "re.compile", "line_number": 19, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 21, "usage_type": "call"}, {"api_name": "streamlink.plugin.api.utils.itertags", "line_number": 37, "usage_type": "call"}, {"api_name": "urllib.parse.urljoin", "line_number": 42, "usage_type": "call"}, {"api_name": "streamlink.stream.HLSStream.parse_variant_playlist", "line_number": 59, "usage_type": "call"}, {"api_name": "streamlink.stream.HLSStream", "line_number": 59, "usage_type": "name"}, {"api_name": "streamlink.stream.HLSStream", "line_number": 62, "usage_type": "call"}, {"api_name": "streamlink.stream.HTTPStream", "line_number": 69, "usage_type": "call"}]} +{"seq_id": "200313210", "text": "# --- coding:utf-8 ---\nimport torch.nn as nn\nfrom transformers import BertForMaskedLM, BertModel\n\n\nclass IntentModel(nn.Module):\n def __init__(self,args):\n super(IntentModel,self).__init__()\n\n self.model = BertModel.from_pretrained(args.pretrained_model_name)\n self.dropout = nn.Dropout(0.1)\n self.mlp = nn.Linear(768, 2)\n\n def forward(self, input_ids, attention_mask=None, token_type_ids=None):\n outputs = self.model(input_ids=input_ids,\n attention_mask=attention_mask,\n token_type_ids=token_type_ids)\n\n cls_out = outputs['pooler_output']\n\n output = self.dropout(cls_out)\n output = self.mlp(output)\n return output", "sub_path": "基于规则的DST对话系统/chatbot/intent/model/bert_model.py", "file_name": "bert_model.py", "file_ext": "py", "file_size_in_byte": 739, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.nn.Module", "line_number": 6, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 6, "usage_type": "name"}, {"api_name": "transformers.BertModel.from_pretrained", "line_number": 10, "usage_type": "call"}, {"api_name": "transformers.BertModel", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "155018305", "text": "#Troy Prince\n#cherrpy primer\n\nimport cherrypy\nimport re, json\n\n\nclass ResetController(object):\n\n def __init__(self, mdb=None):\n self.mdb = mdb\n print(\"Reset Init\")\n \n def PUT(self):\n output = { 'result' : 'success'}\n\n try:\n mdb.load_movies()\n except Exception as ex:\n output['result'] = 'error'\n output['message'] = str(ex)\n\n return json.dumps(output)\n\n def PUT_K(self, key):\n #else:\n output = { 'result' : 'success'}\n \n try:\n mdb.load_one_movie()\n except Exception as ex:\n output['result'] = 'error'\n output['message'] = str(ex)\n \n return json.dumps(output)\n", "sub_path": "ResetController.py", "file_name": "ResetController.py", "file_ext": "py", "file_size_in_byte": 736, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.dumps", "line_number": 23, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 35, "usage_type": "call"}]} +{"seq_id": "26465569", "text": "from PIL import Image\n\nfrom django.forms import ModelChoiceField, ModelForm, ValidationError\nfrom django.contrib import admin\nfrom django.utils.safestring import mark_safe\n\nfrom .models import *\n\n\n# class ShoeAdminForm(ModelForm):\n#\n# def __init__(self, *args, **kwargs):\n# super().__init__(*args, **kwargs)\n# self.fields['image'].help_text = mark_safe(\n# \"\"\"При загрузке изоброжение с разрешением больше {}x{} оно будет обрезать\n# \"\"\".format(\n# *Product.MAX_RESOLUTION\n# )\n# )\n\n # def clean_image(self):\n # image = self.cleaned_data['image']\n # img = Image.open(image)\n # min_height, min_width = Product.MIN_RESOLUTION\n # max_height, max_width = Product.MAX_RESOLUTION\n # if image.size > Product.MAX_IMAGE_SIZE:\n # raise ValidationError('Размер изображение не должен превышать 3MB')\n # if img.height < min_height or img.width < min_width:\n # raise ValidationError('Разрешение изображение меньше минимального')\n # if img.height > max_height or img.width > max_width:\n # raise ValidationError('Разрешение изображение больше максимального')\n # return image\n\n\nclass ShoeAdminForm(ModelForm):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n instance = kwargs.get('instance')\n if not instance.color:\n self.fields['color_volume_max'].vidget.attrs.update({\n 'readonly': True, 'style': 'background: lightgray'\n })\n\n def clean(self):\n if not self .cleaned_data['color']:\n self.cleaned_data['color_volume_max'] = None\n return self.cleaned_data\n\n\nclass ShirtAdminForm(ModelForm):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n instance = kwargs.get('instance')\n if not instance.color:\n self.fields['color_volume_max'].vidget.attrs.update({\n 'readonly': True, 'style': 'background: lightgray'\n })\n\n def clean(self):\n if not self .cleaned_data['color']:\n self.cleaned_data['color_volume_max'] = None\n return self.cleaned_data\n\n\nclass ShortAdminForm(ModelForm):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n instance = kwargs.get('instance')\n if not instance.color:\n self.fields['color_volume_max'].vidget.attrs.update({\n 'readonly': True, 'style': 'background: lightgray'\n })\n\n def clean(self):\n if not self .cleaned_data['color']:\n self.cleaned_data['color_volume_max'] = None\n return self.cleaned_data\n\n\nclass MikeyAdminForm(ModelForm):\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n instance = kwargs.get('instance')\n if not instance.color:\n self.fields['color_volume_max'].vidget.attrs.update({\n 'readonly': True, 'style': 'background: lightgray'\n })\n\n def clean(self):\n if not self .cleaned_data['color']:\n self.cleaned_data['color_volume_max'] = None\n return self.cleaned_data\n\n\nclass ShoeAdmin(admin.ModelAdmin):\n\n # form = ShoeAdminForm\n\n change_form_template = 'admin.html'\n form = ShoeAdminForm\n\n def formfield_for_foreignkey(self, db_field, request, **kwargs):\n if db_field.name == 'category':\n return ModelChoiceField(Category.objects.filter(slug='shoes'))\n return super().formfield_for_foreignkey(db_field, request, **kwargs)\n\n\nclass ShortAdmin(admin.ModelAdmin):\n\n change_form_template = 'admin.html'\n form = ShortAdminForm\n\n def formfield_for_foreignkey(self, db_field, request, **kwargs):\n if db_field.name == 'category':\n return ModelChoiceField(Category.objects.filter(slug='shorts'))\n return super().formfield_for_foreignkey(db_field, request, **kwargs)\n\n\nclass ShirtAdmin(admin.ModelAdmin):\n\n change_form_template = 'admin.html'\n form = ShirtAdminForm\n\n def formfield_for_foreignkey(self, db_field, request, **kwargs):\n if db_field.name == 'category':\n return ModelChoiceField(Category.objects.filter(slug='shirts'))\n return super().formfield_for_foreignkey(db_field, request, **kwargs)\n\n\nclass MikeyAdmin(admin.ModelAdmin):\n\n change_form_template = 'admin.html'\n form = MikeyAdminForm\n\n def formfield_for_foreignkey(self, db_field, request, **kwargs):\n if db_field.name == 'category':\n return ModelChoiceField(Category.objects.filter(slug='mikeys'))\n return super().formfield_for_foreignkey(db_field, request, **kwargs)\n\n\nadmin.site.register(Category)\nadmin.site.register(Shoe, ShoeAdmin)\nadmin.site.register(Short, ShortAdmin)\nadmin.site.register(Shirt, ShirtAdmin)\nadmin.site.register(Mikey, MikeyAdmin)\nadmin.site.register(CartProduct)\nadmin.site.register(Cart)\nadmin.site.register(Customer)\nadmin.site.register(Order)", "sub_path": "main/admin.py", "file_name": "admin.py", "file_ext": "py", "file_size_in_byte": 5226, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.forms.ModelForm", "line_number": 35, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 51, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 67, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 83, "usage_type": "name"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 99, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 99, "usage_type": "name"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 108, "usage_type": "call"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 112, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 112, "usage_type": "name"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 119, "usage_type": "call"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 123, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 123, "usage_type": "name"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 130, "usage_type": "call"}, {"api_name": "django.contrib.admin.ModelAdmin", "line_number": 134, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 134, "usage_type": "name"}, {"api_name": "django.forms.ModelChoiceField", "line_number": 141, "usage_type": "call"}, {"api_name": "django.contrib.admin.site.register", "line_number": 145, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 145, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 145, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 146, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 146, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 146, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 147, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 147, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 147, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 148, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 148, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 148, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 149, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 149, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 149, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 150, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 150, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 150, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 151, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 151, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 151, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 152, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 152, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 152, "usage_type": "name"}, {"api_name": "django.contrib.admin.site.register", "line_number": 153, "usage_type": "call"}, {"api_name": "django.contrib.admin.site", "line_number": 153, "usage_type": "attribute"}, {"api_name": "django.contrib.admin", "line_number": 153, "usage_type": "name"}]} +{"seq_id": "580913797", "text": "# Create your views here.\n\nfrom django.shortcuts import render\nfrom django.template import loader\nfrom django.http import Http404, HttpResponse, HttpResponseRedirect\nfrom django.core.exceptions import ObjectDoesNotExist\nfrom django.contrib.auth.decorators import login_required, permission_required\nfrom .models import Rubro\nfrom .forms import RubroForm\nfrom VeterinariaPatagonica import tools\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n\ndef rubros(request):\n\n context = {}#Defino el contexto.\n template = loader.get_template('GestionDeRubros/GestionDeRubros.html')#Cargo el template desde la carpeta templates/GestionDeRubros.\n return HttpResponse(template.render(context, request))#Devuelvo la url con el template armado.\n\n\n@login_required(redirect_field_name='proxima')\n@permission_required('GestionDeRubros.add_Rubro', raise_exception=True)\ndef modificar(request, id = None):\n\n rubro = Rubro.objects.get(id=id) if id is not None else None\n if (id==None):\n context = {\"titulo\": 1, 'usuario': request.user}\n else:\n context = {\"titulo\": 2, 'usuario': request.user}\n if request.method == 'POST':\n formulario = RubroForm(request.POST, instance=rubro)\n print(formulario)\n if formulario.is_valid():\n rubro = formulario.save()\n return HttpResponseRedirect(\"/GestionDeRubros/ver/{}\".format(rubro.id))\n else:\n context['formulario'] = formulario\n else:\n context['formulario'] = RubroForm(instance=rubro)\n template = loader.get_template('GestionDeRubros/formulario.html')\n return HttpResponse(template.render(context, request))\n\n\n@login_required(redirect_field_name='proxima')\n@permission_required('GestionDeRubros.delete_Rubro', raise_exception=True)\ndef habilitar(request, id):\n try:\n rubro = Rubro.objects.get(id=id)\n except ObjectDoesNotExist:\n raise Http404()\n\n rubro.baja = False\n rubro.save()\n\n return HttpResponseRedirect( \"/GestionDeRubros/verHabilitados/\" )\n\n@login_required(redirect_field_name='proxima')\n@permission_required('GestionDeRubros.delete_Rubro', raise_exception=True)\ndef deshabilitar(request, id):\n\n try:\n rubro = Rubro.objects.get(id=id)\n except ObjectDoesNotExist:\n raise Http404()\n\n rubro.baja = True\n rubro.save()\n\n return HttpResponseRedirect( \"/GestionDeRubros/verDeshabilitados/\" )\n\n@login_required(redirect_field_name='proxima')\n@permission_required('GestionDeRubros.delete_Rubro', raise_exception=True)\ndef eliminar(request, id):\n try:\n rubro = Rubro.objects.get(id=id)\n except ObjectDoesNotExist:\n raise Http404()\n if request.method == 'POST':\n rubro.delete()\n return HttpResponseRedirect( \"/GestionDeRubros/verDeshabilitados/\" )\n else:\n template = loader.get_template('GestionDeRubros/eliminar.html')\n context = {\n 'usuario' : request.user,\n 'id' : id\n }\n return HttpResponse( template.render( context, request) )\n\ndef ver(request, id):\n\n try:\n rubro = Rubro.objects.get(id=id)\n except ObjectDoesNotExist:\n raise Http404(\"No encontrado\", \"El rubro con id={} no existe.\".format(id))\n\n template = loader.get_template('GestionDeRubros/ver.html')\n contexto = {\n 'rubro': rubro,\n 'usuario': request.user\n }\n\n return HttpResponse(template.render(contexto, request))\n\ndef verHabilitados(request):\n rubrosQuery = Rubro.objects.habilitados()\n rubrosQuery = rubrosQuery.filter(tools.paramsToFilter(request.GET, Rubro))\n template = loader.get_template('GestionDeRubros/verHabilitados.html')\n\n paginator = Paginator(rubrosQuery, 3)\n page = request.GET.get('page')\n\n try:\n rubros = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer, deliver first page.\n rubros = paginator.page(1)\n except EmptyPage:\n # If page is out of range (e.g. 9999), deliver last page of results.\n rubros = paginator.page(paginator.num_pages)\n\n contexto = {\n 'rubrosQuery' : rubrosQuery,\n 'usuario' : request.user,\n 'rubros': rubros,\n }\n\n return HttpResponse(template.render(contexto,request))\n\n\ndef verDeshabilitados(request):\n rubrosQuery = Rubro.objects.deshabilitados()\n rubrosQuery = rubrosQuery.filter(tools.paramsToFilter(request.GET, Rubro))\n template = loader.get_template('GestionDeRubros/verDeshabilitados.html')\n\n paginator = Paginator(rubrosQuery, 3)\n page = request.GET.get('page')\n\n try:\n rubros = paginator.page(page)\n except PageNotAnInteger:\n # If page is not an integer, deliver first page.\n rubros = paginator.page(1)\n except EmptyPage:\n # If page is out of range (e.g. 9999), deliver last page of results.\n rubros = paginator.page(paginator.num_pages)\n\n contexto = {\n 'rubrosQuery': rubrosQuery,\n 'usuario': request.user,\n 'rubros': rubros,\n }\n\n return HttpResponse(template.render(contexto,request))\n", "sub_path": "VeterinariaPatagonica/Apps/GestionDeRubros/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 5051, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.template.loader.get_template", "line_number": 16, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 16, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 17, "usage_type": "call"}, {"api_name": "models.Rubro.objects.get", "line_number": 24, "usage_type": "call"}, {"api_name": "models.Rubro.objects", "line_number": 24, "usage_type": "attribute"}, {"api_name": "models.Rubro", "line_number": 24, "usage_type": "name"}, {"api_name": "forms.RubroForm", "line_number": 30, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 34, "usage_type": "call"}, {"api_name": "forms.RubroForm", "line_number": 38, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 39, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 39, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 40, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 20, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 21, "usage_type": "call"}, {"api_name": "models.Rubro.objects.get", "line_number": 47, "usage_type": "call"}, {"api_name": "models.Rubro.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.Rubro", "line_number": 47, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 48, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 49, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 54, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 43, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 44, "usage_type": "call"}, {"api_name": "models.Rubro.objects.get", "line_number": 61, "usage_type": "call"}, {"api_name": "models.Rubro.objects", "line_number": 61, "usage_type": "attribute"}, {"api_name": "models.Rubro", "line_number": 61, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 62, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 63, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 68, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 56, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 57, "usage_type": "call"}, {"api_name": "models.Rubro.objects.get", "line_number": 74, "usage_type": "call"}, {"api_name": "models.Rubro.objects", "line_number": 74, "usage_type": "attribute"}, {"api_name": "models.Rubro", "line_number": 74, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 75, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 76, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 79, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 81, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 81, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 86, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 70, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.permission_required", "line_number": 71, "usage_type": "call"}, {"api_name": "models.Rubro.objects.get", "line_number": 91, "usage_type": "call"}, {"api_name": "models.Rubro.objects", "line_number": 91, "usage_type": "attribute"}, {"api_name": "models.Rubro", "line_number": 91, "usage_type": "name"}, {"api_name": "django.core.exceptions.ObjectDoesNotExist", "line_number": 92, "usage_type": "name"}, {"api_name": "django.http.Http404", "line_number": 93, "usage_type": "call"}, {"api_name": "django.template.loader.get_template", "line_number": 95, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 95, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 101, "usage_type": "call"}, {"api_name": "models.Rubro.objects.habilitados", "line_number": 104, "usage_type": "call"}, {"api_name": "models.Rubro.objects", "line_number": 104, "usage_type": "attribute"}, {"api_name": "models.Rubro", "line_number": 104, "usage_type": "name"}, {"api_name": "VeterinariaPatagonica.tools.paramsToFilter", "line_number": 105, "usage_type": "call"}, {"api_name": "models.Rubro", "line_number": 105, "usage_type": "argument"}, {"api_name": "VeterinariaPatagonica.tools", "line_number": 105, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 106, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 106, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 108, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 113, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 116, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 126, "usage_type": "call"}, {"api_name": "models.Rubro.objects.deshabilitados", "line_number": 130, "usage_type": "call"}, {"api_name": "models.Rubro.objects", "line_number": 130, "usage_type": "attribute"}, {"api_name": "models.Rubro", "line_number": 130, "usage_type": "name"}, {"api_name": "VeterinariaPatagonica.tools.paramsToFilter", "line_number": 131, "usage_type": "call"}, {"api_name": "models.Rubro", "line_number": 131, "usage_type": "argument"}, {"api_name": "VeterinariaPatagonica.tools", "line_number": 131, "usage_type": "name"}, {"api_name": "django.template.loader.get_template", "line_number": 132, "usage_type": "call"}, {"api_name": "django.template.loader", "line_number": 132, "usage_type": "name"}, {"api_name": "django.core.paginator.Paginator", "line_number": 134, "usage_type": "call"}, {"api_name": "django.core.paginator.PageNotAnInteger", "line_number": 139, "usage_type": "name"}, {"api_name": "django.core.paginator.EmptyPage", "line_number": 142, "usage_type": "name"}, {"api_name": "django.http.HttpResponse", "line_number": 152, "usage_type": "call"}]} +{"seq_id": "359560894", "text": "#!/usr/bin/python\n\nfrom __future__ import print_function\n#import pickle\nimport re\nimport math\nfrom PIL import Image, ImageDraw, ImageFont\n\nfileExt = raw_input('Which book? ')\n\nim = Image.open('data/HP' + fileExt + '_Cover.jpg')\nim = im.convert(mode='L')\nim.thumbnail((1000, 1500), Image.ANTIALIAS)\nwidth, height = im.size\nprint (width, height)\n\nnotLetters = ('\\'', '\"', ',', '.', '!', ' ', '\\n', '-', '(', ')')\n#count = 0\n\n\ndef getVal(j, i):\n tot = 0\n for a in range(3):\n for b in range(2):\n tot += im.getpixel((j*2+b, i*3+a))\n return int((tot/24.0)/255.0*100*5)\n\n\ntext = []\nwith open('data/HP' + fileExt + '_1.txt') as f:\n while True:\n c = f.read(1) # format this now\n if not c:\n break\n if re.match('^[a-zA-Z0-9]', c):\n text.append(c)\n#img = Image.new('RGB', (120 * 308, 200*231), color = (255,255,255))\nprint (len(text))\nrowNum = int(math.floor(height / 3))\ncolNum = int(math.floor(width / 2))\nprint (rowNum, colNum)\n#img = Image.new('RGB', (78 * 500, 117*497), color = (255,255,255))\nimg = Image.new('RGB', (78 * (colNum + 1), 117 *\n (rowNum + 1)), color=(255, 255, 255))\npixTotal = []\n# Col limit is width / b range from getVal\n# Row limit is Height / a range from getVal\nfor i in range(0, rowNum):\n # images.append([])\n for j in range(0, colNum):\n #print c.upper()\n #count += 1\n pix = getVal(j, i)\n #print \"\\t\", j\n pixTotal.append(pix)\n\n if text:\n msg = text.pop(0).upper()\n if(msg == 'C' and text[0].upper() == 'H' and text[1].upper() == 'A' and text[2].upper() == 'P' and text[3].upper() == 'T' and text[4].upper() == 'E' and text[5].upper() == 'R'):\n for n in range(0, 15):\n print(text[n], sep='', end='')\n print(' ')\n\n else:\n\n break\n #img = Image.new('RGB', (120,200), color = (255,255,255))\n W, H = (90, 150)\n\n d = ImageDraw.Draw(img)\n if pix > 100:\n pix = 100\n myFont = ImageFont.truetype(\n \"/usr/share/fonts/liberation/LiberationMono-Regular.ttf\", (180 - pix))\n w, h = d.textsize(msg, font=myFont)\n #d.text((j*90 + (W-w)/2, i*150 + (H-h)/2), msg, fill=(0,0,0), font=myFont)\n d.text((j*78 + (W-w)/2, i*117 + (H-h)/2),\n msg, fill=(0, 0, 0), font=myFont)\n\n # images[i].append(img)\n\n print (i)\n if not text:\n\n break\n #print(\"AVG: \", sum(pixTotal) / len(pixTotal))\n #print(\"MAX: \", max(pixTotal))\nprint (len(text))\n# img.save('HP1TextNewLimit100from180.png')\nimg.save('output/HP' + fileExt + '.png')\nimg.thumbnail((14400, 14400), Image.ANTIALIAS)\n# img.save('HP1TextNewLimit100from180_small.png')\nimg.save('output/HP' + fileExt + '_small.png')\n\n'''\nimages = []\nfor i in range(0,231):\n\timages.append([])\n\tfor j in range (0,308):\n\t\t#print c.upper()\n\t\t#count += 1\n\t\tpix = getVal(i,j)\n\n\t\tif text:\n\t\t\tmsg = text.pop(0)\n\t\telse:\n\t\t\tbreak\n\t\timg = Image.new('RGB', (120,200), color = (255,255,255))\n\t\tW, H = (120, 200)\n\n\t\td = ImageDraw.Draw(img)\n\t\tmyFont = ImageFont.truetype(\"/usr/share/fonts/liberation/LiberationMono-Regular.ttf\",(pix + 10))\n\t\tw, h = d.textsize(msg, font=myFont)\n\t\td.text(((W-w)/2, (H-h)/2), msg, fill=(0,0,0), font=myFont)\n\n\t\timages[i].append(img)\n\n\tprint i\n\tif not text:\n\t\tbreak\n\ncombinedImg = []\nfor row in images:\n\t#images = map(Image.open, ['text6.png', 'text6.png', 'text6.png'])\n\twidths, heights = zip(*(i.size for i in row))\n\n\ttotal_width = sum(widths)\n\tmax_height = max(heights)\n\n\tnew_im = Image.new('RGB', (total_width, max_height))\n\n\tx_offset = 0\n\tfor im in row:\n\t new_im.paste(im, (x_offset,0))\n\t x_offset += im.size[0]\n\n\tcombinedImg.append(new_im)\n\n#images = map(Image.open, ['text6.png', 'text6.png', 'text6.png'])\nwidths, heights = zip(*(i.size for i in combinedImg))\n\ntotal_width = sum(widths)\nmax_height = max(heights)\n\nnew_im = Image.new('RGB', (total_width, max_height))\n\ny_offset = 0\nfor im in images:\n new_im.paste(im, (0,y_offset))\n y_offset += im.size[1]\n\nnew_im.save('HP1Text.png')\n#print count '''\n", "sub_path": "makePosterGenLinted.py", "file_name": "makePosterGenLinted.py", "file_ext": "py", "file_size_in_byte": 4097, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PIL.Image.open", "line_number": 11, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 11, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 13, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 13, "usage_type": "name"}, {"api_name": "re.match", "line_number": 35, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 39, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 40, "usage_type": "call"}, {"api_name": "PIL.Image.new", "line_number": 43, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 43, "usage_type": "name"}, {"api_name": "PIL.ImageDraw.Draw", "line_number": 70, "usage_type": "call"}, {"api_name": "PIL.ImageDraw", "line_number": 70, "usage_type": "name"}, {"api_name": "PIL.ImageFont.truetype", "line_number": 73, "usage_type": "call"}, {"api_name": "PIL.ImageFont", "line_number": 73, "usage_type": "name"}, {"api_name": "PIL.Image.ANTIALIAS", "line_number": 91, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 91, "usage_type": "name"}]} +{"seq_id": "374020713", "text": "import os\nimport cv2\nimport csv\nimport numpy as np\nimport easygui\nimport pytesseract\nimport difflib\n\nfrom models import Contours\nfrom models.Contours import sort_contours\nfrom models.Extract import extract, readHV, create\n\ndef convert():\n file = easygui.fileopenbox()\n # files = filedialog.askopenfilenames()\n directory = os.path.dirname(__file__)\n # directory = r'C:\\Users\\USUARIO\\Documents\\UNIVERSIDAD\\DABM\\Proyecto\\data'\n texto = box_extraction(file,directory)\n # disp = Equipo(name,code,rs,brand,model,tipo,series,numAct)\n # disp.create() \n return texto\n\ndef box_extraction(img_for_box_extraction_path, cropped_dir_path): \n img = cv2.imread(img_for_box_extraction_path, 0) # Read the image\n scale_percent = 80 # percent of original size\n width = int(img.shape[1] * scale_percent / 100)\n height = int(img.shape[0] * scale_percent / 100)\n dim = (width, height) \n # resize image\n resized = cv2.resize(img, dim, interpolation = cv2.INTER_AREA)\n # resized = cv2.resize(img, (960,540))\n\n (thresh, img_bin) = cv2.threshold(resized, 150, 255, \n cv2.THRESH_BINARY | cv2.THRESH_OTSU) # Thresholding the image\n img_bin = 255-img_bin # Invert the image\n cv2.imwrite(\"Image_bin.jpg\",img_bin)\n\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,2)) # Operador morfol+ogico de apertura\n img_bin = cv2.morphologyEx(img_bin, cv2.MORPH_OPEN, kernel,iterations=1)\n\n # Defining a kernel length\n kernel_length = np.array(resized).shape[1]//120\n # A verticle kernel of (1 X kernel_length), which will detect all the verticle lines from the image.\n verticle_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1, round(kernel_length*0.89)))\n # A horizontal kernel of (kernel_length X 1), which will help to detect all the horizontal line \n # from the image.\n hori_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (kernel_length, 1))\n # A kernel of (3 X 3) ones.\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3, 3))\n # Morphological operation to detect verticle lines from an image\n img_temp1 = cv2.erode(img_bin, verticle_kernel, iterations=3)\n verticle_lines_img = cv2.dilate(img_temp1, verticle_kernel, iterations=3)\n cv2.imwrite(\"verticle_lines.jpg\",verticle_lines_img)\n # Morphological operation to detect horizontal lines from an image\n img_temp2 = cv2.erode(img_bin, hori_kernel, iterations=3)\n horizontal_lines_img = cv2.dilate(img_temp2, hori_kernel, iterations=3)\n cv2.imwrite(\"horizontal_lines.jpg\",horizontal_lines_img)\n # Weighting parameters, this will decide the quantity of an image to be added to make a new image.\n alpha = 0.5\n beta = 1.0 - alpha\n # This function helps to add two image with specific weight parameter to get a third image as summation of two image.\n img_final_bin = cv2.addWeighted(verticle_lines_img, alpha, horizontal_lines_img, beta, 0.0)\n img_final_bin = cv2.erode(~img_final_bin, kernel, iterations=2)\n (thresh, img_final_bin) = cv2.threshold(img_final_bin, 128, 255, cv2.THRESH_BINARY | cv2.THRESH_OTSU)\n # For Debugging\n # Enable this line to see verticle and horizontal lines in the image which is used to find boxes\n cv2.imwrite(\"img_final_bin.jpg\",img_final_bin)\n # Find contours for image, which will detect all the boxes\n contours, hierarchy = cv2.findContours(img_final_bin, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)\n # cv2.drawContours(img_final_bin,contours,-1,(0,255,0),3)\n # cv2.imshow('image',img_final_bin)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows()\n \n # print(contours)\n # Sort all the contours by top to bottom.\n (contours, boundingBoxes) = sort_contours(contours, method=\"top-to-bottom\")\n idx = 0\n\n pytesseract.pytesseract.tesseract_cmd = r'C:\\Program Files\\Tesseract-OCR\\tesseract.exe'\n text = []\n kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,2))\n for c in contours:\n # Returns the location and width,height for every contour\n x, y, w, h = cv2.boundingRect(c)\n # If the box height is greater then 20, widht is >80, then only save it as a box in \"cropped/\" folder.\n if (w > 20 and h > 10) and w > 4*h:\n idx += 1\n\n new_img = resized[y-3:y+h+3, x-2:x+w]\n # cv2.imshow('image',new_img)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows() \n # # gray = cv2.cvtColor(new_img, cv2.COLOR_BGR2GRAY)\n \n blur = cv2.GaussianBlur(new_img,(3,3),0)\n # cv2.imshow('image',blur)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows() \n \n tresh = cv2.threshold(blur, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]\n # cv2.imshow('image',tresh)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows()\n # kernel = np.ones((1,1),np.uint8)\n # dilation = cv2.dilate(tresh,kernel,iterations = 1) \n \n kernel = np.ones((1,2),np.uint8)\n erosion = cv2.erode(tresh,kernel,iterations = 1)\n # cv2.imshow('image',erosion)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows() \n \n invert = 255 - erosion\n # cv2.imshow('image',invert)\n # cv2.waitKey(0)\n # cv2.destroyAllWindows() \n\n custom_config = r'--oem 3 --psm 6'\n txt = pytesseract.image_to_string(invert,config= custom_config)\n # print(txt)\n\n text.append(txt)\n cv2.imwrite(cropped_dir_path+str(idx) + '.png', invert)\n matx = []\n for e in text:\n mod1 = e.replace('\\n','')\n # print(mod1)\n mod2 = mod1.replace('\\x0c','')\n # print(mod2)\n matx.append(mod2)\n \n return matx\n \n\n # # box_extraction(\"41.jpg\", \"./Cropped/\")\n\ndef get_matches(matrix,refTitle):\n match = difflib.get_close_matches(refTitle,matrix)\n match = match[0]\n return match\n\ndef getData(refMatrix):\n hdv = readHV('HV_BENEHEART_D6.csv')\n headers,values = extract(hdv,refMatrix)\n create(headers,values)\n\n \n \n\n #Extraer fecha de operación\n #Extraer vida util\n #Extraer fecha de vencimiento de garantía\n #Extraer periodicidad de mantenimiento\n #Extraer ultimo mantenimiento\n #Extraer los que tienen x\n #Extraer riesgo", "sub_path": "models/Converter.py", "file_name": "Converter.py", "file_ext": "py", "file_size_in_byte": 6378, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "easygui.fileopenbox", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 24, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 30, "usage_type": "call"}, {"api_name": "cv2.INTER_AREA", "line_number": 30, "usage_type": "attribute"}, {"api_name": "cv2.threshold", "line_number": 33, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 34, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 34, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 36, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 38, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 38, "usage_type": "attribute"}, {"api_name": "cv2.morphologyEx", "line_number": 39, "usage_type": "call"}, {"api_name": "cv2.MORPH_OPEN", "line_number": 39, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 42, "usage_type": "call"}, {"api_name": "cv2.getStructuringElement", "line_number": 44, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 44, "usage_type": "attribute"}, {"api_name": "cv2.getStructuringElement", "line_number": 47, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 47, "usage_type": "attribute"}, {"api_name": "cv2.getStructuringElement", "line_number": 49, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 49, "usage_type": "attribute"}, {"api_name": "cv2.erode", "line_number": 51, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 52, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 53, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 55, "usage_type": "call"}, {"api_name": "cv2.dilate", "line_number": 56, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 57, "usage_type": "call"}, {"api_name": "cv2.addWeighted", "line_number": 62, "usage_type": "call"}, {"api_name": "cv2.erode", "line_number": 63, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 64, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 64, "usage_type": "attribute"}, {"api_name": "cv2.imwrite", "line_number": 67, "usage_type": "call"}, {"api_name": "cv2.findContours", "line_number": 69, "usage_type": "call"}, {"api_name": "cv2.RETR_TREE", "line_number": 69, "usage_type": "attribute"}, {"api_name": "cv2.CHAIN_APPROX_SIMPLE", "line_number": 69, "usage_type": "attribute"}, {"api_name": "models.Contours.sort_contours", "line_number": 77, "usage_type": "call"}, {"api_name": "pytesseract.pytesseract", "line_number": 80, "usage_type": "attribute"}, {"api_name": "cv2.getStructuringElement", "line_number": 82, "usage_type": "call"}, {"api_name": "cv2.MORPH_RECT", "line_number": 82, "usage_type": "attribute"}, {"api_name": "cv2.boundingRect", "line_number": 85, "usage_type": "call"}, {"api_name": "cv2.GaussianBlur", "line_number": 96, "usage_type": "call"}, {"api_name": "cv2.threshold", "line_number": 101, "usage_type": "call"}, {"api_name": "cv2.THRESH_BINARY_INV", "line_number": 101, "usage_type": "attribute"}, {"api_name": "cv2.THRESH_OTSU", "line_number": 101, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 108, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 108, "usage_type": "attribute"}, {"api_name": "cv2.erode", "line_number": 109, "usage_type": "call"}, {"api_name": "pytesseract.image_to_string", "line_number": 120, "usage_type": "call"}, {"api_name": "cv2.imwrite", "line_number": 124, "usage_type": "call"}, {"api_name": "difflib.get_close_matches", "line_number": 139, "usage_type": "call"}, {"api_name": "models.Extract.readHV", "line_number": 144, "usage_type": "call"}, {"api_name": "models.Extract.extract", "line_number": 145, "usage_type": "call"}, {"api_name": "models.Extract.create", "line_number": 146, "usage_type": "call"}]} +{"seq_id": "95865576", "text": "import requests\nimport matplotlib\nimport matplotlib.pyplot as plt\n\n\nsites = ['http://www.google.com', 'http://www.youtube.com', 'http://www.polimi.it']\n\nm = 0\n\nfor site in sites:\n times = [] # List Results\n\n for request in range(20):\n r = requests.get(site) # Store in r request data\n times.append(r.elapsed.microseconds / 1000)\n plt.plot(times, label = site)\n\n print(\"For \", site, \" results are:\")\n print(\"Minimum time: \", min(times), \" ms\")\n print(\"Maximun time: \", max(times), \" ms\")\n print(\"Average time: \", sum(times)/len(times), \" ms\", end=\"\\n\\n\")\n m = max ([m, max(times)])\n\nprint(\"Massimo tra i massimi \", m)\n\n# Plot stuff\nplt.xlabel('ID request')\nplt.ylabel('Time request (ms)')\nplt.title('Multiple server requests')\nplt.ylim([0, 1.1*m])\nplt.legend(loc = 'upper right', fontsize = 10)\nplt.show()", "sub_path": "Python/AnswerTime/Ex1.py", "file_name": "Ex1.py", "file_ext": "py", "file_size_in_byte": 867, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}]} +{"seq_id": "240346515", "text": "# Utilities to deal with the classif2 agrovision dataset\nimport numpy as np\nimport numpy.ma as ma\nimport os\nimport gdal\n\nBASEDIR = os.path.join(os.environ['AGRODATA'], '3_datasets', 'classif2')\n\ndef load_labels(num=1):\n \"\"\"\n Loads the classif2 labels\n Args:\n num: The number of the labels set to load (different sets have different\n folds structure).\n\n Returns:\n labels\n id2label\n folds\n parcel_ids\n \"\"\"\n fname = os.path.join(BASEDIR, 'npy', 'labels_%s.npz' % str(num))\n d = np.load(fname)\n labels = d['labels']\n labels = ma.masked_where(labels == -1, labels)\n\n folds = d['folds']\n folds = ma.masked_where(folds == -1, folds)\n\n return labels, d['id2label'], folds, d['parcel_ids']\n\n\ndef _load_dsm_correction(datestr):\n \"\"\"\n The compute_dsm_correction scripts compute a per-date correction that\n should be subtracted from the DSM to align it with the others\n \"\"\"\n fname = os.path.join(BASEDIR, 'npy', 'per_date_dsm_correction.npz')\n d = np.load(fname)\n dates = d['dates']\n correction = d['correction']\n\n return correction[dates.tolist().index(datestr)]\n\n\ndef load_image(datestr, imgtype, autocorrect_dsm=True):\n \"\"\"\n Loads an image for the given date and type\n Args:\n datestr: Something like 2013_08_21\n imgtype: Either 'rgb' or 'dsm'\n autocorrect_dsm: If true, will apply DSM correction\n Returns:\n array: This is a masked array that contains the data\n - NxMx3 uint8 array with RGB values for 'rgb'\n - NxM float32 array with elevation values for 'dsm'\n \"\"\"\n assert imgtype in ['rgb', 'dsm']\n fname = os.path.join(BASEDIR, 'rasters', '%s_%s.tif' % (datestr, imgtype))\n assert os.path.exists(fname), 'File does not exist : %s' % fname\n\n ds = gdal.Open(fname)\n if imgtype == 'rgb':\n # This should be a RGBA image\n arr = ds.ReadAsArray()\n assert len(arr.shape) == 3\n assert arr.shape[0] == 4\n assert arr.dtype == np.uint8\n arr = np.rollaxis(arr, 0, start=3)\n mask = arr[:,:,3] == 0\n mask = np.dstack([mask, mask, mask])\n return ma.masked_array(arr[:,:,:3], mask=mask)\n else: # dsm\n assert ds.RasterCount == 1\n band = ds.GetRasterBand(1)\n nodata = band.GetNoDataValue()\n arr = band.ReadAsArray()\n arr = ma.masked_where(arr == nodata, arr)\n assert len(arr.shape) == 2\n assert arr.dtype == np.float32\n\n if autocorrect_dsm:\n return arr - _load_dsm_correction(datestr)\n\n return arr\n\n\n", "sub_path": "paper_code/agronn/classif2.py", "file_name": "classif2.py", "file_ext": "py", "file_size_in_byte": 2607, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.ma.masked_where", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 25, "usage_type": "name"}, {"api_name": "numpy.ma.masked_where", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 28, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "numpy.load", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "gdal.Open", "line_number": 62, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 68, "usage_type": "attribute"}, {"api_name": "numpy.rollaxis", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.dstack", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.ma.masked_array", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 72, "usage_type": "name"}, {"api_name": "numpy.ma.masked_where", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.ma", "line_number": 78, "usage_type": "name"}, {"api_name": "numpy.float32", "line_number": 80, "usage_type": "attribute"}]} +{"seq_id": "275595464", "text": "\"\"\"\nCopyright (c) 2015 Red Hat, Inc\nAll rights reserved.\n\nThis software may be modified and distributed under the terms\nof the BSD license. See the LICENSE file for details.\n\"\"\"\nfrom __future__ import print_function, unicode_literals, absolute_import\n\nimport json\nimport logging\nimport os\nimport sys\nimport time\nfrom functools import wraps\n\nfrom .constants import SIMPLE_BUILD_TYPE, PROD_WITHOUT_KOJI_BUILD_TYPE, PROD_WITH_SECRET_BUILD_TYPE\nfrom osbs.build.build_request import BuildManager\nfrom osbs.build.build_response import BuildResponse\nfrom osbs.build.pod_response import PodResponse\nfrom osbs.constants import DEFAULT_NAMESPACE, PROD_BUILD_TYPE\nfrom osbs.core import Openshift\nfrom osbs.exceptions import OsbsException, OsbsValidationException\n# import utils in this way, so that we can mock standalone functions with flexmock\nfrom osbs import utils\n\n\n# Decorator for API methods.\ndef osbsapi(func):\n @wraps(func)\n def catch_exceptions(*args, **kwargs):\n try:\n return func(*args, **kwargs)\n except OsbsException:\n # Re-raise OsbsExceptions\n raise\n except Exception as ex:\n # Convert anything else to OsbsException\n\n # Python 3 has implicit exception chaining and enhanced\n # reporting, so you get the original traceback as well as\n # the one originating here.\n # For Python 2, let's do that explicitly.\n raise OsbsException(cause=ex, traceback=sys.exc_info()[2])\n\n return catch_exceptions\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass OSBS(object):\n \"\"\"\n Note: all API methods return osbs.http.Response object. This is, due to historical\n reasons, untrue for list_builds and get_user, which return list of BuildResponse objects\n and dict respectively.\n \"\"\"\n @osbsapi\n def __init__(self, openshift_configuration, build_configuration):\n \"\"\" \"\"\"\n self.os_conf = openshift_configuration\n self.build_conf = build_configuration\n self.os = Openshift(openshift_api_url=self.os_conf.get_openshift_api_uri(),\n openshift_api_version=self.os_conf.get_openshift_api_version(),\n openshift_oauth_url=self.os_conf.get_openshift_oauth_api_uri(),\n k8s_api_url=self.os_conf.get_k8s_api_uri(),\n verbose=self.os_conf.get_verbosity(),\n username=self.os_conf.get_username(),\n password=self.os_conf.get_password(),\n use_kerberos=self.os_conf.get_use_kerberos(),\n client_cert=self.os_conf.get_client_cert(),\n client_key=self.os_conf.get_client_key(),\n kerberos_keytab=self.os_conf.get_kerberos_keytab(),\n kerberos_principal=self.os_conf.get_kerberos_principal(),\n kerberos_ccache=self.os_conf.get_kerberos_ccache(),\n use_auth=self.os_conf.get_use_auth(),\n verify_ssl=self.os_conf.get_verify_ssl())\n self._bm = None\n\n # some calls might not need build manager so let's make it lazy\n @property\n def bm(self):\n if self._bm is None:\n self._bm = BuildManager(build_json_store=self.os_conf.get_build_json_store())\n return self._bm\n\n @osbsapi\n def list_builds(self, namespace=DEFAULT_NAMESPACE):\n response = self.os.list_builds(namespace=namespace)\n serialized_response = response.json()\n build_list = []\n for build in serialized_response[\"items\"]:\n build_list.append(BuildResponse(None, build))\n return build_list\n\n @osbsapi\n def get_build(self, build_id, namespace=DEFAULT_NAMESPACE):\n response = self.os.get_build(build_id, namespace=namespace)\n build_response = BuildResponse(response)\n return build_response\n\n @osbsapi\n def cancel_build(self, build_id, namespace=DEFAULT_NAMESPACE):\n response = self.os.cancel_build(build_id, namespace=namespace)\n build_response = BuildResponse(response)\n return build_response\n\n @osbsapi\n def get_pod_for_build(self, build_id, namespace=DEFAULT_NAMESPACE):\n \"\"\"\n :return: PodResponse object for pod relating to the build\n \"\"\"\n pods = self.os.list_pods(label='openshift.io/build.name=%s' % build_id,\n namespace=namespace)\n serialized_response = pods.json()\n pod_list = [PodResponse(pod) for pod in serialized_response[\"items\"]]\n if not pod_list:\n raise OsbsException(\"No pod for build\")\n elif len(pod_list) != 1:\n raise OsbsException(\"Only one pod expected but %d returned\",\n len(pod_list))\n return pod_list[0]\n\n @osbsapi\n def get_build_request(self, build_type=None):\n \"\"\"\n return instance of BuildRequest according to specified build type\n\n :param build_type: str, name of build type\n :return: instance of BuildRequest\n \"\"\"\n build_type = build_type or self.build_conf.get_build_type()\n build_request = self.bm.get_build_request_by_type(build_type=build_type)\n\n # Apply configured resource limits.\n cpu_limit = self.build_conf.get_cpu_limit()\n memory_limit = self.build_conf.get_memory_limit()\n storage_limit = self.build_conf.get_storage_limit()\n if (cpu_limit is not None or\n memory_limit is not None or\n storage_limit is not None):\n build_request.set_resource_limits(cpu=cpu_limit,\n memory=memory_limit,\n storage=storage_limit)\n\n return build_request\n\n @osbsapi\n def create_build_from_buildrequest(self, build_request, namespace=DEFAULT_NAMESPACE):\n \"\"\"\n render provided build_request and submit build from it\n\n :param build_request: instance of build.build_request.BuildRequest\n :param namespace: str, place/context where the build should be executed\n :return: instance of build.build_response.BuildResponse\n \"\"\"\n build_request.set_openshift_required_version(self.os_conf.get_openshift_required_version())\n build = build_request.render()\n response = self.os.create_build(json.dumps(build), namespace=namespace)\n build_response = BuildResponse(response)\n return build_response\n\n def _get_running_builds_for_build_config(self, build_config_id, namespace=DEFAULT_NAMESPACE):\n all_builds_for_bc = self.os.list_builds(\n build_config_id=build_config_id,\n namespace=namespace).json()['items']\n running = []\n for b in all_builds_for_bc:\n br = BuildResponse(request=None, build_json=b)\n if br.is_pending() or br.is_running():\n running.append(br)\n return running\n\n def _poll_for_builds_from_buildconfig(self, build_config_id, namespace=DEFAULT_NAMESPACE):\n # try polling for 60 seconds and then fail if build doesn't appear\n deadline = int(time.time()) + 60\n while int(time.time()) < deadline:\n logger.debug('polling for build from BuildConfig \"%s\"' % build_config_id)\n builds = self._get_running_builds_for_build_config(build_config_id, namespace)\n if len(builds) > 0:\n return builds\n # wait for 5 seconds before trying again\n time.sleep(5)\n\n raise OsbsException('Waited for new build from \"%s\", but none was automatically created' %\n build_config_id)\n\n def _panic_msg_for_more_running_builds(self, build_config_name, builds):\n # this should never happen, but if it does, we want to know all the builds\n # that were running at the time\n builds = ', '.join(['%s: %s' % (b.get_build_name(), b.status) for b in builds])\n msg = 'Multiple builds for %s running, can\\'t proceed: %s' % \\\n (build_config_name, builds)\n return msg\n\n def _create_build_config_and_build(self, build_request, namespace):\n # TODO: test this method more thoroughly\n build_json = build_request.render()\n apiVersion = build_json['apiVersion']\n if apiVersion != self.os_conf.get_openshift_api_version():\n raise OsbsValidationException(\"BuildConfig template has incorrect apiVersion (%s)\" %\n apiVersion)\n\n build_config_name = build_json['metadata']['name']\n\n # check if a build already exists for this config; if so then raise\n running_builds = self._get_running_builds_for_build_config(build_config_name, namespace)\n rb_len = len(running_builds)\n if rb_len > 0:\n if rb_len == 1:\n rb = running_builds[0]\n msg = 'Build %s for %s in state %s, can\\'t proceed.' % \\\n (rb.get_build_name(), build_config_name, rb.status)\n else:\n msg = self._panic_msg_for_more_running_builds(build_config_name, running_builds)\n raise OsbsException(msg)\n\n existing_bc = None\n try:\n # see if there's already a build config\n existing_bc = self.os.get_build_config(build_config_name)\n except OsbsException:\n pass # doesn't exist => do nothing\n\n build = None\n if existing_bc is not None:\n utils.deep_update(existing_bc, build_json)\n logger.debug('build config for %s already exists, updating...', build_config_name)\n self.os.update_build_config(build_config_name, json.dumps(existing_bc), namespace)\n else:\n # if it doesn't exist, then create it\n logger.debug('build config for %s doesn\\'t exist, creating...', build_config_name)\n self.os.create_build_config(json.dumps(build_json), namespace=namespace)\n # if there's an \"ImageChangeTrigger\" on the BuildConfig and \"From\" is of type\n # \"ImageStreamTag\", the build will be scheduled automatically\n # see https://github.com/projectatomic/osbs-client/issues/205\n if build_request.is_auto_instantiated():\n builds = self._poll_for_builds_from_buildconfig(build_config_name, namespace)\n if len(builds) > 0:\n if len(builds) > 1:\n raise OsbsException(\n self._panic_msg_for_more_running_builds(build_config_name, builds))\n else:\n build = builds[0].request\n if build is None:\n build = self.os.start_build(build_config_name, namespace=namespace)\n return build\n\n @osbsapi\n def create_prod_build(self, git_uri, git_ref, git_branch, user, component, target,\n architecture, yum_repourls=None, git_push_url=None,\n namespace=DEFAULT_NAMESPACE, **kwargs):\n df_parser = utils.get_df_parser(git_uri, git_ref, git_branch)\n build_request = self.get_build_request(PROD_BUILD_TYPE)\n build_request.set_params(\n git_uri=git_uri,\n git_ref=git_ref,\n git_branch=git_branch,\n user=user,\n component=component,\n base_image=df_parser.baseimage,\n name_label=df_parser.labels['Name'],\n registry_uri=self.build_conf.get_registry_uri(),\n openshift_uri=self.os_conf.get_openshift_base_uri(),\n kojiroot=self.build_conf.get_kojiroot(),\n kojihub=self.build_conf.get_kojihub(),\n sources_command=self.build_conf.get_sources_command(),\n koji_target=target,\n architecture=architecture,\n vendor=self.build_conf.get_vendor(),\n build_host=self.build_conf.get_build_host(),\n authoritative_registry=self.build_conf.get_authoritative_registry(),\n yum_repourls=yum_repourls,\n pulp_secret=self.build_conf.get_pulp_secret(),\n use_auth=self.build_conf.get_builder_use_auth(),\n pulp_registry=self.os_conf.get_pulp_registry(),\n nfs_server_path=self.os_conf.get_nfs_server_path(),\n nfs_dest_dir=self.build_conf.get_nfs_destination_dir(),\n git_push_url=self.build_conf.get_git_push_url(),\n git_push_username=self.build_conf.get_git_push_username(),\n )\n build_request.set_openshift_required_version(self.os_conf.get_openshift_required_version())\n response = self._create_build_config_and_build(build_request, namespace)\n build_response = BuildResponse(response)\n logger.debug(build_response.json)\n return build_response\n\n @osbsapi\n def create_prod_with_secret_build(self, git_uri, git_ref, git_branch, user, component,\n target, architecture, yum_repourls=None,\n namespace=DEFAULT_NAMESPACE, **kwargs):\n return self.create_prod_build(git_uri, git_ref, git_branch, user, component, target,\n architecture, yum_repourls=yum_repourls,\n namespace=namespace, **kwargs)\n\n @osbsapi\n def create_prod_without_koji_build(self, git_uri, git_ref, git_branch, user, component,\n architecture, yum_repourls=None,\n namespace=DEFAULT_NAMESPACE, **kwargs):\n return self.create_prod_build(git_uri, git_ref, git_branch, user, component, None,\n architecture, yum_repourls=yum_repourls,\n namespace=namespace, **kwargs)\n\n @osbsapi\n def create_simple_build(self, git_uri, git_ref, user, component, yum_repourls=None,\n namespace=DEFAULT_NAMESPACE, **kwargs):\n build_request = self.get_build_request(SIMPLE_BUILD_TYPE)\n build_request.set_params(\n git_uri=git_uri,\n git_ref=git_ref,\n user=user,\n component=component,\n registry_uri=self.build_conf.get_registry_uri(),\n openshift_uri=self.os_conf.get_openshift_base_uri(),\n yum_repourls=yum_repourls,\n use_auth=self.build_conf.get_builder_use_auth(),\n )\n build_request.set_openshift_required_version(self.os_conf.get_openshift_required_version())\n response = self._create_build_config_and_build(build_request, namespace)\n build_response = BuildResponse(response)\n logger.debug(build_response.json)\n return build_response\n\n @osbsapi\n def create_build(self, namespace=DEFAULT_NAMESPACE, **kwargs):\n \"\"\"\n take input args, create build request from provided build type and submit the build\n\n :param namespace: str, place/context where the build should be executed\n :param kwargs: keyword args for build\n :return: instance of BuildRequest\n \"\"\"\n build_type = self.build_conf.get_build_type()\n if build_type in (PROD_BUILD_TYPE,\n PROD_WITHOUT_KOJI_BUILD_TYPE,\n PROD_WITH_SECRET_BUILD_TYPE):\n return self.create_prod_build(namespace=namespace, **kwargs)\n elif build_type == SIMPLE_BUILD_TYPE:\n return self.create_simple_build(namespace=namespace, **kwargs)\n elif build_type == PROD_WITH_SECRET_BUILD_TYPE:\n return self.create_prod_with_secret_build(namespace=namespace, **kwargs)\n else:\n raise OsbsException(\"Unknown build type: '%s'\" % build_type)\n\n @osbsapi\n def get_build_logs(self, build_id, follow=False, build_json=None, wait_if_missing=False,\n namespace=DEFAULT_NAMESPACE):\n \"\"\"\n provide logs from build\n\n :param build_id: str\n :param follow: bool, fetch logs as they come?\n :param build_json: dict, to save one get-build query\n :param wait_if_missing: bool, if build doesn't exist, wait\n :param namespace: str\n :return: None, str or iterator\n \"\"\"\n return self.os.logs(build_id, follow=follow, build_json=build_json,\n wait_if_missing=wait_if_missing, namespace=namespace)\n\n @osbsapi\n def get_docker_build_logs(self, build_id, decode_logs=True, build_json=None,\n namespace=DEFAULT_NAMESPACE):\n \"\"\"\n get logs provided by \"docker build\"\n\n :param build_id: str\n :param decode_logs: bool, docker by default output logs in simple json structure:\n { \"stream\": \"line\" }\n if this arg is set to True, it decodes logs to human readable form\n :param build_json: dict, to save one get-build query\n :param namespace: str\n :return: str\n \"\"\"\n if not build_json:\n build = self.os.get_build(build_id, namespace=namespace)\n build_response = BuildResponse(build)\n else:\n build_response = BuildResponse(None, build_json)\n\n if build_response.is_finished():\n logs = build_response.get_logs(decode_logs=decode_logs)\n return logs\n logger.warning(\"build haven't finished yet\")\n\n @osbsapi\n def wait_for_build_to_finish(self, build_id, namespace=DEFAULT_NAMESPACE):\n response = self.os.wait_for_build_to_finish(build_id, namespace=namespace)\n build_response = BuildResponse(None, response)\n return build_response\n\n @osbsapi\n def wait_for_build_to_get_scheduled(self, build_id, namespace=DEFAULT_NAMESPACE):\n response = self.os.wait_for_build_to_get_scheduled(build_id, namespace=namespace)\n build_response = BuildResponse(None, response)\n return build_response\n\n @osbsapi\n def update_labels_on_build(self, build_id, labels,\n namespace=DEFAULT_NAMESPACE):\n response = self.os.update_labels_on_build(build_id, labels,\n namespace=namespace)\n @osbsapi\n def set_labels_on_build(self, build_id, labels, namespace=DEFAULT_NAMESPACE):\n response = self.os.set_labels_on_build(build_id, labels, namespace=namespace)\n return response\n\n @osbsapi\n def update_labels_on_build_config(self, build_config_id, labels,\n namespace=DEFAULT_NAMESPACE):\n response = self.os.update_labels_on_build_config(build_config_id,\n labels,\n namespace=namespace)\n return response\n\n @osbsapi\n def set_labels_on_build_config(self, build_config_id, labels,\n namespace=DEFAULT_NAMESPACE):\n response = self.os.set_labels_on_build_config(build_config_id,\n labels,\n namespace=namespace)\n return response\n\n @osbsapi\n def update_annotations_on_build(self, build_id, annotations,\n namespace=DEFAULT_NAMESPACE):\n return self.os.update_annotations_on_build(build_id, annotations,\n namespace=namespace)\n\n @osbsapi\n def set_annotations_on_build(self, build_id, annotations, namespace=DEFAULT_NAMESPACE):\n return self.os.set_annotations_on_build(build_id, annotations, namespace=namespace)\n\n @osbsapi\n def import_image(self, name, namespace=DEFAULT_NAMESPACE):\n return self.os.import_image(name, namespace=namespace)\n\n @osbsapi\n def get_token(self):\n return self.os.get_oauth_token()\n\n @osbsapi\n def get_user(self, username=\"~\"):\n return self.os.get_user(username).json()\n\n @osbsapi\n def get_image_stream(self, stream_id, namespace=DEFAULT_NAMESPACE):\n return self.os.get_image_stream(stream_id, namespace)\n\n @osbsapi\n def create_image_stream(self, name, docker_image_repository, namespace=DEFAULT_NAMESPACE):\n img_stream_file = os.path.join(self.os_conf.get_build_json_store(), 'image_stream.json')\n stream = json.load(open(img_stream_file))\n stream['metadata']['name'] = name\n stream['spec']['dockerImageRepository'] = docker_image_repository\n return self.os.create_image_stream(json.dumps(stream), namespace=DEFAULT_NAMESPACE)\n", "sub_path": "osbs/api.py", "file_name": "api.py", "file_ext": "py", "file_size_in_byte": 20607, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "osbs.exceptions.OsbsException", "line_number": 34, "usage_type": "name"}, {"api_name": "osbs.exceptions.OsbsException", "line_number": 44, "usage_type": "call"}, {"api_name": "sys.exc_info", "line_number": 44, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 49, "usage_type": "call"}, {"api_name": "osbs.core.Openshift", "line_number": 63, "usage_type": "call"}, {"api_name": "osbs.build.build_request.BuildManager", "line_number": 84, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 88, "usage_type": "name"}, {"api_name": "osbs.build.build_response.BuildResponse", "line_number": 93, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 97, "usage_type": "name"}, {"api_name": "osbs.build.build_response.BuildResponse", "line_number": 99, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 103, "usage_type": "name"}, {"api_name": "osbs.build.build_response.BuildResponse", "line_number": 105, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 109, "usage_type": "name"}, {"api_name": "osbs.build.pod_response.PodResponse", "line_number": 116, "usage_type": "call"}, {"api_name": "osbs.exceptions.OsbsException", "line_number": 118, "usage_type": "call"}, {"api_name": "osbs.exceptions.OsbsException", "line_number": 120, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 149, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 159, "usage_type": "call"}, {"api_name": "osbs.build.build_response.BuildResponse", "line_number": 160, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 163, "usage_type": "name"}, {"api_name": "osbs.build.build_response.BuildResponse", "line_number": 169, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 174, "usage_type": "name"}, {"api_name": "time.time", "line_number": 176, "usage_type": "call"}, {"api_name": "time.time", "line_number": 177, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 183, "usage_type": "call"}, {"api_name": "osbs.exceptions.OsbsException", "line_number": 185, "usage_type": "call"}, {"api_name": "osbs.exceptions.OsbsValidationException", "line_number": 201, "usage_type": "call"}, {"api_name": "osbs.exceptions.OsbsException", "line_number": 216, "usage_type": "call"}, {"api_name": "osbs.exceptions.OsbsException", "line_number": 222, "usage_type": "name"}, {"api_name": "osbs.utils.deep_update", "line_number": 227, "usage_type": "call"}, {"api_name": "osbs.utils", "line_number": 227, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 229, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 233, "usage_type": "call"}, {"api_name": "osbs.exceptions.OsbsException", "line_number": 241, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 252, "usage_type": "name"}, {"api_name": "osbs.utils.get_df_parser", "line_number": 253, "usage_type": "call"}, {"api_name": "osbs.utils", "line_number": 253, "usage_type": "name"}, {"api_name": "osbs.constants.PROD_BUILD_TYPE", "line_number": 254, "usage_type": "argument"}, {"api_name": "osbs.build.build_response.BuildResponse", "line_number": 284, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 291, "usage_type": "name"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 299, "usage_type": "name"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 306, "usage_type": "name"}, {"api_name": "constants.SIMPLE_BUILD_TYPE", "line_number": 307, "usage_type": "argument"}, {"api_name": "osbs.build.build_response.BuildResponse", "line_number": 320, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 325, "usage_type": "name"}, {"api_name": "osbs.constants.PROD_BUILD_TYPE", "line_number": 334, "usage_type": "name"}, {"api_name": "constants.PROD_WITHOUT_KOJI_BUILD_TYPE", "line_number": 335, "usage_type": "name"}, {"api_name": "constants.PROD_WITH_SECRET_BUILD_TYPE", "line_number": 336, "usage_type": "name"}, {"api_name": "constants.SIMPLE_BUILD_TYPE", "line_number": 338, "usage_type": "name"}, {"api_name": "constants.PROD_WITH_SECRET_BUILD_TYPE", "line_number": 340, "usage_type": "name"}, {"api_name": "osbs.exceptions.OsbsException", "line_number": 343, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 347, "usage_type": "name"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 363, "usage_type": "name"}, {"api_name": "osbs.build.build_response.BuildResponse", "line_number": 377, "usage_type": "call"}, {"api_name": "osbs.build.build_response.BuildResponse", "line_number": 379, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 387, "usage_type": "name"}, {"api_name": "osbs.build.build_response.BuildResponse", "line_number": 389, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 393, "usage_type": "name"}, {"api_name": "osbs.build.build_response.BuildResponse", "line_number": 395, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 400, "usage_type": "name"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 404, "usage_type": "name"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 410, "usage_type": "name"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 418, "usage_type": "name"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 426, "usage_type": "name"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 431, "usage_type": "name"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 435, "usage_type": "name"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 447, "usage_type": "name"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 451, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 452, "usage_type": "call"}, {"api_name": "os.path", "line_number": 452, "usage_type": "attribute"}, {"api_name": "json.load", "line_number": 453, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 456, "usage_type": "call"}, {"api_name": "osbs.constants.DEFAULT_NAMESPACE", "line_number": 456, "usage_type": "name"}]} +{"seq_id": "445819979", "text": "# pip3 install attrs\nimport attr\nimport pprint\nimport pygame\nimport time\n\n# Classes (frozen means immutable objects)\n# See attrs docs: https://www.attrs.org\n@attr.s(frozen=True)\nclass Node(object):\n\tx = attr.ib()\n\ty = attr.ib()\n\n@attr.s(frozen=True)\nclass Segment(object):\n\tnodes = attr.ib()\n\ttrans = attr.ib()\n\n@attr.s(frozen=True)\nclass Shape(object):\n\tsegments = attr.ib()\n\n@attr.s(frozen=True)\nclass MoveableNode(object):\n\tsegment_id = attr.ib()\n\tnode_id = attr.ib()\n\tnode = attr.ib()\n\n# Functions\ndef shape_combined_segments(shape: Shape):\n\tsegments = shape.segments\n\n\ttransposed_segments = []\n\tfor segment in segments:\n\t\ttransposed_segments.append(transpose_segment(segment))\n\n\treturn segments + transposed_segments\n\ndef transpose_segment(segment: Segment):\n\tif segment.trans == 'x':\n\t\ttransposed_nodes = [Node(200 + node.x, node.y) for node in segment.nodes] # mirror x coordinate\n\t\ttransposed_nodes.reverse() # reverse node order\n\t\treturn Segment(transposed_nodes, 'x')\n\n\tif segment.trans == 'y':\n\t\ttransposed_nodes = [Node(node.x, node.y - 200) for node in segment.nodes] # mirror y coordinate\n\t\ttransposed_nodes.reverse() # reverse node order\n\t\treturn Segment(transposed_nodes, 'y')\n\ndef print_combined_segments(shape: Shape):\n\tpprint.pp(shape_combined_segments(shape))\n\ndef add_node_to_shape(shape: Shape, segment_id, node_id, node: Node):\n\tsegments = shape.segments\n\tsegment = segments[segment_id]\n\tnodes = segment.nodes\n\tnodes.insert(node_id, node)\n\tsegments[segment_id] = Segment(nodes, segment.trans)\n\treturn Shape(segments)\n\ndef replace_node_in_shape(shape: Shape, segment_id, node_id, node: Node):\n\tsegments = shape.segments\n\tsegment = segments[segment_id]\n\tnodes = segment.nodes\n\tnodes[node_id] = node\n\tsegments[segment_id] = Segment(nodes, segment.trans)\n\treturn Shape(segments)\n\ndef shape_coordinates(shape:Shape):\n\tnodes = []\n\tfor segment in shape_combined_segments(shape):\n\t\tnodes += segment.nodes[:-1] # exclude every last node in segment to prevent overlap\n\tnodes.append(nodes[0]) # Duplicate the start node to the end to close the shape\n\tcoordinates = [(node.x, node.y) for node in nodes]\n\treturn coordinates\n\ndef print_coordinates(shape: Shape):\n\tpprint.pp(shape_coordinates(shape))\n\ndef shape_movable_nodes(shape:Shape):\n\tmoveable_nodes = []\n\tfor segment_id, segment in enumerate(shape.segments):\n\t\tfor node_id, node in enumerate(segment.nodes[1:-1]):\n\t\t\tmoveable_node = MoveableNode(segment_id, node_id + 1, node)\n\t\t\tmoveable_nodes.append(moveable_node)\n\treturn moveable_nodes\n\n# Create start square\ndef create_square_shape():\n\t# Create square\n\tnode1 = Node(-100, -100) # left-bottom\n\tnode2 = Node(-100, 100) # left-top\n\tnode3 = Node( 100, 100) # right-top\n\n\tsegment1 = Segment(\n\t\tnodes = [node1, node2], \n\t\ttrans = 'x'\n\t)\n\tsegment2 = Segment(\n\t\tnodes = [node2, node3], \n\t\ttrans = 'y'\n\t)\n\treturn Shape([segment1, segment2])\n\n\n# Pygame\nfrom pygame.locals import *\npygame.init()\nscreen = pygame.display.set_mode([750, 750])\nclock = pygame.time.Clock()\n\n# Set colors\nblack = (0,0,0)\n#green = (0,255,0)\n#blue = (0,0,255)\ngreybrown = (139,146,154)\n\nwhite = (255,255,255)\nred = (255,25,55)\nlightgreenblue = (182,220,233)\ndarkgreenblue = (48,124,145)\ngreywhite = (229,227,228)\nbrown = (123,92,82)\n\ncolor1 = lightgreenblue\ncolor2 = darkgreenblue\n\nX,Y,Z = 0,1,2\n\n# Set origin (0, 0) in the center of the screen instead of top-left and flip direction of y-axis\ncoord_to_screen = lambda c, center: (c[0] + center[0] + screen.get_width() // 2, - c[1] + center[1] + screen.get_height() // 2)\ncoords_to_screen = lambda l, center: [coord_to_screen(coordinates, center) for coordinates in l]\nscreen_to_coord = lambda s, center: (s[0] - center[0] - screen.get_width() // 2, - s[1] + center[1] + screen.get_height() // 2) \n\n# Set the start shape\nshape = create_square_shape()\nshape = add_node_to_shape(shape, segment_id=0, node_id=1, node=Node(-100, -30))\nshape = add_node_to_shape(shape, segment_id=0, node_id=2, node=Node(-70, 0))\nshape = add_node_to_shape(shape, segment_id=0, node_id=3, node=Node(-70, 30))\nshape = add_node_to_shape(shape, segment_id=0, node_id=4, node=Node(-100, 30))\nshape = add_node_to_shape(shape, segment_id=1, node_id=1, node=Node(-20, 100))\nshape = add_node_to_shape(shape, segment_id=1, node_id=2, node=Node(0, 75))\nshape = add_node_to_shape(shape, segment_id=1, node_id=3, node=Node(20, 100))\nprint(\"Start shape\")\nprint_combined_segments(shape)\nprint_coordinates(shape)\n\n# Select the start node for movement\nselected = None\n\n# Set the texts\nfont = pygame.font.Font(pygame.font.get_default_font(), 14)\ndraw_text = lambda text, pos: screen.blit(font.render(text, True, brown, greywhite), pos)\n\n# Start loop\nrunning = True\nwhile running:\n\tmovable_nodes = shape_movable_nodes(shape)\n\n\t# Single key-press\n\tfor event in pygame.event.get():\n\t\tif event.type == KEYDOWN:\n\t\t\tif event.key == K_ESCAPE:\n\t\t\t\trunning = False\n\n\t\telif event.type == MOUSEBUTTONDOWN and event.button == 1:\n\t\t\tmouse_x, mouse_y = screen_to_coord(pygame.mouse.get_pos(), (0,0))\n\t\t\tprint(pygame.mouse.get_pos(), mouse_x, mouse_y)\n\t\t\tfor m in movable_nodes:\n\t\t\t\tif abs(m.node.x - mouse_x) < 10 and abs(m.node.y - mouse_y) < 10 :\n\t\t\t\t\tselected = m\n\n\t\telif event.type == MOUSEBUTTONUP and event.button == 1:\n\t\t\tselected = None\n\n\t\telif event.type == QUIT:\n\t\t\trunning = False\n\n\tscreen.fill(white)\n\n\tcolor = color1\n\tfor x_center in range(-400,600,200):\n\t\tfor y_center in range(-400,600,200):\n\t\t\tpygame.draw.polygon(screen, color, coords_to_screen(shape_coordinates(shape), (x_center,y_center)))\n\t\t\tcolor = color2 if color == color1 else color1\n\n\tmovable_nodes = shape_movable_nodes(shape)\n\tfor moveable_node in movable_nodes:\n\t\tcoord = (moveable_node.node.x, moveable_node.node.y)\n\t\tpygame.draw.circle(screen, black, coord_to_screen(coord, (0,0)), 1)\n\n\tif selected is not None:\n\t\tmouse_pos = pygame.mouse.get_pos();\n\t\tmouse_x, mouse_y = screen_to_coord(mouse_pos, (0,0))\n\t\tshape = replace_node_in_shape(shape, selected.segment_id, selected.node_id, Node(mouse_x, mouse_y))\n\t\tpygame.draw.circle(screen, red, (mouse_pos[0], mouse_pos[1]), 5)\n\n\tdraw_text(\"ESCHER MAKER\", (10, 10))\n\t# draw_text(\"Select with tab. Move with arrows. Add with a\", (10, 30))\n\t# draw_text(f\"Segment: {selected_segment_id}\", (10, 50))\n\t# draw_text(f\"Node: {selected_node_id}\", (110, 50))\n\t# draw_text(f\"Position: ({selected_node.x}, {selected_node.y})\", (185, 50))\n\n\tpygame.display.update()\n\n\tclock.tick(60)\n\npygame.quit()\n", "sub_path": "backup/escher-pygame.py", "file_name": "escher-pygame.py", "file_ext": "py", "file_size_in_byte": 6394, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "attr.ib", "line_number": 11, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 12, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 9, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 16, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 17, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 14, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 21, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 19, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 25, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 26, "usage_type": "call"}, {"api_name": "attr.ib", "line_number": 27, "usage_type": "call"}, {"api_name": "attr.s", "line_number": 23, "usage_type": "call"}, {"api_name": "pprint.pp", "line_number": 51, "usage_type": "call"}, {"api_name": "pprint.pp", "line_number": 78, "usage_type": "call"}, {"api_name": "pygame.init", "line_number": 108, "usage_type": "call"}, {"api_name": "pygame.display.set_mode", "line_number": 109, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pygame.time.Clock", "line_number": 110, "usage_type": "call"}, {"api_name": "pygame.time", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pygame.font.Font", "line_number": 152, "usage_type": "call"}, {"api_name": "pygame.font", "line_number": 152, "usage_type": "attribute"}, {"api_name": "pygame.font.get_default_font", "line_number": 152, "usage_type": "call"}, {"api_name": "pygame.event.get", "line_number": 161, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 161, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 167, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 167, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 168, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 168, "usage_type": "attribute"}, {"api_name": "pygame.draw.polygon", "line_number": 184, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 184, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 190, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 190, "usage_type": "attribute"}, {"api_name": "pygame.mouse.get_pos", "line_number": 193, "usage_type": "call"}, {"api_name": "pygame.mouse", "line_number": 193, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 196, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 196, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 204, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 204, "usage_type": "attribute"}, {"api_name": "pygame.quit", "line_number": 208, "usage_type": "call"}]} +{"seq_id": "140742878", "text": "import os\nos.environ[\"CUDA_VISIBLE_DEVICES\"]=\"0,1\"\nimport numpy as np\nimport tensorflow as tf\nimport skimage.io\n\nimport argparse\nimport glob\nfrom multiprocessing import Pool, current_process\n\ndef proc_frames(frames_item):\n (in_path, proc_id) = frames_item\n class_name = in_path.split('/')[-2]\n vid_name = in_path.split('/')[-1]\n out_dir = os.path.join(OUT_PATH, class_name, vid_name)\n try:\n os.makedirs(out_dir)\n except:\n pass\n\n # Get frame data\n print(in_path)\n frame_names = glob.glob(in_path+'/*')\n data = [None]*len(frame_names)\n for i, name in enumerate(frame_names):\n data[i] = skimage.io.imread(name)\n data = np.array(data)\n assert len(data.shape) == 4\n\n # Get sobeled data\n print('getting sobel')\n # with tf.Session() as sess:\n feed_dict = {input_plh:data}\n im_data = np.array(sess.run(out_data, feed_dict=feed_dict))\n im_data = np.squeeze(im_data, axis=-1)\n for i, f_data in enumerate(im_data):\n full_name = os.path.join(out_dir, frame_names[i].split('/')[-1])\n skimage.io.imsave(full_name, f_data)\n print('{} {} done'.format(proc_id, vid_name))\n return True\n\n\nif __name__ == '__main__':\n parser = argparse.ArgumentParser(description=\"Do Sobel Operation\")\n parser.add_argument(\"--in_path\", type=str, default='./UCF101',\n help='path to the video frame data')\n parser.add_argument(\"--out_path\", type=str, default='./UCF101_sobeled/',\n help='path to the output sobel dir')\n parser.add_argument(\"--num_worker\", type=int, default=2)\n args = parser.parse_args()\n\n IN_PATH = args.in_path\n OUT_PATH = args.out_path\n num_worker = args.num_worker\n\n sess = tf.InteractiveSession()\n\n input_plh = tf.placeholder(dtype=tf.float32, \n shape=(None, None, None, 3))\n # kernel_h = [[1,0,-1], [2,0,-2], [1,0,-1]]\n # kernel_h_tf = tf.constant(kernel_h, shape=[1,3,3,1], dtype=tf.float32)\n # kernel_v = [[1,2,1], [0,0,0], [-1,-2,-1]]\n # kernel_v_tf = tf.constant(kernel_v, shape=[1,3,3,1], dtype=tf.float32)\n kernel_v_tf = tf.tile(tf.constant([[1,2,1],[0,0,0],[-1,-2,-1]],\n shape=[3,3,1,1],dtype=tf.float32),[1,1,3,1])\n kernel_h_tf = tf.transpose(kernel_v_tf,[1,0,2,3])\n grad_x = tf.nn.conv2d(input_plh, kernel_h_tf, \n [1,1,1,1], padding='SAME')\n grad_y = tf.nn.conv2d(input_plh, kernel_v_tf, \n [1,1,1,1], padding='SAME')\n\n grad = tf.sqrt(tf.add(tf.pow(grad_x, 2), tf.pow(grad_y, 2)))\n # out_data = grad\n grad = tf.clip_by_value(grad, 0., tf.reduce_max(grad))\n out_data = tf.truediv(grad, tf.reduce_max(grad))\n\n vid_list = glob.glob(IN_PATH+'/*/*')\n # pool = Pool(num_worker)\n # pool.map(proc_frames, zip(vid_list, range(len(vid_list))))\n for i in vid_list:\n proc_frames((i, 0))\n\n", "sub_path": "sobel_operation.py", "file_name": "sobel_operation.py", "file_ext": "py", "file_size_in_byte": 2874, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ", "line_number": 2, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 15, "usage_type": "call"}, {"api_name": "os.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 17, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 23, "usage_type": "call"}, {"api_name": "skimage.io.io.imread", "line_number": 26, "usage_type": "call"}, {"api_name": "skimage.io.io", "line_number": 26, "usage_type": "attribute"}, {"api_name": "skimage.io", "line_number": 26, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.squeeze", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "skimage.io.io.imsave", "line_number": 38, "usage_type": "call"}, {"api_name": "skimage.io.io", "line_number": 38, "usage_type": "attribute"}, {"api_name": "skimage.io", "line_number": 38, "usage_type": "name"}, {"api_name": "argparse.ArgumentParser", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.InteractiveSession", "line_number": 56, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 58, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 58, "usage_type": "attribute"}, {"api_name": "tensorflow.tile", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.constant", "line_number": 64, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 65, "usage_type": "attribute"}, {"api_name": "tensorflow.transpose", "line_number": 66, "usage_type": "call"}, {"api_name": "tensorflow.nn.conv2d", "line_number": 67, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.conv2d", "line_number": 69, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 69, "usage_type": "attribute"}, {"api_name": "tensorflow.sqrt", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.add", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.pow", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.clip_by_value", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.reduce_max", "line_number": 74, "usage_type": "call"}, {"api_name": "tensorflow.truediv", "line_number": 75, "usage_type": "call"}, {"api_name": "tensorflow.reduce_max", "line_number": 75, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 77, "usage_type": "call"}]} +{"seq_id": "335881862", "text": "import settings\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nfrom torch.autograd import Variable\nimport numpy as np\nimport torchvision\nfrom torchvision import datasets, models, transforms\nimport matplotlib.pyplot as plt\nimport time\nimport copy\nimport os, glob\nimport cv2\nimport random\nimport argparse\nimport bcolz\nimport pandas as pd\nimport random\nfrom PIL import Image\n#from inception import inception_v3\nfrom vgg import vgg19_bn, vgg16_bn\n#from inceptionresv2 import inceptionresnetv2\n\nMODEL_DIR = settings.MODEL_DIR\nC = settings.NUM_CLASSES\n\nw_files_training = []\n\ndef get_acc_from_w_filename(filename):\n try:\n stracc = filename.split('_')[-2]\n return float(stracc)\n except:\n return 0.\n\ndef load_best_weights(model):\n w_files = glob.glob(os.path.join(MODEL_DIR, model.name) + '_*.pth')\n max_acc = 0\n best_file = None\n saved_epoch = -1\n for w_file in w_files:\n try:\n stracc = w_file.split('_')[-2]\n epoch = w_file.split('_')[-3]\n acc = float(stracc)\n if acc > max_acc:\n best_file = w_file\n max_acc = acc\n saved_epoch = int(epoch)\n w_files_training.append((acc, w_file))\n except:\n continue\n if max_acc > 0:\n print('loading weight: {}'.format(best_file))\n model.load_state_dict(torch.load(best_file))\n return saved_epoch\n\ndef save_weights(acc, model, epoch, max_num=2):\n f_name = '{}_{}_{:.5f}_.pth'.format(model.name, epoch, acc)\n w_file_path = os.path.join(MODEL_DIR, f_name)\n if len(w_files_training) < max_num:\n w_files_training.append((acc, w_file_path))\n torch.save(model.state_dict(), w_file_path)\n return\n min = 10.0\n index_min = -1\n for i, item in enumerate(w_files_training):\n val_acc, fp = item\n if min > val_acc:\n index_min = i\n min = val_acc\n #print(min)\n if acc > min:\n torch.save(model.state_dict(), w_file_path)\n try:\n os.remove(w_files_training[index_min][1])\n except:\n print('Failed to delete file: {}'.format(w_files_training[index_min][1]))\n w_files_training[index_min] = (acc, w_file_path)\n\ndef save_array(fname, arr):\n c=bcolz.carray(arr, rootdir=fname, mode='w')\n c.flush()\n\ndef load_array(fname):\n return bcolz.open(fname)[:]\n\ndef load_weights_file(model, w_file):\n model.load_state_dict(torch.load(w_file))\n\ndef create_res18(load_weights=False, freeze=False):\n model_ft = models.resnet18(pretrained=True)\n if freeze:\n for param in model_ft.parameters():\n param.requires_grad = False\n\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Sequential(nn.Linear(num_ftrs, C)) #, nn.Softmax())\n model_ft = model_ft.cuda()\n\n model_ft.name = 'res18'\n model_ft.batch_size = 256\n return model_ft\n\ndef create_res34(load_weights=False, freeze=False):\n model_ft = models.resnet34(pretrained=True)\n if freeze:\n for param in model_ft.parameters():\n param.requires_grad = False\n\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Sequential(nn.Linear(num_ftrs, C)) #, nn.Softmax())\n model_ft = model_ft.cuda()\n\n model_ft.name = 'res34'\n model_ft.batch_size = 128\n return model_ft\n\ndef create_res50(load_weights=False, freeze=False):\n model_ft = models.resnet50(pretrained=True)\n if freeze:\n for param in model_ft.parameters():\n param.requires_grad = False\n\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Sequential(nn.Linear(num_ftrs, C)) #, nn.Softmax())\n model_ft = model_ft.cuda()\n\n model_ft.name = 'res50'\n model_ft.batch_size = 32\n return model_ft\n\ndef create_res101(load_weights=False, freeze=False):\n model_ft = models.resnet101(pretrained=True)\n if freeze:\n for param in model_ft.parameters():\n param.requires_grad = False\n num_ftrs = model_ft.fc.in_features\n model_ft.fc = nn.Sequential(nn.Linear(num_ftrs, C))\n model_ft = model_ft.cuda()\n\n model_ft.name = 'res101'\n model_ft.batch_size = 32\n return model_ft\n\ndef create_res152(load_weights=False, freeze=False):\n res152 = models.resnet152(pretrained=True)\n if freeze:\n for param in res152.parameters():\n param.requires_grad = False\n num_ftrs = res152.fc.in_features\n res152.fc = nn.Sequential(nn.Linear(num_ftrs, C))\n res152 = res152.cuda()\n\n res152.name = 'res152'\n return res152\n\ndef create_dense161(load_weights=False, freeze=False):\n desnet_ft = models.densenet161(pretrained=True)\n if freeze:\n for param in desnet_ft.parameters():\n param.requires_grad = False\n num_ftrs = desnet_ft.classifier.in_features\n desnet_ft.classifier = nn.Sequential(nn.Linear(num_ftrs, C))\n desnet_ft = desnet_ft.cuda()\n\n desnet_ft.name = 'dense161'\n #desnet_ft.batch_size = 32\n return desnet_ft\n\ndef create_dense169(load_weights=False, freeze=False):\n desnet_ft = models.densenet169(pretrained=True)\n if freeze:\n for param in desnet_ft.parameters():\n param.requires_grad = False\n num_ftrs = desnet_ft.classifier.in_features\n desnet_ft.classifier = nn.Sequential(nn.Linear(num_ftrs, C))\n desnet_ft = desnet_ft.cuda()\n\n desnet_ft.name = 'dense169'\n #desnet_ft.batch_size = 32\n return desnet_ft\n\ndef create_dense121(load_weights=False, freeze=False):\n desnet_ft = models.densenet121(pretrained=True)\n if freeze:\n for param in desnet_ft.parameters():\n param.requires_grad = False\n num_ftrs = desnet_ft.classifier.in_features\n desnet_ft.classifier = nn.Sequential(nn.Linear(num_ftrs, C))\n desnet_ft = desnet_ft.cuda()\n\n desnet_ft.name = 'dense121'\n desnet_ft.batch_size = 32\n return desnet_ft\n\ndef create_dense201(load_weights=False, freeze=False):\n desnet_ft = models.densenet201(pretrained=True)\n if freeze:\n for param in desnet_ft.parameters():\n param.requires_grad = False\n num_ftrs = desnet_ft.classifier.in_features\n desnet_ft.classifier = nn.Sequential(nn.Linear(num_ftrs, C))\n desnet_ft = desnet_ft.cuda()\n \n desnet_ft.name = 'dense201'\n #desnet_ft.batch_size = 32\n return desnet_ft\n\ndef create_vgg19bn(load_weights=False, freeze=False):\n vgg19_bn_ft = vgg19_bn(pretrained=True)\n if freeze:\n for param in vgg19_bn_ft.parameters():\n param.requires_grad = False\n #vgg19_bn_ft.classifier = nn.Linear(25088, 3)\n vgg19_bn_ft.classifier = nn.Sequential(\n nn.Linear(512 * 7 * 7, 4096),\n nn.ReLU(True),\n nn.Dropout(),\n nn.Linear(4096, 4096),\n nn.ReLU(True),\n nn.Dropout(),\n nn.Linear(4096, C))\n\n vgg19_bn_ft = vgg19_bn_ft.cuda()\n\n vgg19_bn_ft.name = 'vgg19bn'\n vgg19_bn_ft.max_num = 1\n #vgg19_bn_ft.batch_size = 32\n return vgg19_bn_ft\n\ndef create_vgg16bn(load_weights=False, freeze=False):\n vgg16_bn_ft = vgg16_bn(pretrained=True)\n if freeze:\n for param in vgg16_bn_ft.parameters():\n param.requires_grad = False\n #vgg16_bn_ft.classifier = nn.Linear(25088, 3)\n vgg16_bn_ft.classifier = nn.Sequential(\n nn.Linear(512 * 7 * 7, 4096),\n nn.ReLU(True),\n nn.Dropout(),\n nn.Linear(4096, 4096),\n nn.ReLU(True),\n nn.Dropout(),\n nn.Linear(4096, C))\n\n vgg16_bn_ft = vgg16_bn_ft.cuda()\n\n vgg16_bn_ft.name = 'vgg16bn'\n vgg16_bn_ft.max_num = 1\n #vgg16_bn_ft.batch_size = 32\n return vgg16_bn_ft\n\ndef create_inceptionv3(load_weights=False, freeze=False):\n incept_ft = models.inception_v3(pretrained=True)\n if freeze:\n for param in incept_ft.parameters():\n param.requires_grad = False\n num_ftrs = incept_ft.fc.in_features\n incept_ft.fc = nn.Sequential(nn.Linear(num_ftrs, C))\n incept_ft.aux_logits=False\n incept_ft = incept_ft.cuda()\n\n incept_ft.name = 'inceptionv3'\n incept_ft.batch_size = 32\n return incept_ft\n\ndef create_inceptionresv2(load_weights=False, freeze=False):\n model_ft = inceptionresnetv2(pretrained=True)\n num_ftrs = model_ft.classif.in_features\n model_ft.classif = nn.Sequential(nn.Linear(num_ftrs, C))\n model_ft = model_ft.cuda()\n\n model_ft.name = 'inceptionresv2'\n model_ft.batch_size = 8\n return model_ft\n\ndef create_model(model_name, freeze=False):\n create_func = 'create_' + model_name\n\n model = eval(create_func)(freeze=freeze)\n if not hasattr(model, 'batch_size'):\n model.batch_size = 16\n return model\n", "sub_path": "utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 8619, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "settings.MODEL_DIR", "line_number": 24, "usage_type": "attribute"}, {"api_name": "settings.NUM_CLASSES", "line_number": 25, "usage_type": "attribute"}, {"api_name": "glob.glob", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 37, "usage_type": "call"}, {"api_name": "os.path", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.load", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "torch.save", "line_number": 63, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 74, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 76, "usage_type": "call"}, {"api_name": "bcolz.carray", "line_number": 82, "usage_type": "call"}, {"api_name": "bcolz.open", "line_number": 86, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 89, "usage_type": "call"}, {"api_name": "torchvision.models.resnet18", "line_number": 92, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 98, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 98, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 98, "usage_type": "call"}, {"api_name": "torchvision.models.resnet34", "line_number": 106, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 106, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 112, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 112, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 112, "usage_type": "call"}, {"api_name": "torchvision.models.resnet50", "line_number": 120, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 120, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 126, "usage_type": "call"}, {"api_name": "torchvision.models.resnet101", "line_number": 134, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 134, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 139, "usage_type": "call"}, {"api_name": "torchvision.models.resnet152", "line_number": 147, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 147, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 152, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 152, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 152, "usage_type": "call"}, {"api_name": "torchvision.models.densenet161", "line_number": 159, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 159, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 164, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 164, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 164, "usage_type": "call"}, {"api_name": "torchvision.models.densenet169", "line_number": 172, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 172, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 177, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 177, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 177, "usage_type": "call"}, {"api_name": "torchvision.models.densenet121", "line_number": 185, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 185, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 190, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 190, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 190, "usage_type": "call"}, {"api_name": "torchvision.models.densenet201", "line_number": 198, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 198, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 203, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 203, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 203, "usage_type": "call"}, {"api_name": "vgg.vgg19_bn", "line_number": 211, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 216, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 216, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 217, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 217, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 218, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 218, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 219, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 219, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 220, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 220, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 221, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 221, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 222, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 222, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 223, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 223, "usage_type": "name"}, {"api_name": "vgg.vgg16_bn", "line_number": 233, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 238, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 238, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 239, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 240, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 241, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 241, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 242, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 242, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 243, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 243, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 244, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 244, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 245, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 245, "usage_type": "name"}, {"api_name": "torchvision.models.inception_v3", "line_number": 255, "usage_type": "call"}, {"api_name": "torchvision.models", "line_number": 255, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 260, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 260, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 260, "usage_type": "call"}, {"api_name": "torch.nn.Sequential", "line_number": 271, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 271, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 271, "usage_type": "call"}]} +{"seq_id": "335296851", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n#Mirrorcast Server for Raspberry Pi.\n#Please use python3 and not 2.7, 2.7 will cause problems\n\nimport socket,subprocess,time,logging, threading\nfrom omx import Omx\n\nlogging.basicConfig(filename='/var/log/mirrorcast_server.log',level=logging.DEBUG,format='%(asctime)s %(message)s', datefmt='%d/%m/%Y %I:%M:%S %p')\nlogging.info(\"Started Server\")\n\ntimestamp = time.localtime()\nconnected = \"\"\nready = False\nplaying = False\ntube = None\n\ndef connection():\n retries = 10\n try:\n sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n host = \"\"\n sock.bind((host,8092))\n \n sock.listen(5)\n \n global connected\n global timestamp\n global ready\n global playing\n \n global tube \n tube = Omx()\n \n while True:\n client, address = sock.accept()\n status = client.recv(8024)\n command = status.decode('ascii')\n command = command.split(\",\")\n #Some else is already connected\n if connected != command[1] and connected != \"\": \n client.send(\"busy\".encode('ascii'))\n logging.info(str(command[1]) + \" tried to connect but \" + str(connected) + \" is already connected\")\n #User started casting/mirroring or reconnected\n if command[0] == \"play\":\n if connected == \"\":\n connected = command[1]\n logging.info(connected + \" has connected\")\n if connected == command[1]:\n ready == False\n timestamp = time.localtime()\n if tube.player != None:\n kill(tube.player)\n tube.player = None\n subprocess.call(\"tvservice -p &\",shell=True) \n tube.mirror()\n time.sleep(1)\n #Inform client that it is now ok to start ffmpeg\n client.send(\"ready\".encode('ascii'))\n \n #Client intiated stop mirroring\n elif command[0] == \"stop\" and connected == command[1]: \n ready = False\n logging.info(connected + \" has disconnected\")\n connected = \"\"\n kill(tube.player)\n subprocess.call(\"tvservice -p &\",shell=True)\n \n #Client wants to freeze the screen\n elif command[0] == \"freeze\" and connected == command[1]: \n ready = False\n connected = \"\"\n if tube.player != None:\n time.sleep(1)\n tube.player.pause()\n logging.info(connected + \" has froozen their screen\")\n client.send(\"paused\".encode('ascii'))\n \n #WIP, for playing youtube videos\n elif \"tube\" in command[0] and connected == \"\":\n if command[0] == \"tube-load\":\n if tube.player != None:\n kill(tube.player)\n tube.url = command[2]\n if tube.youtube() == False:\n client.send(\"error\".encode('ascii'))\n playing == True\n else:\n while True:\n if tube.player.is_playing():\n client.send(\"ready\".encode('ascii'))\n playing == True\n break\n elif command[0] == \"tube-stop\" and tube.player != None:\n kill(tube.player)\n tube.player = None\n elif command[0] == \"tube-forward\" and tube.player != None: \n if tube.player.can_control():\n tube.player.seek(30)\n elif command[0] == \"tube-back\" and tube.player != None:\n if tube.player.can_control():\n tube.player.seek(-30)\n elif command[0] == \"tube-pause\" and tube.player != None:\n if tube.player.can_control():\n tube.player.play_pause()\n elif command[0] == \"tube-up\" and tube.player != None:\n if tube.player.can_control():\n if tube.player.volume() < 700.0:\n tube.player.set_volume(tube.player.volume() + 100.0)\n elif command[0] == \"tube-down\" and tube.player != None:\n if tube.player.can_control():\n if tube.player.volume() > -1550.0:\n tube.player.set_volume(tube.player.volume() - 100.0)\n elif command[0] == \"tube-track-down\" and tube.player != None:\n if tube.player.can_control():\n tube.player.action(6)\n elif command[0] == \"tube-track-up\" and tube.player != None:\n if tube.player.can_control():\n tube.player.action(7)\n elif command[0] == \"tube-vol\" and tube.player != None:\n if tube.player.can_control():\n tube.player.set_volume(float(command[2]))\n \n #This condition is met if the user wants to play a DVD or Media file.\n elif command[0] == \"media\" and connected == \"\":\n logging.info(connected + \" is trying to stream a Media file or DVD\")\n subprocess.call(\"tvservice -p &\",shell=True)\n if tube.player != None:\n kill(tube.player)\n tube.player = None\n #Inform client that it is now ok to start ffmpeg\n client.send(\"ready\".encode('ascii'))\n \n elif command[0] == \"media-start\" and connected == \"\":\n tube.start_media(address[0])\n \n elif command[0] == \"tu-media\" and connected == \"\":\n logging.info(connected + \" is trying to stream a youtube video\")\n subprocess.call(\"tvservice -p &\",shell=True)\n if tube.player != None:\n kill(tube.player)\n tube.player = None\n time.sleep(1)\n #Inform client that it is now ok to start ffmpeg\n client.send(\"ready\".encode('ascii'))\n \n #Check if client is still online\n elif command[0] == \"active\":\n timestamp = time.localtime()\n ready = True\n client.send(\"ok\".encode('ascii'))\n \n client.close()\n retries = 10\n except:\n retries = retries - 1\n #To prevent logs from getting spammed if there is a problem\n if retries > 0:\n logging.warn(\"There was a issue with sockets, will retry in 20 seconds\")\n time.sleep(20)\n return\n\ndef timeout():\n global connected\n global timestamp\n global ready\n while True:\n #Can no longer contact client, kill omxplayer\n now = time.mktime(time.localtime())\n stamp = time.mktime(timestamp)\n if (now - stamp) > 20 and connected != \"\" and ready == True:\n timestamp = time.localtime()\n logging.warn(connected + \" timed out. \" + str(now) + \" :: \" + str(stamp))\n ready = False\n if tube.player != None:\n kill(tube.player)\n tube.player = None\n time.sleep(1)\n connected = \"\"\n return\n \ndef kill(player):\n try:\n player.quit()\n except:\n pass\n \nloop = threading.Thread(target=timeout)\nloop.start()\nwhile True:\n connection()\n", "sub_path": "server/mirrorcast_server_pi.py", "file_name": "mirrorcast_server_pi.py", "file_ext": "py", "file_size_in_byte": 7741, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.basicConfig", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 9, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 10, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 12, "usage_type": "call"}, {"api_name": "socket.socket", "line_number": 21, "usage_type": "call"}, {"api_name": "socket.AF_INET", "line_number": 21, "usage_type": "attribute"}, {"api_name": "socket.SOCK_STREAM", "line_number": 21, "usage_type": "attribute"}, {"api_name": "omx.Omx", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 43, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 48, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 51, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 55, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 57, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 64, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 67, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 76, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 126, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 127, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 138, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 139, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 143, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 149, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 159, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 160, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 169, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 169, "usage_type": "call"}, {"api_name": "time.mktime", "line_number": 170, "usage_type": "call"}, {"api_name": "time.localtime", "line_number": 172, "usage_type": "call"}, {"api_name": "logging.warn", "line_number": 173, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 178, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 188, "usage_type": "call"}]} +{"seq_id": "88547266", "text": "#!/usr/bin/env python\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import absolute_import\nfrom __future__ import unicode_literals\nimport sys\nimport errno\nimport logging\nimport argparse\n\nARG_DEFAULTS = {'single':False, 'log':sys.stderr, 'volume':logging.ERROR}\nDESCRIPTION = \"\"\"Calculate the probability that an error that occurs somewhere during the a PCR\nprocess with k cycles ends up in x reads out of n in a duplex family. Assumes a simple PCR model\nwhere every fragment is doubled every cycle. It prints four tab-delimited columns: k, n, x, and the\nprobability of that x.\"\"\"\n\n\ndef make_argparser():\n\n parser = argparse.ArgumentParser(description=DESCRIPTION)\n parser.set_defaults(**ARG_DEFAULTS)\n\n parser.add_argument('-x', type=int,\n help='Number of reads with the error. If omitted, it will output the probability of every '\n 'x from 1 to n/2 (or n-1 if --one-sided).')\n parser.add_argument('-n', type=int, required=True,\n help='Total number of reads.')\n parser.add_argument('-k', type=int, required=True,\n help='Number of PCR cycles.')\n parser.add_argument('-1', '--1-sided', dest='single', action='store_true',\n help='Only calculate the literal probability of x errors in n reads. Contrast with '\n '--double-sided.')\n parser.add_argument('-2', '--2-sided', dest='single', action='store_false',\n help='Output P(x/n) + P((n-x)/n) (default). This is how real errors will appear in families, '\n 'since errors over 50%% will be considered the \"correct\", consensus base.')\n parser.add_argument('-l', '--log', type=argparse.FileType('w'),\n help='Print log messages to this file instead of to stderr. Warning: Will overwrite the file.')\n parser.add_argument('-q', '--quiet', dest='volume', action='store_const', const=logging.CRITICAL)\n parser.add_argument('-v', '--verbose', dest='volume', action='store_const', const=logging.INFO)\n parser.add_argument('-D', '--debug', dest='volume', action='store_const', const=logging.DEBUG)\n\n return parser\n\n\ndef main(argv):\n\n parser = make_argparser()\n args = parser.parse_args(argv[1:])\n\n logging.basicConfig(stream=args.log, level=args.volume, format='%(message)s')\n tone_down_logger()\n\n if args.x is not None and (args.x < 1 or args.x >= args.n):\n fail('-x must be between 0 and -n (you gave {})'.format(args.x))\n\n if args.x:\n x_values = [args.x]\n else:\n if args.single:\n x_values = range(1, args.n)\n else:\n x_values = range(1, args.n//2+1)\n\n for x in x_values:\n if args.single or x/args.n == 0.5:\n p = get_maf_prob(args.k, args.n, x)\n else:\n p1 = get_maf_prob(args.k, args.n, x)\n p2 = get_maf_prob(args.k, args.n, args.n-x)\n p = p1 + p2\n print(args.k, args.n, x, p, sep='\\t')\n\n\ndef get_maf_prob(k, n, x):\n \"\"\"Calculate the equation:\n $\\frac{\\sum_{i=1}^k 2^i {n \\choose x} \\frac{1}{2^i}^x (1 - \\frac{1}{2^i})^{n - x} }\n {\\sum_{y=1}^{n-1} \\sum_{i=1}^k 2^i {n \\choose y} \\frac{1}{2^i}^y (1 - \\frac{1}{2^i})^{n-y}}$\n Where n is the total number of reads in the family, x is the number of reads containing a given\n error, and k is the number of PCR cycles used. x/n should then be the frequency of the error in\n the family.\n \"\"\"\n numerator = summation(equation1, 1, k, n, x)\n denominator = 0\n for y in range(1, n):\n denominator += summation(equation1, 1, k, n, y)\n return numerator/denominator\n\n\ndef summation(function, start, end, *args):\n sum = 0\n for i in range(start, end+1):\n sum += function(i, *args)\n return sum\n\n\ndef equation1(i, n, x):\n two_i = 2**i\n mult1 = two_i\n mult2 = n_choose_k(n, x)\n mult3 = (1/two_i)**x\n mult4 = (1-(1/two_i))**(n-x)\n return mult1 * mult2 * mult3 * mult4\n\n\ndef n_choose_k(n, k):\n return factorial(n)/(factorial(k)*factorial(n-k))\n\n\ndef factorial(n):\n \"\"\"A non-recursive factorial function. Because why not.\"\"\"\n product = 1\n for i in range(n, 1, -1):\n product *= i\n return product\n\n\ndef tone_down_logger():\n \"\"\"Change the logging level names from all-caps to capitalized lowercase.\n E.g. \"WARNING\" -> \"Warning\" (turn down the volume a bit in your log files)\"\"\"\n for level in (logging.CRITICAL, logging.ERROR, logging.WARNING, logging.INFO, logging.DEBUG):\n level_name = logging.getLevelName(level)\n logging.addLevelName(level, level_name.capitalize())\n\n\ndef fail(message):\n logging.critical(message)\n if __name__ == '__main__':\n sys.exit(1)\n else:\n raise Exception('Unrecoverable error')\n\n\nif __name__ == '__main__':\n try:\n sys.exit(main(sys.argv))\n except IOError as ioe:\n if ioe.errno != errno.EPIPE:\n raise\n", "sub_path": "utils/pcr.py", "file_name": "pcr.py", "file_ext": "py", "file_size_in_byte": 4601, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.stderr", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 11, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 20, "usage_type": "call"}, {"api_name": "argparse.FileType", "line_number": 36, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 38, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 39, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 40, "usage_type": "attribute"}, {"api_name": "logging.basicConfig", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.CRITICAL", "line_number": 120, "usage_type": "attribute"}, {"api_name": "logging.ERROR", "line_number": 120, "usage_type": "attribute"}, {"api_name": "logging.WARNING", "line_number": 120, "usage_type": "attribute"}, {"api_name": "logging.INFO", "line_number": 120, "usage_type": "attribute"}, {"api_name": "logging.DEBUG", "line_number": 120, "usage_type": "attribute"}, {"api_name": "logging.getLevelName", "line_number": 121, "usage_type": "call"}, {"api_name": "logging.addLevelName", "line_number": 122, "usage_type": "call"}, {"api_name": "logging.critical", "line_number": 126, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 128, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 135, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 135, "usage_type": "attribute"}, {"api_name": "errno.EPIPE", "line_number": 137, "usage_type": "attribute"}]} +{"seq_id": "397497580", "text": "import pandas as pd\r\nimport numpy as np\r\nimport torch\r\n\r\n\r\ndef crossEntropyLossValue(tensor1,tensor2):\r\n '''\r\n you must rewrite your own crossEntropyLoss since\r\n the pytorch version of crossEntropyLoss is\r\n (p(x)*log(q(x))).sum()\r\n but the crossEntropyLoss applied in this paper is\r\n (p(x)*log(q(x))+(1-p(x))*log(1-q(x))).sum()\r\n '''\r\n # loss = (-tensor1*torch.log(tensor2)-(1-tensor1)*torch.log(1-tensor2)).sum()/tensor1.shape[0]\r\n loss = ((tensor1-tensor2)*(tensor1-tensor2)).sum()\r\n return loss\r\n\r\ndef read_csv():\r\n loss = 0\r\n data = pd.read_csv('fake_fingerprint.csv',header=None)\r\n mean = pd.read_csv('mean_targetData.csv',header=None)\r\n data_array = np.array(data)\r\n mean_array = np.array(mean)\r\n for i in range(7400):\r\n print(i)\r\n tensor1 = torch.from_numpy(data_array[i])\r\n tensor2 = torch.from_numpy(mean_array[0])\r\n loss = loss+crossEntropyLossValue(tensor1,tensor2)\r\n print(loss/7400)\r\n\r\n\r\nif __name__ == '__main__':\r\n read_csv()", "sub_path": "pandasReadCSV.py", "file_name": "pandasReadCSV.py", "file_ext": "py", "file_size_in_byte": 1022, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 27, "usage_type": "call"}]} +{"seq_id": "489290426", "text": "from django import template\nfrom booking.models import RoomType\nfrom booking.models import Room\nfrom booking.models import Order\n\nregister = template.Library()\n\n\n@register.filter(name='filter_me')\ndef filter_me(data):\n # print(data)\n orders = Order.objects.all()\n diff_day = ''\n user_name = ''\n for i in orders:\n if data == i.start_date:\n myOrder = i\n diff_day = str(myOrder.diff_days)\n user_name = str(myOrder.user_name)\n print(diff_day)\n text = f\" {user_name} \"\n return text\n\n\n@register.simple_tag()\ndef build_key(room, day):\n return \"{}_{:%Y-%m-%d}\".format(room.id, day)\n\n\n@register.simple_tag()\ndef get_item(dictionary, key):\n return dictionary.get(key)\n", "sub_path": "dashboard/templatetags/tags.py", "file_name": "tags.py", "file_ext": "py", "file_size_in_byte": 788, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.template.Library", "line_number": 6, "usage_type": "call"}, {"api_name": "django.template", "line_number": 6, "usage_type": "name"}, {"api_name": "booking.models.Order.objects.all", "line_number": 12, "usage_type": "call"}, {"api_name": "booking.models.Order.objects", "line_number": 12, "usage_type": "attribute"}, {"api_name": "booking.models.Order", "line_number": 12, "usage_type": "name"}]} +{"seq_id": "577934024", "text": "# %%\nfrom matplotlib import pyplot as plt\nimport numpy as np\nimport os\n\nfname = os.path.join('jena_climate', 'jena_climate_2009_2016.csv')\n\nf = open(fname)\ndata = f.read()\nf.close\n\nlines = data.split('\\n')\nheader = lines[0].split(',')\nlines = lines[1:]\n\nprint(header)\nprint(lines)\n# %%\n\nfloat_data = np.zeros((len(lines), len(header)-1))\nfor i, line in enumerate(lines):\n values = [float(x) for x in line.split(',')[1:]]\n float_data[i] = values\n# %%\n\ntemp = float_data[:, 1]\nplt.plot(range(len(temp)), temp)\n# %%\nplt.plot(range(1440), temp[:1440])\n\n# %%\n", "sub_path": "6.3.1.py", "file_name": "6.3.1.py", "file_ext": "py", "file_size_in_byte": 560, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}]} +{"seq_id": "39152842", "text": "#! /Library/Frameworks/Python.framework/Versions/3.9/bin/python3.9\n#Script to take multi-column MTZ from k_optimiser and plot\n#sections of Patterson maps \n#cctbx must be installed / module loaded for Patterson map generation\n\nimport subprocess\nimport numpy as np\nimport gemmi\nimport matplotlib\nmatplotlib.rcParams['text.usetex'] = True\nfrom matplotlib import pyplot as plt\n\n#Input MTZ file name \nmtz_name = 'k_intensity_corrections.mtz'\n#High resolution limit\nhigh_res = 2.0\n#'y' if you want to generate maps, 'n' if they have already been generated\ngenerate_maps = 'n' \n#Base pattern intensities and sigIs. \nI_base = 'I_'\nsig_base = 'sigI_'\n\nk = 0\nmax_dataset = 50\nwhile k <=50:\n\tkk = \"{0:0=3d}\".format(k)\n\t#Run cctbx.patterson_map to generate Patterson map in CCP4 format \n\tresolution_key = 'high_resolution=' + str(high_res)\n\tfile_name_key = 'map_file_name = k_patt_plot_' + str(kk) + '.ccp4'\n\tlabel_key = 'labels =' +'\\'' + I_base + str(kk) + ',' + sig_base + str(kk) + '\\''\n\tif generate_maps == 'y':\n\t\tsubprocess.call(['cctbx.patterson_map', mtz_name, file_name_key, label_key, resolution_key])\n\telse:\n\t\tbreak\n\tk = k + 1\n\n#Read in CCP4 format Patterson maps as 3D Numpy arrays and \n\nccp4 = gemmi.read_ccp4_map('k_patt_plot_000.ccp4')\nccp4.setup()\narr = np.array(ccp4.grid, copy=False)\nx = np.linspace(0, ccp4.grid.unit_cell.a, num=arr.shape[0], endpoint=False)\ny = np.linspace(0, ccp4.grid.unit_cell.b, num=arr.shape[1], endpoint=False)\nplt.plot(y, arr[0,:,0], label='k = 0')\n\nccp4 = gemmi.read_ccp4_map('k_patt_plot_015.ccp4')\nccp4.setup()\narr = np.array(ccp4.grid, copy=False)\nplt.plot(y, arr[0,:,0], label='k = 15')\n\nccp4 = gemmi.read_ccp4_map('k_patt_plot_018.ccp4')\nccp4.setup()\narr = np.array(ccp4.grid, copy=False)\nplt.plot(y, arr[0,:,0], label='k = 18')\n\nccp4 = gemmi.read_ccp4_map('k_patt_plot_021.ccp4')\nccp4.setup()\narr = np.array(ccp4.grid, copy=False)\nplt.plot(y, arr[0,:,0], label='k = 21')\n\nccp4 = gemmi.read_ccp4_map('k_patt_plot_030.ccp4')\nccp4.setup()\narr = np.array(ccp4.grid, copy=False)\nplt.plot(y, arr[0,:,0], label='k = 30')\n#Code to make contour plot of Patterson Map, uncomment and de-indent to use \n\t\t#X, Y = np.meshgrid(x, y, indexing='ij')\n\t\t#plt.contour(X, Y, arr[:,:,100])\n\t\t#plt.gca().set_aspect('equal', adjustable='box')\n\t\t#plt.show()\n\t\t#print(x,y)\n\t\t#print(arr.shape)\n\n\nplt.grid()\nplt.legend()\nplt.xlabel(r'\\textbf{Interatomic a=0 c =0 b-vectors (Angstroms)}')\nplt.ylabel(r'\\textbf{Patterson Peak Height}')\nplt.show()\n", "sub_path": "translational_disorder/k_patt_plot.py", "file_name": "k_patt_plot.py", "file_ext": "py", "file_size_in_byte": 2457, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.rcParams", "line_number": 10, "usage_type": "attribute"}, {"api_name": "subprocess.call", "line_number": 32, "usage_type": "call"}, {"api_name": "gemmi.read_ccp4_map", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 43, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 44, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 44, "usage_type": "name"}, {"api_name": "gemmi.read_ccp4_map", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 48, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 49, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 49, "usage_type": "name"}, {"api_name": "gemmi.read_ccp4_map", "line_number": 51, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 53, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 54, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 54, "usage_type": "name"}, {"api_name": "gemmi.read_ccp4_map", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 59, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 59, "usage_type": "name"}, {"api_name": "gemmi.read_ccp4_map", "line_number": 61, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 74, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 74, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 75, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 75, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 77, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 78, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 78, "usage_type": "name"}]} +{"seq_id": "577579412", "text": "# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this\n# file, You can obtain one at http://mozilla.org/MPL/2.0/.\n\n# This module contains utilities for parsing commit messages.\n\nimport cgi\nimport re\n\n# These regular expressions are not very robust. Specifically, they fail to\n# handle lists well.\n\nBUG_RE = re.compile(\n r'''# bug followed by any sequence of numbers, or\n # a standalone sequence of numbers\n (\n (?:\n bug |\n b= |\n # a sequence of 5+ numbers preceded by whitespace\n (?=\\b\\#?\\d{5,}) |\n # numbers at the very beginning\n ^(?=\\d)\n )\n (?:\\s*\\#?)(\\d+)(?=\\b)\n )''', re.I | re.X)\n\n# Like BUG_RE except it doesn't flag sequences of numbers, only positive\n# \"bug\" syntax like \"bug X\" or \"b=\".\nBUG_CONSERVATIVE_RE = re.compile(\n r'''((?:bug|b=)(?:\\s*)(\\d+)(?=\\b))''', re.I | re.X)\n\nSPECIFIER = r'(?:r|a|sr|rs|ui-r)[=?]'\nR_SPECIFIER = r'\\br[=?]'\nR_SPECIFIER_RE = re.compile(R_SPECIFIER)\nREQUAL_SPECIFIER_RE = re.compile(r'r=')\nRQUESTION_SPECIFIER_RE = re.compile(r'r\\?')\n\nLIST = r'[;,\\/\\\\]\\s*'\nLIST_RE = re.compile(LIST)\n\n# Note that we only allows a subset of legal IRC-nick characters.\n# Specifically we not allow [ \\ ] ^ ` { | }\nIRC_NICK = r'[a-zA-Z0-9\\-\\_]+' # this needs to match irc nicks\nBMO_IRC_NICK_RE = re.compile(r':(' + IRC_NICK + r')')\n\nREVIEWERS_RE = re.compile(\n r'([\\s\\(\\.\\[;,])' + # before 'r' delimiter\n r'(' + SPECIFIER + r')' + # flag\n r'(' + # capture all reviewers\n IRC_NICK + # reviewer\n r'(?:' + # additional reviewers\n LIST + # delimiter\n r'(?![a-z0-9\\.\\-]+[=?])' + # don't extend match into next flag\n IRC_NICK + # reviewer\n r')*' +\n r')?') # noqa\n\nBACKOUT_KEYWORD = r'^(?:backed out|backout|back out)\\b'\nBACKOUT_KEYWORD_RE = re.compile(BACKOUT_KEYWORD, re.I)\nCHANGESET_KEYWORD = r'(?:\\b(?:changeset|revision|change|cset|of)\\b)'\nCHANGESETS_KEYWORD = r'(?:\\b(?:changesets|revisions|changes|csets|of)\\b)'\nSHORT_NODE = r'([0-9a-f]{12}\\b)'\nSHORT_NODE_RE = re.compile(SHORT_NODE, re.I)\n\nBACKOUT_SINGLE_RE = re.compile(\n BACKOUT_KEYWORD + r'\\s+' +\n CHANGESET_KEYWORD + r'?\\s*' +\n r'(?P' + SHORT_NODE + r')',\n re.I\n)\n\nBACKOUT_MULTI_SPLIT_RE = re.compile(\n BACKOUT_KEYWORD + r'\\s+' +\n r'(?P\\d+)\\s+' +\n CHANGESETS_KEYWORD,\n re.I\n)\n\nBACKOUT_MULTI_ONELINE_RE = re.compile(\n BACKOUT_KEYWORD + r'\\s+' +\n CHANGESETS_KEYWORD + r'?\\s*' +\n r'(?P(?:(?:\\s+|and|,)+' + SHORT_NODE + r')+)',\n re.I\n)\n\nSHORT_RE = re.compile('^[0-9a-f]{12}$', re.I)\n\nDIGIT_RE = re.compile('#?\\d+')\n\n# Strip out a white-list of metadata prefixes.\n# Currently just MozReview-Commit-ID\nMETADATA_RE = re.compile('^MozReview-Commit-ID: ')\n\n\ndef parse_bugs(s):\n bugs_with_duplicates = [int(m[1]) for m in BUG_RE.findall(s)]\n bugs_seen = set()\n bugs_seen_add = bugs_seen.add\n bugs = [x for x in bugs_with_duplicates if not (x in bugs_seen or bugs_seen_add(x))]\n return [bug for bug in bugs if bug < 100000000]\n\n\ndef filter_reviewers(s):\n \"\"\"Given a string, extract meaningful reviewer names.\"\"\"\n for word in s.strip().split():\n if not word:\n continue\n\n word = word.strip('\"[]<>.:')\n\n if '=' in word:\n continue\n\n if word.startswith('(') or word.endswith(')'):\n continue\n\n if word == 'DONTBUILD':\n continue\n\n if DIGIT_RE.match(word):\n continue\n\n yield word\n\n\ndef parse_reviewers(commit_description, flag_re=None):\n commit_summary = commit_description.splitlines().pop(0)\n for match in re.finditer(REVIEWERS_RE, commit_summary):\n if not match.group(3):\n continue\n\n for reviewer in re.split(LIST_RE, match.group(3)):\n if flag_re is None:\n yield reviewer\n elif flag_re.match(match.group(2)):\n yield reviewer\n\n\ndef parse_requal_reviewers(commit_description):\n for reviewer in parse_reviewers(commit_description,\n flag_re=REQUAL_SPECIFIER_RE):\n yield reviewer\n\n\ndef parse_rquestion_reviewers(commit_description):\n for reviewer in parse_reviewers(commit_description,\n flag_re=RQUESTION_SPECIFIER_RE):\n yield reviewer\n\n\ndef replace_reviewers(commit_description, reviewers):\n if not reviewers:\n reviewers_str = ''\n else:\n reviewers_str = 'r=' + ','.join(reviewers)\n\n commit_description = commit_description.splitlines()\n commit_summary = commit_description.pop(0)\n commit_description = '\\n'.join(commit_description)\n\n if not R_SPECIFIER_RE.search(commit_summary):\n commit_summary += ' ' + reviewers_str\n else:\n # replace the first r? with the reviewer list, and all subsequent\n # occurences with a marker to mark the blocks we need to remove\n # later\n d = {'first': True}\n\n def replace_first_reviewer(matchobj):\n if R_SPECIFIER_RE.match(matchobj.group(2)):\n if d['first']:\n d['first'] = False\n return matchobj.group(1) + reviewers_str\n else:\n return '\\0'\n else:\n return matchobj.group(0)\n\n commit_summary = re.sub(REVIEWERS_RE, replace_first_reviewer,\n commit_summary)\n\n # remove marker values as well as leading separators. this allows us\n # to remove runs of multiple reviewers and retain the trailing\n # separator.\n commit_summary = re.sub(LIST + '\\0', '', commit_summary)\n commit_summary = re.sub('\\0', '', commit_summary)\n\n if commit_description == \"\":\n return commit_summary.strip()\n else:\n return commit_summary.strip() + \"\\n\" + commit_description\n\n\ndef is_backout(commit_desc):\n \"\"\"Returns True if the first line of the commit description appears to\n contain a backout.\n\n Backout commits should always result in is_backout() returning True,\n and parse_backouts() not returning None. Malformed backouts may return\n True here and None from parse_backouts().\"\"\"\n return BACKOUT_KEYWORD_RE.match(commit_desc) is not None\n\n\ndef parse_backouts(commit_desc, strict=False):\n \"\"\"Look for backout annotations in a string.\n\n Returns a 2-tuple of (nodes, bugs) where each entry is an iterable of\n changeset identifiers and bug numbers that were backed out, respectively.\n Or return None if no backout info is available.\n\n Setting `strict` to True will enable stricter validation of the commit\n description (eg. ensuring N commits are provided when given N commits are\n being backed out).\n \"\"\"\n if not is_backout(commit_desc):\n return None\n\n lines = commit_desc.splitlines()\n first_line = lines[0]\n\n # Single backout.\n m = BACKOUT_SINGLE_RE.match(first_line)\n if m:\n return [m.group('node')], parse_bugs(first_line)\n\n # Multiple backouts, with nodes listed in commit description.\n m = BACKOUT_MULTI_SPLIT_RE.match(first_line)\n if m:\n expected = int(m.group('count'))\n nodes = []\n for line in lines[1:]:\n single_m = BACKOUT_SINGLE_RE.match(line)\n if single_m:\n nodes.append(single_m.group('node'))\n if strict:\n # The correct number of nodes must be specified.\n if expected != len(nodes):\n return None\n return nodes, parse_bugs(commit_desc)\n\n # Multiple backouts, with nodes listed on the first line\n m = BACKOUT_MULTI_ONELINE_RE.match(first_line)\n if m:\n return SHORT_NODE_RE.findall(m.group('nodes')), parse_bugs(first_line)\n\n return None\n\n\ndef strip_commit_metadata(s):\n \"\"\"Strips metadata related to commit tracking.\n\n Will strip lines like \"MozReview-Commit-ID: foo\" from the commit\n message.\n \"\"\"\n # TODO this parsing is overly simplied. There is room to handle\n # empty lines before the metadata.\n lines = [l for l in s.splitlines() if not METADATA_RE.match(l)]\n\n while lines and not lines[-1].strip():\n lines.pop(-1)\n\n if type(s) == str:\n joiner = b'\\n'\n elif type(s) == unicode:\n joiner = u'\\n'\n else:\n raise TypeError('do not know type of commit message: %s' % type(s))\n\n return joiner.join(lines)\n\n\ndef parse_commit_id(s):\n \"\"\"Parse a MozReview-Commit-ID value out of a string.\n\n Returns None if the commit ID is not found.\n \"\"\"\n m = re.search('^MozReview-Commit-ID: ([a-zA-Z0-9]+)$', s, re.MULTILINE)\n if not m:\n return None\n\n return m.group(1)\n\n\nRE_SOURCE_REPO = re.compile('^Source-Repo: (https?:\\/\\/.*)$',\n re.MULTILINE)\nRE_SOURCE_REVISION = re.compile('^Source-Revision: (.*)$', re.MULTILINE)\n\nRE_XCHANNEL_REVISION = re.compile(\n '^X-Channel-Repo: (?P[a-zA-Z0-9/\\-._]+?)\\n'\n 'X-Channel-Converted-Revision: (?P[a-fA-F0-9]{12,40}?)$',\n re.MULTILINE)\n\n\ndef xchannel_link(m):\n s = m.group()[:(m.start('revision') - m.start())]\n l = '{revision}'\n s += l.format(\n repo=m.group('repo'),\n revision=m.group('revision'),\n )\n s += m.group()[(m.end('revision') - m.start()):]\n return s\n\n\ndef add_hyperlinks(s,\n bugzilla_url='https://bugzilla.mozilla.org/show_bug.cgi?id='):\n \"\"\"Add hyperlinks to a commit message.\n\n This is useful to be used as a Mercurial template filter for converting\n plain text into rich HTML.\n \"\"\"\n # Look for annotations saying this commit originally came from elsewhere.\n # If these are present, we are less aggressive about e.g. linking numbers\n # to Bugzilla bugs.\n source_repo = None\n github_repo = None\n\n m = RE_SOURCE_REPO.search(s)\n if m:\n source_repo = m.group(1)\n\n if source_repo.startswith('https://github.com/'):\n github_repo = source_repo[len('https://github.com/'):]\n\n start, end = m.span(1)\n\n s = '%s%s%s' % (\n s[0:start],\n cgi.escape(source_repo),\n cgi.escape(source_repo),\n s[end:])\n\n m = RE_SOURCE_REVISION.search(s)\n if m:\n source_revision = m.group(1)\n\n start, end = m.span(1)\n\n # Hyperlink to GitHub commits.\n if github_repo:\n s = '%s%s%s' % (\n s[0:start],\n cgi.escape(github_repo),\n cgi.escape(source_revision),\n cgi.escape(source_revision),\n s[end:])\n\n # We replace #\\d+ with links to the GitHub issue.\n if github_repo:\n repl = r'#\\1' % github_repo\n s = re.sub(r'#(\\d+)', repl, s)\n\n # Bugzilla linking.\n bugzilla_re = BUG_CONSERVATIVE_RE if github_repo else BUG_RE\n bugzilla_link = r'\\1' % bugzilla_url\n s = bugzilla_re.sub(bugzilla_link, s)\n\n # l10n cross channel linking\n s = RE_XCHANNEL_REVISION.sub(xchannel_link, s)\n\n return s\n", "sub_path": "pylib/mozautomation/mozautomation/commitparser.py", "file_name": "commitparser.py", "file_ext": "py", "file_size_in_byte": 11418, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "re.compile", "line_number": 13, "usage_type": "call"}, {"api_name": "re.I", "line_number": 26, "usage_type": "attribute"}, {"api_name": "re.X", "line_number": 26, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 30, "usage_type": "call"}, {"api_name": "re.I", "line_number": 31, "usage_type": "attribute"}, {"api_name": "re.X", "line_number": 31, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 35, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 36, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 37, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 40, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 45, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 47, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 60, "usage_type": "call"}, {"api_name": "re.I", "line_number": 60, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 64, "usage_type": "call"}, {"api_name": "re.I", "line_number": 64, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 66, "usage_type": "call"}, {"api_name": "re.I", "line_number": 70, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 73, "usage_type": "call"}, {"api_name": "re.I", "line_number": 77, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 80, "usage_type": "call"}, {"api_name": "re.I", "line_number": 84, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 87, "usage_type": "call"}, {"api_name": "re.I", "line_number": 87, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 89, "usage_type": "call"}, {"api_name": "re.compile", "line_number": 93, "usage_type": "call"}, {"api_name": "re.finditer", "line_number": 129, "usage_type": "call"}, {"api_name": "re.split", "line_number": 133, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 180, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 186, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 187, "usage_type": "call"}, {"api_name": "re.search", "line_number": 278, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 278, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 285, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 286, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 287, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 287, "usage_type": "attribute"}, {"api_name": "re.compile", "line_number": 289, "usage_type": "call"}, {"api_name": "re.MULTILINE", "line_number": 292, "usage_type": "attribute"}, {"api_name": "cgi.escape", "line_number": 330, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 331, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 344, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 345, "usage_type": "call"}, {"api_name": "cgi.escape", "line_number": 346, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 352, "usage_type": "call"}]} +{"seq_id": "523058031", "text": "import re\nimport sdist_upip\nfrom setuptools import setup\n\n\ndef long_desc_from_readme():\n with open('README.rst', 'r') as fd:\n long_description = fd.read()\n\n # remove badges\n long_description = re.compile(r'^\\.\\. start-badges.*^\\.\\. end-badges', re.M | re.S).sub('', long_description)\n\n # strip links. keep link name and use literal text formatting\n long_description = re.sub(r'`([^<`]+) ]+>`_', '``\\\\1``', long_description)\n\n return long_description\n\n\nsetup(\n name=\"micropython-py-esp32-ulp\",\n use_scm_version={\n 'local_scheme': 'no-local-version',\n },\n description=\"Assembler toolchain for the ESP32 ULP co-processor, written in MicroPython\",\n long_description=long_desc_from_readme(),\n long_description_content_type='text/x-rst',\n url=\"https://github.com/ThomasWaldmann/py-esp32-ulp\",\n license=\"MIT\",\n author=\"py-esp32-ulp authors\",\n author_email=\"tw@waldmann-edv.de\",\n maintainer=\"py-esp32-ulp authors\",\n maintainer_email=\"tw@waldmann-edv.de\",\n classifiers=[\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: Implementation :: MicroPython',\n ],\n setup_requires=['setuptools_scm'],\n platforms=[\"esp32\", \"linux\", \"darwin\"],\n cmdclass={\"sdist\": sdist_upip.sdist},\n packages=[\"esp32_ulp\"],\n)\n", "sub_path": "setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 1345, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "re.compile", "line_number": 11, "usage_type": "call"}, {"api_name": "re.M", "line_number": 11, "usage_type": "attribute"}, {"api_name": "re.S", "line_number": 11, "usage_type": "attribute"}, {"api_name": "re.sub", "line_number": 14, "usage_type": "call"}, {"api_name": "setuptools.setup", "line_number": 19, "usage_type": "call"}, {"api_name": "sdist_upip.sdist", "line_number": 39, "usage_type": "attribute"}]} +{"seq_id": "32288179", "text": "#coding:utf-8\n\nimport requests\nimport os,re,json\n\n# re_hash=re.compile('\"hash\":\"(.*?)\"',re.S|re.I)\n# url=''\nhash_url='https://searchrecommend.kugou.com/get/complex'\nparams={\n 'callback': 'jQuery1124011578388853206789_1614761739771',\n 'word': '周杰伦',\n '_': '1614761739773'\n}\nhash_html=requests.get(hash_url, params=params).text\nstart=hash_html.find('{\"data\"')\nend=hash_html.find('\"info\":\"\"}')+len('\"info\":\"\"}')\nresult=json.loads(hash_html[start:end])['data']['song']\nfor lis in result:\n song_name=lis['songname']\n songer_name=lis['singername']\n hash=lis['hash']\n albumid=lis['AlbumID']\n\n url='https://wwwapi.kugou.com/yy/index.php'\n params={\n 'r': 'play/getdata',\n 'callback': 'jQuery19104341443083221761_1614761024893',\n 'hash': str(hash),\n 'dfid': '0PVFDZ3qVwt23cY1C93BuBR1',\n 'mid': 'c16afe0890ad8c2a00901214f815242a',\n 'platid': '4',\n 'album_id': albumid,\n '_': '1614761024895'\n }\n html=requests.get(url,params=params).text\n start = html.find('{\"status\"')\n end = html.find('}}}')+len('}}}')\n result = json.loads(html[start:end])['data']\n name = result['audio_name']\n url = result['play_url']\n print('正在下载',name)\n with open('E:\\个人文件\\音乐\\{}.mp3'.format(name),'wb') as f:\n f.write(requests.get(url).content)\n\n\n\n", "sub_path": "爬虫-酷狗音乐.py", "file_name": "爬虫-酷狗音乐.py", "file_ext": "py", "file_size_in_byte": 1352, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 14, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 17, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 35, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 38, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 43, "usage_type": "call"}]} +{"seq_id": "430644522", "text": "import cv2 #computer vision\nimport matplotlib.pyplot as plt \nimport numpy as np\n\n\nimg = cv2.imread('images/Costa.jpg',cv2.IMREAD_COLOR)\nimgRGB=cv2.cvtColor(img,cv2.COLOR_BGR2RGB)\n#m=np.ones(imgRGB.shape,dtype='uint8')*50\n#Mejorar el contraste\nm=np.ones(imgRGB.shape)*0.8\nm2=np.ones(imgRGB.shape)*1.2\n#img1=cv2.add(imgRGB,m)\n#img2=cv2.subtract(imgRGB,m)\n\nimg1=np.uint8(cv2.multiply(np.float64(img),m))\nimg2=np.uint8(np.clip(cv2.multiply(np.float64(img),m2),0,255))#Lo ultimo es para indicar que los valores van de 0 a 255 para que los normalice\n\n\"\"\"plt.subplot(131);plt.imshow(imgRGB);plt.title('Original')\nplt.subplot(132);plt.imshow(img1);plt.title('Clara')\nplt.subplot(133);plt.imshow(img2);plt.title('Oscura')\"\"\"\n\nplt.subplot(131);plt.imshow(img1);plt.title('Bajo contraste')\nplt.subplot(132);plt.imshow(img);plt.title('Original')\nplt.subplot(133);plt.imshow(img2);plt.title('Alto contraste')\n\n#plt.imshow(imgRGB)\nplt.waitforbuttonpress()\n\n\n", "sub_path": "6.Dia_6/5.MejoraImage.py", "file_name": "5.MejoraImage.py", "file_ext": "py", "file_size_in_byte": 944, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "cv2.imread", "line_number": 6, "usage_type": "call"}, {"api_name": "cv2.IMREAD_COLOR", "line_number": 6, "usage_type": "attribute"}, {"api_name": "cv2.cvtColor", "line_number": 7, "usage_type": "call"}, {"api_name": "cv2.COLOR_BGR2RGB", "line_number": 7, "usage_type": "attribute"}, {"api_name": "numpy.ones", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.ones", "line_number": 11, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 15, "usage_type": "call"}, {"api_name": "cv2.multiply", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.uint8", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.clip", "line_number": 16, "usage_type": "call"}, {"api_name": "cv2.multiply", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.float64", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 24, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 24, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.waitforbuttonpress", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "402439344", "text": "from __future__ import print_function\n\nimport sys\nsys.path.append('/opt/pycharm/pycharm-2.7.1/pycharm-debug-py3k.egg')\n\n#import pydevd\n#pydevd.settrace('localhost', port=9989, stdoutToServer=True, stderrToServer=True)\n\nimport ipdb\n#ipdb.set_trace()\n#from ipdb import launch_ipdb_on_exception\n\nimport tornado.ioloop\nimport tornado.web\n\nclass DebuggingLoop(tornado.ioloop.IOLoop):\n def handle_callback_exception(self, callback):\n exc_type, exc_value, tb = sys.exc_info()\n ipdb.post_mortem(tb)\n\nioloop = DebuggingLoop()\nioloop.install()\n\nclass DebuggingRequest(tornado.web.RequestHandler):\n\n def _handle_request_exception(self, e):\n tornado.web.RequestHandler._handle_request_exception(self, e)\n exc_type, exc_value, tb = sys.exc_info()\n ipdb.post_mortem(tb)\n\ndef init_ipython():\n from IPython.config.loader import Config\n from IPython.frontend.terminal.embed import InteractiveShellEmbed\n\n try:\n get_ipython\n except NameError:\n nested = 0\n cfg = Config()\n else:\n print(\"Running nested copies of IPython.\")\n print(\"The prompts for the nested copy have been modified\")\n cfg = Config()\n nested = 1\n\n ipshell = InteractiveShellEmbed(config=cfg,\n banner1 = 'Stopping IO Loop and dropping to ipython')\n\n class shell_wrapper(object):\n\n def __init__(self):\n self.user_wants_out = False\n\n def __call__(self):\n ipshell('Ctrl-D, quit, exit all exit interpreter and continue program\\n'\n 'If you need to kill the program %kill', stack_depth=3)\n return self.user_wants_out\n\n _shell_wrapper = shell_wrapper()\n\n def kill_program(self, parameter_s=''):\n _shell_wrapper.user_wants_out = True\n ipshell.exit()\n\n def really_die(self, etype, value, tb, tb_offset=None):\n _shell_wrapper.user_wants_out = True\n return None\n\n ipshell.define_magic(\"kill\", kill_program)\n ipshell.confirm_exit = False\n ipshell.set_custom_exc((SystemExit,), really_die)\n\n return _shell_wrapper\n\nIPSHELL = init_ipython()\n\ndef drop_to_shell(ipshell=IPSHELL):\n if ipshell:\n exit = ipshell()\n if exit:\n sys.exit(0)\n\ndef run_loop(ioloop):\n while True:\n try:\n ioloop.start()\n except KeyboardInterrupt:\n ioloop.stop()\n drop_to_shell()\n print('Resuming I/O loop')\n\n\n## ALL OF THE ABOVE WILL DISAPPEAR INTO A MODULE\n\nclass MainHandler(DebuggingRequest):\n def get(self):\n something = ['this', 'is', 'the', 'response']\n drop_to_shell()\n self.write(' '.join(something))\n\nclass Broken(DebuggingRequest):\n def get(self):\n raise NotImplemented()\n\napp = tornado.web.Application([\n (r'/', MainHandler),\n (r'/test', Broken)\n], debug=True)\n\nif __name__ == '__main__':\n app.listen(8000)\n ioloop.add_callback(lambda: sys.stdout.write('Started on port 8000 -C to abort\\n'))\n run_loop(ioloop)", "sub_path": "dockerized-gists/5202233/snippet.py", "file_name": "snippet.py", "file_ext": "py", "file_size_in_byte": 3003, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.path.append", "line_number": 4, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 4, "usage_type": "attribute"}, {"api_name": "tornado.ioloop.ioloop", "line_number": 16, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 16, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 18, "usage_type": "call"}, {"api_name": "ipdb.post_mortem", "line_number": 19, "usage_type": "call"}, {"api_name": "tornado.ioloop.web", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 24, "usage_type": "name"}, {"api_name": "tornado.ioloop.web.RequestHandler._handle_request_exception", "line_number": 27, "usage_type": "call"}, {"api_name": "tornado.ioloop.web", "line_number": 27, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 27, "usage_type": "name"}, {"api_name": "sys.exc_info", "line_number": 28, "usage_type": "call"}, {"api_name": "ipdb.post_mortem", "line_number": 29, "usage_type": "call"}, {"api_name": "IPython.config.loader.Config", "line_number": 39, "usage_type": "call"}, {"api_name": "IPython.config.loader.Config", "line_number": 43, "usage_type": "call"}, {"api_name": "IPython.frontend.terminal.embed.InteractiveShellEmbed", "line_number": 46, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 81, "usage_type": "call"}, {"api_name": "tornado.ioloop.web.Application", "line_number": 105, "usage_type": "call"}, {"api_name": "tornado.ioloop.web", "line_number": 105, "usage_type": "attribute"}, {"api_name": "tornado.ioloop", "line_number": 105, "usage_type": "name"}, {"api_name": "sys.stdout.write", "line_number": 112, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 112, "usage_type": "attribute"}]} +{"seq_id": "404489809", "text": "#read in mandel data computed in C\nimport csv\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nxres = 1920\nyres = 1080\nimg = np.zeros( (yres,xres)) \nrowcount = 0\nwith open('mandelbrot.txt', 'r') as f:\n\treader = csv.reader(f, delimiter='\\t')\n\tfor row in reader:\n\t\tcolcount = 0\n\t\tfor col in row[:-1]:\n\t\t\timg[rowcount][colcount] = float(col)\n\t\t\tcolcount += 1\n\t\trowcount += 1\n\nplt.imsave( 'mandel-hot.png', img, cmap='hot')\nplt.imsave( 'mandel-ncar.png', img, cmap='gist_ncar')\nplt.imsave( 'mandel-brbg.png', img, cmap='BrBG')\nplt.imsave( 'mandel-jet.png', img, cmap='jet')\nplt.imsave( 'mandel-prism.png', img, cmap='prism')\n", "sub_path": "Mandelbrot/read_mandel.py", "file_name": "read_mandel.py", "file_ext": "py", "file_size_in_byte": 624, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.zeros", "line_number": 8, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 11, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 21, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 21, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 22, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imsave", "line_number": 23, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 23, "usage_type": "name"}]} +{"seq_id": "652642207", "text": "from __future__ import print_function\r\n\r\nimport numpy as np\r\nnp.random.seed(1337)\r\n\r\nfrom sklearn.preprocessing import Normalizer \r\nfrom keras.models import Sequential\r\nfrom keras.layers import Dense, Dropout, Activation\r\nimport pandas as pd\r\nfrom keras.layers import Convolution2D,Flatten\r\nfrom keras.layers import LSTM, GRU, SimpleRNN\r\nfrom keras.constraints import maxnorm\r\nfrom sklearn.preprocessing import LabelEncoder\r\nfrom sklearn.metrics import confusion_matrix\r\nfrom sklearn.metrics import classification_report \r\nfrom keras.models import model_from_json\r\nimport matplotlib.pyplot as plt\r\n\r\ndataset=pd.read_csv('attackpt1.csv')\r\nX = dataset.iloc[:, :-1].values\r\nY = dataset.iloc[:, 41].values\r\nlabelencoder_x_1 = LabelEncoder()\r\nlabelencoder_x_2 = LabelEncoder()\r\nlabelencoder_x_3 = LabelEncoder()\r\nlabelencoder_y = LabelEncoder()\r\n\r\nlabelencoder_x_1 = labelencoder_x_1.fit(['icmp' 'tcp' 'udp'])\r\nlabelencoder_x_2 = labelencoder_x_2.fit(['IRC' 'X11' 'Z39_50' 'aol' 'auth' 'bgp' 'courier' 'csnet_ns' 'ctf'\r\n 'daytime' 'discard' 'domain' 'domain_u' 'echo' 'eco_i' 'ecr_i' 'efs'\r\n 'exec' 'finger' 'ftp' 'ftp_data' 'gopher' 'harvest' 'hostnames' 'http'\r\n 'http_2784' 'http_443' 'http_8001' 'imap4' 'iso_tsap' 'klogin' 'kshell'\r\n 'ldap' 'link' 'login' 'mtp' 'name' 'netbios_dgm' 'netbios_ns'\r\n 'netbios_ssn' 'netstat' 'nnsp' 'nntp' 'other' 'pm_dump' 'pop_2' 'pop_3'\r\n 'printer' 'private' 'remote_job' 'rje' 'shell' 'smtp' 'sql_net' 'ssh'\r\n 'sunrpc' 'supdup' 'systat' 'telnet' 'tim_i' 'time' 'urp_i' 'uucp'\r\n 'uucp_path' 'vmnet' 'whois'])\r\nlabelencoder_x_3 = labelencoder_x_1.fit(['OTH' 'REJ' 'RSTO' 'RSTOS0' 'RSTR' 'S0' 'S1' 'S2' 'S3' 'SF' 'SH'])\r\nlabelencoder_y = labelencoder_y.fit(['back.' 'buffer_overflow.' 'ftp_write.' 'guess_passwd.' 'imap.'\r\n 'ipsweep.' 'land.' 'loadmodule.' 'multihop.' 'neptune.' 'nmap.' 'perl.'\r\n 'phf.' 'pod.' 'portsweep.' 'rootkit.' 'satan.' 'smurf.' 'spy.'\r\n 'teardrop.' 'warezclient.' 'warezmaster.'])\r\n\r\nX[:, 1] = labelencoder_x_1.fit_transform(X[:, 1])\r\nX[:, 2] = labelencoder_x_2.fit_transform(X[:, 2])\r\nX[:, 3] = labelencoder_x_3.fit_transform(X[:, 3])\r\ntestY= labelencoder_y.fit_transform(Y)\r\n\r\nscaler = Normalizer().fit(X)\r\ntestX= scaler.transform(X)\r\nlabels=['back.' ,'buffer_overflow.', 'ftp_write.', 'guess_passwd.', 'imap.',\r\n 'ipsweep.' ,'land.', 'loadmodule.', 'multihop.', 'neptune.', 'nmap.', 'perl.',\r\n 'phf.', 'pod.', 'portsweep.', 'rootkit.' ,'satan.' ,'smurf.', 'spy.',\r\n 'teardrop.', 'warezclient.' ,'warezmaster.']\r\n\r\ndef con_mat(y_pred,y_test):\r\n cm=confusion_matrix(y_test,y_pred)\r\n print(\"done\") \r\n #print(labels[int(y_pred)])\r\n print(\"\\n\"+classification_report(y_test, y_pred))\r\n from mlxtend.plotting import plot_confusion_matrix\r\n fig,ax=plot_confusion_matrix(conf_mat=cm,figsize=(15,15))\r\n plt.show()\r\n fig,ax=plt.subplots()\r\n ax.scatter(y_test,y_pred )\r\n ax.plot([y_test.min(),y_test.max()],[y_test.min(),y_test.max()],'k--',lw=4)\r\n ax.set_xlabel('Measured')\r\n ax.set_ylabel('predicted')\r\n fig.show()\r\n \r\n \r\n \r\ndef cnnload(testX,op):\r\n cnn = Sequential()\r\n cnn.add(Convolution2D(64, 3,3, border_mode=\"same\",activation=\"relu\",input_shape=(1,41,1),W_constraint=maxnorm(3)))\r\n cnn.add(Convolution2D(64, 3,3, border_mode=\"same\", activation=\"relu\",W_constraint=maxnorm(3)))\r\n cnn.add(Convolution2D(128, 3,3, border_mode=\"same\", activation=\"relu\",W_constraint=maxnorm(3)))\r\n cnn.add(Convolution2D(128, 3,3,border_mode=\"same\", activation=\"relu\",W_constraint=maxnorm(3)))\r\n cnn.add(Flatten())\r\n cnn.add(Dense(128, activation=\"relu\"))\r\n cnn.add(Dropout(0.5))\r\n cnn.add(Dense(op, activation=\"softmax\"))\r\n cnn.load_weights(\"cnn1.hdf5\")\r\n print(\"loaded cnn\")\r\n testX=np.array(testX)\r\n testXR = np.reshape(testX, (testX.shape[0],1,testX.shape[1],1))\r\n y_pred = cnn.predict_classes(testXR)\r\n print(y_pred)\r\n #con_mat(y_pred,testY)\r\n return cnn,y_pred\r\n\r\ndef lstmload(testX,op):\r\n lstm = Sequential()\r\n lstm.add(LSTM(128,input_dim=41, return_sequences=True)) \r\n lstm.add(Dropout(0.1))\r\n lstm.add(LSTM(128,return_sequences=True))\r\n lstm.add(Dropout(0.1))\r\n lstm.add(LSTM(128, return_sequences=True)) \r\n lstm.add(Dropout(0.1))\r\n lstm.add(LSTM(128, return_sequences=False)) \r\n lstm.add(Dropout(0.1))\r\n lstm.add(Dense(op))\r\n lstm.add(Activation('softmax'))\r\n lstm.load_weights(\"lstm1.hdf5\")\r\n print(\"loaded lstm\")\r\n testXR = np.reshape(testX, (testX.shape[0],1,testX.shape[1]))\r\n y_pred = lstm.predict_classes(testXR)\r\n con_mat(y_pred,testY)\r\n return lstm,y_pred\r\n \r\ndef gruload(testX,op):\r\n gru = Sequential()\r\n gru.add(GRU(64,input_dim=41, return_sequences=True)) \r\n gru.add(Dropout(0.1))\r\n gru.add(GRU(64,return_sequences=True)) \r\n gru.add(Dropout(0.1))\r\n gru.add(GRU(64, return_sequences=True)) \r\n gru.add(Dropout(0.1))\r\n gru.add(GRU(64, return_sequences=False)) \r\n gru.add(Dropout(0.1))\r\n gru.add(Dense(op))\r\n gru.add(Activation('softmax'))\r\n gru.load_weights(\"gru1.hdf5\")\r\n print(\"loaded gru\")\r\n testXR = np.reshape(testX, (testX.shape[0],1,testX.shape[1]))\r\n y_pred = gru.predict_classes(testXR)\r\n con_mat(y_pred,testY)\r\n return gru,y_pred\r\n \r\n\r\ndef dnnload(testX,op):\r\n dnn = Sequential()\r\n dnn.add(Dense(1024,input_dim=41,activation='relu')) \r\n dnn.add(Dropout(0.01))\r\n dnn.add(Dense(768,activation='relu')) \r\n dnn.add(Dropout(0.01))\r\n dnn.add(Dense(512,activation='relu')) \r\n dnn.add(Dropout(0.01))\r\n dnn.add(Dense(256,activation='relu')) \r\n dnn.add(Dropout(0.01))\r\n dnn.add(Dense(128,activation='relu')) \r\n dnn.add(Dropout(0.01))\r\n dnn.add(Dense(op))\r\n dnn.add(Activation('softmax'))\r\n dnn.load_weights('dnn1.hdf5')\r\n print(\"loaded dnn\")\r\n testXR = np.reshape(testX, (testX.shape[0],testX.shape[1]))\r\n y_pred = dnn.predict_classes(testXR)\r\n con_mat(y_pred,testY)\r\n return dnn,y_pred\r\n\r\n\r\ndef rnnload(testX,op):\r\n rnn = Sequential()\r\n rnn.add(SimpleRNN(128,input_dim=41, return_sequences=True)) \r\n rnn.add(Dropout(0.1))\r\n rnn.add(SimpleRNN(128,return_sequences=True)) \r\n rnn.add(Dropout(0.1))\r\n rnn.add(SimpleRNN(128, return_sequences=True)) \r\n rnn.add(Dropout(0.1))\r\n rnn.add(SimpleRNN(128, return_sequences=False)) \r\n rnn.add(Dropout(0.1))\r\n rnn.add(Dense(op))\r\n rnn.add(Activation('softmax'))\r\n rnn.load_weights('rnn1.hdf5')\r\n print(\"loaded rnn\")\r\n testXR = np.reshape(testX, (testX.shape[0],1,testX.shape[1]))\r\n y_pred = rnn.predict_classes(testXR)\r\n con_mat(y_pred,testY)\r\n return rnn,y_pred\r\n\r\ndef signatureM(testX):\r\n cnn,yc=cnnload(testX,22)\r\n lstm,yl=lstmload(testX,22)\r\n gru,yg=gruload(testX,22)\r\n dnn,yd=dnnload(testX,22)\r\n rnn,yr=rnnload(testX,22)\r\n '''cnn.summary()\r\n lstm.summary()\r\n gru.summary()\r\n dnn.summary()\r\n rnn.summary()'''\r\n \r\ndef anamoly():\r\n dataset=pd.read_csv('kd10.csv')\r\n X = dataset.iloc[:, :-1].values\r\n labelencoder_x_1 = LabelEncoder()\r\n labelencoder_x_2 = LabelEncoder()\r\n labelencoder_x_3 = LabelEncoder()\r\n labelencoder_x_1 = labelencoder_x_1.fit(['icmp' 'tcp' 'udp'])\r\n labelencoder_x_2 = labelencoder_x_2.fit(['IRC' 'X11' 'Z39_50' 'aol' 'auth' 'bgp' 'courier' 'csnet_ns' 'ctf'\r\n 'daytime' 'discard' 'domain' 'domain_u' 'echo' 'eco_i' 'ecr_i' 'efs'\r\n 'exec' 'finger' 'ftp' 'ftp_data' 'gopher' 'harvest' 'hostnames' 'http'\r\n 'http_2784' 'http_443' 'http_8001' 'imap4' 'iso_tsap' 'klogin' 'kshell'\r\n 'ldap' 'link' 'login' 'mtp' 'name' 'netbios_dgm' 'netbios_ns'\r\n 'netbios_ssn' 'netstat' 'nnsp' 'nntp' 'other' 'pm_dump' 'pop_2' 'pop_3'\r\n 'printer' 'private' 'remote_job' 'rje' 'shell' 'smtp' 'sql_net' 'ssh'\r\n 'sunrpc' 'supdup' 'systat' 'telnet' 'tim_i' 'time' 'urp_i' 'uucp'\r\n 'uucp_path' 'vmnet' 'whois'])\r\n labelencoder_x_3 = labelencoder_x_1.fit(['OTH' 'REJ' 'RSTO' 'RSTOS0' 'RSTR' 'S0' 'S1' 'S2' 'S3' 'SF' 'SH'])\r\n dataset['normal.'] = dataset['normal.'].replace(['back.', 'buffer_overflow.', 'ftp_write.', 'guess_passwd.', 'imap.', 'ipsweep.', 'land.', 'loadmodule.', 'multihop.', 'neptune.', 'nmap.', 'perl.', 'phf.', 'pod.', 'portsweep.', 'rootkit.', 'satan.', 'smurf.', 'spy.', 'teardrop.', 'warezclient.', 'warezmaster.'], 'attack')\r\n T = dataset.iloc[:, 41].values\r\n labelencoder_yBN = LabelEncoder()\r\n labelencoder_yBN=labelencoder_yBN.fit(['attack','normal.'])\r\n y_test=labelencoder_yBN.fit_transform(T)\r\n X[:, 1] = labelencoder_x_1.fit_transform(X[:, 1])\r\n X[:, 2] = labelencoder_x_2.fit_transform(X[:, 2])\r\n X[:, 3] = labelencoder_x_3.fit_transform(X[:, 3])\r\n scaler = Normalizer().fit(X) \r\n testX= scaler.transform(X)\r\n\r\n classifier = Sequential()\r\n classifier.add(Dense(output_dim = 200, init = 'uniform', activation = 'relu', input_dim = 41))\r\n classifier.add(Dense(output_dim = 200, init = 'uniform', activation = 'relu'))\r\n classifier.add(Dense(output_dim = 200, init = 'uniform', activation = 'relu'))\r\n classifier.add(Dense(output_dim = 1, init = 'uniform', activation = 'sigmoid'))\r\n #classifier.summary() \r\n json_file = open('ann1.json', 'r')\r\n loaded_model_json = json_file.read()\r\n json_file.close()\r\n classifier = model_from_json(loaded_model_json)\r\n classifier.load_weights(\"ann1.h5\")\r\n print(\"Loaded model binaryAnn\")\r\n y_pred = classifier.predict(testX)\r\n print(y_pred)\r\n y_pred1= (y_pred > 0.6)\r\n print(y_pred1)\r\n con_mat(y_pred1,y_test)\r\n \r\nanamoly() \r\nsignatureM(testX)", "sub_path": "HIDStesting1.py", "file_name": "HIDStesting1.py", "file_ext": "py", "file_size_in_byte": 9463, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.random.seed", "line_number": 4, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 4, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 19, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 23, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 24, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 25, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Normalizer", "line_number": 48, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 56, "usage_type": "call"}, {"api_name": "sklearn.metrics.classification_report", "line_number": 59, "usage_type": "call"}, {"api_name": "mlxtend.plotting.plot_confusion_matrix", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 63, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 63, "usage_type": "name"}, {"api_name": "keras.models.Sequential", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.constraints.maxnorm", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.constraints.maxnorm", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.constraints.maxnorm", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.constraints.maxnorm", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 81, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 92, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 93, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 95, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 98, "usage_type": "call"}, {"api_name": "keras.layers.LSTM", "line_number": 99, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 100, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 101, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 111, "usage_type": "call"}, {"api_name": "keras.layers.GRU", "line_number": 112, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.GRU", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 115, "usage_type": "call"}, {"api_name": "keras.layers.GRU", "line_number": 116, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 117, "usage_type": "call"}, {"api_name": "keras.layers.GRU", "line_number": 118, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 119, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 120, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 121, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 124, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 131, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 132, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 133, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 134, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 135, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 136, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 137, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 138, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 139, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 140, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 141, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 142, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 146, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 153, "usage_type": "call"}, {"api_name": "keras.layers.SimpleRNN", "line_number": 154, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 155, "usage_type": "call"}, {"api_name": "keras.layers.SimpleRNN", "line_number": 156, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 157, "usage_type": "call"}, {"api_name": "keras.layers.SimpleRNN", "line_number": 158, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 159, "usage_type": "call"}, {"api_name": "keras.layers.SimpleRNN", "line_number": 160, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 161, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 162, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 163, "usage_type": "call"}, {"api_name": "numpy.reshape", "line_number": 166, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 184, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 186, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 187, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 188, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 202, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.Normalizer", "line_number": 208, "usage_type": "call"}, {"api_name": "keras.models.Sequential", "line_number": 211, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 212, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 213, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 214, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 215, "usage_type": "call"}, {"api_name": "keras.models.model_from_json", "line_number": 220, "usage_type": "call"}]} +{"seq_id": "491972097", "text": "import mpmath as mp\nimport math\n\n\ndef f(x):\n return mp.sin(x**4 - 4*x**3 + 7*x**2 - 4)\n\n\ndef deriv(x, n):\n return mp.diff(f, x, n)\n\n\ndef m_abs_diff_n(a, b, n):\n\n if a == b:\n return abs(deriv(a, n))\n\n maximums = []\n curr_point = a\n step = (b - a)/1000 # choosing step by dividing [a, b] into 1000 parts\n while curr_point <= b:\n abs_max = abs(deriv(curr_point, n))\n maximums.append(abs_max)\n curr_point += step\n\n return max(maximums)\n\n\ndef deriv_est(x, h, n):\n if n == 0:\n return (-f(x + 2*h) + 8*f(x + h) - 8*f(x - h) + f(x - 2*h))/(12*h)\n else:\n return (-deriv_est(x + 2*h, h, n - 1) +\n 8*deriv_est(x + h, h, n - 1) -\n 8*deriv_est(x - h, h, n - 1) +\n deriv_est(x - 2*h, h, n - 1))/(12*h)\n\n\ndef deriv_error(x, h, n):\n if n == 0:\n return 0\n a = x - 2*n*h\n b = x + 2*n*h\n m_abs_diff_m = m_abs_diff_n(a, b, n + 2)\n return m_abs_diff_m*h**4/30 + (deriv_error(x + 2*h, h, n - 1) +\n 8*deriv_error(x + h, h, n - 1) +\n 8*deriv_error(x - h, h, n - 1) +\n deriv_error(x - 2*h, h, n - 1)/(12*h))\n\n\ndef optm_h(x, e, h, n):\n a = x - 2*n*h\n b = x + 2*n*h\n m_abs_diff_m = m_abs_diff_n(a, b, n + 2)\n optimal_h = pow(45*e/(4*m_abs_diff_m), 1/3)\n return optimal_h\n\n\nif __name__ == \"__main__\":\n print('Laboratory work #4 \\nSavchuk Ivan KM-73 v.18')\n while(True):\n x0 = (1 - pow(6, 1/2))/2\n h = 2\n e = 0.001\n\n for i in range(1):\n h = optm_h(x0, e, h, i+1)\n d = deriv_est(x0, h, i+1)\n error = deriv_error(x0, h, i+1)\n print(\"\\nOptimal step for calculation of the {} derivative: {}\".format(i+1, h))\n print(\"The {} derivative at point x = {} : {} \".format(i+1, x0, d))\n print(\"Error of calculation of the {} derivative: {}\".format(i+1, error))\n\n answer = input('\\nDo you want to run again?(y/n):')\n if answer == 'y':\n continue\n else:\n print('Bye, bye goody!')\n break\n", "sub_path": "Numerical derivatives/derivative.py", "file_name": "derivative.py", "file_ext": "py", "file_size_in_byte": 2153, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "mpmath.sin", "line_number": 6, "usage_type": "call"}, {"api_name": "mpmath.diff", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "625580698", "text": "#Методы кластеризации\nimport numpy as np \nimport matplotlib.pyplot as plt\nfrom sklearn.neighbors import NearestNeighbors\n\n#входные данные\nA = np.array([[3.1, 2.3], [2.3, 4.2], [3.9, 3.5], [3.7, 6.4], [4.8, 1.9], \n [8.3, 3.1], [5.2, 7.5], [4.8, 4.7], [3.5, 5.1], [4.4, 2.9]])\n\n#определяем ближайших соседей\nk = 3\n\n#тестовые данные\ntest_data = [3.3, 2.9]\n\n#визуализируем входные данные\nplt.figure()\nplt.title(\"input data\")\nplt.scatter(A[:, 0], A[:, 1], marker = \"o\", s = 100, c = \"black\")\n\n#построим ближайшего соседа, обучим его \nknn_model = NearestNeighbors(n_neighbors = k, algorithm = \"auto\").fit(A)\ndistances, indices = knn_model.kneighbors([test_data])\n\n#координаты K ближайших соседей\nprint(\"\\nk ближайших соседей: \")\nfor rank, index in enumerate(indices[0][:k], start = 1):\n print(str(rank) + \" is\", A[index])\n\n#визуализируем K ближайших соседей\nplt.title(\"K Nearest Neighbors\")\nplt.scatter(A[:, 0], A[:, 1], marker=\"o\", s=100, c=\"k\")\nplt.scatter(A[indices][0][:][:, 0], A[indices][0][:][:, 1], marker = \"o\", s=250, facecolors = 'none', edgecolors='purple')\nplt.scatter(test_data[0], test_data[1], marker = \"x\", c = \"purple\", s = 100)\n\nplt.show()\n", "sub_path": "pr_4_3.py", "file_name": "pr_4_3.py", "file_ext": "py", "file_size_in_byte": 1370, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.array", "line_number": 7, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 18, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 19, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "sklearn.neighbors.NearestNeighbors", "line_number": 22, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 33, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 33, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 36, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 36, "usage_type": "name"}]} +{"seq_id": "21240295", "text": "from PyQt5 import QtCore, QtGui, QtWidgets\nfrom Dialogs.Graphs.bloodReportGraph import *\nfrom Dialogs.messageBox import *\nfrom Dialogs.superadmin.BloodBanks.viewBloodBanks import *\nfrom Dialogs.superadmin.BloodBanks.new_bloodBankProfile import *\n\nclass bloodcenterstats(object):\n def setup(self, bloodcenterstats):\n bloodcenterstats.setObjectName(\"bloodcenterstats\")\n bloodcenterstats.resize(393, 409)\n self.titleLabel = QtWidgets.QLabel(bloodcenterstats)\n self.titleLabel.setGeometry(QtCore.QRect(20, 10, 361, 41))\n self.titleLabel.setStyleSheet(\"font-size:14pt;\\n\"\n\"font-weight: bold;\")\n self.titleLabel.setObjectName(\"titleLabel\")\n self.close = QtWidgets.QPushButton(bloodcenterstats)\n self.close.setGeometry(QtCore.QRect(140, 360, 89, 25))\n self.close.setObjectName(\"close\")\n self.searchtcButton = QtWidgets.QPushButton(bloodcenterstats)\n self.searchtcButton.setGeometry(QtCore.QRect(20, 190, 161, 25))\n self.searchtcButton.setObjectName(\"searchtcButton\")\n self.allBillsButton = QtWidgets.QPushButton(bloodcenterstats)\n self.allBillsButton.setGeometry(QtCore.QRect(190, 190, 161, 25))\n self.allBillsButton.setObjectName(\"allBillsButton\")\n self.titleLabel_2 = QtWidgets.QLabel(bloodcenterstats)\n self.titleLabel_2.setGeometry(QtCore.QRect(10, 80, 241, 41))\n self.titleLabel_2.setStyleSheet(\"font-size:14pt;\\n\"\n\"font-weight: bold;\")\n self.titleLabel_2.setObjectName(\"titleLabel_2\")\n self.titleLabel_3 = QtWidgets.QLabel(bloodcenterstats)\n self.titleLabel_3.setGeometry(QtCore.QRect(-30, 130, 241, 41))\n self.titleLabel_3.setStyleSheet(\"font-size:14pt;\\n\"\n\"font-weight: bold;\")\n self.titleLabel_3.setObjectName(\"titleLabel_3\")\n self.frame_2 = QtWidgets.QFrame(bloodcenterstats)\n self.frame_2.setGeometry(QtCore.QRect(20, 240, 341, 101))\n self.frame_2.setFrameShape(QtWidgets.QFrame.StyledPanel)\n self.frame_2.setFrameShadow(QtWidgets.QFrame.Raised)\n self.frame_2.setObjectName(\"frame_2\")\n self.tcid = QtWidgets.QLineEdit(self.frame_2)\n self.tcid.setGeometry(QtCore.QRect(80, 10, 181, 25))\n self.tcid.setObjectName(\"tcid\")\n self.goToProfile = QtWidgets.QPushButton(self.frame_2)\n self.goToProfile.setGeometry(QtCore.QRect(170, 60, 151, 25))\n self.goToProfile.setObjectName(\"goToProfile\")\n self.bloodstatsgraph = QtWidgets.QPushButton(self.frame_2)\n self.bloodstatsgraph.setGeometry(QtCore.QRect(10, 60, 161, 25))\n self.bloodstatsgraph.setObjectName(\"bloodstatsgraph\")\n self.bbcRegistered = QtWidgets.QLabel(bloodcenterstats)\n self.bbcRegistered.setGeometry(QtCore.QRect(270, 90, 67, 17))\n self.bbcRegistered.setObjectName(\"bbcRegistered\")\n self.totalbills = QtWidgets.QLabel(bloodcenterstats)\n self.totalbills.setGeometry(QtCore.QRect(270, 140, 67, 17))\n self.totalbills.setObjectName(\"totalbills\")\n\n self.retranslateUi(bloodcenterstats)\n QtCore.QMetaObject.connectSlotsByName(bloodcenterstats)\n\n def retranslateUi(self, bloodcenterstats):\n _translate = QtCore.QCoreApplication.translate\n bloodcenterstats.setWindowTitle(_translate(\"bloodcenterstats\", \"Stats\"))\n self.titleLabel.setText(_translate(\"bloodcenterstats\", \"

