.*)', line)\n if not match:\n warning_print(\"the bullet screen: ( %s ) happens some error during searching, please check\"%line)\n continue\n try:\n realtime = match.group(\"time\").strip(\"][\")\n author = match.group(\"message\").split(\"\\t\")[0]\n message = match.group(\"message\").split(\"\\t\")[1]\n except:\n warning_print(\"the bullet screen: ( %s ) happens some error during splitting, please check\" % line)\n continue\n messages.append((realtime, author, message))\n return messages\n\n\ndef getFileName(file):\n return file.split('.')[0]\n\ndef checkOrCreateFolder(path):\n if os.path.exists(path):\n if os.path.isdir(path):\n return\n os.mkdir(path)\n\nif __name__ == \"__main__\":\n #test\n convert_file(\"rutusuoshi.lrc\", offset = 30)\n","sub_path":"video_grabber/bullet_screen_converter.py","file_name":"bullet_screen_converter.py","file_ext":"py","file_size_in_byte":8236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"352737591","text":"import eel\nfrom random import randint\nfrom pprint import pprint\n\nfrom pairs import pairs\nimport example as ex\nfrom config import config as CONFIG\n\nPRODUCTION = True\n\neel.init('wgui')\neel.start('', block=False, size=CONFIG['WIN_SIZE'])\n\n@eel.expose\ndef WindowClose():\n\texit()\n\nlastSentTest = [None]\nname, group = '', ''\n\n@eel.expose\ndef GetTime():\n\treturn CONFIG['TIME']\n\ndef GetTable(clk, b=CONFIG['VARS']):\n\tX, Y, Z, W = 0, 0, 0, 0\n\ttable = [['XYZW'[i] for i in range(b)]]\n\ttable[0].append('F')\n\n\tfor i in range(2 ** b):\n\t\tX = int(bool(i & 1)) # черная магия масок\n\t\tY = int(bool(i & 2))\n\t\tZ = int(bool(i & 4))\n\t\tW = int(bool(i & 8))\n\t\trow = []\n\t\tif b > 0:\n\t\t\trow.append(X)\n\t\tif b > 1:\n\t\t\trow.append(Y)\n\t\tif b > 2:\n\t\t\trow.append(Z)\n\t\tif b > 3:\n\t\t\trow.append(W)\n\t\trow.append(int(eval(clk)))\n\t\ttable.append(row)\n\treturn table\n\ndef CAFunc1(clk):\n\ttable = GetTable(clk)\n\tca = []\n\tfor i in range(1, len(table)):\n\t\tca.append(str(table[i].pop()))\n\t\ttable[i].append('?')\n\treturn ' '.join(ca), table\n\ndef CAFunc2(clk): #СДНФ\n\ttable = GetTable(clk)\n\tca = []\n\tfor row in table:\n\t\tif row[len(row) - 1] == 'F' or not int(row[len(row) - 1]):\n\t\t\tcontinue\n\t\tt = []\n\t\tfor i in range(len(row) - 1):\n\t\t\tt.append(('!' if not row[i] else '') + 'XYZW'[i])\n\t\tca.append(' * '.join(t))\n\tca = ' + '.join(ca)\n\tif not ca:\n\t\tca = '0'\n\treturn ca, table\n\t\ndef CAFunc3(clk): #СКНФ\n\ttable = GetTable(clk)\n\tca = []\n\tfor row in table:\n\t\tif row[len(row) - 1] == 'F' or int(row[len(row) - 1]):\n\t\t\tcontinue\n\t\tt = []\n\t\tfor i in range(len(row) - 1):\n\t\t\tt.append(('!' if row[i] else '') + 'XYZW'[i])\n\t\tca.append(f'({\" + \".join(t)})')\n\tca = ' * '.join(ca)\n\tif not ca:\n\t\tca = '1'\n\treturn ca, table\n\ndef CAFunc4(clk, fn):\n\ttable = GetTable(clk, 2)\n\treturn fn.split()[1], table\n\ndef GenTask(text, CAFunc, hint=None, noExpr=False):\n\tfn, clk = BuildExpr(CONFIG['VARS'], CONFIG['CONSTS'])\n\tca, table = CAFunc(clk)\n\tt = {\n\t\t'answer': None,\n\t\t'text': text,\n\t\t'question': {\n\t\t\t'text': text,\n\t\t\t'table': table,\n\t\t\t'expr': fn if not noExpr else '',\n\t\t\t'hint': hint\n\t\t}, \n\t\t'correctAnswer': ca\n\t}\n\treturn t\n\n@eel.expose\ndef GenTest(_name, _group, train, trainNums=None): # генерируем тесты\n\t# not not (not ((not W == 1) <= 1) <= 0) or 1 or 1 or Z == Y and X\n\tglobal name, group\n\tname, group = _name, _group\n\trs = []\n\n\tn1 = int(trainNums and trainNums[0] or CONFIG['TASKS_1'])\n\tn2 = int(trainNums and trainNums[1] or CONFIG['TASKS_2'])\n\tn3 = int(trainNums and trainNums[2] or CONFIG['TASKS_3'])\n\tn4 = int(trainNums and trainNums[3] or CONFIG['TASKS_4'])\n\n\tfor _ in range(n1):\n\t\trs.append(GenTask('Заполните последний столбец таблицы для выражения', CAFunc1))\n\tfor _ in range(n2):\n\t\trs.append(GenTask('Составьте СДНФ для', CAFunc2, noExpr=True))\n\tfor _ in range(n3):\n\t\trs.append(GenTask('Составьте СКНФ для', CAFunc3, noExpr=True))\n\tfor _ in range(n4):\n\t\tfn, clk = BuildExpr(2, 0)\n\t\tfn = fn.replace('!', '')\n\t\tclk = clk.replace('not ', '')\n\t\tca, table = CAFunc4(clk, fn)\n\t\tt = {\n\t\t\t'answer': None,\n\t\t\t'text': f'Введите операцию, соответствующую этой таблице (=>, *, +, =, ^) для выражения {fn.split()[0] + \" ? \" + fn.split()[2]}',\n\t\t\t'question': {\n\t\t\t\t'text': f'Введите операцию, соответствующую этой таблице (=>, *, +, =, ^) для выражения {fn.split()[0] + \" ? \" + fn.split()[2]}',\n\t\t\t\t'table': table,\n\t\t\t\t'expr': '',\n\t\t\t\t'hint': None\n\t\t\t}, \n\t\t\t'correctAnswer': ca\n\t\t}\n\t\trs.append(t)\n\n\tglobal lastSentTest\n\tlastSentTest = []\n\tfor v in rs:\n\t\tlastSentTest.append(dict(v))\n\tif not train:\n\t\tfor i in range(len(rs)):\n\t\t\trs[i]['correctAnswer'] = None\n\treturn rs\n\n\n@eel.expose\ndef Judge(test, train):\n\tglobal lastSentTest\n\ttotal = len(lastSentTest)\n\tdone = 0\n\ttasks = [] # выходная таблица, для GUI (!)\n\tfor i in range(len(lastSentTest)):\n\t\tv = lastSentTest[i]\n\t\tok = isinstance(test[i]['answer'], str) and test[i]['answer'].replace(' ', '') == v['correctAnswer'].replace(' ', '') # верность ответа\n\t\tlastSentTest[i]['answer'] = test[i]['answer'] # пишем ответ в выход\n\t\tif ok:\n\t\t\tdone += 1\n\t\ttasks.append({\n\t\t\t'answer': test[i]['answer'],\n\t\t\t'correctAnswer': v['correctAnswer'],\n\t\t\t'correct': ok\n\t\t})\n\tmark = done / total\n\tif not train:\n\t\tex.SendToServer(CONFIG['IP'], name, group, mark, lastSentTest)\n\tvmark = 0\n\tfor i, threshold in pairs(CONFIG['VRATES']):\n\t\tif mark * 100 >= threshold:\n\t\t\tvmark = 5 - i\n\t\t\tbreak\n\treturn { 'tasks': tasks, 'mark': mark, 'vmark': vmark }\n\n\ndef BuildExpr(_vars, _consts):\n\tvars, consts = _vars, _consts\n\theap = []\n\tstrCalc, strFinal = '', ''\n\n\tfor i in range(vars + consts):\n\t\tconst = vars > 0 and consts > 0\n\t\tif const:\n\t\t\tconst = bool(randint(0, 1))\n\t\telse:\n\t\t\tconst = consts > 0\n\t\tif const:\n\t\t\theap.append(str(randint(0, 1)))\n\t\t\tconsts -= 1\n\t\telse:\n\t\t\theap.append(str('XYZW'[_vars - vars]))\n\t\t\tvars -= 1\n\t\n\tfor i in range(len(heap) - 1):\n\t\tbrackets = len(heap) > 2 and bool(randint(0, 1))\n\t\tinvToken = bool(randint(0, 1))\n\t\toperators = ['=>', '*', '+', '=', '^']\n\t\tif invToken:\n\t\t\theap[0] = '!' + heap[0]\n\t\theap[0] = f'{heap[0]} {operators[randint(0, len(operators) - 1)]} {heap[1]}'\n\t\tdel heap[1]\n\t\tif brackets:\n\t\t\theap[0] = f'({heap[0]})'\n\t\n\tstrFinal = heap[0]\n\tstrCalc = strFinal.replace('!', 'not ').replace('0', 'False').replace('1', 'True')\n\tfor k, v in {\n\t\t'=>': '<=',\n\t\t'*': 'and',\n\t\t'+': 'or',\n\t\t'=': '==',\n\t\t'^': '!='\n\t}.items():\n\t\tstrCalc = strCalc.replace(f' {k} ', f' {v} ')\n\n\treturn strFinal, strCalc\n\t\nwhile True:\n\teel.sleep(10)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"306582305","text":"# -*- coding: utf-8 -*-\nimport datetime\n\nimport my_config\nimport pandas as pd\nimport requests\nimport time\nimport sys\nimport os\n\n# Constants and globals\nURL = \"https://translation.googleapis.com/language/translate/v2\"\nQUOTA = 500\nLANGUAGES = ['de', 'fr', 'it', 'es']\nTRANSDICT = {\n 'description' : {\n 'de': 'Beschreibung',\n 'fr': 'La Description',\n 'it': 'Descrizione',\n 'es': 'Descripción',\n 'en': 'Description'\n },\n 'additional_text' : {\n 'de': 'Sammlungsinformationen',\n 'fr': 'Informations sur la Collecte',\n 'it': 'Informazioni sulla Raccolta',\n 'es': 'Información de la Colección',\n 'en': 'Collection Information'\n },\n 'condition' : {\n 'de': 'Bedingung',\n 'fr': 'Condition',\n 'it': 'Condizione',\n 'es': 'Condición',\n 'en': 'Condition'\n },\n 'biography' : {\n 'de': 'Biografie',\n 'fr': 'La Biographie',\n 'it': 'Biografia',\n 'es': 'Biografía',\n 'en': 'Biography'\n }\n}\npayload = {\n 'key': my_config.config['Google']['api_key'],\n 'source': 'en',\n 'target': 'de'\n}\nlog = []\n\n\nclass Counter:\n\n def __init__(self, length):\n self.length = length\n self.count = 1\n\n def increment(self):\n self.count += 1\n\n def printCount(self):\n print(\"#{:d}, % Complete: {:.2%}\" .format(self.count, float(self.count) / self.length))\n\n\nclass MyTimer:\n\n def __init__(self):\n self.time = time.time()\n self.timeElapsed = 0\n\n def getElapsedTime(self):\n self.timeElapsed = time.time() - self.time\n return self.timeElapsed\n\n def getTimetoWait(self):\n return QUOTA - self.timeElapsed\n\n def resetTimer(self):\n self.time = time.time()\n\n\nclass Translation:\n\n def __init__(self, dataFrame):\n self.cache = {}\n self.translations = []\n self.translation = {}\n self.df = dataFrame\n self.getFields()\n self.payload = {}\n\n def setTargetLang(self, language):\n global payload\n self.language = language\n payload['target'] = language\n\n def doTranslation(self, ):\n\n for index, row in self.df.iterrows():\n\n self.translation = {}\n self.translation['sku'] = index\n\n for field in self.fields:\n\n try:\n self.text = row[field]\n\n if self.text == '':\n counter.printCount()\n counter.increment()\n continue\n\n # if a['Names'].str.contains('Mel').any():\n key = field + '_' + self.language\n\n self.checkForErrors()\n\n #cache check\n if self.text in self.cache.keys():\n self.translation[key] = self.cache[self.text]\n print(\"cache hit\")\n counter.printCount()\n counter.increment()\n continue\n\n #previous translation file check\n if 'dfPartial' in globals():\n if (index in dfPartial.index) and (key in dfPartial.columns) and (dfPartial.loc[index][key] != ''):\n print(\"partial hit\")\n self.cache[self.text] = dfPartial.loc[index][key]\n self.translation[key] = dfPartial.loc[index][key]\n counter.printCount()\n counter.increment()\n continue\n\n self.query = URL + \"?q=\" + self.text\n\n self.sendRequest()\n self.checkResponse()\n\n json = self.response.json()\n translatedText = json['data']['translations'][0]['translatedText']\n self.cache[self.text] = translatedText\n self.translation[key] = translatedText\n counter.printCount()\n counter.increment()\n\n except Exception as e:\n print(\"Error:\", e)\n exc_type, exc_obj, exc_tb = sys.exc_info()\n fname = os.path.split(exc_tb.tb_frame.f_code.co_filename)[1]\n print(exc_type, fname, exc_tb.tb_lineno)\n self.translations.append(self.translation)\n self.joinToOutput()\n writeOutput()\n sys.exit()\n\n self.translations.append(self.translation)\n\n self.dft = pd.DataFrame(self.translations)\n\n def checkForErrors(self):\n if len(self.text) >= 5000:\n raise Exception(\"over 5,000 characters\")\n\n def joinToOutput(self):\n global dfOut\n self.dft = pd.DataFrame(self.translations)\n self.dft.set_index(\"sku\", inplace=True)\n dfOut = dfOut.join(self.dft)\n\n def getFields(self):\n self.fields = list(self.df)\n if 'artist' in self.fields:\n self.fields.remove('artist')\n\n def sendRequest(self):\n self.response = requests.get(self.query, params=payload)\n\n def checkResponse(self):\n if self.response.status_code == 403:\n self.processQutoaExceeded()\n\n if self.response.status_code != 200:\n self.logAndDie(self.response)\n\n def processQutoaExceeded(self):\n # global timer\n timeElapsed = timer.getElapsedTime()\n print(\"usage QUOTA hit, time elapsed: \", timeElapsed, \"s\")\n\n timeToWait = timer.getTimeToWait()\n print(\"Time to wait: {:f}\".format(timeToWait))\n\n time.sleep(timeToWait + 1)\n timer.resetTimer()\n self.sendRequest()\n\n def logAndDie(self, response):\n log = {}\n log['url'] = response.request.url\n log['counter'] = counter\n log['status'] = response.status_code\n log['response'] = response.text\n print(\"skipping\")\n print(\"status: \", log['status'] )\n print(\"content: \", log['response'] )\n sys.exit()\n\n\nclass ListingBuilder:\n\n def __init__(self, dataFrameIn, lang):\n\n self.language = lang\n self.df = dataFrameIn\n self.listings = []\n self.buildListing()\n\n def buildListing(self):\n\n if self.language == 'en':\n suffix = \"\"\n else:\n suffix = \"_\" + self.language\n\n for index, row in self.df.iterrows():\n # print(row)\n listingContent = \"\"\n\n if 'description' + suffix in row.keys() and row['description' + suffix]:\n listingContent += \"\" + TRANSDICT['description'][self.language] + \"
\"\n listingContent += \"\" + row['description' + suffix] + \"
\"\n\n if 'condition' + suffix in row.keys() and row['condition' + suffix]:\n listingContent += \"\" + TRANSDICT['condition'][self.language] + \"
\"\n listingContent += \"\" + row['condition' + suffix] + \"
\"\n\n # print(\"h\")\n # print(row['additional_text' + suffix])\n if 'additional_text' + suffix in row.keys() and row['additional_text' + suffix]:\n listingContent += \"\" + TRANSDICT['additional_text'][self.language] + \"
\"\n listingContent += \"\" + row['additional_text' + suffix] + \"
\"\n\n if 'biography' + suffix in row.keys() and row['biography' + suffix]:\n listingContent += \"\" + TRANSDICT['biography'][self.language] + \"
\"\n listingContent += \"\" + row['biography' + suffix] + \"
\"\n\n self.listings.append({\n \"sku\": index,\n \"listing_content_\" + self.language: listingContent\n })\n\n def joinToOutput(self):\n global dfOut\n\n self.dft = pd.DataFrame(self.listings)\n self.dft.set_index(\"sku\", inplace=True)\n # print self.dft\n # raw_input()\n dfOut = dfOut.join(self.dft)\n\n\ndef writeOutput():\n print(\"saving...\")\n dfOut.to_excel('./out/translated_{}.xlsx'.format(datetime.date.today().strftime(\"%Y-%m-%d\")))\n print(\"saved\")\n\n\ndef read_data():\n df_excel = pd.read_excel('../../Art Import Template.xlsm', sheet_name=\"Master\",\n usecols=\"A:BC\", skiprows=2)[['sku', 'condition', 'Name to Translate',\n 'description', 'additional_text', 'biography']]\n df_excel.dropna(subset=['sku'], inplace=True)\n df_excel.replace({' & ': ' and '}, inplace=True, regex=True)\n df_excel.rename(columns={'Name to Translate': 'name'}, inplace=True)\n df_excel.set_index('sku', inplace=True)\n df_excel.fillna('', inplace=True)\n # df_excel.to_csv('./translate/translate_{}.csv'.format(datetime.date.today().strftime(\"%Y-%m-%d\")), encoding='utf8',\n # index=False)\n return df_excel\n\n\n# Start Execution\nif __name__ == '__main__':\n print(\"Remember to sort by SKU in Excel to have blanks at the end\")\n dfOut = df = read_data()\n\n # dfOut = df = pd.read_csv('./translate/translate_{}.csv'.format(datetime.date.today().strftime(\"%Y-%m-%d\")),\n # encoding='utf8', keep_default_na=False, index_col=0)\n\n # dfOut.replace('nan', '')\n # df.replace('nan', '')\n\n try:\n dfPartial = pd.read_excel('./out/translated_{}.xlsx'.format(datetime.date.today().strftime(\"%Y-%m-%d\")),\n index_col=0)\n # dfPartial.replace('nan', '')\n dfPartial.fillna('', inplace=True)\n except Exception as e:\n print(e)\n pass\n\n length = len(df.index) * len(df.columns) * len(LANGUAGES)\n counter = Counter(length)\n timer = MyTimer()\n\n print(\"Number of Translations: \", length)\n\n listing = ListingBuilder(df, \"en\")\n\n listing.joinToOutput()\n # print(dfOut.loc[:,\"listing_content_en\"])\n # raw_input()\n\n for language in LANGUAGES:\n translation = Translation(df)\n translation.setTargetLang(language)\n translation.doTranslation()\n translation.joinToOutput()\n writeOutput()\n\n # dfOut = dfOut.join(translation.dft )\n translation.dft = translation.dft.fillna('')\n listing = ListingBuilder(translation.dft, language)\n listing.joinToOutput()\n\n dfOut.replace({''': \"'\", '"': '\"',\n 'sammelcode': 'Collection Code',\n 'codice di raccolta': 'Collection Code',\n 'código de colección': 'Collection Code',\n 'Aceite': 'Óleo',\n 'aceite': 'Óleo'}, inplace=True, regex=True)\n\n writeOutput()\n dfOut.filter(like='listing').to_excel('./out/translatedListings_{}.xlsx'.format(datetime.date.today().strftime(\"%Y-%m-%d\")),\n encoding='utf-8')\n dfOut.filter(like='name').to_excel('./out/translatedNames_{}.xlsx'.format(datetime.date.today().strftime(\"%Y-%m-%d\")),\n encoding='utf-8')\n print(\"complete\")\n # pprint(logs)\n\n\n\n","sub_path":"translate.py","file_name":"translate.py","file_ext":"py","file_size_in_byte":11114,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"525435698","text":"review = []\r\n\r\nwith open(\"reviews.txt\", \"r\") as f :\r\n\tfor line in f :\r\n\t\treview.append(line)\r\nprint(len(review))\r\n\r\nx = input(\"Please input how many reviews you want to print :\")\r\nx = int(x)\r\nfor ln in range(x) :\r\n\tprint(ln + 1, \" \", review[ln])\r\n\r\nlimit = input(\"Please input the limit length of each review you want to sort :\")\r\nlimit = int(limit)\r\n\r\nreview_length = [len(d) for d in review]\r\nreview_short = [d for d in review if len(d) < limit]\r\nreview_long = [d for d in review if len(d) >= limit]\r\nreview_good = [d for d in review if \"good\" in d]\r\n\r\nprint(len(review_short))\r\nprint(len(review_long))\r\nprint(len(review_good))\r\n\r\nz = sum(review_length)/len(review)\r\nprint(z)\r\n\r\nsum_length = 0\r\nfor d in review :\r\n\tsum_length += len(d)\r\naverage = sum_length/len(review)\r\nprint(average)\r\n","sub_path":"readv1.2.py","file_name":"readv1.2.py","file_ext":"py","file_size_in_byte":789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"433704682","text":"from sqlalchemy import *\nfrom sqlalchemy.ext.declarative import declarative_base\nfrom sqlalchemy.orm import relation, sessionmaker, relationship\nfrom sqlalchemy import ForeignKey\nfrom sqlalchemy import Enum\nfrom db import Base, Session\nfrom enums import SkillsEnum\n\nclass VolunteerSkills(Base):\n\t__tablename__ = 'volunteerSkills'\n\n\tid = Column(Integer, primary_key=True)\n\tskill = Column(Enum(\"public\",\n\t\t\t\t\t\t\"teaching\",\n\t\t\t\t\t\t\"it\",\n\t\t\t\t\t\t\"administrative\",\n\t\t\t\t\t\t\"legal\",\n\t\t\t\t\t\t\"coaching\",\n\t\t\t\t\t\t\"handiwork\",\n\t\t\t\t\t\t\"arts\",\n\t\t\t\t\t\t\"tefl\",\n\t\t\t\t\t\t\"writing\",\n\t\t\t\t\t\t\"language\",\n\t\t\t\t\t\t\"event\",\n\t\t\t\t\t\t\"management\",\n\t\t\t\t\t\t\"sports\", name=\"skills_enum\"), nullable=False)\n\tvolunteer_id = Column(Integer, ForeignKey('volunteers.id'))\n\n\t#volunteers = relationship(\"Volunteer\", back_populates=\"volunteerSkills\")\n\n\tdef __init__(self, skill, volunteer_id):\n\t\tself.skill = skill\n\t\tself.volunteer_id = volunteer_id\n\n\tdef __repr__(self):\n\t\treturn \"\" % (self.skill)\n\n\tdef createvskills(volunteer_id, skills):\n\t\ts = Session()\n\t\ttry:\n\t\t\tfor sk in skills: \n\t\t\t\tv = VolunteerSkills(sk, volunteer_id)\n\t\t\t\ts.add(v)\n\n\t\t\ts.commit()\n\t\texcept:\n\t\t\treturn False\n\t\tfinally:\n\t\t\ts.close()\n\t\treturn True\n\n\tdef get_skills(id):\n\t\ts = Session()\n\t\tresult = []\n\t\tq = s.query(VolunteerSkills).filter_by(volunteer_id=id)\n\t\tfor sk in q:\n\t\t\t\tresult.append(sk.skill)\n\t\ts.close()\n\t\treturn result\n\n\n\n","sub_path":"volunteerSkills.py","file_name":"volunteerSkills.py","file_ext":"py","file_size_in_byte":1379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"490790420","text":"def enqueue(queue: list[int]):\n \"\"\"Function to enqueue element\"\"\"\n\n item = int(input(\"Enter element to enqueue: \"))\n queue.append(item)\n\n\ndef dequeue(queue: list[int]):\n \"\"\"Function to dequeue element\"\"\"\n\n print(\"Dequeued element: \", queue[0])\n queue.pop(0)\n\n\ndef display(queue: list[int]):\n \"\"\"Function to display elements in a queue\"\"\"\n\n print(queue)\n\n\nif __name__ == \"__main__\":\n my_queue = []\n\n while True:\n choices = [\"1\", \"2\", \"3\", \"4\"]\n\n switcher = {\n \"1\": enqueue,\n \"2\": dequeue,\n \"3\": display\n }\n\n choice = input(\"Enter 1 to enqueue, 2 to dequeue, 3 to display queue, 4 to exit: \")\n\n while choice != \"4\":\n while choice not in choices:\n choice = input(\"Enter 1 to enqueue, 2 to dequeue, 3 to display queue, 4 to exit: \")\n option = switcher.get(choice)\n option(my_queue)\n choice = input(\"Enter 1 to enqueue, 2 to dequeue, 3 to display queue, 4 to exit: \")\n","sub_path":"projects/prajakta_sathe/queue/queue.py","file_name":"queue.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"485467028","text":"import unittest\nimport statistics # python library to compute mean and compare against independently derived value\nfrom SOURCE.Statistics import Statistics\nfrom SOURCE.CsvReaderForStats import CsvReaderForStats\nfrom STATIC_METHODS.randomdata import randomdata\n\n\nclass MyTestCase(unittest.TestCase):\n\n def setUp(self) -> None:\n self.StatCal = Statistics()\n # Setup data source from CSV file\n self.StatData = CsvReaderForStats('DATA/statsdata.csv')\n self.TestValues = self.StatData.column['TestValues']\n # Setup for random data\n SeedRangeStart = 5 # Start of seed range\n SeedRangeEnd = 12 # End of seed range\n Array_Length = 50 # Total number of random values to be produced\n MinValue = 108 # Min possible value to be produced\n MaxValue = 1000 # Max possible value to be produced\n self.RandomData = randomdata(SeedRangeStart, SeedRangeEnd, Array_Length, MinValue, MaxValue)\n\n def test_instantiate_statistics(self):\n self.assertIsInstance(self.StatCal, Statistics)\n\n def test_method_mean(self):\n # Pull expected mean from stats data spreadsheet\n ExpectedMean = float(self.StatData.column['ExpectedMean'][0])\n # Calculate mean using home grown method against test values in stats data CSV file\n # Then determine if the calculated value is same as the expected value in the CSV file\n self.assertEqual(self.StatCal.mean(self.TestValues), ExpectedMean)\n # Calculate mean using home grown method and python library method against randomly generated values\n # Then determine if the result from both methods match\n self.assertEqual(self.StatCal.mean(self.RandomData), round(statistics.mean(self.RandomData), 4))\n\n def test_method_median(self):\n # Pull expected median from stats data spreadsheet\n ExpectedMedian = float(self.StatData.column['ExpectedMedian'][0])\n # Calculate median using home grown method against test values in stats data CSV file\n # Then determine if the calculated value is same as the expected value in the CSV file\n self.assertEqual(self.StatCal.median(self.TestValues), ExpectedMedian)\n # Calculate median using home grown method and python library method against randomly generated values\n # Then determine if the result from both methods match\n self.assertEqual(self.StatCal.median(self.RandomData), statistics.median(self.RandomData))\n\n def test_method_mode(self):\n # Pull expected mode from stats data spreadsheet\n ExpectedMode = float(self.StatData.column['ExpectedMode'][0])\n # Calculate mode using home grown method against test values in stats data CSV file\n # Then determine if the calculated value is same as the expected value in the CSV file\n self.assertEqual(self.StatCal.mode(self.TestValues), ExpectedMode)\n # Calculate mode using home grown method and python library method against randomly generated values\n # Then determine if the result from both methods match\n self.assertEqual(self.StatCal.mode(self.RandomData), statistics.mode(self.RandomData))\n\n def test_method_standard_deviation(self):\n ExpectedStandardDeviation = float(self.StatData.column['ExpectedStandardDeviation'][0])\n self.assertEqual(self.StatCal.standard_deviation(self.TestValues), ExpectedStandardDeviation)\n\n def test_method_variance(self):\n ExpectedVariance = float(self.StatData.column['ExpectedVariance'][0])\n self.assertEqual(self.StatCal.variance(self.TestValues), ExpectedVariance)\n\n\n\n\n","sub_path":"TESTS/test_statistics.py","file_name":"test_statistics.py","file_ext":"py","file_size_in_byte":3611,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"169804485","text":"def nr_to_roman(num):\n rom = ['I', 'IV', 'V', 'IX', 'X', 'XL', 'L','XC', 'C','CD', 'D', 'CM', 'M']\n val = [1, 4, 5, 9, 10, 40, 50, 90, 100, 400, 500, 900, 1000]\n\n i = (len(val)-1)\n roman_numeral = \"\"\n while num > 0:\n if num // val[i] != 0:\n roman_numeral += (num // val[i]) * rom[i]\n num -= (num // val[i]) * val[i]\n i -= 1\n return roman_numeral\n\nprint(nr_to_roman(345))\n\n\n\ndef roman_to_nr(roman):\n rom_special = ['IV', 'IX', 'XL', 'XC', 'CD', 'CM']\n rom_normal = ['I', 'V', 'X', 'L','C','D', 'M']\n val_special = [4, 9, 40, 90, 400, 900]\n val_normal = [1, 5, 10, 50, 100, 500, 1000]\n\n nr = 0\n while len(roman) > 0:\n for x in rom_special:\n if x in roman:\n i = rom_special.index(x)\n nr += val_special[i]\n roman = roman.replace(x, \"\")\n for y in rom_normal:\n if y in roman:\n i = rom_normal.index(y)\n nr += (roman.count(y) * val_normal[i])\n roman = roman.replace(y, \"\")\n return nr\n\n\nprint(roman_to_nr('MMMCMLXXXVI'))\n\n\ndef bracket_validity(string):\n bracket_open = ['(', '{', '[']\n bracket_close = [')', '}', ']']\n for i in range(len(string)):\n if string[i] in bracket_open:\n if string[i+1] != bracket_close[bracket_open.index(string[i])]:\n return \"Invalid\"\n return \"Valid\"\n\n\ndef find_sum(list, target):\n for i in list:\n for y in list:\n if i + y == target and list.index(i) != list.index(y):\n return [list.index(i), list.index(y)]\n pass\n return \"There are no valid combinations\"\n\nprint(find_sum([10,20,10,40,50,60,70], 130))","sub_path":"Roman numerals.py","file_name":"Roman numerals.py","file_ext":"py","file_size_in_byte":1719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"11912753","text":"from pathlib import Path\nfrom setuptools import setup, find_packages\n\nROOT_DIR = Path(__file__).resolve().parent\n\nwith open(ROOT_DIR / \"README.md\", \"r\") as f:\n long_description = f.read().strip()\n\nsetup(\n name=\"sentiment_analysis_model_app\",\n version=0.1,\n description=\"Train and deploy a sentiment analysis model in a Flask application.\",\n long_description=long_description,\n author=\"Bhaven Patel\",\n python_requires= \">=3.6.0\",\n packages=find_packages(),\n include_package_data=True,\n license=\"MIT\",\n classifiers=[\n \"License :: OSI Approved :: MIT License\",\n \"Programming Language :: Python\",\n \"Programming Language :: Python :: 3\",\n \"Programming Language :: Python :: 3.6\"\n ],\n)","sub_path":"ml_service/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":743,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"361461637","text":"#!/usr/bin/env python3\n\n# Definition for singly-linked list.\n# class ListNode:\n# def __init__(self, x):\n# self.val = x\n# self.next = None\n\n\n# You are given two non-empty linked lists representing two non-negative integers. The digits are stored in reverse order and each of their nodes contain a single digit. Add the two numbers and return it as a linked list.\n#\n# You may assume the two numbers do not contain any leading zero, except the number 0 itself.\n#\n# Example\n#\n# Input: (2 -> 4 -> 3) + (5 -> 6 -> 4)\n# Output: 7 -> 0 -> 8\n# Explanation: 342 + 465 = 807.\n\nclass Solution:\n def addTwoNumbers(self, linked1, linked2):\n\n \"\"\"\n :type l1: ListNode\n :type l2: ListNode\n :rtype: ListNode\n \"\"\"\n\n list_one = []\n list_two = []\n\n while linked1.next is not None:\n list_one.insert(0, linked1.val)\n linked1 = linked1.next\n\n list_one.insert(0, linked1.val)\n\n while linked2.next is not None:\n list_two.insert(0, linked2.val)\n linked2 = linked2.next\n\n list_two.insert(0, linked2.val)\n\n val1_string = \"\"\n for this_val in list_one:\n val1_string = val1_string + str(this_val)\n\n val2_string = \"\"\n for this_val in list_two:\n val2_string = val2_string + str(this_val)\n\n int_val = int(val1_string)\n int2_val = int(val2_string)\n total = int_val + int2_val\n\n reversed_total = ''.join(reversed(str(total)))\n\n string_list = list(str(reversed_total))\n\n start_node = ListNode(0)\n previous_node = start_node\n\n string_list = list(str(reversed_total))\n int_list = list(map(int, string_list))\n\n for ind in range(len(int_list)):\n if ind == 0:\n start_node.val = int_list[ind]\n else:\n new_node = ListNode(int_list[ind])\n previous_node.next = new_node\n previous_node = new_node\n # print(\"Int list:\" + str(int_list[ind]) + \".\")\n\n return start_node\n\n # linked3a = ListNode(7)\n # linked3b = ListNode(0)\n # linked3c = ListNode(8)\n # linked3a.next = linked3b\n # linked3b.next = linked3c\n\n # return linked3a\n\n # for i in range(len(int_list), 0, -1):\n # print(\"What is it now?\" + str(int_list[i-1]))","sub_path":"PythonPractice/scripts/linked_list/add_two_numbers_scratchpad.py","file_name":"add_two_numbers_scratchpad.py","file_ext":"py","file_size_in_byte":2386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"239545937","text":"import pytest\nimport boto3\nfrom sqlalchemy import create_engine, MetaData\nfrom sqlalchemy.exc import ProgrammingError\nfrom sqlalchemy_fdw import ForeignDataWrapper\nfrom s3_fdw import S3ForeignDataWrapper\n\n\n@pytest.fixture\n@pytest.helpers.register\ndef options_fixture():\n return {\n 'object_name': 'pytest.csv',\n 'bucket_name': 'pytest',\n 'access_key': 'pytest123',\n 'secret_key': 'pytest123',\n 'endpoint_url': 'http://localhost:9000',\n }\n\n\n@pytest.fixture\ndef sqlalchemy_fdw_fixture(options_fixture):\n url = 'pgfdw://pytest:pytest@localhost:5432/pytest'\n engine = create_engine(url)\n with engine.connect() as con:\n try:\n con.execute('CREATE EXTENSION multicorn;')\n except ProgrammingError as e:\n pass\n metadata = MetaData(bind=engine)\n fdw = ForeignDataWrapper(\n 's3_fdw_srv',\n 'multicorn',\n metadata=metadata,\n options={\n 'wrapper': 's3_fdw.{cls_name}'.format(\n cls_name=S3ForeignDataWrapper.__name__\n )\n },\n )\n fdw.create(checkfirst=True)\n yield fdw\n fdw.drop(checkfirst=True)\n\n\n@pytest.fixture\ndef s3_fixture(options_fixture):\n s3 = boto3.resource(\n 's3',\n endpoint_url=options_fixture['endpoint_url'],\n aws_access_key_id=options_fixture['access_key'],\n aws_secret_access_key=options_fixture['secret_key'],\n )\n bucket = s3.create_bucket(Bucket=options_fixture['bucket_name'])\n yield s3\n bucket.objects.all().delete()\n bucket.delete()\n","sub_path":"test/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":1558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"513822330","text":"#!/usr/bin/python\ndef readFile(fileName):\n paramsFile = open(fileName)\n paramFileLines = paramsFile.readlines()\n return paramFileLines\n\n#number of expected molecular lines\ndef getExpectedLineNum(fileName):\n lines = readFile(fileName)\n print(lines[0])\n return lines[0]\n\n#frequency ranges of all expected molecular lines\n#NOTE: deprecated in cluster environment\ndef getExpectedLines(fileName):\n result = []\n lines = readFile(fileName)\n for i in range(1, len(lines)):\n line = lines[i].split(\" \")\n result.append((line[0], line[1][0:len(line[1]) - 1]))\n print(result)\n return result","sub_path":"InputIngestor.py","file_name":"InputIngestor.py","file_ext":"py","file_size_in_byte":624,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"232056347","text":"from pathlib import Path\n\ndef relative_path(src, dst, through=None):\n \"\"\"\n Like pathlib.Path.relative_to, with the additional possibility to\n define a common parent.\n When provided, the returned path always goes through `through`, even when\n unnecessary.\n\n Examples\n --------\n >>> from smttask.utils import relative_path\n >>> pout = Path(\"/home/User/data/output/file\")\n >>> pin = Path(\"/home/User/data/file\")\n >>> relative_path(pin, pout, through=\"/home/User/data\")\n PosixPath('../output/file')\n \"\"\"\n src=Path(src); dst=Path(dst)\n if through is not None:\n dstrelpath = dst.relative_to(through)\n srcrelpath = src.relative_to(through)\n depth = len(srcrelpath.parents) - 1\n # The number of '..' we need to prepend to the link\n # The last parent is the cwd ('.') and so doesn't count\n uppath = Path('/'.join(['..']*depth))\n return uppath.joinpath(dstrelpath)\n else:\n return dst.relative_to(src)\n\ndef is_valid_desc(desc, required_keys, optional_keys=None,\n expected_types=None, on_wrong_desc_type='false'):\n \"\"\"\n Checks that a description provides a given set of keys.\n Optional keys are not required, but will not trigger a warning if present.\n\n Parameters\n ----------\n desc: dictionary\n required_keys: list of strings\n optional_keys: list of strings\n expected_types: dict\n For each provided key name, test the corresponding value with\n 'isinstance'.\n keys: key names in `required_keys` or `optional_keys`.\n values: types, or tuples of types.\n on_wrong_desc_type: 'false' | 'raise'\n What to do if `desc` is an invalid type.\n 'false': Return False (same as a non-Task desc)\n 'raise': Raise an TypeError exception\n\n Returns\n -------\n bool\n True if desc is a valid Task description. False otherwise.\n\n Raises\n ------\n TypeError\n If `desc` ist not a dictionary and `on_wrong_desc_type=True`.\n \"\"\"\n if optional_keys is None:\n optional_keys = []\n if expected_types is None:\n expected_types = {}\n if not isinstance(desc, dict):\n if not isinstance(on_wrong_desc_type, str):\n warn(\"`on_wrong_desc_type` should be a string.\")\n on_wrong_desc_type = 'false'\n on_wrong_desc_type = on_wrong_desc_type.lower()\n if on_wrong_desc_type == 'raise':\n raise TypeError(\"`desc` must be a dictionary.\")\n elif on_wrong_desc_type != 'false':\n warn(\"`on_wrong_desc_type` should be either 'false' or 'raise'.\")\n else:\n return False\n # Check that required keys are provided\n desc_keys = set(desc)\n required_keys = set(required_keys)\n if not required_keys <= desc_keys:\n return False\n # Check for unexpected keys\n recognized_keys = required_keys | set(optional_keys)\n unrecognized_keys = desc_keys - recognized_keys\n if len(unrecognized_keys) > 0:\n warn(\"Description is valid but provides additional unrecognized \"\n \"keys : {}\".format(','.join(unrecognized_keys)))\n # Check value types\n keys_with_wrong_types = {}\n for key, T in expected_types.items():\n if key not in recognized_keys:\n warn(f\"Can't check type of unrecognized key '{key}'\")\n elif key in desc:\n if not isinstance(desc[key], T):\n keys_with_wrong_types[key] = T\n if len(keys_with_wrong_types) > 0:\n warn(\"Description seems valid, but the values for the following keys \"\n \"have the wrong type:\\n\"\n \"\\n\".join(f\"{key}: {T} (should be {expected_types[key]}\"\n for key, T in keys_with_wrong_types.items()))\n return False\n # If we made it here, all checks succeeded (or only printed warnings)\n return True\n\ndef is_task_desc(desc, tasks=None,\n on_task_not_found='warn', on_wrong_desc_type='false'):\n \"\"\"\n Returns True if the provided `desc` describes a Task.\n Effectively this means checking for the `taskname`, `inputs` and\n `module` keys.\n\n Parameters\n ----------\n desc: dictionary\n tasks: dict of tasks\n If passed, the taskname will be check to see if it is recognized.\n Action taken depends on `on_task_not_found`\n Ignored if `on_task_not_found = True`.\n on_task_not_found: 'false' | 'true' | 'warn'\n What to do if the taskname is not found in `tasks`.\n This parameter is ignored if `tasks` is None.\n 'false': Return False.\n 'true': Return True anyway; no checking is performed.\n 'warn': Return True, but issue a warning.\n on_wrong_desc_type: 'false' | 'raise'\n What to do if `desc` is an invalid type.\n 'false': Return False (same as a non-Task desc)\n 'raise': Raise an TypeError exception\n\n Returns\n -------\n bool\n True if desc is a valid Task description. False otherwise.\n\n Raises\n ------\n TypeError\n If `desc` ist not a dictionary and `on_wrong_desc_type=True`.\n \"\"\"\n return is_valid_desc(desc,\n ['taskname', 'inputs', 'module'],\n expected_types={'taskname': str},\n on_wrong_desc_type=on_wrong_desc_type)\n # if 'taskname' not in desc:\n # return False\n # if 'inputs' not in desc:\n # warn(\"Description defines a task name, but does not define inputs.\")\n # return False\n # if 'module' not in desc:\n # warn(\"Description defines a task name and inputs, but does not \"\n # \"specify the module in which it was defined.\")\n # return False\n # if len(desc) > 3:\n # warn(\"Description seems like a task, but has additional entries \"\n # \"beyond 'taskname' and 'inputs'.\")\n # Let code continue and return True\n # If we made it here, we have both 'taskname' and 'inputs' keys\n taskname = desc['taskname']\n # if not isinstance(taskname, str):\n # warn(\"Description seems like a task, but it's 'taskname' value \"\n # \"is not a string.\")\n # return False\n if (tasks is None\n or taskname in tasks\n or on_task_not_found == 'true'):\n return True\n # Taskname is not in tasks\n if not isinstance(on_task_not_found, str):\n warn(\"`on_task_not_found` should be a string.\")\n on_task_not_found = 'warn'\n on_task_not_found = on_task_not_found.lower()\n if on_task_not_found == 'false':\n return False\n if on_task_not_found != 'warn':\n warn(\"`on_task_not_found` should be one of 'true', 'false' or 'warn'.\")\n # We default to 'warn' by continuing\n warn(\"Description is a valid task name, but the name '{}' was not found \"\n \"in the provided task list.\")\n return True\n","sub_path":"smttask/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":6837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"209689935","text":"import time\nimport random\n\n\nitems = []\nall_monsters = ['Fairy', 'Dragon', 'Ogre', 'Gorgon']\n\nselected_monster = random.choice(list(all_monsters))\n\n\ndef print_pause(message_to_print):\n print(message_to_print)\n time.sleep(2)\n\n\ndef valid_input(prompt, options):\n while True:\n option = input(prompt).lower()\n if option in options:\n return option\n print_pause(f'Sorry, the option \"{options}\"is invalid. Try Again!')\n\n\ndef play_again():\n choice = valid_input(\"Play again? [y|n]\", ['y', 'n'])\n if choice == 'n':\n print('Thanks for playing! Goodbye!')\n exit(0)\n\n\ndef intro():\n print_pause(\"You find yourself standing in an open field, \"\n \"filled with grass and yellow wild flowers. \"\n \"Rumor has it that a wicked \"+selected_monster+\" \"\n \"is somewhere around here \"\n \"and has been terrifying the nearby village.\")\n print_pause(\"In front of you is a house \"\n \"To the right of you is a \"\n \"dark and spooky cave \")\n\n\ndef play_game():\n intro()\n print_pause(\"Enter 1 to knock on the door of the house.\")\n print_pause(\"Enter 2 to to peer into the cave.\")\n print_pause(\"What would you like to do?\")\n choice = valid_input('door or cave? (1|2)', ['1', '2'])\n if choice == '1':\n knock_on_door()\n elif choice == '2':\n peer_into_cave()\n\n\ndef knock_on_door():\n print_pause(\"You step forward and knock on the door of the house \"\n \"An evil \"+selected_monster+\" with \"\n \"stringy white hair and a goti appears \"\n \"The wicked \"+selected_monster+\" ATTACKS YOU!\")\n fight()\n\n\ndef fight():\n choice = valid_input('1-Fight, 2-run or 3-give up? (1|2|3)', ['1', '2', '3'])\n if choice == '1':\n if \"sword\" in items:\n print_pause(\"You take your fighting stance! \"\n \"In your hand is the might 'sword of Thorvath!'\")\n print_pause(\"An evil \"+selected_monster+\" with \"\n \"stringy white hair and a goti appears \"\n \"The wicked \"+selected_monster+\" ATTACKS YOU! \")\n print_pause(\"But he is no match for 'The sword of Thorvath!'\")\n print_pause(\"You DEFEAT the \"+selected_monster+\" ! YOU WIN!\")\n\n else:\n print_pause(\"You take your fight stance \"\n \"In your hand is your trusty \"\n \"and rusty (but ineffective) dagger.\")\n print_pause(\"You do your best... \"\n \"but your dagger is no match \"\n \"for the \"+selected_monster+\".\")\n print_pause(\"You have been DEFEATED!\")\n if choice == '2':\n print_pause(\"(2) Run Away\")\n print_pause(\"You retreat back into the field. \"\n \"Luckily you don't seem to have been followed \")\n if choice == '3':\n print_pause(\"Okay, Goodbye!\")\n exit()\n\n\ndef peer_into_cave():\n print_pause(\"You step back and see a dark, \"\n \"damp cave off into the distance\")\n if \"sword\" in items:\n print_pause(\"You peer cautiously into the cave. \"\n \"You've been here before, \"\n \"and gotten all the good stuff. \"\n \"It's just an empty cave now. \"\n \"You walk back out to the field. \")\n else:\n print_pause(\"You peer cautiously into the cave and \"\n \"you see embedding into a diamond encrusted stone \")\n print_pause(\"The magical sword of Thorvath!\")\n print_pause(\"Throwing aside your useless old dagger, \"\n \"you run to the back of the cave \"\n \"to retrieve the sword \")\n print_pause(\"You take the sword and run \"\n \"out of the cave into the field.\")\n print_pause(\"You feel INVINCIBLE!\")\n items.append(\"sword\")\n\n\ndef game():\n while True:\n\n global selected_monster\n selected_monster = random.choice(list(all_monsters))\n\n play_game()\n\n play_again()\n\n\nif __name__ == '__main__':\n game()\n","sub_path":"adventure_game1.py","file_name":"adventure_game1.py","file_ext":"py","file_size_in_byte":4136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"458855377","text":"@types('float[:,:]','float[:,:]','int','float','float','float','float')\ndef solve_2d_diff_pyccel(u, un, nt, dt, dx, dy, nu):\n row, col = u.shape\n u[int(.5 / dy):int(1 / dy + 1),int(.5 / dx):int(1 / dx + 1)] = 2\n for t in range(nt):\n for i in range(1,row-1):\n for j in range(1,col-1):un[i,j]=u[i,j]\n for i in range(1,row-1):\n for j in range(1,col-1):\n u[i,j]=un[i,j]-nu*dt*((2*un[i,j]-un[i-1,j]-un[i+1,j])/dx**2+(2*un[i,j]-un[i,j-1]-un[i,j+1])/dy**2)\n \n \n return 0\n","sub_path":"notebooks/__epyccel__/mod_me0ez0bk.py","file_name":"mod_me0ez0bk.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"340320209","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sun Dec 8 12:32:47 2019\r\n\r\n@author: BABI\r\n\"\"\"\r\nimport numpy as np\r\nimport pandas as pa\r\nimport matplotlib.pyplot as plt\r\nimport seaborn as sn\r\nimport pickle \r\nfrom flask import Flask,jsonify,request,render_template\r\n\r\napp = Flask(__name__)\r\n\r\nmodel = pickle.load(open('xgboost_model','rb'))\r\nscaler = pickle.load(open('scaler','rb'))\r\n\r\n@app.route('/')\r\ndef home():\r\n return render_template('diabetes_prediction.html')\r\n\r\n\r\n@app.route('/predict',methods=[\"POST\"])\r\ndef predict():\r\n age = float(request.form['Age'])\r\n preg = int(request.form['preg'])\r\n skin = int(request.form['skin'])\r\n pressure = int(request.form['pressure'])\r\n glucose = int(request.form['glucose'])\r\n BMI = float(request.form['BMI'])\r\n Diabetes_pedigree = float(request.form['pedigree'])\r\n \r\n test_data = pa.DataFrame({'Pregnancies':preg,'Glucose':glucose,'BloodPressure':pressure,'SkinThickness':skin,\r\n 'BMI':BMI,'DiabetesPedigreeFunction':Diabetes_pedigree,'Age':age},index=[0])\r\n \r\n\r\n scaled_data= scaler.transform(test_data)\r\n\r\n predict = model.predict(scaled_data)\r\n \r\n if (predict == 0):\r\n return render_template('diabetes_prediction.html',prediction_text='You dont have {}'.format('diabetes'))\r\n else:\r\n return render_template('diabetes_prediction.html',prediction_text='You have {}'.format('diabetes'))\r\n \r\nif __name__ == \"__main__\":\r\n app.run(debug=True) ","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1490,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"632455307","text":"# Ambiente di esecuzione ('PROD' o 'TEST')\nexecution_env = 'PROD' \n# Percorso di origine dei file da elaborare\norig_folder = r\"G:\\COVEPI\\Statistica_e_GIS\\COVID19\"\n# Percorso di destinazione\nif execution_env == 'PROD':\n # su GISVSAT\n dest_folder = r\"D:\\Script\\COVID19\\COVID19-Abruzzo\"\nelif execution_env == 'TEST':\n # su macchina locale per test\n dest_folder = r\"D:\\SVILUPPO\\COVID19-Abruzzo\"\n# Percorso cartella di lavoro\nworkspace = dest_folder\n\n\"\"\"print(orig_folder)\nprint(dest_folder)\nprint(workspace)\"\"\"","sub_path":"config_paths.py","file_name":"config_paths.py","file_ext":"py","file_size_in_byte":518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"323211236","text":"import sys\n\nfrom udapi.core.block import Block\nimport pickle\nimport inspect\nfrom verb_record import Verb_record\nfrom frame_type import Frame_type\nfrom frame_inst_arg import Frame_inst_arg\nfrom frame_type_arg import Frame_type_arg\nfrom frame_inst import Frame_inst\nfrom sent_token import Sent_token\nfrom dict_printer import Dict_printer\nfrom collections import defaultdict, Counter\nfrom random import seed, sample\n\nclass Frame_extractor( Block):\n \"\"\" object for monolingual frame extraction\n extracts frames from each verb node\n\n may be overloaded with language-specific extractors\n redefining appropropriate (u)deprels and genereal classes in __init__\n the rest of extractor is designed language-independent to remain untouched\n\n 2 use cases:\n monolingual - used as separate block\n output: pickle or text\n\n bilingual - used as tool of frame_aligner\n output: frame_insts from each sentece, dict\n \"\"\"\n\n config_codes = [] # overloaded\n\n def __init__( self, output_form=\"text\", output_name=\"\", modals=\"\",\n config_name=\"\", **kwargs):\n \"\"\" called from Frame_aligner.__init__ \"\"\"\n super().__init__( **kwargs)\n self.appropriate_udeprels = \\\n [ \"nsubj\", \"csubj\", \"obj\", \"iobj\", \"obl\", \"ccomp\", \"xcomp\", \"expl\" ]\n #self.appropriate_deprels = [ \"obl:arg\", \"obl:agent\" ] # !! move to Czech module\n #self.possible_udeprels = [ \"obl\" ]\n self.appropriate_child_rels = [ \"case\", \"mark\" ]\n\n self.lang_mark = \"\"\n\n self.verb_record_class = Verb_record\n self.frame_type_class = Frame_type\n self.frame_inst_class = Frame_inst\n self.frame_type_arg_class = Frame_type_arg\n self.frame_inst_arg_class = Frame_inst_arg\n\n\n self.dict_of_verbs = {}\n\n self.verb_records = []\n self.frame_types = []\n self.frame_insts = []\n self.frame_type_args = []\n self.frame_inst_args = []\n self.sent_and_elid_tokens = []\n\n # output form - used in after_process_document\n self.output_form = output_form\n self.output_name = output_name\n\n self.config_dict = defaultdict( bool)\n for config_code in self.config_codes:\n self.config_dict[ config_code ] = True\n if config_name:\n self.process_config_file( config_name)\n\n if modals == \"1\":\n self.modals_inclusion = True\n elif modals == \"0\":\n self.modals_inclusion = False\n # othrewise it is left on language-specific decision\n self.modal_lemmas = []\n\n self.coord_args_num = 0\n self.frame_examples_counter = Counter()\n self.arg_examples_counter = Counter()\n\n def process_config_file( self, config_name):\n with open( config_name, 'r') as config_file:\n for line in config_file:\n if line.rstrip( '\\n') == \"\" or line.startswith( '#'):\n continue\n value, precode, _ = line.split( '\\t')\n code = precode\n if ':' in precode:\n _, code = precode.split( ':', 1)\n self.config_dict[ code ] = bool( int( value))\n\n def process_tree( self, tree): # -> list of Frame_inst\n \"\"\" called as a Block method (monolingual use case)\n or from Frame_aligner.process_bundle\n \"\"\"\n frame_insts = []\n for node in tree.descendants:\n frame_inst = self._process_node( node)\n if frame_inst is not None:\n frame_insts.append( frame_inst)\n bundle_id = tree.bundle.bundle_id\n for index, frame_inst in enumerate( frame_insts):\n frame_inst.bundle_id = bundle_id\n frame_inst.index = index\n #self._check_coherence()\n return frame_insts\n\n def _process_node( self, node): # -> Frame_inst\n \"\"\" called from process_tree\n searching verbs and calling create_frame for them\n \"\"\"\n if self._node_is_appropriate( node):\n if node.lemma in self.dict_of_verbs:\n verb_record = self.dict_of_verbs[ node.lemma ]\n else:\n verb_record = self.verb_record_class( node.lemma)\n self.dict_of_verbs[ node.lemma ] = verb_record\n frame_type, frame_inst = self._process_frame( node)\n\n # adding the frame to the verb_record\n verb_record.consider_new_frame_type( frame_type)\n\n return frame_inst\n #elif node.upos == \"AUX\":\n # parent_node == node.parent\n # if parent_node.upos in [ \"NOUN\", \"PRON\", \"I\n return None\n\n def _node_is_appropriate( self, node):\n return node.upos == \"VERB\"\n\n def _process_frame( self, verb_node): # -> Frame_inst\n \"\"\" called from process_node \"\"\"\n # new frame\n frame_type = self.frame_type_class( self.appropriate_udeprels)\n frame_type.set_verb_features( verb_node)\n frame_inst = self.frame_inst_class()\n\n # creating and adding args to the frame type/inst\n # creating tokens for example sentence and adding them to frame_inst\n frame_type, frame_inst = \\\n self._process_sentence( frame_type, frame_inst, verb_node)\n\n frame_type, frame_inst = \\\n self._after_sent( frame_type, frame_inst, verb_node)\n\n frame_type.sort_args()\n frame_type.add_inst( frame_inst) # connection between frame type and inst\n\n return frame_type, frame_inst\n\n def _process_sentence( self, frame_type, frame_inst, verb_node):\n \"\"\" called from _process_frame\n has several aims:\n - create tokens for being able to reconstruct the example sentcence\n when the dictionary with examples is displayed\n - choose frame arguments according to given requirements\n and connect them with frame objects (both type and inst)\n - create connection between tokens and arguments, so these tokens can be\n marked as arguments when displayed\n \"\"\"\n sent_nodes = verb_node.root.descendants\n sent_tokens = []\n for sent_node in sent_nodes:\n # sentence tokens\n token = self._create_token( sent_node)\n if sent_node is verb_node:\n token.mark_frame_predicate()\n sent_tokens.append( token)\n\n # frame arguments\n if self._node_is_good_arg( sent_node, verb_node):\n #if sent_node.udeprel in self.appropriate_udeprels or \\\n # sent_node.deprel in self.appropriate_deprels:\n frame_type_arg, frame_inst_arg = \\\n self._process_arg( sent_node, verb_node)\n # if sent_node.udeprel == \"obl\":\n # # oblique args may or may not be a part of the frame\n # # will be decided later -> not definitive\n # frame_inst_arg.definitive = False\n if self._could_be_coord_arg( sent_node, verb_node):\n frame_inst_arg.is_coord_arg = True\n self.coord_args_num += 1\n self.arg_examples_counter[ \"coord_possib\"] += 1\n self._connect_arg_with_frame( frame_type, frame_inst,\n frame_type_arg, frame_inst_arg,\n token)\n\n frame_inst.sent_tokens = sent_tokens\n frame_inst.verb_node_ord = verb_node.ord\n verb_parent_node = verb_node.parent\n if verb_parent_node is not None:\n frame_inst.verb_parent_ord = verb_parent_node.ord\n frame_inst.verb_parent_upos = verb_parent_node.upos\n frame_inst.verb_deprel = verb_node.deprel\n frame_inst.verb_depth = int( verb_node.get_attrs( [ \"depth\" ])[ 0 ])\n frame_inst.verb_child_num = int( verb_node.get_attrs( [ \"children\" ])[ 0 ])\n\n return frame_type, frame_inst\n\n def _node_is_good_arg( self, sent_node, verb_node):\n is_arg = sent_node.parent is verb_node or \\\n self._could_be_coord_arg(sent_node, verb_node)\n #is_arg = sent_node.parent is verb_node\n ok_deprel = sent_node.udeprel in self.appropriate_udeprels\n\n result = is_arg and ok_deprel\n return result\n\n def _could_be_coord_arg( self, sent_node, verb_node):\n if verb_node.deprel == \"conj\":\n self.frame_examples_counter[ \"conj\" ] += 1\n coord_head_node = verb_node.parent\n if sent_node.parent is coord_head_node:\n for child_node in verb_node.children:\n if \"subj\" in child_node.deprel and \"subj\" in sent_node.deprel:\n return False\n if \"obj\" in child_node.deprel and \"obj\" in sent_node.deprel:\n return False\n before_coord = sent_node.ord < coord_head_node.ord and \\\n sent_node.ord < verb_node.ord\n after_coord = sent_node.ord > coord_head_node.ord and \\\n sent_node.ord > verb_node.ord\n return before_coord or after_coord\n return False\n\n def _process_arg( self, arg_node, verb_node):\n deprel, form, child_rels, upostag = self._get_arg_features( arg_node)\n frame_type_arg = self.frame_type_arg_class(\n deprel, form, child_rels)\n frame_inst_arg = self.frame_inst_arg_class( upostag)\n frame_type_arg = self._consider_aux_form( frame_type_arg, arg_node)\n\n return frame_type_arg, frame_inst_arg\n\n def _consider_aux_form( self, frame_type_arg, arg_node):\n \"\"\" used in language specific modules \"\"\"\n if \"comp\" in frame_type_arg.deprel or \"csubj\" in frame_type_arg.deprel:\n # and frame_type_arg.form == \"Inf\":\n aux_child_nodes = [ child_node for child_node in arg_node.children\n if child_node.upos == \"AUX\" ]\n if aux_child_nodes != []:\n frame_type_arg.form = aux_child_nodes[ 0 ].feats[ \"VerbForm\" ]\n self.arg_examples_counter[\"aux_form_0\"] += 1\n if frame_type_arg.form != \"Fin\":\n for aux_child_node in aux_child_nodes:\n if aux_child_node.feats[ \"VerbForm\" ] == \"Fin\":\n frame_type_arg.form = \"Fin\"\n self.arg_examples_counter[\"aux_form_non_0\"] += 1\n break\n else:\n self.arg_examples_counter[\"aux_form_0_nonfin\"] += 1\n\n return frame_type_arg\n\n def _after_sent( self, frame_type, frame_inst, verb_node):\n \"\"\" for overloading \"\"\"\n return frame_type, frame_inst\n\n def process_subj_elision( self, frame_type, frame_inst, elided_type_arg,\n elided_inst_arg, subj_token, verb_node):\n \"\"\" for overloading \"\"\"\n return elided_type_arg, elided_inst_arg, subj_token\n\n @staticmethod\n def _connect_arg_with_frame( frame_type, frame_inst,\n frame_type_arg, frame_inst_arg, token):\n token.arg = frame_inst_arg\n frame_inst.add_arg( frame_inst_arg)\n frame_type_arg.add_inst( frame_inst_arg)\n frame_type.add_arg( frame_type_arg)\n\n @staticmethod\n def _delete_arg( frame_type_arg):\n frame_type = frame_type_arg.frame_type\n verb_record = frame_type.verb_record\n frame_type_arg.delete()\n if verb_record is not None:\n verb_record.consider_merging( frame_type)\n\n # frame_type = frame_type_arg.frame_type\n #\n # #assert frame_type is not None\n # #assert frame_type_arg in frame_type.args\n # frame_type.remove_arg( frame_type_arg)\n #\n # for frame_inst_arg in frame_type_arg.insts:\n # #assert frame_inst_arg.type is frame_type_arg\n # frame_inst = frame_inst_arg.frame_inst\n # #assert frame_inst is not None\n # #assert frame_inst.type is frame_type\n # #assert frame_inst_arg in frame_inst.args\n # frame_inst.remove_arg( frame_inst_arg)\n #\n # sent_token = frame_inst_arg.token\n # #assert sent_token is not None\n # #assert sent_token in frame_inst.sent_tokens + frame_inst.elided_tokens\n # #assert sent_token.arg is frame_inst_arg\n # sent_token.arg = None\n #\n # frame_type_arg.deleted = True\n\n # @staticmethod\n # def _disconnect_arg_from_frame( frame_type, frame_type_arg):\n # Frame_extractor._delete_arg(frame_type_arg)\n # return frame_type_arg\n #\n # removed_arg = frame_type.pop_arg( frame_type_arg)\n #\n # for frame_inst_arg in removed_arg.insts:\n # frame_inst_arg.token.arg = None\n # try:\n # frame_inst_arg.frame_inst.remove_arg( frame_inst_arg)\n # frame_type_arg.removed = True\n # except AttributeError:\n # print( frame_inst_arg.used, frame_type_arg.removed)\n # exit()\n # return removed_arg\n\n @staticmethod\n def _substitute_arg( old_frame_arg, new_frame_arg):\n frame_type = old_frame_arg.frame_type\n #assert frame_type is not None\n #assert old_frame_arg in frame_type.args\n frame_type.remove_arg( old_frame_arg)\n\n for frame_inst_arg in old_frame_arg.insts:\n #assert frame_inst_arg.type is old_frame_arg\n new_frame_arg.add_inst( frame_inst_arg) # incl reversed link\n\n frame_type.add_arg( new_frame_arg) # incl reversed link\n\n verb_record = frame_type.verb_record\n if verb_record is not None:\n verb_record.consider_merging( frame_type)\n\n old_frame_arg.deleted = True\n\n def _create_token( self, token_node): # -> Token\n \"\"\" called from _process_sentence\n creation and basic initialization of token\n \"\"\"\n token = Sent_token( token_node.ord, token_node.form)\n\n no_space_after = token_node.no_space_after #or node is sentence_nodes[ -1 ]\n if no_space_after:\n token.unmark_space() # space is default otherwise\n return token\n\n def _udeprel_is_appropriate( self, udeprel): # -> bool\n if udeprel in self.appropriate_udeprels:\n return\n\n def _get_arg_features( self, node): # -> tuple ( string, string, list of strings )\n \"\"\" called from _process_sentence \"\"\"\n deprel = node.deprel\n\n form = node.feats[ \"Case\" ]\n if form in [ \"\", \"0\" ]:\n form = node.feats[ \"VerbForm\"]\n if form == \"0\":\n form = \"\"\n\n child_rels = []\n for child in node.children:\n if child.deprel in self.appropriate_child_rels:\n child_rel = child.deprel + '-' + child.lemma\n child_rels.append( child_rel)\n\n upostag = node.upos\n\n return deprel, form, child_rels, upostag\n\n def get_dict_of_verbs( self): # -> dict\n \"\"\" called from Frame_aligner._pickle_dict \"\"\"\n return self.dict_of_verbs\n\n\n\n def after_process_document( self, doc): # void\n \"\"\" overriden block method - monolingual use case\"\"\"\n if self.config_dict[ \"coord\" ]:\n self._coord_handling()\n self._check_coherence()\n self._postprocessing()\n self._check_coherence()\n if self.config_dict[ \"obliq\" ]:\n self._oblique_handling()\n self._check_coherence()\n\n for verb_record in self.dict_of_verbs.values():\n #verb_record.sort_frames()\n verb_record.build_frame_dag()\n #self._frame_reduction()\n self._check_coherence()\n self._final_control()\n self._print_example_counts()\n\n Dict_printer.print_dict( self.output_form, self.dict_of_verbs, self.output_name)\n #Dict_printer.print_verbs_by_arg_form( self.dict_of_verbs, \"Dat\")\n\n def _check_coherence( self):\n for verb_lemma in self.dict_of_verbs.keys():\n verb_record = self.dict_of_verbs[ verb_lemma ]\n verb_record.check_coherence()\n\n def _oblique_handling( self):\n value_triplets = []\n obl_dict = {}\n for verb_lemma in self.dict_of_verbs.keys():\n verb_record = self.dict_of_verbs[ verb_lemma ]\n for frame_type in verb_record.frame_types:\n for arg in frame_type.args:\n if \"obl\" in arg.deprel:\n if arg.deprel in [ \"obl:arg\", \"obl:agent\" ]:\n continue\n this_verb_portion = round( 100 *\n self._arg_in_this_verb( verb_record, arg), 2)\n all_verbs_portion = round( 100 *\n self._arg_in_all_verbs( obl_dict, arg), 2)\n value_triplets.append( ( arg, this_verb_portion, all_verbs_portion))\n assert arg.frame_type.verb_record is not None\n str_arg = str( arg).split( '-')[1]\n\n #print( str_arg, this_verb_portion, all_verbs_portion, sep='\\t')\n\n try:\n this_verb_portions = [ value for _, value, _ in value_triplets ]\n all_verbs_portions = [ value for _, _, value in value_triplets ]\n frame_type_args = [ arg for arg, _, _ in value_triplets ]\n avg_this = sum( this_verb_portions) / len( this_verb_portions)\n #print( median( this_values))\n avg_all = sum( all_verbs_portions) / len( all_verbs_portions)\n\n #print( median( all_values))\n results = { \"-\": 0, \"?\": 0, \"+\": 0 }\n for arg, this_verb_portion, all_verbs_portion in value_triplets:\n #assert arg.frame_type.verb_record is not None or arg.deleted\n if arg.deleted:\n continue\n if this_verb_portion < avg_this and all_verbs_portion > avg_all:\n #arg.definitive = 2\n self._delete_arg( arg)\n results[ \"-\" ] += 1\n self.arg_examples_counter[ \"obl_adj\"] += 1\n elif this_verb_portion < avg_this or all_verbs_portion > avg_all:\n arg.definitive = False\n self._delete_arg( arg)\n results[ \"?\" ] += 1\n self.arg_examples_counter[ \"obl_???\"] += 1\n else: #if this_verb_portion > avg_this and all_verbs_portion < avg_all:\n #self._delete_arg( arg)\n results[ \"+\" ] += 1\n self.arg_examples_counter[ \"obl_arg\"] += 1\n print( results)\n except ZeroDivisionError:\n pass\n\n def _arg_in_all_verbs( self, deprel_dict, arg):\n arg_str = arg.to_str()\n if arg_str in deprel_dict:\n avg_occur_portion = deprel_dict[ arg_str ]\n else:\n sum_occur_portion = 0\n for verb_lemma in self.dict_of_verbs.keys():\n verb_record = self.dict_of_verbs[ verb_lemma ]\n occur_portion = self._arg_in_this_verb( verb_record, arg)\n sum_occur_portion += occur_portion\n verbs_num = len( self.dict_of_verbs)\n avg_occur_portion = sum_occur_portion / verbs_num\n deprel_dict[ arg_str ] = avg_occur_portion\n #print( arg_str, round( 100 * avg_occur_portion, 3), sep='\\t')\n return avg_occur_portion\n\n def _arg_in_this_verb( self, verb_record, arg):\n all_occurences = 0\n arg_occurences = 0\n for frame_type in verb_record.frame_types:\n insts_num = len( frame_type.insts)\n all_occurences += insts_num\n deprel, form, case_mark_rels = arg.get_description()\n found_args = frame_type.get_arg( deprel, form, case_mark_rels)\n if found_args != []:\n arg_occurences += insts_num\n occurences_portion = arg_occurences / all_occurences\n return occurences_portion\n\n# def _decide_obl_inclusion( self, this_verb_portion, all_verbs_portion):\n\n def _coord_handling( self):\n num_of_successes = 0\n for verb_record in self.dict_of_verbs.values():\n frames_by_arg_num = sorted( verb_record.frame_types,\n key=lambda frame_type : len( frame_type.args), reverse=True)\n for frame_type in frames_by_arg_num:\n success = frame_type.try_delete_coord_args()\n if success:\n self.arg_examples_counter[ \"coord_del\" ] += 1\n num_of_successes += 1\n verb_record.consider_merging( frame_type)\n\n def _postprocessing( self):\n for verb_record in self.dict_of_verbs.values():\n for frame_type in verb_record.frame_types:\n self.specific_postprocessing( frame_type)\n\n def specific_postprocessing( self, frame_type):\n pass\n\n def _frame_reduction( self, only_obl=True):\n for verb_record in self.dict_of_verbs.values():\n verb_record.frame_reduction( only_obl) # reducing number of frames before outputting\n #verb_record.frame_reduction( False)\n\n def _final_control( self):\n formless_args_num = 0\n frame_num = 0\n for verb_record in self.dict_of_verbs.values():\n frame_num += len( verb_record.frame_types)\n for frame_type in verb_record.frame_types:\n for arg in frame_type.args:\n if arg.form == \"\":\n formless_args_num += 1\n print( formless_args_num, frame_num)\n\n #\n # # superframe counter\n # frame_num = 0\n # supernum_counter = Counter()\n # for verb_record in self.dict_of_verbs.values():\n # frame_num += len( verb_record.frame_types)\n # for frame_type in verb_record.frame_types:\n # supernum_counter[ len( frame_type.superframes) ] += 1\n # print( frame_num)\n # print( supernum_counter)\n\n\n # insts = []\n # for verb_record in self.dict_of_verbs.values():\n # for frame_type in verb_record.frame_types:\n # for frame_inst in frame_type.insts:\n # insts.append( frame_inst)\n # new_dict = {}\n # chosen_insts = sample( insts, 100)\n # dict_copy = self.dict_of_verbs.copy()\n # for verb_record in dict_copy.values():\n # frames_copy = verb_record.frame_types.copy()\n # for frame_type in frames_copy:\n # insts_copy = frame_type.insts.copy()\n # for inst in insts_copy:\n # if inst not in chosen_insts:\n # frame_type._insts.remove( inst)\n # if frame_type.insts == []:\n # verb_record.remove_frame( frame_type)\n # if verb_record.frame_types == []:\n # self.dict_of_verbs.pop( verb_record.lemma)\n # instsnum = 0\n # for verb_record in new_dict.values():\n # for frame_type in verb_record.frame_types:\n # for frame_inst in frame_type.insts:\n # instsnum += 1\n # print( instsnum, len( chosen_insts))\n Dict_printer.print_dict( self.output_form, self.dict_of_verbs, self.output_name)\n\n\n\n # multisubj = Counter()\n # dict_copy = self.dict_of_verbs.copy()\n # for verb_record in dict_copy.values():\n # frames_copy = verb_record.frame_types.copy()\n # for frame_type in frames_copy:\n # subj_num = 0\n # for arg in frame_type.args:\n # if \"csubj:pass\" in arg.deprel:\n # pass #print(\"tu\")\n # #subj_num += 1\n # #if subj_num <= 1:\n # # verb_record._frame_types.remove( frame_type)\n # #multisubj[ subj_num ] += len( frame_type.insts)\n # #if verb_record.frame_types == []:\n # # self.dict_of_verbs.pop( verb_record.lemma)\n #for item in multisubj.most_common():\n # print( item)\n # formless_args = Counter()\n # dict_copy = self.dict_of_verbs.copy()\n # for verb_record in dict_copy.values():\n # frames_copy = verb_record.frame_types.copy()\n # for frame_type in frames_copy:\n # for frame_type_arg in frame_type.args:\n # if frame_type_arg.form == \"\":\n # #print( frame_type_arg.deprel, frame_type_arg.case_mark_rels)\n # formless_args[ frame_type_arg.to_str() ] += \\\n # len( frame_type_arg.insts)\n # break\n # else:\n # verb_record._frame_types.remove( frame_type)\n # if verb_record.frame_types == []:\n # self.dict_of_verbs.pop( verb_record.lemma)\n # for item in formless_args.most_common():\n # print( item)\n\n def _print_example_counts( self):\n frame_inst_count = 0\n arg_inst_count = 0\n for verb_record in self.dict_of_verbs.values():\n for frame_type in verb_record.frame_types:\n insts_count = len( frame_type.insts)\n frame_inst_count += insts_count\n arg_inst_count += len( frame_type.args) * insts_count\n\n print( \"\\nframe insts:\", frame_inst_count)\n for key, val in self.frame_examples_counter.items():\n print( key, val, round( val / frame_inst_count * 100, 1))\n\n print( \"\\narg insts:\", arg_inst_count)\n for key, val in self.arg_examples_counter.items():\n print( key, val, round( val / arg_inst_count * 100, 1))\n\n\n # def delete_subframes( self):\n # \"\"\" called from after_process_document on each verb record\n # delete all frames that are a\n # TODO rozmysli si, co vlastne chces, ci mazat subframy ci superframy\n # \"\"\"\n # frame_types = sorted( self.frame_types, key = \\\n # lambda type:type.args_to_one_string())\n # reduced_frame_types = []\n # # comparing frames and marking those to delete\n # for frame_type in frame_types:\n # # a frame should be deleted if it has a superframe\n # best_superframe = frame_type\n # for potential_superframe in frame_types:\n # if potential_superframe.superframe is not None:\n # continue\n # if best_superframe.is_subframe_of( potential_superframe) \\\n # and self.freq_ratio_is_sufficient(\n # best_superframe, potential_superframe):\n # best_superframe = potential_superframe\n # if best_superframe is not frame_type:\n # frame_type.superframe = best_superframe\n #\n # reduced_frame_types = []\n # for frame_type in frame_types:\n # if frame_type.superframe is None:\n # for subframe in frame_type.subframes:\n # for inst in subframe.insts:\n # frame_type.add_inst( inst)\n # reduced_frame_types.append( frame_type)\n # frame_type.subframes = [] # the subframes will be deleted\n #\n # self.frame_types = reduced_frame_types\n #\n # @staticmethod\n # def freq_ratio_is_sufficient( subframe, superframe): # -> bool\n # \"\"\" comparing number of instances of a subframe and a superframe\n # returns if the superframe is sufficiently more frequent than the subframe\n # \"\"\"\n # coef = 2 # !!!\n # if coef * len( subframe.insts) < len( superframe.insts):\n # return True\n # return False\n\n def duplicate_frame_type( self, frame_type):\n new_frame_type = self.frame_type_class( self.appropriate_udeprels)\n for arg in frame_type:\n deprel, form, child_rels = arg.get_description()\n new_arg = frame_type_arg = \\\n self.frame_type_arg_class( deprel, form, child_rels)\n new_frame_type.add_arg( new_arg)","sub_path":"udapi-python/udapi/block/valency/base2_frame_extractor.py","file_name":"base2_frame_extractor.py","file_ext":"py","file_size_in_byte":28164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"19965730","text":"from apscheduler.schedulers.background import BackgroundScheduler\r\nimport os\r\nimport time\r\nimport datetime\r\n\r\n\r\ndef task_book_spider():\r\n \"\"\"\r\n 网站初始url\r\n :return:\r\n \"\"\"\r\n # 你的spider启动命令\r\n current_path = os.path.dirname(os.path.abspath(__file__))\r\n target_path = os.path.join(current_path, 'generate_start_url.py')\r\n print(target_path)\r\n os.system(f'python {target_path}')\r\n\r\nif __name__ == \"__main__\":\r\n scheduler = BackgroundScheduler()\r\n\r\n # 每天9点添加初始url\r\n scheduler.add_job(task_book_spider, 'cron', hour=9, minute=0)\r\n\r\n scheduler.start()\r\n print('Press Ctrl+{0} to exit'.format('Break' if os.name == 'nt' else 'C'))\r\n try:\r\n while True:\r\n time.sleep(2)\r\n except (KeyboardInterrupt, SystemExit):\r\n scheduler.shutdown()","sub_path":"Project/bookcrawler/bookcrawler/scheduler_spider.py","file_name":"scheduler_spider.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"592277439","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.5 (3350)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.macosx-10.11-x86_64/egg/of/common/queue/monitor.py\n# Compiled at: 2016-09-15 19:50:33\n# Size of source mod 2**32: 4715 bytes\n\"\"\"\nThe monitor module implements the Monitor and MonitorThread classes.\n\nCreated on Jan 22, 2016\n\n@author: Nicklas Boerjesson\n\"\"\"\nimport os, traceback, threading\nfrom queue import Empty, Queue\nfrom of.common.logging import write_to_log, EC_UNCATEGORIZED, SEV_DEBUG, SEV_ERROR, EC_INTERNAL\nfrom of.schemas.constants import zero_object_id\n__author__ = 'Nicklas Boerjesson'\n\nclass MonitorThread(threading.Thread):\n terminated = None\n\n def __init__(self, _monitor):\n self.terminated = False\n super(MonitorThread, self).__init__(target=_monitor.monitor, name=_monitor.__class__.__name__ + '_queue monitor thread')\n\n\nclass Monitor(object):\n __doc__ = '\\n The monitor class monitors a given queue for new items and calls a handler when new items appear on the queue.\\n '\n queue = None\n handler = None\n process_id = None\n before_get_queue = None\n log_prefix = None\n monitor_thread = None\n\n def __init__(self, _handler, _queue=None):\n self.log_prefix = self.__class__.__name__ + '(' + str(_handler.__class__.__name__) + '): '\n if _queue:\n self.queue = _queue\n else:\n self.queue = Queue()\n self.handler = _handler\n self.handler.on_monitor_init(self)\n if self.queue:\n self.start()\n\n def write_dbg_info(self, _data):\n write_to_log(self.log_prefix + _data, _category=EC_UNCATEGORIZED, _severity=SEV_DEBUG, _process_id=self.process_id)\n\n def after_get_queue(self):\n \"\"\"\n Implement this for something to happen immidiately after a each queue check has happened.\n \"\"\"\n pass\n\n def monitor(self):\n \"\"\"\n Monitors the queue. Stops when the queue-threads' terminated attribute is set to True\n \"\"\"\n self.write_dbg_info('In monitor thread monitoring queue.')\n while not self.monitor_thread.terminated:\n try:\n _item = self.queue.get(True, 0.1)\n try:\n self.handler.handle(_item)\n except Exception as e:\n write_to_log(self.log_prefix + 'Error handling item:' + str(e) + '\\nData:\\n' + str(_item) + '\\nTraceback:' + traceback.format_exc(), _category=EC_INTERNAL, _severity=SEV_ERROR)\n\n except Empty:\n pass\n except Exception as e:\n self.monitor_thread.terminated = True\n raise Exception(write_to_log(self.log_prefix + 'Terminating, error accessing own queue:' + str(e), _category=EC_INTERNAL, _severity=SEV_ERROR))\n\n self.after_get_queue()\n\n self.write_dbg_info('Monitor thread stopped.')\n\n def stop(self, _user_id=zero_object_id, _reverse_order=None):\n \"\"\"\n Stops the monitor and tells the handler to shut down\n\n :param _user_id: The Optimal BPM user Id that is stopping the monitor\n :param _reverse_order: If true, the monitor is kept running while the handler is shut down.\n \"\"\"\n if not _reverse_order:\n self.write_dbg_info('Told to stop, ceasing monitoring.')\n self.monitor_thread.terminated = True\n self.write_dbg_info('Shutting down handler(' + str(self.handler.__class__.__name__) + ').')\n self.handler.shut_down(_user_id)\n self.write_dbg_info('Handler shut down.')\n if _reverse_order:\n self.write_dbg_info('Told to stop, ceasing monitoring.')\n self.monitor_thread.terminated = True\n\n def start(self):\n \"\"\"\n Start monitoring the queue\n \"\"\"\n if self.monitor_thread and not self.monitor_thread.terminated:\n raise Exception(write_to_log(self.log_prefix + 'The queue monitor is already running.', _category=EC_INTERNAL, _severity=SEV_ERROR))\n self.monitor_thread = MonitorThread(_monitor=self)\n self.monitor_thread.terminated = False\n self.monitor_thread.start()\n self.write_dbg_info('Running, monitoring thread: ' + str(self.monitor_thread.name))","sub_path":"pycfiles/of-1.0.1-py3.5/monitor.cpython-35.py","file_name":"monitor.cpython-35.py","file_ext":"py","file_size_in_byte":4287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"596068039","text":"\n\ndef test_check_type_int_fail():\n test_cases = ({\n 'k1': 'v1',\n }, (b'1', 1), (3.14159, 3), 'b')\n for case in test_cases:\n with pytest.raises(TypeError) as e:\n check_type_int(case)\n assert ('cannot be converted to an int' in to_native(e))\n","sub_path":"Data Set/bug-fixing-1/fa0cd3782fbb819294b2e514dbac2fc741def9b1--fix.py","file_name":"fa0cd3782fbb819294b2e514dbac2fc741def9b1--fix.py","file_ext":"py","file_size_in_byte":281,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"239157171","text":"import os\r\nfrom PIL import Image\r\n\r\npath = 'D:\\git\\Project\\picture\\showcode'\r\n\r\ndef change_picSize(fileName,width = 1136,hight = 640):\r\n img = Image.open(fileName)\r\n\r\n img.thumbnail((width, hight))#只能变小\r\n img.save(fileName)\r\n print (fileName + ' DONE!')\r\n\r\nPHONE = {'iPhone5':(1136,640), 'iPhone6':(1134,750), 'iPhone6P':(2208,1242)}\r\nfiles = os.listdir(path)\r\nwidth,height = PHONE['iPhone6P']\r\nfor fileName in files:\r\n portion = os.path.splitext(fileName)\r\n if portion[1] == \".jpg\" or portion[1] == \".png\":\r\n change_picSize(fileName,width,height)\r\n\r\n","sub_path":"LuHR_showcode/x0005.py","file_name":"x0005.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"157770133","text":"#coding:utf8\nimport random\n\ndef seq(n, s):\n # n个数和是s\n br = []\n for i in xrange(n):\n b = None\n while(b == None or b in br):\n b = random.randint(1, s)\n br.append(b)\n br.sort()\n ans = [br[0]]\n for i in xrange(n - 1):\n ans.append(br[i+1] - br[i])\n return ans\n\ndef datagen():\n fo = open(\"foo.txt\", \"w+\")\n freedom = 10\n for av in xrange(20, 23):\n for k in xrange(20, 30):\n # 分成k组，总共tot个\n tot = (av + freedom) * k\n l = seq(k, tot)\n line = ' '.join([str(x) for x in l])\n fo.write(\"{} {}\\n\".format(k, k))\n fo.write(\"{}\\n\".format(line))\n fo.close()\n\ndatagen()","sub_path":"SRC/ccpc2016hangzhou.1001.dat.gen.py","file_name":"ccpc2016hangzhou.1001.dat.gen.py","file_ext":"py","file_size_in_byte":716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"159772502","text":"# pec\nimport pandas as pd\ncsv = 'auto-mpg.csv'\ndf = pd.read_csv(csv)\nmpg_y = df.groupby('yr')['mpg'].mean()\nfrom bokeh.plotting import figure\nfrom bokeh.io import output_file, show\navg_mpg = figure(x_axis_label='year', y_axis_label='mean mpg', plot_height=150)\navg_mpg.line(mpg_y.index, mpg_y)\nmpg_hp = figure(x_axis_label='hp', y_axis_label='mpg', plot_height=400, plot_width=400)\nmpg_hp.circle(df['hp'],df['mpg'])\nmpg_weight = figure(x_axis_label='weight', y_axis_label='mpg', plot_height=400, plot_width=400)\nmpg_weight.circle(df['weight'], df['mpg'])\n","sub_path":"Jupyter/14. Visualización interactiva de datos con Bokeh/Bokeh_7.py","file_name":"Bokeh_7.py","file_ext":"py","file_size_in_byte":555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"178545203","text":"from psychopy import visual, core, parallel, event, gui, sound\nimport pyglet, os, random, copy\nfrom pyo import *\n\n###ENVIRONMENT AND LOADING###\nwin = visual.Window()\n\n\nloadMessage = visual.TextStim(win, text=\"Loading Stimuli\\n\\nPlease Wait\")\nloadMessage.draw()\n\nwin.flip()\n\n###CONSTANTS AND PARAMETERS###\n\nintroMessage = \"Welcome to our cognitive brain training game! Test your attention abilities and the way you can improve them! If you complete the following tasks successfully, you may improve your cognitive abilities!\\nThe game has 6 rounds, lasting approximately 12 minutes overall. Your task is to pay close attention to the sequences of words. It is important to pay attention to the words as you will be tested on what you have heard later on! We believe that though this task is demanding, it will pay off.\\nYou will be given a break at the end of each round. Please rest for as long as you need to during the breaks given, or continue to the next round if you are able to power through!\\nPlease ensure that you are in a quiet room and have earphones to listen to the words before starting the task.\\nPress SPACEBAR when you are ready.\"\n\nrestMessage = \"You can now take a break for as long as you need to before continuing.\\nPlease press SPACEBAR when you are ready to continue.\"\n\nhalfwayMessage = \"WELL DONE! You are halfway done!\\n\"+restMessage\n\npreTestMessage = \"Well done!\\nThe sequences that you have just heard were generated according to a set of rules that determined the order of words within each sequence.\\nYou will now hear a new set of sequences. Half of these sequences will conform to the same set of rules as before, and the rest will not. Your task is to judge which of the sequences follow the same rules as before and which do not.\\nIf the sequence follows the same roles as before, press Y.\\nIf the sequence does not follow the same rules as before,\\npress N.\\nIf you are unsure, please respond with your gut feeling.\\nPress SPACEBAR when you are ready to begin.\"\n\ntestMessage = \"Did the sequence follow the same rules as before?\\nY (YES) N (NO)\"\n\ngoodbyeMessage = \"You have now come to the end of our experiment.\\nFor more information on our study, please refer to our debrief notes.\\nThank you for your time and participation!\"\n\nintro = visual.TextStim(win, text=introMessage, height = .07, wrapWidth = 1.5)\nrest = visual.TextStim(win, text=restMessage)\nhalfway = visual.TextStim(win, text=halfwayMessage)\npreTest = visual.TextStim(win, text=preTestMessage, height = .07, wrapWidth = 1.5)\ntest = visual.TextStim(win, text=testMessage)\ngoodbye = visual.TextStim(win, text=goodbyeMessage)\n\ngramASymbols = 'M R V T X'.split()\ngramBSymbols = 'P Q W Y Z'.split()\n\nexpDir = '/home/leapadmin/Desktop/leapEEG/'\nstimuliDir = expDir + 'stimuli/'\ngramADir = stimuliDir + 'visual/gram_a/'\ngramBDir = stimuliDir + 'visual/gram_b/'\n\ngramAFiles = os.listdir(gramADir)\ngramBFiles = os.listdir(gramBDir)\n\nblockFile = 'stimuli/metadata/order.txt'\n\ngramAStims = [ visual.ImageStim(win, gramADir+filename) for filename in gramAFiles ]\ngramBStims = [ visual.ImageStim(win, gramBDir+filename) for filename in gramBFiles ]\n\nfixation = visual.ImageStim(win, stimuliDir + 'fix.svg.png')\n\n#enable parallel port access with:\n#sudo modprobe -r lp\n\nport = parallel.ParallelPort('/dev/parport0')\ncore.wait(2)\nport.setData(0)\n\ndata = []\n\n###FUNCTION DEFINITIONS###\ndef parseBlocks(blockFile):\n#Data Types\n blockTemplate = { 'id': 0 , 'bites': [] }\n biteTemplate = { 'grammar': '' , 'symbols': [] }\n parseData = { 'blocks': [], 'line': '', 'reader': open(blockFile) }\n\n#Data Type Functions\n def init(template):\n return lambda : copy.deepcopy(template)\n\n newBlock = init(blockTemplate)\n newBite = init(biteTemplate)\n\n#Processing Functions\n def startsWith(str):\n return lambda l : l.startswith(str)\n\n isBlock = startsWith('b:')\n isGram = startsWith('Gram')\n isUn = startsWith('Un')\n\n def isBite(str):\n if isGram(str) or isUn(str):\n return True\n return False\n\n def nextline():\n parseData['line'] = parseData['reader'].readline()\n \n def getline():\n return parseData['line']\n\n def addBlock(block):\n parseData['blocks'].append(block)\n\n def readBite(block):\n bite = newBite()\n bite['grammar'] = getline().split()[0]\n bite['symbols'] = getline().split()[1:]\n return bite\n\n def readBlock():\n block = newBlock()\n block['id'] = getline().split(':')[1].strip()\n nextline()\n lineNum = 1 #counter for bite number in block (ignored for non-test bites)\n while isBite(getline()):\n block['bites'].append(readBite(block))\n if block['id'] == 'test': #code test bites in sequence in file\n block['bites'][-1]['code'] = lineNum\n lineNum += 1\n elif block['id'] != 'test': #ignore non-test bites and code uniformly\n block['bites'][-1]['code'] = 21\n nextline()\n return block\n\n#Algorithm\n nextline()\n while getline() != '':\n if isBlock(getline()):\n addBlock(readBlock())\n nextline()\n parseData['reader'].close()\n return parseData['blocks']\n\n#Create map between symbols in block and playable stimuli\ndef setSymbols():\n lookupDict = {}\n random.shuffle(gramASymbols)\n random.shuffle(gramBSymbols)\n for symbol, stimuli in zip(gramASymbols, gramAStims):\n lookupDict[symbol] = stimuli\n for symbol, stimuli in zip(gramBSymbols, gramBStims):\n lookupDict[symbol] = stimuli\n return lookupDict\n\n#Experiment control functions\ndef playBite(bite):\n code = bite['code']\n duration = 1\n isi = .2\n if code != 21:\n isi = .8\n for symbol in bite['symbols']:\n stim = lookup[symbol]\n port.setData(code)\n stim.draw()\n win.flip()\n core.wait(duration)\n win.flip()\n core.wait(isi)\n port.setData(0)\n\ndef playTrainingBlock(block):\n for bite in block['bites']:\n playBite(bite)\n win.flip()\n core.wait(1.5)\n\ndef playTestBlock(testBlock):\n global data\n random.shuffle(testBlock['bites'])\n for bite in testBlock['bites']:\n win.flip()\n playBite(bite)\n test.draw()\n win.flip()\n answer = event.waitKeys(keyList=['y','n'])[0]\n data.append([bite['code'], answer])\n\ndef playBlocks(blocks):\n for block in blocks:\n if block['id'] != 'test':\n playTrainingBlock(block)\n if block['id'] == 3:\n halfway.draw()\n elif block['id'] != 3:\n rest.draw()\n win.flip()\n event.waitKeys(keyList=['space'])\n elif block['id'] == 'test':\n preTest.draw()\n win.flip()\n event.waitKeys(keyList=['space'])\n playTestBlock(block)\n output = open('output.txt','w')\n output.write(str(data))\n output.close()\n goodbye.draw()\n win.flip()\n event.waitKeys(keyList=['space'])\n win.close()\n\n###MAIN ROUTINE###\n#Get experiment blocks from file, create symbol map, and play all blocks of experiment\nblocks = parseBlocks(blockFile)\nlookup = setSymbols()\nintro.draw()\nwin.flip()\nevent.waitKeys(keyList=['space'])\nplayBlocks(blocks)\n","sub_path":"dualgram_words_visual.py","file_name":"dualgram_words_visual.py","file_ext":"py","file_size_in_byte":7323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"416645415","text":"from django import template\n\nfrom app.main.models import Type, CourseDiscipline\n\nregister = template.Library()\n\n\n@register.inclusion_tag('inc/filter.html', takes_context=True)\ndef type_choice(context):\n current_type = context['request'].GET.get('typeFilter', -1)\n current_block = context['request'].GET.get('blockFilter', -1)\n return {\n 'type_choices': Type.objects.all(),\n 'current': int(current_type) if current_type else None,\n 'block_choices': CourseDiscipline.objects.filter(course=context['course']) if context.get('course') else None,\n 'current_block': int(current_block) if current_block else None\n }\n","sub_path":"app/main/templatetags/app_extra.py","file_name":"app_extra.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"635942959","text":"# coding=utf-8\nimport os\nfrom distutils.version import LooseVersion\nfrom pathlib import Path\nfrom subprocess import PIPE, run\n\nfrom setuptools import find_packages, setup\n\nproject_root = Path(__file__).parent\n\ninstall_requires = [\n \"audioread>=2.1.9\",\n \"SoundFile>=0.10\",\n \"click>=7.1.1\",\n \"cytoolz>=0.10.1\",\n \"dataclasses\",\n \"h5py>=2.10.0\",\n \"intervaltree>= 3.1.0\",\n \"lilcom>=1.1.0\",\n \"numpy>=1.18.1\",\n \"packaging\",\n \"pyyaml>=5.3.1\",\n \"tqdm\",\n]\n\ntry:\n # A matrix of compatible torch and torchaudio versions.\n # If the user already installed torch, we'll try to find the compatible\n # torchaudio version. If they haven't installed torch, we'll just install\n # the latest torch and torchaudio.\n # This code is partially borrowed from ESPnet's setup.py.\n import torch\n\n torch_ver = LooseVersion(torch.__version__)\n if torch_ver >= LooseVersion(\"1.9.1\"):\n raise NotImplementedError(\"Not yet supported\")\n elif torch_ver >= LooseVersion(\"1.9.0\"):\n install_requires.append(\"torchaudio==0.9.0\")\n elif torch_ver >= LooseVersion(\"1.8.1\"):\n install_requires.append(\"torchaudio==0.8.1\")\n elif torch_ver >= LooseVersion(\"1.8.0\"):\n install_requires.append(\"torchaudio==0.8.0\")\n elif torch_ver >= LooseVersion(\"1.7.1\"):\n install_requires.append(\"torchaudio==0.7.2\")\n else:\n raise ValueError(\n f\"Lhotse requires torch>=1.7.1 and torchaudio 1.7.2 -- \"\n f\"please update your torch (detected version: {torch_ver}).\"\n )\nexcept ImportError:\n install_requires.extend([\"torch\", \"torchaudio\"])\n\ndocs_require = (project_root / 'docs' / 'requirements.txt').read_text().splitlines()\ntests_require = ['pytest==5.4.3', 'flake8==3.8.3', 'coverage==5.1', 'hypothesis==5.41.2']\ndev_requires = sorted(docs_require + tests_require + ['jupyterlab', 'matplotlib', 'isort'])\nall_requires = sorted(dev_requires)\n\nif os.environ.get('READTHEDOCS', False):\n # When building documentation, omit torchaudio installation and mock it instead.\n # This works around the inability to install libsoundfile1 in read-the-docs env,\n # which caused the documentation builds to silently crash.\n install_requires = [\n req for req in install_requires\n if not any(\n req.startswith(dep) for dep in ['torchaudio', 'SoundFile']\n )]\n\ntry:\n git_commit = run(['git', 'rev-parse', '--short', 'HEAD'], check=True, stdout=PIPE).stdout.decode().rstrip(\n '\\n').strip()\n dirty_commit = len(\n run(['git', 'diff', '--shortstat'], check=True, stdout=PIPE).stdout.decode().rstrip('\\n').strip()) > 0\n git_commit = git_commit + '-dirty' if dirty_commit else git_commit + '-clean'\nexcept Exception:\n git_commit = ''\n# See the format https://packaging.python.org/guides/distributing-packages-using-setuptools/#local-version-identifiers\ndev_version = '.dev-' + git_commit\n\nif os.environ.get('RELEASE', False):\n dev_version = ''\n\nsetup(\n name='lhotse',\n version='0.8.0' + dev_version,\n python_requires='>=3.6.0',\n description='Data preparation for speech processing models training.',\n author='The Lhotse Development Team',\n author_email=\"pzelasko@jhu.edu\",\n long_description=(project_root / 'README.md').read_text(encoding='utf-8'),\n long_description_content_type=\"text/markdown\",\n license='Apache-2.0 License',\n packages=find_packages(),\n entry_points={\n \"console_scripts\": [\n \"lhotse=lhotse.bin.lhotse:cli\",\n ]\n },\n install_requires=install_requires,\n extras_require={\n 'docs': docs_require,\n 'tests': tests_require,\n 'dev': dev_requires,\n 'all': all_requires\n },\n classifiers=[\n \"Development Status :: 3 - Alpha\",\n \"Programming Language :: Python :: 3.6\",\n \"Programming Language :: Python :: 3.7\",\n \"Programming Language :: Python :: 3.8\",\n \"Intended Audience :: Science/Research\",\n \"Operating System :: POSIX :: Linux\",\n \"Operating System :: MacOS :: MacOS X\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Topic :: Multimedia :: Sound/Audio :: Speech\",\n \"Topic :: Scientific/Engineering :: Artificial Intelligence\",\n \"Topic :: Software Development :: Libraries :: Python Modules\",\n \"Typing :: Typed\"\n ],\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":4355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"310496499","text":"'''\nWeb crawler 1.0\n\nCreated by Omar Padierna on February 2017\n \n'''\n#Import dependencies\nimport time\nimport scrapy\nfrom selenium import webdriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.common.exceptions import *\nfrom selenium.webdriver.common.action_chains import ActionChains\n\n\n#Define class object in which to save all crawled data\n\nclass Img_Data:\n def __init__ (self):\n self.data = \"\"\n self.img = \"\"\n self.img_url= \"\"\n self.img_path= \"\"\n self.page=\"\"\n self.parsed_data={}\n\n\n\n##This function must return the array in which it saves the info so it can be used for processing in other functions. \n\ndef ISIC_Crawler(lastPage, lastImage, pageDone, firstTime):\n\ttry:\n\t\ttables=[]\n\t\tindex=0\n\t\t#Initialize Selenium webdriver (i.e. start new Firefox instance)\n\t\tdriver = webdriver.Firefox()\n\t\tdriver.implicitly_wait(120)\n\t\t#Indicate driver which website to crawl\n\t\tdriver.get(\"https://isic-archive.com/#images\")\n\n\t\t#Initialize wait property for webdriver. Then tell it to wait until accept button appears. Click button\n\t\tdriver.wait = WebDriverWait(driver, 120)\n\t\tbutton = driver.wait.until(EC.element_to_be_clickable((By.ID, \"isic-terms-accept\")))\n\t\tbutton.click()\n\n\t\t#Assume current page is NOT the last visited page \n\t\tin_page=False\n\n\t\t#Get to the last visited page\n\t\tfirsttimepage= True\n\t\t#Change to false if going forward in the pagination\n\t\tfirstlast=False\n\t\twhile (in_page is False):\n\n\t\t\t#Get current page\n\t\t\t\n\t\t\tif firsttimepage == True:\n\t\t\t\t#once page is reached wait 5 seconds\n\t\t\t\tprint(\"Waiting 5 seconds for page to finish loading\")\n\t\t\t\ttime.sleep(5)\n\t\t\telse:\n\t\t\t\t#Interval of page jumping\n\t\t\t\tprint(\"Waiting 3 seconds for page to finish loading\") #3 seconds seeemed to work\n\t\t\t\ttime.sleep(1)\n\t\t\tdiv=driver.find_element_by_id(\"isic-images-hasPaging\")\n\t\t\tpage=div.find_element_by_class_name(\"page\")\n\t\t\tcurrent_page=page.get_attribute(\"innerHTML\")\n\t\t\tprint(\"This is the current page: \"+current_page)\n\t\t\tprint(\"This is the last visited page: \"+lastPage)\n\n\t\t\t#Check if current page is last (visited) page if not click \"next\" button and repeat process\n\t\t\tif current_page == lastPage:\n\t\t\t\tprint(\"Reached last visited page!\")\n\t\t\t\tif pageDone == True :\n\t\t\t\t\tprint(\"All images in this page have been extracted. Redirecting to next page\")\n\t\t\t\t\tnextbutton=driver.find_element_by_id(\"isic-images-seekNext\")\n\t\t\t\t\tnextbutton.click()\n\t\t\t\t\tfirsttimepage=False\n\t\t\t\t\tprint(\"Waiting 10 seconds for page to finish loading\")\n\t\t\t\t\ttime.sleep(5)\n\t\t\t\tin_page= True\n\t\t\t#If current page is not the last visited then look for next button and click it. Update current_page value to new page. \n\t\t\telse :\n\t\t\t\t\n\t\t\t\tif firstlast == True:\n\t\t\t\t\tprint(\"This is not the last visited page!. Redirecting to last page\")\n\t\t\t\t\tnextbutton=driver.find_element_by_id(\"isic-images-seekLast\")\n\t\t\t\t\tfirstlast= False\n\t\t\t\telse:\n\t\t\t\t\tprint(\"This is not the last visited page!.Redirecting to previous page\")\n\t\t\t\t\t#Change to \"isic-images-seekNext\" if going forward in the pagination\n\t\t\t\t\tnextbutton=driver.find_element_by_id(\"isic-images-seekNext\")\n\t\t\t\tnextbutton.click()\n\t\t\t\tfirsttimepage=False\n\t\t\t\tdiv=driver.find_element_by_id(\"isic-images-hasPaging\")\n\t\t\t\tpage=div.find_element_by_class_name(\"page\")\n\t\t\t\tcurrent_page=page.get_attribute(\"innerHTML\")\n\t\t\t\tprint(\"New page is: \"+ current_page)\n\t\t\t#current page is detected as string, change to integer\n\t\t\tcurrentPage_number=int(current_page.split(\"-\",2)[1])\n\t\t\t#Make sure current page is not the last one \n\t\t\tif currentPage_number == 12086:\n\t\t\t\t#Since pagination is in intervals, change interval to page number (i.e 1-50 is 1, 51-100 is 2, etc)\n\t\t\t\tactual_page = 242\n\t\t\t\tlast_page=actual_page+1\n\t\t\telse:\n\t\t\t\t#Since pagination is in intervals, change interval to page number (i.e 1-50 is 1, 51-100 is 2, etc)\n\t\t\t\tactual_page_float=currentPage_number/50\n\t\t\t\tactual_page=int(actual_page_float)\n\t\t\t\t#create last page to be scraped, adding 3 makes the size chunk to 150 images because each page has 50 images\n\t\t\t\tlast_page=actual_page+1\n\t\t\t\n#################################### Actual crawling ##############################################\n\t\t#Loop through the pages that will be scraped. \n\t\tfor x in range (actual_page,last_page):\n\t\t\t#Wait 5 seconds (so all images load, otherwise box element that has images will be empty)\n\t\t\tprint(\"Waiting for images to load: 5 seconds\")#90 was working\n\t\t\ttime.sleep(6)\n\t\t\tprint(\"Waiting is finished!\")\n\t\t\t#Get box with images\n\t\t\tbox2=driver.find_element_by_id(\"isic-images-imageWall\")\n\t\t\t#limit =0\n\t\t\t#Extract images then iterate on each image\n\t\t\tbuttons = box2.find_elements_by_tag_name(\"img\")\n\t\t\tfor button in buttons:\n\t\t\t\ttry:\n\t\t\t\t\tdriver.wait.until(EC.presence_of_element_located((By.TAG_NAME,\"img\")))\n\t\t\t\t\tprint(\"Found image!\")\n\t\t\t\tfinally:\n\t\t\t\t\tpass\n\t\t\t\t#Scroll up so image is visible\n\t\t\t\tdriver.execute_script(\"return arguments[0].scrollIntoView();\", button)\n\t\t\t\t#Generate Img_Data instance (otherwise elements will be overwritten)\n\t\t\t\tresult=Img_Data()\n\n\t\t\t\t#Get page\n\t\t\t\tdiv=driver.find_element_by_id(\"isic-images-hasPaging\")\n\t\t\t\tstaleness=True\n\t\t\t\twhile (staleness is True):\n\t\t\t\t\tstaleness=EC.staleness_of(div)\n\t\t\t\t\tprint(\"Checking page div is not stale hold on...\")\n\n\t\t\t\tpage=div.find_element_by_class_name(\"page\")\n\t\t\t\tresult.page=page.get_attribute(\"innerHTML\")\n\t\t\t\tprint(\"Got paging!\")\n\n\t\t\t\t#Click on image\n\t\t\t\tbutton.click()\n\n\t\t\t\t#Wait 2 seconds and then look for table with image data\n\t\t\t\tprint(\"Waiting 500 ms in order to avoid getting kicked out...\")\n\t\t\t\ttime.sleep(.7)\n\t\t\t\t#driver.wait.until(EC.element_to_be_clickable((By.ID, \"isic-terms-accept\")))\n\t\t\t\t#image_details=driver.find_element_by_class_name(\"isic-image-details-name\")\n\t\t\t\timage_details=driver.wait.until(EC.presence_of_element_located((By.CLASS_NAME,\"isic-image-details-name\")))\n\t\t\t\timage_details_html=image_details.get_attribute(\"innerHTML\")\n\t\t\t\timage_name=image_details_html[0:12]\n\t\t\t\timage_name_number=image_name.split(\"_\",2)[1]\n\t\t\t\tprint(\"got image details!\")\n\t\t\t\t#If the current image number is larger than the last image in page.txt then scrape it.\n\t\t\t\tif int(image_name_number) > int(lastImage) or firstTime == True:\n\t\t\t\t\t##Begin table extraction \n\t\t\t\t\tprint(\"Getting details of image: \" + image_name)\n\t\t\t\t\tprint(\"Looking for image table\")\n\t\t\t\t\t#table=driver.find_element_by_class_name(\"isic-image-details-table\")\n\t\t\t\t\ttable=driver.wait.until(EC.presence_of_element_located((By.CLASS_NAME, \"isic-image-details-table\")))\n\t\t\t\t\tstaleness=True\n\t\t\t\t\twhile (staleness is True):\n\t\t\t\t\t\tstaleness=EC.staleness_of(table)\n\t\t\t\t\t\tprint(\"Checking image is not stale hold on...\")\n\t\t\t\t\t#Extract data and save it in a tuple\n\t\t\t\t\ttable_html=table.get_attribute(\"innerHTML\")\n\t\t\t\t\tresult.data=table_html\n\t\t\t\t\tprint(\"Got image table, looking for button\")\n\t\t\t\t\t#Click button to open image in new tab then switch focus on that tab\n\t\t\t\t\tbutton=driver.wait.until(EC.presence_of_element_located((By.ID,\"isic-image-details-openwindow\")))\n\t\t\t\t\tstaleness=True\n\t\t\t\t\twhile (staleness is True):\n\t\t\t\t\t\tstaleness=EC.staleness_of(button)\n\t\t\t\t\t\tprint(\"Checking button is not stale hold on...\")\n\t\t\t\t\tprint(\"Button should not be stale anymore\")\n\t\t\t\t\t#button=driver.find_element_by_class_name(\"openwindow\")\n\t\t\t\t\tbutton.click()\n\t\t\t\t\tprint(\"Redirecting to hi-res image: Waiting 300 ms...\")\n\t\t\t\t\ttime.sleep(.4)\n\t\t\t\t\tdriver.switch_to_window(driver.window_handles[-1])\n\t\t\t\t\turl_correct=False\n\t\t\t\t\twhile(url_correct is False):\n\t\t\t\t\t\timageUrl=driver.current_url\n\t\t\t\t\t\t#Get image url\n\t\t\t\t\t\tif imageUrl==\"about:blank\":\n\t\t\t\t\t\t\tprint(\"URl was not received! trying again!\")\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\turl_correct=True\n\t\t\t\t\t#about:blank\n\t\t\t\t\tresult.img_url=imageUrl\n\n\t\t\t\t\t#Get image details and save them in the same tuple \n\t\t\t\t\timage=driver.find_element_by_tag_name(\"title\")\n\t\t\t\t\timage_html=image.get_attribute(\"innerHTML\")\n\t\t\t\t\tresult.img=image_html\n\n\t\t\t\t\t#Save tuple in array, go back to previous tab and wait again\n\t\t\t\t\ttables.append(result) \n\t\t\t\t\tdriver.close()\n\t\t\t\t\tdriver.switch_to_window(driver.window_handles[0])\n\t\t\t\t\n\t\t\t\t#If the current image number is not larger than the last image in page.txt then ignore.\n\t\t\t\telse:\n\t\t\t\t\tprint(\"This is not the latest crawled image: Waiting 300 ms...\")\n\t\t\t\t\ttime.sleep(.7)\n\n\t\t\t#Click next button once all images in that page have been crawled\n\t\t\tif result.page != \"12051 - 12086\":\n\t\t\t\tnextbutton=driver.find_element_by_id(\"isic-images-seekNext\")\n\t\t\t\tnextbutton.click()\n##################################End of crawling process#################################################\n\t\t\n\t\t#Close browser\n\t\tdriver.quit()\n\t\tprint(\"300 images scraped! saving! will restart process again after storing all data\")\n\t\t#Return data\n\t\treturn tables\n\n#Catch exceptions in case job gets cancelled for some reason. \n\texcept StaleElementReferenceException:\n\t\tdriver.quit()\n\t\tprint(\"Oops something went wrong! try again! Error: Stale element\")\n\t\treturn tables\n\n\texcept NoSuchElementException:\n\t\tdriver.quit()\n\t\tprint(\"Oops something went wrong! try again! Error: No such Element\")\n\t\treturn tables\n\n\texcept TimeoutException:\n\t\tdriver.quit()\n\t\tprint(\"Oops something went wrong! try again! Error: Timeout\")\n\t\treturn tables\n\texcept NoSuchWindowException:\n\t\tdriver.quit()\n\t\tprint(\"Oops something went wrong! try again! Error: No such Window\")\n\t\treturn tables\n\texcept WebDriverException:\n\t\tprint(\"Oops something went wrong! try again! Error: WebDriverException\")\n\t\treturn tables","sub_path":"image_scraper/isic_crawler.py","file_name":"isic_crawler.py","file_ext":"py","file_size_in_byte":9497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"346889993","text":"import sys\nimport pandas as pd\nimport numpy as np\nimport sklearn.datasets as sld \nimport sklearn.cluster as slc\nfrom sklearn.metrics import silhouette_score as skl_silScore\nfrom sklearn.metrics import calinski_harabasz_score as skl_calScore\nimport matplotlib.pyplot as plt\nimport mpl_toolkits.mplot3d\nfrom sklearn.preprocessing import StandardScaler\n\nfrom .utils import check\nfrom .mk_pickle import make_pickle\n\n# 对类别进行统计,输出一个包含类别和数目信息的字典\ndef tallyClusters(Clist):\n\t# 将narray数据转化为list\n\tS = Clist.tolist()\n\t# 统计每一类的数量\n\tclassN=dict()\n\tfor i in range(len(S)):\n\t\tclassN[S[i]] = classN.get(S[i], 0) + 1\n\treturn classN\n\n# 对分类的统计结果进行图像化显示\ndef paintResult(resultDict):\n\t# 数值列表\n\tnum_list = []\n\t# 名字列表\n\tname_list = []\n\t# 填充两个列表\n\tfor k,v in resultDict.items():\n\t\tname_list.append(k)\n\t\tnum_list.append(v)\n\t\n\tplt.figure()\n\t# 画出直方图\n\trects = plt.bar(name_list,num_list)\n\tplt.xticks(name_list)\n\t# 标注数据\n\t\n\tfor rect in rects:\n\t\tx = rect.get_x()\n\t\twidth = rect.get_width()\n\t\theight = rect.get_height()\n\t\tplt.text(x+width/2,height,height,ha='center', va='bottom',fontsize=10)\n\t\n\t# 设置打开交互模式而不阻塞\n\tplt.ion()\n\t# show出来\n\tplt.show()\n\n\n# dbscan聚类，\ndef Dbscan(data,maxDis=5,simpleInACluster=10):\n\t# 初始化分类器\n\tdb = slc.DBSCAN(eps=maxDis,min_samples=simpleInACluster,metric='euclidean')\n\t# 使用分类器得到每一条记录对应类别的集合\n\tresult = db.fit_predict(data)\n\t# 返回得到的结果和分类器\n\treturn result,db\n\n\n\n## 对于DBSCAN算法的前验分析\ndef dbscanCMP(data,epsMin,epsMax,smplMin,smplMax,epsStep=1,smplStep=1):\n\tprint(\"半径\\t簇内个数\\t轮廓系数\\tCalinski-Harabasz指数\\t分类结果\")\n\tbestEps_sil=epsMin\n\tbestSmpl_sil=smplMin\n\tbestEps_cal=epsMin\n\tbestSmpl_cal=smplMin\n\t\n\teps = []\n\tsmpl = []\n\tsil = []\n\tcal = []\n\t\n\tbestSilScore=-1\n\tbestCalScore=0\n\t\n\tfig = plt.figure()\n\ttDsil = fig.add_subplot(121,projection='3d')\n\ttDcal = fig.add_subplot(122,projection='3d')\n\ttDsil.set_title(\"silhouette Score\")\n\ttDcal.set_title(\"Calinski-Harabasz Score\")\n\t#改成了兼容float\n\tfor i in np.arange(epsMin,epsMax,epsStep):\n\t\tfor j in np.arange(smplMin,smplMax+1,smplStep):\n\t\t\tdb = slc.DBSCAN(eps=i,min_samples=j,metric='euclidean')\n\t\t\tresult = db.fit_predict(data)\n\t\t\tfinalResult = tallyClusters(result)\n\t\t\tsilScore = skl_silScore(data,result)\n\t\t\tcalScore = skl_calScore(data,result)\n\t\t\teps.append(i)\n\t\t\tsmpl.append(j)\n\t\t\tsil.append(silScore)\n\t\t\tcal.append(calScore)\n\t\t\tprint(i,\"\\t\",j,\"\\t\",silScore,\"\\t\",calScore,\"\\t\", finalResult)\n\t\t\tif silScore > bestSilScore:\n\t\t\t\tbestSilScore = silScore\n\t\t\t\tbestEps_sil = i\n\t\t\t\tbestSmpl_sil =j\n\t\t\tif calScore > bestCalScore:\n\t\t\t\tbestCalScore = calScore\n\t\t\t\tbestEps_cal = i\n\t\t\t\tbestSmpl_cal =j\n\t\n\ttDsil.bar3d([x-0.2*epsStep for x in eps], [x-0.2*smplStep for x in smpl] ,np.zeros_like(eps),dx=0.4*epsStep,dy=0.4*smplStep,dz=sil)\n\ttDsil.set_xlabel(\"eps\")\n\ttDsil.set_ylabel(\"min_samples\")\n\ttDsil.set_zlabel(\"silhouette_score\")\n\t\n\ttDcal.bar3d([x-0.2*epsStep for x in eps], [x-0.2*smplStep for x in smpl] ,np.zeros_like(eps),dx=0.4*epsStep,dy=0.4*smplStep,dz=cal,color=\"yellow\")\n\ttDcal.set_xlabel(\"eps\")\n\ttDcal.set_ylabel(\"min_samples\")\n\ttDcal.set_zlabel(\"Calinski-Harabasz_Score\")\n\t\n\tprint(\"当半径为\",bestEps_sil,\"簇内个数为\",bestSmpl_sil,\"时,轮廓系数最佳\")\n\tprint(\"当半径为\",bestEps_cal,\"簇内个数为\", bestSmpl_cal ,\"时,Calinski-Harabasz指数最佳\")\n\tplt.ion()\n\tplt.show()\n\t\n\n# Main function\ndef main():\n\t# 数据导入\n\n\tprint([check(x) for x in sys.argv])\n\tif len(sys.argv) == 1 :\n\t\tprint(\"Need a file at least\")\n\telse:\n\t\t# 存储属性数据\n\t\tdata = []\n\t\t# 存储公司名称，即键值数据\n\t\tnames = []\n\t\t# 导入数据\n\t\tpos = 2 if sys.argv[1]=='-c' else 1\n\t\ttry:\t\n\t\t\twith open(sys.argv[pos],'r')as f:\n\t\t\t\tlines = f.readlines()\n\t\t\t\tpropertyName = lines.pop(0)\n\t\t\t\tfor line in lines:\n\t\t\t\t\tcells = line.strip().split(',')\n\t\t\t\t\tnames.append(cells[0])\n\t\t\t\t\tdata.append([float(cells[i]) for i in range(1,len(cells))])\n\t\t\t#scaler = StandardScaler()\n\t\t\t#data = scaler.fit_transform(data)\n\t\t\t#print(data)\n\t\t\tif pos == 1 :\n\t\t\t\tif len(sys.argv)==4 and check(sys.argv[2]) in (float,int) and check(sys.argv[3])==int:\n\t\t\t\t\t\n\t\t\t\t\tresult,sorter = Dbscan(data,float(sys.argv[2]),int(sys.argv[3]))\n\t\t\t\t\tpd_data=pd.read_csv(sys.argv[pos])#用于后续输出结果\n\t\t\t\t\tnew_file_name=sys.argv[pos].replace('.','_res_dbscan.')\n\t\t\t\t\t#print(result)\n\t\t\t\t\tlabels = pd.DataFrame(result,columns=['labels'])\n\t\t\t\t\tnew_df=pd.concat([pd_data,labels],axis=1)\n\t\t\t\t\tnew_df.to_csv(new_file_name)\n\t\t\t\t\tprint(\"保存到\"+new_file_name)\n\t\t\t\telse:\n\t\t\t\t\tresult,sorter = Dbscan(data)\n\t\t\t\tfinalResult = tallyClusters(result)\n\t\t\t\tprint(\"[分类及数量]:\",finalResult)\n\t\t\t\tpaintResult(finalResult)\n\t\t\t\tprint(\"[轮廓系数]:\",skl_silScore(data,result))\n\t\t\t\tprint('[Calinski-Harabasz指数]:',skl_calScore(data,result))\n\t\t\t\tplt.show()\n\t\t\t\t#持久化该模型\n\t\t\t\tpkl_filename=sys.argv[pos].split('.')[0]+'.pkl'\n\t\t\t\tmake_pickle(sorter,pkl_filename)\n\t\t\t\tprint(\"模型已保存至\"+pkl_filename)\n\t\t\telse:\n\t\t\t\tif check(sys.argv[3]) in (float,int) and check(sys.argv[4]) in (float,int) and float(sys.argv[3]) numeroPartidos:\n jugadorMasPartidos = jugador\n numeroPartidos = partidosPorJugador[jugador]\n \n return jugadorMasPartidos, numeroPartidos\n \n\n### JUGADORES Y JORNADAS ###\n\njugadores = {\"Juan\":20, \"Pepe\":1, \"Carlos\":8, \"Piños MacMordiscos\": 19, \"Leo Enano\": 14}\njornadasLiga = {1: [\"Juan\", \"Pepe\", \"Carlos\"], 2: [], 3: [], 4:[]}\n\n#a) funcion de añadir por nombres\nnombres = [\"Juan\", \"Piños MacMordiscos\", \"Leo Enano\"]\n\n#jornadasLiga = añadirDatosJornadaNombres(jornadasLiga, 1, nombres)\n#jornadasLiga = añadirDatosJornadaNombres(jornadasLiga, 5, nombres)\n\n#funcion de añadir por dorsales\ndorsales = [1,19,14]\ndorsales2 = [20]\ndorsales3 = [20]\n\njornadasLiga = añadirDatosJornadaDorsales(jornadasLiga, jugadores, 5, dorsales)\njornadasLiga = añadirDatosJornadaDorsales(jornadasLiga, jugadores, 5, dorsales2)\njornadasLiga = añadirDatosJornadaDorsales(jornadasLiga, jugadores, 3, dorsales2)\n\n\n#b) función para comprobar jugadores\nprint(jugadorEnJornada(jornadasLiga, 1, \"Juan\"))\n\n#c) funcion de listar jornadas de un jugador\n\njornadasJugador = listaJornadasJugador(jornadasLiga, jugadores, [1])\n\n#d) funcion para saber el jugador con más partidos\n\njugadorMasPartidos, partidos = mayorNumerosPartidos(jornadasLiga, jugadores)\n\n\n\n\n\n\n\n\n\n\n\n\n \n\n","sub_path":"laboratorio/cuaderno-5/ejercicio-20.py","file_name":"ejercicio-20.py","file_ext":"py","file_size_in_byte":3783,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"218825492","text":"# -*- coding: utf-8 -*-\n\nimport tensorflow as tf\nfrom func import cudnn_gru, native_gru, dot_attention, summ, dropout, ptr_net\nfrom layers import initializer, regularizer, residual_block, highway, conv, mask_logits, trilinear, total_params, \\\n optimized_trilinear_for_attention\n\nclass Model(object):\n \"\"\"\n A CNN for text classification.\n Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.\n \"\"\"\n def __init__(self, config, batch, word_mat=None,char_mat=None, filter_sizes=None, embedding_size=None,num_filters=None,trainable=True, l2_reg_lambda=0.0, keep_prob=0.9, graph=None):\n\n # Placeholders for input, output and dropout\n self.config = config\n self.graph = graph if graph is not None else tf.Graph()\n self.trainable = trainable\n gru = cudnn_gru if config.use_cudnn else native_gru\n self.is_train = tf.get_variable(\"is_train\", shape=[], dtype=tf.bool, trainable=True)\n if trainable == True:\n self.input_x, self.input_x1, self.ch, self.qh, self.input_y, self.qa_id,self.alternatives_tokens = batch.get_next() # self.y1 is (64, 3)self.alterh batch_size is[batch,3,alternative_len,chara_len]\n else:\n self.input_x, self.input_x1, self.ch, self.qh,self.alternatives_tokens= batch.get_next() # self.y1 is (64, 3)self.alterh batch_size is[batch,3,alternative_len,chara_len]\n self.dropout_keep_prob =keep_prob\n self.global_step = tf.get_variable('global_step', shape=[], dtype=tf.int32,\n initializer=tf.constant_initializer(0), trainable=False)\n self.dropout = tf.placeholder_with_default(0.5, (), name=\"dropout\")\n # Keeping track of l2 regularization loss (optional)\n l2_loss = tf.constant(0.0)\n self.c_mask = tf.cast(self.input_x, tf.bool) # 这里是判断出每一个数据集的context对应实际句子长度的位置(64,400)\n self.q_mask = tf.cast(self.input_x1, tf.bool) # 同上(64,50)\n self.c_len = tf.reduce_sum(tf.cast(self.c_mask, tf.int32), axis=1) # 每一个训练数据集实际长度\n self.q_len = tf.reduce_sum(tf.cast(self.q_mask, tf.int32), axis=1) # 每一个问题的实际长度\n self.ch_len = tf.reshape(tf.reduce_sum(tf.cast(tf.cast(self.ch, tf.bool), tf.int32), axis=2), [-1])\n self.qh_len = tf.reshape(tf.reduce_sum(tf.cast(tf.cast(self.qh, tf.bool), tf.int32), axis=2), [-1])\n # Embedding layer\n N, PL, QL, CL, d, dc,dg,nh= config.batch_size,config.para_limit,config.ques_limit,config.char_limit,\\\n config.hidden, config.char_dim,config.char_hidden,config.num_heads\n with tf.variable_scope(\"Input_Embedding_Layer\"):\n self.char_mat = tf.get_variable(\"char_mat\", initializer=tf.constant(char_mat, dtype=tf.float32),trainable=True)\n ch_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.ch), [N * PL, CL, dc])\n qh_emb = tf.reshape(tf.nn.embedding_lookup(self.char_mat, self.qh), [N * QL, CL, dc])\n ch_emb = tf.nn.dropout(ch_emb, 1.0 - 0.5 * self.dropout)\n qh_emb = tf.nn.dropout(qh_emb, 1.0 - 0.5 * self.dropout)\n\n cell_fw = tf.contrib.rnn.GRUCell(dg) # 按照字符有多少个gru神经单元\n cell_bw = tf.contrib.rnn.GRUCell(dg)\n _, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(\n cell_fw, cell_bw, ch_emb, self.ch_len,\n dtype=tf.float32) # self.ch_len表示训练数据集所有字符平摊之后，实际字符的长度,sequence_length=[bacth_size] is N * PL, because\n # char_hidden is 100 so state_fw and state_bw is [N * PL,100]\n ch_emb = tf.concat([state_fw, state_bw], axis=1) # [N * PL,200]\n _, (state_fw, state_bw) = tf.nn.bidirectional_dynamic_rnn(cell_fw, cell_bw, qh_emb, self.qh_len,dtype=tf.float32) # state_* [N*QL]\n qh_emb = tf.concat([state_fw, state_bw], axis=1) # question_emd is [,200]\n\n qh_emb = tf.reshape(qh_emb, [N, QL, 2 * dg]) # [batch_size,que_len,200]\n ch_emb = tf.reshape(ch_emb, [N, PL, 2 * dg]) # 以上过程对应了论文里边的 the character-level embedding are generate by ...in the token\n #这样就把每一个单词的字符转化为单词的字符级别embedding信息，tf.reshape(ch_emb, [N, PL, 2 * dg])\n # 从这里可以看出作者最后那字符的state状态作为字符信息与原始单词embedding进行连接，那么是否可以用拼音\n # 作为汉语的字符级别信息呢，可以尝试\n print(qh_emb,\"llllllllllllll\")\n with tf.name_scope(\"embedding\"):\n\n self.W = tf.get_variable(\"word_mat\", initializer=tf.constant(word_mat, dtype=tf.float32),\n trainable=True)\n self.c_mask = tf.cast(self.input_x, tf.bool) # self.c为填充之后的长度是一致的，用0进行填充\n self.q_mask = tf.cast(self.input_x1, tf.bool)\n if trainable:\n self.c_maxlen, self.q_maxlen, = config.para_limit, config.ques_limit,\n else:\n self.c_maxlen, self.q_maxlen = config.test_para_limit, config.test_ques_limit\n self.embedded_chars = tf.nn.embedding_lookup(self.W, self.input_x)\n self.embedded_chars1 = tf.nn.embedding_lookup(self.W, self.input_x1)\n c_emb = tf.concat([self.embedded_chars, ch_emb], axis=2)\n q_emb= tf.concat([self.embedded_chars1, qh_emb], axis=2)\n # self.embedded_chars_expanded = tf.expand_dims(self.embedded_chars, -1)\n # self.embedded_chars_expanded1 = tf.expand_dims(self.embedded_chars1, -1)\n with tf.variable_scope(\"cnn_predict\"):\n pooled_outputs = []\n c_emb = highway(c_emb, size=d, scope=\"highway\", dropout=self.dropout, reuse=None) # 相当于对信息进行一次筛选,并且让表示的维度降低到75,[batch,sql_len,75]\n q_emb = highway(q_emb, size=d, scope=\"highway\", dropout=self.dropout, reuse=True)\n c = residual_block(c_emb,\n num_blocks=1,\n num_conv_layers=4,\n kernel_size=7,\n mask=self.c_mask,\n num_filters=d,\n num_heads=nh,\n seq_len=self.c_len,\n scope=\"Encoder_Residual_Block\",\n bias=False,\n dropout=self.dropout)\n q = residual_block(q_emb,\n num_blocks=1,\n num_conv_layers=4,\n kernel_size=7,\n mask=self.q_mask,\n num_filters=d,\n num_heads=nh,\n seq_len=self.q_len,\n scope=\"Encoder_Residual_Block\",\n reuse=True, # Share the weights between passage and question\n bias=False,\n dropout=self.dropout)\n qc_att = dot_attention(c, q, mask=self.q_mask, hidden=d,\n keep_prob=config.keep_prob, is_train=self.is_train) # 这个函数实现的是公式（4）中的所有\n rnn = gru(num_layers=1, num_units=d, batch_size=N, input_size=qc_att.get_shape(\n ).as_list()[-1], keep_prob=config.keep_prob, is_train=self.is_train)\n att = rnn(qc_att, seq_len=self.c_len) # this is 公式(3) #[batch,c_maxlen,150]\n print(att,\"111111111111111111111111\")\n c_emb_expanded_shape=att.get_shape().as_list()\n c_emb_expanded=tf.expand_dims(att, -1)\n for i, filter_size in enumerate(filter_sizes):\n with tf.name_scope(\"conv-maxpool-%s\" % filter_size):\n # Convolution Layer\n filter_shape = [filter_size,c_emb_expanded_shape[-1], 1, num_filters]\n W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=\"W\")\n l2_loss += tf.nn.l2_loss(W)\n b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name=\"b\")\n l2_loss += tf.nn.l2_loss(b)\n conv_ouput = tf.nn.conv2d(\n c_emb_expanded,\n W,\n strides=[1, 1, 1, 1],\n padding=\"VALID\",\n name=\"conv\")\n # Apply nonlinearity\n h = tf.nn.relu(tf.nn.bias_add(conv_ouput, b), name=\"relu\")\n # Maxpooling over the outputs\n pooled = tf.nn.max_pool(\n h,\n ksize=[1, c_emb_expanded_shape[1]- filter_size + 1, 1, 1],\n strides=[1, 1, 1, 1],\n padding='VALID',\n name=\"pool\")\n print(pooled,\"222222222222222222222\")\n pooled_outputs.append(pooled)\n\n # Combine all the pooled features\n num_filters_total = num_filters * len(filter_sizes)\n self.h_pool = tf.concat(pooled_outputs, 3)\n self.h_pool_flat_cnn = tf.reshape(self.h_pool, [-1, num_filters_total])\n with tf.variable_scope(\"encoding\"):\n rnn = gru(num_layers=3, num_units=d, batch_size=N, input_size=c_emb.get_shape(\n ).as_list()[-1], keep_prob=config.keep_prob, is_train=self.is_train) #input_size对应embedding的长度,此过程是初始化一个gru，双向lstm，包括他们的初始状态\n c = rnn(c_emb, seq_len=self.c_len) #上下文编码输出为batch ，c_maxlen,以及lstm输出长度 [batch_size,sequncen_length,150*3] num_layers is 3 so concat each layers\n #each layer is 150 because each layers has back_forword and feed_forword(75+75)\n q = rnn(q_emb, seq_len=self.q_len) #问题编码\n with tf.variable_scope(\"attention\"):\n qc_att = dot_attention(c, q, mask=self.q_mask, hidden=d,\n keep_prob=config.keep_prob, is_train=self.is_train) # 这个函数实现的是公式（4）中的所有公式\n rnn = gru(num_layers=1, num_units=d, batch_size=N, input_size=qc_att.get_shape(\n ).as_list()[-1], keep_prob=config.keep_prob, is_train=self.is_train)\n att = rnn(qc_att, seq_len=self.c_len) # this is 公式(3) #[batch,c_maxlen,150]\n # Create a convolution + maxpool layer for each filter size\n input_shape=att.get_shape().as_list()\n print(att,\"rrrr\")\n att=tf.expand_dims(att,-1)\n print(att,\"hhhhhhhhhhhh\")\n pooled_outputs = []\n for i, filter_size in enumerate(filter_sizes):\n with tf.name_scope(\"conv-maxpool-%s\" % filter_size):\n # Convolution Layer\n filter_shape = [filter_size, input_shape[-1], 1, num_filters]\n W = tf.Variable(tf.truncated_normal(filter_shape, stddev=0.1), name=\"W\")\n l2_loss += tf.nn.l2_loss(W)\n b = tf.Variable(tf.constant(0.1, shape=[num_filters]), name=\"b\")\n l2_loss += tf.nn.l2_loss(b)\n conv_ouput = tf.nn.conv2d(\n att,\n W,\n strides=[1, 1, 1, 1],\n padding=\"VALID\",\n name=\"conv\")\n # Apply nonlinearity\n h = tf.nn.relu(tf.nn.bias_add(conv_ouput, b), name=\"relu\")\n # Maxpooling over the outputs\n pooled = tf.nn.max_pool(\n h,\n ksize=[1, config.para_limit - filter_size + 1, 1, 1],\n strides=[1, 1, 1, 1],\n padding='VALID',\n name=\"pool\")\n print(pooled,\"3333333333333333333333333\")\n pooled_outputs.append(pooled)\n\n # Combine all the pooled features\n num_filters_total = num_filters * len(filter_sizes)\n self.h_pool = tf.concat(pooled_outputs, 3)\n self.h_pool_flat = tf.reshape(self.h_pool, [-1, num_filters_total])\n # Add dropout\n with tf.name_scope(\"dropout\"):\n self.h_drop_lstm = tf.nn.dropout(self.h_pool_flat, self.dropout_keep_prob)\n self.h_drop_cnn=tf.nn.dropout(self.h_pool_flat_cnn, self.dropout_keep_prob)\n self.h_drop=tf.concat([self.h_drop_lstm,self.h_drop_cnn],axis=-1)\n # Final (unnormalized) scores and predictions\n with tf.name_scope(\"output\"):\n W = tf.get_variable(\n \"W\",\n shape=[num_filters_total*2, 3],\n initializer=tf.contrib.layers.xavier_initializer())\n b = tf.Variable(tf.constant(0.1, shape=[3]), name=\"b\")\n l2_loss += tf.nn.l2_loss(W)\n l2_loss += tf.nn.l2_loss(b)\n self.scores = tf.nn.xw_plus_b(self.h_drop, W, b, name=\"scores\")\n self.predictions = tf.argmax(self.scores, 1, name=\"predictions\")\n\n # Calculate mean cross-entropy loss\n if trainable:\n with tf.name_scope(\"loss\"):\n print(self.scores,self.input_y, \"llllllllllllllll\")\n losses = tf.nn.softmax_cross_entropy_with_logits(logits=self.scores, labels=self.input_y)\n self.loss = tf.reduce_mean(losses) + l2_reg_lambda * l2_loss\n # Accuracy\n with tf.name_scope(\"accuracy\"):\n correct_predictions = tf.equal(self.predictions, tf.argmax(self.input_y, 1))\n self.accuracy = tf.reduce_mean(tf.cast(correct_predictions, \"float\"), name=\"accuracy\")\n # if config.decay is not None:\n # self.var_ema = tf.train.ExponentialMovingAverage(config.decay)\n # ema_op = self.var_ema.apply(tf.trainable_variables())\n # with tf.control_dependencies([ema_op]):\n # self.loss = tf.identity(self.loss)\n #\n # self.assign_vars = []\n # for var in tf.global_variables():\n # v = self.var_ema.average(var)\n # if v:\n # self.assign_vars.append(tf.assign(var, v))\n self.lr = tf.minimum(config.init_lr,\n 0.001 / tf.log(999.) * tf.log(tf.cast(self.global_step, tf.float32) + 1))\n self.opt = tf.train.AdamOptimizer(learning_rate=self.lr, beta1=0.8, beta2=0.999, epsilon=1e-7)\n grads = self.opt.compute_gradients(self.loss)\n gradients, variables = zip(*grads)\n capped_grads, _ = tf.clip_by_global_norm(\n gradients, config.grad_clip)\n self.train_op = self.opt.apply_gradients(\n zip(capped_grads, variables), global_step=self.global_step)\n self.saver = tf.train.Saver(tf.global_variables(), max_to_keep=3)\n","sub_path":"nlp_distribute/tfjob/vlapha/lstm_cnn_model.py","file_name":"lstm_cnn_model.py","file_ext":"py","file_size_in_byte":15254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"33079213","text":"class Solution:\n def combinationSum(self, candidates, target):\n self.result = []\n candidates.sort()\n self.dfs(candidates, [], target)\n return self.result\n \n def dfs(self, candidates, current, target):\n if target == 0:\n self.result.append(current)\n return\n for i, x in enumerate(candidates):\n if target - x < 0:\n continue\n self.dfs(candidates[i:], current + [x], target - x)\n \n \nclass Solution:\n # @param candidates, a list of integers\n # @param target, integer\n # @return a list of lists of integers\n def _combinationSum(self, arr, target, start):\n if target == 0:\n yield [] \n while start < len(arr) and arr[start] <= target:\n for p in self._combinationSum(arr, target - arr[start], start):\n yield [arr[start]] + p\n start += 1\n \n def combinationSum(self, arr, target):\n return list(self._combinationSum(sorted(arr), target, 0)) \n","sub_path":"leetcode/Combination Sum.py","file_name":"Combination Sum.py","file_ext":"py","file_size_in_byte":1055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"651110214","text":"class Solution:\n # @return a list of lists of string\n def solveNQueens(self, n):\n self.res = []\n queen = [0 for i in xrange(0, n)]\n self.nqueens(queen, 0, n)\n return self.res\n\n def nqueens(self, queen, cur, n):\n if cur == n:\n self.showResult(queen, n)\n else:\n for i in range(0, n):\n queen[cur] = i\n if self.isValid(queen, cur):\n self.nqueens(queen, cur+1, n)\n\n def isValid(self, queen, cur):\n for i in range(0, cur):\n if (queen[i] == queen[cur]) or (cur-i == abs(queen[cur] - queen[i])):\n return False\n return True\n\n def showResult(self, queen, n):\n r = []\n for i in xrange(0, n):\n str = ['.' for j in xrange(0, n)]\n str[queen[i]] = 'Q'\n r.append(''.join(str))\n self.res.append(r)\n\n","sub_path":"Leetcode 2014/1216 N-Queens.py","file_name":"1216 N-Queens.py","file_ext":"py","file_size_in_byte":904,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"129000294","text":"\nimport gspread\nfrom oauth2client.service_account import ServiceAccountCredentials\nfrom datetime import datetime\n\nif __name__ == '__main__':\n from keyloader import keys\n key = keys.get('google')\n scope = ['https://spreadsheets.google.com/feeds']\n credentials = ServiceAccountCredentials.from_json_keyfile_dict(key, scope)\n\n gc = gspread.authorize(credentials)\n wks = gc.open(\"test\").sheet1\n print(wks)\n\n # wks.update_acell('B3', \"using gspread\")\n # val = wks.acell('B3').value\n # val = wks.cell(3, 2).value # same\n\n print('for update_cell')\n start = datetime.now()\n for i in range(1, 21):\n wks.update_cell(i, 1, i)\n print(datetime.now() - start)\n\n print('update_cells')\n start = datetime.now()\n cell_list = wks.range('B1:B20')\n for idx, cell in enumerate(cell_list):\n cell.value = idx\n wks.update_cells(cell_list)\n print(datetime.now() - start)\n\n values_list = wks.row_values(1)\n print(values_list)\n","sub_path":"gspread_exam.py","file_name":"gspread_exam.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"359128638","text":"#!/bin/python2\n\nimport sys, os, subprocess\n\nlists_dir = \"/root/.spotify-ripper/share/lists\"\ndownload_lists = []\n\nfor fname in os.listdir(lists_dir):\n absolute_fname = os.path.join(lists_dir, fname)\n if not os.path.isdir(absolute_fname) :\n download_lists.append(fname)\n\nfor curr_list in download_lists:\n print(\"\\n\\n\\n[ + ] P R O C E S S I N G C U R R E N T L I S T :{}\\n\\n\\n\".format(curr_list))\n subprocess.call('spotify-ripper {} && mv {} done/{}'.format(curr_list,curr_list,curr_list), shell=True, cwd=lists_dir)\n\n","sub_path":"Docker/process_lists.py","file_name":"process_lists.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"196630720","text":"import urllib.request\n\nclass Rusher(object):\n def __init__(self):\n self._NUM_TRIES = 5\n self._USER_AGENT = 'Mozilla/4.0 (compatible; MSIE 5.5; Windows NT 5.0)'\n self.url_list = []\n\n def Rush(self, proxy=None):\n for url in self.url_list:\n succeed = False\n for i in range(self._NUM_TRIES):\n if self.Request(url, proxy):\n succeed = True\n break;\n self._PrintResult(url, proxy, 'succeeds!' if succeed else 'fails......')\n\n def Request(self, url, proxy=None):\n pass\n\n def _BuildRequest(self, proxy=None):\n req = urllib.request.FancyURLopener()\n req.addheader('User-Agent', self._USER_AGENT)\n if proxy:\n req.proxies = {'http' : r'http://' + proxy + '/'}\n return req\n\n def _PrintResult(self, url, proxy, msg):\n if proxy:\n print('Request(\"%s\", \"%s\") %s' % (url, proxy, msg))\n else:\n print('Request(\"%s\") without proxy %s' % (url, msg))\n","sub_path":"ip_rush/rusher.py","file_name":"rusher.py","file_ext":"py","file_size_in_byte":920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"248505180","text":"from decimal import Decimal\n\nfrom lxml import etree\n\nfrom corpora.utils.db_utils import SENTENCE_COLLECTION\nfrom corpora.utils.format_utils import (\n ANNOTATION_OPTION_SEP, ANNOTATION_TAG_SEP,\n TECH_REGEX, get_audio_link, get_audio_annot_div,\n get_annot_div, get_participant_tag_and_status\n)\nfrom corpora.utils.elan_utils import split_ann_for_db\n\n\ndef get_transcript_and_tags_dicts(words):\n transcript = []\n normz_tokens_dict = {}\n annot_tokens_dict = {}\n i = -1\n for w in words:\n transcript.append(w['transcription_view'])\n\n if TECH_REGEX.match(w['transcription_view']) is not None:\n continue\n\n i += 1\n standartization = w.get('standartization_view')\n if standartization is None:\n continue\n\n normz_tokens_dict[i] = [standartization]\n\n lemmata = []\n annots = []\n for ann in w.get('annotations', []): # todo: 'annotations' will now contain only one element\n if ann['lemma_view'] not in lemmata:\n lemmata.append(ann['lemma_view'])\n annots.append(ann['tags_view'])\n\n annot_tokens_dict[i] = [\n ''.join(lemmata),\n ''.join(annots)\n ]\n\n return ' '.join(transcript), normz_tokens_dict, annot_tokens_dict\n\n\ndef db_response_to_html(results, reverse=False):\n if results is None:\n return 'Empty search query.
', {}\n\n item_divs = []\n page_info = {}\n\n for i, item in enumerate(results):\n if not i:\n page_info['min'] = {\n 'elan': item['elan'],\n 'audio_start': item['audio']['start']\n }\n\n transcript, normz_tokens_dict, annot_tokens_dict = get_transcript_and_tags_dicts(item['words'])\n participant, participant_status = get_participant_tag_and_status(item['speaker'], item['tier'])\n annot_div = get_annot_div(\n tier_name=item['tier'],\n dialect=item['dialect'],\n participant=participant,\n transcript=transcript,\n normz_tokens_dict=normz_tokens_dict,\n annot_tokens_dict=annot_tokens_dict,\n elan_file=item['elan']\n )\n\n audio_annot_div = get_audio_annot_div(item['audio']['start'], item['audio']['end'])\n annot_wrapper_div = '%s%s
' % (participant_status, audio_annot_div, annot_div)\n\n audio_div = get_audio_link(item['audio']['file'])\n item_div = audio_div + annot_wrapper_div\n item_divs.append(item_div)\n\n page_info['max'] = {\n 'elan': item['elan'],\n 'audio_start': item['audio']['start']\n }\n\n if reverse and item_divs:\n item_divs = item_divs[::-1]\n page_info['min'], page_info['max'] = page_info['max'], page_info['min']\n\n return ''.join(item_divs) or 'Nothing found.
', page_info\n\n\ndef process_html_token(token_el):\n word_dict = {}\n\n if token_el.tag in ['note', 'tech']:\n trt = token_el.text\n if token_el.tag == 'note':\n trt = '[' + trt + ']'\n word_dict['transcription_view'] = trt\n word_dict['transcription'] = trt\n return word_dict\n\n trt_lst = token_el.xpath('trt/text()')\n nrm_lst = token_el.xpath('nrm/text()')\n lemma_lst = token_el.xpath('lemma/text()')\n morph_lst = token_el.xpath('morph/text()')\n\n if not trt_lst:\n word_dict['transcription_view'] = token_el.text\n word_dict['transcription'] = token_el.text.lower()\n return word_dict\n\n word_dict['transcription_view'] = trt_lst[0]\n word_dict['transcription'] = trt_lst[0].lower()\n\n if nrm_lst:\n word_dict['standartization_view'] = nrm_lst[0]\n word_dict['standartization'] = nrm_lst[0].lower()\n\n if lemma_lst and morph_lst:\n word_dict['annotations'] = split_ann_for_db((lemma_lst[0], morph_lst[0]))\n\n return word_dict\n\n\ndef html_to_db(html_result):\n html_obj = etree.fromstring(html_result)\n for el in html_obj.xpath('//*[contains(@class,\"annot_wrapper\") and contains(@class, \"changed\")]'):\n elan_name = el.xpath('*[@class=\"annot\"]/@elan')[0]\n tier_name = el.xpath('*[@class=\"annot\"]/@tier_name')[0]\n start = int(Decimal(el.xpath('*[@class=\"audiofragment\"]/@starttime')[0]))\n end = int(Decimal(el.xpath('*[@class=\"audiofragment\"]/@endtime')[0]))\n\n words = []\n for token in el.xpath('*//*[self::token or self::tech or self::note]'):\n word_dict = process_html_token(token)\n words.append(word_dict)\n\n filter_query = {'elan': elan_name, 'tier': tier_name, 'audio.start': start, 'audio.end': end}\n update_query = {'$set': {'words': words}}\n SENTENCE_COLLECTION.update_one(filter_query, update_query)\n","sub_path":"trimco/corpora/search_engine/db_to_html.py","file_name":"db_to_html.py","file_ext":"py","file_size_in_byte":4794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"212849352","text":"import matplotlib.pyplot as plt\nimport numpy as np\nimport sys\nfilename = sys.argv[1]\nMaterial = filename[9:-4]\n\nprint(sys.argv) # Stores ARG1 in filename, as in: $ python plot.py ARG1 ARG2 \ndata = np.loadtxt(filename,skiprows=32,delimiter=',')\n#Attempts to load filename into local variable data.\nprint(data)\nStress = -data[:,3]\nStrain = -data[:,7]\nplt.plot(Strain, Stress, color='k', linestyle='-', label='square')\nplt.xlabel('Strain(%)') # adds x axis label\nplt.ylabel('Stress (Mpa)') # adds y axis label\nplt.title('Stress vs Strain Data of '+ Material) # adds the title for the graph\n\nplt.grid()\n\n\nm1,b1=np.polyfit(Strain,Stress,1)\nReg=b1+m1*Strain\nlinearf=np.poly1d(m1,b1)\nplt.plot(Strain,Reg,\"g--\", linestyle='dashed',label=\"Linear fit\")\nplt.savefig(filename+ '.pdf')\nprint(\" Modulus of Elasticicty : \" + str(m1) + str(' Mpa'))\nplt.show()\n\n## Part 0\n# Figure out what arguments to add to the loadtxt function call\n# so that numbers are loaded into the local function 'data'.\n# Hint: look for arguments like 'skiprows' and 'delimiter'\n# Check by running:\n# $ python plot.py raw-data/Sp15_245L_sect-001_group-1_glass.raw\n# at the command line.\n\n\n## Part 1\n# Figure out what columns and rows of data we need to plot\n# Stress (y-axis) vs Strain (x-axis)\n# plot raw-data/Sp15_245L_sect-001_group-1_glass.raw\n# Make sure to include axis labels and units!\n# plt.plot(xdata,ydata, arguments-to-make-plot-pretty)\n\n## Part 2\n# Check to see if your code in part 1 will plot all of the files in raw-data/\n# Edit the files (use git liberally here!) to make them more usable\n\n\n## Part 3\n# Use linear regression to calculate the slope of the linear part of\n# the stress-strain data. Plot your line against the data to make \n# sure it makes sense! Use the slope of this line to calculate and print\n# the Young's modulus (with units!)\n\n## Part 4\n# Modify your code to save your plots to a file and see if you can generate\n# plots and Young's moduli for all of the cleaned up files in your data \n# directory. If you haven't already, this is a good time to add text to \n# your .gitignore file so you're not committing the figures to your repository.\n\n\n\n","sub_path":"plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":2142,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"418200826","text":"class Solution:\n def maximumGap(self, nums: List[int]) -> int:\n if len(nums) < 2:\n return 0\n \n nums.sort() \n differences = list()\n \n for x in range(len(nums)-1):\n differences.append(nums[x+1] - nums[x])\n\n return max(differences)\n ","sub_path":"164-maximum-gap.py","file_name":"164-maximum-gap.py","file_ext":"py","file_size_in_byte":318,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"119253401","text":"import copy\n\nfrom django.conf import settings\nfrom django.db.models import Sum, Count, F\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom usaspending_api.awards.models_matviews import UniversalAwardView\nfrom usaspending_api.awards.v2.filters.matview_filters import matview_search_filter\nfrom usaspending_api.awards.v2.filters.sub_award import subaward_filter\nfrom usaspending_api.awards.v2.filters.view_selector import spending_by_award_count\nfrom usaspending_api.awards.v2.lookups.lookups import (contract_type_mapping, loan_type_mapping,\n non_loan_assistance_type_mapping, grant_type_mapping,\n contract_subaward_mapping, grant_subaward_mapping,\n idv_type_mapping)\nfrom usaspending_api.awards.v2.lookups.matview_lookups import (award_contracts_mapping, loan_award_mapping,\n non_loan_assistance_award_mapping, award_idv_mapping)\nfrom usaspending_api.common.api_versioning import api_transformations, API_TRANSFORM_FUNCTIONS\nfrom usaspending_api.common.cache_decorator import cache_response\nfrom usaspending_api.common.exceptions import InvalidParameterException, UnprocessableEntityException\nfrom usaspending_api.common.validator.award_filter import AWARD_FILTER\nfrom usaspending_api.common.validator.pagination import PAGINATION\nfrom usaspending_api.common.validator.tinyshield import TinyShield\n\n\n@api_transformations(api_version=settings.API_VERSION, function_list=API_TRANSFORM_FUNCTIONS)\nclass SpendingByAwardVisualizationViewSet(APIView):\n \"\"\"\n This route takes award filters and fields, and returns the fields of the filtered awards.\n endpoint_doc: /advanced_award_search/spending_by_award.md\n \"\"\"\n\n @cache_response()\n def post(self, request):\n \"\"\"Return all awards matching the provided filters and limits\"\"\"\n models = [\n {'name': 'fields', 'key': 'fields', 'type': 'array', 'array_type': 'text', 'text_type': 'search', 'min': 1},\n {'name': 'subawards', 'key': 'subawards', 'type': 'boolean', 'default': False}\n ]\n models.extend(copy.deepcopy(AWARD_FILTER))\n models.extend(copy.deepcopy(PAGINATION))\n for m in models:\n if m['name'] in ('award_type_codes', 'fields'):\n m['optional'] = False\n\n json_request = TinyShield(models).block(request.data)\n fields = json_request[\"fields\"]\n filters = json_request.get(\"filters\", {})\n subawards = json_request[\"subawards\"]\n order = json_request[\"order\"]\n limit = json_request[\"limit\"]\n page = json_request[\"page\"]\n\n if \"no intersection\" in filters[\"award_type_codes\"]:\n # \"Special case\": there will never be results when the website provides this value\n return Response({\n \"limit\": limit,\n \"results\": [],\n \"page_metadata\": {\"page\": page, \"hasNext\": False},\n })\n\n sort = json_request.get(\"sort\", fields[0])\n if sort not in fields:\n raise InvalidParameterException(\"Sort value '{}' not found in requested fields: {}\".format(sort, fields))\n\n subawards_values = list(contract_subaward_mapping.keys()) + list(grant_subaward_mapping.keys())\n awards_values = list(award_contracts_mapping.keys()) + list(loan_award_mapping.keys()) + \\\n list(non_loan_assistance_award_mapping.keys()) + list(award_idv_mapping.keys())\n\n msg = \"Sort value '{}' not found in {{}} mappings: {{}}\".format(sort)\n if not subawards and sort not in awards_values:\n raise InvalidParameterException(msg.format(\"award\", awards_values))\n elif subawards and sort not in subawards_values:\n raise InvalidParameterException(msg.format(\"subaward\", subawards_values))\n\n # build sql query filters\n if subawards:\n queryset = subaward_filter(filters)\n values = {'subaward_number', 'piid', 'fain', 'award_type'}\n\n for field in fields:\n if contract_subaward_mapping.get(field):\n values.add(contract_subaward_mapping.get(field))\n if grant_subaward_mapping.get(field):\n values.add(grant_subaward_mapping.get(field))\n else:\n queryset = matview_search_filter(filters, UniversalAwardView).values()\n values = {'award_id', 'piid', 'fain', 'uri', 'type'}\n\n for field in fields:\n if award_contracts_mapping.get(field):\n values.add(award_contracts_mapping.get(field))\n if loan_award_mapping.get(field):\n values.add(loan_award_mapping.get(field))\n if non_loan_assistance_award_mapping.get(field):\n values.add(non_loan_assistance_award_mapping.get(field))\n if award_idv_mapping.get(field):\n values.add(award_idv_mapping.get(field))\n\n # Modify queryset to be ordered by requested \"sort\" in the request or default value(s)\n if sort:\n if subawards:\n if set(filters[\"award_type_codes\"]) <= set(list(contract_type_mapping)\n + list(idv_type_mapping)): # Subaward contracts\n sort_filters = [contract_subaward_mapping[sort]]\n elif set(filters[\"award_type_codes\"]) <= set(grant_type_mapping): # Subaward grants\n sort_filters = [grant_subaward_mapping[sort]]\n else:\n msg = \"\"\"Award Type codes limited for Subawards.\n Only contracts {}, IDVs {}, or grants {} are available\"\"\"\n msg = msg.format(list(contract_type_mapping.keys()),\n list(idv_type_mapping.keys()), list(grant_type_mapping.keys()))\n raise UnprocessableEntityException(msg)\n else:\n if set(filters[\"award_type_codes\"]) <= set(contract_type_mapping): # contracts\n sort_filters = [award_contracts_mapping[sort]]\n elif set(filters[\"award_type_codes\"]) <= set(loan_type_mapping): # loans\n sort_filters = [loan_award_mapping[sort]]\n elif set(filters[\"award_type_codes\"]) <= set(idv_type_mapping): # idvs\n sort_filters = [award_idv_mapping[sort]]\n else: # assistance data\n sort_filters = [non_loan_assistance_award_mapping[sort]]\n\n # Explictly set NULLS LAST in the ordering to encourage the usage of the indexes\n if sort == \"Award ID\" and subawards:\n if order == \"desc\":\n queryset = queryset.order_by(\n F('award__piid').desc(nulls_last=True),\n F('award__fain').desc(nulls_last=True)).values(*list(values))\n else:\n queryset = queryset.order_by(\n F('award__piid').asc(nulls_last=True),\n F('award__fain').asc(nulls_last=True)).values(*list(values))\n elif sort == \"Award ID\":\n if order == \"desc\":\n queryset = queryset.order_by(\n F('piid').desc(nulls_last=True),\n F('fain').desc(nulls_last=True),\n F('uri').desc(nulls_last=True)).values(*list(values))\n else:\n queryset = queryset.order_by(\n F('piid').asc(nulls_last=True),\n F('fain').asc(nulls_last=True),\n F('uri').asc(nulls_last=True)).values(*list(values))\n elif order == \"desc\":\n queryset = queryset.order_by(F(sort_filters[0]).desc(nulls_last=True)).values(*list(values))\n else:\n queryset = queryset.order_by(F(sort_filters[0]).asc(nulls_last=True)).values(*list(values))\n\n limited_queryset = queryset[(page - 1) * limit:page * limit + 1] # lower limit : upper limit\n has_next = len(limited_queryset) > limit\n\n results = []\n for award in limited_queryset[:limit]:\n if subawards:\n row = {\"internal_id\": award[\"subaward_number\"]}\n\n if award['award_type'] == 'procurement':\n for field in fields:\n row[field] = award.get(contract_subaward_mapping[field])\n elif award['award_type'] == 'grant':\n for field in fields:\n row[field] = award.get(grant_subaward_mapping[field])\n else:\n row = {\"internal_id\": award[\"award_id\"]}\n\n if award['type'] in loan_type_mapping: # loans\n for field in fields:\n row[field] = award.get(loan_award_mapping.get(field))\n elif award['type'] in non_loan_assistance_type_mapping: # assistance data\n for field in fields:\n row[field] = award.get(non_loan_assistance_award_mapping.get(field))\n elif award['type'] in idv_type_mapping:\n for field in fields:\n row[field] = award.get(award_idv_mapping.get(field))\n elif (award['type'] is None and award['piid']) or award['type'] in contract_type_mapping:\n # IDV + contract\n for field in fields:\n row[field] = award.get(award_contracts_mapping.get(field))\n\n if \"Award ID\" in fields:\n for id_type in [\"piid\", \"fain\", \"uri\"]:\n if award[id_type]:\n row[\"Award ID\"] = award[id_type]\n break\n results.append(row)\n\n return Response({\"limit\": limit, \"results\": results, \"page_metadata\": {\"page\": page, \"hasNext\": has_next}})\n\n\n@api_transformations(api_version=settings.API_VERSION, function_list=API_TRANSFORM_FUNCTIONS)\nclass SpendingByAwardCountVisualizationViewSet(APIView):\n \"\"\"\n This route takes award filters, and returns the number of awards in each award type (Contracts, Loans, Grants, etc.)\n endpoint_doc: /advanced_award_search/spending_by_award_count.md\n \"\"\"\n @cache_response()\n def post(self, request):\n models = [{'name': 'subawards', 'key': 'subawards', 'type': 'boolean', 'default': False}]\n models.extend(copy.deepcopy(AWARD_FILTER))\n models.extend(copy.deepcopy(PAGINATION))\n json_request = TinyShield(models).block(request.data)\n filters = json_request.get(\"filters\", None)\n subawards = json_request[\"subawards\"]\n if filters is None:\n raise InvalidParameterException(\"Missing required request parameters: 'filters'\")\n\n results = {\n \"contracts\": 0, \"idvs\": 0, \"grants\": 0, \"direct_payments\": 0, \"loans\": 0, \"other\": 0\n } if not subawards else {\n \"subcontracts\": 0, \"subgrants\": 0\n }\n\n if \"award_type_codes\" in filters and \"no intersection\" in filters[\"award_type_codes\"]:\n # \"Special case\": there will never be results when the website provides this value\n return Response({\"results\": results})\n\n if subawards:\n queryset = subaward_filter(filters)\n else:\n queryset, model = spending_by_award_count(filters)\n\n if subawards:\n queryset = queryset.values('award_type').annotate(category_count=Count('subaward_id'))\n elif model == 'SummaryAwardView':\n queryset = queryset.values('category').annotate(category_count=Sum('counts'))\n else:\n queryset = queryset.values('category').annotate(category_count=Count('category'))\n\n categories = {\n 'contract': 'contracts',\n 'idv': 'idvs',\n 'grant': 'grants',\n 'direct payment': 'direct_payments',\n 'loans': 'loans',\n 'other': 'other'\n } if not subawards else {'procurement': 'subcontracts', 'grant': 'subgrants'}\n\n category_name = 'category' if not subawards else 'award_type'\n\n # DB hit here\n for award in queryset:\n if award[category_name] is None:\n result_key = 'other' if not subawards else 'subcontracts'\n elif award[category_name] not in categories.keys():\n result_key = 'other'\n else:\n result_key = categories[award[category_name]]\n\n results[result_key] += award['category_count']\n\n return Response({\"results\": results})\n","sub_path":"usaspending_api/search/v2/views/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":12809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"377067576","text":"import io\nimport numpy as np\nfrom sys import argv\n\ndef readfile(name):\n tweet_assignments = {}\n f = open(name, 'r')\n for line in f.readlines():\n parts = line.split()\n triple = (parts[3], int(parts[1]), int(parts[2]))\n if parts[0] in tweet_assignments:\n if not triple in tweet_assignments[parts[0]]:\n tweet_assignments[parts[0]].append(triple)\n else:\n tweet_assignments[parts[0]] = [triple]\n f.close()\n return tweet_assignments\n\ndef get_results(goldfile, predfile):\n gold = readfile(goldfile)\n pred = readfile(predfile)\n get_results_ass(gold, pred)\n\ndef get_best_prf(ment_ent_scrs, goldfile=\"../data/label-test.tsv\"):\n gold = readfile(goldfile)\n ment_topent_scr, thres = {}, set([0., 1.])\n for mid in ment_ent_scrs:\n ent_lst = sorted(ment_ent_scrs[mid], key=lambda pair: pair[1], reverse=True)\n if ent_lst[0][0] == \"_NULL_\": continue\n ment_topent_scr[mid] = ent_lst[0]\n thres.add(ent_lst[0][1])\n thres = sorted(list(thres))\n P, R, F1 = 0., 0., 0.\n for thre in thres:\n pred = {}\n for mid in ment_topent_scr:\n pair = ment_topent_scr[mid]\n if pair[1] < thre: continue\n tid, sidx, eidx = mid\n triple = (pair[0], sidx, eidx)\n if tid in pred:\n if not triple in pred[tid]:\n pred[tid].append(triple)\n else:\n pred[tid] = [triple]\n p, r, f = get_results_ass(gold, pred)\n if f > F1:\n P, R, F1 = p, r, f\n return P, R, F1\n\ndef get_results_ass(gold, pred):\n gold_total = 0\n predicted_total = 0\n predicted_correct = 0\n\n # merge keys of two dictionaries\n keys = list(set(gold.keys() + pred.keys()))\n for key in keys:\n if key not in gold:\n predicted_total = predicted_total + len(pred[key])\n elif key not in pred:\n gold_total = gold_total + len(gold[key])\n else:\n gold_ass = sorted(gold[key], key=lambda x: x[2])\n pred_ass = sorted(pred[key], key=lambda x: x[2])\n\n match = get_matched_count(gold_ass, pred_ass)\n\n # if len(pred[key]) > match:\n # print pred[key]\n\n predicted_correct = predicted_correct + match\n gold_total = gold_total + len(gold[key])\n predicted_total = predicted_total + len(pred[key])\n\n if predicted_total == 0: return 0., 0., 0.\n precision = float(predicted_correct) / predicted_total\n recall = float(predicted_correct) / gold_total\n f1 = (2.0*precision*recall)/(precision + recall)\n return precision, recall, f1\n\n# print 'Correct: ' + str(predicted_correct) + ' Gold Total: ' + str(gold_total) + ' Predicted Total: ' + str(predicted_total)\n# print 'Precision: ' + str(precision) + ' Recall: ' + str(recall) + ' F1: ' + str(f1)\n\n\ndef get_matched_count(gold_ass, pred_ass):\n lcs_matrix = np.zeros((len(gold_ass), len(pred_ass)))\n\n for i in xrange(len(gold_ass)):\n for j in xrange(len(pred_ass)):\n et1 = gold_ass[i]\n et2 = pred_ass[j]\n\n if not (et1[1] >= et2[2] or et2[1] >= et1[2]) and (et1[0].lower() == et2[0].lower()):\n if i == 0 or j == 0:\n lcs_matrix[i, j] = 1\n else:\n lcs_matrix[i, j] = 1 + lcs_matrix[i - 1, j - 1]\n else:\n if i == 0 and j == 0:\n lcs_matrix[i, j] = 0\n elif i == 0 and j != 0:\n lcs_matrix[i, j] = max(0, lcs_matrix[i, j - 1])\n elif i != 0 and j == 0:\n lcs_matrix[i, j] = max(lcs_matrix[i - 1, j], 0)\n elif i != 0 and j != 0:\n lcs_matrix[i, j] = max(lcs_matrix[i - 1, j], lcs_matrix[i, j - 1])\n\n match_count = lcs_matrix[len(gold_ass) - 1, len(pred_ass) - 1]\n\n return match_count\n\ndef get_ir_prf(gold, pred):\n gc, pc, mc = 0., 0., 0.\n for g in gold:\n if gold[g][1] == \"1\": \n gc += 1\n if gold[g][0] in pred[g]:\n mc += 1\n pc += 1\n else:\n if gold[g][0] in pred[g]:\n pc += 1\n if pc == 0: return 0., 0., 0.\n precision = mc / pc\n recall = mc / gc\n f1 = (2.0*precision*recall)/(precision + recall)\n return precision, recall, f1\n\ndef readirfile(fname):\n tweet_assignments = {}\n with open(fname, \"rb\") as f:\n for line in f:\n parts = line.split()\n tweet_assignments[parts[0]] = (parts[1], parts[2])\n return tweet_assignments\n\nif __name__ == '__main__':\n get_results(argv[1], argv[2])\n","sub_path":"StructMLP/eval.py","file_name":"eval.py","file_ext":"py","file_size_in_byte":4680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"162122097","text":"from youtube_search import YoutubeSearch\nimport sys\n\ntry:\n pesquisa = YoutubeSearch(sys.argv[1], max_results=1).to_dict()\n url = f\"https://youtube.com{pesquisa[0]['url_suffix']}\"\n print(url)\n\nexcept:\n print(\"Não encontrei nada\")\n","sub_path":"lib/music.py","file_name":"music.py","file_ext":"py","file_size_in_byte":242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"83098508","text":"\"\"\"delete playlist field from rankings\n\nRevision ID: a962a44a7eb9\nRevises: \nCreate Date: 2018-04-16 15:03:31.576027\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'a962a44a7eb9'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.drop_table('user_playlist')\n op.drop_constraint('rankings_playlistID_fkey', 'rankings', type_='foreignkey')\n op.drop_column('rankings', 'playlistID')\n op.add_column('song_rankings', sa.Column('ranking_id', sa.Integer(), nullable=True))\n op.drop_constraint('song_rankings_user_id_fkey', 'song_rankings', type_='foreignkey')\n op.drop_constraint('song_rankings_playlist_id_fkey', 'song_rankings', type_='foreignkey')\n op.drop_constraint('song_rankings_rank_id_fkey', 'song_rankings', type_='foreignkey')\n op.create_foreign_key(None, 'song_rankings', 'rankings', ['ranking_id'], ['id'])\n op.drop_column('song_rankings', 'playlist_id')\n op.drop_column('song_rankings', 'rank_id')\n op.drop_column('song_rankings', 'user_id')\n # ### end Alembic commands ###\n\n\ndef downgrade():\n # ### commands auto generated by Alembic - please adjust! ###\n op.add_column('song_rankings', sa.Column('user_id', sa.INTEGER(), autoincrement=False, nullable=True))\n op.add_column('song_rankings', sa.Column('rank_id', sa.INTEGER(), autoincrement=False, nullable=True))\n op.add_column('song_rankings', sa.Column('playlist_id', sa.INTEGER(), autoincrement=False, nullable=True))\n op.drop_constraint(None, 'song_rankings', type_='foreignkey')\n op.create_foreign_key('song_rankings_rank_id_fkey', 'song_rankings', 'rankings', ['rank_id'], ['id'])\n op.create_foreign_key('song_rankings_playlist_id_fkey', 'song_rankings', 'playlists', ['playlist_id'], ['id'])\n op.create_foreign_key('song_rankings_user_id_fkey', 'song_rankings', 'users', ['user_id'], ['id'])\n op.drop_column('song_rankings', 'ranking_id')\n op.add_column('rankings', sa.Column('playlistID', sa.INTEGER(), autoincrement=False, nullable=True))\n op.create_foreign_key('rankings_playlistID_fkey', 'rankings', 'playlists', ['playlistID'], ['id'])\n op.create_table('user_playlist',\n sa.Column('user_id', sa.INTEGER(), autoincrement=False, nullable=True),\n sa.Column('playlist_id', sa.INTEGER(), autoincrement=False, nullable=True),\n sa.ForeignKeyConstraint(['playlist_id'], ['playlists.id'], name='user_playlist_playlist_id_fkey'),\n sa.ForeignKeyConstraint(['user_id'], ['users.id'], name='user_playlist_user_id_fkey')\n )\n # ### end Alembic commands ###\n","sub_path":"migrations/versions/a962a44a7eb9_delete_playlist_field_from_rankings.py","file_name":"a962a44a7eb9_delete_playlist_field_from_rankings.py","file_ext":"py","file_size_in_byte":2656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"559425260","text":"import PyPDF2\nimport pyttsx3\n\nfilepath = input(\"Enter file name: \")\npath = open(filepath + '.pdf.', 'rb')\npdfReader = PyPDF2.PdfFileReader(path)\n\nfrom_page = pdfReader.getPage(2)\n\ntext = from_page.extractText()\n\nspeak = pyttsx3.init()\nspeak.say(text)\nspeak.runAndWait()\n","sub_path":"pdftoaudio/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"571913537","text":"open('processed_events', 'a+') # create file if not existing\n\n# import processed events\nwith open('processed_events', 'r') as f:\n processed_events = f.read().splitlines()\n\n# open event webpage\nfrom selenium import webdriver\nfrom selenium.webdriver.firefox.options import Options\n\noptions = Options()\nprofile = webdriver.FirefoxProfile()\nprofile.set_preference('intl.accept_languages', 'de')\noptions.add_argument(\"--headless\")\ndriver = webdriver.Firefox(options=options, firefox_profile=profile)\n\ndriver.get(\"https://www.facebook.com/pg/Stilbrvch/events/\")\n\ne = driver.find_element_by_id(\"upcoming_events_card\")\nupcoming_events_card = e.get_attribute('outerHTML')\n\nsubstr = \"data-hovercard=\\\"/ajax/hovercard/event.php?id=\"\nb = 0\nevents = []\nwhile upcoming_events_card.find(substr, b) != -1:\n a = upcoming_events_card.find(substr, b) + len(substr)\n b = upcoming_events_card.find(\"\\\"\", a)\n\n events.append(upcoming_events_card[a:b])\n\nimport urllib.request\n\ndef tweetEvent(event):\n # scrape web data\n prefix = \"https://www.facebook.com/events/\"\n eventUrl = prefix + str(event) + \"/\"\n driver.get(eventUrl)\n e = driver.find_element_by_id(\"event_header_primary\")\n event_header_primary = e.get_attribute('outerHTML')\n substr = \"\"\n a = event_header_primary.find(substr) + len(substr)\n b = event_header_primary.find(\"
\", a)\n eventTitle = event_header_primary[a:b].replace('&', '&')\n substr = \"data-ploi=\\\"\"\n a = event_header_primary.find(substr) + len(substr)\n b = event_header_primary.find(\"\\\"\", a)\n imageUrl = event_header_primary[a:b].replace('&', '&')\n urllib.request.urlretrieve(imageUrl, \"image.jpg\")\n e = driver.find_element_by_id(\"event_time_info\")\n event_time_info = e.get_attribute('outerHTML')\n a = event_time_info.find(\"content\")\n a = event_time_info.find(\">\", a) + 1\n b = event_time_info.find(\"<\", a)\n eventDate = event_time_info[a:b]\n # tweet\n print(\"title: \" + eventTitle)\n print(\"date: \" + eventDate)\n print(\"url: \" + eventUrl)\n print(\"image: \" + imageUrl)\n\nfor e in reversed(events):\n if e not in processed_events:\n tweetEvent(e)\n processed_events.append(e)\n print(\"new event \" + str(e) + \" added\\n\")\n\n# export processed events\nf = open('processed_events', 'w')\nfor e in processed_events:\n f.write(e)\n f.write(\"\\n\")\n","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":2442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"133188129","text":"\n\nfrom xai.brain.wordbase.nouns._harbinger import _HARBINGER\n\n#calss header\nclass _HARBINGERS(_HARBINGER, ):\n\tdef __init__(self,): \n\t\t_HARBINGER.__init__(self)\n\t\tself.name = \"HARBINGERS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"harbinger\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_harbingers.py","file_name":"_harbingers.py","file_ext":"py","file_size_in_byte":259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"104486016","text":"import sys\nfrom PyQt5.Qt import *\nfrom ui import win001\n\nif __name__ == '__main__':\n app = QApplication(sys.argv)\n MainWindow = QMainWindow()\n ui = win001.Ui_Form()\n ui.setupUi(MainWindow)\n MainWindow.show()\n sys.exit(app.exec_())\n","sub_path":"004-GUI编程/00-demo/002-加载ui文件生成的类.py","file_name":"002-加载ui文件生成的类.py","file_ext":"py","file_size_in_byte":249,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"293653455","text":"\n# The Interpreter glues together a pipeline to interpret programs either \n# interactively or via the command line.\n\n\nimport sys\nfrom lexer import Lexer\nfrom parser import Parser\nfrom evaluator import Evaluator\n\n\ndef grabInput():\n\t\"\"\"\n\tgrabs the user's code from the command line or file\n\n\treturns: the user's code\n\t\"\"\"\n\tif len(sys.argv) > 2:\n\t\tprint(\"Too many arguments. Usage: \\\"main.py codeFile.txt\\\".\")\n\t# the interpreter operates on either command-line input or loaded files\n\telif len(sys.argv) == 1:\n\t\t# raw_input() prevents arithmetic expressions from being calculated \n\t\t# automatically\n\t\tcode = raw_input(\"Enter your program: \")\n\telse:\n\t\twith open(sys.argv[1], \"r\") as f:\n\t\t\tcode = f.read()\n\n\treturn code\n\n\ndef interpret(code):\n\t\"\"\"\n\tinterprets the code inputted by the user\n\n\tcode: the user's code\n\n\treturns: the result of interpreting the expression\n\t\"\"\"\n\ttokens = Lexer().lex(code)\n\tast = Parser().parse(tokens)\n\tresult = Evaluator().evaluate(ast)\n\n\treturn result\n\n\nif __name__ == '__main__':\n\tcode = grabInput()\n\tresult = interpret(code)\n\tprint(result)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1066,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"67603815","text":"\"\"\"Treadmill identity trace CLI.\n\"\"\"\n\nimport logging\nimport sys\n\nimport click\n\nfrom treadmill import cli\nfrom treadmill.websocket import client as ws_client\n\n\n_LOGGER = logging.getLogger(__name__)\n\n\ndef init():\n \"\"\"Return top level command handler.\"\"\"\n\n ctx = {}\n\n @click.command()\n @click.option('--cell', required=True,\n envvar='TREADMILL_CELL',\n callback=cli.handle_context_opt,\n expose_value=False)\n @click.option('--api', required=False, help='REST API url to use.',\n metavar='URL',\n envvar='TREADMILL_RESTAPI')\n @click.option('--wsapi', required=False, help='WebSocket API url to use.',\n metavar='URL',\n envvar='TREADMILL_WSAPI')\n @click.option('--snapshot', is_flag=True, default=False)\n @click.argument('identity-group')\n def trace(api, wsapi, snapshot, identity_group):\n \"\"\"Trace identity group events.\n\n Invoking treadmill_trace with non existing application instance will\n cause the utility to wait for the specified instance to be started.\n\n Specifying already finished instance of the application will display\n historical trace information and exit status.\n\n Specifying only an application name will list all the instance IDs with\n trace information available.\n \"\"\"\n # Disable too many branches.\n #\n # pylint: disable=R0912\n\n ctx['api'] = api\n ctx['wsapi'] = wsapi\n\n def on_message(result):\n \"\"\"Callback to process trace message.\"\"\"\n host = result.get('host')\n if host is None:\n host = ''\n app = result.get('app')\n if app is None:\n app = ''\n\n identity_group = result['identity-group']\n identity = result['identity']\n\n print('{identity_group}/{identity} {app} {host}'.format(\n identity_group=identity_group,\n identity=identity,\n app=app,\n host=host\n ))\n\n return True\n\n def on_error(result):\n \"\"\"Callback to process errors.\"\"\"\n click.echo('Error: %s' % result['_error'], err=True)\n\n try:\n return ws_client.ws_loop(\n ctx['wsapi'],\n {'topic': '/identity-groups',\n 'identity-group': identity_group},\n snapshot,\n on_message,\n on_error\n )\n except ws_client.ConnectionError:\n click.echo('Could not connect to any Websocket APIs', err=True)\n sys.exit(-1)\n\n return trace\n","sub_path":"treadmill/cli/trace_identity.py","file_name":"trace_identity.py","file_ext":"py","file_size_in_byte":2699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"277404831","text":"\"\"\"\nfunction tools\n\"\"\"\n\n__doc__ = [\"debug_font\", \"me_erina\", \"me_ribbon\", \"ExitCheck\",\n \"snipe\", \"angle\", \"clear_cache\"]\n\nimport pygame\n\ndebug_font = pygame.font.Font(None, 20)\n\n'''\nimport character.erina\nimport character.ribbon\n\nme_erina = character.erina.Erina()\nme_ribbon = character.ribbon.Ribbon()\n'''\n\nimport platform\nimport os\nfrom pygame.sprite import Sprite\nfrom math import sqrt\nfrom math import asin\nfrom math import pi\nfrom math import cos\nfrom math import sin\nfrom random import random\nfrom os import system\nfrom os import remove\nfrom sys import exit\n\ndef ExitCheck():\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n pygame.quit()\n functions.clear_cache()\n exit()\n\nclass HP(object):\n \"\"\"\n HP(max_hp): return HP\n\n HP.add(value)\n HP.sub(value)\n HP.full(value)\n HP.empty()\n HP.maxhp(value)\n \"\"\"\n def __init__(self, maxhp):\n self.maxhp = maxhp\n self.hp = int(self.max_hp)\n\n def add(self, value):\n self.hp += value\n\n def sub(self, value):\n self.hp -= value\n\n def full(self, value):\n self.hp = int(self.maxhp)\n\n def empty(self, value):\n self.hp = 0\n\n def __setattr__(self, name, value):\n if name == 'hp':\n if value > self.maxhp:\n return super().__setattr__('hp', self.maxhp)\n return super().__setattr__(name, value)\n\nclass MP(object):\n \"\"\"\n MP(max_mp):\n\n MP.add(value)\n MP.sub(value)\n MP.full(value)\n MP.empty(value)\n MP.boost()\n \"\"\"\n def __init__(self, max_mp):\n self.maxmp = max_mp\n self.mp = int(self.max_mp)\n\n def add(self, value):\n self.mp += value\n\n def sub(self, value):\n self.mp -= value\n\n def full(self, value):\n self.mp = int(self.maxmp)\n\n def empty(self, value):\n self.mp = 0\n\n def __setattr__(self, name, value):\n if name == 'mp':\n if value > self.maxmp:\n super().__setattr__('hp', self.maxmp)\n return super().__setattr__(name, value)\n\nclass ATK(object):\n \"\"\"\n ?\n \"\"\"\n # under development\n def __init__(self, sprite, atk):\n self.sprite = sprite\n self.atk = atk\n self.base_atk = atk\n\n def __setattr__(self, name, value):\n value = value.__int__()\n return super().__setattr__(name, value)\n\ndef vector(x, y):\n \"\"\"\n vector(x, y): return float\n\n return a radian from a vector\n \"\"\"\n d = sqrt(x**2 + y**2)\n v = asin(y/d)\n if x < 0:\n if y > 0:\n v = pi - v\n elif y < 0:\n v = -pi - v\n elif y == 0:\n v = pi\n elif x > 0:\n pass\n else:\n if y > 0:\n v = pi/2\n elif y < 0:\n v = -pi/2\n return v\n\ndef angle(vector1, vector2):\n \"\"\"\n angle(vector1, vector2): return float\n\n return\n \"\"\"\n if isinsance(vector1, float):\n v1 = vector1\n elif isinstance(vector1, (list, tuple)):\n v1 = vector(*vector1)\n if isinstance(vector2, float):\n v2 = vector2\n elif isinstance(vector2, (list, tuple)):\n v2 = vector(*vector2)\n v = v2 - v1\n while v < -pi:\n v += 2*pi\n while v >= pi:\n v -= 2*pi\n return v\n\nclass SnipeError(TypeError):\n pass\n\ndef snipe(origin, destination, type='rad'):\n \"\"\"\n snipe function here:\n\n snipe(origin, destination, type='rad'): return int\n\n both parament can be a sprite or a direction\n type: 'rad' or 'vector'\n\n return a rad value\n \"\"\"\n if isinstance(origin, (list, tuple)):\n origin_x = origin[0]\n origin_y = origin[1]\n elif isinstance(origin, Sprite):\n origin_x = origin.center[0]\n origin_y = origin.center[1]\n else:\n print(\"snipe parament error\")\n raise TypeError\n if isinstance(destination, (list, tuple)):\n destination_x = destination[0]\n destination_y = destination[1]\n elif isinstance(destination, Sprite):\n destination_x = destination.center[0]\n destination_y = destination.center[1]\n else:\n print(\"snipe parament error\")\n raise TypeError\n delta_x = destination_x - origin_x\n delta_y = destination_y - origin_y\n if delta_x==0 and delta_y==0:\n return random() * 2 * pi\n raise SnipeError\n '''\n distance = sqrt(delta_x ** 2 + delta_y ** 2)\n temp_snipe = asin(delta_y/distance)\n if delta_x < 0:\n if delta_y > 0:\n snipe = pi - temp_snipe\n elif delta_y < 0:\n snipe = -pi - temp_snipe\n elif delta_y == 0:\n snipe = pi\n elif delta_x == 0:\n if delta_y > 0:\n snipe = pi/2\n elif delta_y < 0:\n snipe = -pi/2\n else:\n snipe = temp_snipe\n '''\n snipe = vector(delta_x, delta_y)\n if type == 'rad':\n return snipe\n elif type == 'vector':\n return [cos(snipe), sin(snipe)]\n else:\n print(\"parament error\")\n raise TypeError\n\n'''\ndef angle(direction):\n \"\"\"\n return rad of a direction:\n\n angle(direction): return int\n\n return a rad value\n \"\"\"\n temp_rad = asin(direction[1])\n if direction[0] < 0:\n if direction[1] > 0:\n rad = pi - temp_rad\n elif direction[1] < 0:\n rad = -pi - temp_rad\n else:\n rad = temp_rad\n return rad\n'''\n\nimport time\ndef time_check(fun, *args, **kwargs):\n a = time.time()\n ret = fun(*args, **kwargs)\n print(fun.__name__, time.time()-a)\n return ret\n\ndef clear_cache(*dir):\n '''\n if platform.system() == \"Windows\":\n if dir:\n for each in dir:\n ch = \"del data\\\\tmp\\\\\" + each + \"\\\\*.tmp\"\n system(ch)\n else:\n system(\"del data\\\\tmp\\\\bf\\\\*.tmp\")\n system(\"del data\\\\tmp\\\\bg\\\\*.tmp\")\n system(\"del data\\\\tmp\\\\imgs\\\\*.tmp\")\n system(\"del data\\\\tmp\\\\mid\\\\*.tmp\")\n system(\"del data\\\\tmp\\\\misc\\\\*.tmp\")\n elif platform.system() == \"Linux\" or platform.system()=='Darwin':\n if dir:\n for each in dir:\n ch = \"rm data/tmp/\" + each + \"/*.tmp\"\n system(ch)\n else:\n system(\"rm data/tmp/bf/*.tmp\")\n system(\"rm data/tmp/bg/*.tmp\")\n system(\"rm data/tmp/imgs/*.tmp\")\n system(\"rm data/tmp/mid/*.tmp\")\n system(\"rm data/tmp/misc/*.tmp\")\n '''\n \n \nimport functions.action\nimport functions.values\nimport functions.spell_card\nimport functions.stage_run\nimport functions.buff_debuff\nimport functions.stage\n","sub_path":"rabiribi-danmaku/functions/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6656,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"477005533","text":"import sys\nsys.stdin = open('4875_미로.txt' , 'r')\n\n\ndef mmm(miro):\n\n start = []\n for i in range(num):\n for j in range(num):\n if miro[i][j] == 2:\n start.append(i)\n start.append(j)\n break\n\n dx = [-1, 1, 0, 0] # 상 하 좌 우\n dy = [0, 0, -1, 1]\n\n stack = []\n visited = []\n\n xx = start[0]\n yy = start[1]\n\n stack.append([xx, yy])\n\n while len(stack) != 0:\n xx = stack[-1][0]\n yy = stack[-1][1]\n\n for i in range(len(dx)):\n x = xx + dx[i]\n y = yy + dy[i]\n\n if 0 <= x < len(miro) and 0 <= y < len(miro) and [x, y] not in visited:\n if miro[x][y] == 3:\n return 1\n if miro[x][y] == 0 :\n stack.append([x, y])\n visited.append([x, y])\n break\n\n else:\n stack.pop()\n return 0\n\n\nT = int(input())\n\nfor tc in range(T):\n num = int(input())\n miro = [list(map(int, input())) for _ in range(num)]\n\n result = mmm(miro)\n\n print('#{} {}' .format(tc+1, result))","sub_path":"Algorithm/190826/서울3반_홍수경_4875_미로.py","file_name":"서울3반_홍수경_4875_미로.py","file_ext":"py","file_size_in_byte":1126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"178298481","text":"import numpy as np\nimport scipy.linalg\nimport scipy.spatial\nimport scipy.optimize\nimport copy as cp\n\nfrom . import data, units\n\ndef density_to_spherical_radius(masses, density):\n \"\"\"\n Given list of masses and density, computes radius of sphere in a.u.\n\n Args:\n masses (array_like): in au\n density (float): in grams/cm^3\n\n Returns:\n (float): radius of sphere for given density with given mass\n \"\"\"\n mass = np.sum(masses)\n density_au = density * units.KG_TO_AU * 1.E-3 / ((units.M_TO_AU/100.0)**3) # convert density units\n volume = mass / density_au\n r = pow(volume * 3.0 / 4.0 / np.pi, 1.0/3.0)\n return r\n\ndef compute_center_of_mass(coordinates, masses):\n \"\"\"\n Given coordinates and masses, return center of mass coordinates.\n Also works to compute COM translational motion.\n\n Args:\n coordinates ({nparticle, ndim} ndarray): xyz (to compute COM) or velocities (COM velocity)\n masses ({nparticle,} array_like): masses\n\n Returns:\n ({ndim,} ndarray): center of mass coordinates\n \"\"\"\n coordinates_cp = np.array(coordinates)\n mass_cp = np.reshape(masses, (-1, 1))\n com_coordinates = np.sum(mass_cp * coordinates_cp, axis=0)/np.sum(mass_cp)\n return com_coordinates\n\ndef compute_moment_of_inertia_tensor(coordinates, masses):\n \"\"\"\n Given coordinates and masses, compute 3 x 3 moment of inertia tensor\n\n Args:\n coordinates ({nparticle, 3} ndarray): coordinates\n masses ({nparticle,} ndarray): masses\n\n Returns:\n ({3, 3} ndarray): moment of inertia tensor\n \"\"\"\n X2 = coordinates**2\n I = np.zeros((3, 3))\n I[0, 0] = np.sum((X2[:, 1] + X2[:, 2]) * masses)\n I[1, 1] = np.sum((X2[:, 0] + X2[:, 2]) * masses)\n I[2, 2] = np.sum((X2[:, 0] + X2[:, 1]) * masses)\n I[0, 1] = I[1, 0] = - np.sum(coordinates[:, 0] * coordinates[:, 1] * masses)\n I[0, 2] = I[2, 0] = - np.sum(coordinates[:, 0] * coordinates[:, 2] * masses)\n I[1, 2] = I[2, 1] = - np.sum(coordinates[:, 1] * coordinates[:, 2] * masses)\n return I\n\ndef compute_angular_momentum(coordinates, velocities, masses):\n \"\"\"\n Given coordinates, velocites, and masses, computes total angular momentum.\n\n Args:\n coordinates ({nparticle, 3} ndarray): coordinates\n velocities ({nparticle, 3} ndarray): velocities\n masses ({nparticle,} ndarray): masses\n\n Returns:\n ({3,} ndarray): vector of total angular momenta\n \"\"\"\n linear_momentum = np.array(velocities) * np.reshape(masses, (-1, 1))\n coordinates_mat = cross_product_matrix(coordinates)\n total_angular_momentum = np.einsum('ijk,ik->j', coordinates_mat, linear_momentum)\n return total_angular_momentum\n\ndef compute_kinetic_energy(velocities, masses):\n r\"\"\"\n Compute the kinetic energy using the velocities.\n\n .. math:: K = 1/2 \\sum_i m_i (v_{ix}^2 + v_{iy}^2 + v_{iz}^2)\n\n Args:\n velocities ({nparticle, ndim} ndarray): velocities\n masses ({nparticle,} ndarray): masses\n\n Returns:\n (float): total kinetic energy\n \"\"\"\n kinetic_energy = 0.5 * np.sum(np.sum(velocities**2, axis=1) * np.reshape(masses, (-1, )))\n return kinetic_energy\n\ndef compute_kinetic_energy_momentum(momenta, masses):\n r\"\"\"\n Compute the kinetic energy using the momenta.\n\n .. math:: K = 1/2 \\sum_i m_i^{-1} (p_{ix}^2 + p_{iy}^2 + p_{iz}^2)\n\n Args:\n momenta ({nparticle, ndim} ndarray): momenta\n masses ({nparticle,} ndarray): masses\n\n Returns:\n (float): total kinetic energy\n \"\"\"\n kinetic_energy = 0.5 * np.sum(np.sum(momenta**2, axis=1) / np.reshape(masses, (-1, )))\n return kinetic_energy\n\ndef compute_temperature(velocities, masses, boltzmann_constant=1.0):\n r\"\"\"\n Compute the temperature using the velocities.\n\n .. math:: kT = 1/3N \\sum_i m_i (v_{ix}^2 + v_{iy}^2 + v_{iz}^2)\n\n Args:\n velocities ({nparticle, ndim} ndarray): velocities\n masses ({nparticle,} ndarray): masses\n boltzmann_constant (float): scaling factor of the temperature to energy conversion, defaults to 1.0\n\n Returns:\n (float): temperature\n \"\"\"\n temperature = np.sum(np.sum(velocities**2, axis=1) * np.reshape(masses, (-1, ))) / np.size(velocities) / boltzmann_constant\n return temperature\n\ndef compute_virial_temperature(coordinates, force, boltzmann_constant=1.0):\n r\"\"\"\n Compute virial temperature defined by Clausius virial equation \n\n .. math:: 3NkT = - \\mathbb{E}[\\sum_i r_i^T F_i]\n\n Args:\n coordinates ({nparticle, ndim} ndarray): coordinates\n force ({nparticle, ndim} ndarray): force\n boltzmann_constant (float): scaling factor of the temperature to energy conversion, defaults to 1.0\n\n Returns:\n (float): temperature defined through virial equation\n\n References:\n doi:10.1038/052568a0\n doi:10.1103/PhysRevE.62.4757\n \"\"\"\n temperature = - np.sum(coordinates * force) / np.size(force) / boltzmann_constant\n return temperature\n\ndef compute_configurational_temperature(G, H, G0=None, boltzmann_constant=1.0):\n r\"\"\"\n Compute the instantaneous configurational temperature defined by\n\n .. math:: kT(t) = \\nabla V(t)^T \\nabla V(t) / \\nabla^2 V\n\n Args:\n G (ndarray): gradient of potential to use to compute configurational temp\n H (ndarray): Hessian of potential to use\n G0 (ndarray): model gradient (default None, uses G)\n boltzmann_constant (float): multiplicative factor to convert energy to desired temperature units (default K/au)\n\n Returns:\n float: configurational temperature\n \"\"\"\n if G0 is None:\n G0 = G \n config_temp = np.sum(G*G0) / np.trace(H) * boltzmann_constant\n return config_temp\n\ndef compute_nose_hoover_energy():\n \"\"\"\n Compute the energy associated with the Nose-Hoover chains\n \"\"\"\n raise NotImplementedError\n\ndef compute_spherical_volume(radius):\n \"\"\"\n Given radius, return volume of corresponding sphere.\n\n Args:\n radius (float): radius of sphere\n\n Returns:\n (float): volume of sphere\n \"\"\"\n volume = 4.0/3.0 * np.pi * radius**3\n return volume\n\ndef compute_spherical_radius(volume):\n \"\"\"\n Given volume, return radius of corresponding sphere.\n\n Args:\n volume (float): volume of sphere\n\n Returns:\n (float): radius of sphere\n \"\"\"\n radius = (volume * 0.75 / np.pi) ** (1.0/3.0)\n return radius\n\ndef compute_convex_volume(coordinates):\n \"\"\"\n Compute the volume of the convex hull of the given points.\n\n Args:\n coordinates ({nparticle, ndim} ndarray): coordinates\n\n Returns:\n (float): volume of convex hull of coordinates\n \"\"\"\n ch = scipy.spatial.ConvexHull(coordinates)\n return ch.volume\n\ndef compute_virial_pressure(volume, kinetic_energy, coordinates, force):\n r\"\"\"\n Compute the virial pressure using the virial equation\n\n .. math:: PV = 2/3 KE + 1/3\\sum_i r_i^T F_i\n\n Assumes consistent units, and constant center of mass at origin\n\n Args:\n volume (float): volume of system\n kinetic_energy (float): kinetic_energy of system\n coordinates ({nparticle, ndim} ndarray): coordinates\n force ({nparticle, ndim} ndarray): force\n\n Returns:\n (float): pressure\n \"\"\"\n pv = (2.0 * kinetic_energy + np.sum(coordinates * force)) / np.shape(coordinates)[-1]\n pressure = pv / volume\n return pressure\n\ndef compute_sequential_rmsd(Xs, M=None):\n r\"\"\"\n Compute sequential RMSD for all given frames of X.\n\n .. math:: \\mathrm{RMSD}_i = \\sqrt{1/\\sum_j^{\\mathrm{nparticle}} m_j} || \\mathrm{diag}(M) X_{i+1} - \\mathrm{diag}(M) X_{i} ||\n\n Args:\n Xs ({nframe, nparticle, ndim} ndarray): coordinates \n M ({nparticle,} ndarray): optional masses to use, must be same for all frames, default is equivalent to ones\n\n Returns:\n ({nframe-1,} ndarray): root mean square deviations from frame to frame\n \"\"\"\n if M is None:\n M = np.ones((np.shape(Xs)[1],))\n # Mass weight and reshape\n Xs_r = np.reshape(np.reshape(Xs, (len(Xs), -1, 3)) * np.sqrt(np.reshape(M, (1, -1, 1))), (len(Xs), -1))\n return np.linalg.norm(Xs_r[1:] - Xs_r[:-1], axis=1) / np.sqrt(np.sum(M))\n\ndef subsample_path(Xs, nsample, rmsds=None, interpolate=True):\n \"\"\"\n Use the rmsds along the path to find images that are roughly evenly spaced \n in rmsd including the endpoints\n\n Args:\n Xs ({nframe, nparticle, ndim} ndarray): Original images along path\n nsample (int): number of samples to return\n rmsds ({nframe-1,} ndarray): optional, Use precomputed rmsds, which may use whatever metric you want, should be sorted\n interpolate (bool): default True, interpolate nearest neighbor images weighted by distance to evenly spaced points in rmsd, if False, simply return the nearest neighbor image\n \"\"\"\n if rmsds is None:\n rmsds = compute_rmsd(Xs)\n cs_rmsds = np.cumsum(np.insert(rmsds, 0, 0.0))\n normalized_rmsds = cs_rmsds / cs_rmsds[-1]\n ideal_rmsds = np.linspace(0.0, 1.0, num=nsample, endpoint=True)\n # Find the indices where it could be inserted to maintain sorting, same as the nearest neighbor\n nn_l = np.searchsorted(normalized_rmsds, ideal_rmsds) - 1\n Xs_sub = [Xs[0]]\n # The first and last images are always the original first and last images,\n # this also helps avoid the ideal_rmsd being exactly equal to the normalized\n # rmsd which can throw off how the searchsorted works\n for i in range(1, nsample-1):\n t1p = ideal_rmsds[i] - normalized_rmsds[nn_l[i]]\n t2p = normalized_rmsds[nn_l[i]+1] - ideal_rmsds[i]\n if t1p == 0.0:\n Xs_sub.append(Xs[nn_l[i]])\n elif t2p == 0.0:\n Xs_sub.append(Xs[nn_l[i]+1])\n elif interpolate: # Calculate weight as normalized difference\n w1 = t2p / (t1p + t2p)\n w2 = t1p / (t1p + t2p)\n Xs_sub.append(w1 * Xs[nn_l[i]] + w2 * Xs[nn_l[i]+1])\n else: # Use nearest neighbor\n if t1p < t2p:\n Xs_sub.append(Xs[nn_l[i]])\n else:\n Xs_sub.append(Xs[nn_l[i]+1])\n Xs_sub.append(Xs[-1])\n return np.array(Xs_sub)\n\ndef random_rotation_matrix(deflection=1.0, randnums=None):\n \"\"\"\n Creates a random rotation matrix.\n\n Args:\n deflection (float): the magnitude of the rotation. For 0, no rotation; for 1, competely random rotation. Small deflection => small perturbation.\n randnums ({3,} array_like): 3 random numbers in the range [0, 1]. If `None`, they will be auto-generated.\n\n Returns:\n ({3, 3} ndarray): R random rotation matrix\n \"\"\"\n # from http://www.realtimerendering.com/resources/GraphicsGems/gemsiii/rand_rotation.c\n\n if randnums is None:\n randnums = np.random.uniform(size=(3,))\n\n theta, phi, z = randnums\n\n theta = theta * 2.0*deflection*np.pi # Rotation about the pole (Z).\n phi = phi * 2.0*np.pi # For direction of pole deflection.\n z = z * 2.0*deflection # For magnitude of pole deflection.\n\n # Compute a vector V used for distributing points over the sphere\n # via the reflection I - V Transpose(V). This formulation of V\n # will guarantee that if x[1] and x[2] are uniformly distributed,\n # the reflected points will be uniform on the sphere. Note that V\n # has length sqrt(2) to eliminate the 2 in the Householder matrix.\n\n r = np.sqrt(z)\n Vx, Vy, Vz = V = (\n np.sin(phi) * r,\n np.cos(phi) * r,\n np.sqrt(2.0 - z)\n )\n\n st = np.sin(theta)\n ct = np.cos(theta)\n\n R = np.array(((ct, st, 0), (-st, ct, 0), (0, 0, 1)))\n\n # Construct the rotation matrix ( V Transpose(V) - I ) R.\n\n M = (np.outer(V, V) - np.eye(3)).dot(R)\n return M\n\ndef pairwise_dist_squared(xyz1, xyz2=None):\n \"\"\"\n Compute all squared distances between atoms in one or two molecules\n\n Args:\n xyz1 ({nparticle1, ndim} ndarray): matrix of coordinates\n xyz2 ({nparticle2, ndim} ndarray): matrix of coordinates, if not given xyz1 will be used\n\n Returns:\n ({nparticle1, nparticle2} ndarray): matrix of square distances between atoms\n \"\"\"\n if xyz2 is None:\n xyz2 = xyz1\n xyz1_view = xyz1[:, :, None]\n xyz2_view = np.transpose(xyz2[:, :, None], (2, 1, 0))\n dists = np.sum((xyz1_view - xyz2_view)**2, axis=1)\n return dists\n\ndef pairwise_dist(xyz1, xyz2=None):\n \"\"\"\n Compute all distances between atoms in one or two molecules\n\n Args:\n xyz1 ({nparticle1, ndim} ndarray): matrix of coordinates\n xyz2 ({nparticle2, ndim} ndarray): matrix of coordinates, if not given xyz1 will be used\n\n Returns:\n ({nparticle1, nparticle2} ndarray): matrix of distances between atoms\n \"\"\"\n return np.sqrt(pairwise_dist_squared(xyz1, xyz2))\n\ndef symbol_to_covalent_radius(symbols):\n \"\"\"\n Given iterable of symbols, return numpy array of covalent radii in bohr radius.\n\n Args:\n symbols ({nparticle,} array_like): atomic symbols\n\n Returns:\n ({nparticle,} ndarray): covalent_radii in bohr radius\n\n TODO: Use mtzutils\n \"\"\"\n cov_rad = []\n for s in symbols:\n cov_rad.append(data.element_dict[s].covalent_radius * units.ANGSTROM_TO_AU)\n cov_rad = np.array(cov_rad)\n return cov_rad\n\ndef covalent_dist_matrix(S):\n \"\"\"\n Compute all covalent distances between atoms\n\n Args:\n S ({nparticle,} ndarray): atomic symbols\n\n Returns:\n ({nparticle, nparticle} ndarray): matrix of covalent distances between atoms\n \"\"\"\n covrad = symbol_to_covalent_radius(S)\n return covrad[:, None] + covrad[None, :]\n\ndef symbol_to_mass(symbols):\n \"\"\"\n Given symbols, return array of masses in electron masses.\n\n Args:\n S ({nparticle,} ndarray): atomic symbols\n\n Returns:\n ({nparticle,} ndarray): atomic masses in electron masses\n \"\"\"\n masses = []\n for s in symbols:\n masses.append(data.element_dict[s].exact_mass * units.AMU_TO_AU)\n return np.array(masses)\n\ndef atomic_number_to_mass(Zs):\n \"\"\"\n Given atomic numbers, return array of masses in electron masses.\n\n Args:\n S ({nparticle,} ndarray): atomic numbers\n\n Returns:\n ({nparticle,} ndarray): atomic masses in electron masses\n \"\"\"\n masses = []\n for Z in Zs:\n masses.append(data.element_list[Z].exact_mass * units.AMU_TO_AU)\n return np.array(masses)\n\ndef align_procrustes(X1, X2, M, mass_weight=False):\n \"\"\"\n Align one molecule to the other using orthogonal procrustes algorithm.\n\n Args:\n X1 ({nparticle, ndim} ndarray): Geometry of first molecule (to be aligned)\n X2 ({nparticle, ndim} ndarray): Geometry of second molecule, indexing should be the same\n M ({nparticle,} ndarray): Masses, used to determine center of mass and mass-weighting\n mass_weight (bool): if True, will multiply coordinates by sqrt(M) prior to computing orthogonal procrustes rotation\n\n Returns:\n ({nparticle, ndim} ndarray): Coordinate of first molecule after rotating, reflecting, translating to the second molecule\n \"\"\"\n # Move center of mass to origin\n com1 = compute_center_of_mass(X1, M)\n com2 = compute_center_of_mass(X2, M)\n X1center = X1 - com1\n X2center = X2 - com2\n # Compute optimal orthogonal transformation to overlap\n if mass_weight:\n X1m = X1center * np.sqrt(np.reshape(M, (-1, 1)))\n X2m = X2center * np.sqrt(np.reshape(M, (-1, 1)))\n R, alpha = scipy.linalg.orthogonal_procrustes(X1m, X2m)\n else:\n R, alpha = scipy.linalg.orthogonal_procrustes(X1center, X2center)\n X1rot = np.dot(X1center, R)\n # Move center of mass to second center\n X1trans = X1rot + com2\n return X1trans, R\n\ndef align_principal_axes(X, M):\n \"\"\"\n Align molecule to the principal axes frame.\n\n Args:\n X ({nparticle, 3} ndarray): Coordinates of molecule\n M ({nparticle,} ndarray): Masses (used to determine center of mass)\n\n Returns:\n ({nparticle, 3} ndarray, {3, 3} ndarray): tuple of coordinates after moving COM to origin and rotating axes to align with intertial tensor and rotation matrix used\n \"\"\"\n com = compute_center_of_mass(X, M)\n Xtrans = np.array(X) - com\n I = compute_moment_of_inertia_tensor(Xtrans, M)\n I /= np.sum(M) # Renormalizing shouldn't change eigenvectors\n L, U = np.linalg.eigh(I)\n X_align = np.dot(Xtrans, U)\n return X_align, U\n\ndef align(X1, X2, M1, M2=None, S1=None, S2=None, prealign=False, permutation=False, reflection=False, mass_weight=False):\n \"\"\"\n Align one molecule to another. If permutations are allowed, the molecules \n are first aligned to the Eckart frame, then reflections are checked, and \n then the permutation is found through linear sum assignment with the \n pair distance matrix. Then the molecules are aligned using orthogonal \n procrustes, which can be mass weighted.\n\n Args:\n X1 ({nparticle, 3} ndarray): Coordinates of first configuration\n X2 ({nparticle, 3} ndarray): Coordinates of second configuration\n M1 ({nparticle,} ndarray): masses of first configuration\n M2 ({nparticle,} ndarray): optional masses of second configuration, defaults to M1\n S1 ({nparticle,} ndarray): optional atomic symbols for first configuration; if given, entries with same symbols will only be permuted within their own group; symbols can be arbitrary specific atom subclasses such as CO or CA for better accuracy\n S2 ({nparticle,} ndarray): optional atomic symbols for second configuration; both S1 and S2 must be given to allow class-based permutations\n prealign (bool): True to align molecules to principle axes prior to anything else, default False\n permutation (bool): True to allow permutation of atoms within systems using linear assignment; this is usually good, but may not be perfect, default False\n reflection (bool): True to allow reflections of geometry, default False\n mass_weight (bool): True to mass_weight by sqrt(M) for procrustes alignment, default False\n\n Returns:\n X1_align: X1 after alignment (and permutation/reflection)\n if permutation:\n perm: permutation indices such that X1[perm] ~= X2\n if reflection:\n ref: (1, 3) np.array of 1 if not reflected, -1 if reflected\n\n TODO: Include all permutations of axes after principal axis alignment for cases of symmetry.\n TODO: Rewrite as class, because this is complicated.\n \"\"\"\n M1curr = np.reshape(M1, (-1, 1))\n if M2 is None:\n M2curr = np.copy(M1curr)\n else:\n M2curr = np.reshape(M2, (-1, 1))\n if S1 is not None:\n S1curr = np.array(S1)\n if S2 is not None:\n S2curr = np.array(S2)\n COM1 = compute_center_of_mass(X1, M1curr)\n COM2 = compute_center_of_mass(X2, M2curr)\n if prealign:\n X1curr, _ = align_principal_axes(X1, M1curr)\n X2curr, R_eck = align_principal_axes(X2, M2curr)\n else:\n X1curr = np.copy(X1)\n X2curr = np.copy(X2)\n # Compute permutation and reflection\n if reflection: # Reflection may be necessary for systems with improper rotations as well\n ref = np.ones((1, 3))\n for i in range(3):\n order1 = np.argsort(np.abs(X1curr[:, i]))\n order2 = np.argsort(np.abs(X2curr[:, i]))\n diff = np.sum((X1curr[order1, i] - X2curr[order2, i])**2)\n add = np.sum((X1curr[order1, i] + X2curr[order2, i])**2)\n if add < diff:\n X1curr[:, i] *= -1\n ref[0, i] = -1\n\n if permutation:\n N1 = len(M1)\n D = scipy.spatial.distance.cdist(X1curr, X2curr, \"euclidean\")\n if S1 is not None and S2 is not None:\n col = -np.ones((N1,), dtype=np.int) # Instantiate to -1 to allow checking if nothing is assigned\n unique_symbols = set(S1) \n # Iterate through unique symbols\n for u_s in unique_symbols:\n # Get indices of atoms with corresponding symbol\n i1 = np.array([i for i, s in enumerate(S1) if s == u_s])\n i2 = np.array([i for i, s in enumerate(S2) if s == u_s])\n # Get pairwise dist submatrix with correct elements\n d = D[i1][:, i2]\n # Compute linear assignment\n u_row, u_col = scipy.optimize.linear_sum_assignment(d)\n # Assign permuted indices to subset of indices for final permutation\n col[i1] = i2[u_col]\n \n elif S1 is not None or S2 is not None:\n raise ValueError(\"Symbol lists for both configurations must be given to be used.\")\n\n else:\n # Use linear assignment on distances if symbols not given\n row, col = scipy.optimize.linear_sum_assignment(D)\n\n perm = -np.ones((len(M1), ), dtype=np.int)\n perm[col] = np.arange(N1)\n if -1 in perm:\n raise ValueError(\"Permutation assignment unsuccesful.\")\n X1curr = X1curr[perm]\n M1curr = M1curr[perm]\n if S1 is not None:\n S1curr = S1curr[perm]\n\n # Now that permutations are done, find orthogonal procrustes rotation\n if mass_weight:\n R_pro, _ = scipy.linalg.orthogonal_procrustes(X1curr * np.sqrt(M1curr), X2curr * np.sqrt(M2curr))\n else:\n R_pro, _ = scipy.linalg.orthogonal_procrustes(X1curr, X2curr)\n # Rotate first geometry into second geometry's frame\n X1curr = np.dot(X1curr, R_pro)\n if prealign:\n # Must undo eckart rotation\n X1curr = np.dot(X1curr, R_eck.T)\n # Move center of mass to second center\n X1curr += COM2 \n else:\n X1curr += COM2 - COM1\n \n if permutation and reflection:\n return X1curr, perm, ref\n elif permutation:\n return X1curr, perm\n elif reflection:\n return X1curr, ref\n else:\n return X1curr\n\ndef cross_product_matrix(X):\n \"\"\"\n Returns the skew-symmetric matrix representation of X that when multiplied \n with a 3-vector returns the cross product.\n\n Args:\n X ({..., 3} ndarray): vector or batch to convert\n\n Returns:\n ({..., 3, 3} ndarray): (batch of) matrices representing cross product operation\n \"\"\"\n sh = np.shape(X)\n if sh[-1] != 3:\n raise ValueError(\"Cross product matrix requires the last dimension to be 3\")\n Xmat_sh = tuple(list(sh) + [3])\n Xmat = np.zeros(Xmat_sh)\n Xmat[..., 1, 0] = X[..., 2]\n Xmat[..., 0, 1] = -X[..., 2]\n Xmat[..., 2, 0] = -X[..., 1]\n Xmat[..., 0, 2] = X[..., 1]\n Xmat[..., 2, 1] = X[..., 0]\n Xmat[..., 1, 2] = -X[..., 0]\n return Xmat\n\ndef row_permute(A, Pinds):\n \"\"\"\n Use numpy index slicing to permute a matrix as B = P'A\n where A is the given matrix, P is a permutation matrix and B is returned.\n Because this is done in sparse manner, P is instead given as Pinds which \n should be the permuted indices, with shape (nperm, 2).\n\n Args:\n A ({N, M} ndarray): array to permute\n Pinds ({nperm, 2} ndarray): pairs of indices to permute\n\n Returns:\n ({N, M} ndarray): permuted matrix P'A \n\n Note: \n This routine does not handle resizing if permuting into a larger or smaller matrix.\n \"\"\"\n Anew = np.copy(A)\n Anew[[Pinds[:, 0], Pinds[:, 1]], :] = A[[Pinds[:, 1], Pinds[:, 0]], :]\n return Anew\n\ndef column_permute(A, Pinds):\n \"\"\"\n Use numpy index slicing to permute a matrix as B = AP\n where A is the given matrix, P is a permutation matrix and B is returned.\n Because this is done in sparse manner, P is instead given as Pinds which \n should be the permuted indices, with shape (nperm, 2).\n\n Args:\n A ({N, M} ndarray): array to permute\n Pinds ({nperm, 2} ndarray): pairs of indices to permute\n\n Returns:\n ({N, M} ndarray): permuted matrix AP\n\n Note: \n This routine does not handle resizing if permuting into a larger or smaller matrix.\n \"\"\"\n Anew = np.copy(A)\n Anew[:, [Pinds[:, 0], Pinds[:, 1]]] = Anew[:, [Pinds[:, 1], Pinds[:, 0]]]\n return Anew\n\ndef symmetric_permute(A, Pinds):\n \"\"\"\n Use numpy index slicing to permute a matrix as B = P'AP\n where A is the given matrix, P is a permutation matrix and B is returned.\n Because this is done in sparse manner, P is instead given as Pinds which \n should be the permuted indices, with shape (nperm, 2).\n\n Args:\n A ({N, N} ndarray): array to permute\n Pinds ({nperm, 2} ndarray): pairs of indices to permute\n\n Returns:\n ({N, N} ndarray): permuted matrix P'AP\n\n Note: \n This routine does not handle resizing if permuting into a larger or smaller matrix.\n There is also not error checking for if the same index is included multiple times.\n \"\"\"\n Anew = np.copy(A)\n Anew[[Pinds[:, 0], Pinds[:, 1]], :] = A[[Pinds[:, 1], Pinds[:, 0]], :]\n Anew[:, [Pinds[:, 0], Pinds[:, 1]]] = Anew[:, [Pinds[:, 1], Pinds[:, 0]]]\n return Anew\n\ndef axis_align_matrix(v, axis_ind=0):\n \"\"\"\n Return matrix that yields Rv = v' where v' is aligned to axis_ind axis\n\n Args:\n v ({K,} ndarray): vector to align\n axis_ind (int): index of dimension to align to\n\n Returns:\n ({K, K} ndarray): R, rotation matrix that aligns given vector\n \"\"\"\n vnew = np.zeros_like(v)\n vnew[axis_ind] = 1.0\n u = v/np.linalg.norm(v) - vnew\n R = np.eye(np.size(u)) - 2.0 * np.outer(u, u) / np.sum(u * u)\n R[-1] *= -1.0\n return R\n\ndef time_reversible_propagation(D, Ps):\n \"\"\"\n Use the Niklasson time-reversible propagation with dissipation for extrapolating a density matrix (or other quantity)\n Order of propagation to use is determined by the length of Ps\n\n References:\n https://doi.org/10.1063/1.3148075\n\n Args:\n D ({N, N} ndarray): last converged density matrix\n Ps ({nframe, N, N} ndarray}): last k initial guess density matrices in order of [n-k, n-k+1, ..., n], nframe must be in [4, 10].\n\n Returns:\n ({N, N} ndarray): Reversibly propagated density matrix\n \"\"\"\n order = len(Ps)\n if not (4 <= order <= 10):\n raise ValueError(\"len(Ps) must be in [4, 10] for propagation.\")\n \n # Order 3 to 9\n kappas = [1.69, 1.75, 1.82, 1.84, 1.86, 1.88, 1.89]\n alphas = [0.150, 0.057, 0.018, 0.0055, 0.0016, 0.00044, 0.00012]\n coeffs = [ # Ordered as in the paper, c_i goes with P_n-i, i.e. c_0 with P_n, c_1 P_n-1\n [-2.0, 3.0, 0.0, -1.0],\n [-3.0, 6.0, -2.0, -2.0, 1.0],\n [-6.0, 14.0, -8.0, -3.0, 4.0, -1.0],\n [-14.0, 36.0, -27.0, -2.0, 12.0, -6.0, 1.0],\n [-36.0, 99.0, -88.0, 11.0, 32.0, -25.0, 8.0, -1.0],\n [-99.0, 286.0, -286.0, 78.0, 78.0, -90.0, 42.0, -10.0, 1.0],\n [-286.0, 858.0, -936.0, 364.0, 168.0, -300.0, 184.0, -63.0, 12.0, -1.0],\n ]\n order_ind = order - 4\n kappa = kappas[order_ind]\n alpha = alphas[order_ind]\n coeff = [alpha * c for c in coeffs[order_ind]]\n coeff[0] += 2.0 - kappa \n coeff[1] -= 1.0\n \n P_new = kappa * np.copy(D)\n for c, P in zip(coeff, Ps[::-1]):\n P_new += c * P\n\n return P_new\n\ndef langevin_energy(X1, V1, F1, X2, V2, F2, M, dt):\n \"\"\"\n The change in 'effective energy' arising from integrating an NVT ensemble \n using a langevin thermostat\n\n Args:\n X1 ({nparticle, 3} ndarray): Coordinates of first configuration\n V1 ({nparticle, 3} ndarray): Velocities of first configuration\n F1 ({nparticle, 3} ndarray): Forces acting on first configuration\n X2 ({nparticle, 3} ndarray): Coordinates of second configuration\n V2 ({nparticle, 3} ndarray): Velocities of second configuration\n F2 ({nparticle, 3} ndarray): Forces acting on second configuration\n M ({nparticle,} ndarray): masses\n dt (float): time step used to integrate between X1 and X2\n\n Returns:\n (float): Effective energy change between two configurations that arises from Langevin integration \n \"\"\"\n KE1 = compute_kinetic_energy(V1, M)\n KE2 = compute_kinetic_energy(V2, M)\n dX = X2 - X1\n XF = np.sum(dX * (F1 + F2) * 0.5)\n KEF1 = compute_kinetic_energy_momentum(F1, M)\n KEF2 = compute_kinetic_energy_momentum(F2, M)\n Fsq = 0.25 * dt * dt * (KEF2 - KEF1)\n return KE1 - KE2 + XF + Fsq\n \n\ndef numerical_gradient(inp, func, eps=1.E-6, output_ind=None, *args, **kwargs):\n \"\"\"\n Moves inp by eps in each dimension and calls the function to compute the\n gradient numerically with a symmetric stencil.\n\n Args:\n inp (ndarray): input to function\n func (function): function to differentiate at inp\n eps (float): small number by which to move each element of inp (default 1.E-6)\n output_ind (int): if given, takes the numerical gradient wrt the i'th output of the function given\n *args: additional function arguments\n **kwargs: function keyword arguments\n\n Returns:\n (ndarray): numerical gradient of function at input with shape (inp.shape x function_output.shape)\n \"\"\"\n g = []\n inpr = inp.ravel()\n for i in range(len(inpr)):\n plus = np.copy(inpr)\n plus[i] += eps\n plus = plus.reshape(np.shape(inp))\n minus = np.copy(inpr)\n minus[i] -= eps\n minus = minus.reshape(np.shape(inp))\n if output_ind is not None:\n de = func(plus, *args, **kwargs)[output_ind] - func(minus, *args, **kwargs)[output_ind]\n else:\n de = func(plus, *args, **kwargs) - func(minus, *args, **kwargs)\n dedx = de/2.0/eps\n g.append(dedx)\n dims = []\n if np.shape(inp):\n dims += [d for d in np.shape(inp)]\n if np.shape(g[0]):\n dims += [d for d in np.shape(g[0])]\n grad = np.array(g).reshape(dims)\n return grad\n\ndef np_cache(cache_size=4, arg_ind=0):\n \"\"\"\n Naive LRU cache implementation for functions whose first parameter is a numpy array.\n Makes array_equal comparison, rather than using a hash of a tuple form of the array.\n\n Kwargs:\n cache_size (int): size of cache to save inputs and outputs (default 4)\n arg_ind (int): index of input argument to check to determine if the output should be identical\n\n Note:\n For class methods, the arg_ind should be 1, as argument 0 implies the class instance.\n For inherited class methods, this will not work if there multiple base class instances, unless used very carefully.\n \"\"\"\n def decorator(func):\n def wrapper(*args, **kwargs):\n inp_curr = args[arg_ind]\n is_cached = False\n for i in range(len(wrapper.inp_cache)):\n inp = wrapper.inp_cache[-i-1]\n if np.array_equal(inp_curr, inp):\n is_cached = True\n out = wrapper.out_cache[-i-1]\n if i > 0:\n # Move recalled value to end of cache\n wrapper.inp_cache.append(wrapper.inp_cache.pop(-i-1))\n wrapper.out_cache.append(wrapper.out_cache.pop(-i-1))\n break \n\n if not is_cached:\n out = func(*args, **kwargs)\n wrapper.inp_cache.append(np.copy(inp_curr))\n wrapper.inp_cache = wrapper.inp_cache[-wrapper.cache_size:]\n wrapper.out_cache.append(out)\n wrapper.out_cache = wrapper.out_cache[-wrapper.cache_size:]\n \n return cp.deepcopy(out)\n\n # Bind a reset method to the new function\n def reset_cache(cache_size=cache_size):\n wrapper.inp_cache = []\n wrapper.out_cache = []\n wrapper.cache_size = cache_size\n\n wrapper.reset_cache = reset_cache\n wrapper.reset_cache()\n\n return wrapper\n return decorator\n\ndef sin_sigmoid(x):\n r\"\"\"\n sin sigmoid transfer function\n\n .. math:: \n\n f(x) &= x_c - 1/2\\pi \\sin(2\\pi x_c) \\\\\n x_c &= \\mathrm{min}(\\mathrm{max}(0, x), 1)\n\n Args:\n x (float): input\n Returns:\n (float): output in [0, 1]\n \"\"\"\n xc = np.clip(x, 0.0, 1.0)\n return xc - 0.5 / np.pi * np.sin(2.0 * np.pi * xc)\n\ndef clamp(x):\n r\"\"\"\n Linear transfer function\n\n .. math:: \n\n f(x) &= x_c \\\\\n x_c &= \\mathrm{min}(\\mathrm{max}(0, x), 1)\n\n Args:\n x (float): input\n Returns:\n (float): output in [0, 1]\n \"\"\"\n return np.clip(x, 0.0, 1.0)\n\ndef smoothstep(x):\n r\"\"\"\n Sigmoidal transfer function\n\n .. math:: \n\n f(x) &= 3x_c^2 - 2x_c^3 \\\\\n x_c &= \\mathrm{min}(\\mathrm{max}(0, x), 1)\n\n Args:\n x (float): input\n Returns:\n (float): output in [0, 1]\n \"\"\"\n xc = np.clip(x, 0.0, 1.0)\n return 3.0 * xc**2 - 2.0 * xc**3\n\ndef smootherstep(x):\n r\"\"\"\n Sigmoidal transfer function\n\n .. math:: \n\n f(x) &= 6x_c^5 - 15x_c^4 + 10x_c^3 \\\\\n x_c &= \\mathrm{min}(\\mathrm{max}(0, x), 1)\n\n Args:\n x (float): input\n Returns:\n (float): output in [0, 1]\n \"\"\"\n xc = np.clip(x, 0.0, 1.0)\n return 6.0 * xc**5 - 15 * xc**4 + 10 * xc**3\n","sub_path":"mdprop/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":33062,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"480331748","text":"#!/usr/bin/env python3\n\nimport json\nimport sys\nimport re\nfrom ipaddress import *\nfrom time import time\n\ndef extract_hostname(json):\n try:\n return json['nodeinfo']['hostname'].lower()\n except KeyError:\n return 0\n\nValidHostnameRegex = \"^(([a-zA-Z0-9]|[a-zA-Z0-9][a-zA-Z0-9\\-]*[a-zA-Z0-9])\\.)*([A-Za-z0-9]|[A-Za-z0-9][A-Za-z0-9\\-]*[A-Za-z0-9])$\"\nprefix = IPv6Network('2a03:2267::/64')\n\ndata = list(json.load(sys.stdin)[\"nodes\"].values())\ndata.sort(key=extract_hostname)\n\nprint(\"\"\"$TTL 600 ; 10 minutes\n@ IN SOA srv01.hamburg.freifunk.net. hostmaster.hamburg.freifunk.net. (\n %i ; serial\n 600 ; refresh (10min)\n 30 ; retry (30s)\n 3600 ; expire (1 hour)\n 60 ; minimum (1 minute)\n )\n NS srv01.hamburg.freifunk.net.\n NS named.exosphere.de.\n NS ns.ohrensessel.net.\n \"\"\" % time())\n\nHostnameRegex = re.compile(ValidHostnameRegex)\n\nalready_added = []\n\nfor e in data:\n node = e[\"nodeinfo\"]\n try:\n hostname = node['hostname']\n if HostnameRegex.match(hostname) == None:\n continue\n\n address = None\n\n for a in node['network']['addresses']:\n a = IPv6Address(a)\n if a in prefix:\n address = a\n break\n\n hostname_normalized = hostname.lower()\n\n if address and hostname_normalized not in already_added:\n print(\"%s\\tAAAA\\t%s\" % (hostname_normalized, address))\n already_added.append(hostname_normalized)\n except:\n pass\n","sub_path":"alfred2zone.py","file_name":"alfred2zone.py","file_ext":"py","file_size_in_byte":1499,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"534342133","text":"import Tkinter as tk\nimport datetime\nimport operator\nimport tkMessageBox\n\nif __name__ == '__main__':\n birthdates = {'Patrick': datetime.date(1987, 8, 22),\n 'Markus': datetime.date(1984, 4, 1),\n 'Stefan': datetime.date(1983, 6, 7),\n 'MarkusW': datetime.date(1982, 8, 3),\n 'Leni': datetime.date(1990, 12, 11)}\n\n days_left_dict = {}\n today = datetime.datetime.now().date()\n\n for k, v in birthdates.iteritems():\n age_this_year = today.year - v.year\n days_left = (datetime.date(today.year, v.month, v.day) - today).days\n if days_left > 0:\n days_left_dict[k] = days_left\n\n sorted_birthday_memos = sorted(days_left_dict.items(), key=operator.itemgetter(1))\n\n if len(sorted_birthday_memos) > 0:\n msg = \"This is a list of the next birthdays of your friends: \\n\"\n for name, details in sorted_birthday_memos:\n msg += \"{:20s} {:5s}, {:3d} days\\n\".format(name, birthdates[name].isoformat(), details)\n root = tk.Tk()\n root.withdraw()\n tkMessageBox.showinfo('Upcoming Birthdays', msg)\n","sub_path":"201706_WebDevelopment/Extra/BirthdaysReminder/birthdays_TKinter.py","file_name":"birthdays_TKinter.py","file_ext":"py","file_size_in_byte":1134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"69627277","text":"#\n# Copyright (c) Facebook, Inc. and its affiliates.\n#\n# This source code is licensed under the MIT license found in the\n# LICENSE file in the root directory of this source tree.\n#\n\n\nfrom rlalgos.logger import Logger, TFLogger\nfrom rlstructures import DictTensor, TemporalDictTensor, Trajectories\nfrom rlalgos.tools import weight_init\nfrom rlstructures.rl_batchers import RL_Batcher\nimport torch.nn as nn\nimport copy\nimport torch\nimport time\nimport numpy as np\nimport torch.nn.functional as F\nfrom rlalgos.reinforce.agent import *\nfrom rlstructures import replay_agent\n\n\nclass Reinforce:\n def __init__(self, config, create_env, create_agent):\n self.config = config\n\n # Creation of the Logger (that saves in tensorboard and CSV)\n self.logger = TFLogger(log_dir=self.config[\"logdir\"], hps=self.config)\n\n self._create_env = create_env\n self._create_agent = create_agent\n\n def run(self):\n # Creation of one env instance to get the dimensionnality of observations and number of actions\n env = self._create_env(\n self.config[\"n_envs\"], seed=0, env_name=self.config[\"env_name\"]\n )\n self.n_actions = env.action_space.n\n self.obs_dim = env.reset()[0][\"frame\"].size()[1]\n del env\n\n # Create the agent model\n self.learning_model = self._create_model()\n\n # Create one agent for loss computation (see get_loss)\n self.agent = self._create_agent(\n n_actions=self.n_actions, model=self.learning_model\n )\n\n # We create a batcher dedicated to evaluation\n model = copy.deepcopy(self.learning_model)\n self.evaluation_batcher = RL_Batcher(\n n_timesteps=self.config[\"max_episode_steps\"],\n create_agent=self._create_agent,\n create_env=self._create_env,\n env_args={\n \"n_envs\": self.config[\"n_evaluation_envs\"],\n \"max_episode_steps\": self.config[\"max_episode_steps\"],\n \"env_name\": self.config[\"env_name\"],\n },\n agent_args={\"n_actions\": self.n_actions, \"model\": model},\n n_processes=self.config[\"n_evaluation_processes\"],\n seeds=[\n self.config[\"env_seed\"] + k * 10\n for k in range(self.config[\"n_evaluation_processes\"])\n ],\n agent_info=DictTensor({\"stochastic\": torch.tensor([True])}),\n env_info=DictTensor({}),\n )\n\n # Create a batcher to sample learning trajectories\n model = copy.deepcopy(self.learning_model)\n self.train_batcher = RL_Batcher(\n n_timesteps=self.config[\"max_episode_steps\"],\n create_agent=self._create_agent,\n create_env=self._create_env,\n env_args={\n \"n_envs\": self.config[\"n_envs\"],\n \"max_episode_steps\": self.config[\"max_episode_steps\"],\n \"env_name\": self.config[\"env_name\"],\n },\n agent_args={\"n_actions\": self.n_actions, \"model\": model},\n n_processes=self.config[\"n_processes\"],\n seeds=[\n self.config[\"env_seed\"] + k * 10\n for k in range(self.config[\"n_processes\"])\n ],\n agent_info=DictTensor({\"stochastic\": torch.tensor([True])}),\n env_info=DictTensor({}),\n agent_seeds=[\n self.config[\"env_seed\"] + k * 10\n for k in range(self.config[\"n_processes\"])\n ],\n )\n\n # Creation of the optimizer\n optimizer = torch.optim.RMSprop(\n self.learning_model.parameters(), lr=self.config[\"lr\"]\n )\n\n # Training Loop:\n _start_time = time.time()\n self.iteration = 0\n\n # We launch the evaluation batcher, such that it starts to collect trajectories with the current model\n n_episodes = (\n self.config[\"n_evaluation_processes\"] * self.config[\"n_evaluation_envs\"]\n )\n agent_info = DictTensor(\n {\n \"stochastic\": torch.tensor(\n [self.config[\"evaluation_mode\"] == \"stochastic\"]\n ).repeat(n_episodes)\n }\n )\n self.evaluation_batcher.reset(agent_info=agent_info)\n self.evaluation_batcher.execute()\n self.evaluation_iteration = self.iteration\n\n # Update the batcher with the last version of the learning model\n self.train_batcher.update(self.learning_model.state_dict())\n\n n_interactions = 0\n while time.time() - _start_time < self.config[\"time_limit\"]:\n\n # 1) The policy will be executed in \"stochastic' mode\n n_episodes = self.config[\"n_envs\"] * self.config[\"n_processes\"]\n agent_info = DictTensor(\n {\"stochastic\": torch.tensor([True]).repeat(n_episodes)}\n )\n self.train_batcher.reset(agent_info=agent_info)\n self.train_batcher.execute()\n\n # 2) We get the trajectories (and wait until the trajectories have been sampled)\n trajectories, n_env_running = self.train_batcher.get(blocking=True)\n assert n_env_running == 0 # Assert that all trajectories are finished\n n_interactions += trajectories.trajectories.mask().sum().item()\n self.logger.add_scalar(\n \"n_interactions_per_seconds\",\n n_interactions / (time.time() - _start_time),\n self.iteration,\n )\n\n # 3) Compute the loss\n dt = self.get_loss(trajectories)\n [\n self.logger.add_scalar(\"loss/\" + k, dt[k].item(), self.iteration)\n for k in dt.keys()\n ]\n\n # 4) Compute the final loss by linear combination of the different individual losses\n ld = self.config[\"baseline_coef\"] * dt[\"baseline_loss\"]\n lr = self.config[\"reinforce_coef\"] * dt[\"reinforce_loss\"]\n le = self.config[\"entropy_coef\"] * dt[\"entropy_loss\"]\n floss = ld - le - lr\n\n # 5) Update the parameters of the model\n optimizer.zero_grad()\n floss.backward()\n optimizer.step()\n\n # 6) Update the train batcher with the updated model\n self.train_batcher.update(self.learning_model.state_dict())\n\n # 7) Print some messages\n print(\n \"At iteration %d, avg (discounted) reward is %f\"\n % (self.iteration, dt[\"avg_reward\"].item())\n )\n print(\n \"\\t Avg trajectory length is %f\"\n % (trajectories.trajectories.lengths.float().mean().item())\n )\n print(\n \"\\t Curves can be visualized using 'tensorboard --logdir=%s'\"\n % self.config[\"logdir\"]\n )\n self.iteration += 1\n\n # 8)---- Evaluation\n evaluation_trajectories, n_env_running = self.evaluation_batcher.get(\n blocking=False\n )\n if not evaluation_trajectories is None: # trajectories are available\n assert n_env_running == 0\n # Compute the cumulated reward\n cumulated_reward = (\n (\n evaluation_trajectories.trajectories[\"_observation/reward\"]\n * evaluation_trajectories.trajectories.mask()\n )\n .sum(1)\n .mean()\n )\n self.logger.add_scalar(\n \"evaluation_reward/\" + self.config[\"evaluation_mode\"],\n cumulated_reward.item(),\n self.evaluation_iteration,\n )\n print(\n \"-- Iteration \",\n self.iteration,\n \" Evaluation reward = \",\n cumulated_reward.item(),\n )\n # We reexecute the evaluation batcher to start the acquisition of new trajectories\n self.evaluation_batcher.update(self.learning_model.state_dict())\n self.evaluation_iteration = self.iteration\n n_episodes = (\n self.config[\"n_evaluation_processes\"]\n * self.config[\"n_evaluation_envs\"]\n )\n agent_info = DictTensor(\n {\n \"stochastic\": torch.tensor(\n [self.config[\"evaluation_mode\"] == \"stochastic\"]\n ).repeat(n_episodes)\n }\n )\n self.evaluation_batcher.reset(agent_info=agent_info)\n self.evaluation_batcher.execute()\n\n self.train_batcher.close()\n self.evaluation_batcher.get() # To wait for the last trajectories\n self.evaluation_batcher.close()\n self.logger.update_csv() # To save as a CSV file in logdir\n self.logger.close()\n\n def get_loss(self, trajectories):\n \"\"\"Compute the different individual losses needed for REINFORCE\n - baseline loss for updating the baseline\n - reinforce loss for updating the policy\n - entropy loss for entropy regularization\n \"\"\"\n # Use self.agent to replay the trajectories computation on the batch of trajectories\n replayed = replay_agent(self.agent, trajectories)\n\n info = trajectories.info\n trajectories = trajectories.trajectories\n\n # Compute the cumulated future reward\n reward = trajectories[\"_observation/reward\"]\n mask = trajectories.mask()\n reward = reward * mask\n max_length = trajectories.lengths.max().item()\n cumulated_reward = torch.zeros_like(reward)\n cumulated_reward[:, max_length - 1] = reward[:, max_length - 1]\n for t in range(max_length - 2, -1, -1):\n cumulated_reward[:, t] = (\n reward[:, t]\n + self.config[\"discount_factor\"] * cumulated_reward[:, t + 1]\n )\n\n # Compute reinforce loss\n action_probabilities = replayed[\"action_probabilities\"]\n action_distribution = torch.distributions.Categorical(action_probabilities)\n baseline = replayed[\"baseline\"].squeeze(-1)\n log_proba = action_distribution.log_prob(trajectories[\"action/action\"])\n reinforce_loss = log_proba * (cumulated_reward - baseline).detach()\n reinforce_loss = (reinforce_loss * mask).sum(1) / mask.sum(1)\n avg_reinforce_loss = reinforce_loss.mean()\n\n # Compute entropy loss\n entropy = action_distribution.entropy()\n entropy = (entropy * mask).sum(1) / mask.sum(1)\n avg_entropy = entropy.mean()\n\n # Compute baseline loss\n baseline_loss = (baseline - cumulated_reward) ** 2\n baseline_loss = (baseline_loss * mask).sum(1) / mask.sum(1)\n avg_baseline_loss = baseline_loss.mean()\n\n return DictTensor(\n {\n \"avg_reward\": cumulated_reward[:, 0].mean(),\n \"baseline_loss\": avg_baseline_loss,\n \"reinforce_loss\": avg_reinforce_loss,\n \"entropy_loss\": avg_entropy,\n }\n )\n","sub_path":"rlalgos/reinforce/reinforce.py","file_name":"reinforce.py","file_ext":"py","file_size_in_byte":11193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"218369859","text":"#!/usr/bin/env python3\nimport requests\nimport boto3\nimport redis\nimport json\nimport psutil\nimport os\nimport signal\n\n\nclass identity(object):\n def __init__(self, document=None):\n if document is None:\n # need to follow register.py process for data retrieval\n document = {}\n else:\n identity = document\n for k, v in identity.items():\n if isinstance(k, str):\n setattr(self, k, v)\n else:\n setattr(self, k.decode('utf-8'), v)\n\n def _get_url(self, url):\n response = requests.get(url)\n try:\n response.raise_for_status()\n except:\n return None\n try:\n return response.json()\n except ValueError:\n return response.text\n\n def __call__(self):\n return self.__dict__\n\nclass hosts_file(object):\n def __init__(self, ip_address, hostname, filename='/etc/hosts'):\n hosts = {}\n found = False\n with open(filename) as fh:\n original_hosts = fh.readlines()\n for line in original_hosts:\n try:\n (ip, name) = line.split()\n if hostname == name:\n hosts[name] = ip_address\n else:\n hosts[name] = ip\n except ValueError as err:\n pass\n if not found:\n hosts[hostname] = ip_address\n with open(filename, 'w') as fh:\n for k in hosts.keys():\n if isinstance(k, bytes):\n k = k.decode()\n print('%s\\t%s' % (hosts[k], k), file=fh)\n for pid in psutil.process_iter(['pid', 'name']):\n if pid.info['name'] == \"dnsmasq\":\n os.kill(pid.info['pid'], signal.SIGHUP)\n break\n\n\nclass update_redis(object):\n def __init__(self, redis_session, id):\n self.redis_session = redis_session\n self.identity = identity(id)\n self.redis_instances()\n\n def redis_instances(self):\n old_identity = r.hgetall(self.identity.Name)\n if old_identity:\n self.old_identity = identity(document=old_identity)\n r.srem(self.old_identity.Lab_Group, self.old_identity.ipAddress)\n r.hmset(self.identity.Name, self.identity())\n r.sadd(self.identity.Lab_Group, self.identity.ipAddress)\n r.sadd('groups', self.identity.Lab_Group)\n r.sadd('names', self.identity.Name)\n\n\nif __name__ == '__main__':\n r = redis.StrictRedis(host='localhost', port=6379, db=0)\n p = r.pubsub()\n p.subscribe('instances')\n for m in p.listen():\n if m['type'] == 'message':\n data = json.loads(m['data'].decode('utf-8'))\n id = list(data.keys())[0]\n hosts_file(data[id]['ipAddress'], data[id]['Lab_Name'])\n update_redis(r, data[id])\n r.publish('bootstrap', json.dumps({'bootstrap': data[id]['Lab_Name']}))\n","sub_path":"provisioning/handle_register.py","file_name":"handle_register.py","file_ext":"py","file_size_in_byte":2943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"225852752","text":"#HuntTheWumpus.py\n#Written by Py-Mike\n#Started: 5/10/2021\n#Description: A Hunt the Wumpus Clone in Python\n\n#Imports\nimport random\n\ndef main():\n keep_playing = True\n while keep_playing == True:\n #Calls the main game function\n keep_playing = game()\n\n #Checks to see if the player quit\n if keep_playing == False:\n break\n\n #Checks to see if wants to keep playing\n keep_playing = post_game()\n\ndef game():\n #Initialize Game\n game = game_setup()\n\n #Start Game Loop\n while game['gameover'] == False:\n #Print game screen\n game_screen(game)\n \n #Prompt Player for Action\n action = input('Shoot, Move, or Quit (S-M-Q)? ')\n action = action.lower()\n if action == 's':\n #take the shooting action\n shoot(game)\n if game['gameover'] == True:\n break\n else:\n game['gameover'] = arrow_count_check(game['arrows'])\n elif action == 'm':\n #take the moving action\n move(game)\n hazards(game)\n elif action == 'q':\n return False\n else:\n print('Incorrect Syntax')\n\n print('')\n\ndef post_game():\n a = True\n while a == True:\n play_again = input('Would you like to play again?(y/n) ')\n play_again.lower()\n if play_again == 'y':\n return True\n elif play_again == 'n':\n return False\n else:\n continue\n\ndef game_setup():\n #Create a dictionary with all the game values\n game = {'gameover': False, 'arrows': 5, 'arrow_location': 0, 'player_location': 0, 'wumpus_location': 0,\n 'bat1_location': 0, 'bat2_location': 0, 'pit1_location': 0, 'pit2_location': 0,\n 1: (2,5,8), 2: (1,3,10), 3: (2,4,12), 4: (3,5,15), 5: (1,4,6), 6: (5,7,15),\n 7: (6,8,17), 8: (1,7,9), 9: (8,10,18), 10: (2,9,11), 11: (10,12,19),\n 12: (3,11,13), 13: (12,14,20), 14: (4,13,15), 15: (6,14,16), 16: (15,17,20),\n 17: (7,16,18), 18: (9,17,19), 19: (11,18,20), 20: (13,16,19)}\n\n #Randomize Starting Locations\n locations = [1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20]\n random.shuffle(locations)\n\n #Assign key locations to their respective game variables\n game['player_location'] = locations[0]\n game['wumpus_location'] = locations[1]\n game['bat1_location'] = locations[2]\n game['bat2_location'] = locations[3]\n game['pit1_location'] = locations[4]\n game['pit2_location'] = locations[5]\n return game\n \ndef game_screen(game):\n #Prints the room details\n print('You are in room ' + str(game['player_location']) + '.')\n if game['wumpus_location'] in game[game['player_location']]:\n print(' I smell a Wumpus.')\n if game['bat1_location'] in game[game['player_location']]:\n print(' I hear flapping.')\n if game['bat2_location'] in game[game['player_location']]:\n print(' I hear flapping.')\n if game['pit1_location'] in game[game['player_location']]:\n print(' I feel a draft.')\n if game['pit2_location'] in game[game['player_location']]:\n print(' I feel a draft.')\n print('Tunnels lead to ' + str(game[game['player_location']][0]) +\n ', ' + str(game[game['player_location']][1]) +\n ', ' + str(game[game['player_location']][2]))\n \ndef shoot(game):\n #First Main Action\n #needs to kill the wumpus, scare the wumpus, chance to ricochet and fly through several rooms\n arrow_range = 0\n \n arrow_range = input('How far? [1-5]')\n if arrow_range.isdigit():\n if int(arrow_range) not in range(1,6):\n print('Defaulting to max range.')\n arrow_range = 5\n else:\n print('Defaulting to max range.')\n arrow_range = 5\n #Set the arrow starting position to the players current position\n game['arrow_location'] = game['player_location']\n for a in range(int(arrow_range)):\n room_check = input('Which room #: ')\n if not room_check.isdigit():\n print('Not a valid input.')\n return\n if int(room_check) not in game[game['arrow_location']]:\n ricochet = random.randint(1, 20)\n if ricochet > 15:\n print('You fire your arrow into the wall and it ricochets')\n new_location = random.randint(1,4)\n if new_location == 4 and game['player_location'] == game['arrow_location']:\n print('')\n print('******* Game Over *******')\n print('Upon firing randomly into the wall, your arrow flies back at you.')\n print('Unable to dodge it wounds you greviously. The smell of blood fills the air.')\n print('The Wumpus Has You.')\n game['gameover'] = True\n return\n elif new_location == 4 and game['player_location'] != game['arrow_location']:\n new_location = random.randint(1,3)\n game['arrow_location'] = game['player_location'][new_location]\n else:\n print('Your arrow flies into the wall and shatters.')\n game['arrows'] -= 1\n print('You have ' + str(game['arrows']) + ' arrows remaining.')\n return\n else:\n game['arrow_location'] = int(room_check)\n if game['arrow_location'] == game['wumpus_location']:\n print('')\n print('******* Game Over *******')\n print('Years of paitence has brought you to this moment.')\n print('Your arrow flies true. With a mighty roar the Wumpus')\n print('falls lifeless to the floor. But the Wumpus will get')\n print('you next time.')\n print('')\n game['gameover'] = True\n return\n\n elif game['arrow_location'] == game['player_location'] and a > 0:\n print('')\n print('******* Game Over *******')\n print('Through some unfortunate miracle of chance your arrow flies back')\n print('into the room you are standing in and greviously wounds you. The')\n print('smell of blood fills the air.')\n print('The Wumpus Has You.')\n game['gameover'] = True\n return\n \n elif game['arrow_location'] != game['wumpus_location'] and a != (int(arrow_range)-1):\n print('The crooked arrow continues to fly')\n game['arrow_location'] = int(room_check)\n\n else:\n print('You did not hit anything.')\n if game['wumpus_location'] in game[game['player_location']]:\n print('You hear shuffling, and the scent of wumpus grows fainter.')\n new_wumpus_location = random.randint(1,19)\n if new_wumpus_location >= game['player_location']:\n game['wumpus_location'] = new_wumpus_location + 1\n else:\n game['wumpus_location'] = new_wumpus_location\n\n a += 1\n \n game['arrows'] -= 1\n print('You have ' + str(game['arrows']) + ' arrows remaining.')\n\ndef arrow_count_check(arrows):\n if arrows == 0:\n print('')\n print('******* Game Over *******')\n print('Upon firing your last arrow, the hunter becomes the hunted.')\n print('The Wumpus Has You.')\n return True\n else:\n return False\n\ndef move(game):\n #Second Main Action\n #needs to move through the map, and check to see if bad things happen in the next room\n move = input('Where to? ')\n if not move.isdigit():\n print('That is not a valid input')\n return\n\n #Validate input to prevent players from moving to locations not currently available\n if int(move) not in game[game['player_location']]:\n print('You bump into the wall. That is not a valid exit!')\n return\n else:\n game['player_location'] = int(move)\n\ndef hazards(game):\n #Check for Hazards\n #Check bats first since they update the player location\n if (game['player_location'] == game['bat1_location']) or (game['player_location'] == game['bat2_location']):\n print('The bats swarm and carry you to a random location.')\n game['player_location'] = random.randint(1,20)\n hazards(game)\n return\n if game['player_location'] == game['wumpus_location']:\n wumpus_reaction = random.randint(1,2)\n if wumpus_reaction == 1:\n print('')\n print('******* Game Over *******')\n print('As you enter the room you realize only too late that it was a trap.')\n print('The Wumpus Has You.')\n game['gameover'] = True\n return\n else:\n print('You startled the wumpus. It ran away.')\n game['wumpus_location'] = random.randint(1,20)\n if (game['player_location'] == game['pit1_location']) or (game['player_location'] == game['pit2_location']):\n print('')\n print('******* Game Over *******')\n print('You boldly enter the room, but where you expected floor there is none.')\n print('The Wumpus Has You.')\n game['gameover'] = True\n return\n\n#Calls the main function to start the game.\nmain()\n","sub_path":"HuntTheWumpus.py","file_name":"HuntTheWumpus.py","file_ext":"py","file_size_in_byte":9425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"292343690","text":"from collections import defaultdict\n\nfrom starkware.cairo.lang.vm.cairo_runner import CairoRunner\nfrom starkware.cairo.lang.vm.relocatable import RelocatableValue\n\n\nclass SecurityError(Exception):\n pass\n\n\ndef verify_secure_runner(runner: CairoRunner, verify_builtins=True):\n \"\"\"\n Verifies the complete run in runner is safe to relocate and run by another Cairo program.\n Checks that:\n * No access to pure addresses (The entire run must be relocatable).\n * All segment offsets are non negative.\n * All segment accesses are continuous.\n * No access to builtin segments beyond the allowed region. This region is defined\n by the start ptr (0), and the ptr returned in final stack.\n * No access to program segment beyond program data.\n Note: There is no need to check that builtin segment accesses are continuous. They will be\n accessed continuously in the builtin itself.\n \"\"\"\n\n builtin_segments = runner.get_builtin_segments_info() if verify_builtins else {}\n builtin_segment_names = {seg.index: name for name, seg in builtin_segments.items()}\n builtin_segment_sizes = {seg.index: seg.size for seg in builtin_segments.values()}\n segment_usage = defaultdict(set)\n for addr in runner.vm_memory:\n # Check pure addresses.\n if not isinstance(addr, RelocatableValue):\n raise SecurityError(f'Accessed address {addr} is not relocatable.')\n # Check non negative offset.\n if addr.offset < 0:\n raise SecurityError(f'Accessed address {addr} has negative offset.')\n # Check builtin segment out of bounds.\n if addr.segment_index in builtin_segment_sizes:\n if not addr.offset < builtin_segment_sizes[addr.segment_index]:\n raise SecurityError(\n 'Out of bounds access to builtin segment '\n f'{builtin_segment_names[addr.segment_index]} at {addr}.')\n\n # Check out of bounds for program segment.\n if addr.segment_index == runner.program_base.segment_index:\n if not addr.offset < len(runner.program.data):\n raise SecurityError(f'Out of bounds access to program segment at {addr}.')\n\n # Check usage.\n segment_usage[addr.segment_index].add(addr.offset)\n\n # Check continuity.\n for segment_index, usage in segment_usage.items():\n if len(usage) != max(usage) + 1:\n # Find the non continuity for error message.\n i = 0\n while i in usage:\n i += 1\n raise SecurityError(f'Non continuous segment {segment_index} at offset {i}.')\n","sub_path":"examples/starkex-cairo/starkware/cairo/lang/vm/security.py","file_name":"security.py","file_ext":"py","file_size_in_byte":2605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"502367240","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 ('Catalogue', '0001_initial'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='author',\n name='DateOfBirth',\n field=models.DateField(null=True),\n ),\n migrations.AlterField(\n model_name='author',\n name='Bio',\n field=models.TextField(null=True),\n ),\n migrations.AlterField(\n model_name='post',\n name='text',\n field=models.TextField(null=True),\n ),\n ]\n","sub_path":"Catalogue/migrations/0002_auto_20160717_1254.py","file_name":"0002_auto_20160717_1254.py","file_ext":"py","file_size_in_byte":683,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"627972679","text":"import cv2\nimport numpy as np\nimport sys,os\nfrom glob import glob\nfrom config.resources import video_resource\nfrom config.global_parameters import frameWidth, frameHeight\n\ndef get_frames(videoPath, start_time=5000, end_time=120000, time_step=2000):\n\n print(\"Getting frames for \",videoPath)\n try:\n cap = cv2.VideoCapture(videoPath)\n for k in range(start_time, end_time+1, time_step):\n cap.set(cv2.CAP_PROP_POS_MSEC, k)\n success, frame = cap.read()\n if success:\n frame = cv2.resize(frame,(frameWidth, frameHeight))\n yield frame\n except Exception as e:\n print(e)\n return\nif __name__==\"__main__\":\n get_videos(sys.argv[1])\n","sub_path":"DIP_project/video.py","file_name":"video.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"170809338","text":"from pathlib import Path\nimport yaml\n\n\ndef load_config(config_file=None):\n default_file = Path(__file__).parent / 'config.yaml'\n with open(default_file, 'r') as f:\n config = yaml.safe_load(f)\n\n cf_dict = {}\n if config_file:\n cf_dict = yaml.safe_load(config_file)\n\n config.update(**cf_dict)\n\n return config\n\n\nclass GlobalSettings:\n error_text: str\n user: str\n","sub_path":"blog/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"483973534","text":"# -*- coding: utf-8 -*-\nimport django\nfrom django.contrib.auth.models import User, Group\nfrom rest_framework import viewsets\nfrom .serializers import UserSerializer, GroupSerializer\nfrom django.contrib.auth.decorators import login_required\nfrom django.views.generic import TemplateView\nfrom django.views.generic.base import RedirectView\nfrom django.contrib.auth.forms import AuthenticationForm\nfrom django.contrib.auth import login, authenticate, logout\nfrom django.core.urlresolvers import reverse\nfrom django.shortcuts import redirect, render, get_object_or_404, render_to_response\nfrom django.template import RequestContext\nfrom django.contrib import messages\nfrom django.utils.crypto import random\nfrom django.core.mail import send_mail\nfrom django.utils import timezone\nfrom ..caracterizacion.models import *\nimport hashlib\nimport datetime\nimport os\n\nclass UserViewSet(viewsets.ModelViewSet):\n \"\"\"\n API endpoint that allows users to be viewed or edited.\n \"\"\"\n queryset = User.objects.all().order_by('-date_joined')\n serializer_class = UserSerializer\n\nclass GroupViewSet(viewsets.ModelViewSet):\n \"\"\"\n API endpoint that allows groups to be viewed or edited.\n \"\"\"\n queryset = Group.objects.all()\n serializer_class = GroupSerializer\n\nclass CrearView(TemplateView):\n def get(self, request, *args, **kwargs):\n return render(request, 'registro.html')\n\n def post(self, request, *args, **kwargs):\n usuario = request.POST['username']\n correo = request.POST['correo']\n clave_uno = request.POST['password1']\n clave_dos = request.POST['password2']\n if clave_uno != clave_dos:\n msg = \"las contraseñas no coinciden!\"\n tipo = \"danger\"\n tipomsg = \"Error\"\n ctx = {'msg':msg, 'tipo':tipo,'tipomsg':tipomsg}\n return render(request,'registro.html',ctx)\n form = User.objects.create_user(username=usuario,email=correo,password=clave_uno)\n form.is_active=False\n salt = hashlib.sha1(str(random.random())).hexdigest()[:5]\n activation_key = hashlib.sha1(salt+correo).hexdigest()\n key_expires = timezone.now() + datetime.timedelta(2)\n pk = User.objects.get(pk=form.pk)\n form.save()\n p = Perfil(usuario_id=pk.pk,activation_key=activation_key,key_expires=key_expires)\n p.save()\n # Send email with activation key\n email_subject = 'Confirmacion de la cuenta'\n email_body = \"Hola %s, te damos las gracias por registrarte en REPOSITIO, es un placer contar con tu apoyo. \\nTus datos de acceso son: \\nUsuario: %s \\nContraseña: %s \\nPara completar el registro solo abre el siguiente link que estara habilitado por las sigientes 48 horas \\nhttp://127.0.0.1:8000/sistema/confirmacion/correo/%s\" % (usuario, usuario, clave_uno, activation_key)\n\n send_mail(email_subject, email_body, 'repositio@gmail.com',[correo], fail_silently=False)\n messages.success(request, 'usuario registrado exitosamente!.')\n return redirect('/sistema/sesion/')\n\nclass Register_confirm(TemplateView):\n def get(self, request, activation_key, *args, **kwargs):\n #check if user is already logged in and if he is redirect him to some other url, e.g. home\n # check if there is UserProfile which matches the activation key (if not then display 404)\n profile = get_object_or_404(Perfil, activation_key=activation_key)\n\n #check if the activation key has expired, if it hase then render confirm_expired.html\n if profile.key_expires < django.utils.timezone.now():\n return render_to_response('confirmacion_expirada.html')\n user = profile.usuario\n usuario = get_object_or_404(User,username=user)\n correo = usuario.email\n user.is_active = True\n user.save()\n email_subject = 'Datos del usuario: %s'%(user)\n email_body = \"Hola %s, queremos confirmarte que que pertences a REPOSITIO y puedes hacer uso de la plataforma con el siguiente usuario. \\n\\nNombre de usuario: %s\" % (user, user)\n send_mail(email_subject, email_body, 'repositio@gmail.com',[correo], fail_silently=False)\n return render(request, 'confirmacion.html')\n\nclass Salir(RedirectView):\n\n permanent = False\n query_string = True\n\n def get_redirect_url(self):\n logout(self.request)\n return reverse('inicio')\n\nclass IngresarView(TemplateView):\n def get(self, request, *args, **kwargs):\n return render(request, 'sesion.html')\n\n def post(self, request, *args, **kwargs):\n form = AuthenticationForm(request.POST)\n if form.is_valid:\n usuario = request.POST['username']\n clave = request.POST['password']\n acceso = authenticate(username=usuario, password=clave)\n if acceso is not None:\n if acceso.is_active and acceso.is_staff:\n login(request, acceso)\n return redirect('/')\n elif acceso.is_active and not acceso.is_staff:\n login(request, acceso)\n return redirect('/caracterizacion/perfil/')\n else:\n msg = \"el usuario no esta ativo\"\n tipo = \"warning\"\n tipomsg = \"Ops\"\n else:\n msg = \"el usuario no esta registrado, registrate\"\n tipo = \"info\"\n tipomsg = \"Lo sentimos\"\n ctx = {'msg':msg,'tipo':tipo,'tipomsg':tipomsg}\n return render(request, 'sesion.html', ctx)\n","sub_path":"apps/sistema/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"18631128","text":"from radtrack.util.RbMath import rpn, rpnVariableDict\nimport pytest\n\ndef test_rpn_success():\n testList = [(\"1 2 +\", 3),\n (\"100 10 /\", 10),\n (\"10 17 -\", -7),\n (\"6 9 *\", 54),\n (\"6 9 mult\", 54),\n (\"5 sqr\", 25),\n (\"16 sqr 4 3 -35 * * - sqrt -16 + 2 3 * /\", 5.0/3.0),\n (\"2 10x\", 100),\n (\"1 4 4 0 dist2\", 5.0),\n (\"1 2 2 minN\", 1),\n (\"1 2 3 3 maxN\", 3),\n (\"1.23\", 1.23)]\n\n for test in testList:\n assert rpn(test[0]) == test[1]\n\n# These tests should fail\ndef test_rpn_fail():\n for exp in [\"1 + 2\", \"+ 1 2\", \"1 2 3\", \"a\"]:\n with pytest.raises(ValueError):\n rpn(exp)\n\n# Find RPN expressions in files that can't be processed\ndef test_rpn_files():\n import glob, sys, os\n from radtrack.beamlines.RbElegantElements import fileImporter\n\n for walkTuple in os.walk(os.path.join(os.getcwd(), 'use_cases', 'elegant')):\n dirName = walkTuple[0]\n for fileName in [os.path.join(dirName, name) for name in walkTuple[2] if name.endswith('.lte')]:\n # Load rpn expressions from associated .ele files\n for eleFileName in [os.path.join(dirName, name) for name in walkTuple[2] if name.endswith('.ele')]:\n if 'template' in eleFileName.lower():\n continue\n with open(eleFileName) as f:\n insideRPNsection = False\n insideQuote = False\n for line in f:\n line = line.strip()\n if line == '&rpn_expression':\n insideRPNsection = True\n elif line == '&end' and insideRPNsection:\n break\n elif insideRPNsection:\n if '\"' in line:\n if insideQuote:\n line = line.split('\"')[0]\n else:\n line = line.split('\"')[1]\n insideQuote = not insideQuote\n if line:\n try:\n rpn(line)\n except ValueError:\n pass\n # Test that files load without errors\n elementDictionary, _ = fileImporter(fileName)\n\n for element in elementDictionary.values():\n if element.isBeamline():\n continue\n for index, thing in enumerate(element.data):\n if element.dataType[index].lower() == 'string':\n continue\n try:\n answer = rpn(thing)\n try:\n if answer == float(thing):\n continue # Just a number\n else:\n assert answer == float(thing) # 2 interpretations of value\n except ValueError:\n pass # thing is an unambiguous rpn expression\n except ValueError:\n if thing:\n print(rpnVariableDict)\n print(fileName)\n print(element.name)\n print(type(element))\n print(element.displayLine())\n print(thing)\n print(element.dataType[index])\n assert False\n","sub_path":"tests/util/rpn_test.py","file_name":"rpn_test.py","file_ext":"py","file_size_in_byte":3702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"455828456","text":"import requests\nimport json\nimport pprint \nimport re\n\nINCLUDEPAPERP = re.compile(r\"\\\\includepaper\\{chapters/(.*?)\\}\")\nBOOKAUTHORP = re.compile(r\"\\\\author{(.*?)}\")\nLASTAND = re.compile(r\"(\\\\lastand|\\\\and)\")\nCHAPTERAUTHORP = re.compile(r\"\\\\author{(.*?) *\\\\affiliation{(.*)}\")\nTITLEP = re.compile(r\"\\\\title{(.*?)}\")\nISBNP = re.compile(r\"\\\\lsISBNdigital}{(.*)}\") \nCHAPTERKEYWORDSP = re.compile(r\"\\\\keywords{(.*?)}\")\nABSTRACTP = re.compile(r\"\\\\abstract{(.*?)[}\\n]\")\nBACKBODYP = re.compile(r\"\\\\BackBody{(.*?)[}\\n]\")\nKEYWORDSEPARATOR = re.compile(\"[,;-]\")\nPAGERANGEP = re.compile(\"{([0-9]+--[0-9]+)}\")\nBIBAUTHORP = re.compile(r\"author={([^}]+)\") #current setup adds space after author\nBIBTITLEP = re.compile(r\"title={{([^}]+)}}\")\n\nclass Publication():\n def __init__(self):\n self.metadata={'upload_type': 'publication',\n 'access_right':'open',\n 'license':'cc-by',\n 'imprint_publisher':'Language Science Press',\n 'imprint_place':'Berlin',\n 'communities': [{'identifier':'langscipress'}],\n 'prereserve_doi': True, \n 'language':'eng' \n } \n \n def register(self,token):\n data={\n #'filename': \"test.csv\",\n 'metadata': self.metadata\n } \n pprint.pprint(json.dumps(data)) \n \n r = requests.post('https://zenodo.org/api/deposit/depositions', \n params={'access_token': token}, \n headers = {\"Content-Type\": \"application/json\"},\n data=json.dumps(data)\n )\n pprint.pprint(r.json())\n try:\n return r.json()['metadata'][\"prereserve_doi\"][\"doi\"]\n except KeyError:\n print(r.json()['errors'])\n raise\n\n \nclass Book(Publication): \n def __init__(self):\n Publication.__init__(self)\n self.title=None\n self.authors=[]\n self.abstract=\"Abstract could not be found\"\n self.keywords=None\n self.digitalisbn=None\n self.getBookMetadata()\n self.chapter = []\n self.getChapters()\n self.metadata['publication_type'] = 'book'\n #self.metadata['related_identifiers'] = [{'isAlternateIdentifier':self.digitalisbn}] \n self.metadata['title']=self.title\n self.metadata['description']=self.abstract\n self.metadata['creators']=[{'name':au} for au in self.authors] \n self.metadata['keywords']=self.keywords\n \n def getBookMetadata(self):\n localmetadataf = open('localmetadata.tex')\n localmetadata = localmetadataf.read()\n localmetadataf.close()\n self.title = TITLEP.search(localmetadata).group(1)\n authorstring = BOOKAUTHORP.search(localmetadata).group(1)\n authors = []\n for i, au in enumerate(LASTAND.split(authorstring)):\n if i%2 == 0:#get rid of splitters, i.e. \"and\" and \"lastand\" at odd positions\n authors.append(au.strip())\n self.authors = authors\n self.abstract = BACKBODYP.search(localmetadata).group(1)\n try:\n self.keywords = [x.strip() for x in KEYWORDSEPARATOR.split(CHAPTERKEYWORDSP.search(localmetadata).group(1))]\n except:\n pass\n self.digitalisbn = ISBNP.search(localmetadata).group(1) \n \n def getChapters(self):\n mainf = open('main.tex')\n main = mainf.read()\n mainf.close()\n chapterpaths = INCLUDEPAPERP.findall(main) \n self.chapters = [Chapter(cp,booktitle=self.title,isbn=self.digitalisbn) for cp in chapterpaths]\n \n\nclass Chapter(Publication): \n def __init__ (self,path,booktitle='',isbn=False):\n print(path)\n Publication.__init__(self)\n chapterf = open('chapters/%s.tex'%path)\n chapter = chapterf.read()\n chapterf.close()\n preamble = chapter.split('\\\\begin{document}')[0]\n abstract = ABSTRACTP.search(preamble).group(1)\n keywords = []\n try:\n keywords = [x.strip() for x in CHAPTERKEYWORDSP.search(preamble).group(1).split(',')] \n except:\n pass\n self.path = path.strip() \n self.abstract = abstract \n self.keywords = keywords\n self.pagerange = ''\n for l in open('collection_tmp.bib').readlines(): \n if l.startswith(\"@incollection{chapters/%s,\"%path): \n self.pagerange = PAGERANGEP.search(l).group(1) \n self.authors = [au.strip() for au in BIBAUTHORP.search(l).group(1).split(' and ')]\n self.title = BIBTITLEP.search(l).group(1)\n break \n self.booktitle = booktitle\n if isbn:\n self.bookisbn = isbn \n self.metadata['publication_type'] = 'section' \n self.metadata['imprint_isbn'] = self.bookisbn\n self.metadata['partof_title'] = self.booktitle \n self.metadata['title']=self.title\n self.metadata['description']=self.abstract\n self.metadata['creators']=[{'name':au} for au in self.authors] \n self.metadata['keywords']=self.keywords\n #TODO needs affiliation\n #'creators': [{'name': 'Doe, John',\n #'affiliation': 'Zenodo'}],\n #self.metadata['partof_pages'] = chapter.pagerange\n #self.metadata['related_identifiers'] = [{'hasPart':self.bookisbn}] #unintuitive directionality of hasPart and isPart\n\n\n \n\nif __name__ == \"__main__\":\n book = Book() \n #for c in book.chapters:\n #pprint.pprint(c.metadata) \n tokenfile = open('zenodo.token')\n token = open('zenodo.token').read().strip()\n tokenfile.close()\n print(token) \n #bookdoi = book.register(token)\n #print(\"BookDOI{%s}\"%bookdoi)\n offset = 0\n for ch in book.chapters[offset:]: #for continuation if program stops in the middle of a book\n chapterf = open('chapters/%s.tex'%ch.path)\n chapterlines = chapterf.readlines()\n chapterf.close()\n for line in chapterlines:\n if \"ChapterDOI\" in line:\n print(\"DOI already present in %s\"%ch.path)\n raise IOError\n chapterDOI = ch.register(token) \n insertstring = \"\\\\ChapterDOI{%s}\\n\"%chapterDOI \n chapterf = open('chapters/%s.tex'%ch.path,'w')\n chapterf.write(chapterlines[0]) \n chapterf.write(insertstring) \n chapterf.write(\"\".join(chapterlines[1:])) \n chapterf.close() \n\n \n#check sanity of files before registering\n","sub_path":"zenodo.py","file_name":"zenodo.py","file_ext":"py","file_size_in_byte":5983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"83248998","text":"import sys\nimport rwcsv\n#import rwcsv import *: Dann könnte man auf den Modulnamen verzichten\n#import rwcsv as r\n\nfilename = \"D:/TMP/adr.csv\"\noutfile = \"D:/TMP/out.csv\"\nif (len(sys.argv) > 2):\n filename = sys.argv[1]\n outfile = sys.argv[1]\n\nerg=rwcsv.einlesen(filename=filename)\n(fieldnames,db)=erg\nrwcsv.ausgeben(erg)\nrwcsv.ausgeben(erg,outfile=outfile)\nserg=rwcsv.suchen(erg,\"ort\",\"muenchen\")","sub_path":"Schulung/rwcsv_main.py","file_name":"rwcsv_main.py","file_ext":"py","file_size_in_byte":401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"553137916","text":"from Cimpl import *\r\n\r\n'#Created by Tawseef Patel 101145333'\r\n'#Team Number 65'\r\n\r\n\r\ndef flip_vertical(image: Image) -> Image:\r\n \"\"\"Function takes an image and returns a copy of the image that has been\r\n flipped vertically (across the \"y\" axis in an x-y co-ordinate system)\r\n \"\"\"\r\n flipped_image = copy(image)\r\n middle_pixel = get_width(flipped_image) // 2\r\n width = get_width(flipped_image)\r\n height = get_height(flipped_image)\r\n\r\n for x in range(middle_pixel):\r\n for y in range(height):\r\n r, g, b = get_color(image, x, y)\r\n new_r, new_g, new_b = get_color(image, abs(width - x) - 1, y)\r\n set_color(flipped_image, x, y, create_color(new_r, new_g, new_b))\r\n set_color(flipped_image, width - x - 1, y, create_color(r, g, b))\r\n\r\n return flipped_image\r\n\r\n'#IMAGE LOADING'\r\nif __name__ == \"__main__\":\r\n file = load_image(choose_file())\r\n show(flip_vertical(file))\r\n","sub_path":"Project/Milestone 2/T65_P5_flip_vertical.py","file_name":"T65_P5_flip_vertical.py","file_ext":"py","file_size_in_byte":945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"188750676","text":"#!/usr/bin/python3\nfrom sys import argv\n\n\nclass Chessboard:\n \"\"\"Represents a chessboard.\"\"\"\n def __init__(self, size):\n \"\"\"Initialize the data.\"\"\"\n self.size = size\n self.cols = []\n\n def place_in_next_row(self, col):\n \"\"\"Place in next row.\"\"\"\n self.cols.append(col)\n\n def remove_in_current_row(self):\n \"\"\"Remove in current row.\"\"\"\n return self.cols.pop()\n\n def next_row_safe(self, col):\n \"\"\"Check if current col in the next row is safe.\"\"\"\n row = len(self.cols)\n for q_col in self.cols:\n if col == q_col:\n return False\n\n for q_row, q_col in enumerate(self.cols):\n if q_col - q_row == col - row:\n return False\n\n for q_row, q_col in enumerate(self.cols):\n if self.size - q_col - q_row == self.size - col - row:\n return False\n\n return True\n\n def display(self):\n \"\"\"Display a valid solution.\"\"\"\n print('[', end='')\n for row in range(self.size):\n for col in range(self.size):\n if col == self.cols[row]:\n print('[{}, {}]'.format(row, col), end='')\n if row < self.size - 1:\n print(', ', end='')\n print(']')\n\n\ndef solve(size):\n \"\"\"Solve the N queens problem.\"\"\"\n board = Chessboard(size)\n row = col = 0\n while True:\n while col < size:\n if board.next_row_safe(col):\n board.place_in_next_row(col)\n row += 1\n col = 0\n break\n else:\n col += 1\n\n if col == size or row == size:\n if row == size:\n board.display()\n board.remove_in_current_row()\n row -= 1\n\n try:\n prev_col = board.remove_in_current_row()\n except IndexError:\n break\n\n row -= 1\n col = 1 + prev_col\n\nif len(argv) != 2:\n print('Usage: nqueens N')\n exit(1)\ntry:\n queens = int(argv[1])\nexcept ValueError:\n print('N must be a number')\n exit(1)\nif queens < 4:\n print('N must be at least 4')\n exit(1)\n\nsolve(queens)\n","sub_path":"0x08-python-more_classes/101-nqueens.py","file_name":"101-nqueens.py","file_ext":"py","file_size_in_byte":2232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"28847395","text":"from PhysicsTools.NanoAOD.common_cff import *\nimport FWCore.ParameterSet.Config as cms\n\n\nBToKmumu=cms.EDProducer(\"BToKmumuProducer\",\n beamSpot=cms.InputTag(\"offlineBeamSpot\"),\n vertexCollection=cms.InputTag(\"offlineSlimmedPrimaryVertices\"),\n muonCollection = cms.InputTag(\"linkedObjects\",\"muons\"),\n PFCandCollection=cms.InputTag(\"packedPFCandidates\"),\n lostSubLeadMuonTrackCollection = cms.InputTag(\"lostTracks\"),\n lostChHadrTrackCollection = cms.InputTag(\"lostTracks\"),\n LeadMuonMinPt=cms.double(1.),\n LeadMuonMaxEta=cms.double(2.4),\n SubLeadMuonMinPt=cms.double(1.),\n SubLeadMuonMaxEta=cms.double(2.4), \n KaonMinPt=cms.double(1.),\n KaonMaxEta=cms.double(2.4),\n KaonMinDCASig=cms.double(-1.),\n DiMuonChargeCheck=cms.bool(True),\n JPsiMassConstraint=cms.double(-1), #2-trk refitting uses measured di-muon mass\n save2TrackRefit=cms.bool(False),\n useLostSubLeadMuonTracks=cms.bool(False),\n useLostChHadrTracks=cms.bool(False),\n vtxCL_min=cms.double(1.e-3),\n Bmass_min=cms.double(2.),\n Bmass_max=cms.double(8.)\n )\n\n\n#Example not used for now: could replace BToKmumu collection in BToKmumuTable or be used in a cloned BToKJPsimumuTable\nBToKJPsimumu=BToKmumu.clone(JPsiMassConstraint = cms.double(3.096916))\n\n\n\nBToKmumuTable=cms.EDProducer(\"SimpleCompositeCandidateFlatTableProducer\", \n src=cms.InputTag(\"BToKmumu\"),\n cut=cms.string(\"\"),\n name=cms.string(\"BToKmumu\"),\n doc=cms.string(\"BToKmumu Variable\"),\n singleton=cms.bool(False),\n extension=cms.bool(False),\n variables=cms.PSet(\n mu1_index=Var(\"userInt('mu1_index')\", int,doc=\"index of corresponding leading muon\"),\n mu2_index=Var(\"userInt('mu2_index')\", int,doc=\"index of corresponding subleading muon\"),\n kaon_index=Var(\"userInt('kaon_index')\", int,doc=\"PFCand index of corresponding kaon\"),\n mu2_lostTrack_index=Var(\"userInt('mu2_lostTrack_index')\", int,doc=\"LostTrack index of corresponding subleading muon\"),\n kaon_lostTrack_index=Var(\"userInt('kaon_lostTrack_index')\", int,doc=\"LostTrack index of corresponding kaon\"),\n mu2_isPFCand=Var(\"userInt('mu2_isPFCand')\", int,doc=\"flag is subleading muon from PFCand\"),\n kaon_isPFCand=Var(\"userInt('kaon_isPFCand')\", int,doc=\"flag is kaon from PFCand\"),\n mumuRefit=Var(\"userInt('mumuRefit')\", int,doc=\"result of di muon refit\"),\n mu1_pt=Var(\"userFloat('mu1_pt')\", float,doc=\"pt of leading muon (refitted)\"),\n mu1_eta=Var(\"userFloat('mu1_eta')\", float,doc=\"eta of leading muon (refitted)\"),\n mu1_phi=Var(\"userFloat('mu1_phi')\", float,doc=\"phi of leading muon (refitted)\"),\n mu1_charge=Var(\"userInt('mu1_charge')\", int,doc=\"charge of leading muon\"),\n mu1_dxy=Var(\"daughter('muon1').dB('PV2D')\", float,doc=\"dxy of leading muon (with sign) wrt first PV, in cm\"),\n mu1_dz=Var(\"daughter('muon1').dB('PVDZ')\", float,doc=\"dz of leading muon (with sign) wrt first PV, in cm\"),\n mu2_pt=Var(\"userFloat('mu2_pt')\", float,doc=\"pt of subleading muon (refitted)\"),\n mu2_eta=Var(\"userFloat('mu2_eta')\", float,doc=\"eta of subleading muon (refitted)\"),\n mu2_phi=Var(\"userFloat('mu2_phi')\", float,doc=\"phi of subleading muon (refitted)\"),\n mu2_charge=Var(\"userInt('mu2_charge')\", int,doc=\"charge of subleading muon\"),\n mu2_dxy=Var(\"daughter('muon2').dxy()\", float,doc=\"dxy of subleading muon \"),\n mu2_dz=Var(\"daughter('muon2').dz()\", float,doc=\"dz of subleading muon in cm\"),\n mumuKFit_mumu_mass=Var(\"userFloat('mumuKFit_mumu_mass')\", float, doc=\"dimuon invariant mass post 3tracks refit\"),\n kaon_pt=Var(\"userFloat('kaon_pt')\", float,doc=\"pt of kaon (refitted)\"),\n kaon_eta=Var(\"userFloat('kaon_eta')\", float,doc=\"eta of kaon (refitted)\"),\n kaon_phi=Var(\"userFloat('kaon_phi')\", float,doc=\"phi of kaon (refitted)\"),\n kaon_charge=Var(\"userInt('kaon_charge')\", int,doc=\"charge of kaon\"),\n kaon_DCASig=Var(\"userFloat('kaon_DCASig')\", float,doc=\"significance of xy-distance of closest approach kaon-beamspot\"),\n kaon_dxy=Var(\"daughter('kaon').dxy()\", float,doc=\"dxy of kaon (not refitted)\"),\n kaon_dz=Var(\"daughter('kaon').dz()\", float,doc=\"dz of kaon (not refitted)\"),\n mumu_pt=Var(\"userFloat('mumu_pt')\", float,doc=\"dimuon pt (refitted)\"),\n mumu_eta=Var(\"userFloat('mumu_eta')\", float,doc=\"dimuon eta (refitted)\"),\n mumu_phi=Var(\"userFloat('mumu_phi')\", float,doc=\"dimuon phi (refitted)\"),\n mumu_mass=Var(\"userFloat('mumu_mass')\", float,doc=\"dimuon mass (refitted)\"),\n mumu_mass_err=Var(\"userFloat('mumu_mass_err')\", float,doc=\"error on dimuon mass\"),\n mumu_Lxy=Var(\"userFloat('mumu_Lxy')\", float,doc=\"significance of dimuon vertex-beamspot xy-separation\"),\n mumu_ctxy=Var(\"userFloat('mumu_ctxy')\", float,doc=\"dimuon vertex-beamspot xy-separation/pt\"),\n mumu_Chi2_vtx=Var(\"userFloat('mumu_Chi2_vtx')\", float,doc=\"dimuon vertex chi2\"),\n mumu_CL_vtx=Var(\"userFloat('mumu_CL_vtx')\", float,doc=\"dimuon chi2 vertex probability\"),\n pt=Var(\"userFloat('pt')\", float,doc=\"pt of BToKmumu candidate (refitted)\"),\n eta=Var(\"userFloat('eta')\", float,doc=\"eta of BToKmumu candidate (refitted)\"),\n phi=Var(\"userFloat('phi')\", float,doc=\"phi of BToKmumu candidate (refitted)\"),\n mass=Var(\"userFloat('mass')\", float,doc=\"mass of BToKmumu candidate (refitted)\"),\n mass_err=Var(\"userFloat('mass_err')\", float,doc=\"error on mass of BToKmumu candidate\"), \n Lxy=Var(\"userFloat('Lxy')\", float,doc=\"significance of BToKmumu vertex-beamspot xy-separation\"),\n ctxy=Var(\"userFloat('ctxy')\", float,doc=\"BToKmumu vertex-beamspot xy-separation/pt\"),\n Chi2_vtx=Var(\"userFloat('Chi2_vtx')\", float,doc=\"BToKmumu vertex chi2\"),\n CL_vtx=Var(\"userFloat('CL_vtx')\", float,doc=\"BToKmumu chi2 vertex probability\"),\n cosAlpha=Var(\"userFloat('cosAlpha')\", float,doc=\"cosine of angle between BToKmumu momentum and vertex-beamspot separation\"),\n pt_2trk=Var(\"userFloat('pt_2trk')\", float,doc=\"pt of BToKmumu candidate (2-trk refitted)\"),\n eta_2trk=Var(\"userFloat('eta_2trk')\", float,doc=\"eta of BToKmumu candidate (2-trk refitted)\"),\n phi_2trk=Var(\"userFloat('phi_2trk')\", float,doc=\"phi of BToKmumu candidate (2-trk refitted)\"),\n mass_2trk=Var(\"userFloat('mass_2trk')\", float,doc=\"mass of BToKmumu candidate (2-trk refitted)\"),\n mass_err_2trk=Var(\"userFloat('mass_err_2trk')\", float,doc=\"error on mass of BToKmumu candidate (2-trk refitted)\"),\n Lxy_2trk=Var(\"userFloat('Lxy_2trk')\", float,doc=\"significance of BToKmumu vertex-beamspot xy-separation (2-trk refitted)\"),\n ctxy_2trk=Var(\"userFloat('ctxy_2trk')\", float,doc=\"BToKmumu vertex-beamspot xy-separation/pt (2-trk refitted)\"),\n Chi2_vtx_2trk=Var(\"userFloat('Chi2_vtx_2trk')\", float,doc=\"BToKmumu vertex chi2 (2-trk refitted)\"),\n CL_vtx_2trk=Var(\"userFloat('CL_vtx_2trk')\", float,doc=\"BToKmumu chi2 vertex probability (2-trk refitted)\"),\n cosAlpha_2trk=Var(\"userFloat('cosAlpha_2trk')\", float,doc=\"cosine of angle between BToKmumu momentum and vertex-beamspot separation (2-trk refitted)\"),\n )\n )\n\nBToKmumuSequence=cms.Sequence(BToKmumu)\nBToKmumuTables=cms.Sequence(BToKmumuTable)\n","sub_path":"PhysicsTools/NanoAOD/python/BToKmumu_cff.py","file_name":"BToKmumu_cff.py","file_ext":"py","file_size_in_byte":9351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"147686414","text":"import os\nimport logging\n\nfrom google.appengine.api import users\nfrom google.appengine.api import xmpp\nfrom google.appengine.ext import webapp\n\nfrom roster import *\n\nclass NotificationsHandler(webapp.RequestHandler):\n def get(self):\n logging.info(\"Running notifications task\")\n\n chef = Roster.getTodaysChef()\n\n if chef != None:\n user_address = ADDRESSES[chef]\n chat_message_sent = False\n chat_msg = \"Remember: It's your turn to cook!\"\n status_code = xmpp.send_message(user_address, chat_msg)\n chat_message_sent = (status_code == xmpp.NO_ERROR)\n\n logging.debug(\"Tried to send a chat message to \" + chef)\n\n if not chat_message_sent:\n logging.info(\"Could not send chat message to \" + str(user_address))\n\n message = mail.EmailMessage()\n\n message.sender = \"spagbolroster@appspot.com\"\n message.to = user_address\n message.subject = chat_msg\n message.body = \"If you prefer, you can receive gtalk notifications by adding spagbolroster@appspot.com to your list of chat contacts.\"\n\n message.send()\n logging.info(\"Sent an email notification to \" + str(user_address))\n\n\nclass InvitesHandler(webapp.RequestHandler):\n def get(self):\n logging.info(\"Inviting House Husbands to chat\")\n\n xmpp.send_invite(ADDRESSES[CHRIS])\n xmpp.send_invite(ADDRESSES[NICK])\n xmpp.send_invite(ADDRESSES[STEVE])\n xmpp.send_invite(ADDRESSES[ZAK])\n\n logging.debug(\"Invites sent!\")\n\n\napplication = webapp.WSGIApplication(\n [('/tasks/notifications', NotificationsHandler),\n ('/tasks/invites', InvitesHandler)],\n debug=True)\n\n","sub_path":"tasks.py","file_name":"tasks.py","file_ext":"py","file_size_in_byte":1859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"275271754","text":"#CSCI220L-F16-Lab3-Economics-JBO.py\r\n# Johnny Bello-Ogunu\r\n\r\ndef Inflation_Rate():\r\n currentCPI= eval(input(\"Enter this year's Comsumer's Price Index: \"))\r\n lastCPI=eval(input(\"Enter last year's Comsumer's Price Index: \"))\r\n inflationRate= ((currentCPI-lastCPI)/lastCPI)* 100\r\n print(\"The Inflation Rate: \", inflationRate, \"%\")\r\n\r\nInflation_Rate()\r\n","sub_path":"CSCI220L-F16-Lab3-Economics-JBO.py","file_name":"CSCI220L-F16-Lab3-Economics-JBO.py","file_ext":"py","file_size_in_byte":361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"589342951","text":"import numpy as np\nimport sys, os\n\nimport caffe\n\n# Path to your combined net prototxt files:\nautoencoder_root_path = '/home/kfrey/cnn_prefilter/heatmap_autoencoder/'\nimagenet_root_path = '/home/kfrey/caffe/models/bvlc_alexnet/'\nheatmap_net_root_path= '/home/kfrey/cnn_prefilter/heatmap_alexnet/'\n\n# The pre-trained Caffemodel files:\nimagenet_file = 'bvlc_alexnet.caffemodel'\nautoencoder_file = 'heatmap_autoencoder.caffemodel'\n\n# Their respective prototxt files:\nimagenet_proto = 'deploy.prototxt'\nautoencoder_proto = 'train_val.prototxt'\n\n# Chdir if your prototxt files specify your training and testing files\n# in relative paths:\nos.chdir(autoencoder_root_path)\nautoencoder_net = caffe.Net(autoencoder_proto, autoencoder_file, caffe.TRAIN)\n\nos.chdir(imagenet_root_path)\nimagenet_net = caffe.Net(imagenet_proto, imagenet_file, caffe.TRAIN)\n\nos.chdir(heatmap_net_root_path)\ncomb_net = caffe.Net('train_val.prototxt', caffe.TRAIN)\n\n# The layers you want to combine into the new caffe net:\nlayer_names = ['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'fc_heatmap_decoder']\n\nnets = [autoencoder_net, imagenet_net]\n\n# For each of the pretrained net sides, copy the params to\n# the corresponding layer of the combined net:\nfor net in nets:\n for layer in layer_names:\n if net.params.has_key(layer):\n W = net.params[layer][0].data[...] # Grab the pretrained weights\n b = net.params[layer][1].data[...] # Grab the pretrained bias\n comb_net.params[layer][0].data[...] = W # Insert into new combined net\n comb_net.params[layer][1].data[...] = b\n\n# Save the combined model with pretrained weights to a caffemodel file:\ncomb_net.save('pretrained.caffemodel')","sub_path":"combine_weights.py","file_name":"combine_weights.py","file_ext":"py","file_size_in_byte":1669,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"339537274","text":"import asyncio\nimport uuid\nfrom dataclasses import dataclass\nfrom typing import Union, Optional\n\n\n@dataclass(repr=False, eq=False, frozen=True)\nclass ActorRef: # serializable reference which represents an Actor\n actor_id: str\n name: str\n def __eq__(self, other): return isinstance(other, ActorRef) and self.actor_id == other.actor_id\n def __hash__(self): return hash(self.actor_id)\n def __repr__(self): return self.name\n def __str__(self): return self.name\n\n\nclass Actor: # inheritable Actor class\n def __init__(self):\n self.actor_ref = None\n self.system = None\n\n async def receive(\n self,\n sender: ActorRef,\n message: 'any'\n ) -> None:\n \"\"\"Called every time a message is delivered.\n\n Args:\n sender: Actor that sent the message.\n message: Received message.\n\n Returns:\n None\n \"\"\"\n raise NotImplementedError\n\n # noinspection PyProtectedMember\n def tell(\n self,\n actor: ActorRef,\n message: 'any',\n *,\n delay: Union[None, int, float] = None,\n period: Union[None, int, float] = None\n ) -> asyncio.Task:\n \"\"\"Asynchronously deliver a message to another actor.\n\n Args:\n actor: Anther actor that will receive a message from this actor.\n message: Message to another actor.\n delay: Send message after a delay.\n period: Resend message periodically.\n\n Returns:\n Delivery task that can be cancelled if the message is delayed or periodical.\n \"\"\"\n return self.system._tell(\n actor=actor,\n message=message,\n sender=self.actor_ref,\n delay=delay,\n period=period)\n\n # noinspection PyProtectedMember\n def watch(\n self,\n actor: ActorRef\n ) -> None:\n \"\"\"Watch another actor to get messaged when they are terminated.\n\n When the watched actor dies, this actor will receive a Terminated(actor) message.\n\n Args:\n actor: Actor to watch.\n\n Returns:\n None\n \"\"\"\n self.system._watch(\n actor=self.actor_ref,\n other=actor)\n\n # noinspection PyProtectedMember\n def unwatch(\n self,\n actor: ActorRef\n ) -> None:\n \"\"\"Stop watching another actor.\n\n This function can be called even if the actor is not watched anymore.\n\n Args:\n actor: Watched actor.\n\n Returns:\n None\n \"\"\"\n self.system._unwatch(\n actor=self.actor_ref,\n other=actor)\n\n def stop(self) -> None:\n \"\"\"Asynchronously stop this actor.\n\n Returns:\n None\n \"\"\"\n self.system.stop(actor=self.actor_ref)\n\n async def started(self) -> None:\n \"\"\"Called by the system to let this actor know that they will start receiving messages now.\n\n Returns:\n None\n \"\"\"\n pass\n\n async def stopped(self) -> None:\n \"\"\"Called by the system to let this actor know that they will not receive messages anymore.\n\n Returns:\n None\n \"\"\"\n pass\n\n @property\n def name(self) -> str:\n return self.actor_ref.name\n\n def __str__(self): return self.name\n def __repr__(self): return self.name\n\n\nclass ActorSystem: # ActorSystem controls all the actors\n def __init__(self, *, loop: Optional[asyncio.AbstractEventLoop] = None):\n if loop is None:\n loop = asyncio.get_event_loop()\n self._loop = loop\n self._actors = {}\n self._stopped = asyncio.Event(loop=loop)\n\n def create(\n self,\n actor: Actor,\n name: str = None\n ) -> ActorRef:\n \"\"\"Initialize and asynchronously start an actor.\n\n Args:\n actor: Actor instance to start.\n name: Name of the actor.\n\n Returns:\n Actor reference.\n\n Examples:\n Start an actor by passing the actor instance to this function.\n\n >> class MyActor(Actor):\n ... async def receive(self, sender, message): pass\n\n >> my_actor = ActorSystem().create(MyActor())\n \"\"\"\n if not isinstance(actor, Actor):\n raise ValueError(f'Not an actor: {actor}')\n actor_id = uuid4()\n if not name:\n name = f'{type(actor).__name__}-{actor_id}'\n actor_ref = ActorRef(actor_id, name)\n actor.actor_ref = actor_ref\n actor.system = self\n actor_ctx = _ActorContext()\n actor_ctx.lifecycle = self._loop.create_task(\n self.__start_lifecycle(actor, actor_ref, actor_ctx))\n self._actors[actor_ref] = actor_ctx\n return actor_ref\n\n def tell(\n self,\n actor: Union[Actor, ActorRef],\n message: 'any',\n *,\n delay: Union[None, int, float] = None,\n period: Union[None, int, float] = None\n ) -> asyncio.Task:\n \"\"\"Asynchronously deliver a message to an actor.\n\n Args:\n actor: Actor that will receive a message from the system.\n message: Message to another actor.\n delay: Send message after a delay.\n period: Resend message periodically.\n\n Returns:\n Delivery task that can be cancelled if the message is delayed or periodical.\n \"\"\"\n return self._tell(\n actor=actor,\n message=message,\n delay=delay,\n period=period)\n\n def stop(\n self,\n actor: Union[Actor, ActorRef]\n ) -> None:\n \"\"\"Asynchronously stop an actor.\n\n Args:\n actor: Actor to stop.\n\n Returns:\n None\n \"\"\"\n self._tell(\n actor=actor,\n message=PoisonPill())\n\n async def shutdown(\n self,\n timeout: Union[None, int, float] = None\n ) -> None:\n \"\"\"Initialize a shutdown of the actor system.\n\n Args:\n timeout: If the shutdown should take longer than `timeout`\n then the actors which are still running will be forcibly terminated.\n\n Returns:\n Awaitable until the system is fully shutdown.\n \"\"\"\n self._loop.create_task(self._shutdown(timeout=timeout))\n await self.stopped()\n\n async def stopped(self) -> None:\n \"\"\"Await system shutdown.\n\n Returns:\n Awaitable until the system is fully shutdown.\n \"\"\"\n await self._stopped.wait()\n\n def _tell(\n self,\n actor: Union[Actor, ActorRef],\n message: 'any',\n *,\n sender: Union[None, Actor, ActorRef] = None, # sender is None if the system sends the message\n delay=None,\n period=None\n ) -> asyncio.Task:\n actor = self._validate_actor_ref(actor)\n if sender:\n sender = self._validate_actor_ref(sender)\n ts = (self._loop.time() + delay) if delay else None\n return self._loop.create_task(\n self.__deliver(actor, message, sender=sender, ts=ts, period=period))\n\n def _watch(\n self,\n actor: Union[Actor, ActorRef],\n other: Union[Actor, ActorRef]\n ) -> None:\n actor = self._validate_actor_ref(actor)\n other = self._validate_actor_ref(other)\n if actor == other:\n raise ValueError(f'Actor cannot watch themselves: {actor}')\n actor_ctx = self._actors[actor]\n other_ctx = self._actors[other]\n actor_ctx.watching.append(other)\n other_ctx.watched_by.append(actor)\n\n def _unwatch(\n self,\n actor: Union[Actor, ActorRef],\n other: Union[Actor, ActorRef]\n ) -> None:\n other = self._validate_actor_ref(other, must_exist=False)\n if other not in self._actors:\n return # other actor has been terminated so unwatch is obsolete\n actor = self._validate_actor_ref(actor)\n if actor == other:\n raise ValueError(f'Actor cannot unwatch themselves: {actor}')\n actor_ctx = self._actors[actor]\n other_ctx = self._actors[other]\n if other in actor_ctx.watching:\n actor_ctx.watching.remove(other)\n other_ctx.watched_by.remove(actor)\n\n async def _shutdown(\n self,\n timeout: Union[None, int, float] = None\n ) -> None:\n lifecycle_tasks = []\n for actor_ref, actor_ctx in self._actors.items():\n self.stop(actor_ref)\n lifecycle_tasks.append(actor_ctx.lifecycle)\n done, pending = await asyncio.wait(lifecycle_tasks, timeout=timeout)\n for lifecycle_task in pending:\n lifecycle_task.cancel()\n self._stopped.set()\n\n def _validate_actor_ref(\n self,\n actor: Union[Actor, ActorRef],\n must_exist: bool = True\n ) -> ActorRef:\n if isinstance(actor, Actor):\n actor = actor.actor_ref\n if not isinstance(actor, ActorRef):\n raise ValueError(f'Not an actor: {actor}')\n if must_exist and actor not in self._actors:\n raise ValueError(f'Actor does not exist: {actor}')\n return actor\n\n async def __deliver(\n self,\n actor: ActorRef,\n message: 'any',\n *,\n sender: Optional[ActorRef] = None,\n ts: Union[None, int, float] = None,\n period: Union[None, int, float] = None\n ) -> None:\n delay = (ts - self._loop.time()) if ts else 0\n while True:\n if delay > 0:\n await asyncio.sleep(delay)\n if actor not in self._actors:\n # actor has been stopped, so attempt to send a deadletter to the sender\n if sender in self._actors:\n deadletter = DeadLetter(actor=actor, message=message)\n self._tell(sender, deadletter)\n break\n actor_ctx = self._actors[actor]\n asyncio.create_task(actor_ctx.letterbox.put((sender, message)))\n if not period:\n break\n delay = period\n\n async def __start_lifecycle(\n self,\n actor: Actor,\n actor_ref: ActorRef,\n actor_ctx\n ) -> None:\n try:\n await actor.started()\n while True:\n sender, message = await actor_ctx.letterbox.get()\n if isinstance(message, PoisonPill):\n break\n asyncio.create_task(actor.receive(sender, message))\n await actor.stopped()\n finally:\n # notify others that actor has been terminated\n for other in actor_ctx.watched_by:\n self.tell(other, Terminated(actor_ref))\n other_ctx = self._actors[other]\n other_ctx.watching.remove(actor_ref)\n # unwatch other actors\n for other in actor_ctx.watching:\n other_ctx = self._actors[other]\n other_ctx.watched_by.remove(actor_ref)\n # remove the actor\n del self._actors[actor_ref]\n\n\nclass _ActorContext: # context class that holds the internal state of an actor\n def __init__(self):\n self.letterbox = asyncio.Queue(maxsize=1) # bounded queue\n self.lifecycle = None # main task controlling actor's lifecycle\n self.watching = [] # this actor is watching other actors in the list\n self.watched_by = [] # this actor is watched by other actors in the list\n\n\n# Messages\n\n\n@dataclass(frozen=True)\nclass PoisonPill: # sent to an actor to stop them\n pass\n\n\n@dataclass(frozen=True)\nclass DeadLetter: # sent back to the sender if `actor` cannot receive the `message` because they have been terminated\n actor: ActorRef\n message: 'any'\n\n\n@dataclass(frozen=True)\nclass Terminated: # sent to a watcher when `actor` has been terminated\n actor: ActorRef\n\n\n# Utilities\n\n\ndef uuid4() -> str: return uuid.uuid4().hex\n","sub_path":"pyctor/actor.py","file_name":"actor.py","file_ext":"py","file_size_in_byte":12119,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"179979779","text":"from django.http import Http404\nfrom django.shortcuts import render\nfrom django.urls import reverse\nfrom django.utils.decorators import method_decorator\nfrom django.utils.translation import gettext as _, gettext_lazy\n\nfrom no_exceptions.exceptions import Http403\nimport requests\n\nfrom corehq import toggles\nfrom corehq.apps.reports.models import TableauVisualization\nfrom corehq.apps.domain.views.base import BaseDomainView\n\n\nclass TableauView(BaseDomainView):\n urlname = 'tableau'\n template_name = 'reports/tableau_template.html'\n\n section_name = gettext_lazy(\"Tableau Reports\")\n\n @property\n def section_url(self):\n return reverse('reports_home', args=(self.domain, ))\n\n @property\n def page_title(self):\n return self.visualization.name\n\n @property\n def visualization(self):\n try:\n return TableauVisualization.objects.get(domain=self.domain, id=self.kwargs.get(\"viz_id\"))\n except TableauVisualization.DoesNotExist:\n return None\n\n @property\n def page_url(self):\n return reverse(self.urlname, args=(self.domain, self.visualization.id,))\n\n @method_decorator(toggles.EMBEDDED_TABLEAU.required_decorator())\n def dispatch(self, request, *args, **kwargs):\n if self.visualization is None:\n raise Http404()\n if not self.request.couch_user.can_view_tableau_viz(self.domain, f\"{self.kwargs.get('viz_id')}\"):\n raise Http403\n return super().dispatch(request, *args, **kwargs)\n\n @property\n def page_context(self):\n if self.visualization.server.validate_hostname == '':\n hostname = self.visualization.server.server_name\n else:\n hostname = self.visualization.server.validate_hostname\n return {\n \"domain\": self.visualization.server.domain,\n \"server_type\": self.visualization.server.server_type,\n \"server_address\": self.visualization.server.server_name,\n \"validate_hostname\": hostname,\n \"target_site\": self.visualization.server.target_site,\n \"domain_username\": self.visualization.server.domain_username,\n \"view_url\": self.visualization.view_url,\n }\n\n def get(self, request, *args, **kwargs):\n if self.visualization.server.server_type == 'server':\n return self.tableau_server_response()\n return super().get(request, *args, **kwargs)\n\n def tableau_server_response(self):\n from requests_toolbelt.adapters import host_header_ssl # avoid top-level import that breaks docs build\n context = self.get_context_data()\n tabserver_url = 'https://{}/trusted/'.format(self.visualization.server.server_name)\n post_arguments = {'username': self.visualization.server.domain_username}\n if self.visualization.server.target_site != 'Default':\n post_arguments.update({'target_site': self.visualization.server.target_site})\n\n if self.visualization.server.validate_hostname != '':\n request = requests.Session()\n request.mount('https://', host_header_ssl.HostHeaderSSLAdapter())\n tabserver_response = request.post(tabserver_url,\n post_arguments,\n headers={'Host': self.visualization.server.validate_hostname})\n else:\n tabserver_response = requests.post(tabserver_url, post_arguments)\n\n if tabserver_response.status_code == 200:\n if tabserver_response.content != b'-1':\n context.update({'ticket': tabserver_response.content.decode('utf-8')})\n return render(self.request, self.template_name, context)\n else:\n context.update({\"failure_message\": _(\"Tableau trusted authentication failed\")})\n return render(self.request, 'reports/tableau_server_request_failed.html', context)\n else:\n message = _(\"Request to Tableau failed with status code {}\").format(tabserver_response.status_code)\n context.update({\"failure_message\": message})\n return render(self.request, 'reports/tableau_server_request_failed.html', context)\n","sub_path":"corehq/apps/reports/standard/tableau.py","file_name":"tableau.py","file_ext":"py","file_size_in_byte":4194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"546036443","text":"class Solution:\n def islandPerimeter(self, grid: List[List[int]]) -> int:\n visited = set()\n queue = collections.deque()\n for i in range(len(grid)):\n for j in range(len(grid[0])):\n if grid[i][j] == 1:\n queue.append((i,j))\n visited.add((i,j))\n \n perimeter = 0\n while queue:\n x, y = queue.popleft()\n neighbours = 0\n for dx, dy in [[1,0], [-1,0], [0,1], [0,-1]]:\n xn, yn = x+dx, y+dy\n if xn < 0 or xn >= len(grid) or yn < 0 or yn >=len(grid[0]) or grid[xn][yn] == 0:\n continue\n neighbours += 1\n if not (xn, yn) in visited:\n queue.append((xn,yn))\n visited.add((xn,yn))\n perimeter += 4-neighbours\n return perimeter\n \n# An alternative way (in Java):\n# class Solution {\n# public int islandPerimeter(int[][] grid) {\n# int m = grid.length, n = grid[0].length;\n# int count = 0;\n# int[][] dir = {{0,1},{1,0},{-1,0},{0,-1}};\n# for(int i = 0; i < m; i++){\n# for(int j = 0; j < n; j++){\n# if(grid[i][j] == 1){\n# for(int[] d: dir){\n# int x = i + d[0], y = j + d[1];\n# if(x < 0 || y < 0 || x == m || y == n || grid[x][y] == 0){\n# count++;\n# }\n# } \n# }\n# }\n# }\n# return count; \n# }\n# }","sub_path":"463.IslandPerimeter.py","file_name":"463.IslandPerimeter.py","file_ext":"py","file_size_in_byte":1511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"221328702","text":"import pandas as pd\nimport os\nimport json\nimport datetime\nimport re\nfrom multiprocessing.dummy import Pool as ThreadPool\nimport itertools\n\n\"\"\"\n get news return for one stock\n we only need to load the price csv one time, which gives us a boost in performance\n\"\"\"\n\nDATA_DIR = r\"D:\\data\\stocknet-dataset\"\nWORD_TO_DELETE = [\"AT_USER\", \"URL\", '-']\n\n\ndef clean_tweet(tweet, delete_hashtag=True):\n # TODO\n # 1. will we keep retweet (marked as \"rt\")\n # 2. a complete list of words to delete (AT_USER, URL, etc)\n # 3. how to deal with $\n try:\n cleaned = \"\"\n index_to_delete = []\n for i, w in enumerate(tweet):\n if w in WORD_TO_DELETE:\n index_to_delete += [i]\n continue\n if w == '$':\n if delete_hashtag:\n index_to_delete += [i, i + 1]\n else:\n index_to_delete += [i]\n continue\n if not re.search(r\"[a-zA-Z]\", w):\n # delete word which contains only symbols\n index_to_delete += [i]\n continue\n for i in sorted(index_to_delete, reverse=True):\n del tweet[i]\n except IndexError:\n # error example:\n # ['$', 'aapl', 'commentary', 'in', 'AT_USER', 'opening', 'print', '$', '$']\n print(tweet)\n return ' '.join(tweet)\n\n\ndef read_json_news(ticker, date, keep_rt=True, delete_hashtag=True):\n # type(date) is str\n path = os.path.join(DATA_DIR, \"tweet\", \"preprocessed\",\n ticker.upper(), date)\n with open(path) as f:\n tweets = []\n for line in f.readlines():\n tweets += [json.loads(line)]\n df_news = pd.DataFrame()\n for index, tweet in enumerate(tweets):\n if not keep_rt and tweet[\"text\"][0] == 'rt':\n continue\n s = pd.Series()\n s.set_value(\"Ticker\", ticker)\n s.set_value(\"DateTime\", tweet[\"created_at\"]\n [:-10] + tweet[\"created_at\"][-4:])\n s.set_value(\"Content\", clean_tweet(tweet[\"text\"], delete_hashtag))\n s.name = index\n df_news = df_news.append(s)\n return df_news\n\n\ndef get_all_news(ticker, save_dir, keep_rt=True, delete_hashtag=True):\n print(ticker)\n news_dir = os.path.join(DATA_DIR, \"tweet\", \"preprocessed\", ticker)\n df_all_news = pd.DataFrame()\n for f in os.listdir(news_dir):\n # if not f.startswith(\"2014-01\"):\n # continue\n df_news = read_json_news(\n ticker, f, keep_rt=keep_rt, delete_hashtag=delete_hashtag)\n df_all_news = pd.concat([df_all_news, df_news])\n if df_all_news.shape[0] == 0:\n print(\"%s does not have any entry\" % ticker)\n df_all_news.index = range(df_all_news.shape[0])\n df_all_news[\"DateTime\"] = pd.to_datetime(\n df_all_news[\"DateTime\"], format=\"%a %b %d %H:%M:%S %Y\").dt.tz_localize(\"UTC\").dt.tz_convert(\"US/Eastern\")\n # Attention: the date has already been transformed to US/Eastern time\n df_all_news.to_csv(os.path.join(save_dir, \"%s.tsv\" %\n ticker), date_format=\"%Y-%m-%d %H:%M:%S\", sep='\\t')\n return\n\n\ndef main():\n save_dir = \"D:/data/bert_news_sentiment/tweet\"\n all_tickers = os.listdir(os.path.join(DATA_DIR, \"tweet\", \"preprocessed\"))\n pool = ThreadPool(8)\n _ = pool.starmap(get_all_news, zip(\n all_tickers, itertools.repeat(save_dir), itertools.repeat(True), itertools.repeat(False)))\n pool.close()\n pool.join()\n # get_all_news(\"TM\", save_dir, True, False)\n return\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"data_cleaning/tweet/cat_all_news.py","file_name":"cat_all_news.py","file_ext":"py","file_size_in_byte":3587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"496324250","text":"# SterkeWachtwoorden\n# Boy Bouwense S1102301\n\n\n# een function die de sterkte van een wachtwoord bepaalt\ndef bepaal_wachtwoord_sterkte(wachtwoord):\n # een dictionary waar alle sterkheids factoren in staan\n sterk = { \"lengte\" : False, \"hoofdletter\" : False, \"kleineletter\" : False, \"cijfer\" : False, \"speciaalkarakter\" : False}\n # een if statement die de lengte van het wachtwoord beoordeeldt\n if len(wachtwoord) >= 8:\n sterk[\"lengte\"] = True\n # een for loop die alle tekens van het wachtwoord doorloopt\n for x in wachtwoord:\n # if statement die checkt of het een hoofdletter is\n if x.isupper():\n sterk[\"hoofdletter\"] = True\n # if statement die checkt of het een kleine letter is\n if x.islower():\n sterk[\"kleineletter\"] = True\n # if statement die checkt of het een cijfer is\n if x.isdigit():\n sterk[\"cijfer\"] = True\n # if statement die checkt of het een speciaal teken is\n if not(x.isdigit() or x.isalpha()):\n sterk[\"speciaalkarakter\"] = True\n sterk_counter = 0\n # een for loop die alle sterkheids factoren die true zijn optelt\n for x in sterk:\n if sterk[x]:\n sterk_counter +=1\n # een if statement die voor het aantal sterken factoren de sterkte bepaalt\n if sterk_counter == 5:\n return \"sterk\"\n elif sterk_counter == 3 or sterk_counter == 4:\n return \"matig\"\n else:\n return \"zwak\"\n\n# de main function\ndef main():\n # een lege list waar wachtwoorden aan toegevoegd kunnen worden\n ww_list = []\n # vraag hoeveel wachtwoorden er getest moeten worden\n t = int(input(\"aantal wachtwoorden dat gestest moet worden: \"))\n # een for loop die voor hoeveel wachtwoorden er getest moeten worden wachtwoorden als invoer vraagt\n for x in range(t):\n ww_list.append(input(\"wachtwoord %i: \" % (x+1)))\n # print de uitvoer\n print()\n counter = 1\n # for loop die alle wachtwoorden doorloopt en hun sterkte checkt en de uitvoer print\n for x in ww_list:\n print(\"wachtwoord %i is een %s wachtwoord\" % (counter, bepaal_wachtwoord_sterkte(x)))\n counter += 1\n\n\nif __name__ == \"__main__\":\n main()\n\n","sub_path":"SterkeWachtwoorden.py","file_name":"SterkeWachtwoorden.py","file_ext":"py","file_size_in_byte":2206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"348306658","text":"import numpy as np\nimport tensorflow as tf\nimport time\nimport gc\nimport getopt\nimport sys\nimport matplotlib.pyplot as plt\n\nfrom models.model_basis import BasisModel \nfrom models.model_Bayesian import Bayesian\nfrom data.func_demo import obj\nfrom helper_fns import train \nfrom helper_fns import generatestartx\n\ngc.disable() #disable garbage collector\n\nlogs_path = \"tmp/debug\"\n\n\n\n\n\n\ndef main(argv):\n #default\n basis_training_epochs = 5000\n total_iters = 6 #NUM data points to collect\n num_startx = 4 #number of starting exploration points\n startx_lowrange = -5 #should be chosen according to problem domain\n startx_highrange = 5\n opt_iters = 400\n bayes_alpha = 0.2\n bayes_beta = 4.0\n model_name = \"nothingrn\"\n acq_type = 'EI'\n save_dir = \"/home/anmol/projects/dngo/models/saved_models/\"\n X_dir = ('data/X.npy')\n y_dir = ('data/y.npy')\n\n opts, args = getopt.getopt(argv, \"te:ti:ns:oe:ba:bb:at:sd:sx:sy:mn:\")\n for opt, arg in opts:\n if (opt == \"-te\"):\n basis_training_epochs = arg\n elif (opt == \"-ti\"):\n total_iters = arg\n\n elif (opt == \"-ns\"):\n num_startx = arg \n elif (opt == \"-oe\"):\n opt_iters = arg\n elif(opt == \"-ba\"): \n bayes_alpha = arg\n\n elif (opt == \"-bb\"):\n bayes_beta = arg\n elif (opt == \"-at\"):\n acq_type = arg\n elif (opt == \"-sd\"):\n save_dir = arg\n\n elif (opt == \"-sx\"):\n X_dir = arg\n elif (opt == \"-sy\"):\n y_dir = arg\n elif(opt == \"-mn\"):\n model_name = arg\n #Load data\n x_train = np.load(X_dir)\n dim_inp = x_train.shape[1] \n y_train = np.load(y_dir)\n\n \n sess = tf.Session() #use default session\n\n #Instantiate basis model for training\n m1 = BasisModel(dim_in=dim_inp, train_mode = True)\n x_place,out_t = m1.get_params()\n train(x_place, out_t, x_train, y_train, basis_training_epochs, sess) #FIND BETTER WAY TO DO INitIALIZATION\n save_dir = m1.save_model(sess, save_dir)\n #Instatiante basis model for optimizing EI criterion \n m2 = BasisModel(dim_in=dim_inp, train_mode=False)\n \n \n \n for iter in range(total_iters):\n \n x_train = np.reshape(x_train,(-1,dim_inp))\n y_train = np.reshape(y_train,(-1,1))\n\n train(x_place, out_t, x_train, y_train,basis_training_epochs, sess)\n \n m2.load_model(sess, save_dir)\n xplace_2, x_var, y_hat, basis, var_op, basis_dim = m2.get_params()\n bayes = Bayesian(basis, xplace_2, x_var, x_train, y_train,basis_dim, bayes_alpha,bayes_beta, sess, var_op,acq_type) \n startx = generatestartx(num_startx, np.array([[-6.0, 6.0]])) \n #THIS NEEDS TO BE SPECIFIED BY USER IN SCRIPT#np.random.random_integers(startx_lowrange, startx_highrange, (num_startx, dim_inp)) + np.random.rand()/2.0\n\n if(iter>0): \n startx[-1,:] = next_xsample \n \n ei_min = 990000000 \n \n #try all starting points for optimization -- use the one with the largest EI for sampling\n start_time2 = time.time()\n for start_iter in range(num_startx): \n grad1,grad2, candidate_x = bayes.optimize_acquisition(np.reshape(startx[start_iter,:],(-1,dim_inp)),opt_iters)\n candidate_ei_minus = bayes.get_ei(candidate_x)\n\n if (candidate_ei_minus= 0:\n continue\n else:\n print(\"miss\")\n break\n \n else:\n print(\"hit\")\n \n","sub_path":"training/easy/dead-mens-shot/python3.py","file_name":"python3.py","file_ext":"py","file_size_in_byte":720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"334435742","text":"#%% Imports\nimport numpy as np\nimport scipy.linalg as sla\nimport cvxpy as cvx\nimport copy\nimport matplotlib.pyplot as plt\nfrom matplotlib.patches import Ellipse\nfrom copy import deepcopy\n\n# Riccati recursion\ndef Riccati(A,B,Q,R):\n\n # TODO implement infinite horizon riccati recursion\n P = np.zeros(np.shape(A))\n P_k1 = np.ones(np.shape(A))\n while np.amax(np.absolute(P_k1-P)) > 1e-5:\n P_k1 = deepcopy(P)\n L = -np.linalg.inv(R + B.T.dot(P_k1).dot(B)).dot(B.T).dot(P_k1).dot(A)\n P = Q + A.T.dot(P_k1).dot(A + B.dot(L))\n\n return L,P\n\n#%% Implement one step of MPC using cvxpy\n# Input: x(t), A, B, Q, R, P, N, x0, M, X_f\n# Output: u(t)\n\ndef opt_finite_traj(A, B, Q, R, P, N, x0, M, X_f0, Pf):\n converged = False\n n = Q.shape[0] # state dimension\n m = R.shape[0] # control dimension \n # Initialize variables\n x = cvx.Variable((N+1, n))\n u = cvx.Variable((N, m))\n # Initialize cost, constraint forms\n cost = []\n if Pf:\n cost.append(cvx.quad_form(x[N],P))\n constraints = []\n constraints.append(x[0] == x0)\n for k in range(N):\n cost.append(cvx.quad_form(x[k], Q))\n cost.append(cvx.quad_form(u[k], R))\n constraints.append(cvx.norm(u[k]) <= 1)\n constraints.append(cvx.norm(x[k]) <= 5)\n constraints.append(x[k+1] == (A @ (x[k]) + B @ (u[k])))\n constraints.append(cvx.norm(x[N]) <= 5)\n if X_f0:\n constraints.append(cvx.quad_form(x[N], M) <= 1)\n objective = cvx.Minimize(cvx.sum(cost))\n prob = cvx.Problem(objective, constraints)\n prob.solve()\n u_new = u.value\n if prob.status == cvx.OPTIMAL:\n converged = True\n # Note this is either -Inf or Inf if infeasible or unbounded\n return converged, u_new\n\n#%% Implement entire receding horizon control\ndef mpc(A, B, Q, R, P, N, x0, M, X_f0, Pf):\n x = copy.deepcopy(x0)\n x_hist = copy.deepcopy(x0)\n feas = True\n converged, u = opt_finite_traj(A, B, Q, R, P, N, x0, M, X_f0, Pf)\n if converged:\n x1 = A @ x0 + B @ u[0]\n x2 = A @ x1 + B @ u[1]\n x3 = A @ x2 + B @ u[2]\n x4 = A @ x3 + B @ u[3]\n x_hist = np.vstack([x_hist, x1])\n u_hist = u[0]\n x_traj = np.vstack([x0, x1, x2, x3, x4])\n round = 0\n eps = 1e-5\n # TODO: Collapse this into a while function\n for i in range(100):\n if (np.linalg.norm(x-x1) > eps):\n x = x1\n converged, u = opt_finite_traj(A, B, Q, R, P, N, x, M, X_f0, Pf)\n if converged:\n x1 = A @ x + B @ u[0]\n x2 = A @ x1 + B @ u[1]\n x3 = A @ x2 + B @ u[2]\n x4 = A @ x3 + B @ u[3]\n x_hist = np.vstack([x_hist, x1])\n u_hist = np.vstack([u_hist, u[0]])\n x_traj_i = np.vstack([x, x1, x2, x3, x4])\n x_traj = np.dstack([x_traj, x_traj_i])\n else:\n feas = False\n break\n round += 1\n else:\n break\n else:\n feas = False\n\n if feas == False:\n feasible = False\n else:\n feasible = True\n \n return feasible, x_hist, u_hist, x_traj\n#%% Define problem dynamics and cost functions\nA = np.array([[0.95, 0.5],[0., 0.95]])\nB = np.array([[0.],[1.]])\nQ = np.eye(2)\nR = np.eye(1)\n\nM = np.array([[0.04, 0.],[0., 1.06]])\ntest = M - A.T.dot(M).dot(A)\ntest_eig, _ = np.linalg.eig(test)\n# %%\nL_inf, P_inf = Riccati(A, B, Q, R)\n# %%\nx0 = np.array([-3.0, -2.5])\nN = 4\nfeas, x_hist, u_hist, x_traj = mpc(A, B, Q, R, P_inf, N, x0, M, X_f0=True, Pf=False)\n\n#%% plot trajectory at each timestep\nnum_plots = np.size(u_hist)\ncolormap = plt.cm.gist_ncar\nfig, ax = plt.subplots(figsize=(6, 6))\nM_eig, M_ev = np.linalg.eig(M)\nr1 = 1/np.sqrt(M_eig[0])\nr2 = 1/np.sqrt(M_eig[1])\nellipse = Ellipse((0,0), width=r1*2, height=r2*2, fill=False)\nellipse2 = Ellipse((0,0), width=10, height=10, fill=False, color = 'red')\nax.add_patch(ellipse)\nax.add_patch(ellipse2)\nplt.gca().set_prop_cycle(plt.cycler('color', plt.cm.jet(np.linspace(0, 1, num_plots))))\nfor i in range(num_plots):\n x_tj = x_traj[:,:,i]\n plt.plot(x_tj[:,0], x_tj[:,1])\n plt.scatter(x_hist[i,0], x_hist[i,1])\nplt.xlabel('x1')\nplt.ylabel('x2')\nplt.title('Terminal Constraint, No Terminal Cost')\n#%% Plot control\nplt.plot(u_hist)\nplt.xlabel('iteration')\nplt.ylabel('control')\nplt.title('No Terminal Constraint, No Terminal Cost')\n\n# %%\n","sub_path":"HW3/problem_3/terminal_invariant.py","file_name":"terminal_invariant.py","file_ext":"py","file_size_in_byte":4480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"361546726","text":"import asyncio\r\nimport aiohttp\r\nfrom aiohttp import web\r\nimport ssl\r\nimport os\r\nimport sys\r\n\r\nfrom bot import GetUpdate\r\n\r\nif 'TOKEN' not in os.environ or 'EXTERNAL_ADDRESS' not in os.environ or 'EXTERNAL_PORT' not in os.environ:\r\n print('REQUIRED VARIABLES NOT SET (TOKEN or EXTERNAL_ADDRESS or EXTERNAL_PORT)')\r\n sys.exit()\r\n\r\nasync def set_webhook(token, external_address, external_port):\r\n # set webhook (url with sert for receive updates from telegram)\r\n headers = {'content-type': 'multipart/form-data'}\r\n session = aiohttp.ClientSession(connector = aiohttp.TCPConnector(ssl = False));\r\n api_url = 'https://api.telegram.org/bot{}/'.format(token)\r\n webhook_url = 'https://{}:{}/bot/{}'.format(external_address, external_port, token)\r\n print(webhook_url)\r\n await session.post(api_url + 'setWebhook?url='+webhook_url, data={'certificate':open('ssl/iontzev-bot-public.pem', 'rb')})\r\n await session.close()\r\n\r\nif __name__ == '__main__':\r\n\r\n loop = asyncio.get_event_loop()\r\n\r\n app = web.Application()\r\n\r\n app.token = os.environ.get('TOKEN')\r\n external_address = os.environ.get('EXTERNAL_ADDRESS')\r\n external_port = os.environ.get('EXTERNAL_PORT')\r\n \r\n # set webhook - need once\r\n loop.run_until_complete(set_webhook(app.token, external_address, external_port))\r\n\r\n app.chats = {}\r\n\r\n # make routes\r\n app.router.add_route('POST', '/bot/{}'.format(app.token), GetUpdate)\r\n\r\n ssl_context = ssl.create_default_context(ssl.Purpose.CLIENT_AUTH)\r\n ssl_context.load_cert_chain('ssl/iontzev-bot-public.pem', 'ssl/iontzev-private.key')\r\n\r\n web.run_app(app, port=8443, ssl_context=ssl_context)\r\n\r\n\r\n","sub_path":"backend/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"95841011","text":"komputer = {\n \"procesor\":\"AMD Ryzen 5\",\n \"zasilaczV\":500,\n \"panel_gorny\":[\"USB 3.1 Gen.1\", \"wejscie mikrofonowe\", \"wyjscie słuchawkowe\"],\n \"gwarancja\":True,\n \"porty_wewnetrzne\":{\"PCI-ex16\":1, \"SataIII\":6,\"PCI-ex1\":3}\n}\n'''\nfor k,v in komputer.items():\n print(k,\":\",v)\n '''\nimport json\np=json.dumps(komputer, indent=4)\nprint(p)\nwith open(\"komputer.json\",\"w\") as file:\n file.write(p)","sub_path":"08-DataStructures/json20.py","file_name":"json20.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"137885177","text":"import arrow\nimport uuid\nfrom bloop import (Column, Integer, DateTime, UUID, GlobalSecondaryIndex,\n String, new_base, engine_for_profile)\n\n# ================================================\n# Model setup\n# ================================================\n\nengine = engine_for_profile(\"test-user-bloop\")\nBase = new_base()\n\n\nclass Account(Base):\n class Meta:\n read_units = 5\n write_units = 2\n\n id = Column(UUID, hash_key=True)\n name = Column(String)\n email = Column(String)\n by_email = GlobalSecondaryIndex(\n hash_key='email', projection='keys_only',\n write_units=1, read_units=5)\n\n\nclass Tweet(Base):\n class Meta:\n write_units = 10\n account = Column(UUID, hash_key=True)\n id = Column(String, range_key=True)\n content = Column(String)\n date = Column(DateTime(timezone='EU/Paris'))\n favorites = Column(Integer)\n\n by_date = GlobalSecondaryIndex(\n hash_key='date', projection='keys_only')\n\nengine.bind(base=Base)\n\n\n# ================================================\n# Usage\n# ================================================\n\naccount = Account(\n id=uuid.uuid4(), name='@garybernhardt',\n email='REDACTED')\ntweet = Tweet(\n account=account.id, id='616102582239399936',\n content='today, I wrote a type validator in Python, as you do',\n favorites=9,\n date=arrow.now())\n\nengine.save([account, tweet])\n","sub_path":"examples/tweet.py","file_name":"tweet.py","file_ext":"py","file_size_in_byte":1404,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"565013555","text":"import sys, pygame, random\nfrom pygame.locals import *\n\npygame.init()\nscreen_info = pygame.display.Info()\n\n# set the width and height to the size of the screen\nsize = (width, height) = (screen_info.current_w, screen_info.current_h)\nscreen = pygame.display.set_mode(size)\nclock = pygame.time.Clock()\n\ncolor = (26, 255, 255)\nball_image = pygame.image.load('./basic (no classes)/ball.png')\nball_image = pygame.transform.smoothscale(ball_image, (30, 30))\nball_rect = ball_image.get_rect()\nball_rect.center = (width//2, height//2)\nspeed = pygame.math.Vector2(0, 5)\nspeed.rotate_ip(random.randint(0, 360))\n\n\ndef move_ball():\n # get information from screen in case of resizing\n screen_info = pygame.display.Info()\n ball_rect.move_ip(speed)\n if ball_rect.left < 0 or ball_rect.right > screen_info.current_w:\n speed[0] *= -1\n ball_rect.move_ip(speed[0], 0)\n if ball_rect.top < 0 or ball_rect.bottom > screen_info.current_h:\n speed[1] *= -1\n ball_rect.move_ip(0, speed[1])\n\n\ndef main():\n while True:\n clock.tick(60)\n for event in pygame.event.get():\n if event.type == QUIT:\n sys.exit()\n move_ball()\n screen.fill(color)\n screen.blit(ball_image,ball_rect)\n pygame.display.flip()\n\n\nif __name__ == '__main__':\n main()","sub_path":"cwk5-6532107.20190622T052149228Z.home/home/workspace/cwk5web/Projects/PythonProjects/bouncingball/bouncingball.py","file_name":"bouncingball.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"525496975","text":"# -*- coding: utf-8 -*-\n\nimport socket\nimport sys\nimport os\nimport binascii\n\n# DEFAULT VALUES\nPROTOCOL=\"HADES/1.0\"\nHOST=\"localhost\"\nPORT=1716\n\n# Used to define request method\ndef DefineMethod():\n method = input(\"Give method: \")\n return method\n\n# Used to define request target\ndef DefineTarget():\n target = input(\"Give target: \")\n return target\n\n# Used to define Protocol/version, for example HADES/2.0\ndef DefineProtocol():\n protocol = input(\"Give protocol: \")\n return protocol\n\n# Used to define a filename for filename header\ndef DefineFilename():\n while 1:\n print(\"Add Filename header yes/no:\")\n addHeader=input().lower()\n if addHeader in (\"yes\", \"y\"):\n print(\"Give filename: \")\n filename=input()\n if not filename:\n print(\"Give a valid filename!\")\n continue\n else:\n try:\n f = open(filename, \"r\")\n return filename\n except FileNotFoundError:\n print(\"File not found!\")\n continue\n except:\n print(\"Unexpected error!\")\n continue\n f.close()\n elif addHeader in (\"no\", \"n\"):\n return None\n else:\n continue\n\n# Used to define a key for key header\ndef DefineKey():\n while 1:\n print(\"Add Key header yes/no: \")\n addHeader=input().lower()\n if addHeader in (\"y\", \"yes\"):\n print(\"Give key: \")\n key=input()\n if not key:\n print(\"Give a valid key!\")\n continue\n else:\n return key\n elif addHeader in (\"n\", \"no\"):\n return None\n else:\n continue\n\n# Used to define a host for host header. Currently host header is not used anyway in the server side.\ndef DefineHost():\n while 1:\n print(\"Add host header yes/no\")\n addHeader=input().lower()\n if addHeader in (\"y\", \"yes\"):\n print(\"Give Host: \")\n host=input()\n if not host:\n print(\"Give a valid host!\")\n continue\n else:\n return host\n elif addHeader in (\"n\", \"no\"):\n return None\n else:\n continue\n\n# Used to define host and port to connect \ndef DefineHostAndPort(HOST, PORT):\n while 1:\n print(\"Using host:{0} and port {1} yes/no\".format(HOST,PORT))\n pick = input()\n pick = pick.lower()\n if pick in (\"y\", \"yes\"):\n return HOST, PORT\n elif pick in (\"n\", \"no\"):\n print(\"Give new host: \")\n host=input()\n if not host:\n print(\"Give a valid host!\")\n continue\n print(\"Give new port: \")\n port=input()\n if str(port).isdigit() and not None:\n PORT = int(port)\n HOST = host\n return HOST, PORT\n else:\n print(\"Give a valid port!\")\n continue\n else:\n print(\"Give a valid host\")\n continue\n\n# User interface\ndef ModifyRequest(method=\"LIST\", target=\".\", protocol=PROTOCOL):\n headers={\"Filename\":None, \"Size\":None, \"Key\":None, \"Host\":None}\n\n while 1:\n print(\"\\nHost: {0} Port: {1}\".format(HOST,PORT))\n print(\"Current request:\")\n print(\"{0} {1} {2}\".format(method, target, protocol))\n if(headers[\"Filename\"]):\n filename = headers[\"Filename\"].split(\"/\")\n filename = filename[-1]\n print(\"Filename:{0}\".format(filename))\n print(\"Size:{0}\".format(headers[\"Size\"]))\n if(headers[\"Key\"]):\n print(\"Key:{0}\".format(headers[\"Key\"]))\n if(headers[\"Host\"]):\n print(\"Host:{0}\".format(headers[\"Host\"]))\n\n print(\"\\nOptions:\")\n print(\"0. Send\")\n print(\"1. Method\")\n print(\"2. Target\")\n print(\"3. Protocol\")\n print(\"4. Filename\")\n print(\"5. Key\")\n print(\"6. Host\")\n \n try:\n pick = input()\n except:\n print(\"Error! Shutting down the program!\")\n sys.exit()\n\n if(int(pick)==0):\n return(method, target, protocol, headers)\n elif(int(pick)==1):\n method = DefineMethod()\n elif(int(pick)==2):\n target = DefineTarget()\n elif(int(pick)==3):\n protocol = DefineProtocol()\n elif(int(pick)==4):\n headers[\"Filename\"] = DefineFilename()\n if headers[\"Filename\"] is not None:\n size = os.stat(headers[\"Filename\"])\n headers[\"Size\"] = size.st_size\n elif(int(pick)==5):\n headers[\"Key\"] = DefineKey()\n elif(int(pick)==6):\n headers[\"Host\"] = DefineHost()\n else:\n print(\"Give proper value!\")\n\n# If UPLOAD method is used, Filename header is added to the request and files data is added to body\ndef UploadMethod(request, headers):\n body = \"\"\n try:\n with open(headers[\"Filename\"], \"rb\") as fileToSend:\n body = fileToSend.read()\n except FileNotFoundError:\n print(\"File at path \",headers[\"Filename\"],\" not found!\")\n sys.exit()\n except:\n print(\"Unable to read the file \",headers[\"Filename\"])\n sys.exit()\n finally:\n if \"/\" in headers[\"Filename\"]:\n filename = headers[\"Filename\"].split(\"/\")\n headers[\"Filename\"] = filename[-1]\n else:\n filename = headers[\"Filename\"]\n # Adding all the headers to request\n for header, value in headers.items():\n if value == None:\n continue\n else:\n request += header+\":\"+str(value)+\"\\r\\n\"\n # Adding files data to request body\n request += \"\\r\\n\"\n body = binascii.hexlify(body)\n body = str(body).lstrip(\"b\").strip(\"'\")\n request += str(body)\n fileToSend.close()\n request += \"\\r\\n\\r\\n\\r\\n\"\n return request \n\n# Performs actions depending on the method\ndef CreateRequest(method, target, protocol, headers): \n request = (method+\" \"+target+\" \"+protocol+\"\\r\\n\")\n\n # Adding headers and files data to body if UPLOAD method is used \n if headers[\"Filename\"] is not None and method == \"UPLOAD\":\n request = UploadMethod(request, headers)\n # If LIST, DELETE, DOWNLOAD or any other value as method is used, we add headers to request\n else:\n for header, value in headers.items():\n if value == None:\n continue\n else:\n request += header+\":\"+str(value)+\"\\r\\n\"\n request += \"\\r\\n\\r\\n\"\n return request\n\n# Used to send the request\ndef Send(HOST, PORT, request):\n try:\n connection = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n connection.connect((HOST,int(PORT)))\n except:\n print(\"Failed to make connection!\")\n sys.exit() \n connection.sendto(request.encode(\"utf-8\"),(HOST, PORT))\n return connection\n\n# Used to receive the response\ndef Receive(socket):\n\n response = b\"\"\n while \"\\r\\n\\r\\n\\r\\n\" not in response.decode(\"utf-8\"):\n data = socket.recv(4096)\n response += data\n if not data:\n break\n socket.close()\n\n response = response.decode(\"utf-8\")\n responseMethod=response.split(\" \")[1]\n responseCode=response.split(\" \")[2]\n \n # If response method is ERROR\n if responseMethod == \"ERROR\":\n try:\n print(response.split(\"\\r\\n\\r\\n\\r\\n\")[0])\n except:\n print(response)\n\n # If response method is FILE\n elif responseMethod == \"FILE\":\n filename = None\n size = None\n try:\n responseLineAndHeaders = response.split(\"\\r\\n\\r\\n\")[0]\n body = response.split(\"\\r\\n\\r\\n\")[1].split(\"\\r\\n\\r\\n\\r\\n\")[0]\n body = binascii.unhexlify(str(body))\n except:\n print(\"Double CRLF not found!\")\n sys.exit\n responseLineAndHeaders = responseLineAndHeaders.split(\"\\r\\n\")\n headers = responseLineAndHeaders[0:]\n print(responseLineAndHeaders[0])\n for header in headers:\n keyAndValue = header.split(\":\")\n if keyAndValue[0] == \"Filename\":\n filename = keyAndValue[1]\n elif keyAndValue[0] == \"Size\":\n size = int(keyAndValue[1])\n if filename == None:\n print(\"Missing Filename header!\")\n return\n elif size == None:\n print(\"Missing Size header!\")\n return\n else:\n filename = filename.split(\"/\")\n newFile = open(filename[-1], \"wb+\")\n newFile.write(body[:int(size)])\n newFile.close()\n print(\"File downloaded successfully!\")\n \n # If response method is REPLY\n elif responseMethod == \"REPLY\":\n if responseCode == \"100\" or \"101\":\n print(response.split(\"\\r\\n\\r\\n\\r\\n\")[0])\n elif responseCode == \"102\":\n try:\n responseLineAndHeaders = response.split(\"\\r\\n\\r\\n\")[0]\n body = response[response.find(\"\\r\\n\\r\\n\"):]\n except:\n print(\"CRLF not found!\")\n sys.exit\n responseLineAndHeaders = responseLineAndHeaders.split(\"\\r\\n\")\n print(responseLineAndHeaders[0])\n print(\"\\n\",body)\n \nif __name__ == \"__main__\": \n method,target,protocol,headers = ModifyRequest()\n HOST,PORT = DefineHostAndPort(HOST, PORT)\n request = CreateRequest(method, target, protocol, headers)\n connection = Send(HOST, PORT, request)\n Receive(connection)\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":9749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"240552851","text":"import random\nfrom odoo import api, models, fields, _\n\n\nclass AdvanceReport(models.AbstractModel):\n _name = 'cash.advance.report'\n _description = \"Cash Advance Report\"\n\n name = fields.Char(string=\"Name\")\n\n def _get_report_name(self):\n return _(\"Expense summary\")\n\n @api.multi\n def get_html(self, options=None, line_id=None, additional_context=None):\n '''\n return the html value of report, or html value of unfolded line\n * if line_id is set, the template used will be the line_template\n otherwise it uses the main_template. Reason is for efficiency, when unfolding a line in the report\n we don't want to reload all lines, just get the one we unfolded.\n '''\n\n report = {\n 'name': self._get_report_name(),\n 'company_name': self.env.user.company_id.name,\n }\n\n rcontext = {\n 'report': report,\n # 'lines': {'columns_header': self.get_header(options), 'lines': lines},\n 'values': self._get_lines(),\n 'options': options,\n 'context': self.env.context,\n 'model': self,\n }\n html = self.env['ir.ui.view'].render_template(\"sunray_cash_advance.main_template\", values=dict(rcontext),\n )\n return html\n\n def get_report_informations(self, options=None):\n info = {\n 'options': options,\n 'context': self.env.context,\n 'main_html': self.get_html(options),\n }\n return info\n\n def _get_lines(self):\n \"\"\"REturn the lines to display on the report\n\n Returns:\n dict: description = fields.Char(string='')\n type = fields.Selection(selection=[\n (\"advance\", \"Advance\"),\n (\"retirement\", \"Retirement\")\n ])\n date = fields.Date(string='Date')\n debit = fields.Float(string=\"Debit\")\n credit = fields.Float(string=\"Credit\") \n \"\"\"\n user = self.env.user\n values = {}\n staff_receivable_type = self.env.ref(\n \"sunray_cash_advance.data_account_type_staff_receivable\")\n account_move_lines = self.env['account.move.line'].sudo().search(\n [\n (\"account_id.user_type_id\", \"=\", staff_receivable_type.id), \n (\"partner_id\", \"=\", user.partner_id.id),\n (\"move_id.state\", \"=\", \"posted\")\n ]\n )\n account_move_lines = account_move_lines.sorted(\n key=lambda aml: aml.create_date)\n balance = 0.0\n lines = []\n for aml in account_move_lines:\n report_det = {}\n report_det[\"date\"] = aml.date\n report_det[\"journal\"] = aml.move_id.journal_id.name\n report_det[\"name\"] = aml.name\n report_det[\"partner\"] = aml.partner_id.name\n report_det[\"account\"] = aml.account_id.code\n report_det[\"description\"] = aml.description\n report_det[\"amount\"] = aml.debit - aml.credit\n lines.append(report_det)\n balance += aml.debit - aml.credit\n values['balance'] = balance\n values['lines'] = lines\n return values\n","sub_path":"sunray_cash_advance/report/advance_report.py","file_name":"advance_report.py","file_ext":"py","file_size_in_byte":3291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"303302622","text":"import torch.nn as nn\nimport torch\nfrom utils.config import cfg\nfrom networks.l2norm import L2Norm\nfrom utils.prior_box import PriorBox\n\nclass VGG16(nn.Module):\n\n def __init__(self, in_channels=3):\n super(VGG16, self).__init__()\n self.conv1 = nn.Sequential(nn.Conv2d(in_channels, 64, kernel_size=3, padding=1),\n nn.BatchNorm2d(64),\n nn.ReLU(inplace=True),\n nn.Conv2d(64, 64, kernel_size=3, padding=1),\n nn.BatchNorm2d(64),\n nn.ReLU(inplace=True)\n )\n self.conv2 = nn.Sequential(nn.MaxPool2d(kernel_size=2, stride=2),\n nn.Conv2d(64, 128, kernel_size=3, padding=1),\n nn.BatchNorm2d(128),\n nn.ReLU(inplace=True),\n nn.Conv2d(128, 128, kernel_size=3, padding=1),\n nn.BatchNorm2d(128),\n nn.ReLU(inplace=True))\n\n self.conv3 = nn.Sequential(nn.MaxPool2d(kernel_size=2, stride=2),\n nn.Conv2d(128, 256, kernel_size=3, padding=1),\n nn.BatchNorm2d(256),\n nn.ReLU(inplace=True),\n nn.Conv2d(256, 256, kernel_size=3, padding=1),\n nn.BatchNorm2d(256),\n nn.ReLU(inplace=True),\n nn.Conv2d(256, 256, kernel_size=3, padding=1),\n nn.BatchNorm2d(256),\n nn.ReLU(inplace=True)\n )\n self.conv4 = nn.Sequential(nn.MaxPool2d(kernel_size=2, stride=2),\n nn.Conv2d(256, 512, kernel_size=3, padding=1),\n nn.BatchNorm2d(512),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.BatchNorm2d(512),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.BatchNorm2d(512),\n nn.ReLU(inplace=True)\n )\n\n self.conv5 = nn.Sequential(nn.MaxPool2d(kernel_size=2, stride=2),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.BatchNorm2d(512),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.BatchNorm2d(512),\n nn.ReLU(inplace=True),\n nn.Conv2d(512, 512, kernel_size=3, padding=1),\n nn.BatchNorm2d(512),\n nn.ReLU(inplace=True)\n )\n\n\n def forward(self, x):\n feat1 = self.conv1(x)\n feat2 = self.conv2(feat1)\n feat3 = self.conv3(feat2)\n feat4 = self.conv4(feat3)\n feat5 = self.conv5(feat4)\n return feat5\n\n\nclass Bottleneck_A(nn.Module):\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(Bottleneck_A, self).__init__()\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n padding=2, bias=False, dilation=2)\n self.bn2 = nn.BatchNorm2d(planes)\n self.conv3 = nn.Conv2d(planes, planes, kernel_size=1, bias=False)\n self.bn3 = nn.BatchNorm2d(planes)\n self.relu = nn.ReLU(inplace=True)\n self.downsample = downsample\n self.stride = stride\n\n def forward(self, x):\n residual = x\n\n out = self.conv1(x)\n out = self.bn1(out)\n out = self.relu(out)\n\n out = self.conv2(out)\n out = self.bn2(out)\n out = self.relu(out)\n\n out = self.conv3(out)\n out = self.bn3(out)\n\n if self.downsample is not None:\n residual = self.downsample(x)\n\n out += residual\n out = self.relu(out)\n\n return out\n\n\nclass Bottleneck_B(nn.Module):\n def __init__(self, inplanes, planes, stride=1, downsample=None):\n super(Bottleneck_B, self).__init__()\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n padding=2, bias=False, dilation=2)\n self.bn2 = nn.BatchNorm2d(planes)\n self.conv3 = nn.Conv2d(planes, planes, kernel_size=1, bias=False)\n self.bn3 = nn.BatchNorm2d(planes)\n self.relu = nn.ReLU(inplace=True)\n self.downsample = downsample\n self.stride = stride\n\n self.extra_conv = nn.Sequential(nn.Conv2d(inplanes, planes, kernel_size=1, bias=False),\n nn.BatchNorm2d(planes))\n\n def forward(self, x):\n\n out = self.conv1(x)\n out = self.bn1(out)\n out = self.relu(out)\n\n out = self.conv2(out)\n out = self.bn2(out)\n out = self.relu(out)\n\n out = self.conv3(out)\n out = self.bn3(out)\n residual = self.extra_conv(x)\n\n if self.downsample is not None:\n residual = self.downsample(x)\n\n out += residual\n out = self.relu(out)\n\n return out\n\n\nclass PN_DetNet(nn.Module):\n def __init__(self, device):\n super(PN_DetNet, self).__init__()\n self.device = device\n self.BaseVGG16 = VGG16(in_channels=3)\n self.normal_layer0 = L2Norm(n_channels=256, scale=5)\n self.normal_layer1 = L2Norm(n_channels=256, scale=5)\n self.normal_layer2 = L2Norm(n_channels=256, scale=5)\n self.normal_layer3 = L2Norm(n_channels=256, scale=5)\n self.Extra0 = nn.Sequential(Bottleneck_B(512, 256),\n Bottleneck_A(256, 256),\n Bottleneck_A(256, 256),\n )\n self.Extra1 = nn.Sequential(Bottleneck_B(256, 256),\n Bottleneck_A(256, 256),\n Bottleneck_A(256, 256),\n )\n\n self.Extra2 = nn.Sequential(Bottleneck_B(256, 256),\n Bottleneck_A(256, 256),\n Bottleneck_A(256, 256),\n )\n self.Extra3 = nn.Sequential(Bottleneck_B(256, 256),\n Bottleneck_A(256, 256),\n Bottleneck_A(256, 256),\n )\n self.conv1x1_3 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=3, padding=1, bias=False),\n nn.BatchNorm2d(256),\n nn.ReLU(inplace=True))\n self.conv1x1_2_1 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=1, bias=False),\n nn.BatchNorm2d(256),\n nn.ReLU(inplace=True))\n self.conv1x1_2_2 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1, bias=False),\n nn.BatchNorm2d(256),\n nn.ReLU(inplace=True))\n self.conv1x1_1_1 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=1, bias=False),\n nn.BatchNorm2d(256),\n nn.ReLU(inplace=True))\n self.conv1x1_1_2 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1, bias=False),\n nn.BatchNorm2d(256),\n nn.ReLU(inplace=True))\n self.conv1x1_0_1 = nn.Sequential(nn.Conv2d(256, 256, kernel_size=1, bias=False),\n nn.BatchNorm2d(256),\n nn.ReLU(inplace=True))\n self.conv1x1_0_2 = nn.Sequential(nn.Conv2d(512, 256, kernel_size=3, padding=1, bias=False),\n nn.BatchNorm2d(256),\n nn.ReLU(inplace=True))\n\n cls_layers = []\n loc_layers = []\n kernel_sizes = [3, 5, 9, 17]\n paddings = [1, 2, 4, 8]\n strides = [1, 2, 4, 8]\n for ratio, size, kernel_size, padding, stride in zip(cfg['ratios'], cfg['sizes'], kernel_sizes, paddings, strides):\n num_anchors = len(ratio) + len(size) - 1\n num_classes = cfg['num_classes'] + 1\n num_loc_pred = num_anchors * 4\n num_cls_pred = num_anchors * num_classes\n cls_layers.append(nn.Conv2d(256, num_cls_pred, kernel_size=kernel_size, padding=padding, stride=stride))\n loc_layers.append(nn.Conv2d(256, num_loc_pred, kernel_size=kernel_size, padding=padding, stride=stride))\n self.cls_layers = nn.Sequential(*cls_layers)\n self.loc_layers = nn.Sequential(*loc_layers)\n self.anchor = []\n for index in range(len(cfg['sizes'])):\n self.anchor += [PriorBox(cfg['sizes'][index], cfg['ratios'][index], clip=cfg['clip'], stride=strides[index])]\n\n def forward(self, x):\n batch_size = x.size(0)\n cls_preds = []\n loc_preds = []\n anchor_boxes = []\n features = []\n VGG16_feat = self.BaseVGG16(x)\n feat1 = self.Extra0(VGG16_feat)\n feat2 = self.Extra1(feat1)\n feat3 = self.Extra2(feat2)\n feat4 = self.Extra3(feat3)\n # FPN\n predict_4 = self.conv1x1_3(feat4)\n predict_3 = self.conv1x1_2_2(torch.cat([self.conv1x1_2_1(feat3), predict_4], 1))\n predict_2 = self.conv1x1_1_2(torch.cat([self.conv1x1_1_1(feat2), predict_3], 1))\n predict_1 = self.conv1x1_0_2(torch.cat([self.conv1x1_0_1(feat1), predict_2], 1))\n # L2 Normal\n features.append(self.normal_layer0(predict_1))\n features.append(self.normal_layer1(predict_2))\n features.append(self.normal_layer2(predict_3))\n features.append(self.normal_layer3(predict_4))\n for feature, cls_layer, loc_layer, anchor_box in zip(features, self.cls_layers, self.loc_layers, self.anchor):\n cls_preds.append(cls_layer(feature).permute(0, 2, 3, 1).contiguous().view(batch_size, -1))\n loc_preds.append(loc_layer(feature).permute(0, 2, 3, 1).contiguous().view(batch_size, -1))\n anchor_boxes.append(anchor_box(feature).to(self.device))\n\n cls_preds = torch.cat(cls_preds, 1)\n cls_preds = cls_preds.view(batch_size, -1, cfg['num_classes'] + 1)\n loc_preds = torch.cat(loc_preds, 1).view(batch_size, -1, 4)\n anchor_boxes = torch.cat(anchor_boxes, 0)\n # cls_preds [batch_size, num_anchor, num_classes]\n # loc_preds [batch, num_anchor, 4]\n # anchor_boxes [num_anchor, 4]\n return cls_preds, loc_preds, anchor_boxes\n\nif __name__ == '__main__':\n device = torch.device(\"cuda\")\n input = torch.ones(1, 3, 800, 800).to(device)\n net = PN_Net(device=device).to(device)\n out = net(input)","sub_path":"networks/DetNet_VGG.py","file_name":"DetNet_VGG.py","file_ext":"py","file_size_in_byte":11550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"246892383","text":"import unittest\n\n\nclass Solution(object):\n def removeDuplicates(self, nums):\n \"\"\"\n :type nums: List[int]\n :rtype: int\n \"\"\"\n length=len(nums)\n if not nums:\n return 0\n if len(nums)==1:\n return 1\n i=1\n while i\", \"\")\n strLng = \"\\n\\t\\t\" + self.getTagContent(strPostLatLng, \"\", \"\") + \"\\n\\t\"\n strLat = \"\\n\\t\\t\" + self.getTagContent(strPostLatLng, \"\", \"\") + \"\\n\\t\"\n \n thisProfile = thisProfile.replace(strPostLatLng, \"\")\n \n if thisProfile.find(\"\") == -1: \n thisProfile = thisProfile.replace(\"\\n\", strLng)\n thisProfile = thisProfile.replace(\"\", strLat)\n \n #print thisProfile\n \n self.outputFile.write(thisProfile)\n self.outputFile.flush()\n return True\n \n \n def omit(self, fileLocation):\n \n self.outputFile = open(fileLocation + \"converted\", \"w\")\n self.parse(fileLocation)\n \n \nif __name__ == \"__main__\":\n \n profileLatLngOmitor = ProfileLatLngOmitor()\n profileLatLngOmitor.omit(\"F:\\\\OfficeWork\\\\Development\\\\BusinessSearch\\\\Data\\\\Profiles\\\\Release 04-05-2011\\\\LatLngFind\\\\BusinessProfiles.xml\")\n \n \n \n \n \n \n \n \n \n \n \n ","sub_path":"ProfileLatLngOmitor.py","file_name":"ProfileLatLngOmitor.py","file_ext":"py","file_size_in_byte":1944,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"195253179","text":"import sys\nimport re\nimport gensim\nfrom utils import *\n\nNUM_TOPICS = 50\nNUM_WORDS = 30\nNUM_EPOCHS = 30\n\ndef load_data():\n data = []\n print(\"loading data...\")\n word_to_idx = load_word_to_idx(sys.argv[1])\n word_to_idx = gensim.corpora.dictionary.Dictionary([word_to_idx.keys()])\n fo = open(sys.argv[2])\n for line in fo:\n bow = []\n line = line.strip()\n tokens = line.split()\n for i in range(0, len(tokens), 2):\n bow.append((int(tokens[i]), int(tokens[i + 1])))\n data.append(bow)\n fo.close()\n print(\"data size: %d\" % len(data))\n return data, word_to_idx\n\ndef train():\n data, word_to_idx = load_data()\n lda = gensim.models.ldamodel.LdaModel\n logger = gensim.models.callbacks.PerplexityMetric(corpus = data, logger = \"shell\")\n print(\"training model...\")\n model = lda(\n corpus = data,\n num_topics = NUM_TOPICS,\n id2word = word_to_idx,\n passes = NUM_EPOCHS,\n alpha = [5] * NUM_TOPICS\n )\n for i in range(NUM_TOPICS):\n print(\"\\nTopic %d\" % i)\n for t, f in model.get_topic_terms(i, NUM_WORDS):\n print(\"%s %f\" % (word_to_idx.id2token[t], f))\n\nif __name__ == \"__main__\":\n if len(sys.argv) != 3:\n sys.exit(\"Usage: %s word_to_idx training_data\" % sys.argv[0])\n train()\n","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":1321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"520072162","text":"\n\nfrom xai.brain.wordbase.nouns._jive import _JIVE\n\n#calss header\nclass _JIVING(_JIVE, ):\n\tdef __init__(self,): \n\t\t_JIVE.__init__(self)\n\t\tself.name = \"JIVING\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"jive\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_jiving.py","file_name":"_jiving.py","file_ext":"py","file_size_in_byte":226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"43826376","text":"import komand\nfrom .schema import UnshortenInput, UnshortenOutput\n# Custom imports below\nimport requests\n\n\nclass Unshorten(komand.Action):\n\n def __init__(self):\n super(self.__class__, self).__init__(\n name='unshorten',\n description='Unshorten a shortened URL',\n input=UnshortenInput(),\n output=UnshortenOutput())\n\n def run(self, params={}):\n short_url = params.get('url')\n try:\n r = requests.get('https://unshorten.me/json/' + short_url)\n r.raise_for_status()\n out = r.json()\n except Exception as e:\n self.logger.error(e)\n raise\n\n try:\n if out['error']:\n self.logger.error(out.get('error'))\n except KeyError:\n # All good, no error key is present\n self.logger.info('No errors')\n\n return out\n\n def test(self):\n url = 'https://bit.ly/komand_rocks'\n try:\n r = requests.get('https://unshorten.me/json/' + url)\n r.raise_for_status()\n out = r.json()\n except Exception as e:\n self.logger.error(e)\n raise\n\n # All good\n try:\n if out['error']:\n self.logger.error(out.get('error'))\n except KeyError:\n # All good, no error key is present\n self.logger.info('No errors')\n\n return out\n","sub_path":"unshorten/komand_unshorten/actions/unshorten/action.py","file_name":"action.py","file_ext":"py","file_size_in_byte":1442,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"542548158","text":"import numpy as np\nimport pandas as pd\nfrom joblib import Parallel, delayed\nfrom scipy import stats\n\n\ndef mad(arr, n=1.5):\n x_median = np.median(arr)\n x_mad = np.median(np.abs(arr - x_median))\n return arr[(arr >= x_median - (x_mad * n)) &\n (arr <= x_median + (x_mad * n))]\n\n\ndef speed_up_feature(data):\n data.loc[:, 'speed_up'] = data.Cp_Speed.shift(-1) > data.Cp_Speed\n data.speed_up = data.speed_up.astype('int64')\n for index, row in data.iterrows():\n if row.speed_up == 1:\n end_index = index + 1\n try:\n data.loc[end_index + 10, 'Cp_Speed']\n except KeyError:\n data.loc[index:, 'speed_up'] = 0\n break\n while data.loc[end_index + 10,\n 'Cp_Speed'] > data.loc[end_index,\n 'Cp_Speed']:\n end_index += 1\n try:\n data.loc[end_index + 10, 'Cp_Speed']\n except KeyError:\n data.loc[index:, 'speed_up'] = 0\n break\n data.loc[index, 'speed_up'] = data.loc[end_index,\n 'Cp_Speed'] - data.loc[index, 'Cp_Speed']\n data.loc[index + 1:end_index + 1, 'speed_up'] = 0\n return data\n\n\ndef min_lisg_after_change_speed(data):\n for index, row in data.iterrows():\n temp_value = None\n try:\n if row.speed_up != 0:\n speed = row.Cp_Speed + row.speed_up\n temp_value = data.loc[mad(data.diff().loc[(data.index > index) & (\n data.Cp_Speed == speed), 'LISG_diam'][:20], 1.5).index, 'LISG_diam'].min()\n else:\n if data.loc[index + 20,\n 'Cp_Speed'] == row.Cp_Speed and row.Cp_Speed == data.loc[index - 20,\n 'Cp_Speed']:\n temp_value = data.loc[\n mad(data.diff().loc[index + 1:index + 20, 'LISG_diam'], 1.5).index,\n 'LISG_diam'\n ].min()\n except IndexError:\n temp_value = None\n except KeyError:\n temp_value = None\n data.loc[index, 'min_lisg'] = temp_value\n return data\n\n\ndef drew_fibre_vol(data):\n data.loc[:, 'drew_fibre_vol'] = (\n 3.14 * (0.5 * data.LISG_diam * 1e-3) ** 2) * (\n data.Cp_Speed * 1000 / 60) * 0.5\n return data\n\n\ndef diff_feature(data, feature, n=1):\n new_feature = 'diff' + str(n) + '_' + feature\n data.loc[:, new_feature] = data[feature]\n for _ in range(n):\n data.loc[:, new_feature] = data[new_feature].diff(1)\n return data\n\n\ndef max_min_delta(data, axis):\n return np.max(data, axis=axis) - np.min(data, axis=axis)\n\n\ndef last_n_feature(data, n, feature):\n result = []\n for i in range(len(data)):\n temp = data.loc[i - n + 1: i, feature].tolist()\n if len(temp) < n:\n temp = np.array([None for _ in range(n - len(temp))] + temp)\n result.append(temp)\n return pd.DataFrame(data=np.row_stack(result), columns=[\n feature + '_last_' + str(i + 1) for i in range(n)])\n\n\ndef make_window_feature(data, feature, size, window_size, func):\n for index, i in enumerate(range(1, size, window_size)):\n columns = [feature + '_last_' + str(j)\n for j in range(i, i + window_size)]\n data[feature +\n '_' +\n func.__name__ +\n '_' +\n str(size) +\n 'size' +\n str(window_size) +\n 'w' +\n str(index +\n 1)] = func(data[columns], axis=1)\n return data\n\n\ndef calc_lisg(data):\n for i in range(1, 6, 1):\n data['calc_lisg_last_' + str(i)] = 1000 * 2 * (data['drew_fibre_vol_last_' + str(\n i)] / (3.14 * 0.5 * ((data.Cp_Speed + data.speed_up) * 1000 / 60))) ** 0.5\n return data\n\n\ndef calc_delta_lisg(data):\n for i in range(1, 6, 1):\n data['calc_delta_lisg_last_' +\n str(i)] = data['calc_lisg_last_' + str(i)] - data['LISG_diam_last_' + str(i)]\n return data\n\n\ndef calc_ratio_delta_lisg(data):\n for i in range(1, 6, 1):\n data['calc_ratio_delta_lisg_last_' +\n str(i)] = np.abs(data['calc_delta_lisg_last_' + str(i)]) / data['LISG_diam_last_' + str(i)]\n return data\n\n\ndef make_feature(data):\n funcs = [\n np.mean,\n np.std,\n np.min,\n np.max,\n np.median,\n # max_min_delta,\n stats.skew,\n stats.kurtosis\n ]\n # 抽取原始特征\n column_names = list(filter(lambda x: x.__contains__('Cp_Speed') or x.__contains__(\n 'LISG_diam') or x.__contains__('Furn_Temp'), data.columns))\n result = data[column_names]\n result.columns = [column[4:] for column in result.columns]\n # drew_fibre_vol 特征\n result = drew_fibre_vol(result)\n # diff1_drew_fibre_vol 特征\n result = diff_feature(result, 'drew_fibre_vol', 1)\n result = diff_feature(result, 'drew_fibre_vol', 2)\n # speed_up 特征\n result = speed_up_feature(result)\n # min_lisg 目标值\n result = min_lisg_after_change_speed(result)\n # LISG_diam、drew_fibre_vol、diff1_drew_fibre_vol、diff2_drew_fibre_vol 历史特征\n t1 = last_n_feature(result, 5, 'LISG_diam')\n result.drop('LISG_diam', axis=1, inplace=True)\n t2 = last_n_feature(result, 30, 'drew_fibre_vol')\n result.drop('drew_fibre_vol', axis=1, inplace=True)\n t3 = last_n_feature(result, 30, 'diff1_drew_fibre_vol')\n result.drop('diff1_drew_fibre_vol', axis=1, inplace=True)\n t4 = last_n_feature(result, 30, 'diff2_drew_fibre_vol')\n result.drop('diff2_drew_fibre_vol', axis=1, inplace=True)\n result = pd.concat([t1, t2, t3, t4, result], axis=1)\n del t1, t2, t3, t4\n\n result.dropna(axis=0, inplace=True)\n result = result.astype('float64')\n result.reset_index(drop=True, inplace=True)\n # calc_lisg 特征\n result = calc_lisg(result)\n # calc_delta_lisg 特征\n result = calc_delta_lisg(result)\n # calc_ratio_delta_lisg 特征\n result = calc_ratio_delta_lisg(result)\n\n # window feature\n for func in funcs:\n result = make_window_feature(result, 'LISG_diam', 5, 5, func)\n result = make_window_feature(result, 'calc_lisg', 5, 5, func)\n result = make_window_feature(result, 'calc_delta_lisg', 5, 5, func)\n result = make_window_feature(result, 'calc_ratio_delta_lisg', 5, 5, func)\n for f in [\n 'drew_fibre_vol',\n 'diff1_drew_fibre_vol',\n 'diff2_drew_fibre_vol',]:\n for window_size in [30, 15, 5]:\n result = make_window_feature(result, f, 30, window_size, func)\n result['delta_lisg'] = result.min_lisg - result.LISG_diam_last_5\n return result\n\n\nif __name__ == '__main__':\n import os\n\n os.chdir('../../data/cleaned_data')\n\n file_names = os.listdir()\n\n def my_fun(f):\n print('开始处理' + f)\n try:\n data = make_feature(pd.read_csv(f))\n data.to_csv('../trainable_data20190809/t_' + f, index=False)\n print(f + ' 已处理完毕')\n except Exception as e:\n print(f + '处理过程中有误：' + str(e))\n\n Parallel(n_jobs=-1)(delayed(my_fun)(f) for f in file_names)\n","sub_path":"src/mad_reg_lisg_20190809/feature_engineer.py","file_name":"feature_engineer.py","file_ext":"py","file_size_in_byte":7375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"80934891","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = 'xiaoyureed'\n\n\n# urllib是Python中自带的请求url连接的官方标准库，在Python2中主要为urllib和urllib2，在Python3中整合成了urllib。\n# 在python3中, urllib2 被整合为 urllib.request\n\n# 而urllib3则是增加了连接池等功能\n\ndef quickstart():\n import urllib.request\n\n decode = urllib.request.urlopen(\"https://github.com\").read().decode('utf-8')\n print(decode)\n\n\ndef headers_demo():\n import urllib.request\n url = \"https://github.com\"\n headers = {\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_3) AppleWebKit/537.36 (KHTML, like Gecko) \"\n \"Chrome/73.0.3683.86 Safari/537.36\",\n \"Referer\": \"https://github.com/\",\n \"Host\": \"github.com\"\n }\n req = urllib.request.Request(\n url=url,\n headers=headers\n )\n resp = urllib.request.urlopen(req)\n with open('github.txt', 'wb') as f:\n f.write(resp.read())\n\n\ndef accept_input_word_demo():\n import urllib.request\n import urllib.parse\n\n wd = input('type a key word: ')\n url = 'https://cn.bing.com/search?q='\n headers = {\n \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_3) AppleWebKit/537.36 (KHTML, like Gecko) \"\n \"Chrome/73.0.3683.86 Safari/537.36\",\n \"Referer\": \"https://cn.bing.com/\",\n \"Host\": \"cn.bing.com\"\n }\n req = urllib.request.Request(\n # 对加到url中的关键字进行转义\n url=url + urllib.parse.quote(wd),\n headers=headers\n )\n resp = urllib.request.urlopen(req)\n with open('02.html', 'wb') as f:\n f.write(resp.read())\n\n # print (resp.read().decode('utf-8'))\n\n\ndef download_file_demo():\n from urllib.request import urlretrieve\n import os\n\n image_url = \"https://morvanzhou.github.io/static/img/description/learning_step_flowchart.png\"\n\n if not os.path.exists('./resources/img'): # img exist?\n os.mkdir('./resources/img') # create dir\n else:\n if not os.path.isdir('./resources/img'): # is dir?\n os.mkdir('./resources/img') # create dir\n\n urlretrieve(image_url, './resources/img/demo1.png')\n\n\nif __name__ == '__main__':\n # quickstart()\n # accept_input_word_demo()\n download_file_demo()\n","sub_path":"crawler/urllib2_demo.py","file_name":"urllib2_demo.py","file_ext":"py","file_size_in_byte":2305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"240652202","text":"import sys\n\n\nclass Solution:\n\n def initializeArray(self):\n # try using hash map instead\n return [-1 for x in range(0, 100)]\n\n def lengthOfLongestSubstring(self, s):\n longest_substring = 0\n char_map = self.initializeArray()\n current_len = 0\n start_pos = 0\n for i in range(0, len(s)):\n if(char_map[ord(s[i]) - ord('0')] < start_pos):\n current_len += 1\n char_map[ord(s[i])- ord('0')] = i\n else:\n if(current_len > longest_substring):\n longest_substring = current_len\n current_len = i - char_map[ord(s[i]) - ord('0')]\n start_pos = char_map[ord(s[i]) - ord('0')] + 1\n char_map[ord(s[i]) - ord('0')] = i\n\n if(current_len > longest_substring):\n longest_substring = current_len\n\n return longest_substring\n\n\nif __name__ == \"__main__\":\n string = input().strip()\n print(Solution().lengthOfLongestSubstring(string))\n","sub_path":"LeetCode/longest_substring_without_repeating_chars.py","file_name":"longest_substring_without_repeating_chars.py","file_ext":"py","file_size_in_byte":1020,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"486136379","text":"# -- coding:utf-8 --\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport os\nimport cv2\ndef nms(output, nms_th):\n if len(output) == 0:\n return output\n output = output[np.argsort(-output[:, 0])]\n bboxes = [output[0]]\n for i in np.arange(1, len(output)):\n bbox = output[i]\n flag = 1\n for j in range(len(bboxes)):\n if iou(bbox[1:5], bboxes[j][1:5]) >= nms_th:\n flag = -1\n break\n if flag == 1:\n bboxes.append(bbox)\n bboxes = np.asarray(bboxes, np.float32)\n return bboxes\n\t\ndef iou(box0, box1):\n r0 = box0[3] / 2#半径\n s0 = box0[:3] - r0#xyz的左上角\n e0 = box0[:3] + r0#xyz的右上角\n r1 = box1[3] / 2\n s1 = box1[:3] - r1#xyz的左上角\n e1 = box1[:3] + r1#xyz的右上角\n overlap = []\n for i in range(len(s0)):#3个\n overlap.append(max(0, min(e0[i], e1[i]) - max(s0[i], s1[i])))#最小的右下角-最大的左下角=((x1-x0),(y1-y0),(z1-z0))\n intersection = overlap[0] * overlap[1] * overlap[2]#(x1-x0)*(y1-y0)*(z1-z0)\n union = box0[3] * box0[3] * box0[3] + box1[3] * box1[3] * box1[3] - intersection#体积就是3个直径的乘积\n return intersection / union\nnmsthresh = 0.1\ndetp = -1\ndata_dir = '/home/zhaojie/zhaojie/Lung/DSB_Code/DSB2017-master/training/detector/results/res18/TestBbox/'\nfilenames = os.path.join(data_dir, 'T000807321_pbb.npy')\npbb = np.load(filenames, mmap_mode='r')\n# imgs = np.load('/home/zhaojie/zhaojie/Lung/code/detector_py3/results/dpn3d26/retrft960/train962/1.3.6.1.4.1.14519.5.2.1.6279.6001.102681962408431413578140925249_pbb.npy')\nprint('------0',pbb.shape)\n# print('------0',pbb)\n# pbbold = np.array(pbb[pbb[:,0] > detp])#根据阈值过滤掉概率低的\n# pbbold = np.array(pbbold[pbbold[:,-1] > 3])#根据半径过滤掉小于3mm的\n\n# pbbold = pbbold[np.argsort(-pbbold[:,0])][:1000] #取概率值前1000的结节作为输出，不然直接进行nms耗时太长\npbb = nms(pbb, 0.2)#对输出的结节进行nms\n# pbb = nms(pbbold, nmsthresh)#对输出的结节进行nms\nprint('len(pbb)',pbb.shape,pbb)\n# for i in range(imgs.shape[0]):\n # name = str(i) + '_' + '.png'\n # cv2.imwrite(os.path.join('./CT_cleannp/', name), imgs[i])\n\t\n\t\n","sub_path":"training/detector/results/res18/VisulizePbb.py","file_name":"VisulizePbb.py","file_ext":"py","file_size_in_byte":2232,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"372539859","text":"#!/usr/bin/python\n\nimport sys\nfrom collections import namedtuple\n\nItem = namedtuple('Item', ['index', 'size', 'value'])\n\n# def knapsack_solver(items, capacity):\n# # items is a list of Items\n# # Returns: {'Value': int, 'Chosen': [list of item indices]}\n# # Your code here\n# # First try: top-down recursive exploration of *all* possible selection combinations\n# # Dict 'options' holds possibilities for next item chosen in ready-to-return format\n# # 'Null' option represents no further items to choose.\n# options = {'Null':{'Value': 0, 'Chosen': []}}\n# for item in items:\n# # Iterate through possible items to choose next\n# if item.size <= capacity:\n# # If item can fit, find selections and max value for other items and reduced capacity\n# # Only higher indices used in recursion to prevent duplication (e.g. 1 then 2 vs 2 then 1)\n# # This reduces runtime by ~half and implicitly sorts\n# sub_selection = knapsack_solver([i for i in items if i.index < item.index], capacity-item.size)\n# sub_selection['Chosen'].append(item.index)\n# sub_selection['Value'] += item.value\n# options.update({item.index: sub_selection})\n# # Determine option with maximum value to pass back to parent\n# max_val = 'Null'\n# for key in options.keys():\n# if options[key]['Value'] > options[max_val]['Value']:\n# max_val = key\n# return options[max_val]\n# # Passes for small, slow for medium and large.\n# # Compares *all* possible ways to fill the pack\n# # TO DO: improve runtime, get storage at least somewhat under control\n\n# Second pass: add item with highest value per unit size that fits in remaining space\n# This will NOT always give the optimum solution, just a quick estimate\ndef knapsack_solver(items, capacity, sorted=False):\n if sorted == False:\n # Sort items by descending \"value density\"; selection sort here for simplicity\n # Sort only triggers once\n for i in range(0, len(items) - 1):\n smallest_index = i\n for j in range(i + 1, len(items)):\n if (items[j].value/items[j].size) > (items[smallest_index].value/items[smallest_index].size):\n smallest_index = j\n if smallest_index != i:\n items[i], items[smallest_index] = items[smallest_index], items[i]\n for i in range(len(items)):\n if items[i].size <= capacity:\n # Only the first (most value-dense) item which will fit triggers the next recursion:\n sub_selection = knapsack_solver(items[1:], capacity-items[i].size, True)\n sub_selection['Chosen'].append(items[i].index)\n chosen_items = sub_selection['Chosen']\n sub_selection['Chosen'].sort()\n sub_selection['Value'] += items[i].value\n return sub_selection\n return {'Value': 0, 'Chosen': []}\n # Passes test for medium size, fails for large;\n # For large, returns a solution with Value = 2639 instead of 2640\n # But it takes ~ .09 seconds even for the large version!\n # This estimate is only O(n) runtime complexity, instead of O(2^n)\n\nif __name__ == '__main__':\n if len(sys.argv) > 1:\n capacity = int(sys.argv[2])\n file_location = sys.argv[1].strip()\n file_contents = open(file_location, 'r')\n items = []\n\n for line in file_contents.readlines():\n data = line.rstrip().split()\n items.append(Item(int(data[0]), int(data[1]), int(data[2])))\n \n file_contents.close()\n print(knapsack_solver(items, capacity))\n else:\n print('Usage: knapsack.py [filename] [capacity]')","sub_path":"knapsack/knapsack.py","file_name":"knapsack.py","file_ext":"py","file_size_in_byte":3459,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"295457099","text":"from celery.schedules import crontab\n\nCELERY_BROKER_URL = 'amqp://guest@localhost'\nCELERY_TIMEZONE = 'UTC'\nCELERY_ACCEPT_CONTENT = ['json', 'pickle']\nCELERYBEAT_SCHEDULE = {\n 'daily_update_beat' : {\n 'task' : 'app.tasks.daily_update',\n 'schedule' : crontab(minute=38, hour=17),\n 'args' : []\n }\n}\n\n","sub_path":"server/celeryconfig.py","file_name":"celeryconfig.py","file_ext":"py","file_size_in_byte":324,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"484372856","text":"from __future__ import absolute_import\r\n\r\nfrom celery import Celery\r\n\r\n# instantiate Celery object\r\ncelery = Celery(backend='redis://localhost:6379/1',include=[\r\n 'WSUDOR_Manager.actions.actions' \r\n ])\r\n\r\n# import celery config file\r\ncelery.config_from_object('cl.celeryConfig')\r\n\r\nif __name__ == '__main__':\r\n celery.start()","sub_path":"cl/cl.py","file_name":"cl.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"13492675","text":"import json\r\nimport random\r\nimport tweepy\r\nimport time\r\nimport datetime\r\n\r\n\r\nfrom tweepy.streaming import StreamListener\r\nfrom tweepy import OAuthHandler\r\nfrom tweepy import Stream\r\n# import elasticsearch\r\nimport collections\r\nimport boto3\r\nfrom boto.sqs.message import Message\r\nimport boto.sqs\r\n\r\n# conn = boto.sqs.connect_to_region(\"us-west-2\")\r\n\r\n# myQueue = conn.get_queue('TweetQueue1')\r\n\r\nsqs = boto3.resource('sqs')\r\n\r\n# Get the queue\r\nqueue = sqs.get_queue_by_name(QueueName='TweetQueue')\r\n\r\n\r\nconsumer_key=\"4SXhZPURJLXu9RnhkQkzyuwrM\"\r\nconsumer_secret=\"Q3B85IwmCgRfzF5hESZqRKm1FVbnQqZvesyrj6xvKJsfXyjy4H\"\r\naccess_token=\"465606029-5j06IPwdtoO1vrMA6EzHZhO3c2ryu0YpoJAvKsOu\"\r\naccess_token_secret=\"VT8NcwwT1stSZ0S2XvP8t476kvTOnepRswlZfNZqsd6sG\"\r\nsearch_key_array = [\"TheWalkingDead\", \"Bentancur\", \"india\", \"DDoS\", \"apple\", \"dhoni\", \"chelsea\", \"facebook\", \"trump\", \"election\", \"hillary\",\"RespectJustin\",\"Drake\",\"war\",\"google\"]\r\n#search_key_array = [\"badminton\", \"cricket\", \"hockey\", \"soccer\", \"football\"]\r\ntrack_string = search_key_array[0]\r\nfor index in range(len(search_key_array)-1):\r\n\ttrack_string += \",%s\" % search_key_array[index+1]\r\n\r\n\r\nclass StdOutListener(StreamListener):\r\n\tprint (\"class created\")\r\n\tdef on_data(self, data):\r\n\t\t# es = elasticsearch.Elasticsearch()\r\n\t\tif data is not None:\r\n\t\t\tdict = collections.OrderedDict()\r\n\t\t\tloc = collections.OrderedDict()\r\n\t\t\ttweet = json.loads(data)\r\n\t\t\ttry:\r\n\t\t\t\tif tweet['id']:\r\n\t\t\t\t\ttweet_id = tweet['id']\r\n\t\t\t\tif tweet['text']:\r\n\t\t\t\t\ttext= tweet['text']\r\n\t\t\t\telse :\r\n\t\t\t\t\tprint(\"no text\")\r\n\t\t\t\t\treturn True\r\n\t\t\t\tif tweet['created_at']:\r\n\t\t\t\t\tdateTime = time.strftime('%Y-%m-%d %H:%M:%S', time.strptime(tweet['created_at'],'%a %b %d %H:%M:%S +0000 %Y'))\r\n\t\t\t\t\tdate = str(datetime.datetime.strptime(dateTime, \"%Y-%m-%d %H:%M:%S\"))\r\n\t\t\t\tif tweet['user']:\r\n\t\t\t\t\tuserName= str(tweet['user']['name'])\r\n\t\t\t\t\tuserScreenName= str(tweet['user']['screen_name'])\r\n\t\t\t\tif tweet['coordinates']:\r\n\t\t\t\t\tcoordinates = tweet['coordinates']['coordinates']\r\n\t\t\t\t\tlongitude = coordinates[0]\r\n\t\t\t\t\tlatitude = coordinates[1]\r\n\t\t\t\telif tweet['place'] and tweet['place']['bounding_box'] and tweet['place']['bounding_box']['coordinates']:\r\n\t\t\t\t\tcoordinates = tweet['place']['bounding_box']['coordinates'][0]\r\n\t\t\t\t\tlongitude = (coordinates[0][0] + coordinates[2][0])/2\r\n\t\t\t\t\tlatitude = (coordinates[0][1] + coordinates[2][1])/2\r\n\t\t\t\telse :\r\n\t\t\t\t\tprint(\"no lat long\")\r\n\t\t\t\t\treturn True\r\n\t\t\t\t#temparr=[tweet_id.encode(\"utf-8\"),date.encode(\"utf-8\"),userName.encode(\"utf-8\"),userScreenName.encode(\"utf-8\"),text.encode(\"utf-8\"),longitude.encode(\"utf-8\"),latitude.encode(\"utf-8\")]\r\n\t\t\t\t#dict['tweetId'] = tweet_id\r\n\t\t\t\tdict['text'] = text.replace(\"'\",\"\\'\")\r\n\t\t\t\tdict['date'] = date\r\n\t\t\t\tdict['userName'] = userName\r\n\t\t\t\tdict['userScreenName'] = userScreenName\r\n\t\t\t\tdict['latitude'] = latitude\r\n\t\t\t\tdict['longitude'] = longitude\r\n\t\t\t\t#dict['longitude'] = longitude\r\n\t\t\t\t#loc['location']=[longitude,latitude]\r\n\t\t\t\tdict['pin']={ 'location': str(latitude)+\",\"+str(longitude)}\r\n\t\t\t\tsearch_key = []\r\n\t\t\t\tfor key in search_key_array:\r\n\t\t\t\t\tif key.lower() in text.lower():\r\n\t\t\t\t\t\tsearch_key.append(key.lower())\r\n\t\t\t\tdict['search_key'] = search_key\r\n\t\t\t\tprint (search_key)\r\n\t\t\t\tif (len(search_key)>0):\r\n\t\t\t\t\tjsonArray = json.dumps(dict)\r\n\t\t\t\t\t#print(temparr)\r\n\t\t\t\t\tprint(jsonArray)\t\r\n\t\t\t\t\t#index = index's size \r\n\r\n\t\t\t\t\tresponse = queue.send_message(MessageBody=jsonArray)\r\n\t\t\t\t\t# The response is NOT a resource, but gives you a message ID and MD5\r\n\t\t\t\t\tprint(\"-----------------------------------\")\r\n\t\t\t\t\tprint(response.get('MessageId'))\r\n\t\t\t\t\tprint(response.get('MD5OfMessageBody'))\r\n\t\t\t\t\tprint(\"-----------------------------------\")\r\n\t\t\t\t\t# es.index(index='myposts', doc_type='mytweets' ,id=tweet_id, body= jsonArray)\r\n\t\t\t\t\t# print (\"added into elastic search\")\r\n\r\n\t\t\texcept AttributeError as e:\r\n\t\t\t\tprint (\"Encountered a key AttributeError: \"+str(e))\r\n\t\t\t\treturn True\t\t\r\n\t\t\texcept KeyError as e:\r\n\t\t\t\tprint (\"Encountered a key error: \"+str(e))\r\n\t\t\t\treturn True\t\r\n\t\t\texcept Exception as e:\r\n\t\t\t\tprint(\"err \"+str(e))\r\n\t\t\t\treturn True\r\n\r\n\r\n\t\treturn True\r\n\r\n\tdef on_error(self, status):\r\n\t\tprint (\"err: \"+str(status))\r\n\t\tif status==420:\r\n\t\t\treturn False\r\n\t\treturn True\r\n\r\n\r\n\t\t\r\ndef main():\r\n\tlistener = StdOutListener()\r\n\tauth = OAuthHandler(consumer_key, consumer_secret)\r\n\tauth.set_access_token(access_token, access_token_secret)\r\n\tstream = Stream(auth, listener)\r\n\tprint(\"ready\")\r\n\twhile 1==1:\r\n\t\twhile True:\r\n\t\t\ttry:\r\n\t\t\t\tstream.filter(track=search_key_array, locations=[-180,-90,180,90])\r\n\t\t\t\tprint(\"done\")\r\n\t\t\t\tbreak\r\n\t\t\texcept tweepy.TweepError:\r\n\t\t\t\tprint(\"tweepy error\")\r\n\t\t\t\tprint(\"error. sleeping in for :\"+str(nsecs))\r\n\t\t\t\tnsecs=random.randint(60,63)\r\n\t\t\t\ttime.sleep(nsecs)\r\n\t\tnmsecs=random.randint(60,63)\r\n\t\tprint(\"error. sleeping out for :\"+str(nmsecs))\r\n\t\ttime.sleep(nmsecs)\r\n\t\t\t\r\nif __name__ == '__main__':\r\n\tmain()\r\n","sub_path":"twtani.py","file_name":"twtani.py","file_ext":"py","file_size_in_byte":4848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"124805424","text":"\"\"\"Do I parse bam2mpg right?\"\"\"\nimport sys, nose.tools, csv\nsys.path.append('../')\nimport cmpIndels, flagGgiWithNci, summarizeBam2Mpg\n\nTARGET = '/nas/nbl3/'\nPWD = '/home/evansj/me/projects/diskin/nb_convergence/'\nWORK = PWD + 'working/'\n\ndef loadWeWgSamples(sampleDataFile):\n ls = []\n with open(sampleDataFile) as f:\n reader = csv.DictReader(f, delimiter='\\t')\n for row in reader:\n ls.append( row['wgSamples'] )\n return ls\n\ndef testDeletion():\n # 5:40947770:40947770:T:- PASUCB\n foundIt5, foundItX = False, False\n platypusFile = '/nas/nbl3/nci/TARGET-30-PASUCB_normal.vcf'\n with open(platypusFile) as f:\n for line in f:\n if line[0] != '#':\n (CHROM, POS, ID, REF, ALT, QUAL,\n FILTER, INFO, FORMAT,\n SAMPLE) = line.strip().split('\\t')\n if int(QUAL) >= 10:\n indels, singles = summarizeBam2Mpg.parseIndel(CHROM, POS,\n REF, ALT,\n 'test')\n for indel in indels:\n (indel_sample, indel_chrom,\n indel_st, indel_end, indel_ref,\n indel_alt) = indel\n if indel_chrom == 'chr5' and indel_st == '40947770' and indel_end == '40947770' and indel_ref == 'T' and indel_alt == '-':\n foundIt5 = True\n nose.tools.assert_true(foundIt5, 'fail deletion chr5')\n\ndef printStatus():\n \"\"\"This isn't a great test b/c not all\n indels must be in the platpus file,\n but it gives me an idea of\n what is missing\"\"\"\n samples = [x.split('-')[2] for x in loadWeWgSamples('/home/evansj/me/projects/diskin/recover_trevor/working/wgAndExome.samples.ls')]\n nciIndelFile = TARGET + 'nbl_exome_data/nci/NB222Indels.txt'\n summaryIndelFile = WORK + 'nci.bam2mpg'\n nciIndels = cmpIndels.loadNciIndels(nciIndelFile)\n mpgIndels = flagGgiWithNci.loadParsedNciIndelsFromSingleTool(summaryIndelFile)\n with open('status.bam2mpg', 'w') as fout:\n for indel in nciIndels:\n chrom,st,end,ref,alt = indel.split(':')\n take = True\n if len(ref) == len(alt):\n for n1,n2 in zip(ref, alt):\n if n1 == n2:\n take = False\n if take:\n for sample in nciIndels[indel]:\n if sample in samples:\n status = 'GOOD'\n if not indel in mpgIndels:\n status = 'BAD'\n elif not sample in mpgIndels[indel]:\n status = 'BAD'\n print('\\t'.join((sample, indel, status)),\n file=fout)\n\n# 7:150556055:150556056:CG:GC\n\n#testDeletion() \nprintStatus()\n\n# 22:20920813:20920813:-:CAG PASUCB\n# 17:39925925:39925927:ATC:GTC PASUCB\n# 17:39925925:39925927:ATC:GTA PASUCB\n# X:54982695:54982698:CAAG:- PASUCB\n \n# def testInsertion():\n# myIndelFile = '/home/evansj/me/projects/diskin/nb_convergence/working/nbl.overlap.indel.summary.tumorFlag'\n# myIndels = cmpIndels.loadMySummaryIndels(myIndelFile)\n# nciIndelFile = '/nas/nbl3/nbl_exome_data/nci/NB222Indels.txt'\n# nciIndels = cmpIndels.loadNciIndels(nciIndelFile)\n# indelKey = '1:14106394:14106394:-:CTC'\n# # in PASYLD as cg 14106394 A > ACTC\n# nose.tools.assert_true(indelKey in nciIndels, list(nciIndels.keys())[0])\n# nose.tools.assert_true(indelKey in myIndels, list(myIndels.keys())[0])\n\n# def testDeletion():\n# myIndelFile = '/home/evansj/me/projects/diskin/nb_convergence/working/nbl.overlap.indel.summary.tumorFlag'\n# myIndels = cmpIndels.loadMySummaryIndels(myIndelFile)\n# nciIndelFile = '/nas/nbl3/nbl_exome_data/nci/NB222Indels.txt'\n# nciIndels = cmpIndels.loadNciIndels(nciIndelFile)\n# indelKey = '10:46113657:46113659:TTT:-'\n# # in PASYLD as cg 46113656 CTTT > C\n# nose.tools.assert_true(indelKey in nciIndels, list(nciIndels.keys())[0])\n# nose.tools.assert_true(indelKey in myIndels, list(myIndels.keys())[0])\n\n# def testSubstitution():\n# myIndelFile = '/home/evansj/me/projects/diskin/nb_convergence/working/nbl.overlap.indel.summary.tumorFlag'\n# myIndels = cmpIndels.loadMySummaryIndels(myIndelFile)\n# nciIndelFile = '/nas/nbl3/nbl_exome_data/nci/NB222Indels.txt'\n# nciIndels = cmpIndels.loadNciIndels(nciIndelFile)\n# indelKey = '15:65676617:65676618:TG:CA'\n# nose.tools.assert_true(indelKey in nciIndels, list(nciIndels.keys())[0])\n# nose.tools.assert_true(indelKey in myIndels, list(myIndels.keys())[0])","sub_path":"code/testing/scripts/testBam2pmg.py","file_name":"testBam2pmg.py","file_ext":"py","file_size_in_byte":4754,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"599666794","text":"import os.path\nimport random\nimport sys\n\nimport cv2\nimport h5py\nimport numpy as np\nimport scipy.ndimage as ndimage\nfrom PIL import Image\n\nfrom data.base_dataset import BaseDataset, get_params, get_transform\n\n\nclass BratsDataset(BaseDataset):\n \"\"\"A dataset class for paired image dataset.\n\n It assumes that the directory '/path/to/data/train' contains image pairs in the form of {A,B}.\n During test time, you need to prepare a directory '/path/to/data/test'.\n \"\"\"\n\n def __init__(self, opt, idx=1):\n \"\"\"Initialize this dataset class.\n\n Parameters:\n opt (Option class) -- stores all the experiment flags; needs to be a subclass of BaseOptions\n \"\"\"\n\n self.idx = idx\n\n if idx == 1:\n h5_name = \"train_brats_0.1.h5\"\n elif idx == 2:\n h5_name = \"train_brats_t1_0.1.h5\"\n\n print(f\"Load: {h5_name}\")\n self.is_test = False\n self.real_tumor = False\n self.extend_len = 0\n self.multi_label = True\n BaseDataset.__init__(self, opt)\n self.brats_file = h5py.File(os.path.join(opt.dataroot, h5_name), 'r')\n if 'train' in self.brats_file:\n train_db = self.brats_file['train']\n else:\n train_db = self.brats_file\n self.dcm, self.label, self.seg_label = self.build_pairs(train_db)\n\n # self.dir_AB = os.path.join(opt.dataroot, opt.phase) # get the image directory\n # self.AB_paths = sorted(make_dataset(self.dir_AB, opt.max_dataset_size)) # get image paths\n assert (self.opt.load_size >= self.opt.crop_size) # crop_size should be smaller than the size of loaded image\n self.input_nc = self.opt.output_nc if self.opt.direction == 'BtoA' else self.opt.input_nc\n self.output_nc = self.opt.input_nc if self.opt.direction == 'BtoA' else self.opt.output_nc\n\n def build_pairs(self, dataset):\n dcm_arr = []\n label_arr = []\n seg_label_arr = []\n images = dataset['images']\n labels = dataset['labels']\n\n keys = images.keys()\n for key in keys:\n img = images[key][()]\n label = labels[key][()]\n\n seg_label = (label > 0).astype(np.uint8) * 255\n\n # label = label * (250 / (label.max() + 1e-8)) # multi-label\n label = (label > 0).astype(np.uint8) * 255 # single label\n label = self.merge_skull(img[:, :, 0], label, default_skull_value=255)\n # label = label[:, :, None].repeat(3, axis=2)\n\n dcm_arr.append(img)\n label_arr.append(label)\n seg_label_arr.append(seg_label)\n\n return dcm_arr, label_arr, seg_label_arr\n\n def seg_in_skull(self, seg, mask):\n # ndimage.binary_fill_holes(skull_mask)\n seg = mask * seg\n return seg\n\n def merge_skull(self, skull_mask, slice_label, default_skull_value=5):\n # Add skull structure into label\n skull_mask = ndimage.binary_fill_holes(skull_mask)\n skull_mask = cv2.Laplacian(skull_mask.astype(\"uint8\"), cv2.CV_8U)\n skull_mask[skull_mask > 0] = default_skull_value\n slice_label = slice_label + skull_mask\n\n # slice_label = slice_label * (255 / (slice_label.max() + 1e-8))\n # slice_label = slice_label.astype(\"uint8\")\n\n # slice_label[slice_label > 0] = 255\n\n return slice_label\n\n def __getitem__(self, index):\n \"\"\"Return a data point and its metadata information.\n\n Parameters:\n index - - a random integer for data indexing\n\n Returns a dictionary that contains A, B, A_paths and B_paths\n A (tensor) - - an image in the input domain\n B (tensor) - - its corresponding image in the target domain\n A_paths (str) - - image paths\n B_paths (str) - - image paths (same as A_paths)\n \"\"\"\n A = self.label[index]\n if self.idx == 1:\n A = A // 3 * 2\n\n B = self.dcm[index]\n seg = self.seg_label[index]\n A = Image.fromarray(A).convert('RGB')\n B = Image.fromarray(B).convert('RGB')\n seg = Image.fromarray(seg).convert('RGB')\n\n # apply the same transform to both A and B\n transform_params = get_params(self.opt, A.size)\n if self.opt.phase.lower() != 'train':\n transform_params['crop_pos'] = (0, 0)\n transform_params['vflip'] = False\n transform_params['hflip'] = False\n\n A_transform = get_transform(self.opt, transform_params, grayscale=(self.input_nc == 1), method=Image.NEAREST)\n B_transform = get_transform(self.opt, transform_params, grayscale=(self.output_nc == 1))\n seg_transform = get_transform(self.opt, transform_params, grayscale=(self.input_nc == 1), method=Image.NEAREST)\n\n A = A_transform(A)\n B = B_transform(B)\n seg = seg_transform(seg)\n\n return {'A': A, 'B': B, 'A_paths': str(index), 'B_paths': str(index), 'Seg_label': seg}\n\n def __len__(self):\n \"\"\"Return the total number of images in the dataset.\"\"\"\n return len(self.dcm)\n","sub_path":"data/brats_dataset.py","file_name":"brats_dataset.py","file_ext":"py","file_size_in_byte":5054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"534392511","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 ('scoremgmt', '0007_auto_20151006_2105'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='rounddata',\n name='weather',\n field=models.CharField(blank=True, max_length=5, choices=[('S', '\\u6674\\u308c'), ('C', '\\u66c7\\u308a'), ('R', '\\u96e8'), ('F', '\\u9727'), ('S', '\\u96ea')]),\n ),\n ]\n","sub_path":"scoremgmt/migrations/0008_rounddata_weather.py","file_name":"0008_rounddata_weather.py","file_ext":"py","file_size_in_byte":524,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"435410799","text":"\"\"\"Utilities for import of different file types (csv, docx, pdf, txt)\nand returning quotes and author strings as QuoteModel objects.\n\nQuoteModel: Container class with attributes: body, author\nIngestor: Encapsulates the various Ingestor subclasses\nCSVIngestor: imports CSV files and returns list of QuoteModel objects\nTXTIngestor: imports TXT files and returns list of QuoteModel objects \nDOCXIngestor: imports DOCX files and returns list of QuoteModel objects\nPDFIngestor: imports PDF files and returns list of QuoteModel objects\n\"\"\"\n\nfrom abc import ABC, abstractmethod\nfrom typing import List\nimport docx\nimport os\nimport random\nimport subprocess\nimport pandas as pd\n\n\nclass QuoteModel:\n \"\"\"Container class for imported quotes.\"\"\"\n\n def __init__(self, body: str, author: str):\n \"\"\"Init QuoteModel with body and author attributes.\"\"\"\n self.body = body\n self.author = author \n \n def __repr__(self):\n \"\"\"Output \"body\" - author attributes of QuoteModel.\"\"\"\n return f'\"{self.body}\" - {self.author}'\n \n \nclass IngestorInterface(ABC):\n \"\"\"Abstract base class for Ingestor import classes.\"\"\"\n\n allowed_extensions = []\n\n @classmethod\n def can_ingest(cls, path: str) -> bool:\n \"\"\"Test whether file extensions matches list of allowed extensions.\n \n Arg:\n path = path to file for import\n\n Returns bool.\n \"\"\"\n ext = path.split('.')[-1]\n return ext in cls.allowed_extensions\n\n @classmethod\n @abstractmethod\n def parse(cls, path: str) -> List[QuoteModel]:\n \"\"\"Abstract method to parse files in path (Arg: path) and return list of \n QuoteModel objects.\n \"\"\"\n pass\n\n\nclass CSVIngestor(IngestorInterface):\n \"\"\"Ingestor subclass to parse CSV files.\"\"\"\n\n allowed_extensions = ['csv']\n \n @classmethod\n def parse(cls, path:str) -> List[QuoteModel]:\n \"\"\"Parse CSV files.\"\"\"\n if not cls.can_ingest(path):\n raise Exception(f\"CSVIngestor cannot ingest {path}\") \n quotes = []\n df = pd.read_csv(path, header=0)\n quotes = [QuoteModel(row[0], row[1]) for row in zip(df['body'], df['author'])]\n return quotes\n\nclass TXTIngestor(IngestorInterface):\n \"\"\"Ingestor subclass to parse TXT files.\"\"\"\n\n allowed_extensions = ['txt']\n \n @classmethod\n def parse(cls, path: str) -> List[QuoteModel]:\n \"\"\"Parse TXT files.\"\"\"\n if not cls.can_ingest(path):\n raise Exception(f\"TXTIngestor cannot ingest {path}\") \n quotes = []\n with open(path, \"r\") as file_:\n for line in file_.readlines():\n split_line = line.strip('\\n\\r\"').strip().split(' - ')\n if len(split_line) > 1:\n quote = QuoteModel(split_line[0].strip('\"'), split_line[1])\n quotes.append(quote)\n return quotes\n\nclass DOCXIngestor(IngestorInterface):\n \"\"\"Ingestor subclass to parse DOCX files.\"\"\"\n\n allowed_extensions = ['docx']\n \n @classmethod\n def parse(cls, path:str) -> List[QuoteModel]:\n \"\"\"Parse DOCX files.\"\"\"\n if not cls.can_ingest(path):\n raise Exception(f\"DOCXIngestor cannot ingest {path}\") \n quotes = []\n doc = docx.Document(path)\n for paragraph in doc.paragraphs:\n if paragraph.text != \"\":\n para_text = paragraph.text.split(' - ')\n quote = QuoteModel(para_text[0].strip('\"'), para_text[1].strip())\n quotes.append(quote)\n return quotes\n\nclass PDFIngestor(IngestorInterface):\n \"\"\"Ingestor subclass to parse PDF files.\"\"\"\n\n allowed_extensions = ['pdf']\n\n @classmethod\n def parse(cls, path: str) -> List[QuoteModel]:\n \"\"\"Parse PDF files.\"\"\"\n if not cls.can_ingest(path):\n raise Exception(f\"PDF Ingestor cannot ingest {path}\") \n quotes = []\n tmp = f\"./tmp/{random.randint(0,10000000)}.txt\"\n call = subprocess.call(['pdftotext', '-simple', path, tmp])\n with open(tmp, \"r\") as file_ref:\n for line in file_ref.readlines():\n split_line = line.strip('\\n\\r').strip().split(' - ')\n if len(split_line) > 1:\n quote = QuoteModel(split_line[0].strip('\"'), split_line[1])\n quotes.append(quote)\n os.remove(tmp)\n return quotes\n \n \nclass Ingestor(IngestorInterface):\n \"\"\"Encapsulates the various Ingestor methods defined in ingestors list.\"\"\"\n\n ingestors = [DOCXIngestor, CSVIngestor, PDFIngestor, TXTIngestor]\n \n @classmethod\n def parse(cls, path:str):\n \"\"\"Run parse methods of Ingestor subclasses\"\"\"\n for ingestor in cls.ingestors:\n if ingestor.can_ingest(path):\n return ingestor.parse(path)","sub_path":"meme_project/quote_engine/quote_importer.py","file_name":"quote_importer.py","file_ext":"py","file_size_in_byte":4821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"214175639","text":"'''\nreference: dramatiq.benchmarks.bench\n'''\nimport pika\nimport redis\nimport os\nimport argparse\nimport subprocess\nimport time\n\nfrom magne.coro_consumer.coro_consumer import main as coro_main\n\n\ncounter_key = 'magne_coro_consumer'\n\n\nrouting_key = exchange_name = queue_name = 'magne_latency_bench'.upper()\n\n\ndef en_queue(n):\n print('starting en_queue...')\n parameters = pika.URLParameters('amqp://guest:guest@localhost:5672/%2F')\n\n connection = pika.BlockingConnection(parameters)\n\n channel = connection.channel()\n print('decalaring exchange and queue')\n try:\n channel.exchange_declare(exchange_name)\n channel.queue_declare(queue_name)\n print('bind')\n channel.queue_bind(queue_name, exchange_name, routing_key)\n except Exception as e:\n print('decalare exchange and queue error: %s' % e)\n raise e\n print('config exchange and queue done')\n print('staring send tasks into rabbitmq')\n for _ in range(n):\n channel.basic_publish('MAGNE_LATENCY_BENCH',\n 'MAGNE_LATENCY_BENCH',\n '{\"func\": \"magne_latency_bench\", \"args\": []}',\n )\n print('en_queue done')\n return\n\n\ndef setup(count):\n print('benchmark magne coro_consumer...')\n en_queue(count)\n print('%s tasks in rabbitmq' % count)\n return\n\n\ndef parse_argv():\n parser = argparse.ArgumentParser(prog='magne-bench', description='benchmark magne, reference: dramatiq.benchmarks.bench')\n parser.add_argument('--count', type=int, help='worker count, default: 100',\n default=100,\n )\n parser.add_argument('--only-setup', type=int, help='any non zero means that just push tasks into rabbitmq, do not run bench',\n default=0,\n )\n parser.add_argument('--workers', type=int, help='workers, default 1',\n default=1,\n )\n args = parser.parse_args()\n return args.count, args.only_setup, args.workers\n\n\ndef main():\n print('pid: %s' % os.getpid())\n count, only_setup, workers = parse_argv()\n print('task count: %s' % (count))\n setup(count)\n if only_setup != 0:\n return\n rs = redis.StrictRedis()\n start_time = time.time()\n rs.set(counter_key, 0)\n workers = 1 if workers <= 0 else workers\n if workers > 1:\n cm = ['env', 'PYTHONPATH=/opt/curio:/opt/magne:/opt/magne/magne', 'python3.6',\n '/opt/magne/magne/coro_consumer/run.py', '--task=magne.coro_consumer.bench_tasks', '--log-level=INFO',\n '--curio-debug=0']\n else:\n cm = ['env', 'PYTHONPATH=/opt/curio:/opt/magne:/opt/magne/magne', 'python3.6',\n '/opt/magne/magne/coro_consumer/run.py', '--task=magne.coro_consumer.bench_tasks', '--log-level=INFO',\n '--curio-debug=1']\n print(' '.join(cm))\n procs = []\n for _ in range(workers):\n proc = subprocess.Popen(cm)\n procs.append(proc)\n processed = 0\n while processed < count:\n processed = int(rs.get(counter_key).decode('utf-8'))\n print(f\"{processed}/{count} messages processed\\r\", end=\"\")\n time.sleep(0.1)\n\n duration = time.time() - start_time\n for proc in procs:\n proc.terminate()\n proc.wait()\n print(f\"coro_consumer took {duration} seconds to process {count} messages.\")\n return\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"magne/coro_consumer/bench.py","file_name":"bench.py","file_ext":"py","file_size_in_byte":3455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"558713232","text":"from PIL import Image\n\n\n#open lena image file\n\nimg_name=\"LENA.jpg\"\nimg=Image.open(img_name)\nimg=img.convert(\"L\")\n\n#size of image\n\nw=img.size[0]\nh=img.size[1]\n\n#flip image\npixel = img.load()\nflip=\"flipped_lena.jpg\"\nflipped_img=Image.new(img.mode ,img.size,color=0)\nfor i in range(w):\n for j in range(h):\n flipped_img.putpixel((i,j),pixel[i,w-1-j])\nflipped_img.save(flip)\nflipped_img.show()\n","sub_path":"FLİP_LENA.py","file_name":"FLİP_LENA.py","file_ext":"py","file_size_in_byte":399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"312051885","text":"# -*- coding: utf-8 -*- #\n# Copyright 2017 Google 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\n\"\"\"Tests for `gcloud iot devices create`.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import unicode_literals\n\nimport itertools\nfrom dateutil import tz\n\nfrom googlecloudsdk.api_lib.cloudiot import devices as devices_api\nfrom googlecloudsdk.calliope import base as calliope_base\nfrom googlecloudsdk.core import properties\nfrom googlecloudsdk.core.util import times\nfrom tests.lib import cli_test_base\nfrom tests.lib import parameterized\nfrom tests.lib import test_case\nfrom tests.lib.surface.cloudiot import base\n\n\nclass DevicesCreateTest(base.CloudIotBase, parameterized.TestCase):\n\n def SetUp(self):\n self.devices_client = devices_api.DevicesClient(self.client, self.messages)\n\n self.StartObjectPatch(times, 'LOCAL', tz.tzutc())\n\n properties.VALUES.core.user_output_enabled.Set(False)\n\n def _ExpectCreate(self, device):\n registry_name = ('projects/{}/'\n 'locations/us-central1/'\n 'registries/my-registry').format(self.Project())\n self.client.projects_locations_registries_devices.Create.Expect(\n self.messages.CloudiotProjectsLocationsRegistriesDevicesCreateRequest(\n parent=registry_name,\n device=device),\n device)\n\n @parameterized.parameters(calliope_base.ReleaseTrack.ALPHA,\n calliope_base.ReleaseTrack.BETA,\n calliope_base.ReleaseTrack.GA)\n def testCreate_NoOptions(self, track):\n self.track = track\n device = self.messages.Device(\n id='my-device',\n blocked=False\n )\n self._ExpectCreate(device)\n\n results = self.Run(\n 'iot devices create my-device '\n ' --registry my-registry'\n ' --region us-central1')\n\n self.assertEqual(results, device)\n self.AssertLogContains('Created device [my-device].')\n\n @parameterized.parameters(itertools.product(\n [calliope_base.ReleaseTrack.ALPHA, calliope_base.ReleaseTrack.BETA,\n calliope_base.ReleaseTrack.GA],\n [(True, '--blocked'),\n (False, '--no-blocked')]\n ))\n def testCreate_BlockedFlags(self, track, data):\n blocked, blocked_flag = data\n self.track = track\n device = self.messages.Device(\n id='my-device',\n blocked=blocked,\n )\n self._ExpectCreate(device)\n\n results = self.Run(\n 'iot devices create my-device '\n ' --registry my-registry'\n ' --region us-central1'\n ' {} '.format(blocked_flag)\n )\n\n self.assertEqual(results, device)\n\n @parameterized.parameters(itertools.product(\n [calliope_base.ReleaseTrack.ALPHA, calliope_base.ReleaseTrack.BETA,\n calliope_base.ReleaseTrack.GA],\n [('rsa-pem', 'RSA_PEM', base.CloudIotBase.PUBLIC_KEY_CONTENTS),\n ('RSA_PEM', 'RSA_PEM', base.CloudIotBase.PUBLIC_KEY_CONTENTS),\n ('es256-pem', 'ES256_PEM', base.CloudIotBase.PUBLIC_KEY_CONTENTS),\n ('ES256_PEM', 'ES256_PEM', base.CloudIotBase.PUBLIC_KEY_CONTENTS),\n ('rsa-x509-pem', 'RSA_X509_PEM', base.CloudIotBase.CERTIFICATE_CONTENTS),\n ('RSA_X509_PEM', 'RSA_X509_PEM', base.CloudIotBase.CERTIFICATE_CONTENTS),\n ('es256-x509-pem', 'ES256_X509_PEM',\n base.CloudIotBase.CERTIFICATE_CONTENTS),\n ('ES256_X509_PEM', 'ES256_X509_PEM',\n base.CloudIotBase.CERTIFICATE_CONTENTS),]\n ))\n def testCreate_ValidCredentialTypes(self, track, data):\n key_type, type_enum, key_contents = data\n self.track = track\n key_path = self.Touch(self.temp_path, 'temp.pub', contents=key_contents)\n expiration_time = '2017-01-01T00:00:00.000Z'\n device = self.messages.Device(\n id='my-device',\n blocked=False,\n credentials=[\n self.messages.DeviceCredential(\n expirationTime=expiration_time,\n publicKey=self.messages.PublicKeyCredential(\n format=getattr(self.key_format_enum, type_enum),\n key=key_contents))]\n )\n\n self._ExpectCreate(device)\n\n results = self.Run(\n ('iot devices create my-device '\n ' --registry my-registry'\n ' --region us-central1'\n ' --public-key '\n ' path={},type={},expiration-time={}').format(key_path,\n key_type,\n expiration_time))\n self.assertEqual(results, device)\n self.AssertLogContains('Created device [my-device].')\n\n @parameterized.parameters(calliope_base.ReleaseTrack.ALPHA,\n calliope_base.ReleaseTrack.BETA,\n calliope_base.ReleaseTrack.GA)\n def testCreate_WithCredentialsDeprecatedTypes(self, track):\n self.track = track\n new_credential_cert_path = self.Touch(self.temp_path, 'certificate.pub',\n contents=self.CERTIFICATE_CONTENTS)\n new_credential_key_path = self.Touch(self.temp_path, 'key.pub',\n contents=self.PUBLIC_KEY_CONTENTS)\n device = self.messages.Device(\n id='my-device',\n blocked=False,\n credentials=[\n self.messages.DeviceCredential(\n expirationTime='2017-01-01T00:00:00.000Z',\n publicKey=self.messages.PublicKeyCredential(\n format=self.key_format_enum.RSA_X509_PEM,\n key=self.CERTIFICATE_CONTENTS\n )\n ),\n self.messages.DeviceCredential(\n publicKey=self.messages.PublicKeyCredential(\n format=self.key_format_enum.ES256_PEM,\n key=self.PUBLIC_KEY_CONTENTS\n )\n )\n ]\n )\n self._ExpectCreate(device)\n\n results = self.Run(\n ('iot devices create my-device '\n ' --registry my-registry'\n ' --region us-central1'\n ' --public-key '\n ' path={},type=rs256,expiration-time=2017-01-01T00:00:00Z'\n ' --public-key path={},type=es256').format(new_credential_cert_path,\n new_credential_key_path))\n self.assertEqual(results, device)\n self.AssertLogContains('Created device [my-device].')\n\n @parameterized.parameters(calliope_base.ReleaseTrack.ALPHA,\n calliope_base.ReleaseTrack.BETA,\n calliope_base.ReleaseTrack.GA)\n def testCreate_WithCredentialsBadKeySpecification(self, track):\n self.track = track\n with self.AssertRaisesExceptionMatches(\n cli_test_base.MockArgumentError,\n 'valid keys are [expiration-time, path, type]; received: bad-key'):\n self.Run(\n ('iot devices create my-device '\n ' --registry my-registry'\n ' --region us-central1'\n ' --public-key path={},type=es256,bad-key=bad-value').format(\n self.public_key))\n\n @parameterized.parameters(calliope_base.ReleaseTrack.ALPHA,\n calliope_base.ReleaseTrack.BETA,\n calliope_base.ReleaseTrack.GA)\n def testCreate_WithCredentialsBadKeyType(self, track):\n self.track = track\n new_credential_path = self.Touch(self.temp_path, 'certificate.pub',\n contents=self.PUBLIC_KEY_CONTENTS)\n with self.assertRaisesRegex(\n ValueError, r'Invalid key type \\[bad-key-type\\]'):\n self.Run(\n ('iot devices create my-device '\n ' --registry my-registry'\n ' --region us-central1'\n ' --public-key path={},type=bad-key-type,').format(\n new_credential_path))\n\n @parameterized.parameters(calliope_base.ReleaseTrack.ALPHA,\n calliope_base.ReleaseTrack.BETA,\n calliope_base.ReleaseTrack.GA)\n def testCreate_WithCredentialsBadDateTime(self, track):\n self.track = track\n new_credential_path = self.Touch(self.temp_path, 'certificate.pub',\n contents=self.PUBLIC_KEY_CONTENTS)\n with self.AssertRaisesExceptionMatches(\n cli_test_base.MockArgumentError,\n 'Failed to parse date/time'):\n self.Run(\n ('iot devices create my-device '\n ' --registry my-registry'\n ' --region us-central1'\n ' --public-key path={},type=es256,expiration-time=not-a-datetime,'\n ).format(new_credential_path))\n\n @parameterized.parameters(calliope_base.ReleaseTrack.ALPHA,\n calliope_base.ReleaseTrack.BETA,\n calliope_base.ReleaseTrack.GA)\n def testCreate_WithMetadata(self, track):\n self.track = track\n metadata_path = self.Touch(self.temp_path, 'value.txt', 'file_value')\n device = self.messages.Device(\n id='my-device',\n blocked=False,\n metadata=self.messages.Device.MetadataValue(\n additionalProperties=[\n self._CreateAdditionalProperty('key', 'value'),\n self._CreateAdditionalProperty('file_key', 'file_value')])\n )\n self._ExpectCreate(device)\n\n self.Run(\n ('iot devices create my-device '\n ' --registry my-registry'\n ' --region us-central1'\n ' --metadata=key=value '\n ' --metadata-from-file=file_key={} '.format(metadata_path)))\n\n @parameterized.parameters(calliope_base.ReleaseTrack.ALPHA,\n calliope_base.ReleaseTrack.BETA,\n calliope_base.ReleaseTrack.GA)\n def testCreate_RelativeName(self, track):\n self.track = track\n device = self.messages.Device(\n id='my-device',\n blocked=False\n )\n self._ExpectCreate(device)\n\n device_name = ('projects/{}/'\n 'locations/us-central1/'\n 'registries/my-registry/'\n 'devices/my-device').format(self.Project())\n results = self.Run(\n 'iot devices create {}'.format(device_name))\n\n self.assertEqual(results, device)\n\n\nif __name__ == '__main__':\n test_case.main()\n","sub_path":"google-cloud-sdk/lib/tests/unit/surface/iot/devices/create_test.py","file_name":"create_test.py","file_ext":"py","file_size_in_byte":10706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"263703026","text":"#!