diff --git "a/4380.jsonl" "b/4380.jsonl"
new file mode 100644--- /dev/null
+++ "b/4380.jsonl"
@@ -0,0 +1,699 @@
+{"seq_id":"3491002","text":"import pandas as pd\nimport re\n\nfrom selenium import webdriver\nfrom bs4 import BeautifulSoup\nfrom lmf.dbv2 import db_write,db_command,db_query\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.common.by import By\nfrom selenium.common.exceptions import NoSuchElementException,StaleElementReferenceException\nfrom selenium.common.exceptions import WebDriverException\nfrom selenium.webdriver.support.wait import WebDriverWait\nfrom selenium.webdriver.support import expected_conditions as EC\n\nimport sys\nimport time\n\nimport json\n\nfrom zhulong.util.etl import add_info,est_meta,est_html,est_tbs\n\n\n_name_=\"huzhou\"\n\n\n\n# __conp=[\"postgres\",\"since2015\",\"192.168.3.171\",\"hunan\",\"changsha\"]\n\n\n# url=\"https://ggzy.changsha.gov.cn/spweb/CS/TradeCenter/tradeList.do?Deal_Type=Deal_Type2\"\n# driver=webdriver.Chrome()\n# driver.minimize_window()\n# driver.get(url)\n\n\n\ndef f1(driver, num):\n\n    locator = (By.XPATH, \"(//a[@class='WebList_sub'])[1]\")\n    val = WebDriverWait(driver, 10).until(EC.presence_of_element_located(locator)).text\n\n    locator = (By.XPATH, \"//td[@class='huifont']\")\n    str = WebDriverWait(driver, 10).until(EC.presence_of_element_located(locator)).text\n    cnum = re.findall(r'(\\d+)/', str)[0]\n\n    url = driver.current_url\n    if num != int(cnum):\n        if num == 1:\n            url = re.sub(\"Paging=[0-9]*\", \"Paging=1\", url)\n        else:\n            s = \"Paging=%d\" % (num) if num > 1 else \"Paging=1\"\n            url = re.sub(\"Paging=[0-9]*\", s, url)\n            # print(cnum)\n        driver.get(url)\n        try:\n            locator = (By.XPATH, \"(//a[@class='WebList_sub'])[1][string()!='%s']\" % val)\n            WebDriverWait(driver, 10).until(EC.presence_of_element_located(locator))\n        except:\n            driver.refresh()\n            locator = (By.XPATH, \"(//a[@class='WebList_sub'])[1][string()!='%s']\" % val)\n            WebDriverWait(driver, 3).until(EC.presence_of_element_located(locator))\n\n    page = driver.page_source\n\n    soup = BeautifulSoup(page, 'lxml')\n\n    table = soup.find(\"table\", width='98%')\n\n    trs = table.find_all(\"tr\", height=\"24\")\n    data = []\n    for tr in trs[1:]:\n        a = tr.find(\"a\")\n        try:\n            link = a[\"href\"]\n        except:\n            link = ''\n        td = tr.find(\"td\", width=\"80\").text\n        tmp = [a[\"title\"].strip(), td.strip(), \"http://ggzy.huzhou.gov.cn\" + link.strip()]\n        data.append(tmp)\n\n    df = pd.DataFrame(data)\n    df['info'] = None\n    return df\n\n\ndef f2(driver):\n    locator = (By.XPATH, \"(//a[@class='WebList_sub'])[1]\")\n    WebDriverWait(driver, 10).until(EC.presence_of_element_located(locator))\n    locator = (By.XPATH, '//td[@class=\"huifont\"]')\n    str = WebDriverWait(driver, 10).until(EC.presence_of_element_located(locator)).text\n    num = re.findall(r'/(\\d+)', str)[0]\n\n    driver.quit()\n    return int(num)\n\n\n\ndef f3(driver, url):\n    driver.get(url)\n\n    locator = (By.CLASS_NAME, \"bg\")\n\n    WebDriverWait(driver, 10).until(EC.presence_of_all_elements_located(locator))\n\n    before = len(driver.page_source)\n    time.sleep(0.1)\n    after = len(driver.page_source)\n    i = 0\n    while before != after:\n        before = len(driver.page_source)\n        time.sleep(0.1)\n        after = len(driver.page_source)\n        i += 1\n        if i > 5: break\n\n    page = driver.page_source\n\n    soup = BeautifulSoup(page, 'lxml')\n\n    div = soup.find('td', height='500')\n    # div=div.find_all('div',class_='ewb-article')[0]\n\n    return div\n\n\ndata = [\n    [\"gcjs_jianshe_zhaobiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029001/029001001/029001001001/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"gcjs_jianshe_zhongbiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029001/029001001/029001001002/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"gcjs_jiaotong_zhaobiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029001/029001002/029001002001/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"gcjs_jiaotong_zhongbiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029001/029001002/029001002002/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n\n    [\"gcjs_shuili_zhaobiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029001/029001003/029001003001/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"gcjs_shuli_zhongbiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029001/029001003/029001003002/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"gcjs_xianer_zhaobiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029001/029001004/029001004001/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n\n    [\"zfcg_jizhong_zhaobiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029004/029004001/029004001001/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"zfcg_jizhong_zhongbiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029004/029004001/029004001002/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"zfcg_jizhong_yucai_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029004/029004001/029004001003/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n\n    [\"zfcg_fensan_zhaobiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029004/029004002/029004002001/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"zfcg_fensan_zhongbiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029004/029004002/029004002002/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n    [\"zfcg_fensan_yucai_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029004/029004002/029004002003/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"zfcg_xieyi_cheliangweixiu_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029004/029004004/029004004001/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"zfcg_jizhong_caigoumulu_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029004/029004005/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"ylcg_jizhong_zhaobiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029005/029005001/029005001001/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"ylcg_jizhong_zhongbiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029005/029005001/029005001002/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"],f1,f2],\n\n    [\"ylcg_fensan_zhaobiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029005/029005002/029005002001/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"], f1, f2],\n\n    [\"ylcg_fensan_zhongbiao_gg\",\n     \"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029005/029005002/029005002002/?Paging=1\",\n     [\"name\", \"ggstart_time\", \"href\", \"info\"], f1, f2],\n\n\n]\n\n\ndef work(conp,**args):\n    est_meta(conp,data=data,diqu=\"浙江省湖州市\",**args)\n    est_html(conp,f=f3,**args)\n\n\nif __name__=='__main__':\n    work(conp=[\"postgres\",\"since2015\",\"192.168.3.171\",\"zhejiang\",\"huzhou\"])\n\n\n    # driver=webdriver.Chrome()\n    # url=\"http://ggzy.huzhou.gov.cn/HZfront/jyxx_HuZhou/029001/029001001/029001001001/?Paging=1\"\n    # driver.get(url)\n    # df = f2(driver)\n    # print(df)\n    # for i in range(1, 6):\n    #     df=f1(driver, i)\n    #     print(df)\n","sub_path":"zl_spider/zhejiang/huzhou.py","file_name":"huzhou.py","file_ext":"py","file_size_in_byte":7411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"628420547","text":"from collections import defaultdict\nfrom more_itertools import grouper\nfrom pathlib import Path\n\n\ndef parse_data():\n    with open(Path(__file__).stem + \".txt\") as fp:\n        data = fp.read()\n    return data\n\n\ndef get_direction(direction):\n    x = 0\n    y = 0\n    if direction == \"^\":\n        y += 1\n    elif direction == \"v\":\n        y -= 1\n    elif direction == \"<\":\n        x -= 1\n    elif direction == \">\":\n        x += 1\n    return x, y\n\n\ndef main_a():\n    data = parse_data()\n    x = 0\n    y = 0\n    chart = defaultdict(int)\n    chart[(x, y)] += 1\n    for direction in data:\n        direction = get_direction(direction)\n        x += direction[0]\n        y += direction[1]\n        chart[(x, y)] += 1\n    print(len(chart.keys()))\n\n\ndef main_b():\n    data = parse_data()\n    santa = [0, 0]\n    robo = [0, 0]\n    chart = defaultdict(int)\n    chart[tuple(santa)] += 2\n    for s, r in grouper(data, 2):\n        direction = get_direction(s)\n        santa[0] += direction[0]\n        santa[1] += direction[1]\n        chart[tuple(santa)] += 1\n        direction = get_direction(r)\n        robo[0] += direction[0]\n        robo[1] += direction[1]\n        chart[tuple(robo)] += 1\n    print(len(chart.keys()))\n\n\nif __name__ == \"__main__\":\n    # main_a()\n    main_b()\n","sub_path":"2015/a03.py","file_name":"a03.py","file_ext":"py","file_size_in_byte":1258,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"1897498","text":"# coding=utf-8\n\"\"\"\nInaSAFE Disaster risk assessment tool developed by AusAid - **Help Class.**\n\nContact : ole.moller.nielsen@gmail.com\n\n.. note:: This program is free software; you can redistribute it and/or modify\n     it under the terms of the GNU General Public License as published by\n     the Free Software Foundation; either version 2 of the License, or\n     (at your option) any later version.\n\n\"\"\"\n\n__author__ = 'tim@linfiniti.com'\n__revision__ = '8e90270d76bc3e85f0084bd480f8e977a33cb812'\n__date__ = '20/01/2011'\n__copyright__ = ('Copyright 2012, Australia Indonesia Facility for '\n                 'Disaster Reduction')\n\nimport os\nimport logging\n\n#noinspection PyPackageRequirements\nfrom PyQt4 import (QtGui, QtCore)\n\nfrom safe_qgis.exceptions import HelpFileMissingError\n\nLOGGER = logging.getLogger('InaSAFE')\n\n\ndef show_context_help(context=None):\n    \"\"\"Show help using the user's web browser.\n\n    :type context: str\n    :param context: Page name (without path) of a document in the\n       user-docs subdirectory. e.g. 'keywords'\n    \"\"\"\n\n    try:\n        _show_local_help(context)\n    except HelpFileMissingError:\n        _show_online_help(context)\n\n\ndef _show_local_help(context=None):\n    \"\"\"Show help using the user's web browser - uses local help file.\n\n    :type context: str\n    :param context: Page name (without path) of a document in the\n       user-docs subdirectory. e.g. 'keywords'\n\n    :raises: HelpFileMissingError\n    \"\"\"\n\n    # First we try using local filesystem\n\n    base_url = os.path.abspath(os.path.join(\n        __file__, os.path.pardir, os.path.pardir, os.path.pardir, 'docs'\n    ))\n\n    # set default value for locale\n    locale = 'en'\n    if 'LANG' in os.environ:\n        locale = os.environ['LANG']\n\n    if locale not in ['id', 'en']:\n        locale = 'en'\n\n    base_url = os.path.join(base_url, locale)\n\n    if context is not None:\n        base_url = os.path.join(base_url, context + '.html')\n        LOGGER.debug(os.path.isfile(base_url))\n    else:\n        base_url = os.path.join(base_url, 'index.html')\n\n    if not os.path.exists(base_url):\n        raise HelpFileMissingError('Help file not found: %s' % base_url)\n\n    #noinspection PyTypeChecker,PyArgumentList\n    url = QtCore.QUrl.fromLocalFile(base_url)\n    # noinspection PyCallByClass,PyTypeChecker,PyArgumentList\n    QtGui.QDesktopServices.openUrl(url)\n\n\ndef _show_online_help(context=None):\n    \"\"\"Show help using the user's web browser - uses inasafe web site.\n\n    :type context: str\n    :param context: Page name (without path) of a document in the\n       user-docs subdirectory. e.g. 'keywords'\n    \"\"\"\n\n    # First we try using local filesystem\n\n    base_url = 'http://inasafe.org/'\n\n    # set default value for locale\n    locale = 'en'\n    if 'LANG' in os.environ:\n        locale = os.environ['LANG']\n\n    if locale not in ['id', 'en']:\n        locale = 'en'\n\n    base_url += locale\n    base_url += '/user-docs/'\n\n    if context is not None:\n        base_url += context + '.html'\n        LOGGER.debug(os.path.isfile(base_url))\n    else:\n        base_url += 'index.html'\n    url = QtCore.QUrl(base_url)\n    # noinspection PyCallByClass,PyTypeChecker,PyArgumentList\n    QtGui.QDesktopServices.openUrl(url)\n","sub_path":"safe_qgis/utilities/help.py","file_name":"help.py","file_ext":"py","file_size_in_byte":3213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"190706087","text":"\"\"\"\nhttps://docs.python.org/3/library/multiprocessing.html#multiprocessing.managers.Namespace\n\"\"\"\nimport multiprocessing\nfrom multiprocessing.managers import BaseManager\nimport pprint\n\n\ndef worker(d, key, value):\n    d[key] = value\n\n\n# Shared Namespace\ndef producer(ns, event):\n    # It is important to know that updates to the contents of mutable values\n    # in the namespace are not propagated automatically.\n    #  when using a proxy for a namespace object,\n    # an attribute beginning with '_' will be an attribute of the proxy and not an attribute of the referent:\n    ns.value = 'This is the value'\n    event.set()\n\n\ndef consumer(ns, event):\n    try:\n        print('Before event: {}'.format(ns.value))\n    except Exception as err:\n        print('Before event, error:', str(err))\n    event.wait()\n    print('After event:', ns.value)\n\n\nclass MathsClass:\n    def add(self, x, y):\n        return x + y\n    def mul(self, x, y):\n        return x * y\n\nclass MyManager(BaseManager):\n    pass\n\nMyManager.register('Maths', MathsClass)\n\n\nif __name__ == '__main__':\n    mgr = multiprocessing.Manager()\n    d = mgr.dict()\n    jobs = [\n        multiprocessing.Process(\n            target=worker,\n            args=(d, i, i ** 2),\n        )\n        for i in range(10)\n    ]\n    for j in jobs:\n        j.start()\n    for j in jobs:\n        j.join()\n    print('Results:', d)\n\n    namespace = mgr.Namespace()\n    event = multiprocessing.Event()\n    p = multiprocessing.Process(\n        target=producer,\n        args=(namespace, event),\n    )\n    c = multiprocessing.Process(\n        target=consumer,\n        args=(namespace, event),\n    )\n\n    c.start()\n    p.start()\n\n    c.join()\n    p.join()\n\n    print('Custom manager:')\n    with MyManager() as manager:\n        maths = manager.Maths()\n        print(maths.add(4, 3))\n        print(maths.mul(7, 8))","sub_path":"cheatsheet/multiprocess/manager_shared_state.py","file_name":"manager_shared_state.py","file_ext":"py","file_size_in_byte":1839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"195159084","text":"#!/usr/bin/env python\nimport codefast as cf\nimport numpy as np  # linear algebra\nimport pandas as pd  # data processing, CSV file I/O (e.g. pd.read_csv)\nimport tensorflow as tf\nfrom dofast.toolkits.telegram import messalert\nfrom sklearn.model_selection import train_test_split\nfrom tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\nfrom tensorflow.keras.layers import (GRU, LSTM, Bidirectional, Dense, Dropout,\n                                     Embedding, GlobalMaxPool1D, Input)\nfrom tensorflow.keras.models import Model, Sequential\nfrom tensorflow.keras.preprocessing import sequence\nfrom tensorflow.keras.preprocessing.sequence import pad_sequences\n\nimport premium as pm\nfrom premium.postprocess import get_binary_prediction\nfrom premium.preprocess import label_encode, pad_sequences, tokenize\n\ntf.compat.v1.disable_eager_execution()\n\n\nclass OverFat:\n    def __init__(self,\n                 max_feature: int = 50000,\n                 max_len: int = 300,\n                 embed_size: int = 200,\n                 X=None,\n                 y=None) -> None:\n        self.max_feature = max_feature\n        self.max_len = max_len\n        self.embed_size = embed_size\n        self.X = X\n        self.y = y\n        self.sequence = None\n        self.tokenizer = None\n        self.word_index = None\n\n    def tokenize_sequence(self):\n        self.X, tokenizer = tokenize(self.X, max_feature=self.max_feature)\n        self.sequence = sequence.pad_sequences(self.X, maxlen=self.max_len)\n        self.tokenizer = tokenizer\n        self.word_index = tokenizer.word_index\n        cf.info('{} of unique tokens found'.format(len(self.word_index)))\n        return self.tokenizer\n\n    def embed(self, pretrained_vector: str = 'glove-wiki-gigaword-50'):\n        self.embeddings_index = pm.word2vec.load(pretrained_vector)\n        cf.info('load {} completes'.format(pretrained_vector))\n\n        all_embeddings = np.stack(list(self.embeddings_index.values()))\n        emb_mean, emb_std = all_embeddings.mean(), all_embeddings.std()\n        assert self.word_index is not None, 'Tokenize and sequence text first!'\n\n        self.max_feature = min(self.max_feature, len(self.word_index))\n        self.embedding_matrix = np.random.normal(\n            emb_mean, emb_std, (self.max_feature, self.embed_size))\n\n        hit, missed = 0, 0\n        for word, i in self.word_index.items():\n            if i < self.max_feature:\n                embedding_vector = self.embeddings_index.get(word)\n                if embedding_vector is not None:\n                    self.embedding_matrix[i] = embedding_vector\n                    hit += 1\n                else:\n                    missed += 1\n        cf.info(\n            'embed completes, size of embedding matrix {}. Missed {}, hit {}'.\n            format(self.embedding_matrix.shape, missed, hit))\n\n        return self.embedding_matrix\n\n    def build_model(self):\n        inp = Input(shape=(self.max_len, ))\n        x = Embedding(self.max_feature,\n                      self.embed_size,\n                      weights=[self.embedding_matrix])(inp)\n        # x = Embedding(max_feature, embed_size)(inp)\n        x = Bidirectional(GRU(80, return_sequences=True))(x)\n        x = GlobalMaxPool1D()(x)\n        x = Dropout(0.3)(x)\n        x = Dense(50, activation=\"relu\")(x)\n        x = Dropout(0.3)(x)\n        opt = Dense(6, activation=\"sigmoid\")(x)\n        model = Model(inputs=inp, outputs=opt)\n        model.compile(loss='binary_crossentropy',\n                      optimizer='adam',\n                      metrics=['accuracy'])\n        model.summary()\n        self.model = model\n        cf.info('model was built.')\n        return model\n\n    def fine_tune_model(self, batch_size: int = 1024, epochs: int = 20):\n        file_path = f\"/tmp/best_weights_{cf.uuid()}.hdf5\"\n        checkpoint = ModelCheckpoint(file_path,\n                                     monitor='val_loss',\n                                     verbose=1,\n                                     save_best_only=True,\n                                     mode='min')\n\n        early = EarlyStopping(monitor=\"val_loss\", mode=\"min\", patience=6)\n\n        callbacks_list = [checkpoint, early]     #early\n        self.model.fit(self.sequence,\n                       self.y,\n                       batch_size=batch_size,\n                       epochs=epochs,\n                       validation_split=0.15,\n                       callbacks=callbacks_list,\n                       use_multiprocessing=True)\n\n        self.model.load_weights(file_path)\n        return self.model\n\n    def predict(self, X_te, cats):\n        y_test = self.model.predict(X_te,\n                                    batch_size=1024,\n                                    verbose=1,\n                                    use_multiprocessing=True)\n        sample_submission = pd.read_csv(\"/tmp/sample_submission.csv\")\n        sample_submission[cats] = y_test\n        export_file = f\"tmp/pred_toxic_{cf.uuid()}.csv\"\n        cf.info('Export to file', export_file)\n        sample_submission.to_csv(export_file, index=False)\n        messalert('Toxic Done')\n\n\nclass UnderFat:\n    def __init__(self, train_file, test_file):\n        train = pd.read_csv(train_file)\n        test = pd.read_csv(test_file)\n        text_train = train[\"comment_text\"].fillna(\"CVxTz\").values\n        cats = [\n            \"toxic\", \"severe_toxic\", \"obscene\", \"threat\", \"insult\",\n            \"identity_hate\"\n        ]\n        y = train[cats].values\n\n        max_feature = 10000\n        max_len = 50\n        embed_size = 50\n        fat = OverFat(max_feature, max_len, embed_size, text_train, y)\n        fat.y = y\n        fat.tokenize_sequence()\n        fat.embed('glove-wiki-gigaword-50')\n        fat.build_model()\n        fat.fine_tune_model()\n\n\ndef build_bilstm(maxlen: int, max_feature: int, embed_size: int):\n    inp = Input(shape=(maxlen, ))\n    layer = Embedding(max_feature, embed_size)(inp)\n    layer = Bidirectional(LSTM(50, return_sequences=True))(layer)\n    layer = GlobalMaxPool1D()(layer)\n    layer = Dropout(0.1)(layer)\n    layer = Dense(50, activation=\"relu\")(layer)\n    layer = Dropout(0.1)(layer)\n    opt = Dense(6, activation=\"sigmoid\")(layer)\n    model = Model(inputs=inp, outputs=opt)\n    model.compile(loss='binary_crossentropy',\n                  optimizer='adam',\n                  metrics=['accuracy'])\n    model.summary()\n\n    return model\n\n\ndef optimal_batch_size(sequence_size: int) -> int:\n    import math\n    base = int(math.log(sequence_size + 1, 10))\n    batch_size = 1 << (base + 2)\n    cf.info('Batch size is set to ', batch_size)\n    return batch_size\n\n\nclass NNTouchStone:\n    def __init__(self,\n                 X,\n                 y,\n                 validation_split: float = 0.15,\n                 max_len: int = -1):\n        self.X = X\n        self.y = y\n        self.X_train = None\n        self.y_train = None\n        self.validation_split = validation_split\n        self.max_len = max_len\n        self.epochs = 63\n        self.save_model = False\n\n    def preprocess(self):\n        self.X_train, self.X_test, self.y_train, self.y_test, _i, _j = train_test_split(\n            self.X,\n            self.y,\n            np.arange(len(self.X)),\n            random_state=63,\n            test_size=self.validation_split)\n\n        self.indices = {'train': _i, 'val': _j}\n\n        cf.info('start tokenizing')\n        self.X_train, tokenizer = tokenize(self.X_train)\n        self.X_test = tokenizer.texts_to_sequences(self.X_test)\n        self.tokenizer = tokenizer\n        self.word_index = tokenizer.word_index\n\n        if self.max_len < 0:\n            self.max_len = int(np.percentile(list(map(len, self.X)), 95))\n        cf.info('MAX_LENGTH_SEQUENCE set to {}'.format(self.max_len))\n        assert self.max_len >= 2, 'max length is less than 2, check your data.'\n\n        self.X_train = pad_sequences(self.X_train, maxlen=self.max_len, padding=\"pre\")\n        self.X_test = pad_sequences(self.X_test, maxlen=self.max_len, padding=\"pre\")\n\n        self.input_dim = len(tokenizer.word_index) + 1\n\n    def embed(self, pretrained_vector: str = ''):\n        if pretrained_vector:\n            self.embeddings_index = pm.word2vec.load(pretrained_vector)\n            cf.info('load {} completes'.format(pretrained_vector))\n\n        all_embeddings = np.stack(list(self.embeddings_index.values()))\n        emb_mean, emb_std = all_embeddings.mean(), all_embeddings.std()\n        assert self.word_index is not None, 'Tokenize and sequence text first!'\n\n        self.max_feature = min(self.max_feature, len(self.word_index))\n        self.embedding_matrix = np.random.normal(\n            emb_mean, emb_std, (self.max_feature, self.embed_size))\n\n        missed, hit = 0, 0\n        for word, i in self.word_index.items():\n            if i < self.max_feature:\n                embedding_vector = self.embeddings_index.get(word)\n                if embedding_vector is not None:\n                    self.embedding_matrix[i] = embedding_vector\n                    hit += 1\n                else:\n                    missed += 1\n\n        cf.info(\n            'embed completes, size of embedding matrix {}. Missed {}, hit {}'.\n            format(self.embedding_matrix.shape, missed, hit))\n\n        return self.embedding_matrix\n\n    def build_model(self):\n        cf.info('Building model...')\n        M = Sequential()\n        M.add(Embedding(self.input_dim, 100, input_length=self.max_len))\n\n        # M.add(Bidirectional(LSTM(100)))\n        M.add(Bidirectional(GRU(100)))\n        M.add(Dropout(0.4))\n\n        M.add(Dense(60, activation=\"relu\"))\n        M.add(Dropout(0.4))\n        M.add(Dense(1, activation=\"sigmoid\"))\n\n        M.compile(loss=\"binary_crossentropy\",\n                  optimizer=\"adam\",\n                  metrics=[\"accuracy\"])\n        M.summary()\n        self.model = M\n        return M\n\n    def train(self):\n        cf.info('training model')\n        weight_path = f'/tmp/best_weights_{cf.uuid()}.h5'\n        es = EarlyStopping(monitor='val_loss',\n                           mode='min',\n                           verbose=1,\n                           patience=5)\n        checkpoint = ModelCheckpoint(weight_path,\n                                     monitor='val_loss',\n                                     save_weights_only=True,\n                                     verbose=1,\n                                     save_best_only=True,\n                                     period=1)\n        fit_params = {\n            'batch_size': optimal_batch_size(len(self.X)),\n            'validation_split': 0.1,\n            'epochs': self.epochs,\n            'callbacks': [es, checkpoint]\n        }\n        self.model.fit(self.X_train, self.y_train, **fit_params)\n        self.model.load_weights(weight_path)\n        if self.save_model:\n            self.model.save('/tmp/saved_model.h5')\n\n    def predict(self):\n        y_pred = get_binary_prediction(self.model.predict(self.X_test))\n        pm.libra.metrics(self.y_test, y_pred)\n        return self.y_test, y_pred\n\n    def pipeline(self):\n        self.preprocess()\n        self.build_model()\n        self.train()\n        return self.predict()\n","sub_path":"premium/models/nn.py","file_name":"nn.py","file_ext":"py","file_size_in_byte":11124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"570441943","text":"import socket\nimport time\n\nwith socket.socket(socket.AF_INET, socket.SOCK_DGRAM) as s:\n\n    s.settimeout(1)\n\n    start_t = time.time()\n    max_du = 0;\n    min_du = 1e6;\n    every_t = 0\n    \n    try:\n        while True:\n\n            s.sendto(b'hellohello',(\"192.168.25.238\", 5005))\n            try:\n                data, addr = s.recvfrom(4096)\n            except socket.timeout:\n                print(\"caught a timeout at first echo\")\n\n            #print(repr(data))\n\n            try:\n                data, addr = s.recvfrom(4096)\n            except socket.timeout:\n                print(\"caught a timeout at second echo\")\n\n            #print(int.from_bytes(data, 'little'))\n\n            if every_t > 0:\n                if time.time() - every_t > max_du:\n                    max_du = time.time() - every_t\n\n                if time.time() - every_t < min_du:\n                    min_du = time.time() - every_t\n\n            if time.time() - start_t > 1:\n                print(\"du: \", time.time() - every_t, \", max_du: \", max_du,  \", min_du: \", min_du)\n                start_t = time.time()\n\n\n            every_t = time.time()\n\n            time.sleep(0.001)\n\n    except KeyboardInterrupt:\n        exit()\n","sub_path":"Scripts/socket_udp_send2.py","file_name":"socket_udp_send2.py","file_ext":"py","file_size_in_byte":1201,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"260557016","text":"\n\nimport turtle\nimport math\nimport random\n\n\nSCREEN_WIDTH = 800\nSCREEN_HEIGHT = 600 # setting up the size of the screen\n\nwindow = turtle.Screen() # creating the window\nwindow.setup(SCREEN_WIDTH + 220, SCREEN_HEIGHT + 20)\nwindow.title('Space Arena') # adding the title\nwindow.bgcolor('black') # adding the backgroundcolor\nwindow.tracer(0)\n\npen = turtle.Turtle()\npen.speed(0)\npen.shape('square')\npen.color('white')\npen.penup()\npen.hideturtle() # we won't see the drawed lines \n\nclass Game():\n    def __init__(self, width, height):\n        self.width = width\n        self.height = height\n        self.level = 1\n    \n    def start_level(self):\n        sprites.clear() # clears all the sprites from the list\n\n        # add the player \n        sprites.append(player)\n\n        # add missile\n        sprites.append(missile)\n\n        # add enemy per level\n        for _ in range(self.level):\n            x = random.randint(-self.width/2, self.width/2)\n            y = random.randint(-self.height/2, self.height/2)\n            dx = random.randint(-2, 2)\n            dy = random.randint(-2, -2)\n            sprites.append(Enemy(x, y, 'square', 'red'))\n            sprites[-1].dx = dx # will give us the the last sprite what was added\n            sprites[-1].dy = dy\n\n        # add powerup per level\n        for _ in range(self.level):\n            x = random.randint(-self.width/2, self.width/2)\n            y = random.randint(-self.height/2, self.height/2)\n            dx = random.randint(-2, 2)\n            dy = random.randint(-2, -2)\n            sprites.append(Powerup(x, y, 'circle', 'blue'))\n            sprites[-1].dx = dx # will give us the the last sprite what was added\n            sprites[-1].dy = dy\n\n\n\n\n    def render_border(self, pen, x_offset, y_offset):\n        pen.color('white')\n        pen.width(3)\n        pen.penup()\n\n        left = -self.width / 2 - x_offset# if the screen width is 800 like in  our case its -400 left and 400 by the right side\n        right = self.width / 2 - x_offset\n        top = self.height / 2 - y_offset\n        bottom = -self.height / 2 - y_offset\n\n        pen.goto(left, top) # starting left top\n        pen.pendown() # function needed so we can actually draw the border\n        pen.goto(right, top)\n        pen.goto(right, bottom)\n        pen.goto(left, bottom)\n        pen.goto(left, top)\n        pen.penup()\n\nclass Sprite(): # the class for objects like player, enemy etc.\n    # the constructor\n    def __init__(self, x, y, shape, color):\n        self.x = x\n        self.y = y\n        self.shape = shape\n        self.color = color\n        self.dx = 0 # horizontal speed (if dx is positiv the object moves to the right, if negative left)\n        self.dy = 0 # vertical speed (if dy is positiv the object moves up, if negative down)\n        self.heading = 0\n        self.da = 0\n        self.thrust = 0.0\n        self.acceleration = 0.02\n        self.health = 100\n        self.max_health = 100\n        self.width = 20\n        self.height = 20\n        self.state = 'active'\n        self.radar = 200\n\n\n    def is_collision(self, other):\n        if self.x < other.x + other.width and\\\n            self.x + self.width > other.x and\\\n            self.y < other.y + other.height and\\\n            self.y + self.height > other.y:\n            return True\n        else:\n            return False\n    \n    def bounce(self, other):\n        temp_dx = self.dx\n        temp_dy = self.dy\n\n        self.dx = other.dx\n        self.dy = other.dy\n\n        other.dx = temp_dx\n        other.dy = temp_dy\n\n    \n    def update(self):\n        self.heading += self.da # the heading is gonna change by the delta of the angle\n        self.heading %= 360\n\n        self.dx += math.cos(math.radians(self.heading)) * self.thrust\n        self.dy += math.sin(math.radians(self.heading)) * self.thrust\n    \n        self.x += self.dx\n        self.y += self.dy\n\n        self.border_check()\n    \n    def border_check(self):\n        if self.x > game.width / 2 - 10:\n            self.x = game.width /2 - 10\n            self.dx *= -1 # the sprite bounces off the right wall\n        \n        elif self.x < -game.width / 2 + 10:\n            self.x = -game.width /2 + 10\n            self.dx *= -1 # the sprite bounces off the left wall       \n\n        if self.y > game.height / 2 - 10:\n            self.y = game.height /2 - 10\n            self.dy *= -1 # the sprite bounces off the top wall\n        \n        elif self.y < -game.height / 2 + 10:\n            self.y = -game.height /2 + 10\n            self.dy *= -1 # the sprite bounces off the bottom wall       \n\n\n    def render(self, pen, x_offset, y_offset):\n        if self.state == 'active':\n            pen.goto(self.x - x_offset, self.y - y_offset)\n            pen.setheading(self.heading) # sets the orientation of the turle to east\n            pen.shape(self.shape)\n            pen.color(self.color)\n            pen.stamp() # puts the pen on the screen\n\n            self.render_health_meter(pen, x_offset, y_offset)\n\n    \n    def render_health_meter(self, pen, x_offset, y_offset):\n        # Draw health\n        pen.goto(self.x - x_offset -10, self.y - y_offset + 20)\n        pen.width(3)\n        pen.pendown()\n        pen.setheading(0)\n\n        if self.health/self.max_health < 0.3:\n            pen.color('red') # if the health reaches under 30% the pen color changes to red\n        elif self.health/self.max_health < 0.7:\n            pen.color('yellow')\n        else:\n            pen.color('green')\n        \n        pen.fd(20 * (self.health/self.max_health))\n\n        if self.health != self.max_health: # if function neccessary to get rid of the grey dots at the start\n            pen.color('grey')\n            pen.fd(20 * ((self.max_health - self.health)/self.max_health))\n        \n        pen.penup()\n\nclass Player(Sprite): # inherets the attributes from the parent class\n    def __init__(self, x, y, shape, color):\n        Sprite.__init__(self, 0, 0, shape, color)\n        self.lives = 3 # only the player has lives and score\n        self.score = 0\n        self.heading = 90 #player goes up\n        self.da = 0 # changing the angle\n\n    def rotate_left(self):\n        self.da = 5\n\n    def rotate_right(self):\n        self.da = -5\n\n    def stop_rotation(self):\n        self.da = 0\n    \n    def accelerate(self):\n        self.thrust += self.acceleration\n    \n    def deaccelerate(self):\n        self.thrust = 0.0\n    \n    def fire(self):\n        missile.fire(self.x, self.y, self.heading, self.dx, self.dy) # missile starting on the same position as the player sprites\n    \n    def update(self):\n        if self.state == 'active':\n            self.heading += self.da # the heading is gonna change by the delta of the angle\n            self.heading %= 360\n            self.dx += math.cos(math.radians(self.heading)) * self.thrust\n            self.dy += math.sin(math.radians(self.heading)) * self.thrust\n            self.x += self.dx\n            self.y += self.dy\n            self.border_check()\n            # Check health\n            if self.health <= 0:\n                self.reset()\n    \n    def reset(self): # resetting everything to the default numbers\n        self.x = 0\n        self.y = 0\n        self.health = self.max_health\n        self.heading = 90\n        self.dx = 0\n        self.dy = 0\n        self.lives -= 1\n\n    def render(self, pen, x_offset, y_offset):\n        pen.shapesize(0.5, 1.0, None)\n        pen.goto(self.x - x_offset, self.y - y_offset)\n        pen.setheading(self.heading) # sets the orientation of the turle to east\n        pen.shape(self.shape)\n        pen.color(self.color)\n        pen.stamp() # puts the pen on the screen\n\n        pen.shapesize(1.0, 1.0, None)\n\n        self.render_health_meter(pen, x_offset, y_offset)\n\nclass Missile(Sprite): # inherets the attributes from the parent class\n    def __init__(self, x, y, shape, color):\n        Sprite.__init__(self, x, y, shape, color)\n        self.state = 'ready'\n        self.thrust = 8.0\n        self.max_fuel = 200\n        self.fuel = self.max_fuel\n        self.height = 4\n        self.width = 4\n\n    def fire(self, x, y, heading, dx, dy):\n        if self.state == 'ready':\n            self.state = 'active'\n            self.x = x\n            self.y = y\n            self.heading = heading\n            self.dx = dx\n            self.dy = dy\n\n            self.dx += math.cos(math.radians(self.heading)) * self.thrust # taking the current dx (which is from playwer) and adds own thrust\n            self.dy += math.sin(math.radians(self.heading)) * self.thrust\n\n    def update(self):\n       if self.state == 'active':\n           self.fuel -= self.thrust\n           if self.fuel <= 0:\n               self.reset()\n           self.heading += self.da # the heading is gonna change by the delta of the angle\n           self.heading %= 360\n           \n           self.x += self.dx\n           self.y += self.dy\n           \n           self.border_check()\n    \n    def reset(self): # once its resets we set the fuel back to max fuel and state to ready\n        self.fuel = self.max_fuel\n        self.dx = 0\n        self.dy = 0\n        self.state = 'ready'\n\n\n    def render(self, pen, x_offset, y_offset): # only renders if its active\n        if self.state == 'active':\n            pen.shapesize(0.2, 0.2, None)\n            pen.goto(self.x - x_offset, self.y - y_offset)\n            pen.setheading(self.heading) # sets the orientation of the turle to east\n            pen.shape(self.shape)\n            pen.color(self.color)\n            pen.stamp() # puts the pen on the screen\n    \n            pen.shapesize(1.0, 1.0, None)\n\nclass Enemy(Sprite): # inherets the attributes from the parent class\n    def __init__(self, x, y, shape, color):\n        Sprite.__init__(self, x, y, shape, color)\n        self.max_health = 20\n        self.health = self.max_health\n\n    def update(self):\n        if self.state == 'active':\n            self.heading += self.da # the heading is gonna change by the delta of the angle\n            self.heading %= 360\n\n            self.dx += math.cos(math.radians(self.heading)) * self.thrust\n            self.dy += math.sin(math.radians(self.heading)) * self.thrust\n\n            self.x += self.dx\n            self.y += self.dy\n\n            self.border_check()\n\n            # Check health\n            if self.health <= 0:\n                self.reset()\n    \n    def reset(self):\n        self.state = 'inactive'\n\nclass Powerup(Sprite): # inherets the attributes from the parent class\n    def __init__(self, x, y, shape, color):\n        Sprite.__init__(self, x, y, shape, color)\n\nclass Camera():\n    def __init__(self, x, y):\n        self.x = x\n        self.y = y\n    \n    def update(self, x, y):\n        self.x = x\n        self.y = y\n\nclass Radar():\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    \n    def render(self, pen, sprites):\n\n        # Draw radar circle\n        pen.setheading(90)\n        pen.goto(self.x + self.width / 2, self.y)\n        pen.pendown()\n        pen.circle(self.width / 2)\n        pen.penup()\n\n        # Draw the sprites\n        for sprite in sprites:\n            if sprite.state == 'active': # just want to use the active sprites\n                radar_x = self.x + (sprite.x - player.x) * (self.width / game.width)\n                radar_y = self.y + (sprite.y - player.y) * (self.height / game.height)\n                pen.goto(radar_x, radar_y)\n                pen.color(sprite.color) # getting the actual color of the sprite displayed on radar\n                pen.shape(sprite.shape)\n                pen.setheading(sprite.heading)\n                pen.shapesize(0.1, 0.1, None)\n\n                distance = ((player.x - sprite.x) ** 2 + (player.y - sprite.y) ** 2) ** 0.5\n                if distance < player.radar:\n                    pen.stamp() # stamps a copy of the turtle shape at the current turtle position\n                \n\n\n# Create the border\ngame = Game(600, 400)\n\n# Create the radar\nradar = Radar(400, -200, 200, 200)\n\n# Creating the player sprite as a spaceship\nplayer = Player(0,0, 'triangle', 'white') # putting the player in the center\n\n# Creating the Camera\ncamera = Camera(player.x, player.y)\n\n# Creating missile object\nmissile = Missile(0, 100, 'circle', 'yellow')\n\n#enemy = Enemy(0,100, 'square', 'red')\n#enemy.dx = -1\n#enemy.dy = -0.3\n#\n#enemy2 = Enemy(-100,100, 'square', 'red')\n#enemy2.dx = 1\n#enemy2.dy = 0.3\n\n#powerup = Powerup(0,100, 'circle', 'blue')\n#powerup.dy = 1\n#powerup.dx = 0.1\n#\n#powerup2 = Powerup(-100,100, 'circle', 'blue')\n#powerup2.dy = -1\n#powerup2.dx = -0.1\n\n# Sprites list\nsprites = []\n#sprites.append(player)\n#sprites.append(enemy)\n#sprites.append(powerup)\n#sprites.append(missile)\n#sprites.append(enemy2)\n#sprites.append(powerup2)\n\n# setup the level\ngame.start_level()\n\n# Keyboard bindings\nwindow.listen()\nwindow.onkeypress(player.rotate_left, 'Left')\nwindow.onkeypress(player.rotate_right, 'Right')\n\nwindow.onkeyrelease(player.stop_rotation, 'Left') # the moment you dont press left or right it will stop rotating!\nwindow.onkeyrelease(player.stop_rotation, 'Right')\n\nwindow.onkeypress(player.accelerate, 'Up')\nwindow.onkeyrelease(player.deaccelerate, 'Up')\n\nwindow.onkeypress(player.fire, 'space')\n\n# Main Loop\nwhile True:\n    # Clear the screen\n    pen.clear()\n\n    # Update the Sprites to move around the screen\n    for sprite in sprites:\n        sprite.update()\n    \n    # check for collision\n    for sprite in sprites:\n        if isinstance(sprite, Enemy) and sprite.state == 'active':\n            if player.is_collision(sprite):\n                sprite.health -= 10\n                player.health -= 10\n                player.bounce(sprite)\n\n            if missile.state == 'active' and missile.is_collision(sprite):\n                sprite.health -= 10\n                missile.reset()\n        \n        if isinstance(sprite, Powerup):\n            if player.is_collision(sprite):\n                sprite.x = 100\n                sprite.y = 100\n\n            if missile.state == 'active' and missile.is_collision(sprite):\n                sprite.x = 100\n                sprite.y = -100\n                missile.reset()\n\n\n    # Render the Sprites\n    for sprite in sprites:\n        sprite.render(pen, camera.x + 100, camera.y)\n\n    # Render the borders\n    game.render_border(pen, camera.x + 100, camera.y)\n\n    # Check for end of the level\n    end_of_level = True\n    for sprite in sprites:\n        # First look if an enemy sprite is still active\n        if isinstance(sprite, Enemy) and sprite.state == 'active':\n            end_of_level = False\n    \n    if end_of_level:\n        game.level += 1\n        game.start_level()\n    \n    # Updating the camera\n    camera.update(player.x, player.y)\n\n    # Render the radar\n    radar.render(pen, sprites)\n\n    # Update the Screen\n    window.update()\n\n","sub_path":"Space_Project/space.py","file_name":"space.py","file_ext":"py","file_size_in_byte":14790,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"291540476","text":"from __future__ import absolute_import, division, print_function\n\nimport scipy as sp\nimport pytest\nimport xarray as xr\n\nfrom xrscipy import integrate\nfrom xrscipy.docs import DocParser\nfrom .testings import get_obj\n\n\n_trapz_funcs = [integrate.trapz]\n_trapz_names = ['trapz']\n_cumtrapz_names = ['cumtrapz']\n_simps_names = ['simps']\n_romb_names = ['romb']\nif hasattr(integrate, 'trapezoid'):  # scipy >= 1.6.0\n    _trapz_funcs += [integrate.trapezoid]\n    _trapz_names += ['trapezoid']\n    _cumtrapz_names += ['cumulative_trapezoid']\n    _simps_names += ['simpson']\n\n\n@pytest.mark.parametrize('mode', [1, 1])\n@pytest.mark.parametrize('func', _trapz_names + _cumtrapz_names)\n@pytest.mark.parametrize('dim', ['x', 'time'])\ndef test_integrate(mode, func, dim):\n    da = get_obj(mode)\n\n    axis = da.get_axis_num(da[dim].dims[0])\n    actual = getattr(integrate, func)(da, dim)\n    kwargs = {}\n    if func in _cumtrapz_names:\n        kwargs['initial'] = 0\n    expected = getattr(sp.integrate, func)(da.values, x=da[dim].values,\n                                           axis=axis, **kwargs)\n    assert (actual.values == expected).all()\n\n    # make sure the original data does not change\n    da.values.ndim == get_obj(mode).ndim\n\n    # make sure the coordinate is propagated\n    for key, v in da.coords.items():\n        if 'x' not in v.dims:\n            assert da[key].identical(actual[key])\n\n\n@pytest.mark.parametrize('trapz_func', _trapz_funcs)\ndef test_integrate_dataset(trapz_func):\n    ds = get_obj(mode=3)\n\n    actual = trapz_func(ds, coord='z')\n    assert actual['a'].identical(ds['a'])\n    assert actual['b'].identical(integrate.trapz(ds['b'], coord='z'))\n\n\n@pytest.mark.parametrize('trapz_func', _trapz_funcs)\ndef test_integrate_error(trapz_func):\n    # not sorted\n    da = xr.DataArray([0, 1, 2], dims=['x'], coords={'x': [2, 3, 0]})\n    with pytest.raises(ValueError):\n        trapz_func(da, 'x')\n\n    # wrong argument\n    with pytest.raises(TypeError):\n        trapz_func(da, axis='x')\n\n\n@pytest.mark.parametrize(\n    'func',\n    _trapz_names + _cumtrapz_names + _simps_names + _romb_names)\ndef test_doc_all(func):\n    parser = DocParser(func.__doc__)\n\n    not_included_keys = ['x', 'axis', 'dx']\n    for k in not_included_keys:\n        assert k not in parser.parameters.keys()\n\n\n@pytest.mark.parametrize('trapz_func', _trapz_funcs)\ndef test_doc(trapz_func):\n    parser = DocParser(trapz_func.__doc__)\n\n    not_included_keys = ['x', 'axis', 'dx']\n    for k in not_included_keys:\n        assert k not in parser.parameters.keys()\n","sub_path":"xrscipy/tests/test_integrate.py","file_name":"test_integrate.py","file_ext":"py","file_size_in_byte":2533,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"230969947","text":"from pyspark import SparkConf, SparkContext\nfrom pyspark.streaming import StreamingContext\nfrom nltk.classify import NaiveBayesClassifier\nfrom nltk.corpus import subjectivity\nfrom nltk.sentiment import SentimentAnalyzer\nfrom nltk.sentiment.util import *\nfrom nltk.sentiment.vader import SentimentIntensityAnalyzer\nfrom elasticsearch import Elasticsearch\nimport os\nimport datetime\nimport json\nimport subprocess\n\n\n# set environment variable PYSPARK_SUBMIT_ARGS\nos.environ['PYSPARK_SUBMIT_ARGS'] = '--jars /Users/StevenSYT/Documents/Academic/18_Spring/Big_Data/Homework/HW3/Elastic_Kibana/elasticsearch-hadoop-6.2.3/dist/elasticsearch-spark-20_2.11-6.2.3.jar pyspark-shell'\n\n\n\nTCP_IP = 'localhost'\nTCP_PORT = 3000\n\n# Pyspark\n# # create spark configuration\nconf = SparkConf()\n# conf.setAppName('TwitterApp')\n# conf.setMaster('local[4]')\n# create spark context with the above configuration\nsc = SparkContext(conf=conf)\n\n# use accumulator for id generation\naccum = 0\n\n# create the Streaming Context from spark context with interval size 6 seconds\nssc = StreamingContext(sc, 8)\nssc.checkpoint(\"checkpoint_TwitterApp\")\n# read data from port 9001\ndataStream = ssc.socketTextStream(TCP_IP, TCP_PORT)\n\n# create a sentiment analyzer\nsid = SentimentIntensityAnalyzer()\n\n# connect to our cluster\nes = Elasticsearch([{'host': 'localhost', 'port': 9200}])\nes_write_conf = {\n# specify the node that we are sending data to (this should be the master)\n\"es.nodes\" : 'localhost',\n\n# specify the port in case it is not the default port\n\"es.port\" : '9200',\n\n# specify a resource in the form 'index/doc-type'\n\"es.resource\" : 'tweetmap/tweet',\n\n# is the input JSON?\n\"es.input.json\" : \"yes\",\n\n# # is there a field in the mapping that should be used to specify the ES document ID\n# \"es.mapping.id\": \"doc_id\"\n}\n\nmappings = {\n    \"mappings\": {\n    \"tweet\": {\n      \"properties\": {\n        \"location\": {\n          \"type\": \"geo_point\"\n        }\n      }\n    }\n  }\n}\n\nes.indices.create(index='tweetmap', body=mappings)\n######### your processing here ###################\ndataStream.pprint()\n\n\n\n# sentiment analysis [tweet,\"location.latitude,location.longitude\", str(UID)]\ndef senAnalysis(line):\n   ss = sid.polarity_scores(line[0])\n   result = line[0] + ' -- has '\n   if ss['compound'] > 0:\n      sentiment = 'positive'\n      result += 'positive sentiment \\n'\n   elif ss['compound'] == 0:\n      sentiment = 'neutral'\n      result += 'neutral sentiment \\n'\n   else:\n      sentiment = 'negative'\n      result += 'negative sentiment \\n'\n   return (result, (line[0], sentiment, line[1], line[2]))\n\n# formatData from senResults\ndef formatData(line):\n   jsonLike = {'tweet' : line[1][0], 'sentiment' : line[1][1], 'location' : line[1][2], 'timeStamp' : str(datetime.datetime.now().isoformat())}\n\n   return (line[1][3], json.dumps(jsonLike))\n\ndef sendDataToES(rdd):\n   rdd.saveAsNewAPIHadoopFile(\n   path='',\n   outputFormatClass=\"org.elasticsearch.hadoop.mr.EsOutputFormat\",\n   keyClass=\"org.apache.hadoop.io.NullWritable\",\n   valueClass=\"org.elasticsearch.hadoop.mr.LinkedMapWritable\",\n\n   # critically, we must specify our `es_write_conf`\n   conf=es_write_conf)\n\n# generate the lineArray\ntweets = dataStream.map(lambda x: x.split(\"\\t\"))\n\n#senResults contains (printresults, (tweet, sentiment, geolocation, timeStamp))\nsenResults = tweets.map(senAnalysis)\n\n# print out the results of sentiment\nsenScores = senResults.map(lambda x: x[0])\nsenScores.pprint()\n\n# map the OutPut json format for ES input\ntweetOutPut = senResults.map(formatData)\ntweetOutPut.pprint()\n\n#send data to ES\ntweetOutPut.foreachRDD(sendDataToES)\n\n# tweetTexts.pprint()\n# words = tweets.flatMap(lambda x: x[0].split(' '))\n# wordcount = words.map(lambda x: (x,1)).reduceByKey(lambda x,y: x+y)\n# wordcount.pprint()\n\n\n#################################################\n\nssc.start()\nssc.awaitTermination()\n\n# subprocess, shell daemon\n","sub_path":"spark.py","file_name":"spark.py","file_ext":"py","file_size_in_byte":3859,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"308832610","text":"n = int(input())\nword = input()\nlist = []\nredacted_list = []\nfor i in range(n):\n    list.append(input())\nprint(list)\nfor i in range(n-1, -1, -1):\n    element = list[i]\n    if word not in element:\n        list.remove(element)\nprint(list)","sub_path":"2_fund_python/03_lists_basics_lab/search.py","file_name":"search.py","file_ext":"py","file_size_in_byte":236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"267019357","text":"import os\n\nfrom django.contrib import admin\nfrom django.contrib.admin.util import unquote\nfrom django import forms\nfrom django.conf import settings\n\nfrom mptt.admin import MPTTModelAdmin\nfrom ostinato.statemachine.forms import sm_form_factory\nfrom ostinato.pages.models import Page, PageWorkflow\nfrom ostinato.pages.registry import page_content\n\n\nGRAPPELLI = 'grappelli' in settings.INSTALLED_APPS\n\n\ndef content_inline_factory(page):\n    content_model = page.get_content_model()\n\n    class PageContentInline(admin.StackedInline):\n        model = content_model\n        extra = 1\n        max_num = 1\n        can_delete = False\n        fk_name = 'page'\n\n        ## Check for a custom form and try to load it\n        content_form = getattr(content_model.ContentOptions, 'form', None)\n        if content_form:\n            module_path, form_class = content_form.rsplit('.', 1)\n\n            form = __import__(module_path, locals(), globals(),\n                [form_class], -1).__dict__[form_class]\n\n    return PageContentInline\n\n\n## Admin Models\nclass PageAdminForm(sm_form_factory(sm_class=PageWorkflow)):  # <3 python\n\n    template = forms.ChoiceField()\n    \n    def __init__(self, *args, **kwargs):\n        super(PageAdminForm, self).__init__(*args, **kwargs)\n        self.fields['template'].choices = page_content.get_template_choices()\n\n    class Meta:\n        model = Page\n\n\nclass PageAdmin(MPTTModelAdmin):\n    save_on_top = True\n    form = PageAdminForm\n\n    list_display = ('title', 'reorder', 'slug', 'template', 'author',\n        'page_state', 'show_in_nav', 'show_in_sitemap', 'publish_date')\n\n    list_filter = ('template', 'author', 'show_in_nav', 'show_in_sitemap',\n        'state')\n\n    list_editable = ('show_in_nav', 'show_in_sitemap')\n\n    search_fields = ('title', 'short_title', 'slug', 'author')\n    date_hierarchy = 'publish_date'\n    inlines = ()\n\n    fieldsets = (\n        (None, {\n            'fields': (\n                ('title', 'short_title'), 'slug',\n                'template', 'redirect', 'parent',\n                ('show_in_nav', 'show_in_sitemap'),\n            ),\n        }),\n\n        ('Publication', {\n            'fields': ('state', 'author', 'publish_date'),\n        }),\n\n    )\n    prepopulated_fields = {'slug': ('title',)}\n\n    if GRAPPELLI:\n        change_list_template = 'admin/mptt_grappelli_change_list.html'\n\n    class Media:\n        static_prefix = lambda p: os.path.join(settings.STATIC_URL, '%s' % p)\n\n        css = {\n            'all': (\n                static_prefix('ostinato/css/smoothness/jquery-ui-1.8.18.custom.css'),\n                static_prefix('pages/css/pages_admin.css'),\n            ),\n        }\n\n        js = (\n            static_prefix('ostinato/js/jquery-1.7.1.min.js'),\n            static_prefix('ostinato/js/jquery-ui-1.8.18.custom.min.js'),\n            static_prefix('pages/js/page_admin.js'),\n        )\n\n    def page_state(self, obj):\n        sm = PageWorkflow(instance=obj)\n        return sm.state\n    page_state.short_description = 'State'\n\n    def reorder(self, obj):\n        \"\"\" A List view item that shows the movement actions \"\"\"\n        return '''<span id=\"_page_%s\">\n        <a class=\"ostinato_page_move\" href=\"#\">Move</a>\n        <a class=\"ostinato_move_action _left_of\" href=\"#\">Before</a>\n        <a class=\"ostinato_move_action _right_of\" href=\"#\">After</a>\n        <a class=\"ostinato_move_action _child_of\" href=\"#\">Inside</a>\n        <a class=\"ostinato_cancel_move\" href=\"#\">Cancel</a>\n        </span>\n        ''' % (obj.id)\n    reorder.short_description = 'Re-order'\n    reorder.allow_tags = True\n\n\n    def add_view(self, request, form_url='', extra_context=None):\n        # We need to clear the inlines. Django keeps it cached somewhere\n        self.inlines = ()\n        return super(PageAdmin, self).add_view(request, form_url, extra_context)\n\n\n    def change_view(self, request, object_id, form_url='', extra_context=None):\n        \"\"\"\n        We need to dynamically create the inline models since it\n        changes based on the template.\n        \"\"\"\n        self.inlines = ()\n\n        if object_id:\n            page = self.get_object(request, unquote(object_id))\n\n            if page.template:\n                self.inlines = (content_inline_factory(page),)\n\n            content_model = page.get_content_model()\n            if hasattr(content_model.ContentOptions, 'page_inlines'):\n                for inline in content_model.ContentOptions.page_inlines:\n                    self.inlines += (inline,)\n\n        return super(PageAdmin, self).change_view(\n            request, object_id, form_url, extra_context)\n\n\nadmin.site.register(Page, PageAdmin)\n\n","sub_path":"ostinato/pages/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":4629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"559389157","text":"import xmldict\n\n\nclass XMLSerializer(object):\n    content_type = 'application/xml'\n\n    ITEM_TAG = 'item'\n\n    def to_string(self, data):\n        serialize_data_dict = {}\n        for k, v in data.viewitems():\n            if isinstance(v, (list, tuple, set)):\n                v = {self.ITEM_TAG: v}\n            elif isinstance(v, str):\n                v = v.decode('utf-8')\n            serialize_data_dict[k] = v\n        return xmldict.dict_to_xml({\n            'root': serialize_data_dict\n        })\n\n    def from_string(self, str_data):\n        load_dict = xmldict.xml_to_dict(\n            str_data\n        ).get('root', {})\n        result_dict = {}\n\n        for k, v in load_dict.viewitems():\n            if isinstance(v, dict) and [self.ITEM_TAG] == v.keys():\n                v = v.values()[0]\n            result_dict[k] = v\n\n        for k, v in result_dict.viewitems():\n            if v in ['true', 'false']:\n                result_dict[k] = v == 'true'\n\n        return result_dict\n","sub_path":"butler/jobs/serialization/serializers/xml.py","file_name":"xml.py","file_ext":"py","file_size_in_byte":986,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"195024360","text":"#!/usr/bin/env python3\n\nimport OpenGL.GL as GL\n\nfrom core.shader import Shader\nfrom core.mesh import Mesh\nfrom core.texture import Cubemap\nfrom core.transform import identity, rotate\n\n\nVERTEX_SHADER_NAME = 'skybox/skybox.vert'\nFRAGMENT_SHADER_NAME = 'skybox/skybox.frag'\nTEXTURES_BACK_NAME = ('../assets/skybox/back/{}.png'.format(name)\n                        for name in ('side', 'side', 'top', 'top', 'front', 'side'))\nTEXTURES_FRONT_NAME = ('../assets/skybox/front/{}.png'.format(name)\n                        for name in ('right', 'left', 'top', 'top', 'front', 'back'))\nCLOUDS_ANGULAR_VELOCITY = 0.5\n\n\nclass Skybox(Mesh):\n    def __init__(self):\n        shader = Shader(VERTEX_SHADER_NAME, FRAGMENT_SHADER_NAME)\n\n        position = [(1.0, -1.0, 1.0), (1.0, -1.0, -1.0), (1.0, 1.0, -1.0), (1.0, 1.0, 1.0),\n                    (-1.0, -1.0, -1.0), (-1.0, 1.0, -1.0), (-1.0, 1.0, 1.0), (-1.0, -1.0, 1.0)]\n        index = [2, 1, 0, 0, 3, 2,\n                 4, 5, 7, 7, 5, 6,\n                 2, 3, 5, 5, 3, 6,\n                 0, 1, 4, 4, 7, 0,\n                 0, 7, 3, 3, 7, 6,\n                 1, 2, 4, 4, 2, 5]\n\n        super().__init__(shader, (position,), index)\n\n        self.add_locations('tex_back', 'tex_front')\n\n        self.back = Cubemap(*TEXTURES_BACK_NAME)\n        self.front = Cubemap(*TEXTURES_FRONT_NAME)\n\n        self.angle = 0\n        self.rotation = identity()\n\n    def update(self, delta_time):\n        self.angle += CLOUDS_ANGULAR_VELOCITY * delta_time\n        self.rotation = rotate((0, 1, 0), -self.angle)\n\n    def draw(self, projection, view, model, normal_matrix, camera):\n        GL.glUseProgram(self.shader.glid)\n\n        self.back.bind(0)\n        GL.glUniform1i(self.locations['tex_back'], 0)\n        self.front.bind(1)\n        GL.glUniform1i(self.locations['tex_front'], 1)\n\n        super().draw(projection, view, self.rotation, self.rotation, camera)\n","sub_path":"src/skybox/skybox.py","file_name":"skybox.py","file_ext":"py","file_size_in_byte":1885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"353261687","text":"import re\nimport requests\nfrom bs4 import BeautifulSoup\nimport time\n  \nurl = 'https://www.navitime.co.jp/diagram/stops/00000112/806200ac/?node=00004995&year=2022&month=11&day=07&from=timetable'\nhtml_text = requests.get(url).text\nsoup = BeautifulSoup(html_text, 'html.parser')\nfor page in soup.find_all(class_=\"diagram-link\"):\n    time.sleep(5)\n    url = page.find('a').get('href')+\"&trainType=普通\"\n    html_text = requests.get(url).text\n    soup = BeautifulSoup(html_text, 'html.parser')\n    title = soup.select_one(\"#body-left > div.station-frame > h2 > ruby > rb\").text\n    title = title.replace(\"(静岡県)\",\"\")\n    count = 0\n    for direction in soup.select(\".diagram-frame\"):\n        count += 1\n        if count <=2:\n            if count == 1:\n                f = open('./'+title+'_s.csv', 'w',encoding='UTF-8')\n            if count == 2:\n                f = open('./'+title+'_h.csv', 'w',encoding='UTF-8')\n            for line in direction.select(\"dl\"):\n                hour = line.find(class_=\"diagram-frame__hour\").text\n                if hour[0] == '0':\n                    hour = hour[1:]\n                for cell in line.find_all(class_=\"time-frame\"):\n                    f.writelines(hour+\",\"+cell.find(class_=\"time\").text+\", \"+cell.find(class_=\"ruby-dest\").text[0]+\",\\n\")\n            f.close()\n","sub_path":"DATA2/station.py","file_name":"station.py","file_ext":"py","file_size_in_byte":1311,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"172498101","text":"#!/usr/bin/env python\n\n#\n# LSST Data Management System\n# Copyright 2008, 2009, 2010 LSST Corporation.\n#\n# This product includes software developed by the\n# LSST Project (http://www.lsst.org/).\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the LSST License Statement and\n# the GNU General Public License along with this program.  If not,\n# see <http://www.lsstcorp.org/LegalNotices/>.