Blood Center Stats


\"))\n self.close.setText(_translate(\"bloodcenterstats\", \"close\"))\n self.searchtcButton.setText(_translate(\"bloodcenterstats\", \"Search Blood Centers\"))\n self.allBillsButton.setText(_translate(\"bloodcenterstats\", \"All Blood Bills\"))\n self.titleLabel_2.setText(_translate(\"bloodcenterstats\", \"

Blood Center Registered :

\"))\n self.titleLabel_3.setText(_translate(\"bloodcenterstats\", \"

Total Blood Bills:

\"))\n self.tcid.setPlaceholderText(_translate(\"bloodcenterstats\", \"Enter Blood Center ID\"))\n self.goToProfile.setText(_translate(\"bloodcenterstats\", \"Go To Profile\"))\n self.bloodstatsgraph.setText(_translate(\"bloodcenterstats\", \"See Blood Stats\"))\n self.bbcRegistered.setText(_translate(\"bloodcenterstats\", \"4\"))\n self.totalbills.setText(_translate(\"bloodcenterstats\", \"10\"))\n self.events(bloodcenterstats)\n\n def events(self,parent):\n self.bloodstatsgraph.clicked.connect(lambda : self.clickOnBloodGraph())\n self.goToProfile.clicked.connect(lambda : self.clickOnGotoProfile())\n self.searchtcButton.clicked.connect(lambda : self.clickOnSearchButton())\n\n def clickOnSearchButton(self):\n self.window = QDialog()\n self.dialog = viewBloodBankCenter()\n self.dialog.setup(self.window)\n self.window.setModal(True)\n self.window.show()\n\n\n\n def clickOnBloodGraph(self):\n if not(self.tcid.text().isdigit()):\n self.window = messageBox()\n self.window.infoBox(\"Invalid ID\")\n return\n\n self.window = QDialog()\n self.dialog = bloodbankGraph()\n self.dialog.setup(self.window,int(self.tcid.text()))\n self.window.setModal(True)\n self.window.show()\n\n def clickOnGotoProfile(self):\n if not(self.tcid.text().isdigit()):\n self.window = messageBox()\n self.window.infoBox(\"Invalid ID\")\n return\n import requests\n URL = \"https://mdtouch.herokuapp.com/MDTouch/api/bloodbankcenter/\" + str(self.tcid.text())\n r = requests.get(url=URL)\n l = r.json()\n if l == {\"detail\": \"Not found.\"}:\n self.window = messageBox()\n self.window.infoBox(\"Id Does Not Exits\")\n return\n\n self.window = QDialog()\n self.dialog = new_bloodBankProfile()\n self.dialog.setup(self.window,l)\n self.window.setModal(True)\n self.window.show()\n\n", "sub_path": "Dialogs/superadmin/bloodcenterStats.py", "file_name": "bloodcenterStats.py", "file_ext": "py", "file_size_in_byte": 6083, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 11, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 11, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 12, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 12, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 16, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 16, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 17, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 17, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 19, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 19, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 20, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 20, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 22, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 22, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 23, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 23, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 25, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 25, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 26, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 26, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 30, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 30, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 31, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 31, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 35, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 35, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 36, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 36, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 37, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 37, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QFrame", "line_number": 38, "usage_type": "attribute"}, {"api_name": "PyQt5.QtWidgets", "line_number": 38, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLineEdit", "line_number": 40, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 40, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 41, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 41, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 43, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 43, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 44, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 44, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QPushButton", "line_number": 46, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 46, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 47, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 47, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 49, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 49, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 50, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 50, "usage_type": "name"}, {"api_name": "PyQt5.QtWidgets.QLabel", "line_number": 52, "usage_type": "call"}, {"api_name": "PyQt5.QtWidgets", "line_number": 52, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QRect", "line_number": 53, "usage_type": "call"}, {"api_name": "PyQt5.QtCore", "line_number": 53, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QMetaObject.connectSlotsByName", "line_number": 57, "usage_type": "call"}, {"api_name": "PyQt5.QtCore.QMetaObject", "line_number": 57, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 57, "usage_type": "name"}, {"api_name": "PyQt5.QtCore.QCoreApplication", "line_number": 60, "usage_type": "attribute"}, {"api_name": "PyQt5.QtCore", "line_number": 60, "usage_type": "name"}, {"api_name": "requests.get", "line_number": 108, "usage_type": "call"}]} +{"seq_id": "590534084", "text": "import pkgutil\nimport donica.modules as folder\n\n\nclass ModuleRouter(object):\n def __init__(self):\n self.modules = self.get_modules()\n\n @classmethod\n def get_modules(cls):\n location = folder\n\n modules = []\n for finder, name, ispkg in pkgutil.walk_packages(path=location.__path__,\n prefix=location.__name__+'.'):\n try:\n loader = finder.find_module(name)\n mod = loader.load_module(name)\n except Exception as e:\n raise e\n else:\n if hasattr(mod, 'TITLE'):\n modules.append(mod)\n else:\n print('MODULE ROUTER: Skipping because could not find right format of {}'.format(name))\n return modules\n\n def query(self, titles, message):\n for module in self.modules:\n for title in titles:\n if module.is_valid(title):\n try:\n module.handle(title, message)\n except Exception as e:\n raise e\n else:\n print('MODULE ROUTER: Handling of phrase {} by module {} completed'.format(title,\n module.__name__))\n finally:\n return\n\n", "sub_path": "donica/ModuleRouter.py", "file_name": "ModuleRouter.py", "file_ext": "py", "file_size_in_byte": 1445, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "donica.modules", "line_number": 11, "usage_type": "name"}, {"api_name": "pkgutil.walk_packages", "line_number": 14, "usage_type": "call"}]} +{"seq_id": "262853411", "text": "\"\"\"\n# run celery task\n# celery --app=fufel.celery_app:app worker -l info\n# run celery periodic task\n# celery --app=fufel.celery_app:app beat\n\"\"\"\nfrom __future__ import absolute_import\n\nimport time\nfrom django.utils.timezone import datetime\nfrom fufel.celery_app import app\nfrom Fufel.models import Channel, Video\n\n\n@app.task(bind=True)\ndef debug_task(self):\n print('Request: {0!r}'.format(self.request))\n\n\n@app.task\ndef populate_video(**kwargs):\n channel_inst = Channel.objects.get(pk=2)\n text = \"TEST TEST TEST\"\n for i in range(10):\n video = Video(name='Video {}'.format(i+1), description=text, youtube_id='32145',\n youtube_url='https://www.youtube.com/watch?v=Tj75Arhq5ho', channel=channel_inst,\n uploaded=datetime.now())\n video.save()\n\n\n@app.task\ndef timer(sec):\n print (datetime.now(), ' wait {} sec'.format(sec))\n time.sleep(sec)\n print ('Done')\n", "sub_path": "Fufel/tasks.py", "file_name": "tasks.py", "file_ext": "py", "file_size_in_byte": 928, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "fufel.celery_app.app.task", "line_number": 15, "usage_type": "call"}, {"api_name": "fufel.celery_app.app", "line_number": 15, "usage_type": "name"}, {"api_name": "Fufel.models.Channel.objects.get", "line_number": 22, "usage_type": "call"}, {"api_name": "Fufel.models.Channel.objects", "line_number": 22, "usage_type": "attribute"}, {"api_name": "Fufel.models.Channel", "line_number": 22, "usage_type": "name"}, {"api_name": "Fufel.models.Video", "line_number": 25, "usage_type": "call"}, {"api_name": "django.utils.timezone.datetime.now", "line_number": 27, "usage_type": "call"}, {"api_name": "django.utils.timezone.datetime", "line_number": 27, "usage_type": "name"}, {"api_name": "fufel.celery_app.app.task", "line_number": 20, "usage_type": "attribute"}, {"api_name": "fufel.celery_app.app", "line_number": 20, "usage_type": "name"}, {"api_name": "django.utils.timezone.datetime.now", "line_number": 33, "usage_type": "call"}, {"api_name": "django.utils.timezone.datetime", "line_number": 33, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 34, "usage_type": "call"}, {"api_name": "fufel.celery_app.app.task", "line_number": 31, "usage_type": "attribute"}, {"api_name": "fufel.celery_app.app", "line_number": 31, "usage_type": "name"}]} +{"seq_id": "310055729", "text": "#!/usr/bin/env python3\nimport argparse\nimport configparser\nimport datetime\nimport logging\nimport os\nimport subprocess\nimport sys\n\nfrom flask import Flask\nfrom flask import request\nfrom flask import session\nfrom flask.ext.scrypt import generate_random_salt\n\nimport pajbot.models.apitoken\nimport pajbot.web.common\nimport pajbot.web.routes\nfrom pajbot.bot import Bot\nfrom pajbot.managers import DBManager\nfrom pajbot.managers import RedisManager\nfrom pajbot.managers import TimeManager\nfrom pajbot.models.module import ModuleManager\nfrom pajbot.models.sock import SocketClientManager\nfrom pajbot.streamhelper import StreamHelper\nfrom pajbot.tbutil import init_logging\nfrom pajbot.tbutil import load_config\nfrom pajbot.web.models import errors\nfrom pajbot.web.utils import download_logo\n\ninit_logging('pajbot')\nlog = logging.getLogger('pajbot')\n\napp = Flask(__name__)\napp._static_folder = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'static')\n\nconfig = configparser.ConfigParser()\n\nparser = argparse.ArgumentParser(description='start the web app')\nparser.add_argument('--config', default='config.ini')\nparser.add_argument('--host', default='0.0.0.0')\nparser.add_argument('--port', type=int, default=2325)\nparser.add_argument('--debug', dest='debug', action='store_true')\nparser.add_argument('--no-debug', dest='debug', action='store_false')\nparser.set_defaults(debug=False)\n\nargs = parser.parse_args()\n\nconfig = load_config(args.config)\nconfig.read('webconfig.ini')\n\nif 'web' not in config:\n log.error('Missing [web] section in config.ini')\n sys.exit(1)\n\nif 'api' not in config:\n log.error('Missing [api] section in config.ini, adding it now!')\n config.add_section('api')\n\nif 'pleblist_password_salt' not in config['web']:\n salt = generate_random_salt()\n config.set('web', 'pleblist_password_salt', salt.decode('utf-8'))\n\nif 'secret_key' not in config['web']:\n salt = generate_random_salt()\n config.set('web', 'secret_key', salt.decode('utf-8'))\n\nif 'token_secret' not in config['api']:\n salt = generate_random_salt()\n config.set('api', 'token_secret', salt.decode('utf-8'))\n\nif 'logo' not in config['web']:\n res = download_logo(config['main']['streamer'])\n if res:\n config.set('web', 'logo', 'set')\n\nStreamHelper.init_web(config['main']['streamer'])\n\nredis_options = {}\nif 'redis' in config:\n redis_options = config._sections['redis']\n\nRedisManager.init(**redis_options)\n\nwith open(args.config, 'w') as configfile:\n config.write(configfile)\n\napp.bot_modules = config['web'].get('modules', '').split()\napp.bot_commands_list = []\napp.bot_config = config\napp.secret_key = config['web']['secret_key']\n\n\nif 'sock' in config and 'sock_file' in config['sock']:\n SocketClientManager.init(config['sock']['sock_file'])\n\n\nDBManager.init(config['main']['db'])\nTimeManager.init_timezone(config['main'].get('timezone', 'UTC'))\n\napp.module_manager = ModuleManager(None).load()\n\npajbot.models.apitoken.secret_key = config['api']['token_secret']\n\npajbot.web.routes.admin.init(app)\npajbot.web.routes.api.init(app)\npajbot.web.routes.base.init(app)\n\npajbot.web.common.filters.init(app)\npajbot.web.common.assets.init(app)\npajbot.web.common.tasks.init(app)\npajbot.web.common.menu.init(app)\n\napp.register_blueprint(pajbot.web.routes.clr.page)\napp.register_blueprint(pajbot.web.routes.api.page)\n\nerrors.init(app)\npajbot.web.routes.api.config = config\npajbot.web.routes.clr.config = config\n\nversion = Bot.version\nlast_commit = ''\ncommit_number = 0\ntry:\n current_branch = subprocess.check_output(['git', 'rev-parse', '--abbrev-ref', 'HEAD']).decode('utf8').strip()\n latest_commit = subprocess.check_output(['git', 'rev-parse', 'HEAD']).decode('utf8').strip()[:8]\n commit_number = subprocess.check_output(['git', 'rev-list', 'HEAD', '--count']).decode('utf8').strip()\n last_commit = subprocess.check_output(['git', 'log', '-1', '--format=%cd']).decode('utf8').strip()\n version = '{0} DEV ({1}, {2}, commit {3})'.format(version, current_branch, latest_commit, commit_number)\nexcept:\n pass\n\ndefault_variables = {\n 'version': version,\n 'last_commit': last_commit,\n 'commit_number': commit_number,\n 'bot': {\n 'name': config['main']['nickname'],\n },\n 'site': {\n 'domain': config['web']['domain'],\n 'deck_tab_images': config.getboolean('web', 'deck_tab_images'),\n 'websocket': {\n 'host': config['websocket'].get('host', config['web']['domain']),\n 'port': config['websocket']['port'],\n 'ssl': config.getboolean('websocket', 'ssl')\n }\n },\n 'streamer': {\n 'name': config['web']['streamer_name'],\n 'full_name': config['main']['streamer']\n },\n 'modules': app.bot_modules,\n 'request': request,\n 'session': session,\n 'google_analytics': config['web'].get('google_analytics', None),\n }\n\nif 'streamtip' in config:\n default_variables['streamtip_data'] = {\n 'client_id': config['streamtip']['client_id'],\n 'redirect_uri': config['streamtip']['redirect_uri'],\n }\nelse:\n default_variables['streamtip_data'] = {\n 'client_id': 'MISSING',\n 'redirect_uri': 'MISSING',\n }\n\nif 'twitchalerts' in config:\n default_variables['twitchalerts_data'] = {\n 'client_id': config['twitchalerts']['client_id'],\n 'redirect_uri': config['twitchalerts']['redirect_uri'],\n }\nelse:\n default_variables['twitchalerts_data'] = {\n 'client_id': 'MISSING',\n 'redirect_uri': 'MISSING',\n }\n\n\n@app.context_processor\ndef current_time():\n current_time = {}\n current_time['current_time'] = datetime.datetime.now()\n return current_time\n\n\n@app.context_processor\ndef inject_default_variables():\n return default_variables\n\nif __name__ == '__main__':\n app.run(debug=args.debug, host=args.host, port=args.port)\n", "sub_path": "app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 5998, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pajbot.tbutil.init_logging", "line_number": 30, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 31, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 34, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 36, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 38, "usage_type": "call"}, {"api_name": "pajbot.tbutil.load_config", "line_number": 48, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 53, "usage_type": "call"}, {"api_name": "flask.ext.scrypt.generate_random_salt", "line_number": 60, "usage_type": "call"}, {"api_name": "flask.ext.scrypt.generate_random_salt", "line_number": 64, "usage_type": "call"}, {"api_name": "flask.ext.scrypt.generate_random_salt", "line_number": 68, "usage_type": "call"}, {"api_name": "pajbot.web.utils.download_logo", "line_number": 72, "usage_type": "call"}, {"api_name": "pajbot.streamhelper.StreamHelper.init_web", "line_number": 76, "usage_type": "call"}, {"api_name": "pajbot.streamhelper.StreamHelper", "line_number": 76, "usage_type": "name"}, {"api_name": "pajbot.managers.RedisManager.init", "line_number": 82, "usage_type": "call"}, {"api_name": "pajbot.managers.RedisManager", "line_number": 82, "usage_type": "name"}, {"api_name": "pajbot.models.sock.SocketClientManager.init", "line_number": 94, "usage_type": "call"}, {"api_name": "pajbot.models.sock.SocketClientManager", "line_number": 94, "usage_type": "name"}, {"api_name": "pajbot.managers.DBManager.init", "line_number": 97, "usage_type": "call"}, {"api_name": "pajbot.managers.DBManager", "line_number": 97, "usage_type": "name"}, {"api_name": "pajbot.managers.TimeManager.init_timezone", "line_number": 98, "usage_type": "call"}, {"api_name": "pajbot.managers.TimeManager", "line_number": 98, "usage_type": "name"}, {"api_name": "pajbot.models.module.ModuleManager", "line_number": 100, "usage_type": "call"}, {"api_name": "pajbot.models.apitoken.models", "line_number": 102, "usage_type": "attribute"}, {"api_name": "pajbot.models.apitoken", "line_number": 102, "usage_type": "name"}, {"api_name": "pajbot.models.apitoken.web.routes.admin.init", "line_number": 104, "usage_type": "call"}, {"api_name": "pajbot.models.apitoken.web", "line_number": 104, "usage_type": "attribute"}, {"api_name": "pajbot.models.apitoken", "line_number": 104, "usage_type": "name"}, {"api_name": "pajbot.models.apitoken.web.routes.api.init", "line_number": 105, "usage_type": "call"}, {"api_name": "pajbot.models.apitoken.web", "line_number": 105, "usage_type": "attribute"}, {"api_name": "pajbot.models.apitoken", "line_number": 105, "usage_type": "name"}, {"api_name": "pajbot.models.apitoken.web.routes.base.init", "line_number": 106, "usage_type": "call"}, {"api_name": "pajbot.models.apitoken.web", "line_number": 106, "usage_type": "attribute"}, {"api_name": "pajbot.models.apitoken", "line_number": 106, "usage_type": "name"}, {"api_name": "pajbot.models.apitoken.web.common.filters.init", "line_number": 108, "usage_type": "call"}, {"api_name": "pajbot.models.apitoken.web", "line_number": 108, "usage_type": "attribute"}, {"api_name": "pajbot.models.apitoken", "line_number": 108, "usage_type": "name"}, {"api_name": "pajbot.models.apitoken.web.common.assets.init", "line_number": 109, "usage_type": "call"}, {"api_name": "pajbot.models.apitoken.web", "line_number": 109, "usage_type": "attribute"}, {"api_name": "pajbot.models.apitoken", "line_number": 109, "usage_type": "name"}, {"api_name": "pajbot.models.apitoken.web.common.tasks.init", "line_number": 110, "usage_type": "call"}, {"api_name": "pajbot.models.apitoken.web", "line_number": 110, "usage_type": "attribute"}, {"api_name": "pajbot.models.apitoken", "line_number": 110, "usage_type": "name"}, {"api_name": "pajbot.models.apitoken.web.common.menu.init", "line_number": 111, "usage_type": "call"}, {"api_name": "pajbot.models.apitoken.web", "line_number": 111, "usage_type": "attribute"}, {"api_name": "pajbot.models.apitoken", "line_number": 111, "usage_type": "name"}, {"api_name": "pajbot.models.apitoken.web", "line_number": 113, "usage_type": "attribute"}, {"api_name": "pajbot.models.apitoken", "line_number": 113, "usage_type": "name"}, {"api_name": "pajbot.models.apitoken.web", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pajbot.models.apitoken", "line_number": 114, "usage_type": "name"}, {"api_name": "pajbot.web.models.errors.init", "line_number": 116, "usage_type": "call"}, {"api_name": "pajbot.web.models.errors", "line_number": 116, "usage_type": "name"}, {"api_name": "pajbot.models.apitoken.web", "line_number": 117, "usage_type": "attribute"}, {"api_name": "pajbot.models.apitoken", "line_number": 117, "usage_type": "name"}, {"api_name": "pajbot.models.apitoken.web", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pajbot.models.apitoken", "line_number": 118, "usage_type": "name"}, {"api_name": "pajbot.bot.Bot.version", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pajbot.bot.Bot", "line_number": 120, "usage_type": "name"}, {"api_name": "subprocess.check_output", "line_number": 124, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 125, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 126, "usage_type": "call"}, {"api_name": "subprocess.check_output", "line_number": 127, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 153, "usage_type": "name"}, {"api_name": "flask.session", "line_number": 154, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 184, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 184, "usage_type": "attribute"}]} +{"seq_id": "412215528", "text": "import streamlit as st\r\nimport pandas as pd\r\nimport numpy as np\r\nimport tensorflow as tf\r\nimport tensorflow_hub as hub\r\nfrom PIL import Image\r\nimport io\r\nimport base64\r\nimport time\r\n\r\nst.set_option('deprecation.showfileUploaderEncoding', False)\r\n\r\n# All the function needed \r\n#--------------------------------------------------------------------------------\r\n# Define image size\r\nIMG_SIZE = 224\r\n\r\n# Define a batch size , 32 is a good start \r\nBATCH_SIZE = 32\r\n# Import labels and create an array of 120 dog breeds\r\nlabels_csv = pd.read_csv(\"/home/gagan/Desktop/Ml-Sample/labels.csv\")\r\nlabels = labels_csv[\"breed\"].to_numpy()\r\nunique_breeds = np.unique(labels)\r\n\r\n# Prediction label function\r\ndef get_pred_label(prediction_probabilities):\r\n \"\"\"\r\n Turn an array of prediction probabilities into a label.\r\n \"\"\"\r\n return unique_breeds[np.argmax(prediction_probabilities)]\r\n\r\n#---------------------------------------------------------------------------------\r\n\r\nst.title(\"Welcome to Dog 🐕 Vision 👁️ AI\")\r\nst.write(\"\")\r\nst.write(\"Upload your dog's image\")\r\n\r\nfile = st.file_uploader(\"\", type=[\"jpg\", \"png\"])\r\n\r\nif file is None:\r\n st.text(\"Please upload an image file\")\r\nelse:\r\n custom_image = Image.open(file)\r\n st.text(\"Are you excited?😀...🐶...\")\r\n\r\nif file:\r\n\t# Data preprocessing\r\n\timage = tf.io.decode_image(file.getvalue(), channels=3, dtype=tf.float32)\r\n\timage= tf.image.resize(image, size=[IMG_SIZE, IMG_SIZE])\r\n\tdata = tf.data.Dataset.from_tensor_slices([image])\r\n\tdata_batch = data.batch(BATCH_SIZE)\r\n\r\n\t# Load pretrained model and make predictions\r\n\tloaded_full_model = tf.keras.models.load_model('/home/gagan/Desktop/Ml-Sample/20200727-18521595875929-full-image-set-mobilenetv2-Adam.h5',custom_objects={'KerasLayer':hub.KerasLayer})\r\n\tcustom_preds = loaded_full_model.predict(data_batch)\r\n\t# Get predicted label\r\n\tcustom_pred_labels = [get_pred_label(custom_preds[i]) for i in range(len(custom_preds))]\r\n\t\r\n\t# Starting a long computation...'\r\n\tlatest_iteration = st.empty()\r\n\tbar = st.progress(0)\r\n\r\n\tfor i in range(100):\r\n\t # Update the progress bar with each iteration.\r\n\t latest_iteration.text(f'Hold tight....{i+1}')\r\n\t bar.progress(i + 1)\r\n\t time.sleep(0.1)\r\n\t# '...and now we\\'re done!'\r\n\r\n\tst.title(f'Your dog is a {custom_pred_labels[0]}')\r\n\t# st.write(custom_pred_labels[0])\r\n\tst.image(custom_image, use_column_width=True)\r\n\r\n\r\n\r\n\r\n", "sub_path": "DogVisionAI.py", "file_name": "DogVisionAI.py", "file_ext": "py", "file_size_in_byte": 2377, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "streamlit.set_option", "line_number": 11, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 21, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.argmax", "line_number": 30, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 34, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 35, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 36, "usage_type": "call"}, {"api_name": "streamlit.file_uploader", "line_number": 38, "usage_type": "call"}, {"api_name": "streamlit.text", "line_number": 41, "usage_type": "call"}, {"api_name": "PIL.Image.open", "line_number": 43, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 43, "usage_type": "name"}, {"api_name": "streamlit.text", "line_number": 44, "usage_type": "call"}, {"api_name": "tensorflow.io.decode_image", "line_number": 48, "usage_type": "call"}, {"api_name": "tensorflow.io", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tensorflow.float32", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tensorflow.image.resize", "line_number": 49, "usage_type": "call"}, {"api_name": "tensorflow.image", "line_number": 49, "usage_type": "attribute"}, {"api_name": "tensorflow.data.Dataset.from_tensor_slices", "line_number": 50, "usage_type": "call"}, {"api_name": "tensorflow.data", "line_number": 50, "usage_type": "attribute"}, {"api_name": "tensorflow.keras.models.load_model", "line_number": 54, "usage_type": "call"}, {"api_name": "tensorflow.keras", "line_number": 54, "usage_type": "attribute"}, {"api_name": "tensorflow_hub.KerasLayer", "line_number": 54, "usage_type": "attribute"}, {"api_name": "streamlit.empty", "line_number": 60, "usage_type": "call"}, {"api_name": "streamlit.progress", "line_number": 61, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 67, "usage_type": "call"}, {"api_name": "streamlit.title", "line_number": 70, "usage_type": "call"}, {"api_name": "streamlit.image", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "548359238", "text": "import base64\nfrom flask import request, Flask\nfrom python_fission.lib.atomiq.classes.cloudevent import cloud_event_decorator\nfrom python_fission.lib.atomiq.classes.helper import extract_parameters\nfrom python_fission.lib.ssh.ssh import run_command\nfrom python_fission.lib.atomiq.classes import constants\nfrom python_fission.lib.atomiq.classes.response_helper import success_response,failure_response\n\n# Developers need to make sure the input json will be of below format\n# This is the input template that will be validated\n# Please don't change only keys inside the 'data' dict.\n# Please don't change any naming convention\n# '''\nINPUT_TEMPLATE = {\n \"data\": {\n \"flowName\": \"\",\n \"company\": \"\",\n \"project\": \"\",\n \"parameters\": [\n {\"source_machine\": {\"entity\": \"\"}},\n {\"sender\": \"\"},\n {\"subject\": \"\"},\n {\"recipients\": []},\n {\"body\": \"\"},\n {\"encoded\": \"\"},\n {\"attachments\": []}\n ]\n }\n}\n# main Atom method\n'''\njson_input --> is a request dict.There is no need to run \"json.loads()\" on it\nThe response has to be of type dict/list and we need to return it as \"json_result\" from the\n\"main()\" def All the business logic will be written in \"handle()\"\n'''\n\n\ndef handle(json_input):\n '''\n General description:\n Sends mail to the requested recipients.\n Args:\n param1 json_input(dict) : This is the input json received.\n Returns:\n and return the result as below :\n Success -> JSON response with status as success and message as below\n {\"status\": \"success\", \"message\": \"Mail send successfully\"}\n Failure -> JSON response with status as failure, exit-code as 1\n and Error-details contains error info.\n {\"status\": \"failure\", \"exit-code\": exitcode, \"Error-details\": stderr}\n\n Example :\n handle(json_input)\n '''\n parameters = extract_parameters(json_input)\n config_details = parameters.get('source_machine')\n subject = parameters.get('subject')\n sender = parameters.get('sender')\n encoded = parameters.get('encoded')\n body = parameters.get('body')\n\n if encoded == 'true':\n body = base64.b64decode(body)\n\n attachments = parameters.get('attachments')\n recipients = parameters.get('recipients')\n command = \"echo '\" + str(body) + \"'| mail -r \" + sender\n\n for _s in attachments:\n command = command + \" -a \" + _s\n\n command = command + \" -s \" + \"'\" + subject + \"'\"\n\n for _r in recipients:\n command = command + \" \" + _r\n status, data = run_command(config_details, command)\n\n if status == constants.get_success():\n output = {'output': 'Mail sent successfully'}\n json_result = success_response(output)\n else:\n output = {'output' : str(data)}\n json_result = failure_response(output)\n\n return json_result\n\n\n# '''\n# Fission is invoking the main() method.\n# Please do not change anything below this method declation or body\n# The input payload validation is being done in execut() decorator\n# The main job is being done by the handle definition\n# '''\n\nAPP = Flask(__name__) # USED TO TEST.. COMMENT IT WHILE UPLOADING\n\n\n# USED TO TEST.. COMMENT IT WHILE UPLOADING\n@APP.route('/check', methods=['POST'])\n@cloud_event_decorator(INPUT_TEMPLATE)\ndef main():\n '''\n General description:\n Args:\n\n Returns:\n Returns result entities that were collected by the atom.\n Example :\n main()\n '''\n return handle(request.get_json())\n\n\nif __name__ == '__main__': # USED TO TEST.. COMMENT IT WHILE UPLOADING\n # USED TO TEST.. COMMENT IT WHILE UPLOADING\n APP.run(port=1012, host='0.0.0.0', debug=True)\n", "sub_path": "python_fission/python/atoms/atomiq-py-ssh/send_mail/send_mail.py", "file_name": "send_mail.py", "file_ext": "py", "file_size_in_byte": 3734, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "python_fission.lib.atomiq.classes.helper.extract_parameters", "line_number": 55, "usage_type": "call"}, {"api_name": "base64.b64decode", "line_number": 63, "usage_type": "call"}, {"api_name": "python_fission.lib.ssh.ssh.run_command", "line_number": 76, "usage_type": "call"}, {"api_name": "python_fission.lib.atomiq.classes.constants.get_success", "line_number": 78, "usage_type": "call"}, {"api_name": "python_fission.lib.atomiq.classes.constants", "line_number": 78, "usage_type": "name"}, {"api_name": "python_fission.lib.atomiq.classes.response_helper.success_response", "line_number": 80, "usage_type": "call"}, {"api_name": "python_fission.lib.atomiq.classes.response_helper.failure_response", "line_number": 83, "usage_type": "call"}, {"api_name": "flask.Flask", "line_number": 95, "usage_type": "call"}, {"api_name": "flask.request.get_json", "line_number": 111, "usage_type": "call"}, {"api_name": "flask.request", "line_number": 111, "usage_type": "name"}, {"api_name": "python_fission.lib.atomiq.classes.cloudevent.cloud_event_decorator", "line_number": 100, "usage_type": "call"}]} +{"seq_id": "339832518", "text": "from modules.darkSUSY.classDarkSUSY import *\nfrom modules.general.F_search import *\n\nfrom IPython.display import clear_output\nimport numpy as np\nimport h5py\nimport os\n\nDelphes_DIR = \"/home/franky8939/PROGRAMAS/MG5_aMC/Delphes/\" # Directory local of Delphes\nROOT_DIR = \"/home/franky8939/PROGRAMAS/ROOT/\" # Directory local of Root\nfbash(Delphes_DIR, ROOT_DIR) # path in bash of Delphes and Root\n\n# CLASS DARKSUSY\nDarkFile = DarkSUSY() # initialize the DarkSUSY class\n\n# Create general h5 for all data\nOUTPUT = '/home/franky8939/GITHUP/DarkSUSY-master/data/h5_muon_all/DarkSUSY_all_NMuon.h5'\nFILES_INPUT = \"/media/franky8939/10FE09E910FE09E9/datos_investigacion_grandes/\"\n\n# Si existe el archivo OUTPUT ACTUALIZARLO #\nif os.path.exists(OUTPUT):\n hf = h5py.File(OUTPUT, 'a')\nelse:\n hf = h5py.File(OUTPUT, 'w') # create h5py\n\nfor files_root in os.listdir(FILES_INPUT):\n if \".root\" in files_root: # THE FILE IS *.root\n for i in [1]:\n\n try:\n # Identificar la posicion en *.h5\n var = Ob_Value(files_root)\n name_local_group = \"MNeuL_\" + var[\"MNeuL\"] + \"/MNeuD_\" + var[\"MNeuD\"] + \"/MPhoD_\" + var[\"MPhoD\"] + \\\n \"/TcPhoD_\" + var[\"TcPhoD\"] + \"/\" + var[\"Card\"]\n\n if var[\"Card\"] is \"_HL2_\":\n break\n if np.array(hf.get(name_local_group + \"/Verification\")) == \"ON\":\n print(\" :: INFO OF FILE \" + files_root + \" EXIST, CONTINUE WITH THE NEXT\")\n # continue\n break\n\n hf.require_group(name_local_group) # requerirlo para que lo cree si no existe\n del hf[name_local_group] # borrar siempre para actualizarlo\n local_group = hf.require_group(name_local_group) # volverlo a crear\n # local_group.require_dataset(name=\"Name_of_FileROOT\", data=files_root) # Number of Mu for Event\n local_group.create_dataset(name=\"Name_of_FileROOT\", data=files_root) # Number of Mu for Event\n local_group.create_dataset(name=\"Verification\", data=\"OFF\")\n\n # Variables\n DarkFileTemp = DarkFile # new\n DarkFileTemp.Add_File(FILES_INPUT + files_root) # Add File\n NMu = DarkFileTemp.Mu_for_Event()\n local_group.create_dataset(name='Entries', data=DarkFileTemp.Entries,\n dtype=int) # Number of Event\n local_group.create_dataset(name='Mu_Entries', data=NMu,\n dtype=int) # Number of Mu for Event\n # print(\" :: Finalizo correctamente :: \")\n del local_group[\"Verification\"] # borrar siempre para actualizarlo\n local_group.create_dataset(name=\"Verification\", data=\"ON\")\n # break\n # sys.exit()\n except:\n print(\" :: FILE WITH PROBLEMS :: \" + files_root)\n # sys.exit()\n\nhf.close()\n", "sub_path": "genera_h5_all.py", "file_name": "genera_h5_all.py", "file_ext": "py", "file_size_in_byte": 3030, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.exists", "line_number": 21, "usage_type": "call"}, {"api_name": "os.path", "line_number": 21, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 22, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 24, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 38, "usage_type": "call"}]} +{"seq_id": "652229858", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\n\nclass contentEncoder(nn.Module):\n def __init__(self, weights, method, input_size, hidden_size, output_size):\n super(StyleEncoder, self).__init__()\n self.output_size = output_size\n self.embedding = weights[0] if weights is not None else nn.Embedding(input_size,128)\n self.embLinear = weights[1] if weights is not None else nn.Linear(128, hidden_size)\n if method == \"RNN\":\n self.forward = self.forwardRNN\n self.RNN = nn.RNN(hidden_size, hidden_size, num_layers=2, dropout=0.2, batch_first=True, bidirectional=True)\n if method == \"Transformer\":\n self.forward = self.forwardTransformers\n d_model = 512\n nhead = 8\n num_encoder_layers = 6\n dim_feedforward=2048\n dropout=0.1\n activation=\"relu\"\n encoder_layer = nn.TransformerEncoderLayer(d_model, nhead, dim_feedforward, dropout, activation)\n encoder_norm = nn.LayerNorm(d_model)\n self.Transformer = nn.TransformerEncoder(encoder_layer, num_encoder_layers, encoder_norm)\n\n def forwardRNN(self, seqs):\n embedding = self.embedding(seqs)\n emblinear = self.embLinear(embedding)\n output, _ = self.RNN(emblinear)\n return output[:,-1,:]\n\n def forwardTransformers(self, seqs):\n embedding = self.embedding(seqs)\n emblinear = self.embLinear(embedding)\n output = self.Transformer(emblinear)\n return output.mean(dim=1)\n\n\nclass StyleDisperser(nn.Module):\n def __init__(self, weights, method, input_size, hidden_size, output_size, normalize=100, margin=1):\n super(StyleDisperser, self).__init__()\n self.encoder = StyleEncoder(weights, method, input_size, hidden_size, output_size)\n self.normalize = normalize\n self.margin = margin\n\n def forward(self, batch, same=32):\n ret_z = self.encoder(batch)\n normloss = self.normalize*torch.pow((torch.norm(ret_z, dim=1)-1),2).mean()\n\n true_z, random_z = ret_z[:same], ret_z[same:]\n true_mean = torch.mean(true_z, dim=0)\n true_std = torch.std(true_z, dim=0).sum()\n random_true_std = (torch.mv(random_z,true_mean.T)/torch.norm(random_z,dim=1)/torch.norm(true_mean)).mean()\n stdloss = true_std - random_true_std + self.margin\n return normloss, stdloss\n\n def inference(self, x):\n return self.encoder(x)\n", "sub_path": "networks/main/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 2477, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.Embedding", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.RNN", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.TransformerEncoderLayer", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.TransformerEncoder", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 39, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.pow", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.mean", "line_number": 51, "usage_type": "call"}, {"api_name": "torch.std", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.mv", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.norm", "line_number": 53, "usage_type": "call"}]} +{"seq_id": "31991997", "text": "# -*- coding: utf-8 -*-\n###\n# (C) Copyright (2012-2019) Hewlett Packard Enterprise Development LP\n#\n# Permission is hereby granted, free of charge, to any person obtaining a copy\n# of this software and associated documentation files (the 'Software'), to deal\n# in the Software without restriction, including without limitation the rights\n# to use, copy, modify, merge, publish, distribute, sublicense, and/or sell\n# copies of the Software, and to permit persons to whom the Software is\n# furnished to do so, subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be included in\n# all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n# IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n# FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\n# AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n# LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\n# OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\n# THE SOFTWARE.\n###\n\nfrom unittest import TestCase\n\nimport mock\n\nfrom hpOneView.connection import connection\nfrom hpOneView.resources.servers.logical_enclosures import LogicalEnclosures\nfrom hpOneView.resources.resource import Resource, ResourceHelper, ResourcePatchMixin\n\n\nclass LogicalEnclosuresTest(TestCase):\n def setUp(self):\n self.host = '127.0.0.1'\n self.connection = connection(self.host)\n self._logical_enclosures = LogicalEnclosures(self.connection)\n self.uri = \"/rest/logical-enclosures/ad28cf21-8b15-4f92-bdcf-51cb2042db32\"\n self._logical_enclosures.data = {\"uri\": self.uri}\n\n @mock.patch.object(ResourceHelper, 'create')\n def test_create_called_once(self, mock_create):\n resource = dict(\n enclosureUris=[\n \"/rest/enclosures/0000000000A66101\",\n \"/rest/enclosures/0000000000A66102\",\n \"/rest/enclosures/0000000000A66103\"\n ],\n enclosureGroupUri=\"/rest/enclosure-groups/e41118e4-2233-4b6b-9318-c9982dbf01fa\",\n forceInstallFirmware=False,\n name=\"testLogicalEnclosure\"\n )\n mock_create.return_value = {}\n\n self._logical_enclosures.create(resource)\n mock_create.assert_called_once_with(resource.copy(), None, -1, None, False)\n\n @mock.patch.object(ResourceHelper, 'delete')\n def test_delete_called_once(self, mock_delete):\n self._logical_enclosures.delete(force=False)\n\n mock_delete.assert_called_once_with(self.uri, custom_headers=None,\n force=False, timeout=-1)\n\n @mock.patch.object(ResourceHelper, 'delete')\n def test_delete_called_once_with_force(self, mock_delete):\n self._logical_enclosures.delete(force=True)\n\n mock_delete.assert_called_once_with(self.uri, custom_headers=None,\n force=True, timeout=-1)\n\n @mock.patch.object(ResourceHelper, 'get_all')\n def test_get_all_called_once(self, mock_get_all):\n filter = 'name=TestName'\n sort = 'name:ascending'\n scope_uris = 'rest/scopes/cd237b60-09e2-45c4-829e-082e318a6d2a'\n\n self._logical_enclosures.get_all(2, 500, filter, sort, scope_uris)\n\n mock_get_all.assert_called_once_with(2, 500, filter=filter, sort=sort, scope_uris=scope_uris)\n\n @mock.patch.object(ResourceHelper, 'get_all')\n def test_get_all_called_once_with_default_values(self, mock_get_all):\n self._logical_enclosures.get_all()\n\n mock_get_all.assert_called_once_with(0, -1, filter='', sort='', scope_uris='')\n\n @mock.patch.object(Resource, 'get_by')\n def test_get_by_name_called_once(self, mock_get_by):\n self._logical_enclosures.get_by_name('OneViewSDK-Test-Logical-Enclosure')\n mock_get_by.assert_called_once_with('name', 'OneViewSDK-Test-Logical-Enclosure')\n\n @mock.patch.object(Resource, 'ensure_resource_data')\n @mock.patch.object(ResourceHelper, 'update')\n def test_update_called_once_with_defaults(self, mock_update, mock_ensure_client):\n logical_enclosure = {\n \"name\": \"one_enclosure_le\",\n }\n logical_enclosure[\"uri\"] = self.uri\n self._logical_enclosures.update(logical_enclosure)\n mock_update.assert_called_once_with(logical_enclosure, self.uri, False, -1, None)\n\n @mock.patch.object(Resource, 'ensure_resource_data')\n @mock.patch.object(ResourceHelper, 'update')\n def test_update_called_once(self, mock_update, mock_ensure_client):\n logical_enclosure = {\n \"name\": \"one_enclosure_le\",\n }\n logical_enclosure[\"uri\"] = self.uri\n self._logical_enclosures.update(logical_enclosure, 70)\n mock_update.assert_called_once_with(logical_enclosure, self.uri,\n False, 70, None)\n\n @mock.patch.object(ResourcePatchMixin, 'patch_request')\n def test_patch_should_use_user_defined_values(self, mock_patch):\n mock_patch.return_value = {}\n custom_headers = {'If-Match': '*'}\n\n self._logical_enclosures.patch(\n 'replace', '/name', 'new_name', custom_headers, 1)\n mock_patch.assert_called_once_with(self.uri,\n body=[{'path': '/name',\n 'op': 'replace',\n 'value': 'new_name'}],\n custom_headers={'If-Match': '*'},\n timeout=1)\n\n @mock.patch.object(Resource, 'refresh')\n @mock.patch.object(ResourceHelper, 'update')\n def test_update_configuration(self, mock_update, mock_refresh):\n uri_rest_call = '{}/configuration'.format(self.uri)\n\n self._logical_enclosures.update_configuration()\n\n mock_update.assert_called_once_with(None, uri_rest_call, timeout=-1)\n\n @mock.patch.object(ResourceHelper, 'do_get')\n def test_get_script(self, mock_get):\n uri_rest_call = '{}/script'.format(self.uri)\n\n self._logical_enclosures.get_script()\n\n mock_get.assert_called_once_with(uri_rest_call)\n\n @mock.patch.object(ResourceHelper, 'update')\n def test_update_script(self, mock_update):\n uri_rest_call = '/rest/logical-enclosures/ad28cf21-8b15-4f92-bdcf-51cb2042db32/script'\n information = {\"#TEST COMMAND\": \"\"}\n configuration_rest_call = information.copy()\n\n self._logical_enclosures.update_script(information)\n\n mock_update.assert_called_once_with(\n configuration_rest_call, uri=uri_rest_call, timeout=-1)\n\n @mock.patch.object(ResourceHelper, 'create')\n def test_support_dump_called_once(self, mock_create):\n information = {\n \"errorCode\": \"MyDump16\",\n \"encrypt\": True,\n \"excludeApplianceDump\": False\n }\n uri_rest_call = '{}/support-dumps'.format(self.uri)\n\n mock_create.return_value = {}\n\n self._logical_enclosures.generate_support_dump(information)\n mock_create.assert_called_once_with(\n information.copy(), uri=uri_rest_call, timeout=-1)\n\n @mock.patch.object(ResourceHelper, 'update')\n def test_update_from_group(self, mock_update):\n uri_rest_call = '{}/updateFromGroup'.format(self.uri)\n\n self._logical_enclosures.update_from_group()\n\n mock_update.assert_called_once_with(None, uri_rest_call, timeout=-1)\n", "sub_path": "tests/unit/resources/servers/test_logical_enclosures.py", "file_name": "test_logical_enclosures.py", "file_ext": "py", "file_size_in_byte": 7567, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 33, "usage_type": "name"}, {"api_name": "hpOneView.connection.connection", "line_number": 36, "usage_type": "call"}, {"api_name": "hpOneView.resources.servers.logical_enclosures.LogicalEnclosures", "line_number": 37, "usage_type": "call"}, {"api_name": "mock.patch.object", "line_number": 41, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourceHelper", "line_number": 41, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 41, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 58, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourceHelper", "line_number": 58, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 58, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 65, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourceHelper", "line_number": 65, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 65, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 72, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourceHelper", "line_number": 72, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 72, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 82, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourceHelper", "line_number": 82, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 82, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 88, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.Resource", "line_number": 88, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 88, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 93, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.Resource", "line_number": 93, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 93, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 94, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourceHelper", "line_number": 94, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 94, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 103, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.Resource", "line_number": 103, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 103, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 104, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourceHelper", "line_number": 104, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 104, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 114, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourcePatchMixin", "line_number": 114, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 114, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 128, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.Resource", "line_number": 128, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 128, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 129, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourceHelper", "line_number": 129, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 129, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 137, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourceHelper", "line_number": 137, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 137, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 145, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourceHelper", "line_number": 145, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 145, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 156, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourceHelper", "line_number": 156, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 156, "usage_type": "attribute"}, {"api_name": "mock.patch.object", "line_number": 171, "usage_type": "call"}, {"api_name": "hpOneView.resources.resource.ResourceHelper", "line_number": 171, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 171, "usage_type": "attribute"}]} +{"seq_id": "432861263", "text": "import os\nimport lzma\nimport tarfile\nimport shutil\nimport zipfile\n\nfile_to_dataset = {\n \"Insects_FortyImagesPerCategory_Forty_Images_Per_Category.zip\": \"insects\",\n \"Plankton_FortyImagesPerCategory_Forty_Images_Per_Category.zip\": \"plankton\",\n \"plantvillage_plantvillage-formatted-image (1).zip\": \"plants\",\n \"MedicinalLeaf_medleaf-formatted-image.zip\": \"medleaf\",\n \"Texture_1_FortyImagesPerCategory_Forty_Images_Per_Category.zip\": \"texture1\",\n \"Texture_2_FortyImagesPerCategory_Forty_Images_Per_Category.zip\": \"texture2\",\n \"rsi-cb-128-remotesensing_rsicb128-formatted-image.zip\": \"rsicb\",\n \"resisc45-remotesensing_resisc45-formatted-image (1).zip\": \"resisc\", \n \"OmniPrint_overview_OmniPrint_MetaDL_Ihsan_format_meta-mix_first_set.zip\": \"omniprint1\",\n \"OmniPrint_overview_OmniPrint_MetaDL_Ihsan_format_meta5-bis_first_set.zip\": \"omniprint2\",\n}\n\nall_data = \"publicdata.zip\"\nroot_dir = \"./data/\"\n\nassert os.path.exists(all_data), \"Could not find {} in the current directory\".format(all_data)\n\nif not os.path.isdir(root_dir):\n os.mkdir(root_dir)\n\n# unzip the alldata.zip\nwith zipfile.ZipFile(all_data, 'r') as zip_ref:\n zip_ref.extractall(\"./\")\n\nfor zfile, dirname in file_to_dataset.items():\n print(\"Processing {} files\".format(dirname))\n unzip_location = os.path.join(root_dir, dirname)\n if not os.path.isdir(unzip_location):\n os.mkdir(unzip_location)\n else:\n print(\"\\tDirectory {} already existed. Not touching this and moving to the next one\".format(unzip_location))\n continue\n\n # Check file extension (if .zip -> unzip, if .xz -> convert to tar and untar)\n extension =zfile.split(\".\")[1]\n if extension.lower() == \"zip\":\n # Read zip file and extract it \n with zipfile.ZipFile(zfile, 'r') as zip_ref:\n zip_ref.extractall(unzip_location)\n elif extension.lower() == \"xz\":\n with lzma.open(zfile) as f:\n with tarfile.open(fileobj=f) as tar:\n tar.extractall(unzip_location)\n else:\n print(\"Unknown file extension .{} for {}\".format(extension, dirname))\n\n\n os.remove(zfile)\n\n # Make sure there now is a folder called images in the unzip location\n image_dir = os.path.join(unzip_location, \"images\")\n if not os.path.isdir(image_dir):\n folder_in_zip_loc = os.path.join(unzip_location, os.listdir(unzip_location)[0])\n files_to_move = os.listdir(folder_in_zip_loc)\n for f in files_to_move:\n # unpack the folder\n shutil.move(os.path.join(folder_in_zip_loc, f), os.path.join(unzip_location, f))\n shutil.rmtree(folder_in_zip_loc)\n print(\"\\tSuccess.\")\nprint(\"\\n[*] Everything went well. Data sets are ready!\")\n\n \n", "sub_path": "FewShotBaselines/setup_data.py", "file_name": "setup_data.py", "file_ext": "py", "file_size_in_byte": 2719, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 26, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 35, "usage_type": "call"}, {"api_name": "os.path", "line_number": 35, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 36, "usage_type": "call"}, {"api_name": "zipfile.ZipFile", "line_number": 45, "usage_type": "call"}, {"api_name": "lzma.open", "line_number": 48, "usage_type": "call"}, {"api_name": "tarfile.open", "line_number": 49, "usage_type": "call"}, {"api_name": "os.remove", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 60, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "shutil.move", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 64, "usage_type": "call"}, {"api_name": "os.path", "line_number": 64, "usage_type": "attribute"}, {"api_name": "shutil.rmtree", "line_number": 65, "usage_type": "call"}]} +{"seq_id": "60954705", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import models, migrations\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ('ahtung_api', '0003_auto_20141107_0919'),\n ]\n\n operations = [\n migrations.RenameModel(\n old_name='EnabledSignals',\n new_name='EnabledSignal',\n ),\n ]\n", "sub_path": "ahtung_api/migrations/0004_auto_20141107_0927.py", "file_name": "0004_auto_20141107_0927.py", "file_ext": "py", "file_size_in_byte": 372, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.db.migrations.Migration", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.migrations", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.migrations.RenameModel", "line_number": 14, "usage_type": "call"}, {"api_name": "django.db.migrations", "line_number": 14, "usage_type": "name"}]} +{"seq_id": "286297406", "text": "import time\nimport numpy as np\nimport threading\nimport math\nimport os\nimport datetime\nimport pygame\nimport keyboard\nfrom ctypes import windll\nfrom pylsl import StreamInlet, resolve_stream\nfrom scipy.signal import butter, lfilter\nfrom FBCSP import FBCSP\nfrom CommonSpatialPattern import CommonSpatialPattern\nimport multiprocessing\nfrom multiprocessing import Process, Pipe\nfrom socket import *\nfrom struct import *\n\nEEGdata_size = 60\nfreq = 512\ntotal_ch = 31\nselect_ch = range(1, total_ch + 1)\n\nnum_trials = 8\nnum_tests = 0\nsession_sec = 12 # how long each session is\nwindow_sec = 3\nstride_sec = 1\nwait_time = 0 # how long to wait before starting to collect eeg\n\nnum_classes = 4 # rest, right, left, up\nclass_order = [3, 0, 1, 2]\nnum_iteration = len(class_order)\n\nfolder = \"Data\"\ndata_type = \"EEG_BallFour(training)\"\ndate = datetime.datetime.now().strftime(\"%m%d\")\n\nSetWindowPos = windll.user32.SetWindowPos\nNOSIZE = 1\nNOMOVE = 2\nTOPMOST = -1\nNOT_TOPMOST = -2\ncircle_radius = 20\nXSCREEN = 1920 # 1600\nYSCREEN = 1080 # 900\nscreen = None\nplus_size = XSCREEN / 24\n\nchannel_names=[]\n\n\nclass Marker:\n def __init__(self):\n self.position = 0\n self.points = 0\n self.channel = -1\n self.type = \"\"\n self.description = \"\"\n\nclass BallRecorderFour:\n def __init__(self, subject=\"test\", nChannels=31, frequency=512, streamer_type=\"OpenVibe\", channels=None):\n global screen, total_ch, freq, select_ch, channel_names\n freq = int(frequency)\n channel_names=channels\n total_ch = int(nChannels)\n select_ch = range(1, total_ch + 1)\n\n self.streamer=str(streamer_type)\n self.name = self.__class__.__name__\n self.running = True\n self.edx = 0\n self.global_time = time.clock()\n self.EEGdata = np.zeros((freq * EEGdata_size, total_ch + 1))\n\n if(self.streamer==\"OpenVibe\"):\n print(\"{}: Looking for an EEG stream...\".format(self.name))\n streams = resolve_stream('type', 'signal')\n self.inlet = StreamInlet(streams[0])\n\n i = 1\n fname = \"{}/{}_{}_{}t{}c{}s{}ch_{}\".format(folder, date, data_type, num_trials, num_classes, window_sec,\n len(select_ch), subject)\n self.filename = \"{}_{}.txt\".format(fname, i)\n while os.path.exists(self.filename):\n i += 1\n self.filename = \"{}_{}.txt\".format(fname, i)\n self.file = open(self.filename, \"w\")\n print(\"{}: Writing to {}\".format(self.name, self.filename))\n\n pygame.init()\n pygame.font.init()\n screen = pygame.display.set_mode([XSCREEN, YSCREEN], pygame.FULLSCREEN)\n always_on_top(False)\n self.output_data = []\n self.output_label = []\n self.class_count = [0] * num_classes\n\n self.model = CommonSpatialPattern(augment=False, nChannels=total_ch, chnames=channels)\n\n @staticmethod\n def draw_train(flag):\n screen.fill((0, 0, 0))\n if flag == 0:\n pygame.draw.rect(screen, (255, 255, 255), (XSCREEN / 2 - (plus_size / 2), YSCREEN / 2 - 3, plus_size, 6))\n pygame.draw.rect(screen, (255, 255, 255), (XSCREEN / 2 - 3, YSCREEN / 2 - (plus_size / 2), 6, plus_size))\n elif flag == 1:\n pygame.draw.polygon(screen, (255, 255, 255), (\n (XSCREEN / 2 - (plus_size / 2), YSCREEN / 2 - (plus_size / 2)),\n (XSCREEN / 2 + (plus_size / 2), YSCREEN / 2),\n (XSCREEN / 2 - (plus_size / 2), YSCREEN / 2 + (plus_size / 2))))\n elif flag == 2:\n pygame.draw.polygon(screen, (255, 255, 255), (\n (XSCREEN / 2 - (plus_size / 2), YSCREEN / 2),\n (XSCREEN / 2 + (plus_size / 2), YSCREEN / 2 - (plus_size / 2)),\n (XSCREEN / 2 + (plus_size / 2), YSCREEN / 2 + (plus_size / 2))))\n elif flag == 3:\n pygame.draw.polygon(screen, (255, 255, 255), (\n (XSCREEN / 2, YSCREEN / 2 - (plus_size / 2)),\n (XSCREEN / 2 + (plus_size / 2), YSCREEN / 2 + (plus_size / 2)),\n (XSCREEN / 2 - (plus_size / 2), YSCREEN / 2 + (plus_size / 2))))\n else:\n assert False\n pygame.display.update()\n\n @staticmethod\n def draw_test(flag):\n screen.fill((0, 0, 0))\n if flag == 0:\n pygame.draw.rect(screen, (255, 255, 255),\n (int(XSCREEN / 2) - (plus_size / 2), int(YSCREEN / 2) - 3, plus_size, 6))\n pygame.draw.rect(screen, (255, 255, 255),\n (int(XSCREEN / 2) - 3, int(YSCREEN / 2) - (plus_size / 2), 6, plus_size))\n elif flag == 1:\n pygame.draw.rect(screen, (0, 255, 0), (XSCREEN - circle_radius, 0, circle_radius, YSCREEN))\n pygame.draw.circle(screen, (0, 0, 255), (int(XSCREEN / 2), YSCREEN - circle_radius * 2), circle_radius)\n elif flag == 2:\n pygame.draw.rect(screen, (0, 255, 0), (0, 0, circle_radius, YSCREEN))\n pygame.draw.circle(screen, (0, 0, 255), (int(XSCREEN / 2), YSCREEN - circle_radius * 2), circle_radius)\n elif flag == 3:\n pygame.draw.rect(screen, (0, 255, 0), (0, 0, XSCREEN, circle_radius))\n pygame.draw.circle(screen, (0, 0, 255), (int(XSCREEN / 2), YSCREEN - circle_radius * 2), circle_radius)\n else:\n assert False\n pygame.display.update()\n\n @staticmethod\n def update_circle(x, y, v_x, v_y):\n pygame.draw.circle(screen, (0, 0, 0), (x, y), circle_radius)\n x += v_x\n y += v_y\n pygame.draw.circle(screen, (0, 0, 255), (x, y), circle_radius)\n pygame.display.update()\n return x, y\n\n def collect_data(self):\n print(\"{}: Collection starting\".format(self.name))\n for i in range(num_trials * num_iteration):\n self.edx = 0\n time.sleep(5)\n flag = class_order[i % num_iteration]\n self.draw_train(flag)\n start_time = time.clock()\n prev_time = 0\n while True:\n current_time = int(math.floor(time.clock() - start_time))\n if current_time >= session_sec:\n break\n if current_time >= wait_time + window_sec and current_time >= prev_time + stride_sec:\n prev_time = current_time\n edx=self.edx\n selected_eeg = self.EEGdata[(edx - (freq * window_sec)): edx, select_ch]\n print(selected_eeg)\n self.output_data.append(selected_eeg.T)\n self.output_label.append(flag)\n self.file.write(str(np.ndarray.tolist(selected_eeg)) + '\\n')\n self.file.write(str(flag) + '\\n')\n self.class_count[flag] += 1\n screen.fill((0,0,0))\n pygame.display.update()\n print(\"{}: Collection finished\".format(self.name))\n\n def test_model(self):\n print(\"{}: Started model testing\".format(self.name))\n for t in range(num_tests * 3):\n # collecting direction data\n size_rest, size_right, size_left, size_up = self.class_count\n circle_x = int(XSCREEN / 2)\n circle_y = YSCREEN - circle_radius * 2\n v_x = 0\n v_y = 0\n if size_right <= size_left and size_right <= size_up:\n flag = 1\n elif size_left <= size_right and size_left <= size_up:\n flag = 2\n else:\n flag = 3\n self.draw_test(flag)\n self.edx = 0\n start_time = time.clock()\n prev_time = 0\n print(\"Flag is: {}, class_count is: {}\".format(flag, self.class_count))\n while circle_radius < circle_x < XSCREEN - circle_radius and circle_y > circle_radius:\n current_time = int(math.floor(time.clock() - start_time))\n if keyboard.is_pressed('m'):\n time.sleep(2)\n break\n if keyboard.is_pressed('p'):\n self.file.close()\n time.sleep(3)\n print(\"{}: Forced close model testing\".format(self.name))\n return\n if current_time < 3:\n continue\n\n circle_x, circle_y = self.update_circle(circle_x, circle_y, v_x, v_y)\n time.sleep(0.01)\n\n if current_time >= prev_time + window_sec:\n prev_time = current_time\n edx=self.edx\n selected_eeg = get_eeg(self.EEGdata, edx - (freq * window_sec), edx)\n transformed_eeg = np.asarray(np.transpose(np.asmatrix(selected_eeg)))\n transformed_eeg = np.asarray([transformed_eeg])\n print(transformed_eeg)\n if np.shape(transformed_eeg) != (1, len(select_ch), window_sec * freq):\n print(np.shape(transformed_eeg))\n assert False\n\n predicted_label = self.model.predict(transformed_eeg)\n print(predicted_label)\n if predicted_label[0] == 0:\n v_x = 0\n v_y = 0\n elif predicted_label[0] == 1:\n v_x = 1\n v_y = 0\n elif predicted_label[0] == 2:\n v_x = -1\n v_y = 0\n elif predicted_label[0] == 3:\n v_x = 0\n v_y = -1\n self.output_data.append(selected_eeg.T)\n self.output_label.append(flag)\n self.file.write(str(np.ndarray.tolist(selected_eeg)) + '\\n')\n self.file.write(str(flag) + '\\n')\n self.class_count[flag] += 1\n\n # collecting rest data\n flag = 0\n self.draw_test(flag)\n self.edx = 0\n start_time = time.clock()\n prev_time = 0\n size_rest, size_right, size_left, size_up = self.class_count\n while size_rest < min(size_left, size_right, size_up):\n current_time = int(math.floor(time.clock() - start_time))\n if keyboard.is_pressed('m'):\n time.sleep(2)\n break\n if keyboard.is_pressed('p'):\n self.file.close()\n time.sleep(3)\n print(\"{}: Forced close model testing\".format(self.name))\n return\n\n if current_time >= 3 and current_time >= prev_time + window_sec:\n prev_time = current_time\n edx=self.edx\n selected_eeg = get_eeg(self.EEGdata, edx - (freq * window_sec), edx)\n self.output_data.append(selected_eeg.T)\n self.output_label.append(flag)\n self.file.write(str(np.ndarray.tolist(selected_eeg)) + '\\n')\n self.file.write(str(flag) + '\\n')\n self.class_count[flag] += 1\n size_rest, size_right, size_left, size_up = self.class_count\n\n min_data = []\n min_label = []\n min_count = min(self.class_count)\n count = [0] * num_classes\n print(\"len_output_data: {}, min_count: {}\".format(len(self.output_data), min_count))\n for j in range(len(self.output_data)):\n if count[self.output_label[j]] >= min_count:\n continue\n min_data.append(self.output_data[j])\n min_label.append(self.output_label[j])\n count[self.output_label[j]] += 1\n\n self.model.build_model(min_data, min_label)\n\n def close_recorder(self):\n self.file.close()\n self.running = False\n if(self.streamer==\"OpenVibe\"):\n self.inlet.close_stream()\n pygame.display.quit()\n\n def retrieve_eeg(self):\n if(self.streamer==\"Brainvision Recorder\"):\n parent_conn, child_conn = Pipe()\n eeg_process = multiprocessing.Process(target=retrieve_eeg_BREC, args=(child_conn,))\n eeg_process.start()\n\n while self.running:\n if(self.streamer==\"Brainvision Recorder\"):\n (sample, timestamp) = parent_conn.recv()\n elif(self.streamer==\"OpenVibe\"):\n sample, timestamp = self.inlet.pull_sample()\n\n current_time = time.clock() - self.global_time\n self.EEGdata[self.edx % (freq * EEGdata_size), 0] = current_time\n self.EEGdata[self.edx % (freq * EEGdata_size), 1:total_ch + 1] = sample\n self.edx = self.edx + 1\n if self.edx >= freq * EEGdata_size:\n self.edx = 0\n\n def start(self):\n eeg_thrd = threading.Thread(target=self.retrieve_eeg)\n eeg_thrd.daemon = True\n eeg_thrd.start()\n\n self.collect_data()\n self.model.build_model(self.output_data, self.output_label)\n self.test_model()\n self.close_recorder()\n\n return self.filename\n\n\ndef always_on_top(b):\n zorder = (NOT_TOPMOST, TOPMOST)[b] # choose a flag according to bool\n hwnd = pygame.display.get_wm_info()['window'] # handle to the window\n SetWindowPos(hwnd, zorder, 0, 0, 0, 0, NOMOVE | NOSIZE)\n\n\ndef get_eeg(data, x, y):\n if x < 0:\n if y == 0:\n return data[x:(freq * EEGdata_size), select_ch]\n else:\n return np.concatenate((data[x:(freq * EEGdata_size), select_ch], data[0: y, select_ch]), axis=0)\n return data[x:y, select_ch]\n\n\n######Brainvision Recorder section######\n\n\ndef RecvData(socket, requestedSize):\n returnStream = ''\n while len(returnStream) < requestedSize:\n databytes = socket.recv(requestedSize - len(returnStream))\n if databytes == '':\n raise RuntimeError\n returnStream += databytes\n\n return returnStream\n\n\ndef SplitString(raw):\n stringlist = []\n s = \"\"\n for i in range(len(raw)):\n if raw[i] != '\\x00':\n s = s + raw[i]\n else:\n stringlist.append(s)\n s = \"\"\n\n return stringlist\n\n\ndef GetProperties(rawdata):\n # Extract numerical data\n (channelCount, samplingInterval) = unpack(' 0:\n cfg.CUDA = True\n else:\n raise ValueError(\"Need Cuda device to run !\")\n\n if args.dataset.startswith(\"coco\"):\n dataset = datasets.get_coco_dataset()\n cfg.MODEL.NUM_CLASSES = len(dataset.classes)\n print('cfg.MODEL.NUM_CLASSES:',cfg.MODEL.NUM_CLASSES)\n elif args.dataset.startswith(\"davis\"):\n cfg.MODEL.NUM_CLASSES = 81\n # Load train data, which has the corresponding global id.\n cfg.TRAIN.DATASETS = ('davis_train',)\n dataset = davis_db.DAVIS_imdb(db_name=\"DAVIS\", split = 'train',cls_mapper = None, load_flow=False)\n else:\n raise ValueError('Unexpected dataset name: {}'.format(args.dataset))\n\n #Add unknow class type if necessary.\n if cfg.MODEL.ADD_UNKNOWN_CLASS is True:\n cfg.MODEL.NUM_CLASSES +=1\n\n cfg_from_file(args.cfg_file)\n if args.set_cfgs is not None:\n cfg_from_list(args.set_cfgs)\n \n if cfg.MODEL.IDENTITY_TRAINING and cfg.MODEL.IDENTITY_REPLACE_CLASS:\n cfg.MODEL.NUM_CLASSES = 145\n cfg.MODEL.IDENTITY_TRAINING = False\n cfg.MODEL.ADD_UNKNOWN_CLASS = False\n\n #Add unknow class type if necessary.\n if cfg.MODEL.IDENTITY_TRAINING:\n cfg.MODEL.TOTAL_INSTANCE_NUM = 145\n if cfg.MODEL.ADD_UNKNOWN_CLASS is True:\n cfg.MODEL.NUM_CLASSES +=1 \n\n \n assert bool(args.load_ckpt)\n assert_and_infer_cfg()\n maskRCNN = vos_model_builder.Generalized_VOS_RCNN()\n \n if args.cuda:\n maskRCNN.cuda()\n \n if args.load_ckpt:\n load_name = args.load_ckpt\n print(\"loading checkpoint %s\" % (load_name))\n checkpoint = torch.load(load_name, map_location=lambda storage, loc: storage)\n net_utils.load_ckpt_no_mapping(maskRCNN, checkpoint['model'])\n \n maskRCNN = mynn.DataParallel(maskRCNN, cpu_keywords=['im_info', 'roidb'],minibatch=True, device_ids=[0])\n \n maskRCNN.eval()\n db = davis_db.DAVIS_imdb(db_name=\"DAVIS\", split = 'train', cls_mapper = None)\n \n for seq_idx in range(db.get_num_sequence()):\n db.set_to_sequence(seq_idx)\n seq_name = db.get_current_seq_name()\n cur_output_dir = osp.join(args.output_dir,seq_name)\n if args.no_overwrite is True and osp.exists(osp.join(cur_output_dir,'results.pdf')):\n continue\n if not osp.isdir(cur_output_dir):\n os.makedirs(cur_output_dir)\n assert(cur_output_dir)\n for idx in range(db.get_current_seq_length()):\n im = db.get_image_cv2(idx)\n assert im is not None\n timers = defaultdict(Timer)\n cls_boxes, cls_segms, cls_keyps = im_detect_all(maskRCNN, im, timers=timers)\n im_name = '%03d-%03d'%(seq_idx,idx)\n print(osp.join(seq_name,im_name))\n vis_utils.vis_one_image(\n im[:, :, ::-1], # BGR -> RGB for visualization\n im_name,\n cur_output_dir,\n cls_boxes,\n cls_segms,\n cls_keyps,\n dataset=dataset,\n box_alpha=0.3,\n show_class=True,\n thresh=0.7,\n kp_thresh=2\n )\n\n if args.merge_pdfs:\n merge_out_path = '{}/results.pdf'.format(cur_output_dir)\n if os.path.exists(merge_out_path):\n os.remove(merge_out_path)\n command = \"pdfunite {}/*.pdf {}\".format(cur_output_dir,\n merge_out_path)\n subprocess.call(command, shell=True)\n \n", "sub_path": "lib_vos/tools/predict_davis.py", "file_name": "predict_davis.py", "file_ext": "py", "file_size_in_byte": 5880, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path", "line_number": 10, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path", "line_number": 11, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 11, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path", "line_number": 12, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 12, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path", "line_number": 13, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 14, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 38, "usage_type": "call"}, {"api_name": "distutils.util.util", "line_number": 69, "usage_type": "attribute"}, {"api_name": "distutils.util", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.cuda.is_available", "line_number": 80, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 80, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 81, "usage_type": "call"}, {"api_name": "core.config.cfg.NUM_GPUS", "line_number": 83, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 83, "usage_type": "name"}, {"api_name": "core.config.cfg.CUDA", "line_number": 84, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 84, "usage_type": "name"}, {"api_name": "vos.davis_db.get_coco_dataset", "line_number": 89, "usage_type": "call"}, {"api_name": "vos.davis_db", "line_number": 89, "usage_type": "name"}, {"api_name": "core.config.cfg.MODEL", "line_number": 90, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 90, "usage_type": "name"}, {"api_name": "core.config.cfg.MODEL", "line_number": 91, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 91, "usage_type": "name"}, {"api_name": "core.config.cfg.MODEL", "line_number": 93, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 93, "usage_type": "name"}, {"api_name": "core.config.cfg.TRAIN", "line_number": 95, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 95, "usage_type": "name"}, {"api_name": "vos.davis_db.DAVIS_imdb", "line_number": 96, "usage_type": "call"}, {"api_name": "vos.davis_db", "line_number": 96, "usage_type": "name"}, {"api_name": "core.config.cfg.MODEL", "line_number": 101, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 101, "usage_type": "name"}, {"api_name": "core.config.cfg.MODEL", "line_number": 102, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 102, "usage_type": "name"}, {"api_name": "core.config.cfg_from_file", "line_number": 104, "usage_type": "call"}, {"api_name": "core.config.cfg_from_list", "line_number": 106, "usage_type": "call"}, {"api_name": "core.config.cfg.MODEL", "line_number": 108, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 108, "usage_type": "name"}, {"api_name": "core.config.cfg.MODEL", "line_number": 109, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 109, "usage_type": "name"}, {"api_name": "core.config.cfg.MODEL", "line_number": 110, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 110, "usage_type": "name"}, {"api_name": "core.config.cfg.MODEL", "line_number": 111, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 111, "usage_type": "name"}, {"api_name": "core.config.cfg.MODEL", "line_number": 114, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 114, "usage_type": "name"}, {"api_name": "core.config.cfg.MODEL", "line_number": 115, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 115, "usage_type": "name"}, {"api_name": "core.config.cfg.MODEL", "line_number": 116, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 116, "usage_type": "name"}, {"api_name": "core.config.cfg.MODEL", "line_number": 117, "usage_type": "attribute"}, {"api_name": "core.config.cfg", "line_number": 117, "usage_type": "name"}, {"api_name": "core.config.assert_and_infer_cfg", "line_number": 121, "usage_type": "call"}, {"api_name": "vos_modeling.vos_model_builder.Generalized_VOS_RCNN", "line_number": 122, "usage_type": "call"}, {"api_name": "vos_modeling.vos_model_builder", "line_number": 122, "usage_type": "name"}, {"api_name": "torch.load", "line_number": 130, "usage_type": "call"}, {"api_name": "utils.net.load_ckpt_no_mapping", "line_number": 131, "usage_type": "call"}, {"api_name": "utils.net", "line_number": 131, "usage_type": "name"}, {"api_name": "nn.DataParallel", "line_number": 133, "usage_type": "call"}, {"api_name": "vos.davis_db.DAVIS_imdb", "line_number": 136, "usage_type": "call"}, {"api_name": "vos.davis_db", "line_number": 136, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 141, "usage_type": "call"}, {"api_name": "os.path", "line_number": 141, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path", "line_number": 142, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 142, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 144, "usage_type": "call"}, {"api_name": "os.path", "line_number": 144, "usage_type": "name"}, {"api_name": "os.makedirs", "line_number": 145, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 150, "usage_type": "call"}, {"api_name": "utils.timer.Timer", "line_number": 150, "usage_type": "argument"}, {"api_name": "core.test.im_detect_all", "line_number": 151, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 153, "usage_type": "call"}, {"api_name": "os.path", "line_number": 153, "usage_type": "name"}, {"api_name": "utils.vis.vis_one_image", "line_number": 154, "usage_type": "call"}, {"api_name": "utils.vis", "line_number": 154, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 170, "usage_type": "call"}, {"api_name": "os.path", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 171, "usage_type": "call"}, {"api_name": "subprocess.call", "line_number": 174, "usage_type": "call"}]} +{"seq_id": "444838226", "text": "from struct import *\r\nfrom collections import namedtuple\r\nimport numpy as np\r\n\r\ndef read_snapshot(fname):\r\n with open(fname, 'r') as fp:\r\n data = fp.read()\r\n header = namedtuple(\"header\", \"N Npart Mass Mass0 Mass1 Mass2 Mass3 Mass4 Mass5 a z FlagSfr FlagFeedback Nall0 Nall1 Nall2 Nall3 Nall4 Nall5 FlagCooling NumFiles BoxSize Omega_0 Omega_L h FlagMultphase FlagStellarAge FlagSfrHistogram\")\r\n o = 4\r\n #s = \"%lsf\" % (header.nx)\r\n #x = np.asarray(unpack(s,data[o:o+4*header.nx]))\r\n s = \"%lsi\" % (6)\r\n header.Npart = np.asarray(unpack(s,data[o:o+4*6]),dtype=int)\r\n\r\n #header.Npart0 = int(unpack(\"i\", data[o:o+4])[0])\r\n o += 4*6\r\n #header.Npart1 = int(unpack(\"i\", data[o:o+4])[0])\r\n #o += 4\r\n\r\n #header.Npart2 = int(unpack(\"i\", data[o:o+4])[0])\r\n #o += 4\r\n\r\n #header.Npart3 = int(unpack(\"i\", data[o:o+4])[0])\r\n #o += 4\r\n\r\n #header.Npart4 = int(unpack(\"i\", data[o:o+4])[0])\r\n #o += 4\r\n\r\n #header.Npart5 = int(unpack(\"i\", data[o:o+4])[0])\r\n #o += 4\r\n\r\n s = \"%lsd\" % (6)\r\n header.Mass = np.asarray(unpack(s,data[o:o+8*6]))\r\n o += 8*6\r\n\r\n #header.Mass0 = float(unpack(\"d\", data[o:o+8])[0])\r\n #o += 8\r\n\r\n #header.Mass1 = float(unpack(\"d\", data[o:o+8])[0])\r\n #o += 8\r\n\r\n #header.Mass2 = float(unpack(\"d\", data[o:o+8])[0])\r\n #o += 8\r\n\r\n #header.Mass3 = float(unpack(\"d\", data[o:o+8])[0])\r\n #o += 8\r\n\r\n #header.Mass4 = float(unpack(\"d\", data[o:o+8])[0])\r\n #o += 8\r\n\r\n #header.Mass5 = float(unpack(\"d\", data[o:o+8])[0])\r\n #o += 8\r\n\r\n header.a = float(unpack(\"d\", data[o:o+8])[0])\r\n o += 8\r\n a = header.a\r\n\r\n header.z = float(unpack(\"d\", data[o:o+8])[0])\r\n o += 8\r\n z = header.z\r\n\r\n header.FlagSfr = int(unpack(\"i\", data[o:o+4])[0])\r\n o += 4\r\n\r\n header.FlagFeedback = int(unpack(\"i\", data[o:o+4])[0])\r\n o += 4\r\n\r\n s = \"%lsi\" % (6)\r\n header.Nall = np.asarray(unpack(s,data[o:o+4*6]),dtype=int)\r\n o += 4*6\r\n\r\n #header.Nall0 = int(unpack(\"i\", data[o:o+4])[0])\r\n #o += 4\r\n\r\n #header.Nall1 = int(unpack(\"i\", data[o:o+4])[0])\r\n #o += 4\r\n\r\n #header.Nall2 = int(unpack(\"i\", data[o:o+4])[0])\r\n #o += 4\r\n\r\n #header.Nall3 = int(unpack(\"i\", data[o:o+4])[0])\r\n #o += 4\r\n\r\n #header.Nall4 = int(unpack(\"i\", data[o:o+4])[0])\r\n #o += 4\r\n\r\n #header.Nall5 = int(unpack(\"i\", data[o:o+4])[0])\r\n #o += 4 \r\n\r\n header.FlagCooling = int(unpack(\"i\", data[o:o+4])[0])\r\n o += 4\r\n\r\n header.NumFiles = int(unpack(\"i\", data[o:o+4])[0])\r\n o += 4\r\n\r\n header.BoxSize = float(unpack(\"d\", data[o:o+8])[0])\r\n o += 8\r\n #size = header.BoxSize\r\n\r\n header.Omega_0 = float(unpack(\"d\", data[o:o+8])[0])\r\n o += 8\r\n\r\n header.Omega_L = float(unpack(\"d\", data[o:o+8])[0])\r\n o += 8\r\n\r\n header.h = float(unpack(\"d\", data[o:o+8])[0])\r\n o += 8\r\n\r\n header.FlagMultiphase = int(unpack(\"i\", data[o:o+4])[0])\r\n header.FlagStellarAge = int(unpack(\"i\", data[o:o+4])[0])\r\n header.FlagSfrHistogram = int(unpack(\"i\", data[o:o+4])[0])\r\n\r\n header.N = np.sum(header.Npart,dtype=int)\r\n\r\n #fill\r\n o += 84\r\n o += 4\r\n dummy = int(unpack(\"i\", data[o:o+4])[0])\r\n #print(\"dummy = \",dummy)\r\n\r\n #positions\r\n #dummy\r\n o += 4\r\n s = \"%lsf\" % (3*header.N)\r\n #print(s)\r\n x = np.asarray(unpack(s,data[o:o+3*header.N*4]))\r\n #print(\"x info=\",x[0],x.min(),x.max())\r\n x = np.resize(x,(header.N,3))\r\n o += 3*header.N*4\r\n #dummy\r\n o += 4\r\n\r\n #velocity\r\n #dummy\r\n o += 4\r\n s = \"%lsf\" % (3*header.N)\r\n #print(s)\r\n v = np.asarray(unpack(s,data[o:o+3*header.N*4]))\r\n o += 3*header.N*4\r\n #dummy\r\n o += 4\r\n\r\n #ids\r\n #dummy\r\n o += 4\r\n s = \"%lsi\" % (header.N)\r\n #print(s)\r\n #print(4*header.N)\r\n ids = np.asarray(unpack(s,data[o:o+4*header.N]),dtype=int)\r\n o += header.N*4\r\n #dummy\r\n o += 4\r\n\r\n #dummy\r\n #o += 4\r\n #coordinates\r\n #o = 48\r\n #s = \"%lsf\" % (header.nx)\r\n #x = np.asarray(unpack(s,data[o:o+4*header.nx]))\r\n\r\n #print_header(header)\r\n return header, x, v, ids#, z, a, size\r\n\r\ndef print_header(header):\r\n print(\"N = \",header.N)\r\n print(\"Npart = \",header.Npart)\r\n print(\"Mass = \",header.Mass)\r\n #print(header.Npart0)\r\n #print(header.Npart1)\r\n #print(header.Npart2)\r\n #print(header.Npart3)\r\n #print(header.Npart4)\r\n #print(header.Npart5)\r\n #print(header.Mass0)\r\n #print(header.Mass1)\r\n #print(header.Mass2)\r\n #print(header.Mass3)\r\n #print(header.Mass4)\r\n #print(header.Mass5)\r\n print(\"a = \",header.a)\r\n print(\"z = \",header.z)\r\n print(\"FlagSfr = \",header.FlagSfr)\r\n print(\"FlagFeedback = \",header.FlagFeedback)\r\n print(\"Nall = \",header.Nall)\r\n #print(header.Nall1)\r\n #print(header.Nall2)\r\n #print(header.Nall3)\r\n #print(header.Nall4)\r\n #print(header.Nall5)\r\n print(\"FlagCooling = \",header.FlagCooling)\r\n print(\"Numfiles = \",header.NumFiles)\r\n print(\"BoxSize = \",header.BoxSize)\r\n print(\"Omega_0 = \",header.Omega_0)\r\n print(\"Omega_L = \",header.Omega_L)\r\n print(\"h = \",header.h)\r\n print(\"FlagMultiphase = \",header.FlagMultiphase)\r\n print(\"FlagStellarAge = \",header.FlagStellarAge)\r\n print(\"FlagSfrHistogram = \",header.FlagSfrHistogram)\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "read_snapshot.py", "file_name": "read_snapshot.py", "file_ext": "py", "file_size_in_byte": 5178, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.namedtuple", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 13, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 113, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.resize", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 149, "usage_type": "call"}]} +{"seq_id": "71825215", "text": "\"\"\"Helper function for high-throughput GNN trainings.\"\"\"\r\n\"\"\"Implementation based on the template of ALIGNN.\"\"\"\r\nimport matplotlib.pyplot as plt\r\n\r\n# import numpy as np\r\nimport time\r\n# from matformer.train import train_dgl\r\nimport glob\r\nimport os\r\nfrom collections import defaultdict\r\nimport os\r\nimport argparse\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom jarvis.core.atoms import pmg_to_atoms\r\nfrom jarvis.db.jsonutils import dumpjson, loadjson\r\nfrom sklearn.metrics import mean_absolute_error, roc_auc_score\r\nfrom matbench.bench import MatbenchBenchmark\r\nfrom matbench.constants import CLF_KEY\r\nfrom train_on_folder import train_for_folder\r\n\r\nparser = argparse.ArgumentParser(\r\n description=\"Trainer\"\r\n)\r\nparser.add_argument(\"--single_run\", required=False, help=\"specific part of subset.\", default=None)\r\nparser.add_argument(\"--fold\", required=False, help=\"fold.\", default=None)\r\nparser.add_argument(\"--checkpoint\", required=False, help=\"fold.\", default=None)\r\nparser.add_argument(\"--device\", required=False, help=\"device.\", default=\"gpu:0\")\r\nargs = vars(parser.parse_args())\r\nprint(\"Input of argparse:\", args)\r\n\r\nfold_to_run = int(args[\"fold\"]) if args[\"fold\"] is not None else None\r\n# fold_to_run = \"matbench_mp_is_metal\"\r\ncheckpoint_to_use= args[\"checkpoint\"]\r\ndevice_to_use = args[\"device\"]\r\n\r\nif args[\"single_run\"] is None:\r\n subset =[\r\n \"matbench_jdft2d\",\r\n \"matbench_dielectric\",\r\n \"matbench_phonons\",\r\n \"matbench_perovskites\",\r\n \"matbench_log_gvrh\",\r\n \"matbench_log_kvrh\",\r\n \"matbench_mp_e_form\",\r\n \"matbench_mp_gap\",\r\n \"matbench_mp_is_metal\",\r\n ]\r\nelse:\r\n subset = [args[\"single_run\"]]\r\n\r\nmb = MatbenchBenchmark(\r\n autoload=False,\r\n subset=subset\r\n)\r\n\r\n\r\ndef train_tasks(\r\n mb=None, config_template=\"config_example.json\", file_format=\"poscar\", device=\"gpu:0\"\r\n):\r\n \"\"\"Train MatBench clalssification and regression tasks.\"\"\"\r\n for task in mb.tasks:\r\n task.load()\r\n if task.metadata.task_type == CLF_KEY:\r\n classification = True\r\n else:\r\n classification = False\r\n # Classification tasks\r\n if classification:\r\n # rocs = []\r\n for ii, fold in enumerate(task.folds):\r\n if fold_to_run is not None:\r\n if fold_to_run != ii:\r\n continue\r\n train_df = task.get_train_and_val_data(fold, as_type=\"df\")\r\n test_df = task.get_test_data(\r\n fold, include_target=True, as_type=\"df\"\r\n )\r\n train_df[\"is_metal\"] = train_df[\"is_metal\"].astype(float)\r\n test_df[\"is_metal\"] = test_df[\"is_metal\"].astype(float)\r\n # Name of the target property\r\n target = [\r\n col\r\n for col in train_df.columns\r\n if col not in (\"id\", \"structure\", \"composition\")\r\n ][0]\r\n # Making sure there are spaces or parenthesis which\r\n # can cause issue while creating folder\r\n fold_name = (\r\n task.dataset_name\r\n + \"_\"\r\n + target.replace(\" \", \"_\")\r\n .replace(\"(\", \"-\")\r\n .replace(\")\", \"-\")\r\n + \"_fold_\"\r\n + str(ii)\r\n )\r\n if not os.path.exists(fold_name):\r\n os.makedirs(fold_name)\r\n os.chdir(fold_name)\r\n # ALIGNN requires the id_prop.csv file\r\n f = open(\"id_prop.csv\", \"w\")\r\n for jj, j in train_df.iterrows():\r\n id = j.name\r\n atoms = pmg_to_atoms(j.structure)\r\n pos_name = id\r\n atoms.write_poscar(pos_name)\r\n val = j[target]\r\n line = str(pos_name) + \",\" + str(val) + \"\\n\"\r\n f.write(line)\r\n # There is no pre-defined validation splt, so we will use\r\n # a portion of training set as validation set, and\r\n # keep test set intact\r\n val_df = train_df[0 : len(test_df)]\r\n for jj, j in val_df.iterrows():\r\n # for jj, j in test_df.iterrows():\r\n id = j.name\r\n atoms = pmg_to_atoms(j.structure)\r\n pos_name = id\r\n atoms.write_poscar(pos_name)\r\n val = j[target]\r\n line = str(pos_name) + \",\" + str(val) + \"\\n\"\r\n f.write(line)\r\n for jj, j in test_df.iterrows():\r\n id = j.name\r\n atoms = pmg_to_atoms(j.structure)\r\n pos_name = id\r\n atoms.write_poscar(pos_name)\r\n val = j[target]\r\n line = str(pos_name) + \",\" + str(val) + \"\\n\"\r\n f.write(line)\r\n n_train = len(train_df)\r\n n_val = len(val_df)\r\n n_test = len(test_df)\r\n config = loadjson(config_template)\r\n config[\"n_train\"] = n_train\r\n config[\"n_val\"] = n_val\r\n config[\"n_test\"] = n_test\r\n # Just for testing\r\n # config[\"n_train\"] = 500\r\n # config[\"n_val\"] = 100\r\n # config[\"n_test\"] = 100\r\n config[\"keep_data_order\"] = True\r\n config[\"batch_size\"] = 64\r\n config[\"epochs\"] = 50\r\n config[\"classification_threshold\"] = 0.01\r\n config[\"progress\"] = False\r\n config[\"learning_rate\"] = 0.0005\r\n config[\"criterion\"] = \"BCEWithLogitsLoss\"\r\n config[\"dataset\"] = task.dataset_name\r\n config[\"target\"] = \"target\" # target.replace(\" \", \"_\")\r\n fname = \"config_fold_\" + str(ii) + \".json\"\r\n outdir_name = (\r\n task.dataset_name\r\n + \"_\"\r\n + target.replace(\" \", \"_\")\r\n .replace(\"(\", \"-\")\r\n .replace(\")\", \"-\")\r\n + \"_outdir_\"\r\n + str(ii)\r\n )\r\n config[\"output_dir\"] = outdir_name\r\n dumpjson(data=config, filename=fname)\r\n f.close()\r\n os.chdir(\"..\")\r\n cmd = (\r\n \"train_folder.py --root_dir \"\r\n + fold_name\r\n + \" --config \"\r\n + fold_name\r\n + \"/\"\r\n + fname\r\n + \" --file_format=\"\r\n + file_format\r\n + \" --keep_data_order=True\"\r\n + \" --classification_threshold=0.01\"\r\n + \" --output_dir=\"\r\n + outdir_name\r\n )\r\n print(cmd)\r\n # os.system(cmd)\r\n train_for_folder(root_dir=fold_name,\r\n config_name=fold_name + \"/\" + fname,\r\n file_format=file_format,\r\n output_dir=outdir_name,\r\n keep_data_order=True,\r\n classification_threshold=0.01,\r\n restore_checkpoint=checkpoint_to_use,\r\n device=device\r\n )\r\n test_csv = outdir_name + \"/prediction_results_test_set.csv\"\r\n df = pd.read_csv(test_csv)\r\n target_vals = df.target.values\r\n id_vals = df.id.values\r\n\r\n # Regression tasks\r\n # TODO: shorten the script by taking out repetitive lines\r\n if not classification:\r\n maes = []\r\n for ii, fold in enumerate(task.folds):\r\n if fold_to_run is not None:\r\n if fold_to_run != ii:\r\n continue\r\n train_df = task.get_train_and_val_data(fold, as_type=\"df\")\r\n test_df = task.get_test_data(\r\n fold, include_target=True, as_type=\"df\"\r\n )\r\n # Name of the target property\r\n target = [\r\n col\r\n for col in train_df.columns\r\n if col not in (\"id\", \"structure\", \"composition\")\r\n ][0]\r\n # Making sure there are spaces or parenthesis which\r\n # can cause issue while creating folder\r\n fold_name = (\r\n task.dataset_name\r\n + \"_\"\r\n + target.replace(\" \", \"_\")\r\n .replace(\"(\", \"-\")\r\n .replace(\")\", \"-\")\r\n + \"_fold_\"\r\n + str(ii)\r\n )\r\n if not os.path.exists(fold_name):\r\n os.makedirs(fold_name)\r\n os.chdir(fold_name)\r\n # ALIGNN requires the id_prop.csv file\r\n f = open(\"id_prop.csv\", \"w\")\r\n for jj, j in train_df.iterrows():\r\n id = j.name\r\n atoms = pmg_to_atoms(j.structure)\r\n pos_name = id\r\n atoms.write_poscar(pos_name)\r\n val = j[target]\r\n line = str(pos_name) + \",\" + str(val) + \"\\n\"\r\n f.write(line)\r\n # There is no pre-defined validation splt, so we will use\r\n # a portion of training set as validation set, and\r\n # keep test set intact\r\n val_df = train_df[0 : len(test_df)]\r\n for jj, j in val_df.iterrows():\r\n # for jj, j in test_df.iterrows():\r\n id = j.name\r\n atoms = pmg_to_atoms(j.structure)\r\n pos_name = id\r\n atoms.write_poscar(pos_name)\r\n val = j[target]\r\n line = str(pos_name) + \",\" + str(val) + \"\\n\"\r\n f.write(line)\r\n for jj, j in test_df.iterrows():\r\n id = j.name\r\n atoms = pmg_to_atoms(j.structure)\r\n pos_name = id\r\n atoms.write_poscar(pos_name)\r\n val = j[target]\r\n line = str(pos_name) + \",\" + str(val) + \"\\n\"\r\n f.write(line)\r\n n_train = len(train_df)\r\n n_val = len(val_df)\r\n n_test = len(test_df)\r\n config = loadjson(config_template)\r\n config[\"n_train\"] = n_train\r\n config[\"n_val\"] = n_val\r\n config[\"n_test\"] = n_test\r\n config[\"keep_data_order\"] = True\r\n config[\"batch_size\"] = 64\r\n config[\"epochs\"] = 500\r\n config[\"dataset\"] = task.dataset_name\r\n if task.dataset_name == \"matbench_mp_gap\" or task.dataset_name == \"matbench_mp_e_form\":\r\n config[\"learning_rate\"] = 0.0005\r\n config[\"target\"] = \"target\" # target.replace(\" \", \"_\")\r\n fname = \"config_fold_\" + str(ii) + \".json\"\r\n outdir_name = (\r\n task.dataset_name\r\n + \"_\"\r\n + target.replace(\" \", \"_\")\r\n .replace(\"(\", \"-\")\r\n .replace(\")\", \"-\")\r\n + \"_outdir_\"\r\n + str(ii)\r\n )\r\n config[\"output_dir\"] = outdir_name\r\n dumpjson(data=config, filename=fname)\r\n f.close()\r\n os.chdir(\"..\")\r\n cmd = (\r\n \"train_folder.py --root_dir \"\r\n + fold_name\r\n + \" --config \"\r\n + fold_name\r\n + \"/\"\r\n + fname\r\n + \" --file_format=\"\r\n + file_format\r\n + \" --keep_data_order=True\"\r\n + \" --output_dir=\"\r\n + outdir_name\r\n )\r\n print(cmd)\r\n # os.system(cmd)\r\n train_for_folder(root_dir=fold_name,\r\n config_name=fold_name + \"/\" + fname,\r\n file_format=file_format,\r\n output_dir=outdir_name,\r\n keep_data_order=True,\r\n restore_checkpoint=checkpoint_to_use,\r\n device=device\r\n )\r\n test_csv = outdir_name + \"/prediction_results_test_set.csv\"\r\n df = pd.read_csv(test_csv)\r\n target_vals = df.target.values\r\n # id_vals = df.id.values\r\n pred_vals = df.prediction.values\r\n mae = mean_absolute_error(target_vals, pred_vals)\r\n maes.append(mae)\r\n task.record(fold, pred_vals, params=config)\r\n print(\r\n \"Dataset_name, Fold, MAE=\",\r\n task.dataset_name,\r\n fold,\r\n mean_absolute_error(target_vals, pred_vals),\r\n )\r\n maes = np.array(maes)\r\n print(maes, np.mean(maes), np.std(maes))\r\n print()\r\n print()\r\n print()\r\n\r\n\r\ndef compile_results(key=\"matbench_phonons\", regression=True):\r\n \"\"\"Compile fold based results for each task.\"\"\"\r\n # Some of the jobs such as mp_e_form takes a couple of\r\n # days to complete for each fold\r\n # so we compile the results as follows\r\n maes = []\r\n roc_aucs = []\r\n results = defaultdict()\r\n\r\n for i in glob.glob(key + \"*/prediction_results_test_set.csv\"):\r\n fold = int(os.path.split(i)[0].split(\"_\")[-1])\r\n # fold = int(i.split(\"/\")[0].split(\"_\")[-1])\r\n # print (i,fold)\r\n df = pd.read_csv(i)\r\n\r\n target_vals = df.target.values\r\n # id_vals = df.id.values\r\n pred_vals = df.prediction.values\r\n if regression:\r\n mae = mean_absolute_error(target_vals, pred_vals)\r\n maes.append(mae)\r\n print(\"MAE\", fold, mae)\r\n if not regression:\r\n roc = roc_auc_score(target_vals, pred_vals)\r\n roc_aucs.append(roc)\r\n print(\"ROC\", fold, roc)\r\n # We changed the predictions to sigmoid.\r\n # pred_vals = [True if i == 1 else False for i in pred_vals]\r\n results[fold] = pred_vals\r\n\r\n if regression:\r\n maes = np.array(maes)\r\n print(key, maes, np.mean(maes), np.std(maes))\r\n if not regression:\r\n roc_aucs = np.array(roc_aucs)\r\n print(key, roc_aucs, np.mean(roc_aucs), np.std(roc_aucs))\r\n return results\r\n\r\nrun_training = True\r\n\r\nif __name__ == \"__main__\":\r\n config_template = os.path.abspath(\r\n os.path.join(os.path.dirname(__file__), \"config_example.json\")\r\n )\r\n config = loadjson(config_template)\r\n if run_training:\r\n train_tasks(mb=mb, config_template=config_template, file_format=\"poscar\", device=device_to_use)\r\n\r\n run_dir = \"../matbench\"\r\n\r\n cwd = os.getcwd()\r\n\r\n os.chdir(run_dir)\r\n\r\n results = defaultdict()\r\n for task in mb.tasks:\r\n task.load()\r\n task_name = task.dataset_name\r\n regr = True\r\n if \"is\" in task_name:\r\n regr = False\r\n results = compile_results(task_name, regression=regr)\r\n for ii, fold in enumerate(task.folds):\r\n train_df = task.get_train_and_val_data(fold, as_type=\"df\")\r\n test_df = task.get_test_data(\r\n fold, include_target=True, as_type=\"df\"\r\n )\r\n pred_vals = results[fold]\r\n task.record(fold, pred_vals, params=config)\r\n os.chdir(cwd)\r\n mb.add_metadata({\"algorithm\": \"Matformer\"})\r\n mb.to_file(\"results.json.gz\")\r\n\r\n\r\nfor key, values in mb.scores.items():\r\n factor = 1000.0 if key in [\"matbench_mp_e_form\", \"matbench_mp_gap\", \"matbench_perovskites\"] else 1.0\r\n if key not in [\"matbench_mp_is_metal\"]:\r\n print(key, factor*values[\"mae\"][\"mean\"], factor*values[\"mae\"][\"std\"])\r\n else:\r\n print(key, values[\"rocauc\"][\"mean\"], values[\"rocauc\"][\"std\"])", "sub_path": "benchmarks/matbench_v0.1_matformer/train_matbench.py", "file_name": "train_matbench.py", "file_ext": "py", "file_size_in_byte": 16483, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 22, "usage_type": "call"}, {"api_name": "matbench.bench.MatbenchBenchmark", "line_number": 52, "usage_type": "call"}, {"api_name": "matbench.constants.CLF_KEY", "line_number": 64, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 99, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 100, "usage_type": "call"}, {"api_name": "jarvis.core.atoms.pmg_to_atoms", "line_number": 105, "usage_type": "call"}, {"api_name": "jarvis.core.atoms.pmg_to_atoms", "line_number": 118, "usage_type": "call"}, {"api_name": "jarvis.core.atoms.pmg_to_atoms", "line_number": 126, "usage_type": "call"}, {"api_name": "jarvis.db.jsonutils.loadjson", "line_number": 135, "usage_type": "call"}, {"api_name": "jarvis.db.jsonutils.dumpjson", "line_number": 163, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 165, "usage_type": "call"}, {"api_name": "train_on_folder.train_for_folder", "line_number": 182, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 225, "usage_type": "call"}, {"api_name": "os.path", "line_number": 225, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 226, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 227, "usage_type": "call"}, {"api_name": "jarvis.core.atoms.pmg_to_atoms", "line_number": 232, "usage_type": "call"}, {"api_name": "jarvis.core.atoms.pmg_to_atoms", "line_number": 245, "usage_type": "call"}, {"api_name": "jarvis.core.atoms.pmg_to_atoms", "line_number": 253, "usage_type": "call"}, {"api_name": "jarvis.db.jsonutils.loadjson", "line_number": 262, "usage_type": "call"}, {"api_name": "jarvis.db.jsonutils.dumpjson", "line_number": 284, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 286, "usage_type": "call"}, {"api_name": "train_on_folder.train_for_folder", "line_number": 302, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 311, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 315, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 322, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 325, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 325, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 338, "usage_type": "call"}, {"api_name": "glob.glob", "line_number": 340, "usage_type": "call"}, {"api_name": "os.path.split", "line_number": 341, "usage_type": "call"}, {"api_name": "os.path", "line_number": 341, "usage_type": "attribute"}, {"api_name": "pandas.read_csv", "line_number": 344, "usage_type": "call"}, {"api_name": "sklearn.metrics.mean_absolute_error", "line_number": 350, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_auc_score", "line_number": 354, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 362, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 363, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 363, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 365, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 366, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 366, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 372, "usage_type": "call"}, {"api_name": "os.path", "line_number": 372, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 373, "usage_type": "call"}, {"api_name": "os.path", "line_number": 373, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 373, "usage_type": "call"}, {"api_name": "jarvis.db.jsonutils.loadjson", "line_number": 375, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 381, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 383, "usage_type": "call"}, {"api_name": "collections.defaultdict", "line_number": 385, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 400, "usage_type": "call"}]} +{"seq_id": "54131022", "text": "# keras import\nimport tensorflow as tf\nimport numpy as np\nimport os\nnp.random.seed(42)\nfrom keras.callbacks import Callback, LambdaCallback\nfrom keras.models import Model\nfrom keras.layers.normalization import BatchNormalization\nfrom keras.layers import Input, Dense, Dropout, Activation, Flatten\nfrom keras.layers import Convolution2D, MaxPooling2D\nfrom keras.engine.topology import Layer\nfrom keras.utils import np_utils\nfrom keras import backend as K\nfrom keras.models import load_model\n# big boy utils\nfrom utils.configuration import *\nfrom utils.load_data import *\nfrom utils.dataset import *\nfrom utils.preprocessing import *\nfrom utils.model import *\nfrom utils.reporting import *\nfrom utils.visualization import *\nfrom utils.pca_tsne import *\nfrom utils.load_preprocessed import *\n# sklearn\nfrom sklearn.model_selection import train_test_split as tts\nfrom sklearn.preprocessing import LabelEncoder\nfrom sklearn.preprocessing import OneHotEncoder\nenc = OneHotEncoder()\nlab_enc = LabelEncoder()\n\ninput(\"Press Enter to continue.\")\n\n# Get raw data\nall_images1, all_labels1, all_bottles1 = load_preprocessed_data(corpus_dir, bottle_dir, img_dims)\n\ntrained_dir_2 = \"/mnt/data/corpi/gaussian_masked_clr_20_50/\"\ncorpus_dir_2 = os.path.join(trained_dir_2, \"corpus\")\nbottle_dir_2 = os.path.join(trained_dir_2, \"bottleneck\")\n# Get Raw data 2\nall_images2, all_labels2, all_bottles2 = load_preprocessed_data(corpus_dir_2,\n bottle_dir_2, img_dims)\n\n# Split Data into train_test\ni_train1, i_test1, b_train1, b_test1, l_train1, l_test1 = tts(all_images1, all_bottles1,\n all_labels1, test_size=0.30,\n stratify=all_labels1, random_state=42)\n\ni_train2, i_test2, b_train2, b_test2, l_train2, l_test2 = tts(all_images2, all_bottles2,\n all_labels2, test_size=0.30,\n stratify=all_labels2, random_state=42)\nimgs_train = np.vstack((i_train1, i_train2))\nbots_train = np.vstack((b_train1, b_train2))\nlab_train = np.concatenate((l_train1, l_train2), axis=0)\n#Get test images\nimgs_test = np.vstack((i_test1, i_test2))\nbots_test = np.vstack((b_test1, b_test2))\nlab_test = np.concatenate((l_test1, l_test2), axis=0)\n\n# Perform one-hot encoding on all labels\nlab_train_le = lab_enc.fit_transform(lab_train)\nlab_train_ohe = enc.fit_transform(lab_train_le.reshape(-1,1)).toarray()\nlab_test_le = lab_enc.fit_transform(lab_test)\nlab_test_ohe = enc.fit_transform(lab_test_le.reshape(-1,1)).toarray()\n\n#Split train into train and validation\nimgs_val, imgs_test, bots_val, bots_test, lab_val_ohe, lab_test_ohe = tts(imgs_test, bots_test,\n lab_test_ohe, test_size=0.50,\n stratify=lab_test_ohe, random_state=42)\n\nprint(imgs_train.shape)\nprint(imgs_val.shape)\nprint(imgs_test.shape)\nprint(lab_train_ohe.shape)\nprint(lab_val_ohe.shape)\nprint(lab_test_ohe.shape)\n\ninput(\"Press Enter to continue.\")\n\n# Make a Scheduler:\nepoch_count = K.variable(0)\nbeta = K.variable(1.)\nclass RegScheduler(Callback):\n def __init__(self, beta, epoch_count):\n self.beta = beta\n self.epoch_count = epoch_count\n def on_epoch_begin(self, epoch, logs={}):\n K.set_value(self.epoch_count, epoch)\n def on_epoch_end(self, epoch, logs={}):\n max_epoch= 70\n power = 4\n stop = 0\n K.set_value(self.beta, ((1-(epoch/max_epoch)) ** power ) * (1-stop) + stop )\n print('---current beta: %.3f' % K.get_value(beta))\n\n# checkpoint load\nmodel_dir = \"./mdl/\"\nif not os.path.isdir(model_dir):\n os.makedirs(model_dir)\n\nmodel_path = os.path.join(model_dir, 'model_noreg_150.h5')\nif os.path.exists(model_path):\n print('Loading model...')\n #for name in glob.glob('./mdl/model?.txt'):\n model = load_model(model_path)\nelse:\n print('Building Model..')\n # Make the layers:\n inputs1 = Input(shape=(150, 150, 3))\n\n x = Convolution2D(32, (3, 3), padding='same', name='c0')(inputs1)\n x = BatchNormalization(name='c0_bn')(x)\n x = Activation('relu', name='c0_act')(x)\n x = MaxPooling2D(pool_size=(2, 2), name='c0_max')(x)\n\n x = Convolution2D(32, (3, 3), padding='same', name='c1')(x)\n x = BatchNormalization(name='c1_bn')(x)\n x = Activation('relu', name='c1_act')(x)\n x = MaxPooling2D(pool_size=(2, 2), name='c1_max')(x)\n\n x = Convolution2D(32, (3, 3), padding='same', name='c2')(x)\n x = BatchNormalization(name='c2_bn')(x)\n x = Activation('relu', name='c2_act')(x)\n x = MaxPooling2D(pool_size=(2, 2), name='c2_max')(x)\n\n x = Flatten(name='flat_0')(x)\n\n x = Dense(2048, name='fc_0')(x)\n x = BatchNormalization(name='fc_0_bn')(x)\n x = Activation('sigmoid', name='fc_0_act')(x)\n x = Dropout(0.7, name='fc_0_drop')(x)\n\n x = Dense(2048, name='fc_1')(x)\n x = BatchNormalization(name='fc_1_bn')(x)\n x = Activation('sigmoid', name='fc_1_act')(x)\n\n x = Dense(20, name='fc_2')(x)\n prediction = Activation('softmax')(x)\n\n model = Model(inputs=[inputs1], outputs=[prediction])\n\n model.compile(loss='categorical_crossentropy',\n optimizer='adam', metrics=['accuracy'])\n\nprint('layers made!')\n#model.summary()\n\ninput(\"Press Enter to continue.\")\n# Training:\ntry:\n model.fit([imgs_train], lab_train_ohe,\n batch_size=50, epochs=80, verbose=1,\n validation_data=([imgs_val], lab_val_ohe),\n callbacks=[RegScheduler(beta=beta, epoch_count=epoch_count)])\n\n score, accuracy = model.evaluate([imgs_test], lab_test_ohe, batch_size=100, verbose=0)\n print('Test score:', score)\n print('Test accuracy:', accuracy)\n\n epc_count= int(K.get_value(epoch_count))+1\n file_name = 'model2_noreg_'+str(epc_count)+'.h5'\n model_path_save = os.path.join(model_dir, file_name)\n model.save(model_path_save)\n\n # Layers sizes\n input(\"Press Enter to continue.\")\n bot_lay_size = 2048\n n_train_imgs = imgs_train.shape[0]\n n_test_imgs = imgs_test.shape[0]\n n_classes = lab_train_ohe.shape[1]\n print('train images', n_train_imgs)\n print('test images', n_test_imgs)\n\n # backend function to accesss values from bottle layer\n bottle_tensor_func = K.function([model.layers[0].input, K.learning_phase()],\n [model.get_layer('fc_1_act').output])\n #set up np.array to store values for all images\n bottle_tensor_train = np.zeros(shape=(n_train_imgs, bot_lay_size))\n bottle_labels_train = np.zeros(shape=(n_train_imgs, n_classes))\n bottle_tensor_test = np.zeros(shape=(n_test_imgs, bot_lay_size))\n bottle_labels_test = np.zeros(shape=(n_test_imgs, n_classes))\n\n def batcher(X_train, y_train, size):\n X_batch = [X_train[indx:indx + size] for indx in range(0, len(X_train), size)]\n y_batch = [y_train[indx:indx + size] for indx in range(0, len(y_train), size)]\n return zip(X_batch, y_batch)\n\n counter = 0\n bot_batch = 20\n # get train set bottleneck activation values:\n for batch_x, batch_y in batcher(imgs_train, lab_train_ohe, bot_batch):\n bot_train = bottle_tensor_func([batch_x, 0])[0]\n bottle_tensor_train[counter:counter+bot_batch] = bot_train\n bottle_labels_train[counter:counter+bot_batch] = batch_y\n counter += bot_batch\n\n # get test set bottleneck activation values:\n counter = 0\n bot_batch = 20\n for batch_x, batch_y in batcher(imgs_test, lab_test_ohe, bot_batch):\n bot_train = bottle_tensor_func([batch_x, 0])[0]\n bottle_tensor_test[counter:counter+bot_batch] = bot_train\n bottle_labels_test[counter:counter+bot_batch] = batch_y\n counter += bot_batch\n # stack values in signle np.array\n final_bottle = np.vstack((bottle_tensor_train, bottle_tensor_test))\n final_labels = np.vstack((bottle_labels_train, bottle_labels_test))\n\n tsne_output2(final_bottle, final_labels, 30, 5000, n_train_imgs, filename='merge_tsne.png')\n K.clear_session()\n\nexcept KeyboardInterrupt:\n score, accuracy = model.evaluate([imgs_test1], lab_test_ohe, batch_size=50, verbose=0)\n print('Test score:', score)\n print('Test accuracy:', accuracy)\n epc_count= int(K.get_value(epoch_count))+1\n file_name = 'model2_noreg_'+str(epc_count)+'.h5'\n model_path_save = os.path.join(model_dir, file_name)\n model.save(model_path_save)\n K.clear_session()\n", "sub_path": "bb_sk_corp2_noreg.py", "file_name": "bb_sk_corp2_noreg.py", "file_ext": "py", "file_size_in_byte": 8554, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.random.seed", "line_number": 5, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 5, "usage_type": "attribute"}, {"api_name": "sklearn.preprocessing.OneHotEncoder", "line_number": 29, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 38, "usage_type": "call"}, {"api_name": "os.path", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 45, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 57, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 58, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.backend.variable", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 81, "usage_type": "name"}, {"api_name": "keras.backend.variable", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 82, "usage_type": "name"}, {"api_name": "keras.callbacks.Callback", "line_number": 83, "usage_type": "name"}, {"api_name": "keras.backend.set_value", "line_number": 88, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 88, "usage_type": "name"}, {"api_name": "keras.backend.set_value", "line_number": 93, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 93, "usage_type": "name"}, {"api_name": "keras.backend.get_value", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 94, "usage_type": "name"}, {"api_name": "os.path.isdir", "line_number": 98, "usage_type": "call"}, {"api_name": "os.path", "line_number": 98, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 99, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 101, "usage_type": "call"}, {"api_name": "os.path", "line_number": 101, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 102, "usage_type": "call"}, {"api_name": "os.path", "line_number": 102, "usage_type": "attribute"}, {"api_name": "keras.models.load_model", "line_number": 105, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 109, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 111, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 112, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 113, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 114, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 116, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 117, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 118, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 119, "usage_type": "call"}, {"api_name": "keras.layers.Convolution2D", "line_number": 121, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 122, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 123, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 124, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 126, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 128, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 129, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 130, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 131, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 133, "usage_type": "call"}, {"api_name": "keras.layers.normalization.BatchNormalization", "line_number": 134, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 135, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 137, "usage_type": "call"}, {"api_name": "keras.layers.Activation", "line_number": 138, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 140, "usage_type": "call"}, {"api_name": "keras.backend.get_value", "line_number": 160, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 160, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 162, "usage_type": "call"}, {"api_name": "os.path", "line_number": 162, "usage_type": "attribute"}, {"api_name": "keras.backend.function", "line_number": 175, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 175, "usage_type": "name"}, {"api_name": "keras.backend.learning_phase", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 178, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 179, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 180, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 181, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 207, "usage_type": "call"}, {"api_name": "keras.backend.clear_session", "line_number": 210, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 210, "usage_type": "name"}, {"api_name": "keras.backend.get_value", "line_number": 216, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 216, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 218, "usage_type": "call"}, {"api_name": "os.path", "line_number": 218, "usage_type": "attribute"}, {"api_name": "keras.backend.clear_session", "line_number": 220, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 220, "usage_type": "name"}]} +{"seq_id": "35381860", "text": "# -*- coding: utf-8 -*-\r\n\r\n\"\"\"\r\n 作者: 顾志文\r\n 日期: 2020/03/01\r\n 项目名称：识别Twitter用户性别 (Twitter User Gender Classification)\r\n Kaggle地址：https://www.kaggle.com/crowdflower/twitter-user-gender-classification\r\n\"\"\"\r\nfrom skimage import io\r\nimport os\r\nimport re\r\nfrom nltk.corpus import stopwords\r\nfrom nltk.tokenize import RegexpTokenizer\r\nimport pandas as pd\r\nimport math\r\nimport numpy as np\r\nfrom skimage import exposure, img_as_float\r\n\r\n\r\n# 头像图片保存路径\r\nprofile_image_path = './pro_img/'\r\n\r\n\r\ndef inspect_dataset(df_data):\r\n \"\"\"pytoho\r\n 查看加载的数据基本信息\r\n \"\"\"\r\n print('数据集基本信息：')\r\n print(df_data.info())\r\n print('数据集有{}行，{}列'.format(df_data.shape[0], df_data.shape[1]))\r\n print('数据预览:')\r\n print(df_data.head())\r\n\r\n\r\ndef check_profile_image(img_link):\r\n \"\"\"\r\n 判断头像图片链接是否有效\r\n 如果有效，下载到本地，并且返回保存路径\r\n \"\"\"\r\n save_image_path = ''\r\n # 有效的图片扩展名\r\n valid_img_ext_lst = ['.jpeg', '.png', '.jpg']\r\n\r\n try:\r\n img_data = io.imread(img_link)\r\n image_name = img_link.rsplit('/')[-1]\r\n if any(valid_img_ext in image_name.lower() for valid_img_ext in valid_img_ext_lst):\r\n # 确保图片文件包含有效的扩展名\r\n save_image_path = os.path.join(profile_image_path, image_name)\r\n io.imsave(save_image_path, img_data)\r\n except:\r\n print('头像链接 {} 无效'.format(img_link))\r\n\r\n return save_image_path\r\n\r\n\r\ndef clean_text(text):\r\n \"\"\"\r\n 清洗文本数据\r\n \"\"\"\r\n # just in case\r\n text = text.lower()\r\n\r\n # 去除特殊字符\r\n text = re.sub('\\s\\W', ' ', text)\r\n text = re.sub('\\W\\s', ' ', text)\r\n text = re.sub('\\s+', ' ', text)\r\n\r\n return text\r\n\r\n\r\ndef split_train_test(df_data, size=0.8):\r\n \"\"\"\r\n 分割训练集和测试集\r\n \"\"\"\r\n # 为保证每个类中的数据能在训练集中和测试集中的比例相同，所以需要依次对每个类进行处理\r\n df_train = pd.DataFrame()\r\n df_test = pd.DataFrame()\r\n\r\n labels = [0, 1]\r\n for label in labels:\r\n # 找出gender的记录\r\n text_df_w_label = df_data[df_data['label'] == label]\r\n # 重新设置索引，保证每个类的记录是从0开始索引，方便之后的拆分\r\n text_df_w_label = text_df_w_label.reset_index()\r\n\r\n # 默认按80%训练集，20%测试集分割\r\n # 这里为了简化操作，取前80%放到训练集中，后20%放到测试集中\r\n # 当然也可以随机拆分80%，20%（尝试实现下DataFrame中的随机拆分）\r\n\r\n # 该类数据的行数\r\n n_lines = text_df_w_label.shape[0]\r\n split_line_no = math.floor(n_lines * size)\r\n text_df_w_label_train = text_df_w_label.iloc[:split_line_no, :]\r\n text_df_w_label_test = text_df_w_label.iloc[split_line_no:, :]\r\n\r\n # 放入整体训练集，测试集中\r\n df_train = df_train.append(text_df_w_label_train)\r\n df_test = df_test.append(text_df_w_label_test)\r\n\r\n df_train = df_train.reset_index()\r\n df_test = df_test.reset_index()\r\n return df_train, df_test\r\n\r\n\r\ndef get_word_list_from_data(text_s):\r\n \"\"\"\r\n 将数据集中的单词放入到一个列表中\r\n \"\"\"\r\n word_list = []\r\n for _, text in text_s.iteritems():\r\n word_list += text.split(' ')\r\n return word_list\r\n\r\n\r\ndef proc_text(text):\r\n \"\"\"\r\n 分词+去除停用词\r\n \"\"\"\r\n tokenizer = RegexpTokenizer(r'\\w+')\r\n words = tokenizer.tokenize(text)\r\n filtered_words = [word for word in words if word not in stopwords.words('english')]\r\n return \" \".join(filtered_words)\r\n\r\n\r\ndef extract_tf_idf(text_s, text_collection, common_words_freqs):\r\n \"\"\"\r\n 提取tf-idf特征\r\n \"\"\"\r\n # 这里只选择TF-IDF特征作为例子\r\n # 可考虑使用词频或其他文本特征作为额外的特征\r\n\r\n n_sample = text_s.shape[0]\r\n n_feat = len(common_words_freqs)\r\n\r\n common_words = [word for word, _ in common_words_freqs]\r\n\r\n # 初始化\r\n X = np.zeros([n_sample, n_feat])\r\n\r\n print('提取tf-idf特征...')\r\n for i, text in text_s.iteritems():\r\n feat_vec = []\r\n for word in common_words:\r\n if word in text:\r\n # 如果在高频词中，计算TF-IDF值\r\n tf_idf_val = text_collection.tf_idf(word, text)\r\n else:\r\n tf_idf_val = 0\r\n\r\n feat_vec.append(tf_idf_val)\r\n\r\n # 赋值\r\n X[i, :] = np.array(feat_vec)\r\n\r\n return X\r\n\r\n\r\ndef hex_to_rgb(value):\r\n \"\"\"\r\n 十六进制颜色码转换为RGB值\r\n \"\"\"\r\n rgb_list = list(int(value[i:i + 2], 16) for i in range(0, 6, 2))\r\n return rgb_list\r\n\r\n\r\ndef extract_rgb_feat(hex_color_s):\r\n \"\"\"\r\n 从十六进制颜色码中提取RGB值作为特征\r\n \"\"\"\r\n n_sample = hex_color_s.shape[0]\r\n n_feat = 3\r\n\r\n # 初始化\r\n X = np.zeros([n_sample, n_feat])\r\n\r\n print('提取RGB特征...')\r\n for i, hex_val in hex_color_s.iteritems():\r\n feat_vec = hex_to_rgb(hex_val)\r\n\r\n # 赋值\r\n X[i, :] = np.array(feat_vec)\r\n\r\n return X\r\n\r\n\r\ndef extract_rgb_hist_feat(img_path_s):\r\n \"\"\"\r\n 从图像中提取RGB直方图特征\r\n \"\"\"\r\n n_sample = img_path_s.shape[0]\r\n n_bins = 100 # 每个通道bin的个数\r\n n_feat = n_bins * 3\r\n\r\n # 初始化\r\n X = np.zeros([n_sample, n_feat])\r\n\r\n print('提取RGB直方图特征...')\r\n for i, img_path in img_path_s.iteritems():\r\n # 加载图像\r\n img_data = io.imread(img_path)\r\n img_data = img_as_float(img_data)\r\n\r\n if img_data.ndim == 3:\r\n # 3个通道\r\n hist_r, _ = exposure.histogram(img_data[:, :, 0], nbins=n_bins)\r\n hist_g, _ = exposure.histogram(img_data[:, :, 1], nbins=n_bins)\r\n hist_b, _ = exposure.histogram(img_data[:, :, 2], nbins=n_bins)\r\n else:\r\n # 2个通道\r\n hist, _ = exposure.histogram(img_data, nbins=n_bins)\r\n hist_r = hist.copy()\r\n hist_g = hist.copy()\r\n hist_b = hist.copy()\r\n\r\n feat_vec = np.concatenate((hist_r, hist_b, hist_g))\r\n\r\n # 赋值\r\n X[i, :] = np.array(feat_vec)\r\n\r\n return X\r\n", "sub_path": "pd_tools.py", "file_name": "pd_tools.py", "file_ext": "py", "file_size_in_byte": 6406, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "skimage.io.imread", "line_number": 45, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 45, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 49, "usage_type": "call"}, {"api_name": "os.path", "line_number": 49, "usage_type": "attribute"}, {"api_name": "skimage.io.imsave", "line_number": 50, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 50, "usage_type": "name"}, {"api_name": "re.sub", "line_number": 65, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 66, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 67, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 77, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 78, "usage_type": "call"}, {"api_name": "math.floor", "line_number": 93, "usage_type": "call"}, {"api_name": "nltk.tokenize.RegexpTokenizer", "line_number": 120, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords.words", "line_number": 122, "usage_type": "call"}, {"api_name": "nltk.corpus.stopwords", "line_number": 122, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 139, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 154, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 196, "usage_type": "call"}, {"api_name": "skimage.io.imread", "line_number": 201, "usage_type": "call"}, {"api_name": "skimage.io", "line_number": 201, "usage_type": "name"}, {"api_name": "skimage.img_as_float", "line_number": 202, "usage_type": "call"}, {"api_name": "skimage.exposure.histogram", "line_number": 206, "usage_type": "call"}, {"api_name": "skimage.exposure", "line_number": 206, "usage_type": "name"}, {"api_name": "skimage.exposure.histogram", "line_number": 207, "usage_type": "call"}, {"api_name": "skimage.exposure", "line_number": 207, "usage_type": "name"}, {"api_name": "skimage.exposure.histogram", "line_number": 208, "usage_type": "call"}, {"api_name": "skimage.exposure", "line_number": 208, "usage_type": "name"}, {"api_name": "skimage.exposure.histogram", "line_number": 211, "usage_type": "call"}, {"api_name": "skimage.exposure", "line_number": 211, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 216, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 219, "usage_type": "call"}]} +{"seq_id": "363234558", "text": "from django.shortcuts import render, redirect\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\nfrom django.contrib.auth.decorators import login_required\nfrom django.http import HttpResponse, Http404, JsonResponse\nfrom django.contrib.auth.models import User\nfrom django.db.models import Q, Count\nfrom django.conf import settings\nfrom django.utils import translation\nfrom ..models import Occupation, Artist, Genre, Movie, Profile, MovieRating, Series\nfrom datetime import date\nimport random\n\n## Additional functions\n\ndef is_valid_queryparam(param):\n return param != '' and param is not None\n\ndef getMovieRating(movie_id):\n movie = Movie.objects.get(pk=movie_id)\n ratings = MovieRating.objects.filter(movie=movie)\n count = ratings.count()\n average = 0\n if count > 0:\n sum = 0\n for r in ratings:\n sum += r.rating\n average = sum / count\n return average\n\ndef calculate_age(born):\n today = date.today()\n return today.year - born.year - ((today.month, today.day) < (born.month, born.day))\n\ndef getBirthdate(age):\n today = date.today()\n year = today.year - age\n return date(year, today.month, today.day)\n\n\n## Main views\n\ndef home(request):\n # user_language = 'mn'\n # translation.activate(user_language)\n # request.session[translation.LANGUAGE_SESSION_KEY] = user_language\n latestmovies = Movie.objects.all().order_by('-created_at')[:4]\n latestseries = Series.objects.all().order_by('-created_at')[:4]\n suggestedmovie = random.choice(latestmovies)\n suggestedseries = Series.objects.latest('created_at')\n count_movie = Movie.objects.all().count()\n count_series = Series.objects.all().count()\n count_artist = Artist.objects.all().count()\n # topratedmovies = Movie.objects.all().order_by('-imdb_rating')[:4] \n # mostlikedmovies = Movie.objects.annotate(count_liked=Count('liked_movies')).order_by('-count_liked')[:6]\n # mostwatchedmovies = Movie.objects.annotate(count_watched=Count('moviewatchedlist')).order_by('-count_watched')[:6]\n profile = None\n if request.user.is_authenticated:\n profile = Profile.objects.get(user=request.user)\n context = {\n 'latestmovies': latestmovies,\n 'latestseries': latestseries,\n 'suggestedmovie': suggestedmovie,\n 'suggestedseries': suggestedseries,\n 'count_movie': count_movie,\n 'count_series': count_series,\n 'count_artist': count_artist,\n # 'mostlikedmovies': mostlikedmovies,\n # 'mostwatchedmovies': mostwatchedmovies,\n 'profile': profile\n }\n return render(request, 'home.html', context) \n\n@login_required\ndef profile(request):\n profile = Profile.objects.get(user=request.user) \n movie_favorite = profile.movie_favorite.order_by('name') \n movie_watched = profile.movie_watched.order_by('name')\n movie_watchlist = profile.movie_watchlist.order_by('name')\n context = {\n 'profile': profile,\n 'movie_favorite': movie_favorite,\n 'movie_watched': movie_watched,\n 'movie_watchlist': movie_watchlist\n }\n return render(request, 'profile.html', context)\n", "sub_path": "movies/views/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3153, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "models.Movie.objects.get", "line_number": 19, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 19, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 19, "usage_type": "name"}, {"api_name": "models.MovieRating.objects.filter", "line_number": 20, "usage_type": "call"}, {"api_name": "models.MovieRating.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.MovieRating", "line_number": 20, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 31, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 31, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 35, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 35, "usage_type": "name"}, {"api_name": "datetime.date", "line_number": 37, "usage_type": "call"}, {"api_name": "models.Movie.objects.all", "line_number": 46, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 46, "usage_type": "name"}, {"api_name": "models.Series.objects.all", "line_number": 47, "usage_type": "call"}, {"api_name": "models.Series.objects", "line_number": 47, "usage_type": "attribute"}, {"api_name": "models.Series", "line_number": 47, "usage_type": "name"}, {"api_name": "random.choice", "line_number": 48, "usage_type": "call"}, {"api_name": "models.Series.objects.latest", "line_number": 49, "usage_type": "call"}, {"api_name": "models.Series.objects", "line_number": 49, "usage_type": "attribute"}, {"api_name": "models.Series", "line_number": 49, "usage_type": "name"}, {"api_name": "models.Movie.objects.all", "line_number": 50, "usage_type": "call"}, {"api_name": "models.Movie.objects", "line_number": 50, "usage_type": "attribute"}, {"api_name": "models.Movie", "line_number": 50, "usage_type": "name"}, {"api_name": "models.Series.objects.all", "line_number": 51, "usage_type": "call"}, {"api_name": "models.Series.objects", "line_number": 51, "usage_type": "attribute"}, {"api_name": "models.Series", "line_number": 51, "usage_type": "name"}, {"api_name": "models.Artist.objects.all", "line_number": 52, "usage_type": "call"}, {"api_name": "models.Artist.objects", "line_number": 52, "usage_type": "attribute"}, {"api_name": "models.Artist", "line_number": 52, "usage_type": "name"}, {"api_name": "models.Profile.objects.get", "line_number": 58, "usage_type": "call"}, {"api_name": "models.Profile.objects", "line_number": 58, "usage_type": "attribute"}, {"api_name": "models.Profile", "line_number": 58, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 71, "usage_type": "call"}, {"api_name": "models.Profile.objects.get", "line_number": 75, "usage_type": "call"}, {"api_name": "models.Profile.objects", "line_number": 75, "usage_type": "attribute"}, {"api_name": "models.Profile", "line_number": 75, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 85, "usage_type": "call"}, {"api_name": "django.contrib.auth.decorators.login_required", "line_number": 73, "usage_type": "name"}]} +{"seq_id": "506966488", "text": "import eel\nimport time\nimport serial\nimport time\nimport argparse\n\neel.init('web')\n\n@eel.expose\ndef getTime():\n return time.strftime('%c')\n\n@eel.expose \ndef enviarGcode(gcode):\n\ts = serial.Serial('/dev/ttyUSB0',9600,timeout=5)\n\ttime.sleep(2) \n\t#s = serial.Serial('/dev/ttyACM0',9600)\n\tprint ( 'Abrindo porta serial' )\n\t\n\tf = open(gcode,'r') \n\tprint ( 'Abrindo gcode' ) \n\ttime.sleep(3) \t\t\t\t\t # Wait for Printrbot to initialize\n\ts.flushInput()\n\ts.flushOutput() \t\t\t\t\t # Flush startup text in serial input\n\tprint ( 'Enviando gcode' )\n\t\n\tfor line in f:\n\t\tl = line\n\t\tl = l.rstrip('\\r\\n')\t\t\t\t\t # Strip all EOL characters for streaming\n\t\tif (len(l)>0) :\n\t\t\tfor b in l: \t\t\t\t\t\t\t\t\n\t\t\t\td = bytearray(b'b')\n\t\t\t\ts.write(b)\n\t\t\t\ts.flush()\n\t\t\t\ts.flushInput()\n\t\t\t\ts.flushOutput() \n\t\t\t\t\t\n\t\ts.flush() \n\t\tgrbl_out = s.readline()\n\t\twhile(b'done' not in grbl_out): \n\t\t\tgrbl_out = s.readline()\n\t\t\ttime.sleep(2)\n\t\t\tprint ( ' : ' + grbl_out )\n\t\t\t\n\t\ts.flushInput()\n\t\ts.flushOutput()\n\t\n\t# Close file and serial port\n\tf.close()\n\ts.close()\t\t\n\neel.start('main.html')\n", "sub_path": "EnviarGcode/server.py", "file_name": "server.py", "file_ext": "py", "file_size_in_byte": 1050, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "eel.init", "line_number": 7, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 11, "usage_type": "call"}, {"api_name": "eel.expose", "line_number": 9, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 15, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 16, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 22, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 42, "usage_type": "call"}, {"api_name": "eel.expose", "line_number": 13, "usage_type": "attribute"}, {"api_name": "eel.start", "line_number": 52, "usage_type": "call"}]} +{"seq_id": "436188265", "text": "from spacy import displacy\nimport streamlit as st\nimport requests\nimport json\nimport pandas as pd\nimport numpy as np\nimport ast\nimport seaborn as sn\nimport matplotlib.pyplot as plt\nimport plotly.express as px\n\n\ndef query_df(df, speaker, speech):\n \"\"\"Function to query a dataframe with the given pararmeters\"\"\"\n return df.query(\"speaker == @speaker and text == @speech\")\n\n\ndef display_ner_data(df):\n st.markdown(\"# Natural Entity Recognition (NER)\")\n\n st.markdown(\"\"\"\n NER was used to extract the various entities involved in the discussion.\n We finetuned 2 models (`xlm-roberta-base` and `xlm-roberta-base-ontonotes5`) using manually annotated hansard data to extract the following entity types `PERSON, NORP, FAC, ORG, GPE, LAW, DATE`.\n\n Models were evaluated on Singapore Hansard NER Dataset validation set.\n\n | Model | F1 Score | Precision | Recall | Remark |\n |-------------------------------------------|----------|-----------|--------|------------\n | asahi417/tner-xlm-roberta-base-ontonotes5 | 0.343 | 0.274 | 0.458 |Pretrained model without finetuning|\n | xlm-roberta-base-sh-ner | 0.786 | 0.742 | 0.837 | Pretrained xlm-roberta-base model finetuned on the manually annotated Singapore hansard dataset|\n | xlm-roberta-base-ontonotes5-sh-ner | **0.819**| **0.778** | **0.864** | Pretrained xlm-roberta-base-ontonotes5 model finetuned on the manually annotated Singapore hansard dataset|\n\n ### View our model results using the buttons below:\n \"\"\")\n col1, col2 = st.beta_columns([.35, 1])\n\n with col1:\n speakers = df['speaker'].unique()\n speaker_choice = st.selectbox('Select speaker:', speakers, index=47)\n with col2:\n speeches = df['text'].loc[df[\"speaker\"] == speaker_choice].unique()\n speech_choice = st.selectbox('Select Speech', speeches, index=2)\n\n df_filtered = query_df(df, speaker_choice, speech_choice)\n\n doc = {\"text\": df_filtered['text'].values[0],\n \"ents\": ast.literal_eval(df_filtered['entities'].values[0])}\n HTML_WRAPPER = \"\"\"
{}
\"\"\"\n html = displacy.render(doc, style=\"ent\", manual=True)\n html = html.replace(\"\\n\\n\", \"\\n\")\n st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True)\n\n sequence = \"\"\"Enter some Parliamentary Hansard text here to extract various entities like names (Heng Swee Kwat, K Shanmugam),\n places(Singapore, Malaysia), dates (12th May, 2012-02-03), organisations (SAF, MOH, MHA, Ministry of Finance),\n laws (Adoption of Children Act, Work Injury Compensation Act 2019)\n \"\"\"\n\n st.markdown(\"## **Live Inference**\")\n st.write(\"Please note that loading may take upto 1 min due to deployment cost contraints.\")\n st.markdown(\"### Try out our NER Model `xlm-roberta-base-ontonotes5-sh-ner`\")\n text_box = st.text_area(\"Enter some text for NER\", sequence)\n\n HTML_WRAPPER = \"\"\"
{}