/usr/bin/env python\n#\n# Copyright (c) 2017 10X Genomics, Inc. All rights reserved.\n#\nfrom longranger.cnv import coverage_matrix, contig_manager\nimport json\nfrom crdna.gc_normalizer import gc_normalize\nfrom crdna.utils import load_h5\n\nimport pandas as pd\nimport numpy as np\n\n__MRO__ = \"\"\"\n#\nNormalize GC bias using GC bias parameters from the previous stage\nstage NORMALIZE_PROFILES(\n in h5 raw_profiles,\n in h5 tracks,\n in string reference_path,\n in json gc_norm_params,\n #\n out h5 normalized_profiles,\n #\n src py \"stages/copy_number_processor/normalize_profiles\",\n) split using ()\n\"\"\"\n\n\ndef split(args):\n mat_size_gb = coverage_matrix.get_genome_matrix_size_gb(args.raw_profiles)\n mem_gb = int(np.ceil(3*mat_size_gb + 1))\n return {'chunks': [], 'join': {'__mem_gb' : mem_gb}}\n\n\ndef load_data(profile_name, tracks_name, chroms):\n profile_store = pd.HDFStore(profile_name, \"r\")\n profiles = []\n mask = []\n for chrom in chroms:\n profiles.append(profile_store[\"/contigs/\" + chrom].values)\n mask.append(profile_store[\"/masks/\" + chrom].values) \n profile_store.close()\n\n tracks_store = pd.HDFStore(tracks_name, \"r\")\n gc = []\n for chrom in chroms:\n gc.append(tracks_store[\"/GC/\" + chrom].values)\n tracks_store.close()\n\n return profiles, gc, mask\n\n\ndef join(args, outs, chunk_defs, chunk_outs):\n ref = contig_manager.contig_manager(args.reference_path)\n chroms = ref.primary_contigs(allow_sex_chromosomes=True)\n\n profiles, gc, mask = load_data(args.raw_profiles, args.tracks, chroms)\n gc_norm_params = json.load(open(args.gc_norm_params, \"r\"))\n scale = gc_norm_params[\"scale\"]\n linear = gc_norm_params[\"linear\"]\n quadratic = gc_norm_params[\"quadratic\"]\n\n norm_profiles = gc_normalize(profiles, gc, linear, quadratic, chroms)\n\n bin_size = coverage_matrix.get_bin_size(args.raw_profiles)\n\n coverage_matrix.store_matrix(file_name=outs.normalized_profiles,\n chroms=chroms, profiles=norm_profiles, tracks=None,\n window_size=bin_size, masks=mask, dtype=\"float32\")\n\n store = pd.HDFStore(outs.normalized_profiles, \"a\")\n constants = load_h5(args.raw_profiles, \"constants\")\n store[\"constants\"] = constants\n store.close()\n\n store = pd.HDFStore(outs.normalized_profiles, \"a\")\n store[\"/gc_params/scale\"] = pd.Series(scale)\n store[\"/gc_params/linear\"] = pd.Series(linear)\n store[\"/gc_params/quadratic\"] = pd.Series(quadratic)\n store.close()\n","sub_path":"mro/stages/copy_number_processor/normalize_profiles/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":2511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"408529893","text":"import os\nimport re\nimport warnings\nimport argparse\nimport pandas as pd\nimport numpy as np\nfrom pdfreader import PDFDocument, SimplePDFViewer\n\nwarnings.filterwarnings('ignore')\n\ndef read_file(path_to_file):\n fd = open(path_to_file, \"rb\")\n doc = PDFDocument(fd)\n viewer = SimplePDFViewer(fd)\n viewer.render()\n return viewer\n\ndef get_canvas(viewer):\n page_1_canvas = viewer.canvas.text_content\n\n with open(r\".\\tmp\\page_1_canvas.txt\", \"w\", encoding='utf8') as f:\n f.write(page_1_canvas)\n\n viewer.navigate(2)\n viewer.render()\n page_2_canvas = viewer.canvas.text_content\n\n with open(r\".\\tmp\\page_2_canvas.txt\", \"w\", encoding='utf8') as f:\n f.write(page_2_canvas)\n\n\ndef first_page():\n df = pd.read_csv(r\".\\tmp\\page_1_canvas.txt\", header=[1], sep='\\t')\n df.rename(columns={' q':'col1'}, inplace=True)\n df['col2'] = df['col1'].str.findall(r\"\\)\\] TJ\")\n df.col2 = df.col2.apply(lambda y: np.nan if len(y)==0 else y)\n df_name = df.loc[(df.col2.isnull()!=True)]\n df_name.drop(columns=['col2'], inplace=True)\n df_name.col1 = df_name.col1.astype('str')\n df_name['col2'] = df_name.apply(lambda x: re.sub(\"\\(\\ \\)\", \"_\", x['col1']), axis=1)\n df_name['col2'] = df_name.apply(lambda x: re.sub(\"\\(|\\)|TJ|\\[|\\]|\\ \", '', x['col2']), axis=1)\n df_name = df_name[3:-10]\n df_name.reset_index(drop=True, inplace=True)\n df_name['count_'] = df_name.col2.str.len()\n df_count = df_name.loc[df_name.count_<=3]\n df_name_ = df_name.loc[df_name.count_>3]\n df_count.index = df_count.index-1\n df_name_.count_ = df_count.col2\n df_name_.drop(columns=['col1'], inplace=True)\n df_name_.rename(columns={\n 'col2':'parameter_page1',\n 'count_':'score_page1'\n }, inplace=True)\n df_name_.reset_index(drop=True, inplace=True)\n\n return df_name_\n\n\ndef second_page():\n grey = '0.901 0.905 0.909 rg'\n df = pd.read_csv(r\".\\tmp\\page_2_canvas.txt\", header = [1])\n df.rename(columns={' q':'col1'}, inplace=True)\n df['col2'] = df['col1'].str.findall(r\"\\)\\] TJ\")\n df.col2 = df.col2.apply(lambda y: np.nan if len(y)==0 else y)\n df_name = df.loc[(df.col2.isnull()!=True)]\n df_name.drop(columns=['col2'], inplace=True)\n df_name.col1 = df_name.col1.astype('str')\n df_name['col2'] = df_name.apply(lambda x: re.sub(\"\\(\\ \\)\", \"_\", x['col1']), axis=1)\n df_name['col2'] = df_name.apply(lambda x: re.sub(\"\\(|\\)|TJ|\\[|\\]|\\ \", '', x['col2']), axis=1)\n \n try:\n test_df = pd.DataFrame(index=range(0,100),columns=['A'], dtype='float')\n for ind in range(df_name.shape[0]):\n i = df_name.index[ind]\n j = df_name.index[ind+1]\n name = 'col'+str(i)\n tmp = df.col1[i:j].reset_index(drop=True)\n test_df[name] = tmp\n except:\n pass\n \n test_df.dropna(thresh=15, axis=1, inplace=True)\n count_grey = pd.DataFrame(np.sum(test_df==grey, axis=0), columns=['count'])\n count_grey.index = count_grey.index.str.replace('col', '')\n df_name.index = df_name.index.astype('str')\n df_name['count']=None\n df_name.update(count_grey)\n df_name.fillna(999, inplace=True)\n df_name.dropna(inplace=True)\n df_name['count'] = 4 - df_name['count']\n df_name.replace(-995, ' ', inplace=True)\n df_name.drop(columns=['col1'], inplace=True)\n df_name = df_name[:157]\n df_name.reset_index(drop=True, inplace=True)\n df_name['count'][df_name.col2=='Баллы_по_субшкалам'] = np.nan\n res = pd.concat([df_name[:51].reset_index(), df_name[51:96].reset_index(), df_name[96:].reset_index()], axis=1)\n res.drop(columns=['index'],inplace=True)\n res.columns = pd.Index(['parameter1_page2', 'score1_page2', 'parameter2_page2', 'score2_page2', 'parameter3_page2', 'score3_page2'], \n dtype='object')\n return res\n\ndef concat_frame(page1, page2, name):\n df_ = pd.DataFrame({'parameter_page1':[np.nan, name, 'parameter_page1'],\n 'score_page1':[np.nan, np.nan, 'score_page1'],\n 'parameter1_page2':[np.nan, np.nan, 'HPI'], \n 'score1_page2':[np.nan, np.nan, 'HPI_score'],\n 'parameter2_page2':[np.nan, np.nan, 'HDS'],\n 'score2_page2':[np.nan, np.nan, 'HDS_score'],\n 'parameter3_page2':[np.nan, np.nan, 'MVPI'],\n 'score3_page2':[np.nan, np.nan, 'MVPI_score']})\n df = pd.concat([page1, page2], axis=1)\n df = pd.concat([df_, df], ignore_index=True)\n df.reset_index(drop=True, inplace=True)\n return df\n\ndef write_file(excel_path, frame):\n df_excel = pd.read_excel(excel_path, engine='openpyxl')\n df_excel.reset_index(drop=True, inplace=True)\n result = pd.concat([frame, df_excel],axis=0, ignore_index=True)\n result.to_excel(excel_path, index=False)\n\nif __name__== '__main__':\n # parser = argparse.ArgumentParser()\n # parser.add_argument('path', action='store', help='folder path')\n\n # args = parser.parse_args()\n\n # path_to_file = os.path.normpath(args.path)\n path = r\".\\results\"\n excel_name = 'result.xlsx'\n excel_path = os.path.join(path, excel_name)\n with pd.ExcelWriter(excel_path ,mode='w') as writer:\n pd.DataFrame().to_excel(writer, sheet_name='Sheet_name_1')\n path_to_file = r\".\\examples\\test.pdf\"\n #folder = r\".\\examples\"\n folder = r\".\\uploads\"\n files = [x for x in os.listdir(folder) if x.endswith(\".pdf\")]\n\n for file in files:\n path_to_file = os.path.join(folder, file)\n print(file)\n viewer = read_file(path_to_file)\n get_canvas(viewer)\n df_page1 = first_page()\n df_page2 = second_page()\n fin_df = concat_frame(df_page1, df_page2, path_to_file)\n write_file(excel_path, fin_df)\n print('done')","sub_path":"pdf_reader.py","file_name":"pdf_reader.py","file_ext":"py","file_size_in_byte":5721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"79821361","text":"# -*- coding: utf-8 -*-\nimport numpy as np\n#import pyAgrum as gum\n\nimport utils\n\nclass CdM:\n \"\"\"\n Class virtuelle représentant une Chaîne de Markov\n \"\"\"\n\n def __init__(self):\n \"\"\"\n Constructeur. En particulier, initalise le dictionaire stateToIndex\n\n :warning: doit être appelé en fin de __init__ des classes filles\n avec ` super().__init__()`\n \"\"\"\n self.stateToIndex = dict() \n\n states = self.get_states()\n if (type(self.get_states()) != list):\n states = list(self.get_states())\n \n n = len(states)\n for i in range(n):\n self.stateToIndex[states[i]] = i \n\n def get_state_from_index(self, i):\n for k,v in self.stateToIndex.items():\n if v == i:\n return k\n \n def distribution_to_vector(self, distribution):\n \"\"\"\n :return: la représentation vectorielle des probabilités sur les états \n à partir de la distribution \n \"\"\"\n vector = [0] * len(self.stateToIndex)\n \n for k in distribution.keys():\n vector[self.stateToIndex[k]] = distribution[k]\n \n return np.array(vector)\n \n def vector_to_distribution(self, vector):\n \"\"\"\n :return: la représentation en dictionnaire des probabilités sur les états \n à partir de la représentation vectorielle\n \"\"\"\n vector = vector.tolist()\n distribution = dict()\n n = len(vector) \n \n for i in range(n):\n if vector[i] != 0:\n for k in self.stateToIndex.keys():\n if self.stateToIndex[k] == i:\n break\n \n distribution[k] = vector[i]\n \n return distribution\n\n def show_distribution(self, distribution):\n \"\"\"\n affiche une distribution en le représentant comme son vecteur associé\n \"\"\"\n print(self.distribution_to_vector(distribution))\n\n def get_transition_matrix(self):\n \"\"\"\n :return: la matrice de transition \n \"\"\"\n n = len(self.stateToIndex)\n matrix = [[0.0 for j in range(n)] for i in range(n)] \n \n for s in self.stateToIndex.keys():\n d = self.get_transition_distribution(s) \n for k in d.keys():\n matrix[self.stateToIndex[s]][self.stateToIndex[k]] = d[k]\n \n return np.array(matrix)\n \n\n def get_states(self):\n \"\"\"\n :return: un ensemble d'états énumérable (list, n-uple, etc.)\n \"\"\"\n raise NotImplementedError\n\n def get_transition_distribution(self, state):\n \"\"\"\n :param state: état initial\n :return: un dictionnaire {etat:proba} représentant l'ensemble des états atteignables à partir de state et leurs\n probabilités\n \"\"\"\n raise NotImplementedError\n\n def get_initial_distribution(self):\n \"\"\"\n :return: un dictionnaire représentant la distribution à t=0 {etat:proba}\n \"\"\"\n raise NotImplementedError\n\n def __len__(self):\n \"\"\"\n permet d'utiliser len(CdM) pour avoir le nombre d'état d'un CdM\n\n :warning: peut être surchargée\n :return: le nombre d'état\n \"\"\"\n return len(self.get_states())\n\n def show_transition_matrix(self):\n \"\"\"\n représente la matrice de transition\n :return: \n \"\"\"\n utils.show_matrix(self.get_transition_matrix())\n \n def get_transition_graph(self):\n \"\"\"\n :return: le graphe de transition\n \"\"\"\n g=gum.DiGraph()\n matrix = self.get_transition_matrix().tolist()\n n = len(self.stateToIndex)\n \n for s in self.stateToIndex.keys():\n g.addNode()\n \n for i in range(n):\n for j in range(n):\n if matrix[i][j] != 0.0:\n g.addArc(i, j)\n \n return g\n\n def show_transition_graph(self, gnb):\n \"\"\"\n affiche le graphe de transition\n :return:\n \"\"\"\n s = \"digraph { \"\n matrix = self.get_transition_matrix().tolist()\n n = len(self.stateToIndex)\n \n #for k,v in .items():\n for k in self.stateToIndex.keys():\n s += str(self.stateToIndex[k]) + \" [label=\\\"[\" + str(self.stateToIndex[k]) + \"] \" + str(k) + \"\\\"];\"\n\n for i in range(n):\n for j in range(n):\n if (matrix[i][j] > 0):\n s += str(i) + \"->\" + str(j) + \" [label=\" + str(matrix[i][j]) + \"];\"\n \n s += \"}\"\n gnb.showDot(s)\n \n def get_communication_classes(self):\n \"\"\"\n :return: les classes communicantes du graphe de transition (composantes fortement connexes)\n \"\"\"\n classes = utils.tarjan(self.get_transition_graph())\n classes_states = []\n \n for c in classes:\n new_c = set()\n for i in c:\n #for k,v in self.stateToIndex.items():\n # if v == i:\n # break\n k = self.get_state_from_index(i)\n new_c.add(k)\n \n classes_states.append(new_c)\n return classes_states\n \n def is_irreducible(self):\n \"\"\"\n :return: True si le graphe de transition est irréductible, False sinon\n \"\"\"\n matrix = self.get_transition_matrix().tolist()\n n = len(self.get_states())\n for i in range(n):\n if matrix[i][i] == 1:\n return False\n if all(v == 0 for v in matrix[i]): # pas d'arc sortant\n return False\n return True\n \n def get_absorbing_classes(self):\n \"\"\"\n :return: les classes absorbantes du graphe de transition\n \"\"\"\n if self.is_irreducible():\n return [set(self.get_states())]\n else:\n matrix = self.get_transition_matrix()\n absorbing_c = []\n for i in range(len(self.get_states())):\n if matrix[i][i] == 1:\n #for k,v in self.stateToIndex.items():\n # if v == i:\n # break\n k = self.get_state_from_index(i)\n absorbing_c.append({k})\n return absorbing_c\n\n def get_periodicity(self):\n \"\"\"\n Calcule la périodicité de la chaîne de Markov\n :return: la périodicité de la chaîne de Markov\n \"\"\"\n matrix = self.get_transition_matrix()\n g = self.get_transition_graph()\n n = len(self.get_states())\n \n if not(self.is_irreducible()):\n return 1\n \n for i in range(n):\n if matrix[i][i] > 0: # boucle\n return 1\n \n return utils.get_state_periodicity(g, 0)\n \n def is_aperiodic(self):\n \"\"\"\n :return: True si la chaîne de Markov est apériodique, False sinon\n \"\"\"\n if self.get_periodicity() == 1:\n return True\n else:\n return False\n \n def is_ergodic(self):\n if self.is_irreducible() and self.is_aperiodic():\n return True\n else:\n return False\n \n ","sub_path":"CdM.py","file_name":"CdM.py","file_ext":"py","file_size_in_byte":6364,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"185732662","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n#from __future__ import print_function, division, absolute_import, unicode_literals\n\nimport RPi.GPIO as GPIO\nimport time\nimport sys\nimport os\nimport glob\nimport subprocess\n\nfrom RPLCD import CharLCD, cleared, cursor\nfrom RPLCD import Alignment, CursorMode, ShiftMode\nfrom time import sleep, strftime\nfrom datetime import datetime\n\nGPIO.setwarnings(False)\n\n# -----------------------------------------\n# Temperature from dallas probe DS18B20 - Start\n# -----------------------------------------\n\nos.system('modprobe w1-gpio')\nos.system('modprobe w1-therm')\n \nbase_dir = '/sys/bus/w1/devices/'\ndevice_folder = glob.glob(base_dir + '28*')[0]\ndevice_file = device_folder + '/w1_slave'\n \ndef read_temp_raw():\n f = open(device_file, 'r')\n lines = f.readlines()\n f.close()\n return lines\n \ndef read_temp():\n lines = read_temp_raw()\n while lines[0].strip()[-3:] != 'YES':\n time.sleep(0.2)\n lines = read_temp_raw()\n equals_pos = lines[1].find('t=')\n if equals_pos != -1:\n temp_string = lines[1][equals_pos+2:]\n temp_c = str(int(temp_string) / 1000)\n return temp_c\n\n# ----------------------------------------\n# Temperature from dallas probe DS18B20 - End\n# ----------------------------------------\n\n# ---------------------------------------\n# CPU temperature as a rounded integer - Start \n# ---------------------------------------\n\ndef getCPUtemperature():\n res = os.popen('vcgencmd measure_temp').readline()\n res = float(res.replace(\"temp=\",\"\").replace(\"'C\\n\",\"\"))\n res = int(round(res,0))\n res = str(res)\n return res\n\ngetCPUtemperature()\n\n# --------------------------------------\n# CPU temperature as a rounded integer - End\n# --------------------------------------\n\n# --------------\n# MPD Info - Start\n# --------------\ndef Get_MPC_info():\n subprocess_list = subprocess.check_output(('ps', '-A'))\n subprocess_list_mpd = subprocess_list.find(\"mpd\")\n \n if subprocess_list_mpd == -1:\n mpd_info = \" MPD not started! \"\n return mpd_info\n \n else:\n process = subprocess.Popen('mpc', shell=True, stdout=subprocess.PIPE)\n status = process.communicate()[0]\n statusLines = status.split('\\n')\n #print statusLines\n \n # If MPC was not on \"STOP\"\n if len(statusLines) > 2:\n \n # Extract Volume (be sure that the mpc command return a value in volume (i.e not 'NA')\n mpc_vol= statusLines[2].replace(\"volume:\",\"\").replace(\" \",\"\").split('%')[0] + \" %\"\n #print \"mpc vol = \" + mpc_vol\n if len(str(mpc_vol)) == 3:\n mpc_vol = '0' + str(mpc_vol)\n \n timings_mpd = statusLines[1].split(' ')\n #print \"timing mpd = \" + str(len(timings_mpd))\n index_list = len(timings_mpd) -2\n #print \"index = \" + str(len(timings_mpd)-2)\n \n # Error handling\n if index_list <= 7:\n \n # Extract the song duration and the current duration played\n song_lengh = timings_mpd[index_list].split('/')[1]\n time_play = statusLines[1].split(' ')[index_list].split('/')[0]\n #print song_lengh\n #print time_play\n \n # Convert the durations in seconds\n song_lengh_sec=int(song_lengh.split(':')[0]) * 60 + int(song_lengh.split(':')[1])\n time_play_sec=int(time_play.split(':')[0]) * 60 + int(time_play.split(':')[1])\n\n # Calculate current song \"countdown\"\n count_down = int(song_lengh_sec) - int(time_play_sec)\n \n # Display the song count down (18 characters lengh)\n hours = count_down // 3600\n # Add a '0' if hours between 1-9\n if len(str(hours)) == 1:\n hours = '0' + str(hours)\n \n minutes = int((count_down % 3600) // 60)\n # Add a '0' if minutes between 1-9\n if len(str(minutes)) == 1:\n minutes = '0' + str(minutes)\n \n seconds = int(count_down % 60)\n # Add a '0' if seconds between 1-9\n if len(str(seconds)) == 1:\n seconds = '0' + str(seconds)\n \n if count_down >= 3600:\n count_down_display= '{}:{}:{}'.format(hours, minutes, seconds)\n if len(mpc_vol) > 4: # Volume equals 100%\n spacer = \" \" # 5 spaces\n else : \n spacer = \" \" # 6 spaces\n \n else:\n count_down_display = '{}:{}'.format(minutes, seconds)\n if len(mpc_vol) > 4: # Volume equals 100%\n spacer = \" \" # 8 spaces\n else : \n spacer = \" \" # 9 spaces\n \n mpd_info = \" \" + mpc_vol + spacer + count_down_display\n \n else:\n mpd_info = \" \" # 20 spaces\n \n else:\n mpd_info = \" STOP 00:00 \" \n \n #print \" mpd info = \" + mpd_info\n return mpd_info\n \n# ------------- \n# MPD Info - End\n# -------------\n\n# -------------------\n# Display section - Start\n# -------------------\n\nlcd = CharLCD()\n\n# Define custom character (celcius degre sign)\ndegre_celcius= (0b00000, 0b00110, 0b00110, 0b00000, 0b00000, 0b00000, 0b00000, 0b00000 );\nlcd.create_char(0, degre_celcius)\n\nlcd.clear()\n \nwhile 1:\n # Row 1\n lcd.cursor_pos = (0, 0)\n lcd.write_string(\" \" + datetime.now().strftime('%a %d %b %H:%M') + \" \")\n # Row 2\n lcd.cursor_pos = (1, 0)\n lcd.write_string(\" \" + read_temp() + unichr(0) +\"C\" + \" \" + getCPUtemperature() + unichr(0) +\"C \" )\n # Row 3\n lcd.cursor_pos = (3, 0)\n lcd.write_string(Get_MPC_info())\n #sleep(60)\n\n# ------------------ \n# Display section - End\n# ------------------\n","sub_path":"Lcd.py","file_name":"Lcd.py","file_ext":"py","file_size_in_byte":5991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"315825709","text":"# coding=utf-8\n\"\"\"\nAnalizer instance module\nNOTE: Each analizer instance runs in it's own interpreter\n\"\"\"\nimport os\n\nfrom kumir_constants import *\n\nimport color_marking\nimport static_analisys\n\nSOURCE_DIR_NAME = \"\"\nSOURCE_TEXT = \"\"\nLINE_PROPERTIES = []\nLINE_RANKS = []\n\n\nclass Error:\n # VY: В соответствии с PEP-8 и Google Coding Standards, имена классов - CamelCase\n # В PyCharm есть функция \"умного\" переименования имен: Refactor -> Rename... (Shift+F6)\n # Проверь также и другие файлы\n \"\"\" One error message \"\"\"\n\n def __init__(self, line_no, start_pos, length, message):\n \"\"\"\n line_no -- line number (int)\n start_pos -- start position from 0 (int)\n length -- error block length (int)\n message -- a string (ASCII symbols only) identificating error message\n \"\"\"\n assert isinstance(line_no, int)\n assert isinstance(start_pos, int)\n assert isinstance(length, int)\n assert isinstance(message, str)\n self.line_no = line_no\n self.start_pos = start_pos\n self.length = length\n self.message = message\n\n def __eq__(self, other):\n return self.line_no == other.line_no and self.start_pos == other.start_pos\n\n\ndef set_source_dir_name(path):\n \"\"\"\n Set the source file location (directory name) to help searching imported modules\n path -- directory path in platform dependent format (str)\n \"\"\"\n assert isinstance(path, str)\n global SOURCE_DIR_NAME\n SOURCE_DIR_NAME = path\n\n\n\ndef set_source_text(text):\n \"\"\"\n Set the source text and require complete analisis\n text -- complete python program source; line delimiter is '\\n' (str)\n \"\"\"\n assert isinstance(text, str)\n global SOURCE_TEXT\n SOURCE_TEXT = text\n\n global LINE_PROPERTIES\n global LINE_RANKS\n static_analisys.clear_errors()\n static_analisys.run_static_analisys(text)\n color_marking.set_color_marks_and_ranks(SOURCE_TEXT)\n LINE_RANKS = color_marking.get_ranks()\n # VY: Результат -- это список пар чисел\n # каждое число -- это не количество отступов, а ожидаемое (логически, а не по количеству подсчитанных отступов)\n # изменение отступа. В качестве признака можно использовать, например, двоеточие в конце строки.\n # Эти значения используются редактором для того, чтобы автоматически вставлять нужное количество отступов при\n # нажатии Enter.\n #\n # Пример:\n # def sign(x): # rank = ( 0, +1) # увеличиваем отступ следующей строки, поскольку в конце строки двоеточие\n # print(x) # rank = ( 0, 0) # отступы не меняются\n # if x < 0: # rank = ( 0, +1) # увеличиваем отступ следующей строки, поскольку в конце строки двоеточие\n # return -1 # rank = ( 0, -1) # в блоке кода после return не может быть ничего, поэтому уменьшаем отступ\n # elif x > 0: return 1 # rank = ( 0, 0) # в данном случае elif ... : и return компенсируют друг друга\n # else: # rank = ( 0, +1) # увеличиваем отступ следующей строки, поскольку в конце строки двоеточие\n # return 0 # rank = ( 0, -1) # в блоке кода после return не может быть ничего, поэтому уменьшаем отступ\n #\n # Таким образом, для данной программы ranks должно быть:\n # line_ranks = [ (0,1), (0,0), (0,1), (0,-1), (0,0), (0,1), (0,-1) ]\n #\n # Замечание. Похоже, в Python первое число во всех парах, равно 0. Тем не менее, необходимо соблюдать именно такой\n # интерфейс, который разработан с учетом других ЯП. Например, в Паскале, возможны такие числа:\n #\n # program MySuperProgram; { rank = ( 0, 0) }\n # var { rank = (-1, +1) -- почему в начале -1 -- смотри ниже, станет понятно }\n # i: Integer; { rank = ( 0, 0) }\n # s: String; { rank = ( 0, 0) }\n # begin { rank = (-1, +1) -- begin должен сместиться влево на 1 уровень с var и program,\n # но следующая строка -- уже с отступом на +1. Аналогично и с var,\n # поскольку, в общем случае, описание переменных может следовать\n # после группы описания const.\n # WriteLn('Hello!'); { rank = ( 0, 0) }\n # end. { rank = (-1, 0) -- end сам по себе сдвигается влево (-1), следующие -- на том же уровне\n\n\n LINE_PROPERTIES = color_marking.get_colors()\n\n\ndef get_errors():\n \"\"\"\n Get a list of errors generated while 'set_source_text'\n returns a list of Error class instances\n \"\"\"\n return static_analisys.ERROR_LIST\n\n\ndef get_line_properties():\n \"\"\"\n Get a list of line highlight properties generated while 'set_source_text'\n returns a list:\n - each item corresponds one text line\n - each item is a list:\n - each item corresponds one character in line\n - each item is an integer number, see 'kumir_constants.py'\n \"\"\"\n global LINE_PROPERTIES\n return LINE_PROPERTIES\n\n\ndef get_line_ranks():\n \"\"\"\n Get a list of line indentation ranks generated while 'set_source_text'\n returns a list:\n - each item corresponds one text line\n - each item is a tuple (start, end), where:\n - start is a line start indentation rank\n - end is a terminal indentation rank\n \"\"\"\n global LINE_RANKS\n return LINE_RANKS\n\n\ndef get_line_property(line_no, line_text):\n \"\"\"\n Get line property of one text line, currently editing and possible not complete\n line_no -- editable line number (from 0)\n line_text -- one text line while in edit progress\n returns a list:\n - each item corresponds one character in line\n - each item is an integer number, see 'kumir_constants.py'\n \"\"\"\n assert isinstance(line_no, int)\n assert isinstance(line_text, str)\n global LINE_RANKS\n changes = color_marking.is_change_rank(line_text)\n print(LINE_RANKS[line_no])\n LINE_RANKS[line_no] = (changes[0] + LINE_RANKS[line_no][0], changes[1] + LINE_RANKS[line_no][1])\n print(LINE_RANKS[line_no])\n\n return [LxTypeEmpty] * len(str)\n\n\ndef __run_test(test_name):\n base = os.path.dirname(os.path.abspath(__file__)) + \"/\"\n source_file = open(base + test_name, 'r')\n set_source_text(source_file.read())\n source_file.close()\n\n class LinePropPrintHex(int):\n def __repr__(self):\n return \"0x%x\" % self\n\n lines = SOURCE_TEXT.split('\\n')\n ranks = get_line_ranks()\n props = [[LinePropPrintHex(item) for item in row] for row in get_line_properties()]\n errors = get_errors()\n\n assert len(lines) == len(ranks) == len(props)\n print(\"\\nBegin test \", test_name, \"========================\")\n for no in range(0, len(lines)):\n out = \"{:2d}: {!s:<30} # {!s:<8} {}\".format(no+1, lines[no], ranks[no], props[no])\n print(out)\n if errors:\n print(\"\\n\")\n for error in errors:\n print(\"Error: \", error.message)\n line = lines[error.line_no]\n out = \"{:2d}: {:s}\".format(error.line_no+1, line)\n print(out)\n out = \" \" + \" \" * error.start_pos + \"^\" * error.length\n print(out)\n print(\"End test \", test_name, \"==========================\")\n\nif __name__ == \"__main__\":\n TESTS = [\n \"MyTests/test1.py\",\n \"MyTests/test2.py\",\n \"MyTests/test3.py\",\n \"MyTests/test4.py\",\n \"MyTests/test5.py\",\n \"MyTests/undefined_names.py\"\n ]\n for test in TESTS:\n __run_test(test)\n","sub_path":"analizer_instance.py","file_name":"analizer_instance.py","file_ext":"py","file_size_in_byte":8729,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"531675347","text":"a=[1,2,3,4,6,7,8,9,11,12,13,14,15]\r\ntarget=9\r\nk=0\r\nlow=0\r\nhigh=len(a)-1\r\nwhile(low<=high):\r\n mid=(high+low)//2\r\n if(a[mid]==target):\r\n print(\"element found:\",a[mid])\r\n k=1\r\n\r\n break\r\n elif(target 0:\n previous = cml_effects[ordinal-1]\n if partial:\n return ordinal # that is surpassing_equal\n # previous existent?\n elif previous and surpassing_equal > threshold_value:\n return ordinal-1 # that is previous\n # partial=False; surpassing_equal == threshold_value\n elif surpassing_equal == threshold_value:\n return ordinal # that is surpassing_equal\n # not previous and surpassing_equal >= threshold_value\n # => None\n\n\ndef attain_effects(cml_effects, length, threshold_value,\n partial=True, precision=3):\n \"\"\"Determines causes for specified ratio of effects.\n\n How many causes are at least required for `required_effects`?\n\n Args:\n cml_effects (list[float]): List of cumulative sorted effects.\n length (int): Length of sorted_effects.\n threshold_value: cumulative effects value to attain.\n partial (Optional[bool], default True): Includes value that\n starts before `limit` yet ends after it if True.\n precision (float): Number of decimals to round to.\n\n Returns:\n Minimum ratio of causes required to attain `threshold_value`\n that is ordinal of `cml_effects` as `float`.\n \"\"\"\n ordinal = find_nearest_bisect(threshold_value, cml_effects,\n greater_equal=True)\n the_ordinal = attain_partial_or_impartial(cml_effects, ordinal,\n threshold_value, partial)\n if the_ordinal is not None: # considers 0 to be True\n # + 1 because list indices start from 0 and not 1\n return round((the_ordinal+1)/length, precision)\n else:\n return 0\n\n\ndef attain_causes(cml_effects, length, threshold_value,\n partial=True, precision=3):\n \"\"\"Determines causes for specified ratio of effects.\n\n How many effects are at least effected for `required_causes`?\n\n Args:\n cml_effects (list[float]): List of cumulative sorted effects.\n length (int): Length of sorted_effects.\n threshold_value: Cumulative effects value to attain.\n partial (Optional[bool], default True): Includes value that\n starts before `limit` yet ends after it if True.\n precision (float): Number of decimals to round to.\n\n Returns:\n Minimum ratio of effects required to attain `threshold_value`\n in `cml_effects` as `float`.\n \"\"\"\n ordinal = find_nearest_bisect(threshold_value, range(1, length+1),\n greater_equal=True)\n the_ordinal = attain_partial_or_impartial(cml_effects, ordinal, threshold_value,\n partial, precision)\n if the_ordinal is not None: # considers 0 to be True\n return round(cml_effects[the_ordinal]/cml_effects[-1], precision)\n else:\n return 0\n\n\ndef separate_causes(sorted_effects, required_causes,\n length, partial=True, precision=3):\n \"\"\"Determines threshold value that selects `limit` share of causes.\n\n Which value or greater must an effect be to belong to `limit` of causes?\n\n Args:\n sorted_effects (list[number]): Effects as descending list of numbers.\n required_causes (float): Causes share to attain, between 0 and 1.\n length: Length of `sorted_effects`.\n partial (Optional[bool], default True): Includes value that\n starts before `limit` yet ends after it if True.\n precision (Optional[float], default 3): Decimals to round to.\n\n Returns:\n Tuple(a, b, c).\n `a` is value a effect must be at least to belong to `required_causes`.\n `c` is total count of occurences of that value.\n `b` is number of occurrence up to which selection must span (including)\n to attain `required_causes`.\n\n Examples:\n >>> separate_causes([789, 621, 109, 65, 45, 30, 27, 15, 12, 9], 0.6, 10)\n (30, 1, 1)\n \"\"\"\n limit = round(required_causes, precision)\n # dunno why but -1 is required for the tests to pass\n ordinal = det_causes_ordinal(length, limit, partial, precision)\n left = find_equals_left(sorted_effects, ordinal, length)\n right = find_equals_right(sorted_effects, ordinal, length)\n equals = left + 1\n all = left + right + 1\n return (sorted_effects[ordinal], equals, all)\n\n\ndef separate_effects(cml_effects, sorted_effects, required_effects,\n length, partial=True, precision=3):\n \"\"\"Determines threshold value that selects `limit` share of effects.\n\n Which value or greater must an effect be to belong\n to `limit`?\n\n Args:\n cml_effects (list[number]): Accumulator of `sorted_effects`.\n sorted_effects (list[number]): Effects as descending list of numbers.\n required_effects (float): Causes ratio to attain, between 0 and 1.\n partial (Optional[bool], default True): Includes value that\n starts before `limit` yet ends after it if True.\n precision (Optional[float], default 3): Decimals to round to.\n\n Returns:\n Tuple of (a, b, c).\n `a` is value a effect must be at least to belong to\n `required_effects`.\n `c` is total count of occurences of that value.\n `b` is number of occurrence up to which selection must span (including)\n to attain `required_effects`.\n\n Examples:\n >>> separate_effects([789, 1410, 1519, 1584, 1629, 1659, 1686,\n ... 1701, 1713, 1722], [789, 621, 109, 65,\n ... 45, 30, 27, 15, 12, 9], 0.6, 10)\n (621, 1, 1)\n \"\"\"\n limit = round(required_effects, precision)\n ordinal = det_effects_ordinal(cml_effects, limit, partial, precision)\n left = find_equals_left(sorted_effects, ordinal, length)\n right = find_equals_right(sorted_effects, ordinal, length)\n equals = left + 1\n all = left + right + 1\n return (sorted_effects[ordinal], equals, all)\n\n\ndef get_rule(enriched):\n \"\"\"Returns the rule of 50/5 or any variant of it.\n\n Args:\n enriched (list[tuple]): Enriched relations that is the output of\n :func:`~effectus.core.get_enriched`.\n \"\"\"\n # a generator function can be evaluated once only,\n # hence transform it into a list.\n # only with this the else conditional of\n # :meth:`effectus.Effects.the_rule` does work.\n enriched = list(enriched)\n full_multiples_only = list(filter(lambda x: x[1] % x[0] == 0, enriched))\n retrieved_rule = retrieve_rule(full_multiples_only or enriched) \n if retrieved_rule:\n return 'Rule {}/{} [total ∆: {:.1f} % points]'.format(\n int(retrieved_rule[1]*100),\n int(retrieved_rule[0]*100),\n float(retrieved_rule[4])*100)\n","sub_path":"effectus/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":8923,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"88772703","text":"def leerMatriz(f,c,A):\n #LEER\n \n for i in range(f): #i: 1, n, 1 hacer\n fila = []\n for j in range(c): #j: 1, m, 1 hacer \n fila.append(int(input(f\"Digite valor [{i+1}][{j+1}]: \")))\n\n A.append(fila) \n\ndef escribirMatriz(A):\n #ESCRIBIR\n for i in range(len(A)):\n for j in range(len(A[i])):\n print(f\"{A[i][j]}\", end = \" \")\n\n print()\n\ndef sumarMatriz(n,m,A,B,C): \n for i in range(n):\n fila = []\n for j in range(m):\n fila.append(A[i][j] + B[i][j])\n \n C.append(fila)\n \n\ndef valorMaximoMatriz(A):\n maximo = A[0][0]\n for i in range(len(A)):\n for j in range(len(A[i])):\n if A[i][j] > maximo:\n maximo = A[i][j]\n mI, mJ = i, j\n\n print(f\"Valor máximo es {maximo} en la posición A[{mI+1}][{mJ+1}]\")\n\ndef valorMinimoMatriz(A):\n minimo = A[0][0]\n for i in range(len(A)):\n for j in range(len(A[i])):\n if A[i][j] < minimo:\n minimo = A[i][j]\n mI, mJ = i, j\n \n print(f\"Valor mínimo es {minimo} en la posición A[{mI+1}][{mJ+1}]\")\n\nimport random\ndef llenarMatrizNumAleatorios(f,c,A, rIni, rFin): \n for i in range(f): \n fila = []\n for j in range(c): \n fila.append( random.randint(rIni,rFin) )\n\n A.append(fila) \n\n\n\ndef escalar(matriz, escalar): \n B = []\n for i in range(len(matriz)): \n fila = []\n for j in range(len(matriz[0])): \n fila.append( escalar * matriz[i][j] )\n\n B.append(fila) \n \n escribirMatriz(B)\n\ndef escribirMatrizDiagonalPrincipal(A):\n if len(A) == len(A[0]):\n for i in range(len(A)):\n for j in range(len(A[i])):\n if i == j:\n print(A[i][j], end = \" \")\n else:\n print(\" \", end = \" \")\n print()\n else:\n print(\"La matriz no es cuadrada\")\n\n\n\ndef calcularPromedio(T): \n cantidad = len(T) * len(T[0])\n acumulador = 0\n for i in range(len(T)):\n for j in range(len(T[0])):\n acumulador += T[i][j]\n \n return acumulador / cantidad\n\n","sub_path":"sesion18/matriz.py","file_name":"matriz.py","file_ext":"py","file_size_in_byte":2221,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"577366519","text":"#!/usr/bin/python\n# name file : for5.py\n\nfrom __future__ import print_function\n\ndef main():\n\t# menggunakan for untuk indeks karakter\n\tfor i in range(ord('a'),ord('e')):\n\t \tprint(\"%c\\t: Hello World!\" % chr(i))\n\nif __name__ == '__main__':\n\tmain()","sub_path":"for5.py","file_name":"for5.py","file_ext":"py","file_size_in_byte":244,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"371845438","text":"from turtle import *\nimport random\n\nwindow = Screen()\nwindow.bgcolor('grey')\njohn = Turtle()\njohn.width(5)\ncolours = ['cyan','purple','white','red','blue']\n\ndef draw():\n john.color('cyan')\n for i in range(10):\n for i in range(2):\n john.fd(100)\n john.right(60)\n john.forward(100)\n john.right(120)\n john.right(36)\n john.color(random.choice(colours))\n\ndraw()\nexitonclick()\n","sub_path":"games/game_drawDimond.py","file_name":"game_drawDimond.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"516408448","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport datetime\nimport functools\nimport json\nimport logging\n\nimport requests\nfrom webapp_config import TIMEZONE\n\nYAHOO_API_ENDPOINT = \"https://map.yahooapis.jp/weather/V1/place\"\n\n\ndef get_weather_info_yahoo(config):\n try:\n params = {\n \"appid\": config[\"weather\"][\"yahoo\"][\"id\"],\n \"coordinates\": \",\".join(\n map(\n str,\n [\n config[\"weather\"][\"point\"][\"lon\"],\n config[\"weather\"][\"point\"][\"lat\"],\n ],\n )\n ),\n \"output\": \"json\",\n \"past\": 2,\n }\n\n res = requests.get(YAHOO_API_ENDPOINT, params=params)\n\n if res.status_code != 200:\n logging.warning(\"Failed to fetch weather info from Yahoo\")\n return None\n\n return json.loads(res.content)[\"Feature\"][0][\"Property\"][\"WeatherList\"][\"Weather\"]\n except:\n logging.warning(\"Failed to fetch weather info from Yahoo\")\n return None\n\n\ndef get_rain_fall(config):\n weather_info = get_weather_info_yahoo(config)\n\n if weather_info is None:\n return False\n\n # NOTE: YAhoo の場合，1 時間後までしか情報がとれないので，4 時間前以降を参考にする\n rainfall_list = list(\n map(\n lambda x: x[\"Rainfall\"],\n filter(\n lambda x: (\n datetime.datetime.now(TIMEZONE)\n - datetime.datetime.strptime(x[\"Date\"], \"%Y%m%d%H%M\").replace(tzinfo=TIMEZONE)\n ).total_seconds()\n / (60 * 60)\n < config[\"weather\"][\"rain_fall\"][\"before_hour\"],\n weather_info,\n ),\n )\n )\n\n rainfall_total = functools.reduce(lambda x, y: x + y, rainfall_list)\n\n logging.info(\n \"Rain fall total: {rainfall_total} ({rainfall_list})\".format(\n rainfall_total=rainfall_total, rainfall_list=rainfall_list\n )\n )\n\n rainfall_judge = rainfall_total > config[\"weather\"][\"rain_fall\"][\"threshold\"]\n logging.info(\"Rain fall judge: {rainfall_judge}\".format(rainfall_judge=rainfall_judge))\n\n return rainfall_judge\n\n\nif __name__ == \"__main__\":\n import logger\n from config import load_config\n\n logger.init(\"test\", level=logging.INFO)\n\n config = load_config()\n print(get_rain_fall(config))\n","sub_path":"flask/lib/weather_forecast.py","file_name":"weather_forecast.py","file_ext":"py","file_size_in_byte":2427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"175438988","text":"# use configurator CURRENT ONLY templates\n# egauge_config.py collect-data egauge_id --cfgfile s3://bucket/filename.json\n# --restore --datafile s3://bucket/filename.json\n# egauge_config.py analyze-data --datafile s3://bucket/filename.json --cfgfile fileurl\n# configurator pickups up the file from fileurl and applies the changes\nimport os\nimport uuid\nimport logging\nimport json\nfrom socket import error as SocketError\nfrom urllib2 import HTTPError\n\nimport egauge_collect_phase_data\n\nTIMEOUT_PERIOD = 2\n\nlogging.basicConfig()\nlogger = logging.getLogger(\"collect\")\nlogger.setLevel(logging.DEBUG)\n\nimport egauge_config\nfrom egauge_config import egcfg\n\n\ndef collect(eg, options, scheduler):\n \"\"\"\n top level entry function called by eg_cfg\n \"\"\"\n\n if options.ctgroups is None:\n reg_cfg = json.loads(eg.getregisters(None, skip_write=True))\n options.ctgroups = get_groups(reg_cfg[\"Registers\"])\n elif isinstance(options.ctgroups, basestring):\n options.ctgroups = json.loads(options.ctgroups)\n\n try:\n collector = egauge_collect_phase_data.DataCollector(\n eg, nminutes=options.samples / 60, session=options.data_collection_session)\n reschedule_after = collector.collect(options.datafile,\n reset=options.reset_data_collection,\n mappingfile=options.mappingfile,\n ctgroups=options.ctgroups,\n cycle_exp=options.cycle_count>1)\n # reset retry count on successful operation\n options.retry_count = 0\n except (SocketError, HTTPError):\n if options.retry_count < options.max_retry_count:\n logger.warn(\"Collect operation was unsuccessful, schedule a retry.\")\n reschedule_after = TIMEOUT_PERIOD\n options.retry_count += 1\n else:\n logger.warn(\"Collect operation was unsuccessful, give up.\")\n reschedule_after = -1\n # Don't bother cycling\n options.cycle_count = 0\n\n\n # we should not perpetuate the reset flag\n options.reset_data_collection = False\n if reschedule_after > 0:\n logger.info(\"Rescheduling after {} minutes\".format(reschedule_after))\n scheduler.schedule(reschedule_after, eg, options)\n elif reschedule_after == -1 and options.cycle_count > 1:\n options.cycle_count -= 1\n logger.info(\"Scheduling another cycle. {} remaining\".format(options.cycle_count))\n scheduler.schedule(options.samples // 60, eg, options)\n else:\n logger.info(\"Data collection ending\")\n\n\n\ndef get_groups(regs):\n \"\"\"\n extract group info from register configuration\n \"\"\"\n from collections import defaultdict\n dgrp = defaultdict(list)\n for reg in regs.values():\n if reg['type'] in (\"P\", \"I\"):\n grpname = reg[\"name\"].partition(':')[0].partition(\".\")[0]\n dgrp[grpname].append(reg['val'].partition('*')[0].split(\"-\")[-1])\n\n # group should have at least 2 elements\n return [v for v in dgrp.values() if len(v) > 1]\n","sub_path":"egauge/collect.py","file_name":"collect.py","file_ext":"py","file_size_in_byte":3116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"264936856","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport click\nimport builderutils.parser as parser\nimport builderutils.renderer as renderer\nimport builderutils.dom as dom\n\n\n@click.group()\ndef cli():\n pass\n\n\n@click.command()\n@click.option(\"--configfile\", type=click.Path(), help=\"Builder config\", required=True)\ndef create(configfile):\n print(\"create command!\")\n parserObj = parser.ConfigParser(configfile)\n print(\"Parser Obj: \", parserObj)\n domObj = dom.DomManager(parserObj)\n domObj.buildDomTree()\n dom.DomManager.parseDomTree(dom.SAMPLE_DOM)\n\n # parserObj = parser.BuilderParser(configfile)\n # renderObj = renderer.Renderer()\n # renderObj.build_staging_environment(parserObj.parsedData)\n\n # userConfig = parserObj.parsedData[\"user_config\"]\n # htmlTemplate = parserObj.parsedData[\"html_template\"]\n # flaskTemplate = parserObj.parsedData[\"flask_template\"]\n\n # renderObj.build_html_documents(htmlTemplate, userConfig)\n # renderObj.build_flask_app(flaskTemplate, userConfig)\n\n\ndef main():\n cli.add_command(create)\n cli()\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"builder/builder.py","file_name":"builder.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"145886080","text":"import dash_html_components as html\nimport dash_core_components as dcc\nimport dash_bootstrap_components as dbc\nfrom dash.dependencies import Output, Input\nfrom utils.texts import TEXTS\n\nfrom app import app\n\nlayout = html.Div(\n id=\"div-header\",\n children=[\n html.Div(id=\"home-div-url-redirect-to-about\", style={\"display\": \"none\"}),\n html.Div(id=\"home-div-url-redirect-to-entity-choice\", style={\"display\": \"none\"}),\n dbc.Row(\n dbc.Col(\n [html.H1(\"Outil BEGES\", className=\"my-5\"), dcc.Markdown(TEXTS[\"home\"])], width={\"size\": 6, \"offset\": 3}\n ),\n className=\"mb-5\",\n ),\n dbc.Row(\n [\n dbc.Col(\n dbc.Button(\"En savoir plus\", id=\"button-to-about\", color=\"primary\", outline=True, block=True),\n width={\"size\": 2, \"offset\": 4},\n ),\n dbc.Col(\n dbc.Button(\"C'est parti !\", id=\"button-to-entity-choice\", color=\"primary\", block=True),\n width={\"size\": 2, \"offset\": 0},\n ),\n ],\n className=\"mb-5\",\n ),\n ],\n)\n\n\n@app.callback(\n Output(\"home-div-url-redirect-to-entity-choice\", \"children\"), [Input(\"button-to-entity-choice\", \"n_clicks\")]\n)\ndef on_click_go_to_entity_choice(n_clicks):\n if n_clicks:\n return dcc.Location(id=\"url-redirect-to-entity-choice\", pathname=\"/selection_entite\")\n\n\n@app.callback(Output(\"home-div-url-redirect-to-about\", \"children\"), [Input(\"button-to-about\", \"n_clicks\")])\ndef on_click_go_to_about(n_clicks):\n if n_clicks:\n return dcc.Location(id=\"url-redirect-to-about\", pathname=\"/a_propos\")\n","sub_path":"app/src/apps/home.py","file_name":"home.py","file_ext":"py","file_size_in_byte":1687,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"171842829","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Oct 11 21:46:56 2018\n\n@author: dilipganesan\n\"\"\"\n\nimport subprocess\nimport io\nimport pandas as pd\nimport os\nimport json\n\n# Json file with credentials are stored in github with gitignore.\nwith open(\"kaggle.json\") as json_file:\n json_data = json.load(json_file)\n #print(json_data['username'])\n #print(json_data['key'])\n\nos.environ[\"KAGGLE_USERNAME\"] = json_data['username']\nos.environ[\"KAGGLE_KEY\"] = json_data['key']\ncmd = [\"kaggle\", \"competitions\", \"download\", \"-f\", \"train.csv\",\"-o\", \"titanic\"]\ncmd2 = [\"kaggle\", \"competitions\", \"download\", \"-f\", \"test.csv\",\"-o\", \"titanic\"]\n\nprocess = subprocess.Popen(cmd, stdout=subprocess.PIPE)\ncsv = io.StringIO(process.stdout.read().decode())\ndata = pd.read_csv(csv)\ncsv.close()\n\nprocess2 = subprocess.Popen(cmd2, stdout=subprocess.PIPE)\ncsv2 = io.StringIO(process2.stdout.read().decode())\ndata2 = pd.read_csv(csv2)\ncsv2.close()\n\n## loading the data from kaggle.\ndef loaddataset(target):\n if target == 'train':\n df = pd.read_csv('train.csv')\n elif target == 'test':\n df = pd.read_csv('test.csv')\n else:\n raise ValueError('Not the correct target')\n \n return df\n\n\n## We could have added some validation to the dataset, making sure the rows and columns\n## are as expected. Making sure the Pred column is not there in test.csv\n","sub_path":"Home Work 2/new_pull.py","file_name":"new_pull.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"643120532","text":"import itertools\nimport os\n\nnThreadsList = [8, 16, 32, 64, 120, 160]\n# sizesList = [512, 1024, 1536, 2048, 2560]\nsizesList = [64, 512, 1024, 1536]\n\n# os.system(\"rm *.out *.err\")\n# os.system(\"rm main\")\n\nITER_NUM = 4\n\nfor nThreads in nThreadsList:\n for sz in sizesList:\n for v in range(ITER_NUM):\n # echo 100 10 60 | ./a.out\n os.system(\"echo \" + str(sz) + \" \" + \n str(sz//10) + \" \" + \n str(nThreads) + \" | ./a.out\")\n","sub_path":"openMP/scheduler.py","file_name":"scheduler.py","file_ext":"py","file_size_in_byte":489,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"482571156","text":"\"\"\"Module for functional data manipulation in a basis system.\n\nDefines functional data object in a basis function system representation and\nthe corresponding basis classes.\n\n\"\"\"\nimport copy\nfrom abc import ABC, abstractmethod\n\nimport numpy\nimport scipy.integrate\nimport scipy.interpolate\nimport scipy.linalg\nfrom numpy import polyder, polyint, polymul, polyval\nfrom scipy.interpolate import PPoly\nfrom scipy.interpolate import BSpline as SciBSpline\nfrom scipy.special import binom\n\nfrom . import grid\nfrom . import FData\nfrom .._utils import _list_of_arrays\n\n__author__ = \"Miguel Carbajo Berrocal\"\n__email__ = \"miguel.carbajo@estudiante.uam.es\"\n\nMIN_EVAL_SAMPLES = 201\n\n\n# aux functions\ndef _polypow(p, n=2):\n if n > 2:\n return polymul(p, _polypow(p, n - 1))\n if n == 2:\n return polymul(p, p)\n elif n == 1:\n return p\n elif n == 0:\n return [1]\n else:\n raise ValueError(\"n must be greater than 0.\")\n\n\ndef _check_domain(domain_range):\n for domain in domain_range:\n if len(domain) != 2 or domain[0] >= domain[1]:\n raise ValueError(f\"The interval {domain} is not well-defined.\")\n\n\ndef _same_domain(one_domain_range, other_domain_range):\n return numpy.array_equal(one_domain_range, other_domain_range)\n\n\nclass Basis(ABC):\n \"\"\"Defines the structure of a basis function system.\n\n Attributes:\n domain_range (tuple): a tuple of length 2 containing the initial and\n end values of the interval over which the basis can be evaluated.\n nbasis (int): number of functions in the basis.\n\n \"\"\"\n\n def __init__(self, domain_range=(0, 1), nbasis=1):\n \"\"\"Basis constructor.\n\n Args:\n domain_range (tuple or list of tuples, optional): Definition of the\n interval where the basis defines a space. Defaults to (0,1).\n nbasis: Number of functions that form the basis. Defaults to 1.\n \"\"\"\n\n # TODO: Allow multiple dimensions\n domain_range = _list_of_arrays(domain_range)\n\n # Some checks\n _check_domain(domain_range)\n\n if nbasis < 1:\n raise ValueError(\"The number of basis has to be strictly \"\n \"possitive.\")\n\n self.domain_range = domain_range\n self.nbasis = nbasis\n self._drop_index_lst = []\n\n super().__init__()\n\n @abstractmethod\n def _compute_matrix(self, eval_points, derivative=0):\n \"\"\"Compute the basis or its derivatives given a list of values.\n\n Args:\n eval_points (array_like): List of points where the basis is\n evaluated.\n derivative (int, optional): Order of the derivative. Defaults to 0.\n\n Returns:\n (:obj:`numpy.darray`): Matrix whose rows are the values of the each\n basis function or its derivatives at the values specified in\n eval_points.\n\n \"\"\"\n pass\n\n @abstractmethod\n def _ndegenerated(self, penalty_degree):\n \"\"\"Return number of 0 or nearly 0 eigenvalues of the penalty matrix.\n\n Args:\n penalty_degree (int): Degree of the derivative used in the\n calculation of the penalty matrix.\n\n Returns:\n int: number of close to 0 eigenvalues.\n\n \"\"\"\n pass\n\n @abstractmethod\n def _derivative(self, coefs, order=1):\n pass\n\n def evaluate(self, eval_points, derivative=0):\n \"\"\"Evaluate Basis objects and its derivatives.\n\n Evaluates the basis function system or its derivatives at a list of\n given values.\n\n Args:\n eval_points (array_like): List of points where the basis is\n evaluated.\n derivative (int, optional): Order of the derivative. Defaults to 0.\n\n Returns:\n (numpy.darray): Matrix whose rows are the values of the each\n basis function or its derivatives at the values specified in\n eval_points.\n\n \"\"\"\n eval_points = numpy.asarray(eval_points)\n if numpy.any(numpy.isnan(eval_points)):\n raise ValueError(\"The list of points where the function is \"\n \"evaluated can not contain nan values.\")\n\n return self._compute_matrix(eval_points, derivative)\n\n def plot(self, chart=None, *, derivative=0, **kwargs):\n \"\"\"Plot the basis object or its derivatives.\n\n Args:\n chart (figure object, axe or list of axes, optional): figure over\n with the graphs are plotted or axis over where the graphs are\n plotted.\n derivative (int or tuple, optional): Order of derivative to be\n plotted. Defaults 0.\n **kwargs: keyword arguments to be passed to the fdata.plot function.\n\n Returns:\n fig (figure object): figure object in which the graphs are plotted.\n ax (axes object): axes in which the graphs are plotted.\n\n \"\"\"\n self.to_basis().plot(chart=chart, derivative=derivative, **kwargs)\n\n def _evaluate_single_basis_coefficients(self, coefficients, basis_index, x,\n cache):\n \"\"\"Evaluate a differential operator over one of the basis.\n\n Computes the result of evaluating a the result of applying a\n differential operator over one of the basis functions. It also admits a\n \"cache\" dictionary to store the results for the other basis not\n returned because they are evaluated by the function and may be needed\n later.\n\n Args:\n coefficients (list): List of coefficients representing a\n differential operator. An iterable indicating\n coefficients of derivatives (which can be functions). For\n instance the tuple (1, 0, numpy.sin) means :math:`1\n + sin(x)D^{2}`.\n basis_index (int): index in self.basis of the basis that is\n evaluated.\n x (number): Point of evaluation.\n cache (dict): Dictionary with the values of previous evaluation\n for all the basis function and where the results of the\n evalaution are stored. This is done because later evaluation\n of the same differential operator and same x may be needed\n for other of the basis functions.\n\n \"\"\"\n if x not in cache:\n res = numpy.zeros(self.nbasis)\n for i, k in enumerate(coefficients):\n if callable(k):\n res += k(x) * self._compute_matrix([x], i)[:, 0]\n else:\n res += k * self._compute_matrix([x], i)[:, 0]\n cache[x] = res\n return cache[x][basis_index]\n\n def _numerical_penalty(self, coefficients):\n \"\"\"Return a penalty matrix using a numerical approach.\n\n See :func:`~basis.Basis.penalty`.\n\n Args:\n coefficients (list): List of coefficients representing a\n differential operator. An iterable indicating\n coefficients of derivatives (which can be functions). For\n instance the tuple (1, 0, numpy.sin) means :math:`1\n + sin(x)D^{2}`.\n \"\"\"\n\n # Range of first dimension\n domain_range = self.domain_range[0]\n penalty_matrix = numpy.zeros((self.nbasis, self.nbasis))\n cache = {}\n for i in range(self.nbasis):\n penalty_matrix[i, i] = scipy.integrate.quad(\n lambda x: (self._evaluate_single_basis_coefficients(\n coefficients, i, x, cache) ** 2),\n domain_range[0], domain_range[1]\n )[0]\n for j in range(i + 1, self.nbasis):\n penalty_matrix[i, j] = scipy.integrate.quad(\n lambda x: (self._evaluate_single_basis_coefficients(\n coefficients, i, x, cache)\n * self._evaluate_single_basis_coefficients(\n coefficients, j, x, cache)),\n domain_range[0], domain_range[1]\n )[0]\n penalty_matrix[j, i] = penalty_matrix[i, j]\n return penalty_matrix\n\n @abstractmethod\n def penalty(self, derivative_degree=None, coefficients=None):\n r\"\"\"Return a penalty matrix given a differential operator.\n\n The differential operator can be either a derivative of a certain\n degree or a more complex operator.\n\n The penalty matrix is defined as [RS05-5-6-2]_:\n\n .. math::\n R_{ij} = \\int L\\phi_i(s) L\\phi_j(s) ds\n\n where :math:`\\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis\n functions and :math:`L` is a differential operator.\n\n Args:\n derivative_degree (int): Integer indicating the order of the\n derivative or . For instance 2 means that the differential\n operator is :math:`f''(x)`.\n coefficients (list): List of coefficients representing a\n differential operator. An iterable indicating\n coefficients of derivatives (which can be functions). For\n instance the tuple (1, 0, numpy.sin) means :math:`1\n + sin(x)D^{2}`. Only used if derivative degree is None.\n\n Returns:\n numpy.array: Penalty matrix.\n\n References:\n .. [RS05-5-6-2] Ramsay, J., Silverman, B. W. (2005). Specifying the\n roughness penalty. In *Functional Data Analysis* (pp. 106-107).\n Springer.\n\n \"\"\"\n pass\n\n @abstractmethod\n def basis_of_product(self, other):\n pass\n\n @abstractmethod\n def rbasis_of_product(self, other):\n pass\n\n @staticmethod\n def default_basis_of_product(one, other):\n \"\"\"Default multiplication for a pair of basis\"\"\"\n if not _same_domain(one.domain_range, other.domain_range):\n raise ValueError(\"Ranges are not equal.\")\n\n norder = min(8, one.nbasis + other.nbasis)\n nbasis = max(one.nbasis + other.nbasis, norder + 1)\n return BSpline(one.domain_range, nbasis, norder)\n\n def rescale(self, domain_range=None):\n r\"\"\"Return a copy of the basis with a new domain range, with the\n corresponding values rescaled to the new bounds.\n\n Args:\n domain_range (tuple, optional): Definition of the interval where\n the basis defines a space. Defaults uses the same as the\n original basis.\n \"\"\"\n\n if domain_range is None:\n domain_range = self.domain_range\n\n return type(self)(domain_range, self.nbasis)\n\n def same_domain(self, other):\n r\"\"\"Returns if two basis are defined on the same domain range.\n\n Args:\n other (Basis): Basis to check the domain range definition\n \"\"\"\n return _same_domain(self.domain_range, other.domain_range)\n\n def copy(self):\n \"\"\"Basis copy\"\"\"\n return copy.deepcopy(self)\n\n def to_basis(self):\n return FDataBasis(self.copy(), numpy.identity(self.nbasis))\n\n def _list_to_R(self, knots):\n retstring = \"c(\"\n for i in range(0, len(knots)):\n retstring = retstring + str(knots[i]) + \", \"\n return retstring[0:len(retstring) - 2] + \")\"\n\n def _to_R(self):\n raise NotImplementedError\n\n def inner_product(self, other):\n return numpy.transpose(other.inner_product(self.to_basis()))\n\n def __repr__(self):\n \"\"\"Representation of a Basis object.\"\"\"\n return (f\"{self.__class__.__name__}(domain_range={self.domain_range}, \"\n f\"nbasis={self.nbasis})\")\n\n def __eq__(self, other):\n \"\"\"Equality of Basis\"\"\"\n return (type(self) == type(other)\n and _same_domain(self.domain_range, other.domain_range)\n and self.nbasis == other.nbasis)\n\n\nclass Constant(Basis):\n \"\"\"Constant basis.\n\n Basis for constant functions\n\n Attributes:\n domain_range (tuple): a tuple of length 2 containing the initial and\n end values of the interval over which the basis can be evaluated.\n\n Examples:\n Defines a contant base over the interval :math:`[0, 5]` consisting\n on the constant function 1 on :math:`[0, 5]`.\n\n >>> bs_cons = Constant((0,5))\n\n \"\"\"\n\n def __init__(self, domain_range=(0, 1)):\n \"\"\"Constant basis constructor.\n\n Args:\n domain_range (tuple): Tuple defining the domain over which the\n function is defined.\n\n \"\"\"\n super().__init__(domain_range, 1)\n\n def _ndegenerated(self, penalty_degree):\n \"\"\"Return number of 0 or nearly 0 eigenvalues of the penalty matrix.\n\n Args:\n penalty_degree (int): Degree of the derivative used in the\n calculation of the penalty matrix.\n\n Returns:\n int: number of close to 0 eigenvalues.\n\n \"\"\"\n return penalty_degree\n\n def _derivative(self, coefs, order=1):\n return (self.copy(), coefs.copy() if order == 0\n else self.copy(), numpy.zeros(coefs.shape))\n\n def _compute_matrix(self, eval_points, derivative=0):\n \"\"\"Compute the basis or its derivatives given a list of values.\n\n For each of the basis computes its value for each of the points in\n the list passed as argument to the method.\n\n Args:\n eval_points (array_like): List of points where the basis is\n evaluated.\n derivative (int, optional): Order of the derivative. Defaults to 0.\n\n Returns:\n (:obj:`numpy.darray`): Matrix whose rows are the values of the each\n basis function or its derivatives at the values specified in\n eval_points.\n\n \"\"\"\n return numpy.ones((1, len(eval_points))) if derivative == 0 else numpy.zeros((1, len(eval_points)))\n\n def penalty(self, derivative_degree=None, coefficients=None):\n r\"\"\"Return a penalty matrix given a differential operator.\n\n The differential operator can be either a derivative of a certain\n degree or a more complex operator.\n\n The penalty matrix is defined as [RS05-5-6-2]_:\n\n .. math::\n R_{ij} = \\int L\\phi_i(s) L\\phi_j(s) ds\n\n where :math:`\\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis\n functions and :math:`L` is a differential operator.\n\n Args:\n derivative_degree (int): Integer indicating the order of the\n derivative or . For instance 2 means that the differential\n operator is :math:`f''(x)`.\n coefficients (list): List of coefficients representing a\n differential operator. An iterable indicating\n coefficients of derivatives (which can be functions). For\n instance the tuple (1, 0, numpy.sin) means :math:`1\n + sin(x)D^{2}`. Only used if derivative degree is None.\n\n\n Returns:\n numpy.array: Penalty matrix.\n\n Examples:\n >>> Constant((0,5)).penalty(0)\n array([[5]])\n >>> Constant().penalty(1)\n array([[ 0.]])\n\n References:\n .. [RS05-5-6-2] Ramsay, J., Silverman, B. W. (2005). Specifying the\n roughness penalty. In *Functional Data Analysis* (pp. 106-107).\n Springer.\n\n \"\"\"\n if derivative_degree is None:\n return self._numerical_penalty(coefficients)\n\n return (numpy.full((1, 1), (self.domain_range[0][1] - self.domain_range[0][0]))\n if derivative_degree == 0 else numpy.zeros((1, 1)))\n\n def basis_of_product(self, other):\n \"\"\"Multiplication of a Constant Basis with other Basis\"\"\"\n if not _same_domain(self.domain_range, other.domain_range):\n raise ValueError(\"Ranges are not equal.\")\n\n return other.copy()\n\n def rbasis_of_product(self, other):\n \"\"\"Multiplication of a Constant Basis with other Basis\"\"\"\n return other.copy()\n\n def _to_R(self):\n drange = self.domain_range[0]\n return \"create.constant.basis(rangeval = c(\" + str(drange[0]) + \",\" +\\\n str(drange[1]) + \"))\"\n\n\nclass Monomial(Basis):\n \"\"\"Monomial basis.\n\n Basis formed by powers of the argument :math:`t`:\n\n .. math::\n 1, t, t^2, t^3...\n\n Attributes:\n domain_range (tuple): a tuple of length 2 containing the initial and\n end values of the interval over which the basis can be evaluated.\n nbasis (int): number of functions in the basis.\n\n Examples:\n Defines a monomial base over the interval :math:`[0, 5]` consisting\n on the first 3 powers of :math:`t`: :math:`1, t, t^2`.\n\n >>> bs_mon = Monomial((0,5), nbasis=3)\n\n And evaluates all the functions in the basis in a list of descrete\n values.\n\n >>> bs_mon.evaluate([0, 1, 2])\n array([[ 1., 1., 1.],\n [ 0., 1., 2.],\n [ 0., 1., 4.]])\n\n And also evaluates its derivatives\n\n >>> bs_mon.evaluate([0, 1, 2], derivative=1)\n array([[ 0., 0., 0.],\n [ 1., 1., 1.],\n [ 0., 2., 4.]])\n >>> bs_mon.evaluate([0, 1, 2], derivative=2)\n array([[ 0., 0., 0.],\n [ 0., 0., 0.],\n [ 2., 2., 2.]])\n\n \"\"\"\n\n def _ndegenerated(self, penalty_degree):\n \"\"\"Return number of 0 or nearly 0 eigenvalues of the penalty matrix.\n\n Args:\n penalty_degree (int): Degree of the derivative used in the\n calculation of the penalty matrix.\n\n Returns:\n int: number of close to 0 eigenvalues.\n\n \"\"\"\n return penalty_degree\n\n def _compute_matrix(self, eval_points, derivative=0):\n \"\"\"Compute the basis or its derivatives given a list of values.\n\n For each of the basis computes its value for each of the points in\n the list passed as argument to the method.\n\n Args:\n eval_points (array_like): List of points where the basis is\n evaluated.\n derivative (int, optional): Order of the derivative. Defaults to 0.\n\n Returns:\n (:obj:`numpy.darray`): Matrix whose rows are the values of the each\n basis function or its derivatives at the values specified in\n eval_points.\n\n \"\"\"\n # Initialise empty matrix\n mat = numpy.zeros((self.nbasis, len(eval_points)))\n\n # For each basis computes its value for each evaluation\n if derivative == 0:\n for i in range(self.nbasis):\n mat[i] = eval_points ** i\n else:\n for i in range(self.nbasis):\n if derivative <= i:\n factor = i\n for j in range(2, derivative + 1):\n factor *= (i - j + 1)\n mat[i] = factor * eval_points ** (i - derivative)\n\n return mat\n\n def _derivative(self, coefs, order=1):\n return (Monomial(self.domain_range, self.nbasis - order),\n numpy.array([numpy.polyder(x[::-1], order)[::-1]\n for x in coefs]))\n\n def penalty(self, derivative_degree=None, coefficients=None):\n r\"\"\"Return a penalty matrix given a differential operator.\n\n The differential operator can be either a derivative of a certain\n degree or a more complex operator.\n\n The penalty matrix is defined as [RS05-5-6-2]_:\n\n .. math::\n R_{ij} = \\int L\\phi_i(s) L\\phi_j(s) ds\n\n where :math:`\\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis\n functions and :math:`L` is a differential operator.\n\n Args:\n derivative_degree (int): Integer indicating the order of the\n derivative or . For instance 2 means that the differential\n operator is :math:`f''(x)`.\n coefficients (list): List of coefficients representing a\n differential operator. An iterable indicating\n coefficients of derivatives (which can be functions). For\n instance the tuple (1, 0, numpy.sin) means :math:`1\n + sin(x)D^{2}`. Only used if derivative degree is None.\n\n\n Returns:\n numpy.array: Penalty matrix.\n\n Examples:\n >>> Monomial(nbasis=4).penalty(2)\n array([[ 0., 0., 0., 0.],\n [ 0., 0., 0., 0.],\n [ 0., 0., 4., 6.],\n [ 0., 0., 6., 12.]])\n\n References:\n .. [RS05-5-6-2] Ramsay, J., Silverman, B. W. (2005). Specifying the\n roughness penalty. In *Functional Data Analysis* (pp. 106-107).\n Springer.\n\n \"\"\"\n\n if derivative_degree is None:\n return self._numerical_penalty(coefficients)\n\n integration_domain = self.domain_range[0]\n\n # initialize penalty matrix as all zeros\n penalty_matrix = numpy.zeros((self.nbasis, self.nbasis))\n # iterate over the cartesion product of the basis system with itself\n for ibasis in range(self.nbasis):\n # notice that the index ibasis it is also the exponent of the\n # monomial\n # ifac is the factor resulting of deriving the monomial as many\n # times as indicates de differential operator\n if derivative_degree > 0:\n ifac = ibasis\n for k in range(2, derivative_degree + 1):\n ifac *= ibasis - k + 1\n else:\n ifac = 1\n\n for jbasis in range(self.nbasis):\n # notice that the index jbasis it is also the exponent of the\n # monomial\n # jfac is the factor resulting of deriving the monomial as\n # many times as indicates de differential operator\n if derivative_degree > 0:\n jfac = jbasis\n for k in range(2, derivative_degree + 1):\n jfac *= jbasis - k + 1\n else:\n jfac = 1\n\n # if any of the two monomial has lower degree than the order of\n # the derivative indicated by the differential operator that\n # factor equals 0, so no calculation are needed\n if (ibasis >= derivative_degree\n and jbasis >= derivative_degree):\n # Calculates exactly the result of the integral\n # Exponent after applying the differential operator and\n # integrating\n ipow = ibasis + jbasis - 2 * derivative_degree + 1\n # coefficient after integrating\n penalty_matrix[ibasis, jbasis] = (\n (integration_domain[1] ** ipow\n - integration_domain[0] ** ipow)\n * ifac * jfac / ipow)\n penalty_matrix[jbasis, ibasis] = penalty_matrix[ibasis,\n jbasis]\n\n return penalty_matrix\n\n def basis_of_product(self, other):\n \"\"\"Multiplication of a Monomial Basis with other Basis\"\"\"\n if not _same_domain(self.domain_range, other.domain_range):\n raise ValueError(\"Ranges are not equal.\")\n\n if isinstance(other, Monomial):\n return Monomial(self.domain_range, self.nbasis + other.nbasis)\n\n return other.rbasis_of_product(self)\n\n def rbasis_of_product(self, other):\n \"\"\"Multiplication of a Monomial Basis with other Basis\"\"\"\n return Basis.default_basis_of_product(self, other)\n\n def _to_R(self):\n drange = self.domain_range[0]\n return \"create.monomial.basis(rangeval = c(\" + str(drange[0]) + \",\" +\\\n str(drange[1]) + \"), nbasis = \" + str(self.nbasis) + \")\"\n\n\nclass BSpline(Basis):\n r\"\"\"BSpline basis.\n\n BSpline basis elements are defined recursively as:\n\n .. math::\n B_{i, 1}(x) = 1 \\quad \\text{if } t_i \\le x < t_{i+1},\n \\quad 0 \\text{ otherwise}\n\n .. math::\n B_{i, k}(x) = \\frac{x - t_i}{t_{i+k} - t_i} B_{i, k-1}(x)\n + \\frac{t_{i+k+1} - x}{t_{i+k+1} - t_{i+1}} B_{i+1, k-1}(x)\n\n Where k indicates the order of the spline.\n\n Implementation details: In order to allow a discontinuous behaviour at\n the boundaries of the domain it is necessary to placing m knots at the\n boundaries [RS05]_. This is automatically done so that the user only has to\n specify a single knot at the boundaries.\n\n Attributes:\n domain_range (tuple): A tuple of length 2 containing the initial and\n end values of the interval over which the basis can be evaluated.\n nbasis (int): Number of functions in the basis.\n order (int): Order of the splines. One greather than their degree.\n knots (list): List of knots of the spline functions.\n\n Examples:\n Constructs specifying number of basis and order.\n\n >>> bss = BSpline(nbasis=8, order=4)\n\n If no order is specified defaults to 4 because cubic splines are\n the most used. So the previous example is the same as:\n\n >>> bss = BSpline(nbasis=8)\n\n It is also possible to create a BSpline basis specifying the knots.\n\n >>> bss = BSpline(knots=[0, 0.2, 0.4, 0.6, 0.8, 1])\n\n Once we create a basis we can evaluate each of its functions at a\n set of points.\n\n >>> bss = BSpline(nbasis=3, order=3)\n >>> bss.evaluate([0, 0.5, 1])\n array([[ 1. , 0.25, 0. ],\n [ 0. , 0.5 , 0. ],\n [ 0. , 0.25, 1. ]])\n\n And evaluates first derivative\n\n >>> bss.evaluate([0, 0.5, 1], derivative=1)\n array([[-2., -1., 0.],\n [ 2., 0., -2.],\n [ 0., 1., 2.]])\n\n References:\n .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data\n Analysis*. Springer. 50-51.\n\n \"\"\"\n\n def __init__(self, domain_range=None, nbasis=None, order=4, knots=None):\n \"\"\"Bspline basis constructor.\n\n Args:\n domain_range (tuple, optional): Definition of the interval where\n the basis defines a space. Defaults to (0,1) if knots are not\n specified. If knots are specified defaults to the first and\n last element of the knots.\n nbasis (int, optional): Number of splines that form the basis.\n order (int, optional): Order of the splines. One greater that\n their degree. Defaults to 4 which mean cubic splines.\n knots (array_like): List of knots of the splines. If domain_range\n is specified the first and last elements of the knots have to\n match with it.\n\n \"\"\"\n\n if domain_range is not None:\n domain_range = _list_of_arrays(domain_range)\n\n if len(domain_range) != 1:\n raise ValueError(\"Domain range should be unidimensional.\")\n\n domain_range = domain_range[0]\n\n # Knots default to equally space points in the domain_range\n if knots is None:\n if nbasis is None:\n raise ValueError(\"Must provide either a list of knots or the\"\n \"number of basis.\")\n if domain_range is None:\n domain_range = (0, 1)\n knots = list(numpy.linspace(*domain_range, nbasis - order + 2))\n else:\n knots = list(knots)\n knots.sort()\n if domain_range is None:\n domain_range = (knots[0], knots[-1])\n\n # nbasis default to number of knots + order of the splines - 2\n if nbasis is None:\n nbasis = len(knots) + order - 2\n if domain_range is None:\n domain_range = (knots[0], knots[-1])\n\n if (nbasis - order + 2) < 2:\n raise ValueError(f\"The number of basis ({nbasis}) minus the order \"\n f\"of the bspline ({order}) should be greater than \"\n f\"3.\")\n\n if domain_range[0] != knots[0] or domain_range[1] != knots[-1]:\n raise ValueError(\"The ends of the knots must be the same as \"\n \"the domain_range.\")\n\n # Checks\n if nbasis != order + len(knots) - 2:\n raise ValueError(\"The number of basis has to equal the order \"\n \"plus the number of knots minus 2.\")\n\n self.order = order\n self.knots = list(knots)\n super().__init__(domain_range, nbasis)\n\n def _ndegenerated(self, penalty_degree):\n \"\"\"Return number of 0 or nearly to 0 eigenvalues of the penalty matrix.\n\n Args:\n penalty_degree (int): Degree of the derivative used in the\n calculation of the penalty matrix.\n\n Returns:\n int: number of close to 0 eigenvalues.\n\n \"\"\"\n return penalty_degree\n\n def _compute_matrix(self, eval_points, derivative=0):\n \"\"\"Compute the basis or its derivatives given a list of values.\n\n It uses the scipy implementation of BSplines to compute the values\n for each element of the basis.\n\n Args:\n eval_points (array_like): List of points where the basis system is\n evaluated.\n derivative (int, optional): Order of the derivative. Defaults to 0.\n\n Returns:\n (:obj:`numpy.darray`): Matrix whose rows are the values of the each\n basis function or its derivatives at the values specified in\n eval_points.\n\n Implementation details: In order to allow a discontinuous behaviour at\n the boundaries of the domain it is necessary to placing m knots at the\n boundaries [RS05]_. This is automatically done so that the user only\n has to specify a single knot at the boundaries.\n\n References:\n .. [RS05] Ramsay, J., Silverman, B. W. (2005). *Functional Data\n Analysis*. Springer. 50-51.\n\n \"\"\"\n # Places m knots at the boundaries\n knots = numpy.array([self.knots[0]] * (self.order - 1) + self.knots\n + [self.knots[-1]] * (self.order - 1))\n # c is used the select which spline the function splev below computes\n c = numpy.zeros(len(knots))\n\n # Initialise empty matrix\n mat = numpy.empty((self.nbasis, len(eval_points)))\n\n # For each basis computes its value for each evaluation point\n for i in range(self.nbasis):\n # write a 1 in c in the position of the spline calculated in each\n # iteration\n c[i] = 1\n # compute the spline\n mat[i] = scipy.interpolate.splev(eval_points, (knots, c,\n self.order - 1),\n der=derivative)\n c[i] = 0\n\n return mat\n\n def _derivative(self, coefs, order=1):\n deriv_splines = [self._to_scipy_BSpline(coefs[i]).derivative(order)\n for i in range(coefs.shape[0])]\n\n deriv_coefs = [BSpline._from_scipy_BSpline(spline)[1]\n for spline in deriv_splines]\n\n deriv_basis = BSpline._from_scipy_BSpline(deriv_splines[0])[0]\n\n return deriv_basis, numpy.array(deriv_coefs)[:, 0:deriv_basis.nbasis]\n\n def penalty(self, derivative_degree=None, coefficients=None):\n r\"\"\"Return a penalty matrix given a differential operator.\n\n The differential operator can be either a derivative of a certain\n degree or a more complex operator.\n\n The penalty matrix is defined as [RS05-5-6-2]_:\n\n .. math::\n R_{ij} = \\int L\\phi_i(s) L\\phi_j(s) ds\n\n where :math:`\\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis\n functions and :math:`L` is a differential operator.\n\n Args:\n derivative_degree (int): Integer indicating the order of the\n derivative or . For instance 2 means that the differential\n operator is :math:`f''(x)`.\n coefficients (list): List of coefficients representing a\n differential operator. An iterable indicating\n coefficients of derivatives (which can be functions). For\n instance the tuple (1, 0, numpy.sin) means :math:`1\n + sin(x)D^{2}`. Only used if derivative degree is None.\n\n Returns:\n numpy.array: Penalty matrix.\n\n References:\n .. [RS05-5-6-2] Ramsay, J., Silverman, B. W. (2005). Specifying the\n roughness penalty. In *Functional Data Analysis* (pp. 106-107).\n Springer.\n\n \"\"\"\n if derivative_degree is not None:\n if derivative_degree >= self.order:\n raise ValueError(f\"Penalty matrix cannot be evaluated for \"\n f\"derivative of order {derivative_degree} for \"\n f\"B-splines of order {self.order}\")\n if derivative_degree == self.order - 1:\n # The derivative of the bsplines are constant in the intervals\n # defined between knots\n knots = numpy.array(self.knots)\n mid_inter = (knots[1:] + knots[:-1]) / 2\n constants = self.evaluate(mid_inter,\n derivative=derivative_degree).T\n knots_intervals = numpy.diff(self.knots)\n # Integration of product of constants\n return constants.T @ numpy.diag(knots_intervals) @ constants\n\n if numpy.all(numpy.diff(self.knots) != 0):\n # Compute exactly using the piecewise polynomial\n # representation of splines\n\n # Places m knots at the boundaries\n knots = numpy.array(\n [self.knots[0]] * (self.order - 1) + self.knots\n + [self.knots[-1]] * (self.order - 1))\n # c is used the select which spline the function\n # PPoly.from_spline below computes\n c = numpy.zeros(len(knots))\n\n # Initialise empty list to store the piecewise polynomials\n ppoly_lst = []\n\n no_0_intervals = numpy.where(numpy.diff(knots) > 0)[0]\n\n # For each basis gets its piecewise polynomial representation\n for i in range(self.nbasis):\n # write a 1 in c in the position of the spline\n # transformed in each iteration\n c[i] = 1\n # gets the piecewise polynomial representation and gets\n # only the positions for no zero length intervals\n # This polynomial are defined relatively to the knots\n # meaning that the column i corresponds to the ith knot.\n # Let the ith not be a\n # Then f(x) = pp(x - a)\n pp = (PPoly.from_spline((knots, c, self.order - 1))\n .c[:, no_0_intervals])\n # We need the actual coefficients of f, not pp. So we\n # just recursively calculate the new coefficients\n coeffs = pp.copy()\n for j in range(self.order - 1):\n coeffs[j + 1:] += (\n (binom(self.order - j - 1,\n range(1, self.order - j))\n * numpy.vstack([(-a) ** numpy.array(\n range(1, self.order - j)) for a in\n self.knots[:-1]])\n ).T * pp[j])\n ppoly_lst.append(coeffs)\n c[i] = 0\n\n # Now for each pair of basis computes the inner product after\n # applying the linear differential operator\n penalty_matrix = numpy.zeros((self.nbasis, self.nbasis))\n for interval in range(len(no_0_intervals)):\n for i in range(self.nbasis):\n poly_i = numpy.trim_zeros(ppoly_lst[i][:,\n interval], 'f')\n if len(poly_i) <= derivative_degree:\n # if the order of the polynomial is lesser or\n # equal to the derivative the result of the\n # integral will be 0\n continue\n # indefinite integral\n integral = polyint(_polypow(polyder(\n poly_i, derivative_degree), 2))\n # definite integral\n penalty_matrix[i, i] += numpy.diff(polyval(\n integral, self.knots[interval: interval + 2]))[0]\n\n for j in range(i + 1, self.nbasis):\n poly_j = numpy.trim_zeros(ppoly_lst[j][:,\n interval], 'f')\n if len(poly_j) <= derivative_degree:\n # if the order of the polynomial is lesser\n # or equal to the derivative the result of\n # the integral will be 0\n continue\n # indefinite integral\n integral = polyint(\n polymul(polyder(poly_i, derivative_degree),\n polyder(poly_j, derivative_degree)))\n # definite integral\n penalty_matrix[i, j] += numpy.diff(polyval(\n integral, self.knots[interval: interval + 2])\n )[0]\n penalty_matrix[j, i] = penalty_matrix[i, j]\n return penalty_matrix\n else:\n # if the order of the derivative is greater or equal to the order\n # of the bspline minus 1\n if len(coefficients) >= self.order:\n raise ValueError(f\"Penalty matrix cannot be evaluated for \"\n f\"derivative of order {len(coefficients) - 1} \"\n f\"for B-splines of order {self.order}\")\n\n # compute using the inner product\n return self._numerical_penalty(coefficients)\n\n def rescale(self, domain_range=None):\n r\"\"\"Return a copy of the basis with a new domain range, with the\n corresponding values rescaled to the new bounds.\n The knots of the BSpline will be rescaled in the new interval.\n\n Args:\n domain_range (tuple, optional): Definition of the interval where\n the basis defines a space. Defaults uses the same as the\n original basis.\n \"\"\"\n\n knots = numpy.array(self.knots, dtype=numpy.dtype('float'))\n\n if domain_range is not None: # Rescales the knots\n knots -= knots[0]\n knots *= ((domain_range[1] - domain_range[0]\n ) / (self.knots[-1] - self.knots[0]))\n knots += domain_range[0]\n\n # Fix possible round error\n knots[0] = domain_range[0]\n knots[-1] = domain_range[1]\n\n else:\n # TODO: Allow multiple dimensions\n domain_range = self.domain_range[0]\n\n return BSpline(domain_range, self.nbasis, self.order, knots)\n\n def __repr__(self):\n \"\"\"Representation of a BSpline basis.\"\"\"\n return (f\"{self.__class__.__name__}(domain_range={self.domain_range}, \"\n f\"nbasis={self.nbasis}, order={self.order}, \"\n f\"knots={self.knots})\")\n\n def __eq__(self, other):\n \"\"\"Equality of Basis\"\"\"\n return (super().__eq__(other)\n and self.order == other.order\n and self.knots == other.knots)\n\n def basis_of_product(self, other):\n \"\"\"Multiplication of two Bspline Basis\"\"\"\n if not _same_domain(self.domain_range, other.domain_range):\n raise ValueError(\"Ranges are not equal.\")\n\n if isinstance(other, Constant):\n return other.rbasis_of_product(self)\n\n if isinstance(other, BSpline):\n uniqueknots = numpy.union1d(self.inknots, other.inknots)\n\n multunique = numpy.zeros(len(uniqueknots), dtype=numpy.int32)\n for i in range(len(uniqueknots)):\n mult1 = numpy.count_nonzero(self.inknots == uniqueknots[i])\n mult2 = numpy.count_nonzero(other.inknots == uniqueknots[i])\n multunique[i] = max(mult1, mult2)\n\n m2 = 0\n allknots = numpy.zeros(numpy.sum(multunique))\n for i in range(len(uniqueknots)):\n m1 = m2\n m2 = m2 + multunique[i]\n allknots[m1:m2] = uniqueknots[i]\n\n norder1 = self.nbasis - len(self.inknots)\n norder2 = other.nbasis - len(other.inknots)\n norder = min(norder1 + norder2 - 1, 20)\n\n allbreaks = [self.domain_range[0][0]] + numpy.ndarray.tolist(allknots) + [self.domain_range[0][1]]\n nbasis = len(allbreaks) + norder - 2\n return BSpline(self.domain_range, nbasis, norder, allbreaks)\n else:\n norder = min(self.nbasis - len(self.inknots) + 2, 8)\n nbasis = max(self.nbasis + other.nbasis, norder + 1)\n return BSpline(self.domain_range, nbasis, norder)\n\n def rbasis_of_product(self, other):\n \"\"\"Multiplication of a Bspline Basis with other basis\"\"\"\n\n norder = min(self.nbasis - len(self.inknots) + 2, 8)\n nbasis = max(self.nbasis + other.nbasis, norder + 1)\n return BSpline(self.domain_range, nbasis, norder)\n\n def _to_R(self):\n drange = self.domain_range[0]\n return (\"create.bspline.basis(rangeval = c(\" + str(drange[0]) + \",\" +\n str(drange[1]) + \"), nbasis = \" + str(self.nbasis) +\n \", norder = \" + str(self.order) + \", breaks = \" +\n self._list_to_R(self.knots) + \")\")\n\n def _to_scipy_BSpline(self, coefs):\n\n knots = numpy.concatenate((\n numpy.repeat(self.knots[0], self.order - 1),\n self.knots,\n numpy.repeat(self.knots[-1], self.order - 1)))\n\n return SciBSpline(knots, coefs, self.order - 1)\n\n @staticmethod\n def _from_scipy_BSpline(bspline):\n order = bspline.k\n knots = bspline.t[order: -order]\n coefs = bspline.c\n domain_range = [knots[0], knots[-1]]\n\n return BSpline(domain_range, order=order + 1, knots=knots), coefs\n\n @property\n def inknots(self):\n \"\"\"Return number of basis.\"\"\"\n return self.knots[1:len(self.knots) - 1]\n\n\nclass Fourier(Basis):\n r\"\"\"Fourier basis.\n\n Defines a functional basis for representing functions on a fourier\n series expansion of period :math:`T`. The number of basis is always odd.\n If instantiated with an even number of basis, they will be incremented\n automatically by one.\n\n .. math::\n \\phi_0(t) = \\frac{1}{\\sqrt{2}}\n\n .. math::\n \\phi_{2n -1}(t) = sin\\left(\\frac{2 \\pi n}{T} t\\right)\n\n .. math::\n \\phi_{2n}(t) = cos\\left(\\frac{2 \\pi n}{T} t\\right)\n\n Actually this basis functions are not orthogonal but not orthonormal. To\n achieve this they are divided by its norm: :math:`\\sqrt{\\frac{T}{2}}`.\n\n Attributes:\n domain_range (tuple): A tuple of length 2 containing the initial and\n end values of the interval over which the basis can be evaluated.\n nbasis (int): Number of functions in the basis.\n period (int or float): Period (:math:`T`).\n\n Examples:\n Constructs specifying number of basis, definition interval and period.\n\n >>> fb = Fourier((0, numpy.pi), nbasis=3, period=1)\n >>> fb.evaluate([0, numpy.pi / 4, numpy.pi / 2, numpy.pi]).round(2)\n array([[ 1. , 1. , 1. , 1. ],\n [ 0. , -1.38, -0.61, 1.1 ],\n [ 1.41, 0.31, -1.28, 0.89]])\n\n And evaluate second derivative\n\n >>> fb.evaluate([0, numpy.pi / 4, numpy.pi / 2, numpy.pi],\n ... derivative = 2).round(2)\n array([[ 0. , 0. , 0. , 0. ],\n [ -0. , 54.46, 24.02, -43.37],\n [-55.83, -12.32, 50.4 , -35.16]])\n\n\n\n \"\"\"\n\n def __init__(self, domain_range=(0, 1), nbasis=3, period=None):\n \"\"\"Construct a Fourier object.\n\n It forces the object to have an odd number of basis. If nbasis is\n even, it is incremented by one.\n\n Args:\n domain_range (tuple): Tuple defining the domain over which the\n function is defined.\n nbasis (int): Number of basis functions.\n period (int or float): Period of the trigonometric functions that\n define the basis.\n\n \"\"\"\n\n domain_range = _list_of_arrays(domain_range)\n\n if len(domain_range) != 1:\n raise ValueError(\"Domain range should be unidimensional.\")\n\n domain_range = domain_range[0]\n\n if period is None:\n period = domain_range[1] - domain_range[0]\n self.period = period\n # If number of basis is even, add 1\n nbasis += 1 - nbasis % 2\n super().__init__(domain_range, nbasis)\n\n def _compute_matrix(self, eval_points, derivative=0):\n \"\"\"Compute the basis or its derivatives given a list of values.\n\n Args:\n eval_points (array_like): List of points where the basis is\n evaluated.\n derivative (int, optional): Order of the derivative. Defaults to 0.\n\n Returns:\n (:obj:`numpy.darray`): Matrix whose rows are the values of the each\n basis function or its derivatives at the values specified in\n eval_points.\n\n \"\"\"\n if derivative < 0:\n raise ValueError(\"derivative only takes non-negative values.\")\n\n omega = 2 * numpy.pi / self.period\n omega_t = omega * eval_points\n nbasis = self.nbasis if self.nbasis % 2 != 0 else self.nbasis + 1\n\n # Initialise empty matrix\n mat = numpy.empty((self.nbasis, len(eval_points)))\n if derivative == 0:\n # First base function is a constant\n # The division by numpy.sqrt(2) is so that it has the same norm as\n # the sine and cosine: sqrt(period / 2)\n mat[0] = numpy.ones(len(eval_points)) / numpy.sqrt(2)\n if nbasis > 1:\n # 2*pi*n*x / period\n args = numpy.outer(range(1, nbasis // 2 + 1), omega_t)\n index = range(1, nbasis - 1, 2)\n # odd indexes are sine functions\n mat[index] = numpy.sin(args)\n index = range(2, nbasis, 2)\n # even indexes are cosine functions\n mat[index] = numpy.cos(args)\n # evaluates the derivatives\n else:\n # First base function is a constant, so its derivative is 0.\n mat[0] = numpy.zeros(len(eval_points))\n if nbasis > 1:\n # (2*pi*n / period) ^ n_derivative\n factor = numpy.outer(\n (-1) ** (derivative // 2)\n * (numpy.array(range(1, nbasis // 2 + 1)) * omega)\n ** derivative,\n numpy.ones(len(eval_points)))\n # 2*pi*n*x / period\n args = numpy.outer(range(1, nbasis // 2 + 1), omega_t)\n # even indexes\n index_e = range(2, nbasis, 2)\n # odd indexes\n index_o = range(1, nbasis - 1, 2)\n if derivative % 2 == 0:\n mat[index_o] = factor * numpy.sin(args)\n mat[index_e] = factor * numpy.cos(args)\n else:\n mat[index_o] = factor * numpy.cos(args)\n mat[index_e] = -factor * numpy.sin(args)\n\n # normalise\n mat = mat / numpy.sqrt(self.period / 2)\n return mat\n\n def _ndegenerated(self, penalty_degree):\n \"\"\"Return number of 0 or nearly 0 eigenvalues of the penalty matrix.\n\n Args:\n penalty_degree (int): Degree of the derivative used in the\n calculation of the penalty matrix.\n\n Returns:\n int: number of close to 0 eigenvalues.\n\n \"\"\"\n return 0 if penalty_degree == 0 else 1\n\n def _derivative(self, coefs, order=1):\n\n omega = 2 * numpy.pi / self.period\n deriv_factor = (numpy.arange(1, (self.nbasis+1)/2) * omega) ** order\n\n deriv_coefs = numpy.zeros(coefs.shape)\n\n cos_sign, sin_sign = (-1)**int((order+1)/2), (-1)**int(order/2)\n\n if order % 2 == 0:\n deriv_coefs[:, 1::2] = sin_sign * coefs[:, 1::2] * deriv_factor\n deriv_coefs[:, 2::2] = cos_sign * coefs[:, 2::2] * deriv_factor\n else:\n deriv_coefs[:, 2::2] = sin_sign * coefs[:, 1::2] * deriv_factor\n deriv_coefs[:, 1::2] = cos_sign * coefs[:, 2::2] * deriv_factor\n\n # normalise\n return self.copy(), deriv_coefs\n\n def penalty(self, derivative_degree=None, coefficients=None):\n r\"\"\"Return a penalty matrix given a differential operator.\n\n The differential operator can be either a derivative of a certain\n degree or a more complex operator.\n\n The penalty matrix is defined as [RS05-5-6-2]_:\n\n .. math::\n R_{ij} = \\int L\\phi_i(s) L\\phi_j(s) ds\n\n where :math:`\\phi_i(s)` for :math:`i=1, 2, ..., n` are the basis\n functions and :math:`L` is a differential operator.\n\n Args:\n derivative_degree (int): Integer indicating the order of the\n derivative or . For instance 2 means that the differential\n operator is :math:`f''(x)`.\n coefficients (list): List of coefficients representing a\n differential operator. An iterable indicating\n coefficients of derivatives (which can be functions). For\n instance the tuple (1, 0, numpy.sin) means :math:`1\n + sin(x)D^{2}`. Only used if derivative degree is None.\n\n Returns:\n numpy.array: Penalty matrix.\n\n References:\n .. [RS05-5-6-2] Ramsay, J., Silverman, B. W. (2005). Specifying the\n roughness penalty. In *Functional Data Analysis* (pp. 106-107).\n Springer.\n\n \"\"\"\n if isinstance(derivative_degree, int):\n omega = 2 * numpy.pi / self.period\n # the derivatives of the functions of the basis are also orthogonal\n # so only the diagonal is different from 0.\n penalty_matrix = numpy.zeros(self.nbasis)\n if derivative_degree == 0:\n penalty_matrix[0] = 1\n else:\n # the derivative of a constant is 0\n # the first basis function is a constant\n penalty_matrix[0] = 0\n index_even = numpy.array(range(2, self.nbasis, 2))\n exponents = index_even / 2\n # factor resulting of deriving the basis function the times\n # indcated in the derivative_degree\n factor = (exponents * omega) ** (2 * derivative_degree)\n # the norm of the basis functions is 1 so only the result of the\n # integral is just the factor\n penalty_matrix[index_even - 1] = factor\n penalty_matrix[index_even] = factor\n return numpy.diag(penalty_matrix)\n else:\n # implement using inner product\n return self._numerical_penalty(coefficients)\n\n def basis_of_product(self, other):\n \"\"\"Multiplication of two Fourier Basis\"\"\"\n if not _same_domain(self.domain_range, other.domain_range):\n raise ValueError(\"Ranges are not equal.\")\n\n if isinstance(other, Fourier) and self.period == other.period:\n return Fourier(self.domain_range, self.nbasis + other.nbasis - 1, self.period)\n else:\n return other.rbasis_of_product(self)\n\n def rbasis_of_product(self, other):\n \"\"\"Multiplication of a Fourier Basis with other Basis\"\"\"\n return Basis.default_basis_of_product(other, self)\n\n def rescale(self, domain_range=None, *, rescale_period=False):\n r\"\"\"Return a copy of the basis with a new domain range, with the\n corresponding values rescaled to the new bounds.\n\n Args:\n domain_range (tuple, optional): Definition of the interval where\n the basis defines a space. Defaults uses the same as the\n original basis.\n rescale_period (bool, optional): If true the period will be\n rescaled using the ratio between the lengths of the new\n and old interval. Defaults to False.\n \"\"\"\n\n rescale_basis = super().rescale(domain_range)\n\n if rescale_period == False:\n rescale_basis.period = self.period\n else:\n domain_rescaled = rescale_basis.domain_range[0]\n domain = self.domain_range[0]\n\n rescale_basis.period = self.period * \\\n (domain_rescaled[1] - domain_rescaled[0]\n ) / (domain[1] - domain[0])\n\n return rescale_basis\n\n def _to_R(self):\n drange = self.domain_range[0]\n return (\"create.fourier.basis(rangeval = c(\" + str(drange[0]) + \",\" +\n str(drange[1]) + \"), nbasis = \" + str(self.nbasis) +\n \", period = \" + str(self.period) + \")\")\n\n def __repr__(self):\n \"\"\"Representation of a Fourier basis.\"\"\"\n return (f\"{self.__class__.__name__}(domain_range={self.domain_range}, \"\n f\"nbasis={self.nbasis}, period={self.period})\")\n\n def __eq__(self, other):\n \"\"\"Equality of Basis\"\"\"\n return super().__eq__(other) and self.period == other.period\n\n\nclass FDataBasis(FData):\n r\"\"\"Basis representation of functional data.\n\n Class representation for functional data in the form of a set of basis\n functions multplied by a set of coefficients.\n\n .. math::\n f(x) = \\sum_{k=1}{K}c_k\\phi_k\n\n Where n is the number of basis functions, :math:`c = (c_1, c_2, ...,\n c_K)` the vector of coefficients and :math:`\\phi = (\\phi_1, \\phi_2,\n ..., \\phi_K)` the basis function system.\n\n Attributes:\n basis (:obj:`Basis`): Basis function system.\n coefficients (numpy.darray): List or matrix of coefficients. Has to\n have the same length or number of columns as the number of basis\n function in the basis. If a matrix, each row contains the\n coefficients that multiplied by the basis functions produce each\n functional datum.\n\n Examples:\n >>> basis = Monomial(nbasis=4)\n >>> coefficients = [1, 1, 3, .5]\n >>> FDataBasis(basis, coefficients)\n FDataBasis(\n basis=Monomial(domain_range=[array([0, 1])], nbasis=4),\n coefficients=[[ 1. 1. 3. 0.5]],\n ...)\n\n \"\"\"\n\n def __init__(self, basis, coefficients, *, dataset_label=None,\n axes_labels=None, extrapolation=None, keepdims=False):\n \"\"\"Construct a FDataBasis object.\n\n Args:\n basis (:obj:`Basis`): Basis function system.\n coefficients (array_like): List or matrix of coefficients. Has to\n have the same length or number of columns as the number of\n basis function in the basis.\n \"\"\"\n coefficients = numpy.atleast_2d(coefficients)\n if coefficients.shape[1] != basis.nbasis:\n raise ValueError(\"The length or number of columns of coefficients \"\n \"has to be the same equal to the number of \"\n \"elements of the basis.\")\n self.basis = basis\n self.coefficients = coefficients\n\n #self.dataset_label = dataset_label\n #self.axes_labels = axes_labels\n #self.extrapolation = extrapolation\n #self.keepdims = keepdims\n\n super().__init__(extrapolation, dataset_label, axes_labels, keepdims)\n\n\n @classmethod\n def from_data(cls, data_matrix, sample_points, basis, weight_matrix=None,\n smoothness_parameter=0, penalty_degree=None,\n penalty_coefficients=None, penalty_matrix=None,\n method='cholesky', keepdims=False):\n r\"\"\"Transform raw data to a smooth functional form.\n\n Takes functional data in a discrete form and makes an approximates it\n to the closest function that can be generated by the basis.a\n\n The fit is made so as to reduce the penalized sum of squared errors\n [RS05-5-2-5]_:\n .. math::\n\n PENSSE(c) = (y - \\Phi c)' W (y - \\Phi c) + \\lambda c'Rc\n\n where :math:`y` is the vector or matrix of observations, :math:`\\Phi`\n the matrix whose columns are the basis functions evaluated at the\n sampling points, :math:`c` the coefficient vector or matrix to be\n estimated, :math:`\\lambda` a smoothness parameter and :math:`c'Rc` the\n matrix representation of the roughness penalty :math:`\\int \\left[ L(\n x(s)) \\right] ^2 ds` where :math:`L` is a linear differential operator.\n\n Each element of :math:`R` has the following close form:\n .. math::\n\n R_{ij} = \\int L\\phi_i(s) L\\phi_j(s) ds\n\n By deriving the first formula we obtain the closed formed of the\n estimated coefficients matrix:\n .. math::\n\n \\hat(c) = \\left( |Phi' W \\Phi + \\lambda R \\right)^{-1} \\Phi' W y\n\n The solution of this matrix equation is done using the cholesky\n method for the resolution of a LS problem. If this method throughs a\n rounding error warning you may want to use the QR factorisation that\n is more numerically stable despite being more expensive to compute.\n [RS05-5-2-7]_\n\n Args:\n data_matrix (array_like): List or matrix containing the\n observations. If a matrix each row represents a single\n functional datum and the columns the different observations.\n sample_points (array_like): Values of the domain where the previous\n data were taken.\n basis: (Basis): Basis used.\n weight_matrix (array_like, optional): Matrix to weight the\n observations. Defaults to the identity matrix.\n smoothness_parameter (int or float, optional): Smoothness parameter.\n Trying with several factors in a logarythm scale is suggested.\n If 0 no smoothing is performed. Defaults to 0.\n penalty_degree (int): Integer indicating the order of the\n derivative used in the computing of the penalty matrix. For\n instance 2 means that the differential operator is\n :math:`f''(x)`. If neither penalty_degree nor\n penalty_coefficients are supplied, this defaults to 2.\n penalty_coefficients (list): List of coefficients representing the\n differential operator used in the computing of the penalty\n matrix. An iterable indicating coefficients of derivatives (\n which can be functions). For instance the tuple (1, 0,\n numpy.sin) means :math:`1 + sin(x)D^{2}`. Only used if\n penalty_degree and penalty_matrix are None.\n penalty_matrix (array_like, optional): Penalty matrix. If\n supplied the differential operator is not used and instead\n the matrix supplied by this argument is used.\n method (str): Algorithm used for calculating the coefficients using\n the least squares method. The values admitted are 'cholesky'\n and 'qr' for Cholesky and QR factorisation methods\n respectively.\n\n Returns:\n FDataBasis: Represention of the data in a functional form as\n product of coefficients by basis functions.\n\n Examples:\n >>> import numpy as np\n >>> t = np.linspace(0, 1, 5)\n >>> x = np.sin(2 * np.pi * t) + np.cos(2 * np.pi * t)\n >>> x\n array([ 1., 1., -1., -1., 1.])\n\n >>> basis = Fourier((0, 1), nbasis=3)\n >>> fd = FDataBasis.from_data(x, t, basis)\n >>> fd.coefficients.round(2)\n array([[ 0. , 0.71, 0.71]])\n\n References:\n .. [RS05-5-2-5] Ramsay, J., Silverman, B. W. (2005). How spline\n smooths are computed. In *Functional Data Analysis* (pp. 86-87).\n Springer.\n\n .. [RS05-5-2-7] Ramsay, J., Silverman, B. W. (2005). HSpline\n smoothing as an augmented least squares problem. In *Functional\n Data Analysis* (pp. 86-87). Springer.\n\n\n\n\n \"\"\"\n # TODO add an option to return fit summaries: yhat, sse, gcv...\n if penalty_degree is None and penalty_coefficients is None:\n penalty_degree = 2\n\n # n is the samples\n # m is the observations\n # k is the number of elements of the basis\n\n # Each sample in a column (m x n)\n data_matrix = numpy.atleast_2d(data_matrix).T\n\n # Each basis in a column\n basis_values = basis.evaluate(sample_points).T\n\n # If no weight matrix is given all the weights are one\n if not weight_matrix:\n weight_matrix = numpy.identity(basis_values.shape[0])\n\n # We need to solve the equation\n # (phi' W phi + lambda * R) C = phi' W Y\n # where:\n # phi is the basis_values\n # W is the weight matrix\n # lambda the smoothness parameter\n # C the coefficient matrix (the unknown)\n # Y is the data_matrix\n\n if data_matrix.shape[0] > basis.nbasis or smoothness_parameter > 0:\n method = method.lower()\n if method == 'cholesky':\n right_matrix = basis_values.T @ weight_matrix @ data_matrix\n left_matrix = basis_values.T @ weight_matrix @ basis_values\n\n # Adds the roughness penalty to the equation\n if smoothness_parameter > 0:\n if not penalty_matrix:\n penalty_matrix = basis.penalty(penalty_degree,\n penalty_coefficients)\n left_matrix += smoothness_parameter * penalty_matrix\n\n coefficients = scipy.linalg.cho_solve(scipy.linalg.cho_factor(\n left_matrix, lower=True), right_matrix)\n\n # The ith column is the coefficients of the ith basis for each\n # sample\n coefficients = coefficients.T\n\n elif method == 'qr':\n if weight_matrix is not None:\n # Decompose W in U'U and calculate UW and Uy\n upper = scipy.linalg.cholesky(weight_matrix)\n basis_values = upper @ basis_values\n data_matrix = upper @ data_matrix\n\n if smoothness_parameter > 0:\n # In this case instead of resolving the original equation\n # we expand the system to include the penalty matrix so that\n # the rounding error is reduced\n if not penalty_matrix:\n penalty_matrix = basis.penalty(penalty_degree,\n penalty_coefficients)\n\n w, v = numpy.linalg.eigh(penalty_matrix)\n # Reduction of the penalty matrix taking away 0 or almost\n # zeros eigenvalues\n ndegenerated = basis._ndegenerated(penalty_degree)\n if ndegenerated:\n index = ndegenerated - 1\n else:\n index = None\n w = w[:index:-1]\n v = v[:, :index:-1]\n\n penalty_matrix = v @ numpy.diag(numpy.sqrt(w))\n # Augment the basis matrix with the square root of the\n # penalty matrix\n basis_values = numpy.concatenate([\n basis_values,\n numpy.sqrt(smoothness_parameter) * penalty_matrix.T],\n axis=0)\n # Augment data matrix by n - ndegenerated zeros\n data_matrix = numpy.pad(data_matrix,\n ((0, len(v) - ndegenerated),\n (0, 0)),\n mode='constant')\n\n # Resolves the equation\n # B.T @ B @ C = B.T @ D\n # by means of the QR decomposition\n\n # B = Q @ R\n q, r = numpy.linalg.qr(basis_values)\n right_matrix = q.T @ data_matrix\n\n # R @ C = Q.T @ D\n coefficients = numpy.linalg.solve(r, right_matrix)\n # The ith column is the coefficients of the ith basis for each\n # sample\n coefficients = coefficients.T\n\n else:\n raise ValueError(\"Unknown method.\")\n\n elif data_matrix.shape[0] == basis.nbasis:\n # If the number of basis equals the number of points and no\n # smoothing is required\n coefficients = numpy.linalg.solve(basis_values, data_matrix)\n\n else: # data_matrix.shape[0] < basis.nbasis\n raise ValueError(f\"The number of basis functions ({basis.nbasis}) \"\n f\"exceed the number of points to be smoothed \"\n f\"({data_matrix.shape[0]}).\")\n\n return cls(basis, coefficients, keepdims=keepdims)\n\n @property\n def nsamples(self):\n \"\"\"Return number of samples.\"\"\"\n return self.coefficients.shape[0]\n\n @property\n def ndim_domain(self):\n \"\"\"Return number of dimensions of the domain.\"\"\"\n\n # Only domain dimension equal to 1 is supported\n return 1\n\n @property\n def ndim_image(self):\n \"\"\"Return number of dimensions of the image.\"\"\"\n\n # Only image dimension equal to 1 is supported\n return 1\n\n @property\n def nbasis(self):\n \"\"\"Return number of basis.\"\"\"\n return self.basis.nbasis\n\n @property\n def domain_range(self):\n \"\"\"Definition range.\"\"\"\n return self.basis.domain_range\n\n def _evaluate(self, eval_points, *, derivative=0):\n \"\"\"\"Evaluate the object or its derivatives at a list of values.\n\n Args:\n eval_points (array_like): List of points where the functions are\n evaluated. If a matrix of shape `nsamples` x eval_points is\n given each sample is evaluated at the values in the\n corresponding row.\n derivative (int, optional): Order of the derivative. Defaults to 0.\n\n\n Returns:\n (numpy.darray): Matrix whose rows are the values of the each\n function at the values specified in eval_points.\n\n \"\"\"\n # Only suported 1D objects\n eval_points = eval_points[:, 0]\n\n # each row contains the values of one element of the basis\n basis_values = self.basis.evaluate(eval_points, derivative)\n\n res = numpy.tensordot(self.coefficients, basis_values, axes=(1, 0))\n\n return res.reshape((self.nsamples, len(eval_points), 1))\n\n def _evaluate_composed(self, eval_points, *, derivative=0):\n\n r\"\"\"Evaluate the object or its derivatives at a list of values with a\n different time for each sample.\n\n Returns a numpy array with the component (i,j) equal to :math:`f_i(t_j +\n \\delta_i)`.\n\n This method has to evaluate the basis values once per sample\n instead of reuse the same evaluation for all the samples\n as :func:`evaluate`.\n\n Args:\n eval_points (numpy.ndarray): Matrix of size `nsamples`x n_points\n derivative (int, optional): Order of the derivative. Defaults to 0.\n extrapolation (str or Extrapolation, optional): Controls the\n extrapolation mode for elements outside the domain range.\n By default uses the method defined in fd. See extrapolation to\n more information.\n Returns:\n (numpy.darray): Matrix whose rows are the values of the each\n function at the values specified in eval_points with the\n corresponding shift.\n \"\"\"\n\n eval_points = eval_points[..., 0]\n\n res_matrix = numpy.empty((self.nsamples, eval_points.shape[1]))\n\n _matrix = numpy.empty((eval_points.shape[1], self.nbasis))\n\n for i in range(self.nsamples):\n basis_values = self.basis.evaluate(eval_points[i], derivative).T\n\n numpy.multiply(basis_values, self.coefficients[i], out=_matrix)\n numpy.sum(_matrix, axis=1, out=res_matrix[i])\n\n return res_matrix.reshape((self.nsamples, eval_points.shape[1], 1))\n\n def shift(self, shifts, *, restrict_domain=False, extrapolation=None,\n eval_points=None, **kwargs):\n r\"\"\"Perform a shift of the curves.\n\n Args:\n shifts (array_like or numeric): List with the the shift\n corresponding for each sample or numeric with the shift to apply\n to all samples.\n restrict_domain (bool, optional): If True restricts the domain to\n avoid evaluate points outside the domain using extrapolation.\n Defaults uses extrapolation.\n extrapolation (str or Extrapolation, optional): Controls the\n extrapolation mode for elements outside the domain range.\n By default uses the method defined in fd. See extrapolation to\n more information.\n eval_points (array_like, optional): Set of points where\n the functions are evaluated to obtain the discrete\n representation of the object to operate. If an empty list is\n passed it calls numpy.linspace with bounds equal to the ones\n defined in fd.domain_range and the number of points the maximum\n between 201 and 10 times the number of basis plus 1.\n **kwargs: Keyword arguments to be passed to :meth:`from_data`.\n\n Returns:\n :obj:`FDataBasis` with the shifted data.\n \"\"\"\n\n if self.ndim_image > 1 or self.ndim_domain > 1:\n raise ValueError\n\n domain_range = self.domain_range[0]\n\n if eval_points is None: # Grid to discretize the function\n nfine = max(self.nbasis * 10 + 1, 201)\n eval_points = numpy.linspace(*domain_range, nfine)\n else:\n eval_points = numpy.asarray(eval_points)\n\n if numpy.isscalar(shifts): # Special case, all curves with same shift\n\n _basis = self.basis.rescale((domain_range[0] + shifts,\n domain_range[1] + shifts))\n\n return FDataBasis.from_data(self.evaluate(eval_points,\n keepdims=False),\n eval_points + shifts,\n _basis, **kwargs)\n\n elif len(shifts) != self.nsamples:\n raise ValueError(f\"shifts vector ({len(shifts)}) must have the same\"\n f\" length than the number of samples \"\n f\"({self.nsamples})\")\n\n if restrict_domain:\n a = domain_range[0] - min(numpy.min(shifts), 0)\n b = domain_range[1] - max(numpy.max(shifts), 0)\n domain = (a, b)\n eval_points = eval_points[\n numpy.logical_and(eval_points >= a,\n eval_points <= b)]\n else:\n domain = domain_range\n\n points_shifted = numpy.outer(numpy.ones(self.nsamples),\n eval_points)\n\n points_shifted += numpy.atleast_2d(shifts).T\n\n # Matrix of shifted values\n _data_matrix = self.evaluate(points_shifted,\n aligned_evaluation=False,\n extrapolation=extrapolation,\n keepdims=False)\n\n _basis = self.basis.rescale(domain)\n\n return FDataBasis.from_data(_data_matrix, eval_points,\n _basis, **kwargs)\n\n def derivative(self, order=1):\n r\"\"\"Differentiate a FDataBasis object.\n\n\n Args:\n order (int, optional): Order of the derivative. Defaults to one.\n \"\"\"\n\n if order < 0:\n raise ValueError(\"order only takes non-negative integer values.\")\n\n if order is 0:\n return self.copy()\n\n basis, coefficients = self.basis._derivative(self.coefficients, order)\n\n return FDataBasis(basis, coefficients)\n\n def mean(self):\n \"\"\"Compute the mean of all the samples in a FDataBasis object.\n\n Returns:\n :obj:`FDataBasis`: A FDataBais object with just one sample\n representing the mean of all the samples in the original\n FDataBasis object.\n\n Examples:\n >>> basis = Monomial(nbasis=4)\n >>> coefficients = [[0.5, 1, 2, .5], [1.5, 1, 4, .5]]\n >>> FDataBasis(basis, coefficients).mean()\n FDataBasis(\n basis=Monomial(domain_range=[array([0, 1])], nbasis=4),\n coefficients=[[ 1. 1. 3. 0.5]],\n ...)\n\n \"\"\"\n return self.copy(coefficients=numpy.mean(self.coefficients, axis=0))\n\n def gmean(self, eval_points=None):\n \"\"\"Compute the geometric mean of the functional data object.\n\n A numerical approach its used. The object its transformed into its\n discrete representation and then the geometric mean is computed and\n then the object is taken back to the basis representation.\n\n Args:\n eval_points (array_like, optional): Set of points where the\n functions are evaluated to obtain the discrete\n representation of the object. If none are passed it calls\n numpy.linspace with bounds equal to the ones defined in\n self.domain_range and the number of points the maximum\n between 501 and 10 times the number of basis.\n\n Returns:\n FDataBasis: Geometric mean of the original object.\n\n \"\"\"\n return self.to_grid(eval_points).gmean().to_basis(self.basis)\n\n def var(self, eval_points=None):\n \"\"\"Compute the variance of the functional data object.\n\n A numerical approach its used. The object its transformed into its\n discrete representation and then the variance is computed and\n then the object is taken back to the basis representation.\n\n Args:\n eval_points (array_like, optional): Set of points where the\n functions are evaluated to obtain the discrete\n representation of the object. If none are passed it calls\n numpy.linspace with bounds equal to the ones defined in\n self.domain_range and the number of points the maximum\n between 501 and 10 times the number of basis.\n\n Returns:\n FDataBasis: Variance of the original object.\n\n \"\"\"\n return self.to_grid(eval_points).var().to_basis(self.basis)\n\n def cov(self, eval_points=None):\n \"\"\"Compute the covariance of the functional data object.\n\n A numerical approach its used. The object its transformed into its\n discrete representation and then the covariance matrix is computed.\n\n Args:\n eval_points (array_like, optional): Set of points where the\n functions are evaluated to obtain the discrete\n representation of the object. If none are passed it calls\n numpy.linspace with bounds equal to the ones defined in\n self.domain_range and the number of points the maximum\n between 501 and 10 times the number of basis.\n\n Returns:\n numpy.darray: Matrix of covariances.\n\n \"\"\"\n return self.to_grid(eval_points).cov()\n\n def to_grid(self, eval_points=None):\n \"\"\"Return the discrete representation of the object.\n\n Args:\n eval_points (array_like, optional): Set of points where the\n functions are evaluated. If none are passed it calls\n numpy.linspace with bounds equal to the ones defined in\n self.domain_range and the number of points the maximum\n between 501 and 10 times the number of basis.\n\n Returns:\n FDataGrid: Discrete representation of the functional data\n object.\n\n Examples:\n >>> fd = FDataBasis(coefficients=[[1, 1, 1], [1, 0, 1]],\n ... basis=Monomial((0,5), nbasis=3))\n >>> fd.to_grid([0, 1, 2])\n FDataGrid(\n array([[[ 1.],\n [ 3.],\n [ 7.]],\n \n [[ 1.],\n [ 2.],\n [ 5.]]]),\n sample_points=[array([0, 1, 2])],\n domain_range=array([[0, 5]]),\n ...)\n\n \"\"\"\n\n if self.ndim_image > 1 or self.ndim_domain > 1:\n raise NotImplementedError\n\n if eval_points is None:\n npoints = max(501, 10 * self.nbasis)\n eval_points = numpy.linspace(*self.domain_range[0], npoints)\n\n return grid.FDataGrid(self.evaluate(eval_points, keepdims=False),\n sample_points=eval_points,\n domain_range=self.domain_range,\n keepdims=self.keepdims)\n\n def to_basis(self, basis, eval_points=None, **kwargs):\n \"\"\"Return the basis representation of the object.\n\n Args:\n basis(Basis): basis object in which the functional data are\n going to be represented.\n **kwargs: keyword arguments to be passed to\n FDataBasis.from_data().\n\n Returns:\n FDataBasis: Basis representation of the funtional data\n object.\n \"\"\"\n\n return self.to_grid(eval_points=eval_points).to_basis(basis, **kwargs)\n\n def to_list(self):\n \"\"\"Splits FDataBasis samples into a list\"\"\"\n return [self[i] for i in range(self.nsamples)]\n\n def copy(self, *, basis=None, coefficients=None, dataset_label=None,\n axes_labels=None, extrapolation=None, keepdims=None):\n \"\"\"FDataBasis copy\"\"\"\n\n if basis is None:\n basis = copy.deepcopy(self.basis)\n\n if coefficients is None:\n coefficients = self.coefficients\n\n if dataset_label is None:\n dataset_label = copy.deepcopy(dataset_label)\n\n if axes_labels is None:\n axes_labels = copy.deepcopy(axes_labels)\n\n if extrapolation is None:\n extrapolation = self.extrapolation\n\n if keepdims is None:\n keepdims = self.keepdims\n\n return FDataBasis(basis, coefficients, dataset_label=dataset_label,\n axes_labels=axes_labels, extrapolation=extrapolation,\n keepdims=keepdims)\n\n def times(self, other):\n \"\"\"\"Provides a numerical approximation of the multiplication between\n an FDataObject to other object\n\n Args:\n other (int, list, FDataBasis): Object to multiply with the\n FDataBasis object.\n - int: Multiplies all samples with the value\n - list: multiply each values with the samples respectively.\n Length should match with FDataBasis samples\n - FDataBasis: if there is one sample it multiplies this with\n all the samples in the object. If not, it\n multiplies each sample respectively. Samples\n should match\n\n Returns:\n (FDataBasis): FDataBasis object containing the multiplication\n\n \"\"\"\n if isinstance(other, FDataBasis):\n\n if not _same_domain(self.domain_range, other.domain_range):\n raise ValueError(\"The functions domains are different.\")\n\n basisobj = self.basis.basis_of_product(other.basis)\n neval = max(10 * max(self.nbasis, other.nbasis) + 1,\n MIN_EVAL_SAMPLES)\n (left, right) = self.domain_range[0]\n evalarg = numpy.linspace(left, right, neval)\n\n first = self.copy(coefficients=(numpy.repeat(self.coefficients,\n other.nsamples, axis=0) if self.nsamples == 1 and\n other.nsamples > 1 else self.coefficients.copy()))\n second = other.copy(coefficients=(numpy.repeat(other.coefficients,\n self.nsamples, axis=0) if other.nsamples == 1 and\n self.nsamples > 1 else other.coefficients.copy()))\n\n fdarray = first.evaluate(evalarg) * second.evaluate(evalarg)\n\n return FDataBasis.from_data(fdarray, evalarg, basisobj)\n\n if isinstance(other, int):\n other = [other for _ in range(self.nsamples)]\n\n coefs = numpy.transpose(numpy.atleast_2d(other))\n return self.copy(coefficients=self.coefficients*coefs)\n\n def inner_product(self, other, lfd_self=None, lfd_other=None,\n weights=None):\n r\"\"\"Return an inner product matrix given a FDataBasis object.\n\n The inner product of two functions is defined as\n\n .. math::\n = \\int_a^b x(t)y(t) dt\n\n When we talk abaout FDataBasis objects, they have many samples, so we\n talk about inner product matrix instead. So, for two FDataBasis objects\n we define the inner product matrix as\n\n .. math::\n a_{ij} = = \\int_a^b x_i(s) y_j(s) ds\n\n where :math:`f_i(s), g_j(s)` are the :math:`i^{th} j^{th}` sample of\n each object. The return matrix has a shape of :math:`IxJ` where I and\n J are the number of samples of each object respectively.\n\n Args:\n other (FDataBasis, Basis): FDataBasis object containing the second\n object to make the inner product\n\n lfd_self (Lfd): LinearDifferentialOperator object for the first function evaluation\n\n lfd_other (Lfd): LinearDifferentialOperator object for the second function evaluation\n\n weights(FDataBasis): a FDataBasis object with only one sample that\n defines the weight to calculate the inner product\n\n Returns:\n numpy.array: Inner Product matrix.\n\n \"\"\"\n from ..misc import LinearDifferentialOperator\n\n if not _same_domain(self.domain_range, other.domain_range):\n raise ValueError(\"Both Objects should have the same domain_range\")\n if isinstance(other, Basis):\n other = other.to_basis()\n\n # TODO this will be used when lfd evaluation is ready\n lfd_self = (LinearDifferentialOperator(0) if lfd_self is None\n else lfd_self)\n lfd_other = (LinearDifferentialOperator(0) if (lfd_other is None)\n else lfd_other)\n\n if weights is not None:\n other = other.times(weights)\n\n matrix = numpy.empty((self.nsamples, other.nsamples))\n (left, right) = self.domain_range[0]\n\n for i in range(self.nsamples):\n for j in range(other.nsamples):\n fd = self[i].times(other[j])\n matrix[i, j] = scipy.integrate.quad(\n lambda x: fd.evaluate([x])[0], left, right)[0]\n return matrix\n\n def _to_R(self):\n \"\"\"Gives the code to build the object on fda package on R\"\"\"\n return (\"fd(coef = \" + self._array_to_R(self.coefficients, True) +\n \", basisobj = \" + self.basis._to_R() + \")\")\n\n def _array_to_R(self, coefficients, transpose=False):\n if len(coefficients.shape) == 1:\n coefficients = coefficients.reshape((1, coefficients.shape[0]))\n\n if len(coefficients.shape) > 2:\n return NotImplementedError\n\n if transpose is True:\n coefficients = numpy.transpose(coefficients)\n\n (rows, cols) = coefficients.shape\n retstring = \"matrix(c(\"\n for j in range(cols):\n for i in range(rows):\n retstring = retstring + str(coefficients[i, j]) + \", \"\n\n return (retstring[0:len(retstring) - 2] + \"), nrow = \" + str(rows) +\n \", ncol = \" + str(cols) + \")\")\n\n def __repr__(self):\n \"\"\"Representation of FDataBasis object.\"\"\"\n return (f\"{self.__class__.__name__}(\"\n f\"\\nbasis={self.basis},\"\n f\"\\ncoefficients={self.coefficients},\"\n f\"\\ndataset_label={self.dataset_label},\"\n f\"\\naxes_labels={self.axes_labels},\"\n f\"\\nextrapolation={self.extrapolation},\"\n f\"\\nkeepdims={self.keepdims})\").replace('\\n', '\\n ')\n\n def __str__(self):\n \"\"\"Return str(self).\"\"\"\n\n return (f\"{self.__class__.__name__}(\"\n f\"\\nbasis={self.basis},\"\n f\"\\ncoefficients={self.coefficients})\").replace('\\n', '\\n ')\n\n def __eq__(self, other):\n \"\"\"Equality of FDataBasis\"\"\"\n # TODO check all other params\n return (self.basis == other.basis\n and numpy.all(self.coefficients == other.coefficients))\n\n def concatenate(self, other):\n \"\"\"Join samples from a similar FDataBasis object.\n\n Joins samples from another FDataBasis object if they have the same\n basis.\n\n Args:\n other (:class:`FDataBasis`): another FDataBasis object.\n\n Returns:\n :class:`FDataBasis`: FDataBasis object with the samples from the two\n original objects.\n \"\"\"\n\n if other.basis != self.basis:\n raise ValueError(\"The objects should have the same basis.\")\n\n return self.copy(coefficients=numpy.concatenate((self.coefficients,\n other.coefficients),\n axis=0))\n\n def compose(self, fd, *, eval_points=None, **kwargs):\n \"\"\"Composition of functions.\n\n Performs the composition of functions. The basis is discretized to\n compute the composition.\n\n Args:\n fd (:class:`FData`): FData object to make the composition. Should\n have the same number of samples and image dimension equal to 1.\n eval_points (array_like): Points to perform the evaluation.\n **kwargs: Named arguments to be passed to :func:`from_data`.\n \"\"\"\n\n grid = self.to_grid().compose(fd, eval_points=eval_points)\n\n if fd.ndim_domain == 1:\n basis = self.basis.rescale(fd.domain_range[0])\n composition = grid.to_basis(basis, **kwargs)\n else:\n # Cant be convertered to basis due to the dimensions\n composition = grid\n\n return composition\n\n def __getitem__(self, key):\n \"\"\"Return self[key].\"\"\"\n\n if isinstance(key, int):\n return self.copy(coefficients=self.coefficients[key:key + 1])\n\n else:\n return self.copy(coefficients=self.coefficients[key])\n\n def __add__(self, other):\n \"\"\"Addition for FDataBasis object.\"\"\"\n\n raise NotImplementedError\n\n def __radd__(self, other):\n \"\"\"Addition for FDataBasis object.\"\"\"\n\n return self.__add__(other)\n\n def __sub__(self, other):\n \"\"\"Subtraction for FDataBasis object.\"\"\"\n\n raise NotImplementedError\n\n def __rsub__(self, other):\n \"\"\"Right subtraction for FDataBasis object.\"\"\"\n\n raise NotImplementedError\n\n def __mul__(self, other):\n \"\"\"Multiplication for FDataBasis object.\"\"\"\n\n raise NotImplementedError\n\n def __rmul__(self, other):\n \"\"\"Multiplication for FDataBasis object.\"\"\"\n\n return self.__mul__(other)\n\n def __truediv__(self, other):\n \"\"\"Division for FDataBasis object.\"\"\"\n\n raise NotImplementedError\n\n def __rtruediv__(self, other):\n \"\"\"Right division for FDataBasis object.\"\"\"\n\n raise NotImplementedError\n","sub_path":"skfda/representation/basis.py","file_name":"basis.py","file_ext":"py","file_size_in_byte":89309,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"445852792","text":"# Import module\nimport cv2\nimport numpy as np\nfrom dataPath import DATA_PATH\nimport matplotlib.pyplot as plt\n\n#read the image in BGR format\nbgr = cv2.imread(DATA_PATH+\"images/capsicum.jpg\")\n\n# convert from bgr to HSV format\nhsvImage = cv2.cvtColor(bgr, cv2.COLOR_BGR2HSV)\n\ncv2.imshow(\"hsv\",hsvImage)\n\ncv2.imshow(\"Hue\",hsvImage[:,:,0])\ncv2.imshow(\"Saturation\",hsvImage[:,:,1])\ncv2.imshow(\"Value\",hsvImage[:,:,2])\ncv2.waitKey(0)\n\nfor i in range(0,7):\n # Create 50x50 HSV image with all zeros\n imhsv = np.zeros((50, 50, 3), dtype=np.uint8)\n\n # Set Hue = 0, Saturation = 0, Value = i x 40\n v = i * 40\n imhsv[:,:,:] = (0, 0, v)\n\n # Convert HSV to RGB\n imrgb = cv2.cvtColor(imhsv, cv2.COLOR_HSV2RGB)\n\n # Display image\n ax = plt.subplot(1, 7, i+1)\n plt.imshow(imrgb)\n plt.axis('off')\n ax.set_title('V='+ str(v), fontdict={'fontsize': 10, 'fontweight': 'medium'})\n\nplt.show()\n\n# Saturation test.\n# Set brightness to 128, hue to 0, and change saturation\n\nfor i in range(0,7):\n # Create 50x50 HSV image with all zeros\n imhsv = np.zeros((50, 50, 3), dtype=np.uint8)\n\n # Set Hue = 0, Saturation = i * 40, Value = 128\n s = i * 40\n imhsv[:,:,:] = (0, s, 128)\n\n # Convert HSV to RGB\n imrgb = cv2.cvtColor(imhsv, cv2.COLOR_HSV2RGB)\n\n # Display image\n ax = plt.subplot(1, 7, i+1)\n plt.imshow(imrgb)\n plt.axis('off')\n ax.set_title('S='+ str(s), fontdict={'fontsize': 10, 'fontweight': 'medium'})\n\n\nplt.show()\n\n# Hue Test\n\nfor i in range(0,7):\n imhsv = np.zeros((50, 50, 3), dtype=np.uint8)\n\n # Set Hue = i x 30, Saturation = 128, and Value = 128.\n h = i * 30\n imhsv[:,:,:] = ( h, 128, 128)\n\n # Convert HSV to RGB\n imrgb = cv2.cvtColor(imhsv, cv2.COLOR_HSV2RGB)\n\n # Display image\n ax = plt.subplot(1, 7, i+1)\n plt.imshow(imrgb)\n plt.axis('off')\n ax.set_title('H='+ str(h), fontdict={'fontsize': 10, 'fontweight': 'medium'})\n\nplt.show()\n","sub_path":"week4-python/ColorSpaces/hsvColorSpace.py","file_name":"hsvColorSpace.py","file_ext":"py","file_size_in_byte":1922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"184322578","text":"\n\n#calss header\nclass _TABULATE():\n\tdef __init__(self,): \n\t\tself.name = \"TABULATE\"\n\t\tself.definitions = [u'to show information in the form of a table (= an arrangement of facts and numbers in rows or blocks)']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'verbs'\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/verbs/_tabulate.py","file_name":"_tabulate.py","file_ext":"py","file_size_in_byte":383,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"205106985","text":"import math as m\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import root_scalar,minimize\nimport scipy.integrate as integrate\nimport matplotlib.gridspec as gridspec\nfrom scipy.misc import derivative\nfrom scipy.optimize import fsolve\nimport matplotlib.cm as cmap\nfrom matplotlib.widgets import Slider, Button, RadioButtons\n\n#fig=plt.figure()\n#gs=gridspec.GridSpec(1,2,width_ratios=[1,1],height_ratios=[1])\n#gs.update(wspace=0.5)\n\n#ax1=plt.subplot(gs[0])\n#ax2=plt.subplot(gs[1])\n\ndef F(psi,r,g,K):\n return g*r - (1/2)*np.sin(2*psi) - K*np.tan(2*psi)*np.sin(psi)**2\n\ndef psislider(r,g,K):\n g/=(10**(-6))\n psilist=np.zeros(len(r))\n\n for i in range(len(r)):\n psilist[i] = fsolve(F,psilist[i-1],args=(r[i],g,K))\n \n return psilist\n\n\n#################\nR=15*10**(-9) #nanometers\nN=10000\nylist=[]\n\nr = np.linspace(0.0, R,num=N)\n\nKlist=np.linspace(1,1000,num=200)\nglist=18 + 3.2*Klist\n\nfor i in range(200):\n psi = psislider(r,glist[i],Klist[i])\n ylist.append((psi[-1] - psi[50])/psi[-1])\n\n'''ax2.plot(Klist,ylist,color='blue')\nax2.set_ylabel('$\\\\frac{\\psi_R-\\psi_{R/2}}{\\psi_R}$',rotation=0)\nax2.set_xlabel('$K$')\nax2.set_xlim(-1,)\nax2.set_ylim(0.15,0.5)\nax2.set_title(\"$R=$\"+str(int(R*10**9))+'nm, $\\psi(R)=0.28$')'''\n\n#plt.plot(Klist,ylist,color='blue',label='Cornea')\nplt.plot(r/r[-1],psi/psi[-1],color='blue',label='Cornea')\n\n\n########################\nR=100*10**(-9) #nanometers\nN=300\nylist=[]\n\nr = np.linspace(0.0, R,num=N)\n\nKlist=np.linspace(1,1000,num=200)\nglist=0.9 + 0.015*Klist\n\nfor i in range(200):\n psi = psislider(r,glist[i],Klist[i])\n ylist.append((psi[-1] - psi[50])/psi[-1])\n\n#plt.plot(Klist,ylist,color='orange',label='Tendon')\nplt.plot(r/r[-1],psi/psi[-1],color='orange',label='Tendon')\n\n#plt.ylabel('$\\\\frac{\\psi_R-\\psi_{R/2}}{\\psi_R}$',rotation=0,fontsize=18)\n#plt.xlabel('$K$')\n#plt.xlim(-1,)\n#plt.ylim(0.15,0.5)\n\nplt.xlabel('$r/R$')\nplt.ylabel('$\\psi(r)/\\psi(R)$')\nplt.xscale('log')\nplt.yscale('log')\nplt.legend()\n\n'''ax1.plot(Klist,ylist,color='orange')\nax1.set_ylabel('$\\\\frac{\\psi_R-\\psi_{R/2}}{\\psi_R}$',rotation=0)\nax1.set_xlabel('$K$')\nax1.set_xlim(-1,)\nax1.set_ylim(0.15,0.5)\nax1.set_title(\"$R=$\"+str(int(R*10**9))+'nm, $\\psi(R)=0.08$')'''\n\nplt.show()\n\n\n\n","sub_path":"Scripts/curvatureplots.py","file_name":"curvatureplots.py","file_ext":"py","file_size_in_byte":2234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"476320653","text":"from __future__ import absolute_import, print_function\n\nimport copy\nimport sys\nimport threading\n\n# data_dir = os.path.dirname(os.path.realpath(__file__))\n# sys.path.insert(0, os.path.join(data_dir, 'joinmarket'))\n\nimport time\nfrom optparse import OptionParser\nfrom pprint import pprint\n\nfrom joinmarket import jm_single, Taker, load_program_config, \\\n IRCMessageChannel\nfrom joinmarket import validate_address\nfrom joinmarket import random_nick\nfrom joinmarket import get_log, rand_norm_array, rand_pow_array, \\\n rand_exp_array, choose_orders, weighted_order_choose, choose_sweep_orders, \\\n debug_dump_object\nfrom joinmarket import Wallet\nfrom joinmarket.wallet import estimate_tx_fee\n\nlog = get_log()\n\n\ndef lower_bounded_int(thelist, lowerbound):\n return [int(l) if int(l) >= lowerbound else lowerbound for l in thelist]\n\n\ndef generate_tumbler_tx(destaddrs, options):\n # sends the coins up through a few mixing depths\n # send to the destination addresses from different mixing depths\n\n # simple algo, move coins completely from one mixing depth to the next\n # until you get to the end, then send to destaddrs\n\n # txcounts for going completely from one mixdepth to the next\n # follows a normal distribution\n txcounts = rand_norm_array(options.txcountparams[0],\n options.txcountparams[1], options.mixdepthcount)\n txcounts = lower_bounded_int(txcounts, options.mintxcount)\n tx_list = []\n for m, txcount in enumerate(txcounts):\n if options.mixdepthcount - options.addrcount <= m and m < \\\n options.mixdepthcount - 1:\n #these mixdepths send to a destination address, so their\n # amount_fraction cant be 1.0, some coins must be left over\n if txcount == 1:\n txcount = 2\n # assume that the sizes of outputs will follow a power law\n amount_fractions = rand_pow_array(options.amountpower, txcount)\n amount_fractions = [1.0 - x for x in amount_fractions]\n amount_fractions = [x / sum(amount_fractions) for x in amount_fractions]\n # transaction times are uncorrelated\n # time between events in a poisson process followed exp\n waits = rand_exp_array(options.timelambda, txcount)\n # number of makers to use follows a normal distribution\n makercounts = rand_norm_array(options.makercountrange[0],\n options.makercountrange[1], txcount)\n makercounts = lower_bounded_int(makercounts, options.minmakercount)\n if m == options.mixdepthcount - options.addrcount and options.donateamount:\n tx_list.append({'amount_fraction': 0,\n 'wait': round(waits[0], 2),\n 'srcmixdepth': m + options.mixdepthsrc,\n 'makercount': makercounts[0],\n 'destination': 'internal'})\n for amount_fraction, wait, makercount in zip(amount_fractions, waits,\n makercounts):\n tx = {'amount_fraction': amount_fraction,\n 'wait': round(wait, 2),\n 'srcmixdepth': m + options.mixdepthsrc,\n 'makercount': makercount,\n 'destination': 'internal'}\n tx_list.append(tx)\n\n addrask = options.addrcount - len(destaddrs)\n external_dest_addrs = ['addrask'] * addrask + destaddrs\n for mix_offset in range(options.addrcount):\n srcmix = options.mixdepthsrc + options.mixdepthcount - mix_offset - 1\n for tx in reversed(tx_list):\n if tx['srcmixdepth'] == srcmix:\n tx['destination'] = external_dest_addrs[mix_offset]\n break\n if mix_offset == 0:\n # setting last mixdepth to send all to dest\n tx_list_remove = []\n for tx in tx_list:\n if tx['srcmixdepth'] == srcmix:\n if tx['destination'] == 'internal':\n tx_list_remove.append(tx)\n else:\n tx['amount_fraction'] = 1.0\n [tx_list.remove(t) for t in tx_list_remove]\n return tx_list\n\n\n# thread which does the buy-side algorithm\n# chooses which coinjoins to initiate and when\nclass TumblerThread(threading.Thread):\n def __init__(self, taker):\n threading.Thread.__init__(self, name='TumblerThread')\n self.daemon = True\n self.taker = taker\n self.ignored_makers = []\n self.sweep = False\n self.broadcast_attempts = 0\n self.create_tx_attempts = 0\n\n def unconfirm_callback(self, txd, txid):\n log.debug('that was %d tx out of %d, waiting for confirmation' %\n (self.current_tx + 1, len(self.taker.tx_list)))\n\n def confirm_callback(self, txd, txid, confirmations):\n self.taker.wallet.remove_old_utxos(txd)\n self.taker.wallet.add_new_utxos(txd, txid)\n with self.lockcond:\n self.lockcond.notify()\n\n def timeout_callback(self, confirmed):\n if not confirmed:\n #try rebroadcasting a few times, then create again\n if self.broadcast_attempts == 0:\n log.debug('timed out for unconfirmed tx, recreating')\n self.create_tx()\n #need a countdown here and other places, maybe inside\n #create_tx in case theres some long-running problem\n return\n self.broadcast_attempts -= 1\n log.debug('timed out for unconfirmed tx, rebroadcasting')\n pushed = self.pushtx()\n if not pushed:\n log.debug(\"Failed to push transaction, recreating\")\n self.create_tx()\n else:\n log.debug('timed out waiting for confirmation')\n\n def pushtx(self):\n push_attempts = 3\n while True:\n ret = self.taker.cjtx.push()\n if ret:\n break\n if push_attempts == 0:\n break\n push_attempts -= 1\n time.sleep(10)\n if ret:\n jm_single().bc_interface.add_tx_notify(\n self.taker.cjtx.latest_tx, self.unconfirm_callback,\n self.confirm_callback, self.taker.cjtx.my_cj_addr,\n self.timeout_callback)\n return ret\n\n def finishcallback(self, coinjointx):\n if coinjointx.all_responded:\n self.broadcast_attempts = self.taker.options.maxbroadcasts\n coinjointx.self_sign()\n pushed = self.pushtx()\n if not pushed:\n log.debug(\"Failed to push transaction, recreating\")\n self.create_tx()\n else:\n self.ignored_makers += coinjointx.nonrespondants\n log.debug('recreating the tx, ignored_makers=' + str(\n self.ignored_makers))\n self.create_tx_attempts += 1 #nonrespondants dont count for timeout\n self.create_tx()\n\n def tumbler_choose_orders(self,\n cj_amount,\n makercount,\n nonrespondants=None,\n active_nicks=None):\n if nonrespondants is None:\n nonrespondants = []\n if active_nicks is None:\n active_nicks = []\n self.ignored_makers += nonrespondants\n while True:\n orders, total_cj_fee = choose_orders(\n self.taker.db, cj_amount, makercount, weighted_order_choose,\n self.ignored_makers + active_nicks)\n abs_cj_fee = 1.0 * total_cj_fee / makercount\n rel_cj_fee = abs_cj_fee / cj_amount\n log.debug('rel/abs average fee = ' + str(rel_cj_fee) + ' / ' + str(\n abs_cj_fee))\n\n if rel_cj_fee > self.taker.options.maxcjfee[\n 0] and abs_cj_fee > self.taker.options.maxcjfee[1]:\n log.debug('cj fee higher than maxcjfee, waiting ' + str(\n self.taker.options.liquiditywait) + ' seconds')\n time.sleep(self.taker.options.liquiditywait)\n continue\n if orders is None:\n log.debug('waiting for liquidity ' + str(\n self.taker.options.liquiditywait) +\n 'secs, hopefully more orders should come in')\n time.sleep(self.taker.options.liquiditywait)\n continue\n break\n log.debug('chosen orders to fill ' + str(orders) + ' totalcjfee=' + str(\n total_cj_fee))\n return orders, total_cj_fee\n\n def create_tx(self):\n if self.create_tx_attempts == 0:\n log.debug('reached limit of number of attempts to create tx, quitting')\n self.taker.msgchan.shutdown()\n return\n jm_single().bc_interface.sync_unspent(self.taker.wallet)\n self.create_tx_attempts -= 1\n orders = None\n cj_amount = 0\n change_addr = None\n choose_orders_recover = None\n if self.sweep:\n log.debug('sweeping')\n utxos = self.taker.wallet.get_utxos_by_mixdepth()[self.tx[\n 'srcmixdepth']]\n #do our best to estimate the fee based on the number of\n #our own utxos; this estimate may be significantly higher\n #than the default set in option.txfee * makercount, where\n #we have a large number of utxos to spend. If it is smaller,\n #we'll be conservative and retain the original estimate.\n est_ins = len(utxos)+3*self.tx['makercount']\n log.debug(\"Estimated ins: \"+str(est_ins))\n est_outs = 2*self.tx['makercount'] + 1\n log.debug(\"Estimated outs: \"+str(est_outs))\n estimated_fee = estimate_tx_fee(est_ins, est_outs)\n log.debug(\"We have a fee estimate: \"+str(estimated_fee))\n log.debug(\"And a requested fee of: \"+str(\n self.taker.options.txfee * self.tx['makercount']))\n fee_for_tx = max([estimated_fee,\n self.tx['makercount'] * self.taker.options.txfee])\n fee_for_tx = int(fee_for_tx / self.tx['makercount'])\n total_value = sum([addrval['value'] for addrval in utxos.values()])\n while True:\n orders, cj_amount, total_cj_fee = choose_sweep_orders(\n self.taker.db, total_value, fee_for_tx,\n self.tx['makercount'], weighted_order_choose,\n self.ignored_makers)\n if orders is None:\n log.debug('waiting for liquidity ' + str(\n self.taker.options.liquiditywait) +\n 'secs, hopefully more orders should come in')\n time.sleep(self.taker.options.liquiditywait)\n continue\n abs_cj_fee = 1.0 * total_cj_fee / self.tx['makercount']\n rel_cj_fee = abs_cj_fee / cj_amount\n log.debug(\n 'rel/abs average fee = ' + str(rel_cj_fee) + ' / ' + str(\n abs_cj_fee))\n if rel_cj_fee > self.taker.options.maxcjfee[0] \\\n and abs_cj_fee > self.taker.options.maxcjfee[1]:\n log.debug('cj fee higher than maxcjfee, waiting ' + str(\n self.taker.options.liquiditywait) + ' seconds')\n time.sleep(self.taker.options.liquiditywait)\n continue\n break\n else:\n if self.tx['amount_fraction'] == 0:\n cj_amount = int(self.balance * self.taker.options.donateamount /\n 100.0)\n self.destaddr = None\n else:\n cj_amount = int(self.tx['amount_fraction'] * self.balance)\n if cj_amount < self.taker.options.mincjamount:\n log.debug('cj amount too low, bringing up')\n cj_amount = self.taker.options.mincjamount\n change_addr = self.taker.wallet.get_internal_addr(\n self.tx['srcmixdepth'])\n log.debug('coinjoining ' + str(cj_amount) + ' satoshi')\n orders, total_cj_fee = self.tumbler_choose_orders(\n cj_amount, self.tx['makercount'])\n total_amount = cj_amount + total_cj_fee + \\\n self.taker.options.txfee*self.tx['makercount']\n log.debug('total estimated amount spent = ' + str(total_amount))\n #adjust the required amount upwards to anticipate a tripling of\n #transaction fee after re-estimation; this is sufficiently conservative\n #to make failures unlikely while keeping the occurence of failure to\n #find sufficient utxos extremely rare. Indeed, a tripling of 'normal'\n #txfee indicates undesirable behaviour on maker side anyway.\n try:\n utxos = self.taker.wallet.select_utxos(self.tx['srcmixdepth'],\n total_amount+2*self.taker.options.txfee*self.tx['makercount'])\n except Exception as e:\n #we cannot afford to just throw not enough funds; better to\n #try with a smaller request; it could still fail within\n #CoinJoinTX.recv_txio, but make every effort to avoid stopping.\n if str(e) == \"Not enough funds\":\n log.debug(\"Failed to select total amount + twice txfee from\" +\n \"wallet; trying to select just total amount.\")\n utxos = self.taker.wallet.select_utxos(self.tx['srcmixdepth'],\n total_amount)\n else:\n raise\n fee_for_tx = self.taker.options.txfee\n choose_orders_recover = self.tumbler_choose_orders\n\n self.taker.start_cj(self.taker.wallet, cj_amount, orders, utxos,\n self.destaddr, change_addr,\n fee_for_tx*self.tx['makercount'],\n self.finishcallback, choose_orders_recover)\n\n def init_tx(self, tx, balance, sweep):\n destaddr = None\n if tx['destination'] == 'internal':\n destaddr = self.taker.wallet.get_internal_addr(tx['srcmixdepth'] + 1)\n elif tx['destination'] == 'addrask':\n jm_single().debug_silence[0] = True\n while True:\n destaddr = raw_input('insert new address: ')\n addr_valid, errormsg = validate_address(destaddr)\n if addr_valid:\n break\n print(\n 'Address ' + destaddr + ' invalid. ' + errormsg + ' try again')\n jm_single().debug_silence[0] = False\n else:\n destaddr = tx['destination']\n self.taker.wallet.update_cache_index()\n self.sweep = sweep\n self.balance = balance\n self.tx = tx\n self.destaddr = destaddr\n self.create_tx_attempts = self.taker.options.maxcreatetx\n self.create_tx()\n with self.lockcond:\n self.lockcond.wait()\n log.debug('tx confirmed, waiting for ' + str(tx['wait']) + ' minutes')\n time.sleep(tx['wait'] * 60)\n log.debug('woken')\n\n def run(self):\n log.debug('waiting for all orders to certainly arrive')\n time.sleep(self.taker.options.waittime)\n\n sqlorders = self.taker.db.execute(\n 'SELECT cjfee, ordertype FROM orderbook;').fetchall()\n orders = [o['cjfee'] for o in sqlorders if o['ordertype'] == 'relorder']\n orders = sorted(orders)\n if len(orders) == 0:\n log.debug('There are no orders at all in the orderbook! '\n 'Is the bot connecting to the right server?')\n return\n relorder_fee = float(orders[0])\n log.debug('relorder fee = ' + str(relorder_fee))\n maker_count = sum([tx['makercount'] for tx in self.taker.tx_list])\n log.debug('uses ' + str(maker_count) + ' makers, at ' + str(\n relorder_fee * 100) + '% per maker, estimated total cost ' + str(\n round((1 - (1 - relorder_fee) ** maker_count) * 100, 3)) + '%')\n log.debug('starting')\n self.lockcond = threading.Condition()\n\n self.balance_by_mixdepth = {}\n for i, tx in enumerate(self.taker.tx_list):\n if tx['srcmixdepth'] not in self.balance_by_mixdepth:\n self.balance_by_mixdepth[tx[\n 'srcmixdepth']] = self.taker.wallet.get_balance_by_mixdepth(\n )[tx['srcmixdepth']]\n sweep = True\n for later_tx in self.taker.tx_list[i + 1:]:\n if later_tx['srcmixdepth'] == tx['srcmixdepth']:\n sweep = False\n self.current_tx = i\n self.init_tx(tx, self.balance_by_mixdepth[tx['srcmixdepth']], sweep)\n\n log.debug('total finished')\n self.taker.msgchan.shutdown()\n\nclass Tumbler(Taker):\n def __init__(self, msgchan, wallet, tx_list, options):\n Taker.__init__(self, msgchan)\n self.wallet = wallet\n self.tx_list = tx_list\n self.options = options\n self.tumbler_thread = None\n\n def on_welcome(self):\n Taker.on_welcome(self)\n if not self.tumbler_thread:\n self.tumbler_thread = TumblerThread(self)\n self.tumbler_thread.start()\n\n\ndef main():\n parser = OptionParser(\n usage='usage: %prog [options] [wallet file] [destaddr(s)...]',\n description=\n 'Sends bitcoins to many different addresses using coinjoin in'\n ' an attempt to break the link between them. Sending to multiple '\n ' addresses is highly recommended for privacy. This tumbler can'\n ' be configured to ask for more address mid-run, giving the user'\n ' a chance to click `Generate New Deposit Address` on whatever service'\n ' they are using.')\n parser.add_option(\n '-m',\n '--mixdepthsource',\n type='int',\n dest='mixdepthsrc',\n help=\n 'Mixing depth to spend from. Useful if a previous tumbler run prematurely ended with '\n +\n 'coins being left in higher mixing levels, this option can be used to resume without needing'\n + ' to send to another address. default=0',\n default=0)\n parser.add_option(\n '-f',\n '--txfee',\n action='store',\n type='int',\n dest='txfee',\n default=5000,\n help='number of satoshis per participant to use as the initial estimate '+\n 'for the total transaction fee, default=5000, note that this is adjusted '+\n 'based on the estimated fee calculated after tx construction, based on '+\n 'policy set in joinmarket.cfg.')\n parser.add_option(\n '-a',\n '--addrcount',\n type='int',\n dest='addrcount',\n default=3,\n help=\n 'How many destination addresses in total should be used. If not enough are given'\n ' as command line arguments, the script will ask for more. This parameter is required'\n ' to stop amount correlation. default=3')\n parser.add_option(\n '-x',\n '--maxcjfee',\n type='float',\n dest='maxcjfee',\n nargs=2,\n default=(0.01, 10000),\n help='maximum coinjoin fee and bitcoin value the tumbler is '\n 'willing to pay to a single market maker. Both values need to be exceeded, so if '\n 'the fee is 30% but only 500satoshi is paid the tx will go ahead. default=0.01, 10000 (1%, 10000satoshi)')\n parser.add_option(\n '-N',\n '--makercountrange',\n type='float',\n nargs=2,\n action='store',\n dest='makercountrange',\n help=\n 'Input the mean and spread of number of makers to use. e.g. 3 1.5 will be a normal distribution '\n 'with mean 3 and standard deveation 1.5 inclusive, default=3 1.5',\n default=(3, 1.5))\n parser.add_option(\n '--minmakercount',\n type='int',\n dest='minmakercount',\n default=2,\n help=\n 'The minimum maker count in a transaction, random values below this are clamped at this number. default=2')\n parser.add_option(\n '-M',\n '--mixdepthcount',\n type='int',\n dest='mixdepthcount',\n help='How many mixing depths to mix through',\n default=4)\n parser.add_option(\n '-c',\n '--txcountparams',\n type='float',\n nargs=2,\n dest='txcountparams',\n default=(4, 1),\n help=\n 'The number of transactions to take coins from one mixing depth to the next, it is'\n ' randomly chosen following a normal distribution. Should be similar to --addrask. '\n 'This option controls the parameters of the normal distribution curve. (mean, standard deviation). default=(4, 1)')\n parser.add_option(\n '--mintxcount',\n type='int',\n dest='mintxcount',\n default=1,\n help='The minimum transaction count per mixing level, default=1')\n parser.add_option(\n '--donateamount',\n type='float',\n dest='donateamount',\n default=0,\n help=\n 'percent of funds to donate to joinmarket development, or zero to opt out (default=0%)')\n parser.add_option(\n '--amountpower',\n type='float',\n dest='amountpower',\n default=100.0,\n help=\n 'The output amounts follow a power law distribution, this is the power, default=100.0')\n parser.add_option(\n '-l',\n '--timelambda',\n type='float',\n dest='timelambda',\n default=30,\n help=\n 'Average the number of minutes to wait between transactions. Randomly chosen '\n ' following an exponential distribution, which describes the time between uncorrelated'\n ' events. default=30')\n parser.add_option(\n '-w',\n '--wait-time',\n action='store',\n type='float',\n dest='waittime',\n help='wait time in seconds to allow orders to arrive, default=20',\n default=20)\n parser.add_option(\n '-s',\n '--mincjamount',\n type='int',\n dest='mincjamount',\n default=100000,\n help='minimum coinjoin amount in transaction in satoshi, default 100k')\n parser.add_option(\n '-q',\n '--liquiditywait',\n type='int',\n dest='liquiditywait',\n default=60,\n help=\n 'amount of seconds to wait after failing to choose suitable orders before trying again, default 60')\n parser.add_option(\n '--maxbroadcasts',\n type='int',\n dest='maxbroadcasts',\n default=4,\n help=\n 'maximum amount of times to broadcast a transaction before giving up and re-creating it, default 4')\n parser.add_option(\n '--maxcreatetx',\n type='int',\n dest='maxcreatetx',\n default=9,\n help=\n 'maximum amount of times to re-create a transaction before giving up, default 9')\n (options, args) = parser.parse_args()\n\n if len(args) < 1:\n parser.error('Needs a wallet file')\n sys.exit(0)\n wallet_file = args[0]\n destaddrs = args[1:]\n print(destaddrs)\n\n load_program_config()\n for addr in destaddrs:\n addr_valid, errormsg = validate_address(addr)\n if not addr_valid:\n print('ERROR: Address ' + addr + ' invalid. ' + errormsg)\n return\n\n if len(destaddrs) > options.addrcount:\n options.addrcount = len(destaddrs)\n if options.addrcount + 1 > options.mixdepthcount:\n print('not enough mixing depths to pay to all destination addresses, '\n 'increasing mixdepthcount')\n options.mixdepthcount = options.addrcount + 1\n if options.donateamount > 10.0:\n # fat finger probably, or misunderstanding\n options.donateamount = 0.9\n\n print(str(options))\n tx_list = generate_tumbler_tx(destaddrs, options)\n if not tx_list:\n return\n\n tx_list2 = copy.deepcopy(tx_list)\n tx_dict = {}\n for tx in tx_list2:\n srcmixdepth = tx['srcmixdepth']\n tx.pop('srcmixdepth')\n if srcmixdepth not in tx_dict:\n tx_dict[srcmixdepth] = []\n tx_dict[srcmixdepth].append(tx)\n dbg_tx_list = []\n for srcmixdepth, txlist in tx_dict.iteritems():\n dbg_tx_list.append({'srcmixdepth': srcmixdepth, 'tx': txlist})\n log.debug('tumbler transaction list')\n pprint(dbg_tx_list)\n\n total_wait = sum([tx['wait'] for tx in tx_list])\n print('creates ' + str(len(tx_list)) + ' transactions in total')\n print('waits in total for ' + str(len(tx_list)) + ' blocks and ' + str(\n total_wait) + ' minutes')\n total_block_and_wait = len(tx_list) * 10 + total_wait\n print('estimated time taken ' + str(total_block_and_wait) + ' minutes or ' +\n str(round(total_block_and_wait / 60.0, 2)) + ' hours')\n if options.addrcount <= 1:\n print('=' * 50)\n print('WARNING: You are only using one destination address')\n print('this is very bad for privacy')\n print('=' * 50)\n\n ret = raw_input('tumble with these tx? (y/n):')\n if ret[0] != 'y':\n return\n\n # NOTE: possibly out of date documentation\n # a couple of modes\n # im-running-from-the-nsa, takes about 80 hours, costs a lot\n # python tumbler.py -a 10 -N 10 5 -c 10 5 -l 50 -M 10 wallet_file 1xxx\n #\n # quick and cheap, takes about 90 minutes\n # python tumbler.py -N 2 1 -c 3 0.001 -l 10 -M 3 -a 1 wallet_file 1xxx\n #\n # default, good enough for most, takes about 5 hours\n # python tumbler.py wallet_file 1xxx\n #\n # for quick testing\n # python tumbler.py -N 2 1 -c 3 0.001 -l 0.1 -M 3 -a 0 wallet_file 1xxx 1yyy\n wallet = Wallet(wallet_file,\n max_mix_depth=options.mixdepthsrc + options.mixdepthcount)\n jm_single().bc_interface.sync_wallet(wallet)\n\n jm_single().nickname = random_nick()\n\n log.debug('starting tumbler')\n irc = IRCMessageChannel(jm_single().nickname)\n tumbler = Tumbler(irc, wallet, tx_list, options)\n try:\n log.debug('connecting to irc')\n irc.run()\n except:\n log.debug('CRASHING, DUMPING EVERYTHING')\n debug_dump_object(wallet, ['addr_cache', 'keys', 'seed'])\n debug_dump_object(tumbler)\n debug_dump_object(tumbler.cjtx)\n import traceback\n log.debug(traceback.format_exc())\n\n\nif __name__ == \"__main__\":\n main()\n print('done')\n","sub_path":"tumbler.py","file_name":"tumbler.py","file_ext":"py","file_size_in_byte":27061,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"153178998","text":"# house price predicting\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nhouseData = pd.read_csv(\"input/kc_house_data.csv\").dropna(how=\"any\")\norgPrice = houseData.price\norgBedrooms = houseData.bedrooms\norgBathRooms = houseData.bathrooms\norgSqftLiving = houseData.sqft_living\norgSqftLot = houseData.sqft_lot\norgFloors = houseData.floors # string\norgWaterFront = houseData.waterfront\norgView = houseData.view\norgCondition = houseData.condition\norgGrade = houseData.grade\norgSqftAbove = houseData.sqft_above\norgSqftBasement = houseData.sqft_basement\norgYrBuilt = houseData.yr_built\norgYrRenovated = houseData.yr_renovated\norgZipcode = houseData.zipcode # string\norgLat = houseData.lat\norgLong = houseData.long\norgSqftLiving15 = houseData.sqft_living15\norgSqftLot5 = houseData.sqft_lot15\n\ntestNum = int(orgPrice.size / 3)\ntrainNum = testNum * 2\nfactor = 0.000001\n\ndef calculateCost(x, w, y):\n return np.sum(np.square(x.dot(w) - y)) / (2 * len(x))\n\ndef handleInputs(inputs, fromIdx, offset):\n l = len(inputs)\n ret = []\n for i in np.arange(l):\n input = inputs[i][fromIdx:fromIdx+offset]\n output = input.values.reshape([input.shape[0], -1]) * factor\n ret.append(output)\n return ret\n\norgInputs = [orgBedrooms, orgBathRooms, orgSqftLiving, orgSqftLot, orgSqftBasement, orgGrade]\ndim = len(orgInputs)\n\ntrainInputs = handleInputs(orgInputs, 0, trainNum)\ntestInputs = handleInputs(orgInputs, trainNum, testNum)\ntrainPrice = orgPrice[:trainNum].values.reshape([trainNum, -1]) * factor\ntestPrice = orgPrice[trainNum:trainNum+testNum].values.reshape([testNum, -1]) * factor\nX = np.hstack([trainInputs[0], trainInputs[1], trainInputs[2], trainInputs[3], trainInputs[4], trainInputs[5], np.ones([trainNum, 1])])\nXTest = np.hstack([testInputs[0], testInputs[1], testInputs[2], testInputs[3], testInputs[4], testInputs[5], np.ones([testNum, 1])])\nY = trainPrice\nw = np.zeros([dim + 1, 1]) # Y = X.dot(w)\nlearnRate = 0.01 / len(trainPrice)\nitCount = 2000\nthreshold = 0.001\ncost = 0\nrealItCount = itCount;\n# iterate\nfor i in np.arange(itCount):\n g = X.T.dot(X.dot(w) - Y)\n w = w - learnRate * g\n cost = calculateCost(X, w, Y)\n #print(\"iterate:\", i, cost)\n if (cost < threshold):\n realItCount = i\n break\n\n#print(\"X:\", X)\n\nprint(\"cost/iterate/w:\", cost, realItCount, w)\n\n# Verify with test set: calculate the cost of test set\ntestCost = calculateCost(XTest, w, testPrice)\nprint(\"test cost: \", testCost)\n","sub_path":"ml/lession1/houseprice.py","file_name":"houseprice.py","file_ext":"py","file_size_in_byte":2469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"285433277","text":"from bottle import response,request as req\nimport utils\nimport models\nimport re\nimport utils.rule\n\ndef rest():\n\tres = logic(req.forms.get(\"name\"),req.forms.get(\"text\"))\n\tresponse.status = res[\"code\"]\n\treturn res[\"res\"]\n\ndef logic(name,text):\n\tme = utils.getLoginUser()\n\tif(not me):\n\t\tstatus = 403\n\t\tres = {\"error\":\"not-login\"}\n\telif (len(name)>=30):\n\t\tstatus = 400\n\t\tres = {\"error\":\"name-is-up-to-30chars\"}\n\telif (len(text)>=30000):\n\t\tstatus = 400\n\t\tres = {\"error\":\"text-is-up-to-30000chars\"}\n\telse:\n\t\tnote = models.Note()\n\t\tnote.name = name\n\t\tnote.text = text\n\t\tnote.user = me\n\t\tnote.save()\n\t\tres = note.to_rest()\n\t\tstatus = 200\n\treturn {\"code\":status,\"res\":res}\n\n","sub_path":"src/api/note/create.py","file_name":"create.py","file_ext":"py","file_size_in_byte":665,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"652869071","text":"import os\nimport torchvision.datasets as datasets\nfrom torch.autograd import Variable\nimport cv2\n\n\nclass Preprocessor(object):\n def __init__(self):\n self.dset_dir = '/media/x/datasets/ILSVRC2015/Data/VID'\n self.imsz = 64\n\n dataset_train = datasets.ImageFolder(\n os.path.join(self.dset_dir, 'train')\n )\n self.imgs = dataset_train.imgs\n dataset_val = datasets.ImageFolder(\n os.path.join(self.dset_dir, 'val')\n )\n self.imgs.append(dataset_val.imgs)\n dataset_test = datasets.ImageFolder(\n os.path.join(self.dset_dir, 'test')\n )\n self.imgs.append(dataset_test.imgs)\n\n def run(self):\n seg = 10000\n seg_indx = 0\n\n for i in range(len(self.imgs)):\n img = cv2.imread(self.imgs[i][0])\n img = cv2.resize(img,(self.imsz, self.imsz))\n if i % seg == 0:\n if ~os.path.exists('/media/x/datasets/ILSVRC2015_64/{}'.format(seg_indx)):\n os.mkdir('/media/x/datasets/ILSVRC2015_64/{}'.format(seg_indx))\n seg_indx = seg_indx + 1\n\n cv2.imwrite('/media/x/datasets/ILSVRC2015_64/{0}/{1}.JPEG'.format(seg_indx-1, i), img)\n print('{0}/{1}\\n'.format(i, len(self.imgs)))\n\nif __name__ == '__main__':\n preprocessor = Preprocessor()\n\n preprocessor.run()\n","sub_path":"data_preprocess.py","file_name":"data_preprocess.py","file_ext":"py","file_size_in_byte":1366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"68657017","text":"import csv\nimport codecs\nimport Levenshtein, distance\n\n\n\"\"\"\nThis is to evaluate the performance of active learning algorithm\nby checking if the result should be the same user\n\n# when a user write down these information, it is possible that the contexts\n# will be different in some degree, so just regular expression may not be sufficient\n# String metric analysis may be used\n# maybe two methods:\n# 1. combine different fields;\n# 2. analyze field by field, different matching result will get different marks or weights\n\n\n# calculate the ratio of records pairs correctly classified out of all\n# records pairs which was classified as \"similiar\"\n\n\"\"\"\n\n\n## files\n\n# the input_file is the output_file from dedupe.py, changed the file name\ninput_file = 'result1_output.csv'\nsettings_file = 'csvFormat_learned_settings'\ntraining_file = 'csvFormat_training.json'\nsame_file = 'same_records_from_learning.csv'\ntest_file = 'test.csv'\n\n\n# extract all the records which are labeled \"same user\" and save to another csv file\ndef extract_same_records(fileName):\n with open(same_file,'w', newline = '\\n') as output:\n fieldnames = ['Cluster ID','confidence_score','unique_id','first_name','last_name','address_line','suburb','city','postcode','country','email','phone_main','phone_mobile','phone_fax']\n writer = csv.DictWriter(output, fieldnames=fieldnames, extrasaction='ignore')\n writer.writeheader()\n data = {}\n\n with open(fileName,'r') as file:\n # if add \"delimeter\" within the DictReader, row['first_name'] can not be read\n reader = csv.DictReader(file)\n\n for row in reader:\n score = row['confidence_score']\n # all therecords' confidence_score is a string, no matter has contents or not\n # if score exists\n if score:\n id = row['unique_id']\n data[id] = dict(row)\n writer.writerow(data[id])\n\n gather_same_records(same_file)\n\n\n# choose fields which can be compared\n# first_name, last_name, address_line, city, postcode, country, email, phone_main, phone_mobile\ndef gather_same_records(fileName):\n\n all_data = {}\n exist_id = []\n num_of_valid_predict = 0\n correct_rate = 0\n num_of_pairs = 0\n # read the same_record to a dictionary\n with open(fileName,'r') as file:\n print(\"same\")\n reader = csv.DictReader(file)\n\n # there are two situations: two same records and multiple records\n # create a single dictionry for every similar records block\n id = 0\n for row in reader:\n all_data[id] = dict(row)\n id += 1\n total_count = len(all_data)\n print(\"len(all_data)\", total_count)\n\n # store all the same records to avoid compare twice\n # store as a dictionary so that easy to search\n same_records_id = {}\n\n # find all the same blocks among all the records, and compare them\n for r1 in all_data:\n if r1 not in same_records_id.keys():\n same_records_id[r1] = r1\n index = 0\n same_record = {}\n same_record[index] = all_data[r1]\n for r2 in all_data:\n if r1 != r2:\n if all_data[r1]['Cluster ID'] == all_data[r2]['Cluster ID']:\n index += 1\n same_record[index] = all_data[r2]\n same_records_id[r2] = r2\n if len(same_record) == 2:\n num_of_pairs += int(1)\n else:\n a = len(same_record)\n num_of_pairs += int((a * (a-1))/2)\n # till now, similar record blocks has been found, and every block is stored in a dictionary, can be compared now\n # return the num of records which are classified correctly\n # (use string metrics and the similarity rate is more than 0.5)\n num_of_valid_predict += compare_same_records(same_record)\n print(\"num_of_valid_predict1\", num_of_valid_predict)\n\n # calculate the ratio of records pairs correctly classified out of all\n # records pairs which was classified as \"similiar\"\n print(\"len(all_data)\", total_count)\n print(\"total compare pairs\", num_of_pairs)\n correct_rate = num_of_valid_predict/num_of_pairs\n print(\"correct_pair_rate\", correct_rate)\n\n# used when similiar record blocks are identified, the input is a dictionary of\n# one block of same records\ndef compare_same_records(all_same_records):\n # setting up all the fields into a block\n fields = ['first_name','last_name','address_line','suburb','city','postcode','country','email','phone_main','phone_mobile','phone_fax']\n\n # setting weightes for different fields manually\n # how to revise the weights so that correct preferences during active learning learning precess?\n field_weights = {\n fields[0]:0.7,\n fields[1]:0.7,\n fields[2]:0.7,\n fields[3]:0.6,\n fields[4]:0.6,\n fields[5]:0.6,\n fields[6]:0.6,\n fields[7]:0.9,\n fields[8]:0.9,\n fields[9]:0.9,\n fields[10]:0.8\n }\n\n total_score = 8\n\n # if there are only two same records in the block:\n if len(all_same_records) == 2:\n # return 0\n return compare_two_records(all_same_records, fields, field_weights)\n # if there are more than 2 same records in the block:\n else:\n return compare_more_than_two_records(all_same_records,fields, field_weights)\n\n# calculate the similarity between the two contents using the single feature\n# comparison method, and times the weights to get the final similarity score\n# of the two records\ndef compare_two_records(all_same_records,fields, field_weights):\n\n print(\"start comparing two.....\")\n print(len(all_same_records))\n for re in all_same_records:\n print(all_same_records[re])\n\n lev1 = 0\n lev2 = 0\n sor1 = 0\n sor2 = 0\n jac1 = 0\n jac2 = 0\n\n for i in range(11):\n field = []\n for record in all_same_records:\n name = all_same_records[record][fields[i]]\n field.append(all_same_records[record][fields[i]])\n\n # apply the String_similarity_single_feature to the single field\n # let the similarity times the weight of different fields\n # those with empty and \"null\" seen as 0 similarity\n if field[0] != \"\" and field[0] != \"null\" and field[1] != \"\" and field[1] != \"null\":\n lev1 += (Levenshtein.ratio(field[0],field[1])) * field_weights[fields[i]]\n sor1 += (1 - distance.sorensen(field[0],field[1])) * field_weights[fields[i]]\n jac1 += (1 - distance.jaccard(field[0],field[1])) * field_weights[fields[i]]\n\n# use the sum be divided by the total sum to get the final result\n# see sum/total as the possibility of correct\n lev_ave = lev1/8\n sor_ave = sor1/8\n jac_ave = jac1/8\n final_score = (lev_ave + sor_ave + jac_ave)/3\n print(\"final_score\", final_score)\n\n # can try other threshold\n # use three methods and average the three results\n # if lev_ave > 0.5 and sor_ave > 0.5 and jac_ave > 0.5:\n if final_score > 0.5:\n return 1\n else:\n return 0\n\ndef compare_more_than_two_records(all_same_records,fields, field_weights):\n\n print(\"start comparing more than 2 records....\")\n total = len(all_same_records)\n print(total)\n total_combinations = (total * (total-1))/2\n\n num_of_valid_predict = 0\n\n # record the number of blocks correctly classified\n num_of_correct_sub_blocks = 0\n\n result = []\n\n for i in range(total):\n for j in range(total):\n # compare ith and jth records in the block every time\n if i < j:\n lev1 = 0\n lev2 = 0\n sor1 = 0\n sor2 = 0\n jac1 = 0\n jac2 = 0\n for s in range(11):\n field = []\n field.append(all_same_records[i][fields[s]])\n field.append(all_same_records[j][fields[s]])\n # apply the String_similarity_single_feature to the single field\n # let the similarity times the weight of different fields\n # those with empty and \"null\" seen as 0 similarity\n if field[0] != \"\" and field[0] != \"null\" and field[1] != \"\" and field[1] != \"null\":\n lev1 += (Levenshtein.ratio(field[0],field[1])) * field_weights[fields[i]]\n sor1 += (1 - distance.sorensen(field[0],field[1])) * field_weights[fields[i]]\n jac1 += (1 - distance.jaccard(field[0],field[1])) * field_weights[fields[i]]\n\n # use the sum be divided by the total sum to get the final result\n # see sum/total as the possibility of correct\n lev_ave = lev1/8\n sor_ave = sor1/8\n jac_ave = jac1/8\n final_score = (lev_ave + sor_ave + jac_ave)/3\n\n result.append(final_score)\n\n # if lev_ave > 0.5 or sor_ave > 0.5 or jac_ave > 0.5:\n # combine the three results coming from three methods\n if final_score > 0.5:\n num_of_correct_sub_blocks += 1\n print(\"more than 0.5 small block result...\")\n print(all_same_records[i])\n print(all_same_records[j])\n print(\"final_score\", final_score)\n else:\n print(\"less than 0.5 small block result...\")\n print(all_same_records[i])\n print(all_same_records[j])\n print(\"final_score\", final_score)\n print(\"num_of_correct_classified\", num_of_correct_sub_blocks)\n for a in result:\n print(\"final_score: \", a)\n\n return num_of_correct_sub_blocks\n\n\nextract_same_records(input_file)\n","sub_path":"evaluate.py","file_name":"evaluate.py","file_ext":"py","file_size_in_byte":9974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"257002303","text":"import os\nimport time\nimport argparse\nimport math\nfrom numpy import finfo\n\nimport torch\nimport torch.distributed as dist\nfrom torch.utils.data.distributed import DistributedSampler\nfrom torch.utils.data import DataLoader\n\nfrom model import Tacotron2\nfrom data_utils import TextMelLoader, TextMelCollate\nfrom loss_function import Tacotron2Loss\n#from hparams import create_hparams\nfrom tqdm import tqdm\nimport yaml\nimport logging\n\n#Req. 3-3 학습 로그 기록\n####TODO#### logging을 생성하고 학습 로그를 기록한다.\n\n\n####TODO####\n\n\n# dataloader\ndef prepare_dataloaders(hparams):\n # Get data, data loaders and collate function ready\n trainset = TextMelLoader(hparams['training_files'], hparams)\n valset = TextMelLoader(hparams['validation_files'], hparams)\n collate_fn = TextMelCollate(hparams['n_frames_per_step'])\n\n train_loader = DataLoader(trainset, num_workers=1, shuffle=True,\n sampler=None,\n batch_size=hparams['batch_size'], pin_memory=False,\n drop_last=True, collate_fn=collate_fn)\n return train_loader, valset, collate_fn\n\n# load model\ndef load_model(hparams):\n model = Tacotron2(hparams).cuda() \n return model\n\n# load checkpoint\ndef load_checkpoint(checkpoint_path, model, optimizer):\n print(\"load check\")\n assert os.path.isfile(checkpoint_path)\n print(\"Loading checkpoint '{}'\".format(checkpoint_path))\n \n checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')\n model.load_state_dict(checkpoint_dict['state_dict'])\n optimizer.load_state_dict(checkpoint_dict['optimizer'])\n learning_rate = checkpoint_dict['learning_rate']\n iteration = checkpoint_dict['iteration']\n print(\"Loaded checkpoint '{}' from iteration {}\" .format(\n checkpoint_path, iteration))\n return model, optimizer, learning_rate, iteration\n\n\n\n####TODO#### 3. pre-trained model을 load하여 이어서 학습 진행을 위한 warm_start_model() 함수 구현\n# load_state_dict()로 pre-trained weight을 model에 load\n# optimizer, scheduler등을 load\ndef warm_start_model(checkpoint_path, model, ignore_layers):\n assert os.path.isfile(checkpoint_path)\n \n print(\"Warm starting model from checkpoint '{}'\".format(checkpoint_path))\n checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')\n model_dict = checkpoint_dict['state_dict']\n if len(ignore_layers) > 0:\n model_dict = {k: v for k, v in model_dict.items()\n if k not in ignore_layers}\n dummy_dict = model.state_dict()\n dummy_dict.update(model_dict)\n model_dict = dummy_dict\n model.load_state_dict(model_dict)\n return model\n####TODO####\n\n#Req. 3-2 Save_checkpoint() 함수 구현\n####TODO#### 7. validation 이후 model checkpoint 저장\n# checkpoint path에 iteration,그리고 model, optimizer, scheduler의 statedict를 저장\n# torch.save() 함수 이용하여 딕셔너리 형태로 저장\ndef save_checkpoint(model, optimizer, scheduler, learning_rate, iteration, filepath): \n pass\n\n####TODO####\n\n#Req. 3-1 Validate() 함수 구현\n####TODO#### 6. 주기적으로 validation dataset으로 모델 성능 확인 후 log 기록\ndef validate(model, criterion, valset, iteration, batch_size,collate_fn, epoch, dur):\n # model을 evalutation mode로 전환\n \n # with torch.no_grad로 전체 연산을 묶음\n with torch.no_grad():\n # validation dataset 준비\n val_loader = DataLoader(valset, sampler=None, num_workers=1,\n shuffle=False, batch_size=batch_size,\n pin_memory=False, collate_fn=collate_fn)\n\n # validation loss 계산 (train() 코드와 동일하게 작성하지만 backpropagation을 하면 안된다)\n \n #Req. 3-3 학습 로그 기록\n # validation 결과 출력 및 log 기록 \n \n \n # model을 training mode로 전환\n \n####TODO####\n \n#Req. 2-1 모델 트레이닝 \n####TODO####\ndef train(output_directory, checkpoint_path, warm_start, hparams):\n\n # pytorch random seed 고정\n torch.manual_seed(hparams['seed'])\n torch.cuda.manual_seed(hparams['seed'])\n\n ####TODO#### 1. hyperparameter를 참고하여 model, optimizer, scheduler, loss function 정의\n # optimizer : hparams에서 learning rate, weight decay 참고\n # scheduler : hparams에서 scheduler_step, gamma 참고\n model = load_model(hparams)\n \n optimizer = ''\n scheduler = ''\n \n criterion = Tacotron2Loss() # define loss function \n ####TODO####\n \n \n ####TODO#### 2. prepare_dataloaders 함수를 이용하여 dataset 준비\n # input : hparams\n # output : train_loader, valset, collate_fn 반환\n \n ####TODO####\n \n \n ####TODO#### 3. scratch부터 학습할 것인지, pre-trained model을 load하여 warm start를 할 것인지 결정\n iteration = 0\n epoch_offset = 0\n \n if checkpoint_path is not None:\n # train from pretrained model\n if warm_start:\n # warm_start함수로 이동\n pass\n\n #train from scratch\n ##제공##\n else:\n model, optimizer, _learning_rate, iteration = load_checkpoint(\n checkpoint_path, model, optimizer)\n if hparams['use_saved_learning_rate']:\n learning_rate = _learning_rate\n iteration += 1 # next iteration is iteration + 1\n epoch_offset = max(0, int(iteration / len(train_loader)))\n ##제공## \n ####TODO####\n \n is_overflow = False\n ####TODO#### 4. model을 training mode로 전환 후 main loop 작성\n # hparams에서 epoch을 참고하여 mainloop 구성 \n \n # training time 측정, 최초 시작 시간\n init_start = time.perf_counter()\n \n #----------MainLoop----------\n for epoch in range(epoch_offset, hparams['epochs']): \n for i, batch in enumerate(train_loader): \n \n # iteration start time\n start = time.perf_counter()\n \n # set gradients to zero\n \n\n # loss 계산 후 backpropagation\n \n \n ####TODO####\n \n grad_norm = torch.nn.utils.clip_grad_norm_(model.parameters(), hparams['grad_clip_thresh'])\n \n optimizer.step()\n scheduler.step()\n \n #iteration 별 loss, grad_norm, duration 결과 출력\n if not is_overflow:\n duration = time.perf_counter() - start\n print(\"Train loss {} {:.6f} Grad Norm {:.6f} {:.2f}s/it\".format(\n iteration, reduced_loss, grad_norm, duration),end='\\r') \n \n # validation을 한 뒤 checkpoint model 저장\n if not is_overflow and (iteration % hparams['iters_per_checkpoint'] == 0):\n dur = time.perf_counter() - init_start\n \n # validation 진행\n validate(model, criterion, valset, iteration,hparams['batch_size'], collate_fn, epoch, dur)\n \n # checkpoint model 저장\n checkpoint_path = os.path.join(output_directory, \"checkpoint_{}\".format(iteration))\n save_checkpoint(model, optimizer, scheduler, learning_rate, iteration, checkpoint_path)\n \n iteration += 1\n \n \n \n##제공## \nif __name__ == '__main__':\n parser = argparse.ArgumentParser()\n parser.add_argument('-o', '--output_directory', type=str,\n help='directory to save checkpoints',default = \"/home/multicam/checkpoints/tts_checkpoints\")\n parser.add_argument('-c', '--checkpoint_path', type=str, default=None,\n required=False, help='checkpoint path')\n parser.add_argument('--warm_start', action='store_true',\n help='load model weights only, ignore specified layers')\n parser.add_argument('--hparams', type=str,\n required=False, help='comma separated name=value pairs')\n \n \n #parser 생성 및 hyperparameter 생성\n args = parser.parse_args()\n print(args.hparams)\n \n with open('/home/multicam/samsung_multicam/speak_image/TTS/config.yaml') as f:\n hparams = yaml.load(f)\n \n #pytorch random seed 고정\n torch.backends.cudnn.enabled = hparams['cudnn_enabled']\n torch.backends.cudnn.benchmark = hparams['cudnn_benchmark']\n\n print(\"cuDNN Enabled:\", hparams['cudnn_enabled'])\n print(\"cuDNN Benchmark:\", hparams['cudnn_benchmark'])\n \n \n train(args.output_directory, args.checkpoint_path, args.warm_start, hparams)\n##제공##\n","sub_path":"Sub1/Hyeeun_Lee/skeleton-project/speak_image/TTS/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":8740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"20625967","text":"# -*- coding: UTF-8 -*-\n\nfrom PyQt5.QtWidgets import QApplication\nimport sys\nimport time\nimport os\n\n\ndef screenshots(savepath):\n app = QApplication(sys.argv)\n screen = QApplication.primaryScreen()\n img = screen.grabWindow(QApplication.desktop().winId())\n img.save(savepath)\n\n\ndef take():\n savename = time.strftime(\"%Y%m%d%H%M%S\", time.localtime()) + '.png'\n saveparent = 'd:/screenshot/'\n if not os.path.isdir(saveparent):\n os.makedirs(saveparent)\n savepath = saveparent + savename\n screenshots(savepath)","sub_path":"utils/screenshot.py","file_name":"screenshot.py","file_ext":"py","file_size_in_byte":538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"57951961","text":"\"\"\"\nContour area detection and dry area percentage calculation for filter 04 small platform from GoPro camera.\n\"\"\"\n\nimport os\nimport cv2\nimport numpy as np\nimport pandas as pd\n\nfrom datetime import datetime\nfrom datetime import timedelta\n\nfrom matplotlib import pyplot as plt\nimport matplotlib.dates as mdates\n\n\ndef nothing():\n \"\"\" Empty function to supply to opencv trackbar object\"\"\"\n\n pass\n\n\nip = \"192.168.1.235\"\n# cap = cv2.VideoCapture('rtsp://{0}/11'.format(ip)) # ip camera\n# cap = cv2.VideoCapture(0) # webcam\ninput_path = '/home/dh084/Videos/'\nfilename = 'filter04_small_platform03.m4v'\ncap = cv2.VideoCapture(os.path.join(input_path, filename)) # video file\n\ncv2.namedWindow('Video', cv2.WINDOW_KEEPRATIO)\ncv2.namedWindow('Mask', cv2.WINDOW_KEEPRATIO)\n\n# Set trackbar mode\ntrackbar_mode = True\n\n# Set default target threshold value\ndefault_thresh = 85\ndefault_high_thresh = 150\n\n# Define lists of data\ndry_data = []\ntime_data = []\n\n# Define current frame time\ncurrent_time = datetime.now()\n\nif trackbar_mode is True:\n cv2.createTrackbar('Threshold', 'Video', default_thresh, 255, nothing)\n cv2.createTrackbar('HighThreshold', 'Video', default_high_thresh, 255, nothing)\n\nfourcc = cv2.VideoWriter_fourcc(*'DIVX')\nvid_size = (1080, 1920)\nrecording_fps = 30.0\nout = cv2.VideoWriter('output/gopro_HIGHTHRESH_filter04_small_platform_output.avi', fourcc, recording_fps, vid_size)\n# out = cv2.VideoWriter('output/webcam_output.avi', fourcc, recording_fps, vid_size)\n\nimage_file = 'output/go_pro_HIGHTHRESH_dry_area_graph.png'\n\n\nwhile cap.isOpened():\n start_time = datetime.now()\n\n ret, img = cap.read()\n\n if ret:\n fps = cap.get(cv2.CAP_PROP_FPS)\n print(fps)\n\n # seconds between each frame\n delta_t = 1 / int(fps)\n\n # Get height, width and area of whole image.\n # img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)\n # img = cv2.resize(img, (1090, 1920), interpolation=cv2.INTER_AREA)\n\n # Get height, width and area of whole image.\n height, width = img.shape[:2]\n\n # initialize mask as all zeroes (black)\n mask = np.zeros((height, width), np.uint8) # mask must be np.unint8 not the default np.float65\n\n # Define region of interest polygon\n # roi of polygon points\n offset = 0.20 # fractional offset from edge of screen\n # Box ROI\n # myROI = np.array([[width * offset, height * offset],\n # [(1 - offset) * width, height * offset],\n # [(1 - offset) * width, (1 - offset) * height],\n # [width * offset, (1 - offset) * height]], np.int32)\n\n # Define ROI polygon bounding box\n myROI = np.array([[727, 711],\n [713, 543],\n [1553, 89],\n [1657, 1001]], np.int32)\n cv2.fillPoly(mask, [myROI], 1)\n\n # Get total area of region of interest\n total_area = cv2.contourArea(myROI)\n\n # Convert to grayscale\n img_gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n # apply the mask to the grayscale image\n gray_roi = cv2.bitwise_and(img_gray, img_gray, mask=mask)\n\n # Create threshold binary image\n if trackbar_mode is True:\n thresh_value = cv2.getTrackbarPos('Threshold', 'Video')\n high_thresh = cv2.getTrackbarPos('HighThreshold', 'Video')\n else:\n thresh_value = default_thresh\n high_thresh = default_high_thresh\n\n # retval, thresh = cv2.threshold(gray_roi, thresh_value, 255, cv2.THRESH_BINARY)\n thresh = cv2.inRange(gray_roi, thresh_value, high_thresh)\n\n # Remove noise through morphological transforms, opening and closing\n kernel = np.ones((5, 5), np.uint8)\n thresh = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, kernel)\n thresh = cv2.morphologyEx(thresh, cv2.MORPH_CLOSE, kernel)\n\n ret_image, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE)\n\n # Area of ROI\n print('ROI total area: {}'.format(total_area))\n\n try:\n # Get the level-0 contours indices. Those with Parent hierarchy values of -1\n # hierarchy format: [NEXT, PREVIOUS, FIRST_CHILD, PARENT]\n # Value of -1 for none. (i.e. -1 for NEXT if there are no next\n # contours in that level of the hierarchy\n h0 = np.where(hierarchy[0][:, 3] == -1)\n\n # Create a list of contours from the h0 contour indices.\n contours_white = [contours[i] for i in h0[0]]\n\n # Get the level_1 contours indices. The children of the level_0 contours.\n h1 = np.where(hierarchy[0][:, 3] != -1)\n\n # Create a list of contours from the h1 contour indices.\n contours_black = [contours[i] for i in h1[0]]\n areas_black = [cv2.contourArea(x) for x in contours_black]\n\n # Create list of contour areas sorted descending\n areas = [cv2.contourArea(x) for x in contours_white]\n con_areas = list(zip(contours_white, areas)) # list of tuples of (contour, contour_area)\n con_areas = sorted(con_areas, key=lambda x: x[1], reverse=True)\n\n # Calc total area of white contours\n white_contour_area_total = 0\n for cont, area in con_areas:\n white_contour_area_total += area\n\n # Calc total area of black (holes) contours\n holes_area_total = 0\n for area in areas_black:\n holes_area_total += area\n\n contour_area_total = white_contour_area_total - holes_area_total\n\n print('Total Contour Area: {:.0f}'.format(white_contour_area_total))\n print('Number of contours total: {}'.format(len(contours_white)))\n\n except TypeError as err:\n print('No contours found.')\n print(err)\n\n # When there are no contours set the area to zero\n contour_area_total = 0\n\n print('{} x {}'.format(width, height))\n\n # Draw contours\n cv2.drawContours(img, contours, -1, (0, 255, 0), 2)\n\n # Draw ROI polygon\n cv2.polylines(img, [myROI], True, (0, 0, 255), 2, cv2.LINE_AA) # must be list of polygon points\n\n # Resize video for display on monitor\n # [WARNING] DON'T DETERMINE AREA VALUES AFTER THIS POINT. NUMBER OF PIXELS HAS CHANGED.\n # img = cv2.resize(img, (540, 960), interpolation=cv2.INTER_AREA)\n\n # Rotate image 90 degrees clockwise\n img = cv2.rotate(img, cv2.ROTATE_90_CLOCKWISE)\n\n # Recalc height, width for writing text\n height, width = img.shape[:2]\n\n # Write text to image\n dry_area_percent = contour_area_total / total_area * 100\n text = 'Dry Area: {:02.0f}%'.format(dry_area_percent)\n font = cv2.FONT_HERSHEY_SIMPLEX\n # Draw black background for text\n cv2.rectangle(img, (0, int(0.97 * height)), (width - 1, int(0.96 * height - 65)), (0, 0, 0), -1, cv2.LINE_AA)\n img = cv2.putText(img, text, (int(0.05 * width), int(0.95 * height)), font, 2, (0, 0, 255), 2, cv2.LINE_AA)\n\n # covert BGR to RGB colors\n # img = img[:, :, ::-1]\n\n # Display Image\n cv2.imshow('Video', img)\n\n # Display thresh mask\n thresh = cv2.rotate(thresh, cv2.ROTATE_90_CLOCKWISE)\n cv2.imshow('Mask', thresh)\n\n # Code to plot % Dry relative to time\n dry_data.append(dry_area_percent)\n\n time_data.append(current_time)\n current_time = current_time + timedelta(seconds=delta_t)\n # time_data.append(datetime.now())\n\n # Write video\n out.write(img)\n\n k = cv2.waitKey(1) & 0xFF\n if k == 27:\n break\n\n loop_time = datetime.now() - start_time\n print(loop_time)\n else:\n break\n\n\n# Plot % Area vs Time after video.\nfig = plt.figure()\nax1 = fig.add_subplot(1, 1, 1)\n\nax1.plot(time_data, dry_data)\n\nax1.set_xlabel('Time')\nax1.xaxis.set_major_formatter(mdates.DateFormatter('%H:%M:%S'))\nfig.autofmt_xdate() # Auto rotates/formats date\n\nax1.set_ylabel('% Dry')\n\ntry:\n ax1.set_title('% Dry Area @ Thresh: {}'.format(thresh_value))\nexcept NameError as err:\n print('thresh_value not defined.')\n print(err)\n\nplt.show()\n\nfig.savefig(image_file)\ncap.release()\ncv2.destroyAllWindows()\n","sub_path":"gopro_filter04_small_platform.py","file_name":"gopro_filter04_small_platform.py","file_ext":"py","file_size_in_byte":8363,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"642532610","text":"import re\nimport multiprocessing\n\nimport spacy\nimport nltk\nfrom nltk.corpus import stopwords\nfrom nltk.stem.snowball import SnowballStemmer\n\nPUNCTS = ['-', '$', '&', '.', ',', '\"', \"'\", '?', '!', ':', ';', '(', ')', '[', ']', '{','}']\nSTOP_WORDS = set(stopwords.words('english'))\nSEPERATORS = [\" \", \"\\t\", \"\\n\"]\nSTOP_WORDS.update(PUNCTS)\n\nspacy_model = spacy.load('en')\nstemmer=SnowballStemmer(\"english\")\n\ndef parse(text):\n \"\"\"\n spacy annotations: https://stackoverflow.com/questions/40288323/what-do-spacys-part-of-speech-and-dependency-tags-mean\n \"\"\"\n text = re.sub('[ ]+', ' ', text).strip() # Convert multiple whitespaces into one\n\n doc = spacy_model(text)\n token_tags = []\n for tok in doc:\n tags = {\"text\": tok.text, \"lemma\": tok.lemma_, \"pos\": tok.pos_,\n \"tag\": tok.tag_, \"dep\": tok.dep_, \"shape\": tok.shape_,\n \"is_alpha\": tok.is_alpha, \"is_stop\": tok.is_stop}\n token_tags.append(tags)\n\n return token_tags\n\n\ndef lemmatize(text):\n doc = spacy_model(text)\n return [tok.lemma_ for tok in doc]\n\n\ndef tokenize_and_stem(text):\n # first tokenize by sentence, then by word to ensure that punctuation is caught as it's own token\n tokens = [word for sent in nltk.sent_tokenize(text) for word in nltk.word_tokenize(sent)]\n filtered_tokens = []\n # filter out any tokens not containing letters (e.g., numeric tokens, raw punctuation)\n for token in tokens:\n if re.search('[a-zA-Z]', token):\n filtered_tokens.append(token)\n\n return [stemmer.stem(t) for t in filtered_tokens]\n","sub_path":"nlpwiz/tagging/spacy.py","file_name":"spacy.py","file_ext":"py","file_size_in_byte":1581,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"390449389","text":"import numpy as np\nimport pywt\nimport cv2\n\ndef diff(A, B):\n return np.linalg.norm(A - B)\n\ndef compress_image(A, wavelet='haar'):\n for i in range(A.shape[0]):\n x = A[i].astype(float)\n n = x.shape[0]\n for _ in range(int(np.log2(n))):\n cA, cD = pywt.dwt(x[ :n], wavelet)\n x[0:n/2] = cA\n x[n/2:n] = cD\n n /= 2\n\n A[i] = x\n\n for i in range(A.shape[1]):\n x = A[:, i]\n n = x.shape[0]\n for _ in range(int(np.log2(n))):\n cA, cD = pywt.dwt(x[ :n], wavelet)\n x[0:n/2] = cA\n x[n/2:n] = cD\n n /= 2\n\n A[:, i] = x\n\ndef decompress_image(A, wavelet='haar'):\n for i in range(A.shape[0]):\n x = A[i].astype(float)\n n = 1\n for _ in range(int(np.log2(x.shape))):\n cA = x[0:n]\n cD = x[n:2*n]\n x[ :2*n] = pywt.idwt(cA, cD, wavelet)\n n *= 2\n\n A[i] = x\n\n for i in range(A.shape[1]):\n x = A[:, i].astype(float)\n n = 1\n for _ in range(int(np.log2(x.shape))):\n cA = x[0:n]\n cD = x[n:2*n]\n x[ :2*n] = pywt.idwt(cA, cD, wavelet)\n n *= 2\n\n A[:, i] = x\n\neps = 5\n\nfilename = 'image1'\nA = cv2.imread('images/%s.png' % filename)[:, :, 0].astype(float)\ninitial_nonzeros = np.where(A!=0)[0].shape[0]\n\ncompress_image(A)\n\nA[np.where(np.abs(A) < eps)] = 0\n\napprox_nonzeros = np.where(A!=0)[0].shape[0]\n\nprint(float(initial_nonzeros) / approx_nonzeros)\n\ndecompress_image(A)\n\ncv2.imwrite('%s_compressed.png' % filename, A.astype(np.uint8))\n\n# cv2.imshow('img', A.astype(np.uint8))\n# cv2.waitKey(0)\n","sub_path":"transformations/compression.py","file_name":"compression.py","file_ext":"py","file_size_in_byte":1657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"60343965","text":"from __future__ import absolute_import, division, print_function, unicode_literals\n\n# TensorFlow and tf.keras\nimport tensorflow as tf\nfrom tensorflow import keras\nfrom tensorflow.python.keras.utils.data_utils import Sequence\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Flatten, Conv2D, MaxPooling2D\n\n# Helper libraries\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport math\nimport os\n\nprint(tf.__version__)\n\nimport pathlib\ndata_root = pathlib.Path(r'##PATH TO TRAINING IMAGES##')\ntest_root = pathlib.Path(r'##PATH TO TESTING IMAGES##')\nprint(data_root)\nprint(test_root)\n\nimport random\nall_image_paths = list(data_root.glob('*/*'))\nall_image_paths = [str(path) for path in all_image_paths]\nrandom.shuffle(all_image_paths)\nimage_count = len(all_image_paths)\nprint(image_count)\n\nall_test_paths = list(test_root.glob('*/*'))\nall_test_paths = [str(path) for path in all_test_paths]\nrandom.shuffle(all_test_paths)\n\ntest_count = len(all_test_paths)\nprint(test_count)\n\nimage_size = 192 # All images will be resized to 192x192\nbatch_size = 32\n\n# Rescale all images by 1./255 and apply image augmentation\ntrain_datagen = keras.preprocessing.image.ImageDataGenerator(\n rescale=1./255,\n validation_split=0.2)\n\n\n# These convert the images to tensors to feed into the neural network\ntrain_generator = train_datagen.flow_from_directory(\n directory=data_root,\n target_size=(image_size, image_size),\n color_mode=\"rgb\",\n batch_size=32,\n class_mode=\"categorical\",\n shuffle=True,\n seed=42,\n subset='training'\n)\n\nvalidation_generator = train_datagen.flow_from_directory(\n directory=data_root,\n target_size=(image_size, image_size),\n color_mode=\"rgb\",\n batch_size=32,\n class_mode=\"categorical\",\n shuffle=True,\n seed=42,\n subset='validation'\n)\n\ntest_datagen = keras.preprocessing.image.ImageDataGenerator(\n rescale=1./255)\n\ntest_generator = test_datagen.flow_from_directory(\n directory=test_root,\n target_size=(image_size, image_size),\n color_mode=\"rgb\",\n batch_size=1,\n class_mode=None,\n shuffle=False,\n seed=42\n)\n\n# This section isn't vital but allows performance monitoring and graph plotting of training data over time\nclass CollectBatchStats(tf.keras.callbacks.Callback):\n def __init__(self):\n self.batch_losses = []\n self.batch_acc = []\n\n def on_train_batch_end(self, batch, logs=None):\n self.batch_losses.append(logs['loss'])\n self.batch_acc.append(logs['acc'])\n self.model.reset_metrics()\n \n# Model definition\nmodel = Sequential()\nmodel.add(Conv2D(32, kernel_size=(3, 3),\n activation='relu',\n input_shape=(image_size, image_size, 3)))\nmodel.add(MaxPooling2D(pool_size=(3, 3)))\nmodel.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))\nmodel.add(MaxPooling2D(pool_size=(3, 3)))\nmodel.add(Flatten())\nmodel.add(Dense(128, activation='relu'))\nmodel.add(Dense(10, activation='softmax'))\nmodel.summary()\n\nmodel.compile(loss = keras.losses.categorical_crossentropy,\n optimizer='adam',\n metrics=['accuracy'])\n \n \nbatch_stats_callback = CollectBatchStats()\n\ncheckpoint_path = r\"##TRAINED MODEL SAVE PATH##\\cp.ckpt\"\ncheckpoint_dir = os.path.dirname(checkpoint_path)\n\n# Create checkpoint callback\ncp_callback = tf.keras.callbacks.ModelCheckpoint(checkpoint_path,\n save_weights_only=True,\n verbose=1)\n\n# Use the generators to fit the model \nmodel.fit_generator(train_generator,\n steps_per_epoch = train_generator.samples // batch_size,\n validation_data = validation_generator, \n validation_steps = validation_generator.samples // batch_size,\n epochs = 10,\n callbacks = [cp_callback, batch_stats_callback])\nmodel.save(r'C:\\Users\\EchoY\\OneDrive\\Desktop\\ProjectData\\train\\conv_model\\my_model.h5')\n\nSTEP_SIZE_TRAIN=train_generator.n//train_generator.batch_size\nSTEP_SIZE_VALID=validation_generator.n//validation_generator.batch_size\n\nmodel.evaluate_generator(generator=validation_generator,\nsteps=STEP_SIZE_VALID)\nSTEP_SIZE_TEST=test_generator.n//test_generator.batch_size\ntest_generator.reset()\npred=model.predict_generator(test_generator,\nsteps=STEP_SIZE_TEST,\nverbose=1)\npredicted_class_indices=np.argmax(pred,axis=1)\n\nlabels = (train_generator.class_indices)\nlabels = dict((v,k) for k,v in labels.items())\npredictions = [labels[k] for k in predicted_class_indices]\n\n#Create a new CSV of the file name and it's predicted class. This code is handy for Kaggle submissions\nfilenames=test_generator.filenames\nresults=pd.DataFrame({\"Filename\":filenames,\n \"Predictions\":predictions})\nresults.to_csv(r\"##RESULTS CSV SAVE PATH##\\results.csv\",index=False)\n","sub_path":"conv_model_v1_10epochs.py","file_name":"conv_model_v1_10epochs.py","file_ext":"py","file_size_in_byte":4954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"470626638","text":"import sys\ndef selectsort(nums):\n max_ind = 0\n n = len(nums)\n for i in range(n):\n _max = -sys.maxsize - 1\n for j in range(0, n - i):\n if nums[j] > _max:\n _max = nums[j]\n max_ind = j\n nums[n - i - 1], nums[max_ind] = _max, nums[n - i - 1]\n \na = [3, 9, 0, 2, 6, 8, 1, 7, 4, 5]\nselectsort(a)\nprint(a)","sub_path":"Week_06/select_sort.py","file_name":"select_sort.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"387655159","text":"# while True:\n# print(\"Привет, Богатырь!\")\n# print(\"1: > смерть найдешь\")\n# print(\"2: < полцарства найдешь\")\n# print(\"3: коня потеряешь\")\n# choice = input(\"Твой выбор: ('q' - for exit)\")\n#\n# if choice == 'q':\n# break\n#\n# if int(choice) < 1 or int(choice) > 3:\n# print(\"Учи матчасть!\")\n# continue\n#\n# if int(choice) == 1:\n# print(\"Сам\")\n#\n# elif int(choice) == 2:\n# print(\"Bingo!\")\n#\n# elif int(choice) == 3:\n# print(\"Horse \")\n#\n# # if choice < 1 or choice > 3:\n# # else:\n# # print(\"Учи матчасть!\")\n\nimport random\n\ndef fill_truck(weight_limit):\n total_boxes = 0\n total_weight = 0\n while (total_weight < weight_limit):\n box_weight = random.randint(1, 100)\n free_space = weight_limit - total_weight\n if free_space < box_weight:\n print(box_weight)\n continue\n\n total_weight = total_weight + box_weight\n total_boxes = total_boxes + 1\n print(\"Total weight = %d, num. of boxes = %d\" %\n (total_weight, total_boxes))\n# fill_truck(2000)\n\ndef fill_truck_fixed(weight_limit, box_weight):\n total_weight = 0\n num_boxes = weight_limit // box_weight\n for x in range(1, num_boxes + 1):\n total_weight = total_weight + box_weight\n\n print(total_weight, x)\n# fill_truck_fixed(2000, 50)\n\n# def random_numbers(num_randoms):\n# for i in range(num_randoms):\n# x = random.randint(0, 100)\n#\n# print(x)\n# random_numbers(100)\n\n# def max_random_numbers(num_randoms):\n# max_num = 0\n# for i in range(num_randoms):\n# num = random.randint(0, 100)\n# print(num)\n# if(num > max_num):\n# max_num = num\n#\n# return max_num\n#\n# max_num = max_random_numbers(10)\n# print(\"Max number = \", max_num)\n\n# def min_random_numbers(num_randoms, lower_limit, upper_limit):\n# min_num = upper_limit\n# for i in range(num_randoms):\n# num = random.randint(lower_limit, upper_limit)\n# print(num)\n# if(num < min_num):\n# min_num = num\n# return min_num\n# min_num = min_random_numbers(10, 0, 100)\n# print(\"Min number = \", min_num)\n\n# def avr_random_numbers(num_randoms, lower_limit, upper_limit):\n# avr_num = 0\n# for i in range(num_randoms):\n# num = random.randint(lower_limit, upper_limit)\n# print(num)\n# avr_num = avr_num + num\n# print(\"----------\", avr_num)\n#\n# avr_random_numbers(10, 0, 10)\n\ndef fib(n):\n if n == 1:\n return 1\n elif n == 2:\n return 1\n else:\n return fib(n-1) + fib(n-2)\n\nfib_num = fib(10)\nprint(fib_num)\n","sub_path":"class_work_23_04.py","file_name":"class_work_23_04.py","file_ext":"py","file_size_in_byte":2744,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"565806168","text":"\"\"\"disk information\"\"\"\n\nimport os\nimport sys\nimport stat\n\nclass FSPointException(Exception):\n pass\n\nclass DevException(Exception):\n pass\n\nclass Disk(object):\n def __init__(self, dev, fsid, mount_point):\n dev_stat = os.stat(dev)\n if stat.S_ISBLK(dev_stat.st_mode):\n self.name = dev\n else:\n raise DevException(\"Is not a block device\")\n self.fsid = fsid\n self.mount_point = mount_point\n\nclass FilesystemInfo(object):\n def __init__(self, disk):\n self._inited_fs = False\n self._name = disk.name\n self._uuid = disk.fsid\n self._mount_point = disk.mount_point\n\n self._total_size = None\n self._avail_size = None\n self._used_size = None\n self._used_percentage = None\n\n def init_fs(self):\n try:\n self._fetch_fs_info()\n self._inited_fs = True\n except FSPointException:\n self._inited_fs = False\n\n def get_fs_type(self):\n pass\n\n @property\n def used_size(self):\n # if self._used_size and self._total_size and self._avail_size:\n self._used_size = self.total_size - self.avail_size\n return self._used_size\n\n @property\n def avail_percentage(self):\n if self._avail_size and self._total_size:\n return (1 - (self._avail_size / self._total_size)) * 100\n return\n\n @property\n def avail_size(self):\n if self._avail_size:\n return self._avail_size\n if self._inited_fs:\n self._avail_size = self.fs_bavail * self.fs_bsize\n return self._avail_size\n return\n\n @property\n def total_size(self):\n if self._total_size:\n return self._total_size\n if self._inited_fs:\n self._total_size = self.fs_blocks * self.fs_bsize\n return self._total_size\n else:\n return\n\n @total_size.setter\n def total_size(self, value):\n self._total_size = value\n\n def _fetch_fs_info(self):\n \"\"\"fetch fs information\"\"\"\n if self._mount_point:\n if os.path.exists(self._mount_point):\n try:\n fs_info = os.statvfs(self._mount_point)\n except Exception:\n raise FSPointException(\"syscall statvfs error\")\n self.fs_bsize = fs_info.f_bsize\n self.fs_frsize = fs_info.f_frsize\n self.fs_blocks = fs_info.f_blocks\n self.fs_bfree = fs_info.f_bfree\n self.fs_bavail = fs_info.f_bavail\n self.fs_files = fs_info.f_files\n self.fs_ffree = fs_info.f_ffree\n self.fs_favail = fs_info.f_favail\n self.fs_flag = fs_info.f_flag\n self.fs_namemax = fs_info.f_namemax\n return\n\n raise FSPointException(\"mount point is not exist\")\n\n\nif __name__ == \"__main__\":\n dev = Disk('/dev/sdb1', '159bf3ec-ca95-4695-9319-a4fefbb35d74', '/var/lib/ceph/osd/ceph-1')\n fs = FilesystemInfo(dev)\n fs.init_fs()\n print(\"dev : %(device)s \\nfilesystem id: %(fsid)s \\nmount point: %(mount_point)s \\n \\\n total size: %(total_size)s \\nused size: %(used_size)s \\navail size: %(avail_size)s\\npercentage: %(avail_per)s\"\n % {\"device\":fs._name,\n \"fsid\":fs._uuid,\n \"mount_point\":fs._mount_point,\n \"total_size\":fs.total_size,\n \"used_size\":fs.used_size,\n \"avail_size\":fs.avail_size,\n \"avail_per\":fs.avail_percentage})\n\n","sub_path":"glance/service/utils/disk.py","file_name":"disk.py","file_ext":"py","file_size_in_byte":3573,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"228245821","text":"def fun():\n key = b'5LE2JE5VFMJN8ZGCN9OQLQHQY7JEMCUI'\n packed = 'dgQ+Sy82agkyJSM9Zy0mMBFWOTQ+OiE9NWg5Kj8xLDQ='\n\n def encrypt1(var, key):\n return bytes(a ^ b for a, b in zip(var, key))\n\n print(\"Hello There!\")\n print(\"If you can successfully get the source of this program, you can get the key!\")\n\n m = hashlib.sha256()\n sec = encrypt1(key, base64.b64decode(packed))\n m.update(sec)\n m.update(str(random.randint(0,2*32)).encode('ascii'))\n\n print(base64.b64encode(m.hexdigest().encode('ascii')).decode('ascii'))\n","sub_path":"SorryNotSorry/payload.py","file_name":"payload.py","file_ext":"py","file_size_in_byte":545,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"326377901","text":"#\n# coverage.py\n#\n# Copyright © 2011, 2012 Monotype Imaging Inc. All Rights Reserved.\n#\n\n\"\"\"\nSupport for coverage values associated with 'mort' subtables.\n\"\"\"\n\n# Other imports\nfrom fontio3.fontdata import maskmeta\n\n# -----------------------------------------------------------------------------\n\n#\n# Private functions\n#\n\ndef _validate(obj, **kwArgs):\n logger = kwArgs['logger']\n \n if obj.always and obj.vertical:\n logger.warning((\n 'V0698',\n (),\n \"The Coverage has both the 'always' and 'vertical' bits set. \"\n \"The 'always' bit includes the vertical orientation, so the \"\n \"vertical bit is redundant here.\"))\n \n if obj.reverse and (obj.always or obj.vertical):\n logger.warning((\n 'V0699',\n (),\n \"The 'reverse' bit is set in addition to the 'always' and/or \"\n \"'vertical' bits. This is an unusual combination.\"))\n \n return True\n\ndef _validate_kind(obj, **kwArgs):\n logger = kwArgs['logger']\n \n if obj not in {0, 1, 2, 4, 5}:\n logger.error((\n 'V0697',\n (obj,),\n \"The kind value %d is not a valid subtable kind.\"))\n \n return False\n \n return True\n\n# -----------------------------------------------------------------------------\n\n#\n# Classes\n#\n\nif 0:\n def __________________(): pass\n\nclass Coverage(object, metaclass=maskmeta.FontDataMetaclass):\n \"\"\"\n Objects representing coverage values for 'mort' subtables. These are mask\n values with the following fields:\n \n always A Boolean specifying whether this subtable should always be\n processed, irrespective of horizontal/vertical orientation.\n Default is False.\n \n kind A numeric value determining the kind of subtable.\n \n reverse A Boolean specifying whether this subtable should be\n processed in reverse order (i.e. starting at the semantic\n end of the run/line of text). Default is False.\n \n vertical A Boolean specifying whether this subtable should only be\n applied for vertical runs of text. Default is False.\n \n >>> _testingValues[1].pprint()\n Vertical text\n Subtable kind: 4\n \n >>> logger = utilities.makeDoctestLogger(\"coverage_test\")\n >>> e = utilities.fakeEditor(0x10000)\n >>> d = {'logger': logger, 'editor': e}\n >>> Coverage(kind=7).isValid(**d)\n coverage_test.kind - ERROR - The kind value 7 is not a valid subtable kind.\n False\n \n >>> Coverage(always=True, vertical=True).isValid(**d)\n coverage_test - WARNING - The Coverage has both the 'always' and 'vertical' bits set. The 'always' bit includes the vertical orientation, so the vertical bit is redundant here.\n True\n \n >>> Coverage(reverse=True, vertical=True).isValid(**d)\n coverage_test - WARNING - The 'reverse' bit is set in addition to the 'always' and/or 'vertical' bits. This is an unusual combination.\n True\n \"\"\"\n \n #\n # Class definition variables\n #\n \n maskByteLength = 2\n \n maskControls = dict(\n validatefunc_partial = _validate)\n \n maskSpec = dict(\n vertical = dict(\n mask_isbool = True,\n mask_label = \"Vertical text\",\n mask_rightmostbitindex = 15,\n mask_showonlyiftrue = True),\n \n reverse = dict(\n mask_isbool = True,\n mask_label = \"Process reversed\",\n mask_rightmostbitindex = 14,\n mask_showonlyiftrue = True),\n \n always = dict(\n mask_isbool = True,\n mask_label = \"Process in both orientations\",\n mask_rightmostbitindex = 13,\n mask_showonlyiftrue = True),\n \n kind = dict(\n mask_bitcount = 3,\n mask_label = \"Subtable kind\",\n mask_rightmostbitindex = 0,\n mask_validatefunc = _validate_kind))\n \n maskSorted = ('vertical', 'reverse', 'always', 'kind')\n\n# -----------------------------------------------------------------------------\n\n#\n# Test code\n#\n\nif 0:\n def __________________(): pass\n\nif __debug__:\n from fontio3 import utilities\n \n _testingValues = (\n Coverage(),\n Coverage(vertical=True, kind=4))\n\ndef _test():\n import doctest\n doctest.testmod()\n\nif __name__ == \"__main__\":\n if __debug__:\n _test()\n","sub_path":"fontio3/build/lib.linux-x86_64-3.6/fontio3/mort/coverage.py","file_name":"coverage.py","file_ext":"py","file_size_in_byte":4455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"243773220","text":"\"\"\"\ntest_cards.py\n~~~~~~~~~~~~~\n\nThis module contains the unit tests for the blackjack.cards module.\n\n:copyright: (c) 2020 by Paul J. Iutzi\n:license: MIT, see LICENSE for more details.\n\"\"\"\nimport collections.abc as col\nimport json\nfrom copy import deepcopy\nfrom functools import partial\nfrom itertools import chain\nfrom random import seed\n\nimport pytest\n\nfrom blackjack import cards\nfrom tests.common import deck, hand, hands\n\n\n# Fixtures.\n@pytest.fixture\ndef card():\n \"\"\"An instance of :class:`blackjack.cards.Card` for testing.\"\"\"\n yield cards.Card(10, 'clubs', cards.UP)\n\n\n@pytest.fixture\ndef frenchdeck():\n cards_ = []\n for suit in range(4):\n for rank in range(13, 0, -1):\n cards_.append(cards.Card(rank, suit, cards.DOWN))\n yield cards.Deck(cards_)\n\n\n@pytest.fixture\ndef pile():\n \"\"\"A :class:`blackjack.cards.Pile` for testing.\"\"\"\n yield cards.Pile([\n cards.Card(1, 0),\n cards.Card(2, 0),\n cards.Card(3, 0),\n ])\n\n\n# Utility functions.\ndef raises_test(cls, *args, **kwargs):\n \"\"\"Return the exception raised by the callable.\"\"\"\n try:\n cls(*args, **kwargs)\n except Exception as ex:\n return type(ex)\n return None\n\n\n# Tests for Card class methods.\ndef test_deserialize(card):\n \"\"\"When given a :class:`blackjack.cards.Card` object serialized as\n a json string, :meth:`blackjack.cards.Card.deserialize()`` should\n return the deserialized object.\n \"\"\"\n s = '[\"Card\", 10, \"clubs\", true]'\n assert cards.Card.deserialize(s) == card\n\n\n# Tests for Card initialization.\ndef test_Card__init_default():\n \"\"\"When invoked, an :class:`blackjack.cards.Card` object is created.\"\"\"\n optionals = {\n 'rank': 11,\n 'suit': 'spades',\n 'facing': cards.UP,\n }\n card = cards.Card()\n for attr in optionals:\n assert getattr(card, attr) == optionals[attr]\n\n\ndef test_Card__init_invalid():\n \"\"\"When invoked, an :class:`blackjack.cards.Card` object is created.\n If invalid values for optional parameters are passed, the object\n should raise a :class:`ValueError` exception.\n \"\"\"\n raises = partial(raises_test, cards.Card)\n assert raises(23, 'clubs', True) == ValueError\n assert raises(0, 'clubs', True) == ValueError\n assert raises(2, 'spam', True) == ValueError\n assert raises(2, 5, True) == ValueError\n assert raises(2, -7, True) == ValueError\n assert raises(2, 'clubs', 'spam') == ValueError\n\n\ndef test_Card__init_optional():\n \"\"\"When invoked, an :class:`blackjack.cards.Card` object is created.\n If optional parameters are passed, the relevant attributes are set\n to those values.\n \"\"\"\n optionals = {\n 'rank': 10,\n 'suit': 'hearts',\n 'facing': cards.DOWN,\n }\n card = cards.Card(**optionals)\n for attr in optionals:\n assert getattr(card, attr) == optionals[attr]\n\n\n# Tests for Card dunder methods.\ndef test_Card___repr__(card):\n \"\"\":class:`blackjack.cards.Card.__repr__` should return a string\n useful for debugging.\n \"\"\"\n assert repr(card) == \"Card(rank=10, suit='clubs', facing=True)\"\n\n\ndef test_Card___str__(card):\n \"\"\":class:`blackjack.cards.Card.__str__` should return a string\n useful for printing.\n \"\"\"\n assert str(card) == '10♣︎'\n\n\ndef test_Card___str__(card):\n \"\"\":class:`blackjack.cards.Card.__str__` should return a string\n useful for printing. If the card is face down, the suit and rank\n information should be hidden.\n \"\"\"\n card.facing = cards.DOWN\n assert str(card) == '\\u2500\\u2500'\n\n\ndef test_Card_comparisons(card):\n \"\"\":class:`blackjack.cards.Card` objects should be compared for\n equality based on their rank and suit.\n \"\"\"\n assert card == cards.Card(10, 'clubs', cards.UP)\n assert card == cards.Card(10, 'clubs', cards.DOWN)\n assert card != cards.Card(11, 'clubs')\n assert card != cards.Card(10, 'hearts')\n assert card != cards.Card(9, 'diamonds')\n assert card < cards.Card(11, 'clubs')\n assert card <= cards.Card(12, 'clubs')\n assert card < cards.Card(10, 'diamonds')\n assert card <= cards.Card(10, 'diamonds')\n assert card > cards.Card(9, 'clubs')\n assert card >= cards.Card(9, 'clubs')\n assert cards.Card(10, 'diamonds') > card\n assert cards.Card(10, 'diamonds') >= card\n assert card.__eq__(23) == NotImplemented\n\n\n# Tests for Card._astuple.\ndef test_Card_astuple(card):\n \"\"\"When called, :meth:`blackjack.cards.Card.astuple` should return\n the card's attributes as a tuple for serialization.\n \"\"\"\n assert card.astuple() == ('Card', 10, 'clubs', True)\n\n\n# Tests for Card.flip.\ndef test_Card_flip_up(card):\n \"\"\"When a :class:`blackjack.cards.Card` object is face down,\n :meth:`blackjack.cards.Card.flip` should switch it to\n being face up. If the object is face down, it should switch\n it to face up.\n \"\"\"\n card.flip()\n assert card.facing == cards.DOWN\n card.flip()\n assert card.facing == cards.UP\n\n\n# Tests for Card.serialize.\ndef test_Card_serialize(card):\n \"\"\"When called, :meth:`blackjack.cards.Card.serialize` returns a\n version of the object serialized to a json string.\n \"\"\"\n assert card.serialize() == '[\"Card\", 10, \"clubs\", true]'\n\n\n# Tests for Pile class methods.\ndef test_Pile_deserialize(pile):\n \"\"\"When given a :class:`blackjack.cards.Pile` object serialized\n to a JSON string, :meth:`blackjack.cards.Pile.deserialize` should\n deserialize that object and return it.\n \"\"\"\n s = json.dumps({'class': 'Pile', '_iter_index': 0, 'cards': [\n '[\"Card\", 1, 0, true]',\n '[\"Card\", 2, 0, true]',\n '[\"Card\", 3, 0, true]',\n ],})\n assert cards.Pile.deserialize(s) == pile\n\n\n# Tests for Pile initialization.\ndef test_Pile_init_default():\n \"\"\"Given no parameters, :class:`blackjack.cards.Pile` should\n return a instance that uses default values for its attributes.\n \"\"\"\n optionals = {\n 'cards': (),\n '_iter_index': 0\n }\n pile = cards.Pile()\n for attr in optionals:\n assert getattr(pile, attr) == optionals[attr]\n\n\ndef test_Pile_init_invalid():\n \"\"\"Given an invalid parameter, :class:`Pile` should raise an\n exception.\n \"\"\"\n raises = partial(raises_test, cards.Pile)\n assert raises('spam') == ValueError\n assert raises(6) == ValueError\n assert raises(['spam', 'eggs']) == ValueError\n assert raises(_iter_index='spam') == ValueError\n\n\ndef test_Pile_init_optional():\n \"\"\"Given optional parameters, :class:`blackjack.cards.Pile` should\n return a instance that uses the given values for its attributes.\n \"\"\"\n optionals = {\n 'cards': (\n cards.Card(1, 0),\n cards.Card(2, 0),\n cards.Card(3, 0),\n ),\n '_iter_index': 2\n }\n pile = cards.Pile(**optionals)\n for attr in optionals:\n assert getattr(pile, attr) == optionals[attr]\n\n\n# Tests for Pile dunder methods.\ndef test_Pile_comparisons(pile):\n \"\"\":class:`blackjack.cards.Pile` should be able to be compared with\n other instances of :class:`blackjack.cards.Pile`. The comparison\n should be based on the contents of :attr:`blackjack.cards.Pile.cards`.\n \"\"\"\n assert pile == cards.Pile(pile.cards)\n assert pile != cards.Pile([cards.Card(1, 0),])\n assert pile.__eq__(23) == NotImplemented\n\n\ndef test_Pile_container(pile):\n \"\"\":class:`blackjack.cards.Pile` should implement the `Container`\n protocol.\n \"\"\"\n assert isinstance(pile, col.Container)\n assert cards.Card(2, 0) in pile\n assert cards.Card(4, 0) not in pile\n\n\ndef test_Pile_iterator(pile):\n \"\"\":class:`blackjack.cards.Pile` should implement the iterator\n protocol.\n \"\"\"\n assert isinstance(pile, col.Iterator)\n assert iter(pile) == pile\n assert iter(pile) is not pile\n next(pile)\n assert pile._iter_index == 1\n assert next(pile) == cards.Card(2, 0)\n assert next(pile) == cards.Card(3, 0)\n with pytest.raises(StopIteration):\n next(pile)\n\n\ndef test_Pile_mutablesequence(pile):\n \"\"\":class:`blackjack.cards.Pile` should implement the\n `MutableSequence` protocol.\n \"\"\"\n assert isinstance(pile, col.MutableSequence)\n card = cards.Card(5, 2)\n\n # Test Pile.__set_item__.\n assert pile[1] is not card\n pile[1] = card\n assert pile[1] is card\n\n # Test Pile.__del_item__.\n assert len(pile) == 3\n del pile[1]\n assert pile[1] is not card\n assert len(pile) == 2\n\n # Test Pile.insert.\n pile.insert(1, card)\n assert pile[1] is card\n assert len(pile) == 3\n\n\ndef test_Pile_reversible(pile):\n \"\"\":class:`blackjack.cards.Pile` should implement the `Reversible`\n protocol.\n \"\"\"\n assert isinstance(pile, col.Reversible)\n assert reversed(pile) == cards.Pile(pile.cards[::-1])\n\n\ndef test_Pile_sequence(pile):\n \"\"\":class:`blackjack.cards.Pile` should implement the `Sequence`\n protocol.\n \"\"\"\n assert isinstance(pile, col.Sequence)\n assert pile[1] == cards.Card(2, 0)\n\n\ndef test_Pile_sized(pile):\n \"\"\"Pile should implement the `Sized` protocol.\"\"\"\n assert len(pile) == 3\n\n\n# Tests for Pile.append.\ndef test_Pile_append(pile):\n \"\"\"Given a :class:`Card`, :meth:`Pile.append` should add the card\n to the :class:`Pile`.\n \"\"\"\n card = cards.Card(4, 0)\n assert len(pile) == 3\n assert pile[-1] == cards.Card(3, 0)\n pile.append(card)\n assert len(pile) == 4\n assert pile[-1] == card\n\n\n# Tests for Pile.copy.\ndef test_Pile_copy(pile):\n \"\"\"When called, :meth:`blackjack.cards.Pile.copy` should return\n a shallow copy of the object.\n \"\"\"\n result = pile.copy()\n assert pile == result\n assert pile is not result\n\n\n# Tests for Pile.serialize.\ndef test_Pile_serialize(pile):\n \"\"\"When called, :meth:`blackjack.cards.Pile.serialize` should return\n the Pile object serialized to a JSON string.\n \"\"\"\n assert pile.serialize() == json.dumps({\n 'class': 'Pile',\n '_iter_index': 0,\n 'cards': [\n '[\"Card\", 1, \"clubs\", true]',\n '[\"Card\", 2, \"clubs\", true]',\n '[\"Card\", 3, \"clubs\", true]',\n ]\n })\n\n\n# Tests for Deck class methods and traits.\ndef test_Deck_subclasses_pile(deck):\n \"\"\":class:`blackjack.cards.Deck` should be a subclass of\n :class:`blackjack.cards.Pile`.\n \"\"\"\n assert isinstance(deck, cards.Pile)\n\n\ndef test_Deck_build(frenchdeck):\n \"\"\":meth:`blackjack.cards.Deck.build1 should return an instance\n of :class:`blackjack.cards.Deck` that contains the cards needed\n for a blackjack game.\n \"\"\"\n deck = cards.Deck.build()\n assert deck == frenchdeck\n assert deck.size == 1\n\n\ndef test_Deck_build_casino_deck(frenchdeck):\n \"\"\":meth:`blackjack.cards.Deck.build1 should return an instance\n of :class:`blackjack.cards.Deck` that contains the cards needed\n for a blackjack game. Despite using multiple decks, each card\n in the casino deck should be a unique object.\n \"\"\"\n deck = cards.Deck.build(6)\n assert deck == cards.Deck(chain(*[frenchdeck for _ in range(6)]))\n assert deck.size == 6\n for i in range(52):\n for j in range(1, 6):\n assert deck[i] is not deck[52 * j + i]\n assert deck[i] == deck[52 * j + i]\n\n\ndef test_Deck_deserialize(deck):\n \"\"\"When given a Deck serialized as a JSON string, deserialize()\n should return the deserialized Deck object.\n \"\"\"\n s = json.dumps({'class': 'Deck', '_iter_index': 0, 'size': 1, 'cards': [\n '[\"Card\", 1, 0, true]',\n '[\"Card\", 2, 0, true]',\n '[\"Card\", 3, 0, true]',\n ],})\n assert cards.Deck.deserialize(s) == deck\n\n\n# Tests for Deck initialization.\ndef test_Deck_init_default():\n \"\"\"If no parameters are passed, a :class:`blackjack.cards.Deck`\n object should be initialized with default values.\n \"\"\"\n optionals = {\n 'cards': tuple(),\n 'size': 1,\n '_iter_index': 0\n }\n deck = cards.Deck()\n for attr in optionals:\n assert getattr(deck, attr) == optionals[attr]\n\n\ndef test_Deck_invalid():\n \"\"\"Given an invalid parameter value, :class:`Deck` should raise\n the appropriate exception.\n \"\"\"\n raises = partial(raises_test, cards.Deck)\n assert raises(size='spam') == ValueError\n assert raises(size=-2) == ValueError\n assert raises(size=['spam', 'eggs']) == TypeError\n\n\ndef test_Deck_init_optionals():\n \"\"\"If optional parameters are passed, a :class:`blackjack.cards.Deck`\n object should be initialized with those values.\n \"\"\"\n optionals = {\n 'cards': (cards.Card(1, 0), cards.Card(2, 0),),\n 'size': 2,\n '_iter_index': 1\n }\n deck = cards.Deck(**optionals)\n for attr in optionals:\n assert getattr(deck, attr) == optionals[attr]\n\n\n# Tests for Deck.draw.\ndef test_Deck_draw(deck):\n \"\"\"When called, :meth:`blackjack.cards.Deck.draw` should remove the\n \"top card\" of the deck and return it. For performance reasons, \"top\n card\" is defined as the card at index -1.\n \"\"\"\n assert len(deck) == 3\n assert deck.draw() == cards.Card(3, 0)\n assert len(deck) == 2\n\n\n# Tests for Deck.shuffle.\ndef test_Deck_shuffle(deck):\n \"\"\"When called, :meth:`blackjack.cards.Deck.shuffle` should\n randomize the order of the deck.\n \"\"\"\n seed('spam1')\n deck.shuffle()\n assert deck == cards.Deck([\n cards.Card(1, 0),\n cards.Card(3, 0),\n cards.Card(2, 0),\n ])\n\n\n# Tests for Deck.random_cut.\ndef test_Deck_random_cut(frenchdeck):\n \"\"\"When called, :meth:`blackjack.cards.Deck.random_cut` should\n remove between 60 and 75 cards from the deck.\n \"\"\"\n casinodeck = cards.Deck(chain(*[frenchdeck for _ in range(6)]))\n seed('spam')\n for start in [63, 68, 60, 70]:\n deck = deepcopy(casinodeck)\n deck.random_cut()\n assert deck == casinodeck[start:]\n\n\n# Tests for Deck.serialize.\ndef test_Deck_serialize(deck):\n \"\"\"When called, :meth:`blackjack.cards.Deck.serialize` should return\n the Deck object serialized as a JSON string.\n \"\"\"\n assert deck.serialize() == json.dumps({\n 'class': 'Deck',\n '_iter_index': 0,\n 'cards': [\n '[\"Card\", 1, \"clubs\", true]',\n '[\"Card\", 2, \"clubs\", true]',\n '[\"Card\", 3, \"clubs\", true]',\n ],\n 'size': 1,\n })\n\n\n# Tests for Hand class methods and traits.\ndef test_Hand_subclasses_Pile(hand):\n \"\"\":class:`Hand` should be a subclass of :class:`Pile`.\"\"\"\n assert isinstance(hand, cards.Pile)\n\n\ndef test_Hand_deserialize(hand):\n \"\"\"When given a :class:`Hand` serialized as a JSON string,\n :meth:`Hand.deserialize` should return the deserialized\n :class:`Deck` object.\n \"\"\"\n s = json.dumps({\n 'class': 'Hand',\n '_iter_index': 0,\n 'doubled_down': False,\n 'cards': [\n '[\"Card\", 1, 0, true]',\n '[\"Card\", 2, 0, true]',\n '[\"Card\", 3, 0, true]',\n ],\n })\n assert cards.Hand.deserialize(s) == hand\n\n\n# Tests for Hand initialization.\ndef test_Hand_init_default():\n \"\"\"Given no parameters, :class:`blackjack.cards.Hand` should\n return a instance that uses default values for its attributes.\n \"\"\"\n optionals = {\n 'cards': (),\n '_iter_index': 0,\n 'doubled_down': False,\n }\n hand = cards.Hand()\n for attr in optionals:\n assert getattr(hand, attr) == optionals[attr]\n\n\ndef test_Hand_init_invalid():\n \"\"\"Given an invalid parameter value, :class:`Hand` should raise\n the appropriate exception.\n \"\"\"\n raises = partial(raises_test, cards.Hand)\n assert raises(doubled_down=6) == ValueError\n assert raises(doubled_down='spam') == ValueError\n assert raises(doubled_down=['spam', 'eggs']) == ValueError\n\n\ndef test_Hand_init_default():\n \"\"\"Given parameters, :class:`blackjack.cards.Hand` should return\n a instance that uses given values for its attributes.\n \"\"\"\n optionals = {\n 'cards': (cards.Card(1, 0), cards.Card(2, 0)),\n '_iter_index': 1,\n 'doubled_down': True,\n }\n hand = cards.Hand(**optionals)\n for attr in optionals:\n assert getattr(hand, attr) == optionals[attr]\n\n\n# Tests for Hand_can_split.\n@pytest.mark.hands(\n [[9, 0], [11, 1]],\n [[10, 1], [10, 2]],\n)\ndef test_Hand_can_split(hands):\n \"\"\"When called, :meth:`Hand.can_split` should return whether the\n :class:`Hand` can be split.\n \"\"\"\n hand, hand_20 = hands\n assert hand_20.can_split()\n assert not hand.can_split()\n\n\n# Tests for Hand_is_blackjack.\n@pytest.mark.hands(\n [[11, 0], [1, 0]],\n [[10, 2], [12, 1]],\n [[13, 2], [6, 1], [5, 2]],\n)\ndef test_Hand_is_blackjack(hands):\n \"\"\"When called, :meth:`Hand.is_blackjack` should return whether the\n :class:`Hand` is a natural blackjack.\n \"\"\"\n blackjack, hand_20, hand_21 = hands\n assert blackjack.is_blackjack()\n assert not hand_21.is_blackjack()\n assert not hand_20.is_blackjack()\n\n\n# Tests for Hand.is_bust.\n@pytest.mark.hand([10, 2], [12, 1])\ndef test_Hand_is_bust(hand):\n \"\"\"When called, :meth:`Hand.is_bust` should return whether the\n :class:`Hand` is bust.\n \"\"\"\n assert not hand.is_bust()\n hand.append(cards.Card(10, 2))\n assert hand.is_bust()\n\n\n# Tests for Hand.score.\n@pytest.mark.hands(\n [[11, 0], [1, 0]],\n [[10, 2], [12, 1]],\n [[13, 2], [6, 1], [5, 2]],\n)\ndef test_Hand_score(hand, hands):\n \"\"\"When called, :meth:`Hand.score` should return the score of\n the :class:`Hand`.\n \"\"\"\n blackjack, hand_20, hand_21 = hands\n assert blackjack.score() == [11, 21]\n assert hand.score() == [6, 16]\n assert hand_20.score() == [20,]\n assert hand_21.score() == [21,]\n\n\n# Tests for Hand.serialize.\ndef test_Hand_serialize(hand):\n \"\"\"When called, :meth:`Hand.serialize` should return the\n :class:`Hand` serialized as a JSON string.\n \"\"\"\n assert hand.serialize() == json.dumps({\n 'class': 'Hand',\n '_iter_index': 0,\n 'cards': [\n '[\"Card\", 1, \"clubs\", true]',\n '[\"Card\", 2, \"clubs\", true]',\n '[\"Card\", 3, \"clubs\", true]',\n ],\n 'doubled_down': False,\n })\n\n\n@pytest.mark.hand([11, 0], [11, 1])\ndef test_Hand_split_valid(hand):\n \"\"\"When called on a :class:`Hand` that can be split,\n :meth:`Hand.split` should split the :class:`Hand` and return\n the resulting two :class:`Hand` objects.\n \"\"\"\n assert hand.split() == (\n cards.Hand([cards.Card(11, 0)]),\n cards.Hand([cards.Card(11, 1)]),\n )\n\n\ndef test_Hand_split_invalid(hand):\n \"\"\"When called on a :class:`Hand` that cannot be split,\n :meth:`Hand.split` should raise a ValueError exception.\n \"\"\"\n with pytest.raises(ValueError):\n _ = hand.split()\n","sub_path":"tests/test_cards.py","file_name":"test_cards.py","file_ext":"py","file_size_in_byte":18652,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"391472089","text":"import base64\nimport boto3\nimport datetime\nimport os\nimport random\nimport sys\nimport time\nfrom urllib.parse import urlparse\n\n# The URL of the sample data in S3\nUNSCORED_DATA_S3_URL = \"s3://sample-bucket/banking-batch.csv\"\n\n\ndef use_model(model_id, threshold, ds_id):\n\n ml = boto3.client('machinelearning')\n\n ml.update_ml_model(MLModelId=model_id, ScoreThreshold=threshold)\n\n bp_id = 'bp-' + base64.b32encode(os.urandom(10)).decode()\n ml.create_batch_prediction(\n BatchPredictionId=bp_id,\n BatchPredictionName=\"Batch Prediction for marketing sample\",\n MLModelId=model_id,\n BatchPredictionDataSourceId=ds_id,\n OutputUri=\"s3://ccbda-upc-rui/output\"\n )\n print(\"Batch prediction created\")\n\n# Check model status, wait until it's finished\ndef poll_until_completed(model_id):\n\n ml = boto3.client(\"machinelearning\")\n delay = 2\n while True:\n model = ml.get_ml_model(MLModelId=model_id)\n status = model['Status']\n if status in ['COMPLETED', 'FAILED', 'INVALID']:\n break\n delay *= random.uniform(1.1, 2.0)\n time.sleep(delay)\n\n\ndef create_data_sources(data_s3url, input_data):\n\n ml = boto3.client(\"machinelearning\")\n ds_id = 'ds-' + base64.b32encode(os.urandom(10)).decode()\n\n ml.create_data_source_from_s3(\n DataSourceId=ds_id,\n DataSourceName=\"batch_prediction\",\n DataSpec={\n \"DataLocationS3\": data_s3url,\n \"DataSchema\": open(input_data).read(),\n },\n ComputeStatistics=False\n )\n print(\"Batch datasource created\")\n return ds_id\n\nds_id = create_data_sources(UNSCORED_DATA_S3_URL,\"banking-batch.csv.schema\")\npoll_until_completed(\"ml-W7U36YMOGZBZAMF3\")\nuse_model(\"ml-W7U36YMOGZBZAMF3\", 0.7, ds_id)","sub_path":"behbc/amazon_ml/use_model.py","file_name":"use_model.py","file_ext":"py","file_size_in_byte":1765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"470692755","text":"# Import smtplib for the actual sending function\nimport smtplib\n#import re\n\n#import requests\n#from lxml import html\n\n# Import the email modules we'll need\nfrom email.mime.text import MIMEText\n\ncomment = \"\"\"textfile = \"/tmp/foo\"\n# Open a plain text file for reading. For this example, assume that\n# the text file contains only ASCII characters.\nfp = open(textfile, 'rb')\n# Create a text/plain message\nmsg = MIMEText(fp.read())\nfp.close()\n\nme = \"pfc1@casswitz.com\"\nyou = \"pfcass@casswitz.com\"\n\nmsg['Subject'] = 'The contents of %s' % textfile\nmsg['From'] = me\nmsg['To'] = you\n\n# Send the message via our own SMTP server, but don't include the\n# envelope header.\ns = smtplib.SMTP('smtp.1and1.com', 25)\ns.login('pfcass@casswitz.com','Pfc.Mrw!0')\ns.sendmail(me, [you], msg.as_string())\ns.quit()\"\"\"\n\n\nclass SmtpClient():\n xx = smtplib.SMTP('smtp.1and1.com', 25)\n me = \"\"\n you = \"\"\n def __init__(self, from_addr, to_addr):\n self.xx.login('pfcass@casswitz.com','Pfc.Mrw!0')\n self.me = from_addr\n self.you = to_addr\n\n def __del__(self):\n self.xx.quit()\n\n def send(self, msg):\n msg['From'] = self.me\n msg['To'] = self.you\n msg['Subject'] = \"subject\"\n self.xx.sendmail(self.me, self.you, msg.as_string())\n\nsender = SmtpClient(\"pfcass@gmail.com\", \"pfcass@casswitz.com\")\nmsg = MIMEText(\"now is the time\")\n\nsender.send(msg)\ndel sender","sub_path":"sender.py","file_name":"sender.py","file_ext":"py","file_size_in_byte":1395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"549542380","text":"#!/usr/bin/python\n\nfrom os import system\n\ndef ler_arquivo(nome_arquivo, matriz, quant_ciclos):\n\twith open(nome_arquivo, \"r\") as arquivo:\n\t\tfor linha in arquivo:\n\t\t\tif not linha.startswith(\"#\"):\n\t\t\t\tdados = linha.split(\"\\t\")\n\t\t\t\tdados = [i.replace(\"\\n\", \"\") for i in dados]\n\t\t\t\tciclo = int(dados[0])\n\t\t\t\tif ciclo < quant_ciclos:\n\t\t\t\t\tq_i = float(dados[1])\n\t\t\t\t\tq_s = float(dados[2])\n\t\t\t\t\tq_r = float(dados[3])\n\t\t\t\t\tmatriz[ciclo][0] += q_s \n\t\t\t\t\tmatriz[ciclo][1] += q_i\n\t\t\t\t\tmatriz[ciclo][2] += q_r\n\t\t\t\telse:\n\t\t\t\t\treturn\n\n\ndef escrever_arquivo(nome_arquivo, matriz):\n\twith open(nome_arquivo, \"w\") as arquivo:\n\t\tfor i in matriz:\n\t\t\tarquivo.write(\"{0} {1} {2}\\n\".format(str(i[0]), str(i[1]), str(i[2])))\n\nif __name__ == \"__main__\":\n\tquant_mc = 100\n\tnome_arquivo = \"ac/grafico.txt\"\n\tquant_ciclos = 70\n\tmatriz = [[0 for i in range(3)] for j in range(quant_ciclos)]\n\tfor mc in range(quant_mc):\n\t\tsystem(\"./RODAR_RITA.sh\")\n\t\tler_arquivo(nome_arquivo, matriz, quant_ciclos)\n\tfor i in range(quant_ciclos):\n\t\tfor j in range(3):\n\t\t\tmatriz[i][j] /= quant_mc\n\tescrever_arquivo(\"ResultadoMonteCarlo.txt\", matriz)\n\n","sub_path":"MONTE_CARLO.py","file_name":"MONTE_CARLO.py","file_ext":"py","file_size_in_byte":1099,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"80478052","text":"import json\nfrom pyvis.network import Network\nimport os\n\nclass show_path:\n\n def __init__(self):\n self.graph = Network(height=\"750px\", width=\"100%\", directed=True)\n\n with open('layouts\\path_layout.json') as f:\n self.composite_options = json.load(f)\n\n\n def show_graph(self):\n dirOutput = \"output\"\n if not os.path.exists(dirOutput):\n os.makedirs(\"output\")\n self.graph.show(\"output\\path_graph.html\")\n\n def add_path(self, input, amount):\n\n input_edge = zip(input[0:len(input)-1], input[1:len(input)],amount)\n\n for i in input_edge:\n input = i[0]\n output = i[1]\n weight = i[2]\n self.graph.add_node(input)\n self.graph.add_node(output)\n self.graph.add_edge(input, output, value = weight, title = weight)\n\n self.graph.options = self.composite_options\n\n\n def show(self):\n self.show_graph()\n\n'''\ndef main():\n graph2 = show_path()\n path = [\"a2\",\"a3\",\"a4\",\"a5\",\"a6\",\"a7\", \"a8\",\"a9\",\"a10\"]\n amount = [19,18,18,18,17,16,16,19,20]\n graph2.add_path(path, amount)\n graph2.show_graph()\n\nif __name__== \"__main__\":\n main()\n'''","sub_path":"visuals/show_graph/show_path.py","file_name":"show_path.py","file_ext":"py","file_size_in_byte":1185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"468193731","text":"#!/usr/bin/env python3\n\nimport os\nfrom collections import Counter\nfrom aes import *\nfrom fn import *\n\n## An ECB/CBC detection oracle\n# Now that you have ECB and CBC working:\n\n# Write a function that encrypts data under an unknown key --- that is, a\n# function that generates a random key and encrypts under it.\n# The function should look like:\n# encryption_oracle(your-input)\n# => [MEANINGLESS JIBBER JABBER]\n# Under the hood, have the function append 5-10 bytes (count chosen randomly)\n# before the plaintext and 5-10 bytes after the plaintext. Now, have the\n# function choose to encrypt under ECB 1/2 the time, and under CBC the other\n# half (just use random IVs each time for CBC). Use rand(2) to decide which to\n# use.\n\ndef encryption_oracle(data):\n key = rkey()\n\n # append & prepend data...\n before = os.urandom(ord(os.urandom(1)) % 5 + 5)\n after = os.urandom(ord(os.urandom(1)) % 5 + 5)\n\n data = before + data + after\n\n if ord(os.urandom(1)) > 128:\n return \"ecb\", ecb_encrypt(key, data)\n else:\n iv=os.urandom(16)\n return \"cbc\", cbc_encrypt(key, iv, data)\n\n\n# Detect the block cipher mode the function is using each time. You should end\n# up with a piece of code that, pointed at a block box that might be encrypting\n# ECB or CBC, tells you which one is happening.\n\ndef detect():\n # put a couple blocks of plaintext\n testdata = b\"a\" * 64\n\n spoiler, ct = encryption_oracle(testdata)\n\n # ECB has duplicate blocks\n if check_ecb(bytes(ct)):\n assert spoiler == \"ecb\"\n print(\"correctly guessed ecb\")\n else:\n assert spoiler == \"cbc\"\n print(\"correctly guessed cbc\")\n\nfor _ in range(0, 10):\n detect()\n","sub_path":"c11.py","file_name":"c11.py","file_ext":"py","file_size_in_byte":1633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"341694623","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom numpy.linalg import solve\n\nN=20\n# set random points\nx,y=np.random.uniform(0,5,size=(N,1)),np.random.uniform(0,5,size=(N,1))\nsample=np.concatenate((x,y,np.random.randint(0,2,size=(N,1))),axis=1)\n# set initial line\nw=np.array([np.random.uniform(),np.random.uniform()])\nx1=np.arange(0,6)\ny1=w[0]*x1+w[1]\n\n#calculate dist from point to line\ndist=[]\nfor i in range(len(sample)):\n dist.append(np.dot(w,sample[i,:2])/np.linalg.norm(w,ord=2))\n\nres,result=[],[]\naccuracy=0\nfor i in range(len(sample)):\n # perpendicular condition\n k=-1/w[0]\n b=sample[i,1]-k*sample[i,0]\n #calculate pr from current point to line\n A=np.array([ [k,-1],[w[0],-1]])\n B=np.array([-b,-w[1]])\n dec=solve(A,B)\n res.append(dec)\n result.append(sample[i,1]-dec[1])\n # check labels\n if (sample[i,1]-dec[1])>0:\n sign=1\n else:\n sign=0\n if sign==sample[i,2]:\n accuracy=accuracy+1\n\nplt.plot(x1,y1,'k-')\nplt.scatter(sample[:,0],sample[:,1],c=sample[:,2],linewidths=10)\nplt.xlabel('X')\nplt.ylabel('Y')\nprint('accuracy score','{:.1f}'.format(accuracy/N*100))\n","sub_path":"linear_boarding_2.py","file_name":"linear_boarding_2.py","file_ext":"py","file_size_in_byte":1132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"127296619","text":"# Copyright (c) 2021 MobileCoin. All rights reserved.\n\n\"\"\"\nThe entrypoint for the running MOBot.\n\"\"\"\n\nimport time\n\nfrom django.core.management.base import BaseCommand\nfrom django.conf import settings\n\nfrom mobot_client.models import ChatbotSettings\nfrom mobot_client.mobot import MOBot\nfrom signald_client import Signal\nfrom mobot_client.payments import MCClient\n\n\nclass Command(BaseCommand):\n help = 'Run MOBot Client'\n\n def handle(self, *args, **kwargs):\n cb_settings = ChatbotSettings.load()\n while not cb_settings.store:\n print(\"Awaiting settings... sleeping 60 seconds! Create a store and attach to ChatbotSettings to continue.\")\n time.sleep(60)\n cb_settings.refresh_from_db()\n\n store = cb_settings.store\n bot_avatar_filename = cb_settings.avatar_filename\n bot_name = cb_settings.name\n signal = Signal(str(store.phone_number), socket_path=(settings.SIGNALD_ADDRESS, settings.SIGNALD_PORT))\n mcc = MCClient()\n try:\n mobot = MOBot(bot_name=bot_name,\n bot_avatar_filename=bot_avatar_filename,\n store=store,\n signal=signal,\n mcc=mcc)\n mobot.run_chat()\n except KeyboardInterrupt as e:\n print()\n pass\n","sub_path":"src/mobot_client/management/commands/run_mobot_client.py","file_name":"run_mobot_client.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"288468684","text":"from synaps import flags\nfrom synaps import log as logging\n\nFLAGS = flags.FLAGS\nLOG = logging.getLogger(__name__)\n\nfrom uuid import UUID, uuid4\nimport os\nimport json\nimport traceback\nimport zmq\nfrom synaps import utils\nfrom synaps.cep import storm\nfrom synaps.db import Cassandra\nfrom synaps.utils import validate_email, validate_international_phonenumber\n\n\nclass ActionBolt(storm.BasicBolt):\n BOLT_NAME = \"ActionBolt\"\n \n def initialize(self, stormconf, context):\n self.pid = os.getpid()\n self.cass = Cassandra()\n self.ctx = zmq.Context()\n self.sock = self.ctx.socket(zmq.PUSH)\n self.ENABLE_SEND_MAIL = FLAGS.get('enable_send_mail')\n self.ENABLE_SEND_SMS = FLAGS.get('enable_send_sms')\n self.NOTIFICATION_SERVER = FLAGS.get('notification_server_addr')\n self.sock.connect(self.NOTIFICATION_SERVER)\n \n def log(self, msg):\n LOG.info(msg)\n \n def tracelog(self, e):\n msg = traceback.format_exc(e)\n for line in msg.splitlines():\n self.log(\"TRACE: \" + line)\n \n def get_action_type(self, action):\n if validate_email(action):\n return \"email\"\n elif validate_international_phonenumber(action):\n return \"SMS\"\n \n def do_action(self, action, message):\n \n action_type = self.get_action_type(action)\n if action_type == \"email\":\n self.send_email(action, message)\n elif action_type == \"SMS\":\n self.send_sms(action, message)\n \n def alarm_history_state_update(self, alarmkey, alarm, notification_message):\n# notification_message = {\n# 'method': \"email\",\n# 'receivers': email_receivers,\n# 'subject': message['subject'],\n# 'body': message['body']\n# } \n item_type = 'Action'\n project_id = alarm['project_id']\n history_summary = (\"Message '%(subject)s' is sent via %(method)s\" % \n notification_message)\n timestamp = utils.utcnow()\n \n history_key = uuid4()\n column = {'project_id':project_id,\n 'alarm_key':UUID(alarmkey),\n 'alarm_name':alarm['alarm_name'],\n 'history_data': json.dumps(notification_message),\n 'history_item_type':item_type,\n 'history_summary':history_summary,\n 'timestamp':timestamp}\n \n self.cass.insert_alarm_history(history_key, column)\n storm.log(\"alarm history \\n %s\" % history_summary)\n \n def process_action(self, tup):\n \"\"\"\n message example\n \n msg = {\n 'state': new_state['stateValue'],\n 'subject': \"%s state has been changed from %s to %s\" % \n (alarm['alarm_name'], old_state['stateValue'],\n new_state['stateValue']),\n 'body': new_state['stateReason']\n }\n \"\"\" \n alarm_key = tup.values[0]\n message_buf = tup.values[1]\n message = json.loads(message_buf)\n self.log(\"start processing tup %s\" % (tup))\n \n alarm = self.cass.get_metric_alarm(UUID(alarm_key))\n \n try: \n actions_enabled = alarm['actions_enabled']\n except TypeError:\n msg = \"alarm is not found [\" + alarm_key + \"]\"\n self.log(msg)\n \n return False\n \n if message['state'] == 'OK':\n actions = json.loads(alarm['ok_actions'])\n elif message['state'] == 'INSUFFICIENT_DATA':\n actions = json.loads(alarm['insufficient_data_actions'])\n elif message['state'] == 'ALARM':\n actions = json.loads(alarm['alarm_actions'])\n \n self.log(\"actions enabled: %s actions: %s \" % (actions_enabled,\n actions))\n if actions_enabled and actions:\n if self.ENABLE_SEND_MAIL: \n email_receivers = [action for action in actions \n if self.get_action_type(action) == \"email\"]\n \n notification_message = {\n 'method': \"email\",\n 'receivers': email_receivers,\n 'subject': message['subject'],\n 'body': message['body']\n }\n \n self.sock.send_pyobj(notification_message)\n self.log(\"notify: %s \" % notification_message)\n self.alarm_history_state_update(alarm_key, alarm,\n notification_message)\n \n if self.ENABLE_SEND_SMS:\n sms_receivers = [action for action in actions\n if self.get_action_type(action) == \"SMS\"]\n \n notification_message = {\n 'method': \"SMS\",\n 'receivers': sms_receivers,\n 'subject': message['subject'],\n 'body': message['body']\n }\n \n self.sock.send_pyobj(notification_message)\n self.log(\"notify: %s \" % notification_message)\n self.alarm_history_state_update(alarm_key, alarm,\n notification_message) \n def process(self, tup):\n self.process_action(tup)\n","sub_path":"synaps-api/synaps/cep/bolt/action.py","file_name":"action.py","file_ext":"py","file_size_in_byte":5514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"399203687","text":"#__author__=penxy\n#-*- coding:utf-8-*-\n\nfrom bs4 import BeautifulSoup\nimport re\nimport time\nimport requests\nfrom lxml import etree\nfrom selenium import webdriver\nimport random\nimport json\nimport logging\nimport os,sys\n\n\n\n\nclass music163_spider:\n # 初始化数据\n def __init__(self):\n #def __init__(self,logname,fmt='%(asctime)s %(pathname)s %(logname)s %(levelname)s: %(msg)s'):\n opt = webdriver.chrome.options.Options()\n opt.set_headless()\n self.brower = webdriver.Chrome(chrome_options=opt)\n self.originURL = 'https://music.163.com/#/discover/playlist'\n self.data = list()\n #self.logger = logging.getLogger(logname)\n #format_str = logging.Formatter(fmt,datefmt=\"%Y%M%D\")\n #self.logger.setLevel(logging.DEBUG)\n #ch = logging.StreamHandler() # 输出到控制台\n #ch.setFormatter(format_str) # 输出到控制台的格式\n #fh = logging.FileHandler(logname = logname, encoding='utf-8') # 写入文件\n #fh.setFormatter(format_str) # 写入文件的格式\n #self.logger.addHandler(ch)\n #self.logger.addFilter(fh)\n\n\n\n#爬取网易云音乐中的高播放量的歌单\n\n def get_cur_time():\n #Now_time = time.strftime('%Y.%m.%d %H:%M:%S',time.localtime(time.time()))\n #print('歌单更新时间'+Now_time)\n return time.strftime('%Y.%m.%d %H:%M:%S',time.localtime(time.time()))\n\n\n def get_songslist(self,url):\n #header = {'user-agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/74.0.3729.131 Safari/537.36',\n #'referer': 'https://music.163.com/discover/playlist'}\n\n #response = requests.get(self.originURL,headers = header)\n self.brower.get(url)\n self.brower.switch_to.frame('g_iframe')\n html = self.brower.page_source\n return html\n\n\n def info_songslist(self,html):\n html_elem = etree.HTML(html)\n play_number = html_elem.xpath('//ul[@id=\"m-pl-container\"]/li/div/div/span[@class=\"nb\"]/text()')\n # //*[@id=\"m-pl-container\"]/li[1]/div/div/span[2]\n song_list_title = html_elem.xpath('//ul[@id=\"m-pl-container\"]/li/p[1]/a/@title')\n song_list_url = html_elem.xpath('//ul[@id=\"m-pl-container\"]/li/p[1]/a/@href')\n song_url = ('https://music.163.com/#'+ item for item in song_list_url)\n data = list(map(lambda a,b,c,d:{'播放量':a,'歌单名称':b,'歌单链接':c,'歌曲链接':d},\n play_number,song_list_title,song_list_url,song_url))\n return data\n\n\n def next_page_link(self,html):\n html_elem = etree.HTML(html)\n next_page_link = html_elem.xpath('//div[@id=\"m-pl-pager\"]/div[@class=\"u-page\"]/a[@class=\"zbtn znxt\"]/@href')\n if not next_page_link:\n return None\n else:\n return 'https://music.163.com/#' + next_page_link[0]\n\n #获取当前环境的桌面路径\n #def get_desktop_path(self):运行报错:\"Permission denied\"，尚未想到好的解决办法，只好将保存路径设置在非系统盘下\n #key = winreg.OpenKey(winreg.HKEY_CURRENT_USER,r'Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell Folders')\n #return winreg.QueryValueEx(key, \"Desktop\")[0]\n\n\n def spider_begin(self,path):\n #path = str(spider.get_desktop_path())\n #logger.info(\"爬取歌单列表:\" + path)\n Now_time = time.strftime('%Y.%m.%d %H_%M_%S', time.localtime(time.time()))\n print('歌单获取时间：'+ Now_time)\n time.sleep(2)\n print('开始爬取。')\n time.sleep(2)\n print('正在爬取...')\n\n html = self.get_songslist(self.originURL)\n data = self.info_songslist(html)\n self.data.extend(data)\n link = self.next_page_link(html)\n #index = 1\n #number = self.play_number(data)\n while(link):\n html = self.get_songslist(link)\n data = self.info_songslist(html)\n self.data.extend(data)\n link = self.next_page_link(html)\n #number = self.play_number(data)\n time.sleep(random.random())\n print('数据处理中...')\n time.sleep(3)\n #按播放量排序\n data_after_sort = sorted(self.data,key = lambda item:int(item['播放量'].replace('万','0000')),reverse = True)\n print('写入文件中...')\n time.sleep(5)\n filename = 'D:/'+ Now_time+ r'_163musiclist.json'#这里有个点需要注意，保存时文件名中不允许出现: 所以Now_time定义时如果用了: 在这里需要回去改掉，改成下划线\n with open(filename,'w+',encoding='utf-8') as f:\n #f.write(data.encode('utf-8'))\n #f.write(data)\n for item in data_after_sort:\n #json.dump(item,f,ensure_ascii = False)\n json.dump(item,f,ensure_ascii=False,indent=4) #indent = 4 是dump参数之一，用处是数据格式缩进显示\n #json.write(str(item)+'\\n',f, ensure_ascii=False)\n #for file in item:\n #data.append('\\n' + json.loads(str(item)))\n\n\n\n\n def write_result(self,str):\n Now_time = time.strftime('%Y.%m.%d %H_%M_%S', time.localtime(time.time()))\n filename = 'D:/'+ Now_time+ r'_163musiclist.json'\n write_result = filename\n str1 = write_result.write(str + '\\n')\n write_result.close()\n return str\n\n\n\n'''\n def get_logger(log_dir):\n fh = logging.FileHandler(log_dir, encoding='utf-8', mode='w')\n sh = logging.StreamHandler()\n logger = logging.getLogger()\n logger.setLevel(logging.DEBUG)\n fm = logging.Formatter('%(asctime)s - %(levelname)s: %(message)s')\n logger.addHandler(fh)\n logger.addHandler(sh)\n fh.setFormatter(fm)\n sh.setFormatter(fm)\n return logger\n\n\n\n\n def logging(self,logname,msg):\n Now_time = time.strftime('%Y.%m.%d %H_%M_%S', time.localtime(time.time()))\n self.logger = logging.getLogger(__name__)\n self.logger.setLevel(logging.DEBUG)\n logname = 'D:/' + Now_time + r'_163muscilistlog.txt'\n fh = logging.FileHandler(logname,encoding='utf-8')#写入文件\n ch = logging.StreamHandler(logname)#输出到控制台\n formatter = logging.Formatter(\n fmt= \"%(asctime)s %(name)s %(logname)s %(msg)s\",\n datefmt = \"%Y%M%D\"\n )\n fh.setFormatter(formatter)\n ch.setFormatter(formatter)\n self.logger.addHandler(fh)\n self.logger.addHandler(ch)\n self.logger.info(msg)\n'''\n\n\nif __name__ == '__main__':\n spider = music163_spider()\n #write = write_result()\n path = 'D://'\n #path = str(spider.get_desktop_path()) #调用之前要先实例化\n spider.spider_begin(path)\n print('爬取完成!')\n print('爬取完成的歌单保存在:' + path)\n #差一个日志输出部分\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n'''\nimport requests\nimport re\nimport time\nfrom bs4 import BeautifulSoup\n\ncat ='2'\nimg = 'https://www.dbmeinv.com/index.htm?cid='+ cat\n#https://www.dbmeinv.com/index.htm?cid=2\nend = '/dbgroup/show.htm?cid='+ cat + '&pager_offset=100'\nurls = [ ]\ndef getURLs(mainURL):\n time.sleep(1)\n headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/47.0.2526.80 Safari/537.36'}\n html = requests.get(mainURL).text\n soup = BeautifulSoup(html, 'html.parser')\n picURL = re.findall('![]()
6:\n return urls\n asoup = soup.select('.next a')[0]['href']\n Next_page = 'http://www.dbmeinv.com' + asoup\n if asoup != end:\n getURLs(Next_page)\n else:\n print('链接已处理完毕！')\n return urls\nurl = getURLs(img)\n\ni = 0\nfor each in url:\n pic = requests.get(each, timeout = 10)\n picName = 'pictures/' + str(i) + '.jpg'\n # fp = u\"Picname\"\n fp = open(u\"Picname\", 'w')\n print(pic.content)\n fp.write(pic.content)\n fp.close()\n i += 1\n\nprint('图片下载完成')\n'''\n\n\n\n\n\n","sub_path":"(code)NestMusic.py","file_name":"(code)NestMusic.py","file_ext":"py","file_size_in_byte":8177,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"157503467","text":"#! /data/sever/python/bin/python\n# -*- coding:utf-8 -*-\n\"\"\"\n@file: common.py\n@date: 2016-10-21\n\"\"\"\nimport time\nimport logging\nfrom urllib import quote\n\n__author__ = 'root'\n\nfrom crawl.base import BaseCrawler\n\n\nclass CommonCrawler(BaseCrawler):\n \"\"\"\n CommonCrawler\n \"\"\"\n\n def login(self):\n \"\"\"\n 处理登录\n :return:\n \"\"\"\n self.get('http://pub.alimama.com/')\n self.check_login()\n return self.driver\n\n def check_login(self, count=1):\n \"\"\"\n 判断是否登录\n :return:\n \"\"\"\n while not self._check_login_text:\n logging.info(\"登录失败，等待10s后重新检测！\")\n time.sleep(10)\n return True\n\n @property\n def _check_login_text(self):\n \"\"\"\n 检查登录状态文本\n :return:\n \"\"\"\n try:\n text = self.feb_xpath(self.CHECK_LOGIN_XPATH).text.encode(\"utf-8\")\n logging.info(\"now user_info：%s\", str(text))\n return text\n except Exception as ex:\n logging.error(\"获取检查登录状态文本异常：%s\", str(ex))\n return \"\"\n\n def click_ok(self, count=1, max_count=3):\n \"\"\"\n 处理登录后的提示消息\n :return:\n \"\"\"\n if count > max_count:\n return\n try:\n self.feb_xpath(\".//*[@id='dxjhGuide']/span\").click()\n except Exception as ex:\n logging.error(\"处理登录后的提示消息异常：%s\", str(ex))\n return self.click_ok(count + 1, max_count)\n logging.info(\"自动点击ok确认按钮！\")\n\n def immediate_promotion(self, index, count=1, max_count=10):\n \"\"\"\n 立即推广\n :param index:\n :return:\n \"\"\"\n if count > max_count:\n return False\n try:\n self.feb_xpath(\n \".//*[@id='J_search_results']/div/div[%s]/div[4]/a[1]\" % index\n ).click()\n time.sleep(2)\n except Exception as ex:\n logging.error(\"自动点击立即推广按钮异常：%s\", str(ex))\n return self.immediate_promotion(index, count + 1, max_count)\n else:\n logging.info(\"自动点击立即推广按钮成功！\")\n return True\n\n def determine_promotion(self, count=1, max_count=20):\n \"\"\"\n 选择好推广渠道后，确定推广渠道\n :return:\n \"\"\"\n if count > max_count:\n return False\n try:\n self.feb_xpath(\n \".//*[@id='J_global_dialog']/div/div[3]/button[1]\"\n ).click()\n except Exception as ex:\n logging.error(\"选择好推广渠道后，确定推广渠道异常：%s\", str(ex))\n return self.determine_promotion(count + 1, max_count)\n else:\n logging.info(\"选择好推广渠道后，确定推广渠道成功！\")\n return True\n\n def get_short_link(self, count=1, max_count=10):\n \"\"\"\n 获取短连接\n :return:\n \"\"\"\n if count > max_count:\n return \"\", \"\"\n try:\n try:\n # 获取没有优惠券的淘口令\n commodity_link = self.feb_xpath(\n \".//*[@id='clipboard-target']\").text\n logging.info(\"获取短连接成功！\")\n return commodity_link, \"\"\n except Exception:\n commodity_link = self.feb_xpath(\n \".//*[@id='clipboard-target-1']\"\n ).get_attribute(\"value\")\n coupons_link = self.feb_xpath(\n \".//*[@id='clipboard-target-2']\"\n ).get_attribute(\"value\")\n logging.info(\"获取短连接和券连接成功！\")\n return commodity_link, coupons_link\n except Exception as ex:\n logging.error(\"获取短连接异常：%s\", str(ex))\n return self.get_short_link(count + 1, max_count)\n\n def click_key(self, count=1, max_count=10):\n \"\"\"\n 点击淘口令\n :return:\n \"\"\"\n if count > max_count:\n return self.driver\n try:\n self.feb_xpath(\n \".//*[@id='magix_vf_code']/div/div/ul/li[4]\"\n ).click()\n except Exception as ex:\n logging.error(\"自动点击淘口令异常：%s\", str(ex))\n return self.click_key(count + 1, max_count)\n else:\n logging.info(\"自动点击淘口令成功！\")\n return self.driver\n\n def get_key(self, count=1, max_count=10):\n \"\"\"\n 获取淘口令\n :return:\n \"\"\"\n if count == 1:\n self.click_key()\n if count > max_count:\n return \"\", \"\"\n try:\n try:\n # 获取没有优惠券的淘口令\n commodity_key = self.feb_xpath(\".//*[@id='clipboard-target']\").text\n logging.info(\"获取淘口令成功！\")\n return commodity_key, \"\"\n except Exception:\n commodity_key = self.feb_xpath(\n \".//*[@id='clipboard-target-1']\"\n ).get_attribute(\"value\")\n coupons_key = self.feb_xpath(\n \".//*[@id='clipboard-target-2']\"\n ).get_attribute(\"value\")\n logging.info(\"获取淘口令和券口令成功！\")\n return commodity_key, coupons_key\n except Exception as ex:\n logging.error(\"获取淘口令异常：%s\", str(ex))\n return self.get_key(count + 1, max_count)\n\n def search(self, key):\n \"\"\"\n 搜索商品\n :param key:\n :return:\n \"\"\"\n url = \"http://pub.alimama.com/promo/search/\" \\\n \"index.htm?queryType=2&q={}\".format(quote(key))\n logging.info(\"即将搜索商品：%s\", url)\n return self.get(url)\n\n def confirm_coupons(self, url):\n \"\"\"\n 确认优惠券\n :return:\n \"\"\"\n man, jian = 0.0, 0.0\n try:\n self.get(url)\n man = self.feb_xpath(\n \".//*[@id='mx_6']/div/div/span[2]\"\n ).text.replace(u\"购物满\", \"\").replace(u\"元可使用\", \"\")\n jian = self.feb_xpath(\n \".//*[@id='mx_6']/div/div/span[1]\"\n ).text.replace(u\"¥\", \"\")\n man = float(man)\n jian = float(jian)\n except Exception as ex:\n logging.error(\"确认优惠券异常：%s\", str(ex))\n return {\n \"coupons_man\": man,\n \"coupons_jian\": jian\n }\n","sub_path":"apps/crawler_tbk/common.py","file_name":"common.py","file_ext":"py","file_size_in_byte":6645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"413982466","text":"import pygame\nimport sys\nimport os\nimport random\n\nFPS = 50\n\n\ndef load_image(name, colorkey=None):\n fullname = os.path.join('data', name)\n # если файл не существует, то выходим\n if not os.path.isfile(fullname):\n print(f\"Файл с изображением '{fullname}' не найден\")\n sys.exit()\n image = pygame.image.load(fullname)\n if colorkey is not None:\n # image = image.convert()\n if colorkey == -1:\n colorkey = image.get_at((0, 0))\n image.set_colorkey(colorkey)\n else:\n pass\n # image = image.convert_alpha()\n return image\n\n\ndef load_level(filename):\n filename = \"data/map/\" + filename\n # читаем уровень, убирая символы перевода строки\n with open(filename, 'r') as mapFile:\n level_map = [line.strip() for line in mapFile]\n max_width = max(map(len, level_map))\n return list(map(lambda x: x.ljust(max_width, '.'), level_map))\n\n\ndef terminate():\n pygame.quit()\n sys.exit()\n\n\nclass Tile(pygame.sprite.Sprite):\n def __init__(self, tile_type, pos_x, pos_y, f=False):\n super().__init__(sprite_group)\n self.image = tile_images[tile_type]\n if f:\n self.rect = self.image.get_rect().move(\n tile_width * pos_x - 1, tile_height * pos_y - 61)\n else:\n self.rect = self.image.get_rect().move(\n tile_width * pos_x, tile_height * pos_y)\n self.abs_pos = (self.rect.x, self.rect.y)\n\n\nclass Player(pygame.sprite.Sprite):\n def __init__(self, pos_x, pos_y):\n super().__init__(hero_group)\n self.frames = [pygame.transform.scale(load_image('hero_1.png'), (48, 100)),\n pygame.transform.scale(load_image('hero_2.png'), (48, 100)),\n pygame.transform.scale(load_image('hero_3.png'), (48, 100)),\n pygame.transform.scale(load_image('hero_2.png'), (48, 100))]\n self.cur_frame = 0\n self.image = self.frames[self.cur_frame]\n self.rect = self.image.get_rect().move(\n tile_width * pos_x + 1, tile_height * pos_y - 50)\n self.pos = (pos_x, pos_y)\n\n def move(self, x, y):\n self.pos = pos_x, pos_y = (x, y)\n self.rect = self.image.get_rect().move(\n tile_width * pos_x + 1, tile_height * pos_y - 50)\n\n def update(self):\n self.cur_frame = (self.cur_frame + 1) % len(self.frames)\n self.image = self.frames[self.cur_frame]\n\n\ndef show_task(ques_num):\n image_path = lvl[seed[tek]][0][ques_num]\n question = lvl[seed[tek]][1][ques_num]\n correct_answer = lvl[seed[tek]][2][ques_num].lower()\n\n fon = pygame.transform.scale(load_image('Terminal.jpg'), (width, height))\n screen.blit(fon, (0, 0))\n font = pygame.font.SysFont('Arial', 30)\n question = '>' + question\n quest = font.render(question, False, (255, 255, 255))\n screen.blit(quest, (300, 270))\n hint = font.render('>Ваш ответ', False, (255, 255, 255))\n screen.blit(hint, (300, 550))\n\n all_sprites = pygame.sprite.Group()\n task = pygame.sprite.Sprite()\n task.image = load_image(image_path)\n task.rect = 900, 200\n all_sprites.add(task)\n alphabet = {113: 'й', 119: 'ц', 101: 'у', 114: 'к', 116: 'е', 121: 'н', 117: 'г', 105: 'ш', 111: 'щ', 112: 'з',\n 1093: 'х', 1098: 'ъ', 97: 'ф', 115: 'ы', 100: 'в', 102: 'а', 103: 'п', 104: 'р', 106: 'о', 107: 'л',\n 108: 'д', 1078: 'ж', 1101: 'э', 122: 'я', 120: 'ч', 99: 'с', 118: 'м', 98: 'и', 110: 'т', 109: 'ь',\n 1073: 'б', 1102: 'ю', 1105: 'ё', 49: '1', 50: '2', 51: '3', 52: '4', 53: '5', 54: '6', 55: '7',\n 56: '8', 57: '9', 48: '0', 45: '-', 46: ',', 32: ' '}\n ans = '>'\n corr_flag = ''\n clock = pygame.time.Clock()\n\n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n terminate()\n elif event.type == pygame.KEYDOWN:\n if event.key == 13:\n ans_copy = ans\n ans = '>'\n screen.blit(fon, (0, 0))\n all_sprites.draw(screen)\n if ans_copy[1:] == correct_answer:\n pygame.draw.circle(screen, pygame.Color('green'), (1000, 700), 100)\n lvl[seed[tek]][3][ques_num] = True\n corr_flag = True\n else:\n corr_flag = False\n pygame.draw.circle(screen, pygame.Color('red'), (1000, 700), 100)\n screen.blit(quest, (300, 270))\n screen.blit(hint, (300, 550))\n ans_copy = ''\n elif event.key == 8:\n ans = ans[:-1]\n if '>' not in ans:\n ans = '>' + ans\n screen.blit(fon, (0, 0))\n all_sprites.draw(screen)\n if isinstance(corr_flag, bool):\n if corr_flag:\n pygame.draw.circle(screen, pygame.Color('green'), (1000, 700), 100)\n else:\n pygame.draw.circle(screen, pygame.Color('red'), (1000, 700), 100)\n screen.blit(quest, (300, 270))\n screen.blit(hint, (300, 550))\n elif event.key == 27:\n return\n else:\n try:\n ans += alphabet[event.key]\n except Exception:\n pass\n all_sprites.draw(screen)\n display_ans = font.render(ans, False, (255, 255, 255))\n screen.blit(display_ans, (300, 610))\n pygame.display.flip()\n pygame.display.flip()\n clock.tick(FPS)\n\n\ndef generate_level(level, f=0):\n global sprite_group\n new_player, x, y = None, None, None\n hx, hy = 0, 0\n hx1, hy1, hx2, hy2 = 0, 0, 0, 0\n sprite_group = pygame.sprite.Group()\n for y in range(len(level)):\n for x in range(len(level[y])):\n if level[y][x] == '@':\n hx, hy = x, y\n elif level[y][x] == 'V':\n hx2, hy2 = x, y\n elif level[y][x] == 'X':\n hx1, hy1 = x, y\n elif level[y][x] == 'B':\n Tile('0-0', x, y)\n elif level[y][x] == '1' or level[y][x] == '2':\n Tile('dor', x, y)\n elif level[y][x] == 'D':\n s, k = 0, 0\n for i in lvl[seed[tek]][3]:\n k += 1\n if i:\n s += 1\n st = str(k) + '-' + str(s)\n Tile(st, x, y)\n elif level[y][x] == '?' or level[y][x] == 'f':\n Tile('kvest', x, y, f=True)\n if f == 1:\n return hx1, hy1, x, y\n elif f == 2:\n return hx2, hy2, x, y\n return hx, hy, x, y\n\n\ndef new_lvl(nam):\n global tek, level_map, max_x, max_y, seed, hero, fon\n f = 0\n if nam == '1':\n tek -= 1\n f = 1\n elif nam == '2':\n s, k = 0, 0\n for i in lvl[seed[tek]][3]:\n k += 1\n if i:\n s += 1\n if s == k:\n tek += 1\n f = 2\n if f:\n name = 'map_' + seed[tek] + '.txt'\n level_map = load_level(name)\n name = 'map/' + seed[tek] + '_fon.jpg'\n fon = load_image(name)\n hx, hy, max_x, max_y = generate_level(level_map, f=f)\n hero.move(hx, hy)\n\n\ndef move(heros, movement=None):\n global running\n x, y = heros.pos\n new_x, new_y = x, y\n if movement == 'left':\n if x > 0 and level_map[y][x - 1] != '#':\n new_x, new_y = (x - 1, y)\n elif x > 0 and y - 1 >= 0 and level_map[y - 1][x - 1] != '#':\n new_x, new_y = (x - 1, y - 1)\n elif movement == 'right':\n if x < max_x and level_map[y][x + 1] != '#':\n new_x, new_y = (x + 1, y)\n elif x < max_x and y - 1 >= 0 and level_map[y - 1][x + 1] != '#':\n new_x, new_y = (x + 1, y - 1)\n elif movement == None:\n if y < max_y - 1 and level_map[y + 1][x] != '#':\n new_x, new_y = (x, y + 1)\n nam = level_map[new_y][new_x]\n if nam == '1' or nam == 'B':\n new_lvl('1')\n elif nam == '2' or nam == 'D':\n new_lvl('2')\n elif nam == 'R':\n running = False\n else:\n heros.move(new_x, new_y)\n\n\ndef check_position(hero):\n x, y = hero.pos\n f = False\n if level_map[y][x] == '?':\n ques_num = lvl[seed[tek]][4][(y, x)]\n f = True\n elif x >= 1 and level_map[y][x - 1] == '?':\n ques_num = lvl[seed[tek]][4][(y, x - 1)]\n f = True\n elif x <= max_x and level_map[y][x + 1] == '?':\n ques_num = lvl[seed[tek]][4][(y, x + 1)]\n f = True\n if f:\n show_task(ques_num)\n generate_level(level_map)\n\ndef start_screen():\n fon = load_image('start_screen_1.jpg')\n k = 1\n screen.blit(fon, (0, 0))\n while True:\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n terminate()\n elif event.type == pygame.KEYDOWN or \\\n event.type == pygame.MOUSEBUTTONDOWN:\n if k < 3:\n k += 1\n name = 'start_screen_' + str(k) + '.jpg'\n fon = load_image(name)\n screen.blit(fon, (0, 0))\n else:\n return # начинаем игру\n pygame.display.flip()\n clock.tick(FPS)\n\nif __name__ == '__main__':\n all_sprites = pygame.sprite.Group()\n sprite_group = pygame.sprite.Group()\n hero_group = pygame.sprite.Group()\n tile_images = {\n 'dor': pygame.transform.scale(load_image('box_n.png'), (50, 50)),\n '0-0': pygame.transform.scale(load_image('box_v.png'), (50, 50)),\n '1-0': load_image('box_1_0.png'),\n '1-1': load_image('box_1_1.png'),\n '2-0': load_image('box_2_0.png'),\n '2-1': load_image('box_2_1.png'),\n '2-2': load_image('box_2_2.png'),\n '3-0': load_image('box_3_0.png'),\n '3-1': load_image('box_3_1.png'),\n '3-2': load_image('box_3_2.png'),\n '3-3': load_image('box_3_3.png'),\n 'kvest': pygame.transform.scale(load_image('kvest.png'), (52, 111)),\n 'wall': pygame.transform.scale(load_image('kodred.png'), (50, 50))\n }\n tile_width = tile_height = 50\n pygame.init()\n pygame.display.set_caption('Subvectio certamine')\n size = width, height = 1500, 1000\n FPS = 50\n\n lvl = {\n '0': [['puzzle/lol.jpg', 'puzzle/twitter.png'], ['Как называется этот бренд?'] * 2, ['Яндекс Еда', 'Твиттер'], [False] * 2, {(13, 0): 0, (6, 27): 1}],\n '1': [['puzzle/Big_Tasty.jpg', 'puzzle/cheezburger.jpg'], ['Какое блюдо изображено на картинке?'] * 2,\n ['Биг Тести', 'чизбургер'], [False] * 2, {(15, 14): 0, (13, 23): 1}],\n '2': [['puzzle/vopper_barbeku.png', 'puzzle/king.png'], ['Какое блюдо изображено на картинке?'] * 2,\n ['Воппер барбекю', 'чикин кинг'], [False] * 2, {(9, 8): 0, (9, 26): 1}],\n '3': [['puzzle/twister.png', 'puzzle/shaurma.jpg', 'puzzle/pepperoni.png'],\n ['Какое блюдо изображено на картинке?'] * 3, ['твистер', 'шаурма', 'пепперони'],\n [False] * 3, {(10, 17): 0, (13, 11): 1, (15, 7): 2}],\n '4': [['puzzle/Pepsi.png', 'puzzle/Happy_Meal.png'],\n ['Как называется этот бренд?',\n 'Какое блюдо изображено на картинке?'], ['пепси', 'Хеппи мил'],\n [False] * 2, {(3, 12): 0, (9, 12): 1}],\n '5': [['puzzle/DeliveryClub.png'], ['Кто/что это?'], ['КОНКУРЕНТЫ'], [False], {(17, 18): 0}],\n 'k': [[], [], [False] * 0, {}]\n }\n\n screen = pygame.display.set_mode(size)\n running = True\n seed = ['1', '2', '3', '4', '5']\n random.shuffle(seed)\n seed = ['0'] + seed + ['k']\n clock = pygame.time.Clock()\n level_map = load_level('map_0.txt')\n tek = 0\n hx, hy, max_x, max_y = generate_level(level_map)\n hero = Player(hx, hy)\n ticks, speed = 0, 25\n fon = load_image('map/0_fon.jpg')\n ter = True\n # fon = pygame.transform.scale(load_image('fon3.jpg'), (width, height))\n start_screen()\n while running:\n keys = None\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n running = False\n elif event.type == pygame.KEYDOWN and event.key == pygame.K_e:\n check_position(hero)\n # screen.fill((0, 0, 0))\n screen.blit(fon, (0, 0))\n if ticks == speed:\n hero.update()\n ticks = 0\n if ticks % 6 == 0:\n keys = pygame.key.get_pressed()\n if keys[pygame.K_LEFT] or keys[pygame.K_a]:\n move(hero, 'left')\n elif keys[pygame.K_RIGHT] or keys[pygame.K_d]:\n move(hero, 'right')\n if ticks % 3 == 0 and ter:\n move(hero)\n ticks += 1\n all_sprites.draw(screen)\n sprite_group.draw(screen)\n hero_group.draw(screen)\n clock.tick(FPS)\n pygame.display.flip()\n pygame.quit()\n","sub_path":"Рисуем.py","file_name":"Рисуем.py","file_ext":"py","file_size_in_byte":13542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"652184226","text":"import heapq\nimport random\nimport pygame\n\n# Define some colors\nBLACK = ( 0, 0, 0)\nWHITE = ( 255, 255, 255)\nGREEN = ( 0, 255, 0)\nRED = ( 255, 0, 0)\nBLUE = ( 0, 0, 255)\n \n# This sets the width and height of each grid location\nwidth = 20\nheight = 20\n \n# This sets the margin between each cell\nmargin = 5\n\n# Set the height and width of the screen\nsize = [280, 280]\n\n \nscreen = pygame.display.set_mode(size)\n \nclass Cell(object):\n def __init__(self, x, y, reachable):\n \"\"\"\n Initialize new cell\n\n @param x cell x coordinate\n @param y cell y coordinate\n @param reachable is cell reachable? not a wall?\n \"\"\"\n self.reachable = reachable\n self.visited = False\n self.path = False\n self.x = x\n self.y = y\n self.parent = None\n self.g = 0\n self.h = 0\n self.f = 0\n def __lt__(self, other):\n return self.f < other.f\n \nclass AStar(object):\n def __init__(self):\n self.opened = []\n heapq.heapify(self.opened)\n self.closed = set()\n self.cells = []\n self.grid_height = 11\n self.grid_width = 11\n\n def init_grid(self):\n for x in range(self.grid_width):\n for y in range(self.grid_height):\n reachable = True\n if random.randint(1,10) < 3:\n reachable = False\n \"\"\"if x==1 and y==1:\n reachable = True\n if x==self.grid_width-1 and y==self.grid_height-1:\n reachable = True\"\"\"\n self.cells.append(Cell(x, y, reachable))\n self.start = self.get_cell(0, 0)\n self.end = self.get_cell(10, 10)\n self.start.reachable = True\n self.end.reachable = True\n self.start.path = True\n self.end.path = True\n\n def get_heuristic(self, cell):\n \"\"\"\n Compute the heuristic value H for a cell: distance between\n this cell and the ending cell multiply by 10.\n\n @param cell\n @returns heuristic value H\n \"\"\"\n return 10 * (abs(cell.x - self.end.x) + abs(cell.y - self.end.y))\n\n def get_cell(self, x, y):\n \"\"\"\n Returns a cell from the cells list\n\n @param x cell x coordinate\n @param y cell y coordinate\n @returns cell\n \"\"\"\n return self.cells[x * self.grid_height + y]\n\n def get_adjacent_cells(self, cell):\n \"\"\"\n Returns adjacent cells to a cell. Clockwise starting\n from the one on the right.\n\n @param cell get adjacent cells for this cell\n @returns adjacent cells list \n \"\"\"\n cells = []\n if cell.x < self.grid_width-1:\n cells.append(self.get_cell(cell.x+1, cell.y))\n if cell.y > 0:\n cells.append(self.get_cell(cell.x, cell.y-1))\n if cell.x > 0:\n cells.append(self.get_cell(cell.x-1, cell.y))\n if cell.y < self.grid_height-1:\n cells.append(self.get_cell(cell.x, cell.y+1))\n return cells\n\n def display_path(self):\n cell = self.end\n while cell.parent is not self.start:\n cell = cell.parent\n cell.path = True\n print('path: cell: %d,%d' % (cell.x, cell.y))\n \n \n def display_grid(self):\n pygame.display.set_caption(\"A star implementation\")\n screen.fill(BLACK)\n done = False\n while done == False:\n for event in pygame.event.get(): # User did something\n if event.type == pygame.QUIT: # If user clicked close\n done = True\n else:\n # Draw the grid\n for cell in self.cells:\n color = WHITE\n if cell.reachable == True:\n color = GREEN\n if cell.path == True:\n color = RED\n pygame.draw.rect(screen, color,\n [(margin+width)*cell.x+margin, (margin+height)*cell.y+margin,\n #[(margin+width)*cell.x, (margin+height)*cell.y,\n width, height])\n \n pygame.display.flip()\n pygame.quit()\n \n \"\"\"def compare(self, cell1, cell2):\n \"\"\n Compare 2 cells F values\n\n @param cell1 1st cell\n @param cell2 2nd cell\n @returns -1, 0 or 1 if lower, equal or greater\n \"\"\n if cell1.f < cell2.f:\n return -1\n elif cell1.f > cell2.f:\n return 1\n return 0\n \"\"\"\n def update_cell(self, adj, cell):\n \"\"\"\n Update adjacent cell\n\n @param adj adjacent cell to current cell\n @param cell current cell being processed\n \"\"\"\n adj.g = cell.g + 10\n adj.h = self.get_heuristic(adj)\n adj.parent = cell\n adj.f = adj.h + adj.g\n\n def process(self): \n # add starting cell to open heap queue\n heapq.heappush(self.opened, (self.start.f, self.start))\n while len(self.opened):\n # pop cell from heap queue \n f, cell = heapq.heappop(self.opened)\n # add cell to closed list so we don't process it twice\n self.closed.add(cell)\n # if ending cell, display found path\n if cell is self.end:\n self.display_path()\n break\n # get adjacent cells for cell\n adj_cells = self.get_adjacent_cells(cell)\n for adj_cell in adj_cells:\n if adj_cell.reachable and adj_cell not in self.closed:\n if (adj_cell.f, adj_cell) in self.opened:\n # if adj cell in open list, check if current path is\n # better than the one previously found\n # for this adj cell.\n if adj_cell.g > cell.g + 10:\n self.update_cell(adj_cell, cell)\n else:\n self.update_cell(adj_cell, cell)\n # add adj cell to open list\n heapq.heappush(self.opened, (adj_cell.f, adj_cell))\n\n\ndef main():\n pygame.init()\n \n\n # Set title of screen\n\n a = AStar()\n a.init_grid()\n #a.display_grid()\n a.process()\n a.display_grid()\n \n \nif __name__ == '__main__':\n main()\n\n\"\"\"\n# Used to manage how fast the screen updates\nclock = pygame.time.Clock()\n \n# -------- Main Program Loop -----------\nwhile done == False:\n for event in pygame.event.get(): # User did something\n if event.type == pygame.QUIT: # If user clicked close\n done = True # Flag that we are done so we exit this loop\n \"\"elif event.type == pygame.MOUSEBUTTONDOWN:\n # User clicks the mouse. Get the position\n pos = pygame.mouse.get_pos()\n # Change the x/y screen coordinates to grid coordinates\n column = pos[0] // (width + margin)\n row = pos[1] // (height + margin)\n # Set that location to zero\n grid[row][column] = 1\n print(\"Click \", pos, \"Grid coordinates: \", row, column)\"\"\n \n # Set the screen background\n \n \n # Limit to 60 frames per second\n clock.tick(60)\n\"\"\"\n","sub_path":"AI/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":7326,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"437564250","text":"# coding=utf-8\n# Copyright 2016 Pants project contributors (see CONTRIBUTORS.md).\n# Licensed under the Apache License, Version 2.0 (see LICENSE).\n\nfrom __future__ import (absolute_import, division, generators, nested_scopes, print_function,\n unicode_literals, with_statement)\n\nimport json\nimport os\n\nfrom pants.util.contextutil import temporary_file_path\nfrom pants_test.pants_run_integration_test import PantsRunIntegrationTest\n\n\nclass RunTrackerIntegrationTest(PantsRunIntegrationTest):\n def test_stats_local_json_file(self):\n with temporary_file_path() as tmpfile:\n pants_run = self.run_pants(['test',\n '--run-tracker-stats-local-json-file={}'.format(tmpfile),\n 'testprojects/src/java/org/pantsbuild/testproject/unicode/main'])\n self.assert_success(pants_run)\n\n with open(tmpfile, 'r') as fp:\n stats_json = json.load(fp)\n self.assertIn('outcomes', stats_json)\n self.assertEqual(stats_json['outcomes']['main:test'], 'SUCCESS')\n self.assertIn('artifact_cache_stats', stats_json)\n self.assertIn('run_info', stats_json)\n self.assertIn('self_timings', stats_json)\n self.assertIn('cumulative_timings', stats_json)\n self.assertIn('pantsd_stats', stats_json)\n\n def test_workunit_failure(self):\n pants_run = self.run_pants([\n '--pythonpath={}'.format(os.path.join(os.getcwd(), 'tests', 'python')),\n '--backend-packages={}'.format('pants_test.goal.data'),\n 'run-dummy-workunit',\n '--no-success'\n ])\n # Make sure the task actually happens and of no exception.\n self.assertIn('[run-dummy-workunit]', pants_run.stdout_data)\n self.assertNotIn('Exception', pants_run.stderr_data)\n self.assert_failure(pants_run)\n\n def test_workunit_success(self):\n pants_run = self.run_pants([\n '--pythonpath={}'.format(os.path.join(os.getcwd(), 'tests', 'python')),\n '--backend-packages={}'.format('pants_test.goal.data'),\n 'run-dummy-workunit',\n '--success'\n ])\n self.assert_success(pants_run)\n","sub_path":"tests/python/pants_test/goal/test_run_tracker_integration.py","file_name":"test_run_tracker_integration.py","file_ext":"py","file_size_in_byte":2100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"401072845","text":"import argparse\nimport os\n\nimport matplotlib\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nfrom utils.plot import *\n\nMIN_Y = None\nMAX_Y = None\nIS_ERROR = False\nNUM_ERROR = 25\nFIGSIZE = (6.0, 4.5)\nFONTSIZE = 14\nLEGEND_FONTSIZE = 12\nFIGURE_FORMAT = \"pdf\"\n\nplt.style.use(\"ggplot\")\nset_plot_rc()\n\ndef plot_regrets(results, xlabel, ylabel):\n plt.clf()\n plt.figure(figsize=FIGSIZE)\n plots = {}\n\n for algorithm_name, result in results.items():\n num_repeat, num_iter = result.shape\n cum_regret = np.cumsum(result, axis=1)\n mean = np.mean(cum_regret, axis=0)\n error = None\n if IS_ERROR:\n error = np.std(cum_regret, axis=0) / np.sqrt(num_repeat)\n # error *= (np.arange(0, len(error)) % (num_iter // NUM_ERROR) == 0)\n\n color, linestyle, label = PATTERNS[algorithm_name]\n p = plot_result_uncertainty(mean, color, label, linestyle, error)\n # p = plt.errorbar(range(len(mean)), mean, color=color, yerr=error, label=label, linestyle=linestyle)\n plots[label] = p\n\n handle_labels = [(plots[label], label) for label in LABELS if label in plots]\n handles, labels = zip(*handle_labels)\n renamed_labels = labels\n\n plt.legend(handles, tuple(renamed_labels), fontsize=LEGEND_FONTSIZE)\n plt.grid(axis=\"y\", linestyle=\"-.\")\n plt.ylim(MIN_Y, MAX_Y)\n plt.xlabel(xlabel, fontsize=FONTSIZE)\n plt.ylabel(ylabel, fontsize=FONTSIZE)\n ax = plt.gca()\n ax.xaxis.set_tick_params(labelsize=FONTSIZE)\n ax.yaxis.set_tick_params(labelsize=FONTSIZE)\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n parser.add_argument(\"-i\", \"--input\", dest=\"input\", type=str, required=True, help=\"input folder name\")\n parser.add_argument(\"-o\", \"--output\", dest=\"output\", type=str, required=True, help=\"output folder name\")\n args = parser.parse_args()\n\n datasetname = args.input.strip(\"/\").split(\"/\")[-1]\n filenames = os.listdir(args.input)\n filenames = [filename for filename in filenames if filename.startswith(\"all_round_regrets_\") and filename.endswith(\".npy\")]\n results = {}\n\n for filename in filenames:\n splited_algorithm_name = filename.split(\"_\")[3:-1]\n algorithm_name = \"_\".join(splited_algorithm_name)\n results[algorithm_name] = np.load(os.path.join(args.input, filename))\n\n noshare_res = {algorithm_name: results[algorithm_name] for algorithm_name in PATTERNS_NOSHARE}\n plot_regrets(noshare_res, xlabel=\"Iteration\", ylabel=\"Cumulative Regret\")\n plt.savefig(os.path.join(args.output, \"regret_{}_noshare.{}\".format(datasetname, FIGURE_FORMAT)), bbox_inches=\"tight\", format=FIGURE_FORMAT)\n\n shareattribute_res = {algorithm_name: results[algorithm_name] for algorithm_name in PATTERNS_SHAREATTRIBUTE}\n plot_regrets(shareattribute_res, xlabel=\"Iteration\", ylabel=\"Cumulative Regret\")\n plt.savefig(os.path.join(args.output, \"regret_{}_shareattribute.{}\".format(datasetname, FIGURE_FORMAT)), bbox_inches=\"tight\", format=FIGURE_FORMAT)\n","sub_path":"scripts/plot_regret_all_uncertainty.py","file_name":"plot_regret_all_uncertainty.py","file_ext":"py","file_size_in_byte":2998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"308625923","text":"# __init__.py is a special Python file that allows a directory to become\n# a Python package so it can be accessed using the 'import' statement.\n\nfrom flask import Blueprint\nfrom flask_restplus import Api, Resource, fields, reqparse\nfrom datetime import datetime\n\nfrom util import util\nfrom .model import TaskApp\nfrom tastore import db\nfrom .constants import TASKAPP_STATUS\nfrom settings import PER_PAGE\n\nmain_blueprint = Blueprint('main', __name__, template_folder='templates')\n\n\napi = Api(main_blueprint, version='1.0', title='Task App API',\n description='Task App API')\n\nuser_model = api.model('User Model', {\n 'uid': fields.String(readonly=True,\n description='User ID'),\n 'username': fields.String(readonly=True,\n description='Username')\n})\n\ntask_app_model = api.model('Task App', {\n 'app_id': fields.String(readOnly=True,\n description='The task unique identifier'),\n 'name': fields.String(required=True,\n description='The task name'),\n 'author_id': fields.String(readOnly=True,\n description='The user id of the author'),\n 'author': fields.Nested(user_model),\n 'created_date': fields.DateTime(readOnly=True,\n dt_format='rfc822',\n description='The date time when this task app is created'),\n 'last_update': fields.DateTime(readOnly=True,\n dt_format='rfc822',\n description='The last time when this task app is updated'),\n 'downloads': fields.Integer(readonly=True,\n description='Number of downloads'),\n 'status': fields.String(choices=(\n TASKAPP_STATUS.ACTIVE,\n TASKAPP_STATUS.INACTIVE,\n TASKAPP_STATUS.SUSPENDED),\n description='The status of the Task App. ACTIVE, INACTIVE, SUSPENDED'),\n 'price': fields.Float(description='The price of each task app download'),\n 'description': fields.String(description='The description of the task app'),\n 'current_task': fields.String(description='The tid of the task in the task app')\n})\n\ntask_model = api.model('Task Instance', {\n 'tid': fields.String(readOnly=True,\n description='tid of the task instance'),\n 'name': fields.String(readOnly=True,\n description='Name of the task instance'),\n 'description': fields.String(readOnly=True,\n description='Description of the task instance'),\n 'status': fields.String(readOnly=True,\n description='The status of the task instance'),\n 'roles': fields.List(fields.String, readOnly=True,\n description='User defined roles of the task instance'),\n 'expected_effort_unit': fields.String(readOnly=True,\n description='The unit of the expected effort'),\n 'expected_effort_num': fields.Float(readOnly=True,\n description='The numeric value of the expected effort')\n})\n\n\nns = api.namespace('TaskApp', description='Task App operations')\n\n\ndef register_blueprints(app):\n app.register_blueprint(main_blueprint, url_prefix='/api/v1/tastore')\n\n\ndef get_task_app(app_id):\n task_app = TaskApp.query.filter_by(app_id=app_id).first()\n if task_app is None:\n api.abort(404, \"Cannot find Task App with app_id {}\".format(app_id))\n return task_app\n\n# Basic parser for parsing uid in cookie section\nparser = reqparse.RequestParser()\nparser.add_argument('uid', type=str, location='cookies')\n\ntask_app_parser = reqparse.RequestParser()\ntask_app_parser.add_argument('name', required=True, type=str, location='form')\ntask_app_parser.add_argument('description', type=str, location='form')\n\ntask_app_list_parser = reqparse.RequestParser()\ntask_app_list_parser.add_argument('keyword', type=str, location='args')\ntask_app_list_parser.add_argument('author_id', type=str, location='args')\n\ntid_parser = reqparse.RequestParser()\ntid_parser.add_argument('tid', type=str, location='form', required=True)\n\n\n@ns.route('/')\nclass TaskAppListView(Resource):\n\n @ns.doc('keyword Task App', params={\n 'keyword': 'String for keyword',\n 'author_id': \"Author's uid\"\n })\n @api.marshal_list_with(task_app_model, envelope='task_app_list')\n def get(self):\n \"\"\"\n Only show active task, unless the user is querying his own tasks.\n \"\"\"\n args = task_app_list_parser.parse_args()\n keyword = args['keyword']\n uid = parser.parse_args()['uid']\n author_id = args['author_id']\n query = TaskApp.query\n\n # Check if author is querying his own tasks\n if uid != author_id:\n query = query.filter_by(status=TASKAPP_STATUS.ACTIVE).filter(TaskApp.current_task != None)\n\n # Filter task by author id\n if author_id is not None:\n query = query.filter_by(author_id=author_id)\n\n # Search keywords\n if keyword is not None and keyword != '':\n ilike_query = TaskApp.name.ilike('%{0}%'.format(keyword))\n query = query.filter(ilike_query)\n\n task_apps = query.paginate(per_page=PER_PAGE).items\n task_apps = util.collect_user_info(task_apps)\n return task_apps\n\n @ns.doc('Create Task App', parser=task_app_parser)\n @api.marshal_with(task_app_model, envelope='task_app')\n def post(self):\n \"\"\"\n Create a task app\n \"\"\"\n args = parser.parse_args()\n uid = args['uid']\n task_app = TaskApp()\n task_app.author_id = uid\n task_app.update(task_app_parser.parse_args())\n db.session.commit()\n return task_app\n\n\n@ns.route('//')\nclass TaskAppView(Resource):\n\n @ns.doc('get_task_app', responses={404: 'Task App not found'})\n @api.marshal_with(task_app_model, envelope='task_app')\n def get(self, app_id):\n \"\"\"\n Get the task app by app_id\n \"\"\"\n return get_task_app(app_id)\n\n @ns.doc('partially_update_task_app', responses={404: 'Task App not found'}, parser=task_app_parser)\n @api.marshal_with(task_app_model, envelope='task_app')\n def patch(self, app_id):\n \"\"\"\n Partially update the task app\n \"\"\"\n task_app = get_task_app(app_id)\n task_app.last_update = datetime.utcnow()\n task_app.update(task_app_parser.parse_args())\n db.session.commit()\n return task_app\n\n\n@ns.route('//download/')\nclass DownloadTaskApp(Resource):\n\n @ns.doc('preview_task')\n @api.marshal_with(task_model, envelope='task')\n def get(self, app_id):\n \"\"\"\n Preview the task information of the task app\n \"\"\"\n task_app = get_task_app(app_id)\n return task_app.preview()\n\n @ns.doc('download_task')\n @api.marshal_with(task_model, envelope='task')\n def post(self, app_id):\n \"\"\"\n Download a task from this task app\n \"\"\"\n args = parser.parse_args()\n uid = args['uid']\n task_app = get_task_app(app_id)\n result = task_app.download(uid)\n db.session.commit()\n return result\n\n\n@ns.route('//upload/')\nclass UploadTaskApp(Resource):\n\n @ns.doc('upload_task', parser=tid_parser)\n @api.marshal_with(task_model, envelope='task')\n def post(self, app_id):\n \"\"\"\n Upload a task to Task App\n \"\"\"\n args = parser.parse_args()\n uid = args['uid']\n args = tid_parser.parse_args()\n tid = args['tid']\n task_app = get_task_app(app_id)\n result = task_app.upload(tid, uid)\n db.session.commit()\n return result\n","sub_path":"tastore/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7765,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"395719698","text":"#!/usr/bin/python\n### FKI.PY\n#\n# CLASS of methdos to calculate complete solution using frequency-wavenumber\n# integration using CPS3.30 programs. Everything here is from cps330o.pdf.\n# Wrapper written to read in the ascii output files.\n#\n# METHODS:\n# 1)\n##\n#\n#\n#\n# Nate Lindsey\n# May 2016\n\ndef calcGF(model,distance):\n # note that computational effort is O(f^2) where f is set in distance file\n # but npts. Use -XF in hprep96 to bould f, or -NDEC to decimate npts\n import os\n os.system('rm hspec96.???')\n os.system('hprep96 -M %s -d %s -HS 0.0 -HR 0.0 -ALL' % (model,distance)) #tx=0; rx=0\n os.system('hspec96 > hspec96.out')\n os.system('hpulse96 -D -p -l 4 > synth_fki.96')\n os.system('f96tosac synth_fki.96')\n os.system('mv synth_fki.96 fki_sac/')\n os.system('mv *.sac fki_sac/')\n return\n","sub_path":"progs/fki.py","file_name":"fki.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"424489125","text":"from configparser import ConfigParser\nfrom pathlib import Path\nfrom os import path, system, devnull\nfrom subprocess import Popen, call, STDOUT, PIPE\nimport git\n\nconfig_obj = ConfigParser()\nparser = config_obj.read(path.join(Path.home(), \".pyGinitconfig.ini\"))\n\n\ndef execute_git(username, token, repo_name, remote_name):\n \"\"\"\n initialize git repository and push to remote\n \"\"\"\n remote_name = \"origin\" if not remote_name else remote_name\n url = (\n f'https://{username}:{token}@github.com/{username}/{repo_name.replace(\" \", \"-\")}.git'\n )\n repo = git.Repo.init()\n repo.git.add(\"--all\")\n repo.index.commit(\"initial commit\")\n repo.git.push(url, \"HEAD:master\")\n repo.create_remote(remote_name, url)\n print(f'remote name : {remote_name}')\n","sub_path":"pyGinit/gitCommand.py","file_name":"gitCommand.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"76923145","text":"from __future__ import division\n\nimport logging\nfrom itertools import permutations\nfrom warnings import filterwarnings\nfrom itertools import product\n\nfilterwarnings('ignore', 'Model not reversible', UserWarning)\n\nimport numpy\nfrom numpy.linalg import eig\nfrom cogent import DNA, LoadSeqs, LoadTree\nfrom cogent.evolve.models import GTR, MotifChange, _omega, _gtr_preds\nfrom cogent.evolve.substitution_model import Nucleotide, Codon\nfrom cogent.maths.stats import chisqprob\nfrom cogent.evolve.parameter_controller import AlignmentLikelihoodFunction\nfrom cogent.util.dict_array import DictArrayTemplate\nfrom cogent.core.genetic_code import GeneticCodes, GeneticCode, DEFAULT\nfrom cogent.evolve.predicate import parse, ModelSays\n\nfrom timedec import timed\nimport nest\nassert nest.__version__ == '0.0.31-dev' # a bit hacky but will do for now\n\n__author__ = 'Ben Kaehler'\n__copyright__ = 'Copyright 2016, Ben Kaehler'\n__credits__ = ['Ben Kaehler']\n__license__ = 'GPLv3 or any later version'\n__maintainer__ = 'Ben Kaehler'\n__email__ = 'benjamin.kaehler@anu.edu.au'\n__status__ = 'Development'\n__version__ = '0.0.13-dev'\n\nclass GeneralCalcQ(object):\n def calcQ(self, word_probs, mprobs_matrix, *params):\n Q = self.calcExchangeabilityMatrix(word_probs, *params)\n if self.alphabet[-1] == '---':\n subQ = Q[:-1,:-1]\n v, V = eig(subQ.T)\n pi = V[:,numpy.abs(v).argmin()].real\n pi[pi < 1e-4] = 1e-4\n pi /= pi.sum()\n Q[-1,:-1] = pi*Q[-1,:-1]\n row_totals = Q.sum(axis=1)\n Q -= numpy.diag(row_totals)\n if self._do_scaling:\n Q *= 1.0 / (word_probs * row_totals).sum()\n return Q\n\nclass ReversibleCalcQ(object):\n def calcQ(self, word_probs, mprobs_matrix, *params):\n Q = self.calcExchangeabilityMatrix(word_probs, *params)\n if self.alphabet[-1] == '---':\n Q[:-1,:-1] *= mprobs_matrix[:-1,:-1]\n pi = numpy.array(word_probs)\n pi /= pi.sum()\n Q[-1,:-1] *= pi[:-1]\n Q[:-1,-1] *= pi[-1]\n else:\n Q *= mprobs_matrix\n row_totals = Q.sum(axis=1)\n Q -= numpy.diag(row_totals)\n if self._do_scaling:\n Q *= 1.0 / (word_probs * row_totals).sum()\n return Q\n\n_gap_preds = [parse('--->???'), parse('???>---')]\n_indel = ModelSays('indel')\n\nclass MonkeyPatchModel(object):\n def makeLikelihoodFunction(self, tree, motif_probs_from_align=None,\n optimise_motif_probs=None, aligned=True, expm=None, digits=None,\n space=None, **kw):\n if motif_probs_from_align is None:\n motif_probs_from_align = self.motif_probs_from_align\n \n if optimise_motif_probs is None:\n optimise_motif_probs = self._optimise_motif_probs\n \n kw['optimise_motif_probs'] = optimise_motif_probs\n kw['motif_probs_from_align'] = motif_probs_from_align\n \n if aligned:\n klass = MonkeyPatchLikelihoodFunction\n else:\n alphabet = self.getAlphabet()\n assert alphabet.getGapMotif() not in alphabet\n klass = parameter_controller.SequenceLikelihoodFunction\n \n result = klass(self, tree, **kw)\n \n if self.motif_probs is not None:\n result.setMotifProbs(self.motif_probs, is_constant=\n not optimise_motif_probs, auto=True)\n \n # hack\n mprobs = result.getParamValue('mprobs')\n result.model.setParamControllerMotifProbs(result, mprobs)\n # end hack\n \n if expm is None:\n expm = self._default_expm_setting\n if expm is not None:\n result.setExpm(expm)\n \n if digits or space:\n result.setTablesFormat(digits=digits, space=space)\n \n return result\n\n# This ugly hack is done because MG94GTR is defined as a function in\n# cogent.evolve.models\nclass MG94GTR(ReversibleCalcQ, MonkeyPatchModel, Codon):\n \"\"\"Muse and Gaut 1994 codon substitution model, GTR variant (with params\n analogous to the nucleotide GTR model)\n \n see, Muse and Gaut, 1994, Mol Biol Evol, 11, 715-24\"\"\"\n def __init__(self, model_gaps=False, **kw):\n if model_gaps:\n super(MG94GTR, self).__init__(\n motif_probs = None,\n do_scaling = True,\n model_gaps = True,\n recode_gaps = False,\n name = 'MG94GTR',\n predicates = _gtr_preds+[_indel, _omega],\n mprob_model = 'monomer',\n **kw)\n else:\n super(MG94GTR, self).__init__(\n motif_probs = None,\n do_scaling = True,\n model_gaps = False,\n recode_gaps = True,\n name = 'MG94GTR',\n predicates = _gtr_preds+[_omega],\n mprob_model='monomer',\n **kw)\n\nclass Y98GTR(Codon):\n \"\"\"Yang's 1998 substitution model, a derivative of the GY94, GTR variant\n (with params analogous to the nucleotide GTR model)\n see Z Yang. Mol. Biol. Evol., 15(5):568-73, 1998\"\"\"\n def __init__(self, **kw):\n assert not 'model_gaps' in kw or kw['model_gaps'] == False\n super(Y98GTR, self).__init__(\n motif_probs = None,\n do_scaling = True,\n model_gaps = False,\n recode_gaps = True,\n name = 'Y98GTR',\n predicates = _gtr_preds+[_omega],\n mprob_model = 'tuple',\n **kw)\n\n_general_preds = []\nfor f, t in permutations('ACTG', 2):\n if f != 'T' or t != 'G': # Match GTR's reference cell\n _general_preds.append(MotifChange(f, t, forward_only=True))\n\nclass NG(GeneralCalcQ, Nucleotide):\n def __init__(self, **kw):\n assert not 'model_gaps' in kw or kw['model_gaps'] == False\n super(NG, self).__init__(\n predicates = _general_preds,\n recode_gaps = True,\n model_gaps = False,\n do_scaling = True,\n name = 'NG',\n **kw)\n\nclass GNC(GeneralCalcQ, Codon):\n def __init__(self, model_gaps=False, **kw):\n if model_gaps:\n super(GNC, self).__init__(\n motif_probs = None,\n do_scaling = True,\n model_gaps = True,\n recode_gaps = False,\n name = 'GNC',\n predicates = _general_preds + _gap_preds + [_omega],\n mprob_model = 'tuple',\n **kw)\n else:\n super(GNC, self).__init__(\n motif_probs = None,\n do_scaling = True,\n model_gaps = False,\n recode_gaps = True,\n name = 'GNC',\n predicates = _general_preds + [_omega],\n mprob_model = 'tuple',\n **kw)\n\nclass Y98(Codon):\n def __init__(self, model_gaps=False, **kw):\n if model_gaps:\n super(Y98, self).__init__(\n motif_probs = None,\n do_scaling = True,\n model_gaps = True,\n recode_gaps = False,\n name = 'Y98',\n predicates = {\n 'kappa' : 'transition',\n 'omega' : 'replacement',\n 'indel' : 'indel'\n },\n mprob_model = 'tuple',\n **kw)\n else:\n super(Y98, self).__init__(\n motif_probs = None,\n do_scaling = True,\n model_gaps = False,\n recode_gaps = True,\n name = 'Y98',\n predicates = {\n 'kappa' : 'transition',\n 'omega' : 'replacement',\n },\n mprob_model = 'tuple',\n **kw)\n\nclass CNFGTR(ReversibleCalcQ, Codon):\n def __init__(self, model_gaps=False, **kw):\n if model_gaps:\n super(CNFGTR, self).__init__(\n motif_probs = None,\n do_scaling = True,\n model_gaps = True,\n recode_gaps = False,\n name = 'CNFGTR',\n predicates = _gtr_preds+[_indel, _omega],\n **kw)\n else:\n super(CNFGTR, self).__init__(\n motif_probs = None,\n do_scaling = True,\n model_gaps = False,\n recode_gaps = True,\n name = 'CNFGTR',\n predicates = _gtr_preds+[_omega],\n **kw)\n\nclass NFG(GeneralCalcQ, MonkeyPatchModel, Codon):\n def __init__(self, **kw):\n assert not 'model_gaps' in kw or kw['model_gaps'] == False\n super(NFG, self).__init__(\n motif_probs = None,\n do_scaling = True,\n model_gaps = False,\n recode_gaps = True,\n name = 'NFG',\n predicates = _general_preds + [_omega],\n mprob_model = 'monomers',\n **kw)\n\nclass MG94G(GeneralCalcQ, MonkeyPatchModel, Codon):\n def __init__(self, **kw):\n assert not 'model_gaps' in kw or kw['model_gaps'] == False\n super(MG94G, self).__init__(\n motif_probs = None,\n do_scaling = True,\n model_gaps = False,\n recode_gaps = True,\n name = 'MG94G',\n predicates = _general_preds + [_omega],\n mprob_model = 'monomer',\n **kw)\n\nclass MonkeyPatchLikelihoodFunction(AlignmentLikelihoodFunction):\n def __init__(self, *args, **kwargs):\n super(MonkeyPatchLikelihoodFunction, self).__init__(*args, **kwargs)\n \n def getMotifProbs(self, edge=None, bin=None, locus=None):\n motif_probs_array = self.getParamValue(\n 'mprobs', edge=edge, bin=bin, locus=locus)\n mprob_name = [p for p in self.getParamNames() if 'mprobs' in p][0]\n if 'position' in self.getUsedDimensions(mprob_name):\n positions = self._valuesForDimension('position')\n motifs = self._mprob_motifs\n return DictArrayTemplate(positions, motifs).wrap(motif_probs_array)\n return DictArrayTemplate(self._mprob_motifs).wrap(motif_probs_array)\n \n def setMotifProbs(self, motif_probs, locus=None, bin=None, \n is_constant=None, is_independent=None, auto=False, **kwargs):\n if 'is_const' in kwargs:\n is_constant = kwargs.pop('is_const')\n deprecated('argument', 'is_const', 'is_constant', 1.6)\n \n mprob_name = [p for p in self.getParamNames() if 'mprobs' in p][0]\n # This is a nasty hack. Why does 'position' only show up after a\n # call to setParamControllerMotifProbs?\n if 'position' in self.getUsedDimensions(mprob_name) and \\\n len(motif_probs) == 3:\n # This is a nasty hack. Fix should be in PosnSpecificMonomerProbModel\n motif_probs = [motif_probs[p] \n for p in self._valuesForDimension('position')]\n else:\n motif_probs = self.model.adaptMotifProbs(motif_probs, auto=auto)\n if is_constant is None:\n is_constant = not self.optimise_motif_probs\n self.model.setParamControllerMotifProbs(self, motif_probs, \n is_constant=is_constant, bin=bin, locus=locus, \n is_independent=is_independent, **kwargs)\n if not auto:\n self.mprobs_from_alignment = False # should be done per-locus\n \n def getMotifProbsByNode(self, edges=None, bin=None, locus=None):\n kw = dict(bin=bin, locus=locus)\n mprobs = self.getParamValue('mprobs', **kw)\n mprobs = self._model.calcWordProbs(mprobs)\n result = self._nodeMotifProbs(self._tree, mprobs, kw)\n if edges is None:\n edges = [name for (name, m) in result]\n result = dict(result)\n values = [result[name] for name in edges]\n if len(values[0]) == len(self._mprob_motifs):\n return DictArrayTemplate(edges, self._mprob_motifs).wrap(values)\n return DictArrayTemplate(edges, self._motifs).wrap(values)\n\ndef _fit_init(aln, tree, model, gc, omega_indep, model_gaps, indel_indep, **kw):\n if model == 'NG':\n sm = GTR(optimise_motif_probs=True, model_gaps=model_gaps)\n elif model in ('NFG', 'MG94G', 'MG94GTR', 'GNC', 'Y98GTR'):\n sm = MG94GTR(optimise_motif_probs=True, model_gaps=model_gaps, gc=gc)\n elif model == 'CNFGTR': # CNFGTR nests no models here\n sm = CNFGTR(optimise_motif_probs=True, model_gaps=model_gaps, gc=gc)\n elif model == 'Y98': # No need for nested fitting for Y98\n sm = Y98(optimise_motif_probs=True, model_gaps=model_gaps, gc=gc)\n lf = sm.makeLikelihoodFunction(tree)\n lf.setAlignment(aln)\n with lf.updatesPostponed():\n for param in lf.getParamNames():\n if '/' in param:\n lf.setParamRule(param, **kw)\n if model in ('CNFGTR', 'Y98'): # set the omegas to be independent\n lf.setParamRule('omega', is_independent=omega_indep, upper=2.)\n lf.setParamRule('length', is_independent=True, upper=20.)\n if model_gaps:\n lf.setParamRule('indel', is_independent=indel_indep, upper=2.)\n lf.optimise(local=True, show_progress=False, limit_action='raise')\n return lf\n\ndef _is_param_hard_up(lf, param, **kw):\n defn = lf.defn_for[param]\n posn = defn._getPosnForScope(**kw)\n lc = lf.makeCalculator(variable=defn.uniq[posn])\n for op in lc.opt_pars:\n bounds = op.getOptimiserBounds()\n value = op.transformToOptimiser(lc._getCurrentCellValue(op))\n if value < bounds[0] + 1e-6 or value > bounds[1] - 1e-6:\n return True\n\ndef _is_hard_up(lf):\n \"\"\" Test whether some parameters have ended up at their bounds \"\"\"\n if 'psmprobs' in lf.defn_for:\n for position in lf._valuesForDimension('position'):\n if _is_param_hard_up(lf, 'psmprobs', position=position):\n return True\n elif _is_param_hard_up(lf, 'mprobs'):\n return True\n\n params = (param for param in lf.getParamNames() if '>' in param)\n edges = nest.get_edge_names(lf)\n for param, edge in product(params, edges):\n if _is_param_hard_up(lf, param, edge=edge):\n return True\n\n return False\n\ndef _upsample_mprobs(mprobs, tuples):\n mprobs = [numpy.prod([mprobs[n] for n in w]) for w in tuples]\n mprobs = numpy.array(mprobs)\n mprobs /= mprobs.sum()\n return mprobs\n\ndef _populate_parameters(lf_to, lf_from, **kw):\n mprobs = lf_from['mprobs']\n if lf_to.model.name in ['GNC', 'GNCClock', 'Y98GTR'] \\\n and lf_from['name'] == 'MG94GTR':\n wprobs = _upsample_mprobs(mprobs, lf_to._motifs)\n lf_to.setMotifProbs(wprobs)\n else:\n lf_to.setMotifProbs(mprobs)\n\n edges = set([e.Name for e in lf_to.tree.getEdgeVector(include_root=False)])\n for edge in edges:\n init = lf_from['params']['length'][edge]\n lf_to.setParamRule('length', edge=edge, init=init, upper=20.)\n params = {}\n for param in lf_from['params']:\n value = lf_from['params'][param][edge]\n while not isinstance(value, float):\n value = value.values()[0]\n if '/' in param and len(mprobs) < 6: # len(mprobs) > 5 no nesting\n assert 'GTR' in lf_from['name']\n assert isinstance(mprobs.values()[0], float)\n f, t = param.split('/')\n # Multiply the columns by their mprobs to get unscaled rates,\n # then divide by the mprob for the reference cell so that the\n # hypothetical 'T>G' rate would be unity.\n params['>'.join((f,t))] = value * mprobs[t] / mprobs['G']\n params['>'.join((t,f))] = value * mprobs[f] / mprobs['G']\n elif param == 'indel' and len(mprobs) < 6:\n params['???>---'] = value * wprobs[-1] / mprobs['G']\n params['--->???'] = value * (1. - wprobs[-1]) / mprobs['G']\n params[param] = value\n if 'GTR' in lf_from['name'] and len(mprobs) < 6: # Cover the ref cell\n params['G>T'] = mprobs['T'] / mprobs['G']\n params['T>G'] = 1.\n with lf_to.updatesPostponed():\n for param in lf_to.getParamNames():\n if param.endswith('mprobs') or param=='length' or '/' in param:\n continue\n kwargs = dict(edge=edge, is_independent=False,\n init=params[param])\n if '>' in param and not '-' in param:\n kwargs.update(kw)\n lf_to.setParamRule(param, **kwargs)\n\n if 'GTR' in lf_to.model.name:\n for param in lf_to.getParamNames():\n if '/' in param:\n kwargs = dict(is_independent=False, init=params[param])\n kwargs.update(kw)\n lf_to.setParamRule(param, **kwargs)\n\ndef get_genetic_code(code_name):\n if code_name is None:\n code = DEFAULT\n else:\n code = None\n for gc in GeneticCodes.itervalues():\n if gc.Name == code_name:\n code = gc\n\n if code is None:\n try:\n code = GeneticCode(code_name, Name=code_name)\n except:\n raise RuntimeError('Error selecting genetic code ' + code_name)\n\n return code\n\n@timed\ndef _fit(aln, tree, model, gc, omega_indep, model_gaps, indel_indep):\n sp_kw = dict(upper=20., lower=0.05, is_independent=False) \n\n last_lf = _fit_init(aln, tree, model, gc, omega_indep, model_gaps,\n indel_indep, **sp_kw)\n if model in ('CNFGTR', 'MG94GTR', 'Y98'):\n flat_lf = nest.deflate_likelihood_function(last_lf)\n flat_lf['hard_up'] = _is_hard_up(last_lf)\n return flat_lf\n last_lf = nest.deflate_likelihood_function(last_lf, save_jsd=False)\n\n if model in ('NG', 'NFG', 'MG94G', 'GNC', 'Y98GTR'):\n kwargs = dict(optimise_motif_probs=True, model_gaps=model_gaps)\n if not model.startswith('NG'):\n kwargs['gc'] = gc\n sm = eval(model)(**kwargs)\n else:\n raise NotImplementedError(model + ' not supported')\n lf = sm.makeLikelihoodFunction(tree)\n lf.setAlignment(aln)\n _populate_parameters(lf, last_lf, **sp_kw)\n lf.setParamRule('omega', is_independent=omega_indep)\n if model_gaps:\n if model == 'GNC':\n lf.setParamRule('???>---', is_independent=indel_indep)\n lf.setParamRule('--->???', is_independent=indel_indep)\n else:\n lf.setParamRule('indel', is_independent=indel_indep)\n lf.optimise(local=True, show_progress=False, limit_action='raise')\n flat_lf = nest.deflate_likelihood_function(lf)\n flat_lf['hard_up'] = _is_hard_up(lf)\n return flat_lf\n\ndef ml(doc, model='NG', gc=None, omega_indep=True, model_gaps=False,\n indel_indep=True, **kw):\n aln = LoadSeqs(data=doc['aln'].encode('utf-8'), moltype=DNA)\n tree = LoadTree(treestring=doc['tree'].encode('utf-8'))\n\n code = get_genetic_code(gc)\n if model != 'NG':\n # Trim terminal stop codons\n aln = aln.withoutTerminalStopCodons(code)\n if model_gaps:\n filt = lambda x: set(''.join(x)) <= set(DNA).union({'-'})\n else:\n filt = lambda x: set(''.join(x)) <= set(DNA)\n aln = aln.filtered(filt, motif_length=3)\n\n flat_lf, time = _fit(aln, tree, model, code, omega_indep, model_gaps,\n indel_indep)\n return {'lf' : flat_lf, 'time' : time, 'model' : model, 'gc' : code.Name,\n 'omega_indep' : omega_indep, 'model_gaps' : model_gaps,\n 'indel_indep' : indel_indep}\n\ndef ml_bootstraps(empirical, num_bootstraps=100):\n assert empirical['model'] in \\\n ('NG', 'NFG', 'MG94G', 'GNC', 'Y98GTR', 'CNFGTR', 'MG94GTR', 'Y98')\n gc = get_genetic_code(empirical['gc'].encode('utf-8'))\n model = lambda **kw: eval(empirical['model'])(gc=gc, **kw)\n elf = nest.inflate_likelihood_function(empirical['lf'], model)\n \n aln_length = empirical['lf']['aln_length']\n if empirical['model'] != 'NG': # for unexpected simulateAlignment behaviour\n aln_length = int(aln_length/3)\n assert empirical['lf']['aln_length'] == 3*aln_length\n def bootstrap(empdoc):\n aln = elf.simulateAlignment(aln_length)\n simdoc = {'aln' : str(aln), 'tree' : empdoc['lf']['tree']}\n result = ml(simdoc, **empdoc)\n return result['lf']['gs']\n\n def extract_result(bootstraps):\n egs = empirical['lf']['gs']\n result = {'gstats' : bootstraps, 'gstat' : egs,\n 'pvalue' : sum(g > egs for g in bootstraps)/(num_bootstraps+1)} \n return result\n\n emp_gen = (empirical for i in [None]*num_bootstraps)\n\n return extract_result(map(bootstrap, emp_gen))\n\n","sub_path":"gapped/_ml.py","file_name":"_ml.py","file_ext":"py","file_size_in_byte":21018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"14721287","text":"# -*- coding: utf-8 -*-\n\n#lendo preço de etiqueta\netiqueta = float(input('Preço de etiqueta do produto: '))\n\n#lendo tipo de pagamento\ncodigo = int(input('Codigo de pagamento: '))\n\nif (codigo==1):\n total = etiqueta*0.85\n print('%.2f' % total)\nif (codigo==2):\n total = etiqueta*0.90\n print('%.2f' % total)\nif (codigo==3):\n print('%.2f' % etiqueta)\n\n\n","sub_path":"moodledata/vpl_data/411/usersdata/308/79065/submittedfiles/av1_programa2.py","file_name":"av1_programa2.py","file_ext":"py","file_size_in_byte":365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"339937642","text":"#Robert Bangiyev\r\n#October 23, 2018\r\n#Organize names\r\nnames=str(input(\"Please enter your list of names: \"))\r\nnlist=names.split(\";\")\r\n\r\nfor i in nlist:\r\n nems=str(i)\r\n qname=nems.split(\",\")\r\n for i in range(1):\r\n print(qname[1]+qname[0])\r\n","sub_path":"namesplt.py","file_name":"namesplt.py","file_ext":"py","file_size_in_byte":254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"316823199","text":"import json\nimport os\n\n\ndef labelbox_object_detection_to_sa_vector(json_data, output_dir, id_generator):\n classes = {}\n sa_classes_loader = []\n for d in json_data:\n instances = d[\"Label\"][\"objects\"]\n sa_loader = []\n for instance in instances:\n class_name = instance[\"title\"]\n if class_name in classes.keys():\n index = classes[class_name][\"index\"]\n color = classes[class_name][\"color\"]\n else:\n index = next(id_generator)\n color = instance[\"color\"]\n classes[class_name] = {\"index\": index, \"color\": color}\n\n if 'bbox' in instance.keys():\n x1 = instance['bbox']['left']\n x2 = instance['bbox']['left'] + instance['bbox']['width']\n y1 = instance['bbox']['top']\n y2 = instance['bbox']['top'] + instance['bbox']['height']\n points = {'x1': x1, 'x2': x2, 'y1': y1, 'y2': y2}\n ptype = 'bbox'\n elif 'polygon' in instance.keys():\n ptype = 'polygon'\n points = []\n for point in instance['polygon']:\n points.append(point['x'])\n points.append(point['y'])\n else:\n points = []\n ptype = ''\n\n sa_obj = {\n 'type': ptype,\n 'points': points,\n 'className': class_name,\n 'classId': classes[class_name]['index'],\n 'attributes': [],\n 'probability': 100,\n 'locked': False,\n 'visible': True,\n 'groupId': 0,\n }\n sa_loader.append(sa_obj)\n\n file_name = d['External ID'] + '___objects.json'\n with open(os.path.join(output_dir, file_name), \"w\") as fw:\n json.dump(sa_loader, fw, indent=2)\n\n for key, value in classes.items():\n sa_classes = {\n 'id': value['index'],\n 'name': key,\n 'color': value['color'],\n 'attribute_groups': []\n }\n sa_classes_loader.append(sa_classes)\n\n with open(os.path.join(output_dir, 'classes', 'classes.json'), 'w') as fw:\n json.dump(sa_classes_loader, fw, indent=2)\n","sub_path":"superannotate/input_converters/converters/labelbox_converters/labelbox_to_sa_vector.py","file_name":"labelbox_to_sa_vector.py","file_ext":"py","file_size_in_byte":2300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"639731200","text":"import os\nimport winreg\ndef get_desktop():\n key = winreg.OpenKey(winreg.HKEY_CURRENT_USER,\n r'Software\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Shell Folders') # 利用系统的链表\n return str(winreg.QueryValueEx(key, \"Desktop\")[0]) # 返回的是Unicode类型数据\ndef get_Temp(*file):\n deskdop = get_desktop()\n # 获取临时文件\n temp = os.path.join(deskdop, 'temp')\n # 判断文件是否存在\n if not os.path.exists(temp):\n os.mkdir(temp)\n if file:\n temp = os.path.join(temp, file)\n if not os.path.exists(temp):\n os.mkdir(temp)\n return temp\ndef list_file(path, *ends):\n '''\n 这个列表下的所有文件,不包括文件夹\n :param path:\n :param ends:\n :return:\n '''\n files = os.listdir(path)\n result = set()\n for i in files:\n # 文件也包括快捷方式。（文件夹的快捷方式算文件）\n if os.path.isfile(os.path.join(path, i)):\n # 结束符没有填的话，添加所有\n if len(ends) == 0:\n result.add(os.path.join(path, i))\n else:\n for end in ends:\n if (i.endswith(end)):\n result.add(os.path.join(path, i))\n return result\ndef get_excel(path):\n '''\n 获取该目录下的excel文件的集合\n :param path:\n :return:\n '''\n return list_file(path, 'xls', 'xlsx')\nif __name__ == '__main__':\n # 就是获取当前目录，并组合成新目录\n getcwd = os.getcwd()\n print(getcwd)\n # 当前文件的名称\n print(os.path.basename(__file__))\n # 获取当前文件的绝对路径\n print(__file__)\n # 新建临时文件\n # print(list_dir(get_Temp(), \"xlsx\"))\n print(get_excel(get_Temp()))\n # file = list_file(r'C:\\Users\\Administrator\\Desktop\\1')\n # print(file)\n # 获取当前目录\n print(os.path.abspath('1.jpg'))\n","sub_path":"util/file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":1930,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"196319249","text":"import pytest\nimport torch\n\nimport kornia.augmentation as K\n\n\nclass TestVideoSequential:\n @pytest.mark.parametrize('shape', [(3, 4), (2, 3, 4), (2, 3, 5, 6), (2, 3, 4, 5, 6, 7)])\n @pytest.mark.parametrize('data_format', [\"BCTHW\", \"BTCHW\"])\n def test_exception(self, shape, data_format, device, dtype):\n aug_list = K.VideoSequential(K.ColorJitter(0.1, 0.1, 0.1, 0.1), data_format=data_format, same_on_frame=True)\n with pytest.raises(AssertionError):\n input = torch.randn(*shape, device=device, dtype=dtype)\n output = aug_list(input)\n\n @pytest.mark.parametrize(\n 'augmentation',\n [\n K.RandomAffine(360, p=1.0),\n K.CenterCrop((3, 3), p=1.0),\n K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0),\n K.RandomCrop((5, 5), p=1.0),\n K.RandomErasing(p=1.0),\n K.RandomGrayscale(p=1.0),\n K.RandomHorizontalFlip(p=1.0),\n K.RandomVerticalFlip(p=1.0),\n K.RandomPerspective(p=1.0),\n K.RandomResizedCrop((5, 5), p=1.0),\n K.RandomRotation(360.0, p=1.0),\n K.RandomSolarize(p=1.0),\n K.RandomPosterize(p=1.0),\n K.RandomSharpness(p=1.0),\n K.RandomEqualize(p=1.0),\n K.RandomMotionBlur(3, 35.0, 0.5, p=1.0),\n K.Normalize(torch.tensor([0.5, 0.5, 0.5]), torch.tensor([0.5, 0.5, 0.5]), p=1.0),\n K.Denormalize(torch.tensor([0.5, 0.5, 0.5]), torch.tensor([0.5, 0.5, 0.5]), p=1.0),\n ],\n )\n @pytest.mark.parametrize('data_format', [\"BCTHW\", \"BTCHW\"])\n def test_augmentation(self, augmentation, data_format, device, dtype):\n input = torch.randint(255, (1, 3, 3, 5, 6), device=device, dtype=dtype).repeat(2, 1, 1, 1, 1) / 255.0\n torch.manual_seed(21)\n aug_list = K.VideoSequential(augmentation, data_format=data_format, same_on_frame=True)\n output = aug_list(input)\n\n @pytest.mark.parametrize('augmentations', [[K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.5), K.RandomAffine(360, p=0.5)]])\n @pytest.mark.parametrize('data_format', [\"BCTHW\", \"BTCHW\"])\n def test_p_half(self, augmentations, data_format, device, dtype):\n input = torch.randn(1, 3, 3, 5, 6, device=device, dtype=dtype).repeat(2, 1, 1, 1, 1)\n torch.manual_seed(21)\n aug_list = K.VideoSequential(*augmentations, data_format=data_format, same_on_frame=True)\n output = aug_list(input)\n\n assert not (output[0] == input[0]).all()\n assert (output[1] == input[1]).all()\n\n @pytest.mark.parametrize(\n 'augmentations',\n [\n [K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0), K.RandomAffine(360, p=1.0)],\n [K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0)],\n [K.RandomAffine(360, p=1.0)],\n [K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.0), K.RandomAffine(360, p=0.0)],\n [K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=0.0)],\n [K.RandomAffine(360, p=0.0)],\n ],\n )\n @pytest.mark.parametrize('data_format', [\"BCTHW\", \"BTCHW\"])\n def test_same_on_frame(self, augmentations, data_format, device, dtype):\n aug_list = K.VideoSequential(*augmentations, data_format=data_format, same_on_frame=True)\n\n if data_format == 'BCTHW':\n input = torch.randn(2, 3, 1, 5, 6, device=device, dtype=dtype).repeat(1, 1, 4, 1, 1)\n output = aug_list(input)\n assert (output[:, :, 0] == output[:, :, 1]).all()\n assert (output[:, :, 1] == output[:, :, 2]).all()\n assert (output[:, :, 2] == output[:, :, 3]).all()\n if data_format == 'BTCHW':\n input = torch.randn(2, 1, 3, 5, 6, device=device, dtype=dtype).repeat(1, 4, 1, 1, 1)\n output = aug_list(input)\n assert (output[:, 0] == output[:, 1]).all()\n assert (output[:, 1] == output[:, 2]).all()\n assert (output[:, 2] == output[:, 3]).all()\n\n @pytest.mark.parametrize(\n 'augmentations', [[K.RandomAffine(360, p=1.0)], [K.ColorJitter(0.1, 0.1, 0.1, 0.1, p=1.0)]]\n )\n @pytest.mark.parametrize('data_format', [\"BCTHW\", \"BTCHW\"])\n def test_against_sequential(self, augmentations, data_format, device, dtype):\n aug_list_1 = K.VideoSequential(*augmentations, data_format=data_format, same_on_frame=False)\n aug_list_2 = torch.nn.Sequential(*augmentations)\n\n if data_format == 'BCTHW':\n input = torch.randn(2, 3, 1, 5, 6, device=device, dtype=dtype).repeat(1, 1, 4, 1, 1)\n if data_format == 'BTCHW':\n input = torch.randn(2, 1, 3, 5, 6, device=device, dtype=dtype).repeat(1, 4, 1, 1, 1)\n\n torch.manual_seed(0)\n output_1 = aug_list_1(input)\n param_1 = list(aug_list_1.children())[0]._params\n\n torch.manual_seed(0)\n if data_format == 'BCTHW':\n input = input.transpose(1, 2)\n output_2 = aug_list_2(input.reshape(-1, 3, 5, 6))\n param_2 = list(aug_list_2.children())[0]._params\n output_2 = output_2.view(2, 4, 3, 5, 6)\n if data_format == 'BCTHW':\n output_2 = output_2.transpose(1, 2)\n assert (output_1 == output_2).all()\n\n @pytest.mark.jit\n @pytest.mark.skip(reason=\"turn off due to Union Type\")\n def test_jit(self, device, dtype):\n B, C, D, H, W = 2, 3, 5, 4, 4\n img = torch.ones(B, C, D, H, W, device=device, dtype=dtype)\n op = K.VideoSequential(K.ColorJitter(0.1, 0.1, 0.1, 0.1), same_on_frame=True)\n op_jit = torch.jit.script(op)\n assert_allclose(op(img), op_jit(img))\n","sub_path":"test/augmentation/test_container.py","file_name":"test_container.py","file_ext":"py","file_size_in_byte":5532,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"355591962","text":"#graphics\nimport bpy, bmesh\nimport time\n\n ### SETUP START ###\ntime_start = time.time()\n\ndef reset_scene():\n names = ['Cube', 'Floor', 'Lamp', 'Light', 'Sphere'] #array of object names to be removed\n for item in bpy.data.objects:\n item.select = False\n for j in names:\n if item.name.startswith(j):\n item.select = True\n for material in bpy.data.materials:\n if not material.users:\n bpy.data.materials.remove(material)\n bpy.ops.object.delete()\n bpy.context.scene.render.engine = 'CYCLES'\n ### SETUP END ###\n\ndef set_scene():\n bpy.ops.mesh.primitive_ico_sphere_add(\n subdivisions=8,\n location=(0, 0, 1))\n bpy.context.object.name = 'Sphere'\n\n bpy.ops.mesh.primitive_plane_add(\n radius=3,\n location=(0, 0, 0))\n bpy.context.object.name = 'Floor'\n\n bpy.ops.mesh.primitive_plane_add(\n radius=2,\n location=(1, -1, 10))\n bpy.context.object.name = 'Light'\n\ndef assign_material(obj_name, material_name, material_type):\n #grab object\n obj = bpy.data.objects[obj_name]\n bpy.context.scene.objects.active = bpy.data.objects[obj_name]\n\n #clean object\n for slot in obj.material_slots:\n bpy.ops.object.material_slot_remove()\n\n #add slot and fill with material\n bpy.ops.object.material_slot_add()\n mat = bpy.data.materials.new(material_name)\n obj.material_slots[0].material = bpy.data.materials[material_name]\n\n #use nodes, and establish variables\n mat.use_nodes = True\n nodes = mat.node_tree.nodes\n links = mat.node_tree.links\n\n #deselect and clean up\n obj.select = False\n for link in links:\n links.remove(link)\n for node in nodes:\n nodes.remove(node)\n\n if material_type == 'light':\n light(nodes, links)\n elif material_type == 'ceramic':\n ceramic(nodes, links)\n elif material_type == 'glass':\n glass(nodes, links)\n\n return obj, mat, nodes, links\n\n# MATERIALS\ndef light(nodes, links):\n emission = nodes.new(type='ShaderNodeEmission')\n emission.location = 0, 0\n emission.inputs[1].default_value = 20\n\n output_node = nodes.new(type='ShaderNodeOutputMaterial')\n output_node.location = 200, 0\n\n emit_to_outp = links.new(emission.outputs[0], output_node.inputs[0])\n\ndef ceramic(nodes, links):\n #establish nodes\n subsurf1 = nodes.new(type='ShaderNodeSubsurfaceScattering')\n subsurf1.location = 0, 250\n subsurf1.inputs[1].default_value = .3\n subsurf1.falloff = 'CUBIC'\n\n subsurf2 = nodes.new(type='ShaderNodeSubsurfaceScattering')\n subsurf2.location = 0, 0\n subsurf2.inputs[1].default_value = 1\n subsurf2.falloff = 'CUBIC'\n\n diffuse = nodes.new(type='ShaderNodeBsdfDiffuse')\n diffuse.location = 200, 250\n diffuse.inputs[1].default_value = .7\n\n mixer1 = nodes.new(type='ShaderNodeMixShader')\n mixer1.location = 200, 0\n mixer1.inputs[0].default_value = .35\n\n mixer2 = nodes.new(type='ShaderNodeMixShader')\n mixer2.location = 400, 250\n mixer2.inputs[0].default_value = .35\n\n mixer3 = nodes.new(type='ShaderNodeMixShader')\n mixer3.location = 600, 250\n\n layer_weight = nodes.new(type='ShaderNodeLayerWeight')\n layer_weight.location = 400, 500\n layer_weight.inputs[0].default_value = .35\n\n glossy = nodes.new(type='ShaderNodeBsdfGlossy')\n glossy.location = 400, 0\n glossy.inputs[1].default_value = 0\n\n output_node = nodes.new(type='ShaderNodeOutputMaterial')\n output_node.location = 800, 0\n\n #wire it up\n sub1_to_mix1 = links.new(subsurf1.outputs[0], mixer1.inputs[1])\n sub2_to_mix1 = links.new(subsurf2.outputs[0], mixer1.inputs[2])\n mix1_to_mix2 = links.new(mixer1.outputs[0], mixer2.inputs[2])\n diff_to_mix2 = links.new(diffuse.outputs[0], mixer2.inputs[1])\n mix2_to_mix3 = links.new(mixer2.outputs[0], mixer3.inputs[1])\n layw_to_mix3 = links.new(layer_weight.outputs[0], mixer3.inputs[0])\n glos_to_mix3 = links.new(glossy.outputs[0], mixer3.inputs[2])\n mix3_to_outp = links.new(mixer3.outputs[0], output_node.inputs[0])\n\n return nodes, links\n\ndef glass(nodes, links):\n # http://www.chocofur.com/6-shadersamptextures.html\n rgb1 = nodes.new(type='ShaderNodeRGB')\n rgb1.location = 0, 250\n rgb1.outputs[0].default_value = (.5, 1, 1, 1)\n\n rgb2 = nodes.new(type='ShaderNodeRGB')\n rgb2.location = 0, 0\n rgb2.outputs[0].default_value = (1.5, 2, 2, 1)\n\n mixrgb1 = nodes.new(type='ShaderNodeMixRGB')\n mixrgb1.location = 200, 250\n\n mixrgb2 = nodes.new(type='ShaderNodeMixRGB')\n mixrgb2.location = 200, 0\n\n lightpath = nodes.new(type='ShaderNodeLightPath')\n lightpath.location = 800, 650\n\n huesatval = nodes.new(type='ShaderNodeHueSaturation')\n huesatval.location = 400, 250\n huesatval.inputs[2].default_value = .125\n\n weight = nodes.new(type='ShaderNodeLayerWeight')\n weight.location = 600, 1000\n\n glass = nodes.new(type='ShaderNodeBsdfGlass')\n glass.location = 400, 500\n\n transp1 = nodes.new(type='ShaderNodeBsdfTransparent')\n transp1.location = 600, 150\n\n transp2 = nodes.new(type='ShaderNodeBsdfTransparent')\n transp2.location = 600, 0\n\n curves = nodes.new(type='ShaderNodeRGBCurve')\n curves.location = 800, 1000\n curves.mapping.curves[3].points.new(.70, .30)\n curves.mapping.update()\n\n shader = nodes.new(type='ShaderNodeAddShader')\n shader.location = 800, 250\n\n mixshader1 = nodes.new(type='ShaderNodeMixShader')\n mixshader1.location = 1000, 250\n\n gloss = nodes.new(type='ShaderNodeBsdfGlossy')\n gloss.location = 1000, 0\n\n mixshader2 = nodes.new(type='ShaderNodeMixShader')\n mixshader2.location = 1200, 250\n\n output_node = nodes.new(type='ShaderNodeOutputMaterial')\n output_node.location = 1400, 250\n\n #glass forwards, no xparency\n rgb2_to_mix1 = links.new(rgb2.outputs[0], mixrgb1.inputs[2])\n rgb2_to_mix2 = links.new(rgb2.outputs[0], mixrgb2.inputs[2])\n rgb1_to_mix1 = links.new(rgb1.outputs[0], mixrgb1.inputs[1])\n rgb1_to_mix2 = links.new(rgb1.outputs[0], mixrgb2.inputs[1])\n mix2_to_tra2 = links.new(mixrgb2.outputs[0], transp2.inputs[0])\n mix1_to_glas = links.new(mixrgb1.outputs[0], glass.inputs[0])\n mix1_to_hues = links.new(mixrgb1.outputs[0], huesatval.inputs[4])\n hues_to_tra1 = links.new(huesatval.outputs[0], transp1.inputs[0])\n glas_to_shad = links.new(glass.outputs[0], shader.inputs[0])\n tra1_to_shad = links.new(transp1.outputs[0], shader.inputs[1])\n lpat_to_mix1 = links.new(lightpath.outputs[1], mixshader1.inputs[0])\n shad_to_mix1 = links.new(shader.outputs[0], mixshader1.inputs[1])\n tra2_to_mix1 = links.new(transp2.outputs[0], mixshader1.inputs[2])\n weig_to_curv = links.new(weight.outputs[1], curves.inputs[1])\n curv_to_mix2 = links.new(curves.outputs[0], mixshader2.inputs[0])\n mix1_to_mix2 = links.new(mixshader1.outputs[0], mixshader2.inputs[1])\n glos_to_mix2 = links.new(gloss.outputs[0], mixshader2.inputs[2])\n mix2_to_outp = links.new(mixshader2.outputs[0], output_node.inputs[0])\n\n return nodes, links\n\n ### E.PU START ###\ndef main():\n # kill it with fire\n reset_scene()\n set_scene()\n\n #Test Materials\n assign_material('Light', 'Light', 'light')\n assign_material('Sphere', 'Glass', 'glass')\n\n # FOR DEVELOPMENT START\n print(\"Script time: %.4f sec\" % (time.time() - time_start))\n # FOR DEVELOPMENT END\n\nmain()\n","sub_path":"material_generator.py","file_name":"material_generator.py","file_ext":"py","file_size_in_byte":7422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"379927606","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Aug 4 19:52:49 2018\n\n@author: giulio\n\"\"\"\n#We want to compress integer sequencies through the LZ-78 tree-like algorithm\n\nimport numpy as np\nimport bitstring as bt\n\ndef seqCompress (observations, comparable_size = False):\n#table{} contains known sequencies and matches them to their position in the \n#compressed list.\n table = {}\n currentSeq = []\n comp_list = []\n for idx, integer in enumerate(observations):\n #print(idx,'-th iteration, integer is', integer)\n currentSeq.append(integer)\n #We have to cast to Tuple since lists ain't hashable\n if tuple(currentSeq) in table:\n if (idx == len(observations)-1): \n comp_list.append(table[tuple(currentSeq)])\n #print('LAST')\n #print('Skipping: sequence known so far!')\n continue\n if tuple(currentSeq[0:-1]) in table:\n #print('Recognised', currentSeq[0:-1],'as valid prefix, code', table[tuple(currentSeq[:-1])])\n #print('Adding sequence:', currentSeq)\n comp_list.append([table[tuple(currentSeq[0:-1])], currentSeq[-1]])\n table[tuple(currentSeq)] = len(comp_list)-1\n currentSeq.clear()\n else:\n comp_list.append([-1,currentSeq[-1]])\n table[tuple(currentSeq)] = len(comp_list)-1\n currentSeq.clear()\n if comparable_size:\n res = [item for sublist in comp_list[0:-1] for item in sublist]\n res.append(comp_list[-1])\n return res\n else:\n return comp_list\n \ndef binCompress (observations):\n #table{} contains known sequencies and matches them to their position in the \n #compressed list.\n #Init variables\n table = {}\n currentSeq = bt.Bits()\n comp_list = []\n #Estimate number of bits necessary for each integer\n num_length = int(np.floor(np.log2(max(observations)))+1)\n #Init bitString with integer values\n bitString = bt.Bits().join([bt.Bits(uint = integer, length = num_length) for integer in observations])\n for idx, bit in enumerate(bitString):\n #print('Cycle No.', idx)\n #print(currentSeq.bin)\n currentSeq = bt.Bits().join([currentSeq, bt.Bits(bool = bit)])\n #print(currentSeq.bin)\n #We have to cast to Tuple since lists ain't hashable\n if currentSeq in table:\n if (idx == len(bitString)-1): \n comp_list.append(table[currentSeq])\n #print('LAST')\n #print('Skipping: sequence known so far!')\n continue\n if currentSeq[0:-1] in table:\n #print('Recognised', currentSeq[0:-1],'as valid prefix, code', table[currentSeq[:-1]])\n #print('Adding sequence:', currentSeq)\n comp_list.append([table[currentSeq[0:-1]], currentSeq[-1]])\n table[currentSeq] = len(comp_list)-1\n #print('Assigned code:', table[currentSeq])\n currentSeq = bt.Bits()\n else:\n comp_list.append([-1,currentSeq[-1]])\n table[currentSeq] = len(comp_list)-1\n currentSeq = bt.Bits()\n index_size = int(np.floor(np.log2(len(table))+1))\n print(num_length, index_size)\n single_character_tag = bt.Bits(int = -1, length = index_size) #A suitable -1 is created to represent single letters.\n if type(comp_list[-1]) is int:\n res = bt.Bits().join([single_character_tag if item == -1 else bt.Bits(uint = item, length = index_size) for sublist in comp_list[0:-1] for item in sublist])\n res = bt.Bits().join([res, bt.Bits(uint = comp_list[-1], length = index_size )])\n if type(comp_list[-1]) is list:\n res = bt.Bits().join([single_character_tag if item == -1 else bt.Bits(uint = item, length = index_size) for sublist in comp_list for item in sublist])\n return (res, bitString)","sub_path":"Temp/IntLZ.py","file_name":"IntLZ.py","file_ext":"py","file_size_in_byte":3850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"650799851","text":"import time\nimport services\nclass ClinicError(Exception):\n pass\n\nclass Validacao:\n @staticmethod\n def validarRg(rg):\n if rg == '':\n raise ClinicError('O campo \"rg\" é obrigatório')\n if not rg.isdigit():\n raise ClinicError('Rg deve conter apenas números')\n if len(rg) > 13:\n raise ClinicError('Rg deve conter no máximo 13 caracteres')\n return rg\n\n @staticmethod\n def validarNome(nome, nome_campo):\n limite_char = 100\n if nome == '':\n raise ClinicError(f'O campo \"{nome_campo}\" é obrigatório')\n if len(nome) > limite_char:\n raise ClinicError(f'O campo \"{nome_campo}\" não pode conter mais de {limite_char} caracteres')\n \n for letra in nome:\n if letra.isdigit():\n raise ClinicError(f'O campo \"{nome_campo}\" Não pode conter números')\n \n return nome\n \n def validarRua(rua):\n if rua == '':\n raise ClinicError(f'O campo \"rua\" é obrigatório')\n return rua\n \n @staticmethod\n def validarEstado(estado):\n ESTADOS = ['RN', 'PB', 'PE', 'SE', 'BA', 'MA', 'RO', 'AC', 'PR', 'SP', 'RJ', 'GO', 'MG', 'MT', 'MS']\n if estado == '':\n raise ClinicError('O campo \"estado\" é obrigatório')\n if estado.upper() not in ESTADOS:\n raise ClinicError('Não é um estado válido')\n\n return estado\n\n @staticmethod\n def validarTelefone(telefone):\n limite_digitos = 11\n if telefone == '':\n raise ClinicError('O campo \"telefone\" é obrigatório')\n if len(telefone) != limite_digitos:\n raise ClinicError(f'Telefone deve possuir {limite_digitos} dígitos')\n if not telefone.isdigit():\n raise ClinicError('Telefone deve conter apenas números')\n \n return telefone\n \n @staticmethod\n def validarNumero(numero):\n limite_digitos = 5\n if len(numero) > limite_digitos:\n raise ClinicError(f'O número da casa deve ter no máximo {limite_digitos} dígitos')\n if not numero.isdigit():\n raise ClinicError('O número da casa é inválido')\n \n return int(numero)\n \n @staticmethod\n def validarAno(ano):\n agora = time.gmtime(time.time())\n \n if ano == '':\n raise ClinicError('O campo \"Ano de graduação é obrigatório')\n if not ano.isdigit():\n raise ClinicError('O campo \"Ano de graduação deve conter apenas números\"')\n if int(ano) > agora[0]: #0 é a posicao do ano\n raise ClinicError(f'O campo \"Ano de graduação\" não pode ser maior que {agora[0]}')\n\n return int(ano)\n \n @staticmethod\n def validarSigla(sigla):\n if len(sigla) > 10:\n raise ClinicError('A Sigla deve ter no máximo 10 caracteres.')\n\n @staticmethod\n def validarCRM(crm):\n tamanho_limite = 20\n if not crm.isdigit():\n raise ClinicError('O CRM deve conter apenas números.')\n if len(crm) > tamanho_limite:\n raise ClinicError(f'O CRM deve conter no máximo {tamanho_limite} dígitos.')\n \n @staticmethod\n def validarEspecializacoes(especializacoes):\n tamanho_especializacao = 200\n if len(especializacoes) > tamanho_especializacao:\n raise ClinicError(f'As especializacoes devem ter no máximo {tamanho_especializacao} caracteres') \n \n @staticmethod\n def validarDataEHora(dia, mes, hora):\n\n max_dias = [31, 29, 13, 30, 31, 30, 31, 31, 30, 31, 30, 31] # cada posicao é um mês\n\n if not dia.isdigit():\n raise ClinicError('Dia deve ser um número')\n if not hora.isdigit():\n raise ClinicError('Hora deve ser um número')\n if not mes.isdigit():\n raise ClinicError('Mes deve ser um número')\n if int(hora) < 0 or int(hora) > 23:\n raise ClinicError('A hora deve ser entre 0 e 23')\n if int(mes) < 1 or int(mes) > 12:\n raise ClinicError('O mes deve ser entre 1 e 12')\n if int(dia) < 1:\n raise ClinicError('Insira um dia válido')\n if int(dia) > max_dias[int(mes)-1]:\n raise ClinicError(f'Não existe dia {dia} no mes {mes}')\n \n @staticmethod\n def validarPessoa(pessoa):\n Validacao.validarNome(pessoa.nome, 'nome')\n Validacao.validarRg(pessoa.rg)\n if isinstance(pessoa.telefone, list):\n for telefone in pessoa.telefone:\n Validacao.validarTelefone(telefone)\n else:\n Validacao.validarTelefone(pessoa.telefone)\n \n @staticmethod\n def validarEndereco(endereco):\n Validacao.validarRua(endereco.rua) #rua\n Validacao.validarNome(endereco.cidade, 'cidade') #cidade\n Validacao.validarEstado(endereco.estado)\n Validacao.validarNumero(endereco.numero)\n \n @staticmethod\n def validarEnfermeiro(enfermeiro):\n Validacao.validarPessoa(enfermeiro)\n Validacao.validarAno(enfermeiro.ano_graduacao)\n Validacao.validarNome(enfermeiro.nome_faculdade, \"Faculdade de graduação\")\n \n @staticmethod\n def validarConvenio(convenio):\n Validacao.validarNome(convenio.nome, 'nome')\n Validacao.validarSigla(convenio.sigla)\n Validacao.validarTelefone(convenio.telefone)\n \n @staticmethod\n def validarMedico(medico):\n Validacao.validarPessoa(medico)\n Validacao.validarEspecializacoes(medico.especializacoes)\n Validacao.validarCRM(medico.crm)\n \n @staticmethod\n def validarConsulta(consulta, edit=False):\n Validacao.validarDataEHora(consulta.dia, consulta.mes, consulta.hora)\n erro = services.verificarDisponibilidade(consulta, edit)\n if erro != None:\n raise ClinicError(erro)\n","sub_path":"app/validacao.py","file_name":"validacao.py","file_ext":"py","file_size_in_byte":5852,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"563031826","text":"# ///---- figuritas.py ----///\n# Alumno: Javier C. Rodriguez\n# Mail: javicerodriguez@gmail.com\n\n# ///-------- Datos --------///\n# 1) Álbum con 670 figuritas.\n# 2) Cada figurita se imprime en cantidades iguales y se distribuye aleatoriamente.\n# 3) Cada paquete trae cinco figuritas.\n\n# ///---- Imports ----///\nimport random\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# ///---- Variables ----///\nfigus_total = 670 # Cantidad de figuritas en album\nfigus_paquete = 5 # Cantidad de figuritas en paquete\nn_repeticiones = 100 # Veces para analizar llenado de album\npaquete = [] # Lista para paquete de figuritas\n\n# ///---- Crear album ----///\ndef crear_album(figus_total):\n album_make = np.zeros(figus_total, dtype=np.int64)\n return (album_make)\n\n# ///---- Album incompleto ----///\ndef album_incompleto(A):\n B = A.copy() # Copio la lista para no modificar el original\n B = np.sort(B) # Ordeno de menor a mayor la cantidad de cada figurita\n if B[0] == 0:\n return True # Devuelvo True si faltan\n return False # Devuelvo False si esta completo\n\n# ///---- Comprar figurita----///\ndef comprar_figu(figus_total):\n figu = random.randint(1,figus_total)\n return figu\n\n# ///---- Comprar paquete ----///\ndef comprar_paquete(figus_total, figus_paquete):\n pack = np.array([0]*figus_paquete) # Genero vector para 5 figuritas\n for i in range(figus_paquete-1): # Genero 5 figuritas aleatorias \n pack[i] = random.randint(1,figus_total)\n return pack\n\n# ///---- Compra simulada ----///\ndef cuantas_figus(figus_total):\n cant_compras = 0\n album = crear_album(figus_total) # Creo album\n while album_incompleto(album) == True:\n compra = comprar_figu(figus_total)\n cant_compras += 1\n album[compra-1] += 1\n return cant_compras\n\n# ///---- Compra simulada (Paquete de 5)----///\ndef cuantos_paquetes(figus_total, figus_paquete):\n cant_compras = 0\n album = crear_album(figus_total) # Creo album\n while album_incompleto(album) == True:\n paquete = comprar_paquete(figus_total, figus_paquete)\n cant_compras += 1\n for i in range(figus_paquete-1):\n album[paquete[i]-1] += 1\n return cant_compras\n\n# ///---- Main ----///\n# Experimentos\nexp_figus = [cuantas_figus(figus_total) for i in range(n_repeticiones)]\npromedio_figus = np.mean(exp_figus)\nexp_packs = [cuantos_paquetes(figus_total, figus_paquete) for i in range(n_repeticiones)]\npromedio_packs = np.mean(exp_packs)\npack850 = np.sum([1 for i in range(n_repeticiones) if cuantos_paquetes(figus_total, figus_paquete) <= 850])\n\n# Prints\nprint (f'\\nCompra de figus en promedio: {promedio_figus} figuritas')\nprint (f'Compra de paquetes en promedio: {promedio_packs} paquetes')\nprint (f'De {n_repeticiones} experimentos, en solo {pack850:.0f} se necesitaron hasta 850 paquetes para llenar el album')\nprint (f'La probabilidad de poder comprar hasta 850 paquetes es de {(pack850/n_repeticiones)*100}%')\n\n# Plots\nplt.hist(exp_packs,bins=150)\nplt.ylabel('Probabilidad')\nplt.xlabel('Paquetes')\nplt.show()","sub_path":"04_Random_Plt_Dbg/figuritas.py","file_name":"figuritas.py","file_ext":"py","file_size_in_byte":3038,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"490109025","text":"from requests import get\nfrom random import randint\nfrom os import getenv\n\ntype_ = 'http'\n\n# # free\n# url = 'https://api.proxyscrape.com/v2/?request=getproxies&protocol=http'\\\n# '&timeout=10000&country=all&ssl=yes&anonymity=all&simplified=true'\n\n# premium\nurl = 'https://api.proxyscrape.com/v2/account/datacenter_shared/proxy-list'\\\n f'?auth={getenv(\"PROXYSCRAPE_API\")}&protocol=http'\n\n\ndef refresh():\n \"\"\"\n >>> proxies = refresh()\n \"\"\"\n return get(url).text.splitlines()\n\n\ndef get_nth(proxies, n):\n \"\"\"\n >>> proxies = refresh()\n >>> proxy_dict = proxy.get_nth(proxies, 15)\n \"\"\"\n return {\n 'http': 'http://' + proxies[n],\n 'https': 'http://' + proxies[n]\n }\n\n\ndef get_one_random(proxies):\n \"\"\"\n >>> proxies = refresh()\n >>> proxy_dict = proxy.get_one_random(proxies)\n \"\"\"\n random_index = randint(0, len(proxies)-1)\n return {\n 'http': 'http://' + proxies[random_index],\n 'https': 'http://' + proxies[random_index]\n }\n","sub_path":"twitter/proxy.py","file_name":"proxy.py","file_ext":"py","file_size_in_byte":1001,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"440120399","text":"\"\"\"Functional tests for Airplay.\"\"\"\n\nfrom aiohttp import ClientSession\nfrom aiohttp.test_utils import (AioHTTPTestCase, unittest_run_loop)\n\nfrom pyatv.airplay import player\nfrom tests.airplay.fake_airplay_device import (\n FakeAirPlayDevice, AirPlayUseCases)\n\n\nSTREAM = 'http://airplaystream'\nSTART_POSITION = 0.8\n\n\nclass AirPlayPlayerTest(AioHTTPTestCase):\n\n def setUp(self):\n AioHTTPTestCase.setUp(self)\n\n # This is a hack that overrides asyncio.sleep to avoid making the test\n # slow. It also counts number of calls, since this is quite important\n # to the general function.\n player.asyncio.sleep = self.fake_asyncio_sleep\n self.no_of_sleeps = 0\n\n async def get_application(self, loop=None):\n self.fake_device = FakeAirPlayDevice(self.loop, self)\n self.usecase = AirPlayUseCases(self.fake_device)\n return self.fake_device\n\n async def fake_asyncio_sleep(self, time, loop):\n self.no_of_sleeps += 1\n\n @unittest_run_loop\n async def test_play_video(self):\n self.usecase.airplay_playback_idle()\n self.usecase.airplay_playback_playing()\n self.usecase.airplay_playback_idle()\n\n session = ClientSession(loop=self.loop)\n aplay = player.AirPlayPlayer(\n self.loop, session, '127.0.0.1', port=self.server.port)\n await aplay.play_url(STREAM, position=START_POSITION)\n\n self.assertEqual(self.fake_device.last_airplay_url, STREAM)\n self.assertEqual(self.fake_device.last_airplay_start, START_POSITION)\n self.assertEqual(self.no_of_sleeps, 2) # playback + idle = 3\n\n await session.close()\n","sub_path":"tests/airplay/test_airplay.py","file_name":"test_airplay.py","file_ext":"py","file_size_in_byte":1644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"235549598","text":"from common import prime_table, d_count, Watch\n\nWatch.start()\nlim = 1000000\nprime_t = prime_table(lim)\n\ndef rotate(n):\n q, r = divmod(n, 10 ** (d_count(n) - 1))\n return r * 10 + q\n\ndef circular_prime(n):\n if d_count(n) == 1: return True\n r = rotate(n)\n while r != n:\n if d_count(r) == d_count(n) and prime_t[r]:\n r = rotate(r)\n else: return False\n return True\n\nprimes = [n for n in range(2, lim) if prime_t[n]]\nprint(sum(1 for n in primes if circular_prime(n)))\nWatch.stop()\n ","sub_path":"1_49/src/task35/s35.py","file_name":"s35.py","file_ext":"py","file_size_in_byte":520,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"633367440","text":"import sys \nfrom PyQt5 import QtWidgets, QtGui\n\nclass AppDemo(QtWidgets.QWidget):\n\tdef __init__(self):\n\t\tsuper().__init__()\n\t\tself.resize(300, 200)\n\n\t\tfnt = QtGui.QFont(\"Open Sans\", 12)\n\n\t\tmainLayout = QtWidgets.QVBoxLayout()\n\n\t\t# input field\n\t\tself.input = QtWidgets.QLineEdit()\n\t\tself.input.setFixedHeight(50)\n\t\tself.input.setFont(fnt)\n\t\tself.input.editingFinished.connect(self.addEntry)\n\t\tmainLayout.addWidget(self.input)\n\n\t\tself.model = QtGui.QStandardItemModel()\n\t\tcompleter = QtWidgets.QCompleter(self.model, self)\n\t\tself.input.setCompleter(completer)\n\n\t\tself.console = QtWidgets.QTextEdit()\n\t\tself.console.setFont(fnt)\n\t\tmainLayout.addWidget(self.console)\n\n\t\tself.setLayout(mainLayout)\n\n\tdef addEntry(self):\n\t\tentryItem = self.input.text()\n\t\tself.input.clear()\n\t\tself.console.append(entryItem)\n\n\t\tif not self.model.findItems(entryItem):\n\t\t\tself.model.appendRow(QtGui.QStandardItem(entryItem))\n\n\nif __name__ == \"__main__\":\n\tapp = QtWidgets.QApplication(sys.argv)\n\n\tdemo = AppDemo()\n\tdemo.show()\n\n\tsys.exit(app.exec_())","sub_path":"GUI/AutocompleteFeature/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"6119405","text":"#!/usr/bin/env python2.6\nfrom __future__ import print_function\n\nimport os\nimport sys\nimport subprocess\n\n\nIS_DEBUG = bool(os.environ.get('DEBUG')) or '-D' in sys.argv[1:]\nUSER = os.environ['USER']\nSSHD = 'sshd: %s' % USER\nSSH_AGENT = 'ssh-agent'\nBASH = '-bash'\nPS_CMD = 'ps -o rss,command'\nPER_USER_APACHE = '/usr/local/apache2-mpm-peruser/bin/httpd'\nFILTERS = dict(\n sshd=lambda c: c.startswith(SSHD),\n ssh_agent=lambda c: c == SSH_AGENT,\n bash=lambda c: c == BASH,\n ps_cmd=lambda c: c.startswith(PS_CMD),\n per_user_apache=lambda c: c.startswith(PER_USER_APACHE),\n)\n\n\ndef main():\n mems = []\n cmd = PS_CMD + (' U %s' % USER)\n\n job = subprocess.Popen(cmd, shell=True, stdout=subprocess.PIPE,\n stderr=subprocess.PIPE)\n out = job.communicate()[0].splitlines()\n for line in out:\n if not line.strip():\n continue\n rsz, command = line.lstrip().split(' ', 1)\n debug('rsz=%r, command=%r' % (rsz, command))\n if rsz.isdigit() and not _is_filtered_command(command):\n mems.append(int(rsz))\n print('%s using %.2fMB' % (USER, sum(mems) / 1024.0))\n return 0\n\n\ndef _is_filtered_command(command):\n for func in FILTERS:\n if FILTERS[func](command):\n debug('%r ignoring %s' % (func, command))\n return True\n return False\n\n\ndef debug(*args):\n if IS_DEBUG:\n print(*args)\n\n\nif __name__ == '__main__':\n sys.exit(main())\n","sub_path":"lib/python/mbhlib/mymem.py","file_name":"mymem.py","file_ext":"py","file_size_in_byte":1453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"285973891","text":"from django.db import models\n\nclass Company(models.Model):\n name = models.CharField(max_length=45)\n subcategories = models.ForeignKey('Subcategory',on_delete=models.CASCADE,null=True)\n money = models.ForeignKey('Money',on_delete=models.CASCADE,null=True)\n district = models.ForeignKey('District' , on_delete=models.CASCADE,null=True)\n title = models.CharField (max_length=45)\n dead_line = models.CharField (max_length=45)\n carrer = models.ForeignKey ('Carrer',on_delete=models.CASCADE,null=True)\n image_url = models.CharField (max_length=1000)\n sub_tag = models.ForeignKey('Sub_tag',on_delete=models.CASCADE , related_name='sub_tags',null=True)\n tag_companies = models.ManyToManyField('Sub_tag' , through='Tag_company',related_name='tag_companies')\n\n class Meta:\n db_table = 'companies' \n\nclass Subcategory(models.Model):\n maincategories = models.ForeignKey('Maincategory',on_delete=models.CASCADE)\n name = models.CharField(max_length=45)\n \n class Meta:\n db_table = 'subcategories'\n\nclass Maincategory(models.Model):\n name = models.CharField(max_length=45)\n\n class Meta:\n db_table='maincategories'\n\nclass Money(models.Model):\n recommend = models.DecimalField(max_digits=10, decimal_places=2,null=True,default='')\n applicant = models.DecimalField(max_digits=10, decimal_places=2,null=True,default='')\n\n class Meta:\n db_table='moneys'\n\nclass District(models.Model):\n name = models.CharField(max_length=45)\n district_categories = models.ForeignKey('District_category' , on_delete=models.CASCADE,null=True)\n\n class Meta:\n db_table='districts' \n\nclass District_category(models.Model):\n name = models.CharField(max_length=45)\n\n class Meta:\n db_table='district_categories'\n\nclass Carrer(models.Model):\n carrer = models.CharField(max_length=45)\n\n class Meta:\n db_table='carrers'\n\nclass Sub_tag(models.Model):\n name = models.CharField(max_length=45)\n tag = models.ForeignKey('Tag' , on_delete=models.CASCADE)\n\n class Meta:\n db_table='sub_tag'\n\nclass Tag(models.Model):\n name = models.CharField(max_length=45)\n\n class Meta:\n db_table='tags'\n\nclass Salary(models.Model):\n subcategories = models.ForeignKey('Subcategory', on_delete=models.CASCADE)\n carrer = models.ForeignKey('Carrer' , on_delete=models.CASCADE , null=True)\n maincategories= models.ForeignKey('Maincategory', on_delete=models.CASCADE)\n salary = models.DecimalField(max_digits=10, decimal_places=2,null=True,default='')\n\n class Meta:\n db_table='salaries'\n\nclass Tag_company(models.Model):\n sub_tags = models.ForeignKey('Sub_tag', on_delete=models.CASCADE)\n companies = models.ForeignKey('Company' , on_delete= models.CASCADE)\n\n class Meta:\n db_table='tag_companies'","sub_path":"company/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"603770468","text":"import asyncio\nimport json\nimport websockets\nimport ERParser\nimport importlib\nimport geopy\nimport geopy.distance\n\nhospitals = json.load(open(\"ambulances.json\"))\nambulanceposts = [(a[\"lat\"], a[\"long\"]) for a in hospitals]\n\npts = [geopy.Point(p[0], p[1]) for p in ambulanceposts]\n\n\nasync def main():\n async with websockets.connect('ws://monitor.livep2000.nl/A/websocket') as webs:\n ## Start Connection\n await webs.send('{\"TYP\":\"ANN\",\"COO\":\"5db18e81-901f-42f5-ae96-1c5511341aba\",\"UID\":\"6698853\",\"COM\":6}')\n await webs.recv()\n\n ## Send Start\n await webs.send('{\"FRQ\":true,\"COM\":12}')\n await webs.recv()\n\n while True:\n websocket_data = json.loads(await webs.recv())\n if isinstance(websocket_data, dict):\n pass\n else:\n importlib.reload(ERParser)\n lat, long = ERParser.parse(websocket_data)\n if lat is not None:\n onept = geopy.Point(lat, long)\n alldist = [(p, geopy.distance.distance(p, onept).km) for p in pts]\n nearest_point = min(alldist, key=lambda x: (x[1]))[0]\n print(onept.latitude, onept.longitude, nearest_point.latitude, nearest_point.longitude)\n print(\"https://www.google.nl/maps/dir/{0},{1}/{2},{3}/@{0},{1},18z\".format(onept.latitude,\n onept.longitude,\n nearest_point.latitude,\n nearest_point.longitude))\n\n\nif __name__ == '__main__':\n asyncio.get_event_loop().run_until_complete(main())\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"468386901","text":"'''\n\nKevin Xu\nCS 1114\nsx670\n\nPurpose of program\n'''\n\n\n# Part A\ndef clean_data(complete_weather_filename, cleaned_weather_filename):\n # Complete this function to clean the CSV.\n # It should create a new file as specified in the assignment specs.\n cf=open(cleaned_weather_filename,\"w+\")\n f=open(complete_weather_filename,\"r\")\n #cf.write(\"City,Date,High,Low,Precipitation\\n\")\n for line in f:\n lst=line.split(\",\")\n s=\"\"\n s=s+lst[0]+\",\"+lst[1]+\",\"+lst[2]+\",\"+lst[3]+\",\"\n if(lst[8]=='T'):\n s=s+'0'+'\\n'\n else:\n s=s+lst[8]+'\\n'\n cf.write(s)\n cf.close()\n f.close()\n\n# Part B\ndef f_to_c(f_temperature):\n return ((f_temperature-32)*5)/9 # modify this\n\ndef in_to_cm(inches):\n return inches*2.54 # modify this\n\ndef convert_data_to_metric(imperial_weather_filename, metric_weather_filename):\n # Similar to clean_data() - read in the file and make a new file with metric values.\n wm=open(metric_weather_filename,\"w+\")\n wi=open(imperial_weather_filename,\"r\")\n for line in wi:\n lst=line.split(\",\")\n s=\"\"\n if(lst[2]=='High'):\n s=line\n else:\n s=lst[0]+\",\"+lst[1]+\",\"+str(f_to_c(int(lst[2])))+\",\"+str(f_to_c(int(lst[3])))+\",\"+str(in_to_cm(float(lst[4])))+'\\n'\n wm.write(s)\n wm.close()\n wi.close()\n\n# Part C\ndef print_average_temps_per_month(city, weather_filename, unit_type):\n # prints average highs and lows in each month for the given city\n lst_month=['January','February','March','April','May','June','July','August','September','October','November','December']\n f=open(weather_filename,\"r\")\n print(\"Average temperatures for \",city,\":\\n\",sep=\"\")\n if(unit_type=='imperial'):\n unit='F'\n if(unit_type=='metric'):\n unit='C'\n high_sums=[0,0,0,0,0,0,0,0,0,0,0,0]\n low_sums=[0,0,0,0,0,0,0,0,0,0,0,0]\n day_nums=[0,0,0,0,0,0,0,0,0,0,0,0]\n for line in f:\n lst=line.split(\",\")\n if(lst[1]!='Date'):\n date=lst[1].split(\"/\")\n high_sums[int(date[0])-1]+=float(lst[2])\n low_sums[int(date[0])-1]+=float(lst[3])\n day_nums[int(date[0])-1]+=1\n for i in range(12):\n print(lst_month[i],\": \",high_sums[i]/day_nums[i],unit,\" High, \",low_sums[i]/day_nums[i],unit,\" Low\\n\",sep=\"\")\n f.close()\n \n# Part D\n# Write your question (as a comment), and implement a function to answer it\n# Q: Given two cities, which has higher average rainfall?\ndef higher_rainfall(city1,city2,weather_filename):\n f=open(weather_filename,\"r\")\n rain1=0\n rain2=0\n for line in f:\n lst=line.split(\",\")\n if(lst[0]==city1):\n rain1+=float(lst[4])\n if(lst[0]==city2):\n rain2+=float(lst[4])\n if(rain1>rain2):\n print(city1)\n if(rain1 0:\n ids = ids.flatten()\n for (markerCorner, markerID) in zip(corners, ids):\n obstacles_position.append( ArucoPosition( markerID, markerCorner ) )\n return obstacles_position\n\n def get_acuro_dictionnary(self):\n return cv2.aruco.Dictionary_get(cv2.aruco.DICT_4X4_50)\n\n def generate_empty_obstacle_position(self):\n obstacle_position = []\n for i in range(0, 2):\n obstacle_position.append({f\"obstacle {i + 1}\": {\n \"center\": (0, 0),\n \"top_right\": (0, 0),\n \"top_left\": (0, 0),\n \"bottom_right\": (0, 0),\n \"bottom_left\": (0, 0)\n }})\n return obstacle_position\n\n def calculate_obstacle_position(self, obstacles_position: List[ArucoPosition],\n aruco_marker_width,\n camera_matrix,\n distortion_coefficient) -> List[MarkerPosition]:\n\n aruco_markers_corner = [obstacle_position.get_corner()\n for obstacle_position in\n obstacles_position]\n\n corner_length = len(aruco_markers_corner)\n\n if corner_length < 1:\n return []\n\n rotation_vectors, translation_vectors, objects_points = cv2.aruco.estimatePoseSingleMarkers(\n aruco_markers_corner,\n aruco_marker_width,\n camera_matrix,\n distortion_coefficient\n )\n\n aruco_markers_positions = [\n MarkerPosition(\n markers_points=object_point,\n rotation_vector=rotation_vector,\n translation_vector=translation_vector\n )\n for rotation_vector, translation_vector, object_point\n in zip(rotation_vectors, translation_vectors, objects_points)\n ]\n\n return aruco_markers_positions\n","sub_path":"station/robot_goal_and_obstacle_finder/detection/acuro_markers/obstacle_detection.py","file_name":"obstacle_detection.py","file_ext":"py","file_size_in_byte":2646,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"562698926","text":"#############################\n# -------helper dict----------\n# dictonary der switch-case in python implementieren soll\n# es werden die ASCII werte zwischen 97 und 123 benutzt da dies genau den bereich der kleinbuchstaben a-z abdeckt\n# diese werte werden werden in ASCII buchstaben konvertiert und als key für den dictionary benutzt\n# deren wert minus 97 wird dann als dessen value zugewiesen\n\nswitch = {}\n\nfor i in range(97, 123):\n switch[chr(i)] = i - 97\n\n#############################\n# ------------aufg1-----------\n# prueft ob \"text\" ein string ist\n# danach wird über jedes zeichen in \"text\" iteriert und falls moeglich zu\n# einem kleinbuchstaben konvertiert\n# in der schleife wird getestet ob ob das zeichen ein buchstabe ist\n# falls ja wird der buchstabe kodiert und zu num_list hinzugefuegt\n\n\ndef decode(text):\n num_list = []\n\n if isinstance(text, str):\n for x in text.lower():\n if x.isalpha():\n num_list.append(switch[x])\n else:\n print(\"please enter a string\")\n return num_list\n\n#########################################\n# -------------aufg2----------------------\n# es wird über die liste drüber iteriert\n# innerhalb dieser schleife wird jeder key in switch durch gegeangen\n# nun wird jedes value mit x verglichen\n# falls es eine uebereinstimmung gibt wird der key\n# zum string hinzugefuegt (somit werden die zahlen in charList\n# in die korrespondierenden buchstaben umgewandelt\n\ndef encode(char_list):\n out = \"\"\n\n for x in char_list:\n for y in switch:\n if x == switch[y]:\n out += y\n continue\n\n return out\n\n#########################################\n# -------------aufg3----------------------\n# keytable um die passenden inversen zu finden\n\nkey_table={\n 1: 1,\n 3: 9,\n 5: 21,\n 7: 15,\n 9: 3,\n 11: 19,\n 15: 7,\n 17: 23,\n 19: 11,\n 21: 5,\n 23: 17,\n 25: 25\n}\n\n#########################################\n# -------------aufg4----------------------\n# testet ob a und b Buchstaben, Zahlen oder Zahlen in Strings sind, wenn einer der ersten beiden Faelle\n# zutrifft werden sie zu ihren korrespondierenden Zahlen Werte konvertiert\n# wenn a eine gueltige invertierbare Zahl ist wird der Plaintext unter Verwendung der decode() Funktion\n# in Zahlen umgewandelt\n# wenn nein bricht die Funktion ab und gibt \"invalid key\" auf der Konsole aus,\n# diese werden danach unter der Verwendung von den Schluesseln a und b verschluesselt\n# die verschluesselte Zahlen Liste wird danach wieder in durch die encode() Funtkion in Buchstaben umgewandelt\n# und als Großbuchstaben ausgegeben\n\n\ndef acEncrypt(a, b, plain_text):\n if isinstance(a, str):\n if a.isalpha():\n a = switch[a]\n else:\n a = int(a)\n\n if isinstance(b, str):\n if b.isalpha():\n b = switch[b]\n else:\n b = int(b)\n\n num_list = []\n if a in key_table:\n num_list = decode(plain_text)\n\n for i in range(len(num_list)):\n num_list[i] = (a * num_list[i] + b) % 26\n\n print(encode(num_list).upper())\n\n else:\n print(\"invalid key\")\n return \"\"\n\n\n#########################################\n# ------------aufg5-----------------------\n# es wird wieder geprueft ob a und b Buchstaben, Zahlen oder Zahlen in Strings sind, wenn einer der ersten beiden Faelle\n# zutrifft werden sie zu ihren korrespondierenden Zahlen Werte konvertiert\n# nun wird geprueft ob a ein gueltiger Schluessel ist, wenn nein bricht die Funktion ab und gibt \"invalid key\" auf der Konsole aus\n# und mit encode wird der Text in eine Zahlen Liste umgewandelt\n# die in der acEncrypt Funktion verwendete Gleichung zur Verschluesselung wird nun umgestellt um die Verschluesslung\n# rueckgaengig zu machen\n# anschliessend wird die Zahlen Liste wieder in Kleinbuchstaben umgewandelt und ausgegeben\n\ndef acDecrypt(a, b, cipher_text):\n if isinstance(a, str):\n if a.isalpha():\n a = switch[a]\n else:\n a = int(a)\n\n if isinstance(b, str):\n if b.isalpha():\n b = switch[b]\n else:\n b = int(b)\n\n if a in key_table:\n num_list = decode(cipher_text)\n\n for i in range(len(num_list)):\n num_list[i] = ((num_list[i] - b) * key_table[a]) % 26\n\n print(encode(num_list).lower())\n\n else:\n print(\"invalid key\")\n return \"\"\n\n\n#################################################################\n# diese Funktionen müssen für aufg15 benutzt werden da ich was falsch interpretiert hab als ich acEncrypt und acDecrypt geschrieben hab\n# in der aufgabe heißt es eigentlich das man den Output nicht zurückgeben sondern ausgeben soll...\n# aber in aufg15 darf nichts anderes als die 50 Zeichen ausgegeben werden weshalb man diese leicht abgeänderten Funktionen benutzen muss\n# der einzige Unterschied hier ist lediglich das die return Werte zurückgegeben werden\n\n\ndef acEncrypt2(a, b, plain_text):\n if isinstance(a, str):\n if a.isalpha():\n a = switch[a]\n else:\n a = int(a)\n\n if isinstance(b, str):\n if b.isalpha():\n b = switch[b]\n else:\n b = int(b)\n\n num_list = []\n if a in key_table:\n num_list = decode(plain_text)\n\n for i in range(len(num_list)):\n num_list[i] = (a * num_list[i] + b) % 26\n\n return encode(num_list).upper()\n\n else:\n print(\"invalid key\")\n return \"\"\n\ndef acDecrypt2(a, b, cipher_text):\n if isinstance(a, str):\n if a.isalpha():\n a = switch[a]\n else:\n a = int(a)\n\n if isinstance(b, str):\n if b.isalpha():\n b = switch[b]\n else:\n b = int(b)\n\n if a in key_table:\n num_list = decode(cipher_text)\n\n for i in range(len(num_list)):\n num_list[i] = ((num_list[i] - b) * key_table[a]) % 26\n\n return encode(num_list).lower()\n\n else:\n print(\"invalid key\")\n return \"\"","sub_path":"aclib.py","file_name":"aclib.py","file_ext":"py","file_size_in_byte":5998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"268685121","text":"# -*- coding: utf-8 -*-\n\n# This file is part of python-opsi.\n# Copyright (C) 2010-2019 uib GmbH \n\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as\n# published by the Free Software Foundation, either version 3 of the\n# 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 Affero General Public License for more details.\n\n# You should have received a copy of the GNU Affero General Public License\n# along with this program. If not, see .\n\"\"\"\nDepotserver backend.\n\n:copyright:\tuib GmbH \n:author: Jan Schneider \n:author: Niko Wenselowski \n:license: GNU Affero General Public License version 3\n\"\"\"\n\nimport os\n\nfrom contextlib import contextmanager\n\nfrom OPSI.Exceptions import (\n\tBackendBadValueError, BackendConfigurationError,\n\tBackendError, BackendIOError, BackendMissingDataError,\n\tBackendTemporaryError, BackendUnaccomplishableError)\nfrom OPSI.Logger import Logger, LOG_DEBUG\nfrom OPSI.Types import (\n\tforceBool, forceDict, forceFilename, forceHostId,\n\tforceUnicode, forceUnicodeLower)\nfrom OPSI.Types import forceProductId as forceProductIdFunc\nfrom OPSI.Object import ProductOnDepot, ProductPropertyState\nfrom OPSI.Backend.Backend import LOG_DIR, ExtendedBackend\nfrom OPSI.System import getDiskSpaceUsage\nfrom OPSI.Util.Product import ProductPackageFile\nfrom OPSI.Util import (\n\tcompareVersions, getfqdn, md5sum, librsyncSignature,\n\tlibrsyncPatchFile, librsyncDeltaFile, removeDirectory)\nfrom OPSI.Util.File import ZsyncFile\n\n__all__ = ('DepotserverBackend', 'DepotserverPackageManager')\n\nlogger = Logger()\n\n\nclass DepotserverBackend(ExtendedBackend):\n\tdef __init__(self, backend, **kwargs):\n\t\tself._name = 'depotserver'\n\n\t\tExtendedBackend.__init__(self, backend)\n\n\t\tself._packageLog = os.path.join(LOG_DIR, 'package.log')\n\t\tself._sshRSAPublicKeyFile = u'/etc/ssh/ssh_host_rsa_key.pub'\n\n\t\tself._depotId = forceHostId(getfqdn())\n\t\tif not self._context.host_getIdents(id=self._depotId): # pylint: disable=maybe-no-member\n\t\t\traise BackendMissingDataError(u\"Depot '%s' not found in backend\" % self._depotId)\n\t\tself._packageManager = DepotserverPackageManager(self)\n\n\tdef depot_getHostRSAPublicKey(self):\n\t\twith open(self._sshRSAPublicKeyFile, 'r') as publicKey:\n\t\t\treturn forceUnicode(publicKey.read())\n\n\tdef depot_getMD5Sum(self, filename, forceCalculation=False):\n\t\tchecksum = None\n\t\ttry:\n\t\t\tif not forceBool(forceCalculation):\n\t\t\t\thashFile = filename + '.md5'\n\n\t\t\t\ttry:\n\t\t\t\t\twith open(hashFile) as fileHandle:\n\t\t\t\t\t\tchecksum = fileHandle.read()\n\n\t\t\t\t\tlogger.info(u\"Using pre-calculated MD5sum from '{0}'.\", hashFile)\n\t\t\t\texcept (OSError, IOError):\n\t\t\t\t\tpass\n\n\t\t\tif not checksum:\n\t\t\t\tchecksum = md5sum(filename)\n\n\t\t\tlogger.info(u\"MD5sum of file '{0}' is '{1}'\", filename, checksum)\n\t\t\treturn checksum\n\t\texcept Exception as error:\n\t\t\traise BackendIOError(u\"Failed to get md5sum: %s\" % error)\n\n\tdef depot_librsyncSignature(self, filename):\n\t\ttry:\n\t\t\treturn librsyncSignature(filename)\n\t\texcept Exception as e:\n\t\t\traise BackendIOError(u\"Failed to get librsync signature: %s\" % e)\n\n\tdef depot_librsyncPatchFile(self, oldfile, deltafile, newfile):\n\t\ttry:\n\t\t\treturn librsyncPatchFile(oldfile, deltafile, newfile)\n\t\texcept Exception as e:\n\t\t\traise BackendIOError(u\"Failed to patch file: %s\" % e)\n\n\tdef depot_librsyncDeltaFile(self, filename, signature, deltafile):\n\t\ttry:\n\t\t\tlibrsyncDeltaFile(filename, signature, deltafile)\n\t\texcept Exception as e:\n\t\t\traise BackendIOError(u\"Failed to create librsync delta file: %s\" % e)\n\n\tdef depot_getDiskSpaceUsage(self, path):\n\t\tif os.name != 'posix':\n\t\t\traise NotImplementedError(u\"Not implemented for non-posix os\")\n\n\t\ttry:\n\t\t\treturn getDiskSpaceUsage(path)\n\t\texcept Exception as e:\n\t\t\traise BackendIOError(u\"Failed to get disk space usage: %s\" % e)\n\n\tdef depot_installPackage(self, filename, force=False, propertyDefaultValues={}, tempDir=None, forceProductId=None, suppressPackageContentFileGeneration=False):\n\t\tself._packageManager.installPackage(filename,\n\t\t\tforce=force, propertyDefaultValues=propertyDefaultValues,\n\t\t\ttempDir=tempDir, forceProductId=forceProductId,\n\t\t\tsuppressPackageContentFileGeneration=suppressPackageContentFileGeneration\n\t\t)\n\n\tdef depot_uninstallPackage(self, productId, force=False, deleteFiles=True):\n\t\tself._packageManager.uninstallPackage(productId, force, deleteFiles)\n\n\tdef depot_createMd5SumFile(self, filename, md5sumFilename):\n\t\tif not os.path.exists(filename):\n\t\t\traise BackendIOError(u\"File not found: %s\" % filename)\n\t\tlogger.info(u\"Creating md5sum file '%s'\" % md5sumFilename)\n\t\tmd5 = md5sum(filename)\n\t\twith open(md5sumFilename, 'w') as md5file:\n\t\t\tmd5file.write(md5)\n\n\tdef depot_createZsyncFile(self, filename, zsyncFilename):\n\t\tif not os.path.exists(filename):\n\t\t\traise BackendIOError(u\"File not found: %s\" % filename)\n\t\tlogger.info(u\"Creating zsync file '%s'\" % zsyncFilename)\n\t\tzsyncFile = ZsyncFile(zsyncFilename)\n\t\tzsyncFile.generate(filename)\n\n\nclass DepotserverPackageManager(object):\n\tdef __init__(self, depotBackend):\n\t\tif not isinstance(depotBackend, DepotserverBackend):\n\t\t\traise BackendConfigurationError(u\"DepotserverPackageManager needs instance of DepotserverBackend as backend, got %s\" % depotBackend.__class__.__name__)\n\t\tself._depotBackend = depotBackend\n\t\tlogger.setLogFile(self._depotBackend._packageLog, object=self)\n\n\tdef installPackage(self, filename, force=False, propertyDefaultValues={}, tempDir=None, forceProductId=None, suppressPackageContentFileGeneration=False):\n\n\t\t@contextmanager\n\t\tdef productPackageFile(filename, tempDir, depotId):\n\t\t\ttry:\n\t\t\t\tdepots = self._depotBackend._context.host_getObjects(id=depotId)\n\t\t\t\tdepot = depots[0]\n\t\t\t\tdel depots\n\t\t\texcept IndexError:\n\t\t\t\traise BackendMissingDataError(u\"Depot '%s' not found in backend\" % depotId)\n\n\t\t\tdepotLocalUrl = depot.getDepotLocalUrl()\n\t\t\tif not depotLocalUrl.startswith(u'file:///'):\n\t\t\t\traise BackendBadValueError(u\"Value '%s' not allowed for depot local url (has to start with 'file:///')\" % depotLocalUrl)\n\t\t\tclientDataDir = depotLocalUrl[7:]\n\n\t\t\tppf = ProductPackageFile(filename, tempDir=tempDir)\n\t\t\tppf.setClientDataDir(clientDataDir)\n\t\t\tppf.getMetaData()\n\n\t\t\ttry:\n\t\t\t\tyield ppf\n\t\t\t\tppf.setAccessRights()\n\t\t\tfinally:\n\t\t\t\ttry:\n\t\t\t\t\tppf.cleanup()\n\t\t\t\texcept Exception as cleanupError:\n\t\t\t\t\tlogger.error(\"Cleanup failed: {0!r}\", cleanupError)\n\n\t\t@contextmanager\n\t\tdef lockProduct(backend, product, depotId, forceInstallation):\n\t\t\tproductId = product.getId()\n\t\t\tlogger.debug(\"Checking for locked product '{}' on depot '{}'\", productId, depotId)\n\t\t\tproductOnDepots = backend.productOnDepot_getObjects(depotId=depotId, productId=productId)\n\t\t\ttry:\n\t\t\t\tif productOnDepots[0].getLocked():\n\t\t\t\t\tlogger.notice(u\"Product '{0}' currently locked on depot '{1}'\", productId, depotId)\n\t\t\t\t\tif not forceInstallation:\n\t\t\t\t\t\traise BackendTemporaryError(u\"Product '{}' currently locked on depot '{}'\".format(productId, depotId))\n\t\t\t\t\tlogger.warning(u\"Installation of locked product forced\")\n\t\t\texcept IndexError:\n\t\t\t\tpass\n\n\t\t\tlogger.notice(u\"Locking product '{0}' on depot '{1}'\", productId, depotId)\n\t\t\tproductOnDepot = ProductOnDepot(\n\t\t\t\tproductId=productId,\n\t\t\t\tproductType=product.getType(),\n\t\t\t\tproductVersion=product.getProductVersion(),\n\t\t\t\tpackageVersion=product.getPackageVersion(),\n\t\t\t\tdepotId=depotId,\n\t\t\t\tlocked=True\n\t\t\t)\n\t\t\tlogger.info(u\"Creating product on depot {0}\", productOnDepot)\n\t\t\tbackend.productOnDepot_createObjects(productOnDepot)\n\n\t\t\ttry:\n\t\t\t\tyield productOnDepot\n\t\t\texcept Exception as err:\n\t\t\t\tlogger.warning(\"Installation error. Not unlocking product '{}' on depot '{}'.\", productId, depotId)\n\t\t\t\traise err\n\n\t\t\tlogger.notice(\n\t\t\t\tu\"Unlocking product '{0}' {1}-{2} on depot '{3}'\",\n\t\t\t\tproductOnDepot.getProductId(),\n\t\t\t\tproductOnDepot.getProductVersion(),\n\t\t\t\tproductOnDepot.getPackageVersion(),\n\t\t\t\tdepotId\n\t\t\t)\n\t\t\tproductOnDepot.setLocked(False)\n\t\t\tbackend.productOnDepot_updateObject(productOnDepot)\n\n\t\t@contextmanager\n\t\tdef runPackageScripts(productPackageFile, depotId):\n\t\t\tlogger.info(u\"Running preinst script\")\n\t\t\tfor line in productPackageFile.runPreinst(({'DEPOT_ID': depotId})):\n\t\t\t\tlogger.info(u\"[preinst] {0}\", line)\n\n\t\t\tyield\n\n\t\t\tlogger.info(u\"Running postinst script\")\n\t\t\tfor line in productPackageFile.runPostinst({'DEPOT_ID': depotId}):\n\t\t\t\tlogger.info(u\"[postinst] {0}\", line)\n\n\t\tdef cleanUpProducts(backend, productId):\n\t\t\tproductIdents = set()\n\t\t\tfor productOnDepot in backend.productOnDepot_getObjects(productId=productId):\n\t\t\t\tproductIdent = u\"%s;%s;%s\" % (productOnDepot.productId, productOnDepot.productVersion, productOnDepot.packageVersion)\n\t\t\t\tproductIdents.add(productIdent)\n\n\t\t\tdeleteProducts = set(\n\t\t\t\tproduct\n\t\t\t\tfor product in backend.product_getObjects(id=productId)\n\t\t\t\tif product.getIdent(returnType='unicode') not in productIdents\n\t\t\t)\n\n\t\t\tif deleteProducts:\n\t\t\t\tbackend.product_deleteObjects(deleteProducts)\n\n\t\tdef cleanUpProductPropertyStates(backend, productProperties, depotId, productOnDepot):\n\t\t\tproductPropertiesToCleanup = {}\n\t\t\tfor productProperty in productProperties:\n\t\t\t\tif productProperty.editable or not productProperty.possibleValues:\n\t\t\t\t\tcontinue\n\t\t\t\tproductPropertiesToCleanup[productProperty.propertyId] = productProperty\n\n\t\t\tif productPropertiesToCleanup:\n\t\t\t\tclientIds = set(\n\t\t\t\t\tclientToDepot['clientId']\n\t\t\t\t\tfor clientToDepot in backend.configState_getClientToDepotserver(depotIds=depotId)\n\t\t\t\t)\n\n\t\t\t\tif clientIds:\n\t\t\t\t\tdeleteProductPropertyStates = []\n\t\t\t\t\tupdateProductPropertyStates = []\n\t\t\t\t\tstates = backend.productPropertyState_getObjects(\n\t\t\t\t\t\tobjectId=clientIds,\n\t\t\t\t\t\tproductId=productOnDepot.getProductId(),\n\t\t\t\t\t\tpropertyId=list(productPropertiesToCleanup.keys())\n\t\t\t\t\t)\n\n\t\t\t\t\tfor productPropertyState in states:\n\t\t\t\t\t\tchanged = False\n\t\t\t\t\t\tnewValues = []\n\t\t\t\t\t\tfor value in productPropertyState.values:\n\t\t\t\t\t\t\tproductProperty = productPropertiesToCleanup[productPropertyState.propertyId]\n\t\t\t\t\t\t\tif value in productProperty.possibleValues:\n\t\t\t\t\t\t\t\tnewValues.append(value)\n\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\tif productProperty.getType() == 'BoolProductProperty' and forceBool(value) in productProperty.possibleValues:\n\t\t\t\t\t\t\t\tnewValues.append(forceBool(value))\n\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\tcontinue\n\t\t\t\t\t\t\telif productProperty.getType() == 'UnicodeProductProperty':\n\t\t\t\t\t\t\t\tnewValue = None\n\t\t\t\t\t\t\t\tfor possibleValue in productProperty.possibleValues:\n\t\t\t\t\t\t\t\t\tif forceUnicodeLower(possibleValue) == forceUnicodeLower(value):\n\t\t\t\t\t\t\t\t\t\tnewValue = possibleValue\n\t\t\t\t\t\t\t\t\t\tbreak\n\n\t\t\t\t\t\t\t\tif newValue is not None:\n\t\t\t\t\t\t\t\t\tnewValues.append(newValue)\n\t\t\t\t\t\t\t\t\tchanged = True\n\t\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\tchanged = True\n\n\t\t\t\t\t\tif changed:\n\t\t\t\t\t\t\tif not newValues:\n\t\t\t\t\t\t\t\tlogger.debug(u\"Properties changed: marking productPropertyState {0} for deletion\", productPropertyState)\n\t\t\t\t\t\t\t\tdeleteProductPropertyStates.append(productPropertyState)\n\t\t\t\t\t\t\telse:\n\t\t\t\t\t\t\t\tproductPropertyState.setValues(newValues)\n\t\t\t\t\t\t\t\tlogger.debug(u\"Properties changed: marking productPropertyState {0} for update\", productPropertyState)\n\t\t\t\t\t\t\t\tupdateProductPropertyStates.append(productPropertyState)\n\n\t\t\t\t\tif deleteProductPropertyStates:\n\t\t\t\t\t\tbackend.productPropertyState_deleteObjects(deleteProductPropertyStates)\n\t\t\t\t\tif updateProductPropertyStates:\n\t\t\t\t\t\tbackend.productPropertyState_updateObjects(updateProductPropertyStates)\n\n\t\tdepotId = self._depotBackend._depotId\n\t\tlogger.info(u\"=================================================================================================\")\n\t\tif forceProductId:\n\t\t\tforceProductId = forceProductIdFunc(forceProductId)\n\t\t\tlogger.notice(\n\t\t\t\tu\"Installing package file '{filename}' as '{productId}' on depot '{depotId}'\",\n\t\t\t\tfilename=filename, depotId=depotId, productId=forceProductId\n\t\t\t)\n\t\telse:\n\t\t\tlogger.notice(\n\t\t\t\tu\"Installing package file '{filename}' on depot '{depotId}'\",\n\t\t\t\tfilename=filename, depotId=depotId\n\t\t\t)\n\n\t\ttry:\n\t\t\tfilename = forceFilename(filename)\n\t\t\tforce = forceBool(force)\n\t\t\tpropertyDefaultValues = forceDict(propertyDefaultValues)\n\t\t\tfor propertyId in propertyDefaultValues:\n\t\t\t\tif propertyDefaultValues[propertyId] is None:\n\t\t\t\t\tpropertyDefaultValues[propertyId] = []\n\n\t\t\tif tempDir:\n\t\t\t\ttempDir = forceFilename(tempDir)\n\t\t\telse:\n\t\t\t\ttempDir = None\n\n\t\t\tif not os.path.isfile(filename):\n\t\t\t\traise BackendIOError(u\"Package file '{0}' does not exist or can not be accessed.\".format(filename))\n\t\t\tif not os.access(filename, os.R_OK):\n\t\t\t\traise BackendIOError(u\"Read access denied for package file '%s'\" % filename)\n\n\t\t\ttry:\n\t\t\t\tdataBackend = self._depotBackend._context\n\n\t\t\t\twith productPackageFile(filename, tempDir, depotId) as ppf:\n\t\t\t\t\tproduct = ppf.packageControlFile.getProduct()\n\t\t\t\t\tif forceProductId:\n\t\t\t\t\t\tlogger.info(u\"Forcing product id '{0}'\", forceProductId)\n\t\t\t\t\t\tproduct.setId(forceProductId)\n\t\t\t\t\t\tppf.packageControlFile.setProduct(product)\n\n\t\t\t\t\tproductId = product.getId()\n\n\t\t\t\t\tlogger.info(u\"Creating product in backend\")\n\t\t\t\t\tdataBackend.product_createObjects(product)\n\n\t\t\t\t\twith lockProduct(dataBackend, product, depotId, force) as productOnDepot:\n\t\t\t\t\t\tlogger.info(u\"Checking package dependencies\")\n\t\t\t\t\t\tself.checkDependencies(ppf)\n\n\t\t\t\t\t\twith runPackageScripts(ppf, depotId):\n\t\t\t\t\t\t\tlogger.info(u\"Deleting old client-data dir\")\n\t\t\t\t\t\t\tppf.deleteProductClientDataDir()\n\n\t\t\t\t\t\t\tlogger.info(u\"Unpacking package files\")\n\t\t\t\t\t\t\tppf.extractData()\n\n\t\t\t\t\t\t\tlogger.info(u\"Updating product dependencies of product %s\" % product)\n\t\t\t\t\t\t\tcurrentProductDependencies = {}\n\t\t\t\t\t\t\tfor productDependency in dataBackend.productDependency_getObjects(\n\t\t\t\t\t\t\t\t\t\tproductId=productId,\n\t\t\t\t\t\t\t\t\t\tproductVersion=product.getProductVersion(),\n\t\t\t\t\t\t\t\t\t\tpackageVersion=product.getPackageVersion()):\n\t\t\t\t\t\t\t\tident = productDependency.getIdent(returnType='unicode')\n\t\t\t\t\t\t\t\tcurrentProductDependencies[ident] = productDependency\n\n\t\t\t\t\t\t\tproductDependencies = []\n\t\t\t\t\t\t\tfor productDependency in ppf.packageControlFile.getProductDependencies():\n\t\t\t\t\t\t\t\tif forceProductId:\n\t\t\t\t\t\t\t\t\tproductDependency.productId = productId\n\n\t\t\t\t\t\t\t\tident = productDependency.getIdent(returnType='unicode')\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\tdel currentProductDependencies[ident]\n\t\t\t\t\t\t\t\texcept KeyError:\n\t\t\t\t\t\t\t\t\tpass # Dependency does currently not exist.\n\t\t\t\t\t\t\t\tproductDependencies.append(productDependency)\n\n\t\t\t\t\t\t\tdataBackend.productDependency_createObjects(productDependencies)\n\t\t\t\t\t\t\tif currentProductDependencies:\n\t\t\t\t\t\t\t\tdataBackend.productDependency_deleteObjects(\n\t\t\t\t\t\t\t\t\tcurrentProductDependencies.values()\n\t\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\tlogger.info(u\"Updating product properties of product %s\" % product)\n\t\t\t\t\t\t\tcurrentProductProperties = {}\n\t\t\t\t\t\t\tproductProperties = []\n\t\t\t\t\t\t\tfor productProperty in dataBackend.productProperty_getObjects(\n\t\t\t\t\t\t\t\t\t\tproductId=productId,\n\t\t\t\t\t\t\t\t\t\tproductVersion=product.getProductVersion(),\n\t\t\t\t\t\t\t\t\t\tpackageVersion=product.getPackageVersion()):\n\t\t\t\t\t\t\t\tident = productProperty.getIdent(returnType='unicode')\n\t\t\t\t\t\t\t\tcurrentProductProperties[ident] = productProperty\n\n\t\t\t\t\t\t\tfor productProperty in ppf.packageControlFile.getProductProperties():\n\t\t\t\t\t\t\t\tif forceProductId:\n\t\t\t\t\t\t\t\t\tproductProperty.productId = productId\n\n\t\t\t\t\t\t\t\tident = productProperty.getIdent(returnType='unicode')\n\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\tdel currentProductProperties[ident]\n\t\t\t\t\t\t\t\texcept KeyError:\n\t\t\t\t\t\t\t\t\tpass # Property not found - everyhing okay\n\t\t\t\t\t\t\t\tproductProperties.append(productProperty)\n\t\t\t\t\t\t\tdataBackend.productProperty_createObjects(productProperties)\n\n\t\t\t\t\t\t\tfor productProperty in productProperties:\n\t\t\t\t\t\t\t\t# Adjust property default values\n\t\t\t\t\t\t\t\tif productProperty.editable or not productProperty.possibleValues:\n\t\t\t\t\t\t\t\t\tcontinue\n\n\t\t\t\t\t\t\t\tnewValues = [\n\t\t\t\t\t\t\t\t\tvalue\n\t\t\t\t\t\t\t\t\tfor value in propertyDefaultValues.get(productProperty.propertyId, [])\n\t\t\t\t\t\t\t\t\tif value in productProperty.possibleValues\n\t\t\t\t\t\t\t\t]\n\t\t\t\t\t\t\t\tif not newValues and productProperty.defaultValues:\n\t\t\t\t\t\t\t\t\tnewValues = productProperty.defaultValues\n\t\t\t\t\t\t\t\tpropertyDefaultValues[productProperty.propertyId] = newValues\n\n\t\t\t\t\t\t\tif currentProductProperties.values():\n\t\t\t\t\t\t\t\tdataBackend.productProperty_deleteObjects(\n\t\t\t\t\t\t\t\t\tcurrentProductProperties.values()\n\t\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\tlogger.info(u\"Deleting product property states of product %s on depot '%s'\" % (productId, depotId))\n\t\t\t\t\t\t\tdataBackend.productPropertyState_deleteObjects(\n\t\t\t\t\t\t\t\tdataBackend.productPropertyState_getObjects(\n\t\t\t\t\t\t\t\t\tproductId=productId,\n\t\t\t\t\t\t\t\t\tobjectId=depotId\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t)\n\n\t\t\t\t\t\t\tlogger.info(u\"Deleting not needed property states of product %s\" % productId)\n\t\t\t\t\t\t\tproductPropertyStates = dataBackend.productPropertyState_getObjects(productId=productId)\n\t\t\t\t\t\t\tbaseProperties = dataBackend.productProperty_getObjects(productId=productId)\n\n\t\t\t\t\t\t\tproductPropertyIds = None\n\t\t\t\t\t\t\tproductPropertyStatesToDelete = None\n\t\t\t\t\t\t\tproductPropertyIds = [productProperty.propertyId for productProperty in baseProperties]\n\t\t\t\t\t\t\tproductPropertyStatesToDelete = [ppState for ppState in productPropertyStates if ppState.propertyId not in productPropertyIds]\n\t\t\t\t\t\t\tlogger.debug(u\"Following productPropertyStates are marked to delete: '%s'\" % productPropertyStatesToDelete)\n\t\t\t\t\t\t\tif productPropertyStatesToDelete:\n\t\t\t\t\t\t\t\tdataBackend.productPropertyState_deleteObjects(productPropertyStatesToDelete)\n\n\t\t\t\t\t\t\tlogger.info(u\"Setting product property states in backend\")\n\t\t\t\t\t\t\tproductPropertyStates = [\n\t\t\t\t\t\t\t\tProductPropertyState(\n\t\t\t\t\t\t\t\t\tproductId=productId,\n\t\t\t\t\t\t\t\t\tpropertyId=productProperty.propertyId,\n\t\t\t\t\t\t\t\t\tobjectId=depotId,\n\t\t\t\t\t\t\t\t\tvalues=productProperty.defaultValues\n\t\t\t\t\t\t\t\t) for productProperty in productProperties\n\t\t\t\t\t\t\t]\n\n\t\t\t\t\t\t\tfor productPropertyState in productPropertyStates:\n\t\t\t\t\t\t\t\tif productPropertyState.propertyId in propertyDefaultValues:\n\t\t\t\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\t\t\t\tproductPropertyState.setValues(propertyDefaultValues[productPropertyState.propertyId])\n\t\t\t\t\t\t\t\t\texcept Exception as installationError:\n\t\t\t\t\t\t\t\t\t\tlogger.error(\n\t\t\t\t\t\t\t\t\t\t\tu\"Failed to set default values to {0} for productPropertyState {1}: {2}\",\n\t\t\t\t\t\t\t\t\t\t\tpropertyDefaultValues[productPropertyState.propertyId],\n\t\t\t\t\t\t\t\t\t\t\tproductPropertyState,\n\t\t\t\t\t\t\t\t\t\t\tinstallationError\n\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\tdataBackend.productPropertyState_createObjects(productPropertyStates)\n\n\t\t\t\t\t\tif not suppressPackageContentFileGeneration:\n\t\t\t\t\t\t\tppf.createPackageContentFile()\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tlogger.debug(\"Suppressed generation of package content file.\")\n\n\t\t\t\tcleanUpProducts(dataBackend, productOnDepot.productId)\n\t\t\t\tcleanUpProductPropertyStates(dataBackend, productProperties, depotId, productOnDepot)\n\t\t\texcept Exception as installingPackageError:\n\t\t\t\tlogger.debug(u\"Failed to install the package {!r}\", filename)\n\t\t\t\tlogger.logException(installingPackageError, logLevel=LOG_DEBUG)\n\t\t\t\traise installingPackageError\n\t\texcept Exception as installationError:\n\t\t\tlogger.logException(installationError)\n\t\t\traise BackendError(u\"Failed to install package '%s' on depot '%s': %s\" % (filename, depotId, installationError))\n\n\tdef uninstallPackage(self, productId, force=False, deleteFiles=True):\n\t\tdepotId = self._depotBackend._depotId\n\t\tlogger.info(u\"=================================================================================================\")\n\t\tlogger.notice(u\"Uninstalling product '%s' on depot '%s'\" % (productId, depotId))\n\t\ttry:\n\t\t\tproductId = forceProductIdFunc(productId)\n\t\t\tforce = forceBool(force)\n\t\t\tdeleteFiles = forceBool(deleteFiles)\n\n\t\t\tdataBackend = self._depotBackend._context\n\t\t\tdepot = dataBackend.host_getObjects(type='OpsiDepotserver', id=depotId)[0]\n\t\t\tproductOnDepots = dataBackend.productOnDepot_getObjects(depotId=depotId, productId=productId)\n\t\t\ttry:\n\t\t\t\tproductOnDepot = productOnDepots[0]\n\t\t\texcept IndexError:\n\t\t\t\traise BackendBadValueError(\"Product '%s' is not installed on depot '%s'\" % (productId, depotId))\n\n\t\t\tif productOnDepot.getLocked():\n\t\t\t\tlogger.notice(u\"Product '{}' currently locked on depot '{}'\", productId, depotId)\n\t\t\t\tif not force:\n\t\t\t\t\traise BackendTemporaryError(u\"Product '%s' currently locked on depot '%s'\" % (productId, depotId))\n\t\t\t\tlogger.warning(u\"Uninstallation of locked product forced\")\n\n\t\t\tlogger.notice(u\"Locking product '%s' on depot '%s'\" % (productId, depotId))\n\t\t\tproductOnDepot.setLocked(True)\n\t\t\tdataBackend.productOnDepot_updateObject(productOnDepot)\n\n\t\t\tlogger.debug(\"Deleting product '{}'\", productId)\n\n\t\t\tif deleteFiles:\n\t\t\t\tif not depot.depotLocalUrl.startswith('file:///'):\n\t\t\t\t\traise BackendBadValueError(u\"Value '%s' not allowed for depot local url (has to start with 'file:///')\" % depot.depotLocalUrl)\n\n\t\t\t\tfor element in os.listdir(depot.depotLocalUrl[7:]):\n\t\t\t\t\tif element.lower() == productId.lower():\n\t\t\t\t\t\tclientDataDir = os.path.join(depot.depotLocalUrl[7:], element)\n\t\t\t\t\t\tlogger.info(\"Deleting client data dir '{}'\", clientDataDir)\n\t\t\t\t\t\tremoveDirectory(clientDataDir)\n\n\t\t\tdataBackend.productOnDepot_deleteObjects(productOnDepot)\n\t\texcept Exception as uninstallError:\n\t\t\tlogger.logException(uninstallError)\n\t\t\traise BackendError(u\"Failed to uninstall product '%s' on depot '%s': %s\" % (productId, depotId, uninstallError))\n\n\tdef checkDependencies(self, productPackageFile):\n\t\tfor dependency in productPackageFile.packageControlFile.getPackageDependencies():\n\t\t\tproductOnDepots = self._depotBackend._context.productOnDepot_getObjects(depotId=self._depotBackend._depotId, productId=dependency['package'])\n\t\t\tif not productOnDepots:\n\t\t\t\traise BackendUnaccomplishableError(u\"Dependent package '%s' not installed\" % dependency['package'])\n\n\t\t\tif not dependency['version']:\n\t\t\t\tlogger.info(u\"Fulfilled product dependency '%s'\" % dependency)\n\t\t\t\tcontinue\n\n\t\t\tproductOnDepot = productOnDepots[0]\n\t\t\tavailableVersion = productOnDepot.getProductVersion() + u'-' + productOnDepot.getPackageVersion()\n\n\t\t\tif compareVersions(availableVersion, dependency['condition'], dependency['version']):\n\t\t\t\tlogger.info(u\"Fulfilled package dependency %s (available version: %s)\" % (dependency, availableVersion))\n\t\t\telse:\n\t\t\t\traise BackendUnaccomplishableError(u\"Unfulfilled package dependency %s (available version: %s)\" % (dependency, availableVersion))\n","sub_path":"OPSI/Backend/Depotserver.py","file_name":"Depotserver.py","file_ext":"py","file_size_in_byte":22203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"557769296","text":"from __future__ import division\nimport json\nimport re\nimport os\nfrom collections import OrderedDict\nfrom keyword import kwlist\nfrom StringIO import StringIO\n\nspecial_chars = set(r\" .,<>/?;:'|[{]}=+-)(*&^%$#@!`~\" + '\"')\nregex_chars = set(r\"]\")\npyspecchar = list(special_chars - regex_chars)\nescaped_chars = [\"\\%s\" % c for c in regex_chars]\ninsertion = ''.join(pyspecchar + escaped_chars)\nUNPYTHONIC_REGEX = re.compile(r\"^\\d|[%s\\s]+\" % insertion)\ndel pyspecchar, escaped_chars, insertion, special_chars, regex_chars\n\n\ndef is_name_pythonic(name):\n return not UNPYTHONIC_REGEX.match(name) or name in kwlist\n\n\ndef load_commented_json(reader, **kwargs):\n if isinstance(reader, basestring):\n with open(reader) as file_:\n parsed = _parse_comments(file_)\n else:\n parsed = _parse_comments(reader)\n\n return json.loads(parsed, **kwargs)\n\n\ndef _parse_comments(reader):\n regex = r'\\s*(#|\\/{2}).*$'\n regex_inline = r'(:?(?:\\s)*([A-Za-z\\d\\.{}]*)|((?<=\\\").*\\\"),?)(?:\\s)*(((#|(\\/{2})).*)|)$'\n\n pipe = []\n for line in reader:\n if re.search(regex, line):\n if re.search(r'^' + regex, line, re.IGNORECASE): continue\n elif re.search(regex_inline, line):\n pipe.append(re.sub(regex_inline, r'\\1', line))\n else:\n pipe.append(line)\n return \"\\n\".join(pipe)\n\n\nclass ConfigParseError(IOError):\n pass\n\n\nclass ConfigSpecificationError(AttributeError):\n pass\n\n\nclass Config(object):\n\n @staticmethod\n def fromfile(fp):\n dict_ = load_commented_json(fp, object_pairs_hook=OrderedDict)\n root_name, _ = os.path.splitext(os.path.basename(fp))\n return Config(dict_, file=fp, name=root_name)\n\n @staticmethod\n def fromstring(s, file_name='', root_name=''):\n sio = StringIO(s)\n dict_ = load_commented_json(sio, object_pairs_hook=OrderedDict)\n return Config(dict_, file=file_name, name=root_name)\n\n @staticmethod\n def fromdict(dict_, file_name='', root_name=''):\n return Config(dict_, file=file_name, name=root_name)\n\n def __init__(self, config_dict, name=None, parent=None, file=None):\n self._d = OrderedDict()\n self._attrs = set()\n self._name = name\n self._parent = parent\n self._file = file\n\n existing_vals = set(dir(self))\n for key, val in config_dict.iteritems():\n if key in existing_vals:\n raise ConfigParseError(key)\n\n if isinstance(val, dict):\n val = Config(val, key, self, file)\n elif isinstance(val, list):\n val = [\n Config(item, key + \"[%s]\" % i, self, file)\n if isinstance(item, dict)\n else item\n for i, item in enumerate(val)\n ]\n\n if not is_name_pythonic(key):\n self._d[key] = val\n else:\n setattr(self, key, val)\n self._attrs.add(key)\n self._d[key] = val # Also copy to the dict\n self._attrs = frozenset(self._attrs)\n\n @property\n def asdict(self):\n \"\"\"Getter for the dict of un-pythonic names\"\"\"\n return self._d\n\n @property\n def attrs(self):\n return self._attrs\n\n @property\n def name(self):\n return self._name\n\n @property\n def parent(self):\n return self._parent\n\n @property\n def namespace(self):\n name = self._name if self._name is not None else ''\n if self._parent is None:\n return name\n return '.'.join([self._parent.namespace, name])\n\n def __str__(self):\n if self._parent is None:\n return \"Config @%s\" % self._file\n\n return \"Config(%s) @%s\" % (self.namespace, self._file)\n\n def __getattr__(self, item):\n raise ConfigSpecificationError(\"Item '%s' is missing from config <%s>\" % (item, self.namespace))\n\n def __contains__(self, item):\n return item in self._d\n\n def serialize(self):\n child_dict = OrderedDict()\n for attr, item in self._d.iteritems():\n if isinstance(item, Config):\n child_dict[attr] = item.serialize()\n elif isinstance(item, list):\n child_dict[attr] = [x.serialize() if isinstance(x, Config) else x for x in item]\n else:\n child_dict[attr] = item\n return child_dict\n\n def tofile(self, fp):\n dict_ = self.serialize()\n with open(fp, 'w') as writer:\n json.dump(dict_, writer, indent=2)\n","sub_path":"emme_modelling_tools/configuration.py","file_name":"configuration.py","file_ext":"py","file_size_in_byte":4602,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"493578738","text":"# Local mark to this file\n# =======================\n# What is a quite important part here, this program should be better run\n# in a single thread, with no 2 processes of ``single`` at one time.\n# Otherwise, due to possible multiple realizations of ``single`` at the\n# same time, a correct value of `N` is not guaranteed. So here is a\n# group of action, with the implementation of which the condition of\n# running <= 1 programs at once seems to be fulfilled. To view possible\n# exit codes, see the checker's __doc__ or a source of that lib.\n#\n# Other imports are not needed in this part, so I decided to put them\n# after it.\n\n\nfrom libs.track_processes import local_action as checker\n\n\nINI = 'data/configs.ini'\nSAVE_TO = 'data/pictures_savings'\n\nTEST_MODE = None\n\n\ncode = checker(ini_filename=INI)\nif code != 0:\n import sys\n input('Error! Exit code: {}.'.format(code))\n sys.exit(0)\n\ndel checker, code\n\n\nimport requests\nfrom libs.read_html import Reader\nfrom libs.hasht import save_file, get_hash\n\nimport json\nfrom time import time, sleep\n\nfrom names import URL, c, JSON, HASHING_TYPE\n\n\ndef get_info(text=None):\n # Return is a pair \"(topic, links)\", where ``links`` is a list\n # of the abs. links to the I-net-stored files of the following.\n\n if text is None:\n text = requests.get(URL).text\n\n body = Reader(text).reader('body')\n info = body[body.index('content'):]\n\n topic = Reader(text).reader('strong')[1:-1]\n\n def link(n):\n info1 = Reader(info).between('div', n)\n info2 = info1[info1.index('src'):]\n info3 = info2[info2.index('\"') + 1 :]\n return info3[: info3.index('\"')]\n\n return topic, [URL + link(i) for i in range(1, 10)]\n\n\ndef single(save_it=False):\n c.read(INI)\n N = c.getint('main', 'n')\n\n topic, links = get_info()\n\n # Making the list of all got hashes:\n hashes = []\n for link in links:\n hash_of_it = get_hash(link, hashing_type=HASHING_TYPE)\n if save_it:\n save_file(link, \"{}/{}.png\".format(SAVE_TO, hash_of_it))\n\n hashes.append(hash_of_it)\n\n # Forming the ``dict`` for an appended info:\n added_info = {'N': N, 'topic': topic, 'hashes': hashes}\n\n # Appending the received info to the already stored:\n with open(JSON, encoding='utf-8') as f:\n data = json.load(f)\n\n data.append(added_info)\n\n with open(JSON, 'w', encoding='utf-8') as f:\n json.dump(data, f, indent=4)\n\n c.set('main', 'n', str(N + 1))\n with open(INI, 'w') as f:\n c.write(f)\n\n\ndef main(*args, **kwargs):\n t00 = time()\n mode = \"\"\n\n while True:\n try:\n if mode == \"waiting\":\n sleep(60)\n mode = \"\"\n\n t0 = time()\n single(*args, **kwargs)\n\n print(str(time() - t0).ljust(18), end='; ')\n except KeyboardInterrupt:\n input('\\n'\n + str(time() - t00)\n + '\\nStopped!')\n\n __import__(\"sys\").exit(0)\n except Exception as e:\n print(e)\n mode = \"waiting\" # Being of a `sleep` action here can cause\n # a real `KeyboardInterrupt` in case of it.\n\n\nif TEST_MODE == 1:\n print(*get_info(), sep='\\n', end='\\n\\n') #; 111/0\n\nif TEST_MODE == 2:\n t0 = time()\n single(False); print(time() - t0)\n single(True); print(time() - t0)\n\nif TEST_MODE:\n __import__(\"sys\").exit(\"End of test.\")\n\n\nif __name__ == '__main__':\n print(f\"Action starts. Current time: \\\n{__import__('datetime').datetime.now()}\\n\")\n\n # Saving pictures could be easily enabled or disabled here.\n # Without savings the process is faster.\n main(save_it=False) # Earlier this saving was enabled. Now, seems to be,\n # all pictures are already collected.\n","sub_path":"file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":3785,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"349768610","text":"import sys, sqlite3\nimport jdatetime\nfrom PyQt5 import QtCore, QtGui, QtWidgets\nfrom PyQt5.QtWidgets import QDialog, QApplication\nfrom AddSpecimenDetails import *\nfrom VerificationConcrete_rev01 import *\n\nclass Form_DialogAddSpecimenDetails(QDialog):\n def __init__(self):\n super().__init__()\n self.ui = Ui_DialogAddSpecimenDetails()\n self.ui.setupUi(self)\n self.ui.radioButtonCylindrical.toggled.connect(self.setCylindrical)\n self.ui.radioButtonCubic.toggled.connect(self.setCubic)\n self.ui.comboBoxYearTestDate.currentIndexChanged.connect(self.TrigerMount)\n self.ui.comboBoxMountTestDate.currentIndexChanged.connect(self.TrigerDay)\n self.ui.pushButtonCancelSpecimen.clicked.connect(self.close)\n self.ui.pushButtonCreateSpecimen.clicked.connect(self.CreateNewSpecimen)\n \n def setCylindrical(self):\n self.ui.labelDimention.setText(\" قطر (سانتیمتر)\")\n self.ui.comboBoxDimention.clear()\n for i in ('','10', '15', '20', '25', '30'):\n self.ui.comboBoxDimention.addItem(i)\n\n def setCubic(self):\n self.ui.labelDimention.setText(\" ضلع (سانتیمتر)\")\n self.ui.comboBoxDimention.clear()\n for i in ('','10', '15', '20', '25', '30'):\n self.ui.comboBoxDimention.addItem(i)\n\n def TrigerMount(self):\n if self.ui.comboBoxYearTestDate.currentIndex() == 0:\n self.ui.comboBoxMountTestDate.clear()\n self.ui.comboBoxMountTestDate.addItem(\"ماه\")\n self.ui.comboBoxMountTestDate.setEnabled(False)\n else:\n self.ui.comboBoxMountTestDate.setEnabled(True)\n self.ui.comboBoxMountTestDate.clear()\n self.ui.comboBoxMountTestDate.addItem(\"ماه\")\n for i in range(1, 13):\n self.ui.comboBoxMountTestDate.addItem(str(i).zfill(2))\n\n def TrigerDay(self):\n if self.ui.comboBoxMountTestDate.currentIndex() == 0:\n self.ui.comboBoxDayTestDate.clear()\n self.ui.comboBoxDayTestDate.addItem(\"روز\")\n self.ui.comboBoxDayTestDate.setEnabled(False)\n else:\n self.ui.comboBoxDayTestDate.setEnabled(True)\n self.ui.comboBoxDayTestDate.clear()\n self.ui.comboBoxDayTestDate.addItem(\"روز\")\n cond = self.ui.comboBoxYearTestDate.itemText(self.ui.comboBoxYearTestDate.currentIndex()) not in (\"1399\", \"1403\", \"1408\")\n Mount = self.ui.comboBoxMountTestDate.currentIndex()\n if cond:\n if 0 < Mount < 7:\n for i in range(1,32):\n self.ui.comboBoxDayTestDate.addItem(str(i).zfill(2))\n elif 6 < Mount < 12:\n for i in range(1,31):\n self.ui.comboBoxDayTestDate.addItem(str(i).zfill(2))\n else:\n for i in range(1,30):\n self.ui.comboBoxDayTestDate.addItem(str(i).zfill(2))\n else:\n if 0 < Mount < 7:\n for i in range(1,32):\n self.ui.comboBoxDayTestDate.addItem(str(i).zfill(2))\n elif 6 < Mount < 13:\n for i in range(1,31):\n self.ui.comboBoxDayTestDate.addItem(str(i).zfill(2))\n\n def setSamplingID(self, sampling_id):\n self.sampling_id = sampling_id\n\n def setSamplingData(self, sampling_date):\n self.sampling_date = sampling_date\n \n def setFprimC(self, fc):\n self.fc = fc\n \n def setCementType(self, cement_type):\n self.cement_type = cement_type\n\n def isFill(self):\n fillCondition = (\n len(self.ui.lineEditManualSpecimenID.text()) == 0,\n not(self.ui.radioButtonCylindrical.isChecked() or self.ui.radioButtonCubic.isChecked()),\n self.ui.comboBoxDimention.currentIndex() == 0,\n len(self.ui.lineEditWieght.text()) == 0,\n self.ui.comboBoxDayTestDate.currentIndex() == 0,\n len(self.ui.lineEditWieghtUltimatForce.text()) == 0)\n if any(fillCondition):\n return False\n return True\n def isValidManualSpecimenID(self):\n ValidManualSpecimenID = self.ui.lineEditManualSpecimenID.text().isdigit()\n if not ValidManualSpecimenID:\n return False\n return True\n\n def isValidWieght(self):\n ValidWieght = (\n self.ui.lineEditWieght.text().isdigit()\n or\n self.ui.lineEditWieght.text().replace('.','',1).isdigit())\n if not ValidWieght:\n return False\n return True \n\n \n def isValidUltimatForce(self):\n ValidUltimatForce = (\n self.ui.lineEditWieghtUltimatForce.text().isdigit()\n or\n self.ui.lineEditWieghtUltimatForce.text().replace('.','',1).isdigit())\n if not ValidUltimatForce:\n return False\n return True \n\n def isValidTestDate(self):\n date_1 = jdatetime.datetime(int(self.sampling_date.split(\"/\")[0]),\n int(self.sampling_date.split(\"/\")[1]),\n int(self.sampling_date.split(\"/\")[2]), locale='fa_IR')\n date_2 =jdatetime.datetime(int(self.ui.comboBoxYearTestDate.currentText()),\n int(self.ui.comboBoxMountTestDate.currentText()),\n int(self.ui.comboBoxDayTestDate.currentText()), locale='fa_IR')\n delta = date_2 - date_1\n if delta.days<=0:\n return False\n return True\n \n def CreateNewSpecimen(self):\n if not self.isFill():\n QMB = QtWidgets.QMessageBox.critical(self, \"خطا\", \"لطفاً همه فیلدها را تکمیل نمایید.\")\n return\n else:\n if not self.isValidManualSpecimenID():\n QMB = QtWidgets.QMessageBox.critical(self, \"خطا\", \"شماره نمونه تنها میتواند عدد صحیح باشد.\")\n return\n else:\n if not self.isValidWieght():\n QMB = QtWidgets.QMessageBox.critical(self, \"خطا\", \"وزن وارد شده برای نمونه نامعتبر است.\")\n return\n else:\n if not self.isValidUltimatForce():\n QMB = QtWidgets.QMessageBox.critical(self, \"خطا\", \"نیروی نهایی وارد شده نامعتبر است.\")\n return\n else:\n if not self.isValidTestDate():\n QMB = QtWidgets.QMessageBox.critical(self, \"خطا\", \"تاریخ تست نمونه نمیتواند قبل از تاریخ نمونهگیری باشد.\")\n return\n else:\n try:\n conn = sqlite3.connect(\"MainDB.db\")\n cur = conn.cursor()\n Sp = Specimen()\n if self.ui.radioButtonCubic.isChecked()==True:\n mold_shape = \"Cubic\"\n if self.ui.radioButtonCylindrical.isChecked()==True:\n mold_shape = \"Cylindrical\"\n a0 = Sp.Set_SamplingID(self.sampling_id)\n a1 = Sp.Set_ManualSpecimenID(self.ui.lineEditManualSpecimenID.text())\n a2 = Sp.Set_MoldShape(mold_shape)\n a3 = Sp.Set_Dimension(self.ui.comboBoxDimention.currentText())\n a4 = Sp.Set_Wieght(self.ui.lineEditWieght.text())\n Sp.Set_SamplingDate(int(self.sampling_date.split(\"/\")[0]),\n int(self.sampling_date.split(\"/\")[1]),\n int(self.sampling_date.split(\"/\")[2]))\n a5 = Sp.Set_TestDate(int(self.ui.comboBoxYearTestDate.currentText()),\n int(self.ui.comboBoxMountTestDate.currentText()),\n int(self.ui.comboBoxDayTestDate.currentText()))\n a6 = Sp.Set_UltimatForce(self.ui.lineEditWieghtUltimatForce.text())\n a7 = Sp.Set_Area()\n a8 = Sp.Set_Volume()\n a9 = Sp.Set_Gamma()\n a10 = Sp.Set_Age()\n a11 = Sp.Set_CompressiveStrenght()\n Sp.Set_CementType(self.cement_type)\n a12 = Sp.Set_Name()\n r1 = Sp.Get_r1()\n r2 = Sp.Get_r2()\n r3 = Sp.Get_r3()\n r4 = Sp.Cement_Conversion()\n a13 = Sp.Convert2Standard()\n SpecimenInput = (a0, a1, a2, a3, a4, a5.strftime(\"%Y/%m/%d\"), a6, a7, a8, a9, a10, a11,a12, r1, r2, r3, r4, a13)\n sqlexpr = \"\"\"INSERT INTO Specimen (SamplingID, ManualSpecimenID, MoldShape, Dimension, Wieght, TestDate, UltimatForce,\n Area, Volume, Gamma, Age, CompressiveStrenght, Name, r1, r2, r3, r4, StadardCompressionStrenght)\n VALUES (?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?, ?)\"\"\"\n cur.execute(sqlexpr, SpecimenInput)\n conn.commit()\n print(\"New Specimen Created.\")\n except sqlite3.Error as err:\n print(err)\n finally:\n conn.close()\n self.close()\n \n# if __name__==\"__main__\":\n# app = QApplication(sys.argv)\n# w = Form_DialogAddSpecimenDetails()\n# w.show()\n# sys.exit(app.exec())","sub_path":"AddSpecimenDetailsCall.pyw","file_name":"AddSpecimenDetailsCall.pyw","file_ext":"pyw","file_size_in_byte":10155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"86984789","text":"'''\nGeoLite2-City-Locations-en\ngeoname_id , locale_code , continent_code , continent_name , country_iso_code , country_name , subdivision_1_iso_code , subdivision_1_name , subdivision_2_iso_code , subdivision_2_name , city_name , metro_code , time_zone\n 49518 , en , AF , Africa , RW , Rwanda , , , , , , , Africa/Kigali\n 51537 , en , AF , Africa , SO , Somalia , , , , , , , Africa/Mogadishu\n 53654 , en , AF , Africa , SO , Somalia , BN , Banaadir , , , Mogadishu , , Africa/Mogadishu\n 57289 , en , AF , Africa , SO , Somalia , WO , Woqooyi Galbeed , , , Hargeisa , , Africa/Mogadishu\n'''\n\n'''\nGeoLite2-City-Blocks-IPv4_test\n network , geoname_id , registered_country_geoname_id , represented_country_geoname_id , is_anonymous_proxy , is_satellite_provider , postal_code , latitude , longitude , accuracy_radius\n1.0.0.0/24 , 2151718 , 2077456 , , 0 , 0 , 3095 , -37.7000 , 145.1833 , 1000\n1.0.1.0/24 , 1810821 , 1814991 , , 0 , 0 , , 26.0614 , 119.3061 , 50\n1.0.2.0/23 , 1810821 , 1814991 , , 0 , 0 , , 26.0614 , 119.3061 , 50\n'''\n\n\n\n\n\nfrom time import time\nfrom os import listdir\nfrom os.path import isfile, join\nfrom basic_functions import *\n\ndirPath = \"/Users/AdamLiu/Documents/Deakin/2017_T1/WeblogMining/Data/IP2LOCATION/GeoLite2/\"\ncityBlock = \"GeoLite2-City-Blocks-IPv4_test.csv\"\n\nallCSVs = [f for f in listdir(dirPath) if isfile(join(dirPath, f))]\nallCSVs = [f for f in allCSVs if f[-3:] == \"csv\"]\n\n\ncheck = len(allCSVs)\n\nfor file in allCSVs:\n\n\tfilePath = dirPath + file\n\n\twith open(filePath) as f:\n\n\t\tf = f.read().split(\"\\n\")\n\t\tuniqueIPs = []\n\n\t\tstartTime = time()\n\t\tprint(file + \": Starting.\")\n\n\t\tfor line in f[1:]:\n\t\t\tip = line.split(\" \")[8]\n\t\t\tif ip not in uniqueIPs:\n\t\t\t\tuniqueIPs.append(ip)\n\n\t\tfileSavePath = newPath + file[:-4] + \".txt\"\n\t\twith open(fileSavePath, \"w\") as n:\n\t\t\tn.write(\"\\n\".join(uniqueIPs))\n\t\t\t# n.write(\"\\n\".join(f))\n\t\t\tprint(file + \": Done.\")\n\t\t\tprint(printTime(startTime))\n\t\t\tcheck -= 1\n\t\t\tprint(check, \"files left.\\n\")\n\nprint(\"Total time: \", printTime(theVeryBeginning))","sub_path":"data_cleaning_scripts/07_combine_ip_with_location.py","file_name":"07_combine_ip_with_location.py","file_ext":"py","file_size_in_byte":2947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"541477731","text":"import exotic_options.exotic_option_utilities as exotic_util\nimport Utilities.svi_prepare_vol_data as svi_data\nfrom bs_model import bs_estimate_vol as bs_vol\nfrom Utilities.utilities import *\nimport Utilities.hedging_utility as hedge_util\nimport os\nimport pickle\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport Utilities.plot_util as pu\nimport math\n\nwith open(os.path.abspath('..')+'/intermediate_data/total_hedging_daily_params_calls_1.pickle','rb') as f:\n daily_params = pickle.load(f)[0]\nwith open(os.path.abspath('..') + '/intermediate_data/total_hedging_dates_calls_1.pickle', 'rb') as f:\n dates = pickle.load(f)[0]\nwith open(os.path.abspath('..')+'/intermediate_data/total_hedging_daily_svi_dataset_calls_1.pickle','rb') as f:\n daily_svi_dataset = pickle.load(f)[0]\nwith open(os.path.abspath('..') +'/intermediate_data/total_hedging_bs_estimated_vols.pickle','rb') as f:\n estimated_vols = pickle.load(f)[0]\n\n# Evaluation Settings\nbegDate = ql.Date(19,1,2017)\ncalendar = ql.China()\ndaycounter = ql.ActualActual()\nbegDate = calendar.advance(begDate,ql.Period(1,ql.Days))\n\nfee = 0.6/100\n# Down and out Call\noptiontype = ql.Option.Call\n\n\ncurve = svi_data.get_curve_treasury_bond(begDate,daycounter)\nc, p, maturity_dates, S0, r = daily_svi_dataset.get(to_dt_date(begDate))\nmaturityDate = to_ql_date(maturity_dates[3])\n\n#barriers = S0 - np.arange(0.01,0.1,0.01)\nstrike = S0\n# Knock out : Barrier at OTM\nbarriers = [S0-0.1]\nbarrierType = ql.Barrier.DownOut\n# Reverse knock out : Barrier at ATM\n#barriers = [S0+0.1]\n#barrierType = ql.Barrier.IpOut\n\nrebalance = 1 # days\nunderlying = ql.SimpleQuote(S0)\nplot = True\nprint(\"=\" * 120)\nprint(\"%15s %20s %20s %20s %20s \" % (\"barrier\", \"hedge_error_bs\", \"hedge_error_svi\",\n \"hedge_cost_bs\", \"hedge_cost_svi\"))\nfor barrier in barriers:\n barrier_flag = False\n if plot:\n print(\"=\"*100)\n print(\"%15s %25s %25s %25s\" % (\"maturityDate\",\"barrier\",\"strike\", \"spot\"))\n print(\"%15s %25s %25s %25s\" % (maturityDate,barrier,strike, S0))\n print(\"=\"*100)\n\n if plot:\n print(\"=\"*120)\n print(\"%15s %15s %15s %15s %15s %15s %15s\" % (\"date\",\"spot\",\"hedging_error_bs\",\n \"hedging_error_svi\",\"replicate_bs\",\n \"replicate_svi\",\"reference_npv\"))\n print(\"-\"*120)\n delta_bs_previous = 0.0\n delta_svi_previous = 0.0\n hedging_errors_bs = []\n hedging_errors_svi = []\n hedging_costs_bs = []\n hedging_costs_svi = []\n hist_spots = []\n # Delta Hedge\n c_date = calendar.advance(begDate, ql.Period(-1, ql.Days))\n l_date = calendar.advance(begDate, ql.Period(2, ql.Days))\n while l_date <= maturityDate:\n try:\n c_date = calendar.advance(c_date, ql.Period(1, ql.Days))\n h_date = calendar.advance(c_date, ql.Period(1, ql.Days)) # hedge date\n l_date = calendar.advance(c_date, ql.Period(2, ql.Days)) # liquidation date\n\n exercise = ql.EuropeanExercise(maturityDate)\n payoff = ql.PlainVanillaPayoff(optiontype, strike)\n\n # calibration date\n cal_vols_c, put_vols_c, maturity_dates_c, spot_c, risk_free_rates_c = daily_svi_dataset.get(to_dt_date(c_date))\n calibrated_params = daily_params.get(to_dt_date(c_date)) # on calibrate_date\n black_var_surface_c = hedge_util.get_local_volatility_surface(calibrated_params, to_ql_dates(maturity_dates_c),\n c_date, daycounter, calendar, spot_c,\n risk_free_rates_c)\n const_vol_c = estimated_vols.get(to_dt_date(c_date))\n\n # create hedge portfolio\n curve_h = svi_data.get_curve_treasury_bond(h_date, daycounter)\n rf_h = curve_h.zeroRate(maturityDate, daycounter, ql.Continuous).rate()\n cal_vols_h, put_vols_h, maturity_dates_h, spot_h, risk_free_rates_h = daily_svi_dataset.get(to_dt_date(h_date))\n yield_h = ql.YieldTermStructureHandle(curve_h)\n dividend_h = ql.YieldTermStructureHandle(ql.FlatForward(h_date, 0.0, daycounter))\n underlying.setValue(spot_c)\n process_svi_h = ql.BlackScholesMertonProcess(ql.QuoteHandle(underlying), dividend_h,\n yield_h, ql.BlackVolTermStructureHandle(black_var_surface_c))\n hist_spots.append(spot_c)\n option_price_h = exotic_util.barrier_npv_ql(h_date,hist_spots,barrierType, barrier, payoff, exercise,process_svi_h)\n if option_price_h == 0.0:\n print('0')\n delta_bs = 0.0\n delta_svi = 0.0\n else:\n delta_bs = hedge_util.calculate_delta_bs(h_date, daycounter, calendar,\n const_vol_c, spot_c, rf_h, strike, maturityDate, optiontype)\n delta_svi = hedge_util.calculate_delta_svi(black_var_surface_c, h_date, daycounter, calendar, spot_c,\n curve_h.zeroRate(maturityDate, daycounter, ql.Continuous).rate(),\n strike, maturityDate, optiontype)\n cash_svi_h = option_price_h - delta_svi*spot_h\n cash_bs_h = option_price_h - delta_bs*spot_h\n replicate_svi_h = delta_svi * spot_h + cash_svi_h\n replicate_bs_h = delta_bs * spot_h + cash_bs_h\n #ratio_svi = replicate_svi_h / option_price_h\n #ratio_bs = replicate_bs_h / option_price_h\n #print(ratio_bs,ratio_svi)\n # liquidate replication portfolio\n cal_vols_l, put_vols_l, maturity_dates_l, spot_l, risk_free_rates_l = daily_svi_dataset.get(to_dt_date(l_date))\n params = daily_params.get(to_dt_date(h_date)) # on calibrate_date\n black_var_surface_h = hedge_util.get_local_volatility_surface(calibrated_params, to_ql_dates(maturity_dates_c),\n c_date, daycounter, calendar, spot_c,\n risk_free_rates_c)\n hist_spots.append(spot_h)\n curve_l = svi_data.get_curve_treasury_bond(h_date, daycounter)\n yield_l = ql.YieldTermStructureHandle(curve_l)\n dividend_l = ql.YieldTermStructureHandle(ql.FlatForward(l_date, 0.0, daycounter))\n underlying.setValue(spot_h)\n process_svi_l = ql.BlackScholesMertonProcess(ql.QuoteHandle(underlying), dividend_l,\n yield_l, ql.BlackVolTermStructureHandle(black_var_surface_h))\n option_price_l = exotic_util.barrier_npv_ql(l_date, hist_spots, barrierType, barrier, payoff, exercise,\n process_svi_l)\n\n t = daycounter.yearFraction(h_date, l_date)\n cash_svi_l = cash_svi_h*math.exp(curve_h.zeroRate(maturityDate, daycounter, ql.Continuous).rate()* t)\n cash_bs_l = cash_bs_h*math.exp(curve_h.zeroRate(maturityDate, daycounter, ql.Continuous).rate()* t)\n replicate_svi_l = delta_svi * spot_l + cash_svi_l\n replicate_bs_l = delta_bs * spot_l + cash_bs_l\n hedging_error_bs = replicate_bs_l - option_price_l\n hedging_error_svi = replicate_svi_l - option_price_l\n hedging_errors_bs.append(hedging_error_bs)\n hedging_errors_svi.append(hedging_error_svi)\n #hedging_costs_bs.append(hedge_cost_bs)\n #hedging_costs_svi.append(hedge_cost_svi)\n if plot: print(\"%15s %15s %15s %15s %15s %15s %15s\" % (l_date,\n round(spot_l,4),\n round(hedging_error_bs,4),\n round(hedging_error_svi,4),\n round(replicate_bs_l,4),\n round(replicate_svi_l,4),\n round(option_price_l,4)))\n except Exception as e:\n #print(e)\n continue\n if plot:\n print(\"=\" * 120)\n print(\"%15s %20s %20s %20s %20s \" % (\"barrier\", \"hedge_error_bs\", \"hedge_error_svi\",\n \"hedge_cost_bs\", \"hedge_cost_svi\"))\n print(\"-\" * 120)\n print(\"%15s %20s %20s %20s %20s \" % (barrier, round(sum(hedging_errors_bs), 4),\n round(sum(hedging_errors_svi), 4),\n round(sum(hedging_costs_bs), 4),\n round(sum(hedging_costs_svi), 4)))\nprint(\"=\" * 120)\n\n\n\n\n\n\n\n\n\n","sub_path":"exotic_options/delta_hedge_knock_out_call.py","file_name":"delta_hedge_knock_out_call.py","file_ext":"py","file_size_in_byte":9071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"88646303","text":"#!/usr/bin/python\n\nimport cgi\nimport cgitb\nimport json\nimport requests\n\ncgitb.enable() # for troubleshooting\n\nprint(\"Content-type: text/html\\n\\n\")\nprint()\n\nprint(\"\"\"\n\n\nFoursquare API\n\n\n\n\nVenues Search