\n#\n\nfrom __future__ import print_function\nfrom builtins import range\nfrom builtins import object\nimport os\nimport sys\nimport optparse\nimport lsst.pex.policy as pol\n\n\nclass CondorJobInfo(object):\n\n    class PipelineJob(object):\n\n        def __init__(self, scratchDir, wfName, pipelineName, pipelineNumber):\n            self.wfName = wfName\n            self.pipelineName = pipelineName\n            self.pipelineNumber = pipelineNumber\n\n            self.pipelineIndexedName = \"%s_%s\" % (pipelineName, pipelineNumber)\n            self.wfScratchDir = os.path.join(scratchDir, self.wfName)\n\n        def getWorkflowName(self):\n            return self.wfName\n\n        def getPipelineName(self):\n            return self.pipelineName\n\n        def getPipelineNumber(self):\n            return int(self.pipelineNumber)\n\n        def getFileName(self):\n            pipelineDir = os.path.join(self.wfScratchDir, self.pipelineIndexedName)\n            pipelineWorkDir = os.path.join(pipelineDir, \"work\")\n            pipelineJobDir = os.path.join(pipelineWorkDir, self.pipelineIndexedName)\n            pipelineJobFile = os.path.join(pipelineJobDir, \"%s.job\" % self.pipelineIndexedName)\n            return pipelineJobFile\n\n    class GlideinJob(object):\n\n        def __init__(self, scratchDir, wfName):\n            self.scratchDir = scratchDir\n            self.wfName = wfName\n            wfScratchDir = os.path.join(scratchDir, wfName)\n            self.glideinFileName = os.path.join(wfScratchDir, \"glidein.job\")\n\n        def getWorkflowName(self):\n            return self.wfName\n\n        def getFileName(self):\n            return self.glideinFileName\n\n    def __init__(self, prodPolicy, runid):\n        self.prodPolicy = prodPolicy\n        self.runid = runid\n\n    ##\n    # @brief given a list of pipelinePolicies, number the section we're\n    # interested in based on the order they are in, in the productionPolicy\n    #\n    def getPipelineJobs(self):\n        wfPolicies = self.prodPolicy.getArray(\"workflow\")\n        expanded = []\n        for wfPolicy in wfPolicies:\n            wfShortName = wfPolicy.get(\"shortName\")\n            pipelinePolicies = wfPolicy.getArray(\"pipeline\")\n            localScratch = self.getLocalScratch(wfPolicy)\n            for policy in pipelinePolicies:\n                runCount = 1  # default to 1, if runCount doesn't exist\n                if policy.exists(\"runCount\"):\n                    runCount = policy.get(\"runCount\")\n                for i in range(runCount):\n                    pipelineShortName = policy.get(\"shortName\")\n                    pipelineJob = self.PipelineJob(localScratch, wfShortName, pipelineShortName, i+1)\n                    expanded.append(pipelineJob)\n        return expanded\n\n    ##\n    # @brief given a list of pipelinePolicies, number the section we're\n    # interested in based on the order they are in, in the productionPolicy\n    #\n    def getGlideinJobs(self):\n        wfPolicies = self.prodPolicy.getArray(\"workflow\")\n        glideinJobs = []\n        for wfPolicy in wfPolicies:\n            localScratch = self.getLocalScratch(wfPolicy)\n            wfShortName = wfPolicy.get(\"shortName\")\n            glideinJob = self.GlideinJob(localScratch, wfShortName)\n            glideinJobs.append(glideinJob)\n        return glideinJobs\n\n    def getLocalScratch(self, wfPolicy):\n        configurationPolicy = wfPolicy.get(\"configuration\")\n        condorDataPolicy = configurationPolicy.get(\"condorData\")\n        localScratch = condorDataPolicy.get(\"localScratch\")\n\n        return os.path.join(localScratch, self.runid)\n\n\nclass JobKiller(object):\n\n    def __init__(self, workflowName, pipelineName, pipelineNumber):\n        self.workflowName = workflowName\n        self.pipelineName = pipelineName\n        self.pipelineNumber = pipelineNumber\n        return\n\n    # TODO: make this read multiple lines\n    def killJob(self, filename):\n        print(\"killJob: \", filename)\n        try:\n            input = open(filename, 'r')\n        except Exception:\n            # couldn't find that file, so pass\n            return\n        line = input.readline()\n        line = line.strip('\\n')\n        cmd = [\"condor_rm\", line]\n\n        pid = os.fork()\n        if not pid:\n            os.execvp(cmd[0], cmd)\n        os.wait()[0]\n        return\n\n    def processGlideinJob(self, job):\n        jobFile = job.getFileName()\n        if self.workflowName is None:\n            self.killJob(jobFile)\n        elif self.workflowName == job.getWorkflowName():\n            self.killJob(jobFile)\n        return\n\n    def processPipelineJob(self, job):\n        jobFile = job.getFileName()\n\n        if self.workflowName is None:\n            self.killJob(jobFile)\n        elif self.workflowName == job.getWorkflowName():\n            if self.pipelineName is None:\n                self.killJob(jobFile)\n            elif self.pipelineName == job.getPipelineName():\n                if self.pipelineNumber is None:\n                    self.killJob(jobFile)\n                elif self.pipelineNumber == job.getPipelineNumber():\n                    self.killJob(jobFile)\n        return\n\n\nif __name__ == \"__main__\":\n    usage = \"\"\"usage %prog [-g] [-w workflow[-p pipeline [-n pipelineNum]] productionPolicyFile runId\"\"\"\n\n    parser = optparse.OptionParser(usage)\n\n    parser.add_option(\"-g\", \"--glidein\", action=\"store_true\",\n                      dest=\"killglidein\", default=False, help=\"kill the glidein\")\n    parser.add_option(\"-w\", \"--workflow\", action=\"store\", dest=\"workflowArg\",\n                      default=None, help=\"workflow shortname\")\n    parser.add_option(\"-p\", \"--pipeline\", action=\"store\", dest=\"pipelineArg\",\n                      default=None, help=\"pipeline shortname\")\n    parser.add_option(\"-n\", \"--pipelineNum\", action=\"store\",\n                      dest=\"pipelineNumArg\", default=None, help=\"pipeline number\")\n\n    parser.opts = {}\n    parser.args = []\n\n    (parser.opts, parser.args) = parser.parse_args()\n\n    workflowArg = parser.opts.workflowArg\n    pipelineArg = parser.opts.pipelineArg\n    pipelineNumArg = None\n    if parser.opts.pipelineNumArg is not None:\n        pipelineNumArg = int(parser.opts.pipelineNumArg)\n    killGlidein = parser.opts.killglidein\n\n    if len(parser.args) < 2:\n        print(usage)\n        raise RuntimeError(\"Missing args: productionPolicyFile runId\")\n\n    prodPolicyFile = parser.args[0]\n    runid = parser.args[1]\n\n    prodPolicy = pol.Policy.createPolicy(prodPolicyFile, False)\n\n    jobInfo = CondorJobInfo(prodPolicy, runid)\n\n    killer = JobKiller(workflowArg, pipelineArg, pipelineNumArg)\n\n    if killGlidein:\n        glideinJobs = jobInfo.getGlideinJobs()\n        for job in glideinJobs:\n            killer.processGlideinJob(job)\n        sys.exit(0)\n\n    pipelineJobs = jobInfo.getPipelineJobs()\n    for job in pipelineJobs:\n        killer.processPipelineJob(job)\n","sub_path":"bin.src/killcondor.py","file_name":"killcondor.py","file_ext":"py","file_size_in_byte":7697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"414407135","text":"# Problem from UVA 12207\tThat is Your Queue\n# https://uva.onlinejudge.org/index.php?option=onlinejudge&page=show_problem&problem=3359\n\n# Your government has finally solved the\n# problem of universal health care! Now\n# everyone, rich or poor, will finally have\n# access to the same level of medical care.\n# Hurrah!\n# There’s one minor complication. All\n# of the country’s hospitals have been condensed\n# down into one location, which can\n# only take care of one person at a time.\n# But don’t worry! There is also a plan\n# in place for a fair, efficient computerized\n# system to determine who will be admitted.\n# You are in charge of programming\n# this system.\n# Every citizen in the nation will be assigned\n# a unique number, from 1 to P\n# (where P is the current population). They will be put into a queue, with 1 in front of 2, 2 in front of\n# 3, and so on. The hospital will process patients one by one, in order, from this queue. Once a citizen\n# has been admitted, they will immediately move from the front of the queue to the back.\n# Of course, sometimes emergencies arise; if you’ve just been run over by a steamroller, you can’t wait\n# for half the country to get a routine checkup before you can be treated! So, for these (hopefully rare)\n# occasions, an expedite command can be given to move one person to the front of the queue. Everyone\n# else’s relative order will remain unchanged.\n# Given the sequence of processing and expediting commands, output the order in which citizens will\n# be admitted to the hospital.\n\n# Input\n# Input consists of at most ten test cases. Each test case starts with a line containing P, the population\n# of your country (1 ≤ P ≤ 1000000000), and C, the number of commands to process (1 ≤ C ≤ 1000).\n# The next C lines each contain a command of the form ‘N’, indicating the next citizen is to be\n# admitted, or ‘E x’, indicating that citizen x is to be expedited to the front of the queue.\n# The last test case is followed by a line containing two zeros.\n# Output\n# For each test case print the serial of output. This is followed by one line of output for each ‘N’ command,\n# indicating which citizen should be processed next. Look at the output for sample input for details.\n# Sample Input\n# 3 6\n# N\n# N\n# E 1\n# N\n# N\n# N\n# 10 2\n# N\n# N\n# 0 0\n\n# Sample Output\n# Case 1:\n# 1\n# 2\n# 1\n# 3\n# 2\n# Case 2:\n# 1\n# 2\n\n\nclass Node:\n    def __init__(self, data_val=None):\n        self.data_val = data_val\n        self.next_node = None\n\n\nclass MyLinkedList:\n    def __init__(self):\n        self.head_node = None\n        self.tail_node = self.head_node\n\n    def print_list(self):\n        print_node = self.head_node\n        while True:\n            if print_node.next_node is None:\n                print(print_node.data_val, end='')\n                break\n            else:\n                print(print_node.data_val, end=' ')\n                print_node = print_node.next_node\n        print('---')\n\n    def add_node(self, value=None):\n        new_node = Node(value)\n        if self.tail_node is not None:\n            self.tail_node.next_node = new_node\n            self.tail_node = new_node\n        else:\n            self.head_node = new_node\n            self.tail_node = new_node\n\n    def remove_head(self):\n        head_value = self.head_node.data_val\n        if self.head_node == self.tail_node:\n            self.head_node = None\n            self.tail_node = None\n        else:\n            self.head_node = self.head_node.next_node\n        return head_value\n\n    def remove_node(self, val_to_remove):\n        cur_node = self.head_node\n        cur_pre_node = None\n        while True:\n            if cur_node is None:\n                return\n            if cur_node.data_val == val_to_remove:\n                if cur_pre_node is not None:\n                    cur_pre_node.next_node = cur_node.next_node\n                else:\n                    self.head_node = cur_node.next_node\n                if cur_node.next_node is None:\n                    self.tail_node = cur_pre_node\n                return\n            else:\n                cur_pre_node = cur_node\n                cur_node = cur_node.next_node\n\n    def add_head(self, val_to_add):\n        new_node = Node(val_to_add)\n        if self.head_node is not None:\n            new_node.next_node = self.head_node\n            self.head_node = new_node\n        else:\n            self.head_node = new_node\n            self.tail_node = new_node\n\n\nresults = []\nwhile True:\n    P, C = map(int, input().split())\n\n    if P == 0:\n        break\n    P = min(P, C)\n\n    cur_queue = MyLinkedList()\n    for i in range(P):\n        cur_queue.add_node(i + 1)\n    cur_result = []\n    for i in range(C):\n        Ci = input()\n        if Ci is 'N':\n            cur_treating = cur_queue.remove_head()\n            cur_result.append(cur_treating)\n            cur_queue.add_node(cur_treating)\n        else:\n            expedited_index = int(Ci.split()[1])\n            cur_queue.remove_node(expedited_index)\n            cur_queue.add_head(expedited_index)\n\n    results.append(cur_result)\n\nfor i in range(len(results)):\n    print('Case %d:' %(i + 1))\n    print(*results[i], sep='\\n')\n","sub_path":"Blue/Session 04 - Stack and Queue/UVA_12207 - That is Your Queue.py","file_name":"UVA_12207 - That is Your Queue.py","file_ext":"py","file_size_in_byte":5164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"250133196","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug 25 19:22:08 2015\n\n@author: user\n\"\"\"\nimport csv\n\n\nmovies = ['tt0111161', 'tt1856010', 'tt0096694', 'tt0088763', 'tt1375666']\n\nnumbers = [];\nfor i in movies:\n    numbers.append(i[2:])\nprint(numbers)\n\nnumbers2 = [int(i[1:]) for i in movies];\nprint(numbers2)\n\nint_numbers = [int(i) for i in numbers2];\nsum(int_numbers[0:3])\nsum(int_numbers)\n\n#####################\n\nf = open('airlines.csv', 'rU')\nfile_string = f.read()\nf.close()\n\nwith open('airlines.csv', 'rU') as f:\n    file_nested_list = [row for row in csv.reader(f)]\n    \nheader = file_nested_list[0]\ndata = file_nested_list[1:]\n\n\navg_incidents_yr = [(float(airline[2]) + float(airline[5]))/30 for airline in data]\nname_clean = [airline[0].replace('*','') for airline in data]\nname_star = [0 if airline[0].find('*') == -1 else 1 for airline in data]\ndict_airlines = dict(zip(name_clean, avg_incidents_yr))\n\n##################### HW\n\n# cd ~/Desktop/DAT8/data\nimport csv\nwith open('chipotle.tsv', 'rU') as f:\n    #dialect = csv.Sniffer().sniff(f.read(1024))\n    \n    file_nested_list = [row for row in csv.reader(f, delimiter='\\t')]\n\nheader = file_nested_list[0]\ndata = file_nested_list[1:]\n\nlst_prices = [float(i[4][1:]) for i in data]\nsum_prices = sum(lst_prices)\nlst_order_ids = [int(i[0]) for i in data]\nnum_orders = len(set(lst_order_ids))\navg_order_price = sum_prices/num_orders\n\nlst_canned = [item_order[3] for item_order in data if item_order[2].lower().find('canned') >= 0]\nlst_canned_unique = set(lst_canned)\n\nlst_burrito_topping = [item_order[3] for item_order in data if item_order[2].lower().find('burrito') >= 0]\ncount_burrito_topping = [i.count(',') + 1 for i in lst_burrito_topping]\navg_burrito_topping = float(sum(count_burrito_topping))/len(lst_burrito_topping)\n\nlst_chip_names = set([item_order[2] for item_order in data if item_order[2].lower().find('chip') >= 0])\n\nlst_chip_count = []\n        \nfor chip_name in lst_chip_names:\n    print(chip_name)\n    int_chip_count = 0\n    for item_order in data:\n        if item_order[2] == chip_name:\n            int_chip_count += 1 * int(item_order[1])\n    lst_chip_count.append(int_chip_count)\n    \ndict_chip_count = dict(zip(lst_chip_names, lst_chip_count))\n\n#set([i[3] for i in data])\n# lst_item_name = [i[2].lower() for i in data]\n'''\nfor item_order in data:\n    int_chip_count = 0\n    for chip_name in lst_chip_names:\n        if item_order[2] == chip_name:\n            int_chip_count += 1\n        lst_chip_count.append(int_chip_count)\n'''","sub_path":"code/own_code/class3.py","file_name":"class3.py","file_ext":"py","file_size_in_byte":2498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"129787526","text":"\nfrom mysetup import * #setup code for these sets of simulations\n\nh.dt = 0.1 # set some parameters - dt is time-step in milliseconds\nh.tstop = 400 # tstop is duration of simulation in milliseconds\n\nallsec = [] # list of sections\n\nsoma = h.Section(name=\"soma\") # make a section named soma\nsoma.L = 10 # length in microns \nsoma.diam = 10  # diameter in microns\nsoma.nseg = 1 # number of segments\nallsec.append(soma)\n\ncable = h.Section(name=\"cable\") # make the cable\ncable.L = 10000\ncable.diam = 1\ncable.nseg = 1001\nallsec.append(cable)\n\nsoma.connect(cable,1,0) # connect soma to the cable\n\nhaveHH = False # whether to add a HH channel to soma\n\n# add a passive/leak channel\nfor sec in allsec:\n    sec.insert('pas') \n    sec.e_pas = -65     # mV\n    sec.g_pas = 1/20e3 # 0.1e-3  # S/cm2 \n    if sec == soma and haveHH:\n        # optionally, add a hodgkin-huxley type channel\n        soma.insert('hh')\n\nlvec = [] # list of vectors for recording\n \nvvolt  = h.Vector(int(h.tstop/h.dt)+1) # make a Vector \nvvolt.record(soma(0.5)._ref_v)  # to record voltage from the section\nvvolt.label(\"soma(0.5).v\") # give the vector a label - shows up in plot\nlvec.append(vvolt) # save the vvolt Vector to a list\n\ng = [] # Graphs for plotting the output\nfor i in xrange(0,2):\n    g.append(h.Graph())\n\n# setup space plot\ng.append(h.Graph(0))\ng[2].size(0,10000,-80,-20) # sets viewing size\ng[2].view(0, -80, 10000, 60, 0, 374, 300.48, 200.32)\nh.flush_list.append(g[2]) # makes sure graph gets updated during run\nrvp = h.RangeVarPlot(\"v\") # make it a RangeVarPlot\nrvp.begin(0)\nrvp.end(1)\nrvp.origin(0)\ng[2].addobject(rvp, 2, 1, 0.8, 0.9)\nh.doNotify() # make sure graphics updated during run\n\nasy = h.AlphaSynapse(cable(0.5)) # alpha synapse at soma(0.5)\nasy.onset = 10 # time at which synapse is activated\nasy.tau = 20 # synaptic time constant\nasy.gmax = 0.015 # maximum conductance\nasy.e = 0 # synapse reversal potential\n\nvisy = h.Vector(int(h.tstop/h.dt)+1)\nvisy.record(asy._ref_i) # record synaptic current from the asy AlphaSynapse\nvisy.label(\"asy.i\") # give the vector a label - shows up in plot\nlvec.append(visy) # save the visy Vector to a list\n\n# plot the output\ndef plotout ():\n    for i in xrange(0,2):# clear Graph contents\n        g[i].erase_all()\n        lvec[i].plot(g[i],h.dt,i+1,2) #\n        g[i].exec_menu(\"View = plot\") # set Graph view to what was drawn\n\n# myrun function - runs sim and plots output\ndef myrun ():\n    h.run() # run\n    plotout() # plot the output\n\nmyrun()\n","sub_path":"sam_code/onesecalpha.py","file_name":"onesecalpha.py","file_ext":"py","file_size_in_byte":2468,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"258462025","text":"from django.urls import path\r\nfrom .views import *\r\nfrom . import views\r\n\r\n\r\napp_name = 'app'\r\n\r\nurlpatterns = [\r\n    #path('', HomeView.as_view(), name='home'),you_seller\r\n    path('', home , name='home'),\r\n    path('search/', SearchPage, name='search_result'),\r\n\r\n    path('checkout/', CheckoutView.as_view(), name='checkout'),\r\n    path('payment/<payment_option>/', PaymentView.as_view(), name='payment'),\r\n    path('paymentcod/<payment_option>/', PaymentCOD.as_view(), name='payment-cod'),    \r\n\r\n    path('products/', products, name='products'),\r\n    path('profile/', views.view_profile, name='view_profile'),\r\n    path('profile_edit/', views.update_profile, name='edit_profile'),\r\n\r\n    path('you-seller/', you_seller , name='you-seller'),\r\n    path('seller-register/', seller_register.as_view() , name='seller-register'),\r\n    path('seller-account/', seller_account , name='seller-account'),\r\n    path('sharing/', sharing, name='sharing'),\r\n    path('renting/', renting, name='renting'),\r\n    #path('seller-register/phone-verification/', phone_verification.as_view(), name='phone-verification'),\r\n    path('phone-verification/', ValidatePhoneSendOTP.as_view(), name='phone-verification'),\r\n    path('ValidateOTP/<slug>/', ValidateOTP.as_view(), name='ValidateOTP'),\r\n    path('seller-details/', seller_details.as_view() , name='seller-details'),\r\n\r\n    path('product/<slug>/', ItemDetailView, name='product'),\r\n    path('add-to-cart/<slug>/', add_to_cart,name='add-to-cart'),\r\n    path('remove-from-cart/<slug>/', remove_from_cart,name='remove-from-cart'),\r\n\r\n    path('increase-rent-time-interval/<slug>/', increase_rent_time_interval,name='increase-rent-time-interval'),\r\n    path('reduce-rent-time-interval/<slug>/', reduce_rent_time_interval,name='reduce-rent-time-interval'),\r\n\r\n    path('order-summary/', OrderSummaryView.as_view(), name='order-summary'),\r\n    path('ordered-summary/', OrderedSummaryView.as_view(), name='ordered-summary'),\r\n    path('share-summary/', ShareSummaryView.as_view(), name='share-summary'),\r\n    path('remove-single-item-from-cart/<slug>', remove_single_item_from_cart, name='remove-single-item-from-cart'),\r\n\r\n\r\n    path('image_upload/', sharing, name = 'image_upload'), \r\n    path('success/', success, name = 'success'),\r\n    #path('sharing/', AuthorCreate.as_view(), name = \"sharing\"),\r\n    #path('distance/<slug>/', get_distance, name = 'get-distance'),\r\n\r\n]\r\n\r\n","sub_path":"nearbyshops/app/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":2408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"75537491","text":"from hello import hello\nfrom hello.logger import BuiltinLogger\n\n_g_logger = BuiltinLogger(\"User Test Script\")\n\ndef test_2053(app):\n    \n    n2i = \"n2_i_time_(min)\"\n    n2d = \"n2_d_time (min)\"\n    do_db = \"Deadband (DO%)\"\n    n2_am = \"n2 Auto max (%)\"\n    n2_P = \"n2 p gain (%/DO%)\"\n    \n    # backup settings\n    _g_logger.info(\"Backing up config settings\")\n    app.login()\n    cfg = app.getConfig()\n    old_cfg = {}\n    old_cfg[n2i] = cfg['DO'][n2i]\n    old_cfg[n2d] = cfg['DO'][n2d]\n    old_cfg[do_db] = cfg['DO'][do_db]\n    old_cfg[n2_am] = cfg['DO'][n2_am]\n    old_cfg[n2_P] = cfg['DO'][n2_P]\n    \n    # set test settings\n    _g_logger.info(\"Setting test settings\")\n    app.setconfig('DO', n2i, 0)\n    app.setconfig('DO', n2d, 0)\n    app.setconfig('DO', do_db, 0)\n    app.setconfig('DO', n2_am, 100)\n    app.setconfig('DO', n2_P, -5)\n    \n    do_pv = app.getdopv()\n    app.setdo(0, do_pv - 10)\n    \n    input(\"Press enter when done\")\n    \n    _g_logger.info(\"Restoring old config\")\n    for k, v in old_cfg:\n        app.setconfig('DO', k, v)\n    ","sub_path":"func_test/user_tests/setups.py","file_name":"setups.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"137039938","text":"from random import randrange\nimport pandas as pd\nimport time\nimport random\nimport numpy as np\nfrom datetime import date\nfrom connection import MySQLDatabase\n\n# Input\n#print(\"What's the name of the excel?\")\n#excel_name = input()\n\nexcel_name = \"messages.csv\"\n\n# Read the csv\n# print(excel_name)\ndf = pd.read_csv(excel_name)\n\n\n# Convert dataframe to dict()\nhashmap = df.to_dict('records')\n\ntotalPoints = 0\n# Loop through dictionary\nfor entry in hashmap:\n    totalPoints += entry['Total Points']\n\n# print(totalPoints)\n\nfor entry in hashmap:\n    entry['probability'] = entry['Total Points'] / totalPoints\n\n\ndef get_winner(winning_probability):\n    cumulative_probability = 0\n    for entry in hashmap:\n        cumulative_probability = cumulative_probability + \\\n            entry['probability']\n        if winning_probability <= cumulative_probability and cumulative_probability != 0:\n            return entry['First Name']\n\n    return \"no winner\"\n\n\ndef convertTuple(winner_tuple):\n    winner_tuple_in_str = \"\"\n    winner_tuple_in_str = winner_tuple_in_str.join(str(winner_tuple))\n    return winner_tuple_in_str\n\n\n# Update in database\nx = MySQLDatabase()\nfor entry in hashmap:\n    if (pd.isnull(entry['First Name'])):\n        entry['First Name'] = \"\"\n    x.add_user(entry['First Name'], entry['Email'],\n               entry['Total Points'], entry['probability'])\n\n\n# Compute the winner\nrandom.seed(a=date.today(), version=2)\nwinning_probability = random.uniform(0, 1)\nname_of_winner = get_winner(winning_probability)\nwinner_tuple = x.retrieve_user(name_of_winner)\nx.add_luckydrawrecord(name_of_winner)\nwinner_tuple_in_str = convertTuple(winner_tuple)\n\n\n# Store in database\nx = MySQLDatabase()\n#print(\"The winner is :\", winner_tuple_in_str)\n\n\ndef countdown(t):\n\n    while t:\n        mins, secs = divmod(t, 60)\n        timer = '{:02d}:{:02d}'.format(mins, secs)\n        print(timer, end=\"\\r\")\n        time.sleep(1)\n        t -= 1\n\n    print(\"The winner is :\", winner_tuple[1])\n\n\ncountdown(5)\n","sub_path":"python.py","file_name":"python.py","file_ext":"py","file_size_in_byte":1984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"70798749","text":"import os\nimport sys\nimport re\nimport webbrowser\nimport tkinter\nfrom http.server import HTTPServer,SimpleHTTPRequestHandler\nhost = \"localhost\"\nport = 8000\nurl=host+r\":\"+str(port)\nargc=sys.argv\nif len(argc) > 1:\n\tif os.path.exists(argc[1]):\n\t\tos.chdir(os.path.dirname(argc[1]))\n\t\tif os.path.isfile(argc[1]):\n\t\t\turl=url+\"/\"+os.path.basename(argc[1])\n\t\telse:\n\t\t\tos.chdir(os.path.basename(argc[1]))\nhttpd = HTTPServer((host,port),SimpleHTTPRequestHandler)\nprint(url)\nprint('exit to \"Ctrl + c\"')\nif os.path.exists(\"C:\\\\Program Files (x86)\\\\Mozilla Firefox\\\\firefox.exe\"):\n\twebbrowser.get('\"C:\\\\Program Files (x86)\\\\Mozilla Firefox\\\\firefox.exe\" %s').open_new_tab(url)\nhttpd.serve_forever()","sub_path":"simulator/2015/localserver.py","file_name":"localserver.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"407521908","text":"from flask_restful import Resource\nfrom models.entity import EntityModel\nfrom schemas.entity import EntitySchema\n\nentity_schema = EntitySchema()\nentity_list_schema = EntitySchema(many=True)\n\n\nclass Entity(Resource):\n    @classmethod\n    def get(cls, name):\n        entity = EntityModel.find_by_name(name)\n        if entity:\n            return entity_schema.dump(entity), 200\n        return {'message': 'entity not found'}, 404\n\n    @classmethod\n    def post(cls, name):\n        if EntityModel.find_by_name(name):\n            return {'message': \"A Handler with name '{}' already exists.\".format(name)}, 400\n\n        entity = EntityModel(name=name)\n        try:\n            entity.save_to_db()\n        except:\n            return {'message': 'An error occurred while creating the store.'}, 500\n\n        return entity_schema.dump(entity), 201\n\n    @classmethod\n    def delete(cls, name):\n        entity = EntityModel.find_by_name(name)\n        if entity:\n            entity.delete_from_db()\n        return {'message': \"Handler '{}' deleted.\".format(name)}\n\n\nclass EntityList(Resource):\n    @classmethod\n    def get(cls):\n        return {'handlers': entity_list_schema.dump(EntityModel.find_all())}\n","sub_path":"resources/entity.py","file_name":"entity.py","file_ext":"py","file_size_in_byte":1194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"500918420","text":"import string;\r\ndef limpartela():\r\n    print (\"\\n\"*15)\r\n\r\ndef criptografrar(mensagem,chave):\r\n    if mensagem.isdigit()==True: #Verifica se é numero\r\n        if chave>0:\r\n            mensagem = list(mensagem)\r\n            numeros = str(string.digits)\r\n            num = list(numeros)\r\n            key = len(mensagem)\r\n            for numero_verificar in range(0,len(num)):\r\n                for numero_mensagem in range(0,len(mensagem)):\r\n                    if (numero_mensagem == numero_verificar):\r\n                        mensagem[numero_mensagem] = ((chave+(key+numero_verificar)) + (int(mensagem[numero_mensagem])))\r\n            print(\"Dados criptografados : \",end=\"\")\r\n            for i in range(0,len(mensagem)):\r\n                print((mensagem[i]),end=\",\")\r\n        else:\r\n            print(\"Digite um valor numerico\")\r\n    else:\r\n        alfabeto = list(string.ascii_lowercase*chave)\r\n        mensagem = list(mensagem)\r\n        for i in range(0,len(mensagem)):\r\n            for x in range(0,len(alfabeto)):\r\n                if i<(len(alfabeto)-chave):\r\n                    if (mensagem[i]==alfabeto[x])and(mensagem[i]!=\"\"):\r\n                        mensagem[i] = alfabeto[(x+chave)]\r\n                        break\r\n                else:\r\n                    if (mensagem[i]==alfabeto[x])and(mensagem[i]!=\"\"):\r\n                        mensagem[i] = alfabeto[(x-chave)]\r\n                        break\r\n        print(\"Dados criptografados : \",end=\"\")\r\n        for i in range(0,len(mensagem)):\r\n            print((mensagem[i]),end=\"\")\r\ndef decode(opcao,chave):\r\n    if (opcao.lower()==\"numero\"):\r\n        mais = \"sim\"\r\n        i=0\r\n        mensagem = list()\r\n        while (mais.lower() == \"sim\"):\r\n            mensagem.append ((input(\"Digite o  Numero: \")))\r\n            mais = input(\"Há mais Numeros ? Sim ou Nao ? \") \r\n            i=i+1\r\n        numeros = str(string.digits)\r\n        num = list(numeros)\r\n        key = len(mensagem)\r\n        for numero_verificar in range(0,len(num)):\r\n            for numero_mensagem in range(0,len(mensagem)):\r\n                if (numero_mensagem == numero_verificar):\r\n                    mensagem[numero_mensagem] = ((int(mensagem[numero_mensagem]))-((key+numero_verificar)+chave))\r\n        limpartela()\r\n        print(\"Dados descriptografados : \",end=\"\")\r\n        for i in range(0,len(mensagem)):\r\n            print((mensagem[i]),end=\"\")\r\n    elif (opcao.lower()==\"texto\"):\r\n        alfabeto = list(string.ascii_lowercase*chave)\r\n\r\n        mensagem = list(input(\"Digite a mensagem: \"))\r\n        for i in range(0,len(mensagem)):\r\n            for x in range(0,len(alfabeto)):\r\n                if i<(len(alfabeto)-chave):\r\n                    if (mensagem[i]==alfabeto[x])and(mensagem!=\"\"):\r\n                        mensagem[i] = alfabeto[(x-chave)]\r\n                        break\r\n                else:\r\n                    if (mensagem[i]==alfabeto[x])and(mensagem!=\"\"):\r\n                        mensagem[i] = alfabeto[(x+chave)]\r\n                        break\r\n        print(\"Dados descriptografados : \",end=\"\")\r\n        for i in range(0,len(mensagem)):\r\n            print((mensagem[i]),end=\"\")\r\n    else:\r\n        print(\"Opção Desconhecida\")\r\n        pass\r\ncontinuar = \"sim\"\r\nwhile (continuar.lower()==\"sim\"): #Enquanto o o usuario digitar sim para continuar\r\n    print(\"\\n----------------------------------------------------\")\r\n    print(\"1 - Criptografrar dados\")\r\n    print(\"2 - Descriptografar dados\")\r\n    print(\"3 - Sair\")\r\n    print(\"----------------------------------------------------\")\r\n    i_opcao = int(input())\r\n    if (i_opcao==1):\r\n        limpartela()\r\n        criptografrar(input(\"Mensagem: \"),(int(input(\"Digite a chave: \"))))\r\n    elif (i_opcao==2):\r\n        limpartela()\r\n        decode(input(\"Deseja Descriptografar Texto ou Numero ?\\n\"),int(input(\"Digite a Chave: \")))\r\n    elif(i_opcao==3):\r\n        break\r\n    else:\r\n        limpartela()\r\n        print(\"Opcão Não Conhecida tente novamente\")\r\n        continuar = \"sim\"\r\n","sub_path":"Criptografia1.6.py","file_name":"Criptografia1.6.py","file_ext":"py","file_size_in_byte":3998,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"436463950","text":"from flask import Flask, render_template, request, redirect\r\nfrom flask_mail import Mail, Message\r\n\r\napp=Flask(__name__)\r\n\r\nmail_settings={\r\n    \"MAIL_SERVER\": 'smtp.gmail.com',\r\n    \"MAIL_PORT\": 465,\r\n    \"MAIL_USE_TLS\": False,\r\n    \"MAIL_USE_SSL\": True,\r\n    \"MAIL_USERNAME\": 'rlgbluet3c6@gmail.com',\r\n    \"MAIL_PASSWORD\": '15793*82'\r\n}\r\n\r\napp.config.update(mail_settings)\r\nmail=Mail(app)\r\n\r\nclass Contato:\r\n    def __init__(self, nome, email, mensagem):\r\n        self.nome=nome\r\n        self.email=email\r\n        self.mensagem=mensagem\r\n\r\n@app.route('/')\r\ndef index():\r\n    return render_template('index.html')\r\n\r\n@app.route('/contactform', methods=['GET', 'POST'])\r\ndef send():\r\n    if request.method=='POST':\r\n        formContato=Contato(\r\n            request.form['name'],\r\n            request.form['email'],\r\n            request.form['message']\r\n        )\r\n\r\n        msg=Message(\r\n            subject=\"Contato do seu portfólio\",\r\n            sender=app.config.get(\"MAIL_USERNAME\"),\r\n            recipients=[app.config.get(\"MAIL_USERNAME\")],\r\n            body=f'''{formContato.nome} enviou a seguinte mensagem\r\n            {formContato.mensagem}'''\r\n        )\r\n        mail.send(msg)\r\n    return render_template('contactform.html',\r\n    formContato=formContato)\r\n\r\nif __name__ == '__main__':\r\n    app.run(debug=True)","sub_path":"Projeto02-Portfolio/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1323,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"284148800","text":"from gmplot import gmplot\n\n# PLOTTER CONSTANTS\nCITY = \"Porto Alegre\"\nZOOM = 14\n\n# SCATTER CONSTANTS\nSCATTER_SIZE = 8\nSCATTER_COLOR = \"#3B0B39\"\n\n# HEATMAP CONSTANTS\nRADIUS = 40\nTHRESHOLD = 5\n\n\ndef plot_scatter(latitudes, longitudes, year):\n    # Place map\n    gmap = gmplot.GoogleMapPlotter.from_geocode(CITY, zoom=ZOOM)\n\n    gmap.scatter(latitudes, longitudes, SCATTER_COLOR, size=SCATTER_SIZE, marker=False)\n\n    # Draw\n    gmap.draw(\"results/scatter/\" + str(year) + \".html\")\n\n\ndef plot_heatmap(latitudes, longitudes, year):\n    # Place map\n    gmap = gmplot.GoogleMapPlotter.from_geocode(CITY, zoom=ZOOM)\n\n    gmap.heatmap(latitudes, longitudes, radius=RADIUS, threshold=THRESHOLD)\n\n    # Draw\n    gmap.draw(\"results/heatmaps/\" + str(year) + \".html\")\n","sub_path":"maps_plotting.py","file_name":"maps_plotting.py","file_ext":"py","file_size_in_byte":753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"469479646","text":"# coding:utf-8\n\nimport os\nimport pickle\n\nimport torch\nimport random\nimport warnings\nimport numpy as np\nimport pandas as pd\nfrom tqdm import tqdm\nfrom typing import Dict\nfrom collections import defaultdict\nfrom torch.utils.data import Dataset\n\nfrom transformers import (\n    BertTokenizer,\n    DataCollatorForLanguageModeling,\n    DataCollatorForWholeWordMask,\n    PreTrainedTokenizer,\n    BertConfig,\n    BertForMaskedLM\n)\nfrom transformers.utils import logging\n\nfrom simple_trainer import Trainer\nfrom pretrain_args import TrainingArguments\n\nwarnings.filterwarnings('ignore')\nlogger = logging.get_logger(__name__)\n\n\ndef seed_everything(seed):\n    random.seed(seed)\n    np.random.seed(seed)\n    torch.manual_seed(seed)\n    torch.cuda.manual_seed_all(seed)\n    return seed\n\n\ndef read_data(config, train_file_path, test_file_path, tokenizer: BertTokenizer) -> dict:\n    train_df = pd.read_csv(train_file_path, header=None, sep='\\t')\n    test_df = pd.read_csv(test_file_path, header=None, sep='\\t')\n\n    pretrain_df = pd.concat([train_df, test_df], axis=0)\n\n    inputs = defaultdict(list)\n    for i, row in tqdm(pretrain_df.iterrows(), desc=f'preprocessing pretrain data ... ...', total=len(pretrain_df)):\n        sentence_a, sentence_b = row[0], row[1]\n        inputs_dict = tokenizer.encode_plus(sentence_a, sentence_b, add_special_tokens=True,\n                                            return_token_type_ids=True, return_attention_mask=True)\n        inputs['input_ids'].append(inputs_dict['input_ids'])\n        inputs['token_type_ids'].append(inputs_dict['token_type_ids'])\n        inputs['attention_mask'].append(inputs_dict['attention_mask'])\n\n    data_cache_path = config['data_cache_path']\n\n    if not os.path.exists(os.path.dirname(data_cache_path)):\n        os.makedirs(os.path.dirname(data_cache_path))\n    with open(data_cache_path, 'wb') as f:\n        pickle.dump(inputs, f)\n\n    return inputs\n\n\nclass LineByLineTextDataset(Dataset):\n    def __init__(self, tokenizer: PreTrainedTokenizer, train_file_path: str, test_file_path: str, block_size: int):\n        assert os.path.isfile(train_file_path), f\"Input file path {train_file_path} not found\"\n        logger.info(f\"Creating features from dataset file at {train_file_path}\")\n\n        assert os.path.isfile(test_file_path), f\"Input file path {test_file_path} not found\"\n        logger.info(f\"Creating features from dataset file at {test_file_path}\")\n\n        with open(train_file_path, encoding=\"utf-8\") as f:\n            train_lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]\n\n        with open(test_file_path, encoding=\"utf-8\") as f:\n            test_lines = [line for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())]\n\n        lines = train_lines + test_lines\n\n        batch_encoding = tokenizer(lines, add_special_tokens=True, truncation=True, max_length=block_size)\n        self.examples = batch_encoding[\"input_ids\"]\n        self.examples = [{\"input_ids\": torch.tensor(e, dtype=torch.long)} for e in self.examples]\n\n    def __len__(self):\n        return len(self.examples)\n\n    def __getitem__(self, i) -> Dict[str, torch.tensor]:\n        return self.examples[i]\n\n\ndef main():\n    config = {\n        'pretrain_type': 'dynamic_mask',  # dynamic_mask, whole_word_mask\n        'data_cache_path': '',\n        'train_data_path': '../data/train.txt',\n        'test_data_path': '../data/test.txt',\n    }\n\n    mlm_probability = 0.15\n    num_train_epochs = 150\n    seq_length = 100\n    batch_size = 32\n    learning_rate = 6e-5\n    save_steps = 5000\n    seed = 2021\n\n    \"\"\"\n    if use bert, replace NeZhaForMaskedLM -> BertForMaskedLM, \n                         NeZhaConfig -> BertConfig\n                         model_name -> your pretrain_model name\n\n    example: \n            >>      model_name = bert-base-uncased\n                    model_path = '../pretrain_model/' + model_name + '/pytorch_model.bin'\n                    config_path = '../pretrain_model/' + model_name + '/config.json'\n                    vocab_file = '../pretrain_model/' + model_name + '/vocab.txt'\n                    ... ...\n                    model_config = BertConfig.from_pretrained(config_path)\n                    BertForMaskedLM.from_pretrained(pretrained_model_name_or_path=model_path,\n                                                 config=model_config)\n    \"\"\"\n\n    # put dowm your file path\n    if config['pretrain_type'] == 'whole_word_mask':\n        # https://huggingface.co/hfl/chinese-bert-wwm-ext\n        model_name = 'chinese-bert-wwm-ext'\n    else:\n        # https://huggingface.co/hfl/chinese-macbert-base\n        model_name = 'chinese-macbert-base'\n\n    config['data_cache_path'] = '../user_data/pretrain/' + config['pretrain_type'] + '/data.pkl'\n\n    model_path = '../pretrain_model/' + model_name + '/pytorch_model.bin'\n    config_path = '../pretrain_model/' + model_name + '/config.json'\n\n    vocab_file = '../pretrain_model/' + model_name + '/vocab.txt'\n    tokenizer = BertTokenizer.from_pretrained(vocab_file)\n\n    model_config = BertConfig.from_pretrained(config_path)\n\n    if config['pretrain_type'] == 'dynamic_mask':\n        data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer,\n                                                        mlm=True,\n                                                        mlm_probability=mlm_probability)\n        model = BertForMaskedLM.from_pretrained(pretrained_model_name_or_path=model_path,\n                                                 config=model_config)\n        model_save_path = 'mlm_model'\n\n    if config['pretrain_type'] == 'whole_word_mask':\n        data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer,\n                                                     mlm=True,\n                                                     mlm_probability=mlm_probability)\n        model = BertForMaskedLM.from_pretrained(pretrained_model_name_or_path=model_path,\n                                                 config=model_config)\n        model_save_path = 'whole_word_mask_model'\n\n    dataset = LineByLineTextDataset(tokenizer=tokenizer,\n                                    train_file_path=config['train_data_path'],\n                                    test_file_path=config['test_data_path'],\n                                    block_size=seq_length)\n\n    training_args = TrainingArguments(\n        output_dir='record',\n        num_train_epochs=num_train_epochs,\n        learning_rate=learning_rate,\n        per_device_train_batch_size=batch_size,\n        save_steps=save_steps,\n        logging_steps=500,\n        save_total_limit=5,\n        prediction_loss_only=True,\n        seed=seed\n    )\n\n    trainer = Trainer(\n        model=model,\n        args=training_args,\n        data_collator=data_collator,\n        train_dataset=dataset\n    )\n\n    trainer.train()\n    trainer.save_model(model_save_path)\n    tokenizer.save_pretrained(model_save_path)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"pretrain_code/run_pretrain_bert.py","file_name":"run_pretrain_bert.py","file_ext":"py","file_size_in_byte":6975,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"493951179","text":"#!/usr/bin/env python3\n\nimport os\nSCRIPT_PATH = os.path.dirname(os.path.realpath(__file__))\nFILENAME = '{}/../input.txt'.format(SCRIPT_PATH)\n\n# Read the challenge input\nwith open(FILENAME, 'r') as input_file:\n    PUZZLE_INPUT = input_file.readlines()\n\n# Keypad layout\nlayout = [\n    [1, 2, 3],\n    [4, 5, 6],\n    [7, 8, 9]\n]\n\n# Initial position (number 5)\nstart = (1, 1)\ncode = []\n\nfor line in PUZZLE_INPUT:\n    for char in line:\n        if char == 'L':\n            start = (start[0], max(start[1] - 1, 0))\n        if char == 'U':\n            start = (max(start[0] - 1, 0), start[1])\n        if char == 'R':\n            start = (start[0], min(start[1] + 1, 2))\n        if char == 'D':\n            start = (min(start[0] + 1, 2), start[1])\n\n    code.append(layout[start[0]][start[1]])\n\nprint('The code for the bathroom is', code)\n","sub_path":"2016/day_02/python/p1.py","file_name":"p1.py","file_ext":"py","file_size_in_byte":828,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"9802996","text":"from django.urls import path\nfrom Customer.views import(\n    CustomerCreateAPIView,\n    CustomerListAPIView,\n    CustomerDetailAPIView\n)\n\nurlpatterns = [\npath('create', CustomerCreateAPIView.as_view()),\npath('all', CustomerListAPIView.as_view()),\npath('detail/<int:pk>', CustomerDetailAPIView.as_view())\n]","sub_path":"Customer/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"335309259","text":"import re\n\n\nclass StepFinder():\n    def __init__(self, os_interface):\n        self.os_interface = os_interface\n        self.steps = []\n\n    def find_all_steps(self):\n        self.steps = []\n\n        all_step_files = self.os_interface.get_files()\n        for filename in all_step_files:\n            self._find_steps_in_file(filename)\n\n        return self.steps\n\n    def match(self, text):\n        matches = []\n        for (step, index, filename) in self.steps:\n            class SubstituteCounter:\n                def __init__(self):\n                    self.count = 0\n\n                def substitute_variable(self, m):\n                    self.count += 1\n                    return \"$\" + str(self.count)\n\n            counter = SubstituteCounter()\n\n            step_label = re.sub(r'^@(.*)\\([\"\\'](.*)[\"\\']\\)', r'\\1 \\2', step)\n            step_usage = re.sub(r'\\{[^}]+\\}', counter.substitute_variable, step_label)\n            if self._match_step_to_text(text, step_usage):\n                matches.append((step_label, self._strip_to_text_placement(text, step_usage)))\n        return sorted(matches)\n\n    def _find_steps_in_file(self, filename):\n        lines = self.os_interface.open(filename)\n        for linenumber in range(len(lines)):\n            match = self._line_is_a_step(lines[linenumber])\n            if match:\n                self.steps.append((match.group(), linenumber, filename))\n            elif lines[linenumber].strip() and (linenumber + 1) < len(lines):\n                line = self._double_line_is_a_step(lines[linenumber].strip() + lines[linenumber+1].strip())\n                if line:\n                    self.steps.append((line, linenumber, filename))\n\n    def _line_is_a_step(self, text):\n        pattern = re.compile(r'(@(.+)(\\([\"\\'].+))[\"\\']\\)')\n        return re.match(pattern, text)\n\n    def _double_line_is_a_step(self, double_line):\n        pattern = re.compile(r'@(.+)\\([\"\\'](.+)[\"\\'][\"\\'](.+)[\"\\']\\)')\n        match = re.match(pattern, double_line)\n        if match:\n            return \"@{0}('{1}{2}')\".format(match.group(1), match.group(2), match.group(3))\n        return False\n\n    def _match_step_to_text(self, text, step_usage):\n        step_usage_matcher = step_usage[:len(text)]\n        return text.lower() == step_usage_matcher.lower()\n\n    def _strip_to_text_placement(self, prefix, full_step):\n        \"\"\" Remove last word from prefix \"\"\"\n        if re.search(r'\\s', prefix):\n            prefix = re.sub(r'(.*)\\s[^\\s]+$', r'\\1', prefix)\n            return full_step[len(prefix):].strip()\n        return full_step\n","sub_path":"step_finder.py","file_name":"step_finder.py","file_ext":"py","file_size_in_byte":2549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"637875712","text":"import nltk\nimport pandas as pd\nimport pickle\nimport sys\nfrom collections import defaultdict\nfrom nltk.classify import NaiveBayesClassifier\nfrom nltk.metrics.scores import precision, recall, f_measure\nfrom nltk.stem.porter import PorterStemmer\nfrom sklearn.naive_bayes import MultinomialNB,BernoulliNB\nfrom sklearn.linear_model import LogisticRegression,SGDClassifier\nfrom sklearn.svm import LinearSVC\nfrom random import shuffle\n\n\ninfile = open(f\"Test/all_freq_ngrams\", 'rb')\n\nfreq_ngrams = pickle.load(infile)\nword_features = [w for (w, f) in freq_ngrams.most_common(2000)]\ninfile.close()\n\ninfile = open(\"Test/train_all\", 'rb')\ntrain_ngrams = pickle.load(infile)\n'''tuple with essay_unigrams, essay_bigrams and essay_trigrams\nto access unigrams, just ngram_tuple[0][\"essay0\"]'''\ninfile.close()\n\ninfile = open(\"Test/dev_all\", 'rb')\ndev_ngrams = pickle.load(infile)\ninfile.close()\n\ndef label_func(l, cdl):\n    if l == \"sex\" or cdl == \"white\":\n        return cdl\n    elif l == \"age\":\n        if cdl <= 30:\n            return \"u_30\"\n        else:\n            return \"o_30\"\n    else:\n        return \"n-white\"\n\ndef document_features(document):\n    document_words = set(document)\n    features = {}\n    for word in word_features:\n        features['contains({})'.format(word)] = (word in document_words)\n    return features\n\ndef bayes(essay, label):\n    train_features = [(document_features(t), label_func(label, class_dic[label])) for (t, class_dic) in train_ngrams[essay]]\n    dev_features = [(document_features(t), label_func(label, class_dic[label])) for (t, class_dic) in dev_ngrams[essay]]\n    shuffle(train_features)\n    shuffle(dev_features)\n    training_set, testing_set = train_features, dev_features\n    classifier = nltk.NaiveBayesClassifier.train(training_set)\n    print(\"Naive Bayes accuracy percent:\", (nltk.classify.accuracy(classifier, testing_set))*100)\n    lab = set(classifier.most_informative_features(100))\n    \n\n    '''predictions, gold_labels = defaultdict(set), defaultdict(set)\n    for i, (features, label) in enumerate(testing_set):\n        predictions[classifier.classify(features)].add(i)\n        gold_labels[label].add(i)\n    for label in predictions:\n        print(label, 'Precision:', precision(gold_labels[label], predictions[label]))\n        print(label, 'Recall:', recall(gold_labels[label], predictions[label]))\n        print(label, 'F1-Score:', f_measure(gold_labels[label], predictions[label]))\n        print()\n    \n    MNB_classifier = SklearnClassifier(MultinomialNB())\n    MNB_classifier.train(training_set)\n    print(\"MNB_classifier accuracy percent:\", (nltk.classify.accuracy(MNB_classifier, testing_set))*100)\n\n    BernoulliNB_classifier = SklearnClassifier(BernoulliNB())\n    BernoulliNB_classifier.train(training_set)\n    print(\"BernoulliNB_classifier accuracy percent:\", (nltk.classify.accuracy(BernoulliNB_classifier, testing_set))*100)\n\n    LogisticRegression_classifier = SklearnClassifier(LogisticRegression(max_iter = 150))\n    LogisticRegression_classifier.train(training_set)\n    print(\"LogisticRegression_classifier accuracy percent:\", (nltk.classify.accuracy(LogisticRegression_classifier, testing_set))*100)\n\n    SGDClassifier_classifier = SklearnClassifier(SGDClassifier())\n    SGDClassifier_classifier.train(training_set)\n    print(\"SGDClassifier_classifier accuracy percent:\", (nltk.classify.accuracy(SGDClassifier_classifier, testing_set))*100)\n\n    LinearSVC_classifier = SklearnClassifier(LinearSVC(max_iter=1200))\n    LinearSVC_classifier.train(training_set)\n    print(\"LinearSVC_classifier accuracy percent:\", (nltk.classify.accuracy(LinearSVC_classifier, testing_set))*100)'''\n    return lab\n\ndef main():\n    sex = bayes(\"essay4\", \"sex\")\n    age = bayes(\"essay4\", \"age\")\n    et = bayes(\"essay4\", \"ethnicity\")\n    \n    print(\"Intersection sex-age:\", sex.intersection(age))\n    print(\"Intersection et-age:\", et.intersection(age))\n    print(\"Intersection sex-et:\", sex.intersection(et))\n    print(\"Intersection all:\", et.intersection(sex.intersection(age)))\n\nif __name__ == '__main__':\n    main()","sub_path":"okcupidtest.py","file_name":"okcupidtest.py","file_ext":"py","file_size_in_byte":4051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"278008676","text":"from pajbot.apiwrappers.base import BaseAPI\n\n\nclass TwitchTMIAPI(BaseAPI):\n    def __init__(self):\n        super().__init__(base_url=\"https://tmi.twitch.tv/\")\n\n    def get_chatter_logins_by_login(self, login):\n        response = self.get([\"group\", \"user\", login, \"chatters\"])\n        all_chatters = []\n\n        for chatter_category in response[\"chatters\"].values():\n            all_chatters.extend(chatter_category)\n\n        return all_chatters\n","sub_path":"pajbot/apiwrappers/twitch/tmi.py","file_name":"tmi.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"246160262","text":"import numpy as np\nimport pandas as pd\n\nfrom bs4 import BeautifulSoup\nfrom nltk.corpus import stopwords\nfrom nltk.stem import PorterStemmer\n\nimport re\nfrom scipy.sparse import hstack\n\nfrom sklearn import preprocessing, model_selection, metrics\nfrom sklearn.feature_extraction.text import TfidfVectorizer, CountVectorizer\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.model_selection import train_test_split,cross_val_score\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.metrics import f1_score, make_scorer\n\nimport os\nprint(os.listdir(\"../input/\"))\n\n# Load Data\nprint(\"Loading data...\")\n\ntrain = pd.read_csv('../input/labeledTrainData.tsv', sep=\"\\t\")\nprint(\"Train shape:\", train.shape)\ntest = pd.read_csv('../input/testData.tsv', sep=\"\\t\")\nprint(\"Test shape:\", test.shape)\n\nsample = pd.read_csv('../input/sampleSubmission.csv', sep=\",\")\n\n\nprint(train.head())\nprint(test.head())\nprint(sample.head())\n\nprint(\"Value counts of sentiment class\", train['sentiment'].value_counts()) # balanced dataset\n\n# Check the first review\nprint('The first review is:\\n\\n',train[\"review\"][0])\n\n# clean description\nprint(\"Cleaning train data...\\n\")\ntrain['review'] = train['review'].map(lambda x: BeautifulSoup(x).get_text())\nprint(\"Cleaning test data...\")\ntest['review'] = test['review'].map(lambda x: BeautifulSoup(x).get_text())\n\n\n# function to clean data\n\nstops = set(stopwords.words(\"english\"))\ndef cleanData(text, lowercase = False, remove_stops = False, stemming = False):\n    txt = str(text)\n    txt = re.sub(r'[^A-Za-z0-9\\s]',r'',txt)\n    txt = re.sub(r'\\n',r' ',txt)\n\n    if lowercase:\n        txt = \" \".join([w.lower() for w in txt.split()])\n\n    if remove_stops:\n        txt = \" \".join([w for w in txt.split() if w not in stops])\n\n    if stemming:\n        st = PorterStemmer()\n        txt = \" \".join([st.stem(w) for w in txt.split()])\n\n    return txt\n\ny = train['sentiment']\n\n# Bag of Words (word based)\nctv_word = CountVectorizer(analyzer='word',token_pattern=r'\\w{1,}',min_df = 200, max_features=5000,\n            ngram_range=(1,2), stop_words = 'english')\n\nprint(\"Fitting Bag of Words Model on words...\\n\")\n# Fitting CountVectorizer to both training and test sets\nctv_word.fit(list(train['review']) + list(test['review']))\ntrain_ctv_word =  ctv_word.transform(train['review'])\ntest_ctv_word = ctv_word.transform(test['review'])\n\nprint(\"Fitting Bag of Words Model on characters...\\n\")\n\n# Bag of words (charater based)\nctv_char = TfidfVectorizer(sublinear_tf=True, strip_accents='unicode',analyzer='char',\n    stop_words='english', ngram_range=(2, 6), max_features=10000)\n\n# Fitting CountVectorizer to both training and test sets\nctv_char.fit(list(train['review']) + list(test['review']))\ntrain_ctv_char =  ctv_char.transform(train['review'])\ntest_ctv_char = ctv_char.transform(test['review'])\n\n# TF - IDF (words)\n\nprint(\"Fitting TF-IDF Model on words...\\n\")\ntfv_word = TfidfVectorizer(min_df=150,  max_features= 5000,\n            strip_accents='unicode', analyzer='word',token_pattern=r'\\w{1,}',\n            ngram_range=(1,2),\n            stop_words = 'english')\n\n# Fitting TF-IDF to both training and test sets (semi-supervised learning)\ntfv_word.fit(list(train['review']) + list(test['review']))\ntrain_tfv_word =  tfv_word.transform(train['review'])\ntest_tfv_word = tfv_word.transform(test['review'])\n\n# TF-IDF(char)\nprint(\"Fitting TF - IDF Model on characters...\\n\")\ntfv_char = TfidfVectorizer(sublinear_tf=True,strip_accents='unicode',analyzer='char',\n    stop_words='english',ngram_range=(2, 6),max_features=10000)\ntfv_char.fit(list(train['review']) + list(test['review']))\ntrain_tfv_char = tfv_char.transform(train['review'])\ntest_tfv_char = tfv_char.transform(test['review'])\n\nprint(\"Combining Bag of words for words and characters...\\n\")\n# bag of words for training set (words + char)\ntrain_bow = hstack([train_ctv_word, train_ctv_char])\ntest_bow = hstack([test_ctv_word, test_ctv_char])\n\nprint(\"Combining TF-IDF for words and characters...\\n\")\n\n# TF-IDF for test set (words + char)\ntrain_tfidf = hstack([train_tfv_word, train_tfv_char])\ntest_tfidf = hstack([test_tfv_word, test_tfv_char])\n\nclf_lr = LogisticRegression() # Logistic Regression Model\n\n## 5-fold cross validation\nprint(cross_val_score(clf_lr, train_tfidf, y, cv=5, scoring=make_scorer(f1_score)))\n\n\"\"\"\nWe can see that we are achieving an validation accuracy of 89%.\n\"\"\"\n# Fit the logistic regression model\nclf_lr.fit(train_tfidf,y)\n\n# Make predictions on test data\npreds = clf_lr.predict(test_tfidf)\n\n# Make submission\n\nsample['sentiment'] = preds\nsample = sample[['id','sentiment']]\nsample.to_csv('submissions.csv',index=False)\n","sub_path":"Bag of Words Meets Bag of Popcorn/scripts/bowandtfidf_v1.py","file_name":"bowandtfidf_v1.py","file_ext":"py","file_size_in_byte":4626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"556206113","text":"#!/usr/bin/env python3\n\nimport csv\nimport itertools as it\nimport os\nimport numpy as np\nfrom numpy.random import default_rng\nfrom sklearn.linear_model import Ridge, LinearRegression\nfrom sklearn.preprocessing import MinMaxScaler\nimport matplotlib.pyplot as plt\n\ndef main():\n    X, Y = get_wine_data()\n    # X, Y = get_random_data()\n    X, _ = normalize(X)\n    A, c = RR(X, Y)\n    print(pretty_function(c))\n    print('Q =', quality(Y, A))\n\n    alphas = []\n    qualitiesR = []\n#    qualitiesL = []\n#    qL = quality(Y, LR(X, Y)[0])\n    begin = 0.001\n    end = 1.0\n    steps = 100\n    for i in range(steps):\n        alpha = begin + (end - begin) / steps * i\n        alphas.append(alpha)\n        qualitiesR.append(quality(Y, RR(X, Y, alpha = alpha)[0]))\n#        qualitiesL.append(qL)\n    \n    plt.figure(figsize=(15, 10))\n    plt.plot(alphas, qualitiesR, color = 'magenta', label = 'Q over alpha')\n#    plt.plot(alphas, qualitiesL, color = 'cyan', label = 'Q for LL')\n    plt.legend(loc = 'upper right')\n    plt.show()\n\ndef get_wine_data():\n    # data_path = os.path.join(os.path.dirname(__file__), './LR_wine_data/winequality-red.csv')\n    data_path = os.path.join(os.path.dirname(__file__), './LR_wine_data/winequality-white.csv')\n    data_file = open(data_path,'r')\n    data_reader = csv.reader(data_file, delimiter=';')\n\n    header = data_reader.__next__()\n    # print('header: ', header)\n    wine_data = list(map(lambda c: [float(e) for e in c], data_reader))\n    data_file.close()\n\n    X = np.array(list(map(lambda c: c[:-1], wine_data)))\n    Y = np.array(list(map(lambda c: c[-1], wine_data)))\n    return X, Y\n\ndef get_random_data():\n    n_samples = 500\n    n_features = 5\n    n_redundant = 2\n    rng = default_rng()\n\n    Y = rng.random(n_samples)\n    X = np.zeros(shape = (n_samples, n_features + n_redundant))\n    base = rng.random((n_samples, n_features))\n    X[:n_samples, :n_features] = base\n\n    for i in range(n_redundant):\n        X[:, n_features + i] = X[:, i] + (-0.05 + rng.random(size = n_samples) * 0.1)\n\n    return X, Y\n\ndef quality(A, B):\n    n = len(A)\n\n    err = 0.0\n    for i in range(n):\n        err += pow(A[i] - B[i], 2)\n    \n    return err\n\ndef normalize(X):\n    scaler = MinMaxScaler()\n    scaler.fit(X)\n    return scaler.transform(X), scaler\n\ndef pretty_function(coefs):\n    res = 'y = '\n    res += ' + '.join([f'x{i+1} * {coefs[i]:.3f}' for i in range(len(coefs))])\n    return res\n\ndef RR(X, Y, alpha = 1.0):\n    model = Ridge(alpha = alpha, fit_intercept = False)\n    model.fit(X, Y)\n    \n    return model.predict(X), model.coef_\n\n# def LR(X, Y):\n\n#     model = LinearRegression()\n#     model.fit(X, Y)\n\n#     return model.predict(X), model.coef_\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"prac/RidgeRegression.py","file_name":"RidgeRegression.py","file_ext":"py","file_size_in_byte":2715,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"529931372","text":"import entities\r\nimport occ_grid\r\nimport math\r\n\r\n\r\ndef distance(rob, mon):\r\n   return math.sqrt((rob.position.x-mon.position.x)**2+(rob.position.y-mon.position.y)**2)\r\n\r\ndef farthest(robber, moneys):\r\n   far = robber\r\n   for entitie in moneys:\r\n      if distance(robber, entitie) > distance(robber, far):\r\n         far = entitie\r\n   return far\r\n\r\ndef nearest(robber, moneys):\r\n   near = entities.Money(\"Dollar\", entities.Point(100000000,100000000))\r\n   for entitie in moneys:\r\n      if distance(robber, entitie) < distance(robber, near):\r\n         near = entitie\r\n   return near\r\n\r\ndef makeRobberGo(robbers, Money):\r\n   if robbers.moving:\r\n      if robbers.far:\r\n         goto = robbers.wheretogo\r\n         determineNewGathererPosition(robbers, goto)\r\n      else:\r\n         goto = robbers.wheretogo\r\n         determineNewGathererPosition(robbers, goto)\r\n\r\ndef determineNewGathererPosition(Robbers, Money):\r\n   if Robbers.position.x < Money.position.x:\r\n      Robbers.position.x = Robbers.position.x + 1\r\n   elif Robbers.position.x > Money.position.x:\r\n      Robbers.position.x = Robbers.position.x - 1\r\n   else:\r\n      if Robbers.position.y < Money.position.y:\r\n         Robbers.position.y = Robbers.position.y + 1\r\n      elif Robbers.position.y > Money.position.y:\r\n         Robbers.position.y = Robbers.position.y - 1\r\n         \r\ndef robber_trail(grid):\r\n   for x in range(0, 59):\r\n      for y in range(0, 59):\r\n         if occ_grid.get_cell(grid, entities.Point(x, y)) == occ_grid.GATHERER:\r\n            occ_grid.set_cell(grid, entities.Point(x, y), occ_grid.TRAIL)","sub_path":"hw4/movement.py","file_name":"movement.py","file_ext":"py","file_size_in_byte":1568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"371055462","text":"#App Engine Models\n\nfrom google.appengine.ext import db\nimport string\n\nclass Person(db.Model):\n  \"\"\"Models a person in the RPI directory.\"\"\"\n  first_name      = db.StringProperty()\n  middle_name     = db.StringProperty()\n  last_name       = db.StringProperty()\n  department      = db.StringProperty()\n  email           = db.StringProperty()\n  rcsid           = db.StringProperty()\n  year            = db.StringProperty()\n  major           = db.StringProperty()\n  title           = db.StringProperty()\n  phone           = db.StringProperty()\n  fax             = db.StringProperty()\n  homepage        = db.StringProperty()\n  office_location = db.StringProperty(multiline=True)\n  campus_mailstop = db.StringProperty(multiline=True)\n  mailing_address = db.StringProperty(multiline=True)\n  date_crawled    = db.DateTimeProperty(auto_now=True)\n  directory_id    = 0\n  \n  @staticmethod\n  def buildPerson(d):\n    person = Person()\n    \n    if 'email' in d:\n      rcsid = string.rsplit(d['email'],'@',1)[0]\n      person = Person(key_name = rcsid)\n      person.rcsid = rcsid\n    elif 'name' in d:\n      person = Person(key_name = d['name'])\n      person.rcsid = d['name']\n    else:\n      # No name for the person, no point in making them\n      person = Person()\n      return person\n    \n    if 'name' in d:\n      names = d['name'].split()[:3]\n      if len(names) > 0:\n        person.first_name = names[0]\n      if len(names) > 1:\n        person.last_name = names[-1]\n      #if len(names) > 2:\n        #person.middle_name = names[1]\n    if 'email' in d:\n      person.email = d['email']\n      person.key_name = d['email']\n    if 'year' in d:\n      person.year = d['year']\n    if 'major' in d:\n      person.major = d['major']\n    if 'department' in d:\n      person.department = d['department']\n    if 'title' in d:\n      person.title = d['title']\n    if 'phone' in d:\n      person.phone = d['phone']\n    if 'fax' in d:\n      person.fax = d['fax']\n    if 'homepage' in d:\n      person.homepage = d['homepage']\n    if 'office_location' in d:\n      person.office_location = d['office_location']\n    if 'campus_mailstop' in d:\n      person.campus_mailstop = d['campus_mailstop']\n    if 'mailing_address' in d:\n      person.mailing_address = d['mailing_address']\n    if 'directory_id' in d:\n      person.directory_id = d['directory_id']\n    \n    return person\n  \n  @staticmethod\n  def buildMap(p):\n    \n    d = {}\n    \n    if p.email is not None:\n      d['email'] = p.email\n    if p.rcsid is not None:\n      d['rcsid'] = p.rcsid\n    \n    name = ''\n    if p.first_name is not None:\n      name += p.first_name.title()\n    #if p.middle_name is not None:\n      #name += ' ' + p.middle_name.title()\n    if p.last_name is not None:\n      name += ' ' + p.last_name.title()\n    if name is not \"\":\n      d['name'] = name\n    \n    if p.year is not None:\n      d['year'] = p.year.title()\n    \n    if p.major is not None:\n      d['major'] = p.major.title()\n    \n    if p.department is not None:\n      d['department'] = p.department.title()\n    \n    if p.title is not None:\n      d['title'] = p.title.title()\n    \n    if p.phone is not None:\n      d['phone'] = p.phone.title()\n    \n    if p.fax is not None:\n      d['fax'] = p.fax.title()\n    \n    if p.homepage is not None:\n      d['homepage'] = p.homepage.title()\n    \n    if p.office_location is not None:\n      d['office_location'] = p.office_location.title()\n    \n    if p.campus_mailstop is not None:\n      d['campus_mailstop'] = p.campus_mailstop.title()\n    \n    if p.mailing_address is not None:\n      d['mailing_address'] = p.mailing_address.title()\n    \n    if p.directory_id is not None:\n      d['directory_id'] = p.directory_id\n    \n    return d\n  \n  \n\nclass SearchPosition(db.Model):\n  \"\"\"Model to store Crawler position.