\"\"\"\n\n if text_box:\n url = 'https://hansard-nlp-api-l6lhxur2aq-uc.a.run.app/ner/'\n req_body = json.dumps({'hansard_text': text_box, 'output': {}})\n response = requests.post(url, data=req_body)\n response = json.loads(response.text)['output']\n\n html = displacy.render(response, style=\"ent\", manual=True)\n html = html.replace(\"\\n\\n\", \"\\n\")\n st.write(HTML_WRAPPER.format(html), unsafe_allow_html=True)\n", "sub_path": "ner_info.py", "file_name": "ner_info.py", "file_ext": "py", "file_size_in_byte": 3582, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "streamlit.markdown", "line_number": 19, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 21, "usage_type": "call"}, {"api_name": "streamlit.beta_columns", "line_number": 35, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 39, "usage_type": "call"}, {"api_name": "streamlit.selectbox", "line_number": 42, "usage_type": "call"}, {"api_name": "ast.literal_eval", "line_number": 47, "usage_type": "call"}, {"api_name": "spacy.displacy.render", "line_number": 49, "usage_type": "call"}, {"api_name": "spacy.displacy", "line_number": 49, "usage_type": "name"}, {"api_name": "streamlit.write", "line_number": 51, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 58, "usage_type": "call"}, {"api_name": "streamlit.write", "line_number": 59, "usage_type": "call"}, {"api_name": "streamlit.markdown", "line_number": 60, "usage_type": "call"}, {"api_name": "streamlit.text_area", "line_number": 61, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 67, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 68, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 69, "usage_type": "call"}, {"api_name": "spacy.displacy.render", "line_number": 71, "usage_type": "call"}, {"api_name": "spacy.displacy", "line_number": 71, "usage_type": "name"}, {"api_name": "streamlit.write", "line_number": 73, "usage_type": "call"}]} +{"seq_id": "53870873", "text": "import pygame\nimport math\nimport random\nimport platform\nimport functions\n\nfrom pygame.locals import *\nfrom random import *\nfrom math import *\n\nclass cocoa_danmaku_1(pygame.sprite.Sprite):\n def __init__(self, boss_position):\n pygame.sprite.Sprite.__init__(self)\n if platform.system() == 'Windows':\n self.cocoa_danmaku_type1 = pygame.image.load(\"images\\\\boss\\\\Cocoa\\\\cocoa_danmaku_type1_00.png\").convert_alpha()\n if platform.system() == 'Linux' or platform.system()=='Darwin':\n self.cocoa_danmaku_type1 = pygame.image.load(\"images/boss/Cocoa/cocoa_danmaku_type1_00.png\").convert_alpha()\n self.image = self.cocoa_danmaku_type1\n self.rect = self.image.get_rect()\n self.center = [boss_position[0], boss_position[1]]\n self.rect.left = self.center[0] - 10\n self.rect.top = self.center[1] - 10\n self.direction = [0.0,-1.0]\n \n self.damage = 13\n self.speed = 2.5\n self.birth_life = 6\n self.special_count = 0\n self.radius = 0\n self.effects_time = 60\n \n def move(self):\n self.center[0] += self.speed * self.direction[0]\n self.center[1] += self.speed * self.direction[1]\n self.rect.left = self.center[0] - 10\n self.rect.top = self.center[1] - 10\n if self.effects_time:\n self.effects_time -= 1\n else:\n self.effects_time = 60\n\nclass cocoa_danmaku_1_effects(pygame.sprite.Sprite):\n def __init__(self, danmaku):\n pygame.sprite.Sprite.__init__(self)\n self.cocoa_danmaku_type1_effects = []\n for i in range(0,10):\n ch = \"images/boss/Cocoa/cocoa_danmaku_type2_0\" + str(i) + \".png\"\n self.cocoa_danmaku_type1_effects.append(pygame.image.load(ch).convert_alpha())\n self.image = self.cocoa_danmaku_type1_effects[0]\n self.direction = danmaku.direction\n self.center = danmaku.center\n self.rect = self.cocoa_danmaku_type1_effects[0].get_rect()\n self.rect.left = self.center[0] - 10\n self.rect.top = self.center[1] - 10\n self.speed = danmaku.speed\n self.lifetime = 30\n \n def move(self):\n self.center[0] += self.speed * self.direction[0]\n self.center[1] += self.speed * self.direction[1]\n self.rect.left = self.center[0] - self.lifetime/5 * self.direction[0]\n self.rect.top = self.center[1] - self.lifetime/5 * self.direction[1]\n self.image = self.cocoa_danmaku_type1_effects[9 - self.lifetime//3]\n\nclass cocoa_bomb(pygame.sprite.Sprite):\n def __init__(self):\n pass\n\nclass Cocoa(pygame.sprite.Sprite):\n def __init__(self):\n pygame.sprite.Sprite.__init__(self)\n if platform.system() == 'Windows':\n oimage1 = pygame.image.load(\"images\\\\boss\\\\Cocoa\\\\Cocoa_00.png\").convert_alpha()\n oimage2 = pygame.image.load(\"images\\\\boss\\\\Cocoa\\\\Cocoa_01.png\").convert_alpha()\n oimage3 = pygame.image.load(\"images\\\\boss\\\\Cocoa\\\\Cocoa_02.png\").convert_alpha()\n #self.illustraction = pygame.image.load(\"image\\\\boss\\\\Cocoa\\\\Cocoa_tachie.png\").convert_alpha()\n if platform.system() == 'Linux' or platform.system()=='Darwin':\n oimage1 = pygame.image.load(\"images/boss/Cocoa/Cocoa_00.png\").convert_alpha()\n oimage2 = pygame.image.load(\"images/boss/Cocoa/Cocoa_01.png\").convert_alpha()\n oimage3 = pygame.image.load(\"images/boss/Cocoa/Cocoa_02.png\").convert_alpha()\n #self.illustraction = pygame.image.load(\"images/boss/Cocoa/Cocoa_tachie.png\").convert_alpha()\n \n self.image = pygame.transform.scale(oimage2, (60,75))\n #self.image = oimage2\n self.name = \"cocoa\"\n self.rect = self.image.get_rect()\n self.center = [255.0, 100.0]\n self.temp_position = [255, 100]\n self.direction = [0, -1]\n self.rect.left = self.center[0] - 30\n self.rect.top = self.center[1] - 37\n \n self.speed = 2\n self.radius = 20\n \n self.collide = 1\n self.hp = 1000\n self.max_hp = 1000\n self.spell = 3\n self.crash = 9\n self.energy = 90\n self.spell_time = 0\n \n self.bgm = pygame.mixer.music\n self.bgm.load(\"bgm/Rabi-Ribi Original Soundtrack - 36 Get On With It.ogg\")\n \n def move(self):\n if self.temp_position[0] < 50:\n self.temp_position[0] = 50\n elif self.temp_position[0] > 420:\n self.temp_position[0] = 420\n if self.temp_position[1] < 50:\n self.temp_position[1] = 50\n elif self.temp_position[1] > 200:\n self.temp_position[1] = 200\n \n distance = sqrt( \\\n (self.center[0] - self.temp_position[0]) ** 2 + \\\n (self.center[1] - self.temp_position[1]) ** 2 )\n if distance:\n self.direction = [\n (self.temp_position[0] - self.center[0]) / distance, \n (self.temp_position[1] - self.center[1]) / distance ]\n self.speed = log(distance + 1)/3\n else:\n self.speed = 0\n self.center[0] += self.direction[0] * self.speed\n self.center[1] += self.direction[1] * self.speed\n self.rect.left = self.center[0] - 30\n self.rect.top = self.center[1] - 37\n \n def damage(self, shouting_group):\n for each in shouting_group:\n self.hp -= each.damage\n if self.hp < 0:\n self.hp = 0\n self.spell -= 1\n \n def cocoa_spell_1(self, difficulty, me_erina, boss_group, birth_group, effects_group):\n if self.spell_time < 1800:\n temp_time = self.spell_time % 120\n if temp_time:\n if temp_time%10 == 1 and temp_time<62:\n temp_snipe = functions.snipe(self, me_erina)\n offset = randint(-10,10)\n for i in range(-8,9):\n temp_danmaku = cocoa_danmaku_1(self.center)\n temp_danmaku.center = [self.center[0], self.center[1]]\n temp_danmaku.direction = [cos(temp_snipe + i*pi/32 + pi*offset/320), sin(temp_snipe + i*pi/32 + pi*offset/320)]\n birth_group.add(temp_danmaku)\n else:\n pass\n self.temp_position[0] = randint(50,380)\n self.temp_position[1] = randint(50,160)\n self.spell_time += 1\n", "sub_path": "rabiribi-danmaku/boss/section1/stage1a/cocoa_old.py", "file_name": "cocoa_old.py", "file_ext": "py", "file_size_in_byte": 6475, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pygame.sprite", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 13, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 13, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 14, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 15, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 16, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 17, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 17, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 42, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 44, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 48, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 48, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 69, "usage_type": "attribute"}, {"api_name": "pygame.sprite.Sprite.__init__", "line_number": 71, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 71, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 72, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 73, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 73, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 74, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 74, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 75, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 75, "usage_type": "attribute"}, {"api_name": "platform.system", "line_number": 77, "usage_type": "call"}, {"api_name": "pygame.image.load", "line_number": 78, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 78, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 79, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 79, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 80, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 80, "usage_type": "attribute"}, {"api_name": "pygame.transform.scale", "line_number": 83, "usage_type": "call"}, {"api_name": "pygame.transform", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pygame.mixer", "line_number": 104, "usage_type": "attribute"}, {"api_name": "functions.snipe", "line_number": 144, "usage_type": "call"}]} +{"seq_id": "214092472", "text": "#!/usr/bin/python3\n\nimport os\nimport sys\nimport random\nfrom collections import defaultdict\n\n\ndef derivative_of_cost_function(x, y, dict_len, slope, segment):\n\tfunc = float(segment + float(slope * x))\n\tsegment_val = float(func - y)\n\tslope_val = (float(func - y) * x)\n\t\n\tprint (\"Segment Val: %d :: Slope Val: %d\" % (segment_val, slope_val))\n\treturn (segment_val, slope_val)\n\n\ndef compute_grad_variable(adict):\n\tslope = float(0)\n\tsegment = float(0)\n\tlearning = float(0.001)\n\t\n\tdict_len = len(adict)\n\tprint(adict)\n\tprint(dict_len)\n\tseg_grad = slope_grad = float(0)\n\twhile (True):\n\t\tfor key in adict:\n\t\t\tx = key\n\t\t\talist = adict[key]\n\t\t\tfor idx in range(len(alist)):\n\t\t\t\ty = alist[idx]\n\t\t\t\t(seg_val, slope_val) = derivative_of_cost_function(x, y, dict_len, slope, segment)\n\t\t\t\tseg_grad += seg_val\n\t\t\t\tslope_grad += slope_val\n\n\t\tgrad = float(1 / float(dict_len))\n\t\tprint(grad)\n\t\tseg_grad = float(seg_grad * grad)\n\t\tslope_grad = float(slope_grad * grad)\n\t\tprint (\"GRAD Segment: %d :: Slope: %d\" % (seg_grad, slope_grad))\n\t\ttemp_seg = float(segment - float(learning * seg_grad))\n\t\ttemp_slope = float(slope - float(learning * slope_grad))\n\t\tprint (\"TEMP Segment: %d :: Slope: %d\" % (temp_seg, temp_slope))\n\t\tprint (\"Segment: %d :: Slope: %d\" % (segment, slope))\n\n\t\tif (segment == temp_seg and slope == temp_slope):\n\t\t\tbreak\n\t\telse:\n\t\t\tsegment = temp_seg\n\t\t\tslope = temp_slope\n\t\tprint (\"Segment: %d :: Slope: %d\" % (segment, slope))\n\t\n\treturn(segment, slope)\n\ntheta_segment = 0\ntheta_slope = 0\n\ndef main():\n\tinput_dict = defaultdict(list);\n\tx_list = []\n\ty_list = []\n\tinput_data = True\n\twith open(\"grad.txt\", 'r') as fp:\n\t\tfor line in fp:\n\t\t\tif (input_data):\n\t\t\t\tline = line.rstrip('\\n')\n\t\t\t\tx_list = [i for i in line.split(' ')]\n\t\t\t\tinput_data = False\n\t\t\telse:\n\t\t\t\tline = line.rstrip('\\n')\n\t\t\t\ty_list = [i for i in line.split(' ')]\n\t\t\t\tinput_data = True\n\n\tx_list = list(map(int, x_list))\n\ty_list = list(map(int, y_list))\n\n\tif (len(x_list) != len(y_list)):\n\t\tprint(\"Syncing error in input and output data X : Y\")\n\telse:\n\t\tfor a, b in zip(x_list, y_list):\n\t\t\tinput_dict[a].append(b)\n\n\tsegment, slope = compute_grad_variable(input_dict)\n\ttheta_segment = segment\n\ttheta_slope = slope\n\t\n\tprint(\"$1: %f :: $2: %f\" % (segment, slope))\n\tinput_dict.clear()\n\n\tfp.close()\n\t\t\t\t\n\nmain()\n", "sub_path": "Linear_Regression/gradient_descent/gradient_descent.py", "file_name": "gradient_descent.py", "file_ext": "py", "file_size_in_byte": 2264, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.defaultdict", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "1429202", "text": "from progress.bar import IncrementalBar\nfrom stemming import PorterStemmer\nfrom nltk.tokenize import word_tokenize\nimport os\nimport csv\nimport sys\n\n\ndef main():\n\n stop_words = set()\n if len(sys.argv) > 2:\n print(\"\\nPlease enter the file name correctly\")\n elif not os.path.exists(sys.argv[1]):\n print(\"File does noyt exits, please check\")\n else:\n '''\n Prepare for the stop_words set\n '''\n with open(\"stop_words.lst.txt\", 'r') as f:\n lines = f.readlines()\n for line in lines:\n stop_words.add(line.rstrip())\n\n\n root = './doc/'\n if not os.path.exists(root):\n os.mkdir(root)\n f = sys.argv[1]\n\n file_processing(f,root,stop_words)\n\ndef file_processing(file,root,stop_words):\n p = PorterStemmer()\n with open(file) as f:\n length = len(f.readlines())-1\n bar = IncrementalBar('In progress', max=length)\n\n with open(file, 'r') as csvFile:\n\n reader = csv.reader(csvFile)\n next(reader)\n\n for row ,i in zip(reader,range(1,length+1)):\n if not os.path.exists(root+row[1]):\n os.mkdir(root+row[1])\n\n # Remove stop words first\n example = row[0]\n word_tokens = word_tokenize(example)\n\n filtered_sentence = [w for w in word_tokens if not w in stop_words]\n joined_sentence = (\" \").join(filtered_sentence)+'\\n'\n\n # Do stemming\n\n output = ''\n word = ''\n line = joined_sentence\n if line == '':\n break\n for c in line:\n\n if c.isalpha():\n word += c.lower()\n else:\n if word:\n output += p.stem(word, 0, len(word) - 1)\n word = ''\n output += c.lower()\n\n\n path = root+row[1]+'/'+row[2]+'.txt'\n with open(path, \"w\") as cursor:\n\n # Write file\n cursor.write(output)\n\n bar.next()\n\n\n bar.finish()\n\nif __name__ == '__main__':\n main()", "sub_path": "4412-proj/doc_prepare.py", "file_name": "doc_prepare.py", "file_ext": "py", "file_size_in_byte": 2135, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.argv", "line_number": 12, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 14, "usage_type": "call"}, {"api_name": "os.path", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 27, "usage_type": "call"}, {"api_name": "os.path", "line_number": 27, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 28, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 29, "usage_type": "attribute"}, {"api_name": "stemming.PorterStemmer", "line_number": 34, "usage_type": "call"}, {"api_name": "progress.bar.IncrementalBar", "line_number": 37, "usage_type": "call"}, {"api_name": "csv.reader", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 46, "usage_type": "call"}, {"api_name": "nltk.tokenize.word_tokenize", "line_number": 50, "usage_type": "call"}]} +{"seq_id": "577697015", "text": "import pygame\nimport param as p\nfrom characters import *\nfrom utils import *\n\n\ndef start(screen, clock, bird):\n # draw\n screen.fill(p.background)\n bird.show(screen)\n text = 'Flappy Circular Bird Floating in Vacuum'\n showText(screen, text, p.title_pos, p.font, 50, (0, 0, 0))\n text = 'press \"Space\" to play'\n showText(screen, text, p.subtitle_pos, p.font, 30, (0, 0, 0))\n pygame.display.update()\n\n # detect operation\n events = pygame.event.get()\n for e in events:\n if e.type == pygame.QUIT:\n return 'quit'\n elif e.type == pygame.KEYDOWN and e.key == pygame.K_SPACE:\n print('START!!!')\n return 'game'\n\n # wait\n clock.tick_busy_loop(80)\n return 'start'\n\n\ndef game(screen, clock, bird, obs_list, frame, scoreboard):\n # calculate score\n for obs in obs_list:\n if obs.score():\n scoreboard.gain_point()\n\n # draw\n screen.fill(p.background)\n bird.show(screen)\n for obs in obs_list:\n obs.show(screen)\n scoreboard.show(screen)\n pygame.display.update()\n\n # detect collision\n collision = False\n collision = collision or CollisionDetector.detect(bird, frame)\n for obs in obs_list:\n collision = collision or CollisionDetector.detect(bird, obs)\n if collision:\n print('COLLIDE!!!')\n scoreboard.save_score()\n return 'fail'\n\n # detect operation\n events = pygame.event.get()\n for e in events:\n if e.type == pygame.QUIT:\n return 'quit'\n elif e.type == pygame.KEYDOWN and e.key == pygame.K_SPACE:\n bird.flap()\n print('FLAP!!!')\n elif e.type == pygame.KEYDOWN and e.key == pygame.K_ESCAPE:\n print('PAUSE!!!')\n return 'pause'\n\n # move\n dT = clock.get_time() * .06\n if ObsGenerator.needNewObs(obs_list):\n ObsGenerator.getNewObs(obs_list)\n bird.move(dT)\n for obs in obs_list:\n obs.move(dT)\n\n # wait\n clock.tick_busy_loop(80)\n return 'game'\n\n\ndef pause(screen, clock):\n # draw\n # TODO: Draw a circle with play symbol\n pos = (int(0.5 * p.size[0]), int(0.5 * p.size[1]))\n pygame.draw.circle(screen, p.background, pos, p.pause_rad, 0)\n pygame.draw.circle(screen, (0, 0, 0), pos, p.pause_rad, 5)\n pointlist = ((int(pos[0] - p.pause_lwidth), int(pos[1] - 0.5 * p.pause_hight)),\n (int(pos[0] - p.pause_lwidth), int(pos[1] + 0.5 * p.pause_hight)),\n (int(pos[0] + p.pause_rwidth), int(pos[1])))\n pygame.draw.polygon(screen, (0, 0, 0), pointlist, 0)\n pygame.display.update()\n\n # detect operation\n events = pygame.event.get()\n for e in events:\n if e.type == pygame.QUIT:\n return 'quit'\n elif e.type == pygame.KEYDOWN and e.key == pygame.K_SPACE:\n print('CONTINUE!!!')\n return 'game'\n elif e.type == pygame.KEYDOWN and e.key == pygame.K_ESCAPE:\n print('CONTINUE!!!')\n return 'game'\n\n # wait\n clock.tick_busy_loop(80)\n return 'pause'\n\n\ndef fail(screen, clock, bird, obs_list, scoreboard):\n # draw\n text = 'You Have Failed With a Score of {}'.format(scoreboard.score)\n showText(screen, text, p.title_pos, p.font, 50, (255, 0, 0))\n text = 'press \"Space\" to play again'\n showText(screen, text, p.subtitle_pos, p.font, 30, (0, 0, 0))\n pygame.display.update()\n\n # detect operation\n events = pygame.event.get()\n for e in events:\n if e.type == pygame.QUIT:\n return 'quit'\n elif e.type == pygame.KEYDOWN and e.key == pygame.K_SPACE:\n init(bird, obs_list, scoreboard)\n print('START!!!')\n return 'game'\n elif e.type == pygame.KEYDOWN and e.key == pygame.K_ESCAPE:\n init(bird, obs_list, scoreboard)\n print('MENU!!!')\n return 'start'\n\n # wait\n clock.tick_busy_loop(80)\n return 'fail'\n", "sub_path": "scenes.py", "file_name": "scenes.py", "file_ext": "py", "file_size_in_byte": 3920, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "param.background", "line_number": 9, "usage_type": "attribute"}, {"api_name": "param.title_pos", "line_number": 12, "usage_type": "attribute"}, {"api_name": "param.font", "line_number": 12, "usage_type": "attribute"}, {"api_name": "param.subtitle_pos", "line_number": 14, "usage_type": "attribute"}, {"api_name": "param.font", "line_number": 14, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 15, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 15, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 18, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 18, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 20, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 22, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 22, "usage_type": "attribute"}, {"api_name": "param.background", "line_number": 38, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 43, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 56, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 56, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 58, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 60, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 63, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 63, "usage_type": "attribute"}, {"api_name": "param.size", "line_number": 83, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 84, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 84, "usage_type": "attribute"}, {"api_name": "param.background", "line_number": 84, "usage_type": "attribute"}, {"api_name": "param.pause_rad", "line_number": 84, "usage_type": "attribute"}, {"api_name": "pygame.draw.circle", "line_number": 85, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 85, "usage_type": "attribute"}, {"api_name": "param.pause_rad", "line_number": 85, "usage_type": "attribute"}, {"api_name": "param.pause_lwidth", "line_number": 86, "usage_type": "attribute"}, {"api_name": "param.pause_hight", "line_number": 86, "usage_type": "attribute"}, {"api_name": "param.pause_lwidth", "line_number": 87, "usage_type": "attribute"}, {"api_name": "param.pause_hight", "line_number": 87, "usage_type": "attribute"}, {"api_name": "param.pause_rwidth", "line_number": 88, "usage_type": "attribute"}, {"api_name": "pygame.draw.polygon", "line_number": 89, "usage_type": "call"}, {"api_name": "pygame.draw", "line_number": 89, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 90, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 90, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 93, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 93, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 95, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 100, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 100, "usage_type": "attribute"}, {"api_name": "param.title_pos", "line_number": 112, "usage_type": "attribute"}, {"api_name": "param.font", "line_number": 112, "usage_type": "attribute"}, {"api_name": "param.subtitle_pos", "line_number": 114, "usage_type": "attribute"}, {"api_name": "param.font", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pygame.display.update", "line_number": 115, "usage_type": "call"}, {"api_name": "pygame.display", "line_number": 115, "usage_type": "attribute"}, {"api_name": "pygame.event.get", "line_number": 118, "usage_type": "call"}, {"api_name": "pygame.event", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pygame.QUIT", "line_number": 120, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pygame.K_SPACE", "line_number": 122, "usage_type": "attribute"}, {"api_name": "pygame.KEYDOWN", "line_number": 126, "usage_type": "attribute"}, {"api_name": "pygame.K_ESCAPE", "line_number": 126, "usage_type": "attribute"}]} +{"seq_id": "469715217", "text": "\"\"\"\nThis script runs the application using a development server.\nIt contains the definition of routes and views for the application.\n\"\"\"\n\nfrom flask import Flask, flash, render_template, request, redirect\napp = Flask(__name__)\n\n# Make the WSGI interface available at the top level so wfastcgi can get it.\nwsgi_app = app.wsgi_app\n\n\n@app.route('/')\ndef index():\n return render_template(\"index.html\")\n\n@app.route('/result', methods=['POST'])\ndef create_user():\n print(\"Got Post Info\")\n print(request.form)\n name_from_form = request.form['name']\n location_from_form = request.form['loc']\n language_from_form = request.form['language']\n comment_from_form = request.form['comment']\n return render_template(\"result.html\", name_on_template=name_from_form, location_on_template=location_from_form, language_on_template=language_from_form, comment_on_template=comment_from_form)\n\nif __name__ == '__main__':\n import os\n HOST = os.environ.get('SERVER_HOST', 'localhost')\n try:\n PORT = int(os.environ.get('SERVER_PORT', '5555'))\n except ValueError:\n PORT = 5555\n app.run(HOST, PORT)\n", "sub_path": "python_stack/flask/flask_mysql/DojoSurvey/app.py", "file_name": "app.py", "file_ext": "py", "file_size_in_byte": 1123, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Flask", "line_number": 7, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 20, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 20, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 21, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 21, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 22, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 22, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 23, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 23, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 24, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 24, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 29, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 29, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 31, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 31, "usage_type": "attribute"}]} +{"seq_id": "357998066", "text": "import tkinter as tk\nfrom tkinter import *\nfrom tkinter import filedialog\n\nimport os\nimport shutil\nimport sqlite3\n\n\n\nroot = Tk()\nroot.title('Choose directory')\nroot.geometry('{}x{}'.format(670,250))\n\nsourcePath=StringVar()\ndestPath=StringVar()\n\n\nbtn1 = Button(root, text=\"Source\",width=10,height=1,font=('Times New Roman',13), command=lambda: askdirectory1())\nbtn1.grid(row=0, column=0, padx=(20,0), pady=(100,0))\n\nbtn2 = Button(root, text=\"Destination\",width=10,height=1,font=('Times New Roman',13), command=lambda: askdirectory2())\nbtn2.grid(row=1, column=0, padx=(20,0), pady=(20,0))\n\nbtn3 = Button(root, text=\"Move files\",width=15,height=1,font=('Times New Roman',13), command=lambda: getTxtFile())\nbtn3.grid(row=2, column=1, padx=(0,0), pady=(10,20), sticky=SE)\n\n\ntxt1 = Text(root, height=1, width=55)\ntxt1.grid(row=0,column=1, padx=(20,0), pady=(100,0))\n\ntxt2 = Text(root, height=1, width=55)\ntxt2.grid(row=1,column=1, padx=(20,0), pady=(20,0))\n\n\n\ndef askdirectory1():\n filepath=filedialog.askdirectory()\n sourcePath.set(filepath)\n txt1.insert(END, sourcePath.get())\n\ndef askdirectory2():\n filepath=filedialog.askdirectory()\n destPath.set(filepath)\n txt2.insert(END, destPath.get())\n\n\ndef getTxtFile():\n source = sourcePath.get()\n if source==\"\":\n print(\"Please choose source directory\")\n destination = destPath.get()\n if destination==\"\":\n print(\"Please choose destination directory\")\n files = os.listdir(source)\n \n for i in files:\n name, ext = os.path.splitext(i)\n if ext == \".txt\":\n abspath = os.path.join(source, i)\n dest=shutil.move(abspath,destination)\n\n\n conn = sqlite3.connect(\"test.db\")\n\n with conn:\n cur = conn.cursor()\n cur.execute(\"CREATE TABLE IF NOT EXISTS tbl_info( \\\n ID INTEGER PRIMARY KEY AUTOINCREMENT, \\\n col_txt TEXT, \\\n col_date_time)\")\n conn.commit()\n conn.close()\n\n\n\n conn = sqlite3.connect(\"test.db\")\n\n with conn:\n files = os.listdir(destination)\n for file in files:\n name, ext = os.path.splitext(file)\n if ext == \".txt\":\n filepath = os.path.join(destination, file)\n time = os.path.getmtime(filepath)\n \n cur = conn.cursor()\n cur.execute(\"INSERT INTO tbl_info (col_txt, col_date_time) VALUES (?,?)\", (file,time))\n conn.commit()\n\n cur.execute(\"SELECT col_txt, col_date_time FROM tbl_info\")\n txtList = cur.fetchall()\n print (txtList)\n conn.close()\n \n\n \n\n\nroot.mainloop()\n\n\n\n", "sub_path": "drill_page123.py", "file_name": "drill_page123.py", "file_ext": "py", "file_size_in_byte": 2592, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tkinter.filedialog.askdirectory", "line_number": 38, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 38, "usage_type": "name"}, {"api_name": "tkinter.filedialog.askdirectory", "line_number": 43, "usage_type": "call"}, {"api_name": "tkinter.filedialog", "line_number": 43, "usage_type": "name"}, {"api_name": "os.listdir", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 60, "usage_type": "call"}, {"api_name": "os.path", "line_number": 60, "usage_type": "attribute"}, {"api_name": "shutil.move", "line_number": 61, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 64, "usage_type": "call"}, {"api_name": "sqlite3.connect", "line_number": 77, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 80, "usage_type": "call"}, {"api_name": "os.path.splitext", "line_number": 82, "usage_type": "call"}, {"api_name": "os.path", "line_number": 82, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 84, "usage_type": "call"}, {"api_name": "os.path", "line_number": 84, "usage_type": "attribute"}, {"api_name": "os.path.getmtime", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}]} +{"seq_id": "169901455", "text": "from youtubesearchpython import VideosSearch\nimport youtube_dl, os, subprocess\n\n\ndef run():\n ydl = youtube_dl.YoutubeDL()\n search_query = input(\"(search) > \")\n while( search_query != 'q'):\n if len(search_query) == 0:\n print(\"No search query provided.\")\n print(\"Searching\", search_query)\n try:\n videosSearch = VideosSearch(search_query, limit = 10)\n results = videosSearch.result()['result']\n for i,j in enumerate(results):\n print(i, j['title'])\n selected_video_num = int(input(\"(select) > \"))\n result = results[selected_video_num]\n info_dict = ydl.extract_info(result['id'], download=False)\n url = info_dict['formats'][0]['url']\n print('Playing', result['title'])\n with open(os.devnull, \"w\") as devnull:\n subprocess.run(['vlc', url, '--play-and-exit', '--meta-title=%s' % result['title']], shell=False, stderr=devnull)\n #os.system('/usr/bin/vlc \"%s\" --meta-title=\"%s\"' % (url, result['title']))\n except Exception as ex:\n print(ex)\n search_query = input(\"(search) > \")\n\nif __name__ == '__main__':\n run()\n", "sub_path": "inverminal/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 1212, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "youtube_dl.YoutubeDL", "line_number": 6, "usage_type": "call"}, {"api_name": "youtubesearchpython.VideosSearch", "line_number": 13, "usage_type": "call"}, {"api_name": "os.devnull", "line_number": 22, "usage_type": "attribute"}, {"api_name": "subprocess.run", "line_number": 23, "usage_type": "call"}]} +{"seq_id": "97180166", "text": "'''\npython3 script to construct a tensor network file for T3NS\n'''\n\n\nfrom collections import deque\nimport sys\nimport random\nimport os\nimport os.path as path\nimport shutil\n\n# check the arguments\nif len(sys.argv) != 3: # incl. python script\n error_message = 'Syntaxis: python plant.py FCIDUMP ' + \\\n 'SEED'\n raise IOError(error_message)\n# try interpreting the arguments as files\nfcidump = sys.argv[1]\nseed = sys.argv[2]\n# test wether these files can be opened\ntest = open(sys.argv[1])\ntest.close()\ntest = open(sys.argv[2])\ntest.close()\n\n\n# make temporary files to store the several output pieces\ntmp_dir = 'tmpplant' + str(random.randint(0,999999))\nos.mkdir(tmp_dir)\n# print('tmp_dir was: %s' % tmp_dir)\nheader = path.join(tmp_dir, 'header')\ntree = path.join(tmp_dir, 'tree')\n\n\n# dictionaries to keep track of the labels\ngroups = {}\nbranchtensors = {}\norbsym = []\n# first try to read the groups from the seed\nwith open(seed) as f:\n for line in f:\n words = line.split()\n if len(words) > 0 and 'GROUP' in words[0]:\n orbsym = [words[i] for i in range(1,len(words))]\n# otherwise read in the orbital numbers from the fcidump file\nif len(orbsym) == 0:\n with open(fcidump) as f:\n # parse the orbsym array\n line = next(f).lstrip()\n while not line.startswith('ORBSYM'):\n line = next(f).lstrip()\n key, value = line.split('=')\n orbsym = list(value.strip(',\\n').split(','))\n\n# create a group dictionary from them\nfor i in list(set(orbsym)):\n groups[i] = deque([])\nfor i in range(len(orbsym)):\n groups[orbsym[i]].append(i)\n\n# the groups dictionary can be visually verified\n# print(groups)\n\n\n# help function:\n# get the right orbital number for a certain label\nbranchtensornr = len(orbsym)\ndef nr(label):\n global groups, branchtensors, branchtensornr\n if label in groups:\n if not groups[label]:\n raise ValueError(\"Orbitals of the seed \" \\\n + \"does not match the fcidump!\")\n result = groups[label].popleft()\n elif label in branchtensors:\n result = branchtensors[label]\n else:\n result = branchtensornr\n branchtensors[label] = branchtensornr\n branchtensornr += 1\n return result\n\n# create a temporary file for the actual tree\ntmp_file = 'tmp' + str(random.randint(0,999999))\n# print('tmp_file was: %s' % tmp_dir)\n\n# loop over the seed and construct the tree\nnr_bonds = 0\nwith open(tree, \"w\") as t:\n with open(seed) as f:\n for line in f:\n words = line.split()\n if len(words) > 0 and 'GROUP' not in words[0]:\n nbrs = []\n # give all orbitals a unique label\n if words[0] in groups:\n nbrs.append(-1)\n for tensor in words:\n nbrs.append(nr(tensor))\n # write out the given branch\n for i in range(1, len(nbrs)):\n nr_bonds += 1\n t.write(str(nbrs[i-1]) + ' ' + str(nbrs[i]) + '\\n')\n last = nbrs[-1]\n nr_bonds += 1\n t.write(str(last) + ' -1')\n\nnr_phys_sites = len(orbsym)\nnr_sites = nr_phys_sites + len(branchtensors)\n\nwith open(header, 'w') as f:\n # write out the header\n f.write('NR_SITES = %d\\n' % nr_sites)\n f.write('NR_PHYS_SITES = %d\\n' % nr_phys_sites)\n f.write('NR_BONDS = %d\\n' % nr_bonds)\n f.write('/\\n')\n convertion = [i for i in range(len(orbsym))] + \\\n ['*' for i in range(nr_sites - nr_phys_sites)]\n for orb in range(len(convertion)):\n f.write(str(convertion[orb]) + ' ')\n #f.write(str(*convertion) + '\\n')\n f.write('\\n/END\\n')\n\n# bash way:\n# os.system(\"cat \" + header + \" \" + tree)\n# pythonic way:\nprint(''.join([open(f).read() for f in [header, tree]]))\n# remove the temporary directory\nshutil.rmtree(tmp_dir)\n", "sub_path": "plant.py", "file_name": "plant.py", "file_ext": "py", "file_size_in_byte": 3853, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.argv", "line_number": 14, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 20, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 22, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 24, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 29, "usage_type": "call"}, {"api_name": "os.mkdir", "line_number": 30, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 33, "usage_type": "call"}, {"api_name": "os.path", "line_number": 33, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 58, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 85, "usage_type": "call"}, {"api_name": "shutil.rmtree", "line_number": 130, "usage_type": "call"}]} +{"seq_id": "566236941", "text": "# coding=utf-8\n# pystray\n# Copyright (C) 2016 Moses Palmér\n#\n# This program is free software: you can redistribute it and/or modify it under\n# the terms of the GNU Lesser General Public License as published by the Free\n# Software Foundation, either version 3 of the License, or (at your option) any\n# later version.\n#\n# This program is distributed in the hope that it will be useful, but WITHOUT\n# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more\n# details.\n#\n# You should have received a copy of the GNU Lesser General Public License\n# along with this program. If not, see .\n\nimport ctypes\nimport os\nimport six\nimport sys\nimport threading\nimport tempfile\n\nfrom ctypes import windll, wintypes\nfrom six.moves import queue\n\nfrom . import _base\n\n\nclass Icon(_base.Icon):\n _HWND_TO_ICON = {}\n\n def __init__(self, *args, **kwargs):\n super(Icon, self).__init__(*args, **kwargs)\n\n self._icon_handle = None\n self._hwnd = None\n\n # This is a mapping from win32 event codes to handlers used by the\n # mainloop\n self._message_handlers = {\n WM_STOP: self._on_stop,\n WM_NOTIFY: self._on_notify}\n\n self._queue = queue.Queue()\n\n # Create the message loop\n msg = wintypes.MSG()\n lpmsg = ctypes.byref(msg)\n PeekMessage(lpmsg, None, 0x0400, 0x0400, PM_NOREMOVE)\n\n self._atom = self._register_class()\n self._hwnd = self._create_window(self._atom)\n self._HWND_TO_ICON[self._hwnd] = self\n\n def __del__(self):\n if self._running:\n self._stop()\n if self._thread.ident != threading.current_thread().ident:\n self._thread.join()\n\n def _show(self):\n self._assert_icon_handle()\n self._message(\n NOTIFYICONDATA.NIM_ADD,\n NOTIFYICONDATA.NIF_MESSAGE | NOTIFYICONDATA.NIF_ICON |\n NOTIFYICONDATA.NIF_TIP,\n uCallbackMessage=WM_NOTIFY,\n hIcon=self._icon_handle,\n szTip=self.title)\n\n def _hide(self):\n self._message(\n NOTIFYICONDATA.NIM_DELETE,\n 0)\n\n def _update_icon(self):\n self._icon_handle = None\n self._assert_icon_handle()\n self._message(\n NOTIFYICONDATA.NIM_MODIFY,\n NOTIFYICONDATA.NIF_ICON,\n hIcon=self._icon_handle)\n\n def _update_title(self):\n self._message(\n NOTIFYICONDATA.NIM_MODIFY,\n NOTIFYICONDATA.NIF_TIP,\n szTip=self.title)\n\n def _run(self):\n self._mark_ready()\n\n # Run the event loop\n self._thread = threading.current_thread()\n self._mainloop()\n\n def _stop(self):\n PostMessage(self._hwnd, WM_STOP, 0, 0)\n\n def _mainloop(self):\n \"\"\"The body of the main loop thread.\n\n This method retrieves all events from *Windows* and makes sure to\n dispatch clicks.\n \"\"\"\n # Pump messages\n try:\n while True:\n msg = wintypes.MSG()\n lpmsg = ctypes.byref(msg)\n while True:\n r = GetMessage(lpmsg, None, 0, 0)\n if not r:\n break\n elif r == -1:\n break\n else:\n TranslateMessage(lpmsg)\n DispatchMessage(lpmsg)\n\n # Make sure the icon is removed\n self._hide()\n\n except:\n # TODO: Report errors\n pass\n\n finally:\n try:\n self._hide()\n del self._HWND_TO_ICON[self._hwnd]\n except:\n pass\n\n DestroyWindow(self._hwnd)\n self._unregister_class(self._atom)\n\n def _on_stop(self, wparam, lparam):\n \"\"\"Handles ``WM_STOP``.\n\n This method posts a quit message, causing the mainloop thread to\n terminate.\n \"\"\"\n PostQuitMessage(0)\n\n def _on_notify(self, wparam, lparam):\n \"\"\"Handles ``WM_NOTIFY``.\n\n This method calls the activate callback. It will only be called for\n left button clicks.\n \"\"\"\n if lparam == WM_LBUTTONDOWN:\n self.on_activate(self)\n\n def _create_window(self, atom):\n \"\"\"Creates the system tray icon window.\n\n :param atom: The window class atom.\n\n :return: a window\n \"\"\"\n hwnd = CreateWindowEx(\n 0,\n atom,\n None,\n 0,\n 0, 0, 0, 0,\n HWND_MESSAGE,\n None,\n GetModuleHandle(None),\n None)\n if not hwnd:\n raise ctypes.WinError(wintypes.get_last_error())\n else:\n return hwnd\n\n def _message(self, code, flags, **kwargs):\n \"\"\"Sends a message the the systray icon.\n\n This method adds ``cbSize``, ``hWnd``, ``hId`` and ``uFlags`` to the\n message data.\n\n :param int message: The message to send. This should be one of the\n ``NIM_*`` constants.\n\n :param int flags: The value of ``NOTIFYICONDATA::uFlags``.\n\n :param kwargs: Data for the :class:`NOTIFYICONDATA` object.\n \"\"\"\n r = Shell_NotifyIcon(code, ctypes.byref(NOTIFYICONDATA(\n cbSize=ctypes.sizeof(NOTIFYICONDATA),\n hWnd=self._hwnd,\n hID=id(self),\n uFlags=flags,\n **kwargs)))\n if not r:\n raise ctypes.WinError(wintypes.get_last_error())\n\n def _assert_icon_handle(self):\n \"\"\"Asserts that the cached icon handle exists.\n \"\"\"\n if self._icon_handle:\n return\n\n fd, icon_path = tempfile.mkstemp('.ico')\n try:\n with os.fdopen(fd, 'wb') as f:\n self._icon.save(f, format='ICO')\n hicon = LoadImage(\n None,\n wintypes.LPCWSTR(icon_path),\n IMAGE_ICON,\n 0,\n 0,\n LR_DEFAULTSIZE | LR_LOADFROMFILE)\n if not hicon:\n raise ctypes.WinError(wintypes.get_last_error())\n else:\n self._icon_handle = hicon\n\n finally:\n try:\n os.unlink(icon_path)\n except:\n pass\n\n def _register_class(self):\n \"\"\"Registers the systray window class.\n\n :return: the class atom\n \"\"\"\n window_class = WNDCLASSEX(\n cbSize=ctypes.sizeof(WNDCLASSEX),\n style=0,\n lpfnWndProc=_dispatcher,\n cbClsExtra=0,\n cbWndExtra=0,\n hInstance=GetModuleHandle(None),\n hIcon=None,\n hCursor=None,\n hbrBackground=COLOR_WINDOW + 1,\n lpszMenuName=None,\n lpszClassName='%s%dSystemTrayIcon' % (self.name, id(self)),\n hIconSm=None)\n atom = RegisterClassEx(ctypes.byref(window_class))\n if not atom:\n raise ctypes.WinError(wintypes.get_last_error())\n else:\n return atom\n\n def _unregister_class(self, atom):\n \"\"\"Unregisters the systray window class.\n\n :param atom: The class atom returned by :meth:`_register_class`.\n \"\"\"\n r = UnregisterClassEx(atom, GetModuleHandle(None))\n if not r:\n raise ctypes.WinError(wintypes.get_last_error())\n\n\nWM_CREATE = 0x0001\nWM_NCCREATE = 0x0081\nWM_LBUTTONDOWN = 0x0201\nWM_USER = 0x400\nWM_STOP = WM_USER + 10\nWM_NOTIFY = WM_USER + 11\n\nHWND_MESSAGE = -3\nPM_NOREMOVE = 0\n\nCOLOR_WINDOW = 5\n\nIMAGE_ICON = 1\nLR_LOADFROMFILE = 0x00000010\nLR_DEFAULTSIZE = 0x00000040\n\nNOTIFYICON_VERSION = 3\n\nShell_NotifyIcon = windll.shell32.Shell_NotifyIconW\n\nGetModuleHandle = windll.kernel32.GetModuleHandleW\n\nRegisterClassEx = windll.user32.RegisterClassExW\nCreateWindowEx = windll.user32.CreateWindowExW\nCreateWindowEx.argtypes = [\n wintypes.DWORD,\n wintypes.LPVOID,\n wintypes.LPCWSTR,\n wintypes.DWORD,\n wintypes.INT,\n wintypes.INT,\n wintypes.INT,\n wintypes.INT,\n wintypes.HWND,\n wintypes.HMENU,\n wintypes.HINSTANCE,\n wintypes.LPVOID]\nCreateWindowEx.restype = wintypes.HWND\nDestroyWindow = windll.user32.DestroyWindow\nUnregisterClassEx = windll.user32.UnregisterClassW\n\nLoadImage = windll.user32.LoadImageW\n\nDispatchMessage = windll.user32.DispatchMessageW\nGetMessage = windll.user32.GetMessageW\nPeekMessage = windll.user32.PeekMessageW\nPostMessage = windll.user32.PostMessageW\nPostQuitMessage = windll.user32.PostQuitMessage\nTranslateMessage = windll.user32.TranslateMessage\n\n\nWNDPROC = ctypes.WINFUNCTYPE(\n ctypes.HRESULT,\n wintypes.HWND, wintypes.UINT, wintypes.WPARAM, wintypes.LPARAM)\n\n\nclass WNDCLASSEX(ctypes.Structure):\n _fields_ = [\n ('cbSize', wintypes.UINT),\n ('style', wintypes.UINT),\n ('lpfnWndProc', WNDPROC),\n ('cbClsExtra', wintypes.INT),\n ('cbWndExtra', wintypes.INT),\n ('hInstance', wintypes.HANDLE),\n ('hIcon', wintypes.HICON),\n ('hCursor', wintypes.HANDLE),\n ('hbrBackground', wintypes.HBRUSH),\n ('lpszMenuName', wintypes.LPCWSTR),\n ('lpszClassName', wintypes.LPCWSTR),\n ('hIconSm', wintypes.HICON)]\n\n\n@WNDPROC\ndef _dispatcher(hwnd, uMsg, wParam, lParam):\n try:\n return int(Icon._HWND_TO_ICON[hwnd]._message_handlers.get(\n uMsg, lambda w, l: 0)(wParam, lParam))\n\n except KeyError:\n # Icon._HWND_TO_ICON[hwnd] is not yet set; this message is sent during\n # window creation, so we assume it is WM_CREATE or WM_NCCREATE and\n # return TRUE\n return 1\n\n except:\n # TODO: Report\n return 0\n\n\nclass NOTIFYICONDATA(ctypes.Structure):\n class VERSION_OR_TIMEOUT(ctypes.Union):\n _fields_ = [\n ('uTimeout', wintypes.UINT),\n ('uVersion', wintypes.UINT)]\n\n NIF_MESSAGE = 0x00000001\n NIF_ICON = 0x00000002\n NIF_TIP = 0x00000004\n NIF_STATE = 0x00000008\n NIF_INFO = 0x00000010\n NIF_GUID = 0x00000020\n NIF_REALTIME = 0x00000040\n NIF_SHOWTIP = 0x00000080\n\n NIM_ADD = 0x00000000\n NIM_MODIFY = 0x00000001\n NIM_DELETE = 0x00000002\n NIM_SETFOCUS = 0x00000003\n NIM_SETVERSION = 0x00000004\n\n _fields_ = [\n ('cbSize', wintypes.DWORD),\n ('hWnd', wintypes.HWND),\n ('uID', wintypes.UINT),\n ('uFlags', wintypes.UINT),\n ('uCallbackMessage', wintypes.UINT),\n ('hIcon', wintypes.HICON),\n ('szTip', wintypes.WCHAR * 64),\n ('dwState', wintypes.DWORD),\n ('dwStateMask', wintypes.DWORD),\n ('szInfo', wintypes.WCHAR * 256),\n ('version_or_timeout', VERSION_OR_TIMEOUT),\n ('szInfoTitle', wintypes.WCHAR * 64),\n ('dwInfoFlags', wintypes.DWORD),\n ('guidItem', wintypes.LPVOID),\n ('hBalloonIcon', wintypes.HICON)]\n\n _anonymous_ = [\n 'version_or_timeout']\n", "sub_path": "lib/pystray/_win32.py", "file_name": "_win32.py", "file_ext": "py", "file_size_in_byte": 10975, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "six.moves.queue.Queue", "line_number": 46, "usage_type": "call"}, {"api_name": "six.moves.queue", "line_number": 46, "usage_type": "name"}, {"api_name": "ctypes.wintypes.MSG", "line_number": 49, "usage_type": "call"}, {"api_name": "ctypes.wintypes", "line_number": 49, "usage_type": "name"}, {"api_name": "ctypes.byref", "line_number": 50, "usage_type": "call"}, {"api_name": "threading.current_thread", "line_number": 60, "usage_type": "call"}, {"api_name": "threading.current_thread", "line_number": 96, "usage_type": "call"}, {"api_name": "ctypes.wintypes.MSG", "line_number": 111, "usage_type": "call"}, {"api_name": "ctypes.wintypes", "line_number": 111, "usage_type": "name"}, {"api_name": "ctypes.byref", "line_number": 112, "usage_type": "call"}, {"api_name": "ctypes.WinError", "line_number": 175, "usage_type": "call"}, {"api_name": "ctypes.wintypes.get_last_error", "line_number": 175, "usage_type": "call"}, {"api_name": "ctypes.wintypes", "line_number": 175, "usage_type": "name"}, {"api_name": "ctypes.byref", "line_number": 192, "usage_type": "call"}, {"api_name": "ctypes.sizeof", "line_number": 193, "usage_type": "call"}, {"api_name": "ctypes.WinError", "line_number": 199, "usage_type": "call"}, {"api_name": "ctypes.wintypes.get_last_error", "line_number": 199, "usage_type": "call"}, {"api_name": "ctypes.wintypes", "line_number": 199, "usage_type": "name"}, {"api_name": "tempfile.mkstemp", "line_number": 207, "usage_type": "call"}, {"api_name": "os.fdopen", "line_number": 209, "usage_type": "call"}, {"api_name": "ctypes.wintypes.LPCWSTR", "line_number": 213, "usage_type": "call"}, {"api_name": "ctypes.wintypes", "line_number": 213, "usage_type": "name"}, {"api_name": "ctypes.WinError", "line_number": 219, "usage_type": "call"}, {"api_name": "ctypes.wintypes.get_last_error", "line_number": 219, "usage_type": "call"}, {"api_name": "ctypes.wintypes", "line_number": 219, "usage_type": "name"}, {"api_name": "os.unlink", "line_number": 225, "usage_type": "call"}, {"api_name": "ctypes.sizeof", "line_number": 235, "usage_type": "call"}, {"api_name": "ctypes.byref", "line_number": 247, "usage_type": "call"}, {"api_name": "ctypes.WinError", "line_number": 249, "usage_type": "call"}, {"api_name": "ctypes.wintypes.get_last_error", "line_number": 249, "usage_type": "call"}, {"api_name": "ctypes.wintypes", "line_number": 249, "usage_type": "name"}, {"api_name": "ctypes.WinError", "line_number": 260, "usage_type": "call"}, {"api_name": "ctypes.wintypes.get_last_error", "line_number": 260, "usage_type": "call"}, {"api_name": "ctypes.wintypes", "line_number": 260, "usage_type": "name"}, {"api_name": "ctypes.windll.shell32", "line_number": 281, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 281, "usage_type": "name"}, {"api_name": "ctypes.windll.kernel32", "line_number": 283, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 283, "usage_type": "name"}, {"api_name": "ctypes.windll.user32", "line_number": 285, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 285, "usage_type": "name"}, {"api_name": "ctypes.windll.user32", "line_number": 286, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 286, "usage_type": "name"}, {"api_name": "ctypes.wintypes.DWORD", "line_number": 288, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 288, "usage_type": "name"}, {"api_name": "ctypes.wintypes.LPVOID", "line_number": 289, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 289, "usage_type": "name"}, {"api_name": "ctypes.wintypes.LPCWSTR", "line_number": 290, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 290, "usage_type": "name"}, {"api_name": "ctypes.wintypes.DWORD", "line_number": 291, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 291, "usage_type": "name"}, {"api_name": "ctypes.wintypes.INT", "line_number": 292, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 292, "usage_type": "name"}, {"api_name": "ctypes.wintypes.INT", "line_number": 293, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 293, "usage_type": "name"}, {"api_name": "ctypes.wintypes.INT", "line_number": 294, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 294, "usage_type": "name"}, {"api_name": "ctypes.wintypes.INT", "line_number": 295, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 295, "usage_type": "name"}, {"api_name": "ctypes.wintypes.HWND", "line_number": 296, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 296, "usage_type": "name"}, {"api_name": "ctypes.wintypes.HMENU", "line_number": 297, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 297, "usage_type": "name"}, {"api_name": "ctypes.wintypes.HINSTANCE", "line_number": 298, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 298, "usage_type": "name"}, {"api_name": "ctypes.wintypes.LPVOID", "line_number": 299, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 299, "usage_type": "name"}, {"api_name": "ctypes.wintypes.HWND", "line_number": 300, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 300, "usage_type": "name"}, {"api_name": "ctypes.windll.user32", "line_number": 301, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 301, "usage_type": "name"}, {"api_name": "ctypes.windll.user32", "line_number": 302, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 302, "usage_type": "name"}, {"api_name": "ctypes.windll.user32", "line_number": 304, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 304, "usage_type": "name"}, {"api_name": "ctypes.windll.user32", "line_number": 306, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 306, "usage_type": "name"}, {"api_name": "ctypes.windll.user32", "line_number": 307, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 307, "usage_type": "name"}, {"api_name": "ctypes.windll.user32", "line_number": 308, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 308, "usage_type": "name"}, {"api_name": "ctypes.windll.user32", "line_number": 309, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 309, "usage_type": "name"}, {"api_name": "ctypes.windll.user32", "line_number": 310, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 310, "usage_type": "name"}, {"api_name": "ctypes.windll.user32", "line_number": 311, "usage_type": "attribute"}, {"api_name": "ctypes.windll", "line_number": 311, "usage_type": "name"}, {"api_name": "ctypes.WINFUNCTYPE", "line_number": 314, "usage_type": "call"}, {"api_name": "ctypes.HRESULT", "line_number": 315, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes.HWND", "line_number": 316, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 316, "usage_type": "name"}, {"api_name": "ctypes.wintypes.UINT", "line_number": 316, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes.WPARAM", "line_number": 316, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes.LPARAM", "line_number": 316, "usage_type": "attribute"}, {"api_name": "ctypes.Structure", "line_number": 319, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes.UINT", "line_number": 321, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 321, "usage_type": "name"}, {"api_name": "ctypes.wintypes.UINT", "line_number": 322, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 322, "usage_type": "name"}, {"api_name": "ctypes.wintypes.INT", "line_number": 324, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 324, "usage_type": "name"}, {"api_name": "ctypes.wintypes.INT", "line_number": 325, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 325, "usage_type": "name"}, {"api_name": "ctypes.wintypes.HANDLE", "line_number": 326, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 326, "usage_type": "name"}, {"api_name": "ctypes.wintypes.HICON", "line_number": 327, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 327, "usage_type": "name"}, {"api_name": "ctypes.wintypes.HANDLE", "line_number": 328, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 328, "usage_type": "name"}, {"api_name": "ctypes.wintypes.HBRUSH", "line_number": 329, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 329, "usage_type": "name"}, {"api_name": "ctypes.wintypes.LPCWSTR", "line_number": 330, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 330, "usage_type": "name"}, {"api_name": "ctypes.wintypes.LPCWSTR", "line_number": 331, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 331, "usage_type": "name"}, {"api_name": "ctypes.wintypes.HICON", "line_number": 332, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 332, "usage_type": "name"}, {"api_name": "ctypes.Structure", "line_number": 352, "usage_type": "attribute"}, {"api_name": "ctypes.Union", "line_number": 353, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes.UINT", "line_number": 355, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 355, "usage_type": "name"}, {"api_name": "ctypes.wintypes.UINT", "line_number": 356, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 356, "usage_type": "name"}, {"api_name": "ctypes.wintypes.DWORD", "line_number": 374, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 374, "usage_type": "name"}, {"api_name": "ctypes.wintypes.HWND", "line_number": 375, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 375, "usage_type": "name"}, {"api_name": "ctypes.wintypes.UINT", "line_number": 376, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 376, "usage_type": "name"}, {"api_name": "ctypes.wintypes.UINT", "line_number": 377, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 377, "usage_type": "name"}, {"api_name": "ctypes.wintypes.UINT", "line_number": 378, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 378, "usage_type": "name"}, {"api_name": "ctypes.wintypes.HICON", "line_number": 379, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 379, "usage_type": "name"}, {"api_name": "ctypes.wintypes.WCHAR", "line_number": 380, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 380, "usage_type": "name"}, {"api_name": "ctypes.wintypes.DWORD", "line_number": 381, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 381, "usage_type": "name"}, {"api_name": "ctypes.wintypes.DWORD", "line_number": 382, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 382, "usage_type": "name"}, {"api_name": "ctypes.wintypes.WCHAR", "line_number": 383, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 383, "usage_type": "name"}, {"api_name": "ctypes.wintypes.WCHAR", "line_number": 385, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 385, "usage_type": "name"}, {"api_name": "ctypes.wintypes.DWORD", "line_number": 386, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 386, "usage_type": "name"}, {"api_name": "ctypes.wintypes.LPVOID", "line_number": 387, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 387, "usage_type": "name"}, {"api_name": "ctypes.wintypes.HICON", "line_number": 388, "usage_type": "attribute"}, {"api_name": "ctypes.wintypes", "line_number": 388, "usage_type": "name"}]} +{"seq_id": "156953734", "text": "from tkinter import *\r\nfrom PIL import ImageTk, Image\r\nfrom random import randint\r\nimport random\r\nfrom tkinter import messagebox\r\n\r\n\r\nroot=Tk()\r\nroot.title(\"Flash App\")\r\nroot.geometry(\"810x550\")\r\nload = Image.open(\"C:/gui/india.png\")\r\nrender = ImageTk.PhotoImage(load)\r\nimg = Label(root, image=render)\r\nimg.image = render\r\nimg.place(x=2, y=2)\r\n# alabel=Label(root,text=\"Created by\"+ \" Yash Mehta 1811024 \" +\"Nidhi Nair 1811028\").grid(row=1,column=1,columnspan=3)\r\n\r\n\r\ndef math_random():\r\n # generate a random number\r\n global num_1\r\n global num_2\r\n num_1=randint(0,10)\r\n num_2=randint(0,10)\r\n\r\n global add_image1\r\n global add_image2\r\n card1=\"C:/gui/Flashcards/\" + str(num_1) +\".png\"\r\n card2=\"C:/gui/Flashcards/\" + str(num_2) +\".png\"\r\n add_image1=ImageTk.PhotoImage(Image.open(card1))\r\n add_image2=ImageTk.PhotoImage(Image.open(card2))\r\n \r\n # put flashcard on screen\r\n add_1.config(image=add_image1)\r\n add_2.config(image=add_image2)\r\n\r\n\r\n\r\n\r\n# create addition answer function\r\ndef answer_sub():\r\n answer=num_1-num_2\r\n if sub_answer.get()==\"\":\r\n messagebox.showerror(\"Error\",\"Write a value\")\r\n elif int(sub_answer.get())==answer:\r\n response=\"Correct! \"+str(num_1) +\" - \"+str(num_2)+\" = \"+str(answer)\r\n answer_message.config(text=response)\r\n else:\r\n response=\"Wrong! \"+str(num_1) +\" - \"+str(num_2)+\" = \"+str(answer) +\" Not \"+sub_answer.get()\r\n answer_message.config(text=response)\r\n \r\n sub_answer.delete(0,END)\r\n math_random()\r\n\r\n\r\n\r\n\r\n\r\n\r\n# create addition math flashcard function\r\ndef sub():\r\n hide_all_frames()\r\n sub_frame.pack(fill=\"both\",expand=1)\r\n\r\n add_label=Label(sub_frame,text=\"Subtraction Flashcards\",font=(\"Helvetica\",18)).pack(pady=15)\r\n pic_frame=Frame(sub_frame,width=400,height=300)\r\n pic_frame.pack()\r\n\r\n # generate a random number\r\n global num_1\r\n global num_2\r\n num_1=randint(0,10)\r\n num_2=randint(0,10)\r\n\r\n # create 3 labels inside our pic frame\r\n global add_1\r\n global add_2\r\n add_1=Label(pic_frame)\r\n add_2=Label(pic_frame)\r\n math_sign=Label(pic_frame,text=\"-\",font=(\"Helvetica\",28))\r\n # grid labels\r\n add_1.grid(row=0,column=0)\r\n math_sign.grid(row=0,column=1)\r\n add_2.grid(row=0,column=2)\r\n\r\n global add_image1\r\n global add_image2\r\n card1=\"C:/gui/Flashcards/\" + str(num_1) +\".png\"\r\n card2=\"C:/gui/Flashcards/\" + str(num_2) +\".png\"\r\n add_image1=ImageTk.PhotoImage(Image.open(card1))\r\n add_image2=ImageTk.PhotoImage(Image.open(card2))\r\n \r\n # put flashcard on screen\r\n add_1.config(image=add_image1)\r\n add_2.config(image=add_image2)\r\n\r\n\r\n # create answer box and button\r\n global sub_answer\r\n sub_answer=Entry(sub_frame,font=(\"Helvetica\",18))\r\n sub_answer.pack(pady=50)\r\n\r\n sub_answer_button=Button(sub_frame,text=\"Answer\",command=answer_sub)\r\n sub_answer_button.pack()\r\n\r\n global answer_message\r\n answer_message =Label(sub_frame,text=\"\",font=(\"Helvetica\",18))\r\n answer_message.pack(pady=40)\r\n\r\n\r\n\r\n\r\n# create addition answer function\r\ndef answer_add():\r\n answer=num_1+num_2\r\n if add_answer.get()==\"\":\r\n messagebox.showerror(\"Error\",\"Write a value\")\r\n elif int(add_answer.get())==answer:\r\n response=\"Correct! \"+str(num_1) +\" + \"+str(num_2)+\" = \"+str(answer)\r\n answer_message.config(text=response)\r\n else:\r\n response=\"Wrong! \"+str(num_1) +\" + \"+str(num_2)+\" = \"+str(answer) +\" Not \"+add_answer.get()\r\n answer_message.config(text=response)\r\n \r\n add_answer.delete(0,END)\r\n math_random()\r\n\r\n\r\n\r\n\r\n\r\n# create addition math flashcard function\r\ndef add():\r\n hide_all_frames()\r\n add_frame.pack(fill=\"both\",expand=1)\r\n\r\n add_label=Label(add_frame,text=\"Addition Flashcards\",font=(\"Helvetica\",18)).pack(pady=15)\r\n pic_frame=Frame(add_frame,width=400,height=300)\r\n pic_frame.pack()\r\n\r\n # generate a random number\r\n global num_1\r\n global num_2\r\n num_1=randint(0,10)\r\n num_2=randint(0,10)\r\n\r\n # create 3 labels inside our pic frame\r\n global add_1\r\n global add_2\r\n add_1=Label(pic_frame)\r\n add_2=Label(pic_frame)\r\n math_sign=Label(pic_frame,text=\"+\",font=(\"Helvetica\",28))\r\n # grid labels\r\n add_1.grid(row=0,column=0)\r\n math_sign.grid(row=0,column=1)\r\n add_2.grid(row=0,column=2)\r\n\r\n global add_image1\r\n global add_image2\r\n card1=\"C:/gui/Flashcards/\" + str(num_1) +\".png\"\r\n card2=\"C:/gui/Flashcards/\" + str(num_2) +\".png\"\r\n add_image1=ImageTk.PhotoImage(Image.open(card1))\r\n add_image2=ImageTk.PhotoImage(Image.open(card2))\r\n \r\n # put flashcard on screen\r\n add_1.config(image=add_image1)\r\n add_2.config(image=add_image2)\r\n\r\n\r\n # create answer box and button\r\n global add_answer\r\n add_answer=Entry(add_frame,font=(\"Helvetica\",18))\r\n add_answer.pack(pady=50)\r\n\r\n add_answer_button=Button(add_frame,text=\"Answer\",command=answer_add)\r\n add_answer_button.pack()\r\n\r\n global answer_message\r\n answer_message =Label(add_frame,text=\"\",font=(\"Helvetica\",18))\r\n answer_message.pack(pady=40)\r\n\r\n\r\n\r\n\r\n\r\n# create randomizing state function \r\ndef random_state():\r\n # create a list of our state names\r\n global our_states\r\n our_states=['andhrapradesh','arunachalpradesh','assam','bihar',\r\n 'chattisgarh','goa','gujarat','haryana',\r\n 'himachalpradesh','jharkhand','karnataka','kerala',\r\n 'madhyapradesh','maharashtra','manipur','meghalaya',\r\n 'mizoram','nagaland','odisha','punjab',\r\n 'rajasthan','sikkim','tamilnadu','telangana',\r\n 'tripura','uttarakhand','uttarpradesh','westbengal']\r\n\r\n # generate random number\r\n global rando\r\n rando=randint(0,len(our_states)-1)\r\n state1=\"C:/gui/states/\" + our_states[rando] +\".png\"\r\n\r\n # create state images\r\n global state_img\r\n state_img=ImageTk.PhotoImage(Image.open(state1))\r\n show_state.config(image=state_img,bg=\"white\")\r\n\r\n\r\n\r\n\r\n\r\n# create state capital answers\r\ndef state_capital_answer():\r\n if capital_radio.get() == our_state_capitals[answer]:\r\n response = \"Correct! \"+our_state_capitals[answer].title()+\" is the capital of \"+answer.title()\r\n else:\r\n response=\"Incorrect! \"+our_state_capitals[answer].title()+\" is the capital of \"+answer.title()\r\n\r\n answer_label_capitals.config(text=response)\r\n\r\n\r\n\r\n\r\n# create answer function\r\ndef state_answer():\r\n answer=answer_input.get()\r\n answer=answer.replace(\"\",\"\")\r\n\r\n # determine if our answer is right or wrong\r\n if answer.lower()==\"\":\r\n messagebox.showerror(\"Error\",\"Atleast give a miss!!\")\r\n elif answer.lower()==our_states[rando]:\r\n response=\"correct \" +our_states[rando].title()\r\n answer_label.config(text=response)\r\n else:\r\n response=\"Incorrect! \"+our_states[rando].title()\r\n answer_label.config(text=response) \r\n\r\n\r\n # answer_label.config(text=response)\r\n\r\n # clear the entry box\r\n answer_input.delete(0,END)\r\n\r\n\r\n random_state()\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n# create state flashcard function\r\ndef states():\r\n # hide previous frames\r\n hide_all_frames()\r\n state_frame.pack(fill=\"both\",expand=1)\r\n\r\n global show_state\r\n show_state=Label(state_frame)\r\n show_state.pack(pady=15)\r\n random_state()\r\n\r\n # create answer input box\r\n global answer_input\r\n answer_input=Entry(state_frame,font=(\"Helvetica\",18),bd=2)\r\n answer_input.pack(pady=15)\r\n\r\n # create button to randomize images\r\n rando_btn=Button(state_frame,text=\"Next State\",command=states)\r\n rando_btn.pack(pady=10)\r\n\r\n # create a button to ans the q \r\n answer_button=Button(state_frame,text=\"Answer\",command=state_answer)\r\n answer_button.pack(pady=5)\r\n\r\n # create a label to tell us if we got the right answer or not\r\n global answer_label\r\n answer_label=Label(state_frame,text=\"\",font=(\"Helvetica\",18),bg=\"white\")\r\n answer_label.pack(pady=15)\r\n\r\n\r\n\r\n\r\n\r\n# create state capital flashcard function\r\ndef state_capitals():\r\n hide_all_frames()\r\n state_capital_frame.pack(fill=\"both\",expand=1)\r\n # my_label=Label(state_capital_frame,text=\"Capitals\").pack()\r\n \r\n global show_state\r\n show_state=Label(state_capital_frame)\r\n show_state.pack(pady=15)\r\n\r\n global our_states\r\n our_states=['andhrapradesh','arunachalpradesh','assam','bihar',\r\n 'chattisgarh','goa','gujarat','haryana',\r\n 'himachalpradesh','jharkhand','karnataka','kerala',\r\n 'madhyapradesh','maharashtra','manipur','meghalaya',\r\n 'mizoram','nagaland','odisha','punjab',\r\n 'rajasthan','sikkim','tamilnadu','telangana',\r\n 'tripura','uttarakhand','uttarpradesh','westbengal']\r\n\r\n global our_state_capitals\r\n our_state_capitals={\r\n 'andhrapradesh':\"hyderabad\",\r\n 'arunachalpradesh':\"itanagar\",\r\n 'assam':\"dispur\",\r\n 'bihar':\"patna\",\r\n 'chattisgarh':\"raipur\",\r\n 'goa':\"panaji\",\r\n 'gujarat':\"gandhinagar\",\r\n 'haryana':\"chandigarh\",\r\n 'himachalpradesh':\"shimla\",\r\n 'jharkhand':\"ranchi\",\r\n 'karnataka':\"bangalore\",\r\n 'kerala':\"trivandrum\",\r\n 'madhyapradesh':\"bhopal\",\r\n 'maharashtra':\"mumbai\",\r\n 'manipur':\"imphal\",\r\n 'meghalaya':\"shillong\",\r\n 'mizoram':\"aizawl\",\r\n 'nagaland':\"kohima\",\r\n 'odisha':\"bhubaneshwar\",\r\n 'punjab':\"chandigarh\",\r\n 'rajasthan':\"jaipur\",\r\n 'sikkim':\"gangtok\",\r\n 'tamilnadu':\"chennai\",\r\n 'telangana':\"hyderabad\",\r\n 'tripura':\"agartala\",\r\n 'uttarakhand':\"dehradun\",\r\n 'uttarpradesh':\"lucknow\",\r\n 'westbengal':\"kolkata\"\r\n }\r\n\r\n # create empty answer list and counter\r\n answer_list=[]\r\n count = 1\r\n global answer\r\n # generate 3 random capitals\r\n while count <4:\r\n rando=randint(0,len(our_states)-1)\r\n # if first selection,make it our answer\r\n if count==1:\r\n answer=our_states[rando]\r\n global state_img\r\n state=\"C:/gui/states/\"+our_states[rando]+\".png\"\r\n state_img=ImageTk.PhotoImage(Image.open(state))\r\n show_state.config(image=state_img)\r\n\r\n # add our first selection to a new list\r\n answer_list.append(our_states[rando])\r\n\r\n # remove from old list\r\n our_states.remove(our_states[rando])\r\n\r\n # shuffle original list\r\n random.shuffle(our_states)\r\n\r\n count=count+1\r\n\r\n random.shuffle(answer_list)\r\n\r\n global capital_radio\r\n capital_radio=StringVar()\r\n capital_radio.set(our_state_capitals[answer_list[0]])\r\n\r\n capital_radio_button1=Radiobutton(state_capital_frame,text=our_state_capitals[answer_list[0]].title(),variable=capital_radio,value=our_state_capitals[answer_list[0]]).pack()\r\n capital_radio_button2=Radiobutton(state_capital_frame,text=our_state_capitals[answer_list[1]].title(),variable=capital_radio,value=our_state_capitals[answer_list[1]]).pack()\r\n capital_radio_button3=Radiobutton(state_capital_frame,text=our_state_capitals[answer_list[2]].title(),variable=capital_radio,value=our_state_capitals[answer_list[2]]).pack()\r\n\r\n # add a pass button\r\n pass_button=Button(state_capital_frame,text=\"Next\",command=state_capitals)\r\n pass_button.pack(pady=15)\r\n\r\n # create a button to answer\r\n capital_answer_button=Button(state_capital_frame,text=\"Answer\",command=state_capital_answer)\r\n capital_answer_button.pack(pady=15)\r\n\r\n # create an answer label\r\n global answer_label_capitals\r\n answer_label_capitals=Label(state_capital_frame,text=\"\",font=(\"Helvetica\",12)) \r\n answer_label_capitals.pack(pady=15)\r\n\r\n\r\n\r\n\r\n\r\n# hide all previous frames\r\ndef hide_all_frames():\r\n # loop through and destroy all children in previous frames\r\n for widget in state_frame.winfo_children():\r\n widget.destroy()\r\n\r\n for widget in state_capital_frame.winfo_children():\r\n widget.destroy()\r\n\r\n for widget in add_frame.winfo_children():\r\n widget.destroy() \r\n\r\n for widget in sub_frame.winfo_children():\r\n widget.destroy() \r\n\r\n sub_frame.pack_forget()\r\n add_frame.pack_forget()\r\n state_frame.pack_forget()\r\n state_capital_frame.pack_forget()\r\n\r\n\r\n\r\n# create menu\r\nmy_menu=Menu(root)\r\nroot.config(menu=my_menu)\r\n\r\n# geography menu items\r\nstates_menu=Menu(my_menu)\r\nmy_menu.add_cascade(label=\"Geography\",menu=states_menu)\r\nstates_menu.add_command(label=\"states\",command=states)\r\nstates_menu.add_command(label=\"states capitals\",command=state_capitals)\r\nstates_menu.add_separator()\r\nstates_menu.add_command(label=\"Exit\",command=root.quit)\r\n\r\n# Math flashcard menu\r\nmath_menu=Menu(my_menu)\r\nmy_menu.add_cascade(label=\"Math\",menu=math_menu)\r\nmath_menu.add_command(label=\"Addition\",command=add)\r\nmath_menu.add_command(label=\"Subtraction\",command=sub)\r\nmath_menu.add_separator()\r\nmath_menu.add_command(label=\"Exit\",command=root.quit)\r\n\r\n\r\n# create our frames\r\nstate_frame=Frame(root,width=500,height=500,bg=\"white\")\r\nstate_capital_frame=Frame(root,width=500,height=500)\r\n# addition and subtraction frames\r\nadd_frame=Frame(root,width=500,height=500)\r\nsub_frame=Frame(root,width=500,height=500)\r\n\r\nroot.mainloop()", "sub_path": "p.py", "file_name": "p.py", "file_ext": "py", "file_size_in_byte": 13222, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PIL.Image.open", "line_number": 11, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 11, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 12, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 12, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 23, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 24, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 30, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 30, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 30, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 31, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 31, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 31, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 44, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 44, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 72, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 73, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 90, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 90, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 90, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 90, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 91, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 91, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 91, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 91, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 117, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 117, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 144, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 145, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 162, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 162, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 162, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 162, "usage_type": "name"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 163, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 163, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 163, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 163, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 200, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 205, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 205, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 205, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 205, "usage_type": "name"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 231, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 231, "usage_type": "name"}, {"api_name": "random.randint", "line_number": 344, "usage_type": "call"}, {"api_name": "PIL.ImageTk.PhotoImage", "line_number": 350, "usage_type": "call"}, {"api_name": "PIL.ImageTk", "line_number": 350, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 350, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 350, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 360, "usage_type": "call"}, {"api_name": "random.shuffle", "line_number": 364, "usage_type": "call"}]} +{"seq_id": "280657756", "text": "\"\"\"Keras Sequence object for running BinarySkipGramSequence on texts.\"\"\"\nfrom typing import Tuple\n\nimport numpy as np # type: ignore\nfrom ensmallen_graph import preprocessing # pylint: disable=no-name-in-module\n\nfrom .abstract_word2vec_sequence import AbstractWord2VecSequence\n\n\nclass WordBinarySkipGramSequence(AbstractWord2VecSequence):\n \"\"\"Keras Sequence object for running BinarySkipGramSequence on texts.\"\"\"\n\n def __init__(\n self,\n sequences: np.ndarray,\n batch_size: int,\n vocabulary_size: int,\n negative_samples: float = 7,\n window_size: int = 4,\n shuffle: bool = True,\n seed: int = 42,\n elapsed_epochs: int = 0,\n ):\n \"\"\"Create new Sequence object.\n\n Parameters\n -----------------------------\n sequences: np.ndarray,\n List of encoded texts.\n batch_size: int,\n Number of nodes to include in a single batch.\n negative_samples: float = 7,\n Factor of negative samples to use.\n window_size: int = 4,\n Window size for the local context.\n On the borders the window size is trimmed.\n shuffle: bool = True,\n Wthever to shuffle the vectors.\n seed: int = 42,\n The seed to use to make extraction reproducible.\n elapsed_epochs: int = 0,\n Number of elapsed epochs to init state of generator.\n \"\"\"\n self._negative_samples = negative_samples\n self._vocabulary_size = vocabulary_size\n\n super().__init__(\n sequences=sequences,\n batch_size=batch_size,\n window_size=window_size,\n shuffle=shuffle,\n seed=seed,\n elapsed_epochs=elapsed_epochs\n )\n\n def __getitem__(self, idx: int) -> Tuple[Tuple[np.ndarray, np.ndarray], None]:\n \"\"\"Return batch corresponding to given index.\n\n The return tuple of tuples is composed of an inner tuple, containing\n the words vector and the vector of vectors of the contexts.\n Depending on the order of the input_layers of the models that can\n accept these data format, one of the vectors is used as training\n input and the other one is used as the output for the NCE loss layer.\n\n The words vectors and contexts vectors contain numeric IDs, that\n represent the index of the words' embedding column.\n\n The true output value is None, since no loss function is used after\n the NCE loss, that is implemented as a layer, and this vastly improves\n the speed of the training process since it does not require to allocate\n empty vectors of considerable size for the one-hot encoding process.\n\n Parameters\n ---------------\n idx: int,\n Index corresponding to batch to be rendered.\n\n Returns\n ---------------\n Tuple of tuples with input data.\n \"\"\"\n return preprocessing.binary_skipgrams(\n idx+self.elapsed_epochs,\n self._sequences[idx],\n vocabulary_size=self._vocabulary_size,\n window_size=self._window_size,\n negative_samples=self._negative_samples,\n shuffle=self._shuffle\n )\n", "sub_path": "embiggen/sequences/word_binary_skipgram_sequence.py", "file_name": "word_binary_skipgram_sequence.py", "file_ext": "py", "file_size_in_byte": 3241, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "abstract_word2vec_sequence.AbstractWord2VecSequence", "line_number": 10, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 15, "usage_type": "attribute"}, {"api_name": "ensmallen_graph.preprocessing.binary_skipgrams", "line_number": 82, "usage_type": "call"}, {"api_name": "ensmallen_graph.preprocessing", "line_number": 82, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 56, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 56, "usage_type": "attribute"}]} +{"seq_id": "304292047", "text": "import unittest\n\nfrom dateutil.parser import parse\nfrom google.appengine.ext import testbed\n\nfrom ewentts.models import Event, User\nfrom ewentts.utils import validate_picture_url, return_event, return_user, validate_location\n\n\nclass RequestDecodedTokenTestCase(unittest.TestCase):\n def setUp(self):\n pass\n\n def tearDown(self):\n pass\n\n\nclass RequestUIDTestCase(unittest.TestCase):\n def setUp(self):\n pass\n\n def tearDown(self):\n pass\n\n\nclass ValidatePictureUrlTest(unittest.TestCase):\n def test_validate_string_is_picture(self):\n picture_url1 = \"https://c1.staticflickr.com/2/1520/24330829813_944c817720_b.jpg\"\n picture_url2 = \"https://www.gettyimages.ie/gi-resources/images/Homepage/Hero/UK/CMS_Creative_164657191_Kingfisher.jpg\"\n picture_url3 = \"https://cdn.pixabay.com/photo/2016/06/18/17/42/image-1465348_960_720.jpg\"\n picture_url4 = \"https://upload.wikimedia.org/wikipedia/commons/thumb/4/47/PNG_transparency_demonstration_1.png/280px-PNG_transparency_demonstration_1.png\"\n\n self.assertEqual(validate_picture_url(picture_url1), True)\n self.assertEqual(validate_picture_url(picture_url2), True)\n self.assertEqual(validate_picture_url(picture_url3), True)\n self.assertEqual(validate_picture_url(picture_url4), True)\n\n def test_non_picture_str_raises_error(self):\n picture_url1 = \"abc\"\n picture_url2 = \"www.picture.cz\"\n picture_url3 = \"picture.img\"\n picture_url4 = \".img\"\n picture_url5 = \"www.picture.cz/1234.img/123\"\n\n with self.assertRaises(ValueError):\n validate_picture_url(picture_url1)\n with self.assertRaises(ValueError):\n validate_picture_url(picture_url2)\n with self.assertRaises(ValueError):\n validate_picture_url(picture_url3)\n with self.assertRaises(ValueError):\n validate_picture_url(picture_url4)\n with self.assertRaises(ValueError):\n validate_picture_url(picture_url5)\n\n def test_other_datatype_raise_error(self):\n picture_url1 = 1\n picture_url2 = None\n picture_url3 = [\"picture\", \"img\"]\n\n with self.assertRaises(ValueError):\n validate_picture_url(picture_url1)\n with self.assertRaises(ValueError):\n validate_picture_url(picture_url2)\n with self.assertRaises(ValueError):\n validate_picture_url(picture_url3)\n\n\nclass ReturnEventTestCase(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n pass\n\n def setUp(self):\n self.testbed = testbed.Testbed()\n self.testbed.activate()\n self.testbed.setup_env(overwrite=True)\n self.testbed.init_datastore_v3_stub()\n self.testbed.init_memcache_stub()\n self.user = User(user_names=[\"User\", \"Name\"],\n id=\"ab11\",\n profile_picture_url=\"https://c1.staticflickr.com/2/1520/24330829813_944c817720_b.jpg\",\n user_email=\"email\")\n\n self.event1 = Event(event_name=\"Event Name\",\n status=\"future\",\n start_datetime=parse(\"2100-10-03T10:17:30\"),\n end_datetime=parse(\"2100-11-04T10:17:30\"),\n latitude=49.395470,\n longitude=15.590950,\n event_picture_url=\"https://i.imgur.com/nqTGipe.jpg\",\n private = True,\n organiser = self.user.key)\n\n self.event1.put()\n\n def tearDown(self):\n self.testbed.deactivate()\n\n def test_return_event_woks(self):\n event = return_event(self.event1.key.id())\n self.assertEqual(event, self.event1)\n\n def test_return_fails_wrong_id(self):\n with self.assertRaises(Exception):\n return_event(9887)\n\n\nclass ReturnUserTestCase(unittest.TestCase):\n @classmethod\n def setUpClass(cls):\n pass\n\n def setUp(self):\n self.testbed = testbed.Testbed()\n self.testbed.activate()\n self.testbed.setup_env(overwrite=True)\n self.testbed.init_datastore_v3_stub()\n self.testbed.init_memcache_stub()\n self.user = User(user_names=[\"User\", \"Name\"],\n id=\"ab11\",\n profile_picture_url=\"https://c1.staticflickr.com/2/1520/24330829813_944c817720_b.jpg\",\n user_email=\"email\")\n\n self.user.put()\n\n def tearDown(self):\n self.testbed.deactivate()\n\n def test_return_user_woks(self):\n user = return_user(self.user.key.id())\n\n self.assertEqual(user, self.user)\n\n def test_return_user_wrong_id(self):\n with self.assertRaises(Exception):\n return_user(\"9885\")\n\n\nclass ValidateLocationTest(unittest.TestCase):\n def test_validate_tuple_is_location(self):\n location1 = [0, 0]\n location2 = [90, 180]\n location3 = [-90, -180]\n location4 = [-90, 180]\n location5 = [-40, 20]\n\n self.assertEqual(validate_location(*location1), True)\n self.assertEqual(validate_location(*location2), True)\n self.assertEqual(validate_location(*location3), True)\n self.assertEqual(validate_location(*location4), True)\n self.assertEqual(validate_location(*location5), True)\n\n def test_wrong_location_raises_error(self):\n location1 = [0, 181]\n location2 = [90, -181]\n location3 = [-91, -180]\n location4 = [91, 180]\n location5 = [1000, 20]\n location6 = [100, 200]\n\n with self.assertRaises(ValueError):\n validate_location(*location1)\n with self.assertRaises(ValueError):\n validate_location(*location2)\n with self.assertRaises(ValueError):\n validate_location(*location3)\n with self.assertRaises(ValueError):\n validate_location(*location4)\n with self.assertRaises(ValueError):\n validate_location(*location5)\n with self.assertRaises(ValueError):\n validate_location(*location6)\n\n def test_other_datatype_raise_error(self):\n location1 = 1\n location2 = None\n location3 = [\"picture\", \"img\"]\n location4 = [\"10\", \"10\"]\n\n with self.assertRaises(Exception):\n validate_location(*location1)\n with self.assertRaises(Exception):\n validate_location(*location2)\n with self.assertRaises(Exception):\n validate_location(*location3)\n with self.assertRaises(Exception):\n validate_location(*location4)", "sub_path": "tests/utils_test.py", "file_name": "utils_test.py", "file_ext": "py", "file_size_in_byte": 6588, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "unittest.TestCase", "line_number": 10, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 18, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 26, "usage_type": "attribute"}, {"api_name": "ewentts.utils.validate_picture_url", "line_number": 33, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_picture_url", "line_number": 34, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_picture_url", "line_number": 35, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_picture_url", "line_number": 36, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_picture_url", "line_number": 46, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_picture_url", "line_number": 48, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_picture_url", "line_number": 50, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_picture_url", "line_number": 52, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_picture_url", "line_number": 54, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_picture_url", "line_number": 62, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_picture_url", "line_number": 64, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_picture_url", "line_number": 66, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 69, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.testbed.Testbed", "line_number": 75, "usage_type": "call"}, {"api_name": "google.appengine.ext.testbed", "line_number": 75, "usage_type": "name"}, {"api_name": "ewentts.models.User", "line_number": 80, "usage_type": "call"}, {"api_name": "ewentts.models.Event", "line_number": 85, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 87, "usage_type": "call"}, {"api_name": "dateutil.parser.parse", "line_number": 88, "usage_type": "call"}, {"api_name": "ewentts.utils.return_event", "line_number": 101, "usage_type": "call"}, {"api_name": "ewentts.utils.return_event", "line_number": 106, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 109, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.testbed.Testbed", "line_number": 115, "usage_type": "call"}, {"api_name": "google.appengine.ext.testbed", "line_number": 115, "usage_type": "name"}, {"api_name": "ewentts.models.User", "line_number": 120, "usage_type": "call"}, {"api_name": "ewentts.utils.return_user", "line_number": 131, "usage_type": "call"}, {"api_name": "ewentts.utils.return_user", "line_number": 137, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 140, "usage_type": "attribute"}, {"api_name": "ewentts.utils.validate_location", "line_number": 148, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 149, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 150, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 151, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 152, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 163, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 165, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 167, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 169, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 171, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 173, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 182, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 184, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 186, "usage_type": "call"}, {"api_name": "ewentts.utils.validate_location", "line_number": 188, "usage_type": "call"}]} +{"seq_id": "70983177", "text": "from django.db import models\r\nimport datetime\r\nfrom django.utils.timezone import now\r\n\r\nCHOICES = (\r\n (\"Alin\",\"Aljabar Linear\"),\r\n (\"MPPI\",\"Metodologi Penelitian dan Penulisan Ilmiah\"),\r\n (\"PBP\",\"Pemrograman Berbasis Platform\"),\r\n (\"SOSI\",\"Sistem Operasi untuk Sistem Informasi\"),\r\n (\"SDA\",\"Struktur Data & Algoritma\"),\r\n)\r\n\r\nPRIORITY = (\r\n (\"Tinggi\",\"Tinggi\"),\r\n (\"Sedang\",\"Sedang\"),\r\n (\"Rendah\",\"Rendah\"),\r\n)\r\n\r\nclass JadwalBelajarBareng(models.Model):\r\n def __str__(self): \r\n return self.Topik\r\n Prioritas = models.TextField(max_length = 15, choices=PRIORITY)\r\n Matkul = models.TextField(max_length = 150, choices=CHOICES)\r\n Waktu = models.DateTimeField()\r\n Topik = models.CharField(max_length = 150)\r\n Informasi = models.TextField()\r\n Link = models.URLField(max_length = 200)", "sub_path": "jadwal_belajar_bareng/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 797, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.db.models.Model", "line_number": 19, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 22, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 22, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 23, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 26, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 26, "usage_type": "name"}, {"api_name": "django.db.models.URLField", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}]} +{"seq_id": "610118084", "text": "from collections import OrderedDict\nSHAPES = OrderedDict({\n 'line': {\n 'lines': [\n [(-1, 0, 0), (1, 0, 0)],\n ],\n 'quads': [],\n },\n 'square': {\n 'lines': [\n [(-1, 0, -1), (1, 0, -1), (1, 0, 1), (-1, 0, 1), (-1, 0, -1)]\n ],\n 'quads': [],\n },\n 'shaded_square': {\n 'lines': [\n [(-1, 0, -1), (1, 0, -1), (1, 0, 1), (-1, 0, 1), (-1, 0, -1)]\n ],\n 'quads': [\n [(-1, 0, -1), (1, 0, -1), (1, 0, 1), (-1, 0, 1)],\n ],\n }\n})\n\n\n\ndef get_bounds(shape, scale=1):\n\n lines, quads = shape['lines'], shape['quads']\n\n max_values = []\n min_values = []\n\n if lines:\n flatlines = [t for line in lines for t in line]\n max_values.append(map(max, *flatlines))\n min_values.append(map(min, *flatlines))\n\n if quads:\n flatquads = [t for quad in quads for t in quad]\n max_values.append(map(max, *flatquads))\n min_values.append(map(min, *flatquads))\n\n if len(max_values) < 2:\n min_value = min_values[0]\n max_value = max_values[0]\n else:\n max_value = map(max, *max_values)\n min_value = map(min, *min_values)\n\n return [x*scale for x in min_value], [x*scale for x in max_value]\n\n", "sub_path": "scripts/test_get_bounds.py", "file_name": "test_get_bounds.py", "file_ext": "py", "file_size_in_byte": 1268, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.OrderedDict", "line_number": 2, "usage_type": "call"}]} +{"seq_id": "253005252", "text": "# ##### 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\nbl_info = {\n \"name\": \"Bswap Admin Tools\",\n \"author\": \"Pablo Vazquez, Matthew Muldoon\",\n \"version\": (0, 1),\n \"blender\": (2, 71),\n \"location\": \"Everywhere!\",\n \"description\": \"A collection of tools and settings to improve productivity\",\n \"warning\": \"\",\n \"wiki_url\": \"http://pablovazquez.org/amaranth\",\n \"tracker_url\": \"\",\n \"category\": \"Scene\"}\n\n\nimport bpy\nimport bmesh\nfrom bpy.types import Operator, AddonPreferences, Panel, Menu\nfrom bpy.props import (BoolProperty, EnumProperty,\n FloatProperty, FloatVectorProperty,\n IntProperty, StringProperty)\nfrom mathutils import Vector\nfrom bpy.app.handlers import persistent\nfrom bl_operators.presets import AddPresetBase\n\n# Addon wide, we need to know if cycles is available\ncycles_exists = False\n\n\ndef check_cycles_exists():\n global cycles_exists\n cycles_exists = ('cycles' in dir(bpy.types.Scene)) \n return cycles_exists\n\n\ncheck_cycles_exists()\n\n\n# Preferences\nclass AmaranthToolsetPreferences(AddonPreferences):\n bl_idname = __name__\n use_scene_stats = BoolProperty(\n \tname=\"Extra Scene Statistics\",\n description=\"Display extra scene statistics in Info editor's header\",\n default=True,\n )\n\n# Scene Debug\n # Cycles Node Types\n if check_cycles_exists():\n cycles_shader_node_types = [\n (\"BSDF_DIFFUSE\", \"Diffuse BSDF\", \"\", 0),\n (\"BSDF_GLOSSY\", \"Glossy BSDF\", \"\", 1),\n (\"BSDF_TRANSPARENT\", \"Transparent BSDF\", \"\", 2),\n (\"BSDF_REFRACTION\", \"Refraction BSDF\", \"\", 3),\n (\"BSDF_GLASS\", \"Glass BSDF\", \"\", 4),\n (\"BSDF_TRANSLUCENT\", \"Translucent BSDF\", \"\", 5),\n (\"BSDF_ANISOTROPIC\", \"Anisotropic BSDF\", \"\", 6),\n (\"BSDF_VELVET\", \"Velvet BSDF\", \"\", 7),\n (\"BSDF_TOON\", \"Toon BSDF\", \"\", 8),\n (\"SUBSURFACE_SCATTERING\", \"Subsurface Scattering\", \"\", 9),\n (\"EMISSION\", \"Emission\", \"\", 10),\n (\"BSDF_HAIR\", \"Hair BSDF\", \"\", 11),\n (\"BACKGROUND\", \"Background\", \"\", 12),\n (\"AMBIENT_OCCLUSION\", \"Ambient Occlusion\", \"\", 13),\n (\"HOLDOUT\", \"Holdout\", \"\", 14),\n (\"VOLUME_ABSORPTION\", \"Volume Absorption\", \"\", 15),\n (\"VOLUME_SCATTER\", \"Volume Scatter\", \"\", 16)\n ]\n\n scene.amaranth_cycles_node_types = EnumProperty(\n items=cycles_shader_node_types, name = \"Shader\")\n\n scene.amaranth_cycles_list_sampling = BoolProperty(\n default=False,\n name=\"Samples Per:\")\n\n bpy.types.CyclesRenderSettings.use_samples_final = BoolProperty(\n name=\"Use Final Render Samples\",\n description=\"Use current shader samples as final render samples\",\n default=False)\n\n scene.amaranth_lighterscorner_list_meshlights = BoolProperty(\n default=False,\n name=\"List Meshlights\",\n description=\"Include light emitting meshes on the list\")\n\n scene.amaranth_debug_scene_list_missing_images = BoolProperty(\n default=False,\n name=\"List Missing Images\",\n description=\"Display a list of all the missing images\")\n\n bpy.types.ShaderNodeNormal.normal_vector = prop_normal_vector\n bpy.types.CompositorNodeNormal.normal_vector = prop_normal_vector\n\n bpy.types.Object.is_keyframe = is_keyframe\n\n scene.amth_wire_toggle_scene_all = BoolProperty(\n default=False,\n name=\"All Scenes\",\n description=\"Toggle wire on objects in all scenes\")\n scene.amth_wire_toggle_is_selected = BoolProperty(\n default=False,\n name=\"Only Selected\",\n description=\"Only toggle wire on selected objects\")\n scene.amth_wire_toggle_edges_all = BoolProperty(\n default=True,\n name=\"All Edges\",\n description=\"Draw all edges\")\n scene.amth_wire_toggle_optimal = BoolProperty(\n default=False,\n name=\"Optimal Display\",\n description=\"Skip drawing/rendering of interior subdivided edges \"\n \"on meshes with Subdivision Surface modifier\")\n\ndef clear_properties():\n props = (\n \"use_unsimplify_render\",\n \"simplify_status\",\n \"use_matching_indices\",\n \"use_simplify_nodes_vector\",\n \"status\",\n \"types\",\n \"toggle_mute\",\n \"amaranth_cycles_node_types\",\n \"amaranth_lighterscorner_list_meshlights\",\n \"amaranth_debug_scene_list_missing_images\",\n \"amarath_cycles_list_sampling\",\n \"normal_vector\",\n \"use_samples_final\",\n 'amth_wire_toggle_is_selected',\n 'amth_wire_toggle_scene_all',\n \"amth_wire_toggle_edges_all\",\n \"amth_wire_toggle_optimal\"\n )\n \n wm = bpy.context.window_manager\n for p in props:\n if p in wm:\n del wm[p]\n# FEATURE: Scene Debug\nclass AMTH_SCENE_OT_cycles_shader_list_nodes(Operator):\n \"\"\"List Cycles materials containing a specific shader\"\"\"\n bl_idname = \"scene.cycles_list_nodes\"\n bl_label = \"List Materials\"\n materials = []\n\n @classmethod\n def poll(cls, context):\n return cycles_exists and context.scene.render.engine == 'CYCLES'\n\n def execute(self, context):\n node_type = context.scene.amaranth_cycles_node_types\n roughness = False\n self.__class__.materials = []\n shaders_roughness = ['BSDF_GLOSSY','BSDF_DIFFUSE','BSDF_GLASS']\n\n print(\"\\n=== Cycles Shader Type: %s === \\n\" % node_type)\n\n for ma in bpy.data.materials:\n if ma.node_tree:\n nodes = ma.node_tree.nodes\n \n print_unconnected = ('Note: \\nOutput from \"%s\" node' % node_type,\n 'in material \"%s\"' % ma.name, 'not connected\\n')\n\n for no in nodes:\n if no.type == node_type:\n for ou in no.outputs:\n if ou.links:\n connected = True\n if no.type in shaders_roughness:\n roughness = 'R: %.4f' % no.inputs['Roughness'].default_value\n else:\n roughness = False\n else:\n connected = False\n print(print_unconnected)\n\n if ma.name not in self.__class__.materials:\n self.__class__.materials.append('%s%s [%s] %s%s%s' % (\n '[L] ' if ma.library else '',\n ma.name, ma.users,\n '[F]' if ma.use_fake_user else '',\n ' - [%s]' % roughness if roughness else '',\n ' * Output not connected' if not connected else ''))\n\n elif no.type == 'GROUP':\n if no.node_tree:\n for nog in no.node_tree.nodes:\n if nog.type == node_type:\n for ou in nog.outputs:\n if ou.links:\n connected = True\n if nog.type in shaders_roughness:\n roughness = 'R: %.4f' % nog.inputs['Roughness'].default_value\n else:\n roughness = False\n else:\n connected = False\n print(print_unconnected)\n\n if ma.name not in self.__class__.materials:\n self.__class__.materials.append('%s%s%s [%s] %s%s%s' % (\n '[L] ' if ma.library else '',\n 'Node Group: %s%s -> ' % (\n '[L] ' if no.node_tree.library else '',\n no.node_tree.name),\n ma.name, ma.users,\n '[F]' if ma.use_fake_user else '',\n ' - [%s]' % roughness if roughness else '',\n ' * Output not connected' if not connected else ''))\n\n self.__class__.materials = sorted(list(set(self.__class__.materials)))\n\n if len(self.__class__.materials) == 0:\n self.report({\"INFO\"}, \"No materials with nodes type %s found\" % node_type)\n else:\n print(\"* A total of %d %s using %s was found \\n\" % (\n len(self.__class__.materials),\n \"material\" if len(self.__class__.materials) == 1 else \"materials\",\n node_type))\n\n count = 0\n\n for mat in self.__class__.materials:\n print('%02d. %s' % (count+1, self.__class__.materials[count]))\n count += 1\n print(\"\\n\")\n\n self.__class__.materials = sorted(list(set(self.__class__.materials)))\n\n return {'FINISHED'}\n\nclass AMTH_SCENE_OT_cycles_shader_list_nodes_clear(Operator):\n \"\"\"Clear the list below\"\"\"\n bl_idname = \"scene.cycles_list_nodes_clear\"\n bl_label = \"Clear Materials List\"\n\n @classmethod\n def poll(cls, context):\n return cycles_exists\n\n def execute(self, context):\n AMTH_SCENE_OT_cycles_shader_list_nodes.materials[:] = []\n print(\"* Cleared Cycles Materials List\")\n return {'FINISHED'}\n\nclass AMTH_SCENE_OT_amaranth_object_select(Operator):\n '''Select object'''\n bl_idname = \"scene.amaranth_object_select\"\n bl_label = \"Select Object\"\n object = bpy.props.StringProperty()\n \n def execute(self, context):\n if self.object:\n object = bpy.data.objects[self.object]\n\n bpy.ops.object.select_all(action='DESELECT')\n object.select = True\n context.scene.objects.active = object\n\n return{'FINISHED'}\n\nclass AMTH_SCENE_OT_list_missing_node_links(Operator):\n '''Print a list of missing node links'''\n bl_idname = \"scene.list_missing_node_links\"\n bl_label = \"List Missing Node Links\"\n\n count_groups = 0\n count_images = 0\n count_image_node_unlinked = 0\n\n def execute(self, context):\n missing_groups = []\n missing_images = []\n image_nodes_unlinked = []\n libraries = []\n self.__class__.count_groups = 0\n self.__class__.count_images = 0\n self.__class__.count_image_node_unlinked = 0\n\n for ma in bpy.data.materials:\n if ma.node_tree:\n for no in ma.node_tree.nodes:\n if no.type == 'GROUP':\n if not no.node_tree:\n self.__class__.count_groups += 1\n\n users_ngroup = []\n\n for ob in bpy.data.objects:\n if ob.material_slots and ma.name in ob.material_slots:\n users_ngroup.append(\"%s%s%s\" % (\n \"[L] \" if ob.library else \"\",\n \"[F] \" if ob.use_fake_user else \"\",\n ob.name))\n\n missing_groups.append(\"MA: %s%s%s [%s]%s%s%s\\n\" % (\n \"[L] \" if ma.library else \"\",\n \"[F] \" if ma.use_fake_user else \"\",\n ma.name, ma.users,\n \" *** No users *** \" if ma.users == 0 else \"\",\n \"\\nLI: %s\" % \n ma.library.filepath if ma.library else \"\",\n \"\\nOB: %s\" % ', '.join(users_ngroup) if users_ngroup else \"\"))\n\n if ma.library:\n libraries.append(ma.library.filepath)\n if no.type == 'TEX_IMAGE':\n\n outputs_empty = not no.outputs['Color'].is_linked and not no.outputs['Alpha'].is_linked\n\n if no.image:\n import os.path\n image_path_exists = os.path.exists(\n bpy.path.abspath(\n no.image.filepath, library=no.image.library))\n\n if outputs_empty or not \\\n no.image or not \\\n image_path_exists:\n\n users_images = []\n\n for ob in bpy.data.objects:\n if ob.material_slots and ma.name in ob.material_slots:\n users_images.append(\"%s%s%s\" % (\n \"[L] \" if ob.library else \"\",\n \"[F] \" if ob.use_fake_user else \"\",\n ob.name))\n\n if outputs_empty:\n self.__class__.count_image_node_unlinked += 1\n\n image_nodes_unlinked.append(\"%s%s%s%s%s [%s]%s%s%s%s%s\\n\" % (\n \"NO: %s\" % no.name,\n \"\\nMA: \",\n \"[L] \" if ma.library else \"\",\n \"[F] \" if ma.use_fake_user else \"\",\n ma.name, ma.users,\n \" *** No users *** \" if ma.users == 0 else \"\",\n \"\\nLI: %s\" % \n ma.library.filepath if ma.library else \"\",\n \"\\nIM: %s\" % no.image.name if no.image else \"\",\n \"\\nLI: %s\" % no.image.filepath if no.image and no.image.filepath else \"\",\n \"\\nOB: %s\" % ', '.join(users_images) if users_images else \"\"))\n \n\n if not no.image or not image_path_exists:\n self.__class__.count_images += 1\n\n missing_images.append(\"MA: %s%s%s [%s]%s%s%s%s%s\\n\" % (\n \"[L] \" if ma.library else \"\",\n \"[F] \" if ma.use_fake_user else \"\",\n ma.name, ma.users,\n \" *** No users *** \" if ma.users == 0 else \"\",\n \"\\nLI: %s\" % \n ma.library.filepath if ma.library else \"\",\n \"\\nIM: %s\" % no.image.name if no.image else \"\",\n \"\\nLI: %s\" % no.image.filepath if no.image and no.image.filepath else \"\",\n \"\\nOB: %s\" % ', '.join(users_images) if users_images else \"\"))\n\n if ma.library:\n libraries.append(ma.library.filepath)\n\n # Remove duplicates and sort\n missing_groups = sorted(list(set(missing_groups)))\n missing_images = sorted(list(set(missing_images)))\n image_nodes_unlinked = sorted(list(set(image_nodes_unlinked)))\n libraries = sorted(list(set(libraries)))\n\n print(\"\\n\\n== %s missing image %s, %s missing node %s and %s image %s unlinked ==\" %\n (\"No\" if self.__class__.count_images == 0 else str(self.__class__.count_images),\n \"node\" if self.__class__.count_images == 1 else \"nodes\",\n \"no\" if self.__class__.count_groups == 0 else str(self.__class__.count_groups),\n \"group\" if self.__class__.count_groups == 1 else \"groups\",\n \"no\" if self.__class__.count_image_node_unlinked == 0 else str(self.__class__.count_image_node_unlinked),\n \"node\" if self.__class__.count_groups == 1 else \"nodes\"))\n\n # List Missing Node Groups\n if missing_groups:\n print(\"\\n* Missing Node Group Links\\n\")\n for mig in missing_groups:\n print(mig)\n\n # List Missing Image Nodes\n if missing_images:\n print(\"\\n* Missing Image Nodes Link\\n\")\n\n for mii in missing_images:\n print(mii)\n\n # List Image Nodes with its outputs unlinked\n if image_nodes_unlinked:\n print(\"\\n* Image Nodes Unlinked\\n\")\n\n for nou in image_nodes_unlinked:\n print(nou)\n\n if missing_groups or \\\n missing_images or \\\n image_nodes_unlinked:\n if libraries:\n print(\"\\nThat's bad, run check on %s:\" % (\n \"this library\" if len(libraries) == 1 else \"these libraries\"))\n for li in libraries:\n print(li)\n else:\n self.report({\"INFO\"}, \"Yay! No missing node links\") \n\n print(\"\\n\")\n\n if missing_groups and missing_images:\n self.report({\"WARNING\"}, \"%d missing image %s and %d missing node %s found\" %\n (self.__class__.count_images, \"node\" if self.__class__.count_images == 1 else \"nodes\",\n self.__class__.count_groups, \"group\" if self.__class__.count_groups == 1 else \"groups\"))\n\n return{'FINISHED'}\n\nclass AMTH_SCENE_OT_list_missing_material_slots(Operator):\n '''List objects with empty material slots'''\n bl_idname = \"scene.list_missing_material_slots\"\n bl_label = \"List Empty Material Slots\"\n\n objects = []\n libraries = []\n\n def execute(self, context):\n self.__class__.objects = []\n self.__class__.libraries = []\n\n for ob in bpy.data.objects:\n for ma in ob.material_slots:\n if not ma.material:\n self.__class__.objects.append('%s%s' % (\n '[L] ' if ob.library else '',\n ob.name))\n if ob.library:\n self.__class__.libraries.append(ob.library.filepath)\n\n self.__class__.objects = sorted(list(set(self.__class__.objects)))\n self.__class__.libraries = sorted(list(set(self.__class__.libraries)))\n\n if len(self.__class__.objects) == 0:\n self.report({\"INFO\"}, \"No objects with empty material slots found\")\n else:\n print(\"\\n* A total of %d %s with empty material slots was found \\n\" % (\n len(self.__class__.objects),\n \"object\" if len(self.__class__.objects) == 1 else \"objects\"))\n\n count = 0\n count_lib = 0\n\n for obs in self.__class__.objects:\n print('%02d. %s' % (\n count+1, self.__class__.objects[count]))\n count += 1\n\n if self.__class__.libraries:\n print(\"\\n\\n* Check %s:\\n\" % \n (\"this library\" if len(self.__class__.libraries) == 1\n else \"these libraries\"))\n\n for libs in self.__class__.libraries:\n print('%02d. %s' % (\n count_lib+1, self.__class__.libraries[count_lib]))\n count_lib += 1\n print(\"\\n\")\n\n return{'FINISHED'}\n\nclass AMTH_SCENE_OT_list_missing_material_slots_clear(Operator):\n \"\"\"Clear the list below\"\"\"\n bl_idname = \"scene.list_missing_material_slots_clear\"\n bl_label = \"Clear Empty Material Slots List\"\n \n def execute(self, context):\n AMTH_SCENE_OT_list_missing_material_slots.objects[:] = []\n print(\"* Cleared Empty Material Slots List\")\n return {'FINISHED'}\n\nclass AMTH_SCENE_OT_blender_instance_open(Operator):\n '''Open in a new Blender instance'''\n bl_idname = \"scene.blender_instance_open\"\n bl_label = \"Open Blender Instance\"\n filepath = bpy.props.StringProperty()\n\n def execute(self, context):\n if self.filepath:\n import os.path\n filepath = os.path.normpath(bpy.path.abspath(self.filepath))\n\n import subprocess\n try:\n subprocess.Popen([bpy.app.binary_path, filepath])\n except:\n print(\"Error on the new Blender instance\")\n import traceback\n traceback.print_exc()\n\n return{'FINISHED'}\n\nclass AMTH_SCENE_PT_scene_debug(Panel):\n '''Scene Debug'''\n bl_label = 'Scene Debug'\n bl_space_type = \"PROPERTIES\"\n bl_region_type = \"WINDOW\"\n bl_context = \"scene\"\n\n def draw_header(self, context):\n layout = self.layout\n layout.label(text=\"\", icon=\"RADIO\")\n\n def draw(self, context):\n layout = self.layout\n scene = context.scene\n objects = bpy.data.objects\n ob_act = context.active_object\n images = bpy.data.images\n lamps = bpy.data.lamps\n images_missing = []\n list_missing_images = scene.amaranth_debug_scene_list_missing_images\n materials = AMTH_SCENE_OT_cycles_shader_list_nodes.materials\n materials_count = len(AMTH_SCENE_OT_cycles_shader_list_nodes.materials)\n missing_material_slots_obs = AMTH_SCENE_OT_list_missing_material_slots.objects\n missing_material_slots_count = len(AMTH_SCENE_OT_list_missing_material_slots.objects)\n missing_material_slots_lib = AMTH_SCENE_OT_list_missing_material_slots.libraries\n engine = scene.render.engine\n\n # List Missing Images\n box = layout.box()\n row = box.row(align=True)\n split = row.split()\n col = split.column()\n\n if images:\n import os.path\n\n for im in images:\n if im.type not in ['UV_TEST', 'RENDER_RESULT', 'COMPOSITING']: \n if not os.path.exists(bpy.path.abspath(im.filepath, library=im.library)):\n images_missing.append([\"%s%s [%s]%s\" % (\n '[L] ' if im.library else '',\n im.name, im.users,\n ' [F]' if im.use_fake_user else ''),\n im.filepath if im.filepath else 'No Filepath',\n im.library.filepath if im.library else ''])\n\n if images_missing:\n row = col.row(align=True)\n row.alignment = 'LEFT'\n row.prop(scene, 'amaranth_debug_scene_list_missing_images',\n icon=\"%s\" % 'TRIA_DOWN' if list_missing_images else 'TRIA_RIGHT',\n emboss=False)\n\n split = split.split()\n col = split.column()\n\n col.label(text=\"%s missing %s\" % (\n str(len(images_missing)),\n 'image' if len(images_missing) == 1 else 'images'),\n icon=\"ERROR\")\n\n if list_missing_images:\n col = box.column(align=True)\n for mis in images_missing:\n col.label(text=mis[0],\n icon=\"IMAGE_DATA\")\n col.label(text=mis[1], icon=\"LIBRARY_DATA_DIRECT\")\n if mis[2]:\n row = col.row(align=True)\n row.alignment = \"LEFT\"\n row.operator(AMTH_SCENE_OT_blender_instance_open.bl_idname,\n text=mis[2],\n icon=\"LINK_BLEND\",\n emboss=False).filepath=mis[2]\n col.separator()\n else:\n row = col.row(align=True)\n row.alignment = 'LEFT'\n row.label(text=\"Great! No missing images\", icon=\"RIGHTARROW_THIN\")\n\n split = split.split()\n col = split.column()\n\n col.label(text=\"%s %s loading correctly\" % (\n str(len(images)),\n 'image' if len(images) == 1 else 'images'),\n icon=\"IMAGE_DATA\")\n else:\n row = col.row(align=True)\n row.alignment = 'LEFT'\n row.label(text=\"No images loaded yet\", icon=\"RIGHTARROW_THIN\")\n\n # List Cycles Materials by Shader\n if cycles_exists and engine == 'CYCLES':\n box = layout.box()\n split = box.split()\n col = split.column(align=True)\n col.prop(scene, 'amaranth_cycles_node_types',\n icon=\"MATERIAL\")\n\n row = split.row(align=True)\n row.operator(AMTH_SCENE_OT_cycles_shader_list_nodes.bl_idname,\n icon=\"SORTSIZE\",\n text=\"List Materials Using Shader\")\n if materials_count != 0: \n row.operator(AMTH_SCENE_OT_cycles_shader_list_nodes_clear.bl_idname,\n icon=\"X\", text=\"\")\n col.separator()\n\n try:\n materials\n except NameError:\n pass\n else:\n if materials_count != 0: \n col = box.column(align=True)\n count = 0\n col.label(text=\"%s %s found\" % (materials_count,\n 'material' if materials_count == 1 else 'materials'), icon=\"INFO\")\n for mat in materials:\n count += 1\n col.label(text='%s' % (materials[count-1]), icon=\"MATERIAL\")\n\n # List Missing Node Trees\n box = layout.box()\n row = box.row(align=True)\n split = row.split()\n col = split.column(align=True)\n\n split = col.split()\n split.label(text=\"Node Links\")\n split.operator(AMTH_SCENE_OT_list_missing_node_links.bl_idname,\n icon=\"NODETREE\")\n\n if AMTH_SCENE_OT_list_missing_node_links.count_groups != 0 or \\\n AMTH_SCENE_OT_list_missing_node_links.count_images != 0 or \\\n AMTH_SCENE_OT_list_missing_node_links.count_image_node_unlinked != 0:\n col.label(text=\"Warning! Check Console\", icon=\"ERROR\")\n\n if AMTH_SCENE_OT_list_missing_node_links.count_groups != 0:\n col.label(text=\"%s\" % (\"%s node %s missing link\" % (\n str(AMTH_SCENE_OT_list_missing_node_links.count_groups),\n \"group\" if AMTH_SCENE_OT_list_missing_node_links.count_groups == 1 else \"groups\")),\n icon=\"NODETREE\")\n if AMTH_SCENE_OT_list_missing_node_links.count_images != 0:\n col.label(text=\"%s\" % (\"%s image %s missing link\" % (\n str(AMTH_SCENE_OT_list_missing_node_links.count_images),\n \"node\" if AMTH_SCENE_OT_list_missing_node_links.count_images == 1 else \"nodes\")),\n icon=\"IMAGE_DATA\")\n\n if AMTH_SCENE_OT_list_missing_node_links.count_image_node_unlinked != 0:\n col.label(text=\"%s\" % (\"%s image %s with no output conected\" % (\n str(AMTH_SCENE_OT_list_missing_node_links.count_image_node_unlinked),\n \"node\" if AMTH_SCENE_OT_list_missing_node_links.count_image_node_unlinked == 1 else \"nodes\")),\n icon=\"NODE\")\n\n # List Empty Materials Slots\n box = layout.box()\n split = box.split()\n col = split.column(align=True)\n col.label(text=\"Material Slots\")\n\n row = split.row(align=True)\n row.operator(AMTH_SCENE_OT_list_missing_material_slots.bl_idname,\n icon=\"MATERIAL\",\n text=\"List Empty Materials Slots\")\n if missing_material_slots_count != 0: \n row.operator(AMTH_SCENE_OT_list_missing_material_slots_clear.bl_idname,\n icon=\"X\", text=\"\")\n col.separator()\n\n try:\n missing_material_slots_obs\n except NameError:\n pass\n else:\n if missing_material_slots_count != 0: \n col = box.column(align=True)\n count = 0\n count_lib = 0\n col.label(text=\"%s %s with empty material slots found\" % (\n missing_material_slots_count,\n 'object' if missing_material_slots_count == 1 else 'objects'),\n icon=\"INFO\")\n\n for obs in missing_material_slots_obs:\n count += 1\n\n row = col.row()\n row.alignment = 'LEFT'\n row.label(text='%s' % missing_material_slots_obs[count-1],\n icon=\"OBJECT_DATA\")\n\n if missing_material_slots_lib:\n col.separator()\n col.label(\"Check %s:\" % (\n \"this library\" if\n len(missing_material_slots_lib) == 1\n else \"these libraries\"))\n \n for libs in missing_material_slots_lib:\n count_lib += 1\n row = col.row(align=True)\n row.alignment = \"LEFT\"\n row.operator(AMTH_SCENE_OT_blender_instance_open.bl_idname,\n text=missing_material_slots_lib[count_lib-1],\n icon=\"LINK_BLEND\",\n emboss=False).filepath=missing_material_slots_lib[count_lib-1]\n\n# // FEATURE: Scene Debug", "sub_path": "blendswap_admin_tools.py", "file_name": "blendswap_admin_tools.py", "file_ext": "py", "file_size_in_byte": 30576, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "bpy.types", "line_number": 48, "usage_type": "attribute"}, {"api_name": "bpy.types.AddonPreferences", "line_number": 56, "usage_type": "name"}, {"api_name": "bpy.props.BoolProperty", "line_number": 58, "usage_type": "call"}, {"api_name": "bpy.props.EnumProperty", "line_number": 87, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 90, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 94, "usage_type": "attribute"}, {"api_name": "bpy.props.BoolProperty", "line_number": 94, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 99, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 104, "usage_type": "call"}, {"api_name": "bpy.types", "line_number": 109, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 110, "usage_type": "attribute"}, {"api_name": "bpy.types", "line_number": 112, "usage_type": "attribute"}, {"api_name": "bpy.props.BoolProperty", "line_number": 114, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 118, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 122, "usage_type": "call"}, {"api_name": "bpy.props.BoolProperty", "line_number": 126, "usage_type": "call"}, {"api_name": "bpy.context", "line_number": 153, "usage_type": "attribute"}, {"api_name": "bpy.types.Operator", "line_number": 158, "usage_type": "name"}, {"api_name": "bpy.data", "line_number": 176, "usage_type": "attribute"}, {"api_name": "bpy.types.Operator", "line_number": 251, "usage_type": "name"}, {"api_name": "bpy.types.Operator", "line_number": 265, "usage_type": "name"}, {"api_name": "bpy.props.StringProperty", "line_number": 269, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 269, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 273, "usage_type": "attribute"}, {"api_name": "bpy.ops.object.select_all", "line_number": 275, "usage_type": "call"}, {"api_name": "bpy.ops", "line_number": 275, "usage_type": "attribute"}, {"api_name": "bpy.types.Operator", "line_number": 281, "usage_type": "name"}, {"api_name": "bpy.data", "line_number": 299, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 308, "usage_type": "attribute"}, {"api_name": "os.path.path.exists", "line_number": 332, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 332, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 332, "usage_type": "name"}, {"api_name": "bpy.path.abspath", "line_number": 333, "usage_type": "call"}, {"api_name": "bpy.path", "line_number": 333, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 342, "usage_type": "attribute"}, {"api_name": "bpy.types.Operator", "line_number": 437, "usage_type": "name"}, {"api_name": "bpy.data", "line_number": 449, "usage_type": "attribute"}, {"api_name": "bpy.types.Operator", "line_number": 489, "usage_type": "name"}, {"api_name": "bpy.types.Operator", "line_number": 499, "usage_type": "name"}, {"api_name": "bpy.props.StringProperty", "line_number": 503, "usage_type": "call"}, {"api_name": "bpy.props", "line_number": 503, "usage_type": "attribute"}, {"api_name": "os.path.path.normpath", "line_number": 508, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 508, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 508, "usage_type": "name"}, {"api_name": "bpy.path.abspath", "line_number": 508, "usage_type": "call"}, {"api_name": "bpy.path", "line_number": 508, "usage_type": "attribute"}, {"api_name": "subprocess.Popen", "line_number": 512, "usage_type": "call"}, {"api_name": "bpy.app", "line_number": 512, "usage_type": "attribute"}, {"api_name": "traceback.print_exc", "line_number": 516, "usage_type": "call"}, {"api_name": "bpy.types.Panel", "line_number": 520, "usage_type": "name"}, {"api_name": "bpy.data", "line_number": 534, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 536, "usage_type": "attribute"}, {"api_name": "bpy.data", "line_number": 537, "usage_type": "attribute"}, {"api_name": "os.path.path.exists", "line_number": 558, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 558, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 558, "usage_type": "name"}, {"api_name": "bpy.path.abspath", "line_number": 558, "usage_type": "call"}, {"api_name": "bpy.path", "line_number": 558, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path', 'subprocess': 'subprocess', 'traceback': 'traceback'}.bl_idname", "line_number": 590, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.bl_idname", "line_number": 651, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.count_groups", "line_number": 654, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.count_images", "line_number": 655, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.count_image_node_unlinked", "line_number": 656, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.count_groups", "line_number": 659, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.count_groups", "line_number": 661, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.count_groups", "line_number": 662, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.count_images", "line_number": 664, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.count_images", "line_number": 666, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.count_images", "line_number": 667, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.count_image_node_unlinked", "line_number": 670, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.count_image_node_unlinked", "line_number": 672, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path'}.count_image_node_unlinked", "line_number": 673, "usage_type": "attribute"}, {"api_name": "{'os.path': 'os.path', 'subprocess': 'subprocess', 'traceback': 'traceback'}.bl_idname", "line_number": 724, "usage_type": "attribute"}]} +{"seq_id": "27931214", "text": "import numpy as np\nimport scipy.linalg as sla\nfrom collections import deque, UserDict\n\nimport matplotlib.pyplot as plt\n\nimport ipdb\n\n\nclass System():\n def __init__(self, args):\n self.args = args\n self.unc = Uncertainty(args)\n\n def reset(self):\n return np.zeros(2)\n\n def step(self, t, x, u):\n args = self.args\n\n next_x = x + args.t_step * (\n args.A.dot(x[:, np.newaxis])\n + args.B.dot(self.unc.Lambda).dot(\n (u + self.unc.delta(x))[:, np.newaxis])\n ).ravel()\n\n return next_x\n\n\nclass Uncertainty():\n def __init__(self, args):\n self.W = np.array([-18.59521, 15.162375, -62.45153,\n 9.54708, 21.45291])[:, np.newaxis]\n self.Lambda = np.diag([0.7])\n\n def basis(self, x):\n return np.hstack((x, np.abs(x)*x[1], x[0]**3))\n\n def delta(self, x):\n return self.W.T.dot(self.basis(x))\n\n\nclass DirectMrac():\n def __init__(self, system):\n self.basis = system.unc.basis\n self.args = system.args\n\n self.P = sla.solve_lyapunov(self.args.A.T, self.args.Q_lyap)\n\n\nclass Cmrac():\n def __init__(self, system):\n self.basis = system.unc.basis\n self.args = system.args\n\n delta_num = int(self.args.delta / self.args.t_step)\n self.memory = deque(maxlen=delta_num)\n\n self.P = sla.solve_lyapunov(self.args.A.T, self.args.Q_lyap)\n\n def reset(self):\n args = self.args\n self.xr = np.zeros(args.ndim_state)\n self.v1 = np.zeros(args.ndim_input)\n self.v2 = np.zeros(args.ndim_basis)\n self.v3 = np.zeros(args.ndim_state)\n self.lambdahat = np.eye(args.ndim_input)\n self.vhat = self.lambdahat.dot(\n np.zeros((args.ndim_basis, args.ndim_input)).T\n )\n self.what = np.zeros((args.ndim_basis, args.ndim_input))\n # self.vhat = self.lambdahat.dot(self.what.T)\n\n self.memory.clear()\n\n return self.xr, self.v1, self.v2, self.v3, self.lambdahat, self.vhat\n\n def get_inputs(self, t, x):\n args = self.args\n\n # lambdahat = self.lambdahat\n # vhat = self.vhat\n\n # if args.use_cmrac:\n # what = vhat.T.dot(np.diag(1 / np.diag(lambdahat)))\n # else:\n # what = self.what\n\n what = self.what\n\n c = self.command(t)\n\n # u_n = 0*np.diag(1 / np.diag(lambdahat)).dot(args.Kr).dot(c)\n u_n = args.Kr.dot(c)\n u_a = - what.T.dot(self.basis(x))\n\n return u_n + u_a\n\n def update(self, t, x):\n args = self.args\n\n # realize variables\n xr = self.xr\n v1 = self.v1\n v2 = self.v2\n v3 = self.v3\n lambdahat = self.lambdahat\n vhat = self.vhat\n c = self.command(t)\n\n e = x - xr\n\n # if args.use_cmrac:\n # what = vhat.T.dot(np.diag(1 / np.diag(lambdahat)))\n # else:\n # what = self.what\n\n what = self.what\n\n self.memory.append((x, e, what))\n\n x_delta, e_delta, what_delta = self.memory[0]\n\n y = e - e_delta - args.A.dot(v3[:, np.newaxis]).ravel()\n yhat = args.B.dot(\n - lambdahat.dot(v1[:, np.newaxis])\n + vhat.dot(v2[:, np.newaxis])\n ).ravel()\n\n # update reference model\n next_xr = xr + args.t_step * (\n args.A.dot(xr[:, np.newaxis]) + args.Br.dot(c[:, np.newaxis])\n ).ravel()\n\n next_v1 = v1 + args.t_step * (\n what.T.dot(self.basis(x)[:, np.newaxis])\n - what_delta.T.dot(self.basis(x_delta)[:, np.newaxis])\n ).ravel()\n\n next_v2 = v2 + args.t_step * (\n self.basis(x) - self.basis(x_delta)\n )\n\n next_v3 = v3 + args.t_step * (e - e_delta)\n\n next_lambdahat = lambdahat + args.t_step * (\n args.g1 * np.diag(v1)\n * np.diag(args.B.T.dot((yhat - y)[:, np.newaxis]))\n )\n\n next_vhat = vhat + args.t_step * (\n - args.g2 * args.B.T.dot((yhat - y)[:, np.newaxis]) * v2\n )\n\n next_what = what + args.t_step * (\n args.g3 * self.basis(x)[:, np.newaxis]\n * e.T.dot(self.P).dot(args.B)\n )\n\n self.xr = next_xr\n self.v1 = next_v1\n self.v2 = next_v2\n self.v3 = next_v3\n self.lambdahat = next_lambdahat\n self.vhat = next_vhat\n self.what = next_what\n\n return dict(reference_model=xr, w_hat=what)\n\n # next_what = - args.g1 * what.dot(np.diag(self.\n\n def command(self, t):\n if t < 10:\n c = 1\n elif t < 20:\n c = -1\n elif t < 30:\n c = 1\n elif t < 40:\n c = -1\n else:\n c = 0\n\n return np.deg2rad(5*np.array([c]))\n\n\nclass Arguments(UserDict):\n def __getattr__(self, name):\n return self.data[name]\n\n def __setattr__(self, name, value):\n if name == 'data':\n super().__setattr__(name, value)\n else:\n self.data[name] = value\n\n\nclass Data(Arguments):\n def append(self, name, val):\n val = np.atleast_1d(val)[np.newaxis, :]\n if name not in self:\n self[name] = val\n else:\n self[name] = np.append(self[name], val, axis=0)\n\n def ele_plot(self, name):\n x = self.time\n y = self[name]\n\n plt.plot(x, y)\n\n\n# class Data():\n# def __init__(self, timebase=None, base=None):\n# if type(base) is not int:\n# self.content = np.zeros(timebase.shape + base.shape)\n# else:\n# self.content = np.zeros(timebase.shape + (base, ))\n\n# self.ts = timebase\n\n# def append(self, index, value):\n# self.content[index] = value\n\n# def plot(self):\n# fig, ax = plt.subplots()\n\n# x = self.ts\n# y = self.content\n\n# ax.plot(x, y)\n\n# return fig\n\n\ndef main():\n args = Arguments()\n args.A = np.array([[-2, -1], [1, -1]])\n args.B = np.array([[0, 1]]).T\n args.Br = np.array([[0, 1]]).T\n args.Kr = np.array([[1]])\n args.Q_lyap = np.eye(2)\n args.g1 = 10\n args.g2 = 10\n args.g3 = 100\n args.delta = 3\n args.t_step = 0.01\n args.t_final = 40\n args.ndim_state = 2\n args.ndim_input = 1\n args.ndim_basis = 5\n args.ts = np.arange(0, args.t_final, args.t_step)\n args.comp_gain = 0\n\n system = System(args)\n control = Cmrac(system)\n\n x = system.reset()\n control.reset()\n\n data = Data()\n for i in range(args.ts.size):\n t = args.ts[i]\n u = control.get_inputs(t, x)\n\n # step\n next_x = system.step(t, x, u)\n\n # controller update\n current_data = control.update(t, x)\n\n data.append('time', t)\n data.append('state', x)\n data.append('input', u)\n\n [data.append(*item) for item in current_data.items()]\n\n x = next_x\n\n data.ele_plot('state')\n\n plt.show()\n\n\nif __name__ == '__main__':\n main()\n", "sub_path": "subject/bare-test/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 6936, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.zeros", "line_number": 16, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 22, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 24, "usage_type": "attribute"}, {"api_name": "numpy.array", "line_number": 32, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 34, "usage_type": "call"}, {"api_name": "numpy.hstack", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 37, "usage_type": "call"}, {"api_name": "scipy.linalg.solve_lyapunov", "line_number": 48, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 48, "usage_type": "name"}, {"api_name": "collections.deque", "line_number": 57, "usage_type": "call"}, {"api_name": "scipy.linalg.solve_lyapunov", "line_number": 59, "usage_type": "call"}, {"api_name": "scipy.linalg", "line_number": 59, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 67, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 69, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 124, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 126, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 127, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 132, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 136, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.diag", "line_number": 147, "usage_type": "call"}, {"api_name": "numpy.diag", "line_number": 148, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 148, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 152, "usage_type": "attribute"}, {"api_name": "numpy.newaxis", "line_number": 156, "usage_type": "attribute"}, {"api_name": "numpy.deg2rad", "line_number": 184, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 184, "usage_type": "call"}, {"api_name": "collections.UserDict", "line_number": 187, "usage_type": "name"}, {"api_name": "numpy.atleast_1d", "line_number": 200, "usage_type": "call"}, {"api_name": "numpy.newaxis", "line_number": 200, "usage_type": "attribute"}, {"api_name": "numpy.append", "line_number": 204, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 210, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 210, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 238, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.eye", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 282, "usage_type": "name"}]} +{"seq_id": "351648884", "text": "from typing import TYPE_CHECKING\n\nfrom ..types import TealType\nfrom ..ir import TealOp, Op, TealBlock\nfrom ..errors import TealInputError\nfrom .leafexpr import LeafExpr\n\nif TYPE_CHECKING:\n from ..compiler import CompileOptions\n\n\nclass Arg(LeafExpr):\n \"\"\"An expression to get an argument when running in signature verification mode.\"\"\"\n\n def __init__(self, index: int) -> None:\n \"\"\"Get an argument for this program.\n\n Should only be used in signature verification mode. For application mode arguments, see\n :any:`TxnObject.application_args`.\n\n Args:\n index: The integer index of the argument to get. Must be between 0 and 255 inclusive.\n \"\"\"\n super().__init__()\n\n if type(index) is not int:\n raise TealInputError(\"invalid arg input type {}\".format(type(index)))\n\n if index < 0 or index > 255:\n raise TealInputError(\"invalid arg index {}\".format(index))\n\n self.index = index\n\n def __teal__(self, options: \"CompileOptions\"):\n op = TealOp(self, Op.arg, self.index)\n return TealBlock.FromOp(options, op)\n\n def __str__(self):\n return \"(arg {})\".format(self.index)\n\n def type_of(self):\n return TealType.bytes\n\n\nArg.__module__ = \"pyteal\"\n", "sub_path": "pyteal/ast/arg.py", "file_name": "arg.py", "file_ext": "py", "file_size_in_byte": 1272, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 8, "usage_type": "name"}, {"api_name": "leafexpr.LeafExpr", "line_number": 12, "usage_type": "name"}, {"api_name": "errors.TealInputError", "line_number": 27, "usage_type": "call"}, {"api_name": "errors.TealInputError", "line_number": 30, "usage_type": "call"}, {"api_name": "ir.TealOp", "line_number": 35, "usage_type": "call"}, {"api_name": "ir.Op.arg", "line_number": 35, "usage_type": "attribute"}, {"api_name": "ir.Op", "line_number": 35, "usage_type": "name"}, {"api_name": "ir.TealBlock.FromOp", "line_number": 36, "usage_type": "call"}, {"api_name": "ir.TealBlock", "line_number": 36, "usage_type": "name"}, {"api_name": "types.TealType.bytes", "line_number": 42, "usage_type": "attribute"}, {"api_name": "types.TealType", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "501106995", "text": "import pprint\nimport json\nimport progressbar\nfrom progress.bar import IncrementalBar\n# coding: utf-8\nfrom selenium import webdriver\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import Select\n\n\ndef initialize_browser(url):\n # Initialize Browser Driver\n browser = webdriver.Chrome(executable_path='chromedriver.exe')\n browser.get(url)\n return browser\n\ndef get_def(): \n \n # Initialize Browser\n browser = initialize_browser('https://www.google.com')\n hebrew_data = {\n \"errors\": {\n \"concordance\": [],\n \"verse\": []\n }\n }\n\n dict_num_range = 87\n dict_bar = IncrementalBar('GATHERING HEBREW DICTIONARY', max=dict_num_range)\n for num in range(0, dict_num_range):\n try: \n url = 'http://www.htmlbible.com/sacrednamebiblecom/kjvstrongs/STRHEB'+str(num)+'.htm'\n browser.get(url)\n base_url = '/html/body/center[1]/table/tbody/tr'\n\n num_rows = len(browser.find_elements_by_xpath(base_url)) \n for row in range(2, num_rows + 1):\n column_num = len(browser.find_elements_by_xpath(base_url+'['+str(row)+']/td'))\n if column_num == 1:\n continue\n \n # Store all references into string\n hebrew_number = browser.find_element_by_xpath('/html/body/center[1]/table/tbody/tr['+str(row)+']/td[1]/p/a[2]').text\n language_text = browser.find_element_by_xpath('/html/body/center[1]/table/tbody/tr['+str(row)+']/td[2]/p').text.split('\\n')\n definition = browser.find_element_by_xpath('/html/body/center[1]/table/tbody/tr['+str(row)+']/td[3]/p').text\n\n hebrew_data[hebrew_number] = {\n 'transliteration': language_text[0],\n 'pronunciation': language_text[1],\n 'definition': definition,\n 'verses': {}\n }\n\n except Exception as e:\n print(e)\n hebrew_data['errors']['concordance'].append(hebrew_number)\n continue\n\n finally:\n dict_bar.next()\n\n dict_bar.finish()\n\n range_num = 868\n heb_verse_bar = IncrementalBar('GATHERING CONCORDANCE', max=range_num)\n for num in range(0, range_num):\n try:\n url = 'http://www.htmlbible.com/sacrednamebiblecom/kjvstrongs/CONHEB'+str(num)+'.htm'\n browser.get(url)\n base_url = '/html/body/center[1]/table/tbody/tr'\n\n num_rows = len(browser.find_elements_by_xpath(base_url))\n for row in range(2, num_rows + 1):\n column_num = len(browser.find_elements_by_xpath(base_url+'['+str(row)+']/td'))\n if column_num == 1:\n continue\n \n hebrew_number = browser.find_element_by_xpath('/html/body/center[1]/table/tbody/tr['+str(row)+']/td[1]/p/a').text\n print(hebrew_number)\n num_word_forms = len(browser.find_elements_by_xpath(base_url+'['+str(row)+']/td[3]/p'))\n\n for word_form in range(1, num_word_forms + 1):\n word_form_text = browser.find_element_by_xpath(base_url+'['+str(row)+']/td[3]/p['+str(word_form)+']').text\n num_verses = len(browser.find_elements_by_xpath(base_url+'['+str(row)+']/td[3]/p['+str(word_form)+']/a'))\n print('Word form: {0}, num verses: {1}'.format(word_form_text, num_verses))\n\n word_form_label = word_form_text[:word_form_text.find('\\n')]\n if word_form_label == 'The Following Have Multiple Hebrew Words Associated To A Single English Word':\n continue\n\n verses = []\n print(\"Getting associated verses\")\n for verse in range(1, num_verses + 1):\n verse_text = (browser.find_element_by_xpath(base_url+'['+str(row)+']/td[3]/p['+str(word_form)+']/a['+str(verse)+']').text)\n print(verse_text)\n verses.append(verse_text)\n \n hebrew_data[hebrew_number]['verses'][word_form_label] = verses\n\n except Exception as e:\n print(e)\n hebrew_data['errors']['verse'].append(hebrew_number)\n continue\n\n finally:\n heb_verse_bar.next()\n\n heb_verse_bar.finish()\n browser.quit()\n\n with open('heb_concordance_errors.json', 'w') as fp:\n json.dump(hebrew_data, fp, indent=4)\n\nif __name__ == '__main__':\n get_def()\n\n\n ", "sub_path": "get_hebrew_dictionary.py", "file_name": "get_hebrew_dictionary.py", "file_ext": "py", "file_size_in_byte": 4595, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "selenium.webdriver.Chrome", "line_number": 13, "usage_type": "call"}, {"api_name": "selenium.webdriver", "line_number": 13, "usage_type": "name"}, {"api_name": "progress.bar.IncrementalBar", "line_number": 29, "usage_type": "call"}, {"api_name": "progress.bar.IncrementalBar", "line_number": 65, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "318364399", "text": "import logging\nimport pathlib\nimport random\nfrom typing import Tuple, Generator\n\nfrom keras.utils import Sequence\n\nfrom keras.preprocessing.sequence import TimeseriesGenerator as KerasTimeSeriesGenerator\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import KFold, train_test_split\n\nMAX_TRACKS = 2500\n\ndef get_data(\n dataset_path: pathlib.Path,\n seed: int = 137,\n test_size: float = 0.3\n) -> Tuple[pd.DataFrame, pd.DataFrame]:\n\n logging.info(f\"Reading data from {dataset_path}\")\n train_df = pd.read_csv(dataset_path.as_posix())\n\n # train_df.drop(columns=['index', 'event_id'], inplace=True)\n train_df = train_df.astype({'signal': int})\n train_df, test_df = train_test_split(\n train_df,\n test_size=test_size,\n random_state=seed\n )\n logging.debug(f\"Train set: {train_df.shape} Test set: {test_df.shape}\")\n logging.debug(f\"Train columns: {train_df.columns.values}\")\n return train_df, test_df\n\n\ndef k_folds(data: pd.DataFrame, y_column='signal', n_splits=3, shuffle=False) \\\n -> Generator[Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray], None, None]:\n kf = KFold(n_splits=n_splits, shuffle=shuffle)\n\n for train_index, test_index in kf.split(data):\n logging.info(\"TRAIN:\", train_index, \"TEST:\", test_index)\n\n X: pd.DataFrame = data.drop([y_column], axis=1)\n y: pd.DataFrame = data[[y_column]]\n X_train, X_test = X.loc[train_index].values, X.loc[test_index].values\n y_train, y_test = y.loc[train_index].values, y.loc[test_index].values\n\n yield X_train, y_train, X_test, y_test\n\n\ndef shuffle_arrays(x, y):\n s = np.arange(x.shape[0])\n np.random.shuffle(s)\n return x[s], y[s]\n\n\ndef load_events(dataset_path: pathlib.Path):\n data_df, _ = get_data(dataset_path, test_size=0)\n true_events = data_df[data_df['event_id'] != -999].copy()\n\n events = true_events.drop(['index'], axis=1)\n events.sort_values('event_id')\n events['track_id'] = events.groupby('event_id').cumcount()\n\n index = pd.MultiIndex.from_arrays([events['event_id'], events['track_id']])\n\n new_events = events.set_index(index)\n new_events = new_events.sort_index()\n new_events = new_events.drop(['event_id', 'track_id'], axis='columns')\n\n event_idx = pd.Index(events['event_id'].unique())\n track_idx = pd.RangeIndex(0, MAX_TRACKS)\n\n new_index = pd.MultiIndex.from_product([event_idx, track_idx])\n\n final_events = new_events.reindex(index=new_index)\n fake_events = data_df[data_df['event_id'] == -999][\n ['X', 'Y', 'Z', 'TX', 'TY', 'chi2', 'signal']].copy()\n\n fake_events = fake_events.sample(len(fake_events))\n\n wrong_inputs = fake_events.sample(\n len(np.isnan(final_events)), replace=False\n ).values\n\n new_full_values = np.where(\n np.isnan(final_events.values),\n wrong_inputs,\n final_events.values\n )\n\n new_values_df = pd.DataFrame(\n data=new_full_values,\n index=final_events.index,\n columns=final_events.columns\n )\n\n return new_values_df\n\n\nclass SequenceGenerator(Sequence):\n \"\"\"Utility class for generating batches of temporal data.\n Similar to keras.preprocessing.sequence.TimeseriesGenerator\n\n This class takes in a sequence of data-points gathered at\n equal intervals, along with time series parameters such as\n stride, length of history, etc., to produce batches for\n training/validation.\n\n # Arguments\n data: Indexable generator (such as list or Numpy array)\n containing consecutive data points (timesteps).\n The data should be at 2D, and axis 0 is expected\n to be the time dimension.\n targets: Targets corresponding to timesteps in `data`.\n It should have same length as `data`.\n length: Length of the output sequences (in number of timesteps).\n sampling_rate: Period between successive individual timesteps\n within sequences. For rate `r`, timesteps\n `data[i]`, `data[i-r]`, ... `data[i - length]`\n are used for create a sample sequence.\n stride: Period between successive output sequences.\n For stride `s`, consecutive output samples would\n be centered around `data[i]`, `data[i+s]`, `data[i+2*s]`, etc.\n start_index, end_index: Data points earlier than `start_index`\n or later than `end_index` will not be used in the output sequences.\n This is seful to reserve part of the data for test or validation.\n shuffle: Whether to shuffle output samples,\n or instead draw them in chronological order.\n reverse: Boolean: if `true`, timesteps in each output sample will be\n in reverse chronological order.\n batch_size: Number of timeseries samples in each batch\n (except maybe the last one).\n \"\"\"\n\n def __init__(self, data, targets, length,\n sampling_rate=1,\n stride=1,\n start_index=0,\n end_index=None,\n shuffle=False,\n reverse=False,\n batch_size=128,\n verbose=False\n ):\n\n self.data = data\n self.targets = targets\n self.length = length\n self.sampling_rate = sampling_rate\n self.stride = stride\n self.start_index = start_index\n if end_index is None:\n end_index = len(data) - 1\n self.end_index = end_index\n self.shuffle = shuffle\n self.reverse = reverse\n self.batch_size = batch_size\n self.verbose = verbose\n\n def __len__(self):\n return int(np.ceil(\n (self.end_index - self.start_index) /\n (self.batch_size * self.stride)))\n\n def _empty_batch(self, num_rows):\n samples_shape = [num_rows, self.length // self.sampling_rate]\n samples_shape.extend(self.data.shape[1:])\n targets_shape = [num_rows, self.length // self.sampling_rate]\n targets_shape.extend(self.targets.shape[1:])\n return np.empty(samples_shape), np.empty(targets_shape)\n\n def __getitem__(self, index):\n i = self.start_index + self.batch_size * self.stride * index\n if self.verbose:\n logging.debug(f\"i: {i}\")\n logging.debug(\n f\"{i, (i + self.batch_size * self.stride, self.end_index), self.stride}\")\n rows = np.arange(i, min(i + self.batch_size *\n self.stride, self.end_index), self.stride)\n if self.verbose:\n logging.debug(f\"rows: {rows}\")\n\n samples, targets = self._empty_batch(len(rows))\n for j, row in enumerate(rows):\n indices = np.arange(rows[j], rows[j] + self.length, self.sampling_rate)\n if self.shuffle:\n # indices = (indices)\n np.random.shuffle(indices)\n if self.verbose:\n logging.debug(f\"indices: {indices}\")\n samples[j] = self.data[indices]\n targets[j] = self.targets[indices]\n if self.reverse:\n return samples[:, ::-1, ...], targets\n return samples, targets\n\n# todo def balance_dataset(x,y)\n\n# todo def metrics_report(model, x,y)\n\n# todo def history_plot(model)", "sub_path": "ml_hep_tracking/utils.py", "file_name": "utils.py", "file_ext": "py", "file_size_in_byte": 7375, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pathlib.Path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 22, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 26, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 32, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 19, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 19, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 36, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.KFold", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 41, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 44, "usage_type": "attribute"}, {"api_name": "typing.Generator", "line_number": 37, "usage_type": "name"}, {"api_name": "typing.Tuple", "line_number": 37, "usage_type": "name"}, {"api_name": "numpy.ndarray", "line_number": 37, "usage_type": "attribute"}, {"api_name": "numpy.arange", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 53, "usage_type": "attribute"}, {"api_name": "pathlib.Path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "pandas.MultiIndex.from_arrays", "line_number": 65, "usage_type": "call"}, {"api_name": "pandas.MultiIndex", "line_number": 65, "usage_type": "attribute"}, {"api_name": "pandas.Index", "line_number": 71, "usage_type": "call"}, {"api_name": "pandas.RangeIndex", "line_number": 72, "usage_type": "call"}, {"api_name": "pandas.MultiIndex.from_product", "line_number": 74, "usage_type": "call"}, {"api_name": "pandas.MultiIndex", "line_number": 74, "usage_type": "attribute"}, {"api_name": "numpy.isnan", "line_number": 83, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 86, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 87, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 92, "usage_type": "call"}, {"api_name": "keras.utils.Sequence", "line_number": 101, "usage_type": "name"}, {"api_name": "numpy.ceil", "line_number": 162, "usage_type": "call"}, {"api_name": "numpy.empty", "line_number": 171, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 176, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 177, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 179, "usage_type": "call"}, {"api_name": "logging.debug", "line_number": 182, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 186, "usage_type": "call"}, {"api_name": "numpy.random.shuffle", "line_number": 189, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 189, "usage_type": "attribute"}, {"api_name": "logging.debug", "line_number": 191, "usage_type": "call"}]} +{"seq_id": "554959549", "text": "# This Source Code Form is subject to the terms of the Mozilla Public\n# License, v. 2.0. If a copy of the MPL was not distributed with this file,\n# You can obtain one at http://mozilla.org/MPL/2.0/.\nimport functools\nimport unittest\n\nfrom pyramid import testing\nfrom webtest import TestApp\n\nfrom cornice.tests import CatchErrors\nfrom mozsvc.metrics import MetricsService\n\nservice3 = MetricsService(name=\"service3\", path=\"/service3\")\nservice4 = MetricsService(name=\"service4\", path=\"/service4\")\nservice5 = MetricsService(name=\"service5\", path=\"/service5\")\n\n\ndef wrap_fn(fn):\n if not hasattr(fn, '_wrap_count'):\n fn._wrap_count = 0\n else:\n fn._wrap_count += 1\n\n @functools.wraps(fn)\n def wrapper(*args, **kwargs):\n result = fn(*args, **kwargs)\n result[\"wrapped%d\" % fn._wrap_count] = \"yes\"\n return result\n return wrapper\n\n\n@service3.get(decorators=[wrap_fn])\ndef wrapped_get3(request):\n return {\"test\": \"succeeded\"}\n\n\n@service4.post(decorators=[wrap_fn])\n@service4.get(decorators=[wrap_fn])\ndef wrapped_get4(request):\n return {\"test\": \"succeeded\"}\n\n\n@service5.get(decorators=[wrap_fn])\n@service5.get(accept=\"application/json\", renderer=\"simplejson\")\n@service5.get(accept=\"application/newlines\", renderer=\"newlines\")\n@service5.post(decorators=[wrap_fn])\ndef wrapped_get5(request):\n return {\"test\": \"succeeded\"}\n\n\nclass TestServiceDefinition(unittest.TestCase):\n\n def setUp(self):\n self.config = testing.setUp()\n self.config.include(\"cornice\")\n self.config.scan(\"mozsvc.tests.test_service_definition\")\n self.app = TestApp(CatchErrors(self.config.make_wsgi_app()))\n\n def tearDown(self):\n testing.tearDown()\n\n def test_decorated_view_fn(self):\n # passing a decorator in to the service api call should result in a\n # decorated view callable\n resp = self.app.get(\"/service3\")\n self.assertEquals(resp.json, {'test': 'succeeded', 'wrapped0': 'yes'})\n\n def test_stacked_decorated_view(self):\n # passing a decorator in to the service api call should result in a\n # decorated view callable, ordering of the particular decorators\n # shouldn't break things\n resp = self.app.get(\"/service4\")\n self.assertEquals(resp.json, {'test': 'succeeded', 'wrapped0': 'yes'})\n\n resp = self.app.get(\"/service5\")\n self.assertEquals(resp.json, {'test': 'succeeded', 'wrapped0': 'yes'})\n", "sub_path": "mozsvc/tests/test_service_definition.py", "file_name": "test_service_definition.py", "file_ext": "py", "file_size_in_byte": 2437, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "mozsvc.metrics.MetricsService", "line_number": 13, "usage_type": "call"}, {"api_name": "mozsvc.metrics.MetricsService", "line_number": 14, "usage_type": "call"}, {"api_name": "mozsvc.metrics.MetricsService", "line_number": 15, "usage_type": "call"}, {"api_name": "functools.wraps", "line_number": 24, "usage_type": "call"}, {"api_name": "unittest.TestCase", "line_number": 51, "usage_type": "attribute"}, {"api_name": "pyramid.testing.setUp", "line_number": 54, "usage_type": "call"}, {"api_name": "pyramid.testing", "line_number": 54, "usage_type": "name"}, {"api_name": "webtest.TestApp", "line_number": 57, "usage_type": "call"}, {"api_name": "cornice.tests.CatchErrors", "line_number": 57, "usage_type": "call"}, {"api_name": "pyramid.testing.tearDown", "line_number": 60, "usage_type": "call"}, {"api_name": "pyramid.testing", "line_number": 60, "usage_type": "name"}]} +{"seq_id": "273932378", "text": "import os\nimport time\nimport unicodedata\n\nimport requests\nfrom bs4 import BeautifulSoup\n\nfrom animedb.models import *\n\n\ndef get_content(url, params=None, stream=False):\n r = requests.get(url, params=params, stream=stream)\n while r.status_code != 200:\n time.sleep(3)\n r = requests.get(url, params=params, stream=stream)\n return r\n\ndef get_soup(url, params=None):\n r = get_content(url, params=params, stream=False)\n return BeautifulSoup(r.content, 'html5lib', from_encoding='utf-8')\n\ndef get_safe_str(string):\n string = string.replace('/', '')\n string = string.replace(':', '-')\n string = unicodedata.normalize('NFKC', string)\n return string.strip()\n\ndef get_img(url, dest_dir, file_name):\n img_ext = url.split('.')[-1].lower()\n file_name = '{}.{}'.format(file_name, img_ext)\n img_path = os.path.join(dest_dir, file_name)\n if os.path.exists(img_path):\n return file_name\n\n r = get_content(url, stream=True)\n if r.status_code == 200:\n with open(img_path, 'wb') as f:\n for chunk in r.iter_content(1024):\n f.write(chunk)\n return file_name\n else:\n return 'NA'\n\ndef get_or_create_anime(data_dict):\n if Anime.objects.filter(title=data_dict['title']).exists():\n anime = Anime.objects.get(title=data_dict['title'])\n else:\n genres = data_dict['genres'].split(', ')\n del data_dict['genres']\n anime = Anime.objects.create(**data_dict)\n for genre in genres:\n obj, created = Genre.objects.get_or_create(name=genre.strip())\n anime.genres.add(obj)\n return anime\n", "sub_path": "animedb/updaters/common.py", "file_name": "common.py", "file_ext": "py", "file_size_in_byte": 1626, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 15, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 20, "usage_type": "call"}, {"api_name": "unicodedata.normalize", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 32, "usage_type": "call"}, {"api_name": "os.path", "line_number": 32, "usage_type": "attribute"}]} +{"seq_id": "379287372", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torch.utils.data as Data\nfrom torchvision import datasets, transforms\nfrom torch.autograd import Variable\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\n\nBATCH_SIZE = 128\nKERNEL_SIZE = 3\nEPOCH = 100\ngradient_list = []\nloss_list = []\nNUM_WORKER = 1\n\ndata_train = datasets.MNIST(root = 'MNIST_train.npy', transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize([0.5],[0.5])]), train = True, download = True)\ndata_test = datasets.MNIST(root = 'MNIST_test.npy', transform = transforms.Compose([transforms.ToTensor(),transforms.Normalize([0.5],[0.5])]), train = False, download = True)\n\ndata_loader_train = Data.DataLoader(dataset = data_train, batch_size = BATCH_SIZE, shuffle = True, num_workers = NUM_WORKER)\ndata_loader_test = Data.DataLoader(dataset = data_test, batch_size = BATCH_SIZE, shuffle = True, num_workers = NUM_WORKER)\n\nclass Net(torch.nn.Module):\n\tdef __init__(self):\n\t\tsuper(Net,self).__init__()\n\t\tself.conv = torch.nn.Sequential(nn.Conv2d(1,64,kernel_size = KERNEL_SIZE, stride = 1, padding = 1),\n\t\t\t\t\t\t\t\t\t\tnn.ReLU(),\n\t\t\t\t\t\t\t\t\t\tnn.Conv2d(64,128,kernel_size = KERNEL_SIZE, stride = 1, padding = 1),\n\t\t\t\t\t\t\t\t\t\tnn.ReLU(),\n\t\t\t\t\t\t\t\t\t\tnn.MaxPool2d(stride=2, kernel_size = KERNEL_SIZE-1))\n\t\tself.dense = torch.nn.Sequential(nn.Linear(14*14*128, 1024),\n\t\t\t\t\t\t\t\t\t\tnn.ReLU(),\n\t\t\t\t\t\t\t\t\t\tnn.Dropout(p=0.5),\n\t\t\t\t\t\t\t\t\t\tnn.Linear(1024,10))\n\tdef forward(self,x):\n\t\tx = self.conv(x)\n\t\tx = x.view(-1, 14*14*128)\n\t\tx = self.dense(x)\n\t\treturn x\n\nclass CNN(nn.Module):\n\tdef __init__(self):\n\t\tsuper(CNN, self).__init__()\n\t\tself.conv1 = nn.Sequential(\n\t\t\tnn.Conv2d(\n\t\t\t\tin_channels =1,\n\t\t\t\tout_channels = 16,\n\t\t\t\tkernel_size=5,\n\t\t\t\tstride=1,\n\t\t\t\tpadding=2),\n\t\t\tnn.ReLU(),\n\t\t\tnn.MaxPool2d(kernel_size=2)\n\t\t)\n\t\tself.conv2 = nn.Sequential(\n\t\t\tnn.Conv2d(16,32,5,1,2),\n\t\t\tnn.ReLU(),\n\t\t\tnn.MaxPool2d(2)\n\t\t)\n\t\tself.out = nn.Linear(32*7*7,10)\n\n\tdef forward(self, x):\n\t\tx = self.conv1(x)\n\t\tx = self.conv2(x)\n\t\tx = x.view(x.size(0),-1)\n\t\toutput = self.out(x)\n\t\treturn output\n\t\t\nnet = CNN()\nloss_func = nn.CrossEntropyLoss()\noptimizer = optim.SGD(net.parameters(),lr = 0.2, momentum = 0.9)\n\nprint(net)\n\ndef grad_norm(p):\n grad_sum = 0.0\n for element in net.parameters():\n grad = (element.grad.cpu().data.numpy()**p).sum()\n grad_sum += grad\n return grad_sum ** (1/p)\n\nprint (len(data_loader_train))\nfor epoch in range(EPOCH):\n\tepoch_grad = 0\n\tepoch_loss = 0\n\t'''for i, x in enumerate(data_loader_train):\n\t\tprint (x)'''\n\tfor step, (batch_x,batch_y) in enumerate(data_loader_train):\n\t\tbatch_x,batch_y = Variable(batch_x), Variable(batch_y)\n\t\toptimizer.zero_grad()\n\n\t\tprediction = net(batch_x)\n\n\t\tloss = loss_func(prediction, batch_y)\n\t\tepoch_loss += loss.data.numpy()\n\n\t\tloss.backward()\n\n\t\toptimizer.step()\n\t\tepoch_grad += grad_norm(2)\n\t\tprint ('epoch: %d, loss: %.7f, gradient: %.7f' %(epoch, loss, grad_norm(2)))\n\t\tgradient_list.append(grad_norm(2))\n\t\tloss_list.append(loss.data.numpy())\n\t'''print ('epoch: %d, loss: %.7f, gradient: %.7f' %(epoch, epoch_loss/len(data_loader_train),epoch_grad/len(data_loader_train) ))\n\tgradient_list.append(epoch_grad/len(data_loader_train))\n\tloss_list.append(epoch_loss/len(data_loader_train))'''\n\nprint(sum (p.numel() for p in net.parameters()))\nprint ('TRAINING FINISHING.')\n\ntorch.save(net,'MNIST.save')\nprint('save model.')\n\n\ngradient = np.array(gradient_list)\nx = np.linspace(0,len(data_loader_train),len(data_loader_train))\nx_ep = np.linspace(0,EPOCH,EPOCH)\nplt.subplot(211)\nplt.plot(x,gradient_list,label = 'Gradient Norm')\nplt.legend(loc = 'upper right')\n#plt.show()\nnp.save('gradientNorm_MNIST.npy', gradient)\nloss = np.array(loss_list)\nplt.subplot(212)\nplt.plot(x,loss_list, label = 'Loss', color = 'red')\nplt.legend(loc = 'upper right')\nplt.show()\nnp.save('loss_MNIST.npy',loss)\nprint('save loss.')\n\npd.DataFrame(gradient_list).to_csv('gradientNorm_MNIST.csv')\npd.DataFrame(loss_list).to_csv('loss_MNIST.csv')\n\n\n\n\n", "sub_path": "hw1/hw1-2/observe_gradient_norm/gradient_mnist.py", "file_name": "gradient_mnist.py", "file_ext": "py", "file_size_in_byte": 4007, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torchvision.datasets.MNIST", "line_number": 19, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 19, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 19, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 19, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 19, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 19, "usage_type": "call"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 20, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 20, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 20, "usage_type": "call"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.utils.data", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "attribute"}, {"api_name": "torch.nn.Sequential", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.nn.Conv2d", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 32, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torch.nn.Linear", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn.ReLU", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Dropout", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 46, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 46, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 53, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 54, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.MaxPool2d", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 61, "usage_type": "name"}, {"api_name": "torch.nn.CrossEntropyLoss", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.optim.SGD", "line_number": 72, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 72, "usage_type": "name"}, {"api_name": "torch.autograd.Variable", "line_number": 90, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 116, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 118, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 119, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 120, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 127, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 127, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "numpy.save", "line_number": 129, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 132, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 133, "usage_type": "call"}]} +{"seq_id": "63944990", "text": "#!/usr/bin/env python\n#\n# argdist.py Trace a function and display a distribution of its\n# parameter values as a histogram or frequency count.\n#\n# USAGE: argdist.py [-h] [-p PID] [-z STRING_SIZE] [-i INTERVAL]\n# [-n COUNT] [-C specifier [specifier ...]]\n# [-H specifier [specifier ...]]\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\")\n# Copyright (C) 2016 Sasha Goldshtein.\n\nfrom bcc import BPF\nfrom time import sleep, strftime\nimport argparse\n\nclass Specifier(object):\n text = \"\"\"\nDATA_DECL\n\nint PROBENAME(struct pt_regs *ctx SIGNATURE)\n{\n PID_FILTER\n KEY_EXPR\n if (!(FILTER)) return 0;\n COLLECT\n return 0;\n}\n\"\"\"\n next_probe_index = 0\n aliases = { \"$PID\": \"bpf_get_current_pid_tgid()\" }\n\n def _substitute_aliases(self, expr):\n if expr is None:\n return expr\n for alias, subst in Specifier.aliases.items():\n expr = expr.replace(alias, subst)\n return expr\n\n def __init__(self, type, specifier, pid):\n self.raw_spec = specifier \n spec_and_label = specifier.split(';')\n self.label = spec_and_label[1] \\\n if len(spec_and_label) == 2 else None\n parts = spec_and_label[0].strip().split(':')\n if len(parts) < 3 or len(parts) > 6:\n raise ValueError(\"invalid specifier format\")\n self.type = type # hist or freq\n self.is_ret_probe = parts[0] == \"r\"\n if self.type != \"hist\" and self.type != \"freq\":\n raise ValueError(\"unrecognized probe type\")\n if parts[0] not in [\"r\", \"p\"]:\n raise ValueError(\"unrecognized probe type\")\n self.library = parts[1]\n self.is_user = len(self.library) > 0\n fparts = parts[2].split('(')\n if len(fparts) != 2:\n raise ValueError(\"invalid specifier format\")\n self.function = fparts[0]\n self.signature = fparts[1][:-1]\n self.is_default_expr = len(parts) < 5\n if not self.is_default_expr:\n self.expr_type = parts[3]\n self.expr = parts[4]\n else:\n if not self.is_ret_probe and self.type == \"hist\":\n raise ValueError(\"dist probes must have expr\")\n self.expr_type = \\\n \"u64\" if not self.is_ret_probe else \"int\"\n self.expr = \"1\" if not self.is_ret_probe else \"$retval\"\n self.expr = self.expr.replace(\"$retval\",\n \"(%s)ctx->ax\" % self.expr_type)\n self.filter = None if len(parts) != 6 else parts[5]\n if self.filter is not None:\n self.filter = self.filter.replace(\"$retval\",\n \"(%s)ctx->ax\" % self.expr_type)\n self.expr = self._substitute_aliases(self.expr)\n self.filter = self._substitute_aliases(self.filter)\n self.pid = pid\n self.probe_func_name = \"%s_probe%d\" % \\\n (self.function, Specifier.next_probe_index)\n self.probe_hash_name = \"%s_hash%d\" % \\\n (self.function, Specifier.next_probe_index)\n Specifier.next_probe_index += 1\n\n def _is_string_probe(self):\n return self.expr_type == \"char*\" or self.expr_type == \"char *\"\n\n def generate_text(self, string_size):\n program = self.text.replace(\"PROBENAME\", self.probe_func_name)\n signature = \"\" if len(self.signature) == 0 \\\n else \",\" + self.signature\n program = program.replace(\"SIGNATURE\", signature)\n if self.pid is not None and not self.is_user:\n # kernel probes need to explicitly filter pid\n program = program.replace(\"PID_FILTER\",\n \"u32 pid = bpf_get_current_pid_tgid();\\n\" + \\\n \"if (pid != %d) { return 0; }\" % self.pid)\n else:\n program = program.replace(\"PID_FILTER\", \"\")\n if self._is_string_probe():\n decl = \"\"\"\nstruct %s_key_t { char key[%d]; };\nBPF_HASH(%s, struct %s_key_t, u64);\n\"\"\" \\\n % (self.function, string_size,\n self.probe_hash_name, self.function)\n collect = \"%s.increment(__key);\" % self.probe_hash_name\n key_expr = \"\"\"\nstruct %s_key_t __key = {0};\nbpf_probe_read(&__key.key, sizeof(__key.key), %s);\n\"\"\" \\\n % (self.function, self.expr)\n elif self.type == \"freq\":\n decl = \"BPF_HASH(%s, %s, u64);\" % \\\n (self.probe_hash_name, self.expr_type)\n collect = \"%s.increment(__key);\" % self.probe_hash_name\n key_expr = \"%s __key = %s;\" % \\\n (self.expr_type, self.expr)\n elif self.type == \"hist\":\n decl = \"BPF_HISTOGRAM(%s, %s);\" % \\\n (self.probe_hash_name, self.expr_type)\n collect = \"%s.increment(bpf_log2l(__key));\" % \\\n self.probe_hash_name \n key_expr = \"%s __key = %s;\" % \\\n (self.expr_type, self.expr)\n program = program.replace(\"DATA_DECL\", decl)\n program = program.replace(\"KEY_EXPR\", key_expr) \n program = program.replace(\"FILTER\", self.filter or \"1\") \n program = program.replace(\"COLLECT\", collect)\n return program\n\n def attach(self, bpf):\n self.bpf = bpf\n if self.is_user:\n if self.is_ret_probe:\n bpf.attach_uretprobe(name=self.library,\n sym=self.function,\n fn_name=self.probe_func_name,\n pid=self.pid or -1)\n else:\n bpf.attach_uprobe(name=self.library,\n sym=self.function,\n fn_name=self.probe_func_name,\n pid=self.pid or -1)\n else:\n if self.is_ret_probe:\n bpf.attach_kretprobe(event=self.function,\n fn_name=self.probe_func_name)\n else:\n bpf.attach_kprobe(event=self.function,\n fn_name=self.probe_func_name)\n\n def display(self):\n print(self.label or self.raw_spec)\n data = self.bpf.get_table(self.probe_hash_name)\n if self.type == \"freq\":\n print(\"\\t%-10s %s\" % (\"COUNT\", \"EVENT\"))\n for key, value in sorted(data.items(),\n key=lambda kv: kv[1].value):\n key_val = key.key if self._is_string_probe() \\\n else str(key.value)\n if self.is_default_expr:\n if not self.is_ret_probe:\n key_str = \"total calls\"\n else:\n key_str = \"retval = %s\" % \\\n key_val\n else:\n key_str = \"%s = %s\" % \\\n (self.expr, key_val)\n print(\"\\t%-10s %s\" % \\\n (str(value.value), key_str))\n elif self.type == \"hist\":\n label = self.expr if not self.is_default_expr \\\n else \"retval\"\n data.print_log2_hist(val_type=label)\n\nexamples = \"\"\"\nProbe specifier syntax:\n {p,r}:[library]:function(signature)[:type:expr[:filter]][;label]\nWhere:\n p,r -- probe at function entry or at function exit\n in exit probes, only $retval is accessible\n library -- the library that contains the function\n (leave empty for kernel functions)\n function -- the function name to trace\n signature -- the function's parameters, as in the C header\n type -- the type of the expression to collect\n expr -- the expression to collect\n filter -- the filter that is applied to collected values\n label -- the label for this probe in the resulting output\n\nEXAMPLES:\n\nargdist.py -H 'p::__kmalloc(u64 size):u64:size'\n Print a histogram of allocation sizes passed to kmalloc\n\nargdist.py -p 1005 -C 'p:c:malloc(size_t size):size_t:size:size==16'\n Print a frequency count of how many times process 1005 called malloc\n with an allocation size of 16 bytes\n\nargdist.py -C 'r:c:gets():char*:$retval;snooped strings'\n Snoop on all strings returned by gets()\n\nargdist.py -p 1005 -C 'p:c:write(int fd):int:fd'\n Print frequency counts of how many times writes were issued to a\n particular file descriptor number, in process 1005\n\nargdist.py -p 1005 -H 'r:c:read()'\n Print a histogram of error codes returned by read() in process 1005\n\nargdist.py -H \\\\\n 'p:c:write(int fd, const void *buf, size_t count):size_t:count:fd==1'\n Print a histogram of buffer sizes passed to write() across all\n processes, where the file descriptor was 1 (STDOUT)\n\nargdist.py -C 'p:c:fork();fork calls'\n Count fork() calls in libc across all processes\n Can also use funccount.py, which is easier and more flexible \n\nargdist.py \\\\\n -H 'p:c:sleep(u32 seconds):u32:seconds' \\\\\n -H 'p:c:nanosleep(struct timespec { time_t tv_sec; long tv_nsec; } *req):long:req->tv_nsec'\n Print histograms of sleep() and nanosleep() parameter values\n\nargdist.py -p 2780 -z 120 \\\\\n -C 'p:c:write(int fd, char* buf, size_t len):char*:buf:fd==1'\n Spy on writes to STDOUT performed by process 2780, up to a string size\n of 120 characters \n\"\"\"\n\nparser = argparse.ArgumentParser(description=\n \"Trace a function and display a summary of its parameter values.\",\n formatter_class=argparse.RawDescriptionHelpFormatter,\n epilog=examples)\nparser.add_argument(\"-p\", \"--pid\", type=int,\n help=\"id of the process to trace (optional)\")\nparser.add_argument(\"-z\", \"--string-size\", default=80, type=int,\n help=\"maximum string size to read from char* arguments\")\nparser.add_argument(\"-i\", \"--interval\", default=1, type=int,\n help=\"output interval, in seconds\")\nparser.add_argument(\"-n\", \"--number\", type=int, dest=\"count\",\n help=\"number of outputs\")\nparser.add_argument(\"-H\", \"--histogram\", nargs=\"*\", dest=\"histspecifier\",\n help=\"probe specifier to capture histogram of (see examples below)\")\nparser.add_argument(\"-C\", \"--count\", nargs=\"*\", dest=\"countspecifier\",\n help=\"probe specifier to capture count of (see examples below)\")\nparser.add_argument(\"-v\", \"--verbose\", action=\"store_true\",\n help=\"print resulting BPF program code before executing\")\nargs = parser.parse_args()\n\nspecifiers = []\nfor specifier in (args.countspecifier or []):\n specifiers.append(Specifier(\"freq\", specifier, args.pid))\nfor histspecifier in (args.histspecifier or []):\n specifiers.append(Specifier(\"hist\", histspecifier, args.pid))\nif len(specifiers) == 0:\n print(\"at least one specifier is required\")\n exit(1)\n\nbpf_source = \"#include \\n\"\nfor specifier in specifiers:\n bpf_source += specifier.generate_text(args.string_size)\n\nif args.verbose:\n print(bpf_source)\n\nbpf = BPF(text=bpf_source)\n\nfor specifier in specifiers:\n specifier.attach(bpf)\n\ncount_so_far = 0\nwhile True:\n try:\n sleep(args.interval)\n except KeyboardInterrupt:\n exit()\n print(\"[%s]\" % strftime(\"%H:%M:%S\"))\n for specifier in specifiers:\n specifier.display()\n count_so_far += 1\n if args.count is not None and count_so_far >= args.count:\n exit()\n", "sub_path": "tools/argdist.py", "file_name": "argdist.py", "file_ext": "py", "file_size_in_byte": 13053, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "argparse.ArgumentParser", "line_number": 233, "usage_type": "call"}, {"api_name": "argparse.RawDescriptionHelpFormatter", "line_number": 235, "usage_type": "attribute"}, {"api_name": "bcc.BPF", "line_number": 269, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 277, "usage_type": "call"}, {"api_name": "time.strftime", "line_number": 280, "usage_type": "call"}]} +{"seq_id": "567352077", "text": "from enum import Enum\nfrom core.project_base import Section\n\n\nclass LIDType(Enum):\n # BC for bio-retention cell; RG for rain garden; GR for green roof; PP for porous pavement;\n # IT for infiltration trench; RB for rain barrel;RD for rooftup disconnect; VS for vegetative swale.\n BC = 1\n RG = 2\n GR = 3\n IT = 4\n PP = 5\n RB = 6\n RD = 7\n VS = 8\n\n\nclass LIDControl(Section):\n \"\"\"Defines scale-independent LID controls that can be deployed within subcatchments\"\"\"\n\n LineTypes = (\n (\"has_surface_layer\",\n \"SURFACE\",\n \"surface_layer_storage_depth\",\n \"surface_layer_vegetative_cover_fraction\",\n \"surface_layer_surface_roughness\",\n \"surface_layer_surface_slope\",\n \"surface_layer_swale_side_slope\"),\n (\"has_soil_layer\",\n \"SOIL\",\n \"soil_layer_thickness\",\n \"soil_layer_porosity\",\n \"soil_layer_field_capacity\",\n \"soil_layer_wilting_point\",\n \"soil_layer_conductivity\",\n \"soil_layer_conductivity_slope\",\n \"soil_layer_suction_head\"),\n (\"has_pavement_layer\",\n \"PAVEMENT\",\n \"pavement_layer_thickness\",\n \"pavement_layer_void_ratio\",\n \"pavement_layer_impervious_surface_fraction\",\n \"pavement_layer_permeability\",\n \"pavement_layer_clogging_factor\"),\n (\"has_storage_layer\",\n \"STORAGE\",\n \"storage_layer_height\",\n \"storage_layer_void_ratio\",\n \"storage_layer_filtration_rate\",\n \"storage_layer_clogging_factor\"),\n (\"has_underdrain_system\",\n \"DRAIN\",\n \"drain_coefficient\",\n \"drain_exponent\",\n \"drain_offset_height\",\n \"drain_delay\"),\n (\"has_drainmat_system\",\n \"DRAINMAT\",\n \"drainmat_thickness\",\n \"drainmat_void_fraction\",\n \"drainmat_roughness\"))\n\n def __init__(self):\n Section.__init__(self)\n\n ## Name used to identify the particular LID control\n self.name = \"Unnamed\"\n\n ## Generic type of LID being defined\n self.lid_type = LIDType.BC\n\n ## does lid have surface layer\n self.has_surface_layer = False\n\n ## does lid have pavement layer\n self.has_pavement_layer = False\n\n ## does lid have soil layer\n self.has_soil_layer = False\n\n ## does lid have storage layer\n self.has_storage_layer = False\n\n ## does lid have underdrain system\n self.has_underdrain_system = False\n\n ## does lid have drainmat system\n self.has_drainmat_system = False\n\n ## When confining walls or berms are present this is the maximum depth to\n ## which water can pond above the surface of the unit before overflow\n ## occurs (in inches or mm). For LIDs that experience overland flow it is\n ## the height of any surface depression storage. For swales, it is the height\n ## of its trapezoidal cross section.\n self.surface_layer_storage_depth = \"0.0\"\n\n ## Fraction of the storage area above the surface that is filled with vegetation\n self.surface_layer_vegetative_cover_fraction = \"0.0\"\n\n ## Manning's n for overland flow over the surface of porous pavement or a vegetative swale\n self.surface_layer_surface_roughness = \"0.0\"\n\n ## Slope of porous pavement surface or vegetative swale\n self.surface_layer_surface_slope = \"0.0\"\n\n ## Slope (run over rise) of the side walls of a vegetative swale's cross section\n self.surface_layer_swale_side_slope = \"0.0\"\n\n ## Thickness of the pavement layer\n self.pavement_layer_thickness = \"0.0\"\n\n ## Volume of void space relative to the volume of solids in the pavement\n self.pavement_layer_void_ratio = \"0.0\"\n\n ## Ratio of impervious paver material to total area for modular systems\n self.pavement_layer_impervious_surface_fraction = \"0.0\"\n\n ## Permeability of the concrete or asphalt used in continuous systems or hydraulic\n ## conductivity of the fill material (gravel or sand) used in modular systems\n self.pavement_layer_permeability = \"0.0\"\n\n ## Number of pavement layer void volumes of runoff treated it takes to completely clog the pavement\n self.pavement_layer_clogging_factor = \"0.0\"\n\n ## Thickness of the soil layer\n self.soil_layer_thickness = \"0.0\"\n\n ## Volume of pore space relative to total volume of soil\n self.soil_layer_porosity = \"0.0\"\n\n ## Volume of pore water relative to total volume after the soil has been allowed to drain fully\n self.soil_layer_field_capacity = \"0.0\"\n\n ## Volume of pore water relative to total volume for a well dried soil where only bound water remains\n self.soil_layer_wilting_point = \"0.0\"\n\n ## Hydraulic conductivity for the fully saturated soil\n self.soil_layer_conductivity = \"0.0\"\n\n ## Slope of the curve of log(conductivity) versus soil moisture content\n self.soil_layer_conductivity_slope = \"0.0\"\n\n ## Average value of soil capillary suction along the wetting front\n self.soil_layer_suction_head = \"0.0\"\n\n ## Height of a rain barrel or thickness of a gravel layer\n self.storage_layer_height = \"0.0\"\n\n ## Volume of void space relative to the volume of solids in the layer\n self.storage_layer_void_ratio = \"0.0\"\n\n ## Maximum rate at which water can flow out the bottom of the layer after it is first constructed\n self.storage_layer_filtration_rate = \"0.0\"\n\n ## Total volume of treated runoff it takes to completely clog the bottom of the layer divided by the\n ## void volume of the layer\n self.storage_layer_clogging_factor = \"0.0\"\n\n ## Coefficient that determines the rate of flow through the underdrain as a function of height of\n ## stored water above the drain height\n self.drain_coefficient = \"0.0\"\n\n ## Exponent that determines the rate of flow through the underdrain as a function of height of\n ## stored water above the drain height\n self.drain_exponent = \"0.0\"\n\n ## Height of any underdrain piping above the bottom of a storage layer or rain barrel\n self.drain_offset_height = \"0.0\"\n\n ## Number of dry weather hours that must elapse before the drain line in a rain barrel is opened\n self.drain_delay = \"0.0\"\n\n ## Thickness of the drainage mat (inches or mm)\n self.drainmat_thickness = \"0.0\"\n\n ## Ratio of void volume to total volume in the mat\n self.drainmat_void_fraction = \"0.5\"\n\n ## Manning's n constant used to compute the horizontal flow rate of drained water through the mat\n self.drainmat_roughness = \"0.1\"\n\n\n", "sub_path": "src/core/swmm/hydrology/lidcontrol.py", "file_name": "lidcontrol.py", "file_ext": "py", "file_size_in_byte": 6771, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "enum.Enum", "line_number": 5, "usage_type": "name"}, {"api_name": "core.project_base.Section", "line_number": 18, "usage_type": "name"}, {"api_name": "core.project_base.Section.__init__", "line_number": 64, "usage_type": "call"}, {"api_name": "core.project_base.Section", "line_number": 64, "usage_type": "name"}]} +{"seq_id": "130620947", "text": "# -*- coding: utf-8 -*-\nimport sys\nimport time\n#import r\nimport pexpect\nimport random\nimport string\nimport threading\n\n\nclass Server(threading.Thread):\n def __init__(self, prog_path, port_number):\n super(Server, self).__init__()\n self.prog_path = prog_path\n self.controler = None\n self.port_number = port_number\n\n def run(self):\n if '.py' in prog_path:\n command = \"python3 %s s %d\" % (self.prog_path, self.port_number)\n else:\n command = \"%s s %d\" % (self.prog_path, self.port_number)\n self.controler = pexpect.spawn(command)\n print(\"Server Port:\", self.port_number)\n while True:\n try:\n self.controler.expect(\"\\n\\r\\t\\n\\n\", timeout=2)\n except pexpect.TIMEOUT:\n continue\n except pexpect.EOF:\n break\n\n def close(self):\n self.controler.sendcontrol('c')\n\n\ndef check_binary_valid(prog_path):\n if '.py' in prog_path:\n command = \"python3 %s\" % prog_path\n else:\n command = prog_path\n try:\n client = pexpect.spawn(command)\n except pexpect.ExceptionPexpect:\n return 0\n try:\n client.expect(\"Usage: myprog c