\n\"\"\")\n\nform = cgi.FieldStorage()\nvariable = \"\"\nvalue = \"\"\nr = \"\"\n\nlat = ''\nlon = ''\ncat = ''\nven = ''\n\nfor key in form.keys():\n variable = str(key)\n\n if variable == \"lat\":\n lat = form[key].value\n elif variable == \"lon\":\n lon = form[key].value\n elif variable == 'cat':\n cat = form[key].value\n else:\n print(\"Did not return lat or lon or cat\")\n\n\nif lat != '' and lon != '' and cat != '':\n\n categoryID = ''\n if cat == \"fitness\":\n categoryID = \"4f4528bc4b90abdf24c9de85,4bf58dd8d48988d15e941735\"\n elif cat == \"school\":\n categoryID = \"4bf58dd8d48988d13b941735,4bf58dd8d48988d12f941735,4d4b7105d754a06372d81259\"\n elif cat == \"experience\":\n categoryID = \"4d4b7104d754a06370d81259\"\n else:\n print(\"--CATEGORY DOES NOT EXIST--\")\n\n url = 'https://api.foursquare.com/v2/venues/search'\n s = lat + \",\" + lon\n\n params = dict(\n client_id='MJDWI4DZ2IDHDD5Z2EXFKD4PDW1LRFGTE2R0AE00XQKUFAHU',\n client_secret='ZY11PYIJNJHGXMWGT2WB1APA5IB1ES2MYAPW2DUNR4WDJQE2',\n v='20180323',\n ll=s,\n radius=500,\n intent='checkin',\n categoryId=categoryID,\n limit=1\n )\n\n resp = requests.get(url=url, params=params)\n data = json.loads(resp.text)\n\n if len(data[\"response\"][\"venues\"]) == 0:\n print(\"NO VENUES FOUND\")\n else:\n print(\"VENUE(S) FOUND\")\n\n print(len(data[\"response\"][\"venues\"]))\n print(json.dumps(data[\"response\"][\"venues\"][0:5], sort_keys=True, indent=2, separators=(',', ': ')))\n\nprint(\"\"\"\n\n\n\"\"\")\n\n","sub_path":"server/foursquare2.py","file_name":"foursquare2.py","file_ext":"py","file_size_in_byte":1828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"438932133","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport logging\nimport os\n\nimport pandas as pd\nimport sys\nfrom pandas.util.testing import assert_frame_equal\n\nfrom cbs_utils.misc import range1\n\nlogging.basicConfig(stream=sys.stdout, level=logging.INFO)\n_logger = logging.getLogger(__name__)\n\ntry:\n # this import is used when running python setup.py test or when running from within pycharm\n _logger.debug(sys.path)\n from cbs_utils.readers import SbiInfo\nexcept ImportError:\n # if the import fails we are running this script from the command line and need to include the\n # current path\n real_path = os.path.realpath(os.path.join(os.path.dirname(__file__), \"../src/cbs_utils\"))\n sys.path.insert(0, real_path)\n _logger.debug(\"Import cbs_utils from {}\".format(sys.path[0]))\n # the double mlab_mdfreader is needed in case we are running the script from the command line\n from cbs_utils.readers import SbiInfo\n\n sys.path.pop()\n\nDATA_DIR = \"data\"\nSBI_FILE = \"SBI 2008 versie 2018.xlsx\"\n\n\ndef write_data():\n \"\"\"\n Write the data to a pickle file\n \"\"\"\n\n data_location = os.path.realpath(os.path.join(os.path.dirname(__file__), \"..\", DATA_DIR))\n\n sbi_file_name = os.path.join(data_location, SBI_FILE)\n\n sbi = SbiInfo(sbi_file_name)\n\n # the test file is stored in the same directory as the script\n test_file = os.path.splitext(os.path.join(os.path.dirname(__file__), SBI_FILE))[0] + \".pkl\"\n _logger.info(\"Writing header object to {}\".format(os.path.join(os.path.dirname(__file__),\n test_file)))\n sbi.data.to_pickle(test_file)\n\n\ndef test_sbi_info():\n # name of the example xls file\n data_location = os.path.realpath(os.path.join(os.path.dirname(__file__), \"..\", DATA_DIR))\n sbi_file_name = os.path.join(data_location, SBI_FILE)\n\n # name of the pickle file which was stored to check if the data frame was read correctly\n test_file = os.path.splitext(os.path.join(os.path.dirname(__file__), SBI_FILE))[0] + \".pkl\"\n\n # create the sbi object\n sbi = SbiInfo(sbi_file_name)\n\n # the sbi data frame\n sbi_df = sbi.data\n\n sbi_df_expected = pd.read_pickle(test_file)\n\n # see if the data frames are the same\n assert_frame_equal(sbi_df, sbi_df_expected)\n\n\ndef test_sbi_merge_groups():\n # name of the example xls file\n data_location = os.path.realpath(os.path.join(os.path.dirname(__file__), \"..\", DATA_DIR))\n sbi_file_name = os.path.join(data_location, SBI_FILE)\n\n # name of the pickle file which was stored to check if the data frame was read correctly\n test_file = os.path.splitext(os.path.join(os.path.dirname(__file__), SBI_FILE))[0] + \".pkl\"\n\n _logger.info(range1(10))\n _logger.info(range1(18, 20))\n\n # create the sbi object\n sbi = SbiInfo(sbi_file_name)\n\n # make a selection based on a string with the range.\n # sbi.create_sbi_group(group_name=\"64.19.2-64.92.3\", group_label=\"Banken\")\n sbi.create_sbi_group(group_name=\"26-27\", group_label=\"Electrisch\")\n sbi.create_sbi_group(group_name=\"28\", group_label=\"Machine-industrie\")\n sbi.create_sbi_group(group_name=\"10-12\", group_label=\"Voedings- en genotsmiddelenindustrie\")\n\n sbi.create_sbi_group(group_name=\"Q\", group_label=\"Q\")\n\n # alternatively you can make a selection based on a level range, for the 0, 1, 2 , and 3 level\n # but then is more difficulat to start at a subgroup like 64.19.\n sbi.create_sbi_group(group_name=\"Text13-15\", level_1=range1(13, 15),\n group_label=\"Textiel-, kleding-, en lederindustrie\")\n\n # level based on characters can be done as wel\n sbi.create_sbi_group(group_name=\"AK\", group_label=\"A and K\")\n\n #\n # sbi.create_sbi_group(group_name=\"64.19-64.92\", group_label=\"Banken\",\n # level_1=64, level_2=range1(2, 8), )\n # sbi.create_sbi_group(group_name=\"64.19-64.92\", group_label=\"Banken\",\n # level_1=64, level_2=1, level_3=9)\n # sbi.create_sbi_group(group_name=\"64.19-64.92\", group_label=\"Banken\",\n # level_1=64, level_2=9, level_3=range1(0, 2))\n # to do: implement explicit indices\n # indices=[\"64.19\", \"64.92\"])\n\n pass\n\n\ndef main():\n if \"--debug\" in sys.argv:\n _logger.setLevel(logging.DEBUG)\n write_data()\n\n\nif __name__ == \"__main__\":\n # in case we run the test_mdf_parser as a script from the command line like\n # python.exe tests/test_mdf_parser\n # we call the main routine which will call the routine to create the pkl data from the header.\n # This pickle data is used later by the 'test_header' unit test in order to see if we read the\n # header correctly\n main()\n","sub_path":"tests/test_readers.py","file_name":"test_readers.py","file_ext":"py","file_size_in_byte":4733,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"204424759","text":"from tgt_grease_daemon.BaseCommand import GreaseDaemonCommand\nfrom tgt_grease_core_util.Database import SQLAlchemyConnection\nfrom tgt_grease_core_util import Configuration\nfrom tgt_grease_core_util.RDBMSTypes import JobServers, JobQueue,SourceData, ServerHealth\nfrom sqlalchemy import update, and_, or_\nimport datetime\nimport time\n\n\nclass Section31(GreaseDaemonCommand):\n def __init__(self):\n super(Section31, self).__init__()\n self._config = Configuration()\n self._sql = SQLAlchemyConnection(self._config)\n\n def __del__(self):\n super(Section31, self).__del__()\n self._sql.get_session().close()\n\n def execute(self, context='{}'):\n # first lets get the entire farm\n self._ioc.message().debug(\"Fetching Current Farm Status\", True)\n farm = self._get_farm_status()\n # now lets check each server from the last known state\n self._ioc.message().debug(\"Examining each server. Current servers: [{0}]\".format(len(farm)), True)\n for server in farm:\n if self._server_alive(server):\n # server seems to be alive keep going\n self._ioc.message().debug(\"Server is Alive::Skipping\", True)\n continue\n else:\n self._ioc.message().debug(\"Server is not alive! Attempting to claim\", True)\n # server is not alive, we need to migrate work from it\n # first lets declare ourselves as the doctor\n self._declare_doctor(server[0])\n # now lets sleep to ensure we don't stop on other doctors\n time.sleep(10)\n # okay lets ensure we are still the doctor\n if self._am_i_the_doctor(server[0]):\n # time to reassign\n self._ioc.message().debug(\"I am still the doctor, preparing to cull\", True)\n self._cull_server(server[0])\n else:\n # during our slumber someone else decided to work on this server let them have it\n self._ioc.message().debug(\"Doctor previously declared. Returning\", True)\n continue\n return True\n\n def _get_farm_status(self):\n # type: () -> list\n sql = \"\"\"\n WITH on_demand AS (\n SELECT\n jq.host_name,\n count(jq.id) AS total\n FROM\n job_queue jq\n WHERE\n (\n (\n jq.in_progress = FALSE AND\n jq.completed = FALSE\n ) OR\n jq.in_progress = TRUE\n )\n GROUP BY jq.host_name\n ),\n completed_on_demand AS (\n SELECT\n jt.server_id as host_name,\n count(jt.id) AS total\n FROM\n job_servers js\n LEFT OUTER JOIN job_telemetry jt ON (jt.server_id = js.id)\n GROUP BY jt.server_id\n ),\n completed_daemon AS (\n SELECT\n js.id as host_name,\n count(jtd.id) AS total\n FROM\n job_servers js\n LEFT OUTER JOIN job_telemetry_daemon jtd ON (jtd.server_id = js.id)\n GROUP BY js.id\n ),\n sources AS (\n SELECT\n js.id as host_name,\n count(sf.id) AS total\n FROM\n source_data sf\n INNER JOIN job_servers js ON (js.id = sf.detection_server)\n WHERE\n (\n (\n sf.detection_start_time IS NOT NULL AND\n sf.detection_end_time IS NOT NULL\n ) OR\n (\n sf.detection_start_time IS NOT NULL AND\n sf.detection_end_time IS NULL\n )\n )\n GROUP BY js.id\n ),\n schedules AS (\n SELECT\n js.id as host_name,\n count(sq.id) AS total\n FROM\n source_data sq\n INNER JOIN job_servers js ON (js.id = sq.scheduling_server)\n WHERE\n (\n (\n sq.scheduling_start_time IS NOT NULL AND\n sq.scheduling_end_time IS NOT NULL\n ) OR\n (\n sq.scheduling_start_time IS NOT NULL AND\n sq.scheduling_end_time IS NULL\n )\n )\n GROUP BY js.id\n )\n SELECT\n js.id AS job_server_id,\n js.host_name,\n coalesce(od.total, 0) AS on_demand_total,\n coalesce((cod.total + coda.total), 0) AS completed_total,\n coalesce(s.total, 0) AS sources,\n coalesce(sch.total, 0) AS schedules,\n (coalesce(od.total, 0) || '-' || coalesce((cod.total + coda.total), 0) || '-' || coalesce(s.total, 0) || '-' ||\n coalesce(sch.total, 0)) AS str_hash\n FROM\n job_servers js\n LEFT OUTER JOIN on_demand od ON (od.host_name = js.id)\n LEFT OUTER JOIN completed_on_demand cod ON (cod.host_name = js.id)\n LEFT OUTER JOIN completed_daemon coda ON (coda.host_name = js.id)\n LEFT OUTER JOIN sources s ON (s.host_name = js.id)\n LEFT OUTER JOIN schedules sch ON (sch.host_name = js.id)\n WHERE\n js.active IS TRUE\n ORDER BY js.id\n \"\"\"\n return list(self._sql.get_engine().execute(sql).fetchall())\n\n def _declare_doctor(self, server_id):\n # type: (int) -> None\n # first lets check to ensure someone hasn't already claimed this beast\n result = self._sql.get_session().query(ServerHealth)\\\n .filter(ServerHealth.server == server_id)\\\n .first()\n if result and result.doctor != '':\n self._ioc.message().error(\"SERVER DOCTOR ALREADY DECLARED FOR [{0}]\".format(server_id))\n return\n stmt = update(ServerHealth).where(ServerHealth.server == server_id).values(docter=self._config.node_db_id())\n self._sql.get_session().execute(stmt)\n self._sql.get_session().commit()\n\n def _am_i_the_doctor(self, server_id):\n # type: (int) -> bool\n result = self._sql.get_session().query(ServerHealth)\\\n .filter(ServerHealth.doctor == self._config.node_db_id())\\\n .filter(ServerHealth.server == server_id)\\\n .first()\n if result:\n return True\n else:\n return False\n\n def _server_alive(self, server):\n # type: (list) -> bool\n self._ioc.message().debug(\"data received to be processed [{0}]\".format(str(server)), True)\n result = self._sql.get_session().query(ServerHealth)\\\n .filter(ServerHealth.server == server[0])\\\n .first()\n if not result:\n self._ioc.message().debug(\"Server not in health::registering\", True)\n # if this is a server not checked into health before\n self._register_in_health(server)\n result = self._sql.get_session().query(ServerHealth)\\\n .filter(ServerHealth.server == server[0])\\\n .first()\n # now return to regular logic\n self._ioc.message().debug(\"Inspecting last check timestamp\", True)\n if result.check_time < (datetime.datetime.now(result.check_time.tzinfo) - datetime.timedelta(hours=6)):\n # server status is old\n # lets check the hash though\n if result.job_hash == server[6]:\n # hash hasn't changed time for d-com\n return False\n else:\n # hash is old but changed, lets update and return True\n self._update_hash(server[0], server[6])\n return True\n else:\n # results aren't 12 hrs old yet\n if result.job_hash == server[6]:\n # log but do nothing so we know bad things will happen if we continue\n if result.check_time < (datetime.datetime.now(result.check_time.tzinfo) - datetime.timedelta(minutes=10)):\n self._ioc.message().warning(\n \"Server hash has not changed since last poll ID:[{0}] hash:[{1}]\".format(\n server[0],\n server[6]\n )\n )\n else:\n # if it changed lets update so we see the current hash\n self._update_hash(server[0], server[6])\n return True\n\n def _register_in_health(self, server):\n newServer = ServerHealth(\n server=server[0],\n job_hash=server[6],\n check_time=datetime.datetime.utcnow()\n )\n self._sql.get_session().add(newServer)\n self._sql.get_session().commit()\n\n def _update_hash(self, server_id, hash_str):\n # type: (str, str) -> None\n stmt = update(ServerHealth)\\\n .where(ServerHealth.server == server_id)\\\n .values(job_hash=hash_str, check_time=datetime.datetime.utcnow())\n self._sql.get_session().execute(stmt)\n self._sql.get_session().commit()\n\n def _deactivate_server(self, server_id):\n # type: (int) -> None\n stmt = update(JobServers)\\\n .where(JobServers.id == server_id)\\\n .values(active=False)\n self._sql.get_session().execute(stmt)\n self._sql.get_session().commit()\n\n def _get_new_execution_server(self, server_id):\n # type: (int) -> JobServers\n # first get current env\n old_server = self._sql.get_session().query(JobServers)\\\n .filter(JobServers.id == server_id)\\\n .first()\n new_server = self._sql.get_session().query(JobServers)\\\n .filter(JobServers.execution_environment == old_server.execution_environment)\\\n .filter(JobServers.active == True)\\\n .order_by(JobServers.jobs_assigned)\\\n .first()\n if new_server:\n # we have a server to reassign to\n return new_server\n else:\n # there are no other execution servers for that environment\n self._ioc.message().error(\n \"No other valid execution environment for jobs assigned to server [{0}]\".format(server_id),\n hipchat=True\n )\n return False\n\n def _cull_server(self, server_id):\n # type: (int) -> None\n self._ioc.message().warning(\"DEACTIVATING SERVER [{0}]\".format(server_id), hipchat=True)\n # first lets deactivate the job server\n self._deactivate_server(server_id)\n # next we need to check for any jobs scheduled to that instance and reassign them\n # first lets see if there is another available server\n server = self._get_new_execution_server(server_id)\n if server:\n # get the number of jobs being reassigned\n result = self._sql.get_session().query(JobQueue) \\\n .filter(or_(and_(JobQueue.in_progress == False, JobQueue.completed == False), JobQueue.in_progress == True)) \\\n .filter(JobQueue.host_name == server_id)\\\n .all()\n if result:\n jobs = []\n for job in result:\n jobs.append(job.id)\n # we have jobs to move\n # step one get the list of jobs to reassign\n job_id_group = tuple(jobs)\n self._reassign_on_demand_jobs(server_id, server.id, job_id_group)\n else:\n self._ioc.message().info(\"No jobs to reassign for server [{0}] being decommissioned\".format(server_id))\n else:\n # no other valid server could be found for on-demand work\n self._ioc.message().error(\"Failed reassigning jobs for server [{0}]\".format(server_id))\n # next lets move over any source detection work\n result = self._sql.get_session().query(SourceData)\\\n .filter(SourceData.detection_server == server_id)\\\n .filter(SourceData.detection_start_time == None)\\\n .filter(SourceData.detection_end_time == None)\\\n .filter(SourceData.detection_complete == False)\\\n .all()\n jobs = []\n for job in result:\n jobs.append(job.id)\n source_file_jobs = tuple(jobs)\n if len(source_file_jobs):\n # we have sources to reassign parsing for\n self._reassign_sources(source_file_jobs, server_id)\n else:\n # no source documents to reassign\n self._ioc.message().info(\"No source parsing to reassign for server [{0}]\".format(server_id))\n # finally lets worry about schedules to be reassigned\n result = self._sql.get_session().query(SourceData) \\\n .filter(SourceData.scheduling_server == server_id) \\\n .filter(SourceData.detection_start_time != None)\\\n .filter(SourceData.detection_end_time != None)\\\n .filter(SourceData.detection_complete == True)\\\n .filter(SourceData.scheduling_start_time == None)\\\n .filter(SourceData.scheduling_end_time == None)\\\n .filter(SourceData.scheduling_complete == False)\\\n .all()\n jobs = []\n for job in result:\n jobs.append(job.id)\n schedules = tuple(jobs)\n if len(schedules):\n # we have sources to reassign parsing for\n self._reassign_schedules(schedules, server_id)\n else:\n # no source documents to reassign\n self._ioc.message().info(\"No schedules to reassign for server [{0}]\".format(server_id))\n\n def _reassign_on_demand_jobs(self, server_id, new_server, job_list):\n # type: (int, int, tuple) -> None\n # now lets do the update\n if not job_list:\n job_list = (-1,)\n stmt = update(JobQueue)\\\n .where(JobQueue.id.in_(job_list))\\\n .values(host_name=new_server)\n self._sql.get_session().execute(stmt)\n self._sql.get_session().commit()\n # now lets update the totals on the job servers in question\n self._update_job_assignment_totals(server_id, new_server, len(job_list))\n self._ioc.message().info(\n \"JOB ID SET [{0}] MOVED FROM SERVER [{1}] TO SERVER [{2}]\".format(\n job_list,\n server_id,\n new_server\n )\n )\n\n def _reassign_sources(self, source_file_ids, origin_server):\n # type: (tuple, int) -> None\n # first lets find our applicable detector server\n result = self._sql.get_session().query(JobServers)\\\n .filter(JobServers.active == True)\\\n .filter(JobServers.detector == True)\\\n .order_by(JobServers.jobs_assigned)\\\n .first()\n if result:\n new_server = result.id\n reassign_total = len(source_file_ids)\n # Actual reassignment of sources\n stmt = update(SourceData)\\\n .where(SourceData.id.in_(source_file_ids))\\\n .values(detection_server=new_server)\n self._sql.get_session().execute(stmt)\n self._sql.get_session().commit()\n self._update_job_assignment_totals(origin_server, new_server, reassign_total)\n else:\n # oh crap we have no job detector servers left alive\n # Me IRL Right now:\n # https://68.media.tumblr.com/e59c51080e14cee56ce93416cf8055c8/tumblr_mzncp3nYXh1syplf0o1_250.gif\n self._ioc.message().error(\n \"NO AVAILABLE DETECTOR SERVER CANNOT REASSIGN JOBS [{0}] FROM SERVER [{1}]\".format(\n source_file_ids, origin_server\n ),\n hipchat=True\n )\n # bail because we have no one to reassign to\n return\n\n def _reassign_schedules(self, schedule_ids, origin_server):\n # type: (tuple, int) -> None\n # first lets find our applicable scheduling server\n result = self._sql.get_session().query(JobServers)\\\n .filter(JobServers.active == True)\\\n .filter(JobServers.scheduler == True)\\\n .order_by(JobServers.jobs_assigned)\\\n .first()\n if result:\n new_server = result.id\n reassign_total = len(schedule_ids)\n # Actual reassignment of sources\n stmt = update(SourceData)\\\n .where(SourceData.id.in_(schedule_ids))\\\n .values(scheduling_server=new_server)\n self._sql.get_session().execute(stmt)\n self._sql.get_session().commit()\n self._update_job_assignment_totals(origin_server, new_server, reassign_total)\n else:\n # oh crap we have no job scheduler servers left alive\n # Me IRL Right now: https://media.giphy.com/media/HUkOv6BNWc1HO/giphy.gif\n self._ioc.message().error(\n \"NO AVAILABLE SCHEDULER SERVER CANNOT REASSIGN JOBS [{0}] FROM SERVER [{1}]\".format(\n schedule_ids, origin_server\n ),\n hipchat=True\n )\n # bail because we have no one to reassign to\n return\n\n def _update_job_assignment_totals(self, old_server, new_server, job_count):\n # type: (int, int, int) -> None\n # first lets deduct from the bad server\n stmt1 = update(JobServers)\\\n .where(id=old_server)\\\n .values(jobs_assigned=JobServers.jobs_assigned - job_count)\n stmt2 = update(JobServers)\\\n .where(id=new_server)\\\n .values(jobs_assigned=JobServers.jobs_assigned + job_count)\n self._sql.get_session().execute(stmt1)\n self._sql.get_session().execute(stmt2)\n self._sql.get_session().commit()\n","sub_path":"tgt_grease_enterprise/Article14Section31.py","file_name":"Article14Section31.py","file_ext":"py","file_size_in_byte":18279,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"488510049","text":"import argparse\nimport sys\nfrom pathlib import Path\n\nimport imagesize\nfrom fpdf import FPDF\n\nparser = argparse.ArgumentParser()\nparser.add_argument('--input_dir', type=str, default='.',\n help='Directory with input images.')\nparser.add_argument('--output', type=str, default='document.pdf',\n help='Output document')\nparser.add_argument('--format', type=str, default='jpg',\n help='Input image format that will be used as extension.')\nparser.add_argument('--dpi', type=int, default=300,\n help='DPI of input images.')\nargs = parser.parse_args()\n\nA = {\n 5: (5.8, 8.3),\n 4: (8.3, 11.7),\n}\n\n\ndef get_image_list(image_dir: str, extension: str):\n image_dir = Path(image_dir).resolve()\n images = map(str, image_dir.glob(f'*.{extension}'))\n return sorted(images)\n\n\ndef add_image(pdf: FPDF, image: str, dpi: int):\n w0, h0 = imagesize.get(image)\n\n if w0 < h0:\n orient = 'P'\n else:\n orient = 'L'\n\n keys = list(A.keys())\n keys.sort(key=lambda n: -n)\n\n for k in keys:\n w, h = A[k][0] * dpi, A[k][1] * dpi\n if orient == 'L':\n w, h = h, w\n fmt = f'A{k}'\n if w0 < w and h0 < h:\n break\n else:\n print(f'Image size is too big: {w0}×{h0} (for {w}×{h})')\n w0 = w\n\n pdf.add_page(orientation=orient, format=fmt)\n pdf.image(image, x=(w - w0) / 2 / dpi, y=(h - h0) / 2 / dpi, w=w0 / dpi)\n\n\ndef main():\n images = get_image_list(args.input_dir, args.format)\n\n if len(images) == 0:\n sys.exit('No images were found')\n\n pdf = FPDF(unit='in')\n pdf.set_font('helvetica')\n\n for image in images:\n print(image)\n add_image(pdf, image, args.dpi)\n\n print('Writing', args.output)\n pdf.output(args.output, 'F')\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"scan2pdf.py","file_name":"scan2pdf.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"354928926","text":"#!/usr/bin/env\r\n# -*- coding: utf-8 -*-\r\n\r\n# Copyright 2018 The Google AI Language Team Authors and The HuggingFace Inc. team.\r\n# Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.\r\n# Copyright (c) 2019, Charles\r\n#\r\n# Licensed under the Apache License, Version 2.0 (the \"License\");\r\n# you may not use this file except in compliance with the License.\r\n# You may obtain a copy of the License at\r\n#\r\n# http://www.apache.org/licenses/LICENSE-2.0\r\n#\r\n# Unless required by applicable law or agreed to in writing, software\r\n# distributed under the License is distributed on an \"AS IS\" BASIS,\r\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\r\n# See the License for the specific language governing permissions and\r\n# limitations under the License.\r\n\"\"\"Run BERT on SQuAD.\"\"\"\r\n\r\nfrom __future__ import absolute_import\r\n\r\nfrom pytorch_pretrained_bert.modeling import BertForQuestionAnswering\r\nfrom torch.nn import CrossEntropyLoss\r\n\r\n\r\nclass BertForQA(BertForQuestionAnswering):\r\n \"\"\"Rewrite forward method to support no reduction in loss function.\"\"\"\r\n\r\n def __init__(self, config):\r\n super(BertForQA, self).__init__(config)\r\n\r\n def forward(self, input_ids, token_type_ids=None, attention_mask=None, start_positions=None, end_positions=None, mean=True):\r\n sequence_output, _ = self.bert(input_ids, token_type_ids, attention_mask, output_all_encoded_layers=False)\r\n logits = self.qa_outputs(sequence_output)\r\n start_logits, end_logits = logits.split(1, dim=-1)\r\n start_logits = start_logits.squeeze(-1)\r\n end_logits = end_logits.squeeze(-1)\r\n\r\n if start_positions is not None and end_positions is not None:\r\n # If we are on multi-GPU, split add a dimension\r\n if len(start_positions.size()) > 1:\r\n start_positions = start_positions.squeeze(-1)\r\n if len(end_positions.size()) > 1:\r\n end_positions = end_positions.squeeze(-1)\r\n # sometimes the start/end positions are outside our model inputs, we ignore these terms\r\n ignored_index = start_logits.size(1)\r\n start_positions.clamp_(0, ignored_index)\r\n end_positions.clamp_(0, ignored_index)\r\n\r\n loss_fct = CrossEntropyLoss(ignore_index=ignored_index, reduction='mean' if mean else 'none')\r\n start_loss = loss_fct(start_logits, start_positions)\r\n end_loss = loss_fct(end_logits, end_positions)\r\n total_loss = (start_loss + end_loss) / 2\r\n return total_loss, start_logits, end_logits\r\n else:\r\n return start_logits, end_logits\r\n","sub_path":"paper-qa/models/bert.py","file_name":"bert.py","file_ext":"py","file_size_in_byte":2542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"409955412","text":"import matplotlib as mpl\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport tensorflow as tf\nfrom tensorflow import keras\n\n\"\"\"\n基于tensorflow对服装图片进行分类\n\"\"\"\n\n# 设置中文支持\nmpl.rcParams[\"font.family\"] = 'Arial Unicode MS'\n\n# 读取keras中的进阶版mnist数据集\nfashion_mnist = keras.datasets.fashion_mnist\n\n# 加载数据集，切分为训练集和测试集\n(x_train_all, y_train_all), (x_test, y_test) = fashion_mnist.load_data()\n\n# 从训练集中将后五千张作为验证集，前五千张作为训练集\n# [:5000]默认从头开始，从头开始取5000个\n# [5000:]从第5001开始，结束位置默认为最后\nx_valid, x_train = x_train_all[:5000], x_train_all[5000:]\ny_valid, y_train = y_train_all[:5000], y_train_all[5000:]\n# 打印这些数据集的大小\nprint(x_valid.shape, y_valid.shape)\nprint(x_train.shape, y_train.shape)\nprint(x_test.shape, y_test.shape)\n\n# def show_single_image(img_arr):\n# plt.imshow(img_arr, cmap=\"binary\")\n# plt.show()\n# # 显示训练集第一张图片\n# show_single_image(x_train[0])\n\n\nclass_names = ['T-shirt', 'Trouser', 'Pullover', 'Dress',\n 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag',\n 'Ankle boot']\n# tf.keras.models.Sequential() 构建模型的容器\n\n# 创建一个Sequential的对象,顺序模型，多个网络层的线性堆叠\n# 可使用add方法将各层添加到模块中\nmodel = keras.models.Sequential()\n\n# 添加层次\n# 输入层：Flatten将28*28的图像矩阵展平成为一个一维向量\nmodel.add(keras.layers.Flatten(input_shape=[28, 28]))\n\n# 全连接层（上层所有单元与下层所有单元都连接）：\n# 第一层300个单元，第二层100个单元，激活函数为 relu:\n# relu: y = max(0, x)\nmodel.add(keras.layers.Dense(300, activation=\"relu\"))\nmodel.add(keras.layers.Dense(100, activation=\"relu\"))\n\n# 输出为长度为10的向量，激活函数为 softmax:\n# softmax: 将向量变成概率分布，x = [x1, x2, x3],\n# y = [e^x1/sum, e^x2/sum, e^x3/sum],sum = e^x1+e^x2+e^x3\nmodel.add(keras.layers.Dense(10, activation=\"softmax\"))\n\n# 目标函数的构建与求解方法\n# 为什么使用sparse_categorical_crossentropy :\n# y->是一个数，要用sparse_categorical_crossentropy\n# y->是一个向量，直接用categorical_crossentropy\n\n# loss 损失函数\n# optimizer 优化方式 gradient_descent为梯度下降 adam自适应矩估计 梯度下降的变形 不会因为梯度很大导致步长过大\n# metrics 评估标准 accuracy准确率\nmodel.compile(loss=\"sparse_categorical_crossentropy\",\n optimizer=\"adam\")\n\n\"\"\"\n构建模型也可以这样：\nmodel = keras.models.Sequential([\n keras.layers.Flatten(input_shape=[28,28]),\n keras.layers.Dense(300,activation=\"relu\"),\n keras.layers.Dense(300,activation=\"relu\"),\n keras.layers.Dense(10,activation=\"softmax\")\n])\n\"\"\"\n\n# 看模型的概况\n# model.summary()\n\n# 开启训练\n# epochs:训练集遍历次数\n# validation_data:每个epoch就会用验证集验证\n# 会发现loss和accuracy到后面一直不变，因为用sgd梯度下降法会导致陷入局部最小值点\n# 因此将loss函数的下降方法改为 adam\n# callback 在执行完一次遍历后回调的函数\nhistory = model.fit(x_train, y_train, epochs=10,\n validation_data=(x_valid, y_valid))\n\n\n# 画出训练过程图形\ndef plot_learning_curves(history):\n # 将history.history转换为dataframe格式 表格画画图\n pd.DataFrame(history.history).plot(figsize=(8, 5))\n # 设置显示网格\n plt.grid(True)\n # gca：get current axes 获取当前子图,gcf: get current figure 获取当前图标\n # 设置y值范围\n plt.gca().set_ylim(0, 1)\n plt.show()\n\n\nplot_learning_curves(history)\n\n# 转换为dataframe格式进行查看\npd.DataFrame(history.history)\n","sub_path":"03tensorflow/02tf2/06实现服装分类.py","file_name":"06实现服装分类.py","file_ext":"py","file_size_in_byte":3802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"442013785","text":"'''Given two strings s and t, determine whether some anagram of t is a substring of s.\r\n For example: if s = \"udacity\" and t = \"ad\", then the function returns True. \r\nYour function definition should look like: question1(s, t) and return a boolean True or False'''\r\n\r\ndef question1(s,t):\r\n\r\n #check string s and string t are equals\r\n if s==t:\r\n return True\r\n\r\n # if either of strings( s and t) is null\r\n if s==None or t==None:\r\n return False\r\n\r\n # for t should be substring of s . length of t should be smaller than s\r\n if len(t)>len(s):\r\n return True\r\n\r\n # Checking the character of t in s\r\n s=s.lower()\r\n t=t.lower()\r\n for character in t:\r\n if character in s:\r\n s = s.replace(character, '')\r\n t = t.replace(character, '')\r\n if not t:\r\n return True\r\n return False\r\n\r\n# using Dictionary\r\nprint (question1(\"udAcity\",\"ad\"))\r\nprint (question1(\"udacity\",\"smart\"))\r\nprint (question1(\"udacity\",\"city\"))\r\nprint (question1(\"udaCiTy\",\"city\"))\r\n\r\n","sub_path":"MLND-Interview Questions/question1.py","file_name":"question1.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"430197994","text":"# compare_algorithms.py\n\nimport pandas\nimport matplotlib.pyplot as plt\n\nfrom sklearn import cross_validation\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.tree import DecisionTreeClassifier\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.discriminant_analysis import LinearDiscriminantAnalysis\nfrom sklearn.naive_bayes import GaussianNB\nfrom sklearn.svm import SVC\n\nfilename = \"../data/pima-indians-diabetes.data.csv\"\nnames = [ 'preg','plas','pres','skin','test','mass','pedi','age','class' ]\n\ndataframe = pandas.read_csv(filename, names=names)\narray = dataframe.values\n\nX = array[:,0:8]\nY = array[:,8]\n\n# prepare configuration \nnum_folds = 10\nnum_instances = len(X)\nseed = 7\n\n# prepare models\nmodels = []\nmodels.append(( ' LR ' , LogisticRegression()))\nmodels.append(( ' LDA ' , LinearDiscriminantAnalysis()))\nmodels.append(( ' KNN ' , KNeighborsClassifier()))\nmodels.append(( ' CART ' , DecisionTreeClassifier()))\nmodels.append(( ' NB ' , GaussianNB()))\nmodels.append(( ' SVM ' , SVC()))\n\n\n#valuate each model in turn\nresults = []\nnames = []\nscoring = 'accuracy'\nfor name, model in models:\n\tkfold = cross_validation.KFold(n=num_instances, n_folds=num_folds, random_state=seed)\n\tcv_results = cross_validation.cross_val_score(model, X, Y, cv=kfold, scoring=scoring)\n\tresults.append(cv_results)\n\tnames.append(name)\n\tmsg = \"%s: %f (%f)\" % (name, cv_results.mean(), cv_results.std())\n\tprint(msg)\t\n\n\n# boxplot algorithm comparison\nfig = plt.figure()\nfig.suptitle( ' Algorithm Comparison ' )\nax = fig.add_subplot(111)\nplt.boxplot(results)\nax.set_xticklabels(names)\nplt.show()\n\n","sub_path":"experiment-4/compare_algorithms.py","file_name":"compare_algorithms.py","file_ext":"py","file_size_in_byte":1608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"406870010","text":"import pygame as p\nfrom paddle import Paddle\nfrom ball import Ball\n\ndef main():\n p.init() # Start up Pygame\n\n # Window Settings\n xSize = 700\n ySize = 500\n size = (xSize, ySize)\n screen = p.display.set_mode(size)\n p.display.set_caption('Pong Fun!')\n\n # Colors\n black = (0, 0, 0)\n white = (255, 255, 255)\n cyan = (0, 255, 255)\n\n # Paddle\n paddle = Paddle(white, 10, 100)\n paddle.rect.x = 20\n paddle.rect.y = 200\n\n # Ball\n ball = Ball(cyan, 10, 10)\n ball.rect.x = int(xSize / 2)\n ball.rect.y = int(ySize / 2)\n\n # Adding sprites to a sprite group\n sprites = p.sprite.Group()\n sprites.add(paddle)\n sprites.add(ball)\n\n playing = True # Boolean to check game loop\n clock = p.time.Clock() # Create a clock to manage framerate\n\n score = 0\n\n # Game Loop\n while playing:\n\n # Check if player is exiting the game\n for event in p.event.get():\n if event.type == p.QUIT:\n playing = False\n elif event.type==p.KEYDOWN:\n if event.key == p.K_ESCAPE:\n playing = False\n\n # Check key presses to move paddle.\n keys = p.key.get_pressed()\n if keys[p.K_UP]:\n paddle.moveUp(5)\n if keys[p.K_DOWN]:\n paddle.moveDown(5)\n\n # Update ball's velocity if it hits a wall\n if ball.rect.x >= xSize - 10 or ball.rect.x <= 0:\n ball.velocity[0] = -ball.velocity[0]\n if ball.rect.y > ySize - 10 or ball.rect.y < 0:\n ball.velocity[1] = -ball.velocity[1]\n\n # Check if the ball hits the paddle and bounce it if it does\n if p.sprite.collide_mask(ball, paddle):\n score += 1\n ball.bounce()\n\n sprites.update() # Update sprites \n\n screen.fill(black) # Background of screen\n\n p.draw.line(screen, white, [int(xSize / 2), 0], [int(xSize / 2), ySize], 5) # Middle line\n\n sprites.draw(screen) # Draws sprites to the surface\n\n # Set font for scoreboard\n font = p.font.Font(None, 74)\n text = font.render(str(score), 1, white)\n screen.blit(text, (int(xSize / 4), 10))\n\n p.display.flip() # Updates the display to the new surface (updates the frame)\n\n clock.tick(60) # Update game clock (run no more than 60 frames per second)\n\n p.quit() # Exit the game application\n\nif __name__ == '__main__':\n main()","sub_path":"Pong/pong.py","file_name":"pong.py","file_ext":"py","file_size_in_byte":2414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"11080121","text":"import gc\nfrom math import sqrt\nfrom timeit import default_timer as timer\n\nimport cma\nimport numpy as np\nimport torch\n\nfrom ndes import NDES, SecondaryMutation\nfrom population_initializers import (\n StartFromUniformPopulationInitializer,\n XavierMVNPopulationInitializer,\n)\nfrom utils import seconds_to_human_readable\n\n\nclass FitnessEWMALogger:\n \"\"\"Logger for the fitness values of data batches\"\"\"\n\n def __init__(self, data_gen, model, criterion):\n self.ewma_alpha = 1\n self.iter_counter = 1\n self.num_batches = len(data_gen)\n self.ewma = torch.zeros(self.num_batches)\n # FIXME\n # sum of losses per batch for the current iteration\n self.current_losses = torch.zeros(self.num_batches)\n # count of evaluations per batch for the current iteration\n self.current_counts = torch.zeros(self.num_batches)\n self.set_initial_losses(data_gen, model, criterion)\n\n def set_initial_losses(self, data_gen, model, criterion):\n # XXX this is really ugly\n for batch_idx, (b_x, y) in data_gen:\n out = model(b_x)\n loss = criterion(out, y).item()\n self.ewma[batch_idx] = loss\n if batch_idx >= self.num_batches - 1:\n break\n\n def update_batch(self, batch_idx, loss):\n self.current_losses[batch_idx] += loss\n self.current_counts[batch_idx] += 1\n return loss - self.ewma[batch_idx]\n\n def update_after_iteration(self):\n self.ewma *= 1 - self.ewma_alpha\n # calculate normal average for each batch and include it in the EWMA\n self.ewma += self.ewma_alpha * (self.current_losses / self.current_counts)\n # reset stats for the new iteration\n self.current_losses = torch.zeros(self.num_batches)\n # XXX ones to prevent 0 / 0\n self.current_counts = torch.ones(self.num_batches)\n self.ewma_alpha = 1 / (self.iter_counter ** (1 / 3))\n self.iter_counter += 1\n\n\nclass BasenDESOptimizer:\n \"\"\"Base interface for the nDES optimizer for the neural networks optimization.\"\"\"\n\n def __init__(\n self,\n model,\n criterion,\n data_gen,\n x_val=None,\n y_val=None,\n use_fitness_ewma=False,\n population_initializer=XavierMVNPopulationInitializer,\n restarts=None,\n lr=1e-3,\n **kwargs,\n ):\n \"\"\"\n Args:\n model: ``pytorch``'s model\n criterion: Loss function, must be minimizable.\n data_gen: Data generator, should yield batches: (batch_idx, (x, y))\n x_val: Validation data\n y_val: Validation ground truth\n use_fitness_ewma: is ``True`` will use EWMA fitness loss tracker\n population_initializer: Class of the population initialization strategy\n lr: Learning rate, only used if secondary_mutation is set to gradient\n restarts: Optional number of NDES's restarts.\n **kwargs: Keyword arguments for NDES optimizer\n \"\"\"\n self._layers_offsets_shapes = []\n self.model = model\n self.criterion = criterion\n self.population_initializer = population_initializer\n self.data_gen = data_gen\n self.x_val = x_val\n self.y_val = y_val\n self.use_fitness_ewma = use_fitness_ewma\n self.kwargs = kwargs\n self.restarts = restarts\n self.start = timer()\n if restarts is not None and self.kwargs.get(\"budget\") is not None:\n self.kwargs[\"budget\"] //= restarts\n self.initial_value = self.zip_layers(model.parameters())\n self.xavier_coeffs = self.calculate_xavier_coefficients(model.parameters())\n self.secondary_mutation = kwargs.get(\"secondary_mutation\", None)\n self.lr = lr\n if use_fitness_ewma:\n self.ewma_logger = FitnessEWMALogger(data_gen, model, criterion)\n self.kwargs[\"iter_callback\"] = self.ewma_logger.update_after_iteration\n\n def zip_layers(self, layers_iter):\n \"\"\"Concatenate flattened layers into a single 1-D tensor.\n This method also saves shapes of layers and their offsets in the final\n tensor, allowing for a fast unzip operation.\n\n Args:\n layers_iter: Iterator over model's layers.\n \"\"\"\n self._layers_offsets_shapes = []\n tensors = []\n current_offset = 0\n for param in layers_iter:\n shape = param.shape\n tmp = param.flatten()\n current_offset += len(tmp)\n self._layers_offsets_shapes.append((current_offset, shape))\n tensors.append(tmp)\n return torch.cat(tensors, 0).contiguous()\n\n @staticmethod\n def calculate_xavier_coefficients(layers_iter):\n xavier_coeffs = []\n for param in layers_iter:\n param_num_elements = param.numel()\n if len(param.shape) > 1:\n fan_in, fan_out = torch.nn.init._calculate_fan_in_and_fan_out(param)\n xavier_coeffs.extend(\n [sqrt(6 / (fan_in + fan_out))] * param_num_elements\n )\n else:\n xavier_coeffs.extend([xavier_coeffs[-1]] * param_num_elements)\n return torch.tensor(xavier_coeffs)\n\n def unzip_layers(self, zipped_layers):\n \"\"\"Iterator over 'unzipped' layers, with their proper shapes.\n\n Args:\n zipped_layers: Flattened representation of layers.\n \"\"\"\n start = 0\n for offset, shape in self._layers_offsets_shapes:\n yield zipped_layers[start:offset].view(shape)\n start = offset\n\n # @profile\n def _objective_function(self, weights):\n \"\"\"Custom objective function for the DES optimizer.\"\"\"\n self._reweight_model(weights)\n batch_idx, (b_x, y) = next(self.data_gen)\n if self.secondary_mutation == SecondaryMutation.Gradient:\n gradient = []\n with torch.enable_grad():\n self.model.zero_grad()\n out = self.model(b_x)\n loss = self.criterion(out, y)\n loss.backward()\n for param in self.model.parameters():\n gradient.append(param.grad.flatten())\n gradient = torch.cat(gradient, 0)\n # In-place mutation of the weights\n weights -= self.lr * gradient\n else:\n out = self.model(b_x)\n loss = self.criterion(out, y)\n loss = loss.item()\n if self.use_fitness_ewma:\n return self.ewma_logger.update_batch(batch_idx, loss)\n return loss\n\n # @profile\n def run(self, test_func=None):\n \"\"\"Optimize model's weights wrt. the given criterion.\n\n Returns:\n Optimized model.\n \"\"\"\n self.test_func = test_func\n best_value = self.initial_value\n with torch.no_grad():\n requires_grad = self.secondary_mutation == SecondaryMutation.Gradient\n for param in self.model.parameters():\n param.requires_grad = requires_grad\n population_initializer_args = [\n self.xavier_coeffs,\n self.kwargs[\"device\"],\n self.kwargs.get(\"lambda_\", None),\n ]\n population_initializer = self.population_initializer(\n best_value, *population_initializer_args\n )\n if self.x_val is not None:\n test_func = self.validate_and_test\n else:\n test_func = None\n self.kwargs.update(\n dict(\n initial_value=best_value,\n fn=self._objective_function,\n xavier_coeffs=self.xavier_coeffs,\n population_initializer=population_initializer,\n test_func=test_func,\n )\n )\n if self.restarts is not None:\n for i in range(self.restarts):\n self.kwargs[\"population_initializer\"] = self.population_initializer(\n best_value, *population_initializer_args\n )\n ndes = NDES(log_id=i, **self.kwargs)\n best_value = ndes.run()\n del ndes\n if self.test_func is not None:\n self.test_model(best_value)\n gc.collect()\n torch.cuda.empty_cache()\n else:\n ndes = NDES(**self.kwargs)\n best_value = ndes.run()\n self._reweight_model(best_value)\n return self.model\n\n # @profile\n def _reweight_model(self, weights):\n for param, layer in zip(self.model.parameters(), self.unzip_layers(weights)):\n param.data.copy_(layer)\n\n # @profile\n def test_model(self, weights):\n end = timer()\n model = self.model\n self._reweight_model(weights)\n print(f\"\\nPerf after {seconds_to_human_readable(end - self.start)}\")\n return self.test_func(model)\n\n def iter_callback(self):\n pass\n\n # @profile\n def find_best(self, population):\n min_loss = torch.finfo(torch.float32).max\n best_idx = None\n for i in range(population.shape[1]):\n self._reweight_model(population[:, i])\n out = self.model(self.x_val)\n loss = self.criterion(out, self.y_val).item()\n if loss < min_loss:\n min_loss = loss\n best_idx = i\n return population[:, best_idx].clone()\n\n def validate_and_test(self, population):\n best_individual = self.find_best(population)\n return self.test_model(best_individual), best_individual\n\n\nclass RNNnDESOptimizer(BasenDESOptimizer):\n \"\"\"nDES optimizer for RNNs, uses different initialization strategy than base\n optimizer.\"\"\"\n\n def __init__(\n self,\n *args,\n population_initializer=StartFromUniformPopulationInitializer,\n secondary_mutation=SecondaryMutation.RandomNoise,\n **kwargs,\n ):\n super().__init__(\n *args,\n population_initializer=population_initializer,\n secondary_mutation=secondary_mutation,\n **kwargs,\n )\n\n def calculate_xavier_coefficients(self, layers_iter):\n return torch.ones_like(self.initial_value) * 0.4\n\n\nclass CMAESOptimizerRNN:\n def __init__(self, model, criterion, data_gen, restarts=None, **kwargs):\n self.model = model\n self.criterion = criterion\n self.data_gen = data_gen\n\n def _objective_function(self, weights):\n \"\"\"Custom objective function for the DES optimizer.\"\"\"\n self.model.set_weights(weights)\n _, (x_data, y_data) = next(self.data_gen)\n predicted = np.array([self.model.forward(x) for x in x_data])\n loss = self.criterion(y_data, predicted)\n return loss\n\n def run(self, test_func=None):\n \"\"\"Optimize model's weights wrt. the given criterion.\n\n Returns:\n Optimized model.\n \"\"\"\n es = cma.CMAEvolutionStrategy(11 * [0], 1.5, {\"verb_disp\": 1, \"maxiter\": 1000})\n es.optimize(self._objective_function)\n return es.best.get()[0]\n","sub_path":"src/ndes_optimizer.py","file_name":"ndes_optimizer.py","file_ext":"py","file_size_in_byte":11217,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"81314567","text":"from django.shortcuts import render, redirect\n\n#import necesario para hacer funcionar el buscador\nfrom django.db.models import Q\n\n#importa lo necesario para que funcione el metodo GET y POST\nfrom rest_framework.decorators import api_view\nfrom rest_framework import generics\n\nfrom .models import hoyRX, item\nfrom .serializer import hoyRXSerializer, itemSerializer\nfrom .forms import GenerarPedidosForm,registroUsuarioForm\n\n#importa lo necesario para que funcione la autenticacion\nfrom django.urls import reverse_lazy\nfrom django.utils.decorators import method_decorator\nfrom django.views.decorators.cache import never_cache\nfrom django.views.decorators.csrf import csrf_protect\nfrom django.views.generic.edit import FormView\nfrom django.contrib.auth import login,logout,authenticate\nfrom django.http import HttpResponseRedirect\nfrom django.contrib.auth.forms import AuthenticationForm\nfrom rest_framework.authtoken.models import Token\nfrom rest_framework.permissions import IsAuthenticated\nfrom rest_framework.authentication import TokenAuthentication\nfrom rest_framework import status\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\n\n\nclass hoyRxList(generics.ListCreateAPIView):\n queryset = hoyRX.objects.all()\n serializer_class = hoyRXSerializer\n permission_classes = (IsAuthenticated,)\n authentication_class = (TokenAuthentication,)\n\n\n\n#LOGIN\nclass Login(FormView):\n template_name = 'login.html'\n form_class = AuthenticationForm\n success_url = reverse_lazy('api:hoyRx_List')\n\n @method_decorator(csrf_protect)\n @method_decorator(never_cache)\n def dispatch(self,request,*args,**kwargs):\n if request.user.is_authenticated:\n return HttpResponseRedirect(self.get_success_url())\n\n else:\n return super(Login,self).dispatch(request,*args,*kwargs)\n\n def form_valid(self,form):\n user = authenticate(username=form.cleaned_data['username'], password=form.cleaned_data['password'])\n token,_ = Token.objects.get_or_create(user = user)\n\n if token:\n login(self.request, form.get_user())\n return super(Login, self).form_valid(form)\n\n\n\n\n\n#LOGOUT\n\nclass Logout(APIView):\n def get(self, request, format = None):\n request.user.auth_token.delete()\n logout(request)\n return Response(status = status.HTTP_200_OK)\n\n\n\n\n#funcion de get y post para el modelo item\n@api_view(['GET', 'POST'])\ndef items(request):\n\n if request.method == 'GET':\n Item = item.objects.all()\n serializer = itemSerializer(Item, many=True)\n return Response(serializer.data)\n\n elif request.method == 'POST':\n serializer = itemSerializer(data=request.data)\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n\n\n\n#funcion para mandar informacion al modelo hoyRX\n@api_view(['POST'])\ndef hoyRxPost(request):\n\n request.method == 'POST'\n serializer = hoyRXSerializer(data=request.data)\n\n if serializer.is_valid():\n serializer.save()\n return Response(serializer.data, status=status.HTTP_201_CREATED)\n\n return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n\n\n\n#templates\n\ndef inicio(request):\n #funcion del buscador en la vista pedidos\n queryset = request.GET.get('buscar')\n Item = item.objects.all()\n\n if queryset:\n Item = item.objects.filter(\n Q(date__icontains = queryset)\n )\n\n return render(request, 'index.html', {'item':Item})\n\n\n\n\n Item = item.objects.all()\n\n contexto = {\n 'item':Item,\n }\n\n return render(request, 'index.html', contexto)\n\n\ndef crearPedido(request):\n\n #funcion del formulario\n if request.method == 'GET':\n \n Item = item.objects.all()\n hoyrx = hoyRX.objects.all()\n contexto = {\n 'item':Item,\n 'hoyrx':hoyrx\n }\n\n\n #funcion del buscador en generar pedidos\n queryset = request.GET.get('buscar')\n hoyrx = hoyRX.objects.all()\n\n if queryset:\n hoyrx = hoyRX.objects.filter(\n Q(name__icontains = queryset) |\n Q(email__icontains = queryset) |\n Q(last_name__icontains = queryset)\n ).distinct()\n\n return render(request,'crearPedido.html', {'hoyrx':hoyrx})\n\n\n\n else:\n form = GenerarPedidosForm(request.POST)\n contexto = {\n 'form': form,\n }\n\n if form.is_valid():\n form.save()\n return redirect('index')\n\n\n return render(request, 'crearPedido.html', contexto)\n\n\n\n\ndef regUsuario(request):\n \n if request.method == 'GET':\n Hoyrx = hoyRX.objects.all()\n contexto = {\n 'hoyrx':Hoyrx\n }\n\n\n else:\n form = registroUsuarioForm(request.POST)\n contexto = {\n 'form':form\n }\n\n print(form)\n\n if form.is_valid():\n form.save()\n return redirect('index')\n \n\n\n return render(request, 'regUsuario.html', contexto)\n\n","sub_path":"api/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5180,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"404113529","text":"# импортирую библиотеки необходимые для работы программы\nimport datetime\nimport time\nimport sys\nimport pyglet\n\n# Ипортирую все виджеты для работы qt дизайнера\nfrom PyQt5 import uic\nfrom PyQt5.QtWidgets import QApplication\nfrom PyQt5.QtWidgets import QMainWindow\nfrom PyQt5.QtWidgets import QTextBrowser\n\n\nclass WindowWhitchMessage(QMainWindow):\n def __init__(self):\n # вызываю родительский инит что бы окно работало\n super().__init__()\n # вызываю под функцию где будет\n # описано продолжение работы программы\n self.initUI()\n\n def initUI(self):\n # считываю время на компютере\n now = str(datetime.datetime.now())[:16]\n\n # подключаю мой файл сделаный в pqt\n uic.loadUi('Uwedomlenie.ui', self)\n # кнопки, окна и тд я задал в ui файле\n # что сократило обем программы\n\n # задаю название для окна\n self.setWindowTitle('Уведомление')\n\n # открываю таблицу с сервера\n f = open(\"timing.txt\", mode=\"rb\")\n\n # считываю масив информации\n lines = f.readlines()\n\n # преобразововаю информацию из изночального вида в список\n a = [i[:len(i) - 2] for i in lines]\n a = [str(i) for i in a]\n a = [i[2:len(i) - 1] for i in a]\n a = [i[1:len(i) - 1] for i in a]\n # удаляю лишнии знаки\n # получившиеся при сохранении\n self.a = [i.split(', ') for i in a]\n f.close()\n\n # считываю сообщения которые записал пользователь\n # и преобрахововаю их в список\n self.message = [i for i in self.a]\n self.message = [i[0] for i in self.message]\n self.messageId = [i for i in self.a]\n self.messageId = [i[-2] for i in self.messageId]\n\n # создаю вотрой список с id сообщений что бы определить какое сообзение показывать\n for i in range(len(self.messageId)):\n # задаю переменную с конретным id\n id = self.messageId[i]\n id = id[1:-1]\n # в месседж задаеться нужное сообщение\n if id == now[11:13]:\n message = self.message[i]\n\n # сообщение задаеться в строку\n self.textBrowser.setText(message[1:-1])\n\n\nclass OpenMe:\n def __init__(self):\n # задаю промежуточную переменную\n t = []\n\n # завожу переенную с итоговым временем\n datatime = []\n\n # открываю архив с данными\n f = open(\"timing.txt\", mode=\"rb\")\n # считываю масив информации\n lines = f.readlines()\n # преобразововаю информацию из изночального вида в список\n a = [i[:len(i) - 2] for i in lines]\n a = [str(i) for i in a]\n a = [i[2:len(i) - 1] for i in a]\n a = [i[1:len(i) - 1] for i in a]\n # удаляю лишнии знаки\n # получившиеся при сохранении\n self.a = [i.split(', ') for i in a]\n # закрываю архив\n f.close()\n\n # создаю промежуточный списоки\n # список t идет для формирования списка datatime\n for i in self.a:\n # удаляю первый символ i\n i = i[1:]\n # перевожу дату в викторианкий формат\n year = i[0].split('.')\n # меняю расположение дат\n year = '-'.join([year[2], year[1], year[0]])\n # преобразововаю время в привычную нам форму записи\n i = [(f'{i[1]}:{i[2]}')]\n # собираю вместе дату и время\n t.append([year] + i)\n # бугу по списку t\n for j in t:\n # делаю промежуточный список k\n k = []\n # бегу по дате и времени\n for i in j:\n # убираю лишнии знаки\n i = i.split(\"'\")\n # если перед минутой минутой нету 0 то добавляю его\n if len(i[-2]) == 1:\n i[-2] = '0' + i[-2]\n # соеденяю получившийся результат\n i = ''.join(i)\n # собираю новый список аналогичный t\n k.append(i)\n # соеденяю получившиюсю информацию\n k = [k[0] + ' ' + k[1]]\n # формирую список со всем временем\n datatime.append(k)\n # запускаю бесконечный цикл со считываниемм времени\n while True:\n # в начале цикла считываю время\n # что бы программа знала какое сейчас время\n now = str(datetime.datetime.now())[:16]\n # принчу время что бы понимать что все работает правильно\n print(now)\n # если время находиться в списке нужных нам\n if [now] in datatime:\n # то мы вызываем звук\n # вызываем функцией что бы\n # не завершать наш бесконечный цикл\n Sound()\n # и открываем окно\n # вызываем функцией что бы\n # не завершать наш бесконечный цикл\n Open()\n # ждем 60 секунд что бы обновить время\n # такой перерыв позволяет не нагрушать компютер\n time.sleep(60)\n\n\n\ndef Open():\n # безопасно открываем окно\n # если оно не откроеться звук всеравно будет играть\n app = QApplication(sys.argv)\n ex = WindowWhitchMessage()\n ex.show()\n app.exec()\n\n\ndef Sound():\n # загружаем звук\n song = pyglet.media.load('din-iphone.mp3')\n # играем звук\n song.play()\n\n\n# бесконечный цикл служит для того\n# чтобы если программа закроеться она открывалась снова\nwhile True:\n OpenMe()","sub_path":"basic program.py","file_name":"basic program.py","file_ext":"py","file_size_in_byte":7243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"480361316","text":"from django.shortcuts import render,redirect\nimport tweepy\nfrom django.http import HttpResponseRedirect\nfrom django.contrib.auth import logout as lg\nfrom .models import Tweets\nfrom django.db.models import Count\nfrom datetime import date, timedelta\n\nimport environ\n\nenv = environ.Env(\n DEBUG=(bool, False)\n)\n\nConsumer_Key='WCfsSffpEZoENEiCcmUpWu8vr'\nConsumer_Secret='qRJ8ywu2RMO8zqnqTBxJrPkxMMo4VSLCTCs6YzhCa2fxlmR2XF'\n# Create your views here.\n\ndef api_obj(request):\n\toauth = tweepy.OAuthHandler(Consumer_Key, Consumer_Secret)\n\taccess_key = request.session['access_key_token']\n\taccess_secret = request.session['access_secret_token']\n\toauth.set_access_token(access_key, access_secret)\n\tapi = tweepy.API(oauth, wait_on_rate_limit=True)\n\treturn api\ndef base(request):\n if request.session.get('access_key_token',None) is not None :\n return redirect('Home')\n return render(request,'login.html')\n\ndef login(request):\n if request.session.get('access_key_token',None) is not None :\n return redirect('Home')\n oauth=tweepy.OAuthHandler(Consumer_Key,Consumer_Secret)\n try:\n redirect_url = oauth.get_authorization_url(True)\n except tweepy.TweepError:\n print('Error! Failed to get request token.')\n request.session['request_token']= oauth.request_token \n return HttpResponseRedirect(redirect_url)\n\ndef callback(request):\n verifier = request.GET.get('oauth_verifier')\n oauth = tweepy.OAuthHandler(Consumer_Key, Consumer_Secret)\n token = request.session.get('request_token')\n request.session.delete('request_token')\n oauth.request_token = token\n try:\n oauth.get_access_token(verifier)\n except tweepy.TweepError:\n print('Error! Failed to get access token.')\n request.session['access_key_token']=oauth.access_token\n request.session['access_secret_token']= oauth.access_token_secret\n return redirect('Home')\n\ndef loggedout(request):\n if request.session.get('access_key_token',None) is not None :\n return redirect('Home')\n return redirect('Base')\n\ndef logout(request):\n if request.session.get('access_key_token',None) is not None :\n request.session.clear()\n lg(request) \n return redirect('Loggedout')\n\ndef home(request):\n if request.session.get('access_key_token',None) is not None :\n api=api_obj(request)\n user=api.me()\n context={\n 'username':user.name\n }\n return render(request, 'index.html')\n return redirect('Login')\n\ndef all_twt(request):\n if request.session.get('access_key_token',None) is not None :\n api=api_obj(request)\n user=api.me()\n tweets=Tweets.objects.all()\n context={\n 'my_list':tweets\n }\n return render(request,'tweettimeline.html',context)\n return redirect('Login')\n\ndef top_user(request):\n if request.session.get('access_key_token',None) is not None:\n tweets=Tweets.objects.values('User').order_by().annotate(user_count=Count('User'))\n print(tweets)\n Uid=max(tweets,key=lambda x:x['user_count'])\n api=api_obj(request)\n user=api.me()\n max_user=api.get_user(Uid['User'])\n context={\n 'user':max_user\n }\n return render(request,'topuser.html',context)\n return redirect('Login')\n\ndef top_domain(request):\n if request.session.get('access_key_token',None) is not None:\n tweets=Tweets.objects.values('Domain').order_by().annotate(domain_count=Count('Domain'))\n tweets=sorted(tweets,key=lambda x:x['domain_count'],reverse=True)\n api=api_obj(request)\n user=api.me()\n domains=[]\n for i in range(0,min(3,len(tweets))):\n domains.append([tweets[i]['Domain'],tweets[i]['domain_count']])\n context={\n 'my_list':domains\n \n }\n return render(request,'topdomain.html',context)\n return redirect('Login')\n\nimport json\nclass save_data():\n def get_domain(self,str):\n for x in range(0, len(str)):\n if (str[x] == '/'):\n return str[0:x]\n\n def insert_db(self,ttweet, url):\n tweet = Tweets()\n tweet.id = str(ttweet.id) + str(ttweet.user.id)\n tweet.Name = ttweet.user.name\n tweet.User = ttweet.user.id\n tweet.Tweet_id = ttweet.id\n tweet.Text = ttweet.text\n tweet.Domain = self.get_domain(url[0]['display_url'])\n tweet.Display_picture = ttweet.user.profile_image_url\n tweet.save()\n\ndef link_twt(request):\n if request.session.get('access_key_token',None) is not None:\n api = api_obj(request)\n user = api.me()\n delta = date.today() - timedelta(days=7)\n my_list = []\n for tweet in tweepy.Cursor(api.home_timeline, since_id=delta).items(100):\n url = tweet.entities['urls']\n if len(url) != 0:\n if url[0]['display_url'][0:7] != 'twitter':\n my_list.append(tweet)\n try:\n save_data().insert_db(tweet, url)\n except:\n continue\n \n context={\n 'my_list': my_list\n \n }\n return render(request, 'fetchtweet.html',context)\n return redirect('Login') ","sub_path":"app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":5278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"464239806","text":"import ujson\n\nfrom system.canIf import canIf\nfrom system.hdlc import hdlc\n\n\nclass ifWrapper(object):\n def __init__(self,bitrate,filter,callback):\n self._commIf = canIf()\n self._commIf.bitrate(bitrate)\n self._commIf.filter(filter)\n self._hdlc = hdlc(self._commIf)\n self._callback = callback\n print('start can if')\n\n def run(self):\n while True:\n # (state, data) = self._commIf.rxString()\n (state, data) = self._hdlc.txFrame()\n if 'COMPLET' in state:\n self._callback(ujson.loads(data))\n #\t\t\t\tmsg = ujson.loads(data)\n # object = msg.get('OBJECT')\n # method = msg.get('METHOD')\n # value = msg.get('VALUE')\n # print('DATA', state, data)\n\n yield\n\n def sink(self, msg):\n # self._commIf.txString(ujson.dumps(msg))\n self._hdlc.rxFrame(ujson.dumps(msg))","sub_path":"WIFIatHome/system/ifWrapper.py","file_name":"ifWrapper.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"409111633","text":"from vpython import *\r\nimport numpy as np\r\n\r\nr = 2\r\nomega = 5\r\nt = np.linspace(0,10,1001)\r\ndt = t[1]\r\nv_z = 0.5\r\n\r\n\r\ngaviota = sphere(pos=vector(r,0,0), radius=r/10, color=color.cyan)\r\nx_axis = arrow(pos=vector(0,0,0), axis=vector(1,0,0), color=color.red)\r\ny_axis = arrow(pos=vector(0,0,0), axis=vector(0,1,0), color=color.blue)\r\nz_axis = arrow(pos=vector(0,0,0), axis=vector(0,0,1), color=color.green)\r\n\r\nrarr = arrow(pos=gaviota.pos, axis=vector(1,0,0), color=color.magenta)\r\ntarr = arrow(pos=gaviota.pos, axis=vector(0,1,0), color=color.white)\r\n\r\nscene.autoscale = False\r\n\r\nwhile True:\r\n\tfor this_t in t:\r\n\t\trate(60)\r\n\t\ttheta = omega*this_t\r\n\t\trarr.axis = vector(np.cos(theta),np.sin(theta),0)\r\n\t\ttarr.axis = vector(-np.sin(theta), np.cos(theta), 0)\r\n\t\trarr.pos = tarr.pos = gaviota.pos = vector(r*np.cos(theta), r*np.sin(theta),\r\n\t\t\tv_z*this_t)\r\n\tres = input('Press any key to reset')\r\n\r\n","sub_path":"mcu.py","file_name":"mcu.py","file_ext":"py","file_size_in_byte":892,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"550224477","text":"from django.conf.urls import url\nfrom django.urls import path, include\nfrom . import views\n\n\napp_name = 'graduationManagement'\nurlpatterns = [\n path('', views.welcome, name=\"welcome\"),\n #首页\n path('graduation_home_page', views.graduation_home_page, name=\"graduation_home_page\"),\n path('student_delete',views.student_delete,name=\"student_delete\"),\n path('student_choosing_action',views.student_choosing_action,name=\"student_choosing_action\"),\n #学生查看选题列表并选题\n path('student_choose_project',views.student_choose_project,name=\"student_choose_project\"),\n #学生提交资料\n path('student_submit_project',views.student_submit_project,name=\"student_submit_project\"),\n #学生查看资料\n path('student_view_project',views.student_submit_project,name=\"student_submit_project\"),\n #学生查看成绩\n path('student_view_score',views.student_view_score,name=\"student_view_score\"),\n #学生查看选题详情\n path('student_view_project_detail/',views.student_view_project_detail,name=\"student_view_project_detail\"),\n #学生查看审核结果\n path('student_view_project_ischoosen',views.student_view_project_ischoosen,name=\"student_view_project_ischoosen\"),\n #学生查看审核结果详情\n path('student_view_project_ischoosendetail',views.student_view_project_ischoosendetail,name=\"student_view_project_ischoosendetail\"), \n \n #教师查看选题：教师编辑选题完毕点击“发布”后触发或点击左侧功能栏中“毕业设计”下的“点击查看选题”触发\n path('teacher_submit_project',views.teacher_submit_project,name=\"teacher_submit_project\"), \n #教师编辑选题界面：点击“发布新选题”触发\n path('teacher_edit_project',views.teacher_edit_project,name=\"teacher_edit_project\"),\n #教师查看题目详情：点击查看选题界面中的某一个选题触发\n path('teacher_view_project_detail',views.teacher_view_project_detail,name=\"teacher_view_project_detail\"), \n #教师修改选题界面：在查看题目详情界面点击“修改”按钮触发，修改后点击“确定修改”按钮后跳转到查看题目详情界面\n path('teacher_change_project',views.teacher_change_project,name=\"teacher_change_project\"), \n #教师删除选题功能：在查看题目详情界面点击“删除”按钮触发，删除后跳转到教师查看选题界面\n path('teacher_delete_project',views.teacher_delete_project,name=\"teacher_delete_project\"), \n\n #教师审核学生\n path('teacher_choose_student',views.teacher_choose_student,name=\"teacher_choose_student\"),\n #教师查看资料\n path('teacher_view_projectfiles',views.teacher_view_projectfiles,name=\"teacher_view_projectfiles\"),\n #教师查看资料详情\n path('teacher_view_projectfiles_detail',views.teacher_view_projectfiles_detail,name=\"teacher_view_projectfiles_detail\"),\n #教师提交成绩\n path('teacher_submit_score',views.teacher_submit_score,name=\"teacher_submit_score\"), \n #教师提交成绩\n path('teacher_submit_score_detail',views.teacher_submit_score_detail,name=\"teacher_submit_score_detail\"), \n path('teacher_ref_student',views.teacher_ref_student,name=\"teacher_ref_student\"),\n\n path('teacher_acc_student',views.teacher_acc_student,name=\"teacher_acc_student\"),\n # 管理员查看选题情况\n path('adm_projectSelection', views.adm_projectSelection, name=\"adm_projectSelection\"),\n\n]\n","sub_path":"EMS/graduationManagement/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":4189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"556220197","text":"import math\nfrom statistics import mean\n\n\nimport numpy as np\nfrom PIL import Image\nimport matplotlib.pyplot as plt\n\n\ndef gaussian(img):\n h, w = img.shape\n GaussianKernel = np.array([[1/16, 1/8, 1/16], [1/8, 1/4, 1/8], [1/16, 1/8, 1/16]])\n newImg = np.zeros((h,w))\n for i in range(1, h - 1):\n for j in range(1, w - 1):\n gaussianGrad = (GaussianKernel[0, 0] * img[i - 1, j - 1]) + \\\n (GaussianKernel[0, 1] * img[i - 1, j]) + \\\n (GaussianKernel[0, 2] * img[i - 1, j + 1]) + \\\n (GaussianKernel[1, 0] * img[i, j - 1]) + \\\n (GaussianKernel[1, 1] * img[i, j]) + \\\n (GaussianKernel[1, 2] * img[i, j + 1]) + \\\n (GaussianKernel[2, 0] * img[i + 1, j - 1]) + \\\n (GaussianKernel[2, 1] * img[i + 1, j]) + \\\n (GaussianKernel[2, 2] * img[i + 1, j + 1])\n newImg[i - 1, j - 1] = abs(gaussianGrad)\n return newImg\n\ndef grayscale(image):\n return (0.3 * image[:, :, 0] + 0.59 * image[:, :, 1] + 0.11 * image[:, :, 2]).astype(np.uint8)\n\ndef gem_kleur_van_pixels(picture):\n gem_kleur_van_pixel = []\n\n image = np.array(Image.open(picture)).astype(np.uint8)\n\n for eachRow in image:\n for eachPix in eachRow:\n avgColor = mean(eachPix[:3]) # eerste 3 getallen vd array die de kleur geven\n gem_kleur_van_pixel.append(avgColor)\n\n return gem_kleur_van_pixel\n\ndef iar_reconverted(iar_boolToNum,height,width):\n\n iar_reconverted = []\n index = 0\n for i in range(height): #RANGES HANGEN AF VAN DE GROOTTE VAN DE IMAGE\n iar_reconverted.append([])\n for k in range(width):\n j = iar_boolToNum[index]\n iar_reconverted[i].append([j, j, j])\n index += 1\n return iar_reconverted\n\n\n\n\ndef hyst(x, th_lo, th_hi, initial = False):\n \"\"\"\n x : Numpy Array\n Series to apply hysteresis to.\n th_lo : float or int\n Below this threshold the value of hyst will be False (0).\n th_hi : float or int\n Above this threshold the value of hyst will be True (1).\n \"\"\"\n\n if th_lo > th_hi: # If thresholds are reversed, x must be reversed as well\n x = x[::-1]\n th_lo, th_hi = th_hi, th_lo\n rev = True\n else:\n rev = False\n\n hi = x >= th_hi\n lo_or_hi = (x <= th_lo) | hi\n\n ind = np.nonzero(lo_or_hi)[0] # Index für alle darunter oder darüber\n if not ind.size: # prevent index error if ind is empty\n x_hyst = np.zeros_like(x, dtype=bool) | initial\n else:\n cnt = np.cumsum(lo_or_hi) # from 0 to len(x)\n x_hyst = np.where(cnt, hi[ind[cnt-1]], initial)\n\n if rev:\n x_hyst = x_hyst[::-1]\n\n return x_hyst\n","sub_path":"code/Testing/hulpfunctie_sobel.py","file_name":"hulpfunctie_sobel.py","file_ext":"py","file_size_in_byte":2830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"533334945","text":"\n# coding: utf-8\n\n# In[70]:\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nbrownExt = np.zeros([10,10,4])\nbrownUni = np.zeros([10,10,4])\nbrownMed = np.zeros([10,10,4])\nmacMorExt = np.zeros([10,10,4])\nmacMorUni = np.zeros([10,10,4])\n \nbrownMeans = np.zeros([10,12]) # TokenAcc (Ext, Med, Uni), SentAcc 3x, Unknown 3x, Known 3x\nmacMorMeans = np.zeros([10,8]) # TokenAcc (Ext, Uni), SentAcc 2x, Unknown 2x, Known 2x\n\nfor k in range(10):\n brownExt[k,:,:] = np.loadtxt('RawResults/Brown_extensive_'+str((k+1)/10)+'.txt')\n brownUni[k,:,:] = np.loadtxt('RawResults/Brown_universal_'+str((k+1)/10)+'.txt')\n brownMed[k,:,:] = np.loadtxt('RawResults/Brown_medium_'+str((k+1)/10)+'.txt')\n \n macMorExt[k,:,:] = np.loadtxt('RawResults/macMorpho_extensive_'+str((k+1)/10)+'.txt')\n macMorUni[k,:,:] = np.loadtxt('RawResults/macMorpho_universal_'+str((k+1)/10)+'.txt')\n\n brownMeans[k,0] = np.mean(brownExt[k,:,:],0)[0]\n brownMeans[k,1] = np.mean(brownMed[k,:,:],0)[0] \n brownMeans[k,2] = np.mean(brownUni[k,:,:],0)[0] \n brownMeans[k,3] = np.mean(brownExt[k,:,:],0)[1] \n brownMeans[k,4] = np.mean(brownMed[k,:,:],0)[1] \n brownMeans[k,5] = np.mean(brownUni[k,:,:],0)[1] \n brownMeans[k,6] = np.mean(brownExt[k,:,:],0)[2] \n brownMeans[k,7] = np.mean(brownMed[k,:,:],0)[2] \n brownMeans[k,8] = np.mean(brownUni[k,:,:],0)[2] \n brownMeans[k,9] = np.mean(brownExt[k,:,:],0)[3] \n brownMeans[k,10] = np.mean(brownMed[k,:,:],0)[3] \n brownMeans[k,11] = np.mean(brownUni[k,:,:],0)[3] \n \n macMorMeans[k,0] = np.mean(macMorExt[k,:,:],0)[0]\n macMorMeans[k,1] = np.mean(macMorUni[k,:,:],0)[0]\n macMorMeans[k,2] = np.mean(macMorExt[k,:,:],0)[1]\n macMorMeans[k,3] = np.mean(macMorUni[k,:,:],0)[1]\n macMorMeans[k,4] = np.mean(macMorExt[k,:,:],0)[2]\n macMorMeans[k,5] = np.mean(macMorUni[k,:,:],0)[2]\n macMorMeans[k,6] = np.mean(macMorExt[k,:,:],0)[3]\n macMorMeans[k,7] = np.mean(macMorUni[k,:,:],0)[3]\n\nprint('Brown Ext token std', np.std(brownExt[-1,:,0]))\nprint('Brown Md token std', np.std(brownMed[-1,:,0]))\nprint('Brown Uni token std', np.std(brownUni[-1,:,0]))\n\nprint('Brown Ext sent std', np.std(brownExt[-1,:,1]))\nprint('Brown Md sent std', np.std(brownMed[-1,:,1]))\nprint('Brown Uni sent std', np.std(brownUni[-1,:,1]))\n \nprint('MM Ori token std', np.std(macMorExt[-1,:,0]))\nprint('MM Uni token std', np.std(macMorUni[-1,:,0]))\n\nprint('MM Ori sent std', np.std(macMorExt[-1,:,1]))\nprint('MM Uni sent std', np.std(macMorUni[-1,:,1]))\n\n\nprint('Brown Token Acc', brownMeans[:,0:3])\nprint('Brown Sent Acc', brownMeans[:,3:6])\nprint('MacM Token Acc', macMorMeans[:,0:2])\nprint('MacM Sent Acc', macMorMeans[:,2:4])\n\nprint('Brown Known Acc', brownMeans[:,6:9])\nprint('Brown UnKnown Acc', brownMeans[:,9:12])\nprint('MacM Known Acc', macMorMeans[:,4:6])\nprint('MacM UnKnown Acc', macMorMeans[:,6:8])\n\n\n # In[90]:\nfig = plt.figure()\n\nplt.subplot(2,4,1)\nplt.plot(brownMeans[:,0],'r')\nplt.plot(brownMeans[:,1],'b')\nplt.plot(brownMeans[:,2],'g')\nplt.ylim(0.86, 0.96)\nplt.ylabel('Accuracy',fontsize=14)\nplt.title('Brown: Token Accuracy',fontsize=14)\nplt.xticks(np.arange(10),['',20,'',40,'',60,'',80,'',100])\nplt.legend(['Extensive (472 tags)','Medium (222 tags)','Universal (12 tags)'], loc=4, prop={'size': 9})\nfig.subplots_adjust(hspace=.3)\nfig.subplots_adjust(wspace=.4)\n\n\n# In[89]:\nfig = plt.subplot(2,4,2)\nplt.plot(brownMeans[:,3],'r')\nplt.plot(brownMeans[:,4],'b')\nplt.plot(brownMeans[:,5],'g')\nplt.ylim(0.16, 0.5)\nplt.title('Brown: Sentence Accuracy',fontsize=14)\nplt.xticks(np.arange(10),['',20,'',40,'',60,'',80,'',100])\n\n\n# In[73]:\nfig = plt.subplot(2,4,3)\nplt.plot(brownMeans[:,6],'r')\nplt.plot(brownMeans[:,7],'b')\nplt.plot(brownMeans[:,8],'g')\nplt.ylim(0.91, 0.97)\nplt.title('Brown: known Words',fontsize=14)\nplt.xticks(np.arange(10),['',20,'',40,'',60,'',80,'',100])\n\nfig = plt.subplot(2,4,4)\nplt.plot(brownMeans[:,9],'r')\nplt.plot(brownMeans[:,10],'b')\nplt.plot(brownMeans[:,11],'g')\nplt.ylim(0.23, 0.46)\nplt.title('Brown: unknown Words',fontsize=14)\nplt.xticks(np.arange(10),['',20,'',40,'',60,'',80,'',100])\nplt.savefig('Brown_UnknownWords.png')\n\n# In[75]:\nfig = plt.subplot(2,4,5)\nplt.plot(macMorMeans[:,0],'b')\nplt.plot(macMorMeans[:,1],'g')\nplt.ylim(0.86, 0.96)\nplt.xlabel('% of test data',fontsize=14)\nplt.ylabel('Accuracy',fontsize=14)\nplt.title('MacMorpho: Token Acc.',fontsize=14)\nplt.legend(['Original (82 tags)','Universal (12 tags)'], loc=0, prop={'size': 9})\nplt.xticks(np.arange(10),['',20,'',40,'',60,'',80,'',100])\n\n\n# In[89]:\nfig = plt.subplot(2,4,6)\nplt.plot(macMorMeans[:,2],'b')\nplt.plot(macMorMeans[:,3],'g')\nplt.ylim(0.16, 0.5)\nplt.xlabel('% of test data',fontsize=14)\nplt.title('MacMorpho: Sentence Acc.',fontsize=14)\nplt.xticks(np.arange(10),['',20,'',40,'',60,'',80,'',100])\n\n\nfig = plt.subplot(2,4,7)\nplt.plot(macMorMeans[:,4],'b')\nplt.plot(macMorMeans[:,5],'g')\nplt.ylim(0.91, 0.97)\nplt.xlabel('% of test data',fontsize=14)\nplt.title('MacMorpho: Known Words',fontsize=14)\nplt.xticks(np.arange(10),['',20,'',40,'',60,'',80,'',100])\n\nfig = plt.subplot(2,4,8)\nplt.plot(macMorMeans[:,6],'b')\nplt.plot(macMorMeans[:,7],'g')\nplt.ylim(0.23, 0.46)\nplt.xlabel('% of test data',fontsize=14)\nplt.title('MacMorpho: Unknown Words',fontsize=14)\nplt.xticks(np.arange(10),['',20,'',40,'',60,'',80,'',100])\n\n\nplt.show()\n","sub_path":"Results.py","file_name":"Results.py","file_ext":"py","file_size_in_byte":5322,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"178734316","text":"'''\n\nEntrar com a sigla do estado de uma pessoa e imprimir uma das mensagens\n\n+ Carioca\n+ Paulita\n+ Mineiro\n+ Outros Estados\n\n'''\n\nsigla = str(input(\"Digite a sigla do estado: \"))\n\nif sigla == \"RJ\" or sigla == 'rj' or sigla == 'Rj' or sigla == 'rJ':\n print(\"Você informou |\",sigla,\"|o estado é Rio de Janeiro, Gentilico |Carioca|\")\nelse:\n if sigla == \"SP\" or sigla == 'sp' or sigla == 'Sp' or sigla == 'sP':\n print(\"Você informou |\", sigla, \"|o estado é são Paulo, Gentilico |Paulista|\")\n else:\n if sigla == \"MG\" or sigla == 'mg' or sigla == 'Mg' or sigla == 'mG':\n print(\"Você informou |\", sigla, \"|o estado é Minas Gerais, Gentilico |Mineiro|\")\n else:\n print(\"Outros estados\")","sub_path":"Prog_105_pag_84.py","file_name":"Prog_105_pag_84.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"6307907","text":"#!/usr/bin/env python3\nfrom flask import Flask\nfrom flask import request\nimport pandas as pd\nimport numpy as np\nimport pickle\nfrom flask_cors import CORS\n\n\napp = Flask(__name__)\nCORS(app)\n\ndef enter_here(mileage, year, make_model, years, original_price, Type):\n\tpl = pickle.load(open( \"pl\", \"rb\" ))\n\tdf_ex = pd.DataFrame({'Mileage':float(mileage), 'Year':float(year),'Make_Model':make_model, 'Years': float(years),'Original Price':float(original_price), 'Type':Type},index=[0])\n\treturn 'After %d years and %d mileage, your %s will be worth $%d' % (years, mileage, make_model, np.clip(pl.predict(df_ex).tolist()[0], 0, 1000000))\n\n@app.route(\"/predict\", methods=['GET', 'POST'])\ndef hello():\n\tmileage = request.args.get('mileage')\n\tyear = request.args.get('year')\n\tmake_model = request.args.get('make_model').replace('%20', ' ')\n\tyears = request.args.get('years')\n\toriginal_price = request.args.get('original_price')\n\tType = request.args.get('Type').replace('%20', ' ')\n\t# print(mileage, year, make_model, years, original_price, Type)\n\tresult = enter_here(int(mileage), int(year), make_model, int(years), int(original_price), Type)\n\treturn result\n\n@app.route(\"/main\", methods=['GET'])\ndef main_page():\n\treturn render_template('index.html')\n","sub_path":"predict_server/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":1238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"361974773","text":"import numpy as np\nimport pylab as pl\n\nfilename = 'headtail_for_test/test_protons/SPS_Q20_proton_check_prb.dat'\n\n\nn_part_per_turn = 5000\n\nappo = np.loadtxt(filename)\n\nparid = np.reshape(appo[:,0], (-1, n_part_per_turn))\nx = np.reshape(appo[:,1], (-1, n_part_per_turn))\nxp = np.reshape(appo[:,2], (-1, n_part_per_turn))\ny = np.reshape(appo[:,3], (-1, n_part_per_turn))\nyp =np.reshape(appo[:,4], (-1, n_part_per_turn))\nz = np.reshape(appo[:,5], (-1, n_part_per_turn))\nzp = np.reshape(appo[:,6], (-1, n_part_per_turn))\n\n\npl.close('all')\npl.figure(1)\npl.plot(xp[0,:], '.-')\npl.plot(xp[1,:], 'r.-')\n\n\n\ndxp = xp[1,:]-xp[0,:]\ndyp = yp[1,:]-yp[0,:]\n\npl.figure(2)\nsp1 = pl.subplot(2,1,1)\npl.plot(dxp, '.-')\nsp2 = pl.subplot(2,1,2)\npl.plot(dyp, '.-')\n\n\npl.show()\n\n\nimport ecloud.PyECLOUD_for_PyHEADTAIL as pyecl\nfrom particles.particles import *\nfrom scipy.constants import e, m_e\nimport numpy as np\nfrom particles.slicer import *\n\n\nC = 6911.\nR = C / (2 * np.pi)\ngamma_tr = 18.\ngamma = 27.7\neta = 1 / gamma_tr ** 2 - 1 / gamma ** 2\nQx = 20.13\nQy = 20.18\nQs = 0.017\nbeta_x = 54.6\nbeta_y = 54.6\nbeta_z = np.abs(eta) * R / Qs\nepsn_x = 2.5\nepsn_y = 2.5\nepsn_z = 0.5*(0.2/0.23)**2\n\nn_turns = 1\nbeamslicer = Slicer(64, nsigmaz=3)\n\nL_ecloud = C\n\n# Beam\nbunch = Particles.as_gaussian(100000, e, gamma, 1.15e11, m_p, 0, beta_x, epsn_x, 0, beta_y, epsn_y, beta_z, epsn_z)\nbunch.x[:n_part_per_turn] = x[0,:]\nbunch.xp[:n_part_per_turn] = xp[0,:]\nbunch.y[:n_part_per_turn] = y[0,:]\nbunch.yp[:n_part_per_turn] = yp[0,:]\nbunch.z[:n_part_per_turn] = z[0,:]\nbunch.dp[:n_part_per_turn] =zp[0,:]\n\necloud = pyecl.Ecloud(L_ecloud, beamslicer, Dt_ref = 25e-12, pyecl_input_folder='drift_for_benchmark')\necloud.save_ele_distributions_last_track = True \necloud.save_ele_potential_and_field = True\necloud.save_ele_MP_size = True\necloud.save_ele_MP_position = True\necloud.save_ele_MP_velocity = True\n\nid_before = bunch.id[bunch.id<=n_part_per_turn]\nxp_before = bunch.xp[bunch.id<=n_part_per_turn]\nyp_before = bunch.yp[bunch.id<=n_part_per_turn]\n\necloud.track(bunch)\n\nid_after = bunch.id[bunch.id<=n_part_per_turn]\nxp_after = bunch.xp[bunch.id<=n_part_per_turn]\nz_after = bunch.z[bunch.id<=n_part_per_turn]\nyp_after = bunch.yp[bunch.id<=n_part_per_turn]\n\n\nindsort = np.argsort(id_after)\nid_after = np.take(id_after, indsort)\nxp_after = np.take(xp_after, indsort)\nyp_after = np.take(yp_after, indsort)\nz_after = np.take(z_after, indsort)\n\npl.figure(100)\npl.plot(id_before-1, xp_before, 'r')\npl.plot(xp[0, :], 'ob', mfc=None)\n\n\npl.figure(101)\npl.plot(id_after-1, xp_after, '.r')\npl.plot(xp[1, :], 'ob', mfc=None)\n\n\npl.figure(103)\npl.plot(id_before-1, xp_after-xp_before, '.r')\npl.plot(xp[1, :]-xp[0, :], 'ob', mfc=None)\n\n\npl.figure(104)\nsp1 = pl.subplot(2,1,1)\npl.plot(id_before-1, 100*np.abs((xp_after-xp_before)-(xp[1, :]-xp[0, :]))/np.abs((xp[1, :]-xp[0, :])), '.r')\npl.subplot(2,1,2, sharex=sp1)\npl.plot(id_before-1, np.abs((xp[1, :]-xp[0, :])), '.r')\n\nmask_no_kick = np.abs((xp[1, :]-xp[0, :]))>0.5*np.max(np.abs((xp[1, :]-xp[0, :])))\n\npl.figure(105)\nsp1 = pl.subplot(2,1,1)\npl.plot( (100*np.abs((xp_after-xp_before)-(xp[1, :]-xp[0, :]))/np.abs((xp[1, :]-xp[0, :])))[mask_no_kick], '.r')\npl.subplot(2,1,2, sharex=sp1)\npl.plot( (np.abs((xp[1, :]-xp[0, :])))[mask_no_kick], '.r')\npl.ylim(0, None)\n\n\npl.figure(106)\nsp1 = pl.subplot(2,1,1)\npl.plot(z_after, (100*np.abs((xp_after-xp_before)-(xp[1, :]-xp[0, :]))/np.abs((xp[1, :]-xp[0, :]))), '.r')\npl.grid('on')\npl.subplot(2,1,2, sharex=sp1)\npl.plot(z_after, (xp[1, :]-xp[0, :]), '.r')\npl.plot(z_after, (xp_after-xp_before), '.b')\npl.grid('on')\n\npl.suptitle('%.2f'%(100*pl.norm((xp_after-xp_before)-(xp[1, :]-xp[0, :]))/pl.norm((xp[1, :]-xp[0, :]))))\n\n\npl.figure(116)\nsp1 = pl.subplot(2,1,1)\npl.plot(z_after, (100*np.abs((yp_after-yp_before)-(yp[1, :]-yp[0, :]))/np.abs((yp[1, :]-yp[0, :]))), '.r')\npl.grid('on')\npl.subplot(2,1,2, sharex=sp1)\npl.plot(z_after, (yp[1, :]-yp[0, :]), '.r')\npl.plot(z_after, (yp_after-yp_before), '.b')\npl.grid('on')\npl.suptitle('%.2f'%(100*pl.norm((yp_after-yp_before)-(yp[1, :]-yp[0, :]))/pl.norm((yp[1, :]-yp[0, :]))))\n\n\npl.show()\n\n\n\n\n# Try to plot the pinch\naaa = np.loadtxt(filename.split('_prb.dat')[0]+'_elec.dat');\nhdtl_1st_turn = np.loadtxt(filename.split('_prb.dat')[0]+'_hdtl.dat');\n\nz_hdtl = hdtl_1st_turn[:,0]\npp_bin_hdtl = hdtl_1st_turn[:,1]\n\n#np.ascontiguousarray(\n \nsl_id = aaa[:,0]\nx_e = aaa[:,1]\ny_e = aaa[:,3]\n\n\nx_hdtl = []\ny_hdtl = []\nfor ii in xrange(int(np.max(sl_id)), -1, -1):\n mask_select = np.abs(sl_id-ii)<.1\n x_hdtl.append(x_e[mask_select])\n y_hdtl.append(y_e[mask_select])\n\nz_hdtl = z_hdtl[:len(x_hdtl)]\npp_bin_hdtl = pp_bin_hdtl[:len(x_hdtl)]\nfrom itertools import izip\nimport mystyle as ms\n\n\n\ndef plot_trajectory( x_obs = 0., y_obs = -.005):\n\t \n\n\n\ti_obs_hdtl = np.argmin((x_hdtl[0]-x_obs)**2+(y_hdtl[0]-y_obs)**2)\n\ti_obs = np.argmin((ecloud.x_MP_last_track[-1]-x_hdtl[0][i_obs_hdtl])**2+(ecloud.y_MP_last_track[-1]-y_hdtl[0][i_obs_hdtl])**2)\n\n\t \n\t \n\t#x_arr = np.array(ecloud.x_MP_last_track) \n\t#y_arr = np.array(ecloud.y_MP_last_track)\n\t \n\t#x_arr_hdtl = np.array(x_hdtl) \n\t#y_arr_hdtl = np.array(y_hdtl)\n\t \n\t#vx_arr = np.array(ecloud.vx_MP_last_track) \n\t#vy_arr = np.array(ecloud.vy_MP_last_track)\n\tx_obs = map(lambda v:v[i_obs], ecloud.x_MP_last_track)\n\ty_obs = map(lambda v:v[i_obs], ecloud.y_MP_last_track)\n\n\tx_obs_hdtl = map(lambda v:v[i_obs_hdtl], x_hdtl)\n\ty_obs_hdtl = map(lambda v:v[i_obs_hdtl], y_hdtl)\n\n\t \n\t \n\tpl.figure(2000)\n\tpl.clf()\n\tpl.subplot(2,1,1)\n\tpl.plot(-ecloud.slicer.z_centers/3e8, x_obs, '.-')\n\tpl.plot(-z_hdtl/3e8, x_obs_hdtl, 'o--')\n\tpl.plot(-ecloud.slicer.z_centers/3e8, y_obs, '.-r')\n\tpl.plot(-z_hdtl/3e8, y_obs_hdtl, 'o--r')\n\t \n\t \n\tpl.subplot(2,1,2)\n\tpl.plot(ecloud.slicer.z_centers[::-1]/3e8, ecloud.slicer.n_particles/\\\n\t\t\t\t\t(np.abs(ecloud.slicer.z_centers[1]-ecloud.slicer.z_centers[0])), '.-')\n\tpl.plot(-z_hdtl/3e8, pp_bin_hdtl/np.abs(z_hdtl[1]-z_hdtl[0]), 'r.-')\n\t#pl.plot(ecloud.slicer.z_centers/3e8, vx_arr[:, i_obs], '.-')\n\t#pl.plot(ecloud.slicer.z_centers/3e8, vy_arr[:, i_obs], '.-r')\n\t \n\tpl.show()\n\nplot_trajectory(x_obs = 0., y_obs = -.005 ) \n\n\n\ndec_fact = 50\nimport time\npl.figure(3000, figsize=(12, 12))\nfor ii in xrange(ecloud.slicer.n_slices-1, -1, -4):\n pl.clf()\n pl.subplot(2,2,1)\n pl.imshow(np.log10(-ecloud.rho_ele_last_track[ii]/e).T, origin='lower', aspect='auto', vmin=1, vmax=15,\n extent=(ecloud.spacech_ele.xg[0], ecloud.spacech_ele.xg[-1], ecloud.spacech_ele.yg[0], ecloud.spacech_ele.yg[-1]))\n #~ pl.colorbar()\n pl.axis('equal')\n \n# pl.subplot(2,2,2)\n# pl.imshow(10. * plt.log10( ecloud.phi_ele_last_track[ii]), origin='lower', aspect='auto',\n# extent=(ecloud.poisson.x[0,0], ecloud.poisson.x[0,-1], ecloud.poisson.y[0,0], ecloud.poisson.y[-1,0]))\n\n pl.subplot(2,2,2)\n pl.plot(ecloud.x_MP_last_track[ii][::dec_fact], ecloud.y_MP_last_track[ii][::dec_fact], '.')\n pl.axis('equal')\n \n pl.subplot(2,2,3)\n pl.imshow(ecloud.Ex_ele_last_track[ii].T, origin='lower', aspect='auto',\n extent=(ecloud.spacech_ele.xg[0], ecloud.spacech_ele.xg[-1], ecloud.spacech_ele.yg[0], ecloud.spacech_ele.yg[-1]))\n pl.axis('equal')\n #~ pl.colorbar()\n \n pl.subplot(2,2,4)\n pl.imshow(ecloud.Ey_ele_last_track[ii].T, origin='lower', aspect='auto',\n extent=(ecloud.spacech_ele.xg[0], ecloud.spacech_ele.xg[-1], ecloud.spacech_ele.yg[0], ecloud.spacech_ele.yg[-1]))\n #~ pl.colorbar()\n ms.sciy()\n pl.axis('equal')\n pl.draw()\n pl.ion()\n time.sleep(.1)\n\n","sub_path":"test_pyecloud/000_headtail_kick.py","file_name":"000_headtail_kick.py","file_ext":"py","file_size_in_byte":7514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"442030735","text":"# -*- coding: utf-8 -*-\n#from bson import ObjectId\nimport models\nimport common\nimport qmongo\nimport datetime\n#import TM_SetupProcessApproveLevel\n\ndef get_list_with_process_id(args):\n try:\n if args['data'] != None and args['data'].has_key('process_id'):\n items = qmongo.models.TM_SetupProcess.aggregate\n items.left_join(qmongo.models.auth_user_info, \"created_by\", \"username\", \"uc\")\n items.left_join(qmongo.models.auth_user_info, \"modified_by\", \"username\", \"um\")\n items.project(\n process_id=1,\n process_name=1,\n is_not_apply_process=1,\n max_approve_level=1,\n is_approve_by_dept=1,\n is_require_reason=1,\n is_require_when_approve=1,\n is_require_when_reject=1,\n is_show_list_approver=1,\n is_reselect_approver=1,\n is_require_attach_file=1,\n file_size_limit=1,\n exclude_file_types=1,\n is_email_cancel=1,\n is_email_delete=1,\n is_email_instead=1,\n email_send_code=1,\n email_send_to=1,\n email_send_cc=1,\n email_send_more=1,\n email_cancel_code=1,\n email_cancel_to=1,\n email_cancel_cc=1,\n email_cancel_more=1,\n email_delete_code=1,\n email_delete_to=1,\n email_delete_cc=1,\n email_delete_more=1,\n email_instead_code=1,\n email_instead_to=1,\n email_instead_cc=1,\n email_instead_more=1,\n is_send_email_pronto=1,\n is_send_email_once=1,\n send_email_once_from_hour=1,\n send_email_once_to_hour=1,\n created_by=\"uc.login_account\",\n created_on=1,\n modified_on=\"switch(case(modified_on!='',modified_on),'')\",\n modified_by=\"switch(case(modified_by!='',um.login_account),'')\",\n ).match(\"process_id == {0}\", args['data']['process_id']) \n return items.get_item()\n raise(Exception(\"not found process_id\"))\n except Exception as ex:\n raise(ex)\n\ndef get_list_with_searchtext(args):\n searchText = args['data'].get('search', '')\n pageSize = args['data'].get('pageSize', 0)\n pageIndex = args['data'].get('pageIndex', 20)\n sort = args['data'].get('sort', 20)\n\n pageIndex = (lambda pIndex: pIndex if pIndex != None else 0)(pageIndex)\n pageSize = (lambda pSize: pSize if pSize != None else 20)(pageSize)\n\n items = qmongo.models.TM_SetupProcess.aggregate\n items.left_join(qmongo.models.auth_user_info, \"created_by\", \"username\", \"uc\")\n items.left_join(qmongo.models.auth_user_info, \"modified_by\", \"username\", \"um\")\n items.project(\n process_id=1,\n process_name=1,\n is_not_apply_process=1,\n max_approve_level=1,\n is_approve_by_dept=1,\n is_require_reason=1,\n is_require_when_approve=1,\n is_require_when_reject=1,\n is_show_list_approver=1,\n is_reselect_approver=1,\n is_require_attach_file=1,\n file_size_limit=1,\n exclude_file_types=1,\n is_email_cancel=1,\n is_email_delete=1,\n is_email_instead=1,\n email_send_code=1,\n email_send_to=1,\n email_send_cc=1,\n email_send_more=1,\n email_cancel_code=1,\n email_cancel_to=1,\n email_cancel_cc=1,\n email_cancel_more=1,\n email_delete_code=1,\n email_delete_to=1,\n email_delete_cc=1,\n email_delete_more=1,\n email_instead_code=1,\n email_instead_to=1,\n email_instead_cc=1,\n email_instead_more=1,\n is_send_email_pronto=1,\n is_send_email_once=1,\n send_email_once_from_hour=1,\n send_email_once_to_hour=1,\n created_by=\"uc.login_account\",\n created_on=\"created_on\",\n modified_on=\"switch(case(modified_on!='',modified_on),'')\",\n modified_by=\"switch(case(modified_by!='',um.login_account),'')\"\n )\n\n if(searchText != None) :\n items.match(\"contains(process_name, @name)\", name=searchText)\n\n if(sort != None):\n items.sort(sort)\n \n return items.get_page(pageIndex, pageSize)\n\ndef insert(args):\n if args['data'] != None:\n # Quy trình duyệt cuối kỳ phát sinh data cho UI Tab cấp duyệt/Cách tính điểm đánh giá\n # process_id = \"2\"\n if args['data']['process_id'] == \"2\":\n args['data']['score_by_coeff'] = [\n {\n \"approve_level\": 0,\n \"coeff\": 1\n }\n ]\n for x in range(args['data']['max_approve_level']):\n args['data']['score_by_coeff'].append({\n \"approve_level\":range(args['data']['max_approve_level']).index(x) + 1,\n \"coeff\": 1\n })\n ret = qmongo.models.TM_SetupProcess.insert(args['data'])\n return ret\n return None\n\ndef update(args):\n process_id = \"\"\n current_process = None\n if args['data'] != None:\n if args['data']['process_id'] == None:\n return None\n else:\n if(args['data'].has_key('process_id')):\n process_id = args['data']['process_id']\n if process_id == \"2\":\n current_process = list(common.get_collection(\"TM_SetupProcess\").aggregate(\n [\n {\"$project\":{\n \"process_id\":1,\n \"max_approve_level\": 1,\n \"score_by_coeff\": 1\n }\n },\n {\"$match\":{\n \"process_id\": args['data']['process_id']\n }\n }\n ]\n ))[0]\n #Tăng cấp duyệt\n if current_process['max_approve_level'] < args['data']['max_approve_level']:\n max_level = args['data']['max_approve_level'] - current_process['max_approve_level']\n for x in range(max_level):\n current_process['score_by_coeff'].append({\n \"approve_level\":current_process['max_approve_level'] + range(max_level).index(x) + 1,\n \"coeff\": 1\n })\n qmongo.models.TM_SetupProcess.update(\n {\n \"score_by_coeff\": current_process['score_by_coeff']\n },\n \"process_id == @process_id\",\n process_id = args['data']['process_id']\n )\n #Giảm cấp duyệt\n elif current_process['max_approve_level'] > args['data']['max_approve_level']:\n max_level = current_process['max_approve_level'] - args['data']['max_approve_level']\n for x in range(max_level):\n current_process['score_by_coeff'].pop(current_process['max_approve_level'] - (range(max_level).index(x)))\n qmongo.models.TM_SetupProcess.update(\n {\n \"score_by_coeff\": current_process['score_by_coeff']\n },\n \"process_id == @process_id\",\n process_id=args['data']['process_id']\n )\n\n args['data'].pop('process_id')\n ret = qmongo.models.TM_SetupProcess.update(\n args['data'],\n \"process_id==@process_id\",\n dict(\n process_id = process_id\n ))\n return ret\n return None\n\ndef delete(args):\n if args['data'] != None:\n ret = qmongo.models.TM_SetupProcess.delete(\"process_id in {0}\", [x[\"process_id\"] for x in args['data']])\n return ret\n return None\n\ndef set_dict_data(args):\n data = dict(\n process_id = args['data']['process_id'],\n process_name = args['data']['process_name'],\n is_not_apply_process = (lambda x: x['is_not_apply_process'] if x.has_key('is_not_apply_process') else None)(args['data']),\n function_id = (lambda x: x['function_id'] if x.has_key('function_id') else None)(args['data']),\n max_approve_level = (lambda x: x['max_approve_level'] if x.has_key('max_approve_level') else None)(args['data']),\n is_select_approver = (lambda x: x['is_select_approver'] if x.has_key('is_select_approver') else None)(args['data']),\n is_approve_by_dept = (lambda x: x['is_approve_by_dept'] if x.has_key('is_approve_by_dept') else None)(args['data']),\n is_require_reason = (lambda x: x['is_require_reason'] if x.has_key('is_require_reason') else None)(args['data']),\n is_require_when_approve = (lambda x: x['is_require_when_approve'] if x.has_key('is_require_when_approve') else None)(args['data']),\n is_require_when_reject = (lambda x: x['is_require_when_reject'] if x.has_key('is_require_when_reject') else None)(args['data']),\n sender_value = (lambda x: x['sender_value'] if x.has_key('sender_value') else None)(args['data']),\n file_size_limit = (lambda x: x['department_address'] if x.has_key('file_size_limit') else None)(args['data']),\n exclude_file_types = (lambda x: x['exclude_file_types'] if x.has_key('exclude_file_types') else None)(args['data']),\n email_send_code = (lambda x: x['email_send_code'] if x.has_key('email_send_code') else None)(args['data']),\n email_send_to = (lambda x: x['email_send_to'] if x.has_key('email_send_to') else None)(args['data']),\n email_send_cc = (lambda x: x['email_send_cc'] if x.has_key('email_send_cc') else None)(args['data']),\n is_send_email_pronto = (lambda x: x['is_send_email_pronto'] if x.has_key('is_send_email_pronto') else None)(args['data']),\n is_send_email_once = (lambda x: x['is_send_email_once'] if x.has_key('is_send_email_once') else None)(args['data']),\n is_send_email_once_from_hour = (lambda x: x['is_send_email_once_from_hour'] if x.has_key('is_send_email_once_from_hour') else None)(args['data']),\n is_send_email_once_to_hour = (lambda x: x['is_send_email_once_to_hour'] if x.has_key('is_send_email_once_to_hour') else None)(args['data']), \n )\n return data\n\ndef generate_list_approve_by_max_approve_level(args):\n #số cấp duyệt đã có\n count_approve_level = common.get_collection(\"TM_SetupProcessApproveLevel\").find({\n \"process_id\" : int(args['data']['process_id'])\n }).count()\n \n if args['data'] != None:\n max_approve_level = args['data']['max_approve_level']\n if max_approve_level == None:\n return None\n else:\n if count_approve_level < max_approve_level: #tăng số cấp duyệt\n i = count_approve_level + 1\n while (i <= max_approve_level):\n print(i)\n args['data']['rec_id'] = common.generate_guid()\n args['data']['process_id'] = int(args['data']['process_id'])\n args['data']['approve_level'] = i\n args['data']['approver_value'] = None\n\n args['data']['email_approve_code'] = None\n args['data']['email_approve_to'] = None\n args['data']['email_approve_cc'] = None\n args['data']['email_approve_more'] = None\n\n args['data']['email_reject_code'] = None\n args['data']['email_reject_to'] = None\n args['data']['email_reject_cc'] = None\n args['data']['email_reject_more'] = None\n\n args['data']['email_approve_cancel_code'] = None\n args['data']['email_approve_cancel_to'] = None\n args['data']['email_approve_cancel_cc'] = None\n args['data']['email_approve_cancel_more'] = None\n\n args['data']['default_approver_code'] = None\n args['data']['not_receive_email'] = False\n args['data']['created_on'] = None #str(datetime.datetime.now())\n args['data']['created_by'] = \"admin\" #models.auth_user_info()[\"login_account\"]\n args['data']['modified_on'] = None\n args['data']['modified_by'] = None\n ret = qmongo.models.TM_SetupProcessApproveLevel.insert(args['data'])\n i += 1\n return ret\n elif count_approve_level > max_approve_level: #giảm số cấp duyệt\n ret = qmongo.models.TM_SetupProcessApproveLevel.delete(\"approve_level > {0} and process_id == {1}\", max_approve_level, int(args['data']['process_id']))\n update_data = {}\n for x in range(count_approve_level - max_approve_level):\n update_data.update({\"appover_code.\" + str(max_approve_level + range(count_approve_level - max_approve_level).index(x)): None})\n\n common.get_collection('TM_SetupProcessApproverEmp').update_many(\n {\n 'process_id': args['data']['process_id']\n },\n {\n '$set': update_data\n }\n )\n common.get_collection('TM_SetupProcessApproverDept').update_many(\n {\n 'process_id':args['data']['process_id']\n },\n {\n '$set': update_data\n }\n )\n return ret\n return None\n\ndef get_score_coeff_by_process_id(args):\n try:\n return qmongo.models.TM_SetupProcess.aggregate.project(\n process_id = 1,\n process_level_apply_for = 1,\n score_by = 1,\n score_by_coeff = 1\n ).match(\"process_id == @process_id\", process_id = args['data']['process_id']).get_item()\n except Exception as ex:\n raise ex\n\ndef update_score_by_coeff_by_process_id(args):\n try:\n ret = {}\n process_id = args['data']['process_id']\n del args['data']['process_id']\n if args['data']['score_by'] != 4:\n del args['data']['score_by_coeff']\n ret = qmongo.models.TM_SetupProcess.update(\n args['data'],\n \"process_id == @process\",\n process = process_id\n )\n return ret\n except Exception as ex:\n raise ex","sub_path":"apps/performance/api/TM_SetupProcess.py","file_name":"TM_SetupProcess.py","file_ext":"py","file_size_in_byte":15133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"225339354","text":"\r\narr= list(map(str,input().split(\":\")))\r\n\r\ncount=0\r\n#빈칸이 아닌 수만큼 count++\r\nfor i in range(len(arr)):\r\n\tif arr[i]!='': count+=1\r\n\r\nfor i in range(8):\r\n\tif arr[i]=='':\r\n\t\tfor j in range(8-count):\r\n\t\t\tarr.insert(i, '0'*4)\r\n\t\twhile '' in arr:\r\n\t\t\tarr.remove('')\r\n\telse:\r\n\t\tif len(arr[i]) != 4:\r\n\t\t\tarr[i] = '0'*(4-len(arr[i]))+arr[i]\r\n#:로 나누기 \r\nprint(':'.join(arr))\r\n\t\r\n","sub_path":"study/Python/VCWeek4/BOJ_3107_우창완.py","file_name":"BOJ_3107_우창완.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"111019203","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.4 (62061)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: build/bdist.macosx-10.3-i386/egg/easyshop/order/subscribers/mailing.py\n# Compiled at: 2008-09-03 11:15:08\nfrom zope.component import adapter\nfrom zope.component import getMultiAdapter\nfrom Products.CMFCore.utils import getToolByName\nfrom Products.DCWorkflow.interfaces import IAfterTransitionEvent\nfrom easyshop.core.interfaces import IAddressManagement\nfrom easyshop.core.interfaces import IMailAddresses\nfrom easyshop.core.interfaces import IOrder\nfrom easyshop.core.interfaces import IShopManagement\nfrom easyshop.shop.utilities.misc import sendMultipartMail\n\n@adapter(IOrder, IAfterTransitionEvent)\ndef sendOrderMail(order, event):\n \"\"\"\n \"\"\"\n state = event.new_state.getId()\n if state == 'pending':\n mailOrderSubmitted(order)\n mailOrderReceived(order)\n elif state in ('sent (not payed)', 'sent'):\n mailOrderSent(order)\n\n\ndef mailOrderSent(order):\n \"\"\"Sends email to customer that the order has been sent.\n \"\"\"\n shop = IShopManagement(order).getShop()\n view = getMultiAdapter((order, order.REQUEST), name='mail-order-sent')\n text = view()\n customer = order.getCustomer()\n props = getToolByName(order, 'portal_properties').site_properties\n charset = props.getProperty('default_charset')\n sender = IMailAddresses(shop).getSender()\n sendMultipartMail(context=order, sender=sender, receiver=customer.email, subject='Your order %s has been sent.' % order.getId(), text=text, charset=charset)\n\n\ndef mailOrderSubmitted(order):\n \"\"\"Sends email to shop owner that an order has been submitted.\n \"\"\"\n shop = IShopManagement(order).getShop()\n mail_addresses = IMailAddresses(shop)\n sender = mail_addresses.getSender()\n receivers = mail_addresses.getReceivers()\n if sender and receivers:\n view = getMultiAdapter((order, order.REQUEST), name='mail-order-submitted')\n text = view()\n props = getToolByName(order, 'portal_properties').site_properties\n charset = props.getProperty('default_charset')\n sendMultipartMail(context=order, sender=sender, receiver=(', ').join(receivers), subject='E-Shop: New order', text=text, charset=charset)\n\n\ndef mailOrderReceived(order):\n \"\"\"Sends email to customer that the order has been received.\n \"\"\"\n shop = IShopManagement(order).getShop()\n mail_addresses = IMailAddresses(shop)\n sender = mail_addresses.getSender()\n bcc = mail_addresses.getReceivers()\n customer = order.getCustomer()\n address = IAddressManagement(customer).getShippingAddress()\n receiver = address.email\n if sender and receiver:\n view = getMultiAdapter((order, order.REQUEST), name='mail-order-received')\n text = view()\n props = getToolByName(order, 'portal_properties').site_properties\n charset = props.getProperty('default_charset')\n sendMultipartMail(context=order, sender=sender, receiver=receiver, bcc=bcc, subject='Bestellbestätigung Demmelhuber Holz & Raum', text=text, charset=charset)","sub_path":"pycfiles/easyshop.order-0.1a1-py2.4/mailing.py","file_name":"mailing.py","file_ext":"py","file_size_in_byte":3132,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"602002521","text":"# 큰수의 법칙 p92\n\n# N,M, K를 공백으로 구분하여 입력받기\nn, m, k = map(int, input().split())\n\ndata = list(map(int, input().split()))\ndata.sort() # 데이터 정렬\n\n_k = 0\n\nresult = 0\n# for _ in range(m):\n# if(_k != k):\n# result += data[-1]\n# _k += 1\n# else:\n# result += data[-2]\n# _k = 0\n\n\n# 수열로 파악했을 때\n# m // (k+1) * k\ncount = int(m / (k + 1)) * k\ncount += m % (k + 1)\n\nresult += (count) * data[-1]\nresult += (m - count) * data[-2]\n\nprint(result)\n","sub_path":"textbook/greedy02.py","file_name":"greedy02.py","file_ext":"py","file_size_in_byte":504,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"352978418","text":"#!/usr/bin/python\n# __author__ = 'jasonsheh'\n# -*- coding:utf-8 -*-\nimport os\nimport pymysql\nos.environ.setdefault(\"DJANGO_SETTINGS_MODULE\", \"myblog.settings\")\nimport django\ndjango.setup()\nfrom blog.models import Article\n\ndb = pymysql.connect(host=\"localhost\", user=\"root\", password=\"root\", db=\"wordpress\", port=3306, charset='utf8')\ncur = db.cursor()\nsql = 'select * from wp_posts'\ncur.execute(sql)\nresults = cur.fetchall()\nid = 0\nfor result in results:\n id += 1\n create_time = result[2]\n content = result[4]\n title = result[5]\n post_url = result[11]\n modified_time = result[14]\n\n Article.objects.get_or_create(\n id=id,\n create_time=create_time,\n content=content,\n title=title,\n post_url=post_url,\n modified_time=modified_time\n )\n print(id, create_time, title, modified_time)\n","sub_path":"extra/LoadWpXml.py","file_name":"LoadWpXml.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"241926414","text":"#Player.py\r\n#Player Class\r\n#Alex P.\r\n\r\nimport math\r\n\r\nimport pygame\r\n\r\n\r\nclass Player():\r\n def __init__(self, surf, mPos, xPos, yPos):\r\n\r\n self.SURF = surf\r\n self.MPOS = mPos\r\n self.XPOS = xPos\r\n self.YPOS = yPos\r\n\r\n self.LASTANGLE = None\r\n self.NEWANGLE = None\r\n\r\n self.R = 0\r\n self.RR = True\r\n\r\n self.COUNTER = 0\r\n\r\n def rot(self, image, angle):\r\n rect = image.get_rect()\r\n r_image = pygame.transform.rotate(image, angle)\r\n r_rect = rect.copy()\r\n r_rect.center = r_image.get_rect().center\r\n r_image = r_image.subsurface(r_rect)\r\n return r_image\r\n\r\n def getAngle(self, mPos, gPos):\r\n newAngle = math.atan2(gPos[1] - mPos[1], gPos[0] - mPos[0]) * -180 / 3.1459\r\n self.LASTANGLE = newAngle\r\n return newAngle\r\n\r\n # noinspection PyUnusedLocal\r\n def playerDraw(self, mPos, xPos, yPos):\r\n self.COUNTER += 1\r\n \r\n player = pygame.image.load(\"Resources/robo.png\")\r\n ring = pygame.image.load(\"Resources/ring.png\")\r\n p_size = player.get_rect()[2], player.get_rect()[3]\r\n noRot = 8\r\n rotNumber = 22.5\r\n gPos = xPos + p_size[0] / 2, yPos + p_size[1] / 2\r\n distance = (((mPos[0] - gPos[0]) ** 2) + ((mPos[1] - gPos[1]) ** 2)) ** (1/2)\r\n\r\n rotPlayer = None\r\n lastAngle = 0\r\n if distance >= noRot:\r\n newAngle = self.getAngle(mPos, gPos)\r\n angle = (round(newAngle / rotNumber, 0) * rotNumber)\r\n if distance < noRot:\r\n lastAngle = self.LASTANGLE\r\n angle = (round(lastAngle / rotNumber, 0) * rotNumber)\r\n\r\n rotPlayer = self.rot(player, angle)\r\n \r\n if self.COUNTER >= 10:\r\n self.COUNTER = 0\r\n if self.RR:\r\n self.R += 1\r\n if self.R == 4:\r\n self.RR = False\r\n if not self.RR:\r\n self.R -= 1\r\n if self.R <= 0:\r\n self.RR = True\r\n #print(self.R)\r\n \r\n self.SURF.blit(ring, (xPos - 32, yPos - 32), (self.R * 128, 0, 128, 128))\r\n self.SURF.blit(rotPlayer, (xPos, yPos), (0, 0, 500, 500))","sub_path":"Player.py","file_name":"Player.py","file_ext":"py","file_size_in_byte":2214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"647284043","text":"from __future__ import unicode_literals, print_function, division\nfrom io import open\nimport glob\nimport unicodedata\nimport string\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport random\n\ndef findFiles(path):\n return glob.glob(path)\n\n\nprint(findFiles('data/names/*.txt'))\n\nall_letters = string.ascii_letters + \" .,;'\"\nn_letters = len(all_letters)\n\n\ndef unicodeToASCII(s):\n return ''.join(\n c for c in unicodedata.normalize('NFD', s)\n if unicodedata.category(c) != 'Mn' and c in all_letters\n )\n\n\nprint(unicodeToASCII('Ślusàrski'))\n\n\ncategory_lines = {}\nall_categories = []\n\n\ndef readLines(filename):\n lines = open(filename, encoding='utf-8').read().strip().split('\\n')\n return [unicodeToASCII(line) for line in lines]\n\n\nfor filename in findFiles('data/names/*.txt'):\n category = filename.split('/')[-1].split('.')[0]\n all_categories.append(category)\n lines = readLines(filename)\n category_lines[category] = lines\n\n\nn_categories = len(all_categories)\n\n\ndef letterToIndex(letter):\n return all_letters.find(letter)\n\n\ndef letterToTensor(letter):\n tensor = torch.zeros(1, n_letters)\n tensor[0][letterToIndex(letter)] = 1\n return tensor\n\n\ndef lineToTensor(line):\n tensor = torch.zeros(len(line), 1, n_letters)\n for i, letter in enumerate(line):\n tensor[i][0][letterToIndex(letter)] = 1\n return tensor\n\n\nprint(letterToTensor('J'))\n\nprint(lineToTensor('Jones').size())\n\n\nclass RNN(nn.Module):\n def __init__(self, input_size, hidden_size, output_size):\n super(RNN, self).__init__()\n\n self.hidden_size = hidden_size\n\n self.i2h = nn.Linear(input_size + hidden_size, hidden_size)\n self.i2o = nn.Linear(input_size + hidden_size, output_size)\n self.softmax = nn.LogSoftmax()\n\n\n def forward(self, input, hidden):\n combined = torch.cat((input, hidden), 1)\n hidden = self.i2h(combined)\n output = self.i2o(combined)\n output = self.softmax(output)\n return hidden, output\n\n\n def initHidden(self):\n return Variable(torch.zeros(1, self.hidden_size))\n\n\nn_hidden = 128\nrnn = RNN(n_letters, n_hidden, n_categories)\n\n\ndef categoryFromOutput(output):\n top_n, top_i = output.data.topk(1)\n category_i = top_i[0][0]\n return all_categories[category_i], category_i\n\n\ndef randomChoice(list):\n return list[random.randint(0, len(list) - 1)]\n\n\ndef randomTrainingExample():\n category = randomChoice(all_categories)\n line = randomChoice(category_lines[category])\n category_tensor = Variable(torch.LongTensor([all_categories.index(category)]))\n line_tensor = Variable(lineToTensor(line))\n return category, line, category_tensor, line_tensor\n\nfor i in range(10):\n category, line, category_tensor, line_tensor = randomTrainingExample()\n print('category =', category, '/ line =', line)\n\n\ncriterion = nn.NLLLoss()\nlearning_rate = 0.005\n\n\ndef train(category_tensor, line_tensor):\n hidden = rnn.initHidden()\n rnn.zero_grad()\n\n # For every character in line\n for i in range(line_tensor.size()[0]):\n output, hidden = rnn(line_tensor[i], hidden)\n\n loss = criterion(output, category_tensor)\n loss.backward()\n\n for p in rnn.parameters():\n p.data_add_(-learning_rate, p.grad.data)\n\n return output, loss.data[0]","sub_path":"binaryposeconvlstm/oldcode/simple.py","file_name":"simple.py","file_ext":"py","file_size_in_byte":3315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"215190450","text":"#!/usr/bin/env python3\n\n\"\"\"\npygrade.py\nBy Daniel Sun, April 17 2016\n\nA quick n' dirty script with tab completion for names for reading and entering grades in the myUCLA format.\n\"\"\"\n\n\n\n\nimport csv, sys, readline\n\nfilestring = '1Ngrades' # name of file to be edited\n\ndef openfile(name_of_file):\n with open(name_of_file, newline='') as f:\n reader = csv.reader(f, delimiter='\\t', quoting=csv.QUOTE_NONE)\n gradelist = list(reader) \n return gradelist\n\ndef printfile(gradelist, name_of_file):\n with open(name_of_file, 'w', newline='') as csvfile: \n writer = csv.writer(csvfile, delimiter='\\t', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n for row in gradelist:\n writer.writerow(row) \n\ndef showdict(d):\n for entry in d:\n print(str(entry) + ' ' + str(d[entry]))\n\ndef isfloat(input_string):\n try:\n return float(input_string) != None;\n except ValueError:\n return False;\n\n# gets sanitized key as input\ndef getkey(uidd, named):\n identifier = input(\"Enter a UID or a name. \")\n if(identifier == None):\n return None\n if isfloat(identifier):\n try:\n return uidd[identifier]\n except KeyError:\n print(\"ERROR: That UID isn't in the database.\")\n return None\n else:\n try:\n return named[identifier]\n except KeyError:\n print(\"ERROR: That name doesn't match anything in the database.\")\n return None \n\n# Enters score, returns False on failure.\ndef enterscore(glist, uidd, named, **kwargs):\n key = getkey(uidd,named)\n if key == None:\n return False\n else:\n print(\"Entering data for \" + str(glist[key][1]))\n score = input(\"Enter score. \")\n try:\n score = eval(score) # has ability to accept arithmetic\n \n if isfloat(score):\n glist[key][2] = score\n print(\"Score written: \" + str(glist[key][0]) + \" \" + str(glist[key][1]) + \" \" + str(score) + \"\\n\")\n return True\n else:\n print(\"Improper score input.\")\n return False\n except:\n print(\"Invalid score input.\")\n return False\n\n\n## Beginning script ##\nif __name__ == '__main__':\n \n if len(sys.argv) != 2:\n print(\"Usage: python3 pygrade \")\n sys.exit()\n \n filestring = sys.argv[1]\n\n glist = openfile(filestring)\n print(\"Beginning PyGrade grading script for \" + str(filestring))\n print()\n\n uidd = {} # dictionary of UIDs\n named = {} # dictionary of names\n\n # initialize dictionaries\n for i in range(len(glist)):\n uidd[glist[i][0]] = i\n named[glist[i][1]] = i\n \n \n # set up autocomplete\n name_list = []\n for i in range(len(glist)):\n name_list.append(glist[i][1]) \n\n def name_complete(text,state):\n for name in name_list:\n if name.startswith(text):\n if not state:\n return name\n else:\n state -= 1\n\n comm_list = ['q,','e','r','l','remaining','fill']\n def comm_complete(text,state):\n for comm in comm_list:\n if name.startswith(text):\n if not state:\n return cmd\n else:\n state -= 1\n\n readline.parse_and_bind(\"tab: complete\")\n\n # input grades\n while(True):\n readline.set_completer(comm_complete)\n comm = input(\"Please enter a command. e: enter score | r: repeat enter | l: list | q: quit\\n\")\n printfile(glist,filestring)\n\n if(str(comm) == \"q\"):\n break\n\n if(str(comm) == \"e\"):\n readline.set_completer(name_complete)\n enterscore(glist,uidd,named)\n continue\n\n if(str(comm) == \"r\"):\n readline.set_completer(name_complete)\n while(enterscore(glist,uidd,named)):\n pass\n continue \n\n if(str(comm) == 'l'):\n for row in glist:\n print(row)\n continue\n \n if(str(comm) == 'remaining'):\n count = 0\n for entry in glist:\n if entry[2] == '':\n count += 1\n print(count)\n continue\n\n if(str(comm) == 'fill'):\n fill_no = input(\"Enter number to fill all entries with: \")\n if isfloat(fill_no):\n if(input(\"Are you sure? (y/n) \") == 'y'):\n for entry in glist:\n entry[2] = fill_no\n continue \n \n # default:\n print(\"Invalid input.\") \n\n \n\n\n\n\n\n\n\n\n\n","sub_path":"pygrade.py","file_name":"pygrade.py","file_ext":"py","file_size_in_byte":4680,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"631169273","text":"import datetime\nimport re, ast\nfrom django.db.models import Sum, Max\nfrom django.shortcuts import render\nfrom django.utils.timezone import now\nfrom django.views.generic.base import TemplateView\nfrom django.views.generic.detail import DetailView\nfrom django.views.generic.list import ListView\nfrom jsonview.views import JsonView\nfrom data import models as data_models\nfrom django.contrib.auth.decorators import login_required\n\n\n\n##################\n# Template views #\n##################\n\n#presentation_date = now()\n\n# make a note: dont use naive dates\npresentation_date = datetime.date(2018,10,13)\n\nequity_sector_names = ['Basic Materials',\n 'Communications',\n 'Consumer Cyclical',\n 'Consumer Non-cyclical',\n 'Energy',\n 'Financial',\n 'Industrial',\n 'Investment Instruments',\n 'Technology',\n 'Utilities']\n\n# index page\n#@login_required\nclass IndexView(TemplateView):\n template_name = 'data/index.html'\n\n# calendar page\nclass CalendarView(TemplateView):\n template_name = 'data/calendar.html'\n\n# equities page\nclass EquitiesView(TemplateView):\n template_name = 'data/equities.html'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n #categories = set(data_models.Equities.objects.values_list('sector', flat = True).distinct())\n categories = equity_sector_names\n\n context['categories'] = categories\n\n #print(context)\n return context\n\n# futures page\nclass FuturesView(TemplateView):\n template_name = 'data/futures.html'\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n categories = set(data_models.Futures.objects.values_list('category', flat = True).distinct())\n context['categories'] = categories\n\n print(context)\n return context\n\n# econ page\nclass EconView(TemplateView):\n template_name = 'data/econ.html'\n\n model = data_models.Econ\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n #print(self.request.GET)\n title = self.request.GET.get('title')\n\n series_ticker = data_models.Econ.objects.filter(event=title).values('ticker').first()['ticker']\n\n #print(series_ticker)\n\n impacts = data_models.Impact.objects.filter(ticker=series_ticker)\n impacts = impacts.values('main_tickers_by_volume', 'main_tickers_by_price')[0]\n\n # econ_impacts = {'volume': ast.literal_eval(re.sub(\"' \",\"', \", impacts['main_tickers_by_volume'])), \n # 'price': ast.literal_eval(re.sub(\"' \",\"', \", impacts['main_tickers_by_price']))}\n\n econ_impacts = {'volume': ast.literal_eval(impacts['main_tickers_by_volume']), \n 'price': ast.literal_eval(impacts['main_tickers_by_price'])}\n\n econ_impacts['volume'] = [list(thing) for thing in econ_impacts['volume']]\n econ_impacts['price'] = [list(thing) for thing in econ_impacts['price']]\n\n\n print(econ_impacts)\n context['title'] = title\n # context['volume_impacts'] = econ_impacts['volume']\n # context['price_impacts'] = econ_impacts['price']\n\n context['impacts'] = zip(econ_impacts['volume'],econ_impacts['price'])\n \n\n # title = self.request.GET.get('title')\n\n # econ_series = data_models.Econ.objects.filter(event=title).order_by('datetime')\n\n # econ_series_values = econ_series.values('datetime', 'actual', 'ticker')\n # econ_ticker = econ_series_values[0]['ticker']\n\n # impacts = data_models.Impact.objects.filter(ticker=econ_ticker)\n # impacts = impacts.values('main_tickers_by_volume', 'main_tickers_by_price')[0]\n\n # econ_impacts = {'volume': ast.literal_eval(re.sub(\"' \",\"', \", impacts['main_tickers_by_volume'])), \n # 'price': ast.literal_eval(re.sub(\"' \",\"', \", impacts['main_tickers_by_price']))}\n\n return context\n\n# mortgage page\nclass MortgageView(TemplateView):\n template_name = 'data/mortgages.html'\n\n# bonds page\nclass BondsView(TemplateView):\n template_name = 'data/bonds.html'\n\n# date page\nclass DateView(TemplateView):\n template_name = 'data/dates.html'\n\n# sector page\nclass Equities_SectorView(TemplateView):\n template_name = 'data/sector.html'\n #model = data_models.Futures\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n model = data_models.Equities #namespace\n\n #make only the most recent\n\n start_date = now() - datetime.timedelta(days=90)\n model = model.objects.filter(date__gt=start_date)\n\n #print(self.request.GET)\n sector = self.request.GET.get('sector')\n #print(sector)\n\n sector_tickers = model.filter(sector=sector).values('exchTicker')\n sector_tickers = [ticker['exchTicker'] for ticker in sector_tickers]\n sector_tickers = list(set(sector_tickers))\n\n context['sector'] = sector\n context['sector_tickers'] = sector_tickers\n\n print(context)\n\n return context\n\n\n\n# econ page\nclass TickerView(TemplateView):\n template_name = 'data/ticker.html'\n\n #model = data_models.Equities # what about futures\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n # print(self.request.GET)\n ticker = self.request.GET.get('ticker')\n\n neighbors = data_models.Similarity.objects.filter(ticker = ticker).values('similar')[0]['similar']\n\n print(len(neighbors))\n\n if len(neighbors) != 0:\n neighbors = ast.literal_eval(neighbors)\n\n names = [neighbor[0] for neighbor in neighbors]\n scores = [1-neighbor[1] for neighbor in neighbors]\n context['neighbors'] = zip(names,scores)\n\n neighbors = [list(neighbor) for neighbor in neighbors]\n\n print(neighbors)\n print(names)\n print(scores)\n\n context['ticker'] = ticker\n\n #context['neighbors'] = \n\n return context\n\n\n # def get_queryset(self):\n # queryset = super().get_queryset()\n\n # ticker = self.request.GET.get('title')\n\n # if ticker:\n # queryset = queryset.filter(title = title)\n\n # return queryset\n\n\n#############\n# JsonViews #\n#############\n\n# aggregate data (front page)\nclass AggregatesView(JsonView):\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n end_date = presentation_date\n start_date = end_date - datetime.timedelta(days=360)\n aggregates = data_models.Aggregates.objects.filter(date__gt=start_date, date__lt=end_date).order_by('date')\n \n DATA_TYPES = [ \n 'equities', \n 'corp_bonds', \n 'mortgage_bonds',\n 'commodity_futures',\n 'equity_futures',\n 'fixed_income_futures',\n ]\n\n aggregates = aggregates.values('date', *DATA_TYPES)\n aggregate_data = { i:[] for i in DATA_TYPES }\n for agg in aggregates:\n agg_date = agg['date']\n \n for data_type in DATA_TYPES:\n if agg[data_type] == None:\n aggregate_data[data_type].append([agg_date, 0.0])\n else:\n aggregate_data[data_type].append([agg_date, float(agg[data_type])/1000000])\n context['aggregate_data'] = aggregate_data\n\n print(aggregate_data)\n\n return context\n\n# calendar data view\nclass CalendarDataView(JsonView):\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n start_date = self.request.GET.get('min_timestamp', now() - datetime.timedelta(days=40))\n end_date = self.request.GET.get('max_timestamp', now())\n econ_events = data_models.Econ.objects.filter(datetime__gte=start_date, datetime__lte=end_date).order_by('datetime')\n\n events_values = econ_events.values('pk','datetime', 'event', 'reduced_score', 'ticker')\n events = []\n\n event_colors = ['#ffcc99', #0\n '#eba385', #4\n '#d67a70', #8\n '#eba385', #4\n '#c2525c', #12\n '#990033'] # 20\n\n\n for e in events_values:\n events.append({'title': e['event'], \n 'start': e['datetime'], \n 'score': e['reduced_score'],\n 'color': event_colors[e['reduced_score']], \n 'itemId': e['pk'],\n 'ticker': e['ticker'],})\n\n context['econ'] = events\n\n return context\n\n\n# calendar data view\nclass FrontCalendarDataView(JsonView):\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n start_date = self.request.GET.get('min_timestamp', now() - datetime.timedelta(days=40))\n end_date = self.request.GET.get('max_timestamp', now())\n\n #print(self.request.GET)\n \n\n econ_events = data_models.Econ.objects.filter(datetime__gte=start_date, datetime__lte=end_date).order_by('datetime')\n events_values = econ_events.values('datetime').annotate(Sum('reduced_score'))\n\n\n\n #print(events_values)\n\n events = []\n\n # events_values = econ_events.values('pk','datetime', 'event', 'reduced_score', 'ticker')\n # \n\n # event_colors = ['#b6c2c9', # COLOR_GREY \n # '#929ba1', # COLOR_GREY_DARKER\n # '#575d63', # COLOR_BLACK_LIGHTER\n # '#bfa300', # COLOR_YELLOW_DARKER\n # '#c47d15', # COLOR_ORANGE_DARKER\n # '#cc4946'] # COLOR_RED_DARKER\n\n\n #date = datetime.strptime,\n\n\n for e in events_values:\n print(e)\n event_date = e['datetime']\n # events_for_date = econ_events.filter(datetime=event_date).values_list('event', flat=True)\n events.append({'date': event_date.strftime('%-d/%-m/%-Y'), \n # 'events': list(events_for_date),\n 'score': e['reduced_score__sum']})\n\n context['max_score'] = events\n\n return context\n\nclass EquitiesDataView(JsonView):\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n start_date = now() - datetime.timedelta(days=360)\n\n sectors = data_models.EqSectors.objects.filter(date__gt=start_date).order_by('date')\n\n DATA_TYPES = ['Basic_Materials',\n 'Communications',\n 'Consumer_Cyclical',\n 'Consumer_Noncyclical',\n 'Energy',\n 'Financial',\n 'Industrial',\n 'Investment_Instruments',\n 'Technology',\n 'Utilities']\n\n sectors = sectors.values('date', *DATA_TYPES) #just have everything??\n\n sectors_data = { i:[] for i in DATA_TYPES }\n\n for sector in sectors:\n sector_date = sector['date']\n for data_type in DATA_TYPES:\n if sector[data_type] == None:\n sectors_data[data_type].append([sector_date, 0.0])\n else:\n sectors_data[data_type].append([sector_date, float(sector[data_type])/1000000])\n\n\n\n\n\n\n # equities = data_models.Equities.objects.filter(date__gt=start_date).order_by('date')\n # equities = equities.values('date', 'sector').annotate(Sum('volume'))\n\n # equity_data = {}\n # for item in equities:\n # print(item)\n # key = item['sector']\n # if key not in equity_data:\n # equity_data[key] = []\n # equity_data[key].append([item['date'], float(item['volume__sum'])/1000000])\n\n context['equities'] = sectors_data\n\n return context\n\n\nclass FuturesDataView(JsonView):\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n end_date = presentation_date\n start_date = now() - datetime.timedelta(days=360)\n futures = data_models.Futures.objects.filter(date__gt=start_date, date__lt=end_date).order_by('date')\n \n categories = list(set(futures.values_list('category', flat = True).distinct()))\n print(categories)\n\n #print(futures)\n futures = futures.values('date', 'category').annotate(Sum('dollar_volume'))\n\n futures_data = {category:[] for category in categories}\n\n item_date = None\n\n # To make sure that all categories have the same dates...\n\n categories_copy = categories.copy()\n for item in futures:\n if item_date != item['date']:\n if item_date is not None:\n for remaining_category in categories_copy:\n futures_data[remaining_category].append([item_date, 0])\n categories_copy = categories.copy()\n\n item_date = item['date']\n\n key = item['category']\n categories_copy.remove(key)\n\n futures_data[key].append([item['date'], float(item['dollar_volume__sum'])/1000000])\n\n if item_date is not None:\n for remaining_category in categories_copy:\n futures_data[remaining_category].append([item_date, 0])\n\n context['futures'] = futures_data\n\n return context\n\nclass BondsDataView(JsonView):\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n start_date = now() - datetime.timedelta(days=360)\n bonds = data_models.Bonds.objects.filter(date__gt=start_date).order_by('date')\n\n DATA_TYPES = [\n 'all_high_yield',\n 'all_investment_grade'\n ]\n\n bonds = bonds.values('date', *DATA_TYPES)\n\n bond_data = { i:[] for i in DATA_TYPES }\n\n for bond in bonds:\n bond_date = bond['date']\n for data_type in DATA_TYPES:\n if bond[data_type] == None:\n pass\n #bond_data[data_type].append([bond_date, 0.0])\n else:\n bond_data[data_type].append([bond_date, float(bond[data_type])/1000000])\n\n context['bonds'] = bond_data\n\n return context\n\n# mortgage data view\nclass MortgageDataView(JsonView):\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n end_date = presentation_date\n start_date = now() - datetime.timedelta(days=360)\n mortgages = data_models.MortgageBonds.objects.filter(date__gt=start_date, date__lt=end_date).order_by('date')\n\n DATA_TYPES = [\n 'FNMA_15Y',\n 'FNMA_30Y',\n 'FNMA_15Y_specified',\n 'FNMA_30Y_specified',\n 'FNMA_adjhyb',\n 'FHLMC_15Y',\n 'FHLMC_30Y',\n 'FHLMC_15Y_specified',\n 'FHLMC_30Y_specified',\n 'FHLMC_adjhyb',\n 'GNMA_30Y',\n 'GNMA_other',\n 'GNMA_30Y_specified',\n 'GNMA_adjhyb']\n\n mortgages = mortgages.values('date', *DATA_TYPES)\n\n mortgage_data = { i:[] for i in DATA_TYPES }\n\n for mortgage in mortgages:\n mortgage_date = mortgage['date']\n for data_type in DATA_TYPES:\n if mortgage[data_type] == None:\n mortgage_data[data_type].append([mortgage_date, 0.0])\n else:\n mortgage_data[data_type].append([mortgage_date, float(mortgage[data_type])/1000000])\n\n context['mortgages'] = mortgage_data\n\n return context\n\nclass EconDataView(JsonView):\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n title = self.request.GET.get('title')\n\n econ_series = data_models.Econ.objects.filter(event=title).order_by('datetime')\n\n econ_series_values = econ_series.values('datetime', 'actual')\n \n # econ_ticker = econ_series_values[0]['ticker']\n # impacts = data_models.Impact.objects.filter(ticker=econ_ticker)\n # impacts = impacts.values('main_tickers_by_volume', 'main_tickers_by_price')[0]\n # econ_impacts = {'volume': ast.literal_eval(re.sub(\"' \",\"', \", impacts['main_tickers_by_volume'])), \n # 'price': ast.literal_eval(re.sub(\"' \",\"', \", impacts['main_tickers_by_price']))}\n \n #print(econ_impacts)\n\n # series_values = econ_series.values('datetime', 'actual')\n\n econ_data = {title: []}\n\n for value in econ_series_values:\n # move this regex to the loaddata.py management command at some point\n actual_value = re.sub('[$%kmb]','', value['actual'])\n try:\n econ_data[title].append({'x': value['datetime'], 'y': float(actual_value)})\n except:\n pass\n\n #print(econ_data) \n context['econ_data'] = econ_data\n \n return context\n\nclass Equities_SectorDataView(JsonView):\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n sector = self.request.GET.get('sector')\n\n start_date = now() - datetime.timedelta(days=360)\n\n if sector == 'Basic Materials':\n sector = 'Basic_Materials'\n elif sector == 'Consumer Cyclical':\n sector = 'Consumer_Cyclical'\n elif sector == \"Consumer Non-cyclical\":\n sector = \"Consumer_Noncyclical\"\n elif sector == \"Investment Instruments\":\n sector = \"Investment_Instruments\"\n\n\n\n print(sector)\n\n sector_data = data_models.EqSectors.objects.filter(date__gt=start_date).order_by('date')\n #sector_data = sector_data.filter(date__gt=start_date).order_by('date')\n sector_data = sector_data.values('date', sector)\n\n print(sector_data)\n\n sector_series = {sector: []}\n for item in sector_data:\n sector_series[sector].append([item['date'], float(item[sector])/1000000])\n\n context['sector'] = sector_series\n\n return context\n\nclass TickerDataView(JsonView):\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n ticker = self.request.GET.get('ticker')\n ticker_series = data_models.Equities.objects.filter(exchTicker=ticker).order_by('date')\n ticker_series_values = ticker_series.values('date', 'volume', 'close', \n 'thirty_day_average_price', 'description')\n\n #print(ticker_series_values)\n\n ticker_volume = {'volume': []}\n ticker_price = {'price' : []}\n ticker_price_30 = {'thirty_day_average' : []}\n \n\n for value in ticker_series_values:\n ticker_volume['volume'].append({'x': value['date'], 'y': float(value['volume'])/1000000 })\n ticker_price['price'].append({'x': value['date'], 'y': float(value['close']) })\n ticker_price_30['thirty_day_average'].append({'x': value['date'], 'y': float(value['thirty_day_average_price']) })\n #ticker_volume_30['thirty_day_average'].append({'x': value['date'], 'y': float(value['thirty_day_average_volume'])/1000000 })\n description = ticker_series_values[0]['description']\n\n #print(ticker_volume) \n context['volume'] = ticker_volume\n context['price'] = ticker_price\n context['thirty_day_average_price'] = ticker_price_30\n context['description'] = description\n #context['thirty_day_average_volume'] = ticker_volume_30\n\n\n # ticker_volume = {ticker: []}\n # ticker_price = {ticker: []}\n\n # for value in ticker_series_values:\n # ticker_volume[ticker].append({'x': value['date'], 'y': float(value['dollar_volume'])/1000000 })\n # ticker_price[ticker].append({'x': value['date'], 'y': float(value['close']) })\n\n # #print(ticker_volume) \n # context['volume'] = ticker_volume\n # context['price'] = ticker_price\n\n\n return context\n\n\n\n\nclass DateDataView(JsonView):\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n start_date = now() - datetime.timedelta(days=30)\n equities = data_models.Equities.objects.filter(date__gt=start_date).order_by('date')\n equities = equities.values('date', 'sector').annotate(Sum('dollar_volume'))\n\n equity_data = {}\n for item in equities:\n print(item)\n key = item['sector']\n if key not in equity_data:\n equity_data[key] = []\n equity_data[key].append([item['date'], float(item['dollar_volume__sum'])])\n\n context['equities'] = equity_data\n\n return context\n\n\n# OTHER VIEWS\n\nclass EconDetailView(DetailView):\n model = data_models.Econ\n\nclass EconListView(ListView):\n\n model = data_models.Econ\n\n def get_queryset(self):\n queryset = super().get_queryset()\n\n ticker = self.request.GET.get('ticker')\n\n if ticker:\n queryset = queryset.filter(ticker = ticker)\n\n return queryset\n\n def get_context_data(self, **kwargs):\n context = super().get_context_data(**kwargs)\n\n print(self.request.GET)\n\n title = self.request.GET.get('title')\n\n context['title'] = title\n\n return context\n\n\n# tableau page\nclass TableauView(TemplateView):\n template_name = 'data/tableau.html'\n\n \n \n\t# date = models.DateField(primary_key = True)\n # FNMA_15Y = models.DecimalField(max_digits = 30, decimal_places = 10, null = True, blank = True)\n # FNMA_30Y = models.DecimalField(max_digits = 30, decimal_places = 10, null = True, blank = True)\n # FNMA_other = models.DecimalField(max_digits = 30, decimal_places = 10, null = True, blank = True)\n # FNMA_15Y_specified = models.DecimalField(max_digits = 30, decimal_places = 10, null = True, blank = True)\n # FNMA_30Y_specified = models.DecimalField(max_digits = 30, decimal_places = 10, null = True, blank = True)\n","sub_path":"data/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":22191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"71176813","text":"from parallelpy.utils import Work, Letter\nfrom parallelpy import parallel_evaluate\n\nimport math\nimport constants as c\nimport numpy as np\nimport pyrosim\nfrom copy import deepcopy\n\nclass SIM(Work):\n \"\"\"\n Wrapper for a simulation instance which allows us to parallelize the simulation of aggregates and controllers\n across a cluster of computers.\n \"\"\"\n def __init__(self, aggregate, aggregate_key, controller, controller_key, seed):\n \"\"\"\n :param aggregate: The instance of the aggregate\n :param aggregate_key: The key of the aggregate in the dict of controllers.\n :param controller: The instance of the controller\n :param controller_key: The key of the controller in the dict of controllers\n \"\"\"\n self.aggregate = aggregate\n self.aggregate_key = aggregate_key\n self.controller = controller\n self.controller_key = controller_key\n self.keys = [aggregate_key,controller_key,seed]\n self.fitness = -1\n\n def compute_work(self, serial=False):\n for i in range(10):\n sim = pyrosim.Simulator(eval_steps=COEVOLVE.TIME_STEPS, play_blind=True, play_paused=False, dt=COEVOLVE.DT)\n #print(\"Simulating aggregate\", self.aggregate_key, \"with controller\", self.controller_key)\n self.fitness = self.aggregate.evaluate(sim, self.controller, idNum=self.keys, debug=False)\n if self.fitness > -1:\n break\n #print(\"fitness of aggregate\", self.aggregate_key, \"and controller\", self.controller_key, \"retrieved\")\n if self.fitness < 0: self.fitness = 0\n\n def write_letter(self):\n return Letter((self.fitness, self.aggregate_key, self.controller_key), None)\n\n def open_letter(self, letter):\n self.fitness, _, _ = letter.get_data()\n\nclass COEVOLVE:\n '''\n handles co-evolutionary methods for AGGREGATE and CONTROLLER classes\n \n Attributes\n ----------\n aggrs : POPULATION instance\n Population of aggregate objects\n contrs : POPULATION instance\n Population of controller objects\n '''\n \n DT = 0.01\n TIME_STEPS = 1000\n def __init__(self, aggrs, contrs, mode, seed):\n '''\n initializes the two populations \n '''\n \n self.aggrs = aggrs\n self.contrs = contrs\n self.mode = mode #1 for robustness, 0 for max fitness\n self.fpi = math.ceil(len(self.aggrs.p)*.05)\n self.seed = seed\n \n def non_MPI_exhaustive(self):\n \"\"\"\n non-parallel exhaustive evaluation\n \"\"\"\n \n for j in range(len(self.contrs.p)):\n contr = self.contrs.p[j]\n for i in range(len(self.aggrs.p)):\n aggr = self.aggrs.p[i]\n sim = pyrosim.Simulator(eval_steps=COEVOLVE.TIME_STEPS, play_blind=True, play_paused=False, dt=COEVOLVE.DT)\n fit = aggr.evaluate(sim, self.contrs.p[j], idNum = [i,j,0])\n self.contrs.p[j].scores.append(fit)\n \n self.calculate_fitness()\n\n def exhaustive(self):\n '''\n Evaluates every single aggregate composed with \n every single controller\n '''\n\n # pre allocate work_array to avoid need to expand array upon append.\n work_to_complete = [None]*(len(self.aggrs.p)*len(self.contrs.p))\n work_index = 0 # keep track of which index in the array we are at.\n for j in range(len(self.aggrs.p)):\n aggr = self.aggrs.p[j]\n for i in range(len(self.contrs.p)):\n work_to_complete[work_index] = SIM(aggr, j, self.contrs.p[i], i, self.seed)\n work_index += 1\n print(\"Simulating %d robots\" % len(work_to_complete))\n parallel_evaluate.batch_complete_work(work_to_complete)\n \n #print(\"appending fitnesses\")\n for work in work_to_complete:\n aggr_key = work.aggregate_key\n elmt_key = work.controller_key\n fit = work.fitness\n if c.DEBUG:\n print(\"Sim fit:\",fit)\n\n self.contrs.p[elmt_key].scores.append(fit)\n\n self.calculate_fitness()\n \n def calculate_fitness(self):\n \"\"\"\n Each controller's fitness is set to the sum of the 5th percentile of its scores. If there are too few scores to take a 5th percentile, take the lowest.\n \"\"\"\n if self.mode == 1:\n for i in range(len(self.contrs.p)):\n try:\n self.contrs.p[i].scores.sort()\n fifth_percentile = self.contrs.p[i].scores[0:self.fpi]\n while len(fifth_percentile) != self.fpi:\n fifth_percentile = self.contrs.p[i].scores[0:self.fpi]\n fit = sum(fifth_percentile)/self.fpi\n if (np.isnan(fit) or np.isinf(fit) or len(self.contrs.p[i].scores)==0):\n fit = 0\n self.contrs.p[i].fitness = fit\n print(fit, fifth_percentile)\n except Exception as e:\n print(e)\n raise(e)\n self.contrs.p[i].fitness = 0\n elif self.mode == 0:\n for i in range(len(self.contrs.p)):\n try:\n self.contrs.p[i].scores.sort()\n fit = self.contrs.p[i].scores[-1]\n self.contrs.p[i].fitness = fit\n \n except Exception as e:\n print(e)\n raise(e)\n \n def print_fitness(self):\n '''\n prints the top controller fitness\n '''\n\n print('Best Controller of',len(self.contrs.p),':')\n best = 0\n besti = 0\n for i in range(len(self.contrs.p)):\n if self.contrs.p[i].fitness > best:\n best = self.contrs.p[i].fitness\n besti = i\n \n fpi = math.ceil(len(self.contrs.p[besti].scores)*.05)\n print(besti, \":\", best, self.contrs.p[besti].scores[0:fpi])\n\n def reset(self):\n '''\n calls reset on both populations\n '''\n self.contrs.reset()\n \n def selection(self):\n '''\n calls selection on controllers\n '''\n self.contrs.selection()\n \n def playback(self, play_all=False):\n '''\n for review purposes, plays the best aggregate and controller, with play_blind off\n '''\n\n fit = 0\n eindex = 0\n for j in range(len(self.contrs.p)):\n if abs(self.contrs.p[j].fitness) > abs(fit):\n print(\"fit\")\n print(j)\n fit = self.contrs.p[j].fitness\n eindex = j\n\n if play_all:\n for a in self.aggrs.p:\n aggr = a\n elmt = self.contrs.p[eindex]\n sim = pyrosim.Simulator(eval_steps=1000, play_blind=False, play_paused=False, dt=.01)\n try:\n print(aggr.evaluate(sim, elmt, debug=True))\n except Exception as e:\n print(\"error\")\n pass\n else:\n \n fit = 99999\n aindex = -1\n elmt = self.contrs.p[eindex]\n \n for i in range(len(elmt.scores)):\n if elmt.scores[i] < fit:\n fit = elmt.scores[i]\n aindex = i\n\n aggr = self.aggrs.p[aindex]\n print(aindex, eindex)\n sim = pyrosim.Simulator(eval_steps=1000, play_blind=False, play_paused=True, dt=.01, use_textures=True)\n aggr.evaluate(sim, elmt, debug=False)\n","sub_path":"coevolve.py","file_name":"coevolve.py","file_ext":"py","file_size_in_byte":7657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"328262535","text":"\"\"\"This file contains all extra helper functions that must be used for\nthe novelty search algorithm with the evolution of a CPPN, the majority\nof the functions are utilized in the FULL_CPPN_novelty ea file\n\"\"\"\n\nimport sys\n\nimport numpy as np\n\n\ndef get_euclid_dist(vec_1, vec_2):\n\t\"\"\"Method for calculating the euclidian distance of\n\tvectors using numpy - doesn't square difference so that\n\ta novelty threshold is easier to define\n\t\"\"\"\n\n\treturn np.sqrt(np.sum(np.fabs(vec_1 - vec_2), axis=1)).flatten()\n\ndef get_kNN_measure(curr_vec, pop_vecs, archive_vecs, k):\n\t\"\"\"Method for finding the k nearest distances between the current\n\tresult vector and all existing result vectors in the population\n\tand archive of novel individuals.\n\n\tpre -- archive_vecs should be null if there are no archive vectors yet\n\t\"\"\"\n\n\t# vectorize this computation to do it all at once\n\t# each row of pop_vecs should be an output/phenotype\n\tpop_vec_dist = get_euclid_dist(curr_vec, pop_vecs)\n\tif not archive_vecs.size == 0:\t\n\t\tarchive_vec_dist = get_euclid_dist(curr_vec, archive_vecs)\n\t\t# sort the distances and take the top k for average\n\t\tall_dist = sorted(np.concatenate((pop_vec_dist, archive_vec_dist)))\n\telse:\n\t\tall_dist = sorted(pop_vec_dist)\n\n\t# return average of the k smallest distances\n\treturn (np.mean(all_dist[:k]),)\n\n\ndef get_cross_entropy(actual, expected):\n\t\"\"\"Takes the resulting pixel list and the expected\n\tpixel list and finds the cross entropy value associated\n\twith the predicted pixel values\n\t\n\tParameters:\n\tactual-- pixels predicted by CPPN\n\texpected-- the target pixel list\n\t\n\tpre-- both actual and expected are flattened numpy arrays\n\t\"\"\"\n\n\t# use try except to handle overflow in the log\n\ttry:\t\n\t\t# formula -(y*log(a) + (1-y)log(1-a))\n\t\tones = np.ones(actual.shape) # used to subtract array from one\n\t\tcross_entropy_vec = np.multiply(expected, np.log(actual)) + np.multiply(ones - expected, np.log(ones - actual))\n\t\tcross_entropy = np.sum(cross_entropy_vec)\n\texcept:\n\t\treturn sys.maxsize\n\n\treturn -1.0*cross_entropy\n\n\nif __name__ == \"__main__\":\n\t\"\"\"Used for quick testing for compile errors\"\"\"\n\t\n\tprint(get_cross_entropy(np.array([.6, .9]), np.array([1, 0])))\n\tx = [np.array([0,1,1,1]), np.array([0,1,1,1]), np.array([0,1,1,1])]\n\tx = np.vstack((x))\n\ty = [np.array([1,1,1,1]), np.array([0,0,1,1])]\n\ty = np.stack((y))\n\tprint(x)\n\tprint(y)\n\n\tnn = get_kNN_measure(np.array([[0,0,1,1]]), x, y, 2)\n\tprint(nn)\n","sub_path":"FULL_CPPN_novhelp.py","file_name":"FULL_CPPN_novhelp.py","file_ext":"py","file_size_in_byte":2401,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"461262363","text":"import pandas as pd\nimport numpy as np\nfrom numpy.lib.stride_tricks import as_strided\nimport os\nimport random\nfrom tqdm import tqdm\n\n\npositions = {\n \"ref_021_18450425-G0014_703997_4523\": [[1980, 2100]], # flare_star\n \"ref_031_12010765-G0013_520170_227006\": [[725, 825]],\n \"ref_021_24310595-G0013_746468_14893\": [[1100, 1300]], # flare_star\n \"ref_022_06300085-G0013_1548728_32012\": [[1325, 1700]], # flare_star\n \"ref_022_15730595-G0013_391462_6330\": [[2075, 2350]], # flare_star\n \"ref_022_15730595-G0013_481532_2590\": [[300, 550], [800, 1000]],\n \"ref_033_04500085-G0013_1485992_22874\": [[425, 800]], # flare_star\n \"ref_033_04700255-G0013_25519_18341\": [[1285, 1420]], # flare_star\n \"ref_024_12230255-G0013_303557_17479\": [[625, 750]], # flare_star\n \"ref_033_15300085-G0013_1742006_16050\": [[900, 1050]], # flare_star\n \"ref_024_12230255-G0013_393825_55632\": [[1280, 1380]], # flare_star\n \"ref_033_16810765-G0013_482792_15702\": [[725, 900]], # flare_star\n \"ref_043_04500085-G0013_2358479_2504\": [[4050, 4300]], # flare_star\n \"ref_023_15730595-G0013_378567_3040\": [[550, 750]], # flare_star\n \"ref_044_16280425-G0013_364820_9174\": [[3950, 4100]], # flare_star\n \"ref_041_14110425-G0013_422384_26026\": [[4200, 4550]], # flare_star\n \"ref_024_13500085-G0013_1397642_27534\": [[300, 360]],\n \"ref_043_18590595-G0013_709318_8662\": [[3350, 3600]],\n}\n\n\ndef get_train_filename():\n train_abnormal = pd.read_table(\"../dataset/AstroSet-v0.1/AstroSet/train_abnormal\", header=None, sep=\",\")\n flare_stars, microlensings = [], []\n for index, value in train_abnormal.iterrows():\n if value[1] == 'flare star':\n flare_stars.append(value)\n elif value[1] == 'microlensing':\n microlensings.append(value)\n return flare_stars, microlensings\n\n\ndef crop_data(data):\n background, cropped_data = [], []\n for item in data:\n filename = item[0]\n for position in positions[filename]:\n left, right = position[0], position[1]\n path = \"../dataset/AstroSet-v0.1/\" + item[2] + \"/\" + filename\n table = pd.read_table(path, header=None, sep=\" \")\n x = list(range(len(table)))\n y = table[1] - np.mean(table[1])\n x_, y_ = x[left:right], y[left:right]\n cropped_data.append(y_)\n background += (y[:positions[filename][0][0]].tolist() + y[positions[filename][-1][1]:].tolist())\n return background, cropped_data\n\n\ndef pool2d(A, kernel_size, stride, padding):\n A = np.reshape(A.tolist(), [A.shape[0], 1])\n A = np.pad(A, padding, mode='constant')\n # Window view of A\n output_shape = ((A.shape[0] - kernel_size[0]) // stride + 1, (A.shape[1] - kernel_size[1]) // stride + 1)\n A_w = as_strided(A, shape=output_shape + (kernel_size[0], kernel_size[1]),\n strides=(stride * A.strides[0], stride * A.strides[1]) + A.strides)\n A_w = A_w.reshape(-1, *kernel_size)\n return A_w.mean(axis=(1, 2)).reshape([output_shape[0]])\n\n\ndef data_downsampling(data):\n results = []\n for item in data:\n if len(item) >= 100:\n temp = pool2d(np.array(item), [2, 1], 2, 0)\n results.append(temp)\n return results\n\n\ndef get_all_filename():\n dirs = os.listdir(\"../dataset/AstroSet-v0.1/AstroSet\")\n dirs = [item for item in dirs if item[0] == '0']\n files = []\n for dir_ in dirs:\n filenames = os.listdir(\"../dataset/AstroSet-v0.1/AstroSet/\" + dir_)\n filenames = [\"../dataset/AstroSet-v0.1/AstroSet/\" + dir_ + \"/\" + filename for filename in filenames if filename != 'abstract']\n files += filenames\n return files\n\ndef average(x):\n width = x.shape[0]\n x_move1 = x.reshape([width, 1])\n ones_5 = np.ones((5,)) / 5\n ones_10 = np.ones((10,)) / 10\n ones_20 = np.ones((20,)) / 20\n x_move5 = np.convolve(x, ones_5, mode=\"same\").astype(np.float32).reshape([width, 1])\n x_move10 = np.convolve(x, ones_10, mode=\"same\").astype(np.float32).reshape([width, 1])\n x_move20 = np.convolve(x, ones_20, mode=\"same\").astype(np.float32).reshape([width, 1])\n x_output = np.stack([x_move1, x_move5, x_move10, x_move20], axis=-1)\n del x_move1, x_move5, x_move10, x_move20\n return x_output\n\n\ndef load_all_data():\n data = np.load(\"../dataset/data.npy\")\n data = [item.reshape([item.shape[0], 1, 1]) for item in data]\n pathes = np.load(\"../dataset/pathes.npy\")\n pathes = [np.array(item) for item in pathes]\n assert len(pathes) == len(data), \"error\"\n return data, pathes\n\n\nflare_stars, microlensings = get_train_filename()\nall_files = get_all_filename()\nbackground1, cropped_flare_stars = crop_data(flare_stars)\nbackground2, cropped_microlensings = crop_data(microlensings)\nbackground_all = background1 + background2\nbackground = []\nfor i in range(0, len(background_all), 10):\n array = np.array(background_all[i:i+10])\n array = array - np.mean(array)\n min_ = np.min(array)\n max_ = np.max(array)\n if min_ < -0.05 or max_ > 0.05:\n pass\n else:\n array = array.tolist()\n background += array\nflare_stars_ = cropped_flare_stars + data_downsampling(cropped_flare_stars)\nmicrolensings_ = cropped_microlensings + data_downsampling(cropped_microlensings)\n\n\ndef average(x):\n width = x.shape[0]\n x_move1 = x.reshape([width, 1])\n ones_5 = np.ones((5,)) / 5\n ones_10 = np.ones((10,)) / 10\n ones_20 = np.ones((20,)) / 20\n x_move5 = np.convolve(x, ones_5, mode=\"same\").astype(np.float32).reshape([width, 1])\n x_move10 = np.convolve(x, ones_10, mode=\"same\").astype(np.float32).reshape([width, 1])\n x_move20 = np.convolve(x, ones_20, mode=\"same\").astype(np.float32).reshape([width, 1])\n x_output = np.stack([x_move1, x_move5, x_move10, x_move20], axis=-1)\n del x_move1, x_move5, x_move10, x_move20\n return x_output\n\n\ndef data_augmentation(data):\n start = random.randint(0, len(background) - 400) # 在背景中随机产生一个起点\n data_output = []\n for item in data:\n length = 400 - len(item) # 截取的背景的长度\n rand = random.randint(0, length) # 左侧和右侧至少为20\n rand2 = random.uniform(0.8, 1.2)\n left_part = np.array(background[start:start + rand]) * rand2\n left_part = left_part.tolist()\n right_part = np.array(background[start + rand:start + length]) * rand2\n right_part = right_part.tolist()\n item = np.array(item) * random.uniform(0.75, 1.25)\n item = [_+random.uniform(-0.05, +0.05) for _ in item]\n array = []\n array.extend(left_part)\n array.extend(item)\n array.extend(right_part)\n array = np.array(array).reshape([400, 1, 1])\n data_output.append(array)\n return data_output\n\n\ndef generate_flare_stars(num=104000): #TODO\n flare_stars = []\n for i in tqdm(range(num//24)):\n flare_stars += data_augmentation(flare_stars_)\n return flare_stars\n\n\ndef generate_microlensings(num=104000): #TODO\n microlensings = []\n microlensing_template = (pd.read_table(\"../dataset/microlensing.txt\", header=None)[0] + 1) / 5\n microlensing_template = [item + random.uniform(-0.05, +0.05) for item in microlensing_template]\n for i in tqdm(range(num//24)):\n microlensings += data_augmentation(microlensings_) + \\\n data_augmentation(microlensings_) + \\\n data_augmentation([microlensing_template]*(24-14))\n return microlensings\n\n\ndef generate_class0(num=114000):\n class0 = []\n microlensing_template = (pd.read_table(\"../dataset/microlensing.txt\", header=None)[0] + 1) / 5\n microlensing_template = [item + random.uniform(-0.05, +0.05) for item in microlensing_template]\n for i in tqdm(range(num//38)):\n flare_stars_aug = data_augmentation(flare_stars_) # 24\n microlensings_aug = data_augmentation(microlensings_) + data_augmentation([microlensing_template]*(7)) # 7*2\n class0.extend(flare_stars_aug + microlensings_aug)\n class0 = [np.array(item) for item in class0]\n return class0\n\n\ndef generate_class1(all_data, num=114000):\n def crop_data(y):\n length = len(y)\n data = []\n for i in range(10):\n rand = random.randint(0, length-400)\n data.append(y[rand:rand+400,:,:])\n return data\n files = random.sample(all_data, num//10)\n class1 = []\n for file in tqdm(files):\n class1 += crop_data(file)\n class1 = [np.array(item) for item in class1]\n return class1\n\n\nif __name__ == '__main__':\n data, pathes = load_all_data()\n print(len(data), len(pathes))\n count = 0\n for index, item in enumerate(data):\n\n if item.shape[0] == 0:\n print(item.shape, pathes[index])\n count += 1\n print(count)\n\n","sub_path":"code/data.py","file_name":"data.py","file_ext":"py","file_size_in_byte":8756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"478856659","text":"import logging\nfrom typing import Optional\n\nfrom typing_extensions import Unpack\n\nfrom galaxy.util.drs import fetch_drs_to_file\nfrom . import (\n BaseFilesSource,\n FilesSourceOptions,\n FilesSourceProperties,\n)\n\nlog = logging.getLogger(__name__)\n\n\nclass DRSFilesSource(BaseFilesSource):\n plugin_type = \"drs\"\n\n def __init__(self, **kwd: Unpack[FilesSourceProperties]):\n kwds: FilesSourceProperties = dict(\n id=\"_drs\",\n label=\"DRS file\",\n doc=\"DRS file handler\",\n writable=False,\n )\n kwds.update(kwd)\n props = self._parse_common_config_opts(kwds)\n self._props = props\n\n def _realize_to(self, source_path, native_path, user_context=None, opts: Optional[FilesSourceOptions] = None):\n props = self._serialization_props(user_context)\n headers = props.pop(\"http_headers\", {}) or {}\n fetch_drs_to_file(source_path, native_path, user_context, headers=headers)\n\n def _write_from(self, target_path, native_path, user_context=None, opts: Optional[FilesSourceOptions] = None):\n raise NotImplementedError()\n\n def score_url_match(self, url: str):\n if url.startswith(\"drs://\"):\n return len(\"drs://\")\n else:\n return 0\n\n def _serialization_props(self, user_context=None):\n effective_props = {}\n for key, val in self._props.items():\n effective_props[key] = self._evaluate_prop(val, user_context=user_context)\n return effective_props\n\n\n__all__ = (\"DRSFilesSource\",)\n","sub_path":"lib/galaxy/files/sources/drs.py","file_name":"drs.py","file_ext":"py","file_size_in_byte":1546,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"299429846","text":"# Importation des librairies\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom sklearn.preprocessing import OneHotEncoder, LabelEncoder,StandardScaler\nfrom sklearn.model_selection import train_test_split\nimport keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom sklearn.metrics import confusion_matrix\nfrom keras.wrappers.scikit_learn import KerasClassifier\nfrom sklearn.model_selection import GridSearchCV\nfrom keras.layers import Dropout\n## Préprocessing ..............................................................................................\n\n# Load Data\ndataset = pd.read_csv(\"data/Churn_Modelling.csv\")\n\n# Create features and target\nX = dataset.iloc[:,3:13].values\nY = dataset.iloc[:,13].values\n\n# Encode categorical data and scale continuous data\nlabelencoder_X_1 = LabelEncoder()\nX[:,1] = labelencoder_X_1.fit_transform(X[:,1])\nlabelencoder_X_2 = LabelEncoder()\nX[:,2] = labelencoder_X_2.fit_transform(X[:,2])\n\n# Enode the ['Geography','Gender']\nonehotencoder = OneHotEncoder( categorical_features= [1] )\nX = onehotencoder.fit_transform(X).toarray()\nX = X[:,1:]\n\n# Create Train,Test Datasets\nX_train, X_test, y_train, y_test = train_test_split(X, Y, test_size = 0.2, random_state = 0)\n\n# Normalize\nsc = StandardScaler()\nX_train = sc.fit_transform(X_train)\nX_test = sc.fit_transform(X_test)\n\n# Build Neural Network .................................................................................................\n\n# Initilize\nclassifier = Sequential()\n\n# Add Hiden layer with 6 neurones\nclassifier.add( Dense(units=6,\n activation=\"relu\",\n kernel_initializer=\"uniform\"\n ))\nclassifier.add( Dropout(rate= 0.1))\n\n# Add Hiden layer with 6 neurones\nclassifier.add( Dense(units=6,\n activation=\"relu\",\n kernel_initializer=\"uniform\"\n ))\nclassifier.add( Dropout(rate= 0.1))\n\n# Adding the output layer\nclassifier.add(Dense( units = 1,\n kernel_initializer = 'uniform',\n activation = 'sigmoid'\n ))\n\n# Fitting the ANN to the Training set\nclassifier.compile( optimizer = \"adam\",\n loss = \"binary_crossentropy\",\n metrics=[\"accuracy\"]\n )\n\n# Train the model\nclassifier.fit(X_train,y_train , batch_size=10 , epochs=100)\n\n# Part 3 - Making predictions and evaluating the model\n\n# Predicting the Test set results\ny_pred = classifier.predict(X_test)\ny_pred = (y_pred > 0.5)\n\n# Confusion Matrix\ncm = confusion_matrix(y_test,y_pred)\naccuracy = (cm[0][0] + cm[1][1]) / (cm[0][0] + cm[1][1] + cm[0][1] + cm[1][0] )\n\n\n\n# Méthode Grid Research { Cross Validation (K-fold)}\ndef build_classifier(optimizer):\n classifier = Sequential()\n classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu', input_dim = 11))\n classifier.add(Dense(units = 6, kernel_initializer = 'uniform', activation = 'relu'))\n classifier.add(Dense(units = 1, kernel_initializer = 'uniform', activation = 'sigmoid'))\n classifier.compile(optimizer = optimizer, loss = 'binary_crossentropy', metrics = ['accuracy'])\n return classifier\n\nclassifier = KerasClassifier( build_fn = build_classifier)\nparameters = { \"batch_size\" : [ 25,32 ] , \"epochs\" : [ 100,500 ] , \"optimizer\" : [ \"adam\",\"rmsprop\"] }\n\ngrid_search = GridSearchCV( estimator = classifier,\n param_grid=parameters,\n scoring=\"accuracy\",\n cv = 10\n )\n\ngrid_search = grid_search.fit(X_train,y_train)\n\nbest_parameters = grid_search.best_params_\n\nbest_accuracy = grid_search.best_score_","sub_path":"OVERFIT.py","file_name":"OVERFIT.py","file_ext":"py","file_size_in_byte":3735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"332403485","text":"# Program: Algoritmo243_Para70.py\n# Author: Ramon R. Valeriano\n# Description:\n# Developed: 03/04/202 - 07:14\n# Updated:\n\nname = input(\"Enter with a word: \")\nname = name.lower()\ncont = 0\nquaantity = len(name)\nfor e in range(quaantity):\n n=name[e]\n if(n==\"a\")or(n==\"e\")or(n==\"i\")or(n==\"o\")or(n==\"u\"):\n cont+=1\nprint(cont)\n","sub_path":"Livros/Introdução à Programação - 500 Algoritmos resolvidos/Capitulo 4/Exercicios 4a/Algoritmo243_Para70.py","file_name":"Algoritmo243_Para70.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"378166871","text":"\nfrom setuptools import setup\nfrom os import path\n\n# The directory containing this file\nHERE = path.abspath(path.dirname(__file__))\n\n# The text of the README file\nwith open(path.join(HERE, \"README.rst\")) as fid:\n long_description = fid.read()\n\nwith open(path.join(HERE, \"VERSION\")) as version_fh:\n version = version_fh.read()\n\nsetup(\n author=\"\",\n author_email=\"\",\n long_description=long_description,\n long_description_content_type=\"text/x-rst\",\n classifiers=[\n \"Programming Language :: Python :: 3.6\",\n \"License :: OSI Approved :: MIT License\",\n \"Operating System :: POSIX :: Linux\",\n ],\n license=\"GNU GENERAL PUBLIC LICENSE\",\n name=\"blueprint\",\n packages=[\"blueprint\"],\n url=\"\",\n version=version,\n include_package_data=True,\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":791,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"580264332","text":"import dload\nimport requests\nfrom bs4 import BeautifulSoup\nfrom selenium import webdriver\nimport time\n\ndriver = webdriver.Chrome('chromedriver')\ndriver.get('https://movie.naver.com/movie/running/current.nhn')\ntime.sleep(5)\n\nreq = driver.page_source\nsoup = BeautifulSoup(req,'html.parser')\nthumbnails = soup.select('#content > div.article > div:nth-child(1) > div.lst_wrap > ul > li > div > a > img')\ni = 1\nfor thumbnail in thumbnails:\n img = thumbnail['src']\n print(img)\n dload.save(img,f'img/{i}.jpg')\n i += 1\n\ndriver.quit()","sub_path":"week6/assignment/movie2.py","file_name":"movie2.py","file_ext":"py","file_size_in_byte":537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"358961529","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Google OAuth 1.0 Client.\n#\n# Copyright (C) 2011 Yesudeep Mangalapilly \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.\n\n\"\"\"\n:module: pyoauth.oauth1.client.google\n:synopsis: Google OAuth 1.0 client implementation.\n\n.. autoclass:: GoogleClient\n :members:\n :show-inheritance:\n\"\"\"\n\nfrom pyoauth.error import SignatureMethodNotSupportedError\nfrom pyoauth.oauth1 import SIGNATURE_METHOD_HMAC_SHA1, SIGNATURE_METHOD_PLAINTEXT\n\nfrom pyoauth.oauth1.client import Client\nfrom pyoauth.types import is_bytes_or_unicode\n\nclass GoogleClient(Client):\n \"\"\"\n Creates an instance of a Google OAuth 1.0 client.\n\n :see: http://code.google.com/apis/accounts/docs/OAuth_ref.html\n \"\"\"\n _OAUTH_TEMPORARY_CREDENTIALS_REQUEST_URI = \"https://www.google.com/accounts/OAuthGetRequestToken\"\n _OAUTH_RESOURCE_OWNER_AUTHORIZATION_URI = \"https://www.google.com/accounts/OAuthAuthorizeToken\"\n _OAUTH_TOKEN_CREDENTIALS_REQUEST_URI = \"https://www.google.com/accounts/OAuthGetAccessToken\"\n\n def __init__(self,\n client_credentials,\n use_authorization_header=True):\n super(GoogleClient, self).__init__(\n client_credentials=client_credentials,\n temporary_credentials_request_uri=self._OAUTH_TEMPORARY_CREDENTIALS_REQUEST_URI,\n token_credentials_request_uri=self._OAUTH_TEMPORARY_CREDENTIALS_REQUEST_URI,\n resource_owner_authorization_uri=self._OAUTH_RESOURCE_OWNER_AUTHORIZATION_URI,\n use_authorization_header=use_authorization_header\n )\n\n @classmethod\n def _check_signature_method(cls, signature_method):\n if signature_method == SIGNATURE_METHOD_PLAINTEXT:\n raise SignatureMethodNotSupportedError(\"Google OAuth 1.0 does not support the `%r` signature method.\" % signature_method)\n\n def build_temporary_credentials_request(self,\n scopes,\n xoauth_displayname=None,\n method=\"POST\",\n payload_params=None,\n headers=None,\n realm=None,\n oauth_signature_method=SIGNATURE_METHOD_HMAC_SHA1,\n oauth_callback=\"oob\",\n **extra_oauth_params):\n \"\"\"\n\n :param scopes:\n A list of scopes to use with the credential request.\n :param xoauth_displayname:\n The display name of the application.\n \"\"\"\n payload_params = payload_params or {}\n\n scope = scopes if is_bytes_or_unicode(scopes) else \" \".join(scopes)\n google_params = dict(\n scope=scope,\n )\n if xoauth_displayname:\n google_params[\"xoauth_displayname\"] = xoauth_displayname\n payload_params.update(google_params)\n\n GoogleClient._check_signature_method(oauth_signature_method)\n\n return super(GoogleClient, self).build_temporary_credentials_request(\n method=method,\n payload_params=payload_params,\n headers=headers,\n realm=realm,\n oauth_signature_method=oauth_signature_method,\n oauth_callback=oauth_callback,\n **extra_oauth_params\n )\n\n def build_token_credentials_request(self,\n temporary_credentials,\n oauth_verifier,\n method=\"POST\",\n payload_params=None,\n headers=None,\n realm=None,\n oauth_signature_method=SIGNATURE_METHOD_HMAC_SHA1,\n **extra_oauth_params):\n GoogleClient._check_signature_method(oauth_signature_method)\n\n return super(GoogleClient, self).build_token_credentials_request(\n temporary_credentials=temporary_credentials,\n oauth_verifier=oauth_verifier,\n method=method,\n payload_params=payload_params,\n headers=headers,\n realm=realm,\n oauth_signature_method=oauth_signature_method,\n **extra_oauth_params\n )\n\n def build_resource_request(self,\n token_credentials,\n method,\n url,\n payload_params=None,\n headers=None,\n realm=None,\n oauth_signature_method=SIGNATURE_METHOD_HMAC_SHA1,\n **extra_oauth_params):\n GoogleClient._check_signature_method(oauth_signature_method)\n\n return super(GoogleClient, self).build_resource_request(\n token_credentials=token_credentials,\n method=method,\n url=url,\n payload_params=payload_params,\n headers=headers,\n realm=realm,\n oauth_signature_method=oauth_signature_method,\n **extra_oauth_params\n )\n","sub_path":"pyoauth/oauth1/client/google.py","file_name":"google.py","file_ext":"py","file_size_in_byte":5828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"373521848","text":"\"\"\"\nFlask app 初始化操作\n\"\"\"\nimport logging\nimport os\nimport sys\n\nfrom flask import Flask\n\nfrom app.api import ner_api\n\n# 将工程根目录插入到sys.path中\nproject_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))\nif project_path not in sys.path:\n sys.path.insert(0, project_path)\n\n\ndef create_app():\n \"\"\"\n 创建Flask app，并根据环境加载配置\n :return: Flask对象app\n \"\"\"\n # config = load_config()\n app = Flask(__name__)\n # app.config.from_object(config)\n\n if not hasattr(app, 'production'):\n app.production = not app.debug and not app.testing\n\n if app.debug or app.testing:\n app.logger.addHandler(logging.StreamHandler())\n app.logger.setLevel(logging.ERROR)\n\n # 注册蓝图\n register_blueprint(app)\n\n return app\n\n\ndef register_blueprint(app):\n \"\"\"\n 注册蓝图到给定app上\n :param app: Flask对象\n :return: None\n \"\"\"\n url_prefix = \"/AIMovieNer\"\n app.register_blueprint(ner_api.robot_bp, url_prefix=url_prefix)\n\n return None\n","sub_path":"AIMovieNer/app/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1056,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"131739739","text":"import subprocess\nimport json\nfrom os import system\nimport requests\nimport concurrent.futures\nimport functools\nimport time\nimport platform\nimport logging\nfrom config import config, fake_ips\n\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.DEBUG)\n\nformatter = logging.Formatter('%(asctime)s:%(name)s:%(message)s')\n\nfile_handler = logging.FileHandler('debug.log')\n# file_handler.setLevel(logging.ERROR)\nfile_handler.setFormatter(formatter)\n\nstream_handler = logging.StreamHandler()\nstream_handler.setFormatter(formatter)\n\nlogger.addHandler(file_handler)\n# logger.addHandler(stream_handler)\n\n\nclass Colors:\n HEADER = '\\033[95m'\n OKBLUE = '\\033[94m'\n OKGREEN = '\\033[92m'\n WARNING = '\\033[93m'\n FAIL = '\\033[91m'\n ENDC = '\\033[0m'\n BOLD = '\\033[1m'\n UNDERLINE = '\\033[4m'\n\n ResetAll = \"\\033[0m\"\n\n Bold = \"\\033[1m\"\n Dim = \"\\033[2m\"\n Underlined = \"\\033[4m\"\n Blink = \"\\033[5m\"\n Reverse = \"\\033[7m\"\n Hidden = \"\\033[8m\"\n\n ResetBold = \"\\033[21m\"\n ResetDim = \"\\033[22m\"\n ResetUnderlined = \"\\033[24m\"\n ResetBlink = \"\\033[25m\"\n ResetReverse = \"\\033[27m\"\n ResetHidden = \"\\033[28m\"\n\n Default = \"\\033[39m\"\n Black = \"\\033[30m\"\n Red = \"\\033[31m\"\n Green = \"\\033[32m\"\n Yellow = \"\\033[33m\"\n Blue = \"\\033[34m\"\n Magenta = \"\\033[35m\"\n Cyan = \"\\033[36m\"\n LightGray = \"\\033[37m\"\n DarkGray = \"\\033[90m\"\n LightRed = \"\\033[91m\"\n LightGreen = \"\\033[92m\"\n LightYellow = \"\\033[93m\"\n LightBlue = \"\\033[94m\"\n LightMagenta = \"\\033[95m\"\n LightCyan = \"\\033[96m\"\n White = \"\\033[97m\"\n\n BackgroundDefault = \"\\033[49m\"\n BackgroundBlack = \"\\033[40m\"\n BackgroundRed = \"\\033[41m\"\n BackgroundGreen = \"\\033[42m\"\n BackgroundYellow = \"\\033[43m\"\n BackgroundBlue = \"\\033[44m\"\n BackgroundMagenta = \"\\033[45m\"\n BackgroundCyan = \"\\033[46m\"\n BackgroundLightGray = \"\\033[47m\"\n BackgroundDarkGray = \"\\033[100m\"\n BackgroundLightRed = \"\\033[101m\"\n BackgroundLightGreen = \"\\033[102m\"\n BackgroundLightYellow = \"\\033[103m\"\n BackgroundLightBlue = \"\\033[104m\"\n BackgroundLightMagenta = \"\\033[105m\"\n BackgroundLightCyan = \"\\033[106m\"\n BackgroundWhite = \"\\033[107m\"\n\n\nclass IpHandler:\n ips = []\n conf_id = []\n my_ip = ''\n ips_not_in_dns = list()\n number_of_threads = 0\n up_list = []\n checking_list = []\n down_list = []\n\n def get_ips(self):\n self.check_net_connectivity()\n \"\"\"gets a list of ips related to the servers\"\"\"\n url = config.URLS['get-all-servers']\n # url = \"http://ff18da60.ngrok.io/mtph/get-all-servers\"\n response = requests.get(url)\n\n self.conf_id = response.json()\n self.ips = [item['ip'] for item in self.conf_id]\n logger.info(f'ips fetched: {self.ips}')\n\n def get_id_by_ip(self, ip):\n self.check_net_connectivity()\n for item in self.conf_id:\n if item['ip'] == ip:\n id = item['id']\n return id\n\n def check_ip(self, server, lock):\n \"\"\"\n pings each ip and return a boolean\n :return: Boolean\n \"\"\"\n\n ip = server['ip']\n\n status = self.fping(ip)\n if status:\n if ip not in self.up_list:\n self.up_list.append(ip)\n up_count = len(self.up_list)\n not_count = len(self.ips_not_in_dns)\n failure_stage_count = len(self.checking_list)\n total = up_count + not_count + failure_stage_count\n print(f'{Colors.OKGREEN}U:{up_count}, Fs:{failure_stage_count} N:{not_count}, T:{total} '\n f'*************************{ip} is up ***************************** {Colors.ENDC}')\n if ip in self.ips_not_in_dns:\n print(f\"{Colors.OKBLUE}Adding {ip} to dns was failed before. Now trying to add again.{Colors.ENDC}\")\n res = self.change_dns(action='add', lock=lock, new_ip=ip)\n if ip not in self.up_list:\n self.up_list.append(ip)\n self.ips_not_in_dns.remove(ip)\n logger.info(f'ips not in dns: {self.ips_not_in_dns}')\n print(f'# of ips not in dns: {len(self.ips_not_in_dns)}')\n time.sleep(30)\n return server\n else:\n ip_timing_status = ip in self.ips_not_in_dns\n if ip not in self.checking_list and not ip_timing_status:\n self.checking_list.append(ip)\n timing = {\n 'count': 1 if ip_timing_status else 2,\n 'delay': 1 if ip_timing_status else 2\n }\n\n ip_is_up = self.certainty_check(server, ip, count=timing['count'], delay=timing['delay'])\n if ip_is_up:\n self.checking_list.remove(ip)\n return server\n old_server = server\n # TODO: (3): What if this is the only ip?\n # TODO: Adding error log here.\n if ip_timing_status:\n print(f'{Colors.LightRed}*************************** {ip} is down *****************************{Colors.ENDC}')\n self.ips_not_in_dns.remove(ip)\n else:\n if ip in self.up_list:\n self.up_list.remove(ip)\n\n print(f'{Colors.FAIL}*************************** {ip} is down *****************************{Colors.ENDC}')\n logger.info(f\"Removing {old_server['ip']} from dns\")\n self.change_dns('remove', lock, old_ip=old_server['ip'])\n # todo (5): Handling failure.\n new_server = self.change_server_by_id(old_server['id'])\n logger.info(f\"New Server Created with ip: {new_server['ip']}\")\n if ip in self.checking_list:\n self.checking_list.remove(ip)\n # new_server = self.find_new_drop_by_ip(ip)\n if not new_server:\n logger.info('No server created before. So continue running with old server.')\n # todo (8): Should we add dns again?\n return server\n\n # todo (6): what if first checks fails and other passes.\n self.fping(new_server['ip'])\n time.sleep(5)\n status = self.fping(new_server['ip'])\n if status:\n logger.info(f\"{Colors.OKBLUE}{new_server['ip']} is checked and working. prepare to add to dns{Colors.ENDC}\")\n # else only pass the ip to check and change without changing dns\n\n res = self.change_dns(action='add', lock=lock, new_ip=new_server['ip'])\n time.sleep(30)\n else:\n if new_server['ip'] not in self.ips_not_in_dns:\n logger.info(f\"{new_server['ip']} just created but has failed.\")\n self.ips_not_in_dns.append(new_server['ip'])\n logger.info(f'ips not in dns: {self.ips_not_in_dns}')\n print(f'# of ips not in dns: {len(self.ips_not_in_dns)}')\n\n return [old_server, new_server]\n\n def certainty_check(self, server, ip, count=3, delay=3):\n self.check_net_connectivity()\n additional_check = False\n while count > 0:\n print(f'{Colors.WARNING}checking again for certainty: {count} remaining for ip: {ip}{Colors.ENDC}')\n additional_check = self.fping(ip)\n count -= 1\n time.sleep(delay)\n # if additional_check:\n # return server\n return additional_check\n\n def make_threads(self):\n \"\"\"make threads for each of ips\"\"\"\n import threading\n self.number_of_threads = len(self.ips)\n print('making %s threads' % self.number_of_threads)\n lock = threading.Lock()\n with concurrent.futures.ThreadPoolExecutor(max_workers=30) as executor:\n\n for server in self.conf_id:\n fu = executor.submit(self.check_ip, server, lock)\n fu.add_done_callback(functools.partial(self._future_completed, index=self.conf_id.index(server), lock=lock))\n\n def _future_completed(self, future, **kwargs):\n with concurrent.futures.ThreadPoolExecutor(max_workers=1) as executor:\n index = 'no index'\n if 'index' in kwargs:\n index = kwargs['index']\n lock = kwargs['lock']\n result = future.result()\n if result.__class__.__name__ == 'list':\n # server failed and a new one is created.\n old_server = result[0]\n new_server = result[1]\n logger.info(f'new server is created:{new_server}')\n index = self.conf_id.index(old_server)\n self.conf_id[index] = new_server\n\n fu = executor.submit(self.check_ip, self.conf_id[index], lock)\n fu.add_done_callback(functools.partial(self._future_completed, index=index, lock=lock))\n return fu\n else:\n # server is up\n server = result\n\n index = self.conf_id.index(server)\n fu = executor.submit(self.check_ip, self.conf_id[index], lock)\n fu.add_done_callback(functools.partial(self._future_completed, index=index, lock=lock))\n return fu\n\n def ping(self, ip, check_net=True):\n if check_net:\n self.check_net_connectivity()\n output = subprocess.Popen([\"ping.exe\", ip], stdout=subprocess.PIPE).communicate()[0]\n if str(output).count('Request timed out') > 1:\n return False\n return True\n\n def fping(self, ip, packet_count=15, check_net=True):\n if check_net:\n self.check_net_connectivity()\n cmd = ['fping', '-c', str(packet_count)]\n cmd.append(str(ip))\n ping = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n output = ping.communicate()\n out = str(output[-1])\n if '%,' in out:\n res = out.split('%,')[0]\n res = res.split(\"/\")[-1]\n else:\n res = out.split('=')\n res = res[-1].split('%')[0]\n res = res.split(\"/\")[-1]\n status = True\n if int(res) >= 50:\n status = False\n return status\n return status\n\n def check_net_connectivity(self):\n net_status = self.ping('www.google.com', check_net=False)\n while not net_status:\n print('internet connection problem')\n net_status = self.ping('www.google.com')\n time.sleep(5)\n return net_status\n\n def change_server_by_id(self, id):\n self.check_net_connectivity()\n url = config.URLS['change-server']\n _data = {\n 'id': id,\n }\n data = json.dumps(_data)\n response = requests.post(url, data)\n new_serve = response.json()\n logger.info('New server: ', new_serve)\n return new_serve\n\n def change_dns(self, action, lock, **kwargs):\n self.check_net_connectivity()\n lock.acquire()\n url = config.URLS['change-dns']\n _data = dict()\n if 'old_ip' in kwargs:\n _data['old_ip'] = kwargs['old_ip']\n if 'new_ip' in kwargs:\n _data['new_ip'] = kwargs['new_ip']\n _data['action'] = action\n data = json.dumps(_data)\n response = requests.post(url, data)\n lock.release()\n return response\n\n def find_new_drop_by_ip(self, ip):\n self.check_net_connectivity()\n url = config.URLS['find-new-drop']\n _data = {\n 'old_ip': ip\n }\n data = json.dumps(_data)\n response = requests.post(url, data)\n new_server = response.json()\n return new_server\n\n def api_request(self, url):\n response = requests.get(url)\n return response\n\n\ndef main():\n \"\"\"running part of the script\"\"\"\n ip_handler = IpHandler()\n ip_handler.get_ips()\n\n ip_handler.make_threads()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"518440635","text":"import math\n\nL0 = 2.*math.pi # Wavelength in PIC units\n\nMain(\n\tgeometry = \"1d3v\",\n\t\n\tinterpolation_order = 2,\n\t\n\ttimestep = 0.005 * L0,\n\tsim_time = 0.5 * L0,\n\t\n\tcell_length = [0.01 * L0],\n\tsim_length = [1. * L0],\n\t\n\tnumber_of_patches = [ 4 ],\n\t\n\tbc_em_type_x = [\"periodic\"],\n\t\n\treferenceAngularFrequency_SI = L0 * 3e8 /1.e-6,\n\t\n\tprint_every = 10\n)\n\n# Ion species\nSpecies(\n\tspecies_type = \"ion1\",\n\tinitPosition_type = \"random\",\n\tinitMomentum_type = \"maxwell-juettner\",\n\tn_part_per_cell = 2000,\n\tmass = 1836.0,\n\tcharge = 1.0,\n\tnb_density = 10.,\n\ttemperature = [0.00002],\n\ttime_frozen = 0.0,\n\tbc_part_type_xmin = \"none\",\n\tbc_part_type_xmax = \"none\"\n)\n\n# Electron species\nSpecies(\n\tspecies_type = \"electron1\",\n\tinitPosition_type = \"random\",\n\tinitMomentum_type = \"maxwell-juettner\",\n\tn_part_per_cell= 2000,\n\tmass = 1.0,\n\tcharge = -1.0,\n\tnb_density = 10.,\n\tmean_velocity = [0.05, 0., 0.],\n\ttemperature = [0.00002],\n\ttime_frozen = 0.0,\n\tbc_part_type_xmin = \"none\",\n\tbc_part_type_xmax = \"none\"\n)\n\n\n\n\nDiagScalar(\n\tevery = 1,\n\tvars = ['Utot','Ubal','Ukin']\n)\n\nDiagFields(\n\tevery = 5,\n\tfields = ['Ex','Ey','Ez','Rho_electron1','Rho_ion1']\n)\n\n\nDiagParticles(\n\toutput = \"density\",\n\tevery = 4,\n\ttime_average = 2,\n\tspecies = [\"electron1\"],\n\taxes = [\n\t\t[\"x\", 0.*L0, 1.*L0, 100],\n\t\t[\"vx\", -0.1, 0.1, 100]\n\t]\n)\n\nDiagParticles(\n\toutput = \"density\",\n\tevery = 4,\n\ttime_average = 1,\n\tspecies = [\"ion1\"],\n\taxes = [\n\t\t(\"x\", 0.*L0, 1.*L0, 100),\n\t\t(\"vx\", -0.001, 0.001, 100)\n\t]\n)\n\nDiagParticles(\n\toutput = \"px_density\",\n\tevery = 4,\n\ttime_average = 2,\n\tspecies = [\"electron1\"],\n\taxes = [\n\t\t[\"x\", 0.*L0, 1.*L0, 100],\n\t\t[\"vx\", -0.1, 0.1, 100]\n\t]\n)\n\nDiagParticles(\n\toutput = \"density\",\n\tevery = 1,\n\ttime_average = 1,\n\tspecies = [\"electron1\"],\n\taxes = [\n\t\t[\"ekin\", 0.0001, 0.1, 100, \"logscale\", \"edge_inclusive\"]\n\t]\n)\n","sub_path":"benchmarks/tst1d_6_diagnostics.py","file_name":"tst1d_6_diagnostics.py","file_ext":"py","file_size_in_byte":1790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"508069741","text":"import time\nimport numpy as np\nfrom collections import OrderedDict\n\nfrom .. import datamodels\nfrom .. import assign_wcs\nfrom .. import resample\n\nfrom . import flag_cr\nfrom . import blot_median\nfrom . import create_median\n\nimport logging\nlog = logging.getLogger(__name__)\nlog.setLevel(logging.DEBUG)\n\nDEFAULT_DOMAIN = {'lower': None, 'upper': None, 'includes_lower': True, 'includes_upper': False}\n\nclass OutlierDetection(object):\n \"\"\"\n This is the controlling routine for the outlier detection process.\n It loads and sets the various input data and parameters needed by\n the various functions and then controls the operation of this process\n through all the steps used for the detection.\n\n Notes\n -----\n This routine performs the following operations::\n\n 1. Extracts parameter settings from input model and merges\n them with any user-provided values\n 2. Resamples all input images into grouped observation mosaics.\n 3. Creates a median image from all grouped observation mosaics.\n 4. Blot median image to match each original input image.\n 5. Perform statistical comparison between blotted image and original image\n to identify outliers.\n 6. Updates input data model DQ arrays with mask of detected outliers.\n\n \"\"\"\n outlierpars = {'kernel': 'square', 'pixfrac': 1.0, 'resample_bits': None,\n 'fillval': 'INDEF', 'wht_type': 'exptime',\n 'nlow': 0, 'nhigh': 1,\n 'hthresh': None, 'lthresh': None,\n 'nsigma': '4 3', 'maskpt': 0.7,\n 'grow': 1, 'ctegrow': 0, 'snr': \"4.0 3.0\",\n 'scale': \"0.5 0.4\", 'backg': 0\n }\n\n def __init__(self, input_models, ref_filename=None, to_file=False, **pars):\n \"\"\"\n Parameters\n ----------\n input_models : list of objects or str\n list of data models as ModelContainer or ASN file,\n one data model for each input image\n\n drizzled_models : list of objects\n ModelContainer containing drizzled grouped input images\n\n to_file : bool\n Control whether or not to write out intermediate results to files\n\n \"\"\"\n self.input_models = input_models\n self.ref_filename = ref_filename\n self.to_file = to_file\n\n self.num_groups = len(self.input_models.group_names)\n\n self.outlierpars = {}\n if 'outlierpars' in ref_filename:\n self._get_outlier_pars()\n self.outlierpars.update(pars)\n\n\n def _get_outlier_pars(self):\n \"\"\" Extract outlier detection parameters from reference file\n \"\"\"\n # start by interpreting input data models to define selection criteria\n input_dm = self.input_models[0]\n filtname = input_dm.meta.instrument.filter\n\n ref_model = datamodels.OutlierParsModel(self.ref_filename['outlierpars'])\n\n # look for row that applies to this set of input data models\n # NOTE:\n # This logic could be replaced by a method added to the DrizParsModel object\n # to select the correct row based on a set of selection parameters\n row = None\n outlierpars = ref_model.outlierpars_table\n\n filter_match = False # flag to support wild-card rows in outlierpars table\n for n, filt, num in zip(range(1, outlierpars.numimages.shape[0] + 1), outlierpars.filter,\n outlierpars.numimages):\n # only remember this row if no exact match has already been made for\n # the filter. This allows the wild-card row to be anywhere in the\n # table; since it may be placed at beginning or end of table.\n\n if filt == \"ANY\" and not filter_match and self.num_groups >= num:\n row = n\n # always go for an exact match if present, though...\n if filtname == filt and self.num_groups >= num:\n row = n\n filter_match = True\n\n # With presence of wild-card rows, code should never trigger this logic\n if row is None:\n log.error(\"No row found in %s that matches input data.\", self.ref_filename)\n raise ValueError\n\n # read in values from that row for each parameter\n for kw in list(self.outlierpars.keys()):\n self.outlierpars[kw] = ref_model['outlierpars_table.{0}'.format(kw)]\n\n def do_detection(self):\n \"\"\" Perform drizzling operation on input images's to create a new output\n\n \"\"\"\n pars = self.outlierpars\n\n # Start by creating resampled/mosaic images for each group of exposures\n sdriz = resample.resample.ResampleData(self.input_models, single=True, **pars)\n sdriz.do_drizzle(**pars)\n drizzled_models = sdriz.output_models\n if self.to_file:\n log.info(\"Saving resampled grouped exposures to disk...\")\n drizzled_models.save(None)\n\n # Initialize intermediate products used in the outlier detection\n median_model = datamodels.ImageModel(init=drizzled_models[0].data.shape)\n median_model.meta = drizzled_models[0].meta # provide median with initial metadata\n base_filename = self.input_models[0].meta.filename\n median_filename = '_'.join(base_filename.split('_')[:2] + ['median.fits'])\n median_model.meta.filename = median_filename\n\n # Perform median combination on set of drizzled mosaics\n drizzle_groups_sci = [i.data for i in drizzled_models]\n drizzle_groups_wht = [i.wht for i in drizzled_models]\n median_model.data = create_median.do_median(drizzle_groups_sci,\n drizzle_groups_wht,\n **pars)\n if self.to_file:\n log.info(\"Writing out MEDIAN image to: {}\".format(median_model.meta.filename))\n median_model.save(median_model.meta.filename)\n # Blot the median image back to recreate each input image specified in\n # the original input list/ASN/ModelContainer\n blot_models = blot_median.do_blot(median_model, self.input_models,\n **pars)\n if self.to_file:\n log.info(\"Writing out BLOT input images...\")\n blot_models.save(None)\n\n # Perform outlier detection using statistical comparisons between\n # original input images and their blotted-median images\n flag_cr.do_detection(self.input_models, blot_models,\n self.ref_filename, **pars)\n\n # clean-up (just to be explicit about being finished with these results)\n del median_model, blot_models\n","sub_path":"jwst/outlier_detection/outlier_detection.py","file_name":"outlier_detection.py","file_ext":"py","file_size_in_byte":6647,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"306095548","text":"__author__ = 'joefalkson'\nimport numpy as np\n\ndef hough_circles_acc(BW, radius):\n nR,nC = BW.shape\n H = np.zeros((nR,nC))\n #for each x,y pixel in row/col form we map the point to the sinusoid\n for rowIdx in range(nR):\n for colIdx in range(nC):\n #if it is a 1 (edge) since we have already found the edges\n #in exercise 1\n if BW[rowIdx, colIdx]:\n for theta in range(-89,90):\n #for each possible edge gradient, Theta\n a = colIdx+1 - radius * np.cos(theta*np.pi/180)\n b = rowIdx+1 - radius * np.sin(theta*np.pi/180)\n\n #check for valid point\n if 1 <= a and a < nR and 1 <= b and b < nC:\n #increment vote for radius by 1\n H[a,b] += 1\n\n a = colIdx+1 + radius * np.cos(theta*np.pi/180)\n b = rowIdx+1 + radius * np.sin(theta*np.pi/180)\n if 1 <= a and a < nR and 1 <= b and b < nC:\n H[a,b] += 1\n\n #confirming it is normalized to unint8\n #H.astype(np.uint8)\n return H","sub_path":"ps1_python/hough_circles_acc.py","file_name":"hough_circles_acc.py","file_ext":"py","file_size_in_byte":1153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"588392720","text":"def initCar(color, type, km):\n car = {'color': '', 'type': '', 'km': 0}\n car['color'] = color\n car['type'] = type\n car['km'] = km\n return car\n\ndef ride(car, km):\n car['km'] += km\n\nlada = initCar('red', 'Lada 1200', 40000)\n\ntesla = initCar('pink', 'Tesla S', 1200)\n\nifa = initCar('brown', 'Ifa', 30000000)\n\nride(ifa, 220)\nprint(ifa)\n","sub_path":"week-04/1-objects/project/car.py","file_name":"car.py","file_ext":"py","file_size_in_byte":350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"580157668","text":"class Solution:\n \"\"\"\n @param nums: an array\n @param k: a target value\n @return: the maximum length of a subarray that sums to k\n \"\"\"\n def maxSubArrayLen(self, nums, k):\n # Write your code here\n m = {}\n ans = 0\n m[k] = 0\n n = len(nums)\n sum = [0 for i in range(n+1)]\n for i in range(1,n+1):\n sum[i] = sum[i-1]+nums[i-1]\n if sum[i] in m:\n ans = max(ans, i-m[sum[i]])\n if sum[i] + k not in m:\n m[sum[i]+k] = i\n return ans\n\n\na = Solution()\nprint(a.maxSubArrayLen([1, -1, 5, -2, 3],3))\n\n","sub_path":"lintcode911.py","file_name":"lintcode911.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"327570160","text":"import functools\r\nnum = int(input(\"Enter the length of the list: \"))\r\nlis = []\r\nprint(\"Enter the elements of list:\")\r\nfor i in range(num):\r\n e = eval(input())\r\n lis.append(e)\r\na = lambda x,y:x+y\r\nresult = functools.reduce( a,lis)\r\nprint(result)\r\n","sub_path":"week4(d).py","file_name":"week4(d).py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"369678706","text":"import random\r\nimport winsound\r\nfrequencyV = 3000 # FRECUENCIA PARA VICTORIA.\r\ndurationV = 200 # DURACIÓN PARA VICTORIA.\r\nfrequencyF = 1000 # FRECUENCIA PARA DERROTA.\r\ndurationF = 2000 # DURACIÓN PARA DERROTA.\r\n\r\n\r\n\r\nAHORCADO = ['''\r\n +---+\r\n | |\r\n |\r\n |\r\n |\r\n |\r\n =========''', '''\r\n +---+\r\n | |\r\n O |\r\n |\r\n |\r\n |\r\n =========''', '''\r\n +---+\r\n | |\r\n O |\r\n | |\r\n |\r\n |\r\n =========''', '''\r\n +---+\r\n | |\r\n O |\r\n /| |\r\n |\r\n |\r\n =========''', '''\r\n +---+\r\n | |\r\n O |\r\n /|\\ |\r\n |\r\n |\r\n =========''', '''\r\n +---+\r\n | |\r\n O |\r\n /|\\ |\r\n / |\r\n |\r\n =========''', '''\r\n +---+\r\n | |\r\n O |\r\n /|\\ |\r\n / \\ |\r\n |\r\n =========''']\r\n\r\n\r\ndef palabra_aleatoria(palabras): #ELIGE UNA PALABRA AL AZAR DE LA LISTA DE PALABRAS PARA EL JUEGO.\r\n\r\n\taleatoria=random.randint(0,len(palabras)-1)\r\n\r\n\treturn palabras[aleatoria]\r\n\r\ndef palabra_A_caracteres(palabra): #PASA LA PALABRA A LISTA DE CARACTERES.\r\n\teleccion=[]\r\n\tfor i in palabra:\r\n\t\teleccion+=[i]\r\n\treturn eleccion\r\n\r\ndef juego(vidas,elegida,longitud): # COMPRUEBA SI LETRA ESTÁ EN PALABRA Y SI ES ASÍ SUSTITUYE EL '_' POR LA LETRA.\r\n\r\n\tprint(\"\\n\\n {}\\n\\n\".format(vidas))\r\n\tprint(\"\\nLa palabra contiene {} letras\\n\".format(longitud))\r\n\tcont=0\r\n\tyes=False\r\n\totra=''\r\n\t\r\n\twhile cont<7:\r\n\t\tletra=input(\"\\nIntroduce una letra:\")\r\n\t\te=letra in elegida\r\n\t\tif e:\r\n\t\t\tprint(\"\\nHas dado una\\n\")\r\n\t\t\tposicion=elegida.index(letra)\r\n\t\t\tvidas.pop(posicion)\r\n\t\t\tvidas.insert(posicion,letra)\r\n\t\t\tprint(vidas)\r\n\t\t\to= '_' in vidas\r\n\t\t\tif o is False:\r\n\t\t\t\tyes=True\r\n\t\t\t\tbreak \r\n\t\telse:\r\n\t\t\tprint(\"\\nNo has acertado\\n\")\r\n\t\t\tprint(AHORCADO[cont])\r\n\t\t\tcont+=1\r\n\r\n\tif cont == 7:\r\n\t\tprint(\"**********************************************************************\")\r\n\t\tprint(\"\\n\\n\\n\\nLa palabra era: \\n\\n\\n{}\\n\".format(elegida))\r\n\t\tprint(\"\\n\\n\\n\\n GAME OVER\\n\")\r\n\t\twinsound.Beep(frequencyF, durationF)\r\n\t\twhile otra != 'y' and otra != 'n':\r\n\t\t\totra=input(\"\\n¿Quieres jugar otra partida? (y/n): \")\t\r\n\t\treturn otra\r\n\r\n\tif yes is True:\r\n\t\tprint(\"**********************************************************************\")\r\n\t\tprint(\"\\n\\n\\n\\n HAS GANADO EL JUEGO\\n\")\r\n\t\twinsound.Beep(frequencyV, durationV)\r\n\t\twhile otra != 'y' and otra != 'n':\r\n\t\t\totra=input(\"\\n¿Quieres jugar otra partida? (y/n): \")\r\n\t\treturn otra\t\r\n\r\n#COMIENZO EJECUCIÓN PROGRAMA.\r\n\r\nprint(\"\\n\\n*****************************************EL AHORCADO*******************************************\\n\\n\")\r\npalabras=['monte','culo','hijo','persona','arbol','cantero','entropia','gato','tsunami','truhan','coruña','castigo','oregon','raton','hueco','hielo','rapunsel','celosia','medusa','fusion']\r\notra=''\r\nwhile otra is not 'n':\r\n\teleccion=palabra_aleatoria(palabras)\r\n\tlongitud=len(eleccion)\r\n\tvidas=[]\r\n\tfor i in range(longitud):\r\n\t\tvidas+=['_']\r\n\r\n\telegida=palabra_A_caracteres(eleccion)\r\n\totra=juego(vidas,elegida,longitud)\r\n\r\nprint(\"\t\\n\\nHASTA LUEGO!!!\")","sub_path":"ahorcadocutre.py","file_name":"ahorcadocutre.py","file_ext":"py","file_size_in_byte":3185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"30828287","text":"import pandas as pd\nimport numpy as np\nimport os,sys,ast,requests\nimport json\nfrom math import radians, cos, sin, asin, sqrt\nimport itertools\nimport requests\nimport psycopg2\nimport ast\n#from IPython.display import display,HTML\nos.environ['TZ'] = 'Asia/Kolkata'\n\nclass NextParkingSpace:\n\n def getDBConnection(self, psqlhost, databaseName, userName, password):\n psqlConnection = psycopg2.connect(host=psqlhost, dbname=databaseName, user=userName, password=password)\n marker = psqlConnection.cursor();\n return marker\n\n\n def get_geo_parking(self):\n data_1=json.dumps({\n \"Query\":{\n \"Find\":{\n \"ParkingSpace\":{\n \"sid\":{\n \"ne\":\"\"\n }\n }\n }\n }\n })\n # Real time api for parking\n link='https://cdp-jaipur.cisco.com/deveng/fid-CIMQueryInterface?SensorCustomerKey=500001&AppKey=CDP-App&UserKey=500060'\n headers = {'Content-type': 'application/json', 'body': 'raw'}\n parking_real_time=requests.post(link,data=data_1)\n #print(parking_real_time.status_code)\n data=parking_real_time.json()\n li=[]\n colms=['sid','levelLabel','operatedBy','label','occupied','total','sensorCustomerId','hierId','siblingIndex'\\\n ,'provider','providerId','geo_pts','maxDurationMinutes','parkingRate_durationMinutes'\\\n ,'parkingRate_farePerMinute','zoneType']\n li.append(colms)\n for item in data['Find']['Result']:\n elem=item['ParkingSpace']\n sid=elem['sid']\n try:\n levelLabel=elem['levelLabel']\n except:\n levelLabel=np.nan\n try:\n operatedBy=elem['operatedBy']\n except:\n operatedBy=np.nan\n label=elem['label']\n occupied=elem['state']['occupied']\n total=elem['state']['total'] \n sensorCustomerId=elem['sensorCustomerId']\n hierId=elem['hierId']\n try:\n siblingIndex=elem['siblingIndex']\n except:\n siblingIndex=np.nan \n provider=elem['providerDetails']['provider']\n providerId=elem['providerDetails']['providerId']\n geo_pt=elem['boundary']\n try:\n maxDurationMinutes=elem['opParams']['maxDurationMinutes']\n parkingRate_durationMinutes=elem['opParams']['parkingRate']['durationMinutes']\n parkingRate_farePerMinute=elem['opParams']['parkingRate']['farePerMinute']\n zoneType=elem['opParams']['zoneType']\n except:\n maxDurationMinutes=np.nan\n parkingRate_durationMinutes=np.nan\n parkingRate_farePerMinute=np.nan\n zoneType=np.nan \n geo_pts=[] \n for elem_1 in geo_pt['geoPoint']:\n geo_pts.append([elem_1['latitude'],elem_1['longitude']])\n li.append([sid,levelLabel,operatedBy,label,occupied,total,sensorCustomerId,hierId,siblingIndex,provider,providerId\\\n ,geo_pts,maxDurationMinutes,parkingRate_durationMinutes,parkingRate_farePerMinute,zoneType]) \n\n data_df=pd.DataFrame(li[1:],columns=li[0])\n return data_df\n\n def get_route_info(self, point_1,point_2,mode=['fastest','car'],app_id='QacvSHflGqkVBJGvs9OS',app_code='9dbgDyDrC1ChasubHX7Xfw',traffic_mode='enabled'):\n #mode='list of modes['fastest','car']\n link='https://route.cit.api.here.com/routing/7.2/calculateroute.json?waypoint0='+str(point_1[0][0])+'%2C'+str(point_1[0][1])+'&waypoint1='+str(point_2[0][0])+'%2C'+str(point_2[0][1])+'&mode='+'%3B'.join(str(e) for e in mode)+'%3Btraffic%3A'+traffic_mode+'&app_id='+app_id+'&app_code='+app_code+'&departure=now'\n r=requests.get(link)\n jsn=r.json()\n summary=jsn['response']['route'][0]['summary']\n return summary['trafficTime'],summary['travelTime'],summary['distance']\n\n def get_traffic_info(self,data,grouping_cols='sid'):\n dist_stat={}\n #get traffic info travel time and distance\n sen_pos=data.groupby(grouping_cols).agg({'geo_pts':'first'})\n dist_df=pd.DataFrame(index=sen_pos.index,columns=sen_pos.index)\n travel_time_df=pd.DataFrame(index=sen_pos.index,columns=sen_pos.index)\n traffic_time_df=pd.DataFrame(index=sen_pos.index,columns=sen_pos.index)\n pt1_pt2=[]\n for subset in itertools.combinations(sen_pos.index, 2):\n pt1_pt2.append(subset)\n for i in pt1_pt2:\n temp=self.get_route_info(sen_pos.loc[i[0]]['geo_pts'],sen_pos.loc[i[1]]['geo_pts'])\n temp1=self.get_route_info(sen_pos.loc[i[1]]['geo_pts'],sen_pos.loc[i[0]]['geo_pts'])\n # travelling from a to b\n dist_df.loc[i[0],i[1]]=temp[0]\n travel_time_df.loc[i[0],i[1]]=temp[1]\n traffic_time_df.loc[i[0],i[1]]=temp[2]\n # travelling from b to a\n dist_df.loc[i[1],i[0]]=temp1[0]\n travel_time_df.loc[i[1],i[0]]=temp1[1]\n traffic_time_df.loc[i[1],i[0]]=temp1[2]\n dist_stat['dist']=dist_df\n dist_stat['travel_time']=travel_time_df\n dist_stat['traffic_time']=traffic_time_df\n return dist_stat\n\n def conv_2_list(self,row):\n geo_pts = []\n for elem_1 in row['geoPoint']:\n geo_pts.append([elem_1['latitude'], elem_1['longitude']])\n return geo_pts\n\n\nif __name__ == \"__main__\":\n nps = NextParkingSpace()\n marker = nps.getDBConnection(\"52.55.107.13\", \"cdp\", \"sysadmin\" ,\"sysadmin\")\n '''marker.execute(\"\"\"select sid, state, providerdetails, boundary, label, levellabel, ts from parking_space\"\"\")\n data_db = marker.fetchall()\n data = pd.DataFrame(data_db,columns=['sid', 'state', 'providerdetails', 'geo_pts', 'label', 'levellabel', 'ts'])\n data['geo_pts'] = data['geo_pts'].apply(lambda x: ast.literal_eval(x))\n data['geo_pts'] = data['geo_pts'].apply(lambda x: nps.conv_2_list(x))\n\n #data=nps.get_geo_parking()\n data['mean_position']=data['geo_pts'].apply(lambda x: [np.mean([i[0] for i in x]),np.mean([i[1] for i in x])])\n dist_stat=nps.get_traffic_info(data)\n\n ### minimum distance`\n dist_stat['dist'][dist_stat['dist']==0]=np.nan\n x = dist_stat['dist'].apply(lambda x: np.argmin(x))\n print(x)\n #print(x.loc['ParkingSpace__metro__Bldg1'])\n\n ### minimum travel time\n dist_stat['travel_time'][dist_stat['travel_time']==0]=np.nan\n travel = dist_stat['travel_time'].apply(lambda x: np.argmin(x))\n print(x)\n #print(travel.loc['ParkingSpace__metro__Bldg1'])\n\n ### minimum traffic distance\n dist_stat['traffic_time'][dist_stat['traffic_time']==0]=np.nan\n traffic = dist_stat['traffic_time'].apply(lambda x: np.argmin(x))\n print(traffic) '''\n\n spaceid = 'ParkingSpace__metro__Bldg1Flr6'\n query = \"select nearparkingspace from next_parking_space where parkingsapce ='\"+spaceid + \"'\"\n marker.execute(query)\n out = marker.fetchall()\n for i in out:\n print(i[0])\n #print(out)","sub_path":"app/resources/parkinganalysis/NextParkingSpace.py","file_name":"NextParkingSpace.py","file_ext":"py","file_size_in_byte":6920,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"373065068","text":"from process import remove_artifacts\nimport constants\nimport glob\nimport os\nimport numpy as np\nimport util\n\nfrom argparse import ArgumentParser\n\nparser = ArgumentParser()\nparser.add_argument('model', type=str)\nparser.add_argument('--sample', type=str)\noptions = parser.parse_args()\n\n\ndef main(model):\n folder = options.sample if options.sample else '*'\n files = f'data/predict/{model}/{folder}/*.nii.gz'\n\n for file in glob.glob(files):\n sample, i = os.path.basename(file).split('_')\n i = i[:4]\n header = util.header(file)\n volume = util.read_vol(file)\n\n n = 2 if sample in constants.TWINS else 1\n volume = remove_artifacts(volume, n)\n\n os.makedirs(f'data/predict_cleaned/{model}/{sample}', exist_ok=True)\n util.save_vol(volume, f'data/predict_cleaned/{model}/{sample}/{sample}_{i}.nii.gz', header)\n\n\nif __name__ == '__main__':\n main(options.model)\n","sub_path":"remove_artifacts.py","file_name":"remove_artifacts.py","file_ext":"py","file_size_in_byte":916,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"53328403","text":"#Reading items from a list\ndef append_lists(email): \n if '@' in email:\n email_slicer = email.split('@')\n user_name.append(email_slicer[0])\n domain_name.append(email_slicer[1])\n return user_name, domain_name\n\nif __name__ == '__main__':\n #Delcaring variables\n email_list = ['goku@dbz.com', 2, 3.14, 'vegeta@super.com', 'pq1', True]\n user_name = []\n domain_name = [] \n \n #Loop through the list and apply function\n for email in email_list:\n try:\n append_lists(email)\n except TypeError:\n continue\n print(f'Usernames: {user_name} \\nDomains: {domain_name}')\n","sub_path":"emailslicer.py","file_name":"emailslicer.py","file_ext":"py","file_size_in_byte":638,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"492779265","text":"import sys\nimport click\nimport os\nimport shutil\nimport subprocess\nfrom packageinfo import BUILD, VERSION, NAME\n\n\ndef bootstrap_common():\n if not os.path.exists(\"buildrecipes-common\"):\n subprocess.check_call([\n \"git\", \"clone\",\n \"http://github.com/simphony/buildrecipes-common.git\",\n ])\n sys.path.insert(0, \"buildrecipes-common\")\n\nbootstrap_common()\nimport common\n\nworkspace = common.workspace()\ncommon.edmenv_setup()\nclone_dir = \"simphony-ui-{VERSION}\".format(VERSION=VERSION)\n\n\n@click.group()\ndef cli():\n pass\n\n\n@cli.command()\ndef egg():\n os.makedirs(\"dist\")\n\n common.repo_to_edm_egg(\n \"http://github.com/simphony/simphony-ui.git\",\n clone_dir, NAME, VERSION, BUILD\n )\n\n\n@cli.command()\ndef upload_egg():\n\n egg_path = \"dist/{NAME}-{VERSION}-{BUILD}.egg\".format(\n NAME=NAME,\n VERSION=VERSION,\n BUILD=BUILD)\n\n click.echo(\"Uploading {} to EDM repo\".format(egg_path))\n common.upload_egg(egg_path)\n click.echo(\"Done\")\n\n\n@cli.command()\ndef clean():\n _clean()\n\ndef _clean():\n click.echo(\"Cleaning\")\n common.clean([\"dist\", clone_dir, \"buildrecipes-common\"])\n\n\ncli()\n","sub_path":"builder.py","file_name":"builder.py","file_ext":"py","file_size_in_byte":1174,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"238026541","text":"from tkinter import *\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport re\n\nroot = Tk()\n\n#\t= Pruebas para el circular = \n#\t\n# ...,1,2,*3,4,5,1,2,3,4,5,... | 5\n# ...,1,*2,3,1,2,3,... | 3\n#\n# ...,-1,2,*3,-1,2,3,-1,2,3,-1,... | 3\n# ...,*-4,0,3,2,0,-1,-4,0,3,2,0,-1,... | 6\n#\n#\t= Pruebas Periodica =\n#\n# ...,1,*-6,1,-6,...\t| 2\n# 10,2,*8,-2\n#\n#\t= Pruebas Normales\n#\n# 3,*5,-1,0.5,4,0,11\n# -1,0,*2,5\n#\n#\t\t== UPDATES ==\n#\n#\t>Ya no es necesario limpiar, solo para quitar texto de los campos\n#\t>Ya funcionan los flotantes\n#\t#\tIMPORTANTE\n#\t>En las entradas periodicas se necesita OBLIGATORIAMENTE poner 3 puntos al inicio y al final con sus respectivas comas xd\n#\t...,1,*-6,1,-6,...\n#\t^^^ ^^^\n#\t>con_lista_aux manda 3 veces el periodo de la convolucion periodica xD en una lista\n#\t>Moví los botones para no hacer mas larga la interfaz\n#\n\nXn = []\nHn = []\nmatrizTotal = [[]]\nentradaXn = StringVar()\nentradaHn = StringVar()\n\nentradaNx = StringVar()\nentradaNh = StringVar()\n\n# ConvCircular\nbConv = False\n\n# ConvPeriodica\nbConvPe = False\n\nvarCir = IntVar()\n\nroot.title(\"Convolucion Discreta\")\n\n# ==============================================\n\nmyFrame = Frame(root, width = 500, height = 520)\nmyFrame.pack()\n\nlabelTitle = Label(myFrame, text=\"Convolucion Discreta\", font=(\"Quicksand\",18))\nlabelTitle.place(x=120,y=10)\n\nlabelIns = Label(myFrame,text=\"Indicar el centro de la funcion con el simbolo '*'\",font=(\"Quicksand\",14))\nlabelIns.place(x=30, y=50)\n\nlabelXn = Label(myFrame, text=\"Introduce x(n):\", font=(\"Quicksand\",12))\nlabelXn.place(x=10, y=120)\n\ntxtXn = Entry(myFrame,width = 57,textvariable = entradaXn )\ntxtXn.place(x=10,y=140)\n\nlabelHn = Label(myFrame, text=\"Introduce h(n):\", font=(\"Quicksand\",12))\n\ntxtHn = Entry(myFrame,width = 57,textvariable = entradaHn)\n\nlabelNx = Label(root,text=\"Nx : \",font=(\"Quicksand\",12))\ntxtNx = Entry(myFrame,width = 10,textvariable = entradaNx,state='disabled')\n\nlabelNh = Label(root,text=\"Nh : \",font=(\"Quicksand\",12))\ntxtNh = Entry(myFrame,width = 10,textvariable = entradaNh, state='disabled')\n\nlabelNx.place(x=10, y=170)\ntxtNx.place(x=50, y=170)\n\nlabelNh.place(x=10, y=260)\ntxtNh.place(x=50, y=260)\n\nlablelConv = Label(root,text=\"\",font=(\"Quicksand\",12))\n\nbuttonCal = Button(root,text=\"Calcular\",font=(\"Quicksand\",12),command=lambda:calculaConv(txtXn.get(),txtHn.get()))\n\nbuttonLimp = Button(root,text=\"Limpiar\",font=(\"Quicksand\",12),command=lambda:limpiar())\n\nbuttonGrafS = Button(root,text=\"Salida\",font=(\"Quicksand\",12),command=lambda:graficaSalida(calculaConv(txtXn.get(),txtHn.get())))\n\nbuttonGrafE = Button(root,text=\"Entrada\",font=(\"Quicksand\",12),command=lambda:graficaIn(txtXn.get(),txtHn.get()))\n\n\nlabelHn.place(x=10, y=210)\ntxtHn.place(x=10,y=230)\nlablelConv.place(x=10,y=340)\nbuttonCal.place(x=30,y=300)\nbuttonLimp.place(x=130,y=300)\nbuttonGrafS.place(x=230,y=300)\nbuttonGrafE.place(x=320,y=300)\n# ==============================================\n\ndef redondear(x):\n n = int(x)\n return n if n-1 < x <= n else n+1\n\n#\tFuncion para habilitar o deshabilitar los campos de texto\ndef bloquearTxt(xd):\n\tglobal bConv\n\tglobal bConvPe\n\n\tif xd == 0:\n\t\ttxtNx.config(state='disabled') \n\t\ttxtNh.config(state='disabled') \n\t\tbConv = False\n\t\tbConvPe = False\n\n\telif xd == 1:\n\t\ttxtNx.config(state='normal')\n\t\ttxtNh.config(state='normal')\n\t\tbConv = True\n\t\tbConvPe = False\n\t\t\n\telif xd == 2:\n\t\ttxtNx.config(state='normal')\n\t\ttxtNh.config(state='disabled') \n\t\tbConv = False\n\t\tbConvPe = True\n\n#\tConvolucion Normalona\nconvCir = Radiobutton(myFrame,text=\"Conv. Normal\", variable = varCir, value = 0, command = lambda:bloquearTxt(0))\nconvCir.place(x=10, y=90)\n\n#\tConvolucion Circular\nconvCir = Radiobutton(myFrame,text=\"Conv. Circular\", variable = varCir, value = 1, command = lambda:bloquearTxt(1) )\nconvCir.place(x=140, y=90)\n\n#\tConvolución Periodica\nconvCir = Radiobutton(myFrame,text=\"Conv. Periodica\", variable = varCir, value = 2, command = lambda:bloquearTxt(2) )\nconvCir.place(x=270, y=90)\n\ndef calculaConv(xn,hn):\n\tglobal matrizTotal\n\tglobal Xn \n\tglobal Hn\n\tglobal bConv\n\tglobal bConvPe\n\n\tconv_lista_aux = []\n\ti_c_aux = 0\n\tconvLista = []\n\taux_row = []\n\tconvListaCir = []\n\n\t# ==============================================\n\t\n\t#Guardamos las listas generadas del string para crear la matriz\n\tXn = creaLista(xn)\n\tprint(Xn)\n\tHn = creaLista(hn)\n\tprint(Hn)\n\n\t#Si es convolucion circular, en Xn y Hn guardamos los periodos y la primera fase del metodo es el mismo\n\tif bConv:\n\t\tXn = separaPeriodos(Xn,int(txtNx.get()))\n\t\tHn = separaPeriodos(Hn,int(txtNh.get()))\n\n\t#Si es convolucion periodica, solo obtenemos el periodo de Xn\n\t#Se realiza la convolución normal\n\tif bConvPe:\n\t\tXn = separaPeriodos(Xn,int(txtNx.get()))\n\n\t#Guardamos el tamanio de ambas listas para no llamar a \"len()\"\n\tlen_Xn = len(Xn)\n\tlen_Hn = len(Hn)\n\n\t#Se obtiene el tamanio final de la lista de convolucion\n\tconvLen = len_Xn + len_Hn - 1\n\n\t# ==============================================\n\t#\t\t\t\tCONVOLUCION (convLista)\n\n\tif len_Xn > len_Hn:\n\t\tmultFunc(Xn,Hn,False)\n\t\t#Se realiza la suma de las columnas j veces, que es el tamanio del renglon\n\t\tfor j in range(convLen):\n\t\t\tconvLista.append( sumaColumna(j,len_Hn) )\n\telif len_Xn < len_Hn:\n\t\tmultFunc(Hn,Xn,False)\n\t\tfor j in range(convLen):\n\t\t\tconvLista.append( sumaColumna(j,len_Xn) )\n\telif len_Xn == len_Hn:\n\t\tmultFunc(Xn,Hn,True)\n\t\tfor j in range(convLen):\n\t\t\tconvLista.append( sumaColumna(j,len_Hn) )\n\n\tprint(matrizTotal)\n\n\t# ==============================================\n\n\t# Obtenemos el indice central de la convolucion\n\ti_c = indiceConv(xn,hn)\n\n\t# Obtenemos el indice de la convolcon periodica\n\tif bConvPe:\n\t\ti_c_aux = (i_c % int(txtNx.get())) + 1\n\n\t# ==============================================\n\n\t# Si es circular lo mandamos a la fase 2\n\tif bConv:\n\t\t#Le restamos 2 para quitar los \"...\" que se han tomado como elementos\n\t\ti_c -= 2\n\t\t#De acuerdo al perido más grande se manda a la funcion\n\t\tif int(txtNx.get()) > int(txtNh.get()):\n\t\t\tconvListaCir = fase2(convLista,int(txtNx.get()))\n\t\telif int(txtNx.get()) < int(txtNh.get()):\n\t\t\tconvListaCir = fase2(convLista,int(txtNh.get()))\n\t\telse:\n\t\t\t#Si son iguales\n\t\t\tconvListaCir = fase2(convLista,int(txtNh.get()))\n\t\t#Se asigna el nuevo centro xd\n\t\tconvListaCir[i_c] = \"*\" + str(convListaCir[i_c])\n\n\t# ==============================================\n\n\t#Guardar numero sin asterisco\n\tnum_aux = convLista[i_c]\n\t#Poner asterisco en la convolución\n\tconvLista[i_c] = \"*\" + str(convLista[i_c])\n\tprint(convLista)\n\n\tlablelConv.config(text=\"Convolucion: \\n\\n\"+str(convLista))\n\t\n\t#\t\t\t\tFIN CONV (normal)\n\t# ==============================================\n\n\t# Se limpia para que ya no le piques xd\n\tXn = []\n\tHn = []\n\tmatrizTotal = [[]]\n\n\t# ==============================================\n\n\tif bConvPe:\n\t\t#Quitamos el asterisco y dejamos solo el numero\n\t\tconvLista[i_c] = num_aux\n\n\t\t#Si es convolucion Periodica se realiza al funcion que retorna una lista con el\n\t\t#resultado de la suma por columnas\n\t\tconv_lista_aux = sumaXcolumnas(int(txtNx.get()),convLista)\n\t\t\n\t\t#Aumentamos 3 periodos mas a la suma\n\t\tconv_lista_aux = conv_lista_aux + conv_lista_aux + conv_lista_aux\n\n\t\t#Colocamos el asterisco a los 3 periodos anteriores\n\t\tconv_lista_aux[i_c_aux] = \"*\" + str(conv_lista_aux[i_c_aux])\n\n\t\t#Colocamos el asterisco a la convolución original\n\t\tconvLista[i_c_aux] = \"*\" + str(convLista[i_c_aux])\n\t\tlablelConv.config(text=\"Convolucion: \\n\\n\"+str(convLista)+\"\\n\\n Suma por Columnas: \\n\\n \"+str(conv_lista_aux))\n\n\t# ==============================================\n\n\t#Se imprime de más el y(n)\n\tif bConv:\n\t\tlablelConv.config(text=\"Convolucion: \\n\\n\"+str(convLista)+\"\\n\\ny(n) = \"+str(convListaCir))\n\t\treturn convListaCir\n\telse:\n\t\treturn convLista\n\ndef matrix(datos):\n\tprint(datos)\n\ttam = len(datos)\n\tnewList = []\n\tstrg = StringVar()\n\tfor x in datos:\n\t\tstrg = str(x)\n\t\tprint(strg)\n\n\t\tfor e in strg:\t\n\t\t\tif(e == '*'):\n\t\t\t\tstrg = strg.replace('*','')\n\n\t\taux = float(strg)\n\t\tnewList.append(aux)\n\n\treturn newList\n\ndef centro(strg):\n\tindice_conv = 0\n\tcont = 0\n\n\tfor i in strg:\n\t\tif(i == \"*\"):\n\t\t\tindice_conv = cont\n\t\tif(i == \",\"):\n\t\t\tcont += 1\n\treturn indice_conv\n\ndef graficaSalida(dtS):\n\tdSy = matrix(dtS)\n\tdtS = listToString(dtS)\n\tprint(str(dtS))\n\tc = centro(dtS)\n\tinicio = 0 - c\n\tfin = len(dSy) - c\n\tdSx = np.arange(inicio,fin,1)\n\t\n\tplt.figure('Grafica Salida')\n\tplt.stem(dSx,dSy, use_line_collection = True)\n\tplt.xlabel('x')\n\tplt.ylabel('y')\n\tplt.title('Xn * Hn')\n\tplt.show()\n\n\ndef graficaIn(dtXn, dtHn):\n\tdtXn = dtXn + \",\"\n\tdtXn = creaLista(dtXn)\n\tdXy = matrix(dtXn)\n\tdtXn = listToString(dtXn)\n\tcX = centro(dtXn)\n\tiX = 0 - cX\n\tfX = len(dXy) - cX\n\tdXx = np.arange(iX,fX,1)\n\n\tdtHn = dtHn + \",\"\n\tdtHn = creaLista(dtHn)\n\tdHy = matrix(dtHn)\n\tdtXn = listToString(dtHn)\n\tcH = centro(dtHn)\n\tiH = 0 - cH\n\tfH = len(dHy) - cH\n\tdHx = np.arange(iH,fH,1)\n\n\tplt.figure('Graficas de entrada')\n\tplt.subplot(1,2,1)\n\tplt.stem(dXx,dXy, use_line_collection = True)\n\tplt.title('Grafica Xn')\n\tplt.subplot(1,2,2)\n\tplt.stem(dHx,dHy, use_line_collection = True)\n\tplt.title('Grafica Hn')\n\tplt.show()\n\ndef listToString(s):\n\tstr1 = ','.join([str(elem) for elem in s])\n\treturn str1\n\n#\tFucion que multiplica 2 listas pero F1 tiene que ser mayor que F2\ndef multFunc(*argv):\n\tglobal matrizTotal\n\tmatrizTotal = [[]]\n\n\tlenF1 = argv[0]\n\tlenF2 = argv[1]\n\n\taux = 0\n\tx = 0\n\ty = 0\n\n\t#print(lenF1)\n\t#print(lenF2)\n\n\tconvLen_ = len(lenF1) + len(lenF2) - 1\n\n\t#Creando un arreglo bidimencional lleno de 0's\n\tmatrizTotal = [[0] * convLen_ for z in range( len(lenF2) )]\n\n\t#Guardando la multiplicacion de cada elemento de Hm por todos los elementos de Xn\n\tfor i in lenF2:\n\t\tfor k in lenF1:\n\t\t\taux = float(i) * float(k)\n\t\t\t#Guardando de acuerdo al renglon, al inicio el resultado se pone en la posicion 0\n\t\t\t#despues el resultado empieza a ponerse en 1, después en 2 y así\n\t\t\tmatrizTotal[x][y+x] = aux\n\t\t\t#Cambia de columna\n\t\t\ty += 1\n\t\t#Cambia de renglon\n\t\tx += 1\n\t\t#Reinicia la columna\n\t\ty = 0\n\n# Funcion que realiza la suma por columnas de la convolucion periodica\ndef sumaXcolumnas(p,conv):\n\tmatriz = [[]]\n\tcont = 0\n\tperiodoN = []\n\n\t#Llenamos de 0 la nueva lista que es periodo de la nueva convolucion\n\tfor d in range(p):\n\t\tperiodoN.append(0)\n\n\tprint(periodoN)\n\n\t# x es el numero de renglones de la suma\n\tx = int( redondear(len(conv)/p) )\n\n\t#Creamos la Matriz de suma\n\tmatriz = [[0] * p for z in range( x )]\n\n\t#Asignamos la conv a la nueva matriz de acuerdo al periodo\n\tfor i in range(x):\t\n\t\tfor j in range(p):\n\t\t\tif cont < len(conv):\n\t\t\t\tmatriz[i][j] = conv[cont]\n\t\t\t\tcont += 1\n\t\t\t\tprint(cont)\n\t\t\t\tprint(\"Matriz loka:\")\n\t\t\t\tprint(matriz)\n\n\t#Sumamos ALV\n\tfor a in range(p): \t#Ancho\n\t\tfor b in range(x):\t#Largo\n\t\t\tperiodoN[a] = periodoN[a] + matriz[b][a]\n\t\t\tprint(\"Periodo: \"+str(periodoN))\n\n\tprint(periodoN)\n\treturn periodoN\n\n\n#\tFuncion que suma las columnas de las matrices\ndef sumaColumna(numColumna,tamCna):\n\tglobal matrizTotal\n\n\tsum_ = 0\n\n\tfor i in range(tamCna):\n\t\t#Bajamos de renglon para sumar los valores que estan en \"numColumna\"\n\t\tsum_ = sum_ + matrizTotal[i][numColumna]\n\t\t#print(sum_)\n\n\treturn sum_\n\n#\tFuncion que obtiene el indice de la convolucion\ndef indiceConv(xn_,hn_):\n\t#Tomamos las entradas de string de H(n) y X(n)\n\tindice_x = 0\n\tindice_h = 0\n\tindice_conv = 0\n\tcont = 0\n\n\t#Contamos por comas para diferenciar los elementos\n\tfor i in xn_:\n\t\t#Guardamos el indice donde se encuentre el \"*\"\n\t\tif(i == \"*\"):\n\t\t\tindice_x = cont\n\t\tif(i == \",\"):\n\t\t\tcont += 1\n\n\tcont = 0\n\n\t#Lo mismo para H(n)\n\tfor j in hn_:\n\t\tif(j == \"*\"):\n\t\t\tindice_h = cont\n\t\tif(j == \",\"):\n\t\t\tcont += 1\n\n\t#Para sacar el indice de convolucion se suma ambos indices de xn y hn\n\tindice_conv = indice_x + indice_h\n\tprint(\"Indice Conv: \"+str(indice_conv))\n\n\treturn indice_conv\n\n#Funcion que toma n elementos de \"lista\" y retorna una nueva lista con n elementos\n#\n#\t== SOLO TOMA LOS PRIMEROS N ELEMENTOS DEL ARREGLO ==\n#\t== NO IDENTIFICA EL PERIODO AUTOMATICAMENTE (no pude xd)==\ndef separaPeriodos(lista,n):\n\n\tnuevaLista = []\n\n\tfor i in range(len(lista)):\n\t\t#Mientras sea menor se guarda cada elemento\n\t\tif i < n:\t\n\t\t\tnuevaLista.append(lista[i])\n\t\t#Cuando es mayor, se acaba el periodo deseado, asi que lo mandamoooosh\n\t\telse:\n\t\t\treturn nuevaLista\n\n#Funcion de la fase 2, donde tomamos el periodo de la primera fase y lo sumamos con los elementos\n#restantes y así obtenemos el periodo de la convolución\n#\t_______________________\n#\t4 -8 -15 4 13 7 -2 -3 (0) (0) (0) (0) ....... (f1)\n# |---------------||---------------------|\n#\t Periodo (n) \t Restantes\n#\n#Los paramtros son la convolucion de la fase1 (f1) y el tamaño del periodo mas grande\ndef fase2(listaConv,n):\n\n\tsobrantes = []\n\tnuevaLista = []\n\n\t#Empezamos por el n (tamaño del periodo) para que lo siguiente sean los elementos restantes\n\tfor i in range(n,len(listaConv)):\n\t\tsobrantes.append(listaConv[i])\n\n\t#A la convolucion de la primera fase al tamaño del periodo mas grande le restamos el tamaño de la lista de \n\t#los elementos restantes y así completamos con 0's para sumar\n\t#\t -2 -3 (0) (0) (0) (0)\n\t#\t|---------------------|\n\t#\t\tRestantes xd\n\tfor j in range(n-len(sobrantes)):\n\t\tsobrantes.append(0)\n\n\t#Para todo el periodo se suma uno por uno con los restantes y se guarda en una nueva lista\n\tfor k in range(n):\n\t\tnuevaLista.append(int(sobrantes[k])+int(listaConv[k]))\n\n\treturn nuevaLista\n\n#\tFuncion que convierte la string de entrada a una lista de numeros\ndef creaLista(strg):\n\n\tstrg = strg + \",\"\n\n\tnewList = []\n\taux = \"\"\n\ta = 0\n\n\tfor k in range(len(strg)-1):\n\t\t#Consideramos que la posicion actual no es una coma, ni asterisco, ni punto xd\n\t\tif strg[k] != \",\" and strg[k] != \"*\":\n\n\t\t\tif a == 2 or a == 1:\n\t\t\t\ta -= 1\n\t\t\t\tcontinue\n\n\t\t\tif k == len(strg)-4 or k == 0:\n\t\t\t\taux_p = strg[k] + strg[k+1] + strg[k+1]\n\t\t\t\tprint(\"aux_p: \"+aux_p)\n\t\t\t\tif aux_p == \"...\":\n\t\t\t\t\ta = 2\n\t\t\t\t\tcontinue\n\n\t\t\t#Si no es una coma, verificamos que el siguiente caracter no sea una coma\n\t\t\t#si el siguiente no es una coma signfica que es un numero de mas de 1 digito\n\t\t\t# \t1 , 23 , 456\n\t\t\t#\t^ ^^ ^^^\n\t\t\tif strg[k+1] != \",\":\n\t\t\t\t#Si es mas de un digito, cada digito lo guardamos en la variable aux\n\t\t\t\taux = aux + strg[k]\n\t\t\t\t#Salimos del for para no asignar nada a la lista aun\n\t\t\t\tcontinue\n\t\t\t#Si el siguiente caracter es una coma, significa que el numero ya acabo\n\t\t\taux = aux + strg[k]\n\t\t\t#Agregamos a la lista\n\t\t\tnewList.append(aux)\n\t\t\t#Limpiamos\n\t\t\taux = \"\"\n\n\treturn newList\n\ndef limpiar():\n\n\tlablelConv.config(text=\"\")\n\tentradaXn.set(\"\")\n\tentradaHn.set(\"\")\n\tentradaNx.set(\"\")\n\tentradaNh.set(\"\")\n\nroot.mainloop()\n\n","sub_path":"Practica2/pracS.py","file_name":"pracS.py","file_ext":"py","file_size_in_byte":14403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"479762037","text":"import random\nimport sys\nimport os\nimport datetime\nfrom timeit import default_timer as timer\n\nfrom query_runner import *\nfrom config import *\n\ndef run_bi_4(name_data, tagClass, country, num_tests):\n #create result folder\n if not os.path.exists(os.path.dirname(\"./result/\")):\n try:\n os.makedirs(os.path.dirname(\"./result/\"))\n except OSError as exc: # Guard against race condition\n if exc.errno != errno.EEXIST:\n raise\n\n\n ofile = open(\"result/bi_4\" + \"_\" + name_data, 'a')\n runner = Neo4jQueryRunner();\n\n total_time = 0.0\n total_knsize = 0\n report = \"\\n---------- \" + str(datetime.datetime.now()) + \" \" + \" ----------\\n\"\n\n for i in range(0, num_tests):\n start = timer()\n runner.bi_4(tagClass, country)\n end = timer()\n exe_time = end - start\n total_time += exe_time\n query_param = tagClass + \",\" + country\n line = name_data + \",\" + \"bi_4,\" + str(i) + \",\" + query_param + \",\" + str(exe_time) + \" seconds\"\n print(line)\n report += line + \"\\n\"\n report += \"summary,\" + name_data + \",\" + \"bi_4,\" + query_param + \",\" + str(total_time/num_tests) + \" seconds\"\n ofile.write(report)\n print (report)\n\n\nif __name__ == \"__main__\":\n # kn.py file_name db_name num_iteration\n if len(sys.argv) < 4:\n print(\"Usage: python bi_4.py name_data param num_tests\")\n sys.exit()\n # python bi_4.py snb_1 933 3\n run_bi_4(os.path.basename(sys.argv[1]), sys.argv[2], sys.argv[3], int(sys.argv[4]) if len(sys.argv) == 5 else \"\")\n\n","sub_path":"neo4j/bi_4.py","file_name":"bi_4.py","file_ext":"py","file_size_in_byte":1571,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"515822676","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.conf import settings\nfrom .models import *\n\n\ndef ping_view(request):\n if request.method == \"GET\":\n return render(request, 'control/ping.html',)\n\n if request.method == \"POST\":\n hosts = request.POST.get('data_input', '')\n hosts = hosts.split()\n obj = Control()\n data = json.dumps(obj.ping(hosts))\n print(data)\n return HttpResponse(data, content_type=\"application/json\")\n\n\ndef ip_calc_view(request):\n if request.method == \"GET\":\n return render(request, 'control/ip_calc.html',)\n\n if request.method == \"POST\":\n ip, prefix = request.POST.getlist('data_input[]', [0, 0])\n obj = Control()\n data = json.dumps(obj.info_about_ip(ip, prefix))\n return HttpResponse(data, content_type=\"application/json\")\n\n\ndef snmp_view(request):\n if request.method == \"GET\":\n return render(request, 'control/snmp.html',)\n\n if request.method == \"POST\":\n ip, oid = request.POST.getlist('data_input[]', [0, 0])\n obj = Control()\n data = json.dumps(obj.snmp(ip, oid, settings.COMMUNITY_SNMP))\n return HttpResponse(data, content_type=\"application/json\")\n\n\ndef mac_view(request):\n if request.method == \"GET\":\n return render(request, 'control/mac.html',)\n\n if request.method == \"POST\":\n mac = request.POST.get('data_input', '')\n obj = Mac(mac)\n data = json.dumps(obj.get_vendor())\n return HttpResponse(data, content_type=\"application/json\")\n","sub_path":"main/control/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"428963304","text":"import pygame\nimport constants\nfrom platforms import MovingPlatform\nfrom projectiles import Bullet\nfrom spritesheet_functions import SpriteSheet\n\n\nclass Player(pygame.sprite.Sprite):\n\n WIDTH = 48\n HEIGHT = 48\n\n # -- Methods\n def __init__(self):\n \"\"\" Constructor function \"\"\"\n\n # Call the parent's constructor\n super().__init__()\n\n # Load all of the player's animations\n self.defineAnimations()\n self.image = self.idle[0]\n self.frame_counter = 0\n\n # Set a reference to the image rect.\n self.rect = self.image.get_rect()\n\n # Set speed vector of player\n self.x_change = 0\n self.y_change = 0\n\n self.facing_right = True\n\n self.game = None\n\n # Load sounds\n self.jump_sound = pygame.mixer.Sound(\"jump_sound.ogg\")\n self.double_jump_sound = pygame.mixer.Sound(\"double_jump_sound.ogg\")\n self.shoot_sound = pygame.mixer.Sound(\"shoot.ogg\")\n\n def defineAnimations(self):\n spritesheet = SpriteSheet(\"TheKid.png\")\n\n # Define idle animation\n self.idle = []\n img = spritesheet.get_image(0, 0, Player.WIDTH//2, Player.HEIGHT//2)\n self.idle.append(pygame.transform.scale2x(img))\n img = spritesheet.get_image(27, 0, Player.WIDTH//2, Player.HEIGHT//2)\n self.idle.append(pygame.transform.scale2x(img))\n img = spritesheet.get_image(55, 0, Player.WIDTH//2, Player.HEIGHT//2)\n self.idle.append(pygame.transform.scale2x(img))\n img = spritesheet.get_image(82, 0, Player.WIDTH//2, Player.HEIGHT//2)\n self.idle.append(pygame.transform.scale2x(img))\n\n # Define walking animation\n self.walking = []\n img = spritesheet.get_image(1, 25, Player.WIDTH//2, Player.HEIGHT//2)\n self.walking.append(pygame.transform.scale2x(img))\n img = spritesheet.get_image(31, 25, Player.WIDTH//2, Player.HEIGHT//2)\n self.walking.append(pygame.transform.scale2x(img))\n img = spritesheet.get_image(59, 25, Player.WIDTH//2, Player.HEIGHT//2)\n self.walking.append(pygame.transform.scale2x(img))\n img = spritesheet.get_image(87, 25, Player.WIDTH//2, Player.HEIGHT//2)\n self.walking.append(pygame.transform.scale2x(img))\n img = spritesheet.get_image(118, 25, Player.WIDTH//2, Player.HEIGHT//2)\n self.walking.append(pygame.transform.scale2x(img))\n\n # Define jumping animation\n self.jumping = []\n img = spritesheet.get_image(0, 69, Player.WIDTH//2, Player.HEIGHT//2)\n self.jumping.append(pygame.transform.scale2x(img))\n img = spritesheet.get_image(24, 69, Player.WIDTH//2, Player.HEIGHT//2)\n self.jumping.append(pygame.transform.scale2x(img))\n\n # Define falling animation\n self.falling = []\n img = spritesheet.get_image(4, 101, Player.WIDTH//2, Player.HEIGHT//2)\n self.falling.append(pygame.transform.scale2x(img))\n img = spritesheet.get_image(33, 101, Player.WIDTH//2, Player.HEIGHT//2)\n self.falling.append(pygame.transform.scale2x(img))\n\n def update(self):\n # Gravity\n self.calc_grav()\n\n # Save current x and y value before changes\n previous_x = self.rect.x\n previous_y = self.rect.y\n\n # Move left/right based on colliding Moving Platforms\n self.rect.y += 2\n group = self.game.current_level.platform_list\n block_hit_list = pygame.sprite.spritecollide(self, group, False)\n self.rect.y -= 2\n for block in block_hit_list:\n if isinstance(block, MovingPlatform):\n self.rect.x += block.x_change\n\n # Move left/right\n self.rect.x += self.x_change\n\n # See if we hit anything\n group = self.game.current_level.platform_list\n block_hit_list = pygame.sprite.spritecollide(self, group, False)\n for block in block_hit_list:\n if self.rect.x < previous_x:\n self.rect.left = block.rect.right\n else:\n self.rect.right = block.rect.left\n\n # Move up/down\n self.rect.y += self.y_change\n\n # Check and see if we hit anything\n group = self.game.current_level.platform_list\n block_hit_list = pygame.sprite.spritecollide(self, group, False)\n for block in block_hit_list:\n # Reset our position based on the top/bottom of the object.\n if block.rect.y > previous_y:\n self.rect.bottom = block.rect.top\n self.double_jump = True\n else:\n self.rect.top = block.rect.bottom\n\n # Stop our vertical movement\n self.y_change = 0\n\n # Check and see if we hit any projectiles\n group = self.game.projectile_list\n projectile_hit_list = pygame.sprite.spritecollide(self, group, False)\n\n for projectile in projectile_hit_list:\n if not isinstance(projectile, Bullet):\n self.die()\n\n if self.rect.y > constants.SCREEN_HEIGHT:\n self.die()\n\n group = self.game.current_level.enemy_list\n hit_list = pygame.sprite.spritecollide(self, group, False)\n if len(hit_list) > 0:\n self.die()\n\n # Determine the player's current frame of animation\n if self.y_change < 0:\n self.animate(self.jumping, 4)\n elif self.y_change > 0:\n self.animate(self.falling, 4)\n elif self.x_change == 0:\n self.animate(self.idle, 6)\n else:\n self.animate(self.walking, 2)\n\n def animate(self, animation, buffer):\n if self.frame_counter >= buffer * len(animation):\n self.frame_counter = 0\n self.image = animation[self.frame_counter//buffer]\n self.frame_counter += 1\n\n if not self.facing_right:\n self.image = pygame.transform.flip(self.image, True, False)\n\n def calc_grav(self):\n \"\"\" Calculate effect of gravity. \"\"\"\n if self.y_change == 0:\n self.y_change = 1\n else:\n self.y_change += .35\n\n def jump(self):\n \"\"\" Called when user hits 'jump' button. \"\"\"\n self.rect.y += 2\n group = self.game.current_level.platform_list\n platform_hit_list = pygame.sprite.spritecollide(self, group, False)\n self.rect.y -= 2\n\n # If it is ok to jump, set our speed upwards\n if len(platform_hit_list) > 0:\n self.y_change = -8\n self.double_jump = True\n self.jump_sound.play()\n elif self.double_jump:\n self.y_change = -6\n self.double_jump = False\n self.double_jump_sound.play()\n\n # Player-controlled movement:\n def go_left(self):\n \"\"\" Called when the user hits the left arrow. \"\"\"\n self.x_change = -4\n self.facing_right = False\n\n def go_right(self):\n \"\"\" Called when the user hits the right arrow. \"\"\"\n self.x_change = 4\n self.facing_right = True\n\n def stop(self):\n \"\"\" Called when the user lets off the keyboard. \"\"\"\n self.x_change = 0\n\n def shoot(self):\n if self.facing_right:\n bullet = Bullet(1)\n bullet.rect.x = self.rect.right + self.x_change\n bullet.rect.y = self.rect.centery\n else:\n bullet = Bullet(-1)\n bullet.rect.x = self.rect.x - Bullet.WIDTH + self.x_change\n bullet.rect.y = self.rect.centery\n\n self.game.all_sprites_list.add(bullet)\n self.game.projectile_list.add(bullet)\n self.shoot_sound.play()\n\n def die(self):\n self.game.current_level.load(True)\n","sub_path":"IWishIWasTheGuy/build/exe.win32-3.6/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":7607,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"422155891","text":"from django.test import TestCase,RequestFactory\n\nfrom django.shortcuts import reverse\n\nfrom .models import SubForum,Forum,Topic,Tag,Message\nfrom .forms import MessageForm\nfrom .views import CreateTopicView\n\nfrom django.contrib.auth import get_user_model\nfrom django.test import Client\nUser = get_user_model()\n# Create your tests here.\nclass SectionTests(TestCase):\n def create_tag(self,name):\n return Tag.objects.create(name=name)\n\n def create_topic(self,subforum,creator,tags,theme=\"TopicTheme\"):\n topic = Topic.objects.create(subforum=subforum,theme=theme, creator=creator)\n if tags:\n for tag in tags:\n topic.tags.add(tag)\n return topic\n\n def setUp(self):\n self.tags = [self.create_tag('tag1'),self.create_tag('tag2'),self.create_tag('tag3')]\n self.subforum = SubForum.objects.create( section = Forum.objects.create())\n self.request_factory = RequestFactory()\n self.user = User.objects.create(username=\"User\", password=\"testpassword\")\n self.topic = self.create_topic(self.subforum,self.user,self.tags)\n self.message = Message.objects.create(creator = self.user, topic = self.topic, text = \"TestText\")\n self.client = Client()\n self.client.login(username=\"User\", password=\"testpassword\")\n\n def test_index_page(self):\n response = self.client.get(reverse('index'))\n self.assertEqual(response.status_code,200)\n\n def test_last_themes(self):\n\n self.create_topic(self.subforum,self.user,self.tags,'TopicTheme1')\n response = self.client.get(reverse('index'))\n self.assertQuerysetEqual(response.context['last_topics'],['',''])\n\n def test_subforum_page(self):\n response = self.client.get(reverse('subforum',args=(self.subforum.id,)))\n self.assertEqual(response.status_code,200)\n self.assertContains(response,self.subforum.name)\n def test_suborum_page_contains_topics(self):\n\n response = self.client.get(reverse('subforum', args=(self.subforum.id,)))\n self.assertContains(response,self.topic.theme)\n def test_topic_contains_messages(self):\n\n response = self.client.get(reverse('topic',args=(self.topic.id,)))\n self.assertContains(response,self.message.text)\n def test_message_get_time_created(self):\n\n self.assertEqual(self.message.get_time_created(),self.message.time_created.strftime('%b. %d, %Y, %I:%M %p'))\n def test_anon_user_has_no_access_to_message_form(self):\n\n response = self.client.get(reverse('topic',args=(self.topic.id,)))\n self.assertContains(response,\n 'Please
Login or Register' % (reverse('auth:login'),reverse('auth:register')))\n def test_anon_user_has_no_access_to_create_topic_form(self):\n\n response = self.client.get(reverse('createtopic',args=(self.subforum.id,)))\n self.assertContains(response,\n 'Please
Login or Register' % (reverse('auth:login'),reverse('auth:register')))\n def test_logged_user_access_to_message_form(self):\n\n response = self.client.get(reverse('topic',args=(self.topic.id,)))\n self.assertEqual(response.context['form'],MessageForm)\n\n def test_logged_user_access_to_create_topic_form(self):\n response = self.client.get(reverse('topic', args=(self.topic.id,)))\n self.assertEqual(response.context['form'], MessageForm)\n\n def test_create_topic_form_redirects(self):\n print('tags',','.join([tag.name for tag in self.tags]))\n request = self.request_factory.post(reverse('createtopic',args=(self.subforum.id,)),{\n 'theme': 'Theme',\n 'message': 'Text',\n 'tags': ','.join([tag.name for tag in self.tags])\n })\n request.user = self.user\n response = CreateTopicView.as_view()(request,pk=self.subforum.id)\n self.assertJSONEqual(\n str(response.content, encoding='utf8'),\n {'redirect_url': reverse('topic', args= (Topic.objects.get(theme='Theme').id,))}\n )\n\n def test_create_topic_context_has_tags(self):\n response = self.client.get(reverse('createtopic',args=(self.subforum.id,)))\n self.assertEqual(response.context['tags'], [tag.name for tag in self.tags])\n\n\n\n\n\n\n","sub_path":"sections/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":4400,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"397319580","text":"import os\nimport math\nimport functools\nimport shutil\nfrom contextlib import contextmanager\nfrom threading import Lock\nfrom pathlib import Path\nfrom .. import log\nfrom .fileio import IterStream, Cache, TarExtract, TarExtractAll, RelativePath, GzipExtract, ZipExtract, ZipExtractCache, StringFile, ReTar, Bz2Extract\nfrom .download import Download, DownloadConfig, BaseDownload, RequestsDownload, LocalDownload, _DownloadConfig\nfrom .hash import HashVerificationError, HashVerifier, HashStream\nfrom .registry import Registry\n\n\n_logger = log.easy()\n\n\ndef tmp_path():\n p = Path(os.environ.get('IR_DATASETS_TMP', '/tmp/ir_datasets/'))\n p.mkdir(parents=True, exist_ok=True)\n return p\n\n\ndef home_path():\n p = Path(os.environ.get('IR_DATASETS_HOME', Path.home() / '.ir_datasets'))\n p.mkdir(parents=True, exist_ok=True)\n return p\n\n\n@contextmanager\ndef finialized_file(path, mode):\n if path == os.devnull:\n with open(path, mode) as f:\n yield f\n else:\n try:\n with open(f'{path}.tmp', mode) as f:\n yield f\n os.replace(f'{path}.tmp', path)\n except:\n try:\n os.remove(f'{path}.tmp')\n except:\n pass # ignore\n raise\n\n\nclass Lazy:\n def __init__(self, fn):\n self._lock = Lock()\n self._fn = fn\n self._loaded = False\n self._result = None\n\n def __call__(self):\n if not self._loaded:\n with self._lock:\n if not self._loaded: # repeat condition from above in thread-safe way\n self._result = self._fn()\n self._loaded = True\n return self._result\n\n @property\n def is_loaded(self):\n return self._loaded\n\n\ndef apply_sub_slice(orig_slice: slice, new_slice: slice):\n start, stop, step = None, None, None\n if new_slice.start is not None:\n if isinstance(new_slice.start, int):\n if new_slice.start < 0:\n if orig_slice.stop is None:\n raise ValueError('start cannot be negative with unknown size')\n start = orig_slice.stop + new_slice.start\n else:\n start = orig_slice.start + new_slice.start\n elif isinstance(new_slice.start, float):\n if orig_slice.stop is None:\n raise ValueError('start cannot be float with unknown size')\n if not (0. <= new_slice.stop <= 1.):\n raise ValueError('start must be in interval [0,1] if float')\n size = orig_slice.stop - (orig_slice.start or 0)\n start = (new_slice.start * size) + (orig_slice.start or 0)\n start = math.floor(start)\n else:\n raise TypeError('start must be int or float')\n else:\n start = orig_slice.start\n\n if new_slice.stop is not None:\n if isinstance(new_slice.stop, int):\n if new_slice.stop < 0:\n if orig_slice.stop is None:\n raise ValueError('stop cannot be negative with unknown size')\n stop = orig_slice.stop + new_slice.stop\n else:\n stop = min(orig_slice.start + new_slice.stop, orig_slice.stop)\n elif isinstance(new_slice.stop, float):\n if orig_slice.stop is None:\n raise ValueError('stop cannot be float with unknown size')\n if not (0. <= new_slice.stop <= 1.):\n raise ValueError('stop must be in interval [0,1] if float')\n size = orig_slice.stop - (orig_slice.start or 0)\n stop = (new_slice.stop * size) + (orig_slice.start or 0)\n stop = math.floor(stop)\n else:\n raise TypeError('stop must be int or float')\n else:\n stop = orig_slice.stop\n\n if new_slice.step is not None:\n if isinstance(new_slice.step, int):\n if new_slice.step <= 0:\n raise ValueError('step must be a positive')\n step = (orig_slice.step or 1) * new_slice.step\n else:\n raise TypeError('step must be int')\n else:\n step = orig_slice.step\n return slice(start, stop, step)\n\ndef slice_idx(orig_slice: slice, index: int):\n if index >= 0:\n index = orig_slice.start + index\n else:\n index = orig_slice.stop + index\n return slice(index, min(index + 1, orig_slice.stop))\n\n\nclass DocstoreSplitter:\n def __init__(self, it, docs_store):\n self.it = it\n self.docs_store = docs_store\n\n def __iter__(self):\n return self\n\n def __next__(self):\n return next(self.it)\n\n def __getitem__(self, key):\n return iter(self.docs_store)[key]\n\n\ndef use_docstore(fn):\n # For use as an @annotation\n # use docs_store if it's already built, otherwise only use it if the user\n # specifies a split (docs_it[split])\n @functools.wraps(fn)\n def wrapper(self):\n docs_store = self.docs_store()\n if docs_store.built():\n return iter(docs_store) # iterate from the docstore -- really fast\n return DocstoreSplitter(fn(self), docs_store) # avoid building docstore if not needed\n return wrapper\n\n\nclass Migrator:\n def __init__(self, version_file, version, affected_files, message=None, wrapped=None):\n self._wrapped = wrapped\n self._version_file = Path(version_file)\n self._version = version\n self._affected_files = affected_files\n self._message = message\n self._state = 'NOT_CHECKED'\n\n def __getattr__(self, attr):\n item = getattr(self._wrapped, attr)\n if callable(item):\n item = self._migrate(item)\n return item\n\n def __call__(self, wrapped):\n return Migrator(self._version_file, self._version, self._affected_files, self._message, wrapped)\n\n def _migrate(self, fn):\n # optionally wrap the function to perform cleanup of affected files\n if not self._state == 'OK':\n @functools.wraps(fn)\n def wrapped(*args, **kwargs):\n if not self._state == 'OK':\n self._version_file.parent.mkdir(parents=True, exist_ok=True)\n if not self._version_file.exists() or self._read_version() != self._version:\n self._state = 'IN_PROGRESS'\n paths_to_remove = [f for f in self._affected_files if os.path.exists(f)]\n if paths_to_remove:\n if self._message:\n _logger.info(self._message)\n for file in paths_to_remove:\n if Path(file).is_file():\n os.unlink(file)\n else:\n shutil.rmtree(file)\n with self._version_file.open('wt') as f:\n f.write(self._version)\n self._state = 'OK'\n return fn(*args, **kwargs)\n return wrapped\n return fn\n\n def _read_version(self):\n with self._version_file.open('rt') as f:\n return f.read()\n","sub_path":"ir_datasets/util/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"122893834","text":"import numpy as np\nimport torch\nimport gym\nimport argparse\nimport os\nimport d4rl\nfrom tqdm import tqdm\nimport utils\nimport TD3_BC\n\n\n# Runs policy for X episodes and returns average reward\n# A fixed seed is used for the eval environment\ndef eval_policy(policy, env_name, seed, step, mean, std, seed_offset=100, eval_episodes=10):\n\teval_env = gym.make(env_name)\n\teval_env.seed(seed + seed_offset)\n\n\tep_rewards = []\n\tfor _ in range(eval_episodes):\n\t\ttotal_reward = 0\n\t\tstate, done = eval_env.reset(), False\n\t\twhile not done:\n\t\t\tstate = (np.array(state).reshape(1,-1) - mean)/std\n\t\t\taction = policy.select_action(state)\n\t\t\tstate, reward, done, _ = eval_env.step(action)\n\t\t\ttotal_reward += reward\n\t\tep_rewards.append(total_reward)\n\n\tavg_reward = sum(ep_rewards) / len(ep_rewards)\n\tstd_reward = np.sqrt(sum([(reward - mean) ** 2 for reward in ep_rewards]) / len(ep_rewards))\n\td4rl_score = eval_env.get_normalized_score(avg_reward)\n\td4rl_std = eval_env.get_normalized_score(std_reward)\n\n\tprint(\"---------------------------------------\")\n\tprint(f\"Evaluation over {eval_episodes} episodes: {d4rl_score:.3f}\")\n\tprint(\"---------------------------------------\")\n\treturn d4rl_score\n\n\nif __name__ == \"__main__\":\n\t\n\tparser = argparse.ArgumentParser()\n\t# Experiment\n\tparser.add_argument(\"--policy\", default=\"TD3_BC\") # Policy name\n\tparser.add_argument(\"--env\", default=\"hopper-medium-v0\") # OpenAI gym environment name\n\tparser.add_argument(\"--seed\", default=0, type=int) # Sets Gym, PyTorch and Numpy seeds\n\tparser.add_argument(\"--eval_freq\", default=int(5e3), type=int) # How often (time steps) we evaluate\n\tparser.add_argument(\"--max_timesteps\", default=int(1e6), type=int) # Max time steps to run environment\n\tparser.add_argument(\"--save\", action=\"store_true\") # Save model and optimizer parameters\n\tparser.add_argument(\"--load\", action=\"store_true\") # Model load file name, \"\" doesn't load, \"default\" uses file_name\n\tparser.add_argument(\"--no_tqdm\", action=\"store_true\")\t\n\t# TD3\n\tparser.add_argument(\"--expl_noise\", default=0.1) # Std of Gaussian exploration noise\n\tparser.add_argument(\"--batch_size\", default=256, type=int) # Batch size for both actor and critic\n\tparser.add_argument(\"--discount\", default=0.99) # Discount factor\n\tparser.add_argument(\"--tau\", default=0.005) # Target network update rate\n\tparser.add_argument(\"--policy_noise\", default=0.2) # Noise added to target policy during critic update\n\tparser.add_argument(\"--noise_clip\", default=0.5) # Range to clip target policy noise\n\tparser.add_argument(\"--policy_freq\", default=2, type=int) # Frequency of delayed policy updates\n\tparser.add_argument(\"--output_dir\", type=str, default='/output')\n\t# TD3 + BC\n\tparser.add_argument(\"--alpha\", default=2.5)\n\tparser.add_argument(\"--normalize\", default=True)\n\targs = parser.parse_args()\n\n\tfile_name = f\"td3_bc_seed{args.seed}_{args.env}\"\n\tprint(\"---------------------------------------\")\n\tprint(f\"Policy: {args.policy}, Env: {args.env}, Seed: {args.seed}\")\n\tprint(\"---------------------------------------\")\n\n\tif not os.path.exists(\"./results\"):\n\t\tos.makedirs(\"./results\")\n\n\tif args.save and not os.path.exists(\"./models\"):\n\t\tos.makedirs(\"./models\")\n\n\tenv = gym.make(args.env)\n\n\t# Set seeds\n\tenv.seed(args.seed)\n\tenv.action_space.seed(args.seed)\n\ttorch.manual_seed(args.seed)\n\tnp.random.seed(args.seed)\n\t\n\tstate_dim = env.observation_space.shape[0]\n\taction_dim = env.action_space.shape[0] \n\tmax_action = float(env.action_space.high[0])\n\n\tkwargs = {\n\t\t\"state_dim\": state_dim,\n\t\t\"action_dim\": action_dim,\n\t\t\"max_action\": max_action,\n\t\t\"discount\": args.discount,\n\t\t\"tau\": args.tau,\n\t\t# TD3\n\t\t\"policy_noise\": args.policy_noise * max_action,\n\t\t\"noise_clip\": args.noise_clip * max_action,\n\t\t\"policy_freq\": args.policy_freq,\n\t\t# TD3 + BC\n\t\t\"alpha\": args.alpha\n\t}\n\n\t# Initialize policy\n\tpolicy = TD3_BC.TD3_BC(**kwargs)\n\tmake_empty_dict = lambda: {'critic_loss': [], 'actor_loss': []}\n\tmetrics, epoch_metrics = make_empty_dict(), make_empty_dict()\n\tevaluations = []\n\tT = 0\n\tif args.load and os.path.isfile(os.path.join(args.output_dir, \"step\")):\n\t\tT = policy.load(args.output_dir)\n\t\tevaluations = np.load(os.path.join(args.output_dir, \"reward.npy\")).tolist()\n\t\tfor key, lst in metrics.items():\n\t\t\tlst.extend(np.load(os.path.join(args.output_dir, key + \".npy\"), allow_pickle=True).tolist())\n\n\treplay_buffer = utils.ReplayBuffer(state_dim, action_dim)\n\treplay_buffer.convert_D4RL(d4rl.qlearning_dataset(env))\n\tif args.normalize:\n\t\tmean,std = replay_buffer.normalize_states() \n\telse:\n\t\tmean,std = 0,1\n\t\n\tpbar = tqdm(total=args.eval_freq, disable=args.no_tqdm)\n\tfor t in range(T, int(args.max_timesteps)):\n\t\tbatch_metrics = policy.train(replay_buffer, args.batch_size)\n\t\t#pbar.update(1)\n\t\tfor metric, value in batch_metrics.items():\n\t\t\tepoch_metrics[metric].append(value)\n\t\t# Evaluate episode\n\t\tif (t + 1) % args.eval_freq == 0:\n\t\t\tpbar.close()\n\t\t\tprint(f\"Time steps: {t+1}\")\n\t\t\tevaluations.append(eval_policy(policy, args.env, args.seed, t, mean, std))\n\t\t\tfor key, value in epoch_metrics.items(): \n\t\t\t\tprint(f\"Processing {key}\", flush=True)\n\t\t\t\tvalue = [v.cpu().detach().numpy() for v in value]\n\t\t\t\tif value[0].size == 1:\n\t\t\t\t\tprint(f\"{key}: {sum(value)}\")\n\t\t\t\t\tmetrics[key].append(sum(value))\n\t\t\t\telse:\n\t\t\t\t\tmetrics[key].append(np.concatenate(value, axis=0))\n\t\t\tfor key, value in metrics.items():\n\t\t\t\tnp.save(os.path.join(args.output_dir, key), value)\n\t\t\tnp.save(os.path.join(args.output_dir, \"reward\"), evaluations)\n\t\t\tif args.save: policy.save(args.output_dir, t)\n\t\t\tepoch_metrics = make_empty_dict()\n\t\t\t#pbar.close()\n\t\t\t#pbar = tqdm(total=args.eval_freq, disable=args.no_tqdm)\n","sub_path":"d4rl_evaluations/td3_bc/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5730,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"74472469","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 ('books', '0003_auto_20160218_1202'),\n ]\n\n operations = [\n migrations.AddField(\n model_name='sale',\n name='method_detail',\n field=models.CharField(help_text='Optional detail specific to the payment method. Check# for check payments.', max_length=40, blank=True),\n ),\n ]\n","sub_path":"books/migrations/0004_sale_method_detail.py","file_name":"0004_sale_method_detail.py","file_ext":"py","file_size_in_byte":503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"123118686","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom django.views.decorators.csrf import csrf_protect\n\n# Create your views here.\ndef home(request):\n return render(request, 'home.html')\ndef long_addition(request):\n return render(request,'long_addition.html')\ndef long_additions(request):\n res1=int(request.POST.get('instr1'))\n res2=int(request.POST.get('instr2'))\n res=res1+res2\n e=list(reversed(list(str(res))))\n g=[]\n f=[]\n for i in e:\n g.append(i)\n h=list(reversed(g))\n f.append(''.join(h))\n return render(request,'detail_long_addition.html',{'res1':res1,'res2':res2,'res':res,'e':e,'f':f,'i':i,'g':g})\ndef find_lcm(num1, num2): \n if(num1>num2): \n num = num1 \n den = num2 \n else: \n num = num2 \n den = num1 \n rem = num % den \n while(rem != 0): \n num = den \n den = rem \n rem = num % den \n gcd = den \n lcm = int(int(num1 * num2)/int(gcd)) \n return lcm \ndef prime_factors(n):\n i = 2\n factors = []\n while i * i <= n:\n if n % i:\n i += 1\n else:\n n //= i\n factors.append(str(i))\n if n > 1:\n factors.append(str(n))\n return factors\ndef lcm_more(request):\n return render(request,'lcm_more.html')\ndef lcm_more1(request):\n x=request.POST.get('instr1')\n input_list=x.split(',')\n l=[]\n ll=x.split(',')\n l4=[]\n lll=[]\n for i in ll:\n lll.append(int(i))\n l4.append(str(lll)[1:-1])\n for i in input_list:\n l.append(int(i))\n\n num1 = int(l[0]) \n num2 = int(l[1]) \n lcm = find_lcm(num1, num2) \n \n for i in range(2, len(l)): \n lcm = find_lcm(lcm, int(l[i])) \n fact=prime_factors(lcm)\n l2=[]\n fact1=[]\n \n l6=[]\n for i in fact:\n for j in l:\n if int(j)%int(i)==0:\n l2.append(j)\n if len(l2)>=2:\n for k in l2:\n l[l.index(k)]= int(k)//int(i)\n fact1.append(i)\n l2=[]\n else:\n l2=[]\n if len(fact1)==0:\n fact1=[1]\n for i in fact1:\n for j in input_list:\n if int(j)%int(i)==0:\n \n input_list[input_list.index(j)]=int(j)//int(i)\n\n else:\n input_list[input_list.index(j)]=int(j)\n lx=str(input_list)[1:-1]\n lx2=str(input_list)\n l4.append(lx)\n l6.append(eval(lx2))\n if len(fact1)==1 and fact1[0]==1:\n x2='Given numbers has no common factors except 1. So, there LCM is their product'\n else:\n x2=zip(fact1,l4)\n x3=l4[-1]\n \n list_6=[]\n for i in range(len(l6[-1])):\n list_6.append(str(l6[-1][i]))\n l5=' x '.join(list_6)\n if len(fact1)==1 and fact1[0]==1:\n s1=l5\n else:\n s1=' x '.join(fact1)+' x '+l5\n if len(fact1)==1 and fact1[0]==1:\n return render(request,'lcm-more2.html',{'r':lcm,'z':l,'xx':x2,'xxx':x3,'xxxx':l4[0],'llll':s1})\n else:\n return render(request,'lcm_more1.html',{'r':lcm,'z':l,'xx':x2,'xxx':x3,'xxxx':l4[0],'llll':s1})","sub_path":"pages/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"201457529","text":"# My library\nfrom trectools import TrecPool, TrecRun\n\n# Standard libraries\nimport string\nimport re\n\n# TODO: use logging properly\nimport logging\n\n# External libraries\nimport numpy as np\nimport pandas as pd\nimport scipy as sp\n\ndef remove_punctuation(text):\n t = re.sub('[' + re.escape(''.join(string.punctuation)) + ']', ' ', text)\n return re.sub(' +',' ',t)\n\ndef check_fleish_kappa(tuple_of_judgements):\n items = set()\n categories = set()\n n_ij = {}\n n = len(tuple_of_judgements)\n for judgement in tuple_of_judgements:\n for doc, rel in zip(range(judgement.shape[0]), judgement):\n items.add(doc)\n categories.add(rel)\n n_ij[(doc, rel)] = n_ij.get((doc, rel), 0) + 1\n N = len(items)\n p_j = {}\n for c in categories:\n p_j[c] = sum(n_ij.get((i,c), 0) for i in items) / (1.0*n*N)\n\n P_i = {}\n for i in items:\n P_i[i] = (sum(n_ij.get((i,c), 0)**2 for c in categories)-n) / (n*(n-1.0))\n\n P_bar = sum(P_i.itervalues()) / (1.0*N)\n P_e_bar = sum(p_j[c]**2 for c in categories)\n\n kappa = (P_bar - P_e_bar) / (1 - P_e_bar)\n\n return kappa\n\ndef unique_documents(list_of_runs, cutoff=10):\n # TODO: this should return a , in which for each RUN, we have a list\n # of documents that were uniquely provided by this RUN\n pass\n\ndef make_pool_from_files(filenames, strategy=\"topX\", topX=10, rbp_strategy=\"sum\", rbp_p=0.80, rrf_den=60):\n \"\"\"\n Creates a pool object (TrecPool) from a list of filenames.\n ------\n strategy = (topX, rbp, rrf). Default: topX\n\n * TOP X options:\n topX = Integer Value. The number of documents per query to make the pool.\n\n * RBP options:\n topX = Integer Value. The number of documents per query to make the pool. Default 10.\n rbp_strategy = (max, sum). Only in case strategy=rbp. Default: \"sum\"\n rbp_p = A float value for RBP's p. Only in case strategy=rbp. Default: 0.80\n\n * RRF options:\n rrf_den = value for the Reciprocal Rank Fusion denominator. Default: 60\n \"\"\"\n\n runs = []\n for fname in filenames:\n runs.append(TrecRun(fname))\n return make_pool(runs, strategy, topX=topX, rbp_p=rbp_p, rbp_strategy=rbp_strategy, rrf_den=rrf_den)\n\n\ndef make_pool(list_of_runs, strategy=\"topX\", topX=10, rbp_strategy=\"sum\", rbp_p=0.80, rrf_den=60):\n \"\"\"\n Creates a pool object (TrecPool) from a list of runs.\n ------\n strategy = (topX, rbp). Default: topX\n topX = Integer Value. The number of documents per query to make the pool.\n rbp_strategy = (max, sum). Only in case strategy=rbp. Default: \"sum\"\n rbp_p = A float value for RBP's p. Only in case strategy=rbp. Default: 0.80\n \"\"\"\n\n if strategy == \"topX\":\n return make_pool_topX(list_of_runs, cutoff=topX)\n elif strategy == \"rbp\":\n return make_pool_rbp(list_of_runs, topX=topX, p=rbp_p, strategy=rbp_strategy)\n elif strategy == \"rrf\":\n return make_pool_rrf(list_of_runs, topX=topX, rrf_den=rrf_den)\n\ndef make_pool_rrf(list_of_runs, topX=500, rrf_den=60):\n \"\"\"\n topX = Number of documents per query. Default: 500.\n rrf_den = Value for the Reciprocal Rank Fusion denominator. Default is 60 as in the original paper:\n Reciprocal Rank Fusion outperforms Condorcet and individual Rank Learning Methods. G. V. Cormack. University of Waterloo. Waterloo, Ontario, Canada.\n \"\"\"\n\n big_df = pd.DataFrame(columns=[\"query\",\"docid\",\"rbp_value\"])\n\n for run in list_of_runs:\n df = run.run_data.copy()\n # NOTE: Everything is made based on the rank col. It HAS TO start by '1'\n df[\"rrf_value\"] = 1.0 / (rrf_den + df[\"rank\"])\n # Concatenate all dfs into a single big_df\n big_df = pd.concat((big_df,df[[\"query\",\"docid\",\"rrf_value\"]]))\n\n # Default startegy is the sum.\n grouped_by_docid = big_df.groupby([\"query\",\"docid\"])[\"rrf_value\"].sum().reset_index()\n\n # Sort documents by rbp value inside each qid group\n grouped_by_docid.sort_values(by=[\"query\",\"rrf_value\"], ascending=[True,False], inplace=True)\n\n # Selects only the top X from each query\n result = grouped_by_docid.groupby(\"query\").head(topX)\n\n # Transform pandas data into a dictionary\n pool = {}\n for row in result[[\"query\", \"docid\"]].itertuples():\n q = int(row.query)\n if q not in pool:\n pool[q] = set([])\n pool[q].add(row.docid)\n\n return TrecPool(pool)\n\n\ndef make_pool_rbp(list_of_runs, topX = 100, p=0.80, strategy=\"sum\"):\n \"\"\"\n p = A float value for RBP's p. Default: 0.80\n Strategy = (max, sum). Default: \"sum\"\n topX = Number of documents per query to be used in the pool. Default: 100\n \"\"\"\n\n big_df = pd.DataFrame(columns=[\"query\",\"docid\",\"rbp_value\"])\n\n for run in list_of_runs:\n df = run.run_data.copy()\n # NOTE: Everything is made based on the rank col. It HAS TO start by '1'\n df[\"rbp_value\"] = (1.0-p) * (p) ** (df[\"rank\"]-1)\n # Concatenate all dfs into a single big_df\n big_df = pd.concat((big_df,df[[\"query\",\"docid\",\"rbp_value\"]]))\n\n # Choose strategy for merging the different runs.\n if strategy == \"sum\":\n grouped_by_docid = big_df.groupby([\"query\",\"docid\"])[\"rbp_value\"].sum().reset_index()\n elif strategy == \"max\":\n grouped_by_docid = big_df.groupby([\"query\",\"docid\"])[\"rbp_value\"].max().reset_index()\n else:\n print(\"Strategy '%s' does not exist. Options are 'sum' and 'max'\" % (strategy))\n\n # Sort documents by rbp value inside each qid group\n grouped_by_docid.sort_values(by=[\"query\",\"rbp_value\"], ascending=[True,False], inplace=True)\n\n # Selects only the top X from each query\n result = grouped_by_docid.groupby(\"query\").head(topX)\n\n # Transform pandas data into a dictionary\n pool = {}\n for row in result[[\"query\", \"docid\"]].itertuples():\n q = int(row.query)\n if q not in pool:\n pool[q] = set([])\n pool[q].add(row.docid)\n\n return TrecPool(pool)\n\ndef make_pool_topX(list_of_runs, cutoff=10):\n pool_documents = {}\n if len(list_of_runs) == 0:\n return TrecPool(pool_documents)\n\n topics_seen = set([])\n for run in list_of_runs:\n topics_seen = topics_seen.union(run.topics())\n for t in topics_seen:\n if t not in pool_documents.keys():\n pool_documents[t] = set([])\n pool_documents[t] = pool_documents[t].union(run.get_top_documents(t, n=cutoff))\n\n return TrecPool(pool_documents)\n\ndef sort_systems_by(list_trec_res, metric=\"map\"):\n r = []\n for system in list_trec_res:\n # TODO: check for exceptions\n r.append((system.get_result(metric), system.get_runid()))\n\n # Sorting twice to have first the values sorted descending and then the run name sorted ascending\n return sorted(sorted(r,key=lambda x:x[1]), key=lambda x:x[0], reverse=True)\n\n\ndef get_correlation(sorted1, sorted2, correlation=\"kendall\"):\n \"\"\"\n Use sort_trec_res_by twice to obtain two <\"value\", \"system\"> list of results (sorted1 and sorted2)\n before using this method.\n Correlations implemented: kendalltau, pearson, spearman\n \"\"\"\n def tau_ap(list1, list2):\n # List2 is the ground truth and list 1 is the list that we want to compare the tau_ap with.\n\n N = len(list1)\n # calculate C(i)\n c = [0] * N # C = [0,0,0,0,0,0,....]\n for i, element in enumerate(list1[1:]):\n # c[i] = number of items above rank i and correctly ranked w.r.t.. the item at rank i in list1\n #print \"Checking element\", element, \" ranking\", i + 1\n index_element_in_2 = list2.index(element)\n\n for other_element in list1[:i+1]:\n #print \"Other element\", other_element\n index_other_in_2 = list2.index(other_element)\n if index_element_in_2 > index_other_in_2 or other_element == element: # Check if it is correctly ranked\n c[i] += 1\n #print \"C[\",i + 2,\"]=\", c[i]\n\n summation = 0\n for i in range(1,N):\n summation += (1. * c[i-1] / (i))\n #print c[i+1], (i)\n p = 1. / (N-1) * summation\n #print \"P\", p\n return (2 * p - 1., -1)\n\n if len(sorted1) != len(sorted2):\n print(\"ERROR: Arrays must have the same size. Given arrays have size (%d) and (%d).\" % (len(sorted1), len(sorted2)))\n return np.nan\n\n # Transform a list of names into a list of integers\n s1 = zip(*sorted1)[1]\n s2 = zip(*sorted2)[1]\n m = dict(zip(s1, xrange(len(s2))))\n new_rank = []\n for s in s2:\n new_rank.append(m[s])\n\n if correlation == \"kendall\" or correlation == \"kendalltau\":\n return sp.stats.kendalltau(xrange(len(s1)), new_rank)\n elif correlation == \"pearson\" or correlation == \"spearmanr\":\n return sp.stats.pearsonr(xrange(len(s1)), new_rank)\n elif correlation == \"spearman\" or correlation == \"spearmanr\":\n return sp.stats.spearmanr(xrange(len(s1)), new_rank)\n elif correlation == \"tauap\" or correlation == \"kendalltauap\" or correlation == \"tau_ap\":\n return tau_ap(new_rank, range(len(s1)))\n else:\n print(\"Correlation %s is not implemented yet. Options are: kendall, pearson, spearman, tauap.\" % (correlation))\n return None\n\n\ndef confidence_interval(data, confidence=0.95):\n a = 1.0*np.array(data)\n n = len(a)\n m, se = np.mean(a), sp.stats.sem(a)\n h = se * sp.stats.t._ppf((1+confidence)/2., n-1)\n return h\n\n","sub_path":"trectools/misc.py","file_name":"misc.py","file_ext":"py","file_size_in_byte":9537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"310044113","text":"#Kyle Verhoog\n#August 23, 2014\n\n#Function memorizes previous results, greatly cuts down compute time of large Fibonacci numbers\ndef f(x, mem={0:1,1:1}):\n\tif x in mem:\n\t\treturn mem[x]\n\telse:\n\t\tmem[x] = f(x-1) + f(x-2)\n\t\treturn mem[x]\n\n\n\n","sub_path":"fibonacci/lib_fib.py","file_name":"lib_fib.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"621074767","text":"from bottle import route, request, debug, run, template, redirect\nfrom youtube_client import YoutubeClient\nfrom playlist import Playlist\nfrom video import Video\nfrom user import User\nimport sqlite3\nimport re\n\nsession = {'userID':0, 'username':\"\"}\nvideosToPlay = []\n\n@route('/')\ndef index():\n\n\tglobal session\n\n\tif session['userID'] == 0:\n\n\t\tredirect('/login')\n\n\telse:\n\n\t\tredirect('/playlist/all')\n\t\t\n@route('/login')\ndef login():\n\t\n\tif session['userID'] == 0:\n\t\n\t\treturn template('views/login.tpl', message = \"Please log in to use OurTube\")\n\t\t\n\telse:\n\t\t\n\t\tredirect('/')\n\t\t\n\t\n@route('/login', method = \"post\")\ndef login():\n\t\n\tusername = request.forms.get('username')\n\tpassword = request.forms.get('password')\n\t\n\tif (username and password) != \"\":\n\t\t\n\t\tu = User()\n\t\t\n\t\tuser = u.login(username, password)\n\t\n\t\tif user:\n\t\t\t\n\t\t\tsession['userID'] = user[0]\n\t\t\tsession['username'] = user[1]\n\t\t\t\n\t\t\tredirect('/')\n\t\t\t\n\t\telse:\n\t\t\t\n\t\t\treturn template('views/login.tpl', message = \"Username or Password was not recongnised. Please try again.\")\n\t\n\telse:\n\t\t\n\t\treturn template('views/login.tpl', message = \"Please enter a valid username and password.\")\n\t\n\t\n\t\t\n@route('/register')\ndef register():\n\t\n\treturn template('views/register.tpl', message = \"Type a username and password to register.\")\n\t\n@route('/register', method = \"post\")\ndef register():\n\t\n\tusername = request.forms.get('username')\n\tpassword = request.forms.get('password')\n\t\n\tif (username and password) != \"\":\n\t\t\n\t\tu = User()\n\t\t\n\t\tu.register(username, password)\n\t\tuser = u.login(username, password)\n\t\t\n\t\tsession['userID'] = user[0]\n\t\t\n\t\t# Create new playlist\n\t\tp = Playlist()\n\t\tp.new(session['userID'], \"Recommendations\", \"recommended\")\n\n\t\tredirect('/')\n\t\n\telse:\n\t\t\n\t\treturn template('views/register.tpl', message = \"Please enter a valid username and password.\")\n\n@route('/logout')\ndef logout():\n\t\n\tsession['userID'] = 0\n\tredirect('/')\n\t\ndef key():\n\t\n\tif session['userID'] == 0:\n\t\t\n\t\tredirect('/')\n\t\t\n@route('/hack')\ndef hack():\n\n\tu = User()\n\tusers = u.hack()\n\t\n\treturn template('views/hack.tpl', users = users)\n\t\n\t\n\t\t\n@route('/playlist')\n@route('/playlist/all')\ndef main():\n\t\n\tkey()\n\t\t\n\t# Make user ID Variable\n\tuserID = session['userID']\n\t\n\t# List Playlists\n\tp = Playlist()\n\tplaylists = p.list(userID)\n\n\t# Load main - username - playlists\n\treturn template('views/all.tpl', username = session['username'], playlists = playlists)\n\t\n@route('/playlist/')\n@route('/playlist//show')\ndef showPlaylist(id):\n\n\tkey()\n\n\t# load playlist\n\tp = Playlist()\n\tplaylist = p.find(id)\n\t\n\tif playlist[3] != session['userID']:\n\t\t\n\t\tredirect('/')\n\n\telse:\n\t\t\n\t\tsearch = \"\"\n\n\t\treturn template('views/show.tpl', playlist = playlist, search = 'no')\n\t\n@route('/playlist/new', method = \"post\")\ndef newPlaylist():\n\t\n\tkey()\n\t\n\t# Make user ID Variable\n\tuserID = session['userID']\n\t\n\t# Get the new name for playlist\n\tname = request.forms.get('name')\n\t\n\t# Create new playlist\n\tp = Playlist()\n\tp.new(userID, name, \"standard\")\n\t\n\t# Load main page\n\tredirect('/playlist/all')\n\t\n@route('/playlist//delete')\ndef deletePlaylist(id):\n\t\n\tkey()\n\n\t# Delete playlist\n\tp = Playlist()\n\tp.delete(id)\n\n\t# Load main page\n\tredirect('/playlist/all')\n\t\n@route('/playlist//search', method=\"post\")\ndef searchYoutube(id):\n\t\n\tkey()\n\t\n\tsearchTerm = request.forms.get('searchTerm')\n\tlimit = request.forms.get('limit')\n\t\n\tyt = YoutubeClient()\n\tresults = yt.search(searchTerm, limit)\n\t\n\tp = Playlist()\n\tplaylist = p.find(id)\n\t\n\tsearchResults = []\n\t\n\tfor result in results.entry:\n\t\t\n\t\ttitle = yt.getTitle(result)\n\t\t\n\t\t#title = re.escape(title) Couldnt get this to work.\n\t\t\n\t\tthisResult = [yt.getURL(result), title, yt.getThumbnail(result)]\t\n\t\tsearchResults.append(thisResult)\n\t\t \n\treturn template('views/show.tpl', playlist = playlist, search = 'yes', found = searchResults, searchTerm = searchTerm)\n\n@route('/playlist//fetch')\ndef fetchPlaylist(playlist):\n\t\n\tkey()\n\t\n\tuserID = session['userID']\n\t\n\tu = User()\n\tusers = u.listOther(userID)\n\n\tform = \"\"\n\t\t\n\t# Grab all videos with playlist ID\n\tv = Video()\n\tvideos = v.fetch(playlist)\n\t\n\tp = Playlist()\n\t\n\thtml = \"\"\n\t\n\tif p.checkType(playlist) == \"standard\":\n\t\t\n\t\tif users:\n\t\t\t\n\t\t\tfor user in users:\n\n\t\t\t\tform += \"\"\"\"\"\" \\\n\t\t\t\t% (user[0], user[1])\n\n\t\t\t# Loop through each video and make HTML for it.\n\t\t\tfor video in videos:\n\n\t\t\t\thtml += \"\"\"\t \\\n\t\t\t\t\t\t\t\t\t\t \\\n\t\t\t\t\t\t\t\t\t\t
\\\n\t\t\t\t\t\t\t\t\t\t \\\n\t\t\t\t\t\t\t\t\t\t\tUp |\\\n\t\t\t\t\t\t\t\t\t\t \tDown |\\\n\t\t\t\t\t\t\t\t\t\t\tRemove\\\n\t\t\t\t\t\t\t\t\t\t \\\n\t\t\t\t\t\t\t\t\t\t \\\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\"\"\" \\\n\t\t\t\t% (video[2], video[3], video[0], video[0], video[0], playlist, video[0], form)\n\t\t\t\n\t\telse: \n\t\t\t\n\t\t\tfor user in users:\n\n\t\t\t\tform += \"\"\"\"\"\" \\\n\t\t\t\t% (user[0], user[1])\n\n\t\t\t# Loop through each video and make HTML for it.\n\t\t\tfor video in videos:\n\n\t\t\t\thtml += \"\"\"\t \\\n\t\t\t\t\t\t\t\t\t\t \\\n\t\t\t\t\t\t\t\t\t\t \\\n\t\t\t\t\t\t\t\t\t\t \\\n\t\t\t\t\t\t\t\t\t\t\tUp |\\\n\t\t\t\t\t\t\t\t\t\t \tDown |\\\n\t\t\t\t\t\t\t\t\t\t\tRemove\\\n\t\t\t\t\t\t\t\t\t\t \\\n\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\t\"\"\" \\\n\t\t\t\t% (video[2], video[3], video[0], video[0], video[0])\n\t\t\t\n\telse:\n\t\t\n\t\tfor video in videos:\n\n\t\t\thtml += \"\"\"\t \\\n\t\t\t\t\t\t\t\t\t \\\n\t\t\t\t\t\t\t\t\t \\\n\t\t\t\t\t\t\t\t\t\tUp |\\\n\t\t\t\t\t\t\t\t\t \tDown |\\\n\t\t\t\t\t\t\t\t\t\tRemove\\\n\t\t\t\t\t\t\t\t\t \\\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\"\"\" \\\n\t\t\t% (video[3], video[0], video[0], video[0])\n\t\t\n\treturn html\n\n@route('/playlist//video/add')\ndef addToPlaylist(playlist):\n\t\n\tkey()\n\t\n\t# Get values from url\n\turl = request.query.url\n\ttitle = request.query.title\n\timage = request.query.image\n\t\n\t# Insert video to Database\n\tv = Video()\n\tv.add(url, title, image, playlist)\n\t\n\treturn fetchPlaylist(playlist)\n\t\n@route('/playlist//video/remove')\ndef remove(playlist):\n\t\n\tkey()\n\t\n\tid = request.query.id\n\t\n\tv = Video()\n\tv.delete(id)\n\t\n\treturn fetchPlaylist(playlist)\n\t\n@route('/playlist//video/moveup')\ndef moveUp(playlist):\n\t\n\tkey()\n\n\tid = request.query.id\n\n\tv = Video()\n\tv.moveUp(id)\n\n\treturn fetchPlaylist(playlist)\n\t\n@route('/playlist//video/movedown')\ndef moveDown(playlist):\n\t\n\tkey()\n\t\n\tid = request.query.id\n\t\n\tv = Video()\n\tv.moveDown(id)\n\t\n\treturn fetchPlaylist(playlist)\n\t\n@route('/playlist//video/recommend', method=\"post\")\ndef recommend(playlist):\n\t\n\tkey()\n\t\n\tvideoID = request.forms.get('videoID')\n\tuserID = request.forms.get('userID')\n\t\n\tv = Video()\n\tv.recommend(videoID, userID)\n\t\n\tredirect('/playlist/' + playlist)\n\t\n@route('/playlist//player')\ndef play(playlistID):\n\t\n\tp = Playlist()\n\tp.countPlay(playlistID)\n\tplaylist = p.find(playlistID)\n\t\n\tglobal videosToPlay\n\t\n\tvideosToPlay = []\n\t\n\tif playlist[3] != session['userID']:\n\t\t\n\t\tredirect('/')\n\t\t\n\tv = Video()\n\tvideos = v.fetch(playlist[0])\n\t\n\tfor video in videos:\n\t\t\n\t\tvideoUrl = video[1]\n\t\t\n\t\tcode = videoUrl[videoUrl.find('/v/')+3:videoUrl.find('?')]\n\t\t\n\t\tvideosToPlay.append(code)\n\t\t\n\tvideosToPlay.reverse()\n\t\t\n\t\n\t\n\t#id, url, title, image, position\n\t\n\treturn template('views/player.tpl', playlist = playlist, videos = videos)\n\t\n@route('/playlist//player/next')\ndef fetchNextVideo(playlist):\n\t\n\tcode = videosToPlay[-1]\n\tvideosToPlay.pop()\n\t\n\treturn code\n\t\n\t\ndebug(True)\nrun(reloader=True)","sub_path":"tube.py","file_name":"tube.py","file_ext":"py","file_size_in_byte":7976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"606118885","text":"stuff = {'gold coin': 42, 'sword': 1, 'knigth\\'s armor': 12}\ndragon_loot = ['gold coin', 'gold coin', 'dragon heart', 'dragon wing', 'dragon wing']\n\ndef display_inventory(inventory):\n item_total = 0\n for k, v in inventory.items():\n print(str(v) + ' ' + k)\n item_total += v\n print('Total numberof items: ' + str(item_total))\n\ndef add_to_inventory(inv, loot):\n for k in loot:\n if k in inv:\n inv[k] += 1\n else:\n inv.setdefault(k, 1)\n return inv\n\nprint('Inventory before slaying a dragon:')\ndisplay_inventory(stuff)\n\nprint('\\n')\nprint('Inventory after slaying a dragon:')\nadd_to_inventory(stuff, dragon_loot)\ndisplay_inventory(stuff)","sub_path":"basics/inventory.py","file_name":"inventory.py","file_ext":"py","file_size_in_byte":694,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"204940191","text":"import os\n\nimport cv2\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport torch.backends.cudnn as cudnn\nimport torch.utils.data.distributed\nimport torchvision.transforms as transforms\nfrom PIL import Image, ImageDraw\n\nfrom core.loss import JointsMSELoss\nfrom pose_hrnet import get_pose_net\n\n\ndef plot_result(input_image, output, radius=20):\n draw = ImageDraw.Draw(input_image)\n for heatmap in output:\n heatmap = cv2.resize(heatmap, dsize=(1488, 1999), interpolation=cv2.INTER_CUBIC)\n x = int(max_index_matrix(heatmap, 0))\n y = int(max_index_matrix(heatmap, 1))\n draw.ellipse((y - radius, x - radius, y + radius, x + radius), fill=(255, 0, 0, 255))\n plt.imshow(input_image)\n\n\ndef max_index_matrix(matrix, coordinate):\n result = np.where(matrix == np.amax(matrix))\n return result[coordinate][0]\n\n\ndef create_coordinate_keypoint(model_output, coordinate):\n head_neck = [max_index_matrix(model_output[9], coordinate), max_index_matrix(model_output[8], coordinate)]\n right_hand = [max_index_matrix(model_output[12], coordinate), max_index_matrix(model_output[11],\n coordinate), max_index_matrix(\n model_output[10], coordinate)]\n left_hand = [max_index_matrix(model_output[13], coordinate), max_index_matrix(model_output[14],\n coordinate), max_index_matrix(\n model_output[15], coordinate)]\n torso = [max_index_matrix(model_output[7], coordinate), max_index_matrix(model_output[6], coordinate)]\n right_leg = [max_index_matrix(model_output[2], coordinate), max_index_matrix(model_output[1], coordinate),\n max_index_matrix(\n model_output[0], coordinate)]\n left_leg = [max_index_matrix(model_output[3], coordinate), max_index_matrix(model_output[4], coordinate),\n max_index_matrix(\n model_output[5], coordinate)]\n body = head_neck + right_hand + left_hand + torso + right_leg + left_leg\n return body\n\n\ndef normalize(list):\n return (list - min(list)) / (max(list) - min(list))\n\n\ndef create_keypoints(model_output):\n return [normalize(create_coordinate_keypoint(model_output, 1)),\n normalize(create_coordinate_keypoint(model_output, 0))]\n\n\ncfg = {'OUTPUT_DIR': 'output', 'LOG_DIR': 'log', 'DATA_DIR': '', 'GPUS': (0), 'WORKERS': 24, 'PRINT_FREQ': 100,\n 'AUTO_RESUME': True, 'PIN_MEMORY': True, 'RANK': 0,\n 'CUDNN': {'BENCHMARK': True, 'DETERMINISTIC': False, 'ENABLED': True}, 'MODEL':\n {'NAME': 'pose_hrnet', 'INIT_WEIGHTS': True,\n 'PRETRAINED': '/home/filip/Documents/image_to_2D/pose_hrnet_w32_256x256.pth',\n 'NUM_JOINTS': 16, 'TAG_PER_JOINT': True, 'TARGET_TYPE': 'gaussian', 'IMAGE_SIZE': [256, 256],\n 'HEATMAP_SIZE': [64, 64], 'SIGMA': 2, 'EXTRA': {'PRETRAINED_LAYERS': ['conv1', 'bn1', 'conv2', 'bn2',\n 'layer1', 'transition1',\n 'stage2', 'transition2',\n 'stage3', 'transition3',\n 'stage4'],\n 'FINAL_CONV_KERNEL': 1, 'STAGE2':\n {'NUM_MODULES': 1, 'NUM_BRANCHES': 2, 'BLOCK': 'BASIC',\n 'NUM_BLOCKS': [4, 4], 'NUM_CHANNELS': [32, 64],\n 'FUSE_METHOD': 'SUM'}, 'STAGE3':\n {'NUM_MODULES': 4, 'NUM_BRANCHES': 3, 'BLOCK': 'BASIC',\n 'NUM_BLOCKS': [4, 4, 4],\n 'NUM_CHANNELS': [32, 64, 128], 'FUSE_METHOD': 'SUM'},\n 'STAGE4':\n {'NUM_MODULES': 3, 'NUM_BRANCHES': 4, 'BLOCK': 'BASIC',\n 'NUM_BLOCKS': [4, 4, 4, 4],\n 'NUM_CHANNELS': [32, 64, 128, 256],\n 'FUSE_METHOD': 'SUM'}}},\n 'LOSS': {'USE_OHKM': False, 'TOPK': 8, 'USE_TARGET_WEIGHT': True, 'USE_DIFFERENT_JOINTS_WEIGHT': False},\n 'DATASET':\n {'ROOT': 'data/mpii/', 'DATASET': 'mpii', 'TRAIN_SET': 'train', 'TEST_SET': 'valid', 'DATA_FORMAT': 'jpg',\n 'HYBRID_JOINTS_TYPE': '', 'SELECT_DATA': False, 'FLIP': True, 'SCALE_FACTOR': 0.25, 'ROT_FACTOR': 30,\n 'PROB_HALF_BODY': -1.0, 'NUM_JOINTS_HALF_BODY': 8, 'COLOR_RGB': True}, 'TRAIN':\n {'LR_FACTOR': 0.1, 'LR_STEP': [170, 200], 'LR': 0.001, 'OPTIMIZER': 'adam', 'MOMENTUM': 0.9, 'WD': 0.0001,\n 'NESTEROV': False, 'GAMMA1': 0.99, 'GAMMA2': 0.0, 'BEGIN_EPOCH': 0, 'END_EPOCH': 210, 'RESUME': False,\n 'CHECKPOINT': '', 'BATCH_SIZE_PER_GPU': 32, 'SHUFFLE': True}, 'TEST':\n {'BATCH_SIZE_PER_GPU': 32, 'FLIP_TEST': True, 'POST_PROCESS': True, 'SHIFT_HEATMAP': True,\n 'USE_GT_BBOX': False,\n 'IMAGE_THRE': 0.1, 'NMS_THRE': 0.6, 'SOFT_NMS': False, 'OKS_THRE': 0.5, 'IN_VIS_THRE': 0.0,\n 'COCO_BBOX_FILE': '', 'BBOX_THRE': 1.0,\n 'MODEL_FILE': \"/home/filip/Documents/image_to_2D/pose_hrnet_w32_256x256.pth\"},\n 'DEBUG':\n {'DEBUG': True, 'SAVE_BATCH_IMAGES_GT': True, 'SAVE_BATCH_IMAGES_PRED': True, 'SAVE_HEATMAPS_GT': True,\n 'SAVE_HEATMAPS_PRED': True}}\n\n# cudnn related setting\ncudnn.benchmark = cfg['CUDNN']['BENCHMARK']\ntorch.backends.cudnn.deterministic = cfg['CUDNN']['DETERMINISTIC']\ntorch.backends.cudnn.enabled = cfg['CUDNN']['ENABLED']\nmodel = get_pose_net(cfg, is_train=False).cuda()\nmodel.load_state_dict(torch.load(cfg['TEST']['MODEL_FILE']), strict=False)\n\n# define loss function (criterion) and optimizer\ncriterion = JointsMSELoss(use_target_weight=cfg['LOSS']['USE_TARGET_WEIGHT']).cuda()\n\ntest_transforms = transforms.Compose([\n transforms.Resize((256, 256)),\n transforms.ToTensor(),\n transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])\n\n\ndef result_skeleton(input_image, output, radius=5):\n ids = {9: 0, 8: 1, 12: 2, 11: 3, 10: 4, 13: 5, 14: 6, 15: 7, 7: 8, 6: 9, 2: 10, 1: 11, 0: 12, 3: 13, 4: 14,\n 5: 15}\n sticks = [[0, 1], [1, 8], [8, 2], [2, 3], [3, 4], [8, 5], [5, 6], [6, 7], [8, 9], [9, 10], [10, 11], [11, 12],\n [9, 13], [13, 14], [14, 15]]\n keypoint_locations = [None for index in range(len(output))]\n for index in range(len(output)):\n heatmap = cv2.resize(output[index], dsize=(input_image.size), interpolation=cv2.INTER_CUBIC)\n x = int(max_index_matrix(heatmap, 1))\n y = int(max_index_matrix(heatmap, 0))\n keypoint_locations[ids[index]] = (x, y)\n draw = ImageDraw.Draw(input_image)\n for item in sticks:\n draw.line((keypoint_locations[item[0]], keypoint_locations[item[1]]), fill=(255, 0, 0, 128), width=radius)\n return input_image\n\n\ndef get_all_files_from_path(path, extension):\n files = []\n for r, d, f in os.walk(path):\n for file in f:\n if extension in file:\n files.append(os.path.join(r, file))\n return files\n\n\n# Data loading code\ndef main():\n files = get_all_files_from_path('/media/filip/HDD1/Images/', 'jpg')\n for file in files:\n input = Image.open(file)\n first = input\n input = test_transforms(input)\n input = torch.unsqueeze(input, 0).cuda()\n with torch.no_grad():\n output = model(input).squeeze().detach().cpu().numpy()\n result = result_skeleton(first, output)\n\n plt.imshow(result)\n\n plt.show()\n\n","sub_path":"check_dataset.py","file_name":"check_dataset.py","file_ext":"py","file_size_in_byte":8163,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"452422142","text":"#-*- coding:utf-8 -*-\nimport numpy\nimport matplotlib.pyplot as plt\n\n#读入一些数据，和标签，并且返回数据和标签\ndef loadDataSet(fileName):\n numFeat = len(open(fileName).readline().split('\\t')) #获得数据集中每一个有多少个数据\n dataMat = [] #初始化一个空的列表，用于存储数据集\n labelMat = [] #初始化一个空的列表，用于存储标签集\n fr = open(fileName) #打开文件\n for line in fr.readlines(): #从文件中读取每一行\n lineArr = [] #初始化列表，用于缓存\n curLine = line.strip().split('\\t') #数据分割\n for i in range(numFeat - 1): #轮询每一行数据\n lineArr.append(float(curLine[i])) #最后一个数据是标签，其他是特征\n dataMat.append(lineArr) #生成数据集\n labelMat.append(float(curLine[-1])) #生成标签集\n\n return dataMat,labelMat\n\n\n\n#用于测试是否有某个值小于或者大于我们正在测试的阈值\n#所有在阈值一边的数据会分类到类别-1，而在另一边的数据会被分类到+1\n#dataMatrix 数据集\n#dimen 特征\n#threshVal 阈值\n#threshIneq 比较方式\ndef stumpClassify(dataMatrix,dimen,threshVal,threshIneq):\n retArrray = numpy.ones((numpy.shape(dataMatrix)[0],1)) #将数组的全部元素设置为+1\n #将不满足不等式要求的元素设置为-1\n if threshIneq == \"lt\":\n retArrray[dataMatrix[:,dimen] <= threshVal] = -1.0\n else:\n retArrray[dataMatrix[:,dimen] > threshVal] = -1.0\n\n return retArrray\n\n\n#在一个加权数据集中循环，找到具有最低错误率的单层决策树\n#dataArr 数据集\n#classLabels 标签集\n#D 权重\ndef buildStump(dataArr,classLabels,D):\n dataMatrix = numpy.mat(dataArr) #将数据集转化为矩阵\n labelMat = numpy.mat(classLabels).T #将标签集转化为矩阵，并且转置\n m,n = numpy.shape(dataMatrix) #获得数据集的行、列数据\n numSteps = 10.0 #初始化步长为10，越大则步长越长，分类准确度越高，但运算次数也越多\n bestStump = {} #字典用于保存每个分类器的信息\n bestClasEst = numpy.mat(numpy.zeros((m,1))) #初始化最佳分类器参数为1的矩阵\n minError = numpy.inf #初始化最小误差minError为无穷大\n for i in range(n): #遍历每一个列特征，找到差错最小时特征维数、阈值、以及比较符号\n rangeMin = dataMatrix[:,i].min() #找到列特征的最小值\n rangeMax = dataMatrix[:,i].max() #找到列特征的最大值\n stepSize = (rangeMax - rangeMin) / numSteps #（大-小）/ 分割数 = 步长 得到最小值到最大值需要的每一段距离\n for j in range(-1,int(numSteps) + 1): #遍历步长，获取最佳阈值\n for inequal in ['lt','gt']: #在大于和小于之间切换\n threshVal = (rangeMin + float(j) * stepSize) #最小值+次数*步长 每一次从最小值走的长度\n #获取预测标签列表\n predictedVals = stumpClassify(dataMatrix,i,threshVal,inequal) #最有预测目标值，用于与目标值比较得到误差\n #获取当前列中那些行（1）预测出错，设置1表示出错便于后续量化计算差错权重\n errArr = numpy.mat(numpy.ones((m,1)))\n errArr[predictedVals == labelMat] = 0\n weightedError = D.T * errArr #计算差错权重，计算当前分类方式整体错误率，找出最低错误率\n\n print(\"split:dim %d,thresh %.2f ,thresh ineqal:%s,the weighted error is %.3f\" % (i,threshVal,inequal,weightedError))\n\n if weightedError < minError: #选出最小错误的那个特征\n minError = weightedError #最小误差，后面用于更新D权值\n bestClasEst = predictedVals.copy() #最优化预测值\n bestStump['dim'] = i #特征\n bestStump['thresh'] = threshVal #到最小值的距离\n bestStump['ineq'] = inequal #大于还是小于，最有距离为-1\n\n return bestStump,minError,bestClasEst\n\n\n#基于单层决策树的AdaBoost训练过程\n'''\ndataArr 数据集\nclassLabels 类别标签集\nnumIt 迭代次数 可以用户制定\n任意分类器都可以作为基分类器\n'''\ndef adaBoostTrainDS(dataArr,classLabels,numIt=40):\n weakClassArr = [] #初始化最佳单层决策树组\n m = numpy.shape(dataArr)[0] #获得数据的数量\n D = numpy.mat(numpy.ones((m,1))/m) #初始化权重D 为1/m\n aggClassEst = numpy.mat(numpy.zeros((m,1))) #初始化累计类别估计值\n\n for i in range(numIt):\n bestStump,error,classEst = buildStump(dataArr,classLabels,D) #寻找最佳的单层决策树\n print(\"D:\",D.T)\n #计算alpha max(error,1e-16)用于确保在没有错误时不会发生除零溢出\n alpha = float(0.5*numpy.log((1.0 - error) / max(error,1e-16))) #alpha的计算公式 alpha = 1/2 * ln((1-e)/e)\n bestStump['alpha'] = alpha #记录该决策树的alpha值\n weakClassArr.append(bestStump) #将最佳单层决策树加入到单层决策树组\n print(\"classEst:\",classEst.T)\n #更新样本的权重 D = (D * exp(alpha))/Sum(D)\n #这里需要说明一下，对于classLabels表示数据应该属于哪一类，classEst表示预测的分类结果，两者都为-1，1的list\n #如果分对了，两者相乘结果为1，相反为-1.正好符合提到的公式\n expon = numpy.multiply(-1 * alpha * numpy.mat(classLabels).T,classEst) #计算样本是否被正确分类的alpha值\n D = numpy.multiply(D,numpy.exp(expon))\n D = D / D.sum()\n\n #更新累计类别估计值\n aggClassEst += alpha * classEst\n print(\"aggClassEst:\",aggClassEst.T)\n print(numpy.sign(aggClassEst))\n print(numpy.mat(classLabels).T)\n print(numpy.sign(aggClassEst) != numpy.mat(classLabels).T)\n #调用numpy.sign()函数得到二值分类结果，{x = 1，x > 0;x = 0,x = 0;x = -1,x < 0}\n #应为该集成方法是模型累加式的，所以需要累加各模型的估计值之和，如果最终估计值符号等于真实值符号，表明分队\n #这里!=，的意思是预测和真实值两个不相等就是True（1）.相等就是False（0），错误就是，1乘以任何数等于原数\n aggErrors = numpy.multiply(numpy.sign(aggClassEst) != numpy.mat(classLabels).T,numpy.ones((m,1)))\n errorRate = aggErrors.sum() / m #计算累计错误率（总错误率）\n print(\"total error:\",errorRate,\"\\n\")\n if errorRate == 0.0: #如果错误率等于0.0，则退出循环\n break\n return weakClassArr\n\n#AdaBoost分类函数\n#dataToClass 测试集\n#classifierArr 弱分类器集\n#每个弱分类器的结果以其对应的alpha值作为权重，所有这些弱分类器的结果加权和就是最后的结果\ndef adaClassify(datToClass,classifierArr):\n dataMatrix = numpy.mat(datToClass) #将测试集转成矩阵\n m = numpy.shape(dataMatrix)[0] #计算测试集的个数m\n aggClassEst = numpy.mat(numpy.zeros((m,1))) #初始化m个累加估计值\n for i in range(len(classifierArr)): #遍历每一个弱分类器\n #对每个弱分类器进行类别的估计值计算\n classEst = stumpClassify(dataMatrix,classifierArr[i]['dim'],classifierArr[i][\"thresh\"],classifierArr[i]['ineq'])\n #估计值乘以权重作为单个分类器预测值，对分类器预测值进行累加计算累加估计值\n aggClassEst += classifierArr[i]['alpha'] * classEst\n print(aggClassEst)\n\n return numpy.sign(aggClassEst) #进行二分类求值\n\n\nif __name__ == \"__main__\":\n #获取数据集和标签\n datMat,classLabels = loadDataSet(\"horseColicTraining2.txt\")\n #进行训练，迭代次数大于等于弱分类器个数\n classifierArr = adaBoostTrainDS(datMat,classLabels,50)\n #测试，其中classifierArr是弱分类器集合\n #读取测试集和标签\n testArr,testLabelArr = loadDataSet(\"horseColicTest2.txt\")\n #进行测试\n aggClassEst = adaClassify(testArr,classifierArr)\n errArr = numpy.mat(numpy.ones((67,1)))\n #计算错误率 分错的个数/总个数\n errRate = (errArr[aggClassEst != numpy.mat(testLabelArr).T].sum()) / 67\n print(errRate)\n","sub_path":"集成方法-元算法/基于单层决策树的AdaBoost从疝气病预测病马的死亡率/adaboost.py","file_name":"adaboost.py","file_ext":"py","file_size_in_byte":10189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"141050060","text":"# coding=utf-8\nfrom __future__ import absolute_import\nimport time\n\nimport octoprint.plugin\nimport pigpio\n\nclass EasyservoPlugin(octoprint.plugin.SettingsPlugin, \n octoprint.plugin.AssetPlugin,\n octoprint.plugin.TemplatePlugin,\n octoprint.plugin.StartupPlugin,\n octoprint.plugin.ShutdownPlugin):\n\n\tdef __init__(self):\n\t\tself.pi = None\n\n\t##~~ SettingsPlugin mixin\n\tdef get_settings_defaults(self):\n\t\treturn dict(\n\t\t\tGPIOX=12,\n\t\t\tGPIOY=13,\n\t\t\txAutoAngle=90,\n\t\t\tyAutoAngle=90,\n\t\t\txInvert=False,\n\t\t\tyInvert=False,\n\t\t\tsaved_angle_x=90,\n\t\t\tsaved_angle_y=90\n\t\t)\n\n\tdef on_settings_save(self, data):\n\t\toldGPIOX = self._settings.get_int([\"GPIOX\"])\n\t\toldGPIOY = self._settings.get_int([\"GPIOY\"])\n\n\t\toctoprint.plugin.SettingsPlugin.on_settings_save(self, data)\n\n\t\tnewGPIOX = self._settings.get_int([\"GPIOX\"])\n\t\tnewGPIOY = self._settings.get_int([\"GPIOY\"])\n\n\t\tif oldGPIOX != newGPIOX:\n\t\t\tself._logger.info(\"GPIO x changed, initializing.\")\n\t\t\tself.pi.set_servo_pulsewidth(newGPIOX, 0)\n\t\t\tself._logger.info(\"moving GPIO %d to %d degrees\" % (newGPIOX, self.current_angle_x))\n\t\t\tself.current_angle_x = self.move_servo_by(GPIOX, self.current_angle_x)\n\t\tif oldGPIOY != newGPIOY:\n\t\t\tself._logger.info(\"GPIO y changed, initiliazing.\")\n\t\t\tself.pi.set_servo_pulsewidth(newGPIOY, 0)\n\t\t\tself._logger.info(\"moving GPIO %d to %d degrees\" % (newGPIOY, self.current_angle_y))\n\t\t\tself.current_angle_y = self.move_servo_by(GPIOY, self.current_angle_y)\n\n\n\t##~~ AssetPlugin mixin\n\n\tdef get_assets(self):\n\t\t# Define your plugin's asset files to automatically include in the\n\t\t# core UI here.\n\t\treturn dict(\n\t\t\tjs=[\"js/EasyServo.js\"],\n\t\t)\n\n\t##~~ StartupPlugin mixin\n\n\tdef on_after_startup(self):\n\t\tif self.pi == None:\n\t\t\tself._logger.info(\"Initiliazing pigpio\")\n\t\t\tself.pi = pigpio.pi()\n\t\t\tself._logger.info(self.pi)\n\t\tif not self.pi.connected:\n\t\t\tself._logger.info(\"There was an error initiliazing pigpio\")\n\t\t\treturn\n\n\t\tGPIOX = self._settings.get_int([\"GPIOX\"])\n\t\tGPIOY = self._settings.get_int([\"GPIOY\"])\n\t\txAutoAngle = self._settings.get_int([\"xAutoAngle\"])\n\t\tyAutoAngle = self._settings.get_int([\"yAutoAngle\"])\n\t\tsaved_angle_x = xAutoAngle\n\t\tsaved_angle_y = yAutoAngle\n\t\t#self._logger.info(\"xAutoAngle {} yAutoAngle {}.\".format(xAutoAngle, yAutoAngle))\n\n\t\t\n\t\t# initialize x axis\n\t\tsaved_angle_x = self._settings.get_int([\"saved_angle_x\"])\n\t\tself.pi.set_servo_pulsewidth(GPIOX, 0)\n\t\tif self._settings.get_boolean([\"xInvert\"])==True:\n\t\t\tself._settings.set([\"xInvert\"], False)\n\t\t\tself._settings.save()\n\t\t\tself.current_angle_x = self.move_servo_to_ang(GPIOX, saved_angle_x, xAutoAngle)\n\t\t\tself._settings.set([\"xInvert\"], True)\n\t\t\tself._settings.save()\n\t\telse:\n\t\t\tself.current_angle_x = self.move_servo_to_ang(GPIOX, saved_angle_x, xAutoAngle)\n\t\t# initialize y axis\n\t\tsaved_angle_y = self._settings.get_int([\"saved_angle_y\"])\n\t\tself.pi.set_servo_pulsewidth(GPIOY, 0)\n\t\tif self._settings.get_boolean([\"yInvert\"])==True:\n\t\t\tself._settings.set([\"yInvert\"], False)\n\t\t\tself._settings.save()\n\t\t\tself.current_angle_y = self.move_servo_to_ang(GPIOY, saved_angle_y, yAutoAngle)\n\t\t\tself._settings.set([\"yInvert\"], True)\n\t\t\tself._settings.save()\n\t\telse:\n\t\t\tself.current_angle_y = self.move_servo_to_ang(GPIOY, saved_angle_y, yAutoAngle)\n\n\n\t##~~ ShutdownPlugin mixin\n\n\tdef on_shutdown(self):\n\t\tif not self.pi.connected:\n\t\t\tself._logger.info(\"There was an error on shutdown pigpio not connected\")\n\t\t\treturn\n\n\t\tGPIOX = self._settings.get_int([\"GPIOX\"])\n\t\tGPIOY = self._settings.get_int([\"GPIOY\"])\n\t\tself.pi.set_servo_pulsewidth(GPIOX, 0)\n\t\tself.pi.set_servo_pulsewidth(GPIOY, 0)\n\t\tself.pi.stop()\n\n\t##-- Template hooks\n\n\tdef get_template_configs(self):\n\t\treturn [dict(type=\"settings\",custom_bindings=False)]\n\n\t##~~ Softwareupdate hook\n\n\tdef get_update_information(self):\n\t\treturn dict(\n\t\t\tEasyServo=dict(\n\t\t\t\tdisplayName=\"Easy Servo\",\n\t\t\t\tdisplayVersion=self._plugin_version,\n\n\t\t\t\t# version check: github repository\n\t\t\t\ttype=\"github_release\",\n\t\t\t\tuser=\"iFrostizz\",\n\t\t\t\trepo=\"OctoPrint-EasyServo\",\n\t\t\t\tcurrent=self._plugin_version,\n\n\t\t\t\t# update method: pip\n\t\t\t\tpip=\"https://github.com/iFrostizz/OctoPrint-EasyServo/archive/{target_version}.zip\"\n\t\t\t)\n\t\t)\n\n\t##~~ Utility functions\n\n\tdef angle_to_width(self, ang): #Easier conversion for the angle\n\t\tif ang > 180:\n\t\t\tang = 180\n\t\telif ang < 0:\n\t\t\tang = 0\n\n\t\tratio = (2500 - 500)/180 #Calcul ratio from angle to percent\n\n\t\tangle_as_width = ang * ratio\n\t\treturn int(500 + angle_as_width)\n\n\tdef width_to_angle(self, width):\n\t\tratio = (180.0)/(2500.0-500.0)\n\n\t\twidth_as_angle = float(width) * ratio\n\t\treturn float(width_as_angle - 45.0)\n\n\tdef move_servo_to_ang(self, pin, start_angle, destination_angle):\n\t\tangle_difference = destination_angle - start_angle\n\t\tmove_results = self.move_servo_by(pin, angle_difference)\n\t\treturn move_results\n\n\tdef move_servo_by(self, pin, angle_difference):\n\t\tif angle_difference < 0:\n\t\t\tdirection = -1\n\t\telse:\n\t\t\tdirection = 1\n\n\t\tif self._settings.get_int([\"GPIOX\"]) == int(pin):\n\t\t\tangle_start = self._settings.get_int([\"saved_angle_x\"])\n\t\t\twidth_start = self.pi.get_servo_pulsewidth(int(pin))\n\t\tif self._settings.get_int([\"GPIOY\"]) == int(pin):\n\t\t\tangle_start = self._settings.get_int([\"saved_angle_y\"])\n\t\t\twidth_start = self.pi.get_servo_pulsewidth(int(pin))\n\n\t\tangle_end = angle_start + angle_difference\n\t\twidth_end = self.angle_to_width(angle_end)\n\t\tangle_current = angle_start\n\t\twidth_current = width_start\n\t\tif self._settings.get_boolean([\"xInvert\"])==True and self._settings.get_int([\"GPIOX\"]) == int(pin):\n\t\t\tdirection = direction*-1\n\t\t\tangle_end = -angle_end\n\t\t\tangle_start = -angle_start\n\t\tif self._settings.get_boolean([\"yInvert\"])==True and self._settings.get_int([\"GPIOY\"]) == int(pin):\n\t\t\tdirection = direction*-1\n\t\t\tangle_end = -angle_end\n\t\t\tangle_start = -angle_start\n\t\t#self._logger.info(\"pin {} angle_start {} angle_end {} direction {} angle_difference {} width_start {} width_end {}.\".format(pin, angle_start, angle_end, direction, angle_difference, width_start, width_end))\n\t\tif width_start != width_end:\n\t\t\tfor x in range(0, int(((angle_end - angle_start) * direction) * 10)):\n\t\t\t\tangle_current = angle_current + (0.1 * direction)\n\t\t\t\twidth_current = self.angle_to_width(angle_current)\n\t\t\t\tself.pi.set_servo_pulsewidth(int(pin), width_current)\n\t\t\t\t#self._logger.info(\"Moved GPIO {} to width: {} angle: {}.\".format(pin, width_current, angle_current))\n\t\t\t\ttime.sleep(0.01)\n\t\t\t\tif width_current > 2478 or width_current < 501: #Noticed that the angle was 180.0° for 2479us and 500 was giving strange values .......\n\t\t\t\t\tbreak\n\t\t\tif self._settings.get_int([\"GPIOX\"]) == int(pin):\n\t\t\t\tself._settings.set([\"saved_angle_x\"], round((angle_current), 1))\n\t\t\tif self._settings.get_int([\"GPIOY\"]) == int(pin):\n\t\t\t\tself._settings.set([\"saved_angle_y\"], round((angle_current), 1))\n\t\t\tself._settings.save()\n\t\treturn angle_current\n\n\t##~~ atcommand hook\n\n\tdef processAtCommand(self, comm_instance, phase, command, parameters, tags=None, *args, **kwargs):\n\t\tif command == 'EASYSERVO':\n\t\t\t#get GPIO and angle from parameters\n\t\t\tGPIO,ang= parameters.split(' ')\n\t\t\tif int(GPIO) == self._settings.get_int([\"GPIOX\"]):\n\t\t\t\tself.current_angle_x = self.move_servo_by(int(GPIO), int(ang))\n\t\t\telif int(GPIO) == self._settings.get_int([\"GPIOY\"]):\n\t\t\t\tself.current_angle_y = self.move_servo_by(int(GPIO), int(ang))\n\t\t\telse:\n\t\t\t\tself._logger.info(\"unknown GPIO %d\" % int(GPIO))\n\t\tif command == 'EASYSERVOAUTOHOME':\n\t\t\tself.on_after_startup()\n\n\n__plugin_name__ = \"Easy Servo\"\n__plugin_pythoncompat__ = \">=2.7,<4\"\n\ndef __plugin_load__():\n\tglobal __plugin_implementation__\n\t__plugin_implementation__ = EasyservoPlugin()\n\n\tglobal __plugin_hooks__\n\t__plugin_hooks__ = {\n\t\t\"octoprint.plugin.softwareupdate.check_config\": __plugin_implementation__.get_update_information,\n\t\t\"octoprint.comm.protocol.atcommand.queuing\": __plugin_implementation__.processAtCommand\n\t}","sub_path":"EasyServo/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":7854,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"338183292","text":"import math\n\n\ndef is_prime(n):\n if n <= 1:\n return False\n elif n == 2:\n return True\n else:\n for i in range(2, int(math.sqrt(n)) + 1):\n if n % i == 0:\n return False\n return True\n\n\nx = int(input('x:'))\ny = int(input('y:'))\nfor i in range(x, y):\n if is_prime(i):\n print(i, end=' ')\n","sub_path":"forMath.py","file_name":"forMath.py","file_ext":"py","file_size_in_byte":352,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"325949875","text":"\n\nfrom xai.brain.wordbase.nouns._propagandist import _PROPAGANDIST\n\n#calss header\nclass _PROPAGANDISTS(_PROPAGANDIST, ):\n\tdef __init__(self,): \n\t\t_PROPAGANDIST.__init__(self)\n\t\tself.name = \"PROPAGANDISTS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"propagandist\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_propagandists.py","file_name":"_propagandists.py","file_ext":"py","file_size_in_byte":280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"501474352","text":"\"\"\"ucca_distances_from_path\n\nUsage:\n ucca_distances_from_path.py [--lines] [--input=] [--output=]\n ucca_distances_from_path.py (-h | --help)\n\nOptions:\n -h --help Show this screen.\n\"\"\"\nfrom docopt import docopt\nimport sys\nimport itertools\nimport ijson\nimport jsonlines\nimport networkx\nfrom path_to_re.internal.ucca_types import UccaParsedPassage\n\n\nargs = docopt(__doc__)\n\ninput_stream = open(args['--input'], encoding='utf-8') if args['--input'] is not None else sys.stdin\noutput_stream = open(args['--output'], 'w', encoding='utf-8', newline='', buffering=1) if args['--output'] is not None else sys.stdout\nlines = True if args['--lines'] else False\n\nreader = jsonlines.Reader(input_stream) if lines else ijson.items(input_stream, 'item')\n\nwith jsonlines.Writer(output_stream) as json_write:\n for item in reader:\n\n item['ucca_encodings_min_subtree'] = None\n\n parsed_sentence = UccaParsedPassage.from_serialization(item['ucca_parse'])\n tac_to_ucca = {int(key): val for key, val in item['tac_to_ucca'].items()}\n sent_len = len(item['ucca_tokens'])\n\n subj_start = tac_to_ucca[item['subj_start']][0]\n subj_end = tac_to_ucca[item['subj_end']][-1]\n subj = list(range(subj_start, subj_end+1))\n\n obj_start = tac_to_ucca[item['obj_start']][0]\n obj_end = tac_to_ucca[item['obj_end']][-1]\n obj = list(range(obj_start, obj_end+1))\n\n heads = [int(x) for x in item['ucca_heads']]\n multi_heads = [[int(head) for dep, head in ucca_deps] for ucca_deps in item['ucca_deps']]\n\n edges = {(head_id-1, id): True for id in range(sent_len) for head_id in multi_heads[id]}\n\n extended_edges = edges.copy()\n for edge in itertools.combinations(subj, 2):\n extended_edges[edge] = True\n for edge in itertools.combinations(obj, 2):\n extended_edges[edge] = True\n\n graph = networkx.Graph(list(extended_edges.keys()))\n try:\n on_path = networkx.shortest_path(graph, source=subj[0], target=obj[0])\n except networkx.NetworkXNoPath:\n print('shit')\n continue\n\n all_shortest_paths_lengths = {start: targets for start, targets in networkx.shortest_path_length(graph)}\n\n token_distances = []\n for token_id in range(sent_len):\n distance = 0\n if token_id not in on_path:\n distances_to_path = {target: distance_to_target\n for target, distance_to_target in all_shortest_paths_lengths[token_id].items()\n if target in on_path}\n distance = min(distances_to_path.values(), default=1e12)\n token_distances.append(distance)\n\n item['dist_from_ucca_mh_path'] = token_distances\n\n json_write.write(item)\n\n\n\n","sub_path":"path_to_re/gcn/supplement/ucca_distances_from_path.py","file_name":"ucca_distances_from_path.py","file_ext":"py","file_size_in_byte":2842,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"455405467","text":"input_file='metro-bike-share-trip-data.csv'\r\nimport re\r\nimport numpy as np\r\nimport csv\r\nimport plotly\r\nimport plotly.graph_objs as go\r\nfrom plotly import tools\r\nimport itertools\r\nfrom itertools import groupby\r\nfrom collections import defaultdict, namedtuple\r\n\r\n\r\n\r\n\r\n\r\ndef getElement(line):\r\n result = re.split(r',', line, maxsplit=1)\r\n element = result[0].strip()\r\n return element, result[1]\r\ndef getid(line):\r\n id, line = getElement(line)\r\n return id, line\r\ndef getDuration(line):\r\n Dur, line = getElement(line)\r\n return Dur, line\r\ndef getEnd_Time(line):\r\n ET, line = getElement(line)\r\n return ET, line\r\ndef getStarting_Station_ID(line):\r\n SID, line = getElement(line)\r\n return SID, line\r\ndef getStarting_Station_Latitude(line):\r\n Latitude, line = getElement(line)\r\n return Latitude, line\r\ndef getStarting_Station_Longitude(line):\r\n result=re.split(r',', line, maxsplit=1)\r\n Longitude = re.findall(r'^\\d+.\\d+$', result[0])[0]\r\n return Longitude, result[1]\r\ndef getEnding_Station_ID(line):\r\n result = re.split(r',', line, maxsplit=1)\r\n EID = re.findall(r'^\\-\\d+.\\d+$', result[0])[0]\r\n latitude=EID.replace(\",\",\".\")\r\n return latitude, result[1]\r\ndef read_csv_line(line_number, input_file):\r\n with open(input_file) as fileobj:\r\n for i, line in enumerate(fileobj):\r\n if i == (line_number - 1):\r\n return line\r\n return None\r\n\r\ncolumns = defaultdict(list)\r\na={}\r\nl=[]\r\ntry:\r\n\r\n with open(input_file, encoding=\"utf-8\", mode='r') as file:\r\n line_number = 0\r\n dataset = dict()\r\n file.readline()\r\n i=1\r\n\r\n for line in file:\r\n line = line.strip().rstrip()\r\n line_number += 1\r\n if not line:\r\n continue\r\n if line_number==10:\r\n break\r\n id, line = getElement(line)\r\n Dur, line = getElement(line)\r\n ET, line = getElement(line)\r\n SID, line = getStarting_Station_ID(line)\r\n Latitude, line = getStarting_Station_Latitude(line)\r\n Longitude, line = getStarting_Station_Longitude(line)\r\n EID, line = getEnding_Station_ID(line)\r\n l.append(EID)\r\n #print(city, postalcode, latitude)\r\n if Dur in dataset:\r\n if SID in dataset[Dur]:\r\n dataset[Dur][SID]=[Dur, EID]\r\n if Dur and Dur in dataset[Dur][SID]:\r\n dataset[Dur][SID]=[Longitude, EID]\r\n else:\r\n dataset[Dur][SID]=[]\r\n else:\r\n dataset[Dur]={SID:[Longitude, EID]}\r\n else:\r\n dataset[Dur]={SID:[]}\r\n if i==1:\r\n dataset[Dur]={SID:[Longitude, EID]}\r\n i+=1\r\n print(dataset)\r\n with open(input_file) as file:\r\n reader = csv.DictReader(file)\r\n for row in reader:\r\n for (k, v) in row.items():\r\n columns[k].append(v)\r\n\r\n\r\n\r\nexcept IOError as e:\r\n print (\"I/O error({0}): {1}\".format(e.errno, e.strerror))\r\n\r\nexcept ValueError as ve:\r\n print(\"Value error {0} in line {1}\".format(ve, line_number))\r\nb=list(columns['SID'])\r\nfor x in range(2, 23):\r\n a.update({x:[read_csv_line(x, input_file)]})\r\n\r\n\r\n\r\n\r\ns=list(dataset.keys())\r\n\r\nu=[]\r\n\r\nfor Latitude in list(dataset.keys()):\r\n c=dataset[Latitude]\r\n for SID in list(c.keys()):\r\n u.append(SID)\r\nprint(l)\r\n\r\nbar = [go.Bar(x=u, y=s)]\r\nplotly.offline.plot(bar, filename='bar.html')\r\ntrace = [go.Scatter(x=u, y=l)]\r\nplotly.offline.plot(trace, filename='trace.html')\r\n","sub_path":"aplot.py","file_name":"aplot.py","file_ext":"py","file_size_in_byte":3635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"111653833","text":"import timeit\n\ndef get_sum1(n): # O(n)\n sum = 0\n for i in range(n + 1):\n sum += i\n return sum\n\ndef get_sum2(n): # O(1)\n return n * (n + 1) // 2\n\ndef cal_time(func, d):\n n = int(input(\"숫자를 입력해주세요 : \")) # 5,000,000\n start = timeit.default_timer()\n print(\"{}까지의 합은 {}입니다.\".format(n, func(n)))\n stop = timeit.default_timer()\n print(\"get_sum{} 함수를 실행하는데 걸린 시간 : {}\".format(d, stop - start))\n print(\"----------------------------------------------\")\n return stop-start\n \ndef main():\n t1 = cal_time(get_sum1, 1)\n t2 = cal_time(get_sum2, 2)\n\n if(t1 > t2):\n print(\"get_sum{} 함수가 get_sum{} 함수보다 {}초 빨랐습니다.\".format(2, 1, t1 - t2))\n else:\n print(\"get_sum{} 함수가 get_sum{} 함수보다 {}초 빨랐습니다.\".format(1, 2 , t2 - t1))\n \n # get_sum2 함수가 get_sum1 함수보다 0.8051861999999996초 빨랐습니다.\n \nmain()\n","sub_path":"basics/get_sum.py","file_name":"get_sum.py","file_ext":"py","file_size_in_byte":985,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"120544214","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport os\r\nimport sys\r\nimport unittest\r\n\r\nfrom reportlab.lib.units import cm\r\nfrom reportlab.lib import pagesizes\r\nfrom reportlab.lib import enums\r\nfrom reportlab.lib.colors import blue, red, black, Color, HexColor\r\nfrom reportlab.lib.styles import getSampleStyleSheet\r\nfrom reportlab.pdfbase import pdfmetrics\r\nfrom reportlab.pdfbase.ttfonts import TTFont\r\nfrom reportlab.platypus import \\\r\n Frame, Paragraph, PageTemplate, BaseDocTemplate, NextPageTemplate, PageBreak\r\nfrom reportlab.platypus import Paragraph as NonHyphParagraph\r\nfrom reportlab.pdfgen.canvas import Canvas\r\n\r\n__author__ = \"Dinu Gherman\"\r\n\r\n\r\nUSE_HYPHENATION = True\r\n\r\nif USE_HYPHENATION:\r\n try:\r\n import wordaxe.rl.styles\r\n from wordaxe.rl.paragraph import Paragraph\r\n from wordaxe.DCWHyphenator import DCWHyphenator\r\n wordaxe.hyphRegistry['DE'] = DCWHyphenator('DE', 5)\r\n except SyntaxError:\r\n print(\"could not import wordaxe - try to continue WITHOUT hyphenation!\")\r\n\r\n\r\ntestdata = u\"\"\"Die seit Juni 2006 wöchentlich im Internet abrufbaren Video-Podcasts mit Bundeskanzlerin Angela Merkel (CDU) haben bisher mehr als 550.000 Euro gekostet. Das hat laut dem Magazin Focus das Bundespresseamt auf Anfrage des FDP-Bundestagsabgeordneten Volker Wissing mitgeteilt. Ein Video-Podcast werde demnach für durchschnittlich knapp 10.800 Euro produziert. Im Juni 2006 war noch von 6500 Euro Kosten je Ausgabe die Rede. Jede Woche verzeichne das BPA etwa 200.000 Zugriffe auf Merkels Internet-Ansprachen, ein Zehntel davon Downloads auf Personalcomputer. (anw/c't)\"\"\"\r\n\r\nPAGESIZE = pagesizes.landscape(pagesizes.A4)\r\n\r\n\r\nclass TwoColumnDocTemplate(BaseDocTemplate):\r\n \"Define a simple, two column document.\"\r\n\r\n def __init__(self, filename, **kw):\r\n m = 2*cm\r\n cw, ch = (PAGESIZE[0]-2*m)/2., (PAGESIZE[1]-2*m)\r\n f1 = Frame(m, m+0.5*cm, cw-0.75*cm, ch-1*cm, id='F1',\r\n leftPadding=0, topPadding=0, rightPadding=0, bottomPadding=0,\r\n showBoundary=True\r\n )\r\n f2 = Frame(cw+2.7*cm, m+0.5*cm, cw-0.75*cm, ch-1*cm, id='F2',\r\n leftPadding=0, topPadding=0, rightPadding=0, bottomPadding=0,\r\n showBoundary=True\r\n )\r\n BaseDocTemplate.__init__(self, filename, **kw)\r\n template = PageTemplate('template', [f1, f2])\r\n self.addPageTemplates(template)\r\n\r\n\r\nclass FrameSwitchTestCase(unittest.TestCase):\r\n \"Test hyphenation with switching frames.\"\r\n\r\n def test(self):\r\n stylesheet = getSampleStyleSheet()\r\n normal = stylesheet['BodyText']\r\n normal.fontName = \"Helvetica\"\r\n normal.fontSize = 12\r\n normal.leading = 16\r\n normal.language = 'DE'\r\n normal.hyphenation = True\r\n\r\n story = []\r\n if False:\r\n for i in range(3):\r\n p = Paragraph(testdata, style=normal)\r\n story.append(p)\r\n else:\r\n story.append(Paragraph(testdata*3, style=normal))\r\n doc = TwoColumnDocTemplate(\"test_frames.pdf\", pagesize=PAGESIZE)\r\n doc.build(story)\r\n\r\n\r\nif __name__ == \"__main__\":\r\n unittest.main()\r\n","sub_path":"tests/test_frames.py","file_name":"test_frames.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"467812113","text":"'''\r\n\r\n@author: Youyk\r\n'''\r\nimport zstackwoodpecker.test_util as test_util\r\nimport zstackwoodpecker.test_lib as test_lib\r\nimport test_stub\r\nimport zstackwoodpecker.test_state as test_state\r\nimport time\r\n\r\ntest_obj_dict = test_state.TestStateDict()\r\nvol_num = 19\r\nvolume_list = []\r\n\r\ndef test():\r\n global test_obj_dict\r\n global vol_num\r\n global volume\r\n vm = test_stub.create_vm()\r\n test_obj_dict.add_vm(vm)\r\n vm.check()\r\n for i in range(vol_num):\r\n volume_list.append(test_stub.create_volume())\r\n test_obj_dict.add_volume(volume_list[i])\r\n\r\n additional_vol = test_stub.create_volume()\r\n test_obj_dict.add_volume(additional_vol)\r\n\r\n for i in range(vol_num):\r\n volume_list[i].check()\r\n time.sleep(60)\r\n\r\n test_util.test_dsc('Test attach/detach 19 volumes operations.')\r\n for i in range(vol_num):\r\n volume_list[i].attach(vm)\r\n\r\n for i in range(vol_num):\r\n volume_list[i].check()\r\n\r\n for i in range(vol_num):\r\n volume_list[i].detach()\r\n volume_list[i].check()\r\n\r\n test_util.test_dsc('Redo attach/detach 19 volumes operations.')\r\n\r\n for i in range(vol_num):\r\n volume_list[i].attach(vm)\r\n volume_list[i].check()\r\n\r\n test_util.test_dsc('Try to attach the 19th data volume.')\r\n try:\r\n additional_vol.attach(vm)\r\n except:\r\n test_util.test_logger('Catch expected exception: try to attach the 19th data [volume:] %s to [vm:] %s fail.' % (additional_vol.volume.uuid, vm.vm.uuid))\r\n \r\n for i in range(vol_num):\r\n volume_list[i].detach()\r\n volume_list[i].check()\r\n\r\n for i in range(vol_num):\r\n volume_list[i].delete()\r\n volume_list[i].check()\r\n\r\n vm.destroy()\r\n test_util.test_pass('Create Multi Data Volumes for VM Test Success')\r\n return True\r\n\r\n test_util.test_fail('Fail: could attached the 19th data [volume:] %s to [vm:] %s .' % (additional_vol.volume.uuid, vm.vm.uuid))\r\n return False\r\n\r\ndef error_cleanup():\r\n global test_obj_dict\r\n test_lib.lib_error_cleanup(test_obj_dict)\r\n","sub_path":"integrationtest/vm/basic/test_add_multi_volumes.py","file_name":"test_add_multi_volumes.py","file_ext":"py","file_size_in_byte":2105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"465584538","text":"# -*- coding: utf-8 -*-\r\n\r\nfrom PyQt5 import QtCore, QtGui, QtWidgets\r\n\r\nimport sqlite3\r\n\r\n\r\nclass Ui_ui_new(object):\r\n def setupUi(self, ui_new):\r\n ui_new.setObjectName(\"ui_new\")\r\n ui_new.setEnabled(True)\r\n ui_new.resize(360, 150)\r\n ui_new.setMinimumSize(QtCore.QSize(360, 150))\r\n ui_new.setMaximumSize(QtCore.QSize(500, 200))\r\n ui_new.setWindowIcon(QtGui.QIcon('star.png'))\r\n font = QtGui.QFont()\r\n font.setFamily(\"Comic Sans MS\")\r\n font.setPointSize(10)\r\n ui_new.setFont(font)\r\n self.formLayout = QtWidgets.QFormLayout(ui_new)\r\n self.formLayout.setLabelAlignment(QtCore.Qt.AlignRight|QtCore.Qt.AlignTrailing|QtCore.Qt.AlignVCenter)\r\n self.formLayout.setFormAlignment(QtCore.Qt.AlignRight|QtCore.Qt.AlignTop|QtCore.Qt.AlignTrailing)\r\n self.formLayout.setObjectName(\"formLayout\")\r\n\r\n #label Or title.\r\n self.n_label = QtWidgets.QLabel(ui_new)\r\n self.n_label.setMinimumSize(QtCore.QSize(0, 30))\r\n self.n_label.setAlignment(QtCore.Qt.AlignCenter)\r\n self.n_label.setObjectName(\"n_label\")\r\n self.formLayout.setWidget(3, QtWidgets.QFormLayout.SpanningRole, self.n_label)\r\n\r\n #team name input area\r\n self.n_l_teamname = QtWidgets.QLineEdit(ui_new)\r\n self.n_l_teamname.setMinimumSize(QtCore.QSize(0, 30))\r\n self.n_l_teamname.setMaximumSize(QtCore.QSize(16777215, 30))\r\n font = QtGui.QFont()\r\n font.setFamily(\"Comic Sans MS\")\r\n font.setPointSize(12)\r\n self.n_l_teamname.setFont(font)\r\n self.n_l_teamname.setAlignment(QtCore.Qt.AlignCenter)\r\n self.n_l_teamname.setObjectName(\"n_l_teamname\")\r\n self.formLayout.setWidget(4, QtWidgets.QFormLayout.FieldRole, self.n_l_teamname)\r\n\r\n #create button\r\n self.b_create = QtWidgets.QPushButton(ui_new)\r\n self.b_create.setEnabled(True)\r\n self.b_create.setMinimumSize(QtCore.QSize(0, 0))\r\n self.b_create.setMaximumSize(QtCore.QSize(500, 555))\r\n self.b_create.setObjectName(\"b_create\")\r\n self.formLayout.setWidget(5, QtWidgets.QFormLayout.FieldRole, self.b_create)\r\n\r\n self.retranslateUi(ui_new)\r\n QtCore.QMetaObject.connectSlotsByName(ui_new)\r\n\r\n def retranslateUi(self, ui_new):\r\n _translate = QtCore.QCoreApplication.translate\r\n ui_new.setWindowTitle(_translate(\"ui_new\", \"Create Team\"))\r\n self.n_label.setText(_translate(\"ui_new\", \"Enter a New Team Name\"))\r\n self.n_l_teamname.setPlaceholderText(_translate(\"ui_new\", \"team name\"))\r\n self.b_create.setText(_translate(\"ui_new\", \"Create\"))\r\n\r\n\r\niteam = 'wow'\r\nif __name__ == \"__main__\":\r\n import sys\r\n\r\n app = QtWidgets.QApplication(sys.argv)\r\n ui_new = QtWidgets.QWidget()\r\n ui = Ui_ui_new()\r\n ui.setupUi(ui_new)\r\n ui_new.show()\r\n sys.exit(app.exec_())\r\n","sub_path":"DreamCricketUpdated/UInew.py","file_name":"UInew.py","file_ext":"py","file_size_in_byte":2876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"251408553","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nSpyder Editor\r\n\r\nThis is a temporary script file.\r\n\"\"\"\r\n\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\nfrom sklearn.cluster import KMeans\r\nimport random\r\n\r\n\r\n\r\n\r\ngt_jpg = plt.imread('global_traffic.jpg')\r\ngt = np.array(gt_jpg)\r\npoints = []\r\n\r\nfor i in range(0, 640):\r\n for j in range(0, 1280):\r\n if gt[i][j][0] >= 150 or gt[i][j][1] >= 200:\r\n points.append([j, i])\r\npoints = np.array(points)\r\n\r\nn_clusters = 21\r\nmodel_kmeans = KMeans(n_clusters = n_clusters, random_state=0)\r\nmodel_kmeans.fit(points)\r\ny_pre = model_kmeans.predict(points)\r\n\r\n\r\n\r\n\r\n\r\ncenters = model_kmeans.cluster_centers_\r\ncolors = [\"#\"+''.join([random.choice('0123456789ABCDEF') \r\n for j in range(6)]) for i in range(n_clusters)]\r\nmy_dpi = 96\r\nplt.figure(figsize=(1280/my_dpi, 640/my_dpi), dpi=my_dpi)\r\nfor i in range(n_clusters):\r\n index_sets = np.where(y_pre == i)\r\n cluster = points[index_sets]\r\n plt.scatter(cluster[:, 0], cluster[:, 1], c=colors[i], marker='.', s=1)\r\n plt.plot(centers[i][0], centers[i][1], 'o', \r\n markerfacecolor=colors[i], markeredgecolor='k',\r\n markersize=10)\r\n\r\n\r\nplt.imshow(gt_jpg)\r\nplt.axis('off')\r\nplt.savefig('pre.png')\r\nplt.show","sub_path":"cluster.py","file_name":"cluster.py","file_ext":"py","file_size_in_byte":1233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"644406090","text":"# compiles the new raw file, using the new comed CAPE data and the non-comed planning data\n\n#import cutoutComed\n\nnewdir = 'Final Result'\n\nAllMapFile = 'AllMappedBusData.txt'\nnewGenFile = 'verifiedGenData.txt'\nnewLoadFile = 'newLoadDataConflictResolved.txt'\nnewbranchFile = 'newbranchData.txt'\nnewtfFile = 'TFIter3.txt'\nnewfsFile = 'fixedShuntData.txt'\nnewssFile = 'newShuntData.txt'\nmiscDataFile = 'miscData.txt'\n\n\nPSSErawFile = 'hls18v1dyn_1219.raw'\ncroppedBusFile = 'croppedBusFile.txt'\ncroppedLoadFile = 'croppedLoadFile.txt'\ncroppedoutBranchFile = 'croppedBranchFile.txt'\ncroppedtfFile = 'croppedtfFile.txt'\ncroppedGenFile = 'croppedGenFile.txt'\ncroppedfsFile = 'croppedfsFile.txt'\ncroppedssFile = 'croppedssFile.txt'\n#BoundaryMapClean = 'BoundaryplanningMapCleaned.txt'\n#boundaryFile = 'boundaryFile.txt'\n#BoundaryMapManual = 'BoundaryMapManual.txt'\n\n\ngodFile = 'RAW0406018.raw'\ngodFilePath = newdir + '/' + godFile\n# Bus data\n\nwith open(AllMapFile,'r') as f:\n\tComedBusData = f.read()\nwith open(croppedBusFile,'r') as f:\n\totherBusData = f.read()\n\n\n# Load data\nwith open(newLoadFile,'r') as f:\n\tComedLoadData = f.read()\n\nwith open(croppedLoadFile,'r') as f:\n\totherLoadData = f.read()\t\n\n#Fixed shunt data\nwith open(newfsFile,'r') as f:\n\tComedfsData = f.read()\nwith open(croppedfsFile,'r') as f:\n\totherfsData = f.read()\n\n# Gen data\n\nwith open(newGenFile,'r') as f:\n\tComedGenData = f.read()\nwith open(croppedGenFile,'r') as f:\n\totherGenData = f.read()\n\n# Branch data\nwith open(newbranchFile,'r') as f:\n\tComedBranchData = f.read()\nwith open(croppedoutBranchFile,'r') as f:\n\totherBranchData = f.read()\n\n# Transformer data\nwith open(newtfFile,'r') as f:\n\tComedtfData = f.read()\nwith open(croppedtfFile,'r') as f:\n\tothertfData = f.read()\n\n# Misc data (such as Area, zone)\nwith open(miscDataFile,'r') as f:\n\tmiscData = f.read()\n\n# switched shunt data\nwith open(newssFile,'r') as f:\n\tComedssData = f.read()\nwith open(croppedssFile,'r') as f:\n\totherssData = f.read()\n\nwith open('footer.txt','r') as f:\n\tfooterData = f.read()\n\nwith open(godFile,'w') as f:\n\tf.write('0, 100.00, 33, 1, 1, 60.00 / PSS(R)E-33.3 TUE, DEC 13 2016 22:08')\n\tf.write('\\n')\n\tf.write('COMED 2018, HLS18V1, N18S OUTSIDE AND 18 INTCHNG')\n\tf.write('\\n')\n\tf.write('DYNAMICS REVSION 01')\n\tf.write('\\n')\n\n\tf.write(ComedBusData)\n\tf.write(otherBusData)\n\tf.write('\\n')\n\tf.write('0 / END OF BUS DATA, BEGIN LOAD DATA')\n\tf.write('\\n')\n\tf.write(ComedLoadData)\n\tf.write(otherLoadData)\n\tf.write('\\n')\n\tf.write('0 / END OF LOAD DATA, BEGIN FIXED SHUNT DATA')\n\tf.write('\\n')\n\tf.write(ComedfsData)\n\tf.write(otherfsData)\n\tf.write('\\n')\n\tf.write('0 / END OF FIXED SHUNT DATA, BEGIN GENERATOR DATA')\n\tf.write('\\n')\n\tf.write(ComedGenData)\n\tf.write(otherGenData)\n\tf.write('\\n')\n\tf.write('0 / END OF GENERATOR DATA, BEGIN BRANCH DATA')\n\tf.write('\\n')\n\tf.write(ComedBranchData)\n\tf.write(otherBranchData)\n\tf.write('\\n')\n\tf.write('0 / END OF BRANCH DATA, BEGIN TRANSFORMER DATA')\n\tf.write('\\n')\n\tf.write(ComedtfData)\n\tf.write(othertfData)\n\tf.write('\\n')\n\tf.write('0 / END OF TRANSFORMER DATA, BEGIN AREA DATA')\n\tf.write('\\n')\n\tf.write(miscData)\n\tf.write('\\n')\n\tf.write(ComedssData)\n\tf.write(otherssData)\n\tf.write('\\n')\n\tf.write(footerData)\n\n\n\n\ncleanLines = []\nwith open(godFile,'r') as f:\n\tcontent = f.read()\n\tlines = content.split('\\n')\n\tfor line in lines:\n\t\tif line != '':\n\t\t\tcleanLines.append(line)\ncleangodFile = newdir + '/' + godFile\nwith open(cleangodFile,'w') as f:\n\tfor line in cleanLines:\n\t\tf.write(line)\n\t\tf.write('\\n')\n\n\n\n\n\n\n\n\n\n\n","sub_path":"Scripts/compileAll0322.py","file_name":"compileAll0322.py","file_ext":"py","file_size_in_byte":3495,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"145739693","text":"class UserShips:\r\n def __init__(self, name, size):\r\n self.name=name\r\n self.size=size\r\n\r\n def SelectShip(self):\r\n if BoatInput == 'carrier':\r\n self.size = int(carrier.size)\r\n print(\"size = \",self.size)\r\n carrier.InputPosition()\r\n if BoatInput == 'battleship':\r\n self.size = int(battleship.size)\r\n print(\"size = \",self.size)\r\n battleship.InputPosition()\r\n if BoatInput == 'cruiser':\r\n self.size = int(cruiser.size)\r\n print(\"size = \",self.size)\r\n cruiser.InputPosition()\r\n if BoatInput == 'submarine':\r\n self.size = int(submarine.size)\r\n print(\"size = \",self.size)\r\n submarine.InputPosition()\r\n if BoatInput == 'destroyer':\r\n self.size = int(destroyer.size)\r\n print(\"size = \",self.size)\r\n destroyer.InputPosition()\r\n\r\nclass Position(UserShips):\r\n def __init__(self, name, size, RowHead, ColumnHead):\r\n super().__init__(name,size)\r\n self.RowHead = RowHead\r\n self.ColumnHead = ColumnHead\r\n\r\n def CA(self):\r\n carrier = UserShips('C','5')\r\n carrier.SelectShip()\r\n def BA(self):\r\n battleship = UserShips('B','4')\r\n battleship.SelectShip()\r\n def CR(self):\r\n cruiser = UserShips('R','3')\r\n cruiser.SelectShip()\r\n def SU(self):\r\n submarine = UserShips('S','3')\r\n submarine.SelectShip()\r\n def DE(self):\r\n destroyer = UserShips('D','2')\r\n destroyer.SelectShip()\r\n\r\n def InputPosition(self):\r\n def Coordinates():\r\n for row in Table:\r\n print('\\n')\r\n for column in row:\r\n print(column,end = \" \")\r\n print('\\n') \r\n\r\n def AskDirections():\r\n Coordinates()\r\n rH = 0\r\n cH = 0\r\n while Table[rH][cH] != \".\":\r\n rH = int(input(\"what is row head \"))+1\r\n cH = int(input(\"what is column head \"))+1\r\n\r\n Value = True\r\n while Value == True:\r\n try:\r\n Value,Shape = AskShape(rH,cH)\r\n \r\n except IndexError:\r\n print(\"You cannot place a ship outside the board, try again\")\r\n return rH,cH,Shape\r\n\r\n\r\n\r\n def AskShape(rH,cH):\r\n Shape = \"x\"\r\n Compass =[\"north\",\"east\",\"south\",\"west\",\"res\"]\r\n print(Compass)\r\n print(\"To reselect your coordinate type RES\")\r\n while Shape not in Compass:\r\n Shape = input(\"What direction do you want your ship to go? \").lower()\r\n if Shape in Compass:\r\n if Shape == \"res\":\r\n InputDirections()\r\n \r\n if Shape == \"north\":\r\n for x in range(int(self.size)):\r\n if Table[rH][cH] == \".\":\r\n rH=rH-1\r\n if Table[rH][cH] != \".\":\r\n print(\"Cannot put ship here\")\r\n Shape = \"x\"\r\n break \r\n rH =rH + x + 1\r\n\r\n if Shape == \"south\":\r\n for x in range(int(self.size)):\r\n if Table[rH][cH] == \".\":\r\n rH=rH+1\r\n if Table[rH][cH] != \".\":\r\n print(\"Cannot put ship here\")\r\n Shape = \"x\"\r\n break\r\n rH =rH - x - 1\r\n\r\n if Shape ==\"east\":\r\n for x in range(int(self.size)):\r\n if Table[rH][cH] == \".\":\r\n cH=cH+1\r\n if Table[rH][cH] != \".\":\r\n print(\"Cannot put ship here\")\r\n Shape = \"x\"\r\n break\r\n cH =cH - x - 1\r\n\r\n if Shape ==\"west\":\r\n for x in range(int(self.size)):\r\n if Table[rH][cH] == \".\":\r\n cH=cH-1\r\n if Table[rH][cH] != \".\":\r\n print(\"Cannot put ship here\")\r\n Shape = \"x\"\r\n break\r\n cH =cH + x + 1\r\n Value = False\r\n return Value,Shape\r\n\r\n\r\n def InputDirections():\r\n rH,cH,Shape = AskDirections()\r\n PersonFile = open(AskName.Name +\".txt\",\"a\")\r\n PersonFile.write(str(rH)+\":\"+str(cH)+\",\")\r\n if Shape == \"north\":\r\n for y in range(int(self.size)):\r\n Table[rH][cH]= self.name\r\n rH=rH-1\r\n PersonFile.write(str(rH)+\":\"+str(cH)+\",\")\r\n if Shape == \"south\":\r\n for y in range(int(self.size)):\r\n Table[rH][cH]= self.name\r\n rH=rH+1\r\n PersonFile.write(str(rH)+\":\"+str(cH)+\",\")\r\n if Shape ==\"east\":\r\n for y in range(int(self.size)):\r\n Table[rH][cH]= self.name\r\n cH=cH+1\r\n PersonFile.write(str(rH)+\":\"+str(cH)+\",\")\r\n\r\n if Shape ==\"west\":\r\n for y in range(int(self.size)):\r\n Table[rH][cH]= self.name\r\n cH=cH-1\r\n PersonFile.write(str(rH)+\":\"+str(cH)+\",\")\r\n PersonFile.close()\r\n Coordinates()\r\n InputDirections()\r\n\r\nimport AskName\r\nimport Authentification\r\nAuthentification.PlayerAuthentification()\r\nrH = \"rH\"\r\ncH = \"cH\"\r\nTable =[[\" \",\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\"],\r\n [\" 0\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"],\r\n [\" 1\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"],\r\n [\" 2\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"],\r\n [\" 3\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"],\r\n [\" 4\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"],\r\n [\" 5\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"],\r\n [\" 6\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"],\r\n [\" 7\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"],\r\n [\" 8\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"],\r\n [\" 9\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"],\r\n [\"10\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"],\r\n [\"11\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\",\".\"]]\r\ncarrier = Position('C','5',rH,cH)\r\nbattleship = Position('B','4',rH,cH)\r\ncruiser = Position('R','3',rH,cH)\r\nsubmarine = Position('S','3',rH,cH)\r\ndestroyer = Position('D','2',rH,cH)\r\n\r\nVessel = ['carrier','battleship','cruiser','submarine','destroyer']\r\nEnter = input(\"Press enter to start \")\r\nwhile len(Vessel) != 0:\r\n print(Vessel)\r\n BoatInput = \"x\"\r\n while BoatInput not in Vessel:\r\n BoatInput = input(\"What ship do you want? \").lower()\r\n Vessel.remove(BoatInput)\r\n print(BoatInput,\" selected\")\r\n if BoatInput == 'carrier':\r\n carrier.CA()\r\n if BoatInput == 'battleship':\r\n battleship.BA()\r\n if BoatInput == 'cruiser':\r\n cruiser.CR()\r\n if BoatInput == 'submarine':\r\n submarine.SU()\r\n if BoatInput == 'destroyer':\r\n destroyer.DE()\r\n\r\n","sub_path":"AuthUserBoard.py","file_name":"AuthUserBoard.py","file_ext":"py","file_size_in_byte":7617,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"615978469","text":"from config import cfg\nimport json\nimport numpy as np\nfrom os.path import join\nimport matplotlib.pyplot as plt\nfrom scipy.optimize import curve_fit\nimport pandas as pd\nimport argparse\nimport sys\n\nparser = argparse.ArgumentParser(description='Find working points.')\nparser.add_argument('--default', action='store_true' , help = 'take the default training')\nparser.add_argument('--bayes_opt', action='store_true' , help = 'take the BO training')\nparser.add_argument('--TMVA' , action='store_true' , help = 'take the TMVA training')\nargs = parser.parse_args()\n\nif sum([args.TMVA, args.bayes_opt, args.default]) != 1:\n print(\"You should specify one classifier.\")\n sys.exit(0)\n\ndf_file = 'pt_eta_score.h5'\n\nif args.default:\n score_col = \"bdt_score_default\"\n outfile = \"working_points_default.json\"\n\nif args.bayes_opt:\n score_col = \"bdt_score_optimized\"\n outfile = \"working_points_bayes_opt.json\"\n\nif args.TMVA:\n score_col = \"BDT\"\n outfile = \"working_points_TMVA.json\"\n df_file = join(\"legacy\", df_file)\n\nout_dir_base = join(cfg[\"out_dir\"], cfg['submit_version'])\n\n\n# For p_T scaling cut\ndef wp_func(x, c, tau, A):\n return c - np.exp(-x / tau) * A\n\n\nd = {}\n\ndef load_df(id_name, cat):\n df = pd.read_hdf(join(out_dir_base, id_name, cat, df_file))\n\n # For compatibility with older version of the training framework and for legacy training\n if not \"y\" in df.columns:\n if \"classID\" in df.columns:\n # The TMVA classID 0 is signal\n df.eval('y = 1 - classID', inplace=True)\n else:\n df.eval('y = ({0}) + 2 * ({1}) - 1'.format(cfg[\"selection_bkg\"], cfg[\"selection_sig\"]), inplace=True)\n\n df.rename(index=str, columns={score_col: \"score\"}, inplace=True)\n\n return df\n\n\nfor id_name in cfg['working_points']:\n\n d[id_name] = {}\n print(\"[INFO] Processing {}...\".format(id_name))\n\n for wp_name in cfg['working_points'][id_name]:\n cfg_wp = cfg['working_points'][id_name][wp_name]\n wp_type = cfg_wp['type']\n\n eff_boundaries = {}\n wp = {}\n\n print(\"[INFO] Working point: {}...\".format(wp_name))\n\n for i, cat in enumerate(cfg_wp['categories']):\n print(\"[INFO] Category: \" + cat)\n\n df = load_df(id_name, cat)\n\n y = df[\"y\"].values\n\n if wp_type == 'constant_cut_sig_eff_targets':\n\n wp[cat] = df.score[df.y == True].quantile(1-cfg_wp['targets'][i]) # Find the score for signal events above which cfg_wp['targets'][i]*100% of signal events exist\n\n if 'match_boundary' in cfg_wp and cfg_wp['match_boundary']:\n pt = df[\"ele_pt\"].values\n if '5' in cat:\n sel = np.logical_and.reduce([y == True, pt >= 9.5, pt < 10.0])\n elif '10' in cat:\n sel = np.logical_and.reduce([y == True, pt >= 10.0, pt < 10.5])\n eff_boundaries[cat] = np.sum(np.logical_and(sel, df.score > wp[cat]))*1./np.sum(sel)\n\n\n elif wp_type == 'pt_scaling_cut_sig_eff_targets':\n pt = df[\"ele_pt\"].values\n x = np.zeros(len(cfg_wp['ptbins']))\n x[:] = np.nan\n pt_c = np.mean(cfg_wp['ptbins'], axis=1)\n for k, pt_bin in enumerate(cfg_wp['ptbins']):\n sel = np.logical_and.reduce([y == True, pt >= ptbin[0], pt < ptbin[1]])\n if np.sum(sel) == 0:\n continue\n x[k] = np.percentile(df.score[sel], (1-cfg_wp['targets'][i][k]) * 100)\n\n\n pt_c = pt_c[~np.isnan(x)]\n x = x[~np.isnan(x)]\n\n popt, pcov = curve_fit(wpfunc, pt_c, x, p0=[7, 20, 10], bounds=([0, 0, 0], [100, 100, 100]))\n\n wp[cat] = {'c': popt[0], 'tau': popt[1], 'A': popt[2]}\n\n if 'match_boundary' in cfg_wp and cfg_wp['match_boundary']:\n if '5' in cat:\n sel = np.logical_and.reduce([y == True, pt >= 9.5, pt < 10.0])\n elif '10' in cat:\n sel = np.logical_and.reduce([y == True, pt >= 10.0, pt < 10.5])\n wps = wpfunc(pt, wp[cat]['c'], wp[cat]['tau'], wp[cat]['A'])\n eff_boundaries[cat] = np.sum(np.logical_and(sel, df.score > wps))*1./np.sum(sel)\n\n if 'match_boundary' in cfg_wp and cfg_wp['match_boundary']:\n if wp_type == 'constant_cut_sig_eff_targets':\n for i, cat in enumerate(cfg_wp['categories']):\n if '5' in cat:\n df = load_df(id_name, cat)\n df.query(\"y == 1 & ele_pt >= 9.5 & ele_pt < 10.0\", inplace=True)\n wp[cat] = df.score.quantile(1-eff_boundaries[cat.replace('5', '10')])\n\n if wp_type == 'pt_scaling_cut_sig_eff_targets':\n for i, cat in enumerate(cfg_wp['categories']):\n if '5' in cat:\n df = load_df(id_name, cat)\n df.query(\"y == 1 & ele_pt >= 9.5 & ele_pt < 10.0\", inplace=True)\n wp_boundary = df.score.quantile(1-eff_boundaries[cat.replace('5', '10')])\n wp[cat]['c'] = wp[cat]['c'] - wpfunc(9.75, wp[cat]['c'], wp[cat]['tau'], wp[cat]['A']) + wp_boundary\n\n d[id_name][wp_name] = wp\n\nwith open(join(out_dir_base, outfile), 'w') as fp:\n json.dump(d, fp, indent=4)\n","sub_path":"find_working_points.py","file_name":"find_working_points.py","file_ext":"py","file_size_in_byte":5427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"513754311","text":"#!/usr/local/bin/python3\n\n# Copyright 2016 The Johns Hopkins University Applied Physics Laboratory\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\n### BEGIN INIT INFO\n# Provides: boss-firstboot\n# Required-Start:\n# Required-Stop:\n# Default-Start: 2 3 4 5\n# Default-Stop:\n# Short-Description: boss Python library firstboot script\n# Description: Firstboot service script that configures the BOSS Django install\n# to work correctly.\n#\n### END INIT INFO\n\nimport bossutils\n\nbossutils.utils.set_excepthook()\nlogging = bossutils.logger.BossLogger().logger\n\n\ndef django_initialize():\n logging.info(\"Get migration settings from S3\")\n mm = bossutils.migration_manager.MigrationManager()\n migration_success = mm.get_migrations()\n if not migration_success:\n logging.info(\"getting migrations from s3 failed. Skipping makemigrations, migrate\")\n return False\n else:\n logging.info(\"Finished getting migration settings\")\n\n logging.info(\"Initializing Django\")\n migrate_cmd = \"yes | sudo python3 /srv/www/django/manage.py \"\n bossutils.utils.execute(migrate_cmd + \"makemigrations\", whole=True, shell=True)\n bossutils.utils.execute(migrate_cmd + \"makemigrations bosscore\", whole=True, shell=True) # will hang if it cannot contact the auth server\n bossutils.utils.execute(migrate_cmd + \"makemigrations bossoidc\", whole=True, shell=True)\n bossutils.utils.execute(migrate_cmd + \"makemigrations bossingest\", whole=True, shell=True)\n bossutils.utils.execute(migrate_cmd + \"makemigrations bossmeta\", whole=True, shell=True)\n bossutils.utils.execute(migrate_cmd + \"makemigrations mgmt\", whole=True, shell=True)\n bossutils.utils.execute(migrate_cmd + \"migrate\", whole=True, shell=True)\n bossutils.utils.execute(migrate_cmd + \"collectstatic --no-input\", whole=True, shell=True)\n\n bossutils.utils.execute(\"sudo service uwsgi-emperor reload\")\n bossutils.utils.execute(\"sudo service nginx restart\")\n logging.info(\"Finished Initializing Django\")\n\n logging.info(\"Put migration settings in S3\")\n migration_put_success = mm.put_migrations()\n if not migration_put_success:\n logging.error(\n \"At least one migration failed when putting them in s3.\")\n else:\n logging.info(\"Migrations\")\n return True\n\nif __name__ == '__main__':\n sucessful_initialization = django_initialize()\n\n # Since the service is to be run once, disable it\n if sucessful_initialization:\n bossutils.utils.stop_firstboot()\n","sub_path":"salt_stack/salt/boss/files/firstboot.py","file_name":"firstboot.py","file_ext":"py","file_size_in_byte":3078,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"452528987","text":"\"\"\"Common rpc for NAAS Agent.\"\"\"\n\nimport threading\nfrom simplejson import loads, dumps\n\nfrom kombu import Exchange, Producer, Connection, Queue, Consumer\n\nfrom oslo_config import cfg\nfrom oslo_log import log as logging\n\nfrom naas.agent.exception import NotAnException\n\nrabbitmq_opts = [\n cfg.StrOpt('connection',\n default='localhost',\n help=\"\"),\n cfg.StrOpt('agent_scheduler_exchange',\n default='agent_scheduler_exchange',\n help=''),\n cfg.StrOpt('agent_scheduler_routing_key',\n default='agent_scheduler_routing_key',\n help=''),\n cfg.StrOpt('agent_scheduler_report_exchange',\n default='agent_scheduler_report_exchange',\n help=''),\n cfg.StrOpt('agent_scheduler_report_routing_key',\n default='agent_scheduler_report_routing_key',\n help=''),\n]\n\nCONF = cfg.CONF\nCONF.register_opts(rabbitmq_opts, 'rabbitmq')\n\nLOG = logging.getLogger(__name__)\n\ndef _get_connection_and_channel_and_exchange(target=None, exchange_name=None):\n connection = Connection(target or CONF.rabbitmq.connection)\n channel = connection.channel()\n exchange = Exchange(exchange_name, 'direct', channel)\n return connection, channel, exchange\n\ndef cast(message, exchange_name, routing_key):\n connection, channel, exchange = \\\n _get_connection_and_channel_and_exchange(exchange_name=exchange_name)\n\n if isinstance(message, dict):\n message = dumps(message)\n\n producer = Producer(channel, exchange=exchange, routing_key=routing_key)\n try:\n producer.publish(message)\n except Exception as e:\n LOG.warn('exception: {}, re-publish message: {}'.format(e, message))\n producer.publish(message)\n\ndef cast_to_scheduler(message):\n cast(message=message,\n exchange_name=CONF.rabbitmq.agent_scheduler_exchange,\n routing_key=CONF.rabbitmq.agent_scheduler_routing_key)\n\ndef report_to_scheduler(message):\n cast(message=message,\n exchange_name=CONF.rabbitmq.agent_scheduler_report_exchange,\n routing_key=CONF.rabbitmq.agent_scheduler_report_routing_key)\n\nclass Manager(object):\n\n def __init__(self):\n super(Manager, self).__init__()\n\n def __call__(self, body, message):\n message.ack()\n response = {}\n try:\n LOG.debug('receive: {}'.format(body))\n dict_body = loads(body)\n response['sub_task_id'] = dict_body['sub_task_id']\n method = dict_body['method']\n kwargs = dict_body['body']\n\n func = getattr(self, method)\n result = func(kwargs)\n\n success = 'success'\n msg = result or ''\n except NotAnException as e:\n success = 'success'\n msg = 'info: {}'.format(e)\n except Exception as e:\n success = 'error'\n msg = 'info: {}'.format(e)\n LOG.exception(msg)\n\n response['result'] = success\n response['msg'] = msg\n LOG.debug('send: {}'.format(response))\n cast_to_scheduler(response)\n\n\nclass MessageHandingServer(object):\n\n def __init__(self, routing_key, manager, target=None, exchange_name=None):\n self._is_start = False\n\n if not target:\n target = CONF.rabbitmq.connection\n if not exchange_name:\n exchange_name = CONF.rabbitmq.agent_scheduler_exchange\n\n self._connection, self._channel, self._exchange = \\\n _get_connection_and_channel_and_exchange(target=target, exchange_name=exchange_name)\n\n self._queue_name = routing_key\n self._routing_key = routing_key\n\n self._exchange.declare()\n self._queue = Queue(self._queue_name,\n exchange=self._exchange,\n routing_key=self._routing_key,\n channel=self._channel)\n self._queue.declare()\n\n self._manager = manager\n self._consumer = Consumer(self._channel, queues=[self._queue], callbacks=[self._manager])\n self._executor = threading.Thread(target=self._drain_events)\n\n def _drain_events(self):\n self._consumer.consume()\n while self._is_start:\n self._connection.drain_events()\n self._consumer.cancel()\n\n def start(self):\n self._is_start = True\n self._executor.setDaemon(True)\n self._executor.start()\n\n def stop(self):\n self._is_stop = False\n\n def wait(self):\n pass\n\ndef get_server(routing_key, manager):\n if not isinstance(manager, Manager):\n raise Exception(\"not base rpc Manager\")\n return MessageHandingServer(routing_key, manager)\n","sub_path":"naas/agent/rpc.py","file_name":"rpc.py","file_ext":"py","file_size_in_byte":4679,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"555070971","text":"\"\"\"Test the permuting of images.\n\nTo run this unittest run the command \"python -m unittest Test.test_image_permutation\" from the Code directory.\n\n\"\"\"\n\n# Python imports.\nimport unittest\n\n# 3rd party imports.\nimport numpy as np\n\n# User imports.\nimport Test.permutation_config\nimport Utilities.image_permutation\n\n\nclass CompletionTest(unittest.TestCase):\n \"\"\"Test whether the permuting successfully completes.\"\"\"\n\n def test_no_permute(self):\n print(\"Test\")\n for i in Test.permutation_config.completionNoPermute:\n runParams = Test.permutation_config.completionNoPermute[i]\n image = Utilities.image_permutation.main(\n runParams[\"ImageLocation\"], maxRotation=runParams[\"MaxRotation\"], maxShear=runParams[\"MaxShear\"],\n maxTranslation=runParams[\"MaxTranslation\"], maxScale=runParams[\"MaxScale\"],\n scaleUpProb=runParams[\"ScaleUpProb\"],jointScale=runParams[\"JointScale\"],\n inversionProb=runParams[\"InversionProb\"])\n\n","sub_path":"Code/Test/test_image_permutation.py","file_name":"test_image_permutation.py","file_ext":"py","file_size_in_byte":1008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"410880025","text":"#!/usr/bin/env python\n\nfrom __future__ import print_function\n\nimport unittest\nimport os\nimport config_metplus\nfrom run_tc_pairs import TcPairs\nimport csv\nimport re\nimport shutil\nimport produtil.setup\n\n\nclass TestRunTcPairs(unittest.TestCase):\n def setUp(self):\n \"\"\" Run run_tc_pairs.py based on the metplus.conf file in the\n $METPLUS_BASE/parm directory, then base all unit tests on\n the output from that run.\n \"\"\"\n self.p = self.get_config()\n rtcp = TcPairs(self.p)\n rtcp.main()\n\n def tearDown(self):\n shutil.rmtree(self.p.getdir('TRACK_DATA_SUBDIR_MOD'))\n shutil.rmtree(self.p.getdir('TC_PAIRS_DIR'))\n\n @staticmethod\n def get_config():\n if 'JLOGFILE' in os.environ:\n produtil.setup.setup(send_dbn=False,\n jobname='test run_tc_pairs',\n jlogfile=os.environ['JLOGFILE'])\n else:\n produtil.setup.setup(send_dbn=False,\n jobname='test run_tc_pairs')\n produtil.log.postmsg('unit test for run_tc_pairs is starting')\n\n # Read in the configuration object CONFIG_INST\n config_instance = config_metplus.setup()\n if 'MET_BASE' not in os.environ:\n os.environ['MET_BASE'] = config_instance.getdir('MET_BASE')\n\n return config_instance\n\n def test_no_empty_mod_dir(self):\n \"\"\" Verify that we are creating the ATCF files. \"\"\"\n tc_pairs_dir = self.p.getdir('TC_PAIRS_DIR')\n self.assertTrue(os.listdir(tc_pairs_dir))\n\n def test_no_empty_tcp_dir(self):\n \"\"\" Verify that we are creating tc pair output\"\"\"\n tc_pairs_dir = self.p.getdir('TC_PAIRS_DIR')\n self.assertTrue(os.listdir(tc_pairs_dir))\n\n def test_one_less_column(self):\n \"\"\" Tests the read_modify_write_file() method.\n Verify that the output in the track_data_atcf directory\n has one fewer column than the input track data.\n \"\"\"\n track_data_dir = self.p.getdir('TRACK_DATA_DIR')\n track_data_subdir_mod = self.p.getdir('TRACK_DATA_SUBDIR_MOD')\n\n # So that we have independence of the start and end times indicated\n # in the metplus.conf or any other conf file, perform the test as\n # follows:\n # 1) get a list of directories in the track_data_dir and\n # track_data_subdir_mod\n # 2) use the first directory of each in #1\n # 3) then look at the first file in each of those directories in #2\n # We don't need to have matching dates and filename in the original and\n # modified files, since we are only interested in the *number* of\n # columns in the files.\n orig_dir_list = os.listdir(track_data_dir)\n mod_dir_list = os.listdir(track_data_subdir_mod)\n orig_first_file_list = os.listdir(\n os.path.join(track_data_dir, orig_dir_list[0]))\n mod_first_file_list = os.listdir(\n os.path.join(track_data_subdir_mod, mod_dir_list[0]))\n orig_first_file = orig_first_file_list[0]\n mod_first_file = mod_first_file_list[0]\n orig_tc_file = os.path.join(track_data_dir, orig_dir_list[0],\n orig_first_file)\n mod_tc_file = os.path.join(track_data_subdir_mod, mod_dir_list[0],\n mod_first_file)\n # Get the number of columns from the first row of the original and\n # modified files, respectively.\n with open(orig_tc_file) as f:\n reader = csv.reader(f, delimiter=' ', skipinitialspace=True)\n first_row = next(reader)\n orig_num_cols = len(first_row)\n with open(mod_tc_file) as f:\n reader = csv.reader(f, delimiter=' ', skipinitialspace=True)\n first_row = next(reader)\n mod_num_cols = len(first_row)\n\n self.assertTrue(mod_num_cols == (orig_num_cols - 1))\n\n def test_col2_format_ok(self):\n \"\"\"Tests for proper behavior in the read_modify_write_file() method\n Verify that the second column of one of the modified tc track files\n does indeed have the month followed by the storm id:\n Get the first row of the first file in the same subdirectory of the\n modified and input directory (i.e. same month)\n \"\"\"\n track_data_dir = self.p.getdir('TRACK_DATA_DIR')\n track_data_subdir_mod = self.p.getdir('TRACK_DATA_SUBDIR_MOD')\n mod_dir_list = os.listdir(track_data_subdir_mod)\n\n # Get the first directory in the TRACK_DATA_SUBDIR_MOD directory and\n # the matching year-month dir in the TRACK_DATA_DIR directory.\n year_month_dir = mod_dir_list[0]\n mod_first_file_list = os.listdir(\n os.path.join(track_data_subdir_mod, year_month_dir))\n\n # Get the track file that corresponds to the modified track file's\n # first file.\n first_track_file = mod_first_file_list[0]\n mod_tc_file = os.path.join(track_data_subdir_mod, year_month_dir,\n first_track_file)\n orig_tc_file = os.path.join(track_data_dir, year_month_dir,\n first_track_file)\n\n # Get the month from the year_month_dir\n month_match = re.match(r'[0-9]{4}([0-9]{2})', year_month_dir)\n if month_match:\n month = month_match.group(1)\n\n # Get the number of columns from the first row of the original and\n # modified files, respectively.\n with open(orig_tc_file) as f:\n reader = csv.reader(f, delimiter=' ', skipinitialspace=True)\n first_row = next(reader)\n orig_storm_id = first_row[1]\n with open(mod_tc_file) as f:\n reader = csv.reader(f, delimiter=' ', skipinitialspace=True)\n first_row = next(reader)\n mod_storm_id = first_row[1]\n # Create the expected storm id and compare it to what was produced\n # in the modified file.\n expected_storm_id = month + orig_storm_id\n self.assertEqual(mod_storm_id, expected_storm_id)\n else:\n # If no match, something is wrong, force test to fail.\n self.asssertTrue(False)\n\n # Verify that the second column is the correct month and is the\n # combination of month and storm id. Get the storm id from the\n # second column of the original (input file), create the expected\n # string and compare that to what we observe for the second column\n # in the modified file.\n self.assertTrue(True)\n\n def test_same_dates_in_mod_and_final_dir(self):\n \"\"\" Test that the dirs (i.e. dates) that were found in the modified dir\n are also found in the final track data output dir. Verify that the\n list of dates in the TRACK_DATA_SUBDIR_MOD directory are equal to\n the list of dates in the TRACK_DATA_DIR directory.\n \"\"\"\n\n tc_pairs_dir = self.p.getdir('TC_PAIRS_DIR')\n track_data_subdir_mod = self.p.getdir('TRACK_DATA_SUBDIR_MOD')\n\n # First check that there are the same number of subdirectories in the\n # TC_PAIRS_DIR (the final output dir) and TRACK_DATA_SUBDIR_MOD\n # directories (i.e. the same number of year-month data).\n mod_year_month_list = os.listdir(track_data_subdir_mod)\n final_year_month_list = os.listdir(tc_pairs_dir)\n if len(final_year_month_list) == len(mod_year_month_list):\n # Make sure the dates in the TRACK_DATA_SUBDIR_MOD are found in the\n # TC_PAIRS_DIR.\n for year_month_dir in final_year_month_list:\n if year_month_dir not in mod_year_month_list:\n # If the year_month directory in the final output directory\n # doesn't exist in the mod directory, fail the test.\n self.assertTrue(False)\n else:\n # If the directories don't have the same number of subdirectories,\n # something is wrong, force the test to fail.\n self.assertTrue(False)\n\n # If we get here, then the test passes\n self.assertTrue(True)\n\nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"internal_tests/test_run_tc_pairs.py","file_name":"test_run_tc_pairs.py","file_ext":"py","file_size_in_byte":8280,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"456324024","text":"# =============================================================================\n# Copyright 2020 NVIDIA. 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# =============================================================================\n\nfrom nemo.collections.nlp.data import SquadDataset\nfrom nemo.collections.nlp.nm.data_layers.text_datalayer import TextDataLayer\nfrom nemo.core import ChannelType, LabelsType, NeuralType\n\n__all__ = ['BertQuestionAnsweringDataLayer']\n\n\nclass BertQuestionAnsweringDataLayer(TextDataLayer):\n \"\"\"\n Creates the data layer to use for Question Answering classification task.\n\n Args:\n data_file (str): data_file in *.json.\n tokenizer (obj): Tokenizer object, e.g. NemoBertTokenizer.\n version_2_with_negative (bool): True if training should allow\n unanswerable questions.\n doc_stride (int): When splitting up a long document into chunks,\n how much stride to take between chunks.\n max_query_length (iny): All training files which have a duration less\n than min_duration are dropped. Can't be used if the `utt2dur` file\n does not exist. Defaults to None.\n max_seq_length (int): All training files which have a duration more\n than max_duration are dropped. Can't be used if the `utt2dur` file\n does not exist. Defaults to None.\n mode (str): Use \"train\" or \"dev\" to define between\n training and evaluation.\n batch_size (int): Batch size. Defaults to 64.\n dataset_type (class): Question Answering class.\n Defaults to SquadDataset.\n \"\"\"\n\n @property\n def output_ports(self):\n \"\"\"Returns definitions of module output ports.\n \"\"\"\n return {\n # \"input_ids\": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}),\n # \"input_type_ids\": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}),\n # \"input_mask\": NeuralType({0: AxisType(BatchTag), 1: AxisType(TimeTag)}),\n # \"start_positions\": NeuralType({0: AxisType(BatchTag)}),\n # \"end_positions\": NeuralType({0: AxisType(BatchTag)}),\n # \"unique_ids\": NeuralType({0: AxisType(BatchTag)}),\n \"input_ids\": NeuralType(('B', 'T'), ChannelType()),\n \"input_type_ids\": NeuralType(('B', 'T'), ChannelType()),\n \"input_mask\": NeuralType(('B', 'T'), ChannelType()),\n \"start_positions\": NeuralType(tuple('B'), ChannelType()),\n \"end_positions\": NeuralType(tuple('B'), ChannelType()),\n \"unique_ids\": NeuralType(tuple('B'), ChannelType()),\n }\n\n def __init__(\n self,\n data_file,\n tokenizer,\n version_2_with_negative,\n doc_stride,\n max_query_length,\n max_seq_length,\n mode=\"train\",\n batch_size=64,\n dataset_type=SquadDataset,\n ):\n dataset_params = {\n 'data_file': data_file,\n 'mode': mode,\n 'tokenizer': tokenizer,\n 'version_2_with_negative': version_2_with_negative,\n 'max_query_length': max_query_length,\n 'max_seq_length': max_seq_length,\n 'doc_stride': doc_stride,\n }\n\n super().__init__(dataset_type, dataset_params, batch_size, shuffle=False)\n","sub_path":"nemo/collections/nlp/nm/data_layers/qa_squad_datalayer.py","file_name":"qa_squad_datalayer.py","file_ext":"py","file_size_in_byte":3819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"478068867","text":"from Logger import Logger\nfrom TestTrader import TestTrader\nfrom BacktestMarket import BacktestMarket\nfrom BinanceMarket import BinanceMarket\nfrom Asset import Asset\nfrom Portfolio import Portfolio\nimport datetime\nimport time\nimport Metric\nfrom LinearRegressionTrader import LinearRegressionTrader\n\ndef main():\n exampleBacktesting()\n\ndef exampleFronttesting():\n logger = Logger()\n logger.addTarget('log/fronttestlog.txt')\n\n portfolio = Portfolio(1000)\n portfolio.addAsset(Asset('ethereum', 'ETH'))\n\n end_date = datetime.datetime(2019, 2, 15)\n timeframe = datetime.timedelta(minutes=5)\n\n apiKey = 'SOME BINANCE API KEY'\n privKey = 'SOME BINANCE PRIV KEY'\n\n trader = TestTrader(portfolio, logger, 0)\n trader.fronttest(apiKey, privKey, end_date, timeframe)\n\ndef exampleBacktesting():\n logger = Logger()\n logger.addTarget('log/backtestlog.txt')\n\n start = datetime.datetime(2019, 2, 10)\n end = datetime.datetime(2019, 2, 25)\n interval = datetime.timedelta(days=5)\n\n portfolio = Portfolio(1000)\n portfolio.addAsset(Asset('ethereum', 'ETH'))\n asset = Asset('ethereum', 'ETH')\n\n metrics_list = [\n \"burn_rate\" ,\n \"transaction_volume\" ,\n \"exchange_funds_flow\" ,\n \"price\"\n ]\n start_train = datetime.datetime(2018, 12, 1)\n end_train = datetime.datetime(2019, 1, 30)\n\n trader = LinearRegressionTrader(portfolio, logger, 0, metrics_list, asset)\n trader.train(start_train, end_train, interval)\n\n trader.backtest(start, end, interval)\n\ndef exampleBinance():\n logger = Logger()\n logger.addTarget('log/log.txt', 0)\n\n portfolio = Portfolio()\n portfolio.addAsset(Asset('ethereum', 'ETH'))\n\n apiKey = 'insert api key here'\n privKey = 'insert private key here'\n market = BinanceMarket(apiKey, privKey)\n\n end = datetime.datetime(2019, 1, 25)\n tf = datetime.timedelta(minutes=5)\n\n trader = TestTrader(portfolio, logger, 0)\n trader.trade(market, end, tf)\n\nif __name__ == '__main__':\n main()\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"292166557","text":"from tornado.web import RequestHandler\nimport bugsnag\n\n\nclass BugsnagRequestHandler(RequestHandler):\n def _handle_request_exception(self, exc):\n # Set the request info\n bugsnag.configure_request(\n user_id=self.request.remote_ip,\n context=self._get_context(),\n request_data={\n \"url\": self.request.full_url(),\n \"method\": self.request.method,\n \"arguments\": self.request.arguments,\n },\n )\n\n # Notify bugsnag\n bugsnag.notify(exc)\n\n # Call the parent handler\n RequestHandler._handle_request_exception(self, exc)\n\n def _get_context(self):\n return \"%s %s\" % (self.request.method, self.request.uri.split('?')[0])\n","sub_path":"bugsnag/tornado/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"279068903","text":"import os\n\nimport properties\nimport helpers\nfrom swde_orm import get_swde_columns, check_column_existence, get_table_from_metadata\nfrom geometry_builder import GeometryBuilder\nfrom swde_utils import get_value, GeometryTypes, extract_pzg_ids\n\nLOG = helpers.get_logger(os.path.basename(__file__))\n\n\nclass SWDE():\n\n def __init__(self, import_id, text_rows, parse_description, parse_geometry):\n self.builder = GeometryBuilder()\n self.import_id = import_id\n self.text_rows = text_rows\n self.parse_description = parse_description\n self.parse_geometry = parse_geometry\n\n self.geometryTypes = GeometryTypes()\n\n self.__build()\n\n def get_key(self):\n return self.id, self.type\n\n def clean(self):\n del self.text_rows\n\n def collect_swde_attributes(self):\n for column in get_swde_columns(self.table.name):\n self.insert_data[str(column.name)] = get_value(self.text_rows, column)\n\n def __build(self):\n\n first_line_split = self.text_rows[0].split(properties.SWDE_LINE_SPLITTER)\n self.geom_type = first_line_split[0]\n self.type = '{0}{1}'.format(first_line_split[2][0:2], first_line_split[2][-3:])\n self.id = long(first_line_split[3])\n self.title = first_line_split[4]\n self.table = get_table_from_metadata(self.type)\n\n ''' building dictionary with column name and value - used for inserting '''\n self.insert_data = dict()\n self.insert_data[properties.IMPORT_ID_COLUMN] = self.import_id\n self.insert_data[properties.SWDE_ID_COLUMN] = self.id\n self.insert_data[properties.SWDE_TITLE_COLUMN] = self.title\n\n ''' looking for columns representing g5 swde data '''\n if self.parse_description:\n self.collect_swde_attributes()\n\n ''' importing attributes for selected tables from geometry file'''\n if self.parse_geometry and self.type.lower() in properties.SWDE_TABLES_ATTRIBUTES_GEOMETRY_FILE:\n self.collect_swde_attributes()\n\n ''' filling geometry column '''\n if self.parse_geometry:\n if self.geometryTypes.contains(self.geom_type) \\\n and check_column_existence(self.table, properties.GEOMETRY_COLUMN_NAME):\n\n self.pzg_ids = extract_pzg_ids(self.text_rows)\n if not self.pzg_ids and self.geom_type:\n self.insert_data[properties.GEOMETRY_COLUMN_NAME] = self.builder.build_geometry(self.geom_type, self.text_rows)\n\n ''' looking for columns representing relations '''\n self.relations = []\n if self.parse_description:\n for text_row in self.text_rows:\n if text_row.startswith(properties.SWDE_RELATION_PREFIX):\n text_row = text_row.strip().strip(properties.SWDE_SEPARATOR)\n row_split = text_row.split(properties.SWDE_LINE_SPLITTER)\n relation = dict()\n relation[properties.IMPORT_ID_COLUMN] = self.import_id\n relation[properties.RELATION_RELATION] = row_split[1]\n relation[properties.RELATION_FIELD] = row_split[2]\n relation[properties.RELATION_TID] = self.id\n relation[properties.RELATION_RID] = long(row_split[3].strip().strip(properties.SWDE_SEPARATOR))\n\n self.relations.append(relation)\n\n def set_geometry(self, pzg_array):\n if not pzg_array:\n self.insert_data[properties.GEOMETRY_COLUMN_NAME] = None\n return\n\n if properties.GEOMETRY_COLUMN_NAME not in self.insert_data:\n geometry = self.builder.build_geometry(self.geom_type, self.text_rows, pzg_array)\n self.insert_data[properties.GEOMETRY_COLUMN_NAME] = geometry\n\n self.clean()\n\n def contains_geometry(self):\n \"\"\"\n Informs if swde contains geometry build from coordinates (not PZG)s\n :return: boolean\n \"\"\"\n if properties.GEOMETRY_COLUMN_NAME in self.insert_data:\n return True\n return False\n\n def pzg_enabled(self):\n \"\"\"\n Informs if for swde object its possible to buld geometry based on PZG\n :return: boolean\n \"\"\"\n if not hasattr(self, 'pzg_ids') or len(self.pzg_ids) < 1:\n return False\n return True\n","sub_path":"swde2postgres/swde.py","file_name":"swde.py","file_ext":"py","file_size_in_byte":4339,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"532485615","text":"\"\"\"Functions to support MedPhys Taught Module workshop on\ncalibration and tracking\n\"\"\"\n\nimport math\nimport numpy as np\n\nfrom sksurgerycore.algorithms.procrustes import orthogonal_procrustes\nfrom sksurgerycore.algorithms.errors import compute_tre_from_fle, \\\n compute_fre_from_fle\n\n\nclass PointBasedRegistration:\n \"\"\"\n Does the registration and assoctiated measures\n \"\"\"\n\n def __init__(self, target, fixed_fle_esv, moving_fle_esv):\n \"\"\"\n :params target: 1x3 target point\n :params fixed_fle_esv: the expected squared value of the fixed image fle\n :params moving_fle_esv: the expected squared value of the moving\n image fle\n \"\"\"\n if not moving_fle_esv == 0.0:\n raise NotImplementedError(\"Currently we only support zero\" +\n \"fle on moving image \")\n\n self.target = None\n self.fixed_fle_esv = None\n self.moving_fle_esv = None\n self.reinit(target, fixed_fle_esv, moving_fle_esv)\n\n def reinit(self, target, fixed_fle_esv, moving_fle_esv):\n \"\"\"\n reinitiatilses the target and errors\n \"\"\"\n self.target = target\n self.fixed_fle_esv = fixed_fle_esv\n self.moving_fle_esv = moving_fle_esv\n\n def register(self, fixed_points, moving_points):\n \"\"\"\n Does the registration\n \"\"\"\n success = False\n fre = 0.0\n expected_tre_squared = 0.0\n expected_fre_sq = 0.0\n actual_tre = 0.0\n transformed_target = np.zeros(shape=(1, 3), dtype=np.float64)\n no_fids = fixed_points.shape[0]\n\n if no_fids > 2:\n rotation, translation, fre = orthogonal_procrustes(\n fixed_points, moving_points)\n expected_tre_squared = compute_tre_from_fle(\n moving_points[:, 0:3], self.fixed_fle_esv, self.target[:, 0:3])\n expected_fre_sq = compute_fre_from_fle(moving_points[:, 0:3],\n self.fixed_fle_esv)\n\n transformed_target = np.matmul(rotation,\n self.target.transpose()) + \\\n translation\n actual_tre = np.linalg.norm(\n transformed_target - self.target[:, 0:3].transpose())\n success = True\n\n\n return [success, fre, self.fixed_fle_esv, expected_tre_squared,\n expected_fre_sq, transformed_target[:, 0:3], actual_tre,\n no_fids]\n\n\nclass PlotRegStatistics():\n \"\"\"\n writes the registration statistics\n \"\"\"\n def __init__(self, plot):\n \"\"\"\n The plot to write on\n \"\"\"\n self.plot = plot\n self.fids_text = None\n self.tre_text = None\n self.exp_tre_text = None\n self.fre_text = None\n\n self.props = dict(boxstyle='round', facecolor='wheat', alpha=0.8)\n\n\n\n def update_stats_plot(self, tre, exp_tre, fre, exp_fre):\n \"\"\"\n Updates the statistics display\n \"\"\"\n if self.tre_text is not None:\n self.tre_text.remove()\n if self.exp_tre_text is not None:\n self.exp_tre_text.remove()\n if self.fre_text is not None:\n self.fre_text.remove()\n\n stats_str = ('Expected FRE = {0:.2f}\\n'.format(exp_fre) +\n 'Expected TRE = {0:.2f}'.format(exp_tre))\n\n actual_tre_str = ('Actual TRE = {0:.2f}'.format(tre))\n actual_fre_str = ('Actual FRE = {0:.2f}'.format(fre))\n\n self.exp_tre_text = self.plot.text(-0.90, 1.10, stats_str,\n transform=self.plot.transAxes,\n fontsize=26,\n verticalalignment='top',\n bbox=self.props)\n\n self.tre_text = self.plot.text(-0.05, 1.10, actual_tre_str,\n transform=self.plot.transAxes,\n fontsize=26,\n verticalalignment='top', bbox=self.props)\n\n self.fre_text = self.plot.text(0.65, 1.10, actual_fre_str,\n transform=self.plot.transAxes,\n fontsize=26,\n verticalalignment='top', bbox=self.props)\n\n\n def update_fids_stats(self, no_fids, mean_fle):\n \"\"\"\n Updates the fids stats display\n \"\"\"\n if self.fids_text is not None:\n self.fids_text.remove()\n\n fids_str = ('Number of fids = {0:}\\n'.format(no_fids) +\n 'Expected FLE = {0:.2f}'.format(mean_fle))\n\n self.fids_text = self.plot.text(-1.65, 1.10, fids_str,\n transform=self.plot.transAxes,\n fontsize=26,\n verticalalignment='top',\n bbox=self.props)\n\n\n\nclass PlotRegistrations():\n \"\"\"\n Plots the results of registrations\n \"\"\"\n\n def __init__(self, fixed_plot, moving_plot):\n \"\"\"\n :params fixed_plot: the fixed image subplot\n :params moving_plot: the moving image subplot\n \"\"\"\n\n self.fixed_plot = fixed_plot\n self.moving_plot = moving_plot\n\n self.target_scatter = None\n self.trans_target_plots = [None, None]\n self.fixed_fids_plots = [None, None]\n self.moving_fids_plot = None\n\n self.stats_plot = PlotRegStatistics(fixed_plot)\n\n self.show_actual_positions = True\n self.target_point = None\n\n def initialise_new_reg(self, img, target_point, outline):\n \"\"\"\n resets the registration\n \"\"\"\n self.moving_plot.imshow(img)\n self.fixed_plot.plot(outline[:, 1], outline[:, 0], '-b', lw=3)\n self.fixed_plot.set_ylim([0, img.shape[0]])\n self.fixed_plot.set_xlim([0, img.shape[1]])\n self.fixed_plot.axis([0, img.shape[1], img.shape[0], 0])\n self.fixed_plot.axis('scaled')\n self.target_point = target_point\n\n if self.target_scatter is not None:\n self.target_scatter.remove()\n\n self.target_scatter = self.moving_plot.scatter(self.target_point[0, 0],\n self.target_point[0, 1],\n s=144, c='r')\n if self.trans_target_plots[0] is not None:\n self.trans_target_plots[0].remove()\n self.trans_target_plots[0] = None\n\n if self.trans_target_plots[1] is not None:\n self.trans_target_plots[1].remove()\n self.trans_target_plots[1] = None\n\n self.stats_plot.update_stats_plot(0, 0, 0, 0)\n\n self.moving_plot.set_title('Pre-Operative Image', y=-0.10,\n fontsize=26)\n self.fixed_plot.set_title('Patient in Theatre', y=-0.10,\n fontsize=26)\n\n\n def plot_fiducials(self, fixed_points, moving_points, no_fids, mean_fle):\n \"\"\"\n Updates plot with fiducial data\n \"\"\"\n\n if self.fixed_fids_plots[0] is not None:\n self.fixed_fids_plots[0].remove()\n if self.moving_fids_plot is not None:\n self.moving_fids_plot.remove()\n\n if self.fixed_fids_plots[1] is not None:\n self.fixed_fids_plots[1].remove()\n\n\n self.fixed_fids_plots[0] = self.fixed_plot.scatter(fixed_points[:, 0],\n fixed_points[:, 1],\n s=64, c='g',\n marker='o')\n self.moving_fids_plot = self.moving_plot.scatter(moving_points[:, 0],\n moving_points[:, 1],\n s=64, c='g',\n marker=\"o\")\n\n if self.show_actual_positions:\n self.fixed_fids_plots[1] = self.fixed_plot.scatter(\n moving_points[:, 0],\n moving_points[:, 1],\n s=36, c='black',\n marker='+')\n\n self.stats_plot.update_fids_stats(no_fids, mean_fle)\n\n def plot_registration_result(self, actual_tre, expected_tre,\n fre, expected_fre, transformed_target_2d):\n \"\"\"\n Plots the results of a registration\n \"\"\"\n\n self.stats_plot.update_stats_plot(actual_tre, expected_tre,\n fre, expected_fre)\n\n\n if self.trans_target_plots[0] is not None:\n self.trans_target_plots[0].remove()\n\n if self.trans_target_plots[1] is not None:\n self.trans_target_plots[1].remove()\n\n self.trans_target_plots[0] = self.fixed_plot.scatter(\n transformed_target_2d[0],\n transformed_target_2d[1],\n s=144, c='r', marker='o')\n\n if self.show_actual_positions:\n self.trans_target_plots[1] = self.fixed_plot.scatter(\n self.target_point[0, 0],\n self.target_point[0, 1],\n s=36, c='black', marker='+')\n\nclass AddFiducialMarker:\n \"\"\"\n A class to handle mouse press events, adding a fiducial\n marker.\n \"\"\"\n\n def __init__(self, fig, plotter,\n pbr, logger, fixed_fle_sd, moving_fle_sd):\n \"\"\"\n :params fig: the matplot lib figure to get mouse events from\n :params fixed_plot: the fixed image subplot\n :params moving_plot: the moving image subplot\n :params target: 1x3 target point\n :params fixed_fle: the standard deviations of the fixed image fle\n :params moving_fle: the standard deviations of the moving image fle\n \"\"\"\n\n self.pbr = pbr\n self.plotter = plotter\n self.fig = fig\n self.cid = fig.canvas.mpl_connect('button_press_event', self)\n self.logger = logger\n self.fixed_points = None\n self.moving_points = None\n self.fids_plot = None\n self.fixed_fle_sd = fixed_fle_sd\n self.moving_fle_sd = moving_fle_sd\n\n self.reset_fiducials(0.0)\n\n def __call__(self, event):\n if event.xdata is not None:\n fiducial_location = np.zeros((1, 3), dtype=np.float64)\n fiducial_location[0, 0] = event.xdata\n fiducial_location[0, 1] = event.ydata\n\n if _is_valid_fiducial(fiducial_location):\n fixed_point = _add_guassian_fle_to_fiducial(\n fiducial_location, self.fixed_fle_sd)\n moving_point = _add_guassian_fle_to_fiducial(\n fiducial_location, self.moving_fle_sd)\n self.fixed_points = np.concatenate(\n (self.fixed_points, fixed_point), axis=0)\n self.moving_points = np.concatenate(\n (self.moving_points, moving_point), axis=0)\n\n [success, fre, mean_fle_sq, expected_tre_sq,\n expected_fre_sq, transformed_target_2d,\n actual_tre, no_fids] = self.pbr.register(\n self.fixed_points, self.moving_points)\n\n mean_fle = math.sqrt(mean_fle_sq)\n self.plotter.plot_fiducials(self.fixed_points,\n self.moving_points,\n no_fids,\n mean_fle)\n\n if success:\n expected_tre = math.sqrt(expected_tre_sq)\n expected_fre = math.sqrt(expected_fre_sq)\n self.plotter.plot_registration_result(\n actual_tre, expected_tre,\n fre, expected_fre, transformed_target_2d)\n self.logger.log_result(\n actual_tre, fre, expected_tre, expected_fre, mean_fle,\n no_fids)\n self.fig.canvas.draw()\n\n def reset_fiducials(self, mean_fle_sq):\n \"\"\"\n resets the fiducial markers\n \"\"\"\n self.fixed_points = np.zeros((0, 3), dtype=np.float64)\n self.moving_points = np.zeros((0, 3), dtype=np.float64)\n self.plotter.plot_fiducials(self.fixed_points,\n self.moving_points,\n 0, math.sqrt(mean_fle_sq))\n\n\ndef _is_valid_fiducial(_unused_fiducial_location):\n \"\"\"\n Checks the x, y, and z location of a fiducial\n :returns: true if a valid fiducial\n \"\"\"\n return True\n\ndef _add_guassian_fle_to_fiducial(fiducial, fle_standard_deviation):\n\n moved = np.random.normal(fiducial, fle_standard_deviation)\n return moved\n\ndef make_target_point(outline, edge_buffer=0.9):\n \"\"\"\n returns a target point, that should lie\n within the outline.\n \"\"\"\n #let's assume the anatomy is a circle with\n #centre, and radius\n centre = np.mean(outline, 0)\n max_radius = np.min((np.max(outline, 0) - np.min(outline, 0))/2)*edge_buffer\n radius = np.random.uniform(low=0.0, high=max_radius)\n radius = np.random.uniform(low=0.0, high=max_radius)\n angle = np.random.uniform(low=0.0, high=math.pi*2.0)\n x_ord = radius * math.cos(angle) + centre[0]\n y_ord = radius * math.sin(angle) + centre[1]\n return np.array([[x_ord, y_ord, 0.0]])\n","sub_path":"sksurgeryfred/algorithms/fred.py","file_name":"fred.py","file_ext":"py","file_size_in_byte":13474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"169660474","text":"import datetime\n\nfrom django import forms\nfrom django.db import models\n\nfrom modelcluster.models import ClusterableModel\nfrom wagtail.admin.edit_handlers import FieldPanel, InlinePanel\nfrom wagtail.core.models import (\n Orderable,\n Page,\n ParentalKey,\n ParentalManyToManyField,\n)\nfrom wagtail.snippets.edit_handlers import SnippetChooserPanel\n\nfrom tbx.taxonomy.models import Service\n\n\nclass EventIndexPage(Page):\n template = \"patterns/pages/events/events_listing.html\"\n\n parent_page_types = [\"torchbox.HomePage\"]\n subpage_types = []\n\n content_panels = [\n FieldPanel(\"title\", classname=\"full title\"),\n InlinePanel(\"events\", label=\"events\"),\n ]\n\n def get_events(self, service_filter=None):\n today = datetime.date.today()\n events = self.events.exclude(date__lt=today)\n if service_filter:\n events = events.filter(related_services__slug=service_filter)\n return events.order_by(\"date\")\n\n def get_context(self, request):\n context = super().get_context(request)\n context.update(\n events=self.get_events(request.GET.get(\"filter\")),\n related_services=Service.objects.all(),\n )\n return context\n\n\nclass Event(ClusterableModel, Orderable):\n page = ParentalKey(EventIndexPage, related_name=\"events\")\n title = models.CharField(max_length=255)\n intro = models.TextField(verbose_name=\"Description\")\n link_external = models.URLField(\"External link\")\n date = models.DateField(\"Event date\")\n event_type = models.CharField(max_length=30)\n author = models.ForeignKey(\n \"people.Author\",\n on_delete=models.SET_NULL,\n null=True,\n related_name=\"authors\",\n verbose_name=\"Host\",\n )\n related_services = ParentalManyToManyField(\n \"taxonomy.Service\", related_name=\"events\"\n )\n\n panels = [\n FieldPanel(\"title\"),\n FieldPanel(\"intro\"),\n FieldPanel(\"link_external\"),\n SnippetChooserPanel(\"author\"),\n FieldPanel(\"date\"),\n FieldPanel(\"event_type\"),\n FieldPanel(\"related_services\", widget=forms.CheckboxSelectMultiple),\n ]\n","sub_path":"tbx/events/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":2149,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"321289761","text":"import os\nimport logging\nimport torch\nimport time\nfrom .base_checkpointer import BaseCheckpointer\n\nfrom typing import Union, Dict, Any, List, Tuple\n\nlogger = logging.getLogger(__name__)\n\n\nclass SimpleCheckpointer(BaseCheckpointer):\n \"\"\"\n Simple Checkpointer implements the basic functions\n \"\"\"\n def __init__(self,\n model,\n num_checkpoints_to_keep: int = 1000,\n keep_checkpoint_every_num_seconds: float = 3600,\n storage_dir: str = \"Checkpoints\"):\n self.model = model\n self.storage_dir = storage_dir\n self.current_checkpoint = {}\n self.num_checkpoints_to_keep = num_checkpoints_to_keep\n self.keep_checkpoint_every_num_seconds = keep_checkpoint_every_num_seconds\n self._saved_checkpoint_paths = []\n self._last_checkpoint_time = time.time()\n # initialization\n os.makedirs(self.storage_dir, exist_ok=True)\n\n def save_checkpoint(self, epoch, model_state, training_state):\n checkpoint_path = os.path.join(self.storage_dir,\n f\"model_state_epoch_{epoch}.pth\")\n self.current_checkpoint = {\n \"epoch\": epoch,\n \"model_state\": model_state,\n \"training_state\": training_state\n }\n\n if self.num_checkpoints_to_keep > 0:\n self._saved_checkpoint_paths.append((time.time(), checkpoint_path))\n path_to_remove = self._saved_checkpoint_paths.pop(0)\n\n # check time requirement\n remove_path = True\n if self.keep_checkpoint_every_num_seconds is not None:\n save_time = path_to_remove[0]\n time_since_checkpoint_kept = (save_time -\n self._last_checkpoint_time)\n if time_since_checkpoint_kept > self.keep_checkpoint_every_num_seconds:\n # We want to keep this checkpoint.\n remove_path = False\n self._last_checkpoint_time = save_time\n\n if remove_path:\n for fname in path_to_remove[1:]:\n if os.path.isfile(fname):\n os.remove(fname)\n\n torch.save(self.current_checkpoint, checkpoint_path)\n\n def restore_checkpoint(self):\n latest_checkpoint_path = self.find_latest_checkpoint()\n checkpoint = torch.load(latest_checkpoint_path, map_location=\"cpu\")\n return checkpoint\n\n def find_latest_checkpoint(self) -> Tuple[str, str]:\n \"\"\"\n Return the location of the latest checkpoint file.\n \"\"\"\n return {}\n","sub_path":"torchfly_dev/training/checkpointer/simple_checkpointer.py","file_name":"simple_checkpointer.py","file_ext":"py","file_size_in_byte":2630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"585366832","text":"\ndef craft(recipe_list, recipe, inventory, amount=1, debug=False):\n\tcraftable = True\n\ttmp_storage = inventory.storage[:]\n\n\tfor x in range(amount):\n\t\tfor x in recipe_list[recipe][\"recipe\"]:\n\t\t\tif x in tmp_storage:\n\t\t\t\ttmp_storage.remove(x)\n\t\t\telse:\n\t\t\t\tprint(\"Cannot craft this item.\")\n\t\t\t\treturn\n\t\n\tif debug:\n\t\tprint(\"Confirmed Craftable\")\n\n\tfor x in range(amount):\n\t\tfor x in recipe_list[recipe][\"recipe\"]:\n\t\t\tif x in inventory.storage:\n\t\t\t\tinventory.storage.remove(x)\n\t\t\telse:\n\t\t\t\tprint(\"Cannot craft this item. Some of your stuff burned.\")\n\t\t\t\treturn\n\n\tprint(\"Crafted \" + str(amount) + \" \"+recipe)\t\n\tinventory.add(recipe, recipe_list[recipe][\"type\"])","sub_path":"src_bk/player/craft.py","file_name":"craft.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"77123895","text":"class Solution:\n # @param nums: a list of integer\n # @return: return nothing (void), do not return anything, modify nums in-place instead\n def reverse(self, nums, left, right):\n while left < right:\n temp = nums[left]\n nums[left] = nums[right]\n nums[right] = temp\n left += 1\n right -= 1\n\n def Permutate(self, nums):\n if len(nums) <= 1:\n return False\n pa = len(nums) - 1\n pb = pa - 1\n while pb >= 0 and nums[pa] <= nums[pb]:\n pa -= 1\n pb -= 1\n if pb < 0:\n return False\n pc = len(nums) - 1\n while nums[pc] <= nums[pb]:\n pc -= 1\n temp = nums[pb]\n nums[pb] = nums[pc]\n nums[pc] = temp\n self.reverse(nums, pa, len(nums) - 1)\n return True\n\n def nextPermutation(self, nums):\n # write your code here\n if self.Permutate(nums):\n return nums\n self.reverse(nums, 0, len(nums) - 1)\n return nums\n","sub_path":"Python/Next-Permutation.py","file_name":"Next-Permutation.py","file_ext":"py","file_size_in_byte":1036,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"231250782","text":"# -*- coding: UTF-8 -*- \n#廖沩健\n#自动化65\n#2160504124\n\nfrom __future__ import print_function, division\nimport numpy as np\nimport cv2\nimport math\n\ndef load_file(file):\n img = cv2.imread(file, cv2.IMREAD_GRAYSCALE)\n return img\n\ndef dft(img):\n fimg = np.fft.fft2(img)\n fshiftimg = np.fft.fftshift(fimg)\n return fshiftimg\n\ndef idft(img):\n img = np.fft.ifftshift(img)\n img = np.abs(np.fft.ifft2(img))\n return img\n\ndef extract_from_padded(img, m, n):\n return img[:m, :n]\n\ndef form_padded_img(img):\n return np.pad(img, (0,img.shape[0]), 'constant')\n\ndef filtering(img, kernel):\n return np.multiply(img, kernel)\n\ndef center_distance_matrix(img):\n centerX = img.shape[0] / 2\n centerY = img.shape[1] / 2\n cdm = np.zeros_like(img, dtype=np.float)\n for i in range(img.shape[0]):\n for j in range(img.shape[1]):\n cdm[i,j] = np.sqrt((i - centerX) ** 2 + (j - centerY) ** 2)\n return cdm\n\ndef butterworth_kernel(cdm, n, D0, type='LOW'):\n d = cdm.astype(np.complex)\n h = 1 / ((d / D0) ** (2*n) + 1)\n if type == 'LOW':\n return h\n else:\n return 1 - h\n\ndef gaussian_kernel(cdm, sigma, type='LOW'):\n d = cdm.astype(np.complex)\n h = np.exp(-d**2 / (2*sigma))\n if type == 'LOW':\n return h\n else:\n return 1 - h\n\ndef laplace_kernel(cdm):\n d = cdm.astype(np.complex)\n return 1 + 4 * (math.pi ** 2) * (d ** 2)\n\ndef unmask_sharpen(kernel, k=1):\n return 1 + k * kernel\n\ndef power_spectrum(x):\n ps = 0\n for i in range(x.shape[0]):\n for j in range(x.shape[1]):\n realX = np.real(x[i,j])\n imagX = np.imag(x[i,j])\n ps += realX ** 2 + imagX ** 2\n return ps\n\ndef main(name, method, *args):\n img = load_file(name)\n h, w = img.shape\n F = dft(form_padded_img(img))\n cdm = center_distance_matrix(F)\n print('method:{}===========\\n'.format(method))\n if method == 'butterworth':\n H = butterworth_kernel(cdm, args[0], args[1], type=args[2])\n print('n:{}, D0:{}, type:{}'.format(args[0], args[1], args[2]))\n img_name = name+str('_')+method+str(args[1])\n\n elif method == 'gaussian':\n H = gaussian_kernel(cdm, args[0], type=args[1])\n print('sigma:{}, type:{}'.format(args[0], args[1]))\n img_name = name+str('_')+method+str(args[0])\n\n elif method == 'laplace':\n H = laplace_kernel(cdm)\n img_name = name+str('_')+method\n\n elif method == 'unmask_sharpen':\n H = laplace_kernel(cdm)\n H = unmask_sharpen(H)\n print('kernel: {}'.format('laplace'))\n img_name = name+str('_')+method\n\n G = filtering(F, H)\n res = extract_from_padded(idft(G), h, w)\n #uncomment the following tow line when playing laplace\n # res = (res - np.min(res)) / (np.max(res) - np.min(res)) * 255\n # G = dft(form_padded_img(res))\n ratio = power_spectrum(G) / power_spectrum(F)\n\n cv2.imwrite('./res/{}.bmp'.format(img_name+'_'+str(ratio)), res)\n print('finish {}\\n'.format(name))\n\nif __name__ == '__main__':\n main('test1.pgm', 'butterworth', 2, 300, 'LOW')\n main('test1.pgm', 'butterworth', 2, 30, 'LOW')\n main('test1.pgm', 'gaussian', 300, 'LOW')\n main('test1.pgm', 'gaussian', 30, 'LOW')\n\n main('test2.tif', 'butterworth', 2, 300, 'LOW')\n main('test2.tif', 'butterworth', 2, 30, 'LOW')\n main('test2.tif', 'gaussian', 300, 'LOW')\n main('test2.tif', 'gaussian', 30, 'LOW')\n\n main('test3_corrupt.pgm', 'butterworth', 2, 5, 'HIGH')\n main('test3_corrupt.pgm', 'butterworth', 2, 20, 'HIGH')\n main('test3_corrupt.pgm', 'gaussian', 5, 'HIGH')\n main('test3_corrupt.pgm', 'gaussian', 100, 'HIGH')\n\n main('test4.tif', 'butterworth', 2, 5, 'HIGH')\n main('test4.tif', 'butterworth', 2, 25, 'HIGH')\n main('test4.tif', 'gaussian', 5, 'HIGH')\n main('test4.tif', 'gaussian', 100, 'HIGH')\n\n # main('test3_corrupt.pgm', 'laplace')\n # main('test3_corrupt.pgm', 'unmask_sharpen')\n\n # main('test4.tif', 'laplace')\n # main('test4.tif', 'unmask_sharpen')\n\n\n\n \n \n\n\n\n\n\n\n","sub_path":"lwj_code5.py","file_name":"lwj_code5.py","file_ext":"py","file_size_in_byte":4044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"567634070","text":"def my_sort(one_list):\n\n tmp_list = []\n while len(one_list) > 0:\n min_value = min(one_list)\n tmp_list.append(str(min_value))\n one_list.remove(min_value)\n return tmp_list\n\n\ntries = int(input())\n\n\nfor i in range(1, tries + 1):\n tmp = input()\n num_list = list(map(int, input().split()))\n print('#{} {}'.format(i, ' '.join(my_sort(num_list))))\n","sub_path":"SWEA/D2/D2_sort_num.py","file_name":"D2_sort_num.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"322341168","text":"from scrapy.linkextractors import LinkExtractor\nfrom scrapy.spiders import CrawlSpider, Rule\nfrom ..items import LiNewsSpiderItem\nfrom ftplib import FTP\nfrom io import BytesIO\nimport time,pymysql,scrapy,hashlib\n\nclass Verge_Spider(CrawlSpider):\n name = 'verge'\n allowed_domains = ['theverge.com','vox-cdn.com']\n start_urls = ['https://www.theverge.com/']\n page = 0\n #更新设置--------------------------------------------------\n start_time = time.time()\n up_time = 600 # 更新 秒\n custom_settings = {\n #设置爬取算法模式\n 'SCHEDULER_DISK_QUEUE' : 'scrapy.squeues.PickleFifoDiskQueue',\n 'SCHEDULER_MEMORY_QUEUE' : 'scrapy.squeues.FifoMemoryQueue',\n 'DEPTH_PRIORITY': 1, #0表示深度优先,1表示广度优先\n 'DEPTH_LIMIT' : 5 } #最大深度值\n #-默认入库,入FTP,入分类设置,更新时长---------------------------\n table_name = 'Data_Content_665' #mysql表名\n ftp_name = '' #FTP文件名,只要名为:test则为测试!\n default_category='other' #默认分类\n #--------------------------------------------------------\n rules = (Rule(LinkExtractor(allow=r'https://www.theverge.com/\\d+/\\d+/\\d+/\\d+/.*'), callback='parse_item', follow=True),\n Rule(LinkExtractor(allow=r'https://www.theverge.com/.*'),follow=True),)\n def __init__(self):\n super(Verge_Spider, self).__init__(name='verge')\n # mysql------------------------------------------\n self.conn = pymysql.Connect(\n host='154.212.112.247',\n port=13006,\n # 数据库名：\n db='test',\n user=\"root\",\n passwd='itfkgsbxf3nyw6s1',\n charset='utf8')\n self.cur = self.conn.cursor()\n # ftp---------------------------------------------\n self.ftp = FTP()\n self.ftp.connect('154.86.175.226', 21)\n self.ftp.login(user='img', passwd='W2BpLPnyXbdmWCNd')\n self.ftp.set_pasv(False)\n self.ftp.encoding = 'utf-8'\n\n def parse_item(self, response):\n # 后续更新:启动10分钟后关闭\n if time.time() - self.start_time >= self.up_time:\n self.crawler.engine.close_spider(self, \"更新10分钟完成!!\")\n item = LiNewsSpiderItem()\n url=response.url\n item['url'] = url\n # 标题\n try:\n item['title'] = response.xpath(\"//h1[@class='c-page-title']/text()\").extract_first().replace('\\n','')\n if item['title'] == None:\n item['title'] = ''\n except Exception:\n item['title'] = ''\n # 关键字\n try:\n item['keyword'] = response.xpath(\"//meta[@name='parsely-tags']/@content\").extract_first().replace('verge,front-page,','')\n if item['keyword'] ==None or item['keyword']=='verge,front-page' or len(item['keyword'])<1:\n item['keyword'] = item['title'].replace(' ',',')\n except Exception:\n item['keyword'] = item['title'].replace(' ',',')\n # 摘要\n try:\n item['description'] = response.xpath(\"//meta[@name='description']/@content\").extract_first()\n if item['description'] == None:\n item['description'] = item['title']\n except Exception:\n item['description'] = item['title']\n # 图片\n try:\n img_url=response.xpath(\"//meta[@property='og:image']/@content\").extract_first()\n if 'default' in img_url or img_url == None:\n item['img_src'] = 'https://www.baidu.com/'\n else:item['img_src']=img_url\n except Exception:\n item['img_src'] = 'https://www.baidu.com/'\n # 正文\n try:\n item['content'] =''.join(response.xpath(\"//div[@class='c-entry-content ']/p//text()\").extract()).replace('.','.\\n')\n except Exception:\n item['content'] = ''\n # 作者\n try:\n item['author'] = response.xpath(\"//meta[@name='parsely-author']/@content\").extract_first()\n if item['author'] == None:\n item['author'] = 'POTATO TECHNOLOGY NEWS'\n except Exception:\n item['author'] = 'POTATO TECHNOLOGY NEWS'\n # 发行时间\n try:\n item['release_time'] = response.xpath('//time[@class=\"c-byline__item\"]/text()').extract_first().replace('\\n','').strip()\n except Exception:\n item['release_time'] = ''\n #分类\n try:\n item['category']=self.cate(item_txt=str(item['content']))\n except Exception:\n item['category']=''\n # 来源\n item['be_from'] ='www.theverge.com'\n\n #生成器返回:\n if len(item['title']) >=1 and len(item['content']) >= 50 and item['category'] != '':\n #-----------入库去重--------------------------------------\n find_ex = \"select id,PageUrl from {} where title= %s \".format(self.table_name)\n self.cur.execute(find_ex, (item[\"title\"]))\n if not self.cur.fetchone():\n #封面图片\n #if item['img_src'] != '':\n yield scrapy.Request(url=item['img_src'], callback=self.img_parse, meta={'item': item},dont_filter=True)\n # else:\n # #self.img_parse(response=scrapy.http.HtmlResponse(url='',meta={'item':item},body=))\n # yield scrapy.Request(url='https://www.baidu.com/', callback=self.img_parse, meta={'item': item},dont_filter=True)\n else:print('**数据重复:',item['url'])\n else:\n print('##数据不匹配:标题长度:',len(item['title']),'文本长度:',len(item['content']),'category:',item['category'],response.url)\n def img_parse(self,response):\n item = response.meta['item']\n if 'www.baidu.com' in response.url or response.status >302:\n img_path='![\"{}\"](\"https://img.ksyoume.cn/img_upload/95e3325e00471c3a0705d42e406d69a3.jpg\")