\"\"\"\n  position = db.IntegerProperty()\n\n#class DepartmentKeyword(db.Model):\n  #\"\"\"Model to store a single work from a major, for searching purposes\"\"\"\n  #departments = db.ListProperty\n  \n  #@staticmethod\n  #def buildKeywords(s):\n    #for word in s.split():\n      #d = DepartmentKeyword.get_by_key_name(word)\n      #if not d:\n        #w = DepartmentKeyword(key_name = word)\n        #w.department = [s]\n        #w.put()\n      #elif s not in d.departments:\n        #d.departments.append(s)\n        #d.put()","sub_path":"appengine/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":4256,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"109411359","text":"__author__ = 'ma0722'\nclass Solution:\n\n    def print_range(self, nums, start, end):\n        if start != end:\n            return str(nums[start])+'->'+str(nums[end])\n        else:\n            return str(nums[start])\n\n    # @param {integer[]} nums\n    # @return {string[]}\n    def summaryRanges(self, nums):\n        list_range = []\n        start = 0\n        end = 0\n        if len(nums)-1 == 0:\n                list_range.append(self.print_range(nums,0,0))\n        for i in range(0, len(nums)-1):\n            if nums[i+1] != nums[i]+1:\n                end = i\n                list_range.append(self.print_range(nums,start,end))\n                start = i+1\n            if i == len(nums)-2:\n                end = len(nums)-1\n                list_range.append(self.print_range(nums,start,end))\n        return list_range\n\nif __name__ == '__main__':\n    s = Solution()\n    print(s.summaryRanges([-1,0]))","sub_path":"ok_p_228.py","file_name":"ok_p_228.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"192992247","text":"__author__ = 'Administrator'\nimport cv2\nimport numpy as np\n\nfn=\"620.jpg\"\n\ndef get_EuclideanDistance(x,y):\n    myx = np.array(x)\n    myy = np.array(y)\n    sum = np.sqrt(np.sum(myx-myy)*(myx-myy))\n    return sum\n\nif __name__ == '__main__':\n\n    print('loading %s ....'%fn)\n    print('working')\n    myimg1 = cv2.imread(fn)\n    w = myimg1.shape[1]\n    h = myimg1.shape[0]\n    sz1=w\n    sz0=h\n\n    myimg2 = np.zeros((sz0,sz1,3),np.uint8)\n\n    black = np.array([0,0,0])\n    white = np.array([255,255,255])\n    centercolor = np.array([125,125,125])\n    for y in xrange(0,sz0-1):\n        for x in xrange(0,sz1-1):\n\n            mydown = myimg1[y+1,x,:]\n            myright= myimg1[y,x+1,:]\n\n            myhere = myimg1[y,x,:]\n            lmyhere = myhere\n            lmyright = myright\n            lmydown = mydown\n            if  ((get_EuclideanDistance(lmyhere,lmydown)>16)-(get_EuclideanDistance(lmyhere,lmyright)>16)).all():\n                myimg2[y,x:] = black\n            elif ((get_EuclideanDistance(lmyhere,lmydown)<=16)-(get_EuclideanDistance(lmyhere,lmyright)<=16)).all():\n                myimg2[y,x,:] = white\n            else:\n                myimg2[y,x,:] = centercolor\n\n            print('.')\n\n    cv2.namedWindow('img2')\n    cv2.imshow('img2',myimg2)\n    cv2.waitKey()\n    cv2.destoryAllWindows()\n\n\n\n\n\n\n\n\n\n","sub_path":"remote_cal/org/xu/img/border/blkandwht.py","file_name":"blkandwht.py","file_ext":"py","file_size_in_byte":1312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"307344744","text":"\nimport numpy as np\nimport chainer as ch\n\nclass Model:\n    '''\n    Base class for model objects.\n    '''\n\n    def __init__(self, name=None):\n        self.name = name\n\n    def l_imp(self, w=None, X=None, y=None, lamreg=None):\n        return None\n    \n    def l_tr(self, w, data, n_idx=None, lamreg=None):\n        if n_idx is None:\n            return self.l_imp(w=w, X=data.X_tr,\n                              y=data.y_tr,\n                              lamreg=lamreg)\n        else:\n            return self.l_imp(w=w, X=data.X_tr[n_idx,:],\n                              y=data.y_tr[n_idx,:],\n                              lamreg=lamreg)\n    \n    def l_te(self, w, data, n_idx=None, lamreg=None):\n        if n_idx is None:\n            return self.l_imp(w=w, X=data.X_te,\n                              y=data.y_te,\n                              lamreg=lamreg)\n        else:\n            return self.l_imp(w=w, X=data.X_te[n_idx,:],\n                              y=data.y_te[n_idx,:],\n                              lamreg=lamreg)\n\n    def g_imp(self, w=None, X=None, y=None, lamreg=None):\n        return None\n    \n    def g_tr(self, w, data, n_idx=None, lamreg=None):\n        if n_idx is None:\n            return self.g_imp(w=w, X=data.X_tr,\n                              y=data.y_tr,\n                              lamreg=lamreg)\n        else:\n            return self.g_imp(w=w, X=data.X_tr[n_idx,:],\n                              y=data.y_tr[n_idx,:],\n                              lamreg=lamreg)\n    \n    def g_te(self, w, data, n_idx=None, lamreg=None):\n        if n_idx is None:\n            return self.g_imp(w=w, X=data.X_te,\n                              y=data.y_te,\n                              lamreg=lamreg)\n        else:\n            return self.g_imp(w=w, X=data.X_te[n_idx,:],\n                              y=data.y_te[n_idx,:],\n                              lamreg=lamreg)\n\n\nclass LinReg(Model):\n    '''\n    General-purpose linear regression model.\n    No losses are implemented, just a predict()\n    method.\n    '''\n    \n    def __init__(self, data=None, name=None):\n        super(LinReg, self).__init__(name=name)\n        pass\n\n    def predict(self, w, X):\n        '''\n        Predict real-valued response.\n        w is a (d x 1) array of weights.\n        X is a (k x d) matrix of k observations.\n        Returns array of shape (k x 1) of predicted values.\n        '''\n        return X.dot(w)\n\n\nclass LinearL2(LinReg):\n    '''\n    Orthodox linear regression model, using squared\n    error and regularization via the l2 norm.\n    '''\n    \n    def __init__(self, data=None):\n        super(LinearL2,self).__init__(data=data)\n\n        \n    def l_imp(self, w, X, y, lamreg=None):\n        '''\n        Input:\n        w is a (d x 1) matrix of weights.\n        X is a (k x numfeat) matrix of k observations.\n        y is a (k x 1) matrix of labels in {-1,1}.\n        lamreg is a regularization parameter (l2 penalty).\n\n        Output:\n        A vector of length k with losses evaluated at k points.\n        '''\n        if lamreg is None:\n            return (y-self.predict(w=w,X=X))**2/2\n        else:\n            penalty = lamreg * np.linalg.norm(w)**2\n            return (y-self.predict(w=w,X=X))**2/2 + penalty\n    \n    \n    def g_imp(self, w, X, y, lamreg=None):\n        '''\n        Input:\n        w is a (d x 1) matrix of weights.\n        X is a (k x numfeat) matrix of k observations.\n        y is a (k x 1) matrix of labels in {-1,1}.\n\n        Output:\n        A (k x numfeat) matrix of gradients evaluated\n        at k points.\n        '''\n        if lamreg is None:\n            return (y-self.predict(w=w,X=X))*(-1)*X\n        else:\n            penalty = lamreg*2*w.T\n            return (y-self.predict(w=w,X=X))*(-1)*X + penalty\n\nclass LinearL1(LinReg):\n    '''\n    Orthodox linear regression model, using squared\n    error and regularization via the l1 norm. Good for\n    realizing sparsity without giving up convexity.\n    '''\n    \n    def __init__(self, data=None):\n        super(LinearL1,self).__init__(data=data)\n\n        \n    def l_imp(self, w, X, y, lamreg=None):\n        '''\n        Input:\n        w is a (d x 1) matrix of weights.\n        X is a (k x numfeat) matrix of k observations.\n        y is a (k x 1) matrix of labels in {-1,1}.\n        lamreg is a regularization parameter (l2 penalty).\n\n        Output:\n        A vector of length k with losses evaluated at k points.\n        '''\n        if lamreg is None:\n            return (y-self.predict(w=w,X=X))**2/2\n        else:\n            penalty = lamreg * np.abs(w).sum()\n            return (y-self.predict(w=w,X=X))**2/2 + penalty\n\n        \n    def g_j_imp(self, j, w, X, y, lamreg=None):\n\n        if lamreg is None:\n            return (y-self.predict(w=w,X=X))*(-1)*np.take(a=X,\n                                                          indices=[j],\n                                                          axis=1)\n        else:\n            penalty = lamreg * np.sign(w[j,0])\n            return (y-self.predict(w=w,X=X))*(-1)*np.take(a=X,\n                                                          indices=[j],\n                                                          axis=1) + penalty\n        \n    def g_j_tr(self, j, w, data, n_idx=None, lamreg=None):\n        if n_idx is None:\n            return self.g_j_imp(j=j, w=w, X=data.X_tr,\n                                y=data.y_tr,\n                                lamreg=lamreg)\n        else:\n            return self.g_j_imp(j=j, w=w, X=data.X_tr[n_idx,:],\n                                y=data.y_tr[n_idx,:],\n                                lamreg=lamreg)\n    \n    def g_j_te(self, j, w, data, n_idx=None, lamreg=None):\n        if n_idx is None:\n            return self.g_j_imp(j=j, w=w, X=data.X_te,\n                                y=data.y_te,\n                                lamreg=lamreg)\n        else:\n            return self.g_j_imp(j=j, w=w, X=data.X_te[n_idx,:],\n                                y=data.y_te[n_idx,:],\n                                lamreg=lamreg)\n\n\nimport models\n\nclass Classifier(models.Model):\n    '''\n    Generic classifier model, an object with methods\n    for both training and evaluating classifiers.\n    '''\n\n    def __init__(self, data=None, name=None):\n        super(Classifier, self).__init__(name=name)\n\n        # If given data, collect information about labels.\n        if data is not None:\n            self.labels = self.get_labels(data=data) # all unique labels.\n            self.nc = self.labels.size # number of unique labels.\n\n\n    def onehot(self, y):\n        '''\n        A function for encoding y into a one-hot vector.\n        Inputs:\n        - y is a (k,1) array, taking values in {0,1,...,nc-1}.\n        '''\n        nc = self.nc\n        k = y.shape[0]\n        C = np.zeros((k,nc), dtype=y.dtype)\n\n        for i in range(k):\n            j = y[i,0] # assumes y has only one column.\n            C[i,j] = 1\n\n        return C\n        \n\n    def get_labels(self, data):\n        '''\n        Get all the (unique) labels that appear in the data.\n        '''\n        A = (data.y_tr is None)\n        B = (data.y_te is None)\n\n        if (A and B):\n            raise ValueError(\"No label data provided!\")\n        else:\n            if A:\n                out_labels = np.unique(data.y_te)\n            elif B:\n                out_labels = np.unique(data.y_tr)\n            else:\n                out_labels = np.unique(np.concatenate((data.y_tr,\n                                                       data.y_te), axis=0))\n            count = out_labels.size\n            return out_labels.reshape((count,1))\n\n\n    def classify(self, X):\n        '''\n        Returns None by default.\n        Must be implemented by sub-classes.\n        '''\n        return None\n\n\n    def class_perf(self, y_est, y_true):\n        '''\n        Given class label estimates and true values,\n        compute the fraction of correct classifications\n        made for each label, yielding typical binary\n        classification performance metrics.\n\n        Input:\n        y_est and y_true are (k x 1) matrices of labels.\n\n        Output:\n        Returns a dictionary with two components, (1) being\n        the fraction of correctly classified labels, and\n        (2) being a dict of per-label precison/recall/F1\n        scores. \n        '''\n        \n        # First, get the classification rate.\n        k = y_est.size\n        num_correct = (y_est == y_true).sum()\n        frac_correct = num_correct / k\n        frac_incorrect = 1.0 - frac_correct\n\n        # Then, get precision/recall for each class.\n        prec_rec = { i:None for i in range(self.nc) } # initialize\n\n        for c in range(self.nc):\n\n            idx_c = (y_true == c)\n            idx_notc = (idx_c == False)\n\n            TP = (y_est[idx_c] == c).sum()\n            FN = idx_c.sum() - TP\n            FP = (y_est[idx_notc] == c).sum()\n            TN = idx_notc.sum() - FP\n\n            # Precision.\n            if (TP == 0 and FP == 0):\n                prec = 0\n            else:\n                prec = TP / (TP+FP)\n\n            # Recall.\n            if (TP == 0 and FN == 0):\n                rec = 0\n            else:\n                rec = TP / (TP+FN)\n\n            # F1 (harmonic mean of precision and recall).\n            if (prec == 0 or rec == 0):\n                f1 = 0\n            else:\n                f1 = 2 * prec * rec / (prec + rec)\n\n            prec_rec[c] = {\"P\": prec,\n                           \"R\": rec,\n                           \"F1\": f1}\n\n        return {\"rate\": frac_incorrect,\n                \"PRF1\": prec_rec}\n\n\nclass LogisticReg(Classifier):\n    '''\n    Multi-class logistic regression model.\n    '''\n\n    def __init__(self, data=None):\n        \n        # Given data info, load up the (X,y) data.\n        super(LogisticReg, self).__init__(data=data)\n        \n        # Convert original labels to a one-hot binary representation.\n        if data.y_tr is not None:\n            self.C_tr = self.onehot(y=data.y_tr)\n        if data.y_te is not None:\n            self.C_te = self.onehot(y=data.y_te)\n        \n        \n    def classify(self, w, X):\n        '''\n        Given learned weights (w) and a matrix of one or\n        more observations, classify them as {0,...,nc-1}.\n\n        Input:\n        w is a (d x 1) matrix of weights.\n        X is a (k x numfeat) matrix of k observations.\n        NOTE: k can be anything, the training/test sample size.\n\n        Output:\n        A vector of length k, housing labels in {0,...,nc-1}.\n        '''\n        \n        k, numfeat = X.shape\n        A = np.zeros((self.nc,k), dtype=np.float64)\n        \n        # Get activations, with last row as zeros.\n        A[:-1,:] = w.reshape((self.nc-1, numfeat)).dot(X.T)\n        \n        # Now convert activations to conditional probabilities.\n        A = np.exp(A)\n        A = A / A.sum(axis=0)  # (nc x k).\n        \n        # Assign classes with highest probability, (k x 1) array.\n        return A.argmax(axis=0).reshape((k,1))\n\n\n    def l_imp(self, w, X, C, lamreg=None):\n        '''\n        Implementation of the multi-class logistic regression\n        loss function.\n\n        Input:\n        w is a (d x 1) matrix of weights.\n        X is a (k x numfeat) matrix of k observations.\n        C is a (k x nc) matrix giving a binarized encoding of the\n        class labels for each observation; each row a one-hot vector.\n        lam is a non-negative regularization parameter.\n        NOTE: k can be anything, the training/test sample size.\n\n        Output:\n        A vector of length k with losses evaluated at k points.\n        '''\n        \n        k, numfeat = X.shape\n        A = np.zeros((self.nc,k), dtype=np.float64)\n        \n        # Get activations, with last row as zeros.\n        A[:-1,:] = w.reshape((self.nc-1, numfeat)).dot(X.T)\n        \n        # Raw activations of all the correct weights.\n        cvec = (A*C.T).sum(axis=0)\n        \n        # Compute the negative log-likelihoods.\n        err = np.log(np.exp(A).sum(axis=0))-cvec\n\n        # Return the losses (all data points), with penalty if needed.\n        if lamreg is None:\n            return err\n        else:\n            penalty = lamreg * np.linalg.norm(W)**2\n            return err + penalty\n    \n    \n    def l_tr(self, w, data, n_idx=None, lamreg=None):\n        if n_idx is None:\n            return self.l_imp(w=w, X=data.X_tr,\n                              C=self.C_tr,\n                              lamreg=lamreg)\n        else:\n            return self.l_imp(w=w, X=data.X_tr[n_idx,:],\n                              C=self.C_tr[n_idx,:],\n                              lamreg=lamreg)\n    \n    def l_te(self, w, data, n_idx=None, lamreg=None):\n        if n_idx is None:\n            return self.l_imp(w=w, X=data.X_te,\n                              C=self.C_te,\n                              lamreg=lamreg)\n        else:\n            return self.l_imp(w=w, X=data.X_te[n_idx,:],\n                              C=self.C_te[n_idx,:],\n                              lamreg=lamreg)\n        \n        \n    def g_imp(self, w, X, C, lamreg=0):\n        '''\n        Implementation of the gradient of the loss function used in\n        multi-class logistic regression.\n\n        Input:\n        w is a (d x 1) matrix of weights.\n        X is a (k x numfeat) matrix of k observations.\n        C is a (k x nc) matrix giving a binarized encoding of the\n        class labels for each observation; each row a one-hot vector.\n        lamreg is a non-negative regularization parameter.\n        NOTE: k can be anything, the training/test sample size.\n\n        Output:\n        A (k x d) matrix of gradients eval'd at k points.\n        '''\n        \n        k, numfeat = X.shape\n        A = np.zeros((self.nc,k), dtype=np.float64)\n        \n        # Get activations, with last row as zeros.\n        A[:-1,:] = w.reshape((self.nc-1, numfeat)).dot(X.T)\n        \n        # Now convert activations to conditional probabilities.\n        A = np.exp(A)\n        A = A / A.sum(axis=0) # (nc x k).\n        A = np.float32(A)\n        \n        # Initialize a large matrix (k x d) to house per-point grads.\n        G = np.zeros((k,w.size), dtype=w.dtype)\n\n        for i in range(k):\n            # A very tall vector (i.e., just one \"axis\").\n            G[i,:] = np.kron(a=(A[:-1,i]-C[i,:-1]), b=X[i,:])\n            # Note we carefully remove the last elements.\n        \n        if lamreg is None:\n            return G\n        else:\n            return G + lamreg*2*w.T\n        \n\n    def g_tr(self, w, data, n_idx=None, lamreg=None):\n        if n_idx is None:\n            return self.g_imp(w=w, X=data.X_tr,\n                              C=self.C_tr,\n                              lamreg=lamreg)\n        else:\n            return self.g_imp(w=w, X=data.X_tr[n_idx,:],\n                              C=self.C_tr[n_idx,:],\n                              lamreg=lamreg)\n    \n    def g_te(self, w, data, n_idx=None, lamreg=None):\n        if n_idx is None:\n            return self.g_imp(w=w, X=data.X_te,\n                              C=self.C_te,\n                              lamreg=lamreg)\n        else:\n            return self.g_imp(w=w, X=data.X_te[n_idx,:],\n                              C=self.C_te[n_idx,:],\n                              lamreg=lamreg)\n\n\n# Chainer-related models.\n\n# FunctionNode sub-class, for implementing a fully connected linear layer.\n\nclass LinearFunction(ch.function_node.FunctionNode):\n    '''\n    FunctionNode object, defined on Variable objects,\n    which is the basis for a linear transformation to\n    be wrapped up as a Link object.\n    \n    Take d-dimensional inputs x, and using array W of\n    shape (k,d), map this input to k outputs. That is,\n    we have a fully-connected layer with d input units\n    and k output units.\n    '''\n    \n    def forward(self, inputs):\n        '''\n        Forward computation for both CPU and GPU.\n        '''\n        \n        # Unpack the tuple of inputs.\n        if len(inputs) == 3:\n            x, W, b = inputs\n        else:\n            (x, W), b = inputs, None\n\n        y = x.dot(W.T).astype(x.dtype, copy=False)\n        \n        # Add a bias term, if relevant.\n        if b is not None:\n            y += b\n            \n        # Since backward() depends only on x and W,\n        # we need only retain these two.\n        self.retain_inputs((0,1))\n        \n        # Must return the output as a tuple.\n        return (y,)\n\n    def backward(self, indices, grad_outputs):\n        '''\n        General-purpose computation for both CPU/GPU.\n        '''\n        \n        x, W = self.get_retained_inputs()\n        gy, = grad_outputs # written as gamma in their docs.\n        \n        # Docs say that backward() must return a tuple, but\n        # looking at their source code for linear.py, it\n        # seems like lists are fine.\n        out = []\n        if 0 in indices:\n            gx = gy @ W\n            out.append(ch.functions.cast(gx, x.dtype))\n        if 1 in indices:\n            gW = gy.T @ x\n            out.append(ch.functions.cast(gW, W.dtype))\n        if 2 in indices:\n            # Summing here is simple: for n observations,\n            # gy has shape (n,k), where k is the number of\n            # layer outputs. Summing over axis=0 is summing\n            # over OBSERVATIONS, not over outputs.\n            gb = ch.functions.sum(gy, axis=0)\n            \n        # Return just the relevant gradients we appended.\n        return out\n\n\ndef linear(x, W, b):\n    '''\n    A nice thin wrapper for our linear FunctionNode on\n    Variable objects.\n    '''\n    \n    if b is None:\n        args = (x, W)\n    else:\n        args = (x, W, b)\n    \n    # Don't forget to unpack from the tuple.\n    y, = LinearFunction().apply(args)\n    return y\n\n\n# Link object for our linear FunctionNode.\n\nclass Linear(ch.Link):\n    '''\n    A Link class for our linear transformation, implemented\n    in the LinearFunction class.\n    '''\n    def __init__(self,\n                 in_size, out_size,\n                 init_W=None, init_b=None,\n                 init_delta=None,\n                 nobias=False):\n        super(Linear, self).__init__()\n        \n        # Here we initialize and \"register\" the parameters\n        # of interest. This is critical because when we\n        # call __call__(x) and apply the underlying affine\n        # transformations to input x (both forward pass and\n        # backward pass), the optimization algorithms knows\n        # that we want to optimize W and maybe b, but not x.\n\n        with self.init_scope():\n            \n            # If provided an ndarray, use it.\n            if init_W is not None:\n                self.W = ch.Parameter(initializer=np.copy(init_W))\n            \n            # Else, use a built-in initializer.\n            else:\n                W_initializer = ch.initializers.Uniform(scale=init_delta,\n                                                        dtype=np.float32)\n                self.W = ch.Parameter(initializer=W_initializer,\n                                      shape=(out_size, in_size))\n            \n            if nobias:\n                self.b = None\n            else:\n                if init_b is not None:\n                    self.b = ch.Parameter(initializer=np.copy(init_b))\n                else:\n                    self.b = ch.Parameter(initializer=0,\n                                          shape=(out_size,))\n                \n    def __call__(self, x):\n        '''\n        This method actually applies the linear layer to\n        inputs x.\n        '''\n        return linear(x, self.W, self.b)\n\n\n# Chain object, composed of Link objects. This is a proper model in the\n# sense that it can be fed to optimizers.\n\nclass MyChain(ch.Chain):\n    '''\n    Perhaps the simplest possible model, a\n    feed-forward neural network without any\n    hidden layers. Just one, fully-connected\n    linear layer, aka a classical linear model.\n    \n    out_l0: number of outputs from layer 0.\n    out_l1: number of outputs from layer 1.\n    '''\n    \n    def __init__(self,\n                 out_l0,\n                 out_l1,\n                 init_W=None, init_b=None,\n                 init_delta=1.0,\n                 nobias=False):\n        super(MyChain, self).__init__()\n        \n        with self.init_scope():\n            self.l1 = Linear(in_size=out_l0,\n                             out_size=out_l1,\n                             init_W=init_W,\n                             init_b=init_b,\n                             init_delta=init_delta,\n                             nobias=True)\n\n    def __call__(self, x):\n        return self.l1(x) # parameters are managed by Links.\n","sub_path":"models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":20505,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"310143035","text":"import tensorflow as tf\nimport numpy as np\nfrom DQN import DQN\n\nclass DoubleDQN(DQN):\n    def __init__(\n        self,\n        n_actions,\n        n_features,\n        sess,\n        learning_rate=0.01,\n        reward_decay=0.9,\n        e_greedy=0.9,\n        replace_target_iter=300,\n        memory_size=500,\n        batch_size=32,\n        e_greedy_increment=None,\n        store_q = False\n    ):\n        super(DoubleDQN, self). __init__(\n            n_actions,\n            n_features,\n            sess,\n            learning_rate,\n            reward_decay,\n            e_greedy,\n            replace_target_iter,\n            memory_size,\n            batch_size,\n            e_greedy_increment,\n            store_q\n        )\n        \n    def learn(self):\n        s, a, r, s_ = self.sample()\n        if self.learn_step_counter % self.replace_target_iter == 0:\n            self.sess.run(self.replace_target_op)\n            print('\\ntarget_params_replaced\\n')\n\n        q_next, q_eval = self.sess.run(\n            [self.q_next, self.q_eval],\n            feed_dict={self.s_: s_, self.s: s}\n        )\n        q_target = q_eval.copy()\n\n        batch_index = np.arange(self.batch_size)\n        q_next_new = self.sess.run(self.q_eval, feed_dict={self.s: s_})\n        max_actions = np.argmax(q_next_new, axis=1)\n        q_target[batch_index, a] = r + self.gamma * q_next[batch_index, max_actions]\n\n        _, self.cost = self.sess.run([self._train_op, self.loss], feed_dict={self.s: s, self.q_target: q_target})\n\n        self.epsilon = self.epsilon + self.epsilon_increment if self.epsilon < self.epsilon_max else self.epsilon_max\n        self.learn_step_counter += 1\n\n\n\nif __name__ == '__main__':\n    import gym\n    import matplotlib.pyplot as plt\n\n    env = gym.make(\"Pendulum-v0\")\n    env.seed(1)\n    MEMORY_SIZE = 3000\n    ACTION_SPACE = 11\n\n    sess = tf.Session()\n    with tf.variable_scope(\"Natural_DQN\"):\n        natural_DQN = DQN(\n            n_actions=ACTION_SPACE,\n            n_features=env.observation_space.shape[0],\n            memory_size=MEMORY_SIZE,\n            e_greedy_increment=0.001,\n            sess=sess,\n            store_q = True\n        )\n\n    with tf.variable_scope(\"Double_DQN\"):\n        double_DQN = DoubleDQN(\n            n_actions=ACTION_SPACE,\n            n_features=env.observation_space.shape[0],\n            memory_size=MEMORY_SIZE,\n            e_greedy_increment=0.001,\n            sess=sess,\n            store_q=True\n        )\n    sess.run(tf.global_variables_initializer())\n\n    def train(learner):\n        total_steps = 0\n        s = env.reset()\n        \n        while True:\n            # env.render()\n\n            a = learner.choose_action(s)\n            f_action = (a - (ACTION_SPACE - 1) / 2) / ((ACTION_SPACE - 1) / 4)\n            s_new, reward, done, info = env.step(np.array([f_action]))\n\n            reward /= 10\n\n            learner.store_transition(s, a, reward, s_new)\n\n            if total_steps > MEMORY_SIZE:\n                learner.learn()\n\n            if total_steps - MEMORY_SIZE > 20000:\n                break\n\n            s = s_new\n            total_steps += 1\n\n        return learner.q\n\n    q_natural = train(natural_DQN)\n    q_double = train(double_DQN)\n\n    plt.plot(np.array(q_natural), c='r', label='natural')\n    plt.plot(np.array(q_double), c='b', label='double')\n    plt.legend(loc='best')\n    plt.ylabel('Q eval')\n    plt.xlabel('training steps')\n    plt.grid()\n    plt.show()","sub_path":"Morvan_tutorial/rl/dqn/DDQN.py","file_name":"DDQN.py","file_ext":"py","file_size_in_byte":3426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"579799529","text":"r\"\"\"\nGROMACS 2018.2\n\nContents:\n  Ubuntu 16.04\n  CUDA version 9.0\n  GNU compilers (upstream)\n  OFED (upstream)\n  OpenMPI version 3.0.0\n\"\"\"\n# pylint: disable=invalid-name, undefined-variable, used-before-assignment\n# pylama: ignore=E0602\nimport os\nfrom hpccm.templates.CMakeBuild import CMakeBuild\nfrom hpccm.templates.git import git\n\ngromacs_version = USERARG.get('GROMACS_VERSION', '2018.2')\n\nStage0 += comment(__doc__.strip(), reformat=False)\nStage0.name = 'devel'\nStage0 += baseimage(image='nvidia/cuda:9.0-devel-ubuntu16.04', _as=Stage0.name)\n\nStage0 += python(python3=False)\n\nStage0 += gnu(fortran=False)\n\nStage0 += packages(ospackages=['ca-certificates', 'cmake', 'git'])\n\nStage0 += ofed()\n\nStage0 += openmpi(configure_opts=['--enable-mpi-cxx'],\n               prefix=\"/opt/openmpi\", version='3.0.0')\n\ncm = CMakeBuild()\nbuild_cmds = [git().clone_step(\n                  repository='https://github.com/gromacs/gromacs',\n                  branch='v' + gromacs_version, path='/gromacs',\n                  directory='src'),\n              cm.configure_step(directory='/gromacs/src',\n                                build_directory='/gromacs/build',\n                                opts=[\n                                  '-DCMAKE_BUILD_TYPE=Release',\n                                  '-DCMAKE_INSTALL_PREFIX=/gromacs/install',\n                                  '-DCUDA_TOOLKIT_ROOT_DIR=/usr/local/cuda',\n                                  '-DGMX_BUILD_OWN_FFTW=ON',\n                                  '-DGMX_GPU=ON',\n                                  '-DGMX_MPI=OFF',\n                                  '-DGMX_OPENMP=ON',\n                                  '-DGMX_PREFER_STATIC_LIBS=ON',\n                                  '-DMPIEXEC_PREFLAGS=--allow-run-as-root',\n                                  '-DREGRESSIONTEST_DOWNLOAD=ON']),\n              cm.build_step(),\n              cm.build_step(target='install'),\n              cm.build_step(target='check')]\nStage0 += shell(commands=build_cmds)\n\n# Include examples if they exist in the build context\nif os.path.isdir('recipes/gromacs/examples'):\n    Stage0 += copy(src='recipes/gromacs/examples', dest='/workspace/examples')\n\nStage0 += environment(variables={'PATH': '$PATH:/gromacs/install/bin'})\n\nStage0 += label(metadata={'com.nvidia.gromacs.version': gromacs_version})\n\nStage0 += workdir(directory='/workspace')\n\n######\n# Runtime image stage\n######\nStage1.baseimage('nvidia/cuda:9.0-runtime-ubuntu16.04')\n\nStage1 += Stage0.runtime(_from=Stage0.name)\n\nStage1 += copy(_from=Stage0.name, src='/gromacs/install',\n               dest='/gromacs/install')\n\n# Include examples if they exist in the build context\nif os.path.isdir('recipes/gromacs/examples'):\n    Stage1 += copy(src='recipes/gromacs/examples', dest='/workspace/examples')\n\nStage1 += environment(variables={'PATH': '$PATH:/gromacs/install/bin'})\n\nStage1 += label(metadata={'com.nvidia.gromacs.version': gromacs_version})\n\nStage1 += workdir(directory='/workspace')\n","sub_path":"recipes/gromacs/gromacs.py","file_name":"gromacs.py","file_ext":"py","file_size_in_byte":2969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"217999219","text":"import os\nimport logging\n\nfrom rcGlobalEnv import rcEnv\nimport rcExceptions as ex\nimport rcStatus\nimport resources as Res\nimport time\nimport datetime\nimport resSyncSymclone as symclone\nfrom rcUtilities import which\n\nclass syncSymclone(symclone.syncSymclone):\n    def dev_rescan(self, dev):\n        dev = dev.replace('/dev/', '')\n        sysdev = \"/sys/block/%s/device/rescan\"%dev\n        self.log.info(\"echo 1>%s\"%sysdev)\n        with open(sysdev, 'w') as s:\n            s.write(\"1\")\n\n    def refresh_multipath(self, dev):\n        if which(rcEnv.syspaths.multipath) is None:\n            return\n        cmd = [rcEnv.syspaths.multipath, '-v0', '-r', dev]\n        (ret, out, err) = self.vcall(cmd)\n        if ret != 0:\n            raise ex.excError\n\n    def dev_ready(self, dev):\n        cmd = ['sg_turs', dev]\n        (ret, out, err) = self.vcall(cmd)\n        if ret != 0:\n            return False\n        return True\n\n    def wait_for_dev_ready(self, dev):\n        delay = 1\n        timeout = 5\n        for i in range(timeout/delay):\n            if self.dev_ready(dev):\n                return\n            if i == 0:\n                self.log.info(\"waiting for device %s to become ready (max %i secs)\"%(dev,timeout))\n            time.sleep(delay)\n        self.log.error(\"timed out waiting for device %s to become ready (max %i secs)\"%(dev,timeout))\n        raise ex.excError\n\n    def wait_for_devs_ready(self):\n        for pair in self.pairs:\n            src, dst = self.split_pair(pair)\n            dev = self.showdevs_etree[dst].find('Dev_Info/pd_name').text\n            if \"Not Visible\" in dev:\n                raise ex.excError(\"pd name is 'Not Visible'. please scan scsi buses and run symcfg discover\")\n            self.dev_rescan(dev)\n            self.wait_for_dev_ready(dev)\n            self.refresh_multipath(dev)\n\n    def __init__(self,\n                 rid=None,\n                 type=\"sync.symclone\",\n                 symid=None,\n                 pairs=[],\n                 precopy=True,\n                 consistent=True,\n                 **kwargs):\n        symclone.syncSymclone.__init__(self,\n                                       rid=rid,\n                                       type=type,\n                                       symid=symid,\n                                       pairs=pairs,\n                                       precopy=precopy,\n                                       consistent=consistent,\n                                       **kwargs)\n\n","sub_path":"lib/resSyncSymcloneLinux.py","file_name":"resSyncSymcloneLinux.py","file_ext":"py","file_size_in_byte":2473,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"175467279","text":"from django.db import models\n\nfrom django.core.validators import RegexValidator\n\nfrom prj001.utils.validators_custom import ValidatorModelYear\nfrom datetime import date\n\n\n# 一般基本信息\nclass GeneralInfo(models.Model):\n    TITLE = (\n        (u'主任医师', u'主任医师'),\n        (u'副主任医师', u'副主任医师'),\n        (u'主治医师', u'主治医师'),\n    )\n    NATION = (\n        (u'汉族', u'汉族'),\n        (u'蒙古族', u'蒙古族'),\n        (u'回族', u'回族'),\n        (u'藏族', u'藏族'),\n        (u'维吾尔族', u'维吾尔族'),\n        (u'苗族', u'苗族'),\n        (u'彝族', u'彝族'),\n        (u'壮族', u'壮族'),\n        (u'布依族', u'布依族'),\n        (u'朝鲜族', u'朝鲜族'),\n        (u'满族', u'满族'),\n        (u'侗族', u'侗族'),\n        (u'瑶族', u'瑶族'),\n        (u'白族', u'白族'),\n        (u'土家族', u'土家族'),\n        (u'哈尼族', u'哈尼族'),\n        (u'哈萨克族', u'哈萨克族'),\n        (u'傣族', u'傣族'),\n        (u'黎族', u'黎族'),\n        (u'傈傈族', u'傈傈族'),\n        (u'佤族', u'佤族'),\n        (u'畲族', u'畲族'),\n        (u'高山族', u'高山族'),\n        (u'拉祜族', u'拉祜族'),\n        (u'水族', u'水族'),\n        (u'东乡族', u'东乡族'),\n        (u'纳西族', u'纳西族'),\n        (u'景颇族', u'景颇族'),\n        (u'科尔克孜族', u'科尔克孜族'),\n        (u'土族', u'土族'),\n        (u'达斡尔族', u'达斡尔族'),\n        (u'仫佬族', u'仫佬族'),\n        (u'羌族', u'羌族'),\n        (u'布朗族', u'布朗族'),\n        (u'撒拉族', u'撒拉族'),\n        (u'毛难族', u'毛难族'),\n        (u'仡佬族', u'仡佬族'),\n        (u'锡伯族', u'锡伯族'),\n        (u'阿昌族', u'阿昌族'),\n        (u'普米族', u'普米族'),\n        (u'塔吉克族', u'塔吉克族'),\n        (u'怒族', u'怒族'),\n        (u'乌孜别克族', u'乌孜别克族'),\n        (u'俄罗斯族', u'俄罗斯族'),\n        (u'鄂温克族', u'鄂温克族'),\n        (u'崩龙族', u'崩龙族'),\n        (u'保安族', u'保安族'),\n        (u'裕固族', u'裕固族'),\n        (u'京族', u'京族'),\n        (u'塔塔尔族', u'塔塔尔族'),\n        (u'独龙族', u'独龙族'),\n        (u'鄂伦春族', u'鄂伦春族'),\n        (u'赫哲族', u'赫哲族'),\n        (u'门巴族', u'门巴族'),\n        (u'珞巴族', u'珞巴族'),\n        (u'基诺族', u'基诺族'),\n        (u'其他', u'其他'),\n    )\n    CAREER = (\n        (u'学生', u'学生'),\n        (u'个体', u'个体'),\n        (u'农民', u'农民'),\n        (u'军人', u'军人'),\n        (u'工人', u'工人'),\n        (u'财会人员', u'财会人员'),\n        (u'技术人员', u'技术人员'),\n        (u'服务业', u'服务业'),\n        (u'科教文卫', u'科教文卫'),\n        (u'行政管理', u'行政管理'),\n        (u'无业', u'无业'),\n        (u'其它', u'其它'),\n    )\n    ENTRANCE = (\n        (u'门诊', u'门诊'),\n        (u'病房', u'病房'),\n    )\n    CULTURE = (\n        (u'未接受国家教育(文盲)', u'未接受国家教育(文盲)'),\n        (u'小学及以下', u'小学及以下'),\n        (u'初中', u'初中'),\n        (u'高中/中专', u'高中/中专'),\n        (u'大专', u'大专'),\n        (u'本科', u'本科'),\n        (u'研究生及以上', u'研究生及以上'),\n    )\n\n    owner = models.ForeignKey('myuser.MyUser',\n                              related_name='mygi',\n                              on_delete=models.CASCADE)\n    serial = models.CharField(verbose_name=u'问卷编码',\n                              max_length=13,\n                              unique=True,\n                              blank=True,\n                              help_text=\"问卷编码\")\n    name = models.CharField(verbose_name=u'患者姓名',\n                            max_length=50,\n                            help_text=\"患者姓名\")\n    recdate = models.DateField(verbose_name=u'就诊日期',\n                               default=date.today,\n                               help_text=\"就诊日期\")\n    hospital = models.CharField(verbose_name=u'医院名称',\n                                max_length=100,\n                                help_text=\"医院名称\")\n    telephone = models.CharField(verbose_name=u'电话',\n                                 max_length=11,\n                                 help_text=\"电话\")\n    expert = models.CharField(verbose_name=u'填表专家姓名',\n                              max_length=50,\n                              help_text=\"填表专家姓名\")\n    title = models.CharField(verbose_name=u'职称',\n                             choices=TITLE,\n                             max_length=30,\n                             help_text=\"职称\")\n\n    # age = models.IntegerField(verbose_name=u'年龄',\n    #                           help_text=\"年龄\")\n\n    birth_year = models.CharField(verbose_name='出生年份',\n                                  max_length=4,\n                                  blank=True,\n                                  null=True,\n                                  validators=[RegexValidator(regex=r'^\\d{4}$',\n                                                             message='必须为4位数字',\n                                                             code='year'),\n                                              ValidatorModelYear(4, 'year')],\n                                  default=0000,\n                                  help_text='出生年份')\n    birth_month = models.CharField(verbose_name='出生月份',\n                                   max_length=2,\n                                   default=00,\n                                   blank=True,\n                                   null=True,\n                                   validators=[RegexValidator(regex=r'^\\d{2}$',\n                                                              message='必须为2位数字',\n                                                              code='month'),\n                                               ValidatorModelYear(2, 'month')],\n                                   help_text='出生月份')\n\n    height = models.DecimalField(verbose_name=u'身高cm',\n                                 max_digits=4,\n                                 default='0',\n                                 decimal_places=0,\n                                 help_text=\"身高cm\")\n    weight = models.DecimalField(verbose_name=u'体重kg',\n                                 max_digits=4,\n                                 default='0',\n                                 decimal_places=1,\n                                 help_text=\"体重kg\")\n    waistline = models.DecimalField(verbose_name=u'腰围cm',\n                                    max_digits=4,\n                                    default='0',\n                                    decimal_places=1,\n                                    help_text=\"腰围cm\")\n    hipline = models.DecimalField(verbose_name=u'臀围cm',\n                                  max_digits=4,\n                                  default='0',\n                                  decimal_places=1,\n                                  help_text=\"臀围cm\")\n\n    # blood_type = models.CharField(verbose_name=u'血型',\n    #                               choices=BLOOD_TYPE,\n    #                               max_length=10,\n    #                               help_text=\"血型\")\n    nation = models.CharField(verbose_name=u'民族',\n                              choices=NATION,\n                              max_length=25,\n                              help_text=\"民族(汉字)\")\n    career = models.CharField(verbose_name=u'职业',\n                              choices=CAREER,\n                              max_length=20,\n                              help_text=\"职业\")\n\n    # 一般情况-特殊工作环境\n    gaowen = models.BooleanField(verbose_name='高温',\n                                 default=False,\n                                 help_text='高温', )\n    diwen = models.BooleanField(verbose_name=u'低温',\n                                default=False,\n                                help_text=\"低温\")\n    yeban = models.BooleanField(verbose_name='夜班/熬夜',\n                                default=False,\n                                help_text='夜班/熬夜')\n    zaosheng = models.BooleanField(verbose_name=u'噪声',\n                                   default=False,\n                                   help_text=\"噪声\")\n    fushe = models.BooleanField(verbose_name=u'辐射',\n                                default=False,\n                                help_text=\"辐射\")\n    huagongyinran = models.BooleanField(verbose_name=u'化工印染',\n                                        default=False,\n                                        help_text=\"化工印染\")\n    julieyundong = models.BooleanField(verbose_name=u'剧烈运动',\n                                       default=False,\n                                       help_text=\"剧烈运动\")\n    qiyou = models.BooleanField(verbose_name=u'汽油',\n                                default=False,\n                                help_text=\"汽油\")\n    gaokong = models.BooleanField(verbose_name=u'高空',\n                                  default=False,\n                                  help_text=\"高空\")\n    wu = models.BooleanField(verbose_name=u'无',\n                             default=False,\n                             help_text=\"无\")\n\n    address = models.CharField(verbose_name=u'病人现住址',\n                               max_length=150,\n                               help_text=\"病人住址\")\n    entrance = models.CharField(verbose_name=u'病人来源',\n                                choices=ENTRANCE,\n                                max_length=10,\n                                help_text=\"病人来源\")\n    culture = models.CharField(verbose_name=u'文化程度',\n                               choices=CULTURE,\n                               max_length=30,\n                               help_text=\"文化程度\")\n\n    # 一般情况-饮食偏好\n    wuteshu = models.BooleanField(\n        verbose_name=u'无特殊', default=False, help_text=\"无特殊\")\n    sushi = models.BooleanField(\n        verbose_name=u'素食', default=False, help_text=\"素食\")\n    suan = models.BooleanField(verbose_name=u'酸', default=False, help_text=\"酸\")\n    tian = models.BooleanField(verbose_name=u'甜', default=False, help_text=\"甜\")\n    xian = models.BooleanField(verbose_name=u'咸', default=False, help_text=\"咸\")\n    xinla = models.BooleanField(\n        verbose_name=u'辛辣', default=False, help_text=\"辛辣\")\n    you = models.BooleanField(verbose_name=u'油', default=False, help_text=\"油\")\n    shengleng = models.BooleanField(\n        verbose_name=u'生冷', default=False, help_text=\"生冷\")\n    kafei = models.BooleanField(\n        verbose_name=u'含咖啡因食物或饮品', default=False, help_text=\"咖啡因\")\n    qita = models.CharField(verbose_name=u'其它',\n                            null=True,\n                            blank=True,\n                            max_length=50,\n                            default=None,\n                            help_text=\"其他\")\n\n    degree_of_completion = models.CharField(verbose_name='完成度',\n                                            max_length=8,\n                                            default='0%',\n                                            help_text='完成度')\n\n    CHECKED_CHOICE = (\n        ('未审核', '未审核'),\n        ('审核通过', '审核通过'),\n        ('审核不通过', '审核不通过'),\n    )\n    is_checked = models.CharField(verbose_name='审核状态',\n                                  max_length=5,\n                                  choices=CHECKED_CHOICE,\n                                  default='未审核',\n                                  help_text='审核状态')\n\n    reasons_for_not_passing = models.CharField(verbose_name='审核不通过原因',\n                                               max_length=200,\n                                               default=None,\n                                               help_text='审核不通过原因',\n                                               null=True,)\n\n    def __int__(self):\n        return self.name\n\n    def __str__(self):\n        return '%s' % self.serial\n\n    class Meta:\n        verbose_name = u'基本信息'\n        ordering = ('-recdate', '-pk')\n        permissions = (\n            (\"prj001_operation\", \"prj001_self_permissions\"),\n            ('prj001_patient', 'data_can_be_modified'),\n            ('prj001_all', 'prj001_all_permissions'),\n        )\n\n        verbose_name_plural = verbose_name\n","sub_path":"prj001/models/geninfo.py","file_name":"geninfo.py","file_ext":"py","file_size_in_byte":12830,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"317772877","text":"\"\"\"\nExamples of using extra turboworks features.\n\nIn this task, the calculation and optimization includes a categorical dimension, a function to fetch extra features\n(get_z), a custom predictor function, extra arguments to the workflow creator, duplicate checking enabled, and a custom\nstorage location for the optimization data.\nAlso, a demo of how to use the lpad kwarg to store optimization data based on a Firework's LaunchPad object.\n\"\"\"\n\nfrom fireworks.core.rocket_launcher import rapidfire\nfrom fireworks import Workflow, Firework, LaunchPad\nfrom turboworks.optimize import OptTask\nfrom calculate_task import MixedCalculateTask as CalculateTask\nimport random\n\n\n# use a wf_creator function with more arguments...\ndef wf_creator(x, launchpad, my_arg, my_kwarg=1):\n\n    fw1_spec = {'A': x[0], 'B': x[1], 'C': x[2], 'D': x[3], '_x_opt': x}\n    fw1_dim = [(1, 2), (1, 2), (1, 2), (\"red\", \"green\", \"blue\")]\n\n    # CalculateTask writes _y_opt field to the spec internally.\n\n    firework1 = Firework([CalculateTask(),\n                          OptTask(wf_creator='turboworks_examples.test_extras.wf_creator',\n                                  dimensions=fw1_dim,\n                                  get_z='turboworks_examples.test_extras.get_z',\n                                  predictor='turboworks_examples.test_extras.example_predictor',\n                                  max=True,\n                                  lpad=launchpad,\n                                  wf_creator_args=[launchpad, my_arg * 3],\n                                  wf_creator_kwargs={'my_kwarg': my_kwarg * 2},\n                                  duplicate_check=True,\n                                  opt_label=\"opt_extras_example\",\n                                  retrain_interval=5)],\n                         spec=fw1_spec)\n\n    return Workflow([firework1])\n\n\n# An optional function which returns extra information 'z' from unique vector 'x'\ndef get_z(x):\n    return [x[0] * 2, x[2] ** 3]\n\n# how an example custom optimization function could be used\n# replace the code inside example_predictor with your favorite optimizer\n\ndef example_predictor(X_tot, y, X_space_total):\n    # custom optimizer code goes here\n    return random.choice(X_space_total)\n\n\nif __name__ == \"__main__\":\n\n    TESTDB_NAME = 'turboworks'\n    launchpad = LaunchPad(name=TESTDB_NAME)\n    launchpad.reset(password=None, require_password=False)\n    launchpad.add_wf(wf_creator([1, 1, 2, \"red\"], launchpad, 3, my_kwarg=1))\n\n\n    # if n_launches > 24 for this particular example, the search space will be exhausted and OptTask will throw\n    # an exception\n    rapidfire(launchpad, nlaunches=25, sleep_time=0)\n\n    # tear down database\n    # launchpad.connection.drop_database(TESTDB_NAME)","sub_path":"turboworks_examples/test_extras.py","file_name":"test_extras.py","file_ext":"py","file_size_in_byte":2739,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"525074351","text":"import rospy\nfrom sensor_msgs.msg import Joy\nfrom std_msgs.msg import Int32, Float64, Bool, Int16\nimport math\n\nbuttonA = 0 \nforwardPublisher = 0\n\nDEGREE_45 = 0.785398\nDEGREE_90 = 1.5708\nDEGREE_1 =  0.0174\n\nclass JoyNode:\n    \"Xbox controller for AUV\"\n\n    def __init__(self):\n        \"JoyNode Constructor\"\n        rospy.Subscriber('joy', Joy, self.joyCallBack)\n\n        self.yaw_state = None\n        self.depth_state = None\n        self.yaw_setpoint = None\n        self.depth_setpoint = None\n        rospy.Subscriber('/yaw_control/state', Float64, self.yaw_state_callback)\n        rospy.Subscriber('/depth_control/state', Float64, self.depth_state_callback)\n        rospy.Subscriber('/yaw_control/setpoint', Float64, self.yaw_setpoint_callback)\n        rospy.Subscriber('/depth_control/setpoint', Float64, self.depth_setpoint_callback)\n\n        self.depthPublisher = rospy.Publisher('/depth_pwm', Int16, queue_size=10)\n        self.forwardPublisher = rospy.Publisher('/yaw_pwm', Int16, queue_size=10)\n        self.yawSetpointPublisher = rospy.Publisher('/yaw_control/setpoint', Float64, queue_size=10)\n        self.depthSetpointPublisher = rospy.Publisher('/depth_control/setpoint', Float64, queue_size=10)\n        \n        self.depthPidPublisher = rospy.Publisher('/depth_control/pid_enable', Bool, queue_size=10)\n        self.yawPidPublisher = rospy.Publisher('yaw_control/pid_enable', Bool, queue_size=10)\n\n        # List of buttons and axes for the Microsoft XBox Wired Controller\n        # http://wiki.ros.org/joy\n        self.buttons =  [False for i in range(11)] \n        self.axes = [0 for i in range(8)]\n\n        self.setpoint_button_toggle = False\n        self.saved_angle = None\n\n    def yaw_state_callback(self, msg):\n        self.yaw_state = msg.data\n\n    def depth_state_callback(self, msg):\n        self.depth_state = msg.data\n\n    def yaw_setpoint_callback(self, msg):\n        self.yaw_setpoint = msg.data\n\n    def depth_setpoint_callback(self, msg):\n        self.depth_setpoint = msg.data\n\n    def fix_yaw(self, yaw):\n        if yaw >= 3.14:\n            yaw -= 2 * 3.14\n        elif yaw <= -3.14:\n            yaw += 2 * 3.14\n        return yaw\n\n    def execute(self):\n        buttonIncreaseDepth = self.buttons[0] # Button A\n        buttonDecreaseDepth = self.buttons[3] # Button Y\n        buttonRotateCounterClockwise = self.buttons[2] # Button X\n        buttonRotateClockwise = self.buttons[1] # Button B\n\n        triggerDepth = self.axes[2] # Axis Left Trigger\n        triggerYaw = self.axes[5] # Axis Right Trigger\n\n        axisStrafe = self.axes[6] # Axis Cross Key Left/Right\n        axisForward = self.axes[7] # Axis Cross Key Up/Down\n\n        if buttonIncreaseDepth: # Button A -- Increase depth setpoint\n            print(\"Button A pressed\")\n            if self.depth_state != None:\n                new_depth = self.depth_state + 1\n                depthObj = Float64()\n                depthObj.data = new_depth\n                print(\"Button A -- new depth: {}\".format(new_depth))\n                self.depthSetpointPublisher.publish(depthObj)\n            else:\n                print(\"Button A -- depth state was not published\")\n\n        if buttonRotateClockwise: # Button B -- Rotate clockwise\n            print(\"Button B pressed\")\n            # Increase setpoint clockwise\n            if self.yaw_setpoint != None:\n                new_yaw = self.yaw_setpoint + DEGREE_1 / 50.0\n                new_yaw = self.fix_yaw(new_yaw)\n                yawObj = Float64()\n                yawObj.data = new_yaw\n                print(\"Button B -- {}\".format(new_yaw))\n                self.yawSetpointPublisher.publish(yawObj)\n            else:\n                print(\"Button B -- yaw setpoint was not published. Press the START button\")\n        # else:\n        #     # Stop rotating by setting the setpoint to current rotation\n        #     if self.yaw_state != None:\n        #         yawObj = Float64()\n        #         yawObj.data = self.yaw_state\n        #         self.yawSetpointPublisher.publish(yawObj)\n\n\n        if buttonRotateCounterClockwise: # Button X -- Rotate counter-clockwise\n            print(\"Button X pressed\")\n            # Increase setpoint counter-clockwise\n            if self.yaw_setpoint != None:\n                new_yaw = self.yaw_setpoint - DEGREE_1 / 50.0\n                new_yaw = self.fix_yaw(new_yaw)\n                yawObj = Float64()\n                yawObj.data = new_yaw\n                print(\"Button X -- {}\".format(new_yaw))\n                self.yawSetpointPublisher.publish(yawObj)\n            else:\n                print(\"Button X -- yaw setpoint was not published. Press the START button\")\n\n        # else:\n        #     # Stop rotating by setting the setpoint to current rotation\n        #     if self.yaw_state != None:\n        #         yawObj = Float64()\n        #         yawObj.data = self.yaw_state\n        #         self.yawSetpointPublisher.publish(yawObj)\n\n\n        if buttonDecreaseDepth: # Button Y -- Decrease depth setpoint\n            print(\"Button Y pressed\")\n            if self.depth_state != None:\n                new_depth = self.depth_state - 1\n                depthObj = Float64()\n                depthObj.data = new_depth\n                print(\"Button Y -- new depth: {}\".format(new_depth))\n                self.depthSetpointPublisher.publish(depthObj)\n            else:\n                print(\"Button Y -- depth state was not published\")\n\n\n        if self.buttons[4]:\n            # drop weight LB\n            pass\n\n        if self.buttons[5]:\n            # torpedo launcher RB\n            pass\n\n        if self.buttons[6]: # Button BACK -- Disable PIDs\n            print(\"Button BACK -- PIDs are disabled\")\n            off = Bool()\n            off.data = False\n            zeroFloat = Float64()\n            zeroFloat.data = 0.0\n            zeroInt = Int16()\n            zeroInt.data = 0\n            self.depthPidPublisher.publish(off)\n            self.yawPidPublisher.publish(off)\n            self.forwardPublisher.publish(zeroInt)\n            self.depthPublisher.publish(zeroInt)\n\n        if self.buttons[7]: # Button START -- Enable PIDs\n            print(\"Button START -- PIDs are enabled\")\n            on = Bool()\n            on.data = True\n            self.depthPidPublisher.publish(on)\n            self.yawPidPublisher.publish(on)\n            if self.yaw_state != None:\n                yaw = Float64()\n                yaw.data = self.yaw_state\n                self.yawSetpointPublisher.publish(yaw)\n            else:\n                print(\"Button START -- /yaw_control/state has not been published. Check that AHRS is running?\")\n\n        # Left Trigger -- manually set depth pwm\n        if triggerDepth:\n            value = triggerDepth\n            value -= 1  # adjust from [-1,1] to [-2,0], so can make default zero (depth)\n            value *= -1  # adjust from [-2,0] to [0,2], had to do - 1 and * 1 so not using trigger posts \"0\" instead of \"2\"\n            value /= 2  # scale from [0,2] to [0,1], so button value is a percent of max thrust\n            value *= 150  # scale from [0,1] to [0,150], so thrust scales from 0 to our desired max\n\n            depth_pwm = Int16()\n            depth_pwm = value\n            print(\"Trigger DEPTH -- setting depth_pwm to \" + str(value))\n            self.depthPublisher.publish(depth_pwm)\n            \n            \n        # Right Trigger -- manually set yaw forward pwm\n        if triggerYaw:\n            value = triggerYaw\n            value -= 1  # adjust from [-1,1] to [-2,0], so can make default zero (depth)\n            value *= -1  # adjust from [-2,0] to [0,2], had to do - 1 and * 1 so not using trigger posts \"0\" instead of \"2\"\n            value /= 2  # scale from [0,2] to [0,1], so button value is a percent of max thrust\n            value *= 150  # scale from [0,1] to [0,150], so thrust scales from 0 to our desired max\n\n            yaw_pwm = Int16()\n            yaw_pwm = value\n            print(\"Trigger YAW_FORWARD -- setting yaw to \" + str(value))\n            self.forwardPublisher.publish(yaw_pwm)\n            \n        \n        # Left Stick -- Analog rotation about Y-axis\n        x = -1 * self.axes[3] # axis goes from +1 to -1, so we flip the sign to change it to standard coordinate system\n        y = self.axes[4]\n        left_stick_magnitude = math.sqrt(x**2 + y**2)\n        angle_radians = math.atan2(y,x)\n        new_yaw = Float64()\n        new_yaw.data = 0.0\n        if left_stick_magnitude >= 0.95:\n            new_yaw.data = angle_radians\n            self.yawSetpointPublisher.publish(new_yaw)\n        elif not self.saved_angle is None:\n            new_yaw.data = self.saved_angle\n            self.yawSetpointPublisher.publish(new_yaw)\n        elif not self.yaw_state is None:\n            new_yaw.data = self.yaw_state\n            self.yawSetpointPublisher.publish(new_yaw)\n        \n\n        if self.buttons[8]:\n            # nothing mapped Power\n            pass\n\n        if self.buttons[9]:\n            # nothing mapped Button Stick Left\n            pass\n\n        self.setpoint_button_toggle = False\n        if self.buttons[10]: # Button Stick Right -- toggle saved yaw setpoint\n            if left_stick_magnitude >= 0.95:\n                self.saved_angle = angle_radians\n            else:\n                self.saved_angle = None\n\n        # AXES\n\n        if self.axes[0]:\n            # nothing mapped L/R Axis Stick Left\n            pass\n\n        if self.axes[1]:\n            # camera rotation U/D Axis Stick Left\n            pass\n\n        if self.axes[3]:\n            # rotation about y-axis L/R Axis Stick Right\n            pass\n\n        if self.axes[4]:\n            # rotation about y-axis U/D Axis Stick Right\n            pass\n\n        if self.axes[6]:\n            # nothing mapped Cross Key L/R\n            pass\n\n    def joyCallBack(self, joy):\n        #\"invoked every time a joystick message arrives\"\n        global DEGREE_1, DEGREE_45, DEGREE_90\n        \n        for i in range(len(self.buttons)):\n            self.buttons[i] = joy.buttons[i]\n        for i in range(len(self.axes)):\n            self.axes[i] = joy.axes[i]\n\nimport atexit\n\ndef kill_motors():\n    print(\"killed motors\")\n\n    off = Bool()\n    off.data = False\n    zeroInt = Int16()\n    zeroInt.data = 0\n    zeroFloat = Float64()\n    zeroFloat.data = 0.0\n    yaw_pub = rospy.Publisher('/yaw_pwm', Float64, queue_size=10)\n    depth_pub = rospy.Publisher('/depth_pwm', Int16, queue_size=10)\n    yaw_pid_pub = rospy.Publisher('yaw_control/pid', Bool, queue_size=10)\n    depth_pid_pub = rospy.Publisher('/depth_control/pid', Bool, queue_size=10)\n    yaw_pub.publish(zeroFloat)\n    depth_pub.publish(zeroInt)\n    yaw_pid_pub.publish(off)\n    depth_pid_pub.publish(off)\n\natexit.register(kill_motors)\n\n\ndef main():\n    rospy.init_node('joystickController')\n    delay = rospy.Rate(20)\n\n    joyObject = JoyNode()\n\n    print(\"Python Project Running....\")\n    while not rospy.is_shutdown():\n       joyObject.execute()\n       delay.sleep()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"Mission Control/manual_control/joystickController.py","file_name":"joystickController.py","file_ext":"py","file_size_in_byte":10993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"484912195","text":"# -*- coding: utf-8 -*-\n#########################################################################\n#\n# Copyright (C) 2016 Boundless Spatial\n#\n# This program is free software: you can redistribute it and/or modify\n# it under the terms of the GNU General Public License as published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU General Public License for more details.