or myprog s \", timeout=2)\n client.close()\n return 1\n except (pexpect.TIMEOUT, pexpect.EOF) as e:\n client.close()\n return 0\n\n\nif __name__ == '__main__':\n if len(sys.argv) < 2:\n print('Usage: python3 lab1_test_script.py myprog_path')\n else:\n prog_path = sys.argv[1]\n \n if not check_binary_valid(prog_path):\n print('Please input a valid path of your binary program')\n else:\n messages = [\n 'test case 1',\n 'test case 2: COMPSCI 356 i\\f\\\\\\'\\\"\\as an undergraduate course in computer science teaching the fundamentals of computer networks. We will cover the technologies supporting the Internet, from Ethernet and WiFi through the routing protocols that govern the flow of traffic, and the web technologies that\tgenerate most of it.Topics: The topics we will study include but are not limited to: how to achieve reliable communications over unreliable channels, how to find a good path through a network, how to share networ',\n [],\n \"EOF\"\n ]\n port_number = random.randint(1024, 65535)\n server = Server(prog_path, port_number)\n\n server.start()\n time.sleep(2)\n\n def send_message(client_handle, message):\n if message != 'EOF':\n client_handle.sendline(message)\n else:\n client_handle.sendcontrol('d')\n if message != 'EOF':\n try:\n client_handle.expect(\"Enter message:\", timeout=4)\n new_msg = client_handle.before.decode(\"utf-8\") \n response = new_msg.replace(\"\\r\\n\", \"\\n\")[:-1]\n reply = response.split(\"\\n\")[-1]\n if message == reply:\n return 1\n else:\n return 0\n except (pexpect.TIMEOUT, pexpect.EOF) as e:\n return 0\n else:\n try:\n client_handle.expect('Enter message:', timeout=4)\n return 0\n except pexpect.TIMEOUT:\n return 0\n except pexpect.EOF:\n return 1\n\n def start_client(client_number):\n if '.py' in prog_path:\n command = \"python3 %s c %d 127.0.0.1\" % (prog_path, port_number)\n else:\n command = \"%s c %d 127.0.0.1\" % (prog_path, port_number)\n client = pexpect.spawn(command)\n print(\"client connect to\", port_number)\n try:\n i = client.expect([\"Enter message:\", 'Connection refused'], timeout=4)\n if i == 1:\n print('Client%d: Connection refused.' % client_number)\n return 0\n except (pexpect.TIMEOUT, pexpect.EOF) as e:\n print(e)\n print('Client%d: cannot connect to the server.' % client_number)\n return 0\n results = [1, 1, 1, 1]\n for i in range(len(messages)):\n message_list = [messages[i]]\n if i == 2:\n message_list = []\n for j in range(20):\n message_list.append(''.join(random.SystemRandom().choice(string.ascii_uppercase + string.digits) for _ in range(512)))\n for message in message_list:\n result = send_message(client, message)\n if result == 0:\n results[i] = 0\n break\n for i in range(4):\n if i == 0:\n print('Client%d: Testing short word:' % client_number,end=\" \")\n elif i == 1:\n print(' Testing long sentence:', end=\" \")\n elif i == 2:\n print(' Testing multiple sentences:', end=\" \")\n else:\n print(' Testing EOF:', end=\" \")\n if results[i] == 0:\n print('\\033[1;31;40mFAILED\\033[0m')\n else:\n print('\\033[1;32;40mPASSED\\033[0m')\n client.close()\n if sum(results) == 4:\n return 1\n else:\n return 0\n\n if start_client(1):\n start_client(2)\n\n server.close()\n\n", "sub_path": "lab1_test_script.py", "file_name": "lab1_test_script.py", "file_ext": "py", "file_size_in_byte": 5976, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "threading.Thread", "line_number": 11, "usage_type": "attribute"}, {"api_name": "pexpect.spawn", "line_number": 23, "usage_type": "call"}, {"api_name": "pexpect.TIMEOUT", "line_number": 28, "usage_type": "attribute"}, {"api_name": "pexpect.EOF", "line_number": 30, "usage_type": "attribute"}, {"api_name": "pexpect.spawn", "line_number": 43, "usage_type": "call"}, {"api_name": "pexpect.ExceptionPexpect", "line_number": 44, "usage_type": "attribute"}, {"api_name": "pexpect.TIMEOUT", "line_number": 50, "usage_type": "attribute"}, {"api_name": "pexpect.EOF", "line_number": 50, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 56, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 59, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 70, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 74, "usage_type": "call"}, {"api_name": "pexpect.TIMEOUT", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pexpect.EOF", "line_number": 91, "usage_type": "attribute"}, {"api_name": "pexpect.TIMEOUT", "line_number": 97, "usage_type": "attribute"}, {"api_name": "pexpect.EOF", "line_number": 99, "usage_type": "attribute"}, {"api_name": "pexpect.spawn", "line_number": 107, "usage_type": "call"}, {"api_name": "pexpect.TIMEOUT", "line_number": 114, "usage_type": "attribute"}, {"api_name": "pexpect.EOF", "line_number": 114, "usage_type": "attribute"}, {"api_name": "random.SystemRandom", "line_number": 124, "usage_type": "call"}, {"api_name": "string.ascii_uppercase", "line_number": 124, "usage_type": "attribute"}, {"api_name": "string.digits", "line_number": 124, "usage_type": "attribute"}]} +{"seq_id": "154513748", "text": "from asyncio import gather\nfrom collections import namedtuple\nfrom logging import getLogger\n\nfrom discord.ext.commands import (\n Cog,\n bot_has_permissions,\n group,\n guild_only,\n has_permissions,\n)\n\nfrom ..utils import maybe_send\n\nlogger = getLogger(__name__)\n_perms = namedtuple(\"perms\", [\"send_messages\", \"add_reactions\"])\n\n\ndef is_public(channel):\n \"\"\"\n Tests if a given text channel is public.\n\n Args:\n channel (discord.TextChannel: Channel to check.\n\n Returns:\n bool: `True` when the channel is public, `False` otherwise.\n \"\"\"\n role = channel.guild.default_role\n return channel.overwrites_for(role).read_messages is not False\n\n\nclass Stop(Cog):\n \"\"\"\n Utilities for locking down text channels.\n \"\"\"\n\n def __init__(self):\n self._perm_cache = {}\n\n async def _lock_channel(self, channel):\n \"\"\"\n Locks a text channel, denying Send Messages and Add Reactions.\n\n Args:\n channel (discord.TextChannel): Channel to lock.\n \"\"\"\n role = channel.guild.default_role\n\n await maybe_send(\n channel, \"Sending messages to this channel has been restricted.\"\n )\n\n overwrite = channel.overwrites_for(role)\n self._perm_cache.setdefault(\n channel.id, _perms(overwrite.send_messages, overwrite.add_reactions)\n )\n overwrite.send_messages = False\n overwrite.add_reactions = False\n await channel.set_permissions(role, overwrite=overwrite)\n\n async def _unlock_channel(self, channel):\n \"\"\"\n Unlock a text channel, restoring Send Messages and\n Add Reactions to their previous values.\n\n Args:\n channel (discord.TextChannel): Channel to unlock.\n \"\"\"\n role = channel.guild.default_role\n overwrite = channel.overwrites_for(role)\n\n perms = self._perm_cache.pop(channel.id, _perms(None, None))\n overwrite.send_messages = perms.send_messages\n overwrite.add_reactions = perms.add_reactions\n\n overwrite = (\n overwrite if not overwrite.is_empty() else None\n ) # Clear overwrite if empty\n await channel.set_permissions(role, overwrite=overwrite)\n\n await maybe_send(\n channel, \"Sending messages to this channel has been unrestricted.\"\n )\n\n @group()\n @guild_only()\n @has_permissions(manage_channels=True)\n @bot_has_permissions(manage_channels=True)\n async def stop(self, ctx):\n \"\"\"\n Commands to (un)restrict access to a channel.\n\n Required context: Server\n\n Required permissions:\n - Manage Channels\n\n Required bot permissions:\n - Manage Channels\n \"\"\"\n if ctx.invoked_subcommand is None:\n await maybe_send(\n ctx, 'Invalid subcommand passed. Possible options are \"on\" and \"off\".'\n )\n\n @stop.command(\"on\")\n async def _on_single(self, ctx):\n \"\"\"\n Restrict messaging and reactions to a channel for everyone.\n Note that the user issueing this command should probably have some form of way to\n still write to the channel, or they will need to release the lock manually.\n \"\"\"\n channel = ctx.channel\n await self._lock_channel(channel)\n\n @stop.command(\"off\")\n async def _off_single(self, ctx):\n \"\"\"\n Re-open a channel after it was locked down.\n This restores the default role's modified permissions,\n i.e. Send Messages and Add Reactions, to their previous values.\n If the channel was not previously locked down, nothing happens.\n \"\"\"\n channel = ctx.channel\n\n await self._unlock_channel(channel)\n\n @stop.group()\n async def all(self, ctx):\n \"\"\"\n Commands for (un)locking all public channels at once.\n \"\"\"\n if ctx.invoked_subcommand is None:\n await maybe_send(\n ctx, 'Invalid subcommand passed. Possible options are \"on\" and \"off\".'\n )\n\n @all.command(\"on\")\n async def _on_all(self, ctx):\n \"\"\"\n Works like regular \"stop on\", except it locks all\n public channels instead of just the current one.\n \"\"\"\n # Public channel => @everyone is not denied read perms\n coros = (\n self._lock_channel(channel)\n for channel in ctx.guild.text_channels\n if is_public(channel)\n )\n await gather(*coros)\n\n @all.command(\"off\")\n async def _off_all(self, ctx):\n \"\"\"\n Works like regular \"stop off\", except that it unlocks all\n locked public channels at once.\n \"\"\"\n public_channels = {\n channel.id: channel\n for channel in ctx.guild.text_channels\n if is_public(channel)\n }\n # Select all locked channels in current guild using set intersection\n locked_channel_ids = self._perm_cache.keys() & public_channels.keys()\n\n coros = (\n self._unlock_channel(public_channels[channel_id])\n for channel_id in locked_channel_ids\n )\n await gather(*coros)\n", "sub_path": "src/cardinal/cogs/stop.py", "file_name": "stop.py", "file_ext": "py", "file_size_in_byte": 5143, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 15, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 16, "usage_type": "call"}, {"api_name": "discord.ext.commands.Cog", "line_number": 33, "usage_type": "name"}, {"api_name": "utils.maybe_send", "line_number": 50, "usage_type": "call"}, {"api_name": "utils.maybe_send", "line_number": 82, "usage_type": "call"}, {"api_name": "utils.maybe_send", "line_number": 103, "usage_type": "call"}, {"api_name": "discord.ext.commands.group", "line_number": 86, "usage_type": "call"}, {"api_name": "discord.ext.commands.guild_only", "line_number": 87, "usage_type": "call"}, {"api_name": "discord.ext.commands.has_permissions", "line_number": 88, "usage_type": "call"}, {"api_name": "discord.ext.commands.bot_has_permissions", "line_number": 89, "usage_type": "call"}, {"api_name": "utils.maybe_send", "line_number": 135, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 151, "usage_type": "call"}, {"api_name": "asyncio.gather", "line_number": 171, "usage_type": "call"}]} +{"seq_id": "112751413", "text": "from django.urls import path\nfrom django import forms\nfrom public_app import views\nfrom django.conf.urls import url, include\nfrom site_directory import views\n\nfrom django.conf.urls import url\nfrom haystack.forms import ModelSearchForm\nfrom haystack.generic_views import FacetedSearchView as BaseFacetedSearchView\nfrom haystack.generic_views import SearchView\nfrom haystack.query import SearchQuerySet, EmptySearchQuerySet\n\nfrom admin_app.choices import TIMEESTIMATECHOICES, TYPEOFBEASTCHOICES\n\nclass MySearchForm(ModelSearchForm):\n max_time = forms.TypedChoiceField(choices=[('', '-')]+list(TIMEESTIMATECHOICES)[:-1], initial='', required=False, coerce=int, empty_value=120)\n beast = forms.TypedChoiceField(choices=[('', '-')]+list(TYPEOFBEASTCHOICES), initial='', required=False,\n label='Kind of beast')\n subject = forms.TypedChoiceField(choices=[('', '-')]\n +[(s,s) for s in [\"Quantum mechanics\",\n \"Classical mechanics\",\n \"Electromagnetism\",\n \"Thermal physics\",\n \"Math\",\n ]],\n initial='', required=False)\n\n # The following is needed in order to allow users to browse e.g. a given\n # subject with an empty query string.\n def no_query_found(self):\n return self.searchqueryset.all()\n\n def search(self):\n #First we need to store SearchQuerySet recieved after / from any other processing that's going on\n sqs = super(MySearchForm, self).search()\n\n if self.cleaned_data['max_time']:\n max_time = int(self.cleaned_data['max_time'])\n if max_time != '' and max_time != 0 and max_time < 120:\n sqs = sqs.filter(time_estimate__lte=max_time)\n\n if self.cleaned_data['beast']:\n beast = self.cleaned_data['beast']\n if beast != '':\n sqs = sqs.filter(beast=beast)\n\n if self.cleaned_data['subject']:\n subject = self.cleaned_data['subject']\n if subject != '':\n sqs = sqs.filter(subject=subject)\n\n return sqs\n\n def without_page(self):\n ''' Returns a urlencoded version of this query, but with the page removed. '''\n v = self.data.copy()\n if 'page' in v:\n del v['page']\n return v.urlencode()\n\n# Now create your own that subclasses the base view. Need to figure out faceting.\n# class FacetedSearchView(BaseFacetedSearchView):\nclass FacetedSearchView(SearchView):\n form_class = ModelSearchForm\n facet_fields = ['beast', 'time_estimate', 'topics']\n # template_name = 'search.html'\n context_object_name = 'page_object'\n form_class = MySearchForm\n\n # ... Any other custom methods etc\n def get(self, request, *args, **kwargs):\n if not request.user.has_perm(\"admin_app.change_problem\"):\n self.queryset = SearchQuerySet().filter(content='is_published')\n else:\n self.queryset = SearchQuerySet()\n return super(FacetedSearchView, self).get(request, *args, **kwargs)\n\n\n\nurlpatterns = [\n # url(r'^homework/keyword/(?P\\w+)/$', views.HomeworkKeywordView, name='homework_keyword'),\n \n path('keyword/', views.HomeworkKeywordView, name='homework_keyword'),\n\n path('', FacetedSearchView.as_view(), name='haystack_search'),\n # path('', SearchView.as_view(), name='haystack_search'),\n # path('', include('haystack.urls')),\n # path('', SearchView.as_view(), name='haystack_search'),\n\n]", "sub_path": "osu_www/site_directory/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 3524, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "haystack.forms.ModelSearchForm", "line_number": 15, "usage_type": "name"}, {"api_name": "django.forms.TypedChoiceField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 16, "usage_type": "name"}, {"api_name": "admin_app.choices.TIMEESTIMATECHOICES", "line_number": 16, "usage_type": "argument"}, {"api_name": "django.forms.TypedChoiceField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 17, "usage_type": "name"}, {"api_name": "admin_app.choices.TYPEOFBEASTCHOICES", "line_number": 17, "usage_type": "argument"}, {"api_name": "django.forms.TypedChoiceField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.forms", "line_number": 19, "usage_type": "name"}, {"api_name": "haystack.generic_views.SearchView", "line_number": 63, "usage_type": "name"}, {"api_name": "haystack.forms.ModelSearchForm", "line_number": 64, "usage_type": "name"}, {"api_name": "haystack.query.SearchQuerySet", "line_number": 73, "usage_type": "call"}, {"api_name": "haystack.query.SearchQuerySet", "line_number": 75, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 83, "usage_type": "call"}, {"api_name": "site_directory.views.HomeworkKeywordView", "line_number": 83, "usage_type": "attribute"}, {"api_name": "site_directory.views", "line_number": 83, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 85, "usage_type": "call"}]} +{"seq_id": "64432270", "text": "import os\r\nimport tensorflow as tf\r\nimport numpy as np\r\nimport codecs\r\nfrom keras.utils import to_categorical\r\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'\r\nos.environ['KERAS_BACKEND ']='tensorflow'\r\n\r\ndef load_wsd_train_x():\r\n wsd_train_x = codecs.open('40699_train_data', mode = 'r', encoding= 'utf-8')\r\n line = wsd_train_x.readline()\r\n list1 = []\r\n while line:\r\n a = line.split()\r\n b = a[3:]\r\n list1.append(b)\r\n line = wsd_train_x.readline()\r\n return np.array(list1)\r\n wsd_train_x.close()\r\n\r\n\r\ndef load_wsd_test_x():\r\n wsd_test_x = codecs.open('40699_test_data', mode = 'r', encoding= 'utf-8')\r\n line = wsd_test_x.readline()\r\n list1 = []\r\n while line:\r\n a = line.split()\r\n b = a[3:]\r\n list1.append(b)\r\n line = wsd_test_x.readline()\r\n return np.array(list1)\r\n wsd_test_x.close()\r\n\r\n\r\ndef load_wsd_train_y():\r\n wsd_train_y = codecs.open('40699_train_target', mode = 'r', encoding = 'utf-8')\r\n line = wsd_train_y.readline()\r\n list1 = []\r\n while line:\r\n a = line.split()\r\n b = a[1:2]\r\n list1.append(b)\r\n line = wsd_train_y.readline()\r\n return (np.array(list1)).reshape(50,)\r\n wsd_train_y.close()\r\n\r\n\r\n\r\ndef load_wsd_test_y():\r\n wsd_test_y = codecs.open('40699_test_target', mode = 'r', encoding = 'utf-8')\r\n line = wsd_test_y.readline()\r\n list1 = []\r\n while line:\r\n a = line.split()\r\n b = a[1:2]\r\n list1.append(b)\r\n line = wsd_test_y.readline()\r\n return (np.array(list1)).reshape(50,)\r\n wsd_test_y.close()\r\n\r\n\r\nb = np.zeros(50)\r\n\r\nwsd_train_x = load_wsd_train_x()\r\nwsd_test_x = load_wsd_test_x()\r\n\r\nwsd_train_y = load_wsd_train_y()\r\nwsd_train_y = to_categorical(wsd_train_y)\r\n#wsd_train_y = np.c_[wsd_train_y, b]\r\n\r\nwsd_test_y = load_wsd_test_y()\r\nwsd_test_y = to_categorical(wsd_test_y)\r\n#wsd_test_y = np.c_[wsd_test_y, b]\r\n\r\nmax_epoch = 100\r\ntrain_size = wsd_train_x.shape[0]\r\nbatch_size = 10\r\nn_batch = train_size // batch_size\r\n\r\n\r\nlayer_num = 2\r\ngogi_num = 5\r\n\r\nif layer_num == 3:\r\n\r\n x = tf.placeholder(tf.float32, [None, 768])\r\n y = tf.placeholder(tf.float32, [None, gogi_num])\r\n\r\n W1 = tf.Variable(tf.zeros([768, 50]))\r\n b1 = tf.Variable(tf.zeros([50]))\r\n L1 = tf.nn.sigmoid(tf.matmul(x, W1) + b1)\r\n\r\n W2 = tf.Variable(tf.zeros([50, gogi_num]))\r\n b2 = tf.Variable(tf.zeros[gogi_num])\r\n\r\n predict = tf.nn.softmax(tf.matmul(L1, W2) + b2)\r\n\r\n\r\nif layer_num == 2:\r\n\r\n x = tf.placeholder(tf.float32, [None, 768])\r\n y = tf.placeholder(tf.float32, [None, gogi_num])\r\n\r\n W = tf.Variable(tf.zeros([768, gogi_num]))\r\n b = tf.Variable(tf.zeros([gogi_num]))\r\n\r\n predict = tf.nn.softmax(tf.matmul(x, W) + b)\r\n\r\n\r\nloss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(labels=y, logits=predict))\r\ntrain_step = tf.train.AdamOptimizer().minimize(loss)\r\n\r\ninit = tf.global_variables_initializer()\r\n\r\ncorrect_predict = tf.equal(tf.argmax(y, 1), tf.argmax(predict, 1))\r\naccuracy = tf.reduce_mean(tf.cast(correct_predict, tf.float32))\r\n\r\n\r\nsaver = tf.train.Saver()\r\n\r\n\r\nwith tf.Session() as sess:\r\n sess.run(init)\r\n\r\n for epoch in range(max_epoch):\r\n batch_mask = np.random.choice(train_size, batch_size)\r\n for batch in range(n_batch):\r\n\r\n x_batch = wsd_train_x[batch_mask]\r\n t_batch = wsd_train_y[batch_mask]\r\n\r\n sess.run(train_step, feed_dict={x: x_batch, y: t_batch})\r\n acc = sess.run(accuracy, feed_dict={x:wsd_test_x, y:wsd_test_y})\r\n saver.save(sess, 'model/40699_wsd_model.ckpt')\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n", "sub_path": "01_40699_wsd_train.py", "file_name": "01_40699_wsd_train.py", "file_ext": "py", "file_size_in_byte": 3631, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 7, "usage_type": "attribute"}, {"api_name": "codecs.open", "line_number": 10, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 18, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 44, "usage_type": "call"}, {"api_name": "codecs.open", "line_number": 50, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.utils.to_categorical", "line_number": 72, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 86, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 87, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 87, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.zeros", "line_number": 90, "usage_type": "call"}, {"api_name": "tensorflow.nn.sigmoid", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 91, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 91, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.zeros", "line_number": 93, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 94, "usage_type": "call"}, {"api_name": "tensorflow.zeros", "line_number": 94, "usage_type": "attribute"}, {"api_name": "tensorflow.nn.softmax", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 96, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 96, "usage_type": "call"}, {"api_name": "tensorflow.placeholder", "line_number": 101, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 101, "usage_type": "attribute"}, {"api_name": "tensorflow.placeholder", "line_number": 102, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 102, "usage_type": "attribute"}, {"api_name": "tensorflow.Variable", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.zeros", "line_number": 104, "usage_type": "call"}, {"api_name": "tensorflow.Variable", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.zeros", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tensorflow.matmul", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 110, "usage_type": "call"}, {"api_name": "tensorflow.nn.softmax_cross_entropy_with_logits_v2", "line_number": 110, "usage_type": "call"}, {"api_name": "tensorflow.nn", "line_number": 110, "usage_type": "attribute"}, {"api_name": "tensorflow.train.AdamOptimizer", "line_number": 111, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 111, "usage_type": "attribute"}, {"api_name": "tensorflow.global_variables_initializer", "line_number": 113, "usage_type": "call"}, {"api_name": "tensorflow.equal", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.argmax", "line_number": 115, "usage_type": "call"}, {"api_name": "tensorflow.reduce_mean", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.cast", "line_number": 116, "usage_type": "call"}, {"api_name": "tensorflow.float32", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Saver", "line_number": 119, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 119, "usage_type": "attribute"}, {"api_name": "tensorflow.Session", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.random.choice", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 126, "usage_type": "attribute"}]} +{"seq_id": "507032230", "text": "\nimport requests #installed?\nimport json\nimport time\nimport datetime #installed\nimport csv\nimport re\nfrom area import area\nimport boto3\nfrom boto3.dynamodb.conditions import Key, Attr\n\n\ndef handler(event, context):\n \n res=\"\"\n token = \"\"\n\n \n\n dynamodb = boto3.resource('dynamodb', region_name='us-east-2')#change regions\n table = dynamodb.Table('Activetask')#change table name\n \n #response = table.put_item( Item={ 'taskID' : str(today), 'Data': mass,})\n #return res \n \n try:\n response = table.get_item(\n Key={\n 'taskID': 'miss' \n }\n )\n except ClientError as e:\n print(e.response['Error']['Message'])\n else:\n item = response['Item']\n \n \n mass=response['Item'][\"Data\"]\n\n\n \n headers = {\n 'content-type': \"application/json\",\n \n 'authorization': token #Alex\n }\n massRes=[]\n for k in mass[80:90]:\n results_url = \"https://api.astrodigital.com/v2.0/results?task_id=\"+k\n \n\n results_response = requests.request(\"GET\", results_url, headers=headers, )\n\n results_json_data = json.loads(results_response.text)\n if ('detail' in results_json_data):\n break\n\n for i in range(0,len(results_json_data[\"results\"])):\n\n res+= str(json.dumps(results_json_data[\"results\"][i][\"task\"]))+\"\\n\"\n if 'properties' in results_json_data[\"results\"][i][\"value\"].keys():\n for j in range(0,len(results_json_data[\"results\"][i][\"value\"][\"properties\"][\"ndvi_values\"])):\n \n massRes.append(results_json_data[\"results\"][i][\"value\"][\"properties\"][\"ndvi_values\"][j][\"date\"])\n massRes.sort()\n for k in range(0,len(massRes)-1):\n stime1=datetime.datetime.strptime(massRes[k+1] , '%Y-%m-%d')\n stime2=datetime.datetime.strptime(massRes[k] , '%Y-%m-%d')\n if (stime1-stime2>datetime.timedelta(days=10)):\n res+=str(stime1-stime2)[0:7]+\" \"+str(stime1)[0:10]+\" \"+str(stime2)[0:10]+\"\\n\"#\n \n\n \n #res = response\n\n\n\n\n\n #token = \"\"\n \n dictSlack = {\n \"strChannel\" : \"#random\",\n \"strName\" : \"StatBot\",\n \"strIconUrl\" : \"https://astrodigital.com/images/meta/apple-touch-icon-152x152.png\",\n \"strTitle\" : \"report\",\n \"strHookUrl\" : \"https://hooks.slack.com/services/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\"\n }\n\n def writeToSlack(dictSlack,jsonAttachments):\n jsonPayload = {\n \"channel\": dictSlack[\"strChannel\"],\n \"username\": dictSlack[\"strName\"],\n \"icon_url\": dictSlack[\"strIconUrl\"],\n \"text\": dictSlack[\"strTitle\"],\n \"attachments\": jsonAttachments,\n }\n \n payload = \"-----011000010111000001101001\\r\\nContent-Disposition: form-data; name=\\\"payload\\\"\\r\\n\\r\\n\"+json.dumps(jsonPayload)+\"\\r\\n-----011000010111000001101001--\"\n \n headers = {\n 'content-type': \"multipart/form-data; boundary=---011000010111000001101001\"\n }\n response = requests.request(\"POST\", dictSlack[\"strHookUrl\"], data=payload, headers=headers)\n\n\n jsonAttachments = [{\n \"fallback\": \"Required plain-text summary of the attachment.\",\n \"color\": \"#015752\",\n \"fields\": [{\n \"title\": \"Missed days:\",\n \"value\": str(res),\n \"Date\": \"\"\n \n }]\n }]\n writeToSlack(dictSlack,jsonAttachments)\n\n##\n## jsonAttachments2 = [{\n## \"fallback\": \"Required plain-text summary of the attachment.\",\n## \"color\": \"#015752\",\n## \"fields\": [{\n## \"title\": \"Missed days:\",\n## \"value\": str(event),\n## \"Date\": \"\"\n## \n## }]\n## }]\n## writeToSlack(dictSlack,jsonAttachments2)\n\n res=str(event)+str(res) \n\n\n client = boto3.client('lambda')\n response = client.invoke(\n InvocationType='Event',\n FunctionName='missDayCount11',\n Payload=json.dumps({\"test\": str(res)})\n ) \n return res\n \n\n\n\n", "sub_path": "missDayCountDB10/service.py", "file_name": "service.py", "file_ext": "py", "file_size_in_byte": 4327, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "boto3.resource", "line_number": 20, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 52, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 54, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 60, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 67, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 67, "usage_type": "attribute"}, {"api_name": "datetime.datetime.strptime", "line_number": 68, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 68, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 69, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 99, "usage_type": "call"}, {"api_name": "requests.request", "line_number": 104, "usage_type": "call"}, {"api_name": "boto3.client", "line_number": 135, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 139, "usage_type": "call"}]} +{"seq_id": "437310982", "text": "import pytest\nimport sys\nimport re\nimport uuid\n\n# Python 2.x 3.x compatibility\nfrom six.moves import xrange\n\nfrom socketmsg.utils import config\n\n\nclass TestConfig(object):\n def test_parse_yaml_raises_type_error_if_provided_nonstring_for_path(self):\n with pytest.raises(TypeError):\n # send am IntType object instead of string, 99 is just an integer\n config.parse_yaml(type(99))\n\n def test_parse_yaml_raises_does_not_raise_type_error_if_provided_string_for_path(self):\n try:\n config.parse_yaml(\"./tests/configs/test.yaml\")\n except TypeError:\n pytest.fail(\"Raised a TypeError when provided string for path. If running in test the test file is in tests/configs/test.yaml\")\n\n def test_parse_yaml_returns_dict(self):\n try:\n opts = config.parse_yaml(\"./tests/configs/test.yaml\")\n except TypeError:\n pytest.fail(\"Raised a TypeError when provided string for path. If running in test the test file is in tests/configs/test.yaml\")\n assert type(opts) is dict # test you get a dict back when providing valid yaml\n assert opts == {'log_level': 'INFO', 'version': '0.0.0'} # test the contents are parsed properly\n\n def test_default_config_returns_dict(self):\n assert type(config.default_config()) is dict\n\n # for more info on parameterize\n # https://docs.pytest.org/en/latest/parametrize.html#parametrize\n @pytest.mark.parametrize(\"prop,prop_type\", [\n (\"log_level\", str),\n ], scope=\"class\")\n def test_default_config_has_all_the_right_properties_and_prop_types(self, prop, prop_type):\n \"\"\"overloaded test\"\"\"\n assert prop in config.default_config()\n assert type(config.default_config()[prop]) is prop_type\n", "sub_path": "tests/test_utils_config.py", "file_name": "test_utils_config.py", "file_ext": "py", "file_size_in_byte": 1768, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pytest.raises", "line_number": 14, "usage_type": "call"}, {"api_name": "socketmsg.utils.config.parse_yaml", "line_number": 16, "usage_type": "call"}, {"api_name": "socketmsg.utils.config", "line_number": 16, "usage_type": "name"}, {"api_name": "socketmsg.utils.config.parse_yaml", "line_number": 20, "usage_type": "call"}, {"api_name": "socketmsg.utils.config", "line_number": 20, "usage_type": "name"}, {"api_name": "pytest.fail", "line_number": 22, "usage_type": "call"}, {"api_name": "socketmsg.utils.config.parse_yaml", "line_number": 26, "usage_type": "call"}, {"api_name": "socketmsg.utils.config", "line_number": 26, "usage_type": "name"}, {"api_name": "pytest.fail", "line_number": 28, "usage_type": "call"}, {"api_name": "socketmsg.utils.config.default_config", "line_number": 33, "usage_type": "call"}, {"api_name": "socketmsg.utils.config", "line_number": 33, "usage_type": "name"}, {"api_name": "socketmsg.utils.config.default_config", "line_number": 42, "usage_type": "call"}, {"api_name": "socketmsg.utils.config", "line_number": 42, "usage_type": "name"}, {"api_name": "socketmsg.utils.config.default_config", "line_number": 43, "usage_type": "call"}, {"api_name": "socketmsg.utils.config", "line_number": 43, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 37, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 37, "usage_type": "attribute"}]} +{"seq_id": "467205122", "text": "###########################################\n#\n# API for interfacing firmware > 2.0\n#\n# (c) 2019 Qontrol Systems LLP\n#\n###########################################\n\nfrom __future__ import print_function\nimport serial, re, time\nfrom collections import deque as fifo\nfrom random import shuffle\nfrom serial.tools import list_ports\nimport sys\n\n\nQ8x_ERRORS = {0:'Unknown error.',\n\t1:'Over-voltage error on channel {ch}.',\n\t2:'Over-current error on channel {ch}.',\n\t3:'Power error.',\n\t4:'Calibration error.',\n\t5:'Output error.',\n\t10:'Unrecognised command.',\n\t11:'Unrecognised input parameter.',\n\t12:'Unrecognised channel, {ch}.',\n\t13:'Operation forbidden.',\n\t14:'Serial buffer overflow.',\n\t15:'Serial communication error.',\n\t16:'Command timed out.',\n\t17:'SPI error.',\n\t18:'ADC error.',\n\t19:'I2C error.',\n\t30:'Firmware error.',\n\t90:'Powered up.'}\n\n\n\t\nRESPONSE_OK = 'OK\\n'\nERROR_FORMAT = '[A-Za-z]{1,3}(\\d+):(\\d+)'\n\n\nclass Qontroller(object):\n\t\"\"\"\n\tSuper class which handles serial communication, device identification, and logging.\n\t\n\t\tdevice_id = None\t\t\t\t\tDevice ID\n\t\tserial_port = None\t\t\t\t\tSerial port object\n\t\tserial_port_name = None\t\t\t\tName of serial port, eg 'COM1' or '/dev/tty1'\n\t\terror_desc_dict = Q8x_ERRORS\t\t\tError code descriptions\n\t\tlog = fifo(maxlen = 256)\t\t\tLog FIFO of sent commands and received errors\n\t\tlog_handler = None\t\t\t\t\tFunction which catches log dictionaries\n\t\tlog_to_stdout = True\t\t\t\tCopy new log entries to stdout\n\t\tresponse_timeout = 0.050\t\t\tTimeout for response or error to commands\n\t\tinter_response_timeout = 0.020\t\tTimeout for response or error to get commands\n\t\n\tLog handler:\n\tThe log handler may be used to catch and dynamically handle certain errors, as they arise. In the following example, it is set up to raise a RuntimeError upon reception of errors E01, E02, and E03:\n\t\n\t\tq = Qontroller()\n\t\n\t\tfatal_errors = [1, 2, 3]\n\t\n\t\tdef my_log_handler(err_dict):\n\t\t\tif err_dict['type'] is 'err' and err_dict['id'] in fatal_errors:\n\t\t\t\traise RuntimeError('Caught Qontrol error \"{1}\" at {0} ms'.format(1000*err_dict['proctime'], err_dict['desc']))\n\n\t\tq.log_handler = my_log_handler\n\t\n\t\"\"\"\n\n\n\tdef __init__(self, *args, **kwargs):\n\t\t\"\"\"\n\t\tInitialiser.\n\t\t\"\"\"\n\t\t\n\t\t# Defaults\n\t\t\n\t\tself.device_id = None\t\t\t\t\t\t# Device ID (i.e. [device type]-[device number])\n\t\tself.serial_port = None\t\t\t\t\t\t# Serial port object\n\t\tself.serial_port_name = None\t\t\t\t\t# Name of serial port, eg 'COM1' or '/dev/tty1'\n\t\tself.baudrate = 115200\t\t\t\t\t\t# Serial port baud rate (signalling frequency, Hz)\n\t\tself.error_desc_dict = Q8x_ERRORS\t\t\t\t# Error code descriptions\n\t\tself.log = fifo(maxlen = 512)\t\t\t\t# Log FIFO of sent commands and received errors\n\t\tself.log_handler = None\t\t\t\t\t\t# Function which catches log dictionaries\n\t\n\t\tself.log_to_stdout = False\t\t\t\t\t# Copy new log entries to stdout\n\t\tself.response_timeout = 0.050\t\t\t\t# Timeout for RESPONSE_OK or error to set commands\n\t\tself.inter_response_timeout = 0.020\t\t\t# Timeout between received messages\n\t\t\n\t\t\n\t\t# Setup Rx and Tx logs\n\t\tself.total_rx_str = ''\n\t\tself.total_tx_str = ''\n\t\t\n\t\t# Set a time benchmark\n\t\tself.init_time = time.time()\n\t\t\n\t\t# Get arguments from init\n\t\t\n\t\t# Populate parameters, if provided\n\t\tfor para in ['device_id', 'serial_port_name', 'error_desc_dict', 'log_handler', 'log_to_stdout', 'response_timeout', 'inter_response_timeout', 'baudrate']:\n\t\t\ttry:\n\t\t\t\tself.__setattr__(para, kwargs[para])\n\t\t\texcept KeyError:\n\t\t\t\tcontinue\n\t\t\n\t\t# Find serial port by asking it for its device id\n\t\tif 'device_id' in kwargs:\n\t\t\t# Search for port with matching device ID\n\t\t\tob = re.match('(Q\\w+)-([0-9a-fA-F\\*]+)', self.device_id)\n\t\t\ttarg_dev_type,targ_dev_num = ob.groups()\n\t\t\tif ob is None:\n\t\t\t\traise AttributeException('Entered device ID ({0}) must be of form \"[device type]-[device number]\" where [device number] can be hexadecimal'.format(self.device_id))\n\t\t\t\n\t\t\t# Find serial port based on provided device ID (randomise their order)\n\t\t\tcandidates = []\n\t\t\tpossible_ports = list(list_ports.comports())\n\t\t\tshuffle(possible_ports)\n\t\t\ttries = 0\n\t\t\tfor port in possible_ports:\n\t\t\t\tfor i in range(60):\n\t\t\t\t\tsys.stdout.write(' ')\n\t\t\t\tsys.stdout.write('\\r')\n\t\t\t\tsys.stdout.write('Querying port {:}... '.format(port.device))\n\t\t\t\tsys.stdout.flush()\n\t\t\t\t\n\t\t\t\ttry:\n\t\t\t\t\t# Instantiate the serial port\n\t\t\t\t\tself.serial_port = serial.Serial(port.device, self.baudrate, timeout=0.5)\n\t\t\t\t\tself.serial_port.close()\n\t\t\t\t\tself.serial_port.open()\n\t\t\t\t\t# Clear buffer\n\t\t\t\t\tself.serial_port.reset_input_buffer()\n\t\t\t\t\tself.serial_port.reset_output_buffer()\n\t\t\t\t\t# Transmit our challenge string\n\t\t\t\t\tself.serial_port.write(\"id?\\n\".encode('ascii'))\n\t\t\t\t\t# Receive response\n\t\t\t\t\tresponse = self.serial_port.read(size=64).decode(\"ascii\") \n\t\t\t\t\t# Check if we received a response\n\t\t\t\t\tif response == '':\n\t\t\t\t\t\tsys.stdout.write('No response\\n')\n\t\t\t\t\t\tcontinue\n\t\t\t\t\t# Match the device ID\n\t\t\t\t\tob = re.match('.*((?:'+ERROR_FORMAT+')|(?:Q\\w+-[0-9a-fA-F\\*]+)).*', response)\n\t\t\t\t\tif ob is not None:\n\t\t\t\t\t\tob = re.match('(Q\\w+)-([0-9a-fA-F\\*]+)\\n', response)\n\t\t\t\t\t\tif ob is not None:\n\t\t\t\t\t\t\tsys.stdout.write('{:}\\n'.format(response))\n\t\t\t\t\t\t\tsys.stdout.flush()\n\t\t\t\t\t\t\tdev_type,dev_num = ob.groups()\n\t\t\t\t\t\t\tcandidates.append({'dev_type':dev_type, 'dev_num':dev_num, 'port':port.device})\n\t\t\t\t\t\t\tif dev_type == targ_dev_type and dev_num == targ_dev_num:\n\t\t\t\t\t\t\t\tself.serial_port_name = port.device\n\t\t\t\t\t\t\t\tbreak\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tob = re.match(ERROR_FORMAT, response)\n\t\t\t\t\t\t\tif ob is not None:\n\t\t\t\t\t\t\t\tsys.stdout.write('Error')\n\t\t\t\t\t\t\t\t# Try this port again later\n\t\t\t\t\t\t\t\tif tries < 3:\n\t\t\t\t\t\t\t\t\tsys.stdout.write('. Will try again...')\n\t\t\t\t\t\t\t\t\tpossible_ports.append(port)\n\t\t\t\t\t\t\t\t\ttries += 1\n\t\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\t\tsys.stdout.write('\\n')\n\t\t\t\t\t\t\t\tsys.stdout.flush()\n\t\t\t\t\telse:\n\t\t\t\t\t\tsys.stdout.write('Not a valid device\\n'.format(response))\n\t\t\t\t\t\tsys.stdout.flush()\n\t\t\t\t\t\t\t\n\t\t\t\t\t\n\t\t\t\t\t# Close port\n\t\t\t\t\tself.serial_port.close()\n\t\t\t\t\t\n\t\t\t\texcept serial.serialutil.SerialException:\n\t\t\t\t\tsys.stdout.write('Busy\\n')\n\t\t\t\t\tsys.stdout.flush()\n\t\t\t\t\tcontinue\n\t\t\t\n\t\t\t# If the target device is not found\n\t\t\tif not self.serial_port.is_open:\n\t\t\t\t# Check whether we found another possibility\n\t\t\t\tfor candidate in candidates:\n\t\t\t\t\tif candidate['dev_type'] == targ_dev_type:\n\t\t\t\t\t\tself.device_id = candidate['dev_type']+'-'+candidate['dev_num']\n\t\t\t\t\t\tself.serial_port_name = candidate['port']\n\t\t\t\t\t\tprint ('Qontroller.__init__: Warning: Specified device ID ({0}) could not be found. Using device with matching type ({2}) on port {1}.'.format(kwargs['device_id'], self.serial_port_name, self.device_id))\n\t\t\t\t\t\tbreak\n\t\t\t\t# If no similar device exists, abort\n\t\t\t\tif all([candidate['dev_type'] != targ_dev_type for candidate in candidates]):\n\t\t\t\t\traise AttributeError('Specified device ID ({0}) could not be found.'.format(kwargs['device_id']))\n\t\t\t\n\t\t\tprint ('Using serial port {0}'.format(self.serial_port_name))\n\t\t\t# If serial_port_name was also specified, check that it matches the one we found.\n\t\t\tif ('serial_port_name' in kwargs) and (self.serial_port_name != kwargs['serial_port_name']):\n\t\t\t\tprint ('Qontroller.__init__: Warning: Specified serial port ({0}) does not match the one found based on the specified device ID ({1}, {2}). Using serial port {2}.'.format(kwargs['serial_port_name'], self.device_id, self.serial_port_name))\n\t\t\n\t\t# Open serial port directly, get device id\n\t\telif 'serial_port_name' in kwargs:\n\t\t\t# Open serial communication\n\t\t\t# This will throw a serial.serialutil.SerialException if busy\n\t\t\tself.serial_port = serial.Serial(self.serial_port_name, self.baudrate, timeout = self.response_timeout)\n\t\t\t\n\t\t\t# Get device ID\n\t\t\t# Transmit our challenge string\n\t\t\t# This repeated try mechanism accounts for serial ports with starting hiccups\n\t\t\ttimed_out = True\n\t\t\tfor t in range(3):\n\t\t\t\t# Clear buffer\n\t\t\t\tself.serial_port.reset_input_buffer()\n\t\t\t\tself.serial_port.reset_output_buffer()\n\t\t\t\t# Send challenge\n\t\t\t\tself.serial_port.write('id?\\n'.encode('ascii'))\n\t\t\t\t# Receive response\n\t\t\t\tstart_time = time.time()\n\t\t\t\t# Wait for first byte to arrive\n\t\t\t\twhile (self.serial_port.in_waiting == 0) and (time.time() - start_time < 0.2):\n\t\t\t\t\tpass\n\t\t\t\t# Read response, ignoring unparsable characters\n\t\t\t\ttry:\n\t\t\t\t\tresponse = self.serial_port.read(size=64).decode('ascii')\n\t\t\t\texcept UnicodeDecodeError:\n\t\t\t\t\tresponse = \"\"\n\t\t\t\t# Parse it\n\t\t\t\tob = re.match('.*((?:'+ERROR_FORMAT+')|(?:Q\\w+-[0-9a-fA-F\\*]+)).*', response)\n\t\t\t\t# Check whether it's valid\n\t\t\t\tif ob is not None:\n\t\t\t\t\t# Flag that we have broken out correctly\n\t\t\t\t\ttimed_out = False\n\t\t\t\t\tbreak\n\t\t\t\n\t\t\t# Store the parsed value\n\t\t\tif not timed_out:\n\t\t\t\tself.device_id = ob.groups()[0]\n\t\t\t\t# Check if it was an error, in which case clear the stored value but proceed\n\t\t\t\tob = re.match('((?:'+ERROR_FORMAT+')|(?:Q\\w+-\\*+))', self.device_id)\n\t\t\t\tif ob is not None:\n\t\t\t\t\t# It was an error (no ID assigned yet)\n\t\t\t\t\tself.device_id = None\n\t\t\telse:\n\t\t\t\traise RuntimeError('Qontroller.__init__: Error: Unable to communicate with device on port {0} (received response {1}, \"{2}\").'.format(self.serial_port_name, \":\".join(\"{:02x}\".format(ord(c)) for c in response), response.replace('\\n', '\\\\n')))\n\t\telse:\n\t\t\traise AttributeError('At least one of serial_port_name or device_id must be specified on Qontroller initialisation. Available serial ports are:\\n serial_port_name = {:}'.format('\\n serial_port_name = '.join([port.device for port in list(list_ports.comports())])))\n\t\t\n\t\t\n\t\t# Establish contents of daisy chain\n\t\ttry:\n\t\t\t# Ask for number of upstream devices, parse it\n\t\t\ttry:\n\t\t\t\tchain = self.issue_command('nupall', operator = '?', target_errors = [0,10,11,12,13,14,15,16], output_regex = '(?:([^:\\s]+)\\s*:\\s*(\\d+)\\n*)*')\n\t\t\texcept:\n\t\t\t\tchain = self.issue_command('nup', operator = '?', target_errors = [0,10,11,12,13,14,15,16], output_regex = '(?:([^:\\s]+)\\s*:\\s*(\\d+)\\n*)*')\n\t\t\t# Further parse each found device into a dictionary\n\t\t\tfor i in range(len(chain)):\n\t\t\t\tob = re.match('([^-]+)-([0-9a-fA-F\\*]+)', chain[i][0])\n\t\t\t\n\t\t\t\tdevice_id = chain[i][0]\n\t\t\t\tdevice_type = ob.groups()[0]\n\t\t\t\tdevice_serial = ob.groups()[1]\n\t\t\t\n\t\t\t\ttry:\n\t\t\t\t\tindex = int(chain[i][1])\n\t\t\t\texcept ValueError:\n\t\t\t\t\tindex = -1\n\t\t\t\t\tprint ('Qontroller.__init__: Warning: Unable to determine daisy chain index of device with ID {:}.'.format(device_id))\n\t\t\t\n\t\t\t\t# Scan out number of channels from device type\n\t\t\t\tob = re.match('[^\\d]+(\\d*)[^\\d]*', device_type)\n\t\t\t\n\t\t\t\n\t\t\t\ttry:\n\t\t\t\t\tn_chs = int(ob.groups()[0])\n\t\t\t\texcept ValueError:\n\t\t\t\t\tn_chs = -1\n\t\t\t\t\tprint ('Qontroller.__init__: Warning: Unable to determine number of channels of device at daisy chain index {:}.'.format(index))\n\t\t\t\n\t\t\t\tchain[i] = {\n\t\t\t\t\t'device_id':device_id,\n\t\t\t\t\t'device_type':device_type,\n\t\t\t\t\t'device_serial':device_serial,\n\t\t\t\t\t'n_chs':n_chs,\n\t\t\t\t\t'index':index}\n\t\texcept:\n\t\t\tchain = []\n\t\t\tprint ('Qontroller.__init__: Warning: Unable to determine daisy chain configuration.')\n\t\t\n\t\tself.chain = chain\n\t\n\t\n\tdef __del__(self):\n\t\t\"\"\"\n\t\tDestructor.\n\t\t\"\"\"\n\t\tself.close()\n\t\n\t\n\tdef close(self):\n\t\t\"\"\"\n\t\tRelease resources\n\t\t\"\"\"\n\t\tif self.serial_port is not None and self.serial_port.is_open:\n\t\t\t# Close serial port\n\t\t\tself.serial_port.close()\n\t\n\t\n\tdef transmit (self, command_string):\n\t\t\"\"\"\n\t\tLow-level transmit data method.\n\t\t\"\"\"\n\t\t# Ensure serial port is open\n\t\tif not self.serial_port.is_open:\n\t\t\tself.serial_port.open()\n\t\t\n\t\t# Write to port\n\t\tself.serial_port.write(command_string.encode('ascii'))\n\t\t\n\t\t# Log it\n\t\tself.total_tx_str += command_string\n\t\n\t\n\tdef receive (self):\n\t\t\"\"\"\n\t\tLow-level receive data method which also checks for errors.\n\t\t\"\"\"\n\t\t# Ensure serial port is open\n\t\tif not self.serial_port.is_open:\n\t\t\tself.serial_port.open()\n\t\t\n\t\t# Read from port\n\t\tlines = []\n\t\terrs = []\n\t\t\n\t\t# Check if there's anything in the input buffer\n\t\twhile self.serial_port.in_waiting > 0:\n\t\t\t# Get a line from the receive buffer\n\t\t\tline = str(self.serial_port.readline().decode('ascii'))\n\t\t\t\n\t\t\t# Log it\n\t\t\tself.total_rx_str += line\n\t\t\tself.total_rx_str += '\\n'\n\t\t\t\n\t\t\t# Check if it's an error by parsing it\n\t\t\terr = self.parse_error(line)\n\t\t\tif err is None:\n\t\t\t\t# No error, keep the line\n\t\t\t\tlines.append(line)\n\t\t\telse:\n\t\t\t\t# Line represents an error, add to list\n\t\t\t\terrs.append(err)\n\t\t\n\t\t# Add any errors we found to our log\n\t\tfor err in errs:\n\t\t\tself.log_append(type='err', id=err['id'], ch=err['ch'], desc=err['desc'], raw=err['raw'])\n\t\t\n\t\treturn (lines, errs)\n\t\n\t\n\tdef log_append (self, type='err', id='', ch=0, value=0, desc='', raw=''):\n\t\t\"\"\"\n\t\tAppend an event to the log, adding both a calendar- and a process-timestamp.\"\n\t\t\"\"\"\n\t\t# Append to log fifo\n\t\tself.log.append({'timestamp':time.asctime(), 'proctime':round(time.time()-self.init_time,3), 'type':type, 'id':id, 'ch':ch, 'value':value, 'desc':desc, 'raw':raw})\n\t\t# Send to handler function (if defined)\n\t\tif self.log_handler is not None:\n\t\t\tself.log_handler(self.log[-1])\n\t\t# Send to stdout (if requested)\n\t\tif self.log_to_stdout:\n\t\t\tself.print_log (n = 1)\n\t\n\t\n\tdef print_log (self, n = None):\n\t\t\"\"\"\n\t\tPrint the n last log entries. If n == None, print all log entries.\n\t\t\"\"\"\n\t\tif n is None:\n\t\t\tn = len(self.log)\n\t\t\n\t\tfor i in range(-n,0):\n\t\t\tprint('@ {0: 8.1f} ms, {1} : {2}'.format(1000*self.log[i]['proctime'], self.log[i]['type'], self.log[i]['desc']) )\n\t\n\t\n\tdef parse_error (self, error_str):\n\t\t\"\"\"\n\t\tParse an encoded error (e.g. E02:07) into its code, channel, and human-readable description.\n\t\t\"\"\"\n\t\t# Regex out the error and channel indices from the string\n\t\tob = re.match(ERROR_FORMAT, error_str)\n\t\t\n\t\t# If error_str doesn't match an error, return None\n\t\tif ob is None:\n\t\t\treturn None\n\t\t\n\t\t# Extract the two matched groups (i.e. the error and channel indices)\n\t\terrno,chno = ob.groups()\n\t\terrno = int(errno)\n\t\tchno = int(chno)\n\t\t\n\t\t# Get the error description; if none is defined, mark as unrecognised\n\t\terrdesc = self.error_desc_dict.get(errno, 'Unrecognised error code.').format(ch=chno)\n\t\t\n\t\treturn {'type':'err', 'id':errno, 'ch':chno, 'desc':errdesc, 'raw':error_str}\n\t\n\t\n\tdef wait (self, seconds=0.0):\n\t\t\"\"\"\n\t\tDo nothing while watching for errors on the serial bus.\n\t\t\"\"\"\n\t\tstart_time = time.time()\n\t\twhile time.time() < start_time + seconds:\n\t\t\tself.receive()\n\t\n\t\n\tdef issue_command (self, command_id, ch=None, operator='', value=None, n_lines_requested=2**31, target_errors=None, output_regex='(.*)', special_timeout = None):\n\t\t\"\"\"\n\t\tTransmit command ([command_id][ch][operator][value]) to device, collect response.\n\t\t\n\t\t\tcommand_id\t\t\tCommand header (e.g. 'v' in 'v7=1.0')\n\t\t\tch\t\t\t\t\tChannel index to apply command to (e.g. '7' in 'v7=1.0')\n\t\t\toperator\t\t\tType of command in {?, =} (e.g. '=' in 'v7=1.0')\n\t\t\tvalue\t\t\t\tValue of set command (e.g. '1.0' in 'v7=1.0')\n\t\t\tn_lines_requested\tLines of data (not error) to stop after receiving, or timeout\n\t\t\ttarget_errors\t\tError numbers which will be raised as RuntimeError\n\t\t\tspecial_timeout\t\tTimeout to use for this command only (!= self.response_timeout)\n\t\t\"\"\"\n\t\t# Check for previous errors\n\t\tlines,errs = self.receive()\n\t\t\n\t\t# Transmit command\n\t\tif ch is None:\n\t\t\tch = ''\n\t\tif value is None:\n\t\t\tvalue = ''\n\t\ttx_str = '{0}{1}{2}{3}'.format(command_id, ch, operator, value)\n\t\tself.transmit(tx_str+'\\n')\n\t\t\n\t\t# Log it\n\t\tself.log_append(type= 'set' if operator is '=' else 'get', value=value, id=command_id, ch=ch, desc='Command: \"'+tx_str+'\".')\n\t\t\n\t\t# Receive response\n\t\tlines = []\n\t\terrs = []\n\t\tif target_errors is None:\n\t\t\ttarget_errors = []\n\t\tstart_time = time.time()\n\t\tlast_message_time = start_time\n\t\t\n\t\twhile (True):\n\t\t\t\t\n\t\t\t\t# Break conditions\n\t\t\t\tif (RESPONSE_OK in lines):\n\t\t\t\t\tbreak\n\t\t\t\telif (len(lines) >= n_lines_requested):\n\t\t\t\t\tbreak\n\t\t\t\telif not all([err['id'] not in target_errors for err in errs]):\n\t\t\t\t\tbreak\n\t\t\t\telif (time.time() - start_time > self.response_timeout):\n\t\t\t\t\tif (time.time() - last_message_time > self.inter_response_timeout):\n\t\t\t\t\t\tbreak\n\t\t\t\t\n\t\t\t\t# Receive data\n\t\t\t\trec_lines,rec_errs = self.receive()\n\t\t\t\t\n\t\t\t\t# Update the last time a message was received\n\t\t\t\t# We won't proceed now until self.inter_response_timeout has elapsed\n\t\t\t\tif len(rec_lines) + len(rec_errs) > 0:\n\t\t\t\t\tlast_message_time = time.time()\n\t\t\t\t\t\n\t\t\t\t# Integrate received lines and errors\n\t\t\t\tlines.extend(rec_lines)\n\t\t\t\terrs.extend(rec_errs)\n\t\t\t\t\n\t\t\t\t# Check whether we have received a serial comms error (E15)\n\t\t\t\tif any([err['id'] == 15 for err in errs]):\n\t\t\t\t\t# If we did, we should try issuing the command again, recursively\n\t\t\t\t\treturn self.issue_command (command_id, ch, operator, value, n_lines_requested, target_errors, output_regex)\n\t\t\t\t\n\t\t\t\t# Check whether we have received a fatal error\n\t\t\t\tif any([err['id'] in target_errors for err in errs]):\n\t\t\t\t\traise RuntimeError('Received targetted error code {0}, \"{1}\". Log is: \\n{2}.'.format(err['id'], err['desc'], self.log))\n\t\t\n\t\t# We timed out.\n\t\tif len(lines) == 0 and len(errs) == 0:\n\t\t\tif operator == '?':\n\t\t\t\t# If we are looking for a return value, raise an error\n\t\t\t\traise RuntimeError ('Response to command {0} timed out after {1} ms.'.format(tx_str, 1000*(last_message_time - start_time)))\n\t\t\telse:\n\t\t\t\t# If we are setting something, just warn the user\n\t\t\t\tprint('Qontroller.set_command: Warning: Response to command {0} timed out after {1:.3f} ms.'.format(tx_str, 1000*(last_message_time - start_time)))\n\t\t\n\t\t# Parse the output\n\t\tvalues = []\n\t\tfor line in lines:\n\t\t\top = re.match(output_regex, line)\n\t\t\tif op is None:\n\t\t\t\tvalue = []\n\t\t\telse:\n\t\t\t\tvalue = op.groups()\n\t\t\tvalues.append(value)\n\t\t\n\t\t\n\t\tself.log_append(type= 'rcv', value=None, id=command_id, ch=ch, desc='Received: \"'+str(values)+'\".')\n\t\t\n\t\treturn values\n\t\n\t\n\tdef __getattr__(self, attr):\n\t\t\"\"\"\n\t\tAllow convenience attribute access for certain parameters\n\t\t\"\"\"\n\t\tif (attr in ['firmware', 'vfull', 'ifull', 'lifetime']):\n\t\t\treturn self.issue_command (command_id=attr, ch=None, operator='?', n_lines_requested=1)[0][0]\n\t\telse:\n\t\t\treturn object.__getattr__(self, attr)\n\n\n\nclass ChannelVector(object):\n\t\"\"\"\n\tCustom list class which has a fixed length but mutable (typed) elements, and which phones home when its elements are read or modified.\n\t\"\"\"\n\t\n\tset_handle = None\n\tget_handle = None\n\tvalid_types = (int,float)\n\t\n\tdef __init__(self, base_list):\n\t\tself.list = base_list\n\n\t\n\tdef __len__(self):\n\t\treturn len(self.list)\n\t\t\n\t\n\tdef __getitem__(self, key):\n\t\tif isinstance(key, slice):\n\t\t\t# Handle slice key\n\t\t\treturn [self[k] for k in range(len(self))[key.start:key.stop:key.step]]\n\t\telse:\n\t\t\t# Handle normal key\n\t\t\tif self.get_handle is not None:\n\t\t\t\tget_val = self.get_handle (key, self.list[key])\n\t\t\t\tif get_val is not None:\n\t\t\t\t\tself.list[key] = get_val\n\t\t\treturn self.list[key]\n\t\t\n\t\n\tdef __setitem__(self, key, value):\n\t\tif all([type(value) != valid_type for valid_type in self.valid_types]):\n\t\t\traise TypeError('Attempt to set value to type {0} is forbidden. Valid types are {1}.'.format(type(value), self.valid_types))\n\t\tif isinstance(key, slice):\n\t\t\t# Handle slice key\n\t\t\tfor k in range(len(self))[key.start:key.stop:key.step]:\n\t\t\t\tself[k] = value\n\t\telse:\n\t\t\t# Handle normal key\n\t\t\tif self.set_handle is not None:\n\t\t\t\tself.set_handle (key, value)\n\t\t\tself.list[key] = value\n\t\t\n\t\n\tdef __iter__(self):\n\t\treturn iter(self.list)\n\t\n\t\n\tdef __repr__(self):\n\t\treturn repr([self[i] for i in range(len(self))])\n\n\n\nclass QXOutput(Qontroller):\n\n\n\tdef __init__(self, *args, **kwargs):\n\t\tsuper(type(self), self).__init__(*args, **kwargs)\n\n\t\tself.n_chs = 0\n\t\tself.v_full = 0\n\t\tself.v = None\t\t\t# Channel voltages (direct access)\n\t\tself.i = None\t\t\t# Channel currents (direct access)\n\t\tself.vmax = None\t\t# Channel voltages (direct access)\n\t\tself.imax = None\t\t# Channel currents (direct access)\n\t\t\n\t\t# Get our full-scale voltage (VFULL)\n\t\ttry:\n\t\t\tself.v_full = float(self.issue_command('vfull', operator = '?', n_lines_requested = 1, output_regex='(?:\\+|-|)([\\d\\.]+) V')[0][0])\n\t\texcept:\n\t\t\traise RuntimeError(\"Unable to obtain VFULL from qontroller on port {:}.\".format(self.serial_port_name))\n\t\t\n\t\t# Get our number of channels\n\t\ttry:\n\t\t\t# See if its in the list of kwargs\n\t\t\tself.n_chs = kwargs['n_chs']\n\t\t\tif self.n_chs <= 0 or self.n_chs == None:\n\t\t\t\traise KeyError()\n\t\texcept KeyError:\n\t\t\t# If not in kwargs, try to get it from the chain\n\t\t\ttry:\n\t\t\t\tself.n_chs = sum([device['n_chs'] for device in self.chain])\n\t\t\texcept KeyError:\n\t\t\t\t# If not, just ask the top device how many ports its got\n\t\t\t\ttry:\n\t\t\t\t\tself.n_chs = int(self.issue_command('nchan', operator = '?', n_lines_requested = 1, target_errors = [10], output_regex = '(\\d+)\\n')[0][0])\n\t\t\t\texcept:\n\t\t\t\t\t# If not, just take some random value\n\t\t\t\t\tself.n_chs = 8\n\t\t\t\t\tprint (\"QXOutput.__init__: Warning: Failed to obtain number of daisy-chained channels automatically. Include this as n_chs argument on initialisation to workaround.\")\n\t\t\n\t\t\n\t\t\n\t\t# Set up output direct access\n\t\t# These initialise themselves when they are first used (i.e. the 0 init is OK)\n\t\t\n\t\t# Voltage\n\t\tself.v = ChannelVector([0] * self.n_chs)\n\t\tself.v.set_handle = lambda ch,val: self.set_value(ch,'v',val)\n\t\tself.v.get_handle = lambda ch,val: self.get_value(ch,'v')\n\t\t\n\t\tself.vmax = ChannelVector([0] * self.n_chs)\n\t\tself.vmax.set_handle = lambda ch,val: self.set_value(ch,'vmax',val)\n\t\tself.vmax.get_handle = lambda ch,val: self.get_value(ch,'vmax')\n\t\t\n\t\t# Current\n\t\tself.i = ChannelVector([0] * self.n_chs)\n\t\tself.i.set_handle = lambda ch,val: self.set_value(ch,'i',val)\n\t\tself.i.get_handle = lambda ch,val: self.get_value(ch,'i')\n\t\t\n\t\tself.imax = ChannelVector([0] * self.n_chs)\n\t\tself.imax.set_handle = lambda ch,val: self.set_value(ch,'imax',val)\n\t\tself.imax.get_handle = lambda ch,val: self.get_value(ch,'imax')\n\t\t\n\t\tself.initialised = True\n\t\n\t\n\tdef set_value (self, ch, para='v', new=0):\n\t\tself.issue_command(para, ch=ch, operator='=', value=new)\n\t\n\tdef get_value (self, ch, para='v'):\n\t\tresult = self.issue_command(para, ch = ch, operator = '?', n_lines_requested = 1, output_regex = '((?:\\+|-){0,1}[\\d\\.]+)')\n\t\tif len(result) > 0:\n\t\t\tif len(result[0]) > 0:\n\t\t\t\treturn float(result[0][0])\n\t\treturn None\n\t\n\tdef get_all_values (self, para='v'):\n\t\tresult = self.issue_command(para+'all', operator = '?', n_lines_requested = self.n_chs, output_regex = '(?:\\+|-|)([\\d\\.]+)', special_timeout = 2*self.response_timeout)\n\t\tif len(result) > 0:\n\t\t\tif len(result[0]) > 0:\n\t\t\t\tresult = [float(result[i][0]) for i in range(len(result))]\n\t\t\t\treturn result\n\t\treturn None\n\t\n\tdef __setattr__(self, attr, val):\n\t\t# Prevent overwrite of internal variables\n\t\ttry:\n\t\t\tif (self.initialised is True and attr in ['v', 'i', 'vmax', 'imax', 'v_full', 'n_chs']):\n\t\t\t\tprint (\"QXOutput.__setattr__: Warning: Overwriting of '{:}' is forbidden.\".format(attr) )\n\t\t\t\treturn\n\t\texcept AttributeError:\n\t\t\t# If we are still initialising, carry on setting variable\n\t\t\tpass\n\t\t\n\t\tobject.__setattr__(self, attr, val)\n", "sub_path": ".ipynb_checkpoints/ZeroLevel_Qontrol-checkpoint.py", "file_name": "ZeroLevel_Qontrol-checkpoint.py", "file_ext": "py", "file_size_in_byte": 22559, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.deque", "line_number": 84, "usage_type": "call"}, {"api_name": "time.time", "line_number": 97, "usage_type": "call"}, {"api_name": "re.match", "line_number": 111, "usage_type": "call"}, {"api_name": "serial.tools.list_ports.comports", "line_number": 118, "usage_type": "call"}, {"api_name": "serial.tools.list_ports", "line_number": 118, "usage_type": "name"}, {"api_name": "random.shuffle", "line_number": 119, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 123, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 123, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 124, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 124, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 125, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 125, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 126, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 126, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 130, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 142, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 142, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 145, "usage_type": "call"}, {"api_name": "re.match", "line_number": 147, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 149, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 149, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 150, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 150, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 157, "usage_type": "call"}, {"api_name": "sys.stdout.write", "line_number": 159, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 159, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 162, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 162, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 166, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 166, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 167, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 167, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 169, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 169, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 170, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 170, "usage_type": "attribute"}, {"api_name": "serial.serialutil", "line_number": 176, "usage_type": "attribute"}, {"api_name": "sys.stdout.write", "line_number": 177, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 177, "usage_type": "attribute"}, {"api_name": "sys.stdout.flush", "line_number": 178, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 178, "usage_type": "attribute"}, {"api_name": "serial.Serial", "line_number": 203, "usage_type": "call"}, {"api_name": "time.time", "line_number": 216, "usage_type": "call"}, {"api_name": "time.time", "line_number": 218, "usage_type": "call"}, {"api_name": "re.match", "line_number": 226, "usage_type": "call"}, {"api_name": "re.match", "line_number": 237, "usage_type": "call"}, {"api_name": "serial.tools.list_ports.comports", "line_number": 244, "usage_type": "call"}, {"api_name": "serial.tools.list_ports", "line_number": 244, "usage_type": "name"}, {"api_name": "re.match", "line_number": 256, "usage_type": "call"}, {"api_name": "re.match", "line_number": 269, "usage_type": "call"}, {"api_name": "time.asctime", "line_number": 364, "usage_type": "call"}, {"api_name": "time.time", "line_number": 364, "usage_type": "call"}, {"api_name": "re.match", "line_number": 389, "usage_type": "call"}, {"api_name": "time.time", "line_number": 410, "usage_type": "call"}, {"api_name": "time.time", "line_number": 411, "usage_type": "call"}, {"api_name": "time.time", "line_number": 446, "usage_type": "call"}, {"api_name": "time.time", "line_number": 458, "usage_type": "call"}, {"api_name": "time.time", "line_number": 459, "usage_type": "call"}, {"api_name": "time.time", "line_number": 468, "usage_type": "call"}, {"api_name": "re.match", "line_number": 495, "usage_type": "call"}]} +{"seq_id": "525620083", "text": "from django.shortcuts import render,redirect\nfrom django.utils.http import is_safe_url\n\nfrom .form import AddressForm\nfrom .models import Address\nfrom billing.models import BillingProfile\n\ndef checkout_address_create_view(request):\n\taddress_form=AddressForm(request.POST or None)\n\tcontext={\n\t\t\"Form\":address_form\n\t}\n\tnext_=request.GET.get('next')\n\tnext_post=request.POST.get('next')\n\tredirect_path=next_ or next_post or None\n\tif address_form.is_valid():\n\t\tinstance =address_form.save(commit=False)\n\t\tbilling_profile, billing_profile_created=BillingProfile.objects.new_or_get(request)\n\n\t\tif billing_profile is not None:\n\t\t\taddress_type=request.POST.get('address_type','shipping')\n\t\t\tinstance.billing_profile=billing_profile\n\t\t\tinstance.address_type=address_type\n\t\t\tinstance.save()\n\n\t\t\trequest.session[address_type + \"_address_id\"] =instance.id\n\t\telse:\n\t\t\tprint(\"Error here\")\n\t\t\treturn redirect(\"cart:checkout\")\n\n\t\tif is_safe_url(redirect_path, request.get_host()):\n\t\t\treturn redirect(redirect_path)\n\treturn redirect(\"cart:checkout\")\n\ndef checkout_address_reuse_view(request):\n\tif request.user.is_authenticated():\n\t\tcontext={}\n\t\tnext_=request.GET.get('next')\n\t\tnext_post=request.POST.get('next')\n\t\tredirect_path=next_ or next_post or None\n\t\tif request.method=='POST':\n\t\t\tshipping_address=request.POST.get('shipping_address', None)\n\t\t\taddress_type=request.POST.get('address_type','shipping')\n\t\t\tbilling_profile, billing_profile_created=BillingProfile.objects.new_or_get(request)\n\t\t\tif shipping_address is not None:\n\t\t\t\tqs=Address.objects.filter(billing_profile=billing_profile, id=shipping_address)\n\t\t\t\tif qs.exists():\n\t\t\t\t\trequest.session[address_type + \"_address_id\"] =shipping_address\n\t\t\t\tif is_safe_url(redirect_path, request.get_host()):\n\t\t\t\t\treturn redirect(redirect_path)\n\treturn redirect(\"cart:checkout\")", "sub_path": "ecommerce/src/addresses/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 1809, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "form.AddressForm", "line_number": 9, "usage_type": "call"}, {"api_name": "billing.models.BillingProfile.objects.new_or_get", "line_number": 18, "usage_type": "call"}, {"api_name": "billing.models.BillingProfile.objects", "line_number": 18, "usage_type": "attribute"}, {"api_name": "billing.models.BillingProfile", "line_number": 18, "usage_type": "name"}, {"api_name": "django.shortcuts.redirect", "line_number": 29, "usage_type": "call"}, {"api_name": "django.utils.http.is_safe_url", "line_number": 31, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 32, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 33, "usage_type": "call"}, {"api_name": "billing.models.BillingProfile.objects.new_or_get", "line_number": 44, "usage_type": "call"}, {"api_name": "billing.models.BillingProfile.objects", "line_number": 44, "usage_type": "attribute"}, {"api_name": "billing.models.BillingProfile", "line_number": 44, "usage_type": "name"}, {"api_name": "models.Address.objects.filter", "line_number": 46, "usage_type": "call"}, {"api_name": "models.Address.objects", "line_number": 46, "usage_type": "attribute"}, {"api_name": "models.Address", "line_number": 46, "usage_type": "name"}, {"api_name": "django.utils.http.is_safe_url", "line_number": 49, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 50, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 51, "usage_type": "call"}]} +{"seq_id": "425101259", "text": "# Copyright (c) 2011, Enthought, Ltd.\n# Author: Pietro Berkes \n# License: Modified BSD license (2-clause)\n\n\"\"\"Defines objects to create and manipulate raw annotations.\"\"\"\n\nfrom traits.has_traits import HasStrictTraits, cached_property\nfrom traits.trait_numeric import Array\nfrom traits.trait_types import Str, List, Int\nfrom traits.traits import Property\n\nimport numpy as np\nfrom pyanno.util import MISSING_VALUE, PyannoValueError\n\n\ndef _robust_isnan(x):\n res = False\n\n # workaround for the fact that np.isnan is not defined for non-numerical\n # type, e.g. strings\n try:\n res = np.isnan(x)\n except NotImplementedError:\n pass\n\n return res\n\n\ndef _is_nan_in_list(lst):\n return np.any([_robust_isnan(el) for el in lst])\n\n\nclass AnnotationsContainer(HasStrictTraits):\n \"\"\"Translate from general annotations files and arrays to pyAnno's format.\n\n This class exposes a few methods to import data from files and arrays, and\n converts them to pyAnno's format:\n\n * annotations are 2D integer arrays; rows index items, and columns\n annotators\n\n * label classes are numbered 0 to :attr:`nclasses`-1 . The attribute\n :attr:`labels` defines a mapping from label tokens to label classes\n\n * missing values are defined as :attr:`pyanno.util.MISSING_VALUE`. The\n attribute :attr:`missing_values` contains the missing values tokens\n found in the original, raw data\n\n The converted data can be accessed through the :attr:`annotations` property.\n\n The `AnnotationsContainer` is also used as the format to store annotations\n in :class:`~pyanno.database.PyannoDatabase` objects.\n \"\"\"\n\n DEFAULT_MISSING_VALUES_STR = ['-1', 'NA', 'None', '*']\n DEFAULT_MISSING_VALUES_NUM = [-1, np.nan, None]\n DEFAULT_MISSING_VALUES_ALL = (DEFAULT_MISSING_VALUES_STR +\n DEFAULT_MISSING_VALUES_NUM)\n\n #: raw annotations, as they are imported from file or array\n raw_annotations = List(List)\n\n #: name of file or array from which the annotations were imported\n name = Str\n\n #: list of all labels found in file/array\n labels = List\n\n #: labels corresponding to a missing value\n missing_values = List\n\n #: number of classes found in the annotations\n nclasses = Property(Int, depends_on='labels')\n def _get_nclasses(self):\n return len(self.labels)\n\n #: number of annotators\n nannotators = Property(Int, depends_on='raw_annotations')\n def _get_nannotators(self):\n return len(self.raw_annotations[0])\n\n #: number of annotations\n nitems = Property(Int, depends_on='raw_annotations')\n def _get_nitems(self):\n return len(self.raw_annotations)\n\n #: annotations in pyAnno format\n annotations = Property(Array, depends_on='raw_annotations')\n\n @cached_property\n def _get_annotations(self):\n nitems, nannotators = len(self.raw_annotations), self.nannotators\n anno = np.empty((nitems, nannotators), dtype=int)\n\n # build map from labels and missing values to annotation values\n raw2val = dict(list(zip(self.labels, list(range(self.nclasses)))))\n raw2val.update([(mv, MISSING_VALUE) for mv in self.missing_values])\n\n # translate\n nan_in_missing_values = _is_nan_in_list(self.missing_values)\n for i, row in enumerate(self.raw_annotations):\n for j, lbl in enumerate(row):\n if nan_in_missing_values and _robust_isnan(lbl):\n # workaround for the fact that np.nan cannot be used as\n # the key to a dictionary, since np.nan != np.nan\n anno[i,j] = MISSING_VALUE\n else:\n anno[i,j] = raw2val[lbl]\n\n return anno\n\n\n @staticmethod\n def _from_generator(rows_generator, missing_values, name=''):\n\n missing_set = set(missing_values)\n labels_set = set()\n\n raw_annotations = []\n nannotators = None\n for n, row in enumerate(rows_generator):\n\n # verify that number of lines is consistent in the whole file\n if nannotators is None: nannotators = len(row)\n else:\n if len(row) != nannotators:\n raise PyannoValueError(\n 'File has inconsistent number of entries '\n 'on separate lines (line {})'.format(n))\n\n raw_annotations.append(row)\n labels_set.update(row)\n\n # remove missing values from set of labels\n all_labels = sorted(list(labels_set - missing_set))\n missing_values = sorted(list(missing_set & labels_set))\n\n # workaround for np.nan != np.nan, so intersection does not work\n if _is_nan_in_list(all_labels):\n # uses fact that np.nan < x, for every x\n all_labels = all_labels[1:]\n missing_values.insert(0, np.nan)\n\n # create annotations object\n anno = AnnotationsContainer(\n raw_annotations = raw_annotations,\n labels = all_labels,\n missing_values = missing_values,\n name = name\n )\n\n return anno\n\n @staticmethod\n def _from_file_object(fobj, missing_values=None, name=''):\n \"\"\"Useful for testing, as it can be called using a StringIO object.\n \"\"\"\n\n if missing_values is None:\n missing_values = AnnotationsContainer.DEFAULT_MISSING_VALUES_STR\n\n # generator for rows of file-like object\n def file_row_generator():\n for line in fobj.readlines():\n # remove commas and split in individual tokens\n line = line.strip().replace(',', ' ')\n\n # ignore empty lines\n if len(line) == 0: continue\n\n labels = line.split()\n yield labels\n\n return AnnotationsContainer._from_generator(file_row_generator(),\n missing_values,\n name=name)\n\n\n @staticmethod\n def from_file(filename, missing_values=None):\n \"\"\"Load annotations from a file.\n\n The file is a text file with a columns separated by spaces and/or\n commas, and rows on different lines.\n\n Arguments\n ---------\n filename : string\n File name\n\n missing_values : list\n List of labels that are considered missing values.\n Default is :attr:`DEFAULT_MISSING_VALUES_STR`\n \"\"\"\n\n if missing_values is None:\n missing_values = AnnotationsContainer.DEFAULT_MISSING_VALUES_STR\n\n with open(filename) as fh:\n anno = AnnotationsContainer._from_file_object(fh,\n missing_values=missing_values,\n name=filename)\n\n return anno\n\n\n @staticmethod\n def from_array(x, missing_values=None, name=''):\n \"\"\"Create an annotations object from an array or list-of-lists.\n\n Arguments\n ---------\n x : ndarray or list-of-lists\n Array or list-of-lists containing numerical or string annotations\n\n missing_values : list\n List of values that are considered missing values.\n Default is :attr:`DEFAULT_MISSING_VALUES_ALL`\n\n name : string\n Name of the annotations (for user interaction and used as key in\n databases).\n \"\"\"\n\n if missing_values is None:\n missing_values = AnnotationsContainer.DEFAULT_MISSING_VALUES_ALL\n\n # generator for array objects\n def array_rows_generator():\n for row in x:\n yield list(row)\n\n return AnnotationsContainer._from_generator(array_rows_generator(),\n missing_values, name=name)\n\n\n def save_to(self, filename, set_name=False):\n \"\"\"Save raw annotations to file.\n\n Arguments\n ---------\n filename : string\n File name\n\n set_name : bool\n Set the :attr:`name` of the annotation container to the file name\n \"\"\"\n if set_name:\n self.name = filename\n with open(filename, 'w') as f:\n f.writelines(\n (' '.join(map(str, row))+'\\n'\n for row in self.raw_annotations)\n )\n\n\ndef load_annotations(filename, missing_values=None):\n \"\"\"Load annotations from file.\n\n The file is a text file with a columns separated by spaces and/or\n commas, and rows on different lines.\n\n Arguments\n ---------\n filename : string\n File name\n\n missing_values : list\n List of labels that are considered missing values.\n Default is\n :attr:`~pyanno.AnnotationsContainer.DEFAULT_MISSING_VALUES_STR`\n\n \"\"\"\n anno = AnnotationsContainer.from_file(filename,\n missing_values=missing_values)\n return anno.annotations\n", "sub_path": "pyanno/annotations.py", "file_name": "annotations.py", "file_ext": "py", "file_size_in_byte": 8975, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.isnan", "line_number": 22, "usage_type": "call"}, {"api_name": "numpy.any", "line_number": 30, "usage_type": "call"}, {"api_name": "traits.has_traits.HasStrictTraits", "line_number": 33, "usage_type": "name"}, {"api_name": "numpy.nan", "line_number": 56, "usage_type": "attribute"}, {"api_name": "traits.trait_types.List", "line_number": 61, "usage_type": "call"}, {"api_name": "traits.trait_types.Str", "line_number": 64, "usage_type": "name"}, {"api_name": "traits.trait_types.List", "line_number": 67, "usage_type": "name"}, {"api_name": "traits.trait_types.List", "line_number": 70, "usage_type": "name"}, {"api_name": "traits.traits.Property", "line_number": 73, "usage_type": "call"}, {"api_name": "traits.trait_types.Int", "line_number": 73, "usage_type": "argument"}, {"api_name": "traits.traits.Property", "line_number": 78, "usage_type": "call"}, {"api_name": "traits.trait_types.Int", "line_number": 78, "usage_type": "argument"}, {"api_name": "traits.traits.Property", "line_number": 83, "usage_type": "call"}, {"api_name": "traits.trait_types.Int", "line_number": 83, "usage_type": "argument"}, {"api_name": "traits.traits.Property", "line_number": 88, "usage_type": "call"}, {"api_name": "traits.trait_numeric.Array", "line_number": 88, "usage_type": "argument"}, {"api_name": "numpy.empty", "line_number": 93, "usage_type": "call"}, {"api_name": "pyanno.util.MISSING_VALUE", "line_number": 97, "usage_type": "name"}, {"api_name": "pyanno.util.MISSING_VALUE", "line_number": 106, "usage_type": "name"}, {"api_name": "traits.has_traits.cached_property", "line_number": 90, "usage_type": "name"}, {"api_name": "pyanno.util.PyannoValueError", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 142, "usage_type": "attribute"}]} +{"seq_id": "8112649", "text": "import os, sys, json, datetime, urllib.request, urllib.parse\n\nurl = \"https://api.hipchat.com/v2/room/1393242/notification?auth_token=6QOY1utklXEWKmXzvOudfvNCfaOuQMw8pfIAg8Up\"\njsonData = {\"color\": \"purple\", \"message\": \"test\", \"notify\": \"false\", \"message_format\": \"html\"}\nheaders = {'Content-Type': 'application/json'}\n\n\nrequest = urllib.request.Request(url,data=json.dumps(jsonData).encode('utf-8'),headers=headers,method=\"POST\")\nresponse = urllib.request.urlopen(request)\n\nprint(response.read())\n\n", "sub_path": "bot.py", "file_name": "bot.py", "file_ext": "py", "file_size_in_byte": 497, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "urllib.request.request.Request", "line_number": 8, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 8, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 8, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 8, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 9, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 9, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "590464211", "text": "import requests\nimport sys\nfrom bs4 import BeautifulSoup\n\n\n# def file_write(path_,content):\n# my_file = open(path_, 'a+', encoding='utf-8')\n# my_file.write(content+'\\n')\n# my_file.close()\n\n\ndef make_request(url, header):\n try:\n s = requests.session()\n page_ = s.get(url, headers=header)\n if page_.status_code == 200:\n return page_\n except requests.exceptions.ConnectionError:\n print('Something wrong with your internet connection')\n sys.exit()\n\ndef get_url(lang1, lang2, w):\n url = f'https://context.reverso.net/translation/{lang1.lower()}-{lang2.lower()}/{w}'\n return url\n\n\ndef get_translations(soup):\n word_translation = soup.find_all('a', {'class': 'translation'})\n return [t.text.strip() for t in word_translation][1:]\n\n\ndef get_example(soup):\n examples = soup.find_all('div', {'class': ['src', 'trg']})\n return [e.text.strip().strip('\\n\\n\\n') for e in examples]\n\n\ndef save_translations(tran, lan, path_):\n my_file = open(path_, 'a+', encoding='utf-8')\n\n my_file.write(f'{lan} Translations:\\n')\n\n my_file.write(tran[0] + '\\n')\n\n my_file.write('\\n')\n my_file.close()\n\n\ndef save_example(exam, lan, path_):\n my_file = open(path_, 'a+', encoding='utf-8')\n my_file.write(f'{lan} Examples:\\n')\n\n my_file.write(exam[0] + ':' + '\\n')\n\n if lan == 'Turkish':\n my_file.write(exam[1])\n else:\n my_file.write(exam[1] + '\\n')\n\n if lan != 'Turkish':\n my_file.write('\\n\\n')\n\n my_file.close()\n\n\ndef welcome_message():\n print(\"Hello, you're welcome to the translator.\")\n print('Translator supports:')\n print('1. Arabic')\n print('2. German')\n print('3. English')\n print('4. Spanish')\n print('5. French')\n print('6. Hebrew')\n print('7. Japanese')\n print('8. Dutch')\n print('9. Polish')\n print('10. Portuguese')\n print('11. Romanian')\n print('12. Russian')\n print('13. Turkish')\n\n\nif __name__ == \"__main__\":\n args = sys.argv\n lang_list = ['Arabic', 'German', 'English', 'Spanish', 'French', 'Hebrew', 'Japanese', 'Dutch', 'Polish',\n 'Portuguese', 'Romanian', 'Russian', 'Turkish']\n src_language = args[1]\n\n target_language = args[2]\n\n if src_language.capitalize() not in lang_list:\n print(f\"Sorry,the program doesn't support {src_language}\")\n sys.exit()\n if target_language.capitalize() not in lang_list+['All']:\n print(f\"Sorry,the program doesn't support {target_language}\")\n sys.exit()\n\n word = args[3]\n path = f'{word}.txt'\n headers = {'User-Agent': 'Chrome-Windows'}\n\n if target_language == 'all':\n for k in range(len(lang_list)):\n if lang_list[k].lower() == src_language.lower():\n continue\n url1 = get_url(src_language, lang_list[k], word)\n page = make_request(url1, headers)\n try:\n soup_ = BeautifulSoup(page.content, 'html.parser')\n except AttributeError:\n print(f'Sorry, unable to find {word}')\n sys.exit()\n soup_.prettify()\n translation_ = get_translations(soup_)\n example_ = get_example(soup_)\n\n save_translations(translation_, lang_list[k], path)\n save_example(example_, lang_list[k], path)\n\n else:\n url1 = get_url(src_language, target_language, word)\n page = make_request(url1, headers)\n try:\n soup_ = BeautifulSoup(page.content, 'html.parser')\n except AttributeError:\n print(f'Sorry, unable to find {word}')\n sys.exit()\n soup_.prettify()\n translation_ = get_translations(soup_)\n example_ = get_example(soup_)\n\n save_translations(translation_, target_language, path)\n save_example(example_, target_language, path)\n\n\n file = open(path, 'r', encoding='utf-8')\n print(file.read())\n file.close()\n", "sub_path": "translator.py", "file_name": "translator.py", "file_ext": "py", "file_size_in_byte": 3926, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.session", "line_number": 14, "usage_type": "call"}, {"api_name": "requests.exceptions", "line_number": 18, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 20, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 84, "usage_type": "attribute"}, {"api_name": "sys.exit", "line_number": 93, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 96, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 109, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 112, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 124, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 127, "usage_type": "call"}]} +{"seq_id": "364965650", "text": "# -*- coding: utf-8 -*-\n\"\"\"\nSpyder Editor\n\nThis is a temporary script file.\n\"\"\"\n\n# standard imports \nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Add parent directory to path\nimport sys\nimport os\nparent_path = '..\\\\nistapttools'\nif parent_path not in sys.path:\n sys.path.append(os.path.abspath(parent_path)) \nimport time\n\n# custom imports\nimport apt_fileio\nimport plotting_stuff\nimport initElements_P3\n\nimport peak_param_determination as ppd\n\nfrom histogram_functions import bin_dat\n\n\n\nfns =['R20_18155-v01.epos',\n 'R20_18156-v01.epos',\n 'R20_18157-v01.epos',\n 'R20_18160-v01.epos',\n 'R20_18161-v01.epos',\n 'R20_18162-v01.epos']\n\nfold = \"GaN epos files\"\n\nfig = plt.figure(1)\nfig.clf()\nax = fig.gca()\n\nfor i in range(len(fns)):\n epos = apt_fileio.read_epos_numpy(fold+\"\\\\\"+fns[i])\n plotting_stuff.plot_histo(\n epos['m2q'],\n 1,\n user_label=fns[i],\n clearFigure=False,\n user_xlim=[0, 100],\n user_bin_width=0.03,\n scale_factor=10**i,\n user_color=None,\n )\n\nCSR=[0.038,0.005,0.1,0.09,0.24,0.01]\nN=[34.1,17,41.5,40.5,49.7,23.2]\nGa=[65.9,83,58.5,59.5,53.3,76.8] \nfig = plt.figure(5)\nplt.plot(CSR,Ga,'ko',label=\"Ga\")\nplt.plot(CSR,N,'rs',label=\"N\")\nplt.xscale('log')\nplt.legend()\n \n\n \n \n#fn = fold+\"\\\\\"+'R20_18161-v01.epos'\n#epos = apt_fileio.read_epos_numpy(fn)\n#\n#plotting_stuff.plot_m2q_vs_time(\n# epos['m2q'],\n# epos,\n# 2,\n# clearFigure=True,\n# user_ylim=[0, 100],\n#)\n#\n#\n# \n#pk_lim1 = [22.92, 23.2]\n#pk_lim2 = [23.6, 23.74]\n#\n#idxs = np.where(((epos['m2q']>pk_lim1[0]) & (epos['m2q']pk_lim2[0]) & (epos['m2q']//', auth_views.PasswordResetConfirmView.as_view(), name='password_reset_confirm'),\n # path('reset/done/', auth_views.PasswordResetCompleteView.as_view(), name='password_reset_complete'),\n # alternative url patterns are defined in django.contrib.auth.urls (zamiast powyższych):\n path('', include('django.contrib.auth.urls')),\n\n path('', views.dashboard, name='dashboard'),\n path('register/', views.usersignup, name='register_user'),\n url(r'^activate/(?P[0-9A-Za-z_\\-]+)/(?P[0-9A-Za-z]{1,13}-[0-9A-Za-z]{1,20})/$',\n views.activate_account, name='activate'),\n path('edit/', views.edit, name='edit'),\n path('profile/', views.profile_view, name='profile_view'),\n path('/profile/', views.bidder_view, name='bidder_view'),\n path('api/', include('account.api.urls')),\n\n]", "sub_path": "account/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 1723, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 23, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 27, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 32, "usage_type": "call"}]} +{"seq_id": "86175121", "text": "#!/usr/bin/env python3\n# Author: Bradley Pratt\n# Created: 01/11/2021\n# Last Edit: 01/15/2021\n\nimport PySimpleGUI as sg\nfrom sympy import *\n\n# ########GLOBAL VARIABLES##########\nwelcomeMessage = \"Welcome to my python-based calculator!\"\noperators = [\"^\", \"√\", \"÷\", \"*\", \"+\", \"-\"]\nimplicitMultiply = [\"LOG\", \"LN\", \"SIN\", \"COS\", \"TAN\", \"(\"]\nnumbers = [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\", \".\"]\nhistory = []\n\ncolumn1 = [\n [sg.Button(button_text=\"LOG\", size=(5, 1))],\n [sg.Button(button_text=\"LN\", size=(5, 1))],\n [sg.Button(button_text=pretty(pi), size=(5, 1))],\n [sg.Button(button_text=\"^\", size=(5, 1))],\n [sg.Button(button_text=\"DEL\", size=(5, 1))],\n [sg.Button(button_text=\"OFF\", size=(5, 1))]\n]\n\ncolumn2 = [\n [sg.Button(button_text=\"SIN\", size=(5, 1))],\n [sg.Button(button_text=\"√\", size=(5, 1))],\n [sg.Button(button_text=\"7\", size=(5, 1), button_color=('#12100E', 'white'))],\n [sg.Button(button_text=\"4\", size=(5, 1), button_color=('#12100E', 'white'))],\n [sg.Button(button_text=\"1\", size=(5, 1), button_color=('#12100E', 'white'))],\n [sg.Button(button_text=\"0\", size=(5, 1), button_color=('#12100E', 'white'))]\n]\n\ncolumn3 = [\n [sg.Button(button_text=\"COS\", size=(5, 1))],\n [sg.Button(button_text=\"(\", size=(5, 1))],\n [sg.Button(button_text=\"8\", size=(5, 1), button_color=('#12100E', 'white'))],\n [sg.Button(button_text=\"5\", size=(5, 1), button_color=('#12100E', 'white'))],\n [sg.Button(button_text=\"2\", size=(5, 1), button_color=('#12100E', 'white'))],\n [sg.Button(button_text=\".\", size=(5, 1), button_color=('#12100E', 'white'))]\n]\n\ncolumn4 = [\n [sg.Button(button_text=\"TAN\", size=(5, 1))],\n [sg.Button(button_text=\")\", size=(5, 1))],\n [sg.Button(button_text=\"9\", size=(5, 1), button_color=('#12100E', 'white'))],\n [sg.Button(button_text=\"6\", size=(5, 1), button_color=('#12100E', 'white'))],\n [sg.Button(button_text=\"3\", size=(5, 1), button_color=('#12100E', 'white'))],\n [sg.Button(button_text=\"(-)\", size=(5, 1), button_color=('#12100E', 'white'))]\n]\n\ncolumn5 = [\n [sg.Button(button_text=\"CLEAR\", size=(5, 1))],\n [sg.Button(button_text=\"÷\", size=(5, 1), button_color=('white', '#3A3E5C'))],\n [sg.Button(button_text=\"*\", size=(5, 1), button_color=('white', '#3A3E5C'))],\n [sg.Button(button_text=\"+\", size=(5, 1), button_color=('white', '#3A3E5C'))],\n [sg.Button(button_text=\"-\", size=(5, 1), button_color=('white', '#3A3E5C'))],\n [sg.Button(button_text=\"=\", size=(5, 1), button_color=('white', '#3A3E5C'))]\n]\n\nlayout = [\n [sg.Output(size=(47, 5), key='-DISPLAY-', echo_stdout_stderr=True)],\n [sg.Column(column1),\n sg.VSeperator(pad=(1, 1)),\n sg.Column(column2),\n sg.VSeperator(pad=(1, 1)),\n sg.Column(column3),\n sg.VSeperator(pad=(1, 1)),\n sg.Column(column4),\n sg.VSeperator(pad=(1, 1)),\n sg.Column(column5)]\n]\n\n\n# ########MAIN FUNCTION##########\ndef main():\n global history\n\n # Create the window\n window = sg.Window('Scientific Calculator', layout, no_titlebar=True, grab_anywhere=True)\n print(welcomeMessage)\n equation = []\n\n # Create an event loop\n while True:\n event, values = window.read()\n\n if event == \"OFF\" or event == sg.WIN_CLOSED:\n break\n elif event == \"CLEAR\":\n window['-DISPLAY-'].update('')\n equation = []\n elif event == \"DEL\":\n output = window['-DISPLAY-'].Get()\n window['-DISPLAY-'].update('')\n print(output[0:-2], end=\"\")\n del equation[-1]\n elif event == \"=\":\n if parenthesesChecker(equation):\n answer = calculate(equation)\n print(f\"= {answer}\")\n history.append(answer)\n equation = []\n else:\n print(\"ERROR: missing parenthesis!\")\n else:\n if event == \"LOG\" or event == \"LN\" or event == \"SIN\" or event == \"COS\" or event == \"TAN\" or event == \"√\":\n print(event.lower() + \"(\", end=\"\")\n else:\n if event in operators and event != \"√\" and len(equation) == 0:\n if len(history) == 0:\n print(\"ERROR: no previous history. Cannot perform operation on empty value.\")\n else:\n equation.append(history[-1])\n print(\"ANS\", end=\"\")\n print(event, end=\"\")\n if len(equation) != 0 and equation[-1][-1] in numbers and event in numbers:\n equation[-1] += event\n else:\n equation.append(event)\n\n window.close()\n\n\n# ########ACCESSORY FUNCTION##########\ndef parenthesesChecker(equation):\n stack = []\n\n for element in equation:\n if element in implicitMultiply or element == \"√\":\n stack.append(1)\n if element == ')':\n if len(stack) == 0:\n return False\n else:\n stack.pop()\n return True\n\n\ndef calculate(equation):\n subEq = []\n current = []\n subPar = 0\n tracking = False\n\n if len(equation) == 0:\n return 0\n\n track = 0\n for el in equation:\n # print(current)\n # if el not in operators:\n # if track != len(equation) - 1 and equation[track + 1] in implicitMultiply:\n # current.append(\"*\")\n if tracking and el == \")\" and subPar == 0:\n tracking = False\n if len(subEq) != 0:\n # print(current)\n current.append(funcCalc(equation[track - 2], calculate(subEq)))\n subEq = []\n elif tracking:\n if el in implicitMultiply:\n subPar += 1\n if el == \")\":\n subPar -= 1\n subEq.append(el)\n elif el in implicitMultiply:\n tracking = True\n else:\n current.append(el)\n track += 1\n #print(current)\n # print(current)\n endNotReached = True\n while endNotReached:\n for item in range(len(current)):\n if current[item] == \"^\":\n current[item - 1] = performOperation(float(current[item - 1]), float(current[item + 1]), current[item])\n del current[item + 1]\n del current[item]\n break\n if current[item] == \"√\":\n current[item] = performOperation(float(current[item + 1]), 0, current[item])\n del current[item + 2]\n del current[item + 1]\n break\n if item == len(current) - 1:\n endNotReached = False\n break\n\n endNotReached = True\n while endNotReached:\n for item in range(len(current)):\n if current[item] == \"*\" or current[item] == \"÷\":\n current[item - 1] = performOperation(float(current[item - 1]), float(current[item + 1]), current[item])\n del current[item + 1]\n del current[item]\n break\n if item == len(current) - 1:\n endNotReached = False\n break\n\n endNotReached = True\n while endNotReached:\n for item in range(len(current)):\n if current[item] == \"+\" or current[item] == \"-\":\n current[item - 1] = performOperation(float(current[item - 1]), float(current[item + 1]), current[item])\n del current[item + 1]\n del current[item]\n break\n if item == len(current) - 1:\n endNotReached = False\n break\n\n if len(current) != 1:\n print(f\"Current answer array length: {len(current)}\")\n return \"INTERNAL ERROR: Improper calculation.\"\n else:\n return float(current[0])\n\n\ndef funcCalc(func, value):\n if func == \"(\":\n return value\n elif func == \"LOG\":\n return log(value)\n elif func == \"LN\":\n return ln(value)\n elif func == \"SIN\":\n return sin(value)\n elif func == \"COS\":\n return cos(value)\n elif func == \"TAN\":\n return tan(value)\n else:\n print(f\"ERROR: unrecognized function: {func}. Returning 0.\")\n return 0\n\n\ndef performOperation(num1, num2, op):\n if op == \"^\":\n return exp(num1, num2)\n elif op == \"√\":\n return exp(num1, 1 / 2)\n elif op == \"+\":\n return add(num1, num2)\n elif op == \"-\":\n return add(num1, -num2)\n elif op == \"*\":\n return multiply(num1, num2)\n else:\n return multiply(num1, 1 / num2)\n\n\ndef exp(base, power):\n return base ** power\n\n\ndef add(first, second):\n return first + second\n\n\ndef multiply(first, second):\n return first * second\n\n\n# Call the main function\nif __name__ == \"__main__\":\n main()\n", "sub_path": "scientific_calculator.py", "file_name": "scientific_calculator.py", "file_ext": "py", "file_size_in_byte": 8729, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PySimpleGUI.Button", "line_number": 17, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 18, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 19, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 20, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 21, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 22, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 26, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 27, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 28, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 29, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 30, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 31, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 35, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 36, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 37, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 38, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 39, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 40, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 44, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 45, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 46, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 47, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 48, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 49, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 53, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 54, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 55, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 56, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 57, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 58, "usage_type": "call"}, {"api_name": "PySimpleGUI.Output", "line_number": 62, "usage_type": "call"}, {"api_name": "PySimpleGUI.Column", "line_number": 63, "usage_type": "call"}, {"api_name": "PySimpleGUI.VSeperator", "line_number": 64, "usage_type": "call"}, {"api_name": "PySimpleGUI.Column", "line_number": 65, "usage_type": "call"}, {"api_name": "PySimpleGUI.VSeperator", "line_number": 66, "usage_type": "call"}, {"api_name": "PySimpleGUI.Column", "line_number": 67, "usage_type": "call"}, {"api_name": "PySimpleGUI.VSeperator", "line_number": 68, "usage_type": "call"}, {"api_name": "PySimpleGUI.Column", "line_number": 69, "usage_type": "call"}, {"api_name": "PySimpleGUI.VSeperator", "line_number": 70, "usage_type": "call"}, {"api_name": "PySimpleGUI.Column", "line_number": 71, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 80, "usage_type": "call"}, {"api_name": "PySimpleGUI.WIN_CLOSED", "line_number": 88, "usage_type": "attribute"}]} +{"seq_id": "156629354", "text": "import concurrent.futures\nimport io\nimport json\nimport re\nimport shlex\nfrom collections import deque, OrderedDict\n\nimport twitter\nfrom PIL import Image\n# from googletrans import Translator\n\nfrom api_keys import *\nfrom apis import twitch_rss\nfrom apis.olliebot_web import OllieBotAPI\nfrom apis.worldtime import *\nfrom apis.youtubeapi import YoutubeAPI\nfrom response import *\nfrom util.containers import *\n\n\n# Returns a BotContainer by name\ndef get_bot(name: str):\n global bots\n for b in bots:\n if b.name == name:\n return b\n return None\n\n\ndef proxy_message(bot, channel_id: str, content: str, embed: discord.Embed = None):\n global out_messages\n out_messages.append(ProxyMessage(bot=bot,\n channel=discord.Object(id=channel_id),\n content=content,\n embed=embed))\n\n\ndef get_quote(in_server, in_name, do_spam=False):\n for c in in_server.commands:\n if do_spam:\n if c['name'] == in_name:\n return c\n else:\n if c['name'] == in_name and int(c['timer']) < 1:\n c['timer'] = str(in_server.command_delay * 60)\n return c\n return None\n\n\ndef bc_from_bot(bot):\n global bots\n for b in bots:\n if b.id == bot.user.id:\n return b\n\n\ndef extract_url(arg: str, start: int) -> str:\n out = ''\n for c in arg[start:start + 100]:\n if c == '\"':\n break\n out += c\n return out\n\n\ndef extract_mention_id(id: str):\n out = ''\n for c in id:\n if c.isdigit():\n out += c\n return out\n\n\nurl_regex = re.compile(\n r'^(?:http|ftp)s?://' # http:// or https://\n r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\\.)+(?:[A-Z]{2,6}\\.?|[A-Z0-9-]{2,}\\.?)|' #domain...\n r'localhost|' #localhost...\n r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})' # ...or ip\n r'(?::\\d+)?' # optional port\n r'(?:/?|[/?]\\S+)$', re.IGNORECASE)\n\n\ndef validate_url(url):\n return re.match(url_regex, url)\n\n\ndef yt_shortened_to_long(link: str):\n if 'https://youtu.be' in link:\n link_parts = link.rsplit('/', maxsplit=1)\n try:\n return 'https://www.youtube.com/watch?v={}'.format(link_parts[1])\n except IndexError:\n return None\n return None\n\n\ndef yt_extract_id(url: str):\n if 'youtube' in url and 'v=' in url:\n v_tag = url.rsplit('v=', 1)\n return v_tag[1].split('&')[0]\n elif 'youtu.be' in url:\n return url.rsplit('/', 1)[1]\n return None\n\n\ndef is_num(text: str, base: int = 10):\n try:\n num = int(text, base)\n return num\n except (ValueError, TypeError):\n return None\n\n\ndef safe_list_get(l, idx, default):\n try:\n return l[idx]\n except IndexError:\n return default\n\n\ndef strip_args(args: str) -> list:\n arg_list = shlex.split(args, ' ')\n out_list = []\n for a in arg_list:\n if a:\n pieces = a.split('=')\n out_list.append((pieces[0], pieces[1]))\n return out_list\n\n\ndef flush_delete_queue():\n global delete_queue\n for d in delete_queue: # type: DeleteMessage\n d.timer = 0\n\n\ndef replace_color(img: Image.Image, base_color: int, with_color: int, variance: int):\n red = (base_color & 0xff0000) >> 16\n green = (base_color & 0xff00) >> 8\n blue = base_color & 0xff\n\n with_red = (with_color & 0xff0000) >> 16\n with_green = (with_color & 0xff00) >> 8\n with_blue = with_color & 0xff\n\n pixels = img.load()\n\n for y in range(img.size[1]):\n for x in range(img.size[0]):\n at = pixels[x, y]\n\n # at[0:4] -> [red, green, blue, alpha]\n\n if abs(red - at[0]) <= variance and abs(green - at[1]) <= variance and abs(blue - at[2]) <= variance:\n pixels[x, y] = (with_red, with_green, with_blue, at[3]) # keep original alpha | alpha-blind\n\n\nasync def get_json(page: str) -> dict:\n with aiohttp.ClientSession() as session:\n async with session.get(page) as resp:\n if resp.status == 200:\n d = await resp.json()\n return d\n\n\nasync def get_image(url: str):\n try:\n with aiohttp.ClientSession() as session:\n async with session.get(url) as resp:\n if resp.status == 200:\n image_bytes = await resp.read()\n\n image = Image.open(io.BytesIO(image_bytes))\n image = image.convert('RGBA')\n return image\n except Exception:\n return None\n\n\ndef extract_image_url(arg, msg: discord.Message):\n if type(arg) is str and arg.startswith('http'):\n return arg\n if msg.attachments:\n return msg.attachments[0]['url']\n if msg.embeds:\n return msg.embeds[0]['url']\n\n\ndef extract_filename(path):\n if '.' in path[-5:]:\n matches = re.findall(r'\\b[a-zA-Z]+\\.[a-zA-Z]{3}\\b', path)\n if matches:\n return matches[-1]\n\n matches = re.findall(r'\\b([a-zA-Z]+)', path)\n\n if matches:\n return matches[-1]\n\n return path\n\n\n# I forgot what non-builtin attributes are called so it's \"new\"\ndef get_new_attr(thing, check=None):\n if check:\n return (x for x in thing.__dict__ if not x.startswith('__') and check(getattr(thing, x)))\n return (x for x in thing.__dict__ if not x.startswith('__'))\n\n\n# iterator find help util\ndef iterfind(iterable, check, default=None):\n for i in iterable:\n if check(i):\n return i\n return default\n\n\ndef bool_eval(text):\n if text.lower() in ['yes', 'y', 'ya', 'yea', 'yeah', 'yup', 'true', 't', 'yes please', 'hit me up', 'hell yeah']:\n return True\n elif text.lower() in ['n', 'no', 'nah', 'nah fam', 'please no', 'god no', 'no thanks', 'false', 'f']:\n return False\n # else return None\n\n\n# time in seconds\ndef schedule_delete(bot, msg, time: int):\n delete_queue.append(DeleteMessage(message=msg, bot=bot, timer=time))\n\n\ndef schedule_future(coro, time: int, name: str = ''):\n coro_queue.append(TimedFuture(coro=coro, timer=time, name=name))\n\n\ndef future_is_scheduled(name: str):\n for tf in coro_queue:\n if tf.name and tf.name == name:\n return True\n\n\n# helper function purely for formatting\ndef help_form(text: str):\n return text\n\n\n# global save protection\nsave_in_progress = False\n\n\n# save_in_progress decorator\ndef global_save(func):\n def decorator(*args, **kwargs):\n global save_in_progress\n save_in_progress = True\n func(*args, **kwargs)\n save_in_progress = False\n\n return decorator\n\n\n# str.split but an iter\ndef split_iter(string, include: str = ''):\n return (x.group(0) for x in re.finditer(r\"[A-Za-z0-9{}']+\".format(include), string))\n\n\n# slice an `OrderedDict`\ndef od_slice(od, fr=0, to=0):\n if not to:\n to = len(od)\n desired = list(od)[fr:to]\n return OrderedDict((k, od[k]) for k in desired)\n\n\ndef file_exists(filepath: str):\n try:\n open(filepath, 'r').close()\n return True\n except:\n return False\n\n\nexit_timer = 0\n\nout_messages = deque([]) # for proxy message delivery system\n\ndelete_queue = []\n\ncoro_queue = []\n\nmute_queue = []\n\nbypass_perm = []\n\nalive_timer = 0\n\n# temp storage for temp admin key\nadminKey = ''\n\n# global timer for rss feeds\nrss_timer = 60\n\n# global bad timer\nbad_timer = 0\n\n# global shoe jesus timer\nshoe_jesus_timer = 0\n\ninternal_shutdown = False\n\nsync_shutdown = False\n\n# default executor\ndef_executor = concurrent.futures.ThreadPoolExecutor(max_workers=10)\n\n# -----------\n# CONSTANTS\n# -----------\nCHAR_ZWS = chr(0x200B)\n\nTITLE_BAR = '───────────────────────'\n\nTIME_RESPONSE_EXIT = 300 # in seconds\n\nTIME_RSS_LOOP = 70 # in seconds\n\nTIME_ASYNC_EXIT = 60 # in seconds\n\nTIME_MUSIC_TIMEOUT = 120 # in seconds\n\nTIME_SHOE_JESUS = 2700\n\nOWNER_ID = '305407800778162178'\n\nMUSIC_QUEUE_LIMIT = 50\n\nDATETIME_FORMAT = '%Y-%m-%dT%H:%M:%S'\n\nwith open('resources/emoji_alphabet.json', 'r', encoding='utf8') as f:\n emoji_alphabet = json.load(f)\n\n\n# |--------------[ API Setup ]--------------|\ntwitter_api = twitter.Api(consumer_key=TWITTER_CONSUMER_KEY,\n consumer_secret=TWITTER_CONSUMER_SECRET,\n access_token_key=TWITTER_TOKEN_KEY,\n access_token_secret=TWITTER_TOKEN_SECRET)\n\nyt = YoutubeAPI(key=YOUTUBE_TOKEN)\n\ntwitch = twitch_rss.TwitchRss(client_id=TWITCH_CLIENT_ID,\n client_secret=TWITCH_CLIENT_SECRET,\n oauth=TWITCH_TOKEN)\n\nworldtime = WorldTime(key=GEO_TIME_TOKEN)\n\nolliebot_api = OllieBotAPI(OLLIEBOT_TOKEN)\n\nrss_feeds = ['twitter', 'twitch', 'youtube']\n\nrss_colors = {'twitter': 0x00aced,\n 'twitch': 0x6441a5,\n 'youtube': 0xbb0000}\n\n# shortened music commands to be replaced\nmusic_commands = {'cq': 'queue clear',\n 'qc': 'queue clear',\n 'p': 'play',\n 'q': 'queue',\n 'd': 'disconnect',\n 'sk': 'skip',\n 'se': 'search',\n 'lq': 'queue listall',\n 'ql': 'queue listall',\n 'ps': 'pause',\n 'sh': 'shuffle',\n 'c': 'current track info'}\n\nhug_library = []\npat_library = []\n\nnum2word = {'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", "sub_path": "util/global_util.py", "file_name": "global_util.py", "file_ext": "py", "file_size_in_byte": 9612, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "re.compile", "line_number": 74, "usage_type": "call"}, {"api_name": "re.IGNORECASE", "line_number": 80, "usage_type": "attribute"}, {"api_name": "re.match", "line_number": 84, "usage_type": "call"}, {"api_name": "shlex.split", "line_number": 122, "usage_type": "call"}, {"api_name": "PIL.Image.Image", "line_number": 137, "usage_type": "attribute"}, {"api_name": "PIL.Image", "line_number": 137, "usage_type": "name"}, {"api_name": "PIL.Image.open", "line_number": 173, "usage_type": "call"}, {"api_name": "PIL.Image", "line_number": 173, "usage_type": "name"}, {"api_name": "io.BytesIO", "line_number": 173, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 191, "usage_type": "call"}, {"api_name": "re.findall", "line_number": 195, "usage_type": "call"}, {"api_name": "re.finditer", "line_number": 263, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 271, "usage_type": "call"}, {"api_name": "collections.deque", "line_number": 284, "usage_type": "call"}, {"api_name": "concurrent.futures.futures.ThreadPoolExecutor", "line_number": 313, "usage_type": "call"}, {"api_name": "concurrent.futures.futures", "line_number": 313, "usage_type": "attribute"}, {"api_name": "concurrent.futures", "line_number": 313, "usage_type": "name"}, {"api_name": "json.load", "line_number": 339, "usage_type": "call"}, {"api_name": "twitter.Api", "line_number": 343, "usage_type": "call"}, {"api_name": "apis.youtubeapi.YoutubeAPI", "line_number": 348, "usage_type": "call"}, {"api_name": "apis.twitch_rss.TwitchRss", "line_number": 350, "usage_type": "call"}, {"api_name": "apis.twitch_rss", "line_number": 350, "usage_type": "name"}, {"api_name": "apis.olliebot_web.OllieBotAPI", "line_number": 356, "usage_type": "call"}]} +{"seq_id": "545726383", "text": "from django.urls import path\nfrom apps.sites.views import SitesView, SiteDetailView, SummaryView, SummaryAverageView\n\n\nurlpatterns = [\n path(r'sites/', SitesView.as_view(), name='sites'),\n path(r'sites//', SiteDetailView.as_view(), name='sites'),\n path(r'summary/', SummaryView.as_view(), name='summary'),\n path(r'summary-average/', SummaryAverageView.as_view(), name='summary-average'),\n]\n", "sub_path": "apps/sites/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 415, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "apps.sites.views.SitesView.as_view", "line_number": 6, "usage_type": "call"}, {"api_name": "apps.sites.views.SitesView", "line_number": 6, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "apps.sites.views.SiteDetailView.as_view", "line_number": 7, "usage_type": "call"}, {"api_name": "apps.sites.views.SiteDetailView", "line_number": 7, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "apps.sites.views.SummaryView.as_view", "line_number": 8, "usage_type": "call"}, {"api_name": "apps.sites.views.SummaryView", "line_number": 8, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "apps.sites.views.SummaryAverageView.as_view", "line_number": 9, "usage_type": "call"}, {"api_name": "apps.sites.views.SummaryAverageView", "line_number": 9, "usage_type": "name"}]} +{"seq_id": "499966014", "text": "# Copyright 2015 Red Hat, Inc.\n# All Rights Reserved.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\"); you may\n# not use this file except in compliance with the License. You may obtain\n# 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, WITHOUT\n# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the\n# License for the specific language governing permissions and limitations\n# under the License.\nimport yaml\n\nfrom tripleo_common.core import constants\nfrom tripleo_common.core import exception\n\n\ndef add_key_prefix(source):\n result = dict()\n for keyname, value in source.items():\n new_keyname = \"%s%s\" % (constants.OBJECT_META_KEY_PREFIX, keyname)\n result[new_keyname] = value\n return result\n\n\ndef remove_key_prefix(source):\n result = dict()\n for keyname, value in source.items():\n new_keyname = keyname.replace(constants.OBJECT_META_KEY_PREFIX, '')\n result[new_keyname] = value\n return result\n\n\ndef add_file_metadata(plan_files):\n cm = {k: v for (k, v) in plan_files.items()\n if v.get('meta', {}).get('file-type') == 'capabilities-map'}\n # if there is more than one capabilities-map file, throw an exception\n # if there is a capabilities-map file, then process it and set metadata\n # in files found\n if len(cm) > 1:\n raise exception.TooManyCapabilitiesMapFilesError()\n if len(cm) == 1:\n mapfile = yaml.load(list(cm.items())[0][1]['contents'])\n\n # identify the root template\n if mapfile['root_template']:\n if plan_files[mapfile['root_template']]:\n # if the file exists in the plan and has meta, update it\n # otherwise add meta dict\n if 'meta' in plan_files[mapfile['root_template']]:\n plan_files[mapfile['root_template']]['meta'].update(\n dict(constants.ROOT_TEMPLATE_META)\n )\n else:\n plan_files[mapfile['root_template']]['meta'] =\\\n dict(constants.ROOT_TEMPLATE_META)\n\n # identify all environments\n for topic in mapfile['topics']:\n for eg in topic['environment_groups']:\n for env in eg['environments']:\n if 'meta' in plan_files[env['file']]:\n plan_files[env['file']]['meta'].update(\n dict(constants.ENVIRONMENT_META)\n )\n else:\n plan_files[env['file']]['meta'] =\\\n dict(constants.ENVIRONMENT_META)\n\n # identify the root environment\n if mapfile['root_environment']:\n if plan_files[mapfile['root_environment']]:\n # if the file exists in the plan and has meta, update it\n # otherwise add meta dict\n if 'meta' in plan_files[mapfile['root_environment']]:\n plan_files[mapfile['root_environment']]['meta'].update(\n dict(constants.ROOT_ENVIRONMENT_META)\n )\n else:\n plan_files[mapfile['root_environment']]['meta'] =\\\n dict(constants.ROOT_ENVIRONMENT_META)\n return plan_files\n", "sub_path": "tripleo_common/utils/meta.py", "file_name": "meta.py", "file_ext": "py", "file_size_in_byte": 3436, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tripleo_common.core.constants.OBJECT_META_KEY_PREFIX", "line_number": 24, "usage_type": "attribute"}, {"api_name": "tripleo_common.core.constants", "line_number": 24, "usage_type": "name"}, {"api_name": "tripleo_common.core.constants.OBJECT_META_KEY_PREFIX", "line_number": 32, "usage_type": "attribute"}, {"api_name": "tripleo_common.core.constants", "line_number": 32, "usage_type": "name"}, {"api_name": "tripleo_common.core.exception.TooManyCapabilitiesMapFilesError", "line_number": 44, "usage_type": "call"}, {"api_name": "tripleo_common.core.exception", "line_number": 44, "usage_type": "name"}, {"api_name": "yaml.load", "line_number": 46, "usage_type": "call"}, {"api_name": "tripleo_common.core.constants.ROOT_TEMPLATE_META", "line_number": 55, "usage_type": "attribute"}, {"api_name": "tripleo_common.core.constants", "line_number": 55, "usage_type": "name"}, {"api_name": "tripleo_common.core.constants.ROOT_TEMPLATE_META", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tripleo_common.core.constants", "line_number": 59, "usage_type": "name"}, {"api_name": "tripleo_common.core.constants.ENVIRONMENT_META", "line_number": 67, "usage_type": "attribute"}, {"api_name": "tripleo_common.core.constants", "line_number": 67, "usage_type": "name"}, {"api_name": "tripleo_common.core.constants.ENVIRONMENT_META", "line_number": 71, "usage_type": "attribute"}, {"api_name": "tripleo_common.core.constants", "line_number": 71, "usage_type": "name"}, {"api_name": "tripleo_common.core.constants.ROOT_ENVIRONMENT_META", "line_number": 80, "usage_type": "attribute"}, {"api_name": "tripleo_common.core.constants", "line_number": 80, "usage_type": "name"}, {"api_name": "tripleo_common.core.constants.ROOT_ENVIRONMENT_META", "line_number": 84, "usage_type": "attribute"}, {"api_name": "tripleo_common.core.constants", "line_number": 84, "usage_type": "name"}]} +{"seq_id": "509404953", "text": "import sys\nfrom collections import deque\n\ninput =sys.stdin.readline\n\ndef solution(n, m, A, know):\n s = deque(set(map(int, know)))\n chk = [1]*(n+m+1)\n\n while s:\n x = s.popleft()\n chk[x] = 0\n for j in A[x]:\n if chk[j]==0:\n continue\n s.append(j)\n print(sum(chk[n+1:]))\n\nN, M = map(int, input().split())\nknow = input().split()\narr = [[] for _ in range(N+M+1)]\nif len(know)<=1:\n for _ in range(M): input()\n print(M)\nelse:\n for i in range(1, M+1):\n tmp = list(map(int, input().split()))\n for j in range(1, tmp[0]+1):\n arr[tmp[j]].append(N+i)\n arr[N+i].append(tmp[j])\n solution(N, M, arr, know[1:])", "sub_path": "Graph/[1043]거짓말/[1043]거짓말.py", "file_name": "[1043]거짓말.py", "file_ext": "py", "file_size_in_byte": 709, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.stdin", "line_number": 4, "usage_type": "attribute"}, {"api_name": "collections.deque", "line_number": 7, "usage_type": "call"}]} +{"seq_id": "232659716", "text": "import pickle\nimport string\nimport random\nimport numpy as np\nimport bisect\nimport time\nimport multiprocessing as mp\nimport os\nimport threading\n\n\n# os.system(\"taskset -p 0xff %d\" % os.getpid())\n\n\n\n# class Test:\n# def __init__(self):\n# self.name = 'test'\n# self.id = random.random()\n#\n# dic = Test()\n# with open('test.pickle', 'wb') as f:\n# pickle.dump(dic, f, protocol=pickle.HIGHEST_PROTOCOL)\n#\n# k = open('test.pickle', 'rb')\n# d = pickle.load(k)\n\n# dic = {}\n# with open('../data/word2vec_c', 'r') as f:\n# while True:\n# line = f.readline()\n# if line == '':\n# break\n# ll = line[:-1].split(' ')\n# dic[ll[0]] = np.array(ll[1:]).astype(np.float)\n#\n# with open('../data/w2v.pickle', 'wb') as f:\n# pickle.dump(dic, f)\n\n\n\n\n\nclass My_thread (mp.Process):\n\n def __init__(self, word):\n mp.Process.__init__(self)\n self.word = word\n\n def run(self):\n print(\"Starting {}\".format(self.word))\n output(self.word)\n print(\"Exiting {} \\n \\n\".format(self.word))\n\n\ndef similar_words(word, length):\n tic2 = time.clock()\n with open('../../../data/word2vec/w2v.pickle', 'rb') as f:\n dic = pickle.load(f)\n toc2 = time.clock()\n tot2 = toc2 - tic2\n word = word\n vec = dic[word]\n\n\n\n tot1 = 0\n # tot2 = 0\n minimum = []\n\n for key in dic.keys():\n\n value = dic[key]\n\n if np.shape(value) == np.shape(vec):\n # print(np.dot(dic[key], vec))\n tic = time.clock()\n diff = np.linalg.norm(dic[key] - vec)\n # diff = np.dot(dic[key], vec)\n toc = time.clock()\n tot1 += toc - tic\n if len(minimum) < length:\n minimum.append((diff, key))\n minimum.sort()\n\n else:\n bisect.insort(minimum, (diff, key))\n\n minimum = minimum[:-1]\n\n # print(\"Step1 time for {}: {}\".format(word, tot1))\n # print(\"Step2 time for {}: {} \\n \\n\".format(word, tot2))\n return minimum\n\n\ndef output(word):\n ttic = time.clock()\n print(\"Starting word: {} \\n\".format(word))\n words = similar_words(word, 10)\n print(\"Input word: {} \\n\".format(word))\n for item in words:\n print(\"Word: {} Diff: {}\".format(item[1], item[0]))\n ttoc = time.clock()\n print(\"Total time for {}: {} \\n \\n \".format(word, ttoc - ttic))\n return 0\n\n\n\n# def output1():\n# for i in range(100000000000):\n# j = i**2\n# return 1\n#\n#\n# def output2():\n# for i in range(100000000000):\n# j = i + 2\n# return 2\n\n\nif __name__ == '__main__':\n\n # out = mp.Queue()\n # q = mp.Queue()\n # p1 = mp.Process(target=output1())\n # p2 = mp.Process(target=output2())\n # words = [\"大佬\", \"美女\", \"童年\", \"学习\", \"异常\", \"刺激\", \"色情\", \"抽烟\", \"完美\", ]\n words = [\"财经\", \"金融\", \"股票\", \"证券\", \"异常\", \"刺激\", \"色情\", \"抽烟\", \"完美\", ]\n process_list = []\n for w in words[:6]:\n process_list.append(mp.Process(target=output, args=(w,)))\n\n for p in process_list:\n p.start()\n for p in process_list:\n p.join()\n\n print(\"Exiting main thread \\n\")\n # print(q.get())\n\n\n", "sub_path": "src/scripts/nlp/word2vec/w2v_test.py", "file_name": "w2v_test.py", "file_ext": "py", "file_size_in_byte": 3203, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "multiprocessing.Process", "line_number": 44, "usage_type": "attribute"}, {"api_name": "multiprocessing.Process.__init__", "line_number": 47, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 47, "usage_type": "attribute"}, {"api_name": "time.clock", "line_number": 57, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 59, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 60, "usage_type": "call"}, {"api_name": "numpy.shape", "line_number": 75, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.linalg.norm", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.linalg", "line_number": 78, "usage_type": "attribute"}, {"api_name": "time.clock", "line_number": 80, "usage_type": "call"}, {"api_name": "bisect.insort", "line_number": 87, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 97, "usage_type": "call"}, {"api_name": "time.clock", "line_number": 103, "usage_type": "call"}, {"api_name": "multiprocessing.Process", "line_number": 131, "usage_type": "call"}]} +{"seq_id": "490331157", "text": "from scrapy.spiders import CrawlSpider, Rule, BaseSpider, Spider\nfrom scrapy.linkextractors.lxmlhtml import LxmlLinkExtractor\nfrom scrapy.selector import Selector\nfrom scrapy.http import HtmlResponse\n\nfrom courses.items import Course\n\n\nclass EduSpider(CrawlSpider):\n name = 'bu.edu'\n allowed_domains = ['bu.edu']\n start_urls = ['http://www.bu.edu/academics/']\n\n rules = (\n Rule(LxmlLinkExtractor(\n allow=('.*/academics/[a-z][a-z][a-z]/courses/[a-z][a-z][a-z]-[a-z][a-z]-[0-9][0-9][0-9]/', ),\n ), callback='parse_item'),\n\n Rule(LxmlLinkExtractor(\n allow=('.*/academics/[a-z][a-z][a-z]/', '.*/academics/[a-z][a-z][a-z]/courses/.*'),\n )),\n )\n\n def parse_item(self, response):\n item = Course()\n item[\"institute\"] = 'Boston University'\n item['site'] = 'www.bu.edu'\n item['title'] = response.xpath('//*[@id=\"col1\"]/div/h1/text()').extract()[0]\n item['id'] = response.xpath('//*[@id=\"col1\"]/div/h2/text()').extract()[0]\n item['credits'] = response.xpath('//*[@id=\"info-box\"]/dl/dd[1]/text()').extract()[0]\n item['description'] = response.xpath('//*[@id=\"course-content\"]/p[1]/text()').extract()[0]\n yield item\n", "sub_path": "courses/spiders/bu.py", "file_name": "bu.py", "file_ext": "py", "file_size_in_byte": 1228, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "scrapy.spiders.CrawlSpider", "line_number": 9, "usage_type": "name"}, {"api_name": "scrapy.spiders.Rule", "line_number": 15, "usage_type": "call"}, {"api_name": "scrapy.linkextractors.lxmlhtml.LxmlLinkExtractor", "line_number": 15, "usage_type": "call"}, {"api_name": "scrapy.spiders.Rule", "line_number": 19, "usage_type": "call"}, {"api_name": "scrapy.linkextractors.lxmlhtml.LxmlLinkExtractor", "line_number": 19, "usage_type": "call"}, {"api_name": "courses.items.Course", "line_number": 25, "usage_type": "call"}]} +{"seq_id": "100486813", "text": "# -*- coding: utf-8 -*-\n# /***************************************************************************/\n# * __________________________________\n# * METIS CYBERSPACE TECHNOLOGY S.A.\n# * www.metis.tech\n# * __________________________________\n# * [2019] All Rights Reserved.\n# *\n# * NOTICE: All information contained herein is, and remains\n# * the property of Metis CyberSpace Technology and its suppliers,\n# * if any. The intellectual and technical concepts contained\n# * herein are proprietary to METIS CYBERSPACE TECHNOLOGY\n# * and its suppliers and may be covered by European and Foreign Patents,\n# * patents in process, and are protected by trade secret or copyright law.\n# * Dissemination of this information or reproduction of this material\n# * is strictly forbidden unless prior written permission is obtained\n# * from Metis Cyberspace Technology.\n#\n# /***************************************************************************/\n#\n# Created Date: Monday March 18th 2019\n# Author: Vassilis Lemonidis\n\"\"\"Module holding :class:`TokenGetter` used to retrieve authentication service token\n\"\"\"\nimport os\nfrom requests import exceptions\nfrom metis_pylib.security.Authorization import Authorization\nfrom metis_pylib import PROJECT_CONFIG, LOGGER\n\nclass TokenGetter:\n \"\"\"Class used to retrieve service token\n \"\"\"\n\n def __init__(self):\n self.authorizer = Authorization()\n self._credentials = {'auth_url': PROJECT_CONFIG['AUTH_URL'],\n 'client_id': PROJECT_CONFIG['CLIENT_ID'],\n 'client_secret': PROJECT_CONFIG['CLIENT_SECRET']}\n\n def get_token(self):\n try:\n return self.authorizer.getToken(self._credentials)\n except exceptions.ConnectionError:\n if os.environ['PY_DEPLOYMENT'].startswith('local_'):\n LOGGER.warning('Cound not contact authentication server,'\n ' will raise if error persists in other deployments')\n else:\n raise\n return None", "sub_path": "Metis/file-parser/Model/Authorization/authorization.py", "file_name": "authorization.py", "file_ext": "py", "file_size_in_byte": 2034, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "metis_pylib.security.Authorization.Authorization", "line_number": 35, "usage_type": "call"}, {"api_name": "metis_pylib.PROJECT_CONFIG", "line_number": 36, "usage_type": "name"}, {"api_name": "metis_pylib.PROJECT_CONFIG", "line_number": 37, "usage_type": "name"}, {"api_name": "metis_pylib.PROJECT_CONFIG", "line_number": 38, "usage_type": "name"}, {"api_name": "requests.exceptions.ConnectionError", "line_number": 43, "usage_type": "attribute"}, {"api_name": "requests.exceptions", "line_number": 43, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 44, "usage_type": "attribute"}, {"api_name": "metis_pylib.LOGGER.warning", "line_number": 45, "usage_type": "call"}, {"api_name": "metis_pylib.LOGGER", "line_number": 45, "usage_type": "name"}]} +{"seq_id": "391325754", "text": "from setuptools import setup\n\n__VERSION__ = \"1.0\"\n\nsetup(\n name=\"visdom-pooled\",\n version=__VERSION__,\n description=\"Slightly More Efficient Visdom Wrapper\",\n url=\"https://github.com/kaniblu/visdom-pooled\",\n author=\"Kang Min Yoo\",\n author_email=\"k@nib.lu\",\n license=\"MIT\",\n classifiers=[\n \"Development Status :: 5 - Production/Stable\",\n \"Intended Audience :: Developers\",\n \"Programming Language :: Python :: 3\"\n ],\n keywords=\"visdom deeplearning visualization pooled\",\n packages=[\"visdom_pooled\"],\n install_requires=[\n \"visdom\",\n \"numpy\"\n ]\n)\n", "sub_path": "pypi_install_script/visdom-pooled-1.0.tar/setup.py", "file_name": "setup.py", "file_ext": "py", "file_size_in_byte": 617, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "setuptools.setup", "line_number": 5, "usage_type": "call"}]} +{"seq_id": "382862195", "text": "import PySimpleGUI as sg\n\n\nclass AbstractAddEditPessoa:\n def __init__(self, nome_tela: str, texto_entradas: list, texto_botao: str):\n self.__nome_tela = nome_tela\n self.__texto_botao = texto_botao\n self.__texto_entradas = texto_entradas\n self.__janela = None\n\n def configura(self, texto_entradas, texto_botao):\n sg.ChangeLookAndFeel(\"Reddit\")\n\n layout = [\n [sg.Text('Por favor, informe os dados necessários.')],\n ]\n for i in range(len(texto_entradas)):\n layout.append([sg.Text(texto_entradas[i], size=(15, 1)), sg.InputText()])\n\n layout.append([sg.Button(texto_botao, size=(200, 4),\n button_color=('#000', '#5CBEFF'),\n font=('Helvetica', 14))])\n\n self.__janela = sg.Window(self.__nome_tela, layout, size=(500, 300),\n element_justification=\"center\")\n\n def mostra_opcoes(self):\n self.configura(self.__texto_entradas, self.__texto_botao)\n botao, dict_valores = self.__janela.Read()\n self.__janela.Close()\n nome, cpf, telefone, email = dict_valores.values()\n\n return nome, cpf, telefone, email\n", "sub_path": "limite/abstract_add_edit_pessoa.py", "file_name": "abstract_add_edit_pessoa.py", "file_ext": "py", "file_size_in_byte": 1241, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "PySimpleGUI.ChangeLookAndFeel", "line_number": 12, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 15, "usage_type": "call"}, {"api_name": "PySimpleGUI.Text", "line_number": 18, "usage_type": "call"}, {"api_name": "PySimpleGUI.InputText", "line_number": 18, "usage_type": "call"}, {"api_name": "PySimpleGUI.Button", "line_number": 20, "usage_type": "call"}, {"api_name": "PySimpleGUI.Window", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "464180247", "text": "from django import forms\nfrom django.utils.timezone import datetime\n\nfrom apps.focus.fields import ConceptTagWidget, ConceptTagField\nfrom apps.datasets.fields import PublisherSelectWidget, CatalogRecordSelectMultipleWidget\nfrom .models import Story\n\n\nclass SuggestStoryForm(forms.ModelForm):\n tags = ConceptTagField()\n\n class Meta:\n model = Story\n fields = ['title', 'subheader', 'author', 'organization', 'datasets', 'datasource_urls',\n 'body', 'featured_image', 'featured_image_caption', 'tags']\n widgets = {\n 'tags': ConceptTagWidget,\n 'organization': PublisherSelectWidget,\n 'datasets': CatalogRecordSelectMultipleWidget,\n }\n\n def __init__(self, posted_by, **kwargs):\n self.posted_by = posted_by\n super(SuggestStoryForm, self).__init__(**kwargs)\n\n def save(self):\n instance = super(SuggestStoryForm, self).save(commit=False)\n instance.posted_by = self.posted_by\n instance.save()\n tags = self['tags'].data\n concepts = self.fields['tags'].concepts_from_tags(tags)\n instance.tags.add(*tags)\n instance.concepts.add(*concepts)\n if self.cleaned_data['datasets']:\n instance.datasets.add(*self.cleaned_data['datasets'])\n return instance\n\n\nclass PublishStoryForm(forms.ModelForm):\n class Meta:\n model = Story\n fields = ['title', 'subheader', 'organization', 'datasets',\n 'tags', 'concepts', 'body', 'featured_image']\n\n def __init__(self, approved_by, **kwargs):\n self.approved_by = approved_by\n super(PublishStoryForm, self).__init__(**kwargs)\n\n def save(self):\n instance = super(PublishStoryForm, self).save(commit=False)\n instance.approved_by = self.approved_by\n instance.publish()\n instance.save()\n return instance\n", "sub_path": "apps/stories/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 1868, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.forms.ModelForm", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 9, "usage_type": "name"}, {"api_name": "apps.focus.fields.ConceptTagField", "line_number": 10, "usage_type": "call"}, {"api_name": "models.Story", "line_number": 13, "usage_type": "name"}, {"api_name": "apps.focus.fields.ConceptTagWidget", "line_number": 17, "usage_type": "name"}, {"api_name": "apps.datasets.fields.PublisherSelectWidget", "line_number": 18, "usage_type": "name"}, {"api_name": "apps.datasets.fields.CatalogRecordSelectMultipleWidget", "line_number": 19, "usage_type": "name"}, {"api_name": "django.forms.ModelForm", "line_number": 39, "usage_type": "attribute"}, {"api_name": "django.forms", "line_number": 39, "usage_type": "name"}, {"api_name": "models.Story", "line_number": 41, "usage_type": "name"}]} +{"seq_id": "506641006", "text": "from __future__ import division\nfrom __future__ import print_function\nimport argparse\nimport os\nimport natsort\nimport numpy as np\nimport cv2\nfrom pathlib import Path\nimport torch\nfrom torch.utils import data\nimport random\n\nclass StaticRandomCrop(object):\n \"\"\"\n Helper function for random spatial crop\n \"\"\"\n def __init__(self, size, image_shape):\n h, w = image_shape\n self.th, self.tw = size\n self.h1 = torch.randint(0, h - self.th + 1, (1,)).item()\n self.w1 = torch.randint(0, w - self.tw + 1, (1,)).item()\n\n def __call__(self, img):\n return img[self.h1:(self.h1 + self.th), self.w1:(self.w1 + self.tw), :]\n\ndef trainval_split(data_dir):\n \"\"\"\n get train/valid filenames\n \"\"\"\n\n train_file_names = []\n val_file_names = []\n full_train=[]\n\n for idx in range(1, 9):\n # sort according to filename\n filenames = (Path(data_dir) / ('instrument_dataset_' + str(idx)) / 'images').glob('*')\n filenames = list(sorted(filenames))\n # file_tmp = []\n # for i in range(len(filenames)):\n # if i % 10 == 0:\n # file_tmp.append(filenames[i])\n # set folds[fold] as validation set\n if idx in [1]:\n val_file_names += filenames\n else:\n train_file_names += filenames\n full_train += filenames\n\n return train_file_names, val_file_names\n\nclass FrameLoader(data.Dataset):\n def __init__(self, args, filename, is_training = False, transform=None, back_propagation = False):\n\n self.is_training = is_training\n self.transform = transform\n self.chsize = 3\n\n # carry over command line arguments\n assert args.sequence_length > 1, 'sequence length must be > 1'\n self.sequence_length = args.sequence_length\n\n assert args.sample_rate > 0, 'sample rate must be > 0'\n self.sample_rate = args.sample_rate\n\n self.crop_size = args.crop_size\n self.start_index = args.start_index\n self.stride = args.stride\n\n if self.is_training:\n self.start_index = 0\n\n # collect, colors, motion vectors, and depth\n self.filename = filename\n self.back_propagation = back_propagation\n\n\n def __len__(self):\n return len(self.filename)\n\n def __getitem__(self, index):\n # adjust index\n if self.is_training:\n if not self.back_propagation:\n for i in range(self.sequence_length):\n if (index + i + 1) % 225 ==0:\n index = index - self.sequence_length\n input_files = [str(self.filename[index + offset]) for offset in range(self.sequence_length + 1)]\n else:\n for i in range(self.sequence_length):\n if (index - i) % 225 == 0:\n index = index + self.sequence_length\n input_files = [str(self.filename[index - offset]) for offset in range(self.sequence_length + 1)]\n else:\n input_files = [str(self.filename[index + offset]) for offset in range(self.sequence_length + 1)]\n\n # reverse image order with p=0.5\n if self.is_training and torch.randint(0, 2, (1,)).item():\n input_files = input_files[::-1]\n\n # images = [imageio.imread(imfile)[..., :self.chsize] for imfile in input_files]\n images = [cv2.imread(str(imfile))[..., :self.chsize] for imfile in input_files]\n input_shape = images[0].shape[:2]\n\n\n if self.is_training:\n imgs = []\n for img in images:\n im = cv2.resize(img, (640, 512))\n imgs.append(im)\n cropper = StaticRandomCrop(self.crop_size, (512, 640))\n images = map(cropper, imgs)\n\n # Pad images along height and width to fit them evenly into models.\n height, width = 512, 640\n if (height % self.stride) != 0:\n padded_height = (height // self.stride + 1) * self.stride\n images = [np.pad(im, ((0, padded_height - height), (0, 0), (0, 0)), 'reflect') for im in images]\n\n if (width % self.stride) != 0:\n padded_width = (width // self.stride + 1) * self.stride\n images = [np.pad(im, ((0, 0), (0, padded_width - width), (0, 0)), 'reflect') for im in images]\n\n else:\n height, width = input_shape\n if (height % self.stride) != 0:\n padded_height = (height // self.stride + 1) * self.stride\n images = [np.pad(im, ((0, padded_height - height), (0, 0), (0, 0)), 'reflect') for im in images]\n\n if (width % self.stride) != 0:\n padded_width = (width // self.stride + 1) * self.stride\n images = [np.pad(im, ((0, 0), (0, padded_width - width), (0, 0)), 'reflect') for im in images]\n\n\n input_images = [torch.from_numpy(im.transpose(2, 0, 1)).float() for im in images]\n\n output_dict = {\n 'image': input_images, 'ishape': input_shape, 'input_files': input_files\n }\n\n return output_dict\n\n\n\n\nif __name__ == '__main__':\n root = '../../../data/cropped_train'\n parser = argparse.ArgumentParser(description='A PyTorch Implementation of SDCNet2D')\n parser.add_argument('--sequence_length', default=2, type=int, metavar=\"SEQUENCE_LENGTH\",\n help='number of interpolated frames (default : 7)')\n parser.add_argument(\"--sample_rate\", type=int, default=1, help=\"step size in looping through datasets\")\n parser.add_argument('--crop_size', type=int, nargs='+', default=[448, 448], metavar=\"CROP_SIZE\",\n help=\"Spatial dimension to crop training samples for training (default : [448, 448])\")\n parser.add_argument(\"--start_index\", type=int, default=0, metavar=\"START_INDEX\",\n help=\"Index to start loading input data (default : 0)\")\n parser.add_argument('--stride', default=64, type=int,\n help='The factor for which padded validation image sizes should be evenly divisible. (default: 64)')\n train_filenames, valid_filenames =trainval_split(root)\n args = parser.parse_args()\n train_dataset = FrameLoader(args, filename=train_filenames, is_training=True)\n print(train_dataset[0]['image'])", "sub_path": "motion_learning/datasets/frame_loader2.py", "file_name": "frame_loader2.py", "file_ext": "py", "file_size_in_byte": 6287, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.randint", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.randint", "line_number": 21, "usage_type": "call"}, {"api_name": "pathlib.Path", "line_number": 37, "usage_type": "call"}, {"api_name": "torch.utils.data.Dataset", "line_number": 52, "usage_type": "attribute"}, {"api_name": "torch.utils.data", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.randint", "line_number": 98, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.resize", "line_number": 109, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 128, "usage_type": "call"}, {"api_name": "numpy.pad", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.from_numpy", "line_number": 135, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 148, "usage_type": "call"}]} +{"seq_id": "546422626", "text": "# -*- coding:Utf-8 *-\nimport os.path\nimport json\nfrom collections import namedtuple\nimport sys, os\nsys.path.insert(0, os.path.abspath('../'))\nfrom Model.door import Door\nfrom Model.room import Room\nfrom Model.floor import Floor\nfrom Enum.direction import Direction\n\n# -------------- CREATE FLOOR -------------- #\ndef create_floor_from_json(filename):\n\n file = open_file(filename)\n json_file = parse_file_to_json(file)\n\n floor = Floor(json_file.id, json_file.name)\n for room_json in json_file.rooms:\n floor.add_room(create_room(room_json))\n\n floor.initialize_start_room()\n file.close()\n\n return floor\n\ndef open_file(filename):\n current_path = os.path.abspath(os.path.dirname(__file__))\n path_of_json = os.path.join(current_path, \"../resources/{0}\".format(filename))\n file = open(path_of_json, \"r\")\n return file\n\ndef parse_file_to_json(file):\n content = file.read()\n # Parse JSON into an object with attributes corresponding to dict keys.\n json_file = json.loads(content, object_hook=lambda d: namedtuple('X', d.keys())(*d.values()))\n return json_file\n\ndef create_room(room_json):\n room = Room(room_json.id)\n if room_json.isStart == True:\n room.set_isStart()\n\n if room_json.isEnd == True:\n room.set_isEnd()\n\n for door_json in room_json.doors:\n room.add_door(create_door(door_json))\n\n return room\n\ndef create_door(json_door):\n door = Door(json_door.idNextRoom, get_direction(json_door.direction))\n return door\n\n\ndef get_direction(json_direction):\n if json_direction == Direction.NORTH.value:\n return Direction.NORTH\n elif json_direction == Direction.EAST.value:\n return Direction.EAST\n elif json_direction == Direction.SOUTH.value:\n return Direction.SOUTH\n else :\n return Direction.WEST\n\n\n# -------------- WALK IN FLOOR -------------- #\ndef display_current_room(floor):\n floor.display_current_room()\n\ndef check_door_exist_in_current_room(floor, direction):\n current_room = floor.currentRoom\n for door in current_room.doors:\n if door.direction.value == direction :\n return True\n return False\n\ndef move_to_another_room(floor, direction):\n current_room = floor.currentRoom\n door = list(filter(lambda door: door.direction.value == direction, current_room.doors))[0]\n floor.change_room(door.idRoom)\n", "sub_path": "process/floorProcess.py", "file_name": "floorProcess.py", "file_ext": "py", "file_size_in_byte": 2368, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.path.insert", "line_number": 6, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 6, "usage_type": "call"}, {"api_name": "os.path", "line_number": 6, "usage_type": "attribute"}, {"api_name": "Model.floor.Floor", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 29, "usage_type": "call"}, {"api_name": "os.path", "line_number": 29, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 36, "usage_type": "call"}, {"api_name": "collections.namedtuple", "line_number": 36, "usage_type": "call"}, {"api_name": "Model.room.Room", "line_number": 40, "usage_type": "call"}, {"api_name": "Model.door.Door", "line_number": 53, "usage_type": "call"}, {"api_name": "Enum.direction.Direction.NORTH", "line_number": 58, "usage_type": "attribute"}, {"api_name": "Enum.direction.Direction", "line_number": 58, "usage_type": "name"}, {"api_name": "Enum.direction.Direction.NORTH", "line_number": 59, "usage_type": "attribute"}, {"api_name": "Enum.direction.Direction", "line_number": 59, "usage_type": "name"}, {"api_name": "Enum.direction.Direction.EAST", "line_number": 60, "usage_type": "attribute"}, {"api_name": "Enum.direction.Direction", "line_number": 60, "usage_type": "name"}, {"api_name": "Enum.direction.Direction.EAST", "line_number": 61, "usage_type": "attribute"}, {"api_name": "Enum.direction.Direction", "line_number": 61, "usage_type": "name"}, {"api_name": "Enum.direction.Direction.SOUTH", "line_number": 62, "usage_type": "attribute"}, {"api_name": "Enum.direction.Direction", "line_number": 62, "usage_type": "name"}, {"api_name": "Enum.direction.Direction.SOUTH", "line_number": 63, "usage_type": "attribute"}, {"api_name": "Enum.direction.Direction", "line_number": 63, "usage_type": "name"}, {"api_name": "Enum.direction.Direction.WEST", "line_number": 65, "usage_type": "attribute"}, {"api_name": "Enum.direction.Direction", "line_number": 65, "usage_type": "name"}]} +{"seq_id": "226231456", "text": "import requests\nimport os, re\nfrom bs4 import BeautifulSoup\nimport pandas as pd\nimport numpy as np\nfrom numpy import random as rnd\nimport time\nimport pickle as pkl\nimport re\nimport json\n\n\"\"\"\n##############################################################################################################################\nINPUT:\n 1. biz_reviews_collection.json --collection of reviews\nOUTPUT:\n 1. pickled_user_data.pkl -- scraped data that's saved along the way\n 2. SanDiego_users.csv -- if successful then scraped data is saved into this csv file.\n 3. count.txt -- keeps track of the number of pages processed. \n##############################################################################################################################\nDESCRIPTION:\n Read in user IDs from the reviews, construct URLS, and gather data on each user. \n##############################################################################################################################\n\"\"\"\n\n\n#################################### 0. SETUP ##################################################\ndata_path=\"c:/users/gene/documents//duke/dropbox/gene/yelp_scrapping\"\nos.chdir(data_path)\n\n################################################################################################\n##################################### 1. FUNCTIONS ############################################# \n\n############################### I. Scraping Functions ###############################\ndef fetch_website(url):\n \"\"\"\n To hide that the scraping is being done via Python, I change the user-agent. The numerous user-agents\n included herein are simply the ones most commonly used at the time of scraping. Having multiple agents and\n having ones picked randomly did not help. Yelp still occasionally returned fake websites. \n \"\"\"\n user_agent_list=[\n 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_10_4) AppleWebKit/600.7.12 (KHTML, like Gecko) Version/8.0.7 Safari/600.7.12',\n 'Mozilla/5.0 (Windows NT 6.1; WOW64; rv:39.0) Gecko/20100101 Firefox/39.0',\n 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36',\n 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.125 Safari/537.36',\n 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.130 Safari/537.36',\n 'Mozilla/5.0 (Windows NT 6.3; WOW64; rv:39.0) Gecko/20100101 Firefox/39.0',\n 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10.10; rv:39.0) Gecko/20100101 Firefox/39.0',\n 'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/43.0.2357.134 Safari/537.36',\n 'Mozilla/5.0 (Windows NT 6.1; WOW64; Trident/7.0; rv:11.0) like Gecko',\n 'Mozilla/5.0 (Windows NT 6.1; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/44.0.2403.107 Safari/537.36',\n 'Mozilla/5.0 (Windows NT 6.3; WOW64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/45.0.2454.85 Safari/537.36']\n user_agent=give_rndm_userAgent(user_agent_list)\n print(user_agent['User-agent'])\n r=requests.get(url, headers=user_agent)\n try:\n print(\"Accessed and downloaded URL data\")\n return(r.content)\n except ConnectionError:\n print(\"Incurred the infamous connection error\")\n print(\"Skipping this url\")\n return(\"Skip\")\n\ndef fetch_user_reviews(user_id, num_reviews,base_url=\"http://www.yelp.com/user_details_reviews_self?rec_pagestart=0&userid=\"):\n ##NOTE: some users will probably have hundreds or thousands of reviews. For now, we restrict to collecting at most 50 reviews\n soup=BeautifulSoup(fetch_website(base_url+user_id))\n Master_reviews_list=soup.findAll(\"div\", class_='review')\n \n if (num_reviews>50):\n print(\"Seriously prolific user with [%d] reviews\" %num_reviews)\n if (num_reviews>9) & (num_reviews<200):\n review_links=[x.attrs['href'] for x in soup.findAll('a', class_='page-option available-number')]\n rev_soup_list=[]\n for url in review_links:\n wait()\n rev_soup_list.append(BeautifulSoup(fetch_website(url)))\n list_reviewsList=[rev_soup.findAll(\"div\", class_='review') for rev_soup in rev_soup_list] \n for reviewsList in list_reviewsList:\n Master_reviews_list.extend(reviewsList) \n print(\"# of reviews: %d\\n# reviews got: %d\" %(num_reviews, len(Master_reviews_list))) \n return(Master_reviews_list) \n \n \ndef fetch_user_friends(user_id, num_friends,base_url=\"http://www.yelp.com/user_details_friends?userid=\"):\n #j=0\n friend_set=[]\n friend_list=[]\n wait()\n soup=BeautifulSoup(fetch_website(base_url+user_id))\n master_user_info_list=soup.findAll(\"ul\", class_=\"user-passport-info\")\n master_user_stats_list=soup.findAll('ul', class_='user-passport-stats')\n if len(soup.findAll('a', class_='page-option available-number'))>0:\n friends_links=[x.attrs['href'] for x in soup.findAll('a', class_='page-option available-number')]\n friends_soup_list=[BeautifulSoup(fetch_website(url)) for url in friends_links]\n list_friendsList=[friend_soup.findAll(\"ul\", class_=\"user-passport-info\") for friend_soup in friends_soup_list] \n for friendsList in list_friendsList:\n master_user_info_list.extend(friendsList)\n \n for user_info in master_user_info_list:\n id_link=user_info.find('a').attrs['href']\n friend_list.append(re.search('userid=(\\S+)', id_link).group(1))\n friend_set=list(set(friend_list)) \n print('# of friends: %d\\n# friends found: %d' %(num_friends, len(friend_set))) \n \n return(friend_set)\n\n###################################################################################### \n########################## II. Processing Functions ################################## \ndef extract_data(response, url):\n \"\"\"INPUT: response -- the data given by response.content() from Requests module.\n OUTPUT: 1. data_dict -- the data dictionary of desired data.\n 2. appended reviews file. See [append_reviews_txt()]\n EXTERNAL function\"\"\"\n data_dict={}\n data_dict['url']=[url]\n soup=BeautifulSoup(response)\n user_id=re.search('userid=(\\S+)',url).group(1)\n ##FUNCTION START\n #Corrupted website?\n check1=soup.find(\"li\", class_='miniOrange')\n if check1 is not None:\n print(\"\\n!!!!!Yelp gave a corrupted website!!!!!!\")\n return(\"Bad soup\")\n \n data_dict['user_id']=user_id\n #User location:\n if soup.find('h3', class_='user-location alternate') is not None:\n data_dict['Location']=soup.find('h3', class_='user-location alternate').getText()\n if soup.find('h3', class_='user-location alternate') is None:\n data_dict['Location']=np.nan\n \n #Friend and Review Count:\n for grab in ['friend-count', 'review-count']:\n if soup.find('li', class_=grab) is not None:\n data_dict[grab]=int(re.search('\\d+',str.strip(soup.find('li', class_=grab).getText())).group())\n if soup.find('li', class_=grab) is None:\n data_dict[grab]=np.nan\n \n #Elite Status:\n data_dict['elite_num']=len(soup.findAll('span', class_='elite-badge'))\n \n #Bizs Reviewed:\n if data_dict['review-count']>1:\n Master_reviews_list=fetch_user_reviews(user_id, data_dict['review-count'])\n rvwd_biz_url_list=[review_data.find('a', class_='biz-name').attrs['href'] for review_data in Master_reviews_list]\n rvwd_biz_date_list=[str.strip(review_data.find('span', class_='rating-qualifier').getText()) for review_data in Master_reviews_list]\n data_dict['bizReviewed']=rvwd_biz_url_list\n data_dict['bizRvwDate']=rvwd_biz_date_list\n if data_dict['review-count']==1:\n data_dict['bizReviews']=np.nan\n data_dict['date_rvwd']=np.nan\n #Biz Reviewed Ratings\n \"\"\"\n rvwd_biz_rating_list=[]\n for review_data in Master_reviews_list:\n rvwd_biz_rating_list.append(int(re.search('(\\d+)',review_data.find('i', class_='star-img').attrs['title']).group(1)))\n data_dict['given_stars']=rvwd_biz_rating_list\n \"\"\"\n \n #Friends\n if data_dict['friend-count']>0:\n friend_list=fetch_user_friends(user_id, num_friends=data_dict['friend-count'])\n data_dict['friendIDs']=friend_list\n if data_dict['friend-count']==0:\n data_dict['friend-count']=0\n \n ##FUNCTION END \n return(data_dict)\n \n\n######################################################################################## \n############################ III. Convenience Functions ################################ \ndef make_json(file_name='yelp_user_data.json'):\n \"\"\"file_name -- the file where scraped reviews will be saved.\n EXTERNAL function. \"\"\"\n if os.path.isfile(file_name)==False:\n with open(file_name, 'w') as f:\n json.dump('[', f)\n \ndef make_update_df(data_dict, file_name=\"SanDiego_biz_addendum.csv\"):\n empty_data_dict={}\n for key in data_dict.keys():\n empty_data_dict[key]=[]\n pd.DataFrame(empty_data_dict).to_csv(file_name, index=False) \n \ndef Pickle(data,file_name='business_addendum.pkl'):\n with open(file_name, 'wb') as f:\n pkl.dump(data, f)\n print(\"Downloaded JSON data pickled to [%s]\" %file_name) \n \ndef eat_pickle(file_name='business_addendum.pkl'):\n with open(file_name, 'rb') as f:\n return(pkl.load(f)) \n\ndef write_count(count, file_name=\"count.txt\", start_count='0' ):\n if os.path.isfile(file_name) == False:\n print(\"Creating new count file: [%s]\" %file_name)\n with open(file_name, 'w') as f:\n f.write(start_count)\n \n if os.path.isfile(file_name):\n with open(file_name, 'w') as the_file:\n the_file.write(str(count))\n \ndef read_count(file_name='count.txt'):\n with open(file_name) as f:\n count=f.readline()\n return(int(count)) \n \ndef counter_reset():\n answer='the cake is a lie!'\n while (answer!='Y') & (answer!='N'):\n answer=input(\"Would you like to reset the counter to 0? [Y/N]: \")\n if answer == \"Y\": \n write_count(0)\n print(\"Counter reset\") \n\ndef step_display(i):\n if i%50==0:\n print(\"On number: %s\" %i)\n\ndef wait():\n wait_time=int(rnd.uniform(low=1,high=5))\n print(\"\\nPausing for: %d seconds...\" %wait_time)\n time.sleep(wait_time)\n #print('seconds: [%d%%]\\r' %seconds_elapsed)\n print(\"--\"*20) \n\ndef read_json_as_text(reviews_file='biz_reviews_collection.json'): \n with open(reviews_file) as f:\n user_data=f.read()\n \n try:\n data_dict=json.loads(user_data)\n except Exception:\n user_data2=re.sub(\"}{\",\"},{\" ,user_data)\n data_dict=json.loads(user_data2)\n return(data_dict) \n \"\"\"\n with open( 'biz_reviews_collection_fixed.json', 'w') as f:\n f.write(user_data2)\n reviews_file='biz_reviews_collection_fixed.json'\n with open(reviews_file, 'r').read() as f:\n user_data=json.loads(f) \n \"\"\"\n \ndef extract_user_ids(reviews_list):\n users_list=[]\n for i in range(0,len(reviews_list)):\n reviews_dict=reviews_list[i]\n entries_list=reviews_dict[list(reviews_dict.keys())[0]]\n if len(entries_list)>0:\n reviews_id_list=[re.sub(\"^user_id:\", \"\",review['id']) for review in entries_list]\n users_list.extend(reviews_id_list)\n print(\"Total # of entries found: [%d] \" %len(users_list))\n unique_users_list=list(set(users_list))\n print(\"\\nTotal # of unique users found: [%d] \" %len(unique_users_list))\n return(unique_users_list) \n \ndef construct_user_urls(unique_users_list, base_url='http://www.yelp.com/user_details?userid='):\n users_url_list=[base_url+user_id for user_id in unique_users_list]\n return(users_url_list)\n \ndef load_yelp_user_urls(count ,file_name='yelp_users_url.pkl'): \n if os.path.isfile(file_name)==True:\n print(\"Loading data...\")\n with open(file_name, 'rb') as f:\n users_url_list=pkl.load(f)[count:]\n\n if os.path.isfile(file_name)==False:\n #if the pickle doesn't exist yet, then make one\n print(\"\\nCouldn't find Pickled User URLs so Loading Original JSON Data\")\n reviews_list=read_json_as_text()\n unique_users_list=extract_user_ids(reviews_list)\n users_url_list=construct_user_urls(unique_users_list)\n with open(file_name, 'wb') as f:\n pkl.dump(users_url_list, f)\n print(\"\\nPickled Yelp User URLs to [%s]\" %file_name) \n print(\"Data loaded\")\n print(\"--\"*20)\n return(users_url_list) \n \ndef add_bad_soup(url, file_name='bad_soup_urls.txt'):\n if os.path.isfile(file_name):\n with open(file_name, 'a') as f:\n f.write(','+url)\n if os.path.isfile(file_name)==False:\n with open(file_name, 'w') as f:\n f.write(url)\n print(\"\\nBad soup's URL recorded in: [%s]\" %file_name) \n \ndef give_rndm_userAgent(user_agent_list):\n rnd_agent=np.random.choice(user_agent_list,1)[0]\n user_agent={'User-agent':rnd_agent}\n return(user_agent)\n \n####################################################################################### \n########################### IV. MAIN Function ########################################\ndef main():\n data_path=\"c:/users/gene/documents//duke/dropbox/gene/yelp_scrapping\"\n os.chdir(data_path)\n \n counter_reset()\n count=read_count()\n print(\"--\"*20)\n users_url_list=load_yelp_user_urls(count)\n print(\"--\"*20)\n make_json()\n \n for (i,url) in zip(range(count, len(users_url_list)),users_url_list):\n print(\"--\"*40)\n print(\"--\"*40)\n print(' '*15,url)\n step_display(i)\n response=fetch_website(url)\n Pickle(response, file_name='dwnld_user_profile.pkl')\n #response=eat_pickle('dwnld_user_profile.pkl')\n if response=='Skip':\n pass\n if response !='Skip':\n data_dict=extract_data(response, url)\n if data_dict!='Bad soup':\n with open('yelp_user_data.json', 'a') as f:\n json.dump(',', f, indent=0)\n json.dump({data_dict['user_id']:data_dict}, f, indent=2)\n if data_dict=='Bad soup':\n add_bad_soup(url)\n\n write_count(i)\n #if int(i)%3==0:\n wait()\n\nif \"__main__\"==__name__:\n main()", "sub_path": "YelpScrapeUsers_local.py", "file_name": "YelpScrapeUsers_local.py", "file_ext": "py", "file_size_in_byte": 14416, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.chdir", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 55, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 66, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 76, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 89, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 94, "usage_type": "call"}, {"api_name": "re.search", "line_number": 101, "usage_type": "call"}, {"api_name": "bs4.BeautifulSoup", "line_number": 116, "usage_type": "call"}, {"api_name": "re.search", "line_number": 117, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 130, "usage_type": "attribute"}, {"api_name": "re.search", "line_number": 135, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 137, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 150, "usage_type": "attribute"}, {"api_name": "numpy.nan", "line_number": 151, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 176, "usage_type": "call"}, {"api_name": "os.path", "line_number": 176, "usage_type": "attribute"}, {"api_name": "json.dump", "line_number": 178, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 184, "usage_type": "call"}, {"api_name": "pickle.dump", "line_number": 188, "usage_type": "call"}, {"api_name": "pickle.load", "line_number": 193, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 196, "usage_type": "call"}, {"api_name": "os.path", "line_number": 196, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 201, "usage_type": "call"}, {"api_name": "os.path", "line_number": 201, "usage_type": "attribute"}, {"api_name": "numpy.random.uniform", "line_number": 223, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 223, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 225, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 234, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 236, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 237, "usage_type": "call"}, {"api_name": "re.sub", "line_number": 253, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 265, "usage_type": "call"}, {"api_name": "os.path", "line_number": 265, "usage_type": "attribute"}, {"api_name": "pickle.load", "line_number": 268, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 270, "usage_type": "call"}, {"api_name": "os.path", "line_number": 270, "usage_type": "attribute"}, {"api_name": "pickle.dump", "line_number": 277, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 284, "usage_type": "call"}, {"api_name": "os.path", "line_number": 284, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 287, "usage_type": "call"}, {"api_name": "os.path", "line_number": 287, "usage_type": "attribute"}, {"api_name": "numpy.random.choice", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 293, "usage_type": "attribute"}, {"api_name": "os.chdir", "line_number": 301, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 324, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 325, "usage_type": "call"}]} +{"seq_id": "569123906", "text": "\"\"\"\nCreated on Sat Oct 20 2018\nModules for extracting data from the Chronos database\n\n@author: T.J. Heimovaara\n\"\"\"\n\n\nimport numpy as np\nimport scipy as sp\nimport scipy.stats as stats\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom context import dbl\n# import DataBaseLibrary as dbl\n#from pydream.core import run_dream\nfrom pydream.parameters import SampledParam\n#from pydream.convergence import Gelman_Rubin\n#import inspect\n\n# Meteorological data will be obtained from two sources:\n# 1: a close by weather station (for WM Berkhout, for BB: Lelystad)\n# we will use the evapotranspiration data obtained from the weather station...\n# 2: rainfall from the 1km gridded radar corrected interpolated rainfall data obtained\n# from climate4impact...