'.format(item['keyword'])\n else:\n if self.ftp_name != 'test':\n time_name = time.strftime(\"%Y-%m-%d\", time.localtime(time.time())).replace('-', '')\n try:self.ftp.mkd(time_name)\n except Exception:pass\n else:\n time_name ='test'\n m = hashlib.md5()\n m.update(response.url.encode('utf-8'))\n base_txtname_md5 = m.hexdigest() + '.jpg'\n ftp_path = time_name + '/' + base_txtname_md5\n try:\n fp = BytesIO(response.body)\n self.ftp.storbinary(cmd=\"STOR %s\" % ftp_path,fp=fp ) #上传文件\n img_path= '![\"{}\"'.format(item['keyword'])](\"https://img.ksyoume.cn/img_upload/')
'\n except Exception as e:\n img_path = ''.format(item['keyword'])\n print(item['url'],'!!!图片上传失败:',e)\n self.ftp.close()\n time.sleep(2)\n self.ftp.connect('154.86.175.226', 21)\n self.ftp.login(user='img', passwd='W2BpLPnyXbdmWCNd')\n self.ftp.set_pasv(False)\n self.ftp.encoding = 'utf-8'\n news = \"insert into {}(title,content,author,time,keywords,description,tag,PageUrl,thumbid,category,create_time,be_from) values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\".format(self.table_name)\n self.cur.execute(news, (item['title'], img_path + '\\n' + item['content'], item['author'], item['release_time'], item['keyword'],item['description'], item['keyword'], item['url'], img_path, item['category'], time.time(),item['be_from']))\n self.page += 1\n print(time.strftime('%Y.%m.%d-%H:%M:%S'), '第', self.page, '条抓取成功:',item['url'])\n\n\n #分类区分\n def cate(self,item_txt):\n if ' network ' in item_txt or ' technology ' in item_txt or ' download ' in item_txt or ' Samsung ' in item_txt or ' PC ' in item_txt or ' Technology 'in item_txt or ' Internet ' in item_txt or ' camera 'in item_txt or ' TECH ' in item_txt :\n return 'other'\n elif ' phone 'in item_txt or ' Android 'in item_txt or ' app 'in item_txt or ' Skype ' in item_txt:\n return 'Mobile'\n elif ' TIKTOK 'in item_txt or ' Tik tok 'in item_txt or ' Tik Tok 'in item_txt or ' TIK TOK ' in item_txt:\n return 'TIKTOK'\n elif ' Microsoft 'in item_txt or ' Windows 'in item_txt or ' Office 'in item_txt or ' Outlook ' in item_txt:\n return 'Microsoft'\n elif ' Facebook ' in item_txt:\n return 'Facebook'\n elif ' Tesla ' in item_txt or ' Elon Musk ' in item_txt:\n return 'Tesla'\n elif ' Huawei ' in item_txt:\n return 'Huawei'\n elif ' Amazon 'in item_txt or ' Jeff Bezos ' in item_txt:\n return 'Amazon'\n elif ' AI ' in item_txt:\n return 'AI'\n elif ' Google 'in item_txt:\n return 'Google'\n elif ' iOS 'in item_txt or ' Apple 'in item_txt or ' iPhone 'in item_txt or ' iPad 'in item_txt or ' iMac 'in item_txt or ' MacBook 'in item_txt or ' Watch 'in item_txt or ' iCloud 'in item_txt or ' iTunes ' in item_txt:\n return 'Apple'\n else:\n return self.default_category\n def close(self, reason):\n print(reason,'共抓取:',self.page,'条数据')\n self.conn.close()\n self.ftp.close()\n ##self.crawler.engine.close_spider(self, \"关闭spider\")\n #scrapy crawl verge\n\n","sub_path":"li_news_spider/spiders/li_verge.py","file_name":"li_verge.py","file_ext":"py","file_size_in_byte":9671,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"193494064","text":"__author__ = 'gjosquin'\n\n\nclass SuperRegion(object):\n def __init__(self, id_, reward):\n self.id = id_\n self.reward = reward\n self.sub_regions = []\n\n def all_captured_by(self, bot_name):\n for sub_region in self.sub_regions:\n if not sub_region.owner == bot_name:\n return False\n return True\n\n def get_edge_regions(self):\n edge_regions = []\n for sub_region in self.sub_regions:\n for neighbor in sub_region.neighbors:\n if neighbor.super_region != self:\n edge_regions.append(sub_region)\n continue\n return edge_regions","sub_path":"src/layout/superregion.py","file_name":"superregion.py","file_ext":"py","file_size_in_byte":666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"236457473","text":"import os\nimport sys\nimport pathlib\nimport argparse\nimport warnings\nimport pandas as pd\n\nfrom pycytominer import normalize\n\nsys.path.append(\"config\")\nfrom utils import parse_command_args, process_configuration\n\nargs = parse_command_args()\n\nplate_id = args.plate_id\noptions_config_file = args.options_config_file\nexperiment_config_file = args.experiment_config_file\n\nconfig = process_configuration(\n plate_id,\n options_config=options_config_file,\n experiment_config=experiment_config_file,\n)\n\n# Extract config arguments\nperform = config[\"options\"][\"profile\"][\"normalize\"][\"perform\"]\n\n# check if this step should be performed\nif not perform:\n sys.exit(\"Config file set to perform=False, not performing {}\".format(__file__))\n\nfloat_format = config[\"options\"][\"core\"][\"float_format\"]\ncompression = config[\"options\"][\"core\"][\"compression\"]\n\nnormalize_input_files = config[\"files\"][\"aggregate_files\"]\nnormalize_output_files = config[\"files\"][\"normalize_files\"]\n\nsc_config = config[\"options\"][\"profile\"][\"single_cell\"]\nnormalize_singlecell_from_single_file = sc_config[\"output_one_single_cell_file_only\"]\n\nnormalize_args = config[\"options\"][\"profile\"][\"normalize\"]\nnormalize_levels = normalize_args[\"levels\"]\nnormalize_by_samples = normalize_args[\"by_samples\"]\nnormalize_these_features = normalize_args[\"features\"]\nnormalize_method = normalize_args[\"method\"]\nforce = normalize_args[\"force_overwrite\"]\n\nfor data_level in normalize_levels:\n if data_level == \"single_cell\":\n if not normalize_singlecell_from_single_file:\n continue\n\n file_to_normalize = normalize_input_files[data_level]\n output_file = normalize_output_files[data_level]\n\n print(f\"Now normalizing {data_level}...with operation: {normalize_method}\")\n\n df = pd.read_csv(file_to_normalize)\n\n normalize(\n profiles=df,\n features=normalize_these_features,\n samples=normalize_by_samples,\n method=normalize_method,\n output_file=output_file,\n compression=compression,\n float_format=float_format,\n )\n","sub_path":"1.generate-profiles/2.normalize.py","file_name":"2.normalize.py","file_ext":"py","file_size_in_byte":2046,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"438184784","text":"# lab1_1.py -- template for your Task #1 design file\nimport matplotlib.pyplot as p\nimport lab1\n\n# if you're using iPython, the following call enable matplotlib's\n# interactive mode so plots will appear immediately -- useful\n# when experimenting...\np.ion()\n\ndef plot_data(data):\n\t# takes a numpy array\n\t# plots it as a new figure using routines from matplotlib.pyplot\n\tp.plot(data)\n\tp.title('Receiver data')\n\tp.xlabel('Sample Number')\n\tp.ylabel('Voltage')\n\tp.grid(True)\n\tp.show\n\tp.savefig('receiverData.pdf')\n\t# I chose to use a line since it looks like the picture on the assignment page\n\treturn None\n\n# testing code. Do it this way so we can import this file\n# and use its functions without also running the test code.\nif __name__ == '__main__':\n # supply some test data to plot_data...\n lab1.task1_test(plot_data)\n \n","sub_path":"SWE001/6.02s2009/labs/lab1/lab1_1.py","file_name":"lab1_1.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"597491345","text":"def build_audio_response(url):\n return {\n \"version\": \"1.0\",\n \"response\": {\n \"directives\": [\n {\n \"type\": \"AudioPlayer.Play\",\n \"playBehavior\": \"REPLACE_ALL\",\n \"audioItem\": {\n \"stream\": {\n \"token\": \"12345\",\n \"url\": url,\n \"offsetInMilliseconds\": 0\n }\n }\n }\n ],\n \"shouldEndSession\": True\n }\n }\n\ndef handle_session_end_request():\n return {\n \"version\": \"1.0\",\n \"response\": {\n \"shouldEndSession\": True\n }\n }\n\ndef play_music(intent, session):\n url = \"https://\" + \"MP3 URL\"\n return build_audio_response(url)\n\ndef on_intent(intent_request, session):\n \"\"\" Called when the user specifies an intent for this skill \"\"\"\n\n intent = intent_request['intent']\n intent_name = intent_request['intent']['name']\n\n if intent_name == \"PlayMusic\":\n return play_music(intent, session)\n elif intent_name == \"AMAZON.CancelIntent\" or intent_name == \"AMAZON.StopIntent\":\n return handle_session_end_request()\n else:\n raise ValueError(\"Invalid intent\")\n\ndef lambda_handler(event, context):\n if event['request']['type'] == \"LaunchRequest\":\n return {\n \"version\": \"1.0\",\n \"response\": {\n \"shouldEndSession\": False\n }\n }\n elif event['request']['type'] == \"IntentRequest\":\n return on_intent(event['request'], event['session'])\n elif event['request']['type'] == \"SessionEndedRequest\":\n return handle_session_end_request()\n","sub_path":"playmp3.py","file_name":"playmp3.py","file_ext":"py","file_size_in_byte":1742,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"62351034","text":"\"\"\"\nProblem 34 「桁の階乗」\n\n145は面白い数である. 1! + 4! + 5! = 1 + 24 + 120 = 145となる.\n\n各桁の数の階乗の和が自分自身と一致するような数の和を求めよ.\n\n注: 1! = 1 と 2! = 2 は総和に含めてはならない.\n\n\"\"\"\nimport math\nfrom functools import reduce\n\nketa = []\n\nfor n in range(1, 100000):\n nketa = math.factorial(9) * n\n if n > len(str(nketa)):\n keta.append(n)\n break\n\nprint(keta) # 7桁の数字より以上になると、9!×n の方が小さくなる。\n\nlmf = []\nfor m in range(3, 10**(keta[0] - 1)):\n lm = [int(x) for x in str(m)]\n if m == sum(list(map(lambda x:math.factorial(x), lm))):\n lmf.append(m)\n\nprint(lmf)\nprint(sum(lmf))\n\n# [145, 40585]\n# 40730\n","sub_path":"ProjectEuler/Python/euler034.py","file_name":"euler034.py","file_ext":"py","file_size_in_byte":750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"470832853","text":"# We are given an unsorted array containing ‘n’ numbers taken from the range 1 to ‘n’. The array originally contained all the numbers from 1 to ‘n’, but due to a data error, one of the numbers got duplicated which also resulted in one number going missing. Find both these numbers.\n#\n# Example 1:\n#\n# Input: [3, 1, 2, 5, 2]\n# Output: [2, 4]\n# Explanation: '2' is duplicated and '4' is missing.\n# Example 2:\n#\n# Input: [3, 1, 2, 3, 6, 4]\n# Output: [3, 5]\n# Explanation: '3' is duplicated and '5' is missing.\n\n\ndef find_corrupt_numbers(nums):\n i = 0\n while i < len(nums):\n adjusted_index = nums[i] - 1\n if nums[i] != nums[adjusted_index]:\n nums[i], nums[adjusted_index] = nums[adjusted_index], nums[i]\n else:\n i += 1\n missing = None\n duplicate = None\n\n for i in range(len(nums)):\n if nums[i] != i+1:\n duplicate = nums[i]\n missing = i+1\n return duplicate, missing\n\n\ndef main():\n print(find_corrupt_numbers([3, 1, 2, 5, 2]))\n print(find_corrupt_numbers([3, 1, 2, 3, 6, 4]))\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"revise-daily/educative.io/easy-cyclic-sort/6_find_corrupt_pair.py","file_name":"6_find_corrupt_pair.py","file_ext":"py","file_size_in_byte":1118,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"348899317","text":"#! /usr/bin/env python\n\n#\n# Script to make the trigger SF histogram, provided the trigger efficiencies calculated by the calcTriggerEff.py script\n#\n\n\n#\n# Argument parser and logging\n#\nimport os, argparse\nargParser = argparse.ArgumentParser(description = \"Argument parser\")\nargParser.add_argument('--logLevel', action='store', default='INFO', help='Log level for logging', nargs='?', choices=['CRITICAL', 'ERROR', 'WARNING', 'INFO', 'DEBUG', 'TRACE'])\nargParser.add_argument('--pu', action='store_true', default=False, help='Use pile-up reweighting (no use of CP intervals)')\nargParser.add_argument('--select', action='store', default='', help='Additional selection for systematic studies')\nargs = argParser.parse_args()\n\n\nfrom ttg.tools.logger import getLogger\nlog = getLogger(args.logLevel)\n\nfrom ttg.tools.style import commonStyle, setDefault\nfrom ttg.tools.helpers import printCanvas\nfrom math import sqrt\nimport ROOT\n\nsetDefault()\nROOT.gStyle.SetPadRightMargin(0.15)\nROOT.gStyle.SetPadBottomMargin(0.15)\nROOT.gROOT.ForceStyle()\n\noutFile = ROOT.TFile.Open(os.path.join('data', 'triggerEff', 'triggerSF' + args.select + ('_puWeighted' if args.pu else '') + '.root'), 'update')\ninFile = ROOT.TFile.Open(os.path.join('data', 'triggerEff', 'triggerEfficiencies' + args.select + ('_puWeighted' if args.pu else '') + '.root'))\nfor t in ['', '-l1cl2c', '-l1cl2e', '-l1el2c', '-l1el2e', '-l1c', '-l1e', '-l2c', '-l2e', '-etaBinning', '-integral']:\n for channel in ['ee', 'emu', 'mue', 'mumu']:\n effData = inFile.Get('MET-' + channel + t)\n effMC = inFile.Get('TTGamma-' + channel + t)\n effData.SetStatisticOption(ROOT.TEfficiency.kFCP)\n effMC.SetStatisticOption(ROOT.TEfficiency.kFCP)\n effData.Draw()\n ROOT.gPad.Update() # Important, otherwise ROOT throws null pointers\n hist = effData.GetPaintedHistogram()\n for i in range(1, hist.GetNbinsX()+1):\n for j in range(1, hist.GetNbinsX()+1):\n globalBin = effData.GetGlobalBin(i, j)\n if effMC.GetEfficiency(globalBin) > 0:\n hist.SetBinContent(i, j, effData.GetEfficiency(globalBin)/effMC.GetEfficiency(globalBin))\n errUp = sqrt(effData.GetEfficiencyErrorUp(globalBin)**2+effMC.GetEfficiencyErrorUp(globalBin)**2)\n errLow = sqrt(effData.GetEfficiencyErrorLow(globalBin)**2+effMC.GetEfficiencyErrorLow(globalBin)**2)\n hist.SetBinError(i, j, max(errUp, errLow))\n\n canvas = ROOT.TCanvas(effData.GetName(), effData.GetName())\n canvas.cd()\n ROOT.gStyle.SetPaintTextFormat(\"2.5f\" if 'integral' in t else \"2.2f\")\n commonStyle(hist)\n hist.GetXaxis().SetTitle(\"leading lepton p_{T} [Gev]\")\n hist.GetYaxis().SetTitle(\"trailing lepton p_{T} [Gev]\")\n hist.SetTitle(\"\")\n hist.SetMarkerSize(0.8)\n hist.Draw(\"COLZ TEXT\")\n canvas.RedrawAxis()\n if not ('etaBinning' in t or 'integral' in t):\n canvas.SetLogy()\n canvas.SetLogx()\n hist.GetXaxis().SetMoreLogLabels()\n hist.GetYaxis().SetMoreLogLabels()\n hist.GetXaxis().SetNoExponent()\n hist.GetYaxis().SetNoExponent()\n hist.SetMinimum(0.8)\n hist.SetMaximum(1.2)\n hist.Draw(\"COLZ TEXTE\")\n canvas.RedrawAxis()\n directory = '/user/tomc/www/ttG/triggerEfficiency/' + ('puWeighted/' if args.pu else '') + 'SF' + '/' + args.select\n printCanvas(canvas, directory, channel + t, ['pdf', 'png'])\n outFile.cd()\n hist.Write('SF-' + channel + t)\n\nlog.info('Finished')\n","sub_path":"reduceTuple/makeTriggerSF.py","file_name":"makeTriggerSF.py","file_ext":"py","file_size_in_byte":3422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"652669951","text":"#!/usr/local/bin/env python3\n\nimport sys\nimport time\nfrom datetime import datetime\nfrom phasortoolbox import Client, PDC, DeviceControl\n\n\ndef inline_print_freq(buffer_msgs):\n\n now = time.time()\n\n time_tag = datetime.utcfromtimestamp(\n buffer_msgs[-1][0].time).strftime(\n \"UTC: %m-%d-%Y %H:%M:%S.%f\")\n\n freqlist = [pmu_d.freq for msg in buffer_msgs[-1]\n for pmu_d in msg.data.pmu_data]\n\n freq_str = ' '.join(\"%.4f\" % (\n my_msg) + 'Hz ' if my_msg is not None else\n 'No Data' for\n my_msg in freqlist)\n\n sys.stdout.write(\n \"Network delay:%.4fs Software delay:%.4fs \" % (\n now - buffer_msgs[-1][0].time,\n now - max([_msg._arrtime for _msg in buffer_msgs[-1]])\n ) + time_tag + \" \" + freq_str + \"\\r\"\n )\n sys.stdout.flush()\n return [time_tag]+freqlist\n\n\nclass data_writer(object):\n\n def __init__(self, file):\n self.file = file\n\n def write_(self, buffer_msgs):\n\n freqlist = inline_print_freq(buffer_msgs)\n with open(self.file, 'a') as f:\n f.write(','.join([str(freq) for freq in freqlist]))\n f.write('\\n')\n\n\ndef main():\n\n my_writer = data_writer('freq_sample.txt')\n\n my_pmus = [\n Client(SERVER_IP='10.0.0.5', SERVER_TCP_PORT=4712, IDCODE=5),\n Client(SERVER_IP='10.0.0.6', SERVER_TCP_PORT=4712, IDCODE=6),\n Client(SERVER_IP='10.0.0.7', SERVER_TCP_PORT=4712, IDCODE=7),\n Client(SERVER_IP='10.0.0.8', SERVER_TCP_PORT=4712, IDCODE=8),\n ]\n\n my_pdc = PDC(count=300)\n my_pdc.CALLBACK = my_writer.write_\n\n my_devices = DeviceControl()\n my_devices.device_list = my_pmus + [my_pdc]\n my_devices.connection_list = [[my_pmus, [my_pdc]]]\n\n my_devices.run()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"examples/pmu_to_file_with_time_tag.py","file_name":"pmu_to_file_with_time_tag.py","file_ext":"py","file_size_in_byte":1788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"507138541","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport time\nimport uuid\nimport json\nimport logging\nimport re\nfrom concurrent import futures\nfrom queue import Queue\nfrom threading import Thread\nimport threading\nimport traceback\nfrom datetime import datetime\n# from contextlib import suppress\n# from multiprocessing import Queue\n# from multiprocessing import Process as Thread\n\nimport pymysql\nimport requests\n\nfrom tcrawler import get_prod_info, get_real_pid\nfrom model import Product, db_session, Price, ProductUpdateRecord, Abort\n\n\npymysql.install_as_MySQLdb()\n\nraw_prod_queue = Queue()\nripe_prod_queue = Queue()\n\nlogger = logging.getLogger(__name__)\n\nSQL_READ_LOCK = threading.Lock()\n\nclass suppress():\n def __init__(self, *exceptions):\n self._exceptions = exceptions\n\n def __enter__(self):\n pass\n\n def __exit__(self, exctype, excinst, exctb):\n if exctype is not None:\n logger.error('异常', exc_info=True)\n return exctype is not None and issubclass(exctype, self._exceptions)\n\nclass Crawler:\n API = ('https://ps.pndsn.com/v2/subscribe'\n '/sub-c-4cb8cb8e-9e32-11e7-a3e4-2e10596cd186'\n '/lot_announcements/0?heartbeat=300&tt={}&tr=4'\n '&uuid=anon-edb92648-9e4a-4f71-9297-57f0bf168f42'\n '&pnsdk=PubNub-JS-Web%2F4.14.0'\n '&instanceid=pn-fc1139ef-6497-4f8d-be2e-9a1c629719ba'\n '&requestid={}')\n\n def __init__(self):\n self.r_session = requests.Session()\n headers = {'Host': 'ps.pndsn.com',\n 'Origin': 'https://cn.tophatter.com',\n 'Referer': 'https://cn.tophatter.com/',\n 'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; WOW64) '\n 'AppleWebKit/537.36 (KHTML, like Gecko) '\n 'Chrome/50.0.2661.102 Safari/537.36'}\n self.r_session.headers.update(headers)\n # 初始化时间戳, 保存每一次的服务器返回的时间戳\n self.tt = int(time.time() - 100) * 10000000\n # 拍卖最终价格过滤阈值\n # self.price_threshold = 10\n\n def parse_html(self, text):\n '''获取sold的产品id号\n :return: sold的产品id列表\n '''\n d = json.loads(text)\n logger.info('获取到最新动态产品的总数: %s' % len(d['m']))\n sold_prods = []\n closed_prods = []\n for item in d['m']:\n logger.debug(item)\n # title_key 的状态有sold, close, next, you_won, bid, winning, out_bid\n prod_status = item['d']['slots'][0]['elements'][1]\n # 可能由于平台的更新，多了可能没有title_key的情况\n if not prod_status.get('title_key', None):\n logger.warning('\"title_key\" not found!!')\n continue\n if prod_status['title_key'] == 'th.slots.auction_slot.sold':\n hammer_price = int(prod_status['parameters']['hammer_price']['USD'][1:])\n # if hammer_price >= self.price_threshold:\n p = Product(prod_status['lot_id'])\n p.hammer_price = hammer_price\n sold_prods.append({'type': 'sold', 'data': p})\n elif prod_status['title_key'] == 'th.slots.auction_slot.closed':\n closed_prods.append({'type': 'closed', 'data': prod_status['lot_id']})\n\n logger.info('已经成交的产品数: %s, 流拍产品数: %s' % (len(sold_prods), len(closed_prods)))\n self.tt = d['t']['t']\n return sold_prods + closed_prods\n\n def get_sold_prod(self):\n '''获取sold的产品id号'''\n requestid = uuid.uuid1()\n api = self.API.format(self.tt, requestid)\n\n try:\n resp = self.r_session.get(api, timeout=5)\n except requests.exceptions.RequestException as e:\n logger.warning('获取sold产品失败: %s', e)\n return []\n if resp.status_code == 200:\n return self.parse_html(resp.text)\n else:\n logger.warning('the status_code is not 200')\n return []\n\n def get_sold_prod_for_yield(self):\n '''获取sold的产品, 用yield返回'''\n for prod in self.get_sold_prod():\n yield prod\n\n\n\n# hp --> home_page\nhp_crawler = Crawler()\n\n\ndef get_sold_prod_task():\n def loop(interval=3):\n '''\n :param interval: 获取拍卖最新动态的时间间隔\n '''\n for prod in hp_crawler.get_sold_prod_for_yield():\n raw_prod_queue.put(prod)\n time.sleep(interval)\n while True:\n with suppress(Exception):\n loop()\n\n\ndef get_prod_info_task():\n def loop():\n # logger.warn('当前raw_prod_queue队列长度: %s' % raw_prod_queue.qsize())\n qsize = raw_prod_queue.qsize()\n if qsize and qsize % 10 == 0:\n logger.warn('当前raw_prod_queue队列长度: %s' % qsize)\n raw_prod = raw_prod_queue.get()\n if raw_prod['type'] == 'closed':\n pid = get_real_pid(raw_prod['data'])\n db_session.add(Abort(pid=pid))\n db_session.commit()\n elif raw_prod['type'] == 'sold':\n prod = raw_prod['data']\n prod.pid = get_real_pid(prod.pid)\n ripe_prod = get_prod_info(prod, dl_img=False)\n if ripe_prod:\n ripe_prod_queue.put(ripe_prod)\n while True:\n with suppress(Exception):\n loop()\n\n\ndef save_prod_task():\n def loop():\n # logger.info('当前ripe_prod_queue队列长度: %s' % ripe_prod_queue.qsize())\n prod = ripe_prod_queue.get()\n logger.info('保存 %s !!!!!!!!!!!!!' % prod)\n # 取出prod中的hammer_price\n hammer_price = prod.hammer_price\n delattr(prod, 'hammer_price')\n\n # 对比产品判断是否保存\n try:\n with SQL_READ_LOCK:\n last_prod = db_session.query(Product).filter_by(pid=prod.pid).first()\n if last_prod:\n diff_dict = last_prod.compare(prod)\n if diff_dict:\n db_session.add(last_prod)\n [db_session.add(\n ProductUpdateRecord(\n pid=prod.pid, column=k, value=v,\n history_time=last_prod.updated_time\n )\n )\n for k, v in diff_dict.items()]\n # 复制产品非空属性\n last_prod.copy(prod)\n db_session.add(last_prod)\n else:\n db_session.add(prod)\n # 解决mysql无法延迟外键检查的问题，所以的提前提交\n db_session.commit()\n # 保存Price\n db_session.add(Price(pid=prod.pid, hammer_price=hammer_price,\n shipping_price=prod.shipping_price))\n db_session.commit()\n except Exception as e:\n db_session.rollback()\n logger.warn('错误: %s' % e)\n logger.warn('product id: %s' % prod.pid)\n logger.warn('product desc: %s' % prod.desc)\n # print('product id:', prod.pid)\n while True:\n with suppress(Exception):\n loop()\n\n\ndef get_prod_info_multi_task():\n '''废弃'''\n with futures.ThreadPoolExecutor(5) as executor:\n while True:\n logger.warn('当前raw_prod_queue队列长度: %s' % raw_prod_queue.qsize())\n prod = raw_prod_queue.get()\n time.sleep(5)\n # executor.submit(get_prod_info, prod)\n\n\nif __name__ == \"__main__\":\n import sys\n from config import LOG_CONFIG\n\n logging.basicConfig(**LOG_CONFIG)\n # log_handler = logging.StreamHandler(sys.stdout)\n # log_handler.setFormatter(logging.Formatter(LOG_CONFIG['format']))\n # logger.addHandler(log_handler)\n # logger.setLevel(LOG_CONFIG['level'])\n\n logger.info('begin..')\n\n # get_sold_prod_task()\n t1 = Thread(target=get_sold_prod_task)\n t2 = Thread(target=save_prod_task)\n t1.start()\n t2.start()\n\n for i in range(15):\n t3 = Thread(target=get_prod_info_task)\n t3.start()\n thread_total = threading.active_count()\n while True:\n time.sleep(50)\n if thread_total != threading.active_count():\n error_time = datetime.now()\n while True:\n time.sleep(1)\n logger.error('当前程序出现异常, 异常时间为 %s' % error_time)\n print('over')\n","sub_path":"home_page_crawler.py","file_name":"home_page_crawler.py","file_ext":"py","file_size_in_byte":8560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"232219281","text":"\nimport os\nimport datawizard\nimport export2db\nimport db\nimport cStringIO\nimport utils\nimport datetime\n\ntry:\n import logger\n log = logger.getlogger()\nexcept:\n import logging\n log = logging.getlogger()\n\n\nDEFAULT_TEMPLATE = \"\"\"\n[PREPROCESS TEMPLATE]\nDATABASEINPUT=0\n\n[ASCII]\nFILENAME=%s\nSEPARATOR=TAB\nFIELDNAMEFIRST=1\nFIXEDLENGTH=0\nStartQuoteChar=\"\nEndQuoteChar=\"\nRemoveText=\"\n\"\"\"\n\nACCESSDB_TEMPLATE= \"\"\"\n[PREPROCESS TEMPLATE]\nDATABASEINPUT=1\n\n[DATABASE]\nDATABASENAME=%s\nTABLENAME=%s\n\"\"\"\n\n\nclass DPID(object):\n\n def __init__(self, taskid, conn):\n self.taskid = taskid\n self.conn = conn\n\n def generate_tpl(self, txt_filename, field_list, tpl_filename, src_table=None):\n if txt_filename.endswith('.txt'):\n blink_template = DEFAULT_TEMPLATE % txt_filename\n else:\n blink_template = ACCESSDB_TEMPLATE % (txt_filename, src_table)\n\n extra_list = []\n\n while len(field_list) > 7:\n #postcode\n extra_list.insert(0, field_list.pop())\n\n if extra_list:\n field_list[-1] = field_list[-1]+','+','.join(extra_list)\n\n for idx, a_field in enumerate(field_list):\n blink_template += \"Adr%d=%s\\n\" % (idx+1, a_field)\n\n #write to tpl\n out_file = open(tpl_filename, \"w\")\n\n # Write all the lines at once:\n out_file.writelines(blink_template)\n out_file.close()\n\n log.info(blink_template)\n\n def generate_text_file(self, src_table, filename, cur):\n texter = export2db.textwriter.TextWriter(filename)\n params = \"\"\"WITH DELIMITER AS '\\t' NULL AS '' CSV HEADER\"\"\"\n select_query = \"SELECT * FROM {0}\".format(src_table)\n output = cStringIO.StringIO()\n cur.copy_expert(\"COPY ({0}) TO STDOUT {1}\".format(select_query, params), output)\n texter.write(output)\n texter.save()\n output.close()\n\n\n def run(self):\n \"\"\"\n Run task, dont need to return job_status as previous, let program throw Exception,\n and catch Exception at outer level\n \"\"\"\n cur = self.conn.cursor()\n myvars = dict(taskid=self.taskid)\n # This should run at outer level\n\n #cur.execute(\"\"\"\n # UPDATE tasks\n # set error=null, when_start=now(), status='processing'\n # where id = %(taskid)s\n #\"\"\", myvars)\n\n cur.execute(\"\"\"SELECT a.key_value from vars_tasks a\n INNER JOIN tasks b on a.jobid = b.jobid\n WHERE b.id=%(taskid)s\n \"\"\", myvars)\n\n environment_vars = cur.fetchall()\n\n cur.execute(\"\"\"\n SELECT *\n FROM dpid_tasks\n WHERE taskid = %(taskid)s\n \"\"\", myvars)\n\n result = cur.fetchone()\n\n addresses = result.addresses\n\n suburb = result.suburb\n state = result.state\n postcode = result.postcode\n src_table = result.src_table\n filename = utils.mm_translate(result.dest_file, environment_vars)\n\n FILEEXT = '.txt'\n txt_filename = filename+FILEEXT\n tpl_filename = filename+'.tpl'\n\n #1.export table into text file\n self.generate_text_file(src_table, txt_filename, cur)\n\n #2.export template\n field_list = addresses.split(',')\n field_list.append(suburb)\n field_list.append(state)\n field_list.append(postcode)\n self.generate_tpl(txt_filename, field_list, tpl_filename, src_table)\n\n #3.run data wizard\n if FILEEXT.endswith('.mdb'):\n temp_array = filename.split('\\\\')\n temp_array.pop()\n temp_mdb_file = '\\\\'.join(temp_array) + '\\\\' + src_table + '_matched.mdb'\n else:\n temp_mdb_file = txt_filename.replace(FILEEXT, '_matched.mdb')\n\n try:\n os.remove(temp_mdb_file)\n except WindowsError:\n pass\n\n a_wizard = datawizard.DataWizard()\n a_wizard.run(txt_filename, tpl_filename)\n\n #4.rename mdb to correct mdb file\n if filename.endswith('.mdb'):\n try:\n os.remove(filename)\n except WindowsError:\n pass\n os.rename(temp_mdb_file, filename)\n else:\n pass\n #we need to import back to table\n\nif __name__ == '__main__':\n\n startTime = datetime.datetime.now()\n con = db.get_connection()\n app = DPID(474, con)\n app.run()\n con.commit()\n log.info(datetime.datetime.now() - startTime)\n\n\n","sub_path":"dpid/dpid.py","file_name":"dpid.py","file_ext":"py","file_size_in_byte":4453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"355817865","text":"import asyncio\nimport asyncio.subprocess\n\n\nMESSAGE = \"\"\"\nThis message will be converted\nto all caps.\n\"\"\"\n\n\nasync def to_upper(input_data: str):\n print('-' * 80)\n print('[~] \"to_upper\" coroutine start')\n\n subprocess_create = asyncio.create_subprocess_exec(\n 'tr', '[:lower]', '[:upper]',\n # связываем stdout и stdout с субпроцессом через pipe\n stdout=asyncio.subprocess.PIPE,\n stdin=asyncio.subprocess.PIPE\n )\n print('[~] launching subprocess')\n subprocess = await subprocess_create\n\n print(str.format('[~] subprocess PID: {}', subprocess.pid))\n\n print('[~] communicating with subprocess')\n\n # коммуникация между основной программой и субпроцессом может быть\n # выполнена при помощи метода communicate() объекта субпроцесса\n\n stdout, stdin = await subprocess.communicate(input_data.encode())\n\n # после выполнения коммуникации, необходимо дождаться завершения работы\n # субпроцесса\n await subprocess.wait()\n\n return_code = subprocess.returncode\n print(str.format('[~] subprocess exit code: {}', return_code))\n\n if not return_code:\n results = bytes(stdout).decode()\n else:\n results = ''\n\n print('[~] \"to_upper\" coroutine end')\n print('-' * 80)\n\n return return_code, results\n\n\ndef main():\n print('*' * 80)\n loop = asyncio.get_event_loop()\n\n try:\n return_code, results = loop.run_until_complete(to_upper(MESSAGE))\n finally:\n loop.close()\n\n if return_code:\n # ненулевой статус возврата программы сообщает об аварийном ее\n # завершении\n print(str.format('[#] subprocess error: {}', return_code))\n else:\n print('-' * 40)\n print(str.format('[*] original message: {}', MESSAGE))\n\n print('-' * 40)\n print(str.format('[*] processed message: {}', results))\n\n print('-' * 40)\n print('*' * 80)\n \n \nif __name__ == '__main__':\n main()\n","sub_path":"spl/asyncio_module/python_standard_library_by_examples/app30.py","file_name":"app30.py","file_ext":"py","file_size_in_byte":2186,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"200451838","text":"from app.api.models.Base import Base\nfrom app.api.utils.remoteImageMapper import remoteImagesList\n\nfrom app.lib.conf import Config\nfrom app import __metadata__ as meta\n\nfrom pylxd import Client\nimport requests\nimport logging\n\nimport os\nimport os.path\nfrom collections import namedtuple\nCertRemotePaessler = namedtuple('Cert', ['cert', 'key']) # pragma: no cover\n\n\nlogging = logging.getLogger(__name__)\n\nclass LXDModule(Base):\n # Default 127.0.0.1 -> Move to Config\n def __init__(self, remoteHost='127.0.0.1'):\n logging.info('Accessing PyLXD client')\n verify = False\n\n if Config().get(meta.APP_NAME, '{}.lxd.remote.enable'.format(meta.APP_NAME.lower())) == 'true':\n try:\n remoteHost = Config().get(meta.APP_NAME, '{}.lxd.remote'.format(meta.APP_NAME.lower()))\n verify = False if Config().get(meta.APP_NAME, '{}.lxd.sslverify'.format(meta.APP_NAME.lower())) == 'false' else True\n except:\n pass\n self.client = Client(endpoint=remoteHost, verify=False, cert=None)\n else:\n self.client = Client(verify=False)\n\n\n def listContainers(self):\n try:\n logging.info('Reading container list')\n return self.client.containers.all()\n except Exception as e:\n logging.error('Failed to read container list: ')\n logging.exception(e)\n raise ValueError(e)\n\n def listLocalImages(self):\n try:\n logging.info('Reading local image list')\n results = []\n for image in self.client.api.images.get().json()['metadata']:\n results.append(self.client.api.images[image.split('/')[-1]].get().json()['metadata'])\n\n return results\n except Exception as e:\n logging.error('Failed to read local image list: ')\n logging.exception(e)\n raise ValueError(e)\n\n def listRemoteImages(self):\n try:\n remoteImagesLink = Config().get(meta.APP_NAME, '{}.images.remote'.format(meta.APP_NAME.lower()))\n logging.info('Reading remote image list')\n remoteClient = Client(endpoint=remoteImagesLink)\n return remoteImagesList(remoteClient.api.images.aliases.get().json())\n except Exception as e:\n logging.error('Failed to get remote container images: ')\n logging.exception(e)\n raise ValueError(e)\n\n def listRemotePaesslerImages(self):\n try:\n remotePaesslerImagesLink = Config().get(meta.APP_NAME, '{}.images.remote-paessler'.format(meta.APP_NAME.lower()))\n logging.info('Reading remote image list')\n verify = False if Config().get(meta.APP_NAME, '{}.lxd.sslverify'.format(meta.APP_NAME.lower())) == 'false' else True\n CERTS_PATH = Config().get(meta.APP_NAME, '{}.conf.dir'.format(meta.APP_NAME.lower()))\n cert = CertRemotePaessler(\n cert=os.path.expanduser(os.path.join(CERTS_PATH, 'client.crt')),\n key=os.path.expanduser(os.path.join(CERTS_PATH, 'client.key'))\n )\n remoteClient = Client(endpoint=remotePaesslerImagesLink, verify=verify, cert=cert)\n return remoteImagesList(remoteClient.api.images.aliases.get().json())\n except Exception as e:\n logging.error('Failed to get remote container images: ')\n logging.exception(e)\n raise ValueError(e)\n\n def listNightlyImages(self):\n try:\n logging.info('Reading nightly remote image list')\n images = requests.get(url='https://vhajdari.github.io/lxd-images/images.json')\n return images.json()['images']\n except Exception as e:\n logging.error('Failed to get remote nightly container images: ')\n logging.exception(e)\n raise ValueError(e)\n\n def detailsRemoteImage(self, alias):\n try:\n remoteImagesLink = Config().get(meta.APP_NAME, '{}.images.remote'.format(meta.APP_NAME.lower()))\n remoteClient = Client(endpoint=remoteImagesLink)\n fingerprint = remoteClient.api.images.aliases[alias].get().json()['metadata']['target']\n return remoteClient.api.images[fingerprint].get().json()['metadata']\n except Exception as e:\n raise ValueError(e)\n\n def detailsRemotePaesslerImage(self, alias):\n try:\n remotePaesslerImagesLink = Config().get(meta.APP_NAME, '{}.images.remote-paessler'.format(meta.APP_NAME.lower()))\n verify = False if Config().get(meta.APP_NAME, '{}.lxd.sslverify'.format(meta.APP_NAME.lower())) == 'false' else True\n remoteClient = Client(endpoint=remotePaesslerImagesLink, verify=False)\n fingerprint = remoteClient.api.images.aliases[alias].get().json()['metadata']['target']\n return remoteClient.api.images[fingerprint].get().json()['metadata']\n except Exception as e:\n raise ValueError(e)\n\n\n def downloadImage(self, image):\n try:\n remoteImagesLink = Config().get(meta.APP_NAME, '{}.images.remote'.format(meta.APP_NAME.lower()))\n logging.info('Downloading remote image:', image)\n remoteClient = Client(endpoint=remoteImagesLink)\n try:\n remoteImage = remoteClient.images.get_by_alias(image)\n except:\n remoteImage = remoteClient.images.get(image)\n newImage = remoteImage.copy(self.client, auto_update=False, public=False, wait=True)\n return self.client.api.images[newImage.fingerprint].get().json()['metadata']\n except Exception as e:\n logging.error('Failed to download image:')\n logging.exception(e)\n raise ValueError(e)\n\n def downloadPaesslerImage(self, image):\n try:\n remotePaesslerImagesLink = Config().get(meta.APP_NAME, '{}.images.remote-paessler'.format(meta.APP_NAME.lower()))\n logging.info('Downloading remote image:', image)\n verify = False if Config().get(meta.APP_NAME,'{}.lxd.sslverify'.format(meta.APP_NAME.lower())) == 'false' else True\n remoteClient = Client(endpoint=remotePaesslerImagesLink, verify=False)\n try:\n remoteImage = remoteClient.images.get_by_alias(image)\n except:\n remoteImage = remoteClient.images.get(image)\n newImage = remoteImage.copy(self.client, auto_update=False, public=False, wait=True)\n return self.client.api.images[newImage.fingerprint].get().json()['metadata']\n except Exception as e:\n logging.error('Failed to download image:')\n logging.exception(e)\n raise ValueError(e)\n\n def deleteImage(self):\n pass\n\n def listProfiles(self):\n try:\n results = []\n for profile in self.client.api.profiles.get().json()['metadata']:\n results.append(self.client.api.profiles[profile.split('/')[-1]].get().json()['metadata'])\n\n return results\n except Exception as e:\n raise ValueError(e)\n\n def listStoragePools(self):\n try:\n results = []\n for profile in self.client.api.storage_pools.get().json()['metadata']:\n results.append(self.client.api.storage_pools[profile.split('/')[-1]].get().json()['metadata'])\n\n return results\n except Exception as e:\n raise ValueError(e)\n\n def createProfile(self):\n pass\n\n def deleteProfile(self):\n pass\n\n def updateProfile(self):\n pass\n\n def listNetworks(self):\n try:\n logging.info('Retrieving network list.')\n results = []\n for network in self.client.api.networks.get().json()['metadata']:\n results.append(self.client.api.networks[network.split('/')[-1]].get().json()['metadata'])\n\n return results\n except Exception as e:\n logging.error('Failed to retrieve network list:')\n logging.exception(e)\n raise ValueError(e)\n\n def createNetwork(self):\n pass\n\n def deleteNetwork(self):\n pass\n\n def updateNetwork(self):\n pass\n\n\n def config(self):\n try:\n return self.client.api.get().json()['metadata']\n except Exception as e:\n raise ValueError(e)\n\n def hasImage(self, imageInfo):\n lxdModule = LXDModule()\n for image in lxdModule.listLocalImages():\n if image.get('fingerprint') == imageInfo:\n return 'fingerprint'\n for alias in image.get('aliases'):\n if alias.get('name') == imageInfo:\n return 'alias'\n return None\n\n def containerExists(self, containerName):\n lxdModule = LXDModule()\n try:\n logging.info('Checking if container exists.')\n container = self.client.containers.get(containerName)\n return True\n except Exception as e:\n logging.error('Failed to verify container:')\n logging.exception(e)\n return False\n\n def info(self):\n raise NotImplementedError()\n\n def create(self):\n raise NotImplementedError()\n\n def delete(self):\n raise NotImplementedError()\n\n def start(self):\n raise NotImplementedError()\n\n def stop(self):\n raise NotImplementedError()\n\n def restart(self):\n raise NotImplementedError()\n\n def update(self):\n raise NotImplementedError()\n\n def move(self):\n raise NotImplementedError()\n\n def clone(self):\n raise NotImplementedError()\n\n def snapshot(self):\n raise NotImplementedError()\n\n","sub_path":"app/api/models/LXDModule.py","file_name":"LXDModule.py","file_ext":"py","file_size_in_byte":9706,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"215655650","text":"# This script produces plot 14 in the Linguistics paper.\r\n\r\n# import necessary libraries\r\nimport pandas as pd\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\ndecs = range(1700,1910,10)\r\n\r\n\r\ndef figure_env(ax, name):\r\n ax.grid()\r\n ax.set_title(name, fontweight = \"bold\")\r\n ax.set_xticks(decs[::2])\r\n ax.set_xticklabels(decs[::2], rotation=45)\r\n ax.set_ylim(0,1)\r\n\r\ndef conf_int(df, x, conf, col, plot_x):\r\n nr_sim = df.shape[0]\r\n print(nr_sim)\r\n\r\n conf_max = int(round(nr_sim * conf -1)) # upper boundary. example: p = 0.9, nr_sim =1000. conf_max = 899. In an ascending list of values for each decade, the 899th of 1,000 values is the upper boundary (for p = 0.9)\r\n conf_min = nr_sim - conf_max -1 # lower boundary. example: nr_sim =1000. conf_min = 1000 - 899 -1 = 100\r\n\r\n df = df.transpose()\r\n df.values.sort() # sort the values\r\n line = np.empty([2, len(df)], dtype= float) # set up an empty array that contains values for two lines.\r\n\r\n inx = 0 #set up counter to specify row number\r\n for row in df.iterrows():\r\n if conf_max <= 0: # if conf_max is equal to or below zero, there is no boundary\r\n line[0,inx] = None\r\n line[1,inx] = None\r\n else: # if conf_max is above zero, the line values (upper and lower boundaries) are simply the conf_max-th and conf_min-th value of all simulation values for each decade\r\n df_nan = df.iloc[inx,:].dropna()\r\n if len(df_nan) == 0:\r\n line[0,inx] = None\r\n line[1,inx] = None\r\n else:\r\n if df_nan.shape[0] == nr_sim:\r\n line[0,inx] = df.iloc[inx,conf_min]\r\n line[1,inx] = df.iloc[inx,conf_max]\r\n else:\r\n conf_min_nan = int(round((df_nan.shape[0] * conf_min)/nr_sim))\r\n conf_max_nan = int(round((df_nan.shape[0] * conf_max)/nr_sim))\r\n line[0,inx] = df_nan.iloc[conf_min_nan]\r\n line[1,inx] = df_nan.iloc[conf_max_nan]\r\n inx +=1\r\n plot_x.fill_between(x, line[0,:], line[1,:], facecolor=col, alpha='0.5') # fill the area that is enclosed by the lines\r\n\r\n\r\n# read the necessary files:\r\nbar_hap_neo = pd.read_csv('bar_hapax_and_new_global_100000.csv', sep= ',',index_col=0) # this file contains a line for each text in the corpus, together with the respective decade and the token count\r\nisch_hap_neo = pd.read_csv('isch_hapax_and_new_global_100000.csv', sep= ',',index_col=0) # this file contains a line for each text in the corpus, together with the respective decade and the token count\r\nnis_hap_neo = pd.read_csv('nis_hapax_and_new_global_100000.csv', sep= ',',index_col=0) # this file contains a line for each text in the corpus, together with the respective decade and the token count\r\ntum_hap_neo = pd.read_csv('tum_hapax_and_new_global_100000.csv', sep= ',',index_col=0) # this file contains a line for each text in the corpus, together with the respective decade and the token count\r\nieren_hap_neo = pd.read_csv('ieren_hapax_and_new_global_100000.csv', sep= ',',index_col=0) # this file contains a line for each text in the corpus, together with the respective decade and the token count\r\n\r\nbar_hap = pd.read_csv('bar_hapax_global_100000.csv', sep= ',',index_col=0) # this file contains a line for each text in the corpus, together with the respective decade and the token count\r\nisch_hap = pd.read_csv('isch_hapax_global_100000.csv', sep= ',',index_col=0) # this file contains a line for each text in the corpus, together with the respective decade and the token count\r\nnis_hap = pd.read_csv('nis_hapax_global_100000.csv', sep= ',',index_col=0) # this file contains a line for each text in the corpus, together with the respective decade and the token count\r\ntum_hap = pd.read_csv('tum_hapax_global_100000.csv', sep= ',',index_col=0) # this file contains a line for each text in the corpus, together with the respective decade and the token count\r\nieren_hap = pd.read_csv('ieren_hapax_global_100000.csv', sep= ',',index_col=0) # this file contains a line for each text in the corpus, together with the respective decade and the token count\r\n\r\nbar_hap_vneo = bar_hap_neo/bar_hap\r\nisch_hap_vneo = isch_hap_neo/isch_hap\r\nnis_hap_vneo = nis_hap_neo/nis_hap\r\ntum_hap_vneo = tum_hap_neo/tum_hap\r\nieren_hap_vneo = ieren_hap_neo/ieren_hap\r\n\r\n# plot 11\r\nfig = plt.figure(figsize = (12,9))\r\n\r\nax1 = fig.add_subplot(321)\r\nax1.plot(list(bar_hap_vneo), bar_hap_vneo.mean(axis = 0, skipna = True).values, linewidth = 0.7, color = \"black\")\r\nconf_int(bar_hap_vneo, decs, 0.99, 'grey', ax1)\r\nconf_int(bar_hap_vneo, decs, 0.95,'dimgrey', ax1)\r\nfigure_env(ax1, \"-bar\")\r\nprint(\"bar OK\")\r\n\r\nax2 = fig.add_subplot(322)\r\nax2.plot(list(isch_hap_vneo), isch_hap_vneo.mean(axis = 0, skipna = True).values, linewidth = 0.7, color = \"black\")\r\nconf_int(isch_hap_vneo, decs, 0.99, 'grey', ax2)\r\nconf_int(isch_hap_vneo, decs, 0.95,'dimgrey', ax2)\r\nfigure_env(ax2, \"-isch\")\r\nprint(\"isch OK\")\r\n\r\nax3 = fig.add_subplot(323)\r\nax3.plot(list(nis_hap_vneo), nis_hap_vneo.mean(axis = 0, skipna = True).values, linewidth = 0.7, color = \"black\")\r\nconf_int(nis_hap_vneo, decs, 0.99, 'grey', ax3)\r\nconf_int(nis_hap_vneo, decs, 0.95,'dimgrey', ax3)\r\nfigure_env(ax3, \"-nis\")\r\nprint(\"nis OK\")\r\n\r\nax4 = fig.add_subplot(324)\r\nax4.plot(list(ieren_hap_vneo), ieren_hap_vneo.mean(axis = 0, skipna = True).values, linewidth = 0.7, color = \"black\")\r\nconf_int(ieren_hap_vneo, decs, 0.99, 'grey', ax4)\r\nconf_int(ieren_hap_vneo, decs, 0.95,'dimgrey', ax4)\r\nfigure_env(ax4, \"-ieren\")\r\nprint(\"ieren OK\")\r\n\r\nax5 = fig.add_subplot(325)\r\nax5.plot(list(tum_hap_vneo), tum_hap_vneo.mean(axis = 0, skipna = True).values, linewidth = 0.7, color = \"black\")\r\nconf_int(tum_hap_vneo, decs, 0.99, 'grey', ax5)\r\nconf_int(tum_hap_vneo, decs, 0.95,'dimgrey', ax5)\r\nfigure_env(ax5, \"-tum\")\r\nprint(\"tum OK\")\r\n\r\n\r\n# add legend\r\n#ax1.legend(loc=2, bbox_to_anchor = (0.05,0.95), prop={'size': 14})\r\n\r\nfig.tight_layout(pad = 2)\r\nfig.savefig('plot_14_Linguistics.png', dpi=800)\r\n","sub_path":"scripts/plot_14_Linguistics.py","file_name":"plot_14_Linguistics.py","file_ext":"py","file_size_in_byte":6059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"398059712","text":"import logging\n\nimport synapse.exc as s_exc\nimport synapse.common as s_common\n\nimport synapse.lib.base as s_base\nimport synapse.lib.cache as s_cache\nimport synapse.lib.nexus as s_nexus\nimport synapse.lib.config as s_config\n\nimport synapse.lib.crypto.passwd as s_passwd\n\nlogger = logging.getLogger(__name__)\n\nreqValidRules = s_config.getJsValidator({\n 'type': 'array',\n 'items': {\n 'type': 'array',\n 'items': [\n {'type': 'boolean'},\n {'type': 'array', 'items': {'type': 'string'}},\n ],\n 'minItems': 2,\n 'maxItems': 2,\n }\n})\n\ndef getShadow(passwd): # pragma: no cover\n '''This API is deprecated.'''\n s_common.deprecated('hiveauth.getShadow()', curv='2.110.0')\n salt = s_common.guid()\n hashed = s_common.guid((salt, passwd))\n return (salt, hashed)\n\ndef textFromRule(rule):\n text = '.'.join(rule[1])\n if not rule[0]:\n text = '!' + text\n return text\n\nclass Auth(s_nexus.Pusher):\n '''\n Auth is a user authentication and authorization stored in a Hive. Users\n correspond to separate logins with different passwords and potentially\n different privileges.\n\n Users are assigned \"rules\". These rules are evaluated in order until a rule\n matches. Each rule is a tuple of boolean, and a rule path (a sequence of\n strings). Rules that are prefixes of a privilege match, i.e. a rule\n ('foo',) will match ('foo', 'bar').\n\n Roles are just collections of rules. When a user is \"granted\" a role those\n rules are assigned to that user. Unlike in an RBAC system, users don't\n explicitly assume a role; they are merely a convenience mechanism to easily\n assign the same rules to multiple users.\n\n Authgates are objects that manage their own authorization. Each\n AuthGate has roles and users subkeys which contain rules specific to that\n user or role for that AuthGate. The roles and users of an AuthGate,\n called GateRole and GateUser respectively, contain the iden of a role or\n user defined prior and rules specific to that role or user; they do not\n duplicate the metadata of the role or user.\n\n Node layout::\n\n Auth root (passed into constructor)\n ├ roles\n │ ├ \n │ ├ ...\n │ └ last role\n ├ users\n │ ├ \n │ ├ ...\n │ └ last user\n └ authgates\n ├ \n │ ├ roles\n │ │ ├ \n │ │ ├ ...\n │ │ └ last role\n │ └ users\n │ ├ \n │ ├ ...\n │ └ last user\n ├ \n │ ├ ...\n └ ... last authgate\n\n '''\n\n async def __anit__(self, node, nexsroot=None, seed=None, maxusers=0):\n '''\n Args:\n node (HiveNode): The root of the persistent storage for auth\n '''\n # Derive an iden from the parent\n iden = 'auth:' + ':'.join(node.full)\n await s_nexus.Pusher.__anit__(self, iden, nexsroot=nexsroot)\n\n self.node = node\n\n if seed is None:\n seed = s_common.guid()\n\n self.maxusers = maxusers\n\n self.usersbyiden = {}\n self.rolesbyiden = {}\n self.usersbyname = {}\n self.rolesbyname = {}\n self.authgates = {}\n\n self.allrole = None\n self.rootuser = None\n\n roles = await self.node.open(('roles',))\n for _, node in roles:\n await self._addRoleNode(node)\n\n users = await self.node.open(('users',))\n for _, node in users:\n await self._addUserNode(node)\n\n authgates = await self.node.open(('authgates',))\n for _, node in authgates:\n try:\n await self._addAuthGate(node)\n except Exception: # pragma: no cover\n logger.exception('Failure loading AuthGate')\n\n self.allrole = await self.getRoleByName('all')\n if self.allrole is None:\n # initialize the role of which all users are a member\n guid = s_common.guid((seed, 'auth', 'role', 'all'))\n await self._addRole(guid, 'all')\n self.allrole = self.role(guid)\n\n # initialize an admin user named root\n self.rootuser = await self.getUserByName('root')\n if self.rootuser is None:\n guid = s_common.guid((seed, 'auth', 'user', 'root'))\n await self._addUser(guid, 'root')\n self.rootuser = self.user(guid)\n\n await self.rootuser.setAdmin(True, logged=False)\n await self.rootuser.setLocked(False, logged=False)\n\n async def fini():\n await self.allrole.fini()\n await self.rootuser.fini()\n [await u.fini() for u in self.users()]\n [await r.fini() for r in self.roles()]\n [await a.fini() for a in self.authgates.values()]\n\n self.onfini(fini)\n\n def users(self):\n return self.usersbyiden.values()\n\n def roles(self):\n return self.rolesbyiden.values()\n\n def role(self, iden):\n return self.rolesbyiden.get(iden)\n\n def user(self, iden):\n return self.usersbyiden.get(iden)\n\n async def reqUser(self, iden):\n\n user = self.user(iden)\n if user is None:\n mesg = f'No user with iden {iden}.'\n raise s_exc.NoSuchUser(mesg=mesg, user=iden)\n return user\n\n async def reqRole(self, iden):\n\n role = self.role(iden)\n if role is None:\n mesg = f'No role with iden {iden}.'\n raise s_exc.NoSuchRole(mesg=mesg)\n return role\n\n async def reqUserByName(self, name):\n user = await self.getUserByName(name)\n if user is None:\n mesg = f'No user named {name}.'\n raise s_exc.NoSuchUser(mesg=mesg, username=name)\n return user\n\n async def reqUserByNameOrIden(self, name):\n user = await self.getUserByName(name)\n if user is not None:\n return user\n\n user = self.user(name)\n if user is None:\n mesg = f'No user with name or iden {name}.'\n raise s_exc.NoSuchUser(mesg=mesg)\n return user\n\n async def reqRoleByName(self, name):\n role = await self.getRoleByName(name)\n if role is None:\n mesg = f'No role named {name}.'\n raise s_exc.NoSuchRole(mesg=mesg)\n return role\n\n async def getUserByName(self, name):\n '''\n Get a user by their username.\n\n Args:\n name (str): Name of the user to get.\n\n Returns:\n HiveUser: A Hive User. May return None if there is no user by the requested name.\n '''\n return self.usersbyname.get(name)\n\n async def getUserIdenByName(self, name):\n user = await self.getUserByName(name)\n return None if user is None else user.iden\n\n async def getRoleByName(self, name):\n return self.rolesbyname.get(name)\n\n async def _addUserNode(self, node):\n\n user = await HiveUser.anit(node, self)\n\n self.usersbyiden[user.iden] = user\n self.usersbyname[user.name] = user\n\n return user\n\n @s_nexus.Pusher.onPushAuto('user:name')\n async def setUserName(self, iden, name):\n if not isinstance(name, str):\n raise s_exc.BadArg(mesg='setUserName() name must be a string')\n\n user = self.usersbyname.get(name)\n if user is not None:\n if user.iden == iden:\n return\n raise s_exc.DupUserName(name=name)\n\n user = await self.reqUser(iden)\n\n self.usersbyname.pop(user.name, None)\n self.usersbyname[name] = user\n\n user.name = name\n await user.node.set(name)\n\n beheld = {\n 'iden': iden,\n 'valu': name,\n }\n await self.feedBeholder('user:name', beheld)\n\n @s_nexus.Pusher.onPushAuto('role:name')\n async def setRoleName(self, iden, name):\n if not isinstance(name, str):\n raise s_exc.BadArg(mesg='setRoleName() name must be a string')\n\n role = self.rolesbyname.get(name)\n if role is not None:\n if role.iden == iden:\n return\n raise s_exc.DupRoleName(name=name)\n\n role = await self.reqRole(iden)\n\n if role.name == 'all':\n mesg = 'Role \"all\" may not be renamed.'\n raise s_exc.BadArg(mesg=mesg)\n\n self.rolesbyname.pop(role.name, None)\n self.rolesbyname[name] = role\n\n role.name = name\n await role.node.set(name)\n\n beheld = {\n 'iden': iden,\n 'valu': name,\n }\n await self.feedBeholder('role:name', beheld)\n\n async def feedBeholder(self, evnt, info, gateiden=None, logged=True):\n if self.nexsroot and self.nexsroot.started and logged:\n behold = {\n 'event': evnt,\n 'offset': await self.nexsroot.index(),\n 'info': info\n }\n\n if gateiden:\n gate = self.getAuthGate(gateiden)\n if gate:\n behold['gates'] = [gate.pack()]\n\n await self.fire('cell:beholder', **behold)\n\n @s_nexus.Pusher.onPushAuto('user:info')\n async def setUserInfo(self, iden, name, valu, gateiden=None, logged=True, mesg=None):\n\n user = await self.reqUser(iden)\n\n info = user.info\n if gateiden is not None:\n info = await user.genGateInfo(gateiden)\n\n if name in ('locked', 'archived') and not valu:\n self.checkUserLimit()\n\n await info.set(name, valu)\n\n if mesg is None:\n mesg = {\n 'iden': iden,\n 'name': name,\n }\n if name != 'passwd':\n mesg['valu'] = valu\n\n await self.feedBeholder('user:info', mesg, gateiden=gateiden, logged=logged)\n\n # since any user info *may* effect auth\n user.clearAuthCache()\n\n @s_nexus.Pusher.onPushAuto('role:info')\n async def setRoleInfo(self, iden, name, valu, gateiden=None, logged=True, mesg=None):\n role = await self.reqRole(iden)\n\n info = role.info\n if gateiden is not None:\n info = await role.genGateInfo(gateiden)\n\n await info.set(name, valu)\n\n if mesg is None:\n mesg = {\n 'iden': iden,\n 'name': name,\n 'valu': valu,\n }\n await self.feedBeholder('role:info', mesg, gateiden=gateiden, logged=logged)\n\n role.clearAuthCache()\n\n async def _addRoleNode(self, node):\n\n role = await HiveRole.anit(node, self)\n\n self.rolesbyiden[role.iden] = role\n self.rolesbyname[role.name] = role\n\n return role\n\n async def _addAuthGate(self, node):\n gate = await AuthGate.anit(node, self)\n self.authgates[gate.iden] = gate\n return gate\n\n async def addAuthGate(self, iden, authgatetype):\n '''\n Retrieve AuthGate by iden. Create if not present.\n\n Note:\n Not change distributed\n\n Returns:\n (HiveAuthGate)\n '''\n gate = self.getAuthGate(iden)\n if gate is not None:\n if gate.type != authgatetype:\n raise s_exc.InconsistentStorage(mesg=f'Stored AuthGate is of type {gate.type}, not {authgatetype}')\n return gate\n\n path = self.node.full + ('authgates', iden)\n node = await self.node.hive.open(path)\n await self.node.hive.set(path, authgatetype)\n return await self._addAuthGate(node)\n\n async def delAuthGate(self, iden):\n '''\n Delete AuthGate by iden.\n\n Note:\n Not change distributed\n '''\n gate = self.getAuthGate(iden)\n if gate is None:\n raise s_exc.NoSuchAuthGate(iden=iden)\n\n await gate.fini()\n await gate.delete()\n await gate.node.pop()\n\n del self.authgates[iden]\n\n def getAuthGate(self, iden):\n return self.authgates.get(iden)\n\n def getAuthGates(self):\n return list(self.authgates.values())\n\n def reqAuthGate(self, iden):\n gate = self.authgates.get(iden)\n if gate is None:\n mesg = f'No auth gate found with iden: ({iden}).'\n raise s_exc.NoSuchAuthGate(iden=iden, mesg=mesg)\n return gate\n\n def checkUserLimit(self):\n '''\n Check if we're at the specified user limit.\n\n This should be called right before adding/unlocking/unarchiving a user.\n\n Raises: s_exc.HitLimit if the number of active users is at the maximum.\n '''\n if self.maxusers == 0:\n return\n\n numusers = 0\n\n for user in self.users():\n if user.name == 'root':\n continue\n\n if user.isLocked() or user.isArchived():\n continue\n\n numusers += 1\n\n if numusers >= self.maxusers:\n mesg = f'Cell at maximum number of users ({self.maxusers}).'\n raise s_exc.HitLimit(mesg=mesg)\n\n async def addUser(self, name, passwd=None, email=None, iden=None):\n '''\n Add a User to the Hive.\n\n Args:\n name (str): The name of the User.\n passwd (str): A optional password for the user.\n email (str): A optional email for the user.\n iden (str): A optional iden to use as the user iden.\n\n Returns:\n HiveUser: A Hive User.\n '''\n\n self.checkUserLimit()\n\n if self.usersbyname.get(name) is not None:\n raise s_exc.DupUserName(name=name)\n\n if iden is None:\n iden = s_common.guid()\n else:\n if not s_common.isguid(iden):\n raise s_exc.BadArg(name='iden', arg=iden, mesg='Argument it not a valid iden.')\n\n if self.usersbyiden.get(iden) is not None:\n raise s_exc.DupIden(name=name, iden=iden,\n mesg='User already exists for the iden.')\n\n await self._push('user:add', iden, name)\n\n user = self.user(iden)\n\n if passwd is not None:\n await user.setPasswd(passwd)\n\n if email is not None:\n await self.setUserInfo(user.iden, 'email', email)\n\n # Everyone's a member of 'all'\n await user.grant(self.allrole.iden)\n\n return user\n\n @s_nexus.Pusher.onPush('user:add')\n async def _addUser(self, iden, name):\n\n user = self.usersbyname.get(name)\n if user is not None:\n return user\n\n node = await self.node.open(('users', iden))\n await node.set(name)\n\n await self._addUserNode(node)\n\n user = self.usersbyname.get(name)\n await self.feedBeholder('user:add', user.pack())\n\n async def addRole(self, name, iden=None):\n if self.rolesbyname.get(name) is not None:\n raise s_exc.DupRoleName(name=name)\n\n if iden is None:\n iden = s_common.guid()\n\n await self._push('role:add', iden, name)\n\n return self.role(iden)\n\n @s_nexus.Pusher.onPush('role:add')\n async def _addRole(self, iden, name):\n\n role = self.rolesbyname.get(name)\n if role is not None:\n return role\n\n node = await self.node.open(('roles', iden))\n await node.set(name)\n\n await self._addRoleNode(node)\n\n role = self.rolesbyname.get(name)\n await self.feedBeholder('role:add', role.pack())\n\n async def delUser(self, iden):\n\n await self.reqUser(iden)\n return await self._push('user:del', iden)\n\n @s_nexus.Pusher.onPush('user:del')\n async def _delUser(self, iden):\n\n if iden == self.rootuser.iden:\n mesg = 'User \"root\" may not be deleted.'\n raise s_exc.BadArg(mesg=mesg)\n\n user = self.user(iden)\n if user is None:\n return\n\n self.usersbyiden.pop(user.iden)\n self.usersbyname.pop(user.name)\n\n path = self.node.full + ('users', user.iden)\n\n for gate in self.authgates.values():\n await gate._delGateUser(user.iden)\n\n await user.fini()\n await self.node.hive.pop(path)\n await self.feedBeholder('user:del', {'iden': iden})\n\n def _getUsersInRole(self, role):\n for user in self.users():\n if role.iden in user.info.get('roles', ()):\n yield user\n\n async def delRole(self, iden):\n await self.reqRole(iden)\n return await self._push('role:del', iden)\n\n @s_nexus.Pusher.onPush('role:del')\n async def _delRole(self, iden):\n\n if iden == self.allrole.iden:\n mesg = 'Role \"all\" may not be deleted.'\n raise s_exc.BadArg(mesg=mesg)\n\n role = self.role(iden)\n if role is None:\n return\n\n for user in self._getUsersInRole(role):\n await user.revoke(role.iden, nexs=False)\n\n for gate in self.authgates.values():\n await gate._delGateRole(role.iden)\n\n self.rolesbyiden.pop(role.iden)\n self.rolesbyname.pop(role.name)\n\n await role.fini()\n\n # directly set the node's value and let events prop\n path = self.node.full + ('roles', role.iden)\n await self.node.hive.pop(path)\n await self.feedBeholder('role:del', {'iden': iden})\n\nclass AuthGate(s_base.Base):\n '''\n The storage object for object specific rules for users/roles.\n '''\n async def __anit__(self, node, auth):\n await s_base.Base.__anit__(self)\n self.auth = auth\n\n self.iden = node.name()\n self.type = node.valu\n\n self.node = node\n\n self.gateroles = {} # iden -> HiveRole\n self.gateusers = {} # iden -> HiveUser\n\n for useriden, usernode in await node.open(('users',)):\n\n user = self.auth.user(useriden)\n if user is None: # pragma: no cover\n logger.warning(f'Hive: path {useriden} refers to unknown user')\n continue\n\n userinfo = await usernode.dict()\n self.gateusers[user.iden] = user\n user.authgates[self.iden] = userinfo\n user.clearAuthCache()\n\n for roleiden, rolenode in await node.open(('roles',)):\n\n role = self.auth.role(roleiden)\n if role is None: # pragma: no cover\n logger.warning(f'Hive: path {roleiden} refers to unknown role')\n continue\n\n roleinfo = await rolenode.dict()\n self.gateroles[role.iden] = role\n role.authgates[self.iden] = roleinfo\n\n async def genUserInfo(self, iden):\n node = await self.node.open(('users', iden))\n userinfo = await node.dict()\n\n user = self.auth.user(iden)\n self.gateusers[iden] = user\n user.authgates[self.iden] = userinfo\n\n return userinfo\n\n async def genRoleInfo(self, iden):\n node = await self.node.open(('roles', iden))\n roleinfo = await node.dict()\n\n role = self.auth.role(iden)\n self.gateroles[iden] = role\n role.authgates[self.iden] = roleinfo\n\n return roleinfo\n\n async def _delGateUser(self, iden):\n self.gateusers.pop(iden, None)\n await self.node.pop(('users', iden))\n\n async def _delGateRole(self, iden):\n self.gateroles.pop(iden, None)\n await self.node.pop(('roles', iden))\n\n async def delete(self):\n\n await self.fini()\n\n for _, user in list(self.gateusers.items()):\n user.authgates.pop(self.iden, None)\n user.clearAuthCache()\n\n for _, role in list(self.gateroles.items()):\n role.authgates.pop(self.iden, None)\n role.clearAuthCache()\n\n await self.node.pop()\n\n def pack(self):\n\n users = []\n for user in self.gateusers.values():\n\n gateinfo = user.authgates.get(self.iden)\n if gateinfo is None:\n continue\n\n users.append({\n 'iden': user.iden,\n 'rules': gateinfo.get('rules', ()),\n 'admin': gateinfo.get('admin', False),\n })\n\n roles = []\n for role in self.gateroles.values():\n\n gateinfo = role.authgates.get(self.iden)\n if gateinfo is None:\n continue\n\n roles.append({\n 'iden': role.iden,\n 'rules': gateinfo.get('rules', ()),\n 'admin': gateinfo.get('admin', False),\n })\n\n return {\n 'iden': self.iden,\n 'type': self.type,\n 'users': users,\n 'roles': roles,\n }\n\nclass HiveRuler(s_base.Base):\n '''\n A HiveNode that holds a list of rules. This includes HiveUsers, HiveRoles, and the AuthGate variants of those\n '''\n\n async def __anit__(self, node, auth):\n await s_base.Base.__anit__(self)\n\n self.iden = node.name()\n\n self.auth = auth\n self.node = node\n self.name = node.valu\n self.info = await node.dict()\n\n self.info.setdefault('admin', False)\n self.info.setdefault('rules', ())\n\n self.authgates = {}\n\n async def _setRulrInfo(self, name, valu, gateiden=None, nexs=True, mesg=None): # pragma: no cover\n raise s_exc.NoSuchImpl(mesg='Subclass must implement _setRulrInfo')\n\n def getRules(self, gateiden=None):\n\n if gateiden is None:\n return list(self.info.get('rules', ()))\n\n gateinfo = self.authgates.get(gateiden)\n if gateinfo is None:\n return []\n\n return list(gateinfo.get('rules', ()))\n\n async def setRules(self, rules, gateiden=None, nexs=True, mesg=None):\n reqValidRules(rules)\n return await self._setRulrInfo('rules', rules, gateiden=gateiden, nexs=nexs, mesg=mesg)\n\n async def addRule(self, rule, indx=None, gateiden=None, nexs=True):\n reqValidRules((rule,))\n rules = self.getRules(gateiden=gateiden)\n\n mesg = {\n 'name': 'rule:add',\n 'iden': self.iden,\n 'valu': rule,\n }\n if indx is None:\n rules.append(rule)\n else:\n rules.insert(indx, rule)\n mesg['indx'] = indx\n\n await self.setRules(rules, gateiden=gateiden, nexs=nexs, mesg=mesg)\n\n async def delRule(self, rule, gateiden=None):\n reqValidRules((rule,))\n rules = self.getRules(gateiden=gateiden)\n if rule not in rules:\n return False\n\n mesg = {\n 'name': 'rule:del',\n 'iden': self.iden,\n 'valu': rule,\n }\n rules.remove(rule)\n await self.setRules(rules, gateiden=gateiden, mesg=mesg)\n return True\n\nclass HiveRole(HiveRuler):\n '''\n A role within the Hive authorization subsystem.\n\n A role in HiveAuth exists to bundle rules together so that the same\n set of rules can be applied to multiple users.\n '''\n def pack(self):\n return {\n 'type': 'role',\n 'iden': self.iden,\n 'name': self.name,\n 'rules': self.info.get('rules'),\n 'authgates': {name: info.pack() for (name, info) in self.authgates.items()},\n }\n\n async def _setRulrInfo(self, name, valu, gateiden=None, nexs=True, mesg=None):\n if nexs:\n return await self.auth.setRoleInfo(self.iden, name, valu, gateiden=gateiden, mesg=mesg)\n else:\n return await self.auth._hndlsetRoleInfo(self.iden, name, valu, gateiden=gateiden, logged=nexs, mesg=mesg)\n\n async def setName(self, name):\n return await self.auth.setRoleName(self.iden, name)\n\n def clearAuthCache(self):\n for user in self.auth.users():\n if user.hasRole(self.iden):\n user.clearAuthCache()\n\n async def genGateInfo(self, gateiden):\n info = self.authgates.get(gateiden)\n if info is None:\n gate = self.auth.reqAuthGate(gateiden)\n info = self.authgates[gateiden] = await gate.genRoleInfo(self.iden)\n return info\n\n def allowed(self, perm, default=None, gateiden=None):\n\n perm = tuple(perm)\n if gateiden is not None:\n info = self.authgates.get(gateiden)\n if info is not None:\n for allow, path in info.get('rules', ()):\n if perm[:len(path)] == path:\n return allow\n return default\n\n # 2. check user rules\n for allow, path in self.info.get('rules', ()):\n if perm[:len(path)] == path:\n return allow\n\n return default\n\nclass HiveUser(HiveRuler):\n '''\n A user (could be human or computer) of the system within HiveAuth.\n\n Cortex-wide rules are stored here. AuthGate-specific rules for this user are stored in an GateUser.\n '''\n async def __anit__(self, node, auth):\n await HiveRuler.__anit__(self, node, auth)\n\n self.info.setdefault('roles', ())\n self.info.setdefault('admin', False)\n self.info.setdefault('passwd', None)\n self.info.setdefault('locked', False)\n self.info.setdefault('archived', False)\n\n # arbitrary profile data for application layer use\n prof = await self.node.open(('profile',))\n self.profile = await prof.dict(nexs=True)\n\n # TODO: max size check / max count check?\n varz = await self.node.open(('vars',))\n self.vars = await varz.dict(nexs=True)\n\n self.permcache = s_cache.FixedCache(self._allowed)\n\n def pack(self, packroles=False):\n\n roles = self.info.get('roles', ())\n if packroles:\n roles = [self.auth.role(r).pack() for r in roles]\n\n return {\n 'type': 'user',\n 'iden': self.iden,\n 'name': self.name,\n 'rules': self.info.get('rules', ()),\n 'roles': roles,\n 'admin': self.info.get('admin', ()),\n 'email': self.info.get('email'),\n 'locked': self.info.get('locked'),\n 'archived': self.info.get('archived'),\n 'authgates': {name: info.pack() for (name, info) in self.authgates.items()},\n }\n\n async def _setRulrInfo(self, name, valu, gateiden=None, nexs=True, mesg=None):\n if nexs:\n return await self.auth.setUserInfo(self.iden, name, valu, gateiden=gateiden, mesg=mesg)\n else:\n return await self.auth._hndlsetUserInfo(self.iden, name, valu, gateiden=gateiden, logged=nexs, mesg=mesg)\n\n async def setName(self, name):\n return await self.auth.setUserName(self.iden, name)\n\n async def allow(self, perm):\n if not self.allowed(perm):\n await self.addRule((True, perm), indx=0)\n\n def allowed(self, perm, default=None, gateiden=None):\n perm = tuple(perm)\n return self.permcache.get((perm, default, gateiden))\n\n def _allowed(self, pkey):\n\n perm, default, gateiden = pkey\n\n if self.info.get('locked'):\n return False\n\n if self.info.get('admin'):\n return True\n\n # 1. check authgate user rules\n if gateiden is not None:\n\n info = self.authgates.get(gateiden)\n if info is not None:\n\n if info.get('admin'):\n return True\n\n for allow, path in info.get('rules', ()):\n if perm[:len(path)] == path:\n return allow\n\n # 2. check user rules\n for allow, path in self.info.get('rules', ()):\n if perm[:len(path)] == path:\n return allow\n\n # 3. check authgate role rules\n if gateiden is not None:\n\n for role in self.getRoles():\n\n info = role.authgates.get(gateiden)\n if info is None:\n continue\n\n for allow, path in info.get('rules', ()):\n if perm[:len(path)] == path:\n return allow\n\n # 4. check role rules\n for role in self.getRoles():\n for allow, path in role.info.get('rules', ()):\n if perm[:len(path)] == path:\n return allow\n\n return default\n\n def getAllowedReason(self, perm, gateiden=None, default=False):\n '''\n A non-optimized diagnostic routine which will return a tuple\n of (allowed, reason). This is implemented separately for perf.\n\n NOTE: This must remain in sync with any changes to _allowed()!\n '''\n if self.info.get('locked'):\n return (False, 'The user is locked.')\n\n if self.info.get('admin'):\n return (True, 'The user is a global admin.')\n\n # 1. check authgate user rules\n if gateiden is not None:\n\n info = self.authgates.get(gateiden)\n if info is not None:\n\n if info.get('admin'):\n return (True, f'The user is an admin of auth gate {gateiden}.')\n\n for allow, path in info.get('rules', ()):\n if perm[:len(path)] == path:\n return (allow, f'Matched user rule ({textFromRule((allow, path))}) on gate {gateiden}.')\n\n # 2. check user rules\n for allow, path in self.info.get('rules', ()):\n if perm[:len(path)] == path:\n return (allow, f'Matched user rule ({textFromRule((allow, path))}).')\n\n # 3. check authgate role rules\n if gateiden is not None:\n\n for role in self.getRoles():\n\n info = role.authgates.get(gateiden)\n if info is None:\n continue\n\n for allow, path in info.get('rules', ()):\n if perm[:len(path)] == path:\n return (allow, f'Matched role rule ({textFromRule((allow, path))}) for role {role.name} on gate {gateiden}.')\n\n # 4. check role rules\n for role in self.getRoles():\n for allow, path in role.info.get('rules', ()):\n if perm[:len(path)] == path:\n return (allow, f'Matched role rule ({textFromRule((allow, path))}) for role {role.name}.')\n\n return (default, 'No matching rule found.')\n\n def clearAuthCache(self):\n self.permcache.clear()\n\n async def genGateInfo(self, gateiden):\n info = self.authgates.get(gateiden)\n if info is None:\n gate = self.auth.reqAuthGate(gateiden)\n info = await gate.genUserInfo(self.iden)\n return info\n\n def confirm(self, perm, default=None, gateiden=None):\n if not self.allowed(perm, default=default, gateiden=gateiden):\n self.raisePermDeny(perm, gateiden=gateiden)\n\n def raisePermDeny(self, perm, gateiden=None):\n\n perm = '.'.join(perm)\n if gateiden is None:\n mesg = f'User {self.name!r} ({self.iden}) must have permission {perm}'\n raise s_exc.AuthDeny(mesg=mesg, perm=perm, user=self.iden, username=self.name)\n\n gate = self.auth.reqAuthGate(gateiden)\n mesg = f'User {self.name!r} ({self.iden}) must have permission {perm} on object {gate.iden} ({gate.type}).'\n raise s_exc.AuthDeny(mesg=mesg, perm=perm, user=self.iden, username=self.name)\n\n def getRoles(self):\n for iden in self.info.get('roles', ()):\n role = self.auth.role(iden)\n if role is None:\n logger.warning('user {self.iden} has non-existent role: {iden}')\n continue\n yield role\n\n def hasRole(self, iden):\n return iden in self.info.get('roles', ())\n\n async def grant(self, roleiden, indx=None):\n\n role = await self.auth.reqRole(roleiden)\n\n roles = list(self.info.get('roles'))\n if role.iden in roles:\n return\n\n if indx is None:\n roles.append(role.iden)\n else:\n roles.insert(indx, role.iden)\n\n mesg = {'name': 'role:grant', 'iden': self.iden, 'role': role.pack()}\n await self.auth.setUserInfo(self.iden, 'roles', roles, mesg=mesg)\n\n async def setRoles(self, roleidens):\n '''\n Replace all the roles for a given user with a new list of roles.\n\n Args:\n roleidens (list): A list of roleidens.\n\n Notes:\n The roleiden for the \"all\" role must be present in the new list of roles. This replaces all existing roles\n that the user has with the new roles.\n\n Returns:\n None\n '''\n current_roles = list(self.info.get('roles'))\n\n roleidens = list(roleidens)\n\n if current_roles == roleidens:\n return\n\n if self.auth.allrole.iden not in roleidens:\n mesg = 'Role \"all\" must be in the list of roles set.'\n raise s_exc.BadArg(mesg=mesg)\n\n roles = []\n for iden in roleidens:\n r = await self.auth.reqRole(iden)\n roles.append(r.pack())\n\n mesg = {'name': 'role:set', 'iden': self.iden, 'roles': roles}\n await self.auth.setUserInfo(self.iden, 'roles', roleidens, mesg=mesg)\n\n async def revoke(self, iden, nexs=True):\n\n role = await self.auth.reqRole(iden)\n\n if role.name == 'all':\n mesg = 'Role \"all\" may not be revoked.'\n raise s_exc.BadArg(mesg=mesg)\n\n roles = list(self.info.get('roles'))\n if role.iden not in roles:\n return\n\n roles.remove(role.iden)\n mesg = {'name': 'role:revoke', 'iden': self.iden, 'role': role.pack()}\n if nexs:\n await self.auth.setUserInfo(self.iden, 'roles', roles, mesg=mesg)\n else:\n await self.auth._hndlsetUserInfo(self.iden, 'roles', roles, logged=nexs, mesg=mesg)\n\n def isLocked(self):\n return self.info.get('locked')\n\n def isAdmin(self, gateiden=None):\n\n # being a global admin always wins\n admin = self.info.get('admin', False)\n if admin or gateiden is None:\n return admin\n\n gateinfo = self.authgates.get(gateiden)\n if gateinfo is None:\n return False\n\n return gateinfo.get('admin', False)\n\n def isArchived(self):\n return self.info.get('archived')\n\n async def setAdmin(self, admin, gateiden=None, logged=True):\n if not isinstance(admin, bool):\n raise s_exc.BadArg(mesg='setAdmin requires a boolean')\n if logged:\n await self.auth.setUserInfo(self.iden, 'admin', admin, gateiden=gateiden)\n else:\n await self.auth._hndlsetUserInfo(self.iden, 'admin', admin, gateiden=gateiden, logged=logged)\n\n async def setLocked(self, locked, logged=True):\n if not isinstance(locked, bool):\n raise s_exc.BadArg(mesg='setLocked requires a boolean')\n if logged:\n await self.auth.setUserInfo(self.iden, 'locked', locked)\n else:\n await self.auth._hndlsetUserInfo(self.iden, 'locked', locked, logged=logged)\n\n async def setArchived(self, archived):\n if not isinstance(archived, bool):\n raise s_exc.BadArg(mesg='setArchived requires a boolean')\n await self.auth.setUserInfo(self.iden, 'archived', archived)\n if archived:\n await self.setLocked(True)\n\n async def tryPasswd(self, passwd, nexs=True):\n\n if self.info.get('locked', False):\n return False\n\n if passwd is None:\n return False\n\n onepass = self.info.get('onepass')\n if onepass is not None:\n if isinstance(onepass, dict):\n shadow = onepass.get('shadow')\n expires = onepass.get('expires')\n if expires >= s_common.now():\n if await s_passwd.checkShadowV2(passwd=passwd, shadow=shadow):\n await self.auth.setUserInfo(self.iden, 'onepass', None)\n logger.debug(f'Used one time password for {self.name}',\n extra={'synapse': {'user': self.iden, 'username': self.name}})\n return True\n else:\n # Backwards compatible password handling\n expires, params, hashed = onepass\n if expires >= s_common.now():\n if s_common.guid((params, passwd)) == hashed:\n await self.auth.setUserInfo(self.iden, 'onepass', None)\n logger.debug(f'Used one time password for {self.name}',\n extra={'synapse': {'user': self.iden, 'username': self.name}})\n return True\n\n shadow = self.info.get('passwd')\n if shadow is None:\n return False\n\n if isinstance(shadow, dict):\n return await s_passwd.checkShadowV2(passwd=passwd, shadow=shadow)\n\n # Backwards compatible password handling\n salt, hashed = shadow\n if s_common.guid((salt, passwd)) == hashed:\n logger.debug(f'Migrating password to shadowv2 format for user {self.name}',\n extra={'synapse': {'user': self.iden, 'username': self.name}})\n # Update user to new password hashing scheme.\n await self.setPasswd(passwd=passwd, nexs=nexs)\n\n return True\n\n return False\n\n async def setPasswd(self, passwd, nexs=True):\n # Prevent empty string or non-string values\n if passwd is None:\n shadow = None\n elif passwd and isinstance(passwd, str):\n shadow = await s_passwd.getShadowV2(passwd=passwd)\n else:\n raise s_exc.BadArg(mesg='Password must be a string')\n if nexs:\n await self.auth.setUserInfo(self.iden, 'passwd', shadow)\n else:\n await self.auth._hndlsetUserInfo(self.iden, 'passwd', shadow, logged=nexs)\n","sub_path":"synapse/lib/hiveauth.py","file_name":"hiveauth.py","file_ext":"py","file_size_in_byte":37716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"424878384","text":"from django import forms\n\nclass rule_form(forms.Form):\n CATEGORY_CHOICES = (\n ('Offensive', 'Offensive'),\n ('Defensive', 'Defensive'),\n ('Drinking', 'Drinking'),\n ('Other', 'Other'),\n )\n \n m_name = forms.CharField(label='Rule Name',\n max_length=50,\n widget=forms.TextInput(attrs={'placeholder' : 'Make it epic!'}))\n m_desc = forms.CharField(label='Description',\n max_length=200,\n widget=forms.TextInput(attrs={'placeholder' : 'What kind of crazy stuns you pulling?'}))\n m_loc = forms.CharField(label='Location',\n max_length=100,\n widget=forms.TextInput(attrs={'placeholder' : 'Where art thou?'}))\n m_cat = forms.CharField(label='Category', max_length=9, widget=forms.Select(choices=CATEGORY_CHOICES))\n \nclass vote_form(forms.Form):\n m_voted = forms.BooleanField()\n #m_rating = forms.IntegerField()\n\n \n # Maybe a unique form for each category? ","sub_path":"Assignment_4/Assignment4/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":953,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"650691538","text":"class Invalid(Exception):\n pass\n\n\nurla = \"https://www.lds.org/scriptures/\"\n\nscriptdict = {\n # The Old Testament\n \"Genesis\":\"ot/gen/\",\n \"Exodus\":\"ot/ex/\",\n \"Leviticus\":\"ot/lev/\",\n \"Numbers\":\"ot/num/\",\n \"Deuteronomy\":\"ot/deut/\",\n \"Joshua\":\"ot/josh/\",\n \"Judges\":\"ot/judg/\",\n \"Ruth\":\"ot/ruth/\",\n \"1 Samuel\":\"ot/1-sam/\",\n \"2 Samuel\":\"ot/2-sam/\",\n \"1 Kings\":\"ot/1-kgs/\",\n \"2 Kings\":\"ot/2-kgs/\",\n \"1 Chronicles\":\"ot/1-chr/\",\n \"2 Chronicles\":\"ot/2-chr/\",\n \"Ezra\":\"ot/ezra/\",\n \"Nehemiah\":\"ot/neh/\",\n \"Esther\":\"ot/esth/\",\n \"Job\":\"ot/job/\",\n \"Psalms\":\"ot/ps/\",\n \"Proverbs\":\"ot/prov/\",\n \"Ecclesiastes\":\"ot/eccl/\",\n \"Song of Solomon\":\"ot/song/\",\n \"Isaiah\":\"ot/isa/\",\n \"Jeremiah\":\"ot/jer/\",\n \"Lamentations\":\"ot/lam/\",\n \"Ezekiel\":\"ot/ezek/\",\n \"Daniel\":\"ot/dan\",\n \"Hosea\":\"ot/hosea/\",\n \"Joel\":\"ot/joel/\",\n \"Amos\":\"ot/amos/\",\n \"Obadiah\":\"ot/obad/\",\n \"Jonah\":\"ot/jonah/\",\n \"Micah\":\"ot/micah/\",\n \"Nahum\":\"ot/nahum/\",\n \"Habakkuk\":\"ot/hab/\",\n \"Zephaniah\":\"ot/zeph/\",\n \"Haggai\":\"ot/hag/\",\n \"Zechariah\":\"ot/zech/\",\n \"Malachi\":\"ot/mal\",\n # The New Testament\n \"Matthew\":\"nt/matt/\",\n \"Mark\":\"nt/mark/\",\n \"Luke\":\"nt/luke/\",\n \"John\":\"nt/john/\",\n \"Acts\":\"nt/acts/\",\n \"Romans\":\"nt/rom/\",\n \"1 Corinthians\":\"nt/1-cor/\",\n \"2 Corinthians\":\"nt/2-cor/\",\n \"Galatians\":\"nt/gal/\",\n \"Ephesians\":\"nt/eph/\",\n \"Philippians\":\"nt/philip/\",\n \"Colossians\":\"nt/col/\",\n \"1 Thessalonians\":\"nt/1-thes/\",\n \"2 Thessalonians\":\"nt/2-thes/\",\n \"1 Timothy\":\"nt/1-tim/\",\n \"2 Timothy\":\"nt/2-tim/\",\n \"Titus\":\"nt/titus/\",\n \"Philemon\":\"nt/philem/\",\n \"Hebrews\":\"nt/heb/\",\n \"James\":\"nt/james/\",\n \"1 Peter\":\"nt/1-pet/\",\n \"2 Peter\":\"nt/2-pet/\",\n \"1 John\":\"nt/1-jn/\",\n \"2 John\":\"nt/2-jn/\",\n \"3 John\":\"nt/3-jn/\",\n \"Jude\":\"nt/jude/\",\n \"Revelation\":\"nt/rev/\",\n # The Book of Mormon\n \"1 Nephi\":\"bofm/1-ne/\",\n \"2 Nephi\":\"bofm/2-ne/\",\n \"Jacob\":\"bofm/jacob/\",\n \"Enos\":\"bofm/enos/\",\n \"Jarom\":\"bofm/jarom/\",\n \"Omni\":\"bofm/omni/\",\n \"The Words of Mormon\":\"bofm/w-of-m/\",\n \"Words of Mormon\":\"bofm/w-of-m/\",\n \"Mosiah\":\"bofm/mosiah/\",\n \"Alma\":\"bofm/alma/\",\n \"Helaman\":\"bofm/hel/\",\n \"3 Nephi\": \"bofm/3-ne/\",\n \"3rd Nephi\": \"bofm/3-ne/\",\n \"4 Nephi\": \"bofm/4-ne/\",\n \"Mormon\":\"bofm/morm/\",\n \"Ether\":\"bofm/ether/\",\n \"Moroni\":\"bofm/moro/\",\n # The Doctrine and Covenants\n \"Doctrine and Covenants\": \"dc-testament/dc/\",\n \"D&C\": \"dc-testament/dc/\",\n # The Pearl of Great Price\n \"Moses\":\"pgp/moses/\",\n \"Abraham\":\"pgp/abr/\",\n \"Joseph Smith—Matthew\":\"pgp/js-m/\",\n \"Joseph Smith—History\":\"pgp/js-h/\",\n \"Articles of Faith\":\"pgp/a-of-f/\",\n}\n\n\ndef read_file(rfilename,url_list):\n file = open(rfilename,\"r\")\n for line in file:\n words = line.split(' ')\n try:\n book,rest = get_book(words)\n except Invalid:\n print(\"ERROR: Invalid scripture. Repent or go to Hell.\")\n file.close()\n return\n url_maker(book,rest,url_list)\n file.close()\n\n\ndef write_file(wfilename,url_list):\n file = open(wfilename, \"w\")\n file.writelines(url_list)\n file.close()\n\n\ndef get_book(words):\n book = \"\"\n rest = ' '.join(words).strip()\n for word in words:\n book += word + ' '\n if book.strip() in scriptdict.keys():\n return book.strip(),rest.replace(book.strip(),'').strip()\n raise Invalid\n\n\ndef url_maker(book,rest,url_list):\n url = urla + scriptdict[book]\n if rest.strip() == \"\":\n url_list.append(url + \"1\" + '\\n')\n elif ':' not in rest:\n if '-' not in rest:\n if ',' not in rest:\n url_list.append(url + rest.strip() + '\\n')\n else:\n for i in rest.strip().split(','):\n url_list.append(url + i + '\\n')\n else:\n if ',' not in rest:\n for i in range(int(rest.strip().split('-')[0]),int(rest.strip().split('-')[1])+1):\n url_list.append(url + str(i) + '\\n')\n else:\n for j in rest.strip().split(','):\n if '-' in j:\n for i in range(int(j.strip().split('-')[0]),int(j.strip().split('-')[1])+1):\n url_list.append(url + str(i) + '\\n')\n else:\n url_list.append(url + j.strip() + '\\n')\n else:\n url_list.append(url + rest.split(':')[0].strip() + '.' + rest.split(':')[1].strip() + '\\n')\n\n\ndef main():\n url_list = []\n rfilename = \"REL.txt\"\n wfilename = \"URL.txt\"\n if input(\"Use default filenames? (y/n) \") == 'n':\n rfilename = input(\"Please enter the filename of the file to be opened: \")\n wfilename = input(\"Please enter the filename of the file to be written to: \")\n read_file(rfilename,url_list)\n #print(url_list)\n write_file(wfilename,url_list)\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"GetRelURLS.py","file_name":"GetRelURLS.py","file_ext":"py","file_size_in_byte":4984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"400983583","text":"from django.urls import path\n\nfrom content import views\n\nurlpatterns = [\n # Kurumsal bilgiler(Kurucu,Felsefe,Kurumsal Çözümlemeler)\n path('corporate/index/', views.corporate_view, name='home-corporate'),\n # Yazılım bilgileri(LearnBetter E-Öğrenme,Eudaimonia Health & Fitness,FitSkill Sağlık & Yetenek Karnesi,Fitness Monitor)\n path('learnbetter/index/', views.software_view, name='home-software'),\n\n # About page url\n path('about/index', views.home_about_view, name='home-about-views'),\n # Home Conatct Url\n path('contact/index', views.home_contact, name='home-contact'),\n path('contact/create', views.contactcreate, name='contact-create'),\n # Home Our team Url\n path('team/index', views.home_team, name='home-team')\n]\n","sub_path":"content/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"255064281","text":"# ------------------------------------------------------------\n# Copyright (c) 2017-present, SeetaTech, Co.,Ltd.\n#\n# Licensed under the BSD 2-Clause License.\n# You should have received a copy of the BSD 2-Clause License\n# along with the software. If not, See,\n#\n# \n#\n# Codes are based on:\n#\n# \n#\n# ------------------------------------------------------------\n\"\"\"Basic optimizers.\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\n\nfrom collections import defaultdict\n\nfrom dragon.core import distributed\nfrom dragon.core.framework import workspace\nfrom dragon.vm.torch.core.ops.distributed import functional as distributed_funcs\nfrom dragon.vm.torch.core.ops.training import functional as training_funcs\nfrom dragon.vm.torch.core.tensor import Tensor\n\n# A simple parameter flag.\nrequired = object()\n\n\nclass Optimizer(object):\n \"\"\"The base class of optimizers.\n\n Inherit this class to design a new optimizer:\n\n ```python\n class MyOptimizer(torch.optim.Optimizer):\n def __init__(params, hp1, hp2):\n defaults = dict(hp1=hp1, hp2=hp2)\n super(MyOptimizer, self).__init__(params, defaults)\n ```\n\n \"\"\"\n\n # Store for the global unique handle\n _DEFAULT_UNIQUE_HANDLE_INDEX = 0\n\n def __init__(self, params, defaults):\n \"\"\"Create a ``Optimizer``.\n\n Parameters\n ----------\n params : Sequence[dragon.vm.torch.nn.Parameter]\n The parameters to optimize.\n defaults : dict\n The pre-defined default hyper-parameters.\n\n \"\"\"\n self.defaults = defaults\n if isinstance(params, Tensor):\n raise TypeError(' should be a sequence of tensors.')\n self.state = defaultdict(dict)\n self.param_groups = []\n param_groups = list(params)\n if len(param_groups) == 0:\n raise ValueError('Got an empty parameter list')\n if not isinstance(param_groups[0], dict):\n param_groups = [{'params': param_groups}]\n for param_group in param_groups:\n self.add_param_group(param_group)\n self._op_type = self.__class__.__name__ + 'Update'\n self._process_group = distributed.get_group()\n self._shared_args = {}\n\n def accumulate(self, momentum):\n \"\"\"Accumulate the gradient of params.\n\n Call this method after each ``backward`` pass:\n\n ```python\n x = torch.ones(1, requires_grad=True)\n optimizer = torch.optim.SGD([x], lr=0.1)\n for epoch in range(2):\n for step in range(3):\n y = x + 1\n y.backward()\n # Note to zero the accumulation at the first step\n optimizer.accumulate(momentum=1 if step > 0 else 1)\n optimizer.step()\n print(x) # 0.4\n ```\n\n Parameters\n ----------\n momentum : float, required\n The momentum to the accumulated value.\n\n \"\"\"\n current_ws = workspace.get_workspace()\n for group in self.param_groups:\n group['_internal/grad_accum'] = True\n for param in group['params']:\n grad = self._steal_grad(current_ws, param)\n if grad is not None:\n training_funcs.accumulate_grad(grad)\n\n def add_param_group(self, param_group):\n \"\"\"Add a new param group into the optimizer.\n\n The ``param_group`` should be a dict containing\n the defaults optionally:\n\n ```python\n # A group redefined ``lr`` and ``weight_decay``\n param_group1 = {\n 'params': [],\n 'lr': 0.01,\n 'weight_decay': 0.0001,\n }\n # A group inherits the defaults while using ``multiplier``\n param_group2 = {\n 'params': [],\n 'lr_mult': 1,\n 'decay_mult': 1,\n }\n ```\n\n Parameters\n ----------\n param_group : dict\n The param group to add.\n\n \"\"\"\n if not isinstance(param_group, dict):\n raise TypeError('Param group must be a dict.')\n\n params = param_group['params']\n if isinstance(params, Tensor):\n param_group['params'] = [params]\n elif isinstance(params, (set, dict)):\n raise TypeError('Parameters should be organized in a sequence.')\n else:\n param_group['params'] = list(params)\n\n for param in param_group['params']:\n if not param.requires_grad:\n raise ValueError(\"Optimize a parameter that doesn't require grad.\")\n\n for name, default in self.defaults.items():\n if default is required and name not in param_group:\n raise ValueError(\n \"Parameter group didn't specify a value of \"\n \"required optimization parameter: \" + name)\n else:\n param_group.setdefault(name, default)\n\n if 'name' not in param_group:\n Optimizer._DEFAULT_UNIQUE_HANDLE_INDEX += 1\n param_group['name'] = 'Optimizer_{}'.format(\n Optimizer._DEFAULT_UNIQUE_HANDLE_INDEX)\n\n if '_internal/grad_accum' not in param_group:\n param_group['_internal/grad_accum'] = False\n\n param_set = set()\n for group in self.param_groups:\n param_set.update(set(group['params']))\n\n if not param_set.isdisjoint(set(param_group['params'])):\n raise ValueError('Some parameters appear in more than one parameter group.')\n\n self.param_groups.append(param_group)\n\n def step(self):\n \"\"\"Perform one step update.\n\n Call this method after a ``backward`` pass:\n\n ```python\n x = torch.ones(1, 3, requires_grad=True)\n y = x + 1\n y.backward()\n optimizer.step()\n ```\n\n \"\"\"\n current_ws = workspace.get_workspace()\n for group in self.param_groups:\n self._run_updates(current_ws, group)\n group['_internal/grad_accum'] = False\n\n def zero_grad(self, reset=False):\n \"\"\"Set the gradient of params to zero.\n\n This method is not necessary usually, as we will overwrite\n the gradients in the next computation.\n\n However, if some gradients are not computed every time,\n remember to reset them before ``step(...)``:\n\n ```python\n m1 = torch.nn.Linear(3, 3)\n m2 = torch.nn.Linear(3, 3)\n x = torch.ones(1, 3, requires_grad=True)\n for i in range(10):\n x = m1(x)\n if i in (2, 4, 6):\n x += m2(x)\n optimizer.zero_grad(reset=True)\n x.backward()\n optimizer.step()\n ```\n\n Parameters\n ----------\n reset : bool, optional, default=False\n **True** to reset the memory instead of zeroing.\n\n \"\"\"\n current_ws = workspace.get_workspace()\n for group in self.param_groups:\n for param in group['params']:\n grad = self._steal_grad(current_ws, param)\n if grad is not None:\n current_ws.reset_tensor(grad) if reset else grad.zero_()\n\n def _run_updates(self, ws, group):\n \"\"\"Run updates for the parameter group.\"\"\"\n # Collect params and grads.\n params, grads = [], []\n grad_accum = group['_internal/grad_accum']\n for p in group['params']:\n g = self._steal_grad(ws, p, grad_accum)\n if g is not None:\n params.append(p)\n grads.append(g)\n\n # Reset the shared defaults.\n self._reset_defaults(ws, group)\n\n # Accumulate grads from the current process group.\n if self._process_group is not None:\n distributed_funcs.all_reduce(\n tensor=grads,\n op='MEAN',\n group=self._process_group,\n )\n\n # Apply the specific update.\n for p, g in zip(params, grads):\n training_funcs.update_param(\n p, g,\n op_type=self._op_type,\n op_handle=group['name'],\n lr_mult=group.get('lr_mult', 1),\n decay_mult=group.get('decay_mult', 1),\n )\n\n def _reset_defaults(self, ws, group):\n \"\"\"Reset the defaults to backend.\"\"\"\n template = '/share/hyper/%s/{}' % group['name']\n for name, value in group.items():\n if name in self._shared_args:\n ws.feed_tensor(\n tensor=template.format(self._shared_args[name]),\n value=value,\n dtype='float32',\n enforce_cpu=True,\n )\n\n @staticmethod\n def _steal_grad(ws, param, grad_accum=False):\n \"\"\"Steal the grad from backend.\"\"\"\n impl = ws.GetTensor(param.id + ('_grad[accum]' if grad_accum else '_grad'))\n if impl is not None:\n return Tensor(device=param.device, impl=impl)\n return None\n\n def __repr__(self):\n format_string = self.__class__.__name__ + ' ('\n for i, group in enumerate(self.param_groups):\n format_string += '\\n'\n format_string += 'Parameter Group {0}\\n'.format(i)\n for key in sorted(group.keys()):\n if key != 'params':\n format_string += ' {0}: {1}\\n'.format(key, group[key])\n format_string += ')'\n return format_string\n","sub_path":"torch/core/optim/optimizer.py","file_name":"optimizer.py","file_ext":"py","file_size_in_byte":9556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"194600084","text":"from node import *\nimport argparse\nimport sys\nimport heapq \nimport copy\n\n\ndef make_set(node1):\n node1.parent = node1\n node1.rank = 0\n\ndef union(node1, node2):\n node1Root = find(node1)\n node2Root = find(node2)\n if node1Root == node2Root:\n return False\n if node1Root.rank > node2Root.rank:\n node2Root.parent = node1Root\n elif node1Root.rank < node2Root.rank:\n node1Root.parent = node2Root\n elif node1Root != node2Root: # Unless node1 and node2 are already in same set, merge them\n node2Root.parent = node1Root\n node1Root.rank = node1Root.rank + 1\n return True\n\ndef find(node1):\n if node1.parent == node1:\n return node1\n else:\n node1.parent = find(node1.parent)\n return node1.parent\n\n\ndef get_args():\n parser = argparse.ArgumentParser(description='UDP chat client')\n parser.add_argument(\n '-i', metavar=' ', default='', help='name of the logfile')\n args = parser.parse_args()\n # if len(sys.argv) == 1:\n # parse_args.print_help(sys.stderr)\n # sys.exit(1)\n return args\n\n\ndef readFile(filename):\n maxcost = 1\n nodes = dict()\n f = open(filename)\n num_nodes = int(f.readline())\n for i in range(num_nodes):\n line = f.readline().strip('\\n')\n data = line.split(':')\n n_name, edges = data[0].strip(), data[1][1:]\n edges = edges.split(', ')\n if n_name not in nodes.keys():\n nodes[n_name] = Node(n_name)\n for i in range(len(edges)):\n e_data = edges[i].strip().strip('[]').split(',')\n o_name, weight = e_data[0], int(e_data[1])\n if o_name not in nodes.keys():\n nodes[o_name] = Node(o_name)\n maxcost += weight\n edge1 = Edge(nodes[n_name], nodes[o_name], weight)\n nodes[n_name].edges.append(edge1)\n\n sn = f.readline().strip()\n dn = f.readline().strip() \n return nodes, sn, dn, maxcost \n\n\ndef print_output(result, filename):\n pass\n \n\ndef run_mst(graph, sn, dn, maxcost):\n # Run Algo\n #graph is adjacency list structure\n costs = []\n mst = []\n visited = set()\n for key in graph.keys():\n # print(key, graph[key].edges)\n for edge in graph[key].edges:\n costs.append(edge)\n #create union_find\n make_set(graph[key])\n costs.sort()\n for edge in costs:\n if union(edge.in_node, edge.out_node) == True:\n mst.append(str(edge.weight) + ' ' + str(edge.in_node.name) + ' ' + str(edge.out_node.name))\n if len(mst) == len(graph.keys())-1:\n result = '\\n'.join(mst)\n f = open('output_mst.txt', 'w')\n f.write(result)\n print(result)\n return result\n\n\ndef start_mst():\n args = get_args()\n filename = args.i\n graph, sn, dn, maxcost = readFile(filename)\n result = run_mst(graph, sn, dn, maxcost)\n print_output(result, 'output_mst.txt')\n\n\ndef main():\n start_mst()\n\nif __name__ == '__main__':\n main()\n","sub_path":"pa3/mst.py","file_name":"mst.py","file_ext":"py","file_size_in_byte":3028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"584210835","text":"#!/usr/bin/env python\n\n__author__ = \"Elisa Londero\"\n__email__ = \"elisa.londero@inaf.it\"\n__date__ = \"June 2018\"\n\nimport os\nfrom utilities import VerifyLinux\nfrom preprocessing import SelectEventString \nfrom preprocessing import EventPreprocessing\nfrom utilities import LoggingClass\n\nlog = LoggingClass('',True).get_logger()\npwd = os.getcwd()\n\ndef main():\n\n try:\n VerifyLinux()\n\n selected = SelectEventString().get_selected_events_list()\n\n for i in selected:\n ep = EventPreprocessing(i) \n ep.copy_to_preprocessing_area()\n ep.process_event()\n ep.remove_event()\n os.chdir(pwd)\n\n except Exception as e:\n msg = \"Main exception - main() -- \"\n log.error(\"{0}{1}\".format(msg,e)) \n\nif __name__ == \"__main__\":\n main()\n","sub_path":"events_preprocessor/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":805,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"90513516","text":"import pandas as pd\n\n# read csv\ndf = pd.read_csv('/home/ryan/workspace/work/mustard/data/simple.csv')\n# check na\ndf.isnull().sum()\n\n# remove entries with na\ndf = df.dropna()\n# check na once more\ndf.isnull().sum()\n\n# splitting the data set\nnrow = len(df)\nntrain = int(nrow*0.8)\nntest = nrow - ntrain\n\ndf_train = df.head(ntrain)\ndf_test = df.tail(ntest)\n\noutcomes_df = df_test[['MatchId', 'Home_Corners', 'Away_Corners']]\noutcomes_df = outcomes_df.assign(Total_Corners=outcomes_df['Home_Corners']+outcomes_df['Away_Corners'])\n\ntot_corners, match_id = list(outcomes_df.Total_Corners), list(outcomes_df.MatchId)\n\noutcomes_dict = dict(zip(match_id, tot_corners))\n","sub_path":"scripts/read_and_clean.py","file_name":"read_and_clean.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"372377629","text":"# !/usr/bin/env python\r\n# -- coding: utf-8 --\r\n# @Time : 2020/12/31 18:04\r\n# @Author : liumin\r\n# @File : gfl_head.py\r\n\r\nimport torch\r\nimport torch.nn as nn\r\nimport numpy as np\r\n\r\nfrom ..layers.integral import Integral\r\n\r\n\"\"\"\r\n Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection.\r\n https://arxiv.org/abs/2006.04388\r\n\"\"\"\r\n\r\nclass GFLHead(nn.Module):\r\n \"\"\"Generalized Focal Loss: Learning Qualified and Distributed Bounding\r\n Boxes for Dense Object Detection.\r\n\r\n GFL head structure is similar with ATSS, however GFL uses\r\n 1) joint representation for classification and localization quality, and\r\n 2) flexible General distribution for bounding box locations,\r\n which are supervised by\r\n Quality Focal Loss (QFL) and Distribution Focal Loss (DFL), respectively\r\n\r\n https://arxiv.org/abs/2006.04388\r\n\r\n Args:\r\n num_classes (int): Number of categories excluding the background\r\n category.\r\n in_channels (int): Number of channels in the input feature map.\r\n stacked_convs (int): Number of conv layers in cls and reg tower.\r\n Default: 4.\r\n conv_cfg (dict): dictionary to construct and config conv layer.\r\n Default: None.\r\n norm_cfg (dict): dictionary to construct and config norm layer.\r\n Default: dict(type='GN', num_groups=32, requires_grad=True).\r\n loss_qfl (dict): Config of Quality Focal Loss (QFL).\r\n reg_max (int): Max value of integral set :math: `{0, ..., reg_max}`\r\n in QFL setting. Default: 16.\r\n Example:\r\n >>> self = GFLHead(11, 7)\r\n >>> feats = [torch.rand(1, 7, s, s) for s in [4, 8, 16, 32, 64]]\r\n >>> cls_quality_score, bbox_pred = self.forward(feats)\r\n >>> assert len(cls_quality_score) == len(self.scales)\r\n \"\"\"\r\n def __init__(self,\r\n num_classes,\r\n loss,\r\n input_channel,\r\n stacked_convs=4,\r\n octave_base_scale=4,\r\n scales_per_octave=1,\r\n conv_cfg=None,\r\n norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),\r\n reg_max=16,\r\n **kwargs):\r\n self.stacked_convs = stacked_convs\r\n self.octave_base_scale = octave_base_scale\r\n self.scales_per_octave = scales_per_octave\r\n self.loss_cfg = loss\r\n self.conv_cfg = conv_cfg\r\n self.norm_cfg = norm_cfg\r\n self.reg_max = reg_max\r\n use_sigmoid = True\r\n octave_scales = np.array([2 ** (i / scales_per_octave) for i in range(scales_per_octave)])\r\n anchor_scales = octave_scales * octave_base_scale\r\n super(GFLHead, self).__init__(num_classes, loss, use_sigmoid, input_channel, anchor_scales=anchor_scales, **kwargs)\r\n\r\n self.distribution_project = Integral(self.reg_max)\r\n\r\n # self.loss_qfl = QualityFocalLoss(use_sigmoid=True, beta=2.0, loss_weight=1.0)\r\n # self.loss_dfl = DistributionFocalLoss(loss_weight=0.25)\r\n # self.loss_bbox = GIoULoss(loss_weight=2.0)\r\n self.init_weights()","sub_path":"src/models/heads/gfl_head.py","file_name":"gfl_head.py","file_ext":"py","file_size_in_byte":3237,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"133680311","text":"import requests\nimport logging\nimport datetime\nimport time\nimport json\nimport re\n\nfrom lib.config import settings\n\nlogger = logging.getLogger('public_api')\n\nclass ApiRequest:\n methods = (\"get\", \"put\", \"post\", \"delete\")\n payload = {}\n headers = {}\n reqType = \"\"\n url = \"\"\n\n def _clear_attributes(self):\n for attribute in list(vars(self)):\n delattr(self, attribute)\n\n\n def _dump_request(fn):\n def magic(self, url, reqType):\n req = fn(self, url, reqType)\n\n output = \\\n f\"{self.reqType.upper()}-request will be send to {self.url}\"\n\n headers = json.dumps(self.headers)\n if len(self.headers) == 0:\n output += f\" without any headers\"\n else:\n output += f\" with headers {headers}\"\n\n payload = json.dumps(self.payload)\n if len(self.payload) != 0:\n output += f\" with payload {payload}\"\n\n logger.info(output)\n return req\n return magic\n\n def _dump_response(fn):\n def magic(self):\n response = fn(self)\n logger.info(\\\n f'Response {response.text} with headers {response.headers}')\n return response\n return magic\n\n def set_data(self, payload):\n self.payload = payload\n\n @_dump_request\n def prepare_request(self, url, reqType):\n self.reqType = reqType\n self.url = url\n\n reqType = reqType.lower()\n\n if reqType not in self.methods:\n logger.error(f\"Unknown request type {reqType}\")\n print (\"[ERROR] Unknown request type\")\n raise Exception(f\"Unknown request type {reqType}\")\n\n\n @_dump_response\n def send(self):\n attr = self.reqType.lower()\n method = getattr(requests, attr)\n r = method(self.url, headers=self.headers, json=self.payload)\n\n self._clear_attributes()\n\n return r\n\n def add_headers(self, headers):\n self.headers = {**self.headers, **headers}\n\n def build(self, request_type, request_path, **kwargs):\n uri = url(request_path)\n\n self.prepare_request(uri, request_type)\n\n r = self.send()\n\n return r\n\ndef url(request_path):\n return settings.API_URL + request_path\n\ndef list_to_set(arr):\n dset = set()\n\n for elem in arr:\n dset.add(json.dumps(elem, sort_keys=True))\n\n return dset\n\ndef request_path(request_path, **kwargs):\n return request_path.format(**kwargs)\n\ndef current_date(days=0):\n ts = int(time.time()) + days * 24 * 60 * 60\n res = datetime.datetime.fromtimestamp(ts).strftime('%Y-%m-%d')\n\n return res\n","sub_path":"tests/helpers/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2667,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"459036065","text":"import os #To find out what files we are using \nimport numpy as np\nimport cv2\n\nfilename = 'video.mp4'\nframes_per_seconds = 30;\nmy_res = '720p'\n\n\ndef change_res(cap,width, height):\n cap.set(3, width)\n cap.set(4,height)\n\nSTD_DIMENSIONS = {\n \"480p\": (680, 480),\n \"720p\": (1280, 720),\n \"1080p\":(1920, 1080),\n \"4k\" : (4120, 2160),\n}\n\ndef get_dims(cap, res='1080p'):\n width,height = STD_DIMENSIONS['480p']\n if res in STD_DIMENSIONS:\n width, height = STD_DIMENSIONS[res]\n change_res(cap, width, height)\n return width, height\n\n\nVIDEO_TYPE = {\n 'avi' : cv2.VideoWriter_fourcc(*'MPEG'),\n 'mp4' : cv2.VideoWriter_fourcc(*'MPEG'),\n}\n\ndef get_video_type(filename):\n filename, ext = os.path.splitext(filename)\n if ext in VIDEO_TYPE:\n return VIDEO_TYPE[ext]\n return VIDEO_TYPE['avi']\n\n\n\ncap = cv2.VideoCapture(0)\ndims = get_dims(cap, res=my_res)\nvideo_type_cv2 = get_video_type(filename)\n\nout = cv2.VideoWriter(filename, video_type_cv2, frames_per_seconds, dims) #width, height\n\nwhile True:\n \n #Provides frames \n ret, frame = cap.read()\n out.write(frame)\n cv2.imshow('Frame1', frame) #Displays the feed\n\n if cv2.waitKey(2) & 0xFF == ord('q'):\n break\n\n#Release the feed \ncap.release()\nout.release()\ncv2.destroyAllWindows()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"563021979","text":"# -*- encoding:utf-8 -*-\n\"\"\"\n 突破均线后平仓因子\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nimport numpy as np\nfrom .ABuFactorSellFuturesBase import AbuFactorSellFuturesBase, ESupportDirection\n\n\nclass AbuFactorSellBreakHighClosePut(AbuFactorSellFuturesBase):\n \"\"\"到期平仓卖出因子\"\"\"\n\n def _init_self(self, **kwargs):\n # 卖出仓位的百分比\n\n self.xd = kwargs['xd']\n self.sell_pct = kwargs['sell_pct']\n # 在输出生成的orders_pd中显示的名字\n self.sell_type_extra = '{}:{}'.format(self.__class__.__name__, self.xd)\n\n self.freq = kwargs['freq'].upper()\n self.freq_kl_pd, self.kline_num = self.get_freq_kl_pd(self.kl_pd, self.freq, self.kl_pd_dict)\n\n self.low_ndarray = self.freq_kl_pd.low.values\n self.high_ndarray = self.freq_kl_pd.high.values\n\n self.today_time_str = self.get_today_time_str(self.freq)\n\n self.freq_kl_pd_time_str = self.get_freq_kl_pd_time_str(self.freq)\n self.time_ndarray = self.freq_kl_pd[self.freq_kl_pd_time_str].values\n\n def read_fit_day(self, today, undone_orders, holding_object, buy_factor):\n\n # 今天这个交易日在整个金融时间序列的序号\n self.today_ind = int(today.key)\n\n done_orders = list()\n\n if buy_factor.holding_cnt == 0:\n return done_orders, undone_orders, None\n\n # orders = list(filter(lambda order: order.expect_direction in self.support_direction(), orders))\n return self.fit_day(today, undone_orders, holding_object, buy_factor)\n\n def support_direction(self):\n \"\"\"支持的方向，只支持反向\"\"\"\n return [ESupportDirection.DIRECTION_PUT.value]\n\n def fit_day(self, today, orders, holding_object, buy_factor):\n \"\"\"\n 寻找向下突破作为策略卖出驱动event\n :param today: 当前驱动的交易日金融时间序列数据\n :param holding_object: 卖出交易发生时的持仓量\n :param orders: 买入择时策略中生成的订单序列\n :param buy_factor: 实例化的买入因子\n \"\"\"\n\n today_ind = np.where(self.time_ndarray == today[self.today_time_str])[0][0]\n\n if today_ind < self.xd:\n return [], orders, None\n\n low = self.low_ndarray[today_ind - self.xd: today_ind].min()\n high = self.high_ndarray[today_ind - self.xd: today_ind].max()\n\n condition = today.close > (high + low) / 2\n\n if condition:\n for option in buy_factor.option_list:\n\n option.profit = (np.maximum((today.close - option.k) * option.cp, 0) - option.initial_value) * \\\n option.num\n option.win = True if option.profit > 0 else False\n\n option_cleared_list = buy_factor.option_list\n\n buy_factor.option_list = list()\n\n self.sell_cnt = buy_factor.holding_cnt * self.sell_pct\n\n done_orders, undone_orders = self.sell_tomorrow(orders, holding_object, buy_factor)\n\n return done_orders, undone_orders, option_cleared_list\n\n else:\n return [], orders, None\n\n","sub_path":"abupy/FactorSellFuturesBu/ABuFactorSellBreakHighClosePut.py","file_name":"ABuFactorSellBreakHighClosePut.py","file_ext":"py","file_size_in_byte":3202,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"595375878","text":"# -*- coding: utf-8 -*-\n\n# pylint: disable=E0602\ntry:\n input = raw_input\nexcept:\n pass\n# pylint: enable=E0602\n\nfrom .config import config\n\n\ndef warn(overwrite=False):\n print('CONFIG FILE %s ERROR, NOT APP NAME OR SECRET' % config.filename)\n print('CONFIG FILE Created at %s' % config.filename)\n print('Please make sure follow config is filled:')\n print('- app_key: your dropbox app key')\n print('- app_secret: your dropbox app_secret')\n print('- path_to_watch: your path to watch')\n if overwrite:\n app_key = input('Enter the app_key here: ').strip()\n app_secret = input('Enter the app_secret here: ').strip()\n path_to_watch = input('Enter the watch full path here '\n '(like /home/guojing/somepath):')\n if not app_key:\n print('app key should not be empty')\n return\n if not app_secret:\n print('app secret should not be empty')\n return\n if not path_to_watch:\n print('path to watch should not be empty')\n return\n print('Write app_key %s, app_secret %s, path_to_watch %s '\n 'to config.' % (app_key, app_secret, path_to_watch))\n config.create(app_key=app_key,\n app_secret=app_secret,\n path_to_watch=path_to_watch,\n overwrite=overwrite)\n return\n\n\ndef auth():\n if not config.is_useable():\n warn()\n return\n from dropbox import client\n flow = client.DropboxOAuth2FlowNoRedirect(config.app_key,\n config.app_secret)\n authorize_url = flow.start()\n\n print('1. Go to: ' + authorize_url)\n print('2. Click \"Allow\" (you might have to log in first)')\n print('3. Copy the authorization code.')\n\n code = input('Enter the authorization code here: ').strip()\n\n access_token, user_id = flow.finish(code)\n\n config.token = (access_token, user_id)\n\nif __name__ == '__main__':\n auth()\n","sub_path":"d2pi/auth.py","file_name":"auth.py","file_ext":"py","file_size_in_byte":2015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"457910735","text":"# import data\r\nimport pandas as pd\r\ndf = pd.read_csv('tianchi_fresh_comp_train_user.csv')\r\n#print(df.head())\r\n#print(df.shape)\r\n\r\nprint('-'*118)\r\n# CVR calculation\r\nprint(df['behavior_type'].value_counts())\r\ncount_all,count_4 = 0,0\r\ncount_user = df['behavior_type'].value_counts()\r\ncount_all = count_user[1] + count_user[2] + count_user[3] + count_user[4]\r\ncount_4 += count_user[4]\r\ncvr = count_4 / count_all\r\nprint('CVR: {}%'.format(cvr*100))\r\n\r\nprint('-'*118)\r\n# Data exploring1: time \r\ndf['time'] = pd.to_datetime(df['time']) # 数据列对应的类型是“object”，这样没法对时间数据处理，可以用过pd.to_datetime将该列数据转换为时间类型，即datetime\r\ndf.index = df['time'] # df.index = df'time' -> time列保留 / df.set_index('time') -> time列删除\r\nprint(df.head())\r\n\r\nfrom collections import defaultdict\r\nfrom datetime import datetime,timedelta\r\n\r\n\"\"\"\r\nThe datetime classes in Python are categorized into main 5 classes:\r\ndate – Manipulate just date ( Month, day, year)\r\ntime – Time independent of the day (Hour, minute, second, microsecond)\r\ndatetime – Combination of time and date (Month, day, year, hour, second, microsecond)\r\ntimedelta— A duration of time used for manipulating dates\r\ntzinfo— An abstract class for dealing with time zones\r\ndatetime加减：对日期和时间进行加减实际上就是把datetime往后或往前计算，得到新的datetime。加减可以用+和-运算符，不过需要导入timedalta这个类\r\n注意timedelta 中只支持 days、seconds、microseconds\r\nhttps://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.to_datetime.html\r\n\"\"\"\r\n\r\ndef show_count_day(df):\r\n count_day = defaultdict(int) #2014-11-18 to 2014-12-18\r\n str1 = '2014-11-17' \r\n\r\n temp_date = datetime.strptime(str1,'%Y-%m-%d') # str转换为datetime：用户输入的日期和时间是字符串，要处理日期和时间，首先必须把str转换为datetime。转换方法是通过datetime.strptime()实现，需要一个日期和时间的格式化字符串。https://www.cnblogs.com/pingqiang/p/7812137.html\r\n delta = timedelta(days=1)\r\n for i in range(31):\r\n temp_date = temp_date + delta #时间转化字符串类型\r\n temp_str= temp_date.strftime('%Y-%m-%d') \r\n count_day[temp_str] += df[df['time'] == temp_str].shape[0]\r\n print(temp_date)\r\n print(count_day)\r\n\r\n import matplotlib.pyplot as plt\r\n df_count_day = pd.DataFrame.from_dict(count_day, orient = 'index', columns = ['count'])\r\n df_count_day['count'].plot.bar(color ='black') # https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.bar.html\r\n plt.grid(True)\r\n plt.show()\r\n\r\nshow_count_day(df)\r\n\r\n","sub_path":"移动推荐系统可视化数据探索 V01.py","file_name":"移动推荐系统可视化数据探索 V01.py","file_ext":"py","file_size_in_byte":2691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"625183760","text":"from __future__ import division\nimport os\nimport subprocess\nimport time\nimport shutil\nimport logging\nimport random as rand\nfrom contextlib import contextmanager\nfrom collections import namedtuple\n\nfrom Bio import AlignIO\nfrom Bio import pairwise2\nfrom Bio import SeqIO\nfrom Bio.Seq import Seq\nfrom Bio.SeqRecord import SeqRecord\nfrom Bio.PDB.PDBParser import PDBParser\nfrom Bio.Emboss.Applications import NeedleCommandline\n\nfrom scipy.sparse import csr_matrix as sparsify\nfrom Bio.Alphabet import IUPAC\n\nimport numpy as np\nimport pandas\n\n\nimport sca.exceptions as exception\nfrom sca.utils import init_logger\n\nlog = logging.getLogger(__name__)\ninit_logger(log)\n\n__all__ = [\n 'Alignment',\n 'FastaHeader',\n 'ProteinSequence'\n]\n\n\n# TODO: figure out what min and max SID are\ngap_parameters = namedtuple('GapParameters', ['pos_max_frac', 'seq_max_frac', 'min_sid', 'max_sid'])\nalignment_entry = namedtuple('AlignmentEntry', ['header', 'sequence', 'index'])\ncolumn_descriptor = namedtuple('ColumnDescriptor', ['index', 'gap_total', 'residue_total'])\n\n\nALPHABET = IUPAC.protein\n\n\nclass FastaHeader:\n \"\"\"\n A representational wrapper for the Fasta file header, exposing slots for the string\n and a cleaner repr string for output to the user. Tracks the index from the file that it\n is read from, which is stored on the Alignment object.\n \"\"\"\n __slots__ = (\n '_full',\n '_short',\n 'index'\n )\n\n def __init__(self, header, index):\n \"\"\"\n Requires that the program passes in a header string and a file index.\n :param header: String header separated by | symbols\n :param index: File index of the header, disregarding lines for multi-line sequences\n \"\"\"\n self._full = header\n self._short = header.split('|')[0]\n self.index = index\n\n def __repr__(self):\n return f''\n\n\nclass ProteinSequence:\n \"\"\"\n Basic interface for sequences of amino acids\n \"\"\"\n\n __slots__ = (\n 'gap_char',\n 'alphabet',\n 'n_gaps',\n 'n_residues',\n 'is_alphabetic',\n '_sequence'\n )\n\n def __init__(self, sequence, gap_char='-'):\n \"\"\"\n Initializing the sequence object takes a raw string and runs the super().__init__()\n to create a Series on the string. It checks the string sequence against the passed\n alphabet's `letters` property. This can be anything, but it defaults to the Bio.Alphabet\n IUPAC.protein set. This is used to set the alphabet_matches attribute.\n \"\"\"\n if not isinstance(sequence, (str, np.ndarray)):\n raise exception.SequenceValueError('Sequence must be of type str or np.ndarray')\n if isinstance(sequence, str):\n self._sequence = np.array([char for char in sequence], dtype=np.dtype('U1'))\n elif isinstance(sequence, np.ndarray):\n self._sequence = sequence\n self.gap_char = gap_char\n self.alphabet = ALPHABET\n\n self.is_alphabetic = self._check_alpha(self.alphabet.letters)\n self.n_gaps = self.count_gaps()\n self.n_residues = len(self) - self.n_gaps\n\n def __getitem__(self, item):\n return self._sequence[item]\n\n def __iter__(self):\n for char in self._sequence:\n yield char\n\n def __eq__(self, other):\n determination = False\n if isinstance(other, ProteinSequence):\n determination = all([c1 == c2 for c1, c2 in zip(self._sequence, other._sequence)])\n elif isinstance(other, str):\n determination = ''.join(self._sequence) == other\n return determination\n\n def __len__(self):\n return len(self._sequence)\n\n def __repr__(self):\n return f''\n\n @property\n def sequence(self):\n return self._sequence\n\n @property\n def gap_fraction(self):\n return self.n_gaps / len(self)\n\n def _check_alpha(self, letters):\n for char in self:\n if char not in letters and char != self.gap_char:\n return False\n return True\n\n def count_gaps(self):\n count = 0\n for char in self.sequence:\n if char == self.gap_char:\n count += 1\n return count\n\n def delete_position(self, i):\n return ProteinSequence(np.delete(self.sequence, i), gap_char=self.gap_char)\n\n\nclass Alignment:\n \"\"\"After much fuss and ado, this will be a much more interesting object than originally imagined.\n Strictly, a wrapper, but more a metadata storage and access site for the fasta alignment file.\n \"\"\"\n def __init__(self, alignment, entries=None, gap_char='-', _sequence_matrix=None):\n \"\"\"\n I think that I'll need to expose 3 different methods of initalization:\n\n 1. By file\n 2. By entries\n\n Generates an Alignment instance using a file path or a list of row entries, the row entries list should be a\n list of AlignmentEntry objects. If rows is passed in, then so must an instance of alignment\n so that the _from can be copied over. That mechanic is not really meant to be used outside of methods like\n filter.\n :param alignment:\n :param rows:\n \"\"\"\n self.gap_char = gap_char\n if entries is None:\n # this greatly simplifies the tests\n with open(alignment, 'r') as file:\n file_lines = file.readlines()\n headers, sequences = self._parse(file_lines, self.gap_char)\n\n self._entries = [alignment_entry(header=h,\n sequence=s,\n index=h.index)\n for h, s in zip(headers, sequences)]\n else:\n self._entries = entries\n\n if _sequence_matrix is not None:\n self.sequence_matrix = _sequence_matrix\n else:\n self.sequence_matrix = np.array([entry.sequence for entry in self])\n\n if isinstance(alignment, str):\n _, self._from = os.path.split(alignment)\n else:\n self._from = alignment._from\n\n @staticmethod\n def _parse(file_lines, gap_char):\n \"\"\"\n Reads the file and generates the relevant metadata regarding it.\n :param path_to_alignment:\n :return:\n \"\"\"\n headers = list()\n sequences = list()\n is_first = True\n header_index = 0\n collect_sequence = list()\n for line in file_lines:\n if line[0] == '>':\n if not is_first:\n sequences.append(ProteinSequence(''.join(collect_sequence), gap_char=gap_char))\n collect_sequence = list()\n headers.append(FastaHeader(line[1:].strip(), header_index))\n header_index += 1\n else:\n collect_sequence.append(line.strip())\n is_first = False\n sequences.append(ProteinSequence(''.join(collect_sequence), gap_char=gap_char))\n if len(headers) == 0:\n raise exception.InputAlignmentError(f'Unable to identify headers, ensure the file is fasta format')\n return headers, sequences\n\n def __repr__(self):\n seq, pos = self.shape\n return f'Alignment from file: {self._from}\\n' \\\n f'Shape: n_sequences={seq}, n_positions={pos}'\n\n def __iter__(self):\n \"\"\"\n Iterating over an alignment defaults to returning the sequences, which I think is reasonable.\n :return:\n \"\"\"\n for entry in self._entries:\n yield entry\n\n def __eq__(self, other):\n \"\"\"\n Determine the equality between two Alignment instances using the sequences directly\n \"\"\"\n if not isinstance(other, Alignment):\n raise ValueError(f'Equality not supported between {self.__class__} and {other.__class__}')\n for mine, others in zip(self, other):\n if not mine.sequence == others.sequence:\n return False\n return True\n\n def __getitem__(self, item):\n return self._entries[item]\n\n @property\n def n_records(self):\n \"\"\"\n The total number of records in the alignment\n \"\"\"\n return len(self._entries)\n\n @property\n def n_positions(self):\n return len(self._entries[0].sequence)\n\n @property\n def shape(self):\n \"\"\"\n The shape of the Alignment as though it were a 2D matrix MxL where M is number of sequences\n and L is number of positions per sequence\n \"\"\"\n return self.n_records, self.n_positions\n\n @property\n def is_alphabetic(self):\n \"\"\"\n Checks the sequence against its alphabet and returns True if all characters are letters\n and false otherwise.\n :param sequence:\n :param gap_char:\n :return:\n \"\"\"\n for entry in self:\n if not entry.sequence.is_alphabetic:\n return False\n return True\n\n def row(self, index):\n return self.sequence_matrix[index]\n\n def column(self, index):\n return self.sequence_matrix.T[index]\n\n def row_filter(self, fn):\n \"\"\"\n Returns a new alignment object based on the outcome of on each row of the instance,\n calling fn(entry) should return a boolean\n :function: A function taking an AlignmentEntry as a paramater and returning a boolean\n :return: self\n \"\"\"\n new_entries = list()\n remove_indices = list()\n for i, entry in enumerate(self):\n if fn(entry.sequence):\n new_entries.append(entry)\n else:\n remove_indices.append(i)\n new_mat = np.delete(self.sequence_matrix, remove_indices, 0)\n return Alignment(self, entries=new_entries, _sequence_matrix=new_mat)\n\n def column_filter(self, fn):\n \"\"\"\n Return a new alignment object based on the outcome of on each column of the instance,\n calling fn(column) should return a boolean\n :param function:\n :return:\n \"\"\"\n remove_positions = list()\n for position, column in enumerate(self.sequence_matrix.T):\n if not fn(ProteinSequence(column, gap_char=self.gap_char)):\n remove_positions.append(position)\n return self._remove_columns(remove_positions)\n\n def _remove_columns(self, indices):\n removed_positions = np.delete(self.sequence_matrix, indices, 1)\n new_entries = list()\n for entry, filtered_seq in zip(self, removed_positions):\n new_seq = ProteinSequence(filtered_seq, gap_char=entry.sequence.gap_char)\n new_entry = alignment_entry(header=entry.header, sequence=new_seq, index=entry.index)\n new_entries.append(new_entry)\n return Alignment(self, entries=new_entries, _sequence_matrix=removed_positions)\n\n @contextmanager\n def tempdir(self):\n \"\"\"\n Context manager for working with a temporary directory in the current working context.\n Directory is removed on __exit__\n \"\"\"\n if not os.path.exists('tmp/'):\n os.makedirs('tmp/')\n yield\n shutil.rmtree('tmp')\n\n # public\n def search(self, program=None):\n \"\"\"Identify the sequence in the alignment that most closely corresponds to the species of the reference sequence,\n and return its index.\n\n :param program: The fully qualified path to an executable multiple sequence alignment program.\n If not specified, then ggsearch is attempted assuming that the PATH of the shell\n execution environment is correctly configured.\n :return: Reference sequence index\n :raises: sca.exceptions.InputAlignmentError, sca.exceptions.ProgramConfigurationError\n \"\"\"\n run_failed = False\n with self.tempdir():\n try:\n return self._needle_search(program)\n except Exception as e:\n log.error(f'Needle search failed with: {e}')\n log.info('Continuing execution of possible programs')\n with self.tempdir():\n try:\n return self._ggsearch()\n except Exception as e:\n log.error(f'ggsearch failed with: {e}')\n log.info('ontinuing execution of possible programs')\n run_failed = True\n if run_failed:\n raise exception.SCAError('Unable to complete search for reference sequence. '\n 'Please ensure you have correctly installed a suitable program.')\n\n def _needle_search(self, needle_path):\n with open('tmp/PDB_seq.fasta', 'w') as output_handle:\n SeqIO.write(self._bio_alignment[self.reference_index], output_handle, \"fasta\")\n\n log.debug('second block')\n with open(\"tmp/algn_seq.fasta\", \"w\") as output_handle:\n AlignIO.write(self._bio_alignment, output_handle, \"fasta\")\n\n log.debug('begin cline')\n needle_cline = NeedleCommandline(os.path.join(needle_path, \"needle\"), asequence=\"tmp/PDB_seq.fasta\",\n bsequence=\"tmp/algn_seq.fasta\", gapopen=10, gapextend=0.5,\n outfile=\"tmp/needle.txt\")\n # creates a file with output, no need to capture\n needle_cline()\n # TODO: helper function? or a Search object?\n with open('tmp/needle.txt', 'r') as needle_res:\n res_lines = needle_res.readlines()\n score = list()\n for line in res_lines:\n if line.find('Identity: ') > 0:\n # ok what?\n score.append(int(line.split()[2].split('/')[0]))\n i_0 = score.index(max(score))\n if self.species is not None:\n strseqnum = key_list[i_0]\n else:\n strseqnum = i_0\n return strseqnum\n\n def _ggsearch(self):\n with open('tmp/PDB_seq.fasta', 'w') as output_handle:\n SeqIO.write(self._bio_alignment[self.reference_index], output_handle, \"fasta\")\n\n with open(\"tmp/algn_seq.fasta\", \"w\") as output_handle:\n AlignIO.write(self._bio_alignment, output_handle, \"fasta\")\n\n args = ['ggsearch36', '-M 1- ' + str(len(self.sequences[0])), '-b', '1', '-m 8', 'tmp/PDB_seq.fasta',\n 'tmp/algn_seq.fasta']\n try:\n output = subprocess.check_output(args)\n except Exception as err:\n # log.debug('Failed to run ggsearch')\n # log.debug(err)\n raise err\n i_0 = [i for i in range(len(self.headers)) if output.split('\\t')[1] in self.headers[i]]\n if self.species is not None:\n strseqnum = key_list[i_0[0]]\n else:\n strseqnum = i_0[0]\n return strseqnum\n\n def reference_from_pdb(self, pdbs_path):\n \"\"\"Extract sequence, position labels and matrix of distances from a PDB file.\n\n Arguments:\n Path to an input file in standard protein data bank format\n\n :Example:\n >>> sequence, labels, dist = pdbSeq('/Inputs')\n :return:\n \"\"\"\n if not os.path.isdir(pdbs_path):\n raise Exception\n log.info('extracting sequence information from PDB')\n\n # Table of 3-letter to 1-letter code for amino acids\n aatable = {'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D', 'CYS': 'C', 'GLN': 'Q',\n 'GLU': 'E', 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', 'LEU': 'L', 'LYS': 'K', 'MET': 'M',\n 'PHE': 'F', 'PRO': 'P', 'SER': 'S', 'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V'}\n # Read PDB structure:\n structure = PDBParser().get_structure(pdbid, path2pdb + pdbid + '.pdb')\n # Fill up sequence and label information\n sequence = ''\n labels = list()\n residues = [res for res in structure[0][chain] if res.get_id()[0] == ' ']\n for res in residues:\n labels.append(str(res.get_id()[1]) + str(res.get_id()[2]).strip())\n try:\n sequence += aatable[res.get_resname()]\n except:\n sequence += 'X'\n # Distances between residues (minimal distance between atoms, in angstrom):\n dist = np.zeros((len(residues), len(residues)))\n if calcDist == 1:\n for n0, res0 in enumerate(residues):\n for n1, res1 in enumerate(residues):\n dist[n0, n1] = min([atom0 - atom1 for atom0 in res0 for atom1 in res1])\n return sequence, labels, dist\n else:\n return sequence, labels\n\n\ndef pdbSeq(pdbid, chain='A', path2pdb=None, calcDist=1):\n ''' Extract sequence, position labels and matrix of distances from a PDB file.\n\n **Arguments:**\n - `pdbid` = PDB identifier (four letters/numbers)\n - `chain` = PDB chain identifier\n - `path2pdb` = location of the PDB file\n - `calcDist` = calculate a distance matrix between all pairs of positions, default is 1\n\n :Example:\n >>> sequence, labels, dist = pdbSeq(pdbid, chain='A', path2pdb='/Inputs')\n '''\n if path2pdb is None:\n raise ValueError('Must pass path2pdb')\n log.info('extracting sequence information from PDB')\n\n # Table of 3-letter to 1-letter code for amino acids\n aatable = {'ALA': 'A', 'ARG': 'R', 'ASN': 'N', 'ASP': 'D', 'CYS': 'C', 'GLN': 'Q',\n 'GLU': 'E', 'GLY': 'G', 'HIS': 'H', 'ILE': 'I', 'LEU': 'L', 'LYS': 'K', 'MET': 'M',\n 'PHE': 'F', 'PRO': 'P', 'SER': 'S', 'THR': 'T', 'TRP': 'W', 'TYR': 'Y', 'VAL': 'V'}\n # Read PDB structure:\n structure = PDBParser().get_structure(pdbid, path2pdb + pdbid + '.pdb')\n # Fill up sequence and label information\n sequence = '';\n labels = list()\n residues = [res for res in structure[0][chain] if res.get_id()[0] == ' ']\n for res in residues:\n labels.append(str(res.get_id()[1]) + str(res.get_id()[2]).strip())\n try:\n sequence += aatable[res.get_resname()]\n except:\n sequence += 'X'\n # Distances between residues (minimal distance between atoms, in angstrom):\n dist = np.zeros((len(residues), len(residues)))\n if calcDist == 1:\n for n0, res0 in enumerate(residues):\n for n1, res1 in enumerate(residues):\n dist[n0, n1] = min([atom0 - atom1 for atom0 in res0 for atom1 in res1])\n return sequence, labels, dist\n else:\n return sequence, labels","sub_path":"sca/core/alignment.py","file_name":"alignment.py","file_ext":"py","file_size_in_byte":18558,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"500565706","text":"import recastapi\n\ndef analysis(uuid = None): \n \"\"\"List analysis given uuid or all analyses.\n \n :param uuid: analysis_id.\n \n :return: JSON object containing all analyses retrieved.\n \"\"\"\n \n single_analysis = '/{}'.format(uuid) if uuid else ''\n url = '{}{}'.format(recastapi.ENDPOINTS['ANALYSIS'], single_analysis)\n responses = recastapi.get(url)\n if responses.has_key('_items'):\n return responses['_items']\n else:\n return responses\n \ndef query(query=None):\n \"\"\" customized query. \"\"\"\n url = '{}{}'.format(recastapi.ENDPOINTS['REQUESTS'], query)\n response = recastapi.get(url)\n if response.has_key('_items'):\n return response['_items']\n else:\n return response\n \n","sub_path":"recastapi/analysis/get.py","file_name":"get.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"63574330","text":"from ..base import MLClassifierBase\nfrom ..utils import get_matrix_in_format\n\nfrom sklearn.neighbors import NearestNeighbors\nimport numpy as np\nimport scipy.sparse as sparse\n\nclass MLkNN(MLClassifierBase):\n \"\"\"k Nearest Neighbours multi-label classifier.\"\"\"\n BRIEFNAME = \"MLkNN\"\n\n def __init__(self, k = 10, s = 1.0):\n super(MLkNN, self).__init__()\n self.k = k # Number of neighbours\n self.s = s # Smooth parameter\n self.copyable_attrs = ['k', 's']\n\n def compute_prior(self, y):\n prior_prob_true = np.array((self.s+y.sum(axis=0))/(self.s * 2 + self.num_instances))[0]\n prior_prob_false = 1 - prior_prob_true\n\n return prior_prob_true, prior_prob_false\n\n def compute_cond(self, X, y):\n self.knn = NearestNeighbors(self.k).fit(X)\n c = sparse.lil_matrix((self.num_labels, self.k + 1), dtype='i8')\n cn = sparse.lil_matrix((self.num_labels, self.k + 1), dtype='i8')\n\n label_info = get_matrix_in_format(y, 'dok')\n\n neighbors = self.knn.kneighbors(X, self.k, return_distance=False) \n for instance in xrange(self.num_instances):\n deltas = label_info[neighbors[instance],:].sum(axis=0)\n for label in xrange(self.num_labels):\n if label_info[instance,label] == 1:\n c[label, deltas[0, label]] += 1\n else:\n cn[label, deltas[0, label]] += 1\n\n c_sum = c.sum(axis=1)\n cn_sum = cn.sum(axis=1)\n\n cond_prob_true = sparse.lil_matrix((self.num_labels, self.k + 1), dtype='float')\n cond_prob_false = sparse.lil_matrix((self.num_labels, self.k + 1), dtype='float')\n for label in xrange(self.num_labels):\n for neighbor in xrange(self.k + 1):\n cond_prob_true[label,neighbor] = (self.s + c[label, neighbor]) / (self.s * (self.k + 1) + c_sum[label,0])\n cond_prob_false[label,neighbor] = (self.s + cn[label, neighbor]) / (self.s * (self.k + 1) + cn_sum[label,0])\n return cond_prob_true, cond_prob_false\n\n def fit(self, X, y):\n self.train_labels = get_matrix_in_format(y, 'lil')\n self.num_instances = self.train_labels.shape[0]\n self.num_labels = self.train_labels.shape[1]\n # Computing the prior probabilities\n self.prior_prob_true, self.prior_prob_false = self.compute_prior(self.train_labels)\n # Computing the posterior probabilities\n self.cond_prob_true, self.cond_prob_false = self.compute_cond(X, self.train_labels)\n return self\n\n def predict(self, X):\n result = np.zeros((X.shape[0], self.num_labels), dtype='i8')\n neighbors = self.knn.kneighbors(X, self.k, return_distance=False)\n for instance in xrange(X.shape[0]):\n deltas = self.train_labels[neighbors[instance],].sum(axis=0)\n\n for label in xrange(self.num_labels):\n p_true = self.prior_prob_true[label] * self.cond_prob_true[label,deltas[0, label]]\n p_false = self.prior_prob_false[label] * self.cond_prob_false[label,deltas[0,label]]\n result[instance,label] = int(p_true >= p_false)\n return result\n","sub_path":"skmultilearn/adapt/mlknn.py","file_name":"mlknn.py","file_ext":"py","file_size_in_byte":3170,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"569938524","text":"# -*- coding: utf-8 -*-\r\n\r\n\"\"\"\r\n/***************************************************************************\r\n FQgis\r\n A QGIS plugin\r\n This plugin saves weather station data on a map layer\r\n -------------------\r\n begin : 2016-07-10\r\n copyright : (C) 2016 by Nicola Andreola\r\n email : nicolaandreola59@gmail.com\r\n git sha : $Format:%H$\r\n ***************************************************************************/\r\n\r\n/***************************************************************************\r\n * *\r\n * This program is free software; you can redistribute it and/or modify *\r\n * it under the terms of the GNU General Public License as published by *\r\n * the Free Software Foundation; either version 2 of the License, or *\r\n * (at your option) any later version. *\r\n * *\r\n ***************************************************************************/\r\n \r\n This script create the UploadHandlerWD class to handle Upload WD.\r\n\"\"\"\r\n\r\nimport sys\r\nimport os\r\nimport logging\r\n\r\nfrom codes.entities.weather_data import WeatherData\r\nfrom codes.handlers.database_handler_wd import DatabaseHandlerWD\r\nfrom codes.handlers.upload_handler import UploadHandler\r\n\r\n\r\n\r\n# Upload Handler WD\r\nclass UploadHandlerWD(UploadHandler):\r\n \"\"\"UploadHandlerWD class.\r\n \"\"\"\r\n \r\n logger = None\r\n \r\n _weather_data = None\r\n\r\n #constructor\r\n def __init__(self, file_st=None):\r\n \"\"\"Constructor.\r\n \r\n :param file_st: The File To Read.\r\n :type file_st: string \r\n \"\"\"\r\n \r\n self.logger = logging.getLogger(__name__)\r\n self.logger.debug('%s log setted correctly in %s', self.logger.name, os.path.basename(__file__))\r\n \r\n self.logger.debug('ENTER %s function in %s file.', sys._getframe().f_code.co_name, os.path.basename(__file__))\r\n super(UploadHandler, self).__init__()\r\n \r\n self.logger.debug('Reading File')\r\n self.file_path = file_st\r\n self._read_file()\r\n \r\n self.logger.debug('Creating Weather Station')\r\n self._create_weather_data()\r\n \r\n self.logger.debug('EXIT %s function in %s file.', sys._getframe().f_code.co_name, os.path.basename(__file__))\r\n \r\n \r\n #_create_weather_data\r\n def _create_weather_data(self):\r\n \"\"\"Create the Weather Data.\r\n \"\"\"\r\n self.logger.debug('ENTER %s function in %s file.', sys._getframe().f_code.co_name, os.path.basename(__file__))\r\n \r\n self.logger.debug('Extract Reader Data and Create Weather Data')\r\n data = False\r\n id_st = []\r\n n_row = 0\r\n\r\n for row in self.lines:\r\n n_row += 1\r\n if n_row == 1:\r\n data_type = row[0]\r\n if data_type == 'PLUV':\r\n unit = 'mm'\r\n else:\r\n if row[0] == 'dati':\r\n data = True\r\n continue\r\n \r\n if data:\r\n if row[0] == '#':\r\n n_st = 0\r\n for st in row:\r\n n_st += 1\r\n if n_st != 1:\r\n id_st.append(st) \r\n else :\r\n datas = []\r\n timestamp = None\r\n n_dt = 0\r\n for dt in row:\r\n n_dt += 1\r\n if n_dt != 1:\r\n datas.append(dt)\r\n else:\r\n timestamp = dt\r\n \r\n self.logger.debug('Create Weather Data')\r\n for i in range(len(id_st)):\r\n self.weather_data = WeatherData(id_st[i], timestamp, datas[i], unit, data_type)\r\n self.logger.debug('Weather Data is: %s', self.weather_data)\r\n \r\n self.logger.debug('Add Weather Data to DB')\r\n insert_result = self._insert_data_db()\r\n self.logger.debug('Insert Result is: %s', insert_result)\r\n \r\n self.logger.debug('EXIT %s function in %s file.', sys._getframe().f_code.co_name, os.path.basename(__file__))\r\n \r\n \r\n #_insert_station_db\r\n def _insert_data_db(self): \r\n \"\"\"Insert the Weather Data into DB.\r\n \"\"\"\r\n self.logger.debug('ENTER %s function in %s file.', sys._getframe().f_code.co_name, os.path.basename(__file__))\r\n \r\n self.logger.debug('Insert Weather Data into DB')\r\n dhwd = DatabaseHandlerWD(self.weather_data)\r\n insert_result = dhwd.insert_result\r\n self.logger.debug('Insert Weather Data Result %s', insert_result)\r\n \r\n self.logger.debug('EXIT %s function in %s file.', sys._getframe().f_code.co_name, os.path.basename(__file__))\r\n return insert_result\r\n \r\n \r\n # Properties\r\n @property\r\n def weather_data(self):\r\n self.logger.debug('GETTER %s function in %s file.', sys._getframe().f_code.co_name, os.path.basename(__file__))\r\n self.logger.debug('VALUE RETURNED: %s', self._weather_data)\r\n return self._weather_data\r\n \r\n @weather_data.setter\r\n def weather_data(self, weather_data_st):\r\n self.logger.debug('SETTER %s function in %s file.', sys._getframe().f_code.co_name, os.path.basename(__file__))\r\n self.logger.debug('PARAM weather_data_st: %s', weather_data_st)\r\n\r\n self.logger.debug('CHECK Correctness of weather_data_st: %s', weather_data_st)\r\n if isinstance(weather_data_st, WeatherData):\r\n self.logger.debug('Enter branch IF (weather_data_st WeatherData)')\r\n self._weather_data = weather_data_st\r\n else:\r\n self.logger.debug('Enter branch ELSE (weather_data_st OTHER)')\r\n self._weather_data = None\r\n wd_null = 'Weather Data doesn\\'t exist'\r\n self.logger.error('Error WeatherData: %s', wd_null) \r\n self.logger.exception('Weather Data %s -> Raising Exception...', self._weather_data, exc_info=True) \r\n raise Exception \r\n \r\n self.logger.debug('VALUE ASSIGNED: %s', self._weather_data)","sub_path":"codes/handlers/upload_handler_wd.py","file_name":"upload_handler_wd.py","file_ext":"py","file_size_in_byte":6648,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"284770488","text":"from django.contrib.sessions.models import Session\nfrom django.shortcuts import render, HttpResponse,redirect\n\ndef is_userin(view_func):\n def isloggedin(request, *args, **kwargs):\n if request.session.has_key('user_in'):\n userlogged=request.session['user_in']\n return render(request,'userside.html',{'ul': userlogged})\n else:\n return redirect('userlogin')\n return isloggedin","sub_path":"users/decorators.py","file_name":"decorators.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"609728845","text":"import random\n\n# Helpers\ndef isStartWord(w):\n if w != \"\":\n if w[0] == '\"': return True\n if w[0].isupper(): return True\n return False\n\ndef isStopChar(char):\n for c in \".?!\":\n if char == c:\n return True\n return False\n\ndef isStopWord(w):\n if len(w) >= 2:\n if isStopChar(w[-1]) or (w[-1] == '\"' and isStopChar(w[-2])):\n return True\n return False\n\n# Open and read input text\nd = dict()\nwith open(\"input.txt\") as f:\n words = f.read().split()\n for i in range(0, len(words)):\n w = words[i]\n # Add word to dictionary\n if w not in d.keys():\n # If end of text not reached\n if i+1 != len(words):\n d[w] = [words[i+1]]\n # End of text reached, add a random word as follower\n else:\n d[w] = [random.choice(words)]\n # Word already in dictionary\n else:\n # Add the word following to array\n if i+1 != len(words):\n d[w].append(words[i+1])\n \n# construct random sentence\nstart = \"\"\nwhile isStartWord(start) != True:\n start = random.choice(list(d.keys()))\nrandomWord = start\ni = 0\nwhile i != 5:\n print(randomWord, end=' ')\n randomWord = random.choice(d[randomWord])\n if isStopWord(randomWord):\n i += 1\n print(f'{randomWord}\\n')\n while isStartWord(randomWord) != True:\n randomWord = random.choice(list(d.keys()))\n","sub_path":"applications/markov/markov.py","file_name":"markov.py","file_ext":"py","file_size_in_byte":1462,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"296347607","text":"import numpy as np\n\n# PREPARANDO LOS DATOS\ncoorMov = []\ncadSoltion = []\nyy = []\nm = []\n\nnXn= 6 ######********************* MOVER ************************######\nnumCuadros = (nXn-1)*(nXn-1) \nconstante = 120\n #numero de lineas para 3 24\n #numero de lineas para 4 48\n #numero de lineas para 5 80 \n #numero de lineas para 6 120 \nn = nXn\nlongitud = nXn*nXn\nmn = longitud\n\nfor x in range(0,longitud):\n\tcadSoltion.append(0)\nyy = cadSoltion\t\nfor x in range(0,longitud):\n\tm.append(yy)\n\ncadSoltion = []\ny = []\nbusqueda = []\nfor x in range(0,nXn):\n\tcadSoltion.append(0)\ny = cadSoltion\t\nfor x in range(0,nXn):\n\tbusqueda.append(y)\n\n#llenando unos en movimientos posibles en la cadena \"m\"\n\ndef movimientosPosibles(xDefi):\n\tmovimientos = []\n\t## ORILLAS EN EL TIMBIRICHE\n\t# extremo inferior\n\tif xDefi == 1: \n\t\tmovimientos = [2,1+n]\n\tif xDefi == n: \n\t\tmovimientos = [n-1,n+n]\n\t# extremo superior\n\tif xDefi == (mn-n+1): \n\t\tmovimientos = [(mn-n+1)-n,(mn-n+1)+1]\n\tif xDefi == mn: \n\t\tmovimientos = [mn-n,mn-1]\n\tif xDefi < n and xDefi>1:\n\t\tmovimientos = [xDefi-1,xDefi+1,xDefi+n]\n\tif xDefi > (mn-n+1) and xDefi= len(resultF):\n\t\t\t\tmatrizMov.append(apendice)\n\t\t\t\tbreak\t\t\t\n\t#print(apendice)\n\t#print(matrizMov)\n\n\ndef functionCuadroJugador(coorMov,coorCuadrosReales):\n #formando la subcadena\n subcoorMovJ = []\n print(\"longitud \", len(coorMov))\n x = 0 \n while(x < len(coorMov) ):\n subcoorMovJ.append( [coorMov[x], coorMov[x+1]] )\n x = x+2\n print(\"en dos coorMov: \", subcoorMovJ)\n \n auxCoorCr = np.array([])\n \n cadenaLast = subcoorMovJ[len(subcoorMovJ)-1]\n print(cadenaLast)\n arraySupervision = []\n \n for j1 in range(0,len(coorCuadrosReales)):\n auxCoorCr = coorCuadrosReales[j1]\n for j2 in range(0,4):\n if cadenaLast[0] == auxCoorCr[j2][0] and cadenaLast[1] == auxCoorCr[j2][1]:\n arraySupervision.append(auxCoorCr)\n break\n \n print(\"posibles cuadros: \", arraySupervision)\n \n # buscando si en coorMovJ esta en algun cuadro de arraySupervision\n auxSup = []\n contadorCJ = 0\n for j3 in range(0,len(arraySupervision)):\n auxSup = arraySupervision[j3]\n contadorCJ = 0\n for j4 in range(0,4): # nivel: se mueve en las cordenadas del cuadro 0\n for j5 in range(0,len(subcoorMovJ)): # nivel: lo busca en las coordenadas de movimiento coorMov\n if auxSup[j4][0] == subcoorMovJ[j5][0] and auxSup[j4][1] == subcoorMovJ[j5][1]:\n contadorCJ = contadorCJ + 1\n break\n if contadorCJ == 4:\n #puntosJugador = puntosJugador + 1\n break\n \n if contadorCJ != 4: # ningun cuadro formado por el jugador\n auxSup = []\n \n return auxSup\n\n#################################################\n#### VAMOS A CREAR UNA MATRIZ DE LAS COORDENADAS DE LOS CUADROS \ncoorCuadros = []\n#n = 6\nsalto = 0\nxn = 0\nfor x in range(1,numCuadros+1):\n xn = xn + 1\n if xn%n == 0 :\n xn = xn+1\n coorCuadros.append([xn, xn+1, n+xn, (n+xn)+1])\n\nprint(\"coordenada de cuadros \", coorCuadros)\n\t\ncoorCuadrosReales = []\nayudaCR = []\n\t\nfor ic in range(0,len(coorCuadros)):\n ayudaCR.append([coorCuadros[ic][0], coorCuadros[ic][1]])\n ayudaCR.append([coorCuadros[ic][2], coorCuadros[ic][3]])\n ayudaCR.append([coorCuadros[ic][0], coorCuadros[ic][2]])\n ayudaCR.append([coorCuadros[ic][1], coorCuadros[ic][3]])\n \n coorCuadrosReales.append(ayudaCR)\n ayudaCR = [] \n\nprint(\"coordenada de cuadros Real\", coorCuadrosReales)\n#################################################\n\n## inicio del juego\ncoorMov = []\npuntosComputadora = 0\npuntosJugador = 0\npasos = 0\nOPERADORES = np.array(OPERADORES1)\npasosi = 0\nENDGAME = 0\ncontadorCUADRO = 0\nGANADOR = ''\ncuadrosJugador = []\ncuadrosComputadora = []\n\nprint(\"\")\nprint(\" ************ INICIO DEL JUEGO ************ \")\nprint (\" INGRESE LAS COORDENADASDE MOVIMIENTOS \")\nprint(\"\")\n\nauxSup = []\n\nwhile ENDGAME == 0:\n\tif contadorCUADRO != 3: #funcion de retorno\n\t\txMovs = input(\" introduce inicio de linea: \") \n\t\tyMovs = input(\" introduce final de linea: \")\n\t\t\n\t\txMov = int(xMovs)\n\t\tyMov = int(yMovs)\n\t\t\n\t\taux123 = 0\n\t\t\n\t\tif xMov > yMov:\n\t\t aux123 = xMov\n\t\t xMov = yMov\n\t\t yMov = aux123 \n\n\t\tcoorMov.append(xMov)\n\t\tcoorMov.append(yMov)\n\t\t\n\t\t# checando si acabas de formar un cuadro para así dar otro turno de tiro\n\t\t# la referencia es el ultimo tiro ;)\n\t\t\n\t\tauxSup = functionCuadroJugador(coorMov,coorCuadrosReales)#,puntosJugador)\n\t\t\n\t\twhile auxSup != []: # ha formado un cuadro nuevo\n\t\t if len(coorMov) == constante:\n\t\t ENDGAME = 1\n\t\t if puntosComputadora > puntosJugador :\n\t\t GANADOR = 'COMPUTADORA'\n\t\t if puntosComputadora < puntosJugador :\n\t\t GANADOR = 'GANASTE! :)'\n\t\t auxSup = []\n\t\t print(\"FIN DEL JUEGO\")\n\t\t \n\t\t puntosJugador = puntosJugador + 1\n\t\t print(\"Hizo un punto * \")\n\t\t print(\"Puntos sumados: \", puntosJugador )\n\t\t print(\"Vuelva a tirar\")\n\t\t \n\t\t xMovs = input(\" introduce inicio de linea \") \n\t\t yMovs = input(\" introduce final de linea \")\n\t\t \n\t\t xMov = int(xMovs)\n\t\t yMov = int(yMovs)\n\t\t aux123 = 0\n\t\t if xMov > yMov:\n\t\t aux123 = xMov\n\t\t xMov = yMov\n\t\t yMov = aux123\n\t\t \n\t\t coorMov.append(xMov)\n\t\t coorMov.append(yMov)\n\t\t \n\t\t \n\t\t auxSup = functionCuadroJugador(coorMov,coorCuadrosReales)#,puntosJugador)\n\t\t\n\t\tprint(\"cuadro formado es: \", auxSup)\n\t\t \n\n\tprint(\"sin eliminar: \", OPERADORES1)\n\n \n\tprint(\"movidas\", coorMov)\n\tfor coor in range(pasosi,len(coorMov)-1):\n\t\tfor x in range(0,len(OPERADORES)):\n\t\t\tfor y in range(0,len(OPERADORES[x])):\n\t\t\t\tif (x+1 == coorMov[coor]) and (OPERADORES[x][y] == coorMov[coor+1]):\n\t\t\t\t\tOPERADORES[x] = np.delete(OPERADORES[x],y)\n\t\t\t\t\tbreak\n\t\t\t\tif (x+1 == coorMov[coor+1]) and (OPERADORES[x][y] == coorMov[coor]):\n\t\t\t\t\tOPERADORES[x] = np.delete(OPERADORES[x],y)\n\t\t\t\t\tbreak\n\t\t\t\t\t\n\n\tprint(\"matriz de CONOCIMIENTO: \", OPERADORES) #### MATRIZ DE CONOCIMIENTO ####\n\n\t#HEURISTICA 3 eleccion de movimientos de la computadora xMovComp yMovComp\n\n\t#1era HEURISTICA no entrar a formar parte de las lineas para no formar un cuadro\n\n\tprint(\"coorMov: \", coorMov)\n\tprint(\"coorMov: \", len(coorMov))\n\n\n\tOPheuristica = np.array(OPERADORES)\n\tnumEliminados = 0\n\n\tfor x in range(0,len(coorMov)):\n\t\t#OPheuristica = np.delete(OPheuristica,(coorMov[x] - (x+1)))\n\t\tfor j in range(0,len(OPheuristica)):\n\t\t\tif j+1 == coorMov[x]:\n\t\t\t\tOPheuristica[j] = [] \n\n\tprint(\"OPERADORES ELIM: \", OPheuristica)\n\tcheck = 0\n\tfor x in range(0,len(OPheuristica)):\n\t\tif OPheuristica[x] != []:\n\t\t\tcheck = 1 ## VEMOS SI AÚN ES VALIDA ESTA HEURISTICA \n\t\t\n\tif check == 0:\n\t\tOPheuristica = np.array(OPERADORES)\n\n\tprint(\"OPERADORES ELIM con posible correccion: \", OPheuristica)\n\n\t#2da HEURISTICA los que tengan mayor longitud \n\tmaximo = []\n\tfor x in range(0,len(OPheuristica)):\n\t\tmaximo.append(len(OPheuristica[x]))\n\n\tmaxi = max(maximo) #encontramos el maximo\n\tcadenaMaxi = np.array([])\n\tprint(\"cadena de longitud \", maxi)\n\n\tfor xMaxima in range(0,len(maximo)): #buscamos al maximo e indirectamente el indice para asi encontrar al maz en OPERADOR\n\t\tif maxi == maximo[xMaxima]:\n\t\t\tcadenaMaxi = np.array(OPheuristica[xMaxima])\n\t\t\tpivote = xMaxima + 1 \n\t\t\tbreak\n\tprint(\"cadena a revisar\", cadenaMaxi)\n\t#3er HEURISTICA se elije la linea más alejada a los puntos ya escritos \n\n\t#3.1 que no sea adyacente a ninguno de ellos\n\n\ta = 0\n\tb = 0\n\ti = 0\n\tresultado = []\n\tfor i in range(0,len(coorMov)):\n\t\tfor j in range(0,len(cadenaMaxi)):\n\t\t\tif cadenaMaxi[j] == coorMov[i] :\n\t\t\t\tcadenaMaxi[j] = 0\n\n\tprint(\"cadena a revisar\", cadenaMaxi)\n\tcheck = 0\n\tfor x in range(0,len(cadenaMaxi)): #checamos si la cadena ha quedado borrada y ya no hay más opciones \n\t\tif cadenaMaxi[x] != 0:\n\t\t\tcheck = 1\n\n\tif check == 0: #si la cadena resulto ser vacia usamos la cadena normal \n\t\tcadenaMaxi = OPheuristica[xMaxima]\n\n\tprint(\"cadena a revisar desicion \", cadenaMaxi)\n\n\t#3.2 que no comience a formar un cuadro\n\t\t#### VAMOS A CREAR UNA MATRIZ DE LAS COORDENADAS DE LOS CUADROS \n\tcoorCuadros = []\n\t#n = 6\n\tsalto = 0\n\txn = 0\n\tfor x in range(1,numCuadros+1):\n\t\txn = xn + 1\n\t\tif xn%n == 0 :\n\t\t\txn = xn+1\n\t\tcoorCuadros.append([xn, xn+1, n+xn, (n+xn)+1])\n\t\n\tprint(\"coordenada de cuadros \", coorCuadros)\n\n\t# buscando que no se forme un cuadro\n\tfor jj in range(0,len(coorCuadros)):\n\t\tif (coorMov[0] in coorCuadros[jj]) and (coorMov[1] in coorCuadros[jj]):\n\t\t\tbreak\n\tprint(\"coorCuadros\",coorCuadros[jj])\n\n\tcuadroElim = np.array(coorCuadros[jj])\n\tcuadroElim = np.delete(cuadroElim, coorMov[0])\n\tcuadroElim = np.delete(cuadroElim, coorMov[1])\n\tprint(\"cuadroElim \", cuadroElim)\n\n\tfor x in range(0,len(cadenaMaxi)):\n\t\tif cadenaMaxi[x] in cuadroElim or cadenaMaxi[x] == 0:\n\t\t\tpass\n\t\telse:\n\t\t\tbreak\n\t\t\t\n\tif pivote < cadenaMaxi[x]:\n\t xMovComp = pivote\n\t yMovComp = cadenaMaxi[x]\n\telse:\n\t yMovComp = pivote\n\t xMovComp = cadenaMaxi[x]\n\t\t\n\t#3.3 si se está a punto de formar un cuadro nos ponemos ahí \n\t# buscando si se esta formando un cuadro\"!\n\t# formando coordenadas reales de cada cuadro ;)\n\t\n\tcoorCuadrosReales = []\n\tayudaCR = []\n\t\n\tfor ic in range(0,len(coorCuadros)):\n\t ayudaCR.append([coorCuadros[ic][0], coorCuadros[ic][1]])\n\t ayudaCR.append([coorCuadros[ic][2], coorCuadros[ic][3]])\n\t ayudaCR.append([coorCuadros[ic][0], coorCuadros[ic][2]])\n\t ayudaCR.append([coorCuadros[ic][1], coorCuadros[ic][3]])\n\t \n\t coorCuadrosReales.append(ayudaCR)\n\t ayudaCR = [] \n\t \n\tprint(\"coordenada de cuadros Real\", coorCuadrosReales)\n\t\n\t#formando la subcadena\n\tsubcoorMov = []\n\tprint(\"longitud \", len(coorMov))\n\tx = 0 \n\twhile(x < len(coorMov) ):\n\t subcoorMov.append( [coorMov[x], coorMov[x+1]] )\n\t x = x+2\n\t\n\tprint(\"subcadena de movimientos\", subcoorMov)\t\n\t\n\t#buscando si algun pre cuadro = cuadro - un lado existe en subcoorMov\n\n\tcontadorCUADRO = 0\n\tayudaPreCuadro = []\n\t\n\tfor x in range(0,len(coorCuadrosReales)):\n\t contadorCUADRO = 0\n\t ayudaPreCuadro = []\n\t for y in range(0,len(subcoorMov)):\n\t if subcoorMov[y] in coorCuadrosReales[x]:\n\t contadorCUADRO = contadorCUADRO + 1\n\t ayudaPreCuadro.append(subcoorMov[y])\n\t \n\t if contadorCUADRO == 3:\n\t break\n\t \n\tprint(\"cuadro posible \", coorCuadrosReales[x])\n\tprint(\"pre cuadro exist \", ayudaPreCuadro)\n\t \n\tborr = np.array(coorCuadrosReales[x])\n\tborr1 = np.array([])\n\tbandera = 0\n\t\n\t#tomando la coordenada de jugada para cerrar el cuadro\n \n\tmi = 0\n\tauxBorr = np.array([])\n\tauxPreCuadro = np.array([])\n \n\tif contadorCUADRO == 3:\n\t cuadrosComputadora.append(borr)\n\t for mi in range(0,len(borr)):\n\t bandera = 0\n\t auxBorr = np.array(borr[mi])\n\t for nj in range(0,len(ayudaPreCuadro)):\n\t auxPreCuadro = np.array(ayudaPreCuadro[nj])\n\t if (auxBorr[0] == auxPreCuadro[0]) and (auxBorr[1] == auxPreCuadro[1]) :\n\t bandera = 1 # ya lo encontro\t\n\t if bandera == 1:\n\t break\n\t if bandera == 0:\n\t break\n \n \n\t borr1 = np.array(borr[mi])\n\t xMovComp = borr1[0]\n\t yMovComp = borr1[1]\n\t puntosComputadora = puntosComputadora + 1\n\t \n\t \n\tprint(\"precuadro \", borr[mi])\n\tprint(\"coordenadas cuadro reales limpio \", coorCuadrosReales)\n \n\tcoorMov.append(xMovComp)\n\tcoorMov.append(yMovComp)\n\t\n\n\tprint(\"maximo \", maximo)\n\tprint(\"maxi \", maxi)\n\tprint(\"pivote: \", pivote)\n\tprint(\"maxima cadena \", cadenaMaxi)\n\tprint(coorCuadros)\n\tprint(\"\")\n\tprint(\"**********\")\n\tprint(\"MOVIMIENTOS: \", coorMov)\n\tprint(\"puntos de la computadora: \", \tpuntosComputadora)\n\tprint(\"puntos deL JUGADOR: \", \tpuntosJugador)\n\t\n\t#fin del juego\n\t#checando la matriz de conocimiento SI ESTÁ VACÍA TERMINO EL JUEGO\n\tFINAL = 0\n\tfor x in range(0,len(OPERADORES)):\n\t if OPERADORES[x] != []:\n\t FINAL = 1\n\t \n\tif len(coorMov) == constante:\n\t ENDGAME = 1\n\t if puntosComputadora > puntosJugador :\n\t GANADOR = 'COMPUTADORA'\n\t if puntosComputadora < puntosJugador :\n\t GANADOR = 'GANASTE! :)'\n\t print(\"FIN DEL JUEGO\")\n\n\tpasos = pasos + 1\n\tpasosi = pasosi +2\n\t\n\t\nprint(\"MOVIMIENTOS: \", coorMov )\n\nmovClaros = []\nxf = 0\nwhile(xf < len(coorMov)):\n movClaros.append([coorMov[xf], coorMov[xf+1]])\n xf = xf + 2\n\nprint(\"\")\nprint(\" ******** FIN DEL JUEGO ********* \")\nprint(\"Historial de movimientos\", movClaros)\nprint(\"\")\nprint(\" ******** RESUMEN DEL JUEGO: ********* \")\nprint(\"\")\nprint(\"Puntos del jugador: \", \tpuntosJugador )\nprint(\"Puntos de la computadora: \", \tpuntosComputadora )\nprint(\"GANADOR: \", GANADOR )\n\nprint(\"Cuadros de la computadora: \", cuadrosComputadora)\n\n\n\n\n\n","sub_path":"timbiriche_nxn.py","file_name":"timbiriche_nxn.py","file_ext":"py","file_size_in_byte":13587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"165810877","text":"import simplejson as json\n\nuser=input(\"enter one string\")\nwith open('error.json') as fileObject:\n fileContents = fileObject.read()\n r = json.loads(fileContents)\nj=0\nwhile True:\n a=r['values'][j]['from']\n if user in a:\n print(r['values'][j]['to'])\n break\n j+=1\n#print(r)\n#a=r['values'][0]['from']\n#print(r['values'][0]['from'])\n#if user in a:\n# print(r['values'][0]['to'])\n#data=json.loads(string)\n#print(data)","sub_path":"json.py","file_name":"json.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"415368985","text":"import unittest\nimport datetime\nfrom django.test import Client, RequestFactory, TestCase, tag\nfrom django.urls import reverse_lazy\n\nfrom factories.indicators_models import IndicatorFactory, PeriodicTargetFactory\nfrom factories.workflow_models import (\n ProgramFactory,\n TolaUserFactory,\n UserFactory\n)\nfrom indicators.models import Indicator\nfrom indicators.views.views_reports import IPTTReportQuickstartView, IPTT_Mixin\n\n\n@tag('iptt', 'fast')\nclass IPTTReportQuickstartViewTests(TestCase):\n \"\"\"Unit tests to valid the IPTTReportQuickStartView\"\"\"\n\n def setUp(self):\n self.user = UserFactory(first_name=\"Indicator\", last_name=\"CreateTest\", username=\"IC\")\n self.user.set_password('password')\n self.user.save()\n self.tola_user = TolaUserFactory(user=self.user)\n self.country = self.tola_user.country\n self.program = ProgramFactory(\n funding_status='Funded', reporting_period_start='2016-03-01', reporting_period_end='2020-05-01')\n self.program.country.add(self.country)\n self.program.save()\n self.indicator = IndicatorFactory(\n program=self.program, unit_of_measure_type=Indicator.NUMBER, is_cumulative=False,\n direction_of_change=Indicator.DIRECTION_OF_CHANGE_NONE, target_frequency=Indicator.ANNUAL)\n lop_indicator = IndicatorFactory(\n program=self.program, target_frequency=Indicator.LOP\n )\n self.request_factory = RequestFactory()\n self.client = Client()\n self.client.login(username=\"IC\", password='password')\n\n def test_page_load_returns_200(self):\n \"\"\"Do we return 200?\"\"\"\n\n response = self.client.get(reverse_lazy('iptt_quickstart'))\n self.assertEqual(response.status_code, 200)\n\n def test_page_load_does_not_redirect(self):\n \"\"\"This page should not redirect\"\"\"\n\n response = self.client.get(reverse_lazy('iptt_quickstart'), follow=True)\n self.assertEqual(len(response.redirect_chain), 0)\n\n def test_page_loads_correct_template(self):\n \"\"\"Do we load the right template?\"\"\"\n\n response = self.client.get(reverse_lazy('iptt_quickstart'), follow=True)\n self.assertTemplateUsed(response, 'indicators/iptt_quickstart.html')\n self.assertContains(response, 'Indicator Performance Tracking Table')\n\n def test_get_form_kwargs(self):\n \"\"\"Do we get the correct form kwargs?\"\"\"\n\n data = {'csrfmiddlewaretoken': 'lolwut',\n 'targetperiods-program': self.program.id,\n 'targetperiods-formprefix': IPTTReportQuickstartView.FORM_PREFIX_TARGET,\n 'targetperiods-timeframe': Indicator.LOP,\n 'targetperiods-targetperiods': 1,\n 'targetperiods-numrecentperiods': 1, }\n path = reverse_lazy('iptt_quickstart')\n response = self.client.post(path, data=data, follow=True)\n kwargs = response.resolver_match.kwargs\n self.assertEqual(kwargs['reporttype'], IPTTReportQuickstartView.FORM_PREFIX_TARGET)\n self.assertEqual(int(kwargs['program_id']), self.program.id)\n\n def test_get_context_data(self):\n \"\"\"Do we get the correct context data?\"\"\"\n\n data = {'csrfmiddlewaretoken': 'lolwut',\n 'targetperiods-program': self.program.id,\n 'targetperiods-formprefix': IPTTReportQuickstartView.FORM_PREFIX_TARGET,\n 'targetperiods-timeframe': Indicator.LOP,\n 'targetperiods-targetperiods': 1,\n 'targetperiods-numrecentperiods': 1, }\n path = reverse_lazy('iptt_quickstart')\n response = self.client.post(path, data=data, follow=True)\n context_data = response.context_data\n\n self.assertEqual(int(context_data['program_id']), self.program.id)\n # self.assertEqual(context['report_wide'], ?)\n # self.assertEqual(context['report_date_ranges'], ?)\n # self.assertEqual(context['indicators'], ?)\n self.assertRegex(str(context_data['program']), self.program.name)\n self.assertEqual(str(context_data['reporttype']),\n IPTTReportQuickstartView.FORM_PREFIX_TARGET)\n self.assertEqual(str(context_data['report_end_date']),\n self.program.reporting_period_end)\n self.assertEqual(str(context_data['report_end_date_actual']),\n self.program.reporting_period_end)\n self.assertEqual(str(context_data['report_start_date']),\n self.program.reporting_period_start)\n\n def test_post_with_valid_form(self):\n \"\"\"Does POSTing to iptt_quickstart with valid form data redirect to the\n correct view (iptt_report)?\"\"\"\n\n data = {'csrfmiddlewaretoken': 'lolwut',\n 'targetperiods-program': self.program.id,\n 'targetperiods-formprefix': IPTTReportQuickstartView.FORM_PREFIX_TARGET,\n 'targetperiods-timeframe': Indicator.LOP,\n 'targetperiods-targetperiods': 1,\n 'targetperiods-numrecentperiods': 1, }\n path = reverse_lazy('iptt_quickstart')\n\n response = self.client.post(path, data=data, follow=True)\n self.assertEqual(len(response.redirect_chain), 1)\n self.assertTemplateUsed(response, 'indicators/iptt_report.html')\n self.assertEqual(response.status_code, 200)\n\n def test_post_with_invalid_form(self):\n \"\"\"Does POSTing to iptt_quickstart with crap form data leave us at\n iptt_quickstart?\"\"\"\n\n path = reverse_lazy('iptt_quickstart')\n response = self.client.post(path, data={'foo': 'bar'})\n self.assertEqual(response.status_code, 200)\n self.assertTemplateUsed(response, 'indicators/iptt_quickstart.html')\n\n@tag('targets', 'iptt', 'fast')\nclass TargetFrequencyAPITests(TestCase):\n \"\"\"Tests for the api call /api/programtargetfrequencies/?program_id=#\"\"\"\n def setUp(self):\n self.program = ProgramFactory()\n self.url = '/api/programtargetfrequencies/?program_id={pk}'.format(pk=self.program.pk)\n user = UserFactory()\n self.client = Client()\n self.client.force_login(user=user)\n\n def test_one_indicator_of_each_frequency(self):\n indicator = IndicatorFactory(\n program=self.program\n )\n for frequency, name in Indicator.TARGET_FREQUENCIES:\n if frequency != Indicator.EVENT:\n indicator.target_frequency = frequency\n indicator.save()\n response = self.client.get(self.url).json()\n expected = [{'frequency_name': name, 'target_frequency': frequency}]\n self.assertEqual(\n response, expected,\n \"One {0} indicator expected {1} but got {2}\".format(\n name, expected, response)\n )\n\n def test_multiple_indicators(self):\n for frequency in [Indicator.LOP, Indicator.LOP, Indicator.MID_END, Indicator.SEMI_ANNUAL]:\n IndicatorFactory(program=self.program, target_frequency=frequency)\n response = self.client.get(self.url).json()\n self.assertEqual(len(response), 3)\n self.assertEqual(set([x['target_frequency'] for x in response]),\n set([Indicator.LOP, Indicator.MID_END, Indicator.SEMI_ANNUAL]))\n\n@tag('iptt', 'fast')\nclass TestIPTTGenerateTargetPeriods(TestCase):\n \"\"\"Tests for IPTTMixin._generate_targetperiods function\n \n IPTTMixin.program = program\n IPTTMixin.filter_form_initial_data\n - start_period - blank or ISO date to filter targets after\n - end_period - blank or ISO date to filter targets before\n - timeframe - blank (dates) or 1 (show all) or 2 (most recent)\n - numrecentperiods - show X most recent timeperiods (requires 2 timeframe)\n call with (period = frequency #)\n \n \"\"\"\n def setUp(self):\n pass\n\n def test_generate_target_periods_annual(self):\n # program with 5 year period\n program = ProgramFactory(\n reporting_period_start=datetime.date(2013, 1, 1),\n reporting_period_end=datetime.date(2017, 12, 31)\n )\n \n mixin = IPTT_Mixin()\n mixin.program = program\n mixin.filter_form_initial_data = {\n 'timeframe': 1\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_targetperiods(Indicator.ANNUAL)\n self.assertEqual(end_date, datetime.date(2017, 12, 31))\n self.assertEqual(len(date_ranges), 5)\n self.assertEqual(date_ranges[1]['start'], datetime.date(2014, 1, 1))\n self.assertEqual(len(targetperiods), 5)\n mixin.filter_form_initial_data = {\n 'timeframe': 0,\n 'start_period': '2013-01-01',\n 'end_period': '2016-12-31'\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_targetperiods(Indicator.ANNUAL)\n self.assertEqual(end_date, datetime.date(2016, 12, 31))\n self.assertEqual(len(date_ranges), 5)\n self.assertEqual(len(targetperiods), 4)\n mixin.filter_form_initial_data = {\n 'timeframe': 0,\n 'start_period': '2013-01-01',\n 'end_period': '2014-12-31'\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_targetperiods(Indicator.ANNUAL)\n self.assertEqual(end_date, datetime.date(2014, 12, 31))\n self.assertEqual(len(date_ranges), 5)\n self.assertEqual(len(targetperiods), 2)\n mixin.filter_form_initial_data = {\n 'timeframe': 2,\n 'numrecentperiods': 3\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_targetperiods(Indicator.ANNUAL)\n self.assertEqual(end_date, datetime.date(2017, 12, 31))\n self.assertEqual(len(date_ranges), 5)\n self.assertEqual(len(targetperiods), 3)\n self.assertEqual(targetperiods[0]['start'], datetime.date(2015, 1, 1))\n\n def test_generate_target_periods_quarterly(self):\n program = ProgramFactory(\n reporting_period_start=datetime.date(2015, 4, 1),\n reporting_period_end=datetime.date(2016, 6, 30)\n )\n mixin = IPTT_Mixin()\n mixin.program = program\n mixin.filter_form_initial_data = {\n 'timeframe': 1\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_targetperiods(Indicator.QUARTERLY)\n self.assertEqual(end_date, datetime.date(2016, 6, 30))\n self.assertEqual(len(date_ranges), 5)\n self.assertEqual(date_ranges[1]['start'], datetime.date(2015, 7, 1))\n self.assertEqual(len(targetperiods), 5)\n mixin.filter_form_initial_data = {\n 'timeframe': 0,\n 'start_period': '2015-04-01',\n 'end_period': '2015-12-31'\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_targetperiods(Indicator.QUARTERLY)\n self.assertEqual(end_date, datetime.date(2015, 12, 31))\n self.assertEqual(len(date_ranges), 5)\n self.assertEqual(len(targetperiods), 3)\n mixin.filter_form_initial_data = {\n 'timeframe': 0,\n 'start_period': '2015-10-01',\n 'end_period': '2016-3-31'\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_targetperiods(Indicator.QUARTERLY)\n self.assertEqual(end_date, datetime.date(2016, 3, 31))\n self.assertEqual(targetperiods[1]['name'], 'Quarter 4')\n self.assertEqual(len(date_ranges), 5)\n self.assertEqual(len(targetperiods), 2)\n mixin.filter_form_initial_data = {\n 'timeframe': 2,\n 'numrecentperiods': 3\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_targetperiods(Indicator.QUARTERLY)\n self.assertEqual(end_date, datetime.date(2016, 6, 30))\n self.assertEqual(len(date_ranges), 5)\n self.assertEqual(targetperiods[0]['name'], 'Quarter 3')\n self.assertEqual(len(targetperiods), 3)\n self.assertEqual(targetperiods[0]['start'], datetime.date(2015, 10, 1))\n\n def test_generate_target_periods_lop(self):\n program = ProgramFactory(\n reporting_period_start=datetime.date(2015, 4, 1),\n reporting_period_end=datetime.date(2016, 6, 30)\n )\n mixin = IPTT_Mixin()\n mixin.program = program\n mixin.filter_form_initial_data = {\n 'timeframe': 1\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_targetperiods(Indicator.LOP)\n self.assertEqual(end_date, datetime.date(2016, 6, 30))\n self.assertEqual(len(date_ranges), 1)\n self.assertEqual(date_ranges[0]['start'], datetime.date(2015, 4, 1))\n self.assertEqual(date_ranges[0]['end'], datetime.date(2016, 6, 30))\n self.assertEqual(len(targetperiods), 1)\n\n def test_generate_target_periods_midend(self):\n program = ProgramFactory(\n reporting_period_start=datetime.date(2015, 4, 1),\n reporting_period_end=datetime.date(2016, 6, 30)\n )\n mixin = IPTT_Mixin()\n mixin.program = program\n mixin.filter_form_initial_data = {\n 'timeframe': 1\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_targetperiods(Indicator.MID_END)\n self.assertEqual(end_date, datetime.date(2016, 6, 30))\n self.assertEqual(len(date_ranges), 1)\n self.assertEqual(date_ranges[0]['start'], datetime.date(2015, 4, 1))\n self.assertEqual(date_ranges[0]['end'], datetime.date(2016, 6, 30))\n self.assertEqual(len(targetperiods), 2)\n\n @unittest.skip('generate timeperiods made redundant')\n def test_generate_timeperiods(self):\n program = ProgramFactory(\n reporting_period_start=datetime.date(2015, 1, 1),\n reporting_period_end=datetime.date(2016, 12, 31)\n )\n mixin = IPTT_Mixin()\n mixin.program = program\n mixin.filter_form_initial_data = {\n 'timeframe': 1\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_timeperiods()\n self.assertEqual(end_date, datetime.date(2016, 12, 31))\n self.assertEqual(len(date_ranges), 24)\n self.assertEqual(len(targetperiods), 24)\n self.assertEqual(date_ranges[9]['start'], datetime.date(2015, 10, 1))\n mixin.filter_form_initial_data = {\n 'timeframe': 0,\n 'start_period': '2015-10-1',\n 'end_period': '2016-12-31'\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_timeperiods()\n self.assertEqual(end_date, datetime.date(2016, 12, 31))\n self.assertEqual(len(date_ranges), 24)\n self.assertEqual(len(targetperiods), 15)\n self.assertEqual(targetperiods[9]['start'], datetime.date(2016, 7, 1))\n mixin.filter_form_initial_data = {\n 'timeframe': 0,\n 'start_period': '2015-10-1',\n 'end_period': '2016-10-31'\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_timeperiods()\n self.assertEqual(end_date, datetime.date(2016, 10, 31))\n self.assertEqual(len(date_ranges), 24)\n self.assertEqual(len(targetperiods), 13)\n self.assertEqual(targetperiods[-1]['start'], datetime.date(2016, 10, 1))\n mixin.filter_form_initial_data = {\n 'timeframe': 2,\n 'numrecentperiods': 4\n }\n (end_date, date_ranges, targetperiods) = mixin._generate_timeperiods(Indicator.MONTHLY)\n self.assertEqual(end_date, datetime.date(2016, 12, 31))\n self.assertEqual(len(date_ranges), 24)\n self.assertEqual(len(targetperiods), 4)\n self.assertEqual(targetperiods[0]['start'], datetime.date(2016, 9, 1))\n","sub_path":"indicators/tests/test_iptt_quickstart.py","file_name":"test_iptt_quickstart.py","file_ext":"py","file_size_in_byte":15720,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"557046496","text":"#!/usr/bin/env python\r\nimport requests, csv, sys, re, argparse, random\r\nimport time, glob, urllib3, urllib\r\nfrom bs4 import BeautifulSoup\r\n\r\nurllib3.disable_warnings(urllib3.exceptions.InsecureRequestWarning)\r\n\r\nsrc_url = \"https://www.yelp.com/search?find_desc=tea&find_loc=New%20York%2C%20NY&start=\"\r\noutput_folder = \"./raw/yelp/\"\r\nresult_file = \"./results/yelp.csv\"\r\n\r\ndef parse(url):\r\n print(url)\r\n datarow=[]\r\n headers= {\r\n 'accept':'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',\r\n 'accept-encoding':'gzip, deflate, sdch, br',\r\n 'accept-language':'en-GB,en;q=0.8,en-US;q=0.6,ml;q=0.4',\r\n 'cache-control':'max-age=0',\r\n 'upgrade-insecure-requests':'1',\r\n 'user-agent':'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'\r\n }\r\n response = requests.get(url,headers=headers, verify=False)\r\n #print(response.status_code)\r\n #print(len(response.content))\r\n sys.stdout.flush()\r\n soup = BeautifulSoup(response.text, 'html.parser')\r\n title = \"\"\r\n address = \"\"\r\n phone = \"\"\r\n website = \"\"\r\n if len(soup.select('h1[class*=\"biz-page-title\"]'))>0:\r\n title = soup.select('h1[class*=\"biz-page-title\"]')[0].get_text().replace(\"\\n\",\"\").replace(\" \",\"\")\r\n if len(soup.select('strong[class=\"street-address\"]'))>0:\r\n add1 = soup.select('strong[class=\"street-address\"]')[0]\r\n for br in add1.find_all(\"br\"):\r\n br.replace_with(\" \")\r\n address= add1.get_text().replace(\"\\n\",\"\").replace(\" \",\"\")\r\n if len(soup.select('span[class=\"biz-phone\"]'))>0:\r\n phone = soup.select('span[class=\"biz-phone\"]')[0].get_text().replace(\"\\n\",\"\").replace(\" \",\"\")\r\n if len(soup.select('span[class*=\"biz-website\"]'))>0:\r\n site = soup.select('span[class*=\"biz-website\"]')[0]\r\n if len(site.select('a'))>0:\r\n website = site.select('a')[0].attrs[\"href\"].split(\"=\")[1].split(\"&\")[0]\r\n #print(title)\r\n #print(address)\r\n #print(phone)\r\n #print(urllib.parse.unquote(website))\r\n if title != \"\":\r\n datarow.append(title)\r\n datarow.append(address)\r\n datarow.append(phone)\r\n datarow.append(urllib.parse.unquote(website))\r\n appendToFile(datarow)\r\ndef appendToFile(datarow):\r\n with open(result_file, 'a', encoding = 'utf-8') as output:\r\n writer = csv.writer(output, delimiter=\",\", lineterminator=\"\\n\")\r\n writer.writerow(datarow)\r\ndef crawler():\r\n i = 630 \r\n while True:\r\n url = src_url + str(i)\r\n i += 30\r\n print(url)\r\n sys.stdout.flush()\r\n headers= {\r\n 'accept':'text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,*/*;q=0.8',\r\n 'accept-encoding':'gzip, deflate, sdch, br',\r\n 'accept-language':'en-GB,en;q=0.8,en-US;q=0.6,ml;q=0.4',\r\n 'cache-control':'max-age=0',\r\n 'upgrade-insecure-requests':'1',\r\n 'user-agent':'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/56.0.2924.87 Safari/537.36'\r\n }\r\n response = requests.get(url,headers=headers, verify=False)\r\n print(response.status_code)\r\n print(len(response.content))\r\n if len(response.content) < 400000:\r\n with open(\"screenshot.html\", \"w\") as file:\r\n file.write(str(response.text))\r\n i -= 30\r\n time.sleep(random.randint(50,110))\r\n continue\r\n sys.stdout.flush()\r\n #parsing the text\r\n soup = BeautifulSoup(response.text, 'html.parser')\r\n blocks = soup.select('div[class*=\"businessName\"]')\r\n for block in blocks:\r\n title=block.get_text()\r\n if title[0].isdigit() == False:\r\n continue \r\n link=\"\"\r\n href=\"\"\r\n if len(block.select('a[class*=\"lemon--a\"]')) > 0:\r\n href = block.select('a[class*=\"lemon--a\"]')[0]\r\n link = href.attrs[\"href\"]\r\n parse(\"https://www.yelp.com\" + link)\r\n #print(href)\r\n #print(datarow)\r\n #appendToFile(datarow)\r\n time.sleep(random.randint(1,3))\r\n time.sleep(random.randint(5,10))\r\nif __name__==\"__main__\":\r\n argparser = argparse.ArgumentParser(formatter_class=argparse.RawTextHelpFormatter)\r\n argparser.add_argument('action',help= ' parse, crawl')\r\n argparser.add_argument('--output',nargs= '?', help = 'crawl --output ')\r\n args = argparser.parse_args()\r\n action = args.action\r\n output= args.output\r\n print (\"action: %s\"%(action))\r\n print (\"output:\",output)\r\n sys.stdout.flush()\r\n if not output is None:\r\n result_file = output\r\n if \"parse\" == action:\r\n parse(\"https://www.yelp.com/biz/alices-tea-cup-new-york?osq=tea\")\r\n if \"crawl\" == action:\r\n crawler()\r\n","sub_path":"yelp/crawler_yelp.py","file_name":"crawler_yelp.py","file_ext":"py","file_size_in_byte":4962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"340157218","text":"# =============================================================================\n# Authors: PAR Government\n# Organization: DARPA\n#\n# Copyright (c) 2016 PAR Government\n# All rights reserved.\n# ==============================================================================\n\nfrom maskgen.tool_set import getMilliSecondsAndFrameCount, addFrame\nimport cv2\nfrom maskgen.algorithms.optical_flow import copyFrames\n\n\"\"\"\nReturns the start and end time of the frames inserted\n\"\"\"\n\n\n\ndef transform(img, source, target, **kwargs):\n start_time = getMilliSecondsAndFrameCount(kwargs['Select Start Time']) if 'Select Start Time' in kwargs else (0, 1)\n end_time = addFrame(start_time, int(kwargs['Number of Frames'])-1)\n paste_time = getMilliSecondsAndFrameCount(kwargs['Dest Paste Time'])\n codec = (kwargs['codec']) if 'codec' in kwargs else 'XVID'\n start_frame = copyFrames(source, target,\n start_time,\n end_time,\n paste_time,\n codec=codec) + 1\n return {'Start Time': str(start_frame),\n 'End Time': str(start_frame + int(kwargs['Number of Frames']) - 1),\n 'add type': 'insert'}, None\n\n\ndef suffix():\n return '.avi'\n\n\ndef operation():\n return {'name': 'CopyPaste',\n 'category': 'CopyPaste',\n 'description': 'Copy frames starting point to a destination point.',\n 'software': 'OpenCV',\n 'version': cv2.__version__,\n 'arguments': {\n 'codec': {\n 'type': 'list',\n 'values': ['MPEG', 'XVID', 'AVC1', 'HFYU'],\n 'defaultvalue': 'XVID',\n 'description': 'Codec of output video.'\n }\n },\n 'transitions': [\n 'video.video'\n ]\n }\n","sub_path":"plugins/VideoCopyPasteFrames/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"60078951","text":"# -*- coding: utf-8 -*-\n\n\"\"\"\nmanorm.application\n~~~~~~~~~~~~~~~~~~\n\nMAnorm application/pipeline.\n\"\"\"\n\nfrom __future__ import absolute_import, division\n\nimport logging\nimport sys\nimport time\nfrom os import path\n\nimport numpy as np\n\nimport manorm\nfrom manorm import peak, read\nfrom manorm.compat import range\nfrom manorm.io import mk_dir, write_all_peaks, write_biased_peaks, write_original_peaks, write_wiggle_track\nfrom manorm.logging import setup_logger\nfrom manorm.model import MAmodel\nfrom manorm.peak.utils import generate_random_peaks, overlap_on_single_chr\nfrom manorm.plot import plt_figures\n\nlogger = logging.getLogger(__name__)\n\n\nclass MAnorm(object):\n \"\"\"The application class for MAnorm.\"\"\"\n\n def __init__(self, peak_file1, peak_file2, read_file1, read_file2, peak_format='bed', read_format='bed',\n name1=None, name2=None, shift_size1=100, shift_size2=100, paired=False, window_size=2000,\n summit_dis=None, n_random=10, m_cutoff=1.0, p_cutoff=0.01, write_all=False, output_dir=None,\n verbose=False, *args, **kwargs):\n \"\"\"Initialize MAnorm application.\n\n :param peak_file1: Path of sample 1 peak file.\n :param peak_file2: Path of sample 2 peak file.\n :param read_file1: Path of sample 1 read file.\n :param read_file2: Path of sample 2 read file.\n :param peak_format: Format of peak files.\n :param read_format: Format of read files.\n :param name1: Name of sample 1.\n :param name2: Name of sample 2.\n :param shift_size1: Read shift size of sample 1, only effective in single-end mode.\n :param shift_size2: Read shift size of sample 2, only effective in single-end mode.\n :param paired: Paired-end sequencing or not.\n :param window_size: Window size when counting reads.\n :param summit_dis: Summit distance cutoff for overlapping common peaks when fitting normalization model.\n :param n_random: Number of random simulations to test the enrichment of peak overlapping.\n :param m_cutoff: M value cutoff to define biased (differential binding) peaks.\n :param p_cutoff: P value cutoff to define biased (differential binding) peaks.\n :param write_all: Write two extra output files containing the results of original (unmerged) peaks.\n :param output_dir: Output directory.\n :param verbose: Enable verbose log messages or not.\n :param args: Other positional arguments.\n :param kwargs: Other keyword arguments.\n \"\"\"\n self.start_time = time.time()\n self.end_time = None\n self.version = manorm.__version__\n self.peak_file1 = path.abspath(peak_file1)\n self.peak_file2 = path.abspath(peak_file2)\n self.peak_format = peak_format\n self.read_file1 = path.abspath(read_file1)\n self.read_file2 = path.abspath(read_file2)\n self.read_format = read_format\n self.shift_size1 = shift_size1\n self.shift_size2 = shift_size2\n self.paired = paired\n self.window_size = window_size\n self.summit_dis = window_size // 4 if summit_dis is None else summit_dis\n self.n_random = n_random\n self.n_overlap_rand = None\n self.m_cutoff = m_cutoff\n self.p_cutoff = p_cutoff\n self.name1 = name1 if name1 else path.splitext(path.basename(peak_file1))[0]\n self.name2 = name2 if name2 else path.splitext(path.basename(peak_file2))[0]\n self.output_prefix = self.name1 + \"_vs_\" + self.name2\n self.write_all = write_all\n self.output_dir = path.abspath('') if output_dir is None else path.abspath(output_dir)\n self.verbose = verbose\n self.ma_model = None\n self.num_biased1 = None\n self.num_biased2 = None\n self.num_unbiased = None\n self.args = args\n self.kwargs = kwargs\n\n def print_args(self):\n \"\"\"Log parameters that MAnorm uses.\"\"\"\n logger.info(\"==== INFO ====\")\n logger.info(\"Sample 1 name = {}\".format(self.name1))\n logger.info(\"Sample 1 peak file = {} [{}]\".format(self.peak_file1, self.peak_format))\n logger.info(\"Sample 1 read file = {} [{}]\".format(self.read_file1, self.read_format))\n if not self.paired:\n logger.info(\"Sample 1 read shift size = {}\".format(self.shift_size1))\n logger.info(\"Sample 2 name = {}\".format(self.name2))\n logger.info(\"Sample 2 peak file = {} [{}]\".format(self.peak_file2, self.peak_format))\n logger.info(\"Sample 2 read file = {} [{}]\".format(self.read_file2, self.read_format))\n if not self.paired:\n logger.info(\"Sample 2 read shift size = {}\".format(self.shift_size2))\n if self.paired:\n logger.info(\"Paired-end mode: on\")\n else:\n logger.info(\"Paired-end mode: off\")\n logger.info(\"Window size = {}\".format(self.window_size))\n logger.info(\"Summit distance cutoff = {}\".format(self.summit_dis))\n logger.info(\"Number of random simulation = {}\".format(self.n_random))\n logger.info(\"M-value cutoff = {}\".format(self.m_cutoff))\n logger.info(\"P-value cutoff = {}\".format(self.p_cutoff))\n logger.info(\"Output directory = {}\".format(self.output_dir))\n\n def load_input_data(self):\n \"\"\"Read input data.\"\"\"\n logger.info(\"Loading peaks of sample 1\")\n peaks1 = peak.load_peaks(path=self.peak_file1, format=self.peak_format, name=self.name1)\n logger.info(\"Loading peaks of sample 2\")\n peaks2 = peak.load_peaks(path=self.peak_file2, format=self.peak_format, name=self.name2)\n logger.info(\"Loading reads of sample 1\")\n reads1 = read.load_reads(path=self.read_file1, format=self.read_format, paired=self.paired,\n shift=self.shift_size1, name=self.name1)\n logger.info(\"Loading reads of sample 2\")\n reads2 = read.load_reads(path=self.read_file2, format=self.read_format, paired=self.paired,\n shift=self.shift_size2, name=self.name2)\n return peaks1, peaks2, reads1, reads2\n\n def test_overlap_enrich(self, peaks1, peaks2):\n \"\"\"Given two sets of peaks, test the enrichment of overlap compared to random control peak sets.\"\"\"\n n_overlap_rand = []\n for _ in range(self.n_random):\n peak_rand = generate_random_peaks(peaks2)\n tmp_count = 0\n for chrom in set(peaks1.chroms) | set(peak_rand.chroms):\n flag_overlap, _ = overlap_on_single_chr(peaks1.fetch(chrom), peak_rand.fetch(chrom))\n tmp_count += flag_overlap.sum()\n if tmp_count == 0:\n tmp_count = 1\n n_overlap_rand.append(tmp_count)\n n_overlap_rand = np.array(n_overlap_rand)\n return n_overlap_rand\n\n def run(self):\n \"\"\"Run MAnorm pipeline.\"\"\"\n setup_logger(self.verbose)\n logger.info(\"Running MAnorm v{}\".format(self.version))\n logger.info(\"Command-line arguments: {}\".format(' '.join(sys.argv[1:])))\n self.print_args()\n\n logger.info(\"==== Running ====\")\n logger.info(\"Step 1: Loading input data\")\n peaks1, peaks2, reads1, reads2 = self.load_input_data()\n\n logger.info(\"Step 2: Processing peaks\")\n self.ma_model = MAmodel(peaks1, peaks2, reads1, reads2)\n self.ma_model.process_peaks()\n\n logger.info(\"Step 3: Testing the enrichment of peak overlap\")\n if self.n_random > 0:\n self.n_overlap_rand = self.test_overlap_enrich(peaks1, peaks2)\n else:\n logger.info(\"Skipped\")\n\n logger.info(\"Step 4: Fitting M-A normalization model on common peaks\")\n self.ma_model.fit_model(window_size=self.window_size, summit_dis=self.summit_dis)\n\n logger.info(\"Step 5: Normalizing all peaks\")\n self.ma_model.normalize()\n\n logger.info(\"Step 6: Write output files\")\n self.output()\n\n self.report()\n\n def output(self):\n \"\"\"Write output files.\"\"\"\n mk_dir(self.output_dir)\n if self.write_all:\n write_original_peaks(self.output_dir, self.ma_model.peaks1, self.ma_model.peaks2)\n write_all_peaks(self.output_dir, self.ma_model.peaks1, self.ma_model.peaks2, self.ma_model.peaks_merged)\n write_wiggle_track(self.output_dir, self.ma_model.peaks1, self.ma_model.peaks2, self.ma_model.peaks_merged)\n self.num_biased1, self.num_biased2, self.num_unbiased = write_biased_peaks(self.output_dir,\n self.ma_model.peaks1,\n self.ma_model.peaks2,\n self.ma_model.peaks_merged,\n self.m_cutoff, self.p_cutoff)\n plt_figures(self.output_dir, self.ma_model.peaks1, self.ma_model.peaks2, self.ma_model.peaks_merged,\n self.ma_model.ma_params)\n\n def report(self):\n \"\"\"Report statistics.\"\"\"\n logger.info(\"==== Stats ====\")\n if self.paired:\n logger.info(\"Total read pairs of sample 1: {:,}\".format(self.ma_model.reads1.size))\n logger.info(\"Total read pairs of sample 2: {:,}\".format(self.ma_model.reads2.size))\n else:\n logger.info(\"Total reads of sample 1: {:,}\".format(self.ma_model.reads1.size))\n logger.info(\"Total reads of sample 2: {:,}\".format(self.ma_model.reads2.size))\n logger.info(\"Total peaks of sample 1: {} (unique: {} common: {})\".format(\n self.ma_model.peaks1.size, self.ma_model.peaks1.n_unique, self.ma_model.peaks1.n_common))\n logger.info(\"Total peaks of sample 2: {} (unique: {} common: {})\".format(\n self.ma_model.peaks2.size, self.ma_model.peaks2.n_unique, self.ma_model.peaks2.n_common))\n logger.info(\"Number of merged common peaks: {}\".format(self.ma_model.peaks_merged.size))\n\n logger.info(\"Number of overlapping peaks in random: mean:{:.2f} std:{:.2f}\".format(\n self.n_overlap_rand.mean(), self.n_overlap_rand.std()))\n logger.info(\"Fold change of overlapping peaks compared to random: {:.2f}\".format(\n self.ma_model.peaks1.n_common / self.n_overlap_rand.mean()))\n if self.ma_model.ma_params[0] >= 0:\n logger.info(\"M-A model: M = {:f} * A + {:f}\".format(self.ma_model.ma_params[1],\n self.ma_model.ma_params[0]))\n else:\n logger.info(\"M-A model: M = {:f} * A - {:f}\".format(self.ma_model.ma_params[1],\n abs(self.ma_model.ma_params[0])))\n logger.info(\"{} peaks with |M_value|<{} are filtered as unbiased peaks\".format(\n self.num_unbiased, abs(self.m_cutoff)))\n logger.info(\"{} peaks with M_value>={} are filtered as sample1-biased peaks\".format(\n self.num_biased1, abs(self.m_cutoff)))\n logger.info(\"{} peaks with M_value<=-{} are filtered as sample2-biased peaks\".format(\n self.num_biased2, abs(self.m_cutoff)))\n","sub_path":"manorm/application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":11224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"309234915","text":"class Stack():\n def __init__(self):\n self.stack = []\n\n def push(self, value):\n self.stack.append(value)\n\n def pop(self):\n if self.size() > 0:\n return self.stack.pop()\n else:\n return None\n\n def size(self):\n return len(self.stack)\n\n\nclass Graph:\n \"\"\"\n Represent a graph as a dictionary of vertices mapping labels to edges.\n \"\"\"\n\n def __init__(self):\n self.vertices = {}\n\n def add_vertex(self, vertex_id):\n \"\"\"\n Add a vertex to the graph.\n \"\"\"\n self.vertices[vertex_id] = set()\n\n def add_edge(self, v1, v2):\n \"\"\"\n Add a directed edge to the graph.\n \"\"\"\n if v1 in self.vertices and v2 in self.vertices:\n self.vertices[v1].add(v2)\n else:\n raise IndexError(\"That vertex does not exist.\")\n\n\ndef earliest_ancestor(ancestors, starting_node):\n graph = Graph()\n for parent, child in ancestors:\n graph.add_vertex(parent)\n graph.add_vertex(child)\n # graph.add_edge(child, parent)\n for parent, child in ancestors:\n graph.add_edge(child, parent)\n\n stack = Stack()\n stack.push([starting_node])\n # Create an empty Set to store visited vertices\n visited = set()\n longest_path = [-1]\n # While the que is not empty..\n while stack.size() > 0:\n # pop the first PATH\n path = stack.pop()\n # Grab last vertex from the PATH\n vertex = path[-1]\n # If that vertex hasn't been visited..\n if vertex not in visited:\n if len(path) > len(longest_path):\n longest_path = path.copy()\n elif len(path) == len(longest_path):\n if path[-1] < longest_path[-1]:\n longest_path = path.copy()\n # Mark it as visited\n visited.add(vertex)\n # Then add A PATH to its neighbors to the back of the queue\n for next_vert in graph.vertices[vertex]:\n # COPY THE PATH\n new_path = path.copy()\n # APPEND THE NEIGHBOR TO THE BACK\n new_path.append(next_vert)\n stack.push(new_path)\n\n return longest_path[-1]\n","sub_path":"projects/ancestor/ancestor.py","file_name":"ancestor.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"290179816","text":"import torch.nn as nn\nimport torch\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport math\nimport re\n\nMAX_DOC_LEN = 100\nmaxlen = MAX_DOC_LEN // 2 + 10 # 输入文本的最大词长度，与padding相关\nbatch_size = 32\nmax_pred = MAX_DOC_LEN // 2 + 10 # 最大预测词长度，需要padding和mask\nn_layers = 6\nn_heads = 6\nd_k = d_v = 24 # dimension of K(=Q), V\nd_model = n_heads * d_v # equal to n_heads * d_v\nd_ff = d_model * 4 # 4*d_model, FeedForward dimension\nlower_dimension = 5\nkg_emb_dimension = 5\nalpha = 0.1\nn_graph_heads = 3\n\n\ndef get_attn_pad_mask(seq_q, seq_k):\n batch_size, seq_len = seq_q.size()\n # eq(zero) is PAD token\n pad_attn_mask = seq_q.data.eq(0).unsqueeze(1) # [batch_size, 1, seq_len]\n return pad_attn_mask.expand(\n batch_size, seq_len, seq_len\n ) # [batch_size, seq_len, seq_len]\n\n\ndef gelu(x):\n return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))\n\n\ndef adj_tanh(x, a):\n m = a * x\n return (torch.exp(m) - torch.exp(-m)) / (torch.exp(m) + torch.exp(-m))\n\n\nclass Embedding(nn.Module):\n def __init__(self, vocab_size):\n super(Embedding, self).__init__()\n self.tok_embed = nn.Embedding(vocab_size, d_model) # token embedding\n self.pos_embed = nn.Embedding(maxlen, d_model) # position embedding\n # self.seg_embed = nn.Embedding(n_segments, d_model) # segment(token type) embedding\n self.norm = nn.LayerNorm(d_model)\n\n def forward(self, x):\n seq_len = x.size(1)\n pos = torch.arange(seq_len, dtype=torch.long)\n pos = pos.unsqueeze(0).expand_as(x) # [seq_len] -> [batch_size, seq_len]\n embedding = self.tok_embed(x) + self.pos_embed(pos)\n return self.norm(embedding)\n\n\nclass ScaledDotProductAttention(nn.Module):\n def __init__(self):\n super(ScaledDotProductAttention, self).__init__()\n\n def forward(self, Q, K, V, attn_mask):\n scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(\n d_k\n ) # scores : [batch_size, n_heads, seq_len, seq_len]\n # scores.masked_fill_(attn_mask, -1e9) # Fills elements of self tensor with value where mask is one.\n # attn = nn.Softmax(dim=-1)(scores)\n attn = adj_tanh(scores, a=alpha)\n attn.masked_fill(attn_mask, 0.0)\n context = torch.matmul(attn, V)\n return context\n\n\nclass MultiHeadAttention(nn.Module):\n def __init__(self):\n super(MultiHeadAttention, self).__init__()\n self.W_Q = nn.Linear(d_model, d_k * n_heads)\n self.linear = nn.Linear(n_heads * d_v, d_model)\n self.layernorm = nn.LayerNorm(d_model)\n\n def forward(self, Q, K, V, attn_mask):\n # q: [batch_size, seq_len, d_model], k: [batch_size, seq_len, d_model], v: [batch_size, seq_len, d_model]\n residual, batch_size = Q, Q.size(0)\n # (B, S, D) -proj-> (B, S, D) -split-> (B, S, H, W) -trans-> (B, H, S, W)\n q_s = (\n self.W_Q(Q).view(batch_size, -1, n_heads, d_k).transpose(1, 2)\n ) # q_s: [batch_size, n_heads, seq_len, d_k]\n k_s = K\n v_s = V\n\n attn_mask = attn_mask.unsqueeze(1).repeat(\n 1, n_heads, 1, 1\n ) # attn_mask : [batch_size, n_heads, seq_len, seq_len]\n\n # context: [batch_size, n_heads, seq_len, d_v], attn: [batch_size, n_heads, seq_len, seq_len]\n context = ScaledDotProductAttention()(q_s, k_s, v_s, attn_mask)\n context = (\n context.transpose(1, 2).contiguous().view(batch_size, -1, n_heads * d_v)\n ) # context: [batch_size, seq_len, n_heads, d_v]\n output = self.linear(context)\n return self.layernorm(\n output + residual\n ) # output: [batch_size, seq_len, d_model]\n\n\nclass PoswiseFeedForwardNet(nn.Module):\n def __init__(self):\n super(PoswiseFeedForwardNet, self).__init__()\n self.fc1 = nn.Linear(d_model, d_ff)\n self.fc2 = nn.Linear(d_ff, d_model)\n\n def forward(self, x):\n # (batch_size, seq_len, d_model) -> (batch_size, seq_len, d_ff) -> (batch_size, seq_len, d_model)\n return self.fc2(gelu(self.fc1(x)))\n\n\nclass EncoderLayer(nn.Module):\n def __init__(self):\n super(EncoderLayer, self).__init__()\n self.enc_self_attn = MultiHeadAttention()\n self.pos_ffn = PoswiseFeedForwardNet()\n\n def forward(self, enc_inputs, K, V, enc_self_attn_mask):\n enc_outputs = self.enc_self_attn(\n enc_inputs, K, V, enc_self_attn_mask\n ) # enc_inputs to same Q,K,V\n enc_outputs = self.pos_ffn(\n enc_outputs\n ) # enc_outputs: [batch_size, seq_len, d_model]\n return enc_outputs\n\n\nclass GraphAttn(nn.Module):\n def __init__(self):\n super(GraphAttn, self).__init__()\n self.compress = nn.Linear(3, 1) ## 三元组变一元\n\n def forward(self, query, emb_tri):\n # print(emb_tri.size())\n compressed_emb = self.compress(emb_tri.transpose(-1, -2)).squeeze(dim=-1)\n attn = adj_tanh(\n torch.matmul(query, compressed_emb.transpose(-1, -2))[:, 0, :], alpha\n )\n context = torch.matmul(attn, compressed_emb)[:, 0, :]\n return context\n\n\nclass BERT(nn.Module):\n def __init__(self, vocab_size):\n super(BERT, self).__init__()\n self.embedding = Embedding(vocab_size)\n self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])\n self.fc = nn.Sequential(\n nn.Linear(d_model, d_model),\n nn.Dropout(0.5),\n nn.Tanh(),\n )\n self.classifier = nn.Linear(d_model, 2)\n self.linear = nn.Linear(d_model, d_model)\n self.activ2 = gelu\n # fc2 is shared with embedding layer\n embed_weight = self.embedding.tok_embed.weight\n self.W_K = nn.Linear(d_model, d_k * n_heads)\n self.W_V = nn.Linear(d_model, d_v * n_heads)\n self.fc2 = nn.Linear(d_model, vocab_size, bias=False)\n self.fc2.weight = embed_weight\n self.dense1 = nn.Linear(vocab_size, d_model)\n self.dense2 = nn.Linear(d_model * 2, 2)\n self.graph_layers = nn.ModuleList([GraphAttn() for i in range(n_graph_heads)])\n self.compress = nn.Linear(n_graph_heads, 1)\n self.tri_emb_compress = nn.Linear(d_model, 2)\n\n def forward(self, input_ids, masked_pos, emb_tri, corr_tri, status=\"train\"):\n tri_vec = self.embedding.tok_embed(emb_tri)\n compressed_tri_vec = self.tri_emb_compress(tri_vec) # output picture\n\n if status == \"train\":\n corr_vec = self.embedding.tok_embed(emb_tri)\n compressed_corr_vec = self.tri_emb_compress(tri_vec) # output picture\n\n output = self.embedding(input_ids) # [bach_size, seq_len, d_model]\n enc_self_attn_mask = get_attn_pad_mask(\n input_ids, input_ids\n ) # [batch_size, maxlen, maxlen]\n batch_size = output.size(0)\n K = self.W_K(output).view(batch_size, -1, n_heads, d_k).transpose(1, 2)\n V = self.W_V(output).view(batch_size, -1, n_heads, d_v).transpose(1, 2)\n for layer in self.layers:\n # output: [batch_size, max_len, d_model]\n output = layer(output, K, V, enc_self_attn_mask)\n\n masked_pos = masked_pos[:, :, None].expand(\n -1, -1, d_model\n ) # [batch_size, max_pred, d_model]\n h_masked = torch.gather(\n output, 1, masked_pos\n ) # masking position [batch_size, max_pred, d_model]\n h_masked = self.activ2(self.linear(h_masked)) # [batch_size, max_pred, d_model]\n logits_lm = self.fc2(h_masked) # [batch_size, max_pred, vocab_size]\n Query = self.dense1(logits_lm[:, 0, :])\n\n contexts = [attn_head(Query, tri_vec) for attn_head in self.graph_layers]\n temp = torch.stack(contexts)\n temp = self.compress(temp.transpose(0, -1)).squeeze(dim=-1)\n\n concat = torch.cat([Query, temp.transpose(0, 1)], dim=1)\n logits_pred = self.dense2(concat)\n\n if status == \"train\":\n compressed_corr_vec = self.tri_emb_compress(tri_vec) # output picture\n return (\n logits_lm,\n logits_pred,\n Query,\n compressed_tri_vec,\n compressed_corr_vec,\n )\n else:\n return logits_lm, logits_pred, Query, compressed_tri_vec\n\n\ndef train(model, loader, epoch_num, Corrupt, criterion, optimizer, vocab_size, param):\n from tqdm import tqdm\n\n for epoch in tqdm(range(epoch_num)):\n for input_ids, masked_tokens, masked_pos, emb_tri, label in loader:\n corrupted = Corrupt(emb_tri)\n logits_lm, logits_pred, query, tri_vec, corr_vec = model(\n input_ids, masked_pos, emb_tri, corrupted\n )\n loss_lm = criterion(\n logits_lm.view(-1, vocab_size), masked_tokens.view(-1)\n ) # for masked LM\n loss_lm = (loss_lm.float()).mean()\n loss_pred = criterion(nn.functional.softmax(logits_pred, dim=1), label)\n loss_emb = (\n torch.abs(\n tri_vec[:, :, 0, :] + tri_vec[:, :, 2, :] - tri_vec[:, :, 1, :]\n ).sum()\n - torch.abs(\n corr_vec[:, :, 0, :] + corr_vec[:, :, 2, :] - corr_vec[:, :, 1, :]\n ).sum()\n )\n loss = loss_lm + loss_pred + param * loss_emb\n optimizer.zero_grad()\n loss.backward(retain_graph=True)\n optimizer.step()\n\n\ndef predict(model, batch):\n # 计算模型预测的准确率\n pred_labels = []\n true_labels = []\n model.eval()\n with torch.no_grad():\n for i in range(len(batch)):\n input_ids, masked_tokens, masked_pos, emb_tri, label = batch[i]\n logits_lm, logits_pred, query, tri_vec = model(\n torch.LongTensor([input_ids]).cuda(),\n torch.LongTensor([masked_pos]).cuda(),\n emb_tri.unsqueeze(dim=0).cuda(),\n None,\n status=\"test\",\n )\n logits_pred = nn.functional.softmax(logits_pred, dim=1)\n logits_pred = logits_pred.data.max(1)[1][0].tolist()\n pred_labels.append(logits_pred)\n true_labels.append(label)\n\n from sklearn.metrics import confusion_matrix\n\n cm = confusion_matrix(true_labels, pred_labels)\n\n from mlxtend.plotting import plot_confusion_matrix\n import matplotlib.pyplot as plt\n\n fig, ax = plot_confusion_matrix(conf_mat=cm, figsize=(6, 6), cmap=plt.cm.Greens)\n plt.xlabel(\"Predictions\", fontsize=18)\n plt.ylabel(\"Actuals\", fontsize=18)\n plt.title(\"Confusion Matrix\", fontsize=18)\n plt.show()\n\n accuracy = (cm[0][0] + cm[1][1]) / sum(sum(cm))\n print(\"accuracy:{:.3f}%\".format(accuracy * 100))\n\n\ndef code_triple(model, emb_tri_list, eps):\n emb_loss = []\n good_tri_emb = []\n model.eval()\n with torch.no_grad():\n for item in emb_tri_list:\n for tri in item:\n if tri != [0, 0, 0]:\n # triple_emb = model.tri_emb_compress(model.embedding.tok_embed(torch.tensor(tri).cuda())).cpu().numpy()\n triple_emb = model.tri_emb_compress(\n model.embedding.tok_embed(torch.tensor(tri))\n ).numpy()\n loss = (\n (triple_emb[0, :] + triple_emb[2, :] - triple_emb[1, :]) ** 2\n ).mean()\n emb_loss.append(loss)\n if loss < eps:\n # print(tri)\n if tri not in good_tri_emb:\n good_tri_emb.append(tri)\n return emb_loss, good_tri_emb\n\n\ndef encode_triple_vis(model, good_tri_emb, good_tri, idx):\n import matplotlib.pyplot as plt\n\n show_list = []\n model.eval()\n with torch.no_grad():\n for tri in good_tri_emb:\n temp = (\n model.tri_emb_compress(\n model.embedding.tok_embed(torch.tensor(tri).cuda())\n )\n .cpu()\n .detach()\n .numpy()\n )\n show_list.append(temp)\n\n fig = plt.figure(figsize=(6, 6))\n A = show_list[idx][0]\n B = show_list[idx][1]\n C = show_list[idx][2]\n\n plt.rcParams[\"axes.unicode_minus\"] = False\n plt.arrow(\n 0,\n 0,\n A[0],\n A[1],\n length_includes_head=True,\n head_width=0.05,\n head_length=0.05,\n fc=\"r\",\n ec=\"b\",\n )\n plt.annotate(good_tri[idx][0], ((0 + A[0]) / 2, (0 + A[1]) / 2), fontsize=15)\n\n plt.arrow(\n A[0],\n A[1],\n C[0],\n C[1],\n length_includes_head=True,\n head_width=0.05,\n head_length=0.05,\n fc=\"y\",\n ec=\"g\",\n )\n plt.annotate(\n good_tri[idx][1], ((C[0] + 2 * A[0]) / 2, (C[1] + 2 * A[1]) / 2), fontsize=15\n )\n\n plt.arrow(\n 0,\n 0,\n B[0],\n B[1],\n length_includes_head=True,\n head_width=0.05,\n head_length=0.05,\n fc=\"r\",\n ec=\"b\",\n )\n plt.annotate(good_tri[idx][2], ((0 + B[0]) / 2, (0 + B[1]) / 2), fontsize=15)\n\n # ax.set_xlim(-1.,1.) #设置图形的范围，默认为[0,1]\n # ax.set_ylim(-1.,1.) #设置图形的范围，默认为[0,1]\n plt.grid() # 添加网格\n # ax.set_aspect('equal') #x轴和y轴等比例\n plt.title(\"knowledge embedding visualization\")\n # plt.tight_layout()\n plt.show()\n\n\ndef graph_attn_vis(model, batch, idx, idx2word, n_graph_heads):\n import matplotlib.pyplot as plt\n import numpy as np\n\n model.eval()\n with torch.no_grad():\n input_ids, masked_tokens, masked_pos, emb_tri, label = batch[idx]\n x_axis = list()\n for tri in emb_tri.cpu().numpy():\n for item in tri:\n x_axis.append(idx2word[item])\n logits_lm, logits_pred, query, com_tri_vec = model(\n torch.LongTensor([input_ids]).cuda(),\n torch.LongTensor([masked_pos]).cuda(),\n emb_tri.unsqueeze(dim=0).cuda(),\n None,\n status=\"test\",\n )\n weight_list = []\n for i in range(n_graph_heads):\n tri_vec = model.embedding.tok_embed(emb_tri.cuda())\n compressed_emb = (\n model.graph_layers[i]\n .compress(tri_vec.transpose(-1, -2))\n .squeeze(dim=-1)\n )\n attn = adj_tanh(\n torch.matmul(query, compressed_emb.transpose(-1, -2)), alpha\n )\n token_weight = torch.matmul(\n attn.transpose(-1, -2), model.graph_layers[i].compress.weight.detach()\n ).view(1, -1)\n weight_list.append(list(token_weight.cpu().numpy()[0]))\n import seaborn as sns\n\n plt.figure(figsize=(12, 6))\n plt.rcParams[\"axes.unicode_minus\"] = False\n sns.heatmap(weight_list, cmap=\"bwr\", annot=True)\n plt.xticks(np.arange(len(x_axis)) + 0.5, x_axis, rotation=90)\n plt.yticks(np.arange(3) + 0.5, [\"head1\", \"head2\", \"head3\"], size=15)\n return weight_list\n\n\ndef cal_layer_weight(model):\n import numpy as np\n\n w_layers = list()\n for i in range(len(model.layers)):\n target_weight = model.layers[i].enc_self_attn.W_Q.weight\n w_layers.append(\n torch.sum(torch.abs(target_weight.grad)) / np.sqrt(target_weight.size(0))\n )\n w_across_layers = nn.Softmax(dim=-1)(torch.tensor(w_layers))\n return w_across_layers\n\n\ndef attn_vis(model, example, w, word2idx, pooling=True):\n import matplotlib.pyplot as plt\n import seaborn as sns\n import jieba\n\n token_input = [\"[CLS]\"] + list(jieba.cut(example))\n test_input = [word2idx[item] for item in token_input]\n word_embedding = model.embedding(\n torch.tensor(test_input).unsqueeze(dim=0).cuda()\n ) # batchsize * sequence_len * emb_dim\n K = model.W_K(word_embedding).view(1, -1, n_heads, d_k).transpose(1, 2)\n V = model.W_V(word_embedding).view(1, -1, n_heads, d_k).transpose(1, 2)\n\n model.eval()\n output = word_embedding\n score_list = []\n with torch.no_grad():\n for i in range(n_layers):\n Q = (\n model.layers[i]\n .enc_self_attn.W_Q(output)\n .view(1, -1, n_heads, d_k)\n .transpose(1, 2)\n )\n scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(d_k)\n score_list.append(scores)\n if i == n_layers - 1:\n break\n else:\n attn = adj_tanh(scores, alpha)\n context = torch.matmul(attn, V)\n context = (\n context.transpose(1, 2).contiguous().view(1, -1, n_heads * d_v)\n ) # context: [batch_size, seq_len, n_heads, d_v]\n output = model.layers[i].enc_self_attn.linear(context)\n output = model.layers[i].enc_self_attn.layernorm(\n output + Q.transpose(1, 2).contiguous().view(1, -1, n_heads * d_v)\n )\n output = model.layers[i].pos_ffn.fc2(\n gelu(model.layers[i].pos_ffn.fc1(output))\n )\n res = np.moveaxis(\n torch.cat(score_list).cpu().detach().numpy(), (0, 1, 2, 3), (3, 0, 1, 2)\n )\n final = torch.matmul(torch.from_numpy(res), w)\n if pooling:\n m = list()\n R = final.size(1)\n L = final.size(2)\n score_matrix = final.detach().numpy()\n for i in range(R):\n row = list()\n for j in range(L):\n index = torch.abs(final).argmax(dim=0)[i][j]\n row.append(score_matrix[index][i][j])\n m.append(row)\n\n plt.figure(figsize=(8, 6))\n plt.rcParams[\"axes.unicode_minus\"] = False\n sns.heatmap(m, annot=True, cmap=\"bwr\")\n plt.xticks(np.arange(len(token_input)) + 0.5, token_input, rotation=90, size=10)\n plt.yticks(\n np.arange(len(token_input)) + 0.5,\n token_input,\n rotation=360,\n size=10,\n va=\"center\",\n )\n return m\n else:\n fig = plt.figure(figsize=(27, 18))\n plt.rcParams[\"axes.unicode_minus\"] = False\n n_num = n_heads\n for i in range(n_num):\n ax = fig.add_subplot(n_num // 3, 3, i + 1)\n sns.heatmap(final[i, :, :], annot=True, cmap=\"bwr\")\n plt.xticks(range(len(token_input)), token_input, rotation=90, size=10)\n plt.yticks(range(len(token_input)), token_input, rotation=360, size=10)\n return final[i, :, :]\n","sub_path":"src/AugModel.py","file_name":"AugModel.py","file_ext":"py","file_size_in_byte":18553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"424629341","text":"# from django.shortcuts import render\n#\n# # Create your views here.\n# def hello(request):\n# context = {}\n# context['hello'] = 'hello world!'\n# return render(request,'index.html',context)\n#\n#############################################################################################\n#https://www.jb51.net/article/118632.htm\n\n# from django.shortcuts import render, HttpResponse\n# import os\n# import time\n# import json\n#\n# def test(request):\n# if request.method=='POST':\n# file_obj = request.FILES.get('avatar')\n# print('-' * 20)\n# print(file_obj)\n# return HttpResponse('sss')\n# return render(request, 'test.html')\n#\n#\n# def upload_avatar(request):\n# time.sleep(1)\n# file_obj = request.FILES.get('avatar')\n# print('-'*20)\n# print(file_obj)\n# file_path = os.path.join(r'C:\\Users\\14813\\Desktop\\zhaopian', file_obj.name)\n# print(file_path)\n# with open(file_path, 'wb') as f:\n# for chunk in file_obj.chunks():\n# f.write(chunk)\n# return HttpResponse(file_path)\n\n\n\n\n#####################################################\n#https://blog.csdn.net/kaikai136412162/article/details/79110327\nfrom django.shortcuts import render\nimport os\nfrom project import models\nfrom PIL import Image\n\n\n# Create your views here.\n\ndef uss(req):\n img = models.UserInfor.objects.all()\n return render(req, 'list.html', {'img': img})\n\n\ndef userInfor(req):\n if req.method == \"POST\":\n u = req.POST.get(\"username\", None)\n s = req.POST.get(\"sex\", None)\n e = req.POST.get(\"email\", None)\n p = req.POST.get(\"tt\",None)\n print('#################################')\n print(u,s,e)\n print(os.path.abspath(p))\n print(p)\n im = Image.open(os.path.abspath(p))\n cp = im.copy()\n cp.save('./p')\n\n # ---------表中插入数据方式一\n # info={\"username\":u,\"sex\":s,\"email\":e}\n # models.UserInfor.objects.create(**info)\n\n # ---------表中插入数据方式二\n models.UserInfor.objects.create(\n username=u,\n sex=s,\n email=e,\n photo=p,\n )\n\n info_list = models.UserInfor.objects.all()\n\n return render(req, \"list2.html\", {\"info_list\": info_list})\n\n return render(req, \"list2.html\")","sub_path":"2019325/erweima/project/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2317,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"612593952","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 ('ffsq', '0003_auto_20150223_0517'),\n ]\n\n operations = [\n migrations.RemoveField(\n model_name='ranking',\n name='uses',\n ),\n migrations.RemoveField(\n model_name='review',\n name='uses',\n ),\n migrations.DeleteModel(\n name='Use',\n ),\n migrations.AddField(\n model_name='ranking',\n name='use',\n field=models.CharField(max_length=32, verbose_name='\\u96c6\\u8a08\\u5bfe\\u8c61\\u30d5\\u30a3\\u30fc\\u30eb\\u30c9', blank=True),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='review',\n name='baby',\n field=models.BooleanField(default=False, verbose_name='\\u5e7c\\u5150\\u3068\\u904a\\u3076'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='review',\n name='child',\n field=models.BooleanField(default=False, verbose_name='\\u5c0f\\u5b66\\u751f\\u304c\\u904a\\u3076'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='review',\n name='flower_viewing',\n field=models.BooleanField(default=False, verbose_name='\\u304a\\u82b1\\u898b'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='review',\n name='rest',\n field=models.BooleanField(default=False, verbose_name='\\u4f11\\u61a9\\u3059\\u308b'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='review',\n name='sports',\n field=models.BooleanField(default=False, verbose_name='\\u30b9\\u30dd\\u30fc\\u30c4'),\n preserve_default=True,\n ),\n migrations.AddField(\n model_name='review',\n name='walk',\n field=models.BooleanField(default=False, verbose_name='\\u6563\\u6b69\\u3059\\u308b'),\n preserve_default=True,\n ),\n ]\n","sub_path":"ffsq/migrations/0004_auto_20150223_0534.py","file_name":"0004_auto_20150223_0534.py","file_ext":"py","file_size_in_byte":2176,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"618689007","text":"import os\nimport logging\n\nworking_path = os.path.abspath(\"\")\n\ndef enable_log(log_name):\n \"\"\" Enable logs written to file \"\"\"\n logging.basicConfig(filename= log_name + \".log\",\n level=logging.DEBUG,\n format='%(asctime)s %(levelname)s %(message)s')\n\n\n","sub_path":"src/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"495870388","text":"# 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 json\nimport logging\nimport os\n\nsplit_id = int(os.environ['LIMINAL_SPLIT_ID'])\nnum_splits = int(os.environ['LIMINAL_NUM_SPLITS'])\n\ninputs_dir = f'/mnt/vol1/inputs/{split_id}'\noutputs_dir = '/mnt/vol1/outputs/'\n\nif not os.path.exists(outputs_dir):\n os.makedirs(outputs_dir)\n\nlogging.info(f'Running write_outputs for split id {split_id} [NUM_SPLITS = {num_splits}]')\n\nfor filename in os.listdir(inputs_dir):\n with open(os.path.join(inputs_dir, filename)) as infile, \\\n open(os.path.join(\n outputs_dir,\n filename.replace('input', 'output').replace('.json', '.txt')\n ), 'w') as outfile:\n logging.info(f'Writing output file: {outfile.name}')\n data = json.loads(infile.read())\n outfile.write(data['mykey'])\n","sub_path":"tests/runners/airflow/liminal/write_outputs/write_outputs.py","file_name":"write_outputs.py","file_ext":"py","file_size_in_byte":1330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"328216268","text":"import unittest\n\nfrom cloudrail.knowledge.context.azure.resources.network.azure_vnet_gateway import VirtualNetworkGatewayType, AzureVirtualNetworkGateway\nfrom cloudrail.knowledge.context.azure.azure_environment_context import AzureEnvironmentContext\nfrom cloudrail.knowledge.rules.azure.non_context_aware.vpn_gateway_disallow_basic_sku_rule import VpnGatewayDisallowBasicSkuRule\nfrom cloudrail.knowledge.rules.base_rule import RuleResultType\nfrom cloudrail.knowledge.context.aliases_dict import AliasesDict\nfrom cloudrail.dev_tools.rule_test_utils import create_empty_entity\n\nfrom parameterized import parameterized\n\n\nclass TestVpnGatewayDisallowBasicSku(unittest.TestCase):\n def setUp(self):\n self.rule = VpnGatewayDisallowBasicSkuRule()\n\n @parameterized.expand([\n ['Basic VPN - Should Alert', VirtualNetworkGatewayType.VPN, 'Basic', True],\n ['Basic Express Route - Ok', VirtualNetworkGatewayType.EXPRESS_ROUTE, 'Basic', False],\n ['Standard VPN - Ok', VirtualNetworkGatewayType.VPN, 'Standard', False],\n ])\n def test_vpn_gateway_disallow_basic_sku(self, unused_name: str, gw_type: VirtualNetworkGatewayType, sku: str, should_alert: bool):\n # Arrange\n vpn_gw = create_empty_entity(AzureVirtualNetworkGateway)\n vpn_gw.gateway_type = gw_type\n vpn_gw.sku_tier = sku\n context = AzureEnvironmentContext(vnet_gateways=AliasesDict(vpn_gw))\n # Act\n result = self.rule.run(context, {})\n # Assert\n if should_alert:\n self.assertEqual(RuleResultType.FAILED, result.status)\n self.assertEqual(1, len(result.issues))\n else:\n self.assertEqual(RuleResultType.SUCCESS, result.status)\n","sub_path":"tests/knowledge/rules/azure/non_context_aware/test_vpn_gateway_disallow_basic_sku.py","file_name":"test_vpn_gateway_disallow_basic_sku.py","file_ext":"py","file_size_in_byte":1709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"550014079","text":"import numpy as np\nimport gym\n\nAPI_KEY = \"sk_yy5S2r1SmOL5ZZcNuL5IA\"\n\n\ndef epsilon_greedy_policy(Q, epsilon, actions):\n \"\"\" Q is a numpy array, epsilon between 0,1\n and a list of actions\"\"\"\n\n def policy_fn(state):\n if np.random.rand() > epsilon:\n action = np.argmax(Q[state, :])\n else:\n action = np.random.choice(actions)\n return action\n\n return policy_fn\n\n\nenv = gym.make(\"FrozenLake-v0\")\nenv = gym.wrappers.Monitor(env, 'sarsa', force=True)\nQ = np.random.rand(env.observation_space.n, env.action_space.n)\nR = np.zeros((env.observation_space.n, env.action_space.n))\nN = np.zeros((env.observation_space.n, env.action_space.n))\nactions = range(env.action_space.n)\ngamma = 0.99\n\nn_episodes = 10000\n\n# TO DO:\nfor j in range(n_episodes):\n done = False\n\n policy = epsilon_greedy_policy(Q, epsilon=0.1*(0.9**j), actions=actions)\n state = env.reset()\n action1 = policy(state)\n ### Generate sample episode\n while not done:\n\n new_state, reward, done, _ = env.step(action1)\n new_action = policy(new_state)\n if not done:\n q = Q[new_state, new_action]\n else:\n q = 0.\n Q[state, action1] += (reward + gamma * q - Q[state, action1]) * 0.85\n state = new_state\n action1 = new_action\n\n\n\nenv.close()\n\ngym.upload('sarsa', api_key=API_KEY)\n","sub_path":"Lecture_2/Sarsa.py","file_name":"Sarsa.py","file_ext":"py","file_size_in_byte":1362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"119686323","text":"#!/usr/bin/env python\n\n# FOR THE DIFFERENT WHEEL SETUP WHERE WHEEL 2 IS THE BACK WHEEL\n\nfrom numpy import matrix\nfrom numpy import linalg\nimport math\n\n# Measurements in meters\n\n# radius of wheel\nR = .02712 #tune me #meters\n#radius from center of robot to wheel\nr = .066 #tune me. #meters\n \n# Front-Right wheel\nr3theta = -math.pi/3.0 # angular position\nr3x = math.cos(r3theta)*r # Cartesian x\nr3y = math.sin(r3theta)*r # Cartedsian y\n\n# Front-Left wheel\nr1theta = math.pi/3.0 # angular position\nr1x = math.cos(r1theta)*r # Cartesian x\nr1y = math.sin(r1theta)*r # Cartedsian y\n\n# Back Wheel\nr2theta = math.pi # angular position\nr2x = math.cos(r2theta)*r # Cartesian x\nr2y = math.sin(r2theta)*r # Cartedsian y\n\n# Wheel 1 velocity vector \ns3theta = r3theta + math.pi/2 # angular position vector\ns3x = math.cos(s3theta) # Cartesian x\ns3y = math.sin(s3theta) # Cartedsian y\n\n# Wheel 2 velocity vector \ns1theta = r1theta + math.pi/2 # angular position vector\ns1x = math.cos(s1theta) # Cartesian x\ns1y = math.sin(s1theta) # Cartedsian y\n\n# Wheel 3 velocity vector \ns2theta = r2theta + math.pi/2 # angular position vector\ns2x = math.cos(s2theta) # Cartesian x\ns2y = math.sin(s2theta) # Cartedsian y\n\n# Converts body frame velocities (x,y,omega) to wheel omegas\nM = (1.0/R)*matrix( [[s1x,s1y,(s1y*r1x - s1x*r1y)],\n [s2x,s2y,(s2y*r2x - s2x*r2y)],\n [s3x,s3y,(s3y*r3x - s3x*r3y)]] )\n\n# Rotation matrix from body to world frame\nR = lambda theta: matrix( [[math.cos(theta),math.sin(theta),0.0],\n [-math.sin(theta),math.cos(theta),0.0],\n [0.0,0.0,1.0]] )\n\n# Given an x, y, and omega in the body frame, this function returns the necessary\n# wheel velocites Omega1, Omega2, Omega3\n# @param x: Desired velocity in x direction in body frame\n# @param y: Desired velocity in y direction in body frame\n# @param omega: Desired angular velocity\n# @return: Omega1, Omega2, Omega3 wheel velocities (rads/s)\ndef getWheelVel(x,y,omega):\n \tdesired = matrix( [[x],\n [y],\n [omega]] )\n\t\n\t# Convert from body frame to wheel velocities \n \tresult = M*desired\n\n \treturn result.getA()[0][0], result.getA()[1][0], result.getA()[2][0]\n\n# Given world frame velocities, function will return corresponding body frame velocities\n# @param x: Desired velocity in x direction in world frame\n# @param y: Desired velocity in y direction in world frame\n# @param omega: Desired angular velocity\n# @param theta: angle between body and world frames\n# @return: Velocities in body frame\ndef getBodyXYOmega(x,y,omega,theta):\n \tdesired = matrix( [[x],\n [y],\n [omega]] )\n \tdesired = R(theta)*desired\n \treturn desired\n\ndef getBodyXYOmegaAsTuple(x, y, omega, theta):\n desired = getRobotXYOmega(x, y, omega, theta)\n asArray = desired.getA()\n return asArray[0][0], asArray[1][0], asArray[2][0]\n\n# Wheel speeds from world frame data.\ndef getWheelVelTheta(x,y,omega,theta):\n \tdesired = getBodyXYOmega(x, y, omega, theta)\n \n \tresult = M*desired\n\n \treturn result.getA()[0][0], result.getA()[1][0], result.getA()[2][0]\n\n# Will get the wheel omegas required for world frame movement.\n# @param vx_w: Velocity in the x in the world frame\n# @param vy_w: Velocity in the y in the world frame\n# @param omega: Angular velocity in the world (any) frame\n# @param theta: Angle (degrees) between body and world frames\n# @return: Wheel velocities as array [Omega1,Omega2,Omega3]\ndef getWheelVelFromWorld(vx_w,vy_w,omega=0,theta=0):\n # Desired velocities\n desVel = matrix([[vx_w],\n [vy_w],\n [omega]])\n transferMatrix = M*R(theta)\n wheelVels = transferMatrix*desVel\n\n return wheelVels\n\n","sub_path":"ros_ws/src/robot_soccer/motion_control/library/mat2.py","file_name":"mat2.py","file_ext":"py","file_size_in_byte":3748,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"572691787","text":"import socket\n\ndef main():\n s = socket.socket()\n\n print(\"Socket created successfully\")\n\n port = 12345\n\n # Binding\n\n s.bind(('', port))\n\n print(\"Socket binded to the port %s\" %(port))\n\n s.listen(5)\n\n print(\"Server is listening on the port %s\" %(port))\n\n while True:\n c, addr = s.accept()\n print(\"Got the connection request from \", addr)\n\n encoded_data_from_client = c.recv(2000)\n decoded_data_from_client = encoded_data_from_client.decode()\n\n\n message = \"Recieved message \" + decoded_data_from_client + \" from client\"\n encoded_message = message.encode()\n\n c.send(encoded_message)\n\n c.close()\n\nmain()","sub_path":"socketp/server.py","file_name":"server.py","file_ext":"py","file_size_in_byte":677,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"155346245","text":"import time\nimport png\nimport os\n\nimport numpy as np\n\nfrom thermografree.htpa import *\n\nnp.set_printoptions(threshold=1500, linewidth=150, precision=2)\n\ndef _imageify_array(values, img_range=None):\n \"\"\"Produce a grayscale image (array) from an array with arbitrary values.\n\n Pixel value will be 0 at the minimum range value and 255 at the max range\n value.\n\n Args:\n values (ndarray): Array of arbitrary values (e.g., celsius, cloud height).\n img_range (list(int, int), optional): Set with min/max range values. If\n None then the image range will include the entire range in the array.\n\n Returns:\n ndarray: Greyscale array where black is high side of range.\n \"\"\"\n if img_range is None:\n img_range = (np.nanmin(values), np.nanmax(values))\n\n # subtract the lower bound from the array so that that value becomes 0\n # then multiply everything by the multiplier to distribute the values\n # inside of the range\n scale = 255 / (img_range[1] - img_range[0])\n\n # astype() causes nan's to become 0\n return np.clip((values - img_range[0]) * scale, 0, 255).astype('uint8')\n\n\ndef bigify(nparr):\n new_size = 512\n bigarr = np.zeros((new_size,new_size))\n mult = int(new_size/32)\n for x in range(nparr.shape[0]):\n for y in range(nparr.shape[1]):\n bigarr[x*mult:x*mult+mult, y*mult:y*mult+mult] = nparr[x, y]\n\n return bigarr.astype('uint8')\n\nprint('* Creating HTPA device')\ncnt = 0\nwith HTPA(0x1A, pull_ups=False) as dev:\n while True:\n print(time.ctime())\n ambient = dev.measure_ambient_temperature()\n print('Ambient Temp: {:+.1f}c'.format(to_celsius(ambient)))\n\n im = dev.measure_temperatures(num_frames=15)\n\n #print(_imageify_array(im))\n #print(bigify(_imageify_array(im)))\n fname = 'out{}.png'.format(cnt)\n png.from_array(bigify(_imageify_array(im)), 'L').save(fname)\n\n os.system('imgcat {}'.format(fname))\n\n print('32x32 image (celsius)')\n print(im.astype('int32'))\n\n cnt += 1\n time.sleep(10)\n\n print('\\n\\n')\n","sub_path":"thermografree/capture_display.py","file_name":"capture_display.py","file_ext":"py","file_size_in_byte":1992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"95734288","text":"\"\"\"\n[541] Reverse String II\n\nhttps://leetcode.com/problems/reverse-string-ii/description/\n\n* algorithms\n* Easy (44.25%)\n* Source Code: 541.reverse-string-ii.python3.py\n* Total Accepted: 46.5K\n* Total Submissions: 105.1K\n* Testcase Example: '\"abcdefg\"\\n2'\n\n\nGiven a string and an integer k, you need to reverse the first k characters for every 2k characters counting from the start of the string. If there are less than kcharacters left, reverse all of them. If there are less than 2k but greater than or equal to k characters, then reverse the first k characters and left the other as original.\n\n\nExample:\n\nInput: s = \"abcdefg\", k = 2\nOutput: \"bacdfeg\"\n\n\n\nRestrictions:\n\n The string consists of lower English letters only.\n Length of the given string and k will in the range [1, 10000]\n \"\"\"\n\nclass Solution:\n def reverseStr(self, s, k):\n \"\"\"\n :type s: str\n :type k: int\n :rtype: str\n \"\"\"\n rs = list(s)\n for i in range(0, len(rs), 2 * k):\n rs[i:i+k] = reversed(rs[i:i+k])\n return \"\".join(rs)\n","sub_path":"541.reverse-string-ii.python3.py","file_name":"541.reverse-string-ii.python3.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"581558317","text":"import psutil\n\ndef count():\n cpu_logical = psutil.cpu_count(logical=False)\n cpu_cores = psutil.cpu_count(logical=True)\n\n return {\n 'logical' : cpu_logical,\n 'core' : cpu_cores,\n }\n\ndef procent(interval=0.1):\n cpu_procent_core = psutil.cpu_percent(interval=interval,percpu=True)\n cpu_procent_total = psutil.cpu_percent(interval=interval)\n\n core_load = {}\n\n for inx, core in enumerate(cpu_procent_core):\n core_load['core-{inx}'.format(inx=inx)] = core\n\n return {\n 'total' : cpu_procent_total,\n 'cores' : core_load\n }\n\ndef times():\n cpu_time = psutil.cpu_times()\n\n return {\n 'user' : cpu_time.user,\n 'nice' : cpu_time.nice,\n 'system' : cpu_time.system,\n 'idle' : cpu_time.idle,\n }\n","sub_path":"cpu/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"128729806","text":"import imageprocessing as ip\nimport numpy as np\nimport matplotlib.pyplot as plt \n\nimage, matrix = ip.imageprocessing('groundplanhires.png')\n\nrouter = [500, 200]\nrays = 20\n\nr = 1200\ntheta = np.linspace(0, 2 * np.pi, rays + 1)\n\nfig, ax = plt.subplots(1, 1)\n\n# RaysMat = np.full(())\n\nfor a in theta:\n x = router[0] + r * np.cos(a)\n y = router[1] + r * np.sin(a)\n ax.plot([router[0], x], [router[1], y], 'r-')\n\nax.imshow(matrix, cmap='Greys')\nax.plot(router[0], router[1], 'bs', markersize=10)\nax.axis([-1, len(matrix[0]), -1, len(matrix[:, 0])])\nplt.show() ","sub_path":"WhereDoIPutMyRouter/raytracing.py","file_name":"raytracing.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"82487980","text":"import cv2\nimport numpy as np\nimport pytesseract\nfrom PIL import Image\n\n# Path of working folder on Disk\n\ndef get_string(img_path):\n # Read image with opencv\n img = cv2.imread(img_path)\n\n # Convert to gray\n img = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n\n # Apply dilation and erosion to remove some noise\n kernel = np.ones((1, 1), np.uint8)\n img = cv2.dilate(img, kernel, iterations=1)\n img = cv2.erode(img, kernel, iterations=1)\n\n # Write image after removed noise\n cv2.imwrite(\"removed_noise.png\", img)\n\n # Apply threshold to get image with only black and white\n \n\n # Write the image after apply opencv to do some ...\n cv2.imwrite(img_path, img)\n\n # Recognize text with tesseract for python\n result = pytesseract.image_to_string(Image.open(img_path))\n\n # Remove template file\n \n\n return result\n\n\nprint (\"_____The Recognization Process Of Text From The Input Image Started Successfully______\")\nprint (\"The Recognized Text From The Input Image IS:\")\nprint (get_string(filename))\n\nprint (\"_____Process Finished_______\")\n","sub_path":"file.py","file_name":"file.py","file_ext":"py","file_size_in_byte":1075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"269138485","text":"class TsvDocumentsLoader():\n\n def __init__(self, path_to_cities_tsv):\n self.path_to_data_file = path_to_cities_tsv\n self.documents = []\n\n def load_documents(self):\n with open(self.path_to_data_file, 'r') as file:\n headers = file.readlines(1)[0].split('\\t')\n for line in file.readlines():\n document = {}\n values = line.split('\\t')\n for i, header in enumerate(headers):\n document[header] = values[i]\n self.documents.append(document)\n","sub_path":"suggestionsapi/search/tsv_documents_loader.py","file_name":"tsv_documents_loader.py","file_ext":"py","file_size_in_byte":559,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"252666955","text":"import os\nimport qtui\nimport qt\nimport copy\nimport queue_model_objects_v1 as queue_model_objects\nimport queue_model_enumerables_v1 as queue_model_enumerables\nimport queue_item\nimport ShapeHistory as shape_history\nimport logging\n\nfrom widgets.widget_utils import DataModelInputBinder\nfrom create_task_base import CreateTaskBase\nfrom widgets.data_path_widget import DataPathWidget\nfrom acquisition_widget_simple import AcquisitionWidgetSimple\n\nfrom queue_model_enumerables_v1 import XTAL_SPACEGROUPS\n\n\nclass CreateCharWidget(CreateTaskBase):\n def __init__(self,parent = None,name = None, fl = 0):\n CreateTaskBase.__init__(self, parent, name, fl, 'Characterisation')\n\n if not name:\n self.setName(\"create_char_widget\")\n\n #\n # Data attributes\n #\n self._current_selected_item = None\n self.init_models()\n self._char_params_mib = DataModelInputBinder(self._char_params)\n \n #\n # Layout\n #\n v_layout = qt.QVBoxLayout(self, 2, 6, \"v_layout\")\n \n self._acq_widget = \\\n AcquisitionWidgetSimple(self, acq_params = self._acquisition_parameters,\n path_template = self._path_template)\n self._acq_widget.setFixedHeight(170)\n\n current_dir = os.path.dirname(__file__)\n ui_file = 'ui_files/vertical_crystal_dimension_widget_layout.ui'\n widget = qtui.QWidgetFactory.create(os.path.join(current_dir, ui_file))\n\n widget.reparent(self, qt.QPoint(0,0))\n self._vertical_dimension_widget = widget\n\n ui_file = 'ui_files/characterise_simple_widget_vertical_layout.ui'\n widget = qtui.QWidgetFactory.\\\n create(os.path.join(current_dir, ui_file))\n\n widget.reparent(self, qt.QPoint(0,0))\n self._char_widget = widget\n\n self._data_path_gbox = \\\n qt.QVGroupBox('Data location', self, 'data_path_gbox')\n \n self._data_path_widget = \\\n DataPathWidget(self._data_path_gbox, \n data_model = self._path_template,\n layout = 'vertical')\n\n v_layout.addWidget(self._acq_widget)\n v_layout.addWidget(self._data_path_gbox)\n v_layout.addWidget(self._char_widget)\n v_layout.addWidget(self._vertical_dimension_widget)\n v_layout.addStretch(100)\n\n #\n # Logic\n #\n optimised_sad_cbx = self._char_widget.\\\n child('optimised_sad_cbx')\n account_rad_dmg_cbx = self._char_widget.\\\n child('account_rad_dmg_cbx')\n start_comp_cbox = self._char_widget.child('start_comp_cbox')\n #induced_burn_cbx = self._char_widget.child('induced_burn_cbx')\n max_vdim_ledit = self._vertical_dimension_widget.\\\n child('max_vdim_ledit')\n min_vdim_ledit = self._vertical_dimension_widget.\\\n child('min_vdim_ledit')\n\n min_vphi_ledit = self._vertical_dimension_widget.\\\n child('min_vphi_ledit')\n\n max_vphi_ledit = self._vertical_dimension_widget.\\\n child('max_vphi_ledit')\n\n space_group_ledit = self._vertical_dimension_widget.\\\n child('space_group_ledit')\n \n self._char_params_mib.bind_value_update('opt_sad',\n optimised_sad_cbx,\n bool, None)\n\n self._char_params_mib.bind_value_update('account_rad_damage', \n account_rad_dmg_cbx,\n bool, None)\n\n #self._char_params_mib.bind_value_update('determine_rad_params', \n # induced_burn_cbx,\n # bool, None)\n\n self._char_params_mib.bind_value_update('strategy_complexity',\n start_comp_cbox,\n int, None)\n\n self._char_params_mib.\\\n bind_value_update('max_crystal_vdim',\n max_vdim_ledit, float,\n qt.QDoubleValidator(0.0, 1000, 2, self))\n\n self._char_params_mib.\\\n bind_value_update('min_crystal_vdim',\n min_vdim_ledit, float,\n qt.QDoubleValidator(0.0, 1000, 2, self))\n\n self._char_params_mib.\\\n bind_value_update('min_crystal_vphi',\n min_vphi_ledit, float,\n qt.QDoubleValidator(0.0, 1000, 2, self))\n\n self._char_params_mib.\\\n bind_value_update('max_crystal_vphi',\n max_vphi_ledit, float,\n qt.QDoubleValidator(0.0, 1000, 2, self))\n \n space_group_ledit.insertStrList(XTAL_SPACEGROUPS)\n\n prefix_ledit = self._data_path_widget.\\\n data_path_widget_layout.child('prefix_ledit')\n\n run_number_ledit = self._data_path_widget.\\\n data_path_widget_layout.child('run_number_ledit')\n\n self.connect(prefix_ledit,\n qt.SIGNAL(\"textChanged(const QString &)\"), \n self._prefix_ledit_change)\n\n self.connect(run_number_ledit,\n qt.SIGNAL(\"textChanged(const QString &)\"), \n self._run_number_ledit_change)\n\n self.connect(space_group_ledit,\n qt.SIGNAL(\"activated(int)\"),\n self._space_group_change)\n\n self.connect(self._data_path_widget,\n qt.PYSIGNAL(\"path_template_changed\"),\n self.handle_path_conflict)\n\n #self.connect(induced_burn_cbx, qt.SIGNAL(\"toggled(bool)\"),\n # self.use_induced_burn)\n\n def use_induced_burn(self, state):\n self._acquisition_parameters.induce_burn = state\n\n def _space_group_change(self, index):\n self._char_params.space_group = queue_model_enumerables.\\\n XTAL_SPACEGROUPS[index]\n def _set_space_group(self, space_group):\n index = 0\n \n if space_group in XTAL_SPACEGROUPS:\n index = XTAL_SPACEGROUPS.index(space_group)\n\n self._space_group_change(index)\n self._vertical_dimension_widget.\\\n child('space_group_ledit').setCurrentItem(index)\n\n def init_models(self):\n CreateTaskBase.init_models(self)\n self._init_models()\n\n def _init_models(self):\n self._char = queue_model_objects.Characterisation()\n self._char_params = self._char.characterisation_parameters\n self._processing_parameters = queue_model_objects.ProcessingParameters()\n\n if self._beamline_setup_hwobj is not None: \n self._acquisition_parameters = self._beamline_setup_hwobj.\\\n get_default_char_acq_parameters()\n\n self._char_params = self._beamline_setup_hwobj.\\\n get_default_characterisation_parameters()\n try:\n transmission = self._beamline_setup_hwobj.transmission_hwobj.getAttFactor()\n transmission = round(float(transmission), 1)\n except AttributeError:\n transmission = 0\n\n try:\n resolution = self._beamline_setup_hwobj.resolution_hwobj.getPosition()\n resolution = round(float(resolution), 3)\n except AttributeError:\n resolution = 0\n\n try:\n energy = self._beamline_setup_hwobj.energy_hwobj.getCurrentEnergy()\n energy = round(float(energy), 4)\n except AttributeError:\n energy = 0\n\n self._acquisition_parameters.resolution = resolution\n self._acquisition_parameters.energy = energy\n self._acquisition_parameters.transmission = transmission\n else:\n self._acquisition_parameters = queue_model_objects.AcquisitionParameters()\n \n self._path_template.reference_image_prefix = 'ref'\n # The num images drop down default value is 1\n # we would like it to be 2\n self._acquisition_parameters.num_images = 2\n self._char.characterisation_software =\\\n queue_model_enumerables.COLLECTION_ORIGIN.EDNA\n self._path_template.num_files = 2\n self._acquisition_parameters.shutterless = False\n\n def single_item_selection(self, tree_item):\n CreateTaskBase.single_item_selection(self, tree_item)\n \n if isinstance(tree_item, queue_item.SampleQueueItem):\n logging.info(\"no update, sample level\")\n \"\"\"JN 20140829, keep the values user set up\n self._init_models()\n self._set_space_group(self._char_params.space_group)\n\n self._acq_widget.update_data_model(self._acquisition_parameters,\n self._path_template)\n self._char_params_mib.set_model(self._char_params)\n \"\"\" \n\n #self._char_params = copy.deepcopy(self._char_params)\n #self._acquisition_parameters = copy.deepcopy(self._acquisition_parameters)\n\n elif isinstance(tree_item, queue_item.CharacterisationQueueItem):\n if tree_item.get_model().is_executed():\n self.setDisabled(True)\n else:\n self.setDisabled(False)\n\n self._char = tree_item.get_model()\n\n if self._char.get_path_template():\n self._path_template = self._char.get_path_template()\n\n self._data_path_widget.update_data_model(self._path_template)\n \n data_collection = self._char.reference_image_collection\n\n self._char_params = self._char.characterisation_parameters\n self._char_params_mib.set_model(self._char_params)\n\n self._acquisition_parameters = data_collection.acquisitions[0].\\\n acquisition_parameters\n logging.info(\"resolution is set to %s\", str(self._acquisition_parameters.resolution))\n\n self._acq_widget.update_data_model(self._acquisition_parameters,\n self._path_template)\n self.get_acquisition_widget().use_osc_start(True)\n\n if len(data_collection.acquisitions) == 1:\n self.select_shape_with_cpos(self._acquisition_parameters.\\\n centred_position)\n\n self._processing_parameters = data_collection.processing_parameters\n else:\n self.setDisabled(True)\n\n def update_processing_parameters(self, crystal):\n self._processing_parameters.space_group = crystal.space_group\n self._char_params.space_group = crystal.space_group\n self._processing_parameters.cell_a = crystal.cell_a\n self._processing_parameters.cell_alpha = crystal.cell_alpha\n self._processing_parameters.cell_b = crystal.cell_b\n self._processing_parameters.cell_beta = crystal.cell_beta\n self._processing_parameters.cell_c = crystal.cell_c\n self._processing_parameters.cell_gamma = crystal.cell_gamma\n\n def approve_creation(self):\n return CreateTaskBase.approve_creation(self)\n \n # Called by the owning widget (task_toolbox_widget) to create\n # a collection. when a data collection group is selected.\n def _create_task(self, sample, shape):\n tasks = []\n\n if not shape:\n cpos = queue_model_objects.CentredPosition()\n cpos.snapshot_image = self._shape_history.get_snapshot([])\n else:\n # Shapes selected and sample is mounted, get the\n # centred positions for the shapes\n if isinstance(shape, shape_history.Point):\n snapshot = self._shape_history.\\\n get_snapshot([shape.qub_point])\n\n cpos = shape.get_centred_positions()[0]\n cpos.snapshot_image = snapshot\n\n char_params = copy.deepcopy(self._char_params)\n\n # Acquisition for start position\n acq = queue_model_objects.Acquisition()\n acq.acquisition_parameters = \\\n copy.deepcopy(self._acquisition_parameters)\n acq.acquisition_parameters.collect_agent = \\\n queue_model_enumerables.COLLECTION_ORIGIN.MXCUBE\n acq.acquisition_parameters.centred_position = cpos\n acq.path_template = copy.deepcopy(self._path_template)\n\n if self._beamline_setup_hwobj.in_plate_mode():\n acq.acquisition_parameters.take_snapshots = False\n else:\n acq.acquisition_parameters.take_snapshots = True\n\n if '' in acq.path_template.directory:\n name = sample.get_name().replace(':', '-')\n acq.path_template.directory = acq.path_template.directory.\\\n replace('', name)\n acq.path_template.process_directory = acq.path_template.process_directory.\\\n replace('', name)\n\n if '-' in acq.path_template.base_prefix:\n acq.path_template.base_prefix = self.get_default_prefix(sample)\n acq.path_template.run_numer = self._beamline_setup_hwobj.queue_model_hwobj.\\\n get_next_run_number(acq.path_template)\n\n processing_parameters = copy.deepcopy(self._processing_parameters)\n\n data_collection = queue_model_objects.\\\n DataCollection([acq], sample.crystals[0],\n self._processing_parameters)\n\n # Referance images for characterisations should be taken 90 deg apart\n # this is achived by setting overap to -89\n acq.acquisition_parameters.overlap = -89\n data_collection.acquisitions[0] = acq \n data_collection.experiment_type = queue_model_enumerables.EXPERIMENT_TYPE.EDNA_REF\n\n char = queue_model_objects.Characterisation(data_collection, \n char_params)\n char.set_name(data_collection.acquisitions[0].\\\n path_template.get_prefix())\n char.set_number(data_collection.acquisitions[0].\\\n path_template.run_number)\n\n tasks.append(char)\n self._path_template.run_number += 1\n\n return tasks\n","sub_path":"Bricks/widgets/create_char_widget.py","file_name":"create_char_widget.py","file_ext":"py","file_size_in_byte":14657,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"317057906","text":"import re\nclass CipherBase:\n\n def __init__(self, encrypt_message, frecuency ):\n self.encrypt_message = encrypt_message\n self.decrypt_message = \"\"\n self.frecuency= frecuency\n pass\n\n def __decrypt(self):\n count = {}\n for i in self.encrypt_message.lower():\n if re.match((r'[a-z]'),i)!= None: # Use re.math solo por jugar con .isalpha() salia igual\n if i in count.keys():\n count[i] = count[i] + 1\n else:\n count[i] = 1\n l = list(count.items())\n l.sort(key=lambda x:x[1], reverse=True)\n\n frec = list(zip( self.frecuency.lower(), range(27)))\n\n comparacion = dict((l[i][0], frec[i][0]) for i in range(0, len(l)) )\n\n self.decrypt_message = \"\"\n for i in self.encrypt_message:\n if i.isalpha():\n if i.isupper() :\n self.decrypt_message += str(comparacion[i.lower()]).upper()\n elif i.islower():\n self.decrypt_message += str(comparacion[i])\n else:\n self.decrypt_message += i \n\n return self.decrypt_message \n\n\n\n\n\n def print_decrypt_message(self):\n self.__decrypt()\n print(self.decrypt_message)\n \n\n def __str__(self):\n pass\n\n\nmensaje_cifrado = '''\n Bgc-bfufb tegaedppqna ql aggv zge xof tegaedppfe'l lgjb.\n Xof adpf vflqanfe logjbvn'x hf pdwqna d cgebv qn coqro xof tbdkfe ql mjlx d lpdbb tdex. Xof tbdkfe QL XOF HGLL; qx'l kgje vjxk xg fnxfexdqn oqp ge ofe.\n Zgrjl ql d pdxxfe gz vfrqvqna codx xoqnal kgj def ngx agqna xg vg.\n Xof rglx gz dvvqna d zfdxjef qln'x mjlx xof xqpf qx xdwfl xg rgvf qx. Xof rglx dblg qnrbjvfl xof dvvqxqgn gz dn ghlxdrbf xg zjxjef fstdnlqgn. Xof xeqrw ql xg tqrw xof zfdxjefl xodx vgn'x zqaox fdro gxofe. - Mgon Rdepdrw.\n\n (ccc.adpdljxed.rgp/uqfc/nfcl/234346?utkjpvbjr)\n\n (ccc.hedqnkijgxf.rgp/ijgxfl/djxogel/m/mgon_rdepdrw.oxpb)\n '''\nfrecuencia = \"TEOAISRHNUCMDLGWFPYKJBVQX\"\nexample_cipher = CipherBase(mensaje_cifrado, frecuencia)\nexample_cipher.print_decrypt_message()\n\n","sub_path":"lesson_06/EXERCISE6.py","file_name":"EXERCISE6.py","file_ext":"py","file_size_in_byte":2121,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"334514744","text":"from bs4 import BeautifulSoup\nimport codecs\nimport html\nimport os\nimport re\nfrom collections import defaultdict\nimport json\n\nsharedScenes = defaultdict(lambda : defaultdict(int))\ncharacters = defaultdict(int)\n\nf = codecs.open('/Users/ompatel/Dropbox/seinfeld/data/interim/scene_characters.tsv', 'r', encoding='utf-8')\nepisode = None\nscene = None\ncharacterBuffer = []\nf.readline()\nfor line in f:\n [thisEpisode, thisScene, character, noLines] = line.split('\\t')\n if (episode is None and scene is None):\n episode = thisEpisode\n scene = thisScene\n characterBuffer = [character]\n elif (thisEpisode != episode) or (thisScene != scene):\n characterBuffer.sort()\n for i in range(0, len(characterBuffer)):\n characters[characterBuffer[i]] += 1\n for j in range(i+1, len(characterBuffer)):\n if characterBuffer[i] != characterBuffer[j]:\n sharedScenes[characterBuffer[i]][characterBuffer[j]] += 1\n episode = thisEpisode\n scene = thisScene\n characterBuffer = [character]\n else:\n characterBuffer.append(character)\n# print(characters)\n# print(sharedScenes)\n\noutJSON = {\"nodes\": [], \"links\": []}\n\nfor key, value in characters.items():\n outJSON[\"nodes\"].append({\"id\": key, \"group\": value})\n\nlinks = []\nfor key, value in sharedScenes.items():\n for linkKey, linkValue in value.items():\n outJSON[\"links\"].append({\"source\": key, \"target\": linkKey, \"value\": linkValue})\n\nwith codecs.open('/Users/ompatel/Dropbox/seinfeld/src/seinfeld_data.json', 'w', encoding='utf-8') as o:\n json.dump(outJSON, o)\n","sub_path":"src/scrap.py","file_name":"scrap.py","file_ext":"py","file_size_in_byte":1620,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"610695404","text":"#!/usr/bin/env python\nimport sys, os\nimport rospy\nimport copy\nimport math\nimport time\n\nfrom geometry_msgs.msg import PoseStamped, WrenchStamped\n\nclass exp3():\n\n def __init__(self):\n\n self.pose_ref_pub_ = rospy.Publisher('/uav/impedance_control/pose_stamped_ref_input', PoseStamped, queue_size=1)\n self.force_torque_ref_pub = rospy.Publisher('/uav/impedance_control/force_torque_ref_input', WrenchStamped, queue_size=1)\n # Services for requesting trajectory interpolation\n rospy.sleep(5.)\n self.force_setpoint_flag = [False]\n self.pose_setpoint_flag = [False]\n\n self.force_setpoint = [WrenchStamped()]\n self.pose_setpoint = [PoseStamped()]\n\n self.pose_setpoint[0].header.stamp = rospy.Time.now()\n self.pose_setpoint[0].pose.position.x = 1.00;\n self.pose_setpoint[0].pose.position.y = 0;\n self.pose_setpoint[0].pose.position.z = 1;\n self.pose_setpoint[0].pose.orientation.x = 0;\n self.pose_setpoint[0].pose.orientation.y = 0;\n self.pose_setpoint[0].pose.orientation.z = 0;\n self.pose_setpoint[0].pose.orientation.w = 1;\n\n\n self.force_setpoint[0].wrench.force.x = 1.0\n\n self.ramp_velocity = 0.05\n self.up = False\n self.time = 30\n\n self.run()\n\n def run(self):\n rate = rospy.Rate(100)\n\n time_next = rospy.Time.now().to_sec() + self.time\n\n force_ref = WrenchStamped();\n\n while not rospy.is_shutdown():\n\n time_now = rospy.Time.now().to_sec()\n\n self.pose_setpoint[0].header.stamp = rospy.Time.now()\n self.pose_ref_pub_.publish(self.pose_setpoint[0])\n\n\n if ( not self.pose_setpoint_flag[0]):\n if ((time_next - time_now) < 0.0):\n self.pose_setpoint_flag[0] = True\n time_next = rospy.Time.now().to_sec() + self.time\n\n\n if (self.pose_setpoint_flag[0]):\n if (not self.up):\n for i in range(len(self.force_setpoint)):\n if (not self.force_setpoint_flag[i]):\n if ((time_next - time_now) < 0.0):\n self.force_setpoint_flag[i] = True\n if (i+1 != len(self.force_setpoint)):\n self.force_setpoint_flag[i+1] = False\n else:\n self.up = True\n self.force_setpoint_flag[i] = False\n time_next = rospy.Time.now().to_sec() + self.time\n if (force_ref.wrench.force.x < self.force_setpoint[i].wrench.force.x):\n force_ref.wrench.force.x = force_ref.wrench.force.x + self.ramp_velocity/100.0\n else:\n for i in range(len(self.force_setpoint)-1, -1, -1):\n if (not self.force_setpoint_flag[i]):\n if ((time_next - time_now) < 0.0):\n self.force_setpoint_flag[i] = True\n if (i-1 != -1):\n self.force_setpoint_flag[i-1] = False\n time_next = rospy.Time.now().to_sec() + self.time\n\n if (i-1 != -1):\n if (force_ref.wrench.force.x > (self.force_setpoint[i-1].wrench.force.x + self.ramp_velocity/100.0)):\n force_ref.wrench.force.x = force_ref.wrench.force.x - self.ramp_velocity/100.0\n else:\n if (force_ref.wrench.force.x > 1.5*self.ramp_velocity/100.0):\n force_ref.wrench.force.x = force_ref.wrench.force.x - self.ramp_velocity/100.0\n else:\n force_ref.wrench.force.x = 0.0\n\n force_ref.header.stamp = rospy.Time.now()\n self.force_torque_ref_pub.publish(force_ref)\n\n rate.sleep()\n\n\n\nif __name__==\"__main__\":\n rospy.init_node(\"exp3\")\n exp3()","sub_path":"scripts/exp3.py","file_name":"exp3.py","file_ext":"py","file_size_in_byte":4151,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"8741340","text":"import logging\nfrom typing import List, Type\n\nfrom reporter.core.models import Fact, Message\nfrom reporter.core.realize_slots import SlotRealizerComponent\nfrom reporter.newspaper_message_generator import TaskResult, WrongResourceException\nfrom reporter.resources.processor_resource import ProcessorResource\n\nlog = logging.getLogger(\"root\")\n\n\nTEMPLATE = \"\"\"\nen: the following summary of the contents was automatically created: \"{result_value}\" {analysis_id}\nfi: teksteistä luotiin automaattisesti seuraava tiivistelmä: \"{result_value}\" {analysis_id}\nde: Die folgende Zusammenfassung des Inhalts wurde automatisch erzeugt: \"{result_value}\" {analysis_id}\nfr: le résumé du contenu suivant a automatiquement été créé: \"{result_value}\" {analysis_id}\n| analysis_type = Summarization\n\"\"\"\n\n\nclass SummarizationResource(ProcessorResource):\n def templates_string(self) -> str:\n return TEMPLATE\n\n def parse_messages(self, task_result: TaskResult, context: List[TaskResult], language: str) -> List[Message]:\n if not task_result.processor == \"Summarization\":\n raise WrongResourceException()\n\n corpus, corpus_type = self.build_corpus_fields(task_result)\n\n messages = []\n for summary, interestingness in zip(\n task_result.task_result[\"result\"][\"summary\"], task_result.task_result[\"interestingness\"][\"sentence_scores\"]\n ):\n interestingness *= task_result.task_result[\"interestingness\"][\"overall\"]\n messages.append(\n Message(\n [\n Fact(\n corpus, # corpus\n corpus_type, # corpus_type\n None, # timestamp_from\n None, # timestamp_to\n \"all_time\", # timestamp_type\n \"Summarization\", # analysis_type\n \"Summary\", # result_key\n summary, # result_value\n interestingness, # outlierness\n \"[LINK:{}]\".format(task_result.uuid), # uuid\n )\n ]\n )\n )\n # For now, we limit the summaries to one per result. This needs to be re-evaluated later on.\n return [max(messages, key=lambda m: m.main_fact.outlierness)]\n\n def slot_realizer_components(self) -> List[Type[SlotRealizerComponent]]:\n return []\n","sub_path":"reporter/resources/summarization_resource.py","file_name":"summarization_resource.py","file_ext":"py","file_size_in_byte":2486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"353981590","text":"import psycopg2\nimport os\nimport io\nimport sys\nimport json\nimport datetime\nimport decimal\nimport time\nimport base64\nclass pg_encoder(json.JSONEncoder):\n\tdef default(self, obj):\n\t\tif isinstance(obj, datetime.time) or isinstance(obj, datetime.datetime) or isinstance(obj, datetime.date) or isinstance(obj, decimal.Decimal) or isinstance(obj, datetime.timedelta):\n\t\t\treturn str(obj)\n\t\treturn json.JSONEncoder.default(self, obj)\n\n\nclass pg_connection(object):\n\tdef __init__(self, global_config):\n\t\tself.global_conf=global_config\n\t\tself.pg_conn=self.global_conf.pg_conn\n\t\tself.pg_database=self.global_conf.pg_database\n\t\tself.dest_schema=self.global_conf.my_database\n\t\tself.pg_connection=None\n\t\tself.pg_cursor=None\n\t\tself.pg_charset=self.global_conf.pg_charset\n\t\t\n\t\n\tdef connect_db(self):\n\t\tpg_pars=dict(list(self.pg_conn.items())+ list({'dbname':self.pg_database}.items()))\n\t\tstrconn=\"dbname=%(dbname)s user=%(user)s host=%(host)s password=%(password)s port=%(port)s\" % pg_pars\n\t\tself.pgsql_conn = psycopg2.connect(strconn)\n\t\tself.pgsql_conn .set_isolation_level(psycopg2.extensions.ISOLATION_LEVEL_AUTOCOMMIT)\n\t\tself.pgsql_conn .set_client_encoding(self.pg_charset)\n\t\tself.pgsql_cur=self.pgsql_conn .cursor()\n\t\t\n\t\n\tdef disconnect_db(self):\n\t\tself.pgsql_conn.close()\n\t\t\n\nclass pg_engine(object):\n\tdef __init__(self, global_config, table_metadata, table_file, logger, sql_dir='sql/'):\n\t\tself.sleep_on_reindex = global_config.sleep_on_reindex\n\t\tself.reindex_app_names = global_config.reindex_app_names\n\t\tself.batch_retention = global_config.batch_retention\n\t\tself.logger=logger\n\t\tself.sql_dir=sql_dir\n\t\tself.idx_sequence=0\n\t\tself.pg_conn=pg_connection(global_config)\n\t\tself.pg_conn.connect_db()\n\t\tself.table_metadata=table_metadata\n\t\tself.table_file=table_file\n\t\tself.type_dictionary={\n\t\t\t'integer':'integer',\n\t\t\t'mediumint':'bigint',\n\t\t\t'tinyint':'integer',\n\t\t\t'smallint':'integer',\n\t\t\t'int':'integer',\n\t\t\t'bigint':'bigint',\n\t\t\t'varchar':'character varying',\n\t\t\t'text':'text',\n\t\t\t'char':'character',\n\t\t\t'datetime':'timestamp without time zone',\n\t\t\t'date':'date',\n\t\t\t'time':'time without time zone',\n\t\t\t'timestamp':'timestamp without time zone',\n\t\t\t'tinytext':'text',\n\t\t\t'mediumtext':'text',\n\t\t\t'longtext':'text',\n\t\t\t'tinyblob':'bytea',\n\t\t\t'mediumblob':'bytea',\n\t\t\t'longblob':'bytea',\n\t\t\t'blob':'bytea', \n\t\t\t'binary':'bytea', \n\t\t\t'decimal':'numeric', \n\t\t\t'double':'double precision', \n\t\t\t'double precision':'double precision', \n\t\t\t'float':'float', \n\t\t\t'bit':'integer', \n\t\t\t'year':'integer', \n\t\t\t'enum':'enum', \n\t\t\t'set':'text', \n\t\t\t'json':'text'\n\t\t}\n\t\tself.table_ddl={}\n\t\tself.idx_ddl={}\n\t\tself.type_ddl={}\n\t\tself.pg_charset=self.pg_conn.pg_charset\n\t\tself.cat_version='1.0'\n\t\tself.cat_sql=[\n\t\t\t{'version':'base','script': 'create_schema.sql'}, \n\t\t\t{'version':'0.1','script': 'upgrade/cat_0.1.sql'}, \n\t\t\t{'version':'0.2','script': 'upgrade/cat_0.2.sql'}, \n\t\t\t{'version':'0.3','script': 'upgrade/cat_0.3.sql'}, \n\t\t\t{'version':'0.4','script': 'upgrade/cat_0.4.sql'}, \n\t\t\t{'version':'0.5','script': 'upgrade/cat_0.5.sql'}, \n\t\t\t{'version':'0.6','script': 'upgrade/cat_0.6.sql'}, \n\t\t\t{'version':'0.7','script': 'upgrade/cat_0.7.sql'}, \n\t\t\t{'version':'0.8','script': 'upgrade/cat_0.8.sql'}, \n\t\t\t{'version':'0.9','script': 'upgrade/cat_0.9.sql'}, \n\t\t\t{'version':'1.0','script': 'upgrade/cat_1.0.sql'}, \n\t\t]\n\t\tcat_version=self.get_schema_version()\n\t\tnum_schema=(self.check_service_schema())[0]\n\t\tif cat_version!=self.cat_version and int(num_schema)>0:\n\t\t\tself.upgrade_service_schema()\n\t\tself.table_limit = ['*']\n\t\n\tdef add_source(self, source_name, dest_schema):\n\t\tsql_source = \"\"\"\n\t\t\t\t\tSELECT \n\t\t\t\t\t\tcount(i_id_source)\n\t\t\t\t\tFROM \n\t\t\t\t\t\tsch_chameleon.t_sources \n\t\t\t\t\tWHERE \n\t\t\t\t\t\tt_source=%s\n\t\t\t\t;\n\t\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_source, (source_name, ))\n\t\tsource_data = self.pg_conn.pgsql_cur.fetchone()\n\t\tcnt_source = source_data[0]\n\t\tif cnt_source == 0:\n\t\t\tsql_add = \"\"\"INSERT INTO sch_chameleon.t_sources \n\t\t\t\t\t\t( t_source,t_dest_schema) \n\t\t\t\t\tVALUES \n\t\t\t\t\t\t(%s,%s); \"\"\"\n\t\t\tself.pg_conn.pgsql_cur.execute(sql_add, (source_name, dest_schema ))\n\t\telse:\n\t\t\tprint(\"Source %s already registered.\" % source_name)\n\t\tsys.exit()\n\t\n\tdef get_source_status(self, source_name):\n\t\tsql_source = \"\"\"\n\t\t\t\t\tSELECT \n\t\t\t\t\t\tenm_status\n\t\t\t\t\tFROM \n\t\t\t\t\t\tsch_chameleon.t_sources \n\t\t\t\t\tWHERE \n\t\t\t\t\t\tt_source=%s\n\t\t\t\t;\n\t\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_source, (source_name, ))\n\t\tsource_data = self.pg_conn.pgsql_cur.fetchone()\n\t\tif source_data:\n\t\t\tsource_status = source_data[0]\n\t\telse:\n\t\t\tsource_status = 'Not registered'\n\t\treturn source_status\n\t\t\n\tdef drop_source(self, source_name):\n\t\tsql_delete = \"\"\" DELETE FROM sch_chameleon.t_sources \n\t\t\t\t\tWHERE t_source=%s; \"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_delete, (source_name, ))\n\t\n\t\t\n\t\n\tdef set_source_id(self, source_status):\n\t\tsql_source = \"\"\"\n\t\t\t\t\tUPDATE sch_chameleon.t_sources\n\t\t\t\t\tSET\n\t\t\t\t\t\tenm_status=%s\n\t\t\t\t\tWHERE\n\t\t\t\t\t\tt_source=%s\n\t\t\t\t\tRETURNING i_id_source,t_dest_schema\n\t\t\t\t;\n\t\t\t\"\"\"\n\t\tsource_name=self.pg_conn.global_conf.source_name\n\t\tself.pg_conn.pgsql_cur.execute(sql_source, (source_status, source_name))\n\t\tsource_data=self.pg_conn.pgsql_cur.fetchone()\n\t\ttry:\n\t\t\tself.i_id_source=source_data[0]\n\t\t\tself.dest_schema=source_data[1]\n\t\texcept:\n\t\t\tprint(\"Source %s is not registered.\" % source_name)\n\t\t\tsys.exit()\n\t\n\t\t\t\n\tdef clean_batch_data(self):\n\t\tsql_delete=\"\"\"DELETE FROM sch_chameleon.t_replica_batch \n\t\t\t\t\t\t\t\tWHERE i_id_source=%s;\n\t\t\t\t\t\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_delete, (self.i_id_source, ))\n\t\t\n\t\t\n\tdef create_schema(self):\n\t\t\n\t\tsql_drop=\"DROP SCHEMA IF EXISTS \"+self.dest_schema+\" CASCADE;\"\n\t\tsql_create=\" CREATE SCHEMA IF NOT EXISTS \"+self.dest_schema+\";\"\n\t\tsql_path=\" SET search_path=\"+self.dest_schema+\";\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_drop)\n\t\tself.pg_conn.pgsql_cur.execute(sql_create)\n\t\tself.pg_conn.pgsql_cur.execute(sql_path)\n\t\n\tdef store_table(self, table_name):\n\t\ttable_data=self.table_metadata[table_name]\n\t\tfor index in table_data[\"indices\"]:\n\t\t\tif index[\"index_name\"]==\"PRIMARY\":\n\t\t\t\tsql_insert=\"\"\" INSERT INTO sch_chameleon.t_replica_tables \n\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\t\ti_id_source,\n\t\t\t\t\t\t\t\t\t\t\tv_table_name,\n\t\t\t\t\t\t\t\t\t\t\tv_schema_name,\n\t\t\t\t\t\t\t\t\t\t\tv_table_pkey\n\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\tVALUES (\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t%s,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t%s,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t%s,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\tARRAY[%s]\n\t\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\tON CONFLICT (i_id_source,v_table_name,v_schema_name)\n\t\t\t\t\t\t\t\t\t\t\tDO UPDATE \n\t\t\t\t\t\t\t\t\t\t\t\tSET v_table_pkey=EXCLUDED.v_table_pkey\n\t\t\t\t\t\t\t\t\t\t;\n\t\t\t\t\t\t\t\t\"\"\"\n\t\t\t\tself.pg_conn.pgsql_cur.execute(sql_insert, (self.i_id_source, table_name, self.dest_schema, index[\"index_columns\"].strip()))\t\n\t\n\tdef unregister_table(self, table_name):\n\t\tself.logger.info(\"unregistering table %s from the replica catalog\" % (table_name,))\n\t\tsql_delete=\"\"\" DELETE FROM sch_chameleon.t_replica_tables \n\t\t\t\t\t\t\t\t\tWHERE\n\t\t\t\t\t\t\t\t\t\t\tv_table_name=%s\n\t\t\t\t\t\t\t\t\t\tAND\tv_schema_name=%s\n\t\t\t\t\t\t\t\tRETURNING i_id_table\n\t\t\t\t\t\t\t\t;\n\t\t\t\t\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_delete, (table_name, self.dest_schema))\t\n\t\tremoved_id=self.pg_conn.pgsql_cur.fetchone()\n\t\ttable_id=removed_id[0]\n\t\tself.logger.info(\"renaming table %s to %s_%s\" % (table_name, table_name, table_id))\n\t\tsql_rename=\"\"\"ALTER TABLE IF EXISTS \"%s\".\"%s\" rename to \"%s_%s\"; \"\"\" % (self.dest_schema, table_name, table_name, table_id)\n\t\tself.logger.debug(sql_rename)\n\t\tself.pg_conn.pgsql_cur.execute(sql_rename)\t\n\t\n\tdef create_tables(self):\n\t\t\n\t\t\tfor table in self.table_ddl:\n\t\t\t\t#sql_drop='DROP TABLE IF EXISTS \"'+table+'\" CASCADE ;'\n\t\t\t\t#self.pg_conn.pgsql_cur.execute(sql_drop)\n\t\t\t\ttry:\n\t\t\t\t\tddl_enum=self.type_ddl[table]\n\t\t\t\t\tfor sql_type in ddl_enum:\n\t\t\t\t\t\tself.pg_conn.pgsql_cur.execute(sql_type)\n\t\t\t\texcept psycopg2.Error as e:\n\t\t\t\t\tself.logger.error(\"SQLCODE: %s SQLERROR: %s\" % (e.pgcode, e.pgerror))\n\t\t\t\t\tself.logger.error(sql_type)\n\t\t\t\t\t\n\t\t\t\tsql_create=self.table_ddl[table]\n\t\t\t\ttry:\n\t\t\t\t\tself.pg_conn.pgsql_cur.execute(sql_create)\n\t\t\t\texcept psycopg2.Error as e:\n\t\t\t\t\tself.logger.error(\"SQLCODE: %s SQLERROR: %s\" % (e.pgcode, e.pgerror))\n\t\t\t\t\tself.logger.error(sql_create)\n\t\t\t\tself.store_table(table)\n\t\n\tdef create_indices(self):\n\t\tself.logger.info(\"creating the indices\")\n\t\tfor table in self.idx_ddl:\n\t\t\tidx_ddl= self.idx_ddl[table]\n\t\t\tself.logger.debug(\"processing table %s\" % (table, ))\n\t\t\tfor sql_idx in idx_ddl:\n\t\t\t\tself.pg_conn.pgsql_cur.execute(sql_idx)\n\t\t\t\t\n\t\n\tdef copy_data(self, table, csv_file, my_tables={}):\n\t\tcolumn_copy=[]\n\t\tfor column in my_tables[table][\"columns\"]:\n\t\t\tcolumn_copy.append('\"'+column[\"column_name\"]+'\"')\n\t\tsql_copy=\"COPY \"+'\"'+self.pg_conn.global_conf.dest_schema+'\"'+\".\"+'\"'+table+'\"'+\" (\"+','.join(column_copy)+\") FROM STDIN WITH NULL 'NULL' CSV QUOTE '\\\"' DELIMITER',' ESCAPE '\\\"' ; \"\n\t\tself.pg_conn.pgsql_cur.copy_expert(sql_copy,csv_file)\n\t\t\n\tdef insert_data(self, table, insert_data, my_tables={}):\n\t\tcolumn_copy=[]\n\t\tcolumn_marker=[]\n\t\t\n\t\tfor column in my_tables[table][\"columns\"]:\n\t\t\tcolumn_copy.append('\"'+column[\"column_name\"]+'\"')\n\t\t\tcolumn_marker.append('%s')\n\t\tsql_head=\"INSERT INTO \"+'\"'+self.pg_conn.global_conf.dest_schema+'\"'+\".\"+'\"'+table+'\"'+\" (\"+','.join(column_copy)+\") VALUES (\"+','.join(column_marker)+\");\"\n\t\tfor data_row in insert_data:\n\t\t\tcolumn_values=[]\n\t\t\tfor column in my_tables[table][\"columns\"]:\n\t\t\t\tcolumn_values.append(data_row[column[\"column_name\"]])\n\t\t\ttry:\n\t\t\t\tself.pg_conn.pgsql_cur.execute(sql_head,column_values)\t\n\t\t\texcept psycopg2.Error as e:\n\t\t\t\t\tself.logger.error(\"SQLCODE: %s SQLERROR: %s\" % (e.pgcode, e.pgerror))\n\t\t\t\t\tself.logger.error(self.pg_conn.pgsql_cur.mogrify(sql_head,column_values))\n\t\n\tdef build_idx_ddl(self):\n\t\t\n\t\t\"\"\" the function iterates over the list l_pkeys and builds a new list with the statements for pkeys \"\"\"\n\t\tfor table_name in self.table_metadata:\n\t\t\ttable=self.table_metadata[table_name]\n\t\t\t\n\t\t\ttable_name=table[\"name\"]\n\t\t\tindices=table[\"indices\"]\n\t\t\ttable_idx=[]\n\t\t\tfor index in indices:\n\t\t\t\tindx=index[\"index_name\"]\n\t\t\t\tindex_columns=index[\"index_columns\"]\n\t\t\t\tnon_unique=index[\"non_unique\"]\n\t\t\t\tif indx=='PRIMARY':\n\t\t\t\t\tpkey_name=\"pk_\"+table_name[0:20]+\"_\"+str(self.idx_sequence)\n\t\t\t\t\tpkey_def='ALTER TABLE \"'+table_name+'\" ADD CONSTRAINT \"'+pkey_name+'\" PRIMARY KEY ('+index_columns+') ;'\n\t\t\t\t\ttable_idx.append(pkey_def)\n\t\t\t\telse:\n\t\t\t\t\tif non_unique==0:\n\t\t\t\t\t\tunique_key='UNIQUE'\n\t\t\t\t\telse:\n\t\t\t\t\t\tunique_key=''\n\t\t\t\t\tindex_name='\"idx_'+indx[0:20]+table_name[0:20]+\"_\"+str(self.idx_sequence)+'\"'\n\t\t\t\t\tidx_def='CREATE '+unique_key+' INDEX '+ index_name+' ON \"'+table_name+'\" ('+index_columns+');'\n\t\t\t\t\ttable_idx.append(idx_def)\n\t\t\t\tself.idx_sequence+=1\n\t\t\t\t\t\n\t\t\tself.idx_ddl[table_name]=table_idx\n\n\tdef build_tab_ddl(self):\n\t\t\"\"\" the function iterates over the list l_tables and builds a new list with the statements for tables\"\"\"\n\t\t\n\t\tfor table_name in self.table_metadata:\n\t\t\ttable=self.table_metadata[table_name]\n\t\t\tcolumns=table[\"columns\"]\n\t\t\t\n\t\t\tddl_head=\"CREATE TABLE \"+'\"'+table[\"name\"]+'\" ('\n\t\t\tddl_tail=\");\"\n\t\t\tddl_columns=[]\n\t\t\tddl_enum=[]\n\t\t\tfor column in columns:\n\t\t\t\tif column[\"is_nullable\"]==\"NO\":\n\t\t\t\t\tcol_is_null=\"NOT NULL\"\n\t\t\t\telse:\n\t\t\t\t\tcol_is_null=\"NULL\"\n\t\t\t\tcolumn_type=self.type_dictionary[column[\"data_type\"]]\n\t\t\t\tif column_type==\"enum\":\n\t\t\t\t\tenum_type=\"enum_\"+table[\"name\"]+\"_\"+column[\"column_name\"]\n\t\t\t\t\tsql_drop_enum='DROP TYPE IF EXISTS '+enum_type+' CASCADE;'\n\t\t\t\t\tsql_create_enum=\"CREATE TYPE \"+enum_type+\" AS ENUM \"+column[\"enum_list\"]+\";\"\n\t\t\t\t\tddl_enum.append(sql_drop_enum)\n\t\t\t\t\tddl_enum.append(sql_create_enum)\n\t\t\t\t\tcolumn_type=enum_type\n\t\t\t\tif column_type==\"character varying\" or column_type==\"character\":\n\t\t\t\t\tcolumn_type=column_type+\"(\"+str(column[\"character_maximum_length\"])+\")\"\n\t\t\t\tif column_type=='numeric':\n\t\t\t\t\tcolumn_type=column_type+\"(\"+str(column[\"numeric_precision\"])+\",\"+str(column[\"numeric_scale\"])+\")\"\n\t\t\t\tif column_type=='bit' or column_type=='float':\n\t\t\t\t\tcolumn_type=column_type+\"(\"+str(column[\"numeric_precision\"])+\")\"\n\t\t\t\tif column[\"extra\"]==\"auto_increment\":\n\t\t\t\t\tcolumn_type=\"bigserial\"\n\t\t\t\tddl_columns.append('\"'+column[\"column_name\"]+'\" '+column_type+\" \"+col_is_null )\n\t\t\tdef_columns=str(',').join(ddl_columns)\n\t\t\tself.type_ddl[table[\"name\"]]=ddl_enum\n\t\t\tself.table_ddl[table[\"name\"]]=ddl_head+def_columns+ddl_tail\n\t\n\n\n\t\n\tdef get_schema_version(self):\n\t\t\"\"\"\n\t\t\tGets the service schema version.\n\t\t\"\"\"\n\t\tsql_check=\"\"\"\n\t\t\t\t\t\t\tSELECT \n\t\t\t\t\t\t\t\t\t\t\tt_version\n\t\t\t\t\t\t\tFROM \n\t\t\t\t\t\t\t\t\t\t\tsch_chameleon.v_version \n\t\t\t\t\t\t\t;\n\t\t\t\t\t\t\"\"\"\n\t\ttry:\n\t\t\tself.pg_conn.pgsql_cur.execute(sql_check)\n\t\t\tvalue_check=self.pg_conn.pgsql_cur.fetchone()\n\t\t\tcat_version=value_check[0]\n\t\texcept:\n\t\t\tcat_version='base'\n\t\treturn cat_version\n\t\t\n\tdef upgrade_service_schema(self):\n\t\t\"\"\"\n\t\t\tUpgrade the service schema to the latest version using the upgrade files\n\t\t\"\"\"\n\t\t\n\t\tself.logger.info(\"Upgrading the service schema\")\n\t\tinstall_script=False\n\t\tcat_version=self.get_schema_version()\n\t\t\t\n\t\tfor install in self.cat_sql:\n\t\t\t\tscript_ver=install[\"version\"]\n\t\t\t\tscript_schema=install[\"script\"]\n\t\t\t\tself.logger.info(\"script schema %s, detected schema version %s - install_script:%s \" % (script_ver, cat_version, install_script))\n\t\t\t\tif install_script==True:\n\t\t\t\t\tself.logger.info(\"Installing file version %s\" % (script_ver, ))\n\t\t\t\t\tfile_schema=open(self.sql_dir+script_schema, 'rb')\n\t\t\t\t\tsql_schema=file_schema.read()\n\t\t\t\t\tfile_schema.close()\n\t\t\t\t\tprint(\"=================================================\")\n\t\t\t\t\tself.pg_conn.pgsql_cur.execute(sql_schema)\n\t\t\t\t\tif script_ver=='0.7':\n\t\t\t\t\t\tsql_update=\"\"\"UPDATE sch_chameleon.t_sources\n\t\t\t\t\t\t\t\t\t\t\t\tSET\n\t\t\t\t\t\t\t\t\t\t\t\t\tt_dest_schema=%s \n\t\t\t\t\t\t\t\t\t\t\t\tWHERE i_id_source=(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tSELECT \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ti_id_source\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tFROM\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tsch_chameleon.t_sources\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tWHERE\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tt_source='default'\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tAND t_dest_schema='default'\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t;\n\t\t\t\t\t\t\"\"\"\n\t\t\t\t\t\tself.pg_conn.pgsql_cur.execute(sql_update, (self.pg_conn.global_conf.dest_schema, ))\n\t\t\t\t\n\t\t\t\t\n\t\t\t\tif script_ver==cat_version and not install_script:\n\t\t\t\t\tself.logger.info(\"enabling install script\")\n\t\t\t\t\tinstall_script=True\n\t\t\n\tdef check_service_schema(self):\n\t\tsql_check=\"\"\"\n\t\t\t\t\t\t\t\tSELECT \n\t\t\t\t\t\t\t\t\tcount(*)\n\t\t\t\t\t\t\t\tFROM \n\t\t\t\t\t\t\t\t\tinformation_schema.schemata \n\t\t\t\t\t\t\t\tWHERE \n\t\t\t\t\t\t\t\t\tschema_name='sch_chameleon'\n\t\t\t\t\t\t\"\"\"\n\t\t\t\n\t\tself.pg_conn.pgsql_cur.execute(sql_check)\n\t\tnum_schema=self.pg_conn.pgsql_cur.fetchone()\n\t\treturn num_schema\n\t\n\tdef create_service_schema(self):\n\t\t\n\t\tnum_schema=self.check_service_schema()\n\t\tif num_schema[0]==0:\n\t\t\tfor install in self.cat_sql:\n\t\t\t\tscript_ver=install[\"version\"]\n\t\t\t\tscript_schema=install[\"script\"]\n\t\t\t\tif script_ver=='base':\n\t\t\t\t\tself.logger.info(\"Installing service schema %s\" % (script_ver, ))\n\t\t\t\t\tfile_schema=open(self.sql_dir+script_schema, 'rb')\n\t\t\t\t\tsql_schema=file_schema.read()\n\t\t\t\t\tfile_schema.close()\n\t\t\t\t\tself.pg_conn.pgsql_cur.execute(sql_schema)\n\t\telse:\n\t\t\tself.logger.error(\"The service schema is already created\")\n\t\t\t\n\tdef get_status(self):\n\t\t\"\"\"the function list the sources with the running status and the eventual lag \"\"\"\n\t\tsql_status=\"\"\"\n\t\t\t\t\t\t\t\tSELECT\n\t\t\t\t\t\t\t\t\tt_source,\n\t\t\t\t\t\t\t\t\tt_dest_schema,\n\t\t\t\t\t\t\t\t\tenm_status,\n\t\t\t\t\t\t\t\t\textract(epoch from now()-ts_last_event)::integer as i_seconds_behind_master,\n\t\t\t\t\t\t\t\t\tts_last_event \n\t\t\t\t\t\t\t\tFROM \n\t\t\t\t\t\t\t\t\tsch_chameleon.t_sources\n\t\t\t\t\t\t\t\tORDER BY \n\t\t\t\t\t\t\t\t\tt_source\n\t\t\t\t\t\t\t\t; \"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_status)\n\t\tresults = self.pg_conn.pgsql_cur.fetchall()\n\t\treturn results\n\t\t\n\tdef drop_service_schema(self):\n\t\tfile_schema=open(self.sql_dir+\"drop_schema.sql\", 'rb')\n\t\tsql_schema=file_schema.read()\n\t\tfile_schema.close()\n\t\tself.pg_conn.pgsql_cur.execute(sql_schema)\n\t\n\tdef save_master_status(self, master_status, cleanup=False):\n\t\tnext_batch_id=None\n\t\tsql_tab_log=\"\"\" \n\t\t\t\t\t\t\tSELECT \n\t\t\t\t\t\t\t\tCASE\n\t\t\t\t\t\t\t\t\tWHEN v_log_table='t_log_replica_2'\n\t\t\t\t\t\t\t\t\tTHEN \n\t\t\t\t\t\t\t\t\t\t't_log_replica_1'\n\t\t\t\t\t\t\t\t\tELSE\n\t\t\t\t\t\t\t\t\t\t't_log_replica_2'\n\t\t\t\t\t\t\t\tEND AS v_log_table\n\t\t\t\t\t\t\tFROM\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\tSELECT\n\t\t\t\t\t\t\t\t\t\t\tv_log_table,\n\t\t\t\t\t\t\t\t\t\t\tts_created\n\t\t\t\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t\t\tFROM\n\t\t\t\t\t\t\t\t\t\t\tsch_chameleon.t_replica_batch\n\t\t\t\t\t\t\t\t\tWHERE \n\t\t\t\t\t\t\t\t\t\ti_id_source=%s\n\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\tUNION ALL\n\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\tSELECT\n\t\t\t\t\t\t\t\t\t\t\t't_log_replica_2' AS v_log_table,\n\t\t\t\t\t\t\t\t\t\t\t'1970-01-01'::timestamp as ts_created\n\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\tORDER BY \n\t\t\t\t\t\t\t\t\t\tts_created DESC\n\t\t\t\t\t\t\t\t\tLIMIT 1\n\t\t\t\t\t\t\t\t) tab\n\t\t\t\t\t\t;\n\t\t\t\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_tab_log, (self.i_id_source, ))\n\t\tresults = self.pg_conn.pgsql_cur.fetchone()\n\t\ttable_file = results[0]\n\t\tmaster_data = master_status[0]\n\t\tbinlog_name = master_data[\"File\"]\n\t\tbinlog_position = master_data[\"Position\"]\n\t\ttry:\n\t\t\tevent_time = datetime.datetime.fromtimestamp(master_data[\"Time\"]).isoformat()\n\t\texcept:\n\t\t\tevent_time = None\n\t\tself.logger.debug(\"master data: table file %s, log name: %s, log position: %s \" % (table_file, binlog_name, binlog_position))\n\t\tsql_master=\"\"\"\n\t\t\t\t\t\t\tINSERT INTO sch_chameleon.t_replica_batch\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ti_id_source,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tt_binlog_name, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\ti_binlog_position,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tv_log_table\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\tVALUES (\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t%s,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t%s,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t%s,\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t%s\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t--ON CONFLICT DO NOTHING\n\t\t\t\t\t\t\tRETURNING i_id_batch\n\t\t\t\t\t\t\t;\n\t\t\t\t\t\t\"\"\"\n\t\t\t\t\t\t\n\t\tsql_event=\"\"\"UPDATE sch_chameleon.t_sources \n\t\t\t\t\tSET \n\t\t\t\t\t\tts_last_event=%s \n\t\t\t\t\tWHERE \n\t\t\t\t\t\ti_id_source=%s; \n\t\t\t\t\t\t\"\"\"\n\t\tself.logger.info(\"saving master data id source: %s log file: %s log position:%s Last event: %s\" % (self.i_id_source, binlog_name, binlog_position, event_time))\n\t\t\n\t\t\n\t\ttry:\n\t\t\tif cleanup:\n\t\t\t\tself.logger.info(\"cleaning not replayed batches for source %s\", self.i_id_source)\n\t\t\t\tsql_cleanup=\"\"\" DELETE FROM sch_chameleon.t_replica_batch WHERE i_id_source=%s AND NOT b_replayed; \"\"\"\n\t\t\t\tself.pg_conn.pgsql_cur.execute(sql_cleanup, (self.i_id_source, ))\n\t\t\tself.pg_conn.pgsql_cur.execute(sql_master, (self.i_id_source, binlog_name, binlog_position, table_file))\n\t\t\tresults=self.pg_conn.pgsql_cur.fetchone()\n\t\t\tnext_batch_id=results[0]\n\t\texcept psycopg2.Error as e:\n\t\t\t\t\tself.logger.error(\"SQLCODE: %s SQLERROR: %s\" % (e.pgcode, e.pgerror))\n\t\t\t\t\tself.logger.error(self.pg_conn.pgsql_cur.mogrify(sql_master, (self.i_id_source, binlog_name, binlog_position, table_file)))\n\t\ttry:\n\t\t\tself.pg_conn.pgsql_cur.execute(sql_event, (event_time, self.i_id_source, ))\n\t\t\t\n\t\texcept psycopg2.Error as e:\n\t\t\t\t\tself.logger.error(\"SQLCODE: %s SQLERROR: %s\" % (e.pgcode, e.pgerror))\n\t\t\t\t\tself.pg_conn.pgsql_cur.mogrify(sql_event, (event_time, self.i_id_source, ))\n\t\t\n\t\treturn next_batch_id\n\t\t\n\tdef get_batch_data(self):\n\t\tsql_batch=\"\"\"WITH t_created AS\n\t\t\t\t\t\t(\n\t\t\t\t\t\t\tSELECT \n\t\t\t\t\t\t\t\tmax(ts_created) AS ts_created\n\t\t\t\t\t\t\tFROM \n\t\t\t\t\t\t\t\tsch_chameleon.t_replica_batch \n\t\t\t\t\t\t\tWHERE \n\t\t\t\t\t\t\t\t\t\t\tNOT b_processed\n\t\t\t\t\t\t\t\tAND \tNOT b_replayed\n\t\t\t\t\t\t\t\tAND\t\ti_id_source=%s\n\t\t\t\t\t\t)\n\t\t\t\t\tUPDATE sch_chameleon.t_replica_batch\n\t\t\t\t\t\tSET b_started=True\n\t\t\t\t\t\tFROM \n\t\t\t\t\t\t\tt_created\n\t\t\t\t\t\tWHERE\n\t\t\t\t\t\t\tt_replica_batch.ts_created=t_created.ts_created\n\t\t\t\t\tRETURNING\n\t\t\t\t\t\ti_id_batch,\n\t\t\t\t\t\tt_binlog_name,\n\t\t\t\t\t\ti_binlog_position ,\n\t\t\t\t\t\tv_log_table\n\t\t\t\t\t;\n\t\t\t\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_batch, (self.i_id_source, ))\n\t\treturn self.pg_conn.pgsql_cur.fetchall()\n\t\n\tdef insert_batch(self,group_insert):\n\t\tself.logger.debug(\"starting insert loop\")\n\t\tfor row_data in group_insert:\n\t\t\tglobal_data=row_data[\"global_data\"]\n\t\t\tevent_data=row_data[\"event_data\"]\n\t\t\tevent_update=row_data[\"event_update\"]\n\t\t\tlog_table=global_data[\"log_table\"]\n\t\t\tsql_insert=\"\"\"\n\t\t\t\t\t\t\t\t\tINSERT INTO sch_chameleon.\"\"\"+log_table+\"\"\"\n\t\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t\ti_id_batch, \n\t\t\t\t\t\t\t\t\t\tv_table_name, \n\t\t\t\t\t\t\t\t\t\tv_schema_name, \n\t\t\t\t\t\t\t\t\t\tenm_binlog_event, \n\t\t\t\t\t\t\t\t\t\tt_binlog_name, \n\t\t\t\t\t\t\t\t\t\ti_binlog_position, \n\t\t\t\t\t\t\t\t\t\tjsb_event_data,\n\t\t\t\t\t\t\t\t\t\tjsb_event_update\n\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\tVALUES (%s,%s,%s,%s,%s,%s,%s,%s)\n\t\t\t\t\t\t\t\t\t;\t\t\t\t\t\t\n\t\t\t\t\t\t\t\"\"\"\n\t\t\ttry:\n\t\t\t\tself.pg_conn.pgsql_cur.execute(sql_insert,(\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tglobal_data[\"batch_id\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tglobal_data[\"table\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tglobal_data[\"schema\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tglobal_data[\"action\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tglobal_data[\"binlog\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tglobal_data[\"logpos\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tjson.dumps(event_data, cls=pg_encoder), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tjson.dumps(event_update, cls=pg_encoder))\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\texcept:\n\t\t\t\tself.logger.error(\"error when storing event data. saving the discarded row\")\n\t\t\t\tself.save_discarded_row(row_data,global_data[\"batch_id\"])\n\t\n\tdef save_discarded_row(self,row_data,batch_id):\n\t\tprint(str(row_data))\n\t\tb64_row=base64.b64encode(str(row_data))\n\t\tsql_save=\"\"\"INSERT INTO sch_chameleon.t_discarded_rows(\n\t\t\t\t\t\t\t\t\t\t\ti_id_batch, \n\t\t\t\t\t\t\t\t\t\t\tt_row_data\n\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\tVALUES (%s,%s);\n\t\t\t\t\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_save,(batch_id,b64_row))\n\t\n\tdef write_batch(self, group_insert):\n\t\tcsv_file=io.StringIO()\n\t\t\n\t\tinsert_list=[]\n\t\tfor row_data in group_insert:\n\t\t\tglobal_data=row_data[\"global_data\"]\n\t\t\tevent_data=row_data[\"event_data\"]\n\t\t\tevent_update=row_data[\"event_update\"]\n\t\t\tlog_table=global_data[\"log_table\"]\n\t\t\tinsert_list.append(self.pg_conn.pgsql_cur.mogrify(\"%s,%s,%s,%s,%s,%s,%s,%s\" , (\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tglobal_data[\"batch_id\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tglobal_data[\"table\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tself.dest_schema, \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tglobal_data[\"action\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tglobal_data[\"binlog\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tglobal_data[\"logpos\"], \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tjson.dumps(event_data, cls=pg_encoder), \n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tjson.dumps(event_update, cls=pg_encoder)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\t\t\t\t\n\t\tcsv_data=b\"\\n\".join(insert_list ).decode()\n\t\tcsv_file.write(csv_data)\n\t\tcsv_file.seek(0)\n\t\ttry:\n\t\t\t\n\t\t\t#self.pg_conn.pgsql_cur.execute(sql_insert)\n\t\t\tsql_copy=\"\"\"COPY \"sch_chameleon\".\"\"\"+log_table+\"\"\" (\n\t\t\t\t\t\t\t\t\ti_id_batch, \n\t\t\t\t\t\t\t\t\tv_table_name, \n\t\t\t\t\t\t\t\t\tv_schema_name, \n\t\t\t\t\t\t\t\t\tenm_binlog_event, \n\t\t\t\t\t\t\t\t\tt_binlog_name, \n\t\t\t\t\t\t\t\t\ti_binlog_position, \n\t\t\t\t\t\t\t\t\tjsb_event_data,\n\t\t\t\t\t\t\t\t\tjsb_event_update\n\t\t\t\t\t\t\t\t) FROM STDIN WITH NULL 'NULL' CSV QUOTE '''' DELIMITER ',' ESCAPE '''' ; \"\"\"\n\t\t\tself.pg_conn.pgsql_cur.copy_expert(sql_copy,csv_file)\n\t\texcept psycopg2.Error as e:\n\t\t\tself.logger.error(\"SQLCODE: %s SQLERROR: %s\" % (e.pgcode, e.pgerror))\n\t\t\tself.logger.error(csv_data)\n\t\t\tself.logger.error(\"fallback to inserts\")\n\t\t\t\n\t\t\t\n\t\t\n\tdef set_batch_processed(self, id_batch):\n\t\tself.logger.debug(\"updating batch %s to processed\" % (id_batch, ))\n\t\tsql_update=\"\"\" UPDATE sch_chameleon.t_replica_batch\n\t\t\t\t\t\t\t\t\t\tSET\n\t\t\t\t\t\t\t\t\t\t\t\tb_processed=True,\n\t\t\t\t\t\t\t\t\t\t\t\tts_processed=now()\n\t\t\t\t\t\t\t\tWHERE\n\t\t\t\t\t\t\t\t\t\ti_id_batch=%s\n\t\t\t\t\t\t\t\t;\n\t\t\t\t\t\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_update, (id_batch, ))\n\t\t\n\tdef process_batch(self, replica_batch_size):\n\t\tbatch_loop=True\n\t\tsql_process=\"\"\"SELECT sch_chameleon.fn_process_batch(%s,%s);\"\"\"\n\t\twhile batch_loop:\n\t\t\tself.pg_conn.pgsql_cur.execute(sql_process, (replica_batch_size, self.i_id_source))\n\t\t\tbatch_result=self.pg_conn.pgsql_cur.fetchone()\n\t\t\tbatch_loop=batch_result[0]\n\t\t\tself.logger.debug(\"Batch loop value %s\" % (batch_loop))\n\t\tself.logger.debug(\"Cleaning replayed batches older than %s\" % (self.batch_retention))\n\t\tsql_cleanup=\"\"\"DELETE FROM \n\t\t\t\t\t\t\t\t\tsch_chameleon.t_replica_batch\n\t\t\t\t\t\t\t\tWHERE\n\t\t\t\t\t\t\t\t\t\tb_started\n\t\t\t\t\t\t\t\t\tAND b_processed\n\t\t\t\t\t\t\t\t\tAND b_replayed\n\t\t\t\t\t\t\t\t\tAND now()-ts_replayed>%s::interval\n\t\t\t\t\t\t\t\t\t \"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_cleanup, (self.batch_retention, ))\n\n\tdef add_foreign_keys(self, source_name, fk_metadata):\n\t\t\"\"\"\n\t\t\tthe method creates the foreign keys extracted from the mysql catalog\n\t\t\tthe keys are created initially as invalid then validated. If an error happens\n\t\t\tis displayed on the log destination\n\t\t\t\n\t\t\t:param source_name: the source name, required to determine the destination schema\n\t\t\t:param fk_metadata: the foreign keys metadata extracted from mysql's information schema\n\t\t\"\"\"\n\t\tfk_list = []\n\t\tsql_schema=\"\"\"\n\t\t\tSELECT\n\t\t\t\tt_dest_schema \n\t\t\tFROM\n\t\t\t\tsch_chameleon.t_sources \n\t\t\tWHERE\n\t\t\t\tt_source=%s\n\t\t\t;\n\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_schema, (source_name, ))\n\t\tdschema=self.pg_conn.pgsql_cur.fetchone()\n\t\tdestination_schema = dschema[0]\n\t\tself.logger.info(\"creating the not validated foreign keys in schema %s\" % destination_schema)\n\t\tfk_counter = 0\n\t\tfor foreign_key in fk_metadata:\n\t\t\ttable_name = foreign_key[\"table_name\"]\n\t\t\tfk_name = foreign_key[\"constraint_name\"][0:20] + \"_\" + str(fk_counter)\n\t\t\tfk_cols = foreign_key[\"fk_cols\"]\n\t\t\treferenced_table_name = foreign_key[\"referenced_table_name\"]\n\t\t\tref_columns = foreign_key[\"ref_columns\"]\n\t\t\tfk_list.append({'fkey_name':fk_name, 'table_name':table_name})\n\t\t\tsql_fkey = (\"\"\"ALTER TABLE \"%s\".\"%s\" ADD CONSTRAINT \"%s\" FOREIGN KEY (%s) REFERENCES \"%s\".\"%s\" (%s) NOT VALID;\"\"\" % \n\t\t\t\t(\n\t\t\t\t\tdestination_schema, \n\t\t\t\t\ttable_name, \n\t\t\t\t\tfk_name, \n\t\t\t\t\tfk_cols, \n\t\t\t\t\tdestination_schema, \n\t\t\t\t\treferenced_table_name, \n\t\t\t\t\tref_columns\n\t\t\t\t)\n\t\t\t\t)\n\t\t\tfk_counter+=1\n\t\t\tself.logger.debug(\"creating %s on %s\" % (fk_name, table_name))\n\t\t\ttry:\n\t\t\t\tself.pg_conn.pgsql_cur.execute(sql_fkey)\n\t\t\texcept psycopg2.Error as e:\n\t\t\t\t\tself.logger.error(\"could not create the foreign key %s on table %s\" % (fk_name, table_name))\n\t\t\t\t\tself.logger.error(\"SQLCODE: %s SQLERROR: %s\" % (e.pgcode, e.pgerror))\n\t\t\t\t\tself.logger.error(\"STATEMENT: %s \" % (self.pg_conn.pgsql_cur.mogrify(sql_fkey)))\n\t\t\t\t\t\n\t\tself.logger.info(\"validating the foreign keys in schema %s\" % destination_schema)\n\t\tfor fkey in fk_list:\n\t\t\tsql_validate = 'ALTER TABLE \"%s\".\"%s\" VALIDATE CONSTRAINT \"%s\";' % (destination_schema, fkey[\"table_name\"], fkey[\"fkey_name\"])\n\t\t\ttry:\n\t\t\t\tself.pg_conn.pgsql_cur.execute(sql_validate)\n\t\t\texcept psycopg2.Error as e:\n\t\t\t\t\tself.logger.error(\"could not validate the foreign key %s on table %s\" % (fkey[\"table_name\"], fkey[\"fkey_name\"]))\n\t\t\t\t\tself.logger.error(\"SQLCODE: %s SQLERROR: %s\" % (e.pgcode, e.pgerror))\n\t\t\t\t\tself.logger.error(\"STATEMENT: %s \" % (self.pg_conn.pgsql_cur.mogrify(sql_validate)))\n\t\t\n\tdef reset_sequences(self, source_name):\n\t\t\"\"\" \n\t\t\tthe method resets the sequences to the max value available the associated table \n\t\t\t:param source_name: the source name, required to determine the destination schema\n\t\t\t\n\t\t\"\"\"\n\t\tsql_schema=\"\"\"\n\t\t\tSELECT\n\t\t\t\tt_dest_schema \n\t\t\tFROM\n\t\t\t\tsch_chameleon.t_sources \n\t\t\tWHERE\n\t\t\t\tt_source=%s\n\t\t\t;\n\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_schema, (source_name, ))\n\t\tdschema=self.pg_conn.pgsql_cur.fetchone()\n\t\tdestination_schema = dschema[0]\n\t\tself.logger.info(\"resetting the sequences in schema %s\" % destination_schema)\n\t\tsql_gen_reset=\"\"\" \n\t\t\tSELECT \n\t\t\t\tformat('SELECT setval(%%L::regclass,(select max(%%I) FROM %%I.%%I));',\n\t\t\t\t\treplace(replace(column_default,'nextval(''',''),'''::regclass)',''),\n\t\t\t\t\tcolumn_name,\n\t\t\t\t\ttable_schema,\n\t\t\t\t\ttable_name\n\t\t\t\t),\n\t\t\t\treplace(replace(column_default,'nextval(''',''),'''::regclass)','') as seq_name\n\t\t\tFROM \n\t\t\t\t\tinformation_schema.columns\n\t\t\tWHERE \n\t\t\t\t\t\ttable_schema=%s\n\t\t\t\t\tAND\tcolumn_default like 'nextval%%'\n\t\t;\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_gen_reset, (destination_schema, ))\n\t\tresults=self.pg_conn.pgsql_cur.fetchall()\n\t\ttry:\n\t\t\tfor statement in results:\n\t\t\t\tself.logger.info(\"resetting the sequence %s\" % statement[1])\n\t\t\t\tself.pg_conn.pgsql_cur.execute(statement[0])\n\t\texcept psycopg2.Error as e:\n\t\t\t\t\tself.logger.error(\"SQLCODE: %s SQLERROR: %s\" % (e.pgcode, e.pgerror))\n\t\t\t\t\tself.logger.error(statement)\n\t\texcept:\n\t\t\tpass\n\t\n\n\tdef build_alter_table(self, token):\n\t\t\"\"\" the function builds the alter table statement from the token idata\"\"\"\n\t\talter_cmd=[]\n\t\tddl_enum=[]\n\t\tquery_cmd=token[\"command\"]\n\t\ttable_name=token[\"name\"]\n\t\tfor alter_dic in token[\"alter_cmd\"]:\n\t\t\tif alter_dic[\"command\"] == 'DROP':\n\t\t\t\talter_cmd.append(\"%(command)s %(name)s CASCADE\" % alter_dic)\n\t\t\telif alter_dic[\"command\"] == 'ADD':\n\t\t\t\tcolumn_type=self.type_dictionary[alter_dic[\"type\"]]\n\t\t\t\tif column_type==\"enum\":\n\t\t\t\t\tenum_name=\"enum_\"+table_name+\"_\"+alter_dic[\"name\"]\n\t\t\t\t\tcolumn_type=enum_name\n\t\t\t\t\tsql_drop_enum='DROP TYPE IF EXISTS '+column_type+' CASCADE;'\n\t\t\t\t\tsql_create_enum=\"CREATE TYPE \"+column_type+\" AS ENUM (\"+alter_dic[\"dimension\"]+\");\"\n\t\t\t\t\tddl_enum.append(sql_drop_enum)\n\t\t\t\t\tddl_enum.append(sql_create_enum)\n\t\t\t\tif column_type==\"character varying\" or column_type==\"character\" or column_type=='numeric' or column_type=='bit' or column_type=='float':\n\t\t\t\t\t\tcolumn_type=column_type+\"(\"+str(alter_dic[\"dimension\"])+\")\"\n\t\t\t\talter_cmd.append(\"%s \\\"%s\\\" %s NULL\" % (alter_dic[\"command\"], alter_dic[\"name\"], column_type))\t\n\t\t\telif alter_dic[\"command\"] == 'CHANGE':\n\t\t\t\tsql_rename = \"\"\n\t\t\t\tsql_type = \"\"\n\t\t\t\told_column=alter_dic[\"old\"]\n\t\t\t\tnew_column=alter_dic[\"new\"]\n\t\t\t\tcolumn_type=self.type_dictionary[alter_dic[\"type\"]]\n\t\t\t\tif column_type==\"character varying\" or column_type==\"character\" or column_type=='numeric' or column_type=='bit' or column_type=='float':\n\t\t\t\t\t\tcolumn_type=column_type+\"(\"+str(alter_dic[\"dimension\"])+\")\"\n\t\t\t\tsql_type = \"\"\"ALTER TABLE \"%s\" ALTER COLUMN \"%s\" SET DATA TYPE %s USING \"%s\"::%s ;;\"\"\" % (table_name, old_column, column_type, old_column, column_type)\n\t\t\t\tif old_column != new_column:\n\t\t\t\t\tsql_rename=\"\"\"ALTER TABLE \"%s\" RENAME COLUMN \"%s\" TO \"%s\" ;\"\"\" % (table_name, old_column, new_column)\n\t\t\t\tquery=sql_type+sql_rename\n\t\t\t\treturn query\n\t\t\telif alter_dic[\"command\"] == 'MODIFY':\n\t\t\t\tcolumn_type=self.type_dictionary[alter_dic[\"type\"]]\n\t\t\t\tcolumn_name=alter_dic[\"name\"]\n\t\t\t\tif column_type==\"enum\":\n\t\t\t\t\tenum_name=\"enum_\"+table_name+\"_\"+alter_dic[\"name\"]\n\t\t\t\t\tcolumn_type=enum_name\n\t\t\t\t\tsql_drop_enum='DROP TYPE IF EXISTS '+column_type+' CASCADE;'\n\t\t\t\t\tsql_create_enum=\"CREATE TYPE \"+column_type+\" AS ENUM (\"+alter_dic[\"dimension\"]+\");\"\n\t\t\t\t\tddl_enum.append(sql_drop_enum)\n\t\t\t\t\tddl_enum.append(sql_create_enum)\n\t\t\t\tif column_type==\"character varying\" or column_type==\"character\" or column_type=='numeric' or column_type=='bit' or column_type=='float':\n\t\t\t\t\t\tcolumn_type=column_type+\"(\"+str(alter_dic[\"dimension\"])+\")\"\n\t\t\t\tquery = ' '.join(ddl_enum) + \"\"\"ALTER TABLE \"%s\" ALTER COLUMN \"%s\" SET DATA TYPE %s USING \"%s\"::%s ;\"\"\" % (table_name, column_name, column_type, column_name, column_type)\n\t\t\t\treturn query\n\t\tquery = ' '.join(ddl_enum)+\" \"+query_cmd + ' '+ table_name+ ' ' +', '.join(alter_cmd)+\" ;\"\n\t\treturn query\n\n\n\tdef truncate_tables(self):\n\t\tsql_clean=\"\"\" \n\t\t\t\t\t\tSELECT DISTINCT\n\t\t\t\t\t\t\tformat('SET lock_timeout=''10s'';TRUNCATE TABLE %%I.%%I CASCADE;',v_schema,v_table) v_truncate,\n\t\t\t\t\t\t\tformat('DELETE FROM %%I.%%I;',v_schema,v_table) v_delete,\n\t\t\t\t\t\t\tformat('VACUUM %%I.%%I;',v_schema,v_table) v_vacuum,\n\t\t\t\t\t\t\tformat('%%I.%%I',v_schema,v_table) as v_tab,\n\t\t\t\t\t\t\tv_table\n\t\t\t\t\t\tFROM\n\t\t\t\t\t\t\tsch_chameleon.t_index_def \n\t\t\t\t\t\tWHERE\n\t\t\t\t\t\t\ti_id_source=%s\n\t\t\t\t\t\tORDER BY \n\t\t\t\t\t\t\tv_table\n\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_clean, (self.i_id_source, ))\n\t\ttab_clean=self.pg_conn.pgsql_cur.fetchall()\n\t\tfor stat_clean in tab_clean:\n\t\t\tst_truncate=stat_clean[0]\n\t\t\tst_delete=stat_clean[1]\n\t\t\tst_vacuum=stat_clean[2]\n\t\t\ttab_name=stat_clean[3]\n\t\t\ttry:\n\t\t\t\tself.logger.info(\"truncating table %s\" % (tab_name,))\n\t\t\t\tself.pg_conn.pgsql_cur.execute(st_truncate)\n\t\t\t\t\n\t\t\texcept:\n\t\t\t\tself.logger.info(\"truncate failed, fallback to delete on table %s\" % (tab_name,))\n\t\t\t\tself.pg_conn.pgsql_cur.execute(st_delete)\n\t\t\t\tself.logger.info(\"running vacuum on table %s\" % (tab_name,))\n\t\t\t\tself.pg_conn.pgsql_cur.execute(st_vacuum)\n\n\tdef drop_src_indices(self):\n\t\tsql_idx=\"\"\"SELECT t_drop FROM sch_chameleon.t_index_def WHERE i_id_source=%s;\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_idx, (self.i_id_source, ))\n\t\tidx_drop=self.pg_conn.pgsql_cur.fetchall()\n\t\tfor drop_stat in idx_drop:\n\t\t\tself.pg_conn.pgsql_cur.execute(drop_stat[0])\n\t\t\t\n\tdef create_src_indices(self):\n\t\tsql_idx=\"\"\"SELECT t_create FROM sch_chameleon.t_index_def WHERE i_id_source=%s;\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_idx, (self.i_id_source, ))\n\t\tidx_create=self.pg_conn.pgsql_cur.fetchall()\n\t\tfor create_stat in idx_create:\n\t\t\tself.pg_conn.pgsql_cur.execute(create_stat[0])\n\t\t\n\n\tdef get_index_def(self):\n\t\ttable_limit = ''\n\t\tif self.table_limit[0] != '*':\n\t\t\ttable_limit = self.pg_conn.pgsql_cur.mogrify(\"\"\"WHERE table_name IN (SELECT unnest(%s))\"\"\",(self.table_limit, )).decode()\n\t\t\n\t\tsql_get_idx=\"\"\" \n\t\t\t\tDELETE FROM sch_chameleon.t_index_def WHERE i_id_source=%s;\n\t\t\t\tINSERT INTO sch_chameleon.t_index_def\n\t\t\t\t\t(\n\t\t\t\t\t\ti_id_source,\n\t\t\t\t\t\tv_schema,\n\t\t\t\t\t\tv_table,\n\t\t\t\t\t\tv_index,\n\t\t\t\t\t\tt_create,\n\t\t\t\t\t\tt_drop\n\t\t\t\t\t)\n\t\t\t\tSELECT \n\t\t\t\t\ti_id_source,\n\t\t\t\t\tschema_name,\n\t\t\t\t\ttable_name,\n\t\t\t\t\tindex_name,\n\t\t\t\t\tCASE\n\t\t\t\t\t\tWHEN indisprimary\n\t\t\t\t\t\tTHEN\n\t\t\t\t\t\t\tformat('ALTER TABLE %%I.%%I ADD CONSTRAINT %%I %%s',\n\t\t\t\t\t\t\t\tschema_name,\n\t\t\t\t\t\t\t\ttable_name,\n\t\t\t\t\t\t\t\tindex_name,\n\t\t\t\t\t\t\t\tpg_get_constraintdef(const_id)\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tELSE\n\t\t\t\t\t\t\tpg_get_indexdef(index_id) \n\t\t\t\t\tEND AS t_create,\n\t\t\t\t\tCASE\n\t\t\t\t\t\tWHEN indisprimary\n\t\t\t\t\t\tTHEN\n\t\t\t\t\t\t\tformat('ALTER TABLE %%I.%%I DROP CONSTRAINT %%I',\n\t\t\t\t\t\t\t\tschema_name,\n\t\t\t\t\t\t\t\ttable_name,\n\t\t\t\t\t\t\t\tindex_name\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\n\t\t\t\t\t\tELSE\n\t\t\t\t\t\t\tformat('DROP INDEX %%I.%%I',\n\t\t\t\t\t\t\t\tschema_name,\n\t\t\t\t\t\t\t\tindex_name\n\t\t\t\t\t\t\t\t\n\t\t\t\t\t\t\t)\n\t\t\t\t\tEND AS t_drop\n\t\t\t\t\t\n\t\t\t\tFROM\n\n\t\t\t\t(\n\t\t\t\tSELECT \n\t\t\t\t\ttab.relname AS table_name,\n\t\t\t\t\tindx.relname AS index_name,\n\t\t\t\t\tidx.indexrelid index_id,\n\t\t\t\t\tindisprimary,\n\t\t\t\t\tsch.nspname schema_name,\n\t\t\t\t\tsrc.i_id_source,\n\t\t\t\t\tcns.oid as const_id\n\t\t\t\t\t\n\t\t\t\tFROM\n\t\t\t\t\tpg_index idx\n\t\t\t\t\tINNER JOIN pg_class indx\n\t\t\t\t\tON\n\t\t\t\t\t\tidx.indexrelid=indx.oid\n\t\t\t\t\tINNER JOIN pg_class tab\n\t\t\t\t\tINNER JOIN pg_namespace sch\n\t\t\t\t\tON \n\t\t\t\t\t\ttab.relnamespace=sch.oid\n\t\t\t\t\t\n\t\t\t\t\tON\n\t\t\t\t\t\tidx.indrelid=tab.oid\n\t\t\t\t\tINNER JOIN sch_chameleon.t_sources src\n\t\t\t\t\tON sch.nspname=src.t_dest_schema\n\t\t\t\t\tLEFT OUTER JOIN pg_constraint cns\n\t\t\t\t\tON \n\t\t\t\t\t\t\tindx.relname=cns.conname\n\t\t\t\t\t\tAND cns.connamespace=sch.oid\n\t\t\t\t\t\n\t\t\t\tWHERE\n\t\t\t\t\tsch.nspname=%s\n\t\t\t\t) idx\n\t\t\n\t\t\"\"\" + table_limit\n\t\t\n\t\tself.pg_conn.pgsql_cur.execute(sql_get_idx, (self.i_id_source, self.dest_schema, ))\n\n\tdef drop_primary_key(self, token):\n\t\tself.logger.info(\"dropping primary key for table %s\" % (token[\"name\"],))\n\t\tsql_gen=\"\"\"\n\t\t\t\t\t\tSELECT DISTINCT\n\t\t\t\t\t\t\tformat('ALTER TABLE %%I.%%I DROP CONSTRAINT %%I;',\n\t\t\t\t\t\t\ttable_schema,\n\t\t\t\t\t\t\ttable_name,\n\t\t\t\t\t\t\tconstraint_name\n\t\t\t\t\t\t\t)\n\t\t\t\t\t\tFROM \n\t\t\t\t\t\t\tinformation_schema.key_column_usage \n\t\t\t\t\t\tWHERE \n\t\t\t\t\t\t\t\ttable_schema=%s \n\t\t\t\t\t\t\tAND table_name=%s;\n\t\t\t\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_gen, (self.dest_schema, token[\"name\"]))\n\t\tvalue_check=self.pg_conn.pgsql_cur.fetchone()\n\t\tif value_check:\n\t\t\tsql_drop=value_check[0]\n\t\t\tself.pg_conn.pgsql_cur.execute(sql_drop)\n\t\t\tself.unregister_table(token[\"name\"])\n\t\t\n\n\tdef gen_query(self, token):\n\t\t\"\"\" the function generates the ddl\"\"\"\n\t\tquery=\"\"\n\t\t\n\t\tif token[\"command\"] ==\"DROP TABLE\":\n\t\t\tquery=\" %(command)s IF EXISTS \\\"%(name)s\\\";\" % token\n\t\telif token[\"command\"] ==\"TRUNCATE\":\n\t\t\tquery=\" %(command)s TABLE \\\"%(name)s\\\" CASCADE;\" % token\n\t\telif token[\"command\"] ==\"CREATE TABLE\":\n\t\t\ttable_metadata={}\n\t\t\ttable_metadata[\"columns\"]=token[\"columns\"]\n\t\t\ttable_metadata[\"name\"]=token[\"name\"]\n\t\t\ttable_metadata[\"indices\"]=token[\"indices\"]\n\t\t\tself.table_metadata={}\n\t\t\tself.table_metadata[token[\"name\"]]=table_metadata\n\t\t\tself.build_tab_ddl()\n\t\t\tself.build_idx_ddl()\n\t\t\tquery_type=' '.join(self.type_ddl[token[\"name\"]])\n\t\t\tquery_table=self.table_ddl[token[\"name\"]]\n\t\t\tquery_idx=' '.join(self.idx_ddl[token[\"name\"]])\n\t\t\tquery=query_type+query_table+query_idx\n\t\t\tself.store_table(token[\"name\"])\n\t\telif token[\"command\"] == \"ALTER TABLE\":\n\t\t\tquery=self.build_alter_table(token)\n\t\telif token[\"command\"] == \"DROP PRIMARY KEY\":\n\t\t\tself.drop_primary_key(token)\n\t\treturn query \n\n\n\tdef write_ddl(self, token, query_data):\n\t\tsql_path=\" SET search_path=\"+self.dest_schema+\";\"\n\t\tpg_ddl=sql_path+self.gen_query(token)\n\t\tlog_table=query_data[\"log_table\"]\n\t\tinsert_vals=(\tquery_data[\"batch_id\"], \n\t\t\t\t\t\t\t\ttoken[\"name\"], \n\t\t\t\t\t\t\t\tquery_data[\"schema\"], \n\t\t\t\t\t\t\t\tquery_data[\"binlog\"], \n\t\t\t\t\t\t\t\tquery_data[\"logpos\"], \n\t\t\t\t\t\t\t\tpg_ddl\n\t\t\t\t\t\t\t)\n\t\tsql_insert=\"\"\"\n\t\t\t\t\t\t\t\tINSERT INTO sch_chameleon.\"\"\"+log_table+\"\"\"\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\ti_id_batch, \n\t\t\t\t\t\t\t\t\tv_table_name, \n\t\t\t\t\t\t\t\t\tv_schema_name, \n\t\t\t\t\t\t\t\t\tenm_binlog_event, \n\t\t\t\t\t\t\t\t\tt_binlog_name, \n\t\t\t\t\t\t\t\t\ti_binlog_position, \n\t\t\t\t\t\t\t\t\tt_query\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\t\tVALUES\n\t\t\t\t\t\t\t\t(\n\t\t\t\t\t\t\t\t\t%s,\n\t\t\t\t\t\t\t\t\t%s,\n\t\t\t\t\t\t\t\t\t%s,\n\t\t\t\t\t\t\t\t\t'ddl',\n\t\t\t\t\t\t\t\t\t%s,\n\t\t\t\t\t\t\t\t\t%s,\n\t\t\t\t\t\t\t\t\t%s\n\t\t\t\t\t\t\t\t)\n\t\t\t\t\t\t\"\"\"\n\t\tself.pg_conn.pgsql_cur.execute(sql_insert, insert_vals)\n\t\t\n\t\t\n\t\t\n\tdef check_reindex(self):\n\t\t\"\"\"the function checks if there is any reindex running and holds for the given number of seconds \"\"\"\n\t\tsql_check=\"\"\"SELECT count(*) FROM pg_stat_activity WHERE datname=current_database() AND application_name = ANY(%s) ;\"\"\"\n\t\twhile True:\n\t\t\tself.pg_conn.pgsql_cur.execute(sql_check, (self.reindex_app_names, ))\n\t\t\treindex_tup = self.pg_conn.pgsql_cur.fetchone()\n\t\t\treindex_cnt = reindex_tup[0]\n\t\t\tif reindex_cnt == 0:\n\t\t\t\tbreak;\n\t\t\tself.logger.info(\"reindex detected, sleeping %s second(s)\" % (self.sleep_on_reindex,))\n\t\t\ttime.sleep(self.sleep_on_reindex)\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"pg_chameleon/lib/pg_lib.py","file_name":"pg_lib.py","file_ext":"py","file_size_in_byte":36887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"171394039","text":"\"\"\"\n\nThis module defines several filter classes, together with a function that uses them to filter maps.\n\nThere are two main classes of filter: :obj:`MatchedFilter` and :obj:`RealSpaceMatchedFilter`.\n\nNew base classes can be derived from the overall base class :obj:`MapFilter`. There are also \nbase classes corresponding to filters with different signal templates (:obj:`BeamFilter`,\n:obj:`ArnaudModelFilter`). The actual filters that can be used are derived from these, e.g.:\n\n* :obj:`BeamMatchedFilter`\n* :obj:`ArnaudModelMatchedFilter`\n* :obj:`BeamRealSpaceMatchedFilter`\n* :obj:`ArnaudModelRealSpaceMatchedFilter`\n\n\"\"\"\n\nimport math\nfrom pixell import enmap\nfrom pixell import fft as enfft\nfrom pixell import powspec\nimport astropy.wcs as enwcs\nfrom astLib import *\nimport numpy as np\nfrom numpy import fft\nimport pylab as plt\nimport os\nfrom scipy import interpolate\nfrom scipy import ndimage\nimport astropy.io.fits as pyfits\nimport astropy.stats as apyStats\nimport copy\nimport sys\nimport glob\nimport itertools\nimport pyximport; pyximport.install()\nimport nemoCython\nimport nemo\nfrom . import maps\nfrom . import signals\nfrom . import photometry\nfrom . import catalogs\nfrom . import plotSettings\nfrom . import gnfw\nfrom . import completeness\nimport astropy.table as atpy\nimport time\n#import IPython\n\n#-------------------------------------------------------------------------------------------------------------\ndef filterMaps(unfilteredMapsDictList, filterParams, tileName, filteredMapsDir = '.', diagnosticsDir = '.', \n selFnDir = '.', verbose = True, undoPixelWindow = True, useCachedMaps = True,\n returnFilter = False):\n \"\"\"Build and applies filters to the unfiltered maps(s). The output is a filtered map in yc or uK (this\n can be set with outputUnits in the config file). All filter operations are done in the filter objects, \n even if multifrequency (a change from previous behaviour).\n \n Filtered maps are written to rootOutDir/filteredMaps\n Filters, if stored, are written to rootOutDir/filters\n \n Returns a dictionary containing the filtered map and S/N map etc.\n \n \"\"\"\n \n f=filterParams\n label=f['label']+\"#\"+tileName\n filteredMapFileName=filteredMapsDir+os.path.sep+\"%s_filteredMap.fits\" % (label)\n SNMapFileName=filteredMapsDir+os.path.sep+\"%s_SNMap.fits\" % (label)\n if os.path.exists(filteredMapFileName) == False or useCachedMaps == False:\n \n print(\"... making filtered map %s ...\" % (label))\n filterClass=eval('%s' % (f['class']))\n filterObj=filterClass(f['label'], unfilteredMapsDictList, f['params'], tileName = tileName, \n diagnosticsDir = diagnosticsDir, selFnDir = selFnDir)\n filteredMapDict=filterObj.buildAndApply()\n \n # Keywords we need for photometry later\n filteredMapDict['wcs'].header['BUNIT']=filteredMapDict['mapUnits']\n if 'beamSolidAngle_nsr' in filteredMapDict.keys() and filteredMapDict['beamSolidAngle_nsr'] > 0:\n filteredMapDict['wcs'].header['BEAMNSR']=filteredMapDict['beamSolidAngle_nsr']\n filteredMapDict['wcs'].header['FREQGHZ']=filteredMapDict['obsFreqGHz']\n filteredMapDict['wcs'].updateFromHeader()\n \n # Undo pixel window function using Sigurd's FFT method (takes into account variable pixel scale etc.)\n # We only need to do this for maps of signal (cancels in S/N map)\n # We do this once because it does take some time... \n # ... and then we can forget about if e.g. stacking or doing forced photometry later\n if undoPixelWindow == True:\n mask=np.equal(filteredMapDict['data'], 0)\n filteredMapDict['data']=enmap.apply_window(filteredMapDict['data'], pow=-1.0)\n filteredMapDict['data'][mask]=0 # just in case we rely elsewhere on zero == no data\n\n if 'saveFilteredMaps' in filterObj.params and filterObj.params['saveFilteredMaps'] == True:\n maps.saveFITS(filteredMapFileName, filteredMapDict['data'], filteredMapDict['wcs'])\n maps.saveFITS(SNMapFileName, filteredMapDict['SNMap'], filteredMapDict['wcs']) \n # Uncomment for quicker testing/re-runs...\n #astImages.saveFITS(filteredMapFileName, filteredMapDict['data'], filteredMapDict['wcs'])\n #astImages.saveFITS(SNMapFileName, filteredMapDict['SNMap'], filteredMapDict['wcs']) \n\n else:\n print(\"... loading cached map %s ...\" % (filteredMapFileName))\n filteredMapDict={}\n with pyfits.open(filteredMapFileName) as img:\n filteredMapDict['data']=img[0].data\n filteredMapDict['wcs']=astWCS.WCS(img[0].header, mode = 'pyfits')\n filteredMapDict['mapUnits']=filteredMapDict['wcs'].header['BUNIT']\n if 'BEAMNSR' in filteredMapDict['wcs'].header.keys():\n filteredMapDict['beamSolidAngle_nsr']=filteredMapDict['wcs'].header['BEAMNSR']\n filteredMapDict['obsFreqGHz']=filteredMapDict['wcs'].header['FREQGHZ']\n with pyfits.open(SNMapFileName) as img:\n filteredMapDict['SNMap']=img[0].data\n filteredMapDict['surveyMask'], wcs=completeness.loadAreaMask(tileName, selFnDir)\n filteredMapDict['label']=f['label']\n filteredMapDict['tileName']=tileName\n \n if returnFilter == True:\n return filteredMapDict, filterObj\n \n return filteredMapDict\n \n#------------------------------------------------------------------------------------------------------------\nclass MapFilter(object):\n \"\"\"Generic map filter base class. Defines common interface.\n \n \"\"\"\n def __init__(self, label, unfilteredMapsDictList, paramsDict, tileName = 'PRIMARY', writeFilter = False, \n forceRebuild = False, diagnosticsDir = None, selFnDir = None):\n \"\"\"Initialises a MapFilter. unfilteredMapsDictList describes the input maps, paramsDict describes\n the filter options. The convention is that single frequency only filters (e.g. GaussianWienerFilter)\n only operate on the first map in the unfilteredMapDictList.\n \n label is used to store the filter to save calculating it again. Filters are stored under filters/\n dir. If a filter already exists under this dir with the given label, it is loaded rather than\n recalculated. To force recalculating the filter, set forceRebuild == True.\n \n \"\"\"\n \n self.label=label\n self.params=paramsDict\n \n self.diagnosticsDir=diagnosticsDir\n self.selFnDir=selFnDir\n self.tileName=tileName\n self.filterFileName=self.diagnosticsDir+os.path.sep+\"filter_%s#%s.fits\" % (self.label, self.tileName)\n \n # Prepare all the unfilteredMaps (in terms of cutting sections, masks etc.)\n # NOTE: we're now copying the input unfilteredMapsDictList, for supporting multi-ext tileDir files\n self.unfilteredMapsDictList=[]\n for mapDict in unfilteredMapsDictList: \n mapDict=maps.preprocessMapDict(mapDict.copy(), tileName = tileName, diagnosticsDir = diagnosticsDir)\n self.unfilteredMapsDictList.append(mapDict)\n self.wcs=mapDict['wcs']\n self.shape=mapDict['data'].shape\n \n # Get beam solid angle info (units: nanosteradians)... we'll need for fluxes in Jy later\n self.beamSolidAnglesDict={}\n for mapDict in self.unfilteredMapsDictList: \n if 'solidAngle_nsr' in mapDict.keys():\n solidAngle_nsr=mapDict['solidAngle_nsr']\n else:\n beamFileName=mapDict['beamFileName']\n with open(beamFileName, \"r\") as inFile:\n lines=inFile.readlines()\n foundLine=False\n for line in lines:\n if line.find(\"solid angle\") != -1:\n foundLine=True\n break\n if foundLine == True:\n bits=line.split(\"=\")\n solidAngle_nsr=float(bits[1].split()[0])\n else:\n solidAngle_nsr=0.0\n self.beamSolidAnglesDict[mapDict['obsFreqGHz']]=solidAngle_nsr\n \n # For pixell / enmap\n # NOTE: enki maps can have an additional axis, which we don't want\n enheader=self.wcs.header.copy()\n if 'NAXIS3' in enheader.keys():\n del enheader['NAXIS3']\n enheader['NAXIS']=2\n self.enwcs=enwcs.WCS(enheader)\n\n # We could make this adjustable... added after switch to pixell\n self.apodPix=20\n \n # Sanity check that all maps are the same dimensions\n shape=self.unfilteredMapsDictList[0]['data'].shape\n for mapDict in self.unfilteredMapsDictList:\n if mapDict['data'].shape != shape:\n raise Exception(\"Maps at different frequencies have different dimensions!\")\n \n # This is used by routines that make signal templates\n self.makeRadiansMap()\n \n # This is a default - will get modified (calculated) by buildAndApply\n self.signalNorm=1.0\n self.fRelWeights={}\n \n \n def makeRadiansMap(self):\n \"\"\"Makes a map of distance in radians from centre, for the map being filtered.\n \n \"\"\"\n \n mapDict=self.unfilteredMapsDictList[0]\n \n # NOTE: int conversion for python3\n x0=int(mapDict['data'].shape[1]/2)\n y0=int(mapDict['data'].shape[0]/2)\n ra0, dec0=self.wcs.pix2wcs(x0, y0)\n ra1, dec1=self.wcs.pix2wcs(x0+1, y0+1)\n self.degPerPixX=astCoords.calcAngSepDeg(ra0, dec0, ra1, dec0)\n self.degPerPixY=astCoords.calcAngSepDeg(ra0, dec0, ra0, dec1)\n \n # Real space map of angular distance from centre in radians, used in making filters and beam\n # NOTE: floor and int conversion added for python3\n xRadRange=np.array([np.arange(int(np.floor(-mapDict['data'].shape[1]/2)), int(mapDict['data'].shape[1]/2), \\\n dtype=np.float64)*np.radians(self.degPerPixX)]*mapDict['data'].shape[0])\n yRadRange=np.array([np.arange(int(np.floor(-mapDict['data'].shape[0]/2)), int(mapDict['data'].shape[0]/2), \\\n dtype=np.float64)*np.radians(self.degPerPixY)]*mapDict['data'].shape[1]).transpose()\n rRadRange=np.sqrt(xRadRange**2+yRadRange**2)\n self.radiansMap=rRadRange\n \n \n def buildAndApply(self):\n \"\"\"Builds and applies the filter to the unfiltered map(s). Returns a dictionary, containing\n keys 'data', 'wcs', 'weights', 'obsFreqGHz'. If this routine converts to yc in place, the latter is set\n to 'yc'.\n \n \"\"\"\n \n raise Exception(\"Called a base filter class without a buildAndApply() function implemented.\")\n return None\n \n \n def makeForegroundsPower(self):\n \"\"\"Returns a Power2D object with foregrounds power from the CMB (this could be extended to add other\n foregrounds if necessary).\n \n \"\"\"\n\n # CAMB power spec with Planck 2015 parameters (ish) \n tab=atpy.Table().read(nemo.__path__[0]+os.path.sep+\"data\"+os.path.sep+\"planck_lensedCls.dat\", format = 'ascii')\n tab['TT']=(tab['TT']*2*np.pi)/(tab['L']*(tab['L']+1))\n lmap=enmap.modlmap(self.unfilteredMapsDictList[0]['data'].shape, self.enwcs)\n l2p=interpolate.interp1d(tab['L'], tab['TT'], bounds_error=False, fill_value=0.0)\n fgPower=l2p(lmap)*lmap.shape[0]*lmap.shape[1]\n \n return fgPower\n \n\n def makeRealSpaceFilterProfile(self):\n \"\"\"Makes a 1d real-space profile of the filter. This is normalised to 1 at the frequency of the \n first map given in the unfiltered maps list.\n \n Returns profile, arcminRange\n \n \"\"\"\n realSpace=fft.ifft2(self.filt).real\n realSpace=fft.fftshift(realSpace)\n\n # Changed for python3 - we may want to check this...\n # i.e., force to always be int after /2 without this\n x0=int(realSpace.shape[2]/2)\n y0=int(realSpace.shape[1]/2)\n\n # Arbitrarily normalise to 1 at the maximum (in whichever frequency that occurs)\n normFactor=abs(realSpace[:, y0, x0:]).max() \n prof=realSpace[:, y0, x0:]/normFactor\n #prof=realSpace[0, y0, x0:]/realSpace[0, y0, x0:].max() # normalise for plot\n arcminRange=np.arange(0, prof.shape[1])*self.degPerPixX*60.0\n \n return prof, arcminRange\n \n \n def saveRealSpaceFilterProfile(self):\n \"\"\"Saves a real-space profile of the filter out as a .png plot and 2d .fits image.\n \n \"\"\"\n \n prof, arcminRange=self.makeRealSpaceFilterProfile()\n \n # Measure characteristic FWHM\n #tck=interpolate.splrep(arcminRange, prof)\n #FWHMArcmin=interpolate.splev([0.5], tck)*2 # *2 because otherwise would be half width\n \n fig=plt.figure(figsize=(8,8))\n ax=plt.axes([0.14, 0.11, 0.85, 0.86])\n #fig.canvas.set_window_title('Filter Profile in Real Space')\n #plt.title(\"Filter Profile %s\" % (self.label))\n plt.ylabel(\"Amplitude\")\n plt.xlabel(\"$\\\\theta$ (arcmin)\")\n for row, mapDict in zip(prof, self.unfilteredMapsDictList):\n if mapDict['obsFreqGHz'] is not None:\n label = '%d GHz' % (mapDict['obsFreqGHz'])\n elif mapDict['units'] == 'yc':\n label = 'yc'\n plt.plot(arcminRange, row, label = label)\n plt.xlim(0, 30.0)\n plt.ylim(prof.min(), prof.max()*1.1)\n plt.legend()\n plt.savefig(self.diagnosticsDir+os.path.sep+\"realSpaceProfile1d_\"+self.label+\"#\"+self.tileName+\".png\")\n plt.close()\n \n # Save 2D realspace filter image too\n #astImages.saveFITS(self.diagnosticsDir+os.path.sep+\"realSpaceProfile2d_\"+self.label+\".fits\", \\\n #realSpace, wcs)\n \n\n def makeNoiseMap(self, mapData):\n \"\"\"Estimate the noise map using local RMS measurements on a grid, over the whole filtered map.\n \n \"\"\"\n\n # average all weight maps\n # doesn't matter about spectral weighting, we just want areas with similar characteristics\n # now using this for both 'smart' and original noise grid option\n medWeights=[]\n for mapDict in self.unfilteredMapsDictList:\n medWeights.append(mapDict['weights'])\n medWeights=np.median(np.array(medWeights), axis = 0)\n \n # 'smart option' - measure noise in areas with similar weights (however weights defined)\n if self.params['noiseParams']['noiseGridArcmin'] == \"smart\":\n try:\n numBins=self.params['noiseParams']['numNoiseBins']\n except:\n raise Exception(\"Need to give numNoiseBins in noiseParams when using noiseGridArcmin = 'smart'\")\n binEdges=np.linspace(medWeights.min(), medWeights.max(), numBins)\n RMSMap=np.zeros(medWeights.shape)\n apodMask=np.not_equal(mapData, 0)\n for i in range(len(binEdges)-1):\n # Find area of similar weight\n binMin=binEdges[i]\n binMax=binEdges[i+1]\n weightMask=np.logical_and(medWeights > binMin, medWeights < binMax)\n # Measure noise in that area from filtered map, and fill in noise map\n chunkValues=mapData[weightMask]\n goodAreaMask=np.greater_equal(apodMask[weightMask], 1.0)\n if 'RMSEstimator' in self.params['noiseParams'].keys() and self.params['noiseParams']['RMSEstimator'] == 'biweight':\n if goodAreaMask.sum() >= 10:\n # Astropy version is faster but gives identical results\n chunkRMS=apyStats.biweight_scale(chunkValues[goodAreaMask], c = 9.0, modify_sample_size = True)\n #chunkRMS=astStats.biweightScale(chunkValues[goodAreaMask], 6.0)\n else:\n chunkRMS=0.\n elif 'RMSEstimator' in self.params['noiseParams'].keys() and self.params['noiseParams']['RMSEstimator'] == 'percentile':\n chunkRMS=np.percentile(abs(chunkValues[goodAreaMask]), 68.3)\n else:\n # Default: 3-sigma clipped stdev\n if np.not_equal(chunkValues, 0).sum() != 0:\n goodAreaMask=np.greater_equal(apodMask[weightMask], 1.0)\n chunkMean=np.mean(chunkValues[goodAreaMask])\n chunkRMS=np.std(chunkValues[goodAreaMask])\n sigmaClip=3.0\n for c in range(10):\n mask=np.less(abs(chunkValues), abs(chunkMean+sigmaClip*chunkRMS))\n mask=np.logical_and(goodAreaMask, mask)\n if mask.sum() > 0:\n chunkMean=np.mean(chunkValues[mask])\n chunkRMS=np.std(chunkValues[mask])\n else:\n chunkRMS=0.\n if chunkRMS > 0:\n RMSMap[weightMask]=chunkRMS\n\n # The grid method now recognises numNoiseBins in cells\n else: \n gridSize=int(round((self.params['noiseParams']['noiseGridArcmin']/60.)/self.wcs.getPixelSizeDeg()))\n overlapPix=int(gridSize/2)\n numXChunks=mapData.shape[1]/gridSize\n numYChunks=mapData.shape[0]/gridSize\n yChunks=np.linspace(0, mapData.shape[0], int(numYChunks+1), dtype = int)\n xChunks=np.linspace(0, mapData.shape[1], int(numXChunks+1), dtype = int)\n apodMask=np.not_equal(mapData, 0)\n RMSMap=np.zeros(mapData.shape)\n # For this mode, interpreted as number of noise bins per cell\n if 'numNoiseBins' in self.params['noiseParams'].keys():\n numBins=self.params['noiseParams']['numNoiseBins']\n else:\n numBins=1\n for i in range(len(yChunks)-1):\n for k in range(len(xChunks)-1):\n y0=yChunks[i]-overlapPix\n y1=yChunks[i+1]+overlapPix\n x0=xChunks[k]-overlapPix\n x1=xChunks[k+1]+overlapPix\n if y0 < 0:\n y0=0\n if y1 > mapData.shape[0]:\n y1=mapData.shape[0]\n if x0 < 0:\n x0=0\n if x1 > mapData.shape[1]:\n x1=mapData.shape[1]\n chunkValues=mapData[y0:y1, x0:x1]\n goodAreaMask=np.greater_equal(apodMask[y0:y1, x0:x1], 1.0)\n if goodAreaMask.sum() == 0:\n continue\n # Binning inside cell by weights - to handle sudden noise changes\n weightValues=medWeights[y0:y1, x0:x1]\n percentiles=np.arange(0, 100, 100/numBins)\n binEdges=[]\n for p in percentiles:\n binEdges.append(np.percentile(weightValues[goodAreaMask], p))\n binEdges.append(weightValues[goodAreaMask].max()+1e-6)\n for b in range(len(binEdges)-1):\n binMin=binEdges[b]\n binMax=binEdges[b+1]\n binMask=np.logical_and(weightValues >= binMin, weightValues < binMax)\n binValues=chunkValues[binMask*goodAreaMask]\n if 'RMSEstimator' in self.params['noiseParams'].keys() and self.params['noiseParams']['RMSEstimator'] == 'biweight':\n if (binMask*goodAreaMask).sum() >= 10:\n chunkRMS=apyStats.biweight_scale(binValues, c = 9.0, modify_sample_size = True)\n else:\n chunkRMS=0.\n elif 'RMSEstimator' in self.params['noiseParams'].keys() and self.params['noiseParams']['RMSEstimator'] == 'percentile':\n chunkRMS=np.percentile(abs(binValues), 68.3)\n else:\n # Default: 3-sigma clipped stdev\n if np.not_equal(binValues, 0).sum() != 0:\n chunkMean=np.mean(binValues)\n chunkRMS=np.std(binValues)\n sigmaClip=3.0\n for c in range(10):\n mask=np.less(abs(binValues), abs(chunkMean+sigmaClip*chunkRMS))\n if mask.sum() > 0:\n chunkMean=np.mean(binValues[mask])\n chunkRMS=np.std(binValues[mask])\n else:\n chunkRMS=0.\n if chunkRMS > 0:\n RMSMap[y0:y1, x0:x1][binMask]=chunkRMS\n \n return RMSMap\n \n \n def loadFRelWeights(self):\n \"\"\"Reads frequency weights used for relativistic corrections from filter header.\n \n \"\"\"\n with pyfits.open(self.filterFileName) as img:\n self.fRelWeights={}\n for i in range(1, 10):\n if 'RW%d_GHZ' % (i) in img[0].header.keys():\n freqGHz=img[0].header['RW%d_GHZ' % (i)]\n self.fRelWeights[freqGHz]=img[0].header['RW%d' % (i)]\n \n#------------------------------------------------------------------------------------------------------------\nclass MatchedFilter(MapFilter):\n \"\"\"Multi-frequency matched filter...\n \n \"\"\"\n\n def buildAndApply(self):\n \n fMapsToFilter=[]\n for mapDict in self.unfilteredMapsDictList:\n fMapsToFilter.append(enmap.fft(enmap.apod(mapDict['data'], self.apodPix)))\n fMapsToFilter=np.array(fMapsToFilter)\n \n # NOTE: We've tidied up the config file, so we don't have to feed in surveyMask and psMask like this\n # (see startUp.parseConfig)\n surveyMask=self.unfilteredMapsDictList[0]['surveyMask']\n psMask=self.unfilteredMapsDictList[0]['psMask']\n \n if os.path.exists(self.filterFileName) == False:\n \n fMapsForNoise=[]\n for i in range(len(self.unfilteredMapsDictList)):\n mapDict=self.unfilteredMapsDictList[i]\n d=mapDict['data']\n if self.params['noiseParams']['method'] == 'dataMap':\n if 'noiseMaskCatalog' in mapDict.keys() and mapDict['noiseMaskCatalog'] is not None:\n modelPath=self.diagnosticsDir+os.path.sep+\"noiseMaskModel_%.1f.fits\" % (mapDict['obsFreqGHz'])\n if os.path.exists(modelPath) == True:\n with pyfits.open(modelPath) as img:\n model=img[0].data\n else:\n model=maps.makeModelImage(d.shape, self.wcs, mapDict['noiseMaskCatalog'],\n mapDict['beamFileName'],\n obsFreqGHz = mapDict['obsFreqGHz'],\n minSNR = 5.0)\n maps.saveFITS(modelPath, model, self.wcs)\n if model is not None:\n d=d-model\n fMapsForNoise.append(enmap.fft(enmap.apod(d, self.apodPix)))\n elif self.params['noiseParams']['method'] == 'model':\n # Assuming weights are actually inv var white noise level per pix\n # (which they are for Sigurd's maps)\n valid=np.nonzero(mapDict['weights'])\n RMS=np.mean(1/np.sqrt(mapDict['weights'][valid]))\n if RMS < 10.0: # Minimum level to stop this blowing up\n RMS=10.0\n # Seeds fixed so that outputs are the same on repeated runs\n cmb=maps.simCMBMap(self.shape, self.wcs, beamFileName = mapDict['beamFileName'], \n seed = 3141592654+i, noiseLevel = RMS, fixNoiseSeed = True)\n fMapsForNoise.append(enmap.fft(enmap.apod(cmb, self.apodPix)))\n else:\n raise Exception(\"'%s' is not a valid filter noise method name - fix the .yml config file\" % (self.params['noiseParams']['method']))\n fMapsForNoise=np.array(fMapsForNoise)\n \n # Smoothing noise here is essential\n kernelSize=(3,3)\n noiseCov=[]\n for i in range(len(self.unfilteredMapsDictList)):\n iMap=self.unfilteredMapsDictList[i]\n row=[]\n for j in range(len(self.unfilteredMapsDictList)):\n jMap=self.unfilteredMapsDictList[j]\n if self.params['noiseParams']['method'] in ['dataMap', 'model']:\n NP=np.real(fMapsForNoise[i]*fMapsForNoise[j].conj())\n elif self.params['noiseParams']['method'] == 'max(dataMap,CMB)':\n NP=np.real(fMapsForNoise[i]*fMapsForNoise[j].conj())\n NPCMB=self.makeForegroundsPower() # This needs a beam convolution adding\n NP=np.maximum.reduce([NP, NPCMB])\n else:\n raise Exception(\"Other noise models not yet re-implemented\")\n NP=ndimage.gaussian_filter(NP, kernelSize)\n row.append(NP)\n noiseCov.append(row)\n del fMapsForNoise\n noiseCov=np.array(noiseCov)\n \n # Signal frequency weighting\n w=[]\n for mapDict in self.unfilteredMapsDictList:\n if mapDict['units'] != 'yc':\n # Allows custom weighting in the config file\n if 'specWeight' in mapDict.keys():\n w.append(mapDict['specWeight'])\n # Or standard options (e.g., SZ weighting)\n else:\n if self.params['outputUnits'] == 'yc':\n w.append(signals.fSZ(mapDict['obsFreqGHz']))\n elif self.params['outputUnits'] == 'uK':\n # alpha = spectral index (e.g., -0.8 ish for AGN, +3.8 ish for dusty)\n if 'alpha' in self.params.keys() and self.params['alpha'] is not None:\n w.append(np.power(mapDict['obsFreqGHz']/self.unfilteredMapsDictList[0]['obsFreqGHz'], \n self.params['alpha']) )\n else:\n w.append(1.0)\n else:\n raise Exception('need to specify \"outputUnits\" (\"yc\" or \"uK\") in filter params')\n else:\n w.append(1.0) # For TILe-C: there should only be one map if input units are yc anyway...\n w=np.array(w)\n \n # Make FFTs of unit-normalised signal templates for each band\n signalMapsList=[]\n fSignalsArr=[]\n for mapDict in self.unfilteredMapsDictList:\n signalMap=self.makeSignalTemplateMap(mapDict['beamFileName'])\n fSignal=enmap.fft(signalMap)\n signalMapsList.append(signalMap)\n fSignalsArr.append(fSignal)\n fSignalsArr=np.array(fSignalsArr)\n \n # Build the filter itself\n self.filt=np.zeros([len(self.unfilteredMapsDictList), self.shape[0], self.shape[1]])\n for y in range(0, self.shape[0]):\n for x in range(0, self.shape[1]):\n try:\n self.filt[:, y, x]=np.dot(np.linalg.inv(noiseCov[:, :, y, x]), w*abs(fSignalsArr[:, y, x])) \n except:\n continue\n del fSignalsArr\n del noiseCov\n \n # Use a map with known input signal to figure out how much it has been rolled off by \n if self.params['outputUnits'] == 'yc':\n # Normalise such that peak value in filtered map == y0, taking out the effect of the beam\n signalMaps=[]\n fSignalMaps=[]\n y0=2e-4\n for mapDict in self.unfilteredMapsDictList:\n if mapDict['units'] == 'yc': # For handling TILe-C maps\n signalMap=self.makeSignalTemplateMap(mapDict['beamFileName'], amplitude = y0)\n else: # The normal case\n deltaT0=maps.convertToDeltaT(y0, mapDict['obsFreqGHz'])\n signalMap=self.makeSignalTemplateMap(mapDict['beamFileName'], \n amplitude = deltaT0)\n signalMap=enmap.apply_window(signalMap, pow=1.0) # Needed for clusters, 1.5% effect\n signalMaps.append(signalMap)\n fSignal=enmap.fft(signalMap)\n fSignalMaps.append(fSignal)\n signalMaps=np.array(signalMaps)\n fSignalMaps=np.array(fSignalMaps) \n filteredSignal=self.applyFilter(fSignalMaps)\n self.signalNorm=y0/filteredSignal.max()\n\n # For relativistic corrections (see signals module)\n totalSignal=filteredSignal.flatten()[np.argmax(filteredSignal)]\n filteredSignalCube=np.real(enmap.ifft(fSignalMaps*self.filt, normalize = False))\n self.fRelWeights={}\n for filteredSignalPlane, mapDict in zip(filteredSignalCube, self.unfilteredMapsDictList):\n freqGHz=mapDict['obsFreqGHz']\n fRelWeight=filteredSignalPlane.flatten()[np.argmax(filteredSignal)]/totalSignal\n self.fRelWeights[freqGHz]=fRelWeight\n del fSignalMaps\n self.signalNorm=y0/filteredSignal.max()\n elif self.params['outputUnits'] == 'uK':\n #if len(self.unfilteredMapsDictList) > 1:\n #raise Exception(\"multi-frequency filtering not currently supported for outputUnits 'uK' (point source finding)\")\n combinedObsFreqGHz=float(list(self.beamSolidAnglesDict.keys())[0]) # Make less clunky...\n signalMaps=[]\n fSignalMaps=[]\n for mapDict in self.unfilteredMapsDictList:\n signalMap=self.makeSignalTemplateMap(mapDict['beamFileName'])\n #signalMap=enmap.apply_window(signalMap, pow=1.0) # Tests with nemoModel confirm not needed here\n signalMaps.append(signalMap)\n fSignal=enmap.fft(signalMap)\n fSignalMaps.append(fSignal)\n signalMaps=np.array(signalMaps)\n fSignalMaps=np.array(fSignalMaps) \n filteredSignal=self.applyFilter(fSignalMaps)\n self.signalNorm=1.0/filteredSignal.max()\n del fSignalMaps\n else:\n raise Exception('need to specify \"outputUnits\" (\"yc\" or \"uK\") in filter params')\n else:\n self.loadFilter()\n \n # Apply filter\n filteredMap=self.applyFilter(fMapsToFilter)\n del fMapsToFilter\n \n # Units etc.\n if self.params['outputUnits'] == 'yc':\n mapUnits='yc'\n combinedObsFreqGHz='yc'\n beamSolidAngle_nsr=0.0 # not used for clusters...\n elif self.params['outputUnits'] == 'uK':\n #if len(self.unfilteredMapsDictList) > 1:\n #raise Exception(\"multi-frequency filtering not currently supported for outputUnits 'uK' (point source finding)\")\n combinedObsFreqGHz=float(list(self.beamSolidAnglesDict.keys())[0]) # Make less clunky...\n mapUnits='uK'\n beamSolidAngle_nsr=self.beamSolidAnglesDict[combinedObsFreqGHz]\n else:\n raise Exception('need to specify \"outputUnits\" (\"yc\" or \"uK\") in filter params')\n \n # Apply the point source mask here (before noise estimates etc.) \n filteredMap=filteredMap*psMask\n \n # Make noise and S/N maps\n RMSMap=self.makeNoiseMap(filteredMap)\n validMask=np.greater(RMSMap, 0)\n SNMap=np.zeros(filteredMap.shape)+filteredMap\n SNMap[validMask]=SNMap[validMask]/RMSMap[validMask]\n \n # Use rank filter to zap edges where RMS will be artificially low - we use a bit of a buffer here\n # NOTE: Now point source mask is applied above, we fill the holes back in here when finding edges\n if 'edgeTrimArcmin' in self.params.keys() and self.params['edgeTrimArcmin'] > 0:\n trimSizePix=int(round((self.params['edgeTrimArcmin']/60.)/self.wcs.getPixelSizeDeg()))\n elif 'noiseGridArcmin' in self.params['noiseParams'] and self.params['noiseParams']['noiseGridArcmin'] != \"smart\":\n gridSize=int(round((self.params['noiseParams']['noiseGridArcmin']/60.)/self.wcs.getPixelSizeDeg()))\n trimSizePix=int(round(gridSize*3.0))\n else:\n trimSizePix=0.0\n if trimSizePix > 0:\n edgeCheck=ndimage.rank_filter(abs(filteredMap+(1-psMask)), 0, size = (trimSizePix, trimSizePix))\n edgeCheck=np.array(np.greater(edgeCheck, 0), dtype = float)\n else:\n edgeCheck=np.ones(filteredMap.shape)\n filteredMap=filteredMap*edgeCheck\n apodMask=np.not_equal(filteredMap, 0)\n surveyMask=edgeCheck*surveyMask*psMask\n filteredMap=filteredMap*surveyMask # NOTE: Needed for 2-pass (I think)\n del edgeCheck\n\n # Apply final survey mask to signal-to-noise map and RMS map\n # NOTE: need to avoid NaNs in here, otherwise map interpolation for e.g. S/N will fail later on\n # NOTE: we now save the mask after detecting objects, as we can detect rings around extremely\n # bright sources and add those to the mask there (see pipelines module)\n SNMap=SNMap*surveyMask\n SNMap[np.isnan(SNMap)]=0.