\n#\n# You should have received a copy of the GNU General Public License\n# along with this program. If not, see <http://www.gnu.org/licenses/>.\n#\n#########################################################################\nfrom django import forms\nfrom django.utils.translation import ugettext_lazy as _\nfrom .models import CSWRecord, CSWRecordReference\n\nfield_attrs = {'required': '', 'class': 'form-control'}\n\n\nclass CSWRecordForm(forms.ModelForm):\n    class Meta:\n        model = CSWRecord\n        fields = ('source', 'record_type', 'record_format', 'title', 'category', 'abstract',\n                  'alternative', 'creator', 'contact_email', 'contact_phone', 'gold')\n\n        widgets = {\n            'title': forms.TextInput(attrs=field_attrs),\n            'creator': forms.TextInput(attrs=field_attrs),\n            'abstract': forms.Textarea(attrs=field_attrs),\n            'alternative': forms.TextInput(attrs=field_attrs),\n            'source': forms.TextInput(attrs=field_attrs),\n            'contact_email': forms.TextInput(attrs=field_attrs),\n            'contact_phone': forms.TextInput(attrs=field_attrs),\n            'category': forms.Select(attrs=field_attrs),\n            'record_type': forms.HiddenInput(),\n            'record_format': forms.HiddenInput()\n        }\n\n        labels = {\n            'source': _('Service Url'),\n            'title': _('Title'),\n            'modified': _('Date Last Modified'),\n            'creator': _('Agency'),\n            'record_type': _('Type'),\n            'alternative': _('Layer Identifier'),\n            'abstract': _('Abstract'),\n            'record_format': _('Format'),\n            'bbox_upper_corner': _('Bounding Box: Upper Corner'),\n            'bbox_lower_corner': _('Bounding Box: Lower Corner'),\n            'contact_email': _('Contact Email'),\n            'contact_phone': _('Contact Phone'),\n            'gold': _('Gold'),\n            'category': _('Category')\n        }\n\n        help_texts = {\n            'source': _('e.g. http://example.com/ArcGIS/rest/services/Specialty/ESRI_StateCityHighway_USA/MapServer'),\n            'alternative': 'The layer name or ID assigned to the dataset',\n            'bbox_upper_corner': _('Coordinates for upper left corner'),\n            'bbox_lower_corner': _('Coordinates for lower right corner'),\n        }\n\n\nclass CSWRecordReferenceForm(forms.ModelForm):\n    class Meta:\n        model = CSWRecordReference\n        widgets = {\n            'url': forms.TextInput(attrs={'class': 'form-control'}),\n            'scheme': forms.Select(attrs={'class': 'form-control'})\n        }\n        exclude = ()\n\n\nCSWRecordReferenceFormSet = forms.inlineformset_factory(CSWRecord, CSWRecordReference,\n                                                        form=CSWRecordReferenceForm, extra=1)\n","sub_path":"exchange/core/forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":3411,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"24211836","text":"from jenga.send_money import (\n    SendMoney as BaseSendMoney,\n    Recipient as BaseRecipient,\n    Transaction as BaseTransaction\n)\n\n\nclass Recipient(BaseRecipient):\n    def __init__(self, full_name: str, account_number: str, phone_number: str, bank_code: str,\n                 country_code: str = 'KE'):\n        self.full_name = full_name\n        self.account_number = account_number\n        self.country_code = country_code\n        self.bank_code = bank_code\n        self.phone_number = phone_number\n\n    def to_dict(self):\n        return {\n            \"type\": \"bank\",\n            \"countryCode\": self.country_code,\n            \"name\": self.full_name,\n            \"accountNumber\": self.account_number,\n            \"mobileNumber\": self.phone_number,\n            \"bankCode\": self.bank_code\n        }\n\n\nclass Transaction(BaseTransaction):\n    def to_dict(self):\n        return {\n            \"type\": \"PesaLink\",\n            \"amount\": str(self.amount),\n            \"currencyCode\": self.currency_code,\n            \"reference\": self.transaction_reference,\n            \"date\": self.date,\n            \"description\": self.description\n        }\n\n\nclass SendMoney(BaseSendMoney):\n    \"\"\"\n    This web service enables an application to send money to a PesaLink participating bank.\n    It is restricted to Kenya.\n    \"\"\"\n    transaction_class = Transaction\n    recipient_class = Recipient\n\n    def get_signature_string(self):\n        return f\"{self.transaction.amount}{self.transaction.currency_code}\" \\\n               f\"{self.transaction.transaction_reference}{self.recipient.full_name}\" \\\n               f\"{self.sender.account_number}\"\n","sub_path":"jenga/send_money/pesalink/bank.py","file_name":"bank.py","file_ext":"py","file_size_in_byte":1625,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"334559712","text":"# coding: utf-8\n\n\"\"\"\n    FlashBlade REST API\n\n    A lightweight client for FlashBlade REST API 2.10, developed by Pure Storage, Inc. (http://www.purestorage.com/).\n\n    OpenAPI spec version: 2.10\n    \n    Generated by: https://github.com/swagger-api/swagger-codegen.git\n\"\"\"\n\n\nimport pprint\nimport re\n\nimport six\nimport typing\n\nfrom ....properties import Property\nif typing.TYPE_CHECKING:\n    from pypureclient.flashblade.FB_2_10 import models\n\nclass FileSession(object):\n    \"\"\"\n    Attributes:\n      swagger_types (dict): The key is attribute name\n                            and the value is attribute type.\n      attribute_map (dict): The key is attribute name\n                            and the value is json key in definition.\n    \"\"\"\n    swagger_types = {\n        'name': 'str',\n        'authentication': 'str',\n        'client': 'FixedReferenceNameOnly',\n        'connection_time': 'int',\n        'idle_time': 'int',\n        'opens': 'int',\n        'port': 'int',\n        'protocol': 'str',\n        'time': 'int',\n        'user': 'UserNoId'\n    }\n\n    attribute_map = {\n        'name': 'name',\n        'authentication': 'authentication',\n        'client': 'client',\n        'connection_time': 'connection_time',\n        'idle_time': 'idle_time',\n        'opens': 'opens',\n        'port': 'port',\n        'protocol': 'protocol',\n        'time': 'time',\n        'user': 'user'\n    }\n\n    required_args = {\n    }\n\n    def __init__(\n        self,\n        name=None,  # type: str\n        authentication=None,  # type: str\n        client=None,  # type: models.FixedReferenceNameOnly\n        connection_time=None,  # type: int\n        idle_time=None,  # type: int\n        opens=None,  # type: int\n        port=None,  # type: int\n        protocol=None,  # type: str\n        time=None,  # type: int\n        user=None,  # type: models.UserNoId\n    ):\n        \"\"\"\n        Keyword args:\n            name (str): Name of the object (e.g., a file system or snapshot).\n            authentication (str): Describes how was the user authenticated. Valid values include `KRB` and `NTLMv2`.\n            client (FixedReferenceNameOnly): Client that holds the session.\n            connection_time (int): Connection time in milliseconds since UNIX epoch.\n            idle_time (int): Duration in milliseconds that indicates how long the session has been idle.\n            opens (int): Number of opens for the given session.\n            port (int): Port number the client is connected from.\n            protocol (str): The protocol utilized for obtaining and managing the session. Valid values include `nfs` and `smb`.\n            time (int): Current time in milliseconds since UNIX epoch.\n            user (UserNoId): The user who has created the session.\n        \"\"\"\n        if name is not None:\n            self.name = name\n        if authentication is not None:\n            self.authentication = authentication\n        if client is not None:\n            self.client = client\n        if connection_time is not None:\n            self.connection_time = connection_time\n        if idle_time is not None:\n            self.idle_time = idle_time\n        if opens is not None:\n            self.opens = opens\n        if port is not None:\n            self.port = port\n        if protocol is not None:\n            self.protocol = protocol\n        if time is not None:\n            self.time = time\n        if user is not None:\n            self.user = user\n\n    def __setattr__(self, key, value):\n        if key not in self.attribute_map:\n            raise KeyError(\"Invalid key `{}` for `FileSession`\".format(key))\n        self.__dict__[key] = value\n\n    def __getattribute__(self, item):\n        value = object.__getattribute__(self, item)\n        if isinstance(value, Property):\n            return None\n        else:\n            return value\n\n    def to_dict(self):\n        \"\"\"Returns the model properties as a dict\"\"\"\n        result = {}\n\n        for attr, _ in six.iteritems(self.swagger_types):\n            if hasattr(self, attr):\n                value = getattr(self, attr)\n                if isinstance(value, list):\n                    result[attr] = list(map(\n                        lambda x: x.to_dict() if hasattr(x, \"to_dict\") else x,\n                        value\n                    ))\n                elif hasattr(value, \"to_dict\"):\n                    result[attr] = value.to_dict()\n                elif isinstance(value, dict):\n                    result[attr] = dict(map(\n                        lambda item: (item[0], item[1].to_dict())\n                        if hasattr(item[1], \"to_dict\") else item,\n                        value.items()\n                    ))\n                else:\n                    result[attr] = value\n        if issubclass(FileSession, dict):\n            for key, value in self.items():\n                result[key] = value\n\n        return result\n\n    def to_str(self):\n        \"\"\"Returns the string representation of the model\"\"\"\n        return pprint.pformat(self.to_dict())\n\n    def __repr__(self):\n        \"\"\"For `print` and `pprint`\"\"\"\n        return self.to_str()\n\n    def __eq__(self, other):\n        \"\"\"Returns true if both objects are equal\"\"\"\n        if not isinstance(other, FileSession):\n            return False\n\n        return self.__dict__ == other.__dict__\n\n    def __ne__(self, other):\n        \"\"\"Returns true if both objects are not equal\"\"\"\n        return not self == other\n","sub_path":"pypureclient/flashblade/FB_2_10/models/file_session.py","file_name":"file_session.py","file_ext":"py","file_size_in_byte":5413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"652262913","text":"from django.db import models\nfrom django.db.models import Q, F\nfrom property.models import Property, Council, LandUse, Cell, Village, Sector, District\nfrom citizen.models import Citizen\nfrom citizen.mappers.CitizenMapper import CitizenMapper\nfrom datetime import datetime, timedelta, date\nfrom django.utils.dateformat import DateFormat\nfrom django.utils.formats import get_format\nfrom dev1 import variables\nfrom asset.models import *\nfrom pmauth.models import *\nfrom decimal import Decimal\nfrom dev1 import ThreadLocal\nfrom jtax.shared_functions import calc_interest\nfrom dateutil.relativedelta import relativedelta\nproperty_decorator = property\nimport json\nimport cPickle as pickle\nfrom django.utils import timezone\n\n\n# This is the new IncompletePayment model #\n\nclass IncompletePaymentManager(models.Manager):\n\tdef get_query_set(self):\n\t\tuser = ThreadLocal.get_current_user()\n\t\tif user:\n\t\t\tif user.superuser:\n\t\t\t\treturn super(IncompletePaymentManager,self).get_query_set().all()\n\t\t\telse:\n\t\t\t\treturn super(IncompletePaymentManager,self).get_query_set().filter(district__in = District.objects.all(), sector__in = Sector.objects.all()).distinct()\n\t\telse:\n\t\t\treturn super(IncompletePaymentManager,self).get_query_set().none()\n\n\nclass IncompletePayment(models.Model):\n\ttax_type = models.CharField(choices=variables.tax_and_fee_types, max_length = 100, null = True, blank = True, verbose_name = 'Type of Tax')\n\ttin = models.CharField(max_length = 100, null = True, blank = True, verbose_name = 'TIN')\n\tbusiness = models.ForeignKey(Business, null=True, blank = True)\n\tsubbusiness = models.ForeignKey(SubBusiness, null=True, blank = True)\n\tpaid_amount = models.FloatField(null = True, blank  = True, default = 0, verbose_name = \"Paid amount\")\n\tpaid_date = models.DateField(null=True, blank=True, verbose_name = \"Paid date\")\n\tperiod_from = models.DateField(null=True, blank=True)\n\tperiod_to = models.DateField(null=True, blank=True)\n\tbank = models.CharField(choices=variables.banks,max_length = 100, null = True, blank = True, verbose_name = 'Bank')\n\tbank_receipt = models.CharField(max_length = 100, null = True, blank = True, verbose_name = 'Bank Receipt')\n\tsector_receipt = models.CharField(max_length = 100, null = True, blank = True, verbose_name = 'Sector Receipt/manual receipt')\n\tdistrict = models.ForeignKey(District, null=True, blank=True, verbose_name = 'District')\n\tsector = models.ForeignKey(Sector, null = True, blank = True, verbose_name = 'Sector')\n\tcell = models.ForeignKey(Cell, null = True, blank = True, verbose_name = 'Cell')\n\tvillage = models.ForeignKey(Village, null = True, blank = True, verbose_name = 'Village')\n\tparcel_id = models.CharField(max_length = 100, null = True, blank=True, verbose_name = 'Plot/Parcel ID')\n\ttax_payer = models.CharField(max_length = 100, null = True, blank = True, verbose_name = 'Tax Payer')\n\tcitizen_id = models.CharField(max_length = 50, null = True, blank=True, verbose_name = 'Citizen ID')\n\tdate_of_birth = models.CharField(max_length = 50, null = True, blank=True, verbose_name = 'Date of Birth')\n\tphone = models.CharField(max_length = 100, null = True, blank=True, verbose_name = 'Phone')\n\temail = models.EmailField(max_length = 100, null = True, blank=True, verbose_name = 'Email')\n\tnote = models.TextField(null = True, blank=True, verbose_name = 'Reasons for incomplete payment')\n\tuser = models.ForeignKey(PMUser, null=True, blank = True)\n\tdate_time = models.DateTimeField(help_text='This is the Date and Time the Entry has been entered into the database.',auto_now_add=True)\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\tobjects = IncompletePaymentManager()\n\tobjectsIgnorePermission = models.Manager()\n\n\tdef save(self, *args, **kwargs):\n\t\t\"\"\"\n\t\t1. set user to be the staff\n\t\t\"\"\"\n\t\tuser = ThreadLocal.get_current_user()\n\t\tself.user = user\n\t\tmodels.Model.save(self)\n\n\tdef __unicode__(self):\n\t\treturn \"Incomplete Payment \" + str(self.id)\n\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\n\nclass Historical(models.Model):\n\tcitizen = models.ForeignKey(Citizen, null=True, blank=True)\n\tbusiness = models.ForeignKey(Business, null=True, blank=True)\n\tfee_id = models.IntegerField(null=True, blank=True)\n\tfee_type = models.CharField(max_length=50)\n\tperiod_from = models.DateTimeField(null=True, blank=True)\n\tperiod_to = models.DateTimeField(null=True, blank=True)\n\tdue_date = models.DateTimeField(null=True, blank=True)\n\tamount_due = models.FloatField(null=True, blank=True)\n\tlate_paymemt_penalty = models.FloatField(default=0)\n\tlate_payment_interest = models.FloatField(default=0)\n\tis_paid = models.BooleanField(default = False)\n\tinvoince_no = models.CharField(max_length = 50, null=True, blank = True)\n\n\"\"\"\nThe DeclaredValue Models are based around Declared Values Supplied by Property Owners\nThese need to be checked against the Valuration system and checked if the supplied value is in line\nIf it is not it needs to be flaged and manually checked.\nThe Goverment / Council only has 6 months to Challange this value before it is automatticly accepted.\n\"\"\"\nclass DeclaredValue(models.Model):\n\t\"\"\"\n\tThis is the base class for the Delcated Values of a Plot of Land in Rwandap\n\t\"\"\"\n\t#plot_id = models.CharField(max_length = 50, help_text='This is the ID of the Plot that the Declared Value is for.')\n\t#citizen_id = models.CharField(max_length = 50, help_text='This is the Nation Id of the Cirizen Making the Delcared Value.')\n\tcitizen = models.ForeignKey(Citizen, null=True, blank=True)\n\tcommercial_amount = models.BigIntegerField(help_text='Commecial declared value', null=True, blank=True, default=0)\n\tresidential_amount = models.BigIntegerField(help_text='Residential Declared Value', null=True, blank=True, default=0)\n\tagriculture_amount = models.BigIntegerField(help_text='Agricultural declared value', null=True, blank=True, default=0)\n\tamount = models.BigIntegerField(help_text='This is the amount The Declared Value is been Made for.')\n\tcurrency = models.CharField(max_length=4,choices=variables.currency_types,help_text='This is the Currencey the Amount has been specified in.')\n\tdate_time = models.DateTimeField(help_text='This is the Date and Time the Entry has been entered into the database.',auto_now_add=True)\n\t#staff_id = models.IntegerField(help_text='This is the Id of the Staff Member that Added the Declared Value for the Property.')\n\tuser = models.ForeignKey(PMUser, null=True, blank = True)\n\taccepted = models.CharField(max_length=20,choices=variables.declare_value_statuses,help_text='This is whether the declare Value has been Accepted, rejected, or needs review.')\n\tproperty = models.ForeignKey(Property, null=True, blank=True)\n\tdeclared_on = models.DateField(null=True)\n\n\tdef getLogMessage(self,old_data=None,new_data=None,action=None):\n\t\t\"\"\"\n\t\treturn tailored log message for different actions taken on this group\n\t\t\"\"\"\n\t\tif action == \"add\":\n\t\t\tcitizen = self.citizen\n\t\t\tcitizen_fullname = CitizenMapper.getDisplayName(citizen)\n\t\t\tproperty = self.property\n\t\t\tproperty_info = property.getDisplayName()\n\t\t\tmessage = \"approves Citizen [\"+citizen_fullname+ \"] to declare a value of \"+ self.currency + \" \" + str(self.amount) + \" on Property [\"+ property_info + \"]\"\n\t\t\treturn message\n\n\nclass DeclaredValueNotes(models.Model):\n\t\"\"\"\n\tThis is a table of notes Related to the Declared Values model\n\t\"\"\"\n\tdeclared_value = models.ForeignKey(DeclaredValue)\n\tcitizen = models.ForeignKey(Citizen, null=True, blank=True, help_text='The citizen who adds this note')\n\tuser = models.ForeignKey(PMUser, null=True, blank = True, help_text='The staff who adds this note.')\n\tnote = models.TextField(help_text='This is the Note that is been left for the Declared Value')\n\tdate_time = models.DateTimeField(help_text='This is the Date and Time the Note was added to the Declared Value in the system.',auto_now_add=True)\n\nclass DeclaredValueMedia(models.Model):\n\t\"\"\"\n\tThis is a list of media files that have been attached to the Declared Value Application / Item directly.\n\t\"\"\"\n\tdeclared_value = models.ForeignKey(DeclaredValue)\n\tdescription = models.TextField(null=True, blank = True, help_text = 'Notes/Reminder')\n\tfile_name = models.CharField(max_length = 150)\n\tpath = models.CharField(max_length = 255)\n\tfile_type = models.CharField(max_length = 50)\n\tfile_size = models.CharField(max_length = 50)\n\tuser = models.ForeignKey(PMUser, null=True, blank = True, help_text='The staff who adds this note.')\n\tdate_time = models.DateTimeField(help_text='This is the date and time the file was uploaded',auto_now_add=True)\n\n\tdef __unicode__(self):\n\t\treturn str(self.file_name)\n\n\"\"\"\nThe Assigned Value models are for the assigned Values to a property, this would be the official price of the land/property accepted by the Goverment. All Tax Items based off Property Value should use this table.\nThese values are apparently accepted for 4 years from timme of acceptance.\n\"\"\"\nclass AssignedValue(models.Model):\n\t\"\"\"\n\tThis model is for the offically assigned Values of a plot for the purpose of taxation\n\t\"\"\"\n\tplot_id = models.IntegerField()\n\tamount = models.BigIntegerField(help_text='This is the Offical Property Price')\n\tdate_time = models.DateTimeField(help_text='This is the Date and time that the record for this assigned value was created', auto_now_add=True)\n\tcurrency = models.CharField(max_length=4,choices=variables.currency_types,help_text='this is the Currencey that the assigned Value Amount is in')\n\tstaff_id = models.IntegerField(help_text='This is the System Staff id of the staff member that entered the assigned value into the system.')\n\tcitizen_id = models.CharField(max_length=50,help_text='This is the ID of the Citizen That Provided the Delcared Value that was accepted')\n\tvalid_until = models.DateTimeField('This is when the Assigned Value Record Runs Out.')\n\ton_hold = models.CharField(max_length=4,choices=variables.on_hold,help_text='this will put the record on hold if needed.')\n\tproperty = models.ForeignKey(Property, null=True, blank=True)\n\nclass TaxType(models.Model):\n\tcode = models.CharField(max_length=50, null=True, blank=True)\n\tname = models.CharField(max_length=50)\n\nclass Bank(models.Model):\n\tcode = models.CharField(max_length=10, null=True, blank=True)\n\tname = models.CharField(max_length=50)\n\nclass Currency(models.Model):\n\tcode = models.CharField(max_length=10, null=True, blank=True)\n\tname = models.CharField(max_length=50)\n\n\nclass Tax(models.Model):\n\t\"\"\"\n\tplot_id = models.IntegerField(help_text='This is the ID of the Plot.')\n\ttax_type = models.ForeignKey(TaxType)\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"The amount of tax item.\")\n\tremaining_amount = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"The remaining amount (subtracted past payments).\", null=True, blank = True)\n\tcurrency = models.CharField(max_length=4, choices=variables.currency_types)\n\tperiod_from = models.DateTimeField(help_text=\"The start date of a period that this tax item is for.\")\n\tperiod_to = models.DateTimeField(help_text=\"The end date of a period that this tax item is for.\")\n\tdue_date = models.DateField(help_text=\"The date this tax item is due.\")\n\tis_paid = models.BooleanField(help_text=\"Whether tax is payed.\")\n\tcreated = models.DateTimeField(help_text=\"The date this tax item is generated.\")\n\tproperty = models.ForeignKey(Property, null=True, blank=True)\n\tcitizen = models.ForeignKey(Citizen,null=True,blank=True)\n\tbusiness = models.ForeignKey(Business, null=True, blank=True, help_text=\"Business that have trading license to be taxed.\")\n\tsubbusiness = models.ForeignKey(SubBusiness,  null=True, blank=True, help_text=\"SubBusiness that have trading license to be taxed.\")\n\tis_challenged = models.BooleanField(help_text=\"whether this tax item is challenged.\")\n\tsubmit_date = models.DateTimeField(help_text=\"The date this fee item is submited.\", null=True, blank=True)\n\tsubmit_details = models.CharField(max_length=500, null=True, blank=True)\n\tis_reviewed = models.BooleanField(help_text =\"whether this tax item is reviewed.\")\n\tis_accepted = models.BooleanField(help_text=\"whether this tax item is accepted.\")\n\tstaff_id = models.IntegerField(help_text=\"The staff who generates this property tax item.\", null=True, blank=True)\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\t\"\"\"\n\tclass Meta:\n\t\tabstract = True\n\n\tdef calculatePaymentOld(self, pay_date=None, payment_amount=None):\n\t\tif not pay_date:\n\t\t\tpay_date = date.today()\n\n\t\tpayment = self.calculateTotalPayment(pay_date=pay_date, payment_amount=payment_amount)\n\t\tdue_date = payment['due_date']\n\t\tamount_due = payment['amount_due']\n\t\tinterest = payment['interest']\n\t\tsurcharge = payment['surcharge']\n\t\tlate_fees = payment['late_fees']\n\t\tinterest_rate = payment['interest_rate']\n\t\tmonths_late = payment['months_late']\n\t\tsurcharge_rate = payment['surcharge_rate']\n\t\tsurcharge_max = payment['surcharge_max']\n\t\ttotal = payment['total']\n\n\t\tdefault_installments = []\n\t\tinstallments = self.installments.all().order_by('due')\n\t\tlate_installments = [ installment for installment in installments if installment.due < pay_date and installment.paid < installment.amount ]\n\t\tif installments and not late_installments: # set the next installment date as due date\n\t\t\ttry:\n\t\t\t\tnext_installment = [ installment for installment in installments if installment.due > pay_date and installment.owed > 0 ][0]\n\t\t\t\tdue_date = next_installment.due\n\t\t\t\tamount_due = next_installment.owed\n\t\t\texcept IndexError:\n\t\t\t\tpass\n\n\t\ttotal_due = amount_due + Decimal(late_fees)\n\t\treturn { 'due_date':due_date, 'amount_due':amount_due, 'interest':interest, 'total':total,\n\t\t  'surcharge':surcharge, 'late_fees':late_fees, 'interest_rate':float(interest_rate * 100), 'total_due':total_due, 'months_late':months_late,\n\t\t  'surcharge_rate':surcharge_rate, 'surcharge_max':surcharge_max, 'installments':installments }\n\n\n\tdef calculatePayment(self, pay_date=None, payment_amount=None):\n\t\t\"\"\"\n\t\treturns the next payment amount, due date and interest payment information\n\t\t\"\"\"\n\t\tif not pay_date:\n\t\t\tpay_date = date.today()\n\n\t\tproperty_or_business = None\n\t\ttry:\n\t\t\tproperty_or_business = self.property\n\t\texcept AttributeError:\n\t\t\tpass\n\n\t\ttry:\n\t\t\tproperty_or_business = self.business\n\t\texcept AttributeError:\n\t\t\tpass\n\n\t\tif property_or_business:\n\t\t\tsector = property_or_business.sector\n\t\telse:\n\t\t\tsector = None\n\n\t\tdue_date = self.due_date\n\t\ttax_amount = self.amount or 0\n\t\tamount_paid = tax_amount - ( self.remaining_amount or 0)\n\t\tamount_due = tax_amount - amount_paid\n\t\tif amount_due < 0:\n\t\t\tamount_due = 0\n\n\t\tformula_data = []\n\t\tlate_fees = Decimal(0)\n\t\tmonths_late = Decimal(0)\n\t\ttotal_due = Decimal(0)\n\t\tinterest = Decimal(0)\n\t\tsurcharge = Decimal(0)\n\t\tprinciple = Decimal(0)\n\n\t\t# get the settings to calculate late fee\n\t\ttax_periods = Setting.getTaxPeriods(self.fee_setting, self.period_from, self.period_to, sector=sector)\n\t\ttax_setting = tax_periods[0][2]\n\n\t\tinterest_rate = Decimal(tax_setting['late_fee_interest_rate'])\n\t\tsurcharge_rate = Decimal(tax_setting['late_fee_surcharge_rate'])\n\t\tsurcharge_max = Decimal(tax_setting['late_fee_surcharge_max'])\n\n\t\tinstallments = self.installments.all().order_by('due')\n\t\tlate_installments = False\n\t\tpaid_amount = amount_paid\n\t\tunpaid_installments = []\n\n\t\tfor i in installments:\n\t\t\tif paid_amount < i.amount:\n\t\t\t\tpaid_amount = 0\n\t\t\t\tunpaid_installments.append(i)\n\t\t\telse: # already paid\n\t\t\t\tpaid_amount -= i.amount\n\n\t\tlate_installments = [ i for i in unpaid_installments if pay_date > i.due ]\n\n\t\tif not late_installments and unpaid_installments: # set the next installment date as due date\n\t\t\tdue_date = unpaid_installments[0].due\n\t\t\tamount_due = unpaid_installments[0].remaining\n\n\t\tif due_date and pay_date > due_date:\n\t\t\tlate_fees, months_late, interest, surcharge, principle = calc_interest(due_date, amount_due, surcharge_rate, surcharge_max, interest_rate, payment_amount, pay_date)\n\n\t\treturn { 'due_date':due_date, 'amount_due':amount_due, 'interest':interest,\n\t\t  'surcharge':surcharge, 'late_fees':late_fees, 'interest_rate':float(interest_rate * 100),\n\t\t  'months_late':months_late, 'principle':principle, 'amount_paid':amount_paid,\n\t\t  'surcharge_rate':float(surcharge_rate * 100), 'surcharge_max':surcharge_max }\n\n\n\tdef amount_owing(self, as_at=None):\n\t\tif not as_at:\n\t\t\tas_at = date.today()\n\t\tsector = None\n\t\tif hasattr(self, 'business'):\n\t\t\tsector = self.business.sector\n\t\telif hasattr(self, 'property'):\n\t\t\tsector = self.property.sector\n\n\t\ttax_periods = Setting.getTaxPeriods(self.fee_setting, self.period_from, self.period_to, sector=sector)\n\t\ttax_setting = tax_periods[0][2]\n\n\t\tinterest_rate = Decimal(tax_setting['late_fee_interest_rate'])\n\t\tsurcharge_rate = Decimal(tax_setting['late_fee_surcharge_rate'])\n\t\tsurcharge_max = Decimal(tax_setting['late_fee_surcharge_max'])\n\n\t\tlate_fees, months_late, interest, surcharge, principle = calc_interest(self.due_date, self.remaining_amount, surcharge_rate, surcharge_max, interest_rate, self.remaining_amount, as_at)\n\n\t\treturn self.remaining_amount, (late_fees or 0)\n\n\n\tdef get_installments(self):\n\t\tpaid = (self.amount or 0) - (self.remaining_amount or 0)\n\t\tinstallments = self.installments.all().order_by('due')\n\t\tfor i in installments:\n\t\t\tif paid < i.amount:\n\t\t\t\ti.paid = paid\n\t\t\t\tpaid = 0\n\t\t\telse: # already paid\n\t\t\t\tpaid = paid - i.amount\n\t\t\t\ti.paid = i.amount\n\t\t\ti.owed = i.amount - i.paid\n\t\treturn installments\n\n\n\tdef calculateRemainingAmount(self, amount):\n\t\tfrom django.db.models import Sum\n\t\t\"\"\"given a new amount, calculate the amount remaining\"\"\"\n\t\tif amount is None:\n\t\t\treturn None\n\t\tpaid_amount = self.payments.filter(amount__gt=0).aggregate(sum=Sum('amount'))['sum'] or 0\n\t\tremaining_amount = amount - paid_amount\n\t\treturn remaining_amount\n\n\tdef get_paid_amount(self):\n\t\tfrom django.db.models import Sum\n\t\tpaid = self.payments.filter(amount__gt=0).aggregate(amount=Sum('amount'), fines=Sum('fine_amount'))\n\t\ttotal = paid['amount'] or 0\n\t\tfines = paid['fines'] or 0\n\t\tcapital_amount = total - fines\n\t\treturn capital_amount, fines\n\n\n\tdef get_remaining_amount(self):\n\t\treturn (self.amount - self.get_paid_amount()[0])\n\n\n\tdef reset_tax(self):\n\t\tself.submit_date = None\n\t\tself.amount = self.remaining_amount = 0\n\t\tself.is_paid = False\n\t\tself.save()\n\t\ttry:\n\t\t\tself.formuladata.delete()\n\t\texcept:\n\t\t\tpass\n\n\n\t@property\n\tdef tax_type(self):\n\t\tif type(self) is PropertyTaxItem:\n\t\t\treturn 'fixed_asset_tax'\n\t\telif type(self) is RentalIncomeTax:\n\t\t\treturn 'rental_income_tax'\n\t\telif type(self) is TradingLicenseTax:\n\t\t\treturn 'trading_license_tax'\n\t\telif type(self) is Fee and 'land_lease' in self.fee_type:\n\t\t\treturn 'land_lease_fee'\n\t\telif type(self) is Fee:\n\t\t\treturn 'fee'\n\n\n\t@property\n\tdef fee_setting(self):\n\t\tif type(self) is PropertyTaxItem:\n\t\t\treturn 'fixed_asset_tax'\n\t\telse:\n\t\t\treturn 'general_fee'\n\n\tdef generateInstallments(self):\n\t\tamount = self.remaining_amount\n\t\tmonths = 3\n\t\tno_installments = 12/months\n\t\tif type(self.period_from) is datetime:\n\t\t\tdate_from = self.period_from.astimezone(timezone.get_default_timezone()).date()\n\t\tdue_date = date_from\n\t\tfor i in range(no_installments):\n\t\t\tdue_date = due_date + relativedelta(months=months-1) + relativedelta(day=31)\n\t\t\tamount = round(self.remaining_amount / no_installments)\n\t\t\tInstallment.objects.create(fee=self, amount=amount, paid=0, due=due_date)\n\t\treturn self.installments.all()\n\n\tdef pay_installment(self, amount, paid_on=None):\n\t\tinstallments = self.installments.filter(paid__lt=F('amount')).order_by('due')\n\t\tpaid_on = paid_on or date.today()\n\t\tfor installment in installments:\n\t\t\tunpaid = installment.amount - installment.paid\n\t\t\tif amount <= unpaid:\n\t\t\t\tinstallment.paid += amount\n\t\t\t\tinstallment.paid_on = paid_on\n\t\t\t\tinstallment.save()\n\t\t\t\tbreak\n\t\t\telse:\n\t\t\t\tinstallment.paid = installment.amount\n\t\t\t\tinstallment.paid_on = paid_on\n\t\t\t\tinstallment.save()\n\t\t\t\tamount -= unpaid\n\n\tdef next_outstanding_installment(self):\n\t\tinstallments = self.installments.filter(paid__lt=F('amount')).order_by('due')\n\t\tif installments:\n\t\t\treturn installments[0]\n\t\telse:\n\t\t\treturn None\n\n\tdef calc_tax_period(self):\n\t\tif type(self) is not Fee or type(self) is Fee and self.fee_type == 'land_lease':\n\t\t\tdate_from = date(self.date_from.year, 1, 1)\n\t\t\tdate_to = date(self.date_from.year, 12, 31)\n\t\telse:\n\t\t\tdate_from = self.date_from\n\t\t\tdate_to = self.date_to\n\n\t\towners = None\n\t\tdate_started = None\n\t\tif hasattr(self, 'subbusiness') and self.subbusiness:\n\t\t\towners = self.subbusiness.business.owners.filter(i_status='active').order_by('date_started')\n\t\t\tdate_started = self.subbusiness.business.date_started\n\t\tif hasattr(self, 'business') and self.business:\n\t\t\towners = self.business.owners.filter(i_status='active').order_by('date_started')\n\t\t\tdate_started = self.business.date_started\n\t\t\tif self.business.closed_date and self.business.closed_date < date_to:\n\t\t\t\tdate_to = self.business.closed_date\n\t\telif hasattr(self, 'property') and self.property:\n\t\t\towners = self.property.owners.filter(i_status='active').order_by('date_started')\n\t\tif date_started:\n\t\t\tif date_started > date_from and date_started <= date_to:\n\t\t\t\tdate_from = date_started\n\t\tif owners:\n\t\t\tdate_started = owners[0].date_started\n\t\t\tif date_started > date_from and date_started <= date_to:\n\t\t\t\tdate_from = date_started\n\n\t\t\"\"\"\n\t\tif self.months_exempted:\n\t\t\tdate_from = date_from + relativedelta(months=self.months_exempted)\n\t\t\tif date_from > date_to:\n\t\t\t\tdate_from = date_to\n\t\t\"\"\"\n\n\t\treturn date_from, date_to\n\n\n\"\"\"\nclass Payment(models.Model):\n\t# models.ForeignKey(Tax)\n\tbusiness_id = models.IntegerField(help_text=\"The business who pay this tax item.\", blank = True, null=True)\n\tcitizen_id = models.IntegerField(blank = True, null=True)\n\tstaff  = models.ForeignKey(PMUser, help_text=\"\",blank = True, null=True)\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2)\n\treceipt_no = models.CharField(max_length = 50)\n\tbank =  models.CharField(max_length = 100, choices=variables.banks)\n\tpaid_date = models.DateField(help_text=\"\",default=datetime.now)\n\tfine_amount = models.DecimalField(max_digits = 20, decimal_places = 2, default=0)\n\tfine_description = models.TextField(null=True, blank = True)\n\tmanual_receipt = models.CharField(max_length = 50)\n\tdate_time = models.DateTimeField(help_text=\"The date when this payment is entered into the system.\",auto_now_add=True,auto_now=True)\n\tnote = models.TextField(null=True, blank = True,  help_text=\"note about this payment.\")\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\"\"\"\n\"\"\"\nLand Rental Tax Models\n\"\"\"\nclass LandRentalTax(models.Model):\n\tplot_id = models.IntegerField(help_text='This is the ID of the Plot.')\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"The amount of tax item.\")\n\tcurrency = models.CharField(max_length=4, choices=variables.currency_types)\n\tperiod_from = models.DateTimeField(help_text=\"The start date of a period that this tax item is for.\")\n\tperiod_to = models.DateTimeField(help_text=\"The end date of a period that this tax item is for.\")\n\tdue_date = models.DateField(help_text=\"The date this tax item is due.\")\n\tis_paid = models.BooleanField(help_text=\"Whether tax is payed.\")\n\tdate_time = models.DateTimeField(help_text=\"The date this tax item is generated.\")\n\tproperty = models.ForeignKey(Property, null=True, blank=True)\n\n\nclass LandRentalTaxNotes(models.Model):\n\tland_rental_tax = models.ForeignKey('LandRentalTax')\n\tstaff_id = models.IntegerField(help_text='This is the Id of the Staff Member that created the note.')\n\tnote = models.TextField(help_text='This is the Note for the LandRental Tax Record Itself.')\n\tdate_time = models.DateTimeField(help_text='This is the Date and Time the Note Was Created',auto_now_add=True)\n\nclass LandRentalTaxMedia(models.Model):\n\tland_rental_tax = models.ForeignKey('LandRentalTax')\n\n\"\"\"\nThe following Models are Related to the P = IncompletePayment.objects.all()\n\t\t\tsearch_iroperty Asset Tax Collection and Evalutional\n\"\"\"\n\nclass PropertyTaxItem(Tax):\n\tplot_id = models.CharField(max_length = 50, help_text = 'This is the ID of the Plot that the Declared Value is for.')\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"The amount oIntegerFieldf property tax item.\", null=True, blank = True)\n\tremaining_amount = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"The remaining amount (subtracted past payments).\", null=True, blank = True)\n\tcurrency = models.CharField(max_length=4, choices=variables.currency_types)\n\tperiod_from = models.DateTimeField(help_text=\"The start date of a period that this property tax item is for.\")\n\tperiod_to = models.DateTimeField(help_text=\"The end date of a period that this property tax item is for.\")\n\tdate_from = models.DateField(null=True)\n\tdate_to = models.DateField(null=True)\n\tdue_date = models.DateField(help_text=\"The date this property tax item is due.\", null=True, blank=True, default=None)\n\tdate_time = models.DateTimeField(help_text=\"The date this propert tax item is generated.\", blank=True, default=None)\n\tis_paid = models.BooleanField(help_text=\"Whether tax is payed.\")\n\tis_chanllenged = models.BooleanField(help_text=\"whether this tax item is challenged.\")\n\tis_reviewed = models.BooleanField(help_text =\"whether this tax item is reviewed.\")\n\tis_accepted = models.BooleanField(help_text=\"whether this tax item is accepted.\")\n\tsubmit_date = models.DateTimeField(help_text=\"The date this fee item is submited.\", null=True, blank=True)\n\tsubmit_details = models.CharField(max_length=500, null=True, blank=True)\n\tstaff_id = models.IntegerField(help_text=\"The staff who generates this property tax item.\", null=True, blank=True)\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\tproperty = models.ForeignKey(Property, null=True, blank=True, related_name='fixed_asset_taxes')\n\texempt = models.BooleanField(default=False)\n\tmonths_deferred = models.IntegerField(default=0)\n\tland_use_types = models.ManyToManyField(LandUse)\n\tdeclared_value = models.ForeignKey(DeclaredValue, null=True, blank=True)\n\t#citizen_owners = models.ManyToManyField(Citizen)\n\t#business_owners = models.ManyToManyField(Business)\n\n\n\tdef __unicode__(self):\n\t\t#return 'sd ' + str(self.id)\n\t\tname = \"Property Tax Item (UPI: \" + str(self.property.getUPI()) + \") \"\n\t\tif self.date_from and self.date_to:\n\t\t\tname += \"[\" + DateFormat(self.date_from).format('d/m/Y') + \" - \" + DateFormat(self.date_to).format('d/m/Y') + \"]\"\n\t\treturn name\n\n\tdef calc_tax(self):\n\t\tproperty = self.property\n\t\tdeclared_value = property.declaredValue\n\t\tself.date_from, self.date_to = self.calc_tax_period()\n\n\t\tif declared_value and declared_value.declared_on and declared_value.amount is not None and property.land_use_types.count() > 0:\n\t\t\texpired_declared_value = declared_value.declared_on + relativedelta(years=4) < self.date_from\n\t\t\tif not expired_declared_value: # declared value has not expired, auto submit tax calc\n\t\t\t\tif declared_value.residential_amount + declared_value.agriculture_amount + declared_value.commercial_amount == declared_value.amount:\n\t\t\t\t\ttax_summary = Setting.calculateFixedAssetTax(self.date_from, self.date_to, residential=declared_value.residential_amount, commercial=declared_value.commercial_amount, agricultural=declared_value.agriculture_amount)\n\t\t\t\telse:\n\t\t\t\t\tland_use_codes = [land_use_type.code for land_use_type in property.land_use_types.all()]\n\t\t\t\t\tif 'RES' in land_use_codes:\n\t\t\t\t\t\ttax_summary = Setting.calculateFixedAssetTax(self.date_from, self.date_to, residential=declared_value.amount)\n\t\t\t\t\telif 'AGR' in land_use_codes:\n\t\t\t\t\t\ttax_summary = Setting.calculateFixedAssetTax(self.date_from, self.date_to, agricultural=declared_value.amount)\n\t\t\t\t\telif 'IND' in land_use_codes or 'QRY' in land_use_codes or 'COM' in land_use_codes: # industrial\n\t\t\t\t\t\ttax_summary = Setting.calculateFixedAssetTax(self.date_from, self.date_to, industrial=declared_value.amount)\n\t\t\t\t\telse:\n\t\t\t\t\t\traise Exception(\"Property ID %s does not contain a valid land use\" % property.pk)\n\t\t\t\tif tax_summary:\n\t\t\t\t\tif self.exempt:\n\t\t\t\t\t\tself.amount = 0\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.amount = tax_summary['amount']\n\t\t\t\t\tself.remaining_amount = self.calculateRemainingAmount(self.amount)\n\t\t\t\t\tif self.remaining_amount <= 0:\n\t\t\t\t\t\tself.is_paid = True\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.is_paid = False\n\t\t\t\t\tself.due_date = tax_summary['due_date']\n\t\t\t\t\tself.save()\n\n\t\t\t\t\tfd, created = FormulaData.objects.get_or_create(property_item=self)\n\t\t\t\t\tfd.formula_data = tax_summary\n\t\t\t\t\tfd.save()\n\t\t\t\t\treturn tax_summary\n\t\tself.due_date = Setting.get_due_date('fixed_asset_tax', self.date_to)\n\t\tself.reset_tax()\n\t\treturn None\n\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\n\nclass ChallengePropertyTaxItem(models.Model):\n\tproperty_tax_item = models.ForeignKey(PropertyTaxItem)\n\tcitizen_id = models.IntegerField(help_text=\"The person who pay this tax item.\")\n\tstaff_id = models.IntegerField(help_text=\"The government staff who accepts the payment.\")\n\tperiod_from = models.DateTimeField(help_text=\"The date from which this tax item is challenged.\",auto_now_add=True)\n\tperiod_to = models.DateTimeField(help_text=\"The date till which this tax item is challenged.\")\n\nclass ChallengePropertyTaxItemNote(models.Model):\n\tchallenge_property_tax_item = models.ForeignKey(ChallengePropertyTaxItem)\n\tstaff_id = models.IntegerField(help_text=\"The government staff who records this challenge.\")\n\tnote = models.TextField(help_text=\"Note on why this property tax item is challenged.\")\n\nclass ChallengePropertyTaxItemMedia(models.Model):\n\tchallengepropertytaxitemid = models.ForeignKey(ChallengePropertyTaxItem)\n\tmediatype = models.CharField(max_length=4,choices=variables.media_types,help_text='This is the type of media the file is')\n\tmediafile = models.FileField(help_text='This is the location of the file on the file system.',upload_to='tmp')\n\tstaffid = models.IntegerField(help_text='The ID of the Staff Member who uploaded the file')\n\tmediadatetime = models.DateTimeField(help_text='This is the date and time the file was uploaded',auto_now_add=True)\n\nclass ReviewPropertyTaxItem(models.Model):\n\tchallengepropertytaxitemid = models.ForeignKey(ChallengePropertyTaxItem)\n\tstaffid = models.IntegerField(help_text=\"The government staff who review the challenge.\")\n\treviewdate = models.DateTimeField(help_text=\"The date when this tax item is reviewd.\",auto_now_add=True)\n\tnote = models.CharField(max_length=255, help_text=\"review result.\")\n\n\"\"\"\nThe following Models are Related to the Rental Income Tax Collection and Evalutional\n\"\"\"\nclass RentalIncomeTax(Tax):\n\tplot_id = models.CharField(max_length = 50, help_text='This is the ID of the Plot that the Declared Value is for.')\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"The amount of tax item.\", null=True, blank = True)\n\tremaining_amount = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"The remaining amount (subtracted past payments).\", null=True, blank = True)\n\tcurrency = models.CharField(max_length=4, choices=variables.currency_types)\n\tperiod_from = models.DateTimeField(help_text=\"The start date of a period that this tax item is for.\")\n\tperiod_to = models.DateTimeField(help_text=\"The end date of a period that this tax item is for.\")\n\tdate_from = models.DateField(null=True)\n\tdate_to = models.DateField(null=True)\n\tdue_date = models.DateField(help_text=\"The date this tax item is due.\", null=True, blank=True)\n\tis_paid = models.BooleanField(help_text=\"Whether tax is payed.\")\n\tsubmit_date = models.DateTimeField(help_text=\"The date this fee item is submited.\", null=True, blank=True)\n\tsubmit_details = models.CharField(max_length=500, null=True, blank=True)\n\tdate_time = models.DateTimeField(help_text=\"The date this tax item is generated.\")\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\tproperty = models.ForeignKey(Property, null=True, blank=True, related_name='rental_income_taxes')\n\tstaff_id = models.IntegerField(help_text=\"The staff who generates this property tax item.\", null=True, blank=True)\n\texempt = models.BooleanField(default=False)\n\tmonths_deferred = models.IntegerField(default=0)\n\tdeclared_rental_income = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"Last year declared rental income.\", null=True, blank = True)\n\tdeclared_bank_interest = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"Last year bank interest paid\", null=True, blank = True)\n\t#citizen_owners = models.ManyToManyField(Citizen)\n\t#business_owners = models.ManyToManyField(Business)\n\n\tdef __unicode__(self):\n\t\tname = \"Rental Income Tax (UPI: \" + str(self.property.getUPI()) + \") \"\n\t\tif self.date_from and self.date_to:\n\t\t\tname += \"[\" + DateFormat(self.date_from).format('d/m/Y') + \" - \" + DateFormat(self.date_to).format('d/m/Y') + \"]\"\n\t\treturn name\n\n\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\n\tdef calc_tax(self):\n\t\tself.date_from, self.date_to = self.calc_tax_period()\n\t\tif self.declared_bank_interest and self.declared_rental_income:\n\t\t\ttax_summary = Setting.calculateRentalIncome(self.date_from, self.date_to, self.declared_rental_income, self.declared_bank_interest)\n\t\t\tif tax_summary:\n\t\t\t\tif self.exempt:\n\t\t\t\t\tself.amount = 0\n\t\t\t\telse:\n\t\t\t\t\tself.amount = tax_summary['amount']\n\t\t\t\tself.remaining_amount = self.calculateRemainingAmount(self.amount)\n\t\t\t\tif self.remaining_amount <= 0 and not self.exempt:\n\t\t\t\t\tself.is_paid = True\n\t\t\t\telse:\n\t\t\t\t\tself.is_paid = False\n\t\t\t\tself.due_date = tax_summary['due_date']\n\t\t\t\tself.save()\n\t\t\t\tfd, created = FormulaData.objects.get_or_create(rental_income=self)\n\t\t\t\tfd.formula_data = tax_summary\n\t\t\t\tfd.save()\n\t\t\t\treturn tax_summary\n\n\t\tself.due_date = Setting.get_due_date('rental_income_tax', self.date_to)\n\t\tself.reset_tax()\n\t\treturn None\n\n\nclass RentalIncomeTaxNotes(Tax):\n\trental_income_tax = models.ForeignKey('rentalIncomeTax')\n\tstaff_id = models.IntegerField(help_text='This is the Id of the Staff Member that created the note.')\n\tnote = models.TextField(help_text='This is the Note for the LandRental Tax Record Itself.')\n\tdate_time = models.DateTimeField(help_text='This is the Date and Time the Note Was Created',auto_now_add=True)\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\n\"\"\"\nThe following Models are Related to the Trading License Tax Collection and Evalutional\n\"\"\"\nclass TradingLicenseTax(Tax):\n\tbusiness = models.ForeignKey(Business, null=True, blank=True, help_text=\"Business that have trading license to be taxed.\")\n\tsubbusiness = models.ForeignKey(SubBusiness,  null=True, blank=True, help_text=\"SubBusiness that have trading license to be taxed.\")\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"The amount of property tax item.\", null=True, blank = True)\n\tremaining_amount = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"The remaining amount (subtracted past payments).\", null=True, blank = True)\n\tcurrency = models.CharField(max_length=4, choices=variables.currency_types)\n\tperiod_from = models.DateTimeField(help_text=\"The start date of a period that this property tax item is for.\")\n\tperiod_to = models.DateTimeField(help_text=\"The end date of a period that this property tax item is for.\")\n\tdate_from = models.DateField(null=True)\n\tdate_to = models.DateField(null=True)\n\tdue_date = models.DateField(help_text=\"The date this property tax item is due.\", null=True, blank = True)\n\tis_paid = models.BooleanField(help_text=\"Whether tax is payed.\")\n\tsubmit_date = models.DateTimeField(help_text=\"The date this fee item is submited.\", null=True, blank=True)\n\tsubmit_details = models.CharField(max_length=500, null=True, blank=True)\n\tstaff_id = models.IntegerField(help_text=\"The staff who generates this property tax item.\", null=True, blank = True)\n\tdate_time = models.DateTimeField(help_text=\"The date this propert tax item is generated.\")\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\texempt = models.BooleanField(default=False)\n\tturnover = models.DecimalField(max_digits=20, decimal_places=2, help_text=\"Declared Turnover\", null=True)\n\tmonths_deferred = models.IntegerField(default=0)\n\tactivity_data = models.TextField(blank=False, null=True)\n\n\n\tdef __unicode__(self):\n\t\tif self.business:\n\t\t\tname = \"Trading License Tax (TIN: \" + str(self.business.tin) + \") \"\n\n\t\telif self.subbusiness:\n\t\t\tname = \"Trading License Tax (TIN: \" + str(self.subbusiness.business.tin) + \",branch:\"+self.subbusiness.branch + \") [\" + DateFormat(self.period_from).format('d/m/Y') + \" - \" + DateFormat(self.period_to).format('d/m/Y') + \"]\"\n\t\t\tif self.date_from and self.date_to:\n\t\t\t\tname += \"[\" + DateFormat(self.date_from).format('d/m/Y') + \" - \" + DateFormat(self.date_to).format('d/m/Y') + \"]\"\n\t\treturn name\n\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\n\n\tdef calculateActivityTax(self, *args, **kwargs):\n\t\tif not self.activity_data:\n\t\t\tself.reset_tax()\n\t\t\treturn None\n\t\telse:\n\t\t\tactivity_data = pickle.loads(self.activity_data.decode('base64'))\n\t\ttax_due = 0\n\t\tdate_from = self.date_from\n\t\tdate_to = self.date_to\n\t\tdue_date = self.date_to\n\t\tformula_data = {}\n\t\tcurrent_year = date_from.year\n\t\ttotal_days_in_period = Decimal((date(current_year, 12, 31) - date(current_year, 1, 1)).days + 1)\n\t\tperiod = (date_from, date_to)\n\t\tdays_in_period = Decimal((date_to - date_from).days + 1)\n\t\tactivities = variables.activities\n\t\tformula_data = {}\n\t\tactivity_description = variables.activity_description\n\t\tfor activity, settings in activities:\n\t\t\tfor setting_name, rate in settings:\n\t\t\t\tunits = activity_data.get(\"%s_%s\" % (activity, setting_name), 0)\n\t\t\t\tif not units: continue\n\t\t\t\tamount= Decimal(units * rate).quantize(Decimal('0.01'))\n\t\t\t\ttax_due += amount\n\t\t\t\tformula_data[\"%s - %s\" % (activity_description[activity], setting_name)] = {'units':units, 'rate': rate, 'amount':amount }\n\n\t\ttax_due = Decimal(int(tax_due * days_in_period / total_days_in_period))\n\t\treturn { 'amount':tax_due, 'due_date': due_date, 'due': due_date, 'formula_data':formula_data, 'days':days_in_period, 'total_days':total_days_in_period, 'vat':False }\n\n\n\tdef calc_tax(self):\n\n\t\tif self.subbusiness:\n\t\t\tbusiness = self.subbusiness.business\n\t\telse:\n\t\t\tbusiness = self.business\n\n\t\tself.date_from, self.date_to = self.calc_tax_period()\n\n\t\tif business.vat_register:\n\t\t\tif self.turnover:\n\t\t\t\ttax_summary = Setting.calculateTradingLicenseTax(self.date_from, self.date_to, self.turnover,\n\t\t\t\t\tsector=business.sector)\n\t\t\telse:\n\t\t\t\ttax_summary = None\n\t\telse:\n\t\t\ttax_summary = self.calculateActivityTax()\n\n\t\tif tax_summary:\n\t\t\tif self.exempt:\n\t\t\t\tself.amount = 0\n\t\t\telse:\n\t\t\t\tself.amount = tax_summary['amount']\n\t\t\tself.remaining_amount = self.calculateRemainingAmount(self.amount)\n\t\t\tif self.remaining_amount <= 0:\n\t\t\t\tself.is_paid = True\n\t\t\telse:\n\t\t\t\tself.is_paid = False\n\t\t\tself.due_date = tax_summary['due_date']\n\t\t\tself.save()\n\t\telse:\n\t\t\tself.due_date = Setting.get_due_date('trading_license_tax', self.date_to)\n\t\t\tself.reset_tax()\n\t\t\treturn None\n\n\t\tfd, created = FormulaData.objects.get_or_create(trading_license=self)\n\t\tfd.formula_data = tax_summary\n\t\tfd.save()\n\t\tif self.exempt:\n\t\t\tself.amount = 0\n\t\tself.save()\n\t\treturn tax_summary\n\n\nclass FeeManager(models.Manager):\n\tdef get_query_set(self):\n\t\treturn super(FeeManager,self).get_query_set().filter(status__code='active')\n\n\nclass Fee(Tax):\n\tfee_type = models.CharField(max_length=25, choices=variables.fee_types)\n\t#name = models.CharField(max_length=50, null=True, blank=True)\n\tquantity = models.IntegerField(default=1, null=True, blank=True)\n\t#target = models.CharField(max_length=25, null = True, blank=True, help_text=\"Payer type.\")\n\t#target_id = models.IntegerField(null=True, blank=True, help_text=\"Who will pay.\")\n\t#target_branch_id = models.IntegerField(help_text=\"Branch of target. i.e., branch of business\", null=True, blank=True)\n\tamount = models.IntegerField(help_text=\"The amount of fee item.\")\n\tremaining_amount = models.IntegerField(help_text=\"The remaining amount (subtracted past payments).\", null=True, blank = True)\n\tcurrency = models.CharField(max_length=4, choices=variables.currency_types)\n\tperiod_from = models.DateTimeField(help_text=\"The start date of a period that this fee item is for.\")\n\tperiod_to = models.DateTimeField(help_text=\"The end date of a period that this fee item is for.\")\n\tdate_from = models.DateField(null=True)\n\tdate_to = models.DateField(null=True)\n\tdue_date = models.DateField(help_text=\"The date this fee item is due.\", null=True, blank=True)\n\tis_paid = models.BooleanField(help_text=\"Whether fee is payed.\")\n\tsubmit_date = models.DateTimeField(help_text=\"The date this fee item is submited.\", null=True, blank=True)\n\t#submit_details = models.CharField(max_length=500, null=True, blank=True)\n\tstaff_id = models.IntegerField(help_text=\"The staff who generates this fee item.\", null=True, blank=True)\n\tdate_time = models.DateTimeField(help_text=\"The date this fee item is generated.\",auto_now_add=True)\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\tbusiness = models.ForeignKey(Business, null=True, blank=True, related_name='business_fees')\n\tsubbusiness = models.ForeignKey(SubBusiness,null=True, blank=True)\n\tproperty = models.ForeignKey(Property,null=True,blank=True, related_name='property_fees')\n\tcitizen = models.ForeignKey(Citizen,null=True,blank=True)\n\texempt = models.BooleanField(default=False)\n\tland_lease_type = models.ForeignKey(LandUse, null=True, blank=True, default=None)\n\t#citizen_owners = models.ManyToManyField(Citizen)\n\t#business_owners = models.ManyToManyField(Business)\n\tcategory = models.ForeignKey(CategoryChoice, related_name='fee_category')\n\tstatus = models.ForeignKey(CategoryChoice)\n\tobjects = FeeManager()\n\tall_objects = models.Manager()\n\n\tdef __unicode__(self):\n\t\tif self.category.code == 'land_lease':\n\t\t\treturn \"Land Lease %s - %s\" % (self.date_from.strftime('%d/%m/%Y'), self.date_to.strftime('%d/%m/%Y'))\n\n\t\tif self.category.code == 'cleaning':\n\t\t\treturn \"Cleaning Fee for %s\" % (self.date_from.strftime('%B %Y'))\n\n\t\telse:\n\t\t\tname = self.fee_type.title() + \" Fee \"\n\t\t\tif self.date_from and self.date_to:\n\t\t\t\tname += \"[\" + DateFormat(self.date_from).format('d/m/Y') + \" - \" + DateFormat(self.date_to).format('d/m/Y') + \"]\"\n\t\t\treturn name\n\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\n\n\tdef calc_tax(self):\n\t\tif self.fee_type in ('cleaning_fee', 'cleaning'):\n\t\t\treturn self.calc_cleaningFee()\n\t\telif self.fee_type in ('land_lease', 'land_lease_fee'):\n\t\t\traise Exception('not implemented')\n\t\telse:\n\t\t\treturn None\n\n\tdef calc_landlease(self):\n\t\tself.date_from, self.date_to = self.calc_tax_period()\n\t\tland_size = self.property.get_sq_m()\n\t\tif self.property.land_use_type and land_size:\n\t\t\ttax_summary = Setting.calculateLandLeaseFee(self.date_from, self.date_to, self.property.land_use_type, land_size, district=self.property.sector.district, sector=self.property.sector, cell=self.property.cell, village=self.property.village)\n\t\t\tif tax_summary:\n\t\t\t\tif self.exempt:\n\t\t\t\t\tself.amount = 0\n\t\t\t\telse:\n\t\t\t\t\tself.amount = tax_summary['amount']\n\t\t\t\tself.remaining_amount = self.calculateRemainingAmount(self.amount) or 0\n\t\t\t\tself.due_date = tax_summary['due_date']\n\n\t\t\t\tfd, created = FormulaData.objects.get_or_create(fee=self)\n\t\t\t\tfd.formula_data = tax_summary\n\t\t\t\tfd.save()\n\t\t\t\tself.save()\n\t\t\t\treturn tax_summary\n\n\t\tself.due_date = Setting.get_due_date('land_lease_fee', self.date_to)\n\t\tself.reset_tax()\n\t\treturn None\n\n\n\tdef calc_cleaningFee(self):\n\t\tif self.category.code == 'cleaning':\n\t\t\tbusiness = self.business\n\t\t\tif business.business_category is not None:\n\t\t\t\tif not self.is_paid:\n\t\t\t\t\tif business.business_category_id in range(1,7):\n\t\t\t\t\t\tself.amount = 10000\n\t\t\t\t\telif business.business_category_id == 7:\n\t\t\t\t\t\tself.amount = 5000\n\t\t\t\t\telif business.business_category_id == 8:\n\t\t\t\t\t\tself.amount = 3000\n\t\t\t\t\telse:\n\t\t\t\t\t\tself.amount = 0\n\n\t\t\t\tself.remaining_amount = self.calculateRemainingAmount(self.amount) or 0\n\t\t\t\tif self.remaining_amount <= 0 and self.amount > 0:\n\t\t\t\t\tself.is_paid = True\n\t\t\t\telse:\n\t\t\t\t\tself.is_paid = False\n\n\t\t\t\tself.submit_date = datetime.now()\n\t\t\t\tdue_date = self.date_from + relativedelta(months=1)\n\t\t\t\tself.due_date = date(due_date.year, due_date.month, 5)\n\n\t\t\t\tif self.amount:\n\t\t\t\t\tself.i_status = 'active'\n\t\t\t\t\tself.status = CategoryChoice.objects.get(category__code='status', code='active')\n\t\t\t\tself.save()\n\t\t\t\treturn self.amount, self.due_date\n\n\t\t\tif self.pk:\n\t\t\t\tself.submit_date = None\n\t\t\t\tself.i_status = 'inactive'\n\t\t\t\tself.status = CategoryChoice.objects.get(category__code='status', code='inactive')\n\t\t\t\tself.is_paid = False\n\t\t\t\tself.save()\n\n\t\treturn (None, None)\n\n\tdef medias(self):\n\t\tfrom media.models import Media\n\t\treturn Media.objects.filter(tax_type__in=['cleaning_fee','cleaning','fee'], tax_id=self.pk)\n\n\nclass MiscellaneousFee(models.Model):\n\tfee_type = models.CharField(max_length=250)\n\tfee_sub_type = models.CharField(max_length=250)\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"The amount of fee item.\")\n\tremaining_amount = models.DecimalField(max_digits = 20, decimal_places = 2, help_text=\"The remaining amount (subtracted past payments).\", null=True, blank = True)\n\tcurrency = models.CharField(max_length=4, choices=variables.currency_types)\n\tis_paid = models.BooleanField(help_text=\"Whether fee is paid.\")\n\tsubmit_date = models.DateTimeField(help_text=\"The date this fee item is submited.\", null=True, blank=True)\n\tsubmit_details = models.CharField(max_length=500, null=True, blank=True)\n\tstaff_id = models.IntegerField(help_text=\"The staff who generates this fee item.\", null=True, blank=True)\n\tdate_time = models.DateTimeField(help_text=\"The date this fee item is generated.\",auto_now_add=True)\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\tbusiness = models.ForeignKey(Business, null=True, blank=True)\n\tsubbusiness = models.ForeignKey(SubBusiness,null=True, blank=True)\n\tproperty = models.ForeignKey(Property,null=True,blank=True)\n\tcitizen = models.ForeignKey(Citizen,null=True,blank=True)\n\n\tdef __unicode__(self):\n\t\treturn self.fee_type.title() + \" - \" + self.fee_sub_type.title() + \" Miscellaneous Fee \"\n\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\n\"\"\"\nThe following Models is for saving payments for Tax Items\n\"\"\"\nclass PayRentalIncomeTax(models.Model):\n\trental_income_tax = models.ForeignKey(RentalIncomeTax, help_text=\"\", related_name=\"payments\")\n\tbusiness_id = models.IntegerField(help_text=\"The business who pay this tax item.\", blank = True, null=True)\n\tcitizen_id = models.IntegerField(blank = True, null=True)\n\tstaff  = models.ForeignKey(PMUser, help_text=\"\",blank = True, null=True)\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2)\n\treceipt_no = models.CharField(max_length = 50)\n\tbank =  models.CharField(max_length = 100, choices=variables.banks)\n\tpaid_date = models.DateField(help_text=\"\",default=datetime.now)\n\tfine_amount = models.DecimalField(max_digits = 20, decimal_places = 2, default=0)\n\tfine_description = models.TextField(null=True, blank = True)\n\tmanual_receipt = models.CharField(max_length = 50)\n\tdate_time = models.DateTimeField(help_text=\"The date when this payment is entered into the system.\",auto_now_add=True)\n\tnote = models.TextField(null=True, blank = True,  help_text=\"note about this payment.\")\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\n\tdef __unicode__(self):\n\t\treturn \"Rental Income Tax Payment\"\n\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\n\nclass PayTradingLicenseTax(models.Model):\n\tcitizen_id = models.IntegerField(blank = True, null=True)\n\tbusiness_id = models.IntegerField(help_text=\"The business who pay this tax item.\", blank = True, null=True)\n\t#staff_id = models.IntegerField(help_text=\"The government staff who accepts the payment.\")\n\tstaff  = models.ForeignKey(PMUser, help_text=\"\",blank = True, null=True)\n\ttrading_license_tax = models.ForeignKey(TradingLicenseTax, help_text=\"\", related_name=\"payments\")\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2)\n\treceipt_no = models.CharField(max_length = 50)\n\tbank =  models.CharField(max_length = 100, choices=variables.banks)\n\tpaid_date = models.DateField(help_text=\"\",default=datetime.now)\n\tfine_amount = models.DecimalField(max_digits = 20, decimal_places = 2, default=0)\n\tfine_description = models.TextField(null=True, blank = True)\n\tmanual_receipt = models.CharField(max_length = 50)\n\tdate_time = models.DateTimeField(help_text=\"The date when this payment is entered into the system.\",auto_now_add=True)\n\tnote = models.TextField(null=True, blank = True,  help_text=\"note about this payment.