\n\n# surface areas of Kragge compartment 3 and 4\n\n# In[0]: Import data from KNMI\nweather_station = '269' #Lelystad\nt_range = ['20030101','20210301']\n\npklfile = './DataFiles/meteoLS.gz'\n#path = './MeteoData/WM_Rain_2008-2019.bz2'\n\ninpfile = 'etmgeg_269.txt'\n# Read data from close by meteo station\nmeteo_data_stat = dbl.download_meteoKNMI_etmgeg (t_range, weather_station, pklfile, inpfile)\n\n#meteo_data_stat = dbl.download_meteoKNMI (t_range, weather_station, pklfile)\nmeteo_data_stat = meteo_data_stat.rename(columns={'rain':'rain_station'})\n\n# Read data from gridded radar corrected interpolated rainfall data\n#ain_radar = pd.read_pickle(fpath,compression='infer')\n# transform rain values from kg/m2 (mm) to m water column\n#ain_radar['rain'] = rain_radar['rain']/1e3\n# Merge the station data and the interpolated rain data in to a single dataframe\nmeteo_data = meteo_data_stat\n# meteo_data is top boundary condition. We run the model from 2003 onward\nmeteo_data = meteo_data[slice('2003-01-01','2021-03-01')]\n\n#eteo_data.rain.loc[meteo_data['rain'].isnull()] = \\\n# meteo_data.rain_station.loc[meteo_data['rain'].isnull()]\n\n## Download flow and level data from CHRONOS\npump_code = 'PP-11N'\npklfile = './DataFiles/flowdata_PP-11N.gz'\ndf_inline = dbl.download_flow_level (pump_code, pklfile)\n\n# We create a pivot table based on column cname (component names)\n#inline_par = pd.pivot_table(df_inline, values='rawvalue', index=['datetime'],\n# columns=['cname'], aggfunc=np.sum)\n\ntotF0 = pd.pivot_table(df_inline, values='rawvalue', index=['datetime'],\n columns=['cname'], aggfunc=np.sum)\n\n#totF = dbl.remove_outliers_inline(inline_par)\ntotF = dbl.remove_outliers_inlineBB(totF0)\n\n# as the model allows for leachate recirculation it expects a totIniflF dataset\n# For a situation where no leachate is recirculated, we set the totIniflF to zero\n\ntotF0['totInfilF'] = 0\n\nlevelD = totF0['level']\ninfilF = totF0['totInfilF']\n\n#sensData = dbl.download_sens_data_Kragge (pump_code, tmeas, pklfile)\n\n# We create a pivot table based on column cname (component names)\n#inline_par = pd.pivot_table(df_inline, values='rawvalue', index=['datetime'],\n# columns=['cname'], aggfunc=np.sum)\n#totF = sensData['totalFlow']\n#levelD = sensData['levelD']\n#infilF = sensData['totInfilF']\n\n# Download laboratory data for pump pit\npklfile = './DataFiles/labdata_PP-11N.gz'\n\ndf_lab = dbl.download_lab(pump_code, pklfile)\n\n# We create a pivot table based on column cname (component names)\nlab_data = pd.pivot_table(df_lab, values='value', index=['date'],\n columns=['cname'], aggfunc=np.sum)\n\nlab_data = lab_data.rename(index=str, columns={'Ammonium (als N)': 'NH4',\n 'Bromide': 'Br',\n 'Chloride': 'Cl',\n 'Totaal organisch koolstof (TOC)': 'TOC'})\n\nlab_data.index = pd.to_datetime(lab_data.index)\n\n# meteo_data is top boundary condition. We run the model over 10 years\nmeteo_data = meteo_data[slice('2003-01-01','2021-03-01')]\n\n\n# Define simulation time range (trange)\ntrange = pd.date_range(start='2003-01-01',end = '2021-03-01',freq='D')\n\n# Select measurements, should fall within trange.\n# tmeas contains times where measurements are available!\n# can have multiple tmeas vectors for different types of measurements\n# totF contains measured data from mid 2012. We choose to start on the 2012-07-01\n# Because the outflow is influenced by operator decisions we choose to select weekly\n# cumulative totals...\nmeasFreq = 7\ntmeas = pd.date_range(start='2012-06-14',end = '2021-03-01',freq='7D')\nfinter = sp.interpolate.interp1d(totF.index.astype(int),totF.values)\ntotF_val = finter(tmeas.astype(int))\ntotF2 = pd.DataFrame(data = totF_val, index=tmeas)\ntotF2 = totF2-totF2.iloc[0]\nmeas_data_flow = totF2.rename(columns = {0:'totF'})\n\n# Define calibration time range. This will be used by DREAM to compare\n# simulated values with calibration set...\n# Data set to be matched by modifying parameters...\ntcalib = pd.date_range(start='2012-06-14',end = '2020-01-01',freq='D')\n\n# In order to facilitate quick and easy comparison of simulation with data\n# we need to define the overlapping indices:\n# tmeas_ind: trange[tmeas_ind] = tmeas\n# tcalib_ind: trange[tcalib_ind]=tcalib\n# tcalmeas_ind, tmeascal_ind: tmeas[tcalmeas_ind]=tcalib[tmeascal_ind]\n\n\nxy, ind1, tmeas_ind = np.intersect1d(tmeas, trange,\n return_indices=True)\nxy, ind1, tcalib_ind = np.intersect1d(tcalib, trange,\n return_indices=True)\nxy, tmeascal_ind, tcalmeas_ind = np.intersect1d(tcalib, tmeas,\n return_indices=True)\n\nxy, tlabmeas_ind, tmeaslab_ind = np.intersect1d(lab_data.index, trange,\n return_indices=True)\nxy, tmeascal_lab_ind, tcalmeas_lab_ind = np.intersect1d(tcalib, lab_data.index,\n return_indices=True)\n\n\ntdata = {'trange':trange,\n 'tmeas':tmeas,\n 'tcalib':tcalib,\n 'tmeas_ind':tmeas_ind,\n 'tcalib_ind':tcalib_ind,\n 'tcalmeas_ind':tcalmeas_ind,\n 'tmeascal_ind':tmeascal_ind,\n 'tlabmeas_ind': tlabmeas_ind,\n 'tmeaslab_ind': tmeaslab_ind,\n 'tmeascal_lab_ind': tmeascal_lab_ind,\n 'tcalmeas_lab_ind': tcalmeas_lab_ind}\n\ntdseries = pd.Series(tdata)\n\n\n# Obtain landfill specific properties\ncellIdx = 0 # 11N = 0, 11Z = 1, 12 = 2\nlF = dbl.wastebodyPropertiesBB(cellIdx) #m2\n\n## Run model wil optimal parameter set...\n\n## Prepare DREAM model...\n# Model parameters which are required to calculate fluxes etc. (often need to\n# optimized).\n\n# List of parameters\npar_d = {'dzCL': [0.5, 1.9, 0.9267], #%m\n 'cropFact': [0.75, 1.5, 0.9584], #[-]\n 'cLPor': [0.15, 0.60, 0.1722], #[-]\n 'cLthRes': [0.001, 0.9, 0.0293], # percentage of thTot\n 'cLKSat': [-5, 1, -1.1936], # 10 log\n 'cLbpar': [0, 8, 2.8698], #%10log! [m/d]\n 'wBtau1': [1, 200, 37.5672],\n 'wBsig1': [-5, 1, -0.4642],\n 'wBtau2': [0, 5*365, 170.3241],\n 'wBsig2': [-5, 1, 0.3246],\n 'wBmfrac': [0.01, 1.0, 0.2338],\n 'wBthIni': [0.05, 0.95, 0.5516],\n 'wBfRes' : [0, 0.9, 0.0851],\n 'wBbFlow': [-7,-2,-4.0935], #10log!\n 'cLcIni': [-4, 3, -1.5343], #10log!\n 'wBcIni': [2, 6, 3.1901],\n 'wBvWK': [0, 0.9, 0.1546],\n 'wBalphavW': [-9,2,-4.3908],#, #10log!\n 'wBndEx': [365, 750, 500]\n }\n\n\n\npar_df = pd.DataFrame(data=par_d)\n\nsdData = np.array([1,25])\n\n", "sub_path": "BB11N_noExchange/Initialize_BB11N_DREAM01.py", "file_name": "Initialize_BB11N_DREAM01.py", "file_ext": "py", "file_size_in_byte": 7416, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "context.dbl.download_meteoKNMI_etmgeg", "line_number": 38, "usage_type": "call"}, {"api_name": "context.dbl", "line_number": 38, "usage_type": "name"}, {"api_name": "context.dbl.download_flow_level", "line_number": 58, "usage_type": "call"}, {"api_name": "context.dbl", "line_number": 58, "usage_type": "name"}, {"api_name": "pandas.pivot_table", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 65, "usage_type": "attribute"}, {"api_name": "context.dbl.remove_outliers_inlineBB", "line_number": 68, "usage_type": "call"}, {"api_name": "context.dbl", "line_number": 68, "usage_type": "name"}, {"api_name": "context.dbl.download_lab", "line_number": 90, "usage_type": "call"}, {"api_name": "context.dbl", "line_number": 90, "usage_type": "name"}, {"api_name": "pandas.pivot_table", "line_number": 93, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 94, "usage_type": "attribute"}, {"api_name": "pandas.to_datetime", "line_number": 101, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 108, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 117, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 118, "usage_type": "call"}, {"api_name": "scipy.interpolate", "line_number": 118, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 120, "usage_type": "call"}, {"api_name": "pandas.date_range", "line_number": 127, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 138, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 140, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 143, "usage_type": "call"}, {"api_name": "numpy.intersect1d", "line_number": 145, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 161, "usage_type": "call"}, {"api_name": "context.dbl.wastebodyPropertiesBB", "line_number": 166, "usage_type": "call"}, {"api_name": "context.dbl", "line_number": 166, "usage_type": "name"}, {"api_name": "pandas.DataFrame", "line_number": 198, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 200, "usage_type": "call"}]} +{"seq_id": "127202599", "text": "import matplotlib.pyplot as plt\nimport netCDF4\nimport numpy as np\nimport matplotlib.gridspec as gridspec\nimport math\nfrom matplotlib import colors\n\ndeg = unichr(176)\ndelta = unichr(916)\nk_B = 1.38064852e-23\nN_A = 6.02214086e+23\nR = 8.3144598\nspecies_list = ['atomic_oxygen', 'ozone', 'atomic_hydrogen', 'carbon_dioxide', 'carbon_monoxide', 'temperature', 'density']\nsymbol_list = ['O', 'O3', 'H', 'CO2', 'CO', 'T', 'n']\nunits_list = ['ppmv', '$\\mathregular{cm^{-3}}$', 'K']\n\nfname_uni = netCDF4.Dataset('/nfs/a328/eecwk/earth_system_grid/ccsm4_monthly_ave/zonal_means/f.e20.FXSD.f19_f19.001.cam.h0.2000-01.nc', 'r', format='NETCDF4')\nlats = fname_uni.variables['lat'][:]\nlons = fname_uni.variables['lon'][:]\nfname_uni.close()\n\ndef calc_z3_zon_mer_t_av(levs): \n if levs == 88:\n fname = netCDF4.Dataset('/nfs/a265/earfw/SD_WACCM4/john_ca_paper_JDmif_nad4cad7.cam2.h0.%s-0%s.nc' %(year, month), 'r', format='NETCDF4')\n if levs == 145:\n fname = netCDF4.Dataset('/nfs/a328/eecwk/earth_system_grid/ccsm4_monthly_ave/f.e20.FXSD.f19_f19.001.cam.h0.%s-0%s.nc' %(year, month), 'r', format='NETCDF4')\n z3 = np.zeros([1,levs,96,144])\n z3[:] = fname.variables['Z3'][:]*(1.e-3)\n z3_zon_av = np.mean(z3[:], axis=3)\n z3_zon_mer_av = np.mean(z3_zon_av[:], axis=2)\n z3_zon_mer_t_av = np.mean(z3_zon_mer_av[:], axis=0) \n fname.close()\n return z3_zon_mer_t_av\n\ndef calc_z3_zon_t_av(levs): \n if levs == 88:\n fname = netCDF4.Dataset('/nfs/a265/earfw/SD_WACCM4/john_ca_paper_JDmif_nad4cad7.cam2.h0.%s-0%s.nc' %(year, month), 'r', format='NETCDF4')\n if levs == 145:\n fname = netCDF4.Dataset('/nfs/a328/eecwk/earth_system_grid/ccsm4_monthly_ave/f.e20.FXSD.f19_f19.001.cam.h0.%s-0%s.nc' %(year, month), 'r', format='NETCDF4')\n z3 = np.zeros([1,levs,96,144])\n z3[:] = fname.variables['Z3'][:]*(1.e-3)\n z3_zon_av = np.mean(z3[:], axis=3)\n z3_zon_t_av = np.mean(z3_zon_av[:], axis=0) \n fname.close()\n return z3_zon_t_av\n\ndef calc_cos_factor(param, levs, lowlat, highlat):\n param_weighted = np.zeros(levs) \n for j in range (0, levs): \n sig_cos_x = 0\n sig_cos = 0\n for k in range (lowlat, highlat):\n sig_cos_x = sig_cos_x + (math.cos(math.radians(lats[k])) * param[j][k])\n sig_cos = sig_cos + math.cos(math.radians(lats[k])) \n if k == (highlat - 1):\n param_weighted[j] = sig_cos_x / sig_cos\n return param_weighted\n\ndef calc_species_zon_av(symbol, levs): \n if levs == 88:\n fname = netCDF4.Dataset('/nfs/a265/earfw/SD_WACCM4/john_ca_paper_JDmif_nad4cad7.cam2.h0.%s-0%s.nc' %(year, month), 'r', format='NETCDF4')\n if levs == 145:\n fname = netCDF4.Dataset('/nfs/a328/eecwk/earth_system_grid/ccsm4_monthly_ave/f.e20.FXSD.f19_f19.001.cam.h0.%s-0%s.nc' %(year, month), 'r', format='NETCDF4') \n species_dat = np.zeros([1,levs,96,144])\n if symbol == 'T':\n species_dat = fname.variables[symbol][:]\n elif symbol == 'n':\n species_dat = fname.variables['T'][:]\n else:\n species_dat = fname.variables[symbol][:]*(1.e6) \n species_tm = np.mean(species_dat[:], axis=0)\n species_zon_av = np.mean(species_tm[:], axis=2)\n fname.close()\n return species_zon_av\n\ndef interp_waccmx_species(z3_1, z3_2, species_2):\n species_2_int = np.zeros([88,96])\n species_2_int_rev = np.zeros([88,96])\n z3_1_rev = z3_1[::-1]\n z3_2_rev = z3_2[::-1]\n species_2_rev = species_2[::-1]\n for i in range(0,88): \n for j in range(0,96):\n species_2_int[i,j] = np.interp(z3_1_rev[i], z3_2_rev[:], species_2_rev[:,j]) \n species_2_int_rev = species_2_int[::-1]\n return species_2_int_rev\n\ndef calc_diff(param1, param2): \n diff = np.zeros([88,96])\n for i in range(0,88):\n for j in range(0,96): \n diff[i,j] = ( (param2[i,j] - param1[i,j]) / param1[i,j] ) * 100\n return diff\n\ndef calc_ratio(param1, param2, levs):\n ratio = np.zeros([levs,96])\n for i in range(0,levs):\n for j in range(0,96): \n ratio[i,j] = param1[i,j] / param2[i,j]\n return ratio\n \ndef calc_z3_zon_t_av_weighted(levs, lowlat, highlat):\n z3_zon_t_av = calc_z3_zon_t_av(levs)\n z3_zon_t_av_weighted = calc_cos_factor(z3_zon_t_av, levs, lowlat, highlat)\n return z3_zon_t_av_weighted\n\ndef get_lev(levs):\n if levs == 88:\n fname = netCDF4.Dataset('/nfs/a265/earfw/SD_WACCM4/john_ca_paper_JDmif_nad4cad7.cam2.h0.%s-0%s.nc' %(year, month), 'r', format='NETCDF4')\n if levs == 145:\n fname = netCDF4.Dataset('/nfs/a328/eecwk/earth_system_grid/ccsm4_monthly_ave/f.e20.FXSD.f19_f19.001.cam.h0.%s-0%s.nc' %(year, month), 'r', format='NETCDF4') \n lev = np.zeros([levs])\n lev = fname.variables['lev'][:]\n fname.close()\n return lev\n\ndef calc_density(T, levs, lowlat, highlat):\n lev = get_lev(levs)\n T_weighted = calc_cos_factor(T, levs, lowlat, highlat)\n n_weighted = np.zeros(levs)\n for i in range(0,levs):\n n_weighted[i] = (N_A * 100 * lev[i]) / (R * T_weighted[i]) * (1.e-6)\n return n_weighted\n\ndef calc_profiles(param, levs, lowlat, highlat):\n if symbol == 'n':\n param_weighted = calc_density(param, levs, lowlat, highlat)\n else:\n param_weighted = calc_cos_factor(param, levs, lowlat, highlat)\n return param_weighted\n\ndef calc_conc_profiles(param, levs, lowlat, highlat):\n lev = get_lev(levs)\n T_zon_t_av = calc_species_zon_av('T', levs)\n T_zon_t_av_weighted = calc_cos_factor(T_zon_t_av, levs, lowlat, highlat) \n param_weighted = calc_cos_factor(param, levs, lowlat, highlat)\n param_weighted_conc = np.zeros(levs) \n for i in range(0,levs):\n param_weighted_conc[i] = (param_weighted[i] * 1.e-6 * N_A * 100 * lev[i]) / (R * T_zon_t_av_weighted[i]) * (1.e-6)\n return param_weighted_conc\n\ndef plot_1d_global(name, config, units, z3, species, color, plot_no):\n x = species[::-1]\n y = z3[::-1]\n plt.plot(x, y, color=color, label=config)\n plt.xlabel('%s [%s]' %(name, units), fontsize=12)\n plt.ylabel('Altitude [km]', fontsize=12)\n plt.ylim(60,160)\n if name == 'atomic_oxygen':\n if units == 'ppmv':\n plt.xlim(0,500000)\n if units == '$\\mathregular{cm^{-3}}$': \n plt.xlim(0,8.e+11)\n if name == 'ozone':\n plt.xscale('log')\n if units == 'ppmv':\n plt.xlim(1.e-8,1.e+1)\n if units == '$\\mathregular{cm^{-3}}$': \n plt.xlim(1.e-4,1.e+12)\n if name == 'atomic_hydrogen':\n if units == 'ppmv':\n plt.xlim(0,20)\n if units == '$\\mathregular{cm^{-3}}$': \n plt.xlim(0,5.e+8)\n if name == 'temperature':\n 1==1\n if name == 'density':\n plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n plt.ylim(100,160)\n plt.xlim(0,3.e+12)\n if plot_no == 1:\n plt.legend()\n return\n\ndef plot_1d_multi(name, config, units, z3, species, lowlat, highlat, color, plot_no):\n if plot_no > 5:\n plot_no = plot_no - 6\n plt.subplot(gs1[plot_no])\n plt.title('%s%s to %s%s' %(lowlat_no, deg, highlat_no, deg), fontsize=14)\n x = species[::-1]\n y = z3[::-1]\n plt.plot(x, y, color=color, label=config)\n plt.ylim(60,160)\n if plot_no == 0:\n plt.ylabel('Altitude [km]', fontsize=12)\n plt.tick_params(labelbottom='off')\n if plot_no == 1:\n plt.tick_params(labelleft='off')\n plt.tick_params(labelbottom='off')\n if plot_no == 2:\n plt.tick_params(labelleft='off')\n plt.tick_params(labelbottom='off')\n if plot_no == 3:\n plt.xlabel('%s [%s]' %(name, units), fontsize=12)\n plt.ylabel('Altitude [km]', fontsize=12)\n if plot_no == 4:\n plt.xlabel('%s [%s]' %(name, units), fontsize=12)\n plt.tick_params(labelleft='off')\n if plot_no == 5:\n plt.xlabel('%s [%s]' %(name, units), fontsize=12)\n plt.tick_params(labelleft='off')\n if name == 'atomic_oxygen':\n plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n if units == 'ppmv':\n plt.xlim(0,500000)\n if units == '$\\mathregular{cm^{-3}}$': \n plt.xlim(0,8.e+11)\n if name == 'ozone':\n #plt.xscale('log')\n #plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n if units == 'ppmv':\n plt.xlim(1.e-8,1.e+1)\n if units == '$\\mathregular{cm^{-3}}$':\n plt.ylim(77,100)\n #plt.xlim(1.e-4,1.e+12)\n plt.xlim(0,7.e+8)\n if name == 'atomic_hydrogen':\n if units == 'ppmv':\n plt.xlim(0,20)\n if units == '$\\mathregular{cm^{-3}}$': \n plt.xlim(0,5.e+8)\n if name == 'carbon_dioxide':\n plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n if units == 'ppmv':\n plt.xlim(0,500)\n 1==1\n if units == '$\\mathregular{cm^{-3}}$': \n plt.xlim(0,1.e+13) \n if name == 'temperature':\n plt.xlim(0,800)\n if name == 'density':\n plt.ticklabel_format(style='sci', axis='x', scilimits=(0,0))\n plt.ylim(100,160)\n plt.xlim(0,3.e+12)\n if config == 'waccm-x' and plot_no == 2:\n plt.legend(loc=1)\n return\n\ndef plot_1d_ratio(name, config, units, z3, species, lowlat, highlat, color, plot_no):\n if plot_no > 5:\n plot_no = plot_no - 6\n plt.subplot(gs1[plot_no])\n plt.title('%s%s to %s%s' %(lowlat_no, deg, highlat_no, deg), fontsize=14)\n x = species[::-1]\n y = z3[::-1]\n plt.plot(x, y, color=color, label=config)\n if plot_no == 0:\n plt.ylabel('Altitude [km]', fontsize=12)\n plt.tick_params(labelbottom='off')\n if plot_no == 1:\n plt.tick_params(labelleft='off')\n plt.tick_params(labelbottom='off')\n if plot_no == 2:\n plt.tick_params(labelleft='off')\n plt.tick_params(labelbottom='off')\n if plot_no == 3:\n plt.xlabel('%s' %name, fontsize=12)\n plt.ylabel('Altitude [km]', fontsize=12)\n if plot_no == 4:\n plt.xlabel('%s' %name, fontsize=12)\n plt.tick_params(labelleft='off')\n if plot_no == 5:\n plt.xlabel('%s' %name, fontsize=12)\n plt.tick_params(labelleft='off')\n plt.ylim(60,160)\n plt.xlim(0,10)\n if config == 'waccm-x' and plot_no == 2:\n plt.legend(loc=1)\n return\n\ndef plot_2d(name, z3, species, plot_no):\n plt.subplot(gs1[plot_no])\n x, y = np.meshgrid(lats, z3)\n plt.xlabel('Latitude [%s]' %deg, fontsize=12)\n plt.xticks(np.arange(-90,120,30), fontsize=12) \n plt.yticks(np.arange(0,220,20), fontsize=12) \n plt.ylim(90,200)\n plt.axhline(y=waccm_z3[0], color='w', linewidth=1, linestyle=':')\n if name == 'atomic_oxygen':\n diffs = [1.e+3, 175.e+1, 25.e+2, 325.e+1, 4.e+3, 475.e+1, 55.e+2, 625.e+1, 7.e+3, 775.e+1, 85.e+2, 925.e+1, 1.e+4, 175.e+2, 25.e+3, 325.e+2, 4.e+4, 475.e+2, 55.e+3, 625.e+2, 7.e+4, 775.e+2, 85.e+3, 925.e+2, 1.e+5, 175.e+3, 25.e+4, 325.e+3, 4.e+5, 475.e+3, 55.e+4, 625.e+3, 7.e+5, 775.e+3, 85.e+4, 925.e+3, 1.e+6]\n cbar_ticks = [1.e+3, 1.e+4, 1.e+5, 1.e+6]\n plot = 'log'\n elif name == 'ozone':\n diffs = [1.e-8, 25.e-9, 4.e-8, 55.e-9, 7.e-8, 85.e-9, 1.e-7, 25.e-8, 4.e-7, 55.e-8, 7.e-7, 85.e-8, 1.e-6, 25.e-7, 4.e-6, 55.e-7, 7.e-6, 85.e-7, 1.e-5, 25.e-6, 4.e-5, 55.e-6, 7.e-5, 85.e-6, 1.e-4, 25.e-5, 4.e-4, 55.e-5, 7.e-4, 85.e-5, 1.e-3, 25.e-4, 4.e-3, 55.e-4, 7.e-3, 85.e-4, 1.e-2, 25.e-3, 4.e-2, 55.e-3, 7.e-2, 85.e-3, 1.e-1, 25.e-2, 4.e-1, 55.e-2, 7.e-1, 85.e-2, 1.e+0, 25.e-1, 4.e+0, 55.e-1, 7.e+0, 85.e-1, 1.e+1]\n cbar_ticks = [1.e-8, 1.e-7, 1.e-6, 1.e-5, 1.e-4, 1.e-3, 1.e-2, 1.e-1, 1.e+0, 1.e+1]\n plot = 'log'\n elif name == 'atomic_hydrogen':\n diffs = np.arange(1,91,1)\n cbar_ticks = np.arange(0,100,10)\n plot = 'linear'\n diffs_per = np.arange(-200,201,1)\n if plot_no == 0:\n if plot == 'linear':\n ax = plt.contourf(x[:,:], y[:,:], species[:,:], diffs)\n elif plot == 'log':\n ax = plt.contourf(x[:,:], y[:,:], species[:,:], diffs, norm=colors.LogNorm())\n plt.title('WACCM')\n plt.ylabel('Altitude [km]', fontsize=12)\n elif plot_no == 1:\n if plot == 'linear':\n ax = plt.contourf(x[:,:], y[:,:], species[:,:], diffs)\n elif plot == 'log':\n ax = plt.contourf(x[:,:], y[:,:], species[:,:], diffs, norm=colors.LogNorm())\n plt.title('WACCM-X')\n plt.tick_params(labelleft='off')\n cbar_ax = fig.add_axes([0.94, 0.15, 0.02, 0.7])\n cbar = fig.colorbar(ax, cax=cbar_ax, ticks=cbar_ticks, orientation='vertical')\n cbar.set_label('%s [ppmv]' %name, fontsize=12)\n cbar.ax.tick_params(labelsize=12)\n elif plot_no == 2:\n ax2 = plt.contourf(x[:,:], y[:,:], species[:,:], diffs_per, extend='both', cmap=plt.get_cmap('seismic'))\n plt.title('WACCM to WACCM-X Difference')\n plt.tick_params(labelleft='off')\n cbar_ax2 = fig.add_axes([1.05, 0.15, 0.02, 0.7])\n cbar2 = fig.colorbar(ax2, cax=cbar_ax2, ticks=np.arange(-200,250,50), orientation='vertical')\n cbar2.cmap.set_under('#001648')\n cbar2.set_label('[%]', fontsize=12)\n cbar2.ax.tick_params(labelsize=12)\n return\n\nyear = 2014\nmonth = 1\nname = species_list[0]\nsymbol = symbol_list[0]\nunits = units_list[0]\nchemistry = True\nglobal_only = False\nsave = False\n# For ratio:\nname2 = species_list[3]\nsymbol2 = symbol_list[3]\n\nif units == 'ppmv':\n units_print = 'ppmv'\nelif units == '$\\mathregular{cm^{-3}}$':\n units_print = 'cm-3'\nelif units == 'K':\n units_print = 'K'\n\nif global_only == True:\n if month == 1:\n plt.title('January %s Global' %year , fontsize=16)\n elif month == 7:\n plt.title('July %s Global' %year, fontsize=16)\n step = 96\n a = 0\n b = 1\nelse:\n fig, axes = plt.subplots(nrows=2, ncols=3, figsize=(11,8))\n gs1 = gridspec.GridSpec(2, 3)\n gs1.update(wspace=0.1, hspace=0.1)\n if month == 1:\n plt.suptitle('January %s' %year, fontsize=16)\n elif month == 7:\n plt.suptitle('July %s' %year, fontsize=16)\n step = 16\n a = 0\n b = 6\n\n#waccm_z3 = calc_z3_zon_mer_t_av(88)\n#waccmx_z3 = calc_z3_zon_mer_t_av(145)\n\nwaccm_species = calc_species_zon_av(symbol, 88)\nwaccmx_species = calc_species_zon_av(symbol, 145)\n\n#waccmx_species_int = interp_waccmx_species(waccm_z3, waccmx_z3, waccmx_species)\n#diff = calc_diff(waccm_species, waccmx_species_int)\n\n# 1D Plot Code\nfor i in range(a,b): \n lowlat = i * step\n highlat = (i * step) + step\n lowlat_no = int((lowlat * 1.875) - 90)\n highlat_no = int((highlat * 1.875) - 90)\n waccm_z3_weighted = calc_z3_zon_t_av_weighted(88, lowlat, highlat)\n waccmx_z3_weighted = calc_z3_zon_t_av_weighted(145, lowlat, highlat) \n if chemistry == True:\n if units == 'ppmv':\n waccm_species_profile = calc_profiles(waccm_species, 88, lowlat, highlat)\n waccmx_species_profile = calc_profiles(waccmx_species, 145, lowlat, highlat)\n elif units == '$\\mathregular{cm^{-3}}$':\n waccm_species_profile = calc_conc_profiles(waccm_species, 88, lowlat, highlat)\n waccmx_species_profile = calc_conc_profiles(waccmx_species, 145, lowlat, highlat)\n else:\n if symbol == 'T':\n waccm_species_profile = calc_profiles(waccm_species, 88, lowlat, highlat)\n waccmx_species_profile = calc_profiles(waccmx_species, 145, lowlat, highlat) \n elif symbol == 'n':\n waccm_species_profile = calc_profiles(waccm_species, 88, lowlat, highlat)\n waccmx_species_profile = calc_profiles(waccmx_species, 145, lowlat, highlat) \n if global_only == True:\n plot_1d_global(name, 'waccm', units, waccm_z3_weighted, waccm_species_profile, 'k', 0)\n plot_1d_global(name, 'waccm-x', units, waccmx_z3_weighted, waccmx_species_profile, 'b', 1)\n else:\n plot_1d_multi(name, 'waccm', units, waccm_z3_weighted, waccm_species_profile, lowlat, highlat, 'k', i)\n plot_1d_multi(name, 'waccm-x', units, waccmx_z3_weighted, waccmx_species_profile, lowlat, highlat, 'b', i)\nif save == True:\n if global_only == True:\n plt.savefig('/nfs/a328/eecwk/waccm-x/figures/atomic_oxygen_experiment/john_ca_paper_JDmif_nad4cad7/%s/%s_month%s_profile_global_%s.jpg' %(year, name, month, units_print), bbox_inches='tight', dpi=300)\n else:\n plt.savefig('/nfs/a328/eecwk/waccm-x/figures/atomic_oxygen_experiment/john_ca_paper_JDmif_nad4cad7/%s/%s_month%s_profile_lat_bands_%s.jpg' %(year, name, month, units_print), bbox_inches='tight', dpi=300)\n\n'''\n# 1D Ratio Plot Code: WACCM-X only comparison workaround for missing WACCM species\nwaccmx_species = calc_species_zon_av(symbol, 145)\nwaccmx_species2 = calc_species_zon_av(symbol2, 145)\nwaccmx_ratio = calc_ratio(waccmx_species, waccmx_species2, 145)\nfor i in range(a,b): \n lowlat = i * step\n highlat = (i * step) + step\n lowlat_no = int((lowlat * 1.875) - 90)\n highlat_no = int((highlat * 1.875) - 90)\n waccm_z3_weighted = calc_z3_zon_t_av_weighted(88, lowlat, highlat)\n waccmx_z3_weighted = calc_z3_zon_t_av_weighted(145, lowlat, highlat) \n waccmx_species_profile = calc_profiles(waccmx_ratio, 145, lowlat, highlat) \n plot_1d_ratio('%s / %s ratio' %(symbol, symbol2), 'waccm-x', units, waccmx_z3_weighted, waccmx_species_profile, lowlat, highlat, 'b', i)\nif save == True:\n plt.savefig('/nfs/a328/eecwk/waccm-x/figures/atomic_oxygen_experiment/john_ca_paper_JDmif_nad4cad7/%s/%s_%s_ratio_month%s_profile_lat_bands.jpg' %(year, name, name2, month), bbox_inches='tight', dpi=300)\n'''\n'''\n# 2D Plot Code\n# Currently for chemistry only\nfig, axes = plt.subplots(nrows=1, ncols=3, figsize=(11,5))\ngs1 = gridspec.GridSpec(1, 3)\ngs1.update(wspace=0.1, hspace=0.1)\n\nif month == 1:\n fig.suptitle('January %s' %year, fontsize=16)\nelif month == 7:\n fig.suptitle('July %s' %year, fontsize=16)\n\nplot_2d(name, waccm_z3, waccm_species, 0)\nplot_2d(name, waccmx_z3, waccmx_species, 1)\nplot_2d(name, waccm_z3, diff, 2)\nplt.savefig('/nfs/a328/eecwk/waccm-x/figures/atomic_oxygen_experiment/john_ca_paper_JDmif_nad4cad7/%s/%s_month%s.jpg' %(year, name, month), bbox_inches='tight', dpi=300)\n'''\nplt.show()", "sub_path": "atomic_oxygen_experiment.py", "file_name": "atomic_oxygen_experiment.py", "file_ext": "py", "file_size_in_byte": 18182, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "netCDF4.Dataset", "line_number": 17, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 24, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 31, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 37, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 39, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 40, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 48, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 53, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 53, "usage_type": "call"}, {"api_name": "math.cos", "line_number": 54, "usage_type": "call"}, {"api_name": "math.radians", "line_number": 54, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 61, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 71, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.interp", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 89, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 96, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 109, "usage_type": "call"}, {"api_name": "netCDF4.Dataset", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 112, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 120, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 145, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 145, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 146, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 146, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 147, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 147, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 148, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 148, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 153, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 153, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xscale", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 157, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 157, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ticklabel_format", "line_number": 168, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 168, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 169, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 169, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 170, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 170, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 172, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 172, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 178, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 178, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 179, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 179, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 183, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 183, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 186, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 186, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 188, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 188, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 189, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 189, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 191, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 191, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 192, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 192, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 194, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 194, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 195, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 195, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 197, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 197, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 198, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 198, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 200, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 200, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 201, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 201, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ticklabel_format", "line_number": 203, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 203, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 205, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 205, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 207, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 207, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 212, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 212, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 214, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 214, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 216, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 216, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 219, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 219, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 221, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 221, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ticklabel_format", "line_number": 223, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 223, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 225, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 225, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 228, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 228, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 230, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 230, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ticklabel_format", "line_number": 232, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 232, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 233, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 233, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 234, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 234, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 236, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 236, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 242, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 242, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 243, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 243, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 246, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 246, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 248, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 248, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 249, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 249, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 251, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 251, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 252, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 252, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 254, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 254, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 255, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 255, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 257, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 257, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 258, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 258, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 260, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 260, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 261, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 261, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 263, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 263, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 264, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 264, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 265, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 265, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 266, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 266, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 268, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 268, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 272, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 272, "usage_type": "name"}, {"api_name": "numpy.meshgrid", "line_number": 273, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 274, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 274, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 275, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 275, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.yticks", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 276, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 276, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 277, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 277, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 278, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 278, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 288, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 289, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 291, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 294, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 294, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 296, "usage_type": "name"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 296, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 296, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 297, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 297, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 298, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 298, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 301, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 301, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 303, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 303, "usage_type": "name"}, {"api_name": "matplotlib.colors.LogNorm", "line_number": 303, "usage_type": "call"}, {"api_name": "matplotlib.colors", "line_number": 303, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 304, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 304, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 305, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 305, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 311, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 311, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.get_cmap", "line_number": 311, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 312, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 312, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.tick_params", "line_number": 313, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 313, "usage_type": "name"}, {"api_name": "numpy.arange", "line_number": 315, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.title", "line_number": 342, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 342, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 344, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 344, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 349, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 349, "usage_type": "name"}, {"api_name": "matplotlib.gridspec.GridSpec", "line_number": 350, "usage_type": "call"}, {"api_name": "matplotlib.gridspec", "line_number": 350, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 353, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 353, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.suptitle", "line_number": 355, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 355, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 399, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 399, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 401, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 401, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 437, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 437, "usage_type": "name"}]} +{"seq_id": "191524251", "text": "from web3 import Web3\nimport json\n\nw3 = Web3(Web3.HTTPProvider(\"HTTP://127.0.0.1:8545\"))\nwith open('../conf/contract/PDS.abi', 'r') as myfile:\n abi = myfile.read()\n\nwith open('../conf/contract/PDS.bin', 'r') as myfile:\n binfile = myfile.read()\n bytecode = json.loads(binfile)['object']\n\naccount = w3.eth.accounts[0]\nPDSContract = w3.eth.contract(abi=abi, bytecode=bytecode)\ntx_hash = PDSContract.constructor().transact({'from': account})\ntx_receipt = w3.eth.waitForTransactionReceipt(tx_hash)\naddress = tx_receipt.contractAddress\nprint(address)\nPDSContract_instance = w3.eth.contract(abi=abi, address=address)\ntx_hash = PDSContract_instance.functions.new_token('4qk3Ab43ufPQVif4GAzLUW', w3.toBytes(text='4qk3Ab43ufPQVif4GAzLUW')).transact({'from': account})\nw3.eth.waitForTransactionReceipt(tx_hash)\nDID, enc_token = PDSContract_instance.functions.get_token(0).call()\nprint(DID)", "sub_path": "tests/ganache.py", "file_name": "ganache.py", "file_ext": "py", "file_size_in_byte": 881, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "web3.Web3", "line_number": 4, "usage_type": "call"}, {"api_name": "web3.Web3.HTTPProvider", "line_number": 4, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 10, "usage_type": "call"}]} +{"seq_id": "246841374", "text": "from flask import (\n Blueprint,\n render_template,\n redirect,\n url_for,\n request,\n flash,\n session,\n current_app,\n)\nfrom flask_login import login_required\nfrom sqlalchemy import desc,asc\nimport datetime\n\nfrom project.corsi.forms import CorsiForm, write_to_disk\nfrom project.serate.forms import SerataForm\nfrom project.serate.models import Serata\nfrom project.corsi.models import Corso\nfrom project import db\n\n\n# Define blueprint\ncorsi_blueprint = Blueprint(\n \"corsi\", \n __name__, \n template_folder=\"templates\",\n static_folder='../static'\n)\n\n'''\nLista dei corsi in ordine alfabetico\n'''\n@corsi_blueprint.route(\"/lista\", methods=[\"GET\"])\ndef lista():\n # Ordinamento alfabetico ascendente per titolo\n lista_corsi = Corso.query.order_by(asc(Corso.nome)).all()\n return render_template(\n 'corsi_lista.html', \n lista_corsi=lista_corsi\n )\n\n\n'''\nCreazione di un corso (senza serate e senza tags)\n'''\n@corsi_blueprint.route(\"/create\", methods=[\"GET\", \"POST\"])\n@login_required\ndef create():\n\n form = CorsiForm()\n\n if form.validate_on_submit():\n\n name = form.name.data\n teacher = form.teacher.data\n level = form.level.data\n description = form.description.data\n\n n_course = Corso(name, teacher, level, description)\n db.session.add(n_course)\n\n form.name.data = \"\"\n form.teacher.data = \"\"\n form.level.data = \"\"\n form.description.data = \"\"\n \n try:\n db.session.commit()\n flash('Corso creato correttamente', 'success')\n return redirect(url_for('corsi.lista'))\n except Exception as e:\n db.session.rollback()\n flash(\"Errore durante la creazione del corso: %s\" % str(e), 'danger')\n\n return render_template(\"corsi_create.html\", form=form)\n\n\n'''\nVisualizzazione di un corso (con gestione serate e tags (TODO))\n'''\n@corsi_blueprint.route(\"/\", methods=('GET', 'POST'))\ndef dettaglio_corso(corso_id):\n \n # Gestione aggiunta serate\n form = SerataForm()\n if form.validate_on_submit():\n\n data = form.data.data #date (not datetime!) object\n txt_time = form.txt_time.data #string formato HH:MM\n if not txt_time:\n txt_time = \"00:00\"\n # Converto in oggetto datetime.time per combinarlo con la data\n # in fase di creazione oggetto Serata\n data_time = datetime.datetime.strptime(txt_time, '%H:%M').time()\n nome = form.nome.data\n descrizione = form.descrizione.data\n link_partecipazione = form.link_partecipazione.data\n link_registrazione = form.link_registrazione.data\n\n nuova_serata = Serata(\n nome, \n descrizione, \n datetime.datetime.combine(data,data_time), # Combino data con ore-minuti\n link_partecipazione,\n link_registrazione)\n nuova_serata.corso_id = corso_id\n # Reset dei campi della form\n form.data.data = \"\"\n form.txt_time.data = \"\"\n form.nome.data = \"\"\n form.descrizione.data = \"\"\n form.link_partecipazione.data = \"\"\n form.link_registrazione.data = \"\"\n\n db.session.add(nuova_serata)\n try:\n db.session.commit()\n flash('Inserimento avvenuto con successo.', 'success')\n except Exception as e:\n flash(\"Errore durante l'inserimento della serata: %s\" % str(e), 'error')\n db.session.rollback()\n \n corso = Corso.query.get_or_404(corso_id)\n return render_template('corsi_dettaglio.html', corso=corso, form=form)\n\n\n'''\nCancellazione di un corso\n'''\n@corsi_blueprint.route(\"/delete/\", methods=('GET', 'POST'))\n@login_required\ndef corso_delete(id):\n '''\n Delete corso\n '''\n my_course = Corso.query.filter_by(id=id).first()\n db.session.delete(my_course)\n try:\n db.session.commit()\n flash('Cancellazione avvenuta con successo.', 'success')\n except Exception as e:\n db.session.rollback()\n flash(\"Errore durante la cancellazione del corso: %s\" % str(e), 'danger')\n return redirect(url_for('corsi.lista'))", "sub_path": "Flask/Lezione7/project/corsi/routes.py", "file_name": "routes.py", "file_ext": "py", "file_size_in_byte": 4141, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Blueprint", "line_number": 23, "usage_type": "call"}, {"api_name": "project.corsi.models.Corso.query.order_by", "line_number": 36, "usage_type": "call"}, {"api_name": "project.corsi.models.Corso.query", "line_number": 36, "usage_type": "attribute"}, {"api_name": "project.corsi.models.Corso", "line_number": 36, "usage_type": "name"}, {"api_name": "sqlalchemy.asc", "line_number": 36, "usage_type": "call"}, {"api_name": "project.corsi.models.Corso.nome", "line_number": 36, "usage_type": "attribute"}, {"api_name": "flask.render_template", "line_number": 37, "usage_type": "call"}, {"api_name": "project.corsi.forms.CorsiForm", "line_number": 50, "usage_type": "call"}, {"api_name": "project.corsi.models.Corso", "line_number": 59, "usage_type": "call"}, {"api_name": "project.db.session.add", "line_number": 60, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 60, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 60, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 68, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 68, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 68, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 69, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 70, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 70, "usage_type": "call"}, {"api_name": "project.db.session.rollback", "line_number": 72, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 72, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 72, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 73, "usage_type": "call"}, {"api_name": "flask.render_template", "line_number": 75, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 47, "usage_type": "name"}, {"api_name": "project.serate.forms.SerataForm", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 94, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 94, "usage_type": "attribute"}, {"api_name": "project.serate.models.Serata", "line_number": 100, "usage_type": "call"}, {"api_name": "datetime.datetime.combine", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 103, "usage_type": "attribute"}, {"api_name": "project.db.session.add", "line_number": 115, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 115, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 115, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 117, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 117, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 117, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 118, "usage_type": "call"}, {"api_name": "flask.flash", "line_number": 120, "usage_type": "call"}, {"api_name": "project.db.session.rollback", "line_number": 121, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 121, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 121, "usage_type": "name"}, {"api_name": "project.corsi.models.Corso.query.get_or_404", "line_number": 123, "usage_type": "call"}, {"api_name": "project.corsi.models.Corso.query", "line_number": 123, "usage_type": "attribute"}, {"api_name": "project.corsi.models.Corso", "line_number": 123, "usage_type": "name"}, {"api_name": "flask.render_template", "line_number": 124, "usage_type": "call"}, {"api_name": "project.corsi.models.Corso.query.filter_by", "line_number": 136, "usage_type": "call"}, {"api_name": "project.corsi.models.Corso.query", "line_number": 136, "usage_type": "attribute"}, {"api_name": "project.corsi.models.Corso", "line_number": 136, "usage_type": "name"}, {"api_name": "project.db.session.delete", "line_number": 137, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 137, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 137, "usage_type": "name"}, {"api_name": "project.db.session.commit", "line_number": 139, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 139, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 139, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 140, "usage_type": "call"}, {"api_name": "project.db.session.rollback", "line_number": 142, "usage_type": "call"}, {"api_name": "project.db.session", "line_number": 142, "usage_type": "attribute"}, {"api_name": "project.db", "line_number": 142, "usage_type": "name"}, {"api_name": "flask.flash", "line_number": 143, "usage_type": "call"}, {"api_name": "flask.redirect", "line_number": 144, "usage_type": "call"}, {"api_name": "flask.url_for", "line_number": 144, "usage_type": "call"}, {"api_name": "flask_login.login_required", "line_number": 131, "usage_type": "name"}]} +{"seq_id": "502883695", "text": "from minisom import MiniSom\nimport pandas as pd\nimport numpy as np\n\nbase = pd.read_csv('credit_data.csv')\nbase = base.dropna()\nbase.loc[base.age < 0, 'age'] = 40.92 #Trocamos idades negativas para a media de idade do Banco de dados\n\nX = base.iloc[:, 0:4].values\ny = base.iloc[:, 4].values\n\nfrom sklearn.preprocessing import MinMaxScaler\nnormalizador = MinMaxScaler(feature_range = (0,1))\nX = normalizador.fit_transform(X)\n\nsom = MiniSom(x = 15, y = 15, input_len = 4, random_seed = 0) #Deixaremos o learning_rate e sigma default sendo 0.5 e 0 respectivamente\nsom.random_weights_init(X)\nsom.train_random(data = X, num_iteration = 100)\n\nfrom pylab import pcolor, colorbar, plot\npcolor(som.distance_map().T) #Os amarelos podem ser outliers\ncolorbar()\n\nmarkers = ['o', 's']\ncolors = ['r', 'g']\n\nfor i, x in enumerate(X):\n w = som.winner(x)\n plot(w[0] + 0.5, w[1] + 0.5, markers[y[i]],\n markerfacecolor = 'None', markersize = 10,\n markeredgecolor = colors[y[i]], markeredgewidth = 2)\n\n# ==== Detectando Fraudes ====\n \nmapeamento = som.win_map(X)\nsuspeitos = np.concatenate((mapeamento[(13,9)], mapeamento[(1,10)]), axis = 0) #Pegamos dois quadradinhos amarelos como suspeitos, pode ser diferente dependendo do resultado\nsuspeitos = normalizador.inverse_transform(suspeitos)\n\nclasse = []\nfor i in range(len(base)):\n for j in range(len(suspeitos)):\n if base.iloc[i, 0] == int(round(suspeitos[j,0])): #Roud serve para arredondar\n classe.append(base.iloc[i,4])\nclasse = np.asarray(classe)\n\nsuspeitos_final = np.column_stack((suspeitos, classe))\nsuspeitos_final = suspeitos_final[suspeitos_final[:, 4].argsort()]", "sub_path": "Mapas auto organizaveis/credit_data_mapa.py", "file_name": "credit_data_mapa.py", "file_ext": "py", "file_size_in_byte": 1646, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 5, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.MinMaxScaler", "line_number": 13, "usage_type": "call"}, {"api_name": "minisom.MiniSom", "line_number": 16, "usage_type": "call"}, {"api_name": "pylab.pcolor", "line_number": 21, "usage_type": "call"}, {"api_name": "pylab.colorbar", "line_number": 22, "usage_type": "call"}, {"api_name": "pylab.plot", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.column_stack", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "613733736", "text": "from django.contrib.auth import login, logout\nfrom django.contrib.auth.models import User\nfrom django.core.urlresolvers import reverse\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.template.response import TemplateResponse\nfrom django.views.generic import TemplateView, View\nfrom django.shortcuts import render\n\nfrom subscription.models import Character, Subscription\nfrom .forms import LoginForm, SignupForm, CharacterForm\n\n# Create your views here.\n\n\ndef login_page(request):\n if request.user.is_authenticated():\n return HttpResponseRedirect(reverse('home'))\n return TemplateResponse(request, 'login.html', {'login_form': LoginForm(), 'registration_form': SignupForm()})\n\n\nclass Home(TemplateView):\n template_name = 'home.html'\n\n def get_context_data(self, **kwargs):\n return {'characters': Character.objects.all()}\n\n\n# author sign-in\ndef login_in(request):\n \"\"\"\n Sign in api\n username -- username of a user\n password -- users password\n \"\"\"\n\n if request.method == 'POST':\n form = LoginForm(request.POST or None)\n if form.is_valid():\n user = form.login(request)\n if user is not None:\n login(request, user)\n return HttpResponseRedirect(reverse('home')) # Redirect to a success page.\n return TemplateResponse(request, 'login.html', {'login_form': form, 'registration_form': SignupForm()})\n\n return HttpResponseRedirect(reverse('home'))\n\n\ndef registration(request):\n \"\"\"\n Registration api\n username -- username of a user\n password1 -- users password\n password2 -- users password confirmation\n \"\"\"\n\n if request.method == 'POST':\n form = SignupForm(request.POST or None)\n if form.is_valid():\n user = User.objects.create_user(\n username=form.data['username'],\n password=form.data['password1']\n )\n message = 'Successfully registered now please login'\n else:\n message = form.errors\n return TemplateResponse(request, 'login.html', {'registration_form': form, 'login_form': LoginForm(),\n 'message': message})\n return HttpResponseRedirect(reverse('home'))\n\n\nclass CharacterSubscription(View):\n form_class = CharacterForm\n template_name = 'character_details.html'\n user = None\n\n def get_queryset(self, character_id):\n return Character.objects.get(id=character_id)\n\n def get(self, request, *args, **kwargs):\n obj = self.get_queryset(kwargs.get('character_id'))\n data = {'fields': []}\n subscription = obj.subscribers.filter(subscriber=request.user)\n if subscription.exists():\n data['fields'] = subscription[0].fields.split(',')\n\n form = self.form_class(initial=data)\n return render(request, self.template_name, {'form': form, 'character': obj})\n\n def post(self, request, *args, **kwargs):\n form = self.form_class(request.POST)\n if form.is_valid():\n obj = self.get_queryset(kwargs.get('character_id'))\n subscription = obj.subscribers.filter(subscriber=request.user)\n\n if subscription.exists():\n if form.data.getlist('fields'):\n subscription.update(fields=','.join(form.data.getlist('fields')))\n else:\n subscription.delete() # delete if no fields are subscribed\n else:\n obj.subscribers.create(subscriber=request.user, fields=','.join(form.data.getlist('fields')))\n\n return render(request, self.template_name, {'form': form, 'character': str(obj) + ' saved successfully.'})\n\n\ndef log_out(request):\n \"\"\"\n logs out a user(Author/Publisher)\n \"\"\"\n if request.method == 'POST':\n logout(request)\n return HttpResponseRedirect(reverse('login-page'))\n", "sub_path": "web/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3906, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.http.HttpResponseRedirect", "line_number": 17, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 17, "usage_type": "call"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 18, "usage_type": "call"}, {"api_name": "forms.LoginForm", "line_number": 18, "usage_type": "call"}, {"api_name": "forms.SignupForm", "line_number": 18, "usage_type": "call"}, {"api_name": "django.views.generic.TemplateView", "line_number": 21, "usage_type": "name"}, {"api_name": "subscription.models.Character.objects.all", "line_number": 25, "usage_type": "call"}, {"api_name": "subscription.models.Character.objects", "line_number": 25, "usage_type": "attribute"}, {"api_name": "subscription.models.Character", "line_number": 25, "usage_type": "name"}, {"api_name": "forms.LoginForm", "line_number": 37, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 41, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 42, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 42, "usage_type": "call"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 43, "usage_type": "call"}, {"api_name": "forms.SignupForm", "line_number": 43, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 45, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 45, "usage_type": "call"}, {"api_name": "forms.SignupForm", "line_number": 57, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects.create_user", "line_number": 59, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User.objects", "line_number": 59, "usage_type": "attribute"}, {"api_name": "django.contrib.auth.models.User", "line_number": 59, "usage_type": "name"}, {"api_name": "django.template.response.TemplateResponse", "line_number": 66, "usage_type": "call"}, {"api_name": "forms.LoginForm", "line_number": 66, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 68, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 68, "usage_type": "call"}, {"api_name": "django.views.generic.View", "line_number": 71, "usage_type": "name"}, {"api_name": "forms.CharacterForm", "line_number": 72, "usage_type": "name"}, {"api_name": "subscription.models.Character.objects.get", "line_number": 77, "usage_type": "call"}, {"api_name": "subscription.models.Character.objects", "line_number": 77, "usage_type": "attribute"}, {"api_name": "subscription.models.Character", "line_number": 77, "usage_type": "name"}, {"api_name": "subscription.models", "line_number": 82, "usage_type": "name"}, {"api_name": "subscription.models.exists", "line_number": 83, "usage_type": "call"}, {"api_name": "subscription.models", "line_number": 83, "usage_type": "name"}, {"api_name": "subscription.models", "line_number": 84, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 87, "usage_type": "call"}, {"api_name": "subscription.models", "line_number": 93, "usage_type": "name"}, {"api_name": "subscription.models.exists", "line_number": 95, "usage_type": "call"}, {"api_name": "subscription.models", "line_number": 95, "usage_type": "name"}, {"api_name": "subscription.models.update", "line_number": 97, "usage_type": "call"}, {"api_name": "subscription.models", "line_number": 97, "usage_type": "name"}, {"api_name": "subscription.models.delete", "line_number": 99, "usage_type": "call"}, {"api_name": "subscription.models", "line_number": 99, "usage_type": "name"}, {"api_name": "django.shortcuts.render", "line_number": 103, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 111, "usage_type": "call"}, {"api_name": "django.http.HttpResponseRedirect", "line_number": 112, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 112, "usage_type": "call"}]} +{"seq_id": "458611051", "text": "from typing import List\n\n\nclass Solution:\n def isValidSudoku(self, board: List[List[str]]) -> bool:\n row = [set() for _ in range(9)]\n col = [set() for _ in range(9)]\n block = [set() for _ in range(9)]\n for i in range(9):\n for j in range(9):\n val = board[i][j]\n if val != \".\":\n if val in row[i] | col[j] | block[i // 3 * 3 + j // 3]:\n return False\n else:\n row[i].add(val)\n col[j].add(val)\n block[i // 3 * 3 + j // 3].add(val)\n return True\n", "sub_path": "Week_07/0036_valid_sudoku.py", "file_name": "0036_valid_sudoku.py", "file_ext": "py", "file_size_in_byte": 647, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "typing.List", "line_number": 5, "usage_type": "name"}]} +{"seq_id": "518322391", "text": "import os\n\nfrom flask import Flask\nfrom flask_sqlalchemy import SQLAlchemy\nfrom flask_cors import CORS\n\n# sub classe para poder customizar parâmetros do driver\n# até o momento o Flask SQLAlchemy não suporta uma maneira mais amigável\n# ver issue: https://github.com/mitsuhiko/flask-sqlalchemy/issues/120\n\n\nclass MySQLAlchemy(SQLAlchemy):\n def apply_driver_hacks(self, app, info, options):\n options.update({\n 'isolation_level': 'REPEATABLE READ',\n })\n super().apply_driver_hacks(app, info, options)\n\n\ndb = MySQLAlchemy()\n\n\ndef create_app(test_config=None):\n # create and configure the app\n app = Flask(__name__, instance_relative_config=True)\n CORS(app)\n app.config.from_mapping(\n SECRET_KEY='dev'\n )\n\n if test_config is None:\n # load the instance config, if it exists, when not testing\n app.config.from_envvar('APP_SETTINGS')\n else:\n # load the test config if passed in\n app.config.from_mapping(test_config)\n\n # ensure the instance folder exists\n try:\n os.makedirs(app.instance_path)\n except OSError:\n pass\n\n # database\n db.init_app(app)\n\n from flaskr.apis.auth import auth_api\n from flaskr.apis.usuario import usuario_api\n from flaskr.apis.notificacao import notificacao_api\n\n app.register_blueprint(auth_api.bp)\n app.register_blueprint(usuario_api.bp)\n app.register_blueprint(notificacao_api.bp)\n\n @app.route('/api')\n def check():\n return 'On!'\n\n return app\n", "sub_path": "back/notificacao/flaskr/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 1550, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask_sqlalchemy.SQLAlchemy", "line_number": 12, "usage_type": "name"}, {"api_name": "flask.Flask", "line_number": 25, "usage_type": "call"}, {"api_name": "flask_cors.CORS", "line_number": 26, "usage_type": "call"}, {"api_name": "os.makedirs", "line_number": 40, "usage_type": "call"}, {"api_name": "flaskr.apis.auth.auth_api.bp", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flaskr.apis.auth.auth_api", "line_number": 51, "usage_type": "name"}, {"api_name": "flaskr.apis.usuario.usuario_api.bp", "line_number": 52, "usage_type": "attribute"}, {"api_name": "flaskr.apis.usuario.usuario_api", "line_number": 52, "usage_type": "name"}, {"api_name": "flaskr.apis.notificacao.notificacao_api.bp", "line_number": 53, "usage_type": "attribute"}, {"api_name": "flaskr.apis.notificacao.notificacao_api", "line_number": 53, "usage_type": "name"}]} +{"seq_id": "640414175", "text": "\nimport pandas as pd\nimport warnings\nwarnings.filterwarnings(\"ignore\")\nimport numpy as np\nfrom sklearn.preprocessing import LabelEncoder\ntrain=pd.read_csv('train.csv')\ntest=pd.read_csv('test.csv')\n\n\n# In[191]:\n\ncat_columns=['city', 'state', 'store_location', 'time_zone',\n 'location_employee_code', 'credit_score']\n\n\n# In[192]:\n\ntrain['credit_score_range_max']=0\ntrain['credit_score_range_min']=0\n\n\ntest['credit_score_range_max']=0\ntest['credit_score_range_min']=0\n\n\nfor i in range(len(train)):\n try:\n train['credit_score_range_max'][i]=int(train.credit_score_range[i].split('TO')[1])\n train['credit_score_range_min'][i]=int(train.credit_score_range[i].split('TO')[0])\n train['credit_score_range'][i]=int(int(train.credit_score_range[i].split('TO')[1])-int(train.credit_score_range[i].split('TO')[0]))\n except:\n train['credit_score_range'][i]=0\n train['credit_score_range_max'][i]=0\n train['credit_score_range_min'][i]=0\n \n \nfor i in range(len(test)):\n try:\n test['credit_score_range_max'][i]=int(test.credit_score_range[i].split('TO')[1])\n test['credit_score_range_min'][i]=int(test.credit_score_range[i].split('TO')[0])\n test['credit_score_range'][i]=int(int(test.credit_score_range[i].split('TO')[1])-int(test.credit_score_range[i].split('TO')[0]))\n except:\n test['credit_score_range'][i]=0\n test['credit_score_range_max'][i]=0\n test['credit_score_range_min'][i]=0\n \n\n\n# In[193]:\n\nfor var in cat_columns:\n lb = LabelEncoder()\n full_var_data = pd.concat((train[var],test[var]),axis=0).astype('str')\n temp = lb.fit_transform(np.array(full_var_data))\n train[var] = lb.transform(np.array( train[var] ).astype('str'))\n test[var] = lb.transform(np.array( test[var] ).astype('str'))\n\n\n# In[194]:\n\ntrain['credit_score_range'] = train['credit_score_range'].apply(pd.to_numeric)\ntest['credit_score_range'] = test['credit_score_range'].apply(pd.to_numeric)\n\n\n# In[195]:\n\ndef getCountVar(compute_df, count_df, var_name):\n grouped_df = count_df.groupby(var_name)\n count_dict = {}\n for name, group in grouped_df:\n count_dict[name] = group.shape[0]\n\n count_list = []\n for index, row in compute_df.iterrows():\n name = row[var_name]\n count_list.append(count_dict.get(name, 0))\n return count_list\n\n\n# In[196]:\n\n#store_location\ntrain['store_location_Count']=getCountVar(train,train,'store_location')\ntest['store_location_Count']=getCountVar(test,train,'store_location')\n#time_zone\ntrain['time_zone_Count']=getCountVar(train,train,'time_zone')\ntest['time_zone_Count']=getCountVar(test,train,'time_zone')\n#location_employee_code\ntrain['location_employee_code_Count']=getCountVar(train,train,'location_employee_code')\ntest['location_employee_code_Count']=getCountVar(test,train,'location_employee_code')\n\n\n# In[197]:\n\ntrain['normalized_household_income']=(train['total_household_income']/train['employee_size'])\ntest['normalized_household_income']=(test['total_household_income']/test['employee_size'])\ntrain=train.drop('total_household_income',1)\ntest=test.drop('total_household_income',1)\n\n\n# In[198]:\n\ny=train.total_sales\ntrain=train.drop(['total_sales','outlet_no'],1)\noutlet=test.outlet_no\ntest=test.drop('outlet_no',1)\n\n\n# In[199]:\n\nfrom xgboost import XGBRegressor\nfrom sklearn.cross_validation import StratifiedKFold\nfrom sklearn.cross_validation import cross_val_score\nfrom sklearn.grid_search import GridSearchCV\n\n\n# In[209]:\n\nmodel = XGBRegressor()\nlearning_rate = [0.001, 0.01, 0.1, 0.2, 0.3]\nn_estimators=[100,200,300,400,500]\nparam_grid = dict(learning_rate=learning_rate,n_estimators=n_estimators)\nkfold = StratifiedKFold(y, n_folds=3, shuffle=True, random_state=7)\ngrid_search = GridSearchCV(model, param_grid, scoring=\"mean_absolute_error\", n_jobs=-1, cv=kfold)\n\n\n# In[210]:\n\nresult = grid_search.fit(train,y)\n# summarize results\nprint(\"Best: %f using %s\" % (result.best_score_, result.best_params_))\n\n\n# In[211]:\n\nmodel=XGBRegressor(learning_rate=0.3,n_estimators=100)\nfor traincv,testcv in kfold:\n model.fit(train.iloc[traincv],y.iloc[testcv])\n\n\n\n# In[212]:\n\ny_pred=model.predict(test)\n\n\n# In[213]:\n\noutput2 = pd.DataFrame( data={\"outlet_no\":outlet,\"total_sales_Actual\": y_pred} )\noutput2.to_csv(\"model.csv\", index=False,quoting=3)\n\n\n# In[ ]:\n\n\n\n", "sub_path": "TG_DSchallenge/train.py", "file_name": "train.py", "file_ext": "py", "file_size_in_byte": 4422, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "warnings.filterwarnings", "line_number": 4, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.preprocessing.LabelEncoder", "line_number": 53, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 55, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 56, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 57, "usage_type": "call"}, {"api_name": "pandas.to_numeric", "line_number": 62, "usage_type": "attribute"}, {"api_name": "pandas.to_numeric", "line_number": 63, "usage_type": "attribute"}, {"api_name": "xgboost.XGBRegressor", "line_number": 120, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.StratifiedKFold", "line_number": 124, "usage_type": "call"}, {"api_name": "sklearn.grid_search.GridSearchCV", "line_number": 125, "usage_type": "call"}, {"api_name": "xgboost.XGBRegressor", "line_number": 137, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 150, "usage_type": "call"}]} +{"seq_id": "182054664", "text": "from django.db import models\nfrom entity.models import Item, Personnel\n# Create your models here.\n\n\n# 项目普通人员表\nclass ItemPerson(models.Model):\n item = models.ForeignKey(Item, on_delete=models.CASCADE)\n personnel = models.ForeignKey(Personnel, on_delete=models.CASCADE)\n temp = models.TextField(default='', blank=True)\n\n def to_dict(self):\n return {\n 'id': self.id,\n 'item': self.item.id,\n 'item_name': self.item.name,\n 'personnel': self.personnel.id,\n 'temp': self.temp,\n 'name': self.personnel.name,\n 'account': self.personnel.account,\n 'authority': self.personnel.authority,\n 'team': self.personnel.team, # 所属单位\n }", "sub_path": "server/background/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 764, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.db.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 8, "usage_type": "call"}, {"api_name": "entity.models.Item", "line_number": 8, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.db.models.ForeignKey", "line_number": 9, "usage_type": "call"}, {"api_name": "entity.models.Personnel", "line_number": 9, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.CASCADE", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.db.models.TextField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "356750760", "text": "import xarray as xr\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib\nimport pandas as pd\nimport os\nfrom scipy.interpolate import interp1d\nfrom scipy.ndimage.filters import gaussian_filter1d\nfrom scipy.interpolate import CubicSpline, BSpline\nfrom hapi import *\n\n\nfrom matplotlib.font_manager import FontProperties\n\n### Plot settings\nfont = {'weight' : 'bold',\n 'size' : 12}\nlabel_fontdict = {'weight' : 'bold',\n 'size' : 12}\ntitle_fontdict = {'weight' : 'bold',\n 'size' : 12}\n\nmatplotlib.rc('font', **font)\n\n##### Define constants\n\ngo = 9.8196 #(m/s**2) \nRd = 287.04 # specific gas constant for dry air\nR_universal = 8.314472\nNa = 6.0221415e23\n\n# Some constants and the planck function (as radiance!)\npi = np.pi\nh = 6.62607004e-34 # m^2/kg/s\nc = 299792458 # m/s\nk = 1.380649e-23 # J/K\nstefan_boltzmann_c = 5.670374419*(10**-8)\n\n## Define conversion factors \nW_M_MW_CM = 1e2*1e3\n\n###### Helper function for fundamental equations\ndef planck(wav, T):\n c1 = 2.0*h*c**2\n c2 = h*c/(wav*k*T)\n intensity = c1/ ( (wav**5)*(np.exp(c2) - 1.0) )\n # Convert to W/sr/m^2/µm here directly (it was W/sr/m^2/m)\n return intensity*1.e-6\n# return intensity*1.e-2\n\ndef planck_wavenumber(wavenum, T):\n c1 = 2.0*h*(c**2)*(wavenum**3)\n c2 = (h*c*wavenum)/(k*T)\n intensity = c1/(np.exp(c2) - 1.0)\n return intensity\n# return (c1, c2, intensity)\n\ndef stefan_boltzmann(T):\n return stefan_boltzmann_c*(T**4)\n\ndef compute_rf_from_diff_spec_ds(rad_diff, nu):\n '''Given difference of spectra in \n W*m^-2*sr^-1*cm^-1, compute radiative forcing\n W/m^2'''\n# rad_diff = ds['lw_down_total']\n# nu = ds['nu']\n \n integral_rad_diff = np.trapz(rad_diff, x = nu)\n # factor of pi comes from integrating over half sphere\n return integral_rad_diff * np.pi\n\ndef compute_rf_from_diff_spec(rad_diff,\n nu):\n '''Given difference of spectra in \n W*m^-2*sr^-1*cm^-1, compute radiative forcing\n W/m^2'''\n \n integral_rad_diff = np.trapz(rad_diff, x = nu)\n # factor of pi comes from integrating over half sphere\n return integral_rad_diff * np.pi\n\n\n\ndef _filter_k_range_to_aeri(rad_array, nu):\n '''Filter array of radiances to lie with wavenumber\n range of instrument. \n \n Args\n -----\n rad_array - np.array\n \n nu - np.array\n '''\n nu_inds = np.where((nu > 491.79016) & \n (nu < 1799.8556))\n return (rad_array[nu_inds], nu[nu_inds])\n\ndef compute_mean_rad_800_band(rad_array, nu):\n '''Compute mean radiance in 790 - 810 cm band.'''\n \n nu_inds = np.where((nu > 790.0) & (nu < 810.0))\n return np.nanmean(rad_array[nu_inds])\n\n\n###### Helper function for calculating profile properties\ndef compute_profile_properties_merra2(ds, verbose=True):\n ''' Given single profile from merra2 meteorlogical reanalysis, compute pressure levels, VMR \n for water vapor. Profile should contain variables PS, PL, QV, T, and DELP'''\n # Surface pressure at location\n ps_local = ds['PS'].values\n p_local = ds['PL'].values\n # q and T profiles at location\n q_local = ds['QV'].values\n T_local = ds['T'].values\n\n NLEV = len(T_local)\n\n dz = np.divide(ds['DELP'].values,ds['PL'].values)*(Rd*T_local*(1+0.608*q_local))/go\n rho_N = ds['PL'].values*(1-q_local*1.6068)/(R_universal*T_local)*Na/10000.0\n rho_N_h2o = ds['PL'].values*(q_local*1.6068)/(R_universal*T_local)*Na/10000.0\n vmr_h2o = q_local*1.6068\n\n if verbose:\n print('Total column density of dry air: ' +str(np.sum(dz*rho_N))+' molec/cm^2')\n print('Total column density of water vapor: ' + str(np.sum(dz*rho_N_h2o))+' molec/cm^2')\n VCD_dry = dz*rho_N\n \n return(p_local, T_local, dz, vmr_h2o, VCD_dry, rho_N_h2o, rho_N)\n\n\n\ndef create_cross_section_matrix_hapi(p_prof, T_prof, xmin, xmax, time_i=None, output_path=None):\n '''Given temperature/pressure profile, create cross-section matrix (w/ option to save)\n Args:\n output_path - str\n If not None, save cs matrices as netcdf to specified path.\n \n Returns:\n cs_matrix - xr.Dataset [number of levels, number of wavelengths]\n '''\n nu_, cs_co2 = absorptionCoefficient_Voigt(SourceTables='CO2_S', WavenumberRange=[xmin,xmax],Environment={'p':1,'T':270},IntensityThreshold=1e-27)\n \n NLEV = len(p_prof)\n \n cs_matrix_co2 = np.zeros((len(nu_),NLEV))\n cs_matrix_ch4 = np.zeros((len(nu_),NLEV))\n cs_matrix_h2o = np.zeros((len(nu_),NLEV))\n \n\n # Loop over each layer \n for i in range(NLEV):\n print(str(i)+'/'+str(NLEV), end='\\r')\n p_ = p_prof[i]/101325\n # print(p_)”\n T_ = T_prof[i]\n nu_, cs_co2 = absorptionCoefficient_Voigt(SourceTables='CO2_S', WavenumberRange=[xmin,xmax],Environment={'p':p_,'T':T_},IntensityThreshold=1e-27)\n nu_, cs_ch4 = absorptionCoefficient_Voigt(SourceTables='CH4_S', WavenumberRange=[xmin,xmax],Environment={'p':p_,'T':T_},IntensityThreshold=1e-27)\n nu_, cs_h2o = absorptionCoefficient_Voigt(SourceTables='H2O_S', WavenumberRange=[xmin,xmax],Environment={'p':p_,'T':T_},IntensityThreshold=1e-27)\n cs_matrix_co2[:,i] = cs_co2\n cs_matrix_ch4[:,i] = cs_ch4\n cs_matrix_h2o[:,i] = cs_h2o\n \n \n cs_matrix_ds = xr.Dataset()\n cs_matrix_co2_da = xr.DataArray(cs_matrix_co2, coords = [nu_, p_prof], dims = ['nu','pressure'])\n cs_matrix_ch4_da = xr.DataArray(cs_matrix_ch4, coords = [nu_, p_prof], dims = ['nu','pressure'])\n cs_matrix_h2o_da = xr.DataArray(cs_matrix_h2o, coords = [nu_, p_prof], dims = ['nu','pressure'])\n\n\n cs_matrix_ds['cs_matrix_co2'] = cs_matrix_co2_da\n cs_matrix_ds['cs_matrix_ch4'] = cs_matrix_ch4_da\n cs_matrix_ds['cs_matrix_h2o'] = cs_matrix_h2o_da\n if not (time_i is None):\n cs_matrix_ds['time'] = time_i\n cs_matrix_ds = cs_matrix_ds.assign_coords(time = cs_matrix_ds['time'])\n\n if output_path:\n cs_matrix_ds.to_netcdf(output_path)\n return cs_matrix_ds\n\n######## Functions for performing RT calculations\n\n# def compute_tau_matrix(cs_matrix_co2,\n# cs_matrix_h2o,\n# cs_matrix_ch4,\n# CO2_mr = 400.e-6, \n# CH4_mr = 1.8e-6,\n# AMF=1.0):\n# '''Given cross-section matrices and VMRs of \n# gases, compute matrix of optical depths. \n\n# '''\n\n\n\ndef compute_downwelling_radiation(cs_matrix_co2,\n cs_matrix_h2o,\n cs_matrix_ch4,\n T_prof,\n VCD_dry_prof, \n vmr_h2o_prof,\n nu,\n CO2_mr = 400.e-6, \n CH4_mr = 1.8e-6,\n AMF=1.0):\n '''Compute downwelling radiation from an atmosphere containing \n 3 greenhouse gasses (CO2, CH4, and water vapor).\n \n CO2 and CH4 are assumed to be well-mixed, whereas the vmr of water vapor\n can vary. \n \n '''\n NLEV = cs_matrix_co2.shape[1]\n\n # Generate matrices of optical thickness per layer now for each gas: \n tau_co2 = cs_matrix_co2*VCD_dry_prof*CO2_mr*AMF \n tau_h2o = cs_matrix_h2o*VCD_dry_prof*vmr_h2o_prof*AMF \n tau_ch4 = cs_matrix_ch4*VCD_dry_prof*CH4_mr*AMF \n \n # total transmission\n T = np.exp(-tau_co2)*np.exp(-tau_h2o)*np.exp(-tau_ch4)\n \n # component-by-component transmission \n T_CO2 = np.exp(-tau_co2)\n T_H2O = np.exp(-tau_h2o)\n T_CH4 = np.exp(-tau_ch4)\n \n # Generate Planck curve per layer + surface:\n wl_nu = 1.e7/nu*1.e-9\n wavenum_m = nu*1e2\n # Use skin temperature of 300K\n# B = np.zeros((len(nu_),NLEV))\n\n B = np.zeros(T.shape)\n for i in range(NLEV):\n B[:,i] = planck_wavenumber(wavenum_m,T_prof[i])*1e2\n \n # compute downwelling IR radiation \n Rdown = np.zeros(cs_matrix_co2.shape)\n Rdown_CO2 = np.empty_like(Rdown)\n Rdown_CH4 = np.empty_like(Rdown)\n Rdown_H2O = np.empty_like(Rdown)\n\n\n\n for i in range(NLEV):\n Rdown[:,i] = B[:,i]*(1-T[:,i])*np.prod(T[:,i+1:],axis=1)\n # component-by-component\n Rdown_CO2[:,i] = B[:,i]*(1-T_CO2[:,i])*np.prod(T_CO2[:,i+1:],axis=1)\n Rdown_CH4[:,i] = B[:,i]*(1-T_CH4[:,i])*np.prod(T_CH4[:,i+1:],axis=1)\n Rdown_H2O[:,i] = B[:,i]*(1-T_H2O[:,i])*np.prod(T_H2O[:,i+1:],axis=1)\n \n Surface_Down = np.sum(Rdown,axis=1)\n\n Surface_Down_CO2 = np.sum(Rdown_CO2,axis=1)\n Surface_Down_CH4 = np.sum(Rdown_CH4,axis=1)\n Surface_Down_H2O = np.sum(Rdown_H2O,axis=1)\n \n return (Surface_Down_CO2, Surface_Down_CH4, Surface_Down_H2O, Surface_Down)\n############\n\ndef interpolate_profile(p_prof, \n var_prof, \n p_interp_grid, \n method = 'CubicSpline',\n return_interp_obj = False,\n **kwargs):\n '''\n Interpolate profile to given pressure grid. \n \n Args\n -----\n p_prof - np.array\n pressure profile \n var_prof - np.array\n profile of variable to interpolate\n method - str {'CubicSpline','Linear'}\n interpolation method to use\n \n return_interp_obj - bool\n if True, return scipy.interpolate object along with profile\n \n \n Returns\n -------\n (p_interp_grid, var_prof_interpolated) - Profile interpolated to p_interp_grid\n \n '''\n # ensure coords are increasing \n reversed_coords = False\n if (p_prof[1] < p_prof[0]) & \\\n (var_prof[1] < var_prof[0]):\n# (p_interp_grid[1] < p_interp_grid[0]):\n# print('here')\n p_prof = p_prof[::-1]\n var_prof = var_prof[::-1]\n reversed_coords = True\n \n# return (p_prof, var_prof)\n if method == 'CubicSpline':\n interp_obj = CubicSpline(p_prof,var_prof, bc_type = 'natural')\n var_prof_interpolated = interp_obj(p_interp_grid)\n if method == 'BSpline':\n interp_obj = BSpline(p_prof,var_prof, **kwargs)\n var_prof_interpolated = interp_obj(p_interp_grid)\n \n if method == 'Linear':\n interp_obj = interp1d(p_prof,var_prof, bounds_error = False)\n var_prof_interpolated = interp_obj(p_interp_grid)\n \n# if reversed_coords: \n# var_prof_interpolated = var_prof_interpolated[::-1]\n if return_interp_obj:\n return (p_interp_grid, var_prof_interpolated, interp_obj)\n else:\n return (p_interp_grid, var_prof_interpolated)\n\ndef interpolate_T_prof(T_prof, \n dz_prof,\n num_vertical_points = 3000):\n # interpolate T vs. pressure (to find dT/dz @ tau = 1)\n # interpolate from bottom up\n z_prof = np.cumsum(dz_prof[::-1])\n p_interp_grid = np.linspace(0, z_prof.max(), num_vertical_points)\n \n # make temperature increasing\n T_prof_incr = T_prof[::-1]\n\n bottom_lapse_rate = (T_prof_incr[1] - T_prof_incr[0])/(z_prof[1] - z_prof[0])\n\n # need a level at surface (z=0) to perform integration\n z_prof_0 = np.append(np.array([0]),z_prof)\n # T_prof_0 = np.append(np.array(T_prof_incr[0]),T_prof_incr)\n\n T_prof_0 = np.append(np.array(T_prof_incr[0] - bottom_lapse_rate * (z_prof[0])),T_prof_incr)\n\n interp_prof = interpolate_profile(z_prof_0,\n T_prof_0,\n mehtod = 'CubicSpline',\n p_interp_grid = p_interp_grid,\n return_interp_obj=True)\n \n \n return (z_prof_0, interp_prof[-1])\n\n\ndef calc_emission_and_dT_dz(tau_matrix,\n T_interpolator, \n z_prof_0):\n \n '''Given tau matrix of d_taus through layers,\n compute dT/dz and emission height and emission height.\n \n \n Args\n ---------\n tau_matrix - np.array \n Matrix of taus for a gas\n \n interp_prof - scipy.interpolate._cubic.CubicSpline\n Spline object for T(z) from which dT/dz can be inferred \n \n '''\n\n\n N_ks = tau_matrix.shape[0]\n tau_matrix_cumsum = np.cumsum(tau_matrix, axis = 1)\n\n # add zero column to represent tau @ surface\n zero_col = np.zeros(tau_matrix.shape[0]).reshape((tau_matrix.shape[0],1))\n tau_matrix_cumsum = np.concatenate((zero_col, tau_matrix_cumsum), axis = 1)\n\n\n\n tau_wl = np.zeros((N_ks,))\n dT_dz = np.zeros((N_ks,))\n # we're only going up to tau = 1\n p_interp_grid_tau = np.linspace(0, 5, 1500)\n for k_i in range(N_ks):\n \n tau_cumsum_z = tau_matrix_cumsum[k_i,:]\n # interpolate cumsum(tau) vs. pressure (to find tau = 1)\n interp_tau_cumsum = interpolate_profile(tau_cumsum_z,\n z_prof_0, \n method = 'Linear',\n p_interp_grid = p_interp_grid_tau,\n return_interp_obj=True)\n\n\n z_at_tau_1 = interp_tau_cumsum[-1](1.0)\n # interp to pressure where tau = 1\n tau_wl[k_i] = interp_tau_cumsum[-1](1.0)\n dT_dz[k_i] = T_interpolator(tau_wl[k_i], nu = 1)\n \n return (tau_wl, dT_dz)\n\n\n####### Plotting functions\n\ndef plot_profile(v_coord, temp,\n v_coord_type = 'pressure',\n plot_kind = 'line',\n min_pres = 10, xlabel = \"Temperature [C]\", newfig_bool = True,\n xlim = None,\n ylim = None,\n linewidth = 10,\n figsize = (6,6),\n label = None, rotation = 0):\n '''Given xr.dataset of single profile, plot vertical profile w/ log(p)\n \n Args\n -------\n v_coord - array-like\n vertical coordinate\n v_coord_type - str {'pressure', 'height'}\n Use pressure or height as vertical coordinate\n plot_kind - str {'line', 'scatter'}\n Kind of plot to use.\n \n Returns\n --------\n plt.axis\n \n '''\n\n \n if newfig_bool:\n plt.figure(figsize = figsize)\n if plot_kind == 'line':\n plt.plot(temp, v_coord, linewidth = linewidth, label = label)\n elif plot_kind == 'scatter':\n plt.scatter(temp, v_coord, label = label)\n \n if v_coord_type == 'pressure':\n plt.gca().invert_yaxis()\n plt.ylim([np.nanmax(v_coord), min_pres])\n plt.gca().set_yscale('log')\n plt.ylabel(\"Pressure [Pa]\")\n elif v_coord_type == 'height':\n plt.ylim([np.nanmin(v_coord), np.nanmax(v_coord)])\n plt.ylabel(\"Height [m]\", weight = 'bold')\n \n \n plt.grid()\n plt.xlabel(xlabel, weight = 'bold')\n \n if xlim: \n plt.xlim(xlim)\n if ylim: \n plt.ylim(ylim)\n \n if rotation != 0: \n plt.xticks(rotation=rotation)\n# plt.locator_params(nbins=8)\n# plt.yticks(np.arange(min_pres, pres.max(), 100.0))\n\n return plt.gca()\n\n\ndef plot_emission_height(wl_nm, \n tau_wl, \n T_prof, \n p_prof, \n label, \n tau_wl_2 = None, \n label_2 = None,\n ylim = None , \n xlim = [5,30],\n log_scale = False,\n ave_emmission_pres = None):\n# wl_nm = wl_nu*1e6\n\n plt.figure(figsize = (15,5))\n ax0 = plt.subplot(121)\n plt.plot(wl_nm, tau_wl, label = label)\n if not (tau_wl_2 is None):\n plt.plot(wl_nm, tau_wl_2, label = label_2)\n if log_scale:\n plt.yscale('log')\n plt.grid()\n# plt.gca().set_yscale('log')\n# plt.gca().invert_yaxis()\n\n plt.xlabel(r'Wavenumber $[cm^{-1}]$')\n plt.ylabel(r'$\\tau = 1$ Height [m]')\n plt.legend()\n if ylim:\n# plt.ylim([p_full.max(), 4*10**4])\n plt.ylim(ylim)\n# plt.xlim((12,18))\n if xlim:\n plt.xlim(xlim)\n else:\n plt.xlim([wl_nm.min(), wl_nm.max()])\n plt.subplot(122) #, sharey = ax0)\n\n plt.plot(T_prof, p_prof, '.-')\n if log_scale:\n plt.yscale('log')\n# plt.gca().set_yscale('log')\n# plt.gca().invert_yaxis()\n if ave_emmission_pres:\n plt.axhline(y = ave_emmission_pres, color = 'r', linestyle = '--')\n if ylim:\n plt.ylim(ylim) \n plt.grid()\n \n \ndef plot_downwelling_rad(Down_CO2, \n Down_CH4, \n Down_H2O, \n nu,\n xlims = (500, 1800),\n figsize = (12,7)):\n fig = plt.figure(figsize = figsize)\n ax1 = fig.add_subplot(111) \n ax2 = ax1.twiny()\n \n \n ax1.plot(nu, 1e3*Down_CO2,label='R $CO_2$', alpha=0.7 ,linewidth = 0.5)\n ax1.plot(nu, 1e3*Down_CH4,label='R $CH_4$', alpha=0.7 ,linewidth = 0.5)\n ax1.plot(nu, 1e3*Down_H2O,label='R $H_{2}O$', alpha=0.7 ,linewidth = 0.5)\n wl_nu = 1.e7/nu*1.e-9\n wavenum_m = nu*1e2\n\n ax1.plot(nu, W_M_MW_CM*planck_wavenumber(wavenum_m,244),label='BB @ 244K',alpha=0.8)\n \n ax1.plot(nu, W_M_MW_CM*planck_wavenumber(wavenum_m,249),label='BB @ 249K',alpha=0.8)\n\n ax1.plot(nu, W_M_MW_CM*planck_wavenumber(wavenum_m,230),label='BB @ 230K',alpha=0.8)\n\n \n ax1.plot(nu, W_M_MW_CM*planck_wavenumber(wavenum_m,214),label='BB @ 214K',alpha=0.8)\n plt.legend(loc=0)\n \n ax1.plot(nu, W_M_MW_CM*planck_wavenumber(wavenum_m,200),label='BB @ 200K',alpha=0.8)\n ax1.legend(loc=0)\n\n # plt.xlim((491,1799))\n if not xlims is None:\n ax1.set_xlim(xlims)\n \n def tick_function(X):\n wnum = (1/X)*1e4\n return [\"%.1f\" % z for z in wnum]\n\n ax1Ticks = ax1.get_xticks()\n ax2Ticks = ax1Ticks\n ax2.set_xticks(ax2Ticks)\n ax2.set_xbound(ax1.get_xbound())\n ax2.set_xlabel(r'Wavelength $[\\mu m]$', \n fontsize = 8,\n weight = 'bold')\n ax2.set_xticklabels(tick_function(ax2Ticks))\n \n ax1.set_xlabel('Wavenumber ($cm^{-1}$)', weight = 'bold')\n ax1.set_ylabel(r'Downwelling Radiance [$mW m^{-2} sr^{-1} cm^{-1}$]', weight = 'bold')\n # plt.xlim((4,30))\n ax1.set_title('Downwelling Thermal Radiance at Surface', weight = 'bold')\n ax1.grid()\n # plt.savefig('figs/christian_update_9_14/Rdown_gas_components_zoom.png', dpi = 300)", "sub_path": "rad_transfer_python/.ipynb_checkpoints/simulate_radiances_utils-checkpoint.py", "file_name": "simulate_radiances_utils-checkpoint.py", "file_ext": "py", "file_size_in_byte": 18247, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.rc", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 33, "usage_type": "attribute"}, {"api_name": "numpy.exp", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 54, "usage_type": "call"}, {"api_name": "numpy.trapz", "line_number": 68, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 70, "usage_type": "attribute"}, {"api_name": "numpy.trapz", "line_number": 78, "usage_type": "call"}, {"api_name": "numpy.pi", "line_number": 80, "usage_type": "attribute"}, {"api_name": "numpy.where", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.nanmean", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.divide", "line_number": 118, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 124, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 145, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 146, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 147, "usage_type": "call"}, {"api_name": "xarray.Dataset", "line_number": 164, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 165, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 166, "usage_type": "call"}, {"api_name": "xarray.DataArray", "line_number": 167, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.exp", "line_number": 226, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 234, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 239, "usage_type": "call"}, {"api_name": "numpy.empty_like", "line_number": 240, "usage_type": "call"}, {"api_name": "numpy.empty_like", "line_number": 241, "usage_type": "call"}, {"api_name": "numpy.empty_like", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 249, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 250, "usage_type": "call"}, {"api_name": "numpy.prod", "line_number": 251, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 255, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 256, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 257, "usage_type": "call"}, {"api_name": "scipy.interpolate.CubicSpline", "line_number": 301, "usage_type": "call"}, {"api_name": "scipy.interpolate.BSpline", "line_number": 304, "usage_type": "call"}, {"api_name": "scipy.interpolate.interp1d", "line_number": 308, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 323, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 324, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 332, "usage_type": "call"}, {"api_name": "numpy.append", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 335, "usage_type": "call"}, {"api_name": "numpy.cumsum", "line_number": 367, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 370, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 371, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 375, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 376, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 378, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 428, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 428, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 430, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 430, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 432, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 432, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 435, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 435, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 436, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 436, "usage_type": "name"}, {"api_name": "numpy.nanmax", "line_number": 436, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 437, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 437, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 438, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 438, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 440, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 440, "usage_type": "name"}, {"api_name": "numpy.nanmin", "line_number": 440, "usage_type": "call"}, {"api_name": "numpy.nanmax", "line_number": 440, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 441, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 441, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 444, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 444, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 445, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 445, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 448, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 448, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 450, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 450, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 453, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 453, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 457, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 457, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 473, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 473, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 474, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 474, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 475, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 475, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 477, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 477, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 479, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 479, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 480, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 480, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 484, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 484, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 485, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 485, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 486, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 486, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 489, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 489, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 492, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 492, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlim", "line_number": 494, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 494, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 495, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 495, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 497, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 497, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.yscale", "line_number": 499, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 499, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.axhline", "line_number": 503, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 503, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylim", "line_number": 505, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 505, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.grid", "line_number": 506, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 506, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 515, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 515, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 534, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 534, "usage_type": "name"}]} +{"seq_id": "88962153", "text": "#!/usr/bin/env python3\n\nimport bme680\nimport configparser\nimport datetime\nimport time\nimport logging\nimport grpc\nimport sensor_pb2 as pb\nimport sensor_pb2_grpc\nimport sys\nimport time\n\n\ndef read_bme680(location, filename, stub):\n sensor = bme680.BME680()\n\n # Oversample sets a balance between accuracy of reading and amount of noise.\n # The higher the oversampling, the greater the reduction in noise, but loses accuracy.\n sensor.set_humidity_oversample(bme680.OS_2X)\n sensor.set_pressure_oversample(bme680.OS_4X)\n sensor.set_temperature_oversample(bme680.OS_8X)\n\n # Filter protects transient changes (like a door slamming).\n sensor.set_filter(bme680.FILTER_SIZE_3)\n\n # GAS readings require the plate to be heated. So readings will be much slower.\n sensor.set_gas_status(bme680.ENABLE_GAS_MEAS)\n sensor.set_gas_heater_temperature(320)\n sensor.set_gas_heater_duration(150)\n sensor.select_gas_heater_profile(0)\n\n try:\n while True:\n current = False\n logging.info(\"Taking a measurement\")\n if sensor.get_sensor_data():\n if sensor.data.heat_stable:\n current = pb.current_readings(\n time = int(datetime.datetime.utcnow().timestamp()),\n location = location,\n filename = filename,\n temperature = sensor.data.temperature,\n humidity = sensor.data.humidity,\n pressure = sensor.data.pressure,\n gas = sensor.data.gas_resistance,\n model = pb.BME680,\n )\n if current:\n logging.info(\"Uploading measurements\")\n result = stub.send_readings(current)\n logging.info(result)\n\n time.sleep(30)\n\n except KeyboardInterrupt:\n pass\n\n\nif __name__ == \"__main__\":\n # Load config\n config = configparser.ConfigParser()\n config.read(\"config.ini\")\n\n #Ensure required options are set\n if not config.has_section('sensor'):\n sys.exit(\"Please ensure you have a config.ini with a sensor section\")\n required = ['location', 'filename', 'server', 'port']\n for options in required:\n if not config.has_option('sensor', options):\n sys.exit(\"Missing required options in config.ini\")\n\n # Get options\n location = config.get('sensor', 'location')\n filename = config.get('sensor', 'filename')\n server = config.get('sensor', 'server')\n port = config.get('sensor', 'port')\n log = config.get('sensor', 'logfile')\n\n #Set up GRPC server details\n grpcserver = \"%s:%s\" % (server, port)\n channel = grpc.insecure_channel(grpcserver)\n stub = sensor_pb2_grpc.sensor_dataStub(channel)\n\n # Set up logging\n format_string = '%(levelname)s: %(asctime)s: %(message)s'\n logging.basicConfig(filename=log, level=logging.INFO, format=format_string)\n\n # Start taking measurements\n read_bme680(location, filename, stub)\n\n print(\"Exiting!\")\n", "sub_path": "client/bme680client.py", "file_name": "bme680client.py", "file_ext": "py", "file_size_in_byte": 3053, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "bme680.BME680", "line_number": 16, "usage_type": "call"}, {"api_name": "bme680.OS_2X", "line_number": 20, "usage_type": "attribute"}, {"api_name": "bme680.OS_4X", "line_number": 21, "usage_type": "attribute"}, {"api_name": "bme680.OS_8X", "line_number": 22, "usage_type": "attribute"}, {"api_name": "bme680.FILTER_SIZE_3", "line_number": 25, "usage_type": "attribute"}, {"api_name": "bme680.ENABLE_GAS_MEAS", "line_number": 28, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 36, "usage_type": "call"}, {"api_name": "sensor_pb2.current_readings", "line_number": 39, "usage_type": "call"}, {"api_name": "datetime.datetime.utcnow", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "attribute"}, {"api_name": "sensor_pb2.BME680", "line_number": 47, "usage_type": "attribute"}, {"api_name": "logging.info", "line_number": 50, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 52, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 54, "usage_type": "call"}, {"api_name": "configparser.ConfigParser", "line_number": 62, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 67, "usage_type": "call"}, {"api_name": "sys.exit", "line_number": 71, "usage_type": "call"}, {"api_name": "grpc.insecure_channel", "line_number": 82, "usage_type": "call"}, {"api_name": "sensor_pb2_grpc.sensor_dataStub", "line_number": 83, "usage_type": "call"}, {"api_name": "logging.basicConfig", "line_number": 87, "usage_type": "call"}, {"api_name": "logging.INFO", "line_number": 87, "usage_type": "attribute"}]} +{"seq_id": "475252304", "text": "import json\nimport argparse\nimport sys\nimport time\nfrom marc_to_folio.MtFMapper import MtFMapper\nfrom pymarc import MARCReader\nfrom os import listdir\nfrom os.path import isfile, join\n\n\ndef write_to_file(f, pg_dump, folio_record):\n if(pg_dump):\n f.write('{}\\t{}\\n'.format(folio_record['id'],\n json.dumps(folio_record)))\n else:\n f.write('{}\\n'.format(json.dumps(folio_record)))\n\n\nparser = argparse.ArgumentParser()\nparser.add_argument(\"source_folder\",\n help=\"path of the folder where the marc files resides\")\nparser.add_argument(\"result_path\",\n help=\"path and name of the results file\")\nparser.add_argument(\"okapi_url\",\n help=(\"url of your FOLIO OKAPI endpoint. See settings->\"\n \"software version in FOLIO\"))\nparser.add_argument(\"tenant_id\",\n help=(\"id of the FOLIO tenant. See settings->software \"\n \"version in FOLIO\"))\nparser.add_argument(\"okapi_token\",\n help=(\"the x-okapi-token. Easiest optained via F12 in \"\n \"the webbrowser\"))\nparser.add_argument(\"record_source\",\n help=(\"name of the source system or collection from \"\n \"which the records are added\"))\nparser.add_argument(\"-id_dict_path\", \"-i\",\n help=(\"path to file saving a dictionary of Sierra ids \"\n \"and new InstanceIds to be used for matching the\"\n \"right holdings and items to the right instance.\"))\nparser.add_argument(\"-postgres_dump\",\n \"-p\",\n help=(\"results will be written out for Postgres ingestion.\"\n \" Default is JSON\"),\n action=\"store_true\")\nargs = parser.parse_args()\n\nprint('Will post data to')\nprint('\\tresults file:\\t', args.result_path)\nprint(\"\\tOkapi URL:\\t\", args.okapi_url)\nprint(\"\\tTenanti Id:\\t\", args.tenant_id)\nprint(\"\\tToken: \\t\", args.okapi_token)\nprint(\"\\tRecord source:\\t\", args.record_source)\nprint(\"\\tidMap will get stored at:\\t\", args.id_dict_path)\nid_dict_path = args.id_dict_path\nholdings = 0\nrecords = 0\nstart = time.time()\nfiles = [f for f in listdir(args.source_folder)\n if isfile(join(args.source_folder, f))]\nprint(\"Files to process:\")\nprint(json.dumps(files, sort_keys=True, indent=4))\nidMap = {}\nmapper = MtFMapper(args)\nprint(\"Starting\")\nprint(\"Rec./s\\t\\tHolds\\t\\tTot. recs\\t\\tFile\\t\\t\")\nwith open(args.result_path, 'w+') as results_file:\n for f in files:\n with open(sys.argv[1]+f, 'rb') as fh:\n reader = MARCReader(fh, 'rb',\n hide_utf8_warnings=True,\n utf8_handling='replace')\n for record in reader:\n try:\n records += 1\n if record['004']:\n holdings += 1\n else:\n folio_rec = mapper.parse_bib_record(record,\n args.record_source)\n if(record['907']['a']):\n sierra_id = record['907']['a'].replace('.b', '')[:-1]\n idMap[sierra_id] = folio_rec['id']\n write_to_file(results_file,\n args.postgres_dump,\n folio_rec)\n if records % 1000 == 0:\n elapsed = '{0:.3g}'.format(records/(time.time() - start))\n print_template = \"{}\\t\\t{}\\t\\t{}\\t\\t{}\\t\\t{}\"\n print(print_template.format(elapsed,\n holdings,\n records,\n f,\n len(idMap)), end='\\r')\n except Exception as inst:\n print(type(inst))\n print(inst.args)\n print(inst)\n with open(id_dict_path, 'w+') as json_file:\n json.dump(idMap, json_file, sort_keys=True, indent=4)\n print(\"done\")\n", "sub_path": "main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 4295, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "json.dumps", "line_number": 14, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 16, "usage_type": "call"}, {"api_name": "argparse.ArgumentParser", "line_number": 19, "usage_type": "call"}, {"api_name": "time.time", "line_number": 57, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 59, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 61, "usage_type": "call"}, {"api_name": "marc_to_folio.MtFMapper.MtFMapper", "line_number": 63, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 68, "usage_type": "attribute"}, {"api_name": "pymarc.MARCReader", "line_number": 69, "usage_type": "call"}, {"api_name": "time.time", "line_number": 87, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 99, "usage_type": "call"}]} +{"seq_id": "87887314", "text": "# Copyright lowRISC contributors.\n# Licensed under the Apache License, Version 2.0, see LICENSE for details.\n# SPDX-License-Identifier: Apache-2.0\n\n'''A wrapper around reggen for otbn.hjson'''\n\nimport os\nimport sys\nfrom typing import Optional, Tuple\n\n\n# We use reggen to read the hjson file. Since that lives somewhere completely\n# different from this script (and there aren't __init__.py files scattered all\n# over the OpenTitan repository), we have to do sys.path hacks to find it.\n_OLD_SYS_PATH = sys.path\ntry:\n _UTIL_PATH = os.path.join(os.path.dirname(__file__),\n '..', '..', '..', '..', '..', 'util')\n sys.path = [_UTIL_PATH] + _OLD_SYS_PATH\n import reggen.field # type: ignore\n import reggen.ip_block # type: ignore\n import reggen.reg_block # type: ignore\n import reggen.register # type: ignore\n import reggen.window # type: ignore\nfinally:\n sys.path = _OLD_SYS_PATH\n\n# Re-export some reggen types so that code importing otbn_reggen can get them\n# transitively without having to mess around with sys.path.\nRegister = reggen.register.Register\nField = reggen.field.Field\nWindow = reggen.window.Window\nRegBlock = reggen.reg_block.RegBlock\nIpBlock = reggen.ip_block.IpBlock\n\n_LR_RETVAL = None # type: Optional[Tuple[int, object]]\n\n\ndef load_registers() -> Tuple[int, object]:\n '''Load otbn.hjson with reggen\n\n Returns (width, regs) where width is the register width and regs is a\n list of Register, MultiRegister or Window objects. Memoized.\n\n '''\n global _LR_RETVAL\n if _LR_RETVAL is not None:\n return _LR_RETVAL\n\n path = os.path.join(os.path.dirname(__file__),\n '..', '..', 'data', 'otbn.hjson')\n\n try:\n obj = IpBlock.from_path(path, [])\n except ValueError as err:\n raise RuntimeError('Failed to parse {!r}: {}'.format(path, err))\n\n reg_bit_width = obj.regwidth\n assert isinstance(reg_bit_width, int) and reg_bit_width >= 0\n reg_byte_width = (reg_bit_width + 7) // 8\n\n registers = obj.reg_blocks[None]\n assert isinstance(registers, RegBlock)\n _LR_RETVAL = (reg_byte_width, registers)\n return _LR_RETVAL\n", "sub_path": "hw/ip/otbn/util/shared/otbn_reggen.py", "file_name": "otbn_reggen.py", "file_ext": "py", "file_size_in_byte": 2161, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sys.path", "line_number": 15, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 17, "usage_type": "call"}, {"api_name": "os.path", "line_number": 17, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 17, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "sys.path", "line_number": 26, "usage_type": "attribute"}, {"api_name": "reggen.field.register", "line_number": 30, "usage_type": "attribute"}, {"api_name": "reggen.field", "line_number": 30, "usage_type": "name"}, {"api_name": "reggen.field.field", "line_number": 31, "usage_type": "attribute"}, {"api_name": "reggen.field", "line_number": 31, "usage_type": "name"}, {"api_name": "reggen.field.window", "line_number": 32, "usage_type": "attribute"}, {"api_name": "reggen.field", "line_number": 32, "usage_type": "name"}, {"api_name": "reggen.field.reg_block", "line_number": 33, "usage_type": "attribute"}, {"api_name": "reggen.field", "line_number": 33, "usage_type": "name"}, {"api_name": "reggen.field.ip_block", "line_number": 34, "usage_type": "attribute"}, {"api_name": "reggen.field", "line_number": 34, "usage_type": "name"}, {"api_name": "os.path.join", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 50, "usage_type": "call"}, {"api_name": "typing.Tuple", "line_number": 39, "usage_type": "name"}]} +{"seq_id": "491206615", "text": "# coding: utf-8\nfrom __future__ import unicode_literals\n\nfrom enum import Enum\nfrom yargy.labels import (\n gram,\n gram_not,\n dictionary,\n is_capitalized,\n gnc_match,\n eq,\n)\n\n\nFEDERAL_DISTRICT_DICTIONARY = {\n 'центральный',\n 'северо-западный',\n 'южный',\n 'северо-кавказский',\n 'приволжский',\n 'уральский',\n 'сибирский',\n 'дальневосточный',\n}\n\nREGION_TYPE_DICTIONARY = {\n 'край',\n 'район',\n 'область',\n 'губерния',\n 'уезд',\n}\n\nCOMPLEX_OBJECT_PREFIX_DICTIONARY = {\n 'северный',\n 'северо-западный',\n 'северо-восточный',\n 'южный',\n 'юго-западный',\n 'юго-восточный',\n 'западный',\n 'восточный',\n 'верхний',\n 'вышний',\n 'нижний',\n 'великий',\n 'дальний',\n}\n\nPARTIAL_OBJECT_PREFIX_DICTIONARY = {\n 'север',\n 'северо-восток',\n 'северо-запад',\n 'юг',\n 'юго-восток',\n 'юго-запад',\n 'запад',\n 'восток',\n}\n\nclass Geo(Enum):\n\n FederalDistrict = [\n {\n 'labels': [\n gram('ADJF'),\n dictionary(FEDERAL_DISTRICT_DICTIONARY),\n ],\n },\n {\n 'labels': [\n dictionary({'федеральный', }),\n ],\n },\n {\n 'labels': [\n dictionary({'округ', }),\n ],\n },\n ]\n\n FederalDistrictAbbr = [\n {\n 'labels': [\n gram('ADJF'),\n dictionary(FEDERAL_DISTRICT_DICTIONARY),\n ],\n },\n {\n 'labels': [\n eq('ФО'),\n ],\n },\n ]\n\n Region = [\n {\n 'labels': [\n gram('ADJF'),\n ],\n },\n {\n 'labels': [\n dictionary(REGION_TYPE_DICTIONARY),\n gnc_match(-1, solve_disambiguation=True),\n ],\n },\n ]\n\n ComplexObject = [\n {\n 'labels': [\n gram('ADJF'),\n dictionary(COMPLEX_OBJECT_PREFIX_DICTIONARY),\n ],\n },\n {\n 'labels': [\n gram('NOUN'),\n gram('Geox'),\n gnc_match(-1, solve_disambiguation=True),\n ],\n },\n ]\n\n PartialObject = [\n {\n 'labels': [\n gram('NOUN'),\n dictionary(PARTIAL_OBJECT_PREFIX_DICTIONARY),\n ],\n },\n {\n 'labels': [\n gram('NOUN'),\n gram('Geox'),\n gnc_match(-1, solve_disambiguation=True),\n ],\n },\n ]\n\n Object = [\n {\n 'labels': [\n is_capitalized(True),\n gram('Geox'),\n gram_not('Abbr'),\n ],\n },\n ]\n", "sub_path": "natasha/grammars/geo.py", "file_name": "geo.py", "file_ext": "py", "file_size_in_byte": 3075, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "enum.Enum", "line_number": 61, "usage_type": "name"}, {"api_name": "yargy.labels.gram", "line_number": 66, "usage_type": "call"}, {"api_name": "yargy.labels.dictionary", "line_number": 67, "usage_type": "call"}, {"api_name": "yargy.labels.dictionary", "line_number": 72, "usage_type": "call"}, {"api_name": "yargy.labels.dictionary", "line_number": 77, "usage_type": "call"}, {"api_name": "yargy.labels.gram", "line_number": 85, "usage_type": "call"}, {"api_name": "yargy.labels.dictionary", "line_number": 86, "usage_type": "call"}, {"api_name": "yargy.labels.eq", "line_number": 91, "usage_type": "call"}, {"api_name": "yargy.labels.gram", "line_number": 99, "usage_type": "call"}, {"api_name": "yargy.labels.dictionary", "line_number": 104, "usage_type": "call"}, {"api_name": "yargy.labels.gnc_match", "line_number": 105, "usage_type": "call"}, {"api_name": "yargy.labels.gram", "line_number": 113, "usage_type": "call"}, {"api_name": "yargy.labels.dictionary", "line_number": 114, "usage_type": "call"}, {"api_name": "yargy.labels.gram", "line_number": 119, "usage_type": "call"}, {"api_name": "yargy.labels.gram", "line_number": 120, "usage_type": "call"}, {"api_name": "yargy.labels.gnc_match", "line_number": 121, "usage_type": "call"}, {"api_name": "yargy.labels.gram", "line_number": 129, "usage_type": "call"}, {"api_name": "yargy.labels.dictionary", "line_number": 130, "usage_type": "call"}, {"api_name": "yargy.labels.gram", "line_number": 135, "usage_type": "call"}, {"api_name": "yargy.labels.gram", "line_number": 136, "usage_type": "call"}, {"api_name": "yargy.labels.gnc_match", "line_number": 137, "usage_type": "call"}, {"api_name": "yargy.labels.is_capitalized", "line_number": 145, "usage_type": "call"}, {"api_name": "yargy.labels.gram", "line_number": 146, "usage_type": "call"}, {"api_name": "yargy.labels.gram_not", "line_number": 147, "usage_type": "call"}]} +{"seq_id": "601383706", "text": "\"\"\"\r\npytorch 如何共享参数\r\nhttps://www.cnblogs.com/sdu20112013/p/12134330.html\r\n\"\"\"\r\nimport torch\r\nimport torch.nn as nn\r\nimport torch.nn.init as init\r\n\r\n\r\ndef seq_share_weights():\r\n linear = nn.Linear(1, 1, bias=False)\r\n # 传入 Sequential 的模块是同一个 Module 实例的话参数也是共享的\r\n net = nn.Sequential(linear, linear) # 2个 linear 在内存中对应同一个对象\r\n print(net)\r\n # id -> 139669020411160，对象 id，unique among simultaneously existing objects.\r\n print(id(net[0]) == id(net[1])) # True\r\n print(id(net[0].weight) == id(net[1].weight)) # True\r\n\r\n # y = wx, 初始化 linear 层 w=3; net = 3*3*x = 9x\r\n for name, param in net.named_parameters():\r\n init.constant_(param, val=3)\r\n print(name, param.data)\r\n\r\n x = torch.ones(1, 1) # bs=1\r\n y = net(x).sum()\r\n print(y) # 3*3*1 = 9\r\n y.backward()\r\n print(net[1].weight.grad) # 6 共享参数的 grad 是累加的，相当于更新了2次\r\n print(net[0].weight.grad) # 6\r\n\r\n\r\ndef seq_unique_weights():\r\n linear1 = nn.Linear(1, 1, bias=False)\r\n linear2 = nn.Linear(1, 1, bias=False)\r\n net = nn.Sequential(linear1, linear2)\r\n print(net)\r\n\r\n for name, param in net.named_parameters():\r\n init.constant_(param, val=3)\r\n print(name, param.data)\r\n\r\n x = torch.ones(1, 1)\r\n y = net(x).sum()\r\n print(y)\r\n y.backward()\r\n print(net[1].weight.grad) # 3; 倒数第1层，grad1 = 3x = 3\r\n print(net[0].weight.grad) # 3; 倒数第2层，grad0 = grad1 * x = 3*1 = 3\r\n\r\n\r\nif __name__ == '__main__':\r\n seq_share_weights()\r\n print()\r\n seq_unique_weights()\r\n", "sub_path": "zeros/share_params.py", "file_name": "share_params.py", "file_ext": "py", "file_size_in_byte": 1654, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.nn.Linear", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 21, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 21, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.Linear", "line_number": 33, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.init.constant_", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.ones", "line_number": 42, "usage_type": "call"}]} +{"seq_id": "508484788", "text": "\"\"\"\ncommswave\n=========\nTakes device communications up and down according to a timefunction.\nComms will be working whenever the timefunction returns non-zero.\n\nConfigurable parameters::\n\n {\n \"timefunction\" : A timefunction definition\n \"threshold\" : (optional) Comms will only work when the timefunction is returning >= threshold. If missing then any non-zero value will make comms work.\n }\n\nDevice properties created::\n\n {\n }\n\n\"\"\"\n\nfrom .device import Device\nfrom common import importer\nimport logging\n\nclass Commswave(Device):\n def __init__(self, instance_name, time, engine, update_callback, context, params):\n \"\"\"Take Comms up and down according to some time function\"\"\"\n tf = params[\"commswave\"][\"timefunction\"]\n self.comms_timefunction = importer.get_class(\"timefunction\", list(tf.keys())[0])(engine, self, tf[list(tf.keys())[0]])\n self.comms_tf_threshold = params[\"commswave\"].get(\"threshold\", None)\n self.messages_sent = 0\n self.messages_attempted = 0\n super(Commswave,self).__init__(instance_name, time, engine, update_callback, context, params)\n\n def comms_ok(self):\n self.messages_attempted += 1\n is_ok = super(Commswave, self).comms_ok()\n if self.comms_tf_threshold is not None:\n tf_ok = self.comms_timefunction.state() >= self.comms_tf_threshold\n if not tf_ok:\n pass # logging.info(\"commswave suppressing a communication due to timefunction state\")\n is_ok = is_ok and tf_ok\n else:\n is_ok = is_ok and self.comms_timefunction.state()\n if is_ok:\n self.messages_sent += 1\n return is_ok\n\n def external_event(self, event_name, arg):\n super(Commswave, self).external_event(event_name, arg)\n\n def close(self):\n super(Commswave,self).close()\n logging.info(\"Comms report for \" + str(self.properties[\"$id\"]) + \" \" +\n str(self.messages_sent) + \" sent (\"+str(100 * self.messages_sent/self.messages_attempted) + \"%) from \" +\n str(self.messages_attempted) + \" total\")\n\n\n # Private methods\n\n## (we don't actually need to tick, as we can instantaneously look up timefunction state whenever we need to)\n## def tick_commswave(self, _):\n## self.ok_commswave = self.comms_timefunction.state()\n## self.engine.register_event_at(self.comms_timefunction.next_change(), self.tick_commswave, self, self)\n", "sub_path": "synth/devices/commswave.py", "file_name": "commswave.py", "file_ext": "py", "file_size_in_byte": 2454, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "device.Device", "line_number": 25, "usage_type": "name"}, {"api_name": "common.importer.get_class", "line_number": 29, "usage_type": "call"}, {"api_name": "common.importer", "line_number": 29, "usage_type": "name"}, {"api_name": "logging.info", "line_number": 54, "usage_type": "call"}]} +{"seq_id": "608198478", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch import cuda\nfrom torch import optim\nfrom torch import autograd\nfrom torchvision import transforms\nfrom classify_svhn import get_data_loader\nfrom q3_vae import View\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport samplers\nimport argparse\nimport os\n\n\nclass D(nn.Module):\n def __init__(self, batch_size, dimz):\n super().__init__()\n self.batch_size = batch_size\n self.dimz = dimz\n\n self.convs = nn.Sequential(\n # layer1\n nn.Conv2d(3, 64, 5, padding=2, stride=2),\n nn.LeakyReLU(0.2),\n\n # layer2\n nn.Conv2d(64, 128, 5, padding=2, stride=2),\n nn.BatchNorm2d(128),\n nn.LeakyReLU(0.2),\n\n # layer3\n nn.Conv2d(128, 256, 5, padding=2, stride=2),\n nn.BatchNorm2d(256),\n nn.LeakyReLU(0.2),\n\n # layer 4\n nn.Conv2d(256, 1, 5, padding=2, stride=1),\n View(-1, 4*4*1),\n nn.Sigmoid(),\n )\n\n def forward(self, x):\n out = self.convs(x)[:, 0]\n return out\n\n\nclass G(nn.Module):\n\n def __init__(self, batch_size, dimz):\n super().__init__()\n self.batch_size = batch_size\n self.dimz = dimz\n\n self.deconvs = nn.Sequential(\n # layer 1\n nn.Linear(self.dimz, 4 * 4 * 512),\n nn.BatchNorm1d(4*4*512),\n nn.ReLU(),\n View(-1, 512, 4, 4),\n\n # layer2\n nn.ConvTranspose2d(512, 256, 5, padding=2, stride=2, output_padding=1),\n nn.BatchNorm2d(256),\n nn.ReLU(),\n\n # layer 3\n nn.ConvTranspose2d(256, 128, 5, padding=2, stride=2, output_padding=1),\n nn.BatchNorm2d(128),\n nn.ReLU(),\n\n # layer 4\n nn.ConvTranspose2d(128, 3, 5, padding=2, stride=2, output_padding=1),\n nn.Tanh()\n )\n\n def forward(self, z):\n out = self.deconvs(z)\n return out\n\n def extract_features(self, z):\n return z.view(-1, 3*32*32)\n\n\ndef wgan_gp_loss(real, fake, grad, lam):\n \"\"\"\n Function that computes the WGAN-GP metric given the discriminator's output on real and fake data\n :param real: The output of the discriminator on real data of size [batch_size,]\n :param fake: The output of the generator on fake data of size [batch_size,]\n :param grad: The gradient of the output of the discriminator. Size is [batch_size, 3*32*32]\n :param: The lambda factor applied for regularization\n :return: The WGAN-GP loss over all elements in the mini-batch. Size is [batch_size,]\n \"\"\"\n return fake - real + lam * (torch.norm(grad, dim=1) - 1.)**2\n\n\ndef train_model(g, d, train, valid, save_path):\n \"\"\"\n Function that trains the model\n :param g: The model generator to train\n :param d: The discriminator to train\n :param train: The training set\n :param valid: The validation set\n :return:\n \"\"\"\n # optimizer for the network\n g_optim = optim.Adam(g.parameters(), lr=args.lr, betas=(0, 0.9))\n d_optim = optim.Adam(d.parameters(), lr=args.lr, betas=(0, 0.9))\n\n # print PIL image\n display = transforms.ToPILImage()\n\n for epoch in range(args.nb_epochs):\n for i, (batch, label) in enumerate(train):\n # put batch on device\n batch = batch.to(args.device)\n\n # obtain the discriminator output on real data\n real_prob = d(batch)\n\n # obtain the discriminator output on the fake data\n z = torch.randn(batch.size()[0], g.dimz, device=args.device)\n fake = g(z).detach()\n fake_prob = d(fake)\n\n # obtain the gradient term of the WGAN-GP loss\n a = torch.rand(batch.size()[0], 1, 1, 1, device=args.device)\n conv = a * batch + (1. - a) * fake\n conv.requires_grad = True\n d_conv = d(conv)\n grad = autograd.grad(d_conv, conv, torch.ones_like(d_conv).to(args.device),\n retain_graph=True, create_graph=True, only_inputs=True)[0]\n\n # compute the WGAN-GP loss\n loss = wgan_gp_loss(real_prob, fake_prob, grad.view(-1, 3 * 32 * 32), args.lam).mean()\n\n # minimize the loss\n autograd.backward(loss)\n\n # update the parameters\n d_optim.step()\n d_optim.zero_grad()\n\n if i % args.update_ratio == 0. and i > 0:\n # update the generator\n z = torch.randn(batch.size()[0], g.dimz, device=args.device)\n fake = g(z)\n fake_prob = d(fake)\n loss = - fake_prob.mean()\n loss.backward()\n g_optim.step()\n g.zero_grad()\n\n with torch.no_grad():\n # After training for an epoch, output validation loss\n valid_loss = torch.zeros(1)\n nb_batches = 0\n for i, (batch, label) in enumerate(valid):\n nb_batches += 1\n batch = batch.to(args.device)\n real_prob = d(batch)\n z = torch.randn(batch.size()[0], g.dimz, device=args.device)\n fake = g(z)\n display(((fake[0] + 1.) * 255.).to(device='cpu', copy=True)).show()\n fake_prob = d(fake)\n a = torch.rand(batch.size()[0], 1, 1, 1, device=args.device)\n\n with torch.enable_grad():\n conv = a * batch + (1. - a) * fake\n conv.requires_grad = True\n d_conv = d(conv)\n grad = autograd.grad(d_conv, conv, torch.ones_like(d_conv).to(args.device),\n retain_graph=False, create_graph=True, only_inputs=True)[0]\n batch_loss = wgan_gp_loss(real_prob, fake_prob, grad.view(-1, 3 * 32 * 32), args.lam)\n valid_loss += batch_loss.mean()\n valid_loss /= nb_batches\n print(\"After epoch {} the validation loss is: \".format(epoch + 1), valid_loss.item())\n\n # save the model to be used later\n torch.save(g.state_dict(), save_path)\n\n\ndef evaluation(model):\n \"\"\"\n Function that generates samples for the qualitative evaluation of the model\n :param model: The model from which we pull samples\n :return:\n \"\"\"\n with torch.no_grad():\n transf = transforms.ToPILImage()\n z = torch.randn(model.batch_size, model.dimz, device=args.device)\n samples = model.deconvs(z)\n\n if not os.path.isdir(args.sample_dir):\n os.mkdir(args.sample_dir)\n\n decoder_dir = os.path.join(args.sample_dir, \"decoder_samples\")\n if not os.path.isdir(decoder_dir):\n os.mkdir(decoder_dir)\n\n # save the decoder samples\n for i, sample in enumerate(samples):\n im = transf(sample.to(device='cpu'))\n im.save(os.path.join(decoder_dir, \"img_{}.jpeg\".format(i)))\n\n # perturb the z and get samples\n z_ = z + args.eps\n samples = model.deconvs(z_)\n\n perturb_dir = os.path.join(args.sample_dir, \"perturbed_samples\")\n if not os.path.isdir(perturb_dir):\n os.mkdir(perturb_dir)\n\n # save the perturbed samples\n for i, sample in enumerate(samples):\n im = transf(sample.to(device='cpu'))\n im.save(os.path.join(perturb_dir, \"p_img_{}.jpeg\".format(i)))\n\n int_dir = os.path.join(args.sample_dir, \"interpolated_samples\")\n if not os.path.isdir(int_dir):\n os.mkdir(int_dir)\n\n # interpolate between two z's and generate samples. Save them\n for a in range(11):\n z_a = a / 10. * z[0:1] + (1. - a/10.) * z[1:2]\n gz_a = model.deconvs(z_a)[0]\n im = transf(gz_a.to(device='cpu'))\n im.save(os.path.join(int_dir, \"i1_img_{}.jpeg\".format(a)))\n\n # interpolate the result of the two g(z) values\n g_z = model.deconvs(z[0:2])\n for a in range(11):\n x_a = a / 10. * g_z[0] + (1. - a / 10.) * g_z[1]\n im = transf(x_a.to(device='cpu'))\n im.save(os.path.join(int_dir, \"i2_img_{}.jpeg\".format(a)))\n\n # sample 1000 images to use for FID score\n thousand_dir = os.path.join(args.sample_dir, \"1000_samples\", \"samples\")\n if not os.path.isdir(thousand_dir):\n os.makedirs(thousand_dir)\n\n z = torch.randn(1000, model.dimz, device=args.device)\n gz = model.deconvs(z)\n for i, sample in enumerate(gz):\n im = transf(sample.to(device='cpu'))\n im.save(os.path.join(thousand_dir, \"img_{}.jpeg\".format(i)))\n\n\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-t\", action=\"store_true\", help=\"Flag to specify if we train the model\")\n parser.add_argument(\"--save_path\", type=str, default=\"q3_gan.pt\")\n parser.add_argument(\"--load_path\", type=str, default=\"q3_gan.pt\")\n parser.add_argument(\"--batch_size\", type=int, default=64, help=\"Size of the mini-batches\")\n parser.add_argument(\"--dimz\", type=int, default=100, help=\"Dimension of the latent variables\")\n parser.add_argument(\"--data_dir\", type=str, default=\"svhn.mat\", help=\"SVHN dataset location\")\n parser.add_argument(\"--nb_epochs\", type=int, default=50, help=\"The number of epochs for training\")\n parser.add_argument(\"--lam\", type=int, default=10, help=\"Lambda coefficient for the regularizer in\"\n \"in the WGAN-GP loss\")\n parser.add_argument(\"--lr\", type=float, default=2e-4, help=\"Learning rate for the optimzer\")\n parser.add_argument(\"--update_ratio\", type=int, default=5, help=\"The number of updates to the discriminator\"\n \"before one update to the generator\")\n parser.add_argument(\"--eps\", type=float, default=1e-1, help=\"Perturbation value to the latent when evaluating\")\n parser.add_argument(\"--sample_dir\", type=str, default=\"samples\", help=\"Directory containing samples for\"\n \"evaluation\")\n\n # get the arguments\n args = parser.parse_args()\n args.device = torch.device(\"cuda\") if cuda.is_available() else torch.device('cpu')\n # check for cuda\n device = torch.device(\"cuda\") if cuda.is_available() else torch.device('cpu')\n args.device = device\n\n # load the dataset\n train, valid, test = get_data_loader(args.data_dir, args.batch_size)\n\n # Create model. Load or train depending on choice\n g = G(args.batch_size, args.dimz).to(args.device)\n d = D(args.batch_size, args.dimz).to(args.device)\n if args.t:\n train_model(g, d, train, valid, args.save_path)\n else:\n g.load_state_dict(torch.load(args.load_path))\n g.eval()\n\n evaluation(g)\n", "sub_path": "Q3_WGAN.py", "file_name": "Q3_WGAN.py", "file_ext": "py", "file_size_in_byte": 10892, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.nn.Module", "line_number": 17, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 26, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 26, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 29, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 30, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 30, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 31, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 31, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 35, "usage_type": "name"}, {"api_name": "torch.nn.LeakyReLU", "line_number": 36, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 36, "usage_type": "name"}, {"api_name": "torch.nn.Conv2d", "line_number": 39, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 39, "usage_type": "name"}, {"api_name": "q3_vae.View", "line_number": 40, "usage_type": "call"}, {"api_name": "torch.nn.Sigmoid", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 49, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 49, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 56, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 58, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 58, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 59, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 60, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 60, "usage_type": "name"}, {"api_name": "q3_vae.View", "line_number": 61, "usage_type": "call"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 65, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 66, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 66, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 69, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 69, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm2d", "line_number": 70, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 70, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.ConvTranspose2d", "line_number": 74, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 74, "usage_type": "name"}, {"api_name": "torch.nn.Tanh", "line_number": 75, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 75, "usage_type": "name"}, {"api_name": "torch.norm", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.optim.Adam", "line_number": 108, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 108, "usage_type": "name"}, {"api_name": "torch.optim.Adam", "line_number": 109, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 109, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToPILImage", "line_number": 112, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 112, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 123, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 128, "usage_type": "call"}, {"api_name": "torch.autograd.grad", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 132, "usage_type": "name"}, {"api_name": "torch.ones_like", "line_number": 132, "usage_type": "call"}, {"api_name": "torch.autograd.backward", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 147, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 155, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 157, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 163, "usage_type": "call"}, {"api_name": "torch.rand", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.enable_grad", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.autograd.grad", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.autograd", "line_number": 173, "usage_type": "name"}, {"api_name": "torch.ones_like", "line_number": 173, "usage_type": "call"}, {"api_name": "torch.save", "line_number": 181, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 190, "usage_type": "call"}, {"api_name": "torchvision.transforms.ToPILImage", "line_number": 191, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 191, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 192, "usage_type": "call"}, {"api_name": "os.path.isdir", "line_number": 195, "usage_type": "call"}, {"api_name": "os.path", "line_number": 195, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 196, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 198, "usage_type": "call"}, {"api_name": "os.path", "line_number": 198, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 199, "usage_type": "call"}, {"api_name": "os.path", "line_number": 199, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 200, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 205, "usage_type": "call"}, {"api_name": "os.path", "line_number": 205, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 211, "usage_type": "call"}, {"api_name": "os.path", "line_number": 211, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 212, "usage_type": "call"}, {"api_name": "os.path", "line_number": 212, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 213, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 218, "usage_type": "call"}, {"api_name": "os.path", "line_number": 218, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 220, "usage_type": "call"}, {"api_name": "os.path", "line_number": 220, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 221, "usage_type": "call"}, {"api_name": "os.path", "line_number": 221, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 222, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 229, "usage_type": "call"}, {"api_name": "os.path", "line_number": 229, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 236, "usage_type": "call"}, {"api_name": "os.path", "line_number": 236, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path", "line_number": 239, "usage_type": "attribute"}, {"api_name": "os.path.isdir", "line_number": 240, "usage_type": "call"}, {"api_name": "os.path", "line_number": 240, "usage_type": "attribute"}, {"api_name": "os.makedirs", "line_number": 241, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 243, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 247, "usage_type": "call"}, {"api_name": "os.path", "line_number": 247, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 252, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 271, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 271, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 271, "usage_type": "call"}, {"api_name": "torch.cuda.is_available", "line_number": 273, "usage_type": "call"}, {"api_name": "torch.cuda", "line_number": 273, "usage_type": "name"}, {"api_name": "torch.device", "line_number": 273, "usage_type": "call"}, {"api_name": "classify_svhn.get_data_loader", "line_number": 277, "usage_type": "call"}, {"api_name": "torch.load", "line_number": 285, "usage_type": "call"}]} +{"seq_id": "429191017", "text": "import numpy as np\nimport matplotlib.pyplot as plt\n\nN = 5\nind = np.arange(N) # the x locations for the groups\nwidth = 0.2 # the width of the bars\n\nfig = plt.figure()\nfig.subplots_adjust(bottom=0.25)\nax = fig.add_subplot(111)\n\nyvals = [42, 27, 26, 26, 24]\nrects1 = ax.bar(ind, yvals, width, color='r', label='invalid')\nzvals = [34, 47, 47, 47, 49]\nrects2 = ax.bar(ind + width, zvals, width, color='b', label='direct')\nkvals = [13, 15, 16, 16, 16]\nrects3 = ax.bar(ind + width * 2, kvals, width, color='y',\n label='outlier')\n\nax.set_ylabel('Num of Unique Addresses')\nax.set_xlabel('Minimum threshold (km)')\nax.set_xticks(ind + width)\nax.set_xticklabels(('1', '3', '5', '7', '9'))\nax.legend((rects1[0], rects2[0], rects3[0]),\n ('Multi GC addresses with invalid geocodes',\n 'Multi GC addresses with no outliers',\n 'Multi GC addresses with outliers'),\n loc='upper center', bbox_to_anchor=(0.5, -0.15))\n\n\ndef autolabel(rects):\n for rect in rects:\n h = rect.get_height()\n ax.text(rect.get_x() + rect.get_width() / 2., 1.05 * h, '%d' % int(h),\n ha='center', va='bottom')\n\n\nautolabel(rects1)\nautolabel(rects2)\nautolabel(rects3)\n\nplt.savefig('out/Min_Threshold.png')\n", "sub_path": "Programs/Python/new_gc/result_visualize/multigc.py", "file_name": "multigc.py", "file_ext": "py", "file_size_in_byte": 1229, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.arange", "line_number": 5, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 8, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.savefig", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 42, "usage_type": "name"}]} +{"seq_id": "447827307", "text": "import pandas as pd\r\nimport numpy as np\r\n\r\nfrom .Checker import Checker\r\nfrom .FormatExcelWriter import FormatExcelWriter\r\nfrom .funcs import create_pred_df\r\n\r\nfrom ..utils import calculate_time_execute\r\n\r\n\r\n# PSI Calculation class\r\nclass PSIVariablesChecker(Checker):\r\n \"\"\"\r\n Класс реализации проверки population stability index\r\n по переменным используемым в модели и бинам прогнозов.\r\n\r\n Parameters:\r\n ----------\r\n writer: pd.ExcelWriter\r\n Объект класса excel-writer для записи отчета (файл для отчета должен\r\n быть создан предварительно)\r\n\r\n model_name: str\r\n Имя модели для отображения в названи файлов\r\n\r\n model\r\n Объект scikit-learn like обученной модели\r\n\r\n features_list:list\r\n Список фичей, которые использует модель\r\n\r\n cat_features: list\r\n Список категориальных признаков\r\n\r\n drop_features: list\r\n Список мусорных признаков для исключения из анализа\r\n\r\n current_path: str\r\n Путь к рабочей директории для сохранения изображений и файла с отчетом\r\n \"\"\"\r\n\r\n def __init__(self,\r\n writer: pd.ExcelWriter,\r\n model_name: str,\r\n model,\r\n features_list=list,\r\n cat_features: list = None,\r\n drop_features: list = None,\r\n model_type: str = \"binary_classification\"):\r\n\r\n self.writer = writer\r\n self.model = model\r\n self.features_list = features_list\r\n self.model_name = model_name\r\n self.cat_features = cat_features\r\n self.drop_features = drop_features\r\n self.model_type = model_type\r\n # Датафреймы для хранения результатов проверки \r\n self.psi_short = pd.DataFrame()\r\n self.psi_detailed = pd.DataFrame()\r\n\r\n def _to_excel(self, df: pd.DataFrame, sheet_name: str, fmt=None) -> None:\r\n \"\"\"\r\n Функция записи датафрейма в excel файл на указанный лист и позицию\r\n\r\n Parameters:\r\n ----------\r\n df: pd.DataFrame\r\n Датафрейм для записи в файл\r\n sheet_name: str\r\n Имя листа, на который осуществить запись\r\n plot: bool\r\n Флаг необходимости добавить на страницу с отчетом график из файла\r\n \"\"\"\r\n bold_row = {\"bold\": {\r\n True: df.index[df[\"feature\"] == \"y_pred\"]}\r\n }\r\n\r\n excelWriter = FormatExcelWriter(self.writer)\r\n excelWriter.write_data_frame(df, (0, 0), sheet_name, fmt,\r\n row_formats=bold_row)\r\n\r\n # apply conditional format to highlight validation_report test results\r\n for col in [\"PSI_train_vs_valid_events\",\r\n \"PSI_train_vs_valid_all\",\r\n \"PSI_train_vs_valid_nevents\",\r\n \"PSI_train_vs_OOT_all\",\r\n \"PSI_train_vs_OOT_events\",\r\n \"PSI_train_vs_OOT_nevents\",\r\n \"PSI_train_vs_test_all\",\r\n \"PSI_train_vs_test_events\",\r\n \"PSI_train_vs_test_nevents\",\r\n \"PSI_train_vs_valid\",\r\n \"PSI_train_vs_OOT\",\r\n \"PSI_train_vs_test\",\r\n \"PSI_train_vs_test2_all\",\r\n \"PSI_train_vs_OOT_psi\",\r\n \"PSI_train_vs_OOT_psi_all\",\r\n \"PSI_train_vs_OOT_psi_events\",\r\n \"PSI_train_vs_OOT_psi_nevents\",\r\n ]:\r\n if col in df.columns:\r\n excelWriter.set_col_cond_format(df, (0, 0), col, upper=0.2,\r\n lower=0.1, order=\"straight\")\r\n\r\n def _create_checklist(self, df_list: list):\r\n \"\"\"\r\n Проверка наличия датасетов в словаре и формирование списка для\r\n сравнений с train\r\n\r\n Parameters:\r\n -----------\r\n df_list: list\r\n Список датасетов\r\n\r\n Returns:\r\n list\r\n список датасетов по которым нужно произвести провверку\r\n -------\r\n \"\"\"\r\n check_list = []\r\n\r\n # Создать список для проверки \r\n if \"test\" in df_list:\r\n check_list.append(\"test\")\r\n elif \"valid\" in df_list:\r\n check_list.append(\"valid\")\r\n\r\n if \"OOT\" in df_list:\r\n check_list.append(\"OOT\")\r\n\r\n if \"OOT_psi\" in df_list:\r\n check_list.append(\"OOT_psi\")\r\n\r\n if \"test2\" in df_list:\r\n check_list.append(\"test2\")\r\n\r\n return check_list\r\n\r\n def create_df(self, x: pd.DataFrame,\r\n y: pd.Series, model, features:list) -> dict:\r\n \"\"\"\r\n Функция создает на выходе 3 датафрема: полный, events, no events\r\n\r\n Parameters:\r\n -----------\r\n x:pd.DataFrame\r\n Датафрейм с признаками\r\n y:pd.Series\r\n Истинные значения целевой переменной\r\n model\r\n sklearn-like модель, после применения метода fit\r\n features:list\r\n список переменных используемых в модели\r\n\r\n Returns:\r\n --------\r\n dict\r\n словарь с разбитыми датасетами, ключи:\r\n \"all\", \"events\", \"nevents\"\r\n \"\"\"\r\n y_pred = create_pred_df(model_info=(self.model,\r\n self.features_list),\r\n X=x, y=y)[\"y_pred\"]\r\n if self.model_type == \"binary_classification\":\r\n full_df = pd.concat([x[features], y_pred, y], axis=1)\r\n events_df = full_df[full_df[y.name] == 1]\r\n nevents_df = full_df[full_df[y.name] == 0]\r\n res = {\"all\": full_df,\r\n \"events\": events_df,\r\n \"nevents\": nevents_df}\r\n\r\n elif self.model_type == \"regression\":\r\n full_df = pd.concat([x[features], y_pred, y], axis=1)\r\n res = {\"all\": full_df}\r\n\r\n return res\r\n\r\n def cut_buckets_groups(self, df: pd.DataFrame, df_name: str, perc: dict)\\\r\n -> pd.DataFrame:\r\n \"\"\"\r\n Разбиение всех столбцов в pd.DataFrame по укзаанными в dict пороговым\r\n значениям. Группировка и подсчет доли наблюдений в каждом интервале.\r\n\r\n Parameters:\r\n -----------\r\n df: pd.DataFrame\r\n датафрейм с исходными значениями переменных\r\n\r\n df_name: str\r\n имя набора данных (train, valid, test, oot)\r\n\r\n perc: dict\r\n словарь с необходимыми пороговыми значениями для разбиения\r\n по каждоый фиче вида:\r\n {<название фичи>: [<список пороговых значений>]}\r\n\r\n Returns:\r\n --------\r\n pd.DataFrame\r\n Датафрейм с группировкой фичей по бакетами и долей-количеством\r\n наблюдений в каждом бакете\r\n \"\"\"\r\n\r\n # выходной датафрейм\r\n out_stats = pd.DataFrame()\r\n for col in df.columns:\r\n # для хранения порогов разбиения на перцентили\r\n missings = df[df.isna()[col]][col]\r\n\r\n # выделить отдельно пропуски\r\n missing_cnt = np.nan if len(missings) == 0 else len(missings)\r\n missing_stats = pd.DataFrame({\"feature\": [col],\r\n \"bucket\": [\"MISSING\"],\r\n f\"obs_count_{df_name}\":[missing_cnt],\r\n })\r\n\r\n # разбить на бакеты остальные значения\r\n values = df[df[col].notna()][col]\r\n if col not in perc.keys():\r\n perc[col] = np.unique([np.percentile(values\r\n , interpolation=\"lower\"\r\n , q=q) for q in\r\n np.arange(0, 101, 10)])\r\n buckets = pd.cut(x=values,\r\n bins=perc[col],\r\n duplicates=\"drop\",\r\n include_lowest=True,\r\n labels=False).rename(\"bucket\")\r\n\r\n # Склеить номера бакетов со значениями наблюдений\r\n buckets_group = pd.concat([values, buckets], axis=1)\r\n\r\n # min-max статистики только для train выборки\r\n if df_name == \"train\":\r\n buckets_group = buckets_group.groupby(\"bucket\").agg(\r\n [\"min\", \"max\", \"count\"])[col]\\\r\n .rename(columns={\"min\": \"min_value\",\r\n \"max\": \"max_value\",\r\n \"count\": f\"obs_count_{df_name}\"})\r\n else:\r\n buckets_group = buckets_group.groupby(\"bucket\").agg(\r\n [\"count\"])[col].rename(\r\n columns={\"count\": f\"obs_count_{df_name}\"})\r\n\r\n # buckets_group.columns = buckets_group.columns.droplevel()\r\n buckets_group = buckets_group.reset_index()\\\r\n .rename(columns={\"index\": \"bucket\"})\r\n buckets_group[\"feature\"] = col\r\n\r\n # Добавить пропуски по фиче\r\n if len(missings)>0:\r\n buckets_group = buckets_group.append(missing_stats,\r\n ignore_index=True)\r\n\r\n # Доля наблюдений в каждой выборке\r\n buckets_group[f\"obs_share_{df_name}\"] = \\\r\n buckets_group[f\"obs_count_{df_name}\"] \\\r\n / buckets_group[f\"obs_count_{df_name}\"].sum()\r\n\r\n # Добавить всю статистику по фиче в финальный датасет\r\n out_stats = out_stats.append(buckets_group, ignore_index=True)\r\n return out_stats\r\n\r\n def calc_buckets_categories(self, df: pd.DataFrame, df_name: str)\\\r\n -> pd.DataFrame:\r\n \"\"\"\r\n Группировка признаков по уникальным значениям\r\n и подсчет доли-количества наблюдений в каждом интервале.\r\n\r\n Parameters:\r\n -----------\r\n df: pd.DataFrame\r\n датафрейм с исходными значениями переменных\r\n\r\n df_name: str\r\n имя набора данных (train, valid, test, oot)\r\n\r\n Returns:\r\n --------\r\n pd.DataFrame\r\n Датафрейм с группировкой фичей по значениям и долей-количеством\r\n наблюдений в каждом бакете\r\n \"\"\"\r\n\r\n out_stats = pd.DataFrame()\r\n\r\n for col in df.columns:\r\n missings = df[df.isna()[col]][col]\r\n\r\n # выделить отдельно пропуски\r\n missing_cnt = np.nan if len(missings) == 0 else len(missings)\r\n missing_stats = pd.DataFrame({\"feature\": [col],\r\n \"bucket\": [\"MISSING\"],\r\n f\"obs_count_{df_name}\": [missing_cnt]\r\n })\r\n # категории без пропусков \r\n values = df[df.notna()[col]][col]\r\n\r\n if df_name == \"train\":\r\n groups = values.groupby(by=values)\\\r\n .agg([\"min\", \"max\", \"count\"])\\\r\n .rename(columns={\"min\": \"min_value\",\r\n \"max\": \"max_value\",\r\n \"count\": f\"obs_count_{df_name}\"})\r\n else:\r\n groups = values.groupby(by=values)\\\r\n .agg([\"count\"])\\\r\n .rename(columns={\"count\": f\"obs_count_{df_name}\"})\r\n\r\n groups = groups.reset_index().rename(columns={col: \"bucket\"})\r\n groups[\"feature\"] = col\r\n\r\n # Добавить пропуски\r\n if len(missings) is not None:\r\n groups = groups.append(missing_stats, ignore_index=True)\r\n\r\n # Посчитать доли\r\n groups[f\"obs_share_{df_name}\"] = groups[f\"obs_count_{df_name}\"] \\\r\n / groups[f\"obs_count_{df_name}\"]\\\r\n .sum()\r\n\r\n out_stats = out_stats.append(groups, ignore_index=True)\r\n return out_stats\r\n\r\n def calc_psi_pair(self,\r\n base_df: pd.DataFrame,\r\n base_df_name: str,\r\n diff_df: pd.DataFrame,\r\n diff_df_name: str,\r\n features_type: str = \"numeric\") -> pd.DataFrame:\r\n \"\"\"\r\n Расчет PSI по паре датафреймов: base_df vs diff_df для конкретных\r\n наборов признаков \"numeric\" или \"categorical\"\r\n\r\n Parameters:\r\n -----------\r\n base_df: pd.DataFrame\r\n Датафрейм относительно которого будет считаться PSI\r\n\r\n base_df_name: str\r\n Имя основного датасета\r\n\r\n diff_df: pd.DataFrame\r\n Датафрейм на котором будет считаться PSI\r\n\r\n diff_df_name: str\r\n Имя датасета для расчетаPSI\r\n\r\n features_type: str\r\n Тип признаков в датасете (numeric/categorical)\r\n\r\n Returns:\r\n --------\r\n pd.DataFrame\r\n Датафрейм со значениями PSI на паре наборов данных по\r\n каждой переменной\r\n \"\"\"\r\n if features_type == \"numeric\":\r\n perc = {}\r\n base_stats = self.cut_buckets_groups(base_df,\r\n base_df_name, perc=perc)\r\n diff_stats = self.cut_buckets_groups(diff_df,\r\n diff_df_name, perc=perc)\r\n elif features_type == \"categorical\":\r\n base_stats = self.calc_buckets_categories(base_df, base_df_name)\r\n diff_stats = self.calc_buckets_categories(diff_df, diff_df_name)\r\n\r\n all_stats = pd.concat([base_stats.set_index([\"feature\", \"bucket\"]),\r\n diff_stats.set_index([\"feature\", \"bucket\"])],\r\n axis=1,\r\n join='outer')\r\n\r\n # Заполнить нулями cnt для вновь возникших категорий и\r\n # оч. маленьким числом share\r\n all_stats[f\"obs_share_{base_df_name}\"] = \\\r\n all_stats[f\"obs_share_{base_df_name}\"].fillna(0.001)\r\n all_stats[f\"obs_share_{diff_df_name}\"] = \\\r\n all_stats[f\"obs_share_{diff_df_name}\"].fillna(0.001)\r\n\r\n all_stats[f\"obs_count_{base_df_name}\"] = \\\r\n all_stats[f\"obs_count_{base_df_name}\"].fillna(0)\r\n all_stats[f\"obs_count_{diff_df_name}\"] = \\\r\n all_stats[f\"obs_count_{diff_df_name}\"].fillna(0)\r\n\r\n def _psi(base: pd.Series, diff: pd.Series):\r\n return (diff - base) * np.log(diff / base)\r\n\r\n all_stats[f\"PSI_{base_df_name}_vs_{diff_df_name}\"] =\\\r\n _psi(all_stats[f\"obs_share_{base_df_name}\"],\r\n all_stats[f\"obs_share_{diff_df_name}\"])\r\n\r\n return all_stats\r\n\r\n def calc_psi(self,\r\n base_df: pd.DataFrame,\r\n base_df_name: str,\r\n diff_df: pd.DataFrame,\r\n diff_df_name: str) -> pd.DataFrame:\r\n \"\"\"\r\n Общая функция расчета PSI на всем наборе признаков двух наборов данных.\r\n Управляет запуском расчета PSI для categorical и numeric признаков, а\r\n также объединением результатов в один итоговый датафрейм\r\n\r\n Parameters:\r\n -----------\r\n base_df: pd.DataFrame\r\n Датафрейм относительно которого будет считаться PSI\r\n\r\n base_df_name: str\r\n Имя основного датасета\r\n\r\n diff_df: pd.DataFrame\r\n Датафрейм на котором будет считаться PSI\r\n\r\n diff_df_name: str\r\n Имя датасета для расчетаPSI\r\n\r\n Returns:\r\n --------\r\n pd.DataFrame:\r\n Итоговый датафрейм с PSI между двумя наборами данных на всех\r\n переменных\r\n\r\n \"\"\"\r\n \r\n if self.cat_features is not None:\r\n numeric = set(self.features_list) - set(self.cat_features)\r\n categoric = set(self.features_list)\\\r\n .intersection(set(self.cat_features))\r\n else:\r\n numeric = set(self.features_list)\r\n categoric = set([])\r\n \r\n numeric.add(\"y_pred\")\r\n \r\n\r\n if len(numeric) > 0:\r\n num_stats_psi = self.calc_psi_pair(base_df[numeric]\r\n , base_df_name\r\n , diff_df[numeric]\r\n , diff_df_name\r\n , features_type=\"numeric\")\r\n total_psi = num_stats_psi.reset_index()\r\n \r\n if len(categoric) > 0:\r\n cat_stats_psi = self.calc_psi_pair(base_df[categoric]\r\n , base_df_name\r\n , diff_df[categoric]\r\n , diff_df_name\r\n , features_type=\"categorical\")\r\n\r\n total_psi = pd.concat([num_stats_psi.reset_index()\r\n , cat_stats_psi.reset_index()]\r\n , axis=0)\r\n\r\n return total_psi\r\n\r\n @calculate_time_execute\r\n def validate(self, **kwargs):\r\n \"\"\"\r\n Функция инициации расчетов PSI между наборами данных в словаре dict\r\n Управляет расчетами PSI между парами наборов, объединяет результат в\r\n итоговый датафрем.\r\n Записывает результаты проверки PSI и детали расчетов в excel книгу.\r\n\r\n Parameters:\r\n -----------\r\n **kwargs: Dict[str, Tuple(pd.DataFrame, pd.Series)]\r\n Словарь, где ключ - название датасета, значение -\r\n кортеж из (X, y), X - матрица признаков,\r\n y - вектор истинных ответов.\r\n \"\"\"\r\n print(\"Calculating PSI...