\n RMSMap=RMSMap*surveyMask\n #maskFileName=self.selFnDir+os.path.sep+\"areaMask#%s.fits\" % (self.tileName)\n #surveyMask=np.array(surveyMask, dtype = int)\n #if os.path.exists(maskFileName) == False:\n #maps.saveFITS(maskFileName, surveyMask, self.wcs, compressed = True)\n \n if 'saveRMSMap' in self.params and self.params['saveRMSMap'] == True:\n RMSFileName=self.selFnDir+os.path.sep+\"RMSMap_%s#%s.fits\" % (self.label, self.tileName)\n maps.saveFITS(RMSFileName, RMSMap, self.wcs, compressed = True)\n\n if 'savePlots' in self.params and self.params['savePlots'] == True:\n self.saveRealSpaceFilterProfile()\n \n if 'saveFilter' in self.params and self.params['saveFilter'] == True:\n img=pyfits.PrimaryHDU() \n img.header['SIGNORM']=self.signalNorm\n count=0\n for key in list(self.fRelWeights.keys()):\n count=count+1\n img.header['RW%d_GHZ' % (count)]=key\n img.header['RW%d' % (count)]=self.fRelWeights[key]\n img.data=self.filt \n img.writeto(self.filterFileName, overwrite = True) \n \n # NOTE: What to do about frequency here? Generalise for non-SZ\n return {'data': filteredMap, 'wcs': self.wcs, 'obsFreqGHz': combinedObsFreqGHz, 'SNMap': SNMap, \n 'surveyMask': surveyMask, 'mapUnits': mapUnits, 'beamSolidAngle_nsr': beamSolidAngle_nsr,\n 'label': self.label, 'tileName': self.tileName}\n\n\n def loadFilter(self):\n \"\"\"Loads in a previously saved filter.\n \n \"\"\"\n with pyfits.open(self.filterFileName) as img:\n self.filt=img[0].data\n self.signalNorm=img[0].header['SIGNORM']\n self.loadFRelWeights()\n \n\n def reshapeFilter(self, shape):\n \"\"\"Use linear interpolation to transform the filter to the given shape.\n \n Returns:\n Reshaped filter (2d numpy array)\n \n \"\"\"\n \n # If we feed in a 2d shape, make sure we add an axis to keep generalised to multi-frequency\n if len(shape) == 2:\n shape=[self.filt.shape[0], shape[0], shape[1]]\n assert(len(shape) == 3)\n\n lx, ly=enmap.laxes(self.unfilteredMapsDictList[0]['data'].shape, self.enwcs)\n lxToX=interpolate.interp1d(lx, np.arange(lx.shape[0]), fill_value = 'extrapolate')\n lyToY=interpolate.interp1d(ly, np.arange(ly.shape[0]), fill_value = 'extrapolate')\n lxOut, lyOut=enmap.laxes([shape[1], shape[2]], self.enwcs)\n xOut=lxToX(lxOut) \n yOut=lyToY(lyOut)\n reshapedFilt=np.zeros(shape)\n for i in range(self.filt.shape[0]):\n filtInterp=interpolate.interp2d(np.arange(ly.shape[0]), np.arange(lx.shape[0]), self.filt[i])\n reshapedFilt[i]=filtInterp(yOut, xOut)\n \n return reshapedFilt\n \n \n def applyFilter(self, mapDataToFilter):\n \"\"\"Apply the filter to the given map data (must be a cube - with each plane corresponding to a \n frequency). If the map data is not complex, it will be Fourier transformed. If the map data \n is not the same shape as the filter, the filter will be interpolated to match.\n \n An optional additional high-pass filter can be applied if 'bckSub' and 'bckSubScaleArcmin' are\n given in self.params.\n \n Returns:\n Filtered map (2d numpy array)\n \n \"\"\"\n \n # NOTE: need to check appropriate signalNorm after reshaping\n if mapDataToFilter.shape == self.filt.shape:\n filt=self.filt\n else:\n filt=self.reshapeFilter(mapDataToFilter.shape)\n \n if 'complex' in mapDataToFilter.dtype.name:\n fMapsToFilter=mapDataToFilter\n else:\n fMapsToFilter=enmap.fft(enmap.apod(mapDataToFilter, self.apodPix))\n\n filteredMap=np.real(enmap.ifft(fMapsToFilter*filt, normalize = False)).sum(axis = 0)\n\n # Optional additional high-pass filter\n if 'bckSub' in self.params.keys() and 'bckSubScaleArcmin' in self.params.keys() and self.params['bckSub'] == True:\n filteredMap=maps.subtractBackground(filteredMap, self.wcs, smoothScaleDeg = self.params['bckSubScaleArcmin']/60.)\n \n filteredMap=filteredMap*self.signalNorm\n \n return filteredMap\n \n#------------------------------------------------------------------------------------------------------------\nclass RealSpaceMatchedFilter(MapFilter):\n \"\"\"Makes a matched-filter kernel using the noise properties of a specified region of the map (e.g.\n the deepest part) in Fourier space, which is converted into real space and truncated such that the \n kernel is small enough in footprint to be applied by directly convolving with the map in real space \n in a short amount of time. \n \n First though we high-pass filter the maps on a similar scale using a Mexican hat (difference of Gaussian\n smoothed images), to get rid of large scale noise from CMB and atmosphere.\n \n Tne S/N map is constructed, measuring locally on a scale 3 x that of the filter kernel, with 3-sigma\n clipping applied. This allows us to take into account how the noise level varies across the map.\n \n A rank filter is used to zap noisy regions at the edge of the map, where the RMS values are\n not accurate.\n \n Optionally, a 'surveyMask' can be applied (e.g., for point source masking).\n \n A map of the final area searched for clusters called 'areaMask.fits' is written in the selFn/ \n folder.\n \n \"\"\" \n \n def loadFilter(self):\n \"\"\"Loads a previously cached kernel.\n \n Returns kern2d, signalNorm, bckSubScaleArcmin\n \n \"\"\" \n with pyfits.open(self.filterFileName) as img:\n kern2d=img[0].data\n signalNorm=img[0].header['SIGNORM']\n if 'BCKSCALE' in img[0].header.keys():\n bckSubScaleArcmin=img[0].header['BCKSCALE']\n else:\n bckSubScaleArcmin=0\n self.applyRACentre=img[0].header['APP_RA']\n self.applyDecCentre=img[0].header['APP_DEC']\n self.kern2d=kern2d\n self.signalNorm=signalNorm\n self.bckSubScaleArcmin=bckSubScaleArcmin\n\n \n def buildKernel(self, RADecSection, RADeg = 'centre', decDeg = 'centre'):\n \"\"\"Builds the real space kernel itself. \n \n RADeg, decDeg are used for figuring out pixel scales for background subtraction\n \n Returns kern2d, signalNorm, bckSubScaleArcmin\n \n \"\"\"\n \n if os.path.exists(self.filterFileName) == True:\n return self.loadFilter()\n \n wcs=self.wcs\n \n # Build the matched-filter kernel in a small section of the map\n # Apply the same difference of Gaussians high pass filter here\n # NOTE: we could merge 'bckSubScaleArcmin' and 'maxArcmin' keys here!\n #mapDict['bckSubScaleArcmin']=maxArcmin\n keysWanted=['mapFileName', 'weightsFileName', 'obsFreqGHz', 'units', 'beamFileName', 'addNoise', \n 'pointSourceRemoval', 'weightsType']\n kernelUnfilteredMapsDictList=[]\n for mapDict in self.unfilteredMapsDictList:\n kernelUnfilteredMapsDict={}\n for k in keysWanted:\n if k in list(mapDict.keys()):\n kernelUnfilteredMapsDict[k]=mapDict[k]\n kernelUnfilteredMapsDict['RADecSection']=RADecSection\n kernelUnfilteredMapsDictList.append(kernelUnfilteredMapsDict)\n kernelLabel=\"realSpaceKernel_%s\" % (self.label)\n matchedFilterDir=self.diagnosticsDir+os.path.sep+kernelLabel+\"#\"+self.tileName\n diagnosticsDir=matchedFilterDir+os.path.sep+'diagnostics'\n selFnDir=matchedFilterDir+os.path.sep+'selFn'\n for d in [matchedFilterDir, diagnosticsDir, selFnDir]:\n if os.path.exists(d) == False:\n os.makedirs(d, exist_ok = True)\n matchedFilterClass=eval(self.params['noiseParams']['matchedFilterClass'])\n matchedFilter=matchedFilterClass(kernelLabel, kernelUnfilteredMapsDictList, self.params, \n tileName = mapDict['tileName'],\n diagnosticsDir = matchedFilterDir+os.path.sep+'diagnostics',\n selFnDir = matchedFilterDir+os.path.sep+'selFn')\n filteredMapDict=matchedFilter.buildAndApply() \n \n # Turn the matched filter into a smaller real space convolution kernel\n # This means we have to roll off the kernel to 0 at some radius\n # This is set by maxArcmin in the config file\n kernelMaxArcmin=self.params['noiseParams']['kernelMaxArcmin']\n prof, arcminRange=matchedFilter.makeRealSpaceFilterProfile()\n rIndex=np.where(arcminRange > kernelMaxArcmin)[0][0]\n mask=np.less(arcminRange, kernelMaxArcmin)\n\n # Kernel can be either fully 2d, or be azimuthally averaged... in the ACTPol E-D56 paper, we used the latter\n if 'symmetrize' in self.params['noiseParams'].keys() and self.params['noiseParams']['symmetrize'] == True:\n rRadians=np.radians(arcminRange/60.)\n profile2d=[]\n for i in range(prof.shape[0]):\n r2p=interpolate.interp1d(rRadians[mask], prof[i, mask], bounds_error=False, fill_value=0.0)\n profile2d.append(r2p(matchedFilter.radiansMap))\n profile2d=np.array(profile2d)\n else:\n profile2d=fft.ifft2(matchedFilter.filt).real\n profile2d=fft.fftshift(profile2d)\n \n # z is not needed here - just because we switched to multi-freq throughout\n z, y, x=np.where(abs(profile2d) == abs(profile2d).max()) \n #y, x=np.where(profile2d[0] == profile2d[0].max())\n y=y[0]\n x=x[0]\n yMin=y-rIndex\n yMax=y+rIndex\n xMin=x-rIndex\n xMax=x+rIndex\n if (yMax-yMin) % 2 == 0:\n yMin=yMin+1\n if (xMax-xMin) % 2 == 0:\n xMin=xMin+1\n self.kern2d=profile2d[:, yMin:yMax, xMin:xMax]\n kern2dRadiansMap=matchedFilter.radiansMap[yMin:yMax, xMin:xMax]\n\n # This is what to high pass filter on\n if 'bckSubScaleArcmin' in self.params.keys():\n self.bckSubScaleArcmin=self.params['bckSubScaleArcmin']\n else:\n # This is here just so we can reproduce old results where this was done automatically\n # Now we have to fiddle about a bit to check the sign of the filter \n # (depends on spectral response)\n # We're only considering the first frequency given in a set of multi-freq maps\n if np.greater(prof[0, 0], 0) == True:\n func=np.min\n else:\n func=np.max\n self.bckSubScaleArcmin=arcminRange[prof[0] == func(prof[0])][0]\n \n # Use a map with known input signal to figure out how much it has been rolled off by:\n # 1. The high pass filter (bck sub step)\n # 2. The matched filter itself (includes beam) \n # NOTE: This is SZ-specific again and needs generalising\n signalMaps=[]\n for mapDict in self.unfilteredMapsDictList:\n # This has been made more complicated because of TILe-C\n if self.params['outputUnits'] == 'yc':\n y0=2e-4\n if mapDict['obsFreqGHz'] is not None: \n # Normal case\n deltaT0=maps.convertToDeltaT(y0, mapDict['obsFreqGHz'])\n signalMap=self.makeSignalTemplateMap(mapDict['beamFileName'],\n amplitude = deltaT0)\n else:\n # TILe-C case\n signalMap=self.makeSignalTemplateMap(mapDict['beamFileName'], amplitude = y0)\n elif self.params['outputUnits'] == 'uK':\n signalMap=self.makeSignalTemplateMap(mapDict['beamFileName'])\n else:\n raise Exception('need to specify \"outputUnits\" (\"yc\" or \"uK\") in filter params')\n signalMaps.append(signalMap)\n signalMaps=np.array(signalMaps)\n \n filteredSignal=self.applyFilter(signalMaps, calcFRelWeights = True)\n if self.params['outputUnits'] == 'yc':\n # Normalise such that peak value in filtered map == y0, taking out the effect of the beam\n self.signalNorm=y0/filteredSignal.max() \n elif self.params['outputUnits'] == 'uK':\n self.signalNorm=1.0/filteredSignal.max()\n else:\n raise Exception('need to specify \"outputUnits\" (\"yc\" or \"uK\") in filter params')\n \n # Save 2d kernel - we need this (at least for the photometry ref scale) to calc Q later\n # Add bckSubScaleArcmin to the header\n kernWCS=wcs.copy()\n if self.params['bckSub'] == True:\n kernWCS.header['BCKSCALE']=self.bckSubScaleArcmin\n kernWCS.header['SIGNORM']=self.signalNorm\n kernWCS.header['APP_RA']=self.applyRACentre\n kernWCS.header['APP_DEC']=self.applyDecCentre\n count=0\n for key in list(self.fRelWeights.keys()):\n count=count+1\n kernWCS.header['RW%d_GHZ' % (count)]=key\n kernWCS.header['RW%d' % (count)]=self.fRelWeights[key]\n kernWCS.header['NEMOVER']=nemo.__version__\n maps.saveFITS(self.filterFileName, self.kern2d, kernWCS)\n \n # Filter profile plot \n # Save the stuff we plot first, in case we want to make a plot with multiple filters on later\n np.savez(self.diagnosticsDir+os.path.sep+\"filterProf1D_%s#%s.npz\" % (self.label, self.tileName), \n arcminRange = arcminRange, prof = prof, mask = mask, \n bckSubScaleArcmin = self.bckSubScaleArcmin)\n plotSettings.update_rcParams()\n plt.figure(figsize=(9,6.5))\n ax=plt.axes([0.13, 0.12, 0.86, 0.86])\n #plt.tick_params(axis='both', which='major', labelsize=15)\n #plt.tick_params(axis='both', which='minor', labelsize=15)\n for row, mapDict in zip(prof, self.unfilteredMapsDictList):\n tck=interpolate.splrep(arcminRange[mask], row[mask])\n plotRange=np.linspace(0, arcminRange[mask].max(), 1000)\n #plt.plot(arcminRange[mask], prof[mask])\n if mapDict['obsFreqGHz'] is not None:\n label = '%d GHz' % (mapDict['obsFreqGHz'])\n elif mapDict['units'] == 'yc':\n label = 'yc'\n plt.plot(plotRange, interpolate.splev(plotRange, tck), '-', label = label)\n plt.xlabel(\"$\\\\theta$ (arcmin)\")\n plt.ylabel(\"Amplitude\")\n plt.legend()\n #plt.title(self.label)\n #plt.plot(arcminRange[mask], [0]*len(arcminRange[mask]), 'k--')\n plt.xlim(0, arcminRange[mask].max())\n if self.params['bckSub'] == True:\n plt.plot([self.bckSubScaleArcmin]*3, np.linspace(-1.2, 1.2, 3), 'k--')\n plt.ylim(-1.2, 0.2)\n plt.savefig(self.diagnosticsDir+os.path.sep+\"filterPlot1D_%s#%s.pdf\" % (self.label, self.tileName))\n plt.close()\n\n \n def buildAndApply(self):\n\n surveyMask=self.unfilteredMapsDictList[0]['surveyMask']\n psMask=self.unfilteredMapsDictList[0]['psMask']\n \n if os.path.exists(self.filterFileName) == False:\n \n # Noise region to use\n RAMin, RAMax, decMin, decMax=self.wcs.getImageMinMaxWCSCoords()\n if self.params['noiseParams']['RADecSection'] == 'tileNoiseRegions':\n RADecSection=[self.wcs.header['NRAMIN'], self.wcs.header['NRAMAX'], \n self.wcs.header['NDEMIN'], self.wcs.header['NDEMAX']]\n elif self.params['noiseParams']['RADecSection'] == 'auto':\n cRA, cDec=self.wcs.getCentreWCSCoords()\n halfSizeDeg=2.0\n nRAMin=cRA-halfSizeDeg/np.cos(np.radians(cDec))\n nRAMax=cRA+halfSizeDeg/np.cos(np.radians(cDec))\n nDecMin=cDec-halfSizeDeg\n nDecMax=cDec+halfSizeDeg\n RADecSection=[nRAMin, nRAMax, nDecMin, nDecMax]\n else:\n RADecSection=self.params['noiseParams']['RADecSection']\n self.applyDecCentre=(decMax+decMin)/2.\n self.applyRACentre=(RAMax+RAMin)/2.\n \n # Build kernel \n self.buildKernel(RADecSection, RADeg = self.applyRACentre, decDeg = self.applyDecCentre)\n else:\n self.loadFilter()\n\n # Apply kernel \n mapDataToFilter=[]\n for mapDict in self.unfilteredMapsDictList:\n mapDataToFilter.append(mapDict['data'])\n mapDataToFilter=np.array(mapDataToFilter)\n filteredMap=self.applyFilter(mapDataToFilter)\n \n # Apply the point source mask here (before noise estimates etc.)\n filteredMap=filteredMap*psMask\n \n # Make noise and S/N maps\n RMSMap=self.makeNoiseMap(filteredMap)\n validMask=np.greater(RMSMap, 0)\n SNMap=np.zeros(filteredMap.shape)+filteredMap\n SNMap[validMask]=SNMap[validMask]/RMSMap[validMask]\n \n # Units etc.\n if self.params['outputUnits'] == 'yc':\n mapUnits='yc'\n combinedObsFreqGHz='yc'\n beamSolidAngle_nsr=0.0 # not used for clusters...\n elif self.params['outputUnits'] == 'uK':\n if len(self.unfilteredMapsDictList) > 1:\n raise Exception(\"multi-frequency filtering not currently supported for outputUnits 'uK' (point source finding)\")\n combinedObsFreqGHz=float(list(self.beamSolidAnglesDict.keys())[0]) # Make less clunky...\n mapUnits='uK'\n beamSolidAngle_nsr=self.beamSolidAnglesDict[combinedObsFreqGHz]\n else:\n raise Exception('need to specify \"outputUnits\" (\"yc\" or \"uK\") in filter params')\n\n # Use rank filter to zap edges where RMS will be artificially low - we use a bit of a buffer here\n # NOTE: Now point source mask is applied above, we fill the holes back in here when finding edges\n if 'edgeTrimArcmin' in self.params.keys():\n trimSizePix=int(round((self.params['edgeTrimArcmin']/60.)/self.wcs.getPixelSizeDeg()))\n else:\n gridSize=int(round((self.params['noiseParams']['noiseGridArcmin']/60.)/self.wcs.getPixelSizeDeg()))\n trimSizePix=int(round(gridSize*3.0))\n if trimSizePix > 0:\n edgeCheck=ndimage.rank_filter(abs(filteredMap+(1-psMask)), 0, size = (trimSizePix, trimSizePix))\n else:\n edgeCheck=np.ones(filteredMap.shape)\n edgeCheck=np.array(np.greater(edgeCheck, 0), dtype = float)\n filteredMap=filteredMap*edgeCheck\n apodMask=np.not_equal(filteredMap, 0)\n surveyMask=edgeCheck*surveyMask*psMask\n del edgeCheck\n\n # Apply final survey mask to signal-to-noise map and RMS map\n # NOTE: need to avoid NaNs in here, otherwise map interpolation for e.g. S/N will fail later on\n SNMap=SNMap*surveyMask\n SNMap[np.isnan(SNMap)]=0.\n RMSMap=RMSMap*surveyMask\n\n maskFileName=self.selFnDir+os.path.sep+\"areaMask#%s.fits\" % (self.tileName)\n surveyMask=np.array(surveyMask, dtype = int)\n if os.path.exists(maskFileName) == False:\n maps.saveFITS(maskFileName, surveyMask, self.wcs, compressed = True, compressionType = 'PLIO_1')\n \n if 'saveRMSMap' in self.params and self.params['saveRMSMap'] == True:\n RMSFileName=self.selFnDir+os.path.sep+\"RMSMap_%s#%s.fits\" % (self.label, self.tileName)\n maps.saveFITS(RMSFileName, RMSMap, self.wcs, compressed = True)\n\n return {'data': filteredMap, 'wcs': self.wcs, 'obsFreqGHz': combinedObsFreqGHz, 'SNMap': SNMap, \n 'surveyMask': surveyMask, 'mapUnits': mapUnits, 'beamSolidAngle_nsr': beamSolidAngle_nsr,\n 'label': self.label, 'tileName': self.tileName}\n\n \n def applyFilter(self, mapDataToFilter, calcFRelWeights = False):\n \"\"\"Apply the kernel to the given map data (must be a cube - with each plane corresponding to a \n frequency).\n \n NOTE: calcFRelWeights = True should ONLY be set if this routine is being applied to an ideal\n signal map (when calculating signalNorm). This operation is being done in here to save \n complications / time from doing unnecessary convolution operations elsewhere.\n \n Returns:\n Filtered map (2d numpy array)\n \n \"\"\"\n \n # Apply the high pass filter - subtract background on larger scales using difference of Gaussians \n filteredMap=np.zeros(mapDataToFilter.shape)\n if self.params['bckSub'] == True and self.bckSubScaleArcmin > 0:\n for i in range(mapDataToFilter.shape[0]):\n filteredMap[i]=maps.subtractBackground(mapDataToFilter[i], self.wcs, \n RADeg = self.applyRACentre,\n decDeg = self.applyDecCentre,\n smoothScaleDeg = self.bckSubScaleArcmin/60.)\n else:\n filteredMap=filteredMap+mapDataToFilter\n \n # Apply the kernel\n for i in range(filteredMap.shape[0]):\n filteredMap[i]=ndimage.convolve(filteredMap[i], self.kern2d[i])\n \n # For relativistic corrections (see signals module)\n if calcFRelWeights == True:\n self.fRelWeights={}\n maxIndex=np.argmax(filteredMap.sum(axis = 0))\n totalSignal=filteredMap.sum(axis = 0).flatten()[maxIndex]\n for filteredSignalPlane, mapDict in zip(filteredMap, self.unfilteredMapsDictList):\n freqGHz=mapDict['obsFreqGHz']\n fRelWeight=filteredSignalPlane.flatten()[maxIndex]/totalSignal\n self.fRelWeights[freqGHz]=fRelWeight\n \n filteredMap=filteredMap.sum(axis = 0)\n \n # Apply the normalisation\n filteredMap=filteredMap*self.signalNorm\n \n return filteredMap\n \n \n#------------------------------------------------------------------------------------------------------------\nclass BeamFilter(MapFilter):\n \"\"\"Base class for filters using beam profile files in Matthew + Kavi's format.\n \n \"\"\"\n \n def makeSignalTemplateMap(self, beamFileName, amplitude = None):\n \"\"\"Makes a beam model signal template map.\n \n \"\"\"\n \n signalMap=signals.makeBeamModelSignalMap(np.degrees(self.radiansMap),\n self.wcs, \n beamFileName,\n amplitude = amplitude)\n\n return signalMap\n \n#------------------------------------------------------------------------------------------------------------\nclass ArnaudModelFilter(MapFilter):\n \"\"\"Base class for filters using the GNFW profile as described in Arnaud et al. (2010).\n \n \"\"\"\n \n def makeSignalTemplateMap(self, beamFileName, amplitude = None):\n \"\"\"Makes a model signal template map.\n \n \"\"\"\n \n signalMap=signals.makeArnaudModelSignalMap(self.params['z'], self.params['M500MSun'], \n np.degrees(self.radiansMap),\n self.wcs, beamFileName, \n GNFWParams = self.params['GNFWParams'],\n amplitude = amplitude)\n \n return signalMap\n \n#------------------------------------------------------------------------------------------------------------\n# Definitions of actual filters that can be used\nclass ArnaudModelMatchedFilter(MatchedFilter, ArnaudModelFilter):\n pass\nclass BeamMatchedFilter(MatchedFilter, BeamFilter):\n pass\n\nclass ArnaudModelRealSpaceMatchedFilter(RealSpaceMatchedFilter, ArnaudModelFilter):\n pass\nclass BeamRealSpaceMatchedFilter(RealSpaceMatchedFilter, BeamFilter):\n pass\n","sub_path":"nemo/filters.py","file_name":"filters.py","file_ext":"py","file_size_in_byte":59436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"580691384","text":"import sys\nimport random\nfrom textmagic.rest import TextmagicRestClient\nimport mysql.connector\n\n\n## Google Cloud MySQL credentials\nmydb = mysql.connector.connect(\n host=\"34.76.64.123\",\n user=\"root\",\n passwd=\"v^6R953p3zp#n1u\",\n database=\"shopping_voluenteers\"\n)\nmycursor = mydb.cursor()\n\n# Textmagic SMS credentials\nusername = \"christianhinge1\"\ntoken = \"TDMGxxFCEIUGvZmZoPa9Sd8CdI6BoT\"\n\n## Get pastor based on zip code and last name\ndef FIND_PASTOR(zip_code,name):\n\n sql = \"SELECT * FROM pastors WHERE name LIKE '%{}' AND zip = {};\".format(name,zip_code)\n mycursor.execute(sql)\n pastor = mycursor.fetchall()\n \n return pastor[0]\n\n## Add voluenteer to pastor district\ndef ADD_VOLUENTEER(phone_no, zip_code, name, pastor):\n\n pastor_obj = FIND_PASTOR(zip_code, pastor)\n pastor_id = pastor_obj[0]\n \n sql = \"INSERT INTO voluenteers (name, phone_no, zip, pastor_id) VALUES (%s, %s, %s, %s)\"\n val = (name, phone_no, zip_code, pastor_id)\n mycursor.execute(sql, val)\n mydb.commit()\n \n## Approve voluenteer for helping others\ndef APPROVE_VOLUENTEER(voluenteer_id):\n \n sql = \"UPDATE voluenteers SET approved = TRUE WHERE id = {};\".format(voluenteer_id)\n mycursor.execute(sql);\n mydb.commit()\n\n\n\n## Get one of the voluenteers from a district based on that districts pastor and zip\ndef GET_VOLUENTEER(zip_code,pastor):\n pastor_obj = FIND_PASTOR(zip_code, pastor)\n pastor_id = pastor_obj[0]\n \n sql = \"SELECT * FROM voluenteers WHERE pastor_id = {} AND approved = TRUE\".format(pastor_id)\n \n mycursor.execute(sql);\n voluenteers = mycursor.fetchall()\n \n min_deeds = None\n selected_voluenteer = None\n \n #Choose the voluenteer that has voluenteered the least\n for voluenteer in voluenteers:\n if min_deeds == None or voluenteer[5] < min_deeds:\n selected_voluenteer = voluenteer;\n min_deeds = voluenteer[5]\n \n DO_DEED(selected_voluenteer[5])\n return selected_voluenteer\n\n#Register that a voluenteer has done a deed\ndef DO_DEED(voluenteer_id):\n \n sql = \"UPDATE voluenteers SET deeds = deeds+1 WHERE id = {};\".format(voluenteer_id)\n mycursor.execute(sql);\n mydb.commit()\n\n#Send SMS to voluenteer informing that a person needs help\ndef SEND_SMS_VOLUENTEER(phone_no_v,phone_no_r):\n \n message = \"Hi! A citizen in your area needs help shopping. Please contact him/her via the following phone number: {}. Thank you for helping\".format(phone_no_r)\n client = TextmagicRestClient(username, token)\n client.messages.create(phones=phone_no_v, text=message)\n\n\n\n\n","sub_path":"lib/assets/python/pastor_voluenteer.py","file_name":"pastor_voluenteer.py","file_ext":"py","file_size_in_byte":2588,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"581484474","text":"from collections import Counter\n\ninfo=input().split(',')\na=[int(y) for y in info]\ncount = collections.Counter(deck)\nX = min(count.values())\nfor x in range(2,X+1):\n if all(v % x == 0 for v in count.values()):\n print(True)\nprint(False)\n\n","sub_path":"Code/CodeRecords/2325/59018/275907.py","file_name":"275907.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"521155003","text":"#! python3\n\n\"\"\"this is 8comic module for comiccrawler.\n\t\nhttp://www.comicbus.com/html/197.html\n\n\"\"\"\n\nimport re\nfrom urllib.parse import urljoin\n\nfrom node_vm2 import VM\n\nfrom ..core import Episode, grabhtml, clean_tags\n\ndomain = [\"www.8comic.com\", \"www.comicvip.com\", \"www.comicbus.com\"]\nname = \"無限\"\n\ndef get_title(html, url):\n\treturn re.search(\"([^<]+?)漫畫,\",html).group(1)\n\ndef get_episodes(html, url):\n\thtml = html.replace(\"\\n\", \"\")\n\t\n\tjs = \"\"\"\n\t\tvar output;\n\t\tfunction getCookie() {}\n\t\tvar window = {\n\t\t\topen: function(result){\n\t\t\t\toutput = result;\n\t\t\t}\n\t\t};\n\t\tvar document = {\n\t\t\tlocation: {\n\t\t\t\thref: \"\"\n\t\t\t}\n\t\t};\n\t\"\"\" + grabhtml(urljoin(url, \"/js/comicview.js\"))\n\t\n\ts = []\n\tmatches = re.finditer(\n\t\tr'<a [^>]*?onclick=\"(cview[^\"]+?);[^>]*>(.+?)</a>',\n\t\thtml, re.M\n\t)\n\twith VM(js) as vm:\n\t\tfor match in matches:\n\t\t\tcview, title = match.groups()\n\t\t\t\n\t\t\tvm.run(cview)\n\t\t\tep_url = vm.run(\"output\")\n\t\t\ttitle = clean_tags(title)\n\n\t\t\te = Episode(title, urljoin(url, ep_url))\n\t\t\ts.append(e)\n\treturn s\n\t\ndef get_images(html, url):\n\tm = re.search(\"ch=(\\d+)\", url)\n\tif m is None:\n\t\tch = \"1\"\n\telse:\n\t\tch = m.group(1)\n\n\tdef ss(str): # pylint: disable=invalid-name\n\t\treturn re.sub(\"[a-z]+\", \"\", str)\n\n\tcs = re.search(\"cs='([^']+)\", html).group(1)\n\tti = re.search(\"ti=(\\d+);\", html).group(1)\n\n\ti = 0\n\twhile i < len(cs):\n\t\tif ch == ss(cs[i:i+4]):\n\t\t\tcode = cs[i:i+50]\n\t\t\tbreak\n\t\ti += 50\n\telse:\n\t\tcode = cs[-50:]\n\n\tpages = int(ss(code[7:10]))\n\n\ts = []\n\tfor p in range(1, pages + 1):\n\t\thash = (((p - 1) // 10) % 10) + (((p - 1) % 10) * 3)\n\t\tsrc = \"http://img{}.8comic.com/{}/{}/{}/{:03}_{}.jpg\".format(\n\t\t\t\tss(code[4:6]), code[6:7], ti, ch, p, code[hash + 10:hash + 13])\n\t\ts.append(src)\n\treturn s\n","sub_path":"comiccrawler/mods/eight.py","file_name":"eight.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"647115244","text":"from os.path import join, basename\nimport csv\nDATA_DIR = 'tempdata'\n# as before, we create new headers for our wrangled file\n# though we leave out year because we probably don't care at for our ultimate needs\nWRANGLED_HEADERS = ['name', 'gender' , 'ratio' , 'females', 'males', 'total']\nWRANGLED_DATA_FILENAME = join(DATA_DIR, 'wrangledbabynames.csv')\nSTART_YEAR = 1950\nEND_YEAR = 2014\n# lets just get a list of all decades, between 1950 and 2014:\nyears = list(range(START_YEAR, END_YEAR, 10))\n# and let's tack on the END_YEAR manually:\nyears.append(END_YEAR)\n\n# namesdict sees all names\nnamesdict = {}\nfor year in years:\n # get the file for this particular year\n filename = join(DATA_DIR, 'yob' + str(year) + '.txt')\n print(\"Parsing\", filename)\n # open that file, read it completely in mydict\n with open(filename, 'r') as thefile:\n for line in thefile:\n name, gender, count = line.split(',')\n if not namesdict.get(name): # need to initialize a dict for this name\n namesdict[name] = {'F': 0, 'M': 0}\n # unlike in past exercises, because we're reading multiple years\n # we will be adding to these dicts more than twice (i.e. M and F, and for every year combo)\n # so mind that += operator\n namesdict[name][gender] += int(count)\n\n# Wait till every year is done\nmy_awesome_list = []\n# just the same as it was for g.py, except no \"year\"\nfor name, babiescount in namesdict.items():\n xdict = {'name': name, 'females': babiescount['F'], 'males': babiescount['M']}\n xdict['total'] = xdict['males'] + xdict['females']\n if xdict['females'] >= xdict['males']:\n xdict['gender'] = 'F'\n xdict['ratio'] = round(100 * xdict['females'] / xdict['total'])\n else:\n xdict['gender'] = 'M'\n xdict['ratio'] = round(100 * xdict['males'] / xdict['total'])\n # finally, add our new dict, xdict, to my_awesome_list\n my_awesome_list.append(xdict)\n\n\n# totally the same as g.py\n\n# let's create the new file to write to\nwfile = open(WRANGLED_DATA_FILENAME, 'w')\n# turn it into a DictWriter object, and tell it what the fieldnames are\nwcsv = csv.DictWriter(wfile, fieldnames=WRANGLED_HEADERS)\n# write the headers row\nwcsv.writeheader()\n\n# before we start adding the contents of my_awesome_list to\n# wcsv, we want to sort it in order of:\n# - descending total\n# - ascending name\n# here's one way to do it, make a silly little function:\ndef xfoo(xdict):\n # and return a tuple of negative total, and normal name\n return (-xdict['total'], xdict['name'])\n\n# and now we just iterate through every row and\n# add it to wcsv\nfor row in sorted(my_awesome_list, key=xfoo):\n wcsv.writerow(row)\n# the end...\nwfile.close()\n\n\n# Ohhh but we're not done yet...we have to print\n# the first 5 lines of text (which will include the headers)\n# this is just opening a file and printing the line\nwith open(WRANGLED_DATA_FILENAME, 'r') as f:\n for line in f.readlines()[0:5]:\n print(line.strip())","sub_path":"exercises/0020-gender-detector/j.py","file_name":"j.py","file_ext":"py","file_size_in_byte":3005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"118360074","text":"#Conversions from: http://wiki.seeed.cc/Grove_Starter_Kit_Plus/\r\n\r\nfrom nanpy import (ArduinoApi, SerialManager)\r\nfrom time import sleep\r\nimport math\r\n\r\n#Connect to Arduino. Automatically finds serial port.\r\nconnection = SerialManager()\r\na = ArduinoApi(connection = connection)\r\n\r\nsensor = 14 #A0 on shield\r\nB = 3975 #B value of the thermistor (Grove Starter Kit information)\r\n\r\na.pinMode(sensor, a.INPUT) #Setup sensor\r\n\r\nwhile True:\r\n total = 0 #Each set of readings start with a total of 0\r\n\r\n #Get all the readings:\r\n for i in range(0, 24):\r\n reading = a.analogRead(sensor) #Get reading\r\n resistance = ((1023-reading)*10000/reading) #Find resistance\r\n log = math.log10(resistance/10000)\r\n temp = (1/(log/B+1/298.15)-273.15) #Find temperature\r\n\r\n readings[i] = temp #Place temp reading in i space of array\r\n sleep(0.1) #Time between readings \r\n\r\n #Add the readings:\r\n for i in range(0, 24):\r\n total += readings[i]\r\n\r\n #Find the average and print:\r\n average = total/24\r\n print(\"The average temp is \")\r\n print(average)\r\n \r\n\r\n","sub_path":"5Array/2Temp/Code/averageGrove.py","file_name":"averageGrove.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"643871089","text":"#!/usr/bin/python3\n# -*- coding: utf-8 -*-\n\ndef get(prompt, allow_empty=False):\n try:\n r = input(prompt)\n except EOFError:\n print('')\n return get(prompt)\n except KeyboardInterrupt:\n print('')\n exit()\n return r if r != '' or allow_empty else get(prompt)\n\ndef _repr(g):\n r = '+-+-+-+\\n'\n for y in g:\n for x in y:\n r += '|' + x\n r += '|\\n'\n r += '+-+-+-+\\n'\n return r\n\ndef turn(g, x, y, k):\n if g[y][x] == ' ':\n g[y][x] = k\n else:\n raise Exception\n\ndef over(g):\n _x = any([g[0][x] == g[1][x] == g[2][x] for x in range(3) if g[0][x] != ' '])\n _y = any([y[0] == y[1] == y[2] for y in g if y[0] != ' '])\n _diag = (g[0][0] == g[1][1] == g[2][2] or g[0][2] == g[1][1] == g[2][0]) and g[1][1] != ' '\n return _y or _x or _diag\n\nplayer = lambda ox: 'o' if ox else 'x'\n\ngrid = [[' ' for _ in range(3)] for _ in range(3)]\nO_X = True #True <=> O, False <=> X\nvictory = False\nnb_turns = 0\nwhile not victory and nb_turns < 9:\n print('Turn no : ' + str(nb_turns) + '\\n' + _repr(grid) + '\\n\\nPlaying : ' + player(O_X).upper())\n try:\n x, y = get('Enter case coords: ')\n turn(grid, int(x), int(y), player(O_X))\n except Exception:\n print('Nope! Try again.')\n continue\n O_X = not O_X\n nb_turns += 1\n victory = over(grid)\nif victory:\n print(_repr(grid), '\\n\\nVictory of ' + player(not O_X).upper() + ' !')\nelse:\n print('Blocked!')\n","sub_path":"tic-tac-toe.py","file_name":"tic-tac-toe.py","file_ext":"py","file_size_in_byte":1477,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"210304118","text":"import FWCore.ParameterSet.Config as cms\nimport HLTrigger.HLTfilters.hltHighLevel_cfi as hlt\n\nprocess = cms.Process('TEST')\nprocess.load('JetMETAnalysis.PromptAnalysis.ntuple_cff')\n\nprocess.load(\"Configuration/StandardSequences/Geometry_cff\")\nprocess.load(\"Configuration/StandardSequences/MagneticField_cff\")\nprocess.load(\"Configuration/StandardSequences/FrontierConditions_GlobalTag_cff\")\nprocess.load(\"Configuration/StandardSequences/RawToDigi_Data_cff\")\nprocess.load(\"L1Trigger/Configuration/L1RawToDigi_cff\")\nprocess.load(\"RecoMET/Configuration/RecoMET_BeamHaloId_cff\")\nprocess.load(\"RecoMET/METProducers/BeamHaloSummary_cfi\")\nprocess.load(\"RecoMET/METProducers/CSCHaloData_cfi\")\nprocess.load(\"RecoMET/METProducers/EcalHaloData_cfi\")\nprocess.load(\"RecoMET/METProducers/HcalHaloData_cfi\")\nprocess.load(\"RecoMET/METProducers/GlobalHaloData_cfi\")\n#MC (good for /MinBias/Summer09-STARTUP3X_V8I_900GeV-v2/GEN-SIM-RECO)\nprocess.GlobalTag.globaltag = 'STARTUP3X_V8I::All'\n#DATA (Dec14th rereco)\n#process.GlobalTag.globaltag ='GR09_R_V4::All'\n\nprocess.load(\"Configuration/StandardSequences/ReconstructionCosmics_cff\")\n\nprocess.load(\"RecoMuon/Configuration/RecoMuon_cff\")\n\nprocess.load('Configuration.StandardSequences.Services_cff')\nprocess.add_( cms.Service( \"TFileService\",\n fileName = cms.string( THISROOTFILE ),\n closeFileFast = cms.untracked.bool(True) ) )\n\nprocess.maxEvents = cms.untracked.PSet( input = cms.untracked.int32(1000) )\nprocess.source = cms.Source (\n \"PoolSource\",\n fileNames = cms.untracked.vstring(\n ##STARTUP3X_V8D_900GeV-v1 had incorrect BSC simulation (but this is still available at CERN)\n \"/store/mc/Summer09/MinBias/GEN-SIM-RECO/STARTUP3X_V8D_900GeV-v1/0005/E4590360-4CD7-DE11-8CB4-002618943896.root\"\n ),\n \n duplicateCheckMode = cms.untracked.string(\"noDuplicateCheck\"),\n \n secondaryFileNames = cms.untracked.vstring())\n\n\nprocess.load(\"FWCore.MessageLogger.MessageLogger_cfi\")\nprocess.MessageLogger.cerr.FwkReport.reportEvery = 100\nprocess.MessageLogger.cerr.default.limit = 100\n\n# summary\nprocess.options = cms.untracked.PSet( wantSummary = cms.untracked.bool(True) )\n\n#process.load('L1TriggerConfig.L1GtConfigProducers.L1GtTriggerMaskTechTrigConfig_cff')\n#from HLTrigger.HLTfilters.hltLevel1GTSeed_cfi import hltLevel1GTSeed\n#process.bit40OR41 = hltLevel1GTSeed.clone(L1TechTriggerSeeding = cms.bool(True), L1SeedsLogicalExpression = cms.string('40 OR 41'))\n\n#from HLTrigger.HLTfilters.hltHighLevelDev_cfi import hltHighLevelDev\n#process.physDecl = hltHighLevelDev.clone(HLTPaths = ['HLT_PhysicsDeclared'], HLTPathsPrescales = [1])\n\nprocess.promptanaTree = cms.EDAnalyzer(\"PromptAnaTree\",\n outputCommands = cms.untracked.vstring(\n 'drop *',\n 'keep *_promptanaevent_*_*',\n 'keep *_promptanamet_*_*',\n 'keep *_promptanatcmet_*_*',\n 'keep *_promptanapfmet_*_*',\n 'keep *_promptananohf_*_*',\n #'keep *_promptanaic5calojet_*_*',\n #'keep *_promptanasc5calojet_*_*',\n 'keep *_promptanakt4calojet_*_*',\n 'keep *_promptanaak5calojet_*_*',\n 'keep *_promptanahalo_*_*',\n 'keep *_promptanacalotowers_*_*',\n 'keep *_promptanatrigger_*_*',\n 'keep *_promptanavtx_*_*',\n 'keep *_promptanatrack_*_*',\n 'keep *_promptanacleanup_*_*'\n ))\n\nprocess.theBigNtuple = cms.Path(\n #process.physDecl *\n #process.bit40OR41 *\n process.BeamHaloId *\n (\n process.promptanaevent +\n process.promptanamet +\n process.promptanatcmet +\n process.promptanapfmet +\n process.promptananohf +\n #process.promptanaic5calojet +\n #process.promptanasc5calojet +\n process.promptanakt4calojet +\n process.promptanaak5calojet +\n process.promptanahalo +\n process.promptanacalotowers +\n process.promptanatrigger +\n process.promptanavtx +\n process.promptanatrack +\n process.promptanacleanup\n )\n * process.promptanaTree )\n\n","sub_path":"JetMETAnalysis/PromptAnalysis/test/submitJobsWithCrab/treeMC_cfg.py","file_name":"treeMC_cfg.py","file_ext":"py","file_size_in_byte":3896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"415032882","text":"#!/usr/bin/python3\n#\n# endless-war\n# mperron (2018)\n#\n# a chat bot for the RFCK discord server\n\nimport asyncio\nimport json\nimport logging\nimport os\nimport random\nimport re\nimport shlex\nimport subprocess\nimport sys\nimport time\nimport traceback\n\nimport discord\n\n# Ensure arguments get through before initializations\nimport ew.utils.core as ewutils\nimport ew.static.cfg as ewcfg\n\ndebug = False\ndb_prefix = '--db='\nwhile sys.argv:\n arg_lower = sys.argv[0].lower()\n if arg_lower == '--debug':\n debug = True\n elif arg_lower == '--debugallon': #set all debug option true at startup\n debug = True\n for option in ewutils.DEBUG_OPTIONS:\n ewutils.DEBUG_OPTIONS[option] = True\n elif arg_lower == '--initiate':\n makebp = True\n elif arg_lower.startswith(db_prefix):\n ewcfg.database = arg_lower[len(db_prefix):]\n\n for arg in sys.argv:\n if arg[2:].lower() in ewutils.DEBUG_OPTIONS.keys() and arg[:2] == \"--\":\n ewutils.DEBUG_OPTIONS[arg[2:]] = True\n ewutils.logMsg('Enabled the {} debug option.'.format(arg[2:]))\n\n sys.argv = sys.argv[1:]\n\nfrom ew.cmd import cmd_map, dm_cmd_map, apt_dm_cmd_map\nimport ew.cmd.cmds as ewcmd\ntry:\n from ew.cmd import debug as ewdebug\nexcept:\n from ew.cmd import debug_dummy as ewdebug\nimport ew.cmd.dungeons as ewdungeons\nimport ew.cmd.item as ewitem\n\nimport ew.utils.apt as apt_utils\nimport ew.utils.cmd as cmd_utils\nimport ew.utils.combat as combat_utils\nimport ew.utils.cosmeticitem as cosmetic_utils\nimport ew.utils.dungeons as dungeon_utils\nimport ew.utils.frontend as fe_utils\nimport ew.utils.item as itm_utils\nimport ew.utils.leaderboard as bknd_leaderboard\nimport ew.utils.loop as loop_utils\nimport ew.utils.move as move_utils\nimport ew.utils.rolemgr as ewrolemgr\nimport ew.utils.slimeoid as slimeoid_utils\nimport ew.utils.sports as sports_utils\nimport ew.utils.transport as transport_utils\nimport ew.utils.weather as bknd_weather\nimport ew.utils.mutations as mut_utils\nfrom ew.utils.combat import EwUser\nfrom ew.utils.district import EwDistrict\nfrom ew.utils.user import add_xp\n\nfrom ew.cmd.race.racecmds import ree as racecmdsree\nfrom ew.cmd.slimetwitter.slimetwitterutils import separate_id_from_slimetwitter_embed\nfrom ew.cmd.slimetwitter.slimetwitterutils import send_qrt_command\n\nimport ew.backend.core as bknd_core\nimport ew.backend.farm as bknd_farm\nimport ew.backend.item as bknd_item\nimport ew.backend.player as bknd_player\nimport ew.backend.server as bknd_server\n\nfrom ew.backend.item import EwItem\nfrom ew.backend.market import EwMarket\nfrom ew.backend.player import EwPlayer\nfrom ew.backend.status import EwStatusEffect\nfrom ew.backend.fish import EwRecord\n\nimport ew.static.cosmetics as cosmetics\nimport ew.static.food as static_food\nimport ew.static.items as static_items\nimport ew.static.poi as poi_static\n\newutils.logMsg('Starting up...')\ninit_complete = False\n\n# output discord logs to console\nlogger = logging.getLogger('discord')\nlogger.setLevel(logging.WARNING)\nhandler = logging.StreamHandler(sys.stdout)\nhandler.setFormatter(logging.Formatter('[%(asctime)s]:%(levelname)s:%(name)s: %(message)s'))\nlogger.addHandler(handler)\n\nintents = discord.Intents.all()\n\nclient = discord.Client(intents=intents)\n\n# A map containing user IDs and the last time in UTC seconds since we sent them\n# the help doc via DM. This is to prevent spamming.\nlast_helped_times = {}\n\n# Map of server ID to a map of active users on that server.\nactive_users_map = {}\n\n# When debug is enabled, additional commands are turned on.\nif debug == True:\n ewutils.DEBUG = True\n ewutils.logMsg('Debug mode enabled.')\n\nif ewutils.DEBUG_OPTIONS['trackapi'] == True:\n client._enable_debug_events = True\n\newutils.logMsg('Using database: {}'.format(ewcfg.database))\n\n# This can be done in debug inits please\newcfg.debugpiers = ewdebug.debugpiers\newcfg.debugfish_response = ewdebug.debugfish_response\newcfg.debugfish_goal = ewdebug.debugfish_goal\n\n# ewcfg.debug5 = ewdebug.debug5\n\newcfg.cmd_debug8 = ewcfg.cmd_prefix + ewdebug.cmd_debug8\newcfg.cmd_debug9 = ewcfg.cmd_prefix + ewdebug.cmd_debug9\n\nre_awoo_g = re.compile('.*![a]+[w]+o[o]+.*') #taking these out of on_message so they only need to be compiled once\nre_moan_g = re.compile('.*![b]+[r]+[a]+[i]+[n]+[z]+.*')\nre_measure_g = re.compile('!measure.*')\nre_yoslimernalia_g = re.compile('.*![y]+[o]+[s]+[l]+[i]+[m]+[e]+[r]+[n]+[a]+[l]+[i]+[a]+.*')\nre_ree_g = re.compile('.*![r]+[e]+[e]+.*')\n\n\n@client.event\nasync def on_member_remove(member):\n # Kill players who leave the server.\n try:\n user_data = EwUser(member=member)\n\n # don't kill players who haven't cleared the tutorial yet\n if user_data.poi in poi_static.tutorial_pois:\n return\n\n user_data.trauma = ewcfg.trauma_id_suicide\n await user_data.die(updateRoles=False, cause=ewcfg.cause_leftserver)\n\n ewutils.logMsg('Player killed for leaving the server.')\n except Exception as e:\n ewutils.logMsg(f'Failed to kill member who left the server: {e}')\n\n\n@client.event\nasync def on_presence_update(before, after):\n # update last offline time if they went from offline to online\n try:\n if before.status == discord.Status.offline and after.status != discord.Status.offline:\n user_data = EwUser(member=after)\n user_data.time_lastoffline = int(time.time())\n user_data.persist()\n\n except Exception as e:\n ewutils.logMsg(f'Failed to update member\\'s last offline time: {e}')\n\n\"\"\"\n@client.event\nasync def on_member_update(before, after):\n # update last offline time if they went from offline to online\n try:\n if before.status == discord.Status.offline and after.status != discord.Status.offline:\n user_data = EwUser(member=after)\n user_data.time_lastoffline = int(time.time())\n user_data.persist()\n\n except Exception as e:\n ewutils.logMsg(f'Failed to update member\\'s last offline time: {e}')\n\"\"\"\n\n@client.event\nasync def on_ready():\n\n try:\n await client.change_presence(activity=discord.Game(name=\"EW \" + ewcfg.version))\n except:\n ewutils.logMsg(\"Failed to change_presence!\")\n\n global init_complete\n if init_complete:\n return\n init_complete = True\n ewcfg.set_client(client)\n ewutils.logMsg('Logged in as {} ({}).'.format(client.user.name, client.user.id))\n\n ewutils.logMsg(\"Loaded NLACakaNM world map. ({}x{})\".format(move_utils.map_width, move_utils.map_height))\n move_utils.map_draw()\n\n # Flatten role names to all lowercase, no spaces.\n fake_observer = EwUser()\n fake_observer.life_state = ewcfg.life_state_observer\n for poi in poi_static.poi_list:\n # This just cleans poi roles, why is this here?\n if poi.role != None:\n poi.role = ewutils.mapRoleName(poi.role)\n\n neighbors = []\n neighbor_ids = []\n if len(poi.neighbors.keys()) > 0:\n neighbors = move_utils.path_to(poi_start=poi.id_poi, user_data=fake_observer)\n\n if neighbors != None:\n\n for neighbor in neighbors:\n neighbor_ids.append(neighbor.id_poi)\n\n poi_static.poi_neighbors[poi.id_poi] = set(neighbor_ids)\n\n for id_poi in poi_static.landmark_pois:\n ewutils.logMsg(\"beginning landmark precomputation for \" + id_poi)\n # Set each landmark's value to a dictionary of all pois, and their total cost from that landmark\n move_utils.landmarks[id_poi] = move_utils.score_map_from(\n poi_start=id_poi,\n user_data=fake_observer,\n landmark_mode=True\n )\n\n ewutils.logMsg(\"finished landmark precomputation\")\n\n # Channels in the connected discord servers to send stock market updates to. Map of server ID to channel.\n channels_stockmarket = {}\n dungeon_utils.load_npc_blurbs()\n dungeon_utils.load_other_blurbs()\n\n for server in client.guilds:\n # Force discord to send all users, even offline ones\n await server.chunk()\n\n # Update server data in the database\n bknd_server.server_update(server=server)\n\n # store the list of channels in an ewutils field\n ewcfg.update_server_list(server=server)\n\n # find roles and add them tom the database\n ewrolemgr.setupRoles(client=client, id_server=server.id)\n\n # Grep around for channels\n ewutils.logMsg(\"connected to server: {}\".format(server.name))\n\n # Map a bunch of the main channels\n fe_utils.map_channels(server)\n\n ewdebug.initialize_gamestate(id_server=server.id)\n\n mut_utils.initialize_rotation(id_server=server.id)\n\n # get or make the weapon items for fists and fingernails\n combat_utils.set_unarmed_items(server.id)\n\n # create all the districts in the database\n for poi_object in poi_static.poi_list:\n poi = poi_object.id_poi\n # call the constructor to create an entry if it doesnt exist yet\n dist = EwDistrict(id_server=server.id, district=poi)\n # change the ownership to the faction that's already in control to initialize topic names\n # initialize gang bases\n if poi == ewcfg.poi_id_rowdyroughhouse:\n dist.controlling_faction = ewcfg.faction_rowdys\n elif poi == ewcfg.poi_id_copkilltown:\n dist.controlling_faction = ewcfg.faction_killers\n\n resp_cont = dist.change_ownership(new_owner=dist.controlling_faction, actor=\"init\", client=client)\n dist.persist()\n await resp_cont.post()\n\n asyncio.ensure_future(loop_utils.capture_tick_loop(id_server=server.id))\n\n asyncio.ensure_future(loop_utils.bleed_tick_loop(id_server=server.id))\n\n asyncio.ensure_future(loop_utils.enemy_action_tick_loop(id_server=server.id))\n\n asyncio.ensure_future(loop_utils.burn_tick_loop(id_server=server.id))\n\n asyncio.ensure_future(loop_utils.remove_status_loop(id_server=server.id))\n\n asyncio.ensure_future(loop_utils.event_tick_loop(id_server=server.id))\n\n asyncio.ensure_future(loop_utils.decrease_food_multiplier())\n\n # SWILLDERMUK\n if ewcfg.swilldermuk_active:\n asyncio.ensure_future(loop_utils.spawn_prank_items_tick_loop(id_server = server.id))\n asyncio.ensure_future(loop_utils.generate_credence_tick_loop(id_server = server.id))\n\n # Enemies do spawn randomly\n asyncio.ensure_future(loop_utils.spawn_enemies_tick_loop(id_server=server.id))\n\n await transport_utils.init_transports(id_server=server.id)\n \n asyncio.ensure_future(bknd_weather.weather_tick_loop(id_server=server.id))\n\n asyncio.ensure_future(bknd_farm.farm_tick_loop(id_server=server.id))\n \n asyncio.ensure_future(sports_utils.slimeball_tick_loop(id_server=server.id))\n\n asyncio.ensure_future(loop_utils.clock_tick_loop(id_server=server.id))\n\n asyncio.ensure_future(loop_utils.reset_brick_loop(id_server=server.id))\n\n print('\\nNUMBER OF CHANNELS IN SERVER: {}\\n'.format(len(server.channels)))\n\n\n try:\n ewutils.logMsg('Creating message queue directory.')\n os.mkdir(ewcfg.dir_msgqueue)\n except FileExistsError:\n ewutils.logMsg('Message queue directory already exists.')\n\n ewutils.logMsg('Ready.')\n\n \"\"\"\n Set up for infinite loop to perform periodic tasks.\n \"\"\"\n time_now = int(time.time())\n time_last_pvp = time_now\n\n # Every three hours we log a message saying the periodic task hook is still active. On startup, we want this to happen within about 60 seconds, and then on the normal 3 hour interval.\n time_last_logged = time_now - ewcfg.update_hookstillactive + 60\n\n stream_live = None\n\n ewutils.logMsg('Beginning periodic hook loop.')\n while not ewutils.TERMINATE:\n time_now = int(time.time())\n\n # Periodic message to log that this stuff is still running.\n if (time_now - time_last_logged) >= ewcfg.update_hookstillactive:\n time_last_logged = time_now\n\n ewutils.logMsg(\"Periodic hook still active.\")\n\n # Parse files dumped into the msgqueue directory and send messages as needed.\n try:\n for msg_file in os.listdir(ewcfg.dir_msgqueue):\n fname = \"{}/{}\".format(ewcfg.dir_msgqueue, msg_file)\n\n msg = fe_utils.readMessage(fname)\n os.remove(fname)\n\n msg_channel_names = []\n msg_channel_names_reverb = []\n\n if msg.channel != None:\n msg_channel_names.append(msg.channel)\n\n if msg.poi != None:\n poi = poi_static.id_to_poi.get(msg.poi)\n if poi != None:\n if poi.channel != None and len(poi.channel) > 0:\n msg_channel_names.append(poi.channel)\n\n if msg.reverb == True:\n pois_adjacent = move_utils.path_to(poi_start=msg.poi)\n\n for poi_adjacent in pois_adjacent:\n if poi_adjacent.channel != None and len(poi_adjacent.channel) > 0:\n msg_channel_names_reverb.append(poi_adjacent.channel)\n\n if len(msg_channel_names) == 0:\n ewutils.logMsg('in file {} message for channel {} (reverb {})\\n{}'.format(msg_file, msg.channel, msg.reverb, msg.message))\n else:\n # Send messages to every connected server.\n for server in client.guilds:\n for channel in server.channels:\n if channel.name in msg_channel_names:\n await fe_utils.send_message(client, channel, \"**{}**\".format(msg.message))\n elif channel.name in msg_channel_names_reverb:\n await fe_utils.send_message(client, channel, \"**Something is happening nearby...\\n\\n{}**\".format(msg.message))\n except:\n ewutils.logMsg('An error occurred while trying to process the message queue:')\n traceback.print_exc(file=sys.stdout)\n\n # Wait a while before running periodic tasks.\n await asyncio.sleep(15)\n\n\n@client.event\nasync def on_member_join(member):\n ewutils.logMsg(\"New member \\\"{}\\\" joined. Configuring default roles / permissions now.\".format(member.display_name))\n await ewrolemgr.updateRoles(client=client, member=member)\n bknd_player.player_update(\n member=member,\n server=member.guild\n )\n user_data = EwUser(member=member)\n if member.joined_at.timestamp() - member.created_at.timestamp() < 604800:\n user_data.poi = 'altlock'\n user_data.persist()\n\n\n # attempt to force discord.py to cache the user\n await member.guild.query_members(user_ids=[member.id], presences=True)\n\n if user_data.poi in poi_static.tutorial_pois:\n await dungeon_utils.begin_tutorial(member)\n \n # Assumedly first time joining\n if user_data.poi != \"thesewers\":\n # give the user a game guide\n gameguide = static_items.item_map.get('gameguide')\n item_props = itm_utils.gen_item_props(gameguide)\n bknd_item.item_create(\n id_user=member.id,\n id_server=member.guild.id,\n item_type=ewcfg.it_item,\n item_props=item_props\n )\n # Wait a bit, send a message\n await asyncio.sleep(30)\n try:\n if random.random() < 0.0666:\n await fe_utils.send_message(client, member, fe_utils.formatMessage(member, \"https://cdn.discordapp.com/attachments/431275470902788107/1042615477492535337/jessie.png\"))\n else:\n await fe_utils.send_message(client, member, fe_utils.formatMessage(member, ewcfg.server_join_message))\n except discord.errors.Forbidden:\n ewutils.logMsg(\"New user {} had DMs blocked.\".format(member.id))\n\n\n@client.event\nasync def on_socket_raw_send(payload):\n if ewutils.DEBUG_OPTIONS['trackapi'] == True:\n try:\n f = open(\"apifile.txt\", \"a\")\n f.write(\"{}\\n\".format(str(payload)))\n except:\n pass\n\n\n@client.event\nasync def on_message_delete(message):\n if message != None and message.guild != None and message.author.id != client.user.id and message.content.startswith(ewcfg.cmd_prefix):\n user_data = EwUser(member=message.author)\n mutations = user_data.get_mutations()\n\n if ewcfg.mutation_id_amnesia not in mutations:\n ewutils.logMsg(\"deleted message from {}: {}\".format(message.author.display_name, message.content))\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, '**I SAW THAT.**'))\n\n\n# consolidates the debug conditions into their own function. poorly ordered because no function prototyping in python, boo\nasync def debugHandling(message, cmd, cmd_obj):\n # Test item creation\n time_now = int(time.time())\n market = EwMarket(id_server=cmd_obj.guild.id)\n if cmd == (ewcfg.cmd_prefix + 'enemytick'):\n x = await bknd_leaderboard.make_freshness_top_board(server = cmd_obj.guild.id)\n print(x)\n\n\n elif cmd == (ewcfg.cmd_prefix + 'quickrevive'):\n print(\"Created {} Joined {}\".format(message.author.created_at.timestamp(), message.author.joined_at.timestamp()))\n \"\"\"if cmd.mentions_count == 1 and cmd.tokens_count == 3:\n member = cmd.mentions[0]\n user = EwUser(member = member)\n cmd_fnc = cmd_map.get('!revive')\n await cmd_fnc(cmd=cmd_obj, player_auto = member.id)\n cmd_fnc = cmd_map.get('!tpp')\n await cmd_fnc(cmd=cmd_obj)\"\"\"\n\n\n elif cmd == (ewcfg.cmd_prefix + 'moverelics'):\n await itm_utils.move_relics(id_server=cmd_obj.guild.id)\n\n elif cmd == (ewcfg.cmd_prefix + 'releaseprisoners'):\n await loop_utils.release_timed_prisoners_and_blockparties(market.id_server, market.day)\n elif cmd == (ewcfg.cmd_prefix + 'createtestitem'):\n item_id = bknd_item.item_create(\n item_type='medal',\n id_user=message.author.id,\n id_server=message.guild.id,\n item_props={\n 'medal_name': 'Test Award',\n 'medal_desc': '**{medal_name}**: *Awarded to Krak by Krak for testing shit.*'\n }\n )\n\n ewutils.logMsg('Created item: {}'.format(item_id))\n item = EwItem(id_item=item_id)\n item.item_props['test'] = 'meow'\n item.persist()\n\n item = EwItem(id_item=item_id)\n\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, ewitem.item_look(item)))\n\n # Creates a poudrin\n elif cmd == (ewcfg.cmd_prefix + 'createpoudrin'):\n for item in static_items.item_list:\n if item.context == \"poudrin\":\n poudrin_count = 1\n if cmd_obj.tokens_count > 1:\n try:\n poudrin_count = int(cmd_obj.tokens[1])\n except:\n poudrin_count = 1\n for i in range(poudrin_count):\n bknd_item.item_create(\n item_type=ewcfg.it_item,\n id_user=message.author.id,\n id_server=message.guild.id,\n item_props={\n 'id_item': item.id_item,\n 'context': item.context,\n 'item_name': item.str_name,\n 'item_desc': item.str_desc,\n }\n )\n ewutils.logMsg('Created item: {}'.format(item.id_item))\n else:\n pass\n\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, \"Poudrin(s) created.\"))\n\n # Shows damage\n elif cmd == (ewcfg.cmd_prefix + 'damage'):\n user_data = EwUser(member=message.author, data_level=1)\n slimes_spent = int(ewutils.slime_bylevel(user_data.slimelevel) / 60)\n # disabled until held items update\n # attack_stat_multiplier = 1 + (user_data.attack / 50) # 2% more damage per stat point\n attack_stat_multiplier = 1\n weapon_skill_multiplier = 1 + ((user_data.weaponskill * 5) / 100) # 5% more damage per skill point\n slimes_damage = int(\n 10 * slimes_spent * attack_stat_multiplier * weapon_skill_multiplier) # ten times slime spent, multiplied by both multipliers\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, \"{}\".format(slimes_damage)))\n\n elif cmd == ewcfg.cmd_prefix + 'decaytick':\n #await loop_utils.decaySlimes(id_server=cmd_obj.guild.id)\n #await loop_utils.release_timed_prisoners_and_blockparties(id_server=cmd_obj.guild.id,day = market.day)\n #await move_utils.kick(id_server=cmd_obj.guild.id)\n itm_utils.cull_slime_sea(id_server=message.guild.id)\n #await itm_utils.move_relics(id_server=cmd_obj.guild.id)\n #await move_utils.send_gangbase_messages(server_id=message.guild.id, clock=market.clock)\n\n # Gives the user some slime\n elif cmd == (ewcfg.cmd_prefix + 'getslime'):\n user_data = EwUser(member=message.author)\n user_initial_level = user_data.slimelevel\n\n response = \"You get 1,000,000 slime!\"\n\n levelup_response = user_data.change_slimes(n=1000000)\n\n was_levelup = True if user_initial_level < user_data.slimelevel else False\n\n if was_levelup:\n response += \" {}\".format(levelup_response)\n\n user_data.persist()\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n elif cmd == (ewcfg.cmd_prefix + 'getcoin'):\n user_data = EwUser(member=message.author)\n user_data.change_slimecoin(n=1000000000000, coinsource=ewcfg.coinsource_spending)\n\n response = \"You get 1,000,000,000,000 slimecoin!\"\n\n user_data.persist()\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n\n # Deletes all items in your inventory.\n elif cmd == (ewcfg.cmd_prefix + 'clearinv'):\n user_data = EwUser(member=message.author)\n bknd_item.item_destroyall(id_server=message.guild.id, id_user=message.author.id)\n response = \"You destroy every single item in your inventory.\"\n user_data.persist()\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n\n elif cmd == (ewcfg.cmd_prefix + 'createapple'):\n item_id = bknd_item.item_create(\n id_user=message.author.id,\n id_server=message.guild.id,\n item_type=ewcfg.it_food,\n item_props={\n 'id_food': \"direapples\",\n 'food_name': \"Dire Apples\",\n 'food_desc': \"This sure is a illegal Dire Apple!\",\n 'recover_hunger': 500,\n 'str_eat': \"You chomp into this illegal Dire Apple.\",\n 'time_expir': int(time.time() + ewcfg.farm_food_expir)\n }\n )\n\n ewutils.logMsg('Created item: {}'.format(item_id))\n item = EwItem(id_item=item_id)\n item.item_props['test'] = 'meow'\n item.persist()\n\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, \"Apple created.\"))\n\n elif cmd == (ewcfg.cmd_prefix + 'createhat'):\n patrician_rarity = 20\n patrician_smelted = random.randint(1, patrician_rarity)\n patrician = False\n\n if patrician_smelted == 1:\n patrician = True\n\n items = []\n\n for cosmetic in cosmetics.cosmetic_items_list:\n if patrician and cosmetic.rarity == ewcfg.rarity_patrician:\n items.append(cosmetic)\n elif not patrician and cosmetic.rarity == ewcfg.rarity_plebeian:\n items.append(cosmetic)\n\n item = items[random.randint(0, len(items) - 1)]\n\n item_props = itm_utils.gen_item_props(item)\n\n item_id = bknd_item.item_create(\n item_type=item.item_type,\n id_user=message.author.id,\n id_server=message.guild.id,\n item_props=item_props\n )\n\n ewutils.logMsg('Created item: {}'.format(item_id))\n item = EwItem(id_item=item_id)\n item.item_props['test'] = 'meow'\n item.persist()\n\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, \"Hat created.\"))\n\n elif cmd == (ewcfg.cmd_prefix + 'createfood'):\n item = static_food.food_list[random.randint(0, len(static_food.food_list) - 1)]\n\n item_id = bknd_item.item_create(\n item_type=ewcfg.it_food,\n id_user=message.author.id,\n id_server=message.guild.id,\n item_props={\n 'id_food': item.id_food,\n 'food_name': item.str_name,\n 'food_desc': item.str_desc,\n 'recover_hunger': item.recover_hunger,\n 'str_eat': item.str_eat,\n 'time_expir': item.time_expir\n }\n )\n\n ewutils.logMsg('Created item: {}'.format(item_id))\n item = EwItem(id_item=item_id)\n item.item_props['test'] = 'meow'\n item.persist()\n\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, \"Food created.\"))\n\n elif cmd == (ewcfg.cmd_prefix + 'createdye'):\n item = static_items.dye_list[random.randint(0, len(static_items.dye_list) - 1)]\n\n item_props = itm_utils.gen_item_props(item)\n\n bknd_item.item_create(\n item_type=item.item_type,\n id_user=message.author.id,\n id_server=message.guild.id,\n item_props=item_props\n )\n\n await fe_utils.send_message(client, message.channel,\n fe_utils.formatMessage(message.author, \"{} created.\".format(item.str_name)))\n\n elif cmd == (ewcfg.cmd_prefix + 'createoldhat'):\n patrician_rarity = 20\n patrician_smelted = random.randint(1, patrician_rarity)\n patrician = False\n\n if patrician_smelted == 1:\n patrician = True\n\n cosmetics_list = []\n\n for result in cosmetics.cosmetic_items_list:\n if result.acquisition == ewcfg.acquisition_smelting:\n cosmetics_list.append(result)\n else:\n pass\n\n items = []\n\n for cosmetic in cosmetics_list:\n if patrician and cosmetic.rarity == ewcfg.rarity_patrician:\n items.append(cosmetic)\n elif not patrician and cosmetic.rarity == ewcfg.rarity_plebeian:\n items.append(cosmetic)\n\n item = items[random.randint(0, len(items) - 1)]\n\n bknd_item.item_create(\n item_type=ewcfg.it_cosmetic,\n id_user=message.author.id,\n id_server=message.guild.id,\n item_props={\n 'id_cosmetic': item.id_cosmetic,\n 'cosmetic_name': item.str_name,\n 'cosmetic_desc': item.str_desc,\n 'rarity': item.rarity,\n 'adorned': 'false'\n }\n )\n\n response = \"Success! You've smelted a {}!\".format(item.str_name)\n\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n elif cmd == (ewcfg.cmd_prefix + 'createoldscalp'):\n bknd_item.item_create(\n item_type=ewcfg.it_cosmetic,\n id_user=message.author.id,\n id_server=message.guild.id,\n item_props={\n 'id_cosmetic': 'scalp',\n 'cosmetic_name': \"My scalp\",\n 'cosmetic_desc': \"A scalp.\",\n 'adorned': 'false'\n }\n )\n response = \"Success! You've smelted a scalp!\"\n\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n elif cmd == (ewcfg.cmd_prefix + 'createoldsoul'):\n bknd_item.item_create(\n id_user=message.author.id,\n id_server=message.guild.id,\n item_type=ewcfg.it_cosmetic,\n item_props={\n 'id_cosmetic': \"soul\",\n 'cosmetic_name': \"My soul\",\n 'cosmetic_desc': \"The immortal soul of me. It dances with a vivacious energy inside its jar.\\n If you listen to it closely you can hear it whispering numbers: me.\",\n 'rarity': ewcfg.rarity_patrician,\n 'adorned': 'false',\n 'user_id': \"usermodel.id_user\",\n }\n )\n\n response = \"Success! You've smelted a soul!\"\n\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n\n # FIXME debug\n # Test item deletion\n elif cmd == (ewcfg.cmd_prefix + 'delete'):\n items = bknd_item.inventory(\n id_user=message.author.id,\n id_server=message.guild.id\n )\n\n for item in items:\n bknd_item.item_delete(\n id_item=item.get('id_item')\n )\n\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, 'ok'))\n elif cmd == (ewcfg.cmd_prefix + 'setrole'):\n\n response = \"\"\n\n if cmd_obj.mentions_count == 0:\n response = 'Set who\\'s role?'\n else:\n id_server = cmd_obj.guild.id\n roles_map = ewrolemgr.roles_map[id_server]\n role_target = cmd_obj.tokens[1]\n role = roles_map.get(role_target)\n\n if role != None:\n for user in cmd_obj.mentions:\n try:\n user = await user.edit(roles=[role])\n except:\n ewutils.logMsg('Failed to replace_roles for user {} with {}.'.format(user.display_name, role.name))\n\n response = 'Done.'\n else:\n response = 'Unrecognized role.'\n\n await fe_utils.send_message(client, cmd_obj.message.channel, fe_utils.formatMessage(message.author, response))\n\n elif cmd == (ewcfg.cmd_prefix + 'getrowdy'):\n response = \"You get rowdy. Fuck. YES!\"\n user_data = EwUser(member=message.author)\n user_data.life_state = ewcfg.life_state_enlisted\n user_data.faction = ewcfg.faction_rowdys\n user_data.time_lastenlist = time_now + ewcfg.cd_enlist\n user_data.persist()\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n\n elif cmd == (ewcfg.cmd_prefix + 'getkiller'):\n response = \"You uh... 'get' killer. Sure.\"\n user_data = EwUser(member=message.author)\n user_data.life_state = ewcfg.life_state_enlisted\n user_data.faction = ewcfg.faction_killers\n user_data.time_lastenlist = time_now + ewcfg.cd_enlist\n user_data.persist()\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n\n # Toggles rain on and off\n elif cmd == (ewcfg.cmd_prefix + 'toggledownfall'):\n market_data = EwMarket(id_server=message.guild.id)\n\n if market_data.weather == ewcfg.weather_bicarbonaterain:\n newweather = ewcfg.weather_sunny\n\n market_data.weather = newweather\n response = \"Bicarbonate rain turned OFF. Weather was set to {}.\".format(newweather)\n else:\n market_data.weather = ewcfg.weather_bicarbonaterain\n response = \"Bicarbonate rain turned ON.\"\n\n market_data.persist()\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n\n elif cmd == (ewcfg.cmd_prefix + 'dayforward'):\n market_data = EwMarket(id_server=message.guild.id)\n\n market_data.day += 1\n market_data.persist()\n\n response = \"Time has progressed 1 day forward manually.\"\n\n if ewutils.check_moon_phase(market_data) == ewcfg.moon_full:\n response += \"\\nIt's a full moon!\"\n\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n\n elif cmd == (ewcfg.cmd_prefix + 'hourforward'):\n market_data = EwMarket(id_server=message.guild.id)\n market_data.clock += 1\n response = \"Time has progressed 1 hour forward manually.\"\n\n if market_data.clock >= 24 or market_data.clock < 0:\n market_data.clock = 0\n market_data.day += 1\n response += \"\\nMidnight has come. 1 day progressed forward.\"\n\n if ewutils.check_moon_phase(market_data) == ewcfg.moon_full:\n response += \"\\nIt's a full moon!\"\n\n market_data.persist()\n\n await bknd_weather.weather_cycle(id_server=message.guild.id)\n\n await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n\n elif cmd == (ewcfg.cmd_prefix + 'postleaderboard'):\n try:\n for server in client.guilds:\n await bknd_leaderboard.post_leaderboards(client=client, server=server)\n except:\n pass\n\n elif cmd == (ewcfg.cmd_prefix + 'genslimeoid'):\n user_data = EwUser(member=message.author)\n if user_data.active_slimeoid == -1:\n slimeoid_utils.generate_slimeoid(id_owner=user_data.id_user, id_server=user_data.id_server, persist=True)\n return await fe_utils.send_message(client, message.channel,\n \"Generated slimeoid for user. Take care of them!\")\n else:\n return await fe_utils.send_message(client, message.channel, \"You already have a slimeoid, bub!\")\n\n elif cmd == (ewcfg.cmd_prefix + 'massgenslimeoidnames'):\n response = \"\"\n for sl in range(10):\n new_sl = slimeoid_utils.generate_slimeoid()\n if new_sl.hue != \"\":\n hue_str = \" **\" + new_sl.hue + \"** \"\n else:\n hue_str = \"\"\n response += (\"\\n\" + hue_str + new_sl.name)\n return await fe_utils.send_message(client, message.channel, response)\n\n\n@client.event\nasync def on_message(message):\n time_now = int(time.time())\n ewcfg.set_client(client)\n\n \"\"\" do not interact with our own messages \"\"\"\n if message.author.id == client.user.id or message.author.bot == True:\n return\n\n # Ignore messages in certain channels\n if hasattr(message.channel, \"name\") and message.channel.name in ewcfg.forbidden_channels:\n return\n\n if message.guild is not None:\n # Note that the user posted a message.\n active_map = active_users_map.get(message.guild.id)\n if active_map is None:\n active_map = {}\n active_users_map[message.guild.id] = active_map\n active_map[message.author.id] = True\n\n # Update player information.\n bknd_player.player_update(\n member=message.author,\n server=message.guild\n )\n\n content_tolower = message.content.lower()\n content_tolower_list = content_tolower.split(\" \")\n\n re_awoo = re_awoo_g\n re_moan = re_moan_g\n re_measure = re_measure_g\n re_yoslimernalia = re_yoslimernalia_g\n re_ree = re_ree_g\n\n playermodel = EwPlayer(id_user=message.author.id)\n usermodel = EwUser(id_user=message.author.id, id_server=playermodel.id_server)\n\n # update the player's time_last_action which is used for kicking AFK players out of subzones\n if message.guild is not None:\n\n try:\n usermodel.time_last_action = int(time.time())\n bknd_core.execute_sql_query(\"UPDATE users SET {time_last_action} = %s WHERE id_user = %s AND id_server = %s\".format(\n time_last_action=ewcfg.col_time_last_action\n ), (\n int(time.time()),\n int(message.author.id),\n message.guild.id\n ))\n except Exception as e:\n ewutils.logMsg('server {}: failed to update time_last_action for {}: {}'.format(message.guild.id, message.author.id, e))\n\n statuses = usermodel.getStatusEffects()\n\n if ewcfg.status_strangled_id in statuses:\n strangle_effect = EwStatusEffect(id_status=ewcfg.status_strangled_id, user_data=usermodel)\n source = EwPlayer(id_user=strangle_effect.source, id_server=message.guild.id)\n response = \"You manage to break {}'s garrote wire!\".format(source.display_name)\n usermodel.clear_status(ewcfg.status_strangled_id)\n return await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n\n if ewutils.active_restrictions.get(usermodel.id_user) == 3:\n usermodel.trauma = ewcfg.trauma_id_environment\n die_resp = await usermodel.die(cause=ewcfg.cause_praying)\n await die_resp.post()\n\n response = \"ENDLESS WAR completely and utterly obliterates {} with a bone-hurting beam.\".format(message.author.display_name).replace(\"@\", \"\\{at\\}\")\n return await fe_utils.send_message(client, message.channel, response)\n if str(message.channel) in [\"nurses-office\", \"suggestion-box\", \"detention-center\", \"community-service\", \"playground\", \"graffiti-wall\", \"post-slime-drip\", \"outside-the-lunchroom\", \"outside-the-lunchrooom\", \"outside-the-lunchroooom\"] or message.channel.type in [discord.ChannelType.public_thread, discord.ChannelType.private_thread]:\n if usermodel.hogtied == 1:\n response = random.choice([\"MMMPH!\", \"MBBBBB\", \"HMMHM\", \"MMMMMHMMF!\"])\n await fe_utils.send_message(client, message.channel, response)\n await message.delete()\n return\n\n \"\"\" -------------------------------\n ########## THREAD CUTOFF ##########\n ------------------------------- \"\"\"\n\n # Never treat a message like a command if it's in a thread\n if message.channel.type not in [discord.ChannelType.text, discord.ChannelType.private]:\n return\n\n if message.content.startswith(ewcfg.cmd_prefix) or message.content in ewdebug.debug_content_check or message.guild is None or (any(swear in content_tolower for swear in ewcfg.curse_words.keys())) or message.channel in [\"nurses-office\", \"suggestion-box\", \"detention-center\", \"community-service\", \"playground\", \"graffiti-wall\", \"post-slime-drip\", \"outside-the-lunchroom\", \"outside-the-lunchrooom\", \"outside-the-lunchroooom\"]:\n \"\"\"\n Wake up if we need to respond to messages. If it's in a basic channel, Could be:\n message starts with !\n direct message (server == None)\n Inaccurate - user is new/has no roles (len(roles) < 4)\n user - Inaccurate - is a security officer and - ok - has cussed\n Message is in a designated non-gameplay channel\n \"\"\"\n\n # Ignore users with weird characters in their name\n\n try:\n message.author.display_name[:3].encode('utf-8').decode('ascii')\n except UnicodeError:\n return await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, \"We don't take kindly to moon runes around here.\"))\n\n # tokenize the message. the command should be the first word.\n try:\n tokens = shlex.split(message.content) # it's split with shlex now because shlex regards text within quotes as a single token\n except:\n tokens = message.content.split(' ') # if splitting via shlex doesnt work (odd number of quotes), use the old splitting method so it doesnt give an exception\n\n tokens_count = len(tokens)\n cmd = tokens[0].lower() if tokens_count >= 1 else \"\"\n\n mentions = message.mentions\n mentions_count = len(mentions)\n\n if message.guild is None:\n guild_used = ewcfg.server_list[playermodel.id_server]\n admin_permissions = False\n else:\n guild_used = message.guild\n admin_permissions = message.author.guild_permissions.administrator\n\n # Create command object\n cmd_obj = cmd_utils.EwCmd(\n tokens=tokens,\n message=message,\n client=client,\n mentions=mentions,\n guild=guild_used,\n admin=admin_permissions\n )\n\n # remove mentions to us #moved below cmd_obj because of EwIds #TODO: remove this and move debug commands somewhere else\n mentions = list(filter(lambda user: user.id != client.user.id, message.mentions))\n mentions_count = len(mentions)\n\n \"\"\"\n Punish the user for swearing.\n The swear_jar attribute has been repurposed for SlimeCorp security officers\n \"\"\"\n\n\n usermodel.persist()\n if await ewdebug.contentCheck(cmd=cmd_obj, line=content_tolower) == True:\n return\n usermodel = EwUser(id_user=message.author.id, id_server=playermodel.id_server)\n\n # if the message wasn't a command, we can stop here\n if not message.content.startswith(ewcfg.cmd_prefix):\n return\n\n \"\"\"\n Handle direct messages.\n \"\"\"\n if message.guild is None:\n\n poi = poi_static.id_to_poi.get(usermodel.poi)\n cmd_obj.guild = ewcfg.server_list[playermodel.id_server]\n cmd_obj.message.author = await fe_utils.get_member(cmd_obj.guild, playermodel.id_user)\n\n # Handle DM compatible commands\n if cmd in dm_cmd_map:\n cmd_fnc = dm_cmd_map.get(cmd)\n if cmd_fnc:\n return await cmd_fnc(cmd_obj)\n elif poi.is_apartment and cmd in apt_dm_cmd_map:\n cmd_fnc = apt_dm_cmd_map.get(cmd)\n if cmd_fnc:\n return await cmd_fnc(cmd_obj)\n else:\n # Only send the help response once every thirty seconds. There's no need to spam it.\n # Also, don't send out response if the user doesn't actually type a command.\n if message.content.startswith(ewcfg.cmd_prefix):\n time_last = last_helped_times.get(message.author.id, 0)\n if (time_now - time_last) > 30:\n last_helped_times[message.author.id] = time_now\n direct_help_response = \"ENDLESS WAR doesn't allow you to do that command in DMs.\\nIf you're confused about what you're doing, you might want to get some **!help** over at the server.\"\n await fe_utils.send_message(client, message.channel, direct_help_response)\n else:\n return\n\n # Nothing else to do in a DM.\n return\n\n # assign the appropriate roles to a user with less than @everyone, faction, both location roles\n if usermodel.arrested > 0:\n return\n\n if usermodel.poi == 'altlock':\n response = 'You\\'re locked from the game because your account was created too close to your join date. Dirty little sneak. Talk to a mod if there\\'s some good reason to get it unlocked.'\n return await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n mutations = usermodel.get_mutations()\n # Scold/ignore offline players.\n if message.author.status == discord.Status.offline:\n\n if (ewcfg.mutation_id_chameleonskin not in mutations or cmd not in ewcfg.offline_cmds) and ewutils.DEBUG_OPTIONS['playoffline'] == False:\n response = \"You cannot participate in the ENDLESS WAR while offline.\"\n\n return await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n\n if usermodel.time_lastoffline > time_now - ewcfg.time_offline:\n\n if ewcfg.mutation_id_chameleonskin not in mutations or cmd not in ewcfg.offline_cmds:\n response = \"You are too paralyzed by ENDLESS WAR's judgemental stare to act.\"\n\n return await fe_utils.send_message(client, message.channel, fe_utils.formatMessage(message.author, response))\n\n statuses = usermodel.getStatusEffects()\n # Ignore stunned players\n if ewcfg.status_stunned_id in statuses:\n return\n\n # Check the main command map for the requested command.\n #global cmd_map\n cmd_fn = cmd_map.get(cmd.replace(ewcfg.cmd_prefix, '!', 1))\n\n if usermodel.poi in ewdebug.act_pois.keys():\n keycontent = ewutils.flattenTokenListToString(content_tolower)\n if keycontent in ewdebug.act_pois.get(usermodel.poi).keys() or content_tolower in ewdebug.act_pois.get(usermodel.poi).keys():\n return await ewdebug.act(cmd=cmd_obj, content_tolower=content_tolower)\n #function = ewdebug.act_pois.get(usermodel.poi).get(keycontent)\n #if function is None:\n # function = ewdebug.act_pois.get(usermodel.poi).get(content_tolower)\n #return await function(cmd=cmd_obj)\n\n if cmd_fn is not None:\n # Execute found command\n return await cmd_fn(cmd_obj)\n # AWOOOOO\n elif re_awoo.match(cmd):\n return await ewcmd.cmdcmds.cmd_howl(cmd_obj)\n elif re_moan.match(cmd):\n return await ewcmd.cmdcmds.cmd_moan(cmd_obj)\n elif re_yoslimernalia.match(cmd) and ewcfg.slimernalia_active:\n return await ewcmd.cmdcmds.yoslimernalia(cmd_obj)\n elif re_measure.match(cmd):\n return await ewcmd.cmdcmds.cockdraw(cmd_obj)\n elif re_ree.match(cmd):\n return await racecmdsree(cmd_obj)\n elif debug and cmd in ewcfg.client_debug_commands:\n return await debugHandling(message=message, cmd=cmd, cmd_obj=cmd_obj)\n\n # didn't match any of the command words.\n else:\n \"\"\" couldn't process the command. bail out!! \"\"\"\n \"\"\" bot rule 0: be cute \"\"\"\n randint = random.randint(1, 3)\n msg_mistake = \"ENDLESS WAR is growing frustrated.\"\n if randint == 2:\n msg_mistake = \"ENDLESS WAR denies you his favor.\"\n elif randint == 3:\n msg_mistake = \"ENDLESS WAR pays you no mind.\"\n\n return await fe_utils.send_response(msg_mistake, cmd_obj, 2)\n\n elif content_tolower.find(ewcfg.cmd_howl) >= 0 or content_tolower.find(ewcfg.cmd_howl_alt1) >= 0 or re_awoo.match(content_tolower):\n \"\"\" Howl if !howl is in the message at all. \"\"\"\n return await ewcmd.cmdcmds.cmd_howl(cmd_utils.EwCmd(\n message=message,\n client=client,\n guild=message.guild\n ))\n elif content_tolower.find(ewcfg.cmd_moan) >= 0 or re_moan.match(content_tolower):\n return await ewcmd.cmdcmds.cmd_moan(cmd_utils.EwCmd(\n message=message,\n client=client,\n guild=message.guild\n ))\n elif content_tolower.find(ewcfg.cmd_yoslimernalia) >= 0 or re_yoslimernalia.match(content_tolower):\n if ewcfg.slimernalia_active:\n return await ewcmd.cmdcmds.yoslimernalia(cmd_utils.EwCmd(\n message=message,\n client=client,\n guild=message.guild\n ))\n else:\n return\n elif content_tolower.find(ewcfg.cmd_ree) >= 0 or re_ree.match(content_tolower):\n return await racecmdsree(cmd_utils.EwCmd(\n message=message,\n client=client,\n guild=message.guild\n ))\n\n\n@client.event\nasync def on_raw_reaction_add(payload):\n if ewutils.DEBUG:\n emoji_req = 2\n else:\n emoji_req = 10\n\n # Currently only respond to reactions in the main server\n if payload.guild_id is None:\n # Was a DM\n return\n \n # Don't handle our own reactions\n if payload.user_id == client.user.id:\n return\n\n server = client.get_guild(payload.guild_id)\n\n slime_twitter = fe_utils.get_channel(server, ewcfg.channel_slimetwitter)\n deviant_splaart = fe_utils.get_channel(server, ewcfg.channel_deviantsplaart)\n community_service = fe_utils.get_channel(server, ewcfg.channel_communityservice)\n\n # Slime Twitter Emote Handling\n if slime_twitter is not None and payload.channel_id == slime_twitter.id:\n message = await slime_twitter.fetch_message(payload.message_id)\n if len(message.embeds) > 0:\n embed = message.embeds[0]\n userid = \"<@!{}>\".format(payload.user_id)\n # If the person reacting made the tweet\n if embed.description.startswith(userid):\n # If the reply is :blank:\n if str(payload.emoji) == ewcfg.emote_delete_tweet:\n await message.delete()\n # If person reacting didn't make the tweet\n else:\n # If the reply is :slimetwitterlike:\n if str(payload.emoji) in [ewcfg.emote_slimetwitter_like, ewcfg.emote_slimetwitter_like_debug]:\n try:\n og_id_user = separate_id_from_slimetwitter_embed(embed)\n await add_xp(og_id_user, payload.guild_id, ewcfg.goonscape_clout_stat, 13000)\n except:\n ewutils.logMsg('Failed to increase Clout XP for user with ID {}.'.format(og_id_user))\n # If the reply is :slimeresplat:\n elif str(payload.emoji) in [ewcfg.emote_slimetwitter_resplat, ewcfg.emote_slimetwitter_resplat_debug]:\n try:\n await send_qrt_command(client, server, payload.user_id, payload.message_id)\n og_id_user = separate_id_from_slimetwitter_embed(embed)\n await add_xp(og_id_user, payload.guild_id, ewcfg.goonscape_clout_stat, 4300)\n except:\n ewutils.logMsg('Failed to increase Clout XP for user with ID {}, and failed to send Quote Resplat message to user with ID {}'.format(og_id_user, payload.id_user))\n\n # Deviant Splaart Emote Handling\n elif deviant_splaart is not None and payload.channel_id == deviant_splaart.id:\n message = await deviant_splaart.fetch_message(payload.message_id)\n if str(payload.emoji) == ewcfg.emote_111 or str(payload.emoji) == ewcfg.emote_111_debug:\n for react in message.reactions:\n if react.count >= emoji_req and react.emoji.id in [720412882143150241, 431547758181220377]:\n msgtext = \"--------------------------------------------------------------------------------------------------\\n\" + message.content\n title = message.content.split('::', 1)\n current_record = EwRecord(id_server=payload.guild_id, record_type=title[0])\n current_record.legality = 0\n current_record.persist()\n\n art_exhibits = fe_utils.get_channel(server, ewcfg.channel_artexhibits)\n await fe_utils.send_message(client, art_exhibits, msgtext)\n await message.delete()\n\n # Community Service Clout\n elif community_service is not None and payload.channel_id == community_service.id:\n message = await community_service.fetch_message(payload.message_id)\n await add_xp(message.author.id, payload.guild_id, ewcfg.goonscape_clout_stat, 6500)\n\n# find our REST API token\ntoken = ewutils.getToken()\n\nif token == None or len(token) == 0:\n ewutils.logMsg('Please place your API token in a file called \"token\", in the same directory as this script.')\n sys.exit(0)\n\n# Load the cache before connecting to discord, this way we arent missing heartbeats\newutils.logMsg(\"Initializing caches...\")\n\n# Set all predefined caches to enabled and initialize them\nfor cache_type_name in ewcfg.cacheable_types:\n ewutils.logMsg(\"Initializing {} cache.\".format(cache_type_name))\n bknd_core.enabled_caches.append(cache_type_name)\n bknd_core.ObjCache(ew_obj_type=cache_type_name)\n\n# connect to discord and run indefinitely\ntry:\n client.run(token, log_handler=None)\nfinally:\n ewutils.TERMINATE = True\n ewutils.logMsg(\"main thread terminated.\")\n","sub_path":"client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":52316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"674443","text":"import pygame\r\nfrom math import sqrt\r\n\r\nscreen = pygame.display.set_mode((400, 200))\r\nyellow = (200, 25, 75)\r\n\r\n\r\nclass En:\r\n spd = 0.2\r\n\r\n def __init__(self, x, y):\r\n self.x = x\r\n self.y = y\r\n self.width = 20\r\n self.height = 20\r\n self.raio = 20\r\n\r\n def render(self):\r\n # pygame.draw.circle(screen, yellow, (int(self.x), int(self.y)), self.raio)\r\n pygame.draw.rect(screen, yellow, (self.x, self.y, self.width, self.height))\r\n\r\n def move(self):\r\n print()\r\n\r\n def collidePlayer(self, x, y, r):\r\n raio = self.raio + r\r\n dist = sqrt((self.x - x)*2 + (self.y - y)*2)\r\n if raio > dist:\r\n return True\r\n return False\r\n\r\n","sub_path":"Invaders/En.py","file_name":"En.py","file_ext":"py","file_size_in_byte":722,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"610932441","text":"import sys\n\nwith open(sys.argv[1], 'r') as f:\n for x in f:\n y = x.strip().split(';')\n a, b = list(y[0].split(',')), list(y[1].split(','))\n c = ','.join(sorted(set(a).intersection(b))).strip()\n if c:\n print(c)\n else:\n print()\n","sub_path":"Easy/set_intersection.py","file_name":"set_intersection.py","file_ext":"py","file_size_in_byte":285,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"184988478","text":"from bson import ObjectId\nfrom pymongo import MongoClient\n\nfrom .. import state_changes\n\nclient = MongoClient()\ndb = client.PITherm\n\n\nclass StateChangeWeekly(state_changes.StateChange):\n collection = db.state_changes_weekly\n\n def __init__(self, seconds_into_week, AC_target, heater_target, fan, id=None):\n self.id = id\n self.seconds_into_week = seconds_into_week\n self.AC_target = AC_target\n self.heater_target = heater_target\n self.fan = fan\n\n assert type(seconds_into_week) is int\n assert type(AC_target) is int or float\n assert type(heater_target) is int or float\n assert type(fan) is int or float\n\n @classmethod\n def from_dictionary(cls, json):\n seconds_into_week = json[\"week_time\"]\n\n AC_target = json[\"state\"][\"AC_target\"]\n heater_target = json[\"state\"][\"heater_target\"]\n fan = json[\"state\"][\"fan\"]\n\n try:\n id = ObjectId(json[\"_id\"][\"$oid\"])\n except KeyError:\n id = None\n except TypeError:\n try:\n id = ObjectId(json[\"_id\"])\n except:\n id = None\n\n return cls(seconds_into_week, AC_target, heater_target, fan, id=id)\n\n\n @classmethod\n def get_current(cls, now):\n week_time = now.weekday() * 24 * 60 ** 2 + (now.hour * 60 + now.minute) * 60\n try:\n result = cls.collection.aggregate(\n [\n {\"$project\": {\n \"time_delta\": {\"$mod\": [{\"$add\": [{\"$subtract\": [week_time, \"$week_time\"]}, 24 * 7 * 60 ** 2]},\n 24 * 7 * 60 ** 2]},\n \"state\": 1,\n \"week_time\": 1}\n },\n {\"$sort\": {\"time_delta\": 1}}\n ]).next()\n except StopIteration:\n return None\n return cls.from_dictionary(result)\n\n @classmethod\n def get_next(cls, now):\n week_time = now.weekday() * 24 * 60 ** 2 + (now.hour * 60 + now.minute) * 60\n result = cls.collection.aggregate(\n [\n {\"$project\": {\n \"time_delta\": {\"$mod\": [{\"$add\": [{\"$subtract\": [week_time, \"$week_time\"]}, 24 * 7 * 60 ** 2]},\n 24 * 7 * 60 ** 2]},\n \"state\": 1,\n \"week_time\": 1}\n },\n {\"$sort\": {\"time_delta\": -1}}\n ]).next()\n return cls.from_dictionary(result)\n\n def save(self):\n delayed_state_change = {\n \"week_time\": self.seconds_into_week,\n \"state\": {\"AC_target\": self.AC_target, \"heater_target\": self.heater_target, \"fan\": self.fan}\n }\n if self.id is not None:\n delayed_state_change[\"_id\"] = self.id\n\n return self.collection.save(delayed_state_change)\n\n def to_dictionary(self):\n return {\"week_time\": self.seconds_into_week,\n \"_id\": str(self.id),\n \"state\": {\"AC_target\": self.AC_target,\n \"heater_target\": self.heater_target,\n \"fan\": self.fan}}\n\n @classmethod\n def get_all_dic(cls):\n all_items = cls.get_all()\n result = []\n for item in all_items:\n result.append(item.to_dictionary())\n return result\n\n","sub_path":"src/services/Schedule/src/state_changes_weekly/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":3334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"438708846","text":"import subprocess\nimport time\n\nimport discord, platform\nfrom discord.ext import commands\nfrom uptime import uptime\n\nclass Info(commands.Cog):\n\n def __init__(self, bot):\n self.bot = bot\n\n @commands.command(name='stats')\n async def show_stats(self, ctx):\n\n app_info = await self.bot.application_info()\n\n try:\n app_version = subprocess.check_output([\"git\", \"describe\", \"--tags\"]).decode(\"utf-8\").strip()\n except subprocess.CalledProcessError:\n app_version = 'n/a'\n\n em = {\n \"title\": \"About Senko-san\",\n \"description\": \"Hey, I'm the divine messenger fox, Senko-san!~\\n\"\n \"Your fluffy helpful bot written in [python](https://www.python.org) using [discord.py](https://github.com/Rapptz/discord.py)!\",\n \"color\": 0xf0c561,\n \"thumbnail\": {\n \"url\": str(self.bot.user.avatar_url)\n },\n \"fields\": [\n {\n \"name\": \"Owner\",\n \"value\": \"User: {}#{}\\nId: {}\".format(\n app_info.owner.name,\n app_info.owner.discriminator,\n app_info.owner.id),\n \"inline\": True\n },\n {\n \"name\": \"Versions\",\n \"value\": \"Senko-san {}\\ndiscord.py v{}\\npython {}\".format(\n app_version,\n discord.__version__,\n platform.python_version()),\n \"inline\": True\n },\n {\n \"name\": \"System Info\",\n \"value\": \"OS: {}\\nUptime: {}\".format(\n platform.platform(),\n time.strftime(\"%H:%M:%S\", time.gmtime(uptime()))\n )\n }\n ]\n }\n\n await ctx.send(embed=discord.Embed.from_dict(em))\n\ndef setup(bot):\n bot.add_cog(Info(bot))\n","sub_path":"modules/system/info.py","file_name":"info.py","file_ext":"py","file_size_in_byte":2000,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"236931374","text":"# coding=utf-8\n# Copyright 2018-2022 EVA\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 unittest\nfrom unittest.mock import MagicMock\n\nfrom mock import patch\n\nfrom eva.server.async_protocol import EvaClient, EvaProtocolBuffer\n\n\nclass AsyncProtocolTests(unittest.TestCase):\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n\n def test_empty_buffer(self):\n buf = EvaProtocolBuffer()\n buf.empty()\n self.assertEqual(\"\", buf.buf)\n self.assertEqual(-1, buf.expected_length)\n\n def test_feed_data(self):\n buf = EvaProtocolBuffer()\n data1 = \"4|12\"\n data2 = \"34\"\n\n buf.feed_data(data1)\n self.assertEqual(\"12\", buf.buf)\n self.assertEqual(4, buf.expected_length)\n\n buf.feed_data(data2)\n self.assertEqual(\"1234\", buf.buf)\n self.assertEqual(4, buf.expected_length)\n\n def test_has_complete_message(self):\n buf = EvaProtocolBuffer()\n data1 = \"4|12\"\n data2 = \"34\"\n\n buf.feed_data(data1)\n self.assertFalse(buf.has_complete_message())\n buf.feed_data(data2)\n self.assertTrue(buf.has_complete_message())\n\n def test_read_message_one(self):\n buf = EvaProtocolBuffer()\n data1 = \"4|12\"\n data2 = \"34\"\n buf.feed_data(data1)\n buf.feed_data(data2)\n\n self.assertEqual(\"1234\", buf.read_message())\n self.assertEqual(\"\", buf.buf)\n self.assertEqual(-1, buf.expected_length)\n\n def test_read_message_two(self):\n buf = EvaProtocolBuffer()\n data1 = \"4|123\"\n data2 = \"42|56\"\n buf.feed_data(data1)\n buf.feed_data(data2)\n\n self.assertEqual(\"1234\", buf.read_message())\n self.assertEqual(\"56\", buf.read_message())\n self.assertEqual(\"\", buf.buf)\n self.assertEqual(-1, buf.expected_length)\n\n @patch(\"eva.server.async_protocol.set_socket_io_timeouts\")\n def test_connection_made_time_out(self, mock_set):\n client = EvaClient()\n t = MagicMock()\n mock_set.return_value = False\n\n client.connection_made(t)\n mock_set.assert_called_once_with(t, 60, 0)\n t.abort.assert_called_once_with()\n\n @patch(\"eva.server.async_protocol.set_socket_io_timeouts\")\n def test_connection_made_no_time_out(self, mock_set):\n client = EvaClient()\n t = MagicMock()\n mock_set.return_value = True\n\n client.connection_made(t)\n mock_set.assert_called_once_with(t, 60, 0)\n t.abort.assert_not_called()\n\n def test_connection_lost_no_exception(self):\n client = EvaClient()\n t = MagicMock()\n client.transport = t\n\n client.connection_lost(None)\n t.abort.assert_called_once_with()\n self.assertIsNone(client.transport)\n self.assertIsNone(client.done.result())\n\n def test_connection_lost_raise_exception(self):\n client = EvaClient()\n client.transport = MagicMock()\n client.transport.abort = MagicMock(side_effect=Exception(\"Boom!\"))\n\n with self.assertRaises(Exception) as context:\n client.connection_lost(None)\n self.assertTrue(\"Boom!\" in str(context.exception))\n\n def test_data_received(self):\n client = EvaClient()\n client.queue = MagicMock()\n\n testdata = \"4|1234\".encode(\"ascii\")\n client.data_received(testdata)\n client.queue.put_nowait.assert_called_once_with(\"1234\")\n","sub_path":"test/server/test_async_protocol.py","file_name":"test_async_protocol.py","file_ext":"py","file_size_in_byte":3935,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"247737420","text":"## https://www.acmicpc.net/problem/1932\n\nn = int(input())\ntri = {}\nfor i in range(n):\n temp = list(map(int,input().split()))\n tri[i] = temp\n\nfor i in range(1, len(tri)):\n temp_list =[]\n for j in range(len(tri[i])):\n temp = 0\n if j == 0:\n temp = tri[i-1][0] + tri[i][j]\n elif j == len(tri[i])-1:\n temp = tri[i-1][-1] + tri[i][j]\n else:\n temp = max(tri[i-1][j-1], tri[i-1][j]) + tri[i][j]\n temp_list.append(temp)\n tri[i] = temp_list\nprint(max(tri[n-1]))","sub_path":"baekjoon/dp/1932.정수 삼각형.py","file_name":"1932.정수 삼각형.py","file_ext":"py","file_size_in_byte":534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"178253593","text":"# -*- coding: utf-8 -*-\n\"\"\"\n374. Guess Number Higher or Lower\n\nWe are playing the Guess Game. The game is as follows:\n\nI pick a number from 1 to n. You have to guess which number I picked.\n\nEvery time you guess wrong, I will tell you whether the number I picked is \nhigher or lower than your guess.\n\nYou call a pre-defined API int guess(int num), which returns 3 possible \nresults:\n\n-1: The number I picked is lower than your guess (i.e. pick < num).\n1: The number I picked is higher than your guess (i.e. pick > num).\n0: The number I picked is equal to your guess (i.e. pick == num).\nReturn the number that I picked.\n\nConstraints:\n\n1 <= n <= (2**31) - 1\n1 <= pick <= n\n\nSubmitted Successfully\nCreated on Tue May 18 11:20:00 2021\n\n@author: Christopher Obuchere\n\"\"\"\n\n# The guess API is already defined for you.\n# @param num, your guess\n# @return -1 if my number is lower, 1 if my number is higher, otherwise return 0\n# def guess(num: int) -> int:\n\nclass Solution:\n def guessNumber(self, n: int) -> int:\n state_of = False\n start, end = 0, n\n mid = (start+end)//2\n while state_of == False:\n if guess(mid) == 0:\n state_of = True\n elif guess(mid) == 1:\n start = mid + 1 #raise mid\n else:\n end = mid - 1 #Reduce lowest\n mid = (start+end)//2\n return mid\n\n","sub_path":"LeetCodeChallenges/guessNumberHigherorLower.py","file_name":"guessNumberHigherorLower.py","file_ext":"py","file_size_in_byte":1375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"363048944","text":"from new import*\nfrom new import animate\nimport new as n\nimport naiveworking as nw\nimport Mbayes as mb\nimport Bbayes\nimport LogisticReg\nimport hybridworking\nimport NuSVC_classifier\nimport samm as s\nclass SeaofBTCapp(tk.Tk):\n def __init__(self, *args, **kwargs):\n tk.Tk.__init__(self, *args, **kwargs)\n tk.Tk.wm_title(self, \"SentAna App\")\n\n container = tk.Frame(self)\n container.pack(side=\"top\", fill=\"both\", expand=True)\n container.grid_rowconfigure(0, weight=1)\n container.grid_columnconfigure(0, weight=1)\n\n menubar = tk.Menu(container)\n filemenu = tk.Menu(menubar, tearoff=0)\n filemenu.add_command(label=\"Save settings\", command=lambda: n.popupmessage('Not supported just yet!'))\n filemenu.add_separator()\n filemenu.add_command(label=\"Exit\", command=quit)\n menubar.add_cascade(label=\"File\", menu=filemenu)\n\n exchangeChoice = tk.Menu(menubar, tearoff=1)\n exchangeChoice.add_command(label=\"Naive-Bayes\",\n command=lambda: self.show_frame(NaiveBayesGUI))\n exchangeChoice.add_command(label=\"MultinomialNaivebayes\",\n command=lambda: self.show_frame(MultinomialNaivebayes))\n exchangeChoice.add_command(label=\"BinomialNaivebayes\",\n command=lambda: self.show_frame(BinomialNaivebayes))\n exchangeChoice.add_command(label=\"LogisticRegression\",\n command=lambda: self.show_frame(LogisticRegeration))\n exchangeChoice.add_command(label=\"NuSVC\",\n command=lambda: self.show_frame(NuSVC))\n\n exchangeChoice.add_command(label=\"Bag-Of-Word\",\n command=lambda: self.show_frame(BOW))\n exchangeChoice.add_command(label=\"Hybrid\",\n command=lambda: self.show_frame(Hybrid))\n menubar.add_cascade(label=\"SentAna\", menu=exchangeChoice)\n\n dataTF = tk.Menu(menubar, tearoff=1)\n dataTF.add_command(label=\"Tick\",\n command=lambda: n.popupmessage('tick'))\n dataTF.add_command(label=\"1 day\",\n command=lambda: n.popupmessage('1d'))\n dataTF.add_command(label=\"3 day\",\n command=lambda: n.popupmessage('3d'))\n dataTF.add_command(label=\"1 Week\",\n command=lambda: n.popupmessage('7d'))\n menubar.add_cascade(label=\"Data Time Frame\", menu=dataTF)\n\n TIME_INTR = tk.Menu(menubar, tearoff=1)\n\n TIME_INTR.add_command(label=\"Tick\",\n command=lambda: n.popupmessage('tick'))\n TIME_INTR.add_command(label=\"1 minute\",\n command=lambda: n.popupmessage('1Min'))\n TIME_INTR.add_command(label=\"5 minute\",\n command=lambda: n.popupmessage('5Min'))\n TIME_INTR.add_command(label=\"15 minute\",\n command=lambda: n.popupmessage('15Min'))\n TIME_INTR.add_command(label=\"30 minute\",\n command=lambda: n.popupmessage('30Min'))\n TIME_INTR.add_command(label=\"1 Hour\",\n command=lambda: n.popupmessage('1H'))\n TIME_INTR.add_command(label=\"3 Hour\",\n command=lambda: n.popupmessage('3H'))\n menubar.add_cascade(label=\"Time Interval\", menu=TIME_INTR)\n\n topIndi = tk.Menu(menubar, tearoff=1)\n topIndi.add_command(label=\"None\",\n command=lambda: n.popupmessage('none'))\n topIndi.add_separator()\n topIndi.add_command(label=\"MODI\",\n command=lambda: n.popupmessage('MODI'))\n topIndi.add_command(label=\"RAHUL\",\n command=lambda: n.popupmessage('RAHUL'))\n topIndi.add_command(label=\"Kejriwal\",\n command=lambda: n.popupmessage('Kejriwal'))\n\n menubar.add_cascade(label=\"Top Indicator\", menu=topIndi)\n\n mainI = tk.Menu(menubar, tearoff=1)\n mainI.add_command(label=\"None\",\n command=lambda: n.popupmessage('none'))\n mainI.add_separator()\n mainI.add_command(label=\"BAR\",\n command=lambda: n.popupmessage('BAR'))\n mainI.add_command(label=\"Scatter Plot\",\n command=lambda: n.popupmessage('Scatter Plot'))\n mainI.add_command(label=\"Geographical Indentation\",\n command=lambda: n.popupmessage('Geographical Indentation'))\n menubar.add_cascade(label=\"Graph Indicator\", menu=mainI)\n\n\n startStop = tk.Menu(menubar, tearoff=1)\n startStop.add_command(label=\"Resume\",\n command=lambda: n.popupmessage('start'))\n startStop.add_command(label=\"Pause\",\n command=lambda: n.popupmessage('stop'))\n menubar.add_cascade(label=\"Resume/Pause Client\", menu=startStop)\n\n helpmenu = tk.Menu(menubar, tearoff=0)\n helpmenu.add_command(label=\"Tutorial\", command=lambda :n.popupmessage(\"tutorial\"))\n menubar.add_cascade(label=\"Help\", menu=helpmenu)\n\n tk.Tk.config(self, menu=menubar)\n\n self.frames = {}\n for F in (StartPage, Hybrid,NaiveBayesGUI,MultinomialNaivebayes,BinomialNaivebayes,LogisticRegeration,NuSVC,BOW):\n frame = F(container, self)\n self.frames[F] = frame\n frame.grid(row=0, column=0, sticky=\"nsew\")\n\n self.show_frame(StartPage)\n\n tk.Tk.iconbitmap(self, default='logo.ico')\n\n def show_frame(self, cont):\n frame = self.frames[cont]\n frame.tkraise()\n\n\n\nclass StartPage(tk.Frame):\n file_name = \" \"\n def load_file(self):\n self.file_name = filedialog.askopenfilename(filetypes=([('All files', '*.*'),\n ('Text files', '*.txt'),\n ('CSV files', '*.csv')]))\n print(type(self.file_name))\n def __init__(self, parent, controller):\n tk.Frame.__init__(self, parent)\n label = tk.Label(self, text=(\"\"\"This prediction may be wrong.if you want to contineu press 'agree' else 'disagree'.\"\"\"), font=LARGE_FONT)\n label.pack(pady=10, padx=10)\n\n button1 = ttk.Button(self, text=\"Agree\",\n command=lambda: controller.show_frame(Hybrid))\n button1.pack()\n\n button2 = ttk.Button(self, text=\"Disagree\",\n command=quit)\n button2.pack()\n button3 = ttk.Button(self, text=\"upload file\",\n command=self.load_file)\n button3.pack()\n label1 = tk.Label(self, text= \" \".join(self.file_name), font=LARGE_FONT)\n label1.pack(pady=10, padx=10)\n\n\nclass PageOne(tk.Frame):\n def __init__(self, parent, controller):\n tk.Frame.__init__(self, parent)\n label = tk.Label(self, text=\"Page One!!!\", font=LARGE_FONT)\n label.pack(pady=10, padx=10)\n\n button1 = ttk.Button(self, text=\"Back to Home\",\n command=lambda: controller.show_frame(StartPage))\n button1.pack()\n\n\nclass Hybrid(tk.Frame):\n def __init__(self, parent, controller):\n tk.Frame.__init__(self, parent)\n label = tk.Label(self, text=\"Hybrid Graph Page!\", font=LARGE_FONT)\n label.pack(pady=10, padx=10)\n\n\n canvas = FigureCanvasTkAgg(s.fig, self)\n canvas.show()\n canvas.get_tk_widget().pack(side=tk.BOTTOM, fill=tk.BOTH, expand=True)\n\n toolbar = NavigationToolbar2TkAgg(canvas, self)\n toolbar.update()\n canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n button2 = ttk.Button(self,text = \"execute model\",\n command = lambda: n.popupmessage(hybridworking.Accuracy))\n button2.pack()\n button1 = ttk.Button(self, text=\"Back to Home\",\n command=lambda: controller.show_frame(StartPage))\n button1.pack()\n\nclass NaiveBayesGUI(tk.Frame):\n def __init__(self, parent, controller):\n tk.Frame.__init__(self, parent)\n label = tk.Label(self, text=\"NaiveBayesGUI Graph Page!\", font=LARGE_FONT)\n label.pack(pady=10, padx=10)\n\n\n canvas = FigureCanvasTkAgg(n.f, self)\n canvas.show()\n canvas.get_tk_widget().pack(side=tk.BOTTOM, fill=tk.BOTH, expand=True)\n\n toolbar = NavigationToolbar2TkAgg(canvas, self)\n toolbar.update()\n canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n button2 = ttk.Button(self, text=\"execute model\",\n command=lambda: n.popupmessage(nw.Accuracy))\n button2.pack()\n\n button1 = ttk.Button(self, text=\"Back to Home\",\n command=lambda: controller.show_frame(StartPage))\n button1.pack()\n\n\nclass MultinomialNaivebayes(tk.Frame):\n def __init__(self, parent, controller):\n tk.Frame.__init__(self, parent)\n label = tk.Label(self, text=\"MultinomialNaivebayes Graph Page!\", font=LARGE_FONT)\n label.pack(pady=10, padx=10)\n\n\n canvas = FigureCanvasTkAgg(n.f, self)\n canvas.show()\n canvas.get_tk_widget().pack(side=tk.BOTTOM, fill=tk.BOTH, expand=True)\n\n toolbar = NavigationToolbar2TkAgg(canvas, self)\n toolbar.update()\n canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n button2 = ttk.Button(self, text=\"execute model\",\n command=lambda: n.popupmessage(mb.Accuracy))\n button2.pack()\n\n button1 = ttk.Button(self, text=\"Back to Home\",\n command=lambda: controller.show_frame(StartPage))\n button1.pack()\nclass BinomialNaivebayes(tk.Frame):\n def __init__(self, parent, controller):\n tk.Frame.__init__(self, parent)\n label = tk.Label(self, text=\"BinomialNaivebayes Graph Page!\", font=LARGE_FONT)\n label.pack(pady=10, padx=10)\n\n\n canvas = FigureCanvasTkAgg(n.f, self)\n canvas.show()\n canvas.get_tk_widget().pack(side=tk.BOTTOM, fill=tk.BOTH, expand=True)\n\n toolbar = NavigationToolbar2TkAgg(canvas, self)\n toolbar.update()\n canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n button2 = ttk.Button(self, text=\"execute model\",\n command=lambda: n.popupmessage(Bbayes.Accuracy))\n button2.pack()\n\n button1 = ttk.Button(self, text=\"Back to Home\",\n command=lambda: controller.show_frame(StartPage))\n button1.pack()\nclass LogisticRegeration(tk.Frame):\n def __init__(self, parent, controller):\n tk.Frame.__init__(self, parent)\n label = tk.Label(self, text=\"LogisticRegression Graph Page!\", font=LARGE_FONT)\n label.pack(pady=10, padx=10)\n\n\n canvas = FigureCanvasTkAgg(n.f, self)\n canvas.show()\n canvas.get_tk_widget().pack(side=tk.BOTTOM, fill=tk.BOTH, expand=True)\n\n toolbar = NavigationToolbar2TkAgg(canvas, self)\n toolbar.update()\n canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n button2 = ttk.Button(self, text=\"execute model\",\n command=lambda: n.popupmessage(LogisticReg.Accuracy))\n button2.pack()\n\n button1 = ttk.Button(self, text=\"Back to Home\",\n command=lambda: controller.show_frame(StartPage))\n button1.pack()\n\n\nclass NuSVC(tk.Frame):\n def __init__(self, parent, controller):\n tk.Frame.__init__(self, parent)\n label = tk.Label(self, text=\"NuSVC Graph Page!\", font=LARGE_FONT)\n label.pack(pady=10, padx=10)\n\n canvas = FigureCanvasTkAgg(n.f, self)\n canvas.show()\n canvas.get_tk_widget().pack(side=tk.BOTTOM, fill=tk.BOTH, expand=True)\n\n toolbar = NavigationToolbar2TkAgg(canvas, self)\n toolbar.update()\n canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n button2 = ttk.Button(self, text=\"execute model\",\n command=lambda: n.popupmessage(NuSVC_classifier.Accuracy))\n button2.pack()\n\n button1 = ttk.Button(self, text=\"Back to Home\",\n command=lambda: controller.show_frame(StartPage))\n button1.pack()\n\n\nclass BOW(tk.Frame):\n def __init__(self, parent, controller):\n tk.Frame.__init__(self, parent)\n label = tk.Label(self, text=\"BOW Graph Page!\", font=LARGE_FONT)\n label.pack(pady=10, padx=10)\n\n\n canvas = FigureCanvasTkAgg(n.f, self)\n canvas.show()\n canvas.get_tk_widget().pack(side=tk.BOTTOM, fill=tk.BOTH, expand=True)\n\n toolbar = NavigationToolbar2TkAgg(canvas, self)\n toolbar.update()\n canvas._tkcanvas.pack(side=tk.TOP, fill=tk.BOTH, expand=True)\n button1 = ttk.Button(self, text=\"Back to Home\",\n command=lambda: controller.show_frame(StartPage))\n button1.pack()\n\napp = SeaofBTCapp()\napp.geometry(\"800x650\")\nani = animation.FuncAnimation(n.f, animate, interval=1000)\napp.mainloop()","sub_path":"tt.py","file_name":"tt.py","file_ext":"py","file_size_in_byte":13528,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"139549684","text":"'''\nWrite a script that creates a list of all unique values in a list. For example:\n\nlist_ = [1, 2, 6, 55, 2, 'hi', 4, 6, 1, 13]\nunique_list = [55, 'hi', 4, 13]\n\n\n'''\n\nlist_ = [1, 2, 6, 55, 2, 'hi', 4, 6, 1, 13]\nunique_list=[]\nfor item in list_:\n if list_.count(item) == 1:\n unique_list.append(item)\n else:\n continue\n\nprint(f\"the characters that only appear once are, {unique_list}\")","sub_path":"03_more_datatypes/2_lists/03_10_unique.py","file_name":"03_10_unique.py","file_ext":"py","file_size_in_byte":403,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"387558246","text":"import array\nfrom graphics.gl import *\nfrom graphics.glconstants import *\n\n#Buffer.py\nclass Buffer:\n\n \"\"\"Create a buffer. If arrayOfData==None size must be set for empty allocation\"\"\"\n def __init__(self, data, bindingPoint = GL_ARRAY_BUFFER, usage = GL_STATIC_DRAW, size = None):\n self.length = len(data) if size == None else size\n self.bindingPoint = bindingPoint\n #gen buffer\n tmp = array.array(\"I\", [0])\n glGenBuffers(1, tmp)\n self.buffID = tmp[0]\n #Must start binding to GL_ARRAY_BUFFER to avoid problems generating element array buffers mid run if vaos aren't unbound\n glBindBuffer(GL_ARRAY_BUFFER, self.buffID)\n #Create empty allocated buffer or supplied data buffer\n if data == None:\n glBufferData(GL_ARRAY_BUFFER, size, None, usage)\n else:\n tmp = data.tobytes()\n self.size = len(tmp)\n glBufferData(GL_ARRAY_BUFFER, len(tmp), tmp, usage)\n glBindBuffer(GL_ARRAY_BUFFER, 0)\n\n \"\"\"Bind at GL_ARRAY_BUFFER unless specified in constructor or passed in\"\"\"\n def bind(self, bindingPoint = None):\n #use bindingPoint supplied in constructor\n if bindingPoint == None:\n bindingPoint = self.bindingPoint\n glBindBuffer(bindingPoint, self.buffID)\n\n \"\"\"Bind at point passed in or if pass in None bindingPoint set in constructor\"\"\"\n def bindBase(self, bindingPoint, index):\n #use bindingPoint supplied in constructor\n if bindingPoint == None:\n bindingPoint = self.bindingPoint\n\n glBindBufferBase(bindingPoint, index, self.buffID)\n\n\n \"\"\"Unbind at GL_ARRAY_BUFFER unless specified in constructor or passed in\"\"\"\n def unbind(self, bindingPoint = None):\n if bindingPoint == None:\n bindingPoint = self.bindingPoint\n glBindBuffer(0, bindingPoint)\n\n def getLength(self):\n return self.length\n \n def getSize(self):\n return self.size","sub_path":"graphics/buffer.py","file_name":"buffer.py","file_ext":"py","file_size_in_byte":1976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"472663429","text":"import os\n\nfrom qgis.server import *\n\nclass GetFeatureInfoFilter(QgsServerFilter):\n\n def __init__(self, serverIface):\n super(GetFeatureInfoFilter, self).__init__(serverIface)\n\n def responseComplete(self):\n handler = self.serverInterface().requestHandler()\n params = handler.parameterMap( )\n if params.get('TEST', False):\n handler.clearBody()\n handler.appendBody(b\"ciao\")\n \n if (params.get('SERVICE').upper() == 'WMS' \\\n and params.get('REQUEST', '').upper() == 'GETFEATUREINFO' \\\n and params.get('INFO_FORMAT', '').upper() == 'TEXT/HTML' \\\n and not handler.exceptionRaised() ):\n body = handler.body()\n body.replace(b'<BODY>', b\"\"\"<BODY><STYLE type=\"text/css\">* {font-family: arial, sans-serif; color: #09095e;} table { border-collapse:collapse; } td, tr { border: solid 1px grey; }</STYLE>\"\"\")\n handler.clearBody()\n handler.appendBody(body)\n\n\n\nclass GetFeatureInfo:\n\n def __init__(self, serverIface):\n # Save reference to the QGIS server interface\n serverIface.registerFilter( GetFeatureInfoFilter(serverIface), 100 )\n","sub_path":"resources/web/plugins/getfeatureinfo/getfeatureinfo.py","file_name":"getfeatureinfo.py","file_ext":"py","file_size_in_byte":1198,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"218767544","text":"# Résolution du problème de Didon (avec lagrangien augmenté)\n# Thomas Saigre & Romain Vallet\n# M1 CSMI Année 2019-2020\n\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom time import time\nimport sys\nimport getopt\n\n\n\n## Variables globales et constantes\n\nn = 60\nl = 1\n\n\n\nkmax = 3e4\neps = 1e-3\n\n\nh = 2*l/(n+1)\n\nb = 1\n\n# Les vecteurs sont de taille n+1\noutput = 'aff'\na0 = np.pi/2\nnom = 'uzawa'\nverbose = False\n\n\n\n\n\n# Gestion des arguments\n\ntry:\n opts,args = getopt.getopt(sys.argv[1:],\"hva:b:\",[\"a0=\",\"b=\",\"help\",\"output=\"])\nexcept getopt.GetoptError as err:\n print(err)\n print(\"usage : {} [options]\".format(sys.argv[0]))\n sys.exit(2)\n\n\nfor o,a in opts:\n\n if o == \"-a\" or o == \"--a0\":\n a0 = float(a)\n \n elif o == \"-b\" or o == \"--b\":\n b = float(a)\n\n elif o == \"-h\" or o == \"--help\":\n print(\"Résolution du problème iso-aire avec l'algorithme d'Uzawa\")\n print(\"usage : {} [options]\".format(sys.argv[0]))\n print(\"\\t--a0=Val | -aVal\\tmodifier la valeur de a0 (défaut pi/2)\")\n print(\"\\t--b=Val | -bVal\\tmodifier la valeur de b (défaut 1\")\n print(\"\\t-h | --help\\t\\tafficher l'aide\")\n print(\"\\t-v\\t\\t\\tafficher les itérations\")\n print(\"\\t--output=nom\\t\\tPréfixe des fichiers pdf générés (par défaut, affichage dans la console)\")\n sys.exit(0)\n\n elif o == \"--output\":\n output = \"exp\"\n nom = a\n\n elif o == \"-v\":\n verbose = True\n\n else:\n assert False, \"Option {} non valide\".format(o)\n\n\n\n\n\n\n## Classe fonction pour gérer les vecteurs de R^{n+1} avec des valeurs nulles au bord\n\n\nclass fonction:\n\n def __init__(self, n):\n self.n = n\n self.vals = np.empty(n)\t# les valeurs au bord sont 0\n\n def init_cst(self, c=1):\n self.vals = c * np.ones_like(self.vals)\n\n def init_vals(self, tab):\n self.vals = np.copy(tab)\n \n def get_vals(self, f):\n self.vals[:] = f.vals\n \n def __sub__(self,vct):\n assert(self.n == len(vct))\n dif = fonction(self.n)\n dif.init_vals(self.vals - vct)\n return dif\n\n def aire(self):\n return h * np.sum(self.vals)\n\n def long(self):\n l = np.sqrt(self.vals[0]**2 + h**2)\n for i in range(self.n-1):\n l += np.sqrt((self.vals[i+1]-self.vals[i])**2+h**2)\n l += np.sqrt(self.vals[-1]**2 + h**2)\n return l\n\n def proj(self):\n self.vals = np.maximum(self.vals, np.zeros_like(self.vals))\n\n def plot(self,fen):\n p = np.concatenate(([0],self.vals,[0]))\n x = np.linspace(-l,l,self.n+2)\n fen.plot(x,p,label=\"Solution calculée\")\n\n## Fonctions de l'algorithme d'Uzawa\n\ndef f_uza(f):\n return f.long()\n\ndef h_uza(f):\n return f.aire() - a0\n\n\n## Lagrangien augmenté\n\ndef L_aug(f, lam, b):\n return f.long() + lam * (h_uza(f)) + b/2 * (h_uza(f))**2\n\ndef gradL_aug(f, lam, b):\n x = f.vals\n\n gradlg = np.zeros_like(f.vals)\n gradlg[0] = x[0] / np.sqrt(x[0]**2 + h**2) - (x[1]-x[0]) / np.sqrt((x[1]-x[0])**2 + h**2)\n for i in range(1,f.n-1):\n gradlg[i] = (x[i]-x[i-1]) / np.sqrt((x[i]-x[i-1])**2 + h**2) - (x[i+1]-x[i]) / np.sqrt((x[i+1]-x[i])**2 + h**2)\n gradlg[-1] = (x[-1]-x[-2]) / np.sqrt((x[-1]+x[-2])**2 + h**2) + x[-1] / np.sqrt(x[-1]**2 + h**2)\n\n\n return gradlg + (lam + b*(h_uza(f))) * h\n\n\n\n\n## Algorithme d'Uzawa\n\n\ndef uzawa(f0,lam,pas_grad=0.1,pas_uzawa=0.1):\n\n N = f0.n\n\n f = fonction(N)\n f.init_vals(f0.vals)\n\n f_m1 = fonction(N)\n f_m1.init_vals(2 * f0.vals)\n \n\n crt = 1+eps\n k = 0\n \n aires = []\n per = []\n lag = []\n\n \n while (crt >= eps and k<kmax):\n \n f_tmp = fonction(N)\n \n # Recherche du pas pour la variable primale\n\n pas = pas_grad\n cpt_rho = 0\n Delta_L = 1\n\n while (cpt_rho < 1e2) and (Delta_L >= 0):\n pas /= 1.3\n\n f_tmp.get_vals(f - pas * gradL_aug(f, lam, b))\n Delta_L = L_aug(f_tmp, lam, b) - L_aug(f, lam, b)\n \n cpt_rho += 1\n\n f_tmp.proj()\n\n lam_tmp = lam + b * h_uza(f)\n\n nor_lam = np.linalg.norm(lam_tmp-lam)\n\n # Delta_LA = np.abs(L_aug(f,lam_tmp,b)-L_aug(f_tmp,lam,b))\n\n\n f.get_vals(f_tmp)\n\n # crt = np.linalg.norm(f.vals-f_m1.vals) + nor_lam\n crt = np.abs(Delta_L) + nor_lam\n\n # crt = np.linalg.norm(f.vals-f_m1.vals)+np.linalg.norm(h_uza(f))+Delta_LA\n \n k += 1\n\n \n\n f_m1.get_vals(f)\n lam = lam_tmp\n \n aires.append(f.aire())\n per.append(f.long())\n lag.append(L_aug(f,lam,b))\n\n if verbose:\n print(k, \"Per :\", f.long(), \"\\tAire - a0 :\", np.abs(f.aire() - a0), \"\\t\", crt)\n\n return f,aires,per,lag\n\n\n\n\n\n\n## Exécution\n\n\n\nx_cercle = np.linspace(-l,l,n+2)\ny_cercle = np.sqrt(l**2 - x_cercle**2)\n\n\nf0 = fonction(n)\n# f0.init_vals(y_cercle[1:-1])\nf0.init_cst(a0/(2*l))\nlam0 = 1\n\nt0 = time()\nsol,aires,lg,lag = uzawa(f0,lam0)\nt1 = time()\n\n# Affichage des résultats :\nchn = \"(pi/2)\" if a0==np.pi/2 else ''\nprint(\"Problème isoaire, avec a0 = {}\".format(a0),chn)\nprint(\"Temps d'exécution : {}\".format(t1-t0), \"en {} itérations\".format(len(aires)))\nprint(\"Aire finale : {}\".format(aires[-1]))\nprint(\"Périmètre final : {}\".format(lg[-1]))\n\n\n\n\nif output == 'aff':\n\n fig, ax = plt.subplots(2,2)\n\n ax[0,0].plot(x_cercle, y_cercle, 'r--',label=r\"Solution avc $a_0=\\pi/2$\")\n ax[0,0].set_title(\"Solution\")\n sol.plot(ax[0,0])\n ax[0,0].legend()\n\n ax[0,1].plot(aires)\n ax[0,1].set_title(\"Aires\")\n ax[0,1].plot([0,len(aires)],[a0,a0],label=r\"$a_0$\")\n ax[0,1].legend()\n\n ax[1,0].plot(lg)\n ax[1,0].set_title(\"Longueurs\")\n\n ax[1,1].plot(lag)\n ax[1,1].set_title(\"Lagrangien\")\n\n fig.tight_layout()\n plt.show()\n\nelif output == \"exp\":\n\n figSol = plt.figure()\n plt.plot(x_cercle, y_cercle, 'r--',label=r\"Solution avc $a_0=\\pi/2$\")\n plt.title(\"Solution\")\n sol.plot(plt)\n plt.legend()\n figSol.savefig(nom+\"_sol.pdf\", bbox_inches='tight')\n\n figAires = plt.figure()\n plt.plot(aires)\n plt.title(\"Aires\")\n plt.plot([0,len(aires)],[a0,a0],label=r\"$a_0$\")\n plt.legend()\n figAires.savefig(nom+\"_aires.pdf\", bbox_inches='tight')\n\n figLon = plt.figure()\n plt.plot(lg)\n plt.title(\"Longueurs\")\n figLon.savefig(nom+\"_longueurs.pdf\", bbox_inches='tight')\n\n figLag = plt.figure()\n plt.plot(lag)\n plt.title(\"Lagrangien\")\n figLag.savefig(nom+\"_lagrangien.pdf\", bbox_inches='tight')","sub_path":"aug.py","file_name":"aug.py","file_ext":"py","file_size_in_byte":6498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"246800052","text":"# Program: Algoritmo82_metade.py\n# Author: Ramon R. Valeriano\n# Description:\n# Developed: 23/03/2020 - 16:55\n# Updated:\n\nnumber = float(input(\"Enter with a number:\"))\n\nif number >= 0 :\n if number >= 20:\n result = number/2\n else:\n result = 0\n print(result)\nelse:\n print(\"You are asshole!\")\n","sub_path":"Livros/Introdução à Programação - 500 Algoritmos resolvidos/Capitulo 3/Exemplo 3a/Algoritmo82_metade.py","file_name":"Algoritmo82_metade.py","file_ext":"py","file_size_in_byte":315,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"609596550","text":"import bz2\nimport shutil\nwith open('0.py', 'rb') as f_in:\n with bz2.open('0.py.bz2', 'wb') as f_out:\n shutil.copyfileobj(f_in, f_out)\n\nwith bz2.open('0.py.bz2', 'rb') as f:\n file_content = f.read()\n print(file_content)\n print(bz2.compress(file_content))\n","sub_path":"31/00/0.py","file_name":"0.py","file_ext":"py","file_size_in_byte":273,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"232993616","text":"#!/usr/bin/env python3\n\"\"\"\nAuthor : patricklohr\nDate : 2019-02-12\nPurpose: Rock the Casbah\n\"\"\"\n\nimport argparse\nimport sys\nimport os.path\n\n# --------------------------------------------------\ndef get_args():\n \"\"\"get command-line arguments\"\"\"\n parser = argparse.ArgumentParser(\n description='Argparse Python script',\n formatter_class=argparse.ArgumentDefaultsHelpFormatter)\n\n parser.add_argument(\n 'DNA', metavar='STR', help='DNA/RNA sequence')\n\n parser.add_argument(\n '-c',\n '--codons',\n help='A file with codon translations',\n metavar='str',\n type=str,\n required=True,\n default=None)\n\n parser.add_argument(\n '-o',\n '--outfile',\n help='Output filename',\n metavar='FILE',\n type=argparse.FileType('w'),\n default='out.txt')\n\n return parser.parse_args()\n\n\n# --------------------------------------------------\ndef warn(msg):\n \"\"\"Print a message to STDERR\"\"\"\n print(msg, file=sys.stderr)\n\n\n# --------------------------------------------------\ndef die(msg='Something bad happened'):\n \"\"\"warn() and exit with error\"\"\"\n warn(msg)\n sys.exit(1)\n\n\n# --------------------------------------------------\ndef main():\n \"\"\"Make a jazz noise here\"\"\"\n args = get_args()\n table = args.codons\n string = args.DNA\n output = args.outfile\n d = {}\n\n if os.path.exists(table):\n with open(table, 'r') as table:\n for line in table:\n col = line.split()\n d[col[0]] = col[1]\n\n k = 3\n n = len(string) - k + 1\n\n for i in range(0, n, k):\n codon = string[i:i + k].upper()\n if codon in d:\n output.write(d[codon])\n else:\n output.write('-')\n\n print('Output written to \"{}\"'.format(output.name))\n # Do Stuff with file\n else:\n die(\"--codons \\\"{}\\\" is not a file\".format(table))\n\n\n\n# --------------------------------------------------\nif __name__ == '__main__':\n main()\n","sub_path":"assignments/05-python-proteins/translate_proteins.py","file_name":"translate_proteins.py","file_ext":"py","file_size_in_byte":2086,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"648699312","text":"# -*- coding: utf-8 -*-\n# 第 0017 题： 将 第 0014 题中的 student.xls 文件中的内容写到 student.xml 文件中\nimport json\nimport xml.etree.cElementTree as ET\nfrom collections import OrderedDict\nfrom xml.dom import minidom\n\nimport xlrd\n\nroot = ET.Element(\"root\")\nstudent = ET.SubElement(root, \"students\")\n\nwb = xlrd.open_workbook('student.xls')\nsheet = wb.sheet_by_index(0)\nnumber_of_rows = sheet.nrows\nnumber_of_columns = sheet.ncols\n\nstudent_dict = OrderedDict()\n\nfor row in range(number_of_rows):\n items = [sheet.cell(row, col).value for col in range(1, number_of_columns)]\n student_dict[sheet.cell(row, 0).value] = items\nprint(student_dict)\n\njson_str = json.dumps(student_dict, ensure_ascii=False, indent=4)\nprint(json_str)\n\nstudent.text = json_str\n\ntree = ET.ElementTree(root)\ntree.write('student.xml', encoding='utf-8', xml_declaration=True)\n\nxmlstr = minidom.parseString(ET.tostring(root)).toprettyxml()\nwith open('student1.xml', 'w') as f:\n f.write(xmlstr)\n","sub_path":"darcycool/0017/excel2xml.py","file_name":"excel2xml.py","file_ext":"py","file_size_in_byte":987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"430746033","text":"import subprocess\nimport time\nfrom simplecommand import CommandException\nfrom simplecommand.log import logger\nfrom simplecommand.ssh import get_ssh_command\n\n\ndef rsync(source, dest, user, host, port, keyfile):\n log = logger(name='rsync', logfile='/tmp/rsync_output.%s' % time.strftime(\"%Y%m%d%H%M%S\"))\n ssh_cmd = get_ssh_command(\n user=user,\n host=host,\n port=port,\n keyfile=keyfile)\n\n rsync_cmd = \"/usr/bin/rsync --recursive -av -e '%s' %s root@%s:%s 2>&1\" % (ssh_cmd, source, host, dest)\n output = []\n\n try:\n process = subprocess.Popen(rsync_cmd, shell=True, stdout=subprocess.PIPE, stderr=subprocess.STDOUT)\n\n while True:\n next_line = process.stdout.readline()\n is_done = process.poll()\n if next_line == '' and is_done is not None:\n break\n elif next_line == '\\n' or next_line == '':\n continue\n output.append(next_line.strip())\n log.info('%s - %s' % (host, next_line.strip()))\n\n exitcode = process.returncode\n log.info('Exitcode for rsync was %s' % exitcode)\n return output, exitcode\n except OSError as e:\n raise CommandException(e)\n","sub_path":"simplecommand/rsync.py","file_name":"rsync.py","file_ext":"py","file_size_in_byte":1219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"241763247","text":"import codecs\nimport hashlib\nimport ecdsa\nimport secrets\nimport requests\nimport json\n\ndef private_to_public(private_key):\n private_key_bytes = codecs.decode(private_key, 'hex')\n # Get ECDSA public key\n key = ecdsa.SigningKey.from_string(private_key_bytes, curve=ecdsa.SECP256k1).verifying_key\n key_bytes = key.to_string()\n key_hex = codecs.encode(key_bytes, 'hex')\n # Add bitcoin byte\n bitcoin_byte = b'04'\n public_key = bitcoin_byte + key_hex\n return public_key\n\ndef public_to_address(public_key):\n public_key_bytes = codecs.decode(public_key, 'hex')\n # Run SHA256 for the public key\n sha256_bpk = hashlib.sha256(public_key_bytes)\n sha256_bpk_digest = sha256_bpk.digest()\n # Run ripemd160 for the SHA256\n ripemd160_bpk = hashlib.new('ripemd160')\n ripemd160_bpk.update(sha256_bpk_digest)\n ripemd160_bpk_digest = ripemd160_bpk.digest()\n ripemd160_bpk_hex = codecs.encode(ripemd160_bpk_digest, 'hex')\n # Add network byte\n network_byte = b'00'\n network_bitcoin_public_key = network_byte + ripemd160_bpk_hex\n network_bitcoin_public_key_bytes = codecs.decode(network_bitcoin_public_key, 'hex')\n # Double SHA256 to get checksum\n sha256_nbpk = hashlib.sha256(network_bitcoin_public_key_bytes)\n sha256_nbpk_digest = sha256_nbpk.digest()\n sha256_2_nbpk = hashlib.sha256(sha256_nbpk_digest)\n sha256_2_nbpk_digest = sha256_2_nbpk.digest()\n sha256_2_hex = codecs.encode(sha256_2_nbpk_digest, 'hex')\n checksum = sha256_2_hex[:8]\n # Concatenate public key and checksum to get the address\n address_hex = (network_bitcoin_public_key + checksum).decode('utf-8')\n wallet = base58(address_hex)\n return wallet\n\ndef base58(address_hex):\n alphabet = '123456789ABCDEFGHJKLMNPQRSTUVWXYZabcdefghijkmnopqrstuvwxyz'\n b58_string = ''\n # Get the number of leading zeros and convert hex to decimal\n leading_zeros = len(address_hex) - len(address_hex.lstrip('0'))\n # Convert hex to decimal\n address_int = int(address_hex, 16)\n # Append digits to the start of string\n while address_int > 0:\n digit = address_int % 58\n digit_char = alphabet[digit]\n b58_string = digit_char + b58_string\n address_int //= 58\n # Add '1' for each 2 leading zeros\n ones = leading_zeros // 2\n for one in range(ones):\n b58_string = '1' + b58_string\n return b58_string\n\ndef hex_to_wip(private_key):\n private_key='80'+ private_key+'01'\n bits = codecs.decode(private_key,'hex')\n hashed256=hashlib.sha256(bits).digest()\n hashed256=hashlib.sha256(hashed256).digest()\n hashed256=codecs.encode(hashed256,'hex')\n hashed256=hashed256.decode('utf-8')\n hashed256=list(hashed256)\n checksum=''\n for i in range(0,8):\n checksum=checksum+str(hashed256[i])\n private_key=private_key+checksum\n private_key=base58(private_key)\n return private_key\n\ndef generate_bitcoin_address():\n private_key_in_bits=secrets.randbits(256)\n private_key_in_hex=hex(private_key_in_bits)\n private_key_in_bits=0\n private_key_in_hex=str(private_key_in_hex)[2:]\n private_key_in_wip=hex_to_wip(private_key_in_hex)\n public_key=private_to_public(private_key_in_hex)\n public_address=public_to_address(public_key)\n\n return public_address,private_key_in_wip\n\n[public,private]=generate_bitcoin_address()\n\nprint(public)\nprint(private)\n\n\n\n","sub_path":"wallet.py","file_name":"wallet.py","file_ext":"py","file_size_in_byte":3367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"500766114","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.db import migrations, models\n\n\nclass Migration(migrations.Migration):\n\n dependencies = [\n ]\n\n operations = [\n migrations.CreateModel(\n name='Product',\n fields=[\n ('id', models.AutoField(serialize=False, auto_created=True, verbose_name='ID', primary_key=True)),\n ('name', models.CharField(max_length=254, default='')),\n ('description', models.TextField()),\n ('price', models.DecimalField(decimal_places=2, max_digits=6)),\n ],\n ),\n ]\n","sub_path":"products/migrations/0001_initial.py","file_name":"0001_initial.py","file_ext":"py","file_size_in_byte":628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"251425954","text":"import numpy as np\nimport os\nimport sys\nimport fcntl\nimport copy\nimport pdb\nsys.path.append('/home/local/USHERBROOKE/havm2701/git.repos/semi_bts_svm/semi_bts_svm/generalized_methods/')\nsys.path.append('/home/local/USHERBROOKE/havm2701/git.repos/semi_bts_svm/semi_bts_svm/generalized_methods/journal_paper/')\nfrom string import Template\nimport mlpython.datasets.store as dataset_store\nfrom mlpython.learners.third_party.libsvm.classification import SVMClassifier\nimport compute_statistics\nimport time\nimport data_utils\nfrom model_svm_6d_hyperparameter_Sensitivity import svm_model as svm_model\nfrom model_svm_6d_hyperparameter_Sensitivity import load_data as load_data\nfrom model_svm_6d_hyperparameter_Sensitivity import svm_model2 as svm_model2\n#import ipdb\n\n# make a dictionary of the selected brains with their best c and gamma parameters. \n#brain_list = {'HG_0002': [1,50], 'HG_0001': [1,0.01], 'HG_0003': [1,1], 'HG_0010': [1,50], 'HG_0008': [1,5], 'HG_0012': [50,50], 'HG_0011': [1,5], 'HG_0022': [1,5], 'HG_0025': [1,50], 'HG_0027': [1,10], 'LG_0008': [1,200], 'LG_0001': [1,50], 'LG_0006': [1,1], 'LG_0015': [100,200], 'HG_0014': [1,5]}\n#'HG_0002': [1,50], 'HG_0001': [1,0.01], 'HG_0003': [1,1], 'HG_0004': [1500,10], 'HG_0005': [1,5], 'HG_0006': [1,200], 'HG_0007': [1500,500], 'HG_0009': [1,5], 'HG_0010': [1,50], 'HG_0008': [1,5], 'HG_0012': [50,50], 'HG_0013': [1,5], 'HG_0011': [1,5], 'HG_0014': [1,5], 'HG_0015': [1,5], 'HG_0022': [1,5], 'HG_0024': [1,50], 'HG_0025': [1,50], 'HG_0026': [50,10], 'HG_0027': [1,10], 'LG_0001': [1,50], 'LG_0002': [10,50], 'LG_0004': [1,100], 'LG_0008': [1,200], 'LG_0006': [1,1], 'LG_0011': [1,1], 'LG_0012': [50,100], 'LG_0014': [1,100], 'LG_0013': [1,1], 'LG_0015': [100,200]\nbrain_list = { 'HG_0014': [1,5], 'HG_0015': [1,5], 'HG_0024': [1,50]}\nsys.argv.pop(0); # Remove first argument\n\n# Get arguments\ndataset_directory = sys.argv[0]\n#dataset_name = sys.argv[1]\noutput_folder = sys.argv[1]\n\nresults_path = output_folder + '/libsvm_results/'\nif not os.path.exists(results_path):\n os.makedirs(results_path)\n\ngammaps = [0.001,0.005, 0.01,0.05,0.1,0.5,1,5,10,50,100,150,200,300,400,500,1000]\n#Cs = [1,5,10,25,50,75,100,150,200,250,300,400,500,750,1000,1250,1500]\n\n# measure the sensitivity of gamma for the selected brains and save the text file\n\nbrain_names = brain_list.keys()\nresults_file_c = 'libsvm_measures_C.txt'\nresults_file_g = 'libsvm_measures_gamma.txt'\n\nfor brain in brain_names:\n datasets = load_data(dataset_directory , brain)\n resultc1, resultc2 = '' ,'' \n gamma = brain_list[brain][1]\n C = brain_list[brain][0]\n brain_str = brain + '\\t' + 'gamma=' + str(gamma) + ', \\t'\n brain_str += 'C=' + str(C) + ', \\n'\n for gammap in gammaps:\n paramsc = [ gamma, gammap,C]\n dice_c , processed_timec = svm_model2( paramsc, datasets)\n #if brain == 'LG_0008':\n #\tpdb.set_trace()\n\n resultc1 += \"%.7f\" % dice_c + '\\t'\n resultc2 += \"%.4f\" % processed_timec + '\\t'\n resultc1 += '\\n'\n resultc2 += '\\n'\n \n if not os.path.exists(results_path + results_file_c):\n with open(results_path + results_file_c,'w') as c:\n c.write(brain_str)\n c.write(resultc1)\n c.write(resultc2)\n else:\n with open(results_path + results_file_c,'a') as c:\n c.write(brain_str)\n c.write(resultc1)\n c.write(resultc2)\n","sub_path":"generalized_methods/journal_paper/weighted_SVM/results8/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"327351140","text":"def evenNumbers(arrayOfNums):\n temp = []\n for number in arrayOfNums:\n if number % 2 == 0:\n temp.append(number)\n return temp\n\nevenLambda = lambda num : num % 2 == 0\n\nif __name__ == \"__main__\":\n array = [1, 2, 3, 4]\n print(evenNumbers(array))\n print(list(filter(evenLambda, array)))","sub_path":"functions/even_numbers.py","file_name":"even_numbers.py","file_ext":"py","file_size_in_byte":316,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"350134292","text":"from time import sleep\nfrom datetime import datetime\nfrom decimal import Decimal\n\nfrom selenium import webdriver\nfrom selenium.webdriver.chrome import service\nfrom selenium.webdriver.support.ui import Select\n\nfrom currenciesapp import db, create_app\nfrom currenciesapp.models import Currency, Report\nfrom currenciesapp.config import Config\n\ncurrency_list = [\"CHF\", \"GBP\", \"EUR\", \"USD\", \"SEK\", \"DKK\"]\n\nurl = 'https://www.bankier.pl/narzedzia/archiwum-kursow-walutowych'\n\n# Chrome\n# options = Options()\n# options.add_argument(\"--headless\")\n# browser = webdriver.Chrome(chrome_options=options)\n# browser.get(url)\n\n# Opera\ncapabilities = {\"browserName\": \"opera\",\n \"chromeOptions\": {\"args\": [], \"extensions\": [], \"binary\": Config.BROWSER_LOCATION}}\ndriver_service = service.Service(Config.DRIVER_LOCATION)\n\n\ndef get_data(browser, currency_code, from_date=None):\n select_currency = Select(browser.find_element_by_id('currency'))\n select_currency.select_by_value(currency_code)\n\n select_date = Select(browser.find_element_by_id('set_date'))\n select_date.select_by_value('-1095')\n\n browser.find_element_by_id(\"archiwumKursowWalutSubmit\").click()\n sleep(1)\n container = browser.find_element_by_class_name('hideTableRowContainer')\n container.find_element_by_class_name('showAllTableRow').click()\n table = container.find_element_by_class_name('show10TableRow')\n print(table)\n table_body = table.find_element_by_tag_name('tbody')\n data = []\n rows = table_body.find_elements_by_tag_name('tr')\n for row in rows:\n print(row)\n cols = row.find_elements_by_tag_name('td')\n cols = [ele.text.strip() for ele in cols]\n if from_date is None:\n data.append([ele for ele in cols if ele])\n else:\n date_str = cols[0]\n row_date = datetime.strptime(date_str, \"%Y-%m-%d\").date()\n if row_date > from_date:\n data.append([ele for ele in cols if ele])\n print(data)\n return data\n\n\ndef get_archive(from_max_date=False):\n driver_service.start()\n browser = webdriver.Remote(driver_service.service_url, capabilities)\n browser.get(url)\n\n for currency_code in currency_list:\n currency = Currency.query.filter_by(code=currency_code).first()\n if from_max_date:\n max_date = Report.query.filter_by(currency=currency).order_by(Report.date.desc()).first().date.date()\n data = get_data(browser, currency_code, max_date)\n else:\n data = get_data(browser, currency_code)\n for report in data:\n date_str, price_str, change_str, _ = report\n price_str = price_str.replace(',', '.')\n change_str = change_str.replace(',', '.')\n rdate = datetime.strptime(date_str, \"%Y-%m-%d\").date()\n report = Report(date=rdate, price=Decimal(price_str), change=Decimal(change_str), currency=currency)\n db.session.add(report)\n db.session.commit()\n browser.quit()\n driver_service.stop()\n\n\nif __name__ == '__main__':\n app = create_app()\n with app.app_context():\n get_archive(True)\n","sub_path":"currenciesapp/data/scrapper.py","file_name":"scrapper.py","file_ext":"py","file_size_in_byte":3127,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"304387127","text":"#\n# @lc app=leetcode id=88 lang=python3\n#\n# [88] Merge Sorted Array\n#\nclass Solution:\n def merge(self, nums1: List[int], m: int, nums2: List[int], n: int) -> None:\n \"\"\"\n Do not return anything, modify nums1 in-place instead.\n \"\"\"\n temp = nums1[0:m]\n temp += nums2\n temp = sorted(temp)\n for i in range(len(nums1)):\n nums1[i] = temp[i]\n\n","sub_path":"88.merge-sorted-array.py","file_name":"88.merge-sorted-array.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"616358140","text":"simulation_number = 1\n\nconfigurations_dict = \\\n{\n\"MUTATE_RULE_SET\": 1,\n\"MUTATE_HMM\": 1,\n\n\"EVOLVE_RULES\": True,\n\"EVOLVE_HMM\": False,\n\n\"COMBINE_EMISSIONS\": 1,\n\"ADVANCE_EMISSION\": 1,\n\"CLONE_STATE\": 0,\n\"CLONE_EMISSION\": 1,\n\"ADD_STATE\": 1,\n\"REMOVE_STATE\": 1,\n\"ADD_TRANSITION\": 1,\n\"REMOVE_TRANSITION\": 1,\n\"ADD_SEGMENT_TO_EMISSION\": 1,\n\"REMOVE_SEGMENT_FROM_EMISSION\": 1,\n\"CHANGE_SEGMENT_IN_EMISSION\": 1,\n\"ADD_EMISSION_TO_STATE\": 1,\n\"REMOVE_EMISSION_FROM_STATE\": 1,\n\n\"DATA_ENCODING_LENGTH_MULTIPLIER\": 25,\n\"HMM_ENCODING_LENGTH_MULTIPLIER\": 1,\n\"RULES_SET_ENCODING_LENGTH_MULTIPLIER\": 1,\n\n\"ADD_RULE\": 1,\n\"REMOVE_RULE\": 1,\n\"DEMOTE_RULE\": 1,\n\"CHANGE_RULE\": 1,\n\n\"MUTATE_TARGET\": 1,\n\"MUTATE_CHANGE\": 1,\n\"MUTATE_LEFT_CONTEXT\": 1,\n\"MUTATE_RIGHT_CONTEXT\": 1,\n\"MUTATE_OBLIGATORY\": 1,\n\n\"ADD_FEATURE_BUNDLE\": 1,\n\"REMOVE_FEATURE_BUNDLE\": 1,\n\"CHANGE_EXISTING_FEATURE_BUNDLE\": 1,\n\"CHANGE_KLEENE_VALUE\": 0,\n\n\"ADD_FEATURE\": 1,\n\"REMOVE_FEATURE\": 1,\n\"CHANGE_FEATURE_VALUE\": 1,\n\"CHANGE_KLEENE_VALUE\": 0,\n\n\"MAX_FEATURE_BUNDLE_IN_CONTEXT\": 1,\n\n\"MAX_NUM_OF_INNER_STATES\": 5,\n\"MIN_NUM_OF_INNER_STATES\": 1,\n\n\"MAX_NUMBER_OF_RULES\": 5,\n\"MIN_NUMBER_OF_RULES\": 0,\n\n\"MORPHEME_BOUNDARY_FLAG\": False,\n\"LENGTHENING_FLAG\": False,\n\"WORD_BOUNDARY_FLAG\": False,\n\"UNDERSPECIFICATION_FLAG\": False,\n\"RESTRICTIONS_ON_ALPHABET\": False,\n\n\"CHECK_STALEMATE\": True,\n\"INITIAL_TEMPERATURE\": 75,\n\"THRESHOLD\": 1,\n\"COOLING_RATE\": 0.9999995,\n\"DEBUG_LOGGING_INTERVAL\": 200,\n\"CLEAR_MODULES_CACHING_INTERVAL\": 1000,\n\"STEPS_LIMITATION\": float('inf'),\n\n\"LINEAR_DECAY\": False\n}\n\n\nsegment_table_file_name = \"plural_english_segment_table.txt\"\n\nlog_file_template = \"{}_two_rules_november_{}.txt\"\n\ndata = [u'dagdod', u'daggos', u'dagzook', u'dagsad', u'dag', u'dgodod', u'dgogos', u'dgozook', u'dgosad', u'dgo', u'dottod', u'dotkos', u'dotsook', u'dotsad', u'dot', u'tozazook', u'tozasad', u'tozdod', u'tozgos', u'toz', u'gasazook', u'gasasad', u'gastod', u'gaskos', u'gas', u'gdasazook', u'gdasasad', u'gdastod', u'gdaskos', u'gdas', u'koddod', u'kodgos', u'kodzook', u'kodsad', u'kod', u'ktadod', u'ktagos', u'ktazook', u'ktasad', u'kta', u'kattod', u'katkos', u'katsook', u'katsad', u'kat', u'skozazook', u'skozasad', u'skozdod', u'skozgos', u'skoz']\n\ntarget_hmm = {'q0': ['q1'],\n 'q1': (['q2','qf'], ['dag', 'kat', 'dot', 'kod', 'gas', 'toz', 'kta', 'dgo', 'skoz', 'gdas']),\n 'q2': (['qf'], ['zook', 'gos', 'dod', 'sad'])}\n\nepenthesis_rule = [[], [{\"low\": \"+\"}], [{\"cons\": \"+\", \"cont\": \"+\"}], [{\"cons\": \"+\", \"cont\": \"+\"}], True]\nassimilation_rule = [[{\"cons\": \"+\"}], [{\"voice\": \"-\"}], [{\"voice\": \"-\"}], [], True]\n\nrule_set = [epenthesis_rule, assimilation_rule]\n\ntarget_tuple = (target_hmm, rule_set)\n","sub_path":"source/simulations/dag_zook_two_rules.py","file_name":"dag_zook_two_rules.py","file_ext":"py","file_size_in_byte":2661,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"123239815","text":"# -*- coding: utf-8 -*-\nimport scrapy\nfrom selenium import webdriver\nfrom time import sleep\nimport pandas as pd\nfrom selenium.webdriver.chrome.options import Options\noptions = Options()\n#options.headless = True\nfrom selenium.common.exceptions import NoSuchElementException\n\nclass Alibaba_S6(scrapy.Spider):\n name = 'seleniumspiderA'\n allowed_domains = ['alibaba.com']\n start_urls = ['https://login.alibaba.com']\n \n \n\n def parse(self, response):\n chromedriver = webdriver.Chrome(executable_path = '/Users/swang/scrapy_alibaba/chromedriver')\n login_url = 'https://login.alibaba.com'\n chromedriver.get(login_url)\n \n email = chromedriver.find_element_by_id('fm-login-id')\n email.send_keys('butterbuttscoffee@yahoo.com')\n\n password = chromedriver.find_element_by_id('fm-login-password')\n password.send_keys('jigglypuff1')\n sleep(1)\n \n sign_in = chromedriver.find_element_by_id('fm-login-submit')\n sign_in.click()\n\n sleep(2)\n \n data = {}\n\n df = pd.read_csv('/Users/swang/scrapy_alibaba/Scrapy Data/supplier_names_https.csv')\n url_list = df.contacts_url.tolist()\n start_urls = url_list\n for url in start_urls[0:25000]:\n chromedriver.get(url)\n \n \n try:\n view_details = chromedriver.find_element_by_xpath('//div[@class=\"sens-mask\"]/a')\n except NoSuchElementException:\n data['Url'] = url\n data['Telephone:'] = \"No Contacts\"\n else:\n view_details.click()\n sleep(.300)\n \n \n try:\n tele = chromedriver.find_element_by_xpath('//table[@class=\"info-table\"]//tr/td')\n except NoSuchElementException:\n data['Url'] = url\n data['Telephone:'] = \"No Telephone\"\n else:\n data['Url'] = url\n phone = tele.text\n data['Telephone:'] = phone\n\n yield data\n\n \n\n \n","sub_path":"scrapy_alibaba/spiders/.ipynb_checkpoints/s6-Login-Copy1-checkpoint.py","file_name":"s6-Login-Copy1-checkpoint.py","file_ext":"py","file_size_in_byte":2122,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"376913960","text":"import time\nimport types\nimport collections\nfrom dateutil.parser import parse \nclass Val(object):\n\n def is_num(self,numbers):\n try:\n if type(numbers) == int:\n print(\"its a number of integer\",numbers,type(numbers))\n return True\n elif type(numbers) == float:\n print(\"its float\",numbers)\n return True\n elif type(numbers) == complex :\n print(\"its complex\",numbers)\n return True\n else:\n print(numbers,\"not a num\")\n return False\n except:\n raise\n\n def is_string(self,strings):\n try:\n if type(strings) == str:\n if len(strings) > 0:\n print('its a string',strings,bool(str(strings)),type(strings))\n return True\n elif len(strings) == 0 or strings == \" \":\n print(\"empty\")\n return False\n else:\n print(\"not a string\")\n return False\n\n except:\n raise\n \n def is_list(self,listing):\n if type(listing) is list:\n if len(listing) == 0:\n print(\"list empty\")\n return False\n else:\n print(' its a list',listing,bool(str(listing)))\n return True\n\n def is_tuple(self,tple):\n if type(tple) is tuple:\n if len(tple) > 0:\n print(' its a tuple',tple, bool(str(tple)) ,type(tple))\n return True\n else:\n print(\"empty\",self)\n return False\n \n def is_all(self,listin):\n if type(listin) is list:\n if len(listin) == 0:\n print(\"list empty\",listin,type(listin))\n else:\n print(\" it's a list\",listin,bool(type(listin)),type(listin))\n return True\n elif type(listin) is tuple:\n if len(listin) == 0:\n print(\"tuple is empty\",listin,type(listin))\n else:\n print(\"it's a tuple\",listin,bool(type(listin)))\n return True\n elif type(listin) is dict:\n if len(listin) == 0:\n print(\"dict is empty\",listin)\n else:\n print(\"it's a dict\",listin,bool(type(listin)))\n return True\n else:\n return False\n def datetime(self,dtime):\n try:\n parse(dtime)\n print('its a date',dtime,self)\n return True\n except ValueError:\n print('valueerror',self)\n return False\n \n def funtest(self):\n try:\n from inspect import isfunction\n print('*****',isfunction(self),self)\n if isfunction(self) == False:\n print('its a function',type(self),self)\n else:\n print('its not a function')\n \n #print(bool(str(isinstance(self,types.FunctionType))),'its function')\n except NameError:\n print('its not correct fun name')\n raise\n\n\n def ctest(self):\n try:\n import inspect\n if inspect.isclass(Val):\n print('its a class ',bool(str(Val)),Val,self)\n return True\n else:\n print('not a class',self)\n return False\n except NameError:\n print(\"please give valid class name\")\n","sub_path":"python/validate.py","file_name":"validate.py","file_ext":"py","file_size_in_byte":3509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"25493845","text":"from django.shortcuts import render\nfrom notification.models import Notification\nfrom twitterUser.models import TwitterUser\nfrom django.utils import timezone\nfrom django.contrib.auth.decorators import login_required\n# Create your views here.\n\n@login_required(login_url=\"/welcome/\")\ndef read_notification(request,username):\n all_notifications = Notification.objects.filter(reciver__username=request.user.username)\n tweetlist = []\n pageuser = TwitterUser.objects.filter(username=username).first()\n tweetlist = sorted(tweetlist, key=lambda l: (l.time), reverse=True)\n for notification in all_notifications:\n if not notification.viewed:\n tweetlist.append(notification.tweet)\n notification.viewed = timezone.now()\n notification.save()\n count = len(tweetlist)\n return render(request, \"defaulthome.html\",{\"pageuser\":pageuser,\"tweets\":tweetlist,'count':count})","sub_path":"notification/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"622193904","text":"# coding:utf8\n\n__author__ = u'奶茶哥'\n\nif __name__ == '__main__':\n\n '''for循环'''\n # 遍历元组\n tuple1 = (1,2,3,4)\n for i in tuple1:\n print(i)\n\n # 遍历列表\n list1 = [5,6,7,8]\n for j in list1:\n print(j)\n\n # 遍历字典\n dict1 = {'name':'小花','age':99}\n for key,value in dict1.items():\n print('%s:%s' %(key,value))\n\n # range函数\n for d in range(2,10,3):\n print(d)\n\n '''嵌套'''\n for i in range(1,10):\n for j in range(i,10):\n print('%d * %d = %2d ' %(i,j,j*i), end=' ')\n print('')\n\n '''while'''\n # 计算0-100的所有偶数和\n n = 100\n index = 0\n sum = 0\n while index <= n:\n sum = sum + index\n index = index + 2\n print('偶数和=%d' %sum)\n\n # 九九乘法表\n n = 1\n while n <= 9:\n for m in range(n,10):\n print('%d * %d = %2d ' %(n,m,n*m), end='')\n print('')\n n = n + 1\n\n # break和continue\n for v in range(10):\n print(v)\n if v == 2:\n print('22222222')\n continue\n if v == 5:\n print('55555555555')\n break\n print('#####################')\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"第二期/深圳--奶茶哥/快学Python3/task_xuhuan.py","file_name":"task_xuhuan.py","file_ext":"py","file_size_in_byte":1222,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"483787016","text":"import wsynphot\nimport os\nfrom astropy.io import fits\nfrom astropy import units as u\nfrom wsynphot.spectrum1d import SKSpectrum1D as Spectrum1D\nfrom wsynphot.data.base import ALPHA_LYR_FNAME, ALPHA_LYR_PATH\n\ndefault_vega_path = ALPHA_LYR_PATH\n\ndef get_calibration_dir(fname):\n return os.path.join(wsynphot.__path__[0], 'data', 'calibration', fname)\n\ndef get_vega(vega_file=None):\n if vega_file is None:\n vega_file = default_vega_path\n if not os.path.exists(vega_file):\n raise IOError('Calibration file {0} does not exist - please download by'\n 'using wsynphot.download_calibration() or '\n 'wsynphot.download_all()'.format(vega_file))\n vega_table = fits.getdata(get_calibration_dir(vega_file), extension=1)\n vega = Spectrum1D.from_array(vega_table['wavelength'] * u.angstrom,\n vega_table['flux'] *\n u.erg / u.s/ u.cm**2 / u.angstrom)\n\n return vega","sub_path":"wsynphot/calibration.py","file_name":"calibration.py","file_ext":"py","file_size_in_byte":984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"191743267","text":"import sys, os\nimport numpy as np\n\nsys.path.append(os.pardir)\nfrom gradient import numerical_gradient\n\n\ndef function_2(x):\n return x[0] ** 2 + x[1] ** 2\n\n\ndef gradient_descent(f, init_x, lr=0.01, step_num=100):\n x = init_x\n\n for i in range(step_num):\n grad = numerical_gradient(f, x)\n x -= lr * grad\n return x\n\n\ninit_x = np.array([-3.0, 4.0])\nr = gradient_descent(function_2, init_x, lr=0.1, step_num=100)\nprint(r)\n","sub_path":"ch04/ch04_1.py","file_name":"ch04_1.py","file_ext":"py","file_size_in_byte":441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"41178669","text":"from __future__ import print_function\nimport urllib\nimport urllib2\nimport json\nimport logging\nimport threading\nimport uuid\nlogger=logging.getLogger()\nlogger.setLevel(logging.DEBUG)\n\"\"\" --- Lex part handler --- \"\"\"\n\n\ndef dispatch(intent_request):\n \n raise Exception('Intent with name ' + intent_name + ' not supported')\n\n\n#Main handler\n\ndef lambda_handler(event, context):\n \"\"\" Route the incoming request based on type (LaunchRequest, IntentRequest,\n etc.) The JSON body of the request is provided in the event parameter.\n \"\"\"\n try:\n \n\n if event['request']['type'] == \"LaunchRequest\":\n return on_launch(event['request'], event['session'])\n elif event['request']['type'] == \"IntentRequest\":\n return on_intent(event['request'], event['session'])\n elif event['request']['type'] == \"SessionEndedRequest\":\n return on_session_ended(event['request'], event['session'])\n except:\n \n #logger.debug('event.bot.name={}'.format(event['bot']['name']))\n return dispatch(event)\n \n\ndef on_session_started(session_started_request, session):\n \"\"\" Called when the session starts \"\"\"\n\n print(\"on_session_started requestId=\" + session_started_request['requestId']\n + \", sessionId=\" + session['sessionId'])\n\n\ndef on_launch(launch_request, session):\n \"\"\" Called when the user launches the skill without specifying what they\n want\n \"\"\"\n\n print(\"on_launch requestId=\" + launch_request['requestId'] +\n \", sessionId=\" + session['sessionId'])\n # Dispatch to your skill's launch\n return get_welcome_response()\n\n\ndef on_intent(intent_request, session):\n \"\"\" Called when the user specifies an intent for this skill \"\"\"\n\n print(\"on_intent requestId=\" + intent_request['requestId'] +\n \", sessionId=\" + session['sessionId'])\n\n intent = intent_request['intent']\n intent_name = intent_request['intent']['name']\n\n # Dispatch to your skill's intent handlers\n\n#***************************************************\n#first change is here(add the intent name)\n#****************************************************\n\n if intent_name == \"distance\":\n return run_distance(intent, session)\n elif intent_name == \"presence\":\n return run_presence(intent, session)\n elif intent_name == \"gas\":\n return run_gas(intent, session)\n elif intent_name == \"light\":\n return run_light(intent, session)\n elif intent_name == \"ledon\":\n return run_ledon(intent, session)\n elif intent_name == \"ledoff\":\n return run_ledoff(intent, session)\n elif intent_name == \"AMAZON.HelpIntent\":\n return get_welcome_response()\n elif intent_name == \"AMAZON.CancelIntent\" or intent_name == \"AMAZON.StopIntent\":\n return handle_session_end_request()\n else:\n raise ValueError(\"Invalid intent\")\n\n#***************************************************\n#first change is here(add the intent name)\n#****************************************************\n\ndef on_session_ended(session_ended_request, session):\n \"\"\" Called when the user ends the session.\n\n Is not called when the skill returns should_end_session=true\n \"\"\"\n print(\"on_session_ended requestId=\" + session_ended_request['requestId'] +\n \", sessionId=\" + session['sessionId'])\n\ndef handle_session_end_request():\n card_title = \"Session Ended\"\n speech_output = \"it was nice interacting with you .thank you . \"\n # Setting this to true ends the session and exits the skill.\n should_end_session = True\n return build_response({}, build_speechlet_response(\n card_title, speech_output, None, should_end_session))\n\n# Run the Speed Test\n\n#****************************************************\n#Second change is here(add the intent function)\n#****************************************************\n\n\n\n \ndef run_distance(intent, session):\n session_attributes = {}\n reprompt_text = None\n should_end_session = False\n #code to update gui\n #print \"I am here\"\n url = 'https://api.particle.io/v1/devices/190036001547343339383037/ultrasonicvalue/?access_token=f0ba1e9986df1e186b3869138e10b6fe24c89d7f'\n req = urllib2.Request(url)\n response = urllib2.urlopen(req)\n resp=json.loads(response.read())\n fin = str(resp['result'])\n #print fin\n \n reply=\"the distance of an object is \" + fin + \"centimeter\" \n speech_output = reply\n \n return build_response(session_attributes, build_speechlet_response(\n intent['name'], speech_output, reprompt_text, should_end_session))\n\ndef run_presence(intent, session):\n session_attributes = {}\n reprompt_text = None\n should_end_session = False\n #code to update gui\n #print \"I am here\"\n url = 'https://api.particle.io/v1/devices/190036001547343339383037/presence_mess/?access_token=f0ba1e9986df1e186b3869138e10b6fe24c89d7f'\n req = urllib2.Request(url)\n response = urllib2.urlopen(req)\n resp=json.loads(response.read())\n fin = str(resp['result'])\n #print fin\n \n reply= fin \n speech_output = reply\n \n return build_response(session_attributes, build_speechlet_response(\n intent['name'], speech_output, reprompt_text, should_end_session))\n \ndef run_gas(intent, session):\n session_attributes = {}\n reprompt_text = None\n should_end_session = False\n #code to update gui\n #print \"I am here\"\n url = 'https://api.particle.io/v1/devices/190036001547343339383037/gas_mess/?access_token=f0ba1e9986df1e186b3869138e10b6fe24c89d7f'\n req = urllib2.Request(url)\n response = urllib2.urlopen(req)\n resp=json.loads(response.read())\n fin = str(resp['result'])\n #print fin\n \n reply= fin \n speech_output = reply\n \n return build_response(session_attributes, build_speechlet_response(\n intent['name'], speech_output, reprompt_text, should_end_session))\n \ndef run_light(intent, session):\n session_attributes = {}\n reprompt_text = None\n should_end_session = False\n\n url = 'https://api.particle.io/v1/devices/190036001547343339383037/ldr_mess/?access_token=f0ba1e9986df1e186b3869138e10b6fe24c89d7f'\n req = urllib2.Request(url)\n response = urllib2.urlopen(req)\n resp=json.loads(response.read())\n fin = str(resp['result'])\n #print fin\n \n reply= fin \n speech_output = reply\n \n return build_response(session_attributes, build_speechlet_response(\n intent['name'], speech_output, reprompt_text, should_end_session))\n \ndef run_ledon(intent, session):\n session_attributes = {}\n reprompt_text = None\n should_end_session = False\n reply=\"i have turned on the light .\"\n speech_output = reply\n url = 'https://api.particle.io/v1/devices/190036001547343339383037/led?access_token=f0ba1e9986df1e186b3869138e10b6fe24c89d7f'\n data = urllib.urlencode({'args':'on'})\n req = urllib2.Request(url, data)\n req.add_header('content-type', 'application/x-www-form-urlencoded')\n response = urllib2.urlopen(req)\n resp=json.loads(response.read())\n\n return build_response(session_attributes, build_speechlet_response(\n intent['name'], speech_output, reprompt_text, should_end_session))\n \n \n \n \ndef run_ledoff(intent, session):\n session_attributes = {}\n reprompt_text = None\n should_end_session = False\n reply=\"i have turned off the light .\"\n speech_output = reply\n url = 'https://api.particle.io/v1/devices/190036001547343339383037/led?access_token=f0ba1e9986df1e186b3869138e10b6fe24c89d7f'\n data = urllib.urlencode({'args':'off'})\n req = urllib2.Request(url, data)\n req.add_header('content-type', 'application/x-www-form-urlencoded')\n response = urllib2.urlopen(req)\n resp=json.loads(response.read())\n\n return build_response(session_attributes, build_speechlet_response(\n intent['name'], speech_output, reprompt_text, should_end_session)) \n \n\n\n\n\n\n \n#****************************************************\n#Second change is here(add the intent function)\n#****************************************************\n\n# --------------- Functions that control the skill's behavior ------------------\n\ndef get_welcome_response():\n\n session_attributes = {}\n card_title = \"Welcome\"\n speech_output = \"this is your smart home skill . you can ask me various parameters of your home or to control the lights .\"\n\n \n reprompt_text = \"sorry i did't got that .\"\n should_end_session = False\n return build_response(session_attributes, build_speechlet_response(\n card_title, speech_output, reprompt_text, should_end_session))\n \n \n \n# --------------- Helpers that build all of the responses ----------------------\n\ndef build_speechlet_response(title, output, reprompt_text, should_end_session):\n return {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': output\n },\n 'card': {\n 'type': 'Simple',\n 'title': 'SessionSpeechlet - ' + title,\n 'content': 'SessionSpeechlet - ' + output\n },\n 'reprompt': {\n 'outputSpeech': {\n 'type': 'PlainText',\n 'text': reprompt_text\n }\n },\n 'shouldEndSession': should_end_session\n }\n\n\ndef build_response(session_attributes, speechlet_response):\n return {\n 'version': '1.0',\n 'sessionAttributes': session_attributes,\n 'response': speechlet_response\n }\n\n","sub_path":"lambda.py","file_name":"lambda.py","file_ext":"py","file_size_in_byte":9542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"6480682","text":"import sqlalchemy\nfrom sqlalchemy import *\nfrom sqlalchemy.sql import select\nfrom sqlalchemy.sql import and_, or_\nimport os, pdb, pickle\n\nimport datetime\nimport sys\n\nsys.path.append(\"/Users/abstrackr-user/Hive/abstrackr/abstrackr/lib/curious_snake\")\n# for libsvm\nsys.path.append(\"/Users/abstrackr-user/Hive/abstrackr/abstrackr/lib/curious_snake/learners/libsvm/python\")\n\nimport curious_snake # magic!\n\nengine = create_engine(\"mysql://abstrackr-user:5xl.z=Uy6d@127.0.0.1:3306/abstrackrDBP01?unix_socket=/var/mysql/mysql.sock\")\n\nconn = engine.connect()\nmetadata = MetaData(bind=engine)\n\nencoded_status = Table(\"EncodedStatuses\", metadata, autoload=True)\nprediction_status = Table(\"PredictionStatuses\", metadata, autoload=True)\npredictions_table = Table(\"Predictions\", metadata, autoload=True)\n\n#base_dir=\"/home/byron/abstrackr-web/curious_snake/data\"\n#base_dir=\"C:/dev/abstrackr_web/encode_test\"\nbase_dir = \"/Users/abstrackr-user/Hive/abstrackr/abstrackr/lib/curious_snake/data\"\nfields=[\"title\", \"abstract\", \"keywords\"]\n\n\ndef make_predictions(review_id):\n # we're assuming the review is encoded!\n review_base_dir = os.path.join(base_dir, str(review_id))\n data_paths = []\n for field in fields:\n data_paths.append(os.path.join(review_base_dir, field, \"encoded\", \"%s_encoded\" % field))\n \n pred_results = None\n #try:\n pred_results = curious_snake.abstrackr_predict(data_paths)\n #except:\n # print \"!!!! failed to predict results for \" + review_base_dir\n\n if pred_results is None:\n return None # fail\n\n # otherwise unpack the results\n predictions, train_size, num_pos = pred_results \n # 5/4/12 -- predictions now include probability estimates!\n # just need to add these to the database.\n\n\n ####\n # update the database\n ####\n\n # first, delete all prediction entries associated with this\n # review (these are presumably 'stale' now)\n conn.execute(predictions_table.delete().where(predictions_table.c.project_id == review_id))\n\n # map -1's to 0's; this is because the ORM (sql-alchemy)\n # expects boolean fields to be either 0 or 1, which is apparently\n # a new thing in later releases (oh, well).\n neg_one_to_0 = lambda x : 0 if x < 0 else x\n\n # now re-insert them, reflecting the new prediction\n for study_id, pred_d in predictions.items():\n conn.execute(predictions_table.insert().values(study_id=study_id, project_id=review_id, \\\n prediction=neg_one_to_0(pred_d[\"prediction\"]), num_yes_votes=pred_d[\"num_yes_votes\"]),\n\n predicted_probability=pred_d[\"pred_prob\"])\n \n # delete any existing prediction status entries, should they exist\n conn.execute(prediction_status.delete().where(prediction_status.c.project_id == review_id))\n\n # finally, update the prediction status\n conn.execute(prediction_status.insert().values(project_id=review_id, predictions_exist=True,\\\n predictions_last_made=datetime.datetime.now(), train_set_size=train_size,\\\n num_pos_train=num_pos))\n\n\n\n","sub_path":"abstrackr/lib/make_predictions.py","file_name":"make_predictions.py","file_ext":"py","file_size_in_byte":3034,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"523553483","text":"\n\ndef repalce_input(lst, x, y):\n lst = list(map(int, lst.split(',')))\n lst[1] = x\n lst[2] = y\n return lst\n\n\ndef simulate(lst):\n i = 0\n while i < len(lst):\n if lst[i] == 99:\n break\n elif lst[i] == 1:\n lst[lst[i+3]] = lst[lst[i+1]] + lst[lst[i+2]]\n elif lst[i] == 2:\n lst[lst[i+3]] = lst[lst[i+1]] * lst[lst[i+2]]\n i += 4\n return lst\n\n\ndef naloga1(raw_input):\n lst = repalce_input(raw_input, 12, 2)\n lst = simulate(lst)\n return str(lst[0])\n\n\ndef naloga2(raw_input):\n target = 19690720\n for j in range(0, 100):\n for k in range(0, 100):\n lst = repalce_input(raw_input, j, k)\n if simulate(lst)[0] == target:\n return str(100 * j + k)\n\n\nif __name__ == '__main__':\n with open('day_02.in', encoding='utf-8') as f:\n vsebina_datoteke = f.read()\n odgovor1 = naloga1(vsebina_datoteke)\n with open('day_02_1.out', 'w', encoding='utf-8') as f:\n f.write(odgovor1)\n odgovor2 = naloga2(vsebina_datoteke)\n with open('day_02_2.out', 'w', encoding='utf-8') as f:\n f.write(odgovor2)\n","sub_path":"day_02.py","file_name":"day_02.py","file_ext":"py","file_size_in_byte":1137,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"345714651","text":"def fill_lists_evenly(input_list, col):\n '''\n @input_list : list of int\n @col : int\n\n return : list of lists\n\n функция равномерно распределяет произвольное количество неоднородных данных\n в массиве по подмассивам количеством col (comlumns)\n '''\n col_list = [[] for t in range(col)]\n # заполняем первый ряд\n for k in col_list:\n if input_list:\n val = input_list.pop(0)\n k.append(val)\n\n # пока входной массив не пустой,\n # добавляем из него элементы в последнюю колонку\n while input_list:\n val = input_list.pop(0)\n last_list = col_list[col-1]\n sum_value_to_add = sum(last_list) + len(last_list) + val\n max_weight = max([sum(j) for j in col_list])\n\n # если сумма добавляемого значения и значений последнего массива больше\n # максимального значения суммы самого \"тяжелого\" подмассива, вызываем\n # squeeze_content_in_n_minus_one_lists\n if sum_value_to_add > max_weight:\n col_list = squeeze_content_in_n_minus_one_lists(col_list, val)\n col_list[col-1].append(val)\n\n return col_list\n\n\ndef squeeze_content_in_n_minus_one_lists(col_list, val):\n '''\n @col_list : list of lists\n @val : int\n\n return : list of lists\n\n пока сумма последнего подмассива и val больше суммы самого тяжелого\n подмассива перемещаем первый элемент последнего подмассива в предыдущий и\n распределяем контент с учетом нового максимольного веса самого тяжелого\n подмассива\n '''\n l = len(col_list)\n max_weight = 0\n max_weight = max([sum(j) for j in col_list])\n while sum(col_list[l-1]) + val > max_weight and col_list[l-1]:\n min_sum = sum(col_list[0]) + len(col_list[0]) + col_list[1][0]\n min_idx = 0\n # ищем самую минимальную сумму подмассива и первого элемента следующего\n # за ним подмассива\n for i in range(l-1):\n current_sum = sum(col_list[i]) + len(col_list[i]) + col_list[i+1][0]\n if current_sum < min_sum:\n min_sum = current_sum\n min_idx = i\n # перемещаем первый элемент в предыдущий подмассив\n col_list[min_idx].append(col_list[min_idx+1].pop(0))\n # если следующий подмассив получился пустым, заполняем его из следующего\n # за ним подмассива первым элементом\n if not col_list[min_idx+1]:\n col_list = fill_empty_lists(col_list, min_idx+1)\n\n return col_list\n\n\ndef fill_empty_lists(col_list, idx):\n '''\n @col_list : list of lists\n @idx: int\n\n return : list of lists\n\n функция заполняет пустые массивы первыми значениями из последующих\n подмассивов\n пустым может быть только последний подмассив\n '''\n if col_list[idx] or idx == len(col_list)-1:\n return col_list\n col_list[idx].append(col_list[idx+1].pop(0))\n return fill_empty_lists(col_list, idx+1)\n\n\nif __name__ == '__main__':\n from draw_eq import draw_eq\n i = 2\n\n def draw_columns(s_list, i):\n print('-'*100)\n print('-- ', i, ' comlumns --')\n print(' ')\n print(s_list)\n print(' ')\n print('-'*50)\n print(' ')\n draw_eq(s_list)\n print(' ')\n print('-'*50)\n print('-- ', 'end', ' --')\n print('-'*100)\n print(' ')\n print(' ')\n print(' ')\n\n while i < 10:\n i += 1\n first = [1, 2, 1, 2, 1, 4, 1, 2, 1, 1, 7, 1, 2, 7, 4, 1, 5]\n second = [2, 11, 4, 1, 5, 2, 2, 10, 3, 3, 3, 2, 5, 7, 3, 1, 4, 1, 2, 3]\n third = [1, 1, 1, 1, 4, 1, 2, 2, 1]\n fourth = [2, 1, 2, 2, 2, 1, 1, 3, 1]\n fifth = [1, 7, 1, 2, 1, 6, 2, 1, 3]\n draw_columns(fill_lists_evenly(first, i), i)\n draw_columns(fill_lists_evenly(second, i), i)\n draw_columns(fill_lists_evenly(third, i), i)\n draw_columns(fill_lists_evenly(fourth, i), i)\n draw_columns(fill_lists_evenly(fifth, i), i)\n","sub_path":"evenly_fill_lists.py","file_name":"evenly_fill_lists.py","file_ext":"py","file_size_in_byte":4746,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"579735004","text":"from django.urls import path\nfrom . import views\n\nurlpatterns = [\n path('', views.index_view, name = 'index'),\n path('all_products/', views.all_products, name = 'all_products'),\n path('about_us/', views.about_us, name = 'about_us'),\n path('contact_us/', views.contact_us, name = 'contact_us'),\n path('details/<int:prod_id>/', views.details_view, name = 'details'),\n path('signin/', views.SigninView.as_view(), name = 'signin'),\n path('signup/', views.signup_view, name = 'signup'),\n path('signout/', views.SignoutView.as_view(), name = 'signout'),\n path('forgot/', views.forgot_view, name = 'forgot'),\n path('reset_password/<int:otp>/', views.reset_password_view, name = 'reset_password'),\n path('subscrible/', views.subscribe_newsletter, name = 'subscribe'),\n path('unsubscribe/<str:uid>/', views.unsubscribe_newsletter, name = 'unsubscribe'),\n path('ask_question/<int:prod_id>/', views.ask_question_view, name = 'ask_question'),\n path('cart/', views.cart_view, name = 'cart'),\n path('select_address/<int:prod_id>/<int:buy_type>/', views.select_address, name = 'select_address'),\n path('site_map/', views.site_map, name = 'site_map'),\n path('distributors/', views.distributors, name = 'distributors'),\n path('myaccount/', views.profile, name = 'my_account'),\n path('checkout/', views.checkout, name = 'chcekout'),\n path('buy_now/<int:pid>/', views.buy_now, name = 'buy_now'),\n path('my_orders/', views.my_orders, name='my_orders'),\n path('search_product/<str:prod_name>', views.my_orders, name = 'search_product'),\n]","sub_path":"home/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"442487014","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Oct 3 10:07:10 2017\n\n@author: Charles\n\"\"\"\n\nimport numpy as np\n\n#==============================================================================\n# To extract important information from the model (MDL)\n# Used at the end of inversion routine\n# Arguments: model <pymc model object>, maximum amplitude measured <float>\ndef format_results(M, Z_max):\n var_keys = [s.__name__ for s in M.stochastics] + [d.__name__ for d in M.deterministics]\n var_keys = [s for s in var_keys if s not in [\"zmod\", \"mp\", \"cond\", ]]\n Mst = M.stats(chain=-1)\n pm = {k: Mst[k][\"mean\"] for k in var_keys}\n pm.update({k+\"_std\": Mst[k][\"standard deviation\"] for k in var_keys})\n pm.update({\"R0\": Z_max*pm[\"R0\"],\"R0_std\": Z_max*pm[\"R0_std\"]}) # remove normalization\n pm.update({k.replace(\"log_\", \"\"): 10**pm[k] for k in var_keys if k.startswith(\"log_\")})\n pm.update({(k.replace(\"log_\", \"\"))+\"_std\": abs(pm[k+\"_std\"]/pm[k])*(10**pm[k]) for k in var_keys if k.startswith(\"log_\")})\n pm = {k: v for (k, v) in list(pm.items()) if \"log_\" not in k}\n return pm # returns parameters and uncertainty\n \n#==============================================================================\n# To import data\n# Arguments: file name, number of header lines to skip, phase units\ndef get_data(filename,headers,ph_units):\n # Importation des données .DAT\n dat_file = np.loadtxt(\"%s\"%(filename),skiprows=headers,delimiter=',')\n labels = [\"freq\", \"amp\", \"pha\", \"amp_err\", \"pha_err\"]\n data = {l:dat_file[:,i] for (i,l) in enumerate(labels)}\n if ph_units == \"mrad\":\n data[\"pha\"] = data[\"pha\"]/1000 # mrad to rad\n data[\"pha_err\"] = data[\"pha_err\"]/1000 # mrad to rad\n if ph_units == \"deg\":\n data[\"pha\"] = np.radians(data[\"pha\"]) # deg to rad\n data[\"pha_err\"] = np.radians(data[\"pha_err\"]) # deg to rad\n data[\"phase_range\"] = abs(max(data[\"pha\"])-min(data[\"pha\"])) # Range of phase measurements (used in NRMS error calculation)\n data[\"Z\"] = data[\"amp\"]*(np.cos(data[\"pha\"]) + 1j*np.sin(data[\"pha\"]))\n EI = np.sqrt(((data[\"amp\"]*np.cos(data[\"pha\"])*data[\"pha_err\"])**2)+(np.sin(data[\"pha\"])*data[\"amp_err\"])**2)\n ER = np.sqrt(((data[\"amp\"]*np.sin(data[\"pha\"])*data[\"pha_err\"])**2)+(np.cos(data[\"pha\"])*data[\"amp_err\"])**2)\n data[\"Z_err\"] = ER + 1j*EI\n # Normalization of amplitude\n data[\"Z_max\"] = max(abs(data[\"Z\"])) # Maximum amplitude\n zn, zn_e = data[\"Z\"]/data[\"Z_max\"], data[\"Z_err\"]/data[\"Z_max\"] # Normalization of impedance by max amplitude\n data[\"zn\"] = np.array([zn.real, zn.imag]) # 2D array with first column = real values, second column = imag values\n data[\"zn_err\"] = np.array([zn_e.real, zn_e.imag]) # 2D array with first column = real values, second column = imag values\n return data","sub_path":"bisip/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2875,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"350766704","text":"from main import quicksort\nfrom main import matrix, row\n\nimport time\nstart_time = time.time()\n\ndef bubble_sort(lst):\n for num in range(len(lst) - 1, 0, -1):\n for item in range(num):\n if lst[item] > lst[item + 1]:\n lst[item], lst[item + 1] = lst[item + 1], lst[item]\n\n return lst\n\n\nfor i in range(row):\n print(bubble_sort(matrix[i]))\n# print()\n# for i in range(row):\n# print(quicksort(matrix[i]))\n# print()\n# for i in range(row):\n# print(sorted(matrix[i]))\n\nprint(\"--- %s seconds ---\" % (time.time() - start_time))","sub_path":"laba1/bubble_test.py","file_name":"bubble_test.py","file_ext":"py","file_size_in_byte":563,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"332718381","text":"\"\"\"\nWhen a candle finishes burning it leaves a leftover. makeNew leftovers can be combined to make a new candle, which, \nwhen burning down, will in turn leave another leftover.\n\nYou have candlesNumber candles in your possession. What's the total number of candles you can burn, assuming that you\ncreate new candles as soon as you have enough leftovers?\n\nExample\n\nFor candlesNumber = 5 and makeNew = 2, the output should be\ncandles(candlesNumber, makeNew) = 9.\n\nHere is what you can do to burn 9 candles:\n\n burn 5 candles, obtain 5 leftovers;\n create 2 more candles, using 4 leftovers (1 leftover remains);\n burn 2 candles, end up with 3 leftovers;\n create another candle using 2 leftovers (1 leftover remains);\n burn the created candle, which gives another leftover (2 leftovers in total);\n create a candle from the remaining leftovers;\n burn the last candle.\n\nThus, you can burn 5 + 2 + 1 + 1 = 9 candles, which is the answer.\n\"\"\"\n\ndef candles(candlesNumber, makeNew):\n leftovers = candlesNumber\n totalCandles = leftovers \n \n if candlesNumber < makeNew or candlesNumber == 0:\n return candlesNumber\n if leftovers == 0 or leftovers < makeNew:\n return totalCandles\n \n while True:\n currentCandles = leftovers // makeNew\n leftovers = (leftovers-(currentCandles*makeNew)) + currentCandles \n totalCandles += currentCandles\n if leftovers < makeNew:\n break\n return totalCandles\n \n \n \n \n","sub_path":"LoopTunnel/Candles.py","file_name":"Candles.py","file_ext":"py","file_size_in_byte":1491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"222395412","text":"\"\"\"resume URL Configuration\r\n\r\nThe `urlpatterns` list routes URLs to views. For more information please see:\r\n https://docs.djangoproject.com/en/2.2/topics/http/urls/\r\nExamples:\r\nFunction views\r\n 1. Add an import: from my_app import views\r\n 2. Add a URL to urlpatterns: path('', views.home, name='home')\r\nClass-based views\r\n 1. Add an import: from other_app.views import Home\r\n 2. Add a URL to urlpatterns: path('', Home.as_view(), name='home')\r\nIncluding another URLconf\r\n 1. Import the include() function: from django.urls import include, path\r\n 2. Add a URL to urlpatterns: path('blog/', include('blog.urls'))\r\n\"\"\"\r\nfrom django.views.generic import TemplateView\r\n\r\nimport xadmin\r\nfrom django.contrib import admin\r\nfrom django.urls import path\r\nfrom django.conf.urls import url, include\r\nfrom django.views.static import serve\r\nfrom rest_framework.documentation import include_docs_urls\r\nfrom rest_framework.routers import DefaultRouter\r\nfrom rest_framework_jwt.views import obtain_jwt_token\r\n\r\nfrom myresume.views import UserResumeViewSet, SkillCategoryViewSet, ExpectationViewSet, EducationViewSet, \\\r\n WorkExperienceViewSet, ProjectExperienceViewSet, SkillsViewSet, SelfAppraiseViewSet, GenerateSecret, \\\r\n UserResumeShowView, UserResumeAnthorView\r\nfrom resume.settings import MEDIA_ROOT\r\nfrom user.views import PermissionViewSet, UserViewSet, SmsCodeViewSet, EmailCodeViewSet, ResetPasswordViewSet, \\\r\n CheckEmailCodeView\r\n\r\nrouter = DefaultRouter()\r\n\r\n# 用戶简历\r\nrouter.register(r'userResume', UserResumeViewSet, base_name=\"userResume\")\r\n\r\n# 技术分类\r\nrouter.register(r'skillCategory', SkillCategoryViewSet, base_name=\"skillCategory\")\r\n\r\n# 期望薪资\r\nrouter.register(r'expectation', ExpectationViewSet, base_name=\"expectation\")\r\n\r\n# 教育背景\r\nrouter.register(r'education', EducationViewSet, base_name=\"education\")\r\n\r\n# 工作经验\r\nrouter.register(r'workExperience', WorkExperienceViewSet, base_name=\"workExperience\")\r\n\r\n# 项目经验\r\nrouter.register(r'projectExperience', ProjectExperienceViewSet, base_name=\"projectExperience\")\r\n\r\n# 技能特长\r\nrouter.register(r'skills', SkillsViewSet, base_name=\"skills\")\r\n\r\n# 自我评价\r\nrouter.register(r'selfAppraise', SelfAppraiseViewSet, base_name=\"selfAppraise\")\r\n\r\n# 用户权限\r\nrouter.register(r'permission', PermissionViewSet, base_name=\"permission\")\r\n\r\n# 用户注册，获取个人信息\r\nrouter.register(r'user', UserViewSet, base_name=\"user\")\r\n\r\n# 短信验证码\r\nrouter.register(r'sms', SmsCodeViewSet, base_name=\"sms\")\r\n\r\n# 邮箱验证码\r\nrouter.register(r'email', EmailCodeViewSet, base_name=\"email\")\r\n\r\n# 邮箱验证码\r\nrouter.register(r'resetPas', ResetPasswordViewSet, base_name=\"resetPas\")\r\n\r\n\r\nurlpatterns = [\r\n path('admin/', admin.site.urls),\r\n url(r'^media/(?P<path>.*)$', serve, {\"document_root\": MEDIA_ROOT}),\r\n\r\n url(r'checkPas/$', CheckEmailCodeView.as_view(), name=\"checkPas\"),\r\n url(r'secret/$', GenerateSecret.as_view(), name=\"secret\"),\r\n url(r'showResume/$', UserResumeShowView.as_view(), name=\"showResume\"),\r\n url(r'another/$', UserResumeAnthorView.as_view(), name=\"another\"),\r\n\r\n # url(r'userResume/',UserResume\r\n # ViewSet.as_view()),\r\n # 引入restframework自动生成的文档\r\n url(r'docs/', include_docs_urls(title=\"我的简历\")),\r\n url(r'^xadmin/', xadmin.site.urls),\r\n\r\n url('^', include(router.urls)),\r\n\r\n # jwt的认证接口\r\n url(r'^login/$', obtain_jwt_token),\r\n\r\n # 这个url是为了可以弹出rest_framework提供的登录页面，方便测试的时候操作\r\n url(r'^api-auth/', include('rest_framework.urls')),\r\n\r\n # url(r'^index/', TemplateView.as_view(template_name=\"index.html\"), name=\"index\"),\r\n]\r\n","sub_path":"pycharmProject/resume/resume/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":3716,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"429139545","text":"import re\nfrom defs import *\nfrom values import *\n\n# pick out compartment IDs. E.g. given label=Area_Lumen_Cell, it would return\n# (0,1) for (lumen, cell)\ndef get_interface_id(label):\n\ttmp = (label).split('_')\n\tif len(tmp)==3: # only have two compartment IDs\n\t\tind1,ind2 = compart_id[tmp[1]],compart_id[tmp[2]]\n\t\treturn ind1, ind2\n\telif len(tmp)==4: # solute ID, followed by two compartment IDs\n\t\tsid,ind1,ind2 = solute_id[tmp[1]],compart_id[tmp[2]],compart_id[tmp[3]]\n\t\treturn sid,ind1,ind2\n\n# for coupled transpoters\n# pick out solute IDs, compartment IDs, and coefficients\ndef get_coupled_id(label):\n\ttmp = (label).split('_')\n# print(tmp)\n\tsid1,sid2 = solute_id[tmp[3]],solute_id[tmp[4]]\n\tind1,ind2 = compart_id[tmp[1]],compart_id[tmp[2]]\n\tif len(tmp)==5: # only involves 2 solutes\n\t\treturn ind1,ind2,sid1,sid2\n\telif len(tmp)==6: # involves 3 solutes\n\t\tsid3 = solute_id[tmp[5]]\n\t\treturn ind1,ind2,sid1,sid2,sid3\n\telse:\n\t\tprint(\"Wrong label\",tmp)\n\n# is str_short at the beginning of str_long?\n# case insensitive\ndef compare_string_prefix(str_long,str_short):\n\treturn (str_long.lower())[:len(str_short)] == str_short.lower()\n\ndef compare_sex(sex, cell):\n\tif sex.lower() == 'male' or sex.lower() == 'female':\n\t\treturn cell.sex.lower() == sex.lower()\n\telse:\n\t\treturn True\n\ndef read_params(cell,filename,j):\n# cell = membrane()\n\t#filename=input('Choose a file')\n\t#filename='cTALparams.dat'\n\tfile = open(filename,'r')\n\tcell.segment=filename[0:-12]\n\tdata = []\n\tline = file.readline()\n\twhile (line):\n\t\tline = line.replace('\\t',' ')\n\t\tterms = line.split(' ')\n\t\tif line[0][0]!='#':\n\t\t\tid = terms[0] #re.findall(r'[A-Za-z_]+', line)\n\t\t\tsex = id.split('_')[-1].lower()\n\t\t\tif compare_sex(sex, cell):\n\t\t\t\tid = id.replace('_male', '')\n\t\t\t\tid = id.replace('_female', '')\n\t\t\telse:\n\t\t\t\tline = file.readline()\n\t\t\t\tcontinue;\n\t\t\t# skip over the label, which may contain numbers like in HCO3\n\t\t\tfirst_space_pos = line.index(' ')\n\t\t\tnum = re.findall(r'-?\\d+\\.?\\d*[Ee]?[+-]?\\d*', line[first_space_pos:len(line)])\n\t\t\tif num: # if this line is numerical param\n\t\t\t\tvalue = float(num[0])\n\n\n\t\t\tif id.lower() == \"Sex\".lower():\n\t\t\t\tfind = re.findall(\"Female\".lower(), terms[-1].lower())\n\t\t\t\tif find:\n\t\t\t\t\tcell.sex = \"Female\".lower()\n\t\t\t\n\t\t\telif compare_string_prefix(id,\"Diameter\"):\n\t\t\t\tif cell.diabete == 'No':\n\t\t\t\t\tcell.diam = value\n\t\t\t\telif cell.diabete == 'Yes':\n\t\t\t\t\tif cell.segment == 'PT' or cell.segment == 'S3':\n\t\t\t\t\t\tcell.diam = value*32/25\n\t\t\t\t\telif cell.segment == 'SDL' or cell.segment == 'mTAL' or cell.segment == 'cTAL' or cell.segment == 'DCT' or cell.segment == 'CNT' or cell.segment == 'CCD' or cell.segment == 'OMCD':\n\t\t\t\t\t\tcell.diam = value*1.42\n\t\t\t\t\telse:\n\t\t\t\t\t\tcell.diam = value\n\t\t\t\telse:\n\t\t\t\t\tprint('What is the diabete status?')\n\n\t\t\t\tif cell.inhib == 'NHE3' and cell.inhib_perc == 0.5:\n\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\tif cell.segment == 'CCD' or cell.segment == 'OMCD' or cell.segment == 'IMCD':\n\t\t\t\t\t\t\tcell.diam = value*1.05\n\t\t\t\t\tif cell.sex == 'female':\n\t\t\t\t\t\tif cell.segment == 'CCD' or cell.segment == 'OMCD' or cell.segment == 'IMCD':\n\t\t\t\t\t\t\tcell.diam = value*0.95\n\n\t\t\t\tif cell.inhib == 'NKCC2' and cell.inhib_perc == 1.0:\n\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\tif cell.segment == 'CCD' or cell.segment == 'OMCD' or cell.segment == 'IMCD':\n\t\t\t\t\t\t\tcell.diam = value*0.95\n\t\t\t\t\tif cell.sex == 'female':\n\t\t\t\t\t\tif cell.segment == 'CCD' or cell.segment == 'OMCD' or cell.segment == 'IMCD':\n\t\t\t\t\t\t\tcell.diam = value*0.95\n\n\t\t\telif compare_string_prefix(id,\"Length\"):\n\t\t\t\tif cell.segment == 'LDL' or cell.segment == 'LAL':\n\t\t\t\t\tif cell.type == 'jux1':\n\t\t\t\t\t\tlooplen = 0.2\n\t\t\t\t\telif cell.type == 'jux2':\n\t\t\t\t\t\tlooplen = 0.4\n\t\t\t\t\telif cell.type == 'jux3':\n\t\t\t\t\t\tlooplen = 0.6\n\t\t\t\t\telif cell.type == 'jux4':\n\t\t\t\t\t\tlooplen = 0.8\n\t\t\t\t\telif cell.type == 'jux5':\n\t\t\t\t\t\tlooplen = 1.0\n\n\t\t\t\t\tcell.len = value*looplen\n\t\t\t\telse:\n\t\t\t\t\tcell.len = value\n\n\t\t\t\tif cell.type != 'sup':\n\t\t\t\t\tif cell.segment == 'cTAL':\n\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\tcell.len = 0.05\n\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\tcell.len = 0.05*0.85\n\t\t\t\t\telif cell.segment == 'CNT':\n\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\tcell.len = 0.3\n\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\tcell.len = 0.3*0.85\n\t\t\t\t\n\t\t\t\tif cell.diabete == 'No':\n\t\t\t\t\tcell.len = cell.len\n\t\t\t\telif cell.diabete == 'Yes':\n\t\t\t\t\tif cell.segment == 'PT' or cell.segment == 'S3':\n\t\t\t\t\t\tcell.len = cell.len*1.28\n\t\t\t\t\telif cell.segment == 'SDL' or cell.segment == 'mTAL' or cell.segment == 'cTAL' or cell.segment == 'DCT' or cell.segment == 'CNT' or cell.segment == 'CCD' or cell.segment == 'OMCD':\n\t\t\t\t\t\tcell.len = cell.len*1.07\n\t\t\t\t\telse:\n\t\t\t\t\t\tcell.len = cell.len\n\t\t\t\telse:\n\t\t\t\t\tprint('What is the diabete status?')\n\t\t\t#elif compare_string_prefix(id,\"Total\"):\n\t\t\t# cell.total = value\n\t\t\telif compare_string_prefix(id,\"Pressure\"):\n\t\t\t\tcell.pres[0] = value\n\t\t\t\tif cell.type !='sup' and cell.segment == 'PT':\n\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\tcell.pres[0] = 12.5\n\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\tcell.pres[0] = 12.5\n\n\t\t\telif compare_string_prefix(id,\"pH\"):\n\t\t\t\tfor i in range(6):\n\t\t\t\t\tcell.pH[i] = num[i]\n\t\t\telif compare_string_prefix(id,\"total\"):\n\t\t\t\tcell.total = value\n\t\t\t# surface area\n\t\t\telif compare_string_prefix(id,\"Area\"):\n\t\t\t\tind1,ind2 = get_interface_id(id)\n\t\t\t\tcell.area[ind1][ind2] = value\n\t\t\t\tcell.area[ind2][ind1] = value # symmetry\n\t\t\t\tcell.area_init[ind1][ind2] = value\n\t\t\t\tcell.area_init[ind2][ind1] = value\n\t\t\t\tif cell.type != 'sup':\n\t\t\t\t\tif cell.segment == 'PT' or cell.segment == 'S3':\n\t\t\t\t\t\tcell.area[ind1][ind2] = 1.75*cell.area[ind1][ind2]\n\t\t\t\t\t\tcell.area[ind2][ind1] = cell.area[ind1][ind2]\n\n\t\t\t# water permeabilities\n\t\t\telif compare_string_prefix(id,\"Pf\"):\n\t\t\t\tind1,ind2 = get_interface_id(id)\n\t\t\t\t# Units of dimensional water flux (in 'value'): cm3/s/cm2 epith\n\t\t\t\t# Non-dimensional factor for water flux: (Pfref)*Vwbar*Cref\n\t\t\t\t# Calculate non-dimensional dLPV = Pf*Vwbar*Cref / (Pfref*Vwbar*Cref)\n\t\t\t\t# dLPV = Pf/Pfref\n\t\t\t\tcell.dLPV[ind1][ind2] = value/Pfref\n\t\t\t\t# symmetry\n\t\t\t\tcell.dLPV[ind2][ind1] = value/Pfref\n\t\t\t\tif cell.segment == 'SDL' and cell.type == 'sup':\n\t\t\t\t\tif j>=0.46*cell.total:\n\t\t\t\t\t\tcell.dLPV[0,1]=0.00*cell.dLPV[0,1]\n\t\t\t\t\t\tcell.dLPV[0,4]=0.00*cell.dLPV[0,4]\n\t\t\t\telif cell.segment == 'LDL':\n\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\tif j>=0.4*cell.total:\n\t\t\t\t\t\t\tcell.dLPV[0,1]=0.00*cell.dLPV[0,1]\n\t\t\t\t\t\t\tcell.dLPV[0,4]=0.00*cell.dLPV[0,4]\n\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\tif j>=0.5*cell.total:\n\t\t\t\t\t\t\tcell.dLPV[0,1]=0.00*cell.dLPV[0,1]\n\t\t\t\t\t\t\tcell.dLPV[0,4]=0.00*cell.dLPV[0,4]\n\t\t\t\t#elif cell.segment == 'SDL' and cell.type !='sup' and ind1 == 0 and ind2 == 4:\n\t\t\t\t#\tcell.dLPV[0,4]=10*cell.dLPV[0,4]\n\t\t\t\t\n\t\t\t\tif cell.segment == 'CNT' and cell.type !='sup':\n\t\t\t\t\tif cell.sex == 'female':\n\t\t\t\t\t\tcell.dLPV = cell.dLPV*4/3\n\t\t\t\t\n\t\t\t\tif cell.diabete == 'Yes':\n\t\t\t\t\tif cell.segment == 'CCD' or cell.segment == 'IMCD':\n\t\t\t\t\t\tcell.dLPV[0,1] = cell.dLPV[0,1]*1.4\n\t\t\t\t\t\tcell.dLPV[1,5] = cell.dLPV[1,5]*1.4\n\t\t\t\n\t\t\t# reflective coefficients\n\t\t\telif compare_string_prefix(id,\"sig\"):\n\t\t\t\tsid,ind1,ind2 = get_interface_id(id)\n\t\t\t\tcell.sig[sid][ind1][ind2] = value\n# if sid==0 and ind1==0 and ind2==1:\n# print('got sig[0][0][1]]',cell.sig[0][0][1])\n\t\t\t\t# symmetry\n\t\t\t\tcell.sig[sid][ind2][ind1] = value\n\n\t\t\t# membrane solute permeabilities\n\t\t\telif compare_string_prefix(id,\"perm\"):\n\t\t\t\tsid,ind1,ind2 = get_interface_id(id)\n\t\t\t\tcell.h[sid][ind1][ind2] = value*1.0e-5/href\n\t\t\t\t# symmetry\n\t\t\t\tcell.h[sid][ind2][ind1] = value*1.0e-5/href\n\t\t\t\t# same basolateral around bath or LIS\n\t\t\t\tif ind1==1 and ind2==5:\n\t\t\t\t\tcell.h[sid][ind1][4] = value*1.0e-5/href\n\t\t\t\t\tcell.h[sid][4][ind1] = value*1.0e-5/href\n\t\t\t\telif ind1==1 and ind2==4:\n\t\t\t\t\tcell.h[sid][ind1][5] = value*1.0e-5/href\n\t\t\t\t\tcell.h[sid][5][ind1] = value*1.0e-5/href\n\t\t\t\telif ind1==2 and ind2==4:\n\t\t\t\t\tcell.h[sid][ind1][5] = value*1.0e-5/href\n\t\t\t\telif ind1==3 and ind2==4:\n\t\t\t\t\tcell.h[sid][ind1][5] = value*1.0e-5/href\n\t\t\t\tif cell.segment == 'OMCD':\n\t\t\t\t\tcell.h[sid][0][3] = 0.0\n\t\t\t\t\tcell.h[sid][3][4] = 0.0\n\t\t\t\t\tcell.h[sid][3][5] = 0.0\n\t\t\t\tif cell.segment == 'IMCD':\n\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\tif j>3*cell.total/4-1:\n\t\t\t\t\t\t\tcell.h[8,0,1] = 300.0*1.0e-5/href\n\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\tif j>2*cell.total/3-1:\n\t\t\t\t\t\t\tcell.h[8,0,1] = 300.0*1.0e-5/href\n\t\t\t\tif cell.segment == 'LDL':\n\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\tif j>=0.4*cell.total:\n\t\t\t\t\t\t\tcell.h[0,0,1]=80.0\n\t\t\t\t\t\t\tcell.h[0,0,4]=80.0\n\t\t\t\t\t\t\tcell.h[1,0,1]=100.0\n\t\t\t\t\t\t\tcell.h[1,0,4]=100.0\n\t\t\t\t\t\t\tcell.h[2,0,1]=80.0\n\t\t\t\t\t\t\tcell.h[2,0,4]=80.0\n\t\t\t\t\t\t\tcell.h[3,0,1]=20.0\n\t\t\t\t\t\t\tcell.h[3,0,4]=20.0\n\t\t\t\t\t\t\tcell.h[10,0,1]=20.0\n\t\t\t\t\t\t\tcell.h[10,0,4]=20.0\n\t\t\t\t\t\t\tcell.h[8,0,1]=80.0 #80\n\t\t\t\t\t\t\tcell.h[8,0,4]=80.0 #80\n\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\tif j>=0.5*cell.total:\n\t\t\t\t\t\t\tcell.h[0,0,1]=40.0\n\t\t\t\t\t\t\tcell.h[0,0,4]=80.0\n\t\t\t\t\t\t\tcell.h[1,0,1]=100.0\n\t\t\t\t\t\t\tcell.h[1,0,4]=100.0\n\t\t\t\t\t\t\tcell.h[2,0,1]=40.0\n\t\t\t\t\t\t\tcell.h[2,0,4]=80.0\n\t\t\t\t\t\t\tcell.h[3,0,1]=20.0\n\t\t\t\t\t\t\tcell.h[3,0,4]=20.0\n\t\t\t\t\t\t\tcell.h[10,0,1]=20.0\n\t\t\t\t\t\t\tcell.h[10,0,4]=20.0\n\t\t\t\t\t\t\tcell.h[8,0,1]=80.0\n\t\t\t\t\t\t\tcell.h[8,0,4]=80.0\n\t\t\t\t\t\n\t\t\t\t# if cell.inhib == 'NHE3':\n\t\t\t\t# \tif cell.segment == 'PT':\n\t\t\t\t# \t\tcell.h[8,0,1] = cell.h[8,0,1]*1.2\n\t\t\t\t# \t\tcell.h[8,0,4] = cell.h[8,0,4]*1.2\n\t\t\t\t# \tif cell.segment == 'SDL':\n\t\t\t\t# \t\tcell.h[8,0,1] = 3.0\n\t\t\t\t# \t\tcell.h[8,0,4] = 3.0\n\t\t\t\t# \tif cell.segment == 'LDL':\n\t\t\t\t# \t\tcell.h[8,0,1] = 0.0\n\t\t\t\t# \t\tcell.h[8,0,4] = 0.0\n\t\t\t\t# \tif cell.segment == 'LAL':\n\t\t\t\t# \t\tcell.h[8,0,1] = 0.0\n\t\t\t\t# \t\tcell.h[8,0,4] = 0.0\n\t\t\t# coupled transporters\n\t\t\telif compare_string_prefix(id,\"coupled\"):\n\t\t\t\t# retrieve interface and solute id\n\t\t\t\tvals = get_coupled_id(id)\n\t\t\t\tnewdLA = coupled_transport()\n\t\t\t\tif cell.segment == 'mTAL' and cell.type !='sup':\n\t\t\t\t\tnewdLA.perm = value/(href*Cref)#*1.25\n\t\t\t\telif cell.segment == 'cTAL' and cell.type !='sup':\n\t\t\t\t\tnewdLA.perm = value/(href*Cref)#*1.15\n\t\t\t\telse:\n\t\t\t\t\tnewdLA.perm = value / (href*Cref)\n\t\t\t\tnewdLA.membrane_id = [vals[0],vals[1]]\n\n\t\t\t\tcoef = [] # retrieve coupling coefficients\n# print(num)\n\t\t\t\tfor i in range(1,len(num)):\n\t\t\t\t\tcoef.append(int(num[i]))\n\t\t\t\tnewdLA.coef = coef\n\t\t\t\tnewdLA.solute_id = vals[2:len(vals)]\n\n\t\t\t\tif cell.salt == 'Y':\n\t\t\t\t\tif cell.segment == 'mTAL' or cell.segment == 'cTAL':\n\t\t\t\t\t\tif newdLA.solute_id[-1]=='HPO4':\n\t\t\t\t\t\t\tnewdLA.perm = (1+1)*value/(href*Cref)\n\n\t\t\t\tcell.dLA.append(newdLA)\n\t\t\t\t# same basolateral around bath or LIS\n\t\t\t\t#if vals[0]==1 and vals[1]==5:\n\t\t\t\t# newdLA2 = coupled_transport()\n\t\t\t\t# newdLA2.perm = newdLA.perm\n\t\t\t\t# newdLA2.membrane_id = [1,4]\n\t\t\t\t# newdLA2.coef = coef\n\t\t\t\t# newdLA2.solute_id = vals[2:len(vals)]\n\t\t\t\t# cell.dLA.append(newdLA2)\n\t\t\t\t#elif vals[0]==1 and vals[1]==4: \n\t\t\t\t# newdLA2 = coupled_transport()\n\t\t\t\t# newdLA2.perm = newdLA.perm\n\t\t\t\t# newdLA2.membrane_id = [1,5]\n\t\t\t\t# newdLA2.coef = coef\n\t\t\t\t# newdLA2.solute_id = vals[2:len(vals)]\n\t\t\t\t# cell.dLA.append(newdLA2)\n\t\t\t# specific transporters\n\t\t\telif compare_string_prefix(id,\"transport\"):\n\t\t\t\ttmp = (id).split('_')\n\t\t\t\tind1,ind2 = compart_id[tmp[1]],compart_id[tmp[2]]\n\t\t\t\tnewTransp = transporter()\n\t\t\t\tnewTransp.membrane_id = [ind1,ind2]\n\t\t\t\tnewTransp.type = tmp[3]\n\t\t\t\tnewTransp.act = value/(href*Cref)\n\t\t\t\t#print('transporter')\n\t\t\t\t#print(newTransp.membrane_id,newTransp.type,newTransp.act)\n\t\t\t\tif cell.segment == 'mTAL' and cell.type!='sup':\n\t\t\t\t\tnewTransp.act = value/(href*Cref)#*1.25\n\t\t\t\telif cell.segment == 'mTAL' and cell.type!='sup':\n\t\t\t\t\tnewTransp.act = value/(href*Cref)#*1.15\n\t\t\t\telse:\n\t\t\t\t\tnewTransp.act = value/(href*Cref)\t\t\t\t\n\t\t\t\t#if ind1==1 and ind2==5:\n\t\t\t\t# newTransp2 = transporter()\n\t\t\t\t# newTransp2.membrane_id = [ind1,4]\n\t\t\t\t# newTransp2.type = tmp[3]\n\t\t\t\t# newTransp2.act = newTransp.act\n\t\t\t\t# cell.trans.append(newTransp2)\n\t\t\t\t#elif ind1==1 and ind2==4:\n\t\t\t\t# newTransp2 = transporter()\n\t\t\t\t# newTransp2.membrane_id = [ind1,5]\n\t\t\t\t# newTransp2.type = tmp[3]\n\t\t\t\t# newTransp2.act = newTransp.act\n\t\t\t\t# cell.trans.append(newTransp2)\n\t\t\t\tif cell.type != 'sup' and cell.sex == 'female':\n\t\t\t\t\tif cell.segment == 'mTAL' or cell.segment == 'cTAL':\n\t\t\t\t\t\tif newTransp.type == 'NKCC2A' or newTransp.type == 'NKCC2B' or newTransp.type == 'NKCC2F' or newTransp.type == 'NaKATPase':\n\t\t\t\t\t\t\tnewTransp.act = 1.5*value/(href*Cref)\n\t\t\t\t\t\telif newTransp.type == 'KCC4':\n\t\t\t\t\t\t\tnewTransp.act = 2.0*value/(href*Cref)\n\n\t\t\t\t# NHE3 inhibition parameter settings\n\t\t\t\tif cell.inhib == 'NHE3':\n\t\t\t\t\tif cell.segment == 'PT':\n\t\t\t\t\t\tif newTransp.type == 'NHE3':\n\t\t\t\t\t\t\tnewTransp.act = (1-cell.inhib_perc)*value/(href*Cref)\n\t\t\t\t\t\t#if newTransp.type == 'NaKATPase':\n\t\t\t\t\t\t#\tnewTransp.act = (1+cell.inhib_perc*0.25)*value/(href*Cref)\n\t\t\t\t\telif cell.segment == 'mTAL' or cell.segment == 'cTAL':\n\t\t\t\t\t\tif newTransp.type == 'NHE3':\n\t\t\t\t\t\t\tnewTransp.act = (1-cell.inhib_perc*0.5)*value/(href*Cref)\n\t\t\t\t\t\tif cell.segment == 'mTAL':\n\t\t\t\t\t\t\tif newTransp.type == 'NaKATPase':\n\t\t\t\t\t\t\t\tnewTransp.act = (1+cell.inhib_perc*0.5)*value/(href*Cref)\n\t\t\t\t\t\tif cell.segment == 'cTAL':\n\t\t\t\t\t\t\tif newTransp.type == 'NaKATPase':\n\t\t\t\t\t\t\t\tnewTransp.act = (1-cell.inhib_perc*0.5)*value/(href*Cref)\n\t\t\t\t\telif cell.segment == 'DCT':\n\t\t\t\t\t\tif newTransp.type == 'NHE3':\n\t\t\t\t\t\t\tnewTransp.act = (1-cell.inhib_perc)*value/(href*Cref)\n\t\t\t\telif cell.inhib == 'NKCC2':\n\t\t\t\t\tif cell.segment == 'mTAL' or cell.segment == 'cTAL':\n\t\t\t\t\t\tif newTransp.type == 'NKCC2A' or newTransp.type == 'NKCC2B' or newTransp.type == 'NKCC2F':\n\t\t\t\t\t\t\tnewTransp.act = (1-cell.inhib_perc)*value/(href*Cref)\n\t\t\t\telif cell.inhib == 'NCC':\n\t\t\t\t\tif cell.segment == 'DCT':\n\t\t\t\t\t\tif newTransp.type == 'NCC':\n\t\t\t\t\t\t\tnewTransp.act = (1-cell.inhib_perc)*value/(href*Cref)\n\t\t\t\telif cell.inhib == 'ENaC':\n\t\t\t\t\tif newTransp.type == 'ENaC':\n\t\t\t\t\t\tnewTransp.act = (1-cell.inhib_perc)*value/(href*Cref)\n\t\t\t\telif cell.inhib == 'SNB':\n\t\t\t\t\tif cell.segment == 'mTAL' or cell.segment == 'cTAL':\n\t\t\t\t\t\tif newTransp.type == 'NKCC2A' or newTransp.type == 'NKCC2B' or newTransp.type == 'NKCC2F':\n\t\t\t\t\t\t\tnewTransp.act = (1-cell.inhib_perc)*value/(href*Cref)\n\t\t\t\t\tif cell.segment == 'DCT':\n\t\t\t\t\t\tif newTransp.type == 'NCC':\n\t\t\t\t\t\t\tnewTransp.act = (1-cell.inhib_perc)*value/(href*Cref)\n\t\t\t\t\tif newTransp.type == 'ENaC':\n\t\t\t\t\t\tnewTransp.act = (1-cell.inhib_perc)*value/(href*Cref)\n\n\t\t\t\tif cell.salt == 'Y':\n\t\t\t\t\tif cell.segment == 'mTAL':\n\t\t\t\t\t\tif newTransp.type == 'NaKATPase':\n\t\t\t\t\t\t\tnewTransp.act = (1+0.4)*value/(href*Cref)\n\t\t\t\t\telif cell.segment == 'cTAL':\n\t\t\t\t\t\tif newTransp.type == 'NaKATPase':\n\t\t\t\t\t\t\tnewTransp.act = (1-0.4)*value/(href*Cref)\n\t\t\t\t# elif cell.inhib == 'nhe3 80%':\n\t\t\t\t# \tif cell.segment == 'PT':\n\t\t\t\t# \t\tif newTransp.type == 'NHE3':\n\t\t\t\t# \t\t\tnewTransp.act = 0.2*value/(href*Cref)\n\t\t\t\t# \telif cell.segment == 'mTAL' or cell.segment == 'cTAL':\n\t\t\t\t# \t\tif newTransp.type == 'NHE3':\n\t\t\t\t# \t\t\tnewTransp.act = 0.6*value/(href*Cref)\n\t\t\t\t# \telif cell.segment == 'DCT':\n\t\t\t\t# \t\tif newTransp.type == 'NHE3':\n\t\t\t\t# \t\t\tnewTransp.act = 0.2*value/(href*Cref)\n\n\t\t\t\tcell.trans.append(newTransp)\n\t\t\t# solute concentrations\n\t\t\telif compare_string_prefix(id,\"conc\"):\n\t\t\t\ttmp = (id).split('_')\n\t\t\t\tcell.conc[solute_id[tmp[1]]][0] = float(num[0])\n\t\t\t\tcell.conc[solute_id[tmp[1]]][1] = float(num[1])\n\t\t\t\tcell.conc[solute_id[tmp[1]]][4] = float(num[2])\n\t\t\t\tcell.conc[solute_id[tmp[1]]][5] = float(num[3])\n\t\t\t\tif len(num) > 4:\n\t\t\t\t\tcell.conc[solute_id[tmp[1]]][2] = float(num[4])\n\t\t\t\t\tif len(num) > 5:\n\t\t\t\t\t\tcell.conc[solute_id[tmp[1]]][3] = float(num[5])\n\t\t\t\tif cell.diabete == 'Yes':\n\t\t\t\t\tif cell.segment == 'PT':\n\t\t\t\t\t\tcell.conc[14,0] = 25.0\n\n\t\t\t# reference impermeat concnetration (for cell)\n\t\t\t# or oncotic pressure for lumen/LIS/bath\n\t\t\telif compare_string_prefix(id,\"cimpref\"):\n\t\t\t\ttmp = (id).split('_')\n\t\t\t\tcell.cimpref[compart_id[tmp[1]]] = float(num[0])\n\n\t\t\t# reference volumes\n\t\t\telif compare_string_prefix(id,\"volref\"):\n\t\t\t\ttmp = (id).split('_')\n\t\t\t\tcell.volref[compart_id[tmp[1]]] = float(num[0])\n\n\n\t\t\t# actual volumes\n\t\t\telif compare_string_prefix(id,\"vol\"):\n\t\t\t\ttmp = (id).split('_')\n\t\t\t\tif cell.segment == 'PT' and cell.type != 'sup':\n\t\t\t\t\tif compart_id[tmp[1]] == 0:\t\n\t\t\t\t\t\tif cell.type == 'jux1':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0075\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.006\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux2':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0075\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.006\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\n\t\t\t\t\t\telif cell.type == 'jux3':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0075\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.006\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux4':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0075\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.006\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux5':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0075\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.006\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\telse:\n\t\t\t\t\t\tcell.vol[compart_id[tmp[1]]] = float(num[0])\n\t\t\t\t\t\tcell.vol_init[compart_id[tmp[1]]] = float(num[0])\n\t\t\t\telse:\n\t\t\t\t\tcell.vol[compart_id[tmp[1]]] = float(num[0])\n\t\t\t\t\tcell.vol_init[compart_id[tmp[1]]] = float(num[0])\n\n\t\t\t\tif cell.inhib == 'NHE3' and cell.inhib_perc == 0.5:\n\t\t\t\t\tif cell.segment == 'PT' and compart_id[tmp[1]] == 0:\n\t\t\t\t\t\tif cell.type == 'sup':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.004599\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.00362\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux1':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0065\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.00515\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux2':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0065\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0052\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux3':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0065\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0052\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux4':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0065\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.00521\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux5':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0065\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.00522\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\telif cell.inhib == 'NHE3' and cell.inhib_perc == 0.8:\n\t\t\t\t\tif cell.segment == 'PT' and compart_id[tmp[1]] == 0:\n\t\t\t\t\t\tif cell.type == 'sup':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = float(num[0])*0.725\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0033\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux1':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = float(num[0])*1.41*0.75\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0046\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux2':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = float(num[0])*1.41*0.75\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0046\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux3':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = float(num[0])*1.396*0.75\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0046\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux4':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = float(num[0])*1.393*0.75\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0046\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\t\t\t\t\t\telif cell.type == 'jux5':\n\t\t\t\t\t\t\tif cell.sex == 'male':\n\t\t\t\t\t\t\t\tcell.vol[0] = float(num[0])*1.389*0.75\n\t\t\t\t\t\t\telif cell.sex == 'female':\n\t\t\t\t\t\t\t\tcell.vol[0] = 0.0046\n\t\t\t\t\t\t\tcell.vol_init[0] = cell.vol[0]\n\n\n\t\t\t# membrane potential\n\t\t\telif compare_string_prefix(id,\"ep\"):\n\t\t\t\ttmp = (id).split('_')\n\t\t\t\tcell.ep[compart_id[tmp[1]]] = float(num[0])\n\t\t\t\t\t\t\n\t\t\t# invalue keyword\n# else:\n# print(\"Wrong id\",id)\n\t\t\t\t# if not a comment line\n\t\t\n\t\tline = file.readline()\n\n\t\tfor i in range(NS):\n\t\t\tcell.sig[i,4,5] = 0\n\t\t\tcell.sig[i,5,4] = 0\n\n\t\tif cell.segment=='cTAL' or cell.segment=='mTAL':\n\t\t\tcell.dkd[2]=0\n\t\t\tcell.dkd[3]=0\n\t\t\tcell.dkd[5]=0\n\t\t\tcell.dkh[2]=0\n\t\t\tcell.dkh[3]=0\n\t\t\tcell.dkh[5]=0\n\t\telif cell.segment=='DCT':\n\t\t\tcell.dkd = np.zeros(6)\n\t\t\tcell.dkh = np.zeros(6)\n\t\t\tcell.dkd[0]=496\n\t\t\tcell.dkh[0]=1.45\n\t\t\tcell.dkd[1]=49600\n\t\t\tcell.dkh[1]=145\n\t\t\tcell.dkd[4]=49600\n\t\t\tcell.dkh[4]=145\n\t\telif cell.segment=='PT':\n\t\t\tcell.dkd = 496000 * np.ones(NC)\n\t\t\tcell.dkh = 1450 * np.ones(NC)\n\t\telif cell.segment=='S3':\n\t\t\tcell.dkd = 4960 * np.ones(NC)\n\t\t\tcell.dkh = 14.5 * np.ones(NC)\n\t\telif cell.segment=='CNT' or cell.segment == 'CCD':\n\t\t\tcell.dkd = np.zeros(6)\n\t\t\tcell.dkh = np.zeros(6)\n\t\t\tcell.dkd[0]=496\n\t\t\tcell.dkh[0]=1.45\n\t\t\tcell.dkd[1]=496\n\t\t\tcell.dkh[1]=1.45\n\t\t\tcell.dkd[2]=496000\n\t\t\tcell.dkh[2]=1450\n\t\t\tcell.dkd[3]=496000\n\t\t\tcell.dkh[3]=1450\n\t\t\tcell.dkd[4]=49600\n\t\t\tcell.dkh[4]=145\n\t\telif cell.segment=='OMCD':\n\t\t\tcell.dkd = np.zeros(6)\n\t\t\tcell.dkh = np.zeros(6)\n\t\t\tcell.dkd[0]=49.6\n\t\t\tcell.dkh[0]=0.145\n\t\t\tcell.dkd[1]=496\n\t\t\tcell.dkh[1]=1.45\n\t\t\tcell.dkd[2]=496000\n\t\t\tcell.dkh[2]=1450\n\t\t\tcell.dkd[3]=496000\n\t\t\tcell.dkh[3]=1450\n\t\t\tcell.dkd[4]=49.6\n\t\t\tcell.dkh[4]=0.145\t\t\t\n\t\telif cell.segment == 'IMCD':\n\t\t\tcell.dkd = np.zeros(6)\n\t\t\tcell.dkh = np.zeros(6)\n\t\t\tcell.dkd[0]=49.6\n\t\t\tcell.dkh[0]=0.145\n\t\t\tcell.dkd[1]=4960\n\t\t\tcell.dkh[1]=14.5\n\t\t\tcell.dkd[2]=4960\n\t\t\tcell.dkh[2]=14.5\n\t\t\tcell.dkd[3]=4960\n\t\t\tcell.dkh[3]=14.5\n\t\t\tcell.dkd[4]=49.6\n\t\t\tcell.dkh[4]=0.145\n\tfile.close()\n","sub_path":"set_params.py","file_name":"set_params.py","file_ext":"py","file_size_in_byte":21550,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"635754448","text":"import sys\n\nsys.stdin = open('sample_input.txt', 'r')\n\n\nclass Node:\n\n def __init__(self, value):\n self.value = value\n self.prev = None\n self.next = None\n\n\nclass DoublyLinkedList:\n\n def __init__(self, N, iterable=None):\n self.head = None\n self.tail = None\n self.length = 0\n self.N = N // 4\n\n if iterable:\n for value in iterable:\n self.push(value)\n\n def push(self, value):\n node = Node(value)\n if not self.head:\n self.head = node\n self.tail = node\n else:\n self.tail.next = node\n node.prev = self.tail\n self.tail = node\n\n self.length += 1\n\n def pop(self):\n\n if not self.head:\n return None\n else:\n node = self.tail\n prev_node = self.tail.prev\n prev_node.next = None\n self.tail = prev_node\n\n self.length -= 1\n\n node.prev = None\n\n return node\n\n def pushleft(self, value):\n\n if not self.head:\n self.push(value)\n\n else:\n node = Node(value)\n node.next = self.head\n self.head.prev = node\n self.head = node\n\n self.length += 1\n\n def rotate(self):\n value = self.pop().value\n self.pushleft(value)\n\n def values(self):\n hex_numbers = []\n\n current = self.head\n unit = []\n for i in range(self.length):\n unit.append(current.value)\n current = current.next\n\n for i in range(0, len(unit), self.N):\n hex_numbers.append(''.join(unit[i:i + self.N]))\n\n return hex_numbers\n\n\nT = int(input())\n\nfor tc in range(1, T + 1):\n # N개의 숫자, 내림차순정렬 했을때 K번째 숫자를 구하자.\n N, K = map(int, input().split())\n\n numbers = list(input())\n\n DLL = DoublyLinkedList(N, numbers)\n\n possible_hex = set(DLL.values())\n for i in range(N // 4 - 1):\n DLL.rotate()\n possible_hex.update(DLL.values())\n\n possible_hex = sorted(list(map(lambda x: int(x, 16), possible_hex)), reverse=True)\n print(\"#{} {}\".format(tc, possible_hex[K - 1]))\n","sub_path":"PYTHON/SWEXPERT/5658_보물상자_비밀번호/5658.py","file_name":"5658.py","file_ext":"py","file_size_in_byte":2209,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"79982913","text":"#coding:utf-8\nfrom exception.exceptions import TaskNotFoundError\nfrom dao.task.sql import TaskSQL\nfrom dao.task.latest_sql import LatestTaskSQL\nfrom dao.task.task_find import TaskFindDao\nfrom dao.task.task import TaskDao\nfrom dao.task.task_log import TaskLogDao\nfrom dao.task.serial_number import TaskSerialNumberDao\nfrom model.task import Task\nfrom model.user import User\n\n\nclass TaskRepository(object):\n \n def __init__(self, db):\n self.db = db\n\n def find_one(self, project_name, serial_no):\n sql = LatestTaskSQL()\n sql.where(\"project.name = ?\")\n sql.where(\"task_serial_number.serial_no = ?\")\n sql.where(\"task_log.status = 1\")\n sql.order_by(\"task.timelimit ASC\")\n sql.order_by(\"task_log.timestamp ASC\")\n sql.order_by(\"project.name ASC\")\n param = [project_name, serial_no]\n\n dao = TaskFindDao(self.db, sql.build(), param)\n record = dao.get()\n \n if len(record) == 0:\n return None\n \n for item in record:\n return Task(\n item[\"task_id\"],\n item[\"project_name\"],\n item[\"title\"],\n item[\"serial_no\"],\n item[\"timelimit\"],\n item[\"timestamp\"],\n item[\"note\"],\n item[\"status\"]\n )\n\n def fetch_all(self):\n sql = LatestTaskSQL()\n sql.where(\"task_log.status = 1\")\n sql.order_by(\"task.timelimit ASC\")\n sql.order_by(\"task_log.timestamp ASC\")\n sql.order_by(\"project.name ASC\")\n \n dao = TaskFindDao(self.db, sql.build(), [])\n record = dao.get()\n result = []\n \n for item in record:\n _ = Task(\n item[\"task_id\"],\n item[\"project_name\"],\n item[\"title\"],\n item[\"serial_no\"],\n item[\"timelimit\"],\n item[\"timestamp\"],\n item[\"note\"],\n item[\"status\"]\n )\n result.append(_)\n \n return result\n\n def get_latest(self, task_id):\n sql = LatestTaskSQL()\n sql.where(\"task.id = ?\")\n sql.order_by(\"task.timelimit ASC\")\n sql.order_by(\"task_log.timestamp ASC\")\n sql.order_by(\"project.name ASC\")\n \n dao = TaskFindDao(self.db, sql.build(), [task_id])\n record = dao.get()\n result = []\n \n for item in record:\n return Task(\n item[\"project_name\"],\n item[\"task_id\"],\n item[\"title\"],\n item[\"serial_no\"],\n item[\"timelimit\"],\n item[\"timestamp\"],\n item[\"note\"],\n item[\"status\"]\n )\n result.append(_)\n \n raise TaskNotFoundError(\"Task NotFound\")\n \n\n def create_task(self, project, task, user):\n try:\n #== BEGIN TRANSACTION ==\n self.db.begin_trans()\n #== entry:task ==\n task_dao = TaskDao(self.db, None, task.get_title(), task.get_timelimit())\n task_dao.create()\n #== entry:task_log ==\n id_ = self.db.last_insert_id()\n tasklog_dao = TaskLogDao(self.db, None, id_, None, task.get_note(), 1, user.get_id())\n tasklog_dao.create()\n #== entry:task_serial_number ==\n no_dao = TaskSerialNumberDao(self.db, None, None, project.get_id(), id_)\n no_dao.create()\n #== COMMIT ==\n self.db.commit()\n\n except:\n self.db.rollback()\n raise\n\n def update_task(self, task, user):\n try:\n dao = TaskLogDao(self.db, None, task.get_id(), None, task.get_note(), task.get_status(), user.get_id())\n self.db.begin_trans()\n dao.create()\n self.db.commit()\n\n except:\n self.db.rollback()\n raise\n\n def modify_task(self, task):\n try:\n task_dao = TaskDao(self.db, task.get_id(), task.get_title(), task.get_timelimit())\n self.db.begin_trans()\n task_dao.update()\n self.db.commit()\n\n except:\n self.db.rollback()\n raise\n \n def modify_tasklog(self, tasklog):\n try:\n task_dao = TaskDao(self.db, tasklog.get_id(), tasklog.get_task_id(), tasklog.get_status(), tasklog.get_note(), None, user.get_id())\n self.db.begin_trans()\n task_dao.update()\n self.db.commit()\n\n except:\n self.db.rollback()\n raise\n\n\n def delete(self, task):\n pass\n\n","sub_path":"lib/repository/task.py","file_name":"task.py","file_ext":"py","file_size_in_byte":4658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"268840746","text":"\"\"\"\nProvides functionality for interacting with MASCOT data.\n\"\"\"\n\nfrom __future__ import absolute_import, division\n\nimport logging\nimport xml.etree.ElementTree as ET\n\nfrom . import pep_query, regexes\n\n\nMASCOT_NS = {\n \"mascot\":\n \"http://www.matrixscience.com/xmlns/schema/mascot_search_results_2\",\n}\n\nLOGGER = logging.getLogger(\"pycamv.mascot\")\n\n\ndef _count_instances(pep_seq, letters):\n return sum(\n ([\"N-term\"] + list(pep_seq) + [\"C-term\"]).count(letter)\n for letter in letters\n )\n\n\ndef _parse_letters(letters):\n \"\"\"\n Turns a string of residue letters (i.e. \"STY\") into a list.\n\n Includes special provisions for N- and C-term modifications.\n\n Parameters\n ----------\n letters : str\n\n Returns\n -------\n tuple of str\n \"\"\"\n if letters in [\"N-term\", \"C-term\"]:\n return (letters,)\n\n return tuple(letters)\n\n\ndef _get_fixed_var_mods(root):\n fixed_mods = [\n i.text\n for i in root.findall(\n \"mascot:fixed_mods/mascot:modification/mascot:name\",\n MASCOT_NS,\n )\n ]\n var_mods = [\n i.text\n for i in root.findall(\n \"mascot:variable_mods/mascot:modification/mascot:name\",\n MASCOT_NS,\n )\n ]\n return fixed_mods, var_mods\n\n\ndef _parse_mascot_2_4_1(root):\n filename = root.find(\"mascot:header/mascot:FILENAME\", MASCOT_NS).text\n filename = filename.split(\":\", 1)[1].strip()\n fixed_mods, var_mods = _get_fixed_var_mods(root)\n\n scan_used = {}\n index = 0\n out = []\n\n for hit in root.findall(\"mascot:hits/mascot:hit\", MASCOT_NS):\n accession = hit.find(\"mascot:protein\", MASCOT_NS).get(\"accession\")\n prot_desc = hit.find(\"mascot:protein/mascot:prot_desc\", MASCOT_NS).text\n\n if \"FGR\" in prot_desc.upper() or \"FGR\" in accession.upper():\n LOGGER.info(accession, prot_desc)\n\n match = regexes.RE_MASCOT_DESCRIPTION.match(prot_desc)\n\n if match:\n prot_desc = match.group(1)\n\n for peptide in hit.findall(\"mascot:protein/mascot:peptide\", MASCOT_NS):\n query = int(peptide.get(\"query\"))\n rank = int(peptide.get(\"rank\"))\n pep_score = float(peptide.find(\"mascot:pep_score\", MASCOT_NS).text)\n exp_mz = float(peptide.find(\"mascot:pep_exp_mz\", MASCOT_NS).text)\n exp_z = int(peptide.find(\"mascot:pep_exp_z\", MASCOT_NS).text)\n pep_seq = peptide.find(\"mascot:pep_seq\", MASCOT_NS).text\n\n pep_var_mods = peptide.find(\"mascot:pep_var_mod\", MASCOT_NS).text\n\n if fixed_mods:\n pep_fixed_mods = [\n regexes.RE_DYN_MODS.match(mod.strip()).group(3, 4)\n for mod in fixed_mods\n ]\n pep_fixed_mods = [\n (name, _parse_letters(letters))\n for name, letters in pep_fixed_mods\n ]\n pep_fixed_mods = [\n (_count_instances(pep_seq, letters), name, letters)\n for name, letters in pep_fixed_mods\n ]\n pep_fixed_mods = [\n (count, name, letters)\n for count, name, letters in pep_fixed_mods\n if count > 0\n ]\n else:\n pep_fixed_mods = []\n\n if pep_var_mods:\n # i.e. \"2 Phospho (STY)\"\"\n pep_var_mods = [\n regexes.RE_DYN_MODS.match(mod.strip()).group(2, 3, 4)\n for mod in pep_var_mods.split(\";\")\n ]\n pep_var_mods = [\n (int(count) if count else 1, name, _parse_letters(letters))\n for count, name, letters in pep_var_mods\n ]\n else:\n pep_var_mods = []\n\n scan = int(\n regexes.RE_SCAN_NUM.search(\n peptide.find(\"mascot:pep_scan_title\", MASCOT_NS).text\n ).group(2)\n )\n\n # scan_used\n if scan in scan_used:\n index_match, rank_match = scan_used[scan]\n\n if rank >= rank_match:\n continue\n else:\n del out[index_match]\n\n scan_used[scan] = (index, rank)\n out.append(\n pep_query.PeptideQuery(\n [accession],\n [prot_desc],\n query,\n filename,\n pep_score,\n exp_mz,\n exp_z,\n pep_seq,\n pep_var_mods,\n pep_fixed_mods,\n scan,\n )\n )\n index += 1\n\n out = sorted(out, key=lambda x: x.scan)\n\n return fixed_mods, var_mods, out\n\n\ndef read_mascot_xml(xml_path):\n \"\"\"\n Parse a MASCOT XML file.\n\n Parameters\n ----------\n xml_path : str\n Path to XML file.\n\n Returns\n -------\n fixed_mods : list of str\n var_mods : list of str\n out : list of :class:`PeptideQuery<pycamv.pep_query.PeptideQuery>`\n \"\"\"\n tree = ET.parse(xml_path)\n root = tree.getroot()\n\n ms_version = root.find(\"mascot:header/mascot:MascotVer\", MASCOT_NS).text\n ms_version = tuple(int(i) for i in ms_version.split(\".\"))\n\n if ms_version >= (2, 4, 1):\n return _parse_mascot_2_4_1(root)\n# elif ms_version == \"2.4.0\":\n# return _parse_mascot_2_4_0(root)\n# elif ms_version == \"2.3.02\":\n# return _parse_mascot_2_3_02(root)\n# elif ms_version == \"2.1.03\":\n# return _parse_mascot_2_1_03(root)\n else:\n raise Exception(\"Unsupported Mascot Version: {}\".format(ms_version))\n","sub_path":"pycamv/mascot.py","file_name":"mascot.py","file_ext":"py","file_size_in_byte":5734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"465651945","text":"import sys\r\nfrom sets import Set\r\n\r\n\r\ndef count(num):\r\n \"\"\" Count digits in 1N, 2N, 3N,... until all 10 digits (0-9) appear \"\"\"\r\n s = Set()\r\n counter = 1\r\n while True:\r\n for c in str(counter * num):\r\n s.add(c)\r\n if len(s) == 10:\r\n return counter * num\r\n elif counter > 2000000:\r\n return -1\r\n else:\r\n counter += 1\r\n return counter * num\r\n \r\ndef main():\r\n in_file = open('sheep_in.txt', 'r')\r\n out_file = open('sheep_out.txt', 'w')\r\n list = []\r\n try:\r\n for line in in_file:\r\n print(line)\r\n list.append(int(line.strip()))\r\n except:\r\n pass\r\n in_file.close()\r\n \r\n for x in range(1, list[0] + 1):\r\n r = count(list[x])\r\n out_file.write('Case #' + str(x) + ': ' + (str(r) if r != -1 else 'INSOMNIA') +'\\n')\r\n out_file.close()\r\n\r\nif __name__ == '__main__':\r\n main()","sub_path":"codes/CodeJamCrawler/16_0_1/isitso/sheep.py","file_name":"sheep.py","file_ext":"py","file_size_in_byte":938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"178976384","text":"#\n# Python module dedicated to control Mipow Playbulb LED candles\n# Created by: D3M80L\n#\n# Inspired by the following projects.\n# - https://github.com/amahlaka/python-mipow\n# - https://github.com/Phhere/Playbulb/blob/master/protocols/candle.md\n# - https://github.com/Heckie75/Mipow-Playbulb-BTL201\n# - https://github.com/hohler/hass-mipow-comet/blob/master/mipow_comet.py\n#\n# This code is released under the terms of the MIT license. \n#\n\nfrom bluepy import btle\n\nclass MipowDevice:\n\n\tdef __init__(self, mac):\n\t\tself.mac = mac\n\n\t\tself._rgbwhandle = None\n\t\tself._effecthandle = None\n\t\tself._batteryhandle = None\n\n\tdef connect(self):\n\t\tdevice = btle.Peripheral(self.mac, addrType=btle.ADDR_TYPE_PUBLIC)\n\n\t\tself._rgbwhandle = None\n\t\tself._effecthandle = None\n\t\tself._batteryhandle = None\n\t\t\n\t\thandles = device.getCharacteristics()\n\t\tfor handle in handles:\n\t\t\tif handle.uuid == \"fffb\":\n\t\t\t\tself._effecthandle = handle\n\t\t\tif handle.uuid == \"fffc\":\n\t\t\t\tself._rgbwhandle = handle\n\t\t\tif handle.uuid == \"2a19\":\n\t\t\t\tself._batterlyhandle = handle\n\n\tdef fetch_battery_level(self):\n\t\tif (not self._batterlyhandle):\n\t\t\treturn None\n\n\t\treturn self._batterlyhandle.read()[0]\n\n\tdef fetch_rgbw(self):\n\t\tif (not self._rgbwhandle):\n\t\t\treturn None\n\n\t\treturn self._rgbwhandle.read()\n\n\tdef fetch_effect(self):\n\t\tif (not self._effecthandle):\n\t\t\treturn None\n\n\t\treturn self._effecthandle.read()\n\n\tdef send_packet(self, handle, data):\n\t\treturn handle.write(bytes(data))\n\n\tdef set_effect(self, red, green, blue, white, effect, delay=0x14, repetitions=0):\n\t\tpacket = bytearray([white, red, green, blue, effect, repetitions, delay, 0])\n\t\tself.send_packet(self._effecthandle, packet)\n\n\tdef set_rgbw(self, red, green, blue, white):\n\t\tpacket = bytearray([white, red, green, blue])\n\t\tself.send_packet(self._rgbwhandle, packet)\n","sub_path":"custom_components/mipow/mipow.py","file_name":"mipow.py","file_ext":"py","file_size_in_byte":1790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"450158165","text":"# coding: utf-8\nfrom instagram.client import InstagramAPI\nimport pymorphy2\nfrom nltk.stem.snowball import SnowballStemmer\nimport nltk\nfrom stopwords import stopwords\nfrom app.models import db, User\n#file_with_token = open('app/access_token', \"r\")\n#access_token = file_with_token.readline().split()[0]\n#api = InstagramAPI(access_token=access_token)\nmorph = pymorphy2.MorphAnalyzer()\nstemmer = SnowballStemmer(\"english\")\n\ndef search(username, access_token):\n #user_q = User.query.filter_by(nickname = username).first()\n #if user_q:\n # api = InstagramAPI(access_token=user_q.access_token)\n #else:\n # file_with_token = open('app/access_token', \"r\")\n # access_token = file_with_token.readline().split()[0]\n api = InstagramAPI(access_token=access_token)\n user_id = []\n for user in api.user_search(q=username):\n if user.username == username:\n user_id = [user]\n #print user_id\n #print type(user_id)\n #print 'OK_TEST_1'\n if user_id:\n recent_media, next_ = api.user_recent_media(user_id=str(user_id[0].id), count=11)\n\n liked = api.user_liked_media(user_id=str(user_id[0].id))\n liked = liked[:-1]\n user_liked = {}\n for media in liked[0]:\n if 'user' in dir(media):\n if media.user.username in user_liked:\n user_liked[media.user.username] += 1\n else:\n user_liked[media.user.username] = 1\n user_liked_sorted = []\n for pair in reversed(sorted(user_liked.items(), key=lambda item: item[1])):\n user_liked_sorted.append([pair[0], pair[1]])\n\n locations = {}\n for feed in recent_media:\n if 'location' in dir(feed):\n loc = feed.location.name.lower()\n if loc:\n if loc in locations:\n locations[loc] += 1\n else:\n locations[loc] = 1\n\n locations_sorted = []\n for pair in reversed(sorted(locations.items(), key=lambda item: item[1])):\n locations_sorted.append([pair[0], pair[1]])\n\n tags = {}\n\n for feed in recent_media:\n if 'tags' in dir(feed):\n for tag in feed.tags:\n key = tag.name.lower()\n if key in tags:\n tags[key] +=1\n else:\n tags[key] = 1\n\n tags_sorted = []\n for pair in reversed(sorted(tags.items(), key=lambda item: item[1])):\n tags_sorted.append([pair[0], pair[1]])\n\n captions = {}\n\n for feed in recent_media:\n if 'caption' in dir(feed):\n if 'text' in dir(feed.caption):\n caption = feed.caption.text.lower() + ' '\n word = \"\"\n for symb in caption:\n if (ord(symb) == ord('#')) or (ord(u'a') <= ord(symb) <= ord(u'z')) or (ord(u'а') <= ord(symb) <= ord(u'я')):\n word+=symb\n else:\n if word:\n if not (word[0] == '#'):\n if (ord(u'а') <= ord(word[0]) <= ord(u'я')):\n word = morph.parse(word)[0].normal_form\n type = morph.parse(word)[0].tag.POS\n if type == 'PREP' or type == 'CONJ' or type == 'PRCL' or type == 'INTJ' or type == 'NUMR' or type == 'COMP':\n continue\n elif (ord(u'a') <= ord(word[0]) <= ord(u'z')):\n word = stemmer.stem(word)\n type = nltk.pos_tag(nltk.tokenize.word_tokenize(word))[0][1]\n if type == 'IN' or type == 'CC' or type == 'RB' or type == 'JJR':\n continue\n if word not in stopwords:\n if word in captions:\n captions[word]+=1\n else:\n captions[word]=1\n word = \"\"\n comments = []\n comment_rating = {}\n for media in recent_media:\n for obj in media.comments:\n user_commented = unicode(obj.user.username)\n if user_commented in comment_rating:\n comment_rating[user_commented] += 1\n else:\n comment_rating[user_commented] = 1\n comment = obj.text.lower() + ' '\n comments.append(obj.text)\n word = \"\"\n for symb in comment:\n if (ord(symb) == ord('@')) or (ord(symb) == ord('#')) or (ord(u'a') <= ord(symb) <= ord(u'z')) or (ord(u'а') <= ord(symb) <= ord(u'я')):\n word+=symb\n else:\n if word:\n if not ((word[0] == '#') or (word[0] == '@')):\n if (ord(u'а') <= ord(word[0]) <= ord(u'я')):\n word = morph.parse(word)[0].normal_form\n type = morph.parse(word)[0].tag.POS\n if type == 'PREP' or type == 'CONJ' or type == 'PRCL' or type == 'INTJ' or type == 'NUMR' or type == 'COMP':\n continue\n elif (ord(u'a') <= ord(word[0]) <= ord(u'z')):\n word = stemmer.stem(word)\n type = nltk.pos_tag(nltk.tokenize.word_tokenize(word))[0][1]\n if type == 'IN' or type == 'CC' or type == 'RB' or type == 'JJR':\n continue\n if word not in stopwords:\n if word in captions:\n captions[word]+=1\n else:\n captions[word]=1\n word = \"\"\n\n captions_sorted = []\n for pair in reversed(sorted(captions.items(), key=lambda item: item[1])):\n captions_sorted.append([pair[0], pair[1]])\n comment_rating_sorted = []\n for pair in reversed(sorted(comment_rating.items(), key=lambda item: item[1])):\n comment_rating_sorted.append([pair[0], pair[1]])\n\n images = {}\n videos = []\n filters = {}\n likes_of_users = {}\n count_of_all_likes = 0\n for media in recent_media:\n if 'videos' not in dir(media) and 'images' in dir(media):\n if (media.images['standard_resolution'].url) in images:\n images[media.images['standard_resolution'].url] += media.like_count\n else:\n images[media.images['standard_resolution'].url] = media.like_count\n filter = unicode(media.filter)\n if filter in filters:\n filters[filter] += 1\n else:\n filters[filter] = 1\n\n if 'videos' in dir(media):\n videos.append(media.videos['standard_resolution'].url)\n for usr in media.likes:\n if usr.username in likes_of_users:\n likes_of_users[usr.username] += 1\n else:\n likes_of_users[usr.username] = 1\n count_of_all_likes+=media.like_count\n likes_of_users_sorted = []\n for pair in reversed(sorted(likes_of_users.items(), key=lambda item: item[1])):\n likes_of_users_sorted.append([pair[0], pair[1]])\n images_sorted = []\n for pair in reversed(sorted(images.items(), key=lambda item: item[1])):\n images_sorted.append([pair[0], pair[1]])\n filters_sorted = []\n for pair in reversed(sorted(filters.items(), key=lambda item: item[1])):\n filters_sorted.append([pair[0], pair[1]])\n\n user_bsc = api.user(user_id[0].id)\n basic_information = [user_bsc.id,\n user_bsc.full_name, user_bsc.website, user_bsc.counts[\"media\"], count_of_all_likes, user_bsc.counts[\"follows\"],\n user_bsc.counts[\"followed_by\"], user_bsc.profile_picture, user_bsc.bio]\n #print \"OK_TEST_2\"\n return [basic_information, images_sorted, videos, captions_sorted, tags_sorted,\n locations_sorted, comments, user_liked_sorted, likes_of_users_sorted, comment_rating_sorted, filters_sorted]\n else:\n return []\n# print \"IMAGES\"\n# for obj in images:\n# print obj\n#\n# print \"VIDEOS\"\n# for obj in videos:\n# print obj\n#\n# print \"CAPTIONS:\"\n# for obj in captions_sorted:\n# print obj[0], obj[1]\n#\n# print \"HASHTAGS:\"\n# for obj in tags_sorted:\n# print obj[0], obj[1]\n#\n# print \"PLACES:\"\n# for obj in locations_sorted:\n# print obj[0], obj[1]\n#\n# print \"MOST POPULAR PLACES:\"\n# mstpopular = locations_sorted[:3]\n# for obj in mstpopular:\n# print obj[0], obj[1]","sub_path":"testing.py","file_name":"testing.py","file_ext":"py","file_size_in_byte":9247,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"27106434","text":"l=[]\r\nn=int(input(\"Enter the number of elements - \"))\r\nfor i in range(0,n):\r\n a=int(input(\"Enter the element no %d- \"%(i+1)))\r\n l.append(a)\r\nl.sort()\r\nprint(l)\r\nkey=int(input(\"Enter key element - \"))\r\nlow=0\r\nhigh=n-1\r\nwhile high>=low:\r\n mid=int((high+low)/2)\r\n if l[mid]==key:\r\n print(\"Element found at index \",mid)\r\n break\r\n elif l[mid]<key :\r\n low=mid+1\r\n else:\r\n high=mid-1\r\nif high<low:\r\n print(\"element not found\")\r\n","sub_path":"14.py","file_name":"14.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"210099519","text":"\n# coding: utf-8\n\n# # Solution for Assignment 2\n# ## Image Mosaicing(Image Stitching)\n# \n# ### Problem\n# Given n images of same scene from different viewpoints, stitch those images together.\n# \n# ### Procedure\n# - Points and Feature Detection\n# - Matching\n# - Hommography\n# - Stitch 2 images\n# \n# ### Images\n# 1. img1_* => hill \n# 2. img2_* => taj mahal\n# 3. img3_* => cactus\n# 4. img4_* => text\n# \n\n# In[94]:\n\n\n# Imports \nimport os\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plt\n\n# Dirs \ntest_dir = './test_images/'\nsave_dir = './save_images/'\nif not os.path.exists(save_dir):\n os.mkdir(save_dir)\n\n\n# Parameters\nMIN_MATCH_COUNT = 40\n\nget_ipython().magic('matplotlib inline')\n\n\n# In[214]:\n\n\n# Helper functions\ndef display_images(img_list, shape,fig_size=(16,16),is_gray=None):\n \"\"\"\n Display multple images using matplotlib\n @param img_list:=> mxn matrix of images to be displayed\n @param shape:=> mxn shape\n @param is_gray:=> mxn matrix, is the i,j th the image grayscaled\n \n return None\n \"\"\"\n \n if is_gray is None:\n is_gray = np.zeros(shape)\n \n m,n = shape\n fig = plt.figure(figsize=fig_size)\n \n for i in range(m):\n for j in range(n):\n ax = fig.add_subplot(m,n,i*n + j+1)\n if is_gray[i,j] == 1:\n ax.imshow(img_list[i][j],cmap='gray')\n else:\n img_list[i][j] = cv2.resize(img_list[i][j],(200,200))\n ax.imshow(img_list[i][j]) \n ax.axis('off')\n plt.show()\n return \n\n\n# ## Step 1 -> Feature Matching \n\n# In[215]:\n\n\ndef ORB_features(img_list):\n \"\"\"\n Input: numpy image list\n Returns list sift keypoints and their discriptors for each image\n \"\"\"\n orb = cv2.ORB_create()\n\n \n kp_list = []\n des_list = []\n \n for im in img_list:\n gray = cv2.cvtColor(im, cv2.COLOR_RGB2GRAY)\n kp, des = orb.detectAndCompute(gray,None)\n kp_list.append(kp)\n des_list.append(des)\n \n return kp_list, des_list\n\n\ndef feature_matching(img_list):\n \n # create BFMatcher object\n bf = cv2.BFMatcher(cv2.NORM_HAMMING, crossCheck=True)\n\n # Get features \n kp_list, des_list = ORB_features(img_list)\n\n # Match descriptors.\n matches = bf.match(des_list[0],des_list[1])\n # Sort them in the order of their distance.\n matches = sorted(matches, key = lambda x:x.distance)[:MIN_MATCH_COUNT]\n \n draw_params = dict(matchesMask=None,\n singlePointColor=None,\n matchColor=(0, 255, 0),\n flags=2)\n \n img3 = cv2.drawMatches(img_list[0],kp_list[0],img_list[1],kp_list[1],matches,None, **draw_params)\n \n \n return matches,kp_list,des_list\n\n\n# ## Step 2 -> Homography\n\n# For calculating we are using ransac algorithm, best threashold for ransac is obtained at 30% inliers at 500 iteration\n# \n# For each iteration we randomly take 4 variables \n# Homography is a 3x3 matrix as the camera center is assumed to be same for each image\n# Our objective is the minimize the distance predicted(after transformation using homography) and the real image coordinates. \n# Hence we have a formulation simmilar to DLT. \n# We take the least eigen value vector as the homography in each iteration.\n# Iteration with least error is the most accurate homography matrix\n\n# In[216]:\n\n\ndef find_homogrpahy(correspondences):\n aList = []\n for corr in correspondences:\n \n x1 = corr[0]\n x2 = corr[1]\n a2 = [0, 0, 0, -x1[0], -x1[1], -1, x2[1] *x1[0] , x2[1]*x1[1] , x2[1] ]\n a1 = [ -x1[0], -x1[1], -1, 0, 0, 0, x2[0] * x1[0], x2[0] * x1[1], x2[0] ]\n aList.append(a1)\n aList.append(a2)\n\n matrixA = np.array(aList)\n\n u, d, v = np.linalg.svd(matrixA)\n\n h = np.reshape(v[8], (3, 3))\n\n h = h/h[2,2]\n return h\n\ndef geometricDistance(src,dst, h):\n p1 = np.array([src[0],src[1], 1]).T\n estimatep2 = np.dot(h, p1)\n estimatep2 = estimatep2/estimatep2[2]\n\n p2 = np.matrix([ dst[0], dst[1], 1]).T\n error = p2 - estimatep2\n return np.linalg.norm(error)\n\ndef ransac(src_pts,dst_pts, thresh, dist=5):\n maxInliers = []\n finalH = None\n for i in range(500):\n r4 = np.random.randint(0,len(src_pts),4)\n randomFour = []\n for x in r4:\n randomFour.append((src_pts[x,0,:], dst_pts[x,0,:] ))\n \n h = find_homogrpahy(randomFour)\n \n inliers = []\n\n for i in range(len(src_pts)):\n d = geometricDistance( src_pts[i,0,:], dst_pts[i,0,:] , h)\n if d < dist:\n inliers.append(cr)\n \n if len(inliers) > len(maxInliers):\n maxInliers = inliers\n finalH = h\n \n if len(maxInliers) > len(src_pts)*thresh:\n break\n \n return finalH\n\n\n# In[217]:\n\n\ndef calculate_homography(matches,kp_list):\n \n if len(matches) < MIN_MATCH_COUNT:\n return None\n best_matches = matches[0:MIN_MATCH_COUNT]\n src_pts = np.float32([ kp_list[0][m.queryIdx].pt for m in best_matches ]).reshape(-1,1,2)\n dst_pts = np.float32([ kp_list[1][m.trainIdx].pt for m in best_matches ]).reshape(-1,1,2)\n M = ransac(src_pts, dst_pts,0.1,dist=5.0)\n \n return M\n\n\n# In[219]:\n\n\ndef get_transformation(img_list):\n # Print original images\n# display_images([img_list],(1,2)) \n\n # Feature matching \n matches,kp_list,des_list = feature_matching(img_list)\n\n # Homography\n M = calculate_homography(matches,kp_list)\n\n h,w,c = img_list[0].shape\n pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)\n dst = cv2.perspectiveTransform(pts,M) \n img2 = cv2.polylines(img_list[1].copy(),[np.int32(dst)],True,255,3, cv2.LINE_AA)\n\n h,w,c = img_list[1].shape\n pts = np.float32([ [0,0],[0,h-1],[w-1,h-1],[w-1,0] ]).reshape(-1,1,2)\n dst = cv2.perspectiveTransform(pts,np.linalg.inv(M)) \n img1 = cv2.polylines(img_list[0].copy(),[np.int32(dst)],True,255,3, cv2.LINE_AA)\n\n # Print original images\n display_images([[img1,img2]],(1,2)) \n\n return M\n\n\n# ## Step3 -> Stitch Images\n\n# In[220]:\n\n\ndef stitching(img_list,M):\n h,w,c = img_list[0].shape\n dst = cv2.warpPerspective(img_list[1],np.linalg.inv(M),(2*w,2*h))\n dst[0 : h , 0: w,:] = img_list[0]\n return dst\n\n\n# In[221]:\n\n\ndef stitching(img_list,M):\n dst = cv2.warpPerspective(img_list[1],np.linalg.inv(M),(img_list[0].shape[1]+img_list[1].shape[1],img_list[0].shape[0]))\n dst[0:img_list[0].shape[0],0:img_list[0].shape[1],:] = img_list[0]\n return dst\n\n\n# In[222]:\n\n\n# Remove black region from width\ndef remove_black(final_img):\n r = np.where(np.sum(final_img,(0,2)) == 0)[0][0]\n final_img = final_img[:,0:r,:]\n return final_img\n\n\n# ## Show Results \n\n# In[224]:\n\n\n# Case 1 Hill top\n\n# Load images\nimg_list = []\nfor i in range(1,3):\n im = cv2.imread(os.path.join(test_dir,'img1_{}.png'.format(i)))\n img_list.append(im)\n\nM = get_transformation(img_list) \nfinal_img = stitching(img_list,M)\nfinal_img = remove_black(final_img)\n\ncv2.imwrite(os.path.join(save_dir,'final_img_1.png'),final_img)\n\nfinal_img = cv2.resize(final_img,(400,200))\nfig = plt.figure(figsize=(16,16))\nplt.imshow(final_img)\nplt.axis('off')\nplt.show()\n\n\n# In[225]:\n\n\n# Case 2 Taj Mahal\n\n# Load images\nimg_list = []\nfor i in range(1,7):\n im = cv2.imread(os.path.join(test_dir,'img2_{}.png'.format(i)))\n img_list.append(im)\n\n# 1 & 2 \nM = get_transformation(img_list[0:2]) \nfinal_img = stitching(img_list,M)\nfinal_img = remove_black(final_img)\n\n# fig = plt.figure(figsize=(16,16))\n# plt.imshow(final_img)\n# plt.axis('off')\n# plt.show()\n\n# 3\nM = get_transformation([final_img,img_list[2]]) \nfinal_img = stitching([final_img,img_list[2]],M)\nfinal_img = remove_black(final_img)\n\n# fig = plt.figure(figsize=(16,16))\n# plt.imshow(final_img)\n# plt.axis('off')\n# plt.show()\n\n# 4\nM = get_transformation([img_list[3],final_img]) \nfinal_img = stitching([img_list[3],final_img],M)\nfinal_img = remove_black(final_img)\n\nfig = plt.figure(figsize=(16,16))\nplt.imshow(final_img)\nplt.axis('off')\nplt.show()\n\n# 5\nM = get_transformation([final_img,img_list[4]]) \nfinal_img = stitching([final_img,img_list[4]],M)\nfinal_img = remove_black(final_img)\n\n# fig = plt.figure(figsize=(16,16))\n# plt.imshow(final_img)\n# plt.axis('off')\n# plt.show()\n\n\n# 6\nM = get_transformation([final_img,img_list[5]]) \nfinal_img = stitching([final_img,img_list[5]],M)\nfinal_img = remove_black(final_img)\n\n# fig = plt.figure(figsize=(16,16))\n# plt.imshow(final_img)\n# plt.axis('off')\n# plt.show()\n\n\ncv2.imwrite(os.path.join(save_dir,'final_img_2.png'),final_img) \n\n\n\n\n# In[227]:\n\n\n# Case 3 Cactus\n\n# Load images\nimg_list = []\nfor i in range(1,3):\n im = cv2.imread(os.path.join(test_dir,'img3_{}.png'.format(i)))\n img_list.append(im)\n \nM = get_transformation(img_list)\n# Stitch images\ncv2.imwrite(os.path.join(save_dir,'final_img_3.png'),final_img)\n\nfinal_img = stitching(img_list,M)\n\nfinal_img = cv2.resize(final_img,(400,200))\n\nfig = plt.figure(figsize=(16,16))\nplt.imshow(final_img)\nplt.show()\n\n\n# In[228]:\n\n\n# Case 4 Text\n\n# Load images\nimg_list = []\nfor i in range(1,3):\n im = cv2.imread(os.path.join(test_dir,'img4_{}.jpg'.format(i)))\n img_list.append(im)\n\nM = get_transformation(img_list)\n# Stitch images\nfinal_img = stitching(img_list,M)\n\ncv2.imwrite(os.path.join(save_dir,'final_img_4.png'),final_img)\nfinal_img = cv2.resize(final_img,(400,200))\n\nfig = plt.figure(figsize=(16,16))\nplt.imshow(final_img)\nplt.show()\n\n\n","sub_path":"CV/Assign2/Solution.py","file_name":"Solution.py","file_ext":"py","file_size_in_byte":9616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"87925866","text":"#!/usr/bin/env python\n# -*- encoding: utf-8 -*-\n# Created on 2017-03-24 15:05:41\n# Project: ifengnew2\n\nfrom pyspider.libs.base_handler import *\nimport re\nimport pymysql\n\n\nclass Handler(BaseHandler):\n crawl_config = {}\n\n def __init__(self):\n self.connect = pymysql.connect(host='localhost', port=3306, user='root', passwd='root', db='example',\n charset='utf8')\n\n def insert_mysql(self, title, url):\n try:\n cursor = self.connect.cursor()\n sql = 'insert into test(title,url) values(\"%s\",\"%s\")' % (title, url)\n print(sql)\n cursor.execute(sql)\n self.connect.commit()\n except Exception as e:\n print(e)\n self.connect.rollback()\n\n @every(minutes=24 * 60)\n def on_start(self):\n self.crawl('http://www.ifeng.com/', callback=self.index_page, validate_cert=False)\n\n @config(age=10 * 24 * 60 * 60)\n def index_page(self, response):\n for each in response.doc('a[href^=\"http://news.ifeng.com/a/\"]').items():\n self.crawl(each.attr.href, callback=self.detail_page, validate_cert=False)\n\n @config(priority=2)\n def detail_page(self, response):\n title = response.doc('title').text()\n url = response.url\n self.insert_mysql(title, url)\n return {\n \"url\": response.url,\n \"title\": response.doc('title').text(),\n }","sub_path":"pyBase/ifengnews.py","file_name":"ifengnews.py","file_ext":"py","file_size_in_byte":1432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"338773065","text":"\"\"\"\nThe gametime module handles the global passage of time in the mud.\n\nIt also supplies some useful methods to convert between\nin-mud time and real-world time as well allows to get the\ntotal runtime of the server and the current uptime.\n\"\"\"\nfrom __future__ import division\n\nfrom time import time\nfrom django.conf import settings\nfrom evennia.server.models import ServerConfig\n\n# Speed-up factor of the in-game time compared\n# to real time.\n\nTIMEFACTOR = settings.TIME_FACTOR\n\n# Only set if gametime_reset was called at some point.\nGAME_TIME_OFFSET = ServerConfig.objects.conf(\"gametime_offset\", default=0)\n\n# Common real-life time measure, in seconds.\n# You should not change this.\n\nREAL_MIN = 60.0 # seconds per minute in real world\n\n# Game-time units, in real-life seconds. These are supplied as\n# a convenient measure for determining the current in-game time,\n# e.g. when defining in-game events. The words month, week and year can\n# be used to mean whatever units of time are used in the game.\n\nMIN = settings.TIME_SEC_PER_MIN\nHOUR = MIN * settings.TIME_MIN_PER_HOUR\nDAY = HOUR * settings.TIME_HOUR_PER_DAY\nWEEK = DAY * settings.TIME_DAY_PER_WEEK\nMONTH = WEEK * settings.TIME_WEEK_PER_MONTH\nYEAR = MONTH * settings.TIME_MONTH_PER_YEAR\n\n# these are kept updated by the server maintenance loop\nSERVER_START_TIME = 0.0\nSERVER_RUNTIME_LAST_UPDATED = 0.0\nSERVER_RUNTIME = 0.0\n\n\ndef _format(seconds, *divisors) :\n \"\"\"\n Helper function. Creates a tuple of even dividends given a range\n of divisors.\n\n Args:\n seconds (int): Number of seconds to format\n *divisors (int): a sequence of numbers of integer dividends. The\n number of seconds will be integer-divided by the first number in\n this sequence, the remainder will be divided with the second and\n so on.\n Returns:\n time (tuple): This tuple has length len(*args)+1, with the\n last element being the last remaining seconds not evenly\n divided by the supplied dividends.\n\n \"\"\"\n results = []\n seconds = int(seconds)\n for divisor in divisors:\n results.append(seconds // divisor)\n seconds %= divisor\n results.append(seconds)\n return tuple(results)\n\n\n# Access functions\n\ndef runtime(format=False):\n \"\"\"\n Get the total runtime of the server since first start (minus\n downtimes)\n\n Args:\n format (bool, optional): Format into a time representation.\n\n Returns:\n time (float or tuple): The runtime or the same time split up\n into time units.\n\n \"\"\"\n runtime = SERVER_RUNTIME + (time() - SERVER_RUNTIME_LAST_UPDATED)\n if format:\n return _format(runtime, 31536000, 2628000, 604800, 86400, 3600, 60)\n return runtime\n\n\ndef uptime(format=False):\n \"\"\"\n Get the current uptime of the server since last reload\n\n Args:\n format (bool, optional): Format into time representation.\n\n Returns:\n time (float or tuple): The uptime or the same time split up\n into time units.\n\n \"\"\"\n uptime = time() - SERVER_START_TIME\n if format:\n return _format(uptime, 31536000, 2628000, 604800, 86400, 3600, 60)\n return uptime\n\n\ndef gametime(format=False):\n \"\"\"\n Get the total gametime of the server since first start (minus downtimes)\n\n Args:\n format (bool, optional): Format into time representation.\n\n Returns:\n time (float or tuple): The gametime or the same time split up\n into time units.\n\n \"\"\"\n gametime = (runtime() - GAME_TIME_OFFSET) * TIMEFACTOR\n if format:\n return _format(gametime, YEAR, MONTH, WEEK, DAY, HOUR, MIN)\n return gametime\n\n\ndef reset_gametime():\n \"\"\"\n Resets the game time to make it start from the current time.\n\n \"\"\"\n global GAME_TIME_OFFSET\n GAME_TIME_OFFSET = runtime()\n ServerConfig.objects.conf(\"gametime_offset\", GAME_TIME_OFFSET)\n\n\n# Conversion functions\n\n\ndef gametime_to_realtime(secs=0, mins=0, hrs=0, days=0,\n weeks=0, months=0, yrs=0, format=False):\n \"\"\"\n This method helps to figure out the real-world time it will take until an\n in-game time has passed. E.g. if an event should take place a month later\n in-game, you will be able to find the number of real-world seconds this\n corresponds to (hint: Interval events deal with real life seconds).\n\n Kwargs:\n times (int): The various components of the time.\n format (bool): Formatting the output.\n\n Returns:\n time (float or tuple): The realtime difference or the same\n time split up into time units.\n\n Example:\n gametime_to_realtime(days=2) -> number of seconds in real life from\n now after which 2 in-game days will have passed.\n\n \"\"\"\n realtime = (secs + mins * MIN + hrs * HOUR + days * DAY + weeks * WEEK + \\\n months * MONTH + yrs * YEAR) / TIMEFACTOR\n if format:\n return _format(realtime, 31536000, 2628000, 604800, 86400, 3600, 60)\n return realtime\n\n\ndef realtime_to_gametime(secs=0, mins=0, hrs=0, days=0,\n weeks=0, months=0, yrs=0, format=False):\n \"\"\"\n This method calculates how much in-game time a real-world time\n interval would correspond to. This is usually a lot less\n interesting than the other way around.\n\n Kwargs:\n times (int): The various components of the time.\n format (bool): Formatting the output.\n\n Returns:\n time (float or tuple): The gametime difference or the same\n time split up into time units.\n\n Example:\n realtime_to_gametime(days=2) -> number of game-world seconds\n corresponding to 2 real days.\n\n \"\"\"\n gametime = TIMEFACTOR * (secs + mins * 60 + hrs * 3600 + days * 86400 +\n weeks * 604800 + months * 2628000 + yrs * 31536000)\n if format:\n return _format(gametime, YEAR, MONTH, WEEK, DAY, HOUR, MIN)\n return gametime\n\n","sub_path":"evennia/utils/gametime.py","file_name":"gametime.py","file_ext":"py","file_size_in_byte":5970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"186006231","text":"operator = input(\"Enter the operator you are using:\")\r\n\r\nnum1 = int(input(\"Enter number 1:\"))\r\nnum2 = int(input(\"Enter number 2:\"))\r\n\r\nif operator == \"+\":\r\n if (num1 == 56 and num2 == 9) or (num1 == 9 and num2 == 56):\r\n print(\"The addition is : 77\")\r\n else:\r\n print(\"The addition is :\", num1 + num2)\r\nif operator == \"-\":\r\n print(\"The substration is : \", num1 - num2)\r\nif operator == \"*\":\r\n if (num1 == 45 and num2 == 3) or (num1 == 3 and num2 == 45):\r\n print(\"The multiplication is : 555\")\r\n else:\r\n print(\"The multiplication is :\", num1 * num2)\r\nif operator == \"/\":\r\n if (num1 == 56 and num2 == 6) or (num1 == 6 and num2 == 56):\r\n print(\"The division is : 4\")\r\n else:\r\n print(\"The division is : \", num1 / num2)\r\n","sub_path":"Faulty-calculator.py","file_name":"Faulty-calculator.py","file_ext":"py","file_size_in_byte":777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"441821922","text":"# Copyright (c) 2015 Aptira Pty Ltd.\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.\n\n\"\"\"\nClient side of the migration RPC API.\n\"\"\"\n\nfrom oslo_config import cfg\nimport oslo_messaging as messaging\n\nfrom guts.objects import base as objects_base\nfrom guts import rpc\n\n\nCONF = cfg.CONF\n\n\nclass MigrationAPI(object):\n \"\"\"Client side of the migration rpc API.\"\"\"\n\n BASE_RPC_API_VERSION = '1.0'\n\n def __init__(self, topic=None):\n super(MigrationAPI, self).__init__()\n target = messaging.Target(topic=CONF.migration_topic,\n version=self.BASE_RPC_API_VERSION)\n serializer = objects_base.GutsObjectSerializer()\n\n self.client = rpc.get_client(target, version_cap=None,\n serializer=serializer)\n\n def validate_for_migration(self, ctxt, migration_ref):\n cctxt = self.client.prepare(version='1.8')\n return cctxt.call(ctxt, 'validate_for_migration',\n migration_ref=migration_ref)\n\n def create_migration(self, ctxt, migration_ref):\n cctxt = self.client.prepare(version='1.8')\n cctxt.cast(ctxt, 'create_migration', migration_ref=migration_ref)\n\n def fetch_vms(self, ctxt, source_hypervisor_id):\n cctxt = self.client.prepare(version='1.8')\n cctxt.cast(ctxt, 'fetch_vms',\n source_hypervisor_id=source_hypervisor_id)\n","sub_path":"guts/migration/rpcapi.py","file_name":"rpcapi.py","file_ext":"py","file_size_in_byte":1945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"193030746","text":"#\n# Copyright (C) 2014 Dell, 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#\nfrom setuptools import setup, find_packages\n\ng_version = \"0.3\"\n\nsetup(name='dcmdocker',\n version=g_version,\n description=\"Docker extensions for the dcm-agent\",\n author=\"Dell Software Group\",\n author_email=\"support@enstratius.com\",\n url=\"http://www.enstratius.com/\",\n packages=find_packages(),\n install_requires=[\"docker-py == 1.3.1\"],\n include_package_data=True,\n classifiers=[\n \"Development Status :: 4 - Beta\",\n \"Environment :: Console\",\n \"Intended Audience :: System Administrators\",\n \"License :: OSI Approved :: Apache Software License\",\n \"Operating System :: POSIX :: Linux\",\n \"Programming Language :: Python\",\n \"Topic :: System :: Clustering\",\n \"Topic :: System :: Distributed Computing\"\n ]\n )\n","sub_path":"extensions/docker/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"580874176","text":"import logging\nfrom concurrent.futures import ThreadPoolExecutor\nfrom functools import partial\n\nfrom wqueue.handlers.base_handler import BaseHandler\n\n\nlogger = logging.getLogger(__name__)\n\n\nclass MultiThreadHandler(BaseHandler):\n def __init__(self, message_queue, config=None):\n super(MultiThreadHandler, self).__init__(message_queue, config)\n self.executor = ThreadPoolExecutor(max_workers=self.config['thread_count'])\n\n def stop(self):\n super(MultiThreadHandler, self).stop()\n self.executor.shutdown(wait=True)\n\n def on_event(self, function, event):\n self.executor.submit(\n partial(self._execute_and_log_possible_exception, function, event.data)\n )\n\n def _execute_and_log_possible_exception(self, function, argument):\n try:\n function(argument)\n except Exception as exception:\n logger.exception(\"Got exception when executing function %s\" % function)\n","sub_path":"wqueue/handlers/multi_thread_handler.py","file_name":"multi_thread_handler.py","file_ext":"py","file_size_in_byte":952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"29402191","text":"import pandas as pd\nimport seaborn as sns\nimport os\nimport sys\nimport glob\nimport matplotlib.pyplot as plt\nfrom matplotlib import rc\nrc('font',family ='Malgun Gothic')\n\n# 데이터 input, output 경로 지정\nBASE_DIRECTORY = \"D:\\\\국내주식\"\n\nDATA_PATH = \"stock\"\nDATA_DIRECTORY = os.path.join(BASE_DIRECTORY, DATA_PATH)\n\nOUTPUT_PATH = \"OUTPUT\"\nOUTPUT_DIRECTORY = os.path.join(BASE_DIRECTORY, OUTPUT_PATH)\n\nfiles = glob.glob(os.path.join(DATA_DIRECTORY,'*'))\n\ndef get_merged_df(files):\n \"\"\"\n :param files, csv files:\n :return: DataFrame\n \"\"\"\n df_list = []\n for file in files:\n df = pd.read_csv(file)\n date_str_with_type = os.path.basename(file)\n date_str = date_str_with_type.split('.')[0]\n df['날짜'] = date_str\n df_list.append(df)\n\n merged = pd.concat(df_list)\n return merged\n\ndef set_merged_df(merged):\n result = merged\n\n # 날짜 데이터 변환\n result['날짜'] = pd.to_datetime(merged['날짜'], format='%Y%m%d')\n\n # 문자열 데이터중 숫자형으로 바꾸는 것 변환\n string_columns = ['현재가', '대비', '시가', '고가', '저가', '거래량', '거래대금', '시가총액', '상장주식수']\n for col in string_columns:\n result[col] = result[col].str.replace(\",\", \"\").astype(float)\n\n return result\n\ndef get_related_top(merge, stock_name ,n=30):\n pivot_merge = merge.pivot_table(index='날짜', columns='종목명', values='시가')\n corr_matrix = pivot_merge.corr()\n result = corr_matrix[stock_name].sort_values(ascending=False)[:n].to_frame()\n outpath = os.path.join(OUTPUT_DIRECTORY,stock_name+\"_TOP_\"+str(n)+\".xlsx\")\n result.to_excel(outpath)\n return result\n\n\nmerged = get_merged_df(files)\nmerged = set_merged_df(merged)\nfinished = get_related_top(merged,'셀리드')\n\nprint(finished)\n","sub_path":"src_temp/상관관계분석/TREND분석-12월-셀리드.py","file_name":"TREND분석-12월-셀리드.py","file_ext":"py","file_size_in_byte":1826,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"581712308","text":"\"\"\"\n11. Container With Most Water\nhttps://leetcode.com/problems/container-with-most-water/\n\n_author: Kashif Memon\n_python_version: 3.7.2\n\"\"\"\n\n\nclass Solution:\n def maxArea(self, height: 'List[int]') -> int:\n start = 0\n end = len(height) - 1\n maxA = 0\n\n while start < end:\n breadth = min(height[start], height[end])\n length = end - start\n maxA = max(maxA, length * breadth)\n if height[start] > height[end]:\n end -= 1\n else:\n start += 1\n return maxA\n\n\ndef main():\n print(Solution().maxArea([1, 8, 6, 2, 5, 4, 8, 3, 7]))\n\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"solutions-to-leetcode/11_container_with_most_water.py","file_name":"11_container_with_most_water.py","file_ext":"py","file_size_in_byte":698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"112418846","text":"import numpy as np\nimport pandas\n\nfrom keras.models import Sequential, Model\nfrom keras.layers import Dense, Dropout, Input\nfrom keras.wrappers.scikit_learn import KerasRegressor\nfrom sklearn.preprocessing import StandardScaler\nfrom sql2v_explain import sql_explainer\nfrom sklearn.model_selection import cross_val_score, KFold\n\nq_path = '/home/zxh/join-order-benchmark/'\nNAME_DATASET = 'job_trainData_sql.txt'\nNUM_SQL_FEATURE = 38\nNUM_DB_METRIC = 16\n\n# regression\ndef baseline_model(num_feature = NUM_SQL_FEATURE, num_output = NUM_DB_METRIC):\n # create model\n model = Sequential()\n model.add(Dense(num_feature * 2, input_dim=num_feature, kernel_initializer='normal', activation='relu'))\n model.add(Dense(num_feature, kernel_initializer='normal', activation='relu'))\n model.add(Dense(num_output, kernel_initializer='normal'))\n # Compile model\n model.compile(loss='mean_squared_error', optimizer='adam')\n\n return model\n\n\nclass SqlParser:\n # DML: select delete insert update 1 2 3 4\n\n def __init__(self, benchmark='job', sql = '10a', cur_op='oltp_read_write.lua', num_event=1000, p_r_range=0.6, p_u_index=0.2, p_i=0.1, p_d=0.1):\n\n if benchmark == 'job':\n\n self.SQL_NAMES = [sql]\n\n ########### Convert: sql statement => feature vector\n explainer = sql_explainer(sql)\n self.sql_vector = np.array(explainer.explain())\n\n print(\"### SQL_VECTOR\")\n print(self.sql_vector)\n ################################################################################################################################\n\n ######### Prepare training data\n fs = open(NAME_DATASET, 'r')\n df = pandas.read_csv(fs, sep=' ', header=None)\n lt_sql = df.values\n\n sql_X = lt_sql[:, 0:NUM_SQL_FEATURE] # op_type events table_size\n sql_Y = lt_sql[:, NUM_SQL_FEATURE:]\n print(\"### Format (input, output):\")\n print(sql_X[0])\n print(sql_Y[0])\n\n sc_X = StandardScaler()\n self.X_train = sc_X.fit_transform(sql_X)\n # X_test = X_train[50:]\n # X_train = X_train[:50]\n\n sc_Y = StandardScaler()\n self.Y_train = sc_Y.fit_transform(sql_Y)\n # Y_test = Y_train[20:] # 2:8\n # Y_train = Y_train[:50]\n ################################################################################################################################\n\n def train(self):\n ######### Train model\n seed = 7\n np.random.seed(seed)\n # estimators = []\n # estimators.append(('standardize', StandardScaler()))\n # estimators.append(('mlp', KerasRegressor(build_fn=baseline_model, epochs=50, batch_size=50, verbose=0)))\n self.estimator = KerasRegressor(build_fn=baseline_model, epochs=1000, batch_size=50, verbose=1) # epochs\n kfold = KFold(n_splits=10, random_state=seed) # n_splites packs, with 1 packet as the test set\n results = cross_val_score(self.estimator, self.X_train, self.Y_train, cv=kfold)\n\n self.estimator.fit(self.X_train, self.Y_train)\n ################################################################################################################################\n\n def estimate(self):\n # Estimate the accurancy of this trained model\n pass\n\n\n def predict_sql_resource(self):\n\n return self.estimator.predict(\n self.sql_vector)\n\n def update(self):\n pass\n\n\nparser = SqlParser()\nparser.train()\n\n","sub_path":"code/AITuning_haowen/Train_Estimator/Estimator_sql.py","file_name":"Estimator_sql.py","file_ext":"py","file_size_in_byte":3594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"550017831","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Mon Dec 28 16:29:57 2020\n\n@author: alida\n\"\"\"\n\n#import necessery libraries\n\nfrom sklearn.datasets import load_iris\nfrom sklearn.metrics import accuracy_score\nimport numpy as np\nfrom sklearn.tree import DecisionTreeClassifier\n\niris = load_iris()\n\nx = iris.data\ny = iris.target\ny_names = iris.target_names\n\ntest_ids = np.random.permutation(len(x))\n\n#manually split data to train and test sublists, test wil be the last 10 entries\nx_train = x[test_ids[:-10]]\nx_test = x[test_ids[-10:]]\ny_train= y[test_ids[:-10]]\ny_test = y[test_ids[-10:]]\n\n\n#creating a model as DecisionTreeClassifier\n\nclf = DecisionTreeClassifier()\n\n#training (fitting) the train data with model\nclf.fit(x_train,y_train)\n\n# after training, we are predicting y_Test values with x_test values \npredict = clf.predict(x_test)\n\n# after the prediction is done, lets calculate the accuracy score \n\naccuarcy = accuracy_score(predict, y_test)*100\n\n#now lets print our predicted values, test values and the accuracy score\n\nprint(\"actual values = {act} \".format(act = y_test))\nprint(\"predicted values = {pred}\".format(pred=predict))\nprint(\"the accuracy score is {}\".format(accuarcy))\n\n#actual values = [2 2 2 1 0 1 0 0 1 2] \n#predicted values = [2 2 2 1 0 1 0 0 1 2]\n#the accuracy score is 100.0","sub_path":"İris-DecisionTreeClassifier/src.py","file_name":"src.py","file_ext":"py","file_size_in_byte":1290,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"519774112","text":"import numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torch.autograd as autograd\n\nfrom collections import namedtuple\nfrom itertools import count\nfrom IPython.display import clear_output\nfrom torch.autograd import Variable\nfrom tqdm import tnrange\n\n\nfrom hyper_parametrs import *\n\n\nSavedAction = namedtuple('SavedAction', ['action', 'prob', 'value'])\n\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport torch.optim as optim\nimport torch.autograd as autograd\n\nfrom collections import namedtuple\nfrom itertools import count\nfrom IPython.display import clear_output\nfrom torch.autograd import Variable\nfrom tqdm import tnrange\n\n\nfrom hyper_parametrs import *\n\n\nSavedAction = namedtuple('SavedAction', ['action', 'prob', 'value'])\n\n\nclass Agent(nn.Module):\n def __init__(self, state_dim, action_dim, name, metric_type=\"no\"):\n super(Agent, self).__init__()\n self.name = name\n self.state_dim = state_dim\n self.action_dim = action_dim\n self.metric_type = metric_type\n \n #self.affine1 = nn.Linear(state_dim, hidden_layer1_size) #common\n #nn.init.xavier_normal(self.self.affine1.weight) #common\n \n self.common = nn.Linear(state_dim, hidden_layer1_size)\n \n self.action_head = nn.Sequential(nn.Linear(state_dim, hidden_layer1_size), nn.ReLU(),\n nn.Linear(hidden_layer1_size, hidden_layer2_size), nn.ReLU(),\n #nn.Linear(hidden_layer2_size, hidden_layer3_size), nn.ReLU(),\n nn.Linear(hidden_layer2_size, action_dim))\n \n self.value_head = nn.Sequential(nn.Linear(state_dim, hidden_layer1_size), nn.ReLU(),\n nn.Linear(hidden_layer1_size, hidden_layer2_size), nn.ReLU(),\n #nn.Linear(hidden_layer2_size, hidden_layer3_size), nn.ReLU(),\n nn.Linear(hidden_layer2_size, 1))\n \n# self.base_action = nn.Linear(state_dim, hidden_layer1_size) #separate\n# nn.init.xavier_normal(self.base_action.weight) #separate\n \n# self.base_value = nn.Linear(state_dim, hidden_layer1_size) #separate\n# nn.init.xavier_normal(self.base_value.weight) #separate\n \n# self.action_linear = nn.Linear(hidden_layer1_size, hidden_layer2_size)\n# nn.init.xavier_normal(self.action_linear.weight)\n# self.action_head = nn.Linear(hidden_layer2_size, action_dim)\n# nn.init.xavier_normal(self.action_head.weight)\n \n# self.value_linear = nn.Linear(hidden_layer1_size, hidden_layer2_size)\n# nn.init.xavier_normal(self.value_linear.weight)\n# self.value_head = nn.Linear(hidden_layer2_size, 1)\n# nn.init.xavier_normal(self.value_head.weight)\n\n self.saved_actions = []\n self.rewards = []\n\n def forward(self, s, s_star=None):\n x_onehot = torch.zeros((1, self.state_dim))\n x_onehot[0][s] = 1\n if(not (s_star is None)):\n x_onehot[0][self.state_dim // 2 + s_star] = 1\n x_onehot = Variable(x_onehot)\n \n #x = F.relu(self.affine1(x_onehot)) #common\n# x_action = F.relu(self.base_action(x_onehot))\n# x_value = F.relu(self.base_value(x_onehot))\n \n# action_scores = self.action_head(F.relu(self.action_linear(x_action)))\n# state_values = self.value_head(F.relu(self.value_linear(x_value)))\n action_scores = self.action_head(x_onehot)\n state_values = self.value_head(x_onehot)\n return F.softmax(action_scores), state_values\n \n def select_action(self, state, s_star, cache_action=True):\n #state = torch.from_numpy(state).int().unsqueeze(0)\n probs, state_value = self.forward(state, s_star)\n action = probs.multinomial()\n if(cache_action == True):\n self.saved_actions.append(SavedAction(action, probs, state_value))\n return action.data\n\nclass Expert:\n def __init__(self, Alice_type):\n self.type = Alice_type\n \n def get_action_probs(self, env, s):\n if(self.type == 'left_top_corner'):\n if(s == 0):\n return np.array([0, 0, 0, 0, 1])\n if(s % int(np.sqrt(env.nS)) == 0):\n return np.array([1, 0, 0, 0, 0])\n if(s < int(np.sqrt(env.nS))):\n return np.array([0, 0, 0, 1, 0])\n return np.array([0.5, 0, 0, 0.5, 0])\n if(self.type == 'right_bottom_corner'):\n if(s == env.nS - 1):\n return np.array([0, 0, 0, 0, 1])\n if((s + 1) % int(np.sqrt(env.nS)) == 0):\n return np.array([0, 0, 1, 0, 0])\n if(s + int(np.sqrt(env.nS)) >= env.nS):\n return np.array([0, 1, 0, 0, 0])\n return np.array([0, 0.5, 0.5, 0, 0])\n if(self.type == 'global_optimal'):\n if (s // np.sqrt(env.nS) <= int(np.sqrt(env.nS)) - (s % int(np.sqrt(env.nS))) - 1):\n if(s == 0):\n return np.array([0, 0, 0, 0, 1])\n if s % int(np.sqrt(env.nS)) == 0:\n return np.array([1, 0, 0, 0, 0]) \n if s < int(np.sqrt(env.nS)):\n return np.array([0, 0, 0, 1, 0]) \n return np.array([0.5, 0, 0, 0.5, 0]) \n else:\n if(s == env.nS - 1):\n return np.array([0, 0, 0, 0, 1])\n if (s + 1) % (int(np.sqrt(env.nS))) == 0:\n return np.array([0, 0, 1, 0, 0]) \n if(s + int(np.sqrt(env.nS)) >= env.nS):\n return np.array([0, 1, 0, 0, 0]) \n return np.array([0, 0.5, 0.5, 0, 0]) \n\n def select_action(self, env, s):\n action_probs = self.get_action_probs(env, s)\n action = np.random.choice(np.arange(5), p=action_probs)\n \n return action\n\n def precompute_v_function(self, env):\n self.v = np.zeros(env.nS)\n for s in range(len(self.v)):\n if(self.type == 'left_top_corner'):\n self.v[s] = -(s % int(np.sqrt(env.nS)) + s // int(np.sqrt(env.nS)))\n if(self.type == 'right_bottom_corner'):\n self.v[s] = (-((int(np.sqrt(env.nS)) - (s % int(np.sqrt(env.nS))) - 1) \n + (int(np.sqrt(env.nS)) - s // int(np.sqrt(env.nS)) - 1)))\n if(self.type == 'global_optimal'):\n left_top = s % int(np.sqrt(env.nS)) + s // int(np.sqrt(env.nS))\n right_bottom = ((int(np.sqrt(env.nS)) - (s % int(np.sqrt(env.nS))) - 1) \n + (int(np.sqrt(env.nS)) - s // int(np.sqrt(env.nS)) - 1))\n self.v[s] = -min(left_top, right_bottom)\n \n def get_goal(self, env, s):\n if(self.type == 'left_top_corner'):\n return 0\n if(self.type == 'right_bottom_corner'):\n return env.nS - 1 \n if(self.type == 'global_optimal'):\n if (s // np.sqrt(env.nS) <= int(np.sqrt(env.nS)) - (s % int(np.sqrt(env.nS))) - 1):\n return 0\n if (s // np.sqrt(env.nS) > int(np.sqrt(env.nS)) - (s % int(np.sqrt(env.nS))) - 1):\n return env.nS - 1\n\n def v_function(self, s):\n return self.v[s]\n\n\nclass NovelExpert:\n def __init__(self, agent_type, action_probs, v_function, goal_map):\n self.type = agent_type\n self.action_probs = action_probs\n self.v_function = v_function\n self.goal_map = goal_map\n\n def get_action_probs(self, env, s):\n return self.action_probs[s]\n \n def select_action(self, env, s):\n action_probs = self.action_probs[s]\n action = np.random.choice(np.arange(5), p=action_probs)\n \n return action\n \n def get_goal(self, s):\n return self.goal_map[s]\n\n def get_v_function(self, s):\n return self.v_function[s]\n\n \nfeature_extractor_layer1_size = 100\nfeature_extractor_layer2_size = 100\nsimilarity_layer1_size = 100\n\nclass SiameseNet(nn.Module):\n def __init__(self, state_dim):\n super(SiameseNet, self).__init__()\n \n self.state_dim = state_dim\n \n self.feature_extractor = nn.Sequential(nn.Linear(state_dim, feature_extractor_layer1_size), nn.ReLU(),\n nn.Linear(feature_extractor_layer1_size, feature_extractor_layer2_size), nn.ReLU()\n )\n self.similarity = nn.Sequential(nn.Linear(2 * feature_extractor_layer2_size, similarity_layer1_size), nn.ReLU(),\n nn.Linear(similarity_layer1_size, 1), nn.Sigmoid()\n )\n \n def to_one_hot(self, states):\n states_one_hot = np.zeros((len(states), self.state_dim))\n for i in range(len(states)):\n states_one_hot[i][states[i]] = 1\n\n return Variable(torch.FloatTensor(states_one_hot), requires_grad=False)\n \n def forward(self, X):\n s1_one_hot = self.to_one_hot(X[:, 0])\n s2_one_hot = self.to_one_hot(X[:, 1])\n s1_features = self.feature_extractor(s1_one_hot)\n s2_features = self.feature_extractor(s2_one_hot)\n return self.similarity(torch.cat([s1_features, s2_features], dim=-1))","sub_path":"experts-learning/nets.py","file_name":"nets.py","file_ext":"py","file_size_in_byte":9421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"285956952","text":"import csv\nimport xlrd #importing library\nimport pprint as p #importing library\ncsvfile = open('/users/2020jkapasi/documents/book1.xlsx', \"r\")\nreader = csv.DictReader(csvfile)\n\ndef get_data(file, sheet): #defining function to take in the file and the excel sheet\n book = xlrd.open_workbook(file) #function from xlrd\n sheet = book.sheet_by_name(sheet) #opening the sheet, and then storing it as sheet\n data = [[sheet.cell_value(r, c) for c in range(sheet.ncols)] for r in range(sheet.nrows)]#for the rows and columns within the sheeet, it is getting the value at every single row and column\n return data #return data\n\nsheet = 'Sheet1' #name of sheet\nfile = r'/users/2020jkapasi/documents/book1.xlsx' #file path to get to sheet\np.pprint(get_data(file, sheet)) #print it\n","sub_path":"CompSci_IA/import_data.py","file_name":"import_data.py","file_ext":"py","file_size_in_byte":780,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"592500923","text":"import os\nimport json\nimport pandas as pd\nimport numpy as np\nfrom sklearn import metrics\nimport random\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import StandardScaler\nimport statsmodels.api as sm\n\n\n#record_file = 'record_3000.txt'\n\ndef get_attributes(all_num,record_file):\n\trecord_f = open(record_file,'a')\n\tfor filename in os.listdir(str(all_num)):\n\t\tif '.txt' not in filename:\n\t\t\tcontinue\n\t\tall_num,pos_neg = filename[:-4].split('_')\n\t\tif float(pos_neg) <= 1.0:\n\t\t\tcontinue\n\t\tprint(all_num,pos_neg)\n\n\t\trecall_1_list = []\n\t\twith open(all_num+'/'+filename,'r') as f:\n\t\t\tfor line in f.readlines():\n\t\t\t\tif line.strip() == '':\n\t\t\t\t\tcontinue\n\t\t\t\trecall_1 = line.split('\\t')[1]\n\t\t\t\tstd_recall_1 = line.split('\\t')[4]\n\t\t\t\tif recall_1 not in recall_1_list:\n\t\t\t\t\trecord_f.write(all_num+'\\t'+pos_neg+'\\t'+recall_1+'\\t'+std_recall_1+'\\n')\n\t\t\t\t\trecall_1_list.append(recall_1)\n\trecord_f.close()\n\n\ndef fit_attributes(all_num,record_file):\n\trecord_r = open(record_file,'r')\n\tall_num_list = []\n\tpos_neg_list = []\n\trecall_1_list = []\n\tstd_recall_1_list = []\n\tfor line in record_r.readlines():\n\t\tif line.strip() == '':\n\t\t\tcontinue\n\t\t#print(line)\n\t\tall_num,pos_neg,recall_1,std_recall_1 = line.strip().split('\\t')\n\t\tall_num_list.append(float(all_num))\n\t\tpos_neg_list.append(float(pos_neg))\n\t\trecall_1_list.append(float(recall_1))\n\t\tstd_recall_1_list.append(float(std_recall_1))\n\n\t#print(pos_neg_list)\n\t#print(recall_1_list)\n\t#print(std_recall_1_list)\n\n\treturn all_num_list,pos_neg_list,recall_1_list,std_recall_1_list\n\t'''\n\tx=np.column_stack((all_num_list,pos_neg_list,recall_1_list))\n\ty=np.array(std_recall_1_list)\n\tprint(len(x),len(y))\n \n\t# 线性回归拟合\n\tx_n = sm.add_constant(x) #statsmodels进行回归时，一定要添加此常数项\n\tmodel = sm.OLS(y, x_n) #model是回归分析模型\n\tresults = model.fit() #results是回归分析后的结果\n\t \n\t#输出回归分析的结果\n\tprint(results.summary())\n\tprint('Parameters: ', results.params)\n\tprint('R2: ', results.rsquared)\n\t \n\t#以下用于出图\n\tplt.figure()\n\tplt.title(u\"线性回归预测\")\n\tplt.xlabel(u\"x\")\n\tplt.ylabel(u\"price\")\n\t#plt.axis([0, 3000000, 0, 5000000000])\n\t#plt.scatter(x, y, marker=\"o\",color=\"b\", s=50)\n\tplt.plot(x_n, y, linewidth=3, color=\"r\")\n\tplt.show()\n\t'''\n\t\n\n\n\n\n\ndef get_model_result(model_index,date,topic_name='权力的游戏'):\n\tmodel_result_file = './data/'+topic_name+'/'+date+'/'+str(model_index)+'.txt'\n\n\twith open(model_result_file,'r') as f:\n\t\tdate_result=list(f.readlines())\n\t\tmodel_result = [int(line.strip().split('\\t')[1]) for line in date_result]\n\tf.close()\n\treturn model_result\n\ndef get_samples(list1,list2,rate):\n\tsample1 = []\n\tsample2 = []\n\tassert(len(list1)==len(list2))\n\tlen_sum = len(list2)\n\tsample_size = int(len_sum*rate)\n\tsample_index = random.sample(range(0,len_sum),sample_size)\n\tfor index in sample_index:\n\t\tsample1.append(list1[index])\n\t\tsample2.append(list2[index])\n\treturn sample1,sample2\n\ndef get_result(y_test,predictions):\n\taccuracy = metrics.accuracy_score(y_test, predictions)\n\trecall = metrics.recall_score(y_test, predictions)\n\tprecision = metrics.precision_score(y_test, predictions)\n\tF1 = metrics.f1_score(y_test, predictions)\n\ttn,fp,fn,tp = metrics.confusion_matrix(y_test,predictions,labels=[0,1]).ravel()\n\trecall_1 = tp/(tp+fn)\n\trecall_2 = tn/(tn+fp)\n\n\t#print(\"accuracy:\", accuracy, '\\n', \"precision:\", precision, '\\n', \"recall:\", recall, '\\n', \"recall_1:\",recall_1, \"recall_2:\",recall_2,'\\n',\"F1 :\", F1)\n\treturn accuracy,precision,recall,recall_1,recall_2,tn,fp,fn,tp\n\n\n\ndef main():\n\tall_nums = [1500]\n\tall_all_num_list = []\n\tall_pos_neg_list = []\n\tall_recall_1_list = []\n\tall_std_recall_1_list = []\n\t#get_attributes(all_num)\n\n\tfor all_num in all_nums:\n\t\trecord_file = 'record_'+str(all_num)+'.txt'\n\t\t#record_file = 'record_3000.txt'\n\t\t#get_attributes(all_num,record_file)\n\t\tall_num_list,pos_neg_list,recall_1_list,std_recall_1_list = fit_attributes(all_num,record_file)\n\t\tall_all_num_list.extend(all_num_list)\n\t\tall_pos_neg_list.extend(pos_neg_list)\n\t\tall_recall_1_list.extend(recall_1_list)\n\t\tall_std_recall_1_list.extend(std_recall_1_list)\n\n\tprint(all_all_num_list,all_pos_neg_list,all_recall_1_list,all_std_recall_1_list)\n\n\n\nif __name__ == '__main__':\n\tmain()\n\n\n","sub_path":"simulation/get_attributes.py","file_name":"get_attributes.py","file_ext":"py","file_size_in_byte":4201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"280534872","text":"# -*- coding: utf-8 -*-\nimport os, sys\n\nreload(sys)\nsys.setdefaultencoding(\"utf-8\")\n\nsys.path.append(os.path.join(os.path.split(os.path.realpath(__file__))[0], '../util'))\nimport loghelper, db, util\n\n#logger\nloghelper.init_logger(\"migrate_companyfa_tag\", stream=True)\nlogger = loghelper.get_logger(\"migrate_companyfa_tag\")\n\n'''\ninsert tag(id,name,type,createTime,modifyTime) values(591703,'微软加速器', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591704,'氪空间', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591705,'奥迪创新实验室', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591706,'戈壁创投VCDay', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591707,'天使湾', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591708,'清科', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591709,'投中', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591710,'小饭桌', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591711,'i黑马', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591712,'微链', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591713,'创业邦', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591714,'TechCrunch', 11054, now(),now());\n'''\n\nrules1 = {\n #\"13300\": 591703, #微软加速器\n #\"13001\": 591704, #氪空间\n #\"13500\": 591705, #奥迪创新实验室\n #\"13501\": 591706, #戈壁创投VC Day\n #\"13502\": 591707, #天使湾\n #\"13503\": 591708, #清科\n #\"13504\": 591709, #投中\n #\"13505\": 591710, #小饭桌\n #\"13506\": 591711, #i黑马\n #\"13802\": 591712, #微链\n #\"13806\": 591713, #创业邦\n #\"13815\": 591714 #TechCrunch\n\n}\n\n'''\ninsert tag(id,name,type,createTime,modifyTime) values(591715,'微软加速器第八期', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591716,'微软加速器第九期', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591717,'微软加速器北京第十期', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591718,'微软加速器上海第一期', 11054, now(),now());\n\ninsert tag(id,name,type,createTime,modifyTime) values(591730,'氪空间第七期', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591731,'氪空间第八期', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591732,'氪空间第九期', 11054, now(),now());\n\ninsert tag(id,name,type,createTime,modifyTime) values(591733,'奥迪创新实验室2016', 11054, now(),now());\n\ninsert tag(id,name,type,createTime,modifyTime) values(591734,'天使湾2016秋季观潮会路演', 11054, now(),now());\n\ninsert tag(id,name,type,createTime,modifyTime) values(591735,'清科2016中国最具投资价值企业新芽榜50强', 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591736,'清科2016中国最具投资价值企业50强', 11054, now(),now());\n\ninsert tag(id,name,type,createTime,modifyTime) values(591754,\"投中2016年中国最具潜力成长企业TOP50\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591738,\"投中2016年中国最具成长价值企业TOP50\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591739,\"投中2016年度中国互联网产业最佳互联网/移动互联网领域投资案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591740,\"投中2016年度中国互联网产业最佳大数据/企业服务领域投资案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591741,\"投中2016年度中国互联网产业最佳退出案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591742,\"投中2016年度中国医疗及健康服务产业最佳生物医药领域投资案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591743,\"投中2016年度中国医疗及健康服务产业最佳医疗器械领域投资案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591744,\"投中2016年度中国医疗及健康服务产业最佳医疗服务领域投资案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591745,\"投中2016年度中国医疗及健康服务产业最佳退出案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591746,\"投中2016年度中国消费及升级服务产业最佳品牌消费领域投资案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591747,\"投中2016年度中国消费及升级服务产业最佳消费升级领域投资案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591748,\"投中2016年度中国消费及升级服务产业最佳退出案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591749,\"投中2016年度中国文化及娱乐传媒产业最佳内容领域投资案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591750,\"投中2016年度中国文化及娱乐传媒产业最佳传播领域投资案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591751,\"投中2016年度中国文化及娱乐传媒产业最佳体育领域投资案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591752,\"投中2016年度中国先进制造与高科技产业最佳先进制造领域投资案例TOP10\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591753,\"投中2016年度中国先进制造与高科技产业最佳人工智能领域投资案例TOP10\", 11054, now(),now());\n\ninsert tag(id,name,type,createTime,modifyTime) values(591755,\"小饭桌2016年度新锐创业公司TOP30\", 11054, now(),now());\n\ninsert tag(id,name,type,createTime,modifyTime) values(591756,\"i黑马2016年中国最具潜力创业公司-新锐组\", 11054, now(),now());\ninsert tag(id,name,type,createTime,modifyTime) values(591757,\"i黑马2016年中国最具潜力创业公司-成长组\", 11054, now(),now());\n\ninsert tag(id,name,type,createTime,modifyTime) values(591758,\"微链2017消费升级研究报告暨新品牌100榜单\", 11054, now(),now());\n\ninsert tag(id,name,type,createTime,modifyTime) values(591759,\"创业邦2017中国人工智能创新公司50强榜单\", 11054, now(),now());\n\ninsert tag(id,name,type,createTime,modifyTime) values(591760,\"TechCrunch2016创业大赛\", 11054, now(),now());\n'''\nrules2 = {\n u\"13300-第八期\": 591715,\n u\"13300-第九期\": 591716,\n u\"13300-北京第十期\": 591717,\n u\"13300-上海第一期\": 591718,\n\n u\"13301-第七期\": 591730,\n u\"13301-第八期\": 591731,\n u\"13301-第九期\": 591732,\n\n u\"13500-奥迪创新实验室2016\": 591733,\n\n u\"13501-VC Day\": 591706,\n\n u\"13502-2016秋季观潮会路演\": 591734,\n\n u\"13503-2016中国最具投资价值企业新芽榜50强\": 591735,\n u\"13503-2016中国最具投资价值企业50强\": 591736,\n\n u\"13504-投中2016年中国最具潜力成长企业TOP50\": 591754,\n u\"13504-投中2016年中国最具成长价值企业TOP50\": 591738,\n u\"13504-2016年度中国互联网产业最佳互联网/移动互联网领域投资案例TOP10\": 591739,\n u\"13504-2016年度中国互联网产业最佳大数据/企业服务领域投资案例TOP10\": 591740,\n u\"13504-2016年度中国互联网产业最佳退出案例TOP10\": 591741,\n u\"13504-2016年度中国医疗及健康服务产业最佳生物医药领域投资案例TOP10\": 591742,\n u\"13504-2016年度中国医疗及健康服务产业最佳医疗器械领域投资案例TOP10\": 591743,\n u\"13504-2016年度中国医疗及健康服务产业最佳医疗服务领域投资案例TOP10\": 591744,\n u\"13504-2016年度中国医疗及健康服务产业最佳退出案例TOP10\": 591745,\n u\"13504-2016年度中国消费及升级服务产业最佳品牌消费领域投资案例TOP10\": 591746,\n u\"13504-2016年度中国消费及升级服务产业最佳消费升级领域投资案例TOP10\": 591747,\n u\"13504-2016年度中国消费及升级服务产业最佳退出案例TOP10\": 591748,\n u\"13504-2016年度中国文化及娱乐传媒产业最佳内容领域投资案例TOP10\": 591749,\n u\"13504-2016年度中国文化及娱乐传媒产业最佳传播领域投资案例TOP10\": 591750,\n u\"13504-2016年度中国文化及娱乐传媒产业最佳体育领域投资案例TOP10\": 591751,\n u\"13504-2016年度中国先进制造与高科技产业最佳先进制造领域投资案例TOP10\": 591752,\n u\"13504-2016年度中国先进制造与高科技产业最佳人工智能领域投资案例TOP10\": 591753,\n\n u\"13505-2016年度新锐创业公司TOP30\": 591755,\n\n u\"13506-中国最具潜力创业公司-新锐组\": 591756,\n u\"13506-中国最具潜力创业公司-成长组\": 591757,\n\n u\"13802-2017消费升级研究报告暨新品牌100榜单\": 591758,\n\n u\"13806-2017中国人工智能创新公司50强榜单\": 591759,\n\n u\"13815-2016创业大赛\": 591760\n}\n\n\ndef add_tag(conn, company_id, tag_id):\n logger.info(\"companyId: %s, tagId: %s\", company_id, tag_id)\n if company_id is None or tag_id is None:\n return\n rel = conn.get(\"select * from company_tag_rel where companyId=%s and tagId=%s limit 1\",\n company_id, tag_id)\n if rel is None:\n conn.insert(\"insert company_tag_rel(companyId,tagId,createTime,modifyTime,verify,confidence) values(%s,%s,now(),now(),'Y', 1.0)\",\n company_id, tag_id)\n else:\n if rel[\"active\"] == 'N':\n conn.update(\"update company_tag_rel set active='Y',verify='Y',modifyTime=now(),confidence=1.0 where id=%s\", rel[\"id\"])\n\n\ndef process():\n conn = db.connect_torndb()\n items = conn.query(\"select * from company_fa where (active is null or active='Y') order by id\")\n for item in items:\n company_id = item[\"companyId\"]\n if company_id is None:\n continue\n source = item[\"source\"]\n eventName = item[\"eventName\"]\n if source is None:\n continue\n\n logger.info(\"id: %s, source: %s, eventName: %s, companyId: %s\", item[\"id\"], source, eventName, company_id)\n # tag_id1 = rules1.get(str(source))\n # if tag_id1 is not None:\n # add_tag(conn, company_id, tag_id1)\n # if eventName is not None and eventName != \"\":\n # tag_id2 = rules2.get(str(source) + \"-\" + eventName)\n # if tag_id2 is not None:\n # add_tag(conn, company_id, tag_id2)\n # conn.update(\"update company_fa set active='N',modifyUser=1078 where id=%s\", item[\"id\"])\n if eventName is not None and eventName != \"\":\n tag_id2 = rules2.get(str(source) + \"-\" + eventName)\n if tag_id2 is not None:\n add_tag(conn, company_id, tag_id2)\n # exit(0)\n conn.update(\"update company_fa set active='N',modifyUser=1078 where id=%s\", item[\"id\"])\n conn.close()\n\n\ndef tag2topic():\n conn = db.connect_torndb()\n for key, tag_id in rules2.items():\n t = key.split(\"-\", 1)\n source = int(t[0])\n event_name = t[1]\n\n tag = conn.get(\"select * from tag where id=%s\", tag_id)\n logger.info(tag[\"name\"])\n\n # topic\n topic = conn.get(\"select * from topic where name=%s\", tag[\"name\"])\n if topic is None:\n topic_id = conn.insert(\"insert topic(name,shortName,active,autoPublish,recommend,hot,\"\n \"sectorRelevant,general,onlyForInvestor,type,autoExpand,\"\n \"createTime,modifyTime) values(\"\n \"%s, %s, 'P','N','N','N',\"\n \"'N','N','N',902,'N',\"\n \"now(),now())\", tag[\"name\"], tag[\"name\"])\n else:\n topic_id = topic[\"id\"]\n logger.info(\"topicId: %s\", topic_id)\n\n # topic_tag_rel\n rel = conn.get(\"select * from topic_tag_rel where topicId=%s and tagId=%s\", topic_id, tag_id)\n if rel is None:\n conn.insert(\"insert topic_tag_rel(topicId,tagId,createTime,modifyTime,verify,active) \"\n \"values(%s, %s, now(),now(),'Y','Y')\",\n topic_id, tag_id)\n\n # topic_tab\n tab = conn.get(\"select * from topic_tab where topicId=%s and type=1200\", topic_id)\n if tab is None:\n topicTabId = conn.insert(\"insert topic_tab(topicId,name,type,subType,createTime,modifyTime,sort) \"\n \"values(%s,%s,1200,1200,now(),now(),1)\",\n topic_id, u\"相关公司\")\n else:\n topicTabId = tab[\"id\"]\n\n # topic_company\n cs = conn.query(\"select companyId from company_tag_rel where tagId=%s and (active is null or active='Y')\",\n tag_id)\n for c in cs:\n company_id = c[\"companyId\"]\n logger.info(\"companyId: %s\", company_id)\n tc = conn.get(\"select * from topic_company where topicId=%s and companyId=%s limit 1\",\n topic_id, company_id)\n if tc is None:\n f = conn.get(\"select * from company_fa where companyId=%s and source=%s and eventName=%s \"\n \"order by id desc limit 1\",\n company_id, source, event_name)\n publishTime = f[\"publishDate\"]\n topicCompanyId = conn.insert(\"insert topic_company(topicId,companyId,publishTime,createTime,modifyTime,\"\n \"active,pushStatus,tabStatus,sectorStatus) \"\n \"values(%s,%s,%s,now(),now(),'Y',1,1,1)\",\n topic_id, company_id, publishTime)\n else:\n topicCompanyId = tc[\"id\"]\n\n # topic_tab_company_rel\n rel = conn.get(\"select * from topic_tab_company_rel where topicCompanyId=%s and topicTabId=%s\",\n topicCompanyId, topicTabId)\n if rel is None:\n conn.insert(\"insert topic_tab_company_rel(topicTabId,topicCompanyId,createTime,modifyTime) \"\n \"values(%s,%s,now(),now())\",\n topicTabId, topicCompanyId)\n\n # topic_company_sector_rel\n patch_sector_by_company_sector(topicCompanyId, company_id, conn)\n # exit(0)\n conn.close()\n\n\ndef patch_sector_by_company_sector(topicCompanyId, companyId, conn):\n conn.execute(\"delete from topic_company_sector_rel where topicCompanyId=%s\", topicCompanyId)\n sectors = get_company_sectors(companyId, conn)\n for sector in sectors:\n conn.insert(\"insert topic_company_sector_rel(topicCompanyId,sectorId) values(%s,%s)\",\n topicCompanyId, sector[\"id\"])\n\n\ndef get_company_sectors(company_id, conn):\n sectors = conn.query(\"select s.* from company_tag_rel r join tag t on t.id=r.tagId \"\n \"join sector s on s.tagId=t.Id \"\n \"where (r.active is null or r.active='Y') and (t.active is null or t.active='Y') \"\n \"and s.level=1 and s.active='Y' \"\n \"and r.companyId=%s and t.type=11012\", company_id)\n return sectors\n\n\ndef colletion2topic():\n conn = db.connect_torndb()\n collections = conn.query(\"select * from collection where type=39020 and (active is null or active='Y')\")\n for collection in collections:\n # topic\n topic = conn.get(\"select * from topic where name=%s\", collection[\"name\"])\n if topic is None:\n topic_id = conn.insert(\"insert topic(name,shortName,active,autoPublish,recommend,hot,\"\n \"sectorRelevant,general,onlyForInvestor,type,autoExpand,\"\n \"createTime,modifyTime) values(\"\n \"%s, %s, 'P','N','N','N',\"\n \"'N','N','N',902,'N',\"\n \"now(),now())\", collection[\"name\"], collection[\"name\"])\n else:\n topic_id = topic[\"id\"]\n logger.info(\"topicId: %s\", topic_id)\n\n # topic_tab\n tab = conn.get(\"select * from topic_tab where topicId=%s and type=1200\", topic_id)\n if tab is None:\n topicTabId = conn.insert(\"insert topic_tab(topicId,name,type,subType,createTime,modifyTime,sort) \"\n \"values(%s,%s,1200,1200,now(),now(),1)\",\n topic_id, u\"相关公司\")\n else:\n topicTabId = tab[\"id\"]\n\n # topic_company\n cs = conn.query(\"select c.id, r.createTime from collection_company_rel r join company c on c.id=r.companyId \"\n \"where collectionId=%s and (r.active is null or r.active='Y') \"\n \"and (c.active is null or c.active='Y')\",\n collection[\"id\"])\n for c in cs:\n company_id = c[\"id\"]\n logger.info(\"companyId: %s\", company_id)\n tc = conn.get(\"select * from topic_company where topicId=%s and companyId=%s limit 1\",\n topic_id, company_id)\n if tc is None:\n topicCompanyId = conn.insert(\"insert topic_company(topicId,companyId,publishTime,createTime,modifyTime,\"\n \"active,pushStatus,tabStatus,sectorStatus) \"\n \"values(%s,%s,%s,now(),now(),'Y',1,1,1)\",\n topic_id, company_id, c[\"createTime\"])\n else:\n topicCompanyId = tc[\"id\"]\n\n # topic_tab_company_rel\n rel = conn.get(\"select * from topic_tab_company_rel where topicCompanyId=%s and topicTabId=%s\",\n topicCompanyId, topicTabId)\n if rel is None:\n conn.insert(\"insert topic_tab_company_rel(topicTabId,topicCompanyId,createTime,modifyTime) \"\n \"values(%s,%s,now(),now())\",\n topicTabId, topicCompanyId)\n\n # topic_company_sector_rel\n patch_sector_by_company_sector(topicCompanyId, company_id, conn)\n # exit(0)\n conn.close()\n\n\ndef tag_4_topic(topic_id):\n conn = db.connect_torndb()\n tags = conn.query(\"select * from topic_tag_rel where topicId=%s and (active is null or active='Y')\", topic_id)\n companies = conn.query(\"select * from topic_company where topicId=%s and (active is null or active='Y')\", topic_id)\n for tag in tags:\n for company in companies:\n logger.info(\"companyId: %s\", company[\"companyId\"])\n add_tag(conn, company[\"companyId\"], tag[\"tagId\"])\n # exit()\n conn.close()\n\n\nif __name__ == '__main__':\n # process() # tag\n # tag2topic()\n # colletion2topic()\n tag_4_topic(65)\n\n","sub_path":"data/migrate/migrate_companyfa_tag.py","file_name":"migrate_companyfa_tag.py","file_ext":"py","file_size_in_byte":19350,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"5457320","text":"from django.http import HttpResponse\nfrom django.http import HttpResponseBadRequest\nfrom django.http import HttpResponseForbidden\nfrom django.utils import simplejson\nfrom django.views.decorators.http import require_POST\nfrom django.views.generic import FormView\n\nfrom images.models import Image\nfrom images.models import ImageGroup\nfrom images.forms import ImageGroupForm\n\nclass MTurkImages(FormView):\n form_class = ImageGroupForm\n\n def post(self, request):\n form = self.form_class(request.POST)\n # Checks to make sure that the form has valid input\n if not form.is_valid():\n return HttpResponseBadRequest(\n simplejson.dumps({'errors': form.errors}),\n content_type='application/json'\n )\n form.clean()\n # Find the ImageGroup\n\n image_group = ImageGroup.objects.get(pk = form.data['image_group_id'], status=1)\n image_group.status = 2\n image_group.save()\n\n # Creates Image objects\n image1 = image_group.image_set.get(\n image_id=form.data['p1_id']\n )\n image1.ranking = form.data['p1_score']\n image1.save()\n\n image2 = image_group.image_set.get(\n image_id=form.data['p2_id']\n )\n image2.ranking = form.data['p2_score']\n image2.save()\n\n image3 = image_group.image_set.get(\n image_id=form.data['p3_id']\n )\n image3.ranking = form.data['p3_score']\n image3.save()\n\n image4 = image_group.image_set.get(\n image_id=form.data['p4_id']\n )\n image4.ranking = form.data['p4_score']\n image4.save()\n\n image5 = image_group.image_set.get(\n image_id=form.data['p5_id']\n )\n image5.ranking = form.data['p5_score']\n image5.save()\n\n image6 = image_group.image_set.get(\n image_id=form.data['p6_id']\n )\n image6.ranking = form.data['p6_score']\n image6.save()\n msg = 'Image scores have been saved'\n # ADD some kind of redeem code that allows users to redeem reward on AMT\n return HttpResponse(\n simplejson.dumps(\n {'success': 'true',\n 'msg': msg\n }\n ),\n content_type='application/json'\n )\n","sub_path":"website/images/form_views.py","file_name":"form_views.py","file_ext":"py","file_size_in_byte":2433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"171162809","text":"\n# coding: utf-8\n\n# Count the number of instances of each LR weight per residue\n# ====\n\nimport pyemma\npyemma.__version__\n\n\nimport os\n#get_ipython().magic(u'pylab inline')\n#matplotlib.rcParams.update({'font.size': 12})\nimport numpy as np\n\n# In[4]:\n\nimport pyemma.coordinates as coor\nimport pyemma.msm as msm\nimport pyemma.plots as mplt\nimport msmbuilder\nfrom msmbuilder.msm.ratematrix import ContinuousTimeMSM\nimport scipy\nfrom msmtools.analysis.dense.decomposition import eigenvectors, eigenvalues\nimport operator\n\n\n# Read in the dtrajs\n# ------\n\n# In[7]:\n\nindir = '/data/isilon/rudzinski/cluster_tmp/ALA7/ALA7_hybrid_AMBER_Go/wDB-HP_inter/Find_Tfold/NC_CA/2016_10_11/'\nstate_weights = np.load(indir+'state_LR_weights.npy')\nstate_weights_cap = np.load(indir+'state_LR_weights_cap.npy')\nstate_weights_adj = np.load(indir+'state_LR_weights_adj.npy')\ndtrajs = np.load('../MLE/dtraj_DPCA_CG_mapped_to_rama.npy')\n\n# \n\n\nNres = state_weights.shape[1]\nNw = np.zeros(Nres)\nNv = np.zeros(Nres)\nNcoil = np.zeros(Nres)\nNk = len(dtrajs)*dtrajs[0].shape[0] # total number of samples\nfor traj in range(len(dtrajs)):\n counts = np.bincount(dtrajs[traj])\n for state in range(counts.shape[0]):\n count = counts[state] # how many frames sample this state\n # v\n n_s = np.where(state_weights[state]==1)[0] # which residues have weight v\n if ( len(n_s) != 0 ):\n Nv[n_s] += count\n # w\n n_s = np.where(state_weights[state]==2)[0] # which residues have weight w\n if ( len(n_s) != 0 ):\n Nw[n_s] += count\n # coil\n n_s = np.where(state_weights[state]==0)[0] # which residues have weight w\n if ( len(n_s) != 0 ):\n Ncoil[n_s] += count\n \n# everything is backwards (i.e., N->C-terminal), flip the arrays\n#Nw = Nw[::-1]\n#Nv = Nv[::-1]\n#Ncoil = Ncoil[::-1]\n\nnp.savez('LR_const_params',Nw=Nw,Nv=Nv,Nk=Nk)\n\n\n#\n\n\nNres_cap = state_weights_cap.shape[1]\nNw_cap = np.zeros(Nres_cap)\nNv_cap = np.zeros(Nres_cap)\nNc_cap = np.zeros(Nres_cap)\nNn_cap = np.zeros(Nres_cap)\nfor traj in range(len(dtrajs)):\n counts = np.bincount(dtrajs[traj])\n for state in range(counts.shape[0]):\n count = counts[state] # how many frames sample this state\n # v\n n_s = np.where(state_weights_cap[state]==1)[0] # which residues have weight v\n if ( len(n_s) != 0 ):\n Nv_cap[n_s] += count\n # w\n n_s = np.where(state_weights_cap[state]==2)[0] # which residues have weight w\n if ( len(n_s) != 0 ):\n Nw_cap[n_s] += count\n # c\n n_s = np.where(state_weights_cap[state]==4)[0] # which residues have weight c\n if ( len(n_s) != 0 ):\n Nc_cap[n_s] += count\n # n\n n_s = np.where(state_weights_cap[state]==3)[0] # which residues have weight n\n if ( len(n_s) != 0 ):\n Nn_cap[n_s] += count\n \n# everything is backwards (i.e., N->C-terminal), flip the arrays\n#Nw_cap = Nw_cap[::-1]\n#Nv_cap = Nv_cap[::-1]\n#tmp_Nn_cap = Nc_cap[::-1]\n#tmp_Nc_cap = Nn_cap[::-1]\n#Nn_cap = tmp_Nn_cap\n#Nc_cap = tmp_Nc_cap\n\nnp.savez('LR_const_params_cap',Nw=Nw_cap,Nv=Nv_cap,Nn=Nn_cap,Nc=Nc_cap,Nk=Nk)\n\n\n#\n\n\nNres = state_weights.shape[1]\nNw_adj = np.zeros(Nres)\nNv_adj = np.zeros(Nres)\nNk = len(dtrajs)*dtrajs[0].shape[0] # total number of samples\nfor traj in range(len(dtrajs)):\n counts = np.bincount(dtrajs[traj])\n for state in range(counts.shape[0]):\n count = counts[state] # how many frames sample this state\n # v\n n_s = np.where(state_weights_adj[state]==1)[0] # which residues have weight v\n if ( len(n_s) != 0 ):\n Nv_adj[n_s] += count\n # w\n n_s = np.where(state_weights_adj[state]==2)[0] # which residues have weight w\n if ( len(n_s) != 0 ):\n Nw_adj[n_s] += count\n \n# everything is backwards (i.e., N->C-terminal), flip the arrays\n#Nw_adj = Nw_adj[::-1]\n#Nv_adj = Nv_adj[::-1]\n\nnp.savez('LR_const_params_adj',Nw=Nw_adj,Nv=Nv_adj,Nk=Nk)\n\n\n","sub_path":"gen_analysis/MSM_analysis/LR/Get_LR_const_params.py","file_name":"Get_LR_const_params.py","file_ext":"py","file_size_in_byte":3988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"186160937","text":"#!/usr/bin/env python\n\nimport torch\nfrom torch.autograd import Variable\nimport numpy as np\nimport torch.nn.functional as F\n\n\nclass rbm(torch.nn.Module):\n\tdef __init__(self, num_input, num_hidden, activation, use_denoising=False, sparse=False, learning_rate=0.001, use_cuda=False):\n\t\tsuper(rbm, self).__init__()\n\n\t\tself.l1 = torch.nn.Linear(num_input, num_hidden, bias=True)\n\t\tself.l2 = torch.nn.Linear(num_hidden, num_input, bias=True)\n\n\t\tself.use_denoising = use_denoising\n\t\tself.sparsity = sparse\n\t\tself.criterion = torch.nn.MSELoss()\n\t\t#self.criterion = torch.nn.CrossEntropyLoss(size_average=False)\n\n\t\tself.learning_rate = learning_rate\n\t\tfor param in self.parameters():\n\t\t\tif(len(param.data.numpy().shape)) > 1:\n\t\t\t\ttry:\n\t\t\t\t\ttorch.nn.init.kaiming_normal_(param.data , a=0, mode='fan_in')\t\n\t\t\t\texcept:\n\t\t\t\t\ttorch.nn.init.kaiming_normal(param.data , a=0, mode='fan_in')\t\n\t\t\telse:\n\t\t\t\tpass\n\t\t\t\t#param.data = torch.zeros(param.data.size())\n\t\tself.activation = activation\n\t\tself.use_cuda = use_cuda\n\n\tdef set_ratio(self, ratio):\n\t\tself.beta = torch.from_numpy(np.array([ratio]))\n\n\t\tif self.use_cuda:\n\t\t\tself.beta = Variable(self.beta.type(torch.cuda.FloatTensor), requires_grad=False)\n\t\telse:\n\t\t\tself.beta = Variable(self.beta.type(torch.FloatTensor), requires_grad=False)\n\n\tdef compute_ratio(self, inputs, labels, indices):\n\t\tif self.activation == 'relu':\n\t\t\ty1 = F.relu(self.l1(inputs))\n\t\telif self.activation == 'tanh':\n\t\t\ty1 = F.tanh(self.l1(inputs))\n\t\telif self.activation == 'sigmoid':\n\t\t\ty1 = F.sigmoid(self.l1(inputs))\n\t\telif self.activation == 'softmax':\n\t\t\ty1 = F.softmax(self.l1(inputs))\n\n\t\txout = self.l2(y1)\n\n\t\tnorm_mag = torch.sum(torch.abs(y1))\t\t#\tL1 regularizer\n\t\tratio = torch.abs(self.criterion(xout, inputs)/norm_mag)\n\n\t\tif float(norm_mag) < 0.00001:\n\t\t\tprint('\\n\\nObserve inside rbm that norm_mag is nearly zero, how is this possible???\\n\\n')\n\t\t\timport pdb; pdb.set_trace()\n\n\t\treturn ratio\n\n\tdef get_optimizer(self):\n\t\treturn torch.optim.Adam(self.parameters(), lr=self.learning_rate)\n\t\t\n\n\tdef innerLayer_out(self, x):\n\t\tif self.activation == 'relu':\n\t\t\ty1 = F.relu(self.l1(x))\n\t\telif self.activation == 'sigmoid':\n\t\t\ty1 = F.sigmoid(self.l1(x))\n\t\telif self.activation == 'tanh':\n\t\t\ty1 = F.tanh(self.l1(x))\n#\t\telif self.activation == 'softmax':\n#\t\t\ty1 = F.softmax(self.l1(x), dim=None)\n\t\telif self.activation == 'none':\n\t\t\ty1 = self.l1(x)\n\t\telse:\n\t\t\tprint('Unknow activation function ' + self.activation + '\\n\\n')\n\t\t\traise\n\n\t\treturn y1\n\n\tdef compute_loss(self, inputs, labels, indices):\n\t\tif self.sparsity:\n\t\t\ty1 = self.innerLayer_out(inputs)\n\t\t\txout = self.l2(y1)\n\t\t\tnorm_mag = self.beta*torch.max(torch.sum(torch.abs(y1), dim=0))\t\t#\tL1 regularizer\n\t\t\tcost = self.criterion(xout, inputs)\n\t\t\tloss = cost + norm_mag\n\t\telse:\n\t\t\txout = self.forward(inputs)\n\t\t\tloss = self.criterion(xout, inputs)\n\t\n\t\t#l1_reg = Variable( torch.FloatTensor(1), requires_grad=False)\n\t\t#for W in self.parameters(): \n\t\t#\tl1_reg = l1_reg + W.norm(2)\n\n\t\treturn loss\n\t\t#return self.criterion(xout, inputs) + 0.01*l1_reg\n\n\tdef forward(self, x):\t\n\t\tif self.use_denoising: \n\t\t\tx = F.dropout(x, p=0.2, training=self.training)\t\n\n\t\ty1 = self.innerLayer_out(x)\n\t\ty_pred = self.l2(y1)\n\t\treturn y_pred\n","sub_path":"src/models/rbm.py","file_name":"rbm.py","file_ext":"py","file_size_in_byte":3185,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"164413900","text":"'''\nThis will provide a description of the functions within this file.\n\n\n'''\n\n# Regular EDA and plotting libraries\nimport numpy as np # np is short for numpy\nimport pandas as pd # pandas is so commonly used, it's shortened to pd\nimport matplotlib.pyplot as plt\nimport seaborn as sns # seaborn gets shortened to sns\n\n## Models\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.neighbors import KNeighborsClassifier\nfrom sklearn.ensemble import RandomForestClassifier\n\n## Model evaluators\nfrom sklearn.model_selection import train_test_split, cross_val_score\nfrom sklearn.model_selection import RandomizedSearchCV, GridSearchCV\nfrom sklearn.metrics import confusion_matrix, classification_report\nfrom sklearn.metrics import precision_score, recall_score, f1_score\nfrom sklearn.metrics import plot_roc_curve\n\n\ndef corr_matrix(df):\n \"\"\"\n Plot correlation matrix for numerical DataFrame.\n \n Parameters\n ----------\n df: \n\n \"\"\"\n\n corr_matrix = df.corr()\n fig, ax = plt.subplots(figsize=(15, 10))\n ax = sns.heatmap(corr_matrix,\n annot=True,\n linewidths=0.5,\n fmt=\".2f\",\n cmap=\"YlGnBu\");\n bottom, top = ax.get_ylim()\n ax.set_ylim(bottom + 0.5, top - 0.5)\n\n \n \n##############################################################################\n# Put models in a dictionary\nmodels = {\"Logistic Regression\": LogisticRegression(),\n \"KNN\": KNeighborsClassifier(),\n \"Random Forest\": RandomForestClassifier()}\n\n# Create a function to fit and score models\ndef fit_and_score(models, X_train, X_test, y_train, y_test):\n \"\"\"\n Fits and evaluates given machine learning models, with accuracy score.\n \n Parameters\n ----------\n models : a dict of differetn Scikit-Learn machine learning models\n X_train : training data (no labels)\n X_test : testing data (no labels)\n y_train : training labels\n y_test : test labels\n \n Returns\n -------\n \n Examples\n --------\n An example of the dictionary passed for which models to use:\n models = {\"Logistic Regression\": LogisticRegression(),\n \"KNN\": KNeighborsClassifier(),\n \"Random Forest\": RandomForestClassifier()}\n \n \n \"\"\"\n # Set random seed\n np.random.seed(42)\n # Make a dictionary to keep model scores\n model_scores = {}\n # Loop through models\n for name, model in models.items():\n # Fit the model to the data\n model.fit(X_train, y_train)\n # Evaluate the model and append its score to model_scores\n model_scores[name] = model.score(X_test, y_test)\n return model_scores\n\n\n\ndef plot_conf_mat(y_test, y_preds):\n \"\"\"\n Plots a nice looking confusion matrix using Seaborn's heatmap()\n \n Parameters\n ----------\n y_test:\n y_preds:\n \"\"\"\n fig, ax = plt.subplots(figsize=(3, 3))\n ax = sns.heatmap(confusion_matrix(y_test, y_preds),\n annot=True,\n cbar=False)\n plt.xlabel(\"True label\")\n plt.ylabel(\"Predicted label\")\n \n bottom, top = ax.get_ylim()\n ax.set_ylim(bottom + 0.5, top - 0.5)\n \n ","sub_path":"Mini_Project_4/.ipynb_checkpoints/helper_functions-checkpoint.py","file_name":"helper_functions-checkpoint.py","file_ext":"py","file_size_in_byte":3189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"617405100","text":"import os\nimport sys\nimport yaml\nfrom comm.sc_logger import SCLogger\nimport comm.sc_logger as scl\nfrom datetime import datetime\n\n@scl.SCLogWrite\ndef test(msg):\n msg2 = 'Hello Decorator ' + msg\n print(msg2)\n scl.SCLogger.writeInfo(sys._getframe().f_code.co_name, msg2)\n\n@scl.SCLogWrite\ndef test2(typ, msg=None):\n print('タイプ ' + typ)\n if msg:\n print('メッセージ ' + msg)\n\nclass deco_test :\n @scl.SCLogWriteCL\n def __init__(self, msg=None):\n if msg:\n self.__in_msg = msg\n else:\n self.__in_msg = \"Nooon\"\n\n @scl.SCLogWriteCL\n def disp_msg(self, msg, msg2) :\n print('Message in ' + msg + ' ' + msg2)\n\n @scl.SCLogWriteCL\n def disp_in_msg(self):\n print(\"In Message \" + self.__in_msg)\n\n @scl.SCLogWriteCL\n def msg_rtn(self, msg) -> str:\n retmsg = \"return value \" + msg\n return retmsg\n\n # クラスメソッドのデコレータを先に書く\n @classmethod\n @scl.SCLogWriteCL\n def static_disp(cls, msg):\n print(\"cls名:\" + cls.__name__)\n chk = type(cls) is type\n print(\"clsチェック:\" + str(chk))\n print(\"Static disp \" + msg)\n\n\ndef main():\n print(\"処理開始:\" + datetime.now().strftime(\"%Y-%m-%d %H:%M:%S.%f%z\"))\n\n basedir = os.path.dirname(__file__)\n yml_fil = os.path.join(basedir, \"alert/test.query\")\n\n with open(yml_fil, 'r', encoding='UTF-8') as conf:\n cf = yaml.load(conf, Loader=yaml.FullLoader)\n\n SCLogger.init_logger(\"test_deco\", dirName=cf[\"LOGDIR\"])\n\n test(\"テストメッセージ\")\n\n tc1 = deco_test()\n tc1.disp_msg(\"aaa\", \"bbb\")\n\n tc2 = deco_test(\"construct para\")\n tc2.disp_in_msg()\n rt = tc2.msg_rtn(\"あああ\")\n print(rt)\n deco_test.static_disp(\"いいい\")\n\n SCLogger.destroy_logger()\n print(\"処理終了:\" + datetime.now().strftime(\"%Y-%m-%d %H:%M:%S.%f%z\"))\n\nif __name__ == \"__main__\":\n main()\n","sub_path":"py_work/test_deco_log.py","file_name":"test_deco_log.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"286969667","text":"from django.conf.urls import url\nfrom .views import new,popular,question,ask,signup,login\n\nurlpatterns = [\n url(r'^$', new),\n url(r'^ask',ask),\n url(r'^popular/',popular),\n url(r'^question/(?P<quest_id>\\d+)/$',question),\n url(r'^signup/',signup),\n url(r'^login/',login),\n]\n\n","sub_path":"ask/qa/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":292,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"577993833","text":"'''\n@author: willg\n\nPHY260, Homework #6\n\nThis module plots results for problem 1, part a), which writes:\n\n1. a) Plot <x_n> and <(x_n)^2> up to n = 100 by averaging over at \n least 1e4 different walks for each n > 3. \n\n--> Ns = n elem of {0,1,2,...,100}\n--> Np = 1e4 = 10000\nin this problem (1.a) we want two SCATTER plots on the same figure:\n 1. <x> vs. n\n a. y-axis: \"<x>\"\n a. y-lims: plus or minus 1.2 times the largest magnitude data point.\n 2. <x^2> vs. n\n a. y-axis: \"<x^2>\"\nother notes:\n title: \"PHY260, Homework #6, 1.A: <x>, <x^2> with n.\n x-axis: \"n\"\n rasterized: False\n'''\nfrom hw6_part1.generate_walks import path_for_new_fig_for_newest_pickle, \\\n path_to_newest_pickle\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport pickle\nfrom matplotlib.pyplot import legend\n\nif __name__ == '__main__':\n # get the data from the Random_Walk object in a1_generate.py:\n random_walks = pickle.load( open( path_to_newest_pickle, \"rb\" ) )\n # make a fig for plotting, saving the data in `random_walks`\n fig = plt.figure( figsize = ( 8.5, 11 ) ) # 8.5\" x 11\"\n data = {'<X>' : random_walks.x_means,\n '<R^2>': random_walks.r2_means}\n ns_values = random_walks.ns_values\n colors = ['b', 'r']\n labels_xaxis = ['# of steps', '# of steps']\n labels_yaxis = ['Mean x-coordinate', 'Mean square of total distance from origin']\n symmetrical_yaxis = [True, False]\n add_regression = [False, True]\n for n, title in enumerate( data.keys() ):\n # Make a plot for both <x>, <x^2>\n ax = fig.add_subplot( 2, 1, n + 1 )\n ax.scatter( x = ns_values,\n y = data[title],\n s = 15, # size of dot\n c = colors[n], # color\n marker = 's'\n )\n # Print title, axis labels, \n ax.set_title( title )\n ax.set_xlabel( labels_xaxis[n] )\n ax.set_ylabel( labels_yaxis[n] )\n # Set axis limits. \n ax.set_xlim( 0, random_walks.Ns )\n if symmetrical_yaxis[n]:\n # For the <x> plot, we want y=0 at the middle of the axis.\n ax.set_ylim( -max( -ax.axis()[2], ax.axis()[3] ), max( -ax.axis()[2], ax.axis()[3] ) )\n # Looks nice and symmetrical that way.\n if add_regression[n]:\n # Regress, force y-intercept = 0.\n x = ns_values.reshape( ns_values.size, 1 )\n a, _, _, _ = np.linalg.lstsq( x, data[title] )\n # Calculate R-Squared\n r2 = ( np.corrcoef( data[title], a * ns_values )[0, 1] ) ** 2\n # Plot regression line, annotations with metadata. \n ax.plot( x, a * x, 'y-' )\n x_annotation, y_annotation = 2.5, 102.5\n ax.text( x_annotation, y_annotation , 'Part b)' )\n y_annotation -= 10\n ax.text( x_annotation, y_annotation, 'Regression Coefficient = %8.6f' % a )\n y_annotation -= 10\n ax.text( x_annotation, y_annotation, 'R-squared = %8.6f' % r2 )\n # Give the whole figure a title.\n fig.suptitle( 'PHY260 Homework #6, Part 1: \\n%d Random Walks with %d Steps' % ( random_walks.Np, random_walks.Ns ),\n ha = 'center', size = 'large' )\n legend()\n # Save the figure to a pdf file.\n fig.savefig( path_for_new_fig_for_newest_pickle + '.png' )\n\n\nif False:\n ''' this code is from Random_Walks.py, \n reproduced here as a reminder of some useful plotting tips,\n e.g. using a dict for managing subplots, etc '''\n# data = {'<x>': w1.x_means,\n# '<x^2>':w1.x2_means}\n# # plot the results\n# import matplotlib.pyplot as plt\n# fig = plt.figure()\n# for n, y in enumerate( data ):\n# ax = fig.add_subplot( 2, 1, n + 1 )\n# ax.scatter( w1.ns_values,\n# data[y],\n# c = 'r' )\n# ax.set_xlim( 0, Ns )\n# # ax.set_ylim( -max( -ax.axis()[2], ax.axis()[3] ), max( -ax.axis()[2], ax.axis()[3] ) )\n# plt.show()\n pass\n","sub_path":"src/hw6_part1/plot_walks.py","file_name":"plot_walks.py","file_ext":"py","file_size_in_byte":3996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"369212174","text":"import datetime\n\nfrom Datepicker import SelectDate\nfrom bs4 import BeautifulSoup as bs\nfrom flask import flash\nfrom selenium import webdriver\nfrom selenium.common.exceptions import TimeoutException, StaleElementReferenceException\nfrom selenium.webdriver.chrome.options import Options\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.support import expected_conditions as EC\nfrom selenium.webdriver.support.ui import WebDriverWait\n\n\nclass wait_for_more_than_n_elements_to_be_present(object):\n def __init__(self, locator, count):\n self.locator = locator\n self.count = count\n\n def __call__(self, driver):\n try:\n elements = EC._find_elements(driver, self.locator)\n return len(elements) > self.count\n except StaleElementReferenceException:\n return False\n\n\ndef init_driver():\n # initiate the driver and add options.\n chrome_options = Options()\n chrome_options.add_argument(\"--window-size=1920,1080\")\n chrome_options.add_argument(\"--disable-gpu\")\n chrome_options.add_argument(\"--disable-extensions\")\n chrome_options.add_argument(\"useAutomationExtension\")\n chrome_options.add_argument(\"--proxy-server='direct://'\")\n chrome_options.add_argument(\"--proxy-bypass-list=*\")\n chrome_options.add_argument(\"--start-maximized\")\n # chrome_options.add_argument(\"--headless\")\n\n driver = webdriver.Chrome(chrome_options=chrome_options)\n\n # set a wait time:\n driver.wait = WebDriverWait(driver, 5)\n\n return driver\n\n\ndef close_driver(driver):\n # Close the driver when finished with it.\n driver.close()\n return\n\n\ndef login_twitter(driver, username, password):\n # opens the web page in the browser.\n driver.get(\"https://twitter.com/login\")\n\n # finds the boxes for username and password.\n username_field = driver.find_element_by_class_name(\"js-username-field\")\n password_field = driver.find_element_by_class_name(\"js-password-field\")\n\n # enters username.\n username_field.send_keys(username)\n driver.implicitly_wait(1)\n\n # enters password.\n password_field.send_keys(password)\n driver.implicitly_wait(1)\n\n # clicks login button.\n driver.find_element_by_class_name(\"EdgeButtom--medium\").click()\n\n return True\n\n\ndef Go_to_self(driver, username):\n driver.get(\"https://twitter.com/\" + \"@\" + username)\n\n # wait for results to load\n wait = WebDriverWait(driver, 10)\n\n try:\n # wait until the first search result is found.\n wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, \"li[data-item-id]\")))\n\n # scroll until there are no more tweets:\n while True:\n\n # extract all the tweets:\n tweets = driver.find_elements_by_css_selector(\"li[data-item-id]\")\n\n # find number of visible tweets:\n number_of_tweets = len(tweets)\n\n # keep scrolling:\n driver.execute_script(\"arguments[0].scrollIntoView();\", tweets[-1])\n\n try:\n # wait for more tweets to be visible:\n wait.until(wait_for_more_than_n_elements_to_be_present(\n (By.CSS_SELECTOR, \"li[data-item-id]\"), number_of_tweets))\n\n except TimeoutException:\n # if no more are visible the \"wait.until\" call will timeout.\n print(\"No More tweets available\")\n break\n\n # extract the html\n page_source = driver.page_source\n\n except TimeoutException:\n # if there are no search results then return no html.\n page_source = None\n\n # return html\n return page_source\n\n\ndef Search_HashTag(driver, user, query):\n driver.get(\"https://twitter.com/search-advanced?lang=en\")\n\n # wait until the search box has loaded.\n Hashtagbox = driver.wait.until(EC.presence_of_element_located((By.NAME, \"tag\")))\n\n # find the search box.\n driver.find_element_by_name(\"tag\").clear()\n\n # enter hashtag string.\n Hashtagbox.send_keys(query)\n\n # wait until the search box has loaded.\n FromBox = driver.wait.until(EC.presence_of_element_located((By.NAME, \"from\")))\n\n # find the user box.\n driver.find_element_by_name(\"from\").clear()\n\n # enter user.\n FromBox.send_keys(user)\n\n # Submit.\n FromBox.submit()\n\n # wait for the search results to load.\n wait = WebDriverWait(driver, 10)\n\n try:\n # wait until the first search result is found.\n wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, \"li[data-item-id]\")))\n\n # scroll until there are no more tweets.\n while True:\n\n # extract tweets.\n tweets = driver.find_elements_by_css_selector(\"li[data-item-id]\")\n\n # scrolling.\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\n # find number of visible tweets.\n number_of_tweets = len(tweets)\n\n # Wait for more to load\n wait = WebDriverWait(driver, 5)\n\n try:\n # wait for more tweets to be visible.\n wait.until(wait_for_more_than_n_elements_to_be_present(\n (By.CSS_SELECTOR, \"li[data-item-id]\"), number_of_tweets))\n\n except TimeoutException:\n # if no more tweets \"wait.until\" call will timeout.\n break\n\n # extract the html.\n page_source = driver.page_source\n\n except TimeoutException:\n # if there are no results return None.\n page_source = None\n\n return page_source\n\n\ndef Search_logged_in_User(driver, user, sinceDate='', untilDate='', hashtag='', phrase=''):\n driver.get(\"https://twitter.com/search-advanced?lang=en\")\n\n # wait until the search box has loaded.\n Hashtagbox = driver.wait.until(EC.presence_of_element_located((By.NAME, \"tag\")))\n\n # wait until the user box has loaded.\n User = driver.wait.until(EC.presence_of_element_located((By.NAME, \"from\")))\n\n # find search box for phrase.\n phraseBox = driver.find_element_by_name(\"phrase\")\n\n # find the search box.\n driver.find_element_by_name(\"from\").clear()\n\n # enter user.\n User.send_keys(user)\n\n if hashtag != \"\":\n Hashtagbox.send_keys(hashtag)\n if phrase != \"\":\n phraseBox.send_keys(phrase)\n\n # since box.\n # sinceBox = driver.wait.until(EC.presence_of_element_located((By.NAME, \"since\")))\n\n # until box.\n # untilBox = driver.wait.until(EC.presence_of_element_located((By.NAME, \"until\")))\n\n # driver.execute_script(\"arguments[0].removeAttribute('readonly')\", sinceBox)\n # driver.execute_script(\"arguments[0].removeAttribute('readonly')\", untilBox)\n\n if untilDate == \"\" and sinceDate != \"\":\n flash('Both Dates must be picked!')\n elif sinceDate == \"\" and untilDate != \"\":\n flash('Both Dates must be picked!')\n elif untilDate != \"\" and sinceDate != \"\":\n\n # Getting inputted since date.\n NewSinceDate = datetime.datetime.strptime(sinceDate, \"%Y-%m-%d\")\n\n # splitting it into the right format.\n sinceYear = NewSinceDate.strftime(\"%Y\")\n sinceMonth = NewSinceDate.strftime(\"%b\")\n sinceDay = NewSinceDate.strftime(\"%d\").lstrip(\"0\")\n\n # using dateselector.py to use datepicker.\n SelectDate(driver, \"input-since\", sinceYear, sinceMonth, sinceDay)\n\n # getting inputted until date.\n untilDate = datetime.datetime.strptime(untilDate, \"%Y-%m-%d\")\n\n untilYear = untilDate.strftime(\"%Y\")\n untilMonth = untilDate.strftime(\"%b\")\n untilDay = untilDate.strftime(\"%d\").lstrip(\"0\")\n\n # Selecting until date\n SelectDate(driver, \"input-until\", untilYear, untilMonth, untilDay)\n\n # Submit\n User.submit()\n\n # initial wait for the search results to load\n wait = WebDriverWait(driver, 10)\n\n try:\n # wait until the first search result is found.\n wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, \"li[data-item-id]\")))\n\n # scroll until there are no more tweets.\n while True:\n\n # extract all the tweets.\n tweets = driver.find_elements_by_css_selector(\"li[data-item-id]\")\n\n # keep scrolling.\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\n # find number of visible tweets.\n number_of_tweets = len(tweets)\n\n # Wait for more to load.\n wait = WebDriverWait(driver, 5)\n\n try:\n # wait for more tweets to be visible.\n wait.until(wait_for_more_than_n_elements_to_be_present(\n (By.CSS_SELECTOR, \"li[data-item-id]\"), number_of_tweets))\n\n except TimeoutException:\n break\n\n # extract the html.\n page_source = driver.page_source\n\n except TimeoutException:\n # if there are no search results return none.\n page_source = None\n\n return page_source\n\n\ndef Search_Specific_User(driver, user, sinceDate, untilDate, hashtag=\"\", phrase=''):\n driver.get(\"https://twitter.com/search-advanced?lang=en\")\n\n # wait until the hashtag box has loaded.\n Hashtagbox = driver.wait.until(EC.presence_of_element_located((By.NAME, \"tag\")))\n\n # wait until the user box has loaded.\n User = driver.wait.until(EC.presence_of_element_located((By.NAME, \"from\")))\n\n phraseBox = driver.find_element_by_name(\"phrase\")\n\n # find the search box and clear it.\n driver.find_element_by_name(\"from\").clear()\n\n if hashtag != \"\":\n Hashtagbox.send_keys(hashtag)\n if phrase != \"\":\n phraseBox.send_keys(phrase)\n\n # enter user.\n User.send_keys(user)\n\n # since box.\n # sinceBox = driver.wait.until(EC.presence_of_element_located((By.NAME, \"since\")))\n\n # until box.\n # untilBox = driver.wait.until(EC.presence_of_element_located((By.NAME, \"until\")))\n\n # ActionChains(driver).move_to_element(sinceBox).click().send_keys(sinceDate).perform()\n\n # ActionChains(driver).move_to_element(untilBox).click().send_keys(untilDate).perform()\n\n if untilDate == \"\" and sinceDate != \"\":\n flash('Both Dates must be picked!')\n elif sinceDate == \"\" and untilDate != \"\":\n flash('Both Dates must be picked!')\n elif untilDate != \"\" and sinceDate != \"\":\n\n # getting inputted since date and getting right format.\n\n NewSinceDate = datetime.datetime.strptime(sinceDate, \"%Y-%m-%d\")\n\n # splitting inputted date into the required pieces.\n\n sinceYear = NewSinceDate.strftime(\"%Y\")\n sinceMonth = NewSinceDate.strftime(\"%b\")\n sinceDay = NewSinceDate.strftime(\"%d\").lstrip(\"0\")\n\n # using datepicker.py to select date from datepicker.\n SelectDate(driver, \"input-since\", sinceYear, sinceMonth, sinceDay)\n\n # getting inputted since date and getting right format.\n\n untilDate = datetime.datetime.strptime(untilDate, \"%Y-%m-%d\")\n\n # splitting inputted date into the required pieces.\n\n untilYear = untilDate.strftime(\"%Y\")\n untilMonth = untilDate.strftime(\"%b\")\n untilDay = untilDate.strftime(\"%d\").lstrip(\"0\")\n\n # using datepicker.py to select date from datepicker.\n SelectDate(driver, \"input-until\", untilYear, untilMonth, untilDay)\n\n # Submit.\n User.submit()\n\n # initial wait for the search results to load.\n wait = WebDriverWait(driver, 10)\n\n try:\n # wait until the first search result is found.\n wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, \"li[data-item-id]\")))\n\n # scroll down to the last tweet until there are no more tweets.\n while True:\n\n # extract all the tweets.\n tweets = driver.find_elements_by_css_selector(\"li[data-item-id]\")\n\n # keep scrolling.\n driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\n # find number of visible tweets.\n number_of_tweets = len(tweets)\n\n # Wait for more to load.\n wait = WebDriverWait(driver, 5)\n\n try:\n # wait for more tweets to be visible.\n wait.until(wait_for_more_than_n_elements_to_be_present(\n (By.CSS_SELECTOR, \"li[data-item-id]\"), number_of_tweets))\n\n except TimeoutException:\n # if no more are visible the \"wait.until\" call will timeout.\n break\n\n # extract the html.\n page_source = driver.page_source\n\n except TimeoutException:\n # if there are no tweets then return none.\n page_source = None\n\n return page_source\n\n\n# Normal Search IE: The search bar at the top of the page\n# this is a very general search type and isn't very accurate.\ndef search_twitter(driver, query):\n # wait until the search box has loaded.\n box = driver.wait.until(EC.presence_of_element_located((By.NAME, \"q\")))\n\n # find the search box.\n driver.find_element_by_name(\"q\").clear()\n\n # enter search query.\n box.send_keys(query)\n\n # submit.\n box.submit()\n\n # wait for the results to load\n wait = WebDriverWait(driver, 10)\n\n try:\n # wait until the first search result is found.\n wait.until(EC.visibility_of_element_located((By.CSS_SELECTOR, \"li[data-item-id]\")))\n\n # scroll until there are no more tweets.\n while True:\n\n # extract all the tweets.\n tweets = driver.find_elements_by_css_selector(\"li[data-item-id]\")\n\n # find number of visible tweets.\n number_of_tweets = len(tweets)\n\n # keep scrolling.\n driver.execute_script(\"arguments[0].scrollIntoView();\", tweets[-1])\n\n try:\n # wait for more tweets.\n wait.until(wait_for_more_than_n_elements_to_be_present(\n (By.CSS_SELECTOR, \"li[data-item-id]\"), number_of_tweets))\n\n except TimeoutException:\n # if no more are visible the \"wait.until\" call will timeout.\n break\n\n # extract the html.\n page_source = driver.page_source\n\n except TimeoutException:\n\n # if there are no search results then return none.\n page_source = None\n\n return page_source\n\n\ndef extract_tweets(page_source):\n # get page source\n soup = bs(page_source, 'lxml')\n\n i = 0\n # impressions = []\n # for item in page_source:\n # Open analytics of each tweet\n # driver.find_element_by_xpath(\"/html/body/div[2]/div[2]/div/div[4]/div/div/div[2]/div/div[2]/div[4]/div/div[2]/ol[1]/li[2]/div[1]/div[2]/div[2]/div[2]/div[4]/button\").click()\n # impressions[i] = driver.find_element_by_css_selector(\"ep-MetricAnimation\").getText()\n # i+=1\n # print(impressions[i])\n\n tweets = []\n\n # For every list item in the page source\n for li in soup.find_all(\"li\", class_='js-stream-item'):\n\n totalValue = 0\n totalRetweets = 0\n totalFavourites = 0\n totalReplies = 0\n\n # print(\"\\nStart of item \\n\", li, \"\\nEnd of Item\\n\"\n\n # If list item doesn't have an item ID then it isn't a tweet\n if 'data-item-id' not in li.attrs:\n continue\n else:\n # Making Tweet item\n tweet = {\n 'tweet_id': li['data-item-id'],\n 'text': None,\n 'user_id': None,\n 'user_screen_name': None,\n 'user_name': None,\n 'created_at': None,\n 'retweets': 0,\n 'likes': 0,\n 'replies': 0,\n 'impressions': 0,\n 'value': 0\n }\n\n # Adding details to tweet item from tweet list item\n\n # Tweet Text\n text_p = li.find(\"p\", class_=\"tweet-text\")\n if text_p is not None:\n tweet['text'] = text_p.get_text()\n\n # Tweet User ID, User Screen Name, User Name\n user_details_div = li.find(\"div\", class_=\"tweet\")\n if user_details_div is not None:\n tweet['user_id'] = user_details_div['data-user-id']\n tweet['user_screen_name'] = user_details_div['data-screen-name']\n tweet['user_name'] = user_details_div['data-name']\n\n # Tweet date\n date_span = li.find(\"span\", class_=\"_timestamp\")\n if date_span is not None:\n tweet['created_at'] = float(date_span['data-time-ms'])\n\n # Tweet Retweets\n retweet_span = li.select(\"span.ProfileTweet-action--retweet > span.ProfileTweet-actionCount\")\n if retweet_span is not None and len(retweet_span) > 0:\n tweet['retweets'] = int(retweet_span[0]['data-tweet-stat-count'])\n\n # Tweet Likes\n like_span = li.select(\"span.ProfileTweet-action--favorite > span.ProfileTweet-actionCount\")\n if like_span is not None and len(like_span) > 0:\n tweet['likes'] = int(like_span[0]['data-tweet-stat-count'])\n\n # Tweet Replies\n reply_span = li.select(\"span.ProfileTweet-action--reply > span.ProfileTweet-actionCount\")\n if reply_span is not None and len(reply_span) > 0:\n tweet['replies'] = int(reply_span[0]['data-tweet-stat-count'])\n\n # Attempts to retrieve impressions data\n\n # Tweet impressions\n '''analytics_span = li.select(\"div.ProfileTweet-action.ProfileTweet-action--analytics\")\n if analytics_span is not None and len(analytics_span) > 0:\n analyticsbutton = driver.find_element_by_css_selector(\".ProfileTweet-actionButton\")\n driver.execute_script(\"arguments[0].scrollIntoView();\", analyticsbutton)\n analyticsbutton.click()\n impressions = driver.find_element_by_css_selector(\"ep-MetricValue\").getText()\n print(impressions)'''\n\n # driver.find_element_by_css_selector(\".ProfileTweet-actionButton.u-textUserColorHover.js-actionButton.js-actionQuickPromote\").click()\n # impressions = driver.find_element_by_css_selector(\"ep-MetricValue\").getText()\n # print(\"\\nStart of item \\n\", li, \"\\nEnd of Item\\n\")\n # print(\"Impressions: \", impressions)\n # driver.find_element_by_css_selector(\"QuickPromoteDialog modal-container\").click()\n\n # Tweet VALUE ROI\n tweet['value'] += round((tweet['replies'] * 0.10), 2)\n tweet['value'] += round((tweet['likes'] * 0.15), 2)\n tweet['value'] += round((tweet['retweets'] * 0.25), 2)\n\n tweets.append(tweet)\n\n for tweet in tweets:\n i += 1\n '''Value = round(tweet['value'],2)\n print(\"\\nTweet Number: \", i)\n print(\"Username: \", tweet['user_name'])\n print(\"Text: \", tweet['text'])\n print(\"Retweets: \", tweet['retweets'])\n print(\"Likes: \", tweet['likes'])\n print(\"Replies: \", tweet['replies'])\n print(\"Impressions: \", tweet['impressions'])\n print(\"Value: €\", Value)'''\n\n totalValue += round(tweet['value'], 2)\n totalFavourites += tweet['likes']\n totalReplies += tweet['replies']\n totalRetweets += tweet['retweets']\n\n finalTotalValue = round(totalValue, 2)\n\n # results = [finalTotalValue, totalRetweets, totalFavourites, totalReplies]\n\n # return results\n\n # print(\"\\n******Totals of all Values in ths Ad Campaign*****\\n\")\n # print(\"\\nTotal Value of all tweets: €\", round(totalValue, 2))\n # print(\"\\nTotal number of all replies: \", totalReplies)\n # print(\"\\nTotal number of all favourite: \", totalFavourites)\n # print(\"\\nTotal number of all retweets: \", totalRetweets)\n # print(\"\\nTotal number of all tweets: \", i)\n\n return (finalTotalValue, totalRetweets, totalFavourites, totalReplies, i)\n\n# Below were used in testing:\n\n# start a driver.\n# driver = init_driver()\n\n# Testing log in function.\n\n# username = \"\"\n# password = \"\"\n# login_twitter(driver, username, password)\n\n\n# Testing Searching a specific username.\n\n# Search_username = \"@paddypower\"\n# query = \"#Brexit\"\n\n# Testing searching own account.\n\n# User_Self = \"\"\n# password =\"\"\n# page_source = search_twitter(driver, query)\n\n# Time frames for testing the specific date queries\n\n# SINCE\n# since = \"2015-04-02\"\n\n# TILL\n# till = \"2018-05-24\"\n\n# Full functions\n\n# page_source = Search_HashTag(driver, Search_username, query)\n# page_source = Go_to_self(driver, User_Self, password)\n# page_source = Search_Specific_User(driver, User_Self, password, since, till)\n\n# Extraction\n\n# tweets = extract_tweets(page_source)\n\n# close and quit driver.\n\n# close_driver(driver)\n","sub_path":"TwitterLoginDemo.py","file_name":"TwitterLoginDemo.py","file_ext":"py","file_size_in_byte":20777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"244936637","text":"from flask import Blueprint, request, jsonify\n\nfrom models.category import Category\nfrom models.product import Product\nfrom controllers import auth\n\n\nadmin_product_bp = Blueprint('admin_product_bp', __name__)\n\n\n@admin_product_bp.route('/products', methods=['POST'])\n@auth.requires_token_auth('admin')\ndef new_product(admin):\n data = request.get_json()\n if 'name' not in data or 'color' not in data or 'image' not in data or 'quantity' not in data or 'price' not in data or 'category_id' not in data:\n return jsonify({\"message\": \"Bad request.\"}), 400\n product = Product(data['name'], data['color'], data['image'], data['quantity'], data['price'], data['category_id'])\n product.save_to_db()\n return jsonify({\"message\": \"New product created.\"}), 201\n\n\n@admin_product_bp.route('/products/<int:product_id>', methods=['PUT'])\n@auth.requires_token_auth('admin')\ndef update_product(product_id, admin):\n product = Product.find_by_id(product_id)\n if not product:\n return jsonify({\"message\": \"Product not found.\"})\n data = request.get_json()\n if 'name' not in data and 'color' not in data and 'image' not in data and 'quantity' not in data \\\n and 'price' not in data and 'category_id' not in data:\n return jsonify({\"message\": \"Bad request.\"}), 400\n if 'name' in data:\n product.name = data['name']\n if 'color' in data:\n product.color = data['color']\n if 'image' in data:\n product.image = data['image']\n if 'quantity' in data:\n product.quantity = data['quantity']\n if 'price' in data:\n product.price = data['price']\n if 'category_id' in data:\n product.category_id = data['category_id']\n product.save_to_db()\n return jsonify({\"message\": \"Product updated.\"}), 200\n\n\n@admin_product_bp.route('/products/<int:product_id>', methods=['DELETE'])\n@auth.requires_token_auth('admin')\ndef delete_product(admin, product_id):\n product = Product.find_by_id(product_id)\n if not product:\n return jsonify({\"message\": \"Product not found.\"}), 400\n product.delete_from_db()\n return jsonify({\"message\": \"Product deleted.\"}), 200\n","sub_path":"controllers/admin/product.py","file_name":"product.py","file_ext":"py","file_size_in_byte":2138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"93304674","text":"#!/usr/bin/env python\n\"\"\"\n.. moduleauthor:: Sandeep Nanda <mail: sandeep.nanda@guavus.com> <skype: snanda85>\n\nPotluck server which listens for Sikuli Based Testcase request from harness.\nIt listens on `POTLUCK_SERVER_PORT` defined in settings.py\n\nExample usage:\n\nWindows::\n\n cd Potluck\n server.cmd\n\nMac/OSX::\n\n cd Potluck\n ./server.sh\n\n.. note::\n\n All care has been taken to make this server platform independent. If you find any\n platform related issues, kindly report to the Potluck team\n\"\"\"\n\nimport os\nimport SocketServer\nimport pickle\nfrom threading import Thread\nfrom Queue import Queue\nimport signal\nfrom tempfile import NamedTemporaryFile\nimport pickle\n\nfrom potluck.utils import run_cmd, platform\nfrom potluck.logging import logger\n\nfrom settings import POTLUCK_SERVER_PORT, SCRIPTS_DIR, ROOT_DIR\nSERVER_PATH = os.path.abspath(os.path.dirname(__file__))\n\nif platform() == \"WIN\":\n SIKULI_PATH = os.path.join(SERVER_PATH, \"Sikuli\", \"runScript.cmd\")\n JYTHONPATH = \"%s;%s\" % (SERVER_PATH, SCRIPTS_DIR)\nelse:\n JYTHONPATH = \"%s:%s\" % (SERVER_PATH, SCRIPTS_DIR)\n SIKULI_PATH = os.path.join(SERVER_PATH, \"Sikuli\", \"runScript\")\n\n# To import Potluck modules\n\nclass PotluckRequestHandler(SocketServer.StreamRequestHandler):\n def respond(self, data):\n pickle.dump(data, self.wfile)\n self.wfile.flush()\n\n def receive(self):\n return pickle.load(self.rfile)\n\n def handle(self):\n data = self.receive()\n logger.info(\"Handling request from client: %s\" % data)\n # Data should be a dict with following format\n # data = {\n # \"request\" : \"<ENQUEUE|STATUS>\",\n # \"script\" : \"core/test.sikuli\",\n # \"timeout\" : 600\n # }\n if data[\"request\"] == \"ENQUEUE\":\n request_id = self.server.request_id\n self.server.request_status[request_id] = \"QUEUED\" # Update the testcase status\n data[\"request_id\"] = request_id # Fill in the request id\n self.server.tc_request_queue.put(data) # Submit the testcase request\n self.server.request_id += 1\n ret_val = {\n \"request_id\" : request_id,\n \"success\" : True\n }\n logger.info(\"Testcase queued with request_id: %d\" % request_id)\n self.respond(ret_val)\n elif data[\"request\"] == \"STATUS\":\n request_id = data[\"request_id\"]\n request_status = self.server.request_status.get(request_id, \"INVALID_REQUEST_ID\")\n ret_val = {\n \"success\" : True,\n \"request_id\" : request_id,\n \"status\" : request_status,\n \"logs\" : self.server.request_logs.get(request_id, \"\")\n }\n logger.info(\"Returning status of request_id %d: %s\" % (request_id, request_status))\n self.respond(ret_val)\n elif data[\"request\"] == \"SEND_FILE\":\n filepath = data[\"file\"]\n # If absolute filepath does not exist, consider it relative to Potluck directory\n if not os.path.exists(filepath):\n filepath = os.path.abspath(os.path.join(ROOT_DIR, filepath))\n\n # If the path still does not exist, send back an error\n if not os.path.exists(filepath):\n self.wfile.write(\"ERROR: File does not exist\")\n else:\n try:\n # Open the file to read\n fileToSend = open(filepath, 'r')\n logger.info(\"Sending file '%s' to the client\" % filepath)\n while True:\n data = fileToSend.readline()\n if data:\n self.wfile.write(data)\n else:\n break\n fileToSend.close()\n except Exception as e:\n self.wfile.write(\"ERROR: %s\" % str(e))\n self.wfile.flush()\n\nclass PotluckServer(SocketServer.TCPServer):\n def __init__(self, *args, **kwargs):\n SocketServer.TCPServer.__init__(self, *args, **kwargs)\n self.tc_request_queue = Queue()\n self.tc_response_queue = Queue()\n self.request_status = {}\n self.request_logs = {}\n self.request_id = 1\n self.testcase_executor = TestcaseExecutor(self.tc_request_queue, self.tc_response_queue)\n self.testcase_executor.start()\n self.testcase_updator = Thread(target=self.update_testcase_status)\n self.testcase_updator.start()\n\n def sigBreak(self, signum, f):\n self.tc_request_queue.put(\"SHUTDOWN\")\n\n def update_testcase_status(server):\n for data in iter(server.tc_response_queue.get, \"SHUTDOWN\"):\n # No issue even if this thread blocks\n #data = server.tc_response_queue.get()\n request_id = data.get(\"request_id\")\n if request_id != None:\n request_status = data[\"status\"]\n logger.info(\"Updating Testcase status for request_id %d: %s\" % (request_id, request_status))\n server.request_status[request_id] = request_status\n server.request_logs[request_id] = data.get(\"logs\", \"\")\n\nclass TestcaseExecutor(Thread):\n def __init__(self, request_queue, response_queue, *args, **kwargs):\n super(TestcaseExecutor, self).__init__(*args, **kwargs)\n self.request_queue = request_queue\n self.response_queue = response_queue\n\n def run(self):\n for data in iter(self.request_queue.get, \"SHUTDOWN\"):\n #data = self.request_queue.get()\n #logger.debug(\"Data from parent: %s\" % data)\n script = data[\"script\"]\n request_id = data[\"request_id\"]\n ui_url = data[\"url\"]\n config = data.get(\"config\", None)\n ret_val = {\n \"request_id\" : request_id,\n \"status\" : \"IN_PROGRESS\"\n }\n\n # Inform the parent about progress\n self.response_queue.put(ret_val)\n tc_status, logs = self.execute_tc(script, ui_url, config)\n if tc_status is True:\n ret_val = {\n \"request_id\" : request_id,\n \"status\" : \"PASSED\",\n \"logs\" : logs\n }\n else:\n ret_val = {\n \"request_id\" : request_id,\n \"status\" : \"FAILED\",\n \"logs\" : logs,\n }\n self.response_queue.put(ret_val)\n self.shutdown()\n \n def shutdown(self):\n self.response_queue.put(\"SHUTDOWN\")\n\n def execute_tc(self, script, ui_url, config=None):\n absolute_script_path = os.path.abspath(os.path.join(SCRIPTS_DIR, script))\n if platform() == \"WIN\":\n cmd = \"\"\"cmd /C \"set JYTHONPATH=%s && %s -r %s -- %s\" \"\"\" % (JYTHONPATH, SIKULI_PATH, absolute_script_path, ui_url)\n else:\n cmd = \"JYTHONPATH=%s %s -r %s -- %s\" % (JYTHONPATH, SIKULI_PATH, absolute_script_path, ui_url)\n\n # Dump the config parameters in a temporary file\n # These will be read by the UI testcase\n if config is not None:\n config_fd = NamedTemporaryFile(delete=False)\n pickle.dump(config, config_fd)\n config_fd.close()\n cmd += \" %s\" % config_fd.name\n \n logger.debug(cmd)\n output = run_cmd(cmd)\n #print output\n if \"TESTCASE PASSED\" in output:\n return True, output\n else:\n return False, output\n\nif __name__ == \"__main__\":\n server_address = (\"0.0.0.0\", POTLUCK_SERVER_PORT)\n server = PotluckServer(server_address, PotluckRequestHandler)\n logger.info(\"Listening for connections on %s:%s\" % server_address)\n signal.signal(signal.SIGINT, server.sigBreak)\n server.serve_forever()\n","sub_path":"ciTool/Potluck_cloudera/src/ui_server.py","file_name":"ui_server.py","file_ext":"py","file_size_in_byte":7841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"230390991","text":"#!/usr/bin/python3\n\n# @Project = step_LeetCode\n# @File : 1052_Grumpy_Bookstore_Owner\n# @Author : TCY\n# @Time : 2019/5/27 16:42\n# @Email : tangcaiyuan@hust.edu.cn\n# @Software: PyCharm\n\n# 比赛时的写法还是太过于啰嗦了，完全不用gain数组，直接滑动就行，如果是生气变为不生气则为该滑动窗口的收益，最后保存最大收益，再加上base的收益就行了\n\nclass Solution:\n def maxSatisfied(self, customers: List[int], grumpy: List[int], X: int) -> int:\n \"\"\"计算一个收益数组\"\"\"\n n = len(customers)\n gain = []\n for i in range(n):\n if grumpy[i] == 1:\n gain.append(customers[i])\n else:\n gain.append(0)\n mxi, mxj = 0, X - 1\n mx = 0\n i, j = 0, X - 1\n while j < n:\n tmp = sum(gain[i:j + 1])\n if tmp > mx:\n mxi, mxj = i, j\n mx = tmp\n i += 1\n j += 1\n \"\"\"计算最后的满意顾客数量\"\"\"\n while mxi <= mxj:\n grumpy[mxi] = 0\n mxi += 1\n ans = 0\n for i in range(n):\n if grumpy[i] == 0:\n ans += customers[i]\n return ans\n","sub_path":"Weekly_Contest/Weekly_Contest_138/1052_Grumpy_Bookstore_Owner.py","file_name":"1052_Grumpy_Bookstore_Owner.py","file_ext":"py","file_size_in_byte":1239,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"246038169","text":"# set operations\na = {1, 2, 3, 4, 5, 6, 1, 2, 3}\nprint(set(a))\na.add(11)\na.add(12)\nprint(a)\n\n# adding tuple to a set\nvowels = {'a', 'e', 'u'}\ntup = ('i', 'o')\nvowels.add(tup)\nprint('vowels are:', vowels)\n","sub_path":"set-1.py","file_name":"set-1.py","file_ext":"py","file_size_in_byte":204,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"334328738","text":"import numpy as np\nimport os\nfrom shutil import copy\nimport natsort\nimport sys\nimport pandas as pd\n\n\n\n'''\nGraph for breaking condition\n'''\na=pd.read_csv(\"breaking_result\", sep=\"\\n\", header=None)\n\navg=0.0\nfor each in range((len(a))):\n avg+=a.iloc[each][0] \n\navg=avg/10\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n#sys.exit()\n\n'''\nDELETED seed0, seed1 manually from result file\n'''\n\n'''\na=pd.read_csv(\"result\", sep=\"\\n\", header=None)\nb=pd.DataFrame(columns=a.columns,index=range(50))\ni=0\nfor each in range(len(a)): \n if(each % 2 !=0):\n b.iat[i,0]=a.iloc[each][0]\n i+=1\n \ntot_25=0.0\ntot_50=0.0\ntot_100=0.0\ntot_20=0.0\ntot_200=0.0\nfor i in range(len( b)):\n if(i % 5)==0:\n tot_25+=float (b.iloc[i][0])\n\n if(i % 5)==1:\n tot_50+=float (b.iloc[i][0])\n\n if(i % 5)==2:\n tot_100+=float (b.iloc[i][0])\n\n if(i % 5)==3:\n tot_20+=float (b.iloc[i][0])\n\n if(i % 5)==4:\n tot_200+=float (b.iloc[i][0])\n\n\n\n\ntot_25=tot_25/10\ntot_50=tot_50/10\ntot_100=tot_100/10\ntot_20=tot_20/10\ntot_200=tot_200/10 \n'''\n\n\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nimport sys\n\n\nfig = plt.figure(figsize=(12.6,6))\nax = fig.add_subplot(111)\nax.set_position([0.15,0.15,0.6,0.83])\nind = np.arange(5)\n\navg_bar2 = (avg, .15253) #enchant\npropose_bar1= (tot_25, tot_50,tot_100,tot_20,tot_200 ) #No enchant\n\n\n\nax.bar(ind[0] + 0.05,0.0, color='pink', hatch=\"\\\\\\\\\")\n#ax.bar(ind[2] + 0.60,0.0, color='white',edgecolor=\"black\", label='No Enchant', hatch=\"...\")\n \n \n \n \nrects2 = ax.bar(ind[0], propose_bar1[0], 0.15, color='white',edgecolor=\"black\",hatch= \"\\\\\\\\\",label= \"Baseline\" ) \nrects2 = ax.bar(ind[0] + 0.25, propose_bar1[0], 0.15, color='pink',edgecolor=\"black\",hatch= \"\\\\\\\\\",label= \"SDCS\" ) \n\n\nrects2 = ax.bar(ind[1], propose_bar1[1], 0.15, color='white',edgecolor=\"black\",hatch= \"\\\\\\\\\")\nrects2 = ax.bar(ind[1] + 0.25, propose_bar1[1], 0.15, color='pink',edgecolor=\"black\",hatch= \"\\\\\\\\\")\n\nrects2 = ax.bar(ind[2], propose_bar1[2], 0.15, color='white',edgecolor=\"black\",hatch= \"\\\\\\\\\")\nrects2 = ax.bar(ind[2] + 0.25, avg_bar2 [0], 0.15, color='pink',edgecolor=\"black\",hatch= \"\\\\\\\\\")\n\nrects2 = ax.bar(ind[3], propose_bar1[3], 0.15, color='white',edgecolor=\"black\",hatch= \"\\\\\\\\\") \nrects2 = ax.bar(ind[3]+.25, propose_bar1[3], 0.15, color='pink',edgecolor=\"black\",hatch= \"\\\\\\\\\") \n\n\nrects2 = ax.bar(ind[4], propose_bar1[4], 0.15, color='white',edgecolor=\"black\",hatch= \"\\\\\\\\\") \nrects2 = ax.bar(ind[4] + 0.250, avg_bar2[1], 0.15, color='pink',edgecolor=\"black\",hatch= \"\\\\\\\\\")\n\n\n #1 BBC 2RFC 3 AMAZON \n\n\n\n\n\n\n\n\n#trend_line = plt.plot(high_point_x,high_point_y,marker='o', color='#5b74a8', label='Trend Line')\n\n#plt.xlabel('Propose vs. Encrypted')\nplt.ylabel('Coherence', fontsize=22)\nfor label in ( ax.get_yticklabels()):\n label.set_fontname('Arial')\n label.set_fontsize(14)\nplt.xticks(ind+.13, ('Update1', 'Update2', 'Update3', 'Update4', \"Update5\"), fontsize=16)\n#plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n\nax.legend(bbox_to_anchor=(.65, .98), loc=2, borderaxespad=0., fontsize=14)\n\n\n#fig.savefig('/home/c00300901/Dropbox/semantic search-sailish/publication/new_Journal-Zobaed/Figure/semiDynamic_coherence_bbc.pdf')\nplt.show()","sub_path":"Cluspr_semi_Dynamic/result_semi dyna/bbc/bbc_result.py","file_name":"bbc_result.py","file_ext":"py","file_size_in_byte":3263,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"314033930","text":"import logging\n\nfrom .Context import Context\nfrom inspectortodo.todo import Todo\n\nLINE_BREAK = '\\n'\nlog = logging.getLogger()\n\n\nclass BaseTodoParser(object):\n \"\"\" Finds todos in file \"\"\"\n\n def __init__(self, single_line_comment_start, multi_line_comment_start, multi_line_comment_end, keywords,\n annotations=None):\n self.single_line_comment_start = single_line_comment_start\n self.multi_line_comment_start = multi_line_comment_start\n self.multi_line_comment_end = multi_line_comment_end\n self.keywords = keywords\n self.annotations = annotations\n\n def get_todos(self, absolute_file_name, relative_file_path):\n self._init_variables()\n\n try:\n with(open(absolute_file_name, mode='r', encoding='utf-8')) as file:\n file_iterator = FileIterator(file)\n for ch in file_iterator:\n if self.context == Context.code:\n if self.matches_single_line_comment_start(ch, file_iterator):\n self.context = Context.single_line_comment\n self.comment_content = ch\n elif self.matches_multi_line_comment_start(ch, file_iterator):\n self.context = Context.multi_line_comment\n self.comment_content = ch\n else:\n annotation = self.find_matching_annotations(ch, file_iterator)\n if annotation is not None:\n self.context = Context.annotation\n self.comment_content = ch\n elif self.context == Context.single_line_comment and ch == LINE_BREAK:\n self._store_if_todo_and_reset(file_iterator, relative_file_path)\n elif self.context == Context.annotation and ch == LINE_BREAK:\n self._store_if_todo_and_reset(file_iterator, relative_file_path, True)\n elif self.context == Context.multi_line_comment \\\n and self.matches_multi_line_comment_end(ch, file_iterator):\n self.comment_content += self.multi_line_comment_end\n self._store_if_todo_and_reset(file_iterator, relative_file_path)\n else:\n self.comment_content += ch\n return self.todos\n except Exception as e:\n log.warning('Error parsing file %s: %s', relative_file_path, e)\n return []\n\n def _init_variables(self):\n self.comment_content = ''\n self.context = Context.code\n self.todos = []\n\n def matches_single_line_comment_start(self, character, file_iterator):\n return self._matches(character, self.single_line_comment_start, file_iterator)\n\n def matches_multi_line_comment_start(self, character, file_iterator):\n return self._matches(character, self.multi_line_comment_start, file_iterator)\n\n def matches_multi_line_comment_end(self, character, file_iterator):\n return self._matches(character, self.multi_line_comment_end, file_iterator)\n\n def find_matching_annotations(self, character, file_iterator):\n if self.annotations:\n for annotation in self.annotations:\n if self._matches(character, annotation, file_iterator):\n return annotation\n return None\n\n def _matches(self, character, string_to_match, file_iterator):\n if len(string_to_match) > 0 and character == string_to_match[0]:\n potential_match = character + file_iterator.look_ahead(len(string_to_match) - 1)\n return potential_match == string_to_match\n return False\n\n def _store_if_todo_and_reset(self, file_iterator, file_path, is_annotation=False):\n if is_annotation or self._comment_contains_todo(self.comment_content):\n self.todos.append(Todo(file_path, file_iterator.line_number, self.comment_content))\n self.comment_content = ''\n self.context = Context.code\n\n def _comment_contains_todo(self, content):\n for keyword in self.keywords:\n if keyword in content:\n return True\n return False\n\n\nclass FileIterator:\n\n def __init__(self, file):\n self.file = file\n self.line_number = 1\n self._buffer = []\n\n def __iter__(self):\n return self\n\n def __next__(self):\n ch = self._get_next_char()\n if ch == LINE_BREAK:\n self.line_number += 1\n if ch:\n return ch\n else:\n raise StopIteration()\n\n def _get_next_char(self):\n if len(self._buffer) > 0:\n return self._buffer.pop(0)\n return self.file.read(1)\n\n def look_ahead(self, n):\n buffer_len = len(self._buffer)\n for _ in range(buffer_len, n):\n self._buffer.append(self.file.read(1))\n\n return ''.join(self._buffer[:n])\n","sub_path":"src/inspectortodo/parser/BaseTodoParser.py","file_name":"BaseTodoParser.py","file_ext":"py","file_size_in_byte":4994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"476335548","text":"from django.conf import settings\nfrom getenv import env\n\n\ndef google_analytics(request):\n ga_prop_id = getattr(settings, 'GOOGLE_ANALYTICS_PROPERTY_ID', False)\n if not settings.DEBUG and ga_prop_id:\n return {\n 'GOOGLE_ANALYTICS_PROPERTY_ID': ga_prop_id,\n }\n return {}\n\n\ndef crazy_egg(request):\n crazy_egg_path = getattr(settings, 'CRAZY_EGG', '')\n if not settings.DEBUG and crazy_egg_path:\n return {\n 'CRAZY_EGG': crazy_egg_path\n }\n return {}\n\n\ndef sentry(request):\n sentry_id = ''\n if hasattr(request, 'sentry'):\n sentry_id = request.sentry['id']\n return {\n 'sentry_id': sentry_id,\n 'sentry_public_id': env('SENTRY_PUBLIC_DSN', ''),\n }","sub_path":"insitu/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"355351724","text":"import os\nimport pytest\nimport musdb\nimport simplejson as json\nimport museval\nimport numpy as np\nimport eval\n\ntest_track = 'Al James - Schoolboy Facination'\n\njson_path = os.path.join(\n os.path.dirname(os.path.realpath(__file__)),\n 'data/%s.json' % test_track,\n)\n\n\n@pytest.fixture()\ndef mus():\n return musdb.DB(download=True)\n\n\ndef test_estimate_and_evaluate(mus):\n # return any number of targets\n with open(json_path) as json_file:\n ref = json.loads(json_file.read())\n\n track = [track for track in mus.tracks if track.name == test_track][0]\n\n scores = eval.separate_and_evaluate(\n track,\n targets=['vocals', 'drums', 'bass', 'other'],\n model_name='umx',\n niter=1,\n alpha=1,\n softmask=False,\n output_dir=None,\n eval_dir=None,\n device='cpu'\n )\n\n assert scores.validate() is None\n\n with open(\n os.path.join('.', track.name) + '.json', 'w+'\n ) as f:\n f.write(scores.json)\n\n scores = json.loads(scores.json)\n\n for target in ref['targets']:\n for metric in ['SDR', 'SIR', 'SAR', 'ISR']:\n\n ref = np.array([d['metrics'][metric] for d in target['frames']])\n idx = [t['name'] for t in scores['targets']].index(target['name'])\n est = np.array(\n [\n d['metrics'][metric]\n for d in scores['targets'][idx]['frames']\n ]\n )\n\n assert np.allclose(ref, est, atol=1e-02)\n","sub_path":"open-unmix-pytorch/tests/test_regression.py","file_name":"test_regression.py","file_ext":"py","file_size_in_byte":1502,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"277620317","text":"\"\"\"\n Bird Identifier using Random Forest. \n\"\"\"\n\nimport pandas as pd\nfrom sklearn.ensemble import RandomForestClassifier\nfrom sklearn.metrics import confusion_matrix\nimport matplotlib.pyplot as plt\nimport itertools\nimport numpy as np\nfrom sklearn.model_selection import cross_val_score\n\n# Read the file\nimgatt = pd.read_csv(\"dataset/image_attribute_labels.txt\",\n sep='\\s+', header=None, error_bad_lines=False, warn_bad_lines=False,\n usecols=[0, 1, 2], names=['imgid', 'attid', 'present'])\n\n\"\"\"\n Description from dataset README:\n\n The set of attribute labels as perceived by MTurkers for each image\n is contained in the file attributes/image_attribute_labels.txt, with\n each line corresponding to one image/attribute/worker triplet:\n\n <image_id> <attribute_id> <is_present> <certainty_id> <time>\n\n where <image_id>, <attribute_id>, <certainty_id> correspond to the IDs\n in images.txt, attributes/attributes.txt, and attributes/certainties.txt\n respectively. <is_present> is 0 or 1 (1 denotes that the attribute is\n present). <time> denotes the time spent by the MTurker in seconds.\n\"\"\"\n\n\"\"\"\n We can see how many rows and columns we have by running the following code:\n\n imgatt.shape\n\n This will output (3677856, 3), which means we have about 3.7 million rows and 3 columns.\n\"\"\"\n\n\n# Reorganize the table\nimgatt2 = imgatt.pivot(index='imgid', columns='attid', values='present')\n\n\"\"\"\n Because we want the attributes to be columns, not rows, we need to change the table. For this,\n we have to use pivot:\n\n 1. Pivot on the image ID and make one row for each image ID.\n 2. Turn the attributes into distinct columnt, and the vaues will be ones or twos. \n\n Now, imgatt2.shape will output (11788, 312). \n\"\"\"\n\n\n# Load the true image classes\nimglabels = pd.read_csv(\"dataset/image_class_labels.txt\",\n sep=' ', header=None, names=['imgid', 'label'])\n\nimglabels = imglabels.set_index('imgid')\n\n\"\"\"\n Description from dataset README:\n\n The ground truth class labels (bird species labels) for each image are contained\n in the file image_class_labels.txt, with each line corresponding to one image:\n\n <image_id> <class_id>\n\n where <image_id> and <class_id> correspond to the IDs in images.txt and classes.txt,\n respectively.\n\"\"\"\n\n\n# Attach the labels to the attribute data set\ndf = imgatt2.join(imglabels)\n\n\n# Shuffle the data\ndf = df.sample(frac=1)\n\ndf_att = df.iloc[:, :312]\ndf_label = df.iloc[:, 312:]\n\n# Seperate two sets: a training set and a testing set\ndf_train_att = df_att[:8000]\ndf_train_label = df_label[:8000]\ndf_test_att = df_att[8000:]\ndf_test_label = df_label[8000:]\n\ndf_train_label = df_train_label['label']\ndf_test_label = df_test_label['label']\n\n\n# Create a Random Forest Classifier\nclf = RandomForestClassifier(max_features=50, random_state=0, n_estimators=100)\n\n\"\"\"\n max_features: the number of different columnes each tree can look at\n n_estimators: the number of trees created \n\"\"\"\n\n\n# Build the classifier\nclf.fit(df_train_att, df_train_label)\n\n\n# Predict a few cases and check the accuracy\n\"\"\"\n The following code predicts 5 species based on the attributes from the first 5 rows\n of the training set:\n\n clf.predict(df_train_att.head())\n\n The output is: [ 73 32 86 169 46 ]\n\n \n Now, to check the accuracy of this prediction, the following code is run:\n \n clf.score(df_test_att, df_test_label)\n\n This outputs an accuracy level of 0.447. This is not the best score.\n\n\"\"\"\n\n\n# Create a confusion matrix\npred_labels = clf.predict(df_test_att)\ncm = confusion_matrix(df_test_label, pred_labels)\n\n\n# Visualize the confusion matrix\ndef plot_confusion_matrix(cm, classes,\n normalize=False,\n title='Confusion matrix',\n cmap=plt.cm.Blues):\n \"\"\"\n This function prints and plots the confusion matrix.\n Normalization can be applied by setting `normalize=True`.\n \"\"\"\n if normalize:\n cm = cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]\n print(\"Normalized confusion matrix\")\n else:\n print('Confusion matrix, without normalization')\n\n print(cm)\n\n plt.imshow(cm, interpolation='nearest', cmap=cmap)\n plt.title(title)\n # plt.colorbar()\n tick_marks = np.arange(len(classes))\n plt.xticks(tick_marks, classes, rotation=90)\n plt.yticks(tick_marks, classes)\n\n fmt = '.2f' if normalize else 'd'\n thresh = cm.max() / 2.\n # for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):\n # plt.text(j, i, format(cm[i, j], fmt),\n # horizontalalignment=\"center\",\n # color=\"white\" if cm[i, j] > thresh else \"black\")\n\n plt.tight_layout()\n plt.ylabel('True label')\n plt.xlabel('Predicted label')\n\n\n# Load the names of the birds\nbirds = pd.read_csv(\"dataset/classes.txt\",\n sep='\\s+', header=None, usecols=[1], names=['birdname'])\nbirds = birds['birdname']\n\n\n# Plot the confusion matrix\n\"\"\"\n np.set_printoptions(precision=2)\n plt.figure(figsize=(60, 60), dpi=300)\n plot_confusion_matrix(cm, classes=birds, normalize=True)\n plt.show()\n\"\"\"\n\n\n# Test accuracy of other methods\n\n\"\"\"\n Decision Tree:\n\n from sklearn import tree\n clftree = tree.DecisionTreeClassifier()\n clftree.fit(df_train_att, df_train_label)\n clftree.score(df_test_att, df_test_label)\n\n The accuracy of a decision tree is 0.27 (0.2695353748680042)\n\n\"\"\"\n\n\n\"\"\"\n Support Vector Machine (neural network approach)\n\n from sklearn import svm\n clfsvm = svm.SVC()\n clfsvm.fit(df_train_att, df_train_label)\n clfsvm.score(df_test_att, df_test_label)\n\n The accuracy of SVM is 0.49 (0.48653643083421333)\n * The authors of the book achived an accuracy of 0.29\n\"\"\"\n\n# Perform cross-validation to make sure the accuracy is reliable\n\n\"\"\"\n from sklearn.model_selection import cross_val_score\n\n scores = cross_val_score(clf, df_train_att, df_train_label, cv=5)\n print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scores.mean(), scores.std() * 2))\n\n scorestree = cross_val_score(clftree, df_train_att, df_train_label, cv=5)\n print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scorestree.mean(), scorestree.std() * 2))\n\n scoressvm = cross_val_score(clfsvm, df_train_att, df_train_label, cv=5)\n print(\"Accuracy: %0.2f (+/- %0.2f)\" % (scoressvm.mean(), scoressvm.std() * 2))\n\n This gives the following results:\n Accuracy: 0.44 (+/- 0.01) (random forest)\n Accuracy: 0.26 (+/- 0.01) (simple decision tree)\n Accuracy: 0.47 (+/- 0.02) (svm)\n\n * The authors of the book received the following results: 0.44, 0.25 and 0.27\n\"\"\"\n\n\n# Perform a test to find the best number of attributes and estimators\n\"\"\"\n In order to test the impact of different amount of attributes and estimators, \n the following code is run:\n\n max_features_opts = range(5, 50, 5)\n n_estimators_opts = range(10, 200, 20)\n rf_params = np.empty((len(max_features_opts)*len(n_estimators_opts), 4), float)\n i = 0\n for max_features in max_features_opts:\n for n_estimators in n_estimators_opts:\n clf = RandomForestClassifier(\n max_features=max_features, n_estimators=n_estimators)\n scores = cross_val_score(clf, df_train_att, df_train_label, cv=5)\n rf_params[i, 0] = max_features\n rf_params[i, 1] = n_estimators\n rf_params[i, 2] = scores.mean()\n rf_params[i, 3] = scores.std() * 2\n i += 1\n print(\"Max features: %d, num estimators: %d, accuracy: %0.2f (+/- %0.2f)\" %\n (max_features, n_estimators, scores.mean(), scores.std() * 2))\n\n\n This results in the output:\n {\n Max features: 5, num estimators: 10, accuracy: 0.26 (+/- 0.02)\n Max features: 5, num estimators: 30, accuracy: 0.35 (+/- 0.01)\n Max features: 5, num estimators: 50, accuracy: 0.38 (+/- 0.01)\n Max features: 5, num estimators: 70, accuracy: 0.41 (+/- 0.02)\n Max features: 5, num estimators: 90, accuracy: 0.42 (+/- 0.01)\n Max features: 5, num estimators: 110, accuracy: 0.42 (+/- 0.01)\n Max features: 5, num estimators: 130, accuracy: 0.43 (+/- 0.01)\n Max features: 5, num estimators: 150, accuracy: 0.44 (+/- 0.00)\n Max features: 5, num estimators: 170, accuracy: 0.44 (+/- 0.02)\n Max features: 5, num estimators: 190, accuracy: 0.44 (+/- 0.02)\n Max features: 10, num estimators: 10, accuracy: 0.29 (+/- 0.02)\n Max features: 10, num estimators: 30, accuracy: 0.38 (+/- 0.02)\n Max features: 10, num estimators: 50, accuracy: 0.41 (+/- 0.01)\n Max features: 10, num estimators: 70, accuracy: 0.42 (+/- 0.01)\n Max features: 10, num estimators: 90, accuracy: 0.43 (+/- 0.01)\n Max features: 10, num estimators: 110, accuracy: 0.44 (+/- 0.02)\n Max features: 10, num estimators: 130, accuracy: 0.45 (+/- 0.01)\n Max features: 10, num estimators: 150, accuracy: 0.45 (+/- 0.01)\n Max features: 10, num estimators: 170, accuracy: 0.45 (+/- 0.01)\n Max features: 10, num estimators: 190, accuracy: 0.45 (+/- 0.01)\n Max features: 15, num estimators: 10, accuracy: 0.31 (+/- 0.03)\n Max features: 15, num estimators: 30, accuracy: 0.38 (+/- 0.02)\n Max features: 15, num estimators: 50, accuracy: 0.42 (+/- 0.01)\n Max features: 15, num estimators: 70, accuracy: 0.43 (+/- 0.02)\n Max features: 15, num estimators: 90, accuracy: 0.44 (+/- 0.01)\n Max features: 15, num estimators: 110, accuracy: 0.45 (+/- 0.01)\n Max features: 15, num estimators: 130, accuracy: 0.44 (+/- 0.02)\n Max features: 15, num estimators: 150, accuracy: 0.45 (+/- 0.02)\n Max features: 15, num estimators: 170, accuracy: 0.45 (+/- 0.00)\n Max features: 15, num estimators: 190, accuracy: 0.45 (+/- 0.01)\n Max features: 20, num estimators: 10, accuracy: 0.32 (+/- 0.02)\n Max features: 20, num estimators: 30, accuracy: 0.39 (+/- 0.01)\n Max features: 20, num estimators: 50, accuracy: 0.42 (+/- 0.02)\n Max features: 20, num estimators: 70, accuracy: 0.43 (+/- 0.01)\n Max features: 20, num estimators: 90, accuracy: 0.44 (+/- 0.01)\n Max features: 20, num estimators: 110, accuracy: 0.44 (+/- 0.02)\n Max features: 20, num estimators: 130, accuracy: 0.45 (+/- 0.01)\n Max features: 20, num estimators: 150, accuracy: 0.45 (+/- 0.01)\n Max features: 20, num estimators: 170, accuracy: 0.46 (+/- 0.02)\n Max features: 20, num estimators: 190, accuracy: 0.46 (+/- 0.01)\n Max features: 25, num estimators: 10, accuracy: 0.32 (+/- 0.01)\n Max features: 25, num estimators: 30, accuracy: 0.40 (+/- 0.01)\n Max features: 25, num estimators: 50, accuracy: 0.42 (+/- 0.01)\n Max features: 25, num estimators: 70, accuracy: 0.44 (+/- 0.01)\n Max features: 25, num estimators: 90, accuracy: 0.44 (+/- 0.01)\n Max features: 25, num estimators: 110, accuracy: 0.45 (+/- 0.01)\n Max features: 25, num estimators: 130, accuracy: 0.45 (+/- 0.02)\n Max features: 25, num estimators: 150, accuracy: 0.45 (+/- 0.01)\n Max features: 25, num estimators: 170, accuracy: 0.45 (+/- 0.01)\n Max features: 25, num estimators: 190, accuracy: 0.46 (+/- 0.01)\n Max features: 30, num estimators: 10, accuracy: 0.32 (+/- 0.01)\n Max features: 30, num estimators: 30, accuracy: 0.40 (+/- 0.02)\n Max features: 30, num estimators: 50, accuracy: 0.42 (+/- 0.02)\n Max features: 30, num estimators: 70, accuracy: 0.43 (+/- 0.01)\n Max features: 30, num estimators: 90, accuracy: 0.44 (+/- 0.02)\n Max features: 30, num estimators: 110, accuracy: 0.45 (+/- 0.02)\n Max features: 30, num estimators: 130, accuracy: 0.45 (+/- 0.02)\n Max features: 30, num estimators: 150, accuracy: 0.45 (+/- 0.01)\n Max features: 30, num estimators: 170, accuracy: 0.45 (+/- 0.01)\n Max features: 30, num estimators: 190, accuracy: 0.45 (+/- 0.01)\n Max features: 35, num estimators: 10, accuracy: 0.33 (+/- 0.01)\n Max features: 35, num estimators: 30, accuracy: 0.40 (+/- 0.01)\n Max features: 35, num estimators: 50, accuracy: 0.42 (+/- 0.01)\n Max features: 35, num estimators: 70, accuracy: 0.44 (+/- 0.01)\n Max features: 35, num estimators: 90, accuracy: 0.44 (+/- 0.02)\n Max features: 35, num estimators: 110, accuracy: 0.44 (+/- 0.01)\n Max features: 35, num estimators: 130, accuracy: 0.45 (+/- 0.01)\n Max features: 35, num estimators: 150, accuracy: 0.45 (+/- 0.01)\n Max features: 35, num estimators: 170, accuracy: 0.45 (+/- 0.01)\n Max features: 35, num estimators: 190, accuracy: 0.46 (+/- 0.01)\n Max features: 40, num estimators: 10, accuracy: 0.34 (+/- 0.01)\n Max features: 40, num estimators: 30, accuracy: 0.40 (+/- 0.01)\n Max features: 40, num estimators: 50, accuracy: 0.42 (+/- 0.02)\n Max features: 40, num estimators: 70, accuracy: 0.43 (+/- 0.02)\n Max features: 40, num estimators: 90, accuracy: 0.44 (+/- 0.02)\n Max features: 40, num estimators: 110, accuracy: 0.45 (+/- 0.01)\n Max features: 40, num estimators: 130, accuracy: 0.45 (+/- 0.00)\n Max features: 40, num estimators: 150, accuracy: 0.45 (+/- 0.02)\n Max features: 40, num estimators: 170, accuracy: 0.45 (+/- 0.01)\n Max features: 40, num estimators: 190, accuracy: 0.45 (+/- 0.01)\n Max features: 45, num estimators: 10, accuracy: 0.33 (+/- 0.00)\n Max features: 45, num estimators: 30, accuracy: 0.40 (+/- 0.01)\n Max features: 45, num estimators: 50, accuracy: 0.42 (+/- 0.01)\n Max features: 45, num estimators: 70, accuracy: 0.43 (+/- 0.02)\n Max features: 45, num estimators: 90, accuracy: 0.44 (+/- 0.02)\n Max features: 45, num estimators: 110, accuracy: 0.44 (+/- 0.02)\n Max features: 45, num estimators: 130, accuracy: 0.44 (+/- 0.01)\n Max features: 45, num estimators: 150, accuracy: 0.45 (+/- 0.01)\n Max features: 45, num estimators: 170, accuracy: 0.45 (+/- 0.01)\n Max features: 45, num estimators: 190, accuracy: 0.45 (+/- 0.01)\n }\n\n\n To plot this output, the following code is used:\n\n import matplotlib.pyplot as plt\n from mpl_toolkits.mplot3d import Axes3D\n from matplotlib import cm\n fig = plt.figure()\n fig.clf()\n ax = fig.gca(projection='3d')\n x = rf_params[:,0]\n y = rf_params[:,1]\n z = rf_params[:,2]\n ax.scatter(x, y, z)\n ax.set_zlim(0.2, 0.5)\n ax.set_xlabel('Max features')\n ax.set_ylabel('Num estimators')\n ax.set_zlabel('Avg accuracy')\n plt.show()\n\n\n We can see that increasing the number of trees produces a better outcome. Also, \n increasing the number of features produces better outcomes if we are able to see\n more features, but ultimately, if we're at about 20 to 30 features and we have \n about 75 to 100 trees, that's as good as we're going to get an accuracy of 45%.\n\"\"\"\n","sub_path":"RandomForests/birdIdentifier.py","file_name":"birdIdentifier.py","file_ext":"py","file_size_in_byte":15075,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"149661927","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport logging\r\nimport os\r\n\r\nfrom PIL import Image\r\nimport click\r\n\r\nfrom ximage.utils import mkdirs\r\n\r\nlogger = logging.getLogger(__name__)\r\n\r\nlogger.setLevel(logging.DEBUG)\r\n\r\n\r\ndef resize_image(inputimg, width=0, height=0, outputdir='', outputname=''):\r\n \"\"\"\r\n width and height if you only specify one parameter of them, then the another will dismissed.\r\n if you given a overhigh height, then there maybe output a image not resize at all.\r\n \"\"\"\r\n imgname, imgext = os.path.splitext(os.path.basename(inputimg))\r\n\r\n if not os.path.exists(os.path.abspath(outputdir)):\r\n mkdirs(outputdir)\r\n\r\n if not outputname:\r\n outputname = imgname + '_resized' + imgext\r\n else:\r\n output_imgname, ext = os.path.splitext(outputname)\r\n if not ext:\r\n outputname = output_imgname + '_resized' + imgext\r\n elif ext != imgext:\r\n raise Exception(\r\n 'outputname ext is not the same as the intput image')\r\n\r\n try:\r\n im = Image.open(os.path.abspath(inputimg))\r\n ori_w, ori_h = im.size\r\n\r\n if width == 0 and height != 0: # given height and make width meanful\r\n width = ori_w\r\n elif width != 0 and height == 0:\r\n height = ori_h\r\n elif width == 0 and height == 0:\r\n click.echo('you must give one value , height or width', err=True)\r\n raise IOError\r\n\r\n if width > ori_w:\r\n logger.warning('the target width is larger than origin, '\r\n 'i will use the origin one')\r\n width = ori_w\r\n elif height > ori_h:\r\n logger.warning('the target height is larger than origin, '\r\n 'i will use the origin one')\r\n height = ori_h\r\n\r\n logger.debug(f'pillow resize target ({width},{height})')\r\n im.thumbnail((width, height), Image.ANTIALIAS)\r\n\r\n logger.info(os.path.abspath(inputimg))\r\n\r\n outputimg = os.path.join(os.path.abspath(outputdir), outputname)\r\n\r\n logger.debug(f'pillow resize output image to {outputimg}')\r\n im.save(outputimg)\r\n click.echo('{0} saved.'.format(outputimg))\r\n return outputimg\r\n except IOError:\r\n logging.error('IOError, I can not resize {}'.format(inputimg))\r\n","sub_path":"ximage/resize_image.py","file_name":"resize_image.py","file_ext":"py","file_size_in_byte":2338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"615064864","text":"# Copyright 2013-2015 DataStax, 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\ntry:\n import unittest2 as unittest\nexcept ImportError:\n import unittest\n\nimport os, sys, traceback, logging, ssl\nfrom cassandra.cluster import Cluster\nfrom cassandra import ConsistencyLevel\nfrom cassandra.query import SimpleStatement\nfrom tests.integration import use_singledc, PROTOCOL_VERSION, get_cluster, remove_cluster\n\nlog = logging.getLogger(__name__)\n\nDEFAULT_PASSWORD = \"cassandra\"\nDEFAULT_SERVER_KEYSTORE_PATH = \"tests/integration/long/ssl/keystore.jks\"\nDEFAULT_CLIENT_CA_CERTS = 'tests/integration/long/ssl/driver_ca_cert.pem'\n\n\ndef setup_module():\n \"\"\"\n We need some custom setup for this module. This will start the ccm cluster with basic\n ssl connectivity. No client authentication is performed at this time.\n \"\"\"\n\n use_singledc(start=False)\n ccm_cluster = get_cluster()\n ccm_cluster.stop()\n\n # Fetch the absolute path to the keystore for ccm.\n abs_path_server_keystore_path = os.path.abspath(DEFAULT_SERVER_KEYSTORE_PATH)\n\n # Configure ccm to use ssl.\n config_options = {'client_encryption_options': {'enabled': True,\n 'keystore': abs_path_server_keystore_path,\n 'keystore_password': DEFAULT_PASSWORD}}\n ccm_cluster.set_configuration_options(config_options)\n ccm_cluster.start(wait_for_binary_proto=True, wait_other_notice=True)\n\n\ndef teardown_module():\n \"\"\"\n The rest of the tests don't need ssl enabled, remove the cluster so as to not interfere with other tests.\n \"\"\"\n\n remove_cluster()\n\n\nclass SSLConnectionTests(unittest.TestCase):\n\n def test_can_connect_with_ssl_ca(self):\n \"\"\"\n Test to validate that we are able to connect to a cluster using ssl.\n\n test_can_connect_with_ssl_ca performs a simple sanity check to ensure that we can connect to a cluster with ssl\n authentication via simple server-side shared certificate authority. The client is able to validate the identity\n of the server, however by using this method the server can't trust the client unless additional authentication\n has been provided.\n\n @since 2.6.0\n @jira_ticket PYTHON-332\n @expected_result The client can connect via SSL and preform some basic operations\n\n @test_category connection:ssl\n \"\"\"\n\n # Setup temporary keyspace.\n abs_path_ca_cert_path = os.path.abspath(DEFAULT_CLIENT_CA_CERTS)\n\n tries = 0\n while True:\n if tries > 5:\n raise RuntimeError(\"Failed to connect to SSL cluster after 5 attempts\")\n try:\n cluster = Cluster(protocol_version=PROTOCOL_VERSION, ssl_options={'ca_certs': abs_path_ca_cert_path,\n 'ssl_version': ssl.PROTOCOL_TLSv1})\n session = cluster.connect()\n break\n except Exception:\n ex_type, ex, tb = sys.exc_info()\n log.warn(\"{0}: {1} Backtrace: {2}\".format(ex_type.__name__, ex, traceback.extract_tb(tb)))\n del tb\n tries += 1\n\n # attempt a few simple commands.\n insert_keyspace = \"\"\"CREATE KEYSPACE ssltest\n WITH replication = {'class': 'SimpleStrategy', 'replication_factor': '3'}\n \"\"\"\n statement = SimpleStatement(insert_keyspace)\n statement.consistency_level = 3\n session.execute(statement)\n\n drop_keyspace = \"DROP KEYSPACE ssltest\"\n statement = SimpleStatement(drop_keyspace)\n statement.consistency_level = ConsistencyLevel.ANY\n session.execute(statement)\n\n cluster.shutdown()","sub_path":"tests/integration/long/test_ssl.py","file_name":"test_ssl.py","file_ext":"py","file_size_in_byte":4270,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"189526192","text":"# Exception Handling\n\n# Q-1: Name and handle the exception occured in the following program\n# The Exception in the Code is Zero Division Error\n\ntry:\n a=3\n if a<4:\n a=a/(a-3)\n print(a)\nexcept ZeroDivisionError:\n print(\"You Cannot Deivide By Zero\")\n\n\n# Q-2: Name and handle the exception occurred in the following program:\n# The Error in The Code is INDEX ERROR\n\ntry:\n l=[1,2,3]\n print(l[3])\nexcept:\n print(\"Please Enter an Index which is less than {}\".format(len(l)))\n\n\n# Q-3: Tell The output of the Following Code.\n\n''' O/P: An Exception '''\n\n# Q-4: Output of The FolloWing Code\n\n''' The Result is -5.0 a/b result in 0 '''\n\n\n# Q-5: Write a program to show and handle following exceptions:\n#1. Import Error\n#2. Value Error\n#3. Index Error\n\n# Import Error\ntry:\n import nmb\nexcept ImportError:\n print(\"Please Import Valid Module\")\n \n# Value Error\ntry:\n a=int(input(\"Enter a Number \"))\n # Value Error occurs if we enter an string value in the variable\nexcept:\n print(\"Please Enter a valid Number\")\n \n# Index Error\ntry:\n l=[4,5,6]\n print(l[4])\nexcept:\n print(\"Please Enter A valid Index\")\n","sub_path":"Assignment9.py","file_name":"Assignment9.py","file_ext":"py","file_size_in_byte":1161,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"476172250","text":"\r\nimport numpy as np\r\nfrom numpy import pi as pi\r\nfrom scipy.spatial import Delaunay\r\nfrom scipy.optimize import fmin\r\nimport matplotlib.pylab as plt\r\n\r\nfrom Meshing_Tools import *\r\n\r\n\"\"\"\r\n DISTMESH was developed by Dr Per-Olof Persson, Department of Mathematics,\r\n Massachusetts Institute of Technology\r\n\r\n The original version of DISTMESH was written in MATLAB.