\")\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\tdef __unicode__(self):\n\t\treturn \"Trading License Tax Payment\"\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\nclass PayFeeManager(models.Manager):\n\tdef get_query_set(self):\n\t\treturn super(PayFeeManager,self).get_query_set().filter(status__code='active')\n\nclass PayFee(models.Model):\n\tcitizen_id = models.IntegerField(blank = True, null=True)\n\tbusiness_id = models.IntegerField(help_text=\"The business who pay this tax item.\", blank = True, null=True)\n\tstaff  = models.ForeignKey(PMUser, help_text=\"\",blank = True, null=True)\n\tfee = models.ForeignKey(Fee, help_text=\"\", related_name=\"payments\")\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2)\n\treceipt_no = models.CharField(max_length = 50)\n\tbank =  models.CharField(max_length = 100, choices=variables.banks)\n\tpaid_date = models.DateField(help_text=\"\",default=datetime.now)\n\tfine_amount = models.DecimalField(max_digits = 20, decimal_places = 2, default=0,null=True, blank = True)\n\tfine_description = models.TextField(null=True, blank = True)\n\tmanual_receipt = models.CharField(max_length = 50)\n\tdate_time = models.DateTimeField(help_text=\"The date when this payment is entered into the system.\",auto_now_add=True)\n\tnote = models.TextField(null=True, blank = True,   help_text=\"note about this payment.\")\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\treceipt_id = models.IntegerField(null=True)\n\tstatus = models.ForeignKey(CategoryChoice)\n\tobjects = PayFeeManager()\n\tall_objects = models.Manager()\n\n\tdef __unicode__(self):\n\t\treturn \"Fee Payment\"\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\n\n\nclass PayFixedAssetTax(models.Model):\n\tproperty_tax_item = models.ForeignKey(PropertyTaxItem, related_name=\"payments\")\n\tbusiness_id = models.IntegerField(help_text=\"The business who pay this tax item.\", blank = True, null=True)\n\tcitizen_id = models.IntegerField(help_text=\"The person who pay this tax item.\", blank = True, null=True)\n\t#staff_id = models.IntegerField(help_text=\"The government staff who accepts the payment.\")\n\tstaff  = models.ForeignKey(PMUser, help_text=\"\",blank = True, null=True)\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2)\n\treceipt_no = models.CharField(max_length = 50)\n\tbank =  models.CharField(max_length = 100, choices=variables.banks)\n\tpaid_date = models.DateField(help_text=\"\",default=datetime.now)\n\tfine_amount = models.DecimalField(max_digits = 20, decimal_places = 2, default=0)\n\tfine_description = models.TextField(null=True, blank = True)\n\tmanual_receipt = models.CharField(max_length = 50)\n\tdate_time = models.DateTimeField(help_text=\"The date when this payment is entered into the system.\",auto_now_add=True)\n\tnote = models.TextField(null=True, blank = True, help_text=\"note about this payment.\")\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\tdef __unicode__(self):\n\t\treturn \"Fixed Asset Tax Payment\"\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\n\nclass PayMiscellaneousFee(models.Model):\n\tbusiness = models.ForeignKey(Business, null=True, blank=True)\n\tcitizen = models.ForeignKey(Citizen,null=True,blank=True)\n\t#staff_id = models.IntegerField(help_text=\"The government staff who accepts the payment.\")\n\tstaff  = models.ForeignKey(PMUser, help_text=\"\",blank = True, null=True)\n\tfee = models.ForeignKey(MiscellaneousFee, help_text=\"\", related_name=\"payments\")\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2)\n\treceipt_no = models.CharField(max_length = 50)\n\tbank =  models.CharField(max_length = 100, choices=variables.banks)\n\tpaid_date = models.DateField(help_text=\"\",default=datetime.now)\n\tfine_amount = models.DecimalField(max_digits = 20, decimal_places = 2, default=0,null=True, blank = True)\n\tfine_description = models.TextField(null=True, blank = True)\n\tmanual_receipt = models.CharField(max_length = 50)\n\tdate_time = models.DateTimeField(help_text=\"The date when this payment is entered into the system.\",auto_now_add=True)\n\tnote = models.TextField(null=True, blank = True,   help_text=\"note about this payment.\")\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\n\tdef __unicode__(self):\n\t\treturn \"Fee Payment\"\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\n\n#Pending Payment\nclass PendingPayment(models.Model):\n\tpayment_id = models.CharField(max_length = 50)\n\ttax_type = models.CharField(max_length = 50)\n\ttax_id = models.CharField(max_length = 50)\n\treason = models.CharField(max_length = 250)\n\tnote = models.TextField(null=True, blank = True,   help_text=\"note about this payment.\")\n\tuser = models.ForeignKey(PMUser, null=True, blank = True)\n\tdate_time = models.DateTimeField(help_text=\"The date when this payment is entered into the system.\",auto_now_add=True)\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\n\tdef __unicode__(self):\n\t\treturn \"Pending Payment\"\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\nclass FormulaData(models.Model):\n\n\tdata = models.TextField()\n\tfee = models.OneToOneField(Fee, null=True)\n\tproperty_item = models.OneToOneField(PropertyTaxItem, null=True)\n\ttrading_license = models.OneToOneField(TradingLicenseTax, null=True)\n\trental_income = models.OneToOneField(RentalIncomeTax, null=True)\n\n\tdef get_pickle(self):\n\t\treturn pickle.loads(self.data.decode('base64'))\n\n\tdef set_pickle(self, value):\n\t\tself.data = pickle.dumps(value).encode('base64')\n\n\tdef del_pickle(self):\n\t\tself.data = None\n\n\tformula_data = property_decorator(get_pickle, set_pickle, del_pickle)\n\n\nclass CleaningSchedule(models.Model):\n\tvalid_from = models.DateField(help_text='Date this setting to be valid from.')\n\tcouncil = models.ForeignKey(Council, null=True, blank=True, help_text=\"The council that setting applied to.\")\n\tdistrict = models.ForeignKey(District, null=True, blank=True, help_text=\"\")\n\tsector = models.ForeignKey(Sector, null=True, blank=True, help_text=\"\")\n\tcell = models.ForeignKey(Cell, null=True, blank=True, help_text=\"\")\n\tamount =  models.DecimalField(decimal_places=0, max_digits=9)\n\tdue_date = models.DateField(null=True, blank=True)\n\tbusiness_category = models.ForeignKey(BusinessCategory)\n\tmodified = models.DateTimeField(help_text=\"The date when this setting is entered into the system.\",auto_now_add=True)\n\n\nclass Setting(models.Model):\n\ttax_fee_name = models.CharField(max_length = 50, help_text=\"Tax / Fee Name\")\n\tsetting_name = models.CharField(max_length = 50, help_text=\"Tax / Fee Setting Name\")\n\tsub_type = models.CharField(max_length = 250, blank = True, help_text=\"Tax / Fee Setting Sub Catergories that differentiate the rate / fee\")\n\tvalue = models.CharField(max_length = 50, default='',help_text=\"Setting Value, can be fee/tax rate/date/etc\")\n\tdescription = models.TextField(null=True, blank = True, help_text=\"Description about this payment.\")\n\tvalid_from = models.DateField(help_text='Date this setting to be valid from.')\n\tvalid_to = models.DateField(help_text='Date this setting get deprecated.', null=True, blank = True)\n\tcouncil = models.ForeignKey(Council, null=True, blank=True, help_text=\"The council that setting applied to.\")\n\tdistrict = models.ForeignKey(District, null=True, blank=True, help_text=\"\")\n\tsector = models.ForeignKey(Sector, null=True, blank=True, help_text=\"\")\n\tcell = models.ForeignKey(Cell, null=True, blank=True, help_text=\"\")\n\tvillage = models.ForeignKey(Village, null=True, blank=True, help_text=\"\")\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\tdate_time = models.DateTimeField(help_text=\"The date when this setting is entered into the system.\",auto_now_add=True)\n\n\tdef __unicode__(self):\n\t\treturn \"ID:\" + str(self.id) + \" - \" + str(self.tax_fee_name) + \" \" + str(self.setting_name) + \" - \" + str(self.sub_type) + \"[ \" + str(self.value) + \" ] (\" + self.i_status + \")\"\n\n\tdef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\treturn getLogMessage(self,old_data,new_data, action)\n\n\n\t@classmethod\n\tdef calculateFixedAssetTax(cls, date_from, date_to, residential=0, commercial=0, agricultural=0, land_use_type=None, *args, **kwargs):\n\n\t\tif type(date_from) is datetime:\n\t\t\tdate_from = date_from.astimezone(timezone.get_default_timezone()).date()\n\n\t\tif type(date_to) is datetime:\n\t\t\tdate_to = date_to.astimezone(timezone.get_default_timezone()).date()\n\n\t\ttax = Decimal(0)\n\n\t\tcurrent_year = date_from.year\n\t\ttotal_days_in_period = Decimal((date(current_year, 12, 31) - date(current_year, 1, 1)).days + 1)\n\t\tperiods = cls.getTaxPeriods('fixed_asset_tax', date_from, date_to, *args, **kwargs)\n\t\tif not periods:\n\t\t\traise Exception(\"No tax settings found for %s, %s\" % (date_from, date_to))\n\t\tdue_month, due_day = periods[0][2]['due_date'].split('-')\n\t\tdue_date = date(date_from.year, int(due_month), int(due_day))\n\n\t\tformula_data = {}\n\n\t\tfor date_from, date_to, period_setting in periods:\n\t\t\tperiod = (date_from, date_to)\n\t\t\tformula_data[period] = {}\n\t\t\tdays_in_period = Decimal((date_to - date_from).days + 1)\n\n\n\t\t\tresidential = residential - period_setting['residential_deduction']\n\t\t\tif residential < 0:\n\t\t\t\tresidential = 0\n\t\t\ttaxable = residential + commercial + agricultural\n\t\t\ttaxable = (Decimal(taxable) / 1000).quantize(Decimal(0)) * 1000\n\n\t\t\tif taxable > 0:\n\t\t\t\tamount = (days_in_period / total_days_in_period  * period_setting['tax_rate'] * taxable).quantize(Decimal('.01'))\n\t\t\t\ttax += amount\n\t\t\t\tformula_data[period]['amount'] = amount\n\t\t\telse:\n\t\t\t\tformula_data[period]['amount'] = 0\n\t\t\tformula_data[period]['tax_rate'] = period_setting['tax_rate']\n\t\t\tformula_data[period]['days'] = days_in_period\n\t\t\tformula_data[period]['taxable'] = taxable\n\n\t\ttax = Decimal(int(tax))\n\t\treturn {'amount':tax, 'due':due_date, 'days':total_days_in_period, 'due_date':due_date, 'formula_data':formula_data, 'vat':True}\n\n\n\t@classmethod\n\tdef calculateRentalIncome(cls, date_from, date_to, last_year_rental_income, last_year_bank_interest, sector=None, cell=None, village=None, *args, **kwargs):\n\t\tlast_year_rental_income = Decimal(last_year_rental_income).quantize(Decimal('.01'))\n\t\tlast_year_bank_interest = Decimal(last_year_bank_interest).quantize(Decimal('.01'))\n\t\tif type(date_from) is datetime:\n\t\t\tdate_from = date_from.astimezone(timezone.get_default_timezone()).date()\n\n\t\tif type(date_to) is datetime:\n\t\t\tdate_to = date_to.astimezone(timezone.get_default_timezone()).date()\n\n\t\tcurrent_year = date_from.year\n\t\ttotal_days_in_period = Decimal((date(current_year, 12, 31) - date(current_year, 1, 1)).days + 1)\n\t\tperiods = cls.getTaxPeriods('rental_income_tax', date_from, date_to, sector=sector, cell=cell, village=village, *args, **kwargs)\n\t\tif not periods:\n\t\t\traise Exception(\"No tax settings found for %s, %s, %s, %s\" % (date_from, date_to, sector, cell, village))\n\t\tdue_month, due_day = periods[0][2]['due_date'].split('-')\n\t\tdue_date = date(date_from.year, int(due_month), int(due_day))\n\t\ttax = Decimal(0)\n\n\t\tformula_data = {}\n\t\tfor date_from, date_to, period_setting in periods:\n\t\t\tperiod_total = Decimal(0)\n\t\t\tperiod = (date_from, date_to)\n\t\t\tformula_data[period] = {}\n\t\t\tdays_in_period = Decimal((date_to - date_from).days + 1)\n\t\t\tformula_data[period]['days'] = days_in_period\n\n\t\t\tif last_year_bank_interest > 0:\n\t\t\t\ttaxable = last_year_rental_income - ( last_year_rental_income * period_setting['rate_with_bank_interest']) - last_year_bank_interest\n\t\t\t\tformula_data[period]['taxable_rate'] = period_setting['rate_with_bank_interest']\n\t\t\telse:\n\t\t\t\ttaxable = last_year_rental_income - ( last_year_rental_income * period_setting['rate']) - last_year_bank_interest\n\t\t\t\tformula_data[period]['taxable_rate'] = period_setting['rate']\n\n\t\t\tformula_data[period]['taxable'] = taxable\n\t\t\ttax_ranges = period_setting['tax_ranges']\n\t\t\tamounts = []\n\t\t\tamount = Decimal(0)\n\t\t\tif taxable > 1000000:\n\t\t\t\ttaxable = taxable - 1000000\n\t\t\t\tamount = taxable * tax_ranges['>1000000']\n\t\t\t\tamounts.append({'taxable':taxable, 'rate':tax_ranges['>1000000'], 'amount':amount})\n\t\t\t\ttaxable = 1000000\n\t\t\t\tperiod_total += amount\n\t\t\tif taxable > 180000:\n\t\t\t\ttaxable = taxable - 180000\n\t\t\t\tamount = taxable * tax_ranges['180000-1000000']\n\t\t\t\tamounts.append({'taxable':taxable, 'rate':tax_ranges['180000-1000000'], 'amount':amount})\n\t\t\t\ttaxable = 180000\n\t\t\t\tperiod_total += amount\n\n\t\t\tamount = taxable * tax_ranges['0-180000']\n\t\t\tperiod_total += amount\n\t\t\tperiod_total = (days_in_period / total_days_in_period * period_total).quantize(Decimal('0.01'))\n\t\t\ttax += period_total\n\t\t\tamounts.append({'taxable':taxable, 'rate':tax_ranges['0-180000'], 'amount':amount})\n\n\t\t\tformula_data[period]['amount'] = period_total\n\t\t\tformula_data[period]['amounts'] = amounts\n\n\t\ttax = Decimal(int(tax))\n\t\treturn {'amount':tax, 'due':due_date, 'days':total_days_in_period, 'due_date':due_date, 'rental_income':last_year_rental_income, 'interest_paid':last_year_bank_interest, 'formula_data':formula_data}\n\n\n\t@classmethod\n\tdef calculateLandLeaseFee(cls, date_from, date_to, land_use_type, size, sector=None, cell=None, village=None, *args, **kwargs):\n\t\tif type(date_from) is datetime:\n\t\t\tdate_from = date_from.astimezone(timezone.get_default_timezone()).date()\n\n\t\tif type(date_to) is datetime:\n\t\t\tdate_to = date_to.astimezone(timezone.get_default_timezone()).date()\n\n\t\tsize = Decimal(size).quantize(Decimal('.0001'))\n\t\tland_use_types = None\n\t\tif land_use_type == 'Agricultural':\n\t\t\tunits = 'hectares'\n\t\t\thectares = Decimal(size * 0.0001).quantize(Decimal('.0001'))\n\t\t\tif hectares > 35:\n\t\t\t\tland_use_types = 'Agricultural(>35 ha)'\n\t\t\telif hectares >= 2 and hectares <= 35:\n\t\t\t   land_use_types = 'Agricultural(2-35 ha)'\n\t\t\telif hectares > 2:\n\t\t\t\tland_use_types = 'Agricultural(>2 ha)'\n\t\telif land_use_type == 'Residential':\n\t\t\tland_use_types = 'Residential'\n\t\telif land_use_type == 'Commericial':\n\t\t\tland_use_types = 'Commercial'\n\t\telif land_use_type == 'Industrial':\n\t\t\tland_use_types = 'Industries'\n\t\telif land_use_type == 'Quarry Purpose':\n\t\t\tland_use_types = 'Quarries Exploitation'\n\t\tformula_data = {}\n\t\ttax = Decimal(0)\n\t\tcurrent_year = date_from.year\n\t\ttotal_days_in_period = Decimal((date(current_year, 12, 31) - date(current_year, 1, 1)).days + 1)\n\t\tif land_use_types:\n\t\t\ttax_periods = cls.getTaxPeriods(['land_lease','land_lease_fee'], date_from, date_to, setting_name='area_and_fee_matches', sub_type=land_use_types, sector=sector, cell=cell, village=village, *args, **kwargs)\n\t\telse:\n\t\t\ttax_periods = [(date_from, date_to, { 'value':Decimal(0), 'region':'No Fee applicable' })]\n\t\tif not tax_periods:\n\t\t\ttax_periods = [(date_from, date_to, { 'value':Decimal(0), 'region':'No Fee Found' })]\n\t\tformula_data = {}\n\t\tfor date_from, date_to, values in tax_periods:\n\t\t\tperiod = (date_from, date_to)\n\t\t\trate = values['value']\n\t\t\tdays_in_period = (date_to - date_from).days + 1\n\t\t\tamount = (Decimal(days_in_period) / Decimal(total_days_in_period)  * rate * size).quantize(Decimal('0.1'))\n\t\t\tformula_data[period] = { 'days':days_in_period, 'tax_rate':rate, 'amount':amount, 'size':size, 'region':values['region'] }\n\t\t\ttax += amount\n\n\t\tdue_date = date_to\n\t\ttax = Decimal(int(tax))\n\t\treturn {'amount':tax, 'due':due_date, 'due_date':due_date, 'days':total_days_in_period, 'formula_data':formula_data }\n\n\n\t@classmethod\n\tdef calculateCleaningFee(cls, period_date, business_type, area_type, sector=None, cell=None, village=None):\n\t\t#calculate due date\n\t\ttax_periods = cls.getTaxPeriods('general_fee', period_date, period_date, sector=sector, cell=cell, village=village)\n\t\tif not tax_periods:\n\t\t\traise Exception(\"No tax settings found for %s, %s, %s, %s\" % (period_date, sector, cell, village))\n\t\tfor date_from, date_to, period_setting in tax_periods:\n\t\t\tdue_date_day = period_setting.get('monthly_due_date')\n\t\tdue_date = period_date + relativedelta(months=1)\n\t\tdue_date = date(due_date.year, due_date.month, due_date_day)\n\n\t\t#calculate amount\n\t\tsub_type = \"%s-%s\" % (area_type, business_type)\n\t\ttax_periods = cls.getTaxPeriods('cleaning_fee', period_date, period_date, sector=sector, cell=cell, village=village)\n\t\tif not tax_periods:\n\t\t\traise Exception(\"No tax settings found for %s, %s, %s, %s\" % (period_date, sector, cell, village))\n\t\tif not tax_periods:\n\t\t\treturn None, None\n\t\tfor date_from, date_to, period_setting in tax_periods:\n\t\t\ttry:\n\t\t\t\trate = period_setting['fee_matches'][sub_type]\n\t\t\texcept KeyError:\n\t\t\t\trate = None\n\t\treturn rate, due_date\n\n\t@classmethod\n\tdef calculateTradingLicenseTax(cls, date_from, date_to, turnover, sector=None, *args, **kwargs):\n\t\tif type(date_from) is datetime:\n\t\t\tdate_from = date_from.astimezone(timezone.get_default_timezone()).date()\n\n\t\tif type(date_to) is datetime:\n\t\t\tdate_to = date_to.astimezone(timezone.get_default_timezone()).date()\n\n\t\tturnover = Decimal(turnover).quantize(Decimal('0.01'))\n\t\tcurrent_year = date_from.year\n\t\ttotal_days_in_period = Decimal((date(current_year, 12, 31) - date(current_year, 1, 1)).days + 1)\n\t\ttax = Decimal(0)\n\t\ttax_periods = Setting.getTaxPeriods('trading_license_tax', date_from, date_to, sector=sector, *args, **kwargs)\n\n\t\tif not tax_periods:\n\t\t\traise Exception(\"No tax settings found for %s, %s, %s\" % (date_from, date_to, sector))\n\t\tformula_data = {}\n\t\tfor date_from, date_to, period_setting in tax_periods:\n\t\t\tdays_in_period = Decimal((date_to - date_from).days + 1)\n\t\t\tperiod = (date_from, date_to)\n\t\t\tformula_data[period] = { 'days':days_in_period }\n\t\t\tsettings = period_setting['business_yearly_turnover_and_tax_matches']\n\t\t\tif turnover <= 40000000:\n\t\t\t\trate = Decimal(settings.get('Up to 40,000000 Rwf'))\n\t\t\telif turnover > 40000000 and turnover <= 60000000:\n\t\t\t\trate = Decimal(settings.get('From 40,000,001 to 60,000,000'))\n\t\t\telif turnover > 60000000 and turnover <= 150000000:\n\t\t\t\trate = Decimal(settings.get('From 60,000,001 to 150,000,000'))\n\t\t\telse:\n\t\t\t\trate = Decimal(settings.get('Above 150 Million Rwf'))\n\t\t\tformula_data[period]['rate'] = rate\n\t\t\tamount = (days_in_period / total_days_in_period  * rate).quantize(Decimal('0.01'))\n\t\t\tformula_data[period]['amount'] = amount\n\t\t\ttax += amount\n\t\ttax = Decimal(int(tax))\n\t\treturn {'amount':tax, 'due':date_to, 'due_date':date_to, 'days':total_days_in_period, 'turnover':turnover, 'formula_data':formula_data, 'vat':True}\n\n\t@classmethod\n\tdef get_due_date(cls, tax_fee_name, period_date, sector=None, district=None, *args, **kwargs):\n\t\tcurrent_date = date.today()\n\t\tif tax_fee_name in ('trading_license_tax', 'land_lease_fee'):\n\t\t\treturn date(period_date.year, 12, 31)\n\t\tdue_date = cls.getTaxSetting(tax_fee_name, 'due_date', current_date, current_date, sector, district, *args, **kwargs)\n\t\tdue_month, due_day = due_date.split('-')\n\t\treturn date(period_date.year, int(due_month), int(due_day))\n\n\n\t@classmethod\n\tdef getTaxSetting(cls, tax_fee_name, setting_name, date_from, date_to, sector=None, district=None, *args, **kwargs):\n\t\t\"\"\"gets the latest setting for a tax\"\"\"\n\t\tperiods = cls.getTaxPeriods(tax_fee_name, date_from, date_to, sector, district, *args, **kwargs)\n\t\tif not periods:\n\t\t\traise Exception(\"Could not find setting - %s from %s to %s\" % (tax_fee_name, date_from, date_to))\n\t\treturn periods[0][2][setting_name]\n\n\n\t@classmethod\n\tdef getFees(cls, district=None, sector=None, cell=None, village=None, *args, **kwargs):\n\t\t\"\"\"\n\t\treturns the relevant tax period info as a dictionary\n\t\tin the format: {(date_from, date_to), { setting_name1: value, setting_name2: value2 }, ...}\n\t\teg. {((2013,01,01), (2013,04,31)):{ 'tax_rate': 0.1, 'due_date': (2013,05,31), ...}, ... }\n\t\t( (date_from, date_to, {values}), (date_from, date_to, {values}) )\n\t\tThe order of precedence s Sector tax values, District tax values then\n\t\ttax values with no associated Sector or District\n\t\t\"\"\"\n\n\t\tif district and type(district) is int:\n\t\t\tdistrict = District.objectsIgnorePermission.get(pk=district)\n\n\t\tif sector and type(sector) is int:\n\t\t\tsector = Sector.objectsIgnorePermission.get(pk=sector)\n\n\t\tif cell and type(cell) is int:\n\t\t\tcell = Cell.objects.get(pk=cell)\n\n\t\tif village and type(village) is int:\n\t\t\tvillage = Village.objects.get(pk=village)\n\n\t\tsettings = cls.objects.filter(tax_fee_name='misc_fee', i_status='active')\n\n\t\tif village:\n\t\t\tsettings = settings.filter(Q(village__isnull=True) | Q(village=village))\n\t\t\tcell = village.cell\n\t\telse:\n\t\t\tsettings = settings.filter(village__isnull=True)\n\n\t\tif cell:\n\t\t\tsettings = settings.filter(Q(cell__isnull=True) | Q(cell=cell))\n\t\t\tsector = cell.sector\n\t\telse:\n\t\t\tsettings = settings.filter(cell__isnull=True)\n\n\t\tif sector:\n\t\t\tsettings = settings.filter(Q(sector__isnull=True) | Q(sector=sector))\n\t\t\tdistrict = sector.district\n\t\telse:\n\t\t\tsettings = settings.filter(sector__isnull=True)\n\n\t\tif district:\n\t\t\tsettings = settings.filter(Q(district__isnull=True) | Q(district=district))\n\t\telse:\n\t\t\tsettings = settings.filter(district__isnull=True)\n\n\t\tsettings = settings.select_related('district','sector', 'cell', 'village').order_by('-valid_from', '-village', '-cell', '-sector', '-district', 'sub_type')\n\n\t\tfees = {}\n\t\tfor setting in settings:\n\t\t\tsetting_region = setting.village or setting.cell or setting.sector or setting.district\n\t\t\tcategory = fees.setdefault(setting.setting_name, {})\n\t\t\tsub_type = category.setdefault(setting.sub_type, { 'value': setting.value, 'description':setting.description, 'valid_from':setting.valid_from, 'region': setting_region })\n\t\t\tif setting_region == sub_type['region'] and setting.valid_from >= sub_type['valid_from'] and date.today() >= setting.valid_from or setting_region != sub_type['region']:\n\t\t\t\tsub_type['value'] = Decimal(setting.value)\n\t\t\t\tsub_type['description'] = setting.description\n\t\t\t\tsub_type['region'] = setting_region\n\t\t\t\tsub_type['pk'] = setting.pk\n\n\t\treturn fees\n\n\n\t@classmethod\n\tdef getTaxPeriods(cls, tax_fee_name, date_from, date_to, setting_name=None, sub_type=None, district=None, sector=None, cell=None, village=None, *args, **kwargs):\n\t\t\"\"\"\n\t\treturns the relevant tax period info as a dictionary\n\t\tin the format: {(date_from, date_to), { setting_name1: value, setting_name2: value2 }, ...}\n\t\teg. {((2013,01,01), (2013,04,31)):{ 'tax_rate': 0.1, 'due_date': (2013,05,31), ...}, ... }\n\t\t( (date_from, date_to, {values}), (date_from, date_to, {values}) )\n\t\tThe order of precedence s Sector tax values, District tax values then\n\t\ttax values with no associated Sector or District\n\t\t\"\"\"\n\n\t\tif district and type(district) is int:\n\t\t\tdistrict = District.objectsIgnorePermission.get(pk=district)\n\n\t\tif sector and type(sector) is int:\n\t\t\tsector = Sector.objectsIgnorePermission.get(pk=sector)\n\n\t\tif cell and type(cell) is int:\n\t\t\tcell = Cell.objects.get(pk=cell)\n\n\t\tif village and type(village) is int:\n\t\t\tvillage = Village.objects.get(pk=village)\n\n\t\tif type(date_from) is datetime:\n\t\t\tdate_from = date_from.astimezone(timezone.get_default_timezone()).date()\n\n\t\tif type(date_to) is datetime:\n\t\t\tdate_to = date_to.astimezone(timezone.get_default_timezone()).date()\n\n\t\tif type(tax_fee_name) is not list:\n\t\t\ttax_fee_name = [tax_fee_name]\n\n\t\tsettings = cls.objects.filter(tax_fee_name__in=tax_fee_name, valid_from__lte=date_to, i_status='active')\n\n\t\tif setting_name:\n\t\t\tsettings = settings.filter(setting_name=setting_name)\n\n\t\tif sub_type:\n\t\t\tif not setting_name:\n\t\t\t\traise Exception(\"You must specify a setting name\")\n\t\t\tif hasattr(sub_type, \"__iter__\"):\n\t\t\t\tsettings = settings.filter(sub_type__in=sub_type)\n\t\t\telse:\n\t\t\t\tsettings = settings.filter(sub_type=sub_type)\n\n\t\tif village:\n\t\t\tsettings = settings.filter(Q(village__isnull=True) | Q(village=village))\n\t\t\tcell = village.cell\n\t\telse:\n\t\t\tsettings = settings.filter(village__isnull=True)\n\n\t\tif cell:\n\t\t\tsettings = settings.filter(Q(cell__isnull=True) | Q(cell=cell))\n\t\t\tsector = cell.sector\n\t\telse:\n\t\t\tsettings = settings.filter(cell__isnull=True)\n\n\t\tif sector:\n\t\t\tsettings = settings.filter(Q(sector__isnull=True) | Q(sector=sector))\n\t\t\tdistrict = sector.district\n\t\telse:\n\t\t\tsettings = settings.filter(sector__isnull=True)\n\n\t\tif district:\n\t\t\tsettings = settings.filter(Q(district__isnull=True) | Q(district=district))\n\t\telse:\n\t\t\tsettings = settings.filter(district__isnull=True)\n\n\t\tsettings = settings.order_by('-valid_from', '-village', '-cell', '-sector', '-district')\n\n\t\tsettings = [setting for setting in settings]\n\n\t\tif not settings:\n\t\t\treturn None\n\t\t\t# raise Exception(\"No Settings found for tax fee: %s, from %s to %s, setting name:%s, sub type: %s, district: %s, sector: %s, cell: %s, village: %s\"  % (tax_fee_name, date_from, date_to, setting_name, sub_type, district, sector, cell, village))\n\n\t\t# set the most recent period\n\t\t#import pdb\n\t\t#pdb.set_trace()\n\t\tvalid_from = settings[0].valid_from\n\t\tvalid_to = date_to\n\n\t\tperiods = {}\n\t\tfor setting in settings:\n\t\t\tregion = setting.village or setting.cell or setting.sector or setting.district or None\n\t\t\tif region:\n\t\t\t\tregion_name = region.name\n\t\t\telse:\n\t\t\t\tregion_name = None\n\t\t\t# get the next period setting\n\t\t\tif setting.valid_from < valid_from:\n\t\t\t\tvalid_to = valid_from - timedelta(days=1)\n\t\t\t\t#stop if period is out of range\n\t\t\t\tif valid_to < date_from:\n\t\t\t\t\tbreak;\n\n\t\t\t# set the setting start range if less than date_from\n\t\t\tvalid_from = setting.valid_from\n\t\t\tif valid_from < date_from:\n\t\t\t\tstart_from = date_from\n\t\t\telse:\n\t\t\t\tstart_from = valid_from\n\n\t\t\tperiod = periods.setdefault((start_from, valid_to), {})\n\t\t\ttry:\n\t\t\t\tvalue = Decimal(setting.value)\n\t\t\texcept:\n\t\t\t\tvalue = setting.value\n\n\t\t\tperiod['region'] = region_name\n\t\t\tif sub_type:\n\t\t\t\tif hasattr(sub_type,'__iter__'):\n\t\t\t\t\tperiod[setting.sub_type] = value\n\t\t\t\telse:\n\t\t\t\t\tperiod['value'] = value\n\t\t\telif setting_name:\n\t\t\t\tif setting.sub_type:\n\t\t\t\t\tperiod[setting.sub_type] = value\n\t\t\t\telse: #setting_name provided,  no setting.sub_type\n\t\t\t\t\tperiod['value'] = value\n\t\t\telse: # no setting name\n\t\t\t\tif setting.sub_type:\n\t\t\t\t\tst = period.setdefault(setting.setting_name, {})\n\t\t\t\t\tst[setting.sub_type] = value\n\t\t\t\telse:\n\t\t\t\t\tperiod[setting.setting_name] = value\n\n\t\tperiods = [ (dates[0], dates[1], values) for dates, values in periods.iteritems() ]\n\t\tperiods.sort(key=lambda x:x[0], reverse=True)\n\t\treturn periods\n\n\n# Model for Receipt of Multiple Tax/Fee payment\nclass MultipayReceipt(models.Model):\n\tamount = models.DecimalField(max_digits = 20, decimal_places = 2)\n\tuser = models.ForeignKey(PMUser, null=True, blank = True)\n\tdate_time = models.DateTimeField(help_text='This is the Date and Time the Entry has been entered into the database.',auto_now_add=True)\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\n\n# Model for Receipt of Multiple Tax/Fee payment\nclass MultipayReceiptPaymentRelation(models.Model):\n\tpayfee = models.ForeignKey(PayFee, related_name=\"receipt_relations\")\n\treceipt = models.ForeignKey(MultipayReceipt, related_name=\"payment_relations\")\n\ti_status = models.CharField(max_length = 10, choices = variables.status_choices, default='active', blank = True)\n\n\n\n#General functions used in many models\ndef getLogMessage(self,old_data=None,new_data=None, action=None):\n\t\"\"\"\n\treturn tailored log message for different actions taken on this citizen\n\t\"\"\"\n\tif action == \"view\":\n\t\treturn \"view \" + self.__class__.__name__ + \" [\" + self.__unicode__() + \"]\"\n\tif action == \"delete\":\n\t\treturn \"delete \" + self.__class__.__name__ + \" [\" + self.__unicode__() + \"]\"\n\tif action == \"add\":\n\t\treturn \"add \" + self.__class__.__name__ + \" [\" + self.__unicode__() + \"]\"\n\tif action == \"change\":\n\t\tmessage=\"\"\n\t\tcount = 0\n\t\tif old_data != None:\n\t\t\tfor key, value in old_data.iteritems():\n\t\t\t\tif old_data[key] != new_data[key]:\n\t\t\t\t\tif count != 0:\n\t\t\t\t\t\tmessage = message + \",\"\n\t\t\t\t\tcount = count + 1\n\t\t\t\t\tif type(value) is not list:\n\t\t\t\t\t\tmessage = message + \" change \"+key + \" from '\"+ str(value) + \"' to '\"+str(new_data[key])+\"'\"\n\t\tif message == \"\":\n\t\t\tmessage = \"No change made\"\n\t\tmessage = message + \" on \" + self.__class__.__name__ + \" [\" + self.__unicode__() + \"]\"\n\t\treturn message\n\n\n\nclass Installment(models.Model):\n\tamount = models.DecimalField(max_digits=20, decimal_places=2)\n\tdue = models.DateField()\n\tpropertyTaxItem = models.ForeignKey(PropertyTaxItem, null=True, blank=True, related_name=\"installments\")\n\trentalIncomeTax = models.ForeignKey(RentalIncomeTax, null=True, blank=True, related_name=\"installments\")\n\ttradingLicenseTax = models.ForeignKey(TradingLicenseTax, null=True, blank=True, related_name=\"installments\")\n\tfee = models.ForeignKey(Fee, null=True, help_text=\"\", related_name=\"installments\")\n\t# tax = models.ForeignKey(Tax, null=True, help_text=\"\", related_name=\"installments\")\n\n\tdef __unicode__(self):\n\t\tif hasattr(self, 'paid'):\n\t\t\treturn \"%s due on %s: %s paid\" % (self.amount, self.due, self.paid)\n\t\telse:\n\t\t\treturn \"%s due on %s\" % (self.amount, self.due)\n\n\t@staticmethod\n\tdef previewInstallments(amount, date_from):\n\t\tmonths = 3\n\t\tno_installments = 4\n\t\tinstallments = {}\n\t\tdue_date = date_from\n\n\t\tamount = (amount / no_installments).quantize(Decimal('.01'))\n\t\tfor i in range(no_installments):\n\t\t\tdue_date = due_date + relativedelta(months=months-1) + relativedelta(day=31)\n\t\t\tinstallments[due_date] = amount\n\t\treturn installments\n\n","sub_path":"jtax/models.py","file_name":"models.py","file_ext":"py","file_size_in_byte":78699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"364775612","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Oct 23 17:01:57 2018\n\n@author: macgx\n\"\"\"\nimport pandas as pd\nfrom pandas.api.types import is_string_dtype, is_numeric_dtype\nfrom sklearn.preprocessing import LabelEncoder, scale\nfrom sklearn.model_selection import train_test_split\nimport numpy as np\nimport arff\nimport os\n\n\n\nclass PreprocessData():\n    def __init__(self, dataname):\n        self.dataname = dataname\n        ## get dataset from UCI_data_arff directory\n        current_script_path = os.path.dirname(os.path.abspath(__file__)).split('/')\n        # os.path.dirname does not include file name\n        # current_script_path.pop()\n        current_script_path[-1] = 'UCI_data_arff'\n        self.datapath = '/'.join(current_script_path)   \n\n    def test_preprocess(self):\n        if self.dataname == 'pim':\n            with open('{}/pima.arff'.format(self.datapath), 'r') as f1:\n                data = arff.load(f1)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[8] = data[8].astype('int64')\n            X = data[data.columns[:8]]\n            y = data[data.columns[8]]\n\n            return X, y\n\n    def preprocess(self):\n        if self.dataname == 'adlt':\n            with open('{}/adult_train.arff'.format(self.datapath), 'r') as f1:\n                data = arff.load(f1)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[14] = data[14].astype('int64')\n            \n            # with open('{}/adult_test.arff'.format(self.datapath), 'r') as f2:\n            #     data_test = arff.load(f2)\n            #     data_test = pd.DataFrame(data_test['data'])\n            #     data_test[14] = data_test[14].astype('int64')\n            \n            # data = data.append(data_test, ignore_index=True)\n            X = data[data.columns[:14]]\n            y = data[data.columns[14]]\n            return X, y\n        \n        elif self.dataname == 'pim':\n            with open('{}/pima.arff'.format(self.datapath), 'r') as f3:\n                data = arff.load(f3)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[8] = data[8].astype('int64')\n\n            X = data[data.columns[:8]]\n            y = data[data.columns[8]]\n            \n            return X, y\n        \n        elif self.dataname == 'bnk':\n            with open('{}/bank.arff'.format(self.datapath), 'r') as f4:\n                data = arff.load(f4)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[16] = data[16].astype('int64')\n                   \n            X = data[data.columns[:16]]\n            y = data[data.columns[16]]\n            return X, y  \n        \n        elif self.dataname == 'car':\n            # Characteristics: has missing value\n            with open('{}/car.arff'.format(self.datapath), 'r') as f5:\n                data = arff.load(f5)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[6] = data[6].astype('int64')\n            \n            X = data[data.columns[:6]]\n            y = data[data.columns[6]]\n            return X, y\n        \n        elif self.dataname == 'ches':\n            with open('{}/chess-krvk.arff'.format(self.datapath), 'r') as f6:\n                data = arff.load(f6)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[6] = data[6].astype('int64')\n            \n            X = data[data.columns[:6]]\n            y = data[data.columns[6]] \n            return X, y       \n        \n        elif self.dataname == 'ltr':\n            with open('{}/letter.arff'.format(self.datapath), 'r') as f7:\n                data = arff.load(f7)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[16] = data[16].astype('int64')\n            \n            X = data[data.columns[:16]]\n            y = data[data.columns[16]]\n            return X, y \n        \n        elif self.dataname == 'mgic':\n            with open('{}/magic.arff'.format(self.datapath), 'r') as f8:\n                data = arff.load(f8)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[10] = data[10].astype('int64')\n            \n            X = data[data.columns[:10]]\n            y = data[data.columns[10]]\n            return X, y \n        \n        elif self.dataname == 'msk':\n            with open('{}/musk-2.arff'.format(self.datapath), 'r') as f9:\n                data = arff.load(f9)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[166] = data[166].astype('int64')\n            \n            X = data[data.columns[:166]]\n            y = data[data.columns[166]]\n            return X, y\n        \n        elif self.dataname == 'p-blk':\n            with open('{}/page-blocks.arff'.format(self.datapath), 'r') as f10:\n                data = arff.load(f10)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[10] = data[10].astype('int64')\n            \n            X = data[data.columns[:10]]\n            y = data[data.columns[10]]\n            return X, y\n\n        elif self.dataname == 'sem':\n            with open('{}/semeion.arff'.format(self.datapath), 'r') as f11:\n                data = arff.load(f11)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[256] = data[256].astype('int64')\n            \n            X = data[data.columns[:256]]\n            y = data[data.columns[256]]       \n            return X, y\n        \n        elif self.dataname == 'spam':\n            with open('{}/spambase.arff'.format(self.datapath), 'r') as f12:\n                data = arff.load(f12)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[57] = data[57].astype('int64')\n            \n            X = data[data.columns[:57]]\n            y = data[data.columns[57]]    \n            return X, y\n        \n        elif self.dataname == 's-gc':\n            with open('{}/statlog-german-credit.arff'.format(self.datapath), 'r') as f13:\n                data = arff.load(f13)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[24] = data[24].astype('int64')\n            \n            X = data[data.columns[:24]]\n            y = data[data.columns[24]]\n            return X, y\n        \n        elif self.dataname == 's-im':\n            with open('{}/statlog-image.arff'.format(self.datapath), 'r') as f14:\n                data = arff.load(f14)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[18] = data[18].astype('int64')\n            \n            X = data[data.columns[:18]]\n            y = data[data.columns[18]]\n            return X, y\n        \n        elif self.dataname == 's-sh':\n            #Approximately 80% of the data belongs to class 1. \n            #Therefore the default accuracy is about 80%. \n            #The aim here is to obtain an accuracy of 99 - 99.9%. \n            with open('{}/statlog-shuttle_train.arff'.format(self.datapath), 'r') as f15:\n                data = arff.load(f15)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[9] = data[9].astype('int64')\n            \n            X = data[data.columns[:9]]\n            y = data[data.columns[9]]     \n            return X, y\n        \n        elif self.dataname == 's-pl':\n            with open('{}/steel-plates.arff'.format(self.datapath), 'r') as f16:\n                data = arff.load(f16)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[27] = data[27].astype('int64')\n            \n            X = data[data.columns[:27]]\n            y = data[data.columns[27]]  \n            return X, y\n        \n        elif self.dataname == 'tita':\n            with open('{}/titanic.arff'.format(self.datapath), 'r') as f17:\n                data = arff.load(f17)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[3] = data[3].astype('int64')\n            \n            X = data[data.columns[:3]]\n            y = data[data.columns[3]] \n            return X, y\n        \n        elif self.dataname == 'thy':\n            with open('{}/thyroid_train.arff'.format(self.datapath), 'r') as f18:\n                data = arff.load(f18)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[21] = data[21].astype('int64')\n            \n            X = data[data.columns[:21]]\n            y = data[data.columns[21]]       \n            return X, y\n        \n        elif self.dataname == 'wine':\n            with open('{}/wine-quality-red.arff'.format(self.datapath), 'r') as f19:\n                data = arff.load(f19)\n                data = pd.DataFrame(data['data'])\n                ## convert str to int for target variable, manually modify target column index\n                data[11] = data[11].astype('int64')\n            \n            X = data[data.columns[:11]]\n            y = data[data.columns[11]] \n            return X, y\n\n        elif self.dataname == '1_46':\n            data = pd.read_csv('{}/splice_46.csv'.format(self.datapath), header=0, sep=',')\n            data.drop(['Instance_name'], axis=1, inplace=True)\n        \n            ### my preprocessing strategy: convert categorical variables to [-1, 1]\n            ### and standardize all attributes including previous converted-categorical \n        #    attribute_encoder = {'R': -1, 'S': -5 / 7, 'D': -3 / 7, 'N': 0, 'A': 1 / 7, 'G': 3 / 7, 'T': 5 / 7, 'C': 1}\n        #    target_encoder = {'N': 0, 'EI': 1, 'IE': 2}\n        #    for col in data.columns[:-1]:\n        #        data[col] = data[col].map(attribute_encoder)      \n        #    data['Class'] = data['Class'].map(target_encoder)\n        #    # standardize all attributes\n        #    data[data.columns[:-1]] = scale(data[data.columns[:-1]])\n        \n            ### paper author's strategy: convert categorical variables to integer\n            ### no standardization\n            attribute_encoder = {'R': 1, 'S': 2, 'D': 3, 'N': 4, 'A': 5, 'G': 6, 'T': 7, 'C': 8}\n            target_encoder = {'N': 0, 'EI': 1, 'IE': 2}\n            for col in data.columns[:-1]:\n                data[col] = LabelEncoder().fit_transform(data[col])\n                \n            data['Class'] = data['Class'].map(target_encoder)\n            \n            #TODO: another strategy: data[col] = data[col].map(attribute_encoder)    \n            X = data[data.columns[:-1]]\n            y = data['Class']\n            return X, y\n\n        # TODO: URL ID 184 is kropt, which is same with UCI chess-krvk\n        # URL ID 180 but datafile 184 is covertype.txt\n        elif self.dataname == '2_184':\n            ## OpenML dataset 184 is same as UCI chess-krvk\n            ## paper author's strategy: no standardization\n            with open('{}/dataset_188_kropt.arff'.format(self.datapath), 'r') as f20:\n                data = arff.load(f20)\n                data = pd.DataFrame(data['data'])\n                \n            attribute_encoder = {'a':1, 'b':2, 'c':3, 'd':4, 'e':5, 'f':6, 'g':7, 'h':8}\n            for col in data.columns[:6]:\n                if col in [1,3,5]: ## convert str to int \n                    data[col] = data[col].astype('int64')\n                else:\n                    data[col] = data[col].map(attribute_encoder)    \n            y = LabelEncoder().fit_transform(data[6])\n            X = data[data.columns[:6]]        \n            return X, y\n\n        elif self.dataname == '3_389':\n            with open('{}/fbis.wc.arff'.format(self.datapath), 'r') as f21:\n                data = arff.load(f21)\n                data = pd.DataFrame(data['data'])\n                y = LabelEncoder().fit_transform(data[data.columns[-1]])\n                X = data[data.columns[:-1]]\n    #            X = scale(data[data.columns[:-1]])\n            return X, y\n\n        elif self.dataname == '4_772':\n            # Characteristics: no missing value\n            data = pd.read_csv('{}/quake_772.csv'.format(self.datapath), header=0, sep=',')\n    #        X = scale(data[data.columns[:-1]])\n            X = data[data.columns[:-1]]\n            y = LabelEncoder().fit_transform(data[data.columns[-1]])\n            return X, y\n\n        elif self.dataname == '5_917':\n            data = pd.read_csv('{}/fri_c1_1000_25_917.csv'.format(self.datapath), header=0, sep=',')\n    #        X = scale(data[data.columns[:-1]])\n            X = data[data.columns[:-1]]\n            y = LabelEncoder().fit_transform(data[data.columns[-1]])\n            return X, y\n        # TODO: not sure it's 1049\n        elif self.dataname == '6_1049':\n            # Characteristics: all attributes are numeric, binary class\n            data = pd.read_csv('{}/pc4_1049.csv'.format(self.datapath), header=0, sep=',')\n            y = data['c'].map({True: 1, False: 0})\n    #        X = scale(data[data.columns[:-1]])\n            X = data[data.columns[:-1]]\n            return X, y        \n\n","sub_path":"EGO-ss/PreprocessData.py","file_name":"PreprocessData.py","file_ext":"py","file_size_in_byte":14336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"439908208","text":"# Autor: Jesús Roberto Herrera Vieyra, A01377230\n# Descripcion: Programa que calcula el total de alumos y los porcentajes de cada género\n# Escribe tu programa después de esta línea.\nmujeres = int(input(\"Número de mujeres: \"))\nhombres = int(input(\"Número de hombres:\"))\n\ntotal= hombres+mujeres\nporcentajeH= (hombres*100)/total\nporcentajeM=(mujeres*100)/total\n\nprint(\"Total de alumnos: \",(total))\nprint(\"Porcentaje de mujeres: \",\"{0:.1f}\".format(porcentajeM),\"%\")\nprint(\"Porcentaje de hombres: \",\"{0:.1f}\".format(porcentajeH),\"%\")\n","sub_path":"porcentajes.py","file_name":"porcentajes.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"581622049","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Sat May 20 14:59:49 2017\r\n\r\n@author: JohannesMHeinrich\r\n\"\"\"\r\n\r\nfrom time import localtime, strftime\r\nfrom ctypes import *\r\n\r\nimport time\r\n\r\ndll_HF = windll.LoadLibrary(\"C:\\\\Windows\\\\System32\\\\wlmData.dll\")\r\n\r\n    \r\n#import numpy as np\r\n#import matplotlib.pyplot as plt\r\n#import matplotlib.cm as cm\r\n    \r\nclass HighFinesse_WM():\r\n    \"\"\" Class to interface the HighFinesse_WM\r\n    \r\n        usage:\r\n        to fill later\r\n    \"\"\"\r\n    def __init__(self):\r\n            \r\n        print('HighFinesse WM initialized')\r\n        \r\n        # the ControlWLM/Ex functions start, hide or terminate the WL server application\r\n#        Action = 'cCtrlWLMHide'\r\n#        control_show = dll_HF.ControlWLM(Action, None, None)\r\n#        \r\n#        print(control_show)\r\n\r\n\r\n\r\n\r\n        \r\n    def get_frequency(self):\r\n\r\n        dll_HF.GetFrequency.restype=c_double\r\n        \r\n        frequency = c_double()\r\n        frequency = dll_HF.GetFrequency(frequency)\r\n\r\n        return frequency\r\n\r\n        \r\n        \r\n        \r\n    def get_wavelength(self):\r\n\r\n        dll_HF.GetWavelength.restype=c_double\r\n        \r\n        wavelength = c_double()\r\n        wavelength = dll_HF.GetWavelength(wavelength)\r\n\r\n        return wavelength\r\n\r\n              \r\n              \r\n    def get_temperature(self):\r\n        \r\n        dll_HF.GetTemperature.restype=c_double\r\n        \r\n        temperature = c_double()\r\n        temperature = dll_HF.GetTemperature(temperature)\r\n\r\n        return temperature\r\n\r\n\r\n\r\n\r\n\r\n\r\n#wv = HighFinesse_WM()\r\n#a = []\r\n#for i in range(50):\r\n#    b = wv.get_frequency()\r\n#    time.sleep(0.1)\r\n#    a.append(b)\r\n#    \r\n#import numpy as np\r\n#import matplotlib.pyplot as plt\r\n#\r\n#x = np.arange(0, 50, 1);\r\n#\r\n#plt.plot(x, a)\r\n#plt.show()","sub_path":"library/instr_HighFinesseWM_WS07.py","file_name":"instr_HighFinesseWM_WS07.py","file_ext":"py","file_size_in_byte":1766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"599992754","text":"def print5to50():\n    for num in range(5, 50):\n        div = 2\n        while div < num:\n            if num % div == 0:\n                break\n            div=div+1\n        if div == num:\n            print(str(num) + ' is a prime number')\n\n\nprint5to50()\n","sub_path":"prime5to50.py","file_name":"prime5to50.py","file_ext":"py","file_size_in_byte":252,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"120969240","text":"from django.conf.urls import url\nfrom . import views\n\nurlpatterns = [\n    url(r'^$', views.index, name='index'),\n    url(r'^signup', views.singup, name='signup'),\n    url(r'^login', views.login, name='login'),\n    url(r'^test', views.test_get, name='test_get'),\n    url(r'^receive', views.receive, name='receive'),\n    url(r'^get_login', views.get_login, name='loginGet'),\n]","sub_path":"athenatech/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":374,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"415810489","text":"import csv\r\nimport numpy as np\r\nfrom sklearn.svm import SVR\r\nimport matplotlib.pyplot as plt\r\nimport mysql.connector\r\n\r\n\r\ndef main():\r\n        con=mysql.connector.connect(host='localhost',user='root',passwd='qwerty',database='project')\r\n\r\n        cursor=con.cursor()\r\n        cursor.execute(\"SELECT count(mtr_no) FROM project.data_log where mtr_no=111;\")\r\n        n=cursor.fetchone()[0]\r\n\r\n        dates = [i for i in range(n)]\r\n        prices = []\r\n\r\n        cursor.execute(\"select `reading` from `data_log` where mtr_no=111\")\r\n        l=cursor.fetchall()\r\n        for i in range(365):\r\n                prices.append(l[i][0])\r\n\r\n        def predict_prices(dates, prices, x):\r\n            dates = np.array([dates]).reshape(-1,1)#np.reshape(dates,(len(dates),1))\r\n            #array.reshape(1, -1)\r\n            #svr_lin= SVR(kernel = 'linear',C=1e3)\r\n            #svr_poly=SVR(kernel= 'poly', C=1e3,degree = 2)\r\n            svr_rbf= SVR(kernel= 'rbf', C=1e3,gamma=0.1)\r\n            #svr_lin.fit(dates,prices)\r\n            #svr_poly.fit(dates,prices)\r\n            svr_rbf.fit(dates,prices)\r\n\r\n            plt.scatter(dates,prices,color='black',label='Data')\r\n            plt.plot(dates,svr_rbf.predict(dates),color='red',label='RBF model')\r\n            #plt.plot(dates,svr_lin.predict(dates),color='green',label='Linear model')\r\n            #plt.plot(dates,svr_poly.predict(dates),color='blue',label='Polynomial model')\r\n            plt.xlabel('Date')\r\n            plt.ylabel('Price')\r\n            plt.title('Support Vector Regression')\r\n            plt.legend()\r\n            plt.show()\r\n            return svr_rbf.predict(np.array([x]).reshape(1,1)),#svr_lin.predict(np.array([x]).reshape(1,1)), svr_poly.predict(np.array([x]).reshape(1,1))\r\n\r\n#get_data('C:\\\\Users\\\\Master-Pc\\\\Desktop\\\\a.csv')\r\nmain()\r\npredicted_price= main.predict_prices(dates,prices,366)\r\nprint(predicted_price)\r\n         \r\n\r\n                           \r\n","sub_path":"predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":1924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"581582643","text":"'''\nCreated on May 6, 2019\n\n@author: courtneyfergusonlee\n'''\n\n\ndef get_expected(player_1, player_2):\n    # Returns the expected value for each player\n    expected_1 = 1 / \\\n        (1 + 10**((player_2['rating'] - player_1['rating']) / 400))\n    expected_2 = 1 / \\\n        (1 + 10**((player_1['rating'] - player_2['rating']) / 400))\n\n    return expected_1, expected_2\n\n\ndef get_k_factor(player):\n    # Returns the k-factor from a player dictionary\n    if player['num_games'] < 30:\n        return 40\n    elif player['rating'] < 2400:\n        return 20\n    elif player['rating'] >= 2400:\n        return 10\n\n\ndef get_ratings(player_1, player_2):\n    # Returns player ratings from two player dictionaries\n    outcome_dict = {\n        'win': 1,\n        'loss': 0,\n        'draw': .5\n    }\n    expected_1, expected_2 = get_expected(player_1, player_2)\n    k1 = get_k_factor(player_1)\n    k2 = get_k_factor(player_2)\n\n    player_1_new = player_1['rating'] + k1 * \\\n        (outcome_dict[player_1['outcome']] - expected_1)\n    player_2_new = player_2['rating'] + k2 * \\\n        (outcome_dict[player_2['outcome']] - expected_2)\n\n    return player_1_new, player_2_new\n\n\nif __name__ == '__main__':\n    player_1 = {\n        'rating': 956,\n        'num_games': 100,\n        'outcome': 'win'\n    }\n\n    player_2 = {\n        'rating': 907,\n        'num_games': 100,\n        'outcome': 'loss'\n    }\n\n    ratings = get_ratings(player_1, player_2)\n    print(\"Ratings: {}\".format(ratings))\n","sub_path":"data_utilities/elo_algorithm.py","file_name":"elo_algorithm.py","file_ext":"py","file_size_in_byte":1470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"266928981","text":"\n\"\"\"\nUnitary Event (UE) analysis is a statistical method that\n enables to analyze in a time resolved manner excess spike correlation\n between simultaneously recorded neurons by comparing the empirical\n spike coincidences (precision of a few ms) to the expected number \n based on the firing rates of the neurons.\n\nReferences:\n-----------\nGruen, Diesmann, Grammont, Riehle, Aertsen (1999) J Neurosci Methods, 94(1): 67-79.\nGruen, Diesmann, Aertsen (2002a,b) Neural Comput, 14(1): 43-80; 81-19.\nGruen S, Riehle A, and Diesmann M (2003) Effect of cross-trial nonstationarity \non joint-spike events Biological Cybernetics 88(5):335-351. \nGruen S (2009) Data-driven significance estimation of precise spike correlation. \nJ Neurophysiology 101:1126-1140 (invited review)\n\"\"\"\n\n\n\n\n\nimport numpy as np\nimport quantities as pq\nimport neo\nimport warnings\nimport elephant.conversion as conv\nimport scipy\n\n\ndef hash_from_pattern(m, N, base=2):\n    \"\"\"\n    Calculate for a spike pattern or a matrix of spike patterns\n    (provide each pattern as a column) composed of N neurons a\n    unique number.\n\n\n    Parameters:\n    -----------\n    m: list of integers\n           matrix of 0-1 patterns as columns,\n           shape: (number of neurons, number of patterns)\n    N: integer\n           number of neurons is required to be equal to the number\n           of rows\n    base: integer\n           base for calculation of the number from binary\n           sequences (= pattern).\n           Default is 2\n\n    Returns:\n    --------\n    list of integers:\n           An array containing the hash values of each pattern,\n           shape: (number of patterns)\n\n    Raises:\n    -------\n       ValueError: if matrix m has wrong orientation\n\n    Examples:\n    ---------\n    descriptive example:\n    m = [0\n         1\n         1]\n    N = 3\n    base = 2\n    hash = 0*2^2 + 1*2^1 + 1*2^0 = 3\n\n    second example:\n    >>> import numpy as np\n    >>> m = np.array([[0, 1, 0, 0, 1, 1, 0, 1],\n                         [0, 0, 1, 0, 1, 0, 1, 1],\n                         [0, 0, 0, 1, 0, 1, 1, 1]])\n\n    >>> hash_from_pattern(m,N=3)\n        array([0, 4, 2, 1, 6, 5, 3, 7])\n    \"\"\"\n    # check the consistency between shape of m and number neurons N\n    if N != np.shape(m)[0]:\n        raise ValueError('patterns in the matrix should be column entries')\n\n    # generate the representation for binary system\n    v = np.array([base**x for x in range(N)])\n    # reverse the order\n    v = v[np.argsort(-v)]\n    # calculate the binary number by use of scalar product\n    return np.dot(v,m)\n\n\ndef inverse_hash_from_pattern(h, N, base=2):\n    \"\"\"\n    Calculate the 0-1 spike patterns (matrix) from hash values\n\n    Parameters:\n    -----------\n    h: list of integers\n           list or array of hash values, length: number of patterns\n    N: integer\n           number of neurons\n    base: integer\n           base for calculation of the number from binary\n           sequences (= pattern).\n           Default is 2\n\n    Raises:\n    -------\n       ValueError: if the hash is not compatible with the number\n       of neurons hash value should not be larger than the biggest\n       possible hash number with given number of neurons\n       (e.g. for N = 2, max(hash) = 2^1 + 2^0 = 3\n            , or for N = 4, max(hash) = 2^3 + 2^2 + 2^1 + 2^0 = 15)\n\n    Returns:\n    --------\n       numpy.array:\n           A matrix of shape: (N, number of patterns)\n\n    Examples\n    ---------\n    >>> import numpy as np\n    >>> h = np.array([3,7])\n    >>> N = 4\n    >>> inverse_hash_from_pattern(h,N)\n        array([[1, 1],\n               [1, 1],\n               [0, 1],\n               [0, 0]])\n    \"\"\"\n\n    # check if the hash values are not bigger than possible hash value\n    # for N neuron with basis = base\n    if np.any(h > np.sum([base**x for x in range(N)])):\n        raise ValueError(\n            \"hash value is not compatible with the number of neurons N\")\n    # check if the hash values are integer\n    if not np.all(np.int64(h) == h):\n        raise ValueError(\"hash values are not integers\")\n\n    m = np.zeros((N,len(h)), dtype=int)\n    for j, hh in enumerate(h):\n        i = N-1\n        while i >= 0 and hh != 0:\n            m[i, j] = hh % base\n            hh /= base\n            i -= 1\n    return m\n\n\ndef n_emp_mat(mat, N, pattern_hash, base=2):\n    \"\"\"\n    Calculates empirical number of observed patterns expressed\n    by their hash values\n\n    Parameters:\n    -----------\n    m: list of integers\n           matrix of 0-1 patterns as columns,\n           shape: (number of neurons N, number of patterns)\n    N: integer\n           number of neurons\n    pattern_hash: list of integers\n            array of hash values. Length defines number of patterns\n    base: integer\n           base for calculation of the number from binary\n           sequences (= pattern).\n           Default is 2\n\n    Returns:\n    --------\n    N_emp: list of integers\n           empirical number of each observed pattern.\n           Same length as pattern_hash\n    indices: list of list of integers\n           list of indices of mat per entry of pattern_hash.\n           indices[i] = N_emp[i] = pattern_hash[i]\n\n    Raises:\n    -------\n       ValueError: if mat is not zero-one matrix\n\n    Examples:\n    ---------\n    >>> mat = np.array([[1, 0, 0, 1, 1],\n                        [1, 0, 0, 1, 0]])\n    >>> pattern_hash = np.array([1,3])\n    >>> n_emp, n_emp_indices = N_emp_mat(mat, N,pattern_hash)\n    >>> print n_emp\n    [ 0.  2.]\n    >>> print n_emp_indices\n    [array([]), array([0, 3])]\n    \"\"\"\n    # check if the mat is zero-one matrix\n    if np.any(mat>1) or np.any(mat<0):\n        raise ValueError(\"entries of mat should be either one or zero\")\n    h = hash_from_pattern(mat, N, base = base)\n    N_emp = np.zeros(len(pattern_hash))\n    indices = []\n    for p_h_idx, p_h in enumerate(pattern_hash):\n        indices_tmp = np.nonzero(h == p_h)[0]\n        indices.append(indices_tmp)\n        N_emp_tmp = len(indices_tmp)\n        N_emp[p_h_idx] = N_emp_tmp\n    return N_emp, indices\n\n\ndef n_emp_mat_sum_trial(mat, N, pattern_hash, method='analytic_TrialByTrial'):\n    \"\"\"\n    Calculates empirical number of observed patterns summed across trials\n\n    Parameters:\n    -----------\n    mat: 3d numpy array\n            the entries are zero or one\n            0-axis --> trials\n            1-axis --> neurons\n            2-axis --> time bins\n    N: integer\n            number of neurons\n    pattern_hash: list of integers\n            array of hash values, length: number of patterns\n    method: string\n            method with which the unitary events whould be computed\n            'analytic_TrialByTrial' -- > calculate the expectency\n            (analytically) on each trial, then sum over all trials.\n            'analytic_TrialAverage' -- > calculate the expectency\n            by averaging over trials.\n            (cf. Gruen et al. 2003)\n            'surrogate_TrialByTrial' -- > calculate the distribution \n            of expected coincidences by spike time randomzation in \n            each trial and sum over trials.\n            Default is 'analytic_trialByTrial'\n\n\n\n    Returns:\n    --------\n    N_empL list of integers\n           empirical number of observed pattern summed across trials,\n           length: number of patterns (i.e. len(patter_hash))\n    idx_trials: list of list of integers\n           list of indices of mat for each trial in which\n           the specific pattern has been observed.\n           0-axis --> trial\n           1-axis --> list of indices for the chosen trial per\n           entry of pattern_hash\n\n    Raises:\n    -------\n       ValueError: if matrix mat has wrong orientation\n       ValueError: if mat is not zero-one matrix\n\n    Examples:\n    ---------\n    >>> mat = np.array([[[1, 1, 1, 1, 0],\n                       [0, 1, 1, 1, 0],\n                       [0, 1, 1, 0, 1]],\n\n                       [[1, 1, 1, 1, 1],\n                        [0, 1, 1, 1, 1],\n                        [1, 1, 0, 1, 0]]])\n\n    >>> pattern_hash = np.array([4,6])\n    >>> N = 3\n    >>> n_emp_sum_trial, n_emp_sum_trial_idx =\n                             n_emp_mat_sum_trial(mat, N,pattern_hash)\n    >>> n_emp_sum_trial\n        array([ 1.,  3.])\n    >>> n_emp_sum_trial_idx\n        [[array([0]), array([3])], [array([], dtype=int64), array([2, 4])]]\n    \"\"\"\n    # check the consistency between shape of m and number neurons N\n    if N != np.shape(mat)[1]:\n        raise ValueError('the entries of mat should be a list of a'\n                         'list where 0-axis is trials and 1-axis is neurons')\n\n    num_patt = len(pattern_hash)\n    N_emp = np.zeros(num_patt)\n\n    idx_trials = []\n    for mat_tr in mat:\n        # check if the mat is zero-one matrix\n        if np.any(np.array(mat_tr)>1):\n            raise ValueError(\"entries of mat should be either one or zero\")\n        N_emp_tmp,indices_tmp = n_emp_mat(mat_tr, N, pattern_hash,base=2)\n        idx_trials.append(indices_tmp)\n        N_emp += N_emp_tmp\n    if method == 'analytic_TrialByTrial' or method == 'surrogate_TrialByTrial':\n        return N_emp, idx_trials\n    elif method == 'analytic_TrialAverage':\n        return N_emp/float(len(mat)), idx_trials\n\n\ndef _sts_overlap(sts, t_start=None, t_stop=None):\n    \"\"\"\n    Find the internal range t_start, t_stop where all spike trains are\n    defined; cut all spike trains taking that time range only\n    \"\"\"\n    max_tstart = max([t.t_start for t in sts])\n    min_tstop = min([t.t_stop for t in sts])\n\n    if t_start is None:\n        t_start = max_tstart\n        if not all([max_tstart == t.t_start for t in sts]):\n            warnings.warn(\n                \"Spiketrains have different t_start values -- \"\n                \"using maximum t_start as t_start.\")\n\n    if t_stop is None:\n        t_stop = min_tstop\n        if not all([min_tstop == t.t_stop for t in sts]):\n            warnings.warn(\n                \"Spiketrains have different t_stop values -- \"\n                \"using minimum t_stop as t_stop.