\")\r\n # составить чек-лист датафреймов для сравнения с train\r\n checklist = self._create_checklist(kwargs.keys())\r\n\r\n # считать train\r\n X_train, y_train = kwargs.get(\"train\", (None, None))\r\n # нарезать train на event, nevents, all\r\n train_df = self.create_df(X_train, y_train, self.model,\r\n self.features_list)\r\n\r\n # Для каждого датафрейма из чек-листа\r\n for df_name in checklist:\r\n\r\n X_diff, y_diff = kwargs.get(df_name, (None, None))\r\n # нарезать датафрейм на events, nevents, all\r\n diff_df = self.create_df(X_diff, y_diff, self.model,\r\n self.features_list)\r\n\r\n _psi_df = pd.DataFrame()\r\n # для каждой части events, nevents,all посчитать статистики\r\n for df_part in train_df.keys(): #[\"all\", \"events\", \"nevents\"]:\r\n _psi = self.calc_psi(train_df[df_part], \"train\",\r\n diff_df[df_part], df_name)\r\n _psi[\"data_part\"] = df_part\r\n _psi = _psi.set_index([\"feature\", \"bucket\", \"data_part\"])\r\n\r\n # добавить столбец в датафрейм с итоговым PSI\r\n _psi_grouped = \\\r\n _psi.groupby(\"feature\")[f\"PSI_train_vs_{df_name}\"] \\\r\n .sum().rename(f\"PSI_train_vs_{df_name}_{df_part}\")\r\n self.psi_short = pd.concat([self.psi_short,\r\n _psi_grouped],\r\n axis=1,\r\n sort=True,\r\n join=\"outer\")\r\n\r\n _psi_df = _psi_df.append(_psi)\r\n\r\n new_cols = _psi_df.columns.difference(self.psi_detailed.columns)\r\n self.psi_detailed = pd.concat([self.psi_detailed,\r\n _psi_df[new_cols]],\r\n axis=1,\r\n join=\"outer\")\r\n\r\n self.psi_detailed = self.psi_detailed.reset_index()\r\n self.psi_short = self.psi_short.reset_index()\r\n self.psi_short.rename(columns={\"index\": \"feature\"}, inplace=True)\r\n\r\n # Записать результат в excel файл\r\n # формат\r\n int_number = \"## ##0\"\r\n float_number_high = \"## ##0.00\"\r\n float_number_low = \"## ##0.0000\"\r\n int_percentage = \"0%\"\r\n float_percentage_high = \"0.00%\"\r\n float_percentage_low = \"0.0000%\"\r\n\r\n # Кастомный формат для таблицы\r\n fmt = {\"num_format\": {\r\n int_number: [\"obs_count_train\",\r\n \"obs_count_valid\",\r\n \"obs_count_OOT\",\r\n \"obs_count_test\",\r\n \"obs_count_OOT_psi\"]\r\n , float_percentage_low: [\"obs_share_train\",\r\n \"obs_share_valid\",\r\n \"obs_share_OOT\",\r\n \"obs_share_test\",\r\n \"obs_share_OOT_psi\"]\r\n , float_number_low: [\"PSI_train_vs_valid\",\r\n \"PSI_train_vs_test\",\r\n \"PSI_train_vs_OOT\",\r\n \"PSI_train_vs_OOT_psi\",\r\n \"PSI_train_vs_valid_all\",\r\n \"PSI_train_vs_test_all\",\r\n \"PSI_train_vs_OOT_all\",\r\n \"PSI_train_vs_OOT_psi_all\",\r\n \"PSI_train_vs_valid_events\",\r\n \"PSI_train_vs_test_events\",\r\n \"PSI_train_vs_OOT_events\",\r\n \"PSI_train_vs_OOT_psi_events\",\r\n \"PSI_train_vs_valid_nevents\",\r\n \"PSI_train_vs_test_nevents\",\r\n \"PSI_train_vs_OOT_nevents\",\r\n \"PSI_train_vs_OOT_psi_nevents\"]\r\n }\r\n }\r\n self._to_excel(self.psi_detailed, sheet_name=\"PSI detailed\", fmt=fmt)\r\n self._to_excel(self.psi_short, sheet_name=\"PSI\", fmt=fmt)\r\n", "sub_path": "drafts/dspl/validation_report/.ipynb_checkpoints/PSIVariablesChecker-checkpoint.py", "file_name": "PSIVariablesChecker-checkpoint.py", "file_ext": "py", "file_size_in_byte": 24832, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "Checker.Checker", "line_number": 12, "usage_type": "name"}, {"api_name": "pandas.ExcelWriter", "line_number": 43, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 59, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 60, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 62, "usage_type": "attribute"}, {"api_name": "FormatExcelWriter.FormatExcelWriter", "line_number": 79, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 140, "usage_type": "attribute"}, {"api_name": "pandas.Series", "line_number": 141, "usage_type": "attribute"}, {"api_name": "funcs.create_pred_df", "line_number": 162, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 166, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 174, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 179, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 206, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 212, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 213, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.percentile", "line_number": 221, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 224, "usage_type": "call"}, {"api_name": "pandas.cut", "line_number": 225, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 232, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 180, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 265, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 286, "usage_type": "call"}, {"api_name": "numpy.nan", "line_number": 292, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 293, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 266, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 327, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 329, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 369, "usage_type": "call"}, {"api_name": "pandas.Series", "line_number": 386, "usage_type": "attribute"}, {"api_name": "numpy.log", "line_number": 387, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 331, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 396, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 398, "usage_type": "attribute"}, {"api_name": "pandas.concat", "line_number": 453, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 399, "usage_type": "attribute"}, {"api_name": "pandas.DataFrame", "line_number": 492, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 504, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 513, "usage_type": "call"}, {"api_name": "utils.calculate_time_execute", "line_number": 459, "usage_type": "name"}]} +{"seq_id": "505634938", "text": "__author__ = 'Paul'\n\nfrom django.db import models\nfrom django.contrib.auth.models import User\n\nclass Client(models.Model):\n address = models.CharField(max_length=70)\n phone = models.CharField(max_length=13)\n date_of_birth = models.DateField()\n\n user_fk = models.ForeignKey(User)\n\n# одиниця продукції\n# ціна, об'єм та назва -- обов'язкові атрибути, додаткові атрибути (виробник і тд) в моделі ProductAttributes\nclass Product(models.Model):\n name = models.CharField(max_length=100)\n price = models.DecimalField(max_digits=12, decimal_places=2)\n volume = models.DecimalField(max_digits=8, decimal_places=3)\n is_available = models.BooleanField()\n image_path = models.ImageField()\n\n# атрибути товару\nclass ProductAttributes(models.Model):\n attr_name = models.CharField(max_length=50)\n attr_value = models.CharField(max_length=1000)\n\n product_fk = models.ForeignKey(Product)\n\n# ящик (упаковка) товару\nclass ProductPackage(models.Model):\n total_volume = models.DecimalField(max_digits=12, decimal_places=3)\n total_width = models.DecimalField(max_digits=6, decimal_places=3)\n total_height = models.DecimalField(max_digits=6, decimal_places=3)\n total_length = models.DecimalField(max_digits=6, decimal_places=3)\n image_path = models.ImageField()\n\n consignment_fk = models.ForeignKey(Consignment)\n\n\n# партія товарів\nclass Consignment(models.Model):\n number = models.IntegerField(unique=True)\n creation_date = models.DateTimeField()\n expiration_date = models.DateTimeField()\n\n product_fk = models.ForeignKey(Product)\n\n# коментар до товару\nclass Comments(models.Model):\n text = models.TextField()\n date = models.DateTimeField()\n\n product_fk = models.ForeignKey(Product)\n client_fk = models.ForeignKey(Client)\n\n\n# таблиця для звязку користувача з його лайками (перевіряти чи він уже лайкав той чи інший продукт)\nclass RatedProducts(models.Model):\n product_fk = models.ForeignKey(Product)\n user_fk = models.ForeignKey(User)\n is_rated = models.BooleanField()\n value = models.SmallIntegerField(blank=True)\n\n\nclass Order(models.Model):\n state_choices = ('ACTIVE', 'COMPLETED', 'FROZEN')\n # дата складення замовлення\n initial_date = models.DateTimeField()\n # час в який потрібно доставити товар\n order_date = models.DateTimeField()\n # дата доставки\n delivery_date = models.DateTimeField()\n address = models.CharField(max_length=200)\n state = models.CharField(choices=state_choices)\n\n client_fk = models.ForeignKey(User)\n\nclass ProductsInOrder(models.Model):\n product_fk = models.ForeignKey(Product)\n order_fk = models.ForeignKey(Order)\n\nclass Trucks():\n brand = models.CharField(max_length=25)\n number = models.CharField(max_length=10)\n permissible_load = models.IntegerField(max_length=8)\n width = models.DecimalField(max_digits=10, decimal_places=3)\n height = models.DecimalField(max_digits=10, decimal_places=3)\n length = models.DecimalField(max_digits=10, decimal_places=3)\n\n driver_fk = models.ForeignKey(User)\n\n\n\n\n", "sub_path": "models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 3339, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.db.models.Model", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 6, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 7, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 9, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 9, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 11, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 11, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 11, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 15, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 15, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 16, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 16, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 17, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 17, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 18, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 18, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 19, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 19, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 20, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 20, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 23, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 23, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 24, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 24, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 25, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 25, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 27, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 27, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 30, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 30, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 31, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 31, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 32, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 32, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 33, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 33, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 34, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 34, "usage_type": "name"}, {"api_name": "django.db.models.ImageField", "line_number": 35, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 35, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 37, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 37, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 41, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 41, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 42, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 42, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 43, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 43, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 44, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 44, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 46, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 46, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 49, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 49, "usage_type": "name"}, {"api_name": "django.db.models.TextField", "line_number": 50, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 50, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 51, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 51, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 53, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 53, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 54, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 54, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 58, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 58, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 59, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 59, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 60, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 60, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 60, "usage_type": "name"}, {"api_name": "django.db.models.BooleanField", "line_number": 61, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 61, "usage_type": "name"}, {"api_name": "django.db.models.SmallIntegerField", "line_number": 62, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 62, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 65, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 65, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 68, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 68, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 70, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 70, "usage_type": "name"}, {"api_name": "django.db.models.DateTimeField", "line_number": 72, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 72, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 73, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 73, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 74, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 74, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 76, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 76, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 76, "usage_type": "name"}, {"api_name": "django.db.models.Model", "line_number": 78, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 78, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 79, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 79, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 80, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 80, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 83, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 83, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 84, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 84, "usage_type": "name"}, {"api_name": "django.db.models.IntegerField", "line_number": 85, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 85, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 86, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 86, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 87, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 87, "usage_type": "name"}, {"api_name": "django.db.models.DecimalField", "line_number": 88, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 88, "usage_type": "name"}, {"api_name": "django.db.models.ForeignKey", "line_number": 90, "usage_type": "call"}, {"api_name": "django.contrib.auth.models.User", "line_number": 90, "usage_type": "argument"}, {"api_name": "django.db.models", "line_number": 90, "usage_type": "name"}]} +{"seq_id": "603155225", "text": "from model.information import Information\nfrom model.user import User\nfrom model.time_record import TimeRecord\nfrom logging import getLogger\nimport json\nimport urllib.parse\nimport urllib.request\n\nlogger = getLogger(__name__)\n\nADD_RECORD_STATUS_OK = '1',\nADD_RECORD_STATUS_USER_NOT_FOUND = '5'\n\n\nclass ApiClient(object):\n def __init__(self, options):\n self._options = options\n\n def get_information(self):\n try:\n headers = {\n 'X-API-KEY': self._options.api_key\n }\n\n req = urllib.request.Request(\n self._options.get_information_url, headers=headers)\n\n with urllib.request.urlopen(req) as res:\n body = res.read()\n result = ApiResult(res.code, Information.from_json(body))\n\n return result\n\n except Exception as ex:\n logger.error(ex)\n raise ex\n\n def get_users(self):\n try:\n headers = {\n 'X-API-KEY': self._options.api_key\n }\n\n req = urllib.request.Request(\n self._options.get_users_url, headers=headers)\n\n with urllib.request.urlopen(req) as res:\n body = res.read()\n result = ApiResult(res.code, User.from_json(body))\n\n return result\n\n except Exception as ex:\n logger.error(ex)\n raise ex\n\n def add_time_record(self, time_record):\n try:\n body = time_record.to_json()\n headers = {\n 'Content-Type': 'application/json',\n 'X-API-KEY': self._options.api_key\n }\n\n method = 'POST'\n\n req = urllib.request.Request(\n self._options.add_time_record_url, data=body, method=method, headers=headers)\n\n with urllib.request.urlopen(req) as res:\n body = res.read()\n return AddUserRecordResult.from_json(body)\n\n except Exception as ex:\n logger.error(ex)\n raise ex\n\n\nclass ApiOptions(object):\n\n def __init__(self):\n self.api_key = None\n self.get_information_url = None\n self.get_users_url = None\n self.add_time_record_url = None\n\n\nclass ApiResult(object):\n\n def __init__(self, code, value=None):\n self.code = code\n self.value = value\n\n def is_ok(self):\n return self.code == 200\n\n\nclass AddUserRecordResult(object):\n\n def __init__(self, status):\n self.status = status\n\n def is_ok(self):\n return self.status == ADD_RECORD_STATUS_OK\n\n def is_not_found(self):\n return self.status == ADD_RECORD_STATUS_USER_NOT_FOUND\n\n @staticmethod\n def from_json(data):\n json_data = json.loads(data)\n\n return AddUserRecordResult(json_data['status'])\n", "sub_path": "api/api_client.py", "file_name": "api_client.py", "file_ext": "py", "file_size_in_byte": 2817, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 9, "usage_type": "call"}, {"api_name": "urllib.parse.request.Request", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 25, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 25, "usage_type": "name"}, {"api_name": "urllib.parse.request.urlopen", "line_number": 28, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 28, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 28, "usage_type": "name"}, {"api_name": "model.information.Information.from_json", "line_number": 30, "usage_type": "call"}, {"api_name": "model.information.Information", "line_number": 30, "usage_type": "name"}, {"api_name": "urllib.parse.request.Request", "line_number": 44, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 44, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 44, "usage_type": "name"}, {"api_name": "urllib.parse.request.urlopen", "line_number": 47, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 47, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 47, "usage_type": "name"}, {"api_name": "model.user.User.from_json", "line_number": 49, "usage_type": "call"}, {"api_name": "model.user.User", "line_number": 49, "usage_type": "name"}, {"api_name": "urllib.parse.request.Request", "line_number": 67, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 67, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 67, "usage_type": "name"}, {"api_name": "urllib.parse.request.urlopen", "line_number": 70, "usage_type": "call"}, {"api_name": "urllib.parse.request", "line_number": 70, "usage_type": "attribute"}, {"api_name": "urllib.parse", "line_number": 70, "usage_type": "name"}, {"api_name": "json.loads", "line_number": 111, "usage_type": "call"}]} +{"seq_id": "32347859", "text": "__author__ = 'amrit'\n\nimport math\nimport pygmo as pg\nimport numpy as np\n\nfrom random import randint, uniform, choice, sample\nfrom sklearn.metrics import roc_curve, auc\nfrom sklearn.metrics import confusion_matrix\n\nPRE, REC, SPEC, FPR, NPV, ACC, F1 = 7, 6, 5, 4, 3, 2, 1\n\n\ndef _randint(a=0, b=0):\n return randint(a, b)\n\n\ndef _randchoice(a):\n return choice(a)\n\n\ndef _randuniform(a=0.0, b=0.0):\n return uniform(a, b)\n\n\ndef _randsample(a, b=1):\n return sample(a, b)\n\n\ndef unpack(l):\n tmp = []\n for i in l:\n if type(i) is not list:\n tmp.append(i)\n else:\n for x in i:\n tmp.append(x)\n return tmp\n\n\ndef get_performance(prediction, test_labels):\n tn, fp, fn, tp = confusion_matrix(\n test_labels, prediction, labels=[0, 1]).ravel()\n pre = 1.0 * tp / (tp + fp) if (tp + fp) != 0 else 0\n rec = 1.0 * tp / (tp + fn) if (tp + fn) != 0 else 0\n spec = 1.0 * tn / (tn + fp) if (tn + fp) != 0 else 0\n fpr = 1 - spec\n npv = 1.0 * tn / (tn + fn) if (tn + fn) != 0 else 0\n acc = 1.0 * (tp + tn) / (tp + tn + fp + fn) if (\n tp + tn + fp + fn) != 0 else 0\n f1 = 2.0 * tp / (2.0 * tp + fp + fn) if (2.0 * tp + fp + fn) != 0 else 0\n gm = 2.0 * rec * (1 - fpr) / (rec + 1 - fpr) if (rec + 1 - fpr) != 0 else 0\n\n ifa = 0\n actual_results = np.asarray(test_labels)\n predicted_results = prediction\n for i, j in zip(actual_results, predicted_results):\n if (i == 1) and (j == 1):\n break\n elif (i == 0) and (j == 1):\n ifa += 1\n return [round(x, 3) for x in [pre, rec, spec, fpr, npv, acc, f1, gm, ifa]]\n\n\ndef get_score(criteria, prediction, test_labels, data):\n tn, fp, fn, tp = confusion_matrix(\n test_labels, prediction, labels=[0, 1]).ravel()\n pre, rec, spec, fpr, npv, acc, f1, gm, ifa = get_performance(\n prediction, test_labels)\n all_metrics = [tp, fp, tn, fn, pre, rec, spec, fpr, npv, acc, f1, ifa]\n if criteria == \"Accuracy\":\n score = -all_metrics[-ACC]\n elif criteria == \"d2h\":\n score = all_metrics[-FPR]**2 + (1 - all_metrics[-REC])**2\n score = math.sqrt(score) / math.sqrt(2)\n elif criteria == \"Pf_Auc\":\n score = auc_measure(prediction, test_labels)\n elif criteria == \"popt\":\n score = get_auc(data)\n elif criteria == \"popt20\":\n score = get_popt20(data)\n elif criteria == \"Gini\":\n p1 = all_metrics[-PRE] # target == 1 for the positive split\n p0 = 1 - all_metrics[-NPV] # target == 1 for the negative split\n score = 1 - p0**2 - p1**2\n elif criteria == 'recall':\n score = rec\n elif criteria == 'false_alarm':\n score = fpr\n elif criteria == 'g_measure':\n score = gm\n elif criteria == \"ifa\":\n score = ifa\n else: # Information Gain\n P, N = all_metrics[0] + all_metrics[3], all_metrics[1] + all_metrics[2]\n p = 1.0 * P / (P + N) if P + N > 0 else 0 # before the split\n p1 = all_metrics[-PRE] # the positive part of the split\n p0 = 1 - all_metrics[-NPV] # the negative part of the split\n I, I0, I1 = (-x * np.log2(x) if x != 0 else 0 for x in (p, p0, p1))\n I01 = p * I1 + (1 - p) * I0\n score = -(I - I01) # the smaller the better.\n return round(score, 3)\n\n\ndef auc_measure(prediction, test_labels):\n fpr, tpr, _ = roc_curve(test_labels, prediction, pos_label=1)\n auc1 = auc(fpr, tpr)\n return auc1\n\n\ndef subtotal(x):\n xx = [0]\n for _, t in enumerate(x):\n xx += [xx[-1] + t]\n return xx[1:]\n\n\ndef get_recall(true):\n total_true = float(len([i for i in true if i == 1]))\n hit = 0.0\n recall = []\n for i in range(len(true)):\n if true[i] == 1:\n hit += 1\n recall += [hit / total_true if total_true else 0.0]\n return recall\n\n\ndef get_auc(data):\n \"\"\"The smaller the better\"\"\"\n if len(data) == 1:\n return 0\n x_sum = float(sum(data['loc']))\n x = data['loc'].apply(lambda t: t / x_sum)\n xx = subtotal(x)\n yy = get_recall(data['bug'].values)\n try:\n ret = round(auc(xx, yy), 3)\n except:\n # print\"?\"\n ret = 0\n return ret\n\n\ndef get_popt20(data):\n data.sort_values(by=[\"bug\", \"loc\"], ascending=[0, 1], inplace=True)\n x_sum = float(sum(data['loc']))\n x = data['loc'].apply(lambda t: t / x_sum)\n xx = subtotal(x)\n\n # get AUC_optimal\n yy = get_recall(data['bug'].values)\n xxx = [i for i in xx if i <= 0.2]\n yyy = yy[:len(xxx)]\n s_opt = round(auc(xxx, yyy), 3)\n\n # get AUC_worst\n xx = subtotal(x[::-1])\n yy = get_recall(data['bug'][::-1].values)\n xxx = [i for i in xx if i <= 0.2]\n yyy = yy[:len(xxx)]\n try:\n s_wst = round(auc(xxx, yyy), 3)\n except:\n # print \"s_wst forced = 0\"\n s_wst = 0\n\n # get AUC_prediction\n data.sort_values(by=[\"prediction\", \"loc\"], ascending=[0, 1], inplace=True)\n x = data['loc'].apply(lambda t: t / x_sum)\n xx = subtotal(x)\n yy = get_recall(data['bug'].values)\n xxx = [k for k in xx if k <= 0.2]\n yyy = yy[:len(xxx)]\n try:\n s_m = round(auc(xxx, yyy), 3)\n except:\n return 0\n\n Popt = (s_m - s_wst) / (s_opt - s_wst)\n return round(Popt, 3)\n\n\ndef get_best(values, ignore_idx=None):\n \"\"\"Assumping everything are to MAXIMIZED.\n Return the best value indices\n https://esa.github.io/pagmo2/docs/python/utils/py_mo_utils.html\n \"\"\"\n V = np.array(values)\n for idx in ignore_idx:\n V[:, idx] = 0\n _, _, dc, _ = pg.fast_non_dominated_sorting(points=-1 * V)\n\n return [i for i, v in enumerate(dc) if v == 0]\n", "sub_path": "src/model/utilities.py", "file_name": "utilities.py", "file_ext": "py", "file_size_in_byte": 5611, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "random.randint", "line_number": 15, "usage_type": "call"}, {"api_name": "random.choice", "line_number": 19, "usage_type": "call"}, {"api_name": "random.uniform", "line_number": 23, "usage_type": "call"}, {"api_name": "random.sample", "line_number": 27, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 55, "usage_type": "call"}, {"api_name": "sklearn.metrics.confusion_matrix", "line_number": 66, "usage_type": "call"}, {"api_name": "math.sqrt", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.log2", "line_number": 99, "usage_type": "call"}, {"api_name": "sklearn.metrics.roc_curve", "line_number": 106, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 107, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 138, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 155, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 163, "usage_type": "call"}, {"api_name": "sklearn.metrics.auc", "line_number": 176, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 189, "usage_type": "call"}, {"api_name": "pygmo.fast_non_dominated_sorting", "line_number": 192, "usage_type": "call"}]} +{"seq_id": "652488640", "text": "############################################## IMPORTS ##############################################\n\n# GENERAL\nfrom flask.views import MethodView\nfrom flask_jwt_extended import jwt_required, fresh_jwt_required\nfrom flask_smorest import Blueprint\nimport marshmallow as ma\n\n# MODELS\nfrom app.main.model.group_model import GroupSchema\nfrom app.main.model.utils import GeneralArgumentSchemas\n\n# SERVICES\nfrom app.main.service.group_service import GroupService\n\n# UTILS\nfrom app.main.controller.utils import ErrorDocs, AccessHandler, JWTHandler\n\n############################################## ROUTING ##############################################\n\ngrp_bp = Blueprint('group', 'group', url_prefix='/group', description='Group related operations')\n\ngrp_schema = GroupSchema()\ngrps_schema = GroupSchema(many=True)\ngrp_update_schema = GroupSchema(partial=True)\n\n\n@grp_bp.route('/all')\nclass GroupsAdmin(MethodView):\n \"\"\"\n Methods:\n - GET: get all groups\n \"\"\"\n\n ################################ GET GROUPS ################################\n @fresh_jwt_required\n @AccessHandler.required_access_level('Admin')\n @grp_bp.doc(responses=ErrorDocs.get_error_docs(error_codes=[400, 401]))\n @grp_bp.response(grps_schema, code=200, description='Successfully retrieved groups.')\n def get(self):\n \"\"\"List all groups.\"\"\"\n return GroupService.read_all()\n\n\n@grp_bp.route('/by-user')\nclass GroupsUser(MethodView):\n \"\"\"\n Methods:\n - GET: get all groups of a user\n \"\"\"\n\n ################################ GET GROUPS ################################\n @fresh_jwt_required\n @AccessHandler.required_access_level('User')\n @grp_bp.doc(responses=ErrorDocs.get_error_docs(error_codes=[400, 401]))\n @grp_bp.arguments(GeneralArgumentSchemas.UserIdSchema(), location='query')\n @grp_bp.response(grps_schema, code=200, description='Successfully retrieved groups.')\n def get(self, data):\n \"\"\"List all groups of a user.\"\"\"\n return GroupService.read_all_by_user(data=data)\n\n\n@grp_bp.route('/')\nclass Group(MethodView):\n \"\"\"\n Methods:\n - POST: create a group for a user\n \"\"\"\n\n ################################ POST GROUP ################################\n @fresh_jwt_required\n @AccessHandler.required_access_level('User')\n @grp_bp.doc(responses=ErrorDocs.get_error_docs(error_codes=[400, 401, 409, 422]))\n @grp_bp.arguments(grp_schema, location='json')\n @grp_bp.response(grp_schema, code=201, description='Successfully created group.')\n def post(self, data):\n \"\"\"Create a new group for a user.\"\"\"\n return GroupService.create(data=data)\n\n\n@grp_bp.route('/')\nclass GroupById(MethodView):\n \"\"\"\n Methods:\n - GET: get an group of a user\n - PUT: update an group of a user\n - DELETE: delete an group of a user\n \"\"\"\n\n ################################ GET GROUP ################################\n @fresh_jwt_required\n @AccessHandler.required_access_level('User')\n @grp_bp.doc(responses=ErrorDocs.get_error_docs(error_codes=[400, 401, 404]))\n @grp_bp.response(grp_schema, code=200, description='Successfully retrieved group.')\n def get(self, id):\n \"\"\"Get a group of a user given its id.\"\"\"\n return GroupService.read(id=id)\n\n ################################ PUT GROUP ################################\n @fresh_jwt_required\n @AccessHandler.required_access_level('User')\n @grp_bp.doc(responses=ErrorDocs.get_error_docs(error_codes=[400, 401, 404, 422]))\n @grp_bp.arguments(grp_update_schema, location='json')\n @grp_bp.response(grp_update_schema, code=200, description='Successfully updated group.')\n def put(self, data, id):\n \"\"\"Update a group of a user given its id.\"\"\"\n return GroupService.update(data=data, id=id)\n\n ################################ DELETE GROUP ################################\n @fresh_jwt_required\n @AccessHandler.required_access_level('User')\n @grp_bp.doc(responses=ErrorDocs.get_error_docs(error_codes=[400, 401, 404]))\n @grp_bp.response(grp_schema, code=200, description='Successfully deleted group.')\n def delete(self, id):\n \"\"\"Delete a group of a user given its id.\"\"\"\n return GroupService.delete(id=id)\n", "sub_path": "app/main/controller/group_controller.py", "file_name": "group_controller.py", "file_ext": "py", "file_size_in_byte": 4254, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask_smorest.Blueprint", "line_number": 21, "usage_type": "call"}, {"api_name": "app.main.model.group_model.GroupSchema", "line_number": 23, "usage_type": "call"}, {"api_name": "app.main.model.group_model.GroupSchema", "line_number": 24, "usage_type": "call"}, {"api_name": "app.main.model.group_model.GroupSchema", "line_number": 25, "usage_type": "call"}, {"api_name": "flask.views.MethodView", "line_number": 29, "usage_type": "name"}, {"api_name": "app.main.service.group_service.GroupService.read_all", "line_number": 42, "usage_type": "call"}, {"api_name": "app.main.service.group_service.GroupService", "line_number": 42, "usage_type": "name"}, {"api_name": "flask_jwt_extended.fresh_jwt_required", "line_number": 36, "usage_type": "name"}, {"api_name": "app.main.controller.utils.AccessHandler.required_access_level", "line_number": 37, "usage_type": "call"}, {"api_name": "app.main.controller.utils.AccessHandler", "line_number": 37, "usage_type": "name"}, {"api_name": "app.main.controller.utils.ErrorDocs.get_error_docs", "line_number": 38, "usage_type": "call"}, {"api_name": "app.main.controller.utils.ErrorDocs", "line_number": 38, "usage_type": "name"}, {"api_name": "flask.views.MethodView", "line_number": 46, "usage_type": "name"}, {"api_name": "app.main.service.group_service.GroupService.read_all_by_user", "line_number": 60, "usage_type": "call"}, {"api_name": "app.main.service.group_service.GroupService", "line_number": 60, "usage_type": "name"}, {"api_name": "flask_jwt_extended.fresh_jwt_required", "line_number": 53, "usage_type": "name"}, {"api_name": "app.main.controller.utils.AccessHandler.required_access_level", "line_number": 54, "usage_type": "call"}, {"api_name": "app.main.controller.utils.AccessHandler", "line_number": 54, "usage_type": "name"}, {"api_name": "app.main.controller.utils.ErrorDocs.get_error_docs", "line_number": 55, "usage_type": "call"}, {"api_name": "app.main.controller.utils.ErrorDocs", "line_number": 55, "usage_type": "name"}, {"api_name": "app.main.model.utils.GeneralArgumentSchemas.UserIdSchema", "line_number": 56, "usage_type": "call"}, {"api_name": "app.main.model.utils.GeneralArgumentSchemas", "line_number": 56, "usage_type": "name"}, {"api_name": "flask.views.MethodView", "line_number": 64, "usage_type": "name"}, {"api_name": "app.main.service.group_service.GroupService.create", "line_number": 78, "usage_type": "call"}, {"api_name": "app.main.service.group_service.GroupService", "line_number": 78, "usage_type": "name"}, {"api_name": "flask_jwt_extended.fresh_jwt_required", "line_number": 71, "usage_type": "name"}, {"api_name": "app.main.controller.utils.AccessHandler.required_access_level", "line_number": 72, "usage_type": "call"}, {"api_name": "app.main.controller.utils.AccessHandler", "line_number": 72, "usage_type": "name"}, {"api_name": "app.main.controller.utils.ErrorDocs.get_error_docs", "line_number": 73, "usage_type": "call"}, {"api_name": "app.main.controller.utils.ErrorDocs", "line_number": 73, "usage_type": "name"}, {"api_name": "flask.views.MethodView", "line_number": 82, "usage_type": "name"}, {"api_name": "app.main.service.group_service.GroupService.read", "line_number": 97, "usage_type": "call"}, {"api_name": "app.main.service.group_service.GroupService", "line_number": 97, "usage_type": "name"}, {"api_name": "flask_jwt_extended.fresh_jwt_required", "line_number": 91, "usage_type": "name"}, {"api_name": "app.main.controller.utils.AccessHandler.required_access_level", "line_number": 92, "usage_type": "call"}, {"api_name": "app.main.controller.utils.AccessHandler", "line_number": 92, "usage_type": "name"}, {"api_name": "app.main.controller.utils.ErrorDocs.get_error_docs", "line_number": 93, "usage_type": "call"}, {"api_name": "app.main.controller.utils.ErrorDocs", "line_number": 93, "usage_type": "name"}, {"api_name": "app.main.service.group_service.GroupService.update", "line_number": 107, "usage_type": "call"}, {"api_name": "app.main.service.group_service.GroupService", "line_number": 107, "usage_type": "name"}, {"api_name": "flask_jwt_extended.fresh_jwt_required", "line_number": 100, "usage_type": "name"}, {"api_name": "app.main.controller.utils.AccessHandler.required_access_level", "line_number": 101, "usage_type": "call"}, {"api_name": "app.main.controller.utils.AccessHandler", "line_number": 101, "usage_type": "name"}, {"api_name": "app.main.controller.utils.ErrorDocs.get_error_docs", "line_number": 102, "usage_type": "call"}, {"api_name": "app.main.controller.utils.ErrorDocs", "line_number": 102, "usage_type": "name"}, {"api_name": "app.main.service.group_service.GroupService.delete", "line_number": 116, "usage_type": "call"}, {"api_name": "app.main.service.group_service.GroupService", "line_number": 116, "usage_type": "name"}, {"api_name": "flask_jwt_extended.fresh_jwt_required", "line_number": 110, "usage_type": "name"}, {"api_name": "app.main.controller.utils.AccessHandler.required_access_level", "line_number": 111, "usage_type": "call"}, {"api_name": "app.main.controller.utils.AccessHandler", "line_number": 111, "usage_type": "name"}, {"api_name": "app.main.controller.utils.ErrorDocs.get_error_docs", "line_number": 112, "usage_type": "call"}, {"api_name": "app.main.controller.utils.ErrorDocs", "line_number": 112, "usage_type": "name"}]} +{"seq_id": "362298762", "text": "import os\nimport random\n\nimport cv2\nimport numpy as np \nimport tensorflow as tf \n\ndef _int64_feature(value):\n return tf.train.Feature(int64_list=tf.train.Int64List(value=[value]))\n\n\ndef _bytes_feature(value):\n return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))\n\ndef species_to_num(name):\n species = [0 for _ in range(15)]\n if name.split('_')[0] == '宝贝科':\n species[0] = 1\n elif name.split('_')[0] == '芋螺科':\n species[1] = 1\n elif name.split('_')[0] == '蛾螺科':\n species[2] = 1\n elif name.split('_')[0] == '榧螺科':\n species[3] = 1\n elif name.split('_')[0] == '凤螺科':\n species[4] = 1\n elif name.split('_')[0] == '蚶科':\n species[5] = 1\n elif name.split('_')[0] == '盔螺科':\n species[6] = 1\n elif name.split('_')[0] == '帘蛤科':\n species[7] = 1\n elif name.split('_')[0] == '马蹄螺科':\n species[8] = 1\n elif name.split('_')[0] == '鸟蛤科':\n species[9] = 1\n elif name.split('_')[0] == '细带螺科':\n species[10] = 1\n elif name.split('_')[0] == '玉螺科':\n species[11] = 1\n elif name.split('_')[0] == '贻贝科':\n species[12] = 1\n elif name.split('_')[0] == '砗磲科':\n species[13] = 1\n elif name.split('_')[0] == '扇贝科':\n species[14] = 1\n else:\n raise NameError('No species named %s' % str(file))\n species = bytes(str(species), encoding='utf-8')\n return species\n\ndef create_tfrecords():\n if os.getcwd() != '/home/fish/图片/all_in_one':\n os.chdir('/home/fish/图片/all_in_one')\n #dir = '/home/fish/图片/images_tfrecords'\n dir = '/home/fish/图片/three_tfrecords'\n if not os.path.exists(dir):\n os.mkdir(dir)\n #filename = os.path.join(dir, 'all_pic.tfrecords')\n #writer = tf.python_io.TFRecordWriter(filename)\n #count = 0\n dev_count = 0\n test_count = 0\n train_count = 0\n species = np.zeros((15))\n \"\"\"\n for root, dirs, files in os.walk(os.getcwd()):\n if dirs == []:\n for file in files:\n img = cv2.imread(os.path.join(root, str(file)))\n if img.shape == None:\n print('error')\n continue\n \"\"\"\n for root, dirs, files in os.walk(os.getcwd()):\n if dirs == []:\n num = len(files)\n #dev set\n filename = os.path.join(dir, 'dev.tfrecords')\n writer = tf.python_io.TFRecordWriter(filename)\n for _ in range(int(num*0.2)):\n index = random.randint(0, num-1)\n while (index > len(files)-1):\n index = random.randint(0, num-1)\n img = cv2.imread(os.path.join(root, str(files[index])))\n image_raw = cv2.imencode('.jpg', img)[1].tostring()\n species = species_to_num(str(files[index]))\n example = tf.train.Example(features=tf.train.Features(feature={\n 'label': _bytes_feature(species),\n 'image_raw': _bytes_feature(image_raw)\n }))\n writer.write(example.SerializeToString())\n files.remove(files[index])\n dev_count += 1\n writer.close()\n #test set\n filename = os.path.join(dir, 'test.tfrecords')\n writer = tf.python_io.TFRecordWriter(filename)\n for _ in range(int(num*0.2)):\n index = random.randint(0, int(num*0.8-1))\n while (index > len(files)-1):\n index = random.randint(0, int(num*0.8-1))\n\n img = cv2.imread(os.path.join(root, str(files[index])))\n image_raw = cv2.imencode('.jpg', img)[1].tostring()\n species = species_to_num(str(files[index]))\n example = tf.train.Example(features=tf.train.Features(feature={\n 'label': _bytes_feature(species),\n 'image_raw': _bytes_feature(image_raw)\n }))\n writer.write(example.SerializeToString())\n files.remove(files[index])\n test_count += 1\n writer.close()\n #train set\n filename = os.path.join(dir, 'train.tfrecords')\n writer = tf.python_io.TFRecordWriter(filename)\n for file in files:\n \n img = cv2.imread(os.path.join(root, str(file)))\n image_raw = cv2.imencode('.jpg', img)[1].tostring()\n species = species_to_num(str(file))\n example = tf.train.Example(features=tf.train.Features(feature={\n 'label': _bytes_feature(species),\n 'image_raw': _bytes_feature(image_raw)\n }))\n writer.write(example.SerializeToString())\n train_count += 1\n writer.close() \n \"\"\"\n if img.shape != (256, 256, 3):\n raise ValueError(\"Image size %d doesn't match 256,256,3 \" % img.shape)\n species = species_to_num(str(file))\n image_raw = cv2.imencode('.jpg', img)[1].tostring()\n example = tf.train.Example(features=tf.train.Features(feature={\n 'label': _int64_feature(int(species)),\n 'image_raw': _bytes_feature(image_raw)\n }))\n writer.write(example.SerializeToString())\n print('count==', count)\n count += 1\n writer.close()\n \"\"\"\n print('dev = ', dev_count)\n print('test = ', test_count)\n print('train = ', train_count)\n\ndef main():\n create_tfrecords()\n\nif __name__ == '__main__':\n main()\n ", "sub_path": "create_tfrecords.py", "file_name": "create_tfrecords.py", "file_ext": "py", "file_size_in_byte": 5730, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tensorflow.train.Feature", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 9, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Int64List", "line_number": 9, "usage_type": "call"}, {"api_name": "tensorflow.train.Feature", "line_number": 13, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 13, "usage_type": "attribute"}, {"api_name": "tensorflow.train.BytesList", "line_number": 13, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 53, "usage_type": "call"}, {"api_name": "os.chdir", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 57, "usage_type": "call"}, {"api_name": "os.path", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 58, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 65, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 75, "usage_type": "call"}, {"api_name": "os.getcwd", "line_number": 75, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "tensorflow.python_io.TFRecordWriter", "line_number": 80, "usage_type": "call"}, {"api_name": "tensorflow.python_io", "line_number": 80, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 82, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 84, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 85, "usage_type": "call"}, {"api_name": "os.path", "line_number": 85, "usage_type": "attribute"}, {"api_name": "cv2.imencode", "line_number": 86, "usage_type": "call"}, {"api_name": "tensorflow.train.Example", "line_number": 88, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 88, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Features", "line_number": 88, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 97, "usage_type": "call"}, {"api_name": "os.path", "line_number": 97, "usage_type": "attribute"}, {"api_name": "tensorflow.python_io.TFRecordWriter", "line_number": 98, "usage_type": "call"}, {"api_name": "tensorflow.python_io", "line_number": 98, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 100, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 102, "usage_type": "call"}, {"api_name": "cv2.imread", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 104, "usage_type": "call"}, {"api_name": "os.path", "line_number": 104, "usage_type": "attribute"}, {"api_name": "cv2.imencode", "line_number": 105, "usage_type": "call"}, {"api_name": "tensorflow.train.Example", "line_number": 107, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 107, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Features", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 116, "usage_type": "call"}, {"api_name": "os.path", "line_number": 116, "usage_type": "attribute"}, {"api_name": "tensorflow.python_io.TFRecordWriter", "line_number": 117, "usage_type": "call"}, {"api_name": "tensorflow.python_io", "line_number": 117, "usage_type": "attribute"}, {"api_name": "cv2.imread", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 120, "usage_type": "call"}, {"api_name": "os.path", "line_number": 120, "usage_type": "attribute"}, {"api_name": "cv2.imencode", "line_number": 121, "usage_type": "call"}, {"api_name": "tensorflow.train.Example", "line_number": 123, "usage_type": "call"}, {"api_name": "tensorflow.train", "line_number": 123, "usage_type": "attribute"}, {"api_name": "tensorflow.train.Features", "line_number": 123, "usage_type": "call"}]} +{"seq_id": "83269130", "text": "import matplotlib\n\nmatplotlib.use(\"Qt5Agg\")\n\nfrom matplotlib.backends.backend_qt5agg import FigureCanvasQTAgg as FigureCanvas\nfrom matplotlib.figure import Figure\nimport matplotlib.pyplot as plt\nfrom model import list\nimport pandas as pd\n\n\nclass MyFigure(FigureCanvas):\n\n def __init__(self, width=5, height=4, dpi=100):\n self.fig = Figure(figsize=(width, height), dpi=dpi)\n super(MyFigure, self).__init__(self.fig)\n self.axes = self.fig.add_subplot(111)\n plt.rcParams['font.sans-serif'] = ['SimHei']\n plt.rcParams['axes.unicode_minus'] = False\n\n def draw0(self):\n pro_type = [\"单选题\", \"判断题\"]\n bar = []\n\n for type in pro_type:\n time, correct = list.problem_record[type].getTimes()\n if time == 0:\n rate = 0\n else:\n rate = correct / time\n\n x = [type]\n y = [rate]\n bar.append(self.axes.bar(x, y, alpha=0.5, width=0.3, color='yellow', edgecolor='red', label=type, lw=3))\n\n self.axes.set_ylim([0, 1])\n for bar_container in bar:\n for b in bar_container:\n height = b.get_height()\n self.axes.text(b.get_x() + b.get_width() / 2, b.get_height() + 0.01, '%.2f' % height, ha='center',\n va='bottom')\n\n def draw1(self):\n pro_type = [\"单选题\", \"判断题\"]\n bar = []\n\n for type in pro_type:\n time, correct = list.current_record[type][0], list.current_record[type][1]\n if time == 0:\n rate = 0\n else:\n rate = correct / time\n\n x = [type]\n y = [rate]\n bar.append(self.axes.bar(x, y, alpha=0.5, width=0.3, color='blue', edgecolor='green', label=type, lw=3))\n\n self.axes.set_ylim([0, 1])\n for bar_container in bar:\n for b in bar_container:\n height = b.get_height()\n self.axes.text(b.get_x() + b.get_width() / 2, b.get_height() + 0.01, '%.2f' % height, ha='center',\n va='bottom')\n\n def draw2(self, name):\n data_path = \"./data/user/\"\n current_path = data_path + str(name)\n current_excel = current_path + \"/log.xlsx\"\n\n df = pd.read_excel(current_excel)\n cnt = df.shape[0]\n x = []\n y = []\n bar = []\n\n for i in range(max(0, cnt - 4), cnt):\n line = df.loc[i].values\n x.append(line[1])\n y.append(line[8] / line[7])\n\n bar.append(self.axes.bar(x, y, alpha=0.5, width=0.3, color='blue', edgecolor='green', lw=3))\n self.axes.set_ylim([0, 1])\n for bar_container in bar:\n for b in bar_container:\n height = b.get_height()\n self.axes.text(b.get_x() + b.get_width() / 2, b.get_height() + 0.01, '%.2f' % height, ha='center',\n va='bottom')\n\n def draw3(self, name):\n data_path = \"./data/user/\"\n current_path = data_path + str(name)\n current_excel = current_path + \"/log.xlsx\"\n\n df = pd.read_excel(current_excel)\n cnt = df.shape[0]\n x = []\n y = []\n plot = []\n\n for i in range(max(0, cnt - 4), cnt):\n line = df.loc[i].values\n x.append(line[1])\n y.append(line[6] + line[7])\n\n plot.append(self.axes.plot(x, y))\n\n def draw4(self, name):\n data_path = \"./data/user/\"\n current_path = data_path + str(name)\n current_excel = current_path + \"/log.xlsx\"\n\n labels = ['单选题', '判断题', '简答题']\n tot = 0\n tot_singe = 0\n tot_judge = 0\n tot_easy = 0\n data = pd.read_excel(current_excel)\n for i in range(data.shape[0]):\n line = data.loc[i].values\n tot_singe += line[2]\n tot_judge += line[4]\n tot_easy += line[6]\n tot += line[2] + line[4] + line[6]\n\n size = [tot_singe, tot_judge, tot_easy]\n explode = (0.1, 0.1, 0.1)\n self.axes.pie(size, explode, labels=labels, autopct='%1.1f%%')\n", "sub_path": "model/figure.py", "file_name": "figure.py", "file_ext": "py", "file_size_in_byte": 4142, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.use", "line_number": 3, "usage_type": "call"}, {"api_name": "matplotlib.backends.backend_qt5agg.FigureCanvasQTAgg", "line_number": 12, "usage_type": "name"}, {"api_name": "matplotlib.figure.Figure", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 18, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 18, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.rcParams", "line_number": 19, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 19, "usage_type": "name"}, {"api_name": "model.list.problem_record", "line_number": 26, "usage_type": "attribute"}, {"api_name": "model.list", "line_number": 26, "usage_type": "name"}, {"api_name": "model.list.current_record", "line_number": 48, "usage_type": "attribute"}, {"api_name": "model.list", "line_number": 48, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 70, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 94, "usage_type": "call"}, {"api_name": "pandas.read_excel", "line_number": 117, "usage_type": "call"}]} +{"seq_id": "574205414", "text": "# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreation date: 06.04.2018\r\n\r\nKatarzyna Filipiuk\r\nkatarzyna.filipiuk@student.uw.edu.pl\r\nUrszula Romaniuk\r\nurszula.romaniuk@student.uw.edu.pl\r\nIzabela Szopa\r\nim.szopa@student.uw.edu.pl\r\n\r\nVersion: 3.0\r\nDate: 19.04.2018\r\n\"\"\"\r\nimport numba\r\nfrom timeit import timeit\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom scipy.signal import buttord, butter, filtfilt, hilbert\r\nfrom scipy.io import loadmat\r\n\r\nname = \"AL2442-STA-wires.mat\"\r\nname_of_matlab_matrix = \"rfOut_1\"\r\naperture = 64\r\nf0 = 5.5e6\r\nfs = 50e6\r\npitch = 0.21e-3\r\nc = 1490\r\n\r\ntemp = loadmat(name)\r\ndata = np.array(temp[name_of_matlab_matrix])\r\nno_transducers = data.shape[0]\r\nno_samples = data.shape[1]\r\nno_events = data.shape[2]\r\ndepth = no_samples * c / (2 * fs)\r\ndx = depth / no_samples\r\n\r\n\r\n#@numba.jit\r\ndef _highpass(matrix, wp=6e4, ws=1e4, gpass=3, gstop=20):\r\n \"\"\"\r\n Filtering out low frequencies of signal\r\n :param wp: Passband edge frequency [Hz]\r\n :param ws: Stopband edge frequency [Hz]\r\n :param gpass: The maximum loss in the passband [dB]\r\n :param gstop: The minimum attenuation in the stopband [dB]\r\n :return: filtered matrix\r\n \"\"\"\r\n\r\n n, wn = buttord(wp * 2. / fs, ws * 2. / fs, gpass, gstop)\r\n b, a = butter(n, wn, btype='high')\r\n return filtfilt(b, a, matrix, axis=1)\r\n\r\n\r\n#@numba.jit\r\ndef _hilbert_transform(matrix):\r\n \"\"\"\r\n Calculating an envelope of a RF signal\r\n :param matrix: data which hilbert transform should be calculated\r\n :return: hilbert transform of a given matrix\r\n \"\"\"\r\n\r\n return np.abs(hilbert(matrix, 2 * no_samples,\r\n axis=1))[:, :no_samples]\r\n\r\n\r\n#@numba.jit\r\ndef _delay(column_distance, h):\r\n \"\"\"\r\n Calculate delay for one transducer in aperture\r\n :param column_distance: Distance between middle column of an aperture \r\n and receiver column\r\n :param h: Depth of a receiver focal point\r\n :return: List of delays in samples for every transducer in an aperture\r\n \"\"\"\r\n\r\n delay = (np.sqrt(h ** 2 + (column_distance * pitch) ** 2) - h) / \\\r\n c * fs\r\n delay = int(np.round((-1) * delay, 0))\r\n return delay\r\n\r\n\r\n#@numba.jit\r\ndef _generate_delays_profile(r=16):\r\n \"\"\"\r\n Calculate delays profile for all transducers in aperture\r\n :param r: Distance in samples between emission of a wave form a first\r\n transducer of an aperture and emission of a wave form a \r\n middle transducer\r\n :return: List of delays in samples for every transducer in an aperture\r\n \"\"\"\r\n\r\n t_max = r / fs\r\n focal_point = ((aperture * pitch / 2.) ** 2 - t_max ** 2 * \\\r\n c ** 2) / (2 * c * t_max)\r\n\r\n delay_profile = np.zeros((aperture), dtype=int)\r\n for i in range(aperture):\r\n column_distance = np.abs(aperture / 2. - i)\r\n delay_profile[i] = _delay(column_distance,\r\n focal_point)\r\n return delay_profile\r\n\r\n\r\n#@numba.jit\r\ndef _bfr(matrix):\r\n \"\"\"\r\n Calculate reconstruction of a USG data\r\n :param matrix: filtered USG data\r\n :return: reconstruction of a USG data\r\n \"\"\"\r\n\r\n delay_profile = _generate_delays_profile()\r\n\r\n reconstruction = np.zeros((no_events - aperture,\r\n no_samples))\r\n half_aperture = aperture // 2\r\n\r\n for i in range(half_aperture, no_events - half_aperture):\r\n temp = matrix[i - half_aperture: i + half_aperture, :, i]\r\n for j in range(aperture):\r\n temp[j, :] = np.roll(temp[j, :], delay_profile[j])\r\n reconstruction[i - half_aperture, :] = np.sum(temp, axis=0)\r\n reconstruction_envelope = _hilbert_transform(reconstruction)\r\n return reconstruction_envelope\r\n\r\n\r\n#@numba.jit\r\ndef _db_conversion(matrix):\r\n \"\"\"\r\n Converting a matrix to log scale\r\n :param matrix: data which should be converted to log scale\r\n :return: a given matrix in log scale\r\n \"\"\"\r\n\r\n norm = np.max(matrix)\r\n return 20 * np.log10(matrix / norm)\r\n\r\n\r\n#@numba.jit\r\ndef _plot_reconstruction(matrix, from_sample=300, to_sample=-300,\r\n cutoff=-50, from_transducer=0, to_transducer=0):\r\n \"\"\"\r\n Plot reconstruction of a USG signal\r\n :param matrix:\r\n :param from_sample:\r\n :param cutoff:\r\n :return:\r\n \"\"\"\r\n\r\n plt.figure()\r\n plt.imshow(matrix[:, from_sample:to_sample].T, cmap=\"Greys_r\",\r\n interpolation='bilinear', vmin=cutoff, vmax=0,\r\n extent=[(0 + from_transducer) * pitch * 100,\r\n pitch * (no_transducers + to_transducer) \\\r\n * 100, (no_samples + to_sample) * dx * \\\r\n 100, from_sample * dx * 100])\r\n plt.title(\"Reconstruction\", fontsize=24)\r\n plt.xlabel(\"Horizon [cm]\", fontsize=20)\r\n plt.ylabel(\"Depth [cm]\", fontsize=20)\r\n plt.colorbar()\r\n plt.subplots_adjust(left=0.0, right=0.86)\r\n\r\n\r\ndata_centered_around_0 = _highpass(data)\r\nbfr = _bfr(data_centered_around_0)\r\nbfr = _db_conversion(bfr)\r\n_plot_reconstruction(bfr, cutoff=-40,\r\n from_transducer=aperture // 2,\r\n to_transducer=-(aperture // 2))\r\nplt.show()\r\n", "sub_path": "BFR.py", "file_name": "BFR.py", "file_ext": "py", "file_size_in_byte": 5174, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "scipy.io.loadmat", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 31, "usage_type": "call"}, {"api_name": "scipy.signal.buttord", "line_number": 50, "usage_type": "call"}, {"api_name": "scipy.signal.butter", "line_number": 51, "usage_type": "call"}, {"api_name": "scipy.signal.filtfilt", "line_number": 52, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 63, "usage_type": "call"}, {"api_name": "scipy.signal.hilbert", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.sqrt", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.round", "line_number": 79, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 97, "usage_type": "call"}, {"api_name": "numpy.abs", "line_number": 99, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.roll", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.max", "line_number": 136, "usage_type": "call"}, {"api_name": "numpy.log10", "line_number": 137, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 158, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 158, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 159, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 159, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 160, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 160, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.colorbar", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots_adjust", "line_number": 162, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 162, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}]} +{"seq_id": "503192470", "text": "# app常用操作 滑动 拖拽操作事件\n# 应用场景：做app有些界面或者按钮需要滑动才会出来\n# 滑动：\n# swipe\n# scroll\n# drag_and_drop\n# 参数信息写好\nimport time\n\nfrom appium import webdriver\ninfo={\n # 操作平台 ��作 安卓 苹果 Android不区分大小写，不能写错\n 'platformName':'Android',\n # 版本号 设置 关于平板电脑 版本号\n 'platformVersion':'5.1.1',\n # 设备名 adb devices 检测设备名 可以随意写 不要空 不要中文\n 'deviceName':'127.0.0.1:62001',\n # 包名\n 'appPackage':'com.android.settings',\n # 应用名\n 'appActivity':'com.android.settings.Settings',\n # 不重置\n 'noReset':False\n}\n# 启动程序 Remote(服务器，手机配置信息)\ndriver=webdriver.Remote('http://127.0.0.1:4723/wd/hub',info)\ndriver.implicitly_wait(5)\n\n# swipe 写法 swipe(开始x坐标，开始y坐标，结束x坐标，结束y坐标，持续时间)\n# 产生惯性 持续时间越长，惯性越小\n# driver.swipe(535,1610,579,635)\n# driver.swipe(535,1610,579,635,3000)\n\n# 元素滑动\n# scroll(开始元素，结束元素)\n# scroll滑动：从一个元素滑动到另外一个元素 也有惯性\naddress=driver.find_element_by_xpath('//*[@text=\"位置信息\"]')\nmore=driver.find_element_by_xpath('//*[@text=\"更多\"]')\ndriver.scroll(address,more)\n# drag_and_drop：从一个元素滑动到另外一个元素 没有惯性\n# driver.drag_and_drop(address,more)\n\n# 可以滑动 实现滑动 选择 需不需要惯性 ，选择用坐标 还是用元素\n\n\n\n\n# 等待几秒钟 等待3种方式 强制等待 直男 隐士等待 强迫症 显示等待 正常男\ntime.sleep(3)\n# 关闭\n# driver.quit()", "sub_path": "Appium01/class1/app2.py", "file_name": "app2.py", "file_ext": "py", "file_size_in_byte": 1705, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "appium.webdriver.Remote", "line_number": 26, "usage_type": "call"}, {"api_name": "appium.webdriver", "line_number": 26, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 49, "usage_type": "call"}]} +{"seq_id": "621135331", "text": "from django.shortcuts import render, redirect\nfrom django.views.decorators.http import require_http_methods\nfrom django.contrib.auth.forms import UserCreationForm, AuthenticationForm \n\nfrom django.contrib.auth import login as auth_login, logout as auth_logout\n# 알아서 로그인 해주는 함수 / 이름이 겹치니깐 다르게 부르겠다 (그렇게 안하면 함수에서 재귀함수가 되어버린다. ) \n\n\n@require_http_methods(['GET', 'POST'])\ndef signup(request): # new user\n if request.user.is_authenticated: # 로그인 했으면 signup 못하게 만들기 위해 막아버리기 \n return redirect('sns:posting_list')\n\n # 사용자가 회원가입할 데이터를 보냈다는 뜻 \n if request.method == 'POST': \n form = UserCreationForm(request.POST) # 사용자가 데이터를 넣어 놓은 시험지 / 회원가입하는 form \n if form.is_valid(): # 채점 \n user = form.save()\n return redirect('sns:posting_list')\n # else:\n # return render(request, 'accounts/signup.html', {\n # 'form': form, # 실패된 form 이다. / 망한 시험지 \n # })\n \n else: # 사용자가 회원가입 HTML 을 달라는 뜻 \n form = UserCreationForm() # 시험지 인데 아직 답을 입력하지 않은 셤이다. 새시험지 \n\n return render(request, 'accounts/signup.html', {\n 'form': form, # 새로운 시험지가 나온다. \n })\n\n\n@require_http_methods(['GET', 'POST'])\ndef login(request):\n if request.user.is_authenticated: # 사용자가 로그인한 상태라면, 무시\n return redirect('sns:posting_list') \n\n if request.method == 'POST':\n form = AuthenticationForm(request, request.POST) # 사용자 인증하는 form / 이 form 만 데이터 받아오는 방식이 좀 다르다. \n if form.is_valid():\n\n # 쿠키와 세션을 한꺼번에 세팅해준다. \n auth_login(request, form.get_user()) # form.get_user : form 검증을 통과한 사용자를 꺼내오겠다. == user \n return redirect('sns:posting_list') \n\n # 쿠키세팅\n # response = redirect('sns:posting_list')\n # response.set_cookie(key='nickname', value='idot', max_age=5) # return 이 없다. 원본을 바꿀수 있다. # 쿠키세팅 => 닉네임을 설정했고 5초가 지나면 사라진다. \n # return response\n \n else:\n form = AuthenticationForm()\n return render(request, 'accounts/login.html', {\n 'form': form,\n })\n\n\ndef logout(request):\n auth_logout(request)\n return redirect('sns:posting_list')\n", "sub_path": "06_django_advance/03_IMAGE_UPLOAD/accounts/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 2668, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.shortcuts.redirect", "line_number": 12, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.UserCreationForm", "line_number": 16, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 19, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.UserCreationForm", "line_number": 26, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 28, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 9, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 36, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.AuthenticationForm", "line_number": 39, "usage_type": "call"}, {"api_name": "django.contrib.auth.login", "line_number": 43, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 44, "usage_type": "call"}, {"api_name": "django.contrib.auth.forms.AuthenticationForm", "line_number": 52, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 53, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 33, "usage_type": "call"}, {"api_name": "django.contrib.auth.logout", "line_number": 59, "usage_type": "call"}, {"api_name": "django.shortcuts.redirect", "line_number": 60, "usage_type": "call"}]} +{"seq_id": "79879474", "text": "import os\n\nimport pytest\nimport sqlalchemy as sa\n\nfrom sqlalchemy_continuum.dialects.postgresql import (\n drop_trigger,\n sync_trigger\n)\nfrom tests import (\n get_dns_from_driver,\n get_driver_name,\n QueryPool,\n uses_native_versioning\n)\n\n\n@pytest.mark.skipif('not uses_native_versioning()')\nclass TestTriggerSyncing(object):\n def setup_method(self, method):\n driver = os.environ.get('DB', 'sqlite')\n self.driver = get_driver_name(driver)\n self.engine = sa.create_engine(get_dns_from_driver(self.driver))\n self.connection = self.engine.connect()\n if driver == 'postgres-native':\n self.connection.execute('CREATE EXTENSION IF NOT EXISTS hstore')\n\n self.connection.execute(\n 'CREATE TABLE article '\n '(id INT PRIMARY KEY, name VARCHAR(200), content TEXT)'\n )\n self.connection.execute(\n 'CREATE TABLE article_version '\n '(id INT, transaction_id INT, name VARCHAR(200), '\n 'name_mod BOOLEAN, PRIMARY KEY (id, transaction_id))'\n )\n\n def teardown_method(self, method):\n self.connection.execute('DROP TABLE IF EXISTS article')\n self.connection.execute('DROP TABLE IF EXISTS article_version')\n self.engine.dispose()\n self.connection.close()\n\n def test_sync_triggers(self):\n sync_trigger(self.connection, 'article_version')\n assert (\n 'DROP TRIGGER IF EXISTS article_trigger ON \"article\"'\n in QueryPool.queries[-4]\n )\n assert 'DROP FUNCTION ' in QueryPool.queries[-3]\n assert 'CREATE OR REPLACE FUNCTION ' in QueryPool.queries[-2]\n assert 'CREATE TRIGGER ' in QueryPool.queries[-1]\n sync_trigger(self.connection, 'article_version')\n\n def test_drop_triggers(self):\n drop_trigger(self.connection, 'article')\n assert (\n 'DROP TRIGGER IF EXISTS article_trigger ON \"article\"'\n in QueryPool.queries[-2]\n )\n assert 'DROP FUNCTION ' in QueryPool.queries[-1]\n", "sub_path": "tests/dialects/test_triggers.py", "file_name": "test_triggers.py", "file_ext": "py", "file_size_in_byte": 2045, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ.get", "line_number": 21, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "tests.get_driver_name", "line_number": 22, "usage_type": "call"}, {"api_name": "sqlalchemy.create_engine", "line_number": 23, "usage_type": "call"}, {"api_name": "tests.get_dns_from_driver", "line_number": 23, "usage_type": "call"}, {"api_name": "sqlalchemy_continuum.dialects.postgresql.sync_trigger", "line_number": 45, "usage_type": "call"}, {"api_name": "tests.QueryPool.queries", "line_number": 48, "usage_type": "attribute"}, {"api_name": "tests.QueryPool", "line_number": 48, "usage_type": "name"}, {"api_name": "tests.QueryPool.queries", "line_number": 50, "usage_type": "attribute"}, {"api_name": "tests.QueryPool", "line_number": 50, "usage_type": "name"}, {"api_name": "tests.QueryPool.queries", "line_number": 51, "usage_type": "attribute"}, {"api_name": "tests.QueryPool", "line_number": 51, "usage_type": "name"}, {"api_name": "tests.QueryPool.queries", "line_number": 52, "usage_type": "attribute"}, {"api_name": "tests.QueryPool", "line_number": 52, "usage_type": "name"}, {"api_name": "sqlalchemy_continuum.dialects.postgresql.sync_trigger", "line_number": 53, "usage_type": "call"}, {"api_name": "sqlalchemy_continuum.dialects.postgresql.drop_trigger", "line_number": 56, "usage_type": "call"}, {"api_name": "tests.QueryPool.queries", "line_number": 59, "usage_type": "attribute"}, {"api_name": "tests.QueryPool", "line_number": 59, "usage_type": "name"}, {"api_name": "tests.QueryPool.queries", "line_number": 61, "usage_type": "attribute"}, {"api_name": "tests.QueryPool", "line_number": 61, "usage_type": "name"}, {"api_name": "pytest.mark.skipif", "line_number": 18, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 18, "usage_type": "attribute"}]} +{"seq_id": "430967174", "text": "from django.test import TestCase\nfrom django.contrib.auth import get_user_model\nfrom django.urls import reverse\n\nfrom rest_framework.test import APIClient\nfrom rest_framework import status\n\nfrom ..models import Author\nfrom ..serializers import AuthorSerializer\n\nAUTHOR_LIST_URL = reverse('shelf:author-list')\nAUTHOR_ADD_URL = reverse('shelf:author-add')\n\n\ndef detail_url(author_id):\n return reverse('shelf:author-detail', args=[author_id])\n\n\ndef sample_author(name='Alexander Turgenev', country='Russian'):\n return Author.objects.create(name=name, country=country)\n\n\nclass PublicAuthorApiTests(TestCase):\n\n def setUp(self) -> None:\n self.client = APIClient()\n\n def test_retrieve_author_list(self):\n \"\"\"Test retrieving a list of ingredients\"\"\"\n Author.objects.create(\n name='Ivan Bunin',\n country='USSR'\n )\n\n Author.objects.create(\n name='Alexander Pushkin',\n country='Russian'\n )\n\n res = self.client.get(AUTHOR_LIST_URL)\n\n authors = Author.objects.all().order_by('id')\n serializer = AuthorSerializer(authors, many=True)\n self.assertEqual(res.status_code, status.HTTP_200_OK)\n self.assertEqual(res.data, serializer.data)\n\n def test_author_create_login_required(self):\n \"\"\"Test that login is required to access the endpoint\"\"\"\n res = self.client.get(AUTHOR_ADD_URL)\n\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)\n\n def test_author_detail_login_required(self):\n url = reverse('shelf:author-detail', args=[1])\n res = self.client.get(url)\n\n self.assertEqual(res.status_code, status.HTTP_401_UNAUTHORIZED)\n\n\nclass PrivateAuthorApiTests(TestCase):\n\n def setUp(self) -> None:\n self.client = APIClient()\n self.user = get_user_model().objects.create_user(\n 'test_user@test.com'\n 'password'\n )\n self.client.force_authenticate(user=self.user)\n\n def test_create_author_successful(self):\n payload = {\n 'name': 'Ivan Bunin',\n 'count': 'USSR'\n }\n res = self.client.post(AUTHOR_ADD_URL, payload)\n\n self.assertEqual(res.status_code, status.HTTP_201_CREATED)\n\n exists = Author.objects.filter(\n name=payload['name']\n ).exists()\n\n self.assertTrue(exists)\n\n def test_create_author_invalid(self):\n payload = {\n 'name': '',\n 'count': 'USSR'\n }\n res = self.client.post(AUTHOR_ADD_URL, payload)\n\n self.assertEqual(res.status_code, status.HTTP_400_BAD_REQUEST)\n\n def test_partial_update_author(self):\n author = sample_author()\n\n payload = {\n 'name': 'Ivan Bunin'\n }\n url = detail_url(author.id)\n\n self.client.put(url, payload)\n author.refresh_from_db()\n self.assertEqual(author.name, payload['name'])\n\n def test_full_update_author(self):\n author = sample_author()\n\n payload = {\n 'name': 'Ivan Bunin',\n 'country': 'USSR'\n }\n url = detail_url(author.id)\n\n self.client.put(url, payload)\n author.refresh_from_db()\n for key in payload.keys():\n self.assertEqual(payload[key], getattr(author, key))\n\n def test_remove_author(self):\n author = sample_author()\n url = detail_url(author.id)\n self.client.delete(url)\n\n exists = Author.objects.filter(\n id=author.id\n ).exists()\n\n self.assertFalse(exists)\n", "sub_path": "src/shelf/tests/test_author_api.py", "file_name": "test_author_api.py", "file_ext": "py", "file_size_in_byte": 3559, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.urls.reverse", "line_number": 11, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 12, "usage_type": "call"}, {"api_name": "django.urls.reverse", "line_number": 16, "usage_type": "call"}, {"api_name": "models.Author.objects.create", "line_number": 20, "usage_type": "call"}, {"api_name": "models.Author.objects", "line_number": 20, "usage_type": "attribute"}, {"api_name": "models.Author", "line_number": 20, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 23, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 26, "usage_type": "call"}, {"api_name": "models.Author.objects.create", "line_number": 30, "usage_type": "call"}, {"api_name": "models.Author.objects", "line_number": 30, "usage_type": "attribute"}, {"api_name": "models.Author", "line_number": 30, "usage_type": "name"}, {"api_name": "models.Author.objects.create", "line_number": 35, "usage_type": "call"}, {"api_name": "models.Author.objects", "line_number": 35, "usage_type": "attribute"}, {"api_name": "models.Author", "line_number": 35, "usage_type": "name"}, {"api_name": "models.Author.objects.all", "line_number": 42, "usage_type": "call"}, {"api_name": "models.Author.objects", "line_number": 42, "usage_type": "attribute"}, {"api_name": "models.Author", "line_number": 42, "usage_type": "name"}, {"api_name": "serializers.AuthorSerializer", "line_number": 43, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_200_OK", "line_number": 44, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 44, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 51, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 51, "usage_type": "name"}, {"api_name": "django.urls.reverse", "line_number": 54, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_401_UNAUTHORIZED", "line_number": 57, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 57, "usage_type": "name"}, {"api_name": "django.test.TestCase", "line_number": 60, "usage_type": "name"}, {"api_name": "rest_framework.test.APIClient", "line_number": 63, "usage_type": "call"}, {"api_name": "django.contrib.auth.get_user_model", "line_number": 64, "usage_type": "call"}, {"api_name": "rest_framework.status.HTTP_201_CREATED", "line_number": 77, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 77, "usage_type": "name"}, {"api_name": "models.Author.objects.filter", "line_number": 79, "usage_type": "call"}, {"api_name": "models.Author.objects", "line_number": 79, "usage_type": "attribute"}, {"api_name": "models.Author", "line_number": 79, "usage_type": "name"}, {"api_name": "rest_framework.status.HTTP_400_BAD_REQUEST", "line_number": 92, "usage_type": "attribute"}, {"api_name": "rest_framework.status", "line_number": 92, "usage_type": "name"}, {"api_name": "models.Author.objects.filter", "line_number": 125, "usage_type": "call"}, {"api_name": "models.Author.objects", "line_number": 125, "usage_type": "attribute"}, {"api_name": "models.Author", "line_number": 125, "usage_type": "name"}]} +{"seq_id": "251886982", "text": "from fenx.tools import setup_log\nfrom fenx.config import config\nimport logging as _logging\nimport os\nlogger = _logging.getLogger(__name__)\n\n\ndef icon(name, default=False):\n ico = find_icon(name)\n if ico:\n return ico\n if not default:\n return ''\n else:\n find_icon('nofile')\n\n\ndef find_icon(name, debug=False):\n if not name:\n return ''\n if debug:\n print(os.path.abspath(name))\n if os.path.exists(os.path.abspath(name)) and os.path.isfile(os.path.abspath(name)):\n return name\n expanded = config.expand_var(name)\n if debug:\n print(expanded)\n if os.path.exists(expanded) and os.path.isfile(os.path.abspath(expanded)):\n return expanded\n if config.get('ICONS', {}).get(name, ''):\n ico = config.expand_var(config.get('ICONS', {}).get(name, ''))\n if debug:\n print(ico)\n if os.path.exists(ico) and os.path.isfile(os.path.abspath(ico)):\n return ico\n # else:\n # logger.debug('Custom icon %s not found' % name)\n # return ''\n icons_roots = config.get('RESOURCE_ICON_PATH', [])\n for icons_root in icons_roots:\n if debug:\n print('>', icons_root)\n for module_icon in [\n os.path.normpath(os.path.join(icons_root, name)),\n os.path.normpath(os.path.join(icons_root, name+'.png')),\n os.path.normpath(os.path.join(icons_root, name+'.ico'))\n ]:\n if debug:\n print(module_icon)\n if os.path.exists(module_icon):\n if debug:\n print('RESULT:', module_icon)\n return module_icon\n return ''\n\n\n", "sub_path": "icon_utils.py", "file_name": "icon_utils.py", "file_ext": "py", "file_size_in_byte": 1698, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 5, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 22, "usage_type": "call"}, {"api_name": "os.path", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 23, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 23, "usage_type": "call"}, {"api_name": "fenx.config.config.expand_var", "line_number": 25, "usage_type": "call"}, {"api_name": "fenx.config.config", "line_number": 25, "usage_type": "name"}, {"api_name": "os.path.exists", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 28, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 28, "usage_type": "call"}, {"api_name": "fenx.config.config.get", "line_number": 30, "usage_type": "call"}, {"api_name": "fenx.config.config", "line_number": 30, "usage_type": "name"}, {"api_name": "fenx.config.config.expand_var", "line_number": 31, "usage_type": "call"}, {"api_name": "fenx.config.config", "line_number": 31, "usage_type": "name"}, {"api_name": "fenx.config.config.get", "line_number": 31, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.path.isfile", "line_number": 34, "usage_type": "call"}, {"api_name": "os.path.abspath", "line_number": 34, "usage_type": "call"}, {"api_name": "fenx.config.config.get", "line_number": 39, "usage_type": "call"}, {"api_name": "fenx.config.config", "line_number": 39, "usage_type": "name"}, {"api_name": "os.path.normpath", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path", "line_number": 44, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 44, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 45, "usage_type": "call"}, {"api_name": "os.path.normpath", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path.exists", "line_number": 50, "usage_type": "call"}, {"api_name": "os.path", "line_number": 50, "usage_type": "attribute"}]} +{"seq_id": "32600474", "text": "import numpy as np\nimport keras\nfrom keras.models import Model\nfrom keras.layers import Input\nfrom keras.layers import Conv2D,MaxPooling2D,AveragePooling2D\nfrom keras.layers import Dense,Dropout,Flatten,concatenate\nfrom keras.optimizers import SGD\nfrom keras.callbacks import CSVLogger,TensorBoard\nfrom keras.utils import plot_model\nimport keras.backend.tensorflow_backend as KTF\nimport tensorflow as tf\n\nepochs = 1000\nbatch_size = 16\nlr = 0.01\ndecay = 1e-6\nmomentum = 0.9\n\n##========== data loading ==========##\nanode = np.load(\"../../data/cell_a_MAIKo.npy\")\nanode = anode.reshape(\n (-1,\n anode.shape[1],\n anode.shape[2],\n 1))\ncross_point = np.concatenate(\n (np.load(\"../../data/point_xv_MAIKo.npy\"),\n np.load(\"../../data/point_xs_MAIKo.npy\")),\n axis=1)\ncross_point = np.concatenate(\n (cross_point[:,0:1],\n cross_point[:,2:3],\n cross_point[:,3:4],\n cross_point[:,5:6]),\n axis=1)\nanode_test = np.load(\"../../data/cell_a_MAIKo_test.npy\")\nanode_test = anode_test.reshape(\n (-1,\n anode_test.shape[1],\n anode_test.shape[2],\n 1))\ncross_point_test = np.concatenate(\n (np.load(\"../../data/point_xv_MAIKo_test.npy\"),\n np.load(\"../../data/point_xs_MAIKo_test.npy\")),\n axis=1)\ncross_point_test = np.concatenate(\n (cross_point_test[:,0:1],\n cross_point_test[:,2:3],\n cross_point_test[:,3:4],\n cross_point_test[:,5:6]),\n axis=1)\n\n##========== tensorboard setup ==========##\nold_session = KTF.get_session()\nsession = tf.Session('')\nKTF.set_session(session)\nKTF.set_learning_phase(1)\n\n##========== model building ==========##\nanode_input = Input(shape=anode[0].shape)\nx0 = MaxPooling2D(pool_size=(4,4))(anode_input)\nx1 = Conv2D(filters=32,kernel_size=(16,16),\n padding=\"same\",activation=\"relu\")(x0)\nx2 = MaxPooling2D(pool_size=(2,2))(x1)\nx3 = Conv2D(filters=32,kernel_size=(8,8),\n padding=\"same\",activation=\"relu\")(x2)\nx4 = MaxPooling2D(pool_size=(2,2))(x3)\nz1 = Conv2D(filters=32,kernel_size=(4,4),\n padding=\"same\",activation=\"relu\")(x4)\nz2 = MaxPooling2D(pool_size=(4,4))(z1)\nx6 = Flatten()(x2)\nx7 = Dense(128,activation=\"sigmoid\")(x6)\nx8 = Dropout(0.5)(x7)\nx9 = Flatten()(x4)\nx10 = concatenate([x9,x8])\nx11 = Dense(128,activation=\"sigmoid\")(x10)\nx12 = Dropout(0.5)(x11)\nz3 = Flatten()(z2)\nz4 = concatenate([x12,z3])\nz5 = Dense(512,activation=\"sigmoid\")(z4)\nz6 = Dropout(0.5)(z5)\noutput = Dense(4,activation=\"relu\")(z6)\n\nmodel = Model(inputs=anode_input,outputs=output)\nsgd = SGD(lr=lr,decay=decay,momentum=momentum,nesterov=True)\nmodel.compile(loss=\"mse\",\n optimizer=sgd)\n\n##========== fitting ==========##\ncsvlogger = CSVLogger(\"detector_res.csv\")\nboard = TensorBoard(log_dir=\"log/\",histogram_freq=1)\nmodel.fit(anode,\n cross_point,\n epochs=epochs,\n batch_size=batch_size,\n validation_data=[anode_test,cross_point_test],\n callbacks=[csvlogger,board])\nmodel.save(\"detector_res.h5\")\n\nKTF.set_session(old_session)\n", "sub_path": "keras/pattern_detection/detector_res.py", "file_name": "detector_res.py", "file_ext": "py", "file_size_in_byte": 2977, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.load", "line_number": 20, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 26, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 28, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 30, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 42, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 46, "usage_type": "call"}, {"api_name": "keras.backend.tensorflow_backend.get_session", "line_number": 54, "usage_type": "call"}, {"api_name": "keras.backend.tensorflow_backend", "line_number": 54, "usage_type": "name"}, {"api_name": "tensorflow.Session", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.backend.tensorflow_backend.set_session", "line_number": 56, "usage_type": "call"}, {"api_name": "keras.backend.tensorflow_backend", "line_number": 56, "usage_type": "name"}, {"api_name": "keras.backend.tensorflow_backend.set_learning_phase", "line_number": 57, "usage_type": "call"}, {"api_name": "keras.backend.tensorflow_backend", "line_number": 57, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 60, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 62, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 64, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 65, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 68, "usage_type": "call"}, {"api_name": "keras.layers.MaxPooling2D", "line_number": 70, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 71, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 72, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 73, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 75, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 76, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 77, "usage_type": "call"}, {"api_name": "keras.layers.Flatten", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.layers.concatenate", "line_number": 79, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 80, "usage_type": "call"}, {"api_name": "keras.layers.Dropout", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.layers.Dense", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 84, "usage_type": "call"}, {"api_name": "keras.optimizers.SGD", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.callbacks.CSVLogger", "line_number": 90, "usage_type": "call"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 91, "usage_type": "call"}, {"api_name": "keras.backend.tensorflow_backend.set_session", "line_number": 100, "usage_type": "call"}, {"api_name": "keras.backend.tensorflow_backend", "line_number": 100, "usage_type": "name"}]} +{"seq_id": "378887189", "text": "# -*- coding: utf-8 -*-\n\"\"\"\n@Time : 2018/3/25 下午2:34\n@Author : Nico\n\"\"\"\n# -*- coding: utf-8 -*-\n\"\"\"\n@Time : 2017/12/29 13:31\n@Author : Nico\n\"\"\"\n\nfrom os import path\n\nimport tensorflow as tf\nfrom keras import backend as K\nfrom keras import losses, optimizers, activations\nfrom keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard\nfrom keras.layers import Input, Lambda, Conv2D, Conv2DTranspose\nfrom keras.models import Model\n\nfrom defect_detection.base_model.defect_model import DefectModel, CoreModel\n\n\nclass VAE_FCN(CoreModel):\n def __init__(self, input_size, kernel_num_list, kernel_size,\n z_latent_dim, epsilon_std, kl_alpha,\n learning_rate, n_epoch, batch_size):\n core_name = 'kn%s_ks%d_%s_%s_z%d_kl%g_e%g' % \\\n (kernel_num_list, kernel_size, loss, active, z_latent_dim, kl_alpha, epsilon_std)\n super(VAE_FCN, self).__init__(core_name=core_name)\n self.z_latent_dim = z_latent_dim\n self.epsilon_std = epsilon_std\n self.kl_alpha = kl_alpha\n self.lr = learning_rate\n self.epoch = n_epoch\n self.batch_size = batch_size\n\n x = Input(shape=(input_size, input_size, 1,))\n\n conv1 = Conv2D(kernel_num_list[0], kernel_size=kernel_size, strides=2, padding='same', activation='relu')\n conv2 = Conv2D(kernel_num_list[1], kernel_size=kernel_size, strides=2, padding='same', activation='relu')\n conv3 = Conv2D(kernel_num_list[2], kernel_size=kernel_size, strides=2, padding='same', activation='relu')\n # conv4 = Conv2D(kernel_num[3], kernel_size=kernel_size, strides=2, padding='same', activation='relu')\n # conv5 = Conv2D(kernel_num[4], kernel_size=kernel_size, strides=2, padding='same', activation='relu')\n # conv4 = Conv2D(64, kernel_size=3, strides=2, padding='same', activation='relu')\n\n conv_mean = Conv2D(z_latent_dim, kernel_size=[5, 5], padding='valid', activation=None)\n conv_var = Conv2D(z_latent_dim, kernel_size=[5, 5], padding='valid', activation=None)\n deconv_z = Conv2DTranspose(kernel_num_list[-1], kernel_size=[5, 5], activation='relu')\n\n deconv1 = Conv2DTranspose(kernel_num_list[-2], kernel_size=kernel_size, strides=2, padding='same', activation='relu')\n deconv2 = Conv2DTranspose(kernel_num_list[-3], kernel_size=kernel_size, strides=2, padding='same', activation='relu')\n # deconv3 = Conv2DTranspose(kernel_num[-4], kernel_size=kernel_size, strides=2, padding='same', activation='relu')\n # deconv4 = Conv2DTranspose(kernel_num[-5], kernel_size=kernel_size, strides=2, padding='same', activation='relu')\n deconv5 = Conv2DTranspose(1, kernel_size=kernel_size, strides=2, padding='same', activation=active_func[active])\n\n conv_x = conv3(conv2(conv1(x)))\n self.encode_mean = conv_mean(conv_x)\n self.encode_logvar = conv_var(conv_x)\n\n z = Lambda(self.reparameterize)([self.encode_mean, self.encode_logvar])\n decoded_h = deconv_z(z)\n # decoded = deconv5(deconv3(deconv2(deconv1(decoded_h))))\n decoded = deconv5(deconv2(deconv1(decoded_h)))\n\n self.vae = Model(inputs=x, outputs=decoded)\n self.encoder = Model(inputs=x, outputs=[self.encode_mean, self.encode_logvar])\n\n z_ = Input(shape=(1, 1, z_latent_dim))\n decoded_h_ = deconv_z(z_)\n\n # decoded_ = deconv5(deconv3(deconv2(deconv1(decoded_h_))))\n decoded_ = deconv5(deconv2(deconv1(decoded_h_)))\n self.generator = Model(inputs=z_, outputs=decoded_)\n\n def reparameterize(self, args):\n mean, logvar = args\n learning_phase = K.learning_phase()\n\n if learning_phase == 1:\n epsilon = K.random_normal(\n shape=(K.shape(mean)[0], 1, 1, self.z_latent_dim),\n mean=0., stddev=self.epsilon_std\n )\n return mean + K.exp(0.5 * logvar) * epsilon\n else:\n # return mean + K.exp(0.5 * logvar)\n return mean\n\n def vae_loss(self, x, x_decoded_mean):\n # Because keras did mean(loss, axis=-1) before return loss,\n # which makes loss shape become (N, 28, 28) from (N, 28, 28, 1),\n # we need to sum all dimension error, then loss shape is (N,)\n re_loss = K.sum(loss_func[loss](x, x_decoded_mean), axis=[1, 2])\n # sum all latent dims error, then make loss shape from (N, latent_dim) to (N,)\n kl_loss = -0.5 * K.sum(1 + self.encode_logvar - K.square(self.encode_mean) - K.exp(self.encode_logvar), axis=-1)\n return K.mean(re_loss + kl_loss * self.kl_alpha)\n\n def data_transform_forward(self, data):\n return data[..., None]\n\n def _fit(self, model_dir, model_path, x, y, **kwargs):\n adam = optimizers.Adam(lr=self.lr)\n self.vae.compile(optimizer=adam, loss=self.vae_loss)\n self.vae.summary()\n\n model_tensorboard = TensorBoard(log_dir=path.join(model_dir, 'log/'))\n model_checkpoint = ModelCheckpoint(model_path, verbose=1, save_weights_only=True, save_best_only=True, period=2)\n model_earlystop = EarlyStopping(patience=5, verbose=1)\n\n self.vae.fit(\n x, x,\n shuffle=True,\n verbose=1,\n epochs=self.epoch,\n batch_size=self.batch_size,\n validation_split=0.15,\n callbacks=[model_tensorboard, model_checkpoint, model_earlystop]\n )\n\n def _predict(self, x, **kwargs):\n x = x[..., None]\n return self.vae.predict(x)\n\n def _model_save_func(self, model_path):\n pass\n\n def _model_load_func(self, model_path):\n self.vae.load_weights(model_path)\n\n\nif __name__ == '__main__':\n import os\n\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = '2'\n config = tf.ConfigProto()\n config.gpu_options.per_process_gpu_memory_fraction = 0.3\n K.set_session(tf.Session(config=config))\n\n pattern = 1\n\n # size = (20, 20)\n # stride = (8, 8)\n size = 40\n stride = 15\n hist = False\n scale_factor = 0.5\n\n kernel_num = [32, 32, 64]\n kernel_size = 5\n\n lr = 3e-4\n epoch = 50\n batch_size = 128\n latent_dim = 30\n epsilon_std = 0.5\n kl_alpha = 0.1\n\n eval_patch_size = 10\n threshold = 0.015\n\n loss_func = {'bc': losses.binary_crossentropy, 'mse': losses.mean_squared_error}\n loss = 'mse'\n active_func = {'sigmoid': activations.sigmoid, 'linear': activations.linear}\n active = 'sigmoid'\n\n vae_cnn_core = VAE_FCN(input_size=size, kernel_num_list=kernel_num, kernel_size=kernel_size,\n z_latent_dim=latent_dim, epsilon_std=epsilon_std, kl_alpha=kl_alpha,\n learning_rate=lr, n_epoch=epoch, batch_size=batch_size)\n defect_model = DefectModel(core_model=vae_cnn_core, pattern=pattern, patch_size=size, patch_stride=stride,\n scale_factor=scale_factor, hist_flag=hist, eval_patch_size=eval_patch_size, eval_threshold=threshold)\n\n # defect_model.train_model()\n defect_model.load_model()\n defect_model.test_model()\n", "sub_path": "vae/vae_fcn_patch.py", "file_name": "vae_fcn_patch.py", "file_ext": "py", "file_size_in_byte": 7032, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "defect_detection.base_model.defect_model.CoreModel", "line_number": 24, "usage_type": "name"}, {"api_name": "keras.layers.Input", "line_number": 38, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 40, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 41, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 42, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 47, "usage_type": "call"}, {"api_name": "keras.layers.Conv2D", "line_number": 48, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 49, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 51, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 52, "usage_type": "call"}, {"api_name": "keras.layers.Conv2DTranspose", "line_number": 55, "usage_type": "call"}, {"api_name": "keras.layers.Lambda", "line_number": 61, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 66, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 67, "usage_type": "call"}, {"api_name": "keras.layers.Input", "line_number": 69, "usage_type": "call"}, {"api_name": "keras.models.Model", "line_number": 74, "usage_type": "call"}, {"api_name": "keras.backend.learning_phase", "line_number": 78, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 78, "usage_type": "name"}, {"api_name": "keras.backend.random_normal", "line_number": 81, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 81, "usage_type": "name"}, {"api_name": "keras.backend.shape", "line_number": 82, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 82, "usage_type": "name"}, {"api_name": "keras.backend.exp", "line_number": 85, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 85, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 94, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 94, "usage_type": "name"}, {"api_name": "keras.backend.sum", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 96, "usage_type": "name"}, {"api_name": "keras.backend.square", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.backend.exp", "line_number": 96, "usage_type": "call"}, {"api_name": "keras.backend.mean", "line_number": 97, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 97, "usage_type": "name"}, {"api_name": "keras.optimizers.Adam", "line_number": 103, "usage_type": "call"}, {"api_name": "keras.optimizers", "line_number": 103, "usage_type": "name"}, {"api_name": "keras.callbacks.TensorBoard", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 107, "usage_type": "call"}, {"api_name": "os.path", "line_number": 107, "usage_type": "name"}, {"api_name": "keras.callbacks.ModelCheckpoint", "line_number": 108, "usage_type": "call"}, {"api_name": "keras.callbacks.EarlyStopping", "line_number": 109, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 135, "usage_type": "attribute"}, {"api_name": "tensorflow.ConfigProto", "line_number": 136, "usage_type": "call"}, {"api_name": "keras.backend.set_session", "line_number": 138, "usage_type": "call"}, {"api_name": "keras.backend", "line_number": 138, "usage_type": "name"}, {"api_name": "tensorflow.Session", "line_number": 138, "usage_type": "call"}, {"api_name": "keras.losses.binary_crossentropy", "line_number": 162, "usage_type": "attribute"}, {"api_name": "keras.losses", "line_number": 162, "usage_type": "name"}, {"api_name": "keras.losses.mean_squared_error", "line_number": 162, "usage_type": "attribute"}, {"api_name": "keras.activations.sigmoid", "line_number": 164, "usage_type": "attribute"}, {"api_name": "keras.activations", "line_number": 164, "usage_type": "name"}, {"api_name": "keras.activations.linear", "line_number": 164, "usage_type": "attribute"}, {"api_name": "defect_detection.base_model.defect_model.DefectModel", "line_number": 170, "usage_type": "call"}]} +{"seq_id": "14389097", "text": "# NOTE: Must be before we import or call anything that may be synchronous.\nfrom gevent import monkey\n\nmonkey.patch_all()\n\nimport sys\nimport os\n\nsys.path.append(os.path.join(os.path.dirname(__file__), \"../\"))\n\nimport logging\n\n\nbind = \"unix:/tmp/gunicorn_registry.sock\"\nworkers = 1\nworker_class = \"gevent\"\nworker_connections = 30\npythonpath = \".\"\nreload = True\nreload_engine = \"auto\"\n\n\ndef when_ready(server):\n logger = logging.getLogger(__name__)\n logger.debug(\n \"Starting registry gunicorn with %s workers and %s worker class\", workers, worker_class\n )\n", "sub_path": "local-dev/gunicorn_registry.py", "file_name": "gunicorn_registry.py", "file_ext": "py", "file_size_in_byte": 569, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "gevent.monkey.patch_all", "line_number": 4, "usage_type": "call"}, {"api_name": "gevent.monkey", "line_number": 4, "usage_type": "name"}, {"api_name": "sys.path.append", "line_number": 9, "usage_type": "call"}, {"api_name": "sys.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.join", "line_number": 9, "usage_type": "call"}, {"api_name": "os.path", "line_number": 9, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 9, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 24, "usage_type": "call"}]} +{"seq_id": "650532153", "text": "from collections import deque\n\ndef bfs(row,col): # BFS\n deq = deque()\n deq.append((row,col))\n \n visited[row][col] = 1\n\n v_count = 0\n k_count = 0\n if map_list[row][col] == 'v':\n v_count += 1\n elif map_list[row][col] == 'k':\n k_count += 1\n\n dr = [0,0,-1,1]\n dc = [-1,1,0,0]\n\n while len(deq) > 0:\n row,col = deq.popleft()\n\n for i in range(4):\n nr = row + dr[i]\n nc = col + dc[i]\n \n if 0 <= nr < R and 0 <= nc < C:\n if map_list[nr][nc] != '#':\n if visited[nr][nc] == 0:\n if map_list[nr][nc] == 'v':\n v_count += 1\n visited[nr][nc] = 1\n deq.append((nr,nc))\n\n elif map_list[nr][nc] == 'k':\n k_count += 1\n visited[nr][nc] = 1\n deq.append((nr,nc))\n\n else:\n visited[nr][nc] = 1 \n deq.append((nr,nc))\n if k_count > v_count:\n return 0,k_count\n else:\n return v_count,0\n\n\nR,C = map(int,input().split())\n\nmap_list = [list(input().rstrip()) for _ in range(R)]\nvisited = [[0 for _ in range(C)] for _ in range(R)]\nv,k = 0,0\n\nfor row in range(R):\n for col in range(C):\n if map_list[row][col] != \"#\" and visited[row][col] == 0:\n v1,k1 = bfs(row,col)\n v += v1\n k += k1\nprint(k,v)", "sub_path": "algorithm_search/양치기 꿍 (3187번).py", "file_name": "양치기 꿍 (3187번).py", "file_ext": "py", "file_size_in_byte": 1563, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.deque", "line_number": 4, "usage_type": "call"}]} +{"seq_id": "225084551", "text": "from collections import OrderedDict\nfrom copy import deepcopy\nfrom typing import Union, TypeVar\n\nimport numpy as np\nimport torch\nfrom torch import Tensor\n\nN = TypeVar(\"N\", int, float, Tensor, np.ndarray)\n\n\nclass _BufferMixin:\n \"\"\"\n The buffer in Trainer is for automatic loading and saving.\n \"\"\"\n\n def __init__(self) -> None:\n self._buffers = OrderedDict()\n\n def _register_buffer(self, name: str, value: Union[str, N]):\n r\"\"\"Adds a persistent buffer to the module.\n \"\"\"\n if \"_buffers\" not in self.__dict__:\n raise AttributeError(\"cannot assign buffer before Module.__init__() call\")\n elif not isinstance(name, str):\n raise TypeError(\n \"buffer name should be a string. \" \"Got {}\".format(torch.typename(name))\n )\n elif \".\" in name:\n raise KeyError('buffer name can\\'t contain \".\"')\n elif name == \"\":\n raise KeyError('buffer name can\\'t be empty string \"\"')\n elif hasattr(self, name) and name not in self._buffers:\n raise KeyError(\"attribute '{}' already exists\".format(name))\n else:\n self._buffers[name] = value\n\n def __getattr__(self, name):\n if \"_buffers\" in self.__dict__:\n _buffers = self.__dict__[\"_buffers\"]\n if name in _buffers:\n return _buffers[name]\n raise AttributeError(name)\n\n def __setattr__(self, name, value):\n buffers = self.__dict__.get(\"_buffers\")\n if buffers is not None and name in buffers:\n buffers[name] = value\n else:\n object.__setattr__(self, name, value)\n\n def __delattr__(self, name):\n if name in self._buffers:\n del self._buffers[name]\n else:\n object.__delattr__(self, name)\n\n def _buffer_state_dict(self):\n destination = OrderedDict()\n for name, buf in self._buffers.items():\n value = buf\n if isinstance(buf, Tensor):\n value = buf.detach()\n if isinstance(buf, np.ndarray):\n value = deepcopy(buf)\n destination[name] = value\n return destination\n\n def _load_buffer_from_state_dict(\n self, state_dict, prefix, strict, missing_keys, unexpected_keys, error_msgs\n ):\n\n local_name_params = self._buffers.items()\n local_state = {k: v for k, v in local_name_params if v is not None}\n\n for name, param in local_state.items():\n key = prefix + name\n if key in state_dict:\n input_param = state_dict[key]\n # Backward compatibility: loading 1-dim tensor from 0.3.* to version 0.4+\n with torch.no_grad():\n try:\n if isinstance(input_param, Tensor):\n param.copy_(input_param)\n else:\n self._buffers[name] = input_param\n except Exception as ex:\n error_msgs.append(\n 'While copying the parameter named \"{}\", '\n \"an exception occured : {}.\".format(key, ex.args)\n )\n elif strict:\n missing_keys.append(key)\n\n def _load_buffer_state_dict(self, state_dict):\n r\"\"\"\n \"\"\"\n missing_keys = []\n unexpected_keys = []\n error_msgs = []\n\n # copy state_dict so _load_from_state_dict can modify it\n state_dict = state_dict.copy()\n\n def load(module, prefix=\"\"):\n module._load_buffer_from_state_dict(\n state_dict, prefix, True, missing_keys, unexpected_keys, error_msgs\n )\n\n load(self)\n\n if len(error_msgs) > 0:\n raise RuntimeError(\n \"Error(s) in loading state_dict for {}:\\n\\t{}\".format(\n self.__class__.__name__, \"\\n\\t\".join(error_msgs)\n )\n )\n return missing_keys, unexpected_keys, error_msgs\n", "sub_path": "deepclustering2/trainer/_buffer.py", "file_name": "_buffer.py", "file_ext": "py", "file_size_in_byte": 4040, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "typing.TypeVar", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 9, "usage_type": "argument"}, {"api_name": "numpy.ndarray", "line_number": 9, "usage_type": "attribute"}, {"api_name": "collections.OrderedDict", "line_number": 18, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 20, "usage_type": "name"}, {"api_name": "torch.typename", "line_number": 27, "usage_type": "call"}, {"api_name": "collections.OrderedDict", "line_number": 59, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 62, "usage_type": "argument"}, {"api_name": "numpy.ndarray", "line_number": 64, "usage_type": "attribute"}, {"api_name": "copy.deepcopy", "line_number": 65, "usage_type": "call"}, {"api_name": "torch.no_grad", "line_number": 81, "usage_type": "call"}, {"api_name": "torch.Tensor", "line_number": 83, "usage_type": "argument"}]} +{"seq_id": "406629314", "text": "from simple_pid import PID\nfrom datetime import datetime\n\n\nclass shed():\n\n def __init__(self, name, settings):\n self.settings = settings\n self.request = False\n self.name = name\n self.state = \"off\"\n self.configs = settings['state_settings'] # ['on'], ['off'], ['alarm']\n self.set_temp = settings['set_temp']\n self.set_temp_high = self.set_temp + 3\n self.set_temp_low = self.set_temp - 3\n self.pid_state = \"off\" \n self.dependent = settings['dependent'] # List of dependent variables required to be alarm free for operation \n if \"PID\" in settings:\n self.p = settings[\"PID\"][\"p\"]\n self.i = settings[\"PID\"][\"i\"]\n self.d = settings[\"PID\"][\"d\"]\n self.pid = PID(self.p, self.i, self.d, self.set_temp)\n self.pid_valve_hot = settings[\"PID\"][\"valve_control_hot\"]\n self.pid_valve_cold = settings[\"PID\"][\"valve_control_cold\"]\n self.pid_control = settings[\"PID\"][\"control\"]\n self.pid_state = False\n self.pid.output_limits = (0,10)\n self.timer_start = datetime.now()\n self.timer_elapsed = datetime.now() - self.timer_start\n self.timer_state = 0\n self.timer_output = \"\"\n def change_request(self, value):\n self.request = value\n # print (self.request)\n self.update_state()\n\n\n def update_state(self):\n if self.state != \"alarm\":\n if self.request == \"true\":\n self.state = \"on\"\n if self.request == \"false\":\n self.state = \"off\" \n if self.state == \"alarm\":\n pass # possibly add in fuction to bring up pop up window to clear alarms?\n\n def state_monitor(self, active_alarm):\n count = 0\n if self.request == True or self.request == \"true\":\n # print(self.dependent)\n # print(active_alarm)\n for item in self.dependent:\n if \"Gas\" not in item:\n if item in active_alarm:\n count =+ 1 \n else:\n pass \n else:\n if item in active_alarm:\n count =+ 100\n else:\n pass\n #print(count)\n if count > 100:\n self.state = \"alarm\"\n elif count > 0:\n self.state = \"out_of_range\"\n elif count == 0:\n self.state = \"on\"\n else:\n self.state =\"ERROR!\"\n\n else:\n self.state = \"off\"\n #print(self.name, \"state: \", self.state)\n \n \n\n def new_state_output(self):\n return self.configs[self.state]\n\n def change_set_temp(self, temp_set):\n self.set_temp = float(temp_set)\n \n def change_pid(self, newset):\n self.pid_state = newset\n \n\n def pid_func(self, SHED_temp_current):\n output = {}\n # print(self.pid_state)\n if self.pid_state == True or self.pid_state == \"true\" or self.pid_state == \"True\":\n self.pid.setpoint = float(self.set_temp)\n valve_temp = self.pid(float(SHED_temp_current))\n print(valve_temp)\n output[self.pid_valve_hot] = valve_temp \n print(self.set_temp, SHED_temp_current)\n print(output)\n return output\n\n def timer(self):\n if self.timer_state == 0:\n self.timer_start = datetime.now()\n self.timer_elapsed = datetime.now() - self.timer_start\n weeks = 0\n if self.timer_elapsed.days >= 7:\n weeks = self.timer_elapsed // 7\n days = self.timer_elapsed.days - 7 * weeks\n hours = self.timer_elapsed.seconds // 3600\n minutes = self.timer_elapsed.seconds // 60 % 60\n seconds = self.timer_elapsed.seconds - minutes*60 - hours*3600 - days * 86400\n\n self.timer_output = str(weeks) + \"W \" + str(days) + \"d \" + str(hours) + \"h \" +str(minutes) + \"m \" + str(seconds)\n return self.timer_output\n \n def timer_toggle(self):\n if self.timer_state == 0:\n self.timer_state = 1\n self.timer_start = datetime.now()\n else:\n self.timer_state = 0\n\n\n\nclass alarm():\n def __init__(self, name, settings):\n self.settings = settings\n self.name = name\n self.state = settings[\"state\"]\n self.type = settings[\"limit_type\"]\n self.limit_high = settings[\"limits\"][\"high\"]\n self.limit_low = settings[\"limits\"][\"low\"]\n self.active_config = settings[\"active_config\"]\n\n def update_state(self, reading, pump_state):\n if \"Gas\" in self.name:\n if self.state == 0: \n if self.type == \"inside\":\n if float(reading) > float(self.limit_high) or float(reading) < float(self.limit_low):\n self.state = 1\n elif self.state == 1: \n pass # Alarm will not automatically reset!\n \n else:\n #print(\"pump: \", pump_state)\n if pump_state == 0:\n self.state = 2\n else:\n if self.state == 1: ## change this if disabling alarm is required\n if self.type == \"inside\":\n if float(reading) < float(self.limit_high) and float(reading) > float(self.limit_low):\n self.state = 0\n else:\n self.state = 1\n else: \n if self.type == \"inside\":\n if float(reading) < float(self.limit_high) and float(reading) > float(self.limit_low):\n self.state = 0\n else:\n self.state = 1\n \n \n\n #print(\"state: \",self.state)\n def reset(self):\n self.state = 0\n\n def alarm_output(self):\n return self.active_config\n\n def change_limit(self, limit, lim_set):\n \"\"\"\n lim name: name from javascript including \"high_\" or \"low_\" as the prefix\n lim_set: set limit value entered in web interface\n \"\"\"\n if limit == \"low\" and lim_set.isnumeric():\n self.limit_low = lim_set\n #print(\"Change alarm great success!\")\n if limit == \"high\" and lim_set.isnumeric():\n self.limit_high = lim_set\n \n", "sub_path": "shed.py", "file_name": "shed.py", "file_ext": "py", "file_size_in_byte": 6445, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "simple_pid.PID", "line_number": 22, "usage_type": "call"}, {"api_name": "datetime.datetime.now", "line_number": 28, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 28, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 29, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 103, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 103, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 104, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 104, "usage_type": "name"}, {"api_name": "datetime.datetime.now", "line_number": 119, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 119, "usage_type": "name"}]} +{"seq_id": "547969822", "text": "#!/user/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport yaml\nimport logging\nimport logging.config\nfrom appium import webdriver\nfrom common.common_data import *\nfrom airtest.core.api import auto_setup\n\nlogging.config.fileConfig(CON_LOG)\nlogging = logging.getLogger()\n\n\ndef get_desired_caps():\n \"\"\"\n 读取desired caps并返回\n :return: desired caps\n \"\"\"\n with open(CAPS_YAML, 'r', encoding='utf-8') as file:\n data = yaml.load(file, Loader=yaml.FullLoader)\n print(data)\n return data\n\n\ndef appium_desired():\n data = get_desired_caps()\n desired_caps = data['desired_caps']\n # airtest 输入法禁用 此输入法禁用后，无法使用poco().set_text()\n # yosemite = '?ime_method=None'\n auto_setup(__file__, devices=[\"Android:///%s?ime_method=ADBIME\" % data['desired_caps']['udid']])\n logging.info('start app...')\n driver = webdriver.Remote('http://%s:%s/wd/hub' % (data['ip'], data['port']), desired_caps)\n driver.implicitly_wait(3)\n return driver\n", "sub_path": "common/deserid_caps.py", "file_name": "deserid_caps.py", "file_ext": "py", "file_size_in_byte": 1006, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.config.fileConfig", "line_number": 11, "usage_type": "call"}, {"api_name": "logging.config", "line_number": 11, "usage_type": "attribute"}, {"api_name": "logging.getLogger", "line_number": 12, "usage_type": "call"}, {"api_name": "yaml.load", "line_number": 21, "usage_type": "call"}, {"api_name": "yaml.FullLoader", "line_number": 21, "usage_type": "attribute"}, {"api_name": "airtest.core.api.auto_setup", "line_number": 31, "usage_type": "call"}, {"api_name": "logging.info", "line_number": 32, "usage_type": "call"}, {"api_name": "appium.webdriver.Remote", "line_number": 33, "usage_type": "call"}, {"api_name": "appium.webdriver", "line_number": 33, "usage_type": "name"}]} +{"seq_id": "448878664", "text": "# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\nfrom django.conf.urls import url\nfrom django.http import HttpResponse\nfrom django.shortcuts import render\nfrom GoogleTools.models import *\nimport time,datetime\n\n# Create your views here.\nfrom django.http import StreamingHttpResponse\nfrom django.views.decorators.csrf import csrf_exempt\nfrom django.db.models import Q\nfrom pure_pagination import Paginator, EmptyPage, PageNotAnInteger\n\ndef index(request):\n if request.method == 'GET':\n download = request.GET.get('download','')\n documents = Tool.objects.filter(type='Google-Document').order_by('-id')\n dataList={}\n if request.GET.get('search'):\n documents = documents.filter(Q(toolFile__contains=request.GET.get('search')))\n dataList['search'] = request.GET.get('search')\n if download:\n tool = Tool.objects.get(id=int(download))\n the_file_name = tool.toolFile.path\n response = StreamingHttpResponse(file_iterator(the_file_name))\n response['Content-Type'] = 'application/octet-stream'\n index = str(tool.toolFile.name).rfind('/',0,len(str(tool.toolFile.name))-1)\n tool_name = tool.toolFile.name\n if index:\n tool_name = str(tool.toolFile.name)[index+1:]\n response['Content-Disposition'] = 'attachment;filename=%s' %tool_name\n return response\n documentsPage = pointsPage(request,documents,10)\n dataList['documentsPage']=documentsPage\n return render(request,'document.html',dataList)\n\ndef showAutoTool(request):\n if request.method == 'GET':\n autoTools = Tool.objects.filter(type='Auto-Tools').order_by('-id')\n dataList={}\n if request.GET.get('search'):\n autoTools = autoTools.filter(Q(toolFile__contains=request.GET.get('search')))\n dataList['search'] = request.GET.get('search')\n download = request.GET.get('download','')\n if download:\n tool = Tool.objects.get(id=int(download))\n the_file_name = tool.toolFile.path\n response = StreamingHttpResponse(file_iterator(the_file_name))\n response['Content-Type'] = 'application/octet-stream'\n index = str(tool.toolFile.name).rfind('/',0,len(str(tool.toolFile.name))-1)\n tool_name = tool.toolFile.name\n if index:\n tool_name = str(tool.toolFile.name)[index+1:]\n response['Content-Disposition'] = 'attachment;filename=%s' %tool_name\n return response\n autoToolsPage = pointsPage(request,autoTools,10)\n dataList['autoToolsPage']=autoToolsPage\n return render(request,'auto_tools.html',dataList)\n\n\n@csrf_exempt\ndef addTools(request):\n if request.method=='POST':\n info = request.POST\n if info:\n classification = info['file_classification']\n if classification:\n file = request.FILES.get('cts_docFile')\n t = time.strptime(info['due_time'],\"%Y-%m-%d\")\n y,m,d = t[0:3]\n tool = Tool(type=classification,remarks=info['file_note'],toolFile=file,dueTime=datetime.datetime(y,m,d))\n tool.save()\n return HttpResponse(\"suceess\")\n\ndef file_iterator(file_name, chunk_size=3048):\n with open(file_name) as f:\n while True:\n c = f.read(chunk_size)\n if c:\n yield c\n else:\n break\n\ndef pointsPage(request,tools,number):\n try:\n page = request.GET.get('page', 1)\n except PageNotAnInteger:\n page = 1\n page1 = request.GET.get('page1', 1)\n # Provide Paginator with the request object for complete querystring generation\n p = Paginator(tools, number, request=request)\n return p.page(page)", "sub_path": "GoogleDatas/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 3856, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.db.models.Q", "line_number": 21, "usage_type": "call"}, {"api_name": "django.http.StreamingHttpResponse", "line_number": 26, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models.Q", "line_number": 43, "usage_type": "call"}, {"api_name": "django.http.StreamingHttpResponse", "line_number": 49, "usage_type": "call"}, {"api_name": "django.shortcuts.render", "line_number": 59, "usage_type": "call"}, {"api_name": "time.strptime", "line_number": 70, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 72, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 74, "usage_type": "call"}, {"api_name": "django.views.decorators.csrf.csrf_exempt", "line_number": 62, "usage_type": "name"}, {"api_name": "pure_pagination.PageNotAnInteger", "line_number": 88, "usage_type": "name"}, {"api_name": "pure_pagination.Paginator", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "462627590", "text": "import json\nimport uuid\nimport time\nimport os.path\nfrom copy import deepcopy\n\nfrom config import *\nconfig = Config(os.path.join(\"..\",\"www\",\"config.json\"))\n\nlpri = len(config[\"priorities\"])-1\n\ndef _calcpriority(priority, time):\n\tfor i in config[\"priority_thresh\"]:\n\t\tif time >= i:\n\t\t\tpriority -= 1\n\treturn max(priority, 0)\n\ndef _concatlist(lists):\n\tmasterlist = []\n\tfor i in lists: \n\t\tfor j in i:\n\t\t\tmasterlist.append(j)\n\treturn masterlist\n\ndef _fillblanks(odict, adict):\n\treturn dict(adict, **odict)\n\nclass Queue:\n\trequiredtags = {\n\t\t\"priority\":0,\n\t\t\"name\":\"DEFAULT\",\n\t\t\"material\":\"o\", \n\t\t\"esttime\": 0, \n\t\t\"coachmodified\": False, \n\t\t\"uuid\": \"this object is so old that it should be deleted\", \n\t\t\"sid\": \"this object is so old that it should be deleted\", \n\t\t\"time\": 2**30,\n\t\t\"totaldiff\": 0\n\t}\n\tdef __init__(self):\n\t\tself.queue = [[] for i in config[\"priorities\"]]\n\n\t@classmethod\n\tdef load(cls, fileobj):\n\t\tjdata = json.load(fileobj)\n\t\tself = cls()\n\t\tif type(jdata) is not list:\n\t\t\treturn self\n\t\tif len(jdata) != len(config[\"priorities\"]):\n\t\t\tif len(jdata) > len(config[\"priorities\"]):\n\t\t\t\tself.queue = jdata[:len(config[\"priorities\"])]\n\t\t\telif len(jdata) < len(config[\"priorities\"]):\n\t\t\t\tself.queue = jdata + [[] for i in range(len(config[\"priorities\"])-len(jdata))]\n\t\telse:\n\t\t\tself.queue = jdata\n\t\tfor ii in range(len(self.queue)):\n\t\t\ti = self.queue[ii]\n\t\t\tfor item in i:\n\t\t\t\titem[\"priority\"] = ii\n\t\t\t\titem = _fillblanks(item, Queue.requiredtags)\n\t\treturn self\n\n\tdef metapriority(self):\n\t\tfor i in self.queue:\n\t\t\tfor item in i:\n\t\t\t\tif time.time()-item[\"time\"] > (config[\"metabump\"] + config[\"metabumpmult\"]*item[\"priority\"]) and config[\"metabump\"]:\n\t\t\t\t\tpri = item[\"priority\"]-1\n\t\t\t\t\tif pri < 0:\n\t\t\t\t\t\titem[\"time\"] = time.time()\n\t\t\t\t\t\tcontinue\n\t\t\t\t\ti.remove(item)\n\t\t\t\t\titem[\"time\"] += config[\"metabump\"] + config[\"metabumpmult\"]*item[\"priority\"]\n\t\t\t\t\titem[\"priority\"] = pri\n\t\t\t\t\tself.queue[pri].append(item)\n\n\n\tdef append(self, **kwargs):\n\t\targs, authstate, sid = kwargs[\"args\"], kwargs[\"authstate\"], kwargs[\"sid\"]\n\t\tname, priority, esttime, material = args[0], args[1], args[2], args[3]\n\t\tif not name or material == \"N/A\" or priority == -1:\n\t\t\treturn\n\t\tbounds = config[\"length_bounds\"]\n\t\tif bounds[0] >= 0:\n\t\t\testtime = max(bounds[0], esttime)\n\t\tif bounds[1] >= 0:\n\t\t\testtime = min(bounds[1], esttime)\n\n\t\tif not config[\"priority_selection\"] and not authstate:\n\t\t\tpriority = min(lpri-config[\"default_priority\"], priority)\n\n\t\tif config[\"recalc_priority\"]:\n\t\t\tpriority = _calcpriority(priority, esttime)\n\n\t\tinqueue = False\n\t\tfor i in self.queue:\n\t\t\tfor j in i: \n\t\t\t\tif name.lower() == j[\"name\"].lower() and (material == j[\"material\"] or not config[\"allow_multiple_materials\"]):\n\t\t\t\t\tinqueue = True\n\t\t\t\t\tbreak\n\n\t\tif config[\"recapitalize\"]:\n\t\t\tname = name.title()\n\n\t\tif not inqueue or config[\"allow_multiples\"]:\n\t\t\tself.queue[lpri-priority].append({\n\t\t\t\t\"totaldiff\": 0,\n\t\t\t\t\"priority\": lpri-priority,\n\t\t\t\t\"name\": name.strip().rstrip(),\n\t\t\t\t\"material\": material,\n\t\t\t\t\"esttime\": esttime,\n\t\t\t\t\"coachmodified\": authstate,\n\t\t\t\t\"uuid\": str(uuid.uuid1()),\n\t\t\t\t\"sid\": sid,\n\t\t\t\t\"time\": time.time()\n\t\t\t})\n\n\tdef remove(self, **kwargs):\n\t\targs = kwargs[\"args\"]\n\t\tu = args[0]\n\t\tfor i in self.queue:\n\t\t\tfor j in i:\n\t\t\t\tif j[\"uuid\"] == u:\n\t\t\t\t\ti.remove(j)\n\tdef passoff(self, **kwargs):\n\t\targs, authstate = kwargs[\"args\"], kwargs[\"authstate\"]\n\t\tu = args[0]\n\t\toindex = -1\n\t\tmasterqueue = _concatlist(self.queue)\n\t\tfor i in self.queue:\n\t\t\tfor j in i:\n\t\t\t\tif j[\"uuid\"] == u:\n\t\t\t\t\toindex = masterqueue.index(j)\n\t\tif oindex == -1: return\n\t\tif oindex == len(masterqueue)-1: return\n\t\ttarget = masterqueue[oindex]\n\t\tfor ii in range(len(self.queue)):\n\t\t\ti = self.queue[ii]\n\t\t\tif target in i:\n\t\t\t\ti.remove(target)\n\t\tend = masterqueue[oindex+1]\n\t\tfor ii in range(len(self.queue)):\n\t\t\ti = self.queue[ii]\n\t\t\tif end in i:\n\t\t\t\ttindex = i.index(end)\n\t\t\t\ttpri = lpri-ii\n\t\ttarget[\"time\"] = time.time()\n\t\ttarget[\"priority\"] = lpri-tpri\n\t\tif authstate: target[\"coachmodified\"] = True\n\t\tself.queue[lpri-tpri].insert(tindex+1, target)\n\n\tdef relmove(self, **kwargs):\n\t\targs, authstate = kwargs[\"args\"], kwargs[\"authstate\"]\n\t\tu, nindex = args[0], args[1]\n\t\ttarget = None\n\t\tmasterqueue = _concatlist(self.queue)\n\t\tif len(masterqueue) <= 1: return\n\t\tfor i in self.queue:\n\t\t\tfor j in i:\n\t\t\t\tif j[\"uuid\"] == u:\n\t\t\t\t\ttarget = deepcopy(j)\n\t\t\t\t\ti.remove(j)\n\t\tif not target: return\n\n\t\tmasterqueue = _concatlist(self.queue)\n\n\t\tif nindex <= 0:\n\t\t\tbpri = masterqueue[0][\"priority\"]\n\t\t\tbind = 0\n\t\telif nindex >= len(masterqueue):\n\t\t\tbpri = masterqueue[-1][\"priority\"]\n\t\t\tbind = len(self.queue[bpri])\n\t\telse:\n\t\t\tbtarget = masterqueue[nindex-1]\n\t\t\tbpri = btarget[\"priority\"]\n\t\t\tbind = self.queue[bpri].index(btarget)+1\n\n\t\ttarget[\"time\"] = time.time()\n\t\ttarget[\"priority\"] = bpri\n\t\tif authstate: target[\"coachmodified\"] = True\n\t\tself.queue[bpri].insert(bind, target)\n\n\n\tdef move(self, **kwargs):\n\t\targs, authstate = kwargs[\"args\"], kwargs[\"authstate\"]\n\t\tu, ni, np = args[0], args[1], args[2]\n\t\ttarget = None\n\t\tfor i in self.queue:\n\t\t\tfor j in i:\n\t\t\t\tif j[\"uuid\"] == u:\n\t\t\t\t\ttarget = deepcopy(j)\n\t\t\t\t\ti.remove(j)\n\t\tif not target: return\n\t\ttarget[\"time\"] = time.time()\n\t\tif authstate: target[\"coachmodified\"] = True\n\t\ttarget[\"priority\"] = lpri-np\n\t\tself.queue[lpri-np].insert(ni, target)\n\n\tdef increment(self, **kwargs):\n\t\targs, authstate = kwargs[\"args\"], kwargs[\"authstate\"]\n\t\tu = args[0]\n\t\tindex = -1\n\t\tpriority = -1\n\t\tfor i in self.queue:\n\t\t\tfor j in i:\n\t\t\t\tif j[\"uuid\"] == u:\n\t\t\t\t\tindex = i.index(j)\n\t\t\t\t\tpriority = lpri-self.queue.index(i)\n\t\tif index == -1 and priority == -1: return\n\t\tif priority == lpri and not index:\n\t\t\treturn\n\t\titem = self.queue[lpri-priority].pop(index)\n\t\tindex -= 1\n\t\tif index < 0:\n\t\t\tpriority += 1\n\t\t\tif priority > lpri:\n\t\t\t\tindex = 0\n\t\t\t\tpriority = lpri\n\t\t\telse:\n\t\t\t\tindex = len(self.queue[max(lpri-priority, 0)])\n\t\titem[\"time\"] = time.time()\n\t\tif authstate: item[\"coachmodified\"] = True\n\t\titem[\"priority\"] = lpri-priority\n\t\tself.queue[max(lpri-priority, 0)].insert(min(index, len(self.queue[max(lpri-priority, 0)])),item)\n\n\tdef decrement(self, **kwargs):\n\t\targs, authstate = kwargs[\"args\"], kwargs[\"authstate\"]\n\t\tu = args[0]\n\t\tindex = -1\n\t\tpriority = -1\n\t\tfor i in self.queue:\n\t\t\tfor j in i:\n\t\t\t\tif j[\"uuid\"] == u:\n\t\t\t\t\tindex = i.index(j)\n\t\t\t\t\tpriority = lpri-self.queue.index(i)\n\t\tif index == -1 and priority == -1: return\n\t\tif not priority and len(self.queue[lpri-priority]) < index:\n\t\t\treturn\n\t\titem = self.queue[lpri-priority].pop(index)\n\t\tindex += 1\n\t\tif len(self.queue[lpri-priority]) < index:\n\t\t\tpriority -= 1\n\t\t\tif priority < 0:\n\t\t\t\tindex = len(self.queue[min(lpri-priority, lpri)])\n\t\t\t\tpriority = 0\n\t\t\telse:\n\t\t\t\tindex = 0\n\t\titem[\"time\"] = time.time()\n\t\tif authstate: item[\"coachmodified\"] = True\n\t\titem[\"priority\"] = lpri-priority\n\t\tself.queue[min(lpri-priority, lpri)].insert(max(index, 0),item)\n\n\tdef attr(self, **kwargs):\n\t\targs, authstate = kwargs[\"args\"], kwargs[\"authstate\"]\n\t\tu, attrname, value = args[0], args[1], args[2]\n\t\tif attrname not in self.requiredtags or attrname in [\"uuid\", \"sid\", \"time\", \"totaldiff\"]:\n\t\t\treturn\n\t\tif attrname not in config[\"attr_edit_perms\"] and not authstate:\n\t\t\treturn\n\t\tindex = -1\n\t\tpriority = -1\n\t\tfor i in self.queue:\n\t\t\tfor j in i:\n\t\t\t\tif j[\"uuid\"] == u:\n\t\t\t\t\tindex = i.index(j)\n\t\t\t\t\tpriority = lpri-self.queue.index(i)\n\t\tif index == -1 and priority == -1: return\n\t\titem = self.queue[lpri-priority][index]\n\t\tif attrname not in config[\"attr_edit_perms\"] and attrname != \"coachmodified\":\n\t\t\titem[\"coachmodified\"] = True\n\n\t\tif attrname == \"name\": item[\"name\"] = str(value).strip().rstrip()\n\t\telif attrname == \"material\" and value in config[\"materials\"]: item[\"material\"] = value\n\t\telif attrname == \"esttime\":\n\t\t\tbounds = config[\"length_bounds\"]\n\t\t\tif bounds[0] >= 0:\n\t\t\t\tvalue = max(bounds[0], value)\n\t\t\tif bounds[1] >= 0:\n\t\t\t\tvalue = min(bounds[1], value)\n\t\t\tprevtime = item[\"esttime\"]\n\t\t\titem[\"esttime\"] = value\n\t\t\tif config[\"recalc_priority\"] and not authstate:\n\t\t\t\tnewpriority = priority*1\n\t\t\t\titem[\"totaldiff\"] += value-prevtime\n\t\t\t\titem[\"totaldiff\"] = max(item[\"totaldiff\"], 0)\n\t\t\t\twhile item[\"totaldiff\"] >= 10: \n\t\t\t\t\tnewpriority -= 1\n\t\t\t\t\titem[\"totaldiff\"] -= 10\n\n\t\t\t\tnewpriority = max(newpriority, 0)\n\t\t\t\titem[\"priority\"] = lpri-newpriority\n\t\t\t\tself.queue[lpri-priority].pop(index)\n\t\t\t\tself.queue[lpri-newpriority].append(item)\n\t\t\telif authstate and config[\"recalc_priority\"]:\n\t\t\t\titem[\"coachmodified\"] = True\n\t\telif attrname == \"coachmodified\": item[\"coachmodified\"] = bool(value)\n\n", "sub_path": "scripts/laserqueue.py", "file_name": "laserqueue.py", "file_ext": "py", "file_size_in_byte": 8412, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.path.join", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.path", "line_number": 8, "usage_type": "name"}, {"api_name": "json.load", "line_number": 45, "usage_type": "call"}, {"api_name": "time.time", "line_number": 66, "usage_type": "call"}, {"api_name": "time.time", "line_number": 69, "usage_type": "call"}, {"api_name": "uuid.uuid1", "line_number": 112, "usage_type": "call"}, {"api_name": "time.time", "line_number": 114, "usage_type": "call"}, {"api_name": "time.time", "line_number": 146, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 160, "usage_type": "call"}, {"api_name": "time.time", "line_number": 177, "usage_type": "call"}, {"api_name": "copy.deepcopy", "line_number": 190, "usage_type": "call"}, {"api_name": "time.time", "line_number": 193, "usage_type": "call"}, {"api_name": "time.time", "line_number": 220, "usage_type": "call"}, {"api_name": "time.time", "line_number": 247, "usage_type": "call"}]} +{"seq_id": "362630447", "text": "from bs4 import BeautifulSoup\r\nimport urllib.request\r\nimport re\r\n\r\n## Takes 2 arguments:\r\n## Company name and Company number. Both as strings.\r\n## returns 2 string arrays:\r\n## arr_values an array of values related to given company in format:\r\n## 0: Cash on Hand. 1: Networth. 2: Asset value. 3: Liabilities value.\r\n## arr_changes an arrat of chnages in the above values since last year.\r\n## Null arrays mean there is no financial data avaliable. \r\n\r\ndef scrape_web_page(company_number, company_name):\r\n regex = re.compile(\"red|green\")\r\n \r\n required_page = (\"https://companycheck.co.uk/company/%s/%s/financials#key-financials\") %(company_number, company_name)\r\n page = urllib.request.urlopen(required_page)\r\n soup = BeautifulSoup(page, 'html.parser')\r\n value_box = soup.findAll('div', attrs={'class': 'Four-financial__figure'})\r\n change_dir_box = soup.findAll('section', attrs={'class': 'Four-financials'})\r\n changes_box = soup.findAll('div', attrs={'class': 'Four-financial__change'})\r\n \r\n arr_values = []\r\n arr_changes = []\r\n \r\n for entry in value_box:\r\n arr_values.append(entry.text.strip())\r\n for entry in changes_box:\r\n arr_changes.append(entry.text.strip())\r\n\r\n counter = 0\r\n for entry in regex.findall(str(change_dir_box)):\r\n if entry == \"red\":\r\n arr_changes[counter] = (\"-\" + arr_changes[counter])\r\n counter+=1\r\n \r\n return arr_values, arr_changes\r\n\r\n##scrape_web_page(\"03584121\", \"ASOSCOM-LIMITED\") ## example\r\n##scrape_web_page(\"11104388\", \"ABBOTT-INTERNATIONAL-PLC-LIMITED\")\r\n", "sub_path": "src/finance_scraper.py", "file_name": "finance_scraper.py", "file_ext": "py", "file_size_in_byte": 1583, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "re.compile", "line_number": 14, "usage_type": "call"}, {"api_name": "urllib.request.request.urlopen", "line_number": 17, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 17, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 17, "usage_type": "name"}, {"api_name": "bs4.BeautifulSoup", "line_number": 18, "usage_type": "call"}]} +{"seq_id": "415041112", "text": "import mahotas as mh\nfrom imread import imread\nfrom matplotlib import pyplot as plt\n\nimage = imread('../DATA/simple-dataset/building05.jpg')\nimage = mh.colors.rgb2gray(image)\n\nstandard_deviations = [8, 16, 32]\n\nfig1 = plt.figure()\n\nfor i in range(3):\n im = mh.gaussian_filter(image, standard_deviations[i])\n\n a = fig1.add_subplot(1, 3, i+1) # this line outputs images side-by-side\n plt.imshow(im)\n plt.gray()\n a.set_title('Sigma = ' + str(standard_deviations[i]))\n\nplt.show()\n", "sub_path": "smoothing/gaussian_filtering2.py", "file_name": "gaussian_filtering2.py", "file_ext": "py", "file_size_in_byte": 482, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "imread.imread", "line_number": 5, "usage_type": "call"}, {"api_name": "mahotas.colors.rgb2gray", "line_number": 6, "usage_type": "call"}, {"api_name": "mahotas.colors", "line_number": 6, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 10, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 10, "usage_type": "name"}, {"api_name": "mahotas.gaussian_filter", "line_number": 13, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 16, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 16, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 17, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 17, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 20, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 20, "usage_type": "name"}]} +{"seq_id": "242170787", "text": "# Copyright (c) 2020 Aiven, Helsinki, Finland. https://aiven.io/\n\nimport base64\nimport functools\nimport hashlib\nimport json\nimport logging\nimport os\nimport threading\nfrom concurrent.futures.thread import ThreadPoolExecutor\nfrom datetime import datetime\nfrom tempfile import TemporaryDirectory, NamedTemporaryFile\nfrom typing import Collection, Union, BinaryIO, Dict, Iterable\nfrom urllib.parse import urlparse\n\nimport boto3\nimport botocore.exceptions\nimport time\n\nfrom rpm_s3_mirror.repository import RepodataSection, Package\nfrom rpm_s3_mirror.statsd import StatsClient\nfrom rpm_s3_mirror.util import get_requests_session, validate_checksum, download_file\n\nlock = threading.RLock()\n\n\ndef md5_string(string):\n return hashlib.md5(string.encode(\"utf-8\")).hexdigest()\n\n\nclass S3DirectoryNotFound(Exception):\n def __init__(self, response):\n super().__init__()\n self.response = response\n\n\nclass S3:\n def __init__(\n self,\n aws_access_key_id: str,\n aws_secret_access_key: str,\n bucket_name: str,\n bucket_region: str,\n stats: StatsClient,\n max_workers: int = 8,\n scratch_dir: str = \"/var/tmp/\",\n ):\n self.aws_access_key_id = aws_access_key_id\n self.aws_secret_access_key = aws_secret_access_key\n self.bucket_name = bucket_name\n self.bucket_region = bucket_region\n self.stats = stats\n self.max_workers = max_workers\n self.scratch_dir = scratch_dir\n self._s3 = None\n self.session = get_requests_session()\n self.log = logging.getLogger(type(self).__name__)\n\n def sync_packages(\n self,\n base_url: str,\n upstream_repodata: Dict[str, RepodataSection],\n upstream_packages: Collection[Package],\n skip_existing: bool = False,\n ):\n with TemporaryDirectory(prefix=self.scratch_dir) as temp_dir:\n self._sync_objects(temp_dir, upstream_packages, skip_existing=skip_existing)\n synced_bytes = sum((package.package_size for package in upstream_packages))\n self.stats.gauge(\n metric=\"s3_mirror_sync_bytes\",\n value=synced_bytes,\n tags={\"repo\": urlparse(base_url).path},\n )\n self.stats.gauge(\n metric=\"s3_mirror_sync_packages\",\n value=len(upstream_packages),\n tags={\"repo\": urlparse(base_url).path},\n )\n self._sync_objects(temp_dir=temp_dir, repo_objects=upstream_repodata.values(), skip_existing=skip_existing)\n\n def overwrite_repomd(self, base_url):\n with TemporaryDirectory(prefix=self.scratch_dir) as temp_dir:\n url = f\"{base_url}repodata/repomd.xml\"\n repomd_xml = download_file(temp_dir=temp_dir, url=url, session=self.session)\n path = urlparse(url).path\n self.log.info(\"Overwriting repomd.xml\")\n self.put_object(repomd_xml, path, cache_age=0)\n\n def archive_repomd(self, base_url, location):\n self.log.debug(\"Archiving repomd.xml to %s\", location)\n url = f\"{base_url}repodata/repomd.xml\"\n self.copy_object(source=urlparse(url).path, destination=location)\n\n def put_manifest(self, location, manifest):\n self.log.info(\"Writing manifest to: %s\", location)\n manifest_json = json.dumps(manifest._asdict(), default=lambda x: x.isoformat(), indent=2)\n with NamedTemporaryFile(prefix=self.scratch_dir) as f:\n f.write(manifest_json.encode(\"utf-8\"))\n f.flush()\n self.put_object(local_path=f.name, key=location)\n\n def repomd_update_time(self, base_url: str) -> datetime:\n url = f\"{base_url}repodata/repomd.xml\"\n response = self._head_object(key=self._trim_key(remote_path=urlparse(url).path))\n return response[\"LastModified\"]\n\n def _sync_objects(self, temp_dir: str, repo_objects: Iterable[Package], skip_existing: bool):\n sync = functools.partial(self._sync_object, temp_dir, skip_existing)\n start = time.time()\n self.log.info(\"Beginning sync of %s objects.\", len(repo_objects))\n with ThreadPoolExecutor(max_workers=self.max_workers) as executor:\n # We iterate through the generator to pick up and propagate any Exceptions\n for _ in executor.map(sync, repo_objects):\n pass\n elapsed = int(time.time() - start)\n self.log.info(\"Completed syncing %s objects in %s seconds\", len(repo_objects), elapsed)\n\n # pylint: disable=unsubscriptable-object\n def _sync_object(self, temp_dir: str, skip_existing: bool, repo_object: Union[Package, RepodataSection]):\n # When bootstrapping, support backfilling two versions of problematic packages (see below)\n workaround_destination = repo_object.destination.replace(\"+\", \" \")\n if skip_existing:\n if (\"+\" in repo_object.destination and self._object_exists(workaround_destination)) \\\n and self._object_exists(repo_object.destination):\n self.log.debug(\"SKIP: %s\", repo_object.destination)\n return\n\n package_path = download_file(temp_dir=temp_dir, url=repo_object.url, session=self.session)\n validate_checksum(package_path, checksum_type=repo_object.checksum_type, checksum=repo_object.checksum)\n self.put_object(package_path, repo_object.destination)\n if \"+\" in repo_object.destination:\n # Old versions of DNF did not urlencode plus signs in urls, and s3 always does\n # so we need to upload two versions of these packages, one with the + sign unmodified\n # for newer versions of DNF, and one with the + sign replaced with a space for older\n # versions as s3 interprets a space as a + sign.\n # https://bugzilla.redhat.com/show_bug.cgi?id=1817130\n # https://forums.aws.amazon.com/thread.jspa?threadID=55746\n self.log.debug(\n \"Uploading workaround version of package: %s -> %s\", repo_object.destination, workaround_destination\n )\n self.put_object(package_path, key=workaround_destination)\n try:\n os.unlink(package_path)\n except Exception as e: # pylint: disable=broad-except\n self.log.debug(\"Failed to unlink %s: %s\", package_path, e)\n\n def put_object(self, local_path: str, key: str, cache_age=31536000):\n with open(local_path, \"rb\") as package_fp:\n # We need to seek after this call so boto gets the file pointer at the beginning\n md5_header = self._build_md5_header(fp=package_fp)\n package_fp.seek(0)\n\n key = self._trim_key(key)\n self.log.debug(\"PUT: %s\", key)\n self._client.put_object(\n ACL=\"public-read\",\n Bucket=self.bucket_name,\n CacheControl=f\"max-age={cache_age}\",\n Key=key,\n Body=package_fp,\n ContentMD5=md5_header\n )\n\n def delete_subdirectory(self, subdir):\n objects = []\n for s3_object in self.list(subdir):\n objects.append({\"Key\": s3_object[\"Key\"]})\n self._client.delete_objects(Bucket=self.bucket_name, Delete={\"Objects\": objects, \"Quiet\": True})\n\n def exists(self, prefix):\n try:\n self.list(prefix)\n except S3DirectoryNotFound:\n return False\n return True\n\n def list(self, prefix):\n response = self._client.list_objects_v2(Bucket=self.bucket_name, Prefix=prefix)\n if response.get(\"KeyCount\", 0) == 0:\n raise S3DirectoryNotFound(response=response)\n return response[\"Contents\"]\n\n def copy_object(self, source, destination):\n source, destination = self._trim_key(source), self._trim_key(destination)\n self.log.debug(\"COPY: %s -> %s\", source, destination)\n self._client.copy_object(\n Bucket=self.bucket_name,\n CopySource={\n \"Bucket\": self.bucket_name,\n \"Key\": source,\n },\n ACL=\"public-read\",\n Key=destination,\n CacheControl=\"max-age=0\"\n )\n\n def _object_exists(self, key: str) -> bool:\n try:\n self._head_object(key=self._trim_key(key))\n return True\n except botocore.exceptions.ClientError as e:\n if int(e.response[\"Error\"][\"Code\"]) != 404:\n raise\n return False\n\n def _head_object(self, key: str):\n self.log.debug(\"HEAD: %s\", key)\n return self._client.head_object(\n Bucket=self.bucket_name,\n Key=key,\n )\n\n def _trim_key(self, remote_path: str) -> str:\n # Strip the leading / if present otherwise we end up\n # with an extra root directory in s3 which we don't want.\n if remote_path.startswith(\"/\"):\n remote_path = remote_path[1:]\n return remote_path\n\n @property\n def _client(self):\n if self._s3 is None:\n # The boto3 client call is not threadsafe, so only allow calling it from a singe thread at a time\n with lock:\n self._s3 = boto3.client(\n \"s3\",\n region_name=self.bucket_region,\n aws_access_key_id=self.aws_access_key_id,\n aws_secret_access_key=self.aws_secret_access_key,\n )\n return self._s3\n\n def _build_md5_header(self, fp: BinaryIO) -> str:\n \"\"\"\n ContentMD5 (string) -- The base64-encoded 128-bit MD5 digest of the message (without the headers)\n according to RFC 1864. This header can be used as a message integrity check to verify that the data is the same\n data that was originally sent\n https://boto3.amazonaws.com/v1/documentation/api/latest/reference/services/s3.html#S3.Client.put_object\n \"\"\"\n h = hashlib.md5()\n data = fp.read(1000000)\n while data:\n h.update(data)\n data = fp.read(1000000)\n return base64.b64encode(h.digest()).decode(\"utf-8\")\n", "sub_path": "rpm_s3_mirror/s3.py", "file_name": "s3.py", "file_ext": "py", "file_size_in_byte": 10065, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "threading.RLock", "line_number": 24, "usage_type": "call"}, {"api_name": "hashlib.md5", "line_number": 28, "usage_type": "call"}, {"api_name": "rpm_s3_mirror.statsd.StatsClient", "line_number": 44, "usage_type": "name"}, {"api_name": "rpm_s3_mirror.util.get_requests_session", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.getLogger", "line_number": 57, "usage_type": "call"}, {"api_name": "typing.Dict", "line_number": 62, "usage_type": "name"}, {"api_name": "rpm_s3_mirror.repository.RepodataSection", "line_number": 62, "usage_type": "name"}, {"api_name": "typing.Collection", "line_number": 63, "usage_type": "name"}, {"api_name": "rpm_s3_mirror.repository.Package", "line_number": 63, "usage_type": "name"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 66, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 72, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 77, "usage_type": "call"}, {"api_name": "tempfile.TemporaryDirectory", "line_number": 82, "usage_type": "call"}, {"api_name": "rpm_s3_mirror.util.download_file", "line_number": 84, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 85, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 92, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 96, "usage_type": "call"}, {"api_name": "tempfile.NamedTemporaryFile", "line_number": 97, "usage_type": "call"}, {"api_name": "urllib.parse.urlparse", "line_number": 104, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 102, "usage_type": "name"}, {"api_name": "typing.Iterable", "line_number": 107, "usage_type": "name"}, {"api_name": "rpm_s3_mirror.repository.Package", "line_number": 107, "usage_type": "name"}, {"api_name": "functools.partial", "line_number": 108, "usage_type": "call"}, {"api_name": "time.time", "line_number": 109, "usage_type": "call"}, {"api_name": "concurrent.futures.thread.ThreadPoolExecutor", "line_number": 111, "usage_type": "call"}, {"api_name": "time.time", "line_number": 115, "usage_type": "call"}, {"api_name": "typing.Union", "line_number": 119, "usage_type": "name"}, {"api_name": "rpm_s3_mirror.repository.Package", "line_number": 119, "usage_type": "name"}, {"api_name": "rpm_s3_mirror.repository.RepodataSection", "line_number": 119, "usage_type": "name"}, {"api_name": "rpm_s3_mirror.util.download_file", "line_number": 128, "usage_type": "call"}, {"api_name": "rpm_s3_mirror.util.validate_checksum", "line_number": 129, "usage_type": "call"}, {"api_name": "os.unlink", "line_number": 143, "usage_type": "call"}, {"api_name": "botocore.exceptions.exceptions", "line_number": 201, "usage_type": "attribute"}, {"api_name": "botocore.exceptions", "line_number": 201, "usage_type": "name"}, {"api_name": "boto3.client", "line_number": 225, "usage_type": "call"}, {"api_name": "typing.BinaryIO", "line_number": 233, "usage_type": "name"}, {"api_name": "hashlib.md5", "line_number": 240, "usage_type": "call"}, {"api_name": "base64.b64encode", "line_number": 245, "usage_type": "call"}]} +{"seq_id": "374535457", "text": "import torch\nimport torch.nn as nn\n\nfrom .inv_editor import InvEditorBase, Embedding\n\n\n\nclass LevenshteinInvEditor(InvEditorBase):\n \"\"\"the inverse editor from https://arxiv.org/abs/1709.08878\"\"\"\n def __init__(self, token_embed_dim, edit_embed_dim, hidden_size, \n tgt_dict, edit_dict, num_layers=1, pretrained_token_embed=None):\n super(LevenshteinInvEditor, self).__init__(hidden_size)\n\n\n self.hidden_size = hidden_size\n self.padding_idx = tgt_dict.pad()\n num_token_embeddings = len(tgt_dict)\n num_edit_embeddings = len(edit_dict)\n\n if pretrained_token_embed is None:\n self.embed_tokens = Embedding(num_token_embeddings, token_embed_dim, self.padding_idx)\n else:\n self.embed_tokens = pretrained_token_embed\n\n self.embed_edit = Embedding(num_edit_embeddings, edit_embed_dim, self.padding_idx)\n self.num_layers=num_layers\n\n self.lstm = nn.LSTM(\n input_size=token_embed_dim * 2 + edit_embed_dim,\n hidden_size=hidden_size,\n num_layers=self.num_layers,\n bidirectional=True,\n )\n\n\n def forward(self, src_aligned, tgt_aligned, edit_aligned, aligned_length, **kwargs):\n \"\"\"\n Args: \n src_aligned (LongTensor): (batch, seq_len)\n tgt_aligned (LongTensor): (batch, seq_len)\n\n Returns: Tensor1\n Tensor1: the representation with shape [batch, embed_dim]\n \"\"\"\n\n bsz, seqlen = src_aligned.size()\n\n edit_embed = self.embed_edit(edit_aligned)\n src_embed = self.embed_tokens(src_aligned)\n tgt_embed = self.embed_tokens(tgt_aligned)\n\n x = torch.cat((edit_embed, src_embed, tgt_embed), -1)\n\n # B x T x C -> T x B x C\n x = x.transpose(0, 1)\n\n packed_x = nn.utils.rnn.pack_padded_sequence(x, aligned_length.data.tolist(), enforce_sorted=False)\n state_size = 2 * self.num_layers, bsz, self.hidden_size\n\n h0 = x.new_zeros(*state_size)\n c0 = x.new_zeros(*state_size)\n\n packed_outs, (final_hiddens, final_cells) = self.lstm(packed_x, (h0, c0))\n x, _ = nn.utils.rnn.pad_packed_sequence(packed_outs, padding_value=self.padding_idx)\n\n def combine_bidir(outs):\n out = outs.view(self.num_layers, 2, bsz, -1).transpose(1, 2).contiguous()\n return out.view(self.num_layers, bsz, -1)\n \n\n return combine_bidir(final_hiddens)[-1]\n\n @property\n def output_units(self):\n return 2 * self.hidden_size", "sub_path": "sparse_prototype/inv_editor/inv_editor_levenshtein.py", "file_name": "inv_editor_levenshtein.py", "file_ext": "py", "file_size_in_byte": 2522, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "inv_editor.InvEditorBase", "line_number": 8, "usage_type": "name"}, {"api_name": "inv_editor.Embedding", "line_number": 21, "usage_type": "call"}, {"api_name": "inv_editor.Embedding", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn.LSTM", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn.utils.rnn.pack_padded_sequence", "line_number": 57, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 57, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 57, "usage_type": "name"}, {"api_name": "torch.nn.utils.rnn.pad_packed_sequence", "line_number": 64, "usage_type": "call"}, {"api_name": "torch.nn.utils", "line_number": 64, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 64, "usage_type": "name"}]} +{"seq_id": "515686404", "text": "import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport math\nfrom apex import amp\nimport copy\n\nEPS = 1e-8\n\ndef _clones(module, N):\n return nn.ModuleList([copy.deepcopy(module) for i in range(N)])\n\nclass muse(nn.Module):\n def __init__(self, N, L, B, H, P, X, R, C, M):\n super(muse, self).__init__()\n self.N, self.L, self.B, self.H, self.P, self.X, self.R, self.C = N, L, B, H, P, X, R, C\n \n self.encoder = Encoder(L, N)\n self.separator = TemporalConvNet(N, B, H, P, X, R, C, M)\n self.decoder = Decoder(N, L)\n\n for p in self.parameters():\n if p.dim() > 1:\n nn.init.xavier_normal_(p)\n\n def forward(self, mixture, visual):\n mixture_w = self.encoder(mixture)\n est_a_emb, est_mask = self.separator(mixture_w, visual)\n est_source = self.decoder(mixture_w, est_mask)\n\n # T changed after conv1d in encoder, fix it here\n T_origin = mixture.size(-1)\n T_conv = est_source.size(-1)\n est_source = F.pad(est_source, (0, T_origin - T_conv))\n return est_a_emb, est_source\n\nclass Encoder(nn.Module):\n def __init__(self, L, N):\n super(Encoder, self).__init__()\n self.L, self.N = L, N\n self.conv1d_U = nn.Conv1d(1, N, kernel_size=L, stride=L // 2, bias=False)\n\n def forward(self, mixture):\n mixture = torch.unsqueeze(mixture, 1) # [M, 1, T]\n mixture_w = F.relu(self.conv1d_U(mixture)) # [M, N, K]\n return mixture_w\n\nclass Decoder(nn.Module):\n def __init__(self, N, L):\n super(Decoder, self).__init__()\n self.N, self.L = N, L\n self.basis_signals = nn.Linear(N, L, bias=False)\n\n def forward(self, mixture_w, est_mask):\n est_source = mixture_w * est_mask # [M, N, K]\n est_source = torch.transpose(est_source, 2, 1) # [M, K, N]\n est_source = self.basis_signals(est_source) # [M, K, L]\n est_source = overlap_and_add(est_source, self.L//2) # M x C x T\n return est_source\n\n\nclass TemporalConvNet(nn.Module):\n def __init__(self, N, B, H, P, X, R, C, M):\n super(TemporalConvNet, self).__init__()\n self.C = C\n self.layer_norm = ChannelWiseLayerNorm(N)\n self.bottleneck_conv1x1 = nn.Conv1d(N, B, 1, bias=False)\n\n # Audio TCN\n tcn_blocks = []\n tcn_blocks += [nn.Conv1d(B*3, B, 1, bias=False)]\n for x in range(X):\n dilation = 2**x\n padding = (P - 1) * dilation // 2\n tcn_blocks += [TemporalBlock(B, H, P, stride=1,\n padding=padding,\n dilation=dilation)]\n self.tcn = _clones(nn.Sequential(*tcn_blocks), R)\n \n # visual blocks\n ve_blocks = []\n for x in range(5):\n ve_blocks +=[VisualConv1D()]\n self.visual_conv = nn.Sequential(*ve_blocks)\n\n # Audio and visual seprated layers before concatenation\n self.ve_conv1x1 = _clones(nn.Conv1d(512, B, 1, bias=False),R)\n self.ve_conv1x1_SE = _clones(nn.Conv1d(512, B, 1, bias=False),R)\n\n # speaker embedding extraction and classification\n self.se_net=_clones(SpeakerEmbedding(B), R)\n self.audio_linear=_clones(nn.Linear(B, M),R)\n\n # Mask generation layer\n self.mask_conv1x1 = nn.Conv1d(B, N, 1, bias=False)\n\n\n def forward(self, x, visual):\n visual = visual.transpose(1,2)\n visual = self.visual_conv(visual)\n\n x = self.layer_norm(x)\n x = self.bottleneck_conv1x1(x)\n\n mixture = x\n\n batch, B, K = x.size()\n\n est_a_emb=[]\n\n for i in range(len(self.tcn)):\n v = self.ve_conv1x1[i](visual)\n v = F.interpolate(v, (32*v.size()[-1]), mode='linear')\n v = F.pad(v,(0,K-v.size()[-1]))\n v_2 = self.ve_conv1x1_SE[i](visual)\n v_2 = F.interpolate(v_2, (32*v_2.size()[-1]), mode='linear')\n v_2 = F.pad(v_2,(0,K-v_2.size()[-1]))\n a = mixture*F.relu(x)\n a = self.se_net[i](torch.cat((a,v_2),1))\n est_a_emb.append(self.audio_linear[i](a.squeeze()))\n a = torch.repeat_interleave(a, repeats=K, dim=2)\n x = torch.cat((a, x, v),1)\n x = self.tcn[i](x)\n \n x = self.mask_conv1x1(x)\n x = F.relu(x)\n est_a_emb = torch.stack(est_a_emb)\n return est_a_emb, x\n\nclass SpeakerEmbedding(nn.Module):\n def __init__(self, B, R=3, H=256):\n super(SpeakerEmbedding, self).__init__()\n self.conv_proj = nn.Conv1d(B*2, B, 1, bias=False)\n Conv_1=nn.Conv1d(B, H, 1, bias=False)\n norm_1=nn.BatchNorm1d(H)\n prelu_1=nn.PReLU()\n Conv_2=nn.Conv1d(H, B, 1, bias=False)\n norm_2=nn.BatchNorm1d(B)\n self.resnet=_clones(nn.Sequential(Conv_1, norm_1,\\\n prelu_1, Conv_2, norm_2), R)\n self.prelu=_clones(nn.PReLU(),R)\n self.maxPool=_clones(nn.AvgPool1d(3),R)\n\n self.conv=nn.Conv1d(B,B,1)\n self.avgPool=nn.AdaptiveAvgPool1d(1)\n\n def forward(self, x):\n x = self.conv_proj(x)\n for i in range(len(self.resnet)):\n residual = x\n x = self.resnet[i](x)\n x = self.prelu[i](x+residual)\n x = self.maxPool[i](x)\n\n x = self.conv(x)\n x = self.avgPool(x)\n\n return x\n\n\n\nclass VisualConv1D(nn.Module):\n def __init__(self):\n super(VisualConv1D, self).__init__()\n relu = nn.ReLU()\n norm_1 = nn.BatchNorm1d(512)\n dsconv = nn.Conv1d(512, 512, 3, stride=1, padding=1,dilation=1, groups=512, bias=False)\n prelu = nn.PReLU()\n norm_2 = nn.BatchNorm1d(512)\n pw_conv = nn.Conv1d(512, 512, 1, bias=False)\n\n self.net = nn.Sequential(relu, norm_1 ,dsconv, prelu, norm_2, pw_conv)\n\n def forward(self, x):\n out = self.net(x)\n return out + x\n\nclass TemporalBlock(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size,\n stride, padding, dilation):\n super(TemporalBlock, self).__init__()\n conv1x1 = nn.Conv1d(in_channels, out_channels, 1, bias=False)\n prelu = nn.PReLU()\n norm = GlobalLayerNorm(out_channels)\n dsconv = DepthwiseSeparableConv(out_channels, in_channels, kernel_size,\n stride, padding, dilation)\n # Put together\n self.net = nn.Sequential(conv1x1, prelu, norm, dsconv)\n\n def forward(self, x):\n\n residual = x\n out = self.net(x)\n return out + residual # look like w/o F.relu is better than w/ F.relu\n\n\nclass DepthwiseSeparableConv(nn.Module):\n def __init__(self, in_channels, out_channels, kernel_size,\n stride, padding, dilation):\n super(DepthwiseSeparableConv, self).__init__()\n depthwise_conv = nn.Conv1d(in_channels, in_channels, kernel_size,\n stride=stride, padding=padding,\n dilation=dilation, groups=in_channels,\n bias=False)\n\n prelu = nn.PReLU()\n norm = GlobalLayerNorm(in_channels)\n pointwise_conv = nn.Conv1d(in_channels, out_channels, 1, bias=False)\n self.net = nn.Sequential(depthwise_conv, prelu, norm,\n pointwise_conv)\n\n def forward(self, x):\n return self.net(x)\n\nclass ChannelWiseLayerNorm(nn.LayerNorm):\n @amp.float_function\n def __init__(self, *args, **kwargs):\n super(ChannelWiseLayerNorm, self).__init__(*args, **kwargs)\n\n @amp.float_function\n def forward(self, x):\n if x.dim() != 3:\n raise RuntimeError(\"{} accept 3D tensor as input\".format(\n self.__name__))\n # N x C x T => N x T x C\n x = torch.transpose(x, 1, 2)\n # LN\n x = super().forward(x)\n # N x C x T => N x T x C\n x = torch.transpose(x, 1, 2)\n return x\n\n\nclass GlobalLayerNorm(nn.Module):\n \"\"\"Global Layer Normalization (gLN)\"\"\"\n @amp.float_function\n def __init__(self, channel_size):\n super(GlobalLayerNorm, self).__init__()\n self.gamma = nn.Parameter(torch.Tensor(1, channel_size, 1)) # [1, N, 1]\n self.beta = nn.Parameter(torch.Tensor(1, channel_size,1 )) # [1, N, 1]\n self.reset_parameters()\n\n @amp.float_function\n def reset_parameters(self):\n self.gamma.data.fill_(1)\n self.beta.data.zero_()\n\n @amp.float_function\n def forward(self, y):\n \"\"\"\n Args:\n y: [M, N, K], M is batch size, N is channel size, K is length\n Returns:\n gLN_y: [M, N, K]\n \"\"\"\n # TODO: in torch 1.0, torch.mean() support dim list\n mean = y.mean(dim=1, keepdim=True).mean(dim=2, keepdim=True) #[M, 1, 1]\n var = (torch.pow(y-mean, 2)).mean(dim=1, keepdim=True).mean(dim=2, keepdim=True)\n gLN_y = self.gamma * (y - mean) / torch.pow(var + EPS, 0.5) + self.beta\n return gLN_y\n\n@amp.float_function\ndef overlap_and_add(signal, frame_step):\n \"\"\"Reconstructs a signal from a framed representation.\n Adds potentially overlapping frames of a signal with shape\n `[..., frames, frame_length]`, offsetting subsequent frames by `frame_step`.\n The resulting tensor has shape `[..., output_size]` where\n output_size = (frames - 1) * frame_step + frame_length\n Args:\n signal: A [..., frames, frame_length] Tensor. All dimensions may be unknown, and rank must be at least 2.\n frame_step: An integer denoting overlap offsets. Must be less than or equal to frame_length.\n Returns:\n A Tensor with shape [..., output_size] containing the overlap-added frames of signal's inner-most two dimensions.\n output_size = (frames - 1) * frame_step + frame_length\n Based on https://github.com/tensorflow/tensorflow/blob/r1.12/tensorflow/contrib/signal/python/ops/reconstruction_ops.py\n \"\"\"\n outer_dimensions = signal.size()[:-2]\n frames, frame_length = signal.size()[-2:]\n\n subframe_length = math.gcd(frame_length, frame_step) # gcd=Greatest Common Divisor\n subframe_step = frame_step // subframe_length\n subframes_per_frame = frame_length // subframe_length\n output_size = frame_step * (frames - 1) + frame_length\n output_subframes = output_size // subframe_length\n\n subframe_signal = signal.view(*outer_dimensions, -1, subframe_length)\n\n frame = torch.arange(0, output_subframes).unfold(0, subframes_per_frame, subframe_step)\n frame = signal.new_tensor(frame).long() # signal may in GPU or CPU\n frame = frame.contiguous().view(-1)\n\n result = signal.new_zeros(*outer_dimensions, output_subframes, subframe_length)\n result.index_add_(-2, frame, subframe_signal)\n result = result.view(*outer_dimensions, -1)\n return result\n", "sub_path": "src/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 10805, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.nn.ModuleList", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "copy.deepcopy", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 13, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.init.xavier_normal_", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn.init", "line_number": 24, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.functional.pad", "line_number": 34, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 37, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 37, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 41, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 41, "usage_type": "name"}, {"api_name": "torch.unsqueeze", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 45, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 45, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 48, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 52, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 52, "usage_type": "name"}, {"api_name": "torch.transpose", "line_number": 56, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 62, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 62, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 67, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 67, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 71, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 71, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 78, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 78, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 84, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 84, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 87, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 87, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 88, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 88, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 92, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 92, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 95, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 95, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 113, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 113, "usage_type": "name"}, {"api_name": "torch.nn.functional.pad", "line_number": 114, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 114, "usage_type": "name"}, {"api_name": "torch.nn.functional.interpolate", "line_number": 116, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 116, "usage_type": "name"}, {"api_name": "torch.nn.functional.pad", "line_number": 117, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 117, "usage_type": "name"}, {"api_name": "torch.nn.functional.relu", "line_number": 118, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 118, "usage_type": "name"}, {"api_name": "torch.cat", "line_number": 119, "usage_type": "call"}, {"api_name": "torch.repeat_interleave", "line_number": 121, "usage_type": "call"}, {"api_name": "torch.cat", "line_number": 122, "usage_type": "call"}, {"api_name": "torch.nn.functional.relu", "line_number": 126, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 126, "usage_type": "name"}, {"api_name": "torch.stack", "line_number": 127, "usage_type": "call"}, {"api_name": "torch.nn.Module", "line_number": 130, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 130, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 133, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 133, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 134, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 134, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 135, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 135, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 136, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 136, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 137, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 137, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 138, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 138, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 139, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 139, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 141, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 141, "usage_type": "name"}, {"api_name": "torch.nn.AvgPool1d", "line_number": 142, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 142, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 144, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 144, "usage_type": "name"}, {"api_name": "torch.nn.AdaptiveAvgPool1d", "line_number": 145, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 145, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 162, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 162, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 165, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 165, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 166, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 166, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 167, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 167, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 168, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 168, "usage_type": "name"}, {"api_name": "torch.nn.BatchNorm1d", "line_number": 169, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 169, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 170, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 170, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 172, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 172, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 178, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 178, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 182, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 182, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 183, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 183, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 188, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 188, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 197, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 197, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 201, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 201, "usage_type": "name"}, {"api_name": "torch.nn.PReLU", "line_number": 206, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 206, "usage_type": "name"}, {"api_name": "torch.nn.Conv1d", "line_number": 208, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 208, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 209, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 209, "usage_type": "name"}, {"api_name": "torch.nn.LayerNorm", "line_number": 215, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 215, "usage_type": "name"}, {"api_name": "apex.amp.float_function", "line_number": 216, "usage_type": "attribute"}, {"api_name": "apex.amp", "line_number": 216, "usage_type": "name"}, {"api_name": "torch.transpose", "line_number": 226, "usage_type": "call"}, {"api_name": "torch.transpose", "line_number": 230, "usage_type": "call"}, {"api_name": "apex.amp.float_function", "line_number": 220, "usage_type": "attribute"}, {"api_name": "apex.amp", "line_number": 220, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 234, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 234, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 239, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 239, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 240, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 240, "usage_type": "name"}, {"api_name": "torch.Tensor", "line_number": 240, "usage_type": "call"}, {"api_name": "apex.amp.float_function", "line_number": 236, "usage_type": "attribute"}, {"api_name": "apex.amp", "line_number": 236, "usage_type": "name"}, {"api_name": "apex.amp.float_function", "line_number": 243, "usage_type": "attribute"}, {"api_name": "apex.amp", "line_number": 243, "usage_type": "name"}, {"api_name": "torch.pow", "line_number": 258, "usage_type": "call"}, {"api_name": "torch.pow", "line_number": 259, "usage_type": "call"}, {"api_name": "apex.amp.float_function", "line_number": 248, "usage_type": "attribute"}, {"api_name": "apex.amp", "line_number": 248, "usage_type": "name"}, {"api_name": "math.gcd", "line_number": 280, "usage_type": "call"}, {"api_name": "torch.arange", "line_number": 288, "usage_type": "call"}, {"api_name": "apex.amp.float_function", "line_number": 262, "usage_type": "attribute"}, {"api_name": "apex.amp", "line_number": 262, "usage_type": "name"}]} +{"seq_id": "300848986", "text": "#Segundo Punto Libre\nimport sys\nimport numpy as np\nimport matplotlib.pyplot as plt\n# sys.path.insert(0, '../')\n\nimport oscillator.oscillator as os\nimport forces.forces as fr\nimport solver.solver as sol\n\ng = 9.8\nl = 1\nm = 1\n\ndef restoring_force(state, params):\n x, L, v, vr, t = state\n w0 = params\n dxdt = v\n dvdt = -w0**2 * np.sin(x)\n drdt = vr\n dvrdt = 0\n return dxdt, drdt, dvdt, dvrdt, 1.0\n\ndef energy(v, x):\n ek = (1/2) * m * (l**2) * (v**2)\n ep = m * g * l * (1 - np.cos(x))\n et = ek + ep\n return et, ek, ep\n\ndef integrate(obj):\n xpos, vpos, tpos= [], [], []\n _, _, _, _, tc = obj.objs.get_state()\n while tc < 7: #9.55:\n xc, _, vc, _, tc = obj.objs.get_state()\n xpos.append(xc)\n vpos.append(vc)\n tpos.append(tc)\n obj.do_step()\n return tpos, xpos, vpos\n\ndef energies(tpos, xpos, vpos):\n ek, ep, em= [], [], []\n for i in range(len(tpos)):\n ener_to, ener_ci, ener_po = energy(vpos[i], xpos[i])\n ek.append(ener_ci)\n ep.append(ener_po)\n em.append(ener_to)\n return ek, ep, em\n\n\nx0, v0, w0, t0 = 0.2, 0., 3., 0\nsim_params = w0\ndeltat = 0.05\n\nm1 = \"Euler\"\nm2 = \"Euler-Cromer\"\nm3 = \"Midpoint\"\n\nnum_method = m2\n\npendulo = os.Oscillator(x0, v0, w0, t0, \"P1\")\npendulo_force = fr.Forces(restoring_force, sim_params)\npendulo.set_force(pendulo_force)\neuler = sol.Solver(pendulo, num_method, deltat)\ntvac, xvac, vac= integrate(euler)\nen_ci, en_po, en_to = energies(tvac, xvac, vac)\n\ndelta_e = en_to[-1] - en_to[0]\nprint(\"Theta: \", x0, \", v: \", v0, \", dt: \", deltat, \", de: \", delta_e)\nprint(\"Energia In: \", en_to[0], \", Energia Fin: \", en_to[-1])\nprint(\"Energia Cinetica In: \", en_ci[0], \", Energia Cinetica Fin: \", en_ci[-1])\nprint(\"Energia Potencial In: \", en_po[0], \", Energia Potencial Fin: \", en_po[-1])\n\ndeltae = []\nfor i in range(len(en_to)):\n deltae.append(en_to[i] - en_to[0])\n\n# fig, ax = plt.subplots()\n# ax.plot(tvac, xvac, '--', label='Angle')\n# ax.plot(tvac, vac, '-.', label='Velocity')\n# ax.set(xlabel='time (AU)', ylabel='State (AU)')\n# ax.grid()\n\n\nfig, ax = plt.subplots()\n# ax.plot(tvac, en_to, ls='--', c = 'blueviolet', label='Total Energy')\nax.plot(tvac, en_ci, ls='--', c ='royalblue', label='Cinetic Energy')\nax.plot(tvac, en_po, ls='--', c = 'deeppink', label='Potecial Energy')\nax.set(xlabel='time (AU)', ylabel='Energy (J)')\nax.grid()\n", "sub_path": "libre_p2.py", "file_name": "libre_p2.py", "file_ext": "py", "file_size_in_byte": 2372, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.sin", "line_number": 19, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 26, "usage_type": "call"}, {"api_name": "oscillator.oscillator.Oscillator", "line_number": 61, "usage_type": "call"}, {"api_name": "oscillator.oscillator", "line_number": 61, "usage_type": "name"}, {"api_name": "forces.forces.Forces", "line_number": 62, "usage_type": "call"}, {"api_name": "forces.forces", "line_number": 62, "usage_type": "name"}, {"api_name": "solver.solver.Solver", "line_number": 64, "usage_type": "call"}, {"api_name": "solver.solver", "line_number": 64, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}]} +{"seq_id": "298069014", "text": "# Copyright (c) 2012 The Chromium Authors. All rights reserved.\n# Use of this source code is governed by a BSD-style license that can be\n# found in the LICENSE file.\n\nimport json\nimport logging\nimport operator\n\nfrom appengine_url_fetcher import AppEngineUrlFetcher\nimport url_constants\n\nclass BranchUtility(object):\n def __init__(self, fetch_url, fetcher, object_store_creator):\n self._fetch_url = fetch_url\n self._fetcher = fetcher\n # BranchUtility is obviously cross-channel, so set the channel to None.\n self._object_store = object_store_creator.Create(BranchUtility,\n channel=None)\n\n @staticmethod\n def Create(object_store_creator):\n return BranchUtility(url_constants.OMAHA_PROXY_URL,\n AppEngineUrlFetcher(),\n object_store_creator)\n\n @staticmethod\n def GetAllChannelNames():\n return ['stable', 'beta', 'dev', 'trunk']\n\n @staticmethod\n def SplitChannelNameFromPath(path):\n \"\"\"Splits the channel name out of |path|, returning the tuple\n (channel_name, real_path). If the channel cannot be determined then returns\n (None, path).