\r\n\r\n This Python version of DISTMESH was developed by Dr Jaime Kardontchik,\r\n Sunnyvale, CA\r\n \r\n\"\"\"\r\n\r\n# ---------------------------------------------------------------\r\n# DISTMESH - BASIC SHAPES\r\n# ---------------------------------------------------------------\r\n\r\nclass Circle:\r\n \"\"\"\r\n A circle is defined by its center (xc,yc) and its radius r\r\n \"\"\"\r\n def __init__(self,xc,yc,r):\r\n self.xc, self.yc, self.r = xc, yc, r\r\n def __call__(self,p):\r\n xc, yc, r = self.xc, self.yc, self.r\r\n d = np.sqrt((p[:,0] - xc)**2 + (p[:,1] - yc)**2) - r\r\n return d\r\n\r\nclass Rectangle:\r\n \"\"\"\r\n A rectangle is defined by its vertices (x1,y1) and (x2,y2)\r\n A point in the array p is inside the rectangle if\r\n x1 < p[:,0] < x2 and y1 < p[:,1] < y2\r\n It returns the minimum distance of point p to the edges of\r\n the rectangle\r\n \"\"\"\r\n def __init__(self,x1,x2,y1,y2):\r\n self.x1, self.x2, self.y1, self.y2 = x1,x2,y1,y2\r\n def __call__(self,p):\r\n x1,x2,y1,y2 = self.x1, self.x2, self.y1, self.y2\r\n d1 = p[:,1] - y1 # if p inside d1 > 0\r\n d2 = y2 - p[:,1] # if p inside d2 > 0\r\n d3 = p[:,0] - x1 # if p inside d3 > 0\r\n d4 = x2 - p[:,0] # if p inside d4 > 0\r\n d = -np.minimum(np.minimum(np.minimum(d1,d2),d3),d4)\r\n return d\r\n\r\nclass Polygon:\r\n \"\"\" the vertices of the polygon ('verts') is given as an array\r\n of orderly (counterclockwise) vertices along the polygon\"\r\n \"\"\"\r\n def __init__(self,verts):\r\n self.verts = verts\r\n def __call__(self,p):\r\n verts = self.verts\r\n # close the polygon\r\n cverts = np.zeros((len(verts)+1,2))\r\n cverts[0:-1] = verts\r\n cverts[-1] = verts[0]\r\n # initialize\r\n inside = np.zeros(len(p))\r\n dist = np.zeros(len(p))\r\n Cz = np.zeros(len(verts)) # z-components of the vectorial products\r\n dist_to_edge = np.zeros(len(verts))\r\n in_ref = np.ones(len(verts))\r\n # if np.sign(Cz) == in_ref then point is inside\r\n for j in range(len(p)):\r\n Cz = (cverts[1:,0] - cverts[0:-1,0])*(p[j,1] - cverts[0:-1,1]) - \\\r\n (cverts[1:,1] - cverts[0:-1,1])*(p[j,0] - cverts[0:-1,0])\r\n dist_to_edge = Cz/np.sqrt( \\\r\n (cverts[1:,0] - cverts[0:-1,0])**2 + \\\r\n (cverts[1:,1] - cverts[0:-1,1])**2)\r\n\r\n inside[j] = int(np.array_equal(np.sign(Cz),in_ref))\r\n dist[j] = (1 - 2*inside[j])*np.min(np.abs(dist_to_edge))\r\n return dist\r\n\r\nclass Union:\r\n def __init__(self,fd1,fd2):\r\n self.fd1, self.fd2 = fd1, fd2\r\n def __call__(self,p):\r\n fd1,fd2 = self.fd1, self.fd2\r\n d = np.minimum(fd1(p),fd2(p))\r\n return d\r\n\r\nclass Diff:\r\n def __init__(self,fd1,fd2):\r\n self.fd1, self.fd2 = fd1, fd2\r\n def __call__(self,p):\r\n fd1,fd2 = self.fd1, self.fd2\r\n d = np.maximum(fd1(p),-fd2(p))\r\n return d\r\n\r\nclass Intersect:\r\n def __init__(self,fd1,fd2):\r\n self.fd1, self.fd2 = fd1, fd2\r\n def __call__(self,p):\r\n fd1,fd2 = self.fd1, self.fd2\r\n d = np.maximum(fd1(p),fd2(p))\r\n return d\r\n\r\nclass Protate:\r\n \"\"\"\r\n Rotates points p by angle phi counterclockwise around the origin\r\n \"\"\"\r\n def __init__(self,phi):\r\n self.phi = phi\r\n def __call__(self,p):\r\n phi = self.phi\r\n c = np.cos(phi)\r\n s = np.sin(phi)\r\n temp = np.copy(p[:,0])\r\n rp = np.copy(p)\r\n rp[:,0] = c*p[:,0] - s*p[:,1]\r\n rp[:,1] = s*temp + c*p[:,1]\r\n return rp\r\n\r\nclass Pshift:\r\n \"\"\"\r\n Shifts points p by a constant vector (x0,y0)\r\n \"\"\"\r\n def __init__(self,x0,y0):\r\n self.x0, self.y0 = x0,y0\r\n def __call__(self,p):\r\n x0, y0 = self.x0, self.y0\r\n p[:,0] = p[:,0] + x0\r\n p[:,1] = p[:,1] + y0\r\n return p\r\n\r\n# ------------------------------------------------------------\r\n# ELLIPSE\r\n# ------------------------------------------------------------\r\n\r\ndef Ellipse_dist_to_minimize(t,p,xc,yc,a,b):\r\n \"\"\"\r\n Given a point p in the grid and a point on the ellipse,\r\n defined by its parametric variable t, finds the distance\r\n between these two points.\r\n Since this distance will be used only by the minimizer,\r\n it is more efficient to avoid calculating the sqrt.\r\n Numpy's fmin will vary the variable t to minimize the\r\n distance between the point p in the grid and the Ellipse\r\n \"\"\"\r\n x = xc + a*np.cos(t) # coord x of the point on the ellipse\r\n y = yc + b*np.sin(t) # coord y of the point on the ellipse\r\n dist = (p[0] - x)**2 + (p[1] - y)**2\r\n return dist\r\n\r\nclass Ellipse:\r\n def __init__(self,xc,yc,a,b):\r\n self.xc, self.yc, self.a, self.b = xc, yc, a, b\r\n self.t, self.verts = self.pick_points_on_shape()\r\n\r\n def pick_points_on_shape(self):\r\n \"\"\"\r\n Pick N points, more or less equally spaced, on the ellipse, \r\n (we will call them vertices, because it is equivalent to\r\n defining a polygon circumscribed on the ellipse). They\r\n will be used to quickly find which vertex is closest to a\r\n given point p in the grid. The selected vertex will serve \r\n as an initial reasonable close guess for Numpy's distance\r\n minimizer fmin\r\n \"\"\"\r\n xc, yc, a, b = self.xc, self.yc, self.a, self.b \r\n c = np.array([xc,yc])\r\n t = np.linspace(0,(7.0/4.0)*pi,8)\r\n verts = np.zeros((8,2))\r\n verts[:,0] = c[0] + a*np.cos(t)\r\n verts[:,1] = c[1] + b*np.sin(t)\r\n return t, verts\r\n \r\n def inside_ellipse(self,p):\r\n \"\"\"\r\n returns 1 if the point p is inside, returns 0 if outside\r\n \"\"\"\r\n xc, yc, a, b = self.xc, self.yc, self.a, self.b\r\n c = np.array([xc,yc])\r\n r, phase = self.rect_to_polar(p-c)\r\n r_ellipse = self.rellipse(phase)\r\n in_ref = np.ones(len(p))\r\n inside = 0.5 + 0.5*np.sign(r_ellipse-r)\r\n return inside\r\n\r\n def rect_to_polar(self,p):\r\n \"\"\"\r\n given a point p on the grid with cartesian coordinates (x,y),\r\n find its polar coordinates (r,phase)\r\n \"\"\"\r\n r = np.sqrt(p[:,0]**2 + p[:,1]**2)\r\n phase = np.arctan2(p[:,1],p[:,0])\r\n # note: np.arctan2(y,x) order; phase in +/- pi (+/- 180deg)\r\n return r, phase\r\n\r\n def rellipse(self,phi):\r\n \"\"\"\r\n Given the semi-axes of the ellipse (a,b), a > b, and the angle phi\r\n that a point on the ellipse makes with the semi-axis a ('x' axis)\r\n find the distance r of the point to the center of the ellipse\r\n \"\"\"\r\n a, b = self.a, self.b\r\n r = a*b/np.sqrt((b*np.cos(phi))**2 + (a*np.sin(phi))**2)\r\n return r\r\n\r\n def find_closest_vertex(self,point):\r\n \"\"\"\r\n For a given point in the grid, finds the minimum distance of\r\n the point to the selected points on the ellipse and returns the\r\n parametric variable t0 and the coordinates of the point on\r\n the ellipse. This t0 will be the initial good guess for Numpy's\r\n fmin that will find the exact distance between the point and the\r\n ellipse\r\n \"\"\"\r\n t, verts = self.t, self.verts\r\n \r\n dist = np.zeros(len(t))\r\n for i in range(len(t)):\r\n dist[i] = (point[0] - verts[i,0])**2 + (point[1] - verts[i,1])**2\r\n ind = np.argmin(dist)\r\n t0 = t[ind]\r\n return t0 \r\n \r\n def __call__(self,p):\r\n xc, yc, a, b = self.xc, self.yc, self.a, self.b\r\n t, verts = self.t, self.verts\r\n dist = np.zeros(len(p))\r\n inside = self.inside_ellipse(p)\r\n for j in range(len(p)):\r\n t0 = self.find_closest_vertex(p[j]) # initial guess to minimizer\r\n opt = fmin(Ellipse_dist_to_minimize,t0, \\\r\n args=(p[j],xc,yc,a,b),full_output=1,disp=0)\r\n # add full_output=1 so we can retrieve the min dist(squared)\r\n # (2nd argument of opt array, 1st argument is the optimum t)\r\n min_dist = np.sqrt(opt[1])\r\n dist[j] = min_dist*(1 - 2*inside[j])\r\n return dist\r\n\r\n# --------------------------------------------------------------------\r\n# DISTMESH - core\r\n# --------------------------------------------------------------------\r\n\r\ndef distmesh(fd,fh,h0,xmin,ymin,xmax,ymax,pfix,ttol=0.1,dptol=0.001,Iflag=1,qmin=1.0):\r\n #Iflag = 0: do not print internal sim status and\r\n # do not plot intermediate meshes\r\n #Iflag = 1:(default) print internal simulation status only\r\n #Iflag = 2: plot intermediate meshes only\r\n #Iflag = 3: print internal sim status and plot intermediate meshes\r\n #Iflag = 4: prints the Delaunay iteration, minimum q and angle(deg)\r\n \r\n # Constants\r\n geps = 0.001*h0; deltat = 0.2; Fscale = 1.2\r\n deps = h0 * np.sqrt(np.spacing(1))\r\n\r\n random_seed = 17\r\n\r\n # define the initial number of points in the grid in both directions\r\n h0x = h0; h0y = h0*np.sqrt(3)/2 # to obtain equilateral triangles\r\n Nx = int(np.floor((xmax - xmin)/h0x))\r\n Ny = int(np.floor((ymax - ymin)/h0y))\r\n x = np.linspace(xmin,xmax,Nx)\r\n y = np.linspace(ymin,ymax,Ny)\r\n # create the grid in the (x,y) plane\r\n xx,yy = np.meshgrid(x,y)\r\n # shift the even rows: it will reshape the rectangular grid of points\r\n # into a triangular-like grid (see Fig 1). As in many other areas of\r\n # optimization, providing a good initial guess helps convergence\r\n # into the desired final grid.\r\n xx[1::2] = xx[1::2] + h0x/2.0 # shifts even rows by h0x/2\r\n # p: points of the grid\r\n p = np.zeros((np.size(xx),2))\r\n p[:,0] = np.reshape(xx,np.size(xx))\r\n p[:,1] = np.reshape(yy,np.size(yy))\r\n\r\n # remove points outside the shape boundary with distance > geps\r\n p = np.delete(p,np.where(fd(p) > geps),axis=0)\r\n\r\n # redistribute remaining points according to the weighting function\r\n # and then add the fixed points\r\n np.random.seed(random_seed)\r\n # probability to keep a point:\r\n r0 = 1.0/fh(p)**2\r\n p = np.concatenate((pfix,p[np.random.rand(len(p))<r0/max(r0),:]))\r\n\r\n pold = np.inf\r\n Num_of_Delaunay_triangulations = 0\r\n Num_of_Node_movements = 0 # dp = F*dt\r\n\r\n while (1):\r\n Num_of_Node_movements += 1\r\n if Iflag == 1 or Iflag == 3: # Newton flag\r\n print ('Num_of_Node_movements = %3d' % (Num_of_Node_movements))\r\n if np.max(np.sqrt(np.sum((p - pold)**2,axis = 1))) > ttol:\r\n Num_of_Delaunay_triangulations += 1\r\n if Iflag == 1 or Iflag == 3: # Delaunay flag\r\n print ('Num_of_Delaunay_triangulations = %3d' % \\\r\n (Num_of_Delaunay_triangulations))\r\n pold = p\r\n tri = Delaunay(p) # instantiate a class\r\n t = tri.vertices\r\n pmid = (p[t[:,0]] + p[t[:,1]] + p[t[:,2]])/3.0\r\n # keep the triangles whose geometrical center is inside the shape\r\n t = t[np.where(fd(pmid) < -geps)]\r\n bars = np.concatenate((t[:,[0,1]],t[:,[0,2]], t[:,[1,2]]))\r\n # delete repeated bars\r\n #bars = unique_rows(np.sort(bars))\r\n bars = np.unique(np.sort(bars),axis=0)\r\n print ('bars uniq',bars.shape)\r\n print (bars.shape)\r\n if Iflag == 4:\r\n min_q, min_angle_deg = triqual_flag(p,t)\r\n print ('Del iter: %3d, min q = %5.2f, min angle = %3.0f deg' \\\r\n % (Num_of_Delaunay_triangulations, min_q, min_angle_deg))\r\n if min_q > qmin:\r\n break\r\n if Iflag == 2 or Iflag == 3:\r\n # graphical output of the current mesh\r\n ktrimesh(p,bars)\r\n\r\n # move mesh points based on bar lengths L and forces F\r\n barvec = p[bars[:,0],:] - p[bars[:,1],:]\r\n L = np.sqrt(np.sum(barvec**2,axis=1))\r\n hbars = 0.5*(fh(p[bars[:,0],:]) + fh(p[bars[:,1],:]))\r\n L0 = hbars*Fscale*np.sqrt(np.sum(L**2)/np.sum(hbars**2))\r\n F = np.maximum(L0-L,0)\r\n Fvec = np.column_stack((F,F))*(barvec/np.column_stack((L,L)))\r\n # Fvec: bar forces, (x,y) components\r\n\r\n # Calculate now the total force on a given point p due to its\r\n # interaction with all the bars joining point p to neighboring points\r\n\r\n Ftot = np.zeros((len(p),2))\r\n n = len(bars)\r\n for j in range(n):\r\n # sum of forces on the 1st point of a bar\r\n Ftot[bars[j,0],:] += Fvec[j,:] # the : for the (x,y) components\r\n # but the same point may be on the other side of a bar, so we\r\n # sum of forces on the 2nd point of a bar\r\n Ftot[bars[j,1],:] -= Fvec[j,:]\r\n # the minus sign is because by our convention, Fvec is the force\r\n # on the first node of a bar. Hence, the force on the 2nd node\r\n # of a bar is -Fvec (Netwon's action-reaction law)\r\n\r\n # force = 0 at fixed points, so they do not move:\r\n Ftot[0: len(pfix),:] = 0\r\n\r\n # update the node positions\r\n p = p + deltat*Ftot\r\n\r\n # bring outside points back to the boundary\r\n d = fd(p); ix = d > 0 # find points outside (d > 0)\r\n dpx = np.column_stack((p[ix,0] + deps,p[ix,1]))\r\n dgradx = (fd(dpx) - d[ix])/deps\r\n dpy = np.column_stack((p[ix,0], p[ix,1] + deps))\r\n dgrady = (fd(dpy) - d[ix])/deps\r\n p[ix,:] = p[ix,:] - np.column_stack((dgradx*d[ix], dgrady*d[ix]))\r\n\r\n # termination criterium: all interior nodes move less than dptol:\r\n\r\n if max(np.sqrt(np.sum(deltat*Ftot[d<-geps,:]**2,axis=1))/h0) < dptol:\r\n break\r\n\r\n final_tri = Delaunay(p) # another instantiation of the class\r\n t = final_tri.vertices\r\n pmid = (p[t[:,0]] + p[t[:,1]] + p[t[:,2]])/3.0\r\n # keep the triangles whose geometrical center is inside the shape\r\n t = t[np.where(fd(pmid) < -geps)]\r\n bars = np.concatenate((t[:,[0,1]],t[:,[0,2]], t[:,[1,2]]))\r\n # delete repeated bars\r\n #bars = unique_rows(np.sort(bars))\r\n bars = np.unique(np.sort(bars),axis=0)\r\n # orient all the triangles counterclockwise (ccw)\r\n t = ccw_tri(p,t)\r\n # graphical output of the current mesh\r\n print ('bars 2',bars.shape)\r\n ktrimesh(p,bars)\r\n triqual(p,t,fh)\r\n return p,t,bars\r\n\r\n# ------------------------------------------------------------------\r\n# END OF DISTMESH\r\n# ------------------------------------------------------------------\r\n\r\n# Note from Jaime Kardontchik:\r\n\r\n# In rare situations the Delaunay function 'forgets' to enumerate one\r\n# or a few isolated triangles in the generated mesh.\r\n# A missing triangle in an otherwise complete mesh will appear to the\r\n# Euler solver as an additional boundary (usually internal to the mesh)\r\n# In these rare cases the 'Visual Boundary Integrity Check' plot will\r\n# show up twice: the 1st time with the missing triangle(s). The second\r\n# time with the problem corrected (no missing triangles). \r\n# Sometimes it might be difficult to see the missing triangle in the \r\n# 1st plot (because it might be very tiny) and you will have to use \r\n# the zoom to find it in the plot.\r\n# The following modules were developed to correct this problem:\r\n\r\ndef boundary_bars(t):\r\n \"\"\"\r\n input: t, the triangles generated by a Delaunay triangulation\r\n returns: an array with the boundary bars. Each element of the\r\n array consists of two integers identifying the nodes of the bar\r\n \"\"\" \r\n # create the bars (edges) of every triangle\r\n bars = np.concatenate((t[:,[0,1]],t[:,[0,2]], t[:,[1,2]]))\r\n # sort all the bars\r\n data = np.sort(bars)\r\n # find the bars that are not repeated\r\n Delaunay_bars = dict()\r\n for row in data:\r\n row = tuple(row)\r\n if row in Delaunay_bars:\r\n Delaunay_bars[row] += 1\r\n else:\r\n Delaunay_bars[row] = 1\r\n # return the keys of Delaunay_bars whose value is 1 (non-repeated bars)\r\n bbars = []\r\n for key in Delaunay_bars:\r\n if Delaunay_bars[key] == 1:\r\n bbars.append(key)\r\n bbars = np.asarray(bbars)\r\n return bbars \r\n\r\ndef Delaunay_correction(p,t,bbars):\r\n \"\"\"\r\n Finds missing triangles and eliminates spurious boundary bars.\r\n Appends the missing triangles to the t-array and returns the\r\n correct boundary bars and t-array.\r\n\r\n Algorithm: Find the missing triangles\r\n If a triplet of bbars is made up of only 3 nodes (p points) they\r\n constitute a missing small triangle. Example, if\r\n bbars[59] = np.array([3, 8])\r\n bbars[83] = np.array([8, 17])\r\n bbars[4] = np.array([3, 17])\r\n Then the three bbars form a small missing triangle:\r\n t[missing] = np.array([3,8,17])\r\n Once a missing triangle is found and added, the bars forming this\r\n triangle are not in the boundary anymore, so they are deleted from\r\n the original array of bbars.\r\n \"\"\"\r\n\r\n # the following is a simple one-time operation, so vectorizing it\r\n # would only make it difficult to understand, so it is not worthy\r\n bb_len = len(bbars)\r\n bars_to_delete = []\r\n tri_to_add = []\r\n \r\n for i in range(bb_len):\r\n for j in range(i+1,bb_len):\r\n for k in range(j+1,bb_len):\r\n # create a list of the nodes included in bbars i,j,k\r\n lis = []\r\n lis.extend(list(bbars[i]))\r\n lis.extend(list(bbars[j]))\r\n lis.extend(list(bbars[k]))\r\n # eliminate duplicates in this list\r\n lis = list(set(lis)) # class set removes duplicates from seq\r\n # if len(lis) == 3 we found a missing small triangle\r\n # the indexes of the bbars to delete are i,j,k\r\n # the nodes of the missing triangle are specified in lis\r\n if len(lis) == 3:\r\n bars_to_delete.extend([i,j,k])\r\n tri_to_add.append(lis)\r\n # now we proceed to add triangles to the t-array with nodes specified\r\n # in tri_to_add\r\n\r\n triangles_to_add = np.asarray(tri_to_add)\r\n if np.size(triangles_to_add) == 0:\r\n Tflag = 0\r\n correct_tri = ccw_tri(p,t)\r\n else:\r\n Tflag = 1\r\n correct_tri = np.concatenate((t,triangles_to_add), axis=0)\r\n correct_tri = ccw_tri(p,correct_tri) \r\n\r\n # And delete the bbars whose indexes are specified in bars_to_delete\r\n mask = [0,0]*len(bbars) # element i of bbars is a 2-number array\r\n for i in bars_to_delete:\r\n mask[2*i] = 1\r\n mask[2*i+1] = 1\r\n bbars = np.ma.masked_array(bbars,mask)\r\n boundary_bars = bbars.compressed() # delete the masked elements\r\n # reshape from (2n,0) to (n,2)\r\n len_bb = len(boundary_bars)/2\r\n boundary_bars = np.reshape(boundary_bars,(len_bb,-1))\r\n \r\n return correct_tri,boundary_bars,Tflag\r\n\r\ndef find_boundary(p,t,fh):\r\n bbars = boundary_bars(t)\r\n ktrimesh(p,bbars,pflag=1)\r\n correct_tri, correct_boundary_bars,Tflag = Delaunay_correction(p,t,bbars)\r\n if Tflag == 1:\r\n ktrimesh(p,correct_boundary_bars,pflag=1)\r\n triqual(p,correct_tri,fh)\r\n return correct_tri, correct_boundary_bars\r\n\r\ndef mesh_repair(p,t,fh,indexes):\r\n \"\"\"\r\n Another Delaunay bug is the possible formation of thin spurious\r\n slivers in the boundary with clear violation of the triangulation\r\n rules: spurious bars crossing bars belonging to real triangles to\r\n join far away nodes and creating a spurious triangle. The mesh\r\n can be repaired by entering the indexes of the spurious triangles\r\n as a list, for example:\r\n indexes = [1,11,88,90,131,132]\r\n and then running the 'mesh_repair' module. 'indexes' is used as\r\n an argument to the 'mesh_repair' function.\r\n The mesh_repair module returns the array of true triangles.\r\n \"\"\"\r\n indexes = np.asarray(indexes)\r\n # eliminate the spurious triangles\r\n t = np.delete(t,indexes,axis=0)\r\n # recreate the bars\r\n bars = np.concatenate((t[:,[0,1]],t[:,[0,2]], t[:,[1,2]]))\r\n # delete repeated bars\r\n bars = unique_rows(np.sort(bars))\r\n # recreate the mesh\r\n ktrimesh(p,bars)\r\n # re-calculate the mesh quality\r\n triqual(p,t,fh)\r\n return t\r\n","sub_path":"Introduction_to_CFD_with_Python_kardontchik/paper3_DISTMESH/DISTMESH.py","file_name":"DISTMESH.py","file_ext":"py","file_size_in_byte":20812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"400534091","text":"from http import HTTPStatus\n\nfrom aiohttp.web_exceptions import HTTPNotFound, HTTPBadRequest\nfrom aiohttp.web_response import Response\nfrom aiohttp_validate import validate\nfrom asyncpg import ForeignKeyViolationError\n\nfrom storefront.handlers.base import BaseView\nfrom storefront.models import Product\n\n\nclass ProductsView(BaseView):\n URL_PATH = '/products'\n\n @validate(\n request_schema={\n 'type': 'object',\n 'properties': {\n 'name': {'type': 'string'},\n 'price': {'type': 'number'}\n },\n 'required': ['name', 'price'],\n 'additionalProperties': False\n }\n )\n async def post(self, data, request) -> Response:\n query = Product.__table__.insert().values(data).returning(\n Product.__table__\n )\n data = await self.postgres.fetchrow(query)\n return Response(body={'data': data}, status=HTTPStatus.CREATED)\n\n async def get(self) -> Response:\n query = Product.__table__.select()\n data = await self.postgres.fetch(query)\n return Response(body={'data': data})\n\n\nclass ProductView(BaseView):\n URL_PATH = '/products/{id}'\n\n @property\n def product_id(self) -> int:\n return int(self.request.match_info['id'])\n\n async def get(self) -> Response:\n query = Product.__table__.select().where(\n Product.__table__.c.product_id == self.product_id\n )\n data = await self.postgres.fetchrow(query)\n\n if data is None:\n raise HTTPNotFound()\n return Response(body={'data': data})\n\n @validate(\n request_schema={\n 'type': 'object',\n 'properties': {\n 'name': {'type': 'string'},\n 'price': {'type': 'number'}\n },\n 'required': ['name', 'price'],\n 'additionalProperties': False\n },\n response_schema={\n 'type': 'object',\n 'data': {'type': 'object'},\n 'required': ['data'],\n 'additionalProperties': False\n }\n )\n async def put(self, data, request) -> Response:\n query = Product.__table__.update().values(data).where(\n Product.__table__.c.product_id == self.product_id\n ).returning(Product.__table__)\n data = await self.postgres.fetchrow(query)\n\n if data is None:\n raise HTTPNotFound()\n\n return Response(body={'data': data})\n\n async def delete(self) -> Response:\n try:\n query = Product.__table__.delete().where(\n Product.__table__.c.product_id == self.product_id\n ).returning(Product.__table__)\n data = await self.postgres.fetchrow(query)\n\n if data is None:\n raise HTTPNotFound()\n except ForeignKeyViolationError:\n raise HTTPBadRequest(text=('Product has relations with employees '\n 'and can not be deleted'))\n\n return Response(status=204)\n","sub_path":"storefront/handlers/products.py","file_name":"products.py","file_ext":"py","file_size_in_byte":3018,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"490723316","text":"import requests\n\ndef PDB_Structure_Fetcher(PDB_ID,file):\n\t'''\n\tGet Structure ID with PDB ID\n\t'''\n\n\tr = requests.get(\"\"\"https://www.rcsb.org/pdb/rest/customReport.xml?pdbids={}&customReportColumns=structureTitle&service=wsfile&format=csv\"\"\".format(PDB_ID))\n\t\n\twholeFile = r.text \n\tlinesFile = wholeFile.splitlines()\n\n\tif len(linesFile)>1: \t#first lines is colonne names\n\n\t\tPDBid_Name_Structure = linesFile[1].split(\",\") \n\t\tName_Structure, Def_Structure = PDBid_Name_Structure[0], PDBid_Name_Structure[1]\n\t\tStructure_case = Name_Structure + \" : \" + Def_Structure\n\n\t\tfile.write(Structure_case)\n\t\tfile.write(\"<br>\\n\")\n\n\telse : \n\t\tfile.write(\"Pas de Structure 3D disponible\")\n\t\tfile.write(\"<br>\\n\")\n\ndef PDB_ID(firstUniprotID,file):\n\t'''\n\tConvert Uniprot ID to PDB ID\n\t'''\n\n\turl = \"https://www.rcsb.org/pdb/rest/search\"\n\tdata= \"\"\" \n\t<orgPdbQuery>\n\t<queryType>org.pdb.query.simple.UpAccessionIdQuery</queryType>\n\t<accessionIdList>{}</accessionIdList>\n\t</orgPdbQuery>\"\"\".format(firstUniprotID) #xml query accept by search rest tool\n\n\tr = requests.post(url, data=data ,headers={\"Content-Type\":\"application/x-www-form-urlencoded\"})\n\n\twholeFileIDs = r.text\n\tlinesPDB_ID = wholeFileIDs.splitlines()\n\n\tfile.write(\"<td>\")\n\t\n\tPDB_ID = linesPDB_ID[0].split(\":\")[0] # format is \"structure name : version\", we take only name\n\tPDB_Structure_Fetcher(PDB_ID, file)\n\n\tfile.write(\"</td>\")\n\n","sub_path":"Florian/PDB.py","file_name":"PDB.py","file_ext":"py","file_size_in_byte":1369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"185274587","text":"# Thomas Deranek\n# CSE 30332\n# March 20th, 2015\n# CherryPy WebService, users_controller.py\n\nimport cherrypy\nimport re, json\n\nclass RecommendationsController(object):\n\tdef __init__(self, mdb):\n\t\t# mdb should be the fully initialized and loaded users dictionary\n\t\tself.recommendationsdb = mdb\n\t\t\n\t# Retrieve move recommendation for a user\n\tdef GET(self, user_id):\n\t\tuser_id = int(user_id)\n\t\t# Account for if the uid does not exist\n\t\ttry:\n\t\t\t# retrieve the movie_id of the highest rated movie\n\t\t\tmid = self.recommendationsdb.get_highest_rated_unvoted_movie(user_id)\n\t\t\t# build and return output dictionary\n\t\t\toutput = {'result':'success'}\n\t\t\toutput['movie_id'] = int(mid)\n\t\t\treturn json.dumps(output, encoding='latin-1')\n\t\texcept:\n\t\t\toutput = {'result':'error'}\n\t\t\toutput['message'] ='No ratings available for recommendation'\n\t\t\treturn json.dumps(output, encoding='latin-1')\n\t\t\n\t# Add a new vote for a given user\n\tdef PUT(self, user_id):\n\t\tuser_id = int(user_id)\n\t\t# Account for possible errors\n\t\ttry:\n\t\t\tdata = cherrypy.request.body.read()\n\t\t\tprocessed_data = json.loads(data)\n\t\t\t# Unpack the desired info\n\t\t\tmovie_id = int(processed_data['movie_id'])\n\t\t\trating = int(processed_data['rating'])\n\t\t\t# Call the function to modify the database\n\t\t\tself.recommendationsdb.set_user_movie_rating(user_id, movie_id, rating)\n\t\t\t# Build and return an output dictionary\n\t\t\toutput = {'result':'success'}\n\t\t\treturn json.dumps(output, encoding='latin-1')\n\t\texcept:\n\t\t\toutput = {'result':'error'}\n\t\t\toutput['message'] = 'PUT to /recommendations/ failed'\n\t\t\treturn json.dumps(output, encoding='latin-1')\n\t\t\t\n\tdef DELETE(self):\n\t\t# Account for possible errors\n\t\ttry:\n\t\t\t# Call the function responsible for deleting all ratings\n\t\t\tself.recommendationsdb.delete_all_ratings()\n\t\t\toutput = {'result':'success'}\n\t\t\treturn json.dumps(output, encoding='latin-1')\n\t\texcept:\n\t\t\toutput = {'result':'error'}\n\t\t\toutput['message'] ='Could not delete all ratings'\n\t\t\treturn json.dumps(output, encoding='latin-1')\n\t\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n\t\n","sub_path":"cse30332/python/cherrypy/recommendations_controller.py","file_name":"recommendations_controller.py","file_ext":"py","file_size_in_byte":2008,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"422918971","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[1]:\n\n\nimport cv2\nimport numpy as np\n\n\n# In[2]:\n\n\ncap = cv2.VideoCapture(0)\n\npattern_size = (10, 7)\n\nsamples = []\n\nwhile True:\n ret, frame = cap.read()\n if not ret:\n break\n \n res, corners = cv2.findChessboardCorners(frame, pattern_size)\n \n img_show = np.copy(frame)\n cv2.drawChessboardCorners(img_show, pattern_size, corners, res)\n cv2.putText(img_show, 'Samples captured: %d' % len(samples), (0, 40), \n cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 255, 0), 2)\n cv2.imshow('chessboard', img_show)\n \n wait_time = 0 if res else 30\n k = cv2.waitKey(wait_time)\n \n if k == ord('s') and res:\n samples.append((cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY), corners))\n elif k == 27:\n break\n\ncap.release()\ncv2.destroyAllWindows()\n\n\n# In[3]:\n\n\ncriteria = (cv2.TERM_CRITERIA_EPS + cv2.TERM_CRITERIA_MAX_ITER, 30, 1e-3)\n\nfor i in range(len(samples)):\n img, corners = samples[i]\n corners = cv2.cornerSubPix(img, corners, (10, 10), (-1,-1), criteria)\n\n\n# In[5]:\n\n\npattern_points = np.zeros((np.prod(pattern_size), 3), np.float32)\npattern_points[:, :2] = np.indices(pattern_size).T.reshape(-1, 2)\n\nimages, corners = zip(*samples)\n\npattern_points = [pattern_points]*len(corners)\n\nrms, camera_matrix, dist_coefs, rvecs, tvecs = cv2.calibrateCamera(pattern_points, corners, images[0].shape, None, None)\n\nnp.save('camera_mat.npy', camera_matrix)\nnp.save('dist_coefs.npy', dist_coefs)\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"Chapter09/01 Pinhole camera model calibration.py","file_name":"01 Pinhole camera model calibration.py","file_ext":"py","file_size_in_byte":1498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"289468147","text":"import os\nfrom PIL import Image\nfrom PIL import ImageFilter\n\nfilelist = train['Image'].loc[(train['cnt_freq'] < 10)].tolist()\n\nfor count in range(0, 2):\n for imagefile in filelist:\n os.chdir('/home/paperspace/fastai/courses/dll/data/humpback/train')\n im = Image.open(imagefile)\n im = im.convert('RGB')\n r, g, b = im.split()\n r = r.convert(\"RGB\")\n g = g.convert(\"RGB\")\n b = b.convert(\"RGB\")\n im_blur = im.filer(ImageFilter.GaussianBlur)\n im_unsharp = im.filer(ImageFilter.UnsharpMask)\n\n os.chdir('/home/paperspace/fastai/courses/dll/data/humpback/copy')\n r.save(str(count)+'r_'+imagefile)\n g.save(str(count)+'g_'+imagefile)\n b.save(str(count)+'b_'+imagefile)\n im_blur.save(str(count) + 'bl_' + imagefile)\n im_unsharp.save(str(count) + 'un_' + imagefile)\n","sub_path":"数据集操作/oversampling.py","file_name":"oversampling.py","file_ext":"py","file_size_in_byte":861,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"208201726","text":"import os\nimport numpy as np\nfrom numpy.lib.format import open_memmap\nfrom tqdm import tqdm\n\ndirected_edges = [(i-1, j-1) for i, j in [\n (1, 13), (1, 17), (2, 1), (3, 4), (5, 6),\n (6, 7), (7, 8), (8, 22), (8, 23), (9, 10),\n (10, 11), (11, 12), (12, 24), (12, 25), (13, 14),\n (14, 15), (15, 16), (17, 18), (18, 19), (19, 20),\n (21, 2), (21, 3), (21, 5), (21, 9)\n]]\n\nsets = {'train', 'val'}\ndatasets = {'ntu/xview', 'ntu/xsub'}\n\ndef gen_bone_data():\n \"\"\"Generate bone data from joint data for NTU skeleton dataset\"\"\"\n for dataset in datasets:\n for set in sets:\n print(dataset, set)\n data = np.load('../data/{}/{}_data_joint.npy'.format(dataset, set))\n N, C, T, V, M = data.shape\n fp_sp = open_memmap(\n '../data/{}/{}_data_bone.npy'.format(dataset, set),\n dtype='float32',\n mode='w+',\n shape=(N, 3, T, len(directed_edges), M))\n\n for edge_id, (source_node, target_node) in tqdm(enumerate(directed_edges), total=len(directed_edges)):\n # Assign bones to be joint1 - joint2, the pairs are pre-determined and hardcoded\n # There also happens to be 25 bones\n fp_sp[:, :, :, edge_id, :] = data[:, :, :, source_node, :] - data[:, :, :, target_node, :]\n\n\nif __name__ == '__main__':\n gen_bone_data()\n","sub_path":"data_gen/ntu_gen_bone_data.py","file_name":"ntu_gen_bone_data.py","file_ext":"py","file_size_in_byte":1377,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"604509688","text":"from pprint import pprint, pformat # pylint: disable=unused-import\nimport logging\nimport time\nfrom copy import deepcopy\n\n# from collections import defaultdict\n\nfrom pymongo import UpdateOne, InsertOne\nfrom pymongo.errors import BulkWriteError\n\n\ndef bulk_write(db, item_type, ops, stat=None, retries=3, log_first_failop=True):\n \"\"\"\n Tries to apply `ops` Update operations to `item_type`\n collection with `bulk_write` method.\n Gives up if `retries` retries failed.\n\n Args:\n db - pymongo collection object\n item_type - name of collection\n ops - list of operations like UpdateOne\n stat - instance of `ioweb.stat:Stat`\n retries - number of retries\n \"\"\"\n if stat:\n stat.inc(\"bulk-write-%s\" % item_type)\n for retry in range(retries):\n try:\n res = db[item_type].bulk_write(ops, ordered=False)\n except BulkWriteError as ex:\n # TODO: repeat only failed operations!!!\n # TODO: repeat only in case of DUP (11000) errors\n if retry == (retries - 1):\n if log_first_failop:\n logging.error(\n \"First failed operation:\\n%s\",\n pformat(ex.details[\"writeErrors\"][0]),\n )\n raise\n else:\n if stat:\n stat.inc(\"bulk-write-%s-retry\" % item_type)\n else:\n if stat:\n stat.inc(\n \"bulk-write-%s-upsert\" % item_type, res.bulk_api_result[\"nUpserted\"]\n )\n stat.inc(\n \"bulk-write-%s-change\" % item_type, res.bulk_api_result[\"nModified\"]\n )\n break\n return res\n\n\nclass BulkWriter(object):\n def __init__(self, db, item_type, bulk_size=100, stat=None, retries=3):\n self.database = db\n self.item_type = item_type\n self.stat = stat\n self.retries = retries\n self.bulk_size = bulk_size\n self.ops = []\n\n def _write_ops(self):\n res = bulk_write(self.database, self.item_type, self.ops, self.stat)\n self.ops = []\n return res\n\n def update_one(self, *args, **kwargs):\n self.ops.append(UpdateOne(*args, **kwargs))\n if len(self.ops) >= self.bulk_size:\n return self._write_ops()\n else:\n return None\n\n def insert_one(self, *args, **kwargs):\n self.ops.append(InsertOne(*args, **kwargs))\n if len(self.ops) >= self.bulk_size:\n return self._write_ops()\n else:\n return None\n\n def flush(self):\n if self.ops:\n return self._write_ops()\n else:\n return None\n\n\ndef iterate_collection(\n db,\n item_type,\n query,\n sort_field,\n iter_chunk=1000,\n fields=None,\n infinite=False,\n limit=None,\n):\n \"\"\"\n Iterate over `db[item_type]` collection items matching `query`\n sorted by `sort_field`.\n\n Intenally, it fetches chunk of `iter_chunk` items at once and\n iterates over it. Then fetch next chunk.\n \"\"\"\n recent_id = None\n count = 0\n query = deepcopy(query) # avoid side effects\n if sort_field in query:\n logging.error(\n \"Function `iterate_collection` received query\"\n \" that contains a key same as `sort_field`.\"\n \" That might goes unexpected and weird ways.\"\n )\n while True:\n if recent_id:\n query[sort_field] = {\"$gt\": recent_id}\n else:\n if sort_field in query:\n del query[sort_field]\n items = list(\n db[item_type].find(query, fields, sort=[(sort_field, 1)], limit=iter_chunk)\n )\n if not items:\n if infinite:\n sleep_time = 5\n logging.debug(\"No items to process. Sleeping %d seconds\", sleep_time)\n time.sleep(sleep_time)\n recent_id = None\n else:\n return\n else:\n for item in items:\n yield item\n recent_id = item[sort_field]\n count += 1\n if limit and count >= limit:\n return\n\n\ndef bulk_dup_insert(db, item_type, ops, dup_key, stat=None):\n if stat:\n stat.inc(\"bulk-dup-insert-%s\" % item_type, len(ops))\n all_keys = set()\n uniq_ops = []\n for op in ops:\n if not isinstance(op, InsertOne):\n raise Exception(\n \"Function bulk_dup_insert accepts only\"\n \" InsertOne operations. Got: %s\" % op.__class__.__name__\n )\n if dup_key not in op._doc: # pylint: disable=protected-access\n raise Exception(\n \"Operation for bulk_dup_insert\"\n ' does not have key \"%s\": %s'\n % (dup_key, str(op._doc)[:1000]) # pylint: disable=protected-access\n )\n key = op._doc[dup_key] # pylint: disable=protected-access\n if key not in all_keys:\n all_keys.add(key)\n uniq_ops.append(op)\n\n try:\n db[item_type].bulk_write(uniq_ops, ordered=False)\n except BulkWriteError as ex:\n if (\n all(x[\"code\"] == 11000 for x in ex.details[\"writeErrors\"])\n and not ex.details[\"writeConcernErrors\"]\n ):\n error_keys = set(x[\"op\"][dup_key] for x in ex.details[\"writeErrors\"])\n res_keys = list(all_keys - error_keys)\n if stat:\n stat.inc(\"bulk-dup-insert-%s-inserted\" % item_type, len(res_keys))\n return res_keys\n else:\n raise\n else:\n if stat:\n stat.inc(\"bulk-dup-insert-%s-inserted\" % item_type, len(all_keys))\n return list(all_keys)\n\n\ndef bulk_simple_insert(db, item_type, ops, stat=None):\n if stat:\n stat.inc(\"bulk-dup-insert-%s\" % item_type, len(ops))\n for op in ops:\n if not isinstance(op, InsertOne):\n raise Exception(\n \"Function simple_bulk_insert accepts only\"\n \" InsertOne operations. Got: %s\" % op.__class__.__name__\n )\n try:\n db[item_type].bulk_write(ops, ordered=False)\n except BulkWriteError as ex:\n if (\n all(x[\"code\"] == 11000 for x in ex.details[\"writeErrors\"])\n and not ex.details[\"writeConcernErrors\"]\n ):\n if stat:\n stat.inc(\n \"bulk-dup-insert-%s-inserted\" % item_type,\n len(ops) - len(ex.details[\"writeErrors\"]),\n )\n else:\n raise\n else:\n if stat:\n stat.inc(\"bulk-dup-insert-%s-inserted\" % item_type, len(ops))\n","sub_path":"ioweb/mongodb.py","file_name":"mongodb.py","file_ext":"py","file_size_in_byte":6673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"108255971","text":"\r\n\r\nimport nltk\r\nfrom nltk.corpus import state_union\r\nfrom nltk.tokenize import PunktSentenceTokenizer\r\n\r\ntrain_text = state_union.raw(\"2005-GWBush.txt\")\r\nsample_text = \"Hello Georgia-Pacific Corp. today June, 2008-06-29 is good day since President Obama is visting us. We must eat Apple to stay healthy , as we need to visit Europe soon\"\r\n#sample_text = state_union.raw(\"2006-GWBush.txt\")\r\n\r\n\r\ncustom_sent_tokenizer = PunktSentenceTokenizer(train_text)\r\n\r\ntokenized = custom_sent_tokenizer.tokenize(sample_text)\r\n\r\nfor i in tokenized:\r\n words = nltk.word_tokenize(i)\r\n tagged = nltk.pos_tag(words)\r\n namedEnt = nltk.ne_chunk(tagged)\r\n namedEnt.draw()\r\n\r\n\"\"\"\r\n\r\nNE Type\tExamples\r\nORGANIZATION\tGeorgia-Pacific Corp., WHO\r\nPERSON\tEddy Bonte, President Obama\r\nLOCATION\tMurray River, Mount Everest\r\nDATE\tJune, 2008-06-29\r\nTIME\ttwo fifty a m, 1:30 p.m.\r\nMONEY\t175 million Canadian Dollars, GBP 10.40\r\nPERCENT\ttwenty pct, 18.75 %\r\nFACILITY\tWashington Monument, Stonehenge\r\nGPE\tSouth East Asia, Midlothian\r\n\"\"\"\r\n \r\n","sub_path":"7nameEntittyDetection.py","file_name":"7nameEntittyDetection.py","file_ext":"py","file_size_in_byte":1023,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"46391436","text":"from Introduction_Exercice1 import RequestHTTP\n\t\ndef delete(message):\n array = message.split()\n return ' '.join(array)\n\ndef deleteEspChar(message):\n return ''.join(e for e in string if e.isalnum())\n\ndef domaine(message):\n array = message.split('.')\n del(array[0])\n mesEnd = '.'.join(array).split('/')\n return mesEnd[0]\n \t\nr = RequestHTTP(\"http://www.esiee.fr/\")\nresponse = r.request(5)\nprint(delete(response.content[0:1000]))\nprint(deleteEspChar(response.content[0:1000]))\nprint(domaine(\"http://www.esiee.fr/\"))\n\n","sub_path":"Evaluation/Introduction_Exercice2.py","file_name":"Introduction_Exercice2.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"79782955","text":"from spn.spn import *\nfrom spn.sum_node import SumNode\nfrom spn.normal_leaf_node import NormalLeafNode\nfrom spn.multi_normal_leaf_node import MultiNormalLeafNode\n\n#Simplistic enough. The node counter needs no explanation. Note that a MN leaf node is parametrized by K(K+1)/2 + K parameters, i.e. K(K+3)/2 params - the K-length mean vector and the cov matrix ( which is symmetric and thus requires only the K entries along the diagonal plus K(K-1)/2 off-diagonal entries on any one side )\n\ndef count_nodes(network):\n\tnextnodes = [network.root.children[0]]\n\tcount = 0\n\twhile len(nextnodes) > 0:\n\t\tnode = nextnodes.pop()\n\t\tcount += 1\n\t\tnextnodes.extend(node.children)\n\treturn count\n\ndef count_params(network):\n\tnextnodes = [network.root.children[0]]\n\tcount = 0\n\twhile len(nextnodes) > 0:\n\t\tnode = nextnodes.pop()\n\t\tif type(node) == SumNode:\n\t\t\tcount += len(node.children)\n\t\telif type(node) == NormalLeafNode:\n\t\t\tcount += 2\n\t\telif type(node) == MultiNormalLeafNode:\n\t\t\tk = len(node.scope)\n\t\t\tcount += k*(k+3)//2\n\t\tnextnodes.extend(node.children)\n\treturn count\n\t\n","sub_path":"util/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1058,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"420766601","text":"from socket import socket, SOL_SOCKET, SO_REUSEADDR, SHUT_RDWR, SHUT_WR, AF_INET, SOCK_STREAM\n\n\nclass SerpentineSocket:\n\n def __init__(self, *args, **kwargs):\n self.HOST = kwargs.get('HOST', 'localhost')\n self.PORT = kwargs.get('PORT', 4030)\n self.FAMILY = kwargs.get('FAMILY', AF_INET)\n self.TYPE = kwargs.get('TYPE', SOCK_STREAM)\n self.CONNECTIONS = kwargs.get('CONNECTIONS', 1)\n self.socket = None\n\n def __create_socket__(self, server=None):\n \"\"\"\n :arg server Bool()\n \"\"\"\n self.socket = socket(family=self.FAMILY, type=self.TYPE)\n if server:\n self.socket.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\n\n def __close_socket__(self):\n try:\n self.socket.shutdown(SHUT_WR)\n except (OSError,) as err:\n pass\n finally:\n self.socket.close()","sub_path":"monitor/monitor_socket.py","file_name":"monitor_socket.py","file_ext":"py","file_size_in_byte":879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"216829251","text":"class Solution(object):\n def maximalRectangle(self, matrix):\n \"\"\"\n :type matrix: List[List[str]]\n :rtype: int\n \"\"\"\n if not matrix:\n return 0\n m, n, res = len(matrix), len(matrix[0]), 0\n L, R, H = [0 for i in range(n)], [n for i in range(n)], [0 for i in range(n)]\n for i in range(m):\n curLeft, curRight = 0, n\n for j in range(n):\n if matrix[i][j] == \"1\":\n L[j] = max(L[j], curLeft)\n H[j] += 1\n else:\n L[j], R[j], curLeft = 0, n, j + 1\n H[j] = 0\n for j in range(n, -1, -1):\n if matrix[i][j] == \"1\":\n R[j] = min(R[j], curRight)\n res = max(res, H[j] * (R[j] - L[j]))\n else:\n curRight = j\n return res\n\n","sub_path":"Maximal_Rectangle/solution.py","file_name":"solution.py","file_ext":"py","file_size_in_byte":901,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"443234265","text":"import requests\n\n# define a method called main\ndef main():\n url = 'http://talkpython.fm'\n # download via GET method\n resp = requests.get(url)\n # returns an error to the user if the response is NOT 200\n if resp.status_code != 200:\n print(\"Error requesting URL, {}\".format(resp.status_code))\n return\n # prints out the first 500 characters using a :slice\n print(resp.text[:500])\n# if statement to call main method\nif __name__ == '__main__':\n main()\n","sub_path":"simple_get.py","file_name":"simple_get.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"604682059","text":"#!/usr/bin/python\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.\n#\n# Extract Python package name from setup.cfg\n\nimport ConfigParser\nimport sys\n\nimport wheel.bdist_wheel\n\nsetup_cfg = ConfigParser.SafeConfigParser()\nsetup_cfg.read(\"setup.cfg\")\ndistname = setup_cfg.get(\"metadata\", \"name\")\nassert distname\nif not len(sys.argv) or sys.argv[1] == \"--tarball\":\n print(distname)\nelif sys.argv[1] == \"--wheel\":\n print(wheel.bdist_wheel.safer_name(distname))\nelse:\n sys.stderr.write(\"ERROR: Valid options are --tarball and --wheel\")\n sys.exit(1)\n","sub_path":"jenkins/scripts/pypi-extract-name.py","file_name":"pypi-extract-name.py","file_ext":"py","file_size_in_byte":1050,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"173719430","text":"from numpy.ma import logical_xor\n\n\ndef syndrome(looper, data, check):\n result = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0] # wektor zawierający wynikowy syndrom\n\n\n chunk = (data[looper:looper + 26])\n count =0\n\n if len(chunk)==26:\n # 26 bit vector rds\n for bit_check in range(0, 26): # pętlamnożnącay\n\n if chunk[bit_check]:\n count = count + 1\n result = logical_xor(result, check[bit_check])\n # y*h, sekwencja 26 bitow im macierz parzystosci\n\n return result\n\n\n\n\n\n","sub_path":"cps/syndrome.py","file_name":"syndrome.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"611560986","text":"# 国語の点数の一覧 --- (*1)\npoints = [80, 40, 23, 14, 29, 58]\n\n# # 30点以下のデータだけ選んで赤点リストに追加 --- (*2)\n# akaten = []\n# for p in points:\n# if p < 30:\n# akaten.append(p)\n\n# # 選んだデータを表示 --- (*3)\n# print(akaten)\n\nakaten= []\nfor p in points:\n if p < 30:\n akaten.append(p)\nprint(akaten)\nquit","sub_path":"src/c3/list-akaten.py","file_name":"list-akaten.py","file_ext":"py","file_size_in_byte":369,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"366853766","text":"import re\nfrom datetime import date, datetime\nfrom itertools import repeat\n\nfrom sanic.blueprints import Blueprint\nfrom sanic.response import json\nfrom sanic.views import CompositionView\n\nfrom .doc import route_specs, RouteSpec, serialize_schema, definitions, Field, Integer, Boolean, String, Date, DateTime\n\n\nblueprint = Blueprint('openapi', url_prefix='openapi')\n\n_spec = {}\n\n\n# Removes all null values from a dictionary\ndef remove_nulls(dictionary, deep=True):\n return {\n k: remove_nulls(v, deep) if deep and type(v) is dict else v\n for k, v in dictionary.items()\n if v is not None\n }\n\n\n@blueprint.listener('before_server_start')\ndef build_spec(app, loop):\n _spec['swagger'] = '2.0'\n _spec['info'] = {\n \"version\": getattr(app.config, 'API_VERSION', '1.0.0'),\n \"title\": getattr(app.config, 'API_TITLE', 'API'),\n \"description\": getattr(app.config, 'API_DESCRIPTION', ''),\n \"termsOfService\": getattr(app.config, 'API_TERMS_OF_SERVICE', None),\n \"contact\": {\n \"email\": getattr(app.config, 'API_CONTACT_EMAIL', None)\n },\n \"license\": {\n \"email\": getattr(app.config, 'API_LICENSE_NAME', None),\n \"url\": getattr(app.config, 'API_LICENSE_URL', None)\n }\n }\n _spec['schemes'] = getattr(app.config, 'API_SCHEMES', ['http'])\n\n security_definitions = []\n _spec['securityDefinitions'] = {}\n _security_definitions = getattr(app.config, 'API_SECURITY_DEFINITIONS', '')\n if isinstance(_security_definitions, (tuple, list, set)):\n security_definitions = [_name.lower() for _name in _security_definitions]\n elif isinstance(_security_definitions, str):\n security_definitions.append(_security_definitions.lower())\n\n for security_definition in security_definitions:\n if security_definition == 'basic':\n _spec['securityDefinitions']['basic'] = {'type': 'basic'}\n elif security_definition == 'apikey':\n _spec['securityDefinitions']['apiKey'] = {\n 'type': 'apiKey',\n 'name': getattr(app.config, 'API_SECURITY_DEFINITIONS_NAME', 'token'),\n 'in': getattr(app.config, 'API_SECURITY_DEFINITIONS_IN', 'header')\n }\n elif security_definition == 'oauth2':\n _spec['securityDefinitions']['oauth2'] = {\n 'type': 'oauth2',\n 'authorizationUrl': getattr(app.config, 'API_SECURITY_DEFINITIONS_URL',\n 'http://swagger.io/api/oauth/dialog'),\n 'flow': getattr(app.config, 'API_SECURITY_DEFINITIONS_FLOW', 'implicit'),\n 'scopes': getattr(app.config, 'API_SECURITY_DEFINITIONS_SCOPES',\n {\"write:pets\": \"modify pets in your account\",\n \"read:pets\": \"read your pets\"})\n }\n\n # --------------------------------------------------------------- #\n # Blueprint Tags\n # --------------------------------------------------------------- #\n\n for blueprint in app.blueprints.values():\n if hasattr(blueprint, 'routes'):\n for route in blueprint.routes:\n route_spec = route_specs[route.handler]\n route_spec.blueprint = blueprint\n if not route_spec.tags:\n route_spec.tags.append(blueprint.name)\n\n paths = {}\n for uri, route in app.router.routes_all.items():\n if uri.startswith(\"/swagger\") or uri.startswith(\"/openapi\") \\\n or '<file_uri' in uri:\n # TODO: add static flag in sanic routes\n continue\n\n # --------------------------------------------------------------- #\n # Methods\n # --------------------------------------------------------------- #\n\n # Build list of methods and their handler functions\n handler_type = type(route.handler)\n if handler_type is CompositionView:\n view = route.handler\n method_handlers = view.handlers.items()\n else:\n method_handlers = zip(route.methods, repeat(route.handler))\n\n methods = {}\n for _method, _handler in method_handlers:\n if _method == 'OPTIONS':\n continue\n\n route_spec = route_specs.get(_handler) or RouteSpec()\n consumes_content_types = route_spec.consumes_content_type or \\\n getattr(app.config, 'API_CONSUMES_CONTENT_TYPES', ['application/json'])\n produces_content_types = route_spec.produces_content_type or \\\n getattr(app.config, 'API_PRODUCES_CONTENT_TYPES', ['application/json'])\n\n # Parameters - Path & Query String\n path_parameters = [{\n **serialize_schema(parameter.cast),\n 'required': True,\n 'in': 'path',\n 'name': parameter.name,\n } for parameter in route.parameters]\n query_string_parameters = []\n body_parameters = []\n form_parameters = []\n\n if route_spec.consumes:\n if _method in ('GET', 'DELETE'):\n if hasattr(route_spec.consumes, '__dict__'):\n for name, schema in route_spec.consumes.__dict__.items():\n if isinstance(schema, Field):\n form_parameters.append({\n 'in': 'query',\n **schema.serialize(),\n 'name': name,\n })\n elif not name.startswith('_') and schema is int:\n form_parameters.append({\n 'in': 'query',\n **Integer().serialize(),\n 'name': name,\n })\n elif not name.startswith('_') and schema is str:\n form_parameters.append({\n 'in': 'query',\n **String().serialize(),\n 'name': name,\n })\n elif not name.startswith('_') and schema is bool:\n form_parameters.append({\n 'in': 'query',\n **Boolean().serialize(),\n 'name': name,\n })\n elif not name.startswith('_') and schema is date:\n form_parameters.append({\n 'in': 'query',\n **Date().serialize(),\n 'name': name,\n })\n elif not name.startswith('_') and schema is datetime:\n form_parameters.append({\n 'in': 'query',\n **DateTime().serialize(),\n 'name': name,\n })\n else:\n # not support list or dict\n pass\n else:\n spec = serialize_schema(route_spec.consumes)\n if 'properties' in spec:\n for name, prop_spec in spec['properties'].items():\n query_string_parameters.append({\n 'in': 'query',\n **prop_spec,\n 'name': name,\n })\n elif _method in ('POST', 'PUT', 'PATCH') and hasattr(route_spec.consumes, '__dict__'):\n for name, schema in route_spec.consumes.__dict__.items():\n if isinstance(schema, Field):\n form_parameters.append({\n 'in': 'formData',\n **schema.serialize(),\n 'name': name,\n })\n elif not name.startswith('_') and schema is int:\n form_parameters.append({\n 'in': 'formData',\n **Integer().serialize(),\n 'name': name,\n })\n elif not name.startswith('_') and schema is str:\n form_parameters.append({\n 'in': 'formData',\n **String().serialize(),\n 'name': name,\n })\n elif not name.startswith('_') and schema is bool:\n form_parameters.append({\n 'in': 'formData',\n **Boolean().serialize(),\n 'name': name,\n })\n elif not name.startswith('_') and schema is date:\n form_parameters.append({\n 'in': 'formData',\n **Date().serialize(),\n 'name': name,\n })\n elif not name.startswith('_') and schema is datetime:\n form_parameters.append({\n 'in': 'formData',\n **DateTime().serialize(),\n 'name': name,\n })\n else:\n # not support list or dict\n pass\n else:\n body_parameters.append({\n **serialize_schema(route_spec.consumes),\n 'in': 'body',\n 'name': 'body',\n })\n\n endpoint = remove_nulls({\n 'operationId': route_spec.operation or route.name,\n 'summary': route_spec.summary,\n 'description': route_spec.description,\n 'consumes': consumes_content_types,\n 'produces': produces_content_types,\n 'tags': route_spec.tags or None,\n 'parameters': path_parameters + query_string_parameters + body_parameters + form_parameters,\n 'responses': {\n \"200\": {\n \"description\": None,\n \"examples\": None,\n \"schema\": serialize_schema(route_spec.produces) if route_spec.produces else None\n }\n },\n })\n\n methods[_method.lower()] = endpoint\n\n uri_parsed = uri\n for parameter in route.parameters:\n uri_parsed = re.sub('<'+parameter.name+'.*?>', '{'+parameter.name+'}', uri_parsed)\n\n paths[uri_parsed] = methods\n\n # --------------------------------------------------------------- #\n # Definitions\n # --------------------------------------------------------------- #\n\n _spec['definitions'] = {obj.object_name: definition for cls, (obj, definition) in definitions.items()}\n\n # --------------------------------------------------------------- #\n # Tags\n # --------------------------------------------------------------- #\n\n # TODO: figure out how to get descriptions in these\n tags = {}\n for route_spec in route_specs.values():\n if route_spec.blueprint and route_spec.blueprint.name in ('swagger', 'openapi'):\n # TODO: add static flag in sanic routes\n continue\n for tag in route_spec.tags:\n tags[tag] = True\n _spec['tags'] = [{\"name\": name} for name in tags.keys()]\n\n _spec['paths'] = paths\n\n\n@blueprint.route('/spec.json')\ndef spec(request):\n return json(_spec)\n","sub_path":"sanic_openapi/openapi.py","file_name":"openapi.py","file_ext":"py","file_size_in_byte":12183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"25779251","text":"import keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Activation, Flatten, Reshape\nfrom keras.layers import Conv2D, MaxPooling2D\n\nimport numpy as np\n\ndef create_model():\n\n np.random.seed(100)\n\n\n num_classes = 10\n model = Sequential()\n input_shape = (28, 28)\n model.add(Reshape((28,28,1), input_shape=input_shape))\n model.add(Conv2D(16, (5,5)))\n model.add(Activation('relu'))\n model.add(MaxPooling2D())\n model.add(Conv2D(30, (3,3)))\n model.add(Activation('relu'))\n model.add(MaxPooling2D())\n model.add(Conv2D(50, (3,3)))\n model.add(Activation('relu'))\n model.add(Flatten())\n model.add(Dense(150, activation=\"tanh\"))\n model.add(Dense(num_classes, activation=\"softmax\"))\n\n return model\n\ndef digest(lst):\n if not isinstance(lst, list):\n lst = [lst]\n return [np.mean(w*w) for w in lst]\n \n","sub_path":"ZZZ/ML/tf/model_conv.py","file_name":"model_conv.py","file_ext":"py","file_size_in_byte":886,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"502694091","text":"import json\nimport unittest\n\nimport nap\n\nimport mock\n\nfrom . import SampleResourceModel\n\n\nclass TestResourceModelCreation(object):\n\n def test_mapping_to_fields(self):\n\n dm = SampleResourceModel(title='a', content='b')\n\n assert dm.title == 'a'\n assert dm.content == 'b'\n assert len(dm.extra_data) == 0\n\n def test_extra_data(self):\n\n dm = SampleResourceModel(title='a', note='b', z='c')\n\n assert dm.extra_data['z'] == 'c'\n assert not hasattr(dm, 'z')\n\n def test_api_name(self):\n dm = SampleResourceModel(title='a', note='b', some_field='c')\n\n assert dm.alt_name == 'c'\n\n def test_meta_attached(self):\n\n class SampleMetaDataModel(nap.ResourceModel):\n class Meta:\n resource_name = 'bob'\n root_url = \"http://foo.com/v1/\"\n\n dm = SampleMetaDataModel()\n\n assert dm._meta['resource_name'] == 'bob'\n assert dm._meta['root_url'] == \"http://foo.com/v1/\"\n\n def test_override_root_url(self):\n\n different_url = \"http://www.differenturl.com/v1/\"\n dm = SampleResourceModel(root_url=different_url)\n assert dm._root_url == different_url\n\n\nclass TestResourceModelAccessMethods(object):\n\n def test_get_from_uri(self):\n\n fake_dict = {\n 'title': \"A fake title\",\n 'content': \"isnt this neat\",\n }\n with mock.patch('requests.request') as get:\n stubbed_response = mock.Mock()\n stubbed_response.content = json.dumps(fake_dict)\n\n model_root_url = SampleResourceModel._meta['root_url']\n expected_url = \"xyz/\"\n stubbed_response.url = expected_url\n stubbed_response.status_code = 200\n\n get.return_value = stubbed_response\n obj = SampleResourceModel.get_from_uri('xyz')\n\n assert obj.title == fake_dict['title']\n assert obj.content == fake_dict['content']\n assert obj._root_url == model_root_url\n assert obj._full_url == expected_url\n\n def test_get(self):\n\n with mock.patch('nap.resources.ResourceModel.get_from_uri') as g:\n SampleResourceModel.get('/some/uri/')\n g.assert_called_once\n with mock.patch('nap.resources.ResourceModel.lookup') as lookup:\n SampleResourceModel.get(pk=1)\n lookup.assert_called_once\n\n def test_get_lookup_url(self):\n final_uri = SampleResourceModel.get_lookup_url(hello='1', what='2')\n assert final_uri == \"1/2/\"\n\n final_uri_with_params = SampleResourceModel.get_lookup_url(\n hello='1',\n what='2',\n extra_param='3'\n )\n assert final_uri_with_params == '1/2/?extra_param=3'\n # assert params == {'extra_param': '3'}\n SampleResourceModel._lookup_urls = []\n\n def test_lookup(self):\n with mock.patch('nap.resources.ResourceModel.get_from_uri') as get:\n SampleResourceModel.lookup(hello='hello_test', what='what_test')\n get.assert_called_with('hello_test/what_test/')\n\n SampleResourceModel._lookup_urls = []\n\n def test_lookup_no_valid_urls(self):\n from pytest import raises\n with raises(ValueError):\n SampleResourceModel.get_lookup_url(hello='bad_hello')\n\n SampleResourceModel._lookup_urls = []\n\n def test_generate_url(self):\n rm = SampleResourceModel(hello='1', slug='slug')\n assert rm._generate_url(url_type='create') == 'note/'\n\n # assert that keyword arguments take precedence over meta arguments\n essay_url = rm._generate_url(url_type='create', resource_name='essay')\n assert essay_url == 'essay/'\n\n assert rm._generate_url(url_type='update') == 'note/slug/'\n # assert that keyword arguments take predecnce over field values\n new_slug_url = rm._generate_url(url_type='update', slug='new-slug')\n assert new_slug_url == 'note/new-slug/'\n\n def test_refresh(self):\n rm = SampleResourceModel(pk=5, title=\"bad title\")\n with mock.patch('requests.request') as request:\n r = mock.Mock()\n r.content = json.dumps({'title': 'new title'})\n r.status_code = 200\n request.return_value = r\n rm.refresh()\n assert request.called\n\n assert rm.title == 'new title'\n\n\nclass TestResourceCollectionMethods(object):\n\n def test_collection_field(self):\n\n SampleResourceModel._meta['collection_field'] = 'objects'\n with mock.patch('requests.request') as request:\n r = mock.Mock()\n collection_dict = {\n 'meta': {'something': True},\n 'objects': [\n {'title': 'a'},\n {'title': 'b', 'content': \"b's content\"},\n {'title': 'c'},\n ]\n }\n r.content = json.dumps(collection_dict)\n r.status_code = 200\n request.return_value = r\n objects = SampleResourceModel.all()\n assert request.called\n assert len(objects) == 3\n SampleResourceModel._meta['collection_field'] = None\n\n\nclass TestSerialize(object):\n\n def test_serialize(self):\n dm = SampleResourceModel(\n slug='123',\n title='test title',\n content='test content'\n )\n json_string = dm.serialize()\n obj_dict = json.loads(json_string)\n assert obj_dict['title'] == 'test title'\n assert obj_dict['content'] == 'test content'\n assert obj_dict['alt_name'] == None\n assert 'slug' not in obj_dict\n\n def test_serialize_for_write_false(self):\n dm = SampleResourceModel(\n slug='123',\n title='test title',\n content='test content'\n )\n json_string = dm.serialize(for_read=True)\n obj_dict = json.loads(json_string)\n assert obj_dict['title'] == 'test title'\n assert obj_dict['content'] == 'test content'\n assert obj_dict['alt_name'] == None\n assert obj_dict['slug'] == '123'\n\n\nclass TestResourceModelWriteMethods(unittest.TestCase):\n\n headers = {'content-type': 'application/json'}\n\n def tearDown(self):\n SampleResourceModel._lookup_urls = []\n\n def dont_test_write_url(self):\n\n dm = SampleResourceModel(\n title='expected_title',\n content='Blank Content')\n\n url = dm.get_update_url()\n assert url == u'expected_title/'\n SampleResourceModel._lookup_urls = []\n\n def test_create_url(self):\n\n class CreateURLModel(nap.ResourceModel):\n class Meta:\n root_url = 'http://www.foo.com/api/'\n\n cm = CreateURLModel()\n\n assert cm.get_create_url() == 'createurlmodel/'\n\n class CreateURLModelTwo(nap.ResourceModel):\n class Meta:\n root_url = 'http://www.foo.com/api/'\n resource_name = 'note'\n\n cm2 = CreateURLModelTwo()\n assert cm2.get_create_url() == 'note/'\n\n def test_save(self):\n dm = SampleResourceModel(\n title=None,\n content='Blank Content')\n with mock.patch('nap.resources.ResourceModel.create') as create:\n dm.save()\n assert create.called\n dm._full_url = 'http://www.foo.com/v1/1/'\n with mock.patch('nap.resources.ResourceModel.update') as update:\n dm.save()\n assert update.called\n\n def test_update(self):\n dm = SampleResourceModel(\n title='expected_title',\n content='Blank Content')\n with mock.patch('requests.request') as put:\n r = mock.Mock()\n r.content = ''\n r.status_code = 204\n put.return_value = r\n dm.update()\n put.assert_called_with('PUT', \"http://foo.com/v1/expected_title/\",\n data=dm.serialize(), headers=self.headers, auth=None)\n SampleResourceModel._lookup_urls = []\n\n def test_handle_update_response(self):\n dm = SampleResourceModel(title='old title')\n dm._full_url = 'http://foo.com/v1/random_title/'\n with mock.patch('requests.request') as put:\n r = mock.Mock()\n r.content = json.dumps({'title': 'hello', 'content': 'content'})\n r.status_code = 204\n put.return_value = r\n dm.update()\n assert dm.title == 'hello'\n assert dm.content == 'content'\n\n def test_create(self):\n\n # add a lookup url to ensure it doesn't get used\n dm = SampleResourceModel(\n title='expected_title',\n content='Blank Content')\n with mock.patch('requests.request') as post:\n r = mock.Mock()\n r.content = ''\n r.headers = {'location': 'http://foo.com/v1/random_title/'}\n r.status_code = 201\n post.return_value = r\n dm.create()\n post.assert_called_with('POST', \"http://foo.com/v1/note/\",\n data=dm.serialize(), headers=self.headers, auth=None)\n SampleResourceModel._lookup_urls = []\n\n def test_handle_create_response(self):\n dm = SampleResourceModel(title='old title')\n with mock.patch('requests.request') as post:\n r = mock.Mock()\n r.content = json.dumps({'title': 'hello', 'content': 'content'})\n r.status_code = 201\n post.return_value = r\n dm.create()\n\n assert dm.title == 'hello'\n assert dm.content == 'content'\n\n def test_filter(self):\n with mock.patch('requests.request') as request:\n r = mock.Mock()\n r.status_code = 200\n r.content = json.dumps([\n {'title': 'hello', 'content': 'content'},\n {'title': 'hello', 'content': 'content'}\n ])\n request.return_value = r\n dms = SampleResourceModel.filter(title='title')\n\n assert len(dms) == 2\n\n def test_write_with_no_lookup_url(self):\n\n from pytest import raises\n\n dm = SampleResourceModel(content='what')\n with raises(ValueError):\n dm.update()\n\n\nclass TestResourceID(object):\n\n def test_resource_id_get(self):\n dm = SampleResourceModel(\n title='expected_title',\n content='Blank Content',\n slug='some-slug')\n\n assert dm.resource_id == 'some-slug'\n\n def test_resource_id_set(self):\n dm = SampleResourceModel(\n title='expected_title',\n content='Blank Content',\n slug='some-slug')\n\n dm.resource_id = 'a-new-slug'\n\n assert dm.slug == 'a-new-slug'\n\n def test_resource_id_lookup(self):\n resource_id_url = SampleResourceModel.get_lookup_url(slug='some-slug')\n\n assert resource_id_url == 'note/some-slug/'\n\n\nclass TestReourceEtcMethods(object):\n\n def test_repr(self):\n\n dm = SampleResourceModel(slug='some-slug')\n assert str(dm) == '<SampleResourceModel: some-slug>'\n\n\nclass TestResourceAuth(object):\n\n def test_meta_set(self):\n # SampleResourceModel\n pass\n","sub_path":"tests/test_resources.py","file_name":"test_resources.py","file_ext":"py","file_size_in_byte":11111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"348181691","text":"\nimport time\nfrom typing import List, Tuple, Optional\nimport logging\nfrom logging import basicConfig, getLogger\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.axes import Axes\n\ntry:\n from icecream import ic\nexcept ImportError:\n def ic(*args):\n print(\"ic: \" + str(args))\n return args\n\nfrom abstract_solver import AbstractEquationSolver\n\nclass EquationSolverExp(AbstractEquationSolver):\n # @override\n def calc_fitness(self, w) -> np.ndarray:\n Y_sol = self.calcY(w)\n y_diff = np.gradient(Y_sol, self.x, axis=1)\n y_diff2 = np.gradient(y_diff, self.x, axis=1)\n fitEq = np.abs(\n y_diff2 + 5 * y_diff + 25 * Y_sol\n )\n m = fitEq.shape[1]\n fitEq: np.ndarray = np.sqrt(np.power(fitEq, 2).sum(axis=1) / m).reshape(-1)\n # fitEq = fitEq.sum(axis=1) / m\n # fitEq /= fitEq.std()\n \n gamma = np.cos(self.current_generation / self.max_generation * 2 * np.pi)**2\n \n o = self.origin_points\n y_origin = (Y_sol[:, o[0]] + Y_sol[:, o[1]]) / 2\n y_diff_origin = (y_diff[:, o[0]] + y_diff[:, o[1]]) / 2\n \n fitIni = gamma * np.abs(1 - y_origin) \\\n + (1 - gamma) * np.abs(0 - y_diff_origin)\n fitIni = fitIni.reshape(-1)\n \n return fitEq + fitIni\n\ndef main():\n basicConfig(\n format='\\x1b[34m%(levelname)s\\x1b[0m: [\\x1b[32m%(funcName)s\\x1b[0m] \\x1b[36m%(message)s\\x1b[0m',\n level=logging.INFO\n )\n \n logging.getLogger('matplotlib').setLevel(level=logging.CRITICAL)\n np.set_printoptions(formatter={'float': '{: 0.4f}'.format}) #桁を揃える\n logger = getLogger('exp_cos-solver')\n \n a = -1\n b = 1\n c = 9\n M = 258\n GENERATION = 2**14\n POPULATION = 258\n beta = 0.2\n mu = 0.06\n \n solver = EquationSolverExp(a, b, c, M, population=POPULATION, beta=beta, mu=mu, logger=logger)\n \n def analytical(x):\n omega = 2.5*np.sqrt(3)\n return np.exp(-2.5*x) * (np.cos(omega * x) + np.sin(omega * x) / np.sqrt(3))\n \n st = time.time()\n \n best_w = solver.solve(GENERATION)\n # best_w = solver.solveWithWatching(GENERATION, analytical=analytical)\n \n et = time.time()\n \n logger.info(f\"Finish solving. time: {et - st}\")\n \n y_solve = solver.calcY(best_w)\n \n x = np.linspace(a, b, M)\n\n y_analytical = analytical(x)\n \n mse = solver.score(y_solve, y_analytical)\n logger.info(f\"MSE = {mse:.4e}\")\n \n axs: List[Axes]\n fig, axs = plt.subplots(1, 2)\n \n axs[0].plot(x, analytical(x), label='Analytical')\n axs[0].scatter(x[::6], y_solve[::6], s=12, marker='*', color='green', label='$y_{solve}$')\n axs[0].legend()\n history = solver.fitnessHistory\n axs[1].plot(range(len(history)), history)\n axs[1].set_title(\"Fitness\")\n \n plt.show()\n\nif __name__ == '__main__':\n main()\n","sub_path":"src/equation_solver_ecos.py","file_name":"equation_solver_ecos.py","file_ext":"py","file_size_in_byte":2887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"640093151","text":"import pyautogui\nimport time\nimport numpy as np\nimport cv2\nimport imutils\nimport classes.keyboardEmu as kbe\nimport threading\n\n# Ждем три секунды, успеваем переключиться на окно:\nprint('waiting for 2 seconds...')\ntime.sleep(2)\n\n#ВНИМАНИЕ! PyAutoGUI НЕ ЧИТАЕТ В JPG!\ntitle = './nfs-shift-title.png'\n\n# ВНИМАНИЕ! У вас, скорее всего, будет другое разрешение, т.к. у меня 4К-монитор!\n# Здесь надо выставить те параметры, которые вы задали в игре.\nwindow_resolution = (1920, 1080)\n\nnfs_window_location = None\nsearching_attempt = 1\nwhile searching_attempt <= 5:\n nfs_window_location = pyautogui.locateOnScreen(title)\n\n if nfs_window_location is not None:\n print('nfs_window_location = ', nfs_window_location)\n break\n else:\n searching_attempt += 1\n time.sleep(1)\n print(\"attempt %d...\" % searching_attempt)\n\nif nfs_window_location is None:\n print('NFS Window not found')\n exit(1)\n\n\n# Извлекаем из картинки-скриншота только данные окна NFS.\n# Наша target-картинка - это заголовочная полоска окна. Для получения скриншота, мы берем ее левую точку (0),\n# а к верхней (1) прибавляем высоту (3)\nleft = nfs_window_location[0]\ntop = nfs_window_location[1]+nfs_window_location[3]\n\nwindow = (left, top, left+window_resolution[0], top+window_resolution[1])\n\nranges = {\n 'min_h1': [0, 180],\n 'max_h1': [180,180], # Как ни странно, вроде ни на что не влияет\n\n #'min_h2': [0, 180],\n #'max_h2': [0, 180],\n\n\n 'min_s': [0, 255],\n 'max_s': [8, 255], # Видим только дорогу\n\n\n 'min_v': [0, 255],\n 'max_v': [118, 255], # Видим только дорогу\n\n 'matrix': [9, 255],\n 'tresh': [8, 255],\n 'iterations': [8, 64],\n\n 'wheel': [0,1]\n}\n\n\ndef set_handler_to_trackbar(name):\n def handler(x):\n global ranges\n ranges[name][0] = x\n print(*[(x, ranges[x][0]) for x in ranges if ranges[x][0] != ranges[x][1]])\n return handler\n\n\ncv2.namedWindow('result')\n\nfor name, value in ranges.items():\n cv2.createTrackbar(name, 'result', 1 if name in ['matrix', 'tresh', 'iterations'] else 0, ranges[name][1], set_handler_to_trackbar(name))\n cv2.setTrackbarPos(name, 'result', value[0])\n\n\n\ndef getMask (hsv):\n mask1 = cv2.inRange(hsv, (ranges['min_h1'][0], ranges['min_s'][0], ranges['min_v'][0]), (ranges['max_h1'][0], ranges['max_s'][0], ranges['max_v'][0]))\n #mask2 = cv2.inRange(hsv, (ranges['min_h2'][0], ranges['min_s'][0], ranges['min_v'][0]), (ranges['max_h2'][0], ranges['max_s'][0], ranges['max_v'][0]))\n #return cv2.bitwise_or(mask1, mask2)\n return mask1\n\ncommand = None\n\n\ndef wheel():\n while True:\n #time.sleep(1)\n mouse = pyautogui.position()\n mouse_at_window = mouse[0] >= window[0] and mouse[0] <= window[2] and mouse[1] >= window[1] and mouse[1] <= window[3]\n\n if ranges['wheel'][0] == 1 and mouse_at_window:\n if 'l' == command:\n kbe.keyPress(kbe.SC_LEFT)\n kbe.keyPress(kbe.SC_DOWN, 0.1)\n elif 'l' == command:\n kbe.keyPress(kbe.SC_RIGHT)\n kbe.keyPress(kbe.SC_DOWN, 0.1)\n elif 'f' == command:\n kbe.keyPress(kbe.SC_UP, 0.1)\n\n kbe.keyPress(kbe.SC_UP, 0.1)\n\nControl = threading.Thread(target=wheel)\nControl.start()\n\nwhile True:\n\n pix = pyautogui.screenshot(region=(left, top, window_resolution[0], window_resolution[1]))\n numpix = cv2.cvtColor(np.array(pix), cv2.COLOR_RGB2BGR)\n\n hsv = cv2.cvtColor(numpix, cv2.COLOR_BGR2HSV)\n mask = getMask(hsv)\n\n result = cv2.bitwise_and(numpix, numpix, mask=mask)\n result_copy = imutils.resize(result.copy(), width=800, height=450)\n result_copy = cv2.cvtColor(result_copy, cv2.COLOR_BGR2GRAY)\n\n # Отрезаем нахер нижнюю половину контура — она только шумит и мешает:\n result_copy[result_copy.shape[0]//2:,:] = 0\n\n # Нет эффекта\n # result_copy = cv2.bilateralFilter(result_copy, 5, 175, 175)\n\n\n matrix = (ranges['matrix'][0], ranges['matrix'][0])\n\n # Избавляемся от мелких объектов.\n\n # Обратная функции erode: если есть белый пиксель, весь контур становится белым.\n # Аналогично не работает.\n result_copy = cv2.dilate(result_copy, matrix, iterations=ranges['iterations'][0])\n\n # Уменньшаем контуры белых объектов: если в рамках матрицы есть \"не белый\" пиксель, то все становится черным.\n # Не работает вообще. Тестировать.\n result_copy = cv2.erode(result_copy, matrix, iterations=ranges['iterations'][0])\n\n\n # BLUR\n # В районе 45-45 просто отличный результат:\n # result_copy = cv2.blur(result_copy, matrix)\n # около 9 оптимум, более не надо.\n ret, result_copy = cv2.threshold(result_copy, ranges['tresh'][0], 150, cv2.THRESH_BINARY)\n # ~BLUR\n\n\n # Дает обратный эффект, надо исследовать.\n # result_copy = cv2.adaptiveThreshold(result_copy, 255, cv2.ADAPTIVE_THRESH_GAUSSIAN_C, cv2.THRESH_BINARY, 11, 15)\n\n # Ищем контуры\n # Сами структуры контуров хранятся в начальном элементе возвращаемого значения\n # (Это на винде, на Linux может быть другой индекс):\n contours = cv2.findContours(result_copy, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)[0]\n\n # Их, кстати, может и не быть:\n if contours:\n # Сортируем по убыванию площади контура — хотим один самый большой:\n contours = sorted(contours, key=cv2.contourArea, reverse=True)\n\n # Третий аргумент — это индекс контура, который мы хотим вывести. Мы хотим самый большой.\n # Вывести все можно, передав -1 вместо 0:\n cv2.drawContours(result_copy, contours, 0, (255, ), 8)\n contour = contours[0]\n\n # Получаем прямоугольник, обрамляющий наш контур:\n (x, y, w, h) = cv2.boundingRect(contour)\n\n # И выводим его:\n cv2.rectangle(result_copy, (x, y), (x+w, y+h), (255, ), 1)\n\n\n start_y, start_x = result_copy.shape\n start_x //= 2\n\n\n # считаем моменты контура:\n M = cv2.moments(contour)\n\n # Центр тяжести это первый момент (сумма радиус-векторов), отнесенный к нулевому (площади-массе)\n if 0 != M['m00']:\n cx = int(M['m10'] / M['m00'])\n cy = int(M['m01'] / M['m00'])\n cv2.circle(result_copy, (cx, cy), 5, 255, 2)\n cv2.line(result_copy, (start_y, start_x), (cx, cy), (255, ), 2)\n tangen = start_x - cx\n normal = start_y - cy\n #print(start_x, cx, start_y, cy)\n if normal != 0:\n angle = np.degrees(np.arctan(tangen / normal))\n if angle < -5:\n # print('RIGHT: %d' % angle)\n command = 'r'\n elif angle > 5:\n # print('LEFT: %d' % angle)\n command = 'l'\n else:\n # print('forward')\n command = 'f'\n\n cv2.line(result_copy, (start_y, start_x), (start_y, y+h//2), (255, ), 2)\n\n\n cv2.imshow('mask', imutils.resize(mask, width=1600, height=900))\n\n cv2.imshow('h', imutils.resize(hsv[:,:,0], width=800, height=450))\n cv2.imshow('s', imutils.resize(hsv[:,:,1], width=800, height=450))\n cv2.imshow('v', imutils.resize(hsv[:,:,2], width=800, height=450))\n\n\n cv2.imshow('result', imutils.resize(result_copy, width=1600, height=900))\n if cv2.waitKey(1) == 27:\n break\n\ncv2.destroyAllWindows()","sub_path":"sources/part3.2-Need-4-Speed-Shift/pyautogui-nfs.py","file_name":"pyautogui-nfs.py","file_ext":"py","file_size_in_byte":8479,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"603040384","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\"\"\"\n# author : mazhe\n# Date : 2018/6/20 22:49\n# File : ftp4BackupFile.py\n# Desp : ftp程序用来备份话单到远端主机\n\"\"\"\n\nimport ftplib\nimport os\nimport time\nimport logging\nimport shutil\nimport random\nimport string\nfrom multiprocessing import Pool\nfrom datetime import datetime, timedelta\nimport ConfigParser\nimport sys\n\n\ndef ftp_login(user, passwd, ip, port):\n \"\"\"\n :param user: ftp用户名\n :param passwd: ftp密码\n :param ip: 对端ip地址\n :param port: 对端ftp端口，默认30021\n :return: 返回登录成功后ftp对象\n \"\"\"\n ftp = ftplib.FTP()\n ftp.set_debuglevel(0)\n while True:\n try:\n ftp.connect(ip, port, 300)\n ftp.login(user, passwd)\n break\n except Exception as errmsg:\n logger.error('Login error,relogin in 10s later,reason:' + str(errmsg))\n time.sleep(10) # 10s后重试\n return ftp\n\n\ndef ftp_get_remote_file(ftp, remote_path, size_mod_num=0, mod=0):\n \"\"\"\n :param ftp: ftpclass对象，由ftp_login返回\n :param remote_path: 远端下载目录\n :param size_mod_num: 文件大小取模数，用于多进程\n :param mod: 按size_mod_num取模后的值\n :return: 返回待传输文件列表，元素为文件详细信息\n \"\"\"\n file_list = [] # 初始化获取文件列表\n resul_tlist = []\n try:\n ftp.cwd(remote_path)\n ftp.dir('.', file_list.append)\n except Exception as errmsg:\n logger.error('Get file list error,reason:' + str(errmsg))\n return\n # 判断是否是文件，只传输文件，忽略文件夹,d开头为文件夹不下载\n for item in file_list:\n if item.startswith('d'):\n continue\n # 需要取模数和模值都不为null，只保留按size_mod_num取模后相应mod模值的文件\n if size_mod_num != 0:\n szie = ftp.size(item.split(' ')[-1])\n if (szie % size_mod_num) == mod:\n resul_tlist.append(item)\n else:\n resul_tlist.append(item)\n return resul_tlist\n\n\ndef ftp_get_local_file(local_path, size_mod_num=1, mod=0):\n \"\"\"\n :param local_path: 需要上传文件的目录\n :param size_mod_num: 文件大小取模数，默认值为1，即不分组\n :param mod: 按size_mod_num取模后数值\n :return: 待传输文件列表,绝对路径\n \"\"\"\n result_list = []\n file_list = os.listdir(local_path)\n logger.info('Get %s full file list done.' % (local_path, ))\n for item in file_list:\n file_path = os.path.join(local_path, item)\n # 只上传文件，localpath下的目录剔除\n if os.path.isfile(file_path):\n # 需要将文件列表按大小取模分组\n if size_mod_num != 1:\n size = os.path.getsize(file_path)\n if (size % size_mod_num) == int(mod):\n result_list.append(file_path)\n # 不需要将文件列表按大小取模分组\n else:\n result_list.append(file_path)\n logger.info('Get deal file list done.')\n return result_list\n\n\ndef ftp_download_file(ftp, file_list, local_path):\n \"\"\"\n :param ftp: ftp对象\n :param file_list: 文件列表，由ftp.dir()返回\n :param local_path: 本地下载目录\n :return: 无\n \"\"\"\n buffersize = 1024\n file_num = len(file_list)\n if file_num == 0:\n logger.info('This time has no file to deal,ftp quit .')\n return\n else:\n logger.info('This time download file number: %s.' % (file_num, ))\n # 检查临时目录\n local_tmp = os.path.join(local_path, 'tmp')\n if not os.path.exists(local_tmp):\n os.makedirs(local_tmp)\n if not os.path.exists(local_path):\n os.makedirs(local_path)\n for item in file_list:\n src_file = str(item.split(' ')[-1])\n # 开始下载\n try:\n tmp_dest_file = os.path.join(local_tmp, get_tmp_name(src_file))\n logger.info('Start download %s to %s' % (src_file, tmp_dest_file))\n filehandle = open(tmp_dest_file, 'wb').write\n ftp.retrbinary('RETR ' + src_file, filehandle, buffersize) # 下载文件到临时目录\n shutil.move(tmp_dest_file, local_path) # 移动到正式目录\n logger.info('Move tmpfile %s to %s succed' % (tmp_dest_file, local_path))\n ftp.delete(src_file)\n logger.info('Delete srcfile %s succed' % (src_file, ))\n logger.info('Download %s succed' % (src_file,))\n except Exception as errmsg:\n logger.error(\"Download %s fail,reason:%s\", (src_file, errmsg))\n continue\n ftp.quit()\n\n\ndef ftp_upload_file(user, passwd, ip, port, remote_path, local_path, size_mod_num, mod):\n \"\"\"\n :param user: ftp用户名\n :param passwd: ftp密码\n :param ip: 对端ip地址\n :param port: 对端ftp端口，默认30021\n :return: 返回登录成功后ftp对象\n :param remote_path: 上传的远端目录\n :param local_path: 本地待传输目录\n :param size_mod_num: 文件大小取模数\n :param mod: 取模后数值\n :return:无\n \"\"\"\n buffersize = 1024\n file_list = ftp_get_local_file(local_path, size_mod_num, mod)\n ftp = ftp_login(user, passwd, ip, port)\n file_num = len(file_list)\n if file_num == 0:\n logger.info('This time has no file to deal,ftp quit .')\n return\n else:\n logger.info('This time upload file number: %s.' % (file_num, ))\n remote_tmp = os.path.join(remote_path, 'tmp')\n done_num = 1\n for src_file in file_list:\n # 开始上传\n try:\n tmp_dest_file = os.path.join(remote_tmp, get_tmp_name(os.path.basename(src_file)))\n dest_file = os.path.join(remote_path, os.path.basename(src_file))\n logger.info('[%s/%s] Start upload %s to %s' % (done_num, file_num, src_file, tmp_dest_file))\n with open(src_file, 'rb') as filehandle:\n ftp.storbinary('STOR ' + tmp_dest_file, filehandle, buffersize)\n ftp.rename(tmp_dest_file, dest_file)\n logger.info('[%s/%s] Rename tmpfile %s to %s succed' % (done_num, file_num, tmp_dest_file, dest_file))\n os.remove(src_file)\n logger.info('[%s/%s] Delete srcfile %s succed' % (done_num, file_num, src_file))\n logger.info('[%s/%s] Upload %s succed' % (done_num, file_num, src_file))\n done_num += 1\n except Exception as errmsg:\n logger.error('[%s/%s] Upload %s fail,reason:%s' % (done_num, file_num, src_file, errmsg))\n done_num += 1\n continue\n ftp.quit()\n\n\ndef ftp_check_remote_dir(user, passwd, ip, port, remote_path):\n \"\"\"\n :param user: ftp用户名\n :param passwd: ftp密码\n :param ip: 远端ip\n :param port: ftp端口\n :param remote_path: 远端路径\n :return: NONE\n \"\"\"\n ftp = ftp_login(user, passwd, ip, port)\n # 检查上传临时目录及目标目录是否存在\n remote_tmp = os.path.join(remote_path, 'tmp')\n try:\n ftp.cwd(remote_path)\n except Exception as errmsg:\n logger.error('remote_path directory %s is not exists,create it.reason: %s' % (remote_path, errmsg))\n ftp.mkd(remote_path)\n try:\n ftp.cwd(remote_tmp)\n except Exception as errmsg:\n logger.error('remote_tmp directory %s is not exists,create it.reason: %s' % (remote_tmp, errmsg))\n ftp.mkd(remote_tmp)\n ftp.quit()\n\n\n\ndef get_tmp_name(name):\n \"\"\"\n :param name: 文件名\n :return: 返回增加随机8位英文字符的文件名\n \"\"\"\n # 8位随机英文字符包含大小写\n ran_seq = ''.join(random.sample(string.ascii_letters, 8))\n tmp_name = ran_seq + name\n return tmp_name\n\n\ndef check_local_dir(local_path):\n \"\"\"\n :param local_path: 检查目录\n :return: True目录无文件，False目录有文件\n \"\"\"\n file_list = os.listdir(local_path)\n file_num = len(file_list)\n if file_num == 0:\n logger.info('Local path %s is empty' % (local_path, ))\n return True\n else:\n logger.info('Local path still has %s files' % (file_num, ))\n return False\n\n# 开始上传文件\nif __name__ == '__main__':\n # 读取配置中配置项\n cfg = ConfigParser.ConfigParser()\n # 获取当前执行程序的父目录\n file_path = os.path.split(os.path.abspath(__file__))[0]\n cfg_path = os.path.join(file_path, 'config', 'config_ori_xdr.ini')\n cfg.read(cfg_path)\n deal_type = str(sys.argv[1])\n log_prefix = cfg.get('common', 'log_prefix')\n log_path = cfg.get('common', 'log_path')\n user = cfg.get('common', 'user')\n passwd = cfg.get('common', 'passwd')\n port = int(cfg.get('common', 'port'))\n process_num = int(cfg.get(deal_type, 'process_num'))\n remote_ip = cfg.get(deal_type, 'remote_ip')\n remote_path = cfg.get(deal_type, 'remote_path')\n local_path = cfg.get(deal_type, 'local_path')\n backup_day = cfg.get(deal_type, 'backup_day')\n # deal_day = (datetime.now() + timedelta(days=int(backup_day))).strftime('%Y%m%d')\n deal_day = '20180717'\n # local_path 与 deal_day 拼接为处理目录， remote_path 与 deal_day 拼接为远端目录\n local_path = os.path.join(local_path, deal_day)\n remote_path = os.path.join(remote_path, deal_day)\n # local_path = '/data01/bak/split/ggprs/bakfiles/20180703'\n # remote_path = '/data/ori_xdr_backup/ggprs/20180703'\n # log_name = '%s_%s_%s.log' % (log_prefix, deal_type, datetime.now().strftime('%Y%m%d'))\n log_name = '%s_%s_%s.log' % (log_prefix, deal_type, deal_day)\n # 设置日志文件输出\n log_name = os.path.join(log_path, log_name)\n logger = logging.getLogger(__name__)\n logger.setLevel(logging.INFO)\n formatter = logging.Formatter('%(asctime)s %(processName)s pid:%(process)s [%(levelname)s]: %(message)s')\n filehandle = logging.FileHandler(log_name)\n filehandle.setLevel(logging.INFO)\n filehandle.setFormatter(formatter)\n logger.addHandler(filehandle)\n\n # 检查远端目录\n ftp_check_remote_dir(user, passwd, remote_ip, port, remote_path)\n # 防止因报错导致部分文件没有上传，循环执行，当local_path不再有文件，程序退出\n while True:\n # 一次获取待处理文件列表，按照preocess_num 分组\n p = Pool(process_num)\n for i in range(process_num):\n p.apply_async(ftp_upload_file, args=(user, passwd, remote_ip, port, remote_path, local_path, process_num, i))\n p.close()\n p.join()\n if check_local_dir(local_path):\n break\n logger.info('Delete local path ' + local_path)\n os.rmdir(local_path)\n logger.info('Upload %s to %s is done.' % (local_path, remote_path))\n\n","sub_path":"ftp_get_file/ftp4BackupFile_orixdr.py","file_name":"ftp4BackupFile_orixdr.py","file_ext":"py","file_size_in_byte":10753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"217642672","text":"from smartninja_sql.sqlite import SQLiteDatabase\ndb = SQLiteDatabase(\"Student.sqlite\")\n\ndb.execute(\"CREATE TABLE IF NOT EXISTS Student (id integer primary key autoincrement, name text, grade text);\")\n\n#db.execute(\"INSERT INTO Student(name, grade) VALUES ('TEENA', 'A');\")\n#db.execute(\"INSERT INTO Student(name, grade) VALUES ('ETHAN', 'A');\")\n\n#result= db.execute(\"SELECT * FROM Student;\")\n#print(result)\n\n#db.execute(\"UPDATE Student SET grade='A+' WHERE id=2;\")\n\n#db.execute(\"ALTER TABLE Student ADD age text;\")\n\n#db.execute(\"UPDATE Student SET age=20 WHERE id=2;\")\n\ndb.execute(\"DROP TABLE Student;\")\n\ndb.pretty_print(\"SELECT * FROM Student;\")\n\ndb.print_tables(verbose=True)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"497277002","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# ## Implementación modelo Random Forest datos prueba\n\n# In[42]:\n\n\n# Importamos las librerías necesarias\nimport pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys\nsys.path.insert(1, '../src/')\nfrom modeling import *\n\n\n# In[2]:\n\n\n# Importamos las librerías necesarias\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import label_binarize\nfrom sklearn import tree\nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.metrics import mean_squared_error, r2_score\nfrom sklearn.ensemble import RandomForestRegressor\n\n\n# importamos la libreria pandas y matplotlib, numpy\nfrom itertools import cycle\n\n# modulo de preprocesamiento. Contiene utilidades para escalamiento, transformacion y limpieza de data\nfrom sklearn import preprocessing\n\n#Contiene utilizades que nos ayudan a seleccionar entre modelos.\nfrom sklearn.model_selection import train_test_split\n\n# importamos libreria scikit para el clasificador del random forest\nfrom sklearn.ensemble import RandomForestClassifier\n\n# Importar metricas de evaluacion \nfrom sklearn.metrics import confusion_matrix\nfrom sklearn.metrics import mean_squared_error, r2_score, accuracy_score\nfrom sklearn.metrics import roc_curve, auc, roc_auc_score, classification_report\n\n#importar una persistencia para nuestro modelo, para uso futuro\nfrom sklearn.externals import joblib\nfrom scipy import interp\nfrom collections import OrderedDict\n\n# seteamos el objeto random a 0\nnp.random.seed(0)\n\n\n# #### Hora de 12:00 a.m a 4:00 a.m\n\n# In[3]:\n\n\n#Aplicar el modelo a los datos de prueba y crear un vector con los resultados predichos y el vector de probabilidades\ny_predictH1 = (clf.predict(xTestH1))\ny_scoreH1 = clf.fit(xTrainH1, yTrainH1).predict_proba(xTestH1)\ny_scoreH1\n\n\n# In[4]:\n\n\n#Revisar precisión de clasificación con matrices de confusión\n# creamos una matriz de confusion\npd.crosstab(y_testH1, y_predictH1, rownames=['Actual'], colnames=['Predicted'])\n\n\n# In[5]:\n\n\nprint(confusion_matrix(y_testH1, y_predictH1))\n\n\n# In[6]:\n\n\ny_predictH1\n\n\n# In[7]:\n\n\n# Revisar medidas de error del modelo\n\nprint(\"R^2: \" + str(round(r2_score(yTestH1, y_predictH1),3)))\nprint(\"MSE: \" + str(round(mean_squared_error(yTestH1, y_predictH1),3)))\nprint(\"Accuracy: \" + str(round(accuracy_score(yTestH1, y_predictH1),3)))\n\n\n# #### Resultado de la prueba\n# \n# El ajuste del modelo de con los datos de prueba para la variable \"Hora de 12:00 a.m a 4:00 a.m\" muestra que el puntaje de exactitud es perfecto y que no se produjeron errores en la predicción.\n\n# #### Hora de 4:00 a.m a 8:00 a.m\n\n# In[8]:\n\n\n#Aplicar el modelo a los datos de prueba y crear un vector con los resultados predichos y el vector de probabilidades\ny_predictH2 = (clf.predict(xTestH2))\ny_scoreH2 = clf.fit(xTrainH2, yTrainH2).predict_proba(xTestH2)\ny_scoreH2\n\n\n# In[9]:\n\n\n#Revisar precisión de clasificación con matrices de confusión\n# creamos una matriz de confusion\npd.crosstab(y_testH2, y_predictH2, rownames=['Actual'], colnames=['Predicted'])\n\n\n# In[10]:\n\n\nprint(confusion_matrix(y_testH2, y_predictH2))\n\n\n# In[11]:\n\n\ny_predictH2\n\n\n# In[12]:\n\n\n# Revisar medidas de error del modelo\nprint(\"R^2: \" + str(round(r2_score(yTestH2, y_predictH2),3)))\nprint(\"MSE: \" + str(round(mean_squared_error(yTestH2, y_predictH2),3)))\nprint(\"Accuracy: \" + str(round(accuracy_score(yTestH2, y_predictH2),3)))\n\n\n# #### Resultado de la prueba\n# \n# El ajuste del modelo de con los datos de prueba para la variable \"Hora de 4:00 a.m a 8:00 a.m\" muestra que el puntaje de exactitud es perfecto y que no se produjeron errores en la predicción.\n\n# #### Hora de 8:00 a.m a 12:00 p.m\n\n# In[13]:\n\n\n#Aplicar el modelo a los datos de prueba y crear un vector con los resultados predichos y el vector de probabilidades\ny_predictH3 = (clf.predict(xTestH3))\ny_scoreH3 = clf.fit(xTrainH3, yTrainH3).predict_proba(xTestH3)\ny_scoreH3\n\n\n# In[14]:\n\n\n#Revisar precisión de clasificación con matrices de confusión\n# creamos una matriz de confusion\npd.crosstab(y_testH3, y_predictH3, rownames=['Actual'], colnames=['Predicted'])\n\n\n# In[15]:\n\n\nprint(confusion_matrix(y_testH3, y_predictH3))\n\n\n# In[16]:\n\n\ny_predictH3\n\n\n# In[17]:\n\n\n# Revisar medidas de error del modelo\n# Imprime medidas de error del modelo\n\nprint(\"R^2: \" + str(round(r2_score(yTestH3, y_predictH3),3)))\nprint(\"MSE: \" + str(round(mean_squared_error(yTestH3, y_predictH3),3)))\nprint(\"Accuracy: \" + str(round(accuracy_score(yTestH3, y_predictH3),3)))\n\n\n# #### Resultado de la prueba\n# \n# El ajuste del modelo de con los datos de prueba para la variable \"Hora de 8:00 a.m a 12:00 p.m\" muestra que el puntaje de exactitud es perfecto y que no se produjeron errores en la predicción.\n\n# #### Hora de 12:00 p.m a 04:00 p.m\n\n# In[18]:\n\n\n#Aplicar el modelo a los datos de prueba y crear un vector con los resultados predichos y el vector de probabilidades\ny_predictH4 = (clf.predict(xTestH4))\ny_scoreH4 = clf.fit(xTrainH4, yTrainH4).predict_proba(xTestH4)\ny_scoreH4\n\n\n# In[19]:\n\n\n#Revisar precisión de clasificación con matrices de confusión\n# creamos una matriz de confusion\npd.crosstab(y_testH4, y_predictH4, rownames=['Actual'], colnames=['Predicted'])\n\n\n# In[20]:\n\n\nprint(confusion_matrix(y_testH4, y_predictH4))\n\n\n# In[21]:\n\n\ny_predictH4\n\n\n# In[22]:\n\n\n# Revisar medidas de error del modelo\n# Imprime medidas de error del modelo\n\nprint(\"R^2: \" + str(round(r2_score(yTestH4, y_predictH4),3)))\nprint(\"MSE: \" + str(round(mean_squared_error(yTestH4, y_predictH4),3)))\nprint(\"Accuracy: \" + str(round(accuracy_score(yTestH4, y_predictH4),3)))\n\n\n# #### Resultado de la prueba\n# \n# El ajuste del modelo de con los datos de prueba para la variable \"Hora de 12:00 p.m a 4:00 p.m\" muestra que el puntaje de exactitud es perfecto y que no se produjeron errores en la predicción.\n\n# #### Hora de 04:00 p.m a 08:00 p.m\n\n# In[23]:\n\n\n#Aplicar el modelo a los datos de prueba y crear un vector con los resultados predichos y el vector de probabilidades\ny_predictH5 = (clf.predict(xTestH5))\ny_scoreH5 = clf.fit(xTrainH5, yTrainH5).predict_proba(xTestH5)\ny_scoreH5\n\n\n# In[24]:\n\n\n#Revisar precisión de clasificación con matrices de confusión\n# creamos una matriz de confusion\npd.crosstab(y_testH5, y_predictH5, rownames=['Actual'], colnames=['Predicted'])\n\n\n# In[25]:\n\n\nprint(confusion_matrix(y_testH5, y_predictH5))\n\n\n# In[26]:\n\n\ny_predictH5\n\n\n# In[27]:\n\n\n# Revisar medidas de error del modelo\n# Imprime medidas de error del modelo\n\nprint(\"R^2: \" + str(round(r2_score(yTestH5, y_predictH5),3)))\nprint(\"MSE: \" + str(round(mean_squared_error(yTestH5, y_predictH5),3)))\nprint(\"Accuracy: \" + str(round(accuracy_score(yTestH5, y_predictH5),3)))\n\n\n# #### Resultado de la prueba\n# \n# El ajuste del modelo de con los datos de prueba para la variable \"Hora de 04:00 pa.m a 8:00 p.m\" muestra que el puntaje de exactitud es perfecto y que no se produjeron errores en la predicción.\n\n# #### Hora de 08:00 p.m a 12:00 a.m\n\n# In[28]:\n\n\n#Aplicar el modelo a los datos de prueba y crear un vector con los resultados predichos y el vector de probabilidades\ny_predictH6 = (clf.predict(xTestH6))\ny_scoreH6 = clf.fit(xTrainH6, yTrainH6).predict_proba(xTestH6)\ny_scoreH6\n\n\n# In[29]:\n\n\n#Revisar precisión de clasificación con matrices de confusión\n# creamos una matriz de confusion\npd.crosstab(y_testH6, y_predictH6, rownames=['Actual'], colnames=['Predicted'])\n\n\n# In[30]:\n\n\nprint(confusion_matrix(y_testH6, y_predictH6))\n\n\n# In[31]:\n\n\ny_predictH6\n\n\n# In[32]:\n\n\n# Revisar medidas de error del modelo\n\nprint(\"R^2: \" + str(round(r2_score(yTestH6, y_predictH6),3)))\nprint(\"MSE: \" + str(round(mean_squared_error(yTestH6, y_predictH6),3)))\nprint(\"Accuracy: \" + str(round(accuracy_score(yTestH6, y_predictH6),3)))\n\n\n# #### Resultado de la prueba\n# \n# El ajuste del modelo de con los datos de prueba para la variable \"Hora de 8:00 p.m a 12:00 p.m\" muestra que el puntaje de exactitud es perfecto y que no se produjeron errores en la predicción.\n\n# ## Implementación modelo Random Forest para la regresión datos prueba\n\n# #### Hora de 12:00 a.m a 4:00 a.m\n\n# In[33]:\n\n\n# Buscamos los errores en el modelo a través de la forma de error cuadrático\ndf2Regresion[\"rforest_error_12_4am\"] =(df2Regresion[\"rforest_pred_12_4\"] - df2Regresion[\"Hora_De 12:00 a.m a 4:00 a.m\"])**2\nprint(\"Indice de error:\", str(sum(df2Regresion[\"rforest_error_12_4am\"])/len(df2)))\n# oob_score: similar al coeficiente de determinación o R cuadrado usasdo en la regresión lineal\nprint('Coeficiente de determinación: ', str(forest.oob_score_))\n\n\n# #### Hora de 4:00 a.m a 8:00 a.m\n\n# In[34]:\n\n\n# Buscamos los errores en el modelo a través de la forma de error cuadrático\ndf2Regresion[\"rforest_error_4_8am\"] =(df2Regresion[\"rforest_pred_4_8\"] - df2Regresion[\"Hora_De 4:00 a.m a 8:00 a.m\"])**2\nprint(\"Indice de error:\", str(sum(df2Regresion[\"rforest_error_4_8am\"])/len(df2Regresion)))\n# oob_score: similar al coeficiente de determinación o R cuadrado usasdo en la regresión lineal\nprint('Coeficiente de determinación: ', str(forest.oob_score_))\n\n\n# #### Hora de 8:00 a.m a 12:00 p.m\n\n# In[35]:\n\n\n# Buscamos los errores en el modelo a través de la forma de error cuadrático\ndf2Regresion[\"rforest_error_8_12pm\"] =(df2Regresion[\"rforest_pred_8_12\"] - df2Regresion[\"Hora_De 8:00 a.m a 12:00 p.m\"])**2\nprint(\"Indice de error:\", str(sum(df2Regresion[\"rforest_error_8_12pm\"])/len(df2Regresion)))\n# oob_score: similar al coeficiente de determinación o R cuadrado usasdo en la regresión lineal\nprint('Coeficiente de determinación: ', str(forest.oob_score_))\n\n\n# #### Hora de 12:00 p.m a 04:00 p.m\n\n# In[36]:\n\n\n# Buscamos los errores en el modelo a través de la forma de error cuadrático\ndf2Regresion[\"rforest_error_12_4pm\"] =(df2Regresion[\"rforest_pred_12_4\"] - df2Regresion[\"Hora_De 12:00 p.m a 04:00 p.m\"])**2\nprint(\"Indice de error:\", str(sum(df2Regresion[\"rforest_error_12_4pm\"])/len(df2Regresion)))\n# oob_score: similar al coeficiente de determinación o R cuadrado usasdo en la regresión lineal\nprint('Coeficiente de determinación: ', str(forest.oob_score_))\n\n\n# #### Hora de 04:00 p.m a 08:00 p.m\n\n# In[37]:\n\n\n# Buscamos los errores en el modelo a través de la forma de error cuadrático\ndf2Regresion[\"rforest_error_4_8pm\"] =(df2Regresion[\"rforest_pred_4_8\"] - df2Regresion[\"Hora_De 04:00 p.m a 08:00 p.m\"])**2\nprint(\"Indice de error:\", str(sum(df2Regresion[\"rforest_error_4_8pm\"])/len(df2Regresion)))\n# oob_score: similar al coeficiente de determinación o R cuadrado usasdo en la regresión lineal\nprint('Coeficiente de determinación: ', str(forest.oob_score_))\n\n\n# #### Hora de 08:00 p.m a 12:00 a.m\n\n# In[38]:\n\n\n# Buscamos los errores en el modelo a través de la forma de error cuadrático\ndf2Regresion[\"rforest_error_8_12am\"] =(df2Regresion[\"rforest_pred_8_12\"] - df2Regresion[\"Hora_De 08:00 p.m a 12:00 a.m\"])**2\nprint(\"Indice de error:\", str(sum(df2Regresion[\"rforest_error_8_12am\"])/len(df2Regresion)))\n# oob_score: similar al coeficiente de determinación o R cuadrado usasdo en la regresión lineal\nprint('Coeficiente de determinación: ', str(forest.oob_score_))\n\n\n# In[39]:\n\n\nhora_error = {'Hora':['12am - 4am', '4am - 8am', '8am - 12pm', '12pm - 4pm', '4pm - 8pm', '8pm - 12am'], \n 'Indice_Error': [sum(df2Regresion[\"rforest_error_12_4am\"])/len(df2Regresion), \n sum(df2Regresion[\"rforest_error_4_8am\"])/len(df2Regresion),\n sum(df2Regresion[\"rforest_error_8_12pm\"])/len(df2Regresion),\n sum(df2Regresion[\"rforest_error_12_4pm\"])/len(df2Regresion),\n sum(df2Regresion[\"rforest_error_4_8pm\"])/len(df2Regresion),\n sum(df2Regresion[\"rforest_error_8_12am\"])/len(df2Regresion)]}\nresult = pd.DataFrame(hora_error)\nresult\n\n\n# In[40]:\n\n\nplt.barh(result.Hora, result.Indice_Error, align='center', alpha=0.5)\nplt.xlabel(\"Indice de Error\")\nplt.title('Resultado errores en entrenamiento de modelo hurto por horas')\n\nplt.show()\n\n\n# #### Conclusión general del entrenamiento con Random Forest con regresión\n# El modelo ha sido entrenado con los diferentes rangos de hora y el algoritmo de regresión con RandomForest. En la prueba se tomaron 1000 árboles para el entrenamiento con 2 esfuerzos simultáneos. Este procedimiento redujo el índice de error para todos los rangos de hora siendo el mayor el de 8 a 4pm, pero todos por debajo del 35%. En el caso de 8pm a 12am no se encontraron errores en el entrenamiento. \n","sub_path":"MINERÍA DE DATOS/Proyecto/Entregable/Código Fuente/notebook/evaluation.py","file_name":"evaluation.py","file_ext":"py","file_size_in_byte":12480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"285448684","text":"# PCA : 선형\n# 오토인코더 (autoencoder) : 비선형\n# a11 복붙 --> CNN으로 변경해서 완성하시오\nfrom tensorflow.keras.datasets import mnist\nfrom tensorflow.keras.models import Sequential\nfrom tensorflow.keras.layers import Dense, Conv2D, Conv2DTranspose, UpSampling2D\nimport numpy as np\n\ndef autoencoder(hidden_layer_size):\n \n model = Sequential()\n\n model.add(Conv2D(filters = 256, kernel_size = (3, 3), padding = 'valid', \n input_shape= (28, 28, 1), activation = 'relu'))\n\n model.add(Conv2D(filters = 128, kernel_size = (3, 3), padding = 'valid', activation = 'relu'))\n\n # model.add(UpSampling2D(size = (2, 2)))\n\n model.add(Conv2D(filters = hidden_layer_size, kernel_size = (3, 3), padding = 'valid', activation = 'relu'))\n\n model.add(Conv2DTranspose(filters = 128, kernel_size = (3, 3), padding = 'valid', activation = 'relu'))\n \n model.add(Conv2DTranspose(filters = 256, kernel_size = (3, 3), padding = 'valid', activation = 'relu'))\n\n model.add(Conv2DTranspose(filters = 1, kernel_size = (3, 3), padding = 'valid', activation = 'sigmoid'))\n \n model.summary()\n\n\n return model\n\n\n\ntrain_set, test_set = mnist.load_data()\n\nx_train, y_train = train_set\nx_test, y_test = test_set\n\nprint(x_train.shape[0]) #60000\nprint(x_train.shape[1]) #28\nprint(x_train.shape[2]) #28\n\n\n\n# reshape : 차원을 바꿔주는 것 / shape 숫자의 곱이 항상 같아야한다. \n# 사과 10개는 어떻게 나열하든 총 갯수는 10개이다.\nx_train = x_train.reshape((x_train.shape[0], x_train.shape[1], x_train.shape[2], 1)) #60000 28 28 1\nx_test = x_test.reshape((x_test.shape[0], x_test.shape[1], x_test.shape[2], 1)) #60000 28 28 1\n\nx_train = x_train/255.0\nx_test = x_test/255.0\n\n# noise 추가\n\nx_train_noised = x_train + np.random.normal(0, 0.1, size=x_train.shape)\nx_test_noised = x_test + np.random.normal(0, 0.1, size=x_test.shape)\n# x_train_noised = x_train + np.random.normal(0, 0.5, size=x_train.shape)\n# x_test_noised = x_test + np.random.normal(0, 0.5, size=x_test.shape)\n\nx_train_noised = np.clip(x_train_noised, a_min=0, a_max=1)\nx_test_noised = np.clip(x_test_noised, a_min=0, a_max=1)\n\n\nmodel = autoencoder(hidden_layer_size = 154)\n# model = autoencoder(hidden_layer_size = 32)\n\n# model.compile(optimize = 'adam', loss = 'mse', metrics = ['acc'])\n\n\nmodel.compile(optimize = 'adam', loss = 'binary_crossentropy', metrics = ['acc'])\n\n\nmodel.fit(x_train_noised, x_train, epochs = 20, batch_size = 128) \n# model.fit(x_train_noised, x_train_noised, epochs = 10) # <-- 노이즈 제거 훈련 부분\n\noutput = model.predict(x_test)\n\nfrom matplotlib import pyplot as plt\nimport random\n\nfig, ((ax1, ax2, ax3, ax4, ax5), (ax6, ax7, ax8, ax9,ax10),\n (ax11, ax12, ax13, ax14, ax15)) = \\\n plt.subplots(3, 5, figsize=(20, 7))\n\n# 이미지 다섯 개를 무작위로 고른다\nrandom_images = random.sample(range(output.shape[0]), 5)\n\n# 원본(입력) 이미지를 맨 위에 그린다 / 잡음 없다.\nfor i, ax in enumerate([ax1, ax2, ax3, ax4, ax5]):\n ax.imshow(x_test[random_images[i]].reshape(28, 28), cmap = 'gray')\n # ax.imshow(x_test[random_images[i]].reshape(28, 28), cmap = 'gray')\n if i ==0:\n ax.set_ylabel('INPUT', size = 40)\n ax.grid(False)\n ax.set_xticks([])\n ax.set_yticks([])\n\n# 잡음 있다.\nfor i, ax in enumerate([ax6, ax7, ax8, ax9, ax10]):\n ax.imshow(x_test_noised[random_images[i]].reshape(28, 28), cmap = 'gray')\n # ax.imshow(x_test[random_images[i]].reshape(28, 28), cmap = 'gray')\n if i ==0:\n ax.set_ylabel('NOISE', size = 40)\n ax.grid(False)\n ax.set_xticks([])\n ax.set_yticks([])\n\n# 오토인코더가 출력한 이미지를 마지막에 그린다.\nfor i, ax in enumerate([ax11, ax12, ax13, ax14, ax15]):\n ax.imshow(output[random_images[i]].reshape(28, 28), cmap = 'gray')\n if i ==0:\n ax.set_ylabel('OUTPUT', size = 40)\n ax.grid(False)\n ax.set_xticks([])\n ax.set_yticks([])\n \nplt.show()\n","sub_path":"AE/12_noise2_mnist_cnn.py","file_name":"12_noise2_mnist_cnn.py","file_ext":"py","file_size_in_byte":3972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"523663738","text":"import unittest\nimport string\nimport random\nimport urllib\nfrom main import *\nfrom StringIO import StringIO\nfrom google.appengine.api import memcache\nfrom google.appengine.ext import db\nfrom google.appengine.ext import testbed\nfrom google.appengine.ext import webapp\nfrom django.utils import simplejson as json\nimport logging\n\ndef random_string_generator(size=10, chars=string.ascii_uppercase + string.ascii_lowercase + string.digits):\n return ''.join(random.choice(chars) for x in range(size))\n\nclass APIABHandlerTestCase(unittest.TestCase):\n def setUp(self):\n self.testbed = testbed.Testbed()\n self.testbed.activate()\n self.testbed.init_datastore_v3_stub()\n\n self.abBuildQuestionA = ABBuildQuestion(\n page_id = 0,\n num_visits = 10,\n num_success = 10\n )\n self.abBuildQuestionA.put()\n\n self.abBuildQuestionB = ABBuildQuestion(\n page_id = 1,\n num_visits = 15,\n num_success = 15\n )\n self.abBuildQuestionB.put()\n\n\n def tearDown(self):\n self.testbed.deactivate()\n\n def testGetABBuildQuestionWithPageIDFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"GET\",\n \"PATH_INFO\": \"/api/ABTest/\"+ str(self.abBuildQuestionB.page_id)\n })\n response = webapp.Response()\n handler = APIABHandler()\n handler.initialize(request, response)\n handler.get(str(self.abBuildQuestionB.page_id))\n output = json.loads(response.out.getvalue())\n self.assertEqual(self.abBuildQuestionB.num_visits,output['num_visits'])\n\n def testPostABBuildQuestionWithPageIDFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/ABTest/\"+ str(self.abBuildQuestionB.page_id)\n })\n request.query_string = json.dumps(self.abBuildQuestionB.to_dict())\n response = webapp.Response()\n handler = APIABHandler()\n handler.initialize(request, response)\n handler.post(str(self.abBuildQuestionB.page_id))\n abBuildQuestion = ABBuildQuestion.all().filter('page_id', 1).get()\n self.assertEqual(abBuildQuestion.page_id, 1)\n\n def testPostABBuildQuestionWithPageIDNotFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/ABTest/\"+ str(3)\n })\n request.query_string = json.dumps(self.abBuildQuestionB.to_dict())\n response = webapp.Response()\n handler = APIABHandler()\n handler.initialize(request, response)\n handler.post(str(3))\n abBuildQuestion = ABBuildQuestion.all().filter('page_id', 3).get()\n self.assertEqual(abBuildQuestion.page_id, 3)\n\n\nclass APIMultiplayerHandlerTestCase(unittest.TestCase):\n def setUp(self):\n self.testbed = testbed.Testbed()\n self.testbed.activate()\n self.testbed.init_datastore_v3_stub()\n\n multiplayerGame = MultiplayerGame(\n player_one_fb_id = random_string_generator(),\n player_two_fb_id = random_string_generator(),\n player_one_cards = [random_string_generator()],\n player_two_cards = [random_string_generator()],\n player_one_time = random.uniform(0.1,2),\n player_two_time = random.uniform(0.1,2),\n )\n multiplayerGame.put()\n self.multiplayerGame_dict = multiplayerGame.to_dict()\n self.multiplayerGame_id = self.multiplayerGame_dict['id']\n\n def tearDown(self):\n self.testbed.deactivate()\n\n def testGetMultiplayerGameWithGameIDFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"GET\",\n \"PATH_INFO\": \"/api/multiplayergame/\"+ str(self.multiplayerGame_id)\n })\n response = webapp.Response()\n handler = APIMultiplayerHandler()\n handler.initialize(request, response)\n handler.get(str(self.multiplayerGame_id))\n output = json.loads(response.out.getvalue())\n self.assertEqual(self.multiplayerGame_dict['id'],output['id'])\n\n def testPostMultiplayerGameWithNoGameID(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/multiplayergame/\"\n })\n request.query_string = json.dumps(self.multiplayerGame_dict)\n response = webapp.Response()\n handler = APIMultiplayerHandler()\n handler.initialize(request, response)\n handler.post()\n output = json.loads(response.out.getvalue())\n self.assertNotEqual(output['id'],self.multiplayerGame_id)\n\n def testPostMultiplayerGameWithWithGameIDFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/multiplayergame/\"+ str(self.multiplayerGame_id)\n })\n request.query_string = json.dumps(self.multiplayerGame_dict)\n response = webapp.Response()\n handler = APIMultiplayerHandler()\n handler.initialize(request, response)\n handler.post(str(self.multiplayerGame_id))\n output = json.loads(response.out.getvalue())\n self.assertEqual(output['id'],self.multiplayerGame_id)\n\nclass APIReferralHandlerTestCase(unittest.TestCase):\n def setUp(self):\n self.testbed = testbed.Testbed()\n self.testbed.activate()\n self.testbed.init_datastore_v3_stub()\n\n self.email_addresses = ['jia.luo.2010@sis.smu.edu.sg','xy.peh.2010@sis.smu.edu.sg',\n 'junkit.lee.2008@economics.smu.edu.sg','junhao.peh.2010@sis.smu.edu.sg']\n self.referral_codes = [random_string_generator(),random_string_generator(),random_string_generator(),random_string_generator()]\n\n i = 0\n for email_address in self.email_addresses:\n referral = Referral(\n email_address = email_address,\n referral_code = self.referral_codes[i]\n )\n referral.put()\n i+=1\n\n def tearDown(self):\n self.testbed.deactivate()\n\n def testGetReferralCodeWithReferralCodeFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"GET\",\n \"PATH_INFO\": \"/api/referrals/\"+ self.referral_codes[1] \n })\n response = webapp.Response()\n handler = APIReferralHandler()\n handler.initialize(request, response)\n handler.get(self.referral_codes[1])\n output = json.loads(response.out.getvalue())\n\n self.assertEqual(self.email_addresses[1],output['email_address'])\n\n def testGetReferralWithReferralCodeNotFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"GET\",\n \"PATH_INFO\": \"/api/referrals/abcd\"\n })\n response = webapp.Response()\n handler = APIReferralHandler()\n handler.initialize(request, response)\n handler.get(\"abcd\")\n output = json.loads(response.out.getvalue())\n\n self.assertEqual(output, {'error': 'abcd does not exist.'})\n\n def testGetReferralWithEmailAddressFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"GET\",\n \"PATH_INFO\": \"/api/referrals/\"\n })\n request.query_string = 'email_address=jia.luo.2010@sis.smu.edu.sg'\n response = webapp.Response()\n handler = APIReferralHandler()\n handler.initialize(request, response)\n handler.get()\n output = json.loads(response.out.getvalue())\n\n self.assertEqual(self.referral_codes[0],output['referral_code'])\n\n def testGetReferralWithEmailAddressNotFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"GET\",\n \"PATH_INFO\": \"/api/referrals/\"\n })\n request.query_string = 'email_address=haha@gmail.com'\n response = webapp.Response()\n handler = APIReferralHandler()\n handler.initialize(request, response)\n handler.get()\n output = json.loads(response.out.getvalue())\n\n self.assertEqual(output, {'error': 'haha@gmail.com does not exist.'})\n\n def testGetReferralWithNoParameter(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"GET\",\n \"PATH_INFO\": \"/api/referrals/\"\n })\n response = webapp.Response()\n handler = APIReferralHandler()\n handler.initialize(request, response)\n handler.get()\n output = json.loads(response.out.getvalue())\n\n value = False\n for out in output:\n if self.referral_codes[1] == out['referral_code']:\n value = True\n self.assertTrue(value)\n\n def testPostReferralWithEmailAddressNotFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/referrals/haha@gmail.com\"\n })\n response = webapp.Response()\n handler = APIReferralHandler()\n handler.initialize(request, response)\n handler.post(\"haha@gmail.com\")\n output = json.loads(response.out.getvalue())\n\n value = False\n if \"haha@gmail.com\" == output['email_address']:\n value = True\n self.assertTrue(value)\n\n def testPostReferralWithEmailAddressFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/referrals/\"+self.email_addresses[1]\n })\n response = webapp.Response()\n handler = APIReferralHandler()\n handler.initialize(request, response)\n handler.post(self.email_addresses[1])\n output = json.loads(response.out.getvalue())\n\n self.assertEqual(output, {'error': self.email_addresses[1] + ' already exists.'})\n\nclass APIReferralIncrementHandlerTestCase(unittest.TestCase):\n def setUp(self):\n self.testbed = testbed.Testbed()\n self.testbed.activate()\n self.testbed.init_datastore_v3_stub()\n\n self.email_addresses = ['jia.luo.2010@sis.smu.edu.sg','xy.peh.2010@sis.smu.edu.sg',\n 'junkit.lee.2008@economics.smu.edu.sg','junhao.peh.2010@sis.smu.edu.sg']\n self.referral_codes = [random_string_generator(),random_string_generator(),random_string_generator(),random_string_generator()]\n self.num_referralsList = [random.randint(0,100),random.randint(0,100),random.randint(0,100),random.randint(0,100)]\n\n i = 0\n for referral_code in self.referral_codes:\n referral = Referral(\n referral_code = referral_code,\n conversions_list = [self.email_addresses[i]],\n num_referrals = self.num_referralsList[i]\n )\n referral.put()\n i+=1\n\n def tearDown(self):\n self.testbed.deactivate()\n\n def testGetIncrementalReferralWithReferralCodeFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"GET\",\n \"PATH_INFO\": \"/api/referrals/\"+self.referral_codes[1]+\"/increment\"\n })\n response = webapp.Response()\n handler = APIReferralIncrementHandler()\n handler.initialize(request, response)\n handler.get(self.referral_codes[1])\n output = json.loads(response.out.getvalue())\n self.assertEqual(self.num_referralsList[1]+1, output[\"num_referrals\"])\n\n def testGetIncrementalReferralWithReferralCodeNotFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"GET\",\n \"PATH_INFO\": \"/api/referrals/abcd/increment\"\n })\n response = webapp.Response()\n handler = APIReferralIncrementHandler()\n handler.initialize(request, response)\n handler.get(\"abcd\")\n output = json.loads(response.out.getvalue())\n self.assertEqual(output, {'error': 'abcd does not exist.'})\n\n def testPostIncrementalReferralWithReferralCodeNotFoundNewEmailAdd(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/referrals/\"+self.referral_codes[1]+\"/increment\"\n })\n request.query_string = 'new_email_address=haha@gmail.com'\n response = webapp.Response()\n handler = APIReferralIncrementHandler()\n handler.initialize(request, response)\n handler.post(self.referral_codes[1])\n output = json.loads(response.out.getvalue())\n self.assertTrue(\"haha@gmail.com\" in output[\"conversions_list\"])\n\n def testPostIncrementalReferralWithReferralCodeNotFoundExistingEmailAdd(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/referrals/\"+self.referral_codes[1]+\"/increment\"\n })\n request.query_string = 'new_email_address=xy.peh.2010@sis.smu.edu.sg'\n response = webapp.Response()\n handler = APIReferralIncrementHandler()\n handler.initialize(request, response)\n handler.post(self.referral_codes[1])\n output = json.loads(response.out.getvalue())\n self.assertEqual(output,{'error': 'This new_email_address has already been converted by this user.'})\n \n def testPostIncrementalReferralWithReferralCodeNotFoundNoEmailAdd(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/referrals/\"+self.referral_codes[1]+\"/increment\"\n })\n #request.query_string = 'new_email_address=haha@gmail.com'\n response = webapp.Response()\n handler = APIReferralIncrementHandler()\n handler.initialize(request, response)\n handler.post(self.referral_codes[1])\n output = json.loads(response.out.getvalue())\n self.assertEqual(output,{'error': 'new_email_address POST data does not exist.'})\n\n def testPostIncrementalReferralWithReferralCodeNotFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/referrals/\"+self.referral_codes[1]+\"/increment\"\n })\n response = webapp.Response()\n handler = APIReferralIncrementHandler()\n handler.initialize(request, response)\n handler.post(\"abcd\")\n output = json.loads(response.out.getvalue())\n self.assertEqual(output,{'error': 'abcd does not exist.'})\n\nclass APICardHandlerTestCase(unittest.TestCase):\n def setUp(self):\n self.testbed = testbed.Testbed()\n self.testbed.activate()\n self.testbed.init_datastore_v3_stub()\n\n topics = ['Demand and Supply', 'Monetary Economics', 'International Economics']\n levels = range(1,4) # 1,2,3\n\n # insert mock questions into the database\n for topic in topics:\n for level in levels:\n for num in range(5):\n card = Card(\n level = int(level),\n topic = topic,\n limit = random.randint(5,10),\n question_name = random_string_generator(),\n question_body = random_string_generator(),\n mcq_answers = [random_string_generator() for x in range(4)],\n question_type = 'mcq',\n num_correct_attempts = 0,\n num_wrong_attempts = 0\n )\n card.put()\n card_dict = card.to_dict()\n self.the_id = card_dict['id']\n\n def tearDown(self):\n self.testbed.deactivate()\n\n def testGetCardReturnedWithNoParameter(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"GET\",\n \"PATH_INFO\": \"/api/cards\"\n })\n request.query_string = 'level=3&topic=Demand and Supply&limit=10'\n response = webapp.Response()\n handler = APICardHandler()\n handler.initialize(request, response)\n handler.get()\n output = json.loads(response.out.getvalue())\n\n topic_set = set()\n for card in output:\n topic_set.add(card['topic'])\n\n reference_set = set()\n reference_set.add(u'Demand and Supply')\n\n self.assertEqual(topic_set, reference_set)\n\n def testGetCardReturnedWithCardIdFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"GET\",\n \"PATH_INFO\": \"/api/cards\"\n })\n response = webapp.Response()\n handler = APICardHandler()\n handler.initialize(request, response)\n handler.get(self.the_id)\n output = json.loads(response.out.getvalue())\n self.assertEqual(self.the_id, output['id'])\n\n def testPostCardsWithCardIdFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/cards/1\"\n })\n request.query_string = '{\"num_players_owned\":3, \"graph_data\": \"None\", \"paypal_code\": \"X6B44SQ9WXJP2\", \"level\": 1, \"question_name\": \"Law of Demand\", \"question_body\": \"The law of demand indicates that as price falls,\", \"id\":' + str(self.the_id) + ', \"topic\": \"Demand and Supply\", \"num_wrong_attempts\": 6, \"question_type\": \"mcq\", \"num_correct_attempts\": 10, \"mcq_answers\": [\"the quantity demanded rises.\", \"demand rises.\", \"demand falls.\", \"the quantity demanded falls. \"], \"special_color\": \"Gold\", \"graph_answer\": \"None\"}'\n response = webapp.Response()\n handler = APICardHandler()\n handler.initialize(request, response)\n handler.post()\n output = json.loads(response.out.getvalue())\n self.assertEqual(self.the_id, output['id'])\n\n\nclass APIPlayerHandlerTestCase(unittest.TestCase):\n def setUp(self):\n self.testbed = testbed.Testbed()\n self.testbed.activate()\n self.testbed.init_datastore_v3_stub()\n\n # set up a random facebook id\n #self.facebook_id = random_string_generator()\n self.facebook_ids = [random_string_generator(),random_string_generator(),random_string_generator(),random_string_generator()]\n for facebook_id in self.facebook_ids:\n player = Player(\n facebook_id = facebook_id,\n wins = random.randint(0,100),\n losses = random.randint(0,100),\n )\n player.put()\n\n # insert a player object into datastore\n\n def tearDown(self):\n self.testbed.deactivate()\n\n def testGetPlayerEntityWithFacebookIdFound(self):\n # check that the GET def returns correct json out\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"GET\",\n \"PATH_INFO\": \"/api/player/\" + self.facebook_ids[1]\n })\n response = webapp.Response()\n handler = APIPlayerHandler()\n handler.initialize(request, response)\n handler.get(self.facebook_ids[1])\n\n # check response object for the correct json\n output = json.loads(response.out.getvalue())\n self.assertTrue(output['facebook_id'],self.facebook_ids[1])\n\n def testPostPlayerEntityWithFacebookIdFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/player/\" + self.facebook_ids[1]\n })\n \n request.query_string='{\"wins\": 5, \"losses\": 2, \"cards_owned\": []}'\n response = webapp.Response()\n handler = APIPlayerHandler()\n handler.initialize(request, response)\n handler.post(self.facebook_ids[1])\n\n # now check that there is this Player object in the datastore\n output = json.loads(response.out.getvalue())\n self.assertEqual(output,{'success':1})\n\n def testPostPlayerEntityWithFacebookIdNotFound(self):\n request = webapp.Request({\n \"wsgi.input\": StringIO(),\n \"CONTENT_LENGTH\": 0,\n \"METHOD\": \"POST\",\n \"PATH_INFO\": \"/api/player/abcd\"\n })\n response = webapp.Response()\n handler = APIPlayerHandler()\n handler.initialize(request, response)\n handler.post(\"abcd\")\n\n # now check that there is this Player object in the datastore\n #player = Player.all().filter('facebook_id', 'abcd').get()\n #self.assertEqual(player.facebook_id, 'abcd')\n output = json.loads(response.out.getvalue())\n self.assertEqual(output,{'success':1})\n\n \n\n","sub_path":"GAEtest/unit_tests.py","file_name":"unit_tests.py","file_ext":"py","file_size_in_byte":21590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"148495079","text":"# -*- encoding:utf-8 -*-\nfrom django.core.urlresolvers import reverse_lazy\nfrom django.utils.translation import ugettext_lazy as _\n\nfrom horizon import tables\nfrom horizon import forms\n\nfrom openstack_dashboard.dashboards.drillingsystem.overview import tables as project_tables\nfrom openstack_dashboard.dashboards.drillingsystem.overview import forms as project_forms\nfrom openstack_dashboard.dashboards.drillingsystem.overview import test_service\nfrom django.utils.translation import ugettext_lazy as _\nfrom openstack_dashboard import api\n\nimport logging\n\nLOG = logging.getLogger(__name__)\n\nclass TableData(object):\n def __init__(self, **kwargs):\n self.id = id(self)\n self.name = kwargs['name']\n self.num = kwargs['num']\n\nclass IndexView(tables.DataTableView):\n table_class = project_tables.DataSummaryTable\n template_name = 'drillingsystem/overview/index.html'\n\n def get_data(self):\n try:\n instances, self._more = api.nova.server_list(\n self.request)\n tenant_id = self.request.user.tenant_id\n networks = api.neutron.network_list_for_tenant(\n self.request, tenant_id, include_external=True)\n except Exception:\n self._more = False\n instances = []\n exceptions.handle(self.request,\n _('Unable to retrieve instances.'))\n\n instance_num=0\n router_num=0\n network_num=len(networks)\n\n # instance_typeで判定\n for i in instances:\n try:\n if i.metadata['instance_type'] == 'instance':\n instance_num = instance_num + 1\n else:\n router_num = router_num + 1\n except KeyError:\n pass\n\n _list = []\n _list.append(TableData(name=_('instance'),num=str(instance_num) ))\n _list.append(TableData(name=_('router'),num=str(router_num) ))\n _list.append(TableData(name=_('network'),num=str(network_num) ))\n # return test_service.TestService().list()\n return _list\n\nclass CreateView(forms.ModalFormView):\n form_class = project_forms.CreateForm\n template_name = 'drillingsystem/overview/create.html'\n success_url = reverse_lazy(\"horizon:drillingsystem:overview:index\")\n\nclass DeleteView(forms.ModalFormView):\n form_class = project_forms.DeleteForm\n template_name = 'drillingsystem/overview/delete.html'\n success_url = reverse_lazy(\"horizon:drillingsystem:overview:index\")\n\n def get_context_data(self, **kwargs):\n LOG.info(\"[oxxo] get_context_data\")\n context = super(DeleteView, self).get_context_data(**kwargs)\n context['data_id'] = self.kwargs['data_id']\n\n return context\n\n def get_initial(self):\n LOG.info(\"[oxxo] get_initial\")\n return {'data_id': self.kwargs['data_id']}\n","sub_path":"dashboards/drillingsystem/overview/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"594496431","text":"# -*- coding: utf-8 -*-\n\nimport pytest\nfrom hamcrest import assert_that, equal_to_ignoring_whitespace, is_\n\nfrom hermes.interventions import InterventionsApi\n\n\nclass TestNegativeDeleteInterventions(object):\n\n @pytest.mark.parametrize('intrv_id', [0, 'qwerty', 100500])\n def test_negative_delete_intervention(self, logged_client, intrv_id):\n code, result = InterventionsApi.delete_intervention(intrv_id)\n assert_that(code, is_(404),\n 'Expected that code is \"%s\", but was \"%s\".' % (404, code))\n msg = 'Intervention: %s do not exist' % intrv_id\n assert_that(result['error'], equal_to_ignoring_whitespace(msg),\n 'Expected that error is \"%s\", but was \"%s\".' % (\n msg, result['error']\n ))\n","sub_path":"hermes_api_tests/tests/a-interventions/test_negative_intervention_delete.py","file_name":"test_negative_intervention_delete.py","file_ext":"py","file_size_in_byte":797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"274709202","text":"\"\"\"\n ------ Input file ------\n No. of defender strategies (X)\n No. of attackers (L)\n | Probability for an attacker (p_l)\n | No. of attack strategies for an attacker (Q_l)\n | [\n | Matrix ( X * Q_l) with\n | values r, c\n | ]\n | where r,c are rewards for defender and attacker respectively\n\"\"\"\n\nimport gurobipy\nimport sys\nimport numpy as np\nfrom datetime import datetime\nfrom scipy.special import softmax\nimport random\n\nrandom.seed(a=2021, version=2)\n\n\n\nD = [0, 0] # delta\nTarget_num = 7\nAttacker_type = 2\n\nconst_P = [0.186] * Target_num\n# const_E = [10,10,2,7,4,3,2] # expectation of # of future false positive alerts\nconst_C = [-1,-1,-1,-1,-1,-1,-1] # loss for each quit of a normal user\n\n\n# def bounded_rationality_epsilon(B_now, expected_prob_catch, type, num_alerts_count, attacker_type, E):\ndef bounded_rationality_epsilon_qr(prob_being_caught_list, attacker_type, num_alerts_count, E, lambda_p):\n\n # expected_prob_catch = [0.1, 0.3, 0.05, 0.03, 0.07, 0.1, 0.1]\n\n try:\n # Create a new model\n model = gurobipy.Model(\"MIQP\")\n\n # if len(sys.argv) < 3:\n file = open(str(sys.argv[1]), \"r\")\n\n # # Add resource allocation\n # b_list = []\n # con = gurobipy.LinExpr()\n # for i in range(Target_num):\n # b_list.append(model.addVar(lb=0, ub=B_now, vtype=gurobipy.GRB.CONTINUOUS, name='B_now_t'+str(i)))\n # con.add(b_list[i])\n # model.addConstr(con <= B_now)\n # model.update()\n\n # Add defender strategies (p0 p1 q0 q1)\n # num_defender_strategy = Target_num * 4\n x = []\n for i in range(Target_num):\n name_ = ['p0_t', 'q0_t', 'p1_t', 'q1_t']\n con = gurobipy.LinExpr()\n x_inside = []\n for j in range(len(name_)):\n x_inside.append(model.addVar(lb=0, ub=1, vtype=gurobipy.GRB.CONTINUOUS, name=name_[j]+str(i)))\n con.add(x_inside[-1])\n x.append(x_inside)\n model.addConstr(con == 1)\n model.update()\n con = gurobipy.LinExpr()\n con.add(x_inside[0])\n con.add(x_inside[2])\n con.add(-1 * prob_being_caught_list[i]) ## ********\n model.addConstr(con == 0) ## ********\n model.update()\n\n # # Add defender strategy constraints\n # for i in range(len(x)):\n # con = gurobipy.LinExpr()\n # for j in range(len(x[0])):\n # con.add(x[i][j])\n # model.addConstr(con == 1)\n # con = gurobipy.LinExpr()\n # con.add(x[i][0])\n # con.add(x[i][2])\n # model.addConstr(con == expected_prob_catch)\n # model.update()\n\n \"\"\" Start processing for attacker types \"\"\"\n\n obj = gurobipy.QuadExpr()\n M = 100000000\n obj_update_ind = True\n\n R_a = []\n R_d = []\n\n for l in range(Attacker_type):\n\n # Probability of l-th attacker\n theta = float(file.readline().strip())\n\n # Add A^l\n A_l = model.addVar(lb=-gurobipy.GRB.INFINITY, ub=gurobipy.GRB.INFINITY, vtype=gurobipy.GRB.CONTINUOUS, name='A_l' + str(l))\n\n # Add gamma_l\n gamma_l = model.addVar(lb=-gurobipy.GRB.INFINITY, ub=gurobipy.GRB.INFINITY, vtype=gurobipy.GRB.CONTINUOUS, name='gamma_l' + str(l))\n\n model.update()\n\n # build obj\n obj.add(theta * gamma_l)\n\n\n # Add l-th attacker info to the model\n num_attacher_strategy = int(file.readline())\n z_l_list = []\n y_l_list = []\n cve_names = file.readline().strip().split(\"|\")\n con_z = gurobipy.LinExpr()\n con_y = gurobipy.LinExpr()\n for i in range(num_attacher_strategy):\n n = \"z_l\" + str(l) + \"_\" + cve_names[i]\n z_l_list.append(model.addVar(lb=0, ub=1, vtype=gurobipy.GRB.INTEGER, name=n))\n con_z.add(z_l_list[-1])\n n = \"y_l\" + str(l) + \"_\" + cve_names[i]\n y_l_list.append(model.addVar(lb=0, ub=1, vtype=gurobipy.GRB.INTEGER, name=n))\n con_y.add(y_l_list[-1])\n con_z_y = gurobipy.LinExpr()\n con_z_y.add(z_l_list[-1])\n con_z_y.add(-1*y_l_list[-1])\n model.addConstr(con_z_y <= 0)\n model.update()\n model.addConstr(con_z == 1)\n model.update()\n model.addConstr(con_y >= 1)\n model.update()\n\n # Get reward for attacker and defender\n # the first subvector corresponds to utility of attacker not being caught; the second subvector corresponds to utility of attacker being caught\n R_l_d = []\n R_l_a = []\n for i in range(2): # 1st:\n rewards = file.readline().split()\n r = []\n c = []\n for j in range(Target_num):\n r_and_c = rewards[j].split(\",\")\n r.append(float(r_and_c[0]))\n c.append(float(r_and_c[1]))\n R_l_d.append(r)\n R_l_a.append(c)\n\n R_a.append(R_l_a)\n R_d.append(R_l_d)\n\n for target in range(Target_num):\n # p_1^t U_ac^tl + q_1^t U_au^tl <= delta\n con_force = gurobipy.LinExpr()\n con_force.add(R_l_a[1][target] * x[target][2])\n con_force.add(R_l_a[0][target] * x[target][3])\n con_force.add(-D[l])\n model.addConstr(con_force <= 0)\n model.update()\n\n # A^l - (p_0^t U_ac^tl + q_0^t U_au^tl ) >= 0\n con_A_ll = gurobipy.LinExpr()\n con_A_ll.add(A_l)\n con_A_ll.add(-1 * R_l_a[1][target] * x[target][0])\n con_A_ll.add(-1 * R_l_a[0][target] * x[target][1])\n model.addConstr(con_A_ll >= 0)\n model.update()\n\n # A^l - (p_0^t U_ac^tl + q_0^t U_au^tl ) <= (1 - z_t^l) M\n con_A_lr = gurobipy.LinExpr()\n con_A_lr.add(A_l)\n con_A_lr.add(-1 * R_l_a[1][target] * x[target][0])\n con_A_lr.add(-1 * R_l_a[0][target] * x[target][1])\n con_A_lr.add(-1 * M)\n con_A_lr.add(M * z_l_list[target])\n model.addConstr(con_A_lr <= 0)\n model.update()\n\n # A^l - (p_0^t U_ac^tl + q_0^t U_au^tl ) >= \\epsilon (1-y_t^l)\n con_A_lly = gurobipy.LinExpr()\n con_A_lly.add(A_l)\n con_A_lly.add(-1 * R_l_a[1][target] * x[target][0])\n con_A_lly.add(-1 * R_l_a[0][target] * x[target][1])\n con_A_lly.add(-1 * E[l])\n con_A_lly.add(E[l] * y_l_list[target])\n model.addConstr(con_A_lly >= 0)\n model.update()\n\n # A^l - (p_0^t U_ac^tl + q_0^t U_au^tl ) <= \\epsilon + (1 - y_t^l) M\n con_A_lry = gurobipy.LinExpr()\n con_A_lry.add(A_l)\n con_A_lry.add(-1 * R_l_a[1][target] * x[target][0])\n con_A_lry.add(-1 * R_l_a[0][target] * x[target][1])\n con_A_lry.add(-1 * M)\n con_A_lry.add(-1 * E[l])\n con_A_lry.add(M * y_l_list[target])\n model.addConstr(con_A_lry <= 0)\n model.update()\n\n # M (1 - y_t^l) + p_0^t U_dc^tl + q_0^t U_du^tl >= gamma^l\n con_M = gurobipy.LinExpr()\n con_M.add(M)\n con_M.add(-M * y_l_list[target])\n con_M.add(R_l_d[1][target] * x[target][0])\n con_M.add(R_l_d[0][target] * x[target][1])\n con_M.add(-1 * gamma_l)\n model.addConstr(con_M >= 0)\n model.update()\n\n # # M (1 - z_t^l) + p_0^t U_dc^tl + q_0^t U_du^tl >= gamma^l\n # con_M = gurobipy.LinExpr()\n # con_M.add(M)\n # con_M.add(-M * z_l_list[target])\n # con_M.add(R_l_d[1][target] * x[target][0])\n # con_M.add(R_l_d[0][target] * x[target][1])\n # con_M.add(-1 * gamma_l)\n # model.addConstr(con_M >= 0)\n # model.update()\n\n\n # build obj\n if obj_update_ind == True:\n obj.add(x[target][2] * const_P[target] * num_alerts_count[target] * const_C[target])\n obj.add(x[target][3] * const_P[target] * num_alerts_count[target] * const_C[target])\n\n obj_update_ind = False\n\n # Set objective funcion as all attackers have now been considered\n model.setObjective(obj, gurobipy.GRB.MAXIMIZE)\n # model.setParam(gurobipy.GRB.Param.Seed, 100)\n model.Params.OutputFlag = 0\n model.setParam('IntFeasTol', 1e-9)\n\n model.update()\n\n start = datetime.now()\n\n # Solve MIQP\n model.Params.LogToConsole = 0\n model.optimize()\n\n run_time = (datetime.now() - start).total_seconds()\n\n # print(\"\\nRuning time: \", (datetime.now() - start))\n\n solution = dict()\n for v in model.getVars():\n # print(\"%s -> %g\" % (v.varName, v.x))\n solution[v.varName] = v.x\n\n except gurobipy.GurobiError:\n print(\"Error reported\")\n\n z_list = [[solution['z_l0_T0'], solution['z_l0_T1'], solution['z_l0_T2'], solution['z_l0_T3'], solution['z_l0_T4'],\n solution['z_l0_T5'], solution['z_l0_T6']],\n [solution['z_l1_T0'], solution['z_l1_T1'], solution['z_l1_T2'], solution['z_l1_T3'], solution['z_l1_T4'],\n solution['z_l1_T5'], solution['z_l1_T6']]]\n\n z_list = np.round(z_list).tolist()\n\n y_list = [[solution['y_l0_T0'], solution['y_l0_T1'], solution['y_l0_T2'], solution['y_l0_T3'], solution['y_l0_T4'],\n solution['y_l0_T5'], solution['y_l0_T6']],\n [solution['y_l1_T0'], solution['y_l1_T1'], solution['y_l1_T2'], solution['y_l1_T3'], solution['y_l1_T4'],\n solution['y_l1_T5'], solution['y_l1_T6']]]\n\n y_list = np.round(y_list)\n if np.sum(y_list) > 2:\n a = 1\n\n\n name = ['p0_t', 'q0_t', 'p1_t', 'q1_t']\n\n auditor_util = []\n attacker_util = []\n for type in range(Target_num):\n practice_obj_left = solution[name[0] + str(type)] * R_d[attacker_type][1][type] + solution[name[1] + str(type)] * R_d[attacker_type][0][type]\n practice_obj_right = 0\n for ele in range(Target_num):\n practice_obj_right += (solution[name[2] + str(ele)] + solution[name[3] + str(ele)]) * const_P[ele] * num_alerts_count[ele] * const_C[ele]\n practice_obj = practice_obj_left + practice_obj_right\n auditor_util.append(practice_obj)\n\n attacker_util_ele = solution[name[0] + str(type)] * R_a[attacker_type][1][type] + solution[name[1] + str(type)] * R_a[attacker_type][0][type]\n attacker_util.append(attacker_util_ele)\n\n e_attacker_util = softmax(np.array(attacker_util) * lambda_p)\n type = int(random.choices(list(np.linspace(0, Target_num - 1, Target_num)), e_attacker_util)[0])\n practice_obj = auditor_util[type]\n\n\n type_within_epsilon_types = False\n usefulness = True\n\n if attacker_type == 0:\n if 1.0 in z_list[0]:\n opt_target = z_list[0].index(1.0)\n epsilon_range = len(np.where(y_list[0] == 1.0)[0])\n if type in np.where(y_list[0] == 1.0)[0]:\n type_within_epsilon_types = True\n else:\n usefulness = False\n opt_target = -1\n else:\n if 1.0 in z_list[1]:\n opt_target = z_list[1].index(1.0)\n epsilon_range = len(np.where(y_list[1] == 1.0)[0])\n if type in np.where(y_list[1] == 1.0)[0]:\n type_within_epsilon_types = True\n else:\n usefulness = False\n opt_target = -1\n\n type_match_opt_type = False\n if opt_target == type:\n type_match_opt_type = True\n\n if epsilon_range <= 6:\n a = 1\n\n return solution, z_list, epsilon_range, type_within_epsilon_types, model.objVal, practice_obj, opt_target, type_match_opt_type, run_time, type, usefulness","sub_path":"bounded_rationality_M1_controled_eval_qr.py","file_name":"bounded_rationality_M1_controled_eval_qr.py","file_ext":"py","file_size_in_byte":12214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"74965629","text":"#!/usr/bin/env python\n\"\"\"\nThe MIT License (MIT)\n\nCopyright (c) 2015-2017 Dave Parsons\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the 'Software'), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in\nall copies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN\nTHE SOFTWARE.\n\"\"\"\n\nfrom __future__ import division, print_function\n\nimport os\nimport shutil\nimport sys\nimport tarfile\nimport time\nimport zipfile\nfrom contextlib import closing\n\ntry:\n # For Python 3.0 and later\n # noinspection PyCompatibility\n from urllib.request import urlopen, urlretrieve\n # noinspection PyCompatibility\n from html.parser import HTMLParser\nexcept ImportError:\n # Fall back to Python 2\n # noinspection PyCompatibility\n from urllib2 import urlopen\n # noinspection PyCompatibility\n from HTMLParser import HTMLParser\n # noinspection PyCompatibility\n from urllib import urlretrieve\n\n\n# Parse the Fusion directory page\nclass CDSParser(HTMLParser):\n def __init__(self):\n HTMLParser.__init__(self)\n self.reset()\n self.HTMLDATA = []\n\n def handle_data(self, data):\n # Build a list of numeric data from any element\n if data.find(\"\\n\") == -1:\n if data[0].isdigit():\n self.HTMLDATA.append(data)\n self.HTMLDATA.sort(key=lambda s: [int(u) for u in s.split('.')])\n\n def clean(self):\n self.HTMLDATA = []\n\n\ndef convertpath(path):\n # OS path separator replacement funciton\n return path.replace(os.path.sep, '/')\n\n\ndef download(url, filename, reporthook=None):\n with closing(urlopen(url)) as fp:\n headers = fp.info()\n with open(filename, 'wb') as tfp:\n bs = 1024 * 8\n size = -1\n read = 0\n blocknum = 0\n if \"content-length\" in headers:\n size = int(headers[\"Content-Length\"])\n\n if reporthook:\n reporthook(blocknum, bs, size)\n\n while True:\n block = fp.read(bs)\n if not block:\n break\n read += len(block)\n tfp.write(block)\n blocknum += 1\n if reporthook:\n reporthook(blocknum, bs, size)\n\n\ndef report_hook(blocks_read, block_size, total_byte):\n global start_time\n if blocks_read == 0:\n start_time = time.time()\n return\n duration = time.time() - start_time\n progress_size = int(blocks_read * block_size)\n speed = progress_size / (1024 * 1024 * duration) if duration > 0 else 0\n percent = min(int(blocks_read * block_size * 100 / total_byte), 100)\n time_remaining = (total_byte - progress_size) / (1024 * 1024 * speed) if speed > 0 else 0\n time_remaining = time_remaining if time_remaining > 0 else 0\n progress_bar = '=' * int(percent / 5) + ('>' if percent < 100 else '')\n progress_str = '{:>4}% [{:20}] {:,.2f} MB {:,.2f} MB/s ETA {:.0f} seconds'.format(\n percent, progress_bar, progress_size / (1024 * 1024), speed, time_remaining)\n sys.stdout.write('{:80}\\r'.format(progress_str))\n sys.stdout.flush()\n\n\ndef main():\n # Check minimal Python version is 2.7\n if sys.version_info < (2, 7):\n sys.stderr.write('You need Python 2.7 or later\\n')\n sys.exit(1)\n\n # Setup url and file paths\n url = 'https://softwareupdate.vmware.com/cds/vmw-desktop/fusion/'\n dest = os.path.dirname(os.path.abspath(__file__))\n\n # Re-create the tools folder\n shutil.rmtree(dest + '/tools', True)\n os.mkdir(dest + '/tools')\n\n # Get the list of Fusion releases\n # And get the last item in the ul/li tags\n response = urlopen(url)\n html = response.read()\n parser = CDSParser()\n parser.feed(str(html))\n url = url + parser.HTMLDATA[-1] + '/'\n parser.clean()\n\n # Open the latest release page\n # And build file URL\n response = urlopen(url)\n html = response.read()\n parser.feed(str(html))\n last_version = parser.HTMLDATA[-1]\n parser.clean()\n urlpost15 = url + last_version + '/packages/com.vmware.fusion.tools.darwin.zip.tar'\n urlpre15 = url + last_version + '/packages/com.vmware.fusion.tools.darwinPre15.zip.tar'\n\n # Download the darwin.iso tgz file\n print('Retrieving Darwin tools from: ' + urlpost15)\n try:\n # Try to get tools from packages folder\n download(urlpost15, convertpath(dest + '/tools/com.vmware.fusion.tools.darwin.zip.tar'), report_hook)\n except:\n url_fusion_app = url + last_version + '/core/com.vmware.fusion.zip.tar'\n\n # Get the fusion app file\n print('Tools aren\\'t found. Please wait while downloading from another source.')\n try:\n print('Retrieving Fusion App from: ' + url_fusion_app)\n download(url_fusion_app, convertpath(dest + '/tools/com.vmware.fusion.zip.tar'), report_hook)\n except:\n print('Couldn\\'t find tools')\n return\n\n print()\n\n print('Extracting com.vmware.fusion.zip.tar...')\n tar = tarfile.open(convertpath(dest + '/tools/com.vmware.fusion.zip.tar'), 'r')\n tar.extract('com.vmware.fusion.zip', path=convertpath(dest + '/tools/'))\n tar.close()\n\n # Extract the iso files from zip\n print('Extracting files from com.vmware.fusion.zip...')\n cdszip = zipfile.ZipFile(convertpath(dest + '/tools/com.vmware.fusion.zip'), 'r')\n cdszip.extract('payload/VMware Fusion.app/Contents/Library/isoimages/darwin.iso',\n path=convertpath(dest + '/tools/'))\n cdszip.extract('payload/VMware Fusion.app/Contents/Library/isoimages/darwinPre15.iso',\n path=convertpath(dest + '/tools/'))\n cdszip.close()\n\n # Move the iso and sig files to tools folder\n shutil.move(convertpath(dest + '/tools/payload/VMware Fusion.app/Contents/Library/isoimages/darwin.iso'),\n convertpath(dest + '/tools/darwin.iso'))\n shutil.move(convertpath(dest + '/tools/payload/VMware Fusion.app/Contents/Library/isoimages/darwinPre15.iso'),\n convertpath(dest + '/tools/darwinPre15.iso'))\n\n # Cleanup working files and folders\n shutil.rmtree(convertpath(dest + '/tools/payload'), True)\n os.remove(convertpath(dest + '/tools/com.vmware.fusion.zip.tar'))\n os.remove(convertpath(dest + '/tools/com.vmware.fusion.zip'))\n\n print('Tools retrieved successfully')\n return\n\n # Tools have been found, go with the normal way\n\n # Extract the tar to zip\n tar = tarfile.open(convertpath(dest + '/tools/com.vmware.fusion.tools.darwin.zip.tar'), 'r')\n tar.extract('com.vmware.fusion.tools.darwin.zip', path=convertpath(dest + '/tools/'))\n tar.close()\n\n # Extract the iso and sig files from zip\n cdszip = zipfile.ZipFile(convertpath(dest + '/tools/com.vmware.fusion.tools.darwin.zip'), 'r')\n cdszip.extract('payload/darwin.iso', path=convertpath(dest + '/tools/'))\n cdszip.extract('payload/darwin.iso.sig', path=convertpath(dest + '/tools/'))\n cdszip.close()\n\n # Move the iso and sig files to tools folder\n shutil.move(convertpath(dest + '/tools/payload/darwin.iso'), convertpath(dest + '/tools/darwin.iso'))\n shutil.move(convertpath(dest + '/tools/payload/darwin.iso.sig'), convertpath(dest + '/tools/darwin.iso.sig'))\n\n # Cleanup working files and folders\n shutil.rmtree(convertpath(dest + '/tools/payload'), True)\n os.remove(convertpath(dest + '/tools/com.vmware.fusion.tools.darwin.zip.tar'))\n os.remove(convertpath(dest + '/tools/com.vmware.fusion.tools.darwin.zip'))\n\n # Download the darwinPre15.iso tgz file\n print('Retrieving DarwinPre15 tools from: ' + urlpre15)\n urlretrieve(urlpre15, convertpath(dest + '/tools/com.vmware.fusion.tools.darwinPre15.zip.tar'))\n\n # Extract the tar to zip\n tar = tarfile.open(convertpath(dest + '/tools/com.vmware.fusion.tools.darwinPre15.zip.tar'), 'r')\n tar.extract('com.vmware.fusion.tools.darwinPre15.zip', path=convertpath(dest + '/tools/'))\n tar.close()\n\n # Extract the iso and sig files from zip\n cdszip = zipfile.ZipFile(convertpath(dest + '/tools/com.vmware.fusion.tools.darwinPre15.zip'), 'r')\n cdszip.extract('payload/darwinPre15.iso', path=convertpath(dest + '/tools/'))\n cdszip.extract('payload/darwinPre15.iso.sig', path=convertpath(dest + '/tools/'))\n cdszip.close()\n\n # Move the iso and sig files to tools folder\n shutil.move(convertpath(dest + '/tools/payload/darwinPre15.iso'),\n convertpath(dest + '/tools/darwinPre15.iso'))\n shutil.move(convertpath(dest + '/tools/payload/darwinPre15.iso.sig'),\n convertpath(dest + '/tools/darwinPre15.iso.sig'))\n\n # Cleanup working files and folders\n shutil.rmtree(convertpath(dest + '/tools/payload'), True)\n os.remove(convertpath(dest + '/tools/com.vmware.fusion.tools.darwinPre15.zip.tar'))\n os.remove(convertpath(dest + '/tools/com.vmware.fusion.tools.darwinPre15.zip'))\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"gettools.py","file_name":"gettools.py","file_ext":"py","file_size_in_byte":9828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"458625141","text":"\n\n#calss header\nclass _PRIME():\n\tdef __init__(self,): \n\t\tself.name = \"PRIME\"\n\t\tself.definitions = [u'to tell someone something that will prepare them for a particular situation: ', u'to cover the surface of wood with a special paint before the main paint is put on', u'to make a bomb or gun ready to explode or fire']\n\n\t\tself.parents = []\n\t\tself.childen = []\n\t\tself.properties = []\n\t\tself.jsondata = {}\n\n\n\t\tself.specie = 'verbs'\n\n\tdef run(self, obj1 = [], obj2 = []):\n\t\treturn self.jsondata\n","sub_path":"xai/brain/wordbase/verbs/_prime.py","file_name":"_prime.py","file_ext":"py","file_size_in_byte":491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"417261483","text":"# string_art.py\n# Byrne A. Hollander\n# September 25 2013\n# course: CS 1, Fall 2013, Prof. Cormen. \n# assignment: Short Assignment 4\n# Program that allows you to draw \"string art\".\n\nfrom cs1lib import *\n\n# Request name from user.\nname = str(raw_input(\"Enter your name: \"))\n\n# Function to make the background black\ndef make_background_black():\n set_clear_color (0, 0, 0)\n clear()\n \n# Function to draw the background, sticks, and strings.\ndef draw_everything(x1a, y1a, x2a, y2a, x1b, y1b, x2b, y2b, number_of_strings):\n make_background_black()\n enable_smoothing() # enable smoothing for the graphics\n enable_stroke() # enable stroke\n set_stroke_width(3) # set stroke to 3 for the sticks\n set_stroke_color(1, 0, 0) # set stroke color as red for the sticks\n draw_line(x1a, y1a, x2a, y2a) # draw stick A\n draw_line(x1b, y1b, x2b, y2b) # draw stick B\n set_stroke_width(1) # set stroke for strings\n \n f = 0 # \"f\" helps keep track of number of strings still needed to draw. 'f' is the state variable\n \n while f <= 1: # while-loop that draws strings until the parameter is fulfilled\n set_stroke_color(f * 0.8, f, 1-f) # stroke color changes depending on what string it is\n draw_line(x1a + f * (x2a - x1a), y1a + f * (y2a - y1a), x1b + (1.0 - f) * (x2b - x1b), y1b + (1.0 - f) * (y2b - y1b))\n f = f + (int(number_of_strings) - 1)**(-1) # the string counter increases by 1/(number of strings requested - 1) every time a string is drawn\n \n set_font_size(15)\n set_stroke_color(1, 1, 1) # black\n draw_text(str(name), 15, 385) # sign the artist's name in black once the strings are done being drawn\n \ndef main(): # function so you can actually enter the 9 parameters and run graphics\n draw_everything(25, 50, 50, 200, 350, 180, 200, 350, 25)\n\nstart_graphics(main, str(name) + \"'s String Art\") # open the graphics window, start graphics, and title the window properly","sub_path":"cs1/Short Assignments/SA4/string_art.py","file_name":"string_art.py","file_ext":"py","file_size_in_byte":1942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"580178448","text":"# Python3.4以上环境\n# Requests模块\n# lxml模块\n\n# 1. 分析网页\n\n# 面向对象 --》 集成封装\n\nimport requests\nfrom lxml import etree\n\n\nclass Spider(object):\n def __init__(self):\n self.url = \"http://www.mmjpg.com/\"\n self.headers = {\n \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; WOW64) AppleWebKit/537.36 \"\n \"(KHTML, like Gecko) Chrome/70.0.3538.110 Safari/537.36\",\n \"Referer\": \"http://www.mmjpg.com/\"\n }\n\n def start_requests(self):\n # 2. 获取网页整体数据 Requests\n for i in range(1, 104):\n print(\"正在爬取%d页.....!!!!\" % i)\n if i == 1:\n response = requests.get(self.url)\n html = etree.HTML(response.content.decode())\n self.xpath_data(html)\n else:\n response = requests.get(self.url + \"/home/\" + str(i))\n html = etree.HTML(response.content.decode())\n self.xpath_data(html)\n\n def xpath_data(self, html):\n # 3. 抽取我们想要的数据 lxml\n src_list = html.xpath('//div[@class=\"pic\"]/ul/li/a/img/@src')\n alt_list = html.xpath('//div[@class=\"pic\"]/ul/li/a/img/@alt')\n for src, alt in zip(src_list, alt_list):\n response = requests.get(src, headers=self.headers)\n file_name = alt + \".jpg\"\n print(\"正在抓取\" + file_name)\n # 4. 保存数据 jpg,png\n try:\n with open(file_name, \"wb\") as f:\n f.write(response.content)\n except:\n print(\"保存图片失败！....\")\n\n\nspider = Spider()\nspider.start_requests()\n","sub_path":"tupian.py","file_name":"tupian.py","file_ext":"py","file_size_in_byte":1690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"42526169","text":"from django.contrib.gis.geos import Point\nfrom django.contrib.gis.measure import Distance\nfrom rest_framework import viewsets\nfrom rest_framework.response import Response\nfrom rest_framework.views import APIView\n\nfrom .serializers import RouteSerializer, GetSimilarRoutesSerializer\nfrom .models import Route\nfrom users.models import ServiceUser\nimport datetime\nfrom django.db.models import Q\n\nfrom medic_bot_site.settings import VARS\n\n\nclass RouteViewSet(viewsets.ModelViewSet):\n \"\"\"Endpoint for viewing and creating user routes\"\"\"\n\n queryset = Route.objects.all()\n serializer_class = RouteSerializer\n\n def perform_create(self, serializer):\n \"\"\"Method for creating routes. Takes a user's telegram-id\n to create a one-to-many relationship\"\"\"\n telegram_id = serializer.validated_data['user']\n user = ServiceUser.objects.get(telegram_id=telegram_id)\n serializer.save(user=user)\n\n\nclass GetRoutes(APIView):\n \"\"\"Endpoint to get similar routes\"\"\"\n\n def get_time(self, date_and_time):\n \"\"\"method to create datetime objects to use in filtering\"\"\"\n arrival_time = datetime.datetime.strptime(date_and_time, '%Y-%m-%d %H:%M:%S')\n min_time = arrival_time - datetime.timedelta(minutes=VARS.get('timedelta_in_minutes'))\n max_time = arrival_time + datetime.timedelta(minutes=VARS.get('timedelta_in_minutes'))\n return min_time, max_time\n\n def get_medic_routes(self, **kwargs):\n \"\"\"method to get similar routes for driver\"\"\"\n\n # filter with date_and_time__range, if time is known\n if kwargs.get('min_time'):\n routes = Route.objects.filter(Q(date_and_time__range=(kwargs['min_time'], kwargs['max_time'])) |\n Q(date_and_time=None),\n user__type='doctor',\n start_point__distance_lt=(kwargs['start_point'],\n Distance(km=VARS.get('points_distance'))))\n else:\n routes = Route.objects.filter(user__type='doctor',\n start_point__distance_lt=(kwargs['start_point'],\n Distance(km=VARS.get('points_distance'))))\n return routes\n\n def get_driver_routes(self, **kwargs):\n \"\"\"method to get similar routes for doctor\"\"\"\n\n # filter with date_and_time__range, if time is known\n if kwargs.get('min_time'):\n routes = Route.objects.filter(Q(date_and_time__range=(kwargs['min_time'], kwargs['max_time'])) |\n Q(date_and_time=None),\n user__type='driver',\n start_point__distance_lt=(kwargs['start_point'],\n Distance(km=VARS.get('points_distance'))))\n else:\n routes = Route.objects.filter(user__type='driver',\n start_point__distance_lt=(kwargs['start_point'],\n Distance(km=VARS.get('points_distance'))))\n return routes\n\n def get(self, request):\n \"\"\"Method for getting a list of routes that match at the start point\"\"\"\n\n # getting sequence of request user\n request_user = ServiceUser.objects.get(telegram_id=request.data['telegram_id'])\n # creating a Point object to filter\n start_point = Point(request.data['start_point']['longitude'], request.data['start_point']['latitude'])\n\n # creating a timedelta\n if request.data.get('date_and_time'):\n min_time, max_time = self.get_time(request.data['date_and_time'])\n else:\n min_time, max_time = None, None\n\n # filtering the table with a specific user type\n if request_user.type == 'driver':\n routes = self.get_medic_routes(start_point=start_point,\n min_time=min_time,\n max_time=max_time)\n else:\n routes = self.get_driver_routes(start_point=start_point,\n min_time=min_time,\n max_time=max_time)\n\n serializer = GetSimilarRoutesSerializer(routes, many=True)\n return Response(serializer.data)\n","sub_path":"routes/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"74366043","text":"from docker import Client\nfrom docker.utils import utils\n\n\nclass DockerAdapter:\n \"\"\"Adapter for docker api\"\"\"\n def __init__(self, base_url):\n self.dockerClient = Client(base_url=base_url)\n\n def run_container(self, image_path, run_command, port_bind):\n container = self.dockerClient.create_container(\n image=image_path,\n command=run_command, detach=True,\n host_config=utils.create_host_config(\n network_mode=\"host\"\n )\n )\n self.dockerClient.start(container=container['Id'])\n\n def get_nodes(self):\n return self.dockerClient.containers()\n","sub_path":"app/docker_adapter.py","file_name":"docker_adapter.py","file_ext":"py","file_size_in_byte":640,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"48718857","text":"\nclass Character(object):\n name=\"\" #get normally\n gender=\"\" #get normally\n iop=\"\" #get normally\n dop=\"\" #get normally\n possessive=\"\" #get normally\n adj=[] #get by getAllAdjs()\n race=\"\" #get normally\n ia=\"\" #get by getIA()\n generic=False #get normally\n triggers={}\n def __init__(self, name, gender=\"person\", adj=\"\", race=\"human\"):\n self.name=name\n self.adj = adj.split(\", \")\n self.race = race\n self.gender = gender\n self.iop = \"she\" if gender == \"woman\" else \"he\" if gender == \"man\" else \"they\" #obligatory woman first to avoid hate\n self.dop = \"her\" if gender == \"woman\" else \"him\" if gender == \"man\" else \"them\"\n self.possessive = \"her\" if gender == \"woman\" else \"his\" if gender == \"man\" else \"their\"\n self.generic = True if name == \"\" else False\n self.getIA()\n def getAllAdjs(self):\n if self.adj == []:\n return \"\\b\"\n else:\n ret=\"\"\n for i in self.adj:\n ret+=i+\", \"\n return ret.strip(\", \")\n def getIA(self):\n if not self.adj == [\"\"]:\n if self.adj[0][0] == \"a\" or self.adj[0][0] == \"e\" or self.adj[0][0] == \"i\" or self.adj[0][0] == \"o\" or self.adj[0][0] == \"u\":\n self.ia = \"an\"\n else:\n self.ia = \"a\"\n else:\n if self.race[0] == \"a\" or self.race[0] == \"e\" or self.race[0] == \"i\" or self.race[0] == \"o\" or self.race[0] == \"u\":\n self.ia = \"an\"\n else:\n self.ia = \"a\"\n return self.ia\n\nclass Thing(Character):\n def __init__(self, name, adj=\"\"):\n super(Thing, self).__init__(self, name, \"neuter\", adj, \"object\")\n self.iop = \"it\"\n self.dop = \"it\"\n self.possessive = \"its\"\n\nclass Place(Thing):\n def __init__(self, name, adj=\"\"):\n super(Place, self).__init__(self, name, \"thing\", adj, \"place\")\n","sub_path":"engobjs.py","file_name":"engobjs.py","file_ext":"py","file_size_in_byte":1986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"230073082","text":"import scrapy\nfrom guaiben_redis.items import GuaibenItem\nfrom urllib.parse import parse_qs\nfrom scrapy_redis.spiders import RedisSpider\n\n\nclass IndexSpider(RedisSpider):\n name = 'index'\n #allowed_domains = ['search.jd.com']\n #start_urls = ['https://search.jd.com/Search?keyword=%E4%B9%A6&wq=%E4%B9%A6&pvid=6263ebbb3cf1443295fe83781685cc74&page=3&s=56&click=0']\n redis_key = 'JD'\n def __init__(self,*args,**Kwargs):\n domain = Kwargs.pop('domain','')\n self.allowed_domains = list(filter(None,domain.split(',')))\n super(IndexSpider,self).__init__(*args,**Kwargs)\n def parse(self, response):\n items = JdScrapyItem()\n books_msgs = response.xpath(\"//div[@id='J_goodsList']\")\n books_msg = books_msgs.xpath(\"./ul/li\")\n for i in books_msg:\n items['goods_url'] = 'https:' +i.xpath(\".//div[@class='p-img']/a/@href\").extract_first()\n items['price'] = i.xpath(\".//i/text()\").extract_first()\n items['name'] = ' '.join(i.xpath(\".//div[@class='p-name']/a/em/text()\").extract())\n items['store'] = i.xpath(\".//div[@class='p-shopnum']/a/@title\").extract_first()\n yield items\n\n next_url = str(response.url)\n zd = dict([(k, v[0]) for k, v in parse_qs(next_url.split('?', 1)[1]).items()])\n page_data = int(zd['page'])+2\n if page_data<200:\n next_url = 'https://search.jd.com/Search?keyword=书&wq=书&pvid=6263ebbb3cf1443295fe83781685cc74&page={}'.format(page_data)\n yield scrapy.Request(next_url,callback=self.parse)\n else:\n print('SUCCESS TO THE END PAGE!')\n","sub_path":"guaiben_redis/guaiben_redis/spiders/index.py","file_name":"index.py","file_ext":"py","file_size_in_byte":1628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"2798294","text":"from sklearn.ensemble import (AdaBoostClassifier, ExtraTreesClassifier,\r\n GradientBoostingClassifier,\r\n RandomForestClassifier)\r\nfrom sklearn.linear_model import SGDClassifier\r\nfrom sklearn.metrics import accuracy_score\r\nfrom sklearn.model_selection import (GridSearchCV, RandomizedSearchCV,\r\n StratifiedShuffleSplit, cross_val_score)\r\nfrom sklearn.pipeline import Pipeline\r\nfrom sklearn.preprocessing import MaxAbsScaler, MinMaxScaler, StandardScaler\r\nfrom sklearn.svm import SVC, LinearSVC\r\nfrom gtzantest import Preprocessing\r\nimport pandas as pd\r\n\r\ndef display_scores(scores):\r\n print(\"Scores:\", scores)\r\n print(\"Mean:\", scores.mean())\r\n print(\"Standard deviation:\", scores.std())\r\n\r\nX, y = Preprocessing.load_data()\r\n\r\nsplit = StratifiedShuffleSplit(n_splits=1, train_size=0.8, test_size=0.2, random_state=42)\r\nfor train_indices, test_indices in split.split(X, y):\r\n X_train, X_test = X.loc[train_indices], X.loc[test_indices]\r\n y_train, y_test = y.loc[train_indices], y.loc[test_indices]\r\n\r\npipeline = Pipeline([\r\n ('scaler', StandardScaler()),\r\n ('classifier', LinearSVC())\r\n])\r\nparam_grid = [\r\n {'scaler': [MaxAbsScaler(), MinMaxScaler(), StandardScaler()],\r\n 'classifier': [AdaBoostClassifier(), ExtraTreesClassifier(), GradientBoostingClassifier()],\r\n 'classifier__n_estimators': [10, 100, 1000]},\r\n {'scaler': [MaxAbsScaler(), MinMaxScaler(), StandardScaler()],\r\n 'classifier': [RandomForestClassifier()],\r\n 'classifier__n_estimators': [10, 100, 1000],\r\n 'classifier__max_features': [5, 10, 100],\r\n 'classifier__criterion': ['gini', 'entropy'],\r\n 'classifier__max_features': ['sqrt', 'log2', None]},\r\n {'scaler': [MaxAbsScaler(), MinMaxScaler(), StandardScaler()],\r\n 'classifier': [SGDClassifier()],\r\n 'classifier__max_iter': [100, 1000]},\r\n {'scaler': [MaxAbsScaler(), MinMaxScaler(), StandardScaler()],\r\n 'classifier': [LinearSVC()],\r\n 'classifier__C': [0.01, 0.5, 1.0]},\r\n {'scaler': [MaxAbsScaler(), MinMaxScaler(), StandardScaler()],\r\n 'classifier': [SVC(gamma='auto')],\r\n 'classifier__C': [0.01, 0.5, 1.0],\r\n 'classifier__kernel': ['linear', 'poly', 'rbf', 'sigmoid'],\r\n 'classifier__degree': [1, 3, 30]}\r\n]\r\nparam_dist = {\r\n 'scaler': [MaxAbsScaler(), MinMaxScaler(), StandardScaler()],\r\n 'classifier': [AdaBoostClassifier(), ExtraTreesClassifier(), GradientBoostingClassifier(),\r\n RandomForestClassifier(n_estimators=100), SGDClassifier(), LinearSVC(), SVC(gamma='auto')]\r\n}\r\n\r\nmodel_search = GridSearchCV(pipeline, param_grid, cv=3, iid=False, return_train_score=True)\r\n# model_search = RandomizedSearchCV(pipeline, param_dist, n_iter=100, cv=3)\r\nmodel = model_search.fit(X_train, pd.Series.ravel(y_train))","sub_path":"ancient/gtzantest/Training.py","file_name":"Training.py","file_ext":"py","file_size_in_byte":2840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"476394997","text":"import numpy as np\nfrom imageio import imread\nfrom skimage.color import rgb2gray\nfrom scipy.signal import convolve2d\nfrom os.path import isfile\nfrom scipy.special import binom\n\n# Grayscale representation\nGRAYSCALE = 1\n# RGB representation\nRGB = 2\n# Number of grayscales\nBINS = 256\n# Minimum Dimention\nMIN_DIM = 16\n\n\n# Reads filename to an image (numpy array) and return the image according to the given representation.\n# filename - the filename of an image on disk (could be grayscale or RGB).\n# representation - representation code, either 1 or 2 defining whether the output should be a grayscale\n# image (1) or an RGB image (2). If the input image is grayscale.\ndef read_image(filename, representation):\n if not isfile(filename):\n return None\n im = imread(filename)\n im_float = im.astype(np.float64)\n im_float /= BINS\n if representation == GRAYSCALE:\n if rgb_check(im):\n return rgb2gray(im_float)\n else:\n return im_float\n elif representation == RGB:\n if grayscale_check(im_float):\n return None # should not reach that point\n else:\n return im_float\n else: # should not reach that point\n return None\n\n\n# im – a grayscale image with double values in [0, 1]\n# (e.g. the output of ex1’s read_image with the representation set to 1).\n# max_levels – the maximal number of levels1 in the resulting pyramid.\n# filter_size – the size of the Gaussian filter (an odd scalar that represents a squared filter)\n# to be used in constructing the pyramid filter\n# (e.g for filter_size = 3 you should get [0.25, 0.5, 0.25]).\n# return - A tuple (pyr, filter_vec).\n# pyr - the resulting pyramid as a standard python array (NOT numpy)\n# filter_vec - a normalized row vector of shape (1, filter_size) used for the pyramid construction\ndef build_gaussian_pyramid(im, max_levels, filter_size):\n # Binomial row vec for the filter\n filter_vec = np.array([binom(filter_size - 1, i) for i in range(filter_size)]).reshape((1, filter_size))\n filter_vec *= 1 / np.power((filter_size - 1), 2) # Normalization\n im_i = im\n pyr = [im_i]\n for i in range(1, max_levels):\n min_dim = min(im_i.shape)\n if min_dim <= MIN_DIM:\n break\n im_i = downsample_image(im_i, filter_vec)\n pyr.append(im_i)\n return pyr, filter_vec\n\n\n# Down sample an image.\n# Taking even indices every even row.\n# image - one channel image to down sample\n# filter_vec - filter vector of shape (1, num)\n# return - downsamples image\ndef downsample_image(image, filter_vec):\n # Blur\n blurred_image = blur_image(image, filter_vec)\n # Sub-sample: Taking pixel of even row at even indices\n ds_image = blurred_image[::2, ::2]\n return ds_image\n\n\n# Expand an image.\n# expanded image new shape is (2n, 2m), except odd indices at odd rows.\n# image - one channel image to down sample\n# filter_vec - filter vector of shape (1, num)\n# return - expanded image\ndef expand_image(image, filter_vec):\n n, m = image.shape\n # Expanded zeros array of shape (2n, 2m)\n expanded_image = np.zeros((2 * n, 2 * m), dtype=image.dtype)\n # Pad zeros. Taking pixel of odd row at odd indices\n expanded_image[1::2, 1::2] = image\n # Blur\n return blur_image(expanded_image, filter_vec)\n\n\n# Blur a given image with filter_vec using 2D-convolution.\n# image - one channel image to down sample\n# filter_vec - filter vector of shape (1, num)\n# return - blurred image\ndef blur_image(image, filter_vec):\n blurred_im = convolve2d(image, filter_vec, mode='same') # Conv with row vec\n blurred_im = convolve2d(blurred_im, filter_vec.reshape((filter_vec.size, 1)), mode='same') # Conv with col vec\n return blurred_im\n\n\ndef gaussian_kernel(kernel_size):\n conv_kernel = np.array([1, 1], dtype=np.float64)[:, None]\n conv_kernel = convolve2d(conv_kernel, conv_kernel.T)\n kernel = np.array([1], dtype=np.float64)[:, None]\n for i in range(kernel_size - 1):\n kernel = convolve2d(kernel, conv_kernel, 'full')\n return kernel / kernel.sum()\n\n\ndef blur_spatial(img, kernel_size):\n kernel = gaussian_kernel(kernel_size)\n blur_img = np.zeros_like(img)\n if len(img.shape) == 2:\n blur_img = convolve2d(img, kernel, 'same', 'symm')\n else:\n for i in range(3):\n blur_img[..., i] = convolve2d(img[..., i], kernel, 'same', 'symm')\n return blur_img\n\n\n# Check if image has rgb channels\n# image - image as np array.\ndef rgb_check(image):\n return len(image.shape) == 3 and image.shape[2] == 3\n\n\n# Check if image has grayscale channel\n# image - image as np array.\ndef grayscale_check(image):\n return len(image.shape) == 2\n","sub_path":"ex4/sol4_utils.py","file_name":"sol4_utils.py","file_ext":"py","file_size_in_byte":4672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"645323175","text":"# Copyright (C) 2019 The Raphielscape Company LLC.\n#\n# Licensed under the Raphielscape Public License, Version 1.b (the \"License\");\n# you may not use this file except in compliance with the License.\n#\n\nimport asyncio\nfrom asyncio import wait\n\nfrom userbot import BOTLOG_CHATID as LOGGER_GROUP\nfrom userbot import BOTLOG as LOGGER\nfrom userbot import CMD_HELP\nfrom userbot.events import register\nfrom userbot import gettimemsg\n\n@register(outgoing=True, pattern=\"^.tspam\")\nasync def tmeme(e):\n tspam = str(e.text[7:])\n message = tspam.replace(\" \", \"\")\n for letter in message:\n await e.respond(letter)\n await e.delete()\n\n@register(outgoing=True, pattern=\"^.spam\")\nasync def spammer(e):\n if not e.text[0].isalpha() and e.text[0] not in (\"/\", \"#\", \"@\", \"!\"):\n gx = gettimemsg(e.text)\n message = gx[0]\n counter = int(gx[1])\n #spam_message = str( \" \".join(gx[2:]))\n spam_message = str(gx[2])\n await asyncio.wait([e.respond(spam_message) for i in range(counter)])\n await e.delete()\n if LOGGER:\n await e.client.send_message(\n LOGGER_GROUP,\n \"#SPAM \\n\\n\"\n \"Spam was executed successfully\"\n )\n\n@register(outgoing=True, pattern=\"^.picspam\")\nasync def tiny_pic_spam(e):\n if not e.text[0].isalpha() and e.text[0] not in (\"/\", \"#\", \"@\", \"!\"):\n message = e.text\n text = message.split()\n counter = int(text[1])\n link = str(text[2])\n for i in range(1, counter):\n await e.client.send_file(e.chat_id, link)\n await e.delete()\n if LOGGER:\n await e.client.send_message(\n LOGGER_GROUP,\n \"#PICSPAM \\n\\n\"\n \"PicSpam was executed successfully\"\n )\n\nCMD_HELP.update({\n \"tspam\": \"Usage: .tspam <message>\\n\\nSends each letter of <message> as seperate message.\"\n})\n\nCMD_HELP.update({\n \"spam\": \"Usage: .spam <number> <message>\\n\\nSends <message> <number> times.\"\n})\n\nCMD_HELP.update({\n \"picspam\": \"Usage: .picspam <number> <link_of_pic>\\n\\nSends picture at <link_of_pic> <number> times.\"\n})","sub_path":"userbot/modules/spammer.py","file_name":"spammer.py","file_ext":"py","file_size_in_byte":2148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"464096806","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\n# This file is part of CSTBox.\n#\n# CSTBox is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Lesser General Public License as published\n# by the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# CSTBox 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 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 CSTBox. If not, see <http://www.gnu.org/licenses/>.\n\n\"\"\" Kamstrup Multicanal 601 thermal energy meter low-level interface.\n\nThis modules defines a sub-class of minimalmodbus.Instrument which polls the\nparameters of interest.\n\nIt also defines the input registers so that more specific needs can be\nimplemented by sub-classing and overiding, or direct Modbus register read.\n\nDepends on Jonas Berg's minimalmodbus Python library :\n https://pypi.python.org/pypi/MinimalModbus/\n Version in date of writing: 0.4\n\"\"\"\n\nimport minimalmodbus\nimport struct\nfrom collections import namedtuple\n\nfrom pycstbox.modbus import ModbusRegister\nfrom pycstbox.log import Loggable\n\n__author__ = 'Eric PASCUAL - CSTB (eric.pascual@cstb.fr)'\n__copyright__ = 'Copyright (c) 2013 CSTB'\n__vcs_id__ = '$Id$'\n__version__ = '1.0.0'\n\n\n# Input registers\n\nREG_ENERGY = ModbusRegister(0x80, 2)\nREG_FLOW_V1 = ModbusRegister(0x82, 2)\nREG_VOLUME_V1 = ModbusRegister(0x84, 2)\nREG_POWER = ModbusRegister(0x86, 2)\nREG_TEMP_OUT = ModbusRegister(0x88, 2)\nREG_TEMP_IN = ModbusRegister(0x89, 2)\nREG_WATER_VA = ModbusRegister(0x8A, 2)\nREG_WATER_VB = ModbusRegister(0x8C, 2)\n\nALL_INPUTS_SIZE = reduce(lambda sztot, sz : sztot + sz,\n [reg.size for reg in [ #pylint: disable=E1103\n REG_ENERGY,\n REG_FLOW_V1,\n REG_VOLUME_V1,\n REG_POWER,\n REG_TEMP_OUT,\n REG_TEMP_IN,\n REG_WATER_VA,\n REG_WATER_VB\n ]])\n\nREG_UNIT_ENERGY = ModbusRegister(0x90)\nREG_UNIT_FLOW = ModbusRegister(0x91)\nREG_UNIT_VOLUME = ModbusRegister(0x92)\nREG_UNIT_POWER = ModbusRegister(0x93)\nUNIT_REGS_TOTAL_SIZE = 4\n\n# Units conversion ratios.\n\n# The produced values will be the result of the raw values times the ratios\n# provided by the tables hereafter.\n# Table keys are the codes contained in the units registers group #2\n\n# VERY IMPORTANT: the ratio values must match what is defined in the\n# device meta-data file\n\n# Power (target: kW)\nUNITS_POWER = {\n 0x01: 1.,\n 0x02: 0.001\n}\n\n# Energy (target: MWh)\nUNITS_ENERGY = {\n 0x11: 1000.,\n 0x12: 1.\n}\n\n# Volume (target: m3)\nUNITS_VOLUME = {\n 0x21: 0.001,\n 0x22: 1.,\n 0x23: 10\n}\n\n# Flow (target: l/h)\nUNITS_FLOW = {\n 0x31: 1.,\n 0x32: 1000.\n}\n\n\nclass MC601Instrument(minimalmodbus.Instrument, Loggable):\n \"\"\" minimalmodbus.Instrument sub-class modeling the Kamstrup CM601 thermal\n energy meter.\n\n The supported model is the RTU RS485 one, the RS485 bus being connected\n via a USB.RS485 interface.\n \"\"\"\n\n BAUDRATE = 9600\n\n # Definition of the type of the poll() method result\n\n # VERY IMPORTANT :\n # The name of its items MUST match the name of the outputs described\n # in the metadata stored in devcfg.d directory, since the notification\n # events generation process is based on this.\n # (see pycstbox.hyal.device.PolledDevice.poll() method for details)\n OutputValues = namedtuple('OutputValues', [\n 'energy',\n 'flow_V1',\n 'volume_V1',\n 'power',\n 'temp_out',\n 'temp_in',\n 'water_VA',\n 'water_VB'\n ])\n\n def __init__(self, port, unit_id):\n \"\"\"\n :param port: serial port on which the RS485 interface is connected\n :param unit_id: the address of the device\n \"\"\"\n minimalmodbus.Instrument.__init__(self,\n port=port,\n slaveaddress=int(unit_id))\n\n self.serial.baudrate = self.BAUDRATE\n self._conversion_ratios = None\n\n Loggable.__init__(self, logname='kms%03d' % self.address)\n\n @property\n def unit_id(self):\n \"\"\" The id of the device \"\"\"\n return self.address\n\n def get_units(self):\n \"\"\" Reads the units settings of the device and initializes the inputs\n conversion ratios so that values are always produced using the following\n units :\n energy: MWh\n flow: l/h\n volume: m3\n power: kW\n\n The conversion ratios table maps the OutputValues tuple definition.\n \"\"\"\n u_energy, u_flow, u_volume, u_power = struct.unpack(\n '>HHHH',\n self.read_string(REG_UNIT_ENERGY.addr, UNIT_REGS_TOTAL_SIZE)\n )\n self._conversion_ratios = [\n UNITS_ENERGY[u_energy],\n UNITS_FLOW[u_flow],\n UNITS_VOLUME[u_volume],\n UNITS_POWER[u_power],\n 1., # temperature\n 1., # temperature\n UNITS_VOLUME[u_volume],\n UNITS_VOLUME[u_volume]\n ]\n\n def poll(self):\n \"\"\" Reads the registers operational mode to sensor1 data and returns the\n values as a named tuple.\n\n In case of invalid current mode or error status, resets the device in\n operational mode and return None.\n \"\"\"\n if not self._conversion_ratios:\n self.log_info('first poll -> get used units')\n self.get_units()\n\n s = self.read_string(REG_ENERGY.addr, ALL_INPUTS_SIZE)\n # we need to swap 16 bits words\n s2 = []\n for i in xrange(0, 32, 4):\n s2.extend(s[i+2:i+4])\n s2.extend(s[i:i+2])\n raw_outputs = struct.unpack('>ffffffff', ''.join(s2))\n\n return self.OutputValues(\n *(raw * ratio for raw, ratio in zip(raw_outputs, self._conversion_ratios))\n )\n","sub_path":"extensions/kamstrup/lib/python/pycstbox/kamstrup/mc601.py","file_name":"mc601.py","file_ext":"py","file_size_in_byte":6215,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"461730655","text":"# -*- coding: utf-8 -*-\n\nfrom reedsolo import RSCodec\nimport functools\nimport operator\nimport struct\n\ndef slimdecode(frame, config): \n\trs = RSCodec(config[\"trame\"][\"ecc\"][\"length\"])\n\tframe = rs.decode(frame)\n\n\tcs = frame.pop()\n\n\tif cs != functools.reduce(operator.xor, frame): \n\t\tprint(\"Checksum failed\")\n\t\treturn False\n\n\tframe.pop(0) # Stripping start byte\n\n\tout = []\n\n\tfor fmt in config[\"sensors\"]: \n\t\tl = struct.calcsize(fmt)\n\t\tval, = struct.unpack(\"!\" + fmt, frame[:l])\n\t\tframe = frame[l:]\n\t\tout.append(val)\n\n\n\tprint(len(frame))\n\n\treturn out\n","sub_path":"slimdecoder.py","file_name":"slimdecoder.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"405329924","text":"# Copyright © 2012-2023 jrnl contributors\n# License: https://www.gnu.org/licenses/gpl-3.0.html\n\nimport codecs\nimport fnmatch\nimport os\nfrom typing import TYPE_CHECKING\n\nfrom jrnl import time\n\nfrom .Journal import Journal\n\nif TYPE_CHECKING:\n from jrnl.journals import Entry\n\n\ndef get_files(journal_config: str) -> list[str]:\n \"\"\"Searches through sub directories starting with journal_config and find all text files\"\"\"\n filenames = []\n for root, dirnames, f in os.walk(journal_config):\n for filename in fnmatch.filter(f, \"*.txt\"):\n filenames.append(os.path.join(root, filename))\n return filenames\n\n\nclass Folder(Journal):\n \"\"\"A Journal handling multiple files in a folder\"\"\"\n\n def __init__(self, name: str = \"default\", **kwargs):\n self.entries = []\n self._diff_entry_dates = []\n self.can_be_encrypted = False\n super().__init__(name, **kwargs)\n\n def open(self) -> \"Folder\":\n filenames = []\n self.entries = []\n filenames = get_files(self.config[\"journal\"])\n for filename in filenames:\n with codecs.open(filename, \"r\", \"utf-8\") as f:\n journal = f.read()\n self.entries.extend(self._parse(journal))\n self.sort()\n return self\n\n def write(self) -> None:\n \"\"\"Writes only the entries that have been modified into proper files.\"\"\"\n # Create a list of dates of modified entries. Start with diff_entry_dates\n modified_dates = self._diff_entry_dates\n seen_dates = set(self._diff_entry_dates)\n\n for e in self.entries:\n if e.modified:\n if e.date not in modified_dates:\n modified_dates.append(e.date)\n if e.date not in seen_dates:\n seen_dates.add(e.date)\n\n # For every date that had a modified entry, write to a file\n for d in modified_dates:\n write_entries = []\n filename = os.path.join(\n self.config[\"journal\"],\n d.strftime(\"%Y\"),\n d.strftime(\"%m\"),\n d.strftime(\"%d\") + \".txt\",\n )\n dirname = os.path.dirname(filename)\n # create directory if it doesn't exist\n if not os.path.exists(dirname):\n os.makedirs(dirname)\n for e in self.entries:\n if (\n e.date.year == d.year\n and e.date.month == d.month\n and e.date.day == d.day\n ):\n write_entries.append(e)\n journal = \"\\n\".join([e.__str__() for e in write_entries])\n with codecs.open(filename, \"w\", \"utf-8\") as journal_file:\n journal_file.write(journal)\n # look for and delete empty files\n filenames = []\n filenames = get_files(self.config[\"journal\"])\n for filename in filenames:\n if os.stat(filename).st_size <= 0:\n os.remove(filename)\n\n def delete_entries(self, entries_to_delete: list[\"Entry\"]) -> None:\n \"\"\"Deletes specific entries from a journal.\"\"\"\n for entry in entries_to_delete:\n self.entries.remove(entry)\n self._diff_entry_dates.append(entry.date)\n\n def change_date_entries(self, date: str) -> None:\n \"\"\"Changes entry dates to given date.\"\"\"\n\n date = time.parse(date)\n\n self._diff_entry_dates.append(date)\n\n for entry in self.entries:\n self._diff_entry_dates.append(entry.date)\n entry.date = date\n\n def parse_editable_str(self, edited: str) -> None:\n \"\"\"Parses the output of self.editable_str and updates its entries.\"\"\"\n mod_entries = self._parse(edited)\n diff_entries = set(self.entries) - set(mod_entries)\n for e in diff_entries:\n self._diff_entry_dates.append(e.date)\n # Match those entries that can be found in self.entries and set\n # these to modified, so we can get a count of how many entries got\n # modified and how many got deleted later.\n for entry in mod_entries:\n entry.modified = not any(entry == old_entry for old_entry in self.entries)\n self.entries = mod_entries\n","sub_path":"jrnl/journals/FolderJournal.py","file_name":"FolderJournal.py","file_ext":"py","file_size_in_byte":4231,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"36370882","text":"# coding=utf-8\n\n__author__ = 'LY'\n__time__ = '2018/5/31'\n\n# 二叉树的实现\n\nclass TreeNode:\n\t'''二叉搜索树节点的定义'''\n\tval:int\n\tleft_node:object\n\tright_node:object\n\tdef __init__(self, val):\n\t\tself.val = val\n\t\tself.left_node = None\n\t\tself.right_node = None\n\nclass OperationTree:\n\t'''二叉树操作'''\n\tdef create(self, List):\n\t\t'''二叉搜索树插入操作'''\n\t\troot = TreeNode(List[0])\n\t\tlens = len(List)\n\t\tif lens >= 2:\n\t\t\troot.left_node = self.create(List[1])\n\t\tif lens >= 3:\n\t\t\troot.right_node = self.create(List[2])\n\t\treturn root\n\n\tdef query(self, root, data):\n\t\t'''二叉树查找操作'''\n\t\tif root == None:\n\t\t\treturn False\n\t\tif root.val == data:\n\t\t\treturn True\n\t\telif root.left_node:\n\t\t\treturn self.query(root.left_node, data)\n\t\telif root.right_node:\n\t\t\treturn self.query(root.right_node, data)\n\n\tdef PreOrder(self, root):\n\t\t'''打印二叉树(先序)'''\n\t\tif root == None:\n\t\t\treturn \n\t\tprint(root.val, end=' ')\n\t\tself.PreOrder(root.left_node)\n\t\tself.PreOrder(root.right_node)\n\n\tdef InOrder(self, root):\n\t\t'''中序打印'''\n\t\tif root == None:\n\t\t\treturn\n\t\tself.InOrder(root.left_node)\n\t\tprint(root.val, end=' ')\n\t\tself.InOrder(root.right_node)\n\n\tdef BacOrder(self, root):\n\t\t'''后序打印'''\n\t\tif root == None:\n\t\t\treturn\n\t\tself.BacOrder(root.left_node)\n\t\tself.BacOrder(root.right_node)\n\t\tprint(root.val, end=' ')\n\n\tdef BFS(self, root):\n\t\t'''广度优先'''\n\t\tif root == None:\n\t\t\treturn\n\t\t# queue队列，保存节点\n\t\tqueue = []\n\t\t# res保存节点值，作为结果\n\t\t#vals = []\n\t\tqueue.append(root)\n\n\t\twhile queue:\n\t\t\t# 拿出队首节点\n\t\t\tcurrentNode = queue.pop(0)\n\t\t\t#vals.append(currentNode.val)\n\t\t\tprint(currentNode.val, end=' ')\n\t\t\tif currentNode.left_node:\n\t\t\t\tqueue.append(currentNode.left_node)\n\t\t\tif currentNode.right_node:\n\t\t\t\tqueue.append(currentNode.right_node)\n\t\t#return vals\n\n\tdef DFS(self, root):\n\t\t'''深度优先'''\n\t\tif root == None:\n\t\t\treturn\n\t\t# 栈用来保存未访问节点\n\t\tstack = []\n\t\t# vals保存节点值，作为结果\n\t\t#vals = []\n\t\tstack.append(root)\n\n\t\twhile stack:\n\t\t\t# 拿出栈顶节点\n\t\t\tcurrentNode = stack.pop()\n\t\t\t#vals.append(currentNode.val)\n\t\t\tprint(currentNode.val, end=' ')\n\t\t\tif currentNode.right_node:\n\t\t\t\tstack.append(currentNode.right_node)\n\t\t\tif currentNode.left_node:\n\t\t\t\tstack.append(currentNode.left_node)\t\t\t\n\t\t#return vals\t\t\n\nif __name__ == '__main__':\n\tList1 = [1,\n\t\t\t\t\t\t[2,[4,[8],[9]],[5]],\n\t\t\t\t\t\t[3,[6],[7]]\n\t\t\t\t\t]\n\t\t\t\t\t\t\n\top = OperationTree()\n\ttree1 = op.create(List1)\n\tprint(dir(tree1))\n\tprint(tree1.val)\n\tprint('先序打印：',end = '')\n\top.PreOrder(tree1)\n\tprint(\"\")\n\tprint('中序打印：',end = '')\n\top.InOrder(tree1)\n\tprint(\"\")\n\tprint('后序打印：',end = '')\n\top.BacOrder(tree1)\n\tprint(\"\")\n\tprint('BFS打印 ：',end = '')\n\tbfs = op.BFS(tree1)\n\t#print(bfs)\n\tprint(\"\")\n\tprint('DFS打印 ：',end = '')\n\tdfs = op.DFS(tree1)\n\t#print(dfs)\n\tprint(\"\")","sub_path":"algorithm/binary_tree.py","file_name":"binary_tree.py","file_ext":"py","file_size_in_byte":2850,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"381713302","text":"# Aug 12, 2019 (Ch 7)\ndef isPhoneNumber(text): \n if len(text) != 12: \n return False \n for i in range(0, 3): \n if not text[i].isdecimal(): \n return False\n if text[3] != '-': \n return False\n for i in range(4, 7): \n if not text[i].isdecimal(): \n return False\n if text[7] != '-': \n return False \n for i in range(8, 12): \n if not text[i].isdecimal(): \n return False\n return True \n\n#print('415-555-4242 is a phone number:')\n#print(isPhoneNumber('415-555-4242'))\n#print('Moshi moshi is a phone number:')\n#print(isPhoneNumber('Moshi moshi'))\n\nmessage = 'Call me at 415-555-1011 tomorrow. 415-555-9999 is my office.'\nfor i in range(len(message)): \n chunk = message[i:i+12]\n if isPhoneNumber(chunk):\n print('Phone number found: ' + chunk)\nprint('Done')\n\n#alternatively...\n# import re\n# phoneNumRegex = re.compile(r'\\d\\d\\d-\\d\\d\\d-\\d\\d\\d\\d')\n# mo = phoneNumRegex.search('My number is 415-555-4242.')\n# print('Phone number found: ' + mo.group())\n# should print: Phone number found: 415-555-4242\n# note: that \\d means 'a digit character'","sub_path":"isPhoneNumber.py","file_name":"isPhoneNumber.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"394699020","text":"from __future__ import division\n# The fibonacci sequence\n\n\n# This first attempt works fine for small values of 'n', but as the\n# value of 'n' increases, the number of recursive function calls\n# needed to calculate 'fibonacci(n)' increases exponentially. The\n# problem is that this formulation of the fibonacci function doesn't\n# store previously calculated values to use in future calls to itself.\nprint(\"\\nDirect recursion with no caching of previously calculated values\\n\")\ndef fibonacci(n):\n if n == 1:\n return 1\n elif n == 2:\n return 1\n elif n > 2:\n return fibonacci(n - 1) + fibonacci(n - 2)\n\nfor n in range(1, 11):\n print(\"n: {0:<5} fib({0}): {1}\".format(n, fibonacci(n)))\n\n# In this next version, we will use 'memoization' to speed up our\n# fibonacci function.\n\n# Definition: In computing, memoization is an optimization technique used\n# primarily to speed up computer programs by storing the results of\n# expensive function calls and returning the cached result when the same\n# inputs occur again.\n\n# Using memoization, we no longer have exponential time increases in\n# calculating 'fibonacci(n)' for large values of 'n'.\nprint(\"\\n Using a dictionary to store previously calculated values\\n\")\nfibonacci_cache = {} # A dictionary to store previously calculated values.\n\ndef fibonacci(n):\n # If we have cached the value, then return it\n if n in fibonacci_cache:\n return fibonacci_cache[n]\n\n # Compute the nth term\n if n == 1:\n value = 1\n elif n == 2:\n value = 1\n elif n > 2:\n value = fibonacci(n-1) + fibonacci(n-2)\n\n # Cache the value and return it\n fibonacci_cache[n] = value\n return value\n\nfor n in range(1, 1001):\n print(\"n: {0:<5} fib({0}): {1}\".format(n, fibonacci(n)))\n\n# Finally, we can improve our initial version using the decorator\n# 'lru_cache' ('Least Recently Used Cache') from the 'functools'\n# module.\nprint(\"\\nUsing 'Lru_cache' decorator\\n\")\nfrom functools32 import lru_cache\n\n@lru_cache(maxsize=1000) # Cache 1000 values\ndef fibonacci(n):\n if n == 1:\n return 1\n elif n == 2:\n return 1\n elif n > 2:\n return fibonacci(n - 1) + fibonacci(n - 2)\n\nfor n in range(1, 501):\n print(\"n: {0:<5} fib({0}): {1}\".format(n, fibonacci(n)))\n\n# Let's now add some error checking to make sure the argument is a\n# positive integer. This will prevent a crash in the eventuality that\n# someone tries to call the function without a positive integer input.\nprint(\"\\nAdded some error handling for the input\\n\")\n\n@lru_cache(maxsize=500)\ndef fibonacci(n):\n if type(n) != int:\n raise TypeError(\"n must be a positive integer\")\n if n < 1:\n raise ValueError(\"n must be a positive integer\")\n if n == 1:\n return 1\n elif n == 2:\n return 1\n elif n > 2:\n return fibonacci(n - 1) + fibonacci(n - 2)\n\nfor n in range(1, 51):\n print(\"n: {0:<5} fib({0}): {1}\".format(n, fibonacci(n)))\n\n# The sequence of ratios of consecutive fibonacci numbers converges to\n# the golden ratio.\nfor n in range(1, 51):\n print(fibonacci(n+1)/fibonacci(n))\n","sub_path":"Python/fibonacci.py","file_name":"fibonacci.py","file_ext":"py","file_size_in_byte":3100,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"106310970","text":"from __future__ import division\nimport time\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nimport numpy as np\nimport cv2\nfrom util import *\nimport os\nimport os.path as osp\nfrom darknet import Darknet\nfrom preprocess import prep_image, inp_to_image\nimport pandas as pd\nimport random\nimport pickle as pkl\nimport itertools\n\n\nclass test_net(nn.Module):\n def __init__(self, num_layers, input_size):\n super(test_net, self).__init__()\n self.num_layers = num_layers\n self.linear_1 = nn.Linear(input_size, 5)\n self.middle = nn.ModuleList([nn.Linear(5, 5)\n for x in range(num_layers)])\n self.output = nn.Linear(5, 2)\n\n def forward(self, x):\n x = x.view(-1)\n fwd = nn.Sequential(self.linear_1, *self.middle, self.output)\n return fwd(x)\n\n\ndef get_test_input(input_dim, CUDA, cuda_n):\n img = cv2.imread(\"dog-cycle-car.png\")\n img = cv2.resize(img, (input_dim, input_dim))\n img_ = img[:, :, ::-1].transpose((2, 0, 1))\n img_ = img_[np.newaxis, :, :, :] / 255.0\n img_ = torch.from_numpy(img_).float()\n img_ = Variable(img_)\n\n if CUDA:\n img_ = img_.cuda()\n return img_\n\n\ndef IoU(boxA, boxB):\n xA = max(boxA[0], boxB[0])\n yA = max(boxA[1], boxB[1])\n xB = min(boxA[2], boxB[2])\n yB = min(boxA[3], boxB[3])\n interArea = max(0, xB - xA + 1) * max(0, yB - yA + 1)\n boxAArea = (boxA[2] - boxA[0] + 1) * (boxA[3] - boxA[1] + 1)\n boxBArea = (boxB[2] - boxB[0] + 1) * (boxB[3] - boxB[1] + 1)\n iou = interArea / float(boxAArea + boxBArea - interArea)\n return iou\n\n\ndef load_results(file=\"results.txt\"):\n with open(file, mode='r') as f:\n return f.readlines()\n\n\ndef predict(args):\n cuda_n = int(args.cuda)\n silent = args.silent == \"all\"\n if (silent):\n import sys\n sys.stdout = open(os.devnull, 'w')\n batch_size = int(args.bs)\n confidence = float(args.confidence)\n nms_thesh = float(args.nms_thresh)\n start = 0\n\n os.environ[\"CUDA_DEVICE_ORDER\"] = \"PCI_BUS_ID\"\n os.environ[\"CUDA_VISIBLE_DEVICES\"] = args.cuda\n\n CUDA = torch.cuda.is_available()\n\n classes = load_classes('data/coco.names')\n num_classes = len(classes)\n\n # Set up the neural network\n print(\"Loading network.....\")\n model = Darknet(args.cfgfile)\n model.load_weights(args.weightsfile)\n print(\"Network successfully loaded\")\n\n model.net_info[\"height\"] = args.reso\n inp_dim = int(model.net_info[\"height\"])\n assert inp_dim % 32 == 0\n assert inp_dim > 32\n\n # If there's a GPU availible, put the model on GPU\n if CUDA:\n model.cuda()\n\n # Set the model in evaluation mode\n model.eval()\n\n read_dir = time.time()\n # Detection phase\n\n vot_path = args.vot\n saveto = args.saveto\n\n if vot_path != \"\" and saveto != \"\":\n folders = os.listdir(vot_path)\n else:\n folders = [args.images]\n\n result = dict()\n num_frames = dict()\n\n for n, folder in enumerate(folders): # !\n images = osp.join(vot_path, folder)\n if (not os.path.isdir(images)):\n continue\n\n print(\"processing {}, {}/{}\".format(folder, n + 1, len(folders)))\n if (images.endswith(\".txt\")): # !\n continue\n try:\n imlist = [osp.join(osp.realpath('.'), images, img) for img in sorted(os.listdir(images))[:] if\n os.path.splitext( # sorted() is my modification\n img)[1] == '.png' or os.path.splitext(img)[1] == '.jpeg' or os.path.splitext(img)[\n 1] == '.jpg']\n except NotADirectoryError:\n imlist = []\n imlist.append(osp.join(osp.realpath('.'), images))\n except FileNotFoundError:\n print(\"No file or directory with the name {}\".format(images))\n exit()\n\n num_frames[folder] = len(imlist)\n\n # if not os.path.exists(args.det):\n # os.makedirs(args.det)\n\n load_batch = time.time()\n\n batches = list(\n map(prep_image, imlist, [inp_dim for x in range(len(imlist))]))\n im_batches = [x[0] for x in batches]\n orig_ims = [x[1] for x in batches]\n im_dim_list = [x[2] for x in batches]\n im_dim_list = torch.FloatTensor(im_dim_list).repeat(1, 2)\n\n if CUDA:\n im_dim_list = im_dim_list.cuda()\n\n leftover = 0\n\n if (len(im_dim_list) % batch_size):\n leftover = 1\n\n if batch_size != 1:\n num_batches = len(imlist) // batch_size + leftover\n im_batches = [torch.cat((im_batches[i * batch_size: min((i + 1) * batch_size,\n len(im_batches))])) for i in range(num_batches)]\n\n i = 0\n\n write = False\n model(get_test_input(inp_dim, CUDA, cuda_n), CUDA)\n\n start_det_loop = time.time()\n\n objs = {}\n\n for batch in im_batches:\n # load the image\n start = time.time()\n if CUDA:\n batch = batch.cuda()\n\n # Apply offsets to the result predictions\n # Tranform the predictions as described in the YOLO paper\n # flatten the prediction vector\n # B x (bbox cord x no. of anchors) x grid_w x grid_h --> B x bbox x (all the boxes)\n # Put every proposed box as a row.\n with torch.no_grad():\n prediction = model(Variable(batch), CUDA)\n\n # prediction = prediction[:,scale_indices]\n\n # get the boxes with object confidence > threshold\n # Convert the cordinates to absolute coordinates\n # perform NMS on these boxes, and save the results\n # I could have done NMS and saving seperately to have a better abstraction\n # But both these operations require looping, hence\n # clubbing these ops in one loop instead of two.\n # loops are slower than vectorised operations.\n\n prediction = write_results(\n prediction, confidence, num_classes, nms=True, nms_conf=nms_thesh)\n\n if type(prediction) == int:\n i += 1\n continue\n\n end = time.time()\n\n # print(end - start)\n\n prediction[:, 0] += i * batch_size\n\n if not write:\n output = prediction\n write = 1\n else:\n output = torch.cat((output, prediction))\n\n for im_num, image in enumerate(imlist[i * batch_size: min((i + 1) * batch_size, len(imlist))]):\n # print(image)\n im_id = i * batch_size + im_num\n objs = [classes[int(x[-1])] for x in output if int(x[0]) == im_id]\n # print(\"{0:20s} predicted in {1:6.3f} seconds\".format(\n # image.split(\"/\")[-1], (end - start)/batch_size))\n # print(\"{0:20s} {1:s}\".format(\"Objects Detected:\", \" \".join(objs)))\n # print(\"----------------------------------------------------------\")\n i += 1\n\n if CUDA:\n torch.cuda.synchronize()\n\n try:\n output\n except NameError:\n return list()\n\n im_dim_list = torch.index_select(im_dim_list, 0, output[:, 0].long())\n\n scaling_factor = torch.min(inp_dim / im_dim_list, 1)[0].view(-1, 1)\n\n output[:, [1, 3]] -= (inp_dim - scaling_factor *\n im_dim_list[:, 0].view(-1, 1)) / 2\n output[:, [2, 4]] -= (inp_dim - scaling_factor *\n im_dim_list[:, 1].view(-1, 1)) / 2\n\n output[:, 1:5] /= scaling_factor\n\n for i in range(output.shape[0]):\n output[i, [1, 3]] = torch.clamp(\n output[i, [1, 3]], 0.0, im_dim_list[i, 0])\n output[i, [2, 4]] = torch.clamp(\n output[i, [2, 4]], 0.0, im_dim_list[i, 1])\n\n output_recast = time.time()\n\n class_load = time.time()\n\n colors = pkl.load(open(\"pallete\", \"rb\"))\n\n draw = time.time()\n\n def write(x, batches, results):\n c1 = tuple(x[1:3].int())\n c2 = tuple(x[3:5].int())\n\n img = results[int(x[0])]\n cls = int(x[-1])\n label = \"{0}\".format(classes[cls])\n color = random.choice(colors)\n cv2.rectangle(img, c1, c2, color, 1)\n t_size = cv2.getTextSize(label, cv2.FONT_HERSHEY_PLAIN, 1, 1)[0]\n c2 = c1[0] + t_size[0] + 3, c1[1] + t_size[1] + 4\n cv2.rectangle(img, c1, c2, color, -1)\n cv2.putText(img, label, (c1[0], c1[1] + t_size[1] + 4),\n cv2.FONT_HERSHEY_PLAIN, 1, [225, 255, 255], 1)\n return img\n\n # print(output.tolist(), file=open(\"fuck/before.txt\", 'w+'))\n # if postprocessor is not None:\n # if CUDA:\n # first = first.cuda()\n # # print(first.tolist(), file=open(\"fuck/first.txt\", 'w+'))\n # output = postprocessor(first, output, num_frames, CUDA)\n # # print(output.tolist(), file=open(\"fuck/after.txt\", 'w+'))\n\n list(map(lambda x: write(x, im_batches, orig_ims), output))\n det_names = pd.Series(imlist).apply(\n lambda x: \"{}/{}\".format(args.det, x.split(\"/\")[-1]))\n list(map(cv2.imwrite, det_names, orig_ims))\n\n def save_report(rep, file, format=\"\"):\n if format == \"\":\n with open(file, mode='w') as f:\n print(rep, file=f)\n elif format == \"csv\":\n import csv\n with open(file, 'wb') as f:\n wr = csv.writer(f, quoting=csv.QUOTE_ALL)\n wr.writerow(rep)\n\n # save_report(output.tolist(), \"report.txt\")\n end = time.time()\n\n # print()\n # print(\"SUMMARY\")\n # print(\"----------------------------------------------------------\")\n # print(\"{:25s}: {}\".format(\"Task\", \"Time Taken (in seconds)\"))\n # print()\n # print(\"{:25s}: {:2.3f}\".format(\"Reading addresses\", load_batch - read_dir))\n # print(\"{:25s}: {:2.3f}\".format(\n # \"Loading batch\", start_det_loop - load_batch))\n # print(\"{:25s}: {:2.3f}\".format(\n # \"Detection (\" + str(len(imlist)) + \" images)\", output_recast - start_det_loop))\n # print(\"{:25s}: {:2.3f}\".format(\n # \"Output Processing\", class_load - output_recast))\n # print(\"{:25s}: {:2.3f}\".format(\"Drawing Boxes\", end - draw))\n # print(\"{:25s}: {:2.3f}\".format(\n # \"Average time_per_img\", (end - load_batch)/len(imlist)))\n # print(\"----------------------------------------------------------\")\n\n torch.cuda.empty_cache()\n\n result[folder] = output\n\n return {\"results\": result, \"num_frames\": num_frames, \"CUDA\": CUDA}\n","sub_path":"darknet/detector.py","file_name":"detector.py","file_ext":"py","file_size_in_byte":10849,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"313694611","text":"\nimport os\nimport configparser\nimport copy\nfrom dashboard.bokeh.helper import get_current_process\nimport logging\n\nlogger = logging.getLogger(__name__)\n\ncams_stages_r = list()\nfor i in range(4):\n cams_stages_r.append(\n dict(\n camera=[i for i in range(10)],\n )\n )\n\ncams_stages_b = copy.deepcopy(cams_stages_r)\ncams_stages_z = copy.deepcopy(cams_stages_r)\n\nqlf_root = os.getenv('QLF_ROOT')\ncfg = configparser.ConfigParser()\n\ntry:\n cfg.read('%s/framework/config/qlf.cfg' % qlf_root)\n desi_spectro_redux = cfg.get('namespace', 'desi_spectro_redux')\nexcept Exception as error:\n logger.error(error)\n logger.error(\"Error reading %s/framework/config/qlf.cfg\" % qlf_root)\n\ndef update_camera_status():\n process = get_current_process()\n label_name = list()\n\n for num in range(30):\n if 'z9' not in label_name:\n label_name.append('z' + str(num))\n elif 'r9' not in label_name:\n label_name.append('r' + str(num - 10))\n elif 'b9' not in label_name:\n label_name.append('b' + str(num - 20))\n\n\n for cam in label_name:\n cameralog = None\n log = str()\n try:\n for item in process[0].get(\"process_jobs\", list()):\n if cam == item.get(\"camera\"):\n cameralog = os.path.join(desi_spectro_redux, item.get('logname'))\n break\n if cameralog:\n arq = open(cameralog, 'r')\n log = arq.readlines()\n\n except Exception as e:\n logger.warn(e)\n\n if \"Pipeline completed. Final result\" in ''.join(log):\n update_stage(cam[:1], 0, int(cam[1:]), 'success_stage')\n update_stage(cam[:1], 1, int(cam[1:]), 'success_stage')\n update_stage(cam[:1], 2, int(cam[1:]), 'success_stage')\n update_stage(cam[:1], 3, int(cam[1:]), 'success_stage')\n elif \"Starting to run step SkySub_QL\" in ''.join(log):\n update_stage(cam[:1], 0, int(cam[1:]), 'success_stage')\n update_stage(cam[:1], 1, int(cam[1:]), 'success_stage')\n update_stage(cam[:1], 2, int(cam[1:]), 'success_stage')\n update_stage(cam[:1], 3, int(cam[1:]), 'processing_stage')\n next\n\n elif \"Starting to run step ApplyFiberFlat_QL\" in ''.join(log):\n update_stage(cam[:1], 0, int(cam[1:]), 'success_stage')\n update_stage(cam[:1], 2, int(cam[1:]), 'processing_stage')\n update_stage(cam[:1], 1, int(cam[1:]), 'success_stage')\n next\n\n elif \"Starting to run step BoxcarExtract\" in ''.join(log):\n update_stage(cam[:1], 0, int(cam[1:]), 'success_stage')\n update_stage(cam[:1], 1, int(cam[1:]), 'processing_stage')\n next\n\n elif \"Starting to run step Preproc\" in ''.join(log):\n update_stage(cam[:1], 0, int(cam[1:]), 'processing_stage')\n next\n else:\n update_stage(cam[:1], 0, int(cam[1:]), 'none')\n update_stage(cam[:1], 1, int(cam[1:]), 'none')\n update_stage(cam[:1], 2, int(cam[1:]), 'none')\n update_stage(cam[:1], 3, int(cam[1:]), 'none')\n\ndef update_stage(band, stage, camera, status):\n if band == 'r':\n cams_stages_r[stage]['camera'][camera] = status\n if band == 'z':\n cams_stages_z[stage]['camera'][camera] = status\n if band == 'b':\n cams_stages_b[stage]['camera'][camera] = status\n\ndef get_camera_status():\n update_camera_status()\n return {\"r\": cams_stages_r, \"b\": cams_stages_b, \"z\": cams_stages_z}\n","sub_path":"backend/framework/qlf/ui_channel/camera_status.py","file_name":"camera_status.py","file_ext":"py","file_size_in_byte":3734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"272488165","text":"from typing import List\n\nfrom webdnn.backend.code_generator.allocator import MemoryLayout\nfrom webdnn.backend.fallback.kernel import Kernel\n# assume (batch_size, in_size) * (in_size, out_size) = (batch_size, out_size), C-order\n# EcmaScript3 to support older browsers\nfrom webdnn.graph.operators.scalar_affine import ScalarAffine\n\nsource = \"\"\"\nscalar_affine: function(input_arrays, output_arrays, option) {\nvar x = input_arrays[0];\nvar y = output_arrays[0];\nvar length = option.length | 0;\nvar s = option.s | 0;\nvar b = option.b | 0;\n\nfor (var i = 0; i < length; i++) {\n var val = x[i];\n y[i] = x[i] * s + b;\n}\n\n},\n\n\"\"\"\n\n\ndef scalar_affine(op: ScalarAffine, memory_layout: MemoryLayout) -> List[Kernel]:\n x = op.inputs[\"x\"]\n y = op.outputs[\"y\"]\n\n kernel = Kernel(\n {\"scalar_affine\": source},\n \"scalar_affine\",\n inputs=[x],\n outputs=[y],\n call_option={\n \"length\": x.size,\n \"s\": op.scale,\n \"b\": op.bias\n }\n )\n\n return [kernel]\n","sub_path":"src/graph_transpiler/webdnn/backend/fallback/kernels/scalar_affine.py","file_name":"scalar_affine.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"238752479","text":"import pandas as pd\n\n# import the dataframe of the dataset used in this project\nfrom createDataFrame import df\n\ndef verifyIntegrityOfDataset():\n\n # get the url of known good source for the dataset\n # here I use a popular version of the dataset on Git Hub Gist\n # note that one must use the raw dataset\n url = \"https://gist.githubusercontent.com/curran/a08a1080b88344b0c8a7/raw/639388c2cbc2120a14dcf466e85730eb8be498bb/iris.csv\"\n\n # create a dataframe object of the verified dataset\n verifiedDataSet = pd.read_csv(url)\n\n # check if the verified dataset and the one used in the project are equal.\n # Note that we must use the DataFrame.equals() method\n # rather than the equality operator - == - as DataFrames\n # are ambiguous in respect of boolean values. See here:\n # https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.equals.html\n assert verifiedDataSet.equals(df), \"This dataset would appear to be corrupted.\"\n print(\"The integrity of the data set has been verified against a known good source.\")\n\n\nif __name__ == '__main__':\n verifyIntegrityOfDataset()\n","sub_path":"verifyIntegrityOfDataset.py","file_name":"verifyIntegrityOfDataset.py","file_ext":"py","file_size_in_byte":1123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"199278420","text":"import json\n\nfrom adopt.apps.core.models import Client, Product\nfrom adopt.utils.tests import ViewTestCaseTemplate\nfrom django.test import TestCase\nfrom django.test.client import encode_multipart\nfrom django.test.utils import isolate_apps\nfrom django.urls import reverse\nfrom rest_framework import status\nfrom rest_framework.parsers import JSONParser\nfrom rest_framework.renderers import JSONRenderer\nfrom rest_framework.test import APIClient\n\nfrom ..models import Campaign, DigitalPlacement, DigitalSpotplan\nfrom ..serializers import CampaignSerializer\n\n# from django.contrib.auth import get_user_model\n# from apps.users.serializers import UserSerializer\n\n# User = get_user_model()\n\n\nclass SerializerTestCase(TestCase):\n fixtures = ['test_users', 'test_core', 'test_campaigns']\n\n def setUp(self):\n self.json_render = JSONRenderer()\n self.json_parser = JSONParser()\n self.client = Client.objects.get(name='mercedes')\n\n def test_serializer_can_create_campaign(self):\n cmpg_data = \\\n {\n 'name': 'carshanghai',\n 'client': 'mercedes',\n 'year': 2018,\n 'digital_spotplans': [\n {'name': 'OTV', 'product': 'car1', 'packages': [\n # {\n # 'code':'00213f','name':'sohu_home page','media_cat':'news','media':'sohu',\n # 'package_type':'m', 'package_price': 2000\n # }\n ], 'placements':[\n {'name': 'sohu_home page', 'media_cat': 'news', 'media': 'sohu', 'line_no': 1, 'position': 'home page', 'format': '/', 'size': '200x200 gif', 'device': 'PC', 'target_market': 'Shanghai', 'start_date': '2018-04-13', 'end_date': '2018-06-13', 'media_pkg': '00213f', 'media_cost': 2000, 'spots': [\n {'date': '2018-04-01', 'quantity': 1}\n ]},\n {'name': 'sohu_home page', 'media_cat': 'news', 'media': 'sohu', 'line_no': 2, 'position': 'home page', 'format': '/', 'size': '200x200 gif', 'device': 'PC', 'target_market': 'Shanghai', 'start_date': '2018-04-13', 'end_date': '2018-06-13', 'media_pkg': '00213f', 'quantity': 1, 'spots': [\n {'date': '2018-04-01', 'quantity': 1}\n ]},\n {'name': 'sohu_home page', 'media_cat': 'news', 'media': 'sohu', 'line_no': 3, 'position': 'home page', 'format': '/', 'size': '200x200 gif', 'device': 'PC', 'target_market': 'Shanghai', 'start_date': '2018-04-13', 'end_date': '2018-06-13', 'media_pkg': '00213f', 'quantity': 2, 'total_ratecard_cost': 120000, 'spots': []},\n ]\n }\n ]\n }\n # cmpg = self.json_parser.parse(cmpg_data)\n\n # test create\n serl = CampaignSerializer(\n data=cmpg_data, context={'current_client': self.client})\n serl.is_valid()\n self.assertEqual(\n serl.errors, {}, msg=\"json data isn't valid for serializer!\")\n content = self.json_render.render(cmpg_data)\n self.assertNotEqual(b'{}', content, msg='')\n obj = serl.create(serl.validated_data)\n self.assertTrue(obj.id > 0, 'new campaign should has created')\n self.assertEqual(\n len(obj.digital_spotplans.all()), 1,\n 'should created 1 digital spotplan')\n sp = obj.digital_spotplans.first()\n self.assertIsNotNone(sp.product, \"product should not be empty\")\n plcms = sp.placements.order_by('line_no').all()\n\n self.assertNotEqual(plcms[0].name, plcms[1].name,\n 'should not have same placement name')\n print(plcms[0].name, plcms[1].name)\n print(plcms[0].quantity, plcms[1].quantity)\n plcms\n self.assertEqual(plcms[2].quantity, 2,\n \"the 3rd placement should have quantity 2\")\n self.assertEqual(plcms[2].total_ratecard_cost, 120000,\n \"the 3rd placement should have ratecard 120000\")\n # test update with new spot\n cmpg_data['digital_spotplans'][0]['placements'][0]['spots'].append({'date': '2018-04-02', 'quantity': 1})\n serl = CampaignSerializer(\n data=cmpg_data, context={'client': self.client})\n serl.is_valid()\n self.assertEqual(\n serl.errors, {}, msg=\"json data isn't valid for serializer!\")\n obj = serl.update(obj, serl.validated_data)\n self.assertEqual(\n len(obj.digital_spotplans.all()), 1,\n 'should created 1 digital spotplan')\n sp = obj.digital_spotplans.first()\n plcms = sp.placements.order_by('line_no').all()\n self.assertNotEqual(plcms[0].name, plcms[1].name,\n 'should not have same placement name')\n print(plcms[0].name, plcms[1].name)\n self.assertEqual(plcms[2].total_ratecard_cost, 120000,\n \"the 3rd placement should have ratecard 120000\")\n self.assertTrue(sp.packages.count() != 0, 'should has package price')\n self.assertEqual(sp.packages.first().package_price, 2000)\n self.assertIsNotNone(plcms[0].target_market,\n \"market should not be empty\")\n self.assertEqual(plcms[0].spots.count(), 2,\n \"should have 2 spot\")\n","sub_path":"adopt/apps/campaign/tests/test_serializer.py","file_name":"test_serializer.py","file_ext":"py","file_size_in_byte":5374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"535547012","text":"import string\nimport random\n\n\nfo = open(\"hola.txt\", \"wb\")\nfo.write(\"python Es el mejor lenguaje\")\nfor i in range(10):\n texto = ''.join(random.SystemRandom().choice(string.ascii_uppercase + string.digits) for _ in range(10))\n fo.write(\"%s\" % texto)\n \nfo.close()\n\nfo2 = open(\"hola.txt\",\"r+\")\nsrti = fo2.read()\nprint(srti)\nfo2.close()","sub_path":"archivo.py","file_name":"archivo.py","file_ext":"py","file_size_in_byte":340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"593347076","text":"#!/usr/bin/env python3\n# coding: utf-8\n\nimport itertools\n\nimport plac\n\nfrom ..io import read_jsonl, write_jsonl\nfrom ..logger import logger\n\n\nclass TokenTagger:\n def __init__(self, task=\"splitting\", lowercase=True, text=True):\n \"\"\"\n Converts data in prodigy format with full reference spans to per-token\n spans\n\n Args:\n task (str): One of [\"parsing\", \"splitting\"]. See below further\n explanation.\n lowercase (bool): Automatically convert upper case annotations to\n lowercase under the parsing scenario.\n text (bool): Include the token text in the output span (very useful\n for debugging).\n\n Since the parsing, splitting, and classification tasks have quite\n different labelling requirements, this class behaves differently\n depending on which task is specified in the task argument.\n\n For splitting:\n\n Expects one of four labels for the spans:\n\n * BE: A complete reference\n * BI: A frgament of reference that captures the beginning but not the end\n * IE: A frgament of reference that captures the end but not the beginning\n * II: A fragment of a reference that captures neither the beginning nor the\n end .\n\n Depending on which label is applied the tokens within the span will be\n labelled differently as one of [\"b-r\", \"i-r\", \"e-r\", \"o\"].\n\n For parsing:\n\n Expects any arbitrary label for spans. All tokens within that span will\n be labelled with the same span.\n\n \"\"\"\n\n self.out = []\n self.task = task\n self.lowercase = lowercase\n self.text = text\n\n def tag_doc(self, doc):\n \"\"\"\n Tags a document with appropriate labels for the parsing task\n\n Args:\n doc(dict): A single document in prodigy dict format to be labelled.\n \"\"\"\n\n bie_spans = self.reference_spans(doc[\"spans\"], doc[\"tokens\"], task=self.task)\n o_spans = self.outside_spans(bie_spans, doc[\"tokens\"])\n\n # Flatten into one list.\n\n spans = itertools.chain(bie_spans, o_spans)\n\n # Sort by token id to ensure it is ordered.\n\n spans = sorted(spans, key=lambda k: k[\"token_start\"])\n\n doc[\"spans\"] = spans\n\n return doc\n\n def run(self, docs):\n \"\"\"\n Main class method for tagging multiple documents.\n\n Args:\n docs(dict): A list of docs in prodigy dict format to be labelled.\n \"\"\"\n\n for doc in docs:\n\n self.out.append(self.tag_doc(doc))\n\n return self.out\n\n def reference_spans(self, spans, tokens, task):\n \"\"\"\n Given a whole reference span as labelled in prodigy, break this into\n appropriate single token spans depending on the label that was applied to\n the whole reference span.\n \"\"\"\n split_spans = []\n\n if task == \"splitting\":\n\n for span in spans:\n if span[\"label\"] in [\"BE\", \"be\"]:\n\n split_spans.extend(\n self.split_long_span(tokens, span, \"b-r\", \"e-r\", \"i-r\")\n )\n\n elif span[\"label\"] in [\"BI\", \"bi\"]:\n\n split_spans.extend(\n self.split_long_span(tokens, span, \"b-r\", \"i-r\", \"i-r\")\n )\n\n elif span[\"label\"] in [\"IE\", \"ie\"]:\n\n split_spans.extend(\n self.split_long_span(tokens, span, \"i-r\", \"e-r\", \"i-r\")\n )\n\n elif span[\"label\"] in [\"II\", \"ii\"]:\n\n split_spans.extend(\n self.split_long_span(tokens, span, \"i-r\", \"i-r\", \"i-r\")\n )\n\n elif task == \"parsing\":\n\n for span in spans:\n if self.lowercase:\n label = span[\"label\"].lower()\n else:\n label = span[\"label\"]\n split_spans.extend(\n self.split_long_span(tokens, span, label, label, label)\n )\n\n return split_spans\n\n def outside_spans(self, spans, tokens):\n \"\"\"\n Label tokens with `o` if they are outside a reference\n\n Args:\n spans(list): Spans in prodigy format.\n tokens(list): Tokens in prodigy format.\n\n Returns:\n list: A list of spans in prodigy format that comprises the tokens which\n are outside of a reference.\n \"\"\"\n # Get the diff between inside and outside tokens\n\n span_indices = set([span[\"token_start\"] for span in spans])\n token_indices = set([token[\"id\"] for token in tokens])\n\n outside_indices = token_indices - span_indices\n\n outside_spans = []\n\n for index in outside_indices:\n outside_spans.append(self.create_span(tokens, index, \"o\"))\n\n return outside_spans\n\n def create_span(self, tokens, index, label):\n \"\"\"\n Given a list of tokens, (in prodigy format) and an index relating to one of\n those tokens, and a new label: create a single token span using the new\n label, and the token selected by `index`.\n \"\"\"\n\n token = tokens[index]\n\n span = {\n \"start\": token[\"start\"],\n \"end\": token[\"end\"],\n \"token_start\": token[\"id\"],\n \"token_end\": token[\"id\"],\n \"label\": label,\n }\n\n if self.text:\n span[\"text\"] = token[\"text\"]\n\n return span\n\n def split_long_span(self, tokens, span, start_label, end_label, inside_label):\n \"\"\"\n Split a multi-token span into `n` spans of lengh `1`, where `n=len(tokens)`\n \"\"\"\n spans = []\n start = span[\"token_start\"]\n end = span[\"token_end\"]\n\n span_size = end - start\n\n # Case when there is only one token in the span\n if span_size == 0:\n spans.append(self.create_span(tokens, start, start_label))\n # Case when there are two or more tokens in the span\n else:\n spans.append(self.create_span(tokens, start, start_label))\n spans.append(self.create_span(tokens, end, end_label))\n\n if span_size > 1:\n\n for index in range(start + 1, end):\n spans.append(self.create_span(tokens, index, inside_label))\n\n spans = sorted(spans, key=lambda k: k[\"token_start\"])\n\n return spans\n\n\n@plac.annotations(\n input_file=(\n \"Path to jsonl file containing chunks of references in prodigy format.\",\n \"positional\",\n None,\n str,\n ),\n output_file=(\n \"Path to jsonl file into which fully annotate files will be saved.\",\n \"positional\",\n None,\n str,\n ),\n task=(\n \"Which task is being performed. Either splitting or parsing.\",\n \"positional\",\n None,\n str,\n ),\n lowercase=(\n \"Convert UPPER case reference labels to lower case token labels?\",\n \"flag\",\n \"f\",\n bool,\n ),\n text=(\n \"Output the token text in the span (useful for debugging).\",\n \"flag\",\n \"t\",\n bool,\n ),\n)\ndef reference_to_token_annotations(\n input_file, output_file, task=\"splitting\", lowercase=False, text=False\n):\n \"\"\"\n Creates a span for every token from existing multi-token spans\n\n Converts a jsonl file output by prodigy (using prodigy db-out) with spans\n extending over more than a single token to individual token level spans.\n\n The rationale for this is that reference level annotations are much easier\n for humans to do, but not useful when training a token level model.\n\n This command functions in two ways:\n\n * task=splitting: For the splitting task where we are interested in\n labelling the beginning (b-r) and end (e-r) of references, reference\n spans are labelled with one of BI, BE, IE, II. These are then converted\n to token level spans b-r, i-r, e-r, and o using logic. Symbolically:\n * BE: [BE, BE, BE] becomes [b-r][i-r][e-r]\n * BI: [BI, BI, BI] becomes [b-r][i-r][i-r]\n * IE: [IE, IE, IE] becomes [i-r][i-r][e-r]\n * II: [II, II, II] becomes [i-r][i-r][i-r]\n * All other tokens become [o]\n\n * task=parsing: For the parsing task, multi-task annotations are much\n simpler and would tend to be just 'author', or 'title'. These simple\n labels can be applied directly to the individual tokens contained within\n these multi-token spans; for each token in the multi-token span, a span\n is created with the same label. Symbolically:\n * [author author author] becomes [author][author][author]\n \"\"\"\n\n ref_annotated_docs = read_jsonl(input_file)\n\n # Only run the tagger on annotated examples.\n\n ref_annotated_docs = [doc for doc in ref_annotated_docs if doc.get(\"spans\")]\n\n logger.info(\n \"Loaded %s documents with reference annotations\", len(ref_annotated_docs)\n )\n\n annotator = TokenTagger(task=task, lowercase=lowercase, text=text)\n\n token_annotated_docs = annotator.run(ref_annotated_docs)\n\n write_jsonl(token_annotated_docs, output_file=output_file)\n\n logger.info(\n \"Wrote %s docs with token annotations to %s\",\n len(token_annotated_docs),\n output_file,\n )\n","sub_path":"deep_reference_parser/prodigy/reference_to_token_annotations.py","file_name":"reference_to_token_annotations.py","file_ext":"py","file_size_in_byte":9390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"611083996","text":"from __future__ import unicode_literals\r\nimport os\r\nimport requests # to sent GET requests\r\nfrom bs4 import BeautifulSoup # to parse HTML\r\nfrom tkinter import *\r\nimport tkinter as tk\r\nimport traceback\r\nfrom tkinter import messagebox as m_box\r\nfrom tkinter import filedialog\r\nfrom PIL import Image\r\nimport youtube_dl\r\nfrom PIL import ImageTk\r\n\r\nroot = Tk()\r\nroot.title(\"Media Downloader\")\r\n\r\n \r\nyahoo_img = \\\r\n 'https://in.images.search.yahoo.com/search/images;_ylt=AwrwJSJD2Q1fTlkATCK8HAx.;_ylc=X1MDMjExNDcyMzAwNARfcgMyBGZyAwRncHJpZAN6VDFjeUl0WlFfLnRqMGU1YlNTTGVBBG5fc3VnZwMxMARvcmlnaW4DaW4uaW1hZ2VzLnNlYXJjaC55YWhvby5jb20EcG9zAzAEcHFzdHIDBHBxc3RybAMEcXN0cmwDNARxdWVyeQNkb2dzBHRfc3RtcAMxNTk0NzQzMTEw?fr2=sb-top-in.images.search&'\r\n\r\nuser_agent = {\r\n 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',\r\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\r\n 'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3',\r\n 'Accept-Encoding': 'none',\r\n 'Accept-Language': 'en-US,en;q=0.8',\r\n 'Connection': 'keep-alive',\r\n}\r\nsave_folder = 'images'\r\n\r\n#---------------------------------------------------------------------------------\r\ndef download_n():\r\n \r\n f1=Frame(root)\r\n z = Canvas(f1, width=400,height=280,bg='#0B3680') #2367FA\r\n image = ImageTk.PhotoImage(file='sm1.jpg')\r\n z.create_image(1, 1, image = image, anchor = NW)\r\n \r\n# image1= ImageTk.PhotoImage(file=\"E:\\\\sem4\\\\apsit skills\\\\page1.jpg\")\r\n# label_for_image= Label(f1, image=image1)\r\n# label_for_image.pack()\r\n\r\n# photo = ImageTk.PhotoImage(Image.open('page1.jpg'))\r\n# \r\n# l = Label(image = photo)\r\n# l.pack() \r\n \r\n# Label(f1, text=\"What are you looking for?\", fg = \"Black\",\r\n#font = \"Verdana 10\").place(x=90,y=40)\r\n z.create_text(195,50, text=\"What are you looking for?\", fill = \"White\",font = ('Verdana'))\r\n \r\n\r\n e1=Entry(f1)\r\n e1.place(x=90,y=70)\r\n \r\n# Label(f1, text=\"How many images do you want? \", fg = \"Black\",\r\n#font = \"Verdana 10\").place(x=90,y=110)\r\n z.create_text(225,120, text=\"How many images do you want?\", fill = \"White\",font = ('Verdana'))\r\n \r\n \r\n e2=Entry(f1)\r\n e2.place(x=90,y=140)\r\n \r\n button4 = tk.Button(f1, text='Download', width=17,\r\n bg=\"#D3D3D3\",fg='black',\r\n command=lambda:download_images(e1,e2))\r\n button4.place(x=90,y=190)\r\n \r\n button5 = tk.Button(f1, text='Back', width=10,\r\n bg=\"#D3D3D3\",fg='black',\r\n command=lambda:[f1.destroy(),main()]).place(x=225,y=190)\r\n \r\n z.pack()\r\n f1.pack()\r\n \r\n \r\n \r\ndef download_images(e1,e2):\r\n try:\r\n# f1=Frame(root)\r\n data=e1.get()\r\n n_images=e2.get()\r\n if data=='' or n_images=='':\r\n# root1.withdraw()\r\n m_box.showerror('Error','Please fill both entries ')\r\n else:\r\n data=str(data)\r\n n_images=int(n_images)\r\n # print(data,n_images)\r\n\r\n# z = Canvas(f1, width=260,height=110)\r\n \r\n \r\n \r\n print('Start searching...')\r\n \r\n \r\n \r\n # get url query string\r\n searchurl = yahoo_img + 'p=' + data\r\n #print(searchurl)\r\n \r\n # request url, without user_agent the permission gets denied\r\n response = requests.get(searchurl, headers=user_agent)\r\n html = response.text\r\n \r\n soup = BeautifulSoup(html, 'html.parser')\r\n results = soup.find_all('img',class_= 'process',limit=n_images)\r\n \r\n # extract the link from the img tag\r\n imagelinks= []\r\n \r\n for re in results:\r\n url1=re.attrs.get('data-src')\r\n imagelinks.append(url1)\r\n \r\n print(f'found {len(imagelinks)} images')\r\n# Label(f1, text=f'found {len(imagelinks)} images', fg = \"Black\",\r\n# font = \"Verdana 10\",bg = \"LightGrey\").place(x=70,y=20)\r\n print('Start downloading...')\r\n # Label(root1, text=\"Start downloading...\", fg = \"Black\",\r\n # font = \"Verdana 10\").pack()\r\n \r\n for i, imagelink in enumerate(imagelinks):\r\n # open image link and save as file\r\n response = requests.get(imagelink)\r\n \r\n imagename = save_folder + '/' + data + str(i+1) + '.jpg'\r\n with open(imagename, 'wb') as file:\r\n file.write(response.content)\r\n \r\n print('Done')\r\n# Label(f1, text=\"DOWNLOADING COMPLETE\", fg = \"Black\",\r\n# font = \"Verdana 10\",bg = \"LightGrey\").place(x=40,y=40)\r\n# button5 = tk.Button(f1, text='OK', width=10,\r\n# bg=\"#D3D3D3\",fg='black',\r\n# command=f1.destroy).place(x=90,y=70)\r\n m_box.showinfo(title='Done', message=f'found {len(imagelinks)} images \\nDownloading Complete') \r\n# z.pack()\r\n \r\n except ValueError:\r\n# root1.withdraw()\r\n m_box.showwarning('Error','Enter a Valid Number')\r\n# print(\"enter valid number\")\r\n# root2 = Tk()\r\n# z = Canvas(root2, width=260,height=110)\r\n# Label(root2, text=\"Enter a valid Number\", fg = \"Black\",\r\n# font = \"Verdana 10\").place(x=60,y=30)\r\n# button5 = tk.Button(root2, text='OK', width=10,\r\n# bg=\"#D3D3D3\",fg='black',\r\n# command=root2.destroy).place(x=90,y=70)\r\n# \r\n# z.pack()\r\n#------------------------------------------------------------------------------------\r\n\r\n\r\ndef url_n():\r\n f2=Frame(root)\r\n z = Canvas(f2, width=400,height=280,bg='#0B3680')\r\n# image = ImageTk.PhotoImage(file = \"E:/sem4/apsit skills/page1.jpg\")\r\n# z.create_image(1, 1, image = image, anchor = NW)\r\n# Label(f2, text=\"Enter Url : \", fg = \"Black\",\r\n#font = \"Verdana 10\",bg = \"LightGrey\").place(x=90,y=40)\r\n z.create_text(135,50, text=\"Enter Url : \", fill = \"White\",font = ('Verdana'))\r\n \r\n\r\n e1=Entry(f2,width=35)\r\n e1.place(x=90,y=70)\r\n \r\n# Label(f2, text=\"Name of the image to be saved :\", fg = \"Black\",\r\n#font = \"Verdana 10\",bg = \"LightGrey\").place(x=90,y=110)\r\n z.create_text(225,120, text=\"Name of the image to be saved :\", fill = \"White\",font = ('Verdana'))\r\n \r\n \r\n \r\n e2=Entry(f2)\r\n e2.place(x=90,y=140)\r\n \r\n \r\n button4 = tk.Button(f2, text='Download', width=17,\r\n bg=\"#D3D3D3\",fg='black',\r\n command=lambda:url_images(e1,e2)).place(x=90,y=190)\r\n button5 = tk.Button(f2, text='Back', width=10,\r\n bg=\"#D3D3D3\",fg='black',\r\n command=lambda:[f2.destroy(),main()]).place(x=225,y=190)\r\n \r\n \r\n z.pack()\r\n f2.pack()\r\n \r\ndef url_images(e1,e2):\r\n try:\r\n \r\n# root1 = Tk()\r\n# root1.title(\"Done\")\r\n#\r\n# z = Canvas(root1, width=260,height=110)\r\n \r\n imagelink=e1.get()\r\n data=e2.get()\r\n if imagelink=='' or data=='':\r\n# root1.withdraw()\r\n m_box.showerror('Error','Please fill both entries ')\r\n else:\r\n response = requests.get(imagelink)\r\n imagename = save_folder + '/' + data + '.jpg'\r\n with open(imagename, 'wb') as file:\r\n file.write(response.content)\r\n print('Done')\r\n m_box.showinfo(title='Done', message='Downloading Complete') \r\n# Label(root1, text=\"IMAGE DOWNLOADED\", fg = \"Black\",\r\n# font = \"Verdana 10\",bg = \"LightGrey\").place(x=60,y=30)\r\n# button5 = tk.Button(root1, text='OK', width=10,\r\n# bg=\"#D3D3D3\",fg='black',\r\n# command=root1.destroy).place(x=90,y=70)\r\n \r\n# z.pack()\r\n except :\r\n# root1.withdraw()\r\n m_box.showwarning('Invalid Url','Enter a Valid URL')\r\n \r\n \r\n \r\n#------------------------------------------------------------------------------------------\r\n \r\ndef insta_n():\r\n f3=Frame(root)\r\n \r\n z = Canvas(f3, width=400,height=280,bg = '#0B3680')\r\n image = ImageTk.PhotoImage(file = \"E:\\\\program files\\\\finalmediadownloader\\\\ig.png\")\r\n z.create_image(1, 1, image = image, anchor = NW)\r\n# Label(f3, text=\"Enter Instagram Image link : \", fg = \"Black\",\r\n#font = \"Verdana 10\",bg = \"LightGrey\").place(x=90,y=40)\r\n z.create_text(210,50, text=\"Enter Instagram Image link : \", fill = \"White\",font = ('Verdana'))\r\n \r\n\r\n e1=Entry(f3,width=35)\r\n e1.place(x=90,y=70)\r\n \r\n# Label(f3, text=\"Name of the image to be saved :\", fg = \"Black\",\r\n#font = \"Verdana 10\",bg = \"LightGrey\").place(x=90,y=110)\r\n z.create_text(225,120, text=\"Name of the image to be saved :\", fill = \"White\",font = ('Verdana'))\r\n \r\n \r\n e2=Entry(f3)\r\n e2.place(x=90,y=140)\r\n \r\n button4 = tk.Button(f3, text='Download', width=17,\r\n bg=\"#D3D3D3\",fg='black',\r\n command=lambda:insta_images(e1,e2)).place(x=90,y=190)\r\n button5 = tk.Button(f3, text='Back', width=10,\r\n bg=\"#D3D3D3\",fg='black',\r\n command=lambda:[f3.destroy(),main()]).place(x=225,y=190)\r\n \r\n z.pack()\r\n f3.pack()\r\ndef insta_images(e1,e2):\r\n try:\r\n \r\n url=e1.get()\r\n data=e2.get()\r\n \r\n if data=='' or url=='':\r\n\r\n m_box.showerror('Error','Please fill both entries ')\r\n else:\r\n usr_agent = {\r\n 'User-Agent': 'Mozilla/5.0 (X11; Linux x86_64) AppleWebKit/537.11 (KHTML, like Gecko) Chrome/23.0.1271.64 Safari/537.11',\r\n 'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\r\n 'Accept-Charset': 'ISO-8859-1,utf-8;q=0.7,*;q=0.3',\r\n 'Accept-Encoding': 'none',\r\n 'Accept-Language': 'en-US,en;q=0.8',\r\n 'Connection': 'keep-alive',}\r\n response = requests.get(url, headers=usr_agent)\r\n html = response.text\r\n #soup = BeautifulSoup(html, 'html.parser')\r\n \r\n soup = BeautifulSoup(html,'html.parser')\r\n metaTag = soup.find_all('meta', {'property':'og:image'})\r\n imagelink = metaTag[0]['content']\r\n print(imagelink)\r\n \r\n response = requests.get(imagelink)\r\n imagename = save_folder + '/' + data + '.jpg'\r\n with open(imagename, 'wb') as file:\r\n file.write(response.content)\r\n print('Done')\r\n m_box.showinfo(title='Done', message='Downloading Complete') \r\n \r\n# Label(root1, text=\"IMAGE DOWNLOADED\", fg = \"Black\",\r\n# font = \"Verdana 10\",bg = \"LightGrey\").place(x=60,y=30)\r\n# button5 = tk.Button(root1, text='OK', width=10,\r\n# bg=\"#D3D3D3\",fg='black',\r\n# command=root1.destroy).place(x=90,y=70)\r\n \r\n \r\n\r\n except :\r\n# root1.withdraw()\r\n m_box.showwarning('Invalid Instagram Link','Enter a Valid URL')\r\n# print(\"Invalid Image Url\")\r\n# root2 = Tk()\r\n# z = Canvas(root2, width=260,height=110)\r\n# Label(root2, text=\"Invalid Image Url\", fg = \"Black\",\r\n# font = \"Verdana 10\").place(x=60,y=30)\r\n# button5 = tk.Button(root2, text='OK', width=10,\r\n# bg=\"#D3D3D3\",fg='black',\r\n# command=root2.destroy).place(x=90,y=70) \r\n# z.pack()\r\n \r\n#------------------------------------------------------------------------\r\ndef yt_n():\r\n f4=Frame(root)\r\n\r\n z = Canvas(f4, width=400,height=280,bg='#0B3680')\r\n image = ImageTk.PhotoImage(file = \"E:\\\\program files\\\\finalmediadownloader\\\\ytbg.jpeg\")\r\n z.create_image(0, 0, image = image, anchor = NW)\r\n \r\n# Label(f4, text=\"Enter Url : \", fg = \"Black\",\r\n#font = \"Verdana 10\",bg =\"LightGrey\").place(x=90,y=70)\r\n z.create_text(175,90, text=\"Enter Youtube link :\", fill = \"White\",font = ('Verdana'))\r\n \r\n\r\n e1=Entry(f4,width=35)\r\n e1.place(x=90,y=110)\r\n \r\n# Label(root1, text=\"Name of the image to be saved :\", fg = \"Black\",\r\n# font = \"Verdana 10\").place(x=90,y=90)\r\n# \r\n# e2=Entry(root1)\r\n# e2.place(x=90,y=120)\r\n \r\n \r\n button4 = tk.Button(f4, text='Download', width=17,\r\n bg=\"#D3D3D3\",fg='black',\r\n command=lambda:yt_videos(e1)).place(x=90,y=170)\r\n button5 = tk.Button(f4, text='Back', width=10,\r\n bg=\"#D3D3D3\",fg='black',\r\n command=lambda:[f4.destroy(),main()]).place(x=225,y=170)\r\n \r\n \r\n z.pack()\r\n f4.pack()\r\n \r\ndef yt_videos(e1):\r\n try:\r\n \r\n url=e1.get()\r\n if url=='' :\r\n \r\n m_box.showerror('Error','Please enter url ')\r\n else:\r\n ydl_opts = {}\r\n# root1.withdraw()\r\n f=filedialog.askdirectory(initialdir='C:/')\r\n os.chdir(f)\r\n \r\n \r\n with youtube_dl.YoutubeDL(ydl_opts) as ydl:\r\n ydl.download([url])\r\n m_box.showinfo(title='Done', message='Downloading Complete')\r\n# Label(root1, text=\"VIDEO DOWNLOADED\", fg = \"Black\",\r\n# font = \"Verdana 10\",bg = \"LightGrey\").place(x=60,y=30)\r\n# button5 = tk.Button(root1, text='OK', width=10,\r\n# bg=\"#D3D3D3\",fg='black',\r\n# command=root1.destroy).place(x=90,y=70)\r\n# z.pack()\r\n except:\r\n# root1.withdraw()\r\n m_box.showwarning('Invalid Youtube Link','Enter a Valid URL')\r\n \r\n \r\n#----------------------------------------------------------------------------------------- \r\n \r\n\r\ndef main():\r\n if not os.path.exists(save_folder):\r\n os.mkdir(save_folder)\r\n \r\n f=Frame(root)\r\n \r\n \r\n w = Canvas(f, width=400,height=280,bg = \"black\")\r\n \r\n \r\n# root.wm_attributes('-transparentcolor','black')\r\n image = ImageTk.PhotoImage(file = \"E:\\\\program files\\\\finalmediadownloader\\\\sm1.jpg\")\r\n w.create_image(1, 1, image = image, anchor = NW)\r\n\r\n# background_image=tk.PhotoImage(file=\"E:/sem4/apsit skills/page1.jpg\")\r\n# background_label = Label(root, image=background_image)\r\n# bakcground_label.pack()\r\n \r\n# Label(root, text=\"Media Downloader\", fg = \"Black\",\r\n#font = \"Verdana 14\",pady=10,padx=10,bg = \"LightGrey\").place(x=100,y=20)\r\n \r\n# Label(root, text=\"Media Downloader\", fg = \"White\",bg='black',padx=10,pady=10,\r\n#font = \"Verdana 14\").place(x=100,y=20)\r\n \r\n w.create_text(200,50, text=\"Media Downloader\", fill = \"White\",font = ('Verdana', '18'))\r\n \r\n button1 = tk.Button(f, text='Download n required images', width=35,\r\n command=lambda: [download_n(),f.destroy()]).place(x=75,y=100)\r\n button2 = tk.Button(f, text='Download via url', width=35,\r\n command=lambda: [url_n(),f.destroy()]).place(x=75,y=140)\r\n button3 = tk.Button(f, text='Download instagram images', width=35,\r\n command=lambda: [insta_n(),f.destroy()]).place(x=75,y=180)\r\n button4 = tk.Button(f, text='Download youtube videos', width=35,\r\n command=lambda: [yt_n(),f.destroy()]).place(x=75,y=220)\r\n\r\n w.pack()\r\n f.pack()\r\n mainloop()\r\n \r\n \r\nif __name__ == '__main__':\r\n main()\r\n","sub_path":"finalmediadownloader.py","file_name":"finalmediadownloader.py","file_ext":"py","file_size_in_byte":15666,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"119660521","text":"#coding:utf-8\n\nimport smtplib\nimport weather\nimport news\nimport email\nimport os\nfrom email.mime.text import MIMEText\nfrom email.header import Header\nfrom email.mime.multipart import MIMEMultipart \nfrom email.mime.application import MIMEApplication\nimport random\n# import urllib\n# import urllib2\nimport json\n\n\n_user = \"\"\n_pwd = \"\"\n_to = \"\"\n# _to = \"harrylyx@qq.com\"\nfile_name = \"/chat/static/images/bg-main.jpg\" \n\npro = random.randint(1, 280)\n# url = 'https://ky.crazyc.cn/proverb/%d'%(pro)\n# req = urllib2.Request(url)\n# res_data = urllib2.urlopen(req)\n# res = res_data.read()\n# res = json.loads(res)\n\nweather_data = weather.getweather()\n\n#使用MIMEText构造符合smtp协议的header及body\n#下面构造网页\nmail_msg = \"\"\"\n<html>\n\t<body>\n\t\t<h4>{10}今天的天气</h4>\n\t\t<p>空气质量：{0}，空气质量指数：{1}，PM2.5：{2}</p>\n\t\t<p>实时天气：{3}，最高温度：{4}℃，最低温度：{5}℃</p>\n\t\t<p>{6}</p>\n\t\t<p>风向：{7}，风力：{8}级</p>\n\t\t<hr/>\n\t\t<h4>今天的新闻头条</h4>\n\t\t{9}\n\t</body>\n</html>\n\"\"\".format(weather_data[4], weather_data[2], weather_data[3], \n\t\tweather_data[6], weather_data[11], weather_data[12], \n\t\tweather_data[8], weather_data[9], weather_data[10], \n\t\tnews.getnews(),weather_data[5])\n\nmessage = MIMEMultipart()\n\ntext = MIMEText(mail_msg, 'html', 'utf-8')#内容\nmessage.attach(text)\n\n# 构造MIMEBase对象做为文件附件内容并附加到根容器 \ncontype = 'application/octet-stream' \nmaintype, subtype = contype.split('/', 1) \n \n## 读入文件内容并格式化 \n# data = open(file_name, 'rb') \n# file_msg = email.MIMEBase.MIMEBase(maintype, subtype) \n# file_msg.set_payload(data.read( )) \n# data.close( ) \n# email.Encoders.encode_base64(file_msg) \n \n## 设置附件头 \n# basename = 'Bing.jpg' \n# file_msg.add_header('Content-Disposition','attachment', filename = basename) \n# message.attach(file_msg) \n\nmessage['From'] = _user#发送方\nmessage['To'] = _to#接收方\n\nsubject = 'A New Day'#标题\nmessage['Subject'] = Header(subject, 'utf-8')\n\ns = smtplib.SMTP(\"smtp.163.com\", timeout=300)#连接smtp邮件服务器,端口默认是25\ns.login(_user, _pwd)\ns.sendmail(_user, _to, message.as_string())#发送邮件\ns.close() \n","sub_path":"sendMail.py","file_name":"sendMail.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"346508460","text":"#! /usr/bin/env python\n\n# Script to launch AllenNLP Beaker jobs.\n\nimport argparse\nimport os\nimport json\nimport random\nimport tempfile\nimport subprocess\nimport sys\nimport _jsonnet\n\n# This has to happen before we import spacy (even indirectly), because for some crazy reason spacy\n# thought it was a good idea to set the random seed on import...\nrandom_int = random.randint(0, 2**32)\n\nsys.path.insert(0, os.path.dirname(os.path.abspath(os.path.join(os.path.join(__file__, os.pardir), os.pardir))))\n\ndef main(args):\n # Do steps (1-3) manually\n print(f\"Using the specified image: {args.image}\")\n\n run_command = [\n \"python\",\n \"src/exp.py\"\n ]\n\n for t in args.targ:\n x,y = t.strip().split('=')\n run_command.append(f'-{x}')\n run_command.append(y)\n\n # (4) Automatically make spec file\n # For each dataset, mount to docker container\n dataset_mounts = []\n for source in args.source:\n datasetID, containerPath = source.split(':')\n dataset_mounts.append({\n \"datasetId\": datasetID,\n \"containerPath\": containerPath\n })\n\n env = {}\n\n # Override defaults\n for var in args.env:\n key, value = var.split(\"=\")\n env[key] = value\n\n config_spec = {\n \"image\": args.image,\n \"description\": args.desc,\n \"resultPath\": \"/output\",\n \"args\": run_command,\n \"datasetMounts\": dataset_mounts,\n \"env\": env\n }\n config_task = {\"spec\": config_spec, \"name\": \"training\"}\n\n if args.cluster:\n config_task[\"cluster\"] = args.cluster\n\n config = {\n \"tasks\": [config_task]\n }\n\n output_path = args.spec_output_path if args.spec_output_path else tempfile.mkstemp(\".yaml\",\n \"beaker-config-\")[1]\n with open(output_path, \"w\") as output:\n output.write(json.dumps(config, indent=4))\n print(f\"Beaker spec written to {output_path}.\")\n\n # (5) Create beaker experiment\n experiment_command = [\"beaker\", \"experiment\", \"create\", \"--file\", output_path]\n\n if args.dry_run:\n print(f\"This is a dry run (--dry-run). Launch your job with the following command:\")\n print(f\" \" + \" \".join(experiment_command))\n else:\n print(f\"Running the experiment:\")\n print(f\" \" + \" \".join(experiment_command))\n subprocess.run(experiment_command)\n\nif __name__ == \"__main__\":\n parser = argparse.ArgumentParser()\n\n parser.add_argument('image', type=str, help='Docker image to use')\n parser.add_argument('--cluster', type=str, help='A name for the experiment.')\n parser.add_argument('--desc', type=str, default=\"\", help='A description for the experiment.')\n parser.add_argument('--targ', action='append', default=[], help='Training args (e.g. lr=0.1)')\n parser.add_argument('--config_file', type=str, default='default_beaker_configs.jsonnet', help='Path to json config file')\n parser.add_argument('--spec_output_path', type=str, help='The destination to write the experiment spec.')\n parser.add_argument('--dry-run', action='store_true', help='If specified, an experiment will not be created.')\n parser.add_argument('--source', action='append', default=[], help='Bind a remote data source (e.g. source-id:/target/path)')\n parser.add_argument('--env', action='append', default=[], help='Set environment variables (e.g. NAME=value or NAME)')\n args = parser.parse_args()\n\n main(args)","sub_path":"run_with_beaker.py","file_name":"run_with_beaker.py","file_ext":"py","file_size_in_byte":3423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"226937820","text":"\"\"\"\nTensorFlow 2 OscoNet code\n\"\"\"\nimport numpy as np\nimport pytest\nimport tensorflow as tf\n\nfrom OscopeBootstrap import qvalue\nfrom OscopeBootstrap.create_edge_network_represention import create_edge_network_representation\nfrom OscopeBootstrap.oscope_tf import PRECISION_fp, calc_e2, calc_e2_many_genes, find_best_psi_for_each_gene_pair, \\\n PRECISION_int, get_permuted_cost, get_pvalues, flatten_upper_triangular, get_symmetric_matrix_from_upper_triangular\nfrom OscopeBootstrap.SyntheticDataset import GetSimISyntheticData, true_adj_matrix\nfrom OscopeBootstrap.oscope_tf import bootstrap_hypothesis_test, get_accuracy, get_metrics_for_different_qvalue_thresholds\n\n\ndef calc_e2_np(X, Y, psi):\n return np.sum(np.square(np.square(X) + np.square(Y) - 2 * X * Y * np.cos(psi) - np.square(np.sin(psi))))\n\n\ndef calc_e2_many_genes_np(X_many_genes: np.ndarray, psi_ng: np.ndarray):\n '''\n\n :param X_many_genes: G X N tensor of gene expression\n :param psi_ng: G X G tensor of phase shift - should be symmetric\n :return: total cost across all genes\n '''\n G = X_many_genes.shape[0]\n c = 0\n for ix in range(G):\n for iy in range(G):\n c += calc_e2_np(X_many_genes[ix, :], X_many_genes[iy, :], psi_ng[ix, iy])\n return c\n\n\ndef create_single_group_example(N, std_noise, phase_shift):\n t = np.linspace(0, 2 * np.pi, N)\n G = 4\n data = np.zeros((G, N))\n data[0, :] = np.sin(t) + std_noise * np.random.randn(N)\n data[1, :] = np.sin(t + phase_shift) + std_noise * np.random.randn(N)\n data[2, :] = std_noise * np.random.randn(N)\n data[3, :] = std_noise * np.random.randn(N)\n return data\n\n\ndef test_get_symmetric_matrix_from_upper_triangular():\n flatten_vector = np.array([1, 2, 3, 4, 5, 6])\n a = get_symmetric_matrix_from_upper_triangular(4, flatten_vector)\n np.testing.assert_equal(a, a.T)\n\n\ndef test_calc_e2():\n np.random.seed(42)\n N = 10\n X = tf.constant(np.random.randn(N,), dtype=PRECISION_fp)\n Y = tf.constant(np.random.randn(N, ), dtype=PRECISION_fp)\n psi = tf.constant(np.array(3.), dtype=PRECISION_fp)\n\n assert calc_e2(X, X, tf.constant(np.array(0.), dtype=PRECISION_fp)) == 0, 'must get minimum cost for identical gene with 0 phase'\n\n e_tf = calc_e2(X, Y, psi)\n e_np = calc_e2_np(X.numpy(), Y.numpy(), psi.numpy())\n np.testing.assert_almost_equal(e_tf, e_np, decimal=1)\n\n\ndef test_calc_e2_many_genes():\n G = 5\n N = 10\n X_many_genes = tf.constant(np.random.randn(G, N), dtype=PRECISION_fp)\n psi_ng = tf.constant(np.random.randn(G, G), dtype=PRECISION_fp)\n # make sure we include 0 as possible phase\n cost = calc_e2_many_genes(X_many_genes, psi_ng)\n cost_np = calc_e2_many_genes_np(X_many_genes.numpy(), psi_ng.numpy())\n # np.testing.assert_almost_equal(cost, cost_np) Big differences!\n assert np.all(cost > 0)\n\n\ndef test_find_best_psi_for_each_gene_pair():\n np.random.seed(42)\n tf.random.set_seed(42)\n # construct example\n phase_shift = np.pi\n N = 10\n G = 4\n data_np = create_single_group_example(N, 0.1, phase_shift=phase_shift)\n data = tf.constant(data_np, dtype=PRECISION_fp)\n # candidate_psi = tf.linspace(0, 2 * tf.constant(np.pi), dtype=PRECISION)\n candidate_psi = tf.constant(np.array([phase_shift, phase_shift/2]), dtype=PRECISION_fp)\n\n n_permutations = tf.constant(np.array(20), dtype=PRECISION_int)\n psi_ng = tf.Variable(tf.zeros((G, G), dtype=PRECISION_fp) * tf.constant(np.inf, dtype=PRECISION_fp))\n cost_ng = tf.Variable(tf.ones((G, G), dtype=PRECISION_fp) * tf.constant(np.inf, dtype=PRECISION_fp))\n cost_permuted = tf.Variable(tf.ones((G, G, n_permutations), dtype=PRECISION_fp) * tf.constant(np.inf, dtype=PRECISION_fp))\n pvalues = tf.Variable(tf.ones((G, G), dtype=PRECISION_fp) * tf.constant(np.inf, dtype=PRECISION_fp))\n\n find_best_psi_for_each_gene_pair(psi_ng, cost_ng, data, candidate_psi=candidate_psi)\n assert psi_ng[0, 1] == phase_shift, 'why picked the other phase shift?'\n\n get_permuted_cost(cost_permuted, data, candidate_psi, n_permutations)\n\n get_pvalues(pvalues, cost_ng, cost_permuted)\n\n # then q-values\n # then check we find the right pair\n pvalue_flatten = flatten_upper_triangular(pvalues.numpy())\n qvalues_flatten, pi0 = qvalue.estimate(pvalue_flatten, verbose=True)\n qvalues = get_symmetric_matrix_from_upper_triangular(pvalues.shape[0], qvalues_flatten)\n\n adjacency_matrix = qvalues < 0.01\n assert adjacency_matrix[0, 1]\n assert adjacency_matrix[1, 0]\n assert adjacency_matrix.sum() == 2, 'Only one significant pair should exist (0, 1)'\n\n gene_names = [f'gene{i}' for i in range(4)]\n a = create_edge_network_representation(adjacency_matrix, 1/cost_ng.numpy(), gene_names)\n assert a.shape[1] == 3, 'must have gene1, gene2, weight columns'\n assert a.shape[0] == 1, 'only one gene pair is significant'\n\n\n@pytest.mark.slow\ndef test_bootstrap():\n # This is a slow test (>10 secs) so need to run with `pytest --runslow -rs`\n np.random.seed(42)\n tf.random.set_seed(42)\n NG = 5\n G = 20\n N = 100\n ngroups = 1\n alpha = 0.01 # significance level for test\n\n data_df, phaseG, angularSpeed = GetSimISyntheticData(NG=NG, G=G, ngroups=ngroups,\n N=N, noiseLevel=0)\n\n adjacency_matrix, qvalues, cost = bootstrap_hypothesis_test(n_bootstrap=30,\n data=data_df.values,\n alpha=alpha,\n grid_points_in_search=30)\n\n assert qvalues.shape == (G, G)\n assert adjacency_matrix.shape == (G, G)\n assert np.all(~np.isnan(qvalues))\n assert np.all(~np.isnan(adjacency_matrix))\n assert cost.shape == (G, G)\n\n adjacency_matrix_true = true_adj_matrix(G, angularSpeed)\n correct_ratio = get_accuracy(adjacency_matrix, adjacency_matrix_true)\n assert correct_ratio > .98\n\n TPR, FDR, FPR = get_metrics_for_different_qvalue_thresholds(qvalues,\n adjacency_matrix_true,\n np.array([alpha]))\n # To get appropriate values we need to increase number of bootstrap samples\n assert TPR > 0.75\n assert FDR < 0.3\n assert FPR < 0.1\n","sub_path":"test/test_oscope_tf.py","file_name":"test_oscope_tf.py","file_ext":"py","file_size_in_byte":6373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"561839857","text":"'''\nCreated on May 3, 2016\n\n@author: Julia Yu\n'''\nimport unittest\n\nfrom libs.SkipCase import skip_if_firstrun_fail\nfrom libs.selectDriver import selectDriver\nfrom libs.takeScreenshot import assert_with_screenshot\nfrom tasks.health.logicalHealthView import logicalHealthView\nfrom tasks.health.navHealthPanel import navHealthPanel\nfrom testdata.testdata import testdata\nfrom appobjs.navigation.navigationTree import navigationTree\n\n\nclass testLogicalHealthView(unittest.TestCase):\n def setUp(self):\n self.driver = selectDriver().get_driver(testdata().get_browser_type())\n self.driver.implicitly_wait(5)\n self.base_url = testdata().get_test_url()\n driver = self.driver\n driver.get(self.base_url)\n driver.maximize_window()\n \n \n \n def test_asset_name(self):\n ##Check that asset name is shown correct on logical health page\n driver = self.driver\n skip_if_firstrun_fail(driver)\n navHealthPanel().navHealthPanel(driver)\n navigationTree().get_btn_gotoLogical(driver).click()\n assert_with_screenshot(self, driver, \n logicalHealthView().check_asset_name(driver), \n True, \n \"Logical Health Page: Asset name shows correct\",\n \"Logical Health Page: Asset name does not show correct\")\n #self.assertTrue(logicalHealthView().check_asset_name(driver), \"Logical Health Page: Asset name does not show correct\") \n self.driver.quit()\n \n \n def test_total_capacity(self):\n ##Check that total capacity is shown on logical health page\n driver = self.driver\n skip_if_firstrun_fail(driver)\n navHealthPanel().navHealthPanel(driver)\n navigationTree().get_btn_gotoLogical(driver).click()\n assert_with_screenshot(self, driver, \n logicalHealthView().check_total_capacity(driver), \n True, \n \"Logical Health Page: Total Capacity shows correct\",\n \"Logical Health Page: Total Capacity does not show correct\") \n #self.assertTrue(logicalHealthView().check_total_capacity(driver),\"Logical Health Page: Total Capacity does not show correct\")\n self.driver.quit()\n \n def test_node_name(self): \n ##Check ESXi node name is shown correct\n driver = self.driver\n skip_if_firstrun_fail(driver)\n navHealthPanel().navHealthPanel(driver)\n navigationTree().get_btn_gotoLogical(driver).click()\n assert_with_screenshot(self, driver, \n logicalHealthView().check_node_name(driver), \n True, \n \"Logical Health Page: ESXi node name shows correct\",\n \"Logical Health Page: ESXi node name does not show correct\")\n #self.assertTrue(logicalHealthView().check_node_name(driver), \"Logical Health Page: ESXi node name does not show correct\")\n self.driver.quit()\n \n \nif __name__ == \"__main__\":\n unittest.main()\n","sub_path":"VxRailManager/testcases/health/testLogicalHealthView.py","file_name":"testLogicalHealthView.py","file_ext":"py","file_size_in_byte":3135,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"132621958","text":"import cv2\r\nimport time\r\nimport numpy as np\r\nimport handTrackingModule as htm\r\nimport math\r\nfrom ctypes import cast, POINTER\r\nfrom comtypes import CLSCTX_ALL\r\n# from pycaw.pycaw import AudioUtilities, IAudioEndpointVolume\r\n# import cv2\r\n# from time import sleep\r\n# from cvzone.FaceDetectionModule import FaceDetector\r\nfrom cvzone.SerialModule import SerialObject\r\n\r\narduino = SerialObject()\r\n\r\nwCam, hCam,fcam = 720 ,480 ,60\r\n\r\ncap = cv2.VideoCapture(0)\r\n# cap = cv2.VideoCapture(1)\r\ncap.set(3, wCam)\r\ncap.set(4, hCam)\r\ncap.set(5,fcam)\r\npTime = 0\r\n\r\ndetector = htm.handDetector(detectionCon = 0.3)\r\n\r\n\r\n# devices = AudioUtilities.GetSpeakers()\r\n# interface = devices.Activate(IAudioEndpointVolume._iid_, CLSCTX_ALL, None)\r\n# volume = cast(interface, POINTER(IAudioEndpointVolume))\r\n#volume.GetMute()\r\n#volume.GetMasterVolumeLevel()\r\n# volRange = volume.GetVolumeRange()\r\n\r\n# minVol = volRange[0]\r\n# maxVol = volRange[1]\r\nvol = 1\r\nvolBar =400\r\n\r\nwhile True:\r\n success, img = cap.read()\r\n img = detector.findHands(img) # for finding location of hands we us the function of the handtracking module\r\n lmlist = detector.findPosition(img, draw = False)\r\n if len(lmlist) != 0:\r\n # print(lmlist[4], lmlist[8])q\r\n x1, y1 = lmlist[4][1], lmlist[4][2]\r\n x2, y2 = lmlist[8][1], lmlist[8][2]\r\n cx, cy = (x1+x2) // 2, (y1+y2) // 2\r\n\r\n cv2.circle(img, (x1, y1), 15, (255, 0, 255), cv2.FILLED)\r\n cv2.circle(img, (x2, y2), 15, (255, 0, 255), cv2.FILLED)\r\n cv2.line(img, (x1, y1), (x2, y2), (255, 0, 255), 3)\r\n cv2.circle(img, (cx , cy), 15, (255, 0, 255), cv2.FILLED) #for making a circle in between both the fingers\r\n\r\n length = math.hypot(x2-x1,y2-y1) #distance between both the fingers\r\n vol = np.interp(length, [50, 300], [0,255]) #using numpy to convert our hand range to vol range\r\n volBar = np.interp(length, [50, 300], [400, 150]) #for conversion to use it on the bar\r\n # volume.SetMasterVolumeLevel(vol, None)\r\n\r\n \r\n\r\n # print(int(length))\r\n print(int(vol))\r\n # if length<50:\r\n # cv2.circle(img, (cx,cy), 15, (0, 255, 0), cv2.FILLED)\r\n arduino.sendData([vol])\r\n\r\n cv2.rectangle(img, (50,150), (85,400), (255, 0, 0), 3) #To display the volume change box\r\n cv2.rectangle(img, (50, int(volBar)), (85, 400), (255, 0, 0), cv2.FILLED) #the moving filled part of the box\r\n cTime = time.time()\r\n fps = 1/(cTime-pTime)\r\n pTime = cTime\r\n cv2.putText(img, f'FPS: {int(fps)}', (40,50), cv2.FONT_HERSHEY_COMPLEX, 1, (255,0,0), 3)\r\n\r\n cv2.imshow(\"img\", img)\r\n # cv2.waitKey(1)\r\n if cv2.waitKey(1) & 0xFF == ord(\"q\"):\r\n break\r\n","sub_path":"Gesture-led-Control/VolumeControl.py","file_name":"VolumeControl.py","file_ext":"py","file_size_in_byte":2690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"94008751","text":"import unittest\nimport zope.testing.module\nfrom zope.testing import doctest\nfrom zope.component import testing, eventtesting\nfrom zope.app.container.tests.placelesssetup import PlacelessSetup\n\ncontainer_setup = PlacelessSetup()\n\ndef copierSetUp(test):\n zope.testing.module.setUp(test, 'zc.copy.doctest')\n testing.setUp(test)\n eventtesting.setUp(test)\n container_setup.setUp()\n\ndef copierTearDown(test):\n zope.testing.module.tearDown(test)\n testing.tearDown(test)\n\ndef test_suite():\n return unittest.TestSuite((\n doctest.DocFileSuite(\n 'README.txt',\n setUp=copierSetUp,\n tearDown=copierTearDown),\n ))\n\nif __name__ == '__main__':\n unittest.main(defaultTest='test_suite')\n","sub_path":"zc.copy/tags/1.0/src/zc/copy/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"21021191","text":"#################\n#### imports ####\n#################\n\nimport json\n\nfrom flask import render_template\nfrom flask.ext.login import login_required\nfrom project.user_mgt.utils import team_login_required\n\nimport pandas as pd\nimport numpy as np\nfrom project.models import TeamTask, Contractor, rel_user_team_task, User, TeamTaskHistory\nfrom project.utils import sort_tuple, group_sort_to_list, key_label, get_history_data\nfrom project.utils.analytic import query_to_df\nfrom project.dashboard import dashboard_blueprint\nfrom project.utils.choices import choices_team_task_severity, choices_team_task_category, choices_team_task_class\n\n\"\"\"\nVIEWS FOR ADD FORMS STARTS HERE\n\"\"\"\n@dashboard_blueprint.route('/dashboard', methods=['GET','POST'])\n@dashboard_blueprint.route('/dashboard/<int:record_id>', methods=['GET','POST'])\n@team_login_required\n@login_required\ndef dashboard(record_id=None):\n\n _template = 'dashboard/all.html'\n\n df_team_task = query_to_df(TeamTask.query.all())\n\n df_team_task['str_date_team_task'] = df_team_task['date_team_task'].map(lambda x: x.strftime('%Y-%m-25'))\n df_team_task['open'] = df_team_task['status'].map(lambda x: 1 if x == 1 else 0)\n df_team_task['closed'] = df_team_task['status'].map(lambda x: 1 if x == 2 else 0)\n\n grouped = df_team_task[['open','closed', 'str_date_team_task']].groupby(['str_date_team_task'])\n counted = grouped.sum()\n normalized = counted.reset_index()\n data = normalized.to_dict('records')\n\n table_meta = {'title': 'Teak Task',\n 'description': 'Categorized by Date',\n 'table_id': 'dashboard_amchart_1'}\n\n\n return render_template(_template,\n table_meta = table_meta,\n data = data\n )\n\n@dashboard_blueprint.route('/data', methods=['GET','POST'])\n@team_login_required\n@login_required\ndef data():\n\n df_team_task = query_to_df(TeamTask.query.all())\n\n df_team_task['str_date_team_task'] = df_team_task['date_team_task'].map(lambda x: x.strftime('%Y-%m'))\n df_team_task['open'] = df_team_task['status'].map(lambda x: 1 if x == 1 else 0)\n df_team_task['closed'] = df_team_task['status'].map(lambda x: 1 if x == 2 else 0)\n\n grouped = df_team_task[['open','closed', 'str_date_team_task']].groupby(['str_date_team_task'])\n counted = grouped.sum()\n normalized = counted.reset_index()\n data = normalized.to_dict('records')\n\n return json.dumps(data)\n\n@dashboard_blueprint.route('/report-team-task', methods=['GET','POST'])\n@dashboard_blueprint.route('/report-team-task/<filter_class>/<int:filter_val>', methods=['GET','POST'])\n@team_login_required\n@login_required\ndef report_view_team_task(filter_class=None, filter_val=None):\n summary = {}\n how = 'left' # default merging how\n _field_arrangement = [\n 'contractor_name', 'description', 'action_taken', 'update_date', 'action_by'\n ]\n _field_dictionary = key_label(_field_arrangement)\n _template = 'dashboard/team-task.html'\n _timeline_link_func_name = 'team_mgt.timeline'\n _notify_link_func_name = 'team_mgt.notify'\n _severity = sort_tuple(choices_team_task_severity())\n _category = sort_tuple(choices_team_task_category())\n _classification = sort_tuple(choices_team_task_class())\n\n df_team_task = query_to_df(TeamTask.query.all())\n df_contractor = query_to_df(Contractor.query.all())\n df_history = query_to_df(TeamTaskHistory.query.\\\n order_by(TeamTaskHistory.date_action.desc(),\n TeamTaskHistory.id.desc()).\\\n # limit(1).\n all()\n )\n df_user = query_to_df(User.query.all())\n\n if filter_class == 'Contractor':\n df_contractor = df_contractor[df_contractor['id']==filter_val]\n how = 'right' # to remove other contractors\n\n elif filter_class is not None:\n df_team_task = df_team_task[df_team_task[filter_class.lower()]==filter_val]\n how = 'left' #retain all\n\n df_team_task_open = df_team_task[df_team_task['status']!=2] # filter by status open\n\n mg_history_user = pd.merge(df_history, df_user, left_on='action_taken_by', right_on='id', how='left')\n mg = pd.merge(df_team_task_open, df_contractor, left_on='contractor_id', right_on='id', how=how)\n\n #id_x is the team_task ID\n mg = pd.merge(mg, mg_history_user, left_on='id_x', right_on='team_task_id', how='outer')\n\n group_by_severity = df_team_task_open.groupby('severity')\n group_by_category = df_team_task_open.groupby('category')\n group_by_classification = df_team_task_open.groupby('classification')\n group_by_contractor = mg.groupby('name')\n\n #id_x_x is the team_task ID\n group_by_team_task = mg.groupby('id_x_x')\n summary['total_closed'] = len(df_team_task[df_team_task['status']==2])\n summary['total_open'] = len(df_team_task[df_team_task['status']!=2])\n summary['by_contractor'] = ('Contractor', group_sort_to_list(group_by_contractor, str_list=None))\n summary['by_severity'] = ('Severity', group_sort_to_list(group_by_severity,str_list=_severity))\n summary['by_category'] = ('Category', group_sort_to_list(group_by_category,str_list=_category))\n summary['by_classification'] = ('Classification', group_sort_to_list(group_by_classification,str_list=_classification))\n\n data=[]\n for group in group_by_team_task:\n record = group[1].head(1) # 0 is the group name; 1 is the group data\n index = record.index[0] #takes the index in the Dataframe to access\n try:\n data.append(\n {\n 'id': int(group[0]),\n 'contractor_name': record.loc[index, 'name'],\n 'description': record.loc[index, 'description'],\n 'update_date': record.loc[index, 'date_action'].strftime('%Y-%m-%d'),\n 'action_taken': record.loc[index, 'action_taken'],\n 'action_by': record.loc[index, 'username']\n }\n )\n\n except:\n data.append(\n {\n 'id': int(group[0]),\n 'contractor_name': record.loc[index, 'name'],\n 'description': record.loc[index, 'description'],\n 'latest_action': \"No Update\",\n 'action_taken': \"No Update\",\n 'action_by': \"No Update\"\n }\n )\n\n # records = TeamTask.query.\\\n # filter(TeamTask.status != 2).\\\n # all()\n # data=[]\n # for record in records:\n # try:\n # data.append(\n # {\n # 'id': record.id,\n # 'contractor_name': record.contractor.name,\n # 'description': record.description,\n # 'update_date': record.history[0].date_action.strftime('%Y-%m-%d'),\n # 'action_taken': record.history[0].action_taken,\n # 'action_by': record.history[0].action_taken_by_username\n # }\n # )\n #\n # except IndexError:\n # data.append(\n # {\n # 'id': record.id,\n # 'contractor_name': record.contractor.name,\n # 'description': record.description,\n # 'latest_action': \"No Update\",\n # 'action_taken': \"No Update\",\n # 'action_by': \"No Update\"\n # }\n # )\n\n return render_template(_template,\n# table_meta = table_meta,\n data = data,\n summary=summary,\n columns=_field_dictionary,\n keys=_field_arrangement,\n timeline_link_func=_timeline_link_func_name,\n notify_link_func=_notify_link_func_name\n )\n\n","sub_path":"web/project/dashboard/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"33833156","text":"from PySide import QtGui, QtCore\nimport common\nfrom gui.base.TWizard import TWizard\n\n__author__ = 'Serg'\n\n\nclass SimplexTableWizard(TWizard):\n def runWizard(self):\n return SizeWindow().getValues()\n\n\n\n\nclass SizeWindow(QtGui.QDialog):\n def __init__(self):\n super(SizeWindow, self).__init__()\n self.initUI()\n\n def getValues(self):\n return [self.WidthSB.value(),self.HeightSB.value(),self.RestrCB.currentText()=='max']\n\n def onNextBtn(self):\n self.close()\n\n def initUI(self):\n infoLabel = QtGui.QLabel('Задайте ширину и высоту симплекс-таблицы<br><small><i>Ширина определяется количеством переменных в задаче, высота–количеством ограничений. Целевую функцию считать не следует.</i></small><hr>')\n infoLabel.setAlignment(QtCore.Qt.AlignCenter)\n\n nextBtn = QtGui.QPushButton('Создать симплекс-таблицу')\n nextBtn.clicked.connect(self.onNextBtn)\n self.WidthSB = QtGui.QSpinBox()\n self.HeightSB = QtGui.QSpinBox()\n self.RestrCB = QtGui.QComboBox()\n self.RestrCB.insertItems(0, ['min', 'max'])\n self.WidthSB.setMaximum(100)\n self.WidthSB.setMinimum(1)\n self.WidthSB.setSingleStep(1)\n self.HeightSB.setMaximum(100)\n self.HeightSB.setMinimum(1)\n self.HeightSB.setSingleStep(1)\n\n grid = QtGui.QGridLayout()\n grid.addWidget(infoLabel, 0, 0, 1, 2)\n\n grid.addWidget(QtGui.QLabel('Ширина:'), 1, 0)\n grid.addWidget(QtGui.QLabel('Высота:'), 2, 0)\n grid.addWidget(QtGui.QLabel('L стремится к: '), 3, 0)\n grid.addWidget(self.WidthSB, 1, 1)\n grid.addWidget(self.HeightSB, 2, 1)\n grid.addWidget(self.RestrCB, 3, 1)\n grid.addWidget(nextBtn, 4, 0, 1, 2)\n\n self.setLayout(grid)\n #self.setGeometry(0, 0, 350, 350)\n self.setWindowTitle('Размеры симплекс-таблицы')\n self.centerOnScreen()\n self.exec()\n\n def centerOnScreen (self):\n resolution = QtGui.QDesktopWidget().screenGeometry()\n self.move((resolution.width() / 2) - (self.frameSize().width() / 2),\n (resolution.height() / 2) - (self.frameSize().height() / 2))","sub_path":"gui/misc/simplexTableWizard.py","file_name":"simplexTableWizard.py","file_ext":"py","file_size_in_byte":2378,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"556394019","text":"# Databricks notebook source\ndbutils.widgets.text(\"infilefolder\", \"\", \"In - Folder Path\")\ninfilefolder = dbutils.widgets.get(\"infilefolder\")\n\ndbutils.widgets.text(\"loadid\", \"\", \"Load Id\")\nloadid = dbutils.widgets.get(\"loadid\")\n\n# COMMAND ----------\n\nimport datetime\nimport os\n\n# For testing\n# infilefolder = 'datalake/data/lnd/2019_03_11_01_38_00/'\nload_id = loadid\nloaded_on = datetime.datetime.now()\nbase_path = 'dbfs:/mnt/datalake/data/dw/'\n\n# COMMAND ----------\n\nfrom pyspark.sql.functions import col, lit\nimport ddo_transform.transform as t\nimport ddo_transform.util as util\n\n# Read interim cleansed data\nparkingbay_sdf = spark.read.table(\"interim.parking_bay\").filter(col('load_id') == lit(load_id))\nsensordata_sdf = spark.read.table(\"interim.sensor\").filter(col('load_id') == lit(load_id))\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC ### Transform and load Dimension tables\n\n# COMMAND ----------\n\n# Read existing Dimensions\ndim_parkingbay_sdf = spark.read.table(\"dw.dim_parking_bay\")\ndim_location_sdf = spark.read.table(\"dw.dim_location\")\ndim_st_marker = spark.read.table(\"dw.dim_st_marker\")\n\n# Transform\nnew_dim_parkingbay_sdf = t.process_dim_parking_bay(parkingbay_sdf, dim_parkingbay_sdf, load_id, loaded_on).cache()\nnew_dim_location_sdf = t.process_dim_location(sensordata_sdf, dim_location_sdf, load_id, loaded_on).cache()\nnew_dim_st_marker_sdf = t.process_dim_st_marker(sensordata_sdf, dim_st_marker, load_id, loaded_on).cache()\n\n# Load\nutil.save_overwrite_unmanaged_table(spark, new_dim_parkingbay_sdf, table_name=\"dw.dim_parking_bay\", path=os.path.join(base_path, \"dim_parking_bay\"))\nutil.save_overwrite_unmanaged_table(spark, new_dim_location_sdf, table_name=\"dw.dim_location\", path=os.path.join(base_path, \"dim_location\"))\nutil.save_overwrite_unmanaged_table(spark, new_dim_st_marker_sdf, table_name=\"dw.dim_st_marker\", path=os.path.join(base_path, \"dim_st_marker\"))\n\n# new_dim_parkingbay_sdf.write.mode(\"overwrite\").saveAsTable(\"dw.dim_parking_bay_temp\")\n# new_dim_location_sdf.write.mode(\"overwrite\").saveAsTable(\"dw.dim_location_temp\")\n# new_dim_st_marker_sdf.write.mode(\"overwrite\").saveAsTable(\"dw.dim_st_marker_temp\")\n# spark.read.table(\"dw.dim_parking_bay_temp\").write.mode(\"overwrite\").option(\"path\", os.path.join(base_path, \"dim_parking_bay\")).saveAsTable(\"dw.dim_parking_bay\")\n# spark.read.table(\"dw.dim_location_temp\").write.mode(\"overwrite\").option(\"path\", os.path.join(base_path, \"dim_location\")).saveAsTable(\"dw.dim_location\")\n# spark.read.table(\"dw.dim_st_marker_temp\").write.mode(\"overwrite\").option(\"path\", os.path.join(base_path, \"dim_st_marker\")).saveAsTable(\"dw.dim_st_marker\")\n\n# COMMAND ----------\n\n# MAGIC %md\n# MAGIC ### Transform and load Fact tables\n\n# COMMAND ----------\n\n# Read existing Dimensions\ndim_parkingbay_sdf = spark.read.table(\"dw.dim_parking_bay\")\ndim_location_sdf = spark.read.table(\"dw.dim_location\")\ndim_st_marker = spark.read.table(\"dw.dim_st_marker\")\n\n# Process\nnr_fact_parking = t.process_fact_parking(sensordata_sdf, dim_parkingbay_sdf, dim_location_sdf, dim_st_marker, load_id, loaded_on)\n\n# Insert new rows\nnr_fact_parking.write.mode(\"append\").insertInto(\"dw.fact_parking\")\n\n# COMMAND ----------\n\ndbutils.notebook.exit(\"success\")\n","sub_path":"databricks/notebooks/03_transform.py","file_name":"03_transform.py","file_ext":"py","file_size_in_byte":3193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"609558988","text":"# Anna Sandler 208749648\nimport sys\nimport smtplib\nimport urllib\nfrom email.mime.text import MIMEText\nfrom email.mime.multipart import MIMEMultipart\nfrom email.mime.base import MIMEBase\nfrom email import encoders\nfrom urllib import request\n\n\ndef fill_template(username, jobtitle):\n str = f\"Hello {username}, as the {jobtitle} of you company, I reach out to you with an urgent request.\" \\\n f\"I attached a file of the sales agreement and I need you to sign it ASAP and send it back to me,\" \\\n f\"thank you\"\n\n return str\n\n\ndef main():\n username = sys.argv[1]\n mail_server = sys.argv[2]\n job_title = sys.argv[3]\n\n mail_body = \"\"\n if len(sys.argv) == 5:\n if 'www' in sys.argv[4]: # url\n try:\n with urllib.request.urlopen(sys.argv[4]) as f:\n mail_body = f.read().decode('utf-8')\n except:\n print(\"failed to read from 4th argument - url\")\n\n elif '.txt' in sys.argv[4]: # file\n try:\n with open(sys.argv[4], 'r') as file:\n mail_body = file.read().replace('\\n', '')\n except:\n print(\"failed to read from 4th argument\")\n\n else: # string\n mail_body = sys.argv[4]\n\n else:\n mail_body = fill_template(username, job_title)\n\n message = MIMEMultipart(\"alternative\")\n message[\"Subject\"] = \"Urgent Request\"\n message[\"From\"] = \"managment@arielu.ac.il\"\n message[\"To\"] = username + \"@\" + mail_server\n message.attach(MIMEText(mail_body, \"plain\"))\n\n part = MIMEBase('application', \"octet-stream\")\n part.set_payload(open(\"attachment.py\", \"rb\").read())\n encoders.encode_base64(part)\n part.add_header('Content-Disposition', 'attachment; filename=\"attachment.py\"')\n message.attach(part)\n\n hlp = message[\"To\"]\n with smtplib.SMTP('localhost') as s:\n s.send_message(message)\n print(f\"done. sent to {hlp}\")\n\n\nmain()\n","sub_path":"Phishing.py","file_name":"Phishing.py","file_ext":"py","file_size_in_byte":1956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"361179262","text":"import time\nimport pandas as pd\nimport numpy as np\nimport datetime as dt\nimport calendar\n\n#create library with city data\n\nCITY_DATA = { 'chicago': 'chicago.csv',\n 'new york city': 'new_york_city.csv',\n 'washington': 'washington.csv' }\n\ndef get_filters():\n \"\"\"\n Asks user to specify a city, month, and day to analyze.\n\n Returns:\n (str) city - name of the city to analyze\n (str) month - name of the month to filter by, or \"all\" to apply no month filter\n (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n \"\"\"\n print('Hello!! Let\\'s explore some US bikeshare data!!\\n')\n # TO DO: get user input for city (chicago, new york city, washington). HINT: Use a while loop to handle invalid inputs\n\n while True:\n city = input(\"Let's choose a city! Would you like to see data for Chicago, New York City or Washington?: \").lower()\n if city == \"chicago\" or city == \"ch\" or city == \"chi\" or city== \"c\":\n print (\"You chose Chicago!\\n\")\n city = \"chicago\"\n if city == \"new york city\" or city == \"new york\" or city == \"nyc\" or city == \"ny\":\n print (\"You chose New York!\\n\")\n city = \"new york city\"\n if city == \"washington\" or city == \"wa\" or city == \"wash\" or city==\"w\":\n city = \"washington\"\n print (\"You chose Washington!\\n\")\n if city not in CITY_DATA:\n print(\"Please choose again!\\n\")\n continue\n city = CITY_DATA[city]\n break\n\n # TO DO: get user input for month (all, january, february, ... , june)\n # TO DO: get user input for day of week (all, monday, tuesday, ... sunday)\n\n print(\"Let's filter the data!\\n\")\n\n while True:\n data_filter = input(\"Would you like to filter data by month or day of the week or both?(month, day or both) \\n\").lower()\n month = 'all'\n day = 'all'\n if data_filter == \"month\" or data_filter == \"m\" or data_filter == 'mon':\n print(\"We will filter by month!\\n\")\n month = input(\"Which month would you like to look at? (January to June or all): \").lower()\n print('We will filter by month and see data for {}.\\n'.format(month))\n if month not in ['january', 'february', 'march', 'april', 'may', 'june','all']:\n print(\"Please choose a month between January and June.\\n\")\n continue\n month=month\n elif data_filter == \"day\" or data_filter == 'd':\n print(\"We will filter by day of the week!\\n\")\n day = input(\"Which day of the week would you like to see? (Monday to Sunday or all): \").lower()\n print('We will filter by day of the week and we will see data for {}.\\n'.format(day))\n if day not in ['sunday','monday','tuesday','wednesday','thursday','friday','saturday','all']:\n print(\"Please choose again.\\n\")\n continue\n day=day\n elif data_filter == \"both\" or data_filter == \"b\":\n print(\"We will filter by month and day of the week!\\n\")\n month = input(\"which month would you like to see? (January to June or all): \").lower()\n if month not in ['january', 'february', 'march', 'april', 'may', 'june', 'all']:\n print(\"Please choose a month between January and June.\\n\")\n continue\n month = month\n day = input(\"Which day of the week would you like to see?(Monday to Sunday or all): \").lower()\n if day not in ['sunday','monday','tuesday','wednesday','thursday','friday','saturday', 'all']:\n print(\"Please choose again.\\n\")\n continue\n day = day\n else:\n print(\"Please choose again!\\n\")\n continue\n break\n print(\"We will read data from {}, and filter by {}.\\n\".format(city, data_filter))\n print('-'*40)\n return city, month, day\n\n\ndef load_data(city, month, day):\n \"\"\"\n Loads data for the specified city and filters by month and day if applicable.\n\n Args:\n (str) city - name of the city to analyze\n (str) month - name of the month to filter by, or \"all\" to apply no month filter\n (str) day - name of the day of week to filter by, or \"all\" to apply no day filter\n Returns:\n df - Pandas DataFrame containing city data filtered by month and day\n \"\"\"\n print(\"Loading...\\n\")\n df = pd.read_csv(city)\n\n df['Start Time'] = pd.to_datetime(df['Start Time'])\n df['month'] = df['Start Time'].dt.month\n df['day_of_week'] = df['Start Time'].dt.dayofweek\n\n if month != 'all':\n months = ['january', 'february', 'march', 'april', 'may', 'june']\n month = months.index(month) + 1\n df = df[df['month'] == month]\n\n if day != 'all':\n df = df[df['day_of_week'] == day.title()]\n\n return df\n\ndef time_stats(df):\n \"\"\"Displays statistics on the most frequent times of travel.\"\"\"\n #https://stackoverflow.com/questions/37625334/python-pandas-convert-month-int-to-month-name\n #https://docs.python.org/2/library/calendar.html\n print('\\n****Calculating The Most Frequent Times of Travel...\\n')\n start_time = time.time()\n df['Start Time'] = pd.to_datetime(df[\"Start Time\"])\n df['day_of_week'] = pd.to_datetime(df['Start Time'].dt.dayofweek)\n df['month'] = pd.to_datetime(df['Start Time'].dt.month, format='%m').dt.month_name()\n\n #display total amount of trips\n total_trips = df['Start Time'].count()\n print(\"On the chosen period there were {} trips.\".format(total_trips))\n\n # TO DO: display the most common month\n most_comon_month = df['month'].mode()[0]\n print('The most popular month for travel is {}.'.format(most_comon_month))\n # TO DO: display the most common day of week\n\n most_comon_day = df['day_of_week'].mode()[0].day_name()\n print('The most popular day for travel is {}.'.format(most_comon_day))\n\n # TO DO: display the most common start hour\n df['hour'] = df['Start Time'].dt.hour\n most_comon_hour = df['hour'].mode()[0]\n print('The most common Start time for trips is {} o\\'clock.'.format(most_comon_hour))\n\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)\n\n\ndef station_stats(df):\n \"\"\"Displays statistics on the most popular stations and trip.\"\"\"\n\n print('\\n****Calculating The Most Popular Stations and Trip...\\n')\n start_time = time.time()\n\n # TO DO: display most commonly used start station\n comon_start_station = df['Start Station'].mode()[0]\n print('The most popular start station is {}.'.format(comon_start_station))\n\n # TO DO: display most commonly used end station\n comon_end_station = df['End Station'].mode()[0]\n print('The most popular end station is {}.'.format(comon_end_station))\n\n\n # TO DO: display most frequent combination of start station and end station trip\n df['trip_combination'] = df['Start Station'] + \" -> \" + df['End Station']\n most_frq_trip = df['trip_combination'].mode()[0]\n print('The most popular trip combination is {}.'.format(most_frq_trip))\n\n\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)\n\n\ndef trip_duration_stats(df):\n \"\"\"Displays statistics on the total and average trip duration.\"\"\"\n\n print('\\n****Calculating Trip Duration...\\n')\n start_time = time.time()\n\n # TO DO: display total travel time\n #https://www.w3schools.com/python/numpy_ufunc_rounding_decimals.asp\n\n travel_time = np.round(sum(df['Trip Duration'])/3600,2)\n print('The total travel time was {} hours.'.format(travel_time))\n # TO DO: display mean travel time\n\n mean_travel_time = np.around(np.mean(df['Trip Duration'])/60,2)\n print('The average travel time was {} minutes.'.format(mean_travel_time))\n\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)\n\n\ndef user_stats(df):\n \"\"\"Displays statistics on bikeshare users.\"\"\"\n print('\\n****Calculating User Stats...\\n')\n start_time = time.time()\n\n # TO DO: Display counts of user types\n user_types = df['User Type'].value_counts()\n\n print('The user types are the following:\\n',user_types)\n\n # TO DO: Display counts of gender\n #https://stackoverflow.com/questions/26266362/how-to-count-the-nan-values-in-a-column-in-pandas-dataframe\n print('\\n****Calculating gender data...\\n')\n if 'Gender' in df:\n genders = df['Gender'].value_counts()\n print(\"The user gender distribution is the following:\\n\",genders)\n count_nan = len(df['Gender']) - df['Gender'].count()\n print ('\\n{} users haven\\'t added their gender.'.format(count_nan))\n else:\n print('Sorry! There is no gender data available for Washington.')\n # TO DO: Display earliest, most recent, and most common year of birth\n print ('\\n****Let\\'s look at the user birthdays...\\n')\n if 'Birth Year' in df:\n birth_year = df['Birth Year']\n print('The oldest user was born in {}.'.format(int(min(birth_year))))\n print('The youngest user was born in {}.'.format(int(max(birth_year))))\n print('The most common year of birth is {}.'.format(int(np.mean(birth_year))))\n count_nan_birth = len(df['Birth Year']) - df['Birth Year'].count()\n print ('{} users haven\\'t added their birth year.'.format(count_nan_birth))\n else:\n print('Sorry! There is no birth year data available for Washington.')\n print(\"\\nThis took %s seconds.\" % (time.time() - start_time))\n print('-'*40)\n\ndef raw_data(df):\n print('\\nLet\\'s see some raw data!')\n #https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.iloc.html\n raw = input('Would you like to see 5 rows of raw data?\\n').lower()\n if raw != 'no':\n rows = 0\n while True:\n print(df.iloc[rows:rows+5])\n rows += 5\n extra_data = input('Would you like to see 5 more rows? yes or no.\\n').lower()\n if extra_data not in ('yes','y'):\n break\n\ndef main():\n while True:\n city, month, day = get_filters()\n df = load_data(city, month, day)\n\n time_stats(df)\n station_stats(df)\n trip_duration_stats(df)\n user_stats(df)\n raw_data(df)\n\n restart = input('\\nWould you like to restart? Enter yes or no.\\n')\n if restart.lower() != 'yes':\n break\n\n\nif __name__ == \"__main__\":\n\tmain()\n","sub_path":"bikeshare.py","file_name":"bikeshare.py","file_ext":"py","file_size_in_byte":10503,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"513006664","text":"from collections import defaultdict\nfrom itertools import groupby\nimport re\nfrom arkiv.archive import is_multi_part_extract\n\n\ndef normalize_archive_names(archives):\n extract_files = filter(is_multi_part_extract, archives)\n for extract_file in extract_files:\n volume = extract_file.volume\n archive_groups = _group_archives(volume)\n if len(archive_groups) > 1:\n _rename_to_most_common_archive(archive_groups, volume)\n\n\ndef _group_archives(volume):\n archive_groups = defaultdict(list)\n for archive, archive_group in groupby(volume.values(), key=lambda a: a.archive):\n archive_groups[archive].extend(archive_group)\n return archive_groups\n\n\ndef _rename_to_most_common_archive(archive_groups, volume):\n max_archive, max_archives = max(archive_groups.items(), key=lambda i: len(i[1]))\n for part in volume.values():\n if part.archive != max_archive:\n part.name = re.sub(part.archive.name, max_archive.name, part.name)\n part.archive = max_archive\n","sub_path":"arkiv/util/normalize.py","file_name":"normalize.py","file_ext":"py","file_size_in_byte":1026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"442505779","text":"from mendeleev import element\nfrom .. import ureg\nfrom papermill.iorw import load_notebook_node\nimport papermill as pm\nimport yaml\nimport os\nimport shutil\n\n\ndef run_simtool(nb, outname, inputs, outdir=None, append=True, parallel=False):\n inputs = _get_dict(inputs)\n if outdir is not None:\n if append:\n if not os.path.exists(outdir):\n os.makedirs(outdir)\n else:\n if os.path.exists(outdir):\n shutil.rmtree(outdir)\n os.makedirs(outdir)\n \n pm.execute_notebook(nb, os.path.join(outdir, outname), parameters=inputs)\n\ndef get_inputs(nbname):\n incell = None\n nb = load_notebook_node(nbname)\n for cell in nb.cells:\n if cell['source'].startswith('%%yaml INPUTS'):\n incell = cell['source']\n break\n if incell is None:\n return None\n # remove first line (cell magic)\n incell = incell.split('\\n', 1)[1]\n input_dict = yaml.load(incell)\n return parse(input_dict)\n\n# a javascript-like object. Access using object syntax or as a dictionary\nclass AttrDict(dict):\n def __init__(self, **kwargs):\n super(AttrDict, self).__init__()\n self.__dict__ = self\n self.desc = kwargs.get('desc')\n\n def __repr__(self):\n res = []\n for i in self:\n if i == 'desc':\n continue\n res.append('%s:' % i)\n res.append(self[i].__repr__())\n return '\\n'.join(res)\n\ndef parse(inputs):\n d = AttrDict()\n for i in inputs:\n t = inputs[i]['type']\n if t == 'Text':\n d[i] = Text(**inputs[i])\n elif t == 'Element':\n d[i] = Element(**inputs[i])\n elif t == 'Integer':\n d[i] = Integer(**inputs[i])\n elif t == 'Number':\n d[i] = Number(**inputs[i])\n return d\n\ndef set_variables(inputs):\n for i in inputs:\n val = inputs[i]['value']\n globals()[i] = val\n\ndef _get_dict(inputs):\n if type(inputs) == dict:\n return inputs\n d = {}\n for i in inputs:\n try:\n d[i] = inputs[i].value\n except:\n pass\n # return {'inputs': d}\n return d\n\nclass Integer(AttrDict):\n def __init__(self, value, **kwargs):\n super(Integer, self).__init__(**kwargs)\n self.min = kwargs.get('min')\n self.max = kwargs.get('max')\n self.value = value\n\n @property\n def value(self):\n return self._value\n @value.setter\n def value(self, newval):\n if self.min is not None and newval < self.min:\n raise ValueError(\"Minimum value is %d\" % self.min)\n if self.max is not None and newval > self.max:\n raise ValueError(\"Maximum value is %d\" % self.max)\n self._value = newval\n \n def __repr__(self):\n res = ' value: %s\\n' % self.value\n for i in self:\n if not i.startswith('_') and self[i] is not None:\n res += ' %s: %s\\n' % (i, self[i])\n return res\n\n\nclass Text(AttrDict):\n def __init__(self, value, **kwargs):\n super(Text, self).__init__(**kwargs)\n self.value = value\n self.options = kwargs.get('options')\n self.maxlen = kwargs.get('maxlen')\n\n def __repr__(self):\n res = ''\n for i in self:\n if self[i] is not None:\n res += ' %s: %s\\n' % (i, self[i])\n return res\n\nclass Element(AttrDict):\n def __init__(self, value, property, **kwargs):\n super(Element, self).__init__(**kwargs)\n self.property = property\n self.value = value\n self.options = kwargs.get('options')\n\n @property\n def value(self):\n return self._value\n @value.setter\n def value(self, newval):\n if type(newval) is str:\n self._e = element(newval.title())\n else:\n self._e = element(newval)\n self._value = self._e.__dict__[self.property]\n\n def __repr__(self):\n res = ' value: %s\\n' % self.value\n for i in self:\n if not i.startswith('_') and self[i] is not None:\n res += ' %s: %s\\n' % (i, self[i])\n return res\n\nclass Number(AttrDict):\n def __init__(self, value, *args, **kwargs):\n super(Number, self).__init__()\n self.min = kwargs.get('min')\n if self.min is not None:\n self.min = float(self.min)\n self.max = kwargs.get('max')\n if self.max is not None:\n self.max = float(self.max)\n\n units = kwargs.get('units')\n if units:\n try:\n self.units = ureg.parse_units(units)\n except:\n raise ValueError('Unrecognized units: %s' % units)\n self.value = value\n\n @property\n def value(self):\n return self._value\n\n @value.setter\n def value(self, newval):\n if self.units and type(newval) == str:\n newval = ureg.parse_expression(newval)\n if hasattr(newval, 'units'):\n newval = newval.to(self.units).magnitude\n if self.min is not None and newval < self.min:\n raise ValueError(\"Minimum value is %d\" % self.min)\n if self.max is not None and newval > self.max:\n raise ValueError(\"Maximum value is %d\" % self.max)\n self._value = newval\n\n def __repr__(self):\n res = ' value: %s\\n' % self.value\n for i in self:\n if not i.startswith('_') and self[i] is not None:\n res += ' %s: %s\\n' % (i, self[i])\n return res\n","sub_path":"hublib/tool/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":5505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"573946244","text":"for l in range(0,int(input())):\r\n x,y,z=list(map(int,input().split()))\r\n if x+y-z==0:\r\n possible=True\r\n elif x-y+z==0:\r\n possible=True\r\n elif -x+y+z==0:\r\n possible=True\r\n else :\r\n possible=False\r\n if possible:\r\n print(\"yes\")\r\n else:\r\n print(\"no\")\r\n","sub_path":"Codechef/THREEFR.py","file_name":"THREEFR.py","file_ext":"py","file_size_in_byte":311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"303457387","text":"from __future__ import print_function\nfrom __future__ import absolute_import\n\nimport time\nimport sys\nimport os\nimport json\n\nimport socket\n\nUR_SERVER_PORT = 30002\n\n# shorcut to run the program from anaconda prompt directly\n# python path to your file....\\mini_eggshell\\ur_online_control\\communication\\main_direct_send_group_01.py\n\n# set the paths to find library\nfile_dir = os.path.dirname(__file__)\nparent_dir = os.path.abspath(os.path.join(file_dir, \"..\", \"..\"))\nsys.path.append(file_dir)\nsys.path.append(parent_dir)\n\nfrom ur_online_control.communication.formatting import format_commands\n\n# GLOBALS\n# ===============================================================\nserver_address = \"192.168.10.11\"\nserver_port = 30003\nur_ip = \"192.168.10.10\"\ntool_angle_axis = [-68.7916, -1.0706, 100, 3.1416, 0.0, 0.0]\n# ===============================================================\n\n# COMMANDS\n# ===============================================================\npath = os.path.dirname(os.path.join(__file__))\nfilename = os.path.join(path, \"..\", \"commands_group_01.json\")\nwith open(filename, 'r') as f:\n data = json.load(f)\n# load the commands from the json dictionary\nmove_filament_loading_pt = data['move_filament_loading_pt']\nlen_command = data['len_command']\ngh_commands = data['gh_commands']\ncommands = format_commands(gh_commands, len_command)\nprint(\"We have %d commands to send\" % len(commands))\n# ===============================================================\n\n# UR SCRIPT\n# ===============================================================\ndef movel_commands(server_address, port, tcp, commands):\n script = \"\"\n script += \"def program():\\n\"\n x, y, z, ax, ay, az = tcp\n script += \"\\tset_tcp(p[%.5f, %.5f, %.5f, %.5f, %.5f, %.5f])\\n\" % (x/1000., y/1000., z/1000., ax, ay, az)\n for i in range(len(commands)):\n x, y, z, ax, ay, az, speed, radius = commands[i]\n script += \"\\tmovel(p[%.5f, %.5f, %.5f, %.5f, %.5f, %.5f], v=%f, r=%f)\\n\" % (x/1000., y/1000., z/1000., ax, ay, az, speed/1000., radius/1000.)\n script += \"\\ttextmsg(\\\"sending command number %d\\\")\\n\" % (i)\n script += \"\\tsocket_open(\\\"%s\\\", %d)\\n\" % (server_address, port)\n script += \"\\tsocket_send_string(\\\"c\\\")\\n\"\n script += \"\\tsocket_close()\\n\"\n script += \"end\\n\"\n script += \"program()\\n\\n\\n\"\n script = script.encode()\n return script\n\n# ===============================================================\ndef start_extruder(tcp, movel_command, digital_output):\n script = \"\"\n script += \"def program():\\n\"\n script += \"\\ttextmsg(\\\">> Start extruder.\\\")\\n\"\n x, y, z, ax, ay, az = tcp\n script += \"\\tset_tcp(p[%.5f, %.5f, %.5f, %.5f, %.5f, %.5f])\\n\" % (x/1000., y/1000., z/1000., ax, ay, az)\n x, y, z, ax, ay, az, speed, radius = movel_command\n script += \"\\tmovel(p[%.5f, %.5f, %.5f, %.5f, %.5f, %.5f], v=%f, r=%f)\\n\" % (x/1000., y/1000., z/1000., ax, ay, az, speed/1000., radius/1000.)\n script += \"\\tset_digital_out(%i, True)\\n\" % (int(digital_output))\n script += \"end\\n\"\n script += \"program()\\n\\n\\n\"\n script = script.encode()\n return script\n\n# ===============================================================\ndef stop_extruder(tcp, movel_command, digital_output):\n script = \"\"\n script += \"def program():\\n\"\n script += \"\\ttextmsg(\\\">> Stop extruder.\\\")\\n\"\n x, y, z, ax, ay, az = tcp\n script += \"\\tset_tcp(p[%.5f, %.5f, %.5f, %.5f, %.5f, %.5f])\\n\" % (x/1000., y/1000., z/1000., ax, ay, az)\n script += \"\\tset_digital_out(%i, False)\\n\" % (int(digital_output))\n x, y, z, ax, ay, az, speed, radius = movel_command\n script += \"\\tmovel(p[%.5f, %.5f, %.5f, %.5f, %.5f, %.5f], v=%f, r=%f)\\n\" % (x/1000., y/1000., z/1000., ax, ay, az, speed/1000., radius/1000.)\n script += \"end\\n\"\n script += \"program()\\n\\n\\n\"\n script = script.encode()\n return script\n\n# ===============================================================\ndef main(commands):\n send_socket = socket.create_connection((ur_ip, UR_SERVER_PORT), timeout=2)\n send_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n\n # define i/o digital output numbers\n air_pressure_DO = 0\n # clay_extruder_motor_DO = 0\n #\n # plastic_extruder_motor_DO = 0\n # plastic_extruder_fan_DO = 0\n\n if move_filament_loading_pt:\n first_command = commands[0]\n last_command = commands[-1]\n script = start_extruder(tool_angle_axis, first_command, air_pressure_DO)\n send_socket.send(script)\n # define optimum waiting time according to safe_pt position\n time.sleep(10)\n\n # commands without filament loading points\n commands = commands[1:-1]\n\n script = movel_commands(server_address, server_port, tool_angle_axis, commands)\n print(\"sending commands ...\")\n\n # send file\n send_socket.send(script)\n\n # make server\n recv_socket = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n recv_socket.setsockopt(socket.SOL_SOCKET, socket.SO_REUSEADDR, 1)\n # Bind the socket to the port\n recv_socket.bind((server_address, server_port))\n # Listen for incoming connections\n recv_socket.listen(1)\n while True:\n connection, client_address = recv_socket.accept()\n print(\"client_address\", client_address)\n break\n recv_socket.close()\n\n if move_filament_loading_pt:\n script = stop_extruder(tool_angle_axis, last_command, air_pressure_DO)\n send_socket.send(script)\n time.sleep(1)\n\n send_socket.close()\n print (\"program done ...\")\n\n\nif __name__ == \"__main__\":\n main(commands)\n","sub_path":"ur_online_control/communication/main_direct_send_group_01.py","file_name":"main_direct_send_group_01.py","file_ext":"py","file_size_in_byte":5515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"47757309","text":"# You are given three integers: a, b, and m. Print two lines.\n# On the first line, print the result of pow(a,b). On the second line, print the result of pow(a,b,m).\n\n# Enter your code here. Read input from STDIN. Print output to STDOUT\nimport math\n\na = int(input())\nb = int(input())\nm = int(input())\n\nprint(pow(a,b))\nprint(pow(a,b,m))\n\n# Enter your code here. Read input from STDIN. Print output to STDOUT\n# Task\n# Read four numbers, and print the result of a^b + c^d\n\n# Input Format\n# Integers are given on four separate lines, respectively.\nimport math\n\na = int(input())\nb = int(input())\nc = int(input())\nd = int(input())\n\nprint(pow(a,b) + pow(c,d))\n\n","sub_path":"powInPython.py","file_name":"powInPython.py","file_ext":"py","file_size_in_byte":653,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"549737089","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2011 Odd Simon Simonsen <oddsimons@gmail.com>\n# Copyright (C) 2012-2015 Steffen Hoffmann <hoff.st@web.de>\n# Copyright (C) 2014 Jun Omae <jun66j5@gmail.com>\n# Copyright (C) 2015 Ryan J Ollos <ryan.j.ollos@gmail.com>\n#\n# This software is licensed as described in the file COPYING, which\n# you should have received as part of this distribution.\n#\n\nimport shutil\nimport tempfile\nimport unittest\n\nfrom trac.test import EnvironmentStub, Mock, MockRequest\nfrom trac.web.chrome import Chrome\nfrom trac.web.href import Href\nfrom trac.wiki.test import wikisyntax_test_suite\n\nfrom tractags.db import TagSetup\nfrom tractags.macros import TagWikiMacros, query_realms\n\n\ndef _revert_tractags_schema_init(env):\n with env.db_transaction as db:\n db(\"DROP TABLE IF EXISTS tags\")\n db(\"DROP TABLE IF EXISTS tags_change\")\n db(\"DELETE FROM system WHERE name='tags_version'\")\n db(\"DELETE FROM permission WHERE action %s\" % db.like(),\n ('TAGS_%',))\n\n\ndef _insert_tags(env, tagspace, name, tags):\n args = [(tagspace, name, tag) for tag in tags]\n with env.db_transaction as db:\n db.executemany(\"\"\"\n INSERT INTO tags (tagspace,name,tag) VALUES (%s,%s,%s)\n \"\"\", args)\n\n\nclass _BaseTestCase(unittest.TestCase):\n\n def setUp(self):\n self.env = EnvironmentStub(default_data=True,\n enable=['trac.*', 'tractags.*'])\n self.env.path = tempfile.mkdtemp()\n\n setup = TagSetup(self.env)\n # Current tractags schema is setup with enabled component anyway.\n # Revert these changes for getting default permissions inserted.\n self._revert_tractags_schema_init()\n setup.upgrade_environment()\n\n def tearDown(self):\n self.env.shutdown()\n shutil.rmtree(self.env.path)\n\n def _revert_tractags_schema_init(self):\n _revert_tractags_schema_init(self.env)\n\n def _insert_tags(self, tagspace, name, tags):\n _insert_tags(self.env, tagspace, name, tags)\n\n\nclass TagTemplateProviderTestCase(_BaseTestCase):\n\n def setUp(self):\n _BaseTestCase.setUp(self)\n\n # TagTemplateProvider is abstract, test using a subclass\n self.tag_wm = TagWikiMacros(self.env)\n\n def test_template_dirs_added(self):\n self.assertTrue(self.tag_wm in Chrome(self.env).template_providers)\n\n\nclass ListTaggedMacroTestCase(_BaseTestCase):\n\n def setUp(self):\n _BaseTestCase.setUp(self)\n self.req = MockRequest(self.env, path_info='/wiki/ListTaggedPage',\n authname='user')\n\n self.tag_twm = TagWikiMacros(self.env)\n\n def test_empty_content(self):\n context = Mock(env=self.env, href=Href('/'), req=self.req)\n formatter = Mock(context=context, req=self.req)\n self.assertTrue('No resources found' in\n str(self.tag_twm.expand_macro(formatter,\n 'ListTagged', '')))\n\n def test_listtagged_exclude(self):\n self._insert_tags('wiki', 'InterTrac', ('blah',))\n self._insert_tags('wiki', 'InterWiki', ('blah',))\n self._insert_tags('wiki', 'WikiStart', ('blah',))\n context = Mock(env=self.env, href=Href('/'), req=self.req)\n formatter = Mock(context=context, req=self.req)\n result = unicode(self.tag_twm.expand_macro(formatter, 'ListTagged',\n 'blah,exclude=Inter*'))\n self.assertFalse('InterTrac' in result)\n self.assertFalse('InterWiki' in result)\n self.assertTrue('WikiStart' in result)\n\n result = unicode(self.tag_twm.expand_macro(formatter, 'ListTagged',\n 'blah,exclude=Wi*:*ki'))\n self.assertTrue('InterTrac' in result)\n self.assertFalse('InterWiki' in result)\n self.assertFalse('WikiStart' in result)\n\n def _test_listtagged_paginate(self, page, per_page=2):\n self._insert_tags('wiki', 'InterTrac', ('blah',))\n self._insert_tags('wiki', 'InterWiki', ('blah',))\n self._insert_tags('wiki', 'WikiStart', ('blah',))\n self.req.args['listtagged_per_page'] = per_page\n self.req.args['listtagged_page'] = page\n context = Mock(env=self.env, href=Href('/'), req=self.req)\n formatter = Mock(context=context, req=self.req)\n result = \\\n unicode(self.tag_twm.expand_macro(formatter, 'ListTagged', 'blah'))\n return result\n\n def test_listtagged_paginate_page1(self):\n \"\"\"Paginate results for page 1 has two items.\"\"\"\n result = self._test_listtagged_paginate(1)\n self.assertTrue('InterTrac' in result)\n self.assertTrue('InterWiki' in result)\n self.assertFalse('WikiStart' in result)\n\n def test_listtagged_paginate_page2(self):\n \"\"\"Paginate results for page 2 has one item.\"\"\"\n result = self._test_listtagged_paginate(2)\n self.assertFalse('InterTrac' in result)\n self.assertFalse('InterWiki' in result)\n self.assertTrue('WikiStart' in result)\n\n def test_listtagged_paginate_page_out_of_range(self):\n \"\"\"Out of range page defaults to 1.\"\"\"\n result = self._test_listtagged_paginate(3)\n self.assertTrue('InterTrac' in result)\n self.assertTrue('InterWiki' in result)\n self.assertFalse('WikiStart' in result)\n\n def test_listtagged_paginate_page_invalid(self):\n \"\"\"Invalid page default to 1.\"\"\"\n result = self._test_listtagged_paginate(-1)\n self.assertTrue('InterTrac' in result)\n self.assertTrue('InterWiki' in result)\n self.assertFalse('WikiStart' in result)\n\n def test_listtagged_paginate_per_page_invalid(self):\n \"\"\"Invalid per_page defaults to items_per_page (100).\"\"\"\n result = self._test_listtagged_paginate(2, -1)\n self.assertTrue('InterTrac' in result)\n self.assertTrue('InterWiki' in result)\n self.assertTrue('WikiStart' in result)\n\n\nLISTTAGGED_MACRO_TEST_CASES = u\"\"\"\n============================== invalid operator\n[[ListTagged(xyz:wiki xyz)]]\n------------------------------\n<p>\n</p><div class=\"system-message\">\\\n<strong>ListTagged macro error</strong>\\\n<pre>Invalid attribute \\'xyz\\'</pre>\\\n</div><p>\n</p>\n============================== invalid realm\n[[ListTagged(realm:xyz blah)]]\n------------------------------\n<p>\n</p><div class=\"system-message\">\\\n<strong>ListTagged macro error</strong>\\\n<pre>Tags are not supported on the \\'xyz\\' realm</pre>\\\n</div><p>\n</p>\n\"\"\"\n\n\ndef listtagged_setup(tc):\n _revert_tractags_schema_init(tc.env)\n TagSetup(tc.env).upgrade_environment()\n _insert_tags(tc.env, 'wiki', 'WikiStart', ('abc', 'xyz'))\n\n\ndef listtagged_teardown(tc):\n _revert_tractags_schema_init(tc.env)\n\n\nclass TagCloudMacroTestCase(_BaseTestCase):\n\n def setUp(self):\n _BaseTestCase.setUp(self)\n self.req = MockRequest(self.env, path_info='/wiki/TagCloudPage',\n authname='user')\n self.context = Mock(env=self.env, href=self.req.href, req=self.req)\n self.formatter = Mock(context=self.context, req=self.req)\n\n self.tag_twm = TagWikiMacros(self.env)\n\n # Helpers\n\n def _expand_macro(self, content):\n return self.tag_twm.expand_macro(self.formatter, 'TagCloud', content)\n\n # Tests\n\n def test_normal(self):\n self._insert_tags('wiki', 'CamelCase', ('blah', 'foo', 'bar'))\n self._insert_tags('wiki', 'InterMapTxt', ('blah', 'foo', 'bar'))\n self._insert_tags('wiki', 'InterTrac', ('blah',))\n self._insert_tags('wiki', 'InterWiki', ('blah',))\n self._insert_tags('wiki', 'PageTemplates', ('blah',))\n self._insert_tags('wiki', 'RecentChanges', ('blah', 'foo'))\n self._insert_tags('wiki', 'SandBox', ('blah', 'foo'))\n self._insert_tags('ticket', '1', ('blah',))\n self._insert_tags('ticket', '2', ('blah', 'bar'))\n self._insert_tags('ticket', '3', ('blah', 'bar'))\n self._insert_tags('ticket', '4', ('blah', 'bar'))\n\n result = unicode(self._expand_macro(''))\n self.assertTrue('\">blah</a>' in result, repr(result))\n self.assertTrue('\">foo</a>' in result, repr(result))\n self.assertTrue('\">bar</a>' in result, repr(result))\n\n result = unicode(self._expand_macro('mincount=5'))\n self.assertTrue('\">blah</a>' in result, repr(result))\n self.assertFalse('\">foo</a>' in result, repr(result))\n self.assertTrue('\">bar</a>' in result, repr(result))\n\n result = unicode(self._expand_macro('mincount=6'))\n self.assertTrue('\">blah</a>' in result, repr(result))\n self.assertFalse('\">foo</a>' in result, repr(result))\n self.assertFalse('\">bar</a>' in result, repr(result))\n\n result = unicode(self._expand_macro('realm=ticket|wiki'))\n self.assertTrue('\">blah</a>' in result, repr(result))\n self.assertTrue('\">foo</a>' in result, repr(result))\n self.assertTrue('\">bar</a>' in result, repr(result))\n\n result = unicode(self._expand_macro('realm=ticket'))\n self.assertTrue('\">blah</a>' in result, repr(result))\n self.assertFalse('\">foo</a>' in result, repr(result))\n self.assertTrue('\">bar</a>' in result, repr(result))\n\n result = unicode(self._expand_macro('realm=ticket,mincount=4'))\n self.assertTrue('\">blah</a>' in result, repr(result))\n self.assertFalse('\">foo</a>' in result, repr(result))\n self.assertFalse('\">bar</a>' in result, repr(result))\n\n result = unicode(self._expand_macro('realm=unknown'))\n self.assertEquals('No tags found', result)\n\n result = unicode(self._expand_macro('mincount=100'))\n self.assertEquals('No tags found', result)\n\n\nclass QueryRealmsTestCase(unittest.TestCase):\n def test_query_realms(self):\n all_realms = ['ticket', 'wiki']\n # No tag providers detected.\n self.assertFalse('ticket' in query_realms('', []))\n # No tags query statement used.\n self.assertFalse('ticket' in query_realms('', all_realms))\n self.assertFalse('ticket' in query_realms('ticket', all_realms))\n self.assertFalse('ticket' in query_realms('realm:ticket', ['wiki']))\n self.assertTrue('ticket' in query_realms('realm:ticket', all_realms))\n self.assertFalse('wiki' in query_realms('realm:ticket', all_realms))\n self.assertTrue('wiki' in\n query_realms('onetag realm:wiki 2ndtag', all_realms))\n self.assertFalse('wiki' in\n query_realms('onetag realm=wiki 2ndtag', all_realms))\n\n\ndef test_suite():\n suite = unittest.TestSuite()\n suite.addTest(unittest.makeSuite(TagTemplateProviderTestCase))\n suite.addTest(unittest.makeSuite(ListTaggedMacroTestCase))\n suite.addTest(\n wikisyntax_test_suite(LISTTAGGED_MACRO_TEST_CASES, listtagged_setup,\n __file__, listtagged_teardown,\n enable_components=('trac.*', 'tractags.*')))\n suite.addTest(unittest.makeSuite(TagCloudMacroTestCase))\n suite.addTest(unittest.makeSuite(QueryRealmsTestCase))\n return suite\n\n\nif __name__ == '__main__':\n unittest.main(defaultTest='test_suite')\n","sub_path":"tractags/tests/macros.py","file_name":"macros.py","file_ext":"py","file_size_in_byte":11323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"630748844","text":"from datetime import datetime\nimport speech_recognition as sr\ni=0\nwhile (i==0):\n ch=str(input(\"What's your command: \"))\n if ch==\"hey friday\":\n print(\"What's up, Boss\")\n elif ch==\"hey buddy\":\n print(\"Yes, Boss\")\n elif ch==\"hey bud\":\n print(\"Good to see you too, sir\")\n elif ch==\"fri\":\n time = datetime.now().time()\n print(\"Current time is:\",time)\n elif ch==\"day\":\n today=datetime.today()\n print(\"Today's date is: \",today)\n elif ch==\"stop\":\n i=1\n print(\"Bye Sir\")\n elif ch==\"i said stop it\":\n i=1\n print(\"Okay Sir\")\n elif ch==\"would you stop\":\n i=1\n print(\"Aye aye Captain!!\")\n elif ch==\"can you stop\":\n print(\"But I don't want to ಥ_ಥ\")\n elif ch==\"glad to have you back buddy\":\n print(\"That's my Pleasure, Boss\")\n","sub_path":"Friday.py","file_name":"Friday.py","file_ext":"py","file_size_in_byte":856,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"434649221","text":"from selenium import webdriver\nimport pandas as pd\nimport WhatsappSpammer\nimport time\nfrom notify_run import Notify\n\nnotify = Notify()\ndriver = webdriver.Chrome()\ndriver.get('https://www.google.com/?gws_rd=ssl')\n#DO google search for match\ndef Lookup():\n Match = \"leicester city vs liverpool\"#input(\"Enter the name of tha match: \")\n searchbox = driver.find_element_by_css_selector(\".gLFyf.gsfi\") #gLFyf gsfi\n searchbox.click()\n searchbox.send_keys(Match)\n searchbox.submit()\n try:\n button = driver.find_element_by_class_name(\"PUDfGe\")\n button.click()\n except:\n table = driver.find_element_by_class_name(\"imspo_mt__mit\")\n #liveresults-sports-immersive__match-tile imso-hov \n table.click()\nLookup()\ntime.sleep(1)\nurl = driver.current_url\ndef teams():\n team1 = driver.find_element_by_class_name(\"imso_mh__first-tn-ed\").text\n team2 = driver.find_element_by_class_name(\"imso_mh__second-tn-ed\").text\n return team1 + \"-\" + team2\n\ndef scores():\n score1 = driver.find_element_by_class_name(\"imso_mh__l-tm-sc\").text \n score2 = driver.find_element_by_class_name(\"imso_mh__r-tm-sc\").text\n return score1 + \"-\" + score2\nStats = driver.find_elements_by_class_name(\"MzWkAb\")\ntry:\n league = driver.find_element_by_class_name(\"imso_mh__pst-m-stts-l\").text\nexcept:\n print(\"league not found\")\n league = \"League not found\"\n\n#String handling and beautifying\nmessage = \"\"\"\"\"\"+'{: <25}'.format(league)\nmessage = message + \"\\n\" +'{: <25}'.format(teams()) + \"\\n\" + '{: <25}'.format(scores())\nfor stat in Stats:\n message = message + \"\\n\"+ '{: <25}'.format(stat.text)\nprint(message)\ndriver.close()\nnotify.send(message, url)\n#WhatsappSpammer.StartWhatsapp(\"Contact\", message)\n","sub_path":"PyProjects/MatchInformer/MatchInformer.py","file_name":"MatchInformer.py","file_ext":"py","file_size_in_byte":1735,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"29120124","text":"\"\"\"\n mantiddoc.tools.screenshot\n ~~~~~~~~~~~~~~~~~~~~~~~~~~\n\n Provides functions to take a screenshot of a QWidgets.\n\n It currently assumes that the functions are called within the\n MantidPlot Python environment\n\n :copyright: Copyright 2014\n ISIS Rutherford Appleton Laboratory & NScD Oak Ridge National Laboratory\n\"\"\"\n#--------------------------------------------------------------------------\nclass Screenshot(object):\n \"\"\"\n Takes a screenshot of widget records the filename + meta information\n about it.\n \"\"\"\n\n def __init__(self, widget, filename, directory):\n from mantidplot import screenshot_to_dir, threadsafe_call\n\n self.imgpath = screenshot_to_dir(widget=widget, filename=filename, screenshot_dir=directory)\n self.width = widget.width()\n self.height = widget.height()\n\n#--------------------------------------------------------------------------\n\ndef algorithm_screenshot(name, directory, version = -1, ext = \".png\"):\n \"\"\"\n Takes a snapshot of an algorithm dialog and saves it as an image\n named \"name_dlg.png\"\n\n Args:\n name (str): The name of the algorithm\n directory (str): An directory path where the image should be saved\n version (str): A version of the algorithm to use (default=latest)\n ext (str): An optional extension (including the period). Default=.png\n\n Returns:\n str: A full path to the image file\n \"\"\"\n import mantid\n if not mantid.__gui__:\n raise RuntimeError(\"MantidPlot not available. Cannot take screenshot\")\n\n import mantidqtpython as mantidqt\n from mantidplot import threadsafe_call\n\n iface_mgr = mantidqt.MantidQt.API.InterfaceManager()\n # threadsafe_call required for MantidPlot\n dlg = threadsafe_call(iface_mgr.createDialogFromName, name, version, None, True)\n\n suffix = (\"-v%d\" % version) if version != -1 else \"\"\n filename = \"%s%s_dlg%s\" % (name, suffix, ext)\n\n picture = Screenshot(dlg, filename, directory)\n threadsafe_call(dlg.close)\n return picture\n\n#--------------------------------------------------------------------------\n\ndef custominterface_screenshot(name, directory, ext = \".png\", widget_name = None):\n \"\"\"\n Takes a snapshot of a custom interface and saves it as an image\n named \"name.png\"\n\n Args:\n name (str): The name of the custom interface\n directory (str): An directory path where the image should be saved\n ext (str): An optional extension (including the period). Default=.png\n\n Returns:\n str: A full path to the image file\n \"\"\"\n import mantid\n if not mantid.__gui__:\n raise RuntimeError(\"MantidPlot not available. Cannot take screenshot\")\n\n import mantidqtpython as mantidqt\n from mantidplot import threadsafe_call\n from PyQt4.QtGui import QWidget\n\n iface_mgr = mantidqt.MantidQt.API.InterfaceManager()\n # threadsafe_call required for MantidPlot\n dlg = threadsafe_call(iface_mgr.createSubWindow, name, None)\n\n if dlg is None:\n raise RuntimeError(\"Interface '%s' could not be created\" % name)\n\n if widget_name:\n widget = dlg.findChild(QWidget, widget_name)\n if widget is None:\n raise RuntimeError(\"Widget '%s' does not exist in interface '%s'\" % (widget_name, name))\n picture = Screenshot(widget, name.replace(' ','_') + \"_\" + widget_name + \"_widget\" + ext, directory)\n else:\n picture = Screenshot(dlg, name.replace(' ','_') + \"_interface\" + ext, directory)\n threadsafe_call(dlg.close)\n return picture\n","sub_path":"Code/Mantid/docs/sphinxext/mantiddoc/tools/screenshot.py","file_name":"screenshot.py","file_ext":"py","file_size_in_byte":3523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"114391719","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\"\"\"\nCENTRO DE INVESTIGACION EN MATEMATICAS\nDOCTORADO EN CIENCIAS DE LA COMPUTACION\nFERNANDO CERVANTES SANCHEZ\n\nFILE NAME: visualizator3D.py\n\nPURPOSE: Helps to visualize 3D objects using VTK library\n\nFILE REFERENCES:\nName I/O Description\nNone ---- ----------\n\nABNORMAL TERMINATION CONDITIONS, ERROR AND WARNING MESSAGES:\n\nDEVELOPMENT HISTORY:\nDate Author Change Id Release Description Of Change\n10/Ago/2017 Fernando C. 0 1.0 Creation\n\"\"\"\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom numba import jit\nimport cv2\nimport sys\nimport time\nimport vtk\n\nfrom angio_view import ANGIO_VIEW\n\n# Global look up table used to paint the cells of a structured grid:\nlut = vtk.vtkLookupTable()\n \ndef renderize(renderer):\n renderer.ResetCamera()\n \n renderWindow = vtk.vtkRenderWindow()\n renderWindow.AddRenderer(renderer)\n\n renderWindowInteractor = vtk.vtkRenderWindowInteractor()\n renderWindowInteractor.SetRenderWindow(renderWindow)\n renderWindowInteractor.Initialize()\n\n renderWindowInteractor.Start()\n\n\ndef renderLine(xyz_1, xyz_2, renderer, color = (0.0, 0.5, 0.75), line_width = 1.0):\n lineSource = vtk.vtkLineSource()\n lineSource.SetPoint1(xyz_1)\n lineSource.SetPoint2(xyz_2)\n lineSource.Update()\n\n mapper = vtk.vtkPolyDataMapper()\n mapper.SetInputConnection(lineSource.GetOutputPort())\n \n actor = vtk.vtkActor()\n actor.SetMapper(mapper)\n \n actor.GetProperty().SetLineWidth(line_width)\n actor.GetProperty().SetColor(color)\n renderer.AddActor(actor)\n renderer.ResetCamera()\n\n\ndef renderSphere(xyz, radius, renderer, color = (1.0, 1.0, 0.5), opacity = 1., sphere_resolution = 2): \n sphereSource = vtk.vtkSphereSource()\n\n sphereSource.SetCenter(xyz)\n sphereSource.SetRadius(radius)\n sphereSource.SetThetaResolution(sphere_resolution)\n sphereSource.SetPhiResolution(sphere_resolution)\n\n mapper = vtk.vtkPolyDataMapper()\n mapper.SetInputConnection(sphereSource.GetOutputPort())\n\n actor = vtk.vtkActor()\n actor.SetMapper(mapper)\n\n actor.GetProperty().SetColor(color)\n actor.GetProperty().SetOpacity(opacity)\n\n renderer.AddActor(actor)\n renderer.ResetCamera()\n \n \ndef renderVector(xyz_start, xyz_end, renderer, color = (1.0, 0.5, 0.5)):\n normalized_X = xyz_end - xyz_start\n arrow_length = np.linalg.norm(normalized_X, 2)\n normalized_X /= arrow_length\n\n arbitrary = np.random.rand(3)\n normalized_Z = np.cross(normalized_X, arbitrary)\n normalized_Z /= np.linalg.norm(normalized_Z, 2)\n\n normalized_Y = np.cross(normalized_Z, normalized_X)\n \n arrowSource = vtk.vtkArrowSource()\n arrowSource.SetTipLength(1. / arrow_length)\n arrowSource.SetTipRadius(1. / arrow_length)\n arrowSource.SetTipResolution(5)\n arrowSource.SetShaftRadius(1. / arrow_length)\n arrowSource.SetShaftResolution(5)\n \n # Generate a directon cosine matrix:\n dc_matrix = vtk.vtkMatrix4x4()\n dc_matrix.Identity()\n \n dc_matrix.SetElement(0, 0, normalized_X[0])\n dc_matrix.SetElement(0, 1, normalized_Y[0])\n dc_matrix.SetElement(0, 2, normalized_Z[0])\n dc_matrix.SetElement(1, 0, normalized_X[1])\n dc_matrix.SetElement(1, 1, normalized_Y[1])\n dc_matrix.SetElement(1, 2, normalized_Z[1])\n dc_matrix.SetElement(2, 0, normalized_X[2])\n dc_matrix.SetElement(2, 1, normalized_Y[2])\n dc_matrix.SetElement(2, 2, normalized_Z[2])\n\n transform = vtk.vtkTransform()\n transform.Translate(xyz_start)\n transform.Concatenate(dc_matrix)\n transform.Scale(arrow_length, arrow_length, arrow_length)\n\n transformPD = vtk.vtkTransformPolyDataFilter()\n transformPD.SetTransform(transform)\n transformPD.SetInputConnection(arrowSource.GetOutputPort())\n \n \n mapper = vtk.vtkPolyDataMapper()\n mapper.SetInputConnection(transformPD.GetOutputPort())\n \n actor = vtk.vtkActor()\n actor.SetMapper(mapper)\n actor.GetProperty().SetColor(color)\n \n renderer.AddActor(actor)\n \n\ndef renderIsoRay(xy, view, renderer, color = (0.75, 0.65, 0.25), line_width = 1.):\n x, y = xy\n origin = view.getOrigin()\n dc_matrix = view.getDCMatrix()\n my_height = view.getHeight()\n my_width = view.getWidth()\n pix_X = view.getPixX()\n pix_Y = view.getPixY()\n DDP = view.getDDP()\n \n xyz = np.dot(dc_matrix, np.array([pix_X*(x - my_width/2.), pix_Y*(y - my_height/2.), DDP]))\n \n renderLine(xyz, origin, renderer, color, line_width)\n\n \ndef computeEpipolar(xy, view_1, view_2):\n x, y = xy\n my_height_1 = view_1.getHeight()\n my_width_1 = view_1.getWidth()\n my_width_2 = view_2.getWidth()\n \n DDP_1 = view_1.getDDP()\n pix_X_1 = view_1.getPixX()\n pix_Y_1 = view_1.getPixY()\n \n pix_X_2 = view_2.getPixX()\n pix_Y_2 = view_2.getPixY()\n \n dc_matrix_1 = view_1.getDCMatrix()\n \n # Locate the origin of the x-ray for the first view\n xyz_center_1 = view_1.getCenter()\n origin_1 = view_1.getOrigin()\n normal_1 = view_1.getNormal()\n \n # Locate the origin of the x-ray for the second view\n xyz_center_2 = view_2.getCenter()\n origin_2 = view_2.getOrigin()\n normal_2 = view_2.getNormal()\n \n # Locate the point in the 3D environment\n xyz_1 = np.dot(dc_matrix_1, np.array([pix_X_1 * (x - my_width_1 / 2.), pix_Y_1 * (y - my_height_1 / 2.), DDP_1]))\n \n # Generate 2 vectors from the point xy to the origins of the two views\n V_p_O1 = origin_1 - xyz_1\n V_p_O2 = origin_2 - xyz_1\n \n # Compute the normal vector to the epipolar plane as the cross product of the two generated vectors\n normal_ep = np.cross(V_p_O1, V_p_O2)\n normal_ep /= np.linalg.norm(normal_ep, 2)\n \n # Compute the normal vector of one of the planes ortogonal to the intersection of the second view and the epipolar plane\n normal_intersection = np.cross(normal_ep, normal_2)\n normal_intersection /= np.linalg.norm(normal_intersection, 2)\n \n # Define the equation of the plane ortogonal to the intersection with the upper left corner of the second view\n intersection_plane_constant = np.dot(normal_intersection, origin_2)\n \n # Define the equation of the second view plane and the epipolar plane\n view_2_plane_constant = np.dot(normal_2, xyz_center_2)\n epipolar_plane_constant = np.dot(normal_ep, origin_2)\n \n # Compute the intersection of the three planes:\n normal_matrix = np.array([normal_2, normal_ep, normal_intersection]).reshape(3, 3)\n constants_vector = np.array([view_2_plane_constant, epipolar_plane_constant, intersection_plane_constant])\n \n epl_point = np.linalg.solve(normal_matrix, constants_vector)\n\n epl_point_A = epl_point + normal_intersection * (my_width_2 * pix_X_2 / 2.)\n epl_point_B = epl_point - normal_intersection * (my_width_2 * pix_X_2 / 2.)\n \n return epl_point_A, epl_point_B, normal_ep, epipolar_plane_constant\n\n \ndef renderEpipolar(xy, view_1_information, view_2_information, renderer, color = (1.0, 0.0, 0.0)):\n vec_pos, vec_neg, normal_ep, epipolar_plane_constant = computeEpipolar(xy, view_1_information, view_2_information)\n renderLine(vec_pos, vec_neg, renderer, color, 1.5)\n\n\ndef renderSkeleton(view, renderer, isoRays = False):\n # Render as spheres the features of a skeleton:\n origin = view.getOrigin()\n skeleton_features = view.getFeatures(ANGIO_VIEW.FEAT_BRANCHES | ANGIO_VIEW.FEAT_ENDS | ANGIO_VIEW.FEAT_CROSS)\n \n my_width = view.getWidth()\n my_height = view.getHeight()\n DDP = view.getDDP()\n pix_X = view.getPixX()\n pix_Y = view.getPixY()\n dc_matrix = view.getDCMatrix()\n \n pixel_radius = 0.2\n x = skeleton_features[:, 0]\n y = skeleton_features[:, 1]\n\n xy = np.zeros([3, len(x)])\n xy[0, :] = (x - my_width / 2. + 0.5) * pix_X\n xy[1, :] = (y - my_height / 2. + 0.5) * pix_Y\n xy[2, :] = DDP\n \n xyz_rotated = np.dot(dc_matrix, xy)\n \n for i in range(len(x)):\n sphereSource = vtk.vtkSphereSource()\n sphereSource.SetCenter(xyz_rotated[:, i])\n sphereSource.SetRadius(pixel_radius)\n\n mapper = vtk.vtkPolyDataMapper()\n mapper.SetInputConnection(sphereSource.GetOutputPort())\n\n actor = vtk.vtkActor()\n actor.SetMapper(mapper)\n\n actor.GetProperty().SetColor((1.0, 0.0, 0.5))\n\n renderer.AddActor(actor)\n\n if (isoRays):\n renderVector(xyz_rotated[:, i], origin, renderer, (1.0, 0.0, 0.5))\n\n renderer.ResetCamera()\n\n\ndef computeImageInSpace(view, img_idx = ANGIO_VIEW.IMG_VIEW, show = False):\n try:\n computeImageInSpace.counter += 1\n\n except AttributeError:\n computeImageInSpace.counter = 1\n lut.SetNumberOfTableValues(256)\n lut.Build()\n\n for i in range(256):\n lut.SetTableValue(i, i / 255., i / 255., i / 255.)\n\n my_height = view.getHeight()\n my_width = view.getWidth()\n DDP = view.getDDP()\n pix_X = view.getPixX()\n pix_Y = view.getPixY()\n dc_matrix = view.getDCMatrix()\n\n vertices = vtk.vtkPoints()\n x, y = np.mgrid[0:(my_width+1), 0:(my_height+1)]\n x = x.reshape((my_height+1) * (my_width+1))\n y = y.reshape((my_height+1) * (my_width+1))\n \n xy = np.zeros([3, (my_height+1) * (my_width+1)])\n xy[0, :] = pix_X * (x - my_width / 2.)\n xy[1, :] = pix_Y * (y - my_height / 2.)\n xy[2, :] = DDP\n\n xyz_rotated = np.dot(dc_matrix, xy)\n\n vertices.SetNumberOfPoints((my_height+1) * (my_width+1))\n for i in range((my_height+1) * (my_width+1)):\n vertices.SetPoint(i, xyz_rotated[:, i])\n \n img_intensities = vtk.vtkUnsignedCharArray()\n img_intensities.SetNumberOfComponents(1)\n img_intensities.SetName(\"Intensities\")\n\n img = view.getImageData(img_idx)\n\n for i in range(my_height):\n for j in range(my_width):\n intensity = int(img[j, i])\n img_intensities.InsertNextValue(intensity)\n\n\n structuredGrid = vtk.vtkStructuredGrid()\n structuredGrid.SetDimensions(my_width+1, my_height+1, 1)\n structuredGrid.SetPoints(vertices)\n structuredGrid.GetCellData().SetScalars(img_intensities)\n \n mapper = vtk.vtkDataSetMapper()\n mapper.SetInputData(structuredGrid)\n mapper.SetScalarRange(0, 255)\n mapper.SetLookupTable(lut)\n \n actor = vtk.vtkActor()\n actor.SetMapper(mapper)\n\n if (not(show)):\n return actor\n \n renderer = vtk.vtkRenderer()\n renderer.AddActor(actor)\n renderer.ResetCamera()\n\n renderWindow = vtk.vtkRenderWindow()\n renderWindow.AddRenderer(renderer)\n\n renderWindowInteractor = vtk.vtkRenderWindowInteractor()\n renderWindowInteractor.SetRenderWindow(renderWindow)\n renderWindowInteractor.Initialize()\n renderWindowInteractor.Start()\n \n return actor\n \n\ndef computeImageInIsocenter(view, DDP = 0., img_idx = ANGIO_VIEW.IMG_VIEW, show = False):\n try:\n computeImageInSpace.counter += 1\n\n except AttributeError:\n computeImageInSpace.counter = 1\n lut.SetNumberOfTableValues(256)\n lut.Build()\n\n for i in range(256):\n lut.SetTableValue(i, i / 255., i / 255., i / 255.)\n\n my_height = view.getHeight()\n my_width = view.getWidth()\n\n SID = view.getSID()\n SOD = view.getSOD()\n pix_X = view.getPixX() * (SOD + DDP) / SID\n pix_Y = view.getPixY() * (SOD + DDP) / SID\n dc_matrix = view.getDCMatrix()\n\n vertices = vtk.vtkPoints()\n x, y = np.mgrid[0:(my_width+1), 0:(my_height+1)]\n x = x.reshape((my_height+1) * (my_width+1))\n y = y.reshape((my_height+1) * (my_width+1))\n \n xy = np.zeros([3, (my_height+1) * (my_width+1)])\n xy[0, :] = pix_X * (x - my_width / 2.)\n xy[1, :] = pix_Y * (y - my_height / 2.)\n xy[2, :] = DDP\n\n xyz_rotated = np.dot(dc_matrix, xy)\n\n vertices.SetNumberOfPoints((my_height+1) * (my_width+1))\n for i in range((my_height+1) * (my_width+1)):\n vertices.SetPoint(i, xyz_rotated[:, i])\n \n img_intensities = vtk.vtkUnsignedCharArray()\n img_intensities.SetNumberOfComponents(1)\n img_intensities.SetName(\"Intensities\")\n\n img = view.getImageData(img_idx)\n\n for i in range(my_height):\n for j in range(my_width):\n intensity = int(img[j, i])\n img_intensities.InsertNextValue(intensity)\n\n\n structuredGrid = vtk.vtkStructuredGrid()\n structuredGrid.SetDimensions(my_width+1, my_height+1, 1)\n structuredGrid.SetPoints(vertices)\n structuredGrid.GetCellData().SetScalars(img_intensities)\n \n mapper = vtk.vtkDataSetMapper()\n mapper.SetInputData(structuredGrid)\n mapper.SetScalarRange(0, 255)\n mapper.SetLookupTable(lut)\n \n actor = vtk.vtkActor()\n actor.SetMapper(mapper)\n\n if (not(show)):\n return actor\n \n renderer = vtk.vtkRenderer()\n renderer.AddActor(actor)\n renderer.ResetCamera()\n\n renderWindow = vtk.vtkRenderWindow()\n renderWindow.AddRenderer(renderer)\n\n renderWindowInteractor = vtk.vtkRenderWindowInteractor()\n renderWindowInteractor.SetRenderWindow(renderWindow)\n renderWindowInteractor.Initialize()\n renderWindowInteractor.Start()\n \n return actor\n\n\ndef project3DPointIntoView(xyz, view):\n if (len(xyz.shape)>1):\n xyz_temp = xyz.copy()\n n_points = xyz.shape[1]\n \n else:\n xyz_temp = xyz.copy().reshape(3, 1)\n n_points = 1\n \n xyz_center = view.getCenter()\n normal = view.getNormal()\n origin = view.getOrigin()\n \n DDP = view.getDDP()\n pix_X = view.getPixX()\n pix_Y = view.getPixY()\n \n my_height = view.getHeight()\n my_width = view.getWidth()\n \n dc_matrix = view.getDCMatrix()\n inverse_dc_matrix = view.getDCMatrixInverse()\n d_xyz_origin = xyz_temp - origin.reshape(3, 1)\n \n reference_vectors = np.zeros([3, 3])\n reference_vectors[0, :] = normal\n \n plane_constants = np.zeros(3)\n plane_constants[0] = np.dot(normal, xyz_center)\n \n x_axis_reference_vector = np.dot(dc_matrix, np.array([1., 0., DDP]))\n y_axis_reference_vector = np.dot(dc_matrix, np.array([0., 1., DDP]))\n xyz_2d = np.zeros([2, n_points])\n \n for i in range(n_points):\n reference_vectors[1, :] = np.cross(d_xyz_origin[:, i], x_axis_reference_vector)\n reference_vectors[2, :] = np.cross(d_xyz_origin[:, i], y_axis_reference_vector)\n \n plane_constants[1] = np.dot(reference_vectors[1, :], xyz_temp[:, i])\n plane_constants[2] = np.dot(reference_vectors[2, :], xyz_temp[:, i])\n \n # Determine the xy position on the view as the intersection of the three planes:\n xyz_3d = np.linalg.solve(reference_vectors, plane_constants)\n xyz_2d_scaled = np.dot(inverse_dc_matrix, xyz_3d)\n \n # Re-scale the xy position to the origin view space:\n xyz_2d[:, i] = np.array([1./pix_X, 1./pix_Y]) * xyz_2d_scaled[:-1] + np.array([my_width/2., my_height/2.])\n \n return xyz_2d\n \n \ndef project3DPointIntoIsocenter(xyz, view, DDP = 0.):\n if (len(xyz.shape)>1):\n xyz_temp = xyz.copy()\n n_points = xyz.shape[1]\n \n else:\n xyz_temp = xyz.copy().reshape(3, 1)\n n_points = 1\n\n org_DDP = view.getDDP()\n normal = view.getNormal()\n origin = view.getOrigin()\n SID = view.getSID()\n SOD = view.getSOD()\n \n # Move the center of the projection inthe origin vector direction:\n xyz_center = view.getCenter() + normal * (org_DDP - DDP)\n \n my_height = view.getHeight()\n my_width = view.getWidth()\n \n dc_matrix = view.getDCMatrix()\n d_xyz_origin = xyz_temp - origin.reshape(3, 1)\n \n reference_vectors = np.zeros([3, 3])\n reference_vectors[0, :] = normal\n \n plane_constants = np.zeros(3)\n plane_constants[0] = np.dot(normal, xyz_center)\n \n x_axis_reference_vector = np.dot(dc_matrix, np.array([1., 0., DDP]))\n y_axis_reference_vector = np.dot(dc_matrix, np.array([0., 1., DDP]))\n xyz_3d = np.zeros([3, n_points])\n \n for i in range(n_points):\n reference_vectors[1, :] = np.cross(d_xyz_origin[:, i], x_axis_reference_vector)\n reference_vectors[2, :] = np.cross(d_xyz_origin[:, i], y_axis_reference_vector)\n \n plane_constants[1] = np.dot(reference_vectors[1, :], xyz_temp[:, i])\n plane_constants[2] = np.dot(reference_vectors[2, :], xyz_temp[:, i])\n \n # Determine the xy position on the view as the intersection of the three planes:\n xyz_3d[:, i] = np.linalg.solve(reference_vectors, plane_constants)\n\n return xyz_3d\n\n \ndef distanceToEpipolar(xy, epl_point_A, epl_point_B, view):\n my_height = view.getHeight()\n my_width = view.getWidth()\n DDP = view.getDDP()\n pix_X = view.getPixX()\n pix_Y = view.getPixY()\n dc_matrix = view.getDCMatrix()\n \n x, y = xy\n xyz = np.dot(dc_matrix, np.array([pix_X * (x - my_width / 2.), pix_Y * (y - my_height / 2.), DDP]))\n \n epipolar_vector = epl_point_B - epl_point_A\n vector_to_point = xyz - epl_point_A\n \n projection_on_line = epipolar_vector * np.dot(vector_to_point, epipolar_vector) / np.sum(epipolar_vector**2)\n ortogonal_to_line = vector_to_point - projection_on_line\n \n return np.sum(ortogonal_to_line**2), epl_point_A + projection_on_line\n\n\ndef emphasizePixel(xy, view, renderer, emphasis_type = 0, color = (1., 0., 0.), line_width = 1.):\n my_height = view.getHeight()\n my_width = view.getWidth()\n DDP = view.getDDP()\n pix_X = view.getPixX()\n pix_Y = view.getPixY()\n dc_matrix = view.getDCMatrix()\n \n x, y = xy\n \n if (emphasis_type == 0):\n xy_rect = np.array([[x-0.5, x+0.5, x+0.5, x-0.5], [y-0.5, y-0.5, y+0.5, y+0.5], [0., 0., 0., 0.]]) + np.array([-my_width/2, -my_height/2, DDP]).reshape(3, 1)\n xyz_rect = np.dot(dc_matrix, np.dot(np.array([[pix_X, 0., 0.], [0., pix_Y, 0.], [0., 0., 1.]]), xy_rect))\n \n renderLine(xyz_rect[:, 0], xyz_rect[:, 1], renderer, color, line_width)\n renderLine(xyz_rect[:, 1], xyz_rect[:, 2], renderer, color, line_width)\n renderLine(xyz_rect[:, 2], xyz_rect[:, 3], renderer, color, line_width)\n renderLine(xyz_rect[:, 3], xyz_rect[:, 0], renderer, color, line_width)\n \n else:\n xyz_0 = np.dot(dc_matrix, np.array([pix_X * (x - my_width / 2.), pix_Y * (y - my_height / 2.), DDP]))\n renderSphere(xyz_0, line_width, renderer, color)\n\n \ndef renderCorrespondence(xy_1, xy_2, view_1, view_2, renderer, color = (1., 0.2, 0.2), line_width = 1.):\n x_1, y_1 = xy_1\n x_2, y_2 = xy_2\n \n dc_matrix_1 = view_1.getDCMatrix()\n dc_matrix_2 = view_2.getDCMatrix()\n \n pix_X_1 = view_1.getPixX()\n pix_Y_1 = view_1.getPixY()\n pix_X_2 = view_2.getPixX()\n pix_Y_2 = view_2.getPixY()\n \n my_height_1 = view_1.getHeight()\n my_width_1 = view_1.getWidth()\n my_height_2 = view_2.getHeight()\n my_width_2 = view_2.getWidth()\n \n DDP_1 = view_1.getDDP()\n DDP_2 = view_2.getDDP()\n \n xyz_1 = np.dot(dc_matrix_1, np.array([pix_X_1*(x_1 - my_width_1/2), pix_Y_1*(y_1 - my_height_1/2), DDP_1]))\n xyz_2 = np.dot(dc_matrix_2, np.array([pix_X_2*(x_2 - my_width_2/2), pix_Y_2*(y_2 - my_height_2/2), DDP_2]))\n \n renderLine(xyz_1, xyz_2, renderer, color, line_width)\n renderSphere(xyz_1, 0.75, renderer, (1., 1., 1.))\n renderSphere(xyz_2, 0.75, renderer, (1., 1., 1.))\n \n\n\ndef computeCorrespondence(xy_1, xy_2, view_1, view_2):\n if (len(xy_1.shape) > 1):\n xy_1_temp = xy_1.copy()\n xy_2_temp = xy_2.copy()\n n_points = xy_1_temp.shape[1]\n \n else:\n xy_1_temp = xy_1.copy().reshape(2, 1)\n xy_2_temp = xy_2.copy().reshape(2, 1)\n n_points = 1\n \n my_height_1 = view_1.getHeight()\n my_width_1 = view_1.getWidth()\n my_height_2 = view_2.getHeight()\n my_width_2 = view_2.getWidth()\n\n DDP_1 = view_1.getDDP()\n DDP_2 = view_2.getDDP()\n \n pix_X_1 = view_1.getPixX()\n pix_Y_1 = view_1.getPixX()\n pix_X_2 = view_2.getPixX()\n pix_Y_2 = view_2.getPixY()\n \n origin_1 = view_1.getOrigin()\n origin_2 = view_2.getOrigin()\n\n dc_matrix_1 = view_1.getDCMatrix()\n dc_matrix_2 = view_2.getDCMatrix()\n \n xyz_1 = np.dot(dc_matrix_1, np.concatenate((np.dot(np.array([[pix_X_1, 0.], [0., pix_Y_1]]), xy_1_temp - np.array([[my_width_1/2.], [my_height_1/2.]])), np.array([DDP_1]*n_points).reshape(1, n_points))))\n xyz_2 = np.dot(dc_matrix_2, np.concatenate((np.dot(np.array([[pix_X_2, 0.], [0., pix_Y_2]]), xy_2_temp - np.array([[my_width_2/2.], [my_height_2/2.]])), np.array([DDP_2]*n_points).reshape(1, n_points))))\n\n dir_1 = origin_1.reshape(3, 1) - xyz_1\n dir_2 = origin_2.reshape(3, 1) - xyz_2\n\n # dot product [d]ir [1] with [d]ir [1]\n d1_d1 = np.sum(dir_1 ** 2, 0)\n \n # dot product [d]ir [1] with [d]ir [2]\n d1_d2 = np.sum(dir_1 * dir_2, 0)\n \n # dot product [d]ir [2] with [d]ir [2]\n d2_d2 = np.sum(dir_2 ** 2, 0)\n \n # dot product [d]ir [1] with [x]yz [1]\n d1_x1 = np.sum(dir_1 * xyz_1, 0)\n \n # dot product [d]ir [1] with [x]yz [2]\n d1_x2 = np.sum(dir_1 * xyz_2, 0)\n \n # dot product [d]ir [2] with [x]yz [1]\n d2_x1 = np.sum(dir_2 * xyz_1, 0)\n \n # dot product [d]ir [2] with [x]yz [2]\n d2_x2 = np.sum(dir_2 * xyz_2, 0)\n \n # Compute the scalars that minimize the distance between the lines xyz_1 + dir_1 * t_1 and xyz_2 + dir_2 * t_2\n t1_t2 = np.zeros([2, n_points])\n for i in range(n_points):\n inv_dir_matrix = np.array([[d2_d2[i], d1_d2[i]], [d1_d2[i], d1_d1[i]]]) / (d1_d1[i] * d2_d2[i] - d1_d2[i] * d1_d2[i])\n dir_vector = np.array([d1_x2[i] - d1_x1[i], d2_x1[i] - d2_x2[i]])\n t1_t2[:, i] = np.dot(inv_dir_matrix, dir_vector)\n \n nearest_dir_1 = xyz_1 + dir_1 * t1_t2[0, :]\n nearest_dir_2 = xyz_2 + dir_2 * t1_t2[1, :]\n\n # Compute the nearest point between the two lines as the mean of the two vectors:\n nearest_xyz = (nearest_dir_1 + nearest_dir_2) / 2.\n \n return nearest_xyz","sub_path":"src/visualizator3D.py","file_name":"visualizator3D.py","file_ext":"py","file_size_in_byte":21872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"377354729","text":"from typing import List\n\n\nclass Solution:\n def validTree(self, n: int, edges: List[List[int]]) -> bool:\n roots = [i for i in range(n)]\n connect = n\n for edge in edges:\n left, right = self.find(edge[0], roots), self.find(edge[1], roots)\n if left == right: return False\n roots[right] = left\n connect -= 1\n return connect == 1\n\n def find(self, node, roots):\n root = node\n while root != roots[root]:\n root = roots[root]\n while node != root:\n node, roots[node] = roots[node], root\n return root\n\n\n# class Solution:\n# def validTree(self, n, edges):\n# \"\"\"\n# :type n: int\n# :type edges: List[List[int]]\n# :rtype: bool\n# \"\"\"\n# roots = [i for i in range(n)]\n# count = n\n# for edge in edges:\n# left, right = self.findRoot(roots, edge[0]), self.findRoot(roots, edge[1])\n# if left == right:\n# return False\n# roots[right] = left\n# count -= 1\n# return count == 1\n#\n# def findRoot(self, roots, node):\n# while node != roots[node]:\n# roots[node] = roots[roots[node]]\n# node = roots[roots[node]]\n# return node","sub_path":"261.py","file_name":"261.py","file_ext":"py","file_size_in_byte":1301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"331262080","text":"from collections import OrderedDict\n\n# data preprocessing\n# this function is taken from the recipeNLG codes\ndef data_drop(df):\n\n\n # calculate length of the removal items\n # the conditions are applied to title, ingredients and directions\n remove1 = df.loc[df.title.map(lambda x: len(x)<4 )]\n remove2 = df.loc[df.ingredients.map(lambda x: len(x)<2)]\n remove3 = df.loc[df.directions.map(lambda x: len(x) < 2 or len(''.join(x)) < 30)]\n remove4 = df.loc[df.directions.map(lambda x: re.search('(step|mix all)', ''.join(str(x)), re.IGNORECASE)!=None)]\n len(remove3)+len(remove2)+len(remove1)+len(remove4)\n # remove the items\n df.drop(df[df.title.map(lambda x: len(x)<4)].index, inplace=True)\n df.drop(df[df.ingredients.map(lambda x: len(x)<2)].index, inplace=True)\n df.drop(df[df.directions.map(lambda x: len(x) < 2 or len(''.join(x)) < 30)].index, inplace=True)\n df.drop(df[df.directions.map(lambda x: re.search('(step|mix all)', ''.join(str(x)), re.IGNORECASE)!=None)].index, inplace=True)\n\n return df\n\n\n\ndef df_to_plaintext_file(input_df, output_file, train=True):\n \"\"\"\n prepare data in txt type for further use\n :param input_df:\n :param output_file:\n :param train:\n :return:\n \"\"\"\n print(\"Writing to\", output_file)\n\n with open(output_file, 'w') as f:\n for index, row in input_df.iterrows():\n if index % 100000 == 0:\n print(index)\n print(res)\n if type(row.NER) != str:\n continue\n title = row.title\n directions = json.loads(row.directions)\n if len(directions) <= 1 and train:\n continue\n # print(len(directions))\n ingredients = json.loads(row.ingredients)\n ner = json.loads(row.NER)\n # print(ner)\n res = \"<RECIPE_START> <INPUT_START> \" + \" <NEXT_INPUT> \".join(ner) + \" <INPUT_END> <INGR_START> \" + \\\n \" <NEXT_INGR> \".join(ingredients) + \" <INGR_END> <INSTR_START> \" + \\\n \" <NEXT_INSTR> \".join(\n directions) + \" <INSTR_END> <TITLE_START> \" + title + \" <TITLE_END> <RECIPE_END>\"\n # print(res)\n f.write(\"{}\\n\".format(res))\n\n\ndef datapre_txt2h5_CLM(tokenizer,train_size, test_size):\n \"\"\"\n prepare data from txt to h5 file for casual language modelling\n :param tokenizer: pretrained tokenizer type\n :param train_size: training size\n :param test_size: testing size\n :return: None\n \"\"\"\n end_token_id = tokenizer.convert_tokens_to_ids([\"<RECIPE_END>\"])[0]\n ing_token_id = tokenizer.convert_tokens_to_ids([\"<INPUT_END>\"])[0]\n directions_size = 512\n hf = h5py.File(\"unsupervised.h5\", \"w\")\n for filename in [\"test\", \"train\"]:\n out_np = []\n data = open(\"unsupervised_\" + filename + \".txt\", \"r\")\n num = 0\n rows = 0\n for line in data:\n num += 1\n if num % 10000 == 0:\n print(\"Read \" + str(num) + \" Written: \" + str(rows))\n text_tokens = tokenizer(line)['input_ids']\n if ing_token_id in text_tokens:\n pass\n else:\n continue\n\n # error in one recipe\n # 50273 is the token for <RECIPE_END>\n if len(text_tokens) > directions_size or (50273 not in text_tokens):\n continue\n\n text_tokens_ids = text_tokens\n\n while len(text_tokens_ids) < directions_size:\n text_tokens_ids.append(end_token_id)\n out_np.append(text_tokens_ids)\n rows += 1\n\n if rows == train_size and filename == 'train':\n print(\"training sample enough:\", train_size)\n break\n if rows == test_size and filename == 'test':\n print(\"testing sample enough\", test_size)\n break\n out_mat = np.matrix(out_np)\n print(out_mat.shape)\n hf.create_dataset(filename, data=out_mat)\n hf.close()\n\n\n\n\n\n\ndef datapre_txt2h5_CG(tokenizer, train_size, test_size):\n \"\"\"\n prepare data from txt to h5 file for conditional generation\n :param tokenizer: pretrained tokenizer type\n :param train_size: training size\n :param test_size: testing size\n :return: None\n \"\"\"\n\n end_token_id = tokenizer.convert_tokens_to_ids([\"<RECIPE_END>\"])[0]\n ing_token_id = tokenizer.convert_tokens_to_ids([\"<INPUT_END>\"])[0]\n\n next_input_token_id = tokenizer.convert_tokens_to_ids([\"<NEXT_INPUT>\"])[0]\n\n directions_size = 512\n hf = h5py.File(\"unsupervised.h5\", \"w\")\n for filename in [\"test\", \"train\"]:\n out_np = []\n data = open(\"control_tokens_\" + filename + \".txt\", \"r\")\n num = 0\n rows = 0\n for line in data:\n num += 1\n if num % 10000 == 0:\n print(\"Process \" + str(num) + \"; Valid: \" + str(rows))\n text_tokens = tokenizer(line)['input_ids']\n\n # generate the encoder input\n if ing_token_id in text_tokens:\n ing_idx = text_tokens.index(ing_token_id)\n temp_list = list(OrderedDict.fromkeys(text_tokens[3:ing_idx]))\n\n # if <NEXT_INPUT> not in list, do not need to process\n if next_input_token_id in temp_list:\n temp_list.remove(next_input_token_id)\n else:\n continue\n text_tokens = temp_list + text_tokens\n else:\n continue\n\n # error in one recipe\n if len(text_tokens) > directions_size or (50273 not in text_tokens):\n continue\n\n # text_tokens_ids = tokenizer.convert_tokens_to_ids(text_tokens)\n text_tokens_ids = text_tokens\n\n # append <RECIPDE_END> token to the end\n while len(text_tokens_ids) < directions_size:\n text_tokens_ids.append(end_token_id)\n out_np.append(text_tokens_ids)\n rows += 1\n\n if rows == train_size and filename == 'train':\n print(\"training sample enough:\", train_size)\n break\n if rows == test_size and filename == 'test':\n print(\"testing sample enough\", test_size)\n break\n out_mat = np.matrix(out_np)\n print(out_mat.shape)\n hf.create_dataset(filename, data=out_mat)\n hf.close()\n\n\n# data loading\nclass TextDataset(Dataset):\n\n def __init__(self, tokenizer, file_path='train', block_size=512):\n cached_features_file = \"unsupervised.h5\"\n\n logger.info(\"Loading features from cached file %s\", cached_features_file)\n with h5py.File(cached_features_file, 'r') as f:\n if file_path=='test':\n self.examples = f[file_path][:] #this is a dev set, 10% of a test set\n else:\n self.examples = f[file_path][:]\n\n def __len__(self):\n return len(self.examples)\n\n def __getitem__(self, item):\n return torch.tensor(self.examples[item])\n\ndef load_and_cache_examples(args, tokenizer, evaluate=False):\n dataset = TextDataset(tokenizer, file_path=\"test\" if evaluate else \"train\", block_size=args.block_size)\n print(dataset[0:5])\n return dataset\n\n\ntrain_size = 350000\ntest_size = 100\n\ndf_to_plaintext_file(train, 'unsupervised_train.txt')\ndf_to_plaintext_file(test, 'unsupervised_test.txt',train = False)\n\ntokenizer = BartTokenizer.from_pretrained(\"facebook/bart-base\")\n# tokenizer = GPT2Tokenizer.from_pretrained(\"gpt2\")\nspecial_tokens = {\n \"additional_special_tokens\": [\n \"<TITLE_START>\",\n \"<TITLE_END>\",\n \"<INSTR_START>\",\n \"<NEXT_INSTR>\",\n \"<INSTR_END>\",\n \"<INGR_START>\",\n \"<NEXT_INGR>\",\n \"<INGR_END>\",\n \"<RECIPE_START>\",\n \"<RECIPE_END>\",\n \"<INPUT_START>\",\n \"<INPUT_END>\",\n \"<NEXT_INPUT>\"\n ]\n }\n\ntokenizer.add_special_tokens(special_tokens)\ndata_pre_for_encoder(tokenizer, train_size, test_size)","sub_path":"exp_nbs/helper_functions/data_preprocess.py","file_name":"data_preprocess.py","file_ext":"py","file_size_in_byte":8033,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"40731138","text":"from rest_framework import viewsets\nfrom rest_framework.decorators import action\nfrom rest_framework.generics import get_object_or_404\nfrom rest_framework.response import Response\nfrom rest_framework import permissions, status\nfrom django.db.models import Count\n\nfrom mahi_app.models import Cause\nfrom mahi_auth.models import User\nfrom mahi_app.serializers import CauseSerializer, SuggestionSerializer, \\\n ActivitySerializer\nfrom mahi_app.serializers.cause import CauseDetailSerializer, \\\n CauseCreateSerializer\nfrom mahi_app.permissions import IsVolunteer, CausePermissions\nfrom mahi_app.pagination import SmallResultsSetPagination\n\n\ndef missingDataErrorResponse(message):\n response_data = {\n 'error': message\n }\n return Response(response_data, status=status.HTTP_400_BAD_REQUEST)\n\n\nclass CauseViewSet(viewsets.ModelViewSet):\n permission_classes = [permissions.IsAuthenticatedOrReadOnly & CausePermissions]\n queryset = Cause.objects.filter(is_whitelisted=True)\n serializer_class = CauseSerializer\n\n def get_queryset(self):\n tag = self.request.query_params.get('tag')\n ordering = self.request.query_params.get('ordering')\n pending = self.request.query_params.get('pending')\n try:\n is_volunteer = self.request.user.is_volunteer()\n except AttributeError:\n is_volunteer = False\n if pending == 'true' and is_volunteer:\n queryset = Cause.objects.filter(is_whitelisted=False)\n else:\n queryset = Cause.objects.filter(is_whitelisted=True)\n if tag and int(tag) != 0:\n queryset = queryset.filter(tag=tag)\n if ordering:\n if ordering == '-created_on' or ordering == 'created_on':\n queryset = queryset.order_by(ordering)\n return queryset\n elif ordering == '-supporter_count' or \\\n ordering == 'supporter_count':\n queryset = queryset.annotate(\n supporter_count=Count('liked_by')).\\\n order_by(ordering, '-created_on')\n return queryset\n return queryset\n else:\n return queryset\n\n def get_object(self):\n # Perform the lookup filtering.\n lookup_url_kwarg = self.lookup_url_kwarg or self.lookup_field\n\n assert lookup_url_kwarg in self.kwargs, (\n 'Expected view %s to be called with a URL keyword argument '\n 'named \"%s\". Fix your URL conf, or set the `.lookup_field` '\n 'attribute on the view correctly.' %\n (self.__class__.__name__, lookup_url_kwarg)\n )\n filter_kwargs = {self.lookup_field: self.kwargs[lookup_url_kwarg]}\n\n try:\n is_volunteer = self.request.user.is_volunteer()\n except AttributeError:\n is_volunteer = False\n\n if is_volunteer:\n queryset = Cause.objects.all()\n obj = get_object_or_404(queryset, **filter_kwargs)\n self.check_object_permissions(self.request, obj)\n return obj\n else:\n queryset = Cause.objects.filter(is_whitelisted=True)\n obj = get_object_or_404(queryset, **filter_kwargs)\n self.check_object_permissions(self.request, obj)\n return obj\n\n def retrieve(self, request, *args, **kwargs):\n instance = self.get_object()\n serializer = CauseDetailSerializer(instance, context={'request': request})\n return Response(serializer.data)\n\n @action(detail=True, methods=['GET'], url_name='get_similar_causes',\n url_path='get_similar_causes',\n pagination_class=SmallResultsSetPagination)\n def get_similar_causes(self, request, pk):\n instance = self.get_object()\n queryset = Cause.objects.filter(tag__in=instance.tag.all())\\\n .filter(is_whitelisted=True).exclude(id=pk)\n page = self.paginate_queryset(queryset)\n serializer = CauseSerializer(\n page,\n many=True,\n context={'request': request}\n )\n return self.get_paginated_response(serializer.data)\n\n @action(detail=True, methods=['PATCH'], url_name='update_liked_user',\n url_path='update_liked_user')\n def update_liked_user(self, request, pk):\n user = User.objects.get(id=request.user.id)\n instance = self.get_object()\n if user not in instance.liked_by.all():\n instance.liked_by.add(user)\n else:\n instance.liked_by.remove(user)\n instance.save()\n serializer = CauseDetailSerializer(instance)\n return Response(serializer.data)\n\n @action(detail=True, methods=['PATCH'], url_name='volunteer_request',\n url_path='volunteer_request',\n permission_classes=[permissions.IsAuthenticated, ])\n def volunteer_request(self, request, pk):\n user = request.user\n instance = self.get_object()\n if user not in instance.volunteer_request.all():\n instance.volunteer_request.add(user)\n instance.save()\n response_data = {'message': 'Request successful'}\n else:\n response_data = {\n 'message': 'You have already requested to volunteer'\n }\n\n return Response(response_data, status=status.HTTP_200_OK)\n\n @action(detail=True, methods=['PATCH'], url_name='whitelist_cause',\n url_path='whitelist_cause',\n permission_classes=[permissions.IsAuthenticated & IsVolunteer])\n def whitelist_cause(self, request, pk):\n volunteer = request.user.volunteer\n instance = self.get_object()\n instance.is_whitelisted = True\n instance.associated_volunteers.add(volunteer)\n instance.save()\n response_data = {\n 'message': 'Cause whitelisted successfully',\n }\n return Response(data=response_data, status=status.HTTP_200_OK)\n\n @action(detail=True, methods=['GET'], url_name='get_more_suggestions',\n url_path='get_more_suggestions',\n permission_classes=[permissions.AllowAny, ])\n def get_more_suggestions(self, request, pk):\n instance = self.get_object()\n suggestions = instance.cause_suggestions.all()[5:]\n suggestions_serializer = SuggestionSerializer(suggestions, many=True)\n return Response(suggestions_serializer.data, status=status.HTTP_200_OK)\n\n @action(detail=True, methods=['GET'], url_name='get_more_activities',\n url_path='get_more_activities',\n permission_classes=[permissions.AllowAny, ])\n def get_more_activities(self, request, pk):\n instance = self.get_object()\n activities = instance.cause_activities.all()[3:]\n activities_serializer = ActivitySerializer(activities, many=True)\n return Response(activities_serializer.data, status=status.HTTP_200_OK)\n\n def create(self, request, *args, **kwargs):\n request.data._mutable = True\n data = request.data\n media_files = data.pop('media_files', None)\n benchmark_media = data.pop('benchmark_media', None)\n data['created_by'] = request.user.id\n request.data._mutable = False\n create_serializer = CauseCreateSerializer(data=data)\n create_serializer.is_valid(raise_exception=True)\n tags = request.POST.getlist('tag')\n if benchmark_media is None:\n message = 'Please upload benchmark media.'\n return missingDataErrorResponse(message)\n if not tags:\n message = 'Please add a category for the cause'\n return missingDataErrorResponse(message)\n cause = Cause.objects.create(**create_serializer.validated_data)\n if media_files is not None:\n for file in media_files:\n cause.media_files.create(media=file)\n for file in benchmark_media:\n cause.benchmark_media.create(media=file)\n cause.tag.set(tags)\n serializer = CauseDetailSerializer(cause)\n headers = self.get_success_headers(serializer.data)\n return Response(serializer.data, status=status.HTTP_201_CREATED,\n headers=headers)\n","sub_path":"mahi_care/mahi_app/views/cause.py","file_name":"cause.py","file_ext":"py","file_size_in_byte":8165,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"288839765","text":"import pandas as pd\nfrom typing import Union\nimport datetime\nfrom back_test.model.constant import FrequentType, Util,LongShort,ExecuteType\nfrom back_test.model.base_product import BaseProduct\nfrom back_test.model.trade import Order\nimport numpy as np\n\nclass BaseFuture(BaseProduct):\n \"\"\"\n BaseFuture: For Independent Future.\n \"\"\"\n\n def __init__(self, df_data: pd.DataFrame, df_daily_data: pd.DataFrame = None,\n frequency: FrequentType = FrequentType.DAILY):\n super().__init__(df_data, df_daily_data, frequency)\n self._multiplier = Util.DICT_CONTRACT_MULTIPLIER[self.name_code()]\n self._margin_rate = Util.DICT_FUTURE_MARGIN_RATE[self.name_code()]\n self.fee_rate = Util.DICT_TRANSACTION_FEE_RATE[self.name_code()]\n self.fee_per_unit = Util.DICT_TRANSACTION_FEE[self.name_code()]\n\n def __repr__(self) -> str:\n return 'BaseInstrument(id_instrument: {0},eval_date: {1},frequency: {2})' \\\n .format(self.id_instrument(), self.eval_date, self.frequency)\n\n def is_valid_future(self, eval_date) -> bool:\n return True\n\n\n \"\"\" getters \"\"\"\n\n def contract_month(self) -> Union[str, None]:\n ret = self.current_state[Util.NAME_CONTRACT_MONTH]\n if ret is None or ret == Util.NAN_VALUE or np.isnan(ret):\n ret = int(self._id_instrument.split('_')[1])\n return ret\n\n def get_fund_required(self, long_short: LongShort) -> float:\n return self.get_initial_margin(long_short)\n\n def get_initial_margin(self,long_short:LongShort) -> Union[float, None]:\n # pre_settle_price = self.mktprice_last_settlement()\n margin = self.mktprice_close() * self._margin_rate * self._multiplier\n return margin\n\n def get_maintain_margin(self,long_short:LongShort) -> Union[float, None]:\n margin = self.mktprice_close() * self._margin_rate * self._multiplier\n return margin\n\n def maturitydt(self) -> Union[datetime.date,None]:\n if self.current_state[Util.DT_MATURITY] != Util.NAN_VALUE:\n maturity = self.current_state[Util.DT_MATURITY]\n else:\n maturity = self.df_daily_data.loc[self.nbr_index - 1, Util.DT_DATE]\n return maturity\n\n def multiplier(self) -> Union[int,None]:\n return self._multiplier\n\n def is_margin_trade(self, long_short):\n return True\n\n def is_mtm(self):\n return True\n\n def execute_order(self, order: Order, slippage=0,slippage_rate=0.0, execute_type: ExecuteType = ExecuteType.EXECUTE_ALL_UNITS):\n if order is None or order.trade_unit == 0: return\n # if execute_type == ExecuteType.EXECUTE_ALL_UNITS:\n order.trade_all_unit(slippage=slippage,slippage_rate=slippage_rate)\n # elif execute_type == ExecuteType.EXECUTE_WITH_MAX_VOLUME:\n # order.trade_with_current_volume(int(self.trading_volume()), slippage,slippage_rate)\n # else:\n # return\n execution_record: pd.Series = order.execution_res\n # calculate margin requirement\n margin_requirement = self.get_initial_margin(order.long_short) * execution_record[Util.TRADE_UNIT]\n if self.fee_per_unit is None:\n # 百分比手续费\n transaction_fee = execution_record[Util.TRADE_PRICE] * self.fee_rate * execution_record[\n Util.TRADE_UNIT] * self._multiplier\n else:\n # 每手手续费`\n transaction_fee = self.fee_per_unit * execution_record[Util.TRADE_UNIT]\n execution_record[Util.TRANSACTION_COST] += transaction_fee\n transaction_fee_add_to_price = transaction_fee / (execution_record[Util.TRADE_UNIT] * self._multiplier)\n execution_record[Util.TRADE_PRICE] += execution_record[\n Util.TRADE_LONG_SHORT].value * transaction_fee_add_to_price\n position_size = order.long_short.value * execution_record[Util.TRADE_PRICE] * execution_record[\n Util.TRADE_UNIT] * self._multiplier\n execution_record[Util.TRADE_BOOK_VALUE] = position_size # 头寸规模（含多空符号），例如，空一手豆粕（3000点，乘数10）得到头寸规模为-30000，而建仓时点头寸市值为0。\n execution_record[Util.TRADE_MARGIN_CAPITAL] = margin_requirement\n execution_record[Util.TRADE_MARKET_VALUE] = 0.0 # 建仓时点头寸市值为0\n return execution_record\n\n","sub_path":"back_test/model/base_future.py","file_name":"base_future.py","file_ext":"py","file_size_in_byte":4399,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"603343711","text":"#demonstrate named args and parameters by default\ndef birthday(name,age):\n print(\"Happy Birthday,\",name,\"!\",\" It`s \",age,\". dontcha?\\n\")\ndef birthday2(name=\"camrade Ivanov\", age=1):\n print(\"Happy Birthday,\",name,\"!\",\" It`s \",age,\". dontcha?\\n\")\nbirthday(\"camrade Ivanov\",1)\nbirthday(1,\"camrade Ivanov\")\nbirthday(age=1,name=\"camrade Ivanov\")\nbirthday2()\nbirthday2(name=\"Kate\")\nbirthday2(age=12)\nbirthday2(name=\"KAte\",age=12)\nbirthday2(\"Kate\",12)\ninput(\"\\n\\npress enter to exit\")\n","sub_path":"birthday/birthday.py","file_name":"birthday.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"641178217","text":"from datetime import timedelta, date\n\ndef daterange(start_date, end_date):\n for n in range(int ((end_date - start_date).days)):\n yield start_date + timedelta(n)\n\nstart_date = date(1901, 1, 1)\nend_date = date(2000, 12, 31)\ndates = [0,[]]\nfor single_date in daterange(start_date, end_date):\n if single_date.strftime(\"%A/%d\") == 'Sunday/01':\n dates[1].append(single_date.strftime(\"%d/%m/%Y\"))\n dates[0]+=1\nprint(dates) #171\n","sub_path":"counting_sundays_#19.py","file_name":"counting_sundays_#19.py","file_ext":"py","file_size_in_byte":448,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"328364834","text":"from google.cloud import bigquery\nclient = bigquery.Client()\ndataset_id = 'test_dataset' # replace with your dataset ID\ntable_id = 'test_table' # replace with your table ID\ntable_ref = client.dataset(dataset_id).table(table_id)\ntable = client.get_table(table_ref) # API request\n\nrows_to_insert = [\n ('Phred Phlyntstone', 32),\n ('Wylma Phlyntstone', 29),\n]\n\nerrors = client.insert_rows(table, rows_to_insert) # API request\n\nassert errors == []","sub_path":"bigQueryTest.py","file_name":"bigQueryTest.py","file_ext":"py","file_size_in_byte":452,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"535795512","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# \n# network.py\n# \n# This file is part of the RoboEarth Cloud Engine framework.\n# \n# This file was originally created for RoboEearth\n# http://www.roboearth.org/\n# \n# The research leading to these results has received funding from\n# the European Union Seventh Framework Programme FP7/2007-2013 under\n# grant agreement no248942 RoboEarth.\n# \n# Copyright 2012 RoboEarth\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# \n# \\author/s: Dominique Hunziker \n# \n# \n\nimport socket\nimport fcntl\nimport struct\n\ndef getIP(ifname):\n \"\"\" Get the IP address associated with a network interface.\n \n Based on:\n http://code.activestate.com/recipes/439094-get-the-ip-address-\n associated-with-a-network-inter/\n \n PSF License (Python Software Foundation)\n \n @param ifname: Name of the network interface.\n @type finame: str\n \n @return: IP address as a string, i.e. x.x.x.x\n @rtype: str\n \"\"\"\n s=socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n return socket.inet_ntoa(fcntl.ioctl(\n s.fileno(),\n 0x8915, # SIOCGIFADDR\n struct.pack('256s', ifname[:15])\n )[20:24])\n","sub_path":"framework/util/network.py","file_name":"network.py","file_ext":"py","file_size_in_byte":1863,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"396993143","text":"\"\"\"Программа должна вывести указанную матрицу в соответствии с образцом: разместить единицы на главной и\n побочной диагоналях, остальные позиции матрицы заполнить нулями.\"\"\"\n\nn = int(input())\nmatrix = [[0] * n for _ in range(n)]\n\nfor i in range(n):\n matrix[i][i] = 1\n matrix[i][n - i - 1] = 1\n\nfor r in range(n):\n for c in range(n):\n print(str(matrix[r][c]).ljust(3), end='')\n print()\n","sub_path":"stepik_matrix_14.py","file_name":"stepik_matrix_14.py","file_ext":"py","file_size_in_byte":547,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"493305677","text":"\"\"\"Strategies for notifying users about metrics.\"\"\"\n\nfrom datetime import datetime, timedelta\nfrom typing import Optional\n\nfrom models.notification import Notification\nfrom models.metric_notification_data import MetricNotificationData\n\n\nclass NotificationFinder:\n \"\"\"Handle notification contents and status.\"\"\"\n\n def __init__(self, data_model):\n \"\"\"Initialise already notified list and store data model.\"\"\"\n self.already_notified = []\n self.data_model = data_model\n\n def get_notifications(self, json, most_recent_measurement_seen: datetime) -> list[Notification]:\n \"\"\"Return the reports that have a webhook and metrics that require notifying.\"\"\"\n notifications = []\n for report in json[\"reports\"]:\n notable_metrics = []\n for subject in report[\"subjects\"].values():\n for metric_uuid, metric in subject[\"metrics\"].items():\n notable = self.get_notification(metric, metric_uuid, most_recent_measurement_seen)\n if notable:\n notable_metrics.append(MetricNotificationData(metric, self.data_model, notable))\n if notable_metrics:\n for destination_uuid, destination in report.get(\"notification_destinations\", {}).items():\n notifications.append(Notification(report, notable_metrics, destination_uuid, destination))\n return notifications\n\n def get_notification(self, metric, metric_uuid, most_recent_measurement_seen: datetime) -> Optional[str]:\n \"\"\"Determine whether a notification should be generated for the given metric.\"\"\"\n if (\n len(metric[\"recent_measurements\"]) > 0\n and metric.get(\"status_start\")\n and self.__long_unchanged_status(metric, metric_uuid, most_recent_measurement_seen)\n ):\n return \"status_long_unchanged\"\n if self.status_changed(metric, most_recent_measurement_seen):\n if metric_uuid in self.already_notified:\n self.already_notified.remove(metric_uuid)\n return \"status_changed\"\n return None\n\n @staticmethod\n def status_changed(metric, most_recent_measurement_seen: datetime) -> bool:\n \"\"\"Determine if a metric got a new status after the given timestamp.\"\"\"\n recent_measurements = metric.get(\"recent_measurements\") or []\n if len(recent_measurements) < 2:\n return False # If there are fewer than two measurements, the metric couldn't have recently changed status\n scale = metric[\"scale\"]\n metric_had_other_status = recent_measurements[-2][scale][\"status\"] != recent_measurements[-1][scale][\"status\"]\n change_was_recent = datetime.fromisoformat(recent_measurements[-1][\"start\"]) > most_recent_measurement_seen\n return bool(metric_had_other_status and change_was_recent)\n\n def __long_unchanged_status(self, metric, metric_uuid, most_recent_measurement_seen: datetime) -> bool:\n \"\"\"Determine if a metric has had the same status for 3 weeks.\"\"\"\n status_start = datetime.fromisoformat(metric[\"status_start\"])\n difference = most_recent_measurement_seen - status_start\n if timedelta(days=21) < difference < timedelta(days=22) and metric_uuid not in self.already_notified:\n self.already_notified.append(metric_uuid)\n return True\n return False\n","sub_path":"components/notifier/src/strategies/notification_strategy.py","file_name":"notification_strategy.py","file_ext":"py","file_size_in_byte":3390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"124169052","text":"#!/usr/bin/env python\n# coding=utf-8\n\n\nimport re\nimport json\nimport socket\nimport sys\nimport string\nimport urllib\nimport logging\nimport requests.packages.urllib3\nfrom tldextract import extract, TLDExtract\nimport time\nfrom hashlib import md5\ntry:\n import httplib\nexcept:\n import http.client as httplib\nimport ssl\ntry:\n import urlparse\nexcept:\n from urllib import parse as urlparse\nimport random\nimport requests\nfrom requests import ConnectTimeout\nimport argparse\n\nSTATIC_EXT = [\"f4v\",\"bmp\",\"bz2\",\"css\",\"doc\",\"eot\",\"flv\",\"gif\"]\nSTATIC_EXT += [\"gz\",\"ico\",\"jpeg\",\"jpg\",\"js\",\"less\",\"mp3\", \"mp4\"]\nSTATIC_EXT += [\"pdf\",\"png\",\"rar\",\"rtf\",\"swf\",\"tar\",\"tgz\",\"txt\",\"wav\",\"woff\",\"xml\",\"zip\"]\nSTATIC_EXT += ['a3c', 'ace', 'aif', 'aifc', 'aiff', 'arj', 'asf', 'asx', 'attach', 'au',\n 'avi', 'bin', 'cab', 'cache', 'class', 'djv', 'djvu', 'dwg', 'es', 'esl',\n 'exe', 'fif', 'fvi', 'gz', 'hqx', 'ice', 'ief', 'ifs', 'iso', 'jar', 'kar',\n 'mid', 'midi', 'mov', 'movie', 'mp', 'mp2', 'mp3', 'mp4', 'mpeg',\n 'mpeg2', 'mpg', 'mpg2', 'mpga', 'msi', 'pac', 'pdf', 'ppt', 'pptx', 'psd',\n 'qt', 'ra', 'ram', 'rm', 'rpm', 'snd', 'svf', 'tar', 'tgz', 'tif',\n 'tiff', 'tpl', 'uff', 'wav', 'wma', 'wmv', 'doc', 'docx', 'db', 'jpg']\n\nBLACK_LIST_PATH = ['logout', 'log-out', 'log_out']\n\n\nBLACK_LIST_HOST = ['safebrowsing.googleapis.com', 'shavar.services.mozilla.com',]\nBLACK_LIST_HOST += ['detectportal.firefox.com', 'aus5.mozilla.org', 'incoming.telemetry.mozilla.org',]\nBLACK_LIST_HOST += ['incoming.telemetry.mozilla.org', 'addons.g-fox.cn', 'offlintab.firefoxchina.cn',]\nBLACK_LIST_HOST += ['services.addons.mozilla.org', 'g-fox.cn', 'addons.firefox.com.cn',]\nBLACK_LIST_HOST += ['versioncheck-bg.addons.mozilla.org', 'firefox.settings.services.mozilla.com']\nBLACK_LIST_HOST += ['blocklists.settings.services.mozilla.com', 'normandy.cdn.mozilla.net']\nBLACK_LIST_HOST += ['activity-stream-icons.services.mozilla.com', 'ocsp.digicert.com']\nBLACK_LIST_HOST += ['safebrowsing.clients.google.com', 'safebrowsing-cache.google.com', ]\n# BLACK_LIST_HOST += ['127.0.0.1', 'localhost']\n\nclass TURL(object):\n \"\"\"docstring for TURL\"\"\"\n def __init__(self, url):\n super(TURL, self).__init__()\n self.url = url\n self.format_url()\n self.parse_url()\n if ':' in self.netloc:\n tmp = self.netloc.split(':')\n self.host = tmp[0]\n self.port = int(tmp[1])\n else:\n self.host = self.netloc\n self.port = 80\n if self.start_no_scheme:\n self.scheme_type()\n\n self.final_url = ''\n self.url_string()\n\n def parse_url(self):\n parsed_url = urlparse.urlparse(self.url)\n self.scheme, self.netloc, self.path, self.params, self.query, self.fragment = parsed_url\n\n def format_url(self):\n if (not self.url.startswith('http://')) and (not self.url.startswith('https://')):\n self.url = 'http://' + self.url\n self.start_no_scheme = True\n else:\n self.start_no_scheme = False\n\n def scheme_type(self):\n if is_http(self.host, self.port) == 'http':\n self.scheme = 'http'\n\n if is_https(self.host, 443) == 'https':\n self.scheme = 'https'\n self.port = 443\n\n @property\n def get_host(self):\n return self.host\n\n @property\n def get_port(self):\n return self.port\n\n @property\n def get_scheme(self):\n return self.scheme\n\n @property\n def get_path(self):\n return self.path\n\n @property\n def get_query(self):\n \"\"\"\n return query\n \"\"\"\n return self.query\n\n @property\n def get_dict_query(self):\n \"\"\"\n return the dict type query\n \"\"\"\n return dict(urlparse.parse_qsl(self.query))\n\n @get_dict_query.setter\n def get_dict_query(self, dictvalue):\n if not isinstance(dictvalue, dict):\n raise Exception('query must be a dict object')\n else:\n self.query = urllib.urlencode(dictvalue)\n\n @property\n def get_filename(self):\n \"\"\"\n return url filename\n \"\"\"\n return self.path[self.path.rfind('/')+1:]\n\n @property\n def get_ext(self):\n \"\"\"\n return ext file type\n \"\"\"\n fname = self.get_filename\n ext = fname.split('.')[-1]\n if ext == fname:\n return ''\n else:\n return ext\n\n def is_ext_static(self):\n \"\"\"\n judge if the ext in static file list\n \"\"\"\n if self.get_ext in STATIC_EXT:\n return True\n else:\n return False\n\n def is_block_path(self):\n \"\"\"\n judge if the path in black_list_path\n \"\"\"\n for p in BLACK_LIST_PATH:\n if p in self.path:\n return True\n else:\n return False\n\n def is_block_host(self):\n \"\"\"\n judge if the path in black_list_host\n \"\"\"\n for p in BLACK_LIST_HOST:\n if p in self.host:\n return True\n else:\n return False\n\n def url_string(self):\n data = (self.scheme, self.netloc, self.path, self.params, self.query, self.fragment)\n url = urlparse.urlunparse(data)\n self.final_url = url\n return url\n\n def __str__(self):\n return self.final_url\n\n def __repr__(self):\n return '<TURL for %s>' % self.final_url\n\ndef LogUtil(path='/tmp/test.log', name='test', level='1'):\n levels = {\n '1': logging.INFO,\n '2': logging.WARNING,\n '3': logging.ERROR\n }\n logger = logging.getLogger()\n logger.setLevel(levels[level])\n\n #create formatter\n formatter = logging.Formatter(fmt=u'[%(asctime)s] [%(levelname)s] [%(funcName)s] %(message)s ')\n\n # create console\n console_handler = logging.StreamHandler()\n console_handler.setFormatter(formatter)\n\n # create file\n file_handler = logging.FileHandler(path, encoding='utf-8')\n console_handler.setFormatter(formatter)\n\n logger.addHandler(console_handler)\n logger.addHandler(file_handler)\n return logger\n\n\n# logger = LogUtil()\n\ndef is_http(url, port=None):\n \"\"\"\n judge if the url is http service\n :url the host, like www.iqiyi.com, without scheme\n \"\"\"\n if port is None: port = 80\n service = ''\n try:\n conn = httplib.HTTPConnection(url, port, timeout=10)\n conn.request('HEAD', '/')\n conn.close()\n service = 'http'\n except Exception as e:\n #print \"[lib.common] [is_http] {}\".format(repr(e))\n pass\n\n return service\n\ndef is_https(url, port=None):\n \"\"\"\n judge if the url is https request\n :url the host, like www.iqiyi.com, without scheme\n \"\"\"\n ssl._create_default_https_context = ssl._create_unverified_context\n if port is None: port = 443\n service = ''\n try:\n conn = httplib.HTTPSConnection(url, port, timeout=10)\n conn.request('HEAD', '/')\n conn.close()\n service = 'https'\n except Exception as e:\n #print \"[lib.common] [is_http] {}\".format(repr(e))\n pass\n\n return service\n\ndef hashmd5(string):\n try:\n string = string.encode('utf-8')\n except:\n string = string\n return md5(string).hexdigest()\n\n\ndef get_basedomain(url, basedomain=1):\n try:\n if basedomain == 1: # xxx.iqiyi.com\n return urlparse.urlparse(url).netloc\n elif basedomain == 2: # iqiyi.com\n return extract(url).registered_domain\n elif basedomain == 3: # iqiyi\n return extract(url).domain # 更加有关联性的处理方法\n except Exception as e:\n pass\n\n\n\ndef argsparse():\n parser = argparse.ArgumentParser()\n parser.add_argument(\"--basedomain\", type=int, default=1, help=\"get the domian of url,i.e. url=www.iqiyi.com if value=1, return www.iqiyi.com, if value=2, return iqiyi.com, if value=3 return iqiyi\")\n parser.add_argument(\"--limit\", type=int, default=20, help=\"the time limit default 20m\")\n parser.add_argument(\"-u\", help=\"the url to spider\")\n parser.add_argument(\"--taskname\", default=\"test\", help='the taskname wanna set')\n parser.add_argument(\"--goon\", action='store_true', help=\"continue the spider\")\n parser.add_argument(\"--cookie\", help=\"the cookie file to load\")\n parser.add_argument(\"--wsaddr\", help=\"the websocket address of headless chrome\")\n # add db\n db = parser.add_argument_group(title='Database', description='[optional] options for redis and mongodb')\n db.add_argument('--mongo-db', metavar='STRING', dest='mongo_db', default='spider',\n help='Mongodb database name, default \"spider\"')\n db.add_argument('--redis-db', metavar='NUMBER', dest='redis_db', type=int, default=0,\n help='Redis db index, default 0')\n\n args = parser.parse_args()\n if args.wsaddr is None or args.u is None:\n parser.print_help()\n sys.exit(-1)\n return args\n\nif __name__ == '__main__':\n s = TURL('http://static.iqiyi.com/js/pingback/iwt.js?_=21312312')\n print(get_basedomain(str(s)))\n assert s.is_ext_static() == True\n","sub_path":"lib/commons.py","file_name":"commons.py","file_ext":"py","file_size_in_byte":9152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"99699811","text":"from buildbot.config import BuilderConfig\nfrom buildbot.process.factory import BuildFactory\nfrom buildbot.steps.trigger import Trigger\nfrom rosdistro import get_source_build_files\nfrom buildbot_ros_cfg.distro import RosDistroOracle\n\n## @brief Create build triggeres\n## @param c The Buildmasterconfig\n## @param oracle The rosdistro oracle\n## @param distro The distro to configure for ('groovy', 'hydro', etc)\n## @param builders list of builders that this job can run on\n## @returns A list of build it created\ndef make_build_triggerer(c, oracle, distro, builders):\n build_files = get_source_build_files(oracle.getIndex(), distro)\n jobs = list()\n\n for build_file in build_files:\n for os in build_file.get_target_os_names():\n for code_name in build_file.get_target_os_code_names(os):\n for arch in build_file.get_target_arches(os, code_name):\n jobs.append(build_triggerer(c,\n code_name,\n arch,\n distro,\n builders,\n oracle.getOrderedRepositories(distro)))\n return jobs\n\n## @brief Trigger the build for the topologically sorted repos\n## @param c The Buildmasterconfig\n## @param distro Ubuntu distro to build for (for instance, 'precise')\n## @param arch Architecture to build for (for instance, 'amd64')\n## @param rosdistro ROS distro (for instance, 'groovy')\n## @param machines List of machines this can build on.\n## @param trigger_pkgs List of set of packages names to trigger.\ndef build_triggerer(c, distro, arch, rosdistro, machines, ordered_repos):\n f = BuildFactory()\n for repos in ordered_repos:\n f.addStep(\n Trigger(\n schedulerNames = [t.replace('_','-')+'-'+rosdistro+'-'+distro+'-'+arch+'-debtrigger' for t in repos],\n waitForFinish = True,\n alwaysRun=True\n )\n )\n # Add to builders\n c['builders'].append(\n BuilderConfig(\n name = 'build_triggerer'+'_'+rosdistro+'_'+distro+'_'+arch,\n slavenames = machines,\n factory = f\n )\n )\n return 'build_triggerer'+'_'+rosdistro+'_'+distro+'_'+arch\n","sub_path":"buildbot_ros_cfg/build_triggerer.py","file_name":"build_triggerer.py","file_ext":"py","file_size_in_byte":2332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"646006691","text":"from django.contrib import admin\nfrom .models import New, Event, Paragraph, Albom, Photo, Video\n\nclass ParagraphInline(admin.TabularInline):\n model = Paragraph\n extra = 1\n verbose_name_plural = 'Абзыцы'\n\nclass VideoInline(admin.TabularInline):\n model = Video\n extra = 1\n verbose_name_plural = 'Видео'\n\nclass AlbomInline(admin.TabularInline):\n model = Albom\n extra = 1\n verbose_name_plural = 'Альбом'\n\nclass ProposalSourceAdmin(admin.ModelAdmin):\n inlines = (ParagraphInline, VideoInline, AlbomInline)\n\nclass PhotoInline(admin.TabularInline):\n model = Photo\n extra = 1\n verbose_name_plural = 'Фото'\n\n\nclass GalaryAdmin(admin.ModelAdmin):\n inlines = (PhotoInline, )\n\nadmin.site.register(Albom, GalaryAdmin)\nadmin.site.register(New, ProposalSourceAdmin)\nadmin.site.register(Event)\nadmin.site.register(Video)","sub_path":"rk_dgtu/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":867,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"410415795","text":"#Author: Kyle Marais\n#Script that takes in video from webcam, and then applies basic frame differencing to the frames acquired from the video.\nimport sys\nimport numpy as np\nimport cv2 as cv\n\ndef main(filename):\n\n\t# Open webcam capture\n\tvidcap = cv.VideoCapture(0)\n\tthr = 20;\n\n\t# capture frame at T(i-1)\n\tsuccess, prevBack = vidcap.read()\n\tsize = np.shape(prevBack)\n\t# sclsize = [30,30]\n\tprevBack = cv.cvtColor(prevBack,cv.COLOR_BGR2GRAY)\n\tout=prevBack\n\twhile (True):\n\t\t#capture frame at T(i)\n\t\tsuccess,pCur = vidcap.read()\n\t\tpCur = cv.cvtColor(pCur,cv.COLOR_BGR2GRAY)\n\t\tif success:\n\t\t\tfor i in range(size[0]):\n\t\t\t\tfor j in range(size[1]):\n\t\t\t\t\tcond=abs(int(pCur[i][j])-int(prevBack[i][j]))\n\t\t\t\t\tif cond >thr:\n\t\t\t\t\t\tout[i][j]=255\n\t\t\t\t\telse:\n\t\t\t\t\t\tout[i][j]=0\n\n\n\t\t\tcv.imshow('Frame Differencing',out)\n\t\t\t# update background frame\n\t\t\tprevBack=pCur\n\t\t\t# Save video frames\n\t\t\t#cv.imwrite(\"stvidcap_out/frame%d.ppm\" % count, img) #save frame as PPM file.