\")\n\n    sts_cut = [st.time_slice(t_start=t_start, t_stop=t_stop) for st in sts]\n    return sts_cut\n\n\ndef n_exp_mat(mat, N, pattern_hash, method = 'analytic', **kwargs):\n    \"\"\"\n    Calculates the expected joint probability for each spike pattern\n\n    Parameters:\n    -----------\n    mat: 2d numpy array\n            the entries are zero or one\n            0-axis --> neurons\n            1-axis --> time bins\n    pattern_hash: list of integers\n            array of hash values, length: number of patterns\n    method: string\n            method with which the expectency should be caculated\n            'analytic' -- > analytically\n            'surr' -- > with surrogates (spike time randomization)\n            Default is 'analytic'\n    kwargs:\n    -------\n    n_surr: integer\n            number of surrogate to be used\n            Default is 100\n\n    Raises:\n    -------\n       ValueError: if matrix m has wrong orientation\n\n    Returns:\n    --------\n    if method is analytic:\n        numpy.array:\n           An array containing the expected joint probability of each pattern,\n           shape: (number of patterns,)\n    if method is surr:\n        numpy.ndarray, 0-axis --> different realizations,\n                       length = number of surrogates\n                       1-axis --> patterns\n\n    Examples:\n    ---------\n    >>> mat = np.array([[1, 1, 1, 1],\n                       [0, 1, 0, 1],\n                       [0, 0, 1, 0]])\n    >>> pattern_hash = np.array([5,6])\n    >>> N = 3\n    >>> n_exp_anal = n_exp_mat(mat,N, pattern_hash, method = 'analytic')\n    >>> n_exp_anal\n        [ 0.5 1.5 ]\n    >>>\n    >>>\n    >>> n_exp_surr = n_exp_mat(\n                  mat, N,pattern_hash, method = 'surr', n_surr = 5000)\n    >>> print n_exp_surr\n    [[ 1.  1.]\n     [ 2.  0.]\n     [ 2.  0.]\n     ...,\n     [ 2.  0.]\n     [ 2.  0.]\n     [ 1.  1.]]\n\n    \"\"\"\n    # check if the mat is zero-one matrix\n    if np.any(mat > 1) or np.any(mat < 0):\n        raise ValueError(\"entries of mat should be either one or zero\")\n\n    if method == 'analytic':\n        marg_prob = np.mean(mat,1,dtype=float)\n        # marg_prob needs to be a column vector, so we\n        # build a two dimensional array with 1 column\n        # and len(marg_prob) rows\n        marg_prob = np.reshape(marg_prob,(len(marg_prob),1))\n        m = inverse_hash_from_pattern(pattern_hash, N)\n        nrep = np.shape(m)[1]\n        # multipyling the marginal probability of neurons with regard to the pattern\n        pmat = np.multiply(m,np.tile(marg_prob,(1,nrep))) +\\\n               np.multiply(1-m,np.tile(1-marg_prob,(1,nrep)))\n        return np.prod(pmat,axis=0)*float(np.shape(mat)[1])\n    if method == 'surr':\n        if len(pattern_hash)>1:\n                raise ValueError('surrogate method works only for one pattern!')            \n        if 'n_surr' in kwargs:\n            n_surr = kwargs['n_surr']\n        else:\n            n_surr = 100.\n        N_exp_array = np.zeros(n_surr)\n        for rz_idx, rz in enumerate(np.arange(n_surr)):\n            # shuffling all elements of zero-one matrix\n            mat_surr = np.array(mat)\n            [np.random.shuffle(i) for i in mat_surr]\n            N_exp_array[rz_idx] = n_emp_mat(mat_surr, N, pattern_hash)[0][0]\n        return N_exp_array\n\n\ndef n_exp_mat_sum_trial(mat, N, pattern_hash, method = 'analytic_TrialByTrial', **kwargs):\n    \"\"\"\n    Calculates the expected joint probability\n    for each spike pattern sum over trials\n\n    Parameters:\n    -----------\n    mat: 3d numpy array\n            the entries are zero or one\n            0-axis --> trials\n            1-axis --> neurons\n            2-axis --> time bins\n    N: integer\n           number of neurons\n    pattern_hash: list of integers\n            array of hash values, length: number of patterns\n    method: string\n            method with which the unitary events whould be computed\n            'analytic_TrialByTrial' -- > calculate the expectency\n            (analytically) on each trial, then sum over all trials.\n            'analytic_TrialAverage' -- > calculate the expectency\n            by averaging over trials.\n            (cf. Gruen et al. 2003)\n            'surrogate_TrialByTrial' -- > calculate the distribution \n            of expected coincidences by spike time randomzation in \n            each trial and sum over trials.\n            Default is 'analytic_trialByTrial'\n\n    kwargs:\n    -------\n    n_surr: integer\n            number of surrogate to be used\n            Default is 100\n\n    Returns:\n    --------\n    numpy.array:\n       An array containing the expected joint probability of\n       each pattern summed over trials,shape: (number of patterns,)\n\n    Examples:\n    --------\n    >>> mat = np.array([[[1, 1, 1, 1, 0],\n                       [0, 1, 1, 1, 0],\n                       [0, 1, 1, 0, 1]],\n\n                       [[1, 1, 1, 1, 1],\n                        [0, 1, 1, 1, 1],\n                        [1, 1, 0, 1, 0]]])\n\n    >>> pattern_hash = np.array([5,6])\n    >>> N = 3\n    >>> n_exp_anal = n_exp_mat_sum_trial(mat, N, pattern_hash)\n    >>> print n_exp_anal\n        array([ 1.56,  2.56])\n    \"\"\"\n    # check the consistency between shape of m and number neurons N\n    if N != np.shape(mat)[1]:\n        raise ValueError('the entries of mat should be a list of a'\n                         'list where 0-axis is trials and 1-axis is neurons')\n\n    if method == 'analytic_TrialByTrial':\n        n_exp = np.zeros(len(pattern_hash))\n        for mat_tr in mat:\n            n_exp += n_exp_mat(mat_tr, N, pattern_hash, method='analytic')\n    elif method == 'analytic_TrialAverage':\n        n_exp = n_exp_mat(\n            np.mean(mat,0), N, pattern_hash, method='analytic')*np.shape(mat)[0]\n    elif method == 'surrogate_TrialByTrial':\n        if 'n_surr' in kwargs: \n            n_surr = kwargs['n_surr']\n        else:\n            n_surr = 100.\n        n_exp = np.zeros(n_surr)\n        for mat_tr in mat:\n            n_exp += n_exp_mat(mat_tr, N, pattern_hash, method='surr', n_surr = n_surr)\n    else:\n        raise ValueError(\n            \"The method only works on the zero_one matrix at the moment\")\n    return n_exp\n\n\ndef gen_pval_anal(mat, N, pattern_hash, method='analytic_TrialByTrial',**kwargs):\n    \"\"\"\n    computes the expected coincidences and a function to calculate\n    p-value for given empirical coincidences\n\n    this function generate a poisson distribution with the expected\n    value calculated by mat. it returns a function which gets\n    the empirical coincidences, `n_emp`,  and calculates a p-value\n    as the area under the poisson distribution from `n_emp` to infinity\n\n    Parameters:\n    -----------\n    mat: 3d numpy array\n            the entries are zero or one\n            0-axis --> trials\n            1-axis --> neurons\n            2-axis --> time bins\n    N: integer\n           number of neurons\n    pattern_hash: list of integers\n            array of hash values, length: number of patterns\n    method: string\n            method with which the unitary events whould be computed\n            'analytic_TrialByTrial' -- > calculate the expectency\n            (analytically) on each trial, then sum over all trials.\n            ''analytic_TrialAverage' -- > calculate the expectency\n            by averaging over trials.\n            Default is 'analytic_trialByTrial'\n            (cf. Gruen et al. 2003)\n\n    kwargs:\n    -------\n    n_surr: integer\n            number of surrogate to be used\n            Default is 100\n\n\n    Returns:\n    --------\n    pval_anal:\n            a function which calculates the p-value for\n            the given empirical coincidences\n    n_exp: list of floats\n            expected coincidences\n\n    Examples:\n    --------\n    >>> mat = np.array([[[1, 1, 1, 1, 0],\n                       [0, 1, 1, 1, 0],\n                       [0, 1, 1, 0, 1]],\n\n                       [[1, 1, 1, 1, 1],\n                        [0, 1, 1, 1, 1],\n                        [1, 1, 0, 1, 0]]])\n\n    >>> pattern_hash = np.array([5,6])\n    >>> N = 3\n    >>> pval_anal,n_exp = gen_pval_anal(mat, N,pattern_hash)\n    >>> n_exp\n        array([ 1.56,  2.56])\n    \"\"\"\n    if method == 'analytic_TrialByTrial' or method == 'analytic_TrialAverage':\n        n_exp = n_exp_mat_sum_trial(mat, N, pattern_hash, method = method)\n        def pval(n_emp):\n            p = 1.- scipy.special.gammaincc(n_emp, n_exp)\n            return p\n    elif method ==  'surrogate_TrialByTrial':\n        if 'n_surr' in kwargs: \n            n_surr = kwargs['n_surr'] \n        else:\n            n_surr = 100.\n        n_exp = n_exp_mat_sum_trial(mat, N, pattern_hash, method = method, n_surr = n_surr)\n        def pval(n_emp):\n            hist = np.bincount(np.int64(n_exp))\n            exp_dist = hist/float(np.sum(hist))\n            if len(n_emp)>1:\n                raise ValueError('in surrogate method the p_value can be calculated only for one pattern!')\n            return np.sum(exp_dist[n_emp[0]:])\n\n    return pval, n_exp\n\n\n\ndef jointJ(p_val):\n    \"\"\"Surprise measurement\n\n    logarithmic transformation of joint-p-value into surprise measure\n    for better visualization as the highly significant events are\n    indicated by very low joint-p-values\n\n    Parameters:\n    -----------\n    p_val: list of floats\n        p-values of statistical tests for different pattern.\n\n    Returns:\n    --------\n    J: list of floats\n        list of surprise measure\n\n    Examples:\n    ---------\n    >>> p_val = np.array([0.31271072,  0.01175031])\n    >>> jointJ(p_val)\n        array([0.3419968 ,  1.92481736])\n    \"\"\"\n    p_arr = np.array(p_val)\n\n    try:\n        Js = np.log10(1-p_arr)-np.log10(p_arr)\n    except RuntimeWarning:\n        pass\n    return Js\n\n\ndef _rate_mat_avg_trial(mat):\n    \"\"\"\n    calculates the average firing rate of each neurons across trials\n    \"\"\"\n    num_tr, N, nbins = np.shape(mat)\n    psth = np.zeros(N)\n    for tr,mat_tr in enumerate(mat):\n        psth += np.sum(mat_tr, axis=1)\n    return psth/float(nbins)/float(num_tr)\n\n\ndef _bintime(t, binsize):\n    \"\"\"\n    change the real time to bintime\n    \"\"\"\n    t_dl = t.rescale('ms').magnitude\n    binsize_dl = binsize.rescale('ms').magnitude\n    return np.floor(np.array(t_dl)/binsize_dl).astype(int)\n\n\ndef _winpos(t_start, t_stop, winsize, winstep,position='left-edge'):\n    \"\"\"\n    Calculates the position of the analysis window\n    \"\"\"\n    t_start_dl = t_start.rescale('ms').magnitude\n    t_stop_dl = t_stop.rescale('ms').magnitude\n    winsize_dl = winsize.rescale('ms').magnitude\n    winstep_dl = winstep.rescale('ms').magnitude\n\n    # left side of the window time\n    if position == 'left-edge':\n        ts_winpos = np.arange(t_start_dl, t_stop_dl - winsize_dl + winstep_dl, winstep_dl)*pq.ms\n    else:\n        raise ValueError('the current version only returns left-edge of the window')\n    return ts_winpos\n\n\ndef _UE(mat, N, pattern_hash, method='analytic_TrialByTrial',**kwargs):\n    \"\"\"\n    returns the default results of unitary events analysis\n    (Surprise, empirical coincidences and index of where it happened\n    in the given mat, n_exp and average rate of neurons)\n    \"\"\"\n    rate_avg = _rate_mat_avg_trial(mat)\n    n_emp, indices = n_emp_mat_sum_trial(mat, N, pattern_hash, method)\n    if method == 'surrogate_TrialByTrial':\n        if 'n_surr' in kwargs: \n            n_surr = kwargs['n_surr']\n        else: n_surr = 100\n        dist_exp, n_exp = gen_pval_anal(mat, N, pattern_hash, method, n_surr=n_surr)\n    elif method == 'analytic_TrialByTrial' or method == 'analytic_TrialAverage':\n        dist_exp, n_exp = gen_pval_anal(mat, N, pattern_hash, method)\n    pval = dist_exp(n_emp)\n    Js = jointJ(pval)\n    return Js, rate_avg, n_exp, n_emp,indices\n\n\ndef jointJ_window_analysis(\n        data, binsize, winsize, winstep, pattern_hash,\n        method='analytic_TrialByTrial', t_start=None,\n        t_stop=None, binary=True, **kwargs):\n    \"\"\"\n    Calculates the joint surprise in a sliding window fashion\n\n    Parameters:\n    ----------\n    data: list of neo.SpikeTrain objects\n          list of spike trains in different trials\n                                        0-axis --> Trials\n                                        1-axis --> Neurons\n                                        2-axis --> Spike times\n    binsize: Qunatity scalar with dimension time\n           size of bins for descritizing spike trains\n    winsize: Qunatity scalar with dimension time\n           size of the window of analysis\n    winstep: Qunatity scalar with dimension time\n           size of the window step\n    pattern_hash: list of integers\n           list of interested patterns in hash values\n           (see hash_from_pattern and inverse_hash_from_pattern functions)\n    method: string\n            method with which the unitary events whould be computed\n            'analytic_TrialByTrial' -- > calculate the expectency\n            (analytically) on each trial, then sum over all trials.\n            ''analytic_TrialAverage' -- > calculate the expectency\n            by averaging over trials.\n            (cf. Gruen et al. 2003)\n            'surrogate_TrialByTrial' -- > calculate the distribution \n            of expected coincidences by spike time randomzation in \n            each trial and sum over trials.\n            Default is 'analytic_trialByTrial'\n\n    kwargs:\n    -------\n    n_surr: integer\n            number of surrogate to be used\n            Default is 100\n\n\n    Returns:\n    -------\n    result: dictionary\n          Js: list of float\n                 JointSurprise of different given patterns within each window\n                 shape: different pattern hash --> 0-axis\n                        different window --> 1-axis\n          indices: list of list of integers\n                 list of indices of pattern whithin each window\n                 shape: different pattern hash --> 0-axis\n                        different window --> 1-axis\n          n_emp: list of integers\n                 empirical number of each observed pattern.\n                 shape: different pattern hash --> 0-axis\n                        different window --> 1-axis\n          n_exp: list of floats\n                 expeced number of each pattern.\n                 shape: different pattern hash --> 0-axis\n                        different window --> 1-axis\n          rate_avg: list of floats\n                 average firing rate of each neuron\n                 shape: different pattern hash --> 0-axis\n                        different window --> 1-axis\n\n    \"\"\"\n    if not isinstance(data[0][0],neo.SpikeTrain):\n        raise ValueError(\"structure of the data is not correct: 0-axis should be trials, 1-axis units and 2-axis neo spike trains\")\n\n    if t_start is None: t_start = data[0][0].t_start.rescale('ms')\n    if t_stop is None: t_stop = data[0][0].t_stop.rescale('ms')\n\n    # position of all windows (left edges)\n    t_winpos = _winpos(t_start, t_stop, winsize, winstep,position = 'left-edge')\n    t_winpos_bintime = _bintime(t_winpos, binsize)\n\n    winsize_bintime = _bintime(winsize, binsize)\n    winstep_bintime = _bintime(winstep, binsize)\n\n    if winsize_bintime * binsize != winsize:\n        warnings.warn(\n            \"ratio between winsize and binsize is not integer -- \"\n            \"the actual number for window size is \"+str (winsize_bintime * binsize))\n\n    if winstep_bintime * binsize != winstep:\n        warnings.warn(\n            \"ratio between winsize and binsize is not integer -- \"\n            \"the actual number for window size is\" + str(winstep_bintime * binsize))\n\n    num_tr, N = np.shape(data)[:2]\n\n    n_bins = int((t_stop - t_start)/binsize)\n\n    mat_tr_unit_spt = np.zeros((len(data), N, n_bins))\n    for tr, sts in enumerate(data):\n        bs = conv.BinnedSpikeTrain(sts, t_start=t_start, t_stop=t_stop, binsize=binsize)\n        if binary is True:\n            mat = bs.to_bool_array()\n        else:\n            raise ValueError(\n                \"The method only works on the zero_one matrix at the moment\")\n        mat_tr_unit_spt[tr] = mat\n\n    num_win = len(t_winpos)\n    Js_win, n_exp_win, n_emp_win = (np.zeros(num_win) for _ in xrange(3))\n    rate_avg = np.zeros((num_win,N))\n    indices_win = {}\n    for i in range(num_tr):\n        indices_win['trial'+str(i)]= []\n\n    for i, win_pos in enumerate(t_winpos_bintime):\n        mat_win = mat_tr_unit_spt[:,:,win_pos:win_pos + winsize_bintime]\n        if method == 'surrogate_TrialByTrial':\n            if 'n_surr' in kwargs: \n                n_surr = kwargs['n_surr']\n            else: n_surr = 100\n            Js_win[i], rate_avg[i], n_exp_win[i], n_emp_win[i], indices_lst = _UE(mat_win, N,pattern_hash,method,n_surr=n_surr)\n        else:\n            Js_win[i], rate_avg[i], n_exp_win[i], n_emp_win[i], indices_lst = _UE(mat_win, N,pattern_hash,method)\n        for j in range(num_tr):\n            if len(indices_lst[j][0]) > 0:\n                indices_win['trial'+str(j)] = np.append(indices_win['trial'+str(j)], indices_lst[j][0] + win_pos)\n    return {'Js': Js_win, 'indices':indices_win, 'n_emp': n_emp_win,'n_exp': n_exp_win,'rate_avg':rate_avg/binsize}\n","sub_path":"notebooks/elephant_dev/unitary_event_analysis_dev.py","file_name":"unitary_event_analysis_dev.py","file_ext":"py","file_size_in_byte":26969,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"388047835","text":"from django.shortcuts import render, redirect, get_object_or_404, RequestContext, render_to_response\nfrom datetime import datetime, timedelta\nfrom messaging.models import Message\nfrom .forms import NewMessageForm\n\n######################################\n# Messages\n######################################\n\ndef messages(request):\n    context = RequestContext(request)\n    messages = []\n    user = request.user\n    current_date = datetime.today() - timedelta(days=7)\n    if request.method == 'GET':\n        messages = Message.objects.filter(to_user=user, created__gte=current_date).order_by('-created')\n        #groups=request.user.groups.all()\n    return render_to_response('messaging/messages.html', {'messages': messages }, context)\n    \ndef new_message(request, template_name='dmin/new_message.html'):\n    form = NewMessageForm(request.POST or None)\n    if form.is_valid():\n        frm = form.save(commit=False)\n        frm.from_user = request.user\n        frm.save()\n        return redirect('/a/dmin/profile')\n    return render(request, template_name, {'form':form})\n","sub_path":"tinytotts/messaging/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1068,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"218200676","text":"\"\"\"A database encapsulating collections of near-Earth objects and their\nclose approaches.\n\nA `NEODatabase` holds an interconnected data set of NEOs and close approaches.\nIt provides methods to fetch an NEO by primary designation or by name, as well\nas a method to query the set of close approaches that match a collection of\nuser-specified criteria.\n\nUnder normal circumstances, the main module creates one NEODatabase from the\ndata on NEOs and close approaches extracted by `extract.load_neos` and\n`extract.load_approaches`.\n\nYou'll edit this file in Tasks 2 and 3.\n\"\"\"\n\"\"\"\nA function will be made which makes it possible to link together the neos and\nclose approaches which we have been provided through the load_neos and\nload_approaches. The parameters will be the neos and approaches itself.\nThis is our helper function for linking which we will use for the other\nfunctions in database.py as well.\n\"\"\"\n\n\ndef link_neos_and_approaches(neos, approaches):\n    neosList = {}           # for the neos list\n    approachesList = []    # for the approaches list\n\n    # every close approach has attributes with designation and so does\n    # every neo. We'll check if they match, then it will be linked.\n    # Also, it will searched as well in the list we are making. If it\n    # is already there, the approach will be appended to the neosList\n    # in the approaches. Else, it will be given with a new neo.\n\n    for approach in approaches:\n        for neo in neos:\n            if neo.designation == approach._designation:\n                approach.neo = neo\n                if neo.designation in neosList:\n                    neosList[neo.designation].approaches.append(approach)\n                else:\n                    neo.approaches.append(approach)\n                    neosList[neo.designation] = neo\n                break\n        approachesList.append(approach)\n\n    for neo in neos:\n        if neo.designation not in neosList:\n            neosList[neo.designation] = neo\n\n    return neosList.values(), approachesList\n\n\nclass NEODatabase:\n\n    \"\"\"A database of near-Earth objects and their close approaches.\n\n    A `NEODatabase` contains a collection of NEOs and a collection of close\n    approaches. It additionally maintains a few auxiliary data structures to\n    help fetch NEOs by primary designation or by name and to help speed up\n    querying for close approaches that match criteria.\n    \"\"\"\n\n    def __init__(self, neos, approaches):\n\n        \"\"\"Create a new `NEODatabase`.\n        As a precondition, this constructor assumes that the collections\n        of NEOs and close approaches haven't yet been linked - that is,\n        the `.approaches` attribute of each `NearEarthObject` resolves\n        to an empty collection, and the `.neo` attribute of each\n        `CloseApproach` is None.\n\n        However, each `CloseApproach` has an attribute (`._designation`) that\n        matches the `.designation` attribute of the corresponding NEO. This\n        constructor modifies the supplied NEOs and close approaches to link\n        them together - after it's done, the `.approaches` attribute of each\n        NEO has a collection of that NEO's close approaches, and the `.neo`\n        attribute of each close approach references the appropriate NEO.\n\n        :param neos: A collection of `NearEarthObject`s.\n        :param approaches: A collection of `CloseApproach`es.\n        \"\"\"\n\n        self.neos_name = {}\n        self.neos_designation = {}\n\n        # this will save the values securely\n        self._neos, self._approaches = link_neos_and_approaches(neos,\n                                                                approaches)\n        for neo in self._neos:\n            if neo.name:\n                self.neos_name[neo.name] = neo\n            self.neos_designation[neo.designation] = neo\n\n    def get_neo_by_designation(self, designation):\n\n        \"\"\"Find and return an NEO by its primary designation.\n\n        If no match is found, return `None` instead.\n\n        Each NEO in the data set has a unique primary designation, as a string.\n\n        The matching is exact - check for spelling and capitalization if no\n        match is found.\n\n        :param designation: The primary designation of the NEO to search for.\n        :return: The `NearEarthObject` with the desired primary designation,\n        or `None`.\n        \"\"\"\n\n        return self.neos_designation.get(designation)\n\n    def get_neo_by_name(self, name):\n\n        \"\"\"Find and return an NEO by its name.\n\n        If no match is found, return `None` instead.\n\n        Not every NEO in the data set has a name. No NEOs are associated with\n        the empty string nor with the `None` singleton.\n\n        The matching is exact - check for spelling and capitalization if no\n        match is found.\n\n        :param name: The name, as a string, of the NEO to search for.\n        :return: The `NearEarthObject` with the desired name, or `None`.\n        \"\"\"\n\n        if name in self.neos_name:\n            return self.neos_name[name]\n        return None\n\n    def query(self, filters=()):\n\n        \"\"\"Query close approaches to generate those that match a collection\n        of filters.\n\n        This generates a stream of `CloseApproach` objects that match all\n        of the provided filters.\n\n        If no arguments are provided, generate all known close approaches.\n\n        The `CloseApproach` objects are generated in internal order, which\n        isn't guaranteed to be sorted meaningfully, although is often sorted\n        by time.\n\n        :param filters: A collection of filters capturing user-specified\n        criteria.\n        :return: A stream of matching `CloseApproach` objects.\n        \"\"\"\n\n        for approach in self._approaches:\n            # map(func, iter) will be used to filter out the approaches\n            # for CloseApproach objects.\n            flag = False in map(lambda i: i(approach), filters)\n            if flag is False:\n                yield approach\n","sub_path":"NEO/database.py","file_name":"database.py","file_ext":"py","file_size_in_byte":5955,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"171560816","text":"### Implementing and simulating multiplexers in PyRTL ###\n\nimport pyrtl\n\n# Now, it is time to build and simulate (for 16 cycles) a 3-bit 5:1 MUX.\n# You can develop your design using either Boolean gates as above or PyRTL's\n# conditional assignment.\n\n# Declare data inputs\n# < add your code here >\n\na = pyrtl.Input(bitwidth=3, name='a')\nb = pyrtl.Input(bitwidth=3, name='b')\nc = pyrtl.Input(bitwidth=3, name='c')\nd = pyrtl.Input(bitwidth=3, name='d')\ne = pyrtl.Input(bitwidth=3, name='e')\n\n# Declare control inputs\n# < add your code here >\n\ns = pyrtl.Input(bitwidth=3, name='s')\n\n# Declare outputs \n# < add your code here >\n\no = pyrtl.Output(bitwidth=3, name='o')\n\n\n# Describe your 5:1 MUX implementation\n# < add your code here >\n\nsel0 = s[2]\nsel1 = s[1]\nsel2 = s[0]\n\n\nwith pyrtl.conditional_assignment:\n    with ((~sel0) & (~sel1) & (~sel2)):\n        o |= a\n    with ((~sel0) & (~sel1) & (sel2)):\n        o |= b\n    with ((~sel0) & (sel1) & (~sel2)):\n        o |= c\n    with ((~sel0) & (sel1) & (sel2)):\n        o |= d\n    with ((sel0) & (~sel1) & (~sel2)):\n        o |= e\n\n\n#o <<= temp1 | temp2 | temp3 | temp4 | temp5\n\n# Simulate and test your design for 16 cycles using random inputs\n# < add your code here >\n\n\nsim_trace = pyrtl.SimulationTrace()\nsim = pyrtl.Simulation(tracer=sim_trace)\n\nimport random\nfor cycle in range(16):\n    sim.step({\n        'a': random.choice([0,1,2,3,4,5,6,7]),\n        'b': random.choice([0,1,2,3,4,5,6,7]),\n        'c': random.choice([0,1,2,3,4,5,6,7]),\n        'd': random.choice([0,1,2,3,4,5,6,7]),\n        'e': random.choice([0,1,2,3,4,5,6,7]),\n        's': random.choice([0,1,2,3,4])\n        })\n\n\nprint('--- 3-bit 5:1 MUX Simulation ---')\nsim_trace.render_trace()\n","sub_path":"lab3/lab3_q3.py","file_name":"lab3_q3.py","file_ext":"py","file_size_in_byte":1700,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"468418806","text":"from tensorflow.python.layers import core as layers_core\nfrom nltk.tokenize import word_tokenize\nimport pickle\nimport tensorflow as tf\nimport numpy as np\n\nfrom utils import data_utils\n\n\nclass Chatbot(object):\n\n\tdef __init__(self, params, language_base):\n\n\t\tself.params = params\n\n\t\twith tf.variable_scope(\"chat\"):\n\t\t\tself.train_model = ChatModel(tf.contrib.learn.ModeKeys.TRAIN, params, language_base)\n\t\twith tf.variable_scope('chat', reuse=True):\n\t\t\tself.eval_model = ChatModel(tf.contrib.learn.ModeKeys.EVAL, params, language_base)\n\t\twith tf.variable_scope('chat', reuse=True):\n\t\t\tself.infer_model = ChatModel(tf.contrib.learn.ModeKeys.INFER, params, language_base)\n\n\tdef train(self, data, sess):\n\n\t\treturn self.train_model.train(data, sess)\n\n\tdef infer(self, data, sess):\n\n\t\treturn self.infer_model.infer(data, sess)\n\n\tdef respond(self, text, language_base, sess):\n\n\t\tpad_seq = ['</s>' for _ in range(self.params.src_max_len)]\n\t\twords = word_tokenize(text)\n\t\tsrc_len = len(words)\n\t\twords = [words[i].lower() if i < len(words) else '</s>' for i in range(self.params.src_max_len)]\n\t\tids = language_base.convert_words_to_ids(words)\n\t\tpad_ids = language_base.convert_words_to_ids(pad_seq)\n\t\tencoder_inputs = np.array([ids] + [pad_ids for _ in range(self.params.batch_size-1)])\n\t\tsource_sequence_lengths = np.array([src_len] + [1 for _ in range(self.params.batch_size-1)])\n\t\tbatch_data = (encoder_inputs, None, None, source_sequence_lengths, None)\n\t\ttranslation_ids = self.infer(batch_data, sess)[0][0][0]\n\t\ttranslation_words = language_base.convert_ids_to_words(translation_ids)\n\t\ttranslation_words = data_utils.trim_words(translation_words)\n\t\ttranslation = data_utils.words_to_text(translation_words)\n\n\t\treturn translation.capitalize()\n\n\tdef save(self, directory):\n\n\t\twith open(os.path.join(directory, 'params'), 'wb') as f:\n\t\t\tpickle.dump(self.params, f)\n\n\nclass ChatModel(object):\n\n\tdef __init__(self, mode, params, language_base):\n\n\t\tself.mode = mode\n\n\t\t# Placeholders\n\t\tself.encoder_inputs = tf.placeholder(tf.int32, shape=[params.batch_size, params.src_max_len], name='encoder_inputs')\n\t\tself.source_sequence_lengths = tf.placeholder(tf.int32, shape=[params.batch_size], name='source_sequence_lengths')\n\t\tself.decoder_inputs = tf.placeholder(tf.int32, shape=[params.batch_size, params.tgt_max_len], name='decoder_inputs')\n\t\tself.target_sequence_lengths = tf.placeholder(tf.int32, shape=[params.batch_size], name='target_sequence_lengths')\n\t\tself.decoder_outputs = tf.placeholder(tf.int32, shape=[params.batch_size, params.tgt_max_len], name='decoder_outputs')\n\n\t\t# Look up embedding:\n\t\t#   encoder_inputs: [max_time, batch_size]\n\t\t#   encoder_emb_inp: [max_time, batch_size, embedding_size]\n\t\tencoder_emb_inp = tf.nn.embedding_lookup(language_base.embeddings, self.encoder_inputs)\n\t\tdecoder_emb_inp = tf.nn.embedding_lookup(language_base.embeddings, self.decoder_inputs)\n\n\t\tencoder_emb_inp.set_shape([params.batch_size, params.src_max_len, language_base.word_embedding_size])\n\t\tdecoder_emb_inp.set_shape([params.batch_size, params.tgt_max_len, language_base.word_embedding_size])\n\n\t\tencoder_cell = tf.nn.rnn_cell.BasicLSTMCell(params.num_units)\n\n\t\tencoder_initial_state = encoder_cell.zero_state(params.batch_size, dtype=tf.float32)\n\n\t\tencoder_outputs, encoder_state = tf.nn.dynamic_rnn(\n\t\t\tencoder_cell, encoder_emb_inp, initial_state=encoder_initial_state,\n\t\t\tsequence_length=self.source_sequence_lengths, \n\t\t\ttime_major=False, swap_memory=True)\n\n\t\t# Build RNN cell\n\t\tdecoder_cell = tf.nn.rnn_cell.BasicLSTMCell(params.num_units)\n\n\t\t# attention_states: [batch_size, max_time, num_units]\n\t\t# attention_states = tf.transpose(encoder_outputs, [1, 0, 2])\n\n\t\tsource_seq_lengths = self.source_sequence_lengths\n\n\t\tif self.mode == tf.contrib.learn.ModeKeys.INFER and params.beam_width > 0:\n\t\t\tencoder_outputs = tf.contrib.seq2seq.tile_batch(\n\t\t\t\tencoder_outputs, multiplier=params.beam_width)\n\t\t\tsource_seq_lengths = tf.contrib.seq2seq.tile_batch(\n\t\t\t\tsource_seq_lengths, multiplier=params.beam_width)\n\n\t\t# Create an attention mechanism\n\t\tattention_mechanism = tf.contrib.seq2seq.LuongAttention(\n\t\t\tparams.num_units, encoder_outputs,\n\t\t\tmemory_sequence_length=source_seq_lengths)\n\n\t\tdecoder_cell = tf.contrib.seq2seq.AttentionWrapper(\n\t\t\tdecoder_cell, attention_mechanism,\n\t\t\tattention_layer_size=params.num_units)\n\n\t\tprojection_layer = layers_core.Dense(units=len(language_base.vocabulary), use_bias=True)\n\n\t\tif self.mode == tf.contrib.learn.ModeKeys.INFER:\n\n\t\t\tif params.beam_width > 0:\n\t\t\t\tencoder_state = tf.contrib.seq2seq.tile_batch(\n\t\t\t\t\tencoder_state, multiplier=params.beam_width)\n\t\t\t\tbatch_size = params.batch_size * params.beam_width\n\n\t\t\tdecoder_initial_state = decoder_cell.zero_state(batch_size, tf.float32).clone(cell_state=encoder_state)\n\n\t\t\tmaximum_iterations = tf.round(tf.reduce_max(self.source_sequence_lengths) * 2)\n\n\t\t\ttmp_embeddings = tf.identity(language_base.embeddings)\n\t\t\ttmp_embeddings.set_shape([len(language_base.vocabulary), language_base.word_embedding_size])\n\n\t\t\tdecoder = tf.contrib.seq2seq.BeamSearchDecoder(\n\t\t\t\tcell=decoder_cell,\n\t\t\t\tembedding=tmp_embeddings,\n\t\t\t\tstart_tokens=tf.fill([params.batch_size], language_base.sos_id),\n\t\t\t\tend_token=language_base.eos_id,\n\t\t\t\tinitial_state=decoder_initial_state,\n\t\t\t\tbeam_width=params.beam_width,\n\t\t\t\toutput_layer=projection_layer,\n\t\t\t\tlength_penalty_weight=params.length_penalty_weight)\n\n\t\t\t# Dynamic decoding\n\t\t\toutputs, _, _ = tf.contrib.seq2seq.dynamic_decode(\n\t\t\t\tdecoder, maximum_iterations=maximum_iterations)\n\n\t\t\tself.translations = outputs.predicted_ids\n\t\t\tself.translations = tf.transpose(self.translations, perm=[0, 2, 1])\n\n\t\telse:\n\n\t\t\tself.predict_count = tf.reduce_sum(self.target_sequence_lengths)\n\n\t\t\tdecoder_initial_state = decoder_cell.zero_state(params.batch_size, tf.float32).clone(cell_state=encoder_state)\n\n\t\t\t# Helper\n\t\t\thelper = tf.contrib.seq2seq.TrainingHelper(\n\t\t\t\tdecoder_emb_inp, [params.tgt_max_len for _ in range(params.batch_size)], time_major=False)\n\n\t\t\t# # Decoder\n\t\t\t# if decoder_type == 'basic':\n\t\t\tdecoder = tf.contrib.seq2seq.BasicDecoder(\n\t\t\t\tdecoder_cell, helper, decoder_initial_state,\n\t\t\t\toutput_layer=projection_layer)\n\t\t\t\t\n\t\t\t# elif decoder_type == 'attention':\n\t\t\t#     decoder = tf.contrib.seq2seq.BahdanauAttention(\n\t\t\t#         decoder_cell, helper, encoder_state,\n\t\t\t#         output_layer=projection_layer)\n\t\t\t\t\n\t\t\t# Dynamic decoding\n\t\t\toutputs, _, _ = tf.contrib.seq2seq.dynamic_decode(\n\t\t\t\tdecoder, output_time_major=False,\n\t\t\t\tswap_memory=True\n\t\t\t)\n\n\t\t\tlogits = outputs.rnn_output\n\n\t\t\tcrossent = tf.nn.sparse_softmax_cross_entropy_with_logits(\n\t\t\t\tlabels=self.decoder_outputs, logits=logits)\n\n\t\t\ttarget_weights = tf.sequence_mask(\n\t\t\t\tself.target_sequence_lengths, params.tgt_max_len,\n\t\t\t\tdtype=logits.dtype)\n\n\t\t\tself.loss = (tf.reduce_sum(crossent * target_weights) / tf.to_float(params.batch_size))\n\n\t\t\tself.prediction = outputs.sample_id\n\n\t\t\tglobal_step = tf.Variable(0, trainable=False)\n\t\t\tself.inc_gstep = tf.assign(global_step, global_step + params.batch_size)\n\t\t\tlearning_rate = params.learning_rate\n\t\t\t# learning_rate = tf.train.exponential_decay(\n\t\t\t# \tparams.learning_rate, global_step, decay_steps=10, decay_rate=0.9, staircase=True)\n\n\t\t\tadam_optimizer = tf.train.AdamOptimizer(learning_rate)\n\t\t\tadam_gradients, v = zip(*adam_optimizer.compute_gradients(self.loss))\n\t\t\tadam_gradients, _ = tf.clip_by_global_norm(adam_gradients, params.max_gradient_norm)\n\t\t\tself.adam_optimize = adam_optimizer.apply_gradients(zip(adam_gradients, v))\n\n\tdef _feed_dict(self, data):\n\n\t\tif self.mode == tf.contrib.learn.ModeKeys.INFER:\n\t\t\treturn {\n\t\t\t\tself.encoder_inputs: data[0],\n\t\t\t\tself.source_sequence_lengths: data[3]\n\t\t\t}\n\t\telse:\n\t\t\treturn {\n\t\t\t\tself.encoder_inputs: data[0],\n\t\t\t\tself.source_sequence_lengths: data[3],\n\t\t\t\tself.decoder_inputs: data[1],\n\t\t\t\tself.target_sequence_lengths: data[4],\n\t\t\t\tself.decoder_outputs: data[2],\n\t\t\t}\n\n\tdef train(self, data, sess):\n\n\t\tassert self.mode == tf.contrib.learn.ModeKeys.TRAIN\n\n\t\treturn sess.run([self.loss, self.adam_optimize, self.inc_gstep, self.predict_count], feed_dict=self._feed_dict(data))\n\n\tdef infer(self, data, sess):\n\n\t\tassert self.mode == tf.contrib.learn.ModeKeys.INFER\n\n\t\treturn sess.run([self.translations], feed_dict=self._feed_dict(data))\n\n\n\n\n\n\n\n\n","sub_path":"model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":8236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"268834649","text":"# usage:\n#  python jenkins.py jobname1, jobname2\nimport sys, httplib\n\nJENKINS_HOST = 'localhost:8080'\nURL = '/jenkins/job/%s/build?token=%s&cause=%s'\nJENKINS_AUTH_TOKEN = 'token'\nCAUSE='Requested%20by%20Python'\n\njenkins_job_ids = sys.argv\njenkins_job_ids.pop(0)     # shift operation to reduce this command\n\nfor job_id in jenkins_job_ids:\n  http = httplib.HTTPConnection(JENKINS_HOST)\n  url = URL % (job_id, JENKINS_AUTH_TOKEN, CAUSE)\n  http.request('GET', url)\n  response = http.getresponse()\n  #print response.status, response.reason, url # debug\n","sub_path":"gist.githubuser_daicham/jenkins.py","file_name":"jenkins.py","file_ext":"py","file_size_in_byte":549,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"429993402","text":"import os\nimport math\n\n\n# Assigning Value To variable\na = 10\nb = 20\nname = \"ABC\"\n\ndef my_function(x, y):\n    '''\n    This Function returns Addition\n    '''\n    return x + y\n\t\nans = my_function(a, b)\n\n\"\"\"\nCan Add Multiple Commnets Here\n\"\"\"\nprint(ans)\n","sub_path":"first_program.py","file_name":"first_program.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"377978647","text":"# Algorithm insufficient to solve all test cases\n# the solution requires a Dynamic Programming \n# approach. Pushing off until skills have been \n# acquired. \n\nclass NAndODD:\n\n    def divide_by_two(self, x):\n        return x / 2\n\n    def add_k(self, x, k):\n        return x + k\n\n    def GoF(self, x, K):\n        count = 0\n        if x > K:\n            while x > K:\n                if x % 2 == 0:\n                    x = self.divide_by_two(x)\n                    count += 1\n                else:\n                    x = self.add_k(x, K)\n                    count += 1\n        else:\n            while x < K:\n                if x % 2 == 0:\n                    x = self.divide_by_two(x)\n                    count += 1\n                else:\n                    x = self.add_k(x, K)\n                    count += 1\n        return count\n\n    def bugs(self, x, K):\n        count = 0\n        while x > K:\n            if x % 2 == 0:\n                x = self.divide_by_two(x)\n                count += 1\n            else:\n                x = self.add_k(x, K)\n                count += 1\n        return count\n\n    def solve(self, L, R, K):\n        return self.GoF(L, K) + self.GoF(R, K) if L != R else self.GoF(L, K)\n","sub_path":"NAddOdd.py","file_name":"NAddOdd.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"441814166","text":"# Copyright 2015 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#     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\nimport unittest\n\nimport mock\n\nfrom ._testing import _make_credentials\n\n\nclass MultiCallableStub(object):\n    \"\"\"Stub for the grpc.UnaryUnaryMultiCallable interface.\"\"\"\n\n    def __init__(self, method, channel_stub):\n        self.method = method\n        self.channel_stub = channel_stub\n\n    def __call__(self, request, timeout=None, metadata=None, credentials=None):\n        self.channel_stub.requests.append((self.method, request))\n\n        return self.channel_stub.responses.pop()\n\n\nclass ChannelStub(object):\n    \"\"\"Stub for the grpc.Channel interface.\"\"\"\n\n    def __init__(self, responses=[]):\n        self.responses = responses\n        self.requests = []\n\n    def unary_unary(self, method, request_serializer=None, response_deserializer=None):\n        return MultiCallableStub(method, self)\n\n\nclass TestCluster(unittest.TestCase):\n\n    PROJECT = \"project\"\n    INSTANCE_ID = \"instance-id\"\n    LOCATION_ID = \"location-id\"\n    CLUSTER_ID = \"cluster-id\"\n    LOCATION_ID = \"location-id\"\n    CLUSTER_NAME = (\n        \"projects/\" + PROJECT + \"/instances/\" + INSTANCE_ID + \"/clusters/\" + CLUSTER_ID\n    )\n    LOCATION_PATH = \"projects/\" + PROJECT + \"/locations/\"\n    SERVE_NODES = 5\n    OP_ID = 5678\n    OP_NAME = \"operations/projects/{}/instances/{}/clusters/{}/operations/{}\".format(\n        PROJECT, INSTANCE_ID, CLUSTER_ID, OP_ID\n    )\n\n    @staticmethod\n    def _get_target_class():\n        from google.cloud.bigtable.cluster import Cluster\n\n        return Cluster\n\n    def _make_one(self, *args, **kwargs):\n        return self._get_target_class()(*args, **kwargs)\n\n    @staticmethod\n    def _get_target_client_class():\n        from google.cloud.bigtable.client import Client\n\n        return Client\n\n    def _make_client(self, *args, **kwargs):\n        return self._get_target_client_class()(*args, **kwargs)\n\n    def test_constructor_defaults(self):\n        client = _Client(self.PROJECT)\n        instance = _Instance(self.INSTANCE_ID, client)\n\n        cluster = self._make_one(self.CLUSTER_ID, instance)\n        self.assertEqual(cluster.cluster_id, self.CLUSTER_ID)\n        self.assertIs(cluster._instance, instance)\n        self.assertIsNone(cluster.location_id)\n        self.assertIsNone(cluster.state)\n        self.assertIsNone(cluster.serve_nodes)\n        self.assertIsNone(cluster.default_storage_type)\n\n    def test_constructor_non_default(self):\n        from google.cloud.bigtable.enums import StorageType\n        from google.cloud.bigtable.enums import Cluster\n\n        STATE = Cluster.State.READY\n        STORAGE_TYPE_SSD = StorageType.SSD\n        client = _Client(self.PROJECT)\n        instance = _Instance(self.INSTANCE_ID, client)\n\n        cluster = self._make_one(\n            self.CLUSTER_ID,\n            instance,\n            location_id=self.LOCATION_ID,\n            _state=STATE,\n            serve_nodes=self.SERVE_NODES,\n            default_storage_type=STORAGE_TYPE_SSD,\n        )\n        self.assertEqual(cluster.cluster_id, self.CLUSTER_ID)\n        self.assertIs(cluster._instance, instance)\n        self.assertEqual(cluster.location_id, self.LOCATION_ID)\n        self.assertEqual(cluster.state, STATE)\n        self.assertEqual(cluster.serve_nodes, self.SERVE_NODES)\n        self.assertEqual(cluster.default_storage_type, STORAGE_TYPE_SSD)\n\n    def test_name_property(self):\n        credentials = _make_credentials()\n        client = self._make_client(\n            project=self.PROJECT, credentials=credentials, admin=True\n        )\n        instance = _Instance(self.INSTANCE_ID, client)\n        cluster = self._make_one(self.CLUSTER_ID, instance)\n\n        self.assertEqual(cluster.name, self.CLUSTER_NAME)\n\n    def test_from_pb_success(self):\n        from google.cloud.bigtable_admin_v2.proto import instance_pb2 as data_v2_pb2\n        from google.cloud.bigtable import enums\n\n        client = _Client(self.PROJECT)\n        instance = _Instance(self.INSTANCE_ID, client)\n\n        location = self.LOCATION_PATH + self.LOCATION_ID\n        state = enums.Cluster.State.RESIZING\n        storage_type = enums.StorageType.SSD\n        cluster_pb = data_v2_pb2.Cluster(\n            name=self.CLUSTER_NAME,\n            location=location,\n            state=state,\n            serve_nodes=self.SERVE_NODES,\n            default_storage_type=storage_type,\n        )\n\n        klass = self._get_target_class()\n        cluster = klass.from_pb(cluster_pb, instance)\n        self.assertIsInstance(cluster, klass)\n        self.assertEqual(cluster._instance, instance)\n        self.assertEqual(cluster.cluster_id, self.CLUSTER_ID)\n        self.assertEqual(cluster.location_id, self.LOCATION_ID)\n        self.assertEqual(cluster.state, state)\n        self.assertEqual(cluster.serve_nodes, self.SERVE_NODES)\n        self.assertEqual(cluster.default_storage_type, storage_type)\n\n    def test_from_pb_bad_cluster_name(self):\n        from google.cloud.bigtable_admin_v2.proto import instance_pb2 as data_v2_pb2\n\n        bad_cluster_name = \"BAD_NAME\"\n\n        cluster_pb = data_v2_pb2.Cluster(name=bad_cluster_name)\n\n        klass = self._get_target_class()\n        with self.assertRaises(ValueError):\n            klass.from_pb(cluster_pb, None)\n\n    def test_from_pb_instance_id_mistmatch(self):\n        from google.cloud.bigtable_admin_v2.proto import instance_pb2 as data_v2_pb2\n\n        ALT_INSTANCE_ID = \"ALT_INSTANCE_ID\"\n        client = _Client(self.PROJECT)\n        instance = _Instance(ALT_INSTANCE_ID, client)\n\n        self.assertNotEqual(self.INSTANCE_ID, ALT_INSTANCE_ID)\n        cluster_pb = data_v2_pb2.Cluster(name=self.CLUSTER_NAME)\n\n        klass = self._get_target_class()\n        with self.assertRaises(ValueError):\n            klass.from_pb(cluster_pb, instance)\n\n    def test_from_pb_project_mistmatch(self):\n        from google.cloud.bigtable_admin_v2.proto import instance_pb2 as data_v2_pb2\n\n        ALT_PROJECT = \"ALT_PROJECT\"\n        client = _Client(project=ALT_PROJECT)\n        instance = _Instance(self.INSTANCE_ID, client)\n\n        self.assertNotEqual(self.PROJECT, ALT_PROJECT)\n        cluster_pb = data_v2_pb2.Cluster(name=self.CLUSTER_NAME)\n\n        klass = self._get_target_class()\n        with self.assertRaises(ValueError):\n            klass.from_pb(cluster_pb, instance)\n\n    def test___eq__(self):\n        client = _Client(self.PROJECT)\n        instance = _Instance(self.INSTANCE_ID, client)\n        cluster1 = self._make_one(self.CLUSTER_ID, instance, self.LOCATION_ID)\n        cluster2 = self._make_one(self.CLUSTER_ID, instance, self.LOCATION_ID)\n        self.assertEqual(cluster1, cluster2)\n\n    def test___eq__type_differ(self):\n        client = _Client(self.PROJECT)\n        instance = _Instance(self.INSTANCE_ID, client)\n        cluster1 = self._make_one(self.CLUSTER_ID, instance, self.LOCATION_ID)\n        cluster2 = object()\n        self.assertNotEqual(cluster1, cluster2)\n\n    def test___ne__same_value(self):\n        client = _Client(self.PROJECT)\n        instance = _Instance(self.INSTANCE_ID, client)\n        cluster1 = self._make_one(self.CLUSTER_ID, instance, self.LOCATION_ID)\n        cluster2 = self._make_one(self.CLUSTER_ID, instance, self.LOCATION_ID)\n        comparison_val = cluster1 != cluster2\n        self.assertFalse(comparison_val)\n\n    def test___ne__(self):\n        client = _Client(self.PROJECT)\n        instance = _Instance(self.INSTANCE_ID, client)\n        cluster1 = self._make_one(\"cluster_id1\", instance, self.LOCATION_ID)\n        cluster2 = self._make_one(\"cluster_id2\", instance, self.LOCATION_ID)\n        self.assertNotEqual(cluster1, cluster2)\n\n    def test_reload(self):\n        from google.cloud.bigtable_admin_v2.gapic import bigtable_instance_admin_client\n        from google.cloud.bigtable_admin_v2.proto import instance_pb2 as data_v2_pb2\n        from google.cloud.bigtable.enums import StorageType\n        from google.cloud.bigtable.enums import Cluster\n\n        api = bigtable_instance_admin_client.BigtableInstanceAdminClient(mock.Mock())\n        credentials = _make_credentials()\n        client = self._make_client(\n            project=self.PROJECT, credentials=credentials, admin=True\n        )\n        STORAGE_TYPE_SSD = StorageType.SSD\n        instance = _Instance(self.INSTANCE_ID, client)\n        cluster = self._make_one(\n            self.CLUSTER_ID,\n            instance,\n            location_id=self.LOCATION_ID,\n            serve_nodes=self.SERVE_NODES,\n            default_storage_type=STORAGE_TYPE_SSD,\n        )\n\n        # Create response_pb\n        LOCATION_ID_FROM_SERVER = \"new-location-id\"\n        STATE = Cluster.State.READY\n        SERVE_NODES_FROM_SERVER = 10\n        STORAGE_TYPE_FROM_SERVER = StorageType.HDD\n\n        response_pb = data_v2_pb2.Cluster(\n            name=cluster.name,\n            location=self.LOCATION_PATH + LOCATION_ID_FROM_SERVER,\n            state=STATE,\n            serve_nodes=SERVE_NODES_FROM_SERVER,\n            default_storage_type=STORAGE_TYPE_FROM_SERVER,\n        )\n\n        # Patch the stub used by the API method.\n        client._instance_admin_client = api\n        instance_admin_client = client._instance_admin_client\n        instance_stub = instance_admin_client.transport\n        instance_stub.get_cluster.side_effect = [response_pb]\n\n        # Create expected_result.\n        expected_result = None  # reload() has no return value.\n\n        # Check Cluster optional config values before.\n        self.assertEqual(cluster.location_id, self.LOCATION_ID)\n        self.assertIsNone(cluster.state)\n        self.assertEqual(cluster.serve_nodes, self.SERVE_NODES)\n        self.assertEqual(cluster.default_storage_type, STORAGE_TYPE_SSD)\n\n        # Perform the method and check the result.\n        result = cluster.reload()\n        self.assertEqual(result, expected_result)\n        self.assertEqual(cluster.location_id, LOCATION_ID_FROM_SERVER)\n        self.assertEqual(cluster.state, STATE)\n        self.assertEqual(cluster.serve_nodes, SERVE_NODES_FROM_SERVER)\n        self.assertEqual(cluster.default_storage_type, STORAGE_TYPE_FROM_SERVER)\n\n    def test_exists(self):\n        from google.cloud.bigtable_admin_v2.gapic import bigtable_instance_admin_client\n        from google.cloud.bigtable_admin_v2.proto import instance_pb2 as data_v2_pb2\n        from google.cloud.bigtable.instance import Instance\n        from google.api_core import exceptions\n\n        instance_api = bigtable_instance_admin_client.BigtableInstanceAdminClient(\n            mock.Mock()\n        )\n        credentials = _make_credentials()\n        client = self._make_client(\n            project=self.PROJECT, credentials=credentials, admin=True\n        )\n        instance = Instance(self.INSTANCE_ID, client)\n\n        # Create response_pb\n        cluster_name = client.instance_admin_client.cluster_path(\n            self.PROJECT, self.INSTANCE_ID, self.CLUSTER_ID\n        )\n        response_pb = data_v2_pb2.Cluster(name=cluster_name)\n\n        # Patch the stub used by the API method.\n        client._instance_admin_client = instance_api\n        instance_admin_client = client._instance_admin_client\n        instance_stub = instance_admin_client.transport\n        instance_stub.get_cluster.side_effect = [\n            response_pb,\n            exceptions.NotFound(\"testing\"),\n            exceptions.BadRequest(\"testing\"),\n        ]\n\n        # Perform the method and check the result.\n        non_existing_cluster_id = \"cluster-id-2\"\n        alt_cluster_1 = self._make_one(self.CLUSTER_ID, instance)\n        alt_cluster_2 = self._make_one(non_existing_cluster_id, instance)\n        self.assertTrue(alt_cluster_1.exists())\n        self.assertFalse(alt_cluster_2.exists())\n        with self.assertRaises(exceptions.BadRequest):\n            alt_cluster_1.exists()\n\n    def test_create(self):\n        import datetime\n        from google.api_core import operation\n        from google.longrunning import operations_pb2\n        from google.protobuf.any_pb2 import Any\n        from google.cloud.bigtable_admin_v2.proto import (\n            bigtable_instance_admin_pb2 as messages_v2_pb2,\n        )\n        from google.cloud._helpers import _datetime_to_pb_timestamp\n        from google.cloud.bigtable.instance import Instance\n        from google.cloud.bigtable_admin_v2.types import instance_pb2\n        from google.cloud.bigtable_admin_v2.gapic import bigtable_instance_admin_client\n        from google.cloud.bigtable_admin_v2.proto import (\n            bigtable_instance_admin_pb2 as instance_v2_pb2,\n        )\n        from google.cloud.bigtable.enums import StorageType\n\n        NOW = datetime.datetime.utcnow()\n        NOW_PB = _datetime_to_pb_timestamp(NOW)\n        credentials = _make_credentials()\n        client = self._make_client(\n            project=self.PROJECT, credentials=credentials, admin=True\n        )\n        STORAGE_TYPE_SSD = StorageType.SSD\n        LOCATION = self.LOCATION_PATH + self.LOCATION_ID\n        instance = Instance(self.INSTANCE_ID, client)\n        cluster = self._make_one(\n            self.CLUSTER_ID,\n            instance,\n            location_id=self.LOCATION_ID,\n            serve_nodes=self.SERVE_NODES,\n            default_storage_type=STORAGE_TYPE_SSD,\n        )\n        expected_request_cluster = instance_pb2.Cluster(\n            location=LOCATION,\n            serve_nodes=cluster.serve_nodes,\n            default_storage_type=cluster.default_storage_type,\n        )\n        expected_request = instance_v2_pb2.CreateClusterRequest(\n            parent=instance.name,\n            cluster_id=self.CLUSTER_ID,\n            cluster=expected_request_cluster,\n        )\n\n        metadata = messages_v2_pb2.CreateClusterMetadata(request_time=NOW_PB)\n        type_url = \"type.googleapis.com/{}\".format(\n            messages_v2_pb2.CreateClusterMetadata.DESCRIPTOR.full_name\n        )\n        response_pb = operations_pb2.Operation(\n            name=self.OP_NAME,\n            metadata=Any(type_url=type_url, value=metadata.SerializeToString()),\n        )\n\n        # Patch the stub used by the API method.\n        channel = ChannelStub(responses=[response_pb])\n        api = bigtable_instance_admin_client.BigtableInstanceAdminClient(\n            channel=channel\n        )\n        client._instance_admin_client = api\n\n        # Perform the method and check the result.\n        result = cluster.create()\n        actual_request = channel.requests[0][1]\n\n        self.assertEqual(actual_request, expected_request)\n        self.assertIsInstance(result, operation.Operation)\n        self.assertEqual(result.operation.name, self.OP_NAME)\n        self.assertIsInstance(result.metadata, messages_v2_pb2.CreateClusterMetadata)\n\n    def test_update(self):\n        import datetime\n        from google.api_core import operation\n        from google.longrunning import operations_pb2\n        from google.protobuf.any_pb2 import Any\n        from google.cloud._helpers import _datetime_to_pb_timestamp\n        from google.cloud.bigtable_admin_v2.proto import (\n            bigtable_instance_admin_pb2 as messages_v2_pb2,\n        )\n        from google.cloud.bigtable_admin_v2.types import instance_pb2\n        from google.cloud.bigtable_admin_v2.gapic import bigtable_instance_admin_client\n        from google.cloud.bigtable.enums import StorageType\n\n        NOW = datetime.datetime.utcnow()\n        NOW_PB = _datetime_to_pb_timestamp(NOW)\n\n        credentials = _make_credentials()\n        client = self._make_client(\n            project=self.PROJECT, credentials=credentials, admin=True\n        )\n        STORAGE_TYPE_SSD = StorageType.SSD\n        instance = _Instance(self.INSTANCE_ID, client)\n        cluster = self._make_one(\n            self.CLUSTER_ID,\n            instance,\n            location_id=self.LOCATION_ID,\n            serve_nodes=self.SERVE_NODES,\n            default_storage_type=STORAGE_TYPE_SSD,\n        )\n        # Create expected_request\n        expected_request = instance_pb2.Cluster(\n            name=cluster.name, serve_nodes=self.SERVE_NODES\n        )\n\n        metadata = messages_v2_pb2.UpdateClusterMetadata(request_time=NOW_PB)\n        type_url = \"type.googleapis.com/{}\".format(\n            messages_v2_pb2.UpdateClusterMetadata.DESCRIPTOR.full_name\n        )\n        response_pb = operations_pb2.Operation(\n            name=self.OP_NAME,\n            metadata=Any(type_url=type_url, value=metadata.SerializeToString()),\n        )\n\n        # Patch the stub used by the API method.\n        channel = ChannelStub(responses=[response_pb])\n        api = bigtable_instance_admin_client.BigtableInstanceAdminClient(\n            channel=channel\n        )\n        client._instance_admin_client = api\n\n        # Perform the method and check the result.\n        result = cluster.update()\n        actual_request = channel.requests[0][1]\n\n        self.assertEqual(actual_request, expected_request)\n        self.assertIsInstance(result, operation.Operation)\n        self.assertEqual(result.operation.name, self.OP_NAME)\n        self.assertIsInstance(result.metadata, messages_v2_pb2.UpdateClusterMetadata)\n\n    def test_delete(self):\n        from google.protobuf import empty_pb2\n        from google.cloud.bigtable_admin_v2.gapic import bigtable_instance_admin_client\n\n        api = bigtable_instance_admin_client.BigtableInstanceAdminClient(mock.Mock())\n        credentials = _make_credentials()\n        client = self._make_client(\n            project=self.PROJECT, credentials=credentials, admin=True\n        )\n        instance = _Instance(self.INSTANCE_ID, client)\n        cluster = self._make_one(self.CLUSTER_ID, instance, self.LOCATION_ID)\n\n        # Create response_pb\n        response_pb = empty_pb2.Empty()\n\n        # Patch the stub used by the API method.\n        client._instance_admin_client = api\n        instance_admin_client = client._instance_admin_client\n        instance_stub = instance_admin_client.transport\n        instance_stub.delete_cluster.side_effect = [response_pb]\n\n        # Create expected_result.\n        expected_result = None  # delete() has no return value.\n\n        # Perform the method and check the result.\n        result = cluster.delete()\n\n        self.assertEqual(result, expected_result)\n\n\nclass _Instance(object):\n    def __init__(self, instance_id, client):\n        self.instance_id = instance_id\n        self._client = client\n\n    def __eq__(self, other):\n        return other.instance_id == self.instance_id and other._client == self._client\n\n\nclass _Client(object):\n    def __init__(self, project):\n        self.project = project\n        self.project_name = \"projects/\" + self.project\n        self._operations_stub = mock.sentinel.operations_stub\n\n    def __eq__(self, other):\n        return other.project == self.project and other.project_name == self.project_name\n","sub_path":"bigtable/tests/unit/test_cluster.py","file_name":"test_cluster.py","file_ext":"py","file_size_in_byte":19219,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"486298379","text":"def unify(x, y, s):\n    \"\"\"Unify expressions x,y with substitution s; return a substitution that\n    would make x,y equal, or None if x,y can not unify. x and y can be\n    variables (e.g. Expr('x')), constants, lists, or Exprs. [Fig. 9.1]\n    >>> ppsubst(unify(x + y, y + C, {}))\n    {x: y, y: C}\n    \"\"\"\n    if s is None:\n        return None\n    elif x == y:\n        return s\n    elif is_variable(x):\n        return unify_var(x, y, s)\n    elif is_variable(y):\n        return unify_var(y, x, s)\n    elif isinstance(x, Expr) and isinstance(y, Expr):\n        return unify(x.args, y.args, unify(x.op, y.op, s))\n    elif isinstance(x, str) or isinstance(y, str):\n        return None\n    elif issequence(x) and issequence(y) and len(x) == len(y):\n        if not x: return s\n        return unify(x[1:], y[1:], unify(x[0], y[0], s))\n    else:\n        return None\n\ndef is_variable(x):\n    \"A variable is an Expr with no args and a lowercase symbol as the op.\"\n    return isinstance(x, Expr) and not x.args and is_var_symbol(x.op)\n\ndef unify_var(var, x, s):\n    if var in s:\n        return unify(s[var], x, s)\n    elif occur_check(var, x, s):\n        return None\n    else:\n        return extend(s, var, x)\n\ndef occur_check(var, x, s):\n    \"\"\"Return true if variable var occurs anywhere in x\n    (or in subst(s, x), if s has a binding for x).\"\"\"\n    if var == x:\n        return True\n    elif is_variable(x) and x in s:\n        return occur_check(var, s[x], s)\n    elif isinstance(x, Expr):\n        return (occur_check(var, x.op, s) or\n                occur_check(var, x.args, s))\n    elif isinstance(x, (list, tuple)):\n        return some(lambda element: occur_check(var, element, s), x)\n    else:\n        return False\n\ndef extend(s, var, val):\n    \"\"\"Copy the substitution s and extend it by setting var to val;\n    return copy.\n    >>> ppsubst(extend({x: 1}, y, 2))\n    {x: 1, y: 2}\n    \"\"\"\n    s2 = s.copy()\n    s2[var] = val\n    return s2\n\ndef subst(s, x):\n    \"\"\"Substitute the substitution s into the expression x.\n    >>> subst({x: 42, y:0}, F(x) + y)\n    (F(42) + 0)\n    \"\"\"\n    if isinstance(x, list):\n        return [subst(s, xi) for xi in x]\n    elif isinstance(x, tuple):\n        return tuple([subst(s, xi) for xi in x])\n    elif not isinstance(x, Expr):\n        return x\n    elif is_var_symbol(x.op):\n        return s.get(x, x)\n    else:\n        return Expr(x.op, *[subst(s, arg) for arg in x.args])\n\ndef fol_fc_ask(KB, alpha):\n    \"\"\"Inefficient forward chaining for first-order logic. [Fig. 9.3]\n    KB is a FolKB and alpha must be an atomic sentence.\"\"\"\n    while True:\n        new = {}\n        for r in KB.clauses:\n            ps, q = parse_definite_clause(standardize_variables(r))\n            raise NotImplementedError\n\ndef standardize_variables(sentence, dic=None):\n    \"\"\"Replace all the variables in sentence with new variables.