\n \"\"\"\n if '/' in path:\n first, second = path.split('/', 1)\n else:\n first, second = (path, '')\n if first in ['trunk', 'dev', 'beta', 'stable']:\n return (first, second)\n return (None, path)\n\n def GetBranchForChannel(self, channel_name):\n \"\"\"Returns the branch number for a channel name.\n \"\"\"\n if channel_name == 'trunk':\n return 'trunk'\n\n branch_number = self._object_store.Get(channel_name).Get()\n if branch_number is not None:\n return branch_number\n\n try:\n version_json = json.loads(self._fetcher.Fetch(self._fetch_url).content)\n except Exception as e:\n # This can happen if omahaproxy is misbehaving, which we've seen before.\n # Quick hack fix: just serve from trunk until it's fixed.\n logging.error('Failed to fetch or parse branch from omahaproxy: %s! '\n 'Falling back to \"trunk\".' % e)\n return 'trunk'\n\n branch_numbers = {}\n for entry in version_json:\n if entry['os'] not in ['win', 'linux', 'mac', 'cros']:\n continue\n for version in entry['versions']:\n if version['channel'] != channel_name:\n continue\n branch = version['version'].split('.')[2]\n if branch not in branch_numbers:\n branch_numbers[branch] = 0\n else:\n branch_numbers[branch] += 1\n\n sorted_branches = sorted(branch_numbers.iteritems(),\n None,\n operator.itemgetter(1),\n True)\n self._object_store.Set(channel_name, sorted_branches[0][0])\n\n return sorted_branches[0][0]\n", "sub_path": "chrome/common/extensions/docs/server2/branch_utility.py", "file_name": "branch_utility.py", "file_ext": "py", "file_size_in_byte": 2775, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "url_constants.OMAHA_PROXY_URL", "line_number": 22, "usage_type": "attribute"}, {"api_name": "appengine_url_fetcher.AppEngineUrlFetcher", "line_number": 23, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 55, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 59, "usage_type": "call"}, {"api_name": "operator.itemgetter", "line_number": 78, "usage_type": "call"}]} +{"seq_id": "426927198", "text": "#!/usr/bin/env python\n\n\"\"\"\nAuthor : Tom Dougherty\nDate : 2018 June 25\nDescription : Script to collect data from all FIMO runs\n and create graphs in matplotlib to display\n abundance of TF motifs in genes/protogenes/nongenes.\n\"\"\"\n\nimport numpy as np\nimport matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport seaborn as sns\nimport scipy.stats as stats\n\nfrom graphHelper import *\n\nLENGTH = 500 # Length of promoter to analyze\n\n# Import numpy arrays from fimoData.py\nwith open('Data/fisherData{}.npy'.format(LENGTH), 'rb') as f:\n fisher_data = np.load(f)\nwith open('Data/fisherExact{}.npy'.format(LENGTH), 'rb') as f:\n fisher_exact = np.load(f)\nwith open('Data/proportions{}.npy'.format(LENGTH), 'rb') as f:\n proportions = np.load(f)\nwith open('Data/TFs.npy', 'rb') as f:\n TFs = np.load(f)\nwith open('Data/pvalFisherData{}.npy'.format(LENGTH), 'rb') as f:\n pval_fisher_data = np.load(f)\nwith open('Data/pvalFisherExact{}.npy'.format(LENGTH), 'rb') as f:\n pval_fisher_exact = np.load(f)\n\n# Plot heat map of motif presence fractions\n# Make array (from fisher_data) of ratios of promoters with motifs / total promoters\nfisher_ratios = fisher_data[:,:,0] / (fisher_data[:,:,0] + fisher_data[:,:,1])\n# Sort by increasing ratios for genes\nsorter = fisher_ratios[:,0].argsort()\nfisher_ratios = fisher_ratios[sorter]\n# Create (sorted) labels for heatmap\nTFs_heatmap = np.asarray(TFs)[sorter]\ncategories = ['gene', 'proto-gene', 'non-gene', 'random regions']\n\n# fig, ax = plt.subplots()\n# im = ax.imshow(fisher_ratios, interpolation='none', aspect='auto')\n# # Show all ticks\n# ax.set_xticks(np.arange(len(categories)))\n# ax.set_yticks(np.arange(len(TFs)))\n# # Label them with the respective list entries\n# ax.set_xticklabels(categories)\n# ax.set_yticklabels(TFs, size=5)\n# # Rotate the tick labels and set their alignment.\n# plt.setp(ax.get_xticklabels(), rotation=0, ha=\"right\",\n# rotation_mode=\"anchor\")\n# # Loop over data dimensions and create text annotations.\n# fisher_text = fisher_ratios.astype('\", connectionstyle=\"arc3,rad=.2\"))\nplt.legend(loc='upper left', frameon=False)\nplt.show()\n\n\n# Creating fraction plots for various (FIMO) p-value cutoffs\nfig, axes = plt.subplots(2, 5)\nPVALS = [1e-12, 1e-11, 1e-10, 1e-9, 1e-8, 1e-7, 1e-6, 1e-5, 1e-4, 1e-3]\nfor i, ax in enumerate(axes.flatten()):\n pval_fisher_ratios = pval_fisher_data[i,:,:,0] / (pval_fisher_data[i,:,:,0] + pval_fisher_data[i,:,:,1])\n a = pval_fisher_ratios[sorter] # Sort everything by the order genes are in when all data is included\n labels = TFs_heatmap\n ax.plot(a[:,0], label='gene')\n ax.plot(a[:,1], label='proto-gene')\n ax.plot(a[:,2], label='non-gene')\n ax.plot(a[:,3], label='random regions')\n #ax.xticks(range(len(a)), labels, rotation=45, size=5, rotation_mode=\"anchor\")\n #ax.legend(loc='upper left', frameon=False)\n plt.xlabel('Transcription factors')\n plt.ylabel('Percentage of promoters with TF motif')\n ax.set_title('motifs matches with p-value < {}'.format(PVALS[i]))\n\nplt.legend(loc='upper left', frameon=False)\nplt.show()\n\n\n\n# Heatmap of significance of Fisher's exact test\nsignificance = fisher_exact[:,:,1][sorter]\nfig, ax = plt.subplots()\ncomparisons = ['genes vs. protogenes', 'genes vs. nongenes', 'protogenes vs. nongenes',\n'genes vs. random', 'protogenes vs. random', 'nongenes vs. random']\nim, cbar = heatmap(significance, TFs_heatmap, comparisons, ax=ax,\n cmap=\"YlGn\", cbarlabel=\"P-value\",\n interpolation='none', aspect='auto')\n#im, cbar = heatmap(fisher_exact, TFs_heatmap, categories, ax=ax[1],\n# cmap=\"YlGn\", cbarlabel=\"Significance\")\ntexts = annotate_heatmap(im, valfmt=\"{x:.3f}\", size=5, threshold=0.05)\n# fig.tight_layout()\nplt.show()\n\n# Heatmap of odds ration from Fisher's exact test\nodds = fisher_exact[:,:,0][sorter]\nfig, ax = plt.subplots()\ncomparisons = ['genes vs. protogenes', 'genes vs. nongenes', 'protogenes vs. nongenes',\n'genes vs. random', 'protogenes vs. random', 'nongenes vs. random']\nim, cbar = heatmap(odds, TFs_heatmap, comparisons, ax=ax,\n cmap=\"YlGn\", cbarlabel=\"P-value\",\n interpolation='none', aspect='auto')\n#im, cbar = heatmap(fisher_exact, TFs_heatmap, categories, ax=ax[1],\n# cmap=\"YlGn\", cbarlabel=\"Significance\")\ntexts = annotate_heatmap(im, valfmt=\"{x:.3f}\", size=5, threshold=0.05)\n# fig.tight_layout()\nplt.show()", "sub_path": "FIMO/fimoGraphs.py", "file_name": "fimoGraphs.py", "file_ext": "py", "file_size_in_byte": 7602, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.load", "line_number": 23, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 27, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 29, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 31, "usage_type": "call"}, {"api_name": "numpy.load", "line_number": 33, "usage_type": "call"}, {"api_name": "numpy.asarray", "line_number": 42, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 76, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 76, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 82, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 82, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 83, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 83, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 84, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 84, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 85, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 87, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 87, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 88, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 88, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 90, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 90, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 93, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 93, "usage_type": "name"}, {"api_name": "numpy.corrcoef", "line_number": 94, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 95, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 95, "usage_type": "name"}, {"api_name": "numpy.corrcoef", "line_number": 96, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 97, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 97, "usage_type": "name"}, {"api_name": "numpy.corrcoef", "line_number": 98, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 104, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 104, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 111, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 111, "usage_type": "name"}, {"api_name": "numpy.append", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.where", "line_number": 120, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 121, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 121, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 122, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 122, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 123, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 123, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 124, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 124, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 125, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 125, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xticks", "line_number": 126, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 126, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 128, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 128, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.annotate", "line_number": 129, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 129, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 133, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 133, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 134, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 134, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 138, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 138, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 150, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 150, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 151, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 151, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.legend", "line_number": 154, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 154, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 155, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 155, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 161, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 171, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 171, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 175, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 175, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 185, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 185, "usage_type": "name"}]} +{"seq_id": "284630495", "text": "import torch\nfrom torch import nn\n\n\nclass NetV1(nn.Module):\n\n def __init__(self):\n super(NetV1, self).__init__()\n self.layer = nn.Sequential(\n nn.Linear(784,100),\n nn.ReLU(),\n nn.Linear(100,10),\n nn.Softmax(dim=1)\n )\n\n def forward(self,x):\n return self.layer(x)\n\nclass NetV2(nn.Sequential):\n def __init__(self):\n super(NetV2, self).__init__(\n nn.Linear(784,100,),\n nn.ReLU(),\n nn.Linear(100,10),\n nn.Softmax(dim=1)\n )\n\nclass NetV3(nn.Module):\n\n def __init__(self):\n super(NetV3, self).__init__()\n self.f1 = nn.Linear\n self.relu = nn.ReLU\n self.softmax = nn.Softmax\n\n def forward(self,x):\n h = self.f1(784,100)(x)\n h = self.relu()(h)\n h = self.f1(100,10)(h)\n h = self.softmax(dim=1)(h)\n return h\n\nclass NetV4(nn.Module):\n\n def __init__(self):\n super(NetV4, self).__init__()\n self.w = nn.Parameter(torch.randn(784,100))\n self.w1 = nn.Parameter(torch.randn(100,10))\n\n def forward(self,x):\n h = x@self.w\n h = h@self.w1\n h = torch.exp(h)\n z = torch.sum(h,dim=1,keepdim=True)\n return h/z\n\n\n\nif __name__ == '__main__':\n net = NetV4()\n x = torch.randn(1,784)\n y = net(x)\n print(y.shape)", "sub_path": "DeeplearningStudy/MLP/net.py", "file_name": "net.py", "file_ext": "py", "file_size_in_byte": 1362, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.nn.Module", "line_number": 5, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 5, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 9, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 9, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 10, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 10, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 11, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 13, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 13, "usage_type": "name"}, {"api_name": "torch.nn.Sequential", "line_number": 19, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 19, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 22, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 22, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 23, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 23, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 24, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 24, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 25, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 25, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 28, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 28, "usage_type": "name"}, {"api_name": "torch.nn.Linear", "line_number": 32, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 32, "usage_type": "name"}, {"api_name": "torch.nn.ReLU", "line_number": 33, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 33, "usage_type": "name"}, {"api_name": "torch.nn.Softmax", "line_number": 34, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 34, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 43, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 43, "usage_type": "name"}, {"api_name": "torch.nn.Parameter", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 47, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 47, "usage_type": "call"}, {"api_name": "torch.nn.Parameter", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 48, "usage_type": "name"}, {"api_name": "torch.randn", "line_number": 48, "usage_type": "call"}, {"api_name": "torch.exp", "line_number": 53, "usage_type": "call"}, {"api_name": "torch.sum", "line_number": 54, "usage_type": "call"}, {"api_name": "torch.randn", "line_number": 61, "usage_type": "call"}]} +{"seq_id": "311254171", "text": "from __future__ import absolute_import\nfrom __future__ import print_function\n\nimport unittest2 as unittest\nimport os\n\nimport numpy as np\nfrom pymatgen.util.testing import PymatgenTest\nfrom pymatgen.core.surface import generate_all_slabs\nfrom pymatgen.analysis.adsorption import *\nfrom pymatgen.symmetry.analyzer import SpacegroupAnalyzer\nfrom pymatgen import Structure, Lattice\nimport json\nfrom six.moves import zip\n\ntest_dir = os.path.join(os.path.dirname(__file__), \"..\", \"..\", \"..\", \"..\",\n 'test_files')\n\n\nclass AdsorbateSiteFinderTest(PymatgenTest):\n def setUp(self):\n self.structure = Structure.from_spacegroup(\"Fm-3m\", Lattice.cubic(3.5),\n [\"Ni\"], [[0, 0, 0]])\n slabs = generate_all_slabs(self.structure, max_index=2,\n min_slab_size=6.0, min_vacuum_size=15.0,\n max_normal_search=1, center_slab=True)\n self.slab_dict = {''.join([str(i) for i in slab.miller_index]):\n slab for slab in slabs}\n self.asf_211 = AdsorbateSiteFinder(self.slab_dict[\"211\"])\n self.asf_100 = AdsorbateSiteFinder(self.slab_dict[\"100\"])\n self.asf_111 = AdsorbateSiteFinder(self.slab_dict[\"111\"])\n self.asf_110 = AdsorbateSiteFinder(self.slab_dict[\"110\"])\n\n def test_init(self):\n asf_100 = AdsorbateSiteFinder(self.slab_dict[\"100\"])\n asf_111 = AdsorbateSiteFinder(self.slab_dict[\"111\"])\n\n def test_from_bulk_and_miller(self):\n asf = AdsorbateSiteFinder.from_bulk_and_miller(self.structure, (1, 1, 1))\n sites = asf.find_adsorption_sites()\n self.assertEqual(len(sites), 4)\n asf = AdsorbateSiteFinder.from_bulk_and_miller(self.structure, (1, 0, 0))\n sites = asf.find_adsorption_sites()\n self.assertEqual(len(sites), 3)\n asf = AdsorbateSiteFinder.from_bulk_and_miller(self.structure, (1, 1, 0),\n undercoord_threshold=0.1)\n self.assertEqual(len(asf.surface_sites), 1)\n\n def test_find_adsorption_sites(self):\n sites = self.asf_100.find_adsorption_sites()\n self.assertEqual(len(sites), 3)\n sites = self.asf_100.find_adsorption_sites(positions=\"bridge\")\n self.assertEqual(len(sites), 2)\n sites = self.asf_111.find_adsorption_sites()\n self.assertEqual(len(sites), 4)\n sites = self.asf_110.find_adsorption_sites()\n self.assertEqual(len(sites), 4)\n sites = self.asf_211.find_adsorption_sites()\n\n def test_functions(self):\n slab = self.slab_dict[\"111\"]\n rot = get_rot(slab)\n reoriented = reorient_z(slab)\n self.assertArrayAlmostEqual(slab.frac_coords[0],\n cart_to_frac(slab.lattice, \n slab.cart_coords[0]))\n self.assertArrayAlmostEqual(slab.cart_coords[0],\n frac_to_cart(slab.lattice,\n slab.frac_coords[0]))\n\nif __name__ == '__main__':\n unittest.main()\n", "sub_path": "pymatgen/analysis/tests/test_adsorption.py", "file_name": "test_adsorption.py", "file_ext": "py", "file_size_in_byte": 3135, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 16, "usage_type": "call"}, {"api_name": "os.path", "line_number": 16, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 16, "usage_type": "call"}, {"api_name": "pymatgen.util.testing.PymatgenTest", "line_number": 20, "usage_type": "name"}, {"api_name": "pymatgen.Structure.from_spacegroup", "line_number": 22, "usage_type": "call"}, {"api_name": "pymatgen.Structure", "line_number": 22, "usage_type": "name"}, {"api_name": "pymatgen.Lattice.cubic", "line_number": 22, "usage_type": "call"}, {"api_name": "pymatgen.Lattice", "line_number": 22, "usage_type": "name"}, {"api_name": "pymatgen.core.surface.generate_all_slabs", "line_number": 24, "usage_type": "call"}, {"api_name": "unittest2.main", "line_number": 72, "usage_type": "call"}]} +{"seq_id": "64599440", "text": "import pandas as pd\nimport numpy as np\nimport plotly.plotly as py\nimport plotly.graph_objs as go\n \n# Import data from csv\ndf = pd.read_csv('results.csv')\ndf.head()\n\ntrace = go.Scatter(\n\tx=df['time'], y=df['usd'], name='testplot'\n\t)\n\nlayout = go.Layout(\n\ttitle='Adaptive ETH Short Analysis',\n\n\txaxis=dict(\n autorange=True,\n showgrid=True,\n zeroline=False,\n showline=False,\n autotick=True,\n ticks='',\n showticklabels=False\n ),\n \t# entry point 1\n\n \t# exit point 1\n\n \t# entry point 2\n\n \t# exit point 2\n \t#height = 600,\n \t#width = 1400\n\t)\n\nrender = go.Figure(data=[trace], layout=layout)\n\n# render and publish to plot.ly\npy.plot(render, filename='eth demo', sharing='public') ", "sub_path": "demo/demoPlot.py", "file_name": "demoPlot.py", "file_ext": "py", "file_size_in_byte": 753, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pandas.read_csv", "line_number": 7, "usage_type": "call"}, {"api_name": "plotly.graph_objs.Scatter", "line_number": 10, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 10, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Layout", "line_number": 14, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 14, "usage_type": "name"}, {"api_name": "plotly.graph_objs.Figure", "line_number": 37, "usage_type": "call"}, {"api_name": "plotly.graph_objs", "line_number": 37, "usage_type": "name"}, {"api_name": "plotly.plotly.plot", "line_number": 40, "usage_type": "call"}, {"api_name": "plotly.plotly", "line_number": 40, "usage_type": "name"}]} +{"seq_id": "76205798", "text": "import json\nfrom datetime import timedelta, datetime\n\nimport numpy as np\nimport pandas as pd\nimport requests\nfrom forecast import (\n addMinutes,\n addMonthOfYear,\n add_day_of_week,\n loadModel,\n get_split_indexes,\n)\nfrom forecast_conf import ForecastConfig\nfrom forecast_load_conf import ForecastLoadConfig\nfrom forecast_pv_conf import ForecastPvConfig\nfrom sklearn.externals import joblib\nfrom util import getStepsize, invertScaler, constructTimeStamps\n\nFROM_MEGAWATTHOURS_TO_KILOWATTHOURS = 1000\n\n\nclass NetworkException(Exception):\n pass\n\n\ndef getNinja(filePath, timestamps, offset=timedelta(days=0)):\n with open(filePath, \"r\", encoding=\"utf-8\") as dataFile:\n [dataFile.readline() for i in range(3)]\n data = pd.read_csv(\n dataFile, parse_dates=[\"time\", \"local_time\"], index_col=\"local_time\"\n )\n data = data.loc[\n timestamps[0] + offset : timestamps[-1] + offset + getStepsize(timestamps)\n ]\n origStepsize = getStepsize(data.index)\n wantedStepsize = getStepsize(timestamps)\n if origStepsize > wantedStepsize:\n assert (origStepsize / wantedStepsize).is_integer()\n data = data.resample(wantedStepsize).ffill()\n elif origStepsize < wantedStepsize:\n data = _dropUnfittingValuesAtEndForDownSampling(\n origStepsize, wantedStepsize, timestamps, data\n )\n data = data.resample(wantedStepsize).mean()\n data = data.loc[timestamps[0] + offset : timestamps[-1] + offset]\n\n return data[\"electricity\"]\n\n\ndef getNinjaPvApi(lat, long, timestamps):\n renewNinja = RenewNinja()\n return renewNinja.getPvData(\n lat, long, str(timestamps[0].date()), str(timestamps[-1].date())\n )\n\n\ndef getNinjaWindApi(lat, long, timestamps):\n renewNinja = RenewNinja()\n return renewNinja.getWindData(\n lat, long, str(timestamps[0].date()), str(timestamps[-1].date())\n )\n\n\nclass RenewNinja:\n \"\"\"\n Class to query https://www.renewables.ninja/ API to get pv and wind\n output power based on their dataset + simulation\n\n ...\n\n Attributes\n ----------\n token : API token to access renewables ninja API\n api_base : str\n url of the api of renewables ninja\n s : requests.sessions.Session\n Object to request (package requests)\n\n Methods\n -------\n getPvData(self, lat, long, date_from, date_to, dataset = 'merra2', cap = 1.0, sys_loss = 0.1, track = 0, tilt = 35, azim = 180)\n get the data of the pv from renewables ninja\n \"\"\"\n\n def __init__(self):\n self.token = \"732eaff288b11d478c42381c75173e8e17355fdb\"\n self.api_base = \"https://www.renewables.ninja/api/\"\n self.s = requests.session()\n self.s.headers = {\"Authorization\": \"Token \" + self.token}\n\n def __del__(self):\n self.s.close()\n\n def getPvData(\n self,\n lat,\n long,\n date_from,\n date_to,\n dataset=\"merra2\",\n cap=1.0,\n sys_loss=0.1,\n track=0,\n tilt=35,\n azim=180,\n ):\n \"\"\"Request PV power value\n\n Parameters\n ----------\n lat : float\n latitude of the pv\n long : float\n Longitude of the pv\n date_from : str\n format : year-month-day. Starting date of the requested data\n date_to : str\n format : year-month-day. Ending date of the requested data\n dataset : str, optional\n name of the dataset\n cap : float, optional\n capacity of the pv\n sys_loss : float, optional\n system loss of the pv\n track : bool, optional\n presence of a tracking system\n tilt : int, optional\n azim : int, optional\n\n Returns\n -------\n tuple\n 0 : metadata (dict)\n 1 : data (pandas Dataframe)\n \"\"\"\n\n url = self.api_base + \"data/pv\"\n args = {\n \"lat\": lat,\n \"lon\": long,\n \"date_from\": date_from,\n \"date_to\": date_to,\n \"dataset\": dataset,\n \"capacity\": cap,\n \"system_loss\": sys_loss,\n \"tracking\": track,\n \"tilt\": tilt,\n \"azim\": azim,\n \"format\": \"json\",\n }\n r = self.s.get(url, params=args)\n if r.status_code != 200:\n print(r.text)\n raise NetworkException()\n\n # Parse JSON to get a pandas.DataFrame of data and dict of metadata\n parsed_response = json.loads(r.text)\n\n data = pd.read_json(json.dumps(parsed_response[\"data\"]), orient=\"index\")\n metadata = parsed_response[\"metadata\"]\n return metadata, data\n\n def getWindData(\n self,\n lat,\n long,\n date_from,\n date_to,\n cap=1.0,\n height=100,\n turbine=\"Vestas V80 2000\",\n ):\n \"\"\"Request wind power value\n\n Parameters\n ----------\n lat : float\n latitude of the windmill\n long : float\n Longitude of the windmill\n date_from : str\n format : year-month-day. Starting date of the requested data\n date_to : str\n format : year-month-day. Ending date of the requested data\n cap : float, optional\n capacity of the windmill\n height : int, optional\n height of the windmill\n turbine : str, optional\n type of the turbine\n\n\n Returns\n -------\n tuple\n 0 : metadata (dict)\n 1 : data (pandas Dataframe)\n \"\"\"\n\n url = self.api_base + \"data/wind\"\n args = {\n \"lat\": lat,\n \"lon\": long,\n \"date_from\": date_from,\n \"date_to\": date_to,\n \"capacity\": cap,\n \"height\": height,\n \"turbine\": turbine,\n \"format\": \"json\",\n }\n r = self.s.get(url, params=args)\n if r.status_code != 200:\n print(r.text)\n raise NetworkException()\n\n # Parse JSON to get a pandas.DataFrame of data and dict of metadata\n parsed_response = json.loads(r.text)\n\n data = pd.read_json(json.dumps(parsed_response[\"data\"]), orient=\"index\")\n metadata = parsed_response[\"metadata\"]\n return metadata, data\n\n\ndef resampleData(data, timestamps, offset=timedelta(days=0)):\n origStepsize = getStepsize(data.index)\n wantedStepsize = getStepsize(timestamps)\n if origStepsize > wantedStepsize:\n assert (origStepsize / wantedStepsize).is_integer()\n data = data.resample(wantedStepsize).ffill()\n elif origStepsize < wantedStepsize:\n data = _dropUnfittingValuesAtEndForDownSampling(\n origStepsize, wantedStepsize, timestamps, data\n )\n data = data.resample(wantedStepsize).first()\n data = data.loc[timestamps[0] + offset : timestamps[-1] + offset]\n return data\n\n\ndef getLoadsData(filePath, timestamps):\n with open(filePath, \"r\", encoding=\"utf-8\") as dataFile:\n data = pd.read_csv(\n dataFile,\n parse_dates=[\"DateTime\"],\n index_col=\"DateTime\",\n sep=\";\",\n decimal=\",\",\n )\n data = data.loc[timestamps[0] : timestamps[-1] + getStepsize(timestamps)]\n data = resampleData(data, timestamps)\n data = data.iloc[:, 0]\n data.loc[data <= 0] = 0\n return data\n\n\ndef dateparserWithoutUTC(x):\n d, h = x.split(\" \")[0], x.split(\" \")[1].split(\"-\")[0]\n return pd.datetime.strptime(d + \" \" + h, \"20%y-%m-%d %H:%M:%S\")\n\n\ndef getPecanstreetData(\n filePath,\n timeHeader,\n dataid,\n column,\n timestamps,\n offset=timedelta(days=0),\n nb_rows=20000,\n):\n with open(filePath, \"r\", encoding=\"utf-8\") as dataFile:\n # TODO: read more rows or split dataid into files\n data = pd.read_csv(\n dataFile,\n parse_dates=[timeHeader],\n date_parser=dateparserWithoutUTC,\n nrows=nb_rows,\n )\n\n data = data[data[\"dataid\"] == int(dataid)]\n pd.to_datetime(data[timeHeader])\n data = data.set_index(timeHeader)\n data = data.sort_index()\n if column == \"grid\":\n ev = data.loc[:, [\"car1\"]]\n ev *= -1\n data = data.loc[:, [column, \"solar\", \"solar2\"]]\n data = pd.concat([data, ev], axis=1)\n else:\n data = data.loc[:, [column]]\n stepsize = getStepsize(timestamps)\n if stepsize < timedelta(minutes=15):\n stepsize = timedelta(hours=0)\n\n data = data.loc[timestamps[0] + offset : timestamps[-1] + offset + stepsize]\n data = resampleData(data, timestamps, offset)\n data = data.sum(axis=1)\n min_data_value = min(data)\n for idx, value in enumerate(data):\n if value < 0:\n data[idx] = 0.0\n\n if min_data_value < 0:\n print(\n \"(non-negativity of data) Values in range [{},0) were set to 0\".format(\n min_data_value\n )\n )\n assert all(i >= 0.0 for i in data)\n return data\n\n\ndef splitPecanstreetData(filePath, timeHeader):\n with open(filePath, \"r\", encoding=\"utf-8\") as dataFile:\n data = pd.read_csv(dataFile, parse_dates=[timeHeader])\n current = 0\n # TODO add for loop and store into new csv files\n dataid = data[\"dataid\"][current]\n data = data[data[\"dataid\"] == dataid]\n\n return data\n\n\ndef getPriceData(filePath, timestamps, offset, constantPrice):\n with open(filePath, \"r\", encoding=\"utf-8\") as dataFile:\n data = pd.read_csv(\n dataFile,\n parse_dates=[\"DateTime\"],\n index_col=\"DateTime\",\n sep=\";\",\n decimal=\",\",\n )\n data = data.loc[\n timestamps[0] + offset : timestamps[-1] + offset + getStepsize(timestamps)\n ]\n origStepsize = getStepsize(data.index)\n assert origStepsize == timedelta(hours=1)\n wantedStepsize = getStepsize(timestamps)\n if origStepsize > wantedStepsize:\n assert (origStepsize / wantedStepsize).is_integer()\n data = data.resample(wantedStepsize).asfreq()\n _applyOppositeOfResampleSum(data, timestamps, origStepsize / wantedStepsize)\n elif origStepsize < wantedStepsize:\n data = _dropUnfittingValuesAtEndForDownSampling(\n origStepsize, wantedStepsize, timestamps, data\n )\n data = data.resample(wantedStepsize).sum()\n assert data.shape[1] <= 2\n\n data = data.loc[timestamps[0] + offset : timestamps[-1] + offset]\n return data.iloc[:, 0] / FROM_MEGAWATTHOURS_TO_KILOWATTHOURS + constantPrice / (\n origStepsize / wantedStepsize\n )\n\n\ndef _applyOppositeOfResampleSum(data, timestamps, relation):\n for index in range(len(timestamps)):\n if np.isnan(data.iloc[index, 0]):\n data.iloc[index, 0] = newValue # noqa F821\n else:\n newValue = data.iloc[index, 0] / relation\n data.iloc[index, 0] = newValue\n\n\ndef _dropUnfittingValuesAtEndForDownSampling(\n origStepsize, wantedStepsize, timestamps, data\n):\n relation = _computeIntRelation(wantedStepsize, origStepsize)\n if data.size % relation != 0:\n data = data[: -(data.size % relation)]\n return data\n\n\ndef _computeIntRelation(stepsize1, stepsize2):\n relation = stepsize1 / stepsize2\n assert relation.is_integer(), \"1 stepsize should be a multiple of the other.\"\n return int(relation)\n\n\n# pvValue is at least 3 days\ndef getPredictedPVValue(pvValue, timestamps, delta):\n config_main = ForecastConfig()\n config_pv = ForecastPvConfig(config_main)\n\n config_main.TIMESTAMPS = constructTimeStamps(\n datetime.strptime(config_pv.BEGIN, \"20%y-%m-%d %H:%M:%S\"),\n datetime.strptime(config_pv.END, \"20%y-%m-%d %H:%M:%S\"),\n datetime.strptime(config_pv.STEP_SIZE, \"%H:%M:%S\")\n - datetime.strptime(\"00:00:00\", \"%H:%M:%S\"),\n )\n _, endValidation = get_split_indexes(config_main)\n # we drop the year\n a = datetime.strptime(timestamps[0].strftime(\"%m-%d\"), \"%m-%d\")\n b = datetime.strptime(\n config_main.TIMESTAMPS[endValidation].strftime(\"%m-%d\"), \"%m-%d\"\n )\n assert (a - b).days >= 0\n\n df = addMinutes(pvValue)\n df = addMonthOfYear(df) # , timestamps)\n # datas are normalized\n scaler = joblib.load(config_pv.MODEL_FILE_SC)\n print(scaler.data_max_)\n df = scaler.transform(df)\n\n x = np.empty((len(df) - config_pv.LOOK_BACK, config_pv.LOOK_BACK, df.shape[1]))\n for i in range(len(df) - config_pv.LOOK_BACK):\n x[i] = df[i : i + config_pv.LOOK_BACK, :]\n\n model = loadModel(config_pv)\n res = model.predict(x)\n res = invertScaler(res, scaler)\n\n return res, config_pv.LOOK_BACK, config_pv.OUTPUT_SIZE\n\n\n# loadsData is at least 3 days\ndef getPredictedLoadValue(loadsData, timestamps, timedelta):\n config = ForecastConfig()\n loadConfig = ForecastLoadConfig()\n input_data = addMinutes(loadsData)\n input_data = add_day_of_week(input_data)\n\n config.TIMESTAMPS = constructTimeStamps(\n datetime.strptime(loadConfig.BEGIN, \"20%y-%m-%d %H:%M:%S\"),\n datetime.strptime(loadConfig.END, \"20%y-%m-%d %H:%M:%S\"),\n datetime.strptime(loadConfig.STEPSIZE, \"%H:%M:%S\")\n - datetime.strptime(\"00:00:00\", \"%H:%M:%S\"),\n )\n _, endValidation = get_split_indexes(config)\n # we drop the year\n a = datetime.strptime(timestamps[0].strftime(\"%m-%d\"), \"%m-%d\")\n b = datetime.strptime(config.TIMESTAMPS[endValidation].strftime(\"%m-%d\"), \"%m-%d\")\n assert (a - b).days >= 0\n\n for load in loadConfig.APPLIANCES:\n appliance_data = getPecanstreetData(\n loadConfig.DATA_FILE,\n loadConfig.TIME_HEADER,\n loadConfig.DATAID,\n load,\n timestamps,\n timedelta,\n )\n input_data = pd.concat([input_data, appliance_data], axis=1)\n\n scaler = joblib.load(loadConfig.MODEL_FILE_SC)\n input_data = scaler.transform(input_data)\n\n x = np.empty(\n (\n len(input_data) - loadConfig.LOOK_BACK,\n loadConfig.LOOK_BACK,\n input_data.shape[1],\n )\n )\n for i in range(len(input_data) - loadConfig.LOOK_BACK):\n x[i] = input_data[i : i + loadConfig.LOOK_BACK, :]\n\n model = loadModel(loadConfig)\n res = model.predict(x)\n res = invertScaler(res, scaler)\n return res, loadConfig.LOOK_BACK, loadConfig.OUTPUT_SIZE\n", "sub_path": "code/data.py", "file_name": "data.py", "file_ext": "py", "file_size_in_byte": 14813, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "datetime.timedelta", "line_number": 27, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 30, "usage_type": "call"}, {"api_name": "util.getStepsize", "line_number": 34, "usage_type": "call"}, {"api_name": "util.getStepsize", "line_number": 36, "usage_type": "call"}, {"api_name": "util.getStepsize", "line_number": 37, "usage_type": "call"}, {"api_name": "requests.session", "line_number": 89, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 158, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 160, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 160, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 218, "usage_type": "call"}, {"api_name": "pandas.read_json", "line_number": 220, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 220, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 225, "usage_type": "call"}, {"api_name": "util.getStepsize", "line_number": 226, "usage_type": "call"}, {"api_name": "util.getStepsize", "line_number": 227, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 242, "usage_type": "call"}, {"api_name": "util.getStepsize", "line_number": 249, "usage_type": "call"}, {"api_name": "pandas.datetime.strptime", "line_number": 258, "usage_type": "call"}, {"api_name": "pandas.datetime", "line_number": 258, "usage_type": "attribute"}, {"api_name": "datetime.timedelta", "line_number": 267, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 272, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 280, "usage_type": "call"}, {"api_name": "pandas.concat", "line_number": 287, "usage_type": "call"}, {"api_name": "util.getStepsize", "line_number": 290, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 291, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 292, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 314, "usage_type": "call"}, {"api_name": "pandas.read_csv", "line_number": 325, "usage_type": "call"}, {"api_name": "util.getStepsize", "line_number": 333, "usage_type": "call"}, {"api_name": "util.getStepsize", "line_number": 335, "usage_type": "call"}, {"api_name": "datetime.timedelta", "line_number": 336, "usage_type": "call"}, {"api_name": "util.getStepsize", "line_number": 337, "usage_type": "call"}, {"api_name": "numpy.isnan", "line_number": 357, "usage_type": "call"}, {"api_name": "forecast_conf.ForecastConfig", "line_number": 381, "usage_type": "call"}, {"api_name": "forecast_pv_conf.ForecastPvConfig", "line_number": 382, "usage_type": "call"}, {"api_name": "util.constructTimeStamps", "line_number": 384, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 385, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 385, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 386, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 386, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 387, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 387, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 388, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 388, "usage_type": "name"}, {"api_name": "forecast.get_split_indexes", "line_number": 390, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 392, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 392, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 393, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 393, "usage_type": "name"}, {"api_name": "forecast.addMinutes", "line_number": 398, "usage_type": "call"}, {"api_name": "forecast.addMonthOfYear", "line_number": 399, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 401, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 401, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 405, "usage_type": "call"}, {"api_name": "forecast.loadModel", "line_number": 409, "usage_type": "call"}, {"api_name": "util.invertScaler", "line_number": 411, "usage_type": "call"}, {"api_name": "forecast_conf.ForecastConfig", "line_number": 418, "usage_type": "call"}, {"api_name": "forecast_load_conf.ForecastLoadConfig", "line_number": 419, "usage_type": "call"}, {"api_name": "forecast.addMinutes", "line_number": 420, "usage_type": "call"}, {"api_name": "forecast.add_day_of_week", "line_number": 421, "usage_type": "call"}, {"api_name": "util.constructTimeStamps", "line_number": 423, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 424, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 424, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 425, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 425, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 426, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 426, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 427, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 427, "usage_type": "name"}, {"api_name": "forecast.get_split_indexes", "line_number": 429, "usage_type": "call"}, {"api_name": "datetime.datetime.strptime", "line_number": 431, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 431, "usage_type": "name"}, {"api_name": "datetime.datetime.strptime", "line_number": 432, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 432, "usage_type": "name"}, {"api_name": "datetime.timedelta", "line_number": 442, "usage_type": "argument"}, {"api_name": "pandas.concat", "line_number": 444, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib.load", "line_number": 446, "usage_type": "call"}, {"api_name": "sklearn.externals.joblib", "line_number": 446, "usage_type": "name"}, {"api_name": "numpy.empty", "line_number": 449, "usage_type": "call"}, {"api_name": "forecast.loadModel", "line_number": 459, "usage_type": "call"}, {"api_name": "util.invertScaler", "line_number": 461, "usage_type": "call"}]} +{"seq_id": "439989915", "text": "import numpy as np\nimport skimage.io\nimport matplotlib.pyplot as plt\nimport skimage.segmentation\n\n# from common import *\nfrom encode import *\n\n\n# https://www.kaggle.com/stkbailey/step-by-step-explanation-of-scoring-metric/notebook\ndef evalute_score(true_masks, pred_masks):\n \"\"\"\n Descripition: .\n \n Args\n ----\n .\n \n Returns\n -------\n .\n \"\"\"\n true_masks = image.masks_merge(true_masks, label = True)\n # pred_masks = image.masks_merge(pred_masks, label = True)\n #\n fig = plt.figure()\n plt.subplot(1,2,1)\n plt.imshow(true_masks)\n plt.title(\"Ground truth masks\")\n plt.subplot(1,2,2)\n plt.imshow(pred_masks)\n plt.title(\"Predict masks\")\n\n #\n true_objects = len(np.unique(true_masks))\n pred_objects = len(np.unique(pred_masks))\n print(\"Number of true objects: \", true_objects)\n print(\"Number of predicted objects: \", pred_objects)\n\n # Compute intersection between all objects\n intersection = np.histogram2d(true_masks.flatten(), pred_masks.flatten(), bins=(true_objects, pred_objects))[0]\n\n # Compute areas (needed for finding the union between all objects)\n area_true = np.histogram(true_masks, bins = true_objects)[0]\n area_pred = np.histogram(pred_masks, bins = pred_objects)[0]\n area_true = np.expand_dims(area_true, -1)\n area_pred = np.expand_dims(area_pred, 0)\n\n # Compute union\n union = area_true + area_pred - intersection\n\n # Exclude background from the analysis\n intersection = intersection[1:,1:]\n union = union[1:,1:]\n union[union == 0] = 1e-9\n\n # Compute the intersection over union\n iou = intersection / union\n\n # Precision helper function\n def precision_at(threshold, iou):\n matches = iou > threshold\n true_positives = np.sum(matches, axis=1) == 1 # Correct objects\n false_positives = np.sum(matches, axis=0) == 0 # Missed objects\n false_negatives = np.sum(matches, axis=1) == 0 # Extra objects\n tp, fp, fn = np.sum(true_positives), np.sum(false_positives), np.sum(false_negatives)\n return tp, fp, fn\n\n # Loop over IoU thresholds\n prec = []\n print(\"Thresh\\tTP\\tFP\\tFN\\tPrec.\")\n for t in np.arange(0.5, 1.0, 0.05):\n tp, fp, fn = precision_at(t, iou)\n p = tp / (tp + fp + fn)\n print(\"{:1.3f}\\t{}\\t{}\\t{}\\t{:1.3f}\".format(t, tp, fp, fn, p))\n prec.append(p)\n print(\"AP\\t-\\t-\\t-\\t{:1.3f}\".format(np.mean(prec)))\n\n plt.show()\n\n \n\n\ndef loU():\n \"\"\"\n Descripition: .\n \n Args\n ----\n .\n \n Returns\n -------\n .\n \"\"\"\n pass", "sub_path": "src/utils/evalute.py", "file_name": "evalute.py", "file_ext": "py", "file_size_in_byte": 2592, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.figure", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 27, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 27, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 28, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 28, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 29, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 29, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 30, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 30, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 31, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 31, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 32, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 32, "usage_type": "name"}, {"api_name": "numpy.unique", "line_number": 35, "usage_type": "call"}, {"api_name": "numpy.unique", "line_number": 36, "usage_type": "call"}, {"api_name": "numpy.histogram2d", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 44, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 46, "usage_type": "call"}, {"api_name": "numpy.expand_dims", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 63, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 64, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 65, "usage_type": "call"}, {"api_name": "numpy.sum", "line_number": 66, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 77, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 79, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 79, "usage_type": "name"}]} +{"seq_id": "144386262", "text": "from django.urls import path\nfrom . import views\nfrom .views import CreateWorker \n\nurlpatterns = [\n path('', views.workers_overview, name='workers_overview'),\n path('najdi/', views.workers_overview_search, name='workers_overview_search'),\n path('/', views.worker_details, name='worker_details'),\n path('/izbrisi/', views.delete_worker, name='delete_worker'),\n path('novdelavec/', CreateWorker.as_view(), name='create_worker'),\n path('/uredi/', views.edit_worker_info, name=\"edit_worker_info\"),\n path('test/', views.test),\n path('/dodajdelavca/', views.project_assign_worker),\n path('odstranidelavca//', views.unassign_worker),\n]", "sub_path": "workers/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 732, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.urls.path", "line_number": 6, "usage_type": "call"}, {"api_name": "views.workers_overview", "line_number": 6, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 7, "usage_type": "call"}, {"api_name": "views.workers_overview_search", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 8, "usage_type": "call"}, {"api_name": "views.worker_details", "line_number": 8, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 9, "usage_type": "call"}, {"api_name": "views.delete_worker", "line_number": 9, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 10, "usage_type": "call"}, {"api_name": "views.CreateWorker.as_view", "line_number": 10, "usage_type": "call"}, {"api_name": "views.CreateWorker", "line_number": 10, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 11, "usage_type": "call"}, {"api_name": "views.edit_worker_info", "line_number": 11, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 12, "usage_type": "call"}, {"api_name": "views.test", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 13, "usage_type": "call"}, {"api_name": "views.project_assign_worker", "line_number": 13, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "views.unassign_worker", "line_number": 14, "usage_type": "attribute"}]} +{"seq_id": "549162349", "text": "import requests\nimport json\n#json.dumps() : python transfer to json\n#json.loads() : json transfer to python\n#\nfrom requests.auth import HTTPBasicAuth\nfrom requests.auth import HTTPDigestAuth\nfrom requests_oauthlib import OAuth1\nfrom requests import Request, Session\n\n\ndef try_get():\n r = requests.get('https://api.github.com/user', auth=('user', 'pass'))\n print(r.text)\n print(r.status_code)\n r = requests.get('https://api.github.com/user')\n print(r.text)\n # print(r.json)\n print(r.status_code)\n print(r.headers)\n r = requests.get('https://api.github.com/events')\n print(r.text)\n\ndef try_url():\n payload = {'key1': 'value1', 'key2': 'value2'}\n r = requests.get('http://httpbin.org/get', params=payload)\n print(r.url)\n payload = {'key1': 'value1', 'key2': ['value2', 'value3']}\n r = requests.get('http://httpbin.org/get', params=payload)\n print(r.url)\n\ndef try_practicepython():\n r = requests.get('http://www.practicepython.org/')\n print(r.url)\n print(r.text)\n print(r.status_code)\n\ndef save_raw_stream_as_a_file():\n r = requests.get('http://www.practicepython.org/', stream=True)\n with open('proacticepython.txt', 'wb') as fd:\n for chunk in r.iter_content(chunk_size=128):\n fd.write(chunk)\ndef save_cnn():\n url ='http://edition.cnn.com/2017/02/08/studentnews/ten-content-thurs/index.html'\n r = requests.get(url, stream=True)\n with open('cnn.txt', 'wb') as fd:\n for chunk in r.iter_content(chunk_size=128):\n fd.write(chunk)\n\ndef custom_headers():\n url = 'https://api.github.com/some/endpoint'\n headers = {'user-agent': 'my-app/0.0.1'}\n r = requests.get(url, headers=headers)\n print(r.headers)\n r = requests.get(url)\n print(r.text)\n print(r.headers)\n\ndef Send_form_encoded_data():\n payload = {'key1': 'value1', 'key2': 'value2'}\n r = requests.post(\"http://httpbin.org/post\", data=payload)\n print(r.text)\n print(\"*\"*100)\n r = requests.get(\"http://httpbin.org/post\")\n print(r.text)\n\ndef post_multipart_files():\n url = 'http://httpbin.org/post'\n files = {'file': open('proacticepython.txt', 'rb')}\n r = requests.post(url, files=files)\n print(r.text)\n\ndef cookies():\n url = 'http://example.com/some/cookie/setting/url'\n r = requests.get(url)\n# print(r.text)\n print(r.cookies)\n\ndef send_cookie():\n url = 'http://httpbin.org/cookies'\n cookies = dict(cookies_are='working')\n\n r = requests.get(url, cookies=cookies)\n print(r.text)\n\ndef try_auth():\n r = requests.get('https://api.github.com/user', auth=HTTPBasicAuth('user', 'pass'))\n print(r.status_code)\n# HTTPBasicAuth can be skip\n r = requests.get('https://api.github.com/user', auth=('user', 'pass'))\n print(r.status_code)\n\ndef try_digestAuth():\n url = 'http://httpbin.org/digest-auth/auth/user/pass'\n r = requests.get(url, auth=HTTPDigestAuth('user', 'pass'))\n print(r.status_code)\n\ndef try_oauth():\n # fail\n url = 'https://api.twitter.com/1.1/account/verify_credentials.json'\n auth = OAuth1('YOUR_APP_KEY', 'YOUR_APP_SECRET','USER_OAUTH_TOKEN', 'USER_OAUTH_TOKEN_SECRET')\n r = requests.get(url, auth=auth)\n print(r.status_code)\n\ndef try_session():\n s = requests.Session()\n s.get('http://httpbin.org/cookies/set/sessioncookie/123456789')\n r = s.get('http://httpbin.org/cookies')\n print(r.text)\n\ndef try_prepare():\n s = Session()\n\n req = Request('POST', url, data=data, headers=headers)\n prepped = req.prepare()\n\n # do something with prepped.body\n prepped.body = 'No, I want exactly this as the body.'\n\n # do something with prepped.headers\n del prepped.headers['Content-Type']\n\n resp = s.send(prepped,\n stream=stream,\n verify=verify,\n proxies=proxies,\n cert=cert,\n timeout=timeout\n )\n\n print(resp.status_code)\n\ndef streaming_uploads():\n with open('cnn.txt', 'rb') as f:\n r= requests.post('http://some.url/streamed', data=f)\n print(r.request.headers)\n\ndef gen():\n yield b'a'\n yield b'b'\n\ndef chunk_ended_requests():\n requests.post('http://some.url/chunked', data=gen())\n\n#callback_function\ndef print_url(r, *args, **kwargs):\n print(r.url)\n\ndef event_hooks():\n requests.get('http://httpbin.org', hooks=dict(response=print_url))\n\ndef try_proxies():\n proxies = {\n 'http': 'http://ASIA-PACIFIC\\jerry_chen7:Dell09018@proxy.tpe.apac.dell.com:80',\n 'https': 'https://ASIA-PACIFIC\\jerry_chen7:Dell09018@proxy.tpe.apac.dell.com:80',\n }\n\n requests.get('http://example.org', proxies=proxies)\n\ndef try_socks():\n proxies = {\n 'http': 'socks5://ASIA-PACIFIC\\jerry_chen7:Dell09018@proxy.tpe.apac.dell.com:80',\n 'https': 'socks5://ASIA-PACIFIC\\jerry_chen7:Dell09018@proxy.tpe.apac.dell.com:80',\n }\n requests.get('http://example.org', proxies=proxies)\n\ndef verbs_get():\n r = requests.get(\n 'https://api.github.com/repos/kennethreitz/requests/git/commits/a050faf084662f3a352dd1a941f2c7c9f886d4ad')\n if r.status_code == requests.codes.ok:\n print(r.headers['content-type'])\n\n commit_data = r.json()\n print(commit_data)\n print(commit_data.keys())\n print(commit_data[u'committer'])\n print(commit_data[u'message'])\n\ndef verbs_post():\n r = requests.get('https://api.github.com/repos/kennethreitz/requests/issues/482')\n print(r.status_code)\n\n issue = json.loads(r.text)\n print(issue[u'title'])\n print(issue[u'comments'])\n\n r = requests.get(r.url + u'/comments')\n print(r.status_code)\n comments = r.json()\n print(comments[9])\n print(comments[9].keys())\n print(comments[9][u'body'])\n print(comments[9][u'updated_at'])\n print(comments[9][u'user'][u'login'])\n\n body = json.dumps({u\"body\": u\"Sounds great! I'll get right on it!\"})\n url = u\"https://api.github.com/repos/kennethreitz/requests/issues/482/comments\"\n auth = HTTPBasicAuth('jerry2613@gmail.com', 'jerry2joan')\n r = requests.post(url=url, data=body, auth=auth)\n print(r.status_code)\n r = requests.patch(url=url, data=body, auth=auth)\n print(r.status_code)\n\ndef link_headers():\n url = 'https://api.github.com/users/kennethreitz/repos?page=1&per_page=10'\n r = requests.head(url=url)\n print(r.headers['link'])\n print(r.links[\"next\"])\n print(r.links[\"last\"])\n\nif __name__ == '__main__':\n# r = requests.get('https://api.github.com/events', stream=True)\n# try_practicepython()\n# save_raw_stream_as_a_file()\n# custom_headers()\n# Send_form_encoded_data()\n# post_multipart_files()\n# cookies()\n# send_cookie()\n# try_auth()\n# try_digestAuth()\n# try_oauth()\n# save_cnn()\n# try_session()\n# try_prepare()\n# r = requests.get('https://requestb.in')\n# r = requests.get('https://github.com')\n# print(r.status_code)\n# streaming_uploads()\n# chunk_ended_requests()\n# event_hooks()\n# try_proxies()\n# try_socks()\n# verbs_get()\n# verbs_post()\n link_headers()", "sub_path": "Exercise/Decode_A_Web_Page/requests_test.py", "file_name": "requests_test.py", "file_ext": "py", "file_size_in_byte": 7034, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 13, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 16, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 21, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 26, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 29, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 33, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 39, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 45, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 53, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 55, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 61, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 64, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 70, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 75, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 83, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 87, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 87, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 90, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 95, "usage_type": "call"}, {"api_name": "requests.auth.HTTPDigestAuth", "line_number": 95, "usage_type": "call"}, {"api_name": "requests_oauthlib.OAuth1", "line_number": 101, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 102, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 106, "usage_type": "call"}, {"api_name": "requests.Session", "line_number": 112, "usage_type": "call"}, {"api_name": "requests.Request", "line_number": 114, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 135, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 143, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 150, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 158, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 165, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 168, "usage_type": "call"}, {"api_name": "requests.codes", "line_number": 170, "usage_type": "attribute"}, {"api_name": "requests.get", "line_number": 180, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 183, "usage_type": "call"}, {"api_name": "requests.get", "line_number": 187, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 196, "usage_type": "call"}, {"api_name": "requests.auth.HTTPBasicAuth", "line_number": 198, "usage_type": "call"}, {"api_name": "requests.post", "line_number": 199, "usage_type": "call"}, {"api_name": "requests.patch", "line_number": 201, "usage_type": "call"}, {"api_name": "requests.head", "line_number": 206, "usage_type": "call"}]} +{"seq_id": "137775074", "text": "#Lesson 3 - points of interest\nimport filters as filters\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom scipy.misc import *\nfrom scipy.ndimage import filters\n\n#Image.open('assets/tile2.png').convert('RGB').save('assets/tile2.jpg')\n\n#im = imread('assets/cat.jpg')\n\nim = face()\n\nscales =[0.2989, 0.5870, 0.1140]\nim=np.dot(im,scales)\n\n#im = ascent()\n\ndef get_harris_points(im,min_dist=2,threshold=0.7):\n \"\"\" Return corners from a Harris response image\n min_dist is the minimum number of pixels separating\n corners and image boundary. \"\"\"\n # find top corner candidates above a threshold\n # derivatives\n sigma=3\n imx = np.zeros(im.shape)\n filters.gaussian_filter(im, (sigma,sigma), (0,1), imx)\n imy = np.zeros(im.shape)\n filters.gaussian_filter(im, (sigma,sigma), (1,0), imy)\n\n # compute components of the Harris matrix\n Wxx = filters.gaussian_filter(imx*imx,sigma)\n Wxy = filters.gaussian_filter(imx*imy,sigma)\n Wyy = filters.gaussian_filter(imy*imy,sigma)\n # determinant and trace\n Wdet = Wxx*Wyy - Wxy**2\n print(Wdet[0])\n Wtr = Wxx + Wyy\n harrisim = Wdet / Wtr\n corner_threshold = harrisim.max() * threshold\n harrisim_t = (harrisim > corner_threshold) * 1\n # get coordinates of candidates\n coords = np.array(harrisim_t.nonzero()).T # ...and their values\n candidate_values = [harrisim[c[0],c[1]] for c in coords] # sort candidates\n index = np.argsort(candidate_values)\n # store allowed point locations in array\n allowed_locations = np.zeros(harrisim.shape)\n allowed_locations[min_dist:-min_dist,min_dist:-min_dist] = 1\n # select the best points taking min_distance into account\n filtered_coords = []\n for i in index:\n if allowed_locations[coords[i,0],coords[i,1]] == 1:\n filtered_coords.append(coords[i])\n allowed_locations[(coords[i, 0] - min_dist):(coords[i, 0] + min_dist),\n (coords[i, 1] - min_dist):(coords[i, 1] + min_dist)] = 0\n\n return filtered_coords\n\n\nplt.figure()\nplt.gray()\nplt.imshow(im)\nfiltered_coords = get_harris_points(im)\nplt.plot([p[1] for p in filtered_coords],[p[0] for p in filtered_coords],\"*\")\n\n\ndef appendimages(im1, im2):\n \"\"\" Return a new image that appends the two images side-by-side. \"\"\"\n rows1 = im1.shape[0]\n rows2 = im2.shape[0]\n if rows1 < rows2:\n im1 = np.concatenate((im1, np.zeros((rows2 - rows1, im1.shape[1]))), axis=0)\n elif rows1 > rows2:\n im2 = np.concatenate((im2, np.zeros((rows1 - rows2, im2.shape[1]))), axis=0)\n # if none of these cases they are equal, no filling needed.\n return np.concatenate((im1, im2), axis=1)\n\n\ndef plot_matches(im1, im2, locs1, locs2, matchscores, show_below=True):\n \"\"\" Show a figure with lines joining the accepted matches\n input: im1,im2 (images as arrays), locs1,locs2 (feature locations), matchscores (as output from ’match()’),\n show_below (if images should be shown below matches). \"\"\"\n im3 = appendimages(im1, im2)\n if show_below:\n im3 = np.vstack((im3, im3))\n plt.imshow(im3)\n cols1 = im1.shape[1]\n for i, m in enumerate(matchscores):\n if m > 0: plt.plot([locs1[i][1], locs2[m][1] + cols1], [locs1[i][0], locs2[m][0]],\"c\")\n\n\n\n\ndef get_descriptors(image,filtered_coords,wid=5):\n \"\"\" For each point return pixel values around the point\n using a neighbourhood of width 2*wid+1. (Assume points are\n extracted with min_distance > wid).\n \"\"\"\n desc = []\n for coords in filtered_coords:\n patch = image[coords[0]-wid:coords[0]+wid+1, coords[1]-wid:coords[1]+wid+1].flatten()\n desc.append(patch)\n return desc\n\ndef match(desc1,desc2,threshold=0.5):\n \"\"\" For each corner point descriptor in the first image,\n select its match to second image using\n normalized cross correlation. \"\"\"\n n = len(desc1[0])\n # pair-wise distances\n d = -np.ones((len(desc1),len(desc2)))\n for i in range(len(desc1)):\n for j in range(len(desc2)):\n d1 = (desc1[i] - np.mean(desc1[i])) / np.std(desc1[i])\n d2 = (desc2[j] - np.mean(desc2[j])) / np.std(desc2[j])\n ncc_value = sum(d1 * d2) / (n-1)\n if ncc_value > threshold:\n d[i,j] = ncc_value\n ndx = np.argsort(-d)\n matchscores = ndx[:,0]\n return matchscores\n\n\n\nwid = 5\n\n\nim1 = face()\nim2 = face()\n\n\n\n\n\n\n\n\n\n\ndef paint_over_points(img1,img2):\n filtered_coords1=point_of_intrest(img1,0.3,10)\n filtered_coords2=point_of_intrest(img2,0.3,10)\n ax1=plt.subplot(1,2,1)\n ax2=plt.subplot(1,2,2)\n\n ax1.imshow(img1, cmap='gray')\n ax2.imshow(img2, cmap='gray')\n\n ax1.plot([p[1] for p in filtered_coords1], [p[0] for p in filtered_coords1], \"*\")\n ax2.plot([p[1] for p in filtered_coords2], [p[0] for p in filtered_coords2], \"*\")\n\n plt.show()\n\n\n\n\n\n#im1 = imread('assets/tile1.jpg')\n#im2 = imread('assets/tile2.jpg')\nim1=np.dot(im1,scales)\nim2=np.dot(im2,scales)\n\nax1 = plt.subplot(1, 2, 1)\nax2 = plt.subplot(1, 2, 2)\nax1.imshow(im1)\nfiltered_coords = get_harris_points(im1)\nax1.plot([p[1] for p in filtered_coords],[p[0] for p in filtered_coords],\"*\")\n\nax2.imshow(im2)\nfiltered_coords2 = get_harris_points(im2)\nax2.plot([p[1] for p in filtered_coords2],[p[0] for p in filtered_coords2],\"*\")\n\nplt.show()\n\nfiltered_coords1 = get_harris_points(im1, threshold=0.8)\nd1 = get_descriptors(im1, filtered_coords1)\nfiltered_coords2 = get_harris_points(im2, threshold=0.8)\nd2 = get_descriptors(im2, filtered_coords2)\n\nmatches = match(d1, d2)\nplt.figure()\nplt.gray()\nplot_matches(im1, im2, filtered_coords1, filtered_coords2, matches)\nplt.show()", "sub_path": "ibod tmoona/Lesson3.py", "file_name": "Lesson3.py", "file_ext": "py", "file_size_in_byte": 5606, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.dot", "line_number": 15, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 26, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters.gaussian_filter", "line_number": 27, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters", "line_number": 27, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 28, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters.gaussian_filter", "line_number": 29, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters", "line_number": 29, "usage_type": "name"}, {"api_name": "scipy.ndimage.filters.gaussian_filter", "line_number": 32, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters", "line_number": 32, "usage_type": "name"}, {"api_name": "scipy.ndimage.filters.gaussian_filter", "line_number": 33, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters", "line_number": 33, "usage_type": "name"}, {"api_name": "scipy.ndimage.filters.gaussian_filter", "line_number": 34, "usage_type": "call"}, {"api_name": "scipy.ndimage.filters", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.array", "line_number": 43, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 45, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 47, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 60, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 60, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 61, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 61, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 62, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 62, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 64, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 64, "usage_type": "name"}, {"api_name": "numpy.concatenate", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.concatenate", "line_number": 76, "usage_type": "call"}, {"api_name": "numpy.vstack", "line_number": 85, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.imshow", "line_number": 86, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 86, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.plot", "line_number": 89, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 89, "usage_type": "name"}, {"api_name": "numpy.ones", "line_number": 111, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 114, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 115, "usage_type": "call"}, {"api_name": "numpy.argsort", "line_number": 119, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 143, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 143, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 144, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 144, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 152, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 152, "usage_type": "name"}, {"api_name": "numpy.dot", "line_number": 160, "usage_type": "call"}, {"api_name": "numpy.dot", "line_number": 161, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 163, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 163, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplot", "line_number": 164, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 164, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 173, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 173, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.figure", "line_number": 181, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 181, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gray", "line_number": 182, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 182, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 184, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 184, "usage_type": "name"}]} +{"seq_id": "38850831", "text": "# Copyright 2015-2016 Yelp Inc.\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.\nimport asyncio\n\nfrom mock import Mock\nfrom mock import patch\nfrom pytest import raises\n\nfrom paasta_tools.cli.cmds import mark_for_deployment\nfrom paasta_tools.cli.cmds.mark_for_deployment import NoSuchCluster\nfrom paasta_tools.cli.cmds.wait_for_deployment import get_latest_marked_sha\nfrom paasta_tools.cli.cmds.wait_for_deployment import paasta_wait_for_deployment\nfrom paasta_tools.cli.cmds.wait_for_deployment import validate_git_sha_is_latest\nfrom paasta_tools.cli.utils import NoSuchService\nfrom paasta_tools.marathon_tools import MarathonServiceConfig\nfrom paasta_tools.paastaapi import ApiException\nfrom paasta_tools.remote_git import LSRemoteException\nfrom paasta_tools.utils import TimeoutError\n\n\nclass fake_args:\n deploy_group = \"test_deploy_group\"\n service = \"test_service\"\n git_url = \"\"\n commit = \"d670460b4b4aece5915caf5c68d12f560a9fe3e4\"\n soa_dir = \"fake_soa_dir\"\n timeout = 0\n verbose = False\n polling_interval = 5\n diagnosis_interval = 15\n time_before_first_diagnosis = 15\n\n\n@patch(\"paasta_tools.cli.cmds.mark_for_deployment._log\", autospec=True)\n@patch(\n \"paasta_tools.cli.cmds.mark_for_deployment.client.get_paasta_oapi_client\",\n autospec=True,\n)\ndef test_check_if_instance_is_done(mock_get_paasta_oapi_client, mock__log):\n mock_paasta_api_client = Mock()\n mock_paasta_api_client.api_error = ApiException\n mock_get_paasta_oapi_client.return_value = mock_paasta_api_client\n\n def check_instance(instance_config):\n return mark_for_deployment.check_if_instance_is_done(\n service=\"service1\",\n instance=instance_config.get_instance(),\n cluster=\"cluster\",\n git_sha=\"somesha\",\n instance_config=instance_config,\n )\n\n # valid completed instance\n mock_paasta_api_client.service.status_instance.return_value = Mock(\n git_sha=\"somesha\",\n kubernetes=None,\n marathon=Mock(\n app_count=1,\n active_shas=None,\n deploy_status=\"Running\",\n expected_instance_count=2,\n running_instance_count=2,\n ),\n )\n assert check_instance(mock_marathon_instance_config(\"instance1\"))\n\n # too many marathon apps\n mock_paasta_api_client.service.status_instance.return_value = Mock(\n git_sha=\"somesha\",\n kubernetes=None,\n marathon=Mock(\n app_count=2,\n active_shas=None,\n deploy_status=\"Running\",\n expected_instance_count=2,\n running_instance_count=2,\n ),\n )\n assert not check_instance(mock_marathon_instance_config(\"instance2\"))\n\n # too many running instances\n mock_paasta_api_client.service.status_instance.return_value = Mock(\n git_sha=\"somesha\",\n kubernetes=None,\n marathon=Mock(\n app_count=1,\n active_shas=None,\n deploy_status=\"Running\",\n expected_instance_count=2,\n running_instance_count=4,\n ),\n )\n assert check_instance(mock_marathon_instance_config(\"instance3\"))\n\n # still Deploying\n mock_paasta_api_client.service.status_instance.return_value = Mock(\n git_sha=\"somesha\",\n kubernetes=None,\n marathon=Mock(\n app_count=1,\n active_shas=None,\n deploy_status=\"Deploying\",\n expected_instance_count=2,\n running_instance_count=2,\n ),\n )\n assert check_instance(mock_marathon_instance_config(\"instance4\"))\n\n # still Deploying\n mock_paasta_api_client.service.status_instance.return_value = Mock(\n git_sha=\"somesha\",\n kubernetes=None,\n marathon=Mock(\n app_count=1,\n active_shas=None,\n deploy_status=\"Waiting\",\n expected_instance_count=2,\n running_instance_count=2,\n ),\n )\n assert check_instance(mock_marathon_instance_config(\"instance4.1\"))\n\n # not a marathon instance\n mock_paasta_api_client.service.status_instance.return_value = Mock(\n git_sha=\"somesha\", kubernetes=None, marathon=None,\n )\n assert check_instance(mock_marathon_instance_config(\"instance5\"))\n\n # wrong sha\n mock_paasta_api_client.service.status_instance.return_value = Mock(\n git_sha=\"anothersha\",\n kubernetes=None,\n marathon=Mock(\n app_count=1,\n active_shas=None,\n deploy_status=\"Running\",\n expected_instance_count=2,\n running_instance_count=2,\n ),\n )\n assert not check_instance(mock_marathon_instance_config(\"instance6\"))\n\n # paasta stop'd\n mock_paasta_api_client.service.status_instance.return_value = Mock(\n git_sha=\"somesha\",\n kubernetes=None,\n marathon=Mock(\n app_count=1,\n active_shas=None,\n deploy_status=\"Stopped\",\n expected_instance_count=0,\n running_instance_count=0,\n desired_state=\"stop\",\n ),\n )\n assert check_instance(mock_marathon_instance_config(\"instance7\"))\n\n # paasta has autoscaled to 0\n mock_paasta_api_client.service.status_instance.return_value = Mock(\n git_sha=\"somesha\",\n kubernetes=None,\n marathon=Mock(\n app_count=1,\n active_shas=None,\n deploy_status=\"Stopped\",\n expected_instance_count=0,\n running_instance_count=0,\n ),\n )\n assert check_instance(mock_marathon_instance_config(\"instance8\"))\n\n # not found -> maybe this is the first time we're deploying it, and it's not up yet.\n mock_paasta_api_client.service.status_instance.side_effect = ApiException(\n status=404, reason=\"\"\n )\n assert not check_instance(mock_marathon_instance_config(\"notaninstance\"))\n\n # crash -> consider it not done yet, hope it stops crashing later\n mock_paasta_api_client.service.status_instance.side_effect = ApiException(\n status=500, reason=\"\"\n )\n assert not check_instance(mock_marathon_instance_config(\"api_error\"))\n\n\n@patch(\n \"paasta_tools.cli.cmds.mark_for_deployment.load_system_paasta_config\", autospec=True\n)\n@patch(\n \"paasta_tools.cli.cmds.mark_for_deployment.get_instance_configs_for_service_in_deploy_group_all_clusters\",\n autospec=True,\n)\n@patch(\"paasta_tools.cli.cmds.mark_for_deployment._log\", autospec=True)\n@patch(\n \"paasta_tools.cli.cmds.mark_for_deployment.check_if_instance_is_done\", autospec=True\n)\ndef test_wait_for_deployment(\n mock_check_if_instance_is_done,\n mock__log,\n mock_get_instance_configs_for_service_in_deploy_group_all_clusters,\n mock_load_system_paasta_config,\n):\n mock_get_instance_configs_for_service_in_deploy_group_all_clusters.return_value = {\n \"cluster1\": [\n mock_marathon_instance_config(\"instance1\"),\n mock_marathon_instance_config(\"instance2\"),\n mock_marathon_instance_config(\"instance3\"),\n ],\n }\n\n def check_if_instance_is_done_side_effect(\n service, instance, cluster, git_sha, instance_config, api=None\n ):\n return instance in [\"instance1\", \"instance2\"]\n\n mock_check_if_instance_is_done.side_effect = check_if_instance_is_done_side_effect\n\n mock_load_system_paasta_config.return_value.get_api_endpoints.return_value = {\n \"cluster1\": \"some_url_1\",\n \"cluster2\": \"some_url_2\",\n }\n\n mock_load_system_paasta_config.return_value.get_mark_for_deployment_max_polling_threads.return_value = (\n 4\n )\n\n with raises(TimeoutError):\n with patch(\n \"asyncio.as_completed\", side_effect=[asyncio.TimeoutError], autospec=True\n ):\n asyncio.run(\n mark_for_deployment.wait_for_deployment(\n \"service\", \"fake_deploy_group\", \"somesha\", \"/nail/soa\", 1\n )\n )\n\n mock_get_instance_configs_for_service_in_deploy_group_all_clusters.return_value = {\n \"cluster1\": [\n mock_marathon_instance_config(\"instance1\"),\n mock_marathon_instance_config(\"instance2\"),\n ],\n \"cluster2\": [\n mock_marathon_instance_config(\"instance1\"),\n mock_marathon_instance_config(\"instance2\"),\n ],\n }\n with patch(\"sys.stdout\", autospec=True, flush=Mock()):\n assert (\n asyncio.run(\n mark_for_deployment.wait_for_deployment(\n \"service\", \"fake_deploy_group\", \"somesha\", \"/nail/soa\", 5\n )\n )\n == 0\n )\n\n mock_get_instance_configs_for_service_in_deploy_group_all_clusters.return_value = {\n \"cluster1\": [\n mock_marathon_instance_config(\"instance1\"),\n mock_marathon_instance_config(\"instance2\"),\n ],\n \"cluster2\": [\n mock_marathon_instance_config(\"instance1\"),\n mock_marathon_instance_config(\"instance3\"),\n ],\n }\n with raises(TimeoutError):\n asyncio.run(\n mark_for_deployment.wait_for_deployment(\n \"service\", \"fake_deploy_group\", \"somesha\", \"/nail/soa\", 0\n )\n )\n\n\n@patch(\n \"paasta_tools.cli.cmds.mark_for_deployment.load_system_paasta_config\", autospec=True\n)\n@patch(\n \"paasta_tools.cli.cmds.mark_for_deployment.PaastaServiceConfigLoader\", autospec=True\n)\n@patch(\"paasta_tools.cli.cmds.mark_for_deployment._log\", autospec=True)\ndef test_wait_for_deployment_raise_no_such_cluster(\n mock__log, mock_paasta_service_config_loader, mock_load_system_paasta_config,\n):\n mock_load_system_paasta_config.return_value.get_api_endpoints.return_value = {\n \"cluster1\": \"some_url_1\",\n \"cluster2\": \"some_url_2\",\n }\n\n mock_paasta_service_config_loader.return_value.clusters = [\"cluster3\"]\n with raises(NoSuchCluster):\n asyncio.run(\n mark_for_deployment.wait_for_deployment(\n \"service\", \"deploy_group_3\", \"somesha\", \"/nail/soa\", 0\n )\n )\n\n\n@patch(\"paasta_tools.cli.cmds.wait_for_deployment.validate_service_name\", autospec=True)\n@patch(\"paasta_tools.cli.cmds.mark_for_deployment.wait_for_deployment\", autospec=True)\ndef test_paasta_wait_for_deployment_return_1_when_no_such_service(\n mock_wait_for_deployment, mock_validate_service_name\n):\n mock_validate_service_name.side_effect = NoSuchService(\"Some text\")\n assert paasta_wait_for_deployment(fake_args) == 1\n assert mock_wait_for_deployment.call_args_list == []\n assert mock_validate_service_name.called\n\n\n@patch(\"paasta_tools.cli.cmds.wait_for_deployment.validate_service_name\", autospec=True)\n@patch(\"paasta_tools.cli.cmds.wait_for_deployment.list_deploy_groups\", autospec=True)\n@patch(\"paasta_tools.cli.cmds.mark_for_deployment.wait_for_deployment\", autospec=True)\ndef test_paasta_wait_for_deployment_return_1_when_deploy_group_not_found(\n mock_wait_for_deployment, mock_list_deploy_groups, mock_validate_service_name\n):\n mock_list_deploy_groups.return_value = {\"another_test_deploy_group\"}\n assert paasta_wait_for_deployment(fake_args) == 1\n assert mock_wait_for_deployment.call_args_list == []\n assert mock_validate_service_name.called\n\n\n@patch(\n \"paasta_tools.cli.cmds.mark_for_deployment.load_system_paasta_config\", autospec=True\n)\n@patch(\n \"paasta_tools.cli.cmds.mark_for_deployment.PaastaServiceConfigLoader\", autospec=True\n)\n@patch(\"paasta_tools.cli.cmds.wait_for_deployment.validate_service_name\", autospec=True)\n@patch(\"paasta_tools.cli.cmds.wait_for_deployment.validate_git_sha\", autospec=True)\n@patch(\n \"paasta_tools.cli.cmds.wait_for_deployment.validate_git_sha_is_latest\",\n autospec=True,\n)\n@patch(\"paasta_tools.cli.cmds.wait_for_deployment.list_deploy_groups\", autospec=True)\n@patch(\"paasta_tools.cli.cmds.mark_for_deployment._log\", autospec=True)\n@patch(\"paasta_tools.cli.cmds.wait_for_deployment._log\", autospec=True)\ndef test_paasta_wait_for_deployment_return_0_when_no_instances_in_deploy_group(\n mock__log1,\n mock__log2,\n mock_list_deploy_groups,\n mock_validate_git_sha_is_latest,\n mock_validate_git_sha,\n mock_validate_service_name,\n mock_paasta_service_config_loader,\n mock_load_system_paasta_config,\n system_paasta_config,\n):\n mock__log1.return_value = None\n mock__log2.return_value = None\n mock_load_system_paasta_config.return_value = system_paasta_config\n mock_paasta_service_config_loader.return_value.instance_configs.return_value = [\n mock_marathon_instance_config(\"some_instance\")\n ]\n mock_list_deploy_groups.return_value = {\"test_deploy_group\"}\n assert paasta_wait_for_deployment(fake_args) == 0\n assert mock_validate_service_name.called\n\n\n@patch(\"paasta_tools.cli.cmds.wait_for_deployment.list_remote_refs\", autospec=True)\ndef test_get_latest_marked_sha_good(mock_list_remote_refs):\n mock_list_remote_refs.return_value = {\n \"refs/tags/paasta-fake_group1-20161129T203750-deploy\": \"968b948b3fca457326718dc7b2e278f89ccc5c87\",\n \"refs/tags/paasta-fake_group1-20161117T122449-deploy\": \"eac9a6d7909d09ffec00538bbc43b64502aa2dc0\",\n \"refs/tags/paasta-fake_group2-20161125T095651-deploy\": \"a4911648beb2e53886658ba7ea7eb93d582d754c\",\n \"refs/tags/paasta-fake_group1.everywhere-20161109T223959-deploy\": \"71e97ec397a3f0e7c4ee46e8ea1e2982cbcb0b79\",\n }\n assert (\n get_latest_marked_sha(\"\", \"fake_group1\")\n == \"968b948b3fca457326718dc7b2e278f89ccc5c87\"\n )\n\n\n@patch(\"paasta_tools.cli.cmds.wait_for_deployment.list_remote_refs\", autospec=True)\ndef test_get_latest_marked_sha_bad(mock_list_remote_refs):\n mock_list_remote_refs.return_value = {\n \"refs/tags/paasta-fake_group2-20161129T203750-deploy\": \"968b948b3fca457326718dc7b2e278f89ccc5c87\"\n }\n assert get_latest_marked_sha(\"\", \"fake_group1\") == \"\"\n\n\n@patch(\"paasta_tools.cli.cmds.wait_for_deployment.list_remote_refs\", autospec=True)\ndef test_validate_deploy_group_when_is_git_not_available(mock_list_remote_refs, capsys):\n test_error_message = \"Git error\"\n mock_list_remote_refs.side_effect = LSRemoteException(test_error_message)\n assert (\n validate_git_sha_is_latest(\n \"fake sha\", \"fake_git_url\", \"fake_group\", \"fake_service\"\n )\n is None\n )\n\n\ndef mock_marathon_instance_config(fake_name) -> \"MarathonServiceConfig\":\n return MarathonServiceConfig(\n service=\"fake_service\",\n cluster=\"fake_cluster\",\n instance=fake_name,\n config_dict={\"deploy_group\": \"fake_deploy_group\"},\n branch_dict=None,\n soa_dir=\"fake_soa_dir\",\n )\n\n\ndef test_compose_timeout_message():\n remaining_instances = {\n \"cluster1\": [\"instance1\", \"instance2\"],\n \"cluster2\": [\"instance3\"],\n \"cluster3\": [],\n }\n\n message = mark_for_deployment.compose_timeout_message(\n remaining_instances, 1, \"fake_group\", \"someservice\", \"some_git_sha\"\n )\n assert (\n \" paasta status -c cluster1 -s someservice -i instance1,instance2\" in message\n )\n assert \" paasta status -c cluster2 -s someservice -i instance3\" in message\n assert (\n \" paasta logs -c cluster1 -s someservice -i instance1,instance2 -C deploy -l 1000\"\n in message\n )\n assert (\n \" paasta logs -c cluster2 -s someservice -i instance3 -C deploy -l 1000\"\n in message\n )\n", "sub_path": "tests/cli/test_cmds_wait_for_deployment.py", "file_name": "test_cmds_wait_for_deployment.py", "file_ext": "py", "file_size_in_byte": 15811, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "mock.Mock", "line_number": 51, "usage_type": "call"}, {"api_name": "paasta_tools.paastaapi.ApiException", "line_number": 52, "usage_type": "name"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment.check_if_instance_is_done", "line_number": 56, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment", "line_number": 56, "usage_type": "name"}, {"api_name": "mock.Mock", "line_number": 65, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 68, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 79, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 82, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 93, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 96, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 107, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 110, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 121, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 124, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 135, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 141, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 144, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 155, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 158, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 170, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 173, "usage_type": "call"}, {"api_name": "paasta_tools.paastaapi.ApiException", "line_number": 184, "usage_type": "call"}, {"api_name": "paasta_tools.paastaapi.ApiException", "line_number": 190, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 45, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 46, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 237, "usage_type": "call"}, {"api_name": "paasta_tools.utils.TimeoutError", "line_number": 237, "usage_type": "argument"}, {"api_name": "mock.patch", "line_number": 238, "usage_type": "call"}, {"api_name": "asyncio.TimeoutError", "line_number": 239, "usage_type": "attribute"}, {"api_name": "asyncio.run", "line_number": 241, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment.wait_for_deployment", "line_number": 242, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment", "line_number": 242, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 257, "usage_type": "call"}, {"api_name": "mock.Mock", "line_number": 257, "usage_type": "call"}, {"api_name": "asyncio.run", "line_number": 259, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment.wait_for_deployment", "line_number": 260, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment", "line_number": 260, "usage_type": "name"}, {"api_name": "pytest.raises", "line_number": 277, "usage_type": "call"}, {"api_name": "paasta_tools.utils.TimeoutError", "line_number": 277, "usage_type": "argument"}, {"api_name": "asyncio.run", "line_number": 278, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment.wait_for_deployment", "line_number": 279, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment", "line_number": 279, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 196, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 199, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 203, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 204, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 301, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment.NoSuchCluster", "line_number": 301, "usage_type": "argument"}, {"api_name": "asyncio.run", "line_number": 302, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment.wait_for_deployment", "line_number": 303, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment", "line_number": 303, "usage_type": "name"}, {"api_name": "mock.patch", "line_number": 285, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 288, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 291, "usage_type": "call"}, {"api_name": "paasta_tools.cli.utils.NoSuchService", "line_number": 314, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.wait_for_deployment.paasta_wait_for_deployment", "line_number": 315, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 309, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 310, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.wait_for_deployment.paasta_wait_for_deployment", "line_number": 327, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 320, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 321, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 322, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.wait_for_deployment.paasta_wait_for_deployment", "line_number": 365, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 332, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 335, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 338, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 339, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 340, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 344, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 345, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 346, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.wait_for_deployment.get_latest_marked_sha", "line_number": 378, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 369, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.wait_for_deployment.get_latest_marked_sha", "line_number": 388, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 383, "usage_type": "call"}, {"api_name": "paasta_tools.remote_git.LSRemoteException", "line_number": 394, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.wait_for_deployment.validate_git_sha_is_latest", "line_number": 396, "usage_type": "call"}, {"api_name": "mock.patch", "line_number": 391, "usage_type": "call"}, {"api_name": "paasta_tools.marathon_tools.MarathonServiceConfig", "line_number": 404, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment.compose_timeout_message", "line_number": 421, "usage_type": "call"}, {"api_name": "paasta_tools.cli.cmds.mark_for_deployment", "line_number": 421, "usage_type": "name"}]} +{"seq_id": "419564468", "text": "from rest_framework.routers import DefaultRouter\n\nfrom django.urls import include, path\n\nfrom hotel import views\n\napp_name = 'hotel'\n\nrouter = DefaultRouter()\nrouter.register(r'hotel', views.HotelViewSet)\nrouter.register(r'tax', views.TaxViewSet)\n\nv1_api_urlpatterns = [\n path('', include(router.urls)),\n path('cheapest/', views.Cheapest.as_view(), name='cheapest')\n]", "sub_path": "cheapest_hotel/apps/hotel/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 373, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "rest_framework.routers.DefaultRouter", "line_number": 9, "usage_type": "call"}, {"api_name": "hotel.views.HotelViewSet", "line_number": 10, "usage_type": "attribute"}, {"api_name": "hotel.views", "line_number": 10, "usage_type": "name"}, {"api_name": "hotel.views.TaxViewSet", "line_number": 11, "usage_type": "attribute"}, {"api_name": "hotel.views", "line_number": 11, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.include", "line_number": 14, "usage_type": "call"}, {"api_name": "django.urls.path", "line_number": 15, "usage_type": "call"}, {"api_name": "hotel.views.Cheapest.as_view", "line_number": 15, "usage_type": "call"}, {"api_name": "hotel.views.Cheapest", "line_number": 15, "usage_type": "attribute"}, {"api_name": "hotel.views", "line_number": 15, "usage_type": "name"}]} +{"seq_id": "225275911", "text": "from collections import deque\n\nh, w = map(int, input().split())\ns = [list(input()) for _ in range(h)]\nwhite_count = 0\nfor i in range(h):\n for j in range(w):\n if s[i][j] == '.':\n white_count += 1\n\nd = [[float(\"inf\")] * w for _ in range(h)]\n\nque = deque()\nque.append((0, 0))\nd[0][0] = 1\nwhile que:\n x, y = que.popleft()\n for dx, dy in [(1, 0), (-1, 0), (0, 1), (0, -1)]:\n nx = x + dx\n ny = y + dy\n if 0 <= nx < w and 0 <= ny < h and s[ny][nx] == '.' and d[ny][nx] == float(\"inf\"):\n que.append((nx, ny))\n d[ny][nx] = d[y][x] + 1\n\nres = d[h-1][w-1]\nif res == float(\"inf\"):\n print(-1)\nelse:\n print(white_count - res)\n", "sub_path": "Python_codes/p03436/s159186197.py", "file_name": "s159186197.py", "file_ext": "py", "file_size_in_byte": 688, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "collections.deque", "line_number": 13, "usage_type": "call"}]} +{"seq_id": "602073970", "text": "import numpy as np\nimport pytest\n\nfrom growth import grow\n\n\n@pytest.fixture\ndef test_settings():\n return grow.COOL_SETTINGS[\"snake\"]\n\n\n@pytest.mark.parametrize(\"n_return\", [1, 2, 3, 4, 5])\ndef test_run_reaction_diffusion(test_settings, n_return):\n \"\"\"Make sure the function actually returns the right number of snapshots\"\"\"\n\n results = grow.run_reaction_diffusion(n=10, n_to_return=n_return, **test_settings)\n\n assert len(results) == n_return\n\n\n@pytest.mark.parametrize(\"grid_size\", [(100, 100), (100, 200), (200, 100)])\ndef test_run_reaction_diffusion_grid_size(test_settings, grid_size):\n\n results = grow.run_reaction_diffusion(\n n=10, grid_size=grid_size, n_to_return=1, **test_settings\n )\n assert results[0][0].shape == grid_size\n\n\ndef test_run_reaction_diffusion_raises_grid_size(test_settings):\n\n mask = np.zeros((10, 11))\n with pytest.raises(AssertionError, match=\"The mask size and grid size don't match\"):\n grow.run_reaction_diffusion(\n n=10, grid_size=(10, 10), mask=mask, n_to_return=1, **test_settings\n )\n\n\ndef test_run_reaction_diffusion_raises_param_length(test_settings):\n\n test_settings.update(dict(dA=np.random.random(11)))\n\n with pytest.raises(\n AssertionError, match=\"the wrong dimensions. Should be of length 10\"\n ):\n grow.run_reaction_diffusion(\n n=10, grid_size=(10, 10), n_to_return=1, **test_settings\n )\n", "sub_path": "tests/test_grow.py", "file_name": "test_grow.py", "file_ext": "py", "file_size_in_byte": 1428, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "growth.grow.COOL_SETTINGS", "line_number": 9, "usage_type": "attribute"}, {"api_name": "growth.grow", "line_number": 9, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 7, "usage_type": "attribute"}, {"api_name": "growth.grow.run_reaction_diffusion", "line_number": 16, "usage_type": "call"}, {"api_name": "growth.grow", "line_number": 16, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 12, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 12, "usage_type": "attribute"}, {"api_name": "growth.grow.run_reaction_diffusion", "line_number": 24, "usage_type": "call"}, {"api_name": "growth.grow", "line_number": 24, "usage_type": "name"}, {"api_name": "pytest.mark.parametrize", "line_number": 21, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 21, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 32, "usage_type": "call"}, {"api_name": "pytest.raises", "line_number": 33, "usage_type": "call"}, {"api_name": "growth.grow.run_reaction_diffusion", "line_number": 34, "usage_type": "call"}, {"api_name": "growth.grow", "line_number": 34, "usage_type": "name"}, {"api_name": "numpy.random.random", "line_number": 41, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 41, "usage_type": "attribute"}, {"api_name": "pytest.raises", "line_number": 43, "usage_type": "call"}, {"api_name": "growth.grow.run_reaction_diffusion", "line_number": 46, "usage_type": "call"}, {"api_name": "growth.grow", "line_number": 46, "usage_type": "name"}]} +{"seq_id": "651877822", "text": "#!/usr/bin/env python\n\nimport asyncio\nimport logging\nimport shutil\nimport tempfile\nimport threading\nimport time\nimport traceback\n\nfrom typing import TYPE_CHECKING\nfrom aiohttp import web\n\nfrom opentrons.config import CONFIG\nfrom .rpc import RPCServer\nfrom .http import HTTPServer\nfrom opentrons.api.routers import MainRouter\nfrom opentrons.protocol_api.legacy_wrapper import api\n\nif TYPE_CHECKING:\n from opentrons.hardware_control.types import HardwareAPILike # noqa(F501)\n\nlog = logging.getLogger(__name__)\n\n\n@web.middleware\nasync def error_middleware(request, handler):\n try:\n response = await handler(request)\n except web.HTTPNotFound:\n log.exception(\"Exception handler for request {}\".format(request))\n data = {\n 'message': 'File was not found at {}'.format(request)\n }\n response = web.json_response(data, status=404)\n except Exception as e:\n log.exception(\"Exception in handler for request {}\".format(request))\n data = {\n 'message': 'An unexpected error occured - {}'.format(e),\n 'traceback': traceback.format_exc()\n }\n response = web.json_response(data, status=500)\n\n return response\n\n\nclass ThreadedAsyncLock:\n \"\"\" A thread-safe async lock\n\n This is required to properly lock access to motion calls, which are\n a) done in async contexts (rpc methods and http methods) and should\n block as little as possible\n b) done from several different threads (rpc workers and main thread)\n\n This is a code wart that needs to be removed. It can be removed by\n - making smoothie async so we don't need worker threads anymore\n - removing said threads\n\n This object can be used as either an asynchronous context manager using\n ``async with`` or a synchronous context manager using ``with``.\n \"\"\"\n\n def __init__(self):\n self._thread_lock = threading.RLock()\n\n async def __aenter__(self):\n pref = f\"[ThreadedAsyncLock tid {threading.get_ident()} \"\\\n f\"task {asyncio.Task.current_task()}] \"\n log.debug(pref + 'will acquire')\n then = time.perf_counter()\n while not self._thread_lock.acquire(blocking=False):\n await asyncio.sleep(0.1)\n now = time.perf_counter()\n log.debug(pref + f'acquired in {now-then}s')\n\n async def __aexit__(self, exc_type, exc, tb):\n log.debug(f\"[ThreadedAsyncLock tid {threading.get_ident()} \"\n f\"task {asyncio.Task.current_task()}] will release\")\n self._thread_lock.release()\n\n def __enter__(self):\n self._thread_lock.acquire()\n\n def __exit__(self, exc_type, exc, tb):\n self._thread_lock.release()\n\n\n# Support for running using aiohttp CLI.\n# See: https://docs.aiohttp.org/en/stable/web.html#command-line-interface-cli\ndef init(hardware: 'HardwareAPILike' = None,\n loop: asyncio.AbstractEventLoop = None):\n \"\"\"\n Builds an application and sets up RPC and HTTP servers with it.\n\n :param loop: A specific aiohttp event loop to use. If not specified, the\n server will use the default event loop.\n :param hardware: The hardware manager or hardware adapter to connect to.\n If not specified, the server will use\n :py:attr:`opentrons.hardware`\n \"\"\"\n # Try to migrate containers from database to v2 format\n api.maybe_migrate_containers()\n app = web.Application(middlewares=[error_middleware])\n app['com.opentrons.hardware'] = hardware\n app['com.opentrons.motion_lock'] = ThreadedAsyncLock()\n app['com.opentrons.rpc'] = RPCServer(\n app, MainRouter(\n hardware, lock=app['com.opentrons.motion_lock'], loop=loop))\n app['com.opentrons.response_file_tempdir'] = tempfile.mkdtemp()\n app['com.opentrons.http'] = HTTPServer(app, CONFIG['log_dir'])\n\n async def dispose_response_file_tempdir(app):\n temppath = app.get('com.opentrons.response_file_tempdir')\n if temppath:\n try:\n shutil.rmtree(temppath)\n except Exception:\n log.exception(f\"failed to remove app temp path {temppath}\")\n\n app.on_shutdown.append(dispose_response_file_tempdir)\n app.on_shutdown.freeze()\n return app\n\n\ndef run(hardware: 'HardwareAPILike',\n hostname=None,\n port=None,\n path=None,\n loop=None):\n \"\"\"\n The arguments are not all optional. Either a path or hostname+port should\n be specified; you have to specify one.\n \"\"\"\n if path:\n log.debug(\"Starting Opentrons server application on {}\".format(\n path))\n hostname, port = None, None\n else:\n log.debug(\"Starting Opentrons server application on {}:{}\".format(\n hostname, port))\n path = None\n\n web.run_app(init(hardware=hardware), host=hostname, port=port, path=path)\n", "sub_path": "api/src/opentrons/server/__init__.py", "file_name": "__init__.py", "file_ext": "py", "file_size_in_byte": 4856, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "typing.TYPE_CHECKING", "line_number": 20, "usage_type": "name"}, {"api_name": "logging.getLogger", "line_number": 23, "usage_type": "call"}, {"api_name": "aiohttp.web.HTTPNotFound", "line_number": 30, "usage_type": "attribute"}, {"api_name": "aiohttp.web", "line_number": 30, "usage_type": "name"}, {"api_name": "aiohttp.web.json_response", "line_number": 35, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 35, "usage_type": "name"}, {"api_name": "traceback.format_exc", "line_number": 40, "usage_type": "call"}, {"api_name": "aiohttp.web.json_response", "line_number": 42, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 42, "usage_type": "name"}, {"api_name": "aiohttp.web.middleware", "line_number": 26, "usage_type": "attribute"}, {"api_name": "aiohttp.web", "line_number": 26, "usage_type": "name"}, {"api_name": "threading.RLock", "line_number": 64, "usage_type": "call"}, {"api_name": "threading.get_ident", "line_number": 67, "usage_type": "call"}, {"api_name": "asyncio.Task.current_task", "line_number": 68, "usage_type": "call"}, {"api_name": "asyncio.Task", "line_number": 68, "usage_type": "attribute"}, {"api_name": "time.perf_counter", "line_number": 70, "usage_type": "call"}, {"api_name": "asyncio.sleep", "line_number": 72, "usage_type": "call"}, {"api_name": "time.perf_counter", "line_number": 73, "usage_type": "call"}, {"api_name": "threading.get_ident", "line_number": 77, "usage_type": "call"}, {"api_name": "asyncio.Task.current_task", "line_number": 78, "usage_type": "call"}, {"api_name": "asyncio.Task", "line_number": 78, "usage_type": "attribute"}, {"api_name": "asyncio.AbstractEventLoop", "line_number": 91, "usage_type": "attribute"}, {"api_name": "opentrons.protocol_api.legacy_wrapper.api.maybe_migrate_containers", "line_number": 102, "usage_type": "call"}, {"api_name": "opentrons.protocol_api.legacy_wrapper.api", "line_number": 102, "usage_type": "name"}, {"api_name": "aiohttp.web.Application", "line_number": 103, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 103, "usage_type": "name"}, {"api_name": "rpc.RPCServer", "line_number": 106, "usage_type": "call"}, {"api_name": "opentrons.api.routers.MainRouter", "line_number": 107, "usage_type": "call"}, {"api_name": "tempfile.mkdtemp", "line_number": 109, "usage_type": "call"}, {"api_name": "http.HTTPServer", "line_number": 110, "usage_type": "call"}, {"api_name": "opentrons.config.CONFIG", "line_number": 110, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 116, "usage_type": "call"}, {"api_name": "aiohttp.web.run_app", "line_number": 143, "usage_type": "call"}, {"api_name": "aiohttp.web", "line_number": 143, "usage_type": "name"}]} +{"seq_id": "302502240", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport sklearn\nimport sklearn.datasets\nimport sklearn.linear_model\nimport sklearn.model_selection\nimport h5py\nimport scipy.io\n\n\n# def plot_decision_boundary(model, X, y):\n# # Set min and max values and give it some padding\n# x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1\n# y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1\n# h = 0.01\n# # Generate a grid of points with distance h between them\n# xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n# # Predict the function value for the whole grid\n# Z = model(np.c_[xx.ravel(), yy.ravel()])\n# Z = Z.reshape(xx.shape)\n# # Plot the contour and training examples\n# plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n# plt.ylabel('x2')\n# plt.xlabel('x1')\n# plt.scatter(X[0, :], X[1, :], c=y[0, :], cmap=plt.cm.Spectral)\n\n\n# def plot_decision_boundary(model, X, y):\n# # Set min and max values and give it some padding\n# x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1\n# y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1\n# h = 0.01\n# # Generate a grid of points with distance h between them\n# xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n# # Predict the function value for the whole grid\n# Z = model(np.c_[xx.ravel(), yy.ravel()])\n# Z = Z.reshape(xx.shape)\n# # Plot the contour and training examples\n# plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n# plt.ylabel('x2')\n# plt.xlabel('x1')\n# plt.scatter(X[0, :], X[1, :], c=y[0, :], cmap=plt.cm.Spectral)\n# plt.show()\n\n# Helper function to plot a decision boundary.\n# If you don't fully understand this function don't worry, it just generates the contour plot below.\n# def plot_decision_boundary(pred_func, X, y):\n# # Set min and max values and give it some padding\n# x_min, x_max = X[:, 0].min() - .5, X[:, 0].max() + .5\n# y_min, y_max = X[:, 1].min() - .5, X[:, 1].max() + .5\n# h = 0.01\n# # Generate a grid of points with distance h between them\n# xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n# # Predict the function value for the whole gid\n# Z = pred_func(np.c_[xx.ravel(), yy.ravel()])\n# Z = Z.reshape(xx.shape)\n# # Plot the contour and training examples\n# plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n# plt.scatter(X[:, 0], X[:, 1], c=y[0, :], cmap=plt.cm.Spectral)\n\ndef map_feature(X, degree=2):\n '''\n generate the composition of features of X.\n :param X:\n :param degree:\n :return:\n '''\n assert(X.shape[0]<=2)\n if X.shape[0]==2:\n x1 = X[0,:]\n x2 = X[1,:]\n m = np.sum(np.array([i+1 for i in range(1, degree+1)]))\n out = np.zeros((m, X.shape[1]))\n k = 0\n for i in range(1, degree+1):\n for j in range(0, i+1):\n out[k, :] = np.multiply(np.power(x1, i-j), np.power(x2, j))\n k = k + 1\n return out\n else:\n x1 = X[0, :]\n out = np.zeros((degree, X.shape[1]))\n for i in range(0, degree):\n out[i, :] = np.power(x1, i+1)\n return out\n\n\ndef plot_decision_boundary(model, X, y):\n # Set min and max values and give it some padding\n x_min, x_max = X[0, :].min() - 1, X[0, :].max() + 1\n y_min, y_max = X[1, :].min() - 1, X[1, :].max() + 1\n h = 0.01\n # Generate a grid of points with distance h between them\n xx, yy = np.meshgrid(np.arange(x_min, x_max, h), np.arange(y_min, y_max, h))\n # Predict the function value for the whole grid\n Z = model(np.c_[xx.ravel(), yy.ravel()])\n Z = Z.reshape(xx.shape)\n # Plot the contour and training examples\n plt.contourf(xx, yy, Z, cmap=plt.cm.Spectral)\n plt.ylabel('x2')\n plt.xlabel('x1')\n plt.scatter(X[0, :], X[1, :], c=y[0, :], cmap=plt.cm.Spectral)\n plt.show()\n\n\n# map_feature\ndef plot_decision_boundary_map_feature(predictor, X, y, title=\"decision boundary\", degree=2 ):\n plt.title(title)\n axes = plt.gca()\n min = X.min(axis=1)\n max = X.max(axis=1)\n range = max - min\n min = min - 0.1 * range\n max = max + 0.1 * range\n axes.set_xlim((min[0], max[0]))\n axes.set_ylim((min[1], max[1]))\n plot_decision_boundary(lambda x: predictor.predict(map_feature(x.T, degree)), X, y)\n\n\ndef load_sin_line_dataset(data_path=\"../../data/sin_line.txt\", delimiter=\"\\t\", random_state=3, test_size=0.25):\n '''\n ex1data2.txt contains a training set of housing prices in Portland, Oregon.\n The first column is the size of the house (in square feet), the second column\n is the number of bedrooms, and the third column is the price of the house.\n :param data_path:\n :param delimiter:\n :return:\n '''\n data = np.loadtxt(data_path, delimiter=delimiter)\n X = data[:, :-1]\n y = data[:, -1:]\n train_X, test_X, train_y, test_y = sklearn.model_selection.train_test_split(\n X, y, test_size=test_size, random_state=random_state)\n return train_X.T, train_y.T, test_X.T, test_y.T\n\n\ndef load_flat_dataset(data_path=\"../../data/ex1data1.txt\", delimiter=\",\"):\n '''\n ex1data2.txt contains a training set of housing prices in Portland, Oregon.\n The first column is the size of the house (in square feet), the second column\n is the number of bedrooms, and the third column is the price of the house.\n :param data_path:\n :param delimiter:\n :return:\n '''\n data = np.loadtxt(data_path, delimiter=delimiter)\n X = data[:, :-1]\n y = data[:, -1:]\n return X.T, y.T\n\n\ndef load_regression_dataset(n_samples=400, n_features=1, bias=1, noise=5, random_state=3, test_size=0.25):\n X, y = sklearn.datasets.make_regression(n_samples=n_samples, n_features=n_features, n_targets=1, bias=bias,\n coef=False, noise=noise, random_state=random_state)\n train_X, test_X, train_y, test_y = sklearn.model_selection.train_test_split(\n X, y, test_size = test_size, random_state = random_state)\n return train_X, train_y, test_X, test_y\n\n\ndef load_regression_dataset_coef(n_samples=400, n_features=1, bias=1, noise=5, random_state=3, test_size=0.25, b=5):\n X, y, w = sklearn.datasets.make_regression(n_samples=n_samples, n_features=n_features, n_targets=1, bias=bias,\n coef=True, noise=noise, random_state=random_state)\n train_X, test_X, train_y, test_y = sklearn.model_selection.train_test_split(\n X, y, test_size = test_size, random_state = random_state)\n return train_X, train_y+b, test_X, test_y+b, w, b\n\n\ndef load_happy_dataset(train_data_path='../../data/train_happy.h5', test_data_path='../../data/test_happy.h5'):\n train_dataset = h5py.File(train_data_path, \"r\")\n train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\n train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\n\n test_dataset = h5py.File(test_data_path, \"r\")\n test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\n test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\n\n classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\n\n train_set_y_orig = train_set_y_orig.reshape((1, train_set_y_orig.shape[0]))\n test_set_y_orig = test_set_y_orig.reshape((1, test_set_y_orig.shape[0]))\n\n return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\n\n\ndef load_boston_dataset(test_size=0.25, random_state=3):\n '''\n -Origin\n The origin of the boston housing data is Natural.\n -Usage\n This dataset may be used for Assessment.\n -Number of Cases\n The dataset contains a total of 506 cases.\n -Order\n The order of the cases is mysterious.\n -Variables\n There are 14 attributes in each case of the dataset. They are:\n CRIM - per capita crime rate by town\n ZN - proportion of residential land zoned for lots over 25,000 sq.ft.\n INDUS - proportion of non-retail business acres per town.\n CHAS - Charles River dummy variable (1 if tract bounds river; 0 otherwise)\n NOX - nitric oxides concentration (parts per 10 million)\n RM - average number of rooms per dwelling\n AGE - proportion of owner-occupied units built prior to 1940\n DIS - weighted distances to five Boston employment centres\n RAD - index of accessibility to radial highways\n TAX - full-value property-tax rate per $10,000\n PTRATIO - pupil-teacher ratio by town\n B - 1000(Bk - 0.63)^2 where Bk is the proportion of blacks by town\n LSTAT - % lower status of the population\n MEDV - Median value of owner-occupied homes in $1000's\n :return:\n '''\n boston = sklearn.datasets.load_boston()\n train_X, test_X, train_y, test_y = sklearn.model_selection.train_test_split(\n boston.data, boston.target, test_size = test_size, random_state = random_state)\n return train_X, train_y, test_X, test_y\n\n\ndef load_planar_dataset():\n np.random.seed(1)\n m = 400 # number of examples\n N = int(m / 2) # number of points per class\n D = 2 # dimensionality\n X = np.zeros((m, D)) # data matrix where each row is a single example\n Y = np.zeros((m, 1), dtype='uint8') # labels vector (0 for red, 1 for blue)\n a = 4 # maximum ray of the flower\n\n for j in range(2):\n ix = range(N * j, N * (j + 1))\n t = np.linspace(j * 3.12, (j + 1) * 3.12, N) + np.random.randn(N) * 0.2 # theta\n r = a * np.sin(4 * t) + np.random.randn(N) * 0.2 # radius\n X[ix] = np.c_[r * np.sin(t), r * np.cos(t)]\n Y[ix] = j\n\n X = X.T\n Y = Y.T\n\n return X, Y\n\n\ndef load_petal_dataset(num_example=400, random_state=3):\n np.random.seed(1)\n m = num_example # number of examples\n N = int(m / 2) # number of points per class\n D = 2 # dimensionality\n X = np.zeros((m, D)) # data matrix where each row is a single example\n Y = np.zeros((m, 1), dtype='uint8') # labels vector (0 for red, 1 for blue)\n a = 4 # maximum ray of the flower\n\n for j in range(2):\n ix = range(N * j, N * (j + 1))\n t = np.linspace(j * 3.12, (j + 1) * 3.12, N) + np.random.randn(N) * 0.2 # theta\n r = np.sin(4 * t) + np.random.randn(N) * 0.2 /a # radius\n X[ix] = np.c_[r * np.sin(t), r * np.cos(t)]\n Y[ix] = j\n\n\n train_X, test_X, train_y, test_y = sklearn.model_selection.train_test_split(\n X, Y, test_size=0.25, random_state=random_state)\n\n return train_X.T, train_y.T, test_X.T, test_y.T\n\n\ndef load_extra_datasets():\n N = 200\n noisy_circles = sklearn.datasets.make_circles(n_samples=N, factor=.5, noise=.3)\n noisy_moons = sklearn.datasets.make_moons(n_samples=N, noise=.2)\n blobs = sklearn.datasets.make_blobs(n_samples=N, random_state=5, n_features=2, centers=6)\n gaussian_quantiles = sklearn.datasets.make_gaussian_quantiles(mean=None, cov=0.5, n_samples=N, n_features=2,\n n_classes=2, shuffle=True, random_state=None)\n no_structure = np.random.rand(N, 2), np.random.rand(N, 2)\n\n return noisy_circles, noisy_moons, blobs, gaussian_quantiles, no_structure\n\n\ndef load_dataset():\n np.random.seed(1)\n train_X, train_Y = sklearn.datasets.make_circles(n_samples=300, noise=.05)\n np.random.seed(2)\n test_X, test_Y = sklearn.datasets.make_circles(n_samples=100, noise=.05)\n # Visualize the data\n plt.scatter(train_X[:, 0], train_X[:, 1], c=train_Y, s=40, cmap=plt.cm.Spectral);\n train_X = train_X.T\n train_Y = train_Y.reshape((1, train_Y.shape[0]))\n test_X = test_X.T\n test_Y = test_Y.reshape((1, test_Y.shape[0]))\n return train_X, train_Y, test_X, test_Y\n\n\ndef load_circle_dataset(n_train=300, seed_train=1, noise_train=0.05, n_test=100, seed_test=2, noise_test=0.05):\n np.random.seed(seed_train)\n train_X, train_Y = sklearn.datasets.make_circles(n_samples=n_train, noise=noise_train)\n np.random.seed(seed_test)\n test_X, test_Y = sklearn.datasets.make_circles(n_samples=n_test, noise=noise_test)\n train_X = train_X.T\n train_Y = train_Y.reshape((1, train_Y.shape[0]))\n test_X = test_X.T\n test_Y = test_Y.reshape((1, test_Y.shape[0]))\n return train_X, train_Y, test_X, test_Y\n\n\ndef load_image_data(train_data_path='../../data/train_catvnoncat.h5', test_data_path='../../data/test_catvnoncat.h5'):\n\n print('load ' + train_data_path)\n train_dataset = h5py.File(train_data_path, \"r\")\n train_set_x_orig = np.array(train_dataset[\"train_set_x\"][:]) # your train set features\n train_set_y_orig = np.array(train_dataset[\"train_set_y\"][:]) # your train set labels\n\n print('load ' + test_data_path)\n test_dataset = h5py.File(test_data_path, \"r\")\n test_set_x_orig = np.array(test_dataset[\"test_set_x\"][:]) # your test set features\n test_set_y_orig = np.array(test_dataset[\"test_set_y\"][:]) # your test set labels\n\n classes = np.array(test_dataset[\"list_classes\"][:]) # the list of classes\n\n train_set_y_orig = train_set_y_orig.reshape((train_set_y_orig.shape[0], 1))\n test_set_y_orig = test_set_y_orig.reshape((test_set_y_orig.shape[0], 1))\n\n return train_set_x_orig, train_set_y_orig, test_set_x_orig, test_set_y_orig, classes\n\n\ndef initialize_image_data(X, y=None):\n m = X.shape[0]\n X = X.reshape((m, -1)).T\n if y is not None: y = y.T\n return X/255.0, y\n\n\ndef load_moon_dataset():\n np.random.seed(3)\n train_X, train_Y = sklearn.datasets.make_moons(n_samples=300, noise=.2) # 300 #0.2\n # Visualize the data\n\n train_X = train_X.T\n train_Y = train_Y.reshape((1, train_Y.shape[0]))\n\n return train_X, train_Y\n\n\ndef load_football_dataset(file_path='../../data/football.mat'):\n data = scipy.io.loadmat(file_path)\n train_X = data['X'].T\n train_Y = data['y'].T\n test_X = data['Xval'].T\n test_Y = data['yval'].T\n\n return train_X, train_Y, test_X, test_Y\n\n\nif __name__ == \"__main__\":\n # load_image_data()\n # load_football_dataset()\n # load_image_data()\n # train_X, train_y, test_X, test_y = load_boston_dataset()\n # print(train_X.shape)\n # print(train_y.shape)\n # print(test_X.shape)\n # print(test_y.shape)\n train_X, train_y, test_X, test_y = load_regression_dataset()\n print(train_X.shape)\n print(train_y.shape)\n print(test_X.shape)\n print(test_y.shape)", "sub_path": "arsenal/eipi10/ml/planar_utils.py", "file_name": "planar_utils.py", "file_ext": "py", "file_size_in_byte": 14496, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.sum", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 73, "usage_type": "call"}, {"api_name": "numpy.multiply", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 77, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 82, "usage_type": "call"}, {"api_name": "numpy.power", "line_number": 84, "usage_type": "call"}, {"api_name": "numpy.meshgrid", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 94, "usage_type": "call"}, {"api_name": "numpy.c_", "line_number": 96, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.contourf", "line_number": 99, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 99, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 99, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.ylabel", "line_number": 100, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 100, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.xlabel", "line_number": 101, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 101, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 102, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 102, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 102, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.show", "line_number": 103, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 103, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.title", "line_number": 108, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 108, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.gca", "line_number": 109, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 109, "usage_type": "name"}, {"api_name": "numpy.loadtxt", "line_number": 129, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 132, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 132, "usage_type": "attribute"}, {"api_name": "numpy.loadtxt", "line_number": 146, "usage_type": "call"}, {"api_name": "sklearn.datasets.make_regression", "line_number": 153, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 153, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 155, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 155, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.make_regression", "line_number": 161, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 161, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 163, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 163, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 169, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 170, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 171, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 173, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 174, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 175, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 177, "usage_type": "call"}, {"api_name": "sklearn.datasets.load_boston", "line_number": 213, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 213, "usage_type": "attribute"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 214, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 214, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 220, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 220, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 224, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 225, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 230, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 230, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 231, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 231, "usage_type": "attribute"}, {"api_name": "numpy.c_", "line_number": 232, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 232, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 242, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 242, "usage_type": "attribute"}, {"api_name": "numpy.zeros", "line_number": 246, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 247, "usage_type": "call"}, {"api_name": "numpy.linspace", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 252, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 252, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.random.randn", "line_number": 253, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 253, "usage_type": "attribute"}, {"api_name": "numpy.c_", "line_number": 254, "usage_type": "attribute"}, {"api_name": "numpy.sin", "line_number": 254, "usage_type": "call"}, {"api_name": "numpy.cos", "line_number": 254, "usage_type": "call"}, {"api_name": "sklearn.model_selection.train_test_split", "line_number": 258, "usage_type": "call"}, {"api_name": "sklearn.model_selection", "line_number": 258, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.make_circles", "line_number": 266, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 266, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.make_moons", "line_number": 267, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 267, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.make_blobs", "line_number": 268, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 268, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.make_gaussian_quantiles", "line_number": 269, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 269, "usage_type": "attribute"}, {"api_name": "numpy.random.rand", "line_number": 271, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 271, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 277, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 277, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.make_circles", "line_number": 278, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 278, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 279, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 279, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.make_circles", "line_number": 280, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 280, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot.scatter", "line_number": 282, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 282, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.cm", "line_number": 282, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 291, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 291, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.make_circles", "line_number": 292, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 292, "usage_type": "attribute"}, {"api_name": "numpy.random.seed", "line_number": 293, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 293, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.make_circles", "line_number": 294, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 294, "usage_type": "attribute"}, {"api_name": "h5py.File", "line_number": 305, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 306, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 307, "usage_type": "call"}, {"api_name": "h5py.File", "line_number": 310, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 311, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 312, "usage_type": "call"}, {"api_name": "numpy.array", "line_number": 314, "usage_type": "call"}, {"api_name": "numpy.random.seed", "line_number": 330, "usage_type": "call"}, {"api_name": "numpy.random", "line_number": 330, "usage_type": "attribute"}, {"api_name": "sklearn.datasets.make_moons", "line_number": 331, "usage_type": "call"}, {"api_name": "sklearn.datasets", "line_number": 331, "usage_type": "attribute"}, {"api_name": "scipy.io.io.loadmat", "line_number": 341, "usage_type": "call"}, {"api_name": "scipy.io.io", "line_number": 341, "usage_type": "attribute"}, {"api_name": "scipy.io", "line_number": 341, "usage_type": "name"}]} +{"seq_id": "620889827", "text": "from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n url(r'^$', views.login),\n url(r'^content/$', views.content),\n url(r'^index/$', views.index),\n url(r'^add/$', views.add),\n url(r'^edit/$', views.edit),\n url(r'^delete/$', views.delete),\n url(r'^find/$', views.find),\n url(r'^v_index/$', views.v_index),\n url(r'^v_add/$', views.v_add),\n url(r'^v_edit/$', views.v_edit),\n url(r'^v_delete/$', views.v_delete),\n url(r'^v_find/$', views.v_find),\n url(r'^sign_up/$', views.sign_up),\n url(r'^u_index/$', views.u_index),\n url(r'^u_delete/$', views.u_delete),\n url(r'^u_v_add/$', views.u_v_add),\n]", "sub_path": "sim/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 655, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.conf.urls.url", "line_number": 5, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 6, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 7, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 8, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 9, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 10, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 11, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 12, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 13, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 14, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 15, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 16, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 17, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 18, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 19, "usage_type": "call"}, {"api_name": "django.conf.urls.url", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "471860316", "text": "# -*- coding: utf-8 -*-\n\nimport unittest\nimport os\nimport time\nimport hashlib\nimport hmac\n\nfrom algoliasearch import algoliasearch\n\n\ndef safe_index_name(name):\n if 'TRAVIS' not in os.environ:\n return name\n job = os.environ['TRAVIS_JOB_NUMBER']\n return '%s_travis-%s' % (name, job)\n\n\nclass ClientTest(unittest.TestCase):\n def setUp(self):\n try:\n self.name = unichr(224) + 'lgol?' + unichr(224) + '-python'\n self.name2 = unichr(224) + 'lgol?' + unichr(224) + '2-python'\n self.name_obj = unichr(224) + '/go/?' + unichr(224) + '2-python'\n except Exception:\n self.name = 'àlgol?à-python'\n self.name2 = 'àlgol?à2-python'\n self.name_obj = 'à/go/?à2-python'\n\n self.client = algoliasearch.Client(\n os.environ['ALGOLIA_APPLICATION_ID'],\n os.environ['ALGOLIA_API_KEY'])\n index_name = safe_index_name(self.name)\n try:\n self.client.delete_index(index_name)\n except algoliasearch.AlgoliaException:\n pass\n self.index = self.client.init_index(index_name)\n\n def tearDown(self):\n index_name = safe_index_name(self.name)\n try:\n self.client.delete_index(index_name)\n except algoliasearch.AlgoliaException:\n pass\n index_name2 = safe_index_name(self.name2)\n try:\n self.client.delete_index(index_name2)\n except algoliasearch.AlgoliaException:\n pass\n\n def test_secured_keys(self):\n self.assertEquals(\n '1fd74b206c64fb49fdcd7a5f3004356cd3bdc9d9aba8733656443e64daafc417',\n hmac.new('my_api_key'.encode('utf-8'), '(public,user1)'.encode(\n 'utf-8'), hashlib.sha256).hexdigest())\n key = self.client.generate_secured_api_key('my_api_key',\n '(public,user1)')\n self.assertEquals(key, hmac.new('my_api_key'.encode('utf-8'),\n '(public,user1)'.encode('utf-8'),\n hashlib.sha256).hexdigest())\n key = self.client.generate_secured_api_key('my_api_key',\n '(public,user1)', 42)\n self.assertEquals(key, hmac.new('my_api_key'.encode('utf-8'),\n '(public,user1)42'.encode('utf-8'),\n hashlib.sha256).hexdigest())\n key = self.client.generate_secured_api_key('my_api_key', ['public'])\n self.assertEquals(key, hmac.new('my_api_key'.encode(\n 'utf-8'), 'public'.encode('utf-8'), hashlib.sha256).hexdigest())\n key = self.client.generate_secured_api_key(\n 'my_api_key', ['public', ['premium', 'vip']])\n self.assertEquals(key, hmac.new('my_api_key'.encode('utf-8'),\n 'public,(premium,vip)'.encode('utf-8'),\n hashlib.sha256).hexdigest())\n\n def test_disjunctive_faceting(self):\n self.index.set_settings(\n {'attributesForFacetting': ['city', 'stars', 'facilities']})\n task = self.index.add_objects([{\n 'name': 'Hotel A',\n 'stars': '*',\n 'facilities': ['wifi', 'bath', 'spa'],\n 'city': 'Paris'\n }, {\n 'name': 'Hotel B',\n 'stars': '*',\n 'facilities': ['wifi'],\n 'city': 'Paris'\n }, {\n 'name': 'Hotel C',\n 'stars': '**',\n 'facilities': ['bath'],\n 'city': 'San Francisco'\n }, {\n 'name': 'Hotel D',\n 'stars': '****',\n 'facilities': ['spa'],\n 'city': 'Paris'\n }, {\n 'name': 'Hotel E',\n 'stars': '****',\n 'facilities': ['spa'],\n 'city': 'New York'\n }, ])\n self.index.wait_task(task['taskID'])\n\n answer = self.index.search_disjunctive_faceting(\n 'h', ['stars', 'facilities'], {'facets': 'city'})\n self.assertEquals(answer['nbHits'], 5)\n self.assertEquals(len(answer['facets']), 1)\n self.assertEquals(len(answer['disjunctiveFacets']), 2)\n\n answer = self.index.search_disjunctive_faceting('h', [\n 'stars', 'facilities'\n ], {'facets': 'city'}, {'stars': ['*']})\n self.assertEquals(answer['nbHits'], 2)\n self.assertEquals(len(answer['facets']), 1)\n self.assertEquals(len(answer['disjunctiveFacets']), 2)\n self.assertEquals(answer['disjunctiveFacets']['stars']['*'], 2)\n self.assertEquals(answer['disjunctiveFacets']['stars']['**'], 1)\n self.assertEquals(answer['disjunctiveFacets']['stars']['****'], 2)\n\n answer = self.index.search_disjunctive_faceting('h', [\n 'stars', 'facilities'\n ], {'facets': 'city'}, {'stars': ['*'],\n 'city': ['Paris']})\n self.assertEquals(answer['nbHits'], 2)\n self.assertEquals(len(answer['facets']), 1)\n self.assertEquals(len(answer['disjunctiveFacets']), 2)\n self.assertEquals(answer['disjunctiveFacets']['stars']['*'], 2)\n self.assertEquals(answer['disjunctiveFacets']['stars']['****'], 1)\n\n answer = self.index.search_disjunctive_faceting('h', [\n 'stars', 'facilities'\n ], {'facets': 'city'}, {'stars': ['*', '****'],\n 'city': ['Paris']})\n self.assertEquals(answer['nbHits'], 3)\n self.assertEquals(len(answer['facets']), 1)\n self.assertEquals(len(answer['disjunctiveFacets']), 2)\n self.assertEquals(answer['disjunctiveFacets']['stars']['*'], 2)\n self.assertEquals(answer['disjunctiveFacets']['stars']['****'], 1)\n", "sub_path": "tests/test_old.py", "file_name": "test_old.py", "file_ext": "py", "file_size_in_byte": 5784, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.environ", "line_number": 13, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 15, "usage_type": "attribute"}, {"api_name": "unittest.TestCase", "line_number": 19, "usage_type": "attribute"}, {"api_name": "algoliasearch.algoliasearch.Client", "line_number": 30, "usage_type": "call"}, {"api_name": "algoliasearch.algoliasearch", "line_number": 30, "usage_type": "name"}, {"api_name": "os.environ", "line_number": 31, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 32, "usage_type": "attribute"}, {"api_name": "algoliasearch.algoliasearch.AlgoliaException", "line_number": 36, "usage_type": "attribute"}, {"api_name": "algoliasearch.algoliasearch", "line_number": 36, "usage_type": "name"}, {"api_name": "algoliasearch.algoliasearch.AlgoliaException", "line_number": 44, "usage_type": "attribute"}, {"api_name": "algoliasearch.algoliasearch", "line_number": 44, "usage_type": "name"}, {"api_name": "algoliasearch.algoliasearch.AlgoliaException", "line_number": 49, "usage_type": "attribute"}, {"api_name": "algoliasearch.algoliasearch", "line_number": 49, "usage_type": "name"}, {"api_name": "hmac.new", "line_number": 55, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 56, "usage_type": "attribute"}, {"api_name": "hmac.new", "line_number": 59, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 61, "usage_type": "attribute"}, {"api_name": "hmac.new", "line_number": 64, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 66, "usage_type": "attribute"}, {"api_name": "hmac.new", "line_number": 68, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 69, "usage_type": "attribute"}, {"api_name": "hmac.new", "line_number": 72, "usage_type": "call"}, {"api_name": "hashlib.sha256", "line_number": 74, "usage_type": "attribute"}]} +{"seq_id": "477085248", "text": "import pygame\nimport random\n# 游戏屏幕的大小\nSCREEN_RECT = pygame.Rect(0, 0, 700, 480)\n# 敌机的定时器事件常量\nCREATE_ENEMY_EVENT = pygame.USEREVENT\n\n# 定义一个子弹的常量\nHERO_FIRE_EVENT = pygame.USEREVENT + 1\n\n\nclass PanZhiWei_PlaneGame(pygame.sprite.Sprite):\n \"\"\"游戏精灵的基类\"\"\"\n\n def __init__(self, new_image, new_speed=1):\n # 调用父类的初始化方法\n super().__init__()\n # 图片 速度 位置\n self.image = pygame.image.load(new_image)\n self.speed = new_speed\n # 获取图片的宽和高\n self.rect = self.image.get_rect()\n\n def update(self):\n self.rect.x += self.speed\n\n\nclass PanZhiWei_Background(PanZhiWei_PlaneGame):\n def __init__(self, is_alt=False):\n super().__init__(\"./images/横向的背景.png\",2)\n if is_alt:\n self.rect.right = 0\n\n def update(self):\n super().update()\n if self.rect.left == SCREEN_RECT.width:\n self.rect.right = 0\n\n\nclass PanZhiWei_Enemy(PanZhiWei_PlaneGame):\n \"\"\"敌机精灵类\"\"\"\n\n def __init__(self):\n # 调用父类的方法,创建敌机精灵,并且指定地基的图像\n super().__init__(\"./images/横向的敌机 (复件).png\",6)\n\n # 设置敌机的随机初始速度\n\n self.speed = random.randint(8, 10)\n\n # 设置敌机的随机初始位置\n\n self.rect.right = 0\n\n max_x = SCREEN_RECT.height - self.rect.height\n self.rect.y = random.randint(0, max_x)\n\n def update(self):\n panduan = random.randint(0, 2)\n \n if panduan == 0:\n # 调用父类的方法 让敌机在垂直方向运动\n super().update()\n elif panduan == 1:\n self.rect.x += self.speed\n self.rect.y -= self.speed\n elif panduan == 2:\n self.rect.x += self.speed\n self.rect.y += self.speed\n\n # 判断是否飞出屏幕 如果是 需要将敌机从精灵组删除\n if self.rect.left > SCREEN_RECT.width:\n self.kill()\n\n def __del__(self):\n print(\"敌机挂掉了%s\" % self.rect)\n\n\nclass PanZhiWei_Hero(PanZhiWei_PlaneGame):\n \"\"\"英雄的精灵\"\"\"\n\n def __init__(self):\n\n super().__init__(\"./images/横向的飞机 (1) (复件).png\", 0)\n\n # 给英雄设置一个初始位置\n self.rect.centery = SCREEN_RECT.centery\n self.rect.right = SCREEN_RECT.right - 30\n self.speed1 = 0\n # 创建一个子弹的精灵\n self.bullets = pygame.sprite.Group()\n\n def update(self):\n\n # super().update()\n # 飞机水平移动\n self.rect.x += self.speed\n self.rect.y += self.speed1\n\n # 判断飞机屏幕边界\n if self.rect.left < 0:\n self.rect.left = 0\n\n if self.rect.right > SCREEN_RECT.width:\n self.rect.right = SCREEN_RECT.width\n\n if self.rect.bottom < 0:\n self.rect.top = SCREEN_RECT.height\n if self.rect.top > SCREEN_RECT.height:\n self.rect.bottom = 0\n\n def fire(self):\n print(\"发射子弹\")\n\n for i in (1, 2):\n # 创建子弹\n bullet = PanZhiWei_Bullet()\n bullet1 = PanZhiWei_Bullet()\n bullet2 = PanZhiWei_Bullet()\n # 设置子弹的位置\n bullet.rect.x = self.rect.left\n bullet.rect.centery = self.rect.centery\n bullet1.rect.x = self.rect.left\n bullet1.rect.centery = self.rect.centery + 15\n bullet2.rect.x = self.rect.left\n bullet2.rect.centery = self.rect.centery - 15\n\n # 将子弹添加到精灵组\n self.bullets.add(bullet, bullet1, bullet2)\n\n\nclass PanZhiWei_Bullet(PanZhiWei_PlaneGame):\n \"\"\"子弹精灵类\"\"\"\n\n def __init__(self):\n\n # 调用父类的方法\n super().__init__(\"./images/横向的飞机 (2).png\", -15)\n\n def update(self):\n\n super().update()\n\n # 判断子弹是否超出屏幕 如果是 我们要让子弹从精灵组删除\n\n if self.rect.left > SCREEN_RECT.width:\n self.kill()\n", "sub_path": "面向对象/lala2.py", "file_name": "lala2.py", "file_ext": "py", "file_size_in_byte": 4129, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "pygame.Rect", "line_number": 4, "usage_type": "call"}, {"api_name": "pygame.USEREVENT", "line_number": 6, "usage_type": "attribute"}, {"api_name": "pygame.USEREVENT", "line_number": 9, "usage_type": "attribute"}, {"api_name": "pygame.sprite", "line_number": 12, "usage_type": "attribute"}, {"api_name": "pygame.image.load", "line_number": 19, "usage_type": "call"}, {"api_name": "pygame.image", "line_number": 19, "usage_type": "attribute"}, {"api_name": "random.randint", "line_number": 49, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 56, "usage_type": "call"}, {"api_name": "random.randint", "line_number": 59, "usage_type": "call"}, {"api_name": "pygame.sprite.Group", "line_number": 91, "usage_type": "call"}, {"api_name": "pygame.sprite", "line_number": 91, "usage_type": "attribute"}]} +{"seq_id": "99716257", "text": "# coding: utf-8\nfrom multiprocessing import Process\nimport logging\n\nclass Opc(Process):\n def __init__(self, q_parent, q_self):\n super(Opc, self).__init__()\n self.q = {}\n self.q[\"parent\"] = q_parent\n self.q[\"self\"] = q_self\n\n def run(self):\n \"\"\"Main multiprocess routine\"\"\"\n while True:\n try:\n message = self.q[\"self\"].get()\n self.handler(message)\n except Exception as e:\n logging.error(e)\n\n def handler(self, message):\n \"\"\"Message handler\"\"\"\n try:\n m_type = message[\"type\"]\n if m_type == \"cmd\":\n self.command(message)\n elif m_type == \"req\":\n self.request(message)\n elif m_type == \"rep\":\n self.reponse(message)\n else:\n assert False, \"message type non supporté : %s \" % m_type\n except Exception as e:\n logging.error(e)\n\n def command(self, message):\n \"\"\"Commande message\"\"\"\n res = \"opc : \"+str(message)\n self.q[\"parent\"].put(res)\n\n def request(self, message):\n \"\"\"request message\"\"\"\n res = \"opc : \"+str(message)\n self.q[\"parent\"].put(res)\n\n def reponse(self, message):\n \"\"\"reponse message\"\"\"\n print(\"réponse bien reçu\")\n", "sub_path": "languages/python3/multiprocessing/test_complexe2/service/opc.py", "file_name": "opc.py", "file_ext": "py", "file_size_in_byte": 1350, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "multiprocessing.Process", "line_number": 5, "usage_type": "name"}, {"api_name": "logging.error", "line_number": 19, "usage_type": "call"}, {"api_name": "logging.error", "line_number": 34, "usage_type": "call"}]} +{"seq_id": "520026827", "text": "from kubernetes import client as k8s_client\n\nfrom ..native import deployment\nfrom fairing.backend.kubernetes import TF_JOB_VERSION\n\n\nclass KubeflowDeployment(deployment.NativeDeployment):\n\n def __init__(self, namespace, runs, distribution):\n super(KubeflowDeployment, self).__init__(namespace, runs)\n self.distribution = distribution\n\n def deploy(self):\n self.backend.create_tf_job(self.namespace, self.job_spec)\n\n def generate_job(self, pod_template_spec):\n \"\"\"Returns a TFJob template\"\"\"\n self.set_container_name(pod_template_spec)\n\n worker_replica_spec = {}\n worker_replica_spec['replicas'] = self.distribution['Worker']\n worker_replica_spec['template'] = pod_template_spec\n\n ps_replica_spec = {}\n ps_replica_spec['replicas'] = self.distribution.get('PS', 0)\n ps_replica_spec['template'] = pod_template_spec\n\n chief_replica_spec = {}\n chief_replica_spec['replicas'] = self.distribution.get('Chief', 0)\n chief_replica_spec['template'] = pod_template_spec\n\n spec = {}\n spec['tfReplicaSpecs'] = {}\n spec['tfReplicaSpecs']['Worker'] = worker_replica_spec\n if chief_replica_spec['replicas'] > 0:\n spec['tfReplicaSpecs']['Chief'] = chief_replica_spec\n if ps_replica_spec['replicas'] > 0:\n spec['tfReplicaSpecs']['PS'] = ps_replica_spec\n\n tf_job = {}\n tf_job['kind'] = 'TFJob'\n tf_job['apiVersion'] = 'kubeflow.org/' + TF_JOB_VERSION\n tf_job['metadata'] = k8s_client.V1ObjectMeta(name=self.name)\n tf_job['spec'] = spec\n\n return tf_job\n\n def set_container_name(self, pod_template_spec):\n \"\"\"Sets the name of the main container to `tensorflow`.\n This is required for TfJobs\"\"\"\n pod_template_spec.spec.containers[0].name = 'tensorflow'\n\n def get_logs(self):\n selector = 'tf-replica-index=0,tf-replica-type=worker'\n self.backend.log(self.name, self.namespace, selector)\n", "sub_path": "fairing/training/kubeflow/deployment.py", "file_name": "deployment.py", "file_ext": "py", "file_size_in_byte": 2013, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "native.deployment.NativeDeployment", "line_number": 7, "usage_type": "attribute"}, {"api_name": "native.deployment", "line_number": 7, "usage_type": "name"}, {"api_name": "fairing.backend.kubernetes.TF_JOB_VERSION", "line_number": 42, "usage_type": "name"}, {"api_name": "kubernetes.client.V1ObjectMeta", "line_number": 43, "usage_type": "call"}, {"api_name": "kubernetes.client", "line_number": 43, "usage_type": "name"}]} +{"seq_id": "332692955", "text": "import numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.distributions.multivariate_normal import MultivariateNormal\n\n\nclass NormalizingFlows(nn.Module):\n def __init__(self, transforms, dim=2):\n\n super().__init__()\n if isinstance(transforms, nn.Module):\n self.transforms = nn.ModuleList([transforms, ])\n elif isinstance(transforms, list):\n if not all(isinstance(t, nn.Module) for t in transforms):\n raise ValueError(\"Wrong type of transforms\")\n self.transforms = nn.ModuleList(transforms)\n else:\n raise ValueError(f\"Wrong type of transforms\")\n self.dim = dim\n self.base_dist = MultivariateNormal(torch.zeros(self.dim), torch.eye(self.dim))\n\n def log_prob(self, x):\n\n inv_log_det = 0.0\n for transform in reversed(self.transforms):\n z, inv_log_det_jacobian = transform.inverse(x)\n inv_log_det += inv_log_det_jacobian\n x = z\n log_base = self.base_dist.log_prob(x)\n log_prob = (inv_log_det + log_base)\n\n return log_prob\n\n def sample(self, batch_size):\n\n x = self.base_dist.rsample([batch_size])\n log_base = self.base_dist.log_prob(x)\n log_det = 0.0\n for transform in self.transforms:\n x, log_det_jacobian = transform.forward(x)\n log_det += log_det_jacobian\n log_prob = - log_det + log_base\n\n return x, log_prob\n", "sub_path": "NormalizingFlows.py", "file_name": "NormalizingFlows.py", "file_ext": "py", "file_size_in_byte": 1457, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.nn.Module", "line_number": 7, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 7, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 11, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 11, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 12, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 12, "usage_type": "name"}, {"api_name": "torch.nn.Module", "line_number": 14, "usage_type": "attribute"}, {"api_name": "torch.nn", "line_number": 14, "usage_type": "name"}, {"api_name": "torch.nn.ModuleList", "line_number": 16, "usage_type": "call"}, {"api_name": "torch.nn", "line_number": 16, "usage_type": "name"}, {"api_name": "torch.distributions.multivariate_normal.MultivariateNormal", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.zeros", "line_number": 20, "usage_type": "call"}, {"api_name": "torch.eye", "line_number": 20, "usage_type": "call"}]} +{"seq_id": "524455950", "text": "# -*- coding: utf-8 -*-\n#-------------------------------------------------------------------------------\n# Name: checkUpdates\n# Purpose:\n#\n# Author: Bruce Zhu\n#\n# Created: 23/08/2017\n# Copyright: (c) SQA 2017\n# Licence: \n#-------------------------------------------------------------------------------\nimport os\nimport urllib.request\nimport configparser\nimport zipfile\nimport logging\n\nclass checkUpdates:\n\n def __init__(self):\n pass\n\n def downLoadFromURL(self, url, dest_dir):\n try:\n urllib.request.urlretrieve(url , dest_dir)\n return True\n except Exception as e:\n logging.log(logging.DEBUG, 'Error when downloading: {0}'.format(e))\n return False\n\n def getVer(self, verFile):\n downVer = ''\n conf = configparser.ConfigParser()\n try:\n conf.read(verFile)\n downVer = conf.get(\"version\", \"app\")\n except Exception as e:\n logging.log(logging.DEBUG, 'Error: {0}'.format(e))\n return downVer\n\n def splitVer(self, s):\n ver = s.split('.')\n return ver\n\n def compareVer(self, downVer, currentVer):\n downVersions = self.splitVer(downVer)\n currentVersions = self.splitVer(currentVer)\n for i in range(0, len(currentVersions)):\n if int(downVersions[i]) > int(currentVersions[i]):\n return True\n return False\n\n def unzip_dir(self, zipfilename, unzipdirname):\n fullzipfilename = os.path.abspath(zipfilename)\n fullunzipdirname = os.path.abspath(unzipdirname)\n logging.log(logging.DEBUG, \"Start to unzip file %s to folder %s ...\"% (zipfilename, unzipdirname) )\n #Check input ...\n if not os.path.exists(fullzipfilename):\n logging.log(logging.DEBUG, \"Dir/File %s is not exist, Press any key to quit...\"% fullzipfilename )\n inputStr = input()\n return\n if not os.path.exists(fullunzipdirname):\n os.mkdir(fullunzipdirname)\n else:\n if os.path.isfile(fullunzipdirname):\n logging.log(logging.DEBUG, \"File %s is exist, are you sure to delet it first ? [Y/N]\"% fullunzipdirname)\n while 1:\n inputStr = input()\n if inputStr == \"N\" or inputStr == \"n\":\n return\n else:\n if inputStr == \"Y\" or inputStr == \"y\":\n os.remove(fullunzipdirname)\n logging.log(logging.DEBUG, \"Continue to unzip files ...\")\n break\n #Start extract files ...\n #print(fullzipfilename)\n try:\n zipfiles=zipfile.ZipFile(fullzipfilename,'r')\n zipfiles.extractall(unzipdirname)\n zipfiles.close()\n logging.log(logging.DEBUG, \"Unzip finished!\")\n logging.log(logging.DEBUG, \"Unzip file succeed!\")\n except Exception as e:\n logging.log(logging.DEBUG, e)\n\n\nif __name__ == '__main__':\n dest_dir = './downVer.ini'\n checkUpdates = checkUpdates()\n #checkUpdates.downLoadFromURL('http://sw.tymphany.com/fwupdate/sqa/tool/version.ini', dest_dir)\n #downVer = checkUpdates.getVer(dest_dir)\n #checkUpdates.compareVer(downVer, '1.1.0')\n checkUpdates.unzip_dir('PowerCycle.zip', 'PowerCycle')\n", "sub_path": "src/ui/checkUpdates.py", "file_name": "checkUpdates.py", "file_ext": "py", "file_size_in_byte": 3389, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "urllib.request.request.urlretrieve", "line_number": 25, "usage_type": "call"}, {"api_name": "urllib.request.request", "line_number": 25, "usage_type": "attribute"}, {"api_name": "urllib.request", "line_number": 25, "usage_type": "name"}, {"api_name": "logging.log", "line_number": 28, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 28, "usage_type": "attribute"}, {"api_name": "configparser.ConfigParser", "line_number": 33, "usage_type": "call"}, {"api_name": "logging.log", "line_number": 38, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 54, "usage_type": "call"}, {"api_name": "os.path", "line_number": 54, "usage_type": "attribute"}, {"api_name": "os.path.abspath", "line_number": 55, "usage_type": "call"}, {"api_name": "os.path", "line_number": 55, "usage_type": "attribute"}, {"api_name": "logging.log", "line_number": 56, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 58, "usage_type": "call"}, {"api_name": "os.path", "line_number": 58, "usage_type": "attribute"}, {"api_name": "logging.log", "line_number": 59, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 59, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.mkdir", "line_number": 63, "usage_type": "call"}, {"api_name": "os.path.isfile", "line_number": 65, "usage_type": "call"}, {"api_name": "os.path", "line_number": 65, "usage_type": "attribute"}, {"api_name": "logging.log", "line_number": 66, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.remove", "line_number": 73, "usage_type": "call"}, {"api_name": "logging.log", "line_number": 74, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 74, "usage_type": "attribute"}, {"api_name": "zipfile.ZipFile", "line_number": 79, "usage_type": "call"}, {"api_name": "logging.log", "line_number": 82, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 82, "usage_type": "attribute"}, {"api_name": "logging.log", "line_number": 83, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 83, "usage_type": "attribute"}, {"api_name": "logging.log", "line_number": 85, "usage_type": "call"}, {"api_name": "logging.DEBUG", "line_number": 85, "usage_type": "attribute"}]} +{"seq_id": "602492375", "text": "r# termostato\n\nimport mysql.connector\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nconn = mysql.connector.connect(\n\thost='localhost',\n\tuser='root',\n\tpassword='',\n\tdatabase='db2017',\n)\nrs=conn.cursor()\nrs.execute(\"\"\"\n\tSELECT temperatura FROM temperaturas;\n\t\"\"\")\nr=rs.fetchall()\n\n#for i in r:\tprint(i)\n\n# Data for plotting\n#t = np.arange(0.0, 2.0, 0.01)\n#s = 1 + np.sin(2 * np.pi * t)\n\nfig, ax = plt.subplots()\nax.plot(r)\n\nax.set(xlabel='muestras', ylabel='Tª (ºC)',\n title='Registro de temperaturas')\nax.grid()\n\nfig.savefig(\"test.png\")\nplt.show()", "sub_path": "CURSO-Python-2018/ej50.pyw", "file_name": "ej50.pyw", "file_ext": "pyw", "file_size_in_byte": 578, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "mysql.connector.connector.connect", "line_number": 8, "usage_type": "call"}, {"api_name": "mysql.connector.connector", "line_number": 8, "usage_type": "attribute"}, {"api_name": "mysql.connector", "line_number": 8, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 26, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 26, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.show", "line_number": 34, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 34, "usage_type": "name"}]} +{"seq_id": "6140379", "text": "from django.db import models\nfrom django.db.models.signals import pre_save\nfrom django.db.models import Sum\nfrom django.dispatch import receiver\n\n\nclass Customer(models.Model):\n first_name = models.CharField(\n 'First Name', max_length=255,)\n last_name = models.CharField(\n 'Last Name', max_length=255, null=True, blank=True)\n camp_name = models.CharField('Camp Name', max_length=255,)\n date_created = models.DateField('Date Created', auto_now_add=True)\n\n class Meta:\n db_table = 'customer'\n unique_together = ('first_name', 'last_name', 'camp_name',)\n\n def __unicode__(self):\n fn = ''\n ln = ''\n if self.first_name and self.first_name != self.camp_name:\n fn = ' | %s' % self.first_name\n if self.last_name:\n ln = ' | %s' % self.last_name\n return '%s%s%s' % (self.camp_name, fn, ln,)\n\n def debt(self):\n cash_recvd = self.cash_advances.aggregate(\n total_out=Sum('amount'))['total_out'] or 0\n cocoa_given = self.cocoa_given.aggregate(\n cocoa_given=Sum('value'))['cocoa_given'] or 0\n return cash_recvd - cocoa_given\n\n\n@receiver(pre_save, sender=Customer, dispatch_uid='0982gejb2jcjlelmlmponei')\ndef customer_pre_save(sender, **kwargs):\n self = kwargs['instance']\n self.camp_name = self.camp_name.strip().upper()\n if self.pk is None or not self.pk:\n if not self.first_name:\n self.first_name = self.camp_name\n\n if self.first_name:\n self.first_name = self.first_name.strip().upper()\n\n if self.last_name:\n self.last_name = self.last_name.strip().upper()\n", "sub_path": "customer/models.py", "file_name": "models.py", "file_ext": "py", "file_size_in_byte": 1640, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.db.models.Model", "line_number": 7, "usage_type": "attribute"}, {"api_name": "django.db.models", "line_number": 7, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 8, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 8, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 10, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 10, "usage_type": "name"}, {"api_name": "django.db.models.CharField", "line_number": 12, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 12, "usage_type": "name"}, {"api_name": "django.db.models.DateField", "line_number": 13, "usage_type": "call"}, {"api_name": "django.db.models", "line_number": 13, "usage_type": "name"}, {"api_name": "django.db.models.Sum", "line_number": 30, "usage_type": "call"}, {"api_name": "django.db.models.Sum", "line_number": 32, "usage_type": "call"}, {"api_name": "django.dispatch.receiver", "line_number": 36, "usage_type": "call"}, {"api_name": "django.db.models.signals.pre_save", "line_number": 36, "usage_type": "argument"}]} +{"seq_id": "296491730", "text": "from django.core.management.base import BaseCommand\nfrom django.db.utils import OperationalError\n\nfrom customers.models import Customer\nfrom geolocation.models import Location\n\nimport csv\nimport sys\n\n\nclass Command(BaseCommand):\n \"\"\"\n Command that populates the Customers table\n \"\"\"\n\n def __init__(self, *args, **kwargs):\n super().__init__()\n self.customers = self._get_customer_from_file()\n self.cities = [c['city'] for c in self.customers]\n\n def handle(self, *args, **options):\n sys.stdout.write(\"Populating db...\\n\")\n\n try:\n for customer in self.customers:\n Customer.objects.get_or_create(\n id=customer['id'],\n email=customer['email'],\n first_name=customer['first_name'],\n last_name=customer['last_name'],\n gender=customer['gender'],\n company=customer['company'],\n title=customer['title']\n )\n\n i = 1\n for city in self.cities:\n customer = Customer.objects.get(id=i)\n Location.objects.get_or_create(\n customer=customer,\n city=city,\n latitude=0,\n longitude=0\n )\n\n i += 1\n\n except OperationalError as error:\n raise error\n\n sys.stdout.write(\"Db populated\\n\")\n\n def _get_customer_from_file(self):\n with open('./customers.csv') as file:\n reader = csv.DictReader(file)\n return [{\n 'id': row['id'],\n 'email': row['email'],\n 'first_name': row['first_name'],\n 'last_name': row['last_name'],\n 'gender': row['gender'],\n 'company': row['company'],\n 'title': row['title'],\n 'city': row['city']\n }\n for row in reader]\n", "sub_path": "api/core/management/commands/populate_db.py", "file_name": "populate_db.py", "file_ext": "py", "file_size_in_byte": 2066, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.core.management.base.BaseCommand", "line_number": 11, "usage_type": "name"}, {"api_name": "sys.stdout.write", "line_number": 22, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 22, "usage_type": "attribute"}, {"api_name": "customers.models.Customer.objects.get_or_create", "line_number": 26, "usage_type": "call"}, {"api_name": "customers.models.Customer.objects", "line_number": 26, "usage_type": "attribute"}, {"api_name": "customers.models.Customer", "line_number": 26, "usage_type": "name"}, {"api_name": "customers.models.Customer.objects.get", "line_number": 38, "usage_type": "call"}, {"api_name": "customers.models.Customer.objects", "line_number": 38, "usage_type": "attribute"}, {"api_name": "customers.models.Customer", "line_number": 38, "usage_type": "name"}, {"api_name": "geolocation.models.Location.objects.get_or_create", "line_number": 39, "usage_type": "call"}, {"api_name": "geolocation.models.Location.objects", "line_number": 39, "usage_type": "attribute"}, {"api_name": "geolocation.models.Location", "line_number": 39, "usage_type": "name"}, {"api_name": "django.db.utils.OperationalError", "line_number": 48, "usage_type": "name"}, {"api_name": "sys.stdout.write", "line_number": 51, "usage_type": "call"}, {"api_name": "sys.stdout", "line_number": 51, "usage_type": "attribute"}, {"api_name": "csv.DictReader", "line_number": 55, "usage_type": "call"}]} +{"seq_id": "274408394", "text": "import pika\nimport json\nimport threading\nimport operator\nimport pymongo\nimport time\nimport urllib.request as urllib2\nfrom retry import retry\n\nresponse = urllib2.urlopen(\"https://raw.githubusercontent.com/Annu4git/Tools/master/platform_config.txt\")\npage_source = response.read().decode()\n\npp=page_source.split('\\n')\npp.remove('')\nfor i in pp:\n\tt=i.split(' ')\n\nmyclient = pymongo.MongoClient(t[t.index('mongodb')+1])\n\ndatabase = myclient[\"metadata\"]\n\nservice_metadata = database[\"service_metadata\"]\n\nnodes_metadata = database[\"nodes_metadata\"]\n\nsensors_metadata = database[\"sensors_metadata\"]\n\nrunner_ip = t[t.index('rabbit_server_ip')+1]\nreplica = int(t[t.index('replica_count')+1])\nthreshold = float(t[t.index('threshold')+1])\n\nself_ip = t[t.index('loader')+1]\n\nprint('Load Balancer ip: ',self_ip)\n\n#self_ip = '10.2.138.136'\nserver_loads = {}\n\n@retry(pika.exceptions.AMQPConnectionError, delay=5, jitter=(1, 3))\ndef recieveLoads():\n def onLoadsRecieved(ch, method, props, body):\n global server_loads\n rec_data = json.loads(body.decode())\n #pprint.pprint(rec_data)\n\n if rec_data['msg_type'] == 'serverLoads':\n server_loads = rec_data['data']\n\t\t# elif rec_data['msg_type'] == 'Loads':\n\t\t# \tpass\n #pprint.pprint(server_loads)\n\n queue_name = 'monitoring-loadBalancer'\n connection = pika.BlockingConnection(pika.ConnectionParameters(host=runner_ip))\n loadChannel = connection.channel()\n loadChannel.queue_declare(queue=queue_name, auto_delete = True)\n\n loadChannel.basic_consume(queue=queue_name, on_message_callback=onLoadsRecieved)\n print('Consume for load started')\n loadChannel.start_consuming()\n connection.close()\n\ndef check_temp(curr_temp, high_temp):\n if curr_temp >= high_temp:\n return 0\n else:\n return 1\n\ndef compute_score(cpu_percent, memory_percent, cpu_benchmark, free_memory, current_temp, high_temp):\n score = 40. / ( 3./cpu_percent + 1./memory_percent ) # System load\n score *= float(cpu_benchmark)/1e4 + min(2, free_memory)/10. # System performance\n score *= check_temp(current_temp, high_temp) # System temperature\n return score\n\n\n\ndef get_lowest_load_server(layer, req_replica):\n global server_loads\n\n lowest_load_servers = []\n\n server_load_score = {}\n\n print(server_loads)\n\n #print(layer, server_loads[layer].keys())\n\n for IP in server_loads[layer].keys():\n if server_loads[layer][IP]['isExclusiveServer'] == False:\n cpu_percent = float( server_loads[layer][IP]['cpu_free'] )\n memory_percent = float( server_loads[layer][IP]['mem_free'] )\n cpu_benchmark = float( server_loads[layer][IP]['cpu_performance'] )\n free_memory = float( server_loads[layer][IP]['actual_mem_free'] )\n current_temp = int( server_loads[layer][IP]['temp_current'] )\n high_temp = int( server_loads[layer][IP]['temp_high'] )\n\n score = compute_score(cpu_percent, memory_percent, cpu_benchmark, free_memory, current_temp, high_temp)\n server_load_score[IP] = score\n\n sorted_servers = sorted(server_load_score.items(), key=operator.itemgetter(1))\n\n print(sorted_servers)\n\n itr = [len(sorted_servers) if len(sorted_servers) < req_replica else req_replica]\n\n for i in range(itr[0]):\n if sorted_servers[i][1] > threshold:\n lowest_load_servers.append(sorted_servers[i][0])\n\n return lowest_load_servers\n\n@retry(pika.exceptions.AMQPConnectionError, delay=5, jitter=(1, 3))\ndef retrieve_exclusive_nodes(cpu_free_percent, mem_free, cpu_performance):\n global replica, server_loads, runner_ip\n lowest_load_servers = get_lowest_load_server('1', replica)\n \n exclusive_nodes = []\n\n for server in lowest_load_servers:\n if server_loads['1'][server]['cpu_free'] >= cpu_free_percent and server_loads['1'][server]['actual_mem_free'] >= mem_free and float(server_loads['1'][server]['cpu_performance']) >= float(cpu_performance):\n exclusive_nodes.append(server)\n\n # Notify these servers to reserve resources for exclusive service that is about to deploy\n sending_data = {}\n sending_data['msg_type'] = 'acquire_resources'\n sending_data['request_by'] = 'loadBalancer'\n sending_data['data'] = {}\n sending_data['data']['cpu_percent'] = cpu_free_percent\n sending_data['data']['mem_free'] = mem_free\n\n queue_name = 'server-'+server\n connection = pika.BlockingConnection(pika.ConnectionParameters(host=runner_ip))\n loadBalancer_channel = connection.channel()\n loadBalancer_channel.queue_declare(queue=queue_name, auto_delete = True)\n loadBalancer_channel.basic_publish(exchange='', routing_key=queue_name, body=json.dumps(sending_data))\n\n # Update mapping at loadBalancer\n server_loads['1'][server]['cpu_free'] = server_loads['1'][server]['cpu_free'] - cpu_free_percent\n server_loads['1'][server]['cpu_free'] = server_loads['1'][server]['mem_free'] - mem_free\n server_loads['1'][server]['isExclusiveServer'] = True\n\n # Update the database\n\n if len(exclusive_nodes) < replica:\n # num of exclusive nodes to start = replica-exclusive\n print('Required to up new exclusive servers and return its ips')\n\n\n return exclusive_nodes\n\n@retry(pika.exceptions.AMQPConnectionError, delay=5, jitter=(1, 3))\ndef recieveRequests():\n \n @retry(pika.exceptions.AMQPConnectionError, delay=5, jitter=(1, 3))\n def onRequestsRecieved(ch, method, props, body):\n global server_loads, replica\n rec_data = json.loads(body.decode())\n\n if rec_data['msg_type'] == 'serverLoads':\n server_loads = rec_data['data']\n #if rec_data['msg_type'] == 'Loads':\n #pass\n #pprint.pprint(server_loads)\n\n elif rec_data['msg_type'] == 'scheduleJobs' and rec_data['request_by'] == 'scheduler':\n\n service_id = rec_data['data']['service_id']\n application_id = rec_data['data']['application_id']\n locality_tag = rec_data['data']['locality_tag']\n trigger_type = rec_data['data']['trigger_type']\n\n myquery = { \"service_id\": service_id, \"application_id\": application_id }\n mydoc = service_metadata.find(myquery)\n\n print('Recieved from scheduler')\n \n sensor_query = {\"locality_tag\": locality_tag}\n sensors_data = sensors_metadata.find(sensor_query)\n input_stream_ips = []\n for sensor in sensors_data:\n input_stream_ips.append(sensor[\"ip\"])\n \n if trigger_type == 'stop':\n query = {'service_id': service_id, 'application_id':application_id}\n result = service_metadata.find(query)\n print('Stopping ',service_id, application_id)\n if result.count() == 1:\n for x in result:\n serving_nodes = x['serving_nodes']\n service_state = x['service_state']\n if service_state == 'running':\n sending_data = {}\n sending_data['msg_type'] = 'scheduleJob'\n sending_data['request_by'] = 'loadBalancer'\n sending_data['ip'] = self_ip\n sending_data['data'] = {}\n sending_data['data']['service_id'] = service_id\n sending_data['data']['application_id'] = application_id\n sending_data['data']['input_stream'] = input_stream_ips\n sending_data['data']['trigger_type'] = trigger_type\n \n print('Retrieved serving nodes for stop: ',serving_nodes)\n \n for i in serving_nodes.split(' '):\n if i!