\n\t\t\tif cv.waitKey(2) & 0xFF == 27:\n\t\t\t\tbreak\n\t\telse:\n\t\t\tbreak\n\n\tvidcap.release()\n\tcv.destroyAllWindows()\n\nmain(sys.argv[0])\n","sub_path":"basics/FrameDifference.py","file_name":"FrameDifference.py","file_ext":"py","file_size_in_byte":1072,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"415214994","text":"import os\nfrom sys import platform\n\n# ############################# SOCKETS CONFIG #########################\n\n# socket operation timeout. This will be the minimum granularity for SimpleTimer timeouts\nSOCKET_TIMEOUT = 30\n\n# time to complete a login use in expect login into another account like telnet -> telnet -> telnet\nSOCKET_TIMEOUT_FOR_LOGIN = 0.5\n\n# time to complete a login use in expect login into another account like telnet -> telnet -> telnet\nSOCKET_TIMEOUT_FOR_LOGIN_TELNET = 2\n\n# period to send keep alives set it to 0 to disable keepalives\nSOCKET_KEEPALIVE_PERIOD = 0\n\nSOCKET_OS_CAN_SET_KEEPALIVE_PERIOD = platform != 'win32'\n\nBUFFER_SIZE = 4096 # socket buffer size\n\nSOCKET_TIME_SLEEP_SELECT = 0.5\n\n# ############################# LOCAL CONNECTIONS CONFIG #########################\n\nCONNECTION_LOCAL_TIMEOUT = 0.1\nCONNECTION_LOGIN_LOCAL_TIMEOUT = 0.25\n\nTIMEOUT_FOR_EXEC_OUTPUT = 0.5\nTIMEOUT_FOR_EXPECT = 0 # time to wait for the whole expected list to complete\nSHELL_COLS = 8000 # interactive shell width\nSHELL_ROWS = 300 # interactive shell height\n\nIANA_CSV_FILE = os.path.join(os.path.split(os.path.abspath(__file__))[0], 'service-names-port-numbers.csv')","sub_path":"course/test_envi/connections/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":1241,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"509385901","text":"# -*- coding: utf-8 -*-\n# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-\n# vi: set ft=python sts=4 ts=4 sw=4 et:\n\"\"\"\nResampling workflows\n++++++++++++++++++++\n\n.. autofunction:: init_bold_surf_wf\n.. autofunction:: init_bold_mni_trans_wf\n\n\"\"\"\nimport os.path as op\n\n\nfrom niworkflows.nipype.pipeline import engine as pe\nfrom niworkflows.nipype.interfaces import utility as niu, freesurfer as fs\n\nfrom niworkflows import data as nid\nfrom niworkflows.interfaces.utils import GenerateSamplingReference\nfrom niworkflows.interfaces.fixes import FixHeaderApplyTransforms as ApplyTransforms\n\nfrom ...interfaces import GiftiSetAnatomicalStructure, MultiApplyTransforms\nfrom ...interfaces.nilearn import Merge\n# See https://github.com/poldracklab/fmriprep/issues/768\nfrom ...interfaces.freesurfer import PatchedConcatenateLTA as ConcatenateLTA\n\nDEFAULT_MEMORY_MIN_GB = 0.01\n\n\ndef init_bold_surf_wf(bold_file_size_gb, output_spaces, medial_surface_nan, name='bold_surf_wf'):\n \"\"\"\n This workflow samples functional images to FreeSurfer surfaces\n\n For each vertex, the cortical ribbon is sampled at six points (spaced 20% of thickness apart)\n and averaged.\n\n Outputs are in GIFTI format.\n\n .. workflow::\n :graph2use: colored\n :simple_form: yes\n\n from fmriprep.workflows.bold import init_bold_surf_wf\n wf = init_bold_surf_wf(bold_file_size_gb=0.1,\n output_spaces=['T1w', 'fsnative',\n 'template', 'fsaverage5'],\n medial_surface_nan=False)\n\n **Parameters**\n\n output_spaces : list\n List of output spaces functional images are to be resampled to\n Target spaces beginning with ``fs`` will be selected for resampling,\n such as ``fsaverage`` or related template spaces\n If the list contains ``fsnative``, images will be resampled to the\n individual subject's native surface\n medial_surface_nan : bool\n Replace medial wall values with NaNs on functional GIFTI files\n\n **Inputs**\n\n source_file\n Motion-corrected BOLD series in T1 space\n t1_preproc\n Bias-corrected structural template image\n subjects_dir\n FreeSurfer SUBJECTS_DIR\n subject_id\n FreeSurfer subject ID\n t1_2_fsnative_forward_transform\n LTA-style affine matrix translating from T1w to FreeSurfer-conformed subject space\n\n **Outputs**\n\n surfaces\n BOLD series, resampled to FreeSurfer surfaces\n\n \"\"\"\n workflow = pe.Workflow(name=name)\n inputnode = pe.Node(\n niu.IdentityInterface(fields=['source_file', 't1_preproc', 'subject_id', 'subjects_dir',\n 't1_2_fsnative_forward_transform']),\n name='inputnode')\n\n outputnode = pe.Node(niu.IdentityInterface(fields=['surfaces']), name='outputnode')\n\n spaces = [space for space in output_spaces if space.startswith('fs')]\n\n def select_target(subject_id, space):\n \"\"\" Given a source subject ID and a target space, get the target subject ID \"\"\"\n return subject_id if space == 'fsnative' else space\n\n targets = pe.MapNode(niu.Function(function=select_target),\n iterfield=['space'], name='targets',\n mem_gb=DEFAULT_MEMORY_MIN_GB)\n targets.inputs.space = spaces\n\n # Rename the source file to the output space to simplify naming later\n rename_src = pe.MapNode(niu.Rename(format_string='%(subject)s', keep_ext=True),\n iterfield='subject', name='rename_src', run_without_submitting=True,\n mem_gb=DEFAULT_MEMORY_MIN_GB)\n rename_src.inputs.subject = spaces\n\n resampling_xfm = pe.Node(fs.utils.LTAConvert(in_lta='identity.nofile', out_lta=True),\n name='resampling_xfm')\n set_xfm_source = pe.Node(ConcatenateLTA(out_type='RAS2RAS'), name='set_xfm_source')\n\n sampler = pe.MapNode(\n fs.SampleToSurface(sampling_method='average', sampling_range=(0, 1, 0.2),\n sampling_units='frac', interp_method='trilinear', cortex_mask=True,\n override_reg_subj=True, out_type='gii'),\n iterfield=['source_file', 'target_subject'],\n iterables=('hemi', ['lh', 'rh']),\n name='sampler', mem_gb=bold_file_size_gb * 3)\n\n def medial_wall_to_nan(in_file, subjects_dir, target_subject):\n \"\"\" Convert values on medial wall to NaNs\n \"\"\"\n import nibabel as nb\n import numpy as np\n import os\n\n fn = os.path.basename(in_file)\n if not target_subject.startswith('fs'):\n return in_file\n\n cortex = nb.freesurfer.read_label(os.path.join(\n subjects_dir, target_subject, 'label', '{}.cortex.label'.format(fn[:2])))\n func = nb.load(in_file)\n medial = np.delete(np.arange(len(func.darrays[0].data)), cortex)\n for darray in func.darrays:\n darray.data[medial] = np.nan\n\n out_file = os.path.join(os.getcwd(), fn)\n func.to_filename(out_file)\n return out_file\n\n medial_nans = pe.MapNode(niu.Function(function=medial_wall_to_nan),\n iterfield=['in_file', 'target_subject'], name='medial_nans',\n mem_gb=DEFAULT_MEMORY_MIN_GB)\n\n merger = pe.JoinNode(niu.Merge(1, ravel_inputs=True), name='merger',\n joinsource='sampler', joinfield=['in1'], run_without_submitting=True,\n mem_gb=DEFAULT_MEMORY_MIN_GB)\n\n update_metadata = pe.MapNode(GiftiSetAnatomicalStructure(), iterfield='in_file',\n name='update_metadata', mem_gb=DEFAULT_MEMORY_MIN_GB)\n\n workflow.connect([\n (inputnode, targets, [('subject_id', 'subject_id')]),\n (inputnode, rename_src, [('source_file', 'in_file')]),\n (inputnode, resampling_xfm, [('source_file', 'source_file'),\n ('t1_preproc', 'target_file')]),\n (inputnode, set_xfm_source, [('t1_2_fsnative_forward_transform', 'in_lta2')]),\n (resampling_xfm, set_xfm_source, [('out_lta', 'in_lta1')]),\n (inputnode, sampler, [('subjects_dir', 'subjects_dir'),\n ('subject_id', 'subject_id')]),\n (set_xfm_source, sampler, [('out_file', 'reg_file')]),\n (targets, sampler, [('out', 'target_subject')]),\n (rename_src, sampler, [('out_file', 'source_file')]),\n (merger, update_metadata, [('out', 'in_file')]),\n (update_metadata, outputnode, [('out_file', 'surfaces')]),\n ])\n\n if medial_surface_nan:\n workflow.connect([\n (inputnode, medial_nans, [('subjects_dir', 'subjects_dir')]),\n (sampler, medial_nans, [('out_file', 'in_file')]),\n (targets, medial_nans, [('out', 'target_subject')]),\n (medial_nans, merger, [('out', 'in1')]),\n ])\n else:\n workflow.connect(sampler, 'out_file', merger, 'in1')\n\n return workflow\n\n\ndef init_bold_mni_trans_wf(template, bold_file_size_gb, omp_nthreads,\n name='bold_mni_trans_wf',\n output_grid_ref=None, use_compression=True,\n use_fieldwarp=False):\n \"\"\"\n This workflow samples functional images to the MNI template in a \"single shot\"\n from the original BOLD series.\n\n .. workflow::\n :graph2use: colored\n :simple_form: yes\n\n from fmriprep.workflows.bold import init_bold_mni_trans_wf\n wf = init_bold_mni_trans_wf(template='MNI152NLin2009cAsym',\n bold_file_size_gb=3,\n omp_nthreads=1,\n output_grid_ref=None)\n\n **Parameters**\n\n template : str\n Name of template targeted by `'template'` output space\n bold_file_size_gb : float\n Size of BOLD file in GB\n omp_nthreads : int\n Maximum number of threads an individual process may use\n name : str\n Name of workflow (default: ``bold_mni_trans_wf``)\n output_grid_ref : str or None\n Path of custom reference image for normalization\n use_compression : bool\n Save registered BOLD series as ``.nii.gz``\n use_fieldwarp : bool\n Include SDC warp in single-shot transform from BOLD to MNI\n\n **Inputs**\n\n itk_bold_to_t1\n Affine transform from ``ref_bold_brain`` to T1 space (ITK format)\n t1_2_mni_forward_transform\n ANTs-compatible affine-and-warp transform file\n bold_split\n Individual 3D volumes, not motion corrected\n bold_mask\n Skull-stripping mask of reference image\n name_source\n BOLD series NIfTI file\n Used to recover original information lost during processing\n hmc_xforms\n List of affine transforms aligning each volume to ``ref_image`` in ITK format\n fieldwarp\n a :abbr:`DFM (displacements field map)` in ITK format\n\n **Outputs**\n\n bold_mni\n BOLD series, resampled to template space\n bold_mask_mni\n BOLD series mask in template space\n\n \"\"\"\n workflow = pe.Workflow(name=name)\n inputnode = pe.Node(\n niu.IdentityInterface(fields=[\n 'itk_bold_to_t1',\n 't1_2_mni_forward_transform',\n 'name_source',\n 'bold_split',\n 'bold_mask',\n 'hmc_xforms',\n 'fieldwarp'\n ]),\n name='inputnode'\n )\n\n outputnode = pe.Node(\n niu.IdentityInterface(fields=['bold_mni', 'bold_mask_mni']),\n name='outputnode')\n\n def _aslist(in_value):\n if isinstance(in_value, list):\n return in_value\n return [in_value]\n\n gen_ref = pe.Node(GenerateSamplingReference(), name='gen_ref',\n mem_gb=0.3) # 256x256x256 * 64 / 8 ~ 150MB)\n template_str = nid.TEMPLATE_MAP[template]\n gen_ref.inputs.fixed_image = op.join(nid.get_dataset(template_str), '1mm_T1.nii.gz')\n\n mask_mni_tfm = pe.Node(\n ApplyTransforms(interpolation='NearestNeighbor', float=True),\n name='mask_mni_tfm',\n mem_gb=bold_file_size_gb * 3\n )\n\n # Write corrected file in the designated output dir\n mask_merge_tfms = pe.Node(niu.Merge(2), name='mask_merge_tfms', run_without_submitting=True,\n mem_gb=DEFAULT_MEMORY_MIN_GB)\n\n nxforms = 4 if use_fieldwarp else 3\n merge_xforms = pe.Node(niu.Merge(nxforms), name='merge_xforms',\n run_without_submitting=True, mem_gb=DEFAULT_MEMORY_MIN_GB)\n workflow.connect([(inputnode, merge_xforms, [('hmc_xforms', 'in%d' % nxforms)])])\n\n if use_fieldwarp:\n workflow.connect([(inputnode, merge_xforms, [('fieldwarp', 'in3')])])\n\n workflow.connect([\n (inputnode, gen_ref, [('bold_mask', 'moving_image')]),\n (inputnode, mask_merge_tfms, [('t1_2_mni_forward_transform', 'in1'),\n (('itk_bold_to_t1', _aslist), 'in2')]),\n (mask_merge_tfms, mask_mni_tfm, [('out', 'transforms')]),\n (mask_mni_tfm, outputnode, [('output_image', 'bold_mask_mni')]),\n (inputnode, mask_mni_tfm, [('bold_mask', 'input_image')])\n ])\n\n bold_to_mni_transform = pe.Node(\n MultiApplyTransforms(interpolation=\"LanczosWindowedSinc\", float=True, copy_dtype=True),\n name='bold_to_mni_transform', mem_gb=bold_file_size_gb * 3, n_procs=omp_nthreads)\n\n merge = pe.Node(Merge(compress=use_compression), name='merge',\n mem_gb=bold_file_size_gb * 3)\n\n workflow.connect([\n (inputnode, merge_xforms, [('t1_2_mni_forward_transform', 'in1'),\n (('itk_bold_to_t1', _aslist), 'in2')]),\n (merge_xforms, bold_to_mni_transform, [('out', 'transforms')]),\n (inputnode, merge, [('name_source', 'header_source')]),\n (inputnode, bold_to_mni_transform, [('bold_split', 'input_image')]),\n (bold_to_mni_transform, merge, [('out_files', 'in_files')]),\n (merge, outputnode, [('out_file', 'bold_mni')]),\n ])\n\n if output_grid_ref is None:\n workflow.connect([\n (gen_ref, mask_mni_tfm, [('out_file', 'reference_image')]),\n (gen_ref, bold_to_mni_transform, [('out_file', 'reference_image')]),\n ])\n else:\n mask_mni_tfm.inputs.reference_image = output_grid_ref\n bold_to_mni_transform.inputs.reference_image = output_grid_ref\n return workflow\n","sub_path":"fmriprep/workflows/bold/resampling.py","file_name":"resampling.py","file_ext":"py","file_size_in_byte":12673,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"151890964","text":"# Write a method to decide if two strings are anagrams or not.\n\ndef are_anagrams(string1, string2):\n dic1 = {}\n dic2 = {}\n if len(string1) == len(string2):\n for character in string1:\n if character in dic1:\n dic1[character] += 1\n dic1[character] = 1\n for character in string2:\n if character in dic2:\n dic2[character] += 1\n dic2[character] = 1\n for character in dic1:\n if character not in dic2:\n return False\n if not (dic1[character] == dic2[character]):\n return False\n return True\n return False\n\nstring1 = \"\"\nstring2 = \"\"\nprint(are_anagrams(string1, string2))","sub_path":"are_anagrams.py","file_name":"are_anagrams.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"53439427","text":"# arcpy cursors: search, insert, update\n# SearchCursor, InsertCursor, or UpdateCursor\n#arcpy.da.InsertCursor(in_table, field_names)\n#arcpy.da.SearchCursor(in_table, field_names, {where_clause}, {spatial_reference}, {explode_to_points}, {sql_clause})\n#arcpy.da.UpdateCursor(in_table, field_names, {where_clause}, {spatial_reference}, {explode_to_points}, {sql_clause})\n\nimport arcpy\n\nfc = \"D:/kgeo/kgeo.gdb/SM_Stations\"\n\ncursor = arcpy.da.SearchCursor(fc, ['NAME', 'TYPE'])\nfor row in cursor:\n print(row)\n","sub_path":"arcCursors2.py","file_name":"arcCursors2.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"392440195","text":"import sbol3\n\n# ----------------------------------------------------------------------\n# COMBINE 2020 SBOL 3 Tutorial\n# October, 2020\n#\n# This tutorial code goes with the slides at:\n#\n# https://github.com/SynBioDex/Community-Media/blob/master/2020/IWBDA20/SBOL3-IWBDA-2020.pptx\n# ----------------------------------------------------------------------\n\n# Define a constant that is not defined in pySBOL3\nSO_ENGINEERED_REGION = sbol3.SO_NS + '0000804'\nSO_ASSEMBLY_SCAR = sbol3.SO_NS + '0001953'\n\n# Set the default namespace for new objects and create a document\nsbol3.set_namespace('https://synbiohub.org/public/igem/')\ndoc = sbol3.Document()\n\n# --------------------------------------------------\n# Slide 26: GFP expression cassette\n# --------------------------------------------------\n# Component\n# identity: iGEM#I13504\n# name: \"iGEM 2016 interlab reporter\"\n# description: \"GFP expression cassette used for 2016 iGEM interlab\"\n# type: SBO:0000251 (DNA)\n# role: SO:0000804 (Engineered Region)\nprint('Creating GFP expression cassette')\ni13504 = sbol3.Component('I13504', sbol3.SBO_DNA)\ni13504.name = 'iGEM 2016 interlab reporter'\ni13504.description = 'GFP expression cassette used for 2016 iGEM interlab'\ni13504.roles.append(SO_ENGINEERED_REGION)\n\n# Add the GFP expression cassette to the document\ndoc.add(i13504)\nprint(f'Created GFP expression cassette {i13504.identity}')\n\n# --------------------------------------------------\n# Slide 28: expression cassette parts\n# --------------------------------------------------\n# Add the RBS subcomponent\nrbs = sbol3.Component('B0034', sbol3.SBO_DNA)\nrbs.name = 'RBS (Elowitz 1999)'\nrbs.roles.append(sbol3.SO_RBS)\ndoc.add(rbs)\ni13504.features.append(sbol3.SubComponent(rbs))\nprint(f'Added RBS SubComponent {rbs.identity}')\n\n# Add the GFP subcomponent\ngfp = sbol3.Component('E0040', sbol3.SBO_DNA)\ngfp.name = 'GFP'\ngfp.roles.append(sbol3.SO_CDS)\ndoc.add(gfp)\ni13504.features.append(sbol3.SubComponent(gfp))\nprint(f'Added GFP SubComponent {gfp.identity}')\n\n# Add the terminator\nterm = sbol3.Component('B0015', sbol3.SBO_DNA)\nterm.name = 'double terminator'\nterm.roles.append(sbol3.SO_TERMINATOR)\ndoc.add(term)\ni13504.features.append(sbol3.SubComponent(term))\nprint(f'Added Terminator SubComponent {term.identity}')\n\n\n# --------------------------------------------------\n# Slide 30: Location of a SubComponent\n# --------------------------------------------------\n\n# BBa_I13504_sequence (875 bp)\n# See https://synbiohub.org/public/igem/BBa_I13504_sequence/1\nseq_13504 = ('aaagaggagaaatactagatgcgtaaaggagaagaacttttcactggagttgtcccaattcttgttgaat'\n 'tagatggtgatgttaatgggcacaaattttctgtcagtggagagggtgaaggtgatgcaacatacggaaa'\n 'acttacccttaaatttatttgcactactggaaaactacctgttccatggccaacacttgtcactactttc'\n 'ggttatggtgttcaatgctttgcgagatacccagatcatatgaaacagcatgactttttcaagagtgcca'\n 'tgcccgaaggttatgtacaggaaagaactatatttttcaaagatgacgggaactacaagacacgtgctga'\n 'agtcaagtttgaaggtgatacccttgttaatagaatcgagttaaaaggtattgattttaaagaagatgga'\n 'aacattcttggacacaaattggaatacaactataactcacacaatgtatacatcatggcagacaaacaaa'\n 'agaatggaatcaaagttaacttcaaaattagacacaacattgaagatggaagcgttcaactagcagacca'\n 'ttatcaacaaaatactccaattggcgatggccctgtccttttaccagacaaccattacctgtccacacaa'\n 'tctgccctttcgaaagatcccaacgaaaagagagaccacatggtccttcttgagtttgtaacagctgctg'\n 'ggattacacatggcatggatgaactatacaaataataatactagagccaggcatcaaataaaacgaaagg'\n 'ctcagtcgaaagactgggcctttcgttttatctgttgtttgtcggtgaacgctctctactagagtcacac'\n 'tggctcaccttcgggtgggcctttctgcgtttata')\n\ni13504_seq = sbol3.Sequence('I13504_sequence')\ni13504_seq.elements = seq_13504\ni13504_seq.encoding = sbol3.SBOL_IUPAC_DNA\ni13504.sequences.append(i13504_seq)\n\nloc = sbol3.Range(i13504_seq, 738, 745)\ni13504_seq_feat = sbol3.SequenceFeature([loc])\ni13504_seq_feat.roles = [SO_ASSEMBLY_SCAR]\ni13504.features.append(i13504_seq_feat)\n\n\n# BBa_B0015_sequence (129 bp)\n# From https://synbiohub.org/public/igem/BBa_B0015_sequence/1\nseq_B0015 = ('ccaggcatcaaataaaacgaaaggctcagtcgaaagactgggcctttcgttttatctgttgtttgtcggt'\n 'gaacgctctctactagagtcacactggctcaccttcgggtgggcctttctgcgtttata')\n\nterm_seq = sbol3.Sequence('B0015_sequence')\nterm_seq.elements = seq_B0015\nterm_seq.encoding = sbol3.SBOL_IUPAC_DNA\nterm.sequences.append(term_seq)\n\n# Add the location on to the B0015 SubComponent\nloc = sbol3.Range(i13504_seq, 746, 875)\n\n# pySBOL3 does not yet have an easy way to locate features based on\n# arbitrary criteria so we have to loop over the list to find the\n# SubComponent we are looking for\nfor feature in i13504.features:\n if isinstance(feature, sbol3.SubComponent) and feature.instance_of == term.identity:\n feature.locations.append(loc)\n\n\n# --------------------------------------------------\n# Slide 32: GFP production from expression cassette\n# --------------------------------------------------\n\ni13504_system = sbol3.Component('i13504_system', sbol3.SBO_FUNCTIONAL_ENTITY)\ndoc.add(i13504_system)\n\n# Make a SubComponent referencing i13504\nsubcomp1 = sbol3.SubComponent(i13504)\ni13504_system.features.append(subcomp1)\n\n# pySBOL3 does not yet have an easy way to locate features based on\n# arbitrary criteria so we have to loop over the list to find the\n# SubComponent we are looking for\ngfp_feature = None\nfor feature in i13504.features:\n if isinstance(feature, sbol3.SubComponent) and feature.instance_of == gfp.identity:\n gfp_feature = feature\nif gfp_feature is None:\n raise Exception('Could not find GFP subcomponent')\n\n# Make a component reference for the GFP in i13504\ncompref1 = sbol3.ComponentReference(subcomp1, gfp_feature)\ni13504_system.features.append(compref1)\n\n# GFP Protein\ngfp_protein = sbol3.Component('gfp_protein', sbol3.SBO_PROTEIN)\ni13504_system.features.append(sbol3.SubComponent(gfp_protein))\n\n# Make the template participation\nparticipation1 = sbol3.Participation([sbol3.SBO_TEMPLATE], compref1)\n\n# Make the product participation\nparticipation2 = sbol3.Participation([sbol3.SBO_PRODUCT], gfp_protein)\n\n# Make the interaction\ninteraction1 = sbol3.Interaction([sbol3.SBO_GENETIC_PRODUCTION])\ninteraction1.participations = [participation1, participation2]\n\ni13504_system.interactions.append(interaction1)\n\n\n# --------------------------------------------------\n# Slide 34: Example: concatenating & reusing components\n# --------------------------------------------------\n\n# Left hand side of slide: interlab16device1\nilab16_dev1 = sbol3.Component('interlab16device1', sbol3.SBO_FUNCTIONAL_ENTITY)\ndoc.add(ilab16_dev1)\n\nj23101 = sbol3.Component('j23101', sbol3.SBO_DNA)\nsc_j23101 = sbol3.SubComponent(j23101)\nilab16_dev1.features.append(sc_j23101)\n\nsc_i13504_system = sbol3.SubComponent(i13504_system)\nilab16_dev1.features.append(sc_i13504_system)\n\ncref1 = sbol3.ComponentReference(sc_i13504_system, subcomp1)\nilab16_dev1.features.append(cref1)\n\nilab16_dev1.constraints.append(sbol3.Constraint(sbol3.SBOL_MEETS,\n sc_j23101, cref1))\n\n# Right hand side of slide: interlab16device2\nilab16_dev2 = sbol3.Component('interlab16device2', sbol3.SBO_FUNCTIONAL_ENTITY)\ndoc.add(ilab16_dev2)\n\nj23106 = sbol3.Component('j23106', sbol3.SBO_DNA)\nsc_j23106 = sbol3.SubComponent(j23106)\nilab16_dev2.features.append(sc_j23106)\n\nsc_i13504_system = sbol3.SubComponent(i13504_system)\nilab16_dev2.features.append(sc_i13504_system)\n\ncref2 = sbol3.ComponentReference(sc_i13504_system, subcomp1)\nilab16_dev2.features.append(cref2)\n\nilab16_dev2.constraints.append(sbol3.Constraint(sbol3.SBOL_MEETS,\n sc_j23106, cref2))\n\n\n# --------------------------------------------------\n# Finally, write the data out to a file\n# --------------------------------------------------\ndoc.write('gfp.nt', sbol3.SORTED_NTRIPLES)\n","sub_path":"2020/COMBINE20/tutorial_answer_key.py","file_name":"tutorial_answer_key.py","file_ext":"py","file_size_in_byte":7800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"434659865","text":"from flask import render_template, request, send_from_directory, redirect, url_for\nfrom flask_login import login_required\n\nfrom . import bp, forms, controllers\n\nmethods = ['GET', 'POST']\n\n\n@bp.route('/', methods=methods)\n@bp.route('/index', methods=methods)\ndef index():\n return redirect(url_for('auth.login'))\n\n\n@bp.route('/jxl', methods=methods)\n@login_required\ndef jxl():\n form = forms.Base()\n if form.validate_on_submit():\n project = request.form.get('project')\n version = request.form.get('version')\n filter_by = request.form.get('filter_by')\n\n response = controllers.get_jira_data(project=project,\n version=version,\n filter_by=filter_by)\n return send_from_directory(directory=response.get(\"directory\"),\n filename=response.get(\"filename\"),\n as_attachment=True)\n return render_template(template_name_or_list='index.html',\n title='JXL',\n form=form)\n","sub_path":"client/home/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":1107,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"72483672","text":"# emacs: -*- mode: python-mode; py-indent-offset: 4; tab-width: 4; indent-tabs-mode: nil -*-\n# -*- coding: utf-8 -*-\n# ex: set sts=4 ts=4 sw=4 noet:\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n#\n# See COPYING file distributed along with the datalad package for the\n# copyright and license terms.\n#\n# ## ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ### ##\n\"\"\"Test meta data \"\"\"\n\nimport logging\nimport pickle\nimport os\n\nfrom mock import patch\nfrom operator import itemgetter\nfrom os.path import join as opj, exists\n\nfrom datalad.api import Dataset, aggregate_metadata, install\nfrom datalad.metadata import get_metadata_type, get_metadata\nfrom datalad.metadata import _cached_load_document\nfrom datalad.utils import swallow_logs\nfrom datalad.utils import chpwd\nfrom datalad.utils import assure_unicode\nfrom datalad.dochelpers import exc_str\nfrom datalad.tests.utils import with_tree, with_tempfile\nfrom datalad.tests.utils import assert_not_in\nfrom datalad.tests.utils import assert_in\nfrom datalad.tests.utils import swallow_outputs\nfrom datalad.tests.utils import skip_if_no_network\nfrom datalad.support.exceptions import InsufficientArgumentsError\nfrom datalad.support.gitrepo import GitRepo\nfrom datalad.support.annexrepo import AnnexRepo\n\nfrom nose import SkipTest\nfrom nose.tools import assert_true, assert_equal, assert_raises, assert_false\n\ntry:\n import pyld\n # do here to prevent pyld from being needed\nexcept ImportError as exc:\n raise SkipTest(exc_str(exc))\n\nfrom datalad.api import search\nfrom datalad.api import search_\n\n_dataset_hierarchy_template = {\n 'origin': {\n 'dataset_description.json': \"\"\"\n{\n \"Name\": \"mother_äöü東\"\n}\"\"\",\n 'datapackage.json': \"\"\"\n{\n \"name\": \"MOTHER_äöü東\",\n \"keywords\": [\"example\", \"multitype metadata\"]\n}\"\"\",\n 'sub': {\n 'dataset_description.json': \"\"\"\n{\n \"Name\": \"child_äöü東\"\n}\"\"\",\n 'subsub': {\n 'dataset_description.json': \"\"\"\n{\n \"Name\": \"grandchild_äöü東\"\n}\"\"\"}}}}\n\n\n@with_tempfile(mkdir=True)\ndef test_get_metadata_type(path):\n # nothing set, nothing found\n assert_equal(get_metadata_type(Dataset(path)), [])\n os.makedirs(opj(path, '.datalad'))\n # got section, but no setting\n open(opj(path, '.datalad', 'config'), 'w').write('[metadata]\\n')\n assert_equal(get_metadata_type(Dataset(path)), [])\n # minimal setting\n open(opj(path, '.datalad', 'config'), 'w+').write('[metadata]\\nnativetype = mamboschwambo\\n')\n assert_equal(get_metadata_type(Dataset(path)), ['mamboschwambo'])\n\n\n@with_tree(tree={\n 'dataset_description.json': \"{}\",\n 'datapackage.json': '{\"name\": \"some\"}'\n})\ndef test_get_multiple_metadata_types(path):\n assert_equal(\n sorted(get_metadata_type(Dataset(path), guess=True)),\n ['bids', 'frictionless_datapackage'])\n\n\n@with_tree(tree={\n 'origin': {\n 'dataset_description.json': \"\"\"\n{\n \"Name\": \"the mother\"\n}\"\"\",\n 'sub': {\n 'dataset_description.json': \"\"\"\n{\n \"Name\": \"child\"\n}\"\"\"}}})\ndef test_basic_metadata(path):\n ds = Dataset(opj(path, 'origin'))\n meta = get_metadata(ds)\n assert_equal(sorted(meta[0].keys()),\n ['@context', 'dcterms:conformsTo'])\n ds.create(force=True, save=False)\n # with subdataset\n sub = ds.create('sub', force=True, if_dirty='ignore')\n ds.save()\n meta = get_metadata(ds)\n assert_equal(\n sorted(meta[0].keys()),\n ['@context', '@id', 'availableFrom', 'dcterms:conformsTo',\n 'dcterms:modified', 'type', 'version'])\n assert_equal(meta[0]['type'], 'Dataset')\n # clone and get relationship info in metadata\n sibling = install(opj(path, 'sibling'), source=opj(path, 'origin'))\n sibling_meta = get_metadata(sibling)\n assert_equal(sibling_meta[0]['@id'], ds.id)\n # origin should learn about the clone\n sibling.repo.push(remote='origin', refspec='git-annex')\n meta = get_metadata(ds)\n assert_equal([m['@id'] for m in meta[0]['availableFrom']],\n [m['@id'] for m in sibling_meta[0]['availableFrom']])\n meta = get_metadata(ds, guess_type=True)\n # without aggregation there is not trace of subdatasets in the metadata\n assert_not_in('dcterms:hasPart', meta[0])\n\n\n@skip_if_no_network\n@with_tree(tree=_dataset_hierarchy_template)\ndef test_aggregation(path):\n with chpwd(path):\n assert_raises(InsufficientArgumentsError, aggregate_metadata, None)\n # a hierarchy of three (super/sub)datasets, each with some native metadata\n ds = Dataset(opj(path, 'origin')).create(force=True)\n subds = ds.create('sub', force=True, if_dirty='ignore')\n subsubds = subds.create('subsub', force=True, if_dirty='ignore')\n # aggregate from bottom to top, guess native data, no compacting of graph\n # should yield 6 meta data sets, one implicit, and one native per dataset\n # and a second natiev set for the topmost dataset\n aggregate_metadata(ds, guess_native_type=True, recursive=True)\n # no only ask the top superdataset, no recursion, just reading from the cache\n meta = get_metadata(\n ds, guess_type=False, ignore_subdatasets=False, ignore_cache=False)\n assert_equal(len(meta), 10)\n # same schema\n assert_equal(\n 10,\n sum([s.get('@context', {'@vocab': None})['@vocab'] == 'http://schema.org/'\n for s in meta]))\n # three different IDs\n assert_equal(3, len(set([s.get('@id') for s in meta])))\n # and we know about all three datasets\n for name in ('mother_äöü東', 'child_äöü東', 'grandchild_äöü東'):\n assert_true(sum([s.get('name', None) == assure_unicode(name) for s in meta]))\n #print(meta)\n assert_equal(\n # first implicit, then two natives, then aggregate\n meta[3]['dcterms:hasPart']['@id'],\n subds.id)\n success = False\n for m in meta:\n p = m.get('dcterms:hasPart', {})\n if p.get('@id', None) == subsubds.id:\n assert_equal(opj('sub', 'subsub'), p.get('location', None))\n success = True\n assert_true(success)\n\n # save the toplevel dataset only (see below)\n ds.save('with aggregated meta data', auto_add_changes=True)\n\n # now clone the beast to simulate a new user installing an empty dataset\n clone = install(opj(path, 'clone'), source=ds.path)\n # ID mechanism works\n assert_equal(ds.id, clone.id)\n\n # get fresh meta data, the implicit one for the top-most datasets should\n # differ, but the rest not\n clonemeta = get_metadata(\n clone, guess_type=False, ignore_subdatasets=False, ignore_cache=False)\n\n # make sure the implicit md for the topmost come first\n assert_equal(clonemeta[0]['@id'], clone.id)\n assert_equal(clonemeta[0]['@id'], ds.id)\n assert_equal(clone.repo.get_hexsha(), ds.repo.get_hexsha())\n assert_equal(clonemeta[0]['version'], ds.repo.get_hexsha())\n # all but the implicit is identical\n assert_equal(clonemeta[1:], meta[1:])\n # the implicit md of the clone should list a dataset ID for its subds,\n # although it has not been obtained!\n assert_equal(\n clonemeta[3]['dcterms:hasPart']['@id'],\n subds.id)\n\n # now obtain a subdataset in the clone and the IDs should be updated\n clone.install('sub')\n partial = get_metadata(clone, guess_type=False, ignore_cache=True)\n # ids don't change\n assert_equal(partial[0]['@id'], clonemeta[0]['@id'])\n # datasets are properly connected\n assert_equal(partial[1]['dcterms:hasPart']['@id'],\n partial[2]['@id'])\n\n # query smoke test\n if os.environ.get('DATALAD_TESTS_NONETWORK'):\n raise SkipTest\n\n assert_equal(len(list(clone.search('mother'))), 1)\n assert_equal(len(list(clone.search('MoTHER'))), 1) # case insensitive\n\n child_res = list(clone.search('child'))\n assert_equal(len(child_res), 2)\n\n # little helper to match names\n def assert_names(res, names, path=clone.path):\n assert_equal(list(map(itemgetter(0), res)),\n [opj(path, n) for n in names])\n # should yield (location, report) tuples\n assert_names(child_res, ['sub', 'sub/subsub'])\n\n # result should be identical to invoking search from api\n # and search_ should spit out locations out\n with swallow_outputs() as cmo:\n res = list(search_('child', dataset=clone))\n assert_equal(res, child_res)\n assert_in(res[0][0], cmo.out)\n # and overarching search_ just for smoke testing of processing outputs\n # and not puking (e.g. under PY3)\n with swallow_outputs() as cmo:\n assert list(search_('.', regex=True, dataset=clone))\n assert cmo.out\n\n # test searching among specified properties only\n assert_names(clone.search('i', search='name'), ['sub', 'sub/subsub'])\n assert_names(clone.search('i', search='keywords'), ['.'])\n # case shouldn't matter\n assert_names(clone.search('i', search='Keywords'), ['.'])\n assert_names(clone.search('i', search=['name', 'keywords']),\n ['.', 'sub', 'sub/subsub'])\n\n # without report_matched, we are getting none of the fields\n assert(all([not x for x in map(itemgetter(1), child_res)]))\n # but we would get all if asking for '*'\n assert(all([len(x) >= 9\n for x in map(itemgetter(1),\n list(clone.search('child', report='*')))]))\n # but we would get only the matching name if we ask for report_matched\n assert_equal(\n set(map(lambda x: tuple(x[1].keys()),\n clone.search('child', report_matched=True))),\n set([('name',)])\n )\n # and the additional field we might have asked with report\n assert_equal(\n set(map(lambda x: tuple(sorted(x[1].keys())),\n clone.search('child', report_matched=True,\n report=['schema:type']))),\n set([('name', 'schema:type')])\n )\n # and if we ask report to be 'empty', we should get no fields\n child_res_empty = list(clone.search('child', report=''))\n assert_equal(len(child_res_empty), 2)\n assert_equal(\n set(map(lambda x: tuple(x[1].keys()), child_res_empty)),\n set([tuple()])\n )\n\n # more tests on returned paths:\n assert_names(clone.search('datalad'), ['.', 'sub', 'sub/subsub'])\n # if we clone subdataset and query for value present in it and its kid\n clone_sub = clone.install('sub')\n assert_names(clone_sub.search('datalad'), ['.', 'subsub'], clone_sub.path)\n\n # Test 'and' for multiple search entries\n assert_equal(len(list(clone.search(['child', 'bids']))), 2)\n assert_equal(len(list(clone.search(['child', 'subsub']))), 1)\n assert_equal(len(list(clone.search(['bids', 'sub']))), 2)\n\n res = list(clone.search('.*', regex=True)) # with regex\n assert_equal(len(res), 3) # one per dataset\n\n # we do search, not match\n assert_equal(len(list(clone.search('randchild', regex=True))), 1)\n assert_equal(len(list(clone.search(['gr.nd', 'ch.ld'], regex=True))), 1)\n assert_equal(len(list(clone.search('randchil.', regex=True))), 1)\n assert_equal(len(list(clone.search('^randchild.*', regex=True))), 0)\n assert_equal(len(list(clone.search('^grandchild.*', regex=True))), 1)\n assert_equal(len(list(clone.search('grandchild'))), 1)\n\n\n #TODO update the clone or reclone to check whether saved meta data comes down the pipe\n\n\n@skip_if_no_network\n@with_tree(tree=_dataset_hierarchy_template)\ndef test_aggregate_with_missing_or_duplicate_id(path):\n # a hierarchy of three (super/sub)datasets, each with some native metadata\n ds = Dataset(opj(path, 'origin')).create(force=True)\n subds = ds.create('sub', force=True, if_dirty='ignore')\n subds.repo.remove(opj('.datalad', 'config'))\n subds.save()\n assert_false(exists(opj(subds.path, '.datalad', 'config')))\n subsubds = subds.create('subsub', force=True, if_dirty='ignore')\n # aggregate from bottom to top, guess native data, no compacting of graph\n # should yield 6 meta data sets, one implicit, and one native per dataset\n # and a second native set for the topmost dataset\n aggregate_metadata(ds, guess_native_type=True, recursive=True)\n # no only ask the top superdataset, no recursion, just reading from the cache\n meta = get_metadata(\n ds, guess_type=False, ignore_subdatasets=False, ignore_cache=False)\n # and we know nothing subsub\n for name in ('grandchild_äöü東',):\n assert_true(sum([s.get('name', '') == assure_unicode(name) for s in meta]))\n\n # but search should not fail\n with swallow_outputs():\n res1 = list(search_('.', regex=True, dataset=ds))\n assert res1\n\n # and let's see now if we wouldn't fail if dataset is duplicate if we\n # install the same dataset twice\n subds_clone = ds.install(source=subds.path, path=\"subds2\")\n with swallow_outputs():\n res2 = list(search_('.', regex=True, dataset=ds))\n # TODO: bring back when meta data RF is complete with aggregate\n #assert_equal(len(res1) + 1, len(res2))\n #assert_equal(\n # set(map(itemgetter(0), res1)).union({subds_clone.path}),\n # set(map(itemgetter(0), res2)))\n\n\n@with_tempfile(mkdir=True)\ndef test_cached_load_document(tdir):\n\n target_schema = {'buga': 'duga'}\n cache_filename = opj(tdir, \"crap\")\n\n with open(cache_filename, 'wb') as f:\n f.write(\"CRAPNOTPICKLED\".encode())\n\n with patch('datalad.metadata._get_schema_url_cache_filename',\n return_value=cache_filename):\n with patch('pyld.jsonld.load_document', return_value=target_schema), \\\n swallow_logs(new_level=logging.WARNING) as cml:\n schema = _cached_load_document(\"http://schema.org/\")\n assert_equal(schema, target_schema)\n cml.assert_logged(\"cannot load cache from\", level=\"WARNING\")\n\n # but now pickled one should have been saved\n assert_equal(pickle.load(open(cache_filename, 'rb')), target_schema)\n\n # and if we reload it -- it should be all fine without warnings\n # should come from cache so no need to overload load_document\n with swallow_logs(new_level=logging.WARNING) as cml:\n schema = _cached_load_document(\"http://schema.org/\")\n assert_equal(schema, target_schema)\n assert_not_in(\"cannot load cache from\", cml.out)\n\n\n@with_tempfile(mkdir=True)\ndef test_ignore_nondatasets(path):\n # we want to ignore the version/commits for this test\n def _kill_time(meta):\n for m in meta:\n for k in ('version', 'dcterms:modified'):\n if k in m:\n del m[k]\n return meta\n\n ds = Dataset(path).create()\n meta = _kill_time(get_metadata(ds))\n n_subm = 0\n # placing another repo in the dataset has no effect on metadata\n for cls, subpath in ((GitRepo, 'subm'), (AnnexRepo, 'annex_subm')):\n subm_path = opj(ds.path, subpath)\n r = cls(subm_path, create=True)\n with open(opj(subm_path, 'test'), 'w') as f:\n f.write('test')\n r.add('test')\n r.commit('some')\n assert_true(Dataset(subm_path).is_installed())\n assert_equal(meta, _kill_time(get_metadata(ds)))\n # making it a submodule has no effect either\n ds.save(auto_add_changes=True)\n assert_equal(len(ds.get_subdatasets()), n_subm + 1)\n assert_equal(meta, _kill_time(get_metadata(ds)))\n n_subm += 1\n","sub_path":"datalad/metadata/tests/test_base.py","file_name":"test_base.py","file_ext":"py","file_size_in_byte":15370,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"329002876","text":"import cv2,glob,os\nimport numpy as np\nimport config_3 as cfg\ndef make_video(dir,videoname):\n frames = []\n cap = cv2.VideoCapture(0)\n out = cv2.VideoWriter(videoname,cv2.VideoWriter_fourcc(*'DIVX'),3,(1921,480))\n fights = os.listdir(dir)\n frames = dict()\n lens = []\n for fight in fights:\n imgs = os.listdir(os.path.join(dir,fight))\n frames[fight] = imgs\n lens.append(len(imgs))\n keys = frames.keys()\n for i in range(max(lens)):\n ith_frame = np.zeros((480,1,3))\n for n,k in enumerate(keys):\n if lens[n] <= i:\n img = np.zeros((480,480,3)).astype('float')\n else:\n print(lens[n],i)\n fn = frames[k][i]\n img = cv2.imread(os.path.join(dir,fight,fn))\n ith_frame = cv2.hconcat(ith_frame,img)\n out.write(ith_frame)\n out.release()\n cap.release()\n cv2.destroyAllWindows()\ndef draw_chess(hexes,maxhexes,imgname,attack_infos):\n '''\n center\n - odd : (310+200n,240),(310+200n,640)\n - even : (410+200n,440),(410+200n,840)\n '''\n img = make_chess()\n find_units = np.where(hexes[:,:,0]!=0)\n item_name = [None,'Spatula','Giant Belt','Tear of the Goddeness',0,\n 'Needless Large Rod',0,'BF sword','Recurve bow','Chain vest',\n 'Negatron Clock',0,'Sparring Gloves']\n pairs = [[x,y] for x,y in zip(find_units[0],find_units[1])]\n for p in pairs:\n side = hexes[p[0],p[1],0]\n if p[1] % 2 == 0:\n const = 210\n else:\n const = 310\n st = (200*p[0]+const,140+200*p[1])\n en = (200*p[0]+const+200,340+200*p[1])\n if side == 1:\n clr = (255,0,0)\n else:\n clr = (0,0,255)\n img = cv2.rectangle(img,st,en,clr,2)\n ind = int(hexes[p[0],p[1],1])\n name = find_name(ind)\n hp = hexes[p[0],p[1],2]\n if hp > 500:\n hp_clr = (0,255,0)\n elif hp > 300:\n hp_clr = (10,200,200)\n else:\n hp_clr = (0,0,255)\n cur_mana = hexes[p[0],p[1],3]\n tot_mana = maxhexes[p[0],p[1],3]\n is_skill = hexes[p[0],p[1],9]\n item_ind = hexes[p[0],p[1],19:]\n items = set([item_name[int(ind)] for ind in item_ind])\n if is_skill == 1:\n skill = 'already'\n else:\n skill = 'not yet'\n img = cv2.putText(img,name,(st[0],st[1]+190),cv2.FONT_HERSHEY_SIMPLEX,\n 1,(0,0,0),1)\n img = cv2.putText(img,'health : {}'.format(hp),(st[0],st[1]+170),\n cv2.FONT_HERSHEY_SIMPLEX,0.5,hp_clr,2)\n img = cv2.putText(img,'mana : {}/{}'.format(cur_mana,tot_mana),\n (st[0],st[1]+150),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),2)\n img = cv2.putText(img,'skill : {}'.format(skill),(st[0],st[1]+130),\n cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),2)\n img = cv2.putText(img,'item : {}'.format(items),(st[0],st[1]+110),\n cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),2)\n for info in attack_infos:\n eneind,damage,arr,arrind = info\n if arr[1] % 2 == 0:\n const = 210\n else:\n const = 310\n st = (200*arr[0]+const,140+200*arr[1])\n damaging = find_name(int(arrind))\n damaged = find_name(int(eneind))\n if damage == 0:\n damage ='move to'\n img = cv2.putText(img,'{} {} {}'.format(damaging,damage,damaged),\n (st[0],st[1]+90),cv2.FONT_HERSHEY_SIMPLEX,0.5,(0,0,0),2)\n img = cv2.resize(img,(480,480))\n cv2.imwrite(imgname,img)\ndef make_chess():\n img = np.full((1920,1920,3),255).astype('float')\n for i in range(7):\n img = cv2.rectangle(img,(200*i+210,140),(200*i+410,340),(0,0,0),2)\n img = cv2.rectangle(img,(200*i+210,540),(200*i+410,740),(0,0,0),2)\n img = cv2.rectangle(img,(200*i+310,340),(200*i+510,540),(0,0,0),2)\n img = cv2.rectangle(img,(200*i+310,740),(200*i+510,940),(0,0,0),2)\n img = cv2.rectangle(img,(200*i+210,940),(200*i+410,1140),(0,0,0),2)\n img = cv2.rectangle(img,(200*i+210,1340),(200*i+410,1540),(0,0,0),2)\n img = cv2.rectangle(img,(200*i+310,1140),(200*i+510,1340),(0,0,0),2)\n img = cv2.rectangle(img,(200*i+310,1540),(200*i+510,1740),(0,0,0),2)\n return img\ndef find_name(ind):\n champs = list(cfg.champ_state_info)\n if ind == 100:\n return dict(name='Mech Pilot',cost=0,elem=[])\n return champs[ind-1]\n#if __name__ =='__main__':\n# make_video('./fig/ROUND_5-1','./fig/ex.avi')\n","sub_path":"app/utils/draw.py","file_name":"draw.py","file_ext":"py","file_size_in_byte":4486,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"599304335","text":"def is_armstrong(number):\n\tstrumber = str(number)\n\tnumberofdigits = len(strumber)\n\tsum = 0\n\n\n\tfor strdigit in strumber:\n\t\tdigit = int(strdigit)\n\t\tsum = sum + (digit**numberofdigits)\n\n\tif(sum == int(strumber)):\n\t\t#print(\"Wow, \"+strumber+\" actually is a armstrong number\")\n\t\treturn \tTrue\n\telse:\n\t\t#print(strumber + \" is pretty armweak\")\n\t\treturn False\ndef generate_arnstrong_numbers(highend):\n\tfor x in range(highend):\n\t\tarmstrong = is_armstrong(x)\n\t\tif(armstrong == True):\n\t\t\tprint(str(x) + \" is an armstrong number\")\n\ngenerate_arnstrong_numbers(100000)\ngenerate_arnstrong_numbers(10)\n","sub_path":"armstrong.py","file_name":"armstrong.py","file_ext":"py","file_size_in_byte":584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"199969299","text":"## Kütüphaneler\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nfrom sklearn.impute import SimpleImputer\n\n## Verilerin import edilmesi\nveriler = pd.read_csv('./veriler.csv') \n\n\n#yaş ile alakalı olan sütunu seçiyoruz\nage = veriler.iloc[:,3:4].values \n\nboy_kilo = veriler.iloc[:,1:3].values\ngender = veriler.iloc[:,-1:].values\n\n\n## ÜLKE VERİSİNİ İŞLEME\n\ncountry = veriler.iloc[:,0:1].values\n\n## veri on isleme\nfrom sklearn import preprocessing\n\n# ENCODING\nlabel_encoder = preprocessing.LabelEncoder()\ngender[:,-1] = label_encoder.fit_transform(veriler.iloc[:,-1])\ncountry[:,0] = label_encoder.fit_transform(veriler.iloc[:,0])\n\n# ülke verisini 1 sütundan 3 farklı sütuna ayırma\none_hot_encoder = preprocessing.OneHotEncoder()\ngender = one_hot_encoder.fit_transform(gender).toarray()\ncountry = one_hot_encoder.fit_transform(country).toarray()\n\n\n# ## VERİLERİ PANDAS DATAFRAMELERİNE ÇEVİRME\ncountry_df = pd.DataFrame(data=country,index=range(22),columns= ['fr','tr','us'])\nage_df = pd.DataFrame(data=age,index=range(22),columns=['yas'])\nb_k_df = pd.DataFrame(data=boy_kilo,index=range(22),columns=['boy','kilo'])\ngender_df = pd.DataFrame(data=gender[:,:1],index=range(22),columns=['cinsiyet'])\n\n# ## VERİLERİ BİRLEŞTİRME\nfinal_df = pd.concat([country_df,b_k_df,age_df],axis=1)\n\nfinal_df2 = pd.concat([final_df,gender_df],axis=1)\n\n\n# ## Veriyi eğitim ve test olarak bölme\nfrom sklearn.model_selection import train_test_split\nx_train,x_test,y_train,y_test = train_test_split(final_df,gender_df,test_size=0.33,random_state=0)\n\n# \"\"\"\n# x: bağımsız değişken\n# y: bağımlı değişken\n# \"\"\"\n\nfrom sklearn.linear_model import LinearRegression\nlinear_regressor = LinearRegression()\nlinear_regressor.fit(x_train,y_train)\n\ny_predict = linear_regressor.predict(x_test)\n\n\nheight = final_df2.iloc[:,3:4].values\n\nleft = final_df2.iloc[:,:3]\nright = final_df2.iloc[:,4:]\n\ndataset = pd.concat([left,right],axis=1)\n\nx_train,x_test,y_train,y_test = train_test_split(dataset,height,test_size=0.33,random_state=0)\n\nlinear_regressor2 = LinearRegression()\nlinear_regressor2.fit(x_train,y_train)\n\ny_predict2 = linear_regressor2.predict(x_test)\n\n","sub_path":"dogrusal-regresyon/coklu-dogrusal-regresyon.py","file_name":"coklu-dogrusal-regresyon.py","file_ext":"py","file_size_in_byte":2175,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"192802371","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n#\n# Yadp - Yet Another Discovery Protocol\n# Copyright (C) 2015 Alexander Rüedlinger\n#\n# This file is part of Yadp.\n#\n# Yadp is free software: you can redistribute it and/or modify\n# it under the terms of the GNU Affero General Public License as\n# published by the Free Software Foundation, either version 3 of the\n# License, or (at your option) any later version.\n#\n# Yadp 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 Affero General Public License for more details.\n#\n# You should have received a copy of the GNU Affero General Public License\n# along with Yadp. If not, see <http://www.gnu.org/licenses/>.\n\n__author__ = 'Alexander Rüedlinger'\n\nimport os\nimport glob\nfrom setuptools import setup, find_packages\n\nVERSION = \"1.0.1\"\ndata_dir = os.path.join('frontend', 'public')\ndata_files = [(data_dir, [f for f in glob.glob(os.path.join(data_dir, '*'))])]\n\nsetup(\n name=\"xwot-yadp\",\n packages=find_packages(exclude=['test']),\n version=VERSION,\n entry_points={\n 'console_scripts': [\n 'yadp = yadp.cmd:yadp_listener',\n 'semantic-yadp = yadp.cmd:semantic_yadp_listener',\n 'web-yadp = yadp.cmd:web_yadp'\n ]\n },\n install_requires=['twisted>=15.2', 'klein>=15.0', 'treq>=0.2',\n 'rdflib>=4.2', 'rdfextras>=0.4', 'rdflib-jsonld>=0.3',\n 'xmltodict>=0.9.2', 'stemming==1.0'],\n data_files=data_files,\n description=\"Yet Another Discovery Protocol\",\n author=\"Alexander Rüedlinger\",\n author_email=\"a.rueedlinger@gmail.com\",\n url=\"https://github.com/lexruee/yadp\",\n license=\"AGPL v3\",\n classifiers=['License :: OSI Approved :: GNU Affero General Public License v3'],\n py_modules=[\"\"],\n long_description=\"\"\"\\\nYadp - Yet Another Discovery Protocol\n-------------------------------------\nYadp is a prototype implementation of a Discovery Protocol.\n\n 1) It allows to carry user-defined payload data.\n 2) It's line oriented protocol and looks like HTTP.\n 3) It allows to carry meta headers to encode the content-type of the user-defined payload data,\n a location header to retrieve more information about the discovered service.\n\n\"\"\"\n)\n","sub_path":"pypi_install_script/xwot-yadp-1.0.1.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"466335279","text":"import matplotlib.pyplot as plt\nimport math\n\nv = 20\ntheta = math.pi / 4\nvt = 5.65\ng = 9.81\n\ndef a_x():\n return 0\ndef a_y():\n return -g\n\nX = []\nY = []\n\nx = 0\ny = 0\n\nv_x = v * math.cos(theta)\nv_y = v * math.sin(theta)\n\ndelta = 0.001\n\nwhile y >= 0:\n # Calculating new position\n x = x + v_x * delta\n y = y + v_y * delta\n # Calculating new speeds\n v_x = v_x + a_x() * delta\n v_y = v_y + a_y() * delta\n # Adding x and y coordinates to X and Y lists\n X.append(x)\n Y.append(y)\n\nplt.scatter(x=X, y=Y)\nplt.show()","sub_path":"episode 1/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"233450252","text":"from django.shortcuts import render,redirect\nfrom django.http import HttpResponse\nfrom django.contrib.auth.forms import AuthenticationForm\nfrom django.contrib.auth import login, logout, authenticate\nfrom django.contrib.auth.models import User\nfrom .forms import CustomForm,DoctorProfileForm,SearchDoctorSpForm,PatientProfileForm,SearchDoctorUnForm\nfrom .models import Person, Patient, Doctor\nfrom django.db import connection\nfrom django.contrib import messages\nfrom datetime import datetime\n# Create your views here.\n\ndef homeview(request):\n user=request.user\n if(user.is_authenticated and not (user.is_staff) ):\n cursor=connection.cursor()\n cursor.execute('select * from portal_person WHERE user_id= %s',[user.id])\n person=cursor.fetchone()\n if(person[2]=='doctor'):\n cursor.execute('select p.username,p.name,p.phone_number,p.address,a.time from portal_patient p inner join portal_appointment a on p.id=a.pid_id where did_id= (select id from portal_doctor where username=%s)',[person[1]]) \n else:\n cursor.execute('select d.username,d.name,d.phone_number,d.address,a.time from portal_doctor d inner join portal_appointment a on d.id=a.did_id where pid_id= (select id from portal_patient where username=%s)',[person[1]])\n appointments=cursor.fetchall()\n return render(request, 'portal/home.html', {'person':person,'appointments':appointments})\n return render(request,'portal/home.html',{})\n\ndef register(request):\n if(request.method == 'POST'):\n form = CustomForm(request.POST)\n if(form.is_valid()):\n cursor=connection.cursor()\n form.save()\n occupation=form.cleaned_data.get('occupation')\n email=form.cleaned_data.get('email')\n username=form.cleaned_data.get('username')\n password=form.cleaned_data.get('password1')\n messages.success(request, f\"New account created: {username}\")\n cursor.execute('Select id from auth_user where username=%s',[username])\n userid = cursor.fetchone()\n cursor.execute('INSERT INTO portal_person (user_id,username,email,occupation) values (%s,%s,%s,%s)',[userid[0],username,email,occupation])\n if(occupation == 'doctor'):\n cursor=connection.cursor() \n cursor.execute('INSERT INTO portal_doctor (username,email,user_id) values (%s,%s,%s)',[username,email,userid[0]])\n cursor.execute('select id from portal_doctor where username=%s',[username])\n did=cursor.fetchone()[0]\n tslots=[0 for i in range(24)]\n cursor.execute('insert into portal_freetimings (did_id,t0_1,t1_2,t2_3,t3_4,t4_5,t5_6,t6_7,t7_8,t8_9,t9_10,t10_11,t11_12,t12_13,t13_14,t14_15,t15_16,t16_17,t17_18,t18_19,t19_20,t20_21,t21_22,t22_23,t23_24) values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)',[did,tslots[0],tslots[1],tslots[2],tslots[3],tslots[4],tslots[5],tslots[6],tslots[7],tslots[8],tslots[9],tslots[10],tslots[11],tslots[12],tslots[13],tslots[14],tslots[15],tslots[16],tslots[17],tslots[18],tslots[19],tslots[20],tslots[21],tslots[22],tslots[23]])\n else:\n cursor=connection.cursor()\n cursor.execute('INSERT INTO portal_patient (username,email,user_id) values (%s,%s,%s)',[username,email,userid[0]])\n\n user=authenticate(username=username,password=password)\n login(request, user)\n messages.info(request, f\"You are now logged in as {username}\")\n if(occupation=='doctor'):\n return redirect('editdoctor')\n else:\n return redirect('editpatient')\n else:\n for msg in form.error_messages:\n messages.error(request, f\"{msg}: {form.error_messages[msg]}\")\n\n return render(request = request,\n template_name = \"portal/register.html\",\n context={\"form\":form})\n else:\n form = CustomForm()\n return render(request, 'portal/register.html', {'form': form})\ndef login_req(request):\n if(request.method == 'POST'):\n form = AuthenticationForm(request, data=request.POST)\n if(form.is_valid()):\n username = form.cleaned_data.get('username')\n password = form.cleaned_data.get('password')\n user = authenticate(username=username, password=password)\n if(user is not None):\n login(request, user)\n messages.info(request, f\"You are now logged in as {username}\")\n cursor=connection.cursor()\n cursor.execute('select * from portal_person WHERE user_id= %s',[user.id])\n person=cursor.fetchone()\n return redirect('homepage')\n else:\n messages.error(request,\"Invalid username or password.\")\n else:\n messages.error(request,\"Invalid username or password\")\n else:\n form = AuthenticationForm()\n return render(request, 'portal/login.html', {'form': form})\n\ndef logout_req(request):\n logout(request)\n messages.info(request, \"Logged out successfully.\")\n return redirect('homepage')\n\ndef doctor_search_by(request):\n return render(request,'portal/searchdoctorby.html')\n\ndef doctor_search_by_specialization(request):\n if(request.method==\"GET\"):\n form=SearchDoctorSpForm(request.GET)\n if(form.is_valid()):\n user=request.user\n specialization=request.GET.get('specialization')\n cursor=connection.cursor()\n cursor.execute('select * from portal_doctor d inner join portal_freetimings f on d.id=f.did_id where d.specialization=%s',[specialization])\n doctors=cursor.fetchall()\n print(doctors)\n if(len(doctors)==0):\n numdoctors=0\n else:\n numdoctors=len(doctors)\n return render(request,'portal/selectdoctor.html',{'doctor_list':doctors,'range':range(24),'numdoctors':numdoctors})\n return render(request,'portal/searchdoctorbyspecialization.html',{'form':form})\n \ndef doctor_search_by_username(request):\n if(request.method==\"GET\"):\n form=SearchDoctorUnForm(request.GET)\n if(form.is_valid()):\n user=request.user\n username=request.GET.get('username')\n cursor=connection.cursor()\n cursor.execute('select * from portal_doctor d inner join portal_freetimings f on d.id=f.did_id where d.username=%s',[username])\n doctors=cursor.fetchall()\n #print(doctors)\n if(len(doctors)==0):\n numdoctors=0\n else:\n numdoctors=len(doctors)\n return render(request,'portal/selectdoctor.html',{'doctor_list':doctors,'range':range(24),'numdoctors':numdoctors})\n return render(request,'portal/searchdoctorbyusername.html',{'form':form})\n\ndef profileeditdoctor(request):\n user=request.user\n cursor=connection.cursor()\n cursor.execute('select * from portal_doctor where user_id=%s',[user.id])\n doctor=cursor.fetchone()\n \n if(request.method==\"GET\"):\n form=DoctorProfileForm(request.GET)\n if(form.is_valid()):\n specialization=request.GET.get('specialization')\n name=request.GET.get('name')\n address=request.GET.get('address')\n phone_number=request.GET.get('phone_number')\n tslots=[]\n for i in range(24):\n t='t'+str(i)\n ch=request.GET.get(t)\n if(ch==\"on\"):\n tslots.append(1)\n else:\n tslots.append(0)\n cursor=connection.cursor()\n person=get_person_details(user.id)\n cursor.execute('update portal_doctor set specialization= %s,name= %s,address=%s,phone_number=%s where username=%s',[specialization,name,address,phone_number,user.username])\n cursor.execute('update portal_freetimings set t0_1=%s,t1_2=%s,t2_3=%s,t3_4=%s,t4_5=%s,t5_6=%s,t6_7=%s,t7_8=%s,t8_9=%s,t9_10=%s,t10_11=%s,t11_12=%s,t12_13=%s,t13_14=%s,t14_15=%s,t15_16=%s,t16_17=%s,t17_18=%s,t18_19=%s,t19_20=%s,t20_21=%s,t21_22=%s,t22_23=%s,t23_24=%s where did_id=(select id from portal_doctor where username=%s)',[tslots[0],tslots[1],tslots[2],tslots[3],tslots[4],tslots[5],tslots[6],tslots[7],tslots[8],tslots[9],tslots[10],tslots[11],tslots[12],tslots[13],tslots[14],tslots[15],tslots[16],tslots[17],tslots[18],tslots[19],tslots[20],tslots[21],tslots[22],tslots[23],user.username])\n print(person)\n return redirect('homepage')\n else:\n form=DoctorProfileForm()\n return render(request,\"portal/editdoctorprofile.html\",{'form':form,'doctor':doctor})\n\ndef profileeditpatient(request):\n user=request.user\n cursor=connection.cursor()\n cursor.execute('select * from portal_patient where user_id=%s',[user.id])\n patient=cursor.fetchone()\n if(request.method==\"GET\"):\n form=PatientProfileForm(request.GET)\n if(form.is_valid()):\n \n name=request.GET.get('name')\n address=request.GET.get('address')\n phone_number=request.GET.get('phone_number')\n cursor.execute('update portal_patient set name= %s,address=%s,phone_number=%s where username=%s',[name,address,phone_number,user.username])\n return redirect('homepage')\n else:\n form=PatientProfileForm()\n return render(request,\"portal/editpatientprofile.html\",{'form':form,'patient':patient})\n\ndef get_person_details(username):\n cursor=connection.cursor()\n cursor.execute('select * from portal_person WHERE user_id= %s',[username])\n person=cursor.fetchone()\n return(person)\n\ndef addapointment(request):\n user=request.user\n x = request.GET.get('id')\n did,tme=map(int,x.split(\"-\"))\n tme=\"t\"+str(tme)+\"_\"+str(tme+1)\n print(tme)\n cursor=connection.cursor()\n cursor.execute('select id from portal_patient where user_id=%s',[user.id])\n pid=cursor.fetchone()\n s='update portal_freetimings set '+tme+\"=0 where did_id=\"+str(did)\n cursor.execute(s)\n cursor.execute('insert into portal_appointment (did_id,pid_id,time) values (%s,%s,%s)',[did,pid[0],tme[1:]])\n messages.info(request, f\"Appointment booked.\")\n #print(id)\n return redirect('homepage')\n\ndef cancelAppointment(request):\n user=request.user\n x=request.GET.get('id')\n uname,tme=x.split('-')\n cursor=connection.cursor()\n cursor.execute('select * from portal_person WHERE user_id= %s',[user.id])\n person=cursor.fetchone()\n if(person[2]=='doctor'):\n s=\"delete from portal_appointment where did_id=(select id from portal_doctor where username= '\" + user.username + \"') and pid_id= (select id from portal_patient where username= '\" + uname + \"') and time= '\"+ tme + \"'\"\n cursor.execute(s)\n s='update portal_freetimings set '+\"t\"+tme+\"=0 where did_id=(select id from portal_doctor where username= '\" + user.username + \"')\" \n cursor.execute(s)\n \n else:\n s=\"delete from portal_appointment where pid_id=(select id from portal_patient where username= '\" + user.username + \"') and did_id= (select id from portal_doctor where username= '\" + uname + \"') and time= '\"+ tme + \"'\"\n cursor.execute(s)\n s='update portal_freetimings set '+\"t\"+tme+\"=0 where did_id=(select id from portal_doctor where username= '\" + uname + \"')\" \n cursor.execute(s)\n \n messages.info(request,f'Appointment cancelled')\n return redirect('homepage')\n\ndef doneappointment(request):\n user=request.user\n x=request.GET.get('id')\n uname,tme=x.split('-')\n cursor=connection.cursor()\n cursor.execute('select * from portal_person WHERE user_id= %s',[user.id])\n person=cursor.fetchone()\n if(person[2]=='doctor'):\n s=\"delete from portal_appointment where did_id=(select id from portal_doctor where username= '\" + user.username + \"') and pid_id= (select id from portal_patient where username= '\" + uname + \"') and time= '\"+ tme + \"'\"\n cursor.execute(s)\n s='update portal_freetimings set '+\"t\"+tme+\"=0 where did_id=(select id from portal_doctor where username= '\" + user.username + \"')\" \n cursor.execute(s)\n s=\"insert into portal_appointmenthistory (did_id,pid_id,time) values ((select id from portal_doctor where username= '\" + user.username + \"'),(select id from portal_patient where username= '\" + uname + \"'), 't\"+ tme + \"')\"\n cursor.execute(s)\n else:\n s=\"delete from portal_appointment where pid_id=(select id from portal_patient where username= '\" + user.username + \"') and did_id= (select id from portal_doctor where username= '\" + uname + \"') and time= '\"+ tme + \"'\"\n cursor.execute(s)\n s='update portal_freetimings set '+\"t\"+tme+\"=0 where did_id=(select id from portal_doctor where username= '\" + uname + \"')\" \n cursor.execute(s)\n s=\"insert into portal_appointmenthistory (did_id,pid_id,time) values ((select id from portal_doctor where username= '\" + uname + \"'),(select id from portal_patient where username= '\" + user.username + \"'), 't\"+ tme + \"')\"\n cursor.execute(s)\n messages.info(request,f'Appointment completed')\n return redirect('homepage')\n\ndef appointmenthistory(request):\n\n user=request.user\n cursor=connection.cursor()\n cursor.execute('select * from portal_person WHERE user_id= %s',[user.id])\n person=cursor.fetchone()\n if(person[2]=='doctor'):\n cursor.execute('select p.username,p.name,p.phone_number,p.address,a.time from portal_patient p inner join portal_appointmenthistory a on p.id=a.pid_id where did_id= (select id from portal_doctor where username=%s)',[person[1]]) \n else:\n cursor.execute('select d.username,d.name,d.phone_number,d.address,a.time from portal_doctor d inner join portal_appointmenthistory a on d.id=a.did_id where pid_id= (select id from portal_patient where username=%s)',[person[1]])\n appointments=cursor.fetchall() \n return render(request,'portal/appointmenthistory.html',{'appointments':appointments,'person':person})\n\ndef myaccount(request):\n user=request.user\n cursor=connection.cursor()\n cursor.execute('select * from portal_person where user_id=%s',[user.id])\n person=cursor.fetchone()\n if(person[2]==\"doctor\"):\n cursor.execute('select * from portal_doctor where user_id=%s',[user.id])\n else:\n cursor.execute('select * from portal_patient where user_id=%s',[user.id])\n spperson=cursor.fetchone()\n\n return render(request,'portal/myaccount.html',{'spperson':spperson,'person':person})","sub_path":"dpp/portal/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":14651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"169708174","text":"import wolframalpha\nimport wikipedia\n\nlang = input(\"Choose your language (en for English, pl for Polish): \")\n\nwhile True:\n if lang == 'pl':\n q = input(\"Pytanie: \")\n else:\n q = input(\"Question: \")\n\n try:\n #wikipedia\n wikipedia.set_lang(lang)\n print(wikipedia.summary(q, sentences=3))\n except:\n #wolframalpha\n app_id = \"JWYKGT-PXTAW75528\"\n client = wolframalpha.Client(app_id)\n result = client.query(q)\n answer = next(result.results).text\n print(answer)\n","sub_path":"python_assistant.py","file_name":"python_assistant.py","file_ext":"py","file_size_in_byte":541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"67617657","text":"from django.conf.urls import patterns, include, url\nfrom django.contrib import admin\nfrom eventos.views import certificado_imprimir, certificado_buscar\n\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n url(r'^admin/', include(admin.site.urls)),\n url(r'^certificados/imprimir/(\\d+)', certificado_imprimir),\n url(r'^certificados/(\\d+)', 'eventos.views.certificado_buscar'),\n url(r'^certificados/', certificado_buscar),\n)\n","sub_path":"eventous/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":432,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"565865378","text":"import pygame\n\n\nclass DS4(object):\n def __init__(self):\n pygame.init()\n self.clock = pygame.time.Clock()\n pygame.joystick.init()\n\n def get_button(self):\n for event in pygame.event.get(): # User did something\n if event.type == pygame.QUIT: # If user clicked close\n done = True # Flag that we are done so we exit this loop# Get ready to print\n joystick = pygame.joystick.Joystick(0)\n joystick.init()\n axis_value = [joystick.get_axis(0), joystick.get_axis(1)]\n button_value = [joystick.get_button(0), joystick.get_button(1), joystick.get_button(2), joystick.get_button(3)]\n self.clock.tick(20)\n return axis_value, button_value\n","sub_path":"Experiment/ds4.py","file_name":"ds4.py","file_ext":"py","file_size_in_byte":727,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"172212327","text":"# type: ignore\n# mypy: ignore-errors\n\nfrom typing import Any, Dict, List, Optional, Type, Union\n\nimport copy\nimport datetime\nimport logging\nimport os\nimport time\nfrom pathlib import Path\n\nimport joblib\nimport numpy as np\nfrom ConfigSpace.configuration_space import Configuration\n\nfrom smac.epm.utils import get_rng\nfrom smac.facade.smac_ac_facade import SMAC4AC\nfrom smac.runhistory.runhistory import RunHistory\nfrom smac.scenario.scenario import Scenario\nfrom smac.tae.base import BaseRunner\nfrom smac.tae.execute_ta_run_hydra import ExecuteTARunOld\nfrom smac.utils.io.output_directory import create_output_directory\nfrom smac.utils.io.result_merging import ResultMerger\n\n__author__ = \"Andre Biedenkapp\"\n__copyright__ = \"Copyright 2018, ML4AAD\"\n__license__ = \"3-clause BSD\"\n\n\ndef optimize(\n scenario: Scenario,\n tae_runner: Type[BaseRunner],\n tae_runner_kwargs: Dict,\n rng: Union[np.random.RandomState, int],\n output_dir: str,\n facade_class: Optional[Type[SMAC4AC]] = None,\n **kwargs,\n) -> Configuration:\n \"\"\"\n Unbound method to be called in a subprocess\n\n Parameters\n ----------\n scenario: Scenario\n smac.Scenario to initialize SMAC\n tae_runner: BaseRunner\n Target Algorithm Runner (supports old and aclib format)\n tae_runner_kwargs: Optional[dict]\n arguments passed to constructor of '~tae'\n rng: int/np.random.RandomState\n The randomState/seed to pass to each smac run\n output_dir: str\n The directory in which each smac run should write its results\n\n Returns\n -------\n incumbent: Configuration\n The incumbent configuration of this run\n\n \"\"\"\n if facade_class is None:\n facade_class = SMAC4AC\n solver = facade_class(\n scenario=scenario, tae_runner=tae_runner, tae_runner_kwargs=tae_runner_kwargs, rng=rng, **kwargs\n )\n solver.stats.start_timing()\n solver.stats.print_stats()\n\n incumbent = solver.solver.run()\n solver.stats.print_stats()\n\n if output_dir is not None:\n solver.solver.runhistory.save_json(fn=os.path.join(solver.output_dir, \"runhistory.json\"))\n return incumbent\n\n\nclass PSMAC(object):\n \"\"\"\n Facade to use pSMAC [1]_\n\n With pSMAC you can either run n distinct SMAC optimizations in parallel\n (`shared_model=False`) or you can parallelize the target algorithm evaluations\n (`shared_model=True`).\n In the latter case all SMAC workers share one file directory and communicate via\n the logfiles. You can specify the number of SMAC workers/optimizers with the\n argument `n_workers`.\n\n You can pass all other kwargs for the SMAC4AC facade.\n In addition, you can access the facade's attributes normally (e.g. smac.stats),\n however each time a new SMAC object is built, reading the information from the\n file system.\n\n\n .. [1] Ramage, S. E. A. (2015). Advances in meta-algorithmic software libraries for\n distributed automated algorithm configuration (T). University of British\n Columbia. Retrieved from\n https://open.library.ubc.ca/collections/ubctheses/24/items/1.0167184.\n\n Parameters\n ----------\n scenario : ~smac.scenario.scenario.Scenario\n Scenario object. Note that the budget/number of evaluations (runcount-limit) is\n used for each worker. So if you specify 40 evaluations and 3 workers, 120\n configurations will be evaluated in total.\n n_workers: int\n Number of optimizers to run in parallel per round\n rng: int/np.random.RandomState\n The randomState/seed to pass to each smac run\n run_id: int\n run_id for this hydra run\n tae_runner: BaseRunner\n Target Algorithm Runner (supports old and aclib format as well as AbstractTAFunc)\n tae_runner_kwargs: Optional[dict]\n arguments passed to constructor of '~tae_runner'\n shared_model: bool\n Flag to indicate whether information is shared between SMAC runs or not\n validate: bool / None\n Flag to indicate whether to validate the found configurations or to use the SMAC estimates\n None => neither and return the full portfolio\n val_set: List[str]\n List of instance-ids to validate on\n **kwargs\n Keyword arguments for the SMAC4AC facade\n\n Attributes\n ----------\n # TODO update attributes\n logger\n stats : Stats\n loggs information about used resources\n solver : SMBO\n handles the actual algorithm calls\n rh : RunHistory\n List with information about previous runs\n portfolio : list\n List of all incumbents\n\n \"\"\"\n\n def __init__(\n self,\n scenario: Scenario,\n rng: Optional[Union[np.random.RandomState, int]] = None,\n run_id: int = 1,\n tae_runner: Type[BaseRunner] = ExecuteTARunOld,\n tae_runner_kwargs: Union[dict, None] = None,\n shared_model: bool = True,\n facade_class: Optional[Type[SMAC4AC]] = None,\n validate: bool = True,\n n_workers: int = 2,\n val_set: Union[List[str], None] = None,\n **kwargs,\n ):\n self.logger = logging.getLogger(self.__module__ + \".\" + self.__class__.__name__)\n\n self.scenario = scenario\n self.run_id, self.rng = get_rng(rng, run_id, logger=self.logger)\n self.kwargs = kwargs\n self.output_dir = None\n if facade_class is None:\n facade_class = SMAC4AC\n self.facade_class = facade_class\n self.rh = RunHistory()\n self._tae_runner = tae_runner\n self._tae_runner_kwargs = tae_runner_kwargs\n if n_workers <= 1:\n self.logger.warning(\"Invalid value in %s: %d. Setting to 2\", \"n_workers\", n_workers)\n self.n_workers = max(n_workers, 2)\n self.seeds = np.arange(0, self.n_workers, dtype=int) # seeds for the parallel runs\n self.validate = validate\n self.shared_model = shared_model\n if val_set is None:\n self.val_set = scenario.train_insts\n else:\n self.val_set = val_set\n\n self.result_merger: Optional[ResultMerger] = None\n self.n_incs: int = 1\n\n def optimize(self) -> Configuration:\n \"\"\"\n Optimizes the algorithm provided in scenario (given in constructor)\n\n Returns\n -------\n incumbent : Configuration\n Best configuration across all workers.\n\n \"\"\"\n # Setup output directory\n if self.output_dir is None:\n self.scenario.output_dir = \"psmac3-output_%s\" % (\n datetime.datetime.fromtimestamp(time.time()).strftime(\"%Y-%m-%d_%H:%M:%S_%f\")\n )\n self.output_dir = create_output_directory(self.scenario, run_id=self.run_id, logger=self.logger)\n if self.shared_model:\n self.scenario.shared_model = self.shared_model\n if self.scenario.input_psmac_dirs is None:\n self.scenario.input_psmac_dirs = os.path.sep.join((self.scenario.output_dir, \"run_*\"))\n\n scen = copy.deepcopy(self.scenario)\n scen.output_dir_for_this_run = None\n scen.output_dir = None\n self.logger.info(\"+\" * 120)\n self.logger.info(\"PSMAC run\")\n\n incs = joblib.Parallel(n_jobs=self.n_workers)(\n joblib.delayed(optimize)(\n scenario=self.scenario, # Scenario object\n tae_runner=self._tae_runner, # type of tae_runner to run target with\n tae_runner_kwargs=self._tae_runner_kwargs,\n rng=int(seed), # seed for the rng/run_id\n output_dir=self.output_dir, # directory to create outputs in\n facade_class=self.facade_class,\n **self.kwargs,\n )\n for seed in self.seeds\n )\n\n inc = self.get_best_incumbent(incs=incs)\n return inc\n\n def get_best_incumbent(self, incs: List[Configuration]) -> Configuration:\n \"\"\"\n Determine ID and cost of best configuration (incumbent).\n\n Parameters\n ----------\n incs : List[Configuration]\n List of incumbents from the workers.\n\n Returns\n -------\n Configuration\n Best incumbent from all workers.\n\n \"\"\"\n if self.validate is True:\n mean_costs_conf_valid, cost_per_config_valid = self.validate_incs(incs)\n val_id = list(map(lambda x: x[0], sorted(enumerate(mean_costs_conf_valid), key=lambda y: y[1])))[0]\n inc = incs[val_id]\n else:\n mean_costs_conf_estimate, cost_per_config_estimate = self._get_mean_costs(incs, self.rh)\n est_id = list(map(lambda x: x[0], sorted(enumerate(mean_costs_conf_estimate), key=lambda y: y[1])))[0]\n inc = incs[est_id]\n\n return inc\n\n def _get_mean_costs(self, incs: List[Configuration], new_rh: RunHistory):\n \"\"\"\n Compute mean cost per instance\n\n Parameters\n ----------\n incs : List[Configuration]\n incumbents determined by all parallel SMAC runs\n new_rh : RunHistory\n runhistory to determine mean performance\n\n Returns\n -------\n List[float] means\n Dict(Config -> Dict(inst_id(str) -> float))\n\n \"\"\"\n config_cost_per_inst = {}\n results = []\n for incumbent in incs:\n cost_per_inst = new_rh.get_instance_costs_for_config(config=incumbent)\n config_cost_per_inst[incumbent] = cost_per_inst\n values = list(cost_per_inst.values())\n if values:\n results.append(np.mean(values))\n else:\n results.append(np.nan)\n return results, config_cost_per_inst\n\n def _get_solver(self):\n # TODO: specify one output dir or no output dir\n solver = self.facade_class(scenario=self.scenario, rng=self.rng, run_id=None, **self.kwargs)\n return solver\n\n def validate_incs(self, incs: List[Configuration]):\n \"\"\"Validation of the incumbents.\"\"\"\n solver = self._get_solver()\n self.logger.info(\"*\" * 120)\n self.logger.info(\"Validating\")\n new_rh = solver.validate(\n config_mode=incs,\n instance_mode=self.val_set,\n repetitions=1,\n use_epm=False,\n n_jobs=self.n_workers,\n )\n return self._get_mean_costs(incs, new_rh)\n\n def write_run(self) -> None:\n \"\"\"\n Write all SMAC files to pSMAC dir\n\n Returns\n -------\n None\n\n \"\"\"\n # write runhistory\n # write configspace file .pcs .json\n # write trajectory traj.json\n # write scenario .txt\n # write stats\n raise NotImplementedError\n\n def _check_result_merger(self):\n if self.result_merger is None:\n if self.output_dir is None:\n raise ValueError(\n \"Cannot instantiate `ResultMerger` because `output_dir` \"\n \"is None. In pSMAC `output_dir` is set after \"\n \"`optimize()` has been called. If you already have \"\n \"a pSMAC run or rundirs, please directly use \"\n \"`smac.utils.io.result_merging.ResultMerger`.\"\n )\n self.result_merger = ResultMerger(output_dir=Path(self.output_dir).parent)\n\n def get_runhistory(self) -> Optional[RunHistory]:\n \"\"\"\n Get merged runhistory from pSMAC workers\n\n Returns\n -------\n Optional[RunHistory]\n\n \"\"\"\n self._check_result_merger()\n return self.result_merger.get_runhistory()\n\n def get_trajectory(self) -> Optional[List[Dict[str, Any]]]:\n \"\"\"\n Get trajectory from merged runhistory\n\n Returns\n -------\n Optional[List[Dict[str, Any]]]\n\n \"\"\"\n self._check_result_merger()\n return self.result_merger.get_trajectory()\n","sub_path":"smac/facade/psmac_facade.py","file_name":"psmac_facade.py","file_ext":"py","file_size_in_byte":11810,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"267085633","text":"def\tintToBin(x, n):\n\tif x >= pow(2, n):\n\t\traise Exception()\n\tc = [0] * n\n\tfor j in reversed(range(0, n)):\n\t\tnx = x - pow(2, j)\n\t\tprint(nx)\n\t\tif nx >= 0:\n\t\t\tx = nx\n\t\t\tc[j] = 1\n\treturn c\n\na = intToBin(3, 2)\n\nprint(a)","sub_path":"script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":214,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"393517619","text":"#movingworld.py\nfrom pygame import*\nfrom random import*\nfrom math import*\nfrom colours import*\n\n##initialization of the game\ninit()\nwindow=width,height=800,600\nscreen=display.set_mode((window))\nw,h=20,15\n\n##x,y,vx,vy\nguy=[400,300,0,0]\n\n##font initialization\nfont.init()\nfont=font.SysFont(\"Arial\",16)\n\nmyClock = time.Clock()\n\n##x,y,vx,vy\nx=0\ny=1\nvx=2\nvy=3\n\n##boundary of the camera\nborder=15\n\ndef running():\n ##main game loop\n for evt in event.get():\n if evt.type==QUIT:\n return False\n elif evt.type==KEYDOWN and evt.key==K_ESCAPE:\n return False\n \n return True\n\ndef vectorXY(startptx,startpty,endptx,endpty):\n ##the distance between x coordinates and y coordinates\n distx=startptx-endptx\n disty=startpty-endpty\n ##the hypotenuse\n r=hypot(distx,disty)\n ##calculate the cos and sin of the angle that the two points formed\n vecx=float(distx)/r\n vecy=float(disty)/r\n \n return vecx,vecy\n\ndef moveGuy(list):\n ##get key inputs\n keys = key.get_pressed()\n\n ##adds gravity\n guy[vy] += .05\n ##adds velocity onto current position\n guy[x] += guy[vx]\n guy[y] += guy[vy]\n ##rebound from the edge of the world\n if guy[x] <= border or guy[x] >= width - border:\n guy[vx] *= -0.75\n if guy[y] <= border or guy[y] >= height - border:\n guy[vy] *= -0.75\n ##moves the mining pod\n if keys[K_UP] and guy[y] >= border:\n guy[y] -= 1\n guy[vy] -= .1\n if keys[K_DOWN] and guy[y] <= height - border:\n guy[y] += 1\n guy[vy] += .1\n if keys[K_LEFT] and guy[x] >= border:\n guy[x] -= 1\n guy[vx] -= .1\n if keys[K_RIGHT] and guy[x] <= width - border:\n guy[x] += 1\n guy[vx] += .1\n \n for rect in list:\n if Rect(rect).collidepoint((guy[x],guy[y])):\n ##get the positions of the mining pod and the nearest ore\n endptx=rect[x]+20\n endpty=rect[y]+20\n startptx=guy[x]\n startpty=guy[y]\n ##using vector angles, divide the square into eight equal parts to deal\n ##with different rebound effects\n vectx,vecty=vectorXY(startptx,startpty,endptx,endpty)\n ##rebound from the obstacles\n if vectx>=0.70710678118654746 and vectx<=1.0 and vecty>=0.0 and vecty<=0.70710678118654746:\n guy[x]=rect[x]+42\n guy[vx]*=-0.25\n elif vectx<=0.70710678118654746 and vectx>=0.0 and vecty<=1.0 and vecty>=0.70710678118654746:\n guy[y]=rect[y]+42\n guy[vy]*=-0.25\n elif vectx>=-0.70710678118654746 and vectx<=0.0 and vecty<=1.0 and vecty>=0.70710678118654746:\n guy[y]=rect[y]+42\n guy[vy]*=-0.25\n elif vectx<=-0.70710678118654746 and vectx>=-1.0 and vecty>=0.0 and vecty<=0.70710678118654746:\n guy[x]=rect[x]-2\n guy[vx]*=-0.25\n elif vectx<=-0.70710678118654746 and vectx>=-1.0 and vecty<=0.0 and vecty>=-0.70710678118654746:\n guy[x]=rect[x]-2\n guy[vx]*=-0.25\n elif vectx>=-0.70710678118654746 and vectx<=0.0 and vecty>=-1.0 and vecty<=-0.70710678118654746:\n guy[y]=rect[y]-2\n guy[vy]*=-0.25\n elif vectx<=0.70710678118654746 and vectx>=0.0 and vecty>=-1.0 and vecty<=-0.70710678118654746:\n guy[y]=rect[y]-2\n guy[vy]*=-0.25\n elif vectx>=0.70710678118654746 and vectx<=1.0 and vecty<=0.0 and vecty>=-0.70710678118654746:\n guy[x]=rect[x]+42\n guy[vx]*=-0.25\n\n return guy\n\ndef drilling(list,minerals):\n ##get key inputs\n keys = key.get_pressed()\n ##drilling, test if the mining pod is near an ore\n for rect in list:\n ##the pod can only drill down, left and right, but up\n ##right\n if guy[x]<rect[x]+45 and guy[x]>rect[x] and guy[y]>rect[y] and guy[y]<rect[y]+40:\n if keys[K_a]:\n removal=list.index(rect)\n del list[removal]\n del minerals[removal]\n ##left\n if guy[x]<rect[x]+40 and guy[x]>rect[x]-5 and guy[y]>rect[y] and guy[y]<rect[y]+40:\n if keys[K_d]:\n removal=list.index(rect)\n del list[removal]\n del minerals[removal]\n ##down\n if guy[x]<rect[x]+40 and guy[x]>rect[x] and guy[y]>rect[y]-5 and guy[y]<rect[y]:\n if keys[K_s]:\n removal=list.index(rect)\n del list[removal]\n del minerals[removal]\n \n return guy\n\ndef randomGen():\n worldmap=[]\n for i in range(h):\n worldmap.append([0]*w)\n for value in range(0,5):\n x=randint(0,w-1)\n y=randint(0,h-1)\n for i in range(10):\n while True:\n if worldmap[y][x]==0:\n worldmap[y][x]=value\n break\n else:\n x=randint(0,w-1)\n y=randint(0,h-1)\n \n return worldmap\n\nwmap=randomGen()\n\ndef tranScript(twoDim):\n ores=[]\n for y in twoDim:\n for x in y:\n if x!=0:\n ores.append((y.index(x)*40,twoDim.index(y)*40,40,40))\n \n return ores\n\nsquares=tranScript(wmap)\n\ndef oreType(twoDim):\n oretype=[]\n for y in twoDim:\n for x in y:\n if x!=0:\n oretype.append(x)\n \n return oretype\n\nminerals=oreType(wmap)\n\ncolors=[black,white,green,red,yellow]\n\ndef drawScene(list):\n ##gets mouse input\n mx,my=mouse.get_pos()\n mb=mouse.get_pressed()\n \n ##draws the box and collidepoint effects\n for rect in list:\n draw.rect(screen,colors[minerals[list.index(rect)]],rect,0)\n\n for box in list:\n if Rect(box).collidepoint((mx,my)):\n draw.rect(screen,blue,box,0)\n if mb[0]==1:\n draw.rect(screen,red,box,0)\n \n for rect in list:\n if Rect(rect).collidepoint((guy[x],guy[y])):\n draw.rect(screen,purple,rect,0)\n \n ##draws the current state of the game\n draw.circle(screen,white,(guy[0],guy[1]),5,0)\n \n display.flip()\n screen.blit(backimage,(0,0))\n\nwhile running():\n ##main game loop\n backimage=screen.copy()\n drilling(squares,minerals)\n moveGuy(squares)\n drawScene(squares)\n myClock.tick(60)\n\nquit()\n","sub_path":"movingworld.py","file_name":"movingworld.py","file_ext":"py","file_size_in_byte":6385,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"517273028","text":"# Compute primes up to and including n\r\ndef compute_primes(n):\r\n\tprimes = (n + 1) * [1]\r\n\tprimes_result = []\r\n\r\n\tprimes[0] = 0\r\n\tprimes[1] = 0\r\n\r\n\tfor i in range(2, int(n / 2) + 2):\r\n\t\tif not primes[i]:\r\n\t\t\tcontinue\r\n\t\tprimes_result.append(i)\r\n\t\tindex = 2 * i\r\n\t\twhile index < n + 1:\r\n\t\t\tprimes[index] = 0\r\n\t\t\tindex += i\r\n\r\n\twhile i < n + 1:\r\n\t\tif primes[i]:\r\n\t\t\tprimes_result.append(i)\r\n\t\ti += 1\r\n\treturn primes_result\r\n\r\n\r\nprimes = compute_primes(int(\"1\"*16, 2))\r\n\r\ndef isPossiblyPrime(n):\r\n\tfor i in primes:\r\n\t\tx,y = divmod(n, i)\r\n\t\tif not y:\r\n\t\t\treturn i\r\n\treturn 0\r\n\r\n# Compute if a number is prime\r\ndef isPrime(n):\r\n\tif n > 2**16:\r\n\t\treturn isPossiblyPrime(n)\r\n\tfor i in range(3, int(n**.5 + 1), 2):\r\n\t\tif n % i == 0:\r\n\t\t\treturn i\r\n\treturn 0","sub_path":"codes/CodeJamCrawler/16_0_3_neat/16_0_3_Kyle_W_Adams_Primes.py","file_name":"16_0_3_Kyle_W_Adams_Primes.py","file_ext":"py","file_size_in_byte":747,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"638745521","text":"# Solution for Linear Algebra\n\nimport numpy\n\nif __name__ == '__main__':\n numpy.set_printoptions(legacy=\"1.13\") # see discussion of that challenge...\n\n n = int(input())\n a_rows = [list(map(float, input().split())) for i in range(n)]\n\n print(numpy.linalg.det(a_rows))","sub_path":"Python/Python/Numpy/Linear Algebra.py","file_name":"Linear Algebra.py","file_ext":"py","file_size_in_byte":278,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"382226291","text":"from django.contrib.auth.forms import UserCreationForm\nfrom django.shortcuts import render, redirect, get_object_or_404\n\n# Create your views here.\nfrom user.forms.profile_form import ProfileForm, UserCreateForm\nfrom order.forms.address_form import AddressForm\nfrom user.models import Profile, Search\nfrom order.models import Address, Country, Order, OrderItems\nfrom cart.models import Cart\nfrom django.contrib.auth.models import User\n\n\n\n\ndef register(request):\n \"\"\"This function saves a new User to the database\"\"\"\n # A POST request will save a new user to the database\n if request.method == 'POST':\n form = UserCreateForm(data=request.POST)\n if form.is_valid():\n new_user = form.save()\n\n # Creates empty user profile in database\n profile = Profile(user=new_user)\n profile.email = new_user.email\n profile.save()\n\n # Creates empty cart in database\n cart = Cart(user=new_user)\n cart.save()\n\n return redirect('login')\n # If the request is not a POST a page is rendered to create a new User\n else:\n form = UserCreateForm()\n\n return render(request, 'user/register.html', {\n 'form': form\n })\n\n\ndef view_profile(request):\n \"\"\"This function displays the profile.\"\"\"\n profile_instance = Profile.objects.filter(user=request.user)\n # Profile exists\n if not profile_instance.exists():\n # Profile doesn't exist\n profile = Profile(user=request.user)\n profile.save()\n\n profile = profile_instance.first()\n\n # Address dose not exist\n if profile.address_id is not None:\n address = Address.objects.filter(id=profile.address_id).first()\n # Address exists\n else:\n address = Address()\n\n return render(request, 'user/profile.html', {\n 'profile': profile,\n 'address': address\n })\n\n\ndef edit_profile(request):\n \"\"\"This function sends the form to edit the profile and saves in when returned\"\"\"\n profile = Profile.objects.filter(user=request.user).first()\n # A POST request will update the profile of the user\n if request.method == 'POST':\n form = ProfileForm(instance=profile, data=request.POST)\n if form.is_valid():\n profile = form.save(commit=False)\n profile.user = request.user\n profile.save()\n return redirect('profile')\n # If not a POST request a page will be rendered to update the profile\n return render(request, 'user/edit_profile.html', {\n 'form': ProfileForm(instance=profile)\n })\n\n\ndef edit_address(request):\n \"\"\"This function sends the form to edit the address and saves in when returned\"\"\"\n profile = Profile.objects.filter(user=request.user).first()\n address = Address.objects.filter(id=profile.address_id).first()\n # A POST request will update the address of the user\n if request.method == 'POST':\n form = AddressForm(instance=address, data=request.POST)\n if form.is_valid():\n address = form.save()\n profile.address = address\n profile.save()\n return redirect('profile')\n # If not a POST request a page will be rendered to update the address\n return render(request, 'user/edit_address.html', {\n 'form': AddressForm(instance=address)\n })\n\n\ndef delete_address(request):\n \"\"\"This function deletes the address from a profile\"\"\"\n profile = Profile.objects.filter(user=request.user).first()\n profile.address = None\n profile.save()\n return redirect('profile')\n\n\ndef total_price(items):\n total_price = 0\n for item in items:\n total_price += item.cereal.price * item.quantity\n return total_price\n\n\ndef order_history(request):\n \"\"\"This function displays the order_history of a user\"\"\"\n cereal = []\n orders = []\n for x in Order.objects.filter(user=request.user, order_completed=True).order_by(\"-id\"):\n for j in OrderItems.objects.filter(order_id=x.id):\n cereal += [{'cereal': j.cereal, 'quantity': j.quantity}]\n orders += [{\n 'id': x.id,\n 'address_id': x.address_id,\n 'cereals': cereal,\n 'price': total_price(OrderItems.objects.filter(order_id=x.id))\n }]\n cereal = []\n return render(request, 'user/order_history.html', {\n 'orders': orders\n })\n\n\ndef search_history(request):\n \"\"\"This function returns the search history of a user\"\"\"\n searches = [x.search_query for x in Search.objects.filter(user=request.user)]\n return render(request, 'user/search_history.html', {\n 'searches': searches\n })\n","sub_path":"user/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":4630,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"88827642","text":"'''\nThe MIT License (MIT)\n\nCopyright (c) 2015 Andrew Allan Port\nCopyright (c) 2013-2014 Lennart Regebro\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n'''\n\nfrom .bezier import (bezier_point, bezier2polynomial,\n polynomial2bezier, split_bezier,\n bezier_bounding_box, bezier_intersections,\n bezier_by_line_intersections)\nfrom .path import (Path, Line, QuadraticBezier, CubicBezier, Arc,\n bezier_segment, is_bezier_segment, is_path_segment,\n is_bezier_path, concatpaths, poly2bez, bpoints2bezier,\n closest_point_in_path, farthest_point_in_path,\n path_encloses_pt, bbox2path, polygon, polyline)\nfrom .parser import parse_path\nfrom .paths2svg import disvg, wsvg, paths2Drawing\nfrom .polytools import polyroots, polyroots01, rational_limit, real, imag\nfrom .misctools import hex2rgb, rgb2hex\nfrom .smoothing import smoothed_path, smoothed_joint, is_differentiable, kinks\nfrom .document import Document, CONVERSIONS, CONVERT_ONLY_PATHS, SVG_GROUP_TAG, SVG_NAMESPACE\nfrom .svg_io_sax import SaxDocument\n\ntry:\n from .svg_to_paths import svg2paths, svg2paths2\nexcept ImportError:\n pass\n\n\"\"\"This submodule contains tools that deal with generic, degree n, Bezier\ncurves.\nNote: Bezier curves here are always represented by the tuple of their control\npoints given by their standard representation.\"\"\"\n\n# External dependencies:\nfrom __future__ import division, absolute_import, print_function\nfrom math import factorial as fac, ceil, log, sqrt\nfrom numpy import poly1d\n\n# Internal dependencies\nfrom .polytools import real, imag, polyroots, polyroots01\n\n\n# Evaluation ##################################################################\n\ndef n_choose_k(n, k):\n return fac(n)//fac(k)//fac(n-k)\n\n\ndef bernstein(n, t):\n \"\"\"returns a list of the Bernstein basis polynomials b_{i, n} evaluated at\n t, for i =0...n\"\"\"\n t1 = 1-t\n return [n_choose_k(n, k) * t1**(n-k) * t**k for k in range(n+1)]\n\n\ndef bezier_point(p, t):\n \"\"\"Evaluates the Bezier curve given by it's control points, p, at t.\n Note: Uses Horner's rule for cubic and lower order Bezier curves.\n Warning: Be concerned about numerical stability when using this function\n with high order curves.\"\"\"\n\n # begin arc support block ########################\n try:\n p.large_arc\n return p.point(t)\n except:\n pass\n # end arc support block ##########################\n\n deg = len(p) - 1\n if deg == 3:\n return p[0] + t*(\n 3*(p[1] - p[0]) + t*(\n 3*(p[0] + p[2]) - 6*p[1] + t*(\n -p[0] + 3*(p[1] - p[2]) + p[3])))\n elif deg == 2:\n return p[0] + t*(\n 2*(p[1] - p[0]) + t*(\n p[0] - 2*p[1] + p[2]))\n elif deg == 1:\n return p[0] + t*(p[1] - p[0])\n elif deg == 0:\n return p[0]\n else:\n bern = bernstein(deg, t)\n return sum(bern[k]*p[k] for k in range(deg+1))\n\n\n# Conversion ##################################################################\n\ndef bezier2polynomial(p, numpy_ordering=True, return_poly1d=False):\n \"\"\"Converts a tuple of Bezier control points to a tuple of coefficients\n of the expanded polynomial.\n return_poly1d : returns a numpy.poly1d object. This makes computations\n of derivatives/anti-derivatives and many other operations quite quick.\n numpy_ordering : By default (to accommodate numpy) the coefficients will\n be output in reverse standard order.\"\"\"\n if len(p) == 4:\n coeffs = (-p[0] + 3*(p[1] - p[2]) + p[3],\n 3*(p[0] - 2*p[1] + p[2]),\n 3*(p[1]-p[0]),\n p[0])\n elif len(p) == 3:\n coeffs = (p[0] - 2*p[1] + p[2],\n 2*(p[1] - p[0]),\n p[0])\n elif len(p) == 2:\n coeffs = (p[1]-p[0],\n p[0])\n elif len(p) == 1:\n coeffs = p\n else:\n # https://en.wikipedia.org/wiki/Bezier_curve#Polynomial_form\n n = len(p) - 1\n coeffs = [fac(n)//fac(n-j) * sum(\n (-1)**(i+j) * p[i] / (fac(i) * fac(j-i)) for i in range(j+1))\n for j in range(n+1)]\n coeffs.reverse()\n if not numpy_ordering:\n coeffs = coeffs[::-1] # can't use .reverse() as might be tuple\n if return_poly1d:\n return poly1d(coeffs)\n return coeffs\n\n\ndef polynomial2bezier(poly):\n \"\"\"Converts a cubic or lower order Polynomial object (or a sequence of\n coefficients) to a CubicBezier, QuadraticBezier, or Line object as\n appropriate.\"\"\"\n if isinstance(poly, poly1d):\n c = poly.coeffs\n else:\n c = poly\n order = len(c)-1\n if order == 3:\n bpoints = (c[3], c[2]/3 + c[3], (c[1] + 2*c[2])/3 + c[3],\n c[0] + c[1] + c[2] + c[3])\n elif order == 2:\n bpoints = (c[2], c[1]/2 + c[2], c[0] + c[1] + c[2])\n elif order == 1:\n bpoints = (c[1], c[0] + c[1])\n else:\n raise AssertionError(\"This function is only implemented for linear, \"\n \"quadratic, and cubic polynomials.\")\n return bpoints\n\n\n# Curve Splitting #############################################################\n\ndef split_bezier(bpoints, t):\n \"\"\"Uses deCasteljau's recursion to split the Bezier curve at t into two\n Bezier curves of the same order.\"\"\"\n def split_bezier_recursion(bpoints_left_, bpoints_right_, bpoints_, t_):\n if len(bpoints_) == 1:\n bpoints_left_.append(bpoints_[0])\n bpoints_right_.append(bpoints_[0])\n else:\n new_points = [None]*(len(bpoints_) - 1)\n bpoints_left_.append(bpoints_[0])\n bpoints_right_.append(bpoints_[-1])\n for i in range(len(bpoints_) - 1):\n new_points[i] = (1 - t_)*bpoints_[i] + t_*bpoints_[i + 1]\n bpoints_left_, bpoints_right_ = split_bezier_recursion(\n bpoints_left_, bpoints_right_, new_points, t_)\n return bpoints_left_, bpoints_right_\n\n bpoints_left = []\n bpoints_right = []\n bpoints_left, bpoints_right = \\\n split_bezier_recursion(bpoints_left, bpoints_right, bpoints, t)\n bpoints_right.reverse()\n return bpoints_left, bpoints_right\n\n\ndef halve_bezier(p):\n\n # begin arc support block ########################\n try:\n p.large_arc\n return p.split(0.5)\n except:\n pass\n # end arc support block ##########################\n\n if len(p) == 4:\n return ([p[0], (p[0] + p[1])/2, (p[0] + 2*p[1] + p[2])/4,\n (p[0] + 3*p[1] + 3*p[2] + p[3])/8],\n [(p[0] + 3*p[1] + 3*p[2] + p[3])/8,\n (p[1] + 2*p[2] + p[3])/4, (p[2] + p[3])/2, p[3]])\n else:\n return split_bezier(p, 0.5)\n\n\n# Bounding Boxes ##############################################################\n\ndef bezier_real_minmax(p):\n \"\"\"returns the minimum and maximum for any real cubic bezier\"\"\"\n local_extremizers = [0, 1]\n if len(p) == 4: # cubic case\n a = [p.real for p in p]\n denom = a[0] - 3*a[1] + 3*a[2] - a[3]\n if denom != 0:\n delta = a[1]**2 - (a[0] + a[1])*a[2] + a[2]**2 + (a[0] - a[1])*a[3]\n if delta >= 0: # otherwise no local extrema\n sqdelta = sqrt(delta)\n tau = a[0] - 2*a[1] + a[2]\n r1 = (tau + sqdelta)/denom\n r2 = (tau - sqdelta)/denom\n if 0 < r1 < 1:\n local_extremizers.append(r1)\n if 0 < r2 < 1:\n local_extremizers.append(r2)\n local_extrema = [bezier_point(a, t) for t in local_extremizers]\n return min(local_extrema), max(local_extrema)\n\n # find reverse standard coefficients of the derivative\n dcoeffs = bezier2polynomial(a, return_poly1d=True).deriv().coeffs\n\n # find real roots, r, such that 0 <= r <= 1\n local_extremizers += polyroots01(dcoeffs)\n local_extrema = [bezier_point(a, t) for t in local_extremizers]\n return min(local_extrema), max(local_extrema)\n\n\ndef bezier_bounding_box(bez):\n \"\"\"returns the bounding box for the segment in the form\n (xmin, xmax, ymin, ymax).\n Warning: For the non-cubic case this is not particularly efficient.\"\"\"\n\n # begin arc support block ########################\n try:\n bla = bez.large_arc\n return bez.bbox() # added to support Arc objects\n except:\n pass\n # end arc support block ##########################\n\n if len(bez) == 4:\n xmin, xmax = bezier_real_minmax([p.real for p in bez])\n ymin, ymax = bezier_real_minmax([p.imag for p in bez])\n return xmin, xmax, ymin, ymax\n poly = bezier2polynomial(bez, return_poly1d=True)\n x = real(poly)\n y = imag(poly)\n dx = x.deriv()\n dy = y.deriv()\n x_extremizers = [0, 1] + polyroots(dx, realroots=True,\n condition=lambda r: 0 < r < 1)\n y_extremizers = [0, 1] + polyroots(dy, realroots=True,\n condition=lambda r: 0 < r < 1)\n x_extrema = [x(t) for t in x_extremizers]\n y_extrema = [y(t) for t in y_extremizers]\n return min(x_extrema), max(x_extrema), min(y_extrema), max(y_extrema)\n\n\ndef box_area(xmin, xmax, ymin, ymax):\n \"\"\"\n INPUT: 2-tuple of cubics (given by control points)\n OUTPUT: boolean\n \"\"\"\n return (xmax - xmin)*(ymax - ymin)\n\n\ndef interval_intersection_width(a, b, c, d):\n \"\"\"returns the width of the intersection of intervals [a,b] and [c,d]\n (thinking of these as intervals on the real number line)\"\"\"\n return max(0, min(b, d) - max(a, c))\n\n\ndef boxes_intersect(box1, box2):\n \"\"\"Determines if two rectangles, each input as a tuple\n (xmin, xmax, ymin, ymax), intersect.\"\"\"\n xmin1, xmax1, ymin1, ymax1 = box1\n xmin2, xmax2, ymin2, ymax2 = box2\n if interval_intersection_width(xmin1, xmax1, xmin2, xmax2) and \\\n interval_intersection_width(ymin1, ymax1, ymin2, ymax2):\n return True\n else:\n return False\n\n\n# Intersections ###############################################################\n\nclass ApproxSolutionSet(list):\n \"\"\"A class that behaves like a set but treats two elements , x and y, as\n equivalent if abs(x-y) < self.tol\"\"\"\n def __init__(self, tol):\n self.tol = tol\n\n def __contains__(self, x):\n for y in self:\n if abs(x - y) < self.tol:\n return True\n return False\n\n def appadd(self, pt):\n if pt not in self:\n self.append(pt)\n\n\nclass BPair(object):\n def __init__(self, bez1, bez2, t1, t2):\n self.bez1 = bez1\n self.bez2 = bez2\n self.t1 = t1 # t value to get the mid point of this curve from cub1\n self.t2 = t2 # t value to get the mid point of this curve from cub2\n\n\ndef bezier_intersections(bez1, bez2, longer_length, tol=1e-8, tol_deC=1e-8):\n \"\"\"INPUT:\n bez1, bez2 = [P0,P1,P2,...PN], [Q0,Q1,Q2,...,PN] defining the two\n Bezier curves to check for intersections between.\n longer_length - the length (or an upper bound) on the longer of the two\n Bezier curves. Determines the maximum iterations needed together with tol.\n tol - is the smallest distance that two solutions can differ by and still\n be considered distinct solutions.\n OUTPUT: a list of tuples (t,s) in [0,1]x[0,1] such that\n abs(bezier_point(bez1[0],t) - bezier_point(bez2[1],s)) < tol_deC\n Note: This will return exactly one such tuple for each intersection\n (assuming tol_deC is small enough).\"\"\"\n maxits = int(ceil(1-log(tol_deC/longer_length)/log(2)))\n pair_list = [BPair(bez1, bez2, 0.5, 0.5)]\n intersection_list = []\n k = 0\n approx_point_set = ApproxSolutionSet(tol)\n while pair_list and k < maxits:\n new_pairs = []\n delta = 0.5**(k + 2)\n for pair in pair_list:\n bbox1 = bezier_bounding_box(pair.bez1)\n bbox2 = bezier_bounding_box(pair.bez2)\n if boxes_intersect(bbox1, bbox2):\n if box_area(*bbox1) < tol_deC and box_area(*bbox2) < tol_deC:\n point = bezier_point(bez1, pair.t1)\n if point not in approx_point_set:\n approx_point_set.append(point)\n # this is the point in the middle of the pair\n intersection_list.append((pair.t1, pair.t2))\n\n # this prevents the output of redundant intersection points\n for otherPair in pair_list:\n if pair.bez1 == otherPair.bez1 or \\\n pair.bez2 == otherPair.bez2 or \\\n pair.bez1 == otherPair.bez2 or \\\n pair.bez2 == otherPair.bez1:\n pair_list.remove(otherPair)\n else:\n (c11, c12) = halve_bezier(pair.bez1)\n (t11, t12) = (pair.t1 - delta, pair.t1 + delta)\n (c21, c22) = halve_bezier(pair.bez2)\n (t21, t22) = (pair.t2 - delta, pair.t2 + delta)\n new_pairs += [BPair(c11, c21, t11, t21),\n BPair(c11, c22, t11, t22),\n BPair(c12, c21, t12, t21),\n BPair(c12, c22, t12, t22)]\n pair_list = new_pairs\n k += 1\n if k >= maxits:\n raise Exception(\"bezier_intersections has reached maximum \"\n \"iterations without terminating... \"\n \"either there's a problem/bug or you can fix by \"\n \"raising the max iterations or lowering tol_deC\")\n return intersection_list\n\n\ndef bezier_by_line_intersections(bezier, line):\n \"\"\"Returns tuples (t1,t2) such that bezier.point(t1) ~= line.point(t2).\"\"\"\n # The method here is to translate (shift) then rotate the complex plane so\n # that line starts at the origin and proceeds along the positive real axis.\n # After this transformation, the intersection points are the real roots of\n # the imaginary component of the bezier for which the real component is\n # between 0 and abs(line[1]-line[0])].\n assert len(line[:]) == 2\n assert line[0] != line[1]\n if not any(p != bezier[0] for p in bezier):\n raise ValueError(\"bezier is nodal, use \"\n \"bezier_by_line_intersection(bezier[0], line) \"\n \"instead for a bool to be returned.\")\n\n # First let's shift the complex plane so that line starts at the origin\n shifted_bezier = [z - line[0] for z in bezier]\n shifted_line_end = line[1] - line[0]\n line_length = abs(shifted_line_end)\n\n # Now let's rotate the complex plane so that line falls on the x-axis\n rotation_matrix = line_length/shifted_line_end\n transformed_bezier = [rotation_matrix*z for z in shifted_bezier]\n\n # Now all intersections should be roots of the imaginary component of\n # the transformed bezier\n transformed_bezier_imag = [p.imag for p in transformed_bezier]\n coeffs_y = bezier2polynomial(transformed_bezier_imag)\n roots_y = list(polyroots01(coeffs_y)) # returns real roots 0 <= r <= 1\n\n transformed_bezier_real = [p.real for p in transformed_bezier]\n intersection_list = []\n for bez_t in set(roots_y):\n xval = bezier_point(transformed_bezier_real, bez_t)\n if 0 <= xval <= line_length:\n line_t = xval/line_length\n intersection_list.append((bez_t, line_t))\n return intersection_list\n\n\"\"\"(Experimental) replacement for import/export functionality.\n\nThis module contains the `Document` class, a container for a DOM-style \ndocument (e.g. svg, html, xml, etc.) designed to replace and improve \nupon the IO functionality of svgpathtools (i.e. the svg2paths and \ndisvg/wsvg functions). \n\nAn Historic Note:\n The functionality in this module is meant to replace and improve \n upon the IO functionality previously provided by the the \n `svg2paths` and `disvg`/`wsvg` functions. \n\nExample:\n Typical usage looks something like the following.\n\n >> from svgpathtools import *\n >> doc = Document('my_file.html')\n >> results = doc.flatten_all_paths()\n >> for result in results:\n >> path = result.path\n >> # Do something with the transformed Path object.\n >> element = result.element\n >> # Inspect the raw SVG element. This gives access to the\n >> # path's attributes\n >> transform = result.transform\n >> # Use the transform that was applied to the path.\n >> foo(doc.tree) # do stuff using ElementTree's functionality\n >> doc.display() # display doc in OS's default application\n >> doc.save('my_new_file.html')\n\nA Big Problem: \n Derivatives and other functions may be messed up by \n transforms unless transforms are flattened (and not included in \n css)\n\"\"\"\n\n# External dependencies\nfrom __future__ import division, absolute_import, print_function\nimport os\nimport collections\nimport xml.etree.ElementTree as etree\nfrom xml.etree.ElementTree import Element, SubElement, register_namespace\nimport warnings\n\n# Internal dependencies\nfrom .parser import parse_path\nfrom .parser import parse_transform\nfrom .svg_to_paths import (path2pathd, ellipse2pathd, line2pathd,\n polyline2pathd, polygon2pathd, rect2pathd)\nfrom .misctools import open_in_browser\nfrom .path import *\n\n# To maintain forward/backward compatibility\ntry:\n str = basestring\nexcept NameError:\n pass\n\n# Let xml.etree.ElementTree know about the SVG namespace\nSVG_NAMESPACE = {'svg': 'http://www.w3.org/2000/svg'}\nregister_namespace('svg', 'http://www.w3.org/2000/svg')\n\n# THESE MUST BE WRAPPED TO OUTPUT ElementTree.element objects\nCONVERSIONS = {'path': path2pathd,\n 'circle': ellipse2pathd,\n 'ellipse': ellipse2pathd,\n 'line': line2pathd,\n 'polyline': polyline2pathd,\n 'polygon': polygon2pathd,\n 'rect': rect2pathd}\n\nCONVERT_ONLY_PATHS = {'path': path2pathd}\n\nSVG_GROUP_TAG = 'svg:g'\n\n\ndef flatten_all_paths(group, group_filter=lambda x: True,\n path_filter=lambda x: True, path_conversions=CONVERSIONS,\n group_search_xpath=SVG_GROUP_TAG):\n \"\"\"Returns the paths inside a group (recursively), expressing the\n paths in the base coordinates.\n\n Note that if the group being passed in is nested inside some parent\n group(s), we cannot take the parent group(s) into account, because\n xml.etree.Element has no pointer to its parent. You should use\n Document.flatten_group(group) to flatten a specific nested group into\n the root coordinates.\n\n Args:\n group is an Element\n path_conversions (dict):\n A dictionary to convert from an SVG element to a path data\n string. Any element tags that are not included in this\n dictionary will be ignored (including the `path` tag). To\n only convert explicit path elements, pass in\n `path_conversions=CONVERT_ONLY_PATHS`.\n \"\"\"\n if not isinstance(group, Element):\n raise TypeError('Must provide an xml.etree.Element object. '\n 'Instead you provided {0}'.format(type(group)))\n\n # Stop right away if the group_selector rejects this group\n if not group_filter(group):\n return []\n\n # To handle the transforms efficiently, we'll traverse the tree of\n # groups depth-first using a stack of tuples.\n # The first entry in the tuple is a group element and the second\n # entry is its transform. As we pop each entry in the stack, we\n # will add all its child group elements to the stack.\n StackElement = collections.namedtuple('StackElement',\n ['group', 'transform'])\n\n def new_stack_element(element, last_tf):\n return StackElement(element, last_tf.dot(\n parse_transform(element.get('transform'))))\n\n def get_relevant_children(parent, last_tf):\n children = []\n for elem in filter(group_filter,\n parent.iterfind(group_search_xpath, SVG_NAMESPACE)):\n children.append(new_stack_element(elem, last_tf))\n return children\n\n stack = [new_stack_element(group, np.identity(3))]\n\n FlattenedPath = collections.namedtuple('FlattenedPath',\n ['path', 'element', 'transform'])\n paths = []\n\n while stack:\n top = stack.pop()\n\n # For each element type that we know how to convert into path\n # data, parse the element after confirming that the path_filter\n # accepts it.\n for key, converter in path_conversions.items():\n for path_elem in filter(path_filter, top.group.iterfind(\n 'svg:'+key, SVG_NAMESPACE)):\n path_tf = top.transform.dot(\n parse_transform(path_elem.get('transform')))\n path = transform(parse_path(converter(path_elem)), path_tf)\n paths.append(FlattenedPath(path, path_elem, path_tf))\n\n stack.extend(get_relevant_children(top.group, top.transform))\n\n return paths\n\n\ndef flatten_group(group_to_flatten, root, recursive=True,\n group_filter=lambda x: True, path_filter=lambda x: True,\n path_conversions=CONVERSIONS,\n group_search_xpath=SVG_GROUP_TAG):\n \"\"\"Flatten all the paths in a specific group.\n\n The paths will be flattened into the 'root' frame. Note that root\n needs to be an ancestor of the group that is being flattened.\n Otherwise, no paths will be returned.\"\"\"\n\n if not any(group_to_flatten is descendant for descendant in root.iter()):\n warnings.warn('The requested group_to_flatten is not a '\n 'descendant of root')\n # We will shortcut here, because it is impossible for any paths\n # to be returned anyhow.\n return []\n\n # We create a set of the unique IDs of each element that we wish to\n # flatten, if those elements are groups. Any groups outside of this\n # set will be skipped while we flatten the paths.\n desired_groups = set()\n if recursive:\n for group in group_to_flatten.iter():\n desired_groups.add(id(group))\n else:\n desired_groups.add(id(group_to_flatten))\n\n def desired_group_filter(x):\n return (id(x) in desired_groups) and group_filter(x)\n\n return flatten_all_paths(root, desired_group_filter, path_filter,\n path_conversions, group_search_xpath)\n\n\nclass Document:\n def __init__(self, filename):\n \"\"\"A container for a DOM-style SVG document.\n\n The `Document` class provides a simple interface to modify and analyze \n the path elements in a DOM-style document. The DOM-style document is \n parsed into an ElementTree object (stored in the `tree` attribute).\n\n This class provides functions for extracting SVG data into Path objects.\n The Path output objects will be transformed based on their parent groups.\n \n Args:\n filename (str): The filename of the DOM-style object.\n \"\"\"\n\n # remember location of original svg file\n if filename is not None and os.path.dirname(filename) == '':\n self.original_filename = os.path.join(os.getcwd(), filename)\n else:\n self.original_filename = filename\n\n if filename is not None:\n # parse svg to ElementTree object\n self.tree = etree.parse(filename)\n else:\n self.tree = etree.ElementTree(Element('svg'))\n\n self.root = self.tree.getroot()\n\n def flatten_all_paths(self, group_filter=lambda x: True,\n path_filter=lambda x: True,\n path_conversions=CONVERSIONS):\n \"\"\"Forward the tree of this document into the more general\n flatten_all_paths function and return the result.\"\"\"\n return flatten_all_paths(self.tree.getroot(), group_filter,\n path_filter, path_conversions)\n\n def flatten_group(self, group, recursive=True, group_filter=lambda x: True,\n path_filter=lambda x: True, path_conversions=CONVERSIONS):\n if all(isinstance(s, str) for s in group):\n # If we're given a list of strings, assume it represents a\n # nested sequence\n group = self.get_or_add_group(group)\n elif not isinstance(group, Element):\n raise TypeError(\n 'Must provide a list of strings that represent a nested '\n 'group name, or provide an xml.etree.Element object. '\n 'Instead you provided {0}'.format(group))\n\n return flatten_group(group, self.tree.getroot(), recursive,\n group_filter, path_filter, path_conversions)\n\n def add_path(self, path, attribs=None, group=None):\n \"\"\"Add a new path to the SVG.\"\"\"\n\n # If not given a parent, assume that the path does not have a group\n if group is None:\n group = self.tree.getroot()\n\n # If given a list of strings (one or more), assume it represents\n # a sequence of nested group names\n elif all(isinstance(elem, str) for elem in group):\n group = self.get_or_add_group(group)\n\n elif not isinstance(group, Element):\n raise TypeError(\n 'Must provide a list of strings or an xml.etree.Element '\n 'object. Instead you provided {0}'.format(group))\n\n else:\n # Make sure that the group belongs to this Document object\n if not self.contains_group(group):\n warnings.warn('The requested group does not belong to '\n 'this Document')\n\n # TODO: It might be better to use duck-typing here with a try-except\n if isinstance(path, Path):\n path_svg = path.d()\n elif is_path_segment(path):\n path_svg = Path(path).d()\n elif isinstance(path, str):\n # Assume this is a valid d-string.\n # TODO: Should we sanity check the input string?\n path_svg = path\n else:\n raise TypeError(\n 'Must provide a Path, a path segment type, or a valid '\n 'SVG path d-string. Instead you provided {0}'.format(path))\n\n if attribs is None:\n attribs = {}\n else:\n attribs = attribs.copy()\n\n attribs['d'] = path_svg\n\n return SubElement(group, 'path', attribs)\n\n def contains_group(self, group):\n return any(group is owned for owned in self.tree.iter())\n\n def get_or_add_group(self, nested_names, name_attr='id'):\n \"\"\"Get a group from the tree, or add a new one with the given\n name structure.\n\n `nested_names` is a list of strings which represent group names.\n Each group name will be nested inside of the previous group name.\n\n `name_attr` is the group attribute that is being used to\n represent the group's name. Default is 'id', but some SVGs may\n contain custom name labels, like 'inkscape:label'.\n\n Returns the requested group. If the requested group did not\n exist, this function will create it, as well as all parent\n groups that it requires. All created groups will be left with\n blank attributes.\n\n \"\"\"\n group = self.tree.getroot()\n # Drill down through the names until we find the desired group\n while len(nested_names):\n prev_group = group\n next_name = nested_names.pop(0)\n for elem in group.iterfind(SVG_GROUP_TAG, SVG_NAMESPACE):\n if elem.get(name_attr) == next_name:\n group = elem\n break\n\n if prev_group is group:\n # The group we're looking for does not exist, so let's\n # create the group structure\n nested_names.insert(0, next_name)\n\n while nested_names:\n next_name = nested_names.pop(0)\n group = self.add_group({'id': next_name}, group)\n # Now nested_names will be empty, so the topmost\n # while-loop will end\n return group\n\n def add_group(self, group_attribs=None, parent=None):\n \"\"\"Add an empty group element to the SVG.\"\"\"\n if parent is None:\n parent = self.tree.getroot()\n elif not self.contains_group(parent):\n warnings.warn('The requested group {0} does not belong to '\n 'this Document'.format(parent))\n\n if group_attribs is None:\n group_attribs = {}\n else:\n group_attribs = group_attribs.copy()\n\n return SubElement(parent, '{{{0}}}g'.format(\n SVG_NAMESPACE['svg']), group_attribs)\n\n def save(self, filename):\n with open(filename, 'w') as output_svg:\n output_svg.write(etree.tostring(self.tree.getroot()))\n\n def display(self, filename=None):\n \"\"\"Displays/opens the doc using the OS's default application.\"\"\"\n\n if filename is None:\n filename = self.original_filename\n\n # write to a (by default temporary) file\n with open(filename, 'w') as output_svg:\n output_svg.write(etree.tostring(self.tree.getroot()))\n\n open_in_browser(filename)\n\n\"\"\"This submodule contains miscellaneous tools that are used internally, but\naren't specific to SVGs or related mathematical objects.\"\"\"\n\n# External dependencies:\nfrom __future__ import division, absolute_import, print_function\nimport os\nimport sys\nimport webbrowser\n\n\n# stackoverflow.com/questions/214359/converting-hex-color-to-rgb-and-vice-versa\ndef hex2rgb(value):\n \"\"\"Converts a hexadeximal color string to an RGB 3-tuple\n\n EXAMPLE\n -------\n >>> hex2rgb('#0000FF')\n (0, 0, 255)\n \"\"\"\n value = value.lstrip('#')\n lv = len(value)\n return tuple(int(value[i:i+lv//3], 16) for i in range(0, lv, lv//3))\n\n\n# stackoverflow.com/questions/214359/converting-hex-color-to-rgb-and-vice-versa\ndef rgb2hex(rgb):\n \"\"\"Converts an RGB 3-tuple to a hexadeximal color string.\n\n EXAMPLE\n -------\n >>> rgb2hex((0,0,255))\n '#0000FF'\n \"\"\"\n return ('#%02x%02x%02x' % tuple(rgb)).upper()\n\n\ndef isclose(a, b, rtol=1e-5, atol=1e-8):\n \"\"\"This is essentially np.isclose, but slightly faster.\"\"\"\n return abs(a - b) < (atol + rtol * abs(b))\n\n\ndef open_in_browser(file_location):\n \"\"\"Attempt to open file located at file_location in the default web\n browser.\"\"\"\n\n # If just the name of the file was given, check if it's in the Current\n # Working Directory.\n if not os.path.isfile(file_location):\n file_location = os.path.join(os.getcwd(), file_location)\n if not os.path.isfile(file_location):\n raise IOError(\"\\n\\nFile not found.\")\n\n # For some reason OSX requires this adjustment (tested on 10.10.4)\n if sys.platform == \"darwin\":\n file_location = \"file:///\"+file_location\n\n new = 2 # open in a new tab, if possible\n webbrowser.get().open(file_location, new=new)\n\n\nBugException = Exception(\"This code should never be reached. You've found a \"\n \"bug. Please submit an issue to \\n\"\n \"https://github.com/mathandy/svgpathtools/issues\"\n \"\\nwith an easily reproducible example.\")\n\n\"\"\"This submodule contains the path_parse() function used to convert SVG path\nelement d-strings into svgpathtools Path objects.\nNote: This file was taken (nearly) as is from the svg.path module (v 2.0).\"\"\"\n\n# External dependencies\nfrom __future__ import division, absolute_import, print_function\nimport re\nimport numpy as np\nimport warnings\n\n# Internal dependencies\nfrom .path import Path, Line, QuadraticBezier, CubicBezier, Arc\n\n# To maintain forward/backward compatibility\ntry:\n str = basestring\nexcept NameError:\n pass\n\nCOMMANDS = set('MmZzLlHhVvCcSsQqTtAa')\nUPPERCASE = set('MZLHVCSQTA')\n\nCOMMAND_RE = re.compile(\"([MmZzLlHhVvCcSsQqTtAa])\")\nFLOAT_RE = re.compile(\"[-+]?[0-9]*\\.?[0-9]+(?:[eE][-+]?[0-9]+)?\")\n\n\ndef _tokenize_path(pathdef):\n for x in COMMAND_RE.split(pathdef):\n if x in COMMANDS:\n yield x\n for token in FLOAT_RE.findall(x):\n yield token\n\n\ndef parse_path(pathdef, current_pos=0j, tree_element=None):\n # In the SVG specs, initial movetos are absolute, even if\n # specified as 'm'. This is the default behavior here as well.\n # But if you pass in a current_pos variable, the initial moveto\n # will be relative to that current_pos. This is useful.\n elements = list(_tokenize_path(pathdef))\n # Reverse for easy use of .pop()\n elements.reverse()\n\n if tree_element is None:\n segments = Path()\n else:\n segments = Path(tree_element=tree_element)\n\n start_pos = None\n command = None\n\n while elements:\n\n if elements[-1] in COMMANDS:\n # New command.\n last_command = command # Used by S and T\n command = elements.pop()\n absolute = command in UPPERCASE\n command = command.upper()\n else:\n # If this element starts with numbers, it is an implicit command\n # and we don't change the command. Check that it's allowed:\n if command is None:\n raise ValueError(\"Unallowed implicit command in %s, position %s\" % (\n pathdef, len(pathdef.split()) - len(elements)))\n\n if command == 'M':\n # Moveto command.\n x = elements.pop()\n y = elements.pop()\n pos = float(x) + float(y) * 1j\n if absolute:\n current_pos = pos\n else:\n current_pos += pos\n\n # when M is called, reset start_pos\n # This behavior of Z is defined in svg spec:\n # http://www.w3.org/TR/SVG/paths.html#PathDataClosePathCommand\n start_pos = current_pos\n\n # Implicit moveto commands are treated as lineto commands.\n # So we set command to lineto here, in case there are\n # further implicit commands after this moveto.\n command = 'L'\n\n elif command == 'Z':\n # Close path\n if not (current_pos == start_pos):\n segments.append(Line(current_pos, start_pos))\n segments.closed = True\n current_pos = start_pos\n command = None\n\n elif command == 'L':\n x = elements.pop()\n y = elements.pop()\n pos = float(x) + float(y) * 1j\n if not absolute:\n pos += current_pos\n segments.append(Line(current_pos, pos))\n current_pos = pos\n\n elif command == 'H':\n x = elements.pop()\n pos = float(x) + current_pos.imag * 1j\n if not absolute:\n pos += current_pos.real\n segments.append(Line(current_pos, pos))\n current_pos = pos\n\n elif command == 'V':\n y = elements.pop()\n pos = current_pos.real + float(y) * 1j\n if not absolute:\n pos += current_pos.imag * 1j\n segments.append(Line(current_pos, pos))\n current_pos = pos\n\n elif command == 'C':\n control1 = float(elements.pop()) + float(elements.pop()) * 1j\n control2 = float(elements.pop()) + float(elements.pop()) * 1j\n end = float(elements.pop()) + float(elements.pop()) * 1j\n\n if not absolute:\n control1 += current_pos\n control2 += current_pos\n end += current_pos\n\n segments.append(CubicBezier(current_pos, control1, control2, end))\n current_pos = end\n\n elif command == 'S':\n # Smooth curve. First control point is the \"reflection\" of\n # the second control point in the previous path.\n\n if last_command not in 'CS':\n # If there is no previous command or if the previous command\n # was not an C, c, S or s, assume the first control point is\n # coincident with the current point.\n control1 = current_pos\n else:\n # The first control point is assumed to be the reflection of\n # the second control point on the previous command relative\n # to the current point.\n control1 = current_pos + current_pos - segments[-1].control2\n\n control2 = float(elements.pop()) + float(elements.pop()) * 1j\n end = float(elements.pop()) + float(elements.pop()) * 1j\n\n if not absolute:\n control2 += current_pos\n end += current_pos\n\n segments.append(CubicBezier(current_pos, control1, control2, end))\n current_pos = end\n\n elif command == 'Q':\n control = float(elements.pop()) + float(elements.pop()) * 1j\n end = float(elements.pop()) + float(elements.pop()) * 1j\n\n if not absolute:\n control += current_pos\n end += current_pos\n\n segments.append(QuadraticBezier(current_pos, control, end))\n current_pos = end\n\n elif command == 'T':\n # Smooth curve. Control point is the \"reflection\" of\n # the second control point in the previous path.\n\n if last_command not in 'QT':\n # If there is no previous command or if the previous command\n # was not an Q, q, T or t, assume the first control point is\n # coincident with the current point.\n control = current_pos\n else:\n # The control point is assumed to be the reflection of\n # the control point on the previous command relative\n # to the current point.\n control = current_pos + current_pos - segments[-1].control\n\n end = float(elements.pop()) + float(elements.pop()) * 1j\n\n if not absolute:\n end += current_pos\n\n segments.append(QuadraticBezier(current_pos, control, end))\n current_pos = end\n\n elif command == 'A':\n radius = float(elements.pop()) + float(elements.pop()) * 1j\n rotation = float(elements.pop())\n arc = float(elements.pop())\n sweep = float(elements.pop())\n end = float(elements.pop()) + float(elements.pop()) * 1j\n\n if not absolute:\n end += current_pos\n\n segments.append(Arc(current_pos, radius, rotation, arc, sweep, end))\n current_pos = end\n\n return segments\n\n\ndef _check_num_parsed_values(values, allowed):\n if not any(num == len(values) for num in allowed):\n if len(allowed) > 1:\n warnings.warn('Expected one of the following number of values {0}, but found {1} values instead: {2}'\n .format(allowed, len(values), values))\n elif allowed[0] != 1:\n warnings.warn('Expected {0} values, found {1}: {2}'.format(allowed[0], len(values), values))\n else:\n warnings.warn('Expected 1 value, found {0}: {1}'.format(len(values), values))\n return False\n return True\n\n\ndef _parse_transform_substr(transform_substr):\n\n type_str, value_str = transform_substr.split('(')\n value_str = value_str.replace(',', ' ')\n values = list(map(float, filter(None, value_str.split(' '))))\n\n transform = np.identity(3)\n if 'matrix' in type_str:\n if not _check_num_parsed_values(values, [6]):\n return transform\n\n transform[0:2, 0:3] = np.array([values[0:6:2], values[1:6:2]])\n\n elif 'translate' in transform_substr:\n if not _check_num_parsed_values(values, [1, 2]):\n return transform\n\n transform[0, 2] = values[0]\n if len(values) > 1:\n transform[1, 2] = values[1]\n\n elif 'scale' in transform_substr:\n if not _check_num_parsed_values(values, [1, 2]):\n return transform\n\n x_scale = values[0]\n y_scale = values[1] if (len(values) > 1) else x_scale\n transform[0, 0] = x_scale\n transform[1, 1] = y_scale\n\n elif 'rotate' in transform_substr:\n if not _check_num_parsed_values(values, [1, 3]):\n return transform\n\n angle = values[0] * np.pi / 180.0\n if len(values) == 3:\n offset = values[1:3]\n else:\n offset = (0, 0)\n tf_offset = np.identity(3)\n tf_offset[0:2, 2:3] = np.array([[offset[0]], [offset[1]]])\n tf_rotate = np.identity(3)\n tf_rotate[0:2, 0:2] = np.array([[np.cos(angle), -np.sin(angle)], [np.sin(angle), np.cos(angle)]])\n tf_offset_neg = np.identity(3)\n tf_offset_neg[0:2, 2:3] = np.array([[-offset[0]], [-offset[1]]])\n\n transform = tf_offset.dot(tf_rotate).dot(tf_offset_neg)\n\n elif 'skewX' in transform_substr:\n if not _check_num_parsed_values(values, [1]):\n return transform\n\n transform[0, 1] = np.tan(values[0] * np.pi / 180.0)\n\n elif 'skewY' in transform_substr:\n if not _check_num_parsed_values(values, [1]):\n return transform\n\n transform[1, 0] = np.tan(values[0] * np.pi / 180.0)\n else:\n # Return an identity matrix if the type of transform is unknown, and warn the user\n warnings.warn('Unknown SVG transform type: {0}'.format(type_str))\n\n return transform\n\n\ndef parse_transform(transform_str):\n \"\"\"Converts a valid SVG transformation string into a 3x3 matrix.\n If the string is empty or null, this returns a 3x3 identity matrix\"\"\"\n if not transform_str:\n return np.identity(3)\n elif not isinstance(transform_str, str):\n raise TypeError('Must provide a string to parse')\n\n total_transform = np.identity(3)\n transform_substrs = transform_str.split(')')[:-1] # Skip the last element, because it should be empty\n for substr in transform_substrs:\n total_transform = total_transform.dot(_parse_transform_substr(substr))\n\n return total_transform\n\n\"\"\"This submodule contains the class definitions of the the main five classes\nsvgpathtools is built around: Path, Line, QuadraticBezier, CubicBezier, and\nArc.\"\"\"\n\n# External dependencies\nfrom __future__ import division, absolute_import, print_function\nfrom math import sqrt, cos, sin, acos, asin, degrees, radians, log, pi, ceil\nfrom cmath import exp, sqrt as csqrt, phase\nfrom collections import MutableSequence\nfrom warnings import warn\nfrom operator import itemgetter\nimport numpy as np\ntry:\n from scipy.integrate import quad\n _quad_available = True\nexcept:\n _quad_available = False\n\n# Internal dependencies\nfrom .bezier import (bezier_intersections, bezier_bounding_box, split_bezier,\n bezier_by_line_intersections, polynomial2bezier,\n bezier2polynomial)\nfrom .misctools import BugException\nfrom .polytools import rational_limit, polyroots, polyroots01, imag, real\n\n\n# Default Parameters ##########################################################\n\n# path segment .length() parameters for arc length computation\nLENGTH_MIN_DEPTH = 5\nLENGTH_ERROR = 1e-12\nUSE_SCIPY_QUAD = True # for elliptic Arc segment arc length computation\n\n# path segment .ilength() parameters for inverse arc length computation\nILENGTH_MIN_DEPTH = 5\nILENGTH_ERROR = 1e-12\nILENGTH_S_TOL = 1e-12\nILENGTH_MAXITS = 10000\n\n# compatibility/implementation related warnings and parameters\nCLOSED_WARNING_ON = True\n_NotImplemented4ArcException = \\\n Exception(\"This method has not yet been implemented for Arc objects.\")\n# _NotImplemented4QuadraticException = \\\n# Exception(\"This method has not yet been implemented for QuadraticBezier \"\n# \"objects.\")\n_is_smooth_from_warning = \\\n (\"The name of this method is somewhat misleading (yet kept for \"\n \"compatibility with scripts created using svg.path 2.0). This method \"\n \"is meant only for d-string creation and should NOT be used to check \"\n \"for kinks. To check a segment for differentiability, use the \"\n \"joins_smoothly_with() method instead or the kinks() function (in \"\n \"smoothing.py).\\nTo turn off this warning, set \"\n \"warning_on=False.\")\n\n\n# Miscellaneous ###############################################################\n\ndef bezier_segment(*bpoints):\n if len(bpoints) == 2:\n return Line(*bpoints)\n elif len(bpoints) == 4:\n return CubicBezier(*bpoints)\n elif len(bpoints) == 3:\n return QuadraticBezier(*bpoints)\n else:\n assert len(bpoints) in (2, 3, 4)\n\n\ndef is_bezier_segment(seg):\n return (isinstance(seg, Line) or\n isinstance(seg, QuadraticBezier) or\n isinstance(seg, CubicBezier))\n\n\ndef is_path_segment(seg):\n return is_bezier_segment(seg) or isinstance(seg, Arc)\n\n\ndef is_bezier_path(path):\n \"\"\"Checks that all segments in path are a Line, QuadraticBezier, or\n CubicBezier object.\"\"\"\n return isinstance(path, Path) and all(map(is_bezier_segment, path))\n\n\ndef concatpaths(list_of_paths):\n \"\"\"Takes in a sequence of paths and returns their concatenations into a\n single path (following the order of the input sequence).\"\"\"\n return Path(*[seg for path in list_of_paths for seg in path])\n\n\ndef bbox2path(xmin, xmax, ymin, ymax):\n \"\"\"Converts a bounding box 4-tuple to a Path object.\"\"\"\n b = Line(xmin + 1j*ymin, xmax + 1j*ymin)\n t = Line(xmin + 1j*ymax, xmax + 1j*ymax)\n r = Line(xmax + 1j*ymin, xmax + 1j*ymax)\n l = Line(xmin + 1j*ymin, xmin + 1j*ymax)\n return Path(b, r, t.reversed(), l.reversed())\n\n\ndef polyline(*points):\n \"\"\"Converts a list of points to a Path composed of lines connecting those \n points (i.e. a linear spline or polyline). See also `polygon()`.\"\"\"\n return Path(*[Line(points[i], points[i+1])\n for i in range(len(points) - 1)])\n\n\ndef polygon(*points):\n \"\"\"Converts a list of points to a Path composed of lines connecting those \n points, then closes the path by connecting the last point to the first. \n See also `polyline()`.\"\"\"\n return Path(*[Line(points[i], points[(i + 1) % len(points)])\n for i in range(len(points))])\n\n\n# Conversion###################################################################\n\ndef bpoints2bezier(bpoints):\n \"\"\"Converts a list of length 2, 3, or 4 to a CubicBezier, QuadraticBezier,\n or Line object, respectively.\n See also: poly2bez.\"\"\"\n order = len(bpoints) - 1\n if order == 3:\n return CubicBezier(*bpoints)\n elif order == 2:\n return QuadraticBezier(*bpoints)\n elif order == 1:\n return Line(*bpoints)\n else:\n assert len(bpoints) in {2, 3, 4}\n\n\ndef poly2bez(poly, return_bpoints=False):\n \"\"\"Converts a cubic or lower order Polynomial object (or a sequence of\n coefficients) to a CubicBezier, QuadraticBezier, or Line object as\n appropriate. If return_bpoints=True then this will instead only return\n the control points of the corresponding Bezier curve.\n Note: The inverse operation is available as a method of CubicBezier,\n QuadraticBezier and Line objects.\"\"\"\n bpoints = polynomial2bezier(poly)\n if return_bpoints:\n return bpoints\n else:\n return bpoints2bezier(bpoints)\n\n\ndef bez2poly(bez, numpy_ordering=True, return_poly1d=False):\n \"\"\"Converts a Bezier object or tuple of Bezier control points to a tuple\n of coefficients of the expanded polynomial.\n return_poly1d : returns a numpy.poly1d object. This makes computations\n of derivatives/anti-derivatives and many other operations quite quick.\n numpy_ordering : By default (to accommodate numpy) the coefficients will\n be output in reverse standard order.\n Note: This function is redundant thanks to the .poly() method included\n with all bezier segment classes.\"\"\"\n if is_bezier_segment(bez):\n bez = bez.bpoints()\n return bezier2polynomial(bez,\n numpy_ordering=numpy_ordering,\n return_poly1d=return_poly1d)\n\n\n# Geometric####################################################################\n\ndef rotate(curve, degs, origin=None):\n \"\"\"Returns curve rotated by `degs` degrees (CCW) around the point `origin`\n (a complex number). By default origin is either `curve.point(0.5)`, or in\n the case that curve is an Arc object, `origin` defaults to `curve.center`.\n \"\"\"\n def transform(z):\n return exp(1j*radians(degs))*(z - origin) + origin\n\n if origin is None:\n if isinstance(curve, Arc):\n origin = curve.center\n else:\n origin = curve.point(0.5)\n\n if isinstance(curve, Path):\n return Path(*[rotate(seg, degs, origin=origin) for seg in curve])\n elif is_bezier_segment(curve):\n return bpoints2bezier([transform(bpt) for bpt in curve.bpoints()])\n elif isinstance(curve, Arc):\n new_start = transform(curve.start)\n new_end = transform(curve.end)\n new_rotation = curve.rotation + degs\n return Arc(new_start, radius=curve.radius, rotation=new_rotation,\n large_arc=curve.large_arc, sweep=curve.sweep, end=new_end)\n else:\n raise TypeError(\"Input `curve` should be a Path, Line, \"\n \"QuadraticBezier, CubicBezier, or Arc object.\")\n\n\ndef translate(curve, z0):\n \"\"\"Shifts the curve by the complex quantity z such that\n translate(curve, z0).point(t) = curve.point(t) + z0\"\"\"\n if isinstance(curve, Path):\n return Path(*[translate(seg, z0) for seg in curve])\n elif is_bezier_segment(curve):\n return bpoints2bezier([bpt + z0 for bpt in curve.bpoints()])\n elif isinstance(curve, Arc):\n new_start = curve.start + z0\n new_end = curve.end + z0\n return Arc(new_start, radius=curve.radius, rotation=curve.rotation,\n large_arc=curve.large_arc, sweep=curve.sweep, end=new_end)\n else:\n raise TypeError(\"Input `curve` should be a Path, Line, \"\n \"QuadraticBezier, CubicBezier, or Arc object.\")\n\n\ndef scale(curve, sx, sy=None, origin=0j):\n \"\"\"Scales `curve`, about `origin`, by diagonal matrix `[[sx,0],[0,sy]]`.\n\n Notes:\n ------\n * If `sy` is not specified, it is assumed to be equal to `sx` and \n a scalar transformation of `curve` about `origin` will be returned.\n I.e.\n scale(curve, sx, origin).point(t) == \n ((curve.point(t) - origin) * sx) + origin\n \"\"\"\n\n if sy is None:\n isy = 1j*sx\n else:\n isy = 1j*sy\n\n def _scale(z):\n if sy is None:\n return sx*z\n return sx*z.real + isy*z.imag \n\n def scale_bezier(bez):\n p = [_scale(c) for c in bez2poly(bez)]\n p[-1] += origin - _scale(origin)\n return poly2bez(p)\n\n if isinstance(curve, Path):\n return Path(*[scale(seg, sx, sy, origin) for seg in curve])\n elif is_bezier_segment(curve):\n return scale_bezier(curve)\n elif isinstance(curve, Arc):\n if sy is None or sy == sx:\n return Arc(start=sx*(curve.start - origin) + origin,\n radius=sx*curve.radius,\n rotation=curve.rotation, \n large_arc=curve.large_arc, \n sweep=curve.sweep, \n end=sx*(curve.end - origin) + origin)\n else:\n raise Exception(\"\\nFor `Arc` objects, only scale transforms \"\n \"with sx==sy are implemented.\\n\")\n else:\n raise TypeError(\"Input `curve` should be a Path, Line, \"\n \"QuadraticBezier, CubicBezier, or Arc object.\")\n\n\ndef transform(curve, tf):\n \"\"\"Transforms the curve by the homogeneous transformation matrix tf\"\"\"\n def to_point(p):\n return np.array([[p.real], [p.imag], [1.0]])\n\n def to_vector(z):\n return np.array([[z.real], [z.imag], [0.0]])\n\n def to_complex(v):\n return v.item(0) + 1j * v.item(1)\n\n if isinstance(curve, Path):\n return Path(*[transform(segment, tf) for segment in curve])\n elif is_bezier_segment(curve):\n return bpoints2bezier([to_complex(tf.dot(to_point(p)))\n for p in curve.bpoints()])\n elif isinstance(curve, Arc):\n new_start = to_complex(tf.dot(to_point(curve.start)))\n new_end = to_complex(tf.dot(to_point(curve.end)))\n new_radius = to_complex(tf.dot(to_vector(curve.radius)))\n return Arc(new_start, radius=new_radius, rotation=curve.rotation,\n large_arc=curve.large_arc, sweep=curve.sweep, end=new_end)\n else:\n raise TypeError(\"Input `curve` should be a Path, Line, \"\n \"QuadraticBezier, CubicBezier, or Arc object.\")\n\n\ndef bezier_unit_tangent(seg, t):\n \"\"\"Returns the unit tangent of the segment at t.\n\n Notes\n -----\n If you receive a RuntimeWarning, try the following:\n >>> import numpy\n >>> old_numpy_error_settings = numpy.seterr(invalid='raise')\n This can be undone with:\n >>> numpy.seterr(**old_numpy_error_settings)\n \"\"\"\n assert 0 <= t <= 1\n dseg = seg.derivative(t)\n\n # Note: dseg might be numpy value, use np.seterr(invalid='raise')\n try:\n unit_tangent = dseg/abs(dseg)\n except (ZeroDivisionError, FloatingPointError):\n # This may be a removable singularity, if so we just need to compute\n # the limit.\n # Note: limit{{dseg / abs(dseg)} = sqrt(limit{dseg**2 / abs(dseg)**2})\n dseg_poly = seg.poly().deriv()\n dseg_abs_squared_poly = (real(dseg_poly) ** 2 +\n imag(dseg_poly) ** 2)\n try:\n unit_tangent = csqrt(rational_limit(dseg_poly**2,\n dseg_abs_squared_poly, t))\n except ValueError:\n bef = seg.poly().deriv()(t - 1e-4)\n aft = seg.poly().deriv()(t + 1e-4)\n mes = (\"Unit tangent appears to not be well-defined at \"\n \"t = {}, \\n\".format(t) +\n \"seg.poly().deriv()(t - 1e-4) = {}\\n\".format(bef) +\n \"seg.poly().deriv()(t + 1e-4) = {}\".format(aft))\n raise ValueError(mes)\n return unit_tangent\n\n\ndef segment_curvature(self, t, use_inf=False):\n \"\"\"returns the curvature of the segment at t.\n\n Notes\n -----\n If you receive a RuntimeWarning, run command\n >>> old = np.seterr(invalid='raise')\n This can be undone with\n >>> np.seterr(**old)\n \"\"\"\n\n dz = self.derivative(t)\n ddz = self.derivative(t, n=2)\n dx, dy = dz.real, dz.imag\n ddx, ddy = ddz.real, ddz.imag\n old_np_seterr = np.seterr(invalid='raise')\n try:\n kappa = abs(dx*ddy - dy*ddx)/sqrt(dx*dx + dy*dy)**3\n except (ZeroDivisionError, FloatingPointError):\n # tangent vector is zero at t, use polytools to find limit\n p = self.poly()\n dp = p.deriv()\n ddp = dp.deriv()\n dx, dy = real(dp), imag(dp)\n ddx, ddy = real(ddp), imag(ddp)\n f2 = (dx*ddy - dy*ddx)**2\n g2 = (dx*dx + dy*dy)**3\n lim2 = rational_limit(f2, g2, t)\n if lim2 < 0: # impossible, must be numerical error\n return 0\n kappa = sqrt(lim2)\n finally:\n np.seterr(**old_np_seterr)\n return kappa\n\n\ndef bezier_radialrange(seg, origin, return_all_global_extrema=False):\n \"\"\"returns the tuples (d_min, t_min) and (d_max, t_max) which minimize and\n maximize, respectively, the distance d = |self.point(t)-origin|.\n return_all_global_extrema: Multiple such t_min or t_max values can exist.\n By default, this will only return one. Set return_all_global_extrema=True\n to return all such global extrema.\"\"\"\n\n def _radius(tau):\n return abs(seg.point(tau) - origin)\n\n shifted_seg_poly = seg.poly() - origin\n r_squared = real(shifted_seg_poly) ** 2 + \\\n imag(shifted_seg_poly) ** 2\n extremizers = [0, 1] + polyroots01(r_squared.deriv())\n extrema = [(_radius(t), t) for t in extremizers]\n\n if return_all_global_extrema:\n raise NotImplementedError\n else:\n seg_global_min = min(extrema, key=itemgetter(0))\n seg_global_max = max(extrema, key=itemgetter(0))\n return seg_global_min, seg_global_max\n\n\ndef closest_point_in_path(pt, path):\n \"\"\"returns (|path.seg.point(t)-pt|, t, seg_idx) where t and seg_idx\n minimize the distance between pt and curve path[idx].point(t) for 0<=t<=1\n and any seg_idx.\n Warning: Multiple such global minima can exist. This will only return\n one.\"\"\"\n return path.radialrange(pt)[0]\n\n\ndef farthest_point_in_path(pt, path):\n \"\"\"returns (|path.seg.point(t)-pt|, t, seg_idx) where t and seg_idx\n maximize the distance between pt and curve path[idx].point(t) for 0<=t<=1\n and any seg_idx.\n :rtype : object\n :param pt:\n :param path:\n Warning: Multiple such global maxima can exist. This will only return\n one.\"\"\"\n return path.radialrange(pt)[1]\n\n\ndef path_encloses_pt(pt, opt, path):\n \"\"\"returns true if pt is a point enclosed by path (which must be a Path\n object satisfying path.isclosed==True). opt is a point you know is\n NOT enclosed by path.\"\"\"\n assert path.isclosed()\n intersections = Path(Line(pt, opt)).intersect(path)\n if len(intersections) % 2:\n return True\n else:\n return False\n\n\ndef segment_length(curve, start, end, start_point, end_point,\n error=LENGTH_ERROR, min_depth=LENGTH_MIN_DEPTH, depth=0):\n \"\"\"Recursively approximates the length by straight lines\"\"\"\n mid = (start + end)/2\n mid_point = curve.point(mid)\n length = abs(end_point - start_point)\n first_half = abs(mid_point - start_point)\n second_half = abs(end_point - mid_point)\n\n length2 = first_half + second_half\n if (length2 - length > error) or (depth < min_depth):\n # Calculate the length of each segment:\n depth += 1\n return (segment_length(curve, start, mid, start_point, mid_point,\n error, min_depth, depth) +\n segment_length(curve, mid, end, mid_point, end_point,\n error, min_depth, depth))\n # This is accurate enough.\n return length2\n\n\ndef inv_arclength(curve, s, s_tol=ILENGTH_S_TOL, maxits=ILENGTH_MAXITS,\n error=ILENGTH_ERROR, min_depth=ILENGTH_MIN_DEPTH):\n \"\"\"INPUT: curve should be a CubicBezier, Line, of Path of CubicBezier\n and/or Line objects.\n OUTPUT: Returns a float, t, such that the arc length of curve from 0 to\n t is approximately s.\n s_tol - exit when |s(t) - s| < s_tol where\n s(t) = seg.length(0, t, error, min_depth) and seg is either curve or,\n if curve is a Path object, then seg is a segment in curve.\n error - used to compute lengths of cubics and arcs\n min_depth - used to compute lengths of cubics and arcs\n Note: This function is not designed to be efficient, but if it's slower\n than you need, make sure you have scipy installed.\"\"\"\n\n curve_length = curve.length(error=error, min_depth=min_depth)\n assert curve_length > 0\n if not 0 <= s <= curve_length:\n raise ValueError(\"s is not in interval [0, curve.length()].\")\n\n if s == 0:\n return 0\n if s == curve_length:\n return 1\n\n if isinstance(curve, Path):\n seg_lengths = [seg.length(error=error, min_depth=min_depth)\n for seg in curve]\n lsum = 0\n # Find which segment the point we search for is located on\n for k, len_k in enumerate(seg_lengths):\n if lsum <= s <= lsum + len_k:\n t = inv_arclength(curve[k], s - lsum, s_tol=s_tol,\n maxits=maxits, error=error,\n min_depth=min_depth)\n return curve.t2T(k, t)\n lsum += len_k\n return 1\n\n elif isinstance(curve, Line):\n return s / curve.length(error=error, min_depth=min_depth)\n\n elif (isinstance(curve, QuadraticBezier) or\n isinstance(curve, CubicBezier) or\n isinstance(curve, Arc)):\n t_upper = 1\n t_lower = 0\n iteration = 0\n while iteration < maxits:\n iteration += 1\n t = (t_lower + t_upper)/2\n s_t = curve.length(t1=t, error=error, min_depth=min_depth)\n if abs(s_t - s) < s_tol:\n return t\n elif s_t < s: # t too small\n t_lower = t\n else: # s < s_t, t too big\n t_upper = t\n if t_upper == t_lower:\n warn(\"t is as close as a float can be to the correct value, \"\n \"but |s(t) - s| = {} > s_tol\".format(abs(s_t-s)))\n return t\n raise Exception(\"Maximum iterations reached with s(t) - s = {}.\"\n \"\".format(s_t - s))\n else:\n raise TypeError(\"First argument must be a Line, QuadraticBezier, \"\n \"CubicBezier, Arc, or Path object.\")\n\n# Operations###################################################################\n\n\ndef crop_bezier(seg, t0, t1):\n \"\"\"returns a cropped copy of this segment which starts at self.point(t0)\n and ends at self.point(t1).\"\"\"\n assert t0 < t1\n if t0 == 0:\n cropped_seg = seg.split(t1)[0]\n elif t1 == 1:\n cropped_seg = seg.split(t0)[1]\n else:\n pt1 = seg.point(t1)\n\n # trim off the 0 <= t < t0 part\n trimmed_seg = crop_bezier(seg, t0, 1)\n\n # find the adjusted t1 (i.e. the t1 such that\n # trimmed_seg.point(t1) ~= pt))and trim off the t1 < t <= 1 part\n t1_adj = trimmed_seg.radialrange(pt1)[0][1]\n cropped_seg = crop_bezier(trimmed_seg, 0, t1_adj)\n return cropped_seg\n\n\n# Main Classes ################################################################\n\n\nclass Line(object):\n def __init__(self, start, end):\n self.start = start\n self.end = end\n\n def __repr__(self):\n return 'Line(start=%s, end=%s)' % (self.start, self.end)\n\n def __eq__(self, other):\n if not isinstance(other, Line):\n return NotImplemented\n return self.start == other.start and self.end == other.end\n\n def __ne__(self, other):\n if not isinstance(other, Line):\n return NotImplemented\n return not self == other\n\n def __getitem__(self, item):\n return self.bpoints()[item]\n\n def __len__(self):\n return 2\n\n def joins_smoothly_with(self, previous, wrt_parameterization=False):\n \"\"\"Checks if this segment joins smoothly with previous segment. By\n default, this only checks that this segment starts moving (at t=0) in\n the same direction (and from the same positive) as previous stopped\n moving (at t=1). To check if the tangent magnitudes also match, set\n wrt_parameterization=True.\"\"\"\n if wrt_parameterization:\n return self.start == previous.end and np.isclose(\n self.derivative(0), previous.derivative(1))\n else:\n return self.start == previous.end and np.isclose(\n self.unit_tangent(0), previous.unit_tangent(1))\n\n def point(self, t):\n \"\"\"returns the coordinates of the Bezier curve evaluated at t.\"\"\"\n distance = self.end - self.start\n return self.start + distance*t\n\n def length(self, t0=0, t1=1, error=None, min_depth=None):\n \"\"\"returns the length of the line segment between t0 and t1.\"\"\"\n return abs(self.end - self.start)*(t1-t0)\n\n def ilength(self, s, s_tol=ILENGTH_S_TOL, maxits=ILENGTH_MAXITS,\n error=ILENGTH_ERROR, min_depth=ILENGTH_MIN_DEPTH):\n \"\"\"Returns a float, t, such that self.length(0, t) is approximately s.\n See the inv_arclength() docstring for more details.\"\"\"\n return inv_arclength(self, s, s_tol=s_tol, maxits=maxits, error=error,\n min_depth=min_depth)\n\n def bpoints(self):\n \"\"\"returns the Bezier control points of the segment.\"\"\"\n return self.start, self.end\n\n def poly(self, return_coeffs=False):\n \"\"\"returns the line as a Polynomial object.\"\"\"\n p = self.bpoints()\n coeffs = ([p[1] - p[0], p[0]])\n if return_coeffs:\n return coeffs\n else:\n return np.poly1d(coeffs)\n\n def derivative(self, t=None, n=1):\n \"\"\"returns the nth derivative of the segment at t.\"\"\"\n assert self.end != self.start\n if n == 1:\n return self.end - self.start\n elif n > 1:\n return 0\n else:\n raise ValueError(\"n should be a positive integer.\")\n\n def unit_tangent(self, t=None):\n \"\"\"returns the unit tangent of the segment at t.\"\"\"\n assert self.end != self.start\n dseg = self.end - self.start\n return dseg/abs(dseg)\n\n def normal(self, t=None):\n \"\"\"returns the (right hand rule) unit normal vector to self at t.\"\"\"\n return -1j*self.unit_tangent(t)\n\n def curvature(self, t):\n \"\"\"returns the curvature of the line, which is always zero.\"\"\"\n return 0\n\n # def icurvature(self, kappa):\n # \"\"\"returns a list of t-values such that 0 <= t<= 1 and\n # seg.curvature(t) = kappa.\"\"\"\n # if kappa:\n # raise ValueError(\"The .icurvature() method for Line elements will \"\n # \"return an empty list if kappa is nonzero and \"\n # \"will raise this exception when kappa is zero as \"\n # \"this is true at every point on the line.\")\n # return []\n\n def reversed(self):\n \"\"\"returns a copy of the Line object with its orientation reversed.\"\"\"\n return Line(self.end, self.start)\n\n def intersect(self, other_seg, tol=None):\n \"\"\"Finds the intersections of two segments.\n returns a list of tuples (t1, t2) such that\n self.point(t1) == other_seg.point(t2).\n Note: This will fail if the two segments coincide for more than a\n finite collection of points.\n tol is not used.\"\"\"\n if isinstance(other_seg, Line):\n assert other_seg.end != other_seg.start and self.end != self.start\n assert self != other_seg\n # Solve the system [p1-p0, q1-q0]*[t1, t2]^T = q0 - p0\n # where self == Line(p0, p1) and other_seg == Line(q0, q1)\n a = (self.start.real, self.end.real)\n b = (self.start.imag, self.end.imag)\n c = (other_seg.start.real, other_seg.end.real)\n d = (other_seg.start.imag, other_seg.end.imag)\n denom = ((a[1] - a[0])*(d[0] - d[1]) -\n (b[1] - b[0])*(c[0] - c[1]))\n if np.isclose(denom, 0):\n return []\n t1 = (c[0]*(b[0] - d[1]) -\n c[1]*(b[0] - d[0]) -\n a[0]*(d[0] - d[1]))/denom\n t2 = -(a[1]*(b[0] - d[0]) -\n a[0]*(b[1] - d[0]) -\n c[0]*(b[0] - b[1]))/denom\n if 0 <= t1 <= 1 and 0 <= t2 <= 1:\n return [(t1, t2)]\n return []\n elif isinstance(other_seg, QuadraticBezier):\n t2t1s = bezier_by_line_intersections(other_seg, self)\n return [(t1, t2) for t2, t1 in t2t1s]\n elif isinstance(other_seg, CubicBezier):\n t2t1s = bezier_by_line_intersections(other_seg, self)\n return [(t1, t2) for t2, t1 in t2t1s]\n elif isinstance(other_seg, Arc):\n t2t1s = other_seg.intersect(self)\n return [(t1, t2) for t2, t1 in t2t1s]\n elif isinstance(other_seg, Path):\n raise TypeError(\n \"other_seg must be a path segment, not a Path object, use \"\n \"Path.intersect().\")\n else:\n raise TypeError(\"other_seg must be a path segment.\")\n\n def bbox(self):\n \"\"\"returns the bounding box for the segment in the form\n (xmin, xmax, ymin, ymax).\"\"\"\n xmin = min(self.start.real, self.end.real)\n xmax = max(self.start.real, self.end.real)\n ymin = min(self.start.imag, self.end.imag)\n ymax = max(self.start.imag, self.end.imag)\n return xmin, xmax, ymin, ymax\n\n def point_to_t(self, point):\n \"\"\"If the point lies on the Line, returns its `t` parameter.\n If the point does not lie on the Line, returns None.\"\"\"\n\n # Single-precision floats have only 7 significant figures of\n # resolution, so test that we're within 6 sig figs.\n if np.isclose(point, self.start, rtol=0, atol=1e-6):\n return 0.0\n elif np.isclose(point, self.end, rtol=0, atol=1e-6):\n return 1.0\n\n # Finding the point \"by hand\" here is much faster than calling\n # radialrange(), see the discussion on PR #40:\n # https://github.com/mathandy/svgpathtools/pull/40#issuecomment-358134261\n\n p = self.poly()\n # p(t) = (p_1 * t) + p_0 = point\n # t = (point - p_0) / p_1\n t = (point - p[0]) / p[1]\n if np.isclose(t.imag, 0) and (t.real >= 0.0) and (t.real <= 1.0):\n return t.real\n return None\n\n def cropped(self, t0, t1):\n \"\"\"returns a cropped copy of this segment which starts at\n self.point(t0) and ends at self.point(t1).\"\"\"\n return Line(self.point(t0), self.point(t1))\n\n def split(self, t):\n \"\"\"returns two segments, whose union is this segment and which join at\n self.point(t).\"\"\"\n pt = self.point(t)\n return Line(self.start, pt), Line(pt, self.end)\n\n def radialrange(self, origin, return_all_global_extrema=False):\n \"\"\"returns the tuples (d_min, t_min) and (d_max, t_max) which minimize\n and maximize, respectively, the distance d = |self.point(t)-origin|.\"\"\"\n return bezier_radialrange(self, origin,\n return_all_global_extrema=return_all_global_extrema)\n\n def rotated(self, degs, origin=None):\n \"\"\"Returns a copy of self rotated by `degs` degrees (CCW) around the\n point `origin` (a complex number). By default `origin` is either\n `self.point(0.5)`, or in the case that self is an Arc object,\n `origin` defaults to `self.center`.\"\"\"\n return rotate(self, degs, origin=origin)\n\n def translated(self, z0):\n \"\"\"Returns a copy of self shifted by the complex quantity `z0` such\n that self.translated(z0).point(t) = self.point(t) + z0 for any t.\"\"\"\n return translate(self, z0)\n\n def scaled(self, sx, sy=None, origin=0j):\n \"\"\"Scale transform. See `scale` function for further explanation.\"\"\"\n return scale(self, sx=sx, sy=sy, origin=origin)\n\n\nclass QuadraticBezier(object):\n # For compatibility with old pickle files.\n _length_info = {'length': None, 'bpoints': None}\n\n def __init__(self, start, control, end):\n self.start = start\n self.end = end\n self.control = control\n\n # used to know if self._length needs to be updated\n self._length_info = {'length': None, 'bpoints': None}\n\n def __repr__(self):\n return 'QuadraticBezier(start=%s, control=%s, end=%s)' % (\n self.start, self.control, self.end)\n\n def __eq__(self, other):\n if not isinstance(other, QuadraticBezier):\n return NotImplemented\n return self.start == other.start and self.end == other.end \\\n and self.control == other.control\n\n def __ne__(self, other):\n if not isinstance(other, QuadraticBezier):\n return NotImplemented\n return not self == other\n\n def __getitem__(self, item):\n return self.bpoints()[item]\n\n def __len__(self):\n return 3\n\n def is_smooth_from(self, previous, warning_on=True):\n \"\"\"[Warning: The name of this method is somewhat misleading (yet kept\n for compatibility with scripts created using svg.path 2.0). This\n method is meant only for d string creation and should not be used to\n check for kinks. To check a segment for differentiability, use the\n joins_smoothly_with() method instead.]\"\"\"\n if warning_on:\n warn(_is_smooth_from_warning)\n if isinstance(previous, QuadraticBezier):\n return (self.start == previous.end and\n (self.control - self.start) == (\n previous.end - previous.control))\n else:\n return self.control == self.start\n\n def joins_smoothly_with(self, previous, wrt_parameterization=False,\n error=0):\n \"\"\"Checks if this segment joins smoothly with previous segment. By\n default, this only checks that this segment starts moving (at t=0) in\n the same direction (and from the same positive) as previous stopped\n moving (at t=1). To check if the tangent magnitudes also match, set\n wrt_parameterization=True.\"\"\"\n if wrt_parameterization:\n return self.start == previous.end and abs(\n self.derivative(0) - previous.derivative(1)) <= error\n else:\n return self.start == previous.end and abs(\n self.unit_tangent(0) - previous.unit_tangent(1)) <= error\n\n def point(self, t):\n \"\"\"returns the coordinates of the Bezier curve evaluated at t.\"\"\"\n return (1 - t)**2*self.start + 2*(1 - t)*t*self.control + t**2*self.end\n\n def length(self, t0=0, t1=1, error=None, min_depth=None):\n if t0 == 1 and t1 == 0:\n if self._length_info['bpoints'] == self.bpoints():\n return self._length_info['length']\n a = self.start - 2*self.control + self.end\n b = 2*(self.control - self.start)\n a_dot_b = a.real*b.real + a.imag*b.imag\n\n if abs(a) < 1e-12:\n s = abs(b)*(t1 - t0)\n elif abs(a_dot_b + abs(a)*abs(b)) < 1e-12:\n tstar = abs(b)/(2*abs(a))\n if t1 < tstar:\n return abs(a)*(t0**2 - t1**2) - abs(b)*(t0 - t1)\n elif tstar < t0:\n return abs(a)*(t1**2 - t0**2) - abs(b)*(t1 - t0)\n else:\n return abs(a)*(t1**2 + t0**2) - abs(b)*(t1 + t0) + \\\n abs(b)**2/(2*abs(a))\n else:\n c2 = 4*(a.real**2 + a.imag**2)\n c1 = 4*a_dot_b\n c0 = b.real**2 + b.imag**2\n\n beta = c1/(2*c2)\n gamma = c0/c2 - beta**2\n\n dq1_mag = sqrt(c2*t1**2 + c1*t1 + c0)\n dq0_mag = sqrt(c2*t0**2 + c1*t0 + c0)\n logarand = (sqrt(c2)*(t1 + beta) + dq1_mag) / \\\n (sqrt(c2)*(t0 + beta) + dq0_mag)\n\n s = (t1 + beta)*dq1_mag - (t0 + beta)*dq0_mag + \\\n gamma*sqrt(c2)*log(logarand)\n s /= 2\n\n if t0 == 1 and t1 == 0:\n self._length_info['length'] = s\n self._length_info['bpoints'] = self.bpoints()\n return self._length_info['length']\n else:\n return s\n\n def ilength(self, s, s_tol=ILENGTH_S_TOL, maxits=ILENGTH_MAXITS,\n error=ILENGTH_ERROR, min_depth=ILENGTH_MIN_DEPTH):\n \"\"\"Returns a float, t, such that self.length(0, t) is approximately s.\n See the inv_arclength() docstring for more details.\"\"\"\n return inv_arclength(self, s, s_tol=s_tol, maxits=maxits, error=error,\n min_depth=min_depth)\n\n def bpoints(self):\n \"\"\"returns the Bezier control points of the segment.\"\"\"\n return self.start, self.control, self.end\n\n def poly(self, return_coeffs=False):\n \"\"\"returns the quadratic as a Polynomial object.\"\"\"\n p = self.bpoints()\n coeffs = (p[0] - 2*p[1] + p[2], 2*(p[1] - p[0]), p[0])\n if return_coeffs:\n return coeffs\n else:\n return np.poly1d(coeffs)\n\n def derivative(self, t, n=1):\n \"\"\"returns the nth derivative of the segment at t.\n Note: Bezier curves can have points where their derivative vanishes.\n If you are interested in the tangent direction, use the unit_tangent()\n method instead.\"\"\"\n p = self.bpoints()\n if n == 1:\n return 2*((p[1] - p[0])*(1 - t) + (p[2] - p[1])*t)\n elif n == 2:\n return 2*(p[2] - 2*p[1] + p[0])\n elif n > 2:\n return 0\n else:\n raise ValueError(\"n should be a positive integer.\")\n\n def unit_tangent(self, t):\n \"\"\"returns the unit tangent vector of the segment at t (centered at\n the origin and expressed as a complex number). If the tangent\n vector's magnitude is zero, this method will find the limit of\n self.derivative(tau)/abs(self.derivative(tau)) as tau approaches t.\"\"\"\n return bezier_unit_tangent(self, t)\n\n def normal(self, t):\n \"\"\"returns the (right hand rule) unit normal vector to self at t.\"\"\"\n return -1j*self.unit_tangent(t)\n\n def curvature(self, t):\n \"\"\"returns the curvature of the segment at t.\"\"\"\n return segment_curvature(self, t)\n\n # def icurvature(self, kappa):\n # \"\"\"returns a list of t-values such that 0 <= t<= 1 and\n # seg.curvature(t) = kappa.\"\"\"\n # z = self.poly()\n # x, y = real(z), imag(z)\n # dx, dy = x.deriv(), y.deriv()\n # ddx, ddy = dx.deriv(), dy.deriv()\n #\n # p = kappa**2*(dx**2 + dy**2)**3 - (dx*ddy - ddx*dy)**2\n # return polyroots01(p)\n\n def reversed(self):\n \"\"\"returns a copy of the QuadraticBezier object with its orientation\n reversed.\"\"\"\n new_quad = QuadraticBezier(self.end, self.control, self.start)\n if self._length_info['length']:\n new_quad._length_info = self._length_info\n new_quad._length_info['bpoints'] = (\n self.end, self.control, self.start)\n return new_quad\n\n def intersect(self, other_seg, tol=1e-12):\n \"\"\"Finds the intersections of two segments.\n returns a list of tuples (t1, t2) such that\n self.point(t1) == other_seg.point(t2).\n Note: This will fail if the two segments coincide for more than a\n finite collection of points.\"\"\"\n if isinstance(other_seg, Line):\n return bezier_by_line_intersections(self, other_seg)\n elif isinstance(other_seg, QuadraticBezier):\n assert self != other_seg\n longer_length = max(self.length(), other_seg.length())\n return bezier_intersections(self, other_seg,\n longer_length=longer_length,\n tol=tol, tol_deC=tol)\n elif isinstance(other_seg, CubicBezier):\n longer_length = max(self.length(), other_seg.length())\n return bezier_intersections(self, other_seg,\n longer_length=longer_length,\n tol=tol, tol_deC=tol)\n elif isinstance(other_seg, Arc):\n t2t1s = other_seg.intersect(self)\n return [(t1, t2) for t2, t1 in t2t1s]\n elif isinstance(other_seg, Path):\n raise TypeError(\n \"other_seg must be a path segment, not a Path object, use \"\n \"Path.intersect().\")\n else:\n raise TypeError(\"other_seg must be a path segment.\")\n\n def bbox(self):\n \"\"\"returns the bounding box for the segment in the form\n (xmin, xmax, ymin, ymax).\"\"\"\n return bezier_bounding_box(self)\n\n def split(self, t):\n \"\"\"returns two segments, whose union is this segment and which join at\n self.point(t).\"\"\"\n bpoints1, bpoints2 = split_bezier(self.bpoints(), t)\n return QuadraticBezier(*bpoints1), QuadraticBezier(*bpoints2)\n\n def cropped(self, t0, t1):\n \"\"\"returns a cropped copy of this segment which starts at\n self.point(t0) and ends at self.point(t1).\"\"\"\n return QuadraticBezier(*crop_bezier(self, t0, t1))\n\n def radialrange(self, origin, return_all_global_extrema=False):\n \"\"\"returns the tuples (d_min, t_min) and (d_max, t_max) which minimize\n and maximize, respectively, the distance d = |self.point(t)-origin|.\"\"\"\n return bezier_radialrange(self, origin,\n return_all_global_extrema=return_all_global_extrema)\n\n def rotated(self, degs, origin=None):\n \"\"\"Returns a copy of self rotated by `degs` degrees (CCW) around the\n point `origin` (a complex number). By default `origin` is either\n `self.point(0.5)`, or in the case that self is an Arc object,\n `origin` defaults to `self.center`.\"\"\"\n return rotate(self, degs, origin=origin)\n\n def translated(self, z0):\n \"\"\"Returns a copy of self shifted by the complex quantity `z0` such\n that self.translated(z0).point(t) = self.point(t) + z0 for any t.\"\"\"\n return translate(self, z0)\n\n def scaled(self, sx, sy=None, origin=0j):\n \"\"\"Scale transform. See `scale` function for further explanation.\"\"\"\n return scale(self, sx=sx, sy=sy, origin=origin)\n\n\nclass CubicBezier(object):\n # For compatibility with old pickle files.\n _length_info = {'length': None, 'bpoints': None, 'error': None,\n 'min_depth': None}\n\n def __init__(self, start, control1, control2, end):\n self.start = start\n self.control1 = control1\n self.control2 = control2\n self.end = end\n\n # used to know if self._length needs to be updated\n self._length_info = {'length': None, 'bpoints': None, 'error': None,\n 'min_depth': None}\n\n def __repr__(self):\n return 'CubicBezier(start=%s, control1=%s, control2=%s, end=%s)' % (\n self.start, self.control1, self.control2, self.end)\n\n def __eq__(self, other):\n if not isinstance(other, CubicBezier):\n return NotImplemented\n return self.start == other.start and self.end == other.end \\\n and self.control1 == other.control1 \\\n and self.control2 == other.control2\n\n def __ne__(self, other):\n if not isinstance(other, CubicBezier):\n return NotImplemented\n return not self == other\n\n def __getitem__(self, item):\n return self.bpoints()[item]\n\n def __len__(self):\n return 4\n\n def is_smooth_from(self, previous, warning_on=True):\n \"\"\"[Warning: The name of this method is somewhat misleading (yet kept\n for compatibility with scripts created using svg.path 2.0). This\n method is meant only for d string creation and should not be used to\n check for kinks. To check a segment for differentiability, use the\n joins_smoothly_with() method instead.]\"\"\"\n if warning_on:\n warn(_is_smooth_from_warning)\n if isinstance(previous, CubicBezier):\n return (self.start == previous.end and\n (self.control1 - self.start) == (\n previous.end - previous.control2))\n else:\n return self.control1 == self.start\n\n def joins_smoothly_with(self, previous, wrt_parameterization=False):\n \"\"\"Checks if this segment joins smoothly with previous segment. By\n default, this only checks that this segment starts moving (at t=0) in\n the same direction (and from the same positive) as previous stopped\n moving (at t=1). To check if the tangent magnitudes also match, set\n wrt_parameterization=True.\"\"\"\n if wrt_parameterization:\n return self.start == previous.end and np.isclose(\n self.derivative(0), previous.derivative(1))\n else:\n return self.start == previous.end and np.isclose(\n self.unit_tangent(0), previous.unit_tangent(1))\n\n def point(self, t):\n \"\"\"Evaluate the cubic Bezier curve at t using Horner's rule.\"\"\"\n # algebraically equivalent to\n # P0*(1-t)**3 + 3*P1*t*(1-t)**2 + 3*P2*(1-t)*t**2 + P3*t**3\n # for (P0, P1, P2, P3) = self.bpoints()\n return self.start + t*(\n 3*(self.control1 - self.start) + t*(\n 3*(self.start + self.control2) - 6*self.control1 + t*(\n -self.start + 3*(self.control1 - self.control2) + self.end\n )))\n\n def length(self, t0=0, t1=1, error=LENGTH_ERROR, min_depth=LENGTH_MIN_DEPTH):\n \"\"\"Calculate the length of the path up to a certain position\"\"\"\n if t0 == 0 and t1 == 1:\n if self._length_info['bpoints'] == self.bpoints() \\\n and self._length_info['error'] >= error \\\n and self._length_info['min_depth'] >= min_depth:\n return self._length_info['length']\n\n # using scipy.integrate.quad is quick\n if _quad_available:\n s = quad(lambda tau: abs(self.derivative(tau)), t0, t1,\n epsabs=error, limit=1000)[0]\n else:\n s = segment_length(self, t0, t1, self.point(t0), self.point(t1),\n error, min_depth, 0)\n\n if t0 == 0 and t1 == 1:\n self._length_info['length'] = s\n self._length_info['bpoints'] = self.bpoints()\n self._length_info['error'] = error\n self._length_info['min_depth'] = min_depth\n return self._length_info['length']\n else:\n return s\n\n def ilength(self, s, s_tol=ILENGTH_S_TOL, maxits=ILENGTH_MAXITS,\n error=ILENGTH_ERROR, min_depth=ILENGTH_MIN_DEPTH):\n \"\"\"Returns a float, t, such that self.length(0, t) is approximately s.\n See the inv_arclength() docstring for more details.\"\"\"\n return inv_arclength(self, s, s_tol=s_tol, maxits=maxits, error=error,\n min_depth=min_depth)\n\n def bpoints(self):\n \"\"\"returns the Bezier control points of the segment.\"\"\"\n return self.start, self.control1, self.control2, self.end\n\n def poly(self, return_coeffs=False):\n \"\"\"Returns a the cubic as a Polynomial object.\"\"\"\n p = self.bpoints()\n coeffs = (-p[0] + 3*(p[1] - p[2]) + p[3],\n 3*(p[0] - 2*p[1] + p[2]),\n 3*(-p[0] + p[1]),\n p[0])\n if return_coeffs:\n return coeffs\n else:\n return np.poly1d(coeffs)\n\n def derivative(self, t, n=1):\n \"\"\"returns the nth derivative of the segment at t.\n Note: Bezier curves can have points where their derivative vanishes.\n If you are interested in the tangent direction, use the unit_tangent()\n method instead.\"\"\"\n p = self.bpoints()\n if n == 1:\n return 3*(p[1] - p[0])*(1 - t)**2 + 6*(p[2] - p[1])*(1 - t)*t + 3*(\n p[3] - p[2])*t**2\n elif n == 2:\n return 6*(\n (1 - t)*(p[2] - 2*p[1] + p[0]) + t*(p[3] - 2*p[2] + p[1]))\n elif n == 3:\n return 6*(p[3] - 3*(p[2] - p[1]) - p[0])\n elif n > 3:\n return 0\n else:\n raise ValueError(\"n should be a positive integer.\")\n\n def unit_tangent(self, t):\n \"\"\"returns the unit tangent vector of the segment at t (centered at\n the origin and expressed as a complex number). If the tangent\n vector's magnitude is zero, this method will find the limit of\n self.derivative(tau)/abs(self.derivative(tau)) as tau approaches t.\"\"\"\n return bezier_unit_tangent(self, t)\n\n def normal(self, t):\n \"\"\"returns the (right hand rule) unit normal vector to self at t.\"\"\"\n return -1j * self.unit_tangent(t)\n\n def curvature(self, t):\n \"\"\"returns the curvature of the segment at t.\"\"\"\n return segment_curvature(self, t)\n\n # def icurvature(self, kappa):\n # \"\"\"returns a list of t-values such that 0 <= t<= 1 and\n # seg.curvature(t) = kappa.\"\"\"\n # z = self.poly()\n # x, y = real(z), imag(z)\n # dx, dy = x.deriv(), y.deriv()\n # ddx, ddy = dx.deriv(), dy.deriv()\n #\n # p = kappa**2*(dx**2 + dy**2)**3 - (dx*ddy - ddx*dy)**2\n # return polyroots01(p)\n\n def reversed(self):\n \"\"\"returns a copy of the CubicBezier object with its orientation\n reversed.\"\"\"\n new_cub = CubicBezier(self.end, self.control2, self.control1,\n self.start)\n if self._length_info['length']:\n new_cub._length_info = self._length_info\n new_cub._length_info['bpoints'] = (\n self.end, self.control2, self.control1, self.start)\n return new_cub\n\n def intersect(self, other_seg, tol=1e-12):\n \"\"\"Finds the intersections of two segments.\n returns a list of tuples (t1, t2) such that\n self.point(t1) == other_seg.point(t2).\n Note: This will fail if the two segments coincide for more than a\n finite collection of points.\"\"\"\n if isinstance(other_seg, Line):\n return bezier_by_line_intersections(self, other_seg)\n elif (isinstance(other_seg, QuadraticBezier) or\n isinstance(other_seg, CubicBezier)):\n assert self != other_seg\n longer_length = max(self.length(), other_seg.length())\n return bezier_intersections(self, other_seg,\n longer_length=longer_length,\n tol=tol, tol_deC=tol)\n elif isinstance(other_seg, Arc):\n t2t1s = other_seg.intersect(self)\n return [(t1, t2) for t2, t1 in t2t1s]\n elif isinstance(other_seg, Path):\n raise TypeError(\n \"other_seg must be a path segment, not a Path object, use \"\n \"Path.intersect().\")\n else:\n raise TypeError(\"other_seg must be a path segment.\")\n\n def bbox(self):\n \"\"\"returns the bounding box for the segment in the form\n (xmin, xmax, ymin, ymax).\"\"\"\n return bezier_bounding_box(self)\n\n def split(self, t):\n \"\"\"returns two segments, whose union is this segment and which join at\n self.point(t).\"\"\"\n bpoints1, bpoints2 = split_bezier(self.bpoints(), t)\n return CubicBezier(*bpoints1), CubicBezier(*bpoints2)\n\n def cropped(self, t0, t1):\n \"\"\"returns a cropped copy of this segment which starts at\n self.point(t0) and ends at self.point(t1).\"\"\"\n return CubicBezier(*crop_bezier(self, t0, t1))\n\n def radialrange(self, origin, return_all_global_extrema=False):\n \"\"\"returns the tuples (d_min, t_min) and (d_max, t_max) which minimize\n and maximize, respectively, the distance d = |self.point(t)-origin|.\"\"\"\n return bezier_radialrange(self, origin,\n return_all_global_extrema=return_all_global_extrema)\n\n def rotated(self, degs, origin=None):\n \"\"\"Returns a copy of self rotated by `degs` degrees (CCW) around the\n point `origin` (a complex number). By default `origin` is either\n `self.point(0.5)`, or in the case that self is an Arc object,\n `origin` defaults to `self.center`.\"\"\"\n return rotate(self, degs, origin=origin)\n\n def translated(self, z0):\n \"\"\"Returns a copy of self shifted by the complex quantity `z0` such\n that self.translated(z0).point(t) = self.point(t) + z0 for any t.\"\"\"\n return translate(self, z0)\n\n def scaled(self, sx, sy=None, origin=0j):\n \"\"\"Scale transform. See `scale` function for further explanation.\"\"\"\n return scale(self, sx=sx, sy=sy, origin=origin)\n\n\nclass Arc(object):\n def __init__(self, start, radius, rotation, large_arc, sweep, end,\n autoscale_radius=True):\n \"\"\"\n This should be thought of as a part of an ellipse connecting two\n points on that ellipse, start and end.\n Parameters\n ----------\n start : complex\n The start point of the curve. Note: `start` and `end` cannot be the\n same. To make a full ellipse or circle, use two `Arc` objects.\n radius : complex\n rx + 1j*ry, where rx and ry are the radii of the ellipse (also\n known as its semi-major and semi-minor axes, or vice-versa or if\n rx < ry).\n Note: If rx = 0 or ry = 0 then this arc is treated as a\n straight line segment joining the endpoints.\n Note: If rx or ry has a negative sign, the sign is dropped; the\n absolute value is used instead.\n Note: If no such ellipse exists, the radius will be scaled so\n that one does (unless autoscale_radius is set to False).\n rotation : float\n This is the CCW angle (in degrees) from the positive x-axis of the\n current coordinate system to the x-axis of the ellipse.\n large_arc : bool\n Given two points on an ellipse, there are two elliptical arcs\n connecting those points, the first going the short way around the\n ellipse, and the second going the long way around the ellipse. If\n `large_arc == False`, the shorter elliptical arc will be used. If\n `large_arc == True`, then longer elliptical will be used.\n In other words, `large_arc` should be 0 for arcs spanning less than\n or equal to 180 degrees and 1 for arcs spanning greater than 180\n degrees.\n sweep : bool\n For any acceptable parameters `start`, `end`, `rotation`, and\n `radius`, there are two ellipses with the given major and minor\n axes (radii) which connect `start` and `end`. One which connects\n them in a CCW fashion and one which connected them in a CW\n fashion. If `sweep == True`, the CCW ellipse will be used. If\n `sweep == False`, the CW ellipse will be used. See note on curve\n orientation below.\n end : complex\n The end point of the curve. Note: `start` and `end` cannot be the\n same. To make a full ellipse or circle, use two `Arc` objects.\n autoscale_radius : bool\n If `autoscale_radius == True`, then will also scale `self.radius`\n in the case that no ellipse exists with the input parameters\n (see inline comments for further explanation).\n\n Derived Parameters/Attributes\n -----------------------------\n self.theta : float\n This is the phase (in degrees) of self.u1transform(self.start).\n It is $\\theta_1$ in the official documentation and ranges from\n -180 to 180.\n self.delta : float\n This is the angular distance (in degrees) between the start and\n end of the arc after the arc has been sent to the unit circle\n through self.u1transform().\n It is $\\Delta\\theta$ in the official documentation and ranges from\n -360 to 360; being positive when the arc travels CCW and negative\n otherwise (i.e. is positive/negative when sweep == True/False).\n self.center : complex\n This is the center of the arc's ellipse.\n self.phi : float\n The arc's rotation in radians, i.e. `radians(self.rotation)`.\n self.rot_matrix : complex\n Equal to `exp(1j * self.phi)` which is also equal to\n `cos(self.phi) + 1j*sin(self.phi)`.\n\n\n Note on curve orientation (CW vs CCW)\n -------------------------------------\n The notions of clockwise (CW) and counter-clockwise (CCW) are reversed\n in some sense when viewing SVGs (as the y coordinate starts at the top\n of the image and increases towards the bottom).\n \"\"\"\n assert start != end\n assert radius.real != 0 and radius.imag != 0\n\n self.start = start\n self.radius = abs(radius.real) + 1j*abs(radius.imag)\n self.rotation = rotation\n self.large_arc = bool(large_arc)\n self.sweep = bool(sweep)\n self.end = end\n self.autoscale_radius = autoscale_radius\n\n # Convenience parameters\n self.phi = radians(self.rotation)\n self.rot_matrix = exp(1j*self.phi)\n\n # Derive derived parameters\n self._parameterize()\n\n def __repr__(self):\n params = (self.start, self.radius, self.rotation,\n self.large_arc, self.sweep, self.end)\n return (\"Arc(start={}, radius={}, rotation={}, \"\n \"large_arc={}, sweep={}, end={})\".format(*params))\n\n def __eq__(self, other):\n if not isinstance(other, Arc):\n return NotImplemented\n return self.start == other.start and self.end == other.end \\\n and self.radius == other.radius \\\n and self.rotation == other.rotation \\\n and self.large_arc == other.large_arc and self.sweep == other.sweep\n\n def __ne__(self, other):\n if not isinstance(other, Arc):\n return NotImplemented\n return not self == other\n\n def _parameterize(self):\n # See http://www.w3.org/TR/SVG/implnote.html#ArcImplementationNotes\n # my notation roughly follows theirs\n rx = self.radius.real\n ry = self.radius.imag\n rx_sqd = rx*rx\n ry_sqd = ry*ry\n\n # Transform z-> z' = x' + 1j*y'\n # = self.rot_matrix**(-1)*(z - (end+start)/2)\n # coordinates. This translates the ellipse so that the midpoint\n # between self.end and self.start lies on the origin and rotates\n # the ellipse so that the its axes align with the xy-coordinate axes.\n # Note: This sends self.end to -self.start\n zp1 = (1/self.rot_matrix)*(self.start - self.end)/2\n x1p, y1p = zp1.real, zp1.imag\n x1p_sqd = x1p*x1p\n y1p_sqd = y1p*y1p\n\n # Correct out of range radii\n # Note: an ellipse going through start and end with radius and phi\n # exists if and only if radius_check is true\n radius_check = (x1p_sqd/rx_sqd) + (y1p_sqd/ry_sqd)\n if radius_check > 1:\n if self.autoscale_radius:\n rx *= sqrt(radius_check)\n ry *= sqrt(radius_check)\n self.radius = rx + 1j*ry\n rx_sqd = rx*rx\n ry_sqd = ry*ry\n else:\n raise ValueError(\"No such elliptic arc exists.\")\n\n # Compute c'=(c_x', c_y'), the center of the ellipse in (x', y') coords\n # Noting that, in our new coord system, (x_2', y_2') = (-x_1', -x_2')\n # and our ellipse is cut out by of the plane by the algebraic equation\n # (x'-c_x')**2 / r_x**2 + (y'-c_y')**2 / r_y**2 = 1,\n # we can find c' by solving the system of two quadratics given by\n # plugging our transformed endpoints (x_1', y_1') and (x_2', y_2')\n tmp = rx_sqd*y1p_sqd + ry_sqd*x1p_sqd\n radicand = (rx_sqd*ry_sqd - tmp) / tmp\n try:\n radical = sqrt(radicand)\n except ValueError:\n radical = 0\n if self.large_arc == self.sweep:\n cp = -radical*(rx*y1p/ry - 1j*ry*x1p/rx)\n else:\n cp = radical*(rx*y1p/ry - 1j*ry*x1p/rx)\n\n # The center in (x,y) coordinates is easy to find knowing c'\n self.center = exp(1j*self.phi)*cp + (self.start + self.end)/2\n\n # Now we do a second transformation, from (x', y') to (u_x, u_y)\n # coordinates, which is a translation moving the center of the\n # ellipse to the origin and a dilation stretching the ellipse to be\n # the unit circle\n u1 = (x1p - cp.real)/rx + 1j*(y1p - cp.imag)/ry # transformed start\n u2 = (-x1p - cp.real)/rx + 1j*(-y1p - cp.imag)/ry # transformed end\n\n # clip in case of floating point error\n u1 = np.clip(u1.real, -1, 1) + 1j*np.clip(u1.imag, -1, 1)\n u2 = np.clip(u2.real, -1, 1) + 1j * np.clip(u2.imag, -1, 1)\n\n # Now compute theta and delta (we'll define them as we go)\n # delta is the angular distance of the arc (w.r.t the circle)\n # theta is the angle between the positive x'-axis and the start point\n # on the circle\n if u1.imag > 0:\n self.theta = degrees(acos(u1.real))\n elif u1.imag < 0:\n self.theta = -degrees(acos(u1.real))\n else:\n if u1.real > 0: # start is on pos u_x axis\n self.theta = 0\n else: # start is on neg u_x axis\n # Note: This behavior disagrees with behavior documented in\n # http://www.w3.org/TR/SVG/implnote.html#ArcImplementationNotes\n # where theta is set to 0 in this case.\n self.theta = 180\n\n det_uv = u1.real*u2.imag - u1.imag*u2.real\n\n acosand = u1.real*u2.real + u1.imag*u2.imag\n acosand = np.clip(acosand.real, -1, 1) + np.clip(acosand.imag, -1, 1)\n \n if det_uv > 0:\n self.delta = degrees(acos(acosand))\n elif det_uv < 0:\n self.delta = -degrees(acos(acosand))\n else:\n if u1.real*u2.real + u1.imag*u2.imag > 0:\n # u1 == u2\n self.delta = 0\n else:\n # u1 == -u2\n # Note: This behavior disagrees with behavior documented in\n # http://www.w3.org/TR/SVG/implnote.html#ArcImplementationNotes\n # where delta is set to 0 in this case.\n self.delta = 180\n\n if not self.sweep and self.delta >= 0:\n self.delta -= 360\n elif self.large_arc and self.delta <= 0:\n self.delta += 360\n\n def point(self, t):\n if t == 0:\n return self.start\n if t == 1:\n return self.end\n angle = radians(self.theta + t*self.delta)\n cosphi = self.rot_matrix.real\n sinphi = self.rot_matrix.imag\n rx = self.radius.real\n ry = self.radius.imag\n\n # z = self.rot_matrix*(rx*cos(angle) + 1j*ry*sin(angle)) + self.center\n x = rx*cosphi*cos(angle) - ry*sinphi*sin(angle) + self.center.real\n y = rx*sinphi*cos(angle) + ry*cosphi*sin(angle) + self.center.imag\n return complex(x, y)\n\n def point_to_t(self, point):\n \"\"\"If the point lies on the Arc, returns its `t` parameter.\n If the point does not lie on the Arc, returns None.\n This function only works on Arcs with rotation == 0.0\"\"\"\n\n def in_range(min, max, val):\n return (min <= val) and (max >= val)\n\n # Single-precision floats have only 7 significant figures of\n # resolution, so test that we're within 6 sig figs.\n if np.isclose(point, self.start, rtol=0.0, atol=1e-6):\n return 0.0\n elif np.isclose(point, self.end, rtol=0.0, atol=1e-6):\n return 1.0\n\n if self.rotation != 0.0:\n raise ValueError(\"Arc.point_to_t() only works on non-rotated Arcs.\")\n\n v = point - self.center\n distance_from_center = sqrt((v.real * v.real) + (v.imag * v.imag))\n min_radius = min(self.radius.real, self.radius.imag)\n max_radius = max(self.radius.real, self.radius.imag)\n if (distance_from_center < min_radius) and not np.isclose(distance_from_center, min_radius):\n return None\n if (distance_from_center > max_radius) and not np.isclose(distance_from_center, max_radius):\n return None\n\n # x = center_x + radius_x cos(radians(theta + t delta))\n # y = center_y + radius_y sin(radians(theta + t delta))\n #\n # For x:\n # cos(radians(theta + t delta)) = (x - center_x) / radius_x\n # radians(theta + t delta) = acos((x - center_x) / radius_x)\n # theta + t delta = degrees(acos((x - center_x) / radius_x))\n # t_x = (degrees(acos((x - center_x) / radius_x)) - theta) / delta\n #\n # Similarly for y:\n # t_y = (degrees(asin((y - center_y) / radius_y)) - theta) / delta\n\n x = point.real\n y = point.imag\n\n #\n # +Y points down!\n #\n # sweep mean clocwise\n # sweep && (delta > 0)\n # !sweep && (delta < 0)\n #\n # -180 <= theta_1 <= 180\n #\n # large_arc && (-360 <= delta <= 360)\n # !large_arc && (-180 < delta < 180)\n #\n\n end_angle = self.theta + self.delta\n min_angle = min(self.theta, end_angle)\n max_angle = max(self.theta, end_angle)\n\n acos_arg = (x - self.center.real) / self.radius.real\n if acos_arg > 1.0:\n acos_arg = 1.0\n elif acos_arg < -1.0:\n acos_arg = -1.0\n\n x_angle_0 = degrees(acos(acos_arg))\n while x_angle_0 < min_angle:\n x_angle_0 += 360.0\n while x_angle_0 > max_angle:\n x_angle_0 -= 360.0\n\n x_angle_1 = -1.0 * x_angle_0\n while x_angle_1 < min_angle:\n x_angle_1 += 360.0\n while x_angle_1 > max_angle:\n x_angle_1 -= 360.0\n\n t_x_0 = (x_angle_0 - self.theta) / self.delta\n t_x_1 = (x_angle_1 - self.theta) / self.delta\n\n asin_arg = (y - self.center.imag) / self.radius.imag\n if asin_arg > 1.0:\n asin_arg = 1.0\n elif asin_arg < -1.0:\n asin_arg = -1.0\n\n y_angle_0 = degrees(asin(asin_arg))\n while y_angle_0 < min_angle:\n y_angle_0 += 360.0\n while y_angle_0 > max_angle:\n y_angle_0 -= 360.0\n\n y_angle_1 = 180 - y_angle_0\n while y_angle_1 < min_angle:\n y_angle_1 += 360.0\n while y_angle_1 > max_angle:\n y_angle_1 -= 360.0\n\n t_y_0 = (y_angle_0 - self.theta) / self.delta\n t_y_1 = (y_angle_1 - self.theta) / self.delta\n\n t = None\n if np.isclose(t_x_0, t_y_0):\n t = (t_x_0 + t_y_0) / 2.0\n elif np.isclose(t_x_0, t_y_1):\n t= (t_x_0 + t_y_1) / 2.0\n elif np.isclose(t_x_1, t_y_0):\n t = (t_x_1 + t_y_0) / 2.0\n elif np.isclose(t_x_1, t_y_1):\n t = (t_x_1 + t_y_1) / 2.0\n else:\n # Comparing None and float yields a result in python2,\n # but throws TypeError in python3. This fix (suggested by\n # @CatherineH) explicitly handles and avoids the case where\n # the None-vs-float comparison would have happened below.\n return None\n\n if (t >= 0.0) and (t <= 1.0):\n return t\n\n return None\n\n def centeriso(self, z):\n \"\"\"This is an isometry that translates and rotates self so that it\n is centered on the origin and has its axes aligned with the xy axes.\"\"\"\n return (1/self.rot_matrix)*(z - self.center)\n\n def icenteriso(self, zeta):\n \"\"\"This is an isometry, the inverse of standardiso().\"\"\"\n return self.rot_matrix*zeta + self.center\n\n def u1transform(self, z):\n \"\"\"This is an affine transformation (same as used in\n self._parameterize()) that sends self to the unit circle.\"\"\"\n zeta = (1/self.rot_matrix)*(z - self.center) # same as centeriso(z)\n x, y = real(zeta), imag(zeta)\n return x/self.radius.real + 1j*y/self.radius.imag\n\n def iu1transform(self, zeta):\n \"\"\"This is an affine transformation, the inverse of\n self.u1transform().\"\"\"\n x = real(zeta)\n y = imag(zeta)\n z = x*self.radius.real + y*self.radius.imag\n return self.rot_matrix*z + self.center\n\n def length(self, t0=0, t1=1, error=LENGTH_ERROR, min_depth=LENGTH_MIN_DEPTH):\n \"\"\"The length of an elliptical large_arc segment requires numerical\n integration, and in that case it's simpler to just do a geometric\n approximation, as for cubic bezier curves.\"\"\"\n assert 0 <= t0 <= 1 and 0 <= t1 <= 1\n if _quad_available:\n return quad(lambda tau: abs(self.derivative(tau)), t0, t1,\n epsabs=error, limit=1000)[0]\n else:\n return segment_length(self, t0, t1, self.point(t0), self.point(t1),\n error, min_depth, 0)\n\n def ilength(self, s, s_tol=ILENGTH_S_TOL, maxits=ILENGTH_MAXITS,\n error=ILENGTH_ERROR, min_depth=ILENGTH_MIN_DEPTH):\n \"\"\"Returns a float, t, such that self.length(0, t) is approximately s.\n See the inv_arclength() docstring for more details.\"\"\"\n return inv_arclength(self, s, s_tol=s_tol, maxits=maxits, error=error,\n min_depth=min_depth)\n\n def joins_smoothly_with(self, previous, wrt_parameterization=False,\n error=0):\n \"\"\"Checks if this segment joins smoothly with previous segment. By\n default, this only checks that this segment starts moving (at t=0) in\n the same direction (and from the same positive) as previous stopped\n moving (at t=1). To check if the tangent magnitudes also match, set\n wrt_parameterization=True.\"\"\"\n if wrt_parameterization:\n return self.start == previous.end and abs(\n self.derivative(0) - previous.derivative(1)) <= error\n else:\n return self.start == previous.end and abs(\n self.unit_tangent(0) - previous.unit_tangent(1)) <= error\n\n def derivative(self, t, n=1):\n \"\"\"returns the nth derivative of the segment at t.\"\"\"\n angle = radians(self.theta + t*self.delta)\n phi = radians(self.rotation)\n rx = self.radius.real\n ry = self.radius.imag\n k = (self.delta*2*pi/360)**n # ((d/dt)angle)**n\n\n if n % 4 == 0 and n > 0:\n return rx*cos(phi)*cos(angle) - ry*sin(phi)*sin(angle) + 1j*(\n rx*sin(phi)*cos(angle) + ry*cos(phi)*sin(angle))\n elif n % 4 == 1:\n return k*(-rx*cos(phi)*sin(angle) - ry*sin(phi)*cos(angle) + 1j*(\n -rx*sin(phi)*sin(angle) + ry*cos(phi)*cos(angle)))\n elif n % 4 == 2:\n return k*(-rx*cos(phi)*cos(angle) + ry*sin(phi)*sin(angle) + 1j*(\n -rx*sin(phi)*cos(angle) - ry*cos(phi)*sin(angle)))\n elif n % 4 == 3:\n return k*(rx*cos(phi)*sin(angle) + ry*sin(phi)*cos(angle) + 1j*(\n rx*sin(phi)*sin(angle) - ry*cos(phi)*cos(angle)))\n else:\n raise ValueError(\"n should be a positive integer.\")\n\n def unit_tangent(self, t):\n \"\"\"returns the unit tangent vector of the segment at t (centered at\n the origin and expressed as a complex number).\"\"\"\n dseg = self.derivative(t)\n return dseg/abs(dseg)\n\n def normal(self, t):\n \"\"\"returns the (right hand rule) unit normal vector to self at t.\"\"\"\n return -1j*self.unit_tangent(t)\n\n def curvature(self, t):\n \"\"\"returns the curvature of the segment at t.\"\"\"\n return segment_curvature(self, t)\n\n # def icurvature(self, kappa):\n # \"\"\"returns a list of t-values such that 0 <= t<= 1 and\n # seg.curvature(t) = kappa.\"\"\"\n #\n # a, b = self.radius.real, self.radius.imag\n # if kappa > min(a, b)/max(a, b)**2 or kappa <= 0:\n # return []\n # if a==b:\n # if kappa != 1/a:\n # return []\n # else:\n # raise ValueError(\n # \"The .icurvature() method for Arc elements with \"\n # \"radius.real == radius.imag (i.e. circle segments) \"\n # \"will raise this exception when kappa is 1/radius.real as \"\n # \"this is true at every point on the circle segment.\")\n #\n # # kappa = a*b / (a^2sin^2(tau) + b^2cos^2(tau))^(3/2), tau=2*pi*phase\n # sin2 = np.poly1d([1, 0])\n # p = kappa**2*(a*sin2 + b*(1 - sin2))**3 - a*b\n # sin2s = polyroots01(p)\n # taus = []\n #\n # for sin2 in sin2s:\n # taus += [np.arcsin(sqrt(sin2)), np.arcsin(-sqrt(sin2))]\n #\n # # account for the other branch of arcsin\n # sgn = lambda x: x/abs(x) if x else 0\n # other_taus = [sgn(tau)*np.pi - tau for tau in taus if abs(tau) != np.pi/2]\n # taus = taus + other_taus\n #\n # # get rid of points not included in segment\n # ts = [phase2t(tau) for tau in taus]\n #\n # return [t for t in ts if 0<=t<=1]\n\n\n def reversed(self):\n \"\"\"returns a copy of the Arc object with its orientation reversed.\"\"\"\n return Arc(self.end, self.radius, self.rotation, self.large_arc,\n not self.sweep, self.start)\n\n def phase2t(self, psi):\n \"\"\"Given phase -pi < psi <= pi,\n returns the t value such that\n exp(1j*psi) = self.u1transform(self.point(t)).\n \"\"\"\n def _deg(rads, domain_lower_limit):\n # Convert rads to degrees in [0, 360) domain\n degs = degrees(rads % (2*pi))\n\n # Convert to [domain_lower_limit, domain_lower_limit + 360) domain\n k = domain_lower_limit // 360\n degs += k * 360\n if degs < domain_lower_limit:\n degs += 360\n return degs\n\n if self.delta > 0:\n degs = _deg(psi, domain_lower_limit=self.theta)\n else:\n degs = _deg(psi, domain_lower_limit=self.theta)\n return (degs - self.theta)/self.delta\n\n\n def intersect(self, other_seg, tol=1e-12):\n \"\"\"NOT FULLY IMPLEMENTED. Finds the intersections of two segments.\n returns a list of tuples (t1, t2) such that\n self.point(t1) == other_seg.point(t2).\n Note: This will fail if the two segments coincide for more than a\n finite collection of points.\n\n Note: Arc related intersections are only partially supported, i.e. are\n only half-heartedly implemented and not well tested. Please feel free\n to let me know if you're interested in such a feature -- or even better\n please submit an implementation if you want to code one.\"\"\"\n\n # This special case can be easily solved algebraically.\n if (self.rotation == 0) and isinstance(other_seg, Line):\n a = self.radius.real\n b = self.radius.imag\n\n # Ignore the ellipse's center point (to pretend that it's\n # centered at the origin), and translate the Line to match.\n l = Line(start=(other_seg.start-self.center), end=(other_seg.end-self.center))\n\n # This gives us the translated Line as a parametric equation.\n # s = p1 t + p0\n p = l.poly()\n\n if p[1].real == 0.0:\n # The `x` value doesn't depend on `t`, the line is vertical.\n c = p[0].real\n x_values = [c]\n\n # Substitute the line `x = c` into the equation for the\n # (origin-centered) ellipse.\n #\n # x^2/a^2 + y^2/b^2 = 1\n # c^2/a^2 + y^2/b^2 = 1\n # y^2/b^2 = 1 - c^2/a^2\n # y^2 = b^2(1 - c^2/a^2)\n # y = +-b sqrt(1 - c^2/a^2)\n\n discriminant = 1 - (c * c)/(a * a)\n if discriminant < 0:\n return []\n elif discriminant == 0:\n y_values = [0]\n else:\n val = b * sqrt(discriminant)\n y_values = [val, -val]\n\n else:\n # This is a non-vertical line.\n #\n # Convert the Line's parametric equation to the \"y = mx + c\" format.\n # x = p1.real t + p0.real\n # y = p1.imag t + p0.imag\n #\n # t = (x - p0.real) / p1.real\n # t = (y - p0.imag) / p1.imag\n #\n # (y - p0.imag) / p1.imag = (x - p0.real) / p1.real\n # (y - p0.imag) = ((x - p0.real) * p1.imag) / p1.real\n # y = ((x - p0.real) * p1.imag) / p1.real + p0.imag\n # y = (x p1.imag - p0.real * p1.imag) / p1.real + p0.imag\n # y = x p1.imag/p1.real - p0.real p1.imag / p1.real + p0.imag\n # m = p1.imag/p1.real\n # c = -m p0.real + p0.imag\n m = p[1].imag / p[1].real\n c = (-m * p[0].real) + p[0].imag\n\n # Substitute the line's y(x) equation into the equation for\n # the ellipse. We can pretend the ellipse is centered at the\n # origin, since we shifted the Line by the ellipse's center.\n #\n # x^2/a^2 + y^2/b^2 = 1\n # x^2/a^2 + (mx+c)^2/b^2 = 1\n # (b^2 x^2 + a^2 (mx+c)^2)/(a^2 b^2) = 1\n # b^2 x^2 + a^2 (mx+c)^2 = a^2 b^2\n # b^2 x^2 + a^2(m^2 x^2 + 2mcx + c^2) = a^2 b^2\n # b^2 x^2 + a^2 m^2 x^2 + 2a^2 mcx + a^2 c^2 - a^2 b^2 = 0\n # (a^2 m^2 + b^2)x^2 + 2a^2 mcx + a^2(c^2 - b^2) = 0\n #\n # The quadratic forumla tells us: x = (-B +- sqrt(B^2 - 4AC)) / 2A\n # Where:\n # A = a^2 m^2 + b^2\n # B = 2 a^2 mc\n # C = a^2(c^2 - b^2)\n #\n # The determinant is: B^2 - 4AC\n #\n # The solution simplifies to:\n # x = (-a^2 mc +- a b sqrt(a^2 m^2 + b^2 - c^2)) / (a^2 m^2 + b^2)\n #\n # Solving the line for x(y) and substituting *that* into\n # the equation for the ellipse gives this solution for y:\n # y = (b^2 c +- abm sqrt(a^2 m^2 + b^2 - c^2)) / (a^2 m^2 + b^2)\n\n denominator = (a * a * m * m) + (b * b)\n\n discriminant = denominator - (c * c)\n if discriminant < 0:\n return []\n\n x_sqrt = a * b * sqrt(discriminant)\n x1 = (-(a * a * m * c) + x_sqrt) / denominator \n x2 = (-(a * a * m * c) - x_sqrt) / denominator \n x_values = [x1]\n if x1 != x2:\n x_values.append(x2)\n\n y_sqrt = x_sqrt * m\n y1 = ((b * b * c) + y_sqrt) / denominator\n y2 = ((b * b * c) - y_sqrt) / denominator\n y_values = [y1]\n if y1 != y2:\n y_values.append(y2)\n\n intersections = []\n for x in x_values:\n for y in y_values:\n p = complex(x, y) + self.center\n my_t = self.point_to_t(p)\n if my_t == None:\n continue\n other_t = other_seg.point_to_t(p)\n if other_t == None:\n continue\n intersections.append([my_t, other_t])\n return intersections\n\n elif is_bezier_segment(other_seg):\n u1poly = self.u1transform(other_seg.poly())\n u1poly_mag2 = real(u1poly)**2 + imag(u1poly)**2\n t2s = polyroots01(u1poly_mag2 - 1)\n t1s = [self.phase2t(phase(u1poly(t2))) for t2 in t2s]\n return list(zip(t1s, t2s))\n elif isinstance(other_seg, Arc):\n assert other_seg != self\n # This could be made explicit to increase efficiency\n longer_length = max(self.length(), other_seg.length())\n inters = bezier_intersections(self, other_seg,\n longer_length=longer_length,\n tol=tol, tol_deC=tol)\n\n # ad hoc fix for redundant solutions\n if len(inters) > 2:\n def keyfcn(tpair):\n t1, t2 = tpair\n return abs(self.point(t1) - other_seg.point(t2))\n inters.sort(key=keyfcn)\n for idx in range(1, len(inters)-1):\n if (abs(inters[idx][0] - inters[idx + 1][0])\n < abs(inters[idx][0] - inters[0][0])):\n return [inters[0], inters[idx]]\n else:\n return [inters[0], inters[-1]]\n return inters\n else:\n raise TypeError(\"other_seg should be a Arc, Line, \"\n \"QuadraticBezier, or CubicBezier object.\")\n\n def bbox(self):\n \"\"\"returns a bounding box for the segment in the form\n (xmin, xmax, ymin, ymax).\"\"\"\n # a(t) = radians(self.theta + self.delta*t)\n # = (2*pi/360)*(self.theta + self.delta*t)\n # x'=0: ~~~~~~~~~\n # -rx*cos(phi)*sin(a(t)) = ry*sin(phi)*cos(a(t))\n # -(rx/ry)*cot(phi)*tan(a(t)) = 1\n # a(t) = arctan(-(ry/rx)tan(phi)) + pi*k === atan_x\n # y'=0: ~~~~~~~~~~\n # rx*sin(phi)*sin(a(t)) = ry*cos(phi)*cos(a(t))\n # (rx/ry)*tan(phi)*tan(a(t)) = 1\n # a(t) = arctan((ry/rx)*cot(phi))\n # atanres = arctan((ry/rx)*cot(phi)) === atan_y\n # ~~~~~~~~\n # (2*pi/360)*(self.theta + self.delta*t) = atanres + pi*k\n # Therfore, for both x' and y', we have...\n # t = ((atan_{x/y} + pi*k)*(360/(2*pi)) - self.theta)/self.delta\n # for all k s.t. 0 < t < 1\n from math import atan, tan\n\n if cos(self.phi) == 0:\n atan_x = pi/2\n atan_y = 0\n elif sin(self.phi) == 0:\n atan_x = 0\n atan_y = pi/2\n else:\n rx, ry = self.radius.real, self.radius.imag\n atan_x = atan(-(ry/rx)*tan(self.phi))\n atan_y = atan((ry/rx)/tan(self.phi))\n\n def angle_inv(ang, k): # inverse of angle from Arc.derivative()\n return ((ang + pi*k)*(360/(2*pi)) - self.theta)/self.delta\n\n xtrema = [self.start.real, self.end.real]\n ytrema = [self.start.imag, self.end.imag]\n\n for k in range(-4, 5):\n tx = angle_inv(atan_x, k)\n ty = angle_inv(atan_y, k)\n if 0 <= tx <= 1:\n xtrema.append(self.point(tx).real)\n if 0 <= ty <= 1:\n ytrema.append(self.point(ty).imag)\n xmin = max(xtrema)\n return min(xtrema), max(xtrema), min(ytrema), max(ytrema)\n\n def split(self, t):\n \"\"\"returns two segments, whose union is this segment and which join\n at self.point(t).\"\"\"\n return self.cropped(0, t), self.cropped(t, 1)\n\n def cropped(self, t0, t1):\n \"\"\"returns a cropped copy of this segment which starts at\n self.point(t0) and ends at self.point(t1).\"\"\"\n if abs(self.delta*(t1 - t0)) <= 180:\n new_large_arc = 0\n else:\n new_large_arc = 1\n return Arc(self.point(t0), radius=self.radius, rotation=self.rotation,\n large_arc=new_large_arc, sweep=self.sweep,\n end=self.point(t1), autoscale_radius=self.autoscale_radius)\n\n def radialrange(self, origin, return_all_global_extrema=False):\n \"\"\"returns the tuples (d_min, t_min) and (d_max, t_max) which minimize\n and maximize, respectively, the distance,\n d = |self.point(t)-origin|.\"\"\"\n\n # u1orig = self.u1transform(origin)\n # if abs(u1orig) == 1: # origin lies on ellipse\n # t = self.phase2t(phase(u1orig))\n # d_min = 0\n #\n # # Transform to a coordinate system where the ellipse is centered\n # # at the origin and its axes are horizontal/vertical\n # zeta0 = self.centeriso(origin)\n # a, b = self.radius.real, self.radius.imag\n # x0, y0 = zeta0.real, zeta0.imag\n #\n # # Find t s.t. z'(t)\n # a2mb2 = (a**2 - b**2)\n # if u1orig.imag: # x != x0\n #\n # coeffs = [a2mb2**2,\n # 2*a2mb2*b**2*y0,\n # (-a**4 + (2*a**2 - b**2 + y0**2)*b**2 + x0**2)*b**2,\n # -2*a2mb2*b**4*y0,\n # -b**6*y0**2]\n # ys = polyroots(coeffs, realroots=True,\n # condition=lambda r: -b <= r <= b)\n # xs = (a*sqrt(1 - y**2/b**2) for y in ys)\n #\n # ts = [self.phase2t(phase(self.u1transform(self.icenteriso(\n # complex(x, y))))) for x, y in zip(xs, ys)]\n #\n # else: # This case is very similar, see notes and assume instead y0!=y\n # b2ma2 = (b**2 - a**2)\n # coeffs = [b2ma2**2,\n # 2*b2ma2*a**2*x0,\n # (-b**4 + (2*b**2 - a**2 + x0**2)*a**2 + y0**2)*a**2,\n # -2*b2ma2*a**4*x0,\n # -a**6*x0**2]\n # xs = polyroots(coeffs, realroots=True,\n # condition=lambda r: -a <= r <= a)\n # ys = (b*sqrt(1 - x**2/a**2) for x in xs)\n #\n # ts = [self.phase2t(phase(self.u1transform(self.icenteriso(\n # complex(x, y))))) for x, y in zip(xs, ys)]\n\n raise _NotImplemented4ArcException\n\n def rotated(self, degs, origin=None):\n \"\"\"Returns a copy of self rotated by `degs` degrees (CCW) around the\n point `origin` (a complex number). By default `origin` is either\n `self.point(0.5)`, or in the case that self is an Arc object,\n `origin` defaults to `self.center`.\"\"\"\n return rotate(self, degs, origin=origin)\n\n def translated(self, z0):\n \"\"\"Returns a copy of self shifted by the complex quantity `z0` such\n that self.translated(z0).point(t) = self.point(t) + z0 for any t.\"\"\"\n return translate(self, z0)\n\n def scaled(self, sx, sy=None, origin=0j):\n \"\"\"Scale transform. See `scale` function for further explanation.\"\"\"\n return scale(self, sx=sx, sy=sy, origin=origin)\n\n\ndef is_bezier_segment(x):\n return (isinstance(x, Line) or\n isinstance(x, QuadraticBezier) or\n isinstance(x, CubicBezier))\n\n\ndef is_path_segment(x):\n return is_bezier_segment(x) or isinstance(x, Arc)\n\n\nclass Path(MutableSequence):\n \"\"\"A Path is a sequence of path segments\"\"\"\n\n # Put it here, so there is a default if unpickled.\n _closed = False\n _start = None\n _end = None\n\n def __init__(self, *segments, **kw):\n self._segments = list(segments)\n self._length = None\n self._lengths = None\n if 'closed' in kw:\n self.closed = kw['closed'] # DEPRECATED\n if self._segments:\n self._start = self._segments[0].start\n self._end = self._segments[-1].end\n else:\n self._start = None\n self._end = None\n\n if 'tree_element' in kw:\n self._tree_element = kw['tree_element']\n\n def __getitem__(self, index):\n return self._segments[index]\n\n def __setitem__(self, index, value):\n self._segments[index] = value\n self._length = None\n self._start = self._segments[0].start\n self._end = self._segments[-1].end\n\n def __delitem__(self, index):\n del self._segments[index]\n self._length = None\n self._start = self._segments[0].start\n self._end = self._segments[-1].end\n\n def __iter__(self):\n return self._segments.__iter__()\n\n def __contains__(self, x):\n return self._segments.__contains__(x)\n\n def insert(self, index, value):\n self._segments.insert(index, value)\n self._length = None\n self._start = self._segments[0].start\n self._end = self._segments[-1].end\n\n def reversed(self):\n \"\"\"returns a copy of the Path object with its orientation reversed.\"\"\"\n newpath = [seg.reversed() for seg in self]\n newpath.reverse()\n return Path(*newpath)\n\n def __len__(self):\n return len(self._segments)\n\n def __repr__(self):\n return \"Path({})\".format(\n \",\\n \".join(repr(x) for x in self._segments))\n\n def __eq__(self, other):\n if not isinstance(other, Path):\n return NotImplemented\n if len(self) != len(other):\n return False\n for s, o in zip(self._segments, other._segments):\n if not s == o:\n return False\n return True\n\n def __ne__(self, other):\n if not isinstance(other, Path):\n return NotImplemented\n return not self == other\n\n def _calc_lengths(self, error=LENGTH_ERROR, min_depth=LENGTH_MIN_DEPTH):\n if self._length is not None:\n return\n\n lengths = [each.length(error=error, min_depth=min_depth) for each in\n self._segments]\n self._length = sum(lengths)\n self._lengths = [each/self._length for each in lengths]\n\n def point(self, pos):\n\n # Shortcuts\n if pos == 0.0:\n return self._segments[0].point(pos)\n if pos == 1.0:\n return self._segments[-1].point(pos)\n\n self._calc_lengths()\n # Find which segment the point we search for is located on:\n segment_start = 0\n for index, segment in enumerate(self._segments):\n segment_end = segment_start + self._lengths[index]\n if segment_end >= pos:\n # This is the segment! How far in on the segment is the point?\n segment_pos = (pos - segment_start)/(\n segment_end - segment_start)\n return segment.point(segment_pos)\n segment_start = segment_end\n\n def length(self, T0=0, T1=1, error=LENGTH_ERROR, min_depth=LENGTH_MIN_DEPTH):\n self._calc_lengths(error=error, min_depth=min_depth)\n if T0 == 0 and T1 == 1:\n return self._length\n else:\n if len(self) == 1:\n return self[0].length(t0=T0, t1=T1)\n idx0, t0 = self.T2t(T0)\n idx1, t1 = self.T2t(T1)\n if idx0 == idx1:\n return self[idx0].length(t0=t0, t1=t1)\n return (self[idx0].length(t0=t0) +\n sum(self[idx].length() for idx in range(idx0 + 1, idx1)) +\n self[idx1].length(t1=t1))\n\n def ilength(self, s, s_tol=ILENGTH_S_TOL, maxits=ILENGTH_MAXITS,\n error=ILENGTH_ERROR, min_depth=ILENGTH_MIN_DEPTH):\n \"\"\"Returns a float, t, such that self.length(0, t) is approximately s.\n See the inv_arclength() docstring for more details.\"\"\"\n return inv_arclength(self, s, s_tol=s_tol, maxits=maxits, error=error,\n min_depth=min_depth)\n\n def iscontinuous(self):\n \"\"\"Checks if a path is continuous with respect to its\n parameterization.\"\"\"\n return all(self[i].end == self[i+1].start for i in range(len(self) - 1))\n\n def continuous_subpaths(self):\n \"\"\"Breaks self into its continuous components, returning a list of\n continuous subpaths.\n I.e.\n (all(subpath.iscontinuous() for subpath in self.continuous_subpaths())\n and self == concatpaths(self.continuous_subpaths()))\n )\n \"\"\"\n subpaths = []\n subpath_start = 0\n for i in range(len(self) - 1):\n if self[i].end != self[(i+1) % len(self)].start:\n subpaths.append(Path(*self[subpath_start: i+1]))\n subpath_start = i+1\n subpaths.append(Path(*self[subpath_start: len(self)]))\n return subpaths\n\n def isclosed(self):\n \"\"\"This function determines if a connected path is closed.\"\"\"\n assert len(self) != 0\n assert self.iscontinuous()\n return self.start == self.end\n\n def isclosedac(self):\n assert len(self) != 0\n return self.start == self.end\n\n def _is_closable(self):\n end = self[-1].end\n for segment in self:\n if segment.start == end:\n return True\n return False\n\n @property\n def closed(self, warning_on=CLOSED_WARNING_ON):\n \"\"\"The closed attribute is deprecated, please use the isclosed()\n method instead. See _closed_warning for more information.\"\"\"\n mes = (\"This attribute is deprecated, consider using isclosed() \"\n \"method instead.\\n\\nThis attribute is kept for compatibility \"\n \"with scripts created using svg.path (v2.0). You can prevent \"\n \"this warning in the future by setting \"\n \"CLOSED_WARNING_ON=False.\")\n if warning_on:\n warn(mes)\n return self._closed and self._is_closable()\n\n @closed.setter\n def closed(self, value):\n value = bool(value)\n if value and not self._is_closable():\n raise ValueError(\"End does not coincide with a segment start.\")\n self._closed = value\n\n @property\n def start(self):\n if not self._start:\n self._start = self._segments[0].start\n return self._start\n\n @start.setter\n def start(self, pt):\n self._start = pt\n self._segments[0].start = pt\n\n @property\n def end(self):\n if not self._end:\n self._end = self._segments[-1].end\n return self._end\n\n @end.setter\n def end(self, pt):\n self._end = pt\n self._segments[-1].end = pt\n\n def d(self, useSandT=False, use_closed_attrib=False):\n \"\"\"Returns a path d-string for the path object.\n For an explanation of useSandT and use_closed_attrib, see the\n compatibility notes in the README.\"\"\"\n\n if use_closed_attrib:\n self_closed = self.closed(warning_on=False)\n if self_closed:\n segments = self[:-1]\n else:\n segments = self[:]\n else:\n self_closed = False\n segments = self[:]\n\n current_pos = None\n parts = []\n previous_segment = None\n end = self[-1].end\n\n for segment in segments:\n seg_start = segment.start\n # If the start of this segment does not coincide with the end of\n # the last segment or if this segment is actually the close point\n # of a closed path, then we should start a new subpath here.\n if current_pos != seg_start or \\\n (self_closed and seg_start == end and use_closed_attrib):\n parts.append('M {},{}'.format(seg_start.real, seg_start.imag))\n\n if isinstance(segment, Line):\n args = segment.end.real, segment.end.imag\n parts.append('L {},{}'.format(*args))\n elif isinstance(segment, CubicBezier):\n if useSandT and segment.is_smooth_from(previous_segment,\n warning_on=False):\n args = (segment.control2.real, segment.control2.imag,\n segment.end.real, segment.end.imag)\n parts.append('S {},{} {},{}'.format(*args))\n else:\n args = (segment.control1.real, segment.control1.imag,\n segment.control2.real, segment.control2.imag,\n segment.end.real, segment.end.imag)\n parts.append('C {},{} {},{} {},{}'.format(*args))\n elif isinstance(segment, QuadraticBezier):\n if useSandT and segment.is_smooth_from(previous_segment,\n warning_on=False):\n args = segment.end.real, segment.end.imag\n parts.append('T {},{}'.format(*args))\n else:\n args = (segment.control.real, segment.control.imag,\n segment.end.real, segment.end.imag)\n parts.append('Q {},{} {},{}'.format(*args))\n\n elif isinstance(segment, Arc):\n args = (segment.radius.real, segment.radius.imag,\n segment.rotation,int(segment.large_arc),\n int(segment.sweep),segment.end.real, segment.end.imag)\n parts.append('A {},{} {} {:d},{:d} {},{}'.format(*args))\n current_pos = segment.end\n previous_segment = segment\n\n if self_closed:\n parts.append('Z')\n\n return ' '.join(parts)\n\n def joins_smoothly_with(self, previous, wrt_parameterization=False):\n \"\"\"Checks if this Path object joins smoothly with previous\n path/segment. By default, this only checks that this Path starts\n moving (at t=0) in the same direction (and from the same positive) as\n previous stopped moving (at t=1). To check if the tangent magnitudes\n also match, set wrt_parameterization=True.\"\"\"\n if wrt_parameterization:\n return self[0].start == previous.end and self.derivative(\n 0) == previous.derivative(1)\n else:\n return self[0].start == previous.end and self.unit_tangent(\n 0) == previous.unit_tangent(1)\n\n def T2t(self, T):\n \"\"\"returns the segment index, `seg_idx`, and segment parameter, `t`,\n corresponding to the path parameter `T`. In other words, this is the\n inverse of the `Path.t2T()` method.\"\"\"\n if T == 1:\n return len(self)-1, 1\n if T == 0:\n return 0, 0\n self._calc_lengths()\n # Find which segment self.point(T) falls on:\n T0 = 0 # the T-value the current segment starts on\n for seg_idx, seg_length in enumerate(self._lengths):\n T1 = T0 + seg_length # the T-value the current segment ends on\n if T1 >= T:\n # This is the segment!\n t = (T - T0)/seg_length\n return seg_idx, t\n T0 = T1\n\n assert 0 <= T <= 1\n raise BugException\n\n def t2T(self, seg, t):\n \"\"\"returns the path parameter T which corresponds to the segment\n parameter t. In other words, for any Path object, path, and any\n segment in path, seg, T(t) = path.t2T(seg, t) is the unique\n reparameterization such that path.point(T(t)) == seg.point(t) for all\n 0 <= t <= 1.\n Input Note: seg can be a segment in the Path object or its\n corresponding index.\"\"\"\n self._calc_lengths()\n # Accept an index or a segment for seg\n if isinstance(seg, int):\n seg_idx = seg\n else:\n try:\n seg_idx = self.index(seg)\n except ValueError:\n assert is_path_segment(seg) or isinstance(seg, int)\n raise\n\n segment_start = sum(self._lengths[:seg_idx])\n segment_end = segment_start + self._lengths[seg_idx]\n T = (segment_end - segment_start)*t + segment_start\n return T\n\n def derivative(self, T, n=1):\n \"\"\"returns the tangent vector of the Path at T (centered at the origin\n and expressed as a complex number).\n Note: Bezier curves can have points where their derivative vanishes.\n If you are interested in the tangent direction, use unit_tangent()\n method instead.\"\"\"\n seg_idx, t = self.T2t(T)\n seg = self._segments[seg_idx]\n return seg.derivative(t, n=n)/seg.length()**n\n\n def unit_tangent(self, T):\n \"\"\"returns the unit tangent vector of the Path at T (centered at the\n origin and expressed as a complex number). If the tangent vector's\n magnitude is zero, this method will find the limit of\n self.derivative(tau)/abs(self.derivative(tau)) as tau approaches T.\"\"\"\n seg_idx, t = self.T2t(T)\n return self._segments[seg_idx].unit_tangent(t)\n\n def normal(self, t):\n \"\"\"returns the (right hand rule) unit normal vector to self at t.\"\"\"\n return -1j*self.unit_tangent(t)\n\n def curvature(self, T):\n \"\"\"returns the curvature of this Path object at T and outputs\n float('inf') if not differentiable at T.\"\"\"\n seg_idx, t = self.T2t(T)\n seg = self[seg_idx]\n if np.isclose(t, 0) and (seg_idx != 0 or self.end==self.start):\n previous_seg_in_path = self._segments[\n (seg_idx - 1) % len(self._segments)]\n if not seg.joins_smoothly_with(previous_seg_in_path):\n return float('inf')\n elif np.isclose(t, 1) and (seg_idx != len(self) - 1 or\n self.end == self.start):\n next_seg_in_path = self._segments[\n (seg_idx + 1) % len(self._segments)]\n if not next_seg_in_path.joins_smoothly_with(seg):\n return float('inf')\n dz = self.derivative(T)\n ddz = self.derivative(T, n=2)\n dx, dy = dz.real, dz.imag\n ddx, ddy = ddz.real, ddz.imag\n return abs(dx*ddy - dy*ddx)/(dx*dx + dy*dy)**1.5\n\n # def icurvature(self, kappa):\n # \"\"\"returns a list of T-values such that 0 <= T <= 1 and\n # seg.curvature(t) = kappa.\n # Note: not implemented for paths containing Arc segments.\"\"\"\n # assert is_bezier_path(self)\n # Ts = []\n # for i, seg in enumerate(self):\n # Ts += [self.t2T(i, t) for t in seg.icurvature(kappa)]\n # return Ts\n\n def area(self, chord_length=1e-4):\n \"\"\"Find area enclosed by path.\n \n Approximates any Arc segments in the Path with lines\n approximately `chord_length` long, and returns the area enclosed\n by the approximated Path. Default chord length is 0.01. If Arc\n segments are included in path, to ensure accurate results, make\n sure this `chord_length` is set to a reasonable value (e.g. by\n checking curvature).\n \n Notes\n -----\n * Negative area results from clockwise (as opposed to\n counter-clockwise) parameterization of the input Path.\n \n To Contributors\n ---------------\n This is one of many parts of `svgpathtools` that could be \n improved by a noble soul implementing a piecewise-linear \n approximation scheme for paths (one with controls to guarantee a\n desired accuracy).\n \"\"\"\n\n def area_without_arcs(path):\n area_enclosed = 0\n for seg in path:\n x = real(seg.poly())\n dy = imag(seg.poly()).deriv()\n integrand = x*dy\n integral = integrand.integ()\n area_enclosed += integral(1) - integral(0)\n return area_enclosed\n\n def seg2lines(seg):\n \"\"\"Find piecewise-linear approximation of `seg`.\"\"\"\n num_lines = int(ceil(seg.length() / chord_length))\n pts = [seg.point(t) for t in np.linspace(0, 1, num_lines+1)]\n return [Line(pts[i], pts[i+1]) for i in range(num_lines)]\n\n assert self.isclosed()\n\n bezier_path_approximation = []\n for seg in self:\n if isinstance(seg, Arc):\n bezier_path_approximation += seg2lines(seg)\n else:\n bezier_path_approximation.append(seg)\n return area_without_arcs(Path(*bezier_path_approximation))\n\n def intersect(self, other_curve, justonemode=False, tol=1e-12):\n \"\"\"returns list of pairs of pairs ((T1, seg1, t1), (T2, seg2, t2))\n giving the intersection points.\n If justonemode==True, then returns just the first\n intersection found.\n tol is used to check for redundant intersections (see comment above\n the code block where tol is used).\n Note: If the two path objects coincide for more than a finite set of\n points, this code will fail.\"\"\"\n path1 = self\n if isinstance(other_curve, Path):\n path2 = other_curve\n else:\n path2 = Path(other_curve)\n assert path1 != path2\n intersection_list = []\n for seg1 in path1:\n for seg2 in path2:\n if justonemode and intersection_list:\n return intersection_list[0]\n for t1, t2 in seg1.intersect(seg2, tol=tol):\n T1 = path1.t2T(seg1, t1)\n T2 = path2.t2T(seg2, t2)\n intersection_list.append(((T1, seg1, t1), (T2, seg2, t2)))\n if justonemode and intersection_list:\n return intersection_list[0]\n\n # Note: If the intersection takes place at a joint (point one seg ends\n # and next begins in path) then intersection_list may contain a\n # redundant intersection. This code block checks for and removes said\n # redundancies.\n if intersection_list:\n pts = [seg1.point(_t1)\n for _T1, _seg1, _t1 in list(zip(*intersection_list))[0]]\n indices2remove = []\n for ind1 in range(len(pts)):\n for ind2 in range(ind1 + 1, len(pts)):\n if abs(pts[ind1] - pts[ind2]) < tol:\n # then there's a redundancy. Remove it.\n indices2remove.append(ind2)\n intersection_list = [inter for ind, inter in\n enumerate(intersection_list) if\n ind not in indices2remove]\n return intersection_list\n\n def bbox(self):\n \"\"\"returns a bounding box for the input Path object in the form\n (xmin, xmax, ymin, ymax).\"\"\"\n bbs = [seg.bbox() for seg in self._segments]\n xmins, xmaxs, ymins, ymaxs = list(zip(*bbs))\n xmin = min(xmins)\n xmax = max(xmaxs)\n ymin = min(ymins)\n ymax = max(ymaxs)\n return xmin, xmax, ymin, ymax\n\n def cropped(self, T0, T1):\n \"\"\"returns a cropped copy of the path.\"\"\"\n assert 0 <= T0 <= 1 and 0 <= T1<= 1\n assert T0 != T1\n assert not (T0 == 1 and T1 == 0)\n\n if T0 == 1 and 0 < T1 < 1 and self.isclosed():\n return self.cropped(0, T1)\n\n if T1 == 1:\n seg1 = self[-1]\n t_seg1 = 1\n i1 = len(self) - 1\n else:\n seg1_idx, t_seg1 = self.T2t(T1)\n seg1 = self[seg1_idx]\n if np.isclose(t_seg1, 0):\n i1 = (self.index(seg1) - 1) % len(self)\n seg1 = self[i1]\n t_seg1 = 1\n else:\n i1 = self.index(seg1)\n if T0 == 0:\n seg0 = self[0]\n t_seg0 = 0\n i0 = 0\n else:\n seg0_idx, t_seg0 = self.T2t(T0)\n seg0 = self[seg0_idx]\n if np.isclose(t_seg0, 1):\n i0 = (self.index(seg0) + 1) % len(self)\n seg0 = self[i0]\n t_seg0 = 0\n else:\n i0 = self.index(seg0)\n\n if T0 < T1 and i0 == i1:\n new_path = Path(seg0.cropped(t_seg0, t_seg1))\n else:\n new_path = Path(seg0.cropped(t_seg0, 1))\n\n # T1<T0 must cross discontinuity case\n if T1 < T0:\n if not self.isclosed():\n raise ValueError(\"This path is not closed, thus T0 must \"\n \"be less than T1.\")\n else:\n for i in range(i0 + 1, len(self)):\n new_path.append(self[i])\n for i in range(0, i1):\n new_path.append(self[i])\n\n # T0<T1 straight-forward case\n else:\n for i in range(i0 + 1, i1):\n new_path.append(self[i])\n\n if t_seg1 != 0:\n new_path.append(seg1.cropped(0, t_seg1))\n return new_path\n\n def radialrange(self, origin, return_all_global_extrema=False):\n \"\"\"returns the tuples (d_min, t_min, idx_min), (d_max, t_max, idx_max)\n which minimize and maximize, respectively, the distance\n d = |self[idx].point(t)-origin|.\"\"\"\n if return_all_global_extrema:\n raise NotImplementedError\n else:\n global_min = (np.inf, None, None)\n global_max = (0, None, None)\n for seg_idx, seg in enumerate(self):\n seg_global_min, seg_global_max = seg.radialrange(origin)\n if seg_global_min[0] < global_min[0]:\n global_min = seg_global_min + (seg_idx,)\n if seg_global_max[0] > global_max[0]:\n global_max = seg_global_max + (seg_idx,)\n return global_min, global_max\n\n def rotated(self, degs, origin=None):\n \"\"\"Returns a copy of self rotated by `degs` degrees (CCW) around the\n point `origin` (a complex number). By default `origin` is either\n `self.point(0.5)`, or in the case that self is an Arc object,\n `origin` defaults to `self.center`.\"\"\"\n return rotate(self, degs, origin=origin)\n\n def translated(self, z0):\n \"\"\"Returns a copy of self shifted by the complex quantity `z0` such\n that self.translated(z0).point(t) = self.point(t) + z0 for any t.\"\"\"\n return translate(self, z0)\n\n def scaled(self, sx, sy=None, origin=0j):\n \"\"\"Scale transform. See `scale` function for further explanation.\"\"\"\n return scale(self, sx=sx, sy=sy, origin=origin)\n\n\"\"\"This submodule contains tools for creating svg files from paths and path\nsegments.\"\"\"\n\n# External dependencies:\nfrom __future__ import division, absolute_import, print_function\nfrom math import ceil\nfrom os import getcwd, path as os_path, makedirs\nfrom xml.dom.minidom import parse as md_xml_parse\nfrom svgwrite import Drawing, text as txt\nfrom time import time\nfrom warnings import warn\nimport re\n\n# Internal dependencies\nfrom .path import Path, Line, is_path_segment\nfrom .misctools import open_in_browser\n\n# Used to convert a string colors (identified by single chars) to a list.\ncolor_dict = {'a': 'aqua',\n 'b': 'blue',\n 'c': 'cyan',\n 'd': 'darkblue',\n 'e': '',\n 'f': '',\n 'g': 'green',\n 'h': '',\n 'i': '',\n 'j': '',\n 'k': 'black',\n 'l': 'lime',\n 'm': 'magenta',\n 'n': 'brown',\n 'o': 'orange',\n 'p': 'pink',\n 'q': 'turquoise',\n 'r': 'red',\n 's': 'salmon',\n 't': 'tan',\n 'u': 'purple',\n 'v': 'violet',\n 'w': 'white',\n 'x': '',\n 'y': 'yellow',\n 'z': 'azure'}\n\n\ndef str2colorlist(s, default_color=None):\n color_list = [color_dict[ch] for ch in s]\n if default_color:\n for idx, c in enumerate(color_list):\n if not c:\n color_list[idx] = default_color\n return color_list\n\n\ndef is3tuple(c):\n return isinstance(c, tuple) and len(c) == 3\n\n\ndef big_bounding_box(paths_n_stuff):\n \"\"\"Finds a BB containing a collection of paths, Bezier path segments, and\n points (given as complex numbers).\"\"\"\n bbs = []\n for thing in paths_n_stuff:\n if is_path_segment(thing) or isinstance(thing, Path):\n bbs.append(thing.bbox())\n elif isinstance(thing, complex):\n bbs.append((thing.real, thing.real, thing.imag, thing.imag))\n else:\n try:\n complexthing = complex(thing)\n bbs.append((complexthing.real, complexthing.real,\n complexthing.imag, complexthing.imag))\n except ValueError:\n raise TypeError(\n \"paths_n_stuff can only contains Path, CubicBezier, \"\n \"QuadraticBezier, Line, and complex objects.\")\n xmins, xmaxs, ymins, ymaxs = list(zip(*bbs))\n xmin = min(xmins)\n xmax = max(xmaxs)\n ymin = min(ymins)\n ymax = max(ymaxs)\n return xmin, xmax, ymin, ymax\n\n\ndef disvg(paths=None, colors=None,\n filename=os_path.join(getcwd(), 'disvg_output.svg'),\n stroke_widths=None, nodes=None, node_colors=None, node_radii=None,\n openinbrowser=True, timestamp=False,\n margin_size=0.1, mindim=600, dimensions=None,\n viewbox=None, text=None, text_path=None, font_size=None,\n attributes=None, svg_attributes=None, svgwrite_debug=False, paths2Drawing=False):\n \"\"\"Takes in a list of paths and creates an SVG file containing said paths.\n REQUIRED INPUTS:\n :param paths - a list of paths\n\n OPTIONAL INPUT:\n :param colors - specifies the path stroke color. By default all paths\n will be black (#000000). This paramater can be input in a few ways\n 1) a list of strings that will be input into the path elements stroke\n attribute (so anything that is understood by the svg viewer).\n 2) a string of single character colors -- e.g. setting colors='rrr' is\n equivalent to setting colors=['red', 'red', 'red'] (see the\n 'color_dict' dictionary above for a list of possibilities).\n 3) a list of rgb 3-tuples -- e.g. colors = [(255, 0, 0), ...].\n\n :param filename - the desired location/filename of the SVG file\n created (by default the SVG will be stored in the current working\n directory and named 'disvg_output.svg').\n\n :param stroke_widths - a list of stroke_widths to use for paths\n (default is 0.5% of the SVG's width or length)\n\n :param nodes - a list of points to draw as filled-in circles\n\n :param node_colors - a list of colors to use for the nodes (by default\n nodes will be red)\n\n :param node_radii - a list of radii to use for the nodes (by default\n nodes will be radius will be 1 percent of the svg's width/length)\n\n :param text - string or list of strings to be displayed\n\n :param text_path - if text is a list, then this should be a list of\n path (or path segments of the same length. Note: the path must be\n long enough to display the text or the text will be cropped by the svg\n viewer.\n\n :param font_size - a single float of list of floats.\n\n :param openinbrowser - Set to True to automatically open the created\n SVG in the user's default web browser.\n\n :param timestamp - if True, then the a timestamp will be appended to\n the output SVG's filename. This will fix issues with rapidly opening\n multiple SVGs in your browser.\n\n :param margin_size - The min margin (empty area framing the collection\n of paths) size used for creating the canvas and background of the SVG.\n\n :param mindim - The minimum dimension (height or width) of the output\n SVG (default is 600).\n\n :param dimensions - The (x,y) display dimensions of the output SVG.\n I.e. this specifies the `width` and `height` SVG attributes. Note that \n these also can be used to specify units other than pixels. Using this \n will override the `mindim` parameter.\n\n :param viewbox - This specifies the coordinated system used in the svg.\n The SVG `viewBox` attribute works together with the the `height` and \n `width` attrinutes. Using these three attributes allows for shifting \n and scaling of the SVG canvas without changing the any values other \n than those in `viewBox`, `height`, and `width`. `viewbox` should be \n input as a 4-tuple, (min_x, min_y, width, height), or a string \n \"min_x min_y width height\". Using this will override the `mindim` \n parameter.\n\n :param attributes - a list of dictionaries of attributes for the input\n paths. Note: This will override any other conflicting settings.\n\n :param svg_attributes - a dictionary of attributes for output svg.\n \n :param svgwrite_debug - This parameter turns on/off `svgwrite`'s \n debugging mode. By default svgwrite_debug=False. This increases \n speed and also prevents `svgwrite` from raising of an error when not \n all `svg_attributes` key-value pairs are understood.\n \n :param paths2Drawing - If true, an `svgwrite.Drawing` object is \n returned and no file is written. This `Drawing` can later be saved \n using the `svgwrite.Drawing.save()` method.\n\n NOTES:\n * The `svg_attributes` parameter will override any other conflicting \n settings.\n\n * Any `extra` parameters that `svgwrite.Drawing()` accepts can be \n controlled by passing them in through `svg_attributes`.\n\n * The unit of length here is assumed to be pixels in all variables.\n\n * If this function is used multiple times in quick succession to\n display multiple SVGs (all using the default filename), the\n svgviewer/browser will likely fail to load some of the SVGs in time.\n To fix this, use the timestamp attribute, or give the files unique\n names, or use a pause command (e.g. time.sleep(1)) between uses.\n \"\"\"\n\n\n _default_relative_node_radius = 5e-3\n _default_relative_stroke_width = 1e-3\n _default_path_color = '#000000' # black\n _default_node_color = '#ff0000' # red\n _default_font_size = 12\n\n\n # append directory to filename (if not included)\n if os_path.dirname(filename) == '':\n filename = os_path.join(getcwd(), filename)\n\n # append time stamp to filename\n if timestamp:\n fbname, fext = os_path.splitext(filename)\n dirname = os_path.dirname(filename)\n tstamp = str(time()).replace('.', '')\n stfilename = os_path.split(fbname)[1] + '_' + tstamp + fext\n filename = os_path.join(dirname, stfilename)\n\n # check paths and colors are set\n if isinstance(paths, Path) or is_path_segment(paths):\n paths = [paths]\n if paths:\n if not colors:\n colors = [_default_path_color] * len(paths)\n else:\n assert len(colors) == len(paths)\n if isinstance(colors, str):\n colors = str2colorlist(colors,\n default_color=_default_path_color)\n elif isinstance(colors, list):\n for idx, c in enumerate(colors):\n if is3tuple(c):\n colors[idx] = \"rgb\" + str(c)\n\n # check nodes and nodes_colors are set (node_radii are set later)\n if nodes:\n if not node_colors:\n node_colors = [_default_node_color] * len(nodes)\n else:\n assert len(node_colors) == len(nodes)\n if isinstance(node_colors, str):\n node_colors = str2colorlist(node_colors,\n default_color=_default_node_color)\n elif isinstance(node_colors, list):\n for idx, c in enumerate(node_colors):\n if is3tuple(c):\n node_colors[idx] = \"rgb\" + str(c)\n\n # set up the viewBox and display dimensions of the output SVG\n # along the way, set stroke_widths and node_radii if not provided\n assert paths or nodes\n stuff2bound = []\n if viewbox:\n if not isinstance(viewbox, str):\n viewbox = '%s %s %s %s' % viewbox\n if dimensions is None:\n dimensions = viewbox.split(' ')[2:4]\n elif dimensions:\n dimensions = tuple(map(str, dimensions))\n def strip_units(s):\n return re.search(r'\\d*\\.?\\d*', s.strip()).group()\n viewbox = '0 0 %s %s' % tuple(map(strip_units, dimensions))\n else:\n if paths:\n stuff2bound += paths\n if nodes:\n stuff2bound += nodes\n if text_path:\n stuff2bound += text_path\n xmin, xmax, ymin, ymax = big_bounding_box(stuff2bound)\n dx = xmax - xmin\n dy = ymax - ymin\n\n if dx == 0:\n dx = 1\n if dy == 0:\n dy = 1\n\n # determine stroke_widths to use (if not provided) and max_stroke_width\n if paths:\n if not stroke_widths:\n sw = max(dx, dy) * _default_relative_stroke_width\n stroke_widths = [sw]*len(paths)\n max_stroke_width = sw\n else:\n assert len(paths) == len(stroke_widths)\n max_stroke_width = max(stroke_widths)\n else:\n max_stroke_width = 0\n\n # determine node_radii to use (if not provided) and max_node_diameter\n if nodes:\n if not node_radii:\n r = max(dx, dy) * _default_relative_node_radius\n node_radii = [r]*len(nodes)\n max_node_diameter = 2*r\n else:\n assert len(nodes) == len(node_radii)\n max_node_diameter = 2*max(node_radii)\n else:\n max_node_diameter = 0\n\n extra_space_for_style = max(max_stroke_width, max_node_diameter)\n xmin -= margin_size*dx + extra_space_for_style/2\n ymin -= margin_size*dy + extra_space_for_style/2\n dx += 2*margin_size*dx + extra_space_for_style\n dy += 2*margin_size*dy + extra_space_for_style\n viewbox = \"%s %s %s %s\" % (xmin, ymin, dx, dy)\n\n if dx > dy:\n szx = str(mindim) + 'px'\n szy = str(int(ceil(mindim * dy / dx))) + 'px'\n else:\n szx = str(int(ceil(mindim * dx / dy))) + 'px'\n szy = str(mindim) + 'px'\n dimensions = szx, szy\n\n # Create an SVG file\n if svg_attributes is not None:\n dimensions = (svg_attributes.get(\"width\", dimensions[0]),\n svg_attributes.get(\"height\", dimensions[1]))\n debug = svg_attributes.get(\"debug\", svgwrite_debug)\n dwg = Drawing(filename=filename, size=dimensions, debug=debug,\n **svg_attributes)\n else:\n dwg = Drawing(filename=filename, size=dimensions, debug=svgwrite_debug,\n viewBox=viewbox)\n\n # add paths\n if paths:\n for i, p in enumerate(paths):\n if isinstance(p, Path):\n ps = p.d()\n elif is_path_segment(p):\n ps = Path(p).d()\n else: # assume this path, p, was input as a Path d-string\n ps = p\n\n if attributes:\n good_attribs = {'d': ps}\n for key in attributes[i]:\n val = attributes[i][key]\n if key != 'd':\n try:\n dwg.path(ps, **{key: val})\n good_attribs.update({key: val})\n except Exception as e:\n warn(str(e))\n\n dwg.add(dwg.path(**good_attribs))\n else:\n dwg.add(dwg.path(ps, stroke=colors[i],\n stroke_width=str(stroke_widths[i]),\n fill='none'))\n\n # add nodes (filled in circles)\n if nodes:\n for i_pt, pt in enumerate([(z.real, z.imag) for z in nodes]):\n dwg.add(dwg.circle(pt, node_radii[i_pt], fill=node_colors[i_pt]))\n\n # add texts\n if text:\n assert isinstance(text, str) or (isinstance(text, list) and\n isinstance(text_path, list) and\n len(text_path) == len(text))\n if isinstance(text, str):\n text = [text]\n if not font_size:\n font_size = [_default_font_size]\n if not text_path:\n pos = complex(xmin + margin_size*dx, ymin + margin_size*dy)\n text_path = [Line(pos, pos + 1).d()]\n else:\n if font_size:\n if isinstance(font_size, list):\n assert len(font_size) == len(text)\n else:\n font_size = [font_size] * len(text)\n else:\n font_size = [_default_font_size] * len(text)\n for idx, s in enumerate(text):\n p = text_path[idx]\n if isinstance(p, Path):\n ps = p.d()\n elif is_path_segment(p):\n ps = Path(p).d()\n else: # assume this path, p, was input as a Path d-string\n ps = p\n\n # paragraph = dwg.add(dwg.g(font_size=font_size[idx]))\n # paragraph.add(dwg.textPath(ps, s))\n pathid = 'tp' + str(idx)\n dwg.defs.add(dwg.path(d=ps, id=pathid))\n txter = dwg.add(dwg.text('', font_size=font_size[idx]))\n txter.add(txt.TextPath('#'+pathid, s))\n\n if paths2Drawing:\n return dwg\n \n # save svg\n if not os_path.exists(os_path.dirname(filename)):\n makedirs(os_path.dirname(filename))\n dwg.save()\n\n # re-open the svg, make the xml pretty, and save it again\n xmlstring = md_xml_parse(filename).toprettyxml()\n with open(filename, 'w') as f:\n f.write(xmlstring)\n\n # try to open in web browser\n if openinbrowser:\n try:\n open_in_browser(filename)\n except:\n print(\"Failed to open output SVG in browser. SVG saved to:\")\n print(filename)\n\n\ndef wsvg(paths=None, colors=None,\n filename=os_path.join(getcwd(), 'disvg_output.svg'),\n stroke_widths=None, nodes=None, node_colors=None, node_radii=None,\n openinbrowser=False, timestamp=False,\n margin_size=0.1, mindim=600, dimensions=None,\n viewbox=None, text=None, text_path=None, font_size=None,\n attributes=None, svg_attributes=None, svgwrite_debug=False, paths2Drawing=False):\n \"\"\"Convenience function; identical to disvg() except that\n openinbrowser=False by default. See disvg() docstring for more info.\"\"\"\n disvg(paths, colors=colors, filename=filename,\n stroke_widths=stroke_widths, nodes=nodes,\n node_colors=node_colors, node_radii=node_radii,\n openinbrowser=openinbrowser, timestamp=timestamp,\n margin_size=margin_size, mindim=mindim, dimensions=dimensions,\n viewbox=viewbox, text=text, text_path=text_path, font_size=font_size,\n attributes=attributes, svg_attributes=svg_attributes,\n svgwrite_debug=svgwrite_debug, paths2Drawing=paths2Drawing)\n \n \ndef paths2Drawing(paths=None, colors=None,\n filename=os_path.join(getcwd(), 'disvg_output.svg'),\n stroke_widths=None, nodes=None, node_colors=None, node_radii=None,\n openinbrowser=False, timestamp=False,\n margin_size=0.1, mindim=600, dimensions=None,\n viewbox=None, text=None, text_path=None, font_size=None,\n attributes=None, svg_attributes=None, svgwrite_debug=False, paths2Drawing=True):\n \"\"\"Convenience function; identical to disvg() except that\n paths2Drawing=True by default. See disvg() docstring for more info.\"\"\"\n disvg(paths, colors=colors, filename=filename,\n stroke_widths=stroke_widths, nodes=nodes,\n node_colors=node_colors, node_radii=node_radii,\n openinbrowser=openinbrowser, timestamp=timestamp,\n margin_size=margin_size, mindim=mindim, dimensions=dimensions,\n viewbox=viewbox, text=text, text_path=text_path, font_size=font_size,\n attributes=attributes, svg_attributes=svg_attributes,\n svgwrite_debug=svgwrite_debug, paths2Drawing=paths2Drawing)\n\n\"\"\"This submodule contains tools for working with numpy.poly1d objects.\"\"\"\n\n# External Dependencies\nfrom __future__ import division, absolute_import\nfrom itertools import combinations\nimport numpy as np\n\n# Internal Dependencies\nfrom .misctools import isclose\n\n\ndef polyroots(p, realroots=False, condition=lambda r: True):\n \"\"\"\n Returns the roots of a polynomial with coefficients given in p.\n p[0] * x**n + p[1] * x**(n-1) + ... + p[n-1]*x + p[n]\n INPUT:\n p - Rank-1 array-like object of polynomial coefficients.\n realroots - a boolean. If true, only real roots will be returned and the\n condition function can be written assuming all roots are real.\n condition - a boolean-valued function. Only roots satisfying this will be\n returned. If realroots==True, these conditions should assume the roots\n are real.\n OUTPUT:\n A list containing the roots of the polynomial.\n NOTE: This uses np.isclose and np.roots\"\"\"\n roots = np.roots(p)\n if realroots:\n roots = [r.real for r in roots if isclose(r.imag, 0)]\n roots = [r for r in roots if condition(r)]\n\n duplicates = []\n for idx, (r1, r2) in enumerate(combinations(roots, 2)):\n if isclose(r1, r2):\n duplicates.append(idx)\n return [r for idx, r in enumerate(roots) if idx not in duplicates]\n\n\ndef polyroots01(p):\n \"\"\"Returns the real roots between 0 and 1 of the polynomial with\n coefficients given in p,\n p[0] * x**n + p[1] * x**(n-1) + ... + p[n-1]*x + p[n]\n p can also be a np.poly1d object. See polyroots for more information.\"\"\"\n return polyroots(p, realroots=True, condition=lambda tval: 0 <= tval <= 1)\n\n\ndef rational_limit(f, g, t0):\n \"\"\"Computes the limit of the rational function (f/g)(t)\n as t approaches t0.\"\"\"\n assert isinstance(f, np.poly1d) and isinstance(g, np.poly1d)\n assert g != np.poly1d([0])\n if g(t0) != 0:\n return f(t0)/g(t0)\n elif f(t0) == 0:\n return rational_limit(f.deriv(), g.deriv(), t0)\n else:\n raise ValueError(\"Limit does not exist.\")\n\n\ndef real(z):\n try:\n return np.poly1d(z.coeffs.real)\n except AttributeError:\n return z.real\n\n\ndef imag(z):\n try:\n return np.poly1d(z.coeffs.imag)\n except AttributeError:\n return z.imag\n\n\ndef poly_real_part(poly):\n \"\"\"Deprecated.\"\"\"\n return np.poly1d(poly.coeffs.real)\n\n\ndef poly_imag_part(poly):\n \"\"\"Deprecated.\"\"\"\n return np.poly1d(poly.coeffs.imag)\n\n\"\"\"This submodule contains functions related to smoothing paths of Bezier\ncurves.\"\"\"\n\n# External Dependencies\nfrom __future__ import division, absolute_import, print_function\n\n# Internal Dependencies\nfrom .path import Path, CubicBezier, Line\nfrom .misctools import isclose\nfrom .paths2svg import disvg\n\n\ndef is_differentiable(path, tol=1e-8):\n for idx in range(len(path)):\n u = path[(idx-1) % len(path)].unit_tangent(1)\n v = path[idx].unit_tangent(0)\n u_dot_v = u.real*v.real + u.imag*v.imag\n if abs(u_dot_v - 1) > tol:\n return False\n return True\n\n\ndef kinks(path, tol=1e-8):\n \"\"\"returns indices of segments that start on a non-differentiable joint.\"\"\"\n kink_list = []\n for idx in range(len(path)):\n if idx == 0 and not path.isclosed():\n continue\n try:\n u = path[(idx - 1) % len(path)].unit_tangent(1)\n v = path[idx].unit_tangent(0)\n u_dot_v = u.real*v.real + u.imag*v.imag\n flag = False\n except ValueError:\n flag = True\n\n if flag or abs(u_dot_v - 1) > tol:\n kink_list.append(idx)\n return kink_list\n\n\ndef _report_unfixable_kinks(_path, _kink_list):\n mes = (\"\\n%s kinks have been detected at that cannot be smoothed.\\n\"\n \"To ignore these kinks and fix all others, run this function \"\n \"again with the second argument 'ignore_unfixable_kinks=True' \"\n \"The locations of the unfixable kinks are at the beginnings of \"\n \"segments: %s\" % (len(_kink_list), _kink_list))\n disvg(_path, nodes=[_path[idx].start for idx in _kink_list])\n raise Exception(mes)\n\n\ndef smoothed_joint(seg0, seg1, maxjointsize=3, tightness=1.99):\n \"\"\" See Andy's notes on\n Smoothing Bezier Paths for an explanation of the method.\n Input: two segments seg0, seg1 such that seg0.end==seg1.start, and\n jointsize, a positive number\n\n Output: seg0_trimmed, elbow, seg1_trimmed, where elbow is a cubic bezier\n object that smoothly connects seg0_trimmed and seg1_trimmed.\n\n \"\"\"\n assert seg0.end == seg1.start\n assert 0 < maxjointsize\n assert 0 < tightness < 2\n# sgn = lambda x:x/abs(x)\n q = seg0.end\n\n try: v = seg0.unit_tangent(1)\n except: v = seg0.unit_tangent(1 - 1e-4)\n try: w = seg1.unit_tangent(0)\n except: w = seg1.unit_tangent(1e-4)\n\n max_a = maxjointsize / 2\n a = min(max_a, min(seg1.length(), seg0.length()) / 20)\n if isinstance(seg0, Line) and isinstance(seg1, Line):\n '''\n Note: Letting\n c(t) = elbow.point(t), v= the unit tangent of seg0 at 1, w = the\n unit tangent vector of seg1 at 0,\n Q = seg0.point(1) = seg1.point(0), and a,b>0 some constants.\n The elbow will be the unique CubicBezier, c, such that\n c(0)= Q-av, c(1)=Q+aw, c'(0) = bv, and c'(1) = bw\n where a and b are derived above/below from tightness and\n maxjointsize.\n '''\n# det = v.imag*w.real-v.real*w.imag\n # Note:\n # If det is negative, the curvature of elbow is negative for all\n # real t if and only if b/a > 6\n # If det is positive, the curvature of elbow is negative for all\n # real t if and only if b/a < 2\n\n# if det < 0:\n# b = (6+tightness)*a\n# elif det > 0:\n# b = (2-tightness)*a\n# else:\n# raise Exception(\"seg0 and seg1 are parallel lines.\")\n b = (2 - tightness)*a\n elbow = CubicBezier(q - a*v, q - (a - b/3)*v, q + (a - b/3)*w, q + a*w)\n seg0_trimmed = Line(seg0.start, elbow.start)\n seg1_trimmed = Line(elbow.end, seg1.end)\n return seg0_trimmed, [elbow], seg1_trimmed\n elif isinstance(seg0, Line):\n '''\n Note: Letting\n c(t) = elbow.point(t), v= the unit tangent of seg0 at 1,\n w = the unit tangent vector of seg1 at 0,\n Q = seg0.point(1) = seg1.point(0), and a,b>0 some constants.\n The elbow will be the unique CubicBezier, c, such that\n c(0)= Q-av, c(1)=Q, c'(0) = bv, and c'(1) = bw\n where a and b are derived above/below from tightness and\n maxjointsize.\n '''\n# det = v.imag*w.real-v.real*w.imag\n # Note: If g has the same sign as det, then the curvature of elbow is\n # negative for all real t if and only if b/a < 4\n b = (4 - tightness)*a\n# g = sgn(det)*b\n elbow = CubicBezier(q - a*v, q + (b/3 - a)*v, q - b/3*w, q)\n seg0_trimmed = Line(seg0.start, elbow.start)\n return seg0_trimmed, [elbow], seg1\n elif isinstance(seg1, Line):\n args = (seg1.reversed(), seg0.reversed(), maxjointsize, tightness)\n rseg1_trimmed, relbow, rseg0 = smoothed_joint(*args)\n elbow = relbow[0].reversed()\n return seg0, [elbow], rseg1_trimmed.reversed()\n else:\n # find a point on each seg that is about a/2 away from joint. Make\n # line between them.\n t0 = seg0.ilength(seg0.length() - a/2)\n t1 = seg1.ilength(a/2)\n seg0_trimmed = seg0.cropped(0, t0)\n seg1_trimmed = seg1.cropped(t1, 1)\n seg0_line = Line(seg0_trimmed.end, q)\n seg1_line = Line(q, seg1_trimmed.start)\n\n args = (seg0_trimmed, seg0_line, maxjointsize, tightness)\n dummy, elbow0, seg0_line_trimmed = smoothed_joint(*args)\n\n args = (seg1_line, seg1_trimmed, maxjointsize, tightness)\n seg1_line_trimmed, elbow1, dummy = smoothed_joint(*args)\n\n args = (seg0_line_trimmed, seg1_line_trimmed, maxjointsize, tightness)\n seg0_line_trimmed, elbowq, seg1_line_trimmed = smoothed_joint(*args)\n\n elbow = elbow0 + [seg0_line_trimmed] + elbowq + [seg1_line_trimmed] + elbow1\n return seg0_trimmed, elbow, seg1_trimmed\n\n\ndef smoothed_path(path, maxjointsize=3, tightness=1.99, ignore_unfixable_kinks=False):\n \"\"\"returns a path with no non-differentiable joints.\"\"\"\n if len(path) == 1:\n return path\n\n assert path.iscontinuous()\n\n sharp_kinks = []\n new_path = [path[0]]\n for idx in range(len(path)):\n if idx == len(path)-1:\n if not path.isclosed():\n continue\n else:\n seg1 = new_path[0]\n else:\n seg1 = path[idx + 1]\n seg0 = new_path[-1]\n\n try:\n unit_tangent0 = seg0.unit_tangent(1)\n unit_tangent1 = seg1.unit_tangent(0)\n flag = False\n except ValueError:\n flag = True # unit tangent not well-defined\n\n if not flag and isclose(unit_tangent0, unit_tangent1): # joint is already smooth\n if idx != len(path)-1:\n new_path.append(seg1)\n continue\n else:\n kink_idx = (idx + 1) % len(path) # kink at start of this seg\n if not flag and isclose(-unit_tangent0, unit_tangent1):\n # joint is sharp 180 deg (must be fixed manually)\n new_path.append(seg1)\n sharp_kinks.append(kink_idx)\n else: # joint is not smooth, let's smooth it.\n args = (seg0, seg1, maxjointsize, tightness)\n new_seg0, elbow_segs, new_seg1 = smoothed_joint(*args)\n new_path[-1] = new_seg0\n new_path += elbow_segs\n if idx == len(path) - 1:\n new_path[0] = new_seg1\n else:\n new_path.append(new_seg1)\n\n # If unfixable kinks were found, let the user know\n if sharp_kinks and not ignore_unfixable_kinks:\n _report_unfixable_kinks(path, sharp_kinks)\n\n return Path(*new_path)\n\n\"\"\"(Experimental) replacement for import/export functionality SAX\n\n\"\"\"\n\n# External dependencies\nfrom __future__ import division, absolute_import, print_function\nimport os\nfrom xml.etree.ElementTree import iterparse, Element, ElementTree, SubElement\n\n# Internal dependencies\nfrom .parser import parse_path\nfrom .parser import parse_transform\nfrom .svg_to_paths import (path2pathd, ellipse2pathd, line2pathd,\n polyline2pathd, polygon2pathd, rect2pathd)\nfrom .misctools import open_in_browser\nfrom .path import *\n\n# To maintain forward/backward compatibility\ntry:\n str = basestring\nexcept NameError:\n pass\n\nNAME_SVG = \"svg\"\nATTR_VERSION = \"version\"\nVALUE_SVG_VERSION = \"1.1\"\nATTR_XMLNS = \"xmlns\"\nVALUE_XMLNS = \"http://www.w3.org/2000/svg\"\nATTR_XMLNS_LINK = \"xmlns:xlink\"\nVALUE_XLINK = \"http://www.w3.org/1999/xlink\"\nATTR_XMLNS_EV = \"xmlns:ev\"\nVALUE_XMLNS_EV = \"http://www.w3.org/2001/xml-events\"\nATTR_WIDTH = \"width\"\nATTR_HEIGHT = \"height\"\nATTR_VIEWBOX = \"viewBox\"\nNAME_PATH = \"path\"\nATTR_DATA = \"d\"\nATTR_FILL = \"fill\"\nATTR_STROKE = \"stroke\"\nATTR_STROKE_WIDTH = \"stroke-width\"\nATTR_TRANSFORM = \"transform\"\nVALUE_NONE = \"none\"\n\n\nclass SaxDocument:\n def __init__(self, filename):\n \"\"\"A container for a SAX SVG light tree objects document.\n\n This class provides functions for extracting SVG data into Path objects.\n\n Args:\n filename (str): The filename of the SVG file\n \"\"\"\n self.root_values = {}\n self.tree = []\n # remember location of original svg file\n if filename is not None and os.path.dirname(filename) == '':\n self.original_filename = os.path.join(os.getcwd(), filename)\n else:\n self.original_filename = filename\n\n if filename is not None:\n self.sax_parse(filename)\n\n def sax_parse(self, filename):\n self.root_values = {}\n self.tree = []\n stack = []\n values = {}\n matrix = None\n for event, elem in iterparse(filename, events=('start', 'end')):\n if event == 'start':\n stack.append((values, matrix))\n if matrix is not None:\n matrix = matrix.copy() # copy of matrix\n current_values = values\n values = {}\n values.update(current_values) # copy of dictionary\n attrs = elem.attrib\n values.update(attrs)\n name = elem.tag[28:]\n if \"style\" in attrs:\n for equate in attrs[\"style\"].split(\";\"):\n equal_item = equate.split(\":\")\n values[equal_item[0]] = equal_item[1]\n if \"transform\" in attrs:\n transform_matrix = parse_transform(attrs[\"transform\"])\n if matrix is None:\n matrix = np.identity(3)\n matrix = transform_matrix.dot(matrix)\n if \"svg\" == name:\n current_values = values\n values = {}\n values.update(current_values)\n self.root_values = current_values\n continue\n elif \"g\" == name:\n continue\n elif 'path' == name:\n values['d'] = path2pathd(values)\n elif 'circle' == name:\n values[\"d\"] = ellipse2pathd(values)\n elif 'ellipse' == name:\n values[\"d\"] = ellipse2pathd(values)\n elif 'line' == name:\n values[\"d\"] = line2pathd(values)\n elif 'polyline' == name:\n values[\"d\"] = polyline2pathd(values['points'])\n elif 'polygon' == name:\n values[\"d\"] = polygon2pathd(values['points'])\n elif 'rect' == name:\n values[\"d\"] = rect2pathd(values)\n else:\n continue\n values[\"matrix\"] = matrix\n values[\"name\"] = name\n self.tree.append(values)\n else:\n v = stack.pop()\n values = v[0]\n matrix = v[1]\n\n def flatten_all_paths(self):\n flat = []\n for values in self.tree:\n pathd = values['d']\n matrix = values['matrix']\n parsed_path = parse_path(pathd)\n if matrix is not None:\n transform(parsed_path, matrix)\n flat.append(parsed_path)\n return flat\n\n def get_pathd_and_matrix(self):\n flat = []\n for values in self.tree:\n pathd = values['d']\n matrix = values['matrix']\n flat.append((pathd, matrix))\n return flat\n\n def generate_dom(self):\n root = Element(NAME_SVG)\n root.set(ATTR_VERSION, VALUE_SVG_VERSION)\n root.set(ATTR_XMLNS, VALUE_XMLNS)\n root.set(ATTR_XMLNS_LINK, VALUE_XLINK)\n root.set(ATTR_XMLNS_EV, VALUE_XMLNS_EV)\n width = self.root_values.get(ATTR_WIDTH, None)\n height = self.root_values.get(ATTR_HEIGHT, None)\n if width is not None:\n root.set(ATTR_WIDTH, width)\n if height is not None:\n root.set(ATTR_HEIGHT, height)\n viewbox = self.root_values.get(ATTR_VIEWBOX, None)\n if viewbox is not None:\n root.set(ATTR_VIEWBOX, viewbox)\n identity = np.identity(3)\n for values in self.tree:\n pathd = values.get('d', '')\n matrix = values.get('matrix', None)\n # path_value = parse_path(pathd)\n\n path = SubElement(root, NAME_PATH)\n if matrix is not None and not np.all(np.equal(matrix, identity)):\n matrix_string = \"matrix(\"\n matrix_string += \" \"\n matrix_string += str(matrix[0][0])\n matrix_string += \" \"\n matrix_string += str(matrix[1][0])\n matrix_string += \" \"\n matrix_string += str(matrix[0][1])\n matrix_string += \" \"\n matrix_string += str(matrix[1][1])\n matrix_string += \" \"\n matrix_string += str(matrix[0][2])\n matrix_string += \" \"\n matrix_string += str(matrix[1][2])\n matrix_string += \")\"\n path.set(ATTR_TRANSFORM, matrix_string)\n if ATTR_DATA in values:\n path.set(ATTR_DATA, values[ATTR_DATA])\n if ATTR_FILL in values:\n path.set(ATTR_FILL, values[ATTR_FILL])\n if ATTR_STROKE in values:\n path.set(ATTR_STROKE, values[ATTR_STROKE])\n return ElementTree(root)\n\n def save(self, filename):\n with open(filename, 'wb') as output_svg:\n dom_tree = self.generate_dom()\n dom_tree.write(output_svg)\n\n def display(self, filename=None):\n \"\"\"Displays/opens the doc using the OS's default application.\"\"\"\n if filename is None:\n filename = 'display_temp.svg'\n self.save(filename)\n open_in_browser(filename)\n\n\"\"\"This submodule contains tools for creating path objects from SVG files.\nThe main tool being the svg2paths() function.\"\"\"\n\n# External dependencies\nfrom __future__ import division, absolute_import, print_function\nfrom xml.dom.minidom import parse\nfrom os import path as os_path, getcwd\nimport re\n\n# Internal dependencies\nfrom .parser import parse_path\n\n\nCOORD_PAIR_TMPLT = re.compile(\n r'([\\+-]?\\d*[\\.\\d]\\d*[eE][\\+-]?\\d+|[\\+-]?\\d*[\\.\\d]\\d*)' +\n r'(?:\\s*,\\s*|\\s+|(?=-))' +\n r'([\\+-]?\\d*[\\.\\d]\\d*[eE][\\+-]?\\d+|[\\+-]?\\d*[\\.\\d]\\d*)'\n)\n\ndef path2pathd(path):\n return path.get('d', '')\n\ndef ellipse2pathd(ellipse):\n \"\"\"converts the parameters from an ellipse or a circle to a string for a \n Path object d-attribute\"\"\"\n\n cx = ellipse.get('cx', 0)\n cy = ellipse.get('cy', 0)\n rx = ellipse.get('rx', None)\n ry = ellipse.get('ry', None)\n r = ellipse.get('r', None)\n\n if r is not None:\n rx = ry = float(r)\n else:\n rx = float(rx)\n ry = float(ry)\n\n cx = float(cx)\n cy = float(cy)\n\n d = ''\n d += 'M' + str(cx - rx) + ',' + str(cy)\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(2 * rx) + ',0'\n d += 'a' + str(rx) + ',' + str(ry) + ' 0 1,0 ' + str(-2 * rx) + ',0'\n\n return d\n\n\ndef polyline2pathd(polyline_d, is_polygon=False):\n \"\"\"converts the string from a polyline points-attribute to a string for a\n Path object d-attribute\"\"\"\n points = COORD_PAIR_TMPLT.findall(polyline_d)\n closed = (float(points[0][0]) == float(points[-1][0]) and\n float(points[0][1]) == float(points[-1][1]))\n\n # The `parse_path` call ignores redundant 'z' (closure) commands\n # e.g. `parse_path('M0 0L100 100Z') == parse_path('M0 0L100 100L0 0Z')`\n # This check ensures that an n-point polygon is converted to an n-Line path.\n if is_polygon and closed:\n points.append(points[0])\n\n d = 'M' + 'L'.join('{0} {1}'.format(x,y) for x,y in points)\n if is_polygon or closed:\n d += 'z'\n return d\n\n\ndef polygon2pathd(polyline_d):\n \"\"\"converts the string from a polygon points-attribute to a string \n for a Path object d-attribute.\n Note: For a polygon made from n points, the resulting path will be\n composed of n lines (even if some of these lines have length zero).\n \"\"\"\n return polyline2pathd(polyline_d, True)\n\n\ndef rect2pathd(rect):\n \"\"\"Converts an SVG-rect element to a Path d-string.\n \n The rectangle will start at the (x,y) coordinate specified by the \n rectangle object and proceed counter-clockwise.\"\"\"\n x0, y0 = float(rect.get('x', 0)), float(rect.get('y', 0))\n w, h = float(rect.get('width', 0)), float(rect.get('height', 0))\n x1, y1 = x0 + w, y0\n x2, y2 = x0 + w, y0 + h\n x3, y3 = x0, y0 + h\n\n d = (\"M{} {} L {} {} L {} {} L {} {} z\"\n \"\".format(x0, y0, x1, y1, x2, y2, x3, y3))\n return d\n\ndef line2pathd(l):\n return 'M' + l['x1'] + ' ' + l['y1'] + 'L' + l['x2'] + ' ' + l['y2']\n\ndef svg2paths(svg_file_location,\n return_svg_attributes=False,\n convert_circles_to_paths=True,\n convert_ellipses_to_paths=True,\n convert_lines_to_paths=True,\n convert_polylines_to_paths=True,\n convert_polygons_to_paths=True,\n convert_rectangles_to_paths=True):\n \"\"\"Converts an SVG into a list of Path objects and attribute dictionaries. \n\n Converts an SVG file into a list of Path objects and a list of\n dictionaries containing their attributes. This currently supports\n SVG Path, Line, Polyline, Polygon, Circle, and Ellipse elements.\n\n Args:\n svg_file_location (string): the location of the svg file\n return_svg_attributes (bool): Set to True and a dictionary of\n svg-attributes will be extracted and returned. See also the \n `svg2paths2()` function.\n convert_circles_to_paths: Set to False to exclude SVG-Circle\n elements (converted to Paths). By default circles are included as \n paths of two `Arc` objects.\n convert_ellipses_to_paths (bool): Set to False to exclude SVG-Ellipse\n elements (converted to Paths). By default ellipses are included as \n paths of two `Arc` objects.\n convert_lines_to_paths (bool): Set to False to exclude SVG-Line elements\n (converted to Paths)\n convert_polylines_to_paths (bool): Set to False to exclude SVG-Polyline\n elements (converted to Paths)\n convert_polygons_to_paths (bool): Set to False to exclude SVG-Polygon\n elements (converted to Paths)\n convert_rectangles_to_paths (bool): Set to False to exclude SVG-Rect\n elements (converted to Paths).\n\n Returns: \n list: The list of Path objects.\n list: The list of corresponding path attribute dictionaries.\n dict (optional): A dictionary of svg-attributes (see `svg2paths2()`).\n \"\"\"\n if os_path.dirname(svg_file_location) == '':\n svg_file_location = os_path.join(getcwd(), svg_file_location)\n\n doc = parse(svg_file_location)\n\n def dom2dict(element):\n \"\"\"Converts DOM elements to dictionaries of attributes.\"\"\"\n keys = list(element.attributes.keys())\n values = [val.value for val in list(element.attributes.values())]\n return dict(list(zip(keys, values)))\n\n # Use minidom to extract path strings from input SVG\n paths = [dom2dict(el) for el in doc.getElementsByTagName('path')]\n d_strings = [el['d'] for el in paths]\n attribute_dictionary_list = paths\n\n # Use minidom to extract polyline strings from input SVG, convert to\n # path strings, add to list\n if convert_polylines_to_paths:\n plins = [dom2dict(el) for el in doc.getElementsByTagName('polyline')]\n d_strings += [polyline2pathd(pl['points']) for pl in plins]\n attribute_dictionary_list += plins\n\n # Use minidom to extract polygon strings from input SVG, convert to\n # path strings, add to list\n if convert_polygons_to_paths:\n pgons = [dom2dict(el) for el in doc.getElementsByTagName('polygon')]\n d_strings += [polygon2pathd(pg['points']) for pg in pgons]\n attribute_dictionary_list += pgons\n\n if convert_lines_to_paths:\n lines = [dom2dict(el) for el in doc.getElementsByTagName('line')]\n d_strings += [('M' + l['x1'] + ' ' + l['y1'] +\n 'L' + l['x2'] + ' ' + l['y2']) for l in lines]\n attribute_dictionary_list += lines\n\n if convert_ellipses_to_paths:\n ellipses = [dom2dict(el) for el in doc.getElementsByTagName('ellipse')]\n d_strings += [ellipse2pathd(e) for e in ellipses]\n attribute_dictionary_list += ellipses\n\n if convert_circles_to_paths:\n circles = [dom2dict(el) for el in doc.getElementsByTagName('circle')]\n d_strings += [ellipse2pathd(c) for c in circles]\n attribute_dictionary_list += circles\n\n if convert_rectangles_to_paths:\n rectangles = [dom2dict(el) for el in doc.getElementsByTagName('rect')]\n d_strings += [rect2pathd(r) for r in rectangles]\n attribute_dictionary_list += rectangles\n\n if return_svg_attributes:\n svg_attributes = dom2dict(doc.getElementsByTagName('svg')[0])\n doc.unlink()\n path_list = [parse_path(d) for d in d_strings]\n return path_list, attribute_dictionary_list, svg_attributes\n else:\n doc.unlink()\n path_list = [parse_path(d) for d in d_strings]\n return path_list, attribute_dictionary_list\n\n\ndef svg2paths2(svg_file_location,\n return_svg_attributes=True,\n convert_circles_to_paths=True,\n convert_ellipses_to_paths=True,\n convert_lines_to_paths=True,\n convert_polylines_to_paths=True,\n convert_polygons_to_paths=True,\n convert_rectangles_to_paths=True):\n \"\"\"Convenience function; identical to svg2paths() except that\n return_svg_attributes=True by default. See svg2paths() docstring for more\n info.\"\"\"\n return svg2paths(svg_file_location=svg_file_location,\n return_svg_attributes=return_svg_attributes,\n convert_circles_to_paths=convert_circles_to_paths,\n convert_ellipses_to_paths=convert_ellipses_to_paths,\n convert_lines_to_paths=convert_lines_to_paths,\n convert_polylines_to_paths=convert_polylines_to_paths,\n convert_polygons_to_paths=convert_polygons_to_paths,\n convert_rectangles_to_paths=convert_rectangles_to_paths)\n\n","sub_path":"build/svgpathtools.py","file_name":"svgpathtools.py","file_ext":"py","file_size_in_byte":193428,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"609480061","text":"from typing import List\nfrom collections import deque\n\n\nclass Solution:\n def maxSlidingWindow(self, nums: List[int], k: int) -> List[int]:\n dq = deque()\n res = []\n for i, n in enumerate(nums):\n while dq and dq[0] <= i - k:\n dq.popleft()\n while dq and nums[dq[-1]] < n:\n dq.pop()\n dq.append(i)\n if i >= k - 1:\n res.append(nums[dq[0]])\n return res\n\n def test(self):\n for nums, k, target in [\n ([], 3, []),\n ([1, 2], 1, [1, 2]),\n ([1, 3, -1, -3, 5, 3, 6, 7], 3, [3, 3, 5, 5, 6, 7]),\n ]:\n ans = self.maxSlidingWindow(nums, k)\n assert ans == target, f\"target: {target}, ans: {ans}\"\n print(\"all done\")\n\n\nif __name__ == \"__main__\":\n Solution().test()\n","sub_path":"Week_01/239/sliding_window_maximum.py","file_name":"sliding_window_maximum.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"369438287","text":"# sway\nfrom __future__ import print_function, division\nimport sys\nsys.dont_write_bytecode = True \n\nfrom lib import *\n\n@setting\ndef COUNTS(): return o(\n max=256,\n qs = [0.1, 0.3, 0.5, 0.7, 0.9],\n round = r3s\n)\nclass Some:\n def __init__(i, init=[], max=None):\n i.n, i.all, i.max = 0,[],max or the.COUNTS.max\n map(i.__add__,init)\n def __add__(i,x):\n i.n += 1\n now = len(i.all)\n if now > i.max: return i.afterFull(x)\n if now < i.max: return i.beforeFull(x)\n return i.atFull(x)\n def beforeFull(i,x):\n i.all += [x]\n def atFull(i,x): \n return i.afterFull(x)\n def afterFull(i,x):\n now = len(i.all)\n if r() <= now/i.n:\n i.all[ int(r() * now) ]= x\n\nclass Log: pass\n\nclass Num(Log):\n def __init__(i,inits=[]):\n i.hi = i.lo = None\n i.mu = i.sd = i.m2 = 0 \n i.n = 0\n i.some = Some()\n i._also= None\n map(i.__add__,inits)\n def __add__(i,z):\n i._also = None\n i.n += 1\n i.some + z\n i.lo = min(z,i.lo)\n i.hi = max(z,i.hi)\n delta = z - i.mu;\n i.mu += delta/i.n\n i.m2 += delta*(z - i.mu)\n if i.n > 1:\n i.sd = (i.m2/(i.n - 1))**0.5\n def norm(i,z):\n return div( (z - i.lo), (i.hi - i.lo))\n def also(i):\n if not i._also:\n lst = sorted(i.some.all)\n m = len(lst) \n r = the.COUNTS.round\n i._also = o(some=lst,\n median=lst[int(m/2)],\n range=r([lst[int(m*x)] for x in the.COUNTS.qs]))\n return i._also\n \nclass Logs:\n def __init__(i,logs=[],inits=[]):\n i.logs = logs\n map(i.__add__,inits)\n def __add__(i,x):\n for log in i.logs:\n log + x\n\ndef _counts(): \n reset()\n most = 1000\n want = [0.05, 0.25, 0.5, 0.75,0.95]\n COUNTS(max=most,qs=want)\n got1 = Num([r() for _ in xrange(most)]).also().range\n got2 = Num([r() for _ in xrange(most)]).also().range\n print(\"M P SubSampling ReSampling SubWorseThanReSampling\")\n for few in [32,64,128,256,512]: \n print(\"\")\n COUNTS(max=few,qs=want)\n got0 = Num([r() for _ in xrange(most)]).also().range\n for g0,w,g1,g2 in zip(got0,want,got1,got2):\n subSampling = r3(100*div(g0-w,w))\n reSampling = r3(100*div(g1-g2,g1))\n subSampling = 0 if abs(subSampling) < 5 else subSampling\n reSampling = 0 if abs(reSampling) < 5 else reSampling\n print(few,w,subSampling, reSampling, \n abs(reSampling) < abs(subSampling))\n\nmain(__name__, _counts)\n","sub_path":"sway_old/counts.py","file_name":"counts.py","file_ext":"py","file_size_in_byte":2387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"536957149","text":"import os\nos.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'\n#import sys\nfrom tensorflow.python.keras.layers import Dense, GlobalMaxPooling2D, Dropout\n#from tensorflow.python.keras.optimizers import *\nfrom tensorflow.python.keras.applications import InceptionV3\nfrom tensorflow.python.keras.models import Model\nfrom tensorflow.python.keras.preprocessing.image import ImageDataGenerator\nfrom tensorflow.python.keras.callbacks import ModelCheckpoint, EarlyStopping, TensorBoard\nfrom tensorflow.python.keras import backend as K\n#from tensorflow.python.keras \n\n# fix seed for reproducible results (only works on CPU, not GPU)\n#seed = 9\n#np.random.seed(seed=seed)\n#tf.set_random_seed(seed=seed)\n\n# hyper parameters for model\nnb_classes = 6 # number of classes\nbased_model_last_block_layer_number = 126 # value is based on based model selected.\nimg_width, img_height = 299, 299 # change based on the shape/structure of your images\nbatch_size = 4 # try 4, 8, 16, 32, 64, 128, 256 dependent on CPU/GPU memory capacity (powers of 2 values).\nnb_epoch = 50 # number of iteration the algorithm gets trained.\n#learn_rate = 1e-4 # sgd learning rate\n#momentum = .9 # sgd momentum to avoid local minimum\ntransformation_ratio = .05 # how aggressive will be the data augmentation/transformation\nnb_train_samples = 6286# Total number of train samples. NOT including augmented images\nnb_validation_samples = 1570 # Total number of train samples. NOT including augmented images.\n\nPWD = 'src'\nMODEL = \"{}/model.h5\".format(PWD)\n\n\ndef train(train_data_dir, validation_data_dir, model_path):\n # Pre-Trained CNN Model using imagenet dataset for pre-trained weights\n # base_model = Xception(input_shape=(img_width, img_height, 3), weights='imagenet', include_top=False)\n \n if K.image_data_format() == 'channels_first':\n input_shape = (3, img_width, img_height)\n else:\n input_shape = (img_width, img_height, 3)\n\n if os.path.isfile(MODEL):\n model = load_model(MODEL)\n else:\n base_model = InceptionV3(input_shape=input_shape, weights='imagenet', include_top=False)\n \n x = base_model.output\n x = GlobalMaxPooling2D()(x)\n x = Dense(128, activation='relu', name='fc1')(x)\n x = Dropout(0.2)(x)\n\n predictions = Dense(nb_classes, activation='softmax', name='predictions')(x)\n\n # add your top layer block to your base model\n model = Model(base_model.input, predictions)\n #print(model.summary())\n\n\n # first: train only the top layers (which were randomly initialized)\n # i.e. freeze all layers of the based model that is already pre-trained.\n for layer in base_model.layers:\n layer.trainable = False\n \n model.compile(optimizer='nadam',\n loss='categorical_crossentropy', # categorical_crossentropy if multi-class classifier\n metrics=['accuracy'])\n \n\n # Read Data and Augment it: Make sure to select augmentations that are appropriate to your images.\n # To save augmentations un-comment save lines and add to your flow parameters.\n train_datagen = ImageDataGenerator(rescale=1. / 255,\n rotation_range=transformation_ratio,\n shear_range=transformation_ratio,\n zoom_range=transformation_ratio,\n cval=transformation_ratio,\n horizontal_flip=True,\n vertical_flip=True)\n\n validation_datagen = ImageDataGenerator(rescale=1. / 255)\n\n # os.makedirs(os.path.join(os.path.abspath(train_data_dir), 'preview'), exist_ok=True)\n train_generator = train_datagen.flow_from_directory(train_data_dir,\n target_size=[img_width, img_height],\n batch_size=batch_size,\n class_mode='categorical')\n # save_to_dir=os.path.join(os.path.abspath(train_data_dir), '../preview')\n # save_prefix='aug',\n # save_format='jpeg')\n # use the above 3 commented lines if you want to save and look at how the data augmentations look like\n\n validation_generator = validation_datagen.flow_from_directory(validation_data_dir,\n target_size=[img_width, img_height],\n batch_size=batch_size,\n class_mode='categorical')\n\n\n\n # save weights of best training epoch: monitor either val_loss or val_acc\n\n #top_weights_path = os.path.join(os.path.abspath(model_path), 'top_model_weights.h5')\n callbacks_list = [\n ModelCheckpoint(MODEL, monitor='val_acc', verbose=1, save_best_only=True),\n EarlyStopping(monitor='val_acc', patience=5, verbose=0),\n TensorBoard(log_dir='tensorboard/inception-v3-train-top-layer', histogram_freq=0, write_graph=False, write_images=False)\n ]\n\n # Train Simple CNN\n model.fit_generator(train_generator,\n steps_per_epoch=nb_train_samples // batch_size,\n epochs=nb_epoch / 5,\n validation_data=validation_generator,\n validation_steps=nb_validation_samples // batch_size,\n callbacks=callbacks_list)\n\n # verbose\n #print(\"\\nStarting to Fine Tune Model\\n\")\n\n # add the best weights from the train top model\n # at this point we have the pre-train weights of the base model and the trained weight of the new/added top model\n # we re-load model weights to ensure the best epoch is selected and not the last one.\n #model.load_weights(top_weights_path)\n\n # based_model_last_block_layer_number points to the layer in your model you want to train.\n # For example if you want to train the last block of a 19 layer VGG16 model this should be 15\n # If you want to train the last Two blocks of an Inception model it should be 172\n # layers before this number will used the pre-trained weights, layers above and including this number\n # will be re-trained based on the new data.\n #for layer in model.layers[:based_model_last_block_layer_number]:\n # layer.trainable = False\n #for layer in model.layers[based_model_last_block_layer_number:]:\n # layer.trainable = True\n\n # compile the model with a SGD/momentum optimizer\n # and a very slow learning rate.\n #model.compile(optimizer='nadam',\n # loss='categorical_crossentropy',\n # metrics=['accuracy'])\n \n \n # save weights of best training epoch: monitor either val_loss or val_acc\n #final_weights_path = os.path.join(os.path.abspath(model_path), 'model_weights.h5')\n #callbacks_list = [\n # ModelCheckpoint(final_weights_path, monitor='val_acc', verbose=1, save_best_only=True),\n # EarlyStopping(monitor='val_loss', patience=5, verbose=0),\n #keras.callbacks.TensorBoard(log_dir='tensorboard/inception-v3-fine-tune', histogram_freq=0, write_graph=False, write_images=False)\n #]\n \n\n\n\n # fine-tune the model\n #model.fit_generator(train_generator,\n # steps_per_epoch=nb_train_samples // batch_size,\n # epochs=nb_epoch,\n # validation_data=validation_generator,\n # validation_steps=nb_validation_samples // batch_size)\n\n # save model\n #model_json = model.to_json()\n #with open(os.path.join(os.path.abspath(model_path), 'model.json'), 'w') as json_file:\n # json_file.write(model_json)\n\n\ntrain('downloads/train', 'downloads/test', '.')\n","sub_path":"src/practice.py","file_name":"practice.py","file_ext":"py","file_size_in_byte":7885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"644596020","text":"#-*- coding:utf-8 -*-\nfrom django.shortcuts import render_to_response\nfrom django.http import HttpResponse,HttpResponseRedirect\nimport simplejson,sys,os,urllib,re,datetime,time,md5,hashlib,random,calendar\nimport calendar as cal\nfrom conn import crmdb\nfrom zz91page import *\nfrom sphinxapi import *\nfrom settings import searchconfig\n#from sphinx_rt import spcursor\n#sp=spcursor()\ndb = crmdb()\nreload(sys)\nsys.setdefaultencoding('UTF-8')\nnowpath=os.path.dirname(__file__)\n#行业字典\nINDUSTRY_LABEL={\n '10001000':'废塑料',\n '10001001':'废金属',\n '10001002':'废纸',\n '10001003':'废旧轮胎与废橡胶',\n '10001004':'废纺织品与废皮革',\n '10001005':'废电子电器',\n '10001006':'废玻璃',\n '10001007':'废旧二手设备',\n '10001008':'其他废料',\n '10001009':'服务',\n '10001010':'塑料原料',\n}\nINDUSTRY_LABEL_old={\n '1':'废塑料',\n '2':'废金属',\n '3':'废纸',\n '4':'废旧轮胎与废橡胶',\n '5':'废纺织品与废皮革',\n '6':'废电子电器',\n '10':'废玻璃',\n '12':'废旧二手设备',\n '14':'其他废料',\n '15':'服务',\n}\nexecfile(nowpath+\"/func/company_function.py\")\nexecfile(nowpath+\"/func/crmtools.py\")\nzzc=customer()\n\ndef index(request):\n return render_to_response(\"icdhtml/index.html\",locals())\n#我的所有客户\ndef icdlist(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return HttpResponseRedirect(\"relogin.html\")\n #是否为主管\n has_auth=zzc.is_hasauth(user_id=user_id)\n request_url=request.META.get('HTTP_REFERER','/')\n page=request.GET.get('page')\n isvapflag=None\n if not page:\n page=1\n dotype=request.GET.get('dotype')\n \n nowdint=int(time.strftime('%H%M',time.localtime(time.time())))\n if (dotype==\"gonghai\"):\n wk=getToday().weekday()\n if wk in [0,1,2,3,4]:\n moth=getToday().strftime('%m')\n if str(moth) in ['01','02','03','04','05','11','12']:\n if nowdint > 830 and nowdint<1200:\n return HttpResponse(\"该时间公海不��放！\")\n if nowdint > 1330 and nowdint<1730:\n return HttpResponse(\"该时间公海不开放！\")\n if str(moth) in ['06','07','08','09','10']:\n if nowdint > 830 and nowdint<1200:\n return HttpResponse(\"该时间公海不开放！\")\n if nowdint > 1330 and nowdint<1800:\n return HttpResponse(\"该时间公海不开放！\")\n \n if dotype:\n if (dotype[0:3]==\"vap\"):\n isvapflag=1\n if dotype==\"customin\":\n lucheck=1\n if dotype==\"requestassign\":\n sqcheck=1\n lmaction=request.GET.get('lmaction')\n #搜索\n searchlist={}\n companyname=request.GET.get('companyname')\n contact=request.GET.get('contact')\n mobile=request.GET.get('mobile')\n email=request.GET.get('email')\n business=request.GET.get('business')\n address=request.GET.get('address')\n com_rank=request.GET.get('com_rank')\n \n industry=request.GET.get('industry')\n account=request.GET.get('account')\n industry_txt=''\n if industry:\n industry_txt=INDUSTRY_LABEL[industry]\n last_login_time_begin=request.GET.get('last_login_time_begin')\n last_login_time_end=request.GET.get('last_login_time_end')\n regtime_begin=request.GET.get('regtime_begin')\n regtime_end=request.GET.get('regtime_end')\n company_id=request.GET.get('company_id')\n province=request.GET.get('p')\n city=request.GET.get('c')\n \n ccountry=request.GET.get('ccountry')\n country=request.GET.get('country')\n if not country:\n country=\"1\"\n area=request.GET.get('area')\n if not area:\n area=''\n \n contactnexttime_begin=request.GET.get('contactnexttime_begin')\n contactnexttime_end=request.GET.get('contactnexttime_end')\n comporder=request.GET.get('comporder')\n ascdesc=request.GET.get('ascdesc')\n comporder2=request.GET.get('comporder2')\n ascdesc2=request.GET.get('ascdesc2')\n comporder3=request.GET.get('comporder3')\n ascdesc3=request.GET.get('ascdesc3')\n lastteltime_begin=request.GET.get('lastteltime_begin')\n lastteltime_end=request.GET.get('lastteltime_end')\n servicetag=request.GET.get(\"servicetag\")\n isqianbao=request.GET.get(\"isqianbao\")\n \n adminuser_id=request.GET.get(\"adminuser_id\")\n \n is4star=request.GET.get(\"is4star\")\n is5star=request.GET.get(\"is5star\")\n logincount=request.GET.get(\"logincount\")\n \n companytype=request.GET.get(\"companytype\")\n \n telpersoncount=request.GET.get(\"telpersoncount\")\n telcount=request.GET.get(\"telcount\")\n telnocount=request.GET.get(\"telnocount\")\n \n today=getToday()\n today=date_to_int(today)\n \n today=today-60*60*24*5\n \n today=int_to_str(today)\n \n if company_id:\n searchlist['company_id']=company_id\n if telpersoncount:\n searchlist['telpersoncount']=telpersoncount\n if telcount:\n searchlist['telcount']=telcount\n if telnocount:\n searchlist['telnocount']=telnocount\n if adminuser_id:\n searchlist['adminuser_id']=adminuser_id\n if companytype:\n searchlist['companytype']=companytype\n if is4star:\n searchlist['is4star']=is4star\n if is5star:\n searchlist['is5star']=is5star\n if logincount:\n searchlist['logincount']=logincount\n if comporder:\n searchlist['comporder']=comporder\n if ascdesc:\n searchlist['ascdesc']=ascdesc\n if comporder2:\n searchlist['comporder2']=comporder2\n if ascdesc2:\n searchlist['ascdesc2']=ascdesc2\n if comporder3:\n searchlist['comporder3']=comporder3\n if ascdesc3:\n searchlist['ascdesc3']=ascdesc3\n if servicetag:\n searchlist['servicetag']=servicetag\n if companyname:\n searchlist['companyname']=companyname\n if contact:\n searchlist['contact']=contact\n if mobile:\n searchlist['mobile']=mobile\n if email:\n searchlist['email']=email\n if account:\n searchlist['account']=account\n if address:\n searchlist['address']=address\n if business:\n searchlist['business']=business\n if industry_txt:\n searchlist['industry_txt']=industry_txt\n if industry:\n searchlist['industry']=industry\n if com_rank:\n searchlist['com_rank']=com_rank\n if area:\n searchlist['area']=area\n if province:\n searchlist['p']=province\n if city:\n searchlist['c']=city\n if ccountry:\n searchlist['ccountry']=ccountry\n if country:\n searchlist['country']=country\n if last_login_time_begin:\n searchlist['last_login_time_begin']=last_login_time_begin\n if last_login_time_end:\n searchlist['last_login_time_end']=last_login_time_end\n if regtime_begin:\n searchlist['regtime_begin']=regtime_begin\n if regtime_end:\n searchlist['regtime_end']=regtime_end\n \n if lastteltime_begin:\n searchlist['lastteltime_begin']=lastteltime_begin\n if lastteltime_end:\n searchlist['lastteltime_end']=lastteltime_end\n \n if contactnexttime_begin:\n searchlist['contactnexttime_begin']=contactnexttime_begin\n if contactnexttime_end:\n searchlist['contactnexttime_end']=contactnexttime_end\n if isqianbao:\n searchlist['isqianbao']=isqianbao\n \n if not dotype:\n dotype=\"\"\n if dotype:\n searchlist['dotype']=dotype\n if not lmaction:\n lmaction=\"\"\n if lmaction:\n searchlist['lmaction']=lmaction\n \n searchurl=urllib.urlencode(searchlist)\n #过滤@号\n if searchlist:\n for ss in searchlist:\n values=searchlist[ss]\n values=values.replace(\"@\",\" \")\n searchlist[ss]=values\n #放公海的栏目\n if (dotype in ('my','contact','nocontact','today','allbm','vap_allbm')):\n dropbutton=1\n \n funpage=zz91page()\n limitNum=funpage.limitNum(10)\n nowpage=funpage.nowpage(int(page))\n frompageCount=funpage.frompageCount()\n after_range_num = funpage.after_range_num(3)\n before_range_num = funpage.before_range_num(6)\n #获得客户\n allcustomer=zzc.get_allcustomer(frompageCount=frompageCount,limitNum=limitNum,searchlist=searchlist,user_id=user_id)\n #获得销售人员列表(selection)\n allsalesman=zzc.get_allsalesman(user_id=user_id)\n if allcustomer:\n listcount=allcustomer['listcount']\n listall=allcustomer['listall']\n listcount = funpage.listcount(listcount)\n page_listcount=funpage.page_listcount()\n firstpage = funpage.firstpage()\n lastpage = funpage.lastpage()\n page_range = funpage.page_range()\n if len(page_range)>7:\n page_range=page_range[:7]\n nextpage = funpage.nextpage()\n prvpage = funpage.prvpage()\n provincelist=zzc.getprovincelist()\n #服务列表\n servicelist=zzc.getservicelist()\n guowaiprovincelist=zzc.getguowailist()\n \n return render_to_response(\"icdhtml/icdlist.html\",locals())\n#添加其他联系人页面\ndef otherperson(request):\n #编辑时取值\n editid=request.GET.get('edit')\n company_id=request.GET.get('company_id')\n dotype=request.GET.get('dotype')\n if editid:\n sql=\"select company_id,name,sex,tel,mobile,station,fax,email,address,bz,user_id,gmt_modified,gmt_created from kh_othercontact where id=%s\"\n result=db.fetchonedb(sql,[editid])\n return render_to_response(\"icdhtml/otherperson.html\",locals())\n#添加其他联系人\ndef add_otherperson(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return HttpResponseRedirect(\"relogin.html\")\n company_id=request.POST.get('company_id')\n dotype=request.POST.get('dotype')\n if not dotype:\n dotype=\"\"\n name=request.POST.get('other_name')\n sex=request.POST.get('other_sex')\n tel=request.POST.get('other_tel')\n mobile=request.POST.get('other_mobile')\n station=request.POST.get('other_station')\n fax=request.POST.get('other_fax')\n email=request.POST.get('other_email')\n address=request.POST.get('other_address')\n bz=request.POST.get('other_bz')\n gmt_modified=datetime.datetime.now()\n gmt_created=gmt_modified\n editflag=request.POST.get('editflag')\n if editflag:\n sql='update kh_othercontact set company_id=%s,name=%s,sex=%s,tel=%s,mobile=%s,station=%s,fax=%s,email=%s,address=%s,bz=%s,user_id=%s,gmt_modified=%s where id=%s'\n db.updatetodb(sql,[company_id,name,sex,tel,mobile,station,fax,email,address,bz,user_id,gmt_modified,editflag])\n else:\n sql='insert into kh_othercontact (company_id,name,sex,tel,mobile,station,fax,email,address,bz,user_id,gmt_modified,gmt_created) values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)'\n db.updatetodb(sql,[company_id,name,sex,tel,mobile,station,fax,email,address,bz,user_id,gmt_modified,gmt_created])\n zzc.updatemodifydata(company_id)\n savekhlog(company_id,user_id,user_id,'增加其他联系人')\n return HttpResponseRedirect(\"crm_cominfoedit.html?company_id=\"+str(company_id)+\"&dotype=\"+dotype)\n res={'res':'suc'}\n res=simplejson.dumps(res,ensure_ascii=False)\n return HttpResponse(res)\n#删除其他联系人\ndef del_otherperson(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return HttpResponseRedirect(\"relogin.html\")\n otherperson_id=request.GET.get('otherperson_id')\n dotype=request.GET.get('dotype')\n user_id=request.session.get('user_id',default=None)\n company_id=request.GET.get('company_id')\n sql='delete from kh_othercontact where id=%s and user_id=%s'\n db.updatetodb(sql,[otherperson_id,user_id])\n savekhlog(company_id,user_id,user_id,'删除其他联系人')\n return HttpResponseRedirect(\"crm_cominfoedit.html?company_id=\"+str(company_id)+\"&dotype=\"+dotype)\n\n#销售记录列表(iframe)\ndef tellist(request):\n \"\"\"\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return HttpResponseRedirect(\"relogin.html\")\n \"\"\"\n #翻页\n page=request.GET.get('page')\n if not page:\n page=1\n funpage=zz91page()\n limitNum=funpage.limitNum(10)\n nowpage=funpage.nowpage(int(page))\n frompageCount=funpage.frompageCount()\n after_range_num = funpage.after_range_num(5)\n before_range_num = funpage.before_range_num(9)\n #参数获得\n request_url=request.META.get('HTTP_REFERER','/')\n company_id=request.GET.get(\"company_id\")\n telflag=request.GET.get(\"telflag\")\n \n searchlist={}\n if company_id:\n searchlist['company_id']=company_id\n if telflag:\n searchlist['telflag']=telflag\n \n searchurl=urllib.urlencode(searchlist)\n \n telsalelistall=zzc.gettelsalelist(frompageCount,limitNum,company_id=company_id,telflag=telflag)\n telsalelist=telsalelistall['list']\n listcount=telsalelistall['count']\n if (int(listcount)>1000000):\n listcount=1000000-1\n listcount = funpage.listcount(listcount)\n page_listcount=funpage.page_listcount()\n firstpage = funpage.firstpage()\n lastpage = funpage.lastpage()\n page_range = funpage.page_range()\n if len(page_range)>7:\n page_range=page_range[:7]\n nextpage = funpage.nextpage()\n prvpage = funpage.prvpage()\n \n user_id=request.session.get('user_id',default=None)#销售id\n is_admin=zzc.get_is_admin(user_id)#是否为管理员权限\n #删除销售记录(管理员权限拥有)\n \"\"\"\n del_flag=request.GET.get(\"del\")\n com_id=request.GET.get(\"com_id\")\n id=request.GET.get(\"id\")\n if del_flag:\n sql='delete from kh_tel where id=%s and company_id=%s'\n db.updatetodb(sql,[id,com_id])\n return HttpResponseRedirect(request_url)\n \"\"\"\n return render_to_response(\"icdhtml/tellist.html\",locals())\n#日志列表\ndef khloglist(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return HttpResponseRedirect(\"relogin.html\")\n #翻页\n page=request.GET.get('page')\n if not page:\n page=1\n funpage=zz91page()\n limitNum=funpage.limitNum(10)\n nowpage=funpage.nowpage(int(page))\n frompageCount=funpage.frompageCount()\n after_range_num = funpage.after_range_num(5)\n before_range_num = funpage.before_range_num(9)\n #参数获得\n request_url=request.META.get('HTTP_REFERER','/')\n company_id=request.GET.get(\"company_id\")\n admin_user_id=request.GET.get(\"admin_user_id\")\n old_user_id=request.GET.get(\"old_user_id\")\n dotype=request.GET.get('dotype')\n mydo=request.GET.get('mydo')\n searchlist={}\n if company_id:\n searchlist['company_id']=company_id\n if admin_user_id:\n searchlist['admin_user_id']=admin_user_id\n if old_user_id:\n searchlist['old_user_id']=old_user_id\n if dotype:\n searchlist['dotype']=dotype\n \n if mydo:\n searchlist['admin_user_id']=user_id\n \n searchurl=urllib.urlencode(searchlist)\n \n loglistall=zzc.getloglist(frompageCount,limitNum,searchlist=searchlist,dotype=dotype)\n loglist=loglistall['list']\n listcount=loglistall['count']\n if (int(listcount)>1000000):\n listcount=1000000-1\n listcount = funpage.listcount(listcount)\n page_listcount=funpage.page_listcount()\n firstpage = funpage.firstpage()\n lastpage = funpage.lastpage()\n page_range = funpage.page_range()\n if len(page_range)>7:\n page_range=page_range[:7]\n nextpage = funpage.nextpage()\n prvpage = funpage.prvpage()\n \n \n is_admin=zzc.get_is_admin(user_id)#是否为管理员权限\n return render_to_response(\"icdhtml/log.html\",locals())\n#添加销售记录成功\ndef addtellist(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n company_id=request.POST.get('company_id')\n dotype=request.POST.get('dotype')\n contacttype=request.POST.get('contacttype')\n teltime=datetime.datetime.now()#当前电话时间\n rank=request.POST.get('com_rank')\n telflag=request.POST.get('telflag')\n seoflag=request.POST.get('telflag')\n if not seoflag:\n teltags=0\n else:\n teltags=seoflag\n contactnexttime=request.POST.get('contactnexttime')\n nocontacttype=request.POST.get('c_Nocontact')\n detail=request.POST.get('detail')\n gmt_created=teltime\n sql='insert into kh_tel (company_id,contacttype,teltime,user_id,rank,telflag,contactnexttime,nocontacttype,detail,gmt_created,teltags) values (%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)'\n db.updatetodb(sql,[company_id,contacttype,teltime,user_id,rank,telflag,contactnexttime,nocontacttype,detail,gmt_created,teltags])\n \n \n if dotype[0:3]==\"vap\":\n sql2='select id,rank,lastteltime,is4star,is5star from kh_sales_vap where company_id=%s order by id desc'\n result=db.fetchonedb(sql2,[company_id])\n if not rank:\n rank=0\n is4star=0\n is5star=0\n if result:\n is4star=result['is4star']\n is5star=result['is5star']\n if rank:\n if float(rank)>=4 and float(rank)<5:\n is4star=1\n if float(rank)>=5:\n is5star=1\n if not is4star:\n is4star=0\n if not is5star:\n is5star=0\n oldrank=0\n if result:\n oldrank=result['rank']\n lastteltime=result['lastteltime']\n if str(contacttype)==\"13\":\n lastteltime=gmt_created\n #有，则更新哦\n sql3='update kh_sales_vap set company_id=%s,rank=%s,contactnexttime=%s,lastteltime=%s,contacttype=%s,gmt_modified=%s,is4star=%s,is5star=%s where id=%s'\n db.updatetodb(sql3,[company_id,rank,contactnexttime,lastteltime,contacttype,gmt_created,is4star,is5star,result['id']])\n else:\n #没有,则插入\n lastteltime=None\n if str(contacttype)==\"13\":\n lastteltime=gmt_created\n sql3='insert into kh_sales_vap (company_id,rank,contactnexttime,lastteltime,contacttype,is4star,is5star,gmt_created,gmt_modified) values (%s,%s,%s,%s,%s,%s,%s,%s,%s)'\n db.updatetodb(sql3,[company_id,rank,contactnexttime,lastteltime,contacttype,is4star,is5star,gmt_created,gmt_created])\n if contactnexttime:\n #更新为已联系\n sql=\"update kh_assign_vap set isnew=1 where company_id=%s and user_id=%s\"\n db.updatetodb(sql,[company_id,user_id])\n updatesearchseek(['vap_contactnexttime','vap_lastteltime','vap_isnew','vap_is4star','vap_is5star','vap_rank'], {int(company_id):[str_to_int(contactnexttime),date_to_int(gmt_created),1,is4star,is5star,int(float(rank)*10)]})\n #转4、5星客户\n if rank:\n if float(rank)>float(oldrank) and float(rank)>=4 and float(rank)<5:\n sql=\"insert into kh_changestar(user_id,company_id,telflag,rank,gmt_created) values(%s,%s,%s,%s,%s)\"\n db.updatetodb(sql,[user_id,company_id,4,4,gmt_created])\n if float(rank)>float(oldrank) and int(rank)==5:\n sql=\"insert into kh_changestar(user_id,company_id,telflag,rank,gmt_created) values(%s,%s,%s,%s,%s)\"\n db.updatetodb(sql,[user_id,company_id,4,5,gmt_created])\n else:\n sql2='select id,rank,lastteltime,is4star,is5star from kh_sales where company_id=%s order by id desc'\n result=db.fetchonedb(sql2,[company_id])\n if not rank:\n rank=0\n is4star=0\n is5star=0\n if result:\n is4star=result['is4star']\n is5star=result['is5star']\n if not is4star:\n is4star=0\n if not is5star:\n is5star=0\n if rank:\n if float(rank)>=4 and float(rank)<5:\n is4star=1\n if float(rank)>=5:\n is5star=1\n oldrank=0\n if result:\n oldrank=result['rank']\n lastteltime=result['lastteltime']\n if str(contacttype)==\"13\":\n lastteltime=gmt_created\n #有，则更新哦\n sql3='update kh_sales set company_id=%s,rank=%s,contactnexttime=%s,lastteltime=%s,contacttype=%s,gmt_modified=%s,is4star=%s,is5star=%s where id=%s'\n db.updatetodb(sql3,[company_id,rank,contactnexttime,lastteltime,contacttype,gmt_created,is4star,is5star,result['id']])\n else:\n #没有,则插入\n lastteltime=None\n if str(contacttype)==\"13\":\n lastteltime=gmt_created\n sql3='insert into kh_sales (company_id,rank,contactnexttime,lastteltime,contacttype,is4star,is5star,gmt_created,gmt_modified) values (%s,%s,%s,%s,%s,%s,%s,%s,%s)'\n db.updatetodb(sql3,[company_id,rank,contactnexttime,lastteltime,contacttype,is4star,is5star,gmt_created,gmt_created])\n if contactnexttime:\n #更新为已联系\n sql=\"update kh_assign set isnew=1 where company_id=%s and user_id=%s\"\n db.updatetodb(sql,[company_id,user_id])\n updatesearchseek(['contactnexttime','lastteltime','isnew','is4star','is5star','rank'], {int(company_id):[str_to_int(contactnexttime),date_to_int(gmt_created),1,is4star,is5star,int(float(rank)*10)]})\n #转4、5星客户\n if rank:\n if float(rank)>float(oldrank) and float(rank)>=4 and float(rank)<5:\n sql=\"insert into kh_changestar(user_id,company_id,telflag,rank,gmt_created) values(%s,%s,%s,%s,%s)\"\n db.updatetodb(sql,[user_id,company_id,0,4,gmt_created])\n if float(rank)>float(oldrank) and int(rank)==5:\n sql=\"insert into kh_changestar(user_id,company_id,telflag,rank,gmt_created) values(%s,%s,%s,%s,%s)\"\n db.updatetodb(sql,[user_id,company_id,0,5,gmt_created])\n zzc.updatemodifydata(company_id)\n \n res={'res':'suc'}\n res=simplejson.dumps(res, ensure_ascii=False)\n return HttpResponse(res)\n#放入我的客户库\ndef assign_putmy(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n company_id=request.GET.get(\"company_id\")\n dotype=request.GET.get(\"dotype\")\n gmt_created=datetime.datetime.now()\n if user_id:\n if dotype[0:3]==\"vap\":\n #放VAP客户库\n sql=\"select id,user_id from kh_assign_vap where company_id=%s\"\n result=db.fetchonedb(sql,[company_id])\n if not result:\n sqlt=\"insert into kh_assign_vap (company_id,user_id,gmt_created) values(%s,%s,%s)\"\n db.updatetodb(sqlt,[company_id,user_id,gmt_created])\n ret={'res':'成功，你已成功放入该客户到VAP库里','err':'false'}\n zzc.updatemodifydata(company_id)\n savekhlog(company_id,user_id,user_id,'放入我的VAP客户库')\n sqld=\"update kh_income set assigncheck=0 where company_id=%s\"\n db.updatetodb(sqld,[company_id])\n assign_time=dateall_to_int(gmt_created)\n updatesearchseek(['vap_user_id','vap_assigncheck','vap_assign_time','vap_isnew'], {int(company_id):[int(user_id),0,assign_time,0]})\n \n else:\n vap_user_id=result['user_id']\n if vap_user_id==user_id:\n ret={'res':'失败，该客户已经在你的VAP客户库里','err':'true'}\n else:\n ret={'res':'失败，该客户在其他VAP客户库里','err':'true'}\n else:\n #预录入未审核不能放到我的客户库\n sql=\"select checked from company where id=%s\"\n result=db.fetchonedb(sql,[company_id])\n if result:\n achecked=result['checked']\n if str(achecked)==\"1\":\n ret={'res':'失败，该客户被其他销售申请并未审核，不能放不我库里','err':'true'}\n return HttpResponse(simplejson.dumps(ret, ensure_ascii=False))\n \n sql=\"select id,user_id from kh_assign where company_id=%s\"\n result=db.fetchonedb(sql,[company_id])\n if not result:\n sqlt=\"insert into kh_assign (company_id,user_id,gmt_created) values(%s,%s,%s)\"\n db.updatetodb(sqlt,[company_id,user_id,gmt_created])\n ret={'res':'成功，你已成功放入该客户到库里','err':'false'}\n zzc.updatemodifydata(company_id)\n savekhlog(company_id,user_id,user_id,'放入我的客户库')\n assign_time=dateall_to_int(gmt_created)\n sql=\"update company set checked=0 where id=%s\"\n db.updatetodb(sql,[company_id])\n updatesearchseek(['user_id','assign_time','checked','isnew'], {int(company_id):[int(user_id),assign_time,0,0]})\n \n else:\n icd_user_id=result['user_id']\n if icd_user_id==user_id:\n ret={'res':'失败，该客户已经在你的客户库里','err':'true'}\n else:\n ret={'res':'失败，该客户在其他客户库里','err':'true'}\n else:\n ret={'res':'失败！你还未登录','err':'true'}\n return HttpResponse(simplejson.dumps(ret, ensure_ascii=False))\n#主管或管理员分配\ndef addnew_assign(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n has_auth=zzc.is_hasauth(user_id=user_id)\n if str(has_auth)==\"0\":\n ret={'res':'失败，你没有权限分配客户','err':'false'}\n return simplejson.dumps(ret, ensure_ascii=False)\n companylist=request.GET.getlist('companylist')\n user_id=request.GET.get('user_id')\n dotype=request.GET.get('dotype')\n adminuser_id=request.session.get('user_id',default=None)#销售id\n gmt_created=datetime.datetime.now()\n \n if user_id:\n \n ret={'res':'成功，你已成功放入该客户到库里','err':'false'}\n for company_id in companylist:\n if dotype[0:3]==\"vap\":\n sql=\"select id,user_id from kh_assign_vap where company_id=%s\"\n result=db.fetchonedb(sql,[company_id])\n if not result:\n sqlt=\"insert into kh_assign_vap (company_id,user_id,gmt_created) values(%s,%s,%s)\"\n db.updatetodb(sqlt,[company_id,user_id,gmt_created])\n ret={'res':'成功，你已成功放入该客户到VAP库里','err':'false'}\n zzc.updatemodifydata(company_id)\n savekhlog(company_id,user_id,adminuser_id,'管理员分配到我的VAP客户库')\n sqld=\"update kh_income set assigncheck=0 where company_id=%s\"\n db.updatetodb(sqld,[company_id])\n vap_assign_time=dateall_to_int(gmt_created)\n updatesearchseek(['vap_user_id','vap_assigncheck','vap_assign_time','vap_isnew'], {int(company_id):[int(user_id),0,vap_assign_time,0]})\n else:\n aid=result[\"id\"]\n sqlt=\"update kh_assign_vap set user_id=%s,isnew=0,gmt_created=%s where id=%s\"\n db.updatetodb(sqlt,[user_id,gmt_created,aid])\n ret={'res':'成功，你已成功放入该客户到库里','err':'false'}\n zzc.updatemodifydata(company_id)\n savekhlog(company_id,user_id,adminuser_id,'管理员分配到我的VAP客户库')\n sqld=\"update kh_income set assigncheck=0 where company_id=%s\"\n db.updatetodb(sqld,[company_id])\n vap_assign_time=dateall_to_int(gmt_created)\n updatesearchseek(['vap_user_id','vap_assigncheck','vap_assign_time','vap_isnew'], {int(company_id):[int(user_id),0,vap_assign_time,0]})\n \n ret={'res':'成功，你已成功放入该客户到库里','err':'false'}\n \n else:\n #预录入未审核不能放到我的客户库\n sql=\"select checked from company where id=%s\"\n result=db.fetchonedb(sql,[company_id])\n if result:\n achecked=result['checked']\n if str(achecked)==\"1\":\n ret={'res':'失败，该客户被其他销售申请并未审核，不能放不我库里','err':'true'}\n return HttpResponse(simplejson.dumps(ret, ensure_ascii=False))\n sql=\"select id,user_id from kh_assign where company_id=%s\"\n result=db.fetchonedb(sql,[company_id])\n if not result:\n sqlt=\"insert into kh_assign (company_id,user_id,gmt_created) values(%s,%s,%s)\"\n db.updatetodb(sqlt,[company_id,user_id,gmt_created])\n ret={'res':'成功，你已成功放入该客户到库里','err':'false'}\n #sp.update(\"v_compall\",\"company_rt\",{'user_id':str(user_id)},int(company_id))\n zzc.updatemodifydata(company_id)\n savekhlog(company_id,user_id,adminuser_id,'管理员分配到我的客户库')\n assign_time=dateall_to_int(gmt_created)\n #更新申请分配标识\n sql=\"update company set checked=0 where id=%s\"\n db.updatetodb(sql,[company_id])\n updatesearchseek(['user_id','assign_time','checked','isnew'], {int(company_id):[int(user_id),assign_time,0,0]})\n else:\n aid=result[\"id\"]\n sqlt=\"update kh_assign set user_id=%s,isnew=0,gmt_created=%s where id=%s\"\n db.updatetodb(sqlt,[user_id,gmt_created,aid])\n \n zzc.updatemodifydata(company_id)\n savekhlog(company_id,user_id,adminuser_id,'管理员分配到我的客户库')\n assign_time=dateall_to_int(gmt_created)\n #更新申请分配标识\n sql=\"update company set checked=0 where id=%s\"\n db.updatetodb(sql,[company_id])\n updatesearchseek(['user_id','assign_time','checked','isnew'], {int(company_id):[int(user_id),assign_time,0,0]})\n ret={'res':'成功，你已成功放入该客户到库里','err':'false'}\n if dotype==\"qianbao\":\n sqlt=\"update kh_company_more set isassgin=1 where company_id=%s\"\n db.updatetodb(sqlt,[company_id])\n updatesearchseek(['isassgin'], {int(company_id):[1]})\n zzc.updatemodifydata(company_id)\n else:\n ret={'res':'失败！请选择你要分配的销售人员','err':'true'}\n return HttpResponse(simplejson.dumps(ret, ensure_ascii=False))\n#放入公海\ndef droptogonghai(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n companylist=request.GET.getlist('companylist')\n dotype=request.GET.get('dotype')\n if not dotype:\n dotype=''\n has_auth=zzc.is_hasauth(user_id=user_id)\n \n \n adminuser_id=request.session.get('user_id',default=None)#销售id\n gmt_created=datetime.datetime.now()\n ret={'res':'失败，该客户不在你的库里，或其他错误','err':'true'}\n if companylist:\n for company_id in companylist:\n #管理员放入公海\n if str(has_auth)==\"1\" and (dotype==\"allbm\" or dotype==\"vap_allbm\"):\n if dotype[0:3]==\"vap\":\n sql=\"delete from kh_assign_vap where company_id=%s\"\n result=db.updatetodb(sql,[company_id])\n telflag=4\n else:\n sql=\"delete from kh_assign where company_id=%s\"\n result=db.updatetodb(sql,[company_id])\n telflag=0\n if result:\n ret={'res':'成功，你已成功放入该客户到公海','err':'false'}\n sql=\"select max(id) as maxid from kh_tel where company_id=%s and telflag=%s\"\n resultm=db.fetchonedb(sql,[company_id,telflag])\n if resultm:\n maxtelid=resultm['maxid']\n sql=\"insert into kh_droptogonghai(company_id,tel_id,telflag,user_id,gmt_created) values(%s,%s,%s,%s,%s)\"\n db.updatetodb(sql,[company_id,maxtelid,telflag,adminuser_id,gmt_created])\n \n zzc.updatemodifydata(company_id)\n if dotype[0:3]==\"vap\":\n updatesearchseek(['vap_user_id'], {int(company_id):[0]})\n else:\n updatesearchseek(['user_id'], {int(company_id):[0]})\n savekhlog(company_id,user_id,adminuser_id,'管理员放入公海')\n else:\n if dotype:\n ismycompany=zzc.getismycompany(dotype=dotype,company_id=company_id,user_id=user_id)\n if ismycompany:\n sql=\"select id from kh_tel where company_id=%s\"\n resultb=db.fetchonedb(sql,[company_id])\n if not resultb:\n ret={'res':'失败，新客户请填写小计才可以放公海','err':'true'}\n else:\n if dotype[0:3]==\"vap\":\n sql=\"delete from kh_assign_vap where company_id=%s and user_id=%s\"\n result=db.updatetodb(sql,[company_id,adminuser_id])\n telflag=4\n else:\n sql=\"delete from kh_assign where company_id=%s and user_id=%s\"\n result=db.updatetodb(sql,[company_id,adminuser_id])\n telflag=0\n if result:\n ret={'res':'成功，你已成功放入该客户到公海','err':'false'}\n sql=\"select max(id) as maxid from kh_tel where company_id=%s and telflag=%s\"\n resultm=db.fetchonedb(sql,[company_id,telflag])\n if resultm:\n maxtelid=resultm['maxid']\n sql=\"insert into kh_droptogonghai(company_id,tel_id,telflag,user_id,gmt_created) values(%s,%s,%s,%s,%s)\"\n db.updatetodb(sql,[company_id,maxtelid,telflag,adminuser_id,gmt_created])\n \n \n zzc.updatemodifydata(company_id)\n if dotype[0:3]==\"vap\":\n updatesearchseek(['vap_user_id'], {int(company_id):[0]})\n else:\n updatesearchseek(['user_id'], {int(company_id):[0]})\n savekhlog(company_id,user_id,adminuser_id,'放入公海')\n else:\n ret={'res':'失败，该客户不在你的库里，或其他错误','err':'true'}\n return HttpResponse(simplejson.dumps(ret, ensure_ascii=False))\n#录入客户审核\ndef companychecked(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n companylist=request.GET.getlist('companylist')\n dotype=request.GET.get('dotype')\n if not dotype:\n dotype=''\n adminuser_id=request.session.get('user_id',default=None)#销售id\n gmt_created=datetime.datetime.now()\n ret={'res':'失败，系统出错','err':'true'}\n if companylist:\n for company_id in companylist:\n user_id=None\n sql=\"select user_id from company where id=%s\"\n result=db.fetchonedb(sql,[company_id])\n if result:\n user_id=result['user_id']\n if user_id:\n #更新申请分配/审核标识\n sql=\"update company set checked=0 where id=%s\"\n result=db.updatetodb(sql,[company_id])\n telflag=0\n if result:\n ret={'res':'成功，该客户已经分到录入者库里','err':'false'}\n sql=\"select id from kh_assign where company_id=%s\"\n result=db.fetchonedb(sql,[company_id])\n if not result:\n sqlt=\"insert into kh_assign (company_id,user_id,gmt_created) values(%s,%s,%s)\"\n db.updatetodb(sqlt,[company_id,user_id,gmt_created])\n ret={'res':'成功，该客户已经分到录入者库里','err':'false'}\n savekhlog(company_id,user_id,adminuser_id,'分配到我的客户库')\n updatesearchseek(['user_id','adminuser_id','checked','isnew'], {int(company_id):[int(user_id),int(user_id),0,0]})\n else:\n savekhlog(company_id,user_id,adminuser_id,'分配到我的客户库')\n updatesearchseek(['checked'], {int(company_id):[0]})\n ret={'res':'失败，该客户已经在其他客户库里','err':'true'}\n zzc.updatemodifydata(company_id)\n else:\n ret={'res':'失败，该客户录入者不存在！','err':'true'}\n return HttpResponse(simplejson.dumps(ret, ensure_ascii=False))\n#预申请分配客户审核\ndef companysqchecked(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n companylist=request.GET.getlist('companylist')\n dotype=request.GET.get('dotype')\n if not dotype:\n dotype=''\n adminuser_id=request.session.get('user_id',default=None)#销售id\n gmt_created=datetime.datetime.now()\n ret={'res':'失败，系统出错','err':'true'}\n if companylist:\n for company_id in companylist:\n user_id=None\n sql=\"select user_id from kh_assign_request where company_id=%s\"\n result=db.fetchonedb(sql,[company_id])\n if result:\n user_id=result['user_id']\n if user_id:\n #更新申请分配/审核标识\n sql=\"update company set checked=0 where id=%s\"\n result=db.updatetodb(sql,[company_id])\n telflag=0\n if result:\n ret={'res':'成功，该客户已经分到申请者库里','err':'false'}\n sql=\"select id from kh_assign where company_id=%s\"\n result=db.fetchonedb(sql,[company_id])\n if not result:\n sqlt=\"insert into kh_assign (company_id,user_id,gmt_created) values(%s,%s,%s)\"\n db.updatetodb(sqlt,[company_id,user_id,gmt_created])\n ret={'res':'成功，该客户已经分到申请者库里','err':'false'}\n savekhlog(company_id,user_id,adminuser_id,'分配到我的客户库')\n updatesearchseek(['user_id','checked','isnew'], {int(company_id):[int(user_id),0,0]})\n else:\n savekhlog(company_id,user_id,adminuser_id,'分配到我的客户库')\n updatesearchseek(['checked'], {int(company_id):[0]})\n ret={'res':'失败，该客户已经在其他客户库里','err':'true'}\n zzc.updatemodifydata(company_id)\n else:\n #更新申请分配/审核标识\n sql=\"update company set checked=0 where id=%s\"\n result=db.updatetodb(sql,[company_id])\n updatesearchseek(['checked'], {int(company_id):[0]})\n zzc.updatemodifydata(company_id)\n ret={'res':'失败，该客户申请者不存在！'+str(user_id),'err':'true'}\n return HttpResponse(simplejson.dumps(ret, ensure_ascii=False))\n#该公司详情\ndef crm_cominfoedit(request):\n request_url=request.META.get('HTTP_REFERER','/')\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n gmt_created=formattime(datetime.datetime.now())\n if not username or not user_id:\n return HttpResponseRedirect(\"relogin.html\")\n company_id=request.GET.get('company_id')\n dotype=request.GET.get('dotype')\n isvap=None\n if not dotype:\n return HttpResponse(\"参数错误！\")\n if dotype[0:3]==\"vap\":\n isvap=1\n partcode=zzc.getuserpart(user_id=user_id)\n #再生汇销售可以直接打开所有\n if str(partcode)=='1325':\n iszsh=1\n else:\n if not isvap:\n #是否高会或付费会员\n sql=\"select id from crm_company_service where company_id=%s and apply_status='1'\"\n result=db.fetchonedb(sql,[company_id])\n if result:\n if dotype==\"allall\":\n return HttpResponseRedirect(\"crm_cominfoedit.html?company_id=\"+str(company_id)+\"&dotype=vap_hezuo\")\n return HttpResponse(\"该客户已经转到VAP客户库，你已经不能操作查看！\")\n \n #用户级别\n authlist=zzc.geauthid(user_id=user_id)\n #是否管理员\n isadmin=None\n if \"1\" in authlist:\n isadmin=1\n #是否主管\n iszhuguan=None\n has_auth=zzc.is_hasauth(user_id=user_id)\n if str(has_auth)==\"1\":\n iszhuguan=1\n ismycompany=zzc.getismycompany(dotype=dotype,company_id=company_id,user_id=user_id)\n if not ismycompany:\n ismycompany=0\n if not isadmin:\n #是否我部门客户\n ismysee=zzc.getismysee(dotype=dotype,company_id=company_id,user_id=user_id)\n if not ismysee:\n if iszhuguan:\n ismysee=0\n else:\n return HttpResponse(\"你没有权限查看该信息！\")\n \n companyinfo=zzc.getcompanyinfo(company_id=company_id,user_id=user_id)\n if not companyinfo:\n telflag=0\n sql=\"delete from kh_assign where company_id=%s\"\n result=db.updatetodb(sql,[company_id])\n if result:\n adminuser_id=0\n zzc.updatemodifydata(company_id)\n updatesearchseek(['user_id'], {int(company_id):[0]})\n savekhlog(company_id,user_id,adminuser_id,'放入公海')\n return HttpResponse(\"该用户数据问题，系统已经自动放入公海！\")\n if isvap:\n companyinfo['emphases']=companyinfo['vap_emphases']\n com_rank=''\n if isvap:\n sql='select rank from kh_sales_vap where company_id=%s'\n result=db.fetchonedb(sql,[company_id])\n if result:\n com_rank=result['rank']\n else:\n sql='select rank from kh_sales where company_id=%s'\n result=db.fetchonedb(sql,[company_id])\n if result:\n com_rank=result['rank']\n business_type=companyinfo.get('business_type')\n if not business_type:\n companyinfo['business_type']=\"0\"\n #客户冲突\n searchlist={}\n ctmobile=\"\"\n #撞单客户\n companyinfo['mobile']\n if (companyinfo.has_key(\"mobile\")):\n ctmobile=ctmobile+companyinfo['mobile']\n if (companyinfo.has_key(\"tel\")) and (companyinfo.has_key(\"mobile\")):\n cttel=companyinfo['tel']\n if cttel:\n ctmobile=ctmobile+\"|\"+cttel\n if (companyinfo.has_key(\"tel\")) and (not companyinfo.has_key(\"mobile\")):\n cttel=companyinfo['tel']\n if cttel:\n ctmobile=ctmobile+cttel\n \n otherpersonlist=zzc.getotherperson(company_id=company_id,my_user_id=user_id)\n if otherpersonlist:\n for m in otherpersonlist:\n ccmobile=m['mobile']\n if ccmobile:\n ctmobile=ctmobile+\"|\"+ccmobile\n if ctmobile[0:1]==\"|\":\n ctmobile=ctmobile[1:len(ctmobile)]\n if ctmobile:\n ctmobile=ctmobile.replace(\"-\", '')\n searchlist['mobile']=ctmobile\n searchlist['dotype']='allall'\n searchlist['company_id']=company_id\n ctcompanylist=zzc.get_allcustomer(frompageCount=0,limitNum=50,searchlist=searchlist)\n ctcompanylist['listcount']=ctcompanylist['listcount']-1\n\n provincelist=zzc.getprovincelist()\n guowaiprovincelist=zzc.getguowailist()\n #获得额外联系人\n \n viptype=companyinfo['membership_code']\n #会员判断\n arrviptype={'vipname':'','vipsubname':'','vipcheck':'','ldb':''}\n if (viptype == '10051000'):\n arrviptype['vipname']='普通会员'\n if (viptype == '10051001'):\n arrviptype['vipname']='再生通'\n if (viptype == '100510021000'):\n arrviptype['vipname']='银牌品牌通'\n if (viptype == '100510021001'):\n arrviptype['vipname']='金牌品牌通'\n if (viptype == '100510021002'):\n arrviptype['vipname']='钻石品牌通'\n if (viptype == '10051000'):\n arrviptype['vipcheck']=None\n else:\n arrviptype['vipcheck']=1\n #来电宝客户\n sqll=\"select id from crm_company_service where company_id=%s and crm_service_code in ('1007','1008','1009','1010','1011') and apply_status=1\"\n ldbresult=db.fetchonedb(sqll,[company_id])\n if ldbresult:\n sqlg=\"select front_tel from phone where company_id=%s\"\n phoneresult=db.fetchonedb(sqlg,[company_id])\n if phoneresult:\n arrviptype['ldb']={'ldbphone':phoneresult['front_tel']}\n else:\n arrviptype['ldb']=None\n else:\n arrviptype['ldb']=None\n arrviptype['vipsubname'] = companyinfo['domain_zz91']\n \n sql=\"select id from crm_company_service where company_id=%s and crm_service_code='10001002'\"\n result=db.fetchonedb(sql,[company_id])\n if result:\n loginflag=1\n sql=\"select id from crm_company_service where company_id=%s and crm_service_code in ('1000','1008','1009','1010','10001003','10001004','10001005','1011') and apply_status=1\"\n resultl=db.fetchonedb(sql,[company_id])\n if resultl:\n loginflag=None\n #钱包余额\n fee=zzc.getqianbaoblance(company_id)\n isweixin=zzc.gethaveinstallweixin(company_id)\n isapp=zzc.gethaveinstallapp(company_id)\n \n #是否充值成功\n sql=\"select iszhifu from kh_company_more where company_id=%s\"\n resultl=db.fetchonedb(sql,[company_id])\n if resultl:\n iszhifu=resultl['iszhifu']\n if iszhifu==1:\n iszhifu=1\n else:\n iszhifu=None\n \n if not companyinfo:\n return HttpResponse(\"err\")\n else:\n return render_to_response(\"icdhtml/crm_cominfoedit.html\",locals())\n#保存公司详情\ndef save_crm_cominfoedit(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n company_id=request.POST.get(\"company_id\")\n dotype=request.POST.get(\"dotype\")\n if not dotype:\n dotype=''\n #公司信息\n company_name=request.POST.get(\"company_name\")\n address=request.POST.get(\"address\")\n address_zip=request.POST.get(\"address_zip\")\n website=request.POST.get(\"website\")\n industry_code=request.POST.get(\"industry_code\")\n service_code=request.POST.get(\"service_code\")\n introduction=request.POST.get(\"introduction\")\n business=request.POST.get(\"business\")\n business_type=request.POST.get(\"business_type\")#主营方向\n sale_details=request.POST.get(\"sale_details\")\n buy_details=request.POST.get(\"buy_details\")\n area_code=request.POST.get(\"area_code\")\n #账户信息\n tel_country_code=request.POST.get(\"tel_country_code\")\n tel_area_code=request.POST.get(\"tel_area_code\")\n tel=request.POST.get(\"tel\")\n mobile=request.POST.get(\"mobile\")\n contact=request.POST.get(\"contact\")\n position=request.POST.get(\"position\")\n sex=request.POST.get(\"sex\")\n email=request.POST.get(\"email\")\n fax_country_code=request.POST.get(\"fax_country_code\")\n fax_area_code=request.POST.get(\"fax_area_code\")\n fax=request.POST.get(\"fax\")\n rubbish=request.POST.get(\"rubbish\")\n \n emphases=request.POST.get(\"emphases\")\n islaji=request.POST.get(\"islaji\")\n if not emphases:\n emphases=0\n if not islaji:\n islaji=0\n isdeath=request.POST.get(\"isdeath\")\n if not isdeath:\n isdeath=0\n \n \n sql1=\"update company set name=%s,area_code=%s,address=%s,address_zip=%s,website=%s,industry_code=%s,service_code=%s,introduction=%s,business=%s,business_type=%s,sale_details=%s,buy_details=%s,rubbish=%s,islaji=%s,isdeath=%s where id=%s\"\n db.updatetodb(sql1,[company_name,area_code,address,address_zip,website,industry_code,service_code,introduction,business,business_type,sale_details,buy_details,rubbish,islaji,isdeath,company_id])\n sql2=\"update company_account set tel_country_code=%s,tel_area_code=%s,tel=%s,mobile=%s,contact=%s,position=%s,sex=%s,email=%s,fax_country_code=%s,fax_area_code=%s,fax=%s where company_id=%s\"\n db.updatetodb(sql2, [tel_country_code,tel_area_code,tel,mobile,contact,position,sex,email,fax_country_code,fax_area_code,fax,company_id])\n if user_id:\n if dotype[0:3]==\"vap\":\n sql=\"update kh_assign_vap set emphases=%s where company_id=%s and user_id=%s\"\n db.updatetodb(sql,[emphases,company_id,user_id])\n updatesearchseek(['vap_emphases','islaji','isdeath'], {int(company_id):[int(emphases),int(islaji),int(isdeath)]})\n else:\n sql=\"update kh_assign set emphases=%s where company_id=%s and user_id=%s\"\n db.updatetodb(sql,[emphases,company_id,user_id])\n updatesearchseek(['emphases','islaji','isdeath'], {int(company_id):[int(emphases),int(islaji),int(isdeath)]})\n zzc.updatemodifydata(company_id)\n res={'err':'false'}\n #updatesearchseek([['compname','area_code','address','industry_code','service_code','business','sale_details','buy_details','rubbish','emphases']], {int(company_id):[company_name,area_code,address,industry_code,service_code,business,sale_details,buy_details,rubbish,emphases]})\n res=simplejson.dumps(res, ensure_ascii=False)\n return HttpResponse(res)\n#设置为重点客户\ndef set_emphases(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n company_id=request.POST.get(\"company_id\")\n ispay=zzc.getispaymember(company_id)\n emphases=request.POST.get(\"emphases\")\n if ispay:\n sql=\"update kh_assign_vap set emphases=%s where company_id=%s and user_id=%s\"\n db.updatetodb(sql,[emphases,company_id,user_id])\n updatesearchseek(['vap_emphases'], {int(company_id):[int(emphases)]})\n else:\n sql=\"update kh_assign set emphases=%s where company_id=%s and user_id=%s\"\n db.updatetodb(sql,[emphases,company_id,user_id])\n updatesearchseek(['emphases'], {int(company_id):[int(emphases)]})\n zzc.updatemodifydata(company_id)\n if (emphases==1):\n res={'err':'false','res':'成功！已设置为重点客户！'}\n else:\n res={'err':'false','res':'成功！已经取消设置为重点客户'}\n res=simplejson.dumps(res, ensure_ascii=False)\n return HttpResponse(res)\n#转4、5星客户统计\ndef tj_changestar(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n today=formattime(getToday(),1)\n fromdate=request.GET.get('fromdate')\n todate=request.GET.get('todate')\n if not fromdate:\n fromdate=today\n if not todate:\n todate=today\n todate=formattime(getnextdate(todate),1)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n allsalesman=zzc.get_allsalesman(user_id=user_id)\n return render_to_response(\"icdhtml/tj_changestar.html\",locals())\n#转4、5星客户统计\ndef tj_changestarvap(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n today=formattime(getToday(),1)\n fromdate=request.GET.get('fromdate')\n todate=request.GET.get('todate')\n if not fromdate:\n fromdate=today\n if not todate:\n todate=today\n todate=formattime(getnextdate(todate),1)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n allsalesman=zzc.get_allsalesman(user_id=user_id,vap=1)\n return render_to_response(\"icdhtml/tj_changestarvap.html\",locals())\n\n#联系量统计\ndef tj_contact(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n today=formattime(getToday(),1)\n fromdate=request.GET.get('fromdate')\n todate=request.GET.get('todate')\n if not fromdate:\n fromdate=today\n if not todate:\n todate=today\n todate=formattime(getnextdate(todate),1)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n allsalesman=zzc.get_allsalesman(user_id=user_id)\n return render_to_response(\"icdhtml/tj_contact.html\",locals())\n\n#联系量统计VAP\ndef tj_contactvap(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n today=formattime(getToday(),1)\n fromdate=request.GET.get('fromdate')\n todate=request.GET.get('todate')\n if not fromdate:\n fromdate=today\n if not todate:\n todate=today\n todate=formattime(getnextdate(todate),1)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n allsalesman=zzc.get_allsalesman(user_id=user_id,vap=1)\n return render_to_response(\"icdhtml/tj_contactvap.html\",locals())\ndef tj_company(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n today=formattime(getToday(),1)\n fromdate=request.GET.get('fromdate')\n todate=request.GET.get('todate')\n if not fromdate:\n fromdate=today\n if not todate:\n todate=today\n todate=formattime(getnextdate(todate),1)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n allsalesman=zzc.get_allsalesman(user_id=user_id)\n return render_to_response(\"icdhtml/tj_company.html\",locals())\ndef tj_companyvap(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n today=formattime(getToday(),1)\n fromdate=request.GET.get('fromdate')\n todate=request.GET.get('todate')\n if not fromdate:\n fromdate=today\n if not todate:\n todate=today\n todate=formattime(getnextdate(todate),1)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n allsalesman=zzc.get_allsalesman(user_id=user_id,vap=1)\n return render_to_response(\"icdhtml/tj_companyvap.html\",locals())\n#客户数统计\ndef tj_companyvalue(request):\n user_id=request.GET.get('user_id')\n rank=request.GET.get('rank')\n nocontact=request.GET.get('nocontact')\n gendiu=request.GET.get('gendiu')\n user_category_code=request.GET.get('user_category_code')\n vapflag=request.GET.get('vapflag')\n if user_category_code==\"0\":\n tcount=0\n else:\n tcount=zzc.get_tongjicompany(user_id=user_id,rank=rank,nocontact=nocontact,gendiu=gendiu,user_category_code=user_category_code,vapflag=vapflag)\n tj={'count':tcount}\n res=simplejson.dumps(tj, ensure_ascii=False)\n return HttpResponse(res)\n#转4星客户统计\ndef tj_changestarvalue(request):\n user_id=request.GET.get('user_id')\n rank=request.GET.get('rank')\n telflag=request.GET.get('telflag')\n income=request.GET.get('income')\n fromdate=request.GET.get('fromdate')\n todate=request.GET.get('todate')\n tcount=zzc.get_tongjichangestar(user_id=user_id,rank=rank,telflag=telflag,income=income,fromdate=fromdate,todate=todate)\n tj={'count':tcount}\n res=simplejson.dumps(tj, ensure_ascii=False)\n return HttpResponse(res)\n#公司数据\ndef tj_companylist(request):\n user_id=request.GET.get('user_id')\n rank=request.GET.get('rank')\n nocontact=request.GET.get('nocontact')\n gendiu=request.GET.get('gendiu')\n user_category_code=request.GET.get('user_category_code')\n vapflag=request.GET.get('vapflag')\n page=request.GET.get('page')\n \n if not page:\n page=1\n searchlist={}\n if user_id:\n searchlist['user_id']=user_id\n if rank:\n searchlist['rank']=rank\n if nocontact:\n searchlist['nocontact']=nocontact\n if gendiu:\n searchlist['gendiu']=gendiu\n if user_category_code:\n searchlist['user_category_code']=user_category_code\n if vapflag:\n searchlist['vapflag']=vapflag\n \n searchurl=urllib.urlencode(searchlist)\n funpage=zz91page()\n limitNum=funpage.limitNum(10)\n nowpage=funpage.nowpage(int(page))\n frompageCount=funpage.frompageCount()\n after_range_num = funpage.after_range_num(3)\n before_range_num = funpage.before_range_num(6)\n \n listall=zzc.get_tongjicompanylist(frompageCount,limitNum,user_id=user_id,rank=rank,nocontact=nocontact,gendiu=gendiu,user_category_code=user_category_code,vapflag=vapflag)\n listcount=listall['listcount']\n listall=listall['listall']\n if (int(listcount)>1000000):\n listcount=1000000-1\n listcount = funpage.listcount(listcount)\n page_listcount=funpage.page_listcount()\n firstpage = funpage.firstpage()\n lastpage = funpage.lastpage()\n page_range = funpage.page_range()\n if len(page_range)>7:\n page_range=page_range[:7]\n nextpage = funpage.nextpage()\n prvpage = funpage.prvpage()\n \n return render_to_response(\"icdhtml/tj_companylist.html\",locals())\n#转4、5星客户公司数据\ndef tj_changestarlist(request):\n user_id=request.GET.get('user_id')\n rank=request.GET.get('rank')\n page=request.GET.get('page')\n \n telflag=request.GET.get('telflag')\n income=request.GET.get('income')\n fromdate=request.GET.get('fromdate')\n todate=request.GET.get('todate')\n vapflag=request.GET.get('vapflag')\n \n if not page:\n page=1\n searchlist={}\n if user_id:\n searchlist['user_id']=user_id\n if rank:\n searchlist['rank']=rank\n if telflag:\n searchlist['telflag']=telflag\n if income:\n searchlist['income']=income\n if fromdate:\n searchlist['fromdate']=fromdate\n if todate:\n searchlist['todate']=todate\n \n searchurl=urllib.urlencode(searchlist)\n funpage=zz91page()\n limitNum=funpage.limitNum(10)\n nowpage=funpage.nowpage(int(page))\n frompageCount=funpage.frompageCount()\n after_range_num = funpage.after_range_num(3)\n before_range_num = funpage.before_range_num(6)\n \n listall=zzc.get_tongjichangestarlist(frompageCount,limitNum,user_id=user_id,rank=rank,telflag=telflag,income=income,fromdate=fromdate,todate=todate)\n listcount=listall['listcount']\n listall=listall['listall']\n if (int(listcount)>1000000):\n listcount=1000000-1\n listcount = funpage.listcount(listcount)\n page_listcount=funpage.page_listcount()\n firstpage = funpage.firstpage()\n lastpage = funpage.lastpage()\n page_range = funpage.page_range()\n if len(page_range)>7:\n page_range=page_range[:7]\n nextpage = funpage.nextpage()\n prvpage = funpage.prvpage()\n \n return render_to_response(\"icdhtml/tj_companylist.html\",locals())\n#联系量数据\ndef tj_contactvalue(request):\n user_id=request.GET.get('user_id')\n rank=request.GET.get('rank')\n contacttype=request.GET.get('contacttype')\n telflag=request.GET.get('telflag')\n today=formattime(getToday(),1)\n fromdate=request.GET.get('fromdate')\n todate=request.GET.get('todate')\n teltags=request.GET.get('teltags')\n tomorow=request.GET.get('tomorow')\n if not fromdate:\n fromdate=today\n if not todate:\n todate=today\n todate=formattime(getnextdate(todate),1)\n \n tcount=zzc.get_tongjicontact(user_id=user_id,rank=rank,contacttype=contacttype,telflag=telflag,fromdate=fromdate,todate=todate,teltags=teltags,tomorow=tomorow)\n tj={'count':tcount}\n res=simplejson.dumps(tj, ensure_ascii=False)\n return HttpResponse(res)\n#联系量列表\ndef tj_contactlist(request):\n user_id=request.GET.get('user_id')\n rank=request.GET.get('rank')\n contacttype=request.GET.get('contacttype')\n telflag=request.GET.get('telflag')\n today=formattime(getToday(),1)\n fromdate=request.GET.get('fromdate')\n todate=request.GET.get('todate')\n teltags=request.GET.get('teltags')\n tomorow=request.GET.get('tomorow')\n if not fromdate:\n fromdate=today\n if not todate:\n todate=today\n todate=formattime(getnextdate(todate),1)\n page=request.GET.get('page')\n if not page:\n page=1\n searchlist={}\n if user_id:\n searchlist['user_id']=user_id\n if rank:\n searchlist['rank']=rank\n if contacttype:\n searchlist['contacttype']=contacttype\n if telflag:\n searchlist['telflag']=telflag\n if fromdate:\n searchlist['fromdate']=fromdate\n if todate:\n searchlist['todate']=todate\n if teltags:\n searchlist['teltags']=teltags\n if tomorow:\n searchlist['tomorow']=tomorow\n \n searchurl=urllib.urlencode(searchlist)\n funpage=zz91page()\n limitNum=funpage.limitNum(10)\n nowpage=funpage.nowpage(int(page))\n frompageCount=funpage.frompageCount()\n after_range_num = funpage.after_range_num(3)\n before_range_num = funpage.before_range_num(6)\n \n tjlistall=zzc.get_tongjicontactlist(frompageCount,limitNum,user_id=user_id,rank=rank,contacttype=contacttype,telflag=telflag,fromdate=fromdate,todate=todate,teltags=teltags,tomorow=tomorow)\n listcount=tjlistall['count']\n listall=tjlistall['list']\n if (int(listcount)>1000000):\n listcount=1000000-1\n listcount = funpage.listcount(listcount)\n page_listcount=funpage.page_listcount()\n firstpage = funpage.firstpage()\n lastpage = funpage.lastpage()\n page_range = funpage.page_range()\n if len(page_range)>7:\n page_range=page_range[:7]\n nextpage = funpage.nextpage()\n prvpage = funpage.prvpage()\n \n return render_to_response(\"icdhtml/tj_contactlist.html\",locals())\n#开通单列表\ndef orderlist(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n \n user_category_code=request.GET.get('user_category_code')\n douser_id=request.GET.get('douser_id')\n account=request.GET.get('account')\n mobile=request.GET.get('mobile')\n allsalesman=zzc.get_allsalesman(user_id=user_id)\n \n today=formattime(getToday(),1)\n fromdate=request.GET.get('fromdate')\n todate=request.GET.get('todate')\n service_type=request.GET.get('service_type')\n sales_type=request.GET.get('sales_type')\n sales_priceflag=request.GET.get('sales_priceflag')\n \"\"\"\n if not fromdate:\n fromdate=today\n if not todate:\n todate=today\n todate=formattime(getnextdate(todate),1)\n \"\"\"\n \n page=request.GET.get('page')\n if not page:\n page=1\n searchlist={}\n if douser_id:\n searchlist['douser_id']=douser_id\n if user_category_code:\n searchlist['user_category_code']=user_category_code\n if account:\n searchlist['account']=account\n if mobile:\n searchlist['mobile']=mobile\n if fromdate:\n searchlist['fromdate']=fromdate\n if todate:\n searchlist['todate']=todate\n if service_type:\n searchlist['service_type']=service_type\n if sales_type:\n searchlist['sales_type']=sales_type\n if sales_priceflag:\n searchlist['sales_priceflag']=sales_priceflag\n \n searchurl=urllib.urlencode(searchlist)\n funpage=zz91page()\n limitNum=funpage.limitNum(10)\n nowpage=funpage.nowpage(int(page))\n frompageCount=funpage.frompageCount()\n after_range_num = funpage.after_range_num(3)\n before_range_num = funpage.before_range_num(6)\n \n listall=zzc.getorderlist(frompageCount,limitNum,searchlist=searchlist)\n listcount=listall['count']\n listall=listall['list']\n if (int(listcount)>1000000):\n listcount=1000000-1\n listcount = funpage.listcount(listcount)\n page_listcount=funpage.page_listcount()\n firstpage = funpage.firstpage()\n lastpage = funpage.lastpage()\n page_range = funpage.page_range()\n if len(page_range)>7:\n page_range=page_range[:7]\n nextpage = funpage.nextpage()\n prvpage = funpage.prvpage()\n return render_to_response(\"icdhtml/orderlist.html\",locals())\n\ndef ordercompany(request):\n company_id= request.GET.get(\"company_id\")\n if company_id:\n searchlist={}\n searchlist['company_id']=company_id\n searchurl=urllib.urlencode(searchlist)\n frompageCount=0\n limitNum=20\n listall=zzc.getorderlist(frompageCount,limitNum,searchlist=searchlist)\n listcount=listall['count']\n listall=listall['list']\n return render_to_response(\"icdhtml/ordercompany.html\",locals())\n#销售开通单\ndef order(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n company_id= request.GET.get(\"company_id\")\n mbflag=request.GET.get(\"mbflag\")\n dotype=request.GET.get(\"dotype\")\n if not mbflag:\n if dotype:\n if dotype[0:3]==\"vap\":\n mbflag=\"1\"\n else:\n mbflag=\"2\"\n mbflag=str(mbflag)\n if company_id:\n account=\"\"\n sql=\"select account,contact,mobile from company_account where company_id=%s\"\n result=db.fetchonedb(sql,[company_id])\n if result:\n account=result['account']\n com_contactperson=result['contact']\n com_mobile=result['mobile']\n if not account:\n return HttpResponse(\"该公司账号不存在！\")\n else:\n return HttpResponse(\"错误！\")\n \n allsalesman=zzc.get_allsalesman(user_id=user_id)\n \n service_type1=['再生通','品牌通','展会产品','广告','线下纸媒','百度优化','移动生意管家','国际站','终身服务','商铺服务','诚信会员','定金','微站','来电宝五元','来电宝免月租','来电宝按通','首页直达广告','企业秀','其他']\n service_type2=['再生通','品牌通','展会产品','广告','黄页','展会广告','百度优化','移动生意管家','终身服务','商铺服务','诚信会员','定金','微站','来电宝五元','来电宝免月租','来电宝按通','首页直达广告','企业秀','其他']\n service_type3=['再生通续费','再生通','品牌通续费','展会产品','百度优化','移动生意管家','广告续费','终身服务','商铺服务','诚信会员','定金','微站','来电宝五元','来电宝免月租','来电宝按通','首页直达广告','企业秀','其他']\n \n servicetype=service_type1\n if mbflag==\"1\":\n servicetype=service_type1\n if mbflag==\"2\":\n servicetype=service_type2\n if mbflag==\"3\":\n servicetype=service_type3\n \n typelist=[]\n n=0\n for list in servicetype:\n l={'name':list,'n':n}\n n+=1\n typelist.append(l)\n \n \n \n if mbflag==\"1\":\n return render_to_response(\"icdhtml/order1.html\",locals())\n if mbflag==\"2\":\n return render_to_response(\"icdhtml/order2.html\",locals())\n if mbflag==\"3\":\n return render_to_response(\"icdhtml/order3.html\",locals())\n return render_to_response(\"icdhtml/order1.html\",locals())\ndef ordersave(request):\n dbserver = crmdb(dbtype=\"server\")\n company_id=request.POST.get(\"company_id\")\n user_id = request.POST.get(\"user_id\")\n mtemplates = request.POST.get(\"templates\")\n service_type1 = str(request.POST.get(\"service_type1\"))\n gmt_income=paytime = request.POST.get(\"paytime\")\n user_category_code=request.POST.get(\"user_category_code\")\n \n account=request.POST.get(\"account\")\n amount=payMoney = str(request.POST.get(\"payMoney\"))+'00'\n \n sale_staff=saler=zzc.get_username(user_id)\n \n adkeywords = request.POST.get(\"adkeywords\")\n adfromdate = request.POST.get(\"adfromdate\")\n adtodate = request.POST.get(\"adtodate\")\n adcontent = request.POST.get(\"adcontent\")\n remark = request.POST.get(\"remark\")\n gmt_created=datetime.datetime.now()\n gmt_modified=datetime.datetime.now()\n gmt_date=gmt_created.strftime('%Y-%m-%d')\n mbradio = request.POST.get(\"mbradio\")\n qiyexiu = request.POST.get(\"qiyexiu\")\n remark=\"广告关键字：\"+adkeywords+\"|开始时间：\"+adfromdate+\"|结束时间：\"+adtodate+\"|广告内容：\"+adcontent+\"|备注：\"+remark\n servicestr=request.REQUEST.getlist(\"service_type2\")\n order_nostr=\"\"\n for t in servicestr:\n apply_group=random.randrange(0,1000000000)\n apply_groupstr=str(apply_group)+\"|\"\n order_no=str(gmt_income)+str(mbradio)+str(t)+str(company_id)+str(account)\n order_no = hashlib.md5(str(order_no))\n order_no = order_no.hexdigest()\n order_nostr=order_nostr+order_no+\"|\"\n #判断订单是否已经申请\n errflag=0\n service_type=t\n crm_service_code='1005'\n if (service_type=='再生通' or service_type=='再生通续费' or service_type=='品牌通' or service_type=='品牌通续费'):\n crm_service_code='1000'\n if (service_type=='品牌通'):\n crm_service_code='1000'\n remark1='开通品牌通'\n if (service_type=='广告' or service_type=='广告续费'):\n crm_service_code='1002'\n if (service_type=='黄页'):\n crm_service_code='1003'\n if (service_type=='展会产品' or service_type=='展会广告'):\n crm_service_code='1004'\n if (service_type=='简版再生通' or service_type=='简版续费'):\n crm_service_code='1006'\n if (service_type=='百度优化'):\n crm_service_code='10001002'\n if (service_type=='商铺服务'):\n crm_service_code='10001004'\n if (service_type=='移动生意管家'):\n crm_service_code='10001000'\n if (service_type=='终身服务'):\n crm_service_code='10001003'\n if (service_type=='再生通发起人'):\n crm_service_code='10001001'\n if (service_type=='诚信会员'):\n crm_service_code='10001005'\n if (service_type=='定金'):\n crm_service_code='10001006'\n if (service_type=='微站'):\n crm_service_code='10001007'\n if (service_type=='来电宝(1或2.8)元'):\n crm_service_code='1007'\n if (service_type=='来电宝五元'):\n crm_service_code='1008'\n if (service_type=='首页直达广告'):\n crm_service_code='10001008'\n if (service_type=='来电宝免月租'):\n crm_service_code='1010'\n if (service_type=='来电宝按通'):\n crm_service_code='1011'\n if (service_type=='企业秀'):\n crm_service_code='10001009'\n if (service_type=='移动生意管家'):\n crm_service_code='10001000'\n if service_type==service_type1:\n amount=amount\n else:\n amount=0\n sql=\"select id from crm_service_apply where order_no=%s\"\n result=dbserver.fetchonedb(sql,[order_no])\n if not result:\n value=[apply_group, order_no, gmt_income, account, amount, sale_staff, remark, gmt_created, gmt_modified]\n sql=\"insert into crm_service_apply(apply_group, order_no, gmt_income, email, amount, sale_staff, remark, gmt_created, gmt_modified) values(%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\n dbserver.updatetodb(sql,value)\n remark1=''\n apply_status='0'\n value=[company_id,crm_service_code,apply_group,remark1,gmt_created, gmt_modified,apply_status,mtemplates]\n sql=\"insert into crm_company_service(company_id,crm_service_code,apply_group,remark,gmt_created, gmt_modified,apply_status,mobile_templates) values(%s,%s,%s,%s,%s,%s,%s,%s)\"\n dbserver.updatetodb(sql,value);\n \n if crm_service_code=='10001009':\n sql=\"select id from crm_service_qiyexiu where company_id=%s\"\n resultd=dbserver.fetchonedb(sql,[company_id])\n if not resultd:\n sql=\"insert into crm_service_qiyexiu(company_id,css,html) values(%s,%s,%s)\"\n dbserver.updatetodb(sql,[company_id,qiyexiu,qiyexiu]);\n #保存到本地数据库\n sql=\"select id from kh_income where order_no=%s\"\n result=db.fetchonedb(sql,[order_no])\n if not result:\n realname=saler\n sales_date=paytime\n sales_type=request.POST.get(\"customType\")\n sales_price=request.POST.get(\"payMoney\")\n mobile=request.POST.get(\"com_mobile\")\n bz=request.POST.get(\"remark\")\n contactperson=request.POST.get(\"com_contactperson\")\n com_ly1=request.POST.get(\"com_ly1\")\n \n com_ly1=request.POST.get(\"com_ly1\")\n com_ly2=request.POST.get(\"com_ly2\")\n com_zq=request.POST.get(\"com_zq\")\n com_fwq=request.POST.get(\"com_fwq\")\n com_khdq=request.POST.get(\"com_khdq\")\n com_pro=request.POST.get(\"com_pro\")\n com_cpjb=request.POST.get(\"com_cpjb\")\n com_cxfs=request.POST.get(\"com_cxfs\")\n com_hkfs=request.POST.get(\"com_hkfs\")\n com_gjd=request.POST.get(\"com_gjd\")\n com_servernum=request.POST.get(\"com_servernum\")\n if not user_id:\n user_id=0\n if not com_servernum:\n com_servernum=0\n if not com_gjd:\n com_gjd=''\n if user_id and user_category_code and company_id:\n value=[int(user_id),int(user_category_code),int(company_id),order_no,realname,sales_date,service_type,service_type1,sales_type,sales_price,mobile,bz,contactperson,com_ly1,com_ly2,com_zq,com_fwq,com_khdq,com_pro,com_cpjb,com_cxfs,com_hkfs,com_gjd,com_servernum,gmt_created]\n sqlc=\"insert into kh_income (user_id,user_category_code,company_id,order_no,realname,sales_date,service_type,service_type1,sales_type,sales_price,mobile,bz,contactperson,com_ly1,com_ly2,com_zq,com_fwq,com_khdq,com_pro,com_cpjb,com_cxfs,com_hkfs,com_gjd,com_servernum,gmt_created) values(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\"\n db.updatetodb(sqlc,value)\n return HttpResponse(\"<script>parent.closeorder()</script>\")\ndef ordertongji(request):\n nowdate=request.GET.get(\"nowdate\")\n if not nowdate:\n today=getToday()\n else:\n today=str_to_date(nowdate)\n #fromday=str_to_date(today.strftime('%Y-%m')+\"-1\")\n yearv=request.GET.get(\"yearv\")\n mouthv=request.GET.get(\"mouthv\")\n nowday=None\n if mouthv:\n today=str_to_date(yearv+\"-\"+mouthv+\"-1\")\n d = cal.monthrange(int(yearv), int(mouthv))\n nowday=d[1]\n nowyear=today.strftime('%Y')\n nowmoth=today.strftime('%m')\n if not nowday:\n nowday=today.strftime('%d')\n \n \n datelist=range(1,int(nowday)+1)\n \n \n icd1all=\"%.2f\"%zzc.getincomecount(user_category_code=\"1306\",ndate=date_to_str(today),mflag=1)\n icd2all=\"%.2f\"%zzc.getincomecount(user_category_code=\"1322\",ndate=date_to_str(today),mflag=1)\n icd3all=\"%.2f\"%zzc.getincomecount(user_category_code=\"1307\",ndate=date_to_str(today),mflag=1)\n icdall=\"%.2f\"%float(float(icd1all)+float(icd2all)+float(icd3all))\n vapall=\"%.2f\"%zzc.getincomecount(user_category_code=\"1315\",ndate=date_to_str(today),mflag=1)\n csall=\"%.2f\"%zzc.getincomecount(user_category_code=\"24\",ndate=date_to_str(today),mflag=1)\n ldball=\"%.2f\"%zzc.getincomecount(user_category_code=\"1320\",ndate=date_to_str(today),mflag=1)\n jiaoyiall=\"%.2f\"%zzc.getincomecount(user_category_code=\"1324\",ndate=date_to_str(today),mflag=1)\n allall=\"%.2f\"%zzc.getincomecount(ndate=date_to_str(today),mflag=1)\n \n dlist=[]\n for list in datelist:\n ndate=today.strftime('%Y-%m')+\"-\"+str(list)\n icd1=zzc.getincomecount(user_category_code=\"1306\",ndate=ndate)\n icd2=zzc.getincomecount(user_category_code=\"1322\",ndate=ndate)\n icd3=zzc.getincomecount(user_category_code=\"1307\",ndate=ndate)\n vap=\"%.2f\"%zzc.getincomecount(user_category_code=\"1315\",ndate=ndate)\n cs=\"%.2f\"%zzc.getincomecount(user_category_code=\"24\",ndate=ndate)\n ldb=\"%.2f\"%zzc.getincomecount(user_category_code=\"1320\",ndate=ndate)\n jiaoyi=\"%.2f\"%zzc.getincomecount(user_category_code=\"1324\",ndate=ndate)\n allprice=\"%.2f\"%zzc.getincomecount(ndate=ndate)\n l={'date':ndate,'icd1':\"%.2f\"%icd1,'icd2':\"%.2f\"%icd2,'icd':\"%.2f\"%(icd1+icd2+icd3),'icd3':\"%.2f\"%icd3,'vap':vap,'cs':cs,'ldb':ldb,'jiaoyi':jiaoyi,'allprice':allprice}\n dlist.append(l)\n \n return render_to_response(\"icdhtml/ordertongji.html\",locals())\n#客户添加\ndef companyadd(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n dbserver = crmdb(dbtype=\"server\")\n #客户参加活动列表\n def getactive():\n sql=\"select label,code from category where parent_code='2003'\"\n aclist=dbserver.fetchalldb(sql)\n return aclist\n addtype= request.GET.get(\"addtype\")\n sid = request.GET.get(\"sid\")\n com_mobile = request.GET.get(\"com_mobile\")\n com_contactperson = request.GET.get(\"com_contactperson\")\n activelist=getactive()\n if (com_mobile==None):\n com_mobile=\"\"\n if (com_contactperson==None):\n com_contactperson=\"\"\n if (addtype==\"zst\"):\n zstflag=1\n provincelist=zzc.getprovincelist()\n guowaiprovincelist=zzc.getguowailist()\n host=request.get_host()\n return render_to_response('icdhtml/compadd.html',locals())\n#客户保存\ndef companysave(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n dbserver = crmdb(dbtype=\"server\")\n addtype=request.POST.get(\"addtype\")\n name=cname = request.POST.get(\"cname\")\n contact=ccontactp = request.POST.get(\"ccontactp\")\n cdesi = request.POST.get(\"cdesi\")\n industry_code = request.POST.get(\"industry_code\")\n service_code = request.POST.get(\"service_code\")\n sid = request.POST.get(\"sid\")\n active_flag = request.POST.get(\"active_flag\")\n address=cadd = request.POST.get(\"cadd\")\n address_zip=czip = request.POST.get(\"czip\")[0:6]\n ctel = request.POST.get(\"ctel\")\n mobile=cmobile = request.POST.get(\"cmobile\")\n cfax = request.POST.get(\"cfax\")\n account = request.POST.get(\"account\")\n cemail = request.POST.get(\"cemail\")\n website=domain=cweb = request.POST.get(\"cweb\")\n introduction=cintroduce = request.POST.get(\"cintroduce\")\n business = cproductslist_en = request.POST.get(\"cproductslist_en\")\n personid = request.POST.get(\"personid\")\n countryselect = request.POST.get(\"countryselect\")\n area_code = request.POST.get(\"area_code\")\n account=account.replace(' ','')\n cemail=cemail.replace(' ','')\n \n regtime=gmt_created=datetime.datetime.now()\n gmt_modified=datetime.datetime.now()\n password=sjs=random.randrange(0,100000000)\n md5pwd = hashlib.md5(str(password))\n md5pwd = md5pwd.hexdigest()[8:-8]\n #''判断邮箱帐号是否存在\n sql=\"select id from auth_user where username='\"+str(account)+\"' or email='\"+str(cemail)+\"'\"\n accountlist=dbserver.fetchonedb(sql)\n if (accountlist):\n response = HttpResponse()\n response.write(\"<script>alert('该用户名或邮箱已经存在！');</script>\")\n return response\n \n #''帐号添加\n value=[account,md5pwd,cemail,gmt_created,gmt_modified];\n sql=\"insert into auth_user (username,password,email,gmt_created,gmt_modified) values(%s,%s,%s,%s,%s)\";\n resultauth=dbserver.updatetodb(sql,value)\n auth_user_id=resultauth['id']\n zzc.getserverdbtable(dbserver,\"auth_user\",auth_user_id)\n \n #添加公司信息\n foreign_city=''\n category_garden_id=\"\"\n membership_code='10051000'\n classified_code='10101002'\n regfrom_code='10031023'\n \n value=[name, industry_code, business, service_code, area_code, foreign_city, category_garden_id, membership_code, domain, classified_code, regfrom_code, regtime, gmt_created, gmt_modified, address, address_zip, website, introduction,active_flag]\n sql=\"insert into company (name, industry_code, business, service_code, area_code, foreign_city, category_garden_id, membership_code, domain, classified_code, regfrom_code, regtime, gmt_created, gmt_modified, address, address_zip, website, introduction,active_flag)\"\n sql=sql+\" values(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\";\n result=dbserver.updatetodb(sql,value)\n if result:\n company_id=result['id']\n zzc.getserverdbtable(dbserver,\"company\",company_id)\n is_admin='1'\n tel_country_code=''\n tel_area_code=''\n tel=ctel\n fax_country_code=''\n fax_area_code=''\n fax=cfax\n email=cemail\n sex=cdesi\n #'添加联系方式\n value=[account, company_id, contact, is_admin, tel_country_code, tel_area_code, tel, mobile, fax_country_code, fax_area_code, fax, email, sex, password, gmt_modified, gmt_created]\n sql=\"insert into company_account (account, company_id, contact, is_admin, tel_country_code, tel_area_code, tel, mobile, fax_country_code, fax_area_code, fax, email, sex, password, gmt_modified, gmt_created)\"\n sql=sql+\" values(%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s,%s)\";\n resulta=dbserver.updatetodb(sql,value)\n company_account_id=resulta['id']\n zzc.getserverdbtable(dbserver,\"company_account\",company_account_id)\n savekhlog(company_id,user_id,user_id,'录入客户')\n sql=\"update company set user_id=%s where id=%s\"\n db.updatetodb(sql,[user_id,company_id])\n zzc.updatemodifydata(company_id)\n updateseekoneday()\n #保存到本地\n \n return HttpResponse(\"录入保存成功！<script>alert('录入成功，主管审核后即可放到你的客户库！');parent.addsucss()</script>\")\ndef company_offercount(request):\n dbserver = crmdb(dbtype=\"server\")\n account=request.GET.get(\"account\")\n company_id=request.GET.get(\"company_id\")\n pcount=0\n qcount=0\n rcount=0\n if company_id:\n sql=\"select count(0) as count from products where company_id=%s\"\n result=dbserver.fetchonedb(sql,[company_id])\n pcount=result['count']\n sql=\"select qcount from inquiry_count where company_id=%s\"\n result=dbserver.fetchonedb(sql,[company_id])\n if result:\n qcount=result['qcount']\n if account:\n sql=\"select count(0) as count from inquiry where sender_account=%s\"\n result=dbserver.fetchonedb(sql,[account])\n rcount=result['count']\n list={'pcount':pcount,'qcount':qcount,'rcount':rcount}\n listall=simplejson.dumps(list, ensure_ascii=False)\n return HttpResponse(listall)\n#客户分配设置\ndef user_assign(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n allsalesman=zzc.get_allsalesman(user_id=user_id)\n sql=\"select id from user where assignflag=1\"\n result=db.fetchalldb(sql)\n userassignlist=\"0\"\n for list in result:\n userassignlist+=\",\"+str(list['id'])\n return render_to_response(\"icdhtml/user_assign.html\",locals())\ndef user_assign_ok(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n useridlist=request.POST.getlist('userid')\n sql=\"update user set assignflag=0 where id>%s\"\n db.updatetodb(sql,[0])\n for list in useridlist:\n sql=\"update user set assignflag=1 where id=%s\"\n db.updatetodb(sql,[list])\n return HttpResponse(\"保存成功\")\n#客户分配设置\ndef user_assign_vap(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n allsalesman=zzc.get_allsalesman(user_id=user_id)\n sql=\"select id from user where assignvapflag=1\"\n result=db.fetchalldb(sql)\n userassignlist=\"0\"\n for list in result:\n userassignlist+=\",\"+str(list['id'])\n return render_to_response(\"icdhtml/user_assign_vap.html\",locals())\ndef user_assign_vap_ok(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n useridlist=request.POST.getlist('userid')\n sql=\"update user set assignvapflag=0 where id>%s\"\n db.updatetodb(sql,[0])\n for list in useridlist:\n sql=\"update user set assignvapflag=1 where id=%s\"\n db.updatetodb(sql,[list])\n return HttpResponse(\"保存成功\")\n \n#掉公海设置\ndef user_drop(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n allsalesman=zzc.get_allsalesman(user_id=user_id)\n sql=\"select id from user where dropflag=1\"\n result=db.fetchalldb(sql)\n userassignlist=\"0\"\n for list in result:\n userassignlist+=\",\"+str(list['id'])\n return render_to_response(\"icdhtml/user_drop.html\",locals())\ndef user_drop_ok(request):\n useridlist=request.POST.getlist('userid')\n sql=\"update user set dropflag=0 where id>%s\"\n db.updatetodb(sql,[0])\n for list in useridlist:\n sql=\"update user set dropflag=1 where id=%s\"\n db.updatetodb(sql,[list])\n return HttpResponse(\"保存成功\")\n\n#预申请客户\ndef sqcompany(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n company_id=request.GET.get(\"company_id\")\n gmt_created=datetime.datetime.now()\n if company_id:\n dbserver = crmdb(dbtype=\"server\")\n sql=\"select id from company_account where company_id=%s\"\n result=dbserver.fetchonedb(sql,[company_id])\n if result:\n company_account_id=result['id']\n ca=zzc.getserverdbtable(dbserver,\"company\",company_id)\n cc=zzc.getserverdbtable(dbserver,\"company_account\",company_account_id)\n if ca==1:\n # 预分配客户申请\n sqld=\"replace into kh_assign_request (company_id,user_id,gmt_created) values(%s,%s,%s)\"\n db.updatetodb(sqld,[company_id,user_id,gmt_created])\n \n sqlc=\"update company set checked=1 where id=%s\"\n db.updatetodb(sqlc,[company_id])\n \n zzc.updatemodifydata(company_id)\n savekhlog(company_id,user_id,user_id,'申请分配新客户')\n updateseekoneday()\n updatesearchseek(['checked'], {int(company_id):[1]})\n return HttpResponse(\"申请成功，等待主管审核！\")\n else:\n return HttpResponse(\"该客户已经在本地库，请在本地库搜索！\")\n else:\n return HttpResponse(\"错误！\")\n \n \ndef getnewcompanycount(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n ccount=0\n sql=\"select ccount from kh_assign_count where user_id=%s\"\n result=db.fetchonedb(sql,[user_id])\n if result:\n ccount=result['ccount']\n return HttpResponse(simplejson.dumps({'ccount':ccount}, ensure_ascii=False))\n#获得地点\ndef getsite(request):\n sitecode=request.GET.get('sitecode')\n sitelist=zzc.getsitelist(sitecode=sitecode)\n sitelist=simplejson.dumps(sitelist, ensure_ascii=False)\n return HttpResponse(sitelist)\n#重新登录\ndef relogin(request):\n return render_to_response(\"icdhtml/relogin.html\",locals())\n#返回\ndef returnpage(request):\n request_url=request.GET.get('request_url')\n return HttpResponseRedirect(request_url)\ndef getcompanyid(request):\n account=request.GET.get(\"account\")\n if account:\n dbserver = crmdb(dbtype=\"server\")\n sql=\"select username from auth_user where username=%s or account=%s or email=%s\"\n result=dbserver.fetchonedb(sql,[account,account,account])\n if result:\n account=result[\"username\"]\n sql=\"select company_id from company_account where account=%s\"\n result=dbserver.fetchonedb(sql,[account])\n if result:\n company_id=result[\"company_id\"]\n tourl=\"/icd/tellist.html?company_id=\"+str(company_id)\n return HttpResponseRedirect(tourl)\n else:\n return HttpResponse(\"录入的账号有误\")\n else:\n return HttpResponse(\"录入的账号有误\")\n else:\n return HttpResponse(\"录入的账号有误\")\n \n#再生钱包账单\ndef qianbaobalancelist(request):\n username=request.session.get('username',default=None)\n user_id=request.session.get('user_id',default=None)\n if not username or not user_id:\n return simplejson.dumps({'err':'login'}, ensure_ascii=False)\n \n company_id=request.GET.get('company_id')\n \n page=request.GET.get('page')\n if not page:\n page=1\n searchlist={}\n searchlist['company_id']=company_id\n \n searchurl=urllib.urlencode(searchlist)\n funpage=zz91page()\n limitNum=funpage.limitNum(10)\n nowpage=funpage.nowpage(int(page))\n frompageCount=funpage.frompageCount()\n after_range_num = funpage.after_range_num(3)\n before_range_num = funpage.before_range_num(6)\n \n listall=zzc.getpayfeelist(company_id,frompageCount,limitNum)\n listcount=listall['count']\n listall=listall['list']\n if (int(listcount)>1000000):\n listcount=1000000-1\n listcount = funpage.listcount(listcount)\n page_listcount=funpage.page_listcount()\n firstpage = funpage.firstpage()\n lastpage = funpage.lastpage()\n page_range = funpage.page_range()\n if len(page_range)>7:\n page_range=page_range[:7]\n nextpage = funpage.nextpage()\n prvpage = funpage.prvpage()\n return render_to_response(\"icdhtml/qianbaobalancelist.html\",locals())\n \n ","sub_path":"icdlist.py","file_name":"icdlist.py","file_ext":"py","file_size_in_byte":92700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"331380732","text":"import re\ndef split(word):\n return [char for char in word]\n\nH=int(input())\nT=int(input())\nTsx=input().split()\nfor x in Tsx:\n x=int(x)\nM=int(input())\nS=split(input())\nfor x in range(0,len(S)):\n if(S[x]!='1'):\n S[x]='X'\nS=''.join(S)\ngaps = len(max(re.compile(\"(X+X)*\").findall(S)))\ncho=0\nfor x in Tsx:\n if(int(x)<=gaps):\n cho=int(x)\nif(H%cho == 0):\n print((H//cho))\nelse:\n print((H//cho)+1)\n","sub_path":"CCC/CCC_2020/UCC Coding Compitition/3/tractor.py","file_name":"tractor.py","file_ext":"py","file_size_in_byte":421,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"640144676","text":"from tkinter import *\nfrom PIL import Image, ImageTk\nimport cv2\nimport numpy as np\nimport threading\nimport os\nimport time\nfrom picamera import PiCamera\nfrom picamera.array import PiRGBArray\nimport pickle\n\n\n\n#tao cua so\nwin = Tk() # gọi giao diện\nwin.title('final-project') # đặt tên giao diện\nwin.geometry('1200x680+80+0') # kích thước giao diên\nwin.resizable(width=False,height=False) #ko cho phép thay đổi kích thước\nvar = IntVar()\n\n#giao dien con\ndef child_windows():\n\n\ttop=Toplevel()\t\t\t\t#tạo giao diện con\n\ttop.title('measuring')\t\t# tên giao diện con\n\ttop.geometry('600x200+0+0')\t#kích thước giao diện con\n\n\tframe4 =Frame(top, bg='pink') # tạo frame4\n\tframe4.pack(fill=BOTH, expand='yes')\n\n\tLabel(frame4,text=\"Thời gian bắt đầu:\",justify= LEFT, font = \"TIMES 18\").place(x=10,y=10) #tạo chữ\n\tLabel(frame4,text=\"Thời gian làm việc liên tục:\",justify= LEFT, font = \"TIMES 18\").place(x=10,y=60)\n\tnow = time.gmtime() # xác định thời gian hiện tại \n\tLabel(frame4,text=\"%d giờ %d phút %d giây\"%(now[3]-5, now[4],now[5]) ,justify= LEFT, font = \"TIMES 18\").place(x=200,y=10)\n\n\tlabel_time=Label(frame4,text=\"0 giờ 0 phút 0 giây\" ,justify= LEFT, font = \"TIMES 18\")\n\tlabel_time.place(x=300,y=60)\n\n\n\tlabel_notice=Label(frame4,text=\"Trạng thái: ổn!\" ,justify= LEFT, font = \"TIMES 18\",bg='green')\n\tlabel_notice.place(x=10,y=150)\n\t\n\tlabel_loading=Label(frame4,text=\"đang đợi camera...\" ,justify= LEFT, font = \"TIMES 18\",bg='orange')\n\tlabel_loading.place(x=10,y=100)\n\n\tLabel(frame4,text=\"người dùng :\" ,justify= LEFT, font = \"TIMES 18\",bg='blue').place(x=400,y=100)\n\n\tlabel_user=Label(frame4,text=\"loading...\" ,justify= LEFT, font = \"TIMES 18\",bg='purple')\n\tlabel_user.place(x=400,y=150)\n\n\tdef recognition():\n\t\t#cai dat picamera\n\t\tcamera = PiCamera()\n\t\tcamera.resolution = (640, 480)\n\t\tcamera.framerate = 32\n\t\tcamera.vflip = True\n\t\trawCapture = PiRGBArray(camera, size=(640, 480))\n\t\tgiay=0\n\t\tphut=0\n\t\tgio=0\n\t\tnow = time.time()\n\t\tx,y = [],[]\n\t\tcount=0\n\t\tpath = \"/home/pi/python/cdt/data\"\n\t\tfor dirpath, dirnames, filenames in os.walk(path):\n\t\t\tfor subdirname in dirnames:\n\t\t\t\tsubject_path= os.path.join(dirpath,subdirname)\n\t\t\t\tfor filename in os.listdir(subject_path):\n\t\t\t\t\tim= cv2.imread(os.path.join(subject_path,filename),0)\n\t\t\t\t\tx.append(np.asarray(im,dtype=np.uint8))\n\t\t\t\t\ty.append(count)\n\t\t\t\tcount+=1\n\t\tmodel = cv2.face.createLBPHFaceRecognizer()\n\t\tmodel.train(np.asarray(x),np.asarray(y))\n\t\tnames = ['hieu','trinh','phu','khong biet']\n\t\tface_cascade= cv2.CascadeClassifier('haarcascade_frontalface_default.xml')\n\t\teye_cascade= cv2.CascadeClassifier('haarcascade_eye.xml')\n\t\tfor frame in camera.capture_continuous(rawCapture, format=\"bgr\", use_video_port=True):\n\t\t\timage = frame.array\n\t\t\tgray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)\n\t\t\tfaces =face_cascade.detectMultiScale(gray)\n\t\t\tif not any(map(len, faces)):\n\t\t\t\tif (time.time()-now)>5:\n\t\t\t\t\tgiay=0\n\t\t\t\t\tphut=0\n\t\t\t\t\tgio=0\n\t\t\t\t\tlabel_time.config(text = \"%d giờ %d phút %d giây\"%(gio,phut,giay))\n\t\t\t\t\tlabel_notice.config(text = \"trạng thái: ổn!\",bg='green')\n\t\t\t\t\tnow = time.time()\n\t\t\telse:\n\t\t\t\tfor x2,y2,w,h in faces:\n\t\t\t\t\troi_faces=gray[y2:y2+h,x2:x2+w]\n\t\t\t\t\tthong_so = model.predict(roi_faces)\n\t\t\t\t\tif thong_so[1] <60:\n\t\t\t\t\t#speak_text.speak('hieu')\n\t\t\t\t\t\tcv2.rectangle(image,(x2,y2),(x2+w,y2+h),(0,0,255),2)\n\t\t\t\t\t\tcv2.putText(image,names[thong_so[0]],(x2,y2-20),cv2.FONT_HERSHEY_SIMPLEX,1,(0,255,0))\n\t\t\t\t\t\tlabel_user.config(text = \"%s\"%(names[thong_so[0]])) # update lên giao diện\n\t\t\t\t\t\tif (time.time() - now)>1 :\n\t\t\t\t\t\t\tgiay = giay+1\n\t\t\t\t\t\t\tif giay>=60:\n\t\t\t\t\t\t\t\tgiay=0\n\t\t\t\t\t\t\t\tphut=phut+1\n\t\t\t\t\t\t\tif phut >=60:\n\t\t\t\t\t\t\t\tphut=0\n\t\t\t\t\t\t\t\tgio=gio +1\n\t\t\t\t\t\t\tlabel_time.config(text = \"%d giờ %d phút %d giây\"%(gio,phut,giay)) # hiện thời gian lên giao diên\n\n\t\t\t\t\t\t\t# điều kiện để hiện thông báo \n\t\t\t\t\t\t\tif (15<giay <30): \n\t\t\t\t\t\t\t\tlabel_notice.config(text = \"trạng thái: nên giải lao vài phút!\",bg='yellow')\n\t\t\t\t\t\t\telif (giay>=30):\n\t\t\t\t\t\t\t\tlabel_notice.config(text = \"đã quá mệt mỏi! cần được nghỉ ngơi!\",bg='red')\n\t\t\t\t\t\t\telse : \n\t\t\t\t\t\t\t\tlabel_notice.config(text = \"trạng thái: ổn\",bg='green')\n\t\t\t\t\t\t\tnow=time.time()\n\t\t\tcv2.imshow('face',image)\n\t\t\tlabel_loading.config(text = \"đã mở camera\") # báo đã mở camera\n\t\t\tk = cv2.waitKey(10)\n\t\t\trawCapture.truncate(0)\n\t\t\tif k==27:\n\t\t\t\tbreak\n\tcamera = threading.Thread(target =recognition)\n\tcamera.start()\n\ttop.mainloop()\n\n#doc anh\nclc = cv2.imread('clc.png',1) # đọc các file ảnh\nclc\t= cv2.resize(clc,(200,200))\nspkt = cv2.imread('spkt.png',1)\nspkt= cv2.resize(spkt,(750,200))\nkitty = cv2.imread('kitty.jpg',1)\nkitty\t= cv2.resize(kitty,(200,200)) # chỉnh lại kích thước ảnh \ndoremon= cv2.imread('doremon.jpg',1)\ndoremon\t= cv2.resize(doremon,(200,200))\n\n# sap xep lai kenh mau\nb1,g1,r1 = cv2.split(clc)\nclc = cv2.merge((r1,g1,b1))\nb2,g2,r2 = cv2.split(spkt)\nspkt = cv2.merge((r2,g2,b2))\nb3,g3,r3 = cv2.split(kitty)\nkitty = cv2.merge((r3,g3,b3))\nb4,g4,r4 = cv2.split(doremon)\ndoremon = cv2.merge((r4,g4,b4)) # sắp xếp lại kênh màu\n\n# chuyen tu dang ma tran sang dang anh\nim1 = Image.fromarray(clc)\nimgtk1 = ImageTk.PhotoImage(image=im1) # chuyển thành dạng ảnh \nim2 = Image.fromarray(spkt)\nimgtk2 = ImageTk.PhotoImage(image=im2)\nim3 = Image.fromarray(kitty)\nimgtk3 = ImageTk.PhotoImage(image=im3)\nim4 =Image.fromarray(doremon)\nimgtk4 = ImageTk.PhotoImage(image=im4)\n\n\n\nframe1 =Frame(win, bg='pink',height=250)\nframe1.pack(fill=BOTH, expand='NO')\t\t\t# tạo frame 1\n\nLabel(frame1,image=imgtk1).place(x=950,y=10) # tạo ảnh trên frame1\nLabel(frame1,image=imgtk2).place(x=10,y=10)\n\n\nframe2 =Frame(win,height=400)\nframe2.pack(fill='both', expand='NO')\t\t# tạo frame2\n\nsymbol=Label(frame2,image=imgtk3)\nsymbol.place(x=520,y=100) # tạo ảnh trên frame2\n\n\nLabel(frame2,text=\"KHOA ĐÀO TẠO CHẤT LƯỢNG CAO\",justify= CENTER, font = \"TIMES 18\").place(x=0,y=10) # tạo chữ\nLabel(frame2,text=\"Ngành cơ điện tử\",justify= RIGHT, font = \"TIMES 20\").place(x=1000,y=10)\nLabel(frame2,text=\"Đồ án cơ điện tử\",justify= CENTER, font = \"TIMES 18\").place(x=500,y=30)\nLabel(frame2,text=\"ĐỀ TÀI: ROBOT DỊCH VỤ CHO NHÂN VIÊN VĂN PHÒNG\",justify= CENTER, font = \"TIMES 18\",bg='#F3495E').place(x=300,y=60)\nLabel(frame2,text=\"GVHD: Tưởng Phước Thọ\",justify= CENTER, font = \"TIMES 18\").place(x=0,y=50)\nLabel(frame2,text=\"Nguyễn Thanh Hiếu-MSSV:16146097\",justify= CENTER, font = \"TIMES 18\").place(x=15,y=150)\nLabel(frame2,text=\"Cao Bá Anh-MSSV:16146063\",justify= CENTER, font = \"TIMES 18\").place(x=15,y=190)\nLabel(frame2,text=\"Nguyễn Sỉ Phú-MSSV:16146169\",justify= CENTER, font = \"TIMES 18\").place(x=15,y=230)\n\n\n#gentle\nLabel(frame2,text=\"Giới tính của bạn là :\",justify= CENTER, font = \"TIMES 18\").place(x=950,y=100)\n\ndef sel():\n\tif var.get()==1:\n\t\tsymbol.config(image=imgtk4)\n\t\tframe1.config(bg = 'cyan')\n\t\tframe3.config(bg = '#1F8DEF')\n\telif var.get()==2:\n\t\tsymbol.config(image=imgtk3)\n\t\tframe1.config( bg='pink')\n\t\tframe3.config(bg = '#F349EF')\n\nRadiobutton(frame2,text=\"Nam\",justify=CENTER , font= \"TIMES 15\",variable=var, value=1,command=sel ).place(x=970,y=140)\nRadiobutton(frame2,text=\"Nữ\",justify=CENTER , font= \"TIMES 15\",variable=var, value=2,command=sel ).place(x=1070,y=140)\n\nButton(frame2,text=\"Bắt đầu\",bg='violet', fg='red',font = \"TIMES 30\",command=child_windows).place(x=1000,y=190) # tạo nút nhấn\n\nframe3 =Frame(win,bg='#F349EF')\nframe3.pack(fill='both', expand='yes')\n\nwin.mainloop()\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":7533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"473239548","text":"\n\nfrom xai.brain.wordbase.nouns._werewolf import _WEREWOLF\n\n#calss header\nclass _WEREWOLVES(_WEREWOLF, ):\n\tdef __init__(self,): \n\t\t_WEREWOLF.__init__(self)\n\t\tself.name = \"WEREWOLVES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"werewolf\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_werewolves.py","file_name":"_werewolves.py","file_ext":"py","file_size_in_byte":254,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"25056419","text":"import os\nimport subprocess\nimport posixpath\n\nfrom django.conf import settings\nfrom django.contrib.staticfiles import finders\n\nimport jinja2\nfrom jingo import register, env\n\n\ntry:\n from build import BUILD_ID_CSS, BUILD_ID_JS, BUILD_ID_IMG, BUNDLE_HASHES\nexcept ImportError:\n BUILD_ID_CSS = BUILD_ID_JS = BUILD_ID_IMG = 'dev'\n BUNDLE_HASHES = {}\n\n\ndef path(*a):\n return finders.find(posixpath.normpath(os.path.join(*a))) or \\\n os.path.join(settings.STATIC_ROOT, *a)\n\n\ndef _build_html(items, wrapping):\n \"\"\"\n Wrap `items` in wrapping.\n \"\"\"\n return jinja2.Markup(\"\\n\".join((wrapping % (settings.STATIC_URL + item)\n for item in items)))\n\n@register.function\ndef js(bundle, debug=settings.TEMPLATE_DEBUG):\n \"\"\"\n If we are in debug mode, just output a single script tag for each js file.\n If we are not in debug mode, return a script that points at bundle-min.js.\n \"\"\"\n if debug:\n if getattr(settings, 'COFFEE_PREPROCESS', False):\n items = []\n for item in settings.MINIFY_BUNDLES['js'][bundle]:\n if item.endswith('.coffee'):\n build_coffee(item)\n items.append('%s.js' % item)\n else:\n items.append(item)\n else:\n items = settings.MINIFY_BUNDLES['js'][bundle]\n else:\n build_id = BUILD_ID_JS\n bundle_full = \"js:%s\" % bundle\n if bundle_full in BUNDLE_HASHES:\n build_id = BUNDLE_HASHES[bundle_full]\n items = (\"js/%s-min.js?build=%s\" % (bundle, build_id,),)\n\n return _build_html(items, \"\"\"<script src=\"%s\"></script>\"\"\")\n\n\n@register.function\ndef css(bundle, media=\"screen,projection,tv\", debug=settings.TEMPLATE_DEBUG):\n \"\"\"\n If we are in debug mode, just output a single script tag for each css file.\n If we are not in debug mode, return a script that points at bundle-min.css.\n \"\"\"\n if debug:\n if getattr(settings, 'LESS_PREPROCESS', False):\n items = []\n for item in settings.MINIFY_BUNDLES['css'][bundle]:\n if item.endswith('.less'):\n build_less(item)\n items.append('%s.css' % item)\n else:\n items.append(item)\n else:\n items = settings.MINIFY_BUNDLES['css'][bundle]\n else:\n build_id = BUILD_ID_CSS\n bundle_full = \"css:%s\" % bundle\n if bundle_full in BUNDLE_HASHES:\n build_id = BUNDLE_HASHES[bundle_full]\n\n items = (\"css/%s-min.css?build=%s\" % (bundle, build_id,),)\n\n return _build_html(items,\n \"\"\"<link rel=\"stylesheet\" media=\"%s\" href=\"%%s\" />\"\"\" % media)\n\n\ndef build_less(item):\n path_less = path(item)\n path_css = '%s.css' % path_less\n\n updated_less = os.path.getmtime(path(item))\n updated_css = 0 # If the file doesn't exist, force a refresh.\n if os.path.exists(path_css):\n updated_css = os.path.getmtime(path_css)\n\n # Is the uncompiled version newer? Then recompile!\n if updated_less > updated_css:\n with open(path_css, 'w') as output:\n less_dirs = []\n for finder in finders.get_finders():\n for rel_path, storage in finder.list(''):\n if rel_path.endswith('.less'):\n abs_path = storage.path(rel_path)\n # Compute relative path of less_dir due to lessc\n # peculiarity\n common_prefix = os.path.commonprefix([abs_path,\n path_less])\n base_dir = os.path.dirname(common_prefix)\n less_dir = os.path.relpath(os.path.dirname(abs_path),\n base_dir)\n if not less_dir in less_dirs:\n less_dirs.append(less_dir)\n subprocess.Popen([settings.LESS_BIN,\n '--include-path=%s' % ':'.join(less_dirs),\n path_less],\n stdout=output)\n\n\ndef build_coffee(item):\n path_coffee = path(item)\n path_js = '%s.js' % path_coffee\n\n updated_coffee = os.path.getmtime(path_coffee)\n updated_js = 0 # If the file doesn't exist, force a refresh.\n if os.path.exists(path_js):\n updated_js = os.path.getmtime(path_js)\n\n # Is the uncompiled version newer? Then recompile!\n if updated_coffee > updated_js:\n with open(path_js, 'w') as output:\n subprocess.Popen([settings.COFFEE_BIN,\n '--print',\n path_coffee],\n stdout=output,\n stderr=output)\n\n\ndef build_ids(request):\n \"\"\"A context processor for injecting the css/js build ids.\"\"\"\n return {'BUILD_ID_CSS': BUILD_ID_CSS, 'BUILD_ID_JS': BUILD_ID_JS,\n 'BUILD_ID_IMG': BUILD_ID_IMG}\n","sub_path":"jingo_minify/helpers.py","file_name":"helpers.py","file_ext":"py","file_size_in_byte":5005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"88843329","text":"import os\nimport numpy as np\nimport pandas as pd\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import accuracy_score, classification_report\nfrom sklearn.tree import DecisionTreeClassifier\n# import matplotlib.pyplot as plt\n\n\ndef funcDisplayDecisionTreeClassifier(x_train, y_train, x_test, y_test):\n # Decision Tree Classifier\n dt_fit = DecisionTreeClassifier(\n criterion=\"gini\", max_depth=5, min_samples_split=2, min_samples_leaf=1, random_state=42)\n dt_fit.fit(x_train, y_train)\n\n print(\"\\nDecision Tree - Train Confusion Matrix\\n\\n\", pd.crosstab(y_train,\n dt_fit.predict(x_train), rownames=[\"Actuall\"], colnames=[\"Predicted\"]))\n print(\"\\nDecision Tree - Train accuracy:\",\n round(accuracy_score(y_train, dt_fit.predict(x_train)), 3))\n print(\"\\nDecision Tree - Train Classification Report\\n\",\n classification_report(y_train, dt_fit.predict(x_train)))\n\n print(\"\\n\\nDecision Tree - Test Confusion Matrix\\n\\n\", pd.crosstab(y_test,\n dt_fit.predict(x_test), rownames=[\"Actuall\"], colnames=[\"Predicted\"]))\n print(\"\\nDecision Tree - Test accuracy:\",\n round(accuracy_score(y_test, dt_fit.predict(x_test)), 3))\n print(\"\\nDecision Tree - Test Classification Report\\n\",\n classification_report(y_test, dt_fit.predict(x_test)))\n\n\nif __name__ == \"__main__\":\n hrattr_data = pd.read_csv(\"WA_Fn-UseC_-HR-Employee-Attrition.csv\")\n # iterating the columns\n '''\n for col in hrattr_data.columns:\n print(col)\n '''\n # print (hrattr_data.head())\n\n hrattr_data['Attrition_ind'] = 0\n hrattr_data.loc[hrattr_data['Attrition'] == 'Yes', 'Attrition_ind'] = 1\n\n dummy_busnstrvl = pd.get_dummies(\n hrattr_data['BusinessTravel'], prefix='busns_trvl')\n # print(\"dummy_busnstrvl: \", dummy_busnstrvl)\n dummy_dept = pd.get_dummies(hrattr_data['Department'], prefix='dept')\n # print(\"dummy_dept: \", dummy_dept)\n dummy_edufield = pd.get_dummies(\n hrattr_data['EducationField'], prefix='edufield')\n # print(\"dummy_edufield: \", dummy_edufield)\n dummy_gender = pd.get_dummies(hrattr_data['Gender'], prefix='gend')\n # print(\"dummy_gender: \", dummy_gender)\n dummy_jobrole = pd.get_dummies(hrattr_data['JobRole'], prefix='jobrole')\n # print(\"dummy_jobrole: \", dummy_jobrole)\n dummy_maritstat = pd.get_dummies(\n hrattr_data['MaritalStatus'], prefix='maritalstat')\n # print(\"dummy_maritstat: \", dummy_maritstat)\n dummy_overtime = pd.get_dummies(hrattr_data['OverTime'], prefix='overtime')\n # print(\"dummy_overtime: \", dummy_overtime)\n\n continuous_columns = ['Age', 'DailyRate', 'DistanceFromHome', 'Education', 'EnvironmentSatisfaction',\n 'HourlyRate', 'JobInvolvement', 'JobLevel', 'JobSatisfaction', 'MonthlyIncome', 'MonthlyRate', 'NumCompaniesWorked',\n 'PercentSalaryHike', 'PerformanceRating', 'RelationshipSatisfaction', 'StockOptionLevel', 'TotalWorkingYears',\n 'TrainingTimesLastYear', 'WorkLifeBalance', 'YearsAtCompany', 'YearsInCurrentRole', 'YearsSinceLastPromotion',\n 'YearsWithCurrManager']\n # print(continuous_columns)\n\n hrattr_continuous = hrattr_data[continuous_columns]\n # print(hrattr_continuous)\n\n hrattr_continuous['Age'].describe()\n # print(\"Describe age\\n\" ,hrattr_continuous['Age'].describe())\n\n hrattr_data['BusinessTravel'].value_counts()\n # print(\"BusinessTravel value_counts\\n\", hrattr_data['BusinessTravel'].value_counts())\n\n hrattr_data_new = pd.concat([dummy_busnstrvl, dummy_dept, dummy_edufield, dummy_gender, dummy_jobrole,\n dummy_maritstat, dummy_overtime, hrattr_continuous, hrattr_data['Attrition_ind']], axis=1)\n # print(\"hrattr_data_new: \\n\", hrattr_data_new)\n\n # Train & Test split\n x_train, x_test, y_train, y_test = train_test_split(hrattr_data_new.drop(['Attrition_ind'], axis=1),\n hrattr_data_new['Attrition_ind'], train_size=0.7, random_state=42)\n '''\n print(\"x train: \", x_train)\n print(\"y train: \", y_train)\n print(\"x test: \", x_test)\n print(\"y test: \", y_test)\n '''\n funcDisplayDecisionTreeClassifier(x_train, y_train, x_test, y_test)","sub_path":"Chapter04/prog1.py","file_name":"prog1.py","file_ext":"py","file_size_in_byte":4410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"572844031","text":"# Lint as: python3\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\"\"\"Tests for pyiree.tf.support.trace_utils.