\n    >>> e = expr('F(a, b, c) & G(c, A, 23)')\n    >>> len(variables(standardize_variables(e)))\n    3\n    >>> variables(e).intersection(variables(standardize_variables(e)))\n    set([])\n    >>> is_variable(standardize_variables(expr('x')))\n    True\n    \"\"\"\n    if dic is None: dic = {}\n    if not isinstance(sentence, Expr):\n        return sentence\n    elif is_var_symbol(sentence.op):\n        if sentence in dic:\n            return dic[sentence]\n        else:\n            v = Expr('v_%d' % standardize_variables.counter.next())\n            dic[sentence] = v\n            return v\n    else:\n        return Expr(sentence.op,\n                    *[standardize_variables(a, dic) for a in sentence.args])\n\nstandardize_variables.counter = itertools.count()\n\na = input(\"Enter first exp: \")\nb = input(\"Enter second exp: \")\ns = input(\"Enter substitution: \")\n\nres = unify(a,b,c)\n\nprint(res)\n","sub_path":"unify.py","file_name":"unify.py","file_ext":"py","file_size_in_byte":3719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"525312085","text":"import math\n\nclass Solution:\n    def modInverse(self, a, m):\n        g = self.gcd(a, m)\n        \n        p = self.power(a, m - 2, m)\n        \n        return p\n    # @param A : integer\n    # @return an integer\n    def power(self, x, y, m):\n        if(y ==0):\n            return 1\n        \n        p = self.power(x, y//2, m) % m\n        p = (p*p) % m\n        \n        if(y%2 == 0):\n            return p\n        else:\n            return ((x*p) % m)\n    \n    def gcd(self, a, b):\n        if(a == 0):\n            return b\n        else:\n            return self.gcd(b%a, a)\n    \n    \n    def solve(self, A):\n        if(A == 2):\n            A = 3\n        q = 3 * (3**(A - 2))\n        p = 3 * ((3**(A - 3)) - ((3**(A - 3))/3))\n        #q = q + p \n        \n        if q%p == 0:\n            print(\"q: \" + str(q))\n            print(\"p: \" + str(p))\n            return self.modInverse(q/p, (10**9)+7)\n        else:\n            x = q%p\n            print(\"q: \" + str(q))\n            print(\"p: \" + str(p))\n            print(x)\n            print(q//x)\n            print(p//x)\n            return self.modInverse((q/x)*(p/x), (10**9)+7)\n#https://www.interviewbit.com/test/b1d9dc30a7/?signature=BAhpAwgsCA%3D%3D--3f52f3e83a02d4a97ed6da667a55bee349c7b7b4#/problem_6","sub_path":"Unkown/Basketball.py","file_name":"Basketball.py","file_ext":"py","file_size_in_byte":1243,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"234175085","text":"# Return the two numbers from a sorted array such that they add up to a specific target\n# assume each input have exactly one solution and can not use same number twice\n\n\n# Method-1: using brute force\n# Time complexity: O(n^2)\n# Space complexity: O(1)\n# def two_sum(arr, sum_val):\n#     for i in range(len(arr) - 1):\n#         for j in range(i+1, len(arr)):\n#             if arr[i] + arr[j] == sum_val:\n#                 print((arr[i], arr[j]))\n#                 return True\n#\n#     return False\n\n# Method-2: using hash table\n# Time complexity: O(n)\n# Space complexity: O(n)\n# def two_sum(arr, sum_val):\n#\n#     hast_table = dict()\n#     for i in range(len(arr)):\n#         if arr[i] in hast_table:\n#             print(hast_table[arr[i]], arr[i])\n#             return True\n#         else:\n#             hast_table[sum_val - arr[i]] = arr[i]\n#             print(hast_table)\n#\n#     return False\n\n# Method-3: traverse from both side towards middle\n# Time complexity: O(n)\n# Space complexity: O(1)\ndef two_sum(arr, sum_val):\n    i = 0\n    j = len(arr) - 1\n\n    while i <= j:\n        if arr[i] + arr[j] == sum_val:\n            print(arr[i], arr[j])\n            return True\n        elif arr[i] + arr[j] < sum_val:\n            i += 1\n\n        # arr[i] + arr[j] > sum_val\n        else:\n            j -= 1\n\n    return False\n\n\nif __name__ == \"__main__\":\n\n    a = [2, 3, 4, 6, 7, 9]\n    target = 10\n    print(two_sum(a, target))\n","sub_path":"array/two_sum_problem.py","file_name":"two_sum_problem.py","file_ext":"py","file_size_in_byte":1418,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"487702885","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\nimport argparse\nimport base64\nfrom io import BytesIO\nfrom PIL import Image, ImageChops\nimport random\nfrom selenium import webdriver\nfrom selenium.webdriver.support.ui import WebDriverWait\nfrom selenium.webdriver.common.action_chains import ActionChains\nimport time\n\ndef get_gap_test(picture0, picture1, picture2):\n    position=picture0.getbbox()\n    up=position[3]\n    bottom=position[1]\n    left=position[0]\n    right=position[2]\n\n    #diff_position=ImageChops.difference(picture1, picture2).getbbox()\n    #diff_left=diff_position[0]\n    #diff_right=diff_position[2]\n\n    moved = {}\n    for move in range(0,picture0.size[0]-right):\n        moved[move]=0\n\n    for checkx in range(0,picture0.size[0]):\n        for checky in range(bottom,up):\n            for checkpixel in range(0,3):\n                thisabs=abs(picture2.getpixel((checkx,checky))[checkpixel]-picture1.getpixel((checkx,checky))[checkpixel])\n                for move in range(0,picture0.size[0]-right):\n                    if checkx<move or checkx>move+right:\n                        moved[move]+=thisabs\n                    else:\n                        moved[move]+=abs(picture0.getpixel((checkx-move,checky))[checkpixel]-picture1.getpixel((checkx,checky))[checkpixel])\n\n    minmove=0\n    minmoved=None\n    for move in moved.keys():\n        if minmoved is None or minmoved >= moved[move]:\n            minmoved=moved[move]\n            minmove=move\n    print(minmove)\n    return minmove\n\ndef simpleSimulateDragX(driver, source, targetOffsetX):\n    action_chains = ActionChains(driver)\n    action_chains.click_and_hold(source)\n    action_chains.pause(0.2)\n    action_chains.move_by_offset(targetOffsetX, 0)\n    action_chains.pause(0.6)\n    action_chains.release()\n    action_chains.perform()\n\nheaders = {'User-Agent' : 'Mozilla/5.0 (X11; Ubuntu; Linux x86_64; rv:76.0) Gecko/20100101 Firefox/76.0'}\n\nparser = argparse.ArgumentParser()\nparser.description='please enter parameters ...'\nparser.add_argument('-s', '--search', help='what you want to search', dest='search', type=str, default='lindy')\nparser.add_argument('-o', '--output', help='output file', dest='out', type=str, default='hermes.txt')\nargs = parser.parse_args()\n\nurl='https://www.hermes.com/pl/en/search/?s='+args.search+'#||Category'\n\n# Get the Firefox profile object\nprofile = webdriver.firefox.webdriver.FirefoxProfile()\n# Disable images\n#profile.set_preference('permissions.default.image', 2)\n# Disable Flash\nprofile.set_preference(\n    'dom.ipc.plugins.enabled.libflashplayer.so', 'false'\n)\nprofile.native_events_enabled = True\noptions = webdriver.firefox.options.Options()\noptions.headless = True\nfirefox = webdriver.Firefox(firefox_profile=profile, options=options, timeout=60)\nfirefox.get(url)\n\ngeted = False\nerrors='unknown errors'\ncontent=''\n\ntime.sleep(2)\nwhile 'You have been blocked' in firefox.title:\n    print(firefox.title)\n    firefox.switch_to.frame(0)\n    WebDriverWait(firefox, 30).until(lambda the_driver: the_driver.find_element_by_xpath(\"//div[@class='geetest_holder geetest_wind geetest_ready']\").is_displayed())\n    captcha = firefox.find_element_by_xpath('//div[@class=\"geetest_holder geetest_wind geetest_ready\"]')\n    ActionChains(firefox).move_to_element_with_offset(to_element=captcha, xoffset=0, yoffset=1).perform()\n    ActionChains(firefox).move_to_element_with_offset(to_element=captcha, xoffset=0, yoffset=2).perform()\n    time.sleep(2)\n    #with open('source2.txt', 'w') as filehermes:\n    #    filehermes.write(firefox.page_source)\n    WebDriverWait(firefox, 30).until(lambda the_driver: the_driver.find_element_by_xpath(\"//div[@class='geetest_holder geetest_wind geetest_wait_compute']\").is_displayed())\n    compute = firefox.find_element_by_xpath('//div[@class=\"geetest_holder geetest_wind geetest_wait_compute\"]')\n    compute.click()\n    time.sleep(3)\n    WebDriverWait(firefox, 30).until(lambda the_driver: the_driver.find_element_by_xpath(\"//div[@class='geetest_slider_button']\").is_displayed())\n    img_info = firefox.execute_script('return document.getElementsByClassName(\"geetest_canvas_fullbg geetest_fade geetest_absolute\")[0].toDataURL(\"image/png\");')\n    img_base64 = img_info.split(\",\")[1]\n    img_bytes = base64.b64decode(img_base64)\n    picture1 = Image.open(BytesIO(img_bytes))\n    img_info = firefox.execute_script('return document.getElementsByClassName(\"geetest_canvas_bg geetest_absolute\")[0].toDataURL(\"image/png\");')\n    img_base64 = img_info.split(\",\")[1]\n    img_bytes = base64.b64decode(img_base64)\n    picture2 = Image.open(BytesIO(img_bytes))\n    img_info = firefox.execute_script('return document.getElementsByClassName(\"geetest_canvas_slice geetest_absolute\")[0].toDataURL(\"image/png\");')\n    img_base64 = img_info.split(\",\")[1]\n    img_bytes = base64.b64decode(img_base64)\n    picture0 = Image.open(BytesIO(img_bytes))\n    gap = get_gap_test(picture0, picture1, picture2)\n    slider = firefox.find_element_by_xpath('//div[@class=\"geetest_slider_button\"]')\n    simpleSimulateDragX(firefox, slider, gap)\n    firefox.switch_to_default_content()\n    time.sleep(30)\n    if 'You have been blocked' in firefox.title:\n        firefox.refresh()\n\nsubtitles = firefox.find_elements_by_xpath('//div[@class=\"sub-title\"]')\nif len(subtitles) == 0:\n    geted = True\nelse:\n    errors=subtitles[0].text\n    print(errors)\nmaintitles = firefox.find_elements_by_xpath('//div[@class=\"main-title\"]')\nif len(maintitles) > 0:\n    content=maintitles[0].text\n    print(content)\nelse:\n    print('no main-title')\n    geted = False\nwith open(args.out, 'w') as filehermes:\n    filehermes.write(content+'\\n'+url)\n\nfirefox.close()\nfirefox.quit()\n\nif not geted:\n    raise Exception(errors)\n","sub_path":"hermes.py","file_name":"hermes.py","file_ext":"py","file_size_in_byte":5697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"433669724","text":"import os\nimport numpy as np\nimport librosa as la\n\n\ndef convert_format():\n\tsongs = os.listdir(\"coversongs/covers32k\")\n\tfor i in range(len(songs)):\n\t\tsong = songs[i]\n\t\tcovers = [d for d in os.listdir(\"coversongs/covers32k/{}\".format(song)) if \"mp3\" in d]\n\t\tfor j in range(len(covers)):\n\t\t\tcover = covers[j]\n\t\t\tos.system(\"cp coversongs/covers32k/{}/{} covers80/{}_{}.mp3\".format(song, cover, i, j))\n\n\ndef convert_numpy():\n    songs = [\"covers80/\"+d for d in os.listdir(\"covers80\") if \"mp3\" in d]\n    for i in range(len(songs)):\n    \tname = songs[i].split(\"/\")[-1].split(\".\")[0]\n    \tprint(\"coverting\", i)\n    \taudio = la.load(songs[i])[0]\n    \twith open(\"covers80npy/{}.npy\".format(name), \"wb\") as f:\n    \t\tnp.save(f, la.feature.chroma_cqt(audio))\n\n\ndef load_all():\n\tchromas = list()\n\tsongs = [\"covers80npy/\"+d for d in os.listdir(\"covers80npy\") if \"npy\" in d]\n\tfor s in songs:\n\t\tchromas.append(np.load(s))\n\treturn chromas\n\n","sub_path":"covers80.py","file_name":"covers80.py","file_ext":"py","file_size_in_byte":922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"629109599","text":"## Script maps IP information exchanges to illustrate information flow\n## and centrality metrics on the network. \n## Subnet option allows for filtering for traffic internal to a given subnet\n## Output is an annotated GraphML file that can be imported into Cytoscape.\n## Annotations include centrality calculations based on the completed graph \n\n## This is the only script that uses the .json file generated by the length_preprocess script\n\n# Required Input: .json file from the length_preprocess script\n# Optional Input: Number of clusters, output graphml name\n# Output: GraphML file\n\nimport json\nfrom argparse import ArgumentParser\nfrom scapy.all import *\nimport numpy as np\nfrom sklearn.cluster import KMeans, MiniBatchKMeans\nimport matplotlib.pyplot as plt\n\nimport time\n\ndef parse_length_dict(length_dict, conversation_id = None, index=0, verbose=False):\n\n    if index == 0:\n        reported_lengths = []\n        if verbose:\n            print(f\"Returning only the reported lengths.\")\n    elif index == 1:\n        wire_lengths = []\n        if verbose:\n            print(f\"Returning only the wire lengths.\")\n    elif index == 2:\n        reported_lengths = []\n        wire_lengths = []\n        if verbose:\n            print(f\"Returning a list each for reported length and wire length\")\n    else:\n        print(f\"Provide a valid index for the length lists.\")\n        return\n\n    if conversation_id == None:\n        print(f\"Provide a conversation id to target\")\n        return\n\n    for packet in length_dict[conversation_id]:\n        if index == 0 or index == 2:\n            reported_lengths.append(packet[0])\n        if index == 1 or index == 2:\n            wire_lengths.append(packet[1])\n\n    if index == 0:\n        return reported_lengths\n    elif index == 1:\n        return wire_lengths\n    elif index == 2:\n        return reported_lengths, wire_lengths\n\ndef main():\n    parser = ArgumentParser()\n    parser.add_argument('-f', '--file', help='Path to input json file', required=True)\n    parser.add_argument('-c', '--clusters', help='Number of kmeans clusters to use', default=8)\n    parser.add_argument('-d', '--directory', help='Directory of input json files')\n    parser.add_argument('-o', '--output', help='File name for output graphml file', default= \"output.graphml\")\n    args = parser.parse_args()\n\n    with open(args.file, 'r') as f:\n        print(f\"Loading json file: {args.file}\")\n        length_dict = dict(json.load(f))\n    \n    packet_count = []\n    X = []\n    start = time.time()\n    for key in length_dict:\n        print(f\"Loading conversation: {key}\")\n        reported_lengths = parse_length_dict(length_dict, conversation_id=key, index=0)\n        X += reported_lengths\n    finish = time.time()\n    print(f\"Loading all conversations took {finish - start:.3f} seconds.\")\n\n    X = np.array(X)\n    start = time.time()    \n    # kmeans = KMeans(n_clusters=int(args.clusters))\n    kmeans = MiniBatchKMeans(n_clusters=int(args.clusters))\n    kmeans.fit(X.reshape(-1,1))\n    finish = time.time()\n    print(f\"Kmeans clustering took {finish - start:.3f} seconds.\")\n    # from IPython import embed; embed()\n\n    plt.scatter(X, kmeans.labels_)\n    # print(kmeans.cluster_centers_)\n    # print(kmeans.labels_)\n    plt.show()\n\n\n\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"kmeans.py","file_name":"kmeans.py","file_ext":"py","file_size_in_byte":3274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"259025571","text":"import math\nfrom PyCal.Memory.data_factory import DataFactory\nclass IF92:\n    def __init__(self):\n        self.__data_type = \"DataTab\"\n        self.__data_factory = DataFactory()\n\n    def getScore(self, fid):\n        result = {}\n        eg69 = \"EG69\"\n\n        fl_method_assigment = self.__data_factory.getTab(self.__data_type, \"Method Assignment\", {\"FID\": [fid], \"Assignment ID\": [], \"Method ID\": []})\n        \"\"\"\n        1. Check the METHOD ASSIGNMENT tab\n        A) If there is an input in the METHOD ID column then:\n        \"\"\"\n        fl_method_assigment_mid = fl_method_assigment[fl_method_assigment[\"Method ID\"].notnull()]\n        \"\"\"\n        2. count the number of ASSIGNMENT IDs related to each particular METHOD ID(Methods Assignment tab)\n        \"\"\"\n        result[eg69] = len(fl_method_assigment_mid[\"Assignment ID\"].values)\n        \"\"\"\n        B) If there is no input then:\n        2. count the number of ASSIGNMENT IDs as \"0\" or don't count them.\n        At the end take the results and use the EG to obtain a score for each Method ID (related to a particular Freelancer ID)\n        \"\"\"\n        return result\n# class IF93:\n#     def __init__(self):\n#         self.__data_type = \"DataTab\"\n#         self.__data_factory = DataFactory()\n#\n#     def getScore(self, fid):\n#         result = {}\n#         eg70 = \"EG70\"\n#         fl_job = self.__data_factory.getTab(self.__data_type, \"Job\", {\"FID\": [fid], \"Industry/Sub-Domain\":[]})\n#         fl_assignment = self.__data_factory.getTab(self.__data_type, \"Assignment\", {\"FID\": [fid], \"Industry/Sub-Domain\": []})\n#         fl_job_method = self.__data_factory.getTab(self.__data_type, \"Method Job\", {\"FID\": [fid], \"JID\": [], \"Method\": []})\n#         \"\"\"\n#         1. Check METHOD ID column in the METHOD JOB tab\n#         A) If there is an input then\n#         2. Check INDUSTRY (Sub-Domain) column in the JOB tab\n#         AA) If there is no Input (its because its a consultancy) then:\n#         \"\"\"\n#         fl_job_method_wm = fl_job_method[fl_job_method[\"Method ID\"].notnull()]\n#         fl_job_method_wm_grouped = fl_job_method_wm.groupby(\"JID\")[\"Method\"]\n#         jid_method = {k:v for k,v in fl_job_method_wm_grouped}\n#         \"\"\"\n#         3. go to assignment tab and work only with Assignments related to that specific Consultancy Jobs ID\n#         \"\"\"\n#         fl_job_method_wm_jid = fl_job_method_wm[\"JID\"].values\n#         \"\"\"\n#         4. then take the INDUSTRY (Sub-Domain) related to that particular JOB ID (Assignment tab)\n#         \"\"\"\n#         fl_job_jid = fl_job[fl_job[\"JID\"].isin(fl_job_method_wm_jid)]\n#         fl_job_jid_nosd = fl_job_jid[fl_job_jid[\"Sub-Domain\"].isnull()]\n#         fl_job_jid_nosd_jid = fl_job_jid_nosd[\"JID\"].values\n#         fl_assignment_jid = fl_assignment[fl_assignment[\"JID\"].isin(fl_job_jid_nosd_jid)]\n#         \"\"\"\n#         5. then for each Method ID column(Method Job tab) take the associated JOB ID (Method Job tab) and therefore the associated ASSIGNMENT ID(Assignment tab) and therefore the associated INDUSTRY (Sub-Domain) (Assignment tab)\n#         \"\"\"\n#         fl_assignment_jid[\"JID\"] = fl_assignment_jid[\"JID\"].map(i => jid_method[i])\n#         fl_assignemnt_jid_grouped = fl_assignment_jid.groupby(\"JID\")[\"Industry/Sub-Domain\"]\n#         aa_out = {k: set(v) for k,v in fl_assignment_jid_grouped}\n#         \"\"\"\n#         AB) If there is input (its because its not a consultancy) then:\n#         \"\"\"\n#         fl_job_jid_wsd = fl_job_jid[fl_job_jid[\"Sub-Domain\"].notnull()]\n#         \"\"\"\n#         3. take the INDUSTRY (Sub-Domain) (Job tab) related to that particular JOB ID (Job tab)\n#         \"\"\"\n#         fl_job_jid_wsd[\"JID\"] = fl_job_jid_wsd[\"JID\"].map(i => jid_method[i])\n#         \"\"\"\n#         4. then for each Method ID column(Method Job tab) take the associated JOB ID (Method Job tab) and therefore the associated INDUSTRY (Sub-Domain) (Job tab)\n#         \"\"\"\n#         fl_job_jid_wsd_grouped = fl_job_jid_wsd.groupby(\"JID\")[\"Industry/Sub-Domain\"]\n#         ab_out = {k: set(v) for k,v in fl_job_jid_wsd_grouped}\n#\n#         result[eg70] = {k: len(aa_out.get(k, set()) | ab_out.get(k, set())) for k in set(aa_out) | set(ab_out)}\n#         \"\"\"\n#         B) If there is not input the\n#         Count the number of different INDUSTRIES as \"0\" or don't count them.\n#         ----\n#         -At the end for each FREELANCER ID take all the INDUSTRY (Sub-Domain) obtained for each METHOD ID\n#         -Count the number of different INDUSTRY (Sub-Domain) per METHOD ID\n#         \"\"\"\n#         return result\n#\n# class IF94:\n#     def __init__(self):\n#         self.__data_type = \"DataTab\"\n#         self.__data_factory = DataFactory()\n#\n#     def getScore(self, fid):\n#         result = {}\n#         eg71 = \"EG71\"\n#         \"\"\"\n#         1. Check the METHODS column in the Trainings & Certificates tab\n#         \"\"\"\n#         fl_tnc = self.__data_factory.getTab(self.__data_type, \"Trainings & Certificates\", {\"FID\": [fid], \"TNCID\": [], \"Method\": []})\n#         \"\"\"\n#         2. then in the Trainings & Certificates ID column, count the number of Training & Certificates related to the specific METHOD\n#         \"\"\"\n#         fl_tnc_grouped = fl_tnc.groupby(\"Method\")[\"TNCID\"]\n#         result[eg71] = {k: len(list(v)) for k,v in fl_tnc_grouped}\n#         return result\n","sub_path":"Brain/method/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"214993265","text":"import datetime\n\nclass Patient:\n\n    def __init__(self,ID = 0,firstname = 'First Name',\\\n                middlename = 'Middle Name',lastname = 'Last Name',\\\n                maidenname = 'Maiden Name', gender = 'Gender',\\\n                birthday = '01.01.01',age = 0):\n        \n        self.ID = str(ID)\n        self.FirstName = firstname\n        self.MiddleName = middlename\n        self.LastName = lastname\n        self.MaidenName = maidenname\n        self.Gender = gender\n        self.Birthday = birthday\n        self.Age = str(age)\n        self.LocalPatientID = ''\n        self.createLocalID()\n        \n    def createLocalID(self):\n        \"\"\"encodes the patient information to the LocalPatientID in the EDF header record field\"\"\"\n\n        # ascii-character limit for every patient information (in bytes)\n        lenID = 5\n        lenFirstName = 20\n        lenMiddleName = 12\n        lenLastName = 12\n        lenMaidenName  = 12\n        lenGender = 6\n        lenBirthday = 10\n        lenAge = 3\n\n        LocalPatientIDlist = [self.ID, self.FirstName, self.MiddleName,\\\n                            self.LastName, self.MaidenName, self.Gender,\\\n                            self.Birthday, self.Age]\n        lenLocalPatientID = [lenID, lenFirstName, lenMiddleName,\\\n                            lenLastName, lenMaidenName, lenGender,\\\n                            lenBirthday, lenAge]\n        \n        for i in range(len(LocalPatientIDlist)):\n            maxlen = lenLocalPatientID[i]\n            if len(LocalPatientIDlist[i]) > maxlen:\n                # truncates the string if length is greater than limit\n                LocalPatientIDlist[i] = LocalPatientIDlist[i][:maxlen]      \n            else:                \n                LocalPatientIDlist[i] = LocalPatientIDlist[i].ljust(maxlen)\n                \n        # converts the list to a string with no separator in between elements\n        self.LocalPatientID = ''.join(LocalPatientIDlist)\n\n    def get(self):\n        \"\"\" returns the LocalPatientID attribute \"\"\"\n\n        return self.LocalPatientID\n        \nclass BioSignal:\n    \"\"\"contains the biomedical signals of the patient as well as their technical information\"\"\"\n\n    def __init__(self, Label = 'Label', TransducerType = '', PhyDimension = 'Dimension',\\\n                 PhyMin = -32767, PhyMax = 32767, DigMin = -32767, DigMax = 32767, Prefiltering = '',\\\n                 NRsamples = 1, RawBioSignal = []):\n        # Label = Standard: 'TypeOfSignal Sensor'\n        # DigMin > -32767\n        # DigMax <  32767\n        \n        Reserved = ' '*32\n\n        # fields defined in the EDF format\n        self.Label = Label                           \n        self.TransducerType = TransducerType\n        self.PhyDimension = PhyDimension\n        self.PhyMin = str(PhyMin)   \n        self.PhyMax = str(PhyMax)\n        self.DigMin = str(DigMin)                       \n        self.DigMax = str(DigMax)                       \n        self.Prefiltering = Prefiltering\n        self.NRsamples = str(NRsamples)\n        self.Reserved = Reserved\n        \n        self.TechInfo = []                     # list containing 256 bytes of characters describing the technical characteristics of each signal\n        self.RawBioSignal = RawBioSignal                # the biomedical signal itself\n\n        self.createTechInfo()\n        \n    def createTechInfo(self):\n        \"\"\"encodes the biomedical signal technical information to the EDF header record\"\"\"\n\n        # ascii-character limit for every biosignal information (in bytes)\n        lenLabel = 16\n        lenTransducerType = 80\n        lenPhyDimension = 8\n        lenPhyMin = 8\n        lenPhyMax = 8\n        lenDigMin = 8\n        lenDigMax = 8\n        lenPrefiltering = 80\n        lenNRsamples = 8\n        lenReserved = 32\n\n        self.TechInfo = [self.Label, self.TransducerType, self.PhyDimension, self.PhyMin, self.PhyMax, self.DigMin, self.DigMax, self.Prefiltering,\\\n                             self.NRsamples, self.Reserved]\n        lenTechInfo = [lenLabel, lenTransducerType, lenPhyDimension, lenPhyMin, lenPhyMax, lenDigMin, lenDigMax, lenPrefiltering, lenNRsamples, lenReserved]\n\n        for i in range(len(self.TechInfo)):\n            maxlen = lenTechInfo[i]\n            if len(self.TechInfo[i]) > maxlen:\n                # truncates the string if length is greater than limit\n                self.TechInfo[i] = self.TechInfo[i][:maxlen]      \n            else:\n                \n                self.TechInfo[i] = self.TechInfo[i].ljust(maxlen)\n                \nclass EDF:\n    \"\"\"manipulates the Patient and BioSignal objects to create an EDF file\"\"\"\n\n    def __init__(self,  Patient, BioSignals, StartDate = '01.01.01', StartTime = '01.59.59', Location = '',\\\n                 nDataRecord = -1, DurationDataRecord = 1, ):\n        # StartDate = dd.mm.yy\n        # StartTime = hh.mm.ss\n        \n        Reserved = ' '*44\n\n        # fields defined in the EDF format\n        self.Version = '0'                                   # 0 is the only version number allowed\n        self.LocalPatientID = Patient.get()  \n        self.LocalRecordingID = 'Startdate ' + Location      # Location should contain a date (DD-MMM-YYYY) and the location of the patient\n        self.StartDate = StartDate\n        self.StartTime = StartTime\n        self.nSignals = str(len(BioSignals))\n        self.nBytesHeader = str(256 + 256*int(self.nSignals))\n        self.EDFPlus = Reserved\n        self.nDataRecord = str(nDataRecord)                  # if unknown, default value is -1 (prior or during recording)\n        self.DurationDataRecord = str(DurationDataRecord)\n\n        self.BioSignals = BioSignals\n        self.HeaderRecord = ''                               # contains the header record information of the EDF data\n        self.DataRecord = ''                                 # contains the 2-byte ASCII encided data record values of the BioSignals\n        self.EDFFile = ''                                    # contains the EDF file of a patient and will be saved as .edf\n        \n        self.Starttime = datetime.datetime.today() + datetime.timedelta(seconds = -15)\n        self.timestamp = self.Starttime.strftime(\"%H%M%S\")\n        \n        self.createHeaderRecord()\n        self.createDataRecord()\n\n        \n    def createHeaderRecord(self):\n        \"\"\"creates the header record fields of the EDF file\"\"\"\n\n        # ascii-character limit for every header record information (in bytes)\n        lenVersion = 8\n        lenLocalPatientID = 80\n        lenLocalRecordingID = 80\n        lenStartDate = 8\n        lenStartTime = 8\n        lennBytesHeader = 8\n        lenEDFPlus = 44\n        lennDataRecord = 8\n        lenDurationDataRecord = 8\n        lennSignals = 4\n    \n        HeaderInfolist = [self.Version, self.LocalPatientID, self.LocalRecordingID, self.StartDate, self.StartTime, self.nBytesHeader, self.EDFPlus,\\\n                          self.nDataRecord, self.DurationDataRecord, self.nSignals]\n        lenHeaderInfo = [lenVersion, lenLocalPatientID, lenLocalRecordingID, lenStartDate, lenStartTime, lennBytesHeader, lenEDFPlus, lennDataRecord,\\\n                         lenDurationDataRecord, lennSignals]\n\n        for i in range(len(HeaderInfolist)):\n            maxlen = lenHeaderInfo[i]\n            if len(HeaderInfolist[i]) > maxlen:\n                # truncates the string if length is greater than limit\n                HeaderInfolist[i] = HeaderInfolist[i][:maxlen]      \n                \n            else:\n                HeaderInfolist[i] = HeaderInfolist[i].ljust(maxlen)\n                \n        # converts the list to a string with no separator in between elements\n        self.HeaderRecord = ''.join(HeaderInfolist)                 \n\n        # concatenates each BioSignal TechInfo to the Header Record string\n        for i in range(len(self.BioSignals[0].TechInfo)):\n            for x in range(len(self.BioSignals)):\n                self.HeaderRecord = self.HeaderRecord + self.BioSignals[x].TechInfo[i]\n\n\n    def createDataRecord(self):\n        \"\"\"creates the data record fields of the EDF file\"\"\"\n\n        counter = 0\n        DataRecordlist = []\n        end = []\n        offset = []\n        start = []\n        temp = []\n\n        def DecToBin(num):\n            # converts a decimal number to its 16-bit binary representation\n            \n            BinStr = ''\n            if num == 0: return '0'*16\n            while num > 0:\n                BinStr = str(num % 2) + BinStr\n                num = num >> 1                   # right-shift the num by 1 bit\n            BinStr = BinStr.zfill(16)            # make BinStr a 16-bit string\n            return BinStr\n\n        for i in range(len(self.BioSignals)):\n            offset.append(int(self.BioSignals[i].NRsamples))\n            start.append(0)\n            end.append(0)\n\n        while (counter < int(self.nDataRecord)):\n            \n            for x in range(len(self.BioSignals)):\n\n                end[x] = start[x] + offset[x]\n                temp =  self.BioSignals[x].RawBioSignal[start[x]:end[x]]\n\n                for i in range(len(temp)):\n                    intRawValue = temp[i]\n\n                    if intRawValue >= 0:\n                        # if positive-valued, convert to binary\n                        binRawValue = DecToBin(intRawValue)\n                    else:\n                        # if negative, get the positive representation by adding (2**16-1)\n                        # these are the numbers from 32768-65535\n                        negRawValue = intRawValue + (2**16-1)\n                        # then convert to binary\n                        binRawValue = DecToBin(negRawValue)\n\n                    # divide the 16-bit binary number to two 8-bit binary numbers (MSByte and LSByte)\n                    MSByte = binRawValue[:8]\n                    LSByte = binRawValue[8:]\n\n                    # convert each byte to decimal then get its ASCII representation \n                    chrMSByte = chr(int(MSByte,2))\n                    chrLSByte = chr(int(LSByte,2))\n\n                    # each value in the data record is a 2-byte 2's complement integer represented by its ASCII character\n                    # it is arranged as: Value = LSB,MSB\n                    DataRecordlist.extend([chrLSByte, chrMSByte])\n\n                # update the pointer for the next set of data records\n                start[x] = end[x]\n\n            counter += 1\n\n        # when all BioSignal objects are accessed and encoded, converts the list to a string\n        self.DataRecord = ''.join(DataRecordlist)\n            \n    def get(self,myPatient):\n\n        self.EDFFile = self.HeaderRecord + self.DataRecord\n        edffile   = open(myPatient.LastName + '_' + self.timestamp + '.edf', 'wb+')\n        edffile.write(self.EDFFile)\n        edffile.close()\n","sub_path":"lifelink/rxbox/branches/RxBox v0.2/edf.py","file_name":"edf.py","file_ext":"py","file_size_in_byte":10770,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"321298595","text":"#!/usr/bin/python\nimport sys\nimport numpy as np\n\n\n# ARGUMENTS: <numbersfile> <plumedfile>\n# Takes an input of the carbon indices of a GQD assembly (space delimited, with one newline character between the different GQDs) as the first argument and prints out a plumed input file that calculates the in plane vectors (for the 1.5 nm GQD) and the distance between the GQDs. (NOTE: This uses in plane vectors defined based on the central ring, not the same as the other plumedPrint.py script)\n# input file should be in VMD indices, this automatically adds one to the VMD indices to get the correct plumed ones\n# EBau 8/11/14\n\ndef printComments(output):\n  output.write('# plumed input file to print carbon vectors and distance between GQDs' + 2*'\\n')\n\n\ndef printlist(gqds,output):\n  y=0\n  for x in gqds:\n    numlist = x.split()\n    output.write('cgra%s: COM ATOMS={' % repr(y+1) )\n    z=0\n    for no in numlist:\n      num=int(no)\n      if z < len(numlist)-1:\n        output.write(repr(num+1) + ' ')\n      else:\n        output.write(repr(num+1))\n      z=z+1\n    output.write('}')\n    output.write('\\n' + '\\n')\n    y=y+1\n\ndef distancePrint(gqds,output):\n  for x in range(1,len(gqds)):\n    output.write('cdist%s%s: DISTANCE ATOMS=cgra%s,cgra%s COMPONENTS' % (x, repr(x+1), x, repr(x+1)) + '\\n')\n  output.write('\\n')\n\ndef printCarbons(gqds,output):\n  y=1\n  for x in gqds:\n    z=0\n    numlist = x.split()\n    if y==len(gqds):    \n      output.write('c%sa: DISTANCE ATOMS=%s,%s COMPONENTS' % (repr(y),repr(int(numlist[0])+1),repr(int(numlist[2])+1)) + '\\n')\n      output.write('#c%sb: DISTANCE ATOMS=%s,%s COMPONENTS' % (repr(y),repr(int(numlist[0])+1),repr(int(numlist[5])+1)) + '\\n')\n    else:\n      output.write('c%sa: DISTANCE ATOMS=%s,%s COMPONENTS' % (repr(y),repr(int(numlist[0])+1),repr(int(numlist[2])+1)) + '\\n')\n      output.write('c%sb: DISTANCE ATOMS=%s,%s COMPONENTS' % (repr(y),repr(int(numlist[0])+1),repr(int(numlist[5])+1)) + '\\n')\n    y=y+1\n  output.write('\\n')\n\ndef printPlumed(gqds,output):\n  output.write('PRINT ARG=')\n  for x in range(1,len(gqds)):\n    y=x+1\n    if x < len(gqds)-1:\n      output.write('c%sa.x,c%sa.y,c%sa.z,' % (x,x,x))\n      output.write('c%sb.x,c%sb.y,c%sb.z,' % (x,x,x))\n      output.write('cdist%s%s.x,cdist%s%s.y,cdist%s%s.z' % (x,y,x,y,x,y))\n      output.write(',')\n    else:\n      output.write('c%sa.x,c%sa.y,c%sa.z,' % (x,x,x))\n      output.write('c%sb.x,c%sb.y,c%sb.z,' % (x,x,x))\n      output.write('cdist%s%s.x,cdist%s%s.y,cdist%s%s.z,' % (x,y,x,y,x,y))\n      output.write('c%sa.x,c%sa.y,c%sa.z' % (x+1,x+1,x+1))\n      output.write(' FILE=vectorsHelix')\n\ndef main():\n  numbers = open(sys.argv[1], 'r')\n  input = numbers.read()\n  output = open(sys.argv[2],'w')\n  gqds = input.split('\\n')\n  del gqds[-1]\n  printComments(output)\n  printlist(gqds,output)\n  distancePrint(gqds,output)\n  printCarbons(gqds,output)\n  printPlumed(gqds,output)\n\nif __name__ == '__main__':\n  main()\n","sub_path":"plumedHelixPrint.py","file_name":"plumedHelixPrint.py","file_ext":"py","file_size_in_byte":2911,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"450596268","text":"\"\"\"SAML SOAP Binding Query/Response Interface with service hosted in\nPaste paster web server\n\nNERC DataGrid Project\n\"\"\"\n__author__ = \"P J Kershaw\"\n__date__ = \"01/07/10\"\n__copyright__ = \"(C) 2010 Science and Technology Facilities Council\"\n__license__ = \"http://www.apache.org/licenses/LICENSE-2.0\"\n__contact__ = \"Philip.Kershaw@stfc.ac.uk\"\n__revision__ = '$Id$'\nimport logging\nlogging.basicConfig(level=logging.DEBUG)\n\nimport unittest\nfrom os import path\nfrom ndg.saml import importElementTree\nElementTree = importElementTree()\n\nfrom ndg.soap.utils.etree import prettyPrint\n\nfrom ndg.saml.saml2.core import Attribute, StatusCode\nfrom ndg.saml.xml.etree import ResponseElementTree\nfrom ndg.saml.saml2.binding.soap.client.attributequery import \\\n    AttributeQuerySslSOAPBinding\nfrom ndg.saml.utils.factory import AttributeQueryFactory\nfrom ndg.saml.test.binding.soap import WithPasterBaseTestCase, paste_installed\n    \n    \nclass SamlSslSoapBindingTestCase(WithPasterBaseTestCase):\n    \"\"\"Test SAML SOAP Binding with SSL\"\"\"\n    SERVICE_URI = 'https://localhost:5443/attributeauthority'\n    SUBJECT = \"https://openid.localhost/philip.kershaw\"\n    SUBJECT_FORMAT = \"urn:ndg:saml:openid\"\n    CONFIG_FILENAME = 'attribute-interface.ini'\n    \n    CLIENT_CERT_FILEPATH = path.join(WithPasterBaseTestCase.THIS_DIR, \n                                     'localhost.crt')\n    CLIENT_PRIKEY_FILEPATH = path.join(WithPasterBaseTestCase.THIS_DIR, \n                                       'localhost.key')\n    CLIENT_CACERT_DIR = path.join(WithPasterBaseTestCase.THIS_DIR, 'ca')\n    VALID_DNS = [\n        '/O=NDG/OU=Security/CN=localhost', \n    ]\n    \n    @unittest.skipIf(not paste_installed, 'Need Paste.Deploy to run '\n                     'SamlSslSoapBindingTestCase')\n    \n    def __init__(self, *arg, **kw):\n        kw['withSSL'] = True\n        super(SamlSslSoapBindingTestCase, self).__init__(*arg, **kw)\n                    \n    def test02SendQuery(self):\n        query_binding = AttributeQuerySslSOAPBinding()\n        \n        attribute_query = AttributeQueryFactory.create()\n        attribute_query.subject.nameID.format = self.__class__.SUBJECT_FORMAT\n        attribute_query.subject.nameID.value = self.__class__.SUBJECT\n        attribute_query.issuerName = '/O=Site A/CN=Authorisation Service'\n\n\n        attribute = Attribute()\n        attribute.name = 'urn:ndg:saml:emailaddress'\n        attribute.friendlyName = 'emailAddress'\n        attribute.nameFormat = 'http://www.w3.org/2001/XMLSchema'\n        \n        attribute_query.attributes.append(attribute)\n        \n        query_binding.clockSkewTolerance = 2.\n        query_binding.sslCACertDir = self.__class__.CLIENT_CACERT_DIR\n        query_binding.sslCertFilePath = self.__class__.CLIENT_CERT_FILEPATH\n        query_binding.sslPriKeyFilePath = self.__class__.CLIENT_PRIKEY_FILEPATH\n        query_binding.sslValidDNs = self.__class__.VALID_DNS\n        \n        response = query_binding.send(attribute_query, \n                                      uri=self.__class__.SERVICE_URI)\n        \n        # Convert back to ElementTree instance read for string output\n        samlResponseElem = ResponseElementTree.toXML(response)\n        \n        print(\"SAML Response ...\")\n        print(ElementTree.tostring(samlResponseElem))\n        print(\"Pretty print SAML Response ...\")\n        print(prettyPrint(samlResponseElem))\n        \n        self.assert_(response.status.statusCode.value==StatusCode.SUCCESS_URI)\n  \n \nif __name__ == \"__main__\":\n    if paste_installed:\n        unittest.main()\n    else:\n        import warnings\n        warnings.warn('Skip unittests for %r, Paste package is not installed' %\n                      __name__)\n","sub_path":"ndg/saml/test/binding/soap/test_attributeservice_paster.py","file_name":"test_attributeservice_paster.py","file_ext":"py","file_size_in_byte":3678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"560984084","text":"from .client import Client\nfrom .response import Message\nfrom .util import distribute\nfrom . import logger\n\n\nclass Reader(Client):\n    '''A client meant exclusively for reading'''\n    def __init__(self, topic, channel, lookupd_http_addresses=None,\n        nsqd_tcp_addresses=None, max_in_flight=200):\n        self._channel = channel\n        self._max_in_flight = max_in_flight\n        Client.__init__(self, lookupd_http_addresses, nsqd_tcp_addresses, topic)\n\n    def add(self, connection):\n        '''Add this connection and manipulate its RDY state'''\n        conn = Client.add(self, connection)\n        if conn:\n            conn.sub(self._topic, self._channel)\n            conn.rdy(1)\n\n    def distribute_ready(self):\n        '''Distribute the ready state across all of the connections'''\n        connections = [c for c in self.connections() if c.alive()]\n        if len(connections) > self._max_in_flight:\n            raise NotImplementedError(\n                'Max in flight must be greater than number of connections')\n        else:\n            # Distribute the ready count evenly among the connections\n            for count, conn in distribute(self._max_in_flight, connections):\n                logger.info('Sending RDY %i to %s', count, conn)\n                conn.rdy(count)\n\n    def needs_distribute_ready(self):\n        '''Determine whether or not we need to redistribute the ready state'''\n        # Determine whether or not we need to redistribute the ready state. For\n        # now, we'll just to the simple thing and say that if any of the\n        # conections has a ready state of 0\n        alive = [c for c in self.connections()]\n        # It can happen that a connection receives more messages than we might\n        # believe its RDY state to be. Consider the case where we send one RDY\n        # status, messages are sent back but before we consume them, we send a\n        # second RDY status. In this case, the RDY count might be decremented\n        # below 0.\n        if min([c.ready for c in alive]) <= 0:\n            return True\n\n    def close_connection(self, connection):\n        '''A hook into when connections are closed'''\n        Client.close_connection(self, connection)\n        self.distribute_ready()\n\n    def read(self):\n        '''Read some number of messages'''\n        found = Client.read(self)\n\n        # Redistribute our ready state if necessary\n        if self.needs_distribute_ready():\n            self.distribute_ready()\n\n        # Finally, return all the results we've read\n        return found\n\n    def __iter__(self):\n        while True:\n            for message in self.read():\n                # A reader's only interested in actual messages\n                if isinstance(message, Message):\n                    # We'll probably add a hook in here to track the RDY states\n                    # of our connections\n                    yield message\n","sub_path":"nsq/reader.py","file_name":"reader.py","file_ext":"py","file_size_in_byte":2888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"615860307","text":"#!/usr/bin/python\n\nwords = { \t1: 'one',\n\t\t\t2: 'two',\n\t\t\t3: 'three',\n\t\t\t4: 'four',\n\t\t\t5: 'five',\n\t\t\t6: 'six',\n\t\t\t7: 'seven',\n\t\t\t8: 'eight',\n\t\t\t9: 'nine',\n\t\t\t10: 'ten',\n\t\t\t11: 'eleven',\n\t\t\t12: 'twelve',\n\t\t\t13: 'thirteen',\n\t\t\t14: 'fourteen',\n\t\t\t15: 'fifteen',\n\t\t\t16: 'sixteen',\n\t\t\t17: 'seventeen',\n\t\t\t18: 'eighteen',\n\t\t\t19: 'nineteen',\n\t\t\t20: 'twenty',\n\t\t\t30: 'thirty',\n\t\t\t40: 'forty',\n\t\t\t50: 'fifty',\n\t\t\t60: 'sixty',\n\t\t\t70: 'seventy',\n\t\t\t80: 'eighty',\n\t\t\t90: 'ninety',\n\t\t\t100: 'hundred',\n\t\t\t1000: 'one thousand' }\n\ntotalchars = 0\n\n\nfor x in range(1001):\n\tcurnum = x\n\tcurrentword = \"\"\n\tif curnum / 100 > 0 and curnum != 1000:\n\t\tcurrentword = words[curnum / 100] + \" \" + words[ 100 ]\n\t\tcurnum %= 100\n\t\tif curnum > 0:\n\t\t\tcurrentword += \" and \"\n\tif curnum / 10 > 0 and curnum != 1000 and curnum > 19:\n\t\tcurrentword += words[(curnum/10)*10]\n\t\tcurnum %= 10\n\tif curnum > 0:\n\t\tcurrentword += words[curnum]\n\tprint(currentword)\n\ttotalchars += len(currentword) - currentword.count(' ')\n\t\nprint(totalchars)","sub_path":"problem17.py","file_name":"problem17.py","file_ext":"py","file_size_in_byte":992,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"519944369","text":"# coding: utf-8\n\nfrom .utils import _convert_int_to_float\n\ndef _element_vector_name(i):\n    return 'vec_{i}'.format(i=i)\n\ndef _global_vector_name(i):\n    return 'rhs_{i}'.format(i=i)\n\ndef construct_element_vector_names(n_rows):\n    vec_args = []\n    for i in range(0, n_rows):\n        vec = _element_vector_name(i)\n        vec_args.append(vec)\n\n    return vec_args\n\ndef print_element_vector_args(n_rows, vec_args):\n    vec_args_str = ', '.join(vec for vec in vec_args)\n    return vec_args_str\n\ndef print_element_vector_init(n_rows, vec_args, size, tab):\n    slices = ','.join(':' for i in range(0, size))\n\n    lines = []\n    for i in range(0, n_rows):\n        vec = vec_args[i]\n\n        line = '{vec}[{slices}] = 0.0'.format(vec=vec,slices=slices)\n        line = tab + line\n\n        lines.append(line)\n\n    vec_init_str = '\\n'.join(line for line in lines)\n    return vec_init_str\n\ndef print_accumulation_var_init(n_rows, tab):\n    lines = []\n    for i in range(0, n_rows):\n        line = 'v_{i} = 0.0'.format(i=i)\n        line = tab + line\n\n        lines.append(line)\n\n    accum_init_str = '\\n'.join(line for line in lines)\n    return accum_init_str\n\ndef print_accumulation_var(n_rows, expr, tab):\n    lines = []\n    for i in range(0, n_rows):\n        line = 'v_{i} += ({__WEAK_FORM__}) * wvol'\n        e = _convert_int_to_float(expr[i].evalf())\n        # we call evalf to avoid having fortran doing the evaluation of rational\n        # division\n        line = line.format(i=i, __WEAK_FORM__=e)\n        line = tab + line\n\n        lines.append(line)\n\n    accum_str = '\\n'.join(line for line in lines)\n    return accum_str\n\ndef print_bilinear_accumulation_assign(n_rows, dim, tab):\n    if dim == 1:\n        e_pattern = 'vec_{i}[il_1] = v_{i}'\n\n    elif dim == 2:\n        e_pattern = 'vec_{i}[il_1, il_2] = v_{i}'\n\n    elif dim ==3:\n        e_pattern = 'vec_{i}[il_1, il_2, il_3] = v_{i}'\n\n    else:\n        raise NotImplementedError('only 1d, 2d and 3d are available')\n\n    lines = []\n    for i in range(0, n_rows):\n        line = e_pattern.format(i=i)\n        line = tab + line\n\n        lines.append(line)\n\n    accum_assign_str = '\\n'.join(line for line in lines)\n    return accum_assign_str\n\ndef print_linear_accumulation_assign(n_rows, dim, tab):\n    if dim == 1:\n        e_pattern = 'vec_{i}[il_1] = v_{i}'\n\n    elif dim == 2:\n        e_pattern = 'vec_{i}[il_1, il_2] = v_{i}'\n\n    elif dim ==3:\n        e_pattern = 'vec_{i}[il_1, il_2, il_3] = v_{i}'\n\n    else:\n        raise NotImplementedError('only 1d, 2d and 3d are available')\n\n    lines = []\n    for i in range(0, n_rows):\n        line = e_pattern.format(i=i)\n        line = tab + line\n\n        lines.append(line)\n\n    accum_assign_str = '\\n'.join(line for line in lines)\n    return accum_assign_str\n\n# TODO use space degrees\ndef print_element_vector_decs(n_rows, dim, vec_args, tab):\n    if dim == 1:\n        pattern = 'zeros( test_p1+1 )'\n\n    elif dim == 2:\n        pattern = 'zeros( (test_p1+1, test_p2+1) )'\n\n    elif dim == 3:\n        pattern = 'zeros( (test_p1+1, test_p2+1, test_p3+1) )'\n\n    else:\n        raise NotImplementedError('only 1d, 2d and 3d are available')\n\n    lines = []\n    for i in range(0, n_rows):\n        vec = vec_args[i]\n        line = '{vec} = {pattern}'.format(vec=vec, pattern=pattern)\n        line = tab + line\n        lines.append(line)\n\n    decs = '\\n'.join(i for i in lines)\n    return decs\n\ndef construct_global_vector_names(n_rows):\n    vec_args = []\n    for i in range(0, n_rows):\n        vec = _global_vector_name(i)\n        vec_args.append(vec)\n\n    return vec_args\n\ndef print_global_vector_args(n_rows, vec_args):\n    vec_args_str = ', '.join(vec for vec in vec_args)\n    return vec_args_str\n\n# TODO use spaces\ndef print_global_vector_decs(n_rows, vec_args):\n    pattern = 'StencilVector( test_space.vector_space )'\n\n    lines = []\n    for i in range(0, n_rows):\n        vec = vec_args[i]\n        line = '{vec} = {pattern}'.format(vec=vec, pattern=pattern)\n        lines.append(line)\n\n    decs = '\\n'.join(i for i in lines)\n    return decs\n\n# TODO use spaces\ndef print_global_vector_update(n_rows, dim,\n                               element_vec_args,\n                               global_vec_args, tab):\n\n    lslices = ','.join(':' for i in range(0, dim))\n\n    lines = []\n    for i in range(0, n_rows):\n        lvec = element_vec_args[i]\n        gvec = global_vec_args[i]\n\n        # ... every vector should have access to its corresponding space and\n        # degrees\n        if dim == 1:\n            gslices = 'is1-test_p1:is1+1'\n\n        elif dim == 2:\n            gslices = 'is1-test_p1:is1+1, is2-test_p2:is2+1'\n\n        elif dim == 3:\n            gslices = 'is1-test_p1:is1+1, is2-test_p2:is2+1, is3-test_p3:is3+1'\n        # ...\n\n        line = '{gvec}[{gslices}] += {lvec}[{lslices}]'.format(lvec=lvec,\n                                                               gvec=gvec,\n                                                               lslices=lslices,\n                                                               gslices=gslices)\n        line = tab + line\n        lines.append(line)\n\n    decs = '\\n'.join(i for i in lines)\n    return decs\n\ndef construct_argument_vector_name(n_rows):\n    if (n_rows == 1):\n        return _global_vector_name(0)\n    else:\n        return 'd_vector'\n\ndef print_argument_vector_kwargs(argument_mat):\n    return ', {}=None'.format(argument_mat)\n\ndef print_import_stencil_vector():\n    return 'from spl.linalg.stencil import StencilVector'\n\ndef print_set_dict_vector(n_rows, argument_vec, vec_args):\n    if n_rows == 1:\n        return ''\n\n    lines = [argument_vec + ' = {}']\n    for i in range(0, n_rows):\n        M = _global_vector_name(i)\n        line = '{d}[{i}] = {M}'.format(d=argument_vec, i=i, M=M)\n        lines.append(line)\n\n    return '\\n'.join(i for i in lines)\n\ndef print_get_dict_vector(n_rows, argument_vec, vec_args):\n    if n_rows == 1:\n        return ''\n\n    lines = []\n    for i in range(0, n_rows):\n        M = _global_vector_name(i)\n        line = '{M} = {d}[{i}]'.format(d=argument_vec, i=i, M=M)\n        lines.append(line)\n\n    return '\\n'.join(i for i in lines)\n\n_template_define_global_vector = \"\"\"\nif {__ARGUMENT_VEC__} is None:\n{__IMPORT_STENCIL__}\n{__DECS__}\n{__SET_DICT__}\n{__ELSE__}\n{__GET_DICT__}\n\"\"\"\n# TODO add comment to the generated code\ndef print_define_global_vector(n_rows, global_vec_args, argument_vec, tab):\n    # ...\n    def _indent_block(txt):\n        indent = ' '*4\n\n        lines = []\n        for line in txt.split('\\n'):\n            line = indent + line\n            lines.append(line)\n\n        return '\\n'.join(line for line in lines)\n    # ...\n\n    # ...\n    global_vec_decs_str = print_global_vector_decs(n_rows, global_vec_args)\n    global_vec_decs_str = _indent_block( global_vec_decs_str )\n    # ...\n\n    # ...\n    import_str = print_import_stencil_vector()\n    import_str = _indent_block( import_str )\n    # ...\n\n    # ...\n    set_dict_str = print_set_dict_vector(n_rows, argument_vec, global_vec_args)\n    set_dict_str = _indent_block( set_dict_str )\n    # ...\n\n    # ...\n    get_dict_str = print_get_dict_vector(n_rows, argument_vec, global_vec_args)\n    get_dict_str = _indent_block( get_dict_str )\n    # ...\n\n    # ...\n    if n_rows == 1:\n        else_str = ''\n    else:\n        else_str = 'else:'\n    # ...\n\n    pattern = _template_define_global_vector\n    code = pattern.format(__ARGUMENT_VEC__=argument_vec,\n                          __IMPORT_STENCIL__=import_str,\n                          __DECS__=global_vec_decs_str,\n                          __SET_DICT__=set_dict_str,\n                          __GET_DICT__=get_dict_str,\n                          __ELSE__=else_str)\n\n    lines = []\n    for line in code.split('\\n'):\n        line = tab + line\n        lines.append(line)\n\n    code = '\\n'.join(line for line in lines)\n\n    return code\n","sub_path":"symfe/codegen/vector.py","file_name":"vector.py","file_ext":"py","file_size_in_byte":7840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"535979220","text":"#!/usr/bin/env python\n\"\"\" Simple examples of calling C functions through ctypes module. \"\"\"\nimport ctypes\nfrom ctypes import Structure, c_char_p, c_int, c_float, c_double, byref, POINTER\nimport sys\nimport os\n\nclass MyStruct(Structure):\n    _fields_ =[(\"name\",c_char_p),\n               (\"age\",c_int),\n               (\"mark\",c_double)\n               ]\n\n# Load the shared library into C types.\nif __name__ == \"__main__\":\n    if sys.platform.startswith(\"win\"):\n        c_lib = ctypes.CDLL(\"functions.dll\")\n    else:\n        cur_path = os.getcwd()\n        lib_path = os.path.join (cur_path, \"libfunctions.so\")\n        c_lib = ctypes.CDLL(lib_path)\n\n    print(\"=================================\")\n\n    # Sample data\n    x, y = 5, 3.3\n\n    # You need tell ctypes that the function returns a float (if the function return is different than integer)\n    c_lib.cmult.restype = c_float\n\n    # Example of numeric functions\n    r1 = c_lib.cmult(x, c_float(y))\n    r2 = c_lib.csum(2,5)\n    print(\"Python program : variable r2 contains result from csum\", r2)\n\n    print(\"\\n=================================\")\n\n    # Example of array type function\n    a = (c_int * 3) (-1,  2 ,  5)\n    f_arr = c_lib.cfun_array\n    f_arr.restype = POINTER(c_int * 3)\n    print(\"Python program : previous array: \", [i for i in a])\n    print (\"Python program : new array: \", [i for i in f_arr(a).contents])\n\n    print(\"\\n=================================\")\n\n    # Example of struct type function\n    st = MyStruct(c_char_p(b\"Manolo\"), 37, 6.8)\n    result = c_lib.comprobar_num\n\n    #Sending and receiving pointers\n    result.restype = POINTER(MyStruct)\n    output  = result(byref(st))\n    \n    # Getting content from pointer\n    final_output = output.contents\n    print(\"Python program : \", final_output.name.decode(), \", \", final_output.age, \", \", final_output.mark)\n\n    print(\"\\n=================================\")\n","sub_path":"ctypes_test.py","file_name":"ctypes_test.py","file_ext":"py","file_size_in_byte":1883,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"147488292","text":"# Mary McHale, 14th April 2018\r\n# Project based on Iris Data, Working out the Max\r\n\r\nf = open('data/iris.csv' , 'r')\r\n# This opens the file called iris.csv\r\nprint(\"Max values are below\")\r\nprint(\" pl   pw   sl   sw\")\r\n# pl = short for Petal length\r\n# pw = short for Petal width\r\n# sl = short for Sepal length\r\n# sw = short for Sepal width\r\n\r\nc = 0\r\n# sets the record counter to zero at beginning\r\nmaxsofarpl = 0\r\nmaxsofarpw = 0\r\nmaxsofarsl = 0\r\nmaxsofarsw = 0\r\n\r\n# maximum values so far in the dataset\r\nrecord = \"\"\r\nwhile c < 100000:\r\n    record = f.readline()\r\n    if record == \"\":\r\n\r\n        #print(\"count = \",c)\r\n        #this was a test to ensure that the total of c was in fact 150, otherwise the results of the mean could be wrong\r\n        temp1 = format(maxsofarpl, ' .1f')\r\n        temp2 = format(maxsofarpw, ' .1f')\r\n        temp3 = format(maxsofarsl, ' .1f')\r\n        temp4 = format(maxsofarsw, ' .1f')\r\n        print (temp1, temp2, temp3, temp4)\r\n       \r\n        f.close()\r\n        quit()\r\n\r\n    # Working out the number of characters across each row to put the data into columns\r\n    petallength = (record[0:3])\r\n    petalwidth = (record[4:7])\r\n    sepallength = (record[8:11])\r\n    sepalwidth = (record[12:15])\r\n    name = (record[16:30])\r\n    \r\n    petallength = float(petallength) \r\n    petalwidth = float(petalwidth)\r\n    sepallength = float(sepallength)\r\n    sepalwidth = float(sepalwidth)\r\n    #float is a python function to change a string variable into a decimal number for adding purposes\r\n    \r\n    if petallength > maxsofarpl:\r\n        maxsofarpl = petallength\r\n    if petalwidth > maxsofarpw:\r\n        maxsofarpw = petalwidth\r\n    if sepallength > maxsofarsl:\r\n        maxsofarsl = sepallength  \r\n    if sepalwidth > maxsofarsw:\r\n        maxsofarsw = sepalwidth \r\n    #print(petallength, petalwidth, sepallength, sepalwidth)\r\n    record = \"\"\r\n    c = c + 1","sub_path":"Max.py","file_name":"Max.py","file_ext":"py","file_size_in_byte":1880,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"280086927","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nimport codecs\nimport ujson\nimport random\nfrom pathlib import Path\nfrom enum import IntEnum\nfrom loguru import logger\n\n\nclass Dictionary(object):\n    __phrases = []\n    __names = []\n    __surnames = []\n    __places = []\n    __ue_places = []\n    __ue_vip = []\n    __dictionaries = []\n\n    def __init__(self):\n\n        try:\n            with codecs.open(Path('dictionaries/phrases.json'), encoding='utf8') as data_file:\n                self.__phrases = ujson.load(data_file)\n                self.__dictionaries.append({\n                    \"data\": self.__phrases,\n                    \"path\": Path('dictionaries/phrases.json')\n                })\n                logger.debug('phrases.json loaded')\n\n            # Load first\n            with codecs.open(Path('dictionaries/names.json'), encoding='utf8') as data_file:\n                self.__names = ujson.load(data_file)\n                self.__dictionaries.append({\n                    \"data\": self.__names,\n                    \"path\": Path('dictionaries/names.json')\n                })\n                logger.debug('names.json loaded')\n\n            # Load surnames\n            with codecs.open(Path('dictionaries/surnames.json'), encoding='utf8') as data_file:\n                self.__surnames = ujson.load(data_file)\n                self.__dictionaries.append({\n                    \"data\": self.__surnames,\n                    \"path\": Path('dictionaries/surnames.json')\n                })\n                logger.debug('surnames.json loaded')\n\n            # Load place\n            with codecs.open(Path('dictionaries/places.json'), encoding='utf8') as data_file:\n                self.__places = ujson.load(data_file)\n                self.__dictionaries.append({\n                    \"data\": self.__places,\n                    \"path\": Path('dictionaries/places.json')\n                })\n                logger.debug('places.json loaded')\n\n            # Load users_entries_places\n            with codecs.open(Path('dictionaries/ue_places.json'), encoding='utf8') as data_file:\n                self.__ue_places = ujson.load(data_file)\n                self.__dictionaries.append({\n                    \"data\": self.__ue_places,\n                    \"path\": Path('dictionaries/ue_places.json')\n                })\n                logger.debug('ue_places.json loaded')\n\n            # Load users_entries_vip\n            with codecs.open(Path('dictionaries/ue_vip.json'), encoding='utf8') as data_file:\n                self.__ue_vip = ujson.load(data_file)\n                self.__dictionaries.append({\n                    \"data\": self.__ue_vip,\n                    \"path\": Path('dictionaries/ue_vip.json')\n                })\n                logger.debug('ue_vip.json loaded')\n\n        except OSError or ValueError as err:\n            raise DictionaryErrors.OpeningError(err)\n\n        if len(self.__phrases) == 0 or len(self.__names) == 0 or len(self.__surnames) == 0 or len(self.__places) == 0:\n            raise DictionaryErrors.EmptyDictionaryError\n\n        logger.info('Dictionary class initialized')\n\n    def random_phrase(self):\n        phrase = random.choice(self.__phrases)\n        return self.replace(phrase)\n\n    def random_name(self):\n        if len(self.__ue_vip) > 0 and random.random() < 0.5:\n            name = random.choice(self.__ue_vip)\n        else:\n            name = random.choice(self.__names) + (' ' + random.choice(self.__surnames) if random.random() < 0.5 else '')\n        return name\n\n    def random_place(self):\n        if len(self.__ue_places) > 0 and random.random() < 0.5:\n            name = random.choice(self.__ue_places)\n        else:\n            name = random.choice(self.__places)\n        return name\n\n    def replace(self, string):\n        while string.count('{name}') > 0:\n            string = string.replace('{name}', self.random_name(), 1)\n        while string.count('{place}') > 0:\n            string = string.replace('{place}', self.random_place(), 1)\n        return string\n\n    def add_entity(self, entity_type, entity_text):\n        if entity_type == DictionaryID.PHRASES:\n            self.__phrases.append(entity_text)\n        elif entity_type == DictionaryID.UE_PLACES:\n            self.__ue_places.append(entity_text)\n        elif entity_type == DictionaryID.UE_VIP:\n            self.__ue_vip.append(entity_text)\n        else:\n            return DictionaryErrors.DictionaryIDError('Wrong ID, read-only dictionary')\n        self.save_dictionary(entity_type)\n\n    def save_dictionary(self, dictionary_id):\n        if DictionaryID.read_only(dictionary_id):\n            raise DictionaryErrors.DictionaryIDError('Invalid ID {}'.format(dictionary_id))\n\n        try:\n            with codecs.open(self.__dictionaries[dictionary_id]['path'], 'w', encoding='utf-8') as outfile:\n                ujson.dump(self.__dictionaries[dictionary_id]['data'], outfile, indent=2, ensure_ascii=False)\n        except OSError or ValueError as err:\n            raise DictionaryErrors.OpeningError(err)\n\n\nclass DictionaryID(IntEnum):\n    __length = 6\n    PHRASES, NAMES, SURNAMES, PLACES, UE_PLACES, UE_VIP = range(__length)\n\n    @classmethod\n    def length(cls):\n        return cls.__length.value\n\n    @classmethod\n    def contains(cls, _id):\n        return any(_id == item.value for item in cls)\n\n    @classmethod\n    def read_only(cls, _id):\n        return False if _id in (cls.PHRASES, cls.UE_PLACES, cls.UE_VIP) else True\n\n    @classmethod\n    def max(cls):\n        return cls.__length.value - 1\n\n\nclass DictionaryErrors(Exception):\n    class DictionaryError(Exception):\n        def __init__(self, message=None):\n            if message:\n                super().__init__(message)\n            else:\n                super().