='':\n service_running_ip = i\n queue_name = 'server-'+service_running_ip\n print('sending to: ',i)\n connection = pika.BlockingConnection(pika.ConnectionParameters(host=runner_ip))\n loadBalancer_channel = connection.channel()\n loadBalancer_channel.queue_declare(queue=queue_name, auto_delete = True)\n loadBalancer_channel.basic_publish(exchange='', routing_key=queue_name, body=json.dumps(sending_data))\n else:\n print('No service is running with service_id: ',service_id, ' and application_id: ',application_id)\n elif result.count() > 1:\n print('More than one service with same name stored in the database...')\n elif result.count() == 0:\n print('The service ',service_id,' is not stored in the database...')\n \n elif trigger_type == 'start':\n for x in mydoc:\n deployed_nodes = x['node_ips'].split(' ')\n deployed_node_ips = []\n for i in range(len(deployed_nodes)):\n deployed_node_ips.append(deployed_nodes[i].split(':')[0])\n service_rest_url = x['rest_url']\n \n service_priority = x['priority']\n capable_servers = []\n isNodeUp = False\n print('Following are the ips from service metadata')\n print(deployed_node_ips)\n for node_ip in deployed_node_ips:\n if service_priority == \"high\":\n l = '1'\n elif service_priority == \"low\":\n l = '0'\n cpu_percent = float( server_loads[l][node_ip]['cpu_free'] )\n memory_percent = float( server_loads[l][node_ip]['mem_free'] )\n cpu_benchmark = float( server_loads[l][node_ip]['cpu_performance'] )\n free_memory = float( server_loads[l][node_ip]['actual_mem_free'] )\n current_temp = int( server_loads[l][node_ip]['temp_current'] )\n high_temp = int( server_loads[l][node_ip]['temp_high'] )\n \n calc_threshold = compute_score(cpu_percent, memory_percent, cpu_benchmark, free_memory, current_temp, high_temp)\n \n print(node_ip)\n \n nodes_query = { \"ip\": node_ip }\n responsibleNodes = nodes_metadata.find(nodes_query)\n \n for node in responsibleNodes:\n if node['nodeState'] == \"active\":\n isNodeUp = True\n print('Node: ', node_ip, \" is responsible node!\")\n break\n \n print('Threshold is: ',calc_threshold)\n \n if calc_threshold > threshold and isNodeUp:\n capable_servers.append((calc_threshold, node_ip))\n isNodeUp = False\n \n \n # if any server out of the two is down or unable to handle load\n if len(capable_servers) == 0:\n print('Need to shift models to a less loaded server or start new servers....')\n # lowest_load_servers = get_lowest_load_server(rec_data['data']['layer'])\n # need to move services across servers\n else:\n print('Scheduling start job to the servers....')\n for server in capable_servers:\n sending_data = {}\n sending_data['msg_type'] = 'scheduleJob'\n sending_data['request_by'] = 'loadBalancer'\n sending_data['ip'] = self_ip\n sending_data['data'] = {}\n sending_data['data']['service_id'] = service_id\n sending_data['data']['application_id'] = application_id\n sending_data['data']['input_stream'] = input_stream_ips\n sending_data['data']['trigger_type'] = trigger_type\n queue_name = 'server-'+server[1]\n connection = pika.BlockingConnection(pika.ConnectionParameters(host=runner_ip))\n loadBalancer_channel = connection.channel()\n loadBalancer_channel.queue_declare(queue=queue_name, auto_delete = True)\n loadBalancer_channel.basic_publish(exchange='', routing_key=queue_name, body=json.dumps(sending_data))\n \n if len(capable_servers) < replica:\n print('Start ',replica-len(capable_servers), ' number of servers')\n\n\n elif rec_data['msg_type'] == 'deployServices' and rec_data['request_by'] == 'deployManager':\n\n print('Message Recieved from deploy manager')\n\n if rec_data['data']['service_type'] == 'exclusive':\n\n lowest_load_servers = retrieve_exclusive_nodes(rec_data['data']['service_requirement']['cpu_free'],\n rec_data['data']['service_requirement']['mem_free'],\n rec_data['data']['service_requirement']['cpu_performance'])\n elif rec_data['data']['service_type'] == 'normal':\n if rec_data['data']['layer'] == '0':\n lowest_load_edge = get_lowest_load_server(rec_data['data']['layer'], 1)\n if len(lowest_load_edge) == 0:\n lowest_load_servers = get_lowest_load_server('1', replica)\n if len(lowest_load_servers) < replica:\n print('Required to up a new server and add its ip to lowest load servers')\n else:\n lowest_load_servers = get_lowest_load_server('1', replica-1)\n if len(lowest_load_servers) < replica-1:\n print('Required to up a new server and add its ip to lowest load servers')\n lowest_load_servers.append(lowest_load_edge[0])\n else:\n lowest_load_servers = get_lowest_load_server(rec_data['data']['layer'], replica)\n if len(lowest_load_servers) < replica:\n print('Required to up a new server and add its ip to lowest load servers')\n\n sending_data = {}\n sending_data['msg_type'] = 'deployServicesResp'\n sending_data['request_by'] = 'loadBalancer'\n sending_data['data'] = {}\n sending_data['data']['lowest_load_servers'] = lowest_load_servers\n sending_data['data']['application_id'] = rec_data['data']['application_id']\n sending_data['data']['service_id'] = rec_data['data']['service_id']\n sending_data['data']['nature_of_service'] = rec_data['data']['nature_of_service']\n sending_data['ip'] = self_ip\n\n queue_name = 'deployManager-'+rec_data['ip']\n connection = pika.BlockingConnection(pika.ConnectionParameters(host=runner_ip))\n loadBalancer_channel = connection.channel()\n loadBalancer_channel.queue_declare(queue=queue_name, auto_delete = True)\n loadBalancer_channel.basic_publish(exchange='', routing_key=queue_name, body=json.dumps(sending_data))\n\n queue_name = 'loadBalancer-'+self_ip\n\n connection = pika.BlockingConnection(pika.ConnectionParameters(host=runner_ip))\n reqChannel = connection.channel()\n reqChannel.queue_declare(queue=queue_name, auto_delete = True)\n\n reqChannel.basic_consume(queue=queue_name, on_message_callback=onRequestsRecieved)\n\n print('Consume for other msgs started')\n\n reqChannel.start_consuming()\n connection.close()\n\n@retry(pika.exceptions.AMQPConnectionError, delay=5, jitter=(1, 3))\ndef sendHeartbeat():\n while True:\n serving_data = {}\n serving_data['msg_type'] = 'Heartbeat'\n serving_data['ip'] = self_ip\n serving_data['node_type'] = 'loadBalancer'\n queue_name = 'logging_queue'\n connection1 = pika.BlockingConnection(pika.ConnectionParameters(host=runner_ip))\n channel1 = connection1.channel()\n channel1.queue_declare(queue=queue_name, auto_delete = True)\n channel1.basic_publish(exchange='', routing_key=queue_name, body=json.dumps(serving_data))\n time.sleep(2)\n\n\nthread_recv_loads = threading.Thread(target=recieveLoads, args=())\nthread_recv_requests = threading.Thread(target=recieveRequests, args=())\nthread_send_heartbeat = threading.Thread(target=sendHeartbeat, args=())\n#thread_recv_loads.deamon = True\nthread_recv_loads.start()\n#thread_recv_requests.daemon = True\nthread_recv_requests.start()\n#thread_send_heartbeat.daemon = True\nthread_send_heartbeat.start()\nthread_recv_loads.join()\nthread_recv_requests.join()\nthread_send_heartbeat.join()\n", "sub_path": "platform_old/loadBalancer.py", "file_name": "loadBalancer.py", "file_ext": "py", "file_size_in_byte": 17346, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "urllib.request.urlopen", "line_number": 10, "usage_type": "call"}, {"api_name": "urllib.request", "line_number": 10, "usage_type": "name"}, {"api_name": "pymongo.MongoClient", "line_number": 18, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 43, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 53, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 53, "usage_type": "call"}, {"api_name": "retry.retry", "line_number": 39, "usage_type": "call"}, {"api_name": "pika.exceptions", "line_number": 39, "usage_type": "attribute"}, {"api_name": "operator.itemgetter", "line_number": 99, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 131, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 131, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 134, "usage_type": "call"}, {"api_name": "retry.retry", "line_number": 111, "usage_type": "call"}, {"api_name": "pika.exceptions", "line_number": 111, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 156, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 208, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 208, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 211, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 282, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 282, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 285, "usage_type": "call"}, {"api_name": "pika.BlockingConnection", "line_number": 328, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 328, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 331, "usage_type": "call"}, {"api_name": "retry.retry", "line_number": 153, "usage_type": "call"}, {"api_name": "pika.exceptions", "line_number": 153, "usage_type": "attribute"}, {"api_name": "pika.BlockingConnection", "line_number": 335, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 335, "usage_type": "call"}, {"api_name": "retry.retry", "line_number": 150, "usage_type": "call"}, {"api_name": "pika.exceptions", "line_number": 150, "usage_type": "attribute"}, {"api_name": "pika.BlockingConnection", "line_number": 354, "usage_type": "call"}, {"api_name": "pika.ConnectionParameters", "line_number": 354, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 357, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 358, "usage_type": "call"}, {"api_name": "retry.retry", "line_number": 346, "usage_type": "call"}, {"api_name": "pika.exceptions", "line_number": 346, "usage_type": "attribute"}, {"api_name": "threading.Thread", "line_number": 361, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 362, "usage_type": "call"}, {"api_name": "threading.Thread", "line_number": 363, "usage_type": "call"}]} +{"seq_id": "382225563", "text": "import os\nimport time\nimport pyperclip as pc\nimport string\nimport tkinter\nfrom tkinter import *\nfrom tkinter.ttk import Combobox\nimport tkinter.messagebox as mg\nfrom EasyCode.EasyCode import get_all_drives\navailable_drives = get_all_drives()\navailable_drives.insert(0, \"Select\")\navailable_drives.append(\"Desktop\")\nclass App:\n search_over = False\n def __init__(self, root):\n # initilizing the root as a part of class\n self.root = root\n # making initial size of the root\n self.root.geometry(\"500x250+510+250\")\n # Setting apps title \n self.root.title(\"File Finder\")\n ################ Row 1 ####################\n self.label1 = Label(self.root, text=\"Disk:\", font=(\"times new roman\", 20))\n self.label1.place(x=80, y=50)\n self.com = Combobox(self.root, state=\"readonly\",font = (\"times new roman\", 15))\n self.com.place(x=200, y = 50)\n self.com['values'] = available_drives\n self.com.current(0)\n ################ Row 2 ######################\n self.label2 = Label(self.root, text=\"Filename: \", font=(\"times new roman\", 20))\n self.label2.place(x = 80, y=120)\n self.text = Entry(self.root, font=(\"times new roman\", 20), bg=\"lightgray\", fg=\"black\")\n self.text.place(x=200, y=120, width=250)\n ################ Creating a button for search #########################\n self.button = Button(self.root, text=\"Search \",bd=3, relief=RAISED, command=self.get_info)\n self.button.place(x=190, y=200, width=150)\n \n\n def get_info(self):\n \n drive = self.com.get()+\"\\\\\"\n if \"desktop\" in drive.lower():\n drive = os.path.join(os.environ['USERPROFILE'], \"Desktop\")\n filename = self.text.get()\n self.search(drive, filename)\n\n\n def search(self, drive, fn):\n self.var = StringVar()\n self.var.set(\"Searching...\")\n \n self.label3 = Label(self.root, textvariable=self.var, font=(\"times new roman\", 10))\n self.label3.place(x=0,y=220)\n self.root.update()\n time.sleep(0.8)\n \n self.drive = drive \n self.fn = fn\n for root, dirs, files in os.walk(drive):\n for file in files:\n filename = file\n path_of_file = os.path.join(root, file)\n if fn.lower() in filename.lower():\n self.search_over = True\n os.startfile(path_of_file)\n mg.showinfo(\"Succes\", f\"\"\"\n File was found\n The file was located in : {root}\n The path of the file is : {path_of_file}\"\"\")\n pc.copy(root)\n self.var.set(\"Completed\")\n self.root.update()\n return\n\n mg.showerror(\"No file found\", \"No such file found\")\n def animate(self):\n no=0\n while not self.search_over:\n if no==0:\n self.var.set(\"Searching.\")\n self.root.update()\n time.sleep(0.8)\n no+=1\n elif no==1:\n self.var.set(\"Searching..\")\n self.root.update()\n time.sleep(0.8)\n no+=1\n elif no==2:\n self.var.set(\"Searching...\")\n self.root.update()\n time.sleep(0.8)\n no = 0\n \nroot = Tk()\napp = App(root)\nroot.mainloop()\n", "sub_path": "File Finder.pyw", "file_name": "File Finder.pyw", "file_ext": "pyw", "file_size_in_byte": 3478, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "EasyCode.EasyCode.get_all_drives", "line_number": 10, "usage_type": "call"}, {"api_name": "tkinter.ttk.Combobox", "line_number": 25, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 43, "usage_type": "call"}, {"api_name": "os.path", "line_number": 43, "usage_type": "attribute"}, {"api_name": "os.environ", "line_number": 43, "usage_type": "attribute"}, {"api_name": "time.sleep", "line_number": 55, "usage_type": "call"}, {"api_name": "os.walk", "line_number": 59, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 62, "usage_type": "call"}, {"api_name": "os.path", "line_number": 62, "usage_type": "attribute"}, {"api_name": "os.startfile", "line_number": 65, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showinfo", "line_number": 66, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 66, "usage_type": "name"}, {"api_name": "pyperclip.copy", "line_number": 70, "usage_type": "call"}, {"api_name": "tkinter.messagebox.showerror", "line_number": 75, "usage_type": "call"}, {"api_name": "tkinter.messagebox", "line_number": 75, "usage_type": "name"}, {"api_name": "time.sleep", "line_number": 82, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 87, "usage_type": "call"}, {"api_name": "time.sleep", "line_number": 92, "usage_type": "call"}]} +{"seq_id": "263114141", "text": "#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport tarfile\nimport sys\nimport os\nimport argparse\n\nfrom minelibs import *\n\n### tar.gz\ndef make_tarDir(output_tar_name, source_dir_list):\n with tarfile.open(output_tar_name, \"w:gz\") as tar:\n for item in source_dir_list:\n if item.endswith('/'):\n item = item[:-1]\n print(item)\n tar.add(item, arcname=os.path.basename(item))\n\ndef _argparse(para_list=None):\n parser = argparse.ArgumentParser(description='- Tar file or directory -')\n parser.add_argument('source', metavar='source_name', type=str, nargs='*',\n help='source list')\n\n parser.add_argument('-f', '--tarname', action='store', dest='tar_name',\n help='the Tar filename')\n return parser.parse_args(para_list)\n\nif __name__ == \"__main__\":\n parser = _argparse() if len(sys.argv) > 1 else _argparse(['-h'])\n\n if parser.tar_name:\n if not parser.tar_name.endswith('.tar.gz'):\n tar_name = parser.tar_name + '.tar.gz'\n else:\n tar_name = parser.tar_name\n else:\n if parser.source[0].endswith('/'):\n tar_name = os.path.basename((parser.source[0])[0:-1]) + '.tar.gz'\n else:\n tar_name = os.path.basename(parser.source[0]) + '.tar.gz'\n\n print(tar_name)\n print(get_current_time(True))\n for item in parser.source:\n if not os.path.exists(item):\n print('%s is not exist !' % item)\n raise SystemExit('Exiting ...')\n\n make_tarDir(tar_name, parser.source)\n\n", "sub_path": "x.bin/tarfile_addDir.py", "file_name": "tarfile_addDir.py", "file_ext": "py", "file_size_in_byte": 1571, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "tarfile.open", "line_number": 13, "usage_type": "call"}, {"api_name": "os.path.basename", "line_number": 18, "usage_type": "call"}, {"api_name": "os.path", "line_number": 18, "usage_type": "attribute"}, {"api_name": "argparse.ArgumentParser", "line_number": 21, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 30, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 39, "usage_type": "call"}, {"api_name": "os.path", "line_number": 39, "usage_type": "attribute"}, {"api_name": "os.path.basename", "line_number": 41, "usage_type": "call"}, {"api_name": "os.path", "line_number": 41, "usage_type": "attribute"}, {"api_name": "os.path.exists", "line_number": 46, "usage_type": "call"}, {"api_name": "os.path", "line_number": 46, "usage_type": "attribute"}]} +{"seq_id": "623379799", "text": "from docxcompose.utils import xpath\nfrom lxml.etree import Element\nfrom lxml.etree import QName\n\n\nclass StructuredDocumentTags(object):\n \"\"\"Structured Document Tags (aka Content Controls)\"\"\"\n\n def __init__(self, doc):\n self.doc = doc\n\n def tags_by_alias(self, alias):\n \"\"\"Get Structured Document Tags by alias.\"\"\"\n return xpath(\n self.doc.element.body,\n './/w:sdt/w:sdtPr/w:alias[@w:val=\"%s\"]/ancestor::w:sdt' % alias)\n\n def set_text(self, alias, text):\n \"\"\"Set the text content of all Structured Document Tags identified by\n an alias. Only plain text SDTs are supported.\n\n If the SDT has the 'multiLine' property, newlines in `text` will be\n respected, and the SDTs content will be updated with lines separated\n by line breaks.\n \"\"\"\n text = text.strip()\n tags = self.tags_by_alias(alias)\n for tag in tags:\n # Ignore if it's not a plain text SDT\n plain_text = xpath(tag, './w:sdtPr/w:text')\n if not plain_text:\n continue\n\n nsmap = tag.nsmap\n is_multiline = bool(plain_text[0].xpath('./@w:multiLine', namespaces=nsmap))\n\n properties = xpath(tag, './w:sdtPr')\n content = xpath(tag, './w:sdtContent')\n if not content:\n continue\n\n run_elements = xpath(content[0], './/w:r')\n if not run_elements:\n continue\n\n # First, prepare the SDT for easy updating of its value.\n #\n # We do this by cleaning out the SDT content to only preserve\n # the first of possibly many runs, and remove the contents of\n # that run (except w:rPr formatting properties).\n #\n # That run can then be filled with new text nodes and line breaks\n # as needed. This should allow us to preserve formatting, but\n # otherwise start from a clean slate where we create new nodes\n # instead of having to carefully update an existing structure.\n\n first_run = run_elements[0]\n self._remove_placeholder(properties, content, first_run)\n self._remove_all_runs_except_first(run_elements)\n self._clean_first_run(first_run)\n\n # Now update contents by appending new text nodes.\n #\n # If the SDT has the multiLine property, we respect newlines\n # in the input value string and create text nodes delimited by\n # line breaks.\n if not is_multiline:\n text = text.replace('\\n', ' ')\n\n lines = text.splitlines()\n for i, line in enumerate(lines, start=1):\n txt_node = Element(QName(nsmap['w'], \"t\"))\n txt_node.text = line\n first_run.append(txt_node)\n\n if i != len(lines):\n br = Element(QName(nsmap['w'], \"br\"))\n first_run.append(br)\n\n def _remove_placeholder(self, properties, content, first_run):\n \"\"\"Remove placeholder marker and style.\n \"\"\"\n showing_placeholder = xpath(properties[0], './w:showingPlcHdr')\n if showing_placeholder:\n properties[0].remove(showing_placeholder[0])\n run_props = xpath(first_run, './w:rPr')\n if run_props:\n first_run.remove(run_props[0])\n\n def _remove_all_runs_except_first(self, run_elements):\n \"\"\"Remove all runs except the first one.\n \"\"\"\n for run in run_elements[1:]:\n run.getparent().remove(run)\n\n def _clean_first_run(self, first_run):\n \"\"\"Remove all elements from the first run except run formatting.\n \"\"\"\n for child in first_run.getchildren():\n # Preserve formatting\n if QName(child).localname == 'rPr':\n continue\n first_run.remove(child)\n\n def get_text(self, alias):\n \"\"\"Get the text content of the first Structured Document Tag identified\n by the given alias.\n \"\"\"\n tags = self.tags_by_alias(alias)\n for tag in tags:\n # Ignore if it's not a plain text SDT\n if not xpath(tag, './w:sdtPr/w:text'):\n continue\n\n tokens = []\n text_and_brs = xpath(tag, './w:sdtContent//w:r/*[self::w:t or self::w:br]')\n for el in text_and_brs:\n if QName(el).localname == 't':\n tokens.append(el.text)\n elif QName(el).localname == 'br':\n tokens.append('\\n')\n\n return ''.join(tokens)\n", "sub_path": "docxcompose/sdt.py", "file_name": "sdt.py", "file_ext": "py", "file_size_in_byte": 4643, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "docxcompose.utils.xpath", "line_number": 14, "usage_type": "call"}, {"api_name": "docxcompose.utils.xpath", "line_number": 30, "usage_type": "call"}, {"api_name": "docxcompose.utils.xpath", "line_number": 37, "usage_type": "call"}, {"api_name": "docxcompose.utils.xpath", "line_number": 38, "usage_type": "call"}, {"api_name": "docxcompose.utils.xpath", "line_number": 42, "usage_type": "call"}, {"api_name": "lxml.etree.Element", "line_number": 72, "usage_type": "call"}, {"api_name": "lxml.etree.QName", "line_number": 72, "usage_type": "call"}, {"api_name": "lxml.etree.Element", "line_number": 77, "usage_type": "call"}, {"api_name": "lxml.etree.QName", "line_number": 77, "usage_type": "call"}, {"api_name": "docxcompose.utils.xpath", "line_number": 83, "usage_type": "call"}, {"api_name": "docxcompose.utils.xpath", "line_number": 86, "usage_type": "call"}, {"api_name": "lxml.etree.QName", "line_number": 101, "usage_type": "call"}, {"api_name": "docxcompose.utils.xpath", "line_number": 112, "usage_type": "call"}, {"api_name": "docxcompose.utils.xpath", "line_number": 116, "usage_type": "call"}, {"api_name": "lxml.etree.QName", "line_number": 118, "usage_type": "call"}, {"api_name": "lxml.etree.QName", "line_number": 120, "usage_type": "call"}]} +{"seq_id": "565161935", "text": "import cgi\n\nfrom google.appengine.ext import webapp\nfrom google.appengine.ext.webapp.util import run_wsgi_app\nfrom google.appengine.api import urlfetch\nfrom django.utils import simplejson as json\n\nAPP_ID = '365508406808632'\nAPP_SECRET = 'ac8a79f5b39168d19629644cbf10780a'\nCANVAS_PAGE = 'http://herrpfeffer.appspot.com/'\n\nsigned_request = ''\n\nclass MainPage(webapp.RequestHandler):\n def get(self):\n self.response.headers['Content-Type'] = 'text/plain'\n code = self.request.get('code')\n if code:\n url = ('https://graph.facebook.com/oauth/access_token?'\n + 'client_id=' + APP_ID\n + '&redirect_uri=' + CANVAS_PAGE\n + '&client_secret=' + APP_SECRET\n + '&code=' + code)\n result = urlfetch.fetch(url)\n if result.status_code == 200:\n data = cgi.parse_qs(result.content)\n access_token = ''.join(data['access_token'])\n url = ('https://graph.facebook.com/me?'\n + 'access_token=' + access_token)\n result = urlfetch.fetch(url)\n user = json.loads(result.content)\n self.response.out.write('Hello ' + user['first_name'] + ' '\n + user['last_name'])\n else:\n self.response.out.write(\"Authentication error\")\n else:\n self.redirect('https://www.facebook.com/dialog/oauth?'\n + 'client_id=' + APP_ID\n + '&redirect_uri=' + CANVAS_PAGE)\n\napplication = webapp.WSGIApplication(\n [('/', MainPage)],\n debug=True)\n\ndef main():\n run_wsgi_app(application)\n\nif __name__ == \"__main__\":\n main()\n", "sub_path": "qndfacebook/qndfacebook.py", "file_name": "qndfacebook.py", "file_ext": "py", "file_size_in_byte": 1811, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "google.appengine.ext.webapp.RequestHandler", "line_number": 14, "usage_type": "attribute"}, {"api_name": "google.appengine.ext.webapp", "line_number": 14, "usage_type": "name"}, {"api_name": "google.appengine.api.urlfetch.fetch", "line_number": 24, "usage_type": "call"}, {"api_name": "google.appengine.api.urlfetch", "line_number": 24, "usage_type": "name"}, {"api_name": "cgi.parse_qs", "line_number": 26, "usage_type": "call"}, {"api_name": "google.appengine.api.urlfetch.fetch", "line_number": 30, "usage_type": "call"}, {"api_name": "google.appengine.api.urlfetch", "line_number": 30, "usage_type": "name"}, {"api_name": "django.utils.simplejson.loads", "line_number": 31, "usage_type": "call"}, {"api_name": "django.utils.simplejson", "line_number": 31, "usage_type": "name"}, {"api_name": "google.appengine.ext.webapp.WSGIApplication", "line_number": 41, "usage_type": "call"}, {"api_name": "google.appengine.ext.webapp", "line_number": 41, "usage_type": "name"}, {"api_name": "google.appengine.ext.webapp.util.run_wsgi_app", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "203332600", "text": "#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri Oct 11 21:16:17 2019\n\n@author: kristin.lomicka\n\"\"\"\n\nimport dataset\nimport pandas as pd\nimport numpy as np\n\n#connect to postgres\nds = dataset.connect(\"postgresql://postgres@localhost/hw1_data_warehouse\")\n#export sql tables\ncustomers_a = pd.DataFrame(ds['customers_a'].all())\nemployees_new = pd.DataFrame(ds['employees_new'].all())\noffices_a = pd.DataFrame(ds['offices_a'].all())\norder_metadata_a = pd.DataFrame(ds['order_metadata_a'].all())\norders_new_a = pd.DataFrame(ds['orders_new_a'].all())\nproducts_a = pd.DataFrame(ds['products_a'].all())\n\n#create date dimension table\ndate_d = order_metadata_a[['order_date']].drop_duplicates().reset_index(drop=True)\ndate_d['order_date_time'] = pd.to_datetime(date_d['order_date'])\ndate_d['order_date'] = pd.to_datetime(date_d['order_date']).dt.date\ndate_d['day_of_week'] = date_d['order_date_time'].dt.weekday_name\ndate_d['month'] = date_d['order_date_time'].dt.month\ndate_d['year'] = date_d['order_date_time'].dt.year\ndate_d['quarter']= date_d['order_date_time'].dt.quarter\n#insert unique ID\ndate_d.insert(0, 'date_id', range(1, 1 + len(date_d)))\n\n\n#create remaining dimension tables\nemployees_d = employees_new[['employee_number', 'last_name', 'first_name', 'reports_to', 'job_title', 'office_code']]\noffices_d = offices_a[['office_code', 'city', 'state', 'country', 'office_location']]\nproducts_d = products_a[['product_line', 'product_code', 'product_name', 'product_scale', 'product_vendor', 'product_description', 'quantity_in_stock', 'buy_price', '_m_s_r_p', 'html_description']]\ncustomers_d = customers_a[['customer_number', 'customer_name', 'contact_last_name', 'contact_first_name', 'city', 'state', 'country']]\n\n#check employees_d for duplicate values\ndupes_employees_d = employees_new.pivot_table(index=['employee_number'], aggfunc='size')\nprint(dupes_employees_d)\n\n#create measure table\norders_measure = orders_new_a[['order_number', 'order_line_number', 'customer_number', 'product_code', 'quantity_ordered', 'price_each']]\norders_measure = pd.merge(orders_measure, order_metadata_a[['order_number', 'order_date', 'sales_rep_employee_number']], on='order_number', how='left')\norders_measure.rename(columns={'sales_rep_employee_number':'employee_number'}, inplace=True)\norders_measure = pd.merge(orders_measure, employees_new[['office_code', 'employee_number']], on='employee_number', how='left')\ndate_d['order_date'] = pd.to_datetime(date_d['order_date']).dt.date\n\n#calculate total cost\norders_measure = pd.merge(orders_measure, products_d[['product_code', 'buy_price']], on='product_code', how='left')\norders_measure['total_cost'] = orders_measure['quantity_ordered'] * orders_measure['buy_price']\norders_measure = orders_measure.drop(columns= 'buy_price')\n\n#calculate total revenue\norders_measure['total_revenue'] = orders_measure['quantity_ordered'] * orders_measure['price_each']\n\n\n#calculate total profit\norders_measure['total_profit'] = orders_measure['total_revenue'] - orders_measure['total_cost']\n\n#calculate profit_margin\norders_measure['profit_margin'] = orders_measure['total_profit'] / orders_measure['total_cost'] * 100\n\n#Question: Is there a way to perform the calculation directly from the other dataframe?\n\n#import new dataframes into postgresql\n#import employees_d\nds = dataset.connect(\"postgresql://postgres@localhost/hw2_analytics_db\")\ncs = ds['employees_d'] #python code name#\ncs.insert_many(employees_d.to_dict('records')) #sql code name#\n# # # import offices_d\nds = dataset.connect(\"postgresql://postgres@localhost/hw2_analytics_db\")\ncs = ds['offices_d'] #python code name#\ncs.insert_many(offices_d.to_dict('records')) #sql code name#\n# # #import products_d\nds = dataset.connect(\"postgresql://postgres@localhost/hw2_analytics_db\")\ncs = ds['products_d'] #python code name#\ncs.insert_many(products_d.to_dict('records')) #sql code name#\n# # #import customers_d\nds = dataset.connect(\"postgresql://postgres@localhost/hw2_analytics_db\")\ncs = ds['customers_d'] #python code name#\ncs.insert_many(customers_d.to_dict('records')) #sql code name#\n# # #import date_d\nds = dataset.connect(\"postgresql://postgres@localhost/hw2_analytics_db\")\ncs = ds['date_d'] #python code name#\ncs.insert_many(date_d.to_dict('records')) #sql code name#\n# # #import orders_measure\nds = dataset.connect(\"postgresql://postgres@localhost/hw2_analytics_db\")\ncs = ds['orders_measure'] #python code name#\ncs.insert_many(orders_measure.to_dict('records')) #sql code name#", "sub_path": "homework/data_warehousing_hw_2_KL.py", "file_name": "data_warehousing_hw_2_KL.py", "file_ext": "py", "file_size_in_byte": 4477, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "dataset.connect", "line_number": 14, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 16, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 17, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 18, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 19, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 20, "usage_type": "call"}, {"api_name": "pandas.DataFrame", "line_number": 21, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 25, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 26, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 47, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 49, "usage_type": "call"}, {"api_name": "pandas.to_datetime", "line_number": 50, "usage_type": "call"}, {"api_name": "pandas.merge", "line_number": 53, "usage_type": "call"}, {"api_name": "dataset.connect", "line_number": 71, "usage_type": "call"}, {"api_name": "dataset.connect", "line_number": 75, "usage_type": "call"}, {"api_name": "dataset.connect", "line_number": 79, "usage_type": "call"}, {"api_name": "dataset.connect", "line_number": 83, "usage_type": "call"}, {"api_name": "dataset.connect", "line_number": 87, "usage_type": "call"}, {"api_name": "dataset.connect", "line_number": 91, "usage_type": "call"}]} +{"seq_id": "194459497", "text": "#!/usr/bin/python\n# -*- coding: UTF-8 -*-\nimport serial\nimport json\n\nser = serial.Serial('/dev/ttyACM0',9600)\nwhile True:\n data = ser.readline().decode(\"utf-8\").rstrip('\\r\\n')\n json_data = json.dumps([{'valo': data}])\n with open('/var/www/html/valo.json', 'w') as outfile:\n json.dump(json.JSONDecoder().decode(json_data), outfile)\n \n print (json_data)\nser.close()", "sub_path": "brightnessArvo.py", "file_name": "brightnessArvo.py", "file_ext": "py", "file_size_in_byte": 385, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "serial.Serial", "line_number": 6, "usage_type": "call"}, {"api_name": "json.dumps", "line_number": 9, "usage_type": "call"}, {"api_name": "json.dump", "line_number": 11, "usage_type": "call"}, {"api_name": "json.JSONDecoder", "line_number": 11, "usage_type": "call"}]} +{"seq_id": "339915965", "text": "from django.urls import path\nfrom . import views\nfrom .views import PostListView,PostDetailView,PostCreateView,PostUpdateView,PostDeleteView,UserPostListView\nfrom .views import post_list,post_detail,upvote_post,gallery\nfrom .views import EventDeleteView,EventUpdateView,EventCreateView,event_list,participate\nfrom .views import notice_list,NoticeUpdateView,NoticeDeleteView,NoticeCreateView\n\n# urlpatterns=[\n# path('',PostListView.as_view(),name='blog-home'),\n# path('user/',UserPostListView.as_view(),name='user-posts'),\n# path('post//',PostDetailView.as_view(),name='post-detail'),\n# path('post/new/',PostCreateView.as_view(),name='post-create'),\n# path('post//update/',PostUpdateView.as_view(),name='post-update'),\n# path('post//delete/',PostDeleteView.as_view(),name='post-delete'),\n# path('about/',views.about,name='blog-about'),\n# ]\n # taking some function based views\nurlpatterns=[\n path('',post_list,name='blog-home'),\n path('user//',UserPostListView.as_view(),name='user-posts'),\n path('post//',post_detail,name='post-detail'),\n path('post/upvote/',upvote_post,name='upvote_post'),\n path('post/new/',PostCreateView.as_view(),name='post-create'),\n path('post//update/',PostUpdateView.as_view(),name='post-update'),\n path('post//delete/',PostDeleteView.as_view(),name='post-delete'),\n path('event/new/',EventCreateView.as_view(),name='event-create'),\n path('event//update/',EventUpdateView.as_view(),name='event-update'),\n path('event/list/',event_list,name='event-list'),\n path('event//delete/',EventDeleteView.as_view(),name='event-delete'),\n path('event/participate/',participate,name=\"participate\"),\n path('notice/list/',notice_list,name='notice-list'),\n path('notice/new/',NoticeCreateView.as_view(),name='notice-create'),\n path('notice//update/',NoticeUpdateView.as_view(),name='notice-update'),\n path('notice//delete/',NoticeDeleteView.as_view(),name='notice-delete'),\n path('about/',views.about,name='blog-about'),\n path('gallery/',gallery,name=\"gallery\")\n]\n", "sub_path": "blog/urls.py", "file_name": "urls.py", "file_ext": "py", "file_size_in_byte": 2166, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "django.urls.path", "line_number": 19, "usage_type": "call"}, {"api_name": "views.post_list", "line_number": 19, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 20, "usage_type": "call"}, {"api_name": "views.UserPostListView.as_view", "line_number": 20, "usage_type": "call"}, {"api_name": "views.UserPostListView", "line_number": 20, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 21, "usage_type": "call"}, {"api_name": "views.post_detail", "line_number": 21, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 22, "usage_type": "call"}, {"api_name": "views.upvote_post", "line_number": 22, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 23, "usage_type": "call"}, {"api_name": "views.PostCreateView.as_view", "line_number": 23, "usage_type": "call"}, {"api_name": "views.PostCreateView", "line_number": 23, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 24, "usage_type": "call"}, {"api_name": "views.PostUpdateView.as_view", "line_number": 24, "usage_type": "call"}, {"api_name": "views.PostUpdateView", "line_number": 24, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 25, "usage_type": "call"}, {"api_name": "views.PostDeleteView.as_view", "line_number": 25, "usage_type": "call"}, {"api_name": "views.PostDeleteView", "line_number": 25, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 26, "usage_type": "call"}, {"api_name": "views.EventCreateView.as_view", "line_number": 26, "usage_type": "call"}, {"api_name": "views.EventCreateView", "line_number": 26, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 27, "usage_type": "call"}, {"api_name": "views.EventUpdateView.as_view", "line_number": 27, "usage_type": "call"}, {"api_name": "views.EventUpdateView", "line_number": 27, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 28, "usage_type": "call"}, {"api_name": "views.event_list", "line_number": 28, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 29, "usage_type": "call"}, {"api_name": "views.EventDeleteView.as_view", "line_number": 29, "usage_type": "call"}, {"api_name": "views.EventDeleteView", "line_number": 29, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 30, "usage_type": "call"}, {"api_name": "views.participate", "line_number": 30, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 31, "usage_type": "call"}, {"api_name": "views.notice_list", "line_number": 31, "usage_type": "argument"}, {"api_name": "django.urls.path", "line_number": 32, "usage_type": "call"}, {"api_name": "views.NoticeCreateView.as_view", "line_number": 32, "usage_type": "call"}, {"api_name": "views.NoticeCreateView", "line_number": 32, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 33, "usage_type": "call"}, {"api_name": "views.NoticeUpdateView.as_view", "line_number": 33, "usage_type": "call"}, {"api_name": "views.NoticeUpdateView", "line_number": 33, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 34, "usage_type": "call"}, {"api_name": "views.NoticeDeleteView.as_view", "line_number": 34, "usage_type": "call"}, {"api_name": "views.NoticeDeleteView", "line_number": 34, "usage_type": "name"}, {"api_name": "django.urls.path", "line_number": 35, "usage_type": "call"}, {"api_name": "views.about", "line_number": 35, "usage_type": "attribute"}, {"api_name": "django.urls.path", "line_number": 36, "usage_type": "call"}, {"api_name": "views.gallery", "line_number": 36, "usage_type": "argument"}]} +{"seq_id": "290938856", "text": "from tools import *\n\nfrom variables import *\nfrom cuts import cuts_Bu, prntCuts, mctrue\nfrom model import model_Bu\nfrom data import mc_Pythia6, mc_Pythia8, mc_total\n\n\n\n# Preparation\ntBu = mc_total.data\ncuts_Bu += mctrue\n\nmodel_Bu.b.fix(0)\nmodel_Bu.background.tau.fix(0)\n\nfor i in prntCuts(cuts_Bu, \" CUTS B+ \"):\n logger.info(i)\n\n\n\n\n# logger.info('Fill control B+ histogram (takes some time)')\n# with timing():\n# tBu.Project(h1.GetName(), 'DTFm_b', cuts_Bu)\n\n\n# with rooSilent():\n# logger.info('Fit Bc+ & B+ histogram (check the model)')\n# r, f = model_Bu.fitHisto(h1)\n\n\n\n\nsel_Bu = SelectorWithVars(\n variables=selector_variables,\n selection=cuts_Bu\n)\n\nlogger.info('Build RooFit dataset for B+ , it could take as long as 3-5 minutes')\n\ntBu.process(sel_Bu)\n\nds_Bu = sel_Bu.dataset()\nds_Bu.Print('v')\n\n\n\nlogger.info('Make unbinned fit for B+')\n\nmodel_Bu.s.setMax(1.2 * len(ds_Bu))\nru, fu = model_Bu.fitTo(ds_Bu, draw=True, nbins=nbin_Bu)\n\nmodel_Bu.signal.sigma.release()\nru, fu = model_Bu.fitTo(ds_Bu, draw=True, nbins=nbin_Bu)\n\nmodel_Bu.signal.mean.release()\nru, fu = model_Bu.fitTo(ds_Bu, draw=True, nbins=nbin_Bu)\n\nmodel_Bu.signal.aR.release()\nru, fu = model_Bu.fitTo(ds_Bu, draw=True, nbins=nbin_Bu)\n\nmodel_Bu.signal.aL.release()\nru, fu = model_Bu.fitTo(ds_Bu, draw=True, nbins=nbin_Bu)\n\nmodel_Bu.signal.nR.release()\nru, fu = model_Bu.fitTo(ds_Bu, draw=True, nbins=nbin_Bu)\n\nmodel_Bu.signal.nL.release()\nru, fu = model_Bu.fitTo(ds_Bu, draw=True, nbins=nbin_Bu)\n\n\nfu.SetXTitle('#Inv.\\,mass(J/\\psi\\,K\\pi\\pi), GeV/c^2')\nfu.SetYTitle('Events / (%d \\, MeV/c^{2})' % events_binning)\n\nfu.Draw()\n\n# logger.info('running sPlot')\n# model_Bu.sPlot(ds_Bu)\n\n\n# print 'FIT#2 results for B+ ', ru(model_Bu.s_name)[0]\n# print 'FIT#2 precision:', ru(\"SBu\")[0].prec()\n", "sub_path": "Kpipi/fit/fit_mc.py", "file_name": "fit_mc.py", "file_ext": "py", "file_size_in_byte": 1776, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "data.mc_total.data", "line_number": 11, "usage_type": "attribute"}, {"api_name": "data.mc_total", "line_number": 11, "usage_type": "name"}, {"api_name": "cuts.cuts_Bu", "line_number": 12, "usage_type": "name"}, {"api_name": "cuts.mctrue", "line_number": 12, "usage_type": "name"}, {"api_name": "model.model_Bu.b.fix", "line_number": 14, "usage_type": "call"}, {"api_name": "model.model_Bu.b", "line_number": 14, "usage_type": "attribute"}, {"api_name": "model.model_Bu", "line_number": 14, "usage_type": "name"}, {"api_name": "model.model_Bu.background.tau.fix", "line_number": 15, "usage_type": "call"}, {"api_name": "model.model_Bu.background", "line_number": 15, "usage_type": "attribute"}, {"api_name": "model.model_Bu", "line_number": 15, "usage_type": "name"}, {"api_name": "cuts.prntCuts", "line_number": 17, "usage_type": "call"}, {"api_name": "cuts.cuts_Bu", "line_number": 17, "usage_type": "argument"}, {"api_name": "cuts.cuts_Bu", "line_number": 37, "usage_type": "name"}, {"api_name": "model.model_Bu.s.setMax", "line_number": 51, "usage_type": "call"}, {"api_name": "model.model_Bu.s", "line_number": 51, "usage_type": "attribute"}, {"api_name": "model.model_Bu", "line_number": 51, "usage_type": "name"}, {"api_name": "model.model_Bu.fitTo", "line_number": 52, "usage_type": "call"}, {"api_name": "model.model_Bu", "line_number": 52, "usage_type": "name"}, {"api_name": "model.model_Bu.signal.sigma.release", "line_number": 54, "usage_type": "call"}, {"api_name": "model.model_Bu.signal", "line_number": 54, "usage_type": "attribute"}, {"api_name": "model.model_Bu", "line_number": 54, "usage_type": "name"}, {"api_name": "model.model_Bu.fitTo", "line_number": 55, "usage_type": "call"}, {"api_name": "model.model_Bu", "line_number": 55, "usage_type": "name"}, {"api_name": "model.model_Bu.signal.mean.release", "line_number": 57, "usage_type": "call"}, {"api_name": "model.model_Bu.signal", "line_number": 57, "usage_type": "attribute"}, {"api_name": "model.model_Bu", "line_number": 57, "usage_type": "name"}, {"api_name": "model.model_Bu.fitTo", "line_number": 58, "usage_type": "call"}, {"api_name": "model.model_Bu", "line_number": 58, "usage_type": "name"}, {"api_name": "model.model_Bu.signal.aR.release", "line_number": 60, "usage_type": "call"}, {"api_name": "model.model_Bu.signal", "line_number": 60, "usage_type": "attribute"}, {"api_name": "model.model_Bu", "line_number": 60, "usage_type": "name"}, {"api_name": "model.model_Bu.fitTo", "line_number": 61, "usage_type": "call"}, {"api_name": "model.model_Bu", "line_number": 61, "usage_type": "name"}, {"api_name": "model.model_Bu.signal.aL.release", "line_number": 63, "usage_type": "call"}, {"api_name": "model.model_Bu.signal", "line_number": 63, "usage_type": "attribute"}, {"api_name": "model.model_Bu", "line_number": 63, "usage_type": "name"}, {"api_name": "model.model_Bu.fitTo", "line_number": 64, "usage_type": "call"}, {"api_name": "model.model_Bu", "line_number": 64, "usage_type": "name"}, {"api_name": "model.model_Bu.signal.nR.release", "line_number": 66, "usage_type": "call"}, {"api_name": "model.model_Bu.signal", "line_number": 66, "usage_type": "attribute"}, {"api_name": "model.model_Bu", "line_number": 66, "usage_type": "name"}, {"api_name": "model.model_Bu.fitTo", "line_number": 67, "usage_type": "call"}, {"api_name": "model.model_Bu", "line_number": 67, "usage_type": "name"}, {"api_name": "model.model_Bu.signal.nL.release", "line_number": 69, "usage_type": "call"}, {"api_name": "model.model_Bu.signal", "line_number": 69, "usage_type": "attribute"}, {"api_name": "model.model_Bu", "line_number": 69, "usage_type": "name"}, {"api_name": "model.model_Bu.fitTo", "line_number": 70, "usage_type": "call"}, {"api_name": "model.model_Bu", "line_number": 70, "usage_type": "name"}]} +{"seq_id": "206665174", "text": "# main.py\n\nfrom controller.travel_controller import TravelController\nfrom wx import App # import the wxPython GUI package\n\n\ndef main():\n # Create a wxPython application object and the controller for it\n app = App(False)\n controller = TravelController(app)\n controller.init_ui()\n controller.populate_controls()\n controller.show()\n app.MainLoop() # enters the mainloop\n\nif __name__ == '__main__':\n main()\n", "sub_path": "travel_request/main.py", "file_name": "main.py", "file_ext": "py", "file_size_in_byte": 429, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "wx.App", "line_number": 9, "usage_type": "call"}, {"api_name": "controller.travel_controller", "line_number": 10, "usage_type": "name"}, {"api_name": "controller.travel_controller.TravelController", "line_number": 10, "usage_type": "call"}, {"api_name": "controller.travel_controller.init_ui", "line_number": 11, "usage_type": "call"}, {"api_name": "controller.travel_controller", "line_number": 11, "usage_type": "name"}, {"api_name": "controller.travel_controller.populate_controls", "line_number": 12, "usage_type": "call"}, {"api_name": "controller.travel_controller", "line_number": 12, "usage_type": "name"}, {"api_name": "controller.travel_controller.show", "line_number": 13, "usage_type": "call"}, {"api_name": "controller.travel_controller", "line_number": 13, "usage_type": "name"}]} +{"seq_id": "47162520", "text": "\n\nimport json\nfrom flask import Blueprint, request, abort, jsonify, g, current_app\nfrom API.blibb.blibb import Blibb\nfrom API.event.event import Event\nfrom API.contenttypes.picture import Picture\nfrom API.user.buser import User\nfrom API.utils import is_valid_id, get_user_name, get_key\nfrom bson.objectid import ObjectId\n\nfrom API.decorators import crossdomain, support_jsonp\n\n\nmod = Blueprint('blibb', __name__, url_prefix='/blibb')\n\n\n@mod.route('/favicon.ico')\n@mod.route('/robots.txt')\n@mod.route('/index.html')\n@mod.route('/scripts/')\ndef handle(any=None):\n abort(404)\n\n\n@mod.before_request\ndef before_request():\n g.e = Event(request.path)\n\n\n@mod.teardown_request\ndef teardown_request(exception):\n g.e.save()\n\n#\n# /blibb [POST, DELETE]\n#\n\n\n@mod.route('', methods=['POST'])\n@crossdomain(origin='*')\ndef newBlibb():\n name = request.form['bname']\n desc = request.form['bdesc']\n template = request.form['btemplate']\n key = request.form['bkey']\n user = get_user_name(key)\n image_id = request.form['bimage']\n slug = request.form['slug']\n write_access = request.form['write_access']\n read_access = request.form['read_access']\n\n # check if a blibb with that slug already exists\n blibb = Blibb.get_by_slug(user, slug)\n # return jsonify(blibb)\n\n if not blibb:\n res = {'error': 'None'}\n if is_valid_id(image_id):\n image = Picture.dump_image(image_id)\n else:\n image = 'blibb.png'\n\n new_id = Blibb.insert(user, name, slug, desc, template, image, read_access, write_access)\n res = {'id': new_id}\n else:\n res = {'error': 'Blibb with that slug already exists'}\n return jsonify(res)\n\n\n@mod.route('/view', methods=['PUT'])\n@crossdomain(origin='*')\ndef updateView():\n blibb_id = request.form['blibb_id']\n user = get_user_name(request.form['login_key'])\n view = request.form['viewName']\n html = request.form['viewHtml']\n # current_app.logger.info(user + ' ' + blibb_id + ' ' + view + ' ' + html)\n if is_valid_id(blibb_id):\n if Blibb.can_write(user, '', blibb_id):\n Blibb.update_view(blibb_id, user, view, html)\n return jsonify({'result': 'View Updated'})\n else:\n abort(401)\n abort(404)\n\n\n@mod.route('//', methods=['DELETE'])\n@crossdomain(origin='*')\ndef deleteBlibb(blibb_id=None, login_key=None):\n user = get_user_name(login_key)\n if is_valid_id(blibb_id):\n filter = {'_id': ObjectId(blibb_id), 'u': user}\n Blibb.remove(filter)\n return jsonify({'ret': 1})\n\n\n@mod.route('//p/', methods=['GET'])\n@crossdomain(origin='*')\ndef getBlibb(blibb_id=None, params=None):\n if blibb_id is None:\n abort(404)\n\n if params is None:\n o = Blibb.get_object(blibb_id)\n r = Blibb.flat_object(o)\n else:\n r = Blibb.get_by_id_params(blibb_id, params)\n\n if r != 'null':\n return jsonify(r)\n else:\n abort(404)\n\n@mod.route('/short/', methods=['GET'])\n@support_jsonp\ndef getBlibbShort(short_id=None):\n if short_id is None:\n abort(404)\n\n o = Blibb.get_object({'si': short_id})\n r = Blibb.flat_object(o)\n\n if r != 'null':\n return jsonify(r)\n else:\n abort(404)\n\n\n@mod.route('//template', methods=['GET'])\n@crossdomain(origin='*')\ndef getBlibbTemplate(blibb_id=None):\n b = Blibb()\n r = b.get_template(blibb_id)\n if r != 'null':\n return r\n else:\n abort(404)\n\n\n@mod.route('//view', methods=['GET'])\n@crossdomain(origin='*')\ndef getBlibbView(blibb_id=None, view_name='null'):\n if is_valid_id(blibb_id):\n r = Blibb.get_template_view(blibb_id)\n if r != 'null':\n return jsonify(r)\n else:\n abort(404)\n else:\n abort(400)\n\n\n@mod.route('/', methods=['GET'])\n@crossdomain(origin='*')\n@support_jsonp\ndef getBlibbByUser(username=None):\n b = Blibb()\n if username is None:\n abort(404)\n res = b.get_by_user(username)\n return jsonify(res)\n\n\n@mod.route('//group', methods=['GET'])\n@crossdomain(origin='*')\ndef getGroupBlibbByUser(username=None):\n b = Blibb()\n if username is None:\n abort(404)\n res = b.getByGroupUser(username)\n return res\n\n\n@mod.route('/fork', methods=['POST'])\n@crossdomain(origin='*')\ndef fork():\n key = request.form['login_key']\n user = get_user_name(key)\n target_id = request.form['b']\n Blibb.fork(target_id, user)\n return json.dumps('ok')\n\n\n#####################\n####### TAGS #######\n#####################\n\n@mod.route('/tag', methods=['POST'])\n@crossdomain(origin='*')\ndef newTag():\n target_id = None\n target = None\n key = request.form['k']\n user = get_user_name(key)\n target_id = request.form['b']\n if Blibb.can_write(target_id, user):\n tag = request.form['t']\n target.addTag(target_id, tag)\n\n return json.dumps('ok')\n\n\n@mod.route('/action/image', methods=['POST'])\n@crossdomain(origin='*')\ndef updateImage():\n object_id = request.form['object_id']\n image_id = request.form['image_id']\n if object_id is None:\n abort(404)\n if is_valid_id(image_id) and is_valid_id(object_id):\n Blibb.add_picture({'_id': ObjectId(object_id)}, image_id)\n return 'ok'\n\n\n@mod.route('/actions/webhook', methods=['POST'])\n@crossdomain(origin='*')\ndef add_webhook():\n key = request.form['login_key']\n bid = request.form['blibb_id']\n callback = request.form['callback']\n fields = request.form['fields']\n action = request.form['action']\n user = get_key(key)\n res = dict()\n wb = {'a': action, 'u': callback, 'f': fields}\n if is_valid_id(bid):\n if Blibb.can_write(user, '', bid):\n Blibb.add_webhook(bid, wb)\n res['result'] = 'ok'\n else:\n abort(401)\n else:\n res['error'] = 'Object Id is not valid'\n return jsonify(res)\n\n\n@mod.route('/actions/group', methods=['POST'])\n@crossdomain(origin='*')\ndef add_user_to_group():\n key = request.form['login_key']\n bid = request.form['blibb_id']\n username = request.form['username']\n user = get_key(key)\n res = dict()\n if is_valid_id(bid):\n user_to_add = User.get_by_name(username)\n if user_to_add:\n if Blibb.can_write(user, '', bid):\n Blibb.add_user_to_group(username, bid)\n res['result'] = 'ok'\n else:\n res['error'] = 'Not permissions'\n else:\n res['error'] = 'User not found'\n else:\n res['error'] = 'Object Id is not valid'\n return jsonify(res)\n\n\n@mod.route('/meta/webhooks/', methods=['GET'])\n@crossdomain(origin='*')\ndef getWebhooks(bid=None):\n if is_valid_id(bid):\n b = Blibb()\n fields = b.get_webhooks(bid)\n return jsonify({'webhooks': fields})\n else:\n return jsonify({'error': 'Object id not valid'})\n\n\n@mod.route('/meta/fields/', methods=['GET'])\n@crossdomain(origin='*')\ndef getBlibbFields(bid=None):\n if bid is not None:\n fields = Blibb.get_fields(bid)\n return jsonify({'fields': fields})\n\n\n@mod.route('/object/', methods=['GET'])\n@crossdomain(origin='*')\ndef getObject(bid=None):\n if bid is not None:\n params = request.args.get('fields')\n fields = dict()\n for p in params.split(','):\n fields[p] = 1\n current_app.logger.info(fields)\n doc = Blibb.get_object({'_id': ObjectId(bid)}, fields)\n blibb = Blibb.to_dict(doc)\n #\n return jsonify(Blibb.flat_object(blibb))\n abort(404)\n", "sub_path": "API/blibb/weblibb.py", "file_name": "weblibb.py", "file_ext": "py", "file_size_in_byte": 7607, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask.Blueprint", "line_number": 15, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 23, "usage_type": "call"}, {"api_name": "flask.g.e", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 28, "usage_type": "name"}, {"api_name": "API.event.event.Event", "line_number": 28, "usage_type": "call"}, {"api_name": "flask.request.path", "line_number": 28, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 28, "usage_type": "name"}, {"api_name": "flask.g.e.save", "line_number": 33, "usage_type": "call"}, {"api_name": "flask.g.e", "line_number": 33, "usage_type": "attribute"}, {"api_name": "flask.g", "line_number": 33, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 43, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 43, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 44, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 44, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 45, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 45, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 46, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 46, "usage_type": "name"}, {"api_name": "API.utils.get_user_name", "line_number": 47, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 48, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 48, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 49, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 49, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 50, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 50, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 51, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 51, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb.get_by_slug", "line_number": 54, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 54, "usage_type": "name"}, {"api_name": "API.utils.is_valid_id", "line_number": 59, "usage_type": "call"}, {"api_name": "API.contenttypes.picture.Picture.dump_image", "line_number": 60, "usage_type": "call"}, {"api_name": "API.contenttypes.picture.Picture", "line_number": 60, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb.insert", "line_number": 64, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 64, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 68, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 41, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 74, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 74, "usage_type": "name"}, {"api_name": "API.utils.get_user_name", "line_number": 75, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 75, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 75, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 76, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 76, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 77, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 77, "usage_type": "name"}, {"api_name": "API.utils.is_valid_id", "line_number": 79, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb.can_write", "line_number": 80, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 80, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb.update_view", "line_number": 81, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 81, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 82, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 84, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 85, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 72, "usage_type": "call"}, {"api_name": "API.utils.get_user_name", "line_number": 91, "usage_type": "call"}, {"api_name": "API.utils.is_valid_id", "line_number": 92, "usage_type": "call"}, {"api_name": "bson.objectid.ObjectId", "line_number": 93, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb.remove", "line_number": 94, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 94, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 95, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 89, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 102, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb.get_object", "line_number": 105, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 105, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb.flat_object", "line_number": 106, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 106, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb.get_by_id_params", "line_number": 108, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 108, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 111, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 113, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 99, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 119, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb.get_object", "line_number": 121, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 121, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb.flat_object", "line_number": 122, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 122, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 125, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 127, "usage_type": "call"}, {"api_name": "API.decorators.support_jsonp", "line_number": 116, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 133, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 138, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 131, "usage_type": "call"}, {"api_name": "API.utils.is_valid_id", "line_number": 144, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb.get_template_view", "line_number": 145, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 145, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 147, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 149, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 151, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 142, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 158, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 160, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 162, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 155, "usage_type": "call"}, {"api_name": "API.decorators.support_jsonp", "line_number": 156, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 168, "usage_type": "call"}, {"api_name": "flask.abort", "line_number": 170, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 166, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 178, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 178, "usage_type": "name"}, {"api_name": "API.utils.get_user_name", "line_number": 179, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 180, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 180, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb.fork", "line_number": 181, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 181, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 182, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 176, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 194, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 194, "usage_type": "name"}, {"api_name": "API.utils.get_user_name", "line_number": 195, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 196, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 196, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb.can_write", "line_number": 197, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 197, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 198, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 198, "usage_type": "name"}, {"api_name": "json.dumps", "line_number": 201, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 190, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 207, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 207, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 208, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 208, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 210, "usage_type": "call"}, {"api_name": "API.utils.is_valid_id", "line_number": 211, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb.add_picture", "line_number": 212, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 212, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 212, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 205, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 219, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 219, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 220, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 220, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 221, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 221, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 222, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 222, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 223, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 223, "usage_type": "name"}, {"api_name": "API.utils.get_key", "line_number": 224, "usage_type": "call"}, {"api_name": "API.utils.is_valid_id", "line_number": 227, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb.can_write", "line_number": 228, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 228, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb.add_webhook", "line_number": 229, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 229, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 232, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 235, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 217, "usage_type": "call"}, {"api_name": "flask.request.form", "line_number": 241, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 241, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 242, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 242, "usage_type": "name"}, {"api_name": "flask.request.form", "line_number": 243, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 243, "usage_type": "name"}, {"api_name": "API.utils.get_key", "line_number": 244, "usage_type": "call"}, {"api_name": "API.utils.is_valid_id", "line_number": 246, "usage_type": "call"}, {"api_name": "API.user.buser.User.get_by_name", "line_number": 247, "usage_type": "call"}, {"api_name": "API.user.buser.User", "line_number": 247, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb.can_write", "line_number": 249, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 249, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb.add_user_to_group", "line_number": 250, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 250, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 258, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 239, "usage_type": "call"}, {"api_name": "API.utils.is_valid_id", "line_number": 264, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 265, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 267, "usage_type": "call"}, {"api_name": "flask.jsonify", "line_number": 269, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 262, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb.get_fields", "line_number": 276, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 276, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 277, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 273, "usage_type": "call"}, {"api_name": "flask.request.args.get", "line_number": 284, "usage_type": "call"}, {"api_name": "flask.request.args", "line_number": 284, "usage_type": "attribute"}, {"api_name": "flask.request", "line_number": 284, "usage_type": "name"}, {"api_name": "flask.current_app.logger.info", "line_number": 288, "usage_type": "call"}, {"api_name": "flask.current_app.logger", "line_number": 288, "usage_type": "attribute"}, {"api_name": "flask.current_app", "line_number": 288, "usage_type": "name"}, {"api_name": "API.blibb.blibb.Blibb.get_object", "line_number": 289, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 289, "usage_type": "name"}, {"api_name": "bson.objectid.ObjectId", "line_number": 289, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb.to_dict", "line_number": 290, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 290, "usage_type": "name"}, {"api_name": "flask.jsonify", "line_number": 292, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb.flat_object", "line_number": 292, "usage_type": "call"}, {"api_name": "API.blibb.blibb.Blibb", "line_number": 292, "usage_type": "name"}, {"api_name": "flask.abort", "line_number": 293, "usage_type": "call"}, {"api_name": "API.decorators.crossdomain", "line_number": 281, "usage_type": "call"}]} +{"seq_id": "468873593", "text": "#!/usr/local/bin/python3\n\"\"\"\nPlot the % Population of Somalia that are categorized in each IPC level over\ntime.\nThe IPC (Integrated Food Security Phase Classification) has 5 levels:\n Level 1: Minimal - Level 2: Stressed - Level 3: Crisis - Level 4: Emergency\n - Level 5: Famine\n\"\"\"\n\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nplt.style.use('seaborn-white')\n\n# input_file = 'Dev/IPC Population Figures.xlsx'\ninput_file = 'IPC Population Figures Tracking Sheet.xlsx'\ncountry = \"Somalia\"\nmax_pop = 15500000\ncol_heads = ['country', 'pop', 'date', 'rev_pop', '%pop', 'period',\n 'IPC1-pop', 'IPC1-%rev_pop', 'IPC2-pop', 'IPC2-%rev_pop',\n 'IPC3-pop', 'IPC3-%rev_pop', 'IPC4-pop', 'IPC4-%rev_pop',\n 'IPC5-pop', 'IPC5-%rev_pop', 'IPC3>-pop', 'IPC3>-%rev_pop']\n\n\n# plot_cols = ['date', 'IPC1-pop', 'IPC2-pop', 'IPC3-pop', 'IPC4-pop',\n# 'IPC5-pop']\n\nplot_cols = ['date', 'IPC1-pop']\n\nexcel_dump_df = pd.read_excel(input_file, header=[2], usecols=\"B,D:T\")\n# print(excel_dump_df.head())\nprint(excel_dump_df.columns)\nsomalia_ipc = excel_dump_df.loc[excel_dump_df['Country'] == country]\n# print(\"columns:\", len(somalia_ipc.columns), \"rows:\", len(somalia_ipc.index))\nprint(somalia_ipc)\nsomalia_ipc.columns = col_heads\nprint(somalia_ipc)\nprint(somalia_ipc.loc[:, 'period'])\nsomalia_ipc_chart = somalia_ipc[plot_cols].copy()\nprint(somalia_ipc_chart)\n\nfor idx, row in somalia_ipc_chart.iterrows():\n dt = row['date'].to_pydatetime()\n print('month:', dt.month, type(dt.year), 'year:', dt.year, type(dt.year))\n dt_str = str(dt.month).capitalize() + '-' + str(dt.year)\n print(dt_str, type(dt_str))\n somalia_ipc_chart.ix[idx, 'date'] = dt_str\n\nprint(somalia_ipc_chart)\n\nsomalia_ipc_chart = somalia_ipc_chart.set_index('date')\n\nprint(somalia_ipc_chart)\n\nsomalia_ipc_chart = somalia_ipc_chart.transpose()\n\nprint(somalia_ipc_chart)\n\n# ============================\n\n# somalia_ipc_chart.plot.hist(grid=True, alpha=0.5, # normed=True,\n# histtype='stepfilled', rwidth=0.9,\n# edgecolor='none') # color='steelblue',\n\n# plt.title('Somalia IPC Level Population Count')\n# plt.xlabel('IPC Level')\n# plt.ylabel('Population')\n# # plt.xticks(somalia_ipc_chart['date'])\n# plt.grid(axis='y', alpha=0.75)\n# # plt.text(23, 45, r'$\\mu=15, b=3$')\n\n\n# =============================\n\n\nsns.set(style=\"whitegrid\")\n\ng = sns.catplot(data=somalia_ipc_chart,\n kind=\"bar\", palette=\"muted\")\n\n# g = sns.relplot(kind=\"line\", data=somalia_ipc_chart)\n\ng.despine(left=True)\ng.set_ylabels(\"survival probability\")\n\n\n# ==============================\n\n# sns.kdeplot(somalia_ipc_chart, shade=True)\n\n# chart = sns.load_dataset(somalia_ipc_chart)\n# ax = sns.lineplot(data=chart)\n\n# x = np.linspace(0, 10, 100)\n\n# plt.plot(x, np.sin(x))\n# plt.plot(x, np.cos(x))\n\n# plt.savefig('Dev/fig.png')\n\n# fig, ax = plt.subplots(2)\n# ax[0].plot(x, np.sin(x))\n# ax[1].plot(x, np.cos(x))\n\n# data = np.random.rand(1000)\n\n# plt.hist(data)\n# plt.hist(data, bins=30, normed=True, alpha=0.5, histtype='stepfilled',\n# color='steelblue', edgecolor='none')\n\nplt.show() # should be used only once in a script\n", "sub_path": "somalia/somalia_IPC_chart.py", "file_name": "somalia_IPC_chart.py", "file_ext": "py", "file_size_in_byte": 3222, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "matplotlib.pyplot.style.use", "line_number": 15, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.style", "line_number": 15, "usage_type": "attribute"}, {"api_name": "matplotlib.pyplot", "line_number": 15, "usage_type": "name"}, {"api_name": "pandas.read_excel", "line_number": 32, "usage_type": "call"}, {"api_name": "seaborn.set", "line_number": 78, "usage_type": "call"}, {"api_name": "seaborn.catplot", "line_number": 80, "usage_type": "call"}, {"api_name": "matplotlib.pyplot.show", "line_number": 113, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 113, "usage_type": "name"}]} +{"seq_id": "303621448", "text": "from django.db import transaction as django_db_transaction\r\n\r\nfrom courses import models as courses_models\r\nfrom grades import models as grades_models\r\nfrom grades import non_persistent_models as grades_non_persistent_models\r\n\r\n\r\nclass ExamService(object):\r\n model = grades_models.Exam\r\n\r\n @classmethod\r\n @django_db_transaction.atomic\r\n def get_or_create(\r\n cls,\r\n params: grades_non_persistent_models.CreateExamParams,\r\n ) -> courses_models.CourseInstance:\r\n exam, _ = cls.model.objects.get_or_create(\r\n defaults={},\r\n **{\r\n grades_models.Exam.course_group.field.name: params.course_group,\r\n grades_models.Exam.moed.field.name: params.moed,\r\n grades_models.Exam.students_count.field.name: params.students_count,\r\n grades_models.Exam.failures_count.field.name: params.failures_count,\r\n }\r\n )\r\n\r\n grades_models.ExamStatistics.objects.get_or_create(\r\n defaults={\r\n grades_models.ExamStatistics.mean.field.name: params.mean,\r\n grades_models.ExamStatistics.median.field.name: params.median,\r\n grades_models.ExamStatistics.standard_deviation.field.name: params.standard_deviation,\r\n },\r\n **{\r\n grades_models.ExamStatistics.exam.field.name: exam,\r\n },\r\n )\r\n\r\n grade_ranges = tuple(\r\n grades_models.ExamGradeRange(**{\r\n grades_models.ExamGradeRange.exam.field.name: exam,\r\n grades_models.ExamGradeRange.lowest_grade.field.name: grade_range_params.lowest_grade,\r\n grades_models.ExamGradeRange.highest_grade.field.name: grade_range_params.highest_grade,\r\n grades_models.ExamGradeRange.students_in_range.field.name: grade_range_params.students_in_range,\r\n })\r\n for grade_range_params in params.grade_ranges\r\n )\r\n grades_models.ExamGradeRange.objects.bulk_create(grade_ranges, ignore_conflicts=True)\r\n\r\n return exam\r\n", "sub_path": "grades/services.py", "file_name": "services.py", "file_ext": "py", "file_size_in_byte": 2089, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "grades.models.Exam", "line_number": 9, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 9, "usage_type": "name"}, {"api_name": "grades.non_persistent_models.CreateExamParams", "line_number": 15, "usage_type": "attribute"}, {"api_name": "grades.non_persistent_models", "line_number": 15, "usage_type": "name"}, {"api_name": "grades.models.Exam", "line_number": 20, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 20, "usage_type": "name"}, {"api_name": "grades.models.Exam", "line_number": 21, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 21, "usage_type": "name"}, {"api_name": "grades.models.Exam", "line_number": 22, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 22, "usage_type": "name"}, {"api_name": "grades.models.Exam", "line_number": 23, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 23, "usage_type": "name"}, {"api_name": "grades.models.ExamStatistics.objects.get_or_create", "line_number": 27, "usage_type": "call"}, {"api_name": "grades.models.ExamStatistics", "line_number": 27, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 27, "usage_type": "name"}, {"api_name": "grades.models.ExamStatistics", "line_number": 29, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 29, "usage_type": "name"}, {"api_name": "grades.models.ExamStatistics", "line_number": 30, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 30, "usage_type": "name"}, {"api_name": "grades.models.ExamStatistics", "line_number": 31, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 31, "usage_type": "name"}, {"api_name": "grades.models.ExamStatistics", "line_number": 34, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 34, "usage_type": "name"}, {"api_name": "grades.models.ExamGradeRange", "line_number": 39, "usage_type": "call"}, {"api_name": "grades.models", "line_number": 39, "usage_type": "name"}, {"api_name": "grades.models.ExamGradeRange", "line_number": 40, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 40, "usage_type": "name"}, {"api_name": "grades.models.ExamGradeRange", "line_number": 41, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 41, "usage_type": "name"}, {"api_name": "grades.models.ExamGradeRange", "line_number": 42, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 42, "usage_type": "name"}, {"api_name": "grades.models.ExamGradeRange", "line_number": 43, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 43, "usage_type": "name"}, {"api_name": "grades.models.ExamGradeRange.objects.bulk_create", "line_number": 47, "usage_type": "call"}, {"api_name": "grades.models.ExamGradeRange", "line_number": 47, "usage_type": "attribute"}, {"api_name": "grades.models", "line_number": 47, "usage_type": "name"}, {"api_name": "django.db.transaction.atomic", "line_number": 12, "usage_type": "attribute"}, {"api_name": "django.db.transaction", "line_number": 12, "usage_type": "name"}, {"api_name": "courses.models.CourseInstance", "line_number": 16, "usage_type": "attribute"}, {"api_name": "courses.models", "line_number": 16, "usage_type": "name"}]} +{"seq_id": "39379902", "text": "import urllib\n\nfrom django.core.urlresolvers import reverse\nfrom django.db.models import Count\nfrom django.http import HttpResponse, Http404\nfrom django.views.decorators.http import require_http_methods\nfrom django.views.decorators.clickjacking import xframe_options_exempt\nfrom django.shortcuts import get_object_or_404, HttpResponsePermanentRedirect\nfrom django.utils.translation import ugettext_lazy\nfrom core.lib.datastore import *\nfrom core.cache import Cache\nfrom core.exportDataStream import forms\nfrom core.daos.datastreams import DataStreamDBDAO\nfrom core.engine import invoke as engine_invoke\nfrom core.helpers import jsonToGrid, RequestProcessor\nfrom core.models import DataStreamRevision, DataStreamHits, DataStream\nfrom core.shortcuts import render_to_response\nfrom datetime import date, timedelta\nfrom core.decorators import *\n\n\n@require_http_methods([\"GET\"])\n@datal_cache_page()\ndef invoke(request):\n form = forms.RequestForm(request.GET)\n if form.is_valid():\n query = RequestProcessor(request).get_arguments_no_validation()\n query['pId'] = form.cleaned_data.get('datastream_revision_id')\n limit = form.cleaned_data.get('limit')\n if limit:\n query['pLimit'] = limit\n\n ivk = engine_invoke(query)\n # Sometimes there is no answer. Maybe engine is down\n if ivk is None:\n contents = '{\"Error\":\"No invoke\"}'\n typen = \"json\"\n else:\n contents, typen = ivk\n\n return HttpResponse(contents, mimetype=typen)\n else:\n return HttpResponse('Error! No valid form')\n\n\n@require_http_methods([\"GET\"])\ndef csv(request, id, slug):\n\n contents, type = export_to(id, request, 'csv')\n\n return HttpResponse(contents, mimetype=type)\n\n\n@require_http_methods([\"GET\"])\ndef xls(request, id, slug):\n\n contents, type = export_to(id, request, 'xls')\n\n argument = json.loads(contents)\n\n if argument.get('fType') == 'REDIRECT':\n redirect = HttpResponse(status=302, mimetype='application/vnd.ms-excel')\n redirect['Location'] = argument.get('fUri')\n return redirect\n else:\n return HttpResponse(contents, mimetype=type)\n\n\n@require_http_methods([\"GET\"])\ndef html(request, id, slug):\n contents, type = export_to(id, request, 'html')\n return HttpResponse(contents)\n\n\ndef export_to(datastream_id, request, output):\n\n try:\n datastreamrevision_id = DataStreamRevision.objects.get_last_published_id(datastream_id)\n datastream = DataStreamDBDAO().get(request.auth_manager.language, datastream_revision_id=datastreamrevision_id)\n except:\n raise Http404\n else:\n uri = request.build_absolute_uri()\n \n query = {'pId': datastreamrevision_id, 'pOutput': output.upper()}\n\n arguments = RequestProcessor(request).get_arguments(datastream[\"parameters\"])\n if arguments:\n query.update(arguments)\n\n filter = request.REQUEST.get('pFilter0', None)\n if filter:\n query['pFilter0'] = unicode(filter).encode('utf-8')\n\n return engine_invoke(query, output)\n\n\n@xframe_options_exempt\n@require_http_methods([\"GET\"])\ndef legacy_embed(request):\n form = forms.LegacyEmbedForm(request.GET)\n if form.is_valid():\n datastream_id = form.cleaned_data.get('dataservice_id')\n end_point = form.cleaned_data.get('end_point')\n header_row = form.cleaned_data.get('header_row', 0)\n fixed_column = form.cleaned_data.get('fixed_column', 0)\n\n datastream = get_object_or_404(DataStream, pk = datastream_id)\n query = urllib.urlencode({'end_point': end_point, 'header_row': header_row, 'fixed_column' : fixed_column})\n url = reverse('exportDataStream.action_embed', kwargs={'guid' : datastream.guid}) + '?' + query\n return HttpResponsePermanentRedirect(url)\n else:\n return render_to_response('datastream_manager/embed404.html', {'settings': settings, 'request' : request})\n\n\n@require_http_methods([\"GET\"])\ndef updategrid(request):\n query = dict()\n query['pId'] = request.REQUEST.get('datastream_id')\n query['pLimit'] = request.REQUEST.get('rp')\n query['pPage'] = int(request.REQUEST.get('page')) - 1\n\n search = request.REQUEST.get('query', None)\n if search:\n query['pFilter0'] = '%s[contains]%s' % (request.REQUEST.get('qtype', 'column0'), search)\n\n sortname = request.REQUEST.get('sortname', None)\n sortorder = request.REQUEST.get('sortorder', None)\n if sortname:\n query['pOrderBy'] = sortname\n query['pOrderType'] = {None: 'A', 'asc': 'A', 'desc': 'D'}[sortorder]\n\n contents, mimetype = engine_invoke(RequestProcessor(request).get_arguments_no_validation(query))\n if not contents:\n contents = {\"rows\": [], \"total\": 1, \"page\": 1}\n mimetype = \"application/json\"\n return HttpResponse(jsonToGrid(contents, query['pPage'] + 1), mimetype=mimetype)\n\n\n", "sub_path": "core/exportDataStream/views.py", "file_name": "views.py", "file_ext": "py", "file_size_in_byte": 4892, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "core.exportDataStream.forms.RequestForm", "line_number": 25, "usage_type": "call"}, {"api_name": "core.exportDataStream.forms", "line_number": 25, "usage_type": "name"}, {"api_name": "core.helpers.RequestProcessor", "line_number": 27, "usage_type": "call"}, {"api_name": "core.engine.invoke", "line_number": 33, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 41, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 43, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 22, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 51, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 46, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 62, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 66, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 54, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 72, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 69, "usage_type": "call"}, {"api_name": "core.models.DataStreamRevision.objects.get_last_published_id", "line_number": 78, "usage_type": "call"}, {"api_name": "core.models.DataStreamRevision.objects", "line_number": 78, "usage_type": "attribute"}, {"api_name": "core.models.DataStreamRevision", "line_number": 78, "usage_type": "name"}, {"api_name": "core.daos.datastreams.DataStreamDBDAO", "line_number": 79, "usage_type": "call"}, {"api_name": "django.http.Http404", "line_number": 81, "usage_type": "name"}, {"api_name": "core.helpers.RequestProcessor", "line_number": 87, "usage_type": "call"}, {"api_name": "core.engine.invoke", "line_number": 95, "usage_type": "call"}, {"api_name": "core.exportDataStream.forms.LegacyEmbedForm", "line_number": 101, "usage_type": "call"}, {"api_name": "core.exportDataStream.forms", "line_number": 101, "usage_type": "name"}, {"api_name": "django.shortcuts.get_object_or_404", "line_number": 108, "usage_type": "call"}, {"api_name": "core.models.DataStream", "line_number": 108, "usage_type": "argument"}, {"api_name": "urllib.urlencode", "line_number": 109, "usage_type": "call"}, {"api_name": "django.core.urlresolvers.reverse", "line_number": 110, "usage_type": "call"}, {"api_name": "django.shortcuts.HttpResponsePermanentRedirect", "line_number": 111, "usage_type": "call"}, {"api_name": "core.shortcuts.render_to_response", "line_number": 113, "usage_type": "call"}, {"api_name": "django.views.decorators.clickjacking.xframe_options_exempt", "line_number": 98, "usage_type": "name"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 99, "usage_type": "call"}, {"api_name": "core.engine.invoke", "line_number": 133, "usage_type": "call"}, {"api_name": "core.helpers.RequestProcessor", "line_number": 133, "usage_type": "call"}, {"api_name": "django.http.HttpResponse", "line_number": 137, "usage_type": "call"}, {"api_name": "core.helpers.jsonToGrid", "line_number": 137, "usage_type": "call"}, {"api_name": "django.views.decorators.http.require_http_methods", "line_number": 116, "usage_type": "call"}]} +{"seq_id": "478806919", "text": "import re\nimport itertools\n\nimport lxml.html\nfrom selenium.common.exceptions import NoSuchElementException\n\nfrom legistar.base import Base\nfrom legistar.jurisdictions.utils import try_jxn_delegation\n\n\nclass Form(Base):\n '''Handles posting data to a form and paging through the results.\n '''\n skip_first_submit = False\n\n def __init__(self, view):\n self.view = self.inherit_chainmap_from(view)\n # We need to seed the client with the ASP viewstate nonsense\n # before trying to post to the form. This does that:\n doc = self.doc\n self.count = itertools.count(2)\n self._submitted_first = False\n\n @property\n def formdata(self):\n return dict(self.doc.forms[0].fields)\n\n @try_jxn_delegation\n def before_first_submit(self):\n '''This function runs before the first submit.\n '''\n pass\n\n @try_jxn_delegation\n def submit(self, formdata=None, extra_headers=None):\n # Call the pre-submit hook.\n if not self._submitted_first:\n self.before_first_submit()\n self._submitted_first = True\n\n # Then submit the form.\n self.debug('%r is fetching %s', self, self.url)\n resp = self.cfg.client.post(self.url, formdata, extra_headers)\n doc = lxml.html.fromstring(resp.text)\n doc.make_links_absolute(self.url)\n self.doc = doc\n\n def get_query(self, **kwargs):\n '''This function returns the dictionary of POST data\n the form requires.\n '''\n raise NotImplementedError()\n\n @try_jxn_delegation\n def submit_next_page(self):\n '''Submits the next page in the search results.\n '''\n js = self.doc.xpath(self.cfg.PGN_NEXT_PAGE_XPATH)\n if not js:\n # There are no more pages.\n msg = 'No more pages of search results.'\n self.info(msg)\n raise StopIteration()\n\n # Parse the pagination control id name thingy.\n event_target = js.split(\"'\")[1]\n get_query = getattr(self, 'get_pagination_query', self.get_query)\n\n # Include the pagination target thingy in the query this time.\n formdata = get_query(__EVENTTARGET=event_target)\n\n # Blab.\n msg = '%r requesting page %d of search results: %r'\n formdata_copy = dict(formdata)\n formdata_copy.pop('__VIEWSTATE', None)\n formdata_copy.pop('__EVENTVALIDATION', None)\n self.info(msg, self, next(self.count), formdata_copy)\n\n # Re-submit the form.\n extra_headers = dict(referer=self.url)\n self.submit(formdata, extra_headers)\n\n @try_jxn_delegation\n def __iter__(self):\n yield from self.gen_documents()\n\n def gen_documents(self):\n Table = self.view.viewtype_meta.Table\n if self.skip_first_submit:\n pass\n else:\n self.submit(self.get_query())\n yield from self.make_child(Table, view=self.view)\n while True:\n self.submit_next_page()\n yield from self.make_child(Table, view=self.view)\n\n\nclass FirefoxForm(Form):\n\n def gen_docs_from_lxmldoc(self):\n Table = self.view.viewtype_meta.Table\n doc = self.lxmlize()\n doc.make_links_absolute(self.url)\n self.doc = doc\n table = self.make_child(Table, view=self.view)\n yield from table\n\n def lxmlize(self):\n html = self.firefox.page_source\n doc = lxml.html.fromstring(html)\n return doc\n\n def set_dropdown(self, id, text):\n script = '''\n $find('{id}').findItemByText('{val}').select();\n '''.format(id=id, val=text)\n self.firefox.execute_script(script.strip())\n\n def fill_out_form(self):\n pass\n\n def gen_documents(self):\n self.firefox.get(self.url)\n\n self.fill_out_form()\n\n submit_name = self.get_config_value('submit_button_name')\n button = self.firefox.find_element_by_name(submit_name)\n button.click()\n\n # Yield docs on the first page.\n yield from self.gen_docs_from_lxmldoc()\n\n # Then subsequent pages.\n while True:\n xpath = '//*[@class=\"rgCurrentPage\"]/following-sibling::a'\n try:\n next_page = self.firefox.find_element_by_xpath(xpath)\n except NoSuchElementException:\n return\n next_page.click()\n yield from self.gen_docs_from_lxmldoc()\n", "sub_path": "legistar/forms.py", "file_name": "forms.py", "file_ext": "py", "file_size_in_byte": 4408, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "legistar.base.Base", "line_number": 11, "usage_type": "name"}, {"api_name": "itertools.count", "line_number": 21, "usage_type": "call"}, {"api_name": "legistar.jurisdictions.utils.try_jxn_delegation", "line_number": 28, "usage_type": "name"}, {"api_name": "lxml.html.html.fromstring", "line_number": 44, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 44, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 44, "usage_type": "name"}, {"api_name": "legistar.jurisdictions.utils.try_jxn_delegation", "line_number": 34, "usage_type": "name"}, {"api_name": "legistar.jurisdictions.utils.try_jxn_delegation", "line_number": 54, "usage_type": "name"}, {"api_name": "legistar.jurisdictions.utils.try_jxn_delegation", "line_number": 83, "usage_type": "name"}, {"api_name": "lxml.html.html.fromstring", "line_number": 111, "usage_type": "call"}, {"api_name": "lxml.html.html", "line_number": 111, "usage_type": "attribute"}, {"api_name": "lxml.html", "line_number": 111, "usage_type": "name"}, {"api_name": "selenium.common.exceptions.NoSuchElementException", "line_number": 140, "usage_type": "name"}]} +{"seq_id": "30914370", "text": "from __future__ import print_function\nimport argparse\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nfrom torchvision import datasets, transforms\nfrom torch.optim.lr_scheduler import StepLR\n\n\ndef train(args, model, device, train_loader, optimizer, epoch):\n model.train()\n for batch_idx, (data, target) in enumerate(train_loader):\n data, target = data.to(device), target.to(device)\n optimizer.zero_grad()\n output = model(data)\n loss = F.nll_loss(output, target)\n # mse = nn.MSELoss()\n # loss = mse(output, target)\n loss.backward()\n optimizer.step()\n if batch_idx % args.log_interval == 0:\n print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n epoch, batch_idx * len(data), len(train_loader.dataset),\n 100. * batch_idx / len(train_loader), loss.item()))\n\ndef main():\n torch.manual_seed(1)\n device = torch.device(\"cpu\")\n\n # cuda 使用時の設定\n kwargs = {}\n\n # MNIST データのロード\n train_loader = torch.utils.data.DataLoader(\n datasets.MNIST('../data', train=True, download=True,\n transform=transforms.Compose([\n transforms.ToTensor(),\n transforms.Normalize((0.1307,), (0.3081,))\n ])),\n batch_size=10, shuffle=True, **kwargs)\n\n model = Net().to(device)\n optimizer = optim.Adadelta(model.parameters(), lr=1.0)\n\n scheduler = StepLR(optimizer, step_size=1, gamma=args.gamma)\n for epoch in range(1, args.epochs + 1):\n train(args, model, device, train_loader, optimizer, epoch)\n test(model, device, test_loader)\n scheduler.step() \n \nif __name__ == '__main__':\n main()\n\n", "sub_path": "mnist_continuous/m.py", "file_name": "m.py", "file_ext": "py", "file_size_in_byte": 1817, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "torch.nn.functional.nll_loss", "line_number": 17, "usage_type": "call"}, {"api_name": "torch.nn.functional", "line_number": 17, "usage_type": "name"}, {"api_name": "torch.manual_seed", "line_number": 28, "usage_type": "call"}, {"api_name": "torch.device", "line_number": 29, "usage_type": "call"}, {"api_name": "torch.utils.data.DataLoader", "line_number": 35, "usage_type": "call"}, {"api_name": "torch.utils", "line_number": 35, "usage_type": "attribute"}, {"api_name": "torchvision.datasets.MNIST", "line_number": 36, "usage_type": "call"}, {"api_name": "torchvision.datasets", "line_number": 36, "usage_type": "name"}, {"api_name": "torchvision.transforms.Compose", "line_number": 37, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 37, "usage_type": "name"}, {"api_name": "torchvision.transforms.ToTensor", "line_number": 38, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 38, "usage_type": "name"}, {"api_name": "torchvision.transforms.Normalize", "line_number": 39, "usage_type": "call"}, {"api_name": "torchvision.transforms", "line_number": 39, "usage_type": "name"}, {"api_name": "torch.optim.Adadelta", "line_number": 44, "usage_type": "call"}, {"api_name": "torch.optim", "line_number": 44, "usage_type": "name"}, {"api_name": "torch.optim.lr_scheduler.StepLR", "line_number": 46, "usage_type": "call"}]} +{"seq_id": "154959936", "text": "import os\nimport json\nfrom dotenv import load_dotenv\nfrom pathlib import Path\nfrom .constants import *\n\nenv_path = os.path.join(\n os.path.dirname(__file__),\n os.environ.get('ENVFILE', '.env'))\nload_dotenv(env_path)\n\n\nclass Config:\n # ------------------------------------------------------------------------------------------------------------------\n # GENERAL\n # ------------------------------------------------------------------------------------------------------------------\n\n # General app info\n BASE_DIR = os.path.dirname(os.path.dirname(__file__))\n APP_NAME_SUFFIX = os.environ.get('APP_NAME_SUFFIX')\n DEBUG = os.environ.get('DEBUG', False)\n STAGE = os.environ.get('STAGE', 'dev')\n AWS_ACCOUNT_ID = os.environ.get('AWS_ACCOUNT_ID', '917885688343')\n\n FRONTEND_BASE_URL = os.environ.get('FRONTEND_BASE_URL')\n\n # ------------------------------------------------------------------------------------------------------------------\n # COGNITO\n # ------------------------------------------------------------------------------------------------------------------\n\n # AWS User Pool Configuration\n AWS_COGNITO_USER_POOL_NAME = os.environ.get('AWS_COGNITO_USER_POOL_NAME', 'mpc-dev-chalice-api-user-pool')\n AWS_COGNITO_USER_POOL_ID = os.environ.get('AWS_COGNITO_USER_POOL_ID', 'eu-west-1_UB4WIHfuT')\n AWS_COGNITO_USER_POOL_ARN = os.environ.get(\n 'AWS_COGNITO_USER_POOL_ARN',\n 'arn:aws:cognito-idp:eu-west-1:917885688343:userpool/%s' % AWS_COGNITO_USER_POOL_ID\n )\n AWS_COGNITO_DEFAULT_REGION = os.environ.get('AWS_COGNITO_DEFAULT_REGION', 'eu-west-1')\n\n # ------------------------------------------------------------------------------------------------------------------\n # DYNAMO DB\n # ------------------------------------------------------------------------------------------------------------------\n\n # AWS DYNAMO TABLE CONFIG\n AWS_DYNAMODB_DEFAULT_REGION = os.environ.get('AWS_DYNAMODB_DEFAULT_REGION', 'eu-west-1')\n AWS_DYNAMODB_CMS_TABLE_NAME = os.environ.get('AWS_DYNAMODB_CMS_TABLE_NAME', 'CMS')\n AWS_DYNAMODB_MAGENTO_CUSTOMER_TABLE_NAME = os.environ.get('AWS_DYNAMODB_MAGENTO_CUSTOMER_TABLE_NAME', 'Magento')\n AWS_DYNAMODB_BANNER_TABLE_NAME = os.environ.get('AWS_DYNAMODB_BANNER_TABLE_NAME', 'Banners')\n\n # ------------------------------------------------------------------------------------------------------------------\n # ELASTIC\n # ------------------------------------------------------------------------------------------------------------------\n\n # AWS Elasticsarch Service configuration\n AWS_ELASTICSEARCH_PRODUCTS_REGION = os.environ.get('AWS_ELASTICSEARCH_PRODUCTS_REGION', 'eu-west-1')\n AWS_ELASTICSEARCH_SCHEMA = os.environ.get('AWS_ELASTICSEARCH_SCHEMA', 'https')\n AWS_ELASTICSEARCH_HOST = os.environ.get(\n 'AWS_ELASTICSEARCH_HOST',\n 'search-mpc-domain-qhdgnvecvaqb77evx7i64zbldm.eu-west-1.es.amazonaws.com')\n AWS_ELASTICSEARCH_PORT = int(os.environ.get('AWS_ELASTICSEARCH_PORT', 443))\n AWS_ELASTICSEARCH_ENDPOINT = '{}://{}:{}'.format(\n AWS_ELASTICSEARCH_SCHEMA,\n AWS_ELASTICSEARCH_HOST,\n AWS_ELASTICSEARCH_PORT\n )\n AWS_ELASTICSEARCH_SCROLL_LIFETIME = os.environ.get('AWS_ELASTICSEARCH_SCROLL_LIFETIME', '5m')\n\n # products\n AWS_ELASTICSEARCH_PRODUCTS = os.environ.get('AWS_ELASTICSEARCH_PRODUCTS', 'products')\n\n # Scored Products\n AWS_ELASTICSEARCH_SCORED_PRODUCTS = os.environ.get(\n 'AWS_ELASTICSEARCH_SCORED_PRODUCTS', 'scored_products')\n\n # orders\n AWS_ELASTICSEARCH_PURCHASE_ORDERS = os.environ.get('AWS_ELASTICSEARCH_PURCHASE_ORDERS', 'purchase_orders')\n AWS_ELASTICSEARCH_PURCHASE_ORDERS_CUSTOMER_ORDERS_MAP = os.environ.get(\n 'AWS_ELASTICSEARCH_PURCHASE_ORDERS_CUSTOMER_ORDERS_MAP',\n 'purchase_orders_customer_orders_map'\n )\n\n # credit cards\n AWS_ELASTICSEARCH_PURCHASE_CUSTOMER_CREDIT_CARDS = os.environ.get(\n 'AWS_ELASTICSEARCH_PURCHASE_CUSTOMER_CREDIT_CARDS',\n 'purchase_customer_credit_cards'\n )\n AWS_ELASTICSEARCH_PURCHASE_CUSTOMER_CREDIT_CARDS_CUSTOMER_MAP = os.environ.get(\n 'AWS_ELASTICSEARCH_PURCHASE_CUSTOMER_CREDIT_CARDS_CUSTOMER_MAP',\n 'purchase_customer_credit_cards_customer_map'\n )\n\n # returns\n AWS_ELASTICSEARCH_PURCHASE_RETURN_REQUESTS = os.environ.get(\n 'AWS_ELASTICSEARCH_PURCHASE_RETURN_REQUESTS',\n 'purchase_return_requests'\n )\n AWS_ELASTICSEARCH_PURCHASE_RETURN_REQUESTS_CUSTOMER_MAP = os.environ.get(\n 'AWS_ELASTICSEARCH_PURCHASE_RETURN_REQUESTS_CUSTOMER_MAP',\n 'purchase_return_requests_customer_map'\n )\n\n # cancels\n AWS_ELASTICSEARCH_PURCHASE_CANCEL_REQUESTS = os.environ.get(\n 'AWS_ELASTICSEARCH_PURCHASE_CANCEL_REQUESTS',\n 'purchase_cancel_requests'\n )\n AWS_ELASTICSEARCH_PURCHASE_CANCEL_REQUESTS_ORDERS_MAP = os.environ.get(\n 'AWS_ELASTICSEARCH_PURCHASE_CANCEL_REQUESTS_ORDERS_MAP',\n 'purchase_cancel_requests_orders_map'\n )\n\n # personalization\n AWS_ELASTICSEARCH_PERSONALIZATION_ORDERS = os.environ.get(\n 'AWS_ELASTICSEARCH_PERSONALIZATION_ORDERS',\n 'personalization_orders'\n )\n\n # customer tiers\n AWS_ELASTICSEARCH_CUSTOMER_TIERS_TIERS = os.environ.get(\n 'AWS_ELASTICSEARCH_CUSTOMER_TIERS_TIERS',\n 'customer_tiers_tiers'\n )\n AWS_ELASTICSEARCH_CUSTOMER_TIERS_CUSTOMER_TIERS = os.environ.get(\n 'AWS_ELASTICSEARCH_CUSTOMER_TIERS_CUSTOMER_TIERS',\n 'customer_tiers_customer_tiers'\n )\n AWS_ELASTICSEARCH_CUSTOMER_TIERS_CUSTOMER_INFO_SPENT_AMOUNT = os.environ.get(\n 'AWS_ELASTICSEARCH_CUSTOMER_TIERS_CUSTOMER_INFO_SPENT_AMOUNT',\n 'customer_tiers_customer_info_spent_amount'\n )\n\n # fbucks\n AWS_ELASTICSEARCH_FBUCKS_HANDLED_ORDERS = os.environ.get(\n 'AWS_ELASTICSEARCH_FBUCKS_HANDLED_ORDERS',\n 'fbucks_handled_orders'\n )\n AWS_ELASTICSEARCH_FBUCKS_CUSTOMER_AMOUNT = os.environ.get(\n 'AWS_ELASTICSEARCH_FBUCKS_CUSTOMER_AMOUNT',\n 'fbucks_customer_amount'\n )\n AWS_ELASTICSEARCH_FBUCKS_CUSTOMER_AMOUNT_CHANGES = os.environ.get(\n 'AWS_ELASTICSEARCH_FBUCKS_CUSTOMER_AMOUNT_CHANGES',\n 'fbucks_customer_amount_changes'\n )\n\n # ------------------------------------------------------------------------------------------------------------------\n # SQS QUEUE\n # ------------------------------------------------------------------------------------------------------------------\n\n PORTAL_AWS_ACCOUNT_ID = os.environ.get('PORTAL_AWS_ACCOUNT_ID', AWS_ACCOUNT_ID)\n SQS_REGION = os.environ.get('SQS_REGION', 'eu-west-1')\n def build_sqs_url(\n queue_name: str,\n account_id: str = AWS_ACCOUNT_ID,\n region: str = SQS_REGION\n ) -> str:\n if not os.environ.get('DEBUG', False) and not queue_name or not account_id or not region:\n raise Exception(\"Your configuration has a fatal error.\")\n\n return 'https://sqs.{region}.amazonaws.com/{account_id}/{queue_name}'.format(\n account_id=account_id, region=region, queue_name=queue_name)\n\n\n # Building SQS Queue URL\n SQS_MPC_PORTAL_COMMON = build_sqs_url(os.environ.get('SQS_MPC_PORTAL_COMMON'))\n SQS_MPC_PORTAL_ORDER = build_sqs_url(os.environ.get('SQS_MPC_PORTAL_ORDER'), account_id=PORTAL_AWS_ACCOUNT_ID)\n SQS_MPC_PORTAL_EMAIL_SUBSCRIPTION = build_sqs_url(os.environ.get('SQS_MPC_PORTAL_EMAIL_SUBSCRIPTION'))\n SQS_MPC_PORTAL_CUSTOMER_INFO_REQUEST = build_sqs_url(os.environ.get('SQS_MPC_PORTAL_CUSTOMER_INFO_REQUEST'))\n SQS_MPC_PORTAL_COMMUNICATION_PREFERENCES = build_sqs_url(os.environ.get('SQS_MPC_PORTAL_COMMUNICATION_PREFERENCES'))\n SQS_MPC_MPC_COMMON_URL = build_sqs_url(os.environ.get('SQS_MPC_MPC_COMMON_NAME'))\n SQS_MPC_PORTAL_CUSTOMER_INFO_UPDATE = build_sqs_url(os.environ.get('SQS_MPC_PORTAL_CUSTOMER_INFO_UPDATE'))\n\n # { queues: [{ name: str, batch_size: int }, ...] }\n SQS_LISTENER_CONFIG = {\n 'queues': [\n {'name': os.environ.get('SQS_PORTAL_MPC_COMMON'), 'batch_size': 1},\n {'name': os.environ.get('SQS_PORTAL_MPC_ORDER'), 'batch_size': 1},\n {'name': os.environ.get('SQS_MPC_MPC_COMMON_NAME'), 'batch_size': 1},\n {'name': os.environ.get('SQS_PORTAL_MPC_CUSTOMER_INFO_UPDATE'), 'batch_size': 1},\n ]\n }\n\n # { event_descriptor: { object_type: str, queue_url: str, ... } }\n SQS_SENDER_CONFIG = {\n # can be used for local\n # 'class': 'chalicelib.libs.core.sqs_sender._SqsSenderDummyPrint',\n # 'params': {},\n\n # @TODO : use mpc-portal-common instead of not critical mpc-portal queues\n # @TODO : create single listener of mpc-portal-common for not critical mpc-portal messages\n\n 'class': 'chalicelib.libs.core.sqs_sender._SqsSenderSqs',\n 'params': {\n 'events': {\n 'user_answer': {\n 'object_type': 'user_answer',\n 'queue_url': SQS_MPC_PORTAL_COMMON,\n },\n 'communication_preferences': {\n 'object_type': 'communication_preferences',\n 'queue_url': SQS_MPC_PORTAL_COMMUNICATION_PREFERENCES,\n },\n 'credit_cash_out_request': {\n 'object_type': 'credit_cash_out_request',\n 'queue_url': SQS_MPC_PORTAL_COMMON,\n },\n \"customer_info_request\": {\n \"object_type\": \"customer_info_request\",\n \"queue_url\": SQS_MPC_PORTAL_CUSTOMER_INFO_REQUEST,\n },\n 'contactus_request': {\n 'object_type': 'contactus_request',\n 'queue_url': SQS_MPC_PORTAL_COMMON,\n },\n 'order_change': {\n 'object_type': 'mpc_order',\n 'queue_url': SQS_MPC_PORTAL_ORDER,\n },\n 'eft_proof_uploaded': {\n 'object_type': 'eft_proof_uploaded',\n 'queue_url': SQS_MPC_PORTAL_ORDER,\n },\n 'return_request_change': {\n 'object_type': 'return_request_change',\n 'queue_url': SQS_MPC_PORTAL_ORDER,\n },\n 'fixel_paid_order_cancellation_request': {\n 'object_type': 'fixel_paid_order_cancellation_request',\n 'queue_url': SQS_MPC_PORTAL_ORDER,\n },\n 'subscription_subscribed': {\n 'object_type': 'subscription_subscribed',\n 'queue_url': SQS_MPC_PORTAL_EMAIL_SUBSCRIPTION,\n },\n 'subscription_unsubscribed': {\n 'object_type': 'subscription_unsubscribed',\n 'queue_url': SQS_MPC_PORTAL_EMAIL_SUBSCRIPTION,\n },\n SCORED_PRODUCT_MESSAGE_TYPE.SECRET_KEY: {\n 'object_type': SCORED_PRODUCT_MESSAGE_TYPE.SECRET_KEY,\n 'queue_url': SQS_MPC_MPC_COMMON_URL,\n },\n SCORED_PRODUCT_MESSAGE_TYPE.CALCULATE_FOR_A_CUSTOMER: {\n 'object_type': SCORED_PRODUCT_MESSAGE_TYPE.CALCULATE_FOR_A_CUSTOMER,\n 'queue_url': SQS_MPC_MPC_COMMON_URL,\n },\n 'customer_info_update': {\n 'object_type': 'customer_info_update',\n 'queue_url': SQS_MPC_PORTAL_CUSTOMER_INFO_UPDATE,\n },\n }\n }\n }\n\n # ------------------------------------------------------------------------------------------------------------------\n # MAILER\n # ------------------------------------------------------------------------------------------------------------------\n\n MAILER_CONFIG = json.loads(os.environ.get('MAILER_CONFIG', json.dumps({\n # can be used for local\n # 'class': 'chalicelib.libs.core.mailer._MailerDummyPrint',\n # 'params': {},\n\n # live\n 'class': 'chalicelib.libs.core.mailer._MailerSmtp',\n 'params': {\n 'from_email': 'portal@runwaysale.co.za',\n 'host': 'smtp.mandrillapp.com',\n 'port': 587,\n 'username': 'info@runwaysale.co.za',\n 'password': 'vAkn_tSiZMbqU-KFAZwOlA',\n }\n })))\n\n # ------------------------------------------------------------------------------------------------------------------\n # FILE STORAGE\n # ------------------------------------------------------------------------------------------------------------------\n\n FILE_STORAGE_CONFIG = json.loads(os.environ.get('FILE_STORAGE_CONFIG', json.dumps({\n # This is config example for local environment. Change it in your own run script.\n # Implementations for other environments are defined in config.json.\n #\n # export FILE_STORAGE_CONFIG='{\n # \"class\": \"chalicelib.libs.core.file_storage._FileLocalStorage\",\n # \"params\": {\"root_path\": \"/var/www/html/mpc_api_storage\", \"root_url\": \"http://localhost/mpc_api_storage/\"}\n # }'\n })))\n\n # ------------------------------------------------------------------------------------------------------------------\n # OTHER\n # ------------------------------------------------------------------------------------------------------------------\n\n # Delivery API\n DTD_API_DEFAULT_DTD_URL = os.environ.get('DTD_API_DEFAULT_DTD_URL', 'https://cdt.runway.co.za/sku/DEFAULT')\n DTD_API_DEFAULT_DTD_MIN = os.environ.get('DTD_API_DEFAULT_DTD_MIN', 10) # if default api is unavailable,\n DTD_API_DEFAULT_DTD_MAX = os.environ.get('DTD_API_DEFAULT_DTD_MAX', 25) # we should use hardcoded values\n DTD_API_SKU_BASE_URL = os.environ.get('DTD_API_SKU_BASE_URL', 'https://cdt.runway.co.za/sku/')\n\n # Product filtering meta data\n NEW_PRODUCT_THRESHOLD = int(os.environ.get('NEW_PRODUCT_THRESHOLD', 1600)) # Should be 7 days in production\n LAST_CHANCE_STOCK_THRESHOLD = os.environ.get('LAST_CHANCE_STOCK_THRESHOLD', 10) # Stock Number\n LAST_CHANCE_END_DATE_THRESHOLD = os.environ.get('LAST_CHANCE_END_DATE_THRESHOLD', 30)\n PRODUCT_VISIT_LOG_MAX = os.environ.get('PRODUCT_VISIT_LOG_MAX', 10)\n PRODUCT_VISIT_LOG_THRESHOLD = os.environ.get('PRODUCT_VISIT_LOG_THRESHOLD', 7)\n\n # READ API\n READ_API_HEADER_NAME = os.environ.get('READ_API_HEADER_NAME', 'Identification')\n READ_API_HEADER_VALUE = os.environ.get('READ_API_HEADER_VALUE', 'RunwaySale::ReadAPI')\n\n # PEACH PAYMENT\n # https://peachpayments.docs.oppwa.com/\n # Attention! Default values are for tests here (see doc/examples).\n PEACH_PAYMENT_BASE_URL = os.environ.get('PEACH_PAYMENT_BASE_URL', 'https://test.oppwa.com/v1/')\n PEACH_PAYMENT_ENTITY_ID = os.environ.get('PEACH_PAYMENT_ENTITY_ID', '8a8294174e735d0c014e78cf26461790')\n PEACH_PAYMENT_ACCESS_TOKEN = os.environ.get(\n 'PEACH_PAYMENT_ACCESS_TOKEN',\n 'OGE4Mjk0MTc0ZTczNWQwYzAxNGU3OGNmMjY2YjE3OTR8cXl5ZkhDTjgzZQ=='\n )\n PEACH_PAYMENT_WEBHOOKS_DECRYPTION_KEY = os.environ.get('PEACH_PAYMENT_WEBHOOKS_DECRYPTION_KEY', \"need_real_value\")\n\n # ------------------------------------------------------------------------------------------------------------------\n\n # CRITICAL! FOR THE DATA LAKE\n DATALAKE_AWS_ACCOUNT_ACCESS_KEY_ID = os.environ.get(\n 'DATALAKE_AWS_ACCOUNT_ACCESS_KEY_ID')\n DATALAKE_AWS_ACCOUNT_SECRET_KEY_ID = os.environ.get(\n 'DATALAKE_AWS_ACCOUNT_SECRET_KEY_ID')\n DATALAKE_USERTRACK_DELIVERY_STREAM_NAME = os.environ.get(\n 'DATALAKE_USERTRACK_DELIVERY_STREAM_NAME')\n\n # When you need to create sqs lambda function, consider the following\n STAGES_TO_BIND_LAMBDA_WITH_AWS_RESOURCES = ['dev', 'stage', 'production']\n if isinstance(os.environ.get('STAGES_TO_BIND_LAMBDA_WITH_AWS_RESOURCES'), str):\n STAGES_TO_BIND_LAMBDA_WITH_AWS_RESOURCES += os.environ.get('STAGES_TO_BIND_LAMBDA_WITH_AWS_RESOURCES')\n\n CALCULATE_SCORE_BATCH_SIZE = os.environ.get('CALCULATE_SCORE_BATCH_SIZE', 20)\n SCORE_CALCULATE_INTERVAL = os.environ.get('SCORE_CALCULATE_INTERVAL', 20)\n CALCULATE_SCORE_CHUNK_SIZE = os.environ.get('CALCULATE_SCORE_CHUNK_SIZE', 5)\n\nsettings = Config()\n\n", "sub_path": "chalicelib/settings.py", "file_name": "settings.py", "file_ext": "py", "file_size_in_byte": 16615, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "os.path.join", "line_number": 7, "usage_type": "call"}, {"api_name": "os.path", "line_number": 7, "usage_type": "attribute"}, {"api_name": "os.path.dirname", "line_number": 8, "usage_type": "call"}, {"api_name": "os.path", "line_number": 8, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 9, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 9, "usage_type": "attribute"}, {"api_name": "dotenv.load_dotenv", "line_number": 10, "usage_type": "call"}, {"api_name": "os.path.dirname", "line_number": 19, "usage_type": "call"}, {"api_name": "os.path", "line_number": 19, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 20, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 20, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 21, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 21, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 22, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 22, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 23, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 23, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 25, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 25, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 32, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 32, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 33, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 33, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 34, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 34, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 38, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 38, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 45, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 45, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 46, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 46, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 47, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 47, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 48, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 48, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 55, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 55, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 56, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 56, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 57, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 57, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 60, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 60, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 66, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 66, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 69, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 69, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 72, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 72, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 76, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 76, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 77, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 77, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 83, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 83, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 87, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 87, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 93, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 93, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 97, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 97, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 103, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 103, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 107, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 107, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 113, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 113, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 119, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 119, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 123, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 123, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 127, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 127, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 133, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 133, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 137, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 137, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 141, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 141, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 150, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 150, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 151, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 151, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 157, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 157, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 165, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 165, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 166, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 166, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 167, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 167, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 168, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 168, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 169, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 169, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 170, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 170, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 171, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 171, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 176, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 176, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 177, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 177, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 178, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 178, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 179, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 179, "usage_type": "attribute"}, {"api_name": "json.loads", "line_number": 259, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 259, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 259, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 259, "usage_type": "call"}, {"api_name": "json.loads", "line_number": 279, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 279, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 279, "usage_type": "attribute"}, {"api_name": "json.dumps", "line_number": 279, "usage_type": "call"}, {"api_name": "os.environ.get", "line_number": 294, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 294, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 295, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 295, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 296, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 296, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 297, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 297, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 300, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 300, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 301, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 301, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 302, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 302, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 303, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 303, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 304, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 304, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 307, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 307, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 308, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 308, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 313, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 313, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 314, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 314, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 315, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 315, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 319, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 319, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 324, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 324, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 326, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 326, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 328, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 328, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 333, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 333, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 334, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 334, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 336, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 336, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 337, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 337, "usage_type": "attribute"}, {"api_name": "os.environ.get", "line_number": 338, "usage_type": "call"}, {"api_name": "os.environ", "line_number": 338, "usage_type": "attribute"}]} +{"seq_id": "514651380", "text": "__author__ = 'allen'\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn import cross_validation\n\ndef applyModel(data, answer, test):\n model1 = RandomForestClassifier(random_state=10, n_estimators=80, max_features='auto', criterion='entropy', max_depth=5)\n print(cross_validation.cross_val_score(model1, data, answer, cv=10))\n model2 = LogisticRegression(random_state=10, penalty='l1', tol=0.05)\n print(cross_validation.cross_val_score(model2, data, answer, cv=10))\n model2.fit(data, answer)\n test_answer = model2.predict(test)\n return test_answer", "sub_path": "Titanic/model.py", "file_name": "model.py", "file_ext": "py", "file_size_in_byte": 633, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "sklearn.ensemble.RandomForestClassifier", "line_number": 7, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.cross_val_score", "line_number": 8, "usage_type": "call"}, {"api_name": "sklearn.cross_validation", "line_number": 8, "usage_type": "name"}, {"api_name": "sklearn.linear_model.LogisticRegression", "line_number": 9, "usage_type": "call"}, {"api_name": "sklearn.cross_validation.cross_val_score", "line_number": 10, "usage_type": "call"}, {"api_name": "sklearn.cross_validation", "line_number": 10, "usage_type": "name"}]} +{"seq_id": "278097706", "text": "import sys, requests\nfrom OpenSSL import crypto\nfrom cryptography.fernet import Fernet\n\ndef redirectToLeader(server_address, message):\n type = message[\"type\"]\n # looping until someone tells he is the leader\n while True:\n # switching between \"get\" and \"put\"\n if type == \"get\":\n try:\n response = requests.get(server_address,\n json=message,\n timeout=1)\n except Exception as e:\n return e\n \n else:\n try:\n response = requests.put(server_address,\n json=message,\n timeout=1)\n except Exception as e:\n return e\n\n # if valid response and an address in the \"message\" section in reply\n # redirect server_address to the potential leader\n \n # if the message we get from client contains \"payload\" entry \n # and the \"payload\" contains \"message\" entry, then we know that \n # the client needs to redirect\n if response.status_code == 200 and \"payload\" in response.json():\n payload = response.json()[\"payload\"]\n if \"message\" in payload:\n # payload[\"message\"] contains the address of the LEADER\n server_address = payload[\"message\"] + \"/request\" \n else:\n break\n else:\n break\n \n return response.json()\n \n\n# client put request\ndef put(addr, key, value):\n server_address = addr + \"/request\"\n payload = {'key': key, 'value': value}\n message = {\"type\": \"put\", \"payload\": payload}\n \n #encrypting the message\n file_key = open('encode_key.key', 'rb') \n key = file_key.read()\n file_key.close()\n encoded = message[\"payload\"][\"key\"].encode()\n f1 = Fernet(key)\n message_encrypt = f1.encrypt(encoded)\n message[\"payload\"][\"key\"] = message_encrypt.decode()\n \n # redirecting till we find the leader, in case of request during election\n print(redirectToLeader(server_address, message))\n\n\n# client get request\ndef get(addr, key):\n print(\"Inside get\\n\")\n server_address = addr + \"/request\"\n payload = {'key': key}\n message = {\"type\": \"get\", \"payload\": payload}\n \n # redirecting till we find the leader, in case of request during election\n print(redirectToLeader(server_address, message))\n\n\nif __name__ == \"__main__\":\n if len(sys.argv) == 3:\n # addr, key\n # get\n addr = sys.argv[1]\n key = sys.argv[2]\n get(addr, key)\n elif len(sys.argv) == 4:\n # addr, key value\n # put\n addr = sys.argv[1]\n key = sys.argv[2]\n val = sys.argv[3]\n put(addr, key, val)\n else:\n print(\"PUT usage: python3 client.py address 'key' 'value'\")\n print(\"GET usage: python3 client.py address 'key'\")\n print(\"Format: address: http://ip:port\")\n", "sub_path": "client.py", "file_name": "client.py", "file_ext": "py", "file_size_in_byte": 3014, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "requests.get", "line_number": 12, "usage_type": "call"}, {"api_name": "requests.put", "line_number": 20, "usage_type": "call"}, {"api_name": "cryptography.fernet.Fernet", "line_number": 56, "usage_type": "call"}, {"api_name": "sys.argv", "line_number": 76, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 79, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 80, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 82, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 85, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 86, "usage_type": "attribute"}, {"api_name": "sys.argv", "line_number": 87, "usage_type": "attribute"}]} +{"seq_id": "113521235", "text": "import boto.s3.connection\nimport mimetypes\nimport os\nimport shutil\nimport tempfile\nimport urllib\nimport urlparse\nimport logging\n\nfrom boto.s3.key import Key\nfrom StringIO import StringIO\n\nlogger = logging.getLogger(__name__)\n\nMAX_FILE_SPLITS = 9999\nDEFAULT_FILE_SPLIT_SIZE = 6291456\nDEFAULT_MINIMUM_SPLIT_AT_SIZE = 20000000\n\n\nclass S3Client(object):\n \"\"\"A client that helps user to send and get files from S3\"\"\"\n s3_connection = None\n bucket = None\n\n def __init__(self, bucket):\n \"\"\"\n Creates the logger and sets the bucket name that will be used throughout\n :param\n - bucket: string - The name of the bucket you will be working with\n \"\"\"\n self.bucket_name = bucket\n\n def connect(self):\n \"\"\"Start the amazon connection using the system's boto.cfg file to retrieve the credentials\"\"\"\n if self.s3_connection:\n return\n\n try:\n # - Amazon S3 credentials will use Boto's fall back config, looks for boto.cfg then environment variables\n self.s3_connection = boto.s3.connection.S3Connection(\n is_secure=False)\n self.bucket = self.s3_connection.get_bucket(\n self.bucket_name, validate=False)\n\n except Exception as s3_connection_exception:\n # - Reset the variables on failure to allow a reconnect\n self.s3_connection = None\n self.bucket = None\n message = \"Exception while connecting to S3: {0}\".format(s3_connection_exception)\n raise S3ClientException(message)\n\n def store_file(self, s3_path, file_to_store, filename, return_url=False, mime_type=None,\n chunk_at_size=DEFAULT_MINIMUM_SPLIT_AT_SIZE):\n \"\"\"\n Pushes the desired file up to S3 (e.g. log file).\n :param\n - s3_path: string - The S3 path to the folder in which you'd like to store the file\n - file_to_store: StringIO or string - The fileIO or file local file path for the file to be sent\n - filename: string - The name the file will have when on S3. Should include the file extension\n - return_url: boolean - Whether to return the path to the file on S3\n - mime_type: string - the mime type the file should be saved as, ex: text/html or image/png\n - chunk_at_size: int - the size of which the file should be split to multi-upload (default ~ 20 mb)\n :return\n - file_url: string - The path to the file on S3. This is returned only is return_url is set to true\n \"\"\"\n self.connect()\n\n try:\n s3_file = Key(self.bucket)\n s3_file.key = self._generate_file_path(s3_path, filename)\n # --- Set the Content type for the file being sent (so that it downloads properly)\n # - content_type can be 'image/png', 'application/pdf', 'text/plain', etc.\n mime_type = mimetypes.guess_type(filename) if mime_type is None else mime_type\n s3_file.set_metadata('Content-Type', mime_type)\n\n # - Check if file is a buffer or disk file and if file that is getting uploaded is greater than\n # chunk_at_size then upload cool multi style\n mutli_part_upload_successful = False\n if isinstance(file_to_store, str) and os.path.getsize(file_to_store) > chunk_at_size:\n split_file_dir = None\n multipart_file = self.bucket.initiate_multipart_upload(key_name=s3_file.key, metadata=s3_file.metadata)\n\n try:\n # - Split the file and get it chunky\n split_file_dir = self._split_file(file_to_store)\n\n # - Upload the file parts\n file_count = 0\n for files in os.listdir(split_file_dir):\n file_count += 1\n file_part = open(os.path.join(split_file_dir, files), 'rb')\n multipart_file.upload_part_from_file(file_part, file_count)\n\n # - Complete the upload\n multipart_file.complete_upload()\n mutli_part_upload_successful = True\n except boto.s3.connection.S3ResponseError as s3_error:\n logger.warning(\"A S3 Response error was caught while attempting to chunk and upload the PDF | {}\\n\"\n \"Will now attempt to send the file as a whole...\".format(s3_error))\n multipart_file.cancel_upload()\n except Exception as s3_error:\n logger.warning(\"Unexpected Error encountered an issue while chunking and uploading the PDF | {}\\n\"\n \"Will now attempt to send the file as a whole...\".format(s3_error))\n multipart_file.cancel_upload()\n finally:\n # - Remove the folder from splitting the file\n if split_file_dir:\n shutil.rmtree(split_file_dir)\n\n # - Upload the file as a whole\n if not mutli_part_upload_successful:\n file_type = type(file_to_store)\n if file_type in [str, unicode]:\n s3_file.set_contents_from_filename(file_to_store)\n else:\n s3_file.set_contents_from_file(file_to_store)\n\n if return_url:\n file_key = self.bucket.get_key(s3_file.key)\n file_key.set_acl('public-read')\n file_url = file_key.generate_url(0, query_auth=False)\n\n # - Certain server side permissions might cause a x-amz-security-token parameter to be added to the url\n # Split the url into its pieces\n scheme, netloc, path, params, query, fragment = urlparse.urlparse(file_url)\n # Check whether the x-amz-security-token parameter was appended to the url and remove it\n params = urlparse.parse_qs(query)\n if 'x-amz-security-token' in params:\n del params['x-amz-security-token']\n # Rebuild the params without the x-amz-security-token\n query = urllib.urlencode(params)\n\n return urlparse.urlunparse((scheme, netloc, path, params, query, fragment))\n\n except Exception as store_file_exception:\n message = \"Exception while storing file on S3: {0}\".format(store_file_exception)\n raise S3ClientException(message)\n\n def get_file(self, s3_path, file_to_get):\n \"\"\"\n Stores the desired file locally (e.g. configuration file).\n :param\n - s3_path: string - The S3 path to the folder which contains the file\n - file_to_get: string - The name of the file you are looking for in the folder\n :return\n - retrieved_file StringIO - an IO object containing the content of the file retrieved from S3\n \"\"\"\n self.connect()\n\n try:\n if self.verify_file(s3_path, file_to_get):\n retrieved_file = StringIO()\n s3_file = self.bucket.get_key(\n self._generate_file_path(s3_path, file_to_get))\n s3_file.get_contents_to_file(retrieved_file)\n return retrieved_file\n else:\n raise S3ClientException(\"File not found in S3\")\n\n except Exception as get_file_exception:\n message = \"Exception while retrieving file from S3: {0}\".format(get_file_exception)\n raise S3ClientException(message)\n\n def verify_file(self, s3_path, file_to_verify):\n \"\"\"\n Verifies a file (e.g. configuration file) is on S3 and returns\n \"True\" or \"False\".\n :param\n - s3_path: string - The S3 path to the folder which contains the file\n - file_to_verify: string - The name of the file you are looking for in the folder\n :return\n - boolean: True if .get_key returns an instance of a Key object and False if .get_key returns None:\n \"\"\"\n self.connect()\n try:\n file_path = self._generate_file_path(s3_path, file_to_verify)\n s3_file = self.bucket.get_key(file_path)\n if s3_file:\n return True\n else:\n return False\n\n except Exception as verify_file_exception:\n message = \"Exception while verifying file on S3: {0}\".format(verify_file_exception)\n raise S3ClientException(message)\n\n def _generate_file_path(self, s3_path, file_to_store):\n \"\"\"\n Ensures that the / situation creates a proper path by removing any double slash possibilities\n :param\n - s3_path: string - The path to the folder you wish to store the file in\n - file_to_store: string - The name of the file you wish to store\n :return\n - string: The concatenated version of the /folder/filename path\n \"\"\"\n return \"{0}/{1}\".format(s3_path.strip(\"/\"), file_to_store.strip(\"/\"))\n\n def get_all_filenames_in_folder(self, path_to_folder):\n \"\"\"\n Retrieves a list of the files/keys in a folder on S3\n :param\n - path_to_folder: string - The path to the folder on S3. This should start after the bucket name\n :return\n - key_list: list - The list of keys in the folder\n \"\"\"\n self.connect()\n\n s3_folder_path = str(path_to_folder)\n key_list = self.bucket.list(prefix=s3_folder_path)\n return key_list\n\n def get_most_recent_file_from_s3_key_list(self, key_list):\n \"\"\"\n Sorts through the list of files in s3 key list object and returns the most recently modified file in the list\n :param\n - key_list: list - The list of files returned from a s3.bucket.list() operation\n :return\n - key boto.s3.Key - The most recently modified file in the key list\n \"\"\"\n most_recent_key = None\n for key in key_list:\n if not most_recent_key or key.last_modified > most_recent_key.last_modified:\n most_recent_key = key\n return most_recent_key\n\n def _split_file(self, from_file, file_chunk_size=DEFAULT_FILE_SPLIT_SIZE):\n \"\"\"\n Split a given file into smaller chunks named partXXXX into a temp at a default size of ~ 6 mb. The temp\n folder should be deleted after use.\n\n WARNING: You cannot split into more than 9999 files.\n\n :param\n - from_file: string - the file to split up\n - file_chunk_size: int - number of Bytes each split should contain (Should be > 5 MB for Amazon S3 minimum)\n :return:\n - temp_dir: string - temp folder location of split file, use to iterate through the split files\n \"\"\"\n if os.path.getsize(from_file) > (MAX_FILE_SPLITS * file_chunk_size):\n raise S3ClientException(\"Could not split the file.\\nError: Input file is too large!\\n\")\n elif os.path.getsize(from_file) < DEFAULT_FILE_SPLIT_SIZE:\n raise S3ClientException(\"Could not split the file.\\nError: Input file is too small!\\n\")\n\n try:\n temp_dir = tempfile.mkdtemp()\n part_num = 0\n with open(from_file, 'rb') as input_file:\n chunk = input_file.read(file_chunk_size)\n while chunk:\n part_num += 1\n open(os.path.join(temp_dir, ('part%04d' % part_num)), 'wb').write(chunk)\n chunk = input_file.read(file_chunk_size)\n\n return temp_dir\n except Exception as e:\n raise S3ClientException(\"Could not split the file.\\nError: {}\\n\".format(e))\n\n\nclass S3ClientException(Exception):\n def __init__(self, message):\n self.msg = message\n\n def __str__(self):\n return self.msg\n", "sub_path": "the_ark/s3_client.py", "file_name": "s3_client.py", "file_ext": "py", "file_size_in_byte": 11958, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "logging.getLogger", "line_number": 13, "usage_type": "call"}, {"api_name": "boto.s3.connection.s3.connection.S3Connection", "line_number": 40, "usage_type": "call"}, {"api_name": "boto.s3.connection.s3", "line_number": 40, "usage_type": "attribute"}, {"api_name": "boto.s3.connection", "line_number": 40, "usage_type": "name"}, {"api_name": "boto.s3.key.Key", "line_number": 69, "usage_type": "call"}, {"api_name": "mimetypes.guess_type", "line_number": 73, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 79, "usage_type": "call"}, {"api_name": "os.path", "line_number": 79, "usage_type": "attribute"}, {"api_name": "os.listdir", "line_number": 89, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 91, "usage_type": "call"}, {"api_name": "os.path", "line_number": 91, "usage_type": "attribute"}, {"api_name": "boto.s3.connection.s3", "line_number": 97, "usage_type": "attribute"}, {"api_name": "boto.s3.connection", "line_number": 97, "usage_type": "name"}, {"api_name": "shutil.rmtree", "line_number": 108, "usage_type": "call"}, {"api_name": "urlparse.urlparse", "line_number": 125, "usage_type": "call"}, {"api_name": "urlparse.parse_qs", "line_number": 127, "usage_type": "call"}, {"api_name": "urllib.urlencode", "line_number": 131, "usage_type": "call"}, {"api_name": "urlparse.urlunparse", "line_number": 133, "usage_type": "call"}, {"api_name": "StringIO.StringIO", "line_number": 152, "usage_type": "call"}, {"api_name": "os.path.getsize", "line_number": 239, "usage_type": "call"}, {"api_name": "os.path", "line_number": 239, "usage_type": "attribute"}, {"api_name": "os.path.getsize", "line_number": 241, "usage_type": "call"}, {"api_name": "os.path", "line_number": 241, "usage_type": "attribute"}, {"api_name": "tempfile.mkdtemp", "line_number": 245, "usage_type": "call"}, {"api_name": "os.path.join", "line_number": 251, "usage_type": "call"}, {"api_name": "os.path", "line_number": 251, "usage_type": "attribute"}]} +{"seq_id": "252379135", "text": "from datetime import datetime, date\nimport pytest\nfrom unittest import mock\nfrom typing import Dict\n\nfrom flask_app.infra.darksky import DarkSkyRepository\nfrom flask_app.models.weather import WeatherCondition\n\nON_MAKING = 1\n\n\nclass TestDarkSkyRepository(object):\n\n @classmethod\n def setup_class(cls):\n cls.dsr = DarkSkyRepository()\n cls.patcher1 = mock.patch('flask_app.utils.util.to_latlng')\n cls.patcher2 = mock.patch('flask_app.infra.darksky.DarkSkyRepository.get_response_from_darksky_api')\n\n @classmethod\n def teardown_class(cls):\n del cls.dsr\n del cls.patcher1\n del cls.patcher2\n\n def setup_method(self):\n pass\n\n @pytest.fixture()\n def dark_sky_dummy_data(request):\n yield {'timezone': 'Asia/Tokyo',\n 'daily': {'data': [{\n 'temperatureMax': 1.1,\n 'temperatureMin': 1.2,\n 'cloudCover': 1.3,\n 'humidity': 1.4,\n 'pressure': 1.5,\n 'ozone': 1.6,\n 'precipProbability': 1.7,\n 'date': datetime.today(),\n 'time': 15247100000,\n }]\n }\n }\n\n ### get_repository_name ###\n def test_get_repository_name(self):\n assert self.dsr.get_repository_name() == 'DarkSkyRepository'\n\n ### get_past_condition_by_city ###\n # 都市名　空白\n def test_get_past_condition_by_city_no_city(self):\n err, conditions, location = self.dsr.get_past_condition_by_city(city_name=\"\", from_date=date.today(),\n to_date=date.today())\n assert err == 'no city name'\n assert conditions == []\n assert location == {}\n\n # 都市名　存在しない\n def test_get_past_condition_by_city_not_exist_city(self):\n err, conditions, location = self.dsr.get_past_condition_by_city(city_name=\"ほげほげ\", from_date=date.today(),\n to_date=date.today())\n assert err == 'not exist city name'\n assert conditions == []\n assert location == {}\n\n # 都市 > 緯度・経度変換(googleMapエラー）\n def test_get_past_condition_by_city_to_latlng_failure(self):\n mock_to_latlng = self.patcher1.start()\n mock_to_latlng.return_value = 'access failure', {}\n err, conditions, location = self.dsr.get_past_condition_by_city(city_name=\"東京\", from_date=date.today(),\n to_date=date.today())\n assert err == 'access failure'\n assert conditions == []\n assert location == {}\n self.patcher1.stop()\n\n # 期間　開始日None\n def test_get_past_condition_by_city_from_date_none(self, request, dark_sky_dummy_data):\n mock_use = request.config.getoption('--mock-use')\n if mock_use == 'True':\n self.dsr.get_response_from_darksky_api = mock.MagicMock(return_value=('', dark_sky_dummy_data))\n\n err, conditions, location = self.dsr.get_past_condition_by_city(city_name=\"東京\", from_date=None,\n to_date=date.today())\n assert err == 'success'\n assert isinstance(conditions[0], WeatherCondition)\n # assert isinstance(location, Dict[str, float])\n\n # 期間　終了日None\n def test_get_past_condition_by_city_to_date_none(self, request, dark_sky_dummy_data):\n mock_use = request.config.getoption('--mock-use')\n if mock_use == 'True':\n print('##### darksky_api mock used. ######')\n mock_get_response_from_darksky_api = self.patcher2.start()\n mock_get_response_from_darksky_api.return_value = ('', dark_sky_dummy_data)\n\n err, conditions, location = self.dsr.get_past_condition_by_city(city_name=\"東京\", from_date=date.today(),\n to_date=None)\n assert err == 'success'\n assert isinstance(conditions[0], WeatherCondition)\n\n if mock_use == 'True':\n self.patcher2.stop()\n\n # 都市名　正常、期間　１日\n def test_get_past_condition_by_city_success(self, request, dark_sky_dummy_data):\n mock_use = request.config.getoption('--mock-use')\n if mock_use == 'True':\n print('##### darksky_api mock used. ######')\n mock_get_response_from_darksky_api = self.patcher2.start()\n mock_get_response_from_darksky_api.return_value = ('', dark_sky_dummy_data)\n\n err, conditions, location = self.dsr.get_past_condition_by_city(city_name=\"東京\", from_date=date.today(),\n to_date=date.today())\n assert err == 'success'\n assert isinstance(conditions[0], WeatherCondition)\n\n if mock_use == 'True':\n self.patcher2.stop()\n\n # 都市名　正常、期間　30日\n @pytest.mark.skipif(ON_MAKING=1, reason='作成中はスキップ')\n def test_get_past_condition_by_city_success_multi_days(self, request, dark_sky_dummy_data):\n mock_use = request.config.getoption('--mock-use')\n if mock_use == 'True':\n print('##### darksky_api mock used. ######')\n mock_get_response_from_darksky_api = self.patcher2.start()\n mock_get_response_from_darksky_api.return_value = ('', dark_sky_dummy_data)\n\n err, conditions, location = self.dsr.get_past_condition_by_city(city_name=\"東京\", from_date=date(2019, 11, 1),\n to_date=date(2019, 11, 30))\n assert err == 'success'\n assert isinstance(conditions[0], WeatherCondition)\n assert len(conditions) == 30\n\n if mock_use == 'True':\n self.patcher2.stop()\n\n ### get_past_condition_by_latlng ###\n # 緯度・経度なし\n def test_get_past_condition_by_latlng_no_latlng(self):\n assert self.dsr.get_past_condition_by_latlng(latitude=None, longitude=139) == ('no latitude', [])\n assert self.dsr.get_past_condition_by_latlng(latitude=35, longitude=None) == ('no longitude', [])\n\n # darksky apiエラー\n def test_get_past_condition_by_latlng_api_error(self):\n mock_get_response_from_darksky_api = self.patcher2.start()\n mock_get_response_from_darksky_api.return_value = ('HTTP Error', {})\n assert self.dsr.get_past_condition_by_latlng(latitude=35, longitude=139) == ('HTTP Error', [])\n mock_get_response_from_darksky_api.return_value = ('URL Error', {})\n assert self.dsr.get_past_condition_by_latlng(latitude=35, longitude=139) == ('URL Error', [])\n self.patcher2.stop()\n\n # 正常１日\n def test_get_past_condition_by_latlng(self):\n err, conditions = self.dsr.get_past_condition_by_latlng(latitude=35, longitude=139)\n assert err == 'success'\n assert isinstance(conditions[0], WeatherCondition)\n\n # 正常複数日\n @pytest.mark.skipif(ON_MAKING=1, reason='作成中はスキップ')\n def test_get_past_condition_by_latlng_multi_days(self):\n err, conditions = self.dsr.get_past_condition_by_latlng(latitude=35, longitude=139,\n from_date=date(2019, 11, 1),\n to_date=date(2019, 11, 30))\n assert err == 'success'\n assert isinstance(conditions[0], WeatherCondition)\n assert len(conditions) == 30\n\n", "sub_path": "source/application/tests/infra/test_darksky.py", "file_name": "test_darksky.py", "file_ext": "py", "file_size_in_byte": 7698, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "flask_app.infra.darksky.DarkSkyRepository", "line_number": 16, "usage_type": "call"}, {"api_name": "unittest.mock.patch", "line_number": 17, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 17, "usage_type": "name"}, {"api_name": "unittest.mock.patch", "line_number": 18, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 18, "usage_type": "name"}, {"api_name": "datetime.datetime.today", "line_number": 40, "usage_type": "call"}, {"api_name": "datetime.datetime", "line_number": 40, "usage_type": "name"}, {"api_name": "pytest.fixture", "line_number": 29, "usage_type": "call"}, {"api_name": "datetime.date.today", "line_number": 53, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 53, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 54, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 54, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 61, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 61, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 62, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 62, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 71, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 71, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 72, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 72, "usage_type": "name"}, {"api_name": "unittest.mock.MagicMock", "line_number": 82, "usage_type": "call"}, {"api_name": "unittest.mock", "line_number": 82, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 85, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 85, "usage_type": "name"}, {"api_name": "flask_app.models.weather.WeatherCondition", "line_number": 87, "usage_type": "argument"}, {"api_name": "datetime.date.today", "line_number": 98, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 98, "usage_type": "name"}, {"api_name": "flask_app.models.weather.WeatherCondition", "line_number": 101, "usage_type": "argument"}, {"api_name": "datetime.date.today", "line_number": 114, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 114, "usage_type": "name"}, {"api_name": "datetime.date.today", "line_number": 115, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 115, "usage_type": "name"}, {"api_name": "flask_app.models.weather.WeatherCondition", "line_number": 117, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 131, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 132, "usage_type": "call"}, {"api_name": "flask_app.models.weather.WeatherCondition", "line_number": 134, "usage_type": "argument"}, {"api_name": "pytest.mark.skipif", "line_number": 123, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 123, "usage_type": "attribute"}, {"api_name": "flask_app.models.weather.WeatherCondition", "line_number": 159, "usage_type": "argument"}, {"api_name": "datetime.date", "line_number": 165, "usage_type": "call"}, {"api_name": "datetime.date", "line_number": 166, "usage_type": "call"}, {"api_name": "flask_app.models.weather.WeatherCondition", "line_number": 168, "usage_type": "argument"}, {"api_name": "pytest.mark.skipif", "line_number": 162, "usage_type": "call"}, {"api_name": "pytest.mark", "line_number": 162, "usage_type": "attribute"}]} +{"seq_id": "353300946", "text": "import matplotlib.pyplot as plt\nimport numpy as np\nimport scipy.io as sio\nimport math\nimport entropy_estimators as ee\n\ndef num_bins_calculator(num_points):\n return round(np.sqrt(num_points/5))\n\n\ndef calc_entropy(x,bins):\n c_x = np.histogram(x, bins)[0]/sum(np.histogram(x, 5)[0])\n\n entropy=0\n for prob in c_x:\n if prob!=0:\n\n entropy += - prob * math.log(prob, 2)\n\n\n return entropy\n\n\ndef calc_MI(x, y, bins):\n c_xy = np.histogram2d(x, y, bins)[0]\n mi = mutual_info_score(None, None, contingency=c_xy)\n return mi\n\ndef mutual_info_score(labels_true, labels_pred, contingency=None):\n\n # if contingency is None:\n # labels_true, labels_pred = check_clusterings(labels_true, labels_pred)\n # contingency = contingency_matrix(labels_true, labels_pred, sparse=True)\n\n if isinstance(contingency, np.ndarray):\n # For an array\n nzx, nzy = np.nonzero(contingency)\n nz_val = contingency[nzx, nzy]\n elif sp.issparse(contingency):\n # For a sparse matrix\n nzx, nzy, nz_val = sp.find(contingency)\n else:\n raise ValueError(\"Unsupported type for 'contingency': %s\" %\n type(contingency))\n\n contingency_sum = contingency.sum()\n pi = np.ravel(contingency.sum(axis=1))\n pj = np.ravel(contingency.sum(axis=0))\n log_contingency_nm = np.log(nz_val)\n contingency_nm = nz_val / contingency_sum\n # Don't need to calculate the full outer product, just for non-zeroes\n outer = pi.take(nzx) * pj.take(nzy)\n log_outer = -np.log(outer) + np.log(pi.sum()) + np.log(pj.sum())\n mi = (contingency_nm * (log_contingency_nm - np.log(contingency_sum)) +\n contingency_nm * log_outer)\n return mi.sum()\n\nif __name__ == \"__main__\":\n from os import listdir\n\n names = listdir('output_files')\n # for name in names:\n\n\n mat_contents = sio.loadmat('output_files/' + names[0])\n color_mat = mat_contents['colors']\n fig, ax = plt.subplots()\n fig2, ax1 = plt.subplots()\n ax2 = ax.twinx()\n t_len = mat_contents['colors'].shape[1]\n print(t_len)\n t_vect = np.linspace(0, (t_len - 1) * 3, t_len)\n # ax3 = fig2.add_subplot(111, label=\"1\")\n entropy_mat = np.zeros((3, t_len))\n mi_mat = np.zeros((3, t_len))\n\n # for cell in range(color_mat.shape[0]):\n # ax2.plot(t_vect, color_mat[cell, :, 0], linewidth=3, color='orange', alpha=0.25)\n # # ax3.plot(color_mat[cell, :, 1], color_mat[cell, :, 2], linewidth=3, color='orange', alpha=0.25)\n\n for j in np.arange(3):\n\n mat_contents = sio.loadmat('output_files/' + names[j])\n color_mat = mat_contents['colors']\n t_len = color_mat.shape[1]\n print(t_len)\n # for cell in range(color_mat.shape[0]):\n # ax.plot(t_vect,color_mat[cell, :, 0],linewidth=3,color='teal',alpha=0.25)\n bins = num_bins_calculator(color_mat.shape[0])\n entropies = []\n mis = []\n for t in range(t_len):\n entropies.append(calc_entropy(color_mat[:, t, 0], 10))\n\n mis.append(calc_MI(color_mat[:, t, 1], color_mat[:, t, 2], bins))\n\n\n entropy_mat[j, :] = entropies\n mi_mat[j, :] = mis\n\n entropy_mat2 = np.zeros((3, t_len))\n mi_mat2 = np.zeros((3, t_len))\n for k in np.arange(3):\n\n mat_contents = sio.loadmat('output_files/' + names[k+3])\n color_mat = mat_contents['colors']\n t_len = color_mat.shape[1]\n print(t_len)\n # for cell in range(color_mat.shape[0]):\n # ax.plot(t_vect,color_mat[cell, :, 0],linewidth=3,color='teal',alpha=0.25)\n bins = num_bins_calculator(color_mat.shape[0])\n entropies = []\n mis = []\n for t in range(t_len):\n entropies.append(calc_entropy(color_mat[:, t, 0], 10))\n\n mis.append(calc_MI(color_mat[:, t, 1], color_mat[:, t, 2], bins))\n\n entropy_mat2[k, :] = entropies\n mi_mat2[k, :] = mis\n\n ax.errorbar(t_vect, np.mean(entropy_mat, 0), yerr=np.std(entropy_mat, 0), linewidth=3, color='teal')\n ax.errorbar(t_vect, np.mean(entropy_mat2, 0), yerr=np.std(entropy_mat2, 0), linewidth=3, color='orange')\n\n ax1.errorbar(t_vect, np.mean(mi_mat, 0), yerr=np.std(mi_mat, 0), linewidth=3, color='teal')\n ax1.errorbar(t_vect, np.mean(mi_mat2, 0), yerr=np.std(mi_mat2, 0), linewidth=3, color='orange')\n ax1.set_ylabel('Mutual Information (bits)')\n ax1.set_xlabel('Time (Minutes)')\n ax.set_xlim([-5, 253])\n ax1.set_xlim([-5, 253])\n ax.set_xlabel('time (minutes)')\n ax.set_ylabel('Entropy (bits)')\n ax2.set_ylabel('Flourescence (AU)')\n fig.savefig('figures/2a.png', bbox_inches='tight')\n\n fig2.savefig('figures/2b.png', bbox_inches='tight')", "sub_path": "comparative_plots.py", "file_name": "comparative_plots.py", "file_ext": "py", "file_size_in_byte": 4683, "program_lang": "python", "lang": "en", "doc_type": "code", "dataset": "code-starcoder2", "pt": "17", "api": [{"api_name": "numpy.sqrt", "line_number": 8, "usage_type": "call"}, {"api_name": "numpy.histogram", "line_number": 12, "usage_type": "call"}, {"api_name": "math.log", "line_number": 18, "usage_type": "call"}, {"api_name": "numpy.histogram2d", "line_number": 25, "usage_type": "call"}, {"api_name": "numpy.ndarray", "line_number": 35, "usage_type": "attribute"}, {"api_name": "numpy.nonzero", "line_number": 37, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 47, "usage_type": "call"}, {"api_name": "numpy.ravel", "line_number": 48, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 49, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 53, "usage_type": "call"}, {"api_name": "numpy.log", "line_number": 54, "usage_type": "call"}, {"api_name": "os.listdir", "line_number": 61, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 65, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 65, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 67, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 67, "usage_type": "name"}, {"api_name": "matplotlib.pyplot.subplots", "line_number": 68, "usage_type": "call"}, {"api_name": "matplotlib.pyplot", "line_number": 68, "usage_type": "name"}, {"api_name": "numpy.linspace", "line_number": 72, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 74, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 75, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 81, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 83, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 83, "usage_type": "name"}, {"api_name": "numpy.zeros", "line_number": 101, "usage_type": "call"}, {"api_name": "numpy.zeros", "line_number": 102, "usage_type": "call"}, {"api_name": "numpy.arange", "line_number": 103, "usage_type": "call"}, {"api_name": "scipy.io.loadmat", "line_number": 105, "usage_type": "call"}, {"api_name": "scipy.io", "line_number": 105, "usage_type": "name"}, {"api_name": "numpy.mean", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 122, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 123, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 125, "usage_type": "call"}, {"api_name": "numpy.mean", "line_number": 126, "usage_type": "call"}, {"api_name": "numpy.std", "line_number": 126, "usage_type": "call"}]} +{"seq_id": "51263927", "text": "import os\r\nimport json\r\nimport codecs\r\nimport flask\r\nfrom werkzeug.contrib.fixers import ProxyFix\r\n\r\napp = flask.Flask(__name__)\r\napp.jinja_env.add_extension('pyjade.ext.jinja.PyJadeExtension')\r\n\r\nconfig = json.loads(codecs.open(\"config.json\", \"r\", \"utf-8\").read())\r\n\r\n@app.route(\"/page/