\"\"\"\n\nimport os\nimport tempfile\n\nfrom absl.testing import parameterized\nimport numpy as np\nfrom pyiree.tf.support import module_utils\nfrom pyiree.tf.support import trace_utils\nimport tensorflow as tf\n\n\nclass StatefulCountingModule(tf.Module):\n\n def __init__(self):\n self.count = tf.Variable([0.])\n\n @tf.function(input_signature=[])\n def increment(self):\n self.count.assign_add(tf.constant([1.]))\n\n @tf.function(input_signature=[])\n def get_count(self):\n return self.count\n\n @tf.function(input_signature=[tf.TensorSpec([1])])\n def increment_by(self, value):\n self.count.assign_add(value)\n\n @tf.function(input_signature=[tf.TensorSpec([1]), tf.TensorSpec([1])])\n def increment_by_max(self, a, b):\n result = tf.maximum(a, b)\n self.count.assign_add(result)\n return result\n\n @tf.function(input_signature=[])\n def decrement(self):\n self.count.assign_sub(tf.constant([1.]))\n\n\nclass TestUtilsTests(tf.test.TestCase, parameterized.TestCase):\n\n def test_trace_inputs_and_outputs(self):\n\n def trace_function(module):\n # No inputs or outputs\n module.increment()\n # Only inputs\n module.increment_by(np.array([81.], dtype=np.float32))\n # Only outputs\n module.get_count()\n\n module = module_utils.TfCompiledModule.create_from_class(\n StatefulCountingModule, module_utils.BackendInfo('tf'))\n trace = trace_utils.Trace(module, trace_function)\n trace_function(trace_utils.TracedModule(module, trace))\n\n self.assertIsInstance(trace.calls[0].inputs, tuple)\n self.assertEmpty(trace.calls[0].inputs)\n self.assertIsInstance(trace.calls[0].outputs, tuple)\n self.assertEmpty(trace.calls[0].outputs)\n\n self.assertAllClose(trace.calls[1].inputs[0], [81.])\n self.assertAllClose(trace.calls[2].outputs[0], [82.])\n\n def test_nonmatching_methods(self):\n\n def tf_function(module):\n module.increment()\n module.increment()\n\n def vmla_function(module):\n module.increment()\n module.decrement()\n\n tf_module = module_utils.TfCompiledModule.create_from_class(\n StatefulCountingModule, module_utils.BackendInfo('tf'))\n tf_trace = trace_utils.Trace(tf_module, tf_function)\n tf_function(trace_utils.TracedModule(tf_module, tf_trace))\n\n vmla_module = module_utils.IreeCompiledModule.create_from_class(\n StatefulCountingModule, module_utils.BackendInfo('iree_vmla'))\n vmla_trace = trace_utils.Trace(vmla_module, vmla_function)\n vmla_function(trace_utils.TracedModule(vmla_module, vmla_trace))\n\n with self.assertRaises(ValueError):\n trace_utils.compare_traces(tf_trace, vmla_trace)\n\n def test_nonmatching_inputs(self):\n\n def tf_function(module):\n module.increment_by(np.array([42.], dtype=np.float32))\n\n def vmla_function(module):\n module.increment_by(np.array([22.], dtype=np.float32))\n\n tf_module = module_utils.TfCompiledModule.create_from_class(\n StatefulCountingModule, module_utils.BackendInfo('tf'))\n tf_trace = trace_utils.Trace(tf_module, tf_function)\n tf_function(trace_utils.TracedModule(tf_module, tf_trace))\n\n vmla_module = module_utils.IreeCompiledModule.create_from_class(\n StatefulCountingModule, module_utils.BackendInfo('iree_vmla'))\n vmla_trace = trace_utils.Trace(vmla_module, vmla_function)\n vmla_function(trace_utils.TracedModule(vmla_module, vmla_trace))\n\n same, error_messages = trace_utils.compare_traces(tf_trace, vmla_trace)\n self.assertFalse(same)\n\n def test_trace_serialize_and_load(self):\n\n def trace_function(module):\n module.increment()\n module.increment_by(np.array([81.], dtype=np.float32))\n module.increment_by_max(np.array([81], dtype=np.float32),\n np.array([92], dtype=np.float32))\n module.get_count()\n\n module = module_utils.IreeCompiledModule.create_from_class(\n StatefulCountingModule, module_utils.BackendInfo('iree_vmla'))\n trace = trace_utils.Trace(module, trace_function)\n trace_function(trace_utils.TracedModule(module, trace))\n\n with tempfile.TemporaryDirectory() as artifacts_dir:\n trace_function_dir = trace_utils.get_trace_dir(artifacts_dir, trace)\n trace.serialize(trace_function_dir)\n self.assertTrue(\n os.path.exists(os.path.join(trace_function_dir, 'metadata.pkl')))\n loaded_trace = trace_utils.Trace.load(trace_function_dir)\n\n # Check all calls match.\n self.assertTrue(trace_utils.compare_traces(trace, loaded_trace))\n\n # Check all other metadata match.\n self.assertAllEqual(trace.__dict__.keys(), loaded_trace.__dict__.keys())\n for key in trace.__dict__.keys():\n if key != 'calls':\n self.assertEqual(trace.__dict__[key], loaded_trace.__dict__[key])\n\n\nif __name__ == '__main__':\n tf.test.main()\n","sub_path":"integrations/tensorflow/bindings/python/pyiree/tf/support/trace_utils_test.py","file_name":"trace_utils_test.py","file_ext":"py","file_size_in_byte":5408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"455164962","text":"import pygame\r\nfrom pygame.draw import *\r\nfrom random import randint\r\nimport numpy as np\r\nimport matplotlib as mp\r\npygame.init()\r\nloose=0\r\nWIDTH=1000\r\nHEIGHT=600\r\nBULLETS=[]\r\n\r\nENEMIES=[]\r\n\r\nTIME=0\r\nTIME_last_shoot=0\r\nclass Enemy:\r\n def __init__(self):\r\n self.type=\"usual\"\r\n self.x=0\r\n self.y=0\r\n self.Vx=0\r\n self.Vy=5\r\n self.radius=10\r\n self.exist=1\r\n self.hp=10\r\n self.destroyer_Vy=7.5\r\n def move(self):\r\n if self.exist ==1:\r\n if self.type == \"usual\":\r\n self.x+=self.Vx\r\n self.y+=self.Vy\r\n circle(screen, (BLUE), (round(self.x), round(self.y)), round(self.radius)) \r\n if self.type == \"destroyer\":\r\n self.y+=self.destroyer_Vy\r\n rect(screen, (YELLOW), (self.x,self.y, self.radius, self.radius), )\r\n def check(self):\r\n if self.y<HEIGHT:\r\n del self\r\n\r\n \r\n\r\nclass Hero:\r\n def __init__(self):\r\n self.weapon=\"medium\"\r\n self.V=8\r\n self.size=120\r\n self.x=0\r\n self.y=HEIGHT-self.size//2\r\n self.dx=0\r\n self.dy=0\r\n self.gun_width=20\r\n self.gunsize=100\r\n \r\n def move_right(self):\r\n self.x+=self.V\r\n \r\n def move_left(self):\r\n self.x-=self.V\r\n \r\n def draw(self):\r\n rect(screen, (183, 196, 200), (self.x,self.y, self.size, self.size//2), )\r\n line(screen, RED,(self.x+self.size//2,self.y),(self.x+self.size//2+self.dx,self.y+self.dy),round(self.gun_width))\r\n \r\n def SHOOT(self):\r\n a = Bullet()\r\n if hero.weapon == \"small\":\r\n a.type=\"small\"\r\n a.Vx = self.dx / self.gunsize *a.speed*1.3\r\n a.Vy = -((a.speed*1.3) ** 2 - a.Vx ** 2) ** 0.5\r\n a.x = self.x+self.size//2+self.dx \r\n a.y = self.y+self.dy\r\n BULLETS.append(a)\r\n a.radius=10\r\n if hero.weapon == \"medium\":\r\n a.type=\"medium\"\r\n a.Vx = self.dx / self.gunsize *a.speed\r\n a.Vy = -(a.speed ** 2 - a.Vx ** 2) ** 0.5\r\n a.x = self.x+self.size//2+self.dx \r\n a.y = self.y+self.dy\r\n BULLETS.append(a)\r\n a.radius=20\r\n if hero.weapon == \"big\":\r\n a.type=\"big\"\r\n a.Vx = self.dx / self.gunsize *a.speed*0.5\r\n a.Vy = -((a.speed*0.5) ** 2 - a.Vx ** 2) ** 0.5\r\n a.x = self.x+self.size//2+self.dx \r\n a.y = self.y+self.dy\r\n a.radius=40\r\n BULLETS.append(a)\r\n \r\n \r\nclass Bullet:\r\n \r\n def __init__(self):\r\n self.type=\"medium\"\r\n self.x=0\r\n self.y=0\r\n self.speed = 30\r\n self.Vy=0\r\n self.Vx=0\r\n self.radius=10\r\n self.g=0.3\r\n self.exist=1\r\n \r\n def check(self):\r\n if self.x > WIDTH:\r\n del self\r\n\r\n\r\n \r\n def move(self):\r\n if self.exist == 1:\r\n self.x+=self.Vx\r\n self.y+=self.Vy\r\n circle(screen, (RED), (round(self.x), round(self.y)), self.radius)\r\n if self.type==\"big\" or self.type == \"medium\":\r\n self.Vy+=self.g\r\n \r\n \r\ndef spawner():\r\n random = randint(1,1000)\r\n if random >= 990:\r\n e=Enemy()\r\n e.hp=randint(1,10)+10\r\n e.radius=e.hp*2.5\r\n e.x=randint(1,round(WIDTH-2*e.radius))+e.radius\r\n e.y=-e.radius\r\n ENEMIES.append(e)\r\n if random <= 4:\r\n e=Enemy()\r\n e.type=\"destroyer\"\r\n e.hp=100\r\n e.radius=50\r\n e.x=hero.x\r\n e.y=-e.radius\r\n ENEMIES.append(e)\r\n \r\n\r\nRED = (255, 0, 0)\r\nBLUE = (0, 0, 255)\r\nYELLOW = (255, 255, 0)\r\nGREEN = (0, 255, 0)\r\nMAGENTA = (255, 0, 255)\r\nCYAN = (0, 255, 255)\r\nBLACK = (0, 0, 0)\r\nWIDTH=1000\r\nHEIGHT=700\r\nFPS = 30\r\nloose=0\r\nscreen = pygame.display.set_mode(( WIDTH, HEIGHT))\r\n\r\nrandom=0\r\ndel BULLETS[:]\r\ndel ENEMIES [:]\r\n\r\nball = Enemy()\r\nhero = Hero()\r\n#ball.move()\r\n\r\npygame.display.update()\r\nclock = pygame.time.Clock()\r\nfinished = False\r\n\r\nwhile not finished:\r\n clock.tick(FPS)\r\n TIME+=1\r\n for event in pygame.event.get():\r\n if event.type == pygame.MOUSEMOTION:\r\n dx = event.pos[0]-(hero.x+hero.size//2)\r\n dy = event.pos[1]-hero.y\r\n if dy<0 :\r\n hero.dx = dx/np.abs(dx)*(hero.gunsize**2/(1+(dy/(dx+0.001))**2))**0.5\r\n hero.dy = (-1)*(hero.gunsize**2 - hero.dx**2)**0.5\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_SPACE:\r\n if hero.weapon == \"big\":\r\n if TIME - TIME_last_shoot>=60:\r\n hero.SHOOT()\r\n TIME_last_shoot=TIME\r\n else:\r\n hero.SHOOT()\r\n if event.type == pygame.KEYDOWN:\r\n if event.key == pygame.K_2:\r\n hero.weapon=\"medium\"\r\n hero.gun_width=20\r\n if event.key == pygame.K_3:\r\n hero.weapon=\"big\"\r\n hero.gun_width=40\r\n if event.key == pygame.K_1:\r\n hero.weapon=\"small\"\r\n hero.gun_width=10\r\n \r\n \r\n if event.type == pygame.QUIT:\r\n finished = True\r\n keys = pygame.key.get_pressed()\r\n if keys[pygame.K_LEFT]:\r\n hero.move_left()\r\n if keys[pygame.K_SPACE]:\r\n if hero.weapon == \"small\":\r\n if TIME%3==0:\r\n hero.SHOOT()\r\n \r\n elif keys[pygame.K_RIGHT]:\r\n hero.move_right()\r\n \r\n ball.move()\r\n hero.draw()\r\n for b in BULLETS:\r\n b.move()\r\n if b.x > WIDTH:\r\n b.exist = 0\r\n if b.x < 0:\r\n b.exist = 0\r\n if b.y < 0:\r\n b.exist = 0\r\n if b.y > HEIGHT:\r\n b.exist = 0\r\n \r\n \r\n \r\n for e in ENEMIES:\r\n e.move()\r\n if e.exist == 1:\r\n if e.type == \"destroyer\":\r\n if e.y >= HEIGHT - hero.size//1.5:\r\n if np.abs(e.x-(hero.x+hero.size//2))<=hero.size//2:\r\n pygame.quit()\r\n if e.y > HEIGHT:\r\n if e.type ==\"usual\":\r\n e.exist=0\r\n loose+=1\r\n \r\n\r\n \r\n\r\n \r\n spawner()\r\n for b in BULLETS:\r\n for e in ENEMIES:\r\n if b.exist ==1:\r\n if e.exist == 1:\r\n if (b.x-e.x)**2+(b.y-e.y)**2 <= (e.radius + b.radius)**2:\r\n if b.type == \"big\":\r\n e.exist = 0\r\n else:\r\n if b.exist==1:\r\n if b.type == \"small\":\r\n e.hp-=4\r\n if b.type == \"medium\":\r\n e.hp-=11\r\n b.exist=0\r\n if e.hp<=0:\r\n e.exist=0\r\n \r\n \r\n \r\n\r\n spawner()\r\n pygame.display.update()\r\n screen.fill(BLACK)\r\n if loose >=5:\r\n pygame.quit()\r\n\r\npygame.quit() \r\n \r\n \r\n \r\n ","sub_path":"TANK.py","file_name":"TANK.py","file_ext":"py","file_size_in_byte":7282,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"583089330","text":"from articles.models import Article\n\n\ndef drafts(request):\n \"\"\"\n Context processor for Actions count\n :param request:\n :return: Count of user's actions, which follows current user\n \"\"\"\n if not request.user.is_anonymous:\n draft_articles = Article.objects.get_draft(request).only('id').exists()\n else:\n draft_articles = False\n\n return {'drafts': draft_articles}\n","sub_path":"articles/context_processors.py","file_name":"context_processors.py","file_ext":"py","file_size_in_byte":398,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"533033282","text":"\"\"\"\r\n Convex Hull Assignment: COSC262 (2018)\r\n Student Name: Corbyn Greenwood\r\n Usercode: ctg31\r\n\"\"\"\r\nimport math\r\nfrom timeit import default_timer as timer\r\nimport operator\r\n\r\ndef readDataPts(filename, N):\r\n \"\"\"Reads the first N lines of data from the input file\r\n and returns a list of N tuples\r\n [(x0,y0), (x1, y1), ...]\r\n \"\"\"\r\n listPts = list()\r\n file = open(filename, 'r')\r\n for i in range(0, N):\r\n point = file.readline().strip().split(\" \")\r\n pointx, pointy = float(point[0]), float(point[1])\r\n listPts.append((pointx, pointy))\r\n return listPts\r\n\r\ndef isCCW(ptA, ptB, ptC):\r\n return lineFn(ptA, ptB, ptC) > 0\r\n\r\ndef lineFn(ptA, ptB, ptC):\r\n return ((ptB[0]-ptA[0])*(ptC[1]-ptA[1]) - (ptB[1]-ptA[1])*(ptC[0]-ptA[0]))\r\n\r\ndef theta(pointA, pointB):\r\n \"\"\"computes approximate angle between line AB and a horizontal line through A\"\"\"\r\n dx = pointB[0] - pointA[0]\r\n dy = pointB[1] - pointA[1]\r\n if abs(dx) < 1.e-6 and abs(dy) < 1.e-6:\r\n t = 0\r\n else:\r\n t = dy/(abs(dx) + abs(dy))\r\n if dx < 0:\r\n t = 2 - t\r\n elif dy < 0:\r\n t = 4 + t\r\n return t*90\r\n\r\ndef lowest_rightmost(listPts):\r\n \"\"\"gets the lowest and rightmost point in the list of points\"\"\"\r\n min_point = [float('inf'), float('inf')]\r\n k = 0\r\n for point in range(len(listPts)):\r\n if listPts[point][1] < min_point[1]:\r\n min_point = listPts[point]\r\n k = point\r\n elif listPts[point][1] == min_point[1]:\r\n if listPts[point][0] > min_point[0]:\r\n min_point = listPts[point]\r\n k = point\r\n return (min_point, k)\r\n\r\ndef find_distance(pointA, pointB):\r\n \"\"\"The function takes two points with x and y values and return the distance between two points.\"\"\"\r\n dx = pointB[0] - pointA[0]\r\n dy = pointB[1] - pointA[1]\r\n distance = ((dx**2) + (dy**2))**1/2\r\n return abs(distance)\r\n\r\ndef giftwrap(listPts):\r\n \"\"\"Returns the convex hull vertices computed using the\r\n giftwrap algorithm as a list of m tuples\r\n [(u0,v0), (u1,v1), ...] \r\n \"\"\"\r\n #Your implementation goes here\r\n chull = []\r\n # selects the lowest point (min y value) \r\n min_pt, k = lowest_rightmost(listPts)\r\n chull.append(min_pt)\r\n next_point = gifting(min_pt, listPts, 0)\r\n chull.append(next_point[0])\r\n prev_angle = next_point[1]\r\n while min_pt != chull[-1] and chull[-1] != None:\r\n next_point = gifting(chull[-1], listPts, prev_angle)\r\n chull.append(next_point[0])\r\n prev_angle = next_point[1]\r\n chull = chull[:-1]\r\n return chull\r\n\r\ndef gifting(convex_point, listPts, previous):\r\n \"\"\"Finds the next point in the convex hull\"\"\"\r\n new_point = None\r\n prev_angle = 0\r\n for point in range(len(listPts)):\r\n current_angle = theta(convex_point, listPts[point])\r\n if prev_angle > current_angle and current_angle > previous or prev_angle == 0 and current_angle > previous:\r\n prev_angle = current_angle\r\n new_point = listPts[point]\r\n return new_point , prev_angle\r\n\r\ndef grahamscan(listPts):\r\n \"\"\"Returns the convex hull vertices computed using the\r\n Graham-scan algorithm as a list of m tuples\r\n [(u0,v0), (u1,v1), ...] \r\n \"\"\"\r\n #Your implementation goes here\r\n min_point, k = lowest_rightmost(listPts)\r\n listPts_copy = list(listPts)\r\n n = len(listPts_copy)\r\n listPts_copy = sorted(listPts_copy, key = lambda x: theta(min_point, x))\r\n chull = [listPts_copy[0], listPts_copy[1], listPts_copy[2]]\r\n for i in range(3, n):\r\n while not isCCW(chull[-2], chull[-1], listPts_copy[i]):\r\n chull.pop()\r\n chull.append(listPts_copy[i])\r\n return chull \r\n\r\ndef amethod(listPts):\r\n \"\"\"Returns the convex hull vertices computed using \r\n a third algorithm\r\n \"\"\"\r\n #Your implementation goes here\r\n #Using Chans algorithm we split the graph into parts then grahams'\r\n #scan each part. adding each of those grahams scanned parts together\r\n #we then gift wrap the final result\r\n n = len(listPts)\r\n number = math.ceil(n / 5000)\r\n segments = []\r\n split_parts = []\r\n grahams = []\r\n start = 0\r\n scanned_total = []\r\n for i in range(1, number + 1):\r\n segment = int(n * ((1/number)) * i)\r\n part = listPts[start:segment]\r\n grahams = grahamscan(part)\r\n scanned_total += grahams\r\n start = segment\r\n chull = giftwrap(scanned_total)\r\n return chull\r\n\r\n\r\ndef main():\r\n file = \"B_3000.dat\"\r\n num_points = 3000\r\n average_times = []\r\n times = []\r\n total_gift = 0\r\n total_grahams = 0\r\n total_chans = 0\r\n tests = 15\r\n for i in range(10):\r\n listPts = readDataPts(file, num_points) #File name, numPts given as example only\r\n times = []\r\n total_gift = 0\r\n total_chans = 0\r\n total_grahams = 0\r\n for q in range(tests):\r\n start = timer()\r\n gifted = giftwrap(listPts) #You may replace these three print statements\r\n end = timer()\r\n gift_time = end - start\r\n start = timer()\r\n grahams = grahamscan(listPts) #with any code for validating your outputs\r\n end = timer()\r\n grahams_time = end - start\r\n start = timer()\r\n chans = amethod(listPts)\r\n end = timer()\r\n chans_time = end - start\r\n times.append((gift_time, grahams_time, chans_time))\r\n #print(\"Giftwrap time: \", round(gift_time, 5))\r\n #print(\"Grahams Scan time: \", round(grahams_time, 5))\r\n #print(\"Chans time: \", round(chans_time, 5))\r\n #print(times)\r\n for numbers in times:\r\n total_gift += numbers[0]\r\n total_grahams += numbers[1]\r\n total_chans += numbers[2]\r\n total_gift = total_gift / tests\r\n total_grahams = total_grahams / tests\r\n total_chans = total_chans / tests\r\n average_times.append((file, num_points, (total_gift), (total_grahams), (total_chans)))\r\n num_points += 3000\r\n file = \"B_\" + str(num_points) + \".dat\" \r\n print(\"FILE \" + \" GIFT \" + \" GRAHAMS\" + \" CHANS\")\r\n double = \" || \"\r\n for files, num_points, gift, grahams, chans in average_times:\r\n if num_points < 10000:\r\n double = \" || \"\r\n else:\r\n double = \" || \"\r\n print(files, double, round(gift, 5), round(grahams, 5), round(chans, 5))\r\n\r\n \r\nif __name__ == \"__main__\":\r\n main()\r\n ","sub_path":"convexhull_time.py","file_name":"convexhull_time.py","file_ext":"py","file_size_in_byte":6618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"460416872","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2017 Red Hat, Inc\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.\n\n\nfrom dci.identity import Identity\n\n\ndef identity_factory(role_label):\n user = {'role_label': role_label, 'team_id': 'abc'}\n team = {'id': 'abc', 'parent_id': None}\n return Identity(user, [team])\n\n\ndef test_is_super_admin():\n super_admin = identity_factory('SUPER_ADMIN')\n assert super_admin.is_super_admin()\n\n\ndef test_is_admin():\n admin = identity_factory('ADMIN')\n assert admin.is_admin()\n\n\ndef test_is_product_owner():\n product_owner = identity_factory('PRODUCT_OWNER')\n assert product_owner.is_product_owner()\n\n\ndef test_is_feeder():\n feeder = identity_factory('FEEDER')\n assert feeder.is_feeder()\n\n\ndef test_is_remoteci():\n remoteci = identity_factory('REMOTECI')\n assert remoteci.is_remoteci()\n\n\ndef test_is_regular_user():\n user = identity_factory('USER')\n assert user.is_regular_user()\n\n\ndef test_user_is_member_of():\n team = {'id': 'id-1', 'parent_id': 'id-0'}\n user = Identity({'role_label': 'USER', 'team_id': 'id-1'}, [team])\n assert user.is_member_of(team)\n assert user.is_member_of({'id': 'another_team_id'}) is False\n\n\ndef test_super_admin_is_member_of_all_teams():\n super_admin = identity_factory('SUPER_ADMIN')\n assert super_admin.is_member_of({'id': 'id-1'})\n assert super_admin.is_member_of({'id': 'id-2'})\n\n\ndef test_product_owner_is_member_of_all_child_teams():\n product_owner = {'role_label': 'PRODUCT_OWNER', 'team_id': 'abc'}\n teams = [\n {'id': 'abc', 'parent_id': None},\n {'id': 'def', 'parent_id': 'abc'},\n {'id': 'ghi', 'parent_id': None}\n ]\n user = Identity(product_owner, teams)\n assert user.is_member_of({'id': 'abc'})\n assert user.is_member_of({'id': 'def'})\n\n\ndef test_filter_teams():\n teams = [\n {'id': 'abc', 'parent_id': None},\n {'id': 'cde', 'parent_id': 'abc'}\n ]\n user = Identity({'role_label': 'USER', 'team_id': 'abc'}, teams)\n assert user.team['id'] == 'abc'\n assert user.team['parent_id'] is None\n # TODO remove user.teams\n assert user.teams[0] == 'abc'\n assert len(user.partner_teams) == 1\n\n\ndef test_filter_teams_with_partner_teams():\n product_owner = {'role_label': 'PRODUCT_OWNER', 'team_id': 'abc'}\n teams = [\n {'id': 'abc', 'parent_id': None},\n {'id': 'def', 'parent_id': 'abc'},\n {'id': 'ghi', 'parent_id': None}\n ]\n\n user = Identity(product_owner, teams)\n assert user.team['id'] == 'abc'\n assert user.team['parent_id'] is None\n assert len(user.partner_teams) == 1\n # TODO remove user.teams\n assert user.teams[0] == 'abc'\n assert user.teams[1] == 'def'\n\n\ndef test_ensure_any_parent_team_member_has_access_to_subteam():\n user = {'role_label': None, 'team_id': 'abc'}\n teams = [\n {'id': 'abc', 'parent_id': None},\n {'id': 'def', 'parent_id': 'abc'},\n {'id': 'ghi', 'parent_id': None}\n ]\n\n user = Identity(user, teams)\n assert user.team['id'] == 'abc'\n assert user.team['parent_id'] is None\n assert len(user.partner_teams) == 1\n","sub_path":"tests/test_identity.py","file_name":"test_identity.py","file_ext":"py","file_size_in_byte":3614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"226154939","text":"import os\nimport uuid\nimport re\nimport six\nfrom heatmap.models import Submission\nfrom django.utils import timezone\nimport datetime\nfrom django.db.models import DateTimeField, CharField, FileField, BooleanField, TextField, FloatField\nfrom datawebtools.fields import DataFrameField\nfrom .stats import get_pvals_df\nfrom datawebtools.exceptions import DataNotProvidedError, InputValidationError\nimport pandas as pd\nimport io\nfrom io import StringIO\nimport numpy as np\n\nclass CmpSubmission(Submission):\n stat_test_choices = (('MannWhitney2sided', 'Mann-Whitney U-test'), \\\n ('Wilcoxon', 'Wilcoxon test'), \\\n ('Ttest_ind', 'independent T-test'), \\\n ('Ttest_rel', 'paired T-test'))\n pval_thresh_choices = ((0.05, '* ('+six.unichr(8804)+' 0.05)'), (0.01, '** ('+six.unichr(8804)+' 0.01)'), (0.001, '*** ('+six.unichr(8804)+' 0.001)'))\n \n pvals = DataFrameField()\n stat_test = CharField(max_length = 20, default = '', choices = stat_test_choices)\n vars_in_rows = BooleanField(default=False, blank=True)\n pval_thresh = FloatField(default=0.05, choices=pval_thresh_choices)\n\n @classmethod\n def create(cls, cln_data, csv_file=None, cli='127.0.0.1'):\n submsn = cls(uploaded_file=csv_file, paste_data=cln_data['paste_data'], stat_test=cln_data['stat_test'], vars_in_rows=cln_data['vars_in_rows'], \\\n pval_thresh=cln_data['pval_thresh'], rownames=cln_data['rownames'], csv_separator=cln_data['csv_separator'], cli_addr=cli)\n if ((not submsn.uploaded_file) and (submsn.paste_data=='')):\n raise DataNotProvidedError('CSV file should be uploaded or text with data pasted in the relevant section\\n')\n elif (submsn.uploaded_file):\n submsn.file_uploaded = True\n elif (submsn.paste_data != ''):\n submsn.file_uploaded = False\n submsn.save()\n return submsn\n\n\n def run_analysis(self):\n if self.rownames == True:\n index_col = 0\n else:\n index_col = False\n if (self.file_uploaded):\n data = pd.read_csv(self.uploaded_file, index_col = index_col, sep = self.csv_separator, skipinitialspace=True)\n self.uploaded_file.seek(0)\n else:\n data = pd.read_csv(StringIO(self.paste_data), index_col = index_col, sep = self.csv_separator, skipinitialspace=True)\n self.parsed_data = data.dropna(how='all')\n axis = 0 if self.vars_in_rows == False else 1\n pvals = get_pvals_df(data, axis = axis, test = self.stat_test)\n self.pvals = pvals\n self.save()\n \n def get_pvals_html(self):\n def _color_if_significant(x):\n if (x <= self.pval_thresh):\n clr = 'yellow'\n else:\n clr = 'white'\n return 'background-color: ' + clr\n s = self.pvals.style\n s.applymap(_color_if_significant)\n s.set_table_attributes('border=\"1\"')\n s.set_table_attributes('class=\"restable\"')\n return s.render()\n \n def validate(self):\n if self.file_uploaded:\n input_buf = self.uploaded_file\n input_buf.open(mode='rt')\n else:\n input_buf = io.StringIO(self.paste_data)\n cnt=0\n for line in input_buf:\n cnt +=1 \n if cnt==1:\n continue # Skip header\n values = line.split(self.csv_separator)\n if (len(values) < 2):\n raise InputValidationError('Line \"'+line+'\" does not contain specified CSV separator')\n first_value = 1 if self.rownames else 0\n for value in values[first_value:]:\n value = value.strip()\n if re.search(r'[^0-9.\\-e]', value):\n raise InputValidationError('Value '+str(value)+' from line \"'+line+'\" is not valid')\n if self.file_uploaded:\n self.uploaded_file.seek(0)\n","sub_path":"allcmp/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":3955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"567924817","text":"\"\"\"\nBinary Tree Level Sum\n\nGiven a binary tree and an integer which is the depth of the target level.\n\nCalculate the sum of the nodes in the target level.\n\nExample\nGiven a binary tree:\n\n 1\n / \\\n 2 3\n / \\ / \\\n4 5 6 7\n / \\\n 8 9\nFor depth = 2, return 5.\nFor depth = 3, return 22.\nFor depth = 4, return 17.\n\nTags\nBinary Tree\n\"\"\"\n\n\"\"\"\nDefinition of TreeNode:\nclass TreeNode:\n def __init__(self, val):\n this.val = val\n this.left, this.right = None, None\n\"\"\"\nclass Solution:\n # @param {TreeNode} root the root of the binary tree\n # @param {int} level the depth of the target level\n # @return {int} an integer\n sum = 0\n def levelSum(self, root, level):\n # Write your code here\n self.helper(root, 1, level)\n return self.sum\n\n def helper(self, root, depth, level):\n if root is None:\n return;\n\n if depth == level:\n self.sum += root.val\n\n self.helper(root.left, depth + 1, level)\n self.helper(root.right, depth + 1, level)\n return","sub_path":"Binary_Tree_Level_Sum.py","file_name":"Binary_Tree_Level_Sum.py","file_ext":"py","file_size_in_byte":1067,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"12268042","text":"##############################################################################\n# Copyright (c) 2017 Huawei Technologies Co.,Ltd and others.\n#\n# All rights reserved. This program and the accompanying materials\n# are made available under the terms of the Apache License, Version 2.0\n# which accompanies this distribution, and is available at\n# http://www.apache.org/licenses/LICENSE-2.0\n##############################################################################\nimport unittest\nimport mock\n\nfrom yardstick.common import openstack_utils\nfrom yardstick.common import exceptions\nfrom yardstick.benchmark.scenarios.lib import delete_image\n\n\nclass DeleteImageTestCase(unittest.TestCase):\n\n def setUp(self):\n self._mock_delete_image = mock.patch.object(\n openstack_utils, 'delete_image')\n self.mock_delete_image = (\n self._mock_delete_image.start())\n self._mock_get_shade_client = mock.patch.object(\n openstack_utils, 'get_shade_client')\n self.mock_get_shade_client = self._mock_get_shade_client.start()\n self._mock_log = mock.patch.object(delete_image, 'LOG')\n self.mock_log = self._mock_log.start()\n self.args = {'options': {'name_or_id': 'yardstick_image'}}\n self.result = {}\n\n self.delimg_obj = delete_image.DeleteImage(self.args, mock.ANY)\n\n self.addCleanup(self._stop_mock)\n\n def _stop_mock(self):\n self._mock_delete_image.stop()\n self._mock_get_shade_client.stop()\n self._mock_log.stop()\n\n def test_run(self):\n self.mock_delete_image.return_value = True\n self.assertIsNone(self.delimg_obj.run(self.result))\n self.assertEqual({'delete_image': 1}, self.result)\n self.mock_log.info.assert_called_once_with('Delete image successful!')\n\n def test_run_fail(self):\n self.mock_delete_image.return_value = False\n with self.assertRaises(exceptions.ScenarioDeleteImageError):\n self.delimg_obj.run(self.result)\n self.assertEqual({'delete_image': 0}, self.result)\n self.mock_log.error.assert_called_once_with('Delete image failed!')\n","sub_path":"yardstick/tests/unit/benchmark/scenarios/lib/test_delete_image.py","file_name":"test_delete_image.py","file_ext":"py","file_size_in_byte":2126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"111778924","text":"#!/usr/bin/env python\n\"\"\"Tests for the polytope subpackage.\"\"\"\nimport logging\n\nimport numpy as np\nfrom numpy.testing import assert_allclose\nimport polytope as pc\nfrom polytope.polytope import solve_rotation_ap, givens_rotation_matrix\n\nlog = logging.getLogger('polytope.polytope')\nlog.setLevel(logging.INFO)\n\n\ndef test_polytope_str():\n polys = dict(\n p1d=[[0, 1]],\n p2d=[[0, 1], [0, 2]],\n p3d=[[0, 1], [0, 2], [0, 3]])\n strings = dict(\n p1d='Single polytope \\n [[ 1.] x <= [[ 1.]\\n [-1.]]| [ 0.]]\\n',\n p2d=(\n 'Single polytope \\n [[ 1. 0.] | [[ 1.]\\n [ 0. 1.] '\n 'x <= [ 2.]\\n [-1. -0.] | [ 0.]\\n [-0. -1.]]|'\n ' [ 0.]]\\n'),\n p3d=(\n 'Single polytope \\n [[ 1. 0. 0.] | [[ 1.]\\n '\n '[ 0. 1. 0.] | [ 2.]\\n [ 0. 0. 1.] x <= [ 3.]\\n'\n ' [-1. -0. -0.] | [ 0.]\\n [-0. -1. -0.] |'\n ' [ 0.]\\n [-0. -0. -1.]]| [ 0.]]\\n'))\n for name, poly in polys.items():\n p = pc.Polytope.from_box(poly)\n s = str(p)\n s_ = strings[name]\n assert s == s_, (s, s_)\n\n\nclass operations_test(object):\n def setUp(self):\n # unit square in first quadrant\n self.Ab = np.array([[0.0, 1.0, 1.0],\n [0.0, -1.0, 0.0],\n [1.0, 0.0, 1.0],\n [-1.0, 0.0, 0.0]])\n\n # unit square in second quadrant\n self.Ab2 = np.array([[-1.0, 0.0, 1.0],\n [1.0, 0.0, 0.0],\n [0.0, 1.0, 1.0],\n [0.0, -1.0, 0.0]])\n\n # unit square in third quadrant\n self.Ab3 = np.array([[0.0, 1.0, 0.0],\n [0.0, -1.0, 1.0],\n [1.0, 0.0, 0.0],\n [-1.0, 0.0, 1.0]])\n\n # unit square in fourth quadrant\n self.Ab4 = np.array([[0.0, 1.0, 0.0],\n [0.0, -1.0, 1.0],\n [1.0, 0.0, 1.0],\n [-1.0, 0.0, 0.0]])\n\n self.A = self.Ab[:, 0:2]\n self.b = self.Ab[:, 2]\n\n def tearDown(self):\n pass\n\n\n def comparison_test(self):\n p = pc.Polytope(self.A, self.b)\n p2 = pc.Polytope(self.A, 2*self.b)\n\n assert(p <= p2)\n assert(not p2 <= p)\n assert(not p2 == p)\n\n r = pc.Region([p])\n r2 = pc.Region([p2])\n\n assert(r <= r2)\n assert(not r2 <= r)\n assert(not r2 == r)\n\n # test H-rep -> V-rep -> H-rep\n v = pc.extreme(p)\n p3 = pc.qhull(v)\n assert(p3 == p)\n\n # test V-rep -> H-rep with d+1 points\n p4 = pc.qhull(np.array([[0, 0], [1, 0], [0, 1]]))\n assert(p4 == pc.Polytope(\n np.array([[1, 1], [0, -1], [0, -1]]),\n np.array([1, 0, 0])))\n\n\n def region_rotation_test(self):\n p = pc.Region([pc.Polytope(self.A, self.b)])\n p1 = pc.Region([pc.Polytope(self.A, self.b)])\n p2 = pc.Region([pc.Polytope(self.Ab2[:, 0:2], self.Ab2[:, 2])])\n p3 = pc.Region([pc.Polytope(self.Ab3[:, 0:2], self.Ab3[:, 2])])\n p4 = pc.Region([pc.Polytope(self.Ab4[:, 0:2], self.Ab4[:, 2])])\n\n p = p.rotation(0, 1, np.pi/2)\n print(p.bounding_box)\n assert(p == p2)\n assert(not p == p3)\n assert(not p == p4)\n assert(not p == p1)\n assert_allclose(p.chebXc, [-0.5, 0.5])\n\n p = p.rotation(0, 1, np.pi/2)\n assert(p == p3)\n assert_allclose(p.chebXc, [-0.5, -0.5])\n\n p = p.rotation(0, 1, np.pi/2)\n assert(p == p4)\n assert_allclose(p.chebXc, [0.5, -0.5])\n\n p = p.rotation(0, 1, np.pi/2)\n assert(p == p1)\n assert_allclose(p.chebXc, [0.5, 0.5])\n\n\n def polytope_rotation_test(self):\n p = pc.Polytope(self.A, self.b)\n p1 = pc.Polytope(self.A, self.b)\n p2 = pc.Polytope(self.Ab2[:, 0:2], self.Ab2[:, 2])\n p3 = pc.Polytope(self.Ab3[:, 0:2], self.Ab3[:, 2])\n p4 = pc.Polytope(self.Ab4[:, 0:2], self.Ab4[:, 2])\n\n p = p.rotation(0, 1, np.pi/2)\n print(p.bounding_box)\n assert(p == p2)\n assert(not p == p3)\n assert(not p == p4)\n assert(not p == p1)\n assert_allclose(p.chebXc, [-0.5, 0.5])\n\n p = p.rotation(0, 1, np.pi/2)\n assert(p == p3)\n assert_allclose(p.chebXc, [-0.5, -0.5])\n\n p = p.rotation(0, 1, np.pi/2)\n assert(p == p4)\n assert_allclose(p.chebXc, [0.5, -0.5])\n\n p = p.rotation(0, 1, np.pi/2)\n assert(p == p1)\n assert_allclose(p.chebXc, [0.5, 0.5])\n\n\n def region_translation_test(self):\n p = pc.Region([pc.Polytope(self.A, self.b)])\n p1 = pc.Region([pc.Polytope(self.A, self.b)])\n p2 = pc.Region([pc.Polytope(self.Ab2[:, 0:2], self.Ab2[:, 2])])\n\n p = p.translation([-1, 0])\n assert(p == p2)\n assert(not p == p1)\n p = p.translation([1, 0])\n assert(p == p1)\n\n\n def polytope_translation_test(self):\n p = pc.Polytope(self.A, self.b)\n p1 = pc.Polytope(self.A, self.b)\n p2 = pc.Polytope(self.Ab2[:, 0:2], self.Ab2[:, 2])\n\n p = p.translation([-1, 0])\n assert(p == p2)\n assert(not p == p1)\n p = p.translation([1, 0])\n assert(p == p1)\n\n def region_empty_test(self):\n # Note that as of commit a037b555758ed9ee736fa7cb324d300b8d622fb4\n # Region.__init__ deletes empty polytopes from\n # the given list of polytopes at instantiation.\n reg = pc.Region()\n reg.list_poly = [pc.Polytope(), pc.Polytope()]\n assert len(reg) > 0\n assert pc.is_empty(reg)\n\n def polytope_full_dim_test(self):\n assert pc.is_fulldim(pc.Polytope(self.A, self.b))\n assert pc.is_fulldim(pc.Polytope(self.Ab2[:, 0:2], self.Ab2[:, 2]))\n assert not pc.is_fulldim(pc.Polytope())\n assert not pc.is_fulldim(pc.Polytope(self.A, self.b - 1e3))\n\n def region_full_dim_test(self):\n assert not pc.is_fulldim(pc.Region())\n\n p1 = pc.Polytope(self.A, self.b)\n p2 = pc.Polytope(self.Ab2[:, 0:2], self.Ab2[:, 2])\n reg = pc.Region([p1, p2])\n assert pc.is_fulldim(reg)\n\n # Adding empty polytopes should not affect the\n # full-dimensional status of this region.\n reg.list_poly.append(pc.Polytope())\n assert pc.is_fulldim(reg)\n reg.list_poly.append(pc.Polytope(self.A, self.b - 1e3))\n assert pc.is_fulldim(reg)\n\n def polytope_intersect_test(self):\n p1 = pc.Polytope(self.A, self.b)\n p2 = pc.Polytope(self.Ab2[:, 0:2], self.Ab2[:, 2])\n p3 = p1.intersect(p2)\n assert pc.is_fulldim(p1)\n assert pc.is_fulldim(p2)\n assert not pc.is_fulldim(p3)\n\n # p4 is the unit square with center at the origin.\n p4 = pc.Polytope(np.array([[ 1., 0.],\n [ 0., 1.],\n [-1., 0.],\n [ 0., -1.]]),\n np.array([0.5, 0.5, 0.5, 0.5]))\n p5 = p2.intersect(p4)\n assert pc.is_fulldim(p4)\n assert pc.is_fulldim(p5)\n\n\n\ndef solve_rotation_test_090(atol=1e-15):\n g1 = np.array([0, 1, 1, 0])\n g2 = np.array([0, 1, 0, 0])\n R = solve_rotation_ap(g1, g2)\n\n e0 = np.array([0, 1, 1, 1])\n e1 = np.array([0, 0, -1, 0])\n e2 = np.array([0, 0, 0, 0])\n\n t0 = np.array([0, 1, -1, 1])\n t1 = np.array([0, -1, 0, 0])\n t2 = np.array([0, 0, 0, 0])\n\n assert_allclose(R.dot(e0), t0, atol=atol)\n assert_allclose(R.dot(e1), t1, atol=atol)\n assert_allclose(R.dot(e2), t2, atol=atol)\n\n\ndef solve_rotation_test_180(atol=1e-15):\n g1 = np.array([0, 1, 0, 0])\n g2 = np.array([0, 0, 1, 0])\n R = solve_rotation_ap(g1, g2)\n\n e0 = np.array([0, 1, 1, 1])\n e1 = np.array([0, 0, -1, 0])\n e2 = np.array([0, 0, 0, 0])\n\n t0 = np.array([0, -1, -1, 1])\n t1 = np.array([0, 0, 1, 0])\n t2 = np.array([0, 0, 0, 0])\n\n assert_allclose(R.dot(e0), t0, atol=atol)\n assert_allclose(R.dot(e1), t1, atol=atol)\n assert_allclose(R.dot(e2), t2, atol=atol)\n\n\ndef solve_rotation_test_270R(atol=1e-15):\n g1 = np.array([0, -1, 0, 0])\n g2 = np.array([0, 1, 1, 0])\n R = solve_rotation_ap(g1, g2)\n\n e0 = np.array([0, 1, 1, 1])\n e1 = np.array([0, 0, -1, 0])\n e2 = np.array([0, 0, 0, 0])\n\n t0 = np.array([0, -1, 1, 1])\n t1 = np.array([0, 1, 0, 0])\n t2 = np.array([0, 0, 0, 0])\n\n assert_allclose(R.dot(e0), t0, atol=atol)\n assert_allclose(R.dot(e1), t1, atol=atol)\n assert_allclose(R.dot(e2), t2, atol=atol)\n\n\ndef solve_rotation_test_270L(atol=1e-15):\n g1 = np.array([0, -1, 0, 0])\n g2 = np.array([0, 1, -1, 0])\n R = solve_rotation_ap(g1, g2)\n\n e0 = np.array([0, 1, 1, 1])\n e1 = np.array([0, 0, -1, 0])\n e2 = np.array([0, 0, 0, 0])\n\n t0 = np.array([0, 1, -1, 1])\n t1 = np.array([0, -1, 0, 0])\n t2 = np.array([0, 0, 0, 0])\n\n assert_allclose(R.dot(e0), t0, atol=atol)\n assert_allclose(R.dot(e1), t1, atol=atol)\n assert_allclose(R.dot(e2), t2, atol=atol)\n\n\ndef givens_rotation_test_180(atol=1e-15):\n R = givens_rotation_matrix(1, 2, np.pi, 4)\n\n e0 = np.array([0, 1, 1, 1])\n e1 = np.array([0, 0, -1, 0])\n e2 = np.array([0, 0, 0, 0])\n\n t0 = np.array([0, -1, -1, 1])\n t1 = np.array([0, 0, 1, 0])\n t2 = np.array([0, 0, 0, 0])\n\n assert_allclose(R.dot(e0), t0, atol=atol)\n assert_allclose(R.dot(e1), t1, atol=atol)\n assert_allclose(R.dot(e2), t2, atol=atol)\n\n\ndef givens_rotation_test_270L(atol=1e-15):\n g1 = np.array([0, -1, 0, 0])\n g2 = np.array([0, 1, -1, 0])\n R = givens_rotation_matrix(1, 2, 3*np.pi/2, 4)\n\n e0 = np.array([0, 1, 1, 1])\n e1 = np.array([0, 0, -1, 0])\n e2 = np.array([0, 0, 0, 0])\n\n t0 = np.array([0, 1, -1, 1])\n t1 = np.array([0, -1, 0, 0])\n t2 = np.array([0, 0, 0, 0])\n\n assert_allclose(R.dot(e0), t0, atol=atol)\n assert_allclose(R.dot(e1), t1, atol=atol)\n assert_allclose(R.dot(e2), t2, atol=atol)\n\n\nif __name__ == '__main__':\n pass\n","sub_path":"tests/polytope_test.py","file_name":"polytope_test.py","file_ext":"py","file_size_in_byte":10090,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"470126379","text":"from django.core.context_processors import csrf\nfrom django.http.response import HttpResponse\nfrom django.shortcuts import render\nfrom django.shortcuts import get_object_or_404, Http404\nfrom django.views.defaults import page_not_found\nfrom .models import Articulo, ArticuloForm\nfrom pprint import pprint\nfrom django.db.models import Q\n\nfrom .models import Articulo\nfrom .utils import *\n\n# vista principal\ndef home(request):\n return render(request, 'inventario/base.html')\n\n\n# vista del articulo\ndef ver(request, question_id):\n item = get_object_or_404(Articulo, pk=question_id)\n if request.user.is_authenticated() and item.usuario == request.user:\n return render(request, 'inventario/view_item.html', {'item': item})\n else:\n return page_not_found()\n\n\ndef crear(request):\n form = ArticuloForm()\n if (request.POST and request.user.is_authenticated()):\n f = ArticuloForm(request.POST)\n j = f.save(commit=False)\n j.usuario = request.user\n j.save()\n return HttpResponse('ok')\n\n data = {'form': form}\n\n\ndef editar(request, id):\n item = get_object_or_404(Articulo, pk=id)\n\n form = ArticuloForm()\n if request.user.is_authenticated() and item.usuario == request.user:\n if request.POST:\n f = ArticuloForm(request.POST, instance=item)\n f.save()\n data = {}\n else:\n data = {'form': ArticuloForm(instance=item)}\n data.update(csrf(request))\n return render(request, 'inventario/item.html', data)\n\n else:\n return page_not_found()\n\n\ndef listado(request):\n resp = {}\n query = request.GET.get('query')\n pag = string_to_positive_int_or_default(request.GET.get('pag'), 1)\n cant = string_to_positive_int_or_default(request.GET.get('cant'), 10)\n if query != None:\n count = Articulo.objects.filter(usuario=request.user).filter(Q(titulo__icontains=query) | Q(descripcion__icontains=query)).count()\n resp['cantidad'] = count\n obj = Articulo.objects.filter(usuario=request.user).filter(Q(titulo__icontains=query) | Q(descripcion__icontains=query))[(pag-1)*cant:pag*cant]\n else:\n obj = Articulo.objects.filter(usuario=request.user)[(pag - 1) * cant:pag * cant]\n count = Articulo.objects.filter(usuario=request.user).count()\n resp['cantidad'] = count\n resp['lista'] = obj\n return render(request, 'inventario/list.html', resp)","sub_path":"inventario/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2422,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"76264816","text":"# coding: utf-8\n\"\"\"\nBase para desarrollo de modulos externos.\nPara obtener el modulo/Funcion que se esta llamando:\n GetParams(\"module\")\n\nPara obtener las variables enviadas desde formulario/comando Rocketbot:\n var = GetParams(variable)\n Las \"variable\" se define en forms del archivo package.json\n\nPara modificar la variable de Rocketbot:\n SetVar(Variable_Rocketbot, \"dato\")\n\nPara obtener una variable de Rocketbot:\n var = GetVar(Variable_Rocketbot)\n\nPara obtener la Opcion seleccionada:\n opcion = GetParams(\"option\")\n\n\nPara instalar librerias se debe ingresar por terminal a la carpeta \"libs\"\n\n pip install <package> -t .\n\n\"\"\"\n\nimport os\nimport glob\n\nbase_path = tmp_global_obj[\"basepath\"]\ncur_path = base_path + 'modules' + os.sep + 'SplitPdfMerge' + os.sep + 'libs' + os.sep\nif cur_path not in sys.path:\n sys.path = [cur_path] + sys.path\n \n\n\nfrom PyPDF2 import PdfFileReader, PdfFileWriter\nglobal txt\n\ndef reset_eof_of_pdf_return_stream(pdf_stream_in:list):\n # find the line position of the EOF\n for i, x in enumerate(txt[::-1]):\n if b'%%EOF' in x:\n actual_line = len(pdf_stream_in)-i\n print(f'EOF found at line position {-i} = actual {actual_line}, with value {x}')\n break\n\n # return the list up to that point\n return pdf_stream_in[:actual_line]\n\n\"\"\"\n Obtengo el modulo que fueron invocados\n\"\"\"\nmodule = GetParams(\"module\")\n\nif module == \"split_pdf\":\n path = GetParams(\"pdf\").replace(\"/\", os.sep)\n folder = GetParams(\"folder\")\n step = GetParams(\"step\") or 1\n step = int(step)\n\n r = True\n try:\n fname = os.path.splitext(os.path.basename(path))[0]\n pdf = PdfFileReader(path)\n page_number = pdf.getNumPages()\n start = 0\n while start < page_number:\n end = start + step\n if end > page_number:\n end = page_number\n pdf_writer = PdfFileWriter()\n for page in range(start, end):\n pdf_writer.addPage(pdf.getPage(page))\n output_filename = '{}_page_{}.pdf'.format(fname, start + 1)\n start = end\n with open(folder + os.sep + output_filename, 'wb') as out:\n pdf_writer.write(out)\n print('Created: {}'.format(output_filename))\n except Exception as e:\n raise Exception(e)\n\nif module == \"merge_pdf\":\n\n input_ = GetParams(\"pdfs_folder\")\n output = GetParams(\"output_folder\")\n\n output += \".pdf\"\n pdf_writer = PdfFileWriter()\n pdfs = glob.glob(input_ + os.sep + \"*.pdf\")\n pdfs.sort()\n\n for pdf in pdfs:\n\n pdf = pdf.replace(\"\\\\\", \"/\")\n\n with open(pdf, 'rb') as p:\n txt = (p.readlines())\n\n txtx = reset_eof_of_pdf_return_stream(txt)\n\n with open(pdf, 'wb') as f:\n f.writelines(txtx)\n pdf_reader = PdfFileReader(pdf, strict=False)\n for page in range(pdf_reader.getNumPages()):\n pdf_writer.addPage(pdf_reader.getPage(page))\n\n with open(output, 'wb') as fh:\n pdf_writer.write(fh)\n\nif module == \"encrypt_pdf\":\n path = GetParams(\"path\")\n out = GetParams(\"out\")\n password = GetParams(\"pass\")\n\n try:\n with open(path, \"rb\") as pdf:\n input_pdf = PdfFileReader(pdf)\n\n output = PdfFileWriter()\n output.appendPagesFromReader(input_pdf)\n output.encrypt(password)\n\n with open(out, \"wb\") as out_pdf:\n output.write(out_pdf)\n\n except Exception as e:\n PrintException()\n raise e\n\nif module == \"read_pdf\":\n path = GetParams(\"path\")\n password = GetParams(\"pass\")\n result = GetParams(\"result\")\n\n try:\n text = \"\"\n with open(path, \"rb\") as pdf:\n reader = PdfFileReader(pdf)\n\n if reader.isEncrypted:\n reader.decrypt(password)\n\n page_number = reader.numPages\n\n for i in range(page_number):\n page = reader.getPage(i)\n text += page.extractText()\n\n SetVar(result, text)\n except Exception as e:\n PrintException()\n raise e\n\nif module == \"decrypt_pdf\":\n\n in_path = GetParams(\"in_path\")\n pass_pdf = GetParams(\"pass_pdf\")\n out_path = GetParams(\"out_path\")\n\n try:\n try:\n out = PdfFileWriter()\n file = PdfFileReader(in_path, password=pass_pdf)\n if file.isEncrypted:\n\n file.decrypt('')\n for idx in range(file.numPages):\n page = file.getPage(idx)\n out.addPage(page)\n\n with open(out_path, \"wb\") as f:\n out.write(f)\n\n print(\"File decrypted Successfully.\")\n else:\n print(\"File already decrypted.\")\n except:\n import subprocess\n FNULL = open(os.devnull, 'w') #use this if you want to suppress output to stdout from the subprocess\n bin_path = cur_path + os.sep + \"bin_qpdf\" + os.sep + \"qpdf\"\n args = bin_path + \" --decrypt --password={pass_pdf} {in_path} {out_path} \".format(pass_pdf=pass_pdf, in_path=in_path, out_path=out_path)\n subprocess.call(args, stdout=FNULL, stderr=FNULL, shell=False)\n\n\n except Exception as e:\n PrintException()\n raise e\n\nif (module == \"SplitPdfMergeSpecificStep\"):\n path = GetParams(\"pdf\").replace(\"/\", os.sep)\n folder = GetParams(\"folder\")\n step = GetParams(\"step\")\n step = eval(step)\n fname = os.path.splitext(os.path.basename(path))[0]\n pdf = PdfFileReader(path)\n page_number = pdf.getNumPages()\n\n try:\n for each in step:\n\n realStep = each.split(\"-\")\n pdf_writer = PdfFileWriter()\n\n for page in range((int(realStep[0])) - 1, int(realStep[1])):\n pdf_writer.addPage(pdf.getPage(page))\n output_filename = '{}_page_{}.pdf'.format(fname, f\"{realStep[0]}-{realStep[1]}\")\n\n with open(folder + os.sep + output_filename, 'wb') as out:\n pdf_writer.write(out)\n print('Created: {}'.format(output_filename))\n\n except Exception as e:\n PrintException()\n raise e","sub_path":"__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":6182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"100378835","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\n# Import library\nimport pygpseq as gp\n\n# Create pyGPSeq analyzer instance\ngpi = gp.Main(ncores = 5)\n\n# Steps to be skipped\n# 1\t : skip instantiation (need gpi.inst.cpickle, otherwise unskips)\n# 2\t : skip segmentation (need gpi.seg.cpickle, otherwise unskips)\n# 3\t : skip analysis (need gpi.an.cpickle, otherwise unskips)\n# 3.5 : skip single-nuclei boxplot (end of step 3)\n# 4\t : final plots\n# 5 : final report\ngpi.skip = []\n\n# Data directory\ngpi.basedir = '/home/gire/Desktop/BiCro-Data/Imaging/iTK_61_66_deconvolved'\n\n# Output directory\ngpi.outdir = '/home/gire/Desktop/BiCro-Analysis/Imaging/iTK_61_66_deconvolved'\n\n# Segmentation type for nuclear identification\n# SEG_SUM_PROJ\t: through sum Z projection\n# SEG_MAX_PROJ\t: through max Z projection\n# SEG_3D\t\t: 3D segmentation\ngpi.seg_type = gp.const.SEG_3D\n\n# Single-nucleus analysis type\n# AN_SUM_PROJ\t: on nucleus sum Z projection\n# AN_MAX_PROJ\t: on nucleus max Z projection\n# AN_3D\t\t\t: on whole and partial nuclear volume\n# AN_MID\t\t: only on nucleus mid-section (highest sum of DNA staining intensity)\ngpi.an_type = gp.const.AN_MID\n\n# Voxel aspect proportions (or sizes, ZYX)\ngpi.aspect = (300., 216.6, 216.6)\n\n# Minimum percentage of stack to be occupied by a cell\ngpi.min_z_size = .25\n\n# Nuclear selection\n# NSEL_SIZE\t\t\t: nuclear size (either volume or area)\n# NSEL_SURF\t\t\t: nuclear surface (only if AN_3D)\n# NSEL_SHAPE\t\t: nuclear shape (either sphericity or circularity)\n# NSEL_SUMI\t\t\t: nuclear DNA intensity sum\n# NSEL_MEANI\t\t: nuclear DNA intensity average\n# NSEL_FLAT_SIZE\t: nuclear Z-projected area\ngpi.nsf = (gp.const.NSEL_FLAT_SIZE, gp.const.NSEL_SUMI)\n\n# Regular expression to identify image files\ngpi.reg = '^(?P<' + gp.const.REG_CHANNEL_NAME + '>[^/]*)'\ngpi.reg += '\\.(?P<' + gp.const.REG_CHANNEL_ID + '>channel[0-9]+)'\ngpi.reg += '\\.(?P<' + gp.const.REG_SERIES_ID + '>series[0-9]+)'\ngpi.reg += '(?P<' + gp.const.REG_EXT + '>(_cmle)?\\.tif)$'\n\n# Where to save the run log\ngpi.logpath = '/home/gire/Desktop/BiCro-Analysis/Imaging/iTK_61_66_deconvolved/log'\n#gpi.logpath += gpi.gen_log_name()\n\n# Perform deconvolved image rescaling?\ngpi.rescale_deconvolved = True\n\n# Normalize distance?\ngpi.normalize_distance = True\n\n# Better condition naming\ngpi.cdescr['iTK61_031116_001'] = '0N'\ngpi.cdescr['iTK62_031116_001'] = '30min'\ngpi.cdescr['iTK63_031116_001'] = '15min'\ngpi.cdescr['iTK64_031116_001'] = '5min'\ngpi.cdescr['iTK65_031116_001'] = '1min'\ngpi.cdescr['iTK66_031116_001'] = 'negative'\n\n# Notes\ngpi.notes = 'HeLa cells, 200U HindIII, super long Y-FISH linker'\n\n# Start the analysis\ngpi = gpi.run()\n","sub_path":"sample/pyGPSeq_run.py","file_name":"pyGPSeq_run.py","file_ext":"py","file_size_in_byte":2612,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"109754476","text":"#!/usr/bin/env python3\n\n\"\"\" 用Python画个美队盾牌 \"\"\"\nimport turtle as t\nimport logging\n\nlogging.basicConfig(level=logging.INFO)\n\n# 「抬笔」->「移动画笔」->「落笔」函数\ndef setpen(x,y):\n # 抬笔\n t.penup()\n # 移动画笔到x,y\n t.goto(x,y)\n # 落笔\n t.pendown()\n # 未知\n t.setheading(0)\n\n# 画盾牌\ndef circle(x,y,r,color):\n # 为了保证画出的圆够圆，所以我们把圆的边设置的多一些\n n=36 # by Peter: 此处3为三角形，4为正方形\n angle=360/n\n pi=3.1415926\n # 周长\n c=2*pi*r\n # 每条边的长度\n l=c/n\n # 起始位置\n start_x=x-1/2\n start_y=y+r\n # 移动画笔\n setpen(start_x,start_y)\n # 选择画笔颜色\n t.pencolor(color)\n # 选择背景色\n t.fillcolor(color)\n # 填充\n t.begin_fill()\n # t.hideturtle()\n # t.penup()\n logging.info('边数:{} 转边角度:{} 周长:{} 边长:{} x:{} y:{}'.format(n,angle,c,l,start_x,start_y))\n for i in range(n):\n # 画线\n t.forward(l)\n # 转角\n t.right(angle)\n t.end_fill()\n\n# 画五角星(未理解)\ndef five_star(l):\n setpen(0, 0)\n t.setheading(162)\n # add by peter: 看画线路径\n t.pencolor('white')\n\n t.forward(150)\n t.setheading(0)\n t.fillcolor('WhiteSmoke')\n t.begin_fill()\n t.hideturtle()\n # add by peter: 看画线路径\n # t.penup()\n\n for i in range(5):\n t.forward(l)\n # 五角星的五个顶角各是36°, 180-36=144,转边角度144\n t.right(144)\n t.end_fill()\n\n# 主函数\ndef shield():\n circle(0,0,300,'red')\n circle(0,0,250,'white')\n circle(0,0,200,'red')\n circle(0,0,150,'blue')\n five_star(284)\n\nif __name__=='__main__':\n shield()\n # 结束乌龟图\n t.done()\n # 最后结果图有偏差，原因未知 \n","sub_path":"python-sample/turtle_draw/draw_shield.py","file_name":"draw_shield.py","file_ext":"py","file_size_in_byte":1837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"232109850","text":"import logging\nlog = logging.getLogger('StreamTransport')\n\nimport Queue\nimport gevent\nfrom gevent import socket\nfrom . import ServerTransport, ClientTransport\n\n\nclass StreamServerTransport(ServerTransport):\n \"\"\"TCP socket transport.\n\n This transport has a few peculiarities: It must be run in a thread,\n greenlet or some other form of concurrent execution primitive.\n\n This is due to\n :py:func:`~tinyrpc.transports.socket.StreamServerTransport.handle` blocking\n while waiting for a call to\n :py:func:`~tinyrpc.transports.socket.StreamServerTransport.send_reply`.\n\n The parameter ``queue_class`` must be used to supply a proper queue class\n for the chosen concurrency mechanism (i.e. when using :py:mod:`gevent`,\n set it to :py:class:`gevent.queue.Queue`).\n\n :param queue_class: The Queue class to use.\n \"\"\"\n\n def __init__(self, queue_class=Queue.Queue):\n self._config_buffer = 4096\n self._config_timeout = 90\n self._socket_error = False\n self._queue_class = queue_class\n self.messages = queue_class()\n\n def receive_message(self):\n return self.messages.get()\n\n def send_reply(self, context, reply):\n if not isinstance(reply, basestring):\n raise TypeError('string expected')\n\n context.put(reply)\n\n def _get_data(self, sock, address):\n \"\"\" Retrieves a data chunk from the socket. \"\"\"\n sock_error = False\n try:\n data = sock.recv(self._config_buffer)\n except socket.timeout:\n sock_error = True\n data = None\n log.debug('StreamServerTransport:socket timeout from %s', address)\n except socket.error:\n sock_error = True\n data = None\n log.debug('StreamServerTransport:socket error from %s', address)\n\n return data, sock_error\n\n def _get_msg(self, sock, address):\n sock_error = False\n chunks = []\n while True:\n data, sock_error = self._get_data(sock, address)\n if not data:\n break\n chunks.append(data)\n if len(data) < self._config_buffer:\n break\n\n msg = ''.join(chunks)\n return msg, sock_error\n\n def handle(self, sock, address):\n \"\"\"StreamServer handler function.\n\n The transport will serve a request by reading the message and putting\n it into an internal buffer. It will then block until another\n concurrently running function sends a reply using\n :py:func:`~tinyrpc.transports.socket.StreamServerTransport.send_reply`.\n\n The reply will then be sent to the client being handled and handle will\n return.\n \"\"\"\n\n sock.settimeout(self._config_timeout)\n\n while True:\n msg, sock_error = self._get_msg(sock, address)\n if msg and len(msg):\n log.debug('StreamServerTransport:%s', msg)\n\n # create new context\n context = self._queue_class()\n self.messages.put((context, msg))\n # ...and send the reply\n response = context.get()\n sock.send(response)\n\n if sock_error:\n log.debug('')\n sock.close()\n break\n\n\nclass StreamClientTransport(ClientTransport):\n \"\"\"TCP socket based client transport.\n\n Requires :py:mod:`websocket-python`. Submits messages to a server using the body of\n an ``HTTP`` ``WebSocket`` message. Replies are taken from the response of the websocket.\n\n The connection is establish on the ``__init__`` because the protocol is connection oriented,\n you need to close the connection calling the close method.\n\n :param endpoint: The URL to connect the websocket.\n :param kwargs: Additional parameters for :py:func:`websocket.send`.\n \"\"\"\n def __init__(self, endpoint, **kwargs):\n self._config_timeout = 5\n self._config_buffer = 4096\n self.endpoint = endpoint\n self.request_kwargs = kwargs\n self.sock = gevent.socket.create_connection(self.endpoint, **kwargs)\n self.sock.settimeout(self._config_timeout)\n\n def send_message(self, message, expect_reply=True):\n if not isinstance(message, basestring):\n raise TypeError('str expected')\n\n self.sock.send(message)\n if expect_reply:\n chunks = []\n while True:\n try:\n data = self.sock.recv(self._config_buffer)\n except socket.timeout:\n log.debug('StreamClientTransport:socket timeout from server')\n break\n if not data:\n break\n chunks.append(data)\n if len(data) < self._config_buffer:\n break\n response = ''.join(chunks)\n return response\n\n def close(self):\n if self.sock is not None:\n self.sock.close()\n","sub_path":"tinyrpc/transports/tcp.py","file_name":"tcp.py","file_ext":"py","file_size_in_byte":4970,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"95736889","text":"import pandas as pd\nimport numpy as np\n\npd.set_option('display.max_columns', 15)\npd.set_option('display.width', 600)\n\ndf = pd.read_csv(\"../data/auto-mpg.csv\", header=None)\ndf.columns = ['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n 'acceleration', 'model year', 'origin', 'name']\n\nprint(\"df[horsepower]\", \"\\n\", df['horsepower'], '\\n')\n\n# horsepower 열의 누락 데이터('?') 삭제하고 실수형으로 변환\ndf['horsepower'] = df['horsepower'].replace('?', np.nan)\ndf = df.dropna(subset=['horsepower'], axis=0)\ndf['horsepower'] = df['horsepower'].astype('float')\n\nprint(\"# np.histogram 함수로 3개의 bin으로 나누는 경계 값의 리스트 구하기\")\ncount, bin_dividers = np.histogram(df['horsepower'], bins=3)\nprint(\"count = \", count)\nprint(\"bin_dividers = \", bin_dividers, '\\n')\n\n# 3개의 bin에 이름 지정\nbin_names = ['저출력', '보통출력', '고출력']\n\n# pd.cut 함수로 각 데이터를 3개의 bin에 할당\ndf['hp_bin'] = pd.cut(x=df['horsepower'], # 데이터 배열\n bins=bin_dividers, # 경계 값 리스트\n labels=bin_names, # bin 이름\n include_lowest=True) # 첫 경계값 포함\n\nprint(\"# horsepower 열, hp_bin 열의 첫 15행을 출력\")\nprint(df[['horsepower', 'hp_bin']].head(15), '\\n')\n\nprint(df['hp_bin'].cat.categories)","sub_path":"data_preprocess/data_binning.py","file_name":"data_binning.py","file_ext":"py","file_size_in_byte":1373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"363975793","text":"from sklearn.utils import resample\nfrom numpy import arange,sqrt,fliplr,argsort\nfrom numpy.linalg import norm\nfrom scipy.linalg import eigh\nfrom collections import OrderedDict,Iterable\n\nimport numpy as np\n\nclass KernelCoorSys:\n\n\tdef __init__(self,bipart_node_selector= lambda n:n.startswith('u'),truncate=False,normalize=False):\n\t\tif bipart_node_selector:\n\t\t\tself.bipart_node_selector = bipart_node_selector\n\t\telse:\n\t\t\tself.bipart_node_selector = lambda n:True\n\t\tself.truncate = truncate\n\t\tself.normalize = normalize\n\n\tdef select_nodes(self):\n\t\tfor n in self.g.nodes():\n\t\t\tif self.bipart_node_selector(n):\n\t\t\t\tyield n \n\n\tdef fit(self,g,kernel,landmarks='high-degree',k_lmrk=5,k_dim=5):\n\t\t'''\n\t\tlandmarks: list of node_names, 'high-degree','centrality', 'random'\n\t\t'''\n\t\tassert \t(landmarks in ('high-degree','random','centrality')) \\\n\t\t\tor \t(type(landmarks) is list) \n\t\tassert (0 < k_dim) and (k_dim <= k_lmrk)\n\n\t\tself.g = g\n\t\tself.kernel =kernel\n\t\tself.k_dim = k_dim\n\t\tself.node2idx = OrderedDict([(n,i) for i,n in enumerate(self.select_nodes())])\n\n\t\tif landmarks in ['high-degree','centrality', 'random']:\n\t\t\tself.landmarks = self.__get_landmarks__(landmarks,k_lmrk)\n\t\telse:\n\t\t\tself.landmarks = landmarks\n\n\t\tlmrk_idx = [self.node2idx[n] for n in self.landmarks]\n\t\tA2L = kernel.fit_with_g(g,src=self.landmarks,dst=self.node2idx.keys())\n\t\tif self.truncate:\n\t\t\t# L2L is A2L[lmrk_idx,:]\n\t\t\t#truncate:\n\t\t\tSig,U = eigh(A2L[lmrk_idx,:])\n\t\t\tmin_eigval = min(Sig[Sig>0])\n\t\t\tA2L[lmrk_idx,arange(k_lmrk)] = A2L[lmrk_idx,arange(k_lmrk)] - min_eigval\n\n\t\tL2L = A2L[lmrk_idx,:]\n\t\t\n\t\tif self.normalize:\n\t\t\t#find eigenpairs of normalized L2L\n\t\t\tL2L = A2L[lmrk_idx,:]\n\t\t\tsqrt_d = sqrt(L2L.diagonal())\t\t\n\t\t\tL2L = (L2L / sqrt_d) / sqrt_d[:,None]\n\t\t\tprint(L2L)\n\t\t\t#half-normlize A2L:\t\t\n\t\t\tA2L = A2L / sqrt_d\n\t\t\t\n\t\tself.A2L = A2L\n\t\tSig,U = eigh(L2L,eigvals=(k_lmrk-k_dim,k_lmrk-1))\n\t\tself.Sig = Sig[::-1]\n\t\tself.U = fliplr(U)\n\n\t\treturn self\n\n\tdef __get_landmarks__(self,strategy,k_lmrk):\n\t\t'''\n\t\tstrategy: how to choose landmarks. Must be one of ['high-degree','centrality', 'random']\n\t\t'''\n\t\tg = self.g\n\t\tkernel = self.kernel\n\t\tif strategy =='high-degree':\t\t\t\n\t\t\tnode_degree = g.degree(self.select_nodes())\n\t\t\treturn [n for n,_ in sorted(node_degree,key=lambda x:x[1])][-k_lmrk:]\n\t\telif strategy == 'random':\n\t\t\treturn resample([n for n in self.select_nodes()],n_samples=k_lmrk,replace=False)\n\t\telif strategy == 'centrality':\n\t\t\tnodes = [n for n in self.select_nodes()]\n\t\t\tcentrality = kernel.fit_with_g(g,src=None,dst=None)\t\t\t\n\t\t\treturn [n for n,_ in sorted(zip(g.nodes(),centrality), key=lambda x:x[1]) if self.bipart_node_selector(n)][-k_lmrk:]\n\t\t\t\n\tdef __getitem__(self,node):\n\t\tif isinstance(node,str):\n\t\t\treturn self.__get_coor__([node])\n\t\telif node is None or isinstance(node,Iterable):\n\t\t\treturn self.__get_coor__(node)\n\t\telse:\n\t\t\treturn None\n\n\tdef __get_coor__(self,nodes):\n\t\tif nodes:\n\t\t\tidx = [self.node2idx[n] for n in nodes]\n\t\t\tX2L = self.A2L[idx,:]\n\t\telse:\n\t\t\tX2L = self.A2L\n\n\t\tif self.normalize:\t\t\n\t\t\tnorms = norm(X2L,axis=1)\n\t\t\tX2L = X2L / norms[:,None]\t\t\n\t\tX2L = X2L.dot(self.U[:,:self.k_dim]) / sqrt(self.Sig[:self.k_dim])\n\t\treturn X2L\n","sub_path":"networkx/applications/coorsys.py","file_name":"coorsys.py","file_ext":"py","file_size_in_byte":3134,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"576019508","text":"from __future__ import print_function, division\n\nimport numpy as np\nimport pandas as pd\nimport pickle\nimport yaml\nimport matplotlib.pyplot as plt\nfrom os import path, makedirs\nimport copy\nfrom scipy.interpolate import UnivariateSpline\nimport multiprocessing as mp\nfrom multiprocessing import Pool\nfrom functools import partial\n\nimport timeit\n\nfrom .dataset import save_obj, load_obj, update_progress\nfrom .functions_nz import compute_statistics,covariance_jck,Silence\n\n\n\n\n\n\n\ndef compare(labels=['BNZ_Newman'],\n shift_pz=[0.],\n cov_mode='diag',\n model_kwargs={'z0': 0.8},\n priors={\n 'gamma': {'kind': 'uniform', 'weight': 1,\n 'kwargs': {'loc': -2, 'scale': 4}},\n 'deltaz': {'kind': 'truncnorm', 'weight': 1,\n 'kwargs': {'a': -8.0, 'b': 8.0,\n 'loc': 0, 'scale': 0.05}},\n 'amplitude': {'kind': 'truncnorm', 'weight': 1,\n 'kwargs': {'a': -5.0, 'b': 5.0,\n 'loc': 0, 'scale': 1.0}},\n },\n nwalkers=32, nburnin=100, nrun=500, live_dangerously=False,\n time0=0,number_of_cores=1):\n\n update_progress(0.)\n\n start_time=timeit.default_timer()\n for i in range(len(labels)):\n with Silence(stdout='./compare/compare_log.txt', mode='w'):\n compare_single(labels,shift_pz,cov_mode,model_kwargs,priors,nwalkers,nburnin,nrun,live_dangerously,time0,i)\n update_progress(np.float((i+1.)/len(labels)),timeit.default_timer(),start_time)\n\n '''\n pool = mp.Pool(number_of_cores)\n iterable = range(len(labels))\n func = partial(compare_single,labels,shift_pz,cov_mode,model_kwargs,priors,nwalkers,nburnin,nrun,live_dangerously,time0)\n\n pool.map(func, iterable)\n\n pool.close()\n pool.join()\n '''\n\n #for i,label_1 in enumerate(label):\n\n #shift_pz_1=shift_pz[i]\n\n #update_progress(np.float((i+1.)/len(label)),timeit.default_timer(),start_time)\n #return\n\ndef compare_single(label_multi='BNZ_Newman',\n shift_multi=0.,\n cov_mode='diag',\n model_kwargs={'z0': 0.8},\n priors={\n 'gamma': {'kind': 'uniform', 'weight': 1,\n 'kwargs': {'loc': -2, 'scale': 4}},\n 'deltaz': {'kind': 'truncnorm', 'weight': 1,\n 'kwargs': {'a': -8.0, 'b': 8.0,\n 'loc': 0, 'scale': 0.05}},\n 'amplitude': {'kind': 'truncnorm', 'weight': 1,\n 'kwargs': {'a': -5.0, 'b': 5.0,\n 'loc': 0, 'scale': 1.0}},\n },\n nwalkers=32, nburnin=100, nrun=500, live_dangerously=False,\n time0=0,ik=1):\n \"\"\"Open up catalog and create dndz from it\n\n Parameters\n ----------\n label : Where we extract the N(z)\n cov_mode : string [diag, tomo, full] The kind of covariance\n matrix we will use for the fits from the WZ\n nwalkers : int [default 32] number of walkers emcee uses\n nburnin : int [default 200] number of initial steps to take in\n emcee fitter\n nrun : int [default 2000] number of steps each walker will\n take in sampling the parameter space\n live_dangerously : override emcee's concerns about size of nwalkers\n relative to ndims of the model\n priors : dict of dicts. Each key corresponds to a different term\n in equation. kind is the kind of scipy.stats function.\n Weight is the relative weight of the prior (higher =\n weight heavier). kwargs is a dictionary of kwargs\n passed to construction of the stats function.\n model_kwargs : dictionary of things to pass to the specific model for\n fitting.\n\n Here we have one kwarg: z0. float [default 0.8] pivot\n point for clustering bias term. Multiplies WZ by ((1 +\n z) / (1 + z0)) ** gamma\n time0 : float [default: 0] If non-zero, will give updates on\n progress\n\n Notes\n -----\n\n \"\"\"\n\n label=label_multi[ik]\n print('{0} sample running'.format(label))\n shift_pz_1=shift_multi[ik]\n\n if time0:\n update_progress(0, timeit.default_timer(), time0)\n\n compare_path = './compare/'\n compare_label = 'compare_{0}'.format(label)\n\n if not path.exists(compare_path):\n makedirs(compare_path)\n # load wz\n wz, wz_zbins, wz_zcenters, wz_cov, wz_jackknives = load_wz(label, cov_mode=cov_mode)\n\n # load true redshift distribution in finer broad bins\n pz, pz_zbins, pz_zcenters,pz_binned,norm,zp_t_TOT = load_pz(wz_zbins,shift_pz_1)\n\n # we'll use the priors to create p0\n p0 = []\n\n # create the redshift bias object\n sampler = RedshiftBiasSampler(\n wz=wz, wz_zbins=wz_zbins, wz_zcenters=wz_zcenters, wz_cov=wz_cov,\n pz=pz, pz_zbins=pz_zbins, pz_zcenters=pz_zcenters,\n cov_mode=cov_mode,\n nwalkers=nwalkers, nburnin=nburnin, nrun=nrun,\n live_dangerously=live_dangerously,\n p0=p0, model_kwargs=model_kwargs, priors=priors, time0=time0)\n\n # run sampler\n sampler.walk()\n\n # save sampler\n save_sampler(sampler, compare_label, compare_path)\n\n # correct samples\n p = sampler.median_sample()\n biases = p[:-1:2]\n zp_t_TOT_corrected=zp_t_TOT-biases\n\n ntomo = len(wz)\n\n # correct PZ to be the same binning as WZ\n pz_corrected = sampler.correct_pz(pz,\n # [pz_zbins] * ntomo, [pz_zcenters] * ntomo,\n [wz_zbins] * ntomo, [pz_zcenters] * ntomo,\n p)\n\n\n wz_corrected,wz_jackknives_corrected = sampler.correct_wz_jck(wz,wz_jackknives,\n [wz_zbins] * ntomo, [wz_zcenters] * ntomo,\n p)\n\n cov_index = []\n for i in range(len(wz) + 1):\n cov_index.append(len(wz_zcenters) * i)\n cov_full2 = _make_cov_array(wz_jackknives)\n wz_cov_corrected= convert_cov(cov_full2,\n from_mode='full', to_mode=cov_mode, cov_index=cov_index)\n\n '''\n #just for testing purposes:\n wz_jackknives_corrected=wz_jackknives\n wz_corrected=wz\n zp_t_TOT_corrected=zp_t_TOT\n wz_cov_corrected=wz_cov\n '''\n fig = sampler.plot_flatchain()\n fig.savefig('./compare/countour{0}.pdf'.format(compare_label))\n\n\n #save pz corrected\n pd.DataFrame(zp_t_TOT).to_hdf('{0}{1}_pzcorrected.h5'.format(compare_path,label),'full_pz_uncorrected')\n pd.DataFrame(zp_t_TOT_corrected).to_hdf('{0}{1}_pzcorrected.h5'.format(compare_path,label),'full_pz_corrected')\n pd.DataFrame(pz_binned).to_hdf('{0}{1}_pzcorrected.h5'.format(compare_path,label),'pz_uncorrected')\n pd.DataFrame(pz_corrected).to_hdf('{0}{1}_pzcorrected.h5'.format(compare_path,label),'pz_corrected')\n pd.DataFrame(wz_zcenters).to_hdf('{0}{1}_pzcorrected.h5'.format(compare_path,label),'z')\n pd.DataFrame(wz_zbins).to_hdf('{0}{1}_pzcorrected.h5'.format(compare_path,label),'z_edges')\n # save wz corrected\n\n\n #plot\n plot(norm,wz_zcenters,wz_zbins,zp_t_TOT,zp_t_TOT_corrected,np.array(wz)[0],np.array(wz_cov)[0],np.array(wz_corrected)[0],np.array(wz_cov_corrected)[0],'{0}'.format(compare_label),'./compare/',cov_mode)\n\n\n # save wz\n # save_dndz(wz_corrected)\ndef save_wz(Nz,cov,z,z_bin,label_save):\n pd.DataFrame(Nz[:,0]).to_hdf(label_save, 'results')\n pd.DataFrame(Nz[:,1:]).to_hdf(label_save, 'jackknife')\n pd.DataFrame(cov).to_hdf(label_save, 'cov')\n pd.DataFrame(z).to_hdf(label_save, 'z')\n pd.DataFrame(z_bin).to_hdf(label_save, 'z_edges')\n\n\n\n return locals()\n\ndef load_wz(label, cov_mode='diag'):\n hpath = './output_dndz/best_Nz/{0}.h5'.format(label)\n # Ntomo, Nz\n \n wz = pd.read_hdf(hpath, 'results').values.T[0][None, :]\n # (Njack, Ntomo, Nz)\n wz_samples = pd.read_hdf(hpath, 'jackknife').values.T[:, None, :]\n wz_samples1 = pd.read_hdf(hpath, 'jackknife')\n \n #reference_bins_interval=load_obj('./pairscount/reference_bins_interval')\n\n wz_zcenters = pd.read_hdf(hpath, 'z').values.T[0][None, :][0]\n wz_zbins = pd.read_hdf(hpath, 'z_edges').values.T[0][None, :][0]\n\n #print (wz_zcenters,wz_zbins)\n # normalize wz and wz_samples\n \n \n #wz = wz / np.sum(wz)\n norm_wz = normalize(wz[0], wz_zbins)\n wz = wz / norm_wz\n #norm_wz_samples = normalize(wz_samples[:,0,:], wz_zbins)\n for i in range(wz_samples.shape[0]):\n norm_wz_samples = normalize(wz_samples[i,0,:], wz_zbins)\n wz_samples[i,0,:] = wz_samples[i,0,:] /norm_wz_samples\n\n\n # create the cov\n cov_index = []\n for i in range(len(wz) + 1):\n cov_index.append(len(wz_zcenters) * i)\n cov_full = _make_cov_array(wz_samples)\n wz_cov = convert_cov(cov_full,\n from_mode='full', to_mode=cov_mode, cov_index=cov_index)\n\n return wz, wz_zbins, wz_zcenters, wz_cov, wz_samples\n\ndef load_pz(z_bins,shift_pz_1):\n hdd=pd.read_hdf('./pairscount/dataset.h5', 'unk')\n\n z = pd.read_hdf('./pairscount/dataset.h5', 'unk')['Z_T'].values\n z=z+shift_pz_1\n mask_z=z>=0.\n z=z[mask_z]\n tomo_bins = pd.read_hdf('./pairscount/dataset.h5', 'unk')['bins'].values\n\n # cuts the tomographic bin\n unknown_bins_interval=load_obj('./pairscount/unknown_bins_interval')\n unkn_z=unknown_bins_interval['z']\n tomo_bins=tomo_bins[mask_z]\n mask=(0.5<tomo_bins) & (tomo_bins<unkn_z.shape[0]+0.5)\n z=z[mask]\n norm_tomo=z.shape[0]\n pz_zbins = np.linspace(-0.5, 2.5, 15001)\n\n pz_zcenters = 0.5 * (pz_zbins[1:] + pz_zbins[:-1])\n\n pz, centers_out, bins_out, norm_out = catalog_to_histogram(z, pz_zbins, cut_to_range=False)\n pz_binned,_,_,_= catalog_to_histogram(z, z_bins, cut_to_range=False)\n\n # blow pz size\n pz = pz[None, :]\n\n return pz, pz_zbins, pz_zcenters, pz_binned,norm_tomo,z\n\ndef catalog_to_histogram(z, bins, weights=None, z_min=0, z_max=10, cut_to_range=True):\n conds = (z >= z_min) & (z < z_max)\n if sum(conds) == 0:\n bins_out = bins\n pdf = np.zeros(len(bins) - 1)\n else:\n # bin\n pdf, bins_out = np.histogram(z, bins, weights=weights)\n\n if np.any(bins != bins_out):\n print('bins:')\n print(bins)\n print('bins_out:')\n print(bins_out)\n raise Exception('bins not equal to bins_out?!')\n\n centers = 0.5 * (bins[1:] + bins[:-1])\n conds = (bins[:-1] >= z_min) & (bins[1:] < z_max)\n conds_bins = (bins >= z_min) & (bins < z_max)\n\n # normalize with trapezoidal\n norm = normalize(pdf, bins, z_min=z_min, z_max=z_max)\n if norm == 0:\n norm = 1\n\n if cut_to_range:\n centers = centers[conds]\n pdf = pdf[conds]\n bins = bins[conds_bins]\n\n return pdf / norm, centers, bins, norm\n\ndef normalize(pdf, bins, z_min=0, z_max=10):\n # integrate via trapezoidal rule\n\n centers = 0.5 * (bins[1:] + bins[:-1])\n conds = (bins[:-1] >= z_min) & (bins[1:] < z_max)\n norm = np.trapz(pdf[conds], centers[conds])\n return norm\n\ndef convert_cov(covs, from_mode, to_mode, cov_index=None):\n \"\"\"Code to go from one way of presenting covs to another\n\n Parameters\n ----------\n covs : Input covariance matrix or matrices\n from/to_mode : string, must be in ['full', 'tomo', 'diag']\n cov_index : List of ints; says when each tomo bin stars in the full cov\n matrix. Obviously not needed it not converting from full\n\n Returns\n -------\n covs_to : Converted covariance matrix or matrices in appropriate format\n\n Notes\n -----\n Here are the three covariance modes:\n\n diag : A list of arrays of the same format as the dndz. Each entry\n corresponds to the dndz's sigma^2 error.\n tomo : A list of 2d arrays. These are the covariance matrices within each\n tomographic bin.\n full : A single 2d array. This is the full covariance\n\n \"\"\"\n if from_mode == to_mode:\n return covs\n modes = ['full', 'tomo', 'diag']\n if from_mode not in modes or to_mode not in modes:\n raise Exception('from or to mode not available! from: {0}, to: {1}'.format(from_mode, to_mode))\n\n if to_mode == 'full':\n _cov_index = np.cumsum([0] + [len(covi) for covi in covs])\n ndim = sum([len(covi) for covi in covs])\n covs_to = np.zeros((ndim, ndim))\n for ith in range(len(_cov_index) - 1):\n i_start = _cov_index[ith]\n i_end = _cov_index[ith + 1]\n if from_mode == 'tomo':\n covi = covs[ith]\n elif from_mode == 'diag':\n covi = np.eye(len(covs[ith])) * covs[ith]\n covs_to[i_start:i_end] = covi\n elif to_mode == 'tomo':\n if from_mode == 'full':\n covs_to = []\n for ith in range(len(cov_index) - 1):\n i_start = cov_index[ith]\n i_end = cov_index[ith + 1]\n covs_to.append(covs[i_start:i_end, i_start:i_end])\n elif from_mode == 'diag':\n covs_to = [np.eye(len(covi)) * covi for covi in covs]\n elif to_mode == 'diag':\n if from_mode == 'full':\n covs_to = []\n for ith in range(len(cov_index) - 1):\n i_start = cov_index[ith]\n i_end = cov_index[ith + 1]\n covs_to.append(np.diag(covs[i_start:i_end, i_start:i_end]))\n elif from_mode == 'tomo':\n covs_to = [np.diag(covi) for covi in covs]\n\n return covs_to\n\n\ndef _make_cov_array(samples):\n # samples: [Njackknife, Ntomo, Nredshift]\n N_jackknife, N_tomo, N_redshift = samples.shape\n # create df from samples\n samples_flattened = np.array([sample.flatten() for sample in samples])\n\n jackknife, iz_and_tomo = np.mgrid[0:N_jackknife, 0:N_tomo * N_redshift]\n df = pd.DataFrame({'w': samples_flattened.flatten(),\n 'jackknife': jackknife.flatten(),\n 'iz_and_tomo': iz_and_tomo.flatten()})\n\n # too clever by half.\n # note that Cov_JK -> cov_ij = E((Xi - E(Xi))(Xj - E(Xj))) len(Nij - 1)\n # also note that this ends up being considerably faster than a nested\n # for-loop\n cov = df.pivot(index='jackknife', columns='iz_and_tomo', values='w').cov().values * (N_jackknife - 1)\n\n return cov\n\n\n\ndef save_sampler(sampler, label, sample_path):\n h5_path = '{0}/compare.h5'.format(sample_path)\n priors_pickle_path = '{0}/compare_priors_{1}.pkl'.format(sample_path, label)\n # save the chain\n chain = sampler.sampler._chain.copy()\n pd.Panel(chain).to_hdf(h5_path, '/{0}/chain'.format(label))\n\n kwargs = {'nwalkers': sampler.nwalkers,\n 'nburnin': sampler.nburnin,\n 'nrun': sampler.nrun,\n 'live_dangerously': sampler.live_dangerously,\n 'time0': sampler.time0}\n pd.Series(kwargs).to_hdf(h5_path, '/{0}/kwargs'.format(label))\n\n pd.Series(sampler.model_kwargs).to_hdf(h5_path, '/{0}/model_kwargs'.format(label))\n\n save_pickle(sampler.priors, priors_pickle_path)\n\n # write human readable output, too\n text_path = '{0}/{1}_results.txt'.format(sample_path, label)\n results = {key: {} for key in sampler.labels}\n # median, error, asymmetric error, argmax, two samples\n N_samples = 5\n results_str = '{0:+.3e}'\n sample_median = sampler.median_sample()\n sample_error = sampler.error_sample()\n sample_asymmetric_error = sampler.error_asymmetric_sample()\n sample_argmax = sampler.best_sample()\n sample_samples = sampler.sample(N_samples)\n lnprior = sampler.lnprior(sample_median) # for each param\n for key, med, err, asym, arg, sam, lnpri in zip(sampler.labels, sample_median, sample_error, sample_asymmetric_error, sample_argmax, sample_samples.T, lnprior):\n # put in as strings\n results[key]['median'] = results_str.format(med)\n results[key]['error='] = results_str.format(err)\n results[key]['error+'] = results_str.format(asym[1])\n results[key]['error-'] = results_str.format(asym[0])\n results[key]['argmax'] = results_str.format(arg)\n results[key]['sample'] = [results_str.format(sam_i) for sam_i in sam]\n results[key]['lnprior'] = results_str.format(lnpri)\n\n # get the tomo chi2 and dof as well\n chi2 = -2 * sampler.lnlike(sample_median) # for each tomo\n lnprob = sampler.lnprob(sample_median)\n lnprior_tot = np.sum(lnprior)\n results['chi2'] = {}\n results['chi2']['lnprior'] = results_str.format(lnprior_tot)\n results['chi2']['lnprob'] = results_str.format(lnprob)\n for chi2_i, chi2_val in enumerate(chi2):\n results['chi2']['tomo_{0}'.format(chi2_i)] = results_str.format(chi2_val)\n results['chi2']['dof_{0}'.format(chi2_i)] = '{0}'.format(len(sampler.wz[chi2_i]))\n\n with open(text_path, 'w') as f:\n f.write(yaml.dump(results, default_flow_style=False))\n\ndef load_sampler(label, sample_path):\n h5_path = '{0}/compare.h5'.format(sample_path)\n priors_pickle_path = '{0}/compare_priors_{1}.pkl'.format(sample_path, label)\n\n kwargs = pd.read_hdf(h5_path, '/{0}/kwargs'.format(label))\n\n model_kwargs = pd.read_hdf(h5_path, '/{0}/model_kwargs'.format(label)).to_dict()\n\n priors = load_pickle(priors_pickle_path)\n\n\n sampler = RedshiftBiasSampler(model_kwargs=model_kwargs, priors=priors,\n **kwargs)\n\n # add chain to sampler\n chain_key = '/{0}/chain'.format(label)\n chain = pd.read_hdf(h5_path, chain_key).values\n\n sampler.sampler._chain = chain\n\n return sampler\n\ndef save_pickle(obj, name):\n with open(name, 'wb') as f:\n pickle.dump(obj, f, pickle.HIGHEST_PROTOCOL)\n\ndef load_pickle(name):\n with open(name, 'rb') as f:\n return pickle.load(f)\n\ndef _chi2(estimate, truth, cov):\n from scipy import linalg\n cv_chol = linalg.cholesky(cov, lower=True)\n cv_sol = linalg.solve(cv_chol, estimate - truth, lower=True)\n chi2_val = 0.5 * np.sum(cv_sol ** 2)\n return chi2_val\n\ndef _tomo_chi2(wz, pz, cov, cov_mode):\n # returns chi2s but unsummed\n if cov_mode == 'full':\n chi2 = [_chi2(np.array(wz).flatten(), np.array(pz).flatten(), cov)]\n elif cov_mode == 'tomo':\n chi2 = [_chi2(wzi, pzi, covi) for wzi, pzi, covi in zip(wz, pz, cov)]\n elif cov_mode == 'diag':\n chi2 = [np.sum((wzi - pzi) ** 2 / covi) for wzi, pzi, covi in zip(wz, pz, cov)]\n return np.array(chi2)\n\ndef _compare_indices(self_z, other_z, other_index_in=None):\n \"\"\"Check that the z match up, and return the subset that are close.\n \"\"\"\n if not other_index_in:\n other_index_in = np.array([True] * len(other_z))\n # adjust indices based on redshifts.\n self_index = np.array([np.any(np.abs(zi - other_z[other_index_in]) < 0.0001)\n for zi in self_z])\n other_index = np.array([np.any(np.abs(zi - self_z[self_index]) < 0.0001)\n for zi in other_z])\n return self_index, other_index\n\nclass RedshiftBiasSampler(object):\n \"\"\"Model a set of pz and wz as redshift bias\n \"\"\"\n\n def __init__(self, wz=None, wz_zbins=None, wz_zcenters=None, wz_cov=None,\n pz=None, pz_zbins=None, pz_zcenters=None,\n cov_mode='full',\n nwalkers=64, nburnin=100, nrun=500, live_dangerously=False,\n p0=[],\n model_kwargs={'z0': 0.8},\n priors={\n 'gamma': {'kind': 'uniform', 'weight': 1,\n 'kwargs': {'loc': -2, 'scale': 4}},\n 'deltaz': {'kind': 'truncnorm', 'weight': 1,\n 'kwargs': {'a': -8.0, 'b': 8.0,\n 'loc': 0, 'scale': 0.05}},\n 'amplitude': {'kind': 'truncnorm', 'weight': 1,\n 'kwargs': {'a': -5.0, 'b': 5.0,\n 'loc': 0, 'scale': 1.0}},\n },\n time0=0,\n **kwargs):\n \"\"\"Combine PZ and WZ estimates\n\n Parameters\n ----------\n wz : Estimation of redshift distribution from clustering\n methods. A list of arrays for the different\n samples.\n wz_cov : Estimation of the covariance of the wz\n measurements. These can be provided in three\n formats:\n - If cov is a single 2d array, then it is\n assumed to be the full covariance of all\n the wz values flattened to one list\n - If the cov is a list of 2d arrays, then it\n is assumed to be the covariance matrix of\n each wz sample, so each wz sample is\n treated as independent of each other\n - If the cov is a list of 1d arrays, then it\n is assumed to be the diag component of\n the covariance, sigma2\n wz_zbins : The redshift bins used for the wz estimates. If it\n is a single array, it is assumed that all wz\n measurements share the same redshift bins. If it is\n a list of arrays then each entry corresponds to the\n redshift bins for the corresponding wz\n wz_zcenters : The center location of the redshift bins\n pz: List of array of redshifts from the photoz code for\n each sample.\n pz_zbins : The redshift bins used for the pz estimates.\n Note: Comparisons are done in the wz basis.\n pz_zcenters : The center location of the redshift bins\n nwalkers : int [default 32] number of walkers emcee uses\n nburnin : int [default 200] number of initial steps to take\n in emcee fitter\n nrun : int [default 2000] number of steps each walker will\n take in sampling the parameter space\n live_dangerously : override emcee's concerns about size of nwalkers\n relative to ndims of the model\n p0: list [Default: empty] List of initial guesses. Must\n be nwalkers length. If it is not, then the initial\n guesses will come from samples of the prior\n distribution.\n priors : dict of dicts. Each key corresponds to a different\n term in equation. kind is the kind of scipy.stats\n function. Weight is the relative weight of the\n prior (higher = weight heavier). kwargs is a\n dictionary of kwargs passed to construction of the\n stats function.\n z0: Float [Default: 0.5] Redshift pivot for bias\n evolution function.\n\n \"\"\"\n self.time0 = time0\n\n self.wz = wz\n self.wz_cov = wz_cov\n self.pz = pz\n\n self.ndim = len(self.wz)\n # deal with the redshift bins\n if len(wz_zbins) != self.ndim:\n # blow up wz_zbins\n self.wz_zbins = [wz_zbins] * self.ndim\n else:\n self.wz_zbins = wz_zbins\n if len(wz_zcenters) != self.ndim:\n # blow up wz_zcenters\n self.wz_zcenters = [wz_zcenters] * self.ndim\n else:\n self.wz_zcenters = wz_zcenters\n if len(pz_zbins) != self.ndim:\n # blow up pz_zbins\n self.pz_zbins = [pz_zbins] * self.ndim\n else:\n self.pz_zbins = pz_zbins\n if len(pz_zcenters) != self.ndim:\n # blow up pz_zcenters\n self.pz_zcenters = [pz_zcenters] * self.ndim\n else:\n self.pz_zcenters = pz_zcenters\n\n # determine cov mode\n if len(self.wz_cov) == len(self.wz):\n if self.wz_cov[0].shape == self.wz[0].shape:\n self.wz_cov_mode = 'diag'\n else:\n self.wz_cov_mode = 'tomo'\n else:\n self.wz_cov_mode = 'full'\n\n # set up model kwargs\n self.model_kwargs = self.setup_model_kwargs(**model_kwargs)\n\n # set up labels\n self.labels = self.setup_labels()\n\n # set up priors\n self.priors = priors\n self.prior_function, self.prior_weight = self.setup_prior(priors)\n self.nparams = len(self.prior_function)\n\n # set up emcee\n self.nwalkers = nwalkers\n self.nburnin = nburnin\n self.nrun = nrun\n self.live_dangerously = live_dangerously\n self.p0, self.sampler = self.setup_sampler(nwalkers, p0)\n\n def lnlike(self, p):\n \"\"\"Function that evaluates the goodness of fit of parameters\n\n Parameters\n ----------\n p : array\n Parameter values\n\n Returns\n -------\n chi squares\n \"\"\"\n # we use wz_zbins (instead of pz_zbins) to put pz_corrected in same shape as output wz\n pz = self.correct_pz_full(self.pz, self.wz_zbins, self.pz_zcenters,\n self.wz_zbins, self.wz_zcenters, p)\n chi2 = self.evaluate(self.wz, pz, self.wz_cov)\n\n return -0.5 * chi2\n\n def lnprior(self, p):\n logprior = [prior.logpdf(pi) * wi for pi, prior, wi in zip(p, self.prior_function, self.prior_weight)]\n return logprior\n\n def lnprob(self, p):\n self.nsteps += 1\n\n if self.time0:\n progress = self.nsteps / self.nwalkers / (self.nburnin + self.nrun)\n update_progress(progress, timeit.default_timer(), self.time0)\n\n lp = np.sum(self.lnprior(p))\n if not np.isfinite(lp):\n return -np.inf\n\n ll = np.sum(self.lnlike(p))\n return lp + ll\n\n def evaluate(self, wz, pz, cov):\n # how well do wz and pz compare?\n return self._tomo_chi2(wz, pz, cov)\n\n # chi2\n @classmethod\n def _chi2(cls, estimate, truth, cov):\n return _chi2(estimate, truth, cov)\n\n def _tomo_chi2(self, estimate, truth, cov):\n return _tomo_chi2(estimate, truth, cov, self.wz_cov_mode)\n\n @classmethod\n def rebin(cls, z_old, pdf_old, zbins):\n # spline\n kwargs_spline = {'s': 0, # force spline to go through data points\n 'ext': 'zeros', # ext=0 means extrapolate, =1 means return 0\n 'k': 3,\n }\n spline = UnivariateSpline(z_old, pdf_old, **kwargs_spline)\n pdf = np.zeros(len(zbins) - 1)\n for i in xrange(len(zbins) - 1):\n zmin = zbins[i]\n zmax = zbins[i + 1]\n pdf[i] = spline.integral(zmin, zmax) / (zmax - zmin)\n return pdf\n\n def setup_sampler(self, nwalkers=32, p0=[]):\n import emcee\n\n if len(p0) != nwalkers:\n # set p0 from the prior if no p0 given\n p0 = self.prior_sample(nwalkers)\n\n # set up the sampler\n sampler = emcee.EnsembleSampler(nwalkers, self.nparams, self.lnprob,\n live_dangerously=self.live_dangerously)\n\n return p0, sampler\n\n def walk(self, nburnin=-1, nrun=-1):\n if nburnin < 0:\n nburnin = self.nburnin\n else:\n self.nburnin = nburnin\n if nrun < 0:\n nrun = self.nrun\n else:\n self.nrun = nrun\n self.nsteps = 0\n # burn-in\n stuff = self.sampler.run_mcmc(self.p0, nburnin)\n pos = stuff[0]\n # run\n self.sampler.reset()\n stuff = self.sampler.run_mcmc(pos, nrun)\n\n # update timer to remove progress bar\n if self.time0:\n self.time0 = 0\n\n return stuff\n\n def plot_flatchain(self, mad_cut=0, **kwargs):\n # a useful kwarg is labels\n # plot with dfm's corner.py, potentially cutting on median absolute deviation\n flatchain = self.sampler.flatchain.copy()\n import corner\n if mad_cut > 0:\n # remove median absolute deviations\n med = np.median(flatchain, axis=0)\n mad = np.median(np.abs(flatchain - med), axis=0)\n flatchain = flatchain[np.all(np.abs(flatchain - med) < mad_cut * mad, axis=1)]\n\n return corner.corner(flatchain, labels=self.labels, **kwargs)\n\n def sample(self, size=1, **kwargs):\n if hasattr(self, 'sampler'):\n # if we have run the chain, sample from there\n return self.sampler.flatchain[\n np.random.choice(len(self.sampler.flatchain), size=size, **kwargs)]\n else:\n # else, sample from the prior\n return self.prior_sample(size=size)\n\n def median_sample(self):\n return np.median(self.sampler.flatchain, axis=0)\n\n def error_sample(self, percent=34):\n # default is 1sigma bounds\n # average, upper, lower\n vals = np.array(map(lambda v: 0.5 * (v[2] - v[0]),\n zip(*np.percentile(self.sampler.flatchain,\n [50 - percent, 50, 50 + percent], axis=0))))\n return vals\n\n def error_asymmetric_sample(self, percent=34):\n # default is 1sigma bounds\n # upper, lower\n vals = np.array(map(lambda v: (v[2] - v[1], v[1] - v[0]),\n zip(*np.percentile(self.sampler.flatchain,\n [50 - percent, 50, 50 + percent], axis=0))))\n return vals\n\n def best_sample(self, size=1):\n if size != 1:\n indx = np.argsort(self.sampler.flatlnprobability)[::-1]\n return self.sampler.flatchain[indx[:size]]\n else:\n return self.sampler.flatchain[self.sampler.flatlnprobability.argmax()]\n\n def prior_sample(self, size=1):\n if size != 1:\n # transpose to put in (nwalkers, ndim)\n samples = np.array([prior.rvs(size) for prior in self.prior_function]).T\n else:\n samples = np.array([prior.rvs() for prior in self.prior_function])\n return samples\n\n @property\n def chain(self):\n return self.sampler.chain\n\n @property\n def flatchain(self):\n return self.sampler.flatchain\n\n @classmethod\n def interpret_prior(cls, kind, kwargs):\n from scipy import stats\n # if kind == 'uniform':\n # return stats.uniform(**kwargs)\n # elif kind == 'truncnorm':\n # return stats.truncnorm(**kwargs)\n # elif kind == 'norm':\n # return stats.norm(**kwargs)\n # else:\n # raise Exception('Stats object not allowed!')\n return getattr(stats, kind)(**kwargs)\n\n # during fitting it is easier to lop all corrections on pz\n def correct_pz_full(self, pz, pz_zbins, pz_zcenters, wz_zbins, wz_zcenters,\n p, **kwargs):\n # do both correct_pz and correct_wz on ONLY the pz\n pz_corrected = self.correct_pz(pz, pz_zbins, pz_zcenters, p, **kwargs)\n # by construction, pz correction and wz correction are just opposite\n pz_corrected_wz = self.correct_wz(pz_corrected, wz_zbins, wz_zcenters,\n -p, **kwargs)\n return pz_corrected_wz\n\n def correct_wz_full(self, wz, pz_zbins, pz_zcenters, wz_zbins, wz_zcenters,\n p, **kwargs):\n # MARCO: I saw this is not used. I\n # TODO: Do I need to also add extra correction for cov?\n # do both correct_wz and correct_pz on ONLY the wz\n wz_corrected = self.correct_wz(wz, wz_zbins, wz_zcenters, p, **kwargs)\n # by construction, wz correction and pz correction are just opposite\n # we use wz_zbins to put wz_corrected_pz in same shape as output wz\n wz_corrected_pz = self.correct_pz(wz_corrected, pz_zbins, pz_zcenters,\n -p, **kwargs)\n return wz_corrected_pz\n\n ###########################################################################\n # TODO: In the future, the below functions are what we define for each\n # sampler class!\n ###########################################################################\n\n # p to params\n @classmethod\n def params(cls, p):\n # go from p to meaningful params\n bias = p[:-1:2]\n amplitude = np.exp(p[1:-1:2])\n gamma = p[-1]\n return {'bias': bias, 'amplitude': amplitude, 'gamma': gamma}\n\n def setup_labels(self, ndim=-1):\n if ndim < 1:\n ndim = self.ndim\n labels = np.array([['$\\Delta z_{{{0}}}$'.format(i + 1),\n '$k_{{{0}}}$'.format(i + 1)]\n for i in range(ndim)]).flatten().tolist()\n labels += ['$\\gamma$']\n\n return labels\n\n def setup_model_kwargs(self, z0):\n return {'z0': z0}\n\n def setup_prior(self, priors):\n # convert prior from dictionary to appropriate list\n prior_function = []\n prior_weight = []\n for i in range(self.ndim):\n for key in ['deltaz', 'amplitude']:\n prior_function.append(self.interpret_prior(priors[key]['kind'], priors[key]['kwargs']))\n prior_weight.append(priors[key]['weight'])\n prior_function.append(self.interpret_prior(priors['gamma']['kind'], priors['gamma']['kwargs']))\n prior_weight.append(priors['gamma']['weight'])\n\n return prior_function, prior_weight\n\n # photoz\n def correct_pz_catalog(self, zcats, p, **kwargs):\n params = self.params(p)\n biases = params['bias']\n zcats_prime = [zcat - bias for zcat, bias in zip(zcats, biases)]\n return zcats_prime\n\n def correct_pz(self, pz, zbins, zcenters, p, catalog=False, **kwargs):\n \"\"\"\n NOTE: zbins -> final binning scheme\n NOTE: zcenters -> initial redshifts of pz (shape should match pz)\n \"\"\"\n if catalog:\n # if zcats are just catalogs, then apply catalog correction\n # TODO: weights are not included in here currently...\n pdfs = [catalog_to_histogram(zcat, zbin,\n z_min=min(zbin), z_max=max(zbin),\n cut_to_range=False)[0] # TODO: cut_to_range=False?\n for zcat, zbin\n in zip(self.correct_pz_catalog(pz, p), zbins)]\n else:\n # if zcats are already binned pdfs, apply bias and rebin\n params = self.params(p)\n biases = params['bias']\n pdfs = []\n for pzi, zbin, zcenter, bias in zip(pz, zbins, zcenters, biases):\n pdf = self.rebin(zcenter - bias, pzi, zbin)\n pdfs.append(pdf)\n\n return pdfs\n\n # clustering\n def correct_wz_jck(self, wz, wz_jackknives,zbins, zcenters, p, **kwargs):\n \"\"\"\n NOTE: zbins -> not used\n NOTE: zcenters -> should match with input wz shape\n \"\"\"\n params = self.params(p)\n amplitudes = params['amplitude']\n gamma = params['gamma']\n z0 = self.model_kwargs['z0']\n wzs_prime = [((1 + z) / (1 + z0)) ** gamma * amplitude * wzi\n for z, wzi, amplitude in zip(zcenters, wz, amplitudes)]\n\n #wzs_prime = [wzi\n # for z, wzi, amplitude in zip(zcenters, wz, amplitudes)]\n\n\n\n wz_jackknives_prime=copy.deepcopy(wz_jackknives)\n for i in range(wz_jackknives.shape[0]):\n wz_jackknives_prime[i,:] = [((1 + z) / (1 + z0)) ** gamma * amplitude * wzi for z, wzi, amplitude in zip(zcenters, wz_jackknives[i,:], amplitudes)]\n #for i in range(wz_jackknives.shape[0]):\n # wz_jackknives_prime[i,:] = [wzi for z, wzi, amplitude in zip(zcenters, wz_jackknives[i,:], amplitudes)]\n wz_jackknives_prime=np.array(wz_jackknives_prime)[:,0,:]\n #print(np.vstack((wzs_prime,wz_jackknives_prime)).shape)\n #return wzs_prime,np.array(np.vstack((wzs_prime,wz_jackknives_prime)))\n\n return wzs_prime,np.array(wz_jackknives_prime)\n\n\n def correct_wz(self, wz, zbins, zcenters, p, **kwargs):\n \"\"\"\n NOTE: zbins -> not used\n NOTE: zcenters -> should match with input wz shape\n \"\"\"\n params = self.params(p)\n amplitudes = params['amplitude']\n gamma = params['gamma']\n z0 = self.model_kwargs['z0']\n wzs_prime = [((1 + z) / (1 + z0)) ** gamma * amplitude * wzi\n for z, wzi, amplitude in zip(zcenters, wz, amplitudes)]\n\n\n '''\n if wz_jackknives:\n wz_jackknives_prime=wzs_prime = [((1 + z) / (1 + z0)) ** gamma * amplitude * wzi\n for z, wzi, amplitude in zip(zcenters, wz_jackknives, amplitudes)]\n '''\n\n return wzs_prime\n\n def correct_wz_cov(self, covs, zbins, zcenters, p, **kwargs):\n \"\"\"\n NOTE: zbins -> not used\n NOTE: zcenters -> should match with input wz shape\n \"\"\"\n params = self.params(p)\n amplitudes = params['amplitude']\n gamma = params['gamma']\n z0 = self.model_kwargs['z0']\n\n if self.wz_cov_mode == 'full':\n # need to keep track of length of different pieces:\n _cov_index = np.cumsum([0] + [len(wzi) for wzi in self.wz])\n covs_prime = covs.copy()\n for i, z_i, amplitude_i in zip(range(len(zcenters)), zcenters, amplitudes):\n mult_i = ((1 + z_i) / (1 + z0)) ** gamma * amplitude_i\n i_start = _cov_index[i]\n i_end = _cov_index[i + 1]\n for j, z_j, amplitude_j in zip(range(len(zcenters)), zcenters, amplitudes):\n mult_j = ((1 + z_j) / (1 + z0)) ** gamma * amplitude_j\n j_start = _cov_index[j]\n j_end = _cov_index[j + 1]\n\n covs_prime[i_start:i_end, j_start:j_end] = covs[i_start:i_end, j_start:j_end] * mult_i[None] * mult_j[:, None]\n\n elif self.wz_cov_mode == 'tomo':\n # gotta deal with the shape of cov\n covs_prime = []\n for z, cov, amplitude in zip(zcenters, covs, amplitudes):\n mult = ((1 + z) / (1 + z0)) ** gamma * amplitude\n covs_prime.append(cov * mult[None] * mult[:, None])\n elif self.wz_cov_mode == 'diag':\n covs_prime = [((1 + z) / (1 + z0)) ** gamma * amplitude * cov\n for z, cov, amplitude in zip(zcenters, covs, amplitudes)]\n return covs_prime\n\n\n\n\n\ndef plot(norm,z,z_bin,zp_t_TOT,zp_t_TOT_corrected,wz,cov,wz_corrected,cov_corrected,label_save,output,cov_mode):\n\n #renormalize\n mute1=norm/np.sum(wz)\n wz=wz*mute1\n mute2=norm/np.sum(wz_corrected)\n wz_corrected=wz_corrected*mute2\n cov=cov*mute1*mute1\n cov_corrected=cov_corrected*mute2*mute2\n\n\n\n\n if cov_mode=='diag':\n err=np.sqrt(cov)\n err_corrected=np.sqrt(cov_corrected)\n cov=copy.deepcopy(np.diag(err*err))\n cov_corrected=copy.deepcopy(np.diag(err_corrected*err_corrected))\n else:\n err=np.zeros(cov.shape[0])\n err_corrected=np.zeros(cov.shape[0])\n for i in range(cov.shape[0]):\n err[i]=np.sqrt(cov[i,i])\n err_corrected[i]=np.sqrt(cov_corrected[i,i])\n\n fig= plt.figure()\n ax = fig.add_subplot(111)\n plt.hist(zp_t_TOT,bins=z_bin,color='blue',alpha=0.4,label='photo_z',histtype='step',edgecolor='r')\n plt.hist(zp_t_TOT_corrected,bins=z_bin,color='blue',alpha=0.4,label='photo_z_corrected',histtype='step',edgecolor='b')\n\n\n\n\n plt.errorbar(z,wz,err,fmt='o',color='red',label='clustz')\n plt.errorbar(z,wz_corrected,err_corrected,fmt='o',color='blue',label='clustz corrected')\n\n\n plt.xlim(min(z-0.1),max(z+0.4))\n plt.xlabel('$z$')\n plt.ylabel('$N(z)$')\n\n N,_=np.histogram(zp_t_TOT,bins=z_bin)\n N1,_=np.histogram(zp_t_TOT_corrected,bins=z_bin)\n dict_stat1=compute_statistics(z,N,wz,cov)\n dict_stat2=compute_statistics(z,N1,wz_corrected,cov_corrected)\n\n #put text where I want\n mute_phi=max(wz)\n mute_z=max(z)\n\n\n label_diag=''\n if cov_mode=='diag':\n label_diag='_diag'\n\n\n\n\n ax.text(0.8, 0.95,'<z>_pdf_bin='+str((\"%.3f\" % dict_stat1['mean_true'])),fontsize=12 , ha='center', transform=ax.transAxes)\n ax.text(0.8, 0.9,'<z>_pdf_bin_corr='+str((\"%.3f\" % dict_stat2['mean_true'])),fontsize=12 , ha='center', transform=ax.transAxes)\n ax.text(0.8, 0.85,'<z>_clustz='+str((\"%.3f\" % dict_stat1['mean_rec']))+'+-'+str((\"%.3f\" % dict_stat1['mean_rec_err'+label_diag])),fontsize=12, ha='center', transform=ax.transAxes)\n ax.text(0.8, 0.8,'<z>_clustz_corr='+str((\"%.3f\" % dict_stat2['mean_rec']))+'+-'+str((\"%.3f\" % dict_stat2['mean_rec_err'+label_diag])),fontsize=12, ha='center', transform=ax.transAxes)\n\n '''\n ax.text(0.8, 0.8,'std_pdf='+str((\"%.3f\" % dict_stat['std_true'])),fontsize=12 , ha='center', transform=ax.transAxes)\n ax.text(0.8, 0.75,'std_clustz='+str((\"%.3f\" % dict_stat['std_rec']))+'+-'+str((\"%.3f\" % dict_stat['std_rec_err'+label_diag])),fontsize=12 , ha='center', transform=ax.transAxes)\n ax.text(0.8, 0.7,'$\\chi^2/dof=$'+str((\"%.3f\" % dict_stat['chi_reduced'])),fontsize=12 , ha='center', transform=ax.transAxes)\n '''\n plt.legend(loc=2,prop={'size':10},fancybox=True)\n\n\n plt.savefig((output+'/{0}.pdf').format(label_save), format='pdf', dpi=100)\n plt.close()\n\n\ndef covariance_jck_2(TOTAL_PHI,jk_r):\n\n # Covariance estimation\n\n average=np.zeros(TOTAL_PHI.shape[1])\n cov_jck=np.zeros((TOTAL_PHI.shape[1],TOTAL_PHI.shape[1]))\n err_jck=np.zeros(TOTAL_PHI.shape[1])\n\n\n for kk in range(jk_r):\n average+=TOTAL_PHI[kk,:]\n average=average/(jk_r)\n\n # print average\n for ii in range(TOTAL_PHI.shape[1]):\n for jj in range(ii+1):\n for kk in range(jk_r):\n cov_jck[jj,ii]+=TOTAL_PHI[kk,ii,]*TOTAL_PHI[kk,jj]\n\n cov_jck[ii,jj]=(-average[jj]*average[ii]*jk_r+cov_jck[ii,jj])*(jk_r-1)/(jk_r)\n cov_jck[jj,ii]=cov_jck[ii,jj]\n\n for ii in range(TOTAL_PHI.shape[1]):\n err_jck[ii]=np.sqrt(cov_jck[ii,ii])\n # print err_jck\n\n #compute correlation\n corr=np.zeros((TOTAL_PHI.shape[1],TOTAL_PHI.shape[1]))\n for i in range(TOTAL_PHI.shape[1]):\n for j in range(TOTAL_PHI.shape[1]):\n corr[i,j]=cov_jck[i,j]/(np.sqrt(cov_jck[i,i]*cov_jck[j,j]))\n\n average=average*(jk_r)/(jk_r-1)\n return {'cov' : cov_jck,\n 'err' : err_jck,\n 'corr':corr,\n 'mean':average}\n","sub_path":"wizard/compare.py","file_name":"compare.py","file_ext":"py","file_size_in_byte":42938,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"589013706","text":"import json\nimport requests\nimport statistics\nfrom bs4 import BeautifulSoup\nfrom datetime import datetime\n\n\nclass TopMutualFunds:\n\n def __init__(self):\n pass\n\n\nclass MoneyControl:\n\n def __init__(self):\n json_data = open(\"../config/config.json\").read()\n self.config_json = json.loads(json_data)\n\n def get_list_of_funds(self):\n current_holding_map = {}\n for url in self.config_json.get(\"funds_url\"):\n html_data = self._get_data_from_url(url)\n funds_list = self._get_fund_list_from_html(html_data)\n current_holding_map = self.update_holding_map(funds_list,\n current_holding_map)\n final_stocks_map = self.stock_stats(current_holding_map)\n self.write_output_to_file(final_stocks_map)\n\n def write_output_to_file(self, final_stocks_map):\n file_name = \"../../output/\" + \\\n str(datetime.now().strftime(\"%Y%m%d-%H%M%S\")) + \".json\"\n map_file = open(file_name, \"w\")\n map_file.write(\"\\n\\n\")\n map_file.write(str(datetime.now()))\n map_file.write(str(final_stocks_map))\n map_file.close()\n\n def stock_stats(self, stock_info_map):\n stocks_above_sd = {}\n for key in stock_info_map:\n try:\n stock_percents = stock_info_map.get(key).get(\"percent\")\n sd = 1\n median = 0\n if stock_percents and len(stock_percents) > 2:\n sd = statistics.stdev(stock_percents)\n median = statistics.median(stock_percents)\n stocks_above_sd[key] = {\"sd\": sd, \"count\": 0, \"median\": median}\n stocks_above_sd.get(key)[\"count\"] = len(stock_percents)\n except:\n pass\n stocks_above_sd = sorted(\n stocks_above_sd.items(), key=lambda x: x[1][\"count\"], reverse=True)\n return stocks_above_sd\n\n def update_holding_map(self, funds_list, current_holding_map):\n count = 0\n for fund in funds_list:\n fund_holding_url = self.config_json.get(\"holding_base_url\") \\\n + \"/\" \\\n + fund\n holding_html = self._get_data_from_url(fund_holding_url)\n holding_list = self._get_holding_list_from_html(holding_html)\n current_holding_map = self._update_holding_map(\n current_holding_map, holding_list)\n count += 1\n return current_holding_map\n\n def _update_holding_map(self, holding_map, holding_list):\n for holding in holding_list:\n # holding_map[holding] = holding_map[holding] + 1 if \\\n # holding_map.get(holding) else holding_map[holding] = 1\n if holding_map.get(holding.get(\"title\")):\n share_info = holding_map.get(holding.get(\"title\"))\n share_info[\"count\"] = share_info.get(\"count\") + 1\n share_info.get(\"percent\").append(holding.get(\"%\"))\n holding_map[holding.get(\"title\")] = share_info\n else:\n share_info = {\"count\": 1, \"percent\": [holding.get(\"%\", 0)]}\n holding_map[holding.get(\"title\")] = share_info\n return holding_map\n\n\n def _get_data_from_url(self, url_to_call):\n return requests.get(url_to_call).text\n\n def _get_fund_list_from_html(self, html_data):\n soup = BeautifulSoup(html_data)\n funds_table = soup.find(\"table\", attrs={\"class\":\"gry_t thdata\"})\n funds_list = []\n for row in funds_table.find_all(\"tr\")[1:]:\n fund_full_name = row.find(attrs={\"class\": \"bl_12\"})\n if fund_full_name and str(fund_full_name).__contains__(\"href\"):\n fund_url = fund_full_name.get(\"href\")\n fund_id = str(fund_url).split(\"/\")[-1]\n funds_list.append(fund_id)\n return funds_list\n\n def _get_holding_list_from_html(self, holding_html):\n holding_list = []\n soup = BeautifulSoup(holding_html)\n holding_table = soup.find(\"table\", attrs={\"class\": \"tblporhd\"})\n if not holding_table:\n return holding_list\n for row in holding_table.find_all(\"tr\")[1:]:\n share_obj={}\n for column in row.find_all(\"td\"):\n for a in column.find_all(\"a\"):\n if a and str(a).__contains__(\"title\"):\n share_title = a.get(\"title\")\n share_obj[\"title\"] = share_title\n for column in row.find_all(\"td\")[1:]:\n try:\n share_obj[\"%\"] = float(column.get_text())\n except:\n share_obj[\"%\"] = float(\"0\")\n holding_list.append(share_obj)\n return holding_list\n\nif __name__ == \"__main__\":\n money_control = MoneyControl()\n money_control.get_list_of_funds()","sub_path":"api/lead_generator/mutual_funds.py","file_name":"mutual_funds.py","file_ext":"py","file_size_in_byte":4905,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"2975558","text":"import pygame, math, random\nfrom pygame.locals import *\n\n#Les couleurs a utiliser\nBLANC = (255, 255, 255)\nNOIR = (0, 0, 0)\n\n#Les dimensions de la fenetre qui affiche\nTAILLEFENETRE = (500, 500)\nTAILLEFENETRE_X = TAILLEFENETRE[0]\nTAILLEFENETRE_Y = TAILLEFENETRE[1]\n\n#Les dimensions de la cellule\nTAILLECARRE = 25\nNMBCASEX = 25\nNMBCASEY = 25\n\n#Rapidite de l'image (Quand on utilise play et pause)\nFPS = 60\nclock = pygame.time.Clock()\n\n#Initialisation de PYGAME\npygame.init()\nfenetre = pygame.display.set_mode(TAILLEFENETRE)\n\nclass Cell:\n\tdef __init__(self, x, y, state):\n\t\tself.x = x\n\t\tself.y = y\n\t\tself.state = state #0 si elle est morte et 1 si elle est vivante\n\t\tself.nmbVoisine = 0 #pour calculer la somme des nbrs voisines\n\n\t#Fonction pour trouver le nombre des cellules voisines qui prend en parametre numbX et numbY qui sont les coordonées de la cellule courante\n\tdef voisine(self, listCells, nmbX, nmbY):\n\t\tvoisine = 0\n\t\tif self.x+1<nmbX:\n\t\t\tif listCells[self.x+1][self.y].state == 1:\n\t\t\t\tvoisine += 1\n\t\tif 0<=self.x-1:\n\t\t\tif listCells[self.x-1][self.y].state == 1:\n\t\t\t\tvoisine += 1\n\t\tif self.y+1<nmbY:\n\t\t\tif listCells[self.x][self.y+1].state == 1:\n\t\t\t\tvoisine += 1\n\t\tif 0<=self.y-1:\n\t\t\tif listCells[self.x][self.y-1].state == 1:\n\t\t\t\tvoisine += 1\n\t\tif self.x+1<nmbX and self.y+1<nmbY:\n\t\t\tif listCells[self.x+1][self.y+1].state == 1:\n\t\t\t\tvoisine += 1\n\t\tif 0<=self.x-1 and self.y+1<nmbY:\n\t\t\tif listCells[self.x-1][self.y+1].state == 1:\n\t\t\t\tvoisine += 1\n\t\tif 0<=self.x-1 and 0<=self.y-1:\n\t\t\tif listCells[self.x-1][self.y-1].state == 1:\n\t\t\t\tvoisine += 1\n\t\tif self.x+1<nmbX and 0<=self.y-1:\n\t\t\tif listCells[self.x+1][self.y-1].state == 1:\n\t\t\t\tvoisine += 1\n\n\t\tself.nmbVoisine = voisine\n\n\tdef calcule(self):\n\t\t#Definire l'etat de la cellule selon les rules du jeu\n\n\t\tif self.state == 0:\n\t\t\tif self.nmbVoisine == 3:\n\t\t\t\tself.state = 1\n\n\t\tif self.state == 1:\n\t\t\tif self.nmbVoisine < 2:\n\t\t\t\tself.state = 0\n\t\t\telif self.nmbVoisine == 2:\n\t\t\t\tself.state = 1\n\t\t\telif self.nmbVoisine > 3:\n\t\t\t\tself.state = 0\n\n\t#Definition des lignes et des colonnes\n\tdef draw(self, fenetre):\n\t\tif self.state == 0: #morte = BLANC\n\t\t\tpygame.draw.rect(fenetre, BLANC, (self.x * TAILLECARRE, self.y * TAILLECARRE, TAILLECARRE, TAILLECARRE), 0)\n\t\t\tpygame.draw.line(fenetre, NOIR, (self.x * TAILLECARRE, self.y * TAILLECARRE), (self.x * TAILLECARRE+TAILLECARRE, self.y * TAILLECARRE), 1)\n\t\t\tpygame.draw.line(fenetre, NOIR, (self.x * TAILLECARRE, self.y * TAILLECARRE), (self.x * TAILLECARRE, self.y * TAILLECARRE+TAILLECARRE), 1)\n\t\t\tpygame.draw.line(fenetre, NOIR, (self.x * TAILLECARRE, self.y * TAILLECARRE+TAILLECARRE), (self.x * TAILLECARRE+TAILLECARRE, self.y * TAILLECARRE+TAILLECARRE), 1)\n\t\t\tpygame.draw.line(fenetre, NOIR, (self.x * TAILLECARRE+TAILLECARRE, self.y * TAILLECARRE), (self.x * TAILLECARRE+TAILLECARRE, self.y * TAILLECARRE+TAILLECARRE), 1)\n\t\tif self.state == 1: #vivante = NOIRE\n\t\t\tpygame.draw.rect(fenetre, NOIR, (self.x * TAILLECARRE, self.y * TAILLECARRE, TAILLECARRE, TAILLECARRE), 0)\n\t\t\t\n\nclass World:\n\tdef __init__(self, nmbX, nmbY):\n\t\tself.nmbX = nmbX\n\t\tself.nmbY = nmbY\n\t\tself.listCells = []\n\t\tself.gen = 0\n\n\tdef init(self):\n\t\tfor x in range(self.nmbX):\n\t\t\tself.listCells.append([])\n\n\t\t#Initialization des cellules avant que le jeu commence par default, toutes les celllues sont mortes \n\t\tfor x in range(self.nmbX):\n\t\t\tfor y in range(self.nmbY):\n\t\t\t\tnewCell = Cell(x,y,0)\n\t\t\t\t#La liste listCells contient toutes les cellules\n\t\t\t\tself.listCells[x].append(newCell)\n\t\t\t\t\n\t\tfor x in range(len(self.listCells)):\n\t\t\tfor y in range(len(self.listCells[x])):\n\t\t\t\tself.listCells[x][y].voisine(self.listCells, self.nmbX, self.nmbY)\n\n\t\t\n\n\tdef calcule(self):\n\t\tself.gen += 1\n\t\tfor x in range(len(self.listCells)):\n\t\t\tfor y in range(len(self.listCells[x])):\n\t\t\t\tself.listCells[x][y].calcule() #Parcourir chaque cellule et calculer sont état, mort ou vivant\n\n\tdef calculeVoisine(self):\n\t\tfor x in range(len(self.listCells)):\n\t\t\tfor y in range(len(self.listCells[x])):\n\t\t\t\tself.listCells[x][y].voisine(self.listCells, self.nmbX, self.nmbY)#Parcourir chaque cellule et calculer ces voisines\n\n\n\tdef draw(self, fenetre, pause):\n\t\tfor x in range(len(self.listCells)):\n\t\t\tfor y in range(len(self.listCells[x])):\n\t\t\t\tself.listCells[x][y].draw(fenetre)\n\n\t\t\n\npygame.time.set_timer(USEREVENT, 200)\n\nw = World(NMBCASEX,NMBCASEY)\nw.init()\n\npause = True\n\nwhile True:\n\t#Les evenements\n\t#Cas 1, si on a cliqué sur le boutton X\n\tfor event in pygame.event.get():\n\t\tif event.type == QUIT:\n\t\t\tpygame.quit()\n\t\t\tbreak\n\n\t\t#Clavier appuyé\n\t\tif event.type == KEYDOWN:\n\t\t\tif event.key == K_ESCAPE: #Touche escape\n\t\t\t\tpygame.quit()\n\t\t\t\tbreak\n\t\t\tif event.key == K_RIGHT: #Touche fleche droite\n\t\t\t\tw.calcule()\n\t\t\t\tw.calculeVoisine()\n\t\t\tif event.key == K_p: #Touche p\n\t\t\t\tif pause == False:\n\t\t\t\t\tpause = True\n\t\t\t\telse:\n\t\t\t\t\tpause = False\n\t\n\t\tif event.type == MOUSEBUTTONDOWN:\n\t\t\tmX = event.pos[0]\n\t\t\tmY = event.pos[1]\n\t\t\tif event.button == 1: #clique souris left click\n\n\t\t\t\tif 0<=mX<=TAILLECARRE*NMBCASEX and 0<=mY<=TAILLECARRE*NMBCASEY:\n\t\t\t\t\tif w.listCells[int(mX/TAILLECARRE)][int(mY/TAILLECARRE)].state == 1:\n\t\t\t\t\t\tw.listCells[int(mX/TAILLECARRE)][int(mY/TAILLECARRE)].state = 0\n\t\t\t\t\telse:\n\t\t\t\t\t\tw.listCells[int(mX/TAILLECARRE)][int(mY/TAILLECARRE)].state = 1\n\t\t\t\t\tw.calculeVoisine()\n\n\t\tif pause == False:\n\t\t\tif event.type == USEREVENT:\n\t\t\t\tw.calcule()\n\t\t\t\tw.calculeVoisine()\n\t\n\tw.draw(fenetre, pause)\n\n\t#Refresh\n\tpygame.display.flip()\n\tclock.tick(FPS)","sub_path":"jeu_de_la_vie.py","file_name":"jeu_de_la_vie.py","file_ext":"py","file_size_in_byte":5412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"463360096","text":"from decimal import Decimal\nimport numpy\n\n\n# nprtt but have some experience about others\ndef strategy(history, memory):\n # num_rounds == 0\n if memory == None:\n memory = {}\n memory[\"sin\"] = 0\n memory[\"battle_state\"] = \"first_peace\"\n memory[\"strategy\"] = None\n memory[\"counter\"] = 0\n memory[\"strategy_history\"] = []\n choice = 1\n\n return choice, memory\n\n MAX_DEFECTION_THRESHOLD = Decimal(1) / Decimal(2)\n MAX_DEFECTION_THRESHOLD_PEACEFULL = Decimal(3) / Decimal(4)\n DETECTIVE_WINDOW = 3\n TEST_DETECTIVE_SCHEDULE = [0]\n SHORT_WINDOW = 4\n MEDIUM_WINDOW = 8\n LONG_WINDOW = 16\n VERY_LONG_WINDOW = 32\n RANDOM_DEFECTION_RATE_THRESHOLD = Decimal(0.6)\n num_rounds = history.shape[1] # elapsed round\n \n # get oponets last move\n if num_rounds >= 1:\n our_last_move = history[0, -1]\n opponents_last_move = history[1, -1]\n else:\n our_last_move = 1\n opponents_last_move = 1\n\n if num_rounds >= 2:\n our_second_last_move = history[0, -2]\n else:\n our_second_last_move = 1\n\n # enemy defect in the last move\n if opponents_last_move == 0:\n # add sin counter\n memory[\"sin\"] += 1\n\n if memory[\"sin\"] == 1:\n memory[\"battle_state\"] = \"first_war\"\n choice = 0\n memory[\"strategy\"] = \"peaceMaker\"\n return choice, memory\n\n # second betrayal?\n if memory[\"battle_state\"] == \"second_peace\":\n memory[\"battle_state\"] = \"second_war\"\n memory[\"strategy\"] = \"nprtt-peacefull\"\n \n if memory[\"battle_state\"] == \"third_peace\":\n memory[\"battle_state\"] = \"third_war\"\n memory[\"strategy\"] = \"nprtt\"\n\n # maybe fighting defector?\n if num_rounds == SHORT_WINDOW:\n if memory[\"sin\"] == num_rounds:\n memory[\"strategy\"] = \"fight_defector\"\n memory[\"strategy_history\"].extend(memory[\"strategy\"])\n\n # maybe fighting random?\n if num_rounds >= VERY_LONG_WINDOW:\n enemy_moves = history[1, :]\n flipped_enemy_move = numpy.abs(enemy_moves - 1)\n defection_rate_medium_window = numpy.average(flipped_enemy_move)\n if defection_rate_medium_window > (RANDOM_DEFECTION_RATE_THRESHOLD):\n memory[\"strategy\"] = \"fight_random\"\n\n # I've asking for peace\n if (\n memory[\"strategy\"] == \"peaceMaker\"\n and our_last_move == 1\n ):\n memory[\"strategy\"] = \"nprtt\"\n\n if memory[\"battle_state\"] == \"first_war\":\n # peace achived\n if opponents_last_move == 1:\n memory[\"battle_state\"] = \"second_peace\"\n memory[\"strategy\"] = None\n\n if memory[\"battle_state\"] == \"second_war\":\n if our_last_move == 1 and opponents_last_move == 1:\n memory[\"battle_state\"] = \"third_peace\"\n memory[\"strategy\"] = None\n\n if memory[\"strategy\"] == \"fight_defector\":\n # want to make up?\n if opponents_last_move == 1:\n choice = 1\n memory[\"strategy\"] == None\n else:\n choice = 0\n\n if memory[\"strategy\"] == \"fight_random\":\n choice = 0\n enemy_moves = history[1, -MEDIUM_WINDOW:]\n\n # random shold never always defect\n if numpy.sum(enemy_moves) <= 0.1: # <= 0 but avoiding floating error\n memory[\"strategy\"] == nprtt\n\n if memory[\"strategy\"] == \"nprtt-peacefull\":\n choice, _ = nprtt(\n history, \n None, \n num_rounds, \n opponents_last_move, \n our_second_last_move, \n MAX_DEFECTION_THRESHOLD_PEACEFULL\n )\n\n # default strategy\n if (\n (num_rounds <= LONG_WINDOW and memory[\"strategy\"] == None and \"peaceMaker\" not in memory[\"strategy_history\"])\n or memory[\"strategy\"] == \"peaceMaker\"\n ):\n # peaceMaker\n choice, _ = peaceMaker(history, memory)\n elif (\n memory[\"strategy\"] == None\n or memory[\"strategy\"] == \"nprtt\"\n ):\n\n choice, _ = nprtt(\n history, \n None, \n num_rounds, \n opponents_last_move, \n our_second_last_move, \n MAX_DEFECTION_THRESHOLD\n )\n \n return choice, memory\n\ndef nprtt(history, memory, num_rounds, opponents_last_move, our_second_last_move, defection_threshold=0.5):\n \"\"\"\n Nice Patient Reflective Tit for Tat (NPRTT):\n 1. Nice: Never initiate defection, else face the wrath of the Grudge.\n 2. Patient: Respond to defection with defection, unless it was in possibly\n response to my defection. Give opponent a chance to cooperate again since,\n even if they backstab me a few more times, we'll both come out ahead.\n I don't have to worry about this causing my opponent to actually win\n because the Grudge and Tit for Tat will penalize them heavily for\n initiating defection.\n 3. Reflective: Before cooperating in forgiveness, we check whether the opponent\n has defected so far more than 1/2 of the time. If they have, then we'd\n probably lose out by cooperating.\n 4. Tit for Tat: (see Patient)\n\n This strategy generates interesting results. If you look at head-to-head matchups,\n for example, it \"loses\" to strategies like joss. However, compare that to Tit for\n Tat: Tit for Tat has a low-scoring \"win\" vs. joss. NPRTT, on the other hand, has\n a high-scoring \"loss\" vs. joss.\n\n A cycle of mutual defection is costly because C/C is worth 2 more points than D/D.\n So even if we suffer one D/C (0) for every 2 C/C's (+6), that's still 2 points on\n average for that group of 3, vs. 1 point on average for a series of D/D. This also\n guides the 1/2 cutoff for the Reflective trait.\n \"\"\"\n opponent_history = history[1, 0:num_rounds]\n if num_rounds == 0:\n opponent_defection_rate = 0\n else:\n opponent_stats = dict(zip(*numpy.unique(opponent_history, return_counts=True)))\n opponent_defection_rate = Decimal(int(opponent_stats.get(0, 0))) / Decimal(\n num_rounds\n )\n\n # be patient?\n if opponent_defection_rate <= defection_threshold:\n be_patient = True\n else:\n be_patient = False\n\n choice = (\n 1\n if (\n opponents_last_move == 1 \n or (be_patient and our_second_last_move == 0)\n )\n else 0\n )\n\n return choice, None\n\ndef peaceMaker(history, memory):\n num_rounds = history.shape[1]\n choice = 1 # default is play nice\n if history[1, -1] == 0:\n # [\n # [X],\n # [0]\n # ]\n choice = 0\n if num_rounds > 3 and choice == 0:\n # forgive\n if history[0, -2] == 0 or history[0, -1]:\n # [\n # [0, X],\n # [1, X]\n # ]\n # or\n # [\n # [0],\n # [X]\n # ]\n choice = 1\n\n return choice, memory\n","sub_path":"code/strats/nprttStudent.py","file_name":"nprttStudent.py","file_ext":"py","file_size_in_byte":7053,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"563229495","text":"import os\nfrom uuid import uuid4\nfrom vcore.configuration.conf_loader import Settings\nfrom vcore.api.backends.storage.base_storage_api import StorageApiInterface\nfrom vcore.api.backends.storage.exceptions import UnknownEncoding, FileNotFound\n\nUPLOAD_DIR = Settings.settings.DISK_API_SETTINGS[\"directory\"]\n\n\ndef encode(stream, encoding):\n if encoding == \"hex\":\n return bytes.fromhex(stream)\n\n raise UnknownEncoding(encoding)\n\n\nclass DiskApi(StorageApiInterface):\n def get_archive(self, archive_id):\n path = os.path.join(UPLOAD_DIR, archive_id)\n if not os.path.exists(path):\n raise FileNotFound(archive_id) # don't use full path for security reasons !\n\n with open(path, \"rb\") as archive:\n return archive.read()\n\n def store_archive(self, local_path=None, stream=None, encoding=None, ext=None):\n archive_id = str(uuid4()) + ext\n while os.path.exists(archive_id):\n archive_id = str(uuid4())\n\n if local_path:\n with open(local_path, \"rb\") as remote_file:\n stream = remote_file.read()\n\n with open(os.path.join(UPLOAD_DIR, archive_id), \"wb\") as local_file:\n if encoding:\n stream = encode(stream, encoding)\n local_file.write(stream)\n\n return archive_id\n\n\ndef create_api():\n if not os.path.isdir(UPLOAD_DIR):\n os.mkdir(UPLOAD_DIR)\n return DiskApi()\n","sub_path":"vcore/api/backends/storage/disk.py","file_name":"disk.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"217999877","text":"import random\nimport json\nimport boto3\n\nclass AnswerHelp:\n def __init__(self,intent_request):\n self.intent_request = intent_request\n\n def execute(self):\n question = self.intent_request['inputTranscript'].lower()\n response = \"\"\n\n #emr\n if len(response) < 1:\n for h in ['emr','what is emr']:\n if h in question:\n response = \"Amazon EMR is the industry-leading cloud big data platform for processing vast amounts of data using open source tools such as Apache Spark, Apache Hive, Apache HBase, Apache Flink, Apache Hudi, and Presto. Amazon EMR makes it easy to set up, operate, and scale your big data environments by automating time-consuming tasks like provisioning capacity and tuning clusters.\"\n\n #annotation\n if len(response) < 1:\n for h in ['annotation','define annotation']:\n if h in question:\n response = \"DNA annotation or genome annotation is the process of identifying the locations of genes and all of the coding regions in a genome and determining what those genes do.\"\n\n #genome sequencing\n if len(response) < 1:\n for h in ['emr','what is genome sequencing']:\n if h in question:\n response = \"The sequence tells scientists the kind of genetic information that is carried in a particular DNA segment.\"\n\n #variant\n if len(response) < 1:\n for h in ['emr','what is a variant']:\n if h in question:\n response = \"An alteration in the most common DNA nucleotide sequence. The term variant can be used to describe an alteration that may be benign, pathogenic, or of unknown significance.\"\n\n return response\n","sub_path":"chatBot/intents/answer_help.py","file_name":"answer_help.py","file_ext":"py","file_size_in_byte":1777,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"166476709","text":"#https://wiki.python.org/moin/UdpCommunication\n#https://www.geeksforgeeks.org/socket-programming-python/\n\nimport numpy as np\n#import scipy\n#from scipy.ndimage import measurements\nimport socket\nimport time\nfrom socket import AF_INET, SOCK_DGRAM\nimport fcntl\nimport struct\nimport threading\nserv = socket.socket(AF_INET, SOCK_DGRAM)\n\nserv.settimeout(10) #10 second delay per connection request\n\nmaxStudents = 84\nmaxRows = 20\nmaxCols = 20\ndeltaTime = 0.5\nipArr = np.empty([maxRows,maxCols],dtype=object)\ndone = False\n\nipArrBin = np.zeros([maxRows,maxCols],dtype=int)\n\n#Gets IP address of server so others can connect (server's should be known by everyone beforehand)\ndef get_ip_address(ifname):\n s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\n return socket.inet_ntoa(fcntl.ioctl(\n s.fileno(),\n 0x8915, # SIOCGIFADDR\n struct.pack('256s'.encode(), ifname[:15].encode())\n )[20:24])\n\n#Parse the position and IP address String. Returns a list.\ndef parseIP(data): \n parsed = data.split(',')\n return parsed\n\n#Updates the IP array with the correct position\ndef updateIP(posR,posC,ipAddress):\n ipArr[posR-1,posC-1] = ipAddress #Index\n return\n\n#Sends message to the Client in byte form\ndef sendMess(message,ipAddress):\n serv.sendto(message,(ipAddress,8080))\n return\n\n#Create array of simple binary 0s and 1s to determine locations of where the IP addresses are\n#def createBinaryArray(ipArr):\n# for r in range (0,maxRows):\n# for c in range (0,maxCols):\n# if(ipArr[r,c] == None):\n# ipArrBin[r,c] == 0\n# else:\n# ipArrBin[r,c] == 1\n# return ipArrBin\n\n#Use built in functions in \"measurements\" library to determine clusters. These clusters will be cycled through\n#def clusterData():\n# ipArrBin = createBinaryArray(ipArr)\n# clustered, numberOfClusters = measurements.label(ipArrBin)\n# return [clustered,numberOfClusters]\n\n#Thread 1: Focuses on connecting Clients and adding their IP addresses to the overall IP array\ndef establishClientConnections():\n global done\n global ipArr\n\n while True:\n try:\n data, _ = serv.recvfrom(4096) #Sets up try/except block to ensure wait time isn't too long (cycles every 10 seconds)\n except socket.timeout:\n print(\"Timeout from establishing connection with a Client\")\n continue\n if not data: continue\n data = data.decode()\n\n if(data == \"iterDone\"): #See if it is confirmation by the Client\n done = True\n data = ''\n continue\n \n else:\n if(data == \"RESET\"): #See if the whole IP array must be RESET because of a mistake\n ipArr = np.empty([maxRows,maxCols],dtype=object)\n \n else: #If all is good, parse the information, update the array, and confirm with that IP address\n [posR,posC,ipAddress] = parseIP(data)\n posRow = int(posR)\n posCol = int(posC)\n updateIP(posRow,posCol,ipAddress)\n\n mess = posR + ',' + posC\n message = mess.encode()\n sendMess(message,ipAddress)\n \n data = ''\n\n#Thread 2: Focuses on sending messages to the Clients while everything is still happening\ndef propagateDisplayMessages():\n global done\n global ipArr\n iter = 0\n\n while True:\n numberOfClusters = 1\n #[clusteredData,numberOfClusters] = clusterData()\n #area = measurements.sum(ipArrBin, clusteredData, index=np.arange(clusteredData.max() + 1))\n for clustNum in range (1,numberOfClusters+1): #Move throughout clusters of students\n amountInClust = 3\n numWithinClust = 0\n for posR in range (0,maxRows): #Move throughout the IP address loop\n for posC in range (0,maxCols):\n if(not(ipArr[posR,posC] == None)): #\"None\" will define all the locations that are not connected\n if(True):\n numWithinClust = numWithinClust + 1\n ipAddress = ipArr[posR,posC]\n mess = str(clustNum) + ',' + str(amountInClust) + ',' + str(numWithinClust) #Sends them the code to start their LED run\n message = mess.encode()\n try: \n sendMess(message,ipAddress) #If they are not connected, this will be problematic and will cause the IP to be removed\n except socket.timeout:\n ipArr[posR,posC] = None\n print(\"Socket Timeout from IP address \" + ipAddress)\n continue\n\n \n startTime = time.time()\n while(not done):\n if(time.time()-startTime > deltaTime): #If there is no response for longer than maxTime iterations, it will be removed (failsafe)\n ipArr[posR,posC] = None\n print(\"Time Timeout from IP address \" + ipAddress)\n break\n \n if(iter == 5):\n print(ipArr[posR,posC])\n print(numWithinClust)\n print(\"____________\")\n iter = 0\n iter = iter + 1\n done = False\n\n\n# Main function\nif __name__ == \"__main__\":\n ip = get_ip_address('wlan0') #'172.20.10.5'\n print(ip)\n serv.bind((ip, 8080))\n\n # creating thread \n t1 = threading.Thread(target=establishClientConnections, args=()) \n t2 = threading.Thread(target=propagateDisplayMessages, args=()) \n \n # starting thread 1 \n t1.start() \n # starting thread 2 \n t2.start()\n\n # wait until thread 1 is completely executed \n t1.join() \n # wait until thread 2 is completely executed \n t2.join() \n \n # both threads completely executed \n print(\"Done!\")\n","sub_path":"GradCapCode/Server/serverThreadsNoGroup.py","file_name":"serverThreadsNoGroup.py","file_ext":"py","file_size_in_byte":6190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"360231094","text":"import tensorflow as tf\n\nclass TfModel(object):\n \"\"\"A deep RNN model, for use of acoustic or duration modeling.\"\"\"\n\n def __init__(self, rnn_cell, dnn_depth, dnn_num_hidden, rnn_depth, rnn_num_hidden,\n output_size, bidirectional=False, rnn_output=False, cnn_output=False,\n look_ahead=5, mdn_output=False, mix_num=1, name=\"acoustic_model\"):\n\n self._cell_fn = tf.contrib.rnn.LSTMBlockCell\n\n self._rnn_cell = rnn_cell\n self._dnn_depth = dnn_depth\n self._dnn_num_hidden = dnn_num_hidden\n self._rnn_depth = rnn_depth\n self._rnn_num_hidden = rnn_num_hidden\n self._output_size = output_size\n self._bidirectional = bidirectional\n self._rnn_output = rnn_output\n self._cnn_output = cnn_output\n self._look_ahead = look_ahead\n self._mdn_output = mdn_output\n self._mix_num = mix_num\n self.collector = {}\n\n self._input_module = [\n tf.layers.Dense(units=self._dnn_num_hidden,\n activation=tf.nn.relu,\n name=\"linear_input_{}\".format(i))\n for i in range(self._dnn_depth)\n ]\n self._rnns = tf.nn.rnn_cell.MultiRNNCell([\n self._cell_fn(self._rnn_num_hidden,\n name=\"{0}_{1}\".format(rnn_cell, i))\n for i in range(self._rnn_depth)\n ])\n \n self._output_module = tf.layers.Dense(output_size, name=\"linear_output\")\n\n\n def __call__(self, input_sequence, input_length):\n\n output_sequence = input_sequence\n output_sequence = tf.transpose(output_sequence, [1, 0, 2])\n\n output_sequence, final_state = tf.nn.dynamic_rnn(\n cell=self._rnns,\n inputs=output_sequence,\n sequence_length=input_length,\n dtype=tf.float32)\n\n\n \n\n output_sequence_logits = self._output_module(output_sequence)\n\n return output_sequence_logits, final_state\n \n def _unpack_cell(self, cell):\n \"\"\"Unpack the cells because the stack_bidirectional_dyargument 'cell' (<tensorflow.python.ops.rnn_cell_impl.MultiRNNCell object at 0x7f40d1384748>) is not an RNNCell: 'output_size' property is missing, 'state_size' property isnamic_rnn\n expects a list of cells, one per layer.\"\"\"\n if isinstance(cell, tf.nn.rnn_cell.MultiRNNCell):\n return cell._cells\n else:\n return [cell]\n\n\ninput_sequence = tf.placeholder(shape=(32, 600, 297), dtype=tf.float32)\ninput_sequence_length = tf.placeholder(shape=(32,), dtype=tf.int32)\ntarget_sequence =tf.placeholder(shape=(32, 600, 75), dtype=tf.float32)\ntarget_sequence_length = tf.placeholder(shape=(32, ), dtype=tf.int32)\n\n\nclass Object:\n pass\n\n\nFLAGS = Object()\nroot = \"/mnt/sdb/zhengnianzu/spss-prepoc/generated_king003/\"\nFLAGS.config_dir = root + 'config/'\nFLAGS.data_dir = root + 'data/'\nFLAGS.batch_size = 32\nFLAGS.input_dim = 297\nFLAGS.output_dim = 75\nFLAGS.num_threads = 8\nFLAGS.rnn_cell = \"fused_lstm\"\nFLAGS.dnn_depth = 3\nFLAGS.dnn_num_hidden = 256\nFLAGS.rnn_depth = 3\nFLAGS.rnn_num_hidden = 256\nFLAGS.bidirectional = True\nFLAGS.rnn_output = False\nFLAGS.cnn_output = False\nFLAGS.look_ahead = 5\nFLAGS.mdn_output = False\nFLAGS.mix_num = 1\nFLAGS.learning_rate = 0.001\nFLAGS.reduce_learning_rate_multiplier = 0.5\nFLAGS.max_grad_norm = 5.0\nFLAGS.max_epochs = 30\nFLAGS.save_dir = 'exp/acoustic/'\nFLAGS.resume_training = False\n\nm = TfModel(\n rnn_cell=\"fused_lstm\",\n dnn_depth=3,\n dnn_num_hidden=256,\n rnn_depth=3,\n rnn_num_hidden=256,\n output_size=75,\n bidirectional=True,\n rnn_output=False,\n cnn_output=False,\n look_ahead=5,\n mdn_output=False,\n mix_num=1,\n name=\"tf_model\")\n\n\noutput_sequence = input_sequence\ninput_length = input_sequence_length\n\n\noutput_sequence, final_state = tf.nn.dynamic_rnn(\n cell=m._rnns,\n inputs=output_sequence,\n sequence_length=input_length,\n dtype=tf.float32)\n\noutput_sequence_logits = m._output_module(output_sequence)\n\n\n","sub_path":"Example/LSTM with LSTM cell.py","file_name":"LSTM with LSTM cell.py","file_ext":"py","file_size_in_byte":4011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"626981818","text":"import pickle\nimport os\n\nimport joblib\nimport numpy as np\nimport glob\nimport cv2\nfrom scipy.spatial.transform import Rotation as R\n\n\nclass FeatureExtractor:\n def __init__(self):\n # self.check_frames = [0, -5, -9, -14, -20, -27, -35, -44, -54, -65]\n self.check_frames = [0, -3, -2, -1, 1, 2, 3] #, -2, -3, -4, -5, -6]#, -44, -54, -65]\n self.check_joints = [0, 3, 5, 6, 7, 9, 10, 11, 13, 15, 17, 19, 21, 22, 24]\n self.check_head_joints = [20, 21, 22, 24]\n self.check_head_joints_no_chin = [21, 22, 24]\n self.anchor_joints = [1, 20, 7, 11]\n self.shift = [0, 0, 50, 50]\n self.box_size = [50, 50, 50, 50]\n winSize = (32, 32)\n blockSize = (16, 16)\n blockStride = (8, 8)\n cellSize = (16, 16)\n nbins = 9\n self.hog = cv2.HOGDescriptor(winSize, blockSize, blockStride, cellSize, nbins)\n # self.hog = cv2.HOGDescriptor()\n self.event_seg_battery = {\n 'test_9434_18': [[0, 96, 0], [97, 361, 0], [362, 528, 0], [529, 608, 0], [609, 825, 0], [864, 1041, 0]],\n 'test_94342_1': [[0, 751, 0], [752, 876, 0], [877, 1167, 0], [1168, 1386, 0]],\n 'test_94342_6': [[0, 95, 0], [836, 901, 0]],\n 'test_94342_10': [[0, 156, 0], [157, 169, 0], [245, 274, 0], [275, 389, 0], [390, 525, 0], [526, 665, 0],\n [666, 680, 0]],\n 'test_94342_21': [[0, 13, 0], [1098, 1133, 0]],\n 'test1': [[0, 94, 0], [95, 155, 0], [156, 225, 0], [226, 559, 0], [690, 698, 0]],\n 'test6': [[0, 488, 0], [489, 541, 0], [542, 672, 0], [672, 803, 0]],\n 'test7': [[0, 70, 0], [71, 100, 0], [221, 226, 0]],\n 'test_boelter_2': [[0, 318, 0], [319, 458, 0], [459, 543, 0], [544, 606, 0]],\n 'test_boelter_7': [[0, 69, 0], [119, 133, 0], [134, 187, 0], [188, 239, 0], [329, 376, 0], [398, 491, 0],\n [492, 564, 0], [689, 774, 0], [775, 862, 0], [863, 897, 0], [959, 1000, 0],\n [1001, 1178, 0], [1268, 1307, 0], [1307, 1327, 0]],\n 'test_boelter_24': [[0, 62, 0], [293, 314, 0]],\n 'test_boelter_12': [[48, 219, 0], [220, 636, 0]],\n 'test_9434_1': [[0, 67, 0], [68, 124, 0], [252, 343, 0], [344, 380, 0], [381, 417, 0]],\n 'test_94342_16': [[0, 84, 0], [201, 214, 0]],\n 'test_boelter4_12': [[0, 32, 0], [33, 141, 0], [463, 519, 0], [520, 597, 0], [598, 605, 0],\n [1233, 1293, 0]],\n 'test_boelter4_9': [[0, 221, 0], [308, 466, 0], [1215, 1270, 0]],\n 'test_boelter4_4': [[0, 183, 0], [281, 529, 0], [530, 714, 0]],\n 'test_boelter4_3': [[0, 117, 0]],\n 'test_boelter4_1': [[0, 252, 0], [253, 729, 0], [730, 1202, 0], [1203, 1237, 0]],\n 'test_boelter3_13': [],\n 'test_boelter3_11': [[255, 424, 0], [599, 692, 0]],\n 'test_boelter3_6': [[281, 498, 0], [499, 639, 0], [696, 748, 0], [749, 788, 0]],\n 'test_boelter3_4': [[781, 817, 0]],\n 'test_boelter3_0': [[0, 102, 0], [103, 480, 0], [481, 703, 0], [704, 768, 0]],\n 'test_boelter2_15': [[0, 46, 0]],\n 'test_boelter2_12': [[0, 163, 0], [445, 519, 0], [584, 660, 0]],\n 'test_boelter2_5': [[0, 94, 0], [655, 1206, 0]],\n 'test_boelter2_4': [],\n 'test_boelter2_2': [[0, 145, 0], [146, 224, 0], [225, 271, 0], [272, 392, 0], [393, 454, 0],\n [455, 762, 0], [763, 982, 0], [983, 1412, 0]],\n 'test_boelter_21': [[239, 285, 0], [286, 310, 0], [374, 457, 0]],\n }\n self.trackers = {'test1': 'skele1.p', 'test2': 'skele2.p', 'test6': 'skele2.p', 'test7': 'skele1.p',\n 'test_9434_1': 'skele2.p', 'test_9434_3': 'skele2.p', 'test_9434_18': 'skele1.p',\n 'test_94342_0': 'skele2.p', 'test_94342_1': 'skele2.p', 'test_94342_2': 'skele2.p',\n 'test_94342_3': 'skele2.p', 'test_94342_4': 'skele1.p', 'test_94342_5': 'skele1.p',\n 'test_94342_6': 'skele1.p', 'test_94342_7': 'skele1.p', 'test_94342_8': 'skele1.p',\n 'test_94342_10': 'skele2.p', 'test_94342_11': 'skele2.p', 'test_94342_12': 'skele1.p',\n 'test_94342_13': 'skele2.p', 'test_94342_14': 'skele1.p', 'test_94342_15': 'skele2.p',\n 'test_94342_16': 'skele1.p', 'test_94342_17': 'skele2.p', 'test_94342_18': 'skele1.p',\n 'test_94342_19': 'skele2.p', 'test_94342_20': 'skele1.p', 'test_94342_21': 'skele2.p',\n 'test_94342_22': 'skele1.p', 'test_94342_23': 'skele1.p', 'test_94342_24': 'skele1.p',\n 'test_94342_25': 'skele2.p', 'test_94342_26': 'skele1.p',\n 'test_boelter_1': 'skele2.p', 'test_boelter_2': 'skele2.p', 'test_boelter_3': 'skele2.p',\n 'test_boelter_4': 'skele1.p', 'test_boelter_5': 'skele1.p', 'test_boelter_6': 'skele1.p',\n 'test_boelter_7': 'skele1.p', 'test_boelter_9': 'skele1.p', 'test_boelter_10': 'skele1.p',\n 'test_boelter_12': 'skele2.p', 'test_boelter_13': 'skele1.p', 'test_boelter_14': 'skele1.p',\n 'test_boelter_15': 'skele1.p', 'test_boelter_17': 'skele2.p', 'test_boelter_18': 'skele1.p',\n 'test_boelter_19': 'skele2.p', 'test_boelter_21': 'skele1.p', 'test_boelter_22': 'skele2.p',\n 'test_boelter_24': 'skele1.p', 'test_boelter_25': 'skele1.p',\n 'test_boelter2_0': 'skele1.p', 'test_boelter2_2': 'skele1.p', 'test_boelter2_3': 'skele1.p',\n 'test_boelter2_4': 'skele1.p', 'test_boelter2_5': 'skele1.p', 'test_boelter2_6': 'skele1.p',\n 'test_boelter2_7': 'skele2.p', 'test_boelter2_8': 'skele2.p', 'test_boelter2_12': 'skele2.p',\n 'test_boelter2_14': 'skele2.p', 'test_boelter2_15': 'skele2.p', 'test_boelter2_16': 'skele1.p',\n 'test_boelter2_17': 'skele1.p',\n 'test_boelter3_0': 'skele1.p', 'test_boelter3_1': 'skele2.p', 'test_boelter3_2': 'skele2.p',\n 'test_boelter3_3': 'skele2.p', 'test_boelter3_4': 'skele1.p', 'test_boelter3_5': 'skele2.p',\n 'test_boelter3_6': 'skele2.p', 'test_boelter3_7': 'skele1.p', 'test_boelter3_8': 'skele2.p',\n 'test_boelter3_9': 'skele2.p', 'test_boelter3_10': 'skele1.p', 'test_boelter3_11': 'skele2.p',\n 'test_boelter3_12': 'skele2.p', 'test_boelter3_13': 'skele2.p',\n 'test_boelter4_0': 'skele2.p', 'test_boelter4_1': 'skele2.p', 'test_boelter4_2': 'skele2.p',\n 'test_boelter4_3': 'skele2.p', 'test_boelter4_4': 'skele2.p', 'test_boelter4_5': 'skele2.p',\n 'test_boelter4_6': 'skele2.p', 'test_boelter4_7': 'skele2.p', 'test_boelter4_8': 'skele2.p',\n 'test_boelter4_9': 'skele2.p', 'test_boelter4_10': 'skele2.p', 'test_boelter4_11': 'skele2.p',\n 'test_boelter4_12': 'skele2.p', 'test_boelter4_13': 'skele2.p',\n }\n\n def check_norm(self, features):\n if np.linalg.norm(features) > 0:\n features = features / np.linalg.norm(features)\n return features\n\n def extract_body_motion_features(self, skeletons):\n feature_dim = len(self.check_joints) * len(self.check_frames) * 3\n video_features = np.empty((0, feature_dim))\n for frame_id, skeleton in enumerate(skeletons):\n skeleton = np.array(skeleton)\n frame_feature = np.empty((1, 0))\n torso = skeleton[1]\n for check_frame in self.check_frames:\n if frame_id + check_frame == frame_id:\n features = skeleton[self.check_joints] - torso\n # features = self.check_norm(features)\n features = features.reshape(1, -1)\n frame_feature = np.hstack([frame_feature, features])\n else:\n if check_frame < 0:\n pre_frame_id = max(0, check_frame + frame_id)\n else:\n pre_frame_id = min(len(skeletons) - 1, check_frame + frame_id)\n pre_skele = skeletons[pre_frame_id]\n features = skeleton - pre_skele\n features = features[self.check_joints]\n # features = self.check_norm(features)\n features = features.reshape(1, -1)\n frame_feature = np.hstack([frame_feature, features])\n video_features = np.vstack([video_features, frame_feature])\n return video_features\n\n def extract_hand_features(self, skeletons):\n feature_dim = 6 + 6\n video_feature = np.empty((0, feature_dim))\n for frame_id, skeleton in enumerate(skeletons):\n skeleton = np.array(skeleton)\n frame_feature = np.empty((1, 0))\n torso = skeleton[1]\n head = skeleton[20]\n left_hand = skeleton[11]\n right_hand = skeleton[7]\n\n left_head = left_hand - head\n left_head = left_head.reshape((1, -1))\n # left_head = self.check_norm(left_head).reshape(1, -1)\n left_torso = left_hand - torso\n left_torso = left_torso.reshape((1, -1))\n # left_torso = self.check_norm(left_torso).reshape(1, -1)\n right_head = right_hand - head\n right_head = right_head.reshape((1, -1))\n # right_head = self.check_norm(right_head).reshape(1, -1)\n right_torso = right_hand - torso\n right_torso = right_torso.reshape((1, -1))\n # right_torso = self.check_norm(right_torso).reshape(1, -1)\n frame_feature = np.hstack([frame_feature, left_head, left_torso, right_head, right_torso])\n video_feature = np.vstack([video_feature, frame_feature])\n return video_feature\n\n def extract_foot_features(self, skeletons):\n feature_dim = 6 + 6\n video_feature = np.empty((0, feature_dim))\n for frame_id, skeleton in enumerate(skeletons):\n skeleton = np.array(skeleton)\n frame_feature = np.empty((1, 0))\n torso = skeleton[1]\n head = skeleton[20]\n left_foot = skeleton[19]\n right_foot = skeleton[15]\n left_head = left_foot - head\n left_head = left_head.reshape((1, -1))\n # left_head = self.check_norm(left_head).reshape(1, -1)\n left_torso = left_foot - torso\n left_torso = left_torso.reshape((1, -1))\n # left_torso = self.check_norm(left_torso).reshape(1, -1)\n right_head = right_foot - head\n right_head = right_head.reshape((1, -1))\n # right_head = self.check_norm(right_head).reshape(1, -1)\n right_torso = right_foot - torso\n right_torso = right_torso.reshape((1, -1))\n # right_torso = self.check_norm(right_torso).reshape(1, -1)\n frame_feature = np.hstack([frame_feature, left_head, left_torso, right_head, right_torso])\n video_feature = np.vstack([video_feature, frame_feature])\n return video_feature\n\n def point2screen(self, points):\n K = [607.13232421875, 0.0, 638.6468505859375, 0.0, 607.1067504882812, 367.1607360839844, 0.0, 0.0, 1.0]\n K = np.reshape(np.array(K), [3, 3])\n rot_points = np.array(points) + np.array([0, 0.2, 0])\n rot_points = rot_points\n points_camera = rot_points.reshape(3, 1)\n\n project_matrix = np.array(K).reshape(3, 3)\n points_prj = project_matrix.dot(points_camera)\n points_prj = points_prj.transpose()\n if not points_prj[:, 2][0] == 0.0:\n points_prj[:, 0] = points_prj[:, 0] / points_prj[:, 2]\n points_prj[:, 1] = points_prj[:, 1] / points_prj[:, 2]\n points_screen = points_prj[:, :2]\n assert points_screen.shape == (1, 2)\n points_screen = points_screen.reshape(-1)\n return points_screen\n\n def extract_hog_features(self, skeletons, img_names, depths):\n feature_dim = 648\n video_feature = np.empty((0, feature_dim))\n for frame_id, skeleton in enumerate(skeletons):\n skeleton = np.array(skeleton)\n frame_feature = np.empty((1, 0))\n img_name = img_names[frame_id]\n to_draw_img = cv2.imread(img_name)\n depth_name = depths[frame_id]\n depth_img = np.load(depth_name)\n for ind, joint_id in enumerate(self.anchor_joints):\n joint = skeleton[joint_id]\n screen_joint = self.point2screen(joint)\n x, y = screen_joint[0], screen_joint[1] + self.shift[ind]\n width = self.box_size[ind]\n y_min = min(max(y - width, 0), to_draw_img.shape[0] - 100)\n y_max = max(min(y + width, to_draw_img.shape[0]), 100)\n x_min = min(max(x - width, 0), to_draw_img.shape[1] - 100)\n x_max = max(min(x + width, to_draw_img.shape[1]), 100)\n\n box = [x_min, y_min, x_max, y_max]\n cropped_image = to_draw_img[int(y_min):int(y_max), int(x_min):int(x_max), :]\n cropped_image = cv2.resize(cropped_image, self.hog.winSize)\n hog_feature = self.hog.compute(cropped_image).reshape(1, -1)\n frame_feature = np.hstack([frame_feature, hog_feature])\n temp = to_draw_img.copy()\n # cv2.circle(temp, (int(joint[0]), int(joint[1])), 3, (255, 0, 0), thickness=1)\n # cv2.rectangle(temp, (int(box[0]), int(box[1])), (int(box[2]), int(box[3])), (255, 0, 0))\n # cv2.imshow('img', cropped_image)\n # cv2.waitKey(20)\n cropped_depth_img = depth_img[int(y_min):int(y_max), int(x_min):int(x_max)]\n cropped_depth_img = cropped_depth_img.astype(np.uint8)\n cropped_depth_img = cv2.resize(cropped_depth_img, self.hog.winSize)\n hog_feature = self.hog.compute(cropped_depth_img).reshape(1, -1)\n frame_feature = np.hstack([frame_feature, hog_feature])\n video_feature = np.vstack([video_feature, frame_feature])\n return video_feature\n\n def check_location(self, clip):\n names = clip.split('_')\n print(names)\n if len(names) == 1:\n return 'video_lib'\n else:\n name = names[1]\n if name == '9434':\n return 'video_9434'\n elif name == '94342':\n return 'video_94342'\n elif name == 'boelter':\n return 'video_bhlib'\n elif name == 'boelter2':\n return 'video_bhlib2'\n elif name == 'boelter3':\n return 'video_bhlib3'\n elif name == 'boelter4':\n return 'video_bhlib4'\n\n def get_orientation(self, tracker):\n a = tracker[9] - tracker[5]\n b = tracker[0] - tracker[5]\n c = np.cross(a, b)\n c_norm = np.linalg.norm(c)\n if c_norm > 0:\n c = c / c_norm\n return [c[0], c[1], 0]\n\n def cal_angle(self, orientation):\n theta = np.arctan2(orientation[1], orientation[0])\n return theta\n\n def cal_hand_relative(self, tracker_t, battery_t, frame_feature, joint_id):\n battery_shoulder = battery_t[joint_id]\n tracker_shoulder = tracker_t[joint_id]\n battery_hand = battery_t[joint_id + 2] - battery_t[joint_id]\n tracker_hand = tracker_t[joint_id + 2] - tracker_t[joint_id]\n shoulder_relative_dirct = (battery_shoulder - tracker_shoulder).reshape((1, -1))\n shoulder_relative_dirct = self.check_norm(shoulder_relative_dirct)\n tracker_hand = self.check_norm(tracker_hand)\n angle = shoulder_relative_dirct.dot(tracker_hand).reshape((1, -1))\n frame_feature = np.hstack([frame_feature, angle])\n battery_hand = self.check_norm(battery_hand)\n angle = -shoulder_relative_dirct.dot(battery_hand).reshape((1, -1))\n frame_feature = np.hstack([frame_feature, angle])\n if joint_id == 9:\n battery_right_hand = battery_t[7] - battery_t[5]\n tracker_right_hand = tracker_t[7] - tracker_t[5]\n battery_right_hand = self.check_norm(battery_right_hand)\n tracker_right_hand = self.check_norm(tracker_right_hand)\n angle1 = tracker_hand.dot(battery_hand).reshape((1, -1))\n angle2 = tracker_hand.dot(battery_right_hand).reshape((1, -1))\n angle3 = tracker_right_hand.dot(battery_hand).reshape((1, -1))\n angle4 = tracker_right_hand.dot(battery_right_hand).reshape((1, -1))\n frame_feature = np.hstack([frame_feature, angle1, angle2, angle3, angle4])\n return frame_feature\n\n def extract_tracker_gaze_hog_feature_per_frame(self, img_name, gaze):\n img = cv2.imread(img_name)\n box_height = img.shape[0]/6\n box_width = img.shape[1]/6\n gaze_center = gaze\n top = gaze_center[1] - box_height\n left = gaze_center[0] - box_width\n top = max(0, top)\n left = max(0, left)\n bottom = gaze_center[1] + box_height\n right = gaze_center[0] + box_width\n bottom = min(img.shape[0], bottom)\n right = min(img.shape[1], right)\n\n # cv2.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), color=(255,0, 0))\n # cv2.imshow('img', img)\n # cv2.waitKey(20)\n img = img[int(top):int(bottom), int(left):int(right)]\n if img.shape[0] == 0 or img.shape[1] == 0:\n return\n else:\n cropped_img = img.astype(np.uint8)\n cropped_img = cv2.resize(cropped_img, self.hog.winSize)\n hog_feature = self.hog.compute(cropped_img).reshape(1, -1)\n for i in range(0, 3):\n channel = img[:, :, i]\n hist, _ = np.histogram(channel.reshape(-1), bins=27, density=True)\n hog_feature = np.hstack([hog_feature, hist.reshape((1, -1))])\n return hog_feature\n\n def extract_battery_gaze_hog_feature_per_frame(self, img_name):\n img = cv2.imread(img_name)\n top = img.shape[0] / 3 * 2\n left = img.shape[1] / 3\n bottom = img.shape[0]\n right = img.shape[1] / 3 * 2\n img = img[top:bottom, left:right]\n cropped_img = img.astype(np.uint8)\n cropped_img = cv2.resize(cropped_img, self.hog.winSize)\n hog_feature = self.hog.compute(cropped_img).reshape(1, -1)\n for i in range(0, 3):\n channel = img[:, :, i]\n hist, _ = np.histogram(channel.reshape(-1), bins=27, density=True)\n hog_feature = np.hstack([hog_feature, hist.reshape((1, -1))])\n return hog_feature\n\n def extract_pair_features(self, trackers, batteries, battery_gazes, tracker_gazes, battery_img_names, tracker_img_names):\n video_feature = np.empty((0, len(self.check_joints)*3 + 7 + 1 + 4 + 162*2))\n for frame_id, tracker in enumerate(trackers):\n battery = batteries[frame_id]\n tracker = np.array(tracker)\n battery = np.array(battery)\n # transform\n tracker_center = np.mean(tracker, axis=0)\n orientation = self.get_orientation(tracker)\n angle = self.cal_angle(orientation)\n tracker_t = tracker - tracker_center\n r = R.from_euler('z', angle)\n tracker_t = r.apply(tracker_t)\n\n battery_t = battery - tracker_center\n battery_center = np.mean(battery_t, axis=0)\n battery_temp = battery_t - battery_center\n battery_temp = r.apply(battery_temp)\n battery_t = battery_temp + battery_center\n\n frame_feature = np.empty((1, 0))\n\n # relative joint\n dirct = battery_t[self.check_joints] - tracker_t[self.check_joints]\n dirct = dirct.reshape((1, -1))\n # dirct = self.check_norm(dirct)\n frame_feature = np.hstack([frame_feature, dirct])\n\n # relative gaze\n # if battery_gazes[frame_id]:\n # battery_gaze = battery_gazes[frame_id]\n # else:\n # battery_gaze = np.array([0, 0, 0])\n # if tracker_gazes[frame_id]:\n # tracker_gaze = tracker_gazes[frame_id]\n # else:\n # tracker_gaze = np.array([0, 0, 0])\n battery_gaze = battery_gazes[frame_id]\n tracker_gaze = tracker_gazes[frame_id]\n battery_gaze = self.check_norm(battery_gaze)\n tracker_gaze = self.check_norm(tracker_gaze)\n battery_gaze_t = r.apply(battery_gaze)\n tracker_gaze_t = r.apply(tracker_gaze)\n\n battery_head = battery_t[20]\n tracker_head = tracker_t[20]\n head_relative_dirct = battery_head - tracker_head\n head_relative_dirct = self.check_norm(head_relative_dirct)\n angle = head_relative_dirct.dot(tracker_gaze_t).reshape((1, -1))\n frame_feature = np.hstack([frame_feature, angle])\n angle = -head_relative_dirct.dot(battery_gaze_t).reshape((1, -1))\n frame_feature = np.hstack([frame_feature, angle])\n angle = tracker_gaze_t.dot(battery_gaze_t).reshape((1, -1))\n frame_feature = np.hstack([frame_feature, angle])\n # relative dist\n dist = np.linalg.norm(battery_center - tracker_center).reshape((1, -1))\n frame_feature = np.hstack([frame_feature, dist])\n\n # left hand feature\n frame_feature = self.cal_hand_relative(tracker_t, battery_t, frame_feature, 9)\n\n # right hand feature\n frame_feature = self.cal_hand_relative(tracker_t, battery_t, frame_feature, 5)\n\n # hog feature\n tracker_hog = self.extract_tracker_gaze_hog_feature_per_frame(tracker_img_names[frame_id], tracker_gazes[frame_id])\n if tracker_hog is not None:\n frame_feature = np.hstack([frame_feature, tracker_hog.reshape((1, -1))])\n else:\n frame_feature = np.hstack([frame_feature, video_feature[frame_id - 1][-162*2:-162]])\n battery_hog = self.extract_battery_gaze_hog_feature_per_frame(battery_img_names[frame_id])\n if battery_hog is not None:\n frame_feature = np.hstack([frame_feature, battery_hog.reshape((1, -1))])\n else:\n frame_feature = np.hstack([frame_feature, video_feature[frame_id - 1][-162:]])\n video_feature = np.vstack([video_feature, frame_feature])\n return video_feature\n\n def extract_head_motion_features(self, skeletons):\n feature_dim = 3*3\n video_feature = np.empty((0, feature_dim))\n for frame_id, skeleton in enumerate(skeletons):\n skeleton = np.array(skeleton)\n frame_feature = np.empty((1, 0))\n head = skeleton[20]\n direct = skeleton[self.check_head_joints_no_chin] - head\n # direct = self.check_norm(direct).reshape((1, -1))\n direct = direct.reshape((1, -1))\n frame_feature = np.hstack([frame_feature, direct])\n\n video_feature = np.vstack([video_feature, frame_feature])\n return video_feature\n\n def check_gaze(self, gaze):\n if gaze:\n gaze_t = gaze[0] - gaze[1]\n else:\n gaze_t = np.array([0, 0, 0])\n return gaze_t\n\n def extract_gaze_motion_features(self, gazes):\n feature_dim = 3*7\n video_feature = np.empty((0, feature_dim))\n for frame_id in range(len(gazes)):\n frame_feature = np.empty(((1, 0)))\n gaze = gazes[frame_id]\n # gaze = self.check_gaze(gaze)\n gaze_t = self.check_norm(gaze).reshape((1, -1))\n frame_feature = np.hstack([frame_feature, gaze_t])\n for check_frame in range(-3, 3, 1):\n if check_frame < 0:\n pre_frame_id = max(0, check_frame + frame_id)\n elif check_frame > 0:\n pre_frame_id = min(len(gazes) - 1, check_frame + frame_id)\n else:\n pre_frame_id = frame_id\n pre_gaze = gazes[pre_frame_id]\n # pre_gaze = self.check_gaze(pre_gaze).reshape((1, -1))\n # pre_gaze = self.check_norm(pre_gaze).reshape((1, -1))\n features = gaze_t - pre_gaze\n # features = self.check_norm(features)\n frame_feature = np.hstack([frame_feature, features])\n video_feature = np.vstack([video_feature, frame_feature])\n return video_feature\n\n def extract_obj_features(self, skeletons, gazes, cate_file, mask_names):\n feature_dim = 10\n video_feature = np.empty((0, feature_dim))\n\n with open(cate_file, 'rb') as f:\n category = joblib.load(f)\n for frame_id, skeleton in enumerate(skeletons):\n frame_features = np.empty((1, 0))\n skeleton = np.array(skeleton)\n left_hand = skeleton[7]\n right_hand = skeleton[11]\n mask_name = mask_names[frame_id]\n with open(mask_name, 'rb') as f:\n masks = joblib.load(f)\n distance = np.empty((0, 4))\n for obj_name, frame_cates in category.items():\n frame_cate = frame_cates[frame_id]\n if frame_cate[0]:\n cate_id = frame_cate[1]\n sub_id = frame_cate[2]\n mask = masks[cate_id][1][sub_id]\n avg_col = np.array(mask).mean(axis=1)\n if np.array(mask)[avg_col != 0, :].shape[0] == 0:\n distance = np.vstack([distance, np.array([0, 0, 0, 100])])\n else:\n obj_center = np.array(mask)[avg_col != 0, :].mean(axis=0)\n dist = (np.linalg.norm(obj_center - left_hand) + np.linalg.norm(obj_center - right_hand))/2\n obj_center = np.hstack([obj_center, dist])\n distance = np.vstack([distance, obj_center])\n else:\n distance = np.vstack([distance, np.array([0, 0, 0, 1000])])\n\n idx = np.argsort(distance[:, 3])[:10]\n top_dists = distance[idx][:, 3]\n frame_features = np.hstack([frame_features, top_dists.reshape((1, -1))])\n video_feature = np.vstack([video_feature, frame_features])\n return video_feature\n\n def extract_obj_hog_feature(self, skeletons, bounding_box_file, img_names, cate_file):\n with open(cate_file, 'rb') as f:\n category = joblib.load(f)\n obj_names = sorted(glob.glob(os.path.join(bounding_box_file, '*.p')))\n objs = dict()\n for i in range(len(obj_names)):\n objs[i] = [[],[]]\n with open(obj_names[i], 'rb') as f:\n objs[i][0] = joblib.load(f)\n objs[i][1] = obj_names[i].split('/')[-2:]\n video_feature = np.empty((0, 81))\n\n for frame_id, skeleton in enumerate(skeletons):\n img = cv2.imread(img_names[frame_id])\n left_hand = self.point2screen(skeleton[7])\n right_hand = self.point2screen(skeleton[11])\n min_dist = 1000\n min_id = -1\n min_box = None\n for obj_id, [obj, names] in objs.items():\n cate = category['./post_box_reid/' + names[0] + '/' + names[1]][frame_id][0]\n obj_curr = obj[frame_id]\n center = np.array([int(obj_curr[0] + obj_curr[2] / 2), int(obj_curr[1] + obj_curr[3] / 2)])\n #dist = np.linalg.norm(center - left_hand) + np.linalg.norm(center - right_hand)\n dist1=np.linalg.norm(center - left_hand)\n dist2=np.linalg.norm(center - right_hand)\n dist=min(dist1, dist2)\n # cv2.rectangle(img, (int(obj_curr[0]), int(obj_curr[1])),\n # (int(obj_curr[0] + obj_curr[2]), int(obj_curr[1] + obj_curr[3])),\n # (255, 0, 0), thickness=3)\n # img = cv2.circle(img, (center[0], center[1]), color = (255, 0, 0), radius = 3, thickness=2)\n # img = cv2.circle(img, (int(left_hand[0]), int(left_hand[1])), color = (0, 0, 255), radius = 3, thickness=2)\n # img = cv2.circle(img, (int(right_hand[0]), int(right_hand[1])), color=(0, 0, 255), radius=3, thickness=2)\n if dist < min_dist and cate != 'person':\n min_dist = dist\n min_id = obj_id\n min_box = obj_curr\n # cv2.imshow('img', img)\n # cv2.waitKey(20)\n if min_id > -1 and min_box.mean() != 0:\n top = max(0, min_box[1])\n left = max(0, min_box[0])\n bottom = min(min_box[3] + min_box[1], img.shape[0])\n right = min(min_box[2] + min_box[0], img.shape[1])\n cropped_img = img[int(top):int(bottom), int(left):int(right)]\n # cv2.imshow('img', cropped_img)\n # cv2.waitKey(20)\n # print([top, left, bottom, right], cropped_img.shape)\n cropped_img = cropped_img.astype(np.uint8)\n cropped_img = cv2.resize(cropped_img, self.hog.winSize)\n hog_feature = self.hog.compute(cropped_img).reshape(1, -1)\n video_feature = np.vstack([video_feature, hog_feature])\n else:\n video_feature = np.vstack([video_feature, np.zeros((1, 81))])\n return video_feature\n\n def extract_tracker_gaze_hog_feature(self, img_names, gaze_file):\n with open(gaze_file, 'rb') as f:\n gazes = pickle.load(f)\n video_features = np.empty((0, 81*2))\n for frame_id, img_name in enumerate(img_names):\n img = cv2.imread(img_name)\n box_height = img.shape[0]/6\n box_width = img.shape[1]/6\n gaze_center = gazes[frame_id]\n top = gaze_center[1] - box_height\n left = gaze_center[0] - box_width\n top = max(0, top)\n left = max(0, left)\n bottom = gaze_center[1] + box_height\n right = gaze_center[0] + box_width\n bottom = min(img.shape[0], bottom)\n right = min(img.shape[1], right)\n\n # cv2.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), color=(255,0, 0))\n # cv2.imshow('img', img)\n # cv2.waitKey(20)\n img = img[int(top):int(bottom), int(left):int(right)]\n if img.shape[0] == 0 or img.shape[1] == 0:\n if frame_id == 0:\n video_features = np.vstack([video_features, np.zeros((1, 81*2))])\n else:\n video_features = np.vstack([video_features, video_features[frame_id - 1]])\n else:\n cropped_img = img.astype(np.uint8)\n cropped_img = cv2.resize(cropped_img, self.hog.winSize)\n hog_feature = self.hog.compute(cropped_img).reshape(1, -1)\n for i in range(0, 3):\n channel = img[:, :, i]\n hist, _ = np.histogram(channel.reshape(-1), bins=27, density=True)\n hog_feature = np.hstack([hog_feature, hist.reshape((1, -1))])\n video_features = np.vstack([video_features, hog_feature])\n # cv2.imshow('img', img)\n # cv2.waitKey(20)\n return video_features\n\n def extract_battery_gaze_hog_feature(self, img_names):\n video_features = np.empty((0, 81*2))\n for img_name in img_names:\n img = cv2.imread(img_name)\n top = img.shape[0] / 3 * 2\n left = img.shape[1] / 3\n bottom = img.shape[0]\n right = img.shape[1] / 3 * 2\n img = img[top:bottom, left:right]\n cropped_img = img.astype(np.uint8)\n cropped_img = cv2.resize(cropped_img, self.hog.winSize)\n hog_feature = self.hog.compute(cropped_img).reshape(1, -1)\n for i in range(0, 3):\n channel = img[:, :, i]\n hist, _ = np.histogram(channel.reshape(-1), bins=27, density=True)\n hog_feature = np.hstack([hog_feature, hist.reshape((1, -1))])\n video_features = np.vstack([video_features, hog_feature])\n # cv2.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), color=(255, 0, 0))\n # cv2.imshow('img', img)\n # cv2.waitKey(20)\n return video_features\n\n\n def load_file(self):\n data_path = '/home/shuwen/data/data_preprocessing2/post_skeletons/'\n img_path = '/home/shuwen/data/data_preprocessing2/annotations'\n depth_path = '/home/shuwen/data/data_preprocessing2/post_images/kinect'\n save_folder = '/home/shuwen/data/data_preprocessing2/feature_single/'\n save_pair_folder = '/home/shuwen/data/data_preprocessing2/feature_pair/'\n battery_gaze_path = '/home/shuwen/data/data_preprocessing2/gaze_smooth_360_battery/'\n tracker_gaze_path = '/home/shuwen/data/data_preprocessing2/gaze_smooth_360_tracker/'\n mask_path = '/home/shuwen/Downloads/pointclouds/'\n cate_path = '/home/shuwen/data/data_preprocessing2/track_cate_with_frame/'\n bounding_box_path = '/home/shuwen/data/data_preprocessing2/post_neighbor_smooth_newseq/'\n tracker_gt_path = '/home/shuwen/data/data_preprocessing2/tracker_gt_smooth/'\n if not os.path.exists(save_folder):\n os.makedirs(save_folder)\n if not os.path.exists(save_pair_folder):\n os.makedirs(save_pair_folder)\n\n clips = os.listdir(data_path)\n # clips = self.event_seg_battery.keys()\n # clips = ['test_boelter2_2']\n # clips=['test_boelter4_1']\n # clips=['test_94342_16', 'test_boelter_12', 'test_boelter2_5', 'test_boelter3_4', 'test_boelter4_9']\n # clips=['test_boelter_17']\n # clips=['test_boelter2_2']\n\n for clip in clips:\n if os.path.exists(save_folder + clip + '.p'):\n continue\n print('*******{}*******'.format(clip))\n location = self.check_location(clip)\n print(location)\n depths = sorted(glob.glob(os.path.join(depth_path, location, clip, '*.npy')))\n img_names = sorted(glob.glob(os.path.join(img_path, clip, 'kinect', '*jpg')))\n cate_file = os.path.join(cate_path, clip, clip + '.p')\n bounding_box_file = os.path.join(bounding_box_path, clip)\n mask_names = sorted(glob.glob(os.path.join(mask_path, clip, '*.p')))\n print(os.path.join(img_path, clip, 'kinect', '*.jpg'))\n features = {}\n with open(os.path.join(data_path, clip, 'skele1.p')) as f:\n skeletons1 = pickle.load(f)\n with open(os.path.join(data_path, clip, 'skele2.p')) as f:\n skeletons2 = pickle.load(f)\n with open('../3d_pose2gaze/tracker_record_bbox/' + clip + '.p', 'rb') as f:\n tracker_tracking_id = joblib.load(f)\n with open('../3d_pose2gaze/record_bbox/' + clip + '.p', 'rb') as f:\n battery_tracking_id = joblib.load(f)\n if self.trackers[clip] == 'skele1.p':\n trackers = skeletons1\n battery = skeletons2\n else:\n trackers = skeletons2\n battery = skeletons1\n with open(os.path.join(battery_gaze_path, clip + '.p'), 'rb') as f:\n battery_gaze = joblib.load(f)\n with open(os.path.join(tracker_gaze_path, clip + '.p'), 'rb') as f:\n tracker_gaze = joblib.load(f)\n # pair_features = self.extract_pair_features(trackers, battery, battery_gaze, tracker_gaze,\n # sorted(glob.glob(os.path.join(img_path, clip, 'battery', '*jpg'))),\n # sorted(glob.glob(os.path.join(img_path, clip, 'tracker', '*jpg'))))\n # pair_features = (pair_features - pair_features.mean())/pair_features.std()\n # print(pair_features.shape)\n # with open(save_pair_folder + clip + '.p', 'wb') as f:\n # pickle.dump(pair_features, f)\n total = {'tracker': [trackers, tracker_gaze, tracker_tracking_id],\n 'battery': [battery, battery_gaze, battery_tracking_id]}\n for i in [1, 2]:\n if str(i) == self.trackers[clip].split('.')[0][-1]:\n p_type = 'tracker'\n skeletons, gaze, tracking_id = total['tracker']\n else:\n p_type = 'battery'\n skeletons, gaze, tracking_id = total['battery']\n # head features\n head_features = self.extract_head_motion_features(skeletons)\n gaze_features = self.extract_gaze_motion_features(gaze)\n\n\n # skeletons features\n body_motion_features = self.extract_body_motion_features(skeletons)\n hand_features = self.extract_hand_features(skeletons)\n foot_features = self.extract_foot_features(skeletons)\n\n # object features\n object_feature = self.extract_obj_features(skeletons, gaze, cate_file, mask_names)\n object_hog_feature = self.extract_obj_hog_feature(skeletons, bounding_box_file, img_names, cate_file)\n\n #gaze hog feature\n if p_type == 'tracker':\n tracker_img_names = sorted(glob.glob(os.path.join(img_path, clip, 'tracker', '*jpg')))\n gaze_file = os.path.join(tracker_gt_path, clip +'.p')\n gaze_hog_feature = self.extract_tracker_gaze_hog_feature(tracker_img_names, gaze_file)\n else:\n battery_img_names = sorted(glob.glob(os.path.join(img_path, clip, 'battery', '*jpg')))\n gaze_hog_feature = self.extract_battery_gaze_hog_feature(battery_img_names)\n\n\n # visual features\n # hog_features = self.extract_hog_features(skeletons, img_names, depths)\n # print(body_motion_features.shape[1] + hand_features.shape[1] + foot_features.shape[1], hog_features.shape[1], pair_features.shape[1])\n # total_feature = np.hstack([body_motion_features, hand_features, foot_features, hog_features, pair_features])\n\n head_features=(head_features-head_features.mean())/head_features.std()\n gaze_features = (gaze_features - gaze_features.mean()) / gaze_features.std()\n body_motion_features = (body_motion_features - body_motion_features.mean()) / body_motion_features.std()\n hand_features = (hand_features - hand_features.mean()) / hand_features.std()\n foot_features = (foot_features - foot_features.mean()) / foot_features.std()\n object_hog_feature = (object_hog_feature - object_hog_feature.mean()) / object_hog_feature.std()\n gaze_hog_feature = (gaze_hog_feature - gaze_hog_feature.mean()) / gaze_hog_feature.std()\n object_feature = (object_feature - object_feature.mean()) / object_feature.std()\n\n total_feature = np.hstack(\n [head_features, gaze_features, body_motion_features, hand_features, foot_features,\n object_hog_feature, gaze_hog_feature, object_feature])\n\n # mean = total_feature.mean()\n # std = total_feature.std()\n # total_feature = (total_feature - mean)/std\n print(head_features.shape[1], gaze_features.shape[1], body_motion_features.shape[1], hand_features.shape[1],\n foot_features.shape[1], object_hog_feature.shape[1], gaze_hog_feature.shape[1])\n features[i] = total_feature\n with open(save_folder + clip + '.p', 'wb') as f:\n pickle.dump(features, f)\n\n\nif __name__ == \"__main__\":\n feature_extractor = FeatureExtractor()\n feature_extractor.load_file()","sub_path":"src/data_processing_scripts/skeleton_feature_extractor.py","file_name":"skeleton_feature_extractor.py","file_ext":"py","file_size_in_byte":40431,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"368154841","text":"#\r\n# Copyright (c) 2019-2020, RTE (https://www.rte-france.com)\r\n# See AUTHORS.txt\r\n#\r\n# This Source Code Form is subject to the terms of the Apache License, version 2.0.\r\n# If a copy of the Apache License, version 2.0 was not distributed with this file,\r\n# you can obtain one at http://www.apache.org/licenses/LICENSE-2.0.\r\n# SPDX-License-Identifier: Apache-2.0\r\n#\r\n# This file is part of [R#SPACE], [IEC61850 Digital Contronl System testing.\r\n#\r\n\r\n\r\nimport xml.dom.minidom as dom\r\nimport time\r\nfrom IEC_FileListe import FileListe as FL\r\n\r\nfrom IEC_LNodeType import Parse_LNodeType\r\nfrom IEC_DOType import Parse_DOType\r\nfrom IEC_DAType import Parse_DAType\r\nfrom IEC_EnumType import Parse_EnumType\r\nfrom IEC_Services import Test_Services\r\nfrom IEC_Communication import ParseCommunication\r\nfrom IEC_IED_Server import Parse_Server\r\n\r\nfrom IEC_Trace import Trace as TConsole\r\nfrom IEC_Trace import Level as TL\r\nfrom IEC_TypeSimpleCheck import Check\r\n\r\nfrom IEC61850_XML_Class import DataTypeTemplates as IecType\r\n\r\n\r\nglobal SEP1\r\nglobal SEP2\r\n\r\nSEP1 = '$' # Standard MMS separator.\r\nSEP2 = '/' # MMS Separator for system testing\r\n\r\n##\r\n# \\b IECda: handle a single final data attribute\r\n#\r\n# This class is used to store information about a DA with its value adn MMS adress\r\n# The constructor is mainly building the MMS Adress of the DA, so that it can be\r\n# used by ACSI services. In particular, it placing the FC at the appropriate place.\r\n#\r\n#\r\n# @param Texte - only for debugging purpose, it precises the type of data..\r\n# @param _mmAdr the MMS address as built by BrowseTypeSimple\r\n# @param _fc the FC of the data point\r\n# @param _BasicType the IEC basicType as defined in DataTypeTemplates.bType\r\n# (excluding Enum and Struct which need some specific treatment).\r\n# @param _EnumpType in case on Enum, the enum type (will be used to check Enum range)\r\n# @param _Value The expected value of the data point (from the type definition or from DAi)\r\n# @param _Valkind The 'valKind' associated to the DA.\r\n#\r\nclass IECda:\r\n def __init__(self, Texte, _mmsAdr, _u_mmsAdr , _fc, _BasicType, _EnumType, _Value, _Valkind):\r\n self.fc = _fc ## Functional Constrain\r\n self.BasicType = _BasicType ## 'bType' see IEC61850_XML_Class (enum and struct are not treated as bType)\r\n self.EnumType = _EnumType ## EnumType\r\n self.TypeValue = _Value ## Value from DATA TYPE DEFINITION\r\n self.ValKind = _Valkind ## ValKlind from DATA TYPE DEFINITION\r\n self.IntAdr = '' ## Internal addresse (without the FC), to get a direct access to the local data model.\r\n self.mmsAdr = _mmsAdr ## mms adress with the FC, using SEP1, the FC is before the LN_ID (LN_Prefix_LN_Class_\r\n self.u_mmsAdr = _u_mmsAdr ## mms adress for U_TEST with with the FC, using SEP1, the FC is before the LN_ID (LN_Prefix_LN_Class_\r\n# if _Value == '__None__':\r\n# self.TypeValue = None\r\n\r\n if _BasicType == \"Quality\" or _BasicType == \"Timestamp\":\r\n self.IntAdr = None\r\n\r\n mmsAdrSplit = _mmsAdr.split(SEP1)\r\n self.mmsAdr = mmsAdrSplit[1]+SEP1+ mmsAdrSplit[2]+SEP1 + mmsAdrSplit[0] # LDName$LN$FC.\r\n self.IntAdr = mmsAdrSplit[1]+'.'+ mmsAdrSplit[2] # Exclude FC.\r\n\r\n for i in range(3, len(mmsAdrSplit)):\r\n field = mmsAdrSplit[i]\r\n self.mmsAdr=self.mmsAdr + SEP1 + field # mmsAdrSplit[i]\r\n self.IntAdr=self.IntAdr + '.' + field # mmsAdrSplit[i]\r\n\r\n if _BasicType in IecType.bType.Simple:\r\n iecType = _BasicType\r\n else:\r\n if _EnumType is not None:\r\n iecType = \"Enum: \"+_EnumType\r\n\r\n##\r\n#\r\n# \\b globalDataModel Collect all Data Type Templates (LnodeType, DoType, Datype, EnumType)\r\n#\r\n# Function Hierarchy\r\n# - LoadSCLModel - Invoke all class/Method to build the full DataModel\r\n# - getIED_withComm - Browse IED and AccessPoint, get the IP from communication part and store it in the AccessPoint Class\r\n# - BrowseDataModel - for a IED instance, browse all AccessPoint and Logical Device\r\n# - BrowseDataModel_LD - for a Logical Device instance, browse all LN and their DO\r\n# - BrowseDA - for a given DO, SDO, BDA, browse the DA\r\n# - BrowseTypeSimple - for a given DA if a type is 'final' build the MMS adresses, otherwise recusively call BrowseDA\r\n# - TraceDataPoint - For debugging purpose, trace to the console the details of a given data point\r\n# - GetIPAddress - Search the IP adresse of a given IED/AccessPoint\r\n#\r\n# @param _TR _TR instance of the trace system\r\n# @param file SCL file to use (SCD, IID, ICD, ...)\r\n\r\nclass globalDataModel:\r\n def __init__(self, _TR, file, _SCL):\r\n self.TR = _TR ##_TR instance of the trace system\r\n self.tIED = []\r\n\r\n self.scl = self.LoadSCLModel(file, _SCL)\r\n\r\n\r\n DataType = self.scl.getElementsByTagName(\"DataTypeTemplates\")\r\n\r\n self.LNode = Parse_LNodeType(self.scl,self.TR) # class init\r\n self.LNode.Create_LNodeType_Dict(DataType) # Create the 'dictionary' of LNODE\r\n\r\n self.DOType = Parse_DOType(self.scl, self.TR) # class init\r\n self.DOType.Create_DOType_Dict(DataType) # Create the 'dictionary' of DoType\r\n\r\n self.DAType = Parse_DAType(self.scl, self.TR)\r\n self.DAType.Create_DAType_Dict(DataType)\r\n\r\n self.EnumType = Parse_EnumType(self.scl , self.TR)\r\n self.EnumType.Create_EnumType_Dict(DataType)\r\n ##\r\n # \\b LoadSCLModel\r\n #\r\n # Load the SCL file and parse the communication structure\r\n #\r\n # @param file the SCL file name, without the directory.\r\n #\r\n\r\n def LoadSCLModel(self, fileName, _SCL):\r\n t0 = time.time()\r\n if _SCL is None:\r\n _SCL = dom.parse(fileName)\r\n t1 = time.time()\r\n deltaT = t1 - t0\r\n self.TR.Trace(\"Time to load the SCL file: \" + fileName + ':' + str(deltaT),TL.GENERAL)\r\n\r\n self.tIED = self.getIED_withComm(_SCL) # Expect a file list\r\n\r\n comm = _SCL.getElementsByTagName(\"Communication\")\r\n subNetWork = ParseCommunication(comm, self.TR)\r\n tNetWork = subNetWork.ParseCommSection(comm) # <SubNetWork>\r\n tServices = Test_Services.main('', fileName, _SCL)\r\n\r\n return _SCL\r\n\r\n #<IED name=\"AUT1A_SITE_1\" type=\"..\" manufacturer=\"...\" configVersion=\"...\" originalSclVersion=\"2007\" ...>\r\n # #\t\t<AccessPoint name=\"ADMINISTRATION_AP\"></AccessPoint>\r\n # #\t\t<AccessPoint name=\"PROCESS_AP\"></AccessPoint>\r\n\r\n #< Communication >\r\n # < SubNetwork type = \"8-MMS\" name = \"RSPACE_PROCESS_NETWORK\" >\r\n # < ConnectedAP iedName = \"AUT1A_SITE_1\" apName = \"PROCESS_AP\" redProt = \"prp\" >\r\n\r\n ##\r\n # \\b getIED_withComm Browse IED and AccessPoint hierachy, get the IP from communication part and store it in the AccessPoint Class\r\n #\r\n # @param scl The scl as loaded in memory\r\n\r\n def getIED_withComm(self, scl):\r\n\r\n comm = scl.getElementsByTagName(\"Communication\")\r\n tIEDNet = ParseCommunication(scl, self.TR) # Analyse de la section <Communication>\r\n tNetWork = tIEDNet.ParseCommSection(comm) # <SubNetWork>\r\n # <ConnectedAP...>\r\n ## Gather information on server from the data model aspect\r\n tIEDComm = Parse_Server(scl, self.TR) # IED / SERVER / LD\r\n tIED = tIEDComm.Parse_IED(self.TR)\r\n\r\n self.TR.Trace((\"nombre de IED/server: \"+str(len(tIED))) , TL.DETAIL) # 42\r\n self.TR.Trace((\"nombre de subnetWork: \"+str(len(tNetWork))) , TL.DETAIL)\r\n\r\n # Supposition: un server par IED ! # TODO extend to n Server\r\n for i in range (0,len(tIED)):\r\n iedNameSrv = tIED[i].name # Nom de l'IED dans la partie IED\r\n apNameSrv = tIED[i].tAccessPoint[0].name # Nom de l'access point dans la partie IED\r\n # Recherche de l'addresse IP dans la partie réseau\r\n\r\n for j in range(0,len(tNetWork)): # Level 1: browsin 'subNetWork' (Ex: STATION-BUS / PROCESS-BUS\r\n name = tNetWork[j].name # network name\r\n\r\n # Il faut que tAdress ne soit pas 'None'.\r\n iNetwork = tNetWork[j] # Instance du réseau\r\n for k in range(0,len(iNetwork.tConnectedAP)): # Niveau 2: parcours des ConnectedAP\r\n\r\n iedNameAP = iNetwork.tConnectedAP[k].iedName\r\n apName = iNetwork.tConnectedAP[k].apName\r\n\r\n if (iedNameAP==iedNameSrv) and (apNameSrv==apName): # Trouvé l'IED et l'access Point ?\r\n if iNetwork.tConnectedAP[k].tAddress is not None:\r\n IP= self.GetIPAddress(iNetwork.tConnectedAP[k].tAddress)\r\n if IP is None:\r\n IP='0.0.0.0' # Cas des template IED: pas d'adresse IP\r\n tIED[i].tAccessPoint[0].tServer[0].IP = IP\r\n tIED[i].IP = IP\r\n tIED[i].tAccessPoint[0].tServer[0].tAddress = iNetwork.tConnectedAP[k].tAddress\r\n self.TR.Trace((\"iedNameAP:\" + iedNameAP + \"iedNameSRV:\"+iedNameSrv +\" , apName:\" + apName + \" adresse IP:\" + IP), TL.DETAIL)\r\n break\r\n self.tIED = tIED\r\n return tIED\r\n ##\r\n #\r\n # \\b BrowseDataModel\r\n #\r\n # Going through all AccessPoint, Servers and logical device\r\n #\r\n # @param IEDinstance the IED concerned\r\n # @return tIEC_adresse the table of IEC/MMS adresse (empty at this stage)\r\n\r\n def BrowseDataModel(self, IEDinstance):\r\n\r\n tIEC_adresse=[]\r\n IEDName = IEDinstance.name\r\n\r\n for i in range (len(IEDinstance.tAccessPoint)):\r\n for j in range (len(IEDinstance.tAccessPoint[i].tServer)):\r\n NbLdevice = len(IEDinstance.tAccessPoint[i].tServer[j].tLDevice)\r\n for k in range(NbLdevice): # Browsing all LDevice of one IED\r\n LD = IEDinstance.tAccessPoint[i].tServer[j].tLDevice[k]\r\n tIEC_adresse= self.BrowseDataModel_LD(tIEC_adresse, IEDName, LD)\r\n\r\n return tIEC_adresse\r\n\r\n ##\r\n #\r\n # \\b BrowseDataModel_LD\r\n #\r\n # Going through all LN and DO of an IED\r\n #\r\n # @param tIEC_adresse the table of IEC/MMS address for a given IED\r\n # @param IEDName the name of IED concerned\r\n # @param LD the logical device to be parsed\r\n # @return tIEC_adresse the table of IEC/MMS filled.\r\n def BrowseDataModel_LD(self, tIEC_adresse, IEDName, LD):\r\n for j in range(len(LD.LN)): # Browsing LN du LDEVICE\r\n LN = LD.LN[j]\r\n txtLN = LD.LN[j].lnPrefix + LD.LN[j].lnClass + LD.LN[j].lnInst\r\n self.TR.Trace((\"Browsing LD:\" + LD.inst + \" LN:\" + txtLN ) , TL.GENERAL)\r\n LNodeType = self.LNode.getIEC_LNodeType(LN.lnType) # Look-up for LNType\r\n if (LNodeType) is None:\r\n self.TR.Trace((\"Missing LNodeType, IED Name\" + IEDName + \" LN Type \" + LN.lnType),TL.ERROR)\r\n# exit(-1)\r\n else:\r\n LD.LN[j].tDO = LNodeType.tDO\r\n #TODO traiter le cas ou on le trouve pas !!!\r\n for k in range(len(LNodeType.tDO)): # Browsing DO\r\n DO = LN.tDO[k]\r\n iDO = self.DOType.getIEC_DoType(DO.type) # Look-up for DO Type\r\n tDA = iDO.tDA\r\n## No need for IEDName DO_Name = IEDName + SEP1 + LD.inst + SEP1 + LN.lnPrefix + LN.lnClass + LN.lnInst + SEP1 + LN.tDO[k].name\r\n DO_Name1 = LD.inst + SEP1 + LN.lnPrefix + LN.lnClass + LN.lnInst + SEP1 + LN.tDO[k].name\r\n DO_Name2 = LD.inst + SEP2 + LN.lnPrefix + LN.lnClass + LN.lnInst + SEP2 + LN.tDO[k].name\r\n self.BrowseDA(tIEC_adresse, DO_Name1, DO_Name2, tDA)\r\n\r\n return(tIEC_adresse)\r\n\r\n ##\r\n #\r\n # \\b BrowseDA\r\n #\r\n # Going through all the DA of a given DO\r\n #\r\n # @param tIEC_adresse the table of all IEC/mms adresses for one IED\r\n # @param DO_Name Name of the DO\r\n # @param DA instance of a DA\r\n # @param fc Functional Constraint of the data point\r\n def BrowseDA(self, tIEC_adresse, DO_Name1 , DO_Name2, DA):\r\n\r\n if DA is None:\r\n self.TR.Trace((\"DA is NONE in BrowseDA DO_Name\"+DO_Name1),TL.ERROR)\r\n return\r\n\r\n for i in range(len(DA)): # Browse des DA composants le DO.\r\n type1 = DA[i].type\r\n bType1 = DA[i].bType\r\n value = DA[i].value\r\n valKind = DA[i].valKind\r\n FC = DA[i].fc\r\n try:\r\n count = DA[i].count\r\n except:\r\n count = ''\r\n\r\n if count is not None and count !='':\r\n cpt = int(count)\r\n for j in range(0,cpt):\r\n DataName1 = FC + SEP1 + DO_Name1 + SEP1 + DA[i].name + str(j)\r\n DataName2 = DO_Name2 + SEP2 + DA[i].name + str(j) + '['+ FC + ']'\r\n bType2 = self.BrowseTypeSimple(tIEC_adresse, type1, bType1, i, DataName1, DataName2, FC, DA, value, valKind )\r\n else:\r\n DataName1 = FC + SEP1 + DO_Name1 + SEP1 + DA[i].name\r\n DataName2 = DO_Name2 + SEP2 + DA[i].name + '['+ FC + ']'\r\n bType2 = self.BrowseTypeSimple(tIEC_adresse, type1, bType1, i, DataName1, DataName2, FC, DA, value, valKind)\r\n\r\n ##\r\n #\r\n # \\b BrowseTypeSimple\r\n #\r\n # Recursive function which browse a given DA, down the 'stVal', 'q' / 't' depth.\r\n #\r\n # @param tIEC_adresse the table of all IEC/mms adresses for one IED\r\n # @param DA_Type Data Attribute level, DA_name (depends on the DO complexity...)\r\n # @param bType2 Data Type or Struct field\r\n # @param bType Data Type or Struct field\r\n # @param idx Index for the table of IEC adresses\r\n # @param DataName\r\n # @pamra fc Functional Constraint of the data point\r\n # @param DA\r\n # @param value Actual if defined by SCL or type declaration\r\n # @param valkind What actions are possible on the data or not.\r\n def BrowseTypeSimple(self, tIEC_adresse, DA_type, bType, idx, DataName1, DataName2, fc, DA, value, valKind):\r\n\r\n if bType in IecType.bType.Simple: # Type de base ?\r\n _iecAdr = IECda(\"Simple-0 : \", DataName1, DataName2, fc, bType, None, value, valKind)\r\n tIEC_adresse.append(_iecAdr)\r\n return None\r\n\r\n elif (bType == 'Struct'):\r\n DA_type = DA[idx].type\r\n SDA = self.DAType.getIEC_DaType(DA_type)\r\n\r\n for m in range(len(SDA.tBDA)):\r\n bType2 = SDA.tBDA[m].type\r\n if bType2 in IecType.bType.Simple:\r\n BaseName1 = DataName1 + SEP1 + SDA.tBDA[m].name\r\n BaseName2 = DataName2 + SEP2 + SDA.tBDA[m].name\r\n _iecAdr = IECda(\"Simple-xx-1 : \", BaseName1, BaseName2, fc, bType2, None, value, valKind)\r\n tIEC_adresse.append(_iecAdr)\r\n continue\r\n\r\n elif (bType2 == 'Struct'):\r\n DA = self.DAType.getIEC_DaType(DA_type) # DataName SDA.BDA_lst[m].bType)\r\n### self.BrowseDA(tIEC_adresse, DataName, DA, None)\r\n for n in range(len(DA.tBDA)):\r\n DA1 = DA.tBDA[n]\r\n type1 = DA1.type\r\n bType1 = DA1.bType\r\n bName = DA1.name\r\n bValue = DA1.value\r\n bValKind = DA1.valKind\r\n BaseName1 = DataName1 + SEP1 + DA1.name\r\n BaseName2 = DataName2 + SEP2 + DA1.name\r\n\r\n if (DA1.type in IecType.bType.Simple):\r\n# BaseName1 =\r\n# BaseName2\r\n _iecAdr = IECda(\"Simple-3 : \", BaseName1, BaseName2, fc, DA1.type, None, bValue, bValKind)\r\n tIEC_adresse.append(_iecAdr)\r\n elif (DA1.type == 'Enum'):\r\n _iecAdr = IECda(\"Enum-2 : \", BaseName1, BaseName2, fc, DA1.bType, type1, bValue, bValKind)\r\n tIEC_adresse.append(_iecAdr)\r\n elif (DA1.type == 'Struct'):\r\n DA2 = self.DAType.getIEC_DaType(DA1.bType)\r\n for k in range(len(DA2.tBDA)):\r\n DataName3 = BaseName1 + SEP1 + DA2.tBDA[k].name\r\n DataName4 = BaseName2 + SEP2 + DA2.tBDA[k].name\r\n bValue = DA2.tBDA[k].value\r\n bValKind = DA2.tBDA[k].valKind\r\n _iecAdr = IECda(\"Struct-3 : \", DataName3, DataName4, fc, DA2.tBDA[k].type, DA2.tBDA[k].bType,\r\n bValue, bValKind)\r\n tIEC_adresse.append(_iecAdr)\r\n continue\r\n elif (bType2 == 'Enum'):\r\n DataName1 = DataName1 + SEP1 + SDA.tBDA[m].name\r\n DataName2 = DataName2 + SEP2 + SDA.tBDA[m].name\r\n bValue = SDA.tBDA[m].value\r\n bValKind = SDA.tBDA[m].valKind\r\n _iecAdr = IECda(\"Enum-1 : \", DataName1, DataName2, fc, SDA.tBDA[m].type, SDA.tBDA[m].bType, bValue,\r\n bValKind)\r\n tIEC_adresse.append(_iecAdr)\r\n continue\r\n else:\r\n self.TraceDataPoint(\"ELSE :\", DA_type, bType, bType2, DataName, fc)\r\n DA = self.DAType.getIEC_DaType(bType2)\r\n self.BrowseDA(tIEC_adresse, DataName1, DataName2, DA)\r\n return None\r\n elif (bType == 'Enum'):\r\n bValue = DA[idx].value\r\n bValKind = DA[idx].valKind\r\n _iecAdr = IECda(\"Enum-0 : \", DataName1, DataName2, fc, bType, DA[idx].type, bValue, bValKind)\r\n tIEC_adresse.append(_iecAdr)\r\n return None\r\n\r\n elif (bType is not None):\r\n\r\n DO = self.DOType.getIEC_DoType(bType)\r\n if DO is not None:\r\n self.BrowseDA(tIEC_adresse, DataName1, DataName2, DO.tDA)\r\n else:\r\n DA = self.DAType.getIEC_DaType(bType)\r\n self.BrowseDA(tIEC_adresse, DataName1, DataName2, DA)\r\n\r\n ##\r\n #\r\n # \\b TraceDataPoint\r\n #\r\n # For debugging purpose, trace the details of a given data point\r\n #\r\n # @param header Text like Struct-1 , Enum-2, Simple...\r\n # @param file SCL file to use (SCD, IID, ICD, ...)\r\n # @param DA_Type Data Attribute level, DA_name (depends on the DO complexity...)\r\n # @param bType2 Data Type or Struct field\r\n # @param bType Data Type or Struct field\r\n # @param DataName\r\n # @pamra fc Functional Constraint of the data point\r\n\r\n def TraceDataPoint(self,header, DA_type, bType2, bType, DataName, fc):\r\n self.TR.Trace((header + \" DA_type:\" + DA_type + ', bType:' + bType + ', bType2:' + bType2+\r\n ', DataName: ' + DataName + ', FC:' + fc), TL.GENERAL)\r\n\r\n ##\r\n # \\b GetIPAddress(tAddress):\r\n #\r\n # Look_up for an IP address in a ConnectedAP structure\r\n #\r\n # @param tAddress Table of network addresses ConnectedAP.PhysConn.PType\r\n #\r\n def GetIPAddress(self, tAddress):\r\n for i in range (0,len(tAddress)):\r\n self.TR.Trace((\"tAddress.type:\"+tAddress[i].type + \"tAddress.value=\"+tAddress[i].value),TL.DETAIL)\r\n if (tAddress[i].type==\"IP\"):\r\n return(tAddress[i].value)\r\n return(None)\r\n\r\n\r\n\r\n# def CheckDatapointSCL(self, iec):\r\n# bType = iec.BasicType\r\n# if iec.TypeValue != None:\r\n# if bType != None:\r\n# if bType == 'Enum':\r\n# iEnum = GM.EnumType.getIEC_EnumType(iec.EnumType)\r\n# Check.Enum(iEnum, iec.TypeValue)\r\n# # TODO Trace\r\n# else:\r\n# Check.Type(bType, iec.TypeValue)\r\n\r\n##\r\n# \\b Test_DOType: unitary test for Test_ParcoursDataModel\r\nclass Test_ParcoursDataModel:\r\n def main(directory, file, scl):\r\n\r\n TX = TConsole.File(TL.GENERAL, \"dump data.txt\")\r\n TR = TConsole.Console(TL.GENERAL)\r\n GM = globalDataModel(TX, directory + file, scl)\r\n iec_BasicType = ''\r\n iec_TypeValue = ''\r\n iec_EnumType = ''\r\n\r\n\r\n T0_Global = time.time()\r\n for ied in GM.tIED:\r\n t0_ied = time.time()\r\n tIEC_adresse = GM.BrowseDataModel(ied)\r\n if ied.IP is None:\r\n ip = '0.0.0.0'\r\n else:\r\n ip = ied.IP\r\n nbDa = len(GM.tIED)\r\n Resultat = str(time.time() - t0_ied)\r\n TX.Trace((\"Time for IED:\" + ied.name + '(' + ip + \") Number of DA:\" + str(nbDa) + \"Time\" + Resultat),TL.GENERAL)\r\n\r\n index = 0\r\n IED_ID = ied.name + ied.tAccessPoint[0].name # ied.name # TODO ou ied.name+AP_Name\r\n for iec in tIEC_adresse:\r\n\r\n if iec.BasicType is None:\r\n iec_BasicType = \" - \"\r\n if iec.TypeValue is None:\r\n iec_TypeValue = \" - \"\r\n if iec.EnumType is None:\r\n iec_EnumType = \" - \"\r\n\r\n TX.Trace(( \"MMS_ADR\" + iec.mmsAdr + \" FC:\" + iec.fc + \" bType:\" + iec_BasicType + \" EnumType:\" + iec_EnumType + \"Value:\" + iec_TypeValue + \"\\n\" ), TL.GENERAL)\r\n index = index + 1\r\n T1 = time.time()\r\n TempsTotal = str(time.time() - T0_Global)\r\n TX.Close()\r\n TR.Trace((\"Total execution time\" + file + ':' + TempsTotal) ,TL.GENERAL)\r\n TR.Trace((\"*** finished ***\"),TL.GENERAL)\r\n\r\n##\r\n# \\b MAIN call the unitary test 'Test_ParcoursDataModel'\r\nif __name__ == '__main__':\r\n fileliste = FL.lstFull # System level file list\r\n for file in fileliste:\r\n Test_ParcoursDataModel.main('SCL_files/', file, None)\r\n\r\n fileliste = FL.lstIED # IED level file list\r\n for file in fileliste:\r\n Test_ParcoursDataModel.main('SCL_files/', file, None)\r\n\r\n","sub_path":"IEC_ParcoursDataModel.py","file_name":"IEC_ParcoursDataModel.py","file_ext":"py","file_size_in_byte":23261,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"166882231","text":"import os\r\nimport datetime as datetime\r\nfrom werkzeug.utils import secure_filename\r\nimport config as config\r\n\r\ndef allowed_photo(filename):\r\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in config.ALLOWED_PHOTO_EXTENTIONS\r\n\r\ndef allowed_document(filename):\r\n return '.' in filename and filename.rsplit('.', 1)[1].lower() in config.ALLOWED_DOCUMENT_EXTENTIONS\r\n\r\ndef upload_file(file, file_type):\r\n if file.filename == '':\r\n return {\r\n 'status':'BAD REQUEST',\r\n 'message':'NO FILE SELECTED'\r\n }\r\n if (file_type == 'IMG' and allowed_photo(file.filename)) or (file_type == 'DOC' and allowed_document(file.filename)):\r\n filename = secure_filename(file.filename)\r\n # upload_time = datetime.datetime.timestamp(datetime.datetime.now())\r\n # filename = str(upload_time) + '_' + filename\r\n if(file_type == 'IMG'):\r\n file.save(os.path.join(config.PHOTOS_UPLOAD_FOLDER, filename))\r\n else:\r\n file.save(os.path.join(config.DOCUMENTS_UPLOAD_FOLDER, filename))\r\n # return redirect('/dashboard')\r\n return {\r\n 'status':'OK',\r\n 'message':'FILE SAVED',\r\n 'filename':filename,\r\n # 'upload_time':upload_time\r\n }\r\n else:\r\n return {\r\n 'status':'BAD REQUEST',\r\n 'message':'INVALID FILE'\r\n }\r\n","sub_path":"my_routes/upload_file.py","file_name":"upload_file.py","file_ext":"py","file_size_in_byte":1393,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"438669728","text":"import matplotlib.pyplot as plt\n\nx_values = list(range(1001))\ny_values = [x**2 for x in x_values]\n# 绘制散点图，设置点的颜色为(0, 0, 0.8),点的外围颜色为黑色，指定点的大小为40\n# plt.scatter(x_value, y_value, c=(0, 0, 0.8), edgecolors='black', s=40)\n# 使用颜色映射\nplt.scatter(x_values, y_values, c=y_values, cmap=plt.cm.Blues, edgecolors='none', s=40)\n\n# 设置标题并给坐标轴加上标签\nplt.title(\"Square Number\", fontsize=24)\nplt.xlabel(\"Value\", fontsize=14)\nplt.ylabel(\"Square of Value\", fontsize=24)\n\n# 设置刻度标记的大小\nplt.tick_params(axis='both', which='major', labelsize=14)\n\n# 自动保存图表\nplt.savefig('squares_plot.png', bbox_inches='tight')\n\nplt.show()","sub_path":"matplotlib_practice/mat_scatter.py","file_name":"mat_scatter.py","file_ext":"py","file_size_in_byte":721,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"516609172","text":"import pygame\npygame.init()\n\nwindow = pygame.display.set_mode((1150, 670))\npygame.display.set_caption(\"Spy\")\n\nwalkRight = [pygame.image.load('R1.png'), pygame.image.load('R2.png'), pygame.image.load('R3.png'),\n pygame.image.load('R4.png')]\nwalkLeft = [pygame.image.load('L1.png'), pygame.image.load('L2.png'), pygame.image.load('L3.png'),\n pygame.image.load('L4.png')]\nbg = pygame.image.load('bg(1200*675).png')\nstand = pygame.image.load('R1.png')\n\nclock = pygame.time.Clock()\n\nscore = 0\n\n\nclass Player(object):\n def __init__(self, x, y, width, height):\n self.x = x\n self.y = y\n self.width = width\n self.height = height\n self.move = 7\n self.jumping = False\n self.jumpHeight = 8\n self.left = False\n self.right = False\n self.walkCount = 0\n self.standing = True\n self.hitbox = (self.x + 28, self.y, 35, 50)\n\n def draw(self, window):\n if self.walkCount + 1 > 8:\n self.walkCount = 0\n\n if not(self.standing):\n if self.right:\n window.blit(walkRight[self.walkCount // 2], (self.x, self.y))\n self.walkCount += 1\n elif self.left:\n window.blit(walkLeft[self.walkCount // 2], (self.x, self.y))\n self.walkCount += 1\n\n else:\n if self.left:\n window.blit(walkLeft[0], (self.x, self.y))\n else:\n window.blit(walkRight[0], (self.x, self.y))\n self.hitbox = (self.x + 17, self.y + 10, 78, 140)\n\n\nclass Enemy(object):\n walkRight = [pygame.image.load('RE1'), pygame.image.load('RE2'), pygame.image.load('RE3'),\n pygame.image.load('RE4'), pygame.image.load('RE5'), pygame.image.load('RE6')]\n walkLeft = [pygame.image.load('LE1'), pygame.image.load('LE2'), pygame.image.load('LE3'),\n pygame.image.load('LE4'), pygame.image.load('LE5'), pygame.image.load('LE6')]\n\n def __init__(self, x, y, width, height, end):\n self.x = x\n self.y = y\n self.width = width\n self.height = height\n self.end = end\n self.walkCount = 0\n self.speed = 5\n self.path = [self.x, self.end]\n self.hitbox = (self.x + 20, self.y, 28, 60)\n self.health = 2\n self.visible = True\n\n def draw(self, window):\n self.move()\n if self.visible:\n if self.walkCount + 1 >= 16:\n self.walkCount = 0\n\n if self.speed > 0:\n window.blit(self.walkRight[self.walkCount // 3], (self.x, self.y))\n self.walkCount += 1\n else:\n window.blit(self.walkLeft[self.walkCount // 3], (self.x, self.y))\n self.walkCount += 1\n self.hitbox = (self.x + 15, self.y + 12, 50, 85)\n pygame.draw.rect(window, (255, 0, 0), (self.hitbox[0], self.hitbox[1] - 30, 50, 10))\n pygame.draw.rect(window, (0, 255, 0), (self.hitbox[0], self.hitbox[1] - 30, 50 - ((50/3) * (2 - self.health)), 10))\n\n def move(self):\n if self.speed > 0:\n if self.x + self.speed < self.path[1]:\n self.x += self.speed\n else:\n self.speed = self.speed * -1\n self.walkCount = 0\n else:\n if self.x - self.speed > self.path[0]:\n self.x += self.speed\n else:\n self.speed = self.speed * -1\n self.walkCount = 0\n\n def hit(self):\n if self.health > 0:\n self.health -= 1\n else:\n self.health -= 1\n self.visible = False\n print(\"HIT!\")\n\n\nclass Weapon(object):\n def __init__(self, x, y, radius, color, direction):\n self.x = x\n self.y = y\n self.radius = radius\n self.color = color\n self.direction = direction\n self.speed = 20 * direction\n\n def draw(self, window):\n pygame.draw.circle(window, self.color, (self.x, self.y), self.radius)\n\n\ndef update_game_window():\n window.blit(bg, (0, 0))\n spy.draw(window)\n soldier.draw(window)\n text = font.render('Score: ' + str(score), 1, (5, 5, 5))\n window.blit(text, (0, 10))\n\n for bullet in bullets:\n bullet.draw(window)\n\n pygame.display.update()\n\n\nfont = pygame.font.SysFont('comicsans', 40, True, True)\nspy = Player(300, 410, 64, 64)\nsoldier = Enemy(100, 475, 64, 64, 1000)\nbullets = []\nrun = True\nwhile run:\n clock.tick(40)\n\n for event in pygame.event.get():\n if event.type == pygame.QUIT:\n run = False\n\n for bullet in bullets:\n if bullet.y - bullet.radius < soldier.hitbox[1] + soldier.hitbox[3] and bullet.y + bullet.radius > soldier.hitbox[1]:\n if bullet.x + bullet.radius > soldier.hitbox[0] and bullet.x - bullet.radius < soldier.hitbox[0] + soldier.hitbox[2]:\n soldier.hit()\n score += 1\n if soldier.health < 0:\n soldier = Enemy(0, 0, 0, 0, 0)\n soldier.visible = False\n bullets.pop(bullets.index(bullet))\n\n if 15 < bullet.x < 1145:\n bullet.x += bullet.speed\n else:\n bullets.pop(bullets.index(bullet))\n\n buttom = pygame.key.get_pressed()\n\n if buttom[pygame.K_SPACE]:\n if spy.right:\n direction = 1\n elif spy.left:\n direction = -1\n else:\n direction = 1\n\n if len(bullets) < 1:\n bullets.append(Weapon(round(spy.x + spy.width), round(spy.y + spy.height // 0.73), 5, (0, 100, 200), direction))\n\n if buttom[pygame.K_LEFT] and spy.x > spy.move:\n spy.x -= spy.move\n spy.left = True\n spy.right = False\n spy.standing = False\n\n elif buttom[pygame.K_RIGHT] and spy.x < 1140 - spy.move - spy.width:\n spy.x += spy.move\n spy.left = False\n spy.right = True\n spy.standing = False\n else:\n spy.standing = True\n spy.walkCount = 0\n\n if not spy.jumping:\n if buttom[pygame.K_UP]:\n spy.jumping = True\n spy.right = False\n spy.left = False\n spy.walkCount = 0\n else:\n if spy.jumpHeight >= -8:\n neg = 1\n if spy.jumpHeight < 0:\n neg = -1\n spy.y -= (spy.jumpHeight ** 2) / 2 * neg\n spy.jumpHeight -= 1\n else:\n spy.jumping = False\n spy.jumpHeight = 8\n\n update_game_window()\n\npygame.quit()\n","sub_path":"SpyGame.py","file_name":"SpyGame.py","file_ext":"py","file_size_in_byte":6493,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"243507887","text":"\"\"\"\nreverse: Reverse the list.\"\"\"\n\n\ndef insert_list(lst, i, e):\n # print(\"inside insert: inserting\", e, \"at position\", i, \"current list len\", len(lst))\n if i >= len(lst):\n lst.append(e)\n new_list = lst\n # print(\"inside appending at end of the list\")\n else:\n new_list = lst[0:i] + [e] + lst[i:len(lst)]\n # print(\"returning \", new_list)\n return new_list\n\n\ndef print_list(lst):\n print(lst)\n\n\ndef move_elems(lst, start_pos):\n for x in range(start_pos, len(lst)):\n if x == len(lst) - 1:\n lst[x] = \"\"\n else:\n lst[x] = lst[x + 1]\n return lst\n\n\ndef remove_lst(lst, e):\n # print(\"from remove list\", lst)\n result = []\n for idx in range(0, len(lst)):\n if lst[idx] == e:\n # print(\"element found\")\n result = lst[0:idx]+lst[idx+1:len(lst)]\n # print(\"printing modified result\")\n break\n # print(\"returning \", result)\n return result\n\ndef append_list(lst, elem):\n # print(\"inside append\", lst)\n if len(lst) > 0:\n # print(\"inside > 0\")\n lst.append(elem)\n new_list = lst\n # print(new_list)\n else:\n new_list = [elem]\n # print(\"returning \", new_list)\n return new_list\n\n\ndef sort_list(lst):\n # print(\"returning\", sorted(lst))\n return sorted(lst)\n\n\ndef pop_list(lst):\n lst.pop()\n return lst\n\n\ndef reverse_list(lst):\n return lst[::-1]\n\n\nif __name__ == '__main__':\n final_list = []\n N = int(input())\n for i in range(N):\n command = input().split()\n if command[0] == 'insert':\n final_list = insert_list(final_list, int(command[1]), int(command[2]))\n elif command[0] == 'print':\n print_list(final_list)\n elif command[0] == 'append':\n final_list = append_list(final_list, int(command[1]))\n elif command[0] == 'sort':\n final_list = sort_list(final_list)\n elif command[0] == 'pop':\n final_list = pop_list(final_list)\n elif command[0] == 'reverse':\n final_list = reverse_list(final_list)\n elif command[0] == 'remove':\n final_list = remove_lst(final_list, int(command[1]))\n","sub_path":"Lists.py","file_name":"Lists.py","file_ext":"py","file_size_in_byte":2200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"477414128","text":"#! /usr/bin/env python3\nimport sys\nimport requests\nimport bs4\nimport re\nimport logging\nimport pymongo\nimport threading\nimport queue\nimport threading\nfrom database import JeopardyDatabase\n\nlogger = logging.getLogger(__name__)\nhdlr = logging.FileHandler(\"scraper.log\")\nformatter = logging.Formatter(\"%(asctime)s - %(levelname)s - %(message)s\")\nhdlr.setFormatter(formatter)\nlogger.addHandler(hdlr)\nlogger.setLevel(logging.DEBUG)\n\n\nBASE_URL = \"http://j-archive.com\"\nANSWER_REGEX = re.compile(r'''<em class=\"correct_response\">(.+)</em>''')\nGAME_ID_REGEX = re.compile(r'''.php\\?game_id=(\\d{1,5})''')\n\nclass IncompleteClueError(Exception):\n \"\"\"Raise when a game clue does not\n have: a value AND question AND answer.\n \"\"\"\n pass\n\nclass MalformedBoardHTMLError(Exception):\n \"\"\"Raise when the HTML of a JArchvie gameboard is not properly\n formatted.\n\n The scraper excepts the gameboard to contain 6 category nodes and 30 clue\n nodes. Some Jarchive gameboards contain mismatched tags, usually involving\n nested <a> and <i> tags in the question text of a clue. Beautiful Soup cannot \n properly parse this HTML, so the scraper is unable to process the game.\n \"\"\"\n pass\n\n\nclass ScraperWorker(threading.Thread):\n\n def __init__(self, queue, database):\n threading.Thread.__init__(self)\n self.queue = queue\n self.database = database\n\n def run(self):\n while True:\n game_url = self.queue.get()\n game_rounds = scrape_game(game_url) # game_rounds, final_jeopardy = \n if game_rounds:\n self.database.write_game_rounds(game_rounds)\n game_id = get_game_id(game_url)\n self.database.save_game_id(game_id)\n self.queue.task_done()\n\ndef init_workers(url_queue, database):\n for x in range(8):\n worker = ScraperWorker(url_queue, database)\n worker.daemon = True\n worker.start()\n\ndef remove_html_markup(text):\n return re.sub(r'(<.*?>|\\\\)', '', text)\n\ndef get_game_id(url):\n # TODO: error handling\n try:\n game_id = GAME_ID_REGEX.search(url).group(1)\n except:\n print('Error getting game id from url: {}'.format(url))\n return game_id\n\n\n\ndef get_page_soup(url):\n \"\"\"Return BS4 soup of page at url\"\"\"\n # TODO: This is time consuming step. Print 'getting page' / scraping game message here?\n print('Requesting page at {}...'.format(url))\n \n req = requests.get(url)\n try:\n req.raise_for_status()\n except requests.exceptions.HTTPError:\n print(\"Problem requesting resource at {}\".format(url))\n raise\n soup = bs4.BeautifulSoup(req.text, 'html.parser')\n return soup\n\n\ndef get_game_date(page_soup):\n title = page_soup.select('#game_title h1')[0].text\n return title\n\ndef get_game_boards(page_soup):\n return page_soup.find_all(\"table\", class_ = \"round\")\n\n\ndef get_current_season_number():\n homepage_soup = get_page_soup(BASE_URL)\n current_season_page_href = homepage_soup.find(\"a\")[\"href\"]\n current_season_number = re.search(r'''\\d{1,3}''', current_season_page_href).group() # TODO: error handling\n return current_season_number\n\n\ndef get_season_game_urls(season_number):\n game_hrefs = []\n season_page_url = \"{}/showseason.php?season={}\".format(BASE_URL, season_number)\n season_page_soup = get_page_soup(season_page_url)\n for td_tag in season_page_soup.find_all(\"td\", {\"align\":\"left\", \"style\":\"width:140px\", \"valign\":\"top\"}):\n game_href = td_tag.find(\"a\")[\"href\"]\n game_hrefs.append(game_href)\n return game_hrefs\n\n\ndef parse_clue_value(clue_node):\n value_node = clue_node.find(\"td\", \"clue_value\") or clue_node.find(\"td\", \"clue_value_daily_double\")\n if not value_node:\n return None\n value = value_node.text[1:]\n value = \"DD\" if value.startswith(\"D\") else int(value)\n return value\n\ndef parse_clue_answer(clue_node):\n answer_node = clue_node.find('div')\n try:\n answer = ANSWER_REGEX.search(answer_node['onmouseover']).group(1) # Correct answer in part of inline JavaScript event listener. Raises AttributeError if not found\n except AttributeError: # Search returned None\n return None\n return answer\n\ndef parse_clue_question(clue_node):\n question_node = clue_node.find(\"td\", class_=\"clue_text\")\n if question_node is None:\n return None\n question = question_node.text\n if not question:\n return None\n return question\n \n\ndef scrape_clue_data(clue_node):\n \"\"\"Scrape a clue's value, question, and answer, and return as a dictionary\"\"\"\n value = parse_clue_value(clue_node)\n if value is None:\n raise IncompleteClueError\n question = parse_clue_question(clue_node)\n if question is None:\n raise IncompleteClueError\n answer = parse_clue_answer(clue_node)\n if answer is None:\n raise IncompleteClueError\n question, answer = map(lambda string: remove_html_markup(string), (question, answer))\n clue_data = {\"value\":value, \"question\":question, \"answer\":answer}\n return clue_data\n\n\ndef scrape_category_clues(clue_nodes, categoryindex):\n \"\"\"Scrape a given category's clues from the collection of all game clues\"\"\"\n category_clues = []\n for i in range(categoryindex, categoryindex+30, 6):\n clue_node = clue_nodes[i] # Empty clues are <td class='clue'></td> {'class':'clue_value'}) \n try:\n clue = scrape_clue_data(clue_node)\n except IncompleteClueError:\n return None\n category_clues.append(clue)\n return category_clues\n\n\n\ndef scrape_round(board): \n \"\"\"Scrape a JArchive game round and return as a dict\"\"\"\n game_round = {}\n category_nodes = board.find_all('td', 'category_name') \n clue_nodes = board.find_all('td', class_ = \"clue\")\n if not (len(category_nodes)==6 and len(clue_nodes)==30):\n raise MalformedBoardHTMLError\n category_titles = [c.text for c in category_nodes or None]\n for i,category in enumerate(category_titles): # i is each category's index in the game, from left to right on the board. Used to iterate over all game clues by category.\n if category is not None:\n category_clues = scrape_category_clues(clue_nodes, i) # None returned if category contains an incomplete clue.\n if category_clues is not None:\n game_round[category] = category_clues\n return game_round\n \n\n\n\ndef scrape_game(url):\n \"\"\"Return list of [ {round1dict}, {round2dict}]\"\"\"\n try:\n page_soup = get_page_soup(url)\n except requests.exceptions.HTTPError:\n print(\"Unable to scrape game at {}\".format(url))\n return \n game_boards = get_game_boards(page_soup)\n if not game_boards:\n print(\"Game at {} does not have any rounds. Skipping\".format(url))\n return\n\n game_rounds = []\n for board in game_boards:\n try:\n round_dict = scrape_round(board)\n except MalformedBoardHTMLError:\n continue\n game_rounds.append(round_dict)\n return game_rounds\n\n\ndef begin_scraper(season_number):\n # TODO: new game only mode; quit when reach game already scraped\n try:\n database = JeopardyDatabase(host=\"localhost\", port=27017,dbname=\"jeopardy\", conn_timeout=2)\n except pymongo.errors.ServerSelectionTimeoutError:\n print(\"Aborting...\")\n sys.exit(1)\n url_queue = queue.Queue()\n init_workers(url_queue, database)\n while True: # TODO: limit number to scrape at time before exit/asking to continue\n print(\"Scraping season {}\".format(season_number))\n game_urls = get_season_game_urls(season_number)\n for url in game_urls:\n url_queue.put(url)\n url_queue.join()\n season_number -= 1 # Continue with the previous season.\n\nif __name__ == \"__main__\":\n begin_scraper(32)\n \n\n\n\n\n","sub_path":"scraper/jarchivescraper.py","file_name":"jarchivescraper.py","file_ext":"py","file_size_in_byte":7820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"12221038","text":"import easypost\nfrom rest_framework.views import APIView\nfrom rest_framework.response import Response\nfrom rest_framework import status\nfrom jmi.settings import EASYPOST\n\n\n\nclass APIGetRates(APIView):\n\tdef post(self,request):\n\t\tpost = request.POST\n\t\teasypost.api_key = EASYPOST['test']\n\t\t\n\t\tto_address = easypost.Address.create(\n\t\t\tstreet1= post['address[street]'],\n\t\t\tstreet2= post['address[line_2]'],\n\t\t\tcity= post['address[city]'],\n\t\t\tstate= post['address[state]'],\n\t\t\tzip= post['address[zip_code]'],\n\t\t\tcountry= 'US',\n\t\t\tcompany= post['address[organization]'],\n\t\t)\n\n\t\tfrom_address = easypost.Address.create(\n\t\t\tstreet= 'P.O. Box 1061 ',\n\t\t\tcity= 'Zanesville',\n\t\t\tstate= 'OH',\n\t\t\tzip= '43701'\n\t\t)\t\n\n\t\tparcel={\n\t\t\t\"length\": post['parcel[length]'],\n\t\t \"width\": post['parcel[width]'],\n\t\t \"height\": post['parcel[height]'],\n\t\t \"weight\": post['parcel[weight]']\n\t\t} \n\n\t\t\n\t\tif to_address and from_address and parcel:\n\t\t\tshipment = easypost.Shipment.create(\n\t\t\t to_address=to_address,\n\t\t\t from_address=from_address,\n\t\t\t parcel=parcel,\n\t\t\t)\n\n\t\t\treturn Response(shipment.to_dict())\n\n\n\t\treturn Response('there was a problem in the to address fields')\n","sub_path":"jmi/api/shipment/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1157,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"466613660","text":"# coding:utf-8\nimport os\nimport sys\nimport unittest\nimport argparse\nfrom requests.compat import StringIO, is_py26, str\n\n\nTESTS_ROOT = os.path.dirname(__file__)\nsys.path.insert(0, os.path.realpath(os.path.join(TESTS_ROOT, '..')))\nfrom httpie import __main__\nfrom httpie import cli\n\n\nTEST_FILE = os.path.join(TESTS_ROOT, 'file.txt')\nTERMINAL_COLOR_PRESENCE_CHECK = '\\x1b['\n\n\ndef http(*args, **kwargs):\n http_kwargs = {\n 'stdin_isatty': True,\n 'stdout_isatty': False\n }\n http_kwargs.update(kwargs)\n stdout = http_kwargs.setdefault('stdout', StringIO())\n __main__.main(args=args, **http_kwargs)\n return stdout.getvalue()\n\n\nclass BaseTest(unittest.TestCase):\n\n if is_py26:\n def assertIn(self, member, container, msg=None):\n self.assert_(member in container, msg)\n\n def assertNotIn(self, member, container, msg=None):\n self.assert_(member not in container, msg)\n\n def assertDictEqual(self, d1, d2, msg=None):\n self.assertEqual(set(d1.keys()), set(d2.keys()), msg)\n self.assertEqual(sorted(d1.values()), sorted(d2.values()), msg)\n\n\nclass TestItemParsing(BaseTest):\n\n def setUp(self):\n self.kv = cli.KeyValueType(\n cli.SEP_HEADERS,\n cli.SEP_DATA,\n cli.SEP_DATA_RAW_JSON,\n cli.SEP_FILES,\n )\n\n def test_invalid_items(self):\n items = ['no-separator']\n for item in items:\n self.assertRaises(argparse.ArgumentTypeError,\n lambda: self.kv(item))\n\n def test_valid_items(self):\n headers, data, files = cli.parse_items([\n self.kv('string=value'),\n self.kv('header:value'),\n self.kv('list:=[\"a\", 1, {}, false]'),\n self.kv('obj:={\"a\": \"b\"}'),\n self.kv('eh:'),\n self.kv('ed='),\n self.kv('bool:=true'),\n self.kv('test-file@%s' % TEST_FILE),\n ])\n self.assertDictEqual(headers, {\n 'header': 'value',\n 'eh': ''\n })\n self.assertDictEqual(data, {\n \"ed\": \"\",\n \"string\": \"value\",\n \"bool\": True,\n \"list\": [\"a\", 1, {}, False],\n \"obj\": {\"a\": \"b\"}\n })\n self.assertIn('test-file', files)\n\n\nclass TestHTTPie(BaseTest):\n\n def test_get(self):\n http('GET', 'http://httpbin.org/get')\n\n def test_verbose(self):\n r = http('--verbose', 'GET', 'http://httpbin.org/get', 'test-header:__test__')\n self.assertEqual(r.count('__test__'), 2)\n\n def test_json(self):\n response = http('POST', 'http://httpbin.org/post', 'foo=bar')\n self.assertIn('\"foo\": \"bar\"', response)\n\n def test_form(self):\n response = http('--form', 'POST', 'http://httpbin.org/post', 'foo=bar')\n self.assertIn('\"foo\": \"bar\"', response)\n\n def test_headers(self):\n response = http('GET', 'http://httpbin.org/headers', 'Foo:bar')\n self.assertIn('\"User-Agent\": \"HTTPie', response)\n self.assertIn('\"Foo\": \"bar\"', response)\n\n\nclass TestPrettyFlag(BaseTest):\n \"\"\"Test the --pretty / --ugly flag handling.\"\"\"\n\n def test_pretty_enabled_by_default(self):\n r = http('GET', 'http://httpbin.org/get', stdout_isatty=True)\n self.assertIn(TERMINAL_COLOR_PRESENCE_CHECK, r)\n\n def test_pretty_enabled_by_default_unless_stdin_redirected(self):\n r = http('GET', 'http://httpbin.org/get', stdout_isatty=False)\n self.assertNotIn(TERMINAL_COLOR_PRESENCE_CHECK, r)\n\n def test_force_pretty(self):\n r = http('--pretty', 'GET', 'http://httpbin.org/get', stdout_isatty=False)\n self.assertIn(TERMINAL_COLOR_PRESENCE_CHECK, r)\n\n def test_force_ugly(self):\n r = http('--ugly', 'GET', 'http://httpbin.org/get', stdout_isatty=True)\n self.assertNotIn(TERMINAL_COLOR_PRESENCE_CHECK, r)\n\n\nclass TestFileUpload(BaseTest):\n\n def test_non_existent_file_raises_parse_error(self):\n self.assertRaises(cli.ParseError, http,\n '--form', '--traceback',\n 'POST', 'http://httpbin.org/post',\n 'foo@/__does_not_exist__')\n\n def test_upload_ok(self):\n r = http('--form', 'POST', 'http://httpbin.org/post',\n 'test-file@%s' % TEST_FILE)\n self.assertIn('\"test-file\": \"__test_file_content__', r)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"tests/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":4373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"653275947","text":"#!/usr/bin/env python3\n#\n# To run the test cases:\n# python3 test_EBI_SRA_Downloader.py\n#\n# conda command to install all dependencies:\n# conda create -n ebi_sra_importer pandas requests entrez-direct sra-tools xmltodict lxml -c bioconda -c conda-forge -y\n# pip command to install all dependencies:\n# pip install csv glob requests subprocess xmltodict sys lxml os urllib\n# pip install argparse pandas bioconda sra-tools entrez-direct\n#\n# We also need EBI_SRA_Downloader.py to be under the same directory\n#\n# libraries used\nimport glob\nimport unittest\nfrom shutil import rmtree\nfrom pandas import read_csv\nfrom os import path, remove\nfrom EBI_SRA_Downloader import (ebi_create_details_file,\n sra_create_details_file, ebi_create_info_file,\n sra_create_info_file, create_prep_file,\n ebi_create_sample_file, sra_create_sample_file,\n ebi_fetch_data_file, sra_fetch_data_file)\n\n\nclass EBI_SRA_DownloaderTests(unittest.TestCase):\n def setUp(self):\n pass\n\n def test_ebi_create_details_file(self):\n # invalid accession\n with self.assertRaises(Exception):\n ebi_create_details_file(\"0\")\n # valid accession but no entry\n with self.assertRaises(Exception):\n ebi_create_details_file(\"ERP0\")\n # valid accession but not from Illumia\n with self.assertRaises(Exception):\n ebi_create_details_file(\"ERP012804\")\n # valid accession\n detail_file = ebi_create_details_file(\"PRJNA390646\")\n self.assertTrue(path.exists(detail_file)\n and path.getsize(detail_file) > 0)\n if path.exists(detail_file):\n remove(detail_file)\n\n def test_sra_create_details_file(self):\n # invalid accession\n with self.assertRaises(Exception):\n sra_create_details_file(\"0\")\n # valid accession but not metagenomic\n with self.assertRaises(Exception):\n sra_create_details_file(\"PRJNA312948\")\n # valid accession\n detail_file = sra_create_details_file(\"PRJNA545409\")\n self.assertTrue(path.exists(detail_file)\n and path.getsize(detail_file) > 0)\n if path.exists(detail_file):\n remove(detail_file)\n\n def test_ebi_create_info_file(self):\n # invalid accession\n info_file = \"0_study_info.txt\"\n ebi_create_info_file(info_file, \"0\")\n self.assertFalse(path.exists(info_file))\n # valid accession but no entry\n info_file = \"PRJNA295847_study_info.txt\"\n ebi_create_info_file(info_file, \"PRJNA295847\")\n self.assertFalse(path.exists(info_file))\n self.assertFalse(path.exists('feed.xml'))\n # valid accession\n info_file = \"PRJNA390646_study_info.txt\"\n ebi_create_info_file(info_file, \"PRJNA390646\")\n self.assertTrue(path.exists(info_file)\n and path.getsize(info_file) > 0)\n if path.exists(info_file):\n remove(info_file)\n self.assertTrue(path.exists('feed.xml')\n and path.getsize('feed.xml') > 0)\n if path.exists('feed.xml'):\n remove('feed.xml')\n\n def test_sra_create_info_file(self):\n # invalid accession\n info_file = \"0_study_info.txt\"\n with self.assertRaises(Exception):\n sra_create_info_file(info_file, \"0\")\n # valid accession\n info_file = \"PRJNA545409_study_info.txt\"\n sra_create_info_file(info_file, \"PRJNA545409\")\n self.assertTrue(path.exists(info_file)\n and path.getsize(info_file) > 0)\n if path.exists(info_file):\n remove(info_file)\n\n def test_create_prep_file(self):\n # invalid study detail file\n prep_file = \"PRJNA390646_prep_info.txt\"\n with self.assertRaises(FileNotFoundError):\n create_prep_file(prep_file, \"PRJNA390646_detail.txt\")\n # valid study detail file\n detail_file = ebi_create_details_file(\"PRJNA390646\")\n create_prep_file(prep_file, detail_file)\n prep_fname = [fp for fp in glob.glob('PRJNA390646_prep_info*.txt')]\n self.assertTrue(len(prep_fname) > 0)\n for fp in prep_fname:\n self.assertTrue(path.getsize(fp) > 0)\n if path.exists(fp):\n remove(fp)\n remove(detail_file)\n\n # invalid study detail filfale\n prep_file = \"0_prep_info.txt\"\n with self.assertRaises(FileNotFoundError):\n create_prep_file(prep_file, \"0_detail.txt\")\n # valid study detail file\n detail_file = sra_create_details_file(\"PRJNA545409\")\n prep_file = \"PRJNA545409_prep_info.txt\"\n create_prep_file(prep_file, detail_file)\n prep_fname = [fp for fp in glob.glob('PRJNA545409_prep_info*.txt')]\n self.assertTrue(len(prep_fname) > 0)\n for fp in prep_fname:\n self.assertTrue(path.getsize(fp) > 0)\n if path.exists(fp):\n remove(fp)\n remove(detail_file)\n\n def test_ebi_create_sample_file(self):\n # invalid study detail file\n sample_file = \"0_sample_info.txt\"\n with self.assertRaises(FileNotFoundError):\n ebi_create_sample_file(sample_file, \"0\",\n \"0_detail.txt\")\n # valid study detail file\n sample_file = \"ERP106503_sample_info.txt\"\n detail_file = ebi_create_details_file(\"ERP106503\")\n ebi_create_sample_file(sample_file, \"ERP106503\", detail_file)\n remove(detail_file)\n self.assertTrue(path.exists(sample_file)\n and path.getsize(sample_file) > 0)\n if path.exists(sample_file):\n remove(sample_file)\n sample_fname = [fp for fp in glob.glob('ERP106503/*/*')]\n self.assertTrue(len(sample_fname) > 0)\n for fp in sample_fname:\n self.assertTrue(path.getsize(fp) > 0)\n if path.exists(fp):\n remove(fp)\n rmtree(\"ERP106503\")\n\n def test_sra_create_sample_file(self):\n # invalid study detail file\n sample_file = \"0_sample_info.txt\"\n with self.assertRaises(FileNotFoundError):\n sra_create_sample_file(sample_file, \"0\",\n \"0_detail.txt\")\n # valid study detail file\n sample_file = \"PRJNA545409_sample_info.txt\"\n detail_file = sra_create_details_file(\"PRJNA545409\")\n sra_create_sample_file(sample_file, \"PRJNA545409\", detail_file)\n remove(detail_file)\n self.assertTrue(path.exists(sample_file)\n and path.getsize(sample_file) > 0)\n if path.exists(sample_file):\n remove(sample_file)\n sample_fname = [fp for fp in glob.glob('PRJNA545409/*/*')]\n self.assertTrue(len(sample_fname) > 0)\n for fp in sample_fname:\n self.assertTrue(path.getsize(fp) > 0)\n if path.exists(fp):\n remove(fp)\n rmtree(\"PRJNA545409\")\n\n def test_ebi_fetch_data_file(self):\n # invalid study detail file\n with self.assertRaises(FileNotFoundError):\n ebi_fetch_data_file(\"ERP106503\", \"ERP106503_detail.txt\")\n # valid study detail file\n detail_file = ebi_create_details_file(\"ERP106503\")\n ebi_fetch_data_file(\"ERP106503\", detail_file)\n remove(detail_file)\n fastq_fname = [fp for fp in glob.glob('ERP106503/*/*')]\n self.assertTrue(len(fastq_fname) > 0)\n for fp in fastq_fname:\n self.assertTrue(path.getsize(fp) > 0)\n if path.exists(fp):\n remove(fp)\n rmtree(\"ERP106503\")\n\n def test_sra_fetch_data_file(self):\n # invalid study detail file\n with self.assertRaises(FileNotFoundError):\n sra_fetch_data_file(\"0_detail.txt\")\n # valid study detail file\n detail_file = ebi_create_details_file(\"PRJNA533871\")\n sra_fetch_data_file(detail_file)\n details_df = read_csv(detail_file, sep='\\t', header=None)\n run_accession = []\n for row in details_df.iterrows():\n run_accession.append(row[1][2])\n remove(detail_file)\n fastq_fname = [fp for fp in glob.glob('*')\n if fp[:fp.rfind(\"_\")] in run_accession]\n self.assertTrue(len(fastq_fname) > 0)\n for fp in fastq_fname:\n self.assertTrue(path.getsize(fp) > 0)\n if path.exists(fp):\n remove(fp)\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"ebi_sra_importer/test_EBI_SRA_Downloader.py","file_name":"test_EBI_SRA_Downloader.py","file_ext":"py","file_size_in_byte":8567,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"507478855","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Mar 11 17:55:10 2020\r\n\r\n@author: Dell\r\n\"\"\"\r\n\r\nli = [x for x in range(51)]\r\nfinal_list = list(filter(lambda x : (x%5==0),li))\r\nprint(final_list)","sub_path":"OOPS/Python/Assignment 1/A8.py","file_name":"A8.py","file_ext":"py","file_size_in_byte":188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"275311115","text":"# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom django.shortcuts import render\nfrom .models import Article\nfrom django.http import HttpResponse\nfrom django.contrib.auth.decorators import login_required\nfrom . import forms\n\ndef article_list(request):\n\tarticles = Article.objects.all().order_by('date')\n\treturn render(request,'articles/articles_list.html',{'articles':articles})\n\n\ndef articles_detail(request,slug):\n\t#return HttpResponse(slug)\n\ttry:\n\t\tarticle =Article.objects.get(slug=slug)\n\texcept Article.DoesNotExist:\n\t\tarticle = None\n\treturn render(request,'articles/articles_detail.html',{'article':article})\n\n\n@login_required(login_url=\"/accounts/login\")\ndef article_create(request):\n\tif request.method =='POST':\n\t\tform = forms.CreateArticle(request.POST,request.FILES)\n\t\tif forms.is_valid():\n\t\t\tinstance = form.save(commit=False)\n\t\t\tinstance.author = request.user\n\t\t\tinstance.save()\n\n\t\t\treturn redirect('article:list')\n\telse:\n\t\tform =forms.CreateArticle()\n\treturn render(request,'articles/articles_create.html',{'form':form})\n","sub_path":"articles/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1054,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"16098849","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"test\")\n\nprocess.source = cms.Source(\"PoolSource\",\n duplicateCheckMode = cms.untracked.string(\"noDuplicateCheck\"),\n fileNames = cms.untracked.vstring(\n # \"root://cmsxrootd.fnal.gov//store/user/dgulhan/PYTHIA_QCD_TuneCUETP8M1_cfi_GEN_SIM_5020GeV/PYTHIA_QCD120_TuneCUETP8M1_cfi_RECODEBUGpp_757p1_timeslew_HcalRespCorrs_v4_00_mc/151215_074107/0000/step3_1.root\",\n \"file:/home/peng43/scratch/Jet_FlavorID/CMSSW_7_5_8/src/HeavyIonsAnalysis/JetAnalysis/test/sample_input/PYTHIA_QCD120_TuneCUETP8M1_cfi_RECODEBUGpp_757p1_timeslew_HcalRespCorrs_v4_00_mc_pp/step3_1.root\",\n )\n # SkipEvents = cms.untracked.uint32(2)\n\n)\n\nprocess.maxEvents = cms.untracked.PSet(\n input = cms.untracked.int32(2))\n\nprocess.load(\"SimGeneral.HepPDTESSource.pythiapdt_cfi\") \n\nprocess.printTree = cms.EDAnalyzer(\"ParticleListDrawer\",\n maxEventsToPrint = cms.untracked.int32(1),\n printVertex = cms.untracked.bool(False),\n printOnlyHardInteraction = cms.untracked.bool(False),\n src = cms.InputTag(\"genParticles\")\n)\n\nprocess.p = cms.Path( process.printTree )\n","sub_path":"JetAnalysis/test/particleUtil1.py","file_name":"particleUtil1.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"80974472","text":"from detector import Detector\nfrom recognition import TextRecognition\nfrom utils.image_utils import align_image, sort_text\nimport cv2\nimport time\nimport numpy as np\n\n\nclass CompletedModel(object):\n def __init__(self):\n self.corner_detection_model = Detector(path_to_model='./config_corner_detection/Corner/model.tflite',\n path_to_labels='./config_corner_detection/label_map.pbtxt',\n nms_threshold=0.2, score_threshold=0.3)\n self.text_detection_model = Detector(path_to_model='./config_text_detection/Text/model.tflite',\n path_to_labels='./config_text_detection/label_map.pbtxt',\n nms_threshold=0.2, score_threshold=0.2)\n self.text_recognition_model = TextRecognition(path_to_checkpoint='./config_text_recognition/transformerocr.pth')\n\n # init boxes\n self.id_boxes = None\n self.name_boxes = None\n self.birth_boxes = None\n self.add_boxes = None\n self.home_boxes = None\n\n def detect_corner(self, image):\n detection_boxes, detection_classes, category_index = self.corner_detection_model.predict(image)\n\n coordinate_dict = dict()\n height, width, _ = image.shape\n\n for i in range(len(detection_classes)):\n label = str(category_index[detection_classes[i]]['name'])\n real_ymin = int(max(1, detection_boxes[i][0]))\n real_xmin = int(max(1, detection_boxes[i][1]))\n real_ymax = int(min(height, detection_boxes[i][2]))\n real_xmax = int(min(width, detection_boxes[i][3]))\n coordinate_dict[label] = (real_xmin, real_ymin, real_xmax, real_ymax)\n\n # align image\n cropped_img = align_image(image, coordinate_dict)\n cv2.imwrite('./test.png', cropped_img)\n\n return cropped_img\n\n def detect_text(self, image):\n # detect text boxes\n detection_boxes, detection_classes, category_index = self.text_detection_model.predict(image)\n\n # sort text boxes according to coordinate\n self.id_boxes, self.name_boxes, self.birth_boxes, self.home_boxes, self.add_boxes = sort_text(detection_boxes, detection_classes)\n\n def text_recognition(self, image):\n field_dict = dict()\n\n # crop boxes according to coordinate\n def crop_and_recog(boxes):\n crop = []\n if len(boxes) == 1:\n ymin, xmin, ymax, xmax = boxes[0]\n crop.append(image[ymin:ymax, xmin:xmax])\n else:\n for box in boxes:\n ymin, xmin, ymax, xmax = box\n # cv2.imwrite('./crop/test_' + str(ymin) + '_' + str(ymax) + '.png', image[ymin:ymax, xmin:xmax])\n crop.append(image[ymin:ymax, xmin:xmax])\n\n return crop\n\n list_ans = list(crop_and_recog(self.id_boxes))\n list_ans.extend(crop_and_recog(self.name_boxes))\n list_ans.extend(crop_and_recog(self.birth_boxes))\n list_ans.extend(crop_and_recog(self.add_boxes))\n list_ans.extend(crop_and_recog(self.home_boxes))\n\n start1 = time.time()\n result = self.text_recognition_model.predict_on_batch(np.array(list_ans))\n end1 = time.time()\n print(\"predicted time: \", end1 - start1)\n field_dict['id'] = result[0]\n field_dict['name'] = ' '.join(result[1:len(self.name_boxes) + 1])\n field_dict['birth'] = result[len(self.name_boxes) + 1]\n field_dict['home'] = ' '.join(result[len(self.name_boxes) + 2: -len(self.home_boxes)])\n field_dict['add'] = ' '.join(result[-len(self.home_boxes):])\n print(field_dict)\n\n def predict(self, image):\n cropped_image = self.detect_corner(image)\n self.detect_text(cropped_image)\n self.text_recognition(cropped_image)\n\n\nimg = cv2.imread('test.png')\nmodel = CompletedModel()\nstart = time.time()\nmodel.predict(img)\nend = time.time()\nprint(\"Required time: \", end - start)\n","sub_path":"complete/merged_model.py","file_name":"merged_model.py","file_ext":"py","file_size_in_byte":4051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"594307607","text":"from pygame import mixer\nfrom src import options\nimport json\nimport time\n\n\nmixer.pre_init()\nmixer.init()\n\n\n# use the QUEUE and CHANNEL tools\nclass Player:\n volume = 0\n channels = []\n\n def create_channels(self, channel_count):\n for i in range(channel_count):\n self.channels.append(mixer.Channel(i))\n mixer.set_num_channels(channel_count)\n\n def set_volume(self, vol):\n \"\"\"\n\n :param vol: should be 0.0-1.0\n :type vol: float\n :return:\n \"\"\"\n self.volume = vol\n for channel in self.channels:\n channel.set_volume(self.volume)\n\n def play_note(self, notes, channel_id):\n \"\"\"\n\n :param notes: note to play\n :param channel_id: id of the channel to play on\n :type notes: Sound\n :return:\n \"\"\"\n try:\n try:\n self.channels[channel_id].queue(notes.sound)\n except AttributeError:\n print(\"Invalid note\")\n except IndexError:\n self.channels[channel_id - 1].queue(notes.sound)\n\n def play_notes(self, notes):\n \"\"\"\n\n :param notes: notes to play\n :type notes: Sound\n :return:\n \"\"\"\n i = 0\n for note in notes.notes:\n self.play_note(note, i)\n i += 1\n time.sleep(options.hold_time)\n self.stop_channel(i)\n if i > len(self.channels) - 1:\n i = 0\n\n def play_progression(self, progression):\n for chord in progression:\n self.queue_chord(chord)\n time.sleep(options.pause_time)\n time.sleep(options.pause_time)\n time.sleep(options.sleep_time)\n self.stop()\n\n def queue_chord(self, chord_to_divide):\n i = 0\n to_add = []\n for notes in chord_to_divide.notes:\n to_add.append(notes)\n for channel in self.channels:\n try:\n try:\n channel.queue(to_add[i].sound)\n i += 1\n except AttributeError:\n print(\"Invalid chord\")\n except IndexError:\n pass\n\n def stop_channel(self, channel):\n if channel in self.channels:\n channel.stop()\n\n def init(self):\n try:\n with open(\"res/settings.json\", encoding=\"utf-8\") as data_file:\n s = json.load(data_file)\n self.create_channels(s[\"channels\"])\n self.set_volume(s[\"default playback volume\"])\n except KeyError:\n print(\"LoadingError: Loading player\")\n\n def test_play(self, notes):\n \"\"\"Tests the play_notes method\n\n :param notes: List of notes to go through\n :return: none\n \"\"\"\n print(\"Testing\")\n i = 0\n for note in notes:\n self.play_note(note, i)\n i += 1\n time.sleep(options.hold_time)\n self.stop_channel(i)\n if i > len(self.channels) - 1:\n i = 0\n\n @staticmethod\n def stop():\n mixer.stop()\n\n @staticmethod\n def quit():\n mixer.quit()\n\n\nmidi_player = Player()\n","sub_path":"src/player.py","file_name":"player.py","file_ext":"py","file_size_in_byte":3155,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"637306535","text":"import itertools\n\nimport matplotlib.pyplot as plt\nfrom datetime import date, timedelta\nfrom matplotlib.rcsetup import cycler\nfrom numpy import array, pi, exp, lcm, floor, log, sqrt\nfrom typing import Tuple\n\nNPOINTS = 10\nRESFACT = 3\nMAP = 'cool'\nRUNS = 1.5\n\n\ndef highresLine(x1: int, x2: int, n: int) -> array:\n return [x1 + k / n * (x2 - x1) for k in range(0, n + 1)]\n\n\ndef highResPoints(x: array, y: array, factor=10) -> Tuple[array, array]:\n xmod = list(itertools.chain(\n *(highresLine(x[i], x[i+1], factor) for i in range(0, len(x) - 1))))\n ymod = list(itertools.chain(\n *(highresLine(y[i], y[i+1], factor) for i in range(0, len(y) - 1))))\n return xmod, ymod\n\n\ndef f(n: int, m: int, d: int, y: int) -> int:\n return n/m + n**2/d + n**3/y\n\n\ndef g(n: int, m: int, d: int, y: int) -> int:\n return log(n)**4.1 + n**2/(0.05/d)\n\n\ndef index(i: int, total: int) -> float:\n if RUNS*i/(total-1) < 1:\n return RUNS * i / (total - 1)\n else:\n return RUNS*i/(total-1) - floor(RUNS*i/(total-1))\n\n\ndef create_svg_d(d: int, m: int, y: int, date=True, ind=0) -> None:\n # N = lcm.reduce([d, m, y])\n N = 200\n\n # z = array([exp(2*pi*1j*f(n, d, m, y)) for n in range(0, N+1)])\n z = array([exp(2*pi*1j*sqrt(n)*sqrt(2)) for n in range(1, N+2)])\n z = z.cumsum()\n\n cm = plt.get_cmap(MAP)\n ax = plt.gca()\n\n xHiRes, yHiRes = highResPoints(z.real, z.imag, RESFACT)\n # xHiRes, yHiRes = z.real, z.imag\n npointsHiRes = len(xHiRes)\n\n # print(str(N) + ' ' + str(npointsHiRes))\n\n ax.set_prop_cycle(cycler(color=[cm(index(i, npointsHiRes))\n for i in range(npointsHiRes - 1)]))\n for i in range(npointsHiRes - 1):\n ax.plot(xHiRes[i], yHiRes[i], 'o')\n plt.axes().set_aspect(1)\n plt.axis('off')\n plt.axes().get_xaxis().set_visible(False)\n plt.axes().get_yaxis().set_visible(False)\n\n name = 'sum-'+str(d) + '-'+str(m) + '-'+str(y) + \\\n '.svg' if date else 'sum_' + str(ind) + '.svg'\n\n plt.savefig(name, bbox_inches='tight',\n transparent=True, dpi=300)\n plt.show()\n plt.clf()\n\n\ndef advance_day(day: date) -> Tuple[int, int, int, date]:\n next = day + timedelta(days=1)\n d = next.day\n m = next.month\n y = next.year % 100\n return d, m, y, next\n\n\ndef create_svg(advande: int, pre: int) -> None:\n today = date.today()\n d, m, y = today.day, today.month, today.year % 100\n for i in range(pre):\n d, m, y, today = advance_day(today)\n for i in range(advande):\n print('creating sum for: ' + str(d) + \".\" + str(m) + \".\" + str(y))\n create_svg_d(d, m, y, date=True, ind=i)\n print(\"Created sum for: \" + str(d) + \".\" + str(m) + \".\" + str(y))\n d, m, y, today = advance_day(today)\n\n\nprint('SUM_OF_TODAY')\ncreate_svg(1, 0)\n","sub_path":"scripts/sum_of_today.py","file_name":"sum_of_today.py","file_ext":"py","file_size_in_byte":2804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"58016298","text":"# -*- coding: utf-8 -*-\nimport config as cfg\nimport tushare as ts\nimport datetime\nimport time\nimport random\nimport logging\nimport json\nimport operator\n\nfrom bson.objectid import ObjectId\nfrom error import trace_log\nfrom Database import DB\n\nclass ShareDB():\n\tdef __init__(self, db = \"MyShare\", output = None):\n\t\tself.Output = output\n\t\tself.StopOutput = False\n\t\tself._db = db\n\t\tself.RecordDB = DB(db = self._db, col = \"Record\")\n\t\tself.TradeDayList = None\n\n\tdef isTradeDay(self, date):\n\t\tif self.TradeDayList == None:\n\t\t\tdf = ts.trade_cal()\n\t\t\tself.TradeDayList = df.loc[df[\"isOpen\"] == 1][\"calendarDate\"].tolist()\n\n\t\tif date in self.TradeDayList:\n\t\t\treturn True\n\t\telse:\n\t\t\treturn False\n\n\n\tdef OutputText(self, s):\n\t\tif self.StopOutput:\n\t\t\treturn 0\n\t\tif self.Output is not None:\n\t\t\tself.Output.AppendText(s)\n\n\tdef stop(self, event):\n\t\tif event is None:\n\t\t\treturn False\n\t\telse:\n\t\t\tif event.isSet():\n\t\t\t\treturn False\n\t\t\telse:\n\t\t\t\tself.StopOutput = True\n\t\t\t\treturn True\n\n\tdef GetStockInfo(self):\n\t\tStockInfoDB = DB(db = self._db, col = \"Stockinfo\")\n\t\tdoc = { \"_id\" : None,\n\t\t\t\t\"code\" : None,\n\t\t\t\t\"name\" : None,\n\t\t\t\t\"timeToMarket\": None,\n\t\t\t\t\"basics\": [],\n\t\t\t\t\"report\": [],\n\t\t\t\t\"profit\": [],\n\t\t\t\t\"growth\": [],\n\t\t\t\t}\n\t\tbasics_doc = {\"CollectDate\": None,\n\t\t\t\t\t#\n\t\t\t\t\t# Other key : value will be inster dynamiclly \n\t\t\t\t\t#\t\n\t\t\t\t}\n\n\t\tdf = ts.get_stock_basics()\n\t\tbasics_clm = [ \"name\",#股票名称\n\t\t\t\t\"pe\", #市盈率\n\t\t\t\t\"outstanding\",#流通股本(亿)\n\t\t\t\t\"totals\",#总股本(亿)\n\t\t\t\t\"totalAssets\",#总资产(万)\n\t\t\t\t\"liquidAssets\",#流动资产\n\t\t\t\t\"fixedAssets\",#固定资产\n\t\t\t\t\"reserved\",#公积金\n\t\t\t\t\"reservedPerShare\",#每股公积金\n\t\t\t\t\"esp\",#每股收益\n\t\t\t\t\"bvps\",#每股净资\n\t\t\t\t\"pb\",#市净率\n\t\t\t\t\"timeToMarket\",#上市日期\n\t\t\t\t\"undp\",#未分利润\n\t\t\t\t\"perundp\",#每股未分配\n\t\t\t\t\"rev\",#收入同比(%)\n\t\t\t\t\"profit\",#利润同比(%)\n\t\t\t\t\"gpr\",#毛利率(%)\n\t\t\t\t\"npr\",#净利润率(%)\n\t\t\t\t\"holders\"#股东人数\n\t\t\t\t]\n\n\t\t# Record_doc = {\n\t\t# \t\t\"type\": \"StockInfo\",\n\t\t# \t\t\"last_success_quarter\": None,\n\t\t# }\n\t\tfor index in df.index:\n\t\t\ti, result = StockInfoDB.find(_filter = {\"code\": index})\n\t\t\tif i == 0:\n\t\t\t\tdoc[\"code\"] = str(index)\n\t\t\t\tdoc[\"_id\"] = ObjectId()\n\t\t\t\ttoday = datetime.datetime.now().strftime('%Y-%m-%d')\n\t\t\t\tbasics_doc[\"CollectDate\"] = datetime.datetime.strptime(today, \"%Y-%m-%d\")\n\t\t\t\tfor cl in basics_clm:\n\t\t\t\t\tif cl == \"timeToMarket\":\n\t\t\t\t\t\ttry:\n\t\t\t\t\t\t\tdoc[\"timeToMarket\"] = datetime.datetime.strptime(str(df.loc[index][\"timeToMarket\"]), \"%Y%m%d\")\n\t\t\t\t\t\texcept:\n\t\t\t\t\t\t\tbreak\n\t\t\t\t\telif cl == \"name\":\n\t\t\t\t\t\tdoc[\"name\"] = df.loc[index][\"name\"]\n\t\t\t\t\telse:\n\t\t\t\t\t\tbasics_doc[cl] = df.loc[index][cl]\n\t\t\t\tdoc[\"basics\"] = []\n\t\t\t\tdoc[\"basics\"].append(basics_doc)\n\t\t\t\tStockInfoDB.insert_one(doc)\n\t\t\t\ttime.sleep(0.2)\n\t\t\telse:\n\t\t\t\ttoday = datetime.datetime.now().strftime('%Y-%m-%d')\n\t\t\t\tfor cl in basics_clm:\n\t\t\t\t\tif cl not in [\"timeToMarket\", \"name\"]:\n\t\t\t\t\t\tbasics_doc[cl] = df.loc[index][cl]\n\n\t\t\t\ttmp_basics = result[0][\"basics\"]\n\t\t\t\tfor i in range(0, len(tmp_basics)):\n\t\t\t\t\ttmp_basics[i][\"CollectDate\"] = None\n\t\t\t\tbasics_doc[\"CollectDate\"] = None\n\n\t\t\t\tif basics_doc not in tmp_basics:\n\t\t\t\t\tbasics_doc[\"CollectDate\"] = datetime.datetime.strptime(today, \"%Y-%m-%d\")\n\t\t\t\t\tStockInfoDB.update(_filter = {\"code\": index}, _update = {\"$push\": {\"basics\": basics_doc}})\n\t\t\t\t\ttime.sleep(0.2)\n\n\tdef GetBasicInfomation(self, year = None, quarter = None, retry = 3):\n\t\tdef template(db = None, y = None, q = None, fun = None, k = None, doc = None):\n\t\t\ttry:\n\t\t\t\tlogging.debug(\"Collecting %s data for %s-%s\" %(k, str(y), str(q)))\n\t\t\t\tdf = fun(y,q)\n\t\t\t\tfor index in df.index:\n\t\t\t\t\tcode = df.loc[index][\"code\"]\n\t\t\t\t\ti, result = db.find(_filter = {\"code\": code})\n\t\t\t\t\tif i != 0:\n\t\t\t\t\t\tdoc[\"CollectQuarter\"] = str(y) + \"-\" + str(q)\n\t\t\t\t\t\tselecter = \"%s.CollectQuarter\" % k\n\t\t\t\t\t\ti, result = db.find(_filter = {\"code\": code, selecter: doc[\"CollectQuarter\"]})\n\t\t\t\t\t\tif i == 0:\n\t\t\t\t\t\t\tfor key in doc.keys():\n\t\t\t\t\t\t\t\tif key != \"CollectQuarter\":\n\t\t\t\t\t\t\t\t\tdoc[key] = df.loc[index][key]\n\t\t\t\t\t\t\tlogging.debug (\"Stock %s match, update DB\" % (code))\n\t\t\t\t\t\t\tdb.update(_filter = {\"code\": code}, _update = {\"$push\": {k: doc}})\n\t\t\t\t\t\t\ttime.sleep(0.2)\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tfor key in doc.keys():\n\t\t\t\t\t\t\t\tdoc[key] = None\n\t\t\t\t\t\t\tcontinue\n\t\t\texcept:\n\t\t\t\ttrace_log()\n\t\t\ttime.sleep(random.uniform(1, 5))\n\n\t\tStockInfoDB = DB(db = self._db, col = \"Stockinfo\")\n\t\tif year == None:\n\t\t\tstart_year = 2015\n\t\telse:\n\t\t\tstart_year = year\n\n\t\tif quarter == None:\n\t\t\tquarter_list = [1, 2, 3, 4]\n\t\telse:\n\t\t\tif type(quarter) == list:\n\t\t\t\tquarter_list = quarter\n\t\t\telse:\n\t\t\t\tquarter_list = list(quarter)\n\n\t\treport_doc = {\"CollectQuarter\": None,\n\t\t\t\t\t \"eps\": None,#每股收益\n\t\t\t\t\t \"eps_yoy\": None,#每股收益同比(%)\n\t\t\t\t\t \"bvps\": None,#每股净资产\n\t\t\t\t\t \"roe\": None,#净资产收益率(%)\n\t\t\t\t\t \"epcf\": None,#每股现金流量(元)\n\t\t\t\t\t \"net_profits\": None,#净利润(万元)\n\t\t\t\t\t \"profits_yoy\": None,#净利润同比(%)\n\t\t\t\t\t \"distrib\": None,#分配方案\n\t\t\t\t\t \"report_date\": None,#发布日期\n\t\t\t\t\t }\n\n\t\tprofit_doc = {\"CollectQuarter\": None,\n\t\t\t\t\t \"net_profit_ratio\": None,#净利率(%)\n\t\t\t\t\t \"gross_profit_rate\": None,#毛利率(%)\n\t\t\t\t\t \"business_income\": None,#营业收入(百万元)\n\t\t\t\t\t \"bips\": None,#每股主营业务收入(元)\n\t\t\t\t\t }\n\n\t\tgrowth_doc = {\"CollectQuarter\": None,\n\t\t\t\t\t \"mbrg\": None,#主营业务收入增长率(%)\n\t\t\t\t\t \"nprg\": None,#净利润增长率(%)\n\t\t\t\t\t \"nav\": None,#净资产增长率\n\t\t\t\t\t \"targ\": None,#总资产增长率\n\t\t\t\t\t \"epsg\": None,#每股收益增长率\n\t\t\t\t\t \"seg\": None,#股东权益增长率\n\t\t\t\t\t }\n\t\twhile retry:\n\t\t\twhile start_year <= datetime.datetime.now().year:\n\t\t\t\tfor quarter in quarter_list:\n\t\t\t\t\ttemplate(db = StockInfoDB, y = start_year, q = quarter, fun = ts.get_report_data, k = \"report\", doc = report_doc)\n\n\t\t\t\tfor quarter in quarter_list:\n\t\t\t\t\ttemplate(db = StockInfoDB, y = start_year, q = quarter, fun = ts.get_profit_data, k = \"profit\", doc = profit_doc)\n\n\t\t\t\tfor quarter in quarter_list:\n\t\t\t\t\ttemplate(db = StockInfoDB, y = start_year, q = quarter, fun = ts.get_growth_data, k = \"growth\", doc = growth_doc)\n\n\t\t\t\tstart_year = start_year + 1\n\n\t\t\tretry = retry - 1\n\t\t\tif year == None:\n\t\t\t\tstart_year = 2015\n\t\t\telse:\n\t\t\t\tstart_year = year\n\n\tdef GetHistoryData(self, sharecode = None, startdate = None, event = None):\n\t\tdelta = datetime.timedelta(days=1)\n\t\tStockDB = DB(db = \"MyShare_Test\", col = sharecode)\n\t\tlogging.info(\"Getting historyData for %s\" % (sharecode))\n\n\t\tShare_doc = {\n\t\t\"_id\": None,\n\t\t\"date\": None,\n\t\t\"k_data\": None,\n\t\t\"tick\": None\n\t\t}\n\n\t\tHistDataRec_doc = {\n\t\t\t\"type\": \"Record\",\n\t\t\t\"last_success\": None,\n\t\t\t\"fail_list\": None\n\t\t}\n\n\t\ti = 0\n\t\tcount = 0\n\t\ttry:\n\t\t\tcount, result = StockDB.find(_filter = {\"type\": \"Record\"})\n\t\t\tif count != 0:\n\t\t\t\tdate = result[0][\"last_success\"] + delta\n\t\t\telse:\n\t\t\t\tdate = datetime.datetime.strptime(startdate, \"%Y-%m-%d\")\n\t\t\t\t#\n\t\t\t\t# Don't have the record data create one\n\t\t\t\t#\n\t\t\t\tHistDataRec_doc['last_success'] = date\n\t\t\t\tHistDataRec_doc['fail_list'] = []\n\t\t\t\tStockDB.insert_one(HistDataRec_doc)\n\t\texcept:\n\t\t\tdate = datetime.datetime.strptime(startdate, \"%Y-%m-%d\")\n\n\t\tfor _ in range(3):\n\t\t\ttry:\n\t\t\t\tk_df = ts.get_k_data(sharecode, start=date.strftime(\"%Y-%m-%d\"), autype = None, retry_count=10, pause=4)\n\t\t\texcept:\n\t\t\t\tlogging.error(\"Exception!!!\")\n\t\t\t\ttrace_log()\n\t\t\t\tyield 1\n\t\t\tif k_df is not None and len(k_df) != 0:\n\t\t\t\tbreak\n\t\t\ttime.sleep(random.uniform(1, 5))\n\n\t\tif k_df is None or len(k_df) == 0:\n\t\t\tbase = date\n\t\t\tdays = (datetime.datetime.now().strftime('%Y-%m-%d') - date).days\n\t\t\tdate_list = [base + datetime.timedelta(days=x) for x in range(0, days + 1)]\n\t\t\tfor day in date_list:\n\t\t\t\tif self.isTradeDay(day.strftime('%Y-%m-%d')):\n\t\t\t\t\tlogging.error(\"！！！k_df is None, skip this stock\")\n\t\t\t\t\tbreak\n\t\t\treturn 0\n\n\t\twhile not self.stop(event):\n\t\t\tif date.strftime(\"%Y-%m-%d\") >= datetime.datetime.today().strftime(\"%Y-%m-%d\"):\n\t\t\t\tlogging.info(\"All the share data were collected, exit the collection progress!\")\n\t\t\t\tbreak\n\t\t\ttry:\n\t\t\t\tShare_doc['date'] = date\n\t\t\t\tlogging.info(Share_doc['date'])\n\t\t\t\tdf = k_df.loc[k_df[\"date\"] == date.strftime(\"%Y-%m-%d\")]\n\n\t\t\t\tif df is not None and len(df) != 0:\n\t\t\t\t\tlogging.debug(\"Get K data successful\")\n\t\t\t\t\tShare_doc['_id'] = ObjectId()\n\t\t\t\t\tShare_doc['k_data'] = json.loads(df.to_json(orient = \"records\"))[0]\n\n\t\t\t\t\t#获取复权数据\n\t\t\t\t\t# time.sleep(random.uniform(1, 10))\n\t\t\t\t\t# qfq_df = ts.get_k_data(sharecode, start=date.strftime(\"%Y-%m-%d\"), end=date.strftime(\"%Y-%m-%d\"), retry_count=10, pause=4)\n\t\t\t\t\t# if qfq_df is not None and len(qfq_df) != 0:\n\t\t\t\t\t# \tShare_doc['k_data_qfq'] = json.loads(qfq_df.to_json(orient = \"records\"))[0]\n\t\t\t\t\t# else:\n\t\t\t\t\t# \tlogging.debug(\"Get K_qfq data fail\")\n\n\t\t\t\t\t#获取历史分笔数据\n\t\t\t\t\tdf = ts.get_tick_data(sharecode, date=date.strftime(\"%Y-%m-%d\"), retry_count=10, pause=4)\n\t\t\t\t\tif len(df) > 3: # src = \"sn\"\n\t\t\t\t\t\tlogging.debug(\"Getting tick data successful\")\n\t\t\t\t\t\tSortDf = df.sort_values(by = 'time', axis = 0, ascending = True)#.sort_index(ascending=False,inplace=False)\n\t\t\t\t\t\tSortDf.reset_index(drop = True, inplace = True)\n\t\t\t\t\t\tSortDf['type'] = SortDf['type'].replace(\"买盘\", 1).replace(\"卖盘\", -1).replace(\"中性盘\", 0)\n\t\t\t\t\t\tSortDf['change'] = SortDf['change'].replace('--', '0').astype('float')\n\t\t\t\t\t\tShare_doc['tick'] = json.loads(SortDf.to_json(orient = \"index\"))\n\n\t\t\t\t\t\t#将获取到的数据插入数据库\n\t\t\t\t\t\tInsertResult = StockDB.insert_one(Share_doc)\n\t\t\t\t\t\tif InsertResult.acknowledged:\n\t\t\t\t\t\t\tStockDB.update(_filter = {\"type\": \"Record\"}, _update = {\"$set\": {\"last_success\": date}})\n\t\t\t\t\t\t\tlogging.debug(\"Insert data successful ObjectId = %s\" %(InsertResult.inserted_id))\n\t\t\t\t\t\telse:\n\t\t\t\t\t\t\tlogging.error(\"Insert data to DB fail\")\n\t\t\t\t\t\t\tStockDB.update(_filter = {\"type\": \"Record\"}, _update = {\"$push\": {\"fail_list\": date}})\n\t\t\t\t\t\tfor k in Share_doc.keys():\n\t\t\t\t\t\t\tShare_doc[k] = None\n\t\t\t\t\telse:\n\t\t\t\t\t\t# 获取数据失败，添加失败记录\n\t\t\t\t\t\tlogging.error(\"Getting tick data fail\")\n\t\t\t\t\t\tStockDB.insert_one(Share_doc)\n\t\t\t\t\t\tStockDB.update(_filter = {\"type\": \"Record\"}, _update = {\"$push\": {\"fail_list\": date}})\n\t\t\t\t\t\t\n\t\t\t\telse:\n\t\t\t\t\tStockDB.update(_filter = {\"type\": \"Record\"}, _update = {\"$set\": {\"last_success\": date}})\n\t\t\t\t\tlogging.info(\"No k_data, pass\")\n\n\t\t\t\ttime.sleep(random.uniform(1, 5))\n\t\t\t\tdate = date + delta\n\n\t\t\t\t#每收集10次数据延迟5秒\n\t\t\t\ti = i + 1\n\t\t\t\tif i == 10:\n\t\t\t\t\ti = 0\n\t\t\t\t\ttime.sleep(10)\n\t\t\texcept:\n\t\t\t\tlogging.error(\"Exception!!!\")\n\t\t\t\ttrace_log()\n\t\t\t\tyield 1\n\t\tStockDB.logout()\n\n\t\treturn 0\n\n\n#Test code >>>\nif __name__ == '__main__':\n\tlog_file = \"ShareDB.log\"\n\tlogging.basicConfig(\n level=logging.DEBUG,\n format=\"%(message)s\",\n filename=log_file)\n\tconsole_logger = logging.StreamHandler()\n\tconsole_logger.setLevel(logging.DEBUG)\n\tconsole_logger.setFormatter(logging.Formatter(\"%(message)s\"))\n\tlogging.getLogger().addHandler(console_logger)\n\n\tShare = ShareDB()\n\t# Share.GetStockInfo()\n\t# Share.GetBasicInfomation(year = 2015)\n\t# Share.GetHistoryData(sharecode = \"600050\", startdate = \"2015-01-05\")\n\t# qfq_df = ts.get_k_data(\"603713\", start=\"2015-01-05\", autype = None, retry_count=10, pause=4)\n\t# print (qfq_df)\n\t# if qfq_df is not None and len(qfq_df) != 0:\n\t# \tdf = qfq_df.loc[qfq_df[\"date\"] == \"2015-01-05\"]\n\t# \tprint (df)\n\t# \tif df is not None and len(df) != 0:\n\t# \t\tprint(json.loads(df.to_json(orient = \"records\"))[0])\n\n\tdf = ts.get_k_data(\"600050\", start = \"2015-01-05\", autype = None, retry_count=10, pause=4)\n\tprint (df)\n\t# print (ts.get_profit_data(2015,1))\n#Test code<<<","sub_path":"ShareDB.py","file_name":"ShareDB.py","file_ext":"py","file_size_in_byte":11492,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"221692137","text":"\"\"\"\r\nScript Name : *.py\r\nDescription : This code is to process the file from given\r\nsource bucket and write to the given S3 bucket in SCD concept.\r\nAuthor : Gayathri, Sarath, Chandan\r\nVersion : 1.0\r\nLast Modified Date : 04/09/2018\r\n\"\"\"\r\nimport sys\r\nfrom urlparse import urlparse\r\nfrom os.path import normpath, basename\r\nfrom pyspark.sql import SparkSession\r\nfrom pyspark.sql.functions import col, hash, lit\r\nimport boto3\r\n\r\n\r\nclass StageToContract:\r\n \"\"\"\r\n This can be used for parsing the source file\r\n which will be used to rename the file header\r\n to the standard.\r\n \"\"\"\r\n\r\n def __init__(self):\r\n self.app_name = self.__class__.__name__\r\n self.spark_session = SparkSession.builder.appName(self.app_name).getOrCreate()\r\n self.log4j_logger = self.spark_session.sparkContext._jvm.org.apache.log4j\r\n self.log = self.log4j_logger.LogManager.getLogger(self.app_name)\r\n self.client = boto3.client('s3')\r\n \"\"\"\r\n selectcolumnlist= (previos-version-effectstartdate-hashcolumn)\r\n columnshistory(above lsit,version_no.hash_column)\r\n columnscurrent(collist)\r\n hash =columnlist(columnist exclude start_Date,v)no needed to ecluse\r\n for current from eff_start_dttm fro mdaga run date\r\n \"\"\"\r\n self.curatedColumnsCurrent = \\\r\n \"contract_cd,proposal_cd,src_sys_id,contract_status_id,advertiser_id,advertiser_cd,\" \\\r\n \"agency_id,agency_cd,contract_type_id,rating_type_id,primary_demo_id,revenue_type_id,\" \\\r\n \"traffic_status_id,stew_method_id,guarantee_ind,agency_comm_pct,total_budget_amt,\" \\\r\n \"total_contract_amt,hold_experation_dttm,contract_year_cd,\" \\\r\n \"contract_acct_exec_party_id,contract_planner_party_id,contract_exec_party_id,\" \\\r\n \"hh_stew_method_cd,demo_stew_method_cd,broadcast_quarter_id,src_sys_row_id\"\r\n self.curatedColumnsPrevious = self.curatedColumnsCurrent+\",hash_column, file_date,version_no\"\r\n\r\n self.input_path_adsales_rev_goal = sys.argv[1]\r\n self.input_path_adsales_proj_rev = sys.argv[2]\r\n self.input_path_adsales_calender = sys.argv[3]\r\n self.output_curated_path = sys.argv[4]\r\n pathbreakup = urlparse(self.output_curated_path)\r\n self.historyPath = \\\r\n pathbreakup.scheme + '://' + pathbreakup.netloc + '/' \\\r\n + pathbreakup.path.split('/')[1] + '/*'\r\n self.bucket = urlparse(self.output_curated_path).netloc\r\n self.prefix = urlparse(self.output_curated_path).path.split('/')[1]+'/'\r\n self.file_date = basename(normpath(pathbreakup.path))\r\n\r\n def load_file(self):\r\n \"\"\"This is the main method.\"\"\"\r\n print(\"Printing buckets\")\r\n print(self.bucket)\r\n print(self.prefix)\r\n # all_objects = self.client.list_objects(Bucket='datg-daa-adsales-dev-curated', Prefix='contract/')\r\n all_objects = self.client.list_objects(Bucket=self.bucket, Prefix=self.prefix)\r\n print(\"Printing all_objects\")\r\n print(all_objects)\r\n count = 0\r\n for fold in all_objects['Contents']:\r\n print(fold['Key'])\r\n count = count + 1\r\n\r\n print(count)\r\n # self.log.info(\"source file path is %s:\" % self.input_absolute_source_path)\r\n # self.log.info(\"output path is %s:\" % self.output_absolute_path)\r\n\r\n self.log.info(\"Going to load the first source file :%s\" % self.input_path_adsales_rev_goal)\r\n df_adsales_rev_goal = self.spark_session.read.format(\"com.databricks.spark.csv\").\\\r\n option(\"header\", \"true\"). \\\r\n option(\"treatEmptyValuesAsNulls\", \"true\"). \\\r\n option(\"delimiter\", \"|\"). \\\r\n option(\"multiline\", \"true\").\\\r\n option(\"quote\", \"\\\"\").\\\r\n load(self.input_path_adsales_rev_goal)\r\n self.log.info(\"Going to load the second source file :%s\" % self.input_path_adsales_proj_rev)\r\n df_adsales_proj_rev = self.spark_session.read.format(\"com.databricks.spark.csv\"). \\\r\n option(\"header\", \"true\"). \\\r\n option(\"treatEmptyValuesAsNulls\", \"true\"). \\\r\n option(\"multiline\", \"true\"). \\\r\n option(\"quote\", \"\\\"\"). \\\r\n option(\"delimiter\", \"|\"). \\\r\n load(self.input_path_adsales_proj_rev)\r\n df_adsales_calender = self.spark_session.read.format(\"com.databricks.spark.csv\"). \\\r\n option(\"header\", \"true\"). \\\r\n option(\"treatEmptyValuesAsNulls\", \"true\"). \\\r\n option(\"delimiter\", \"|\"). \\\r\n option(\"multiline\", \"true\"). \\\r\n option(\"quote\", \"\\\"\"). \\\r\n load(self.input_path_adsales_calender)\r\n df_adsales_rev_goal.registerTempTable(\"REVENUE_GOAL_PLACEMENT_REPORT\")\r\n print(\"printing REVENUE_GOAL_PLACEMENT_REPORT\")\r\n df_adsales_rev_goal.printSchema()\r\n df_adsales_proj_rev.registerTempTable(\"PROJECTED_REV_REPORT\")\r\n print(\"printing PROJECTED_REV_REPORT\")\r\n df_adsales_proj_rev.printSchema()\r\n df_adsales_calender.registerTempTable(\"S_BROADCAST_CALENDAR\")\r\n print(\"printing S_BROADCAST_CALENDAR\")\r\n df_adsales_calender.printSchema()\r\n df_current_file = self.spark_session.sql(\r\n \"SELECT DISTINCT (CASE when insertion_order_approval_status='APPROVED'\"\r\n \" then gpr.insertion_order_id else '' end) contract_cd,\"\r\n \"gpr.insertion_order_id proposal_cd,gpr.src_sys_id, 1 AS contract_status_id,\"\r\n \" '' AS advertiser_id, gpr.advertiser_id AS advertiser_cd, '' AS agency_id,\"\r\n \" gpr.agency_id AS agency_cd, 2 AS contract_type_id, '' AS rating_type_id, \"\r\n \"'' AS primary_demo_id, 3 AS revenue_type_id, '' AS traffic_status_id, \"\r\n \"'' AS stew_method_id,(case when gpr.placement_meta_data_4=NULL then 'N' else 'Y' end) \"\r\n \"AS guarantee_ind, '' AS agency_comm_pct, '' AS total_budget_amt, \"\r\n \"pr.io_gross_cost AS total_contract_amt, '' AS hold_experation_dttm, \"\r\n \"'' AS contract_year_cd, gpr.io_default_role_1 AS contract_acct_exec_party_id,\"\r\n \" gpr.io_default_role_2 AS contract_planner_party_id, gpr.io_default_role_4 \"\r\n \"AS contract_exec_party_id, '' AS hh_stew_method_cd, '' AS demo_stew_method_cd,\"\r\n \" bc.broadcast_quarter_id, \"\r\n \" '' AS src_sys_row_id FROM \"\r\n \"REVENUE_GOAL_PLACEMENT_REPORT gpr left join PROJECTED_REV_REPORT pr \"\r\n \"on gpr.insertion_order_id=pr.io_id inner join S_BROADCAST_CALENDAR bc on \"\r\n \"gpr.event_quarter=bc.calendar_dt\").\\\r\n registerTempTable(\"current\")\r\n\r\n self.spark_session.sql(\"select \" + self.curatedColumnsCurrent + \" from current\"). \\\r\n withColumn(\"Hash_Column\",\r\n hash(\"contract_cd\", \"proposal_cd\", \"src_sys_id\", \"contract_status_id\",\r\n \"advertiser_id\", \"advertiser_cd\", \"agency_id\", \"agency_cd\",\r\n \"contract_type_id\", \"rating_type_id\", \"primary_demo_id\",\r\n \"revenue_type_id\", \"traffic_status_id\", \"stew_method_id\",\r\n \"guarantee_ind\", \"agency_comm_pct\", \"total_budget_amt\",\r\n \"total_contract_amt\", \"hold_experation_dttm\", \"contract_year_cd\",\r\n \"contract_acct_exec_party_id\", \"contract_planner_party_id\",\r\n \"contract_exec_party_id\", \"hh_stew_method_cd\", \"demo_stew_method_cd\",\r\n \"broadcast_quarter_id\", \"src_sys_row_id\")).\\\r\n registerTempTable(\"currenthash\")\r\n\r\n # Loading historical file.\r\n if count < 2:\r\n print(\"This is first run.\")\r\n dfcuratedNew = self.spark_session.sql(\r\n \"select a.*, 1 as version_no from currenthash a\"). \\\r\n withColumn(\"CDC_Flag\", lit('I')).\\\r\n withColumn(\"file_date\", lit(self.file_date))\r\n # unionfinal = dfcuratedNew.drop('Hash_Column')\r\n dfcuratedNew.coalesce(1).select(\"*\"). \\\r\n write.format(\"com.databricks.spark.csv\"). \\\r\n option(\"header\", \"true\"). \\\r\n mode(\"overwrite\"). \\\r\n save(self.output_curated_path)\r\n else:\r\n print(\"inside else block.\")\r\n print(self.historyPath)\r\n dfInputFile = self.spark_session.read.format(\"com.databricks.spark.csv\"). \\\r\n option(\"header\", \"true\"). \\\r\n option(\"treatEmptyValuesAsNulls\", \"true\"). \\\r\n option(\"inferSchema\", \"true\"). \\\r\n option(\"quote\", \"\\\"\"). \\\r\n option(\"multiLine\", \"true\"). \\\r\n option(\"spark.read.simpleMode\", \"true\"). \\\r\n option(\"delimiter\", \",\"). \\\r\n option(\"useHeader\", \"true\"). \\\r\n load(self.historyPath)\r\n dfInputFile.registerTempTable(\"previous\")\r\n dfInputFile.show(10)\r\n dfInputFile.printSchema()\r\n self.log.info(\r\n \"The curated file from %s has been \"\r\n \"registered temporarily.\" % self.output_curated_path)\r\n self.spark_session.sql(\r\n \"select * from (select \" + self.curatedColumnsPrevious\r\n + \",row_number() over (partition by PROPOSAL_CD,BROADCAST_QUARTER_ID \"\r\n \"order by version_no desc) as rnum from previous )aa where rnum = 1\").\\\r\n registerTempTable(\"previoushash\")\r\n\r\n \"\"\"\r\n dfcuratedUpdated = self.spark_session.sql(\r\n \"select b.* , a.version_no+1 from previoushash a \"\r\n \"left join currenthash b on a.proposal_cd = b.proposal_cd \"\r\n \"and a.BROADCAST_QUARTER_ID = b.BROADCAST_QUARTER_ID \"\r\n \"where a.Hash_Column <> b.Hash_Column \"\r\n \"and a.CONTRACT_STATUS_ID = b.CONTRACT_STATUS_ID\").\\\r\n withColumn(\"CDC_Flag\", lit('U'))\r\n dfcuratedUI = self.spark_session.sql(\r\n \"select b.*, a.version_no+1 as version_no from previoushash a \"\r\n \"left join currenthash b on a.proposal_cd = b.proposal_cd \"\r\n \"and a.BROADCAST_QUARTER_ID = b.BROADCAST_QUARTER_ID \"\r\n \"where a.Hash_Column <> b.Hash_Column \"\r\n \"and a.CONTRACT_STATUS_ID <> b.CONTRACT_STATUS_ID\").\\\r\n withColumn(\"CDC_Flag\", lit('UI'))\r\n \"\"\"\r\n print(\"dfprevioushash printing.\")\r\n dfprevioushash = self.spark_session.sql(\"select * from previoushash\")\r\n dfprevioushash.show(10)\r\n dfcuratedUI = self.spark_session.sql(\r\n \"select b.*, a.version_no+1 as version_no from previoushash a \"\r\n \"left join currenthash b on a.proposal_cd = b.proposal_cd \"\r\n \"and a.BROADCAST_QUARTER_ID = b.BROADCAST_QUARTER_ID \"\r\n \"where a.Hash_Column <> b.Hash_Column \"). \\\r\n withColumn(\"CDC_Flag\", lit('UI')). \\\r\n withColumn(\"file_date\", lit(self.file_date))\r\n print(\"dfcuratedUI print\")\r\n dfcuratedUI.show(10)\r\n dfcuratedNew = self.spark_session.sql(\r\n \"select a.*, 1 as version_no from currenthash a \"\r\n \"left join previoushash b on a.proposal_cd = b.proposal_cd \"\r\n \"and a.BROADCAST_QUARTER_ID = b.BROADCAST_QUARTER_ID \"\r\n \"where b.proposal_cd is null \"\r\n \"or b.BROADCAST_QUARTER_ID is null\"). \\\r\n withColumn(\"CDC_Flag\", lit('I')). \\\r\n withColumn(\"file_date\", lit(self.file_date))\r\n\r\n\r\n print(\"dfcuratedNew print\")\r\n dfcuratedNew.show(10)\r\n\r\n dfcuratedUI.printSchema()\r\n dfcuratedNew.printSchema()\r\n\r\n unionfinal = dfcuratedUI.union(dfcuratedNew)\r\n unionfinal.coalesce(1).select(\"*\").\\\r\n write.format(\"com.databricks.spark.csv\").\\\r\n option(\"header\", \"true\"). \\\r\n option(\"multiline\", \"true\"). \\\r\n option(\"delimiter\", \"|\").\\\r\n option(\"quote\", \"\\\"\"). \\\r\n mode(\"overwrite\"). \\\r\n save(self.output_curated_path)\r\n\r\n self.spark_session.stop()\r\n\r\n\r\nif __name__ == \"__main__\":\r\n StageToContract().load_file()\r\n","sub_path":"Curated/adsales_freewheel_scd_curated_contract.py","file_name":"adsales_freewheel_scd_curated_contract.py","file_ext":"py","file_size_in_byte":12355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"56329783","text":"# Ref: https://medium.com/@ermolushka/text-clusterization-using-python-and-doc2vec-8c499668fa61\n\n# Requirements:\n# gensim==3.8.3\n# scikit-learn==0.24.2\n\n# Imports\nimport json\nimport nltk\nfrom gensim.models.doc2vec import Doc2Vec, TaggedDocument\nfrom nltk.tokenize import word_tokenize\nimport matplotlib.pyplot as plt\nfrom sklearn.decomposition import PCA\nfrom sklearn.cluster import KMeans\nfrom sklearn.metrics import silhouette_score\nimport pickle\n\n# Constants\nANN_FILES = [\"/mydata/MSRVTT_Annotations/test_videodatainfo.json\", \"/mydata/MSRVTT_Annotations/train_val_videodatainfo.json\"]\nCLUSTERS = [10, 20, 30, 40, 50]\n\nif __name__ == \"__main__\":\n nltk.download('punkt')\n\n # Initializing the ground truth dictionary and a list of captions.\n data_dict = {}\n cat_dict = {}\n label_dict = {}\n ground_truth_dict = {}\n\n # Reading the annotations\n for ann in ANN_FILES:\n with open(ann) as file:\n data = json.load(file)\n\n # Obtaining all the captions\n sentences = data[\"sentences\"]\n videos = data[\"videos\"]\n\n # Iterating over the captions\n for sentence in sentences:\n video_id = sentence[\"video_id\"]\n caption = sentence[\"caption\"]\n\n # If video in the dictionary, append the caption to the list of captions.\n if video_id in data_dict:\n sent = data_dict[video_id]\n data_dict[video_id] = sent + \" \" + caption + \".\"\n else:\n # If the video is not in dictionary, add to it.\n data_dict[video_id] = caption + \".\"\n\n for video_id in data_dict:\n for video in videos:\n if video[\"video_id\"] == video_id:\n cat_dict[data_dict[video_id]] = video[\"category\"]\n\n tagged_data = [TaggedDocument(words=word_tokenize(_d.lower()), tags=[str(i)]) for i, _d in enumerate(data_dict.values())]\n\n # With detailed hyper parameters provided.\n # d2v_model = Doc2Vec(tagged_data, size=1000, window=10, min_count=500, workers=7, dm=1, alpha=0.025, min_alpha=0.001)\n # d2v_model.train(tagged_data, total_examples=d2v_model.corpus_count, epochs=1000, start_alpha=0.002, end_alpha=-0.016)\n\n d2v_model = Doc2Vec(tagged_data, size=300, workers=25)\n d2v_model.train(tagged_data, total_examples=d2v_model.corpus_count, epochs=1000)\n\n for c in CLUSTERS:\n kmeans_model = KMeans(n_clusters=c, init='k-means++', max_iter=100)\n X = kmeans_model.fit(d2v_model.docvecs.doctag_syn0)\n labels = kmeans_model.labels_.tolist()\n\n # Computing the silhouette score\n silhouette_avg = silhouette_score(d2v_model.docvecs.doctag_syn0, labels)\n print(\"Silhouette score (n=\", c, \"): \", silhouette_avg)\n\n # Plotting the clusters\n pca = PCA(n_components=2).fit(d2v_model.docvecs.doctag_syn0)\n datapoint = pca.transform(d2v_model.docvecs.doctag_syn0)\n plt.scatter(datapoint[:, 0], datapoint[:, 1], c=labels)\n centroids = kmeans_model.cluster_centers_\n centroid_point = pca.transform(centroids)\n plt.scatter(centroid_point[:, 0], centroid_point[:, 1], marker='^', s=20, c='#000000')\n plt.savefig(\"clusters_\" + str(c) + \".png\")\n\n # Obtaining the cluster for each video\n for index, (key, value) in enumerate(data_dict.items()):\n label_dict[key] = labels[index]\n\n # Creating the ground truth\n for q_video_id in label_dict:\n results = []\n\n for c_video_id in label_dict:\n if label_dict[c_video_id] == label_dict[q_video_id]:\n results.append(c_video_id)\n\n ground_truth_dict[q_video_id] = results\n\n # Creating the pickle file of the ground truth.\n with open(\"Clustered_Ground_Truth\" + str(c) + \".pkl\", \"wb\") as f:\n pickle.dump(ground_truth_dict, f)\n","sub_path":"QIK-Videos/Evaluate/MSR_VTT/create_clusters.py","file_name":"create_clusters.py","file_ext":"py","file_size_in_byte":3903,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"356558186","text":"import testmatplotlib\nimport tensorflow as tf\nfrom sklearn.model_selection import train_test_split\nimport matplotlib\nmatplotlib.use('TkAgg')\nimport matplotlib.pyplot as plt\n\nlearning_rate = 0.5#学习率\nepochs = 300#训练次数\nbatch_size = 100#每次取的样本数量\n\n# loading dataset\ndataset = testmatplotlib.Toarray()\n# print(type(dataset))\n# print(dataset.keys())\ndata_X = dataset['data']\ndata_y = dataset['target']\nprint(data_X)\nprint(data_y)\n\nX_train, X_test, y_train, y_test = train_test_split(data_X, data_y, test_size=0.2)\n\ndef add_layer(inputs, in_size, out_size, activation_function=None):\n Weights = tf.Variable(tf.random_normal([in_size, out_size]))\n biases = tf.Variable(tf.zeros([1, out_size]) + 0.1)\n Wx_plus_b = tf.matmul(inputs, Weights) + biases\n if activation_function == None:\n outputs = Wx_plus_b\n else:\n outputs = activation_function(Wx_plus_b)\n return outputs\n\nxs = tf.placeholder(tf.float32, [None, 3])\nys = tf.placeholder(tf.float32, [None, 1])\n\nl1 = add_layer(xs, 3, 5, activation_function=None)\n# l2 = add_layer(l1, 10, 10, activation_function=tf.nn.relu)\nprediction = add_layer(l1, 5, 1, activation_function=None)\n\nloss = tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction), reduction_indices=[1]))\ntrain_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)\n\nsess = tf.Session()\nsess.run(tf.global_variables_initializer())\n\nfor i in range(300):\n start = i*batch_size\n end = (i+1)*batch_size\n sess.run(train_step, feed_dict={xs: X_train[start:end], ys: y_train[start:end]})\n if i % 50 == 0:\n print(\"loss\", sess.run(loss, feed_dict={xs: X_train[start:end], ys: y_train[start:end]}))\n # print(\"prediction\", sess.run(prediction, feed_dict={xs: X_test, ys: y_test}))\n print(\"prediction\", sess.run(prediction, feed_dict={xs: X_train[start:end], ys: y_train[start:end]}))\n\n# plt.figure(1)\n# plt.scatter(x, y, c='b') # plot data: c-color\n# plt.plot(x, result, 'r-', lw=2) # plot line fitting\n\n\n# A = [[1, 3, 4, 5, 6]]\n# B = [[1, 3, 4], [2, 4, 1]]\n#\n# with tf.Session() as sess:\n# print(sess.run(tf.argmax(A, 0)))\n# print(sess.run(tf.argmax(B, 1)))","sub_path":"TFpointcloud.py","file_name":"TFpointcloud.py","file_ext":"py","file_size_in_byte":2172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"90870668","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n\nimport socketserver\nimport threading\n\nHOST = ''\nPORT = 9009\nlock = threading.Lock()\n\n\nclass UserManager:\n def __init__(self):\n self.users = {}\n\n def add_user(self, username, conn, addr):\n if username in self.users:\n conn.send('이미 등록된 사용자입니다.\\n'.encode())\n return None\n\n # 새로운 사용자를 등록함\n lock.acquire()\n self.users[username] = (conn, addr)\n lock.release()\n\n self.send_message_to_all('[%s]님이 입장했습니다.' % username)\n print('+++ 대화 참여자 수 [%d]' %len(self.users))\n \n return username\n\n def remove_user(self, username):\n if username not in self.users:\n return\n\n lock.acquire()\n del self.users[username]\n lock.release()\n\n self.send_message_to_all('[%s]님이 퇴장했습니다.' % username)\n print('--- 대화 참여자 수 [%d]' %len(self.users))\n\n def message_handler(self, username, msg):\n if msg[0] != '/':\n self.send_message_to_all('[%s] %s' % (username, msg))\n return\n\n if msg.strip() == '/quit':\n self.remove_user(username)\n return -1\n\n def send_message_to_all(self, msg):\n for conn, addr in self.users.values():\n conn.send(msg.encode())\n \n\nclass MyTcpHandler(socketserver.BaseRequestHandler):\n userman = UserManager()\n \n def handle(self): \n print('[%s] 연결됨' %self.client_address[0])\n\n try:\n username = self.register_username()\n msg = self.request.recv(1024)\n while msg:\n print(msg.decode())\n if self.userman.message_handler(username, msg.decode()) == -1:\n self.request.close()\n break\n msg = self.request.recv(1024)\n \n except Exception as e:\n print(e)\n\n print('[%s] 접속종료' %self.client_address[0])\n self.userman.remove_user(username)\n\n def register_username(self):\n while True:\n self.request.send('로그인ID:'.encode())\n username = self.request.recv(1024)\n username = username.decode().strip()\n if self.userman.add_user(username, self.request, self.client_address):\n return username\n\n\nclass ChatingServer(socketserver.ThreadingMixIn, socketserver.TCPServer):\n pass\n\n\ndef run_server():\n print('+++ 채팅 서버를 시작합니다.')\n print('+++ 채텅 서버를 끝내려면 Ctrl-C를 누르세요.')\n\n try:\n server = ChatingServer((HOST, PORT), MyTcpHandler)\n server.serve_forever()\n except KeyboardInterrupt:\n print('--- 채팅 서버를 종료합니다.')\n server.shutdown()\n server.server_close()\n\nrun_server()\n","sub_path":"deliberate-learning/snippet/part-09/178_chat_server.py","file_name":"178_chat_server.py","file_ext":"py","file_size_in_byte":2758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"241218173","text":"from flask import Flask, render_template, request, redirect\nfrom datetime import date, timedelta\nimport sqlite3\n\napp = Flask(__name__)\n\n# create a global variable for the tank number that defaults to 0\ntankNum = \"0\"\ntankName = \"0\"\n# create global variable for the number of tables in the db\nnumTables = 18\n\n# method to get date from the db\ndef getHistData(startDate, endDate, tankNum):\n # create a string to send to the db to retreive data from a specified table between 2 dates\n sqlTableString = \"SELECT * FROM Tank\" + tankNum + \"Data WHERE timestamp BETWEEN '\" + startDate + \"' AND '\" + endDate + \"'\"\n # create a connection to the db\n con = sqlite3.connect(\"../data.db\")\n # get the db cursor\n curs = con.cursor()\n # get data from the db\n curs.execute(sqlTableString)\n # store the data from the db into data[]\n data = curs.fetchall()\n # create lists for each row in the table\n dates = []\n sats = []\n online = []\n floatAlarm = []\n o2Alarm = []\n pressure = []\n for row in data:\n # append the data in each row to their corresponding list\n dates.append(row[0])\n sats.append(row[1])\n online.append(row[2])\n floatAlarm.append(row[3])\n o2Alarm.append(row[4])\n pressure.append(row[5])\n # commit the transaction\n con.commit()\n # close the db connection\n con.close()\n # return all the lists\n return dates, sats, online, floatAlarm, o2Alarm, pressure\n\n# when at the root of the url (this is the overview page)\n@app.route(\"/\")\n# mex how do i luk in mai new pants\ndef beepBeepLettuce():\n # initialize an empty dictionary to store tank data\n tanks = {\n }\n # get the global varaible numTables\n global numTables\n # set startDate to yesterday\n startDate = str(date.today() - timedelta(1))\n # set endDate to tomorrow\n endDate = str(date.today() + timedelta(1))\n for x in range(numTables):\n # set tankNum to the iteration of the loop\n tankNum = str(x)\n # retreive data from table for specified start date, end date, and tank number\n dates, sats, online, floatAlarm, o2Alarm, pressure = getHistData(startDate, endDate, tankNum)\n # add the last value of each list to the dictionary with their corresponding keys\n # example entry: tanks[sat0 : 100]\n tanks[\"sat\" + str(x)] = sats[-1]\n tanks[\"online\" + str(x)] = online[-1]\n tanks[\"floatAlarm\" + str(x)] = floatAlarm[-1]\n tanks[\"o2Alarm\" + str(x)] = o2Alarm[-1]\n tanks[\"pressure\" + str(x)] = pressure[-1]\n # return the page 'main.html' along with access to the tanks dictionary\n return render_template(\"main.html\", data = tanks)\n\n# when the user clicks on 'Show Detail'\n@app.route(\"/\", methods=[\"POST\"])\ndef sendTank():\n # get the global tankNum and tankName\n global tankNum\n global tankName\n # set tankNum to the value retreived from the html form\n tankNum = str(request.form[\"tankNum\"])\n tankName = str(request.form[\"tankName\"])\n # redirect the user to route '/detail'\n return redirect(\"/detail\")\n\n# when the user submits the date form on the details page\n@app.route(\"/detail\", methods=[\"POST\"])\ndef formPost():\n # set startDate to the date selected from the date form\n startDate = str(request.form[\"startDate\"])\n # set the endDate to the date selected from the date form\n endDate = str(request.form[\"endDate\"])\n # get the global tankNum and tankName\n global tankNum\n global tankName\n # retreive data from table for specified start date, end date, and tank number\n dates, sats, online, floatAlarm, o2Alarm, pressure = getHistData(startDate, endDate, tankNum)\n # set legend to 'Tank x Saturation (%)' where x is the tankNum + 1\n # example string if tankNum were equal to 0: Tank 1 Saturation\n legend = \"Tank \" + tankName + \" Saturation (%)\"\n # just reassigning variables for use in the html templating\n labels = dates\n values = sats\n # set maxDate to tomorrow\n maxDate = date.today() + timedelta(1)\n # return 'data.html' with access to allllllll the variables\n # any that have a [-1] are a list and only the last element is being returned\n return render_template(\"detail.html\", online = online, floatAlarm = floatAlarm, tankName = tankName, o2Alarm = o2Alarm, values = values, labels = labels, legend = legend, tankNum = str(int(tankNum) + 1), startDate = startDate, endDate = endDate, maxDate = maxDate, pressure = pressure)\n\n# do the same as the previous method but automatically set the start and end date rather then getting them from a form\n@app.route(\"/detail\")\ndef detail():\n # set startDate to 2 days ago\n startDate = str(date.today() - timedelta(3))\n # set endDate to tomorrow\n endDate = str(date.today() + timedelta(1))\n global tankNum\n global tankName\n dates, sats, online, floatAlarm, o2Alarm, pressure = getHistData(startDate, endDate, tankNum)\n legend = \"Tank \" + tankName + \" Saturation (%)\"\n labels = dates\n values = sats\n return render_template(\"detail.html\", values = values, labels = labels, legend = legend, tankName= tankName, tankNum = str(int(tankNum) + 1), maxDate = endDate, startDate = startDate, endDate = endDate, online = online, o2Alarm = o2Alarm, floatAlarm = floatAlarm, pressure = pressure)\n\nif __name__ == \"__main__\":\n app.run()","sub_path":"webServer/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"177313085","text":"#!/usr/bin/env python\n# coding=utf-8\n\nmain_str = list('01011100010111000101110101000111011101010100')\n\nprint(str(main_str))\nprint('Length: ' + str(len(main_str)))\nprint(format(43, '06b'))\nprint('=' * 70)\nprint(' | c | d | e | f || ')\n\nfor i in range(0, 64):\n\n if len(list(main_str)) > i:\n beacon = main_str[i]\n else:\n beacon = 'X'\n\n a = int(format(i, '06b')[0])\n b = int(format(i, '06b')[1])\n c = int(format(i, '06b')[2])\n d = int(format(i, '06b')[3])\n e = int(format(i, '06b')[4])\n f = int(format(i, '06b')[5])\n\n # (f and not c) or (f and not e) or (f and d) or (d and not c and not e) or (e and d and c)\n if (f and not c) or (e and not c and not d) or (f and not e and not d): # (f and not d) or (d and not e)\n res = 1\n else:\n res = 0\n print(str(format(i, '02d')) +\n # ' | ' + str(a) +\n # ' | ' + str(b) +\n ' | ' + str(c) +\n ' | ' + str(d) +\n ' | ' + str(e) +\n ' | ' + str(f) +\n ' || ' + str(beacon) +\n ' | ' + str(res)\n )\n\n if (i + 1) % 16 == 0:\n print('=' * 70)\n print(' | c | d | e | f || ')\n","sub_path":"Programs/Tests/karnagh_maps.py","file_name":"karnagh_maps.py","file_ext":"py","file_size_in_byte":1167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"31846096","text":"# coding=utf-8\nimport Config\nimport Common\nimport Course.database as dao\n\n\ndef update(config, semester, version):\n # 更新全体名单\n tag_meaning = {\n \"序号\": \"id\",\n \"课程号\": \"code\",\n \"课程名称\": \"name\",\n \"课程大类\": \"type\",\n \"开课单位\": \"department\",\n }\n if Common.fetch_list_data(config, \"课程列表\", \"course_list\", tag_meaning, \"kcmd\", 100):\n Common.update_page_info(config, \"课程列表\", \"course_list\", dao.write_course_info)\n\n # 更新活跃教室\n active_list = Common.fetch_active_list(config, version, \"可用课程\", \"act_course\", \"kclb\", semester)\n Common.update_active_list(config, \"可用课程\", \"act_course\", \"course\", semester, active_list)\n\n # 更新教室课表\n Common.fetch_class_table_oc(config, version, \"课程课表\", \"course_table\", \"course\", \"kckb\", semester, active_list)\n Common.update_table_info_oc(config, version, \"课程课表\", \"course_table\", \"course\", semester)\n\n\nif __name__ == \"__main__\":\n _config = Config.load_config(\"../Config\")\n main(_config, \"2019-2020-1\", \"2020-02-16\")\n","sub_path":"Course/course_main.py","file_name":"course_main.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"516817538","text":"import copy\nfrom cereal import car\nfrom selfdrive.car.hyundai.values import DBC, STEER_THRESHOLD, FEATURES, EV_CAR, HYBRID_CAR, Buttons\nfrom selfdrive.car.interfaces import CarStateBase\nfrom opendbc.can.parser import CANParser\nfrom opendbc.can.can_define import CANDefine\nfrom selfdrive.config import Conversions as CV\n\nimport common.loger as trace1\n\nGearShifter = car.CarState.GearShifter\n\n\n\nclass CarState(CarStateBase):\n def __init__(self, CP):\n super().__init__(CP)\n can_define = CANDefine(DBC[CP.carFingerprint][\"pt\"])\n\n if self.CP.carFingerprint in FEATURES[\"use_cluster_gears\"]:\n self.shifter_values = can_define.dv[\"CLU15\"][\"CF_Clu_Gear\"]\n elif self.CP.carFingerprint in FEATURES[\"use_tcu_gears\"]:\n self.shifter_values = can_define.dv[\"TCU12\"][\"CUR_GR\"]\n else: # preferred and elect gear methods use same definition\n self.shifter_values = can_define.dv[\"LVR12\"][\"CF_Lvr_Gear\"]\n\n # atom\n self.cruise_buttons = 0\n self.cruise_buttons_time = 0\n self.time_delay_int = 0\n self.enable_status = 0\n self.VSetDis = 0\n self.clu_Vanz = 0\n\n # acc button \n self.prev_clu_CruiseSwState = 0\n self.prev_acc_active = 0\n self.prev_acc_set_btn = False\n self.acc_active = 0\n self.cruise_set_speed_kph = 0\n self.cruise_set_mode = 0 # 모드 설정.\n self.gasPressed = False\n\n\n\n #@staticmethod\n def cruise_speed_button( self ):\n if self.prev_acc_active != self.acc_active:\n self.prev_acc_active = self.acc_active\n self.cruise_set_speed_kph = self.clu_Vanz\n\n set_speed_kph = self.cruise_set_speed_kph\n if not self.prev_acc_set_btn:\n self.prev_acc_set_btn = self.acc_active\n if self.cruise_buttons == Buttons.RES_ACCEL: # up \n self.cruise_set_speed_kph = self.VSetDis\n else:\n self.cruise_set_speed_kph = self.clu_Vanz\n\n if self.prev_clu_CruiseSwState != self.cruise_buttons:\n self.prev_clu_CruiseSwState = self.cruise_buttons\n if self.cruise_buttons == Buttons.GAP_DIST:\n self.cruise_set_mode += 1\n if self.cruise_set_mode > 2:\n self.cruise_set_mode = 0\n return self.cruise_set_speed_kph\n\n elif self.prev_acc_set_btn != self.acc_active:\n self.prev_acc_set_btn = self.acc_active\n\n if self.cruise_buttons:\n self.cruise_buttons_time += 1\n else:\n self.cruise_buttons_time = 0\n \n \n if self.cruise_buttons_time >= 60:\n self.cruise_set_speed_kph = self.VSetDis\n\n if self.prev_clu_CruiseSwState == self.cruise_buttons:\n return set_speed_kph\n self.prev_clu_CruiseSwState = self.cruise_buttons\n\n if self.cruise_buttons == Buttons.RES_ACCEL: # up \n set_speed_kph += 1\n elif self.cruise_buttons == Buttons.SET_DECEL: # dn\n if self.gasPressed:\n set_speed_kph = self.clu_Vanz + 1\n else:\n set_speed_kph -= 1\n\n if set_speed_kph < 30:\n set_speed_kph = 30\n\n self.cruise_set_speed_kph = set_speed_kph\n return set_speed_kph\n\n\n\n def cruise_enabled_btn( self, main_on, vEgo ):\n if main_on == False:\n self.time_delay_int = 0\n elif not self.enable_status:\n self.time_delay_int = 2000\n elif vEgo > 5: # 15 km/h\n self.time_delay_int = 0\n elif self.time_delay_int > 0:\n self.time_delay_int -= 1\n\n if self.time_delay_int <= 0:\n enabled = main_on\n else:\n enabled = False\n\n return enabled\n\n # TPMS code added from OPKR\n def update_tpms(self, cp, ret ):\n unit_ratio = 1.0\n unit = cp.vl[\"TPMS11\"][\"UNIT\"]\n ret.tpms.fl = cp.vl[\"TPMS11\"]['PRESSURE_FL']\n ret.tpms.fr = cp.vl[\"TPMS11\"]['PRESSURE_FR']\n ret.tpms.rl = cp.vl[\"TPMS11\"]['PRESSURE_RL']\n ret.tpms.rr = cp.vl[\"TPMS11\"]['PRESSURE_RR']\n\n if unit == 1.0:\n unit_ratio = 0.72519\n elif unit == 2.0:\n unit_ratio = 1.45038\n\n ret.tpms.fl *= unit_ratio\n ret.tpms.fr *= unit_ratio\n ret.tpms.rl *= unit_ratio\n ret.tpms.rr *= unit_ratio\n return ret\n\n def update(self, cp, cp_cam):\n ret = car.CarState.new_message()\n\n ret.doorOpen = any([cp.vl[\"CGW1\"][\"CF_Gway_DrvDrSw\"], cp.vl[\"CGW1\"][\"CF_Gway_AstDrSw\"],\n cp.vl[\"CGW2\"][\"CF_Gway_RLDrSw\"], cp.vl[\"CGW2\"][\"CF_Gway_RRDrSw\"]])\n\n ret.seatbeltUnlatched = cp.vl[\"CGW1\"][\"CF_Gway_DrvSeatBeltSw\"] == 0\n\n ret.wheelSpeeds.fl = cp.vl[\"WHL_SPD11\"][\"WHL_SPD_FL\"] * CV.KPH_TO_MS\n ret.wheelSpeeds.fr = cp.vl[\"WHL_SPD11\"][\"WHL_SPD_FR\"] * CV.KPH_TO_MS\n ret.wheelSpeeds.rl = cp.vl[\"WHL_SPD11\"][\"WHL_SPD_RL\"] * CV.KPH_TO_MS\n ret.wheelSpeeds.rr = cp.vl[\"WHL_SPD11\"][\"WHL_SPD_RR\"] * CV.KPH_TO_MS\n ret.vEgoRaw = (ret.wheelSpeeds.fl + ret.wheelSpeeds.fr + ret.wheelSpeeds.rl + ret.wheelSpeeds.rr) / 4.\n ret.vEgo, ret.aEgo = self.update_speed_kf(ret.vEgoRaw)\n\n ret.standstill = ret.vEgoRaw < 0.1\n\n ret.steeringAngleDeg = cp.vl[\"SAS11\"][\"SAS_Angle\"]\n ret.steeringRateDeg = cp.vl[\"SAS11\"][\"SAS_Speed\"]\n ret.yawRate = cp.vl[\"ESP12\"][\"YAW_RATE\"]\n ret.leftBlinker, ret.rightBlinker = self.update_blinker_from_lamp(\n 50, cp.vl[\"CGW1\"][\"CF_Gway_TurnSigLh\"], cp.vl[\"CGW1\"][\"CF_Gway_TurnSigRh\"])\n ret.steeringTorque = cp.vl[\"MDPS12\"][\"CR_Mdps_StrColTq\"]\n ret.steeringTorqueEps = cp.vl[\"MDPS12\"][\"CR_Mdps_OutTq\"]\n ret.steeringPressed = abs(ret.steeringTorque) > STEER_THRESHOLD\n ret.steerWarning = cp.vl[\"MDPS12\"][\"CF_Mdps_ToiUnavail\"] != 0 or cp.vl[\"MDPS12\"][\"CF_Mdps_ToiFlt\"] != 0\n\n # cruise state\n self.VSetDis = cp.vl[\"SCC11\"][\"VSetDis\"] # kph 크루즈 설정 속도.\n self.clu_Vanz = cp.vl[\"CLU11\"][\"CF_Clu_Vanz\"] #kph 현재 차량의 속도.\n self.acc_active = (cp.vl[\"SCC12\"]['ACCMode'] != 0)\n ret.vEgo = self.clu_Vanz * CV.KPH_TO_MS\n ret.cruiseState.accActive = self.acc_active\n ret.cruiseState.gapSet = cp.vl[\"SCC11\"]['TauGapSet']\n ret.cruiseState.cruiseSwState = self.cruise_buttons\n ret.cruiseState.modeSel = self.cruise_set_mode\n #if self.CP.openpilotLongitudinalControl:\n # ret.cruiseState.available = cp.vl[\"TCS13\"][\"ACCEnable\"] == 0\n # ret.cruiseState.enabled = cp.vl[\"TCS13\"][\"ACC_REQ\"] == 1\n # ret.cruiseState.standstill = False\n #else:\n ret.cruiseState.available = cp.vl[\"SCC11\"][\"MainMode_ACC\"] == 1\n ret.cruiseState.enabled = ret.cruiseState.available # cp.vl[\"SCC12\"][\"ACCMode\"] != 0\n ret.cruiseState.standstill = cp.vl[\"SCC11\"][\"SCCInfoDisplay\"] == 4.\n\n set_speed = self.cruise_speed_button()\n if self.acc_active:\n speed_conv = CV.MPH_TO_MS if cp.vl[\"CLU11\"][\"CF_Clu_SPEED_UNIT\"] else CV.KPH_TO_MS\n ret.cruiseState.speed = set_speed * speed_conv\n else:\n ret.cruiseState.speed = 0\n\n # TODO: Find brake pressure\n ret.brake = 0\n ret.brakePressed = cp.vl[\"TCS13\"][\"DriverBraking\"] != 0\n ret.brakeLightsDEPRECATED = bool(cp.vl[\"TCS13\"]['BrakeLight'])\n\n if self.CP.carFingerprint in (HYBRID_CAR | EV_CAR):\n if self.CP.carFingerprint in HYBRID_CAR:\n ret.gas = cp.vl[\"E_EMS11\"][\"CR_Vcu_AccPedDep_Pos\"] / 254.\n else:\n ret.gas = cp.vl[\"E_EMS11\"][\"Accel_Pedal_Pos\"] / 254.\n ret.gasPressed = ret.gas > 0\n else:\n ret.gas = cp.vl[\"EMS12\"][\"PV_AV_CAN\"] / 100.\n ret.gasPressed = bool(cp.vl[\"EMS16\"][\"CF_Ems_AclAct\"])\n\n self.gasPressed = ret.gasPressed\n # Gear Selection via Cluster - For those Kia/Hyundai which are not fully discovered, we can use the Cluster Indicator for Gear Selection,\n # as this seems to be standard over all cars, but is not the preferred method.\n if self.CP.carFingerprint in FEATURES[\"use_cluster_gears\"]:\n gear = cp.vl[\"CLU15\"][\"CF_Clu_Gear\"]\n elif self.CP.carFingerprint in FEATURES[\"use_tcu_gears\"]:\n gear = cp.vl[\"TCU12\"][\"CUR_GR\"]\n elif self.CP.carFingerprint in FEATURES[\"use_elect_gears\"]:\n gear = cp.vl[\"ELECT_GEAR\"][\"Elect_Gear_Shifter\"]\n else:\n gear = cp.vl[\"LVR12\"][\"CF_Lvr_Gear\"]\n\n ret.gearShifter = self.parse_gear_shifter(self.shifter_values.get(gear))\n\n if self.CP.carFingerprint in FEATURES[\"use_fca\"]:\n ret.stockAeb = cp.vl[\"FCA11\"][\"FCA_CmdAct\"] != 0\n ret.stockFcw = cp.vl[\"FCA11\"][\"CF_VSM_Warn\"] == 2\n else:\n ret.stockAeb = cp.vl[\"SCC12\"][\"AEB_CmdAct\"] != 0\n ret.stockFcw = cp.vl[\"SCC12\"][\"CF_VSM_Warn\"] == 2\n\n if self.CP.enableBsm:\n ret.leftBlindspot = cp.vl[\"LCA11\"][\"CF_Lca_IndLeft\"] != 0\n ret.rightBlindspot = cp.vl[\"LCA11\"][\"CF_Lca_IndRight\"] != 0\n\n ret = self.update_tpms( cp, ret )\n # save the entire LKAS11 and CLU11\n self.lkas11 = copy.copy(cp_cam.vl[\"LKAS11\"])\n self.clu11 = copy.copy(cp.vl[\"CLU11\"])\n self.mdps12 = copy.copy(cp.vl[\"MDPS12\"])\n self.park_brake = cp.vl[\"TCS13\"][\"PBRAKE_ACT\"] == 1\n self.steer_state = cp.vl[\"MDPS12\"][\"CF_Mdps_ToiActive\"] # 0 NOT ACTIVE, 1 ACTIVE\n self.lead_distance = cp.vl[\"SCC11\"][\"ACC_ObjDist\"]\n self.brake_hold = cp.vl[\"TCS15\"][\"AVH_LAMP\"] == 2 # 0 OFF, 1 ERROR, 2 ACTIVE, 3 READY\n self.brake_error = cp.vl[\"TCS13\"][\"ACCEnable\"] != 0 # 0 ACC CONTROL ENABLED, 1-3 ACC CONTROL DISABLED\n self.prev_cruise_buttons = self.cruise_buttons\n self.cruise_buttons = cp.vl[\"CLU11\"][\"CF_Clu_CruiseSwState\"]\n\n self.lkas_button_on = cp_cam.vl[\"LKAS11\"][\"CF_Lkas_LdwsSysState\"]\n self.is_highway = cp.vl[\"SCC11\"][\"Navi_SCC_Camera_Act\"] != 0.\n if ret.gearShifter != GearShifter.drive or ret.seatbeltUnlatched or ret.doorOpen:\n self.enable_status = False\n else:\n self.enable_status = ret.cruiseState.enabled\n\n if not self.cruise_enabled_btn( ret.cruiseState.enabled, ret.vEgo ):\n ret.cruiseState.enabled = False \n \n return ret\n\n @staticmethod\n def get_can_parser(CP):\n signals = [\n # sig_name, sig_address, default\n (\"WHL_SPD_FL\", \"WHL_SPD11\", 0),\n (\"WHL_SPD_FR\", \"WHL_SPD11\", 0),\n (\"WHL_SPD_RL\", \"WHL_SPD11\", 0),\n (\"WHL_SPD_RR\", \"WHL_SPD11\", 0),\n\n (\"YAW_RATE\", \"ESP12\", 0),\n\n (\"CF_Gway_DrvSeatBeltInd\", \"CGW4\", 1),\n\n (\"CF_Gway_DrvSeatBeltSw\", \"CGW1\", 0),\n (\"CF_Gway_DrvDrSw\", \"CGW1\", 0), # Driver Door\n (\"CF_Gway_AstDrSw\", \"CGW1\", 0), # Passenger door\n (\"CF_Gway_RLDrSw\", \"CGW2\", 0), # Rear reft door\n (\"CF_Gway_RRDrSw\", \"CGW2\", 0), # Rear right door\n (\"CF_Gway_TurnSigLh\", \"CGW1\", 0),\n (\"CF_Gway_TurnSigRh\", \"CGW1\", 0),\n (\"CF_Gway_ParkBrakeSw\", \"CGW1\", 0),\n\n (\"CYL_PRES\", \"ESP12\", 0),\n\n (\"CF_Clu_CruiseSwState\", \"CLU11\", 0),\n (\"CF_Clu_CruiseSwMain\", \"CLU11\", 0),\n (\"CF_Clu_SldMainSW\", \"CLU11\", 0),\n (\"CF_Clu_ParityBit1\", \"CLU11\", 0),\n (\"CF_Clu_VanzDecimal\" , \"CLU11\", 0),\n (\"CF_Clu_Vanz\", \"CLU11\", 0),\n (\"CF_Clu_SPEED_UNIT\", \"CLU11\", 0),\n (\"CF_Clu_DetentOut\", \"CLU11\", 0),\n (\"CF_Clu_RheostatLevel\", \"CLU11\", 0),\n (\"CF_Clu_CluInfo\", \"CLU11\", 0),\n (\"CF_Clu_AmpInfo\", \"CLU11\", 0),\n (\"CF_Clu_AliveCnt1\", \"CLU11\", 0),\n\n (\"ACCEnable\", \"TCS13\", 0),\n (\"ACC_REQ\", \"TCS13\", 0),\n (\"DriverBraking\", \"TCS13\", 0),\n (\"BrakeLight\", \"TCS13\", 0), \n (\"StandStill\", \"TCS13\", 0),\n (\"PBRAKE_ACT\", \"TCS13\", 0),\n\n (\"ESC_Off_Step\", \"TCS15\", 0),\n (\"AVH_LAMP\", \"TCS15\", 0),\n\n (\"CR_Mdps_StrColTq\", \"MDPS12\", 0),\n (\"CF_Mdps_ToiActive\", \"MDPS12\", 0),\n (\"CF_Mdps_ToiUnavail\", \"MDPS12\", 0),\n (\"CF_Mdps_ToiFlt\", \"MDPS12\", 0),\n (\"CR_Mdps_OutTq\", \"MDPS12\", 0),\n\n (\"CF_Mdps_MsgCount2\", \"MDPS12\", 0), #\n (\"CF_Mdps_Chksum2\", \"MDPS12\", 0), #\n\n\n\n (\"SAS_Angle\", \"SAS11\", 0),\n (\"SAS_Speed\", \"SAS11\", 0),\n\n (\"MainMode_ACC\", \"SCC11\", 0),\n (\"VSetDis\", \"SCC11\", 0),\n (\"SCCInfoDisplay\", \"SCC11\", 0),\n (\"ACC_ObjDist\", \"SCC11\", 0),\n (\"ACCMode\", \"SCC12\", 1),\n\n (\"Navi_SCC_Camera_Act\", \"SCC11\", 0),\n (\"TauGapSet\", \"SCC11\", 4),\n\n # TPMS\n (\"UNIT\", \"TPMS11\", 0),\n (\"PRESSURE_FL\", \"TPMS11\", 0),\n (\"PRESSURE_FR\", \"TPMS11\", 0),\n (\"PRESSURE_RL\", \"TPMS11\", 0),\n (\"PRESSURE_RR\", \"TPMS11\", 0), \n ]\n\n checks = [\n # address, frequency\n (\"MDPS12\", 50),\n (\"TCS13\", 50),\n (\"TCS15\", 10),\n (\"CLU11\", 50),\n (\"ESP12\", 100),\n (\"CGW1\", 10),\n (\"CGW2\", 5),\n (\"CGW4\", 5),\n (\"WHL_SPD11\", 50),\n (\"SAS11\", 100),\n\n (\"SCC11\", 50),\n (\"SCC12\", 50),\n\n (\"TPMS11\", 1),\n ]\n\n\n\n if CP.enableBsm:\n signals += [\n (\"CF_Lca_IndLeft\", \"LCA11\", 0),\n (\"CF_Lca_IndRight\", \"LCA11\", 0),\n ]\n checks += [(\"LCA11\", 50)]\n\n if CP.carFingerprint in (HYBRID_CAR | EV_CAR):\n if CP.carFingerprint in HYBRID_CAR:\n signals += [\n (\"CR_Vcu_AccPedDep_Pos\", \"E_EMS11\", 0)\n ]\n else:\n signals += [\n (\"Accel_Pedal_Pos\", \"E_EMS11\", 0)\n ]\n checks += [\n (\"E_EMS11\", 50),\n ]\n else:\n signals += [\n (\"PV_AV_CAN\", \"EMS12\", 0),\n (\"CF_Ems_AclAct\", \"EMS16\", 0),\n ]\n checks += [\n (\"EMS12\", 100),\n (\"EMS16\", 100),\n ]\n\n if CP.carFingerprint in FEATURES[\"use_cluster_gears\"]:\n signals += [\n (\"CF_Clu_Gear\", \"CLU15\", 0),\n ]\n checks += [\n (\"CLU15\", 5)\n ]\n elif CP.carFingerprint in FEATURES[\"use_tcu_gears\"]:\n signals += [\n (\"CUR_GR\", \"TCU12\", 0)\n ]\n checks += [\n (\"TCU12\", 100)\n ]\n elif CP.carFingerprint in FEATURES[\"use_elect_gears\"]:\n signals += [(\"Elect_Gear_Shifter\", \"ELECT_GEAR\", 0)]\n checks += [(\"ELECT_GEAR\", 20)]\n else:\n signals += [\n (\"CF_Lvr_Gear\", \"LVR12\", 0)\n ]\n checks += [\n (\"LVR12\", 100)\n ]\n\n if CP.carFingerprint in FEATURES[\"use_fca\"]:\n signals += [\n (\"FCA_CmdAct\", \"FCA11\", 0),\n (\"CF_VSM_Warn\", \"FCA11\", 0),\n ]\n if not CP.openpilotLongitudinalControl:\n checks += [(\"FCA11\", 50)]\n else:\n signals += [\n (\"AEB_CmdAct\", \"SCC12\", 0),\n (\"CF_VSM_Warn\", \"SCC12\", 0),\n ]\n\n return CANParser(DBC[CP.carFingerprint][\"pt\"], signals, checks, 0)\n\n @staticmethod\n def get_cam_can_parser(CP):\n\n signals = [\n # sig_name, sig_address, default\n (\"CF_Lkas_LdwsActivemode\", \"LKAS11\", 0),\n (\"CF_Lkas_LdwsSysState\", \"LKAS11\", 0),\n (\"CF_Lkas_SysWarning\", \"LKAS11\", 0),\n (\"CF_Lkas_LdwsLHWarning\", \"LKAS11\", 0),\n (\"CF_Lkas_LdwsRHWarning\", \"LKAS11\", 0),\n (\"CF_Lkas_HbaLamp\", \"LKAS11\", 0),\n (\"CF_Lkas_FcwBasReq\", \"LKAS11\", 0),\n (\"CF_Lkas_HbaSysState\", \"LKAS11\", 0),\n (\"CF_Lkas_FcwOpt\", \"LKAS11\", 0),\n (\"CF_Lkas_HbaOpt\", \"LKAS11\", 0),\n (\"CF_Lkas_FcwSysState\", \"LKAS11\", 0),\n (\"CF_Lkas_FcwCollisionWarning\", \"LKAS11\", 0),\n (\"CF_Lkas_MsgCount\", \"LKAS11\", 0), # append\n (\"CF_Lkas_FusionState\", \"LKAS11\", 0),\n (\"CF_Lkas_FcwOpt_USM\", \"LKAS11\", 0),\n (\"CF_Lkas_LdwsOpt_USM\", \"LKAS11\", 0),\n ]\n\n checks = [\n (\"LKAS11\", 100)\n ]\n\n return CANParser(DBC[CP.carFingerprint][\"pt\"], signals, checks, 2)\n","sub_path":"selfdrive/car/hyundai/carstate.py","file_name":"carstate.py","file_ext":"py","file_size_in_byte":14795,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"451031732","text":"from binascii import hexlify, unhexlify\n\nfrom base58 import b58encode_check\n\nfrom binary.unsigned_integer.reader import read_bit16, read_bit64\n\nfrom crypto.transactions.deserializers.base import BaseDeserializer\n\n\nclass MultiPaymentDeserializer(BaseDeserializer):\n\n def deserialize(self):\n starting_position = int(self.asset_offset / 2)\n\n payment_length = read_bit16(self.serialized, starting_position) & 0xff\n\n self.transaction.asset['payments'] = []\n\n index = 0\n\n for payment in range(payment_length):\n amount = read_bit64(self.serialized, offset=starting_position + 2 + index)\n\n recipient_start_index = (starting_position + 10 + index) * 2\n recipientId = hexlify(self.serialized)[recipient_start_index:recipient_start_index + 42]\n recipientId = b58encode_check(unhexlify(recipientId)).decode()\n\n self.transaction.asset['payments'].append({'amount': amount, 'recipientId': recipientId})\n\n index += 21 + 8\n\n self.transaction.parse_signatures(\n hexlify(self.serialized).decode(),\n self.asset_offset + 4 + (payment_length * (21 + 8)) * 2\n )\n\n return self.transaction\n","sub_path":"crypto/transactions/deserializers/multi_payment.py","file_name":"multi_payment.py","file_ext":"py","file_size_in_byte":1213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"226394497","text":"from bs4 import BeautifulSoup\r\nimport datetime\r\nimport pandas as pd\r\nimport numpy as np\r\nimport IDRdrop\r\nimport EPOwebscrape\r\n#import emailscrape\r\nimport json\r\nimport os\r\n\r\ndef main():\r\n\r\n #output_dict, warnings, filename = emailscrape.get_emails()\r\n filename = 'bodies.json'\r\n\r\n try:\r\n emails = output_dict\r\n \r\n except:\r\n \r\n with open(filename, 'r') as f:\r\n emails = json.load(f)\r\n emails = json.loads(emails)\r\n\r\n for item in emails.values():\r\n \r\n username = item['user']\r\n pwd = item['pw']\r\n accts_to_find = item['accts']\r\n\r\n NGrid = ('SUEZ' in username)\r\n\r\n scrape_data(username, pwd, NGrid, accts_to_find)\r\n split_data()\r\n filter_data()\r\n\r\ndef scrape_data(user_, pw_, ngrid, acct_list):\r\n\r\n browser, url = EPOwebscrape.logon(user_, pw_, ngrid)\r\n\r\n AIDs = []\r\n\r\n soup = BeautifulSoup(browser.page_source, features = 'html5lib')\r\n table = soup.find('tbody', {'role' : 'rowgroup'})\r\n\r\n #get EPO AID value for every account\r\n for accts in acct_list:\r\n results = EPOwebscrape.big_match(accts, table)\r\n AIDs.append(results)\r\n\r\n #make list of AIDs, split into list of lists of 5\r\n final = []\r\n\r\n for aid_list in AIDs:\r\n for aid in aid_list:\r\n final.append(aid)\r\n\r\n n = 5\r\n final2 = [final[i * n:(i + 1) * n] for i in range((len(final) + n - 1) // n )] \r\n\r\n for elem in final2:\r\n EPOwebscrape.export_data(elem, browser)\r\n \r\n\r\ndef split_data():\r\n \r\n day_before = datetime.datetime.now() - datetime.timedelta(days = 3)\r\n\r\n #readpath = 'C:\\\\Users\\wb5888\\Downloads'\r\n readpath = '/Users/stevenhurwitt/Downloads'\r\n\r\n myfiles = IDRdrop.show_dir(readpath, 20)\r\n\r\n rec_files = myfiles[myfiles.time > day_before]\r\n csv_ind = [file.split('.')[1] == 'csv' for file in rec_files.files]\r\n rec_files_csv = rec_files[csv_ind]\r\n index = rec_files_csv.index.values.astype(int)\r\n\r\n # Choose files to split into Raw IDR files.\r\n\r\n splitfiles = list(myfiles.files[index])\r\n print('files to split: ')\r\n print(splitfiles)\r\n\r\n\r\n #Batch process downloaded EPO files into Raw IDRs\r\n\r\n #writepath = 'C:\\\\Users\\\\wb5888\\\\Documents\\\\Python Code\\\\IDR_Drop\\\\Raw IDR Data'\r\n writepath = '/Volumes/USB30FD/Python Code/IDR_Drop/Raw IDR Data'\r\n errorlog = []\r\n\r\n for file in splitfiles:\r\n try:\r\n os.chdir(readpath)\r\n filedf = pd.read_csv(file, sep = \",\", header = 0)\r\n \r\n IDRdrop.raw_split(filedf, readpath, writepath)\r\n print('success, file: ', file)\r\n \r\n except:\r\n errorlog.append(file)\r\n print('error, file: ', file)\r\n return(errorlog)\r\n\r\n\r\ndef filter_data():\r\n #Show Raw IDR files based on utility\r\n\r\n #rawpath = 'C:\\\\Users\\\\wb5888\\\\Documents\\\\Python Code\\\\IDR_Drop\\\\Raw IDR Data'\r\n rawpath = '/Volumes/USB30FD/Python Code/IDR_Drop/Raw IDR Data'\r\n\r\n day_before = datetime.datetime.now() - datetime.timedelta(days = 3)\r\n rawfiles = IDRdrop.show_dir(rawpath, 50)\r\n rec_raw = rawfiles[rawfiles.time > day_before]\r\n csv_ind = [file.split('.')[1] == 'csv' for file in rec_raw.files]\r\n rec_raw_csv = rec_raw[csv_ind]\r\n index_raw = rec_raw_csv.index.values.astype(int)\r\n\r\n #Choose Raw IDRs to filter into IDR files.\r\n\r\n processfiles = list(rawfiles.files[index_raw])\r\n print('files to process: ')\r\n print(processfiles)\r\n\r\n\r\n #Batch filter Raw IDR into IDR files to be dropped\r\n\r\n #writepath = 'C:\\\\Users\\\\wb5888\\\\Documents\\\\IDR Data\\\\NEPOOL\\\\'\r\n writepath = '/Volumes/USB30FD/Python Code/IDR_Drop/IDR Data/NEPOOL'\r\n error_log2 = []\r\n\r\n for dropfile in processfiles:\r\n try:\r\n IDRdrop.data_drop(dropfile, rawpath, writepath)\r\n print('success, file: ', dropfile)\r\n \r\n except:\r\n error_log2.append(dropfile)\r\n print(\"error, file: \", dropfile)\r\n\r\n\r\n return(error_log2)\r\n\r\n\r\nmain()\r\n\r\n","sub_path":"IDR_Drop/IDR Drop Main.py","file_name":"IDR Drop Main.py","file_ext":"py","file_size_in_byte":4005,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"501954221","text":"# coding=utf-8\n# --------------------------------------------------------------------------\n# Copyright (c) Microsoft Corporation. All rights reserved.\n# Licensed under the MIT License. See License.txt in the project root for license information.\n# Code generated by Microsoft (R) AutoRest Code Generator.\n# Changes may cause incorrect behavior and will be lost if the code is regenerated.\n# --------------------------------------------------------------------------\nimport datetime\nfrom typing import Any, Callable, Dict, Generic, Optional, TypeVar\nimport warnings\n\nfrom azure.core.exceptions import ClientAuthenticationError, HttpResponseError, ResourceExistsError, ResourceNotFoundError, map_error\nfrom azure.core.pipeline import PipelineResponse\nfrom azure.core.pipeline.transport import AsyncHttpResponse, HttpRequest\nfrom azure.mgmt.core.exceptions import ARMErrorFormat\n\nfrom ... import models as _models\n\nT = TypeVar('T')\nClsType = Optional[Callable[[PipelineResponse[HttpRequest, AsyncHttpResponse], T, Dict[str, Any]], Any]]\n\nclass PredictiveMetricOperations:\n \"\"\"PredictiveMetricOperations async operations.\n\n You should not instantiate this class directly. Instead, you should create a Client instance that\n instantiates it for you and attaches it as an attribute.\n\n :ivar models: Alias to model classes used in this operation group.\n :type models: ~$(python-base-namespace).v2021_05_01_preview.models\n :param client: Client for service requests.\n :param config: Configuration of service client.\n :param serializer: An object model serializer.\n :param deserializer: An object model deserializer.\n \"\"\"\n\n models = _models\n\n def __init__(self, client, config, serializer, deserializer) -> None:\n self._client = client\n self._serialize = serializer\n self._deserialize = deserializer\n self._config = config\n\n async def get(\n self,\n resource_group_name: str,\n autoscale_setting_name: str,\n timespan: str,\n interval: datetime.timedelta,\n metric_namespace: str,\n metric_name: str,\n aggregation: str,\n **kwargs: Any\n ) -> \"_models.PredictiveResponse\":\n \"\"\"get predictive autoscale metric future data.\n\n :param resource_group_name: The name of the resource group.\n :type resource_group_name: str\n :param autoscale_setting_name: The autoscale setting name.\n :type autoscale_setting_name: str\n :param timespan: The timespan of the query. It is a string with the following format\n 'startDateTime_ISO/endDateTime_ISO'.\n :type timespan: str\n :param interval: The interval (i.e. timegrain) of the query.\n :type interval: ~datetime.timedelta\n :param metric_namespace: Metric namespace to query metric definitions for.\n :type metric_namespace: str\n :param metric_name: The names of the metrics (comma separated) to retrieve. Special case: If a\n metricname itself has a comma in it then use %2 to indicate it. Eg: 'Metric,Name1' should be\n **'Metric%2Name1'**.\n :type metric_name: str\n :param aggregation: The list of aggregation types (comma separated) to retrieve.\n :type aggregation: str\n :keyword callable cls: A custom type or function that will be passed the direct response\n :return: PredictiveResponse, or the result of cls(response)\n :rtype: ~$(python-base-namespace).v2021_05_01_preview.models.PredictiveResponse\n :raises: ~azure.core.exceptions.HttpResponseError\n \"\"\"\n cls = kwargs.pop('cls', None) # type: ClsType[\"_models.PredictiveResponse\"]\n error_map = {\n 401: ClientAuthenticationError, 404: ResourceNotFoundError, 409: ResourceExistsError\n }\n error_map.update(kwargs.pop('error_map', {}))\n api_version = \"2021-05-01-preview\"\n accept = \"application/json\"\n\n # Construct URL\n url = self.get.metadata['url'] # type: ignore\n path_format_arguments = {\n 'subscriptionId': self._serialize.url(\"self._config.subscription_id\", self._config.subscription_id, 'str', min_length=1),\n 'resourceGroupName': self._serialize.url(\"resource_group_name\", resource_group_name, 'str'),\n 'autoscaleSettingName': self._serialize.url(\"autoscale_setting_name\", autoscale_setting_name, 'str'),\n }\n url = self._client.format_url(url, **path_format_arguments)\n\n # Construct parameters\n query_parameters = {} # type: Dict[str, Any]\n query_parameters['timespan'] = self._serialize.query(\"timespan\", timespan, 'str')\n query_parameters['interval'] = self._serialize.query(\"interval\", interval, 'duration')\n query_parameters['metricNamespace'] = self._serialize.query(\"metric_namespace\", metric_namespace, 'str')\n query_parameters['metricName'] = self._serialize.query(\"metric_name\", metric_name, 'str')\n query_parameters['aggregation'] = self._serialize.query(\"aggregation\", aggregation, 'str')\n query_parameters['api-version'] = self._serialize.query(\"api_version\", api_version, 'str')\n\n # Construct headers\n header_parameters = {} # type: Dict[str, Any]\n header_parameters['Accept'] = self._serialize.header(\"accept\", accept, 'str')\n\n request = self._client.get(url, query_parameters, header_parameters)\n pipeline_response = await self._client._pipeline.run(request, stream=False, **kwargs)\n response = pipeline_response.http_response\n\n if response.status_code not in [200]:\n map_error(status_code=response.status_code, response=response, error_map=error_map)\n error = self._deserialize.failsafe_deserialize(_models.AutoscaleErrorResponse, response)\n raise HttpResponseError(response=response, model=error, error_format=ARMErrorFormat)\n\n deserialized = self._deserialize('PredictiveResponse', pipeline_response)\n\n if cls:\n return cls(pipeline_response, deserialized, {})\n\n return deserialized\n get.metadata = {'url': '/subscriptions/{subscriptionId}/resourcegroups/{resourceGroupName}/providers/Microsoft.Insights/autoscalesettings/{autoscaleSettingName}/predictiveMetrics'} # type: ignore\n","sub_path":"sdk/monitor/azure-mgmt-monitor/azure/mgmt/monitor/v2021_05_01_preview/aio/operations/_predictive_metric_operations.py","file_name":"_predictive_metric_operations.py","file_ext":"py","file_size_in_byte":6265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"95710605","text":"from snactor.executors.default import Executor, registered_executor\nfrom snactor.registry import must_get_actor\n\n\nclass GroupExecutorDefinition(Executor.Definition):\n def __init__(self, init):\n super(GroupExecutorDefinition, self).__init__(init)\n self.actors = map(must_get_actor, init.get('actors', ()))\n\n\n@registered_executor('group')\nclass GroupExecutor(Executor):\n Definition = GroupExecutorDefinition\n\n def __init__(self, definition):\n super(GroupExecutor, self).__init__(definition)\n\n def execute(self, data):\n restricted = {}\n for port in self.definition.inputs:\n restricted.update({port['name']: data[port['name']]})\n\n ret = True\n for actor in self.definition.executor.actors:\n ret = actor().execute(restricted)\n if not ret:\n break\n\n for port in self.definition.outputs:\n data.update({port['name']: restricted[port['name']]})\n return ret\n","sub_path":"snactor/executors/group.py","file_name":"group.py","file_ext":"py","file_size_in_byte":980,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"317213767","text":"import os\r\nimport re\r\nimport analyseUtil as util\r\nimport constants as const\r\nimport matplotlib.pyplot as plt\r\n\r\ndef getCompTimes(testFolder, numTester):\r\n homeDirName = os.path.dirname(__file__)\r\n folderPath = os.path.join(homeDirName, testFolder)\r\n\r\n groupedTime = []\r\n for _ in range(numTester):\r\n groupedTime.append([])\r\n\r\n for folder in os.listdir(folderPath):\r\n currPath = os.path.join(folderPath, folder)\r\n for fileName in os.listdir(currPath):\r\n filePath = os.path.join(currPath, fileName)\r\n\r\n with open(filePath, 'r') as analyseFile:\r\n blocks = util.splitResults(analyseFile)\r\n\r\n # fill groupedTime with data\r\n for x in range(numTester):\r\n if(not util.foundValidPlan(blocks[x])):\r\n #continue\r\n groupedTime[x].append(const.timeLimit * 10)\r\n else:\r\n groupedTime[x].append(util.getCalcTime(blocks[x]))\r\n\r\n for x in range(numTester):\r\n groupedTime[x] = sorted(groupedTime[x])\r\n return groupedTime\r\n\r\ndef plotCompTimes(listsToShow):\r\n markers = ['.', '+', 'x', '1', '2', '3', '4']\r\n for x in range(len(showResult)):\r\n plt.plot(showResult[x], marker = markers[x], linestyle = '-', label = x)\r\n plt.xlabel('Test file instance')\r\n plt.ylabel('Time in seconds (logarithmic scale)')\r\n plt.yscale(\"symlog\")\r\n plt.title('Computation time on solving different test instances sorted by time')\r\n plt.legend()\r\n plt.show()\r\n\r\ndef plotSpeedUp(list1, list2):\r\n speedUp = [s/p for (s,p) in zip(list1, list2)]\r\n plt.plot(speedUp, marker = '.', linestyle = 'none')\r\n\r\n plt.axhline(y=1, color='black', linestyle='-')\r\n plt.axhline(y=2, color='black', linestyle='-')\r\n plt.axhline(y=4, color='black', linestyle='-')\r\n\r\n plt.xlabel('Test file instance')\r\n plt.ylabel('Speedup t_1/t_2')\r\n plt.title('Speedup relative to two different solver instances')\r\n plt.show()\r\n\r\nresultSE = getCompTimes('resultSequential', 1)\r\nresultBSE = getCompTimes('bigSequentialTest', 1)\r\nresultFGB = getCompTimes('FullGreedyBanditTest', 4)\r\n\r\nresultSSE = getCompTimes('smallSequentialTest', 1)\r\n#resultB1 = getCompTimes('resultBandit', 4)\r\n#resultB2 = getCompTimes('resultBandit2', 4)\r\n#resultGB = getCompTimes('resultGreedyBandit', 4)\r\n#resultGBCA = getCompTimes('GreedyBanditCubeAmountTest', 4)\r\n#resultFI = getCompTimes('resultForcedImprovement', 4)\r\nresultNTC = getCompTimes('NodeTypeComparison', 2)\r\nresultSP = getCompTimes('SparseTest', 6)\r\nresultRSP = getCompTimes('CubeRandomSparseTest', 6)\r\nresultPARG = getCompTimes('CubeFindPortfolioAndRandom', 8)\r\nresultSVS = getCompTimes('ShareVisitedStates', 4)\r\n\r\n\r\n#showResult = resultFGB # Different sched Time intervals -schedT=4000, 1000, 400, 100\r\n#showResult = resultSE + [resultFGB[3]] # compare sequential with the greedy bandit approach\r\n#plotSpeedUp(resultSE[0], resultFGB[3]) # look at speedup for sequential and parallel\r\n\r\n#showResult = resultGBCA # -c=8000, 2000, 800, 80\r\n#showResult = resultSSE + [resultSP[4]]\r\n#showResult = resultNTC + [resultGBCA[3]] # nodes open and closed (-c=2000) and best cube amount open (-c=80)\r\n#plotSpeedUp(resultNTC[0], resultNTC[1]) # look at speedup for open and closed nodes\r\n#showResult = [resultSP[0]] + [resultNTC[1]] #sanity check with node type closed\r\n#showResult = resultSP[0:3] # -c=2000, 200, 20 -interval=1, 10, 100\r\n#showResult = resultSP[3:6] # -c=80 -interval=1, 10, 100\r\n#showResult = [resultSP[1]] + [resultSP[4]] # compare -c=200, 80 and -interval=10\r\n\r\nshowResult = resultSE + resultBSE + [resultFGB[3]] #we didnt get twice as fast but made the sequential one twice as slow\r\nshowResult = [resultPARG[0], resultPARG[4]] + resultSSE # Portfolio with 2000 and 800 Cubes\r\nshowResult = resultPARG[1:4] + resultPARG[5:8] + resultSSE # randomGreedy with 2000 cubes and 1, 5, 20 descents and 800 cubes\r\nshowResult = resultSP[0:3] + resultRSP[0:3] # normal forward search and taking random x% of cubes\r\n#showResult = resultSP[3:6] + resultRSP[3:6]\r\nshowResult = [resultRSP[2]] + resultSSE # best finder so far?\r\n#showResult = resultSVS # sharing visited States\r\nshowResult = resultSSE + [resultNTC[1]] + [resultSVS[1]] # comparing it with closed\r\n\r\nplotCompTimes(showResult)\r\n\r\n\r\n","sub_path":"automatedTesting/analyseFolder.py","file_name":"analyseFolder.py","file_ext":"py","file_size_in_byte":4331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"590780266","text":"try:\n _not = int(input(\"Lütfen notunuzu giriniz : \"))\n if(_not >= 0 and _not <= 100):\n result = \"harf notunuz : \"\n if _not <= 30:\n result += \"FF\"\n elif _not <= 50:\n result += \"DD\"\n elif _not <= 75:\n result += \"CC\"\n elif _not <= 85:\n result += \"BB\"\n else:\n result += \"AA\"\n print(result)\n else:\n print(\"Geçersiz not girişi\")\nexcept ValueError as mahmud:\n print(\"ne diyem, mahmud mu diyem\")","sub_path":"Lesson 3/kararyapilari7.py","file_name":"kararyapilari7.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"292912773","text":"#!/usr/bin/env python\nfrom sympy import *\nfrom sympy.matrices import *\n\nGT = \"GT\"\nLT = \"LT\"\n\n\n\n\n\ndef mk_standard_form_constraints(A, signs, b):\n \"\"\"\n input: EQNS\n output: (A, b) such that Ax <= b\n \"\"\"\n\n assert(NVARS > 0)\n\n assert(len(signs) == len(A))\n assert(len(b) == len(A))\n\n for i in range(len(A)):\n # flip the sign | ax >= b | -ax <= -b\n if signs[i] == GT:\n A[i] = [c * -1 for c in A[i]]\n b[i] *= -1\n\n A = Matrix(A)\n b = Matrix(b)\n\n return (A, b)\n\n\neqns = [[4, 2, GT, 10], [-3, 2, LT, 3], [1, 1, LT, 3], [1, 0, GT, 0], [1, 0, GT, 0]]\nif __name__ == \"__main__\":\n NVARS = len(eqns[0]) - 2\n A = [c[0:-2] for c in eqns]\n signs = [c[-2] for c in eqns]\n b = [c[-1] for c in eqns]\n\n A, b = mk_standard_form_constraints(A, signs, b)\n print(A)\n print(b)\n\n vars = Matrix([Symbol(\"x\" + str(i)) for i in range(A.shape[1])])\n print(vars)\n print(A * vars)\n","sub_path":"optimisation/assignments/duality-2/plot.py","file_name":"plot.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"358516064","text":"from . import launchable\n\n@launchable.subset\ndef subset(client):\n for case in client.stdin():\n # Avoid last line such as `ok github.com/launchableinc/rocket-car-gotest 0.268s`\n if not ' ' in case:\n client.test_path([{'type': 'testcase', 'name': case.rstrip('\\n')}])\n\n client.formatter = lambda x: \"^{}$\".format(x[0]['name'])\n client.separator = '|'\n client.run()\n\n\nrecord_tests = launchable.CommonRecordTestImpls(__name__).report_files()\n","sub_path":"launchable/test_runners/go_test.py","file_name":"go_test.py","file_ext":"py","file_size_in_byte":485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"604741878","text":"'''\nCreated on 2020年2月13日\n@author: ZhaoGuiFu\n'''\n\nimport tkinter as tk\nimport tkinter.messagebox as mb\n\n'''主窗口'''\nWindow = tk.Tk()\n'''主窗口标题'''\nWindow.title('登录')\n'''主窗口大小及位置'''\nWindow.geometry('250x200+500+200')\n\ntk.Label(Window,text='用户名：',height=4).grid(row=0,column=0)\ntk.Label(Window,text='密 码：',height=4).grid(row=1,column=0)\nstr1 = tk.StringVar()\nstr2 = tk.StringVar()\n'''从文件读取用户名与密码'''\ntry:\n with open(file='user_psw.txt',mode='r') as f:\n str1.set(f.readline())\n str2.set(f.readline())\nexcept:\n pass\n'''创建Entry对象'''\ne1 = tk.Entry(Window,textvariable=str1)\ne2 = tk.Entry(Window,textvariable=str2,show='*')\n'''以grid布局显示'''\ne1.grid(row=0,column=1)\ne2.grid(row=1,column=1)\ne1.focus_set()\n'''e1回车事件'''\ndef e1_e2_Return(event):\n if str1.get().strip() != '' and str2.get().strip() != '':\n login()\n elif str1.get().strip() != '' and str2.get().strip() == '':\n e2.focus_set()\n elif str1.get().strip() == '':\n e1.focus_set()\n'''事件绑定'''\ne1.bind('<Return>',e1_e2_Return)\ne2.bind('<Return>',e1_e2_Return)\n\n'''登录事件'''\ndef login():\n if str1.get().strip() == str2.get().strip():\n '''保存用户名与密码'''\n with open(file='user_psw.txt',mode='w') as f:\n f.write(str1.get())\n f.write('\\n'+str2.get())\n '''销毁登录窗口'''\n Window.destroy()\n '''创建顶层窗口'''\n top = tk.Tk()\n top.title('问卷调查')\n top.geometry('800x400+200+100')\n \n '''创建Menu'''\n MenuBar = tk.Menu(top)\n '''设置为顶级菜单'''\n top.config(menu=MenuBar)\n '''创建子菜单'''\n MenuFile = tk.Menu(MenuBar)\n MenuBar.add_cascade(label='文件',menu=MenuFile)\n MenuEdit = tk.Menu(MenuBar)\n MenuBar.add_cascade(label='编辑',menu=MenuEdit)\n '''向子菜单添加选项'''\n def ToExit():\n choice = mb.askquestion('提示', '是否退出？')\n if choice == 'yes':\n top.destroy()\n else:\n pass\n def ToSave():\n with open(file='info.txt',mode='w') as f:\n f.write(str(v1.get()))\n MenuFile.add_command(label='退出',command=ToExit)\n MenuEdit.add_command(label='保存',command=ToSave)\n \n '''创建LabelFrame对象'''\n group1 = tk.LabelFrame(top,text='你最喜欢哪门编程语言？（单选）',padx=10,pady=10)\n '''以pack()布局显示该对象'''\n group1.grid(row=0,column=0,padx=10,pady=10)\n '''选项列表'''\n Langs=[('PYTHON',1),('JAVA',2),('C++',3),('C',4)]\n '''整数对象，表示选中的num'''\n v1 = tk.IntVar()\n '''从文件读取内容'''\n try:\n with open(file='info.txt',mode='r') as f:\n v1.set(f.readline())\n except:\n with open(file='info.txt',mode='w') as f:\n pass\n '''循环向group1添加内容'''\n for lang,num in Langs:\n '''创建Radiobutton'''\n bt = tk.Radiobutton(group1,text=lang,variable=v1,value=num)\n '''以pack()布局显示'''\n bt.pack(anchor=tk.W)\n\n '''创建LabelFrame对象'''\n group2 = tk.LabelFrame(top,text='你学过下列哪些编程语言？（多选）',padx=10,pady=10)\n '''以pack()布局显示该对象'''\n group2.grid(row=0,column=1,padx=10,pady=10)\n '''循环向group2添加内容'''\n for lang,num in Langs:\n '''创建Checkbutton'''\n bt = tk.Checkbutton(group2,text=lang,variable=tk.IntVar())\n '''以pack()布局显示'''\n bt.pack(anchor=tk.W)\n \n '''创建LabelFrame对象'''\n group3 = tk.LabelFrame(top,text='你学的第一门编程语言？',padx=10,pady=10)\n '''以pack()布局显示该对象'''\n group3.grid(row=0,column=2,padx=10,pady=10)\n '''创建OptionMenu'''\n options = ['PYTHON','JAVA','C++','C']\n sv = tk.StringVar()\n sv.set(options[0])\n om = tk.OptionMenu(group3,sv,*options).pack()\n \n top.mainloop()\n else:\n str2.set('')\n'''登录按钮'''\ntk.Button(Window,text='登录',width=8,command=login).grid(row=2,column=1)\n\nWindow.mainloop()\n","sub_path":"Python/问卷调查（GUI）/my_first_GUI.py","file_name":"my_first_GUI.py","file_ext":"py","file_size_in_byte":4391,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"634674754","text":"import time\nimport pyttsx3\nWAITING_SEC = 3\nengine = pyttsx3.init()\nengine.say('开始听写，每隔' + str(WAITING_SEC) +'秒听写下一个')\ni = 1\nwith open('wordsOfGrade3.txt', 'r', encoding='gb18030', errors='ignore') as f:\n for line in f:\n engine.say('第' + str(i) +'个词：' + line)\n i+=1\n engine.runAndWait()\n time.sleep(WAITING_SEC)\nengine.say('听写完毕, Break a leg!')\nengine.runAndWait()\nengine.stop()\n\n\n\n","sub_path":"scripts/Voice.py","file_name":"Voice.py","file_ext":"py","file_size_in_byte":454,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"601156732","text":"from bs4 import BeautifulSoup \nimport requests \nimport pickle\n\nurl = \"https://filmarks.com/\"\n\nr = requests.get(url)\nsoup = BeautifulSoup(r.text, \"lxml\")\n\nc_list_line = soup.find_all('ul', class_='c-list-line')\n\ngenre = soup.find_all('ul', class_='c-list-line')[-2]\n# a属性をすべて取得\ngenre_links = genre.find_all('a')\n# ジャンル名：URL となる辞書を作成する\ngenre_link_dic = {}\nfor link in genre_links:\n genre_url = url + link.get('href')\n name = link.text\n genre_link_dic[name] = genre_url # 辞書に追加\n\npath = r'C:\\Users\\mkou0\\Desktop\\film_search\\genre_url.pickle'\n\nwith open(path, mode='wb') as f:\n pickle.dump(genre_link_dic, f)\n\n#Load \n#with open(path, mode='rb') as f:\n# data = pickle.load(f)\n# print(data)\n# print(type(data))\n\n","sub_path":"film_search.py","file_name":"film_search.py","file_ext":"py","file_size_in_byte":786,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"315148741","text":"import unittest\nfrom django.test import TestCase\nfrom cognation.scripts.tests import GetData\n# import logging\n# logger = logging.getLogger('django.db.backends')\n# logger.setLevel(logging.DEBUG)\n# logger.addHandler(logging.StreamHandler())\n\n\nclass TestFormula(TestCase):\n def setUp(self):\n self.reference_paths = [\n 'brother_1/brother_1_ref',\n 'brother_2/brother_2_ref',\n 'zero_cases/zero_cases_ref'\n ]\n\n self.test_paths = [\n 'brother_1/brother_1_test',\n 'brother_2/brother_2_test',\n 'zero_cases/zero_cases_test'\n ]\n\n short_path = 'cognation/scripts/tests/test_cases/two_brothers_cases/'\n get_ref = GetData()\n self.overall_ref_dict, self.overall_test_dict = {}, {}\n\n for i in range(len(self.reference_paths)):\n ref_path, test_path = self.reference_paths[i], self.test_paths[i]\n self.overall_ref_dict[ref_path] = get_ref.get_reference_data(short_path, ref_path)\n self.overall_test_dict[test_path] = get_ref.get_test_data(short_path, test_path, 13)\n\n def test_formula(self):\n for i in range(len(self.reference_paths)):\n ref_path, test_path = self.reference_paths[i], self.test_paths[i]\n ref_tuple, test_tuple = self.overall_ref_dict[ref_path], self.overall_test_dict[test_path]\n dict_loci_lrs_ref, dict_loci_lrs_test = ref_tuple[0], test_tuple[0]\n\n for key in dict_loci_lrs_ref.keys():\n lr_ref, lr_test = dict_loci_lrs_ref[key], dict_loci_lrs_test[key]\n self.assertEqual(lr_ref, lr_test, key)\n\n cpi_ref, cpi_test = ref_tuple[1], test_tuple[1]\n p_ref, p_test = float(ref_tuple[2]), float(test_tuple[2])\n self.assertEqual(cpi_ref, cpi_test)\n self.assertEqual(p_ref, p_test)\n\n def tearDown(self):\n pass\n\n\nif __name__ == '__main__':\n unittest.main()\n","sub_path":"cognation/scripts/tests/test_cases/two_brothers_cases/test_two_brothers.py","file_name":"test_two_brothers.py","file_ext":"py","file_size_in_byte":1942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"540012132","text":"\n\nfrom xai.brain.wordbase.verbs._miscast import _MISCAST\n\n#calss header\nclass _MISCASTS(_MISCAST, ):\n\tdef __init__(self,): \n\t\t_MISCAST.__init__(self)\n\t\tself.name = \"MISCASTS\"\n\t\tself.specie = 'verbs'\n\t\tself.basic = \"miscast\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/verbs/_miscasts.py","file_name":"_miscasts.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"532736530","text":"\"\"\"\nКористувач вводить текст. Вивести усі слова цього тексту,що містить велику букву, у вигляді списку.\n\"\"\"\n\n\ntext = str(input(\"Введіть текст: \"))\nlst = []\nfor count in range(0, len(text) + 1):\n if text[count].isupper():\n count2 = count + 1\n while text[count2] != \" \":\n lst += text[count2]\n count2 += 1\n","sub_path":"zaytseved/first/task1.py","file_name":"task1.py","file_ext":"py","file_size_in_byte":436,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"420610513","text":"# prediction:\n#patientID = \"440_OD_5194\"\n#OCTSlice = 16\n\n# 120030_OD_3477_OCT15_Image_GT_Predict.png\npatientID = \"120030_OD_3477\"\nOCTSlice = 15\n\npredDir= \"/home/hxie1/data/OCT_Tongren/numpy/10FoldCVForMultiSurfaceNet/log/SurfacesUnet/expUnetTongren_20200313_9Surfaces_CV5_Sigma20/testResult\"\npatientID_Index = patientID+f\"_OCT{OCTSlice:02d}\"\npredImagePath = predDir+ \"/\"+ patientID_Index+\"_Image_GT_Predict.png\"\n# \"/home/hxie1/data/OCT_Tongren/numpy/10FoldCVForMultiSurfaceNet/log/OCTUnetTongren/expUnetTongren_9Surfaces_20200215_CV5/testResult/440_OD_5194_OCT16_Image_GT_Predict.png\"\n\npatientSegFile = \"/home/hxie1/data/OCT_Tongren/OCTExplorerOutput_W512/Control/\"+ patientID+\"_Volume_Sequence_Surfaces_Iowa.xml\"\noutputDir = \"/home/hxie1/data/OCT_Tongren/paper\"\n\nimport sys\nsys.path.append(\".\")\nfrom TongrenFileUtilities import *\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom imageio import imread\nimport os\n\n\nslicesPerPatient = 31\nW = 512\nH = 496\n\n# read OCTExplorer output\npatientSurfacesArray = getSurfacesArray(patientSegFile)\n\nZ, Num_Surfaces, X = patientSurfacesArray.shape\nassert 11 == Num_Surfaces and Z == slicesPerPatient\nif 11 == Num_Surfaces:\n patientSurfacesArray = np.delete(patientSurfacesArray, 8, axis=1) # delete inaccurate surface 8\n B, Num_Surfaces, X = patientSurfacesArray.shape\n assert B == 31 and Num_Surfaces == 10\n\n patientSurfacesArray = np.delete(patientSurfacesArray, 2, axis=1) # delete inaccurate surface 2\n B, Num_Surfaces, X = patientSurfacesArray.shape\n assert B == 31 and Num_Surfaces == 9\n\n# remove the leftmost and rightmost 128 columns for each B-scans as the segmentation is not accurate\nif 768 == X:\n if \"5363_OD_25453\" == patientID:\n patientSurfacesArray = patientSurfacesArray[:, :, 103:615] # left shift 25 pixels for case 5363_OD_25453\n else:\n patientSurfacesArray = patientSurfacesArray[:, :, 128:640]\n\n# read prediction image\nraWGTPredImage = imread(predImagePath)\nrawImage = raWGTPredImage[:,0:W]\ngtImage = raWGTPredImage[:,W:2*W]\npredImage = raWGTPredImage[:,2*W:]\nS = Num_Surfaces\n\n\n# draw output image\nf = plt.figure(frameon=False)\nDPI = f.dpi\nsubplotRow = 1\nsubplotCol = 3\nf.set_size_inches(W*subplotCol/float(DPI), H*subplotRow/float(DPI))\n\npltColors = ['tab:blue', 'tab:orange', 'tab:green', 'tab:purple', 'tab:olive', 'tab:brown', 'tab:pink', 'tab:red', 'tab:cyan']\nsurfaceNames =['ILM', 'RNFL-GCL', 'IPL-INL', 'INL-OPL', 'OPL-ONL', 'IB-EZ', 'OB-EZ', 'IB-RPE', 'BM-CC boundary']\nassert S <= len(pltColors)\n\nplt.margins(0)\nplt.subplots_adjust(left=0, bottom=0, right=1, top=1, wspace=0,hspace=0) # very important for erasing unnecessary margins.\n\nsubplot2 = plt.subplot(subplotRow, subplotCol, 1)\nsubplot2.imshow(gtImage)\nsubplot2.axis('off')\n\nsubplot3 = plt.subplot(subplotRow, subplotCol, 2)\nsubplot3.imshow(rawImage, cmap='gray')\nfor s in range(0, S):\n subplot3.plot(range(0, W), patientSurfacesArray[OCTSlice-1, s, :], pltColors[s], linewidth=0.9)\nsubplot3.legend(surfaceNames, loc='lower center', ncol=4)\nsubplot3.axis('off')\n\nsubplot4 = plt.subplot(subplotRow, subplotCol, 3)\nsubplot4.imshow(predImage)\nsubplot4.axis('off')\n\n\nplt.savefig(os.path.join(outputDir, patientID_Index + \"_GT_OCTExplorer_Predict.png\"), dpi='figure', bbox_inches='tight', pad_inches=0)\nplt.close()\n","sub_path":"OCTMultiSurfaces/dataPrepare_Tongren/combinePredictionComparison_withoutRawImage.py","file_name":"combinePredictionComparison_withoutRawImage.py","file_ext":"py","file_size_in_byte":3289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"18375547","text":"from Lemmatizer import Lemmatizer\n\n\nclass TokenSplitter():\n def __init__(self):\n self.lemmatizer = Lemmatizer()\n self.lemmatize = self.lemmatizer.lemmatize\n self.words = self.lemmatizer.words\n\n def split_token_words(self, token):\n candidates = []\n if '' in token:\n candidates.append(tuple(token.split('')))\n\n splits = [(token[:s], token[s:]) for s in range(1, len(token)) if token[s - 1] != '' and\n token[s] != ''] + [token]\n candidates.extend(list(filter(lambda tokens: set(map(self.lemmatize, tokens)).issubset(self.words), splits)))\n\n return candidates\n","sub_path":"TokenSplitter.py","file_name":"TokenSplitter.py","file_ext":"py","file_size_in_byte":658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"646719839","text":"# -*- coding: utf-8 -*-\nimport scrapy\nimport bs4\nimport re\nfrom gginfo import items\nclass InfoSpider(scrapy.Spider):\n name = 'capitalmuseum'\n # allowed_domains = ['www.dpm.org.cn/collection/ceramic/']\n # start_urls = ['http://www.dpm.org.cn/collection/ceramic//']\n def start_requests(self):\n start_page=226704\n end_page=227204\n url=\"https://baike.baidu.com/item/%E9%A6%96%E9%83%BD%E5%8D%9A%E7%89%A9%E9%A6%86\"\n count=0\n yield scrapy.Request(url=url, callback=self.parse, meta={'id':count})\n def parse(self, response):\n if(response.status=='404'):\n return\n Items = items.GginfoItem()\n Items['id']= response.meta['id']\n data=[]\n name=[]\n text=[]\n data=response.css('body > div.body-wrapper > div.content-wrapper > div > div.main-content > table > tr > td::text').extract()\n print(data)\n flag = True\n s1=\"\"\n for i in range(len(data)) :\n if i<3:\n continue\n s=str(data[i]).strip()\n if(len(s)>0 and len(s)<=8):\n if(flag):\n flag = False\n else:\n text.append(s1)\n s1=\"\"\n name.append(s)\n elif(len(s)>8):\n s1+=s\n for i in range(min(len(name),len(text))):\n print(name[i])\n print(text[i])\n picurl=[]\n picurl=response.css('body > div > div > div > div > table > tr > td > div > div > a::attr(href)').extract()\n exp=3\n for i in range(-1+min(len(text),len(name),len(picurl))-1):\n Items['name']=name[i+1]\n Items['text']=text[i+1]\n Items['pic']=\"https://baike.baidu.com\"+picurl[i+exp]\n if(i==4):\n exp=4\n yield Items\n","sub_path":"gginfo/spiders/capitalmuseum.py","file_name":"capitalmuseum.py","file_ext":"py","file_size_in_byte":1832,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"564594398","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 7 19:57:03 2019\n\n@author: ivanbarrientos\n\"\"\"\n\nimport time\nimport numpy as np\n#import pandas as pd\nfrom matplotlib import pyplot as plt\nfrom random import random as unif_rand\n\ndef play_game():\n player_amt = -5 # player initially pays 5 units\n u = unif_rand()\n success_counter = 0\n payout = 1\n while u > 0.5:\n success_counter += 1\n payout = 2*payout\n player_amt += payout\n u = unif_rand()\n return player_amt\n \n \ndef simulate_trials(n_trials):\n player_amt_list = [play_game() for _ in range(n_trials)]\n return player_amt_list\n\nn_trials_list = [10**n for n in range(1, 8)]\navg_payout_list = []\nfor n_trials in n_trials_list:\n player_amt_list = simulate_trials(n_trials)\n #plt.hist(player_amt_list)\n avg_payout_list.append(np.mean(player_amt_list))\n \nplt.plot(avg_payout_list)","sub_path":"Ch5/MonteCarloExercises/st_petersburg.py","file_name":"st_petersburg.py","file_ext":"py","file_size_in_byte":912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"260920138","text":"import unittest\nimport transaction\n\nfrom pyramid import testing\nimport zope.sqlalchemy\n\n\ndef dummy_request(dbsession):\n return testing.DummyRequest(dbsession=dbsession)\n\n\nclass BaseTest(unittest.TestCase):\n def setUp(self):\n from ..models import get_tm_session\n self.config = testing.setUp(settings={\n 'sqlalchemy.url': 'sqlite:///:memory:'\n })\n self.config.include('..models')\n self.config.include('..routes')\n\n session_factory = self.config.registry['dbsession_factory']\n self.session = get_tm_session(session_factory, transaction.manager)\n\n self.init_database()\n\n def init_database(self):\n from ..models.meta import Base\n session_factory = self.config.registry['dbsession_factory']\n engine = session_factory.kw['bind']\n Base.metadata.create_all(engine)\n\n def tearDown(self):\n testing.tearDown()\n transaction.abort()\n\n def makeBook(self, author, title, review):\n from ..models import Book\n return Book(author=author, title=title, review=review)\n\n\nclass ViewWikiTests(BaseTest):\n def _callFUT(self, request):\n from tutorial.views.default import view_wiki\n return view_wiki(request)\n\n def test_it(self):\n request = dummy_request(self.session)\n response = self._callFUT(request)\n print(response)\n self.assertNotEqual(None, response)\n\nclass ViewBookTests(BaseTest):\n def _callFUT(self, request):\n from tutorial.views.default import view_book\n return view_book(request)\n\n\n def test_it(self):\n # add a book to the db\n book = self.makeBook('Bill Bryson', 'Around the world', 'I just made it up');\n self.session.add(book)\n # create a request asking for the book we've created\n request = dummy_request(self.session)\n request.matchdict['book_id'] = 1\n\n # call the view we're testing and check its behavior\n info = self._callFUT(request)\n self.assertEqual(info['book'], book)\n\n\nclass AddPageTests(BaseTest):\n def _callFUT(self, request):\n from tutorial.views.default import add_entry\n return add_entry(request)\n\n def test_it_notsubmitted(self):\n request = dummy_request(self.session)\n request.book = self.makeBook('Bill Bryson', 'Around the world', 'I just made it up')\n\n info = self._callFUT(request)\n self.assertEqual(info['author'], '')\n self.assertEqual(info['save_url'], 'http://example.com/add_entry')\n\n\n def test_it_submitted(self):\n from ..models import Book\n request = testing.DummyRequest({'form.submitted': True,\n 'author': 'Bill Bryson',\n 'title': 'Around the world',\n 'review': 'It is a silly book'},\n dbsession=self.session)\n self._callFUT(request)\n book = self.session.query(Book).filter_by(author='Bill Bryson').one()\n self.assertEqual(book.title, 'Around the world')\n\n\nclass EditPageTests(BaseTest):\n def _callFUT(self, request):\n from tutorial.views.default import edit_entry\n return edit_entry(request)\n\n def test_it_notsubmitted(self):\n book = self.makeBook('Bill Bryson', 'Around the world', 'I just made it up');\n self.session.add(book)\n request = dummy_request(self.session)\n request.matchdict['book_id'] = 1\n info = self._callFUT(request)\n self.assertEqual(info['author'], 'Bill Bryson')\n self.assertEqual(info['save_url'],\n 'http://example.com/1/edit_entry')\n\n def test_it_submitted(self):\n from ..models import Book\n book = self.makeBook('Bill Bryson', 'Around the world', 'I just made it up');\n self.session.add(book)\n request = testing.DummyRequest({'form.submitted': True,\n 'author': 'Bill Bryson',\n 'title': 'Around the world',\n 'review': 'It is a silly book'},\n dbsession=self.session)\n request.matchdict['book_id'] = 1\n self._callFUT(request)\n book = self.session.query(Book).filter_by(author='Bill Bryson').one()\n self.assertEqual(book.title, 'Around the world')","sub_path":"tutorial/Tests/test_views.py","file_name":"test_views.py","file_ext":"py","file_size_in_byte":4410,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"483658312","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom modules.partialconv2d import PartialConv2d\nfrom modules.Attention import AttentionModule\nfrom torchvision import models\n\nclass VGG16FeatureExtractor(nn.Module):\n def __init__(self):\n super().__init__()\n vgg16 = models.vgg16(pretrained=True)\n self.enc_1 = nn.Sequential(*vgg16.features[:5])\n self.enc_2 = nn.Sequential(*vgg16.features[5:10])\n self.enc_3 = nn.Sequential(*vgg16.features[10:17])\n\n # fix the encoder\n for i in range(3):\n for param in getattr(self, 'enc_{:d}'.format(i + 1)).parameters():\n param.requires_grad = False\n\n def forward(self, image):\n results = [image]\n for i in range(3):\n func = getattr(self, 'enc_{:d}'.format(i + 1))\n results.append(func(results[-1]))\n return results[1:]\n \nclass Bottleneck(nn.Module):\n expansion = 4\n\n def __init__(self, inplanes, planes, stride=1):\n super(Bottleneck, self).__init__()\n self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)\n self.bn1 = nn.BatchNorm2d(planes)\n self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,\n padding=1, bias=False)\n self.bn2 = nn.BatchNorm2d(planes)\n self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)\n self.bn3 = nn.BatchNorm2d(planes * 4)\n self.relu = nn.ReLU(inplace = True)\n\n def forward(self, x):\n residual = x\n\n out = self.conv1(x)\n out = self.bn1(out)\n out = self.relu(out)\n\n out = self.conv2(out)\n out = self.bn2(out)\n out = self.relu(out)\n\n out = self.conv3(out)\n out = self.bn3(out)\n\n out += residual\n out = self.relu(out)\n\n return out\n\nclass RFRModule(nn.Module):\n def __init__(self, layer_size=6, in_channel = 64):\n super(RFRModule, self).__init__()\n self.freeze_enc_bn = False\n self.layer_size = layer_size\n for i in range(3):\n name = 'enc_{:d}'.format(i + 1)\n out_channel = in_channel * 2\n block = [nn.Conv2d(in_channel, out_channel, 3, 2, 1, bias = False),\n nn.BatchNorm2d(out_channel),\n nn.ReLU(inplace = True)]\n in_channel = out_channel\n setattr(self, name, nn.Sequential(*block))\n \n for i in range(3, 6):\n name = 'enc_{:d}'.format(i + 1)\n block = [nn.Conv2d(in_channel, out_channel, 3, 1, 2, dilation = 2, bias = False),\n nn.BatchNorm2d(out_channel),\n nn.ReLU(inplace = True)]\n setattr(self, name, nn.Sequential(*block))\n self.att = AttentionModule(512)\n for i in range(5, 3, -1):\n name = 'dec_{:d}'.format(i)\n block = [nn.Conv2d(in_channel + in_channel, in_channel, 3, 1, 2, dilation = 2, bias = False),\n nn.BatchNorm2d(in_channel),\n nn.LeakyReLU(0.2, inplace = True)]\n setattr(self, name, nn.Sequential(*block))\n \n\n block = [nn.ConvTranspose2d(1024, 512, 4, 2, 1, bias = False),\n nn.BatchNorm2d(512),\n nn.LeakyReLU(0.2, inplace = True)]\n self.dec_3 = nn.Sequential(*block)\n \n block = [nn.ConvTranspose2d(768, 256, 4, 2, 1, bias = False),\n nn.BatchNorm2d(256),\n nn.LeakyReLU(0.2, inplace = True)]\n self.dec_2 = nn.Sequential(*block)\n \n block = [nn.ConvTranspose2d(384, 64, 4, 2, 1, bias = False),\n nn.BatchNorm2d(64),\n nn.LeakyReLU(0.2, inplace = True)]\n self.dec_1 = nn.Sequential(*block)\n \n def forward(self, input, mask):\n\n h_dict = {} # for the output of enc_N\n\n h_dict['h_0']= input\n\n h_key_prev = 'h_0'\n for i in range(1, self.layer_size + 1):\n l_key = 'enc_{:d}'.format(i)\n h_key = 'h_{:d}'.format(i)\n h_dict[h_key] = getattr(self, l_key)(h_dict[h_key_prev])\n h_key_prev = h_key\n \n h = h_dict[h_key]\n for i in range(self.layer_size - 1, 0, -1):\n enc_h_key = 'h_{:d}'.format(i)\n dec_l_key = 'dec_{:d}'.format(i)\n h = torch.cat([h, h_dict[enc_h_key]], dim=1)\n h = getattr(self, dec_l_key)(h)\n if i == 3:\n h = self.att(h, mask)\n return h\n\nclass RFRNet(nn.Module):\n def __init__(self):\n super(RFRNet, self).__init__()\n self.Pconv1 = PartialConv2d(3, 64, 7, 2, 3, multi_channel = True, bias = False)\n self.bn1 = nn.BatchNorm2d(64)\n self.Pconv2 = PartialConv2d(64, 64, 5, 1, 3, multi_channel = True, bias = False)\n self.bn20 = nn.BatchNorm2d(64)\n self.Pconv21 = PartialConv2d(64, 64, 5, 1, 3, multi_channel = True, bias = False)\n self.Pconv22 = PartialConv2d(64, 64, 5, 1, 3, multi_channel = True, bias = False)\n self.bn2 = nn.BatchNorm2d(64)\n self.RFRModule = RFRModule()\n self.Tconv = nn.ConvTranspose2d(64, 64, 4, 2, 1, bias = False)\n self.bn3 = nn.BatchNorm2d(64)\n self.tail1 = PartialConv2d(67, 32, 3, 1, 1, multi_channel = True, bias = False)\n self.tail2 = Bottleneck(32,8)\n self.out = nn.Conv2d(64,3,3,1,1, bias = False)\n\n def forward(self, in_image, mask):\n x1, m1 = self.Pconv1(in_image, mask)\n x1 = F.relu(self.bn1(x1), inplace = True)\n x1, m1 = self.Pconv2(x1, m1)\n x1 = F.relu(self.bn20(x1), inplace = True)\n x2 = x1\n x2, m2 = x1, m1\n n, c, h, w = x2.size()\n feature_group = [x2.view(n, c, 1, h, w)]\n mask_group = [m2.view(n, c, 1, h, w)]\n self.RFRModule.att.att.att_scores_prev = None\n self.RFRModule.att.att.masks_prev = None\n\n for i in range(6):\n x2, m2 = self.Pconv21(x2, m2)\n x2, m2 = self.Pconv22(x2, m2)\n x2 = F.leaky_relu(self.bn2(x2), inplace = True)\n x2 = self.RFRModule(x2, m2[:,0:1,:,:])\n x2 = x2 * m2\n feature_group.append(x2.view(n, c, 1, h, w))\n mask_group.append(m2.view(n, c, 1, h, w))\n x3 = torch.cat(feature_group, dim = 2)\n m3 = torch.cat(mask_group, dim = 2)\n amp_vec = m3.mean(dim = 2)\n x3 = (x3*m3).mean(dim = 2) /(amp_vec+1e-7)\n x3 = x3.view(n, c, h, w)\n m3 = m3[:,:,-1,:,:]\n x4 = self.Tconv(x3)\n x4 = F.leaky_relu(self.bn3(x4), inplace = True)\n m4 = F.interpolate(m3, scale_factor = 2)\n x5 = torch.cat([in_image, x4], dim = 1)\n m5 = torch.cat([mask, m4], dim = 1)\n x5, _ = self.tail1(x5, m5)\n x5 = F.leaky_relu(x5, inplace = True)\n x6 = self.tail2(x5)\n x6 = torch.cat([x5,x6], dim = 1)\n output = self.out(x6)\n return output, None\n \n def train(self, mode=True, finetune = False):\n super().train(mode)\n if finetune:\n for name, module in self.named_modules():\n if isinstance(module, nn.BatchNorm2d):\n module.eval()","sub_path":"modules/RFRNet_Smaller_Hole.py","file_name":"RFRNet_Smaller_Hole.py","file_ext":"py","file_size_in_byte":7173,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"270499313","text":"u\"\"\"ДЗ №5.\n\n# 1. задание со студентами делим на несколько\n# модулей с функциями + main.py\n\n# 2. срезы и индексы с использованием try-except\n\"\"\"\n# -*- coding: utf-8 -*-\n\n# Делаем программу \"students_list.py\".\n# 1. Она должна сделать следующее:\n# Создаем список.\n# Заполняем его нашими именами (+фамилиями).\nimport utils\n\nnames = [u'Иванов Иван',\n u'Кротов Илья',\n u'Сергеев Алексей',\n u'Маринина Наталья',\n u'Голиков Илья',\n u'Степанова Екатерина',\n u'Трепач Наталья',\n u'Прыгунов Иван']\n\nutils.sort_names(names)\n\nutils.search_by_id(names)\n\nutils.search_by_range(names)\n\nutils.search_by_letter(names)\n\nutils.make_groups(names)\n","sub_path":"hw5/hw5_1/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"395130054","text":"\"\"\"\nList the Documents and Folders\n\"\"\"\n\nfrom paper_to_git.commands.base import BaseCommand\nfrom paper_to_git.models import PaperDoc, PaperFolder\n\n__all__ = [\n 'ListCommand',\n ]\n\n\nclass ListCommand(BaseCommand):\n \"\"\"List the PaperDocs and Folders\n \"\"\"\n\n name = 'list'\n\n def add(self, parser, command_parser):\n self.parser = parser\n command_parser.add_argument('-d', '--docs',\n default=False, action='store_true',\n help=(\"\"\"\\\n List all the documents currently stored.\"\"\"))\n command_parser.add_argument('-fd', '--folders',\n default=False, action='store_true',\n help=(\"\"\"List all folders in Dropbox Paper\"\"\"))\n\n def process(self, args):\n if args.docs:\n for doc in PaperDoc.select():\n print(doc)\n\n if args.folders:\n for folder in PaperFolder.select():\n print(folder)\n for doc in folder.docs:\n print('|----{}'.format(doc))\n\n if not (args.docs or args.folders):\n print(\"Please provide atleast one of the --docs or --folders flags\")\n","sub_path":"paper_to_git/commands/list_command.py","file_name":"list_command.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"566486910","text":"import torch\nfrom torch._C import device\nimport torch.nn as nn\n\nimport torchvision\nfrom torchvision.datasets.mnist import FashionMNIST\nimport torchvision.transforms as transforms\n\nimport torch.nn.functional as F\nimport torch.optim as optim\n\n\n\ndef get_correct_preds(preds, labels):\n return preds.argmax(dim=1).eq(labels).sum().item()\n\ndef save_checkpoint(state, filename=\"./checkpoints/my_checkpoint.pth.tar\"):\n print(\"Saving Checkpoint\")\n torch.save(state,filename)\n\n\ndef load_checkpoint(network, path):\n print(\"Loading Checkpoint\")\n optimizer = optim.Adam(network.parameters(), lr = 0.01)\n \n checkpoint = torch.load(path)\n network.load_state_dict(checkpoint['model_state_dict'])\n optimizer.load_state_dict(checkpoint['optimizer_state_dict'])\n epoch = checkpoint['epoch']\n loss = checkpoint['loss']\n\n \n #optimizer.load_state_dict(checkpoint['optimizer'])\n #for state in optimizer.state.values():\n #for k, v in state.items():\n #if isinstance(v, torch.Tensor):\n #state[k] = v.cuda()\n\ndef Train(network, train_loader, device, epochs=20, load_model = False):\n\n load_model = load_model\n\n\n optimizer = optim.Adam(network.parameters(), lr = 0.01)\n if load_model:\n load_checkpoint(network, path = \"./checkpoints/my_checkpoint.pth.tar\")\n\n for epoch in range(epochs):\n total_loss = 0\n total_correct = 0\n\n checkpoint = {\n 'epoch': epoch,\n 'model_state_dict': network.state_dict(),\n 'optimizer_state_dict': optimizer.state_dict(),\n 'loss': total_loss,\n }\n \n if epoch % 10 == 0:\n save_checkpoint(checkpoint) \n\n for batch in train_loader:\n images,labels = batch\n images, labels = images.to(device), labels.to(device)\n\n preds = network(images)\n loss = F.cross_entropy(preds, labels)\n\n optimizer.zero_grad()\n loss.backward()\n optimizer.step()\n\n total_loss += loss.item()\n total_correct += get_correct_preds(preds, labels)\n\n print(\"epoch: \",epoch, \"total_correct: \", total_correct, \"loss: \", total_loss)\n\n\n\n\ndef evaluate(loader, network,device):\n num_correct = 0\n num_samples = 0\n network.eval()\n\n with torch.no_grad():\n for x, y in loader:\n x = x.to(device=device)\n y = y.to(device=device)\n\n scores = network(x)\n _, predictions = scores.max(1)\n num_correct += (predictions == y).sum()\n num_samples += predictions.size(0)\n accuracy = (num_correct/num_samples)*100\n print(f'accuracy {accuracy}')\n","sub_path":"utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"478918329","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\n__author__ = 'ipetrash'\n\n\nif __name__ == '__main__':\n from olxua_site_ad_parser import OlxUa_SiteAdParser\n\n # Создаем парсер и устанавливаем настройки\n parser = OlxUa_SiteAdParser()\n parser.process_config('config.yaml')\n parser.run()\n parser.save()\n","sub_path":"olx_ua_phone_parser/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"263197340","text":"# coding: utf-8\nimport sys, os\nsys.path.append(os.pardir) # 부모 디렉터리의 파일을 가져올 수 있도록 설정\nimport numpy as np\nimport pickle\nfrom dataset.mnist import load_mnist\nfrom common.functions import sigmoid, softmax\n\ndef get_data():\n\t(x_train, t_train),(x_test, t_test) = load_mnist(normalize=True, flatten = True, one_hot_label=False)\n\treturn x_test, t_test\n\ndef init_network():\n\twith open(\"sample_weight.pkl\",'rb') as f:\n\t\tnetwork = pickle.load(f)\n\treturn network\n\ndef predict(network, x):\n\tW1, W2, W3 = network['W1'], network['W2'], network['W3']\n\tb1, b2, b3 = network['b1'], network['b2'], network['b3']\n\n\ta1 = np.dot(x1,W1) + b1\n\tz1 = sigmoid(a1)\n\ta2 = np.dot(z1,W2) + b2\n\tz2 = sigmoid(a2)\n\ta3 = np.dot(z2.W3) + b3\n\ty = softmax(a3)\n\n\treturn y","sub_path":"mnist_set0.py","file_name":"mnist_set0.py","file_ext":"py","file_size_in_byte":772,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"347231424","text":"import time\n#import math\n#import sys\n#import fractions\n\ndebug = True\ninFile = \"A-large.in\"\noutFile = inFile.rstrip(\".in\") + \".out\"\n\ndef battleship(testNum, fin):\n dbgPrint(\"\")\n \n dbgPrint(\"Test %d\" % testNum)\n dbgPrint(\"----------\")\n \n rows, cols, ship = fin.readline().rstrip(\"\\n\").split(\" \")\n dbgPrint(rows)\n dbgPrint(cols)\n dbgPrint(ship)\n rows = int(rows)\n cols = int(cols)\n ship = int(ship)\n \n shots = (cols//ship) * (rows-1)\n shots += (cols//ship) + (ship-1)\n if cols%ship != 0:\n shots += 1\n \n return shots\n\ndef dbgPrint(string):\n if debug:\n print(string)\n\ndef go():\n fin = open(inFile, \"rU\")\n fout = open(outFile, \"w\")\n \n numTests = int(fin.readline())\n print(\"Number of tests = %d\" % numTests)\n \n programStartTime = time.clock()\n \n for testNum in range(1,numTests+1):\n caseStartTime = time.clock()\n \n number = battleship(testNum, fin)\n outStr = \"Case #%d: %s\" % (testNum, number)\n \n print(\"%s\" % outStr)\n fout.write(\"%s\\n\" % outStr)\n \n caseEndTime = time.clock()\n print(\"time = %s\" % (caseEndTime - caseStartTime))\n \n programEndTime = time.clock()\n print(\"Total time = %s\" % (programEndTime - programStartTime))\n \n fout.close()\n fin.close()\n\nif __name__ == \"__main__\":\n go()\n","sub_path":"solutions_5640146288377856_1/Python/TheOther1/1C-A-battleship.py","file_name":"1C-A-battleship.py","file_ext":"py","file_size_in_byte":1376,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"546695288","text":"#!/usr/bin/python\n\nimport sys\nimport os\n\nfrom tornado.options import options, define, parse_command_line\nimport tornado.httpserver\nimport tornado.ioloop\nimport tornado.web\nimport tornado.wsgi\n\nfrom django.core.wsgi import get_wsgi_application\n\n\ndefine('port', type=int, default=8080)\n\n\nclass HelloHandler(tornado.web.RequestHandler):\n def get(self):\n self.write('Hello from tornado')\n\n\ndef main():\n os.environ['DJANGO_SETTINGS_MODULE'] = 'haldir.settings'\n sys.path.append('./haldir')\n\n parse_command_line()\n\n wsgi_app = get_wsgi_application()\n container = tornado.wsgi.WSGIContainer(wsgi_app)\n\n settings = {\n 'debug': True,\n 'autoreload': True,\n # 'static_path': '/tmp/org.au9ustine.haldir/static'\n }\n \n tornado_app = tornado.web.Application(\n [\n (r'/hello-tornado', HelloHandler),\n (r'/static/(.*)', tornado.web.StaticFileHandler, {'path': '/tmp/org.au9ustine.haldir/static'}),\n (r'.*', tornado.web.FallbackHandler, dict(fallback=container)),\n ], **settings)\n server = tornado.httpserver.HTTPServer(tornado_app)\n server.listen(options.port)\n \n tornado.ioloop.IOLoop.instance().start()\n\n\nif __name__ == '__main__':\n main()\n","sub_path":"tornado_main.py","file_name":"tornado_main.py","file_ext":"py","file_size_in_byte":1224,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"279016584","text":"from __future__ import unicode_literals\nfrom unittest import TestCase\ntry:\n from unittest.mock import patch, MagicMock\nexcept ImportError:\n from mock import patch, MagicMock\n\nfrom . import SagepayProvider\nfrom .. import PaymentStatus\nfrom ..testcommon import create_test_payment\n\n\nVENDOR = 'abcd1234'\nENCRYPTION_KEY = '1234abdd1234abcd'\n\n\nPayment = create_test_payment()\n\n\nclass TestSagepayProvider(TestCase):\n\n def setUp(self):\n self.payment = Payment()\n self.provider = SagepayProvider(\n vendor=VENDOR, encryption_key=ENCRYPTION_KEY)\n\n @patch('payments.sagepay.redirect')\n def test_provider_raises_redirect_needed_on_success(self, mocked_redirect):\n data = {'Status': 'OK'}\n data = \"&\".join(u\"%s=%s\" % kv for kv in data.items())\n with patch.object(SagepayProvider, 'aes_dec', return_value=data):\n self.provider.process_data(self.payment, MagicMock())\n self.assertEqual(self.payment.status, PaymentStatus.CONFIRMED)\n self.assertEqual(self.payment.captured_amount, self.payment.total)\n\n @patch('payments.sagepay.redirect')\n def test_provider_raises_redirect_needed_on_failure(self, mocked_redirect):\n data = {'Status': ''}\n data = \"&\".join(u\"%s=%s\" % kv for kv in data.items())\n with patch.object(SagepayProvider, 'aes_dec', return_value=data):\n self.provider.process_data(self.payment, MagicMock())\n self.assertEqual(self.payment.status, PaymentStatus.REJECTED)\n self.assertEqual(self.payment.captured_amount, 0)\n\n def test_provider_encrypts_data(self):\n data = self.provider.get_hidden_fields(self.payment)\n decrypted_data = self.provider.aes_dec(data['Crypt'])\n self.assertIn(self.payment.billing_first_name, str(decrypted_data))\n\n def test_encrypt_method_returns_valid_data(self):\n encrypted = self.provider.aes_enc('mirumee')\n self.assertEqual(encrypted, b'@e63c293672f50b9c8e291831facb4e4f')\n","sub_path":"payments/sagepay/test_sagepay.py","file_name":"test_sagepay.py","file_ext":"py","file_size_in_byte":1993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"330312128","text":"#!/usr/bin/env python\n\nimport sys\nimport math\nimport itertools\nimport pandas as pd\nimport numpy as np\nimport Bio.Phylo\nfrom Bio.Phylo.TreeConstruction import _Matrix, _DistanceMatrix\nfrom Bio.Phylo.TreeConstruction import _Matrix, _DistanceMatrix\nfrom Bio.Phylo.TreeConstruction import DistanceTreeConstructor\n\nclass Munkres:\n \"\"\"\n Modified from Munkres version 1.0.5.4 http://bmc.github.com/munkres/\n \"\"\"\n \"\"\"\n Calculate the Munkres solution to the classical assignment problem.\n See the module documentation for usage.\n \"\"\"\n def __init__(self):\n \"\"\"Create a new instance\"\"\"\n self.C = None\n self.row_covered = []\n self.col_covered = []\n self.n = 0\n self.Z0_r = 0\n self.Z0_c = 0\n self.marked = None\n self.path = None\n def make_cost_matrix(profit_matrix, inversion_function):\n \"\"\"\n **DEPRECATED**\n\n Please use the module function ``make_cost_matrix()``.\n \"\"\"\n import munkres\n return munkres.make_cost_matrix(profit_matrix, inversion_function)\n make_cost_matrix = staticmethod(make_cost_matrix)\n def pad_matrix(self, matrix, pad_value=0):\n \"\"\"\n Pad a possibly non-square matrix to make it square.\n :Parameters:\n matrix : list of lists\n matrix to pad\n\n pad_value : int\n value to use to pad the matrix\n\n :rtype: list of lists\n :return: a new, possibly padded, matrix\n \"\"\"\n max_columns = 0\n total_rows = len(matrix)\n for row in matrix:\n max_columns = max(max_columns, len(row))\n total_rows = max(max_columns, total_rows)\n new_matrix = []\n for row in matrix:\n row_len = len(row)\n new_row = row[:]\n if total_rows > row_len:\n # Row too short. Pad it.\n new_row += [0] * (total_rows - row_len)\n new_matrix += [new_row]\n while len(new_matrix) < total_rows:\n new_matrix += [[0] * total_rows]\n return new_matrix\n def compute(self, cost_matrix):\n \"\"\"\n Compute the indexes for the lowest-cost pairings between rows and\n columns in the database. Returns a list of (row, column) tuples\n that can be used to traverse the matrix.\n :Parameters:\n cost_matrix : list of lists\n The cost matrix. If this cost matrix is not square, it\n will be padded with zeros, via a call to ``pad_matrix()``.\n (This method does *not* modify the caller's matrix. It\n operates on a copy of the matrix.)\n\n **WARNING**: This code handles square and rectangular\n matrices. It does *not* handle irregular matrices.\n\n :rtype: list\n :return: A list of ``(row, column)`` tuples that describe the lowest\n cost path through the matrix\n \"\"\"\n self.C = self.pad_matrix(cost_matrix)\n self.n = len(self.C)\n self.original_length = len(cost_matrix)\n self.original_width = len(cost_matrix[0])\n self.row_covered = [False for i in range(self.n)]\n self.col_covered = [False for i in range(self.n)]\n self.Z0_r = 0\n self.Z0_c = 0\n self.path = self.__make_matrix(self.n * 2, 0)\n self.marked = self.__make_matrix(self.n, 0)\n done = False\n step = 1\n steps = { 1 : self.__step1,\n 2 : self.__step2,\n 3 : self.__step3,\n 4 : self.__step4,\n 5 : self.__step5,\n 6 : self.__step6 }\n while not done:\n try:\n func = steps[step]\n step = func()\n except KeyError:\n done = True\n # Look for the starred columns\n results = []\n for i in range(self.original_length):\n for j in range(self.original_width):\n if self.marked[i][j] == 1:\n results += [(i, j)]\n return results\n def __copy_matrix(self, matrix):\n \"\"\"Return an exact copy of the supplied matrix\"\"\"\n return copy.deepcopy(matrix)\n def __make_matrix(self, n, val):\n \"\"\"Create an *n*x*n* matrix, populating it with the specific value.\"\"\"\n matrix = []\n for i in range(n):\n matrix += [[val for j in range(n)]]\n return matrix\n def __step1(self):\n \"\"\"\n For each row of the matrix, find the smallest element and\n subtract it from every element in its row. Go to Step 2.\n \"\"\"\n C = self.C\n n = self.n\n for i in range(n):\n minval = min(self.C[i])\n # Find the minimum value for this row and subtract that minimum\n # from every element in the row.\n for j in range(n):\n self.C[i][j] -= minval\n return 2\n def __step2(self):\n \"\"\"\n Find a zero (Z) in the resulting matrix. If there is no starred\n zero in its row or column, star Z. Repeat for each element in the\n matrix. Go to Step 3.\n \"\"\"\n n = self.n\n for i in range(n):\n for j in range(n):\n if (self.C[i][j] == 0) and \\\n (not self.col_covered[j]) and \\\n (not self.row_covered[i]):\n self.marked[i][j] = 1\n self.col_covered[j] = True\n self.row_covered[i] = True\n\n self.__clear_covers()\n return 3\n def __step3(self):\n \"\"\"\n Cover each column containing a starred zero. If K columns are\n covered, the starred zeros describe a complete set of unique\n assignments. In this case, Go to DONE, otherwise, Go to Step 4.\n \"\"\"\n n = self.n\n count = 0\n for i in range(n):\n for j in range(n):\n if self.marked[i][j] == 1:\n self.col_covered[j] = True\n count += 1\n if count >= n:\n step = 7 # done\n else:\n step = 4\n return step\n def __step4(self):\n \"\"\"\n Find a noncovered zero and prime it. If there is no starred zero\n in the row containing this primed zero, Go to Step 5. Otherwise,\n cover this row and uncover the column containing the starred\n zero. Continue in this manner until there are no uncovered zeros\n left. Save the smallest uncovered value and Go to Step 6.\n \"\"\"\n step = 0\n done = False\n row = -1\n col = -1\n star_col = -1\n while not done:\n (row, col) = self.__find_a_zero()\n if row < 0:\n done = True\n step = 6\n else:\n self.marked[row][col] = 2\n star_col = self.__find_star_in_row(row)\n if star_col >= 0:\n col = star_col\n self.row_covered[row] = True\n self.col_covered[col] = False\n else:\n done = True\n self.Z0_r = row\n self.Z0_c = col\n step = 5\n return step\n def __step5(self):\n \"\"\"\n Construct a series of alternating primed and starred zeros as\n follows. Let Z0 represent the uncovered primed zero found in Step 4.\n Let Z1 denote the starred zero in the column of Z0 (if any).\n Let Z2 denote the primed zero in the row of Z1 (there will always\n be one). Continue until the series terminates at a primed zero\n that has no starred zero in its column. Unstar each starred zero\n of the series, star each primed zero of the series, erase all\n primes and uncover every line in the matrix. Return to Step 3\n \"\"\"\n count = 0\n path = self.path\n path[count][0] = self.Z0_r\n path[count][1] = self.Z0_c\n done = False\n while not done:\n row = self.__find_star_in_col(path[count][1])\n if row >= 0:\n count += 1\n path[count][0] = row\n path[count][1] = path[count-1][1]\n else:\n done = True\n if not done:\n col = self.__find_prime_in_row(path[count][0])\n count += 1\n path[count][0] = path[count-1][0]\n path[count][1] = col\n self.__convert_path(path, count)\n self.__clear_covers()\n self.__erase_primes()\n return 3\n def __step6(self):\n \"\"\"\n Add the value found in Step 4 to every element of each covered\n row, and subtract it from every element of each uncovered column.\n Return to Step 4 without altering any stars, primes, or covered\n lines.\n \"\"\"\n minval = self.__find_smallest()\n for i in range(self.n):\n for j in range(self.n):\n if self.row_covered[i]:\n self.C[i][j] += minval\n if not self.col_covered[j]:\n self.C[i][j] -= minval\n return 4\n def __find_smallest(self):\n \"\"\"Find the smallest uncovered value in the matrix.\"\"\"\n minval = sys.maxint\n for i in range(self.n):\n for j in range(self.n):\n if (not self.row_covered[i]) and (not self.col_covered[j]):\n if minval > self.C[i][j]:\n minval = self.C[i][j]\n return minval\n def __find_a_zero(self):\n \"\"\"Find the first uncovered element with value 0\"\"\"\n row = -1\n col = -1\n i = 0\n n = self.n\n done = False\n while not done:\n j = 0\n while True:\n if (self.C[i][j] == 0) and \\\n (not self.row_covered[i]) and \\\n (not self.col_covered[j]):\n row = i\n col = j\n done = True\n j += 1\n if j >= n:\n break\n i += 1\n if i >= n:\n done = True\n return (row, col)\n def __find_star_in_row(self, row):\n \"\"\"\n Find the first starred element in the specified row. Returns\n the column index, or -1 if no starred element was found.\n \"\"\"\n col = -1\n for j in range(self.n):\n if self.marked[row][j] == 1:\n col = j\n break\n\n return col\n def __find_star_in_col(self, col):\n \"\"\"\n Find the first starred element in the specified row. Returns\n the row index, or -1 if no starred element was found.\n \"\"\"\n row = -1\n for i in range(self.n):\n if self.marked[i][col] == 1:\n row = i\n break\n return row\n def __find_prime_in_row(self, row):\n \"\"\"\n Find the first prime element in the specified row. Returns\n the column index, or -1 if no starred element was found.\n \"\"\"\n col = -1\n for j in range(self.n):\n if self.marked[row][j] == 2:\n col = j\n break\n return col\n def __convert_path(self, path, count):\n for i in range(count+1):\n if self.marked[path[i][0]][path[i][1]] == 1:\n self.marked[path[i][0]][path[i][1]] = 0\n else:\n self.marked[path[i][0]][path[i][1]] = 1\n def __clear_covers(self):\n \"\"\"Clear all covered matrix cells\"\"\"\n for i in range(self.n):\n self.row_covered[i] = False\n self.col_covered[i] = False\n def __erase_primes(self):\n \"\"\"Erase all prime markings\"\"\"\n for i in range(self.n):\n for j in range(self.n):\n if self.marked[i][j] == 2:\n self.marked[i][j] = 0\n\ndef make_cost_matrix(profit_matrix, inversion_function):\n \"\"\"\n Create a cost matrix from a profit matrix by calling\n 'inversion_function' to invert each value. The inversion\n function must take one numeric argument (of any type) and return\n another numeric argument which is presumed to be the cost inverse\n of the original profit.\n This is a static method. Call it like this:\n .. python::\n cost_matrix = Munkres.make_cost_matrix(matrix, inversion_func)\n For example:\n .. python::\n cost_matrix = Munkres.make_cost_matrix(matrix, lambda x : sys.maxint - x)\n :Parameters:\n profit_matrix : list of lists\n The matrix to convert from a profit to a cost matrix\n\n inversion_function : function\n The function to use to invert each entry in the profit matrix\n :rtype: list of lists\n :return: The converted matrix\n \"\"\"\n cost_matrix = []\n for row in profit_matrix:\n cost_matrix.append([inversion_function(value) for value in row])\n return cost_matrix\n\ndef print_matrix(matrix, msg=None):\n \"\"\"\n Convenience function: Displays the contents of a matrix of integers.\n :Parameters:\n matrix : list of lists\n Matrix to print\n msg : str\n Optional message to print before displaying the matrix\n \"\"\"\n import math\n if msg is not None:\n print(msg)\n # Calculate the appropriate format width.\n width = 0\n for row in matrix:\n for val in row:\n width = max(width, int(math.log10(val)) + 1)\n # Make the format string\n format = '%%%dd' % width\n # Print the matrix\n for row in matrix:\n sep = '['\n for val in row:\n sys.stdout.write(sep + format % val)\n sep = ', '\n sys.stdout.write(']\\n')\n\n\ndef calculate_GK(A, B, nbhood): #nbhood = 5, can be changed\n # calculate the Goodman-Kruskal gamma index\n GK = 0.\n if len(set(A) & set(B)) > 1:\n pairsA = set( [(A[i],A[j]) for i in xrange(len(A)-nbhood) for j in xrange(i+1,i+nbhood)] )\n pairsB = set( [(B[i],B[j]) for i in xrange(len(B)-nbhood) for j in xrange(i+1,i+nbhood)] )\n allPairs = set(list(pairsA) + list(pairsB))\n Ns, Nr = 0.,0.\n for p in allPairs:\n if p in pairsA and p in pairsB: Ns += 1\n elif p in pairsA and tuple(p[::-1]) in pairsB: Nr += 1\n elif tuple(p[::-1]) in pairsA and p in pairsB: Nr += 1\n else: pass\n if (Nr + Ns) == 0:\n gamma = 0\n else:\n gamma = abs(Nr-Ns) / (Nr+Ns)\n GK = (1+gamma)/2.\n return GK\n\ndef cluster_distance(A, B, nbhood, pathways, dist, annotation):\n clusterA = pathways[A]\n clusterB = pathways[B]\n try:\n Jaccard = len(set(clusterA.keys()) & set(clusterB.keys())) / float( len(set(clusterA.keys())) + len(set(clusterB.keys())) - len(set(clusterA.keys()) & set(clusterB.keys())))\n except ZeroDivisionError:\n print(\"Zerodivisionerror during the Jaccard distance calculation. Can only happen when one or more clusters contains no domains.\")\n print(\"keys of clusterA\", A, clusterA.keys())\n print(\"keys of clusterB\", B, clusterB.keys())\n intersect = set(clusterA.keys() ).intersection(clusterB.keys()) #shared domains\n not_intersect = []\n DDS,S = 0,0\n SumDistance = 0\n pair = \"\"\n for domain in set(clusterA.keys() + clusterB.keys()):\n if domain not in intersect:\n not_intersect.append(domain)\n for unshared_domain in not_intersect: \n dom_set = []\n try:\n dom_set = clusterA[unshared_domain]\n except KeyError:\n dom_set = clusterB[unshared_domain]\n DDS += len(dom_set)\n S += len(dom_set)\n for shared_domain in intersect:\n seta = clusterA[shared_domain]\n setb = clusterB[shared_domain]\n if len(seta+setb) == 2: #The domain occurs only once in both clusters\n pair = tuple(sorted([seta[0],setb[0]]))\n for p in pair:\n try:\n S += max(len(seta),len(setb))\n DDS += dist[p[0]][p[1]]\n except KeyError:\n print(\"KeyError on \"+\" \".join([str(pair), str(p[0]), str(p[1])]))\n else: #The domain occurs more than once in both clusters\n DistanceMatrix = [[1 for col in range(len(setb))] for row in range(len(seta))]\n for domsa in range(len(seta)):\n for domsb in range(domsa, len(setb)):\n pair = tuple(sorted([seta[domsa], setb[domsb]]))\n for p in pair:\n try:\n DistanceMatrix[domsa][domsb] = dist[p[0]][p[1]]\n except KeyError:\n print(\"KeyError on \"+str(pair))\n #Only use the best scoring pairs\n Hungarian = Munkres()\n #print \"DistanceMatrix\", DistanceMatrix\n BestIndexes = Hungarian.compute(DistanceMatrix)\n # print \"BestIndexes\", BestIndexes\n accumulated_distance = sum([DistanceMatrix[bi[0]][bi[1]] for bi in BestIndexes])\n # print \"accumulated_distance\", accumulated_distance\n SumDistance = (abs(len(seta)-len(setb)) + accumulated_distance) #diff in abundance + sequence distance\n # print 'SumDistance2', SumDistance\n S += max(len(seta),len(setb))\n DDS += SumDistance\n\n## HERE\n # calculate the Goodman-Kruskal gamma index\n A_pseudo_seq = annotation[A]\n B_pseudo_seq = annotation[B]\n Ar = [item for item in A_pseudo_seq]\n Ar.reverse()\n GK = max([calculate_GK(A_pseudo_seq, B_pseudo_seq, nbhood = nbhood), calculate_GK(Ar, B_pseudo_seq, nbhood = nbhood)])\n DDS /= float(S)\n DDS /= float(S)\n DDS = math.exp(-DDS) #transform from distance to similarity score\n Distance = 1 - (Jaccardw * Jaccard) - (DDSw * DDS) - (GKw * GK)\n Similarity_score = (Jaccardw * Jaccard) + (DDSw * DDS) + (GKw * GK)\n if Distance < 0:\n print(\"\\t\".join([\"negative distance\", str(Distance), \"DDS\", str(DDS), str(pair)]))\n print(\"Probably a rounding issue\")\n print(\"Distance is set to 0 for these clusters\")\n Distance = 0\n return Similarity_score\n\ndef generate_distance_matrix(pathways, domain_names, annotation,\n dist, Jaccardw, GKw, DDSw,\n scale, nbhood, outfile):\n pnames = pathways.keys()\n r = len(pnames)\n c = len(pnames)\n Dist = [[0 for x in range(r)] for y in range(c)]\n df = pd.DataFrame(Dist, index=pnames, columns=pnames)\n pairs = list(itertools.combinations(pnames, 2))\n for p in pairs:\n sim_score = cluster_distance(p[1], p[0], nbhood, pathways, dist, annotation)\n rowname = p[1]\n colname = p[0]\n df.ix[rowname, colname] = 1- sim_score/scale\n df.to_csv(outfile)\n return df\n\n\n\"\"\"\n This assumes that the annotation matrix col names are:\n 0: pathway name\n 1: number of domains in pathway\n 2: first domain\n n: last domain\n\n Headers for the blast/diamond queries/hits are assumed to be\n pathway|domain\n where both pathway and domain match the annotation file (row and col names, respectively)\n\n\"\"\"\n\n## Set the weights\nJaccardw = 0.5\nGKw = 0.25\nDDSw = 0.25\n\n## Define inputs\nannotation_matrix = \"./annotateKS_per_pred_transATPKS_mc_manualderep.txt\"\nblasttable = \"./full.dbp\" ## Tab blast\n\n## Define outputs\noutfile = \"distance.txt\"\ntree_outfile = \"upgmma.nwk\"\n\n## Parse inputs\npathways = {}\ndomain_names = []\nannotation = {}\nwith open(annotation_matrix) as a:\n l = 0 \n for ln in a.read().splitlines():\n s = ln.split(\"\\t\")\n if(l==0):\n domain_names = s[2:]\n else:\n if s[0] not in pathways:\n pathways[s[0]] = {}\n d = s[2:]\n for i, spec in enumerate(d):\n if spec == 'NA':\n continue\n domain = '|'.join([s[0], domain_names[i]])\n if s[0] not in annotation:\n annotation[s[0]] = []\n annotation[s[0]].append(spec)\n if spec not in pathways[s[0]]:\n pathways[s[0]][spec] = []\n pathways[s[0]][spec].append(domain)\n l += 1\ndist = {} \nwith open(blasttable) as b:\n for ln in b.read().splitlines():\n s = ln.split(\"\\t\")\n if s[0] not in dist:\n dist[s[0]] = {}\n dist[s[0]][s[1]] = 1-float(s[2])\n\n## Distance matrix\ndist_score_assembly_line = generate_distance_matrix(pathways, domain_names, annotation,\n dist, Jaccardw, GKw, DDSw,\n scale=1, nbhood=3, outfile=outfile)\n\n#-- Plot the tree\nnames = pathways.keys()\nscore = [s for s in open(outfile, 'r').read().split('\\n')[1:] if s != '']\nmatrix = []\nfor i in range(len(score)):\n input_i = score[i].split(',')[1:(i+2)]\n input_i_int = [float(n) for n in input_i]\n matrix.append(input_i_int)\nm = _DistanceMatrix(names, matrix)\nconstructor = DistanceTreeConstructor()\ntree1 = constructor.upgma(m)\n#Bio.Phylo.draw_ascii(tree1)\nBio.Phylo.write(tree1, tree_outfile, 'newick')\n\n\n","sub_path":"generate_dendrogram.py","file_name":"generate_dendrogram.py","file_ext":"py","file_size_in_byte":21128,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"104512710","text":"# saved as server.py\nimport Pyro4\n\n@Pyro4.expose\nclass JustHungry(object):\n\n # List of categories and products\n categories = [\"starter\", \"main course\", \"drink\", \"dessert\"]\n products = [[\"salad\", categories[0] , 4],\n [\"soup\", categories[0], 4],\n [\"salmon\", categories[0], 3],\n [\"sandwich\", categories[1], 7],\n [\"mac and cheese\", categories[1], 11],\n [\"peperoni pizza\",categories[1], 10],\n [\"coca-cola\", categories[2], 1],\n [\"fanta\", categories[2], 1.3],\n [\"water\", categories[2], 1.5], \n [\"yogurt\", categories[3], 3],\n [\"apple\", categories[3], 2],\n [\"chocolate cake\", categories[3], 4]]\n\n orders = []\n primary = 0\n def setPrimary(self):\n self.primary = 1\n def setSlave(self):\n self.primary = 0\n def isPrimary(self):\n return self.primary\n # Get method for categories\n def getCategories(self):\n return self.categories\n # Get method for products of a specific category\n def getProducts(self,category):\n category_products = []\n for product in self.products:\n if product[1]==category:\n category_products.append(product)\n return category_products\n \n # Get method for the last Id in the orders array\n def getLastId(self):\n # Check if orders is not empty\n if len(self.orders)>0:\n return self.orders[len(self.orders)-1][0]\n else:\n return 0\n\n # Sets an order (only used for the primary server) and then replicates the data onto the other 2 servers\n def setOrder(self,order_Id, order_list, order_price,order_time,name,postcode):\n # Sets the order\n self.orders.append([order_Id,order_list, order_price,order_time,name,postcode])\n print(\"An order was added\")\n print(self.orders)\n # Update the other 2 servers\n try:\n server = Pyro4.Proxy(\"PYRONAME:server1\")\n server.updateOrders(self.orders)\n except:\n print(\"Can't update Server1 as it is not running\")\n pass\n try:\n server = Pyro4.Proxy(\"PYRONAME:server3\")\n server.updateOrders(self.orders)\n except:\n print(\"Can't update Server3 as it is not running\")\n pass\n return \"Your order has been placed\"\n\n # Get method for the orders\n def getOrders(self):\n return self.orders\n \n # Update orders from primary server onto a secondary one\n def updateOrders(self, orders):\n try:\n self.orders.extend([order for order in orders if order not in self.orders])\n print(self.orders)\n return \"ok\"\n except:\n return \"Error\"\n\n\n\n\n\ndaemon = Pyro4.Daemon() # make a Pyro daemon\nns = Pyro4.locateNS() # find the name server\nuri = daemon.register(JustHungry) # register the greeting maker as a Pyro object\nns.register(\"server2\", uri) # register the object with a name in the name server\n\nprint(\"Ready\") # print the uri so we can use it in the client later\ndaemon.requestLoop() # start the event loop of the server to wait for calls","sub_path":"server2.py","file_name":"server2.py","file_ext":"py","file_size_in_byte":3235,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}
+{"seq_id":"1740101","text":"from __future__ import division\n__author__ = 'ywang'\n\"\"\"fold all the necessary return measurement\"\"\"\nimport pandas as pd\nimport numpy as np\n\nMONTH_IN_YEAR = 12\nMONTH_IN_QUARTER = 3\nDAY_IN_YEAR = 252\nPCT_MULTIPLIER = 100\n\nSMOOTHING_FACTOR = 24 #24 Month\n##############\n### not really useful- we need return perspective\n###############\ndef drawdown(prices):\n \"\"\"\n Returns start and end index of maximum drawdown of given series\n \"\"\"\n prevmaxi = 0\n prevmini = 0\n maxi = 0\n\n for i in range(len(prices))[1:]:\n if prices[i] >= prices[maxi]:\n maxi = i\n else:\n if (prices[maxi] - prices[i]) > (prices[prevmaxi] - prices[prevmini]):\n prevmaxi = maxi\n prevmini = i\n\n return prices.index[prevmaxi], prices.index[prevmini]\n\ndef cum_ret(df):\n return (df/PCT_MULTIPLIER+1).cumprod()\n\ndef anualize_ret(gross_ret,duration):\n \"\"\"duration default in # of months\"\"\"\n # print gross_ret\n # print MONTH_IN_YEAR/duration\n # print np.power(float(gross_ret), float(MONTH_IN_YEAR/duration))\n return (np.power(float(gross_ret), float(MONTH_IN_YEAR/duration))-1)*PCT_MULTIPLIER\n\ndef anualize_vol(std_dev):\n return np.sqrt(MONTH_IN_YEAR) * std_dev\n\ndef max_dd(ser):\n \"\"\"calculate maximum drawdown - return the duration of maximum draw down\"\"\"\n max2here = pd.expanding_max(ser)\n dd2here = ser - max2here\n return dd2here.min()\n\ndef _stat(return_series, cum_ret_series, duration, bm_ret):\n \"\"\"return the basic distribution files\"\"\"\n start_date = return_series.index[0] #aviod the 1st zero\n end_date = return_series.index[-1]\n return_series = return_series.dropna()\n window = len(return_series)\n mean = pd.rolling_mean(return_series, window)[-1]\n median = pd.rolling_median(return_series, window)[-1]\n std_dev = pd.rolling_std(return_series, window)[-1]\n skew = pd.rolling_skew(return_series, window)[-1]\n kurtosis = pd.rolling_kurt(return_series, window)[-1]\n total_return = cum_ret_series.dropna()[-1]/cum_ret_series.dropna()[0] #this is the gross multiplier of the fund\n corr = pd.rolling_corr(return_series, bm_ret,len(return_series))[-1]\n\n return {'start': start_date,\n 'end': end_date,\n 'mean':mean,\n 'median': median,\n 'std_dev': std_dev,\n 'skew': skew,\n 'kurt': kurtosis,\n 'peak return': pd.expanding_max(return_series.cumsum()).max(),\n 'max drawdown': max_dd(return_series.cumsum()),\n 'rolling drawdown one year': pd.rolling_apply(return_series.cumsum(), 4, max_dd, min_periods=0).dropna().resample('Q').min(),\n 'rolling drawdown two year': pd.rolling_apply(return_series.cumsum(), 8, max_dd, min_periods=0).dropna().resample('Q').min(),\n 'rolling drawdown three year': pd.rolling_apply(return_series.cumsum(), 12, max_dd, min_periods=0).dropna().resample('Q').min(),\n 'total return': (total_return-1) * PCT_MULTIPLIER,\n 'annualized vol': anualize_vol(std_dev),\n 'annualized return': anualize_ret(total_return, duration),\n 'cover period': duration/MONTH_IN_YEAR,\n 'correlation': corr\n }\n\n\n# def annualized_ret(df, Smooth = SMOOTHING_FACTOR):\n# \"\"\" default to annualized return\n# smoothing parameter is the rolling windows being acessed\n# \"\"\"\n# for col in df.columns:\n\n","sub_path":"research/optimizer/perf_util.py","file_name":"perf_util.py","file_ext":"py","file_size_in_byte":3414,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"1"}