__init__()\n\n    class OpeningError(DictionaryError):\n        pass\n\n    class EmptyDictionaryError(DictionaryError):\n        pass\n\n    class DictionaryIDError(DictionaryError):\n        pass\n","sub_path":"dictionaries/dictionary.py","file_name":"dictionary.py","file_ext":"py","file_size_in_byte":5912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"515126792","text":"####################################\n# 1. package import\nfrom flask import Flask, render_template, g, request, session, flash, redirect, url_for\nimport pandas as pd\nimport pymysql\nimport re\n####################################\n\n\n####################################\n# 2. config\nDEBUG = True\nUSERNAME = \"admin\"\nPASSWORD = \"888\"\nSECRET_KEY = \"development key~~\"\n####################################\n\n\n####################################\n# 3. initialize\napp = Flask(__name__)\napp.config.from_object(__name__)\n\nkDbName = \"db_for_test\"\nkInitTableName = \"coupang_product_table\"\nkTableNameForSelectedDatas = \"selected_datas_table_revised\"\nkPathToCsv = \\\n    \"\"\"\n    ../../data/backup_dry_tissue_page8.csv\n    \"\"\".strip()\n\n\ndef create_table(table_name):\n    create_sql = \\\n        \"\"\"\n        CREATE TABLE {} (\n        id INTEGER PRIMARY KEY NOT NULL AUTO_INCREMENT,\n        code varchar(20),\n        name varchar(200),\n        brand_name varchar(20),\n        hash_tag varchar(20),\n        star varchar(20),\n        star_count varchar(20),\n        weight varchar(50),\n        ml varchar(300),\n        product_count varchar(50),\n        price varchar(50),\n        date varchar(30),\n        flag varchar(20),\n        ingredient_name varchar(200),\n        view varchar(20),\n        product_img_path varchar(200),\n        product_detail_img_paths varchar(5000),\n        product_url varchar(200)\n        );\n        \"\"\".format(table_name)\n    with g.db.cursor() as cursor:\n        cursor.execute(create_sql)\n        g.db.commit()\n\n\ndef get_insert_sql(table_name):\n    result = \\\n        \"\"\"\n        INSERT INTO {} (id, code, name, brand_name, hash_tag, star, star_count, weight, ml, product_count, price, date, flag, ingredient_name, view, product_img_path, product_detail_img_paths, product_url)\n        VALUES (%s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s, %s);\n        \"\"\".format(table_name)\n    return result\n\n\ndef connect_db():\n    return pymysql.connect(host='localhost',\n                           db=kDbName,\n                           user='root',\n                           password='babylab123',\n                           port=3306,\n                           charset='utf8')\n\n\ndef init_db():\n    with connect_db() as db:\n        with db.cursor() as cursor:\n            try:\n                create_table(kInitTableName)\n            except Exception as e:\n                print(str(e))\n            finally:\n                cursor.execute(\"SELECT * FROM {}\".format(kInitTableName))\n                datas_from_db_table = cursor.fetchall()\n\n                if not datas_from_db_table:\n                    df = pd.read_csv(kPathToCsv, index_col=0)\n\n                    for col in df.columns:\n                        df[col] = df[col].apply(lambda x: \"\" if str(x) == 'nan' else str(x))\n                        df[col] = df[col].apply(lambda x: x.replace(\" , \", \",\") if \" , \" in x else x)\n\n                    df2numpy = df.to_numpy()\n                    df2numpy2list = []\n                    for data in df2numpy:\n                        df2numpy2list.append(data.tolist())\n\n                    insert_sql = get_insert_sql(kInitTableName)\n                    cursor.executemany(insert_sql, df2numpy2list)\n                    db.commit()\n                cursor.close()\n####################################\n\n\n####################################\n# 4. before_request & teardown_request\n@app.before_request\ndef before_request():\n    # HTTP 요청이 들어올때마다 실행\n    g.db = connect_db()\n    init_db()\n\n\n@app.teardown_request\ndef teardown_request(exception):\n    # HTTP 요청 결과가 브라우저에 응답한 다음 실행\n    g.db.close()\n####################################\n\n\n####################################\n# 5. views\n@app.route(\"/\")\ndef init_login():\n    # 제일 처음에 뜨는 페이지로 로그인을 먼저 요구한다.\n    return render_template(\"login.html\")\n\n\n@app.route(\"/login\", methods=['GET', 'POST'])\ndef login():\n    # 로그인 버튼 클릭시 실행되는 함수.\n    error = None\n    if request.method == 'POST':\n        if request.form['username'] != app.config['USERNAME']:\n            error = \"Invalid username!\"\n        elif request.form['password'] != app.config['PASSWORD']:\n            error = \"Invalid password!\"\n        else:\n            session['logged_in'] = True\n            flash(\"login success\")\n            return redirect(url_for(\"show_selectable_elements\"))\n    return render_template(\"login.html\", error=error)\n\n\n@app.route(\"/logout\", methods=['POST'])\ndef logout():\n    # 로그아웃 버튼 클릭시 실행됨.\n    session.pop(\"logged_in\", None)\n    flash(\"logged out!\")\n    return redirect(url_for(\"login\"))\n\n\n@app.route(\"/main\", methods=['GET', 'POST'])\ndef show_selectable_elements():\n    # 선택할 수 있는 데이터를 띄워줌.\n    datas = None\n    with g.db.cursor() as cursor:\n        cursor.execute(\"SELECT id, name, date FROM {};\".format(kInitTableName))\n        datas = cursor.fetchall()\n        cursor.close()\n    return render_template('show_selectable_elements.html', datas=datas)\n\n\n@app.route(\"/main/revise\", methods=['GET'])\ndef show_revising_element_from_main():\n    # 수정 중인 데이터를 띄워줌. (데이터는 main 에서 나온 경우)\n    full_path = request.full_path\n    before_parsing_id = re.compile(\"id=[0-9]+\").search(full_path).group()\n    after_parsing_id = before_parsing_id.split(\"=\")[-1]\n\n    select_sql = \\\n        \"\"\"\n        SELECT * FROM {}\n        WHERE id = {};\n        \"\"\".format(kInitTableName, after_parsing_id).strip()\n\n    selected_data = None\n    with g.db.cursor(pymysql.cursors.DictCursor) as cursor:\n        cursor.execute(select_sql)\n        selected_data = cursor.fetchone()\n\n    return render_template(\"show_revising_data.html\", datas=selected_data)\n\n\n@app.route(\"/already_revised/revise\", methods=['GET'])\ndef show_revising_element_from_already_revised():\n    # 수정 중인 데이터를 띄워줌. (데이터는 already_revised 에서 나온 경우)\n    full_path = request.full_path\n    before_parsing_id = re.compile(\"id=[0-9]+\").search(full_path).group()\n    after_parsing_id = before_parsing_id.split(\"=\")[-1]\n\n    select_sql = \\\n        \"\"\"\n        SELECT * FROM {}\n        WHERE id = {};\n        \"\"\".format(kTableNameForSelectedDatas, after_parsing_id).strip()\n\n    selected_data = None\n    with g.db.cursor(pymysql.cursors.DictCursor) as cursor:\n        cursor.execute(select_sql)\n        selected_data = cursor.fetchone()\n\n    return render_template(\"show_revising_data.html\", datas=selected_data)\n\n\n@app.route(\"/main/revising\", methods=['POST'])\n@app.route(\"/already_revised/revising\", methods=['POST'])\ndef revising():\n    # 수정 완료 버튼 클릭시 실행되는 함수\n    revised_product_detail_paths = request.form.getlist(\"revised_product_detail_paths\")\n    revised_product_detail_path = ','.join(revised_product_detail_paths)\n\n    revised_data_without_product_detail_paths = request.form.getlist(\"revised_element\")\n    revised_data = revised_data_without_product_detail_paths[:]\n    for idx, element in enumerate(revised_data_without_product_detail_paths):\n        if \"/product_detail/\" in element:\n            revised_data[idx] = revised_product_detail_path\n            break\n\n    id = revised_data[0]\n    insert_sql = get_insert_sql(kTableNameForSelectedDatas)\n\n    try:\n        create_table(kTableNameForSelectedDatas)\n    finally:\n        with g.db.cursor() as cursor:\n            delete_sql = \\\n                \"\"\"\n                DELETE FROM {} WHERE id = {};\n                \"\"\".format(kTableNameForSelectedDatas, id).strip()\n            cursor.execute(delete_sql)\n            cursor.execute(insert_sql, revised_data)\n\n            g.db.commit()\n            cursor.close()\n        return redirect(url_for(\"already_revised\"))\n\n\n@app.route(\"/already_revised\", methods=['GET', 'POST'])\ndef already_revised():\n    # 선택, 수정된 데이터만을 모아서 모두 띄워준다.\n    try:\n        create_table(kTableNameForSelectedDatas)\n    finally:\n        showing_datas = None\n        with g.db.cursor(pymysql.cursors.DictCursor) as cursor:\n            select_sql = \\\n                \"\"\"\n                SELECT id, name, price, date, ingredient_name FROM {}\n                \"\"\".format(kTableNameForSelectedDatas).strip()\n            cursor.execute(select_sql)\n            showing_datas = cursor.fetchall()\n        return render_template(\"show_revised_data.html\", datas=showing_datas)\n\n\n@app.route(\"/main/just_view\", methods=['GET'])\ndef just_view_for_init():\n    # 데이터의 이름을 클릭했을때,\n    full_path = request.full_path\n    print(full_path)\n    id = re.compile(\"id=[0-9]+\").search(full_path).group().split(\"=\")[-1]\n\n    selected_sql = \\\n        \"\"\"\n        SELECT * FROM {}\n        WHERE id = {}\n        \"\"\".format(kInitTableName, id)\n\n    selected_data = None\n    with g.db.cursor(pymysql.cursors.DictCursor) as cursor:\n        cursor.execute(selected_sql)\n        selected_data = cursor.fetchone()\n    return render_template('just_view_for_selected_data.html', datas=selected_data)\n\n\n@app.route(\"/already_revised/just_view\", methods=['GET'])\ndef just_view_for_selected():\n    full_path = request.full_path\n    id = re.compile(\"id=[0-9]+\").search(full_path).group().split(\"=\")[-1]\n    select_sql = \\\n        \"\"\"\n        SELECT * FROM {}\n        WHERE id = {}\n        \"\"\".format(kTableNameForSelectedDatas, id)\n\n    selected_data = None\n    with g.db.cursor(pymysql.cursors.DictCursor) as cursor:\n        cursor.execute(select_sql)\n        selected_data = cursor.fetchone()\n    return render_template('just_view_for_selected_data.html', datas=selected_data)\n\n\n@app.route(\"/delete_data\", methods=['GET', 'POST'])\ndef delete_data():\n    delete_data_id = request.form.get(\"deleted_data_id\")\n\n    delete_sql = \\\n        \"\"\"\n        DELETE FROM {}\n        WHERE id = {}\n        \"\"\".format(kTableNameForSelectedDatas, delete_data_id)\n\n    with g.db.cursor() as cursor:\n        cursor.execute(delete_sql)\n        g.db.commit()\n\n    return redirect(url_for(\"already_revised\"))\n####################################\n\n\n####################################\n# 6. template_filter\n@app.template_filter(\"is_name\")\ndef is_name(any_element):\n    try:\n        int(any_element)\n        return False\n    except:\n        try:\n            int(any_element[:4])\n            return False\n        except:\n            return True\n\n\n@app.template_filter(\"to_string\")\ndef to_string(any_element):\n    return str(any_element)\n\n\n@app.template_filter(\"split_by_comma\")\ndef split_by_comma(img_paths):\n    return img_paths.split(\",\")\n\n\n@app.template_filter(\"is_empty\")\ndef is_empty(a_list):\n    return False if a_list else True\n####################################\n\n\nif __name__ == '__main__':\n    app.run()\n\n","sub_path":"toy_project_product_manage_with_flask/tutorial/5_coupang_imgs/5_main.py","file_name":"5_main.py","file_ext":"py","file_size_in_byte":10766,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"550163849","text":"import threading\nfrom queue import Queue\n\nclass threadingOperation:\n    dest=False #holds a pointer to function to be multithreaded.\n    thCount=1 #specifies number of threads to be created\n    myQueue=False\n    results=[] #holds results of threading\n    outputType=\"\" #stores type of results, could be list or dict\n    def __init__(self,*args):\n        if len(args)<2:\n            raise ValueError\n        destFunc=args[0]\n        thCount=args[1]\n        self.dest=destFunc\n        self.thCount=thCount\n        if len(args)>2:\n            if args[2]==\"list\":\n                self.outputType=\"list\"\n            if args[2]==\"dict\":\n                self.outputType=\"dict\"\n    def worker(self): #one worker per thread, each one will work until all tasks done, then dies\n        if self.outputType==\"\":\n            while True:\n                self.dest( self.myQueue.get() ) #passes task to multied function\n                self.myQueue.task_done() #signals to queue that particular task is finished\n        elif self.outputType==\"list\":\n            while True:\n                self.results.append( self.dest( self.myQueue.get() ) ) #passes task to multied function\n                self.myQueue.task_done() #signals to queue that particular task is finished\n        elif self.outputType==\"dict\":\n            while True:\n                key=self.myQueue.get()\n                self.results[key]=self.dest( key ) #passes task to multied function\n                self.myQueue.task_done() #signals to queue that particular task is finished\n    def thread(self,taskList):\n        #taskList is a list of stuff\n        #each element of the list is passed to an instance of the function\n        #named in dest\n        if self.outputType==\"list\":\n            self.results=[]\n        elif self.outputType==\"dict\":\n            self.results=dict()\n        self.myQueue=Queue() #make queue\n        for i in range(self.thCount):\n            t=threading.Thread(target=self.worker)\n            t.daemon=True\n            t.start()\n        for x in taskList:\n            self.myQueue.put(x)\n        self.myQueue.join()\n        if self.outputType!=\"\":\n            return self.results\n","sub_path":"current/easyMultiThread.py","file_name":"easyMultiThread.py","file_ext":"py","file_size_in_byte":2160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"73698019","text":"#!/usr/bin/python3\nimport sys\n\nsave = __import__('7-save_to_json_file').save_to_json_file\nload = __import__('8-load_from_json_file').load_from_json_file\n\n\n\"\"\" Adds all arguments to a Python list, and then save them to a file \"\"\"\ntry:\n    list = load(\"add_item.json\")\nexcept Exception:\n    list = []\n\nfor i in sys.argv[1:]:\n        list.append(i)\n\nsave(list, \"add_item.json\")\n","sub_path":"0x0B-python-input_output/9-add_item.py","file_name":"9-add_item.py","file_ext":"py","file_size_in_byte":375,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"211258158","text":"import json\nimport logging\nimport os\nimport subprocess\n\nlogger = logging.getLogger()\nlogger.setLevel(logging.INFO)\n\n# these are coming from the kubectl layer\nos.environ['PATH'] = '/opt/kubectl:/opt/awscli:' + os.environ['PATH']\n\noutdir = os.environ.get('TEST_OUTDIR', '/tmp')\nkubeconfig = os.path.join(outdir, 'kubeconfig')\n\n\ndef apply_handler(event, context):\n    logger.info(json.dumps(dict(event, ResponseURL='...')))\n\n    request_type = event['RequestType']\n    props = event['ResourceProperties']\n\n    # resource properties (all required)\n    cluster_name  = props['ClusterName']\n    manifest_text = props['Manifest']\n    role_arn      = props['RoleArn']\n    prune_label   = props.get('PruneLabel', None)\n    overwrite     = props.get('Overwrite', 'false').lower() == 'true'\n    skip_validation = props.get('SkipValidation', 'false').lower() == 'true'\n\n    # \"log in\" to the cluster\n    cmd = [ 'aws', 'eks', 'update-kubeconfig',\n        '--role-arn', role_arn,\n        '--name', cluster_name,\n        '--kubeconfig', kubeconfig\n    ]\n    logger.info(f'Running command: {cmd}')\n    subprocess.check_call(cmd)\n\n    if os.path.isfile(kubeconfig):\n        os.chmod(kubeconfig, 0o600)\n\n    # write resource manifests in sequence: { r1 }{ r2 }{ r3 } (this is how\n    # a stream of JSON objects can be included in a k8s manifest).\n    manifest_list = json.loads(manifest_text)\n    manifest_file = os.path.join(outdir, 'manifest.yaml')\n    with open(manifest_file, \"w\") as f:\n        f.writelines(map(lambda obj: json.dumps(obj), manifest_list))\n\n    logger.info(\"manifest written to: %s\" % manifest_file)\n\n    kubectl_opts = []\n    if skip_validation:\n        kubectl_opts.extend(['--validate=false'])\n\n    if request_type == 'Create':\n        # if \"overwrite\" is enabled, then we use \"apply\" for CREATE operations\n        # which technically means we can determine the desired state of an\n        # existing resource.\n        if overwrite:\n            kubectl('apply', manifest_file, *kubectl_opts)\n        else:\n            # --save-config will allow us to use \"apply\" later\n            kubectl_opts.extend(['--save-config'])\n            kubectl('create', manifest_file, *kubectl_opts)\n    elif request_type == 'Update':\n        if prune_label is not None:\n            kubectl_opts.extend(['--prune', '-l', prune_label])\n\n        kubectl('apply', manifest_file, *kubectl_opts)\n    elif request_type == \"Delete\":\n        try:\n            kubectl('delete', manifest_file)\n        except Exception as e:\n            logger.info(\"delete error: %s\" % e)\n\n\ndef kubectl(verb, file, *opts):\n    maxAttempts = 3\n    retry = maxAttempts\n    while retry > 0:\n        try:\n            cmd = ['kubectl', verb, '--kubeconfig', kubeconfig, '-f', file] + list(opts)\n            logger.info(f'Running command: {cmd}')\n            output = subprocess.check_output(cmd, stderr=subprocess.STDOUT)\n        except subprocess.CalledProcessError as exc:\n            output = exc.output\n            if b'i/o timeout' in output and retry > 0:\n                retry = retry - 1\n                logger.info(\"kubectl timed out, retries left: %s\" % retry)\n            else:\n                raise Exception(output)\n        else:\n            logger.info(output)\n            return\n    raise Exception(f'Operation failed after {maxAttempts} attempts: {output}')\n","sub_path":"packages/@aws-cdk-testing/framework-integ/test/aws-stepfunctions-tasks/test/emrcontainers/integ.start-job-run.js.snapshot/asset.9017774b84ae2457b1b2ad6fcbb4860d8ce2537062c77010b24d9b156ced5a1b/apply/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":3330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"407691023","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        ('lingua', '0010_auto_20160109_1105'),\n    ]\n\n    operations = [\n        migrations.CreateModel(\n            name='Device',\n            fields=[\n                ('id', models.AutoField(verbose_name='ID', serialize=False, auto_created=True, primary_key=True)),\n                ('dev_id', models.CharField(unique=True, max_length=50, verbose_name='Device ID')),\n                ('reg_id', models.CharField(unique=True, max_length=255, verbose_name='Registration ID')),\n                ('email', models.CharField(max_length=255, null=True, verbose_name='Email', blank=True)),\n                ('user_type', models.CharField(max_length=255, null=True, verbose_name='User Type', blank=True)),\n                ('creation_date', models.DateTimeField(auto_now_add=True, verbose_name='Creation date')),\n                ('modified_date', models.DateTimeField(auto_now=True, verbose_name='Modified date')),\n                ('is_active', models.BooleanField(default=False, verbose_name='Is active?')),\n            ],\n        ),\n        migrations.AlterField(\n            model_name='client',\n            name='gender',\n            field=models.CharField(default='male', max_length=10, choices=[('male', 'male'), ('female', 'female')]),\n        ),\n    ]\n","sub_path":"lingua_vista/lingua/migrations/0011_auto_20160109_1745.py","file_name":"0011_auto_20160109_1745.py","file_ext":"py","file_size_in_byte":1417,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"649470897","text":"\"\"\"\n    给定一个二叉树，找出其最大深度。\n    二叉树的深度为根节点到最远叶子节点的最长路径上的节点数。\n    说明: 叶子节点是指没有子节点的节点。\n\n    示例：\n        给定二叉树 [3,9,20,null,null,15,7]，\n            3\n           / \\\n          9  20\n            /  \\\n           15   7\n        返回它的最大深度 3 。\n\"\"\"\n\n\nclass TreeNode:\n    def __init__(self, x):\n        self.val = x\n        self.left = None\n        self.right = None\n\n\nclass Solution:\n    def maxDepth(self, root: TreeNode) -> int:\n        return self.iteration(root)\n\n    @classmethod\n    def recursive(cls, root: TreeNode) -> int:\n        if root is None:\n            return 0\n        left_height = cls.recursive(root.left)\n        right_height = cls.recursive(root.right)\n        return max(left_height, right_height) + 1\n\n    @classmethod\n    def iteration(cls, root: TreeNode) -> int:\n        depth = 0\n        if root:\n            stack = [(root, 1)]\n\n            while stack:\n                node, tmp_depth = stack.pop()\n                if node:\n                    depth = max(depth, tmp_depth)\n                    stack.append((node.left, tmp_depth+1))\n                    stack.append((node.right, tmp_depth+1))\n        return depth\n","sub_path":"algorithm/LeetCode_104_二叉树的最大深度.py","file_name":"LeetCode_104_二叉树的最大深度.py","file_ext":"py","file_size_in_byte":1289,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"194853453","text":"import socket\r\n\r\nclient_socket = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)\r\n\r\n# Server address and port number, and message to be sent\r\nserver_address = (\"localhost\", 50000)\r\n\r\nmessage = \"I love socket programming in Python!\"\r\nbytes_send = client_socket.sendto(message.encode(\"utf-8\"), server_address)\r\n\r\n# Close the socket\r\nclient_socket.close()\r\n","sub_path":"_site/scripts/udp_simple_client.py","file_name":"udp_simple_client.py","file_ext":"py","file_size_in_byte":355,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"203200844","text":"import numpy as np\r\nimport cv2 as cv\r\n\r\nimg1 = './img1.png'\r\nimg2 = './img2.png'\r\nimg3 = './img3.png'\r\nimg4 = './img4.png'\r\nimg5 = './img5.png'\r\nimg6 = './img6.png'\r\nimg7 = './img7.png'\r\nimg8 = './img8.png'\r\n\r\nmain_read = img7\r\nmain_test = cv.imread('digits.png')\r\n\r\n# img = cv.imread(main_read)\r\nimg = cv.imread('digits.png')\r\n# width = 2500\r\n# height = 400  # keep original height\r\n# dim = (width, height)\r\n#\r\n# # resize image\r\n# img = cv.resize(img, dim, interpolation=cv.INTER_AREA)\r\n\r\n# gray = cv.cvtColor(img,cv.COLOR_BGR2GRAY)\r\n# rows = np.vsplit(img, 50)\r\n# cells = []\r\n# for row in rows:\r\n#     row_cells = np.hsplit(row, 50)\r\n#     for cell in row_cells:\r\n#         cell = cell.flatten()\r\n#         cells.append(cell)\r\n# cells = np.array(cells, dtype=np.float32)\r\n\r\ngray = cv.cvtColor(img, cv.COLOR_BGR2GRAY)\r\n\r\n# Now we split the image to 5000 cells, each 20x20 size\r\ncells = [np.hsplit(row, 100) for row in np.vsplit(gray, 50)]\r\n# Make it into a Numpy array: its size will be (50,100,20,20)\r\nx = np.array(cells)\r\n# Now we prepare the training data and test data\r\ntrain = x[:, :50].reshape(-1, 400).astype(np.float32)  # Size = (2500,400)\r\ntest = x[:, 50:100].reshape(-1, 400).astype(np.float32)  # Size = (2500,400)\r\n# Create labels for train and test data\r\nk = np.arange(10)\r\ntrain_labels = np.repeat(k, 250)[:, np.newaxis]\r\ntest_labels = train_labels.copy()\r\n# Initiate kNN, train it on the training data, then test it with the test data with k=1\r\nknn = cv.ml.KNearest_create()\r\nknn.train(train, cv.ml.ROW_SAMPLE, train_labels)\r\nret, result, neighbours, dist = knn.findNearest(test, k=5)\r\n# Now we check the accuracy of classification\r\n# For that, compare the result with test_labels and check which are wrong\r\nmatches = result == test_labels\r\ncorrect = np.count_nonzero(matches)\r\naccuracy = correct * 100.0 / result.size\r\nprint('accuracy', accuracy)\r\n# Save the data\r\nnp.savez('knn_data.npz', train=train, train_labels=train_labels)\r\n# Now load the data\r\nwith np.load('knn_data.npz') as data:\r\n    print(data.files)\r\n    train = data['train']\r\n    train_labels = data['train_labels']\r\n\r\nknn = cv.ml.KNearest_create()\r\nknn.train(train, cv.ml.ROW_SAMPLE, train_labels)\r\n\r\ntest_img = cv.imread(main_read)\r\n\r\ntest_img = cv.cvtColor(test_img, cv.COLOR_BGR2GRAY)\r\ntest_img = cv.resize(test_img, (20, 20))\r\nx = np.array(test_img)\r\ntest_img = x.reshape(-1, 400).astype(np.float32)\r\nret, result, neighbours, distance = knn.findNearest(test_img, k=1)\r\n# Print the predicted number\r\nprint('result', result, 'neighbours', neighbours, 'distance', distance)\r\n","sub_path":"OpticalCharacterRecognition/OCRKnn.py","file_name":"OCRKnn.py","file_ext":"py","file_size_in_byte":2560,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"346345049","text":"from setuptools import setup, find_packages\nimport sys, os\n\nversion = '0.2'\n\ntry:\n    from mercurial import ui, hg, error\n    repo = hg.repository(ui.ui(), \".\")\n    ver = repo[version]\nexcept ImportError:\n    pass\nexcept error.RepoLookupError:\n    tip = repo[\"tip\"]\n    version = version + \".%s.%s\" % (tip.rev(), tip.hex()[:12])\nexcept error.RepoError:\n    pass\n\nsetup(\n    name='unstats_rdf',\n    version=version,\n    description=\"RDF API for UN Statistical Division Data\",\n    long_description=\"\"\"\\\nRDF API for UN Statistical Division Data\"\"\",\n    # Get strings from http://pypi.python.org/pypi?%3Aaction=list_classifiers\n    classifiers=[\n        \"Development Status :: 3 - Alpha\",\n        \"Environment :: Console\",\n        \"Environment :: Web Environment\",\n        \"Framework :: Paste\",\n        \"Intended Audience :: Developers\",\n        \"Intended Audience :: Information Technology\",\n        \"License :: OSI Approved :: GNU Affero General Public License v3\",\n        \"Operating System :: POSIX\",\n        \"Programming Language :: Python :: 2.6\",\n        \"Topic :: Internet\",\n        \"Topic :: Software Development :: Libraries :: Python Modules\",\n    ],\n    keywords=\"unstats rdf\",\n    author='Open Knowledge Foundation',\n    author_email='okfn-help@lists.okfn.org',\n    url=\"http://pypi.python.org/pypi/unstats_rdf_data\",\n    license='BSD',\n    packages=find_packages(exclude=['ez_setup', 'examples', 'tests']),\n    include_package_data=True,\n    zip_safe=False,\n    setup_requires=[\n        \"datapkg\",\n    ],\n    install_requires=[\n        \"Paste\",\n        \"setuptools\",\n        \"ordf\",\n    ],\n    datapkg_index = \"\"\"\n        [cofog99_en]\n        title=COFOG 1999 via the EU in XML format (English labels)\n        download_url=http://bitbucket.org/ww/unstats_rdf/raw/tip/unstats/data/cofog/COFOG99_EN.xml\n\n        [cofog99_fr]\n        title=COFOG 1999 via the EU in XML format (French labels)\n        download_url=http://bitbucket.org/ww/unstats_rdf/raw/tip/unstats/data/cofog/COFOG99_FR.xml\n\n        [cofog99_de]\n        title=COFOG 1999 via the EU in XML format (German labels)\n        download_url=http://bitbucket.org/ww/unstats_rdf/raw/tip/unstats/data/cofog/COFOG99_DE.xml\n    \"\"\",\n    entry_points = \"\"\"\n    \"\"\",\n)\n","sub_path":"pypi_install_script/unstats_rdf-0.2.tar/setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":2228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"101612398","text":"#!/usr/bin/env python\n\nfrom socket import *\nfrom addressbook_pb2 import *\nimport time\n\nsock = socket( AF_INET, SOCK_DGRAM )\n\nmsg = AddressBook()\nperson = msg.person.add()\nperson.name = 'J Doe'\nperson.id = 12345\nperson.email = 'j@doe.net'\nphone = person.phone.add()\nphone.number = '123-456-7890'\nphone.type = 0\nphone = person.phone.add()\nphone.number = '111-111-1111'\nphone.type = 1\n\nrunning = True\n\nwhile running:\n    try:\n        sock.sendto( msg.SerializeToString(), (\"233.0.0.1\",33445) )\n        time.sleep( 10 )\n    except KeyboardInterrupt:\n        running = False\n        continue\n        \n    \n","sub_path":"runtime/addressbook_test.py","file_name":"addressbook_test.py","file_ext":"py","file_size_in_byte":601,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"386122351","text":"import numpy as np\r\nimport pylab as pl\r\nimport math\r\n\r\ndots = 1000000\r\n# Make an array of x values\r\n\r\nlow = 1.0\r\nhigh = 9.0\r\n\r\nstep = (high-low)/dots\r\n\r\nx = np.arange(low+step, high, step)\r\n\r\nc = 1.0\r\n# Make an array of y values for each x value\r\ny1 = [math.sqrt(16.0 - math.pow(i-5.0, 2)) + 5.0 for i in x]\r\ny2 = [-1.0*math.sqrt(16.0 - math.pow(i-5.0, 2)) + 5.0 for i in x]\r\n#y = y1+y2\r\n\r\n#x = [i for i in x for _ in range(2)]\r\n#print(len(x), \" \", len(y))\r\n# use pylab to plot x and y\r\npl.plot(x, y1, 'r+')\r\npl.plot(x, y2, 'r+')\r\n# give plot a title\r\npl.title('Plot of y vs. x')\r\n# make axis labels\r\npl.xlabel('x axis')\r\npl.ylabel('y axis')\r\n# set axis limits\r\n#pl.xlim(0.0, 0.0001)\r\n#pl.ylim(0.0, 1.0)\r\n# show the plot on the screen\r\npl.show()\r\n\r\n","sub_path":"Plotting.py","file_name":"Plotting.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"516363975","text":"import sys\r\n\r\n\r\ndef get_largest_prime_below(n):\r\n    \"\"\"\r\n    returneaza ultimul nr prim mai mic decat cel dat\r\n    :param n: un nr intreg\r\n    :return: numarul cautat\r\n    \"\"\"\r\n    for i in range(n - 1, 2, -1):\r\n        if is_Prime(i):\r\n            return i\r\n\r\ndef is_Prime(n):\r\n    \"\"\"\r\n    verifica daca un nr este prim\r\n    :param n: un nr intreg\r\n    :return: True, daca nr este prim. False, daca nr nu este prim\r\n    \"\"\"\r\n    if n < 2:\r\n        return False\r\n    for i in range(2, n // 2 + 1):\r\n        if n % i == 0:\r\n            return False\r\n    return True\r\n\r\ndef test_is_Prime():\r\n    assert is_Prime(4) == False\r\n    assert is_Prime(7) == True\r\n    assert is_Prime(13) == True\r\n    assert is_Prime(6886) == False\r\n\r\ndef test_get_largest_prime_below():\r\n    assert get_largest_prime_below(60) == 59\r\n    assert get_largest_prime_below(8) == 7\r\n    assert get_largest_prime_below(10) == 7\r\n    assert get_largest_prime_below(34) == 31\r\n    assert get_largest_prime_below(94) == 89\r\n\r\ndef is_palindrome(n):\r\n    \"\"\"\r\n    verifica daca un nr este palindrom\r\n    :param n: un nr intreg\r\n    :return: True, daca nr este palindrom, False in caz contrar\r\n    \"\"\"\r\n    inverse = 0\r\n    copy = n\r\n    while copy > 0:\r\n        inverse = inverse * 10 + copy % 10\r\n        copy = copy // 10\r\n    if inverse == n:\r\n        return True\r\n    else:\r\n        return False\r\n\r\ndef main():\r\n    test_get_largest_prime_below()\r\n    test_is_Prime()\r\n    print(\"Testele au trecut cu succes\")\r\n    while True:\r\n        print(\"1. Ultimul nr prim\")\r\n        print(\"2. Palindrom\")\r\n        print(\"3. Iesire\")\r\n        optiune = input(\"Alege optiunea: \")\r\n        if optiune == '1':\r\n            n = int(input('Dati numarul n: '))\r\n            valoare = get_largest_prime_below(n)\r\n            print(f'numarul prim cautat este {valoare}')\r\n        elif optiune == '2':\r\n            n = int(input('Dati numarul n: '))\r\n            if is_palindrome(n):\r\n                print('Numarul este palindrom')\r\n            elif not is_palindrome(n):\r\n                print('Numarul nu este palindrom')\r\n        elif optiune == '3':\r\n            sys.exit()\r\n        else:\r\n            print(\"optiune invalida\")\r\n\r\nmain()\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2194,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"264795668","text":"def mergeSort(n, S):\r\n    h = n // 2\r\n    m = n - h\r\n\r\n    if n > 1:\r\n        U = S[:h]\r\n        V = S[h:]\r\n        #분할 전 필요한 추가 메모리의 총합을 계산\r\n        global memory, max_memory\r\n        memory += len(U) + len(V)\r\n        #합병정렬 중에 순간 추가 메모리의 최대값를 계산\r\n        max_memory = max(memory, max_memory)\r\n        mergeSort(h, U)                     #divide\r\n        mergeSort(m, V)                     #divide\r\n        merge(h,m,U,V,S)                    #combine\r\n        #합병 후 사용한 추가 메모리는 총합에서 제외\r\n        memory -= (len(U) + len(V))\r\n        \r\ndef merge(h,m,U,V,S):\r\n    i = 0; j = 0; k = 0\r\n\r\n    while(i < h and j < m):\r\n        if U[i] < V[j]:\r\n            S[k] = U[i]\r\n            i += 1\r\n        else:\r\n            S[k] = V[j]\r\n            j += 1\r\n        k += 1\r\n    if i >= h:\r\n        S[k:] = V[j:]\r\n    else:\r\n        S[k:] = U[i:]\r\n\r\nS=[3,5,2,9,10,14,4,8]\r\nmemory = 0 ; max_memory = 0\r\nmergeSort(8, S)\r\nprint(S)\r\nprint(\"Additional Memory : \", max_memory)\r\n","sub_path":"Python/2020-2_알고리즘분석/2_DivideConquer/2_2_MergeSort.py","file_name":"2_2_MergeSort.py","file_ext":"py","file_size_in_byte":1069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"501266660","text":"import os\nimport argparse\nimport tensorflow as tf\nfrom Transformer_softmax_lstm import Transformer, Config\nfrom data_process import train_dev_split\nfrom data_process import load_data\nparser = argparse.ArgumentParser()\nparser.add_argument('model_path', type=str)\n\n\n\ndef main(test_model):\n\n    config = Config()\n    config.batch_size = 1024\n    config.hidden_size = 64\n    config.vocab_size = 26\n    config.embed_size = 320\n    config.max_epochs = 100\n    config.label_kinds = 2\n    config.if_train = True\n    config.if_test = True\n    config.is_biLSTM = True\n    config.max_seqlen = 20\n\n    config.original_file = '../data/most_frequent_words_label.txt'\n    config.train_file = '../data/most_frequent_words_label_train.txt'\n    config.dev_file = '../data/most_frequent_words_label_dev'\n    config.vocab_file = '../data/vocab.txt'\n    config.model_path = 'models/Transformer_softmax_lstm/'\n\n    config.split_ratio = 0.8\n\n    print('Prepare data for train and dev ... ')\n    train_dev_split(config.original_file, config.train_file, config.dev_file,\n                    config.vocab_file, config.split_ratio)\n    print('Prepare data sucessfully!')\n\n    model = Transformer(config)\n    gpu_options = tf.GPUOptions(allow_growth=True)\n    gpu_options =tf.GPUOptions(per_process_gpu_memory_fraction=0.5, allow_growth=True) ##每个gpu占用0.8                                                                              的显存\n    tf_config=tf.ConfigProto(gpu_options=gpu_options,allow_soft_placement=True)\n    with tf.Session(config=tf_config) as sess:\n        if config.if_test:\n            init=tf.global_variables_initializer()\n            sess.run(init)\n            (X_test, y_test) = load_data(config.dev_file)\n\n            if len(X_test) < config.batch_size:\n                for i in range(0, config.batch_size - len(X_test)):\n                    X_test.append([0])\n                    y_test.append([0])\n\n            seq_len_test = list(map(lambda x: len(x), X_test))\n\n            print('Target to special model to test')\n            print('Start do predicting...')\n            model.test(sess, test_model, X_test, y_test, seq_len_test,\n                             config.vocab_file, config.model_path + 'result/')\n\n\n\n    print('Success for preparing data')\n\n\nif __name__ == '__main__':\n    args = parser.parse_args()\n    main(args.model_path)\n","sub_path":"subword/test_Transformer_softmax_lstm.py","file_name":"test_Transformer_softmax_lstm.py","file_ext":"py","file_size_in_byte":2349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"606977981","text":"#!/usr/bin/env python\nu\"\"\"\nplot_global_grid_5maps.py\nWritten by Tyler Sutterley (05/2023)\nCreates 5 GMT-like plots in a Plate Carree (Equirectangular) projection\n\nPYTHON DEPENDENCIES:\n    numpy: Scientific Computing Tools For Python\n        http://www.numpy.org\n        http://www.scipy.org/NumPy_for_Matlab_Users\n    scipy: Scientific Tools for Python\n        http://www.scipy.org/\n    netCDF4: Python interface to the netCDF C library\n        https://unidata.github.io/netcdf4-python/netCDF4/index.html\n    h5py: Python interface for Hierarchal Data Format 5 (HDF5)\n        http://h5py.org\n    matplotlib: Python 2D plotting library\n        http://matplotlib.org/\n        https://github.com/matplotlib/matplotlib\n    cartopy: Python package designed for geospatial data processing\n        https://scitools.org.uk/cartopy\n    pyshp: Python read/write support for ESRI Shapefile format\n        https://github.com/GeospatialPython/pyshp\n\nUPDATE HISTORY:\n    Updated 05/2023: use pathlib to define and operate on paths\n        added option to set the input variable names or column order\n    Updated 03/2023: switch from parameter files to argparse arguments\n        updated inputs to spatial from_ascii function\n    Updated 07/2022: place some imports behind try/except statements\n    Updated 05/2022: use argparse descriptions within documentation\n        use mask, shift grid and interpolation functions from tools\n    Updated 12/2021: added color palette table (cpt) file reader from tools\n    Updated 05/2021: define int/float precision to prevent deprecation warning\n    Updated 10/2020: using spatial utilities for reading\n        using argparse to set command-line parameters\n    Updated 10/2019: changing Y/N flags to True/False\n    Updated 09/2019: added parameter for specifying if netCDF4 or HDF5\n    Updated 04/2019: set cap style of cartopy geoaxes outline patch\n    Updated 03/2019: replacing matplotlib basemap with cartopy\n    Updated 02/2019: saving metadata to output figure\n    Updated 12/2018: added parameter CBEXTEND for colorbar extension triangles\n    Updated 09/2018: use a custom mask_oceans function instead of default maskoceans\n        added REMOVE parameter to subtract a spatial field from each input map\n    Updated 05/2018: using __future__ print function for python3 compatibility\n    Updated 11/2017: can plot a contour of the global average with MEAN\n    Written 08/2017\n\"\"\"\nfrom __future__ import print_function\n\nimport sys\nimport os\nimport copy\nimport logging\nimport pathlib\nimport argparse\nimport warnings\nimport traceback\nimport numpy as np\nimport scipy.ndimage\nimport scipy.interpolate\nimport gravity_toolkit as gravtk\n\n# attempt imports\ntry:\n    import cartopy.crs as ccrs\nexcept ModuleNotFoundError:\n    warnings.filterwarnings(\"module\")\n    warnings.warn(\"cartopy not available\", ImportWarning)\ntry:\n    import matplotlib\n    import matplotlib.pyplot as plt\n    import matplotlib.cm as cm\n    from matplotlib import gridspec\n    import matplotlib.colors as colors\n    import matplotlib.ticker as ticker\n    import matplotlib.offsetbox as offsetbox\n    matplotlib.rcParams['axes.linewidth'] = 2.0\n    matplotlib.rcParams['font.family'] = 'sans-serif'\n    matplotlib.rcParams['font.sans-serif'] = ['Helvetica']\n    matplotlib.rcParams['mathtext.default'] = 'regular'\nexcept ModuleNotFoundError:\n    warnings.filterwarnings(\"module\")\n    warnings.warn(\"matplotlib not available\", ImportWarning)\ntry:\n    import shapefile\nexcept ModuleNotFoundError:\n    warnings.filterwarnings(\"module\")\n    warnings.warn(\"shapefile not available\", ImportWarning)\n# ignore warnings\nwarnings.filterwarnings(\"ignore\")\n\n# cartopy transform for Equirectangular Projection\ntry:\n    projection = ccrs.PlateCarree()\nexcept (NameError,ValueError) as exc:\n    pass\n\n# PURPOSE: keep track of threads\ndef info(args):\n    logging.info(pathlib.Path(sys.argv[0]).name)\n    logging.info(args)\n    logging.info(f'module name: {__name__}')\n    if hasattr(os, 'getppid'):\n        logging.info(f'parent process: {os.getppid():d}')\n    logging.info(f'process id: {os.getpid():d}')\n\n# PURPOSE plot coastlines and islands (GSHHS with G250 Greenland)\ndef plot_coastline(ax, base_dir, LINEWIDTH=0.5):\n    # read the coastline shape file\n    coastline_dir = base_dir.joinpath('masks','G250')\n    coastline_shape_files = []\n    coastline_shape_files.append('GSHHS_i_L1_no_greenland.shp')\n    coastline_shape_files.append('greenland_coastline_islands.shp')\n    for fi,S in zip(coastline_shape_files,[1000,200]):\n        coast_shapefile = coastline_dir.joinpath(*fi)\n        logging.debug(str(coast_shapefile))\n        shape_input = shapefile.Reader(str(coast_shapefile))\n        shape_entities = shape_input.shapes()\n        # for each entity within the shapefile\n        for c,ent in enumerate(shape_entities[:S]):\n            # extract coordinates and plot\n            lon,lat = np.transpose(ent.points)\n            ax.plot(lon, lat, c='k', lw=LINEWIDTH, transform=projection)\n\n# PURPOSE: plot Antarctic grounded ice delineation\ndef plot_grounded_ice(ax, base_dir, LINEWIDTH=0.5):\n    coast_file = ['masks','IceBoundaries_Antarctica_v02',\n        'ant_ice_sheet_islands_v2.shp']\n    coast_shapefile = base_dir.joinpath(*coast_file)\n    logging.debug(str(coast_shapefile))\n    shape_input = shapefile.Reader(str(coast_shapefile))\n    shape_entities = shape_input.shapes()\n    shape_attributes = shape_input.records()\n    i = [i for i,e in enumerate(shape_entities) if (np.ndim(e.points) > 1)]\n    # cartopy transform for NSIDC polar stereographic south\n    projection = ccrs.Stereographic(central_longitude=0.0,\n        central_latitude=-90.0,true_scale_latitude=-71.0)\n    for indice in i:\n        # extract Polar-Stereographic coordinates for record\n        pts = np.array(shape_entities[indice].points)\n        ax.plot(pts[:,0], pts[:,1], c='k', lw=LINEWIDTH, transform=projection)\n\n# plot grid program\ndef plot_grid(base_dir, FILENAMES,\n    DATAFORM=None,\n    VARIABLES=[],\n    MASK=None,\n    INTERPOLATION=None,\n    DDEG=None,\n    INTERVAL=None,\n    SCALE_FACTOR=1.0,\n    REMOVE_FILE=None,\n    COLOR_MAP=None,\n    CPT_FILE=None,\n    PLOT_RANGE=None,\n    BOUNDARY=None,\n    ALPHA=1.0,\n    CONTOURS=False,\n    CONTOUR_RANGE=None,\n    MEAN_CONTOUR=False,\n    TITLES=None,\n    LABELS=None,\n    CBEXTEND=None,\n    CBTITLE=None,\n    CBUNITS=None,\n    CBFORMAT=None,\n    DRAW_GRID_LINES=False,\n    GRID=None,\n    FIGURE_FILE=None,\n    FIGURE_FORMAT=None,\n    FIGURE_DPI=None,\n    MODE=0o775):\n\n    # extend list if a single format was entered for all files\n    if len(DATAFORM) < len(FILENAMES):\n        DATAFORM = DATAFORM*len(FILENAMES)\n\n    # read CPT or use color map\n    if CPT_FILE is not None:\n        # cpt file\n        cmap = gravtk.tools.from_cpt(CPT_FILE)\n    else:\n        # colormap\n        cmap = copy.copy(cm.get_cmap(COLOR_MAP))\n    # grey color map for bad values\n    cmap.set_bad('w',0.5)\n\n    # set transparency ALPHA\n    if BOUNDARY is None:\n        # contours\n        levels = np.arange(PLOT_RANGE[0], PLOT_RANGE[1]+PLOT_RANGE[2],\n            PLOT_RANGE[2])\n        norm = colors.Normalize(vmin=PLOT_RANGE[0], vmax=PLOT_RANGE[1])\n    else:\n        # boundary between contours\n        levels = np.array(BOUNDARY, dtype=np.float64)\n        norm = colors.BoundaryNorm(BOUNDARY, ncolors=256)\n\n    # convert degree spacing and interval parameters\n    # Grid spacing\n    dlon,dlat = (DDEG[0],DDEG[0]) if (len(DDEG) == 1) else (DDEG[0],DDEG[1])\n    # Grid dimensions\n    if (INTERVAL == 1):# (0:360, 90:-90)\n        nlon = np.int64((360.0/dlon)+1.0)\n        nlat = np.int64((180.0/dlat)+1.0)\n    elif (INTERVAL == 2):# degree spacing/2\n        nlon = np.int64((360.0/dlon))\n        nlat = np.int64((180.0/dlat))\n\n    # interpolation method for image background using transform_scalar\n    if (INTERPOLATION == 'nearest'):\n        order = 0\n    elif (INTERPOLATION == 'bilinear'):\n        order = 1\n    elif (INTERPOLATION == 'cubic'):\n        order = 3\n\n    # create masked array if missing values\n    if MASK is not None:\n        # Read Land-Sea Mask of specified input file\n        # 0=Ocean, 1=Land, 2=Lake, 3=Small Island, 4=Ice Shelf\n        # Open the land-sea NetCDF file for reading\n        landsea = gravtk.spatial().from_netCDF4(MASK,\n            date=False, varname='LSMASK')\n        # create land function\n        nth,nphi = landsea.shape\n        mask = np.zeros((nth,nphi),dtype=bool)\n        # combine land and island levels for land function\n        indx,indy = np.nonzero((landsea.data >= 1) & (landsea.data <= 3))\n        mask[indx,indy] = True\n\n    # remove a spatial field from each input map\n    if REMOVE_FILE is not None:\n        REMOVE = gravtk.spatial().from_netCDF4(REMOVE_FILE,\n            date=False).data[:,:]\n    else:\n        REMOVE = 0.0\n\n    # image extents\n    ax = {}\n    # setup Plate Carree projection\n    fig = plt.figure(figsize=(10.375,12.5))\n    gs = gridspec.GridSpec(3, 2,  height_ratios=[2,1,1])\n    ax[0] = plt.subplot(gs[0,:], projection=projection)\n    ax[1] = plt.subplot(gs[1,0], projection=projection)\n    ax[2] = plt.subplot(gs[1,1], projection=projection)\n    ax[3] = plt.subplot(gs[2,0], projection=projection)\n    ax[4] = plt.subplot(gs[2,1], projection=projection)\n    # WGS84 Ellipsoid parameters\n    a_axis = 6378137.0# [m] semimajor axis of the ellipsoid\n    flat = 1.0/298.257223563# flattening of the ellipsoid\n    # (4pi/3)R^3 = (4pi/3)(a^2)b = (4pi/3)(a^3)(1 -f)\n    rad_e = a_axis*(1.0 - flat)**(1.0/3.0)\n\n    for i,ax1 in ax.items():\n\n        # input ascii/netCDF4/HDF5 file\n        if (DATAFORM[i] == 'ascii'):\n            # ascii (.txt)\n            dinput = gravtk.spatial().from_ascii(FILENAMES[i], date=False,\n                columns=VARIABLES, spacing=[dlon,dlat], nlat=nlat, nlon=nlon)\n        elif (DATAFORM[i] == 'netCDF4'):\n            # netCDF4 (.nc)\n            field_mapping = gravtk.spatial().default_field_mapping(VARIABLES)\n            dinput = gravtk.spatial().from_netCDF4(FILENAMES[i], date=False,\n                field_mapping=field_mapping)\n        elif (DATAFORM[i] == 'HDF5'):\n            # HDF5 (.H5)\n            field_mapping = gravtk.spatial().default_field_mapping(VARIABLES)\n            dinput = gravtk.spatial().from_HDF5(FILENAMES[i], date=False,\n                field_mapping=field_mapping)\n\n        # remove offset and scale to units\n        if (REMOVE != 0.0) or (SCALE_FACTOR != 1.0):\n            dinput = dinput.offset(-REMOVE).scale(SCALE_FACTOR)\n\n        # update masked values\n        if MASK is not None:\n            dinput.replace_invalid(fill_value=dinput.fill_value, mask=mask)\n\n        # if dlat is negative\n        if (np.sign(dlat) == -1):\n            dinput = dinput.flip(axis=0)\n\n        # calculate image coordinates\n        xmin,xmax,ymin,ymax = ax1.get_extent()\n        mx = np.int64((xmax-xmin)/0.5)+1\n        my = np.int64((ymax-ymin)/0.5)+1\n        X = np.linspace(xmin,xmax,mx)\n        Y = np.linspace(ymin,ymax,my)\n        gridx,gridy = np.meshgrid(X,Y)\n        # create mesh lon/lat\n        points = projection.transform_points(projection,\n            gridx.flatten(), gridy.flatten())\n        lonsin = points[:,0].reshape(my,mx)\n        latsin = points[:,1].reshape(my,mx)\n\n        # interpolate to image coordinates\n        if (INTERVAL == 1) and (np.max(dinput.lon) > 180):# (0:360, 90:-90)\n            shift_data,lon180 = gravtk.tools.shift_grid(180.0,\n                dinput.data,dinput.lon)\n            shift_mask,lon180 = gravtk.tools.shift_grid(180.0,\n                dinput.mask,dinput.lon)\n            img = gravtk.tools.interp_grid(shift_data,lon180,\n                dinput.lat,lonsin,latsin,order)\n            msk = gravtk.tools.interp_grid(shift_mask,lon180,\n                dinput.lat,lonsin,latsin,order)\n        elif (INTERVAL == 2) and (np.max(dinput.lon) > 180):# DDEG/2\n            shift_data,lon180 = gravtk.tools.shift_grid(180.0+dlon,\n                dinput.data,dinput.lon)\n            shift_mask,lon180 = gravtk.tools.shift_grid(180.0+dlon,\n                dinput.mask,dinput.lon)\n            img = gravtk.tools.interp_grid(shift_data,\n                lon180,dinput.lat,lonsin,latsin,order)\n            msk = gravtk.tools.interp_grid(shift_mask,\n                lon180,dinput.lat,lonsin,latsin,order)\n        else:# -180:180 or modification of there of\n            img = gravtk.tools.interp_grid(dinput.data,\n                dinput.lon,dinput.lat,lonsin,latsin,order)\n            msk = gravtk.tools.interp_grid(dinput.mask,\n                dinput.lon,dinput.lat,lonsin,latsin,order)\n        # create masked array of image\n        img = np.ma.array(img, mask=msk.astype(bool))\n\n        # plot only grounded points\n        if MASK is not None:\n            img = gravtk.tools.mask_oceans(lonsin,latsin,\n                data=img,order=order,iceshelves=False)\n        # plot image with transparency using normalization\n        im = ax1.imshow(img, interpolation='nearest',\n            extent=(xmin,xmax,ymin,ymax),\n            cmap=cmap, norm=norm, alpha=ALPHA,\n            origin='lower', transform=projection)\n\n        # create mesh lon/lat\n        lon, lat = np.meshgrid(dinput.lon,dinput.lat)\n        # recalculate data at zoomed coordinates\n        data = np.ma.array(scipy.ndimage.zoom(img.data,5,order=1))\n        mask = scipy.ndimage.zoom(np.invert(img.mask),5,order=1,output=bool)\n        data.mask = np.invert(mask)\n        # plot line contours\n        if CONTOURS:\n            clevs = np.arange(CONTOUR_RANGE[0],\n                CONTOUR_RANGE[1] + CONTOUR_RANGE[2],\n                CONTOUR_RANGE[2])\n            # remove 0 (will plot in red)\n            reduce_clevs = clevs[np.nonzero(clevs)]\n            # plot contours\n            ax1.contour(data,reduce_clevs,colors='0.2',linestyles='solid',\n                extent=(xmin,xmax,ymin,ymax),origin='lower',\n                transform=projection)\n            ax1.contour(data,[0],colors='red',linestyles='solid',linewidths=1.5,\n                extent=(xmin,xmax,ymin,ymax),origin='lower',\n                transform=projection)\n\n        # plot line contour for global average\n        if MEAN_CONTOUR and CONTOURS:\n            # calculate areas of each grid cell\n            dphi,dth = (dlon*np.pi/180.0,dlat*np.pi/180.0)\n            indy,indx = np.nonzero(np.logical_not(dinput.mask))\n            area = (rad_e**2)*dth*dphi*np.cos(lat[indy,indx]*np.pi/180.0)\n            # calculate average\n            ave = np.sum(area*dinput.data[indy,indx])/np.sum(area)\n            # plot line contour of global average\n            ax1.contour(data,[ave],colors='blue',linestyles='solid',linewidths=1.5,\n                extent=(xmin,xmax,ymin,ymax),origin='lower',\n                transform=projection)\n\n        # draw coastlines\n        plot_coastline(ax1, base_dir)\n        # plot grounded ice exterior for Antarctica\n        plot_grounded_ice(ax1, base_dir)\n\n        # draw lat/lon grid lines\n        if DRAW_GRID_LINES:\n            # meridian and parallel grid spacing\n            llx,lly = (GRID[0],GRID[0]) if (len(GRID) == 1) else (GRID[0],GRID[1])\n            grid_meridians = np.arange(-180, 180 + llx, llx)\n            grid_parallels = np.arange(-90, 90 + lly, lly)\n            gl = ax1.gridlines(crs=projection, draw_labels=False,\n                linewidth=0.1, color='0.25', linestyle='-')\n            gl.xlocator = ticker.FixedLocator(grid_meridians)\n            gl.ylocator = ticker.FixedLocator(grid_parallels)\n\n        # add title for each subplot\n        if TITLES is not None:\n            TITLE = ' '.join(TITLES[i].split('_'))\n            ax1.set_title(TITLE.replace('-',u'\\u2013'), fontsize=18)\n            ax1.title.set_y(1.01)\n        # Add figure label for each subplot\n        if LABELS is not None:\n            at = offsetbox.AnchoredText(LABELS[i],\n                loc=2, pad=0, borderpad=0.25, frameon=True,\n                prop=dict(size=18,weight='bold',color='k'))\n            at.patch.set_boxstyle(\"Square,pad=0.1\")\n            at.patch.set_edgecolor(\"white\")\n            ax1.axes.add_artist(at)\n\n        # axis = equal\n        ax1.set_aspect('equal', adjustable='box')\n        # no ticks on the x and y axes\n        ax1.get_xaxis().set_ticks([])\n        ax1.get_yaxis().set_ticks([])\n        # stronger linewidth on frame\n        ax1.spines['geo'].set_linewidth(2.0)\n        ax1.spines['geo'].set_zorder(10)\n        ax1.spines['geo'].set_capstyle('projecting')\n\n    # Add colorbar\n    # Add an axes at position rect [left, bottom, width, height]\n    cbar_ax = fig.add_axes([0.095, 0.065, 0.81, 0.025])\n    # extend = add extension triangles to upper and lower bounds\n    # options: neither, both, min, max\n    cbar = fig.colorbar(im, cax=cbar_ax, extend=CBEXTEND,\n        extendfrac=0.0375, drawedges=False, orientation='horizontal')\n    # rasterized colorbar to remove lines\n    cbar.solids.set_rasterized(True)\n    # Add label to the colorbar\n    cbar.ax.set_title(CBTITLE, fontsize=18, rotation=0, y=-1.65, va='top')\n    cbar.ax.set_xlabel(CBUNITS, fontsize=18, rotation=0, va='center')\n    cbar.ax.xaxis.set_label_coords(1.075, 0.5)\n    # Set the tick levels for the colorbar\n    cbar.set_ticks(levels)\n    cbar.set_ticklabels([CBFORMAT.format(ct) for ct in levels])\n    # ticks lines all the way across\n    cbar.ax.tick_params(which='both', width=1, length=22, labelsize=18,\n        direction='in')\n\n    # adjust subplots within figure\n    fig.subplots_adjust(left=0.01, right=0.99, bottom=0.10, top=0.97,\n        hspace=0.1, wspace=0.05)\n    # create output directory if non-existent\n    FIGURE_FILE.parent.mkdir(mode=MODE, parents=True, exist_ok=True)\n    # save to file\n    logging.info(str(FIGURE_FILE))\n    plt.savefig(FIGURE_FILE,\n        metadata={'Title':pathlib.Path(sys.argv[0]).name},\n        dpi=FIGURE_DPI, format=FIGURE_FORMAT)\n    plt.clf()\n    # change the permissions mode\n    FIGURE_FILE.chmod(mode=MODE)\n\n# PURPOSE: create argument parser\ndef arguments():\n    parser = argparse.ArgumentParser(\n        description=u\"\"\"Creates 5 GMT-like plots on a global Plate Carr\\u00E9e\n            (Equirectangular) projection\n            \"\"\",\n        fromfile_prefix_chars=\"@\"\n    )\n    parser.convert_arg_line_to_args = gravtk.utilities.convert_arg_line_to_args\n    # command line parameters\n    parser.add_argument('infile', nargs=5,\n        type=pathlib.Path,\n        help='Input grid files')\n    # working data directory\n    parser.add_argument('--directory','-D',\n        type=pathlib.Path, default=pathlib.Path.cwd(),\n        help='Working data directory')\n    # Input data format (ascii, netCDF4, HDF5)\n    parser.add_argument('--format','-F',\n        type=str, nargs='+',\n        default='netCDF4', choices=['ascii','netCDF4','HDF5'],\n        help='Input data format')\n    # variable names (for ascii names of columns)\n    parser.add_argument('--variables','-v',\n        type=str, nargs='+', default=['lon','lat','z'],\n        help='Variable names of data in input file')\n    # land-sea mask\n    lsmask = gravtk.utilities.get_data_path(['data','landsea_hd.nc'])\n    parser.add_argument('--mask',\n        type=pathlib.Path, default=lsmask,\n        help='Land-sea mask')\n    # output grid parameters\n    parser.add_argument('--spacing',\n        type=float, nargs='+', default=[0.5,0.5], metavar=('dlon','dlat'),\n        help='Spatial resolution of input data')\n    parser.add_argument('--interval',\n        type=int, default=2, choices=[1,2],\n        help=('Input grid interval (1: global, 2: centered global)'))\n    # Interpolation method\n    parser.add_argument('--interpolation','-I',\n        type=str, default='bilinear', choices=['nearest','bilinear','cubic'],\n        help='Interpolation method')\n    # scale factor\n    parser.add_argument('--scale-factor','-s',\n        type=float, default=1.0,\n        help='Multiplicative scale factor for converting to plot units')\n    # plot range\n    parser.add_argument('--plot-range','-R',\n        type=float, nargs=3, metavar=('MIN','MAX','STEP'),\n        help='Plot range and step size for normalization')\n    parser.add_argument('--boundary','-B',\n        type=float, nargs='+',\n        help='Plot boundary for normalization')\n    # color palette table or named color map\n    try:\n        cmap_set = set(cm.datad.keys()) | set(cm.cmaps_listed.keys())\n    except (ValueError, NameError) as exc:\n        cmap_set = []\n    parser.add_argument('--colormap','-m',\n        metavar='COLORMAP', type=str, default='viridis',\n        choices=sorted(cmap_set),\n        help='Named Matplotlib colormap')\n    parser.add_argument('--cpt-file','-c',\n        type=pathlib.Path,\n        help='Input Color Palette Table (.cpt) file')\n    # color map alpha\n    parser.add_argument('--alpha','-a',\n        type=float, default=1.0,\n        help='Named Matplotlib colormap')\n    # plot contour parameters\n    parser.add_argument('--plot-contours',\n        default=False, action='store_true',\n        help='Plot contours')\n    parser.add_argument('--contour-range',\n        type=float, nargs=3, metavar=('MIN','MAX','STEP'),\n        help='Contour range and step size')\n    parser.add_argument('--mean-contour',\n        default=False, action='store_true',\n        help='Plot contours for mean of dataset')\n    # title and label\n    parser.add_argument('--plot-title', nargs=5,\n        type=str, help='Plot title')\n    parser.add_argument('--plot-label', nargs=5,\n        type=str, help='Plot label')\n    # colorbar parameters\n    parser.add_argument('--cbextend',\n        type=str, default='both',\n        choices=['neither', 'both', 'min', 'max'],\n        help='Add extension triangles to colorbar')\n    parser.add_argument('--cbtitle',\n        type=str, default='',\n        help='Title label for colorbar')\n    parser.add_argument('--cbunits',\n        type=str, default='',\n        help='Units label for colorbar')\n    parser.add_argument('--cbformat',\n        type=str, default='{0:3.0f}',\n        help='Tick format for colorbar')\n    # additional parameters\n    parser.add_argument('--draw-grid-lines',\n        default=False, action='store_true',\n        help='Add map grid lines')\n    parser.add_argument('--grid-lines',\n        type=float, nargs='+', default=(15,15),\n        help='Input grid spacing for meridians and parallels')\n    # output file, format and dpi\n    parser.add_argument('--figure-file','-O',\n        type=pathlib.Path,\n        help='Output figure file')\n    parser.add_argument('--figure-format','-f',\n        type=str, default='png', choices=('pdf','png','jpg','svg'),\n        help='Output figure format')\n    parser.add_argument('--figure-dpi','-d',\n        type=int, default=180,\n        help='Output figure resolution in dots per inch (dpi)')\n    # print information about each input and output file\n    parser.add_argument('--verbose','-V',\n        action='count', default=0,\n        help='Verbose output of run')\n    # permissions mode of the local directories and files (number in octal)\n    parser.add_argument('--mode','-M',\n        type=lambda x: int(x,base=8), default=0o775,\n        help='Permissions mode of output files')\n    # return the parser\n    return parser\n\n# This is the main part of the program that calls the individual functions\ndef main():\n    # Read the system arguments listed after the program\n    parser = arguments()\n    args,_ = parser.parse_known_args()\n\n    # create logger\n    loglevels = [logging.CRITICAL, logging.INFO, logging.DEBUG]\n    logging.basicConfig(level=loglevels[args.verbose])\n\n    # try to run the analysis with listed parameters\n    try:\n        info(args)\n        # run plot program with parameters\n        plot_grid(args.directory, args.infile,\n            DATAFORM=args.format,\n            VARIABLES=args.variables,\n            DDEG=args.spacing,\n            INTERVAL=args.interval,\n            INTERPOLATION=args.interpolation,\n            SCALE_FACTOR=args.scale_factor,\n            CPT_FILE=args.cpt_file,\n            COLOR_MAP=args.colormap,\n            PLOT_RANGE=args.plot_range,\n            BOUNDARY=args.boundary,\n            ALPHA=args.alpha,\n            CONTOURS=args.plot_contours,\n            CONTOUR_RANGE=args.contour_range,\n            MEAN_CONTOUR=args.mean_contour,\n            TITLES=args.plot_title,\n            LABELS=args.plot_label,\n            CBEXTEND=args.cbextend,\n            CBTITLE=args.cbtitle,\n            CBUNITS=args.cbunits,\n            CBFORMAT=args.cbformat,\n            DRAW_GRID_LINES=args.draw_grid_lines,\n            GRID=args.grid_lines,\n            FIGURE_FILE=args.figure_file,\n            FIGURE_FORMAT=args.figure_format,\n            FIGURE_DPI=args.figure_dpi,\n            MODE=args.mode)\n    except Exception as exc:\n        # if there has been an error exception\n        # print the type, value, and stack trace of the\n        # current exception being handled\n        logging.critical(f'process id {os.getpid():d} failed')\n        logging.error(traceback.format_exc())\n\n# run main program\nif __name__ == '__main__':\n    main()\n","sub_path":"scripts/plot_global_grid_5maps.py","file_name":"plot_global_grid_5maps.py","file_ext":"py","file_size_in_byte":24924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"74927101","text":"#!/usr/bin/python\n# -*- coding:utf-8 -*-\n#    @fileName : import_log.py\n#    @Author   : sf_xu\n#    @Time     : 2019/12/11 10:00\n#\n#    @Description  :\n\n\nimport os\nimport time\n\n\nclass VisitLogIoc(object):\n    def __init__(self, log_content_list=None):\n        self._log_content_list = log_content_list\n\n    def save(self):\n        u\"\"\"\n        :return\n        \"\"\"\n        # project_name = self._request_obj.project_name\n        # log_type = self._request_obj.log_type\n        # title = self._request_obj.title\n        # content = self._request_obj.content\n\n        for item in self._log_content_list:\n            log_path = os.getcwd() + '/Logs/' + f\"/{item['project_name']}/\" + f\"/{item['log_type']}/\"\n            if not os.path.exists(log_path):  # 判断文件路径是否存在\n                os.makedirs(log_path)\n            date = time.strftime('%Y%m%d', time.localtime(time.time()))\n            log_name = log_path + date + '.log'\n            data = f\"{item['date_time']} - [title]:{item['title']} - [content]:{item['content']}\"\n            with open(log_name, mode=\"a+\", encoding=\"utf8\") as f:\n                f.writelines(data + \"\\n\")\n                f.flush()\n\n        return True\n\n\n","sub_path":"projects/log_record/ioc/import_log.py","file_name":"import_log.py","file_ext":"py","file_size_in_byte":1195,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"89722221","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Fri May 24 17:50:19 2019\n\n@author: akmil\n\"\"\"\n\n'''\nExercise 2: Write another program that prompts for a list of numbers\nas above and at the end prints out both the maximum and minimum of\nthe numbers instead of the average.\n'''\n\nnumbers = []\ntotal = 0\nwhile True:\n    try:\n        line = input(\"Enter a number: \")\n        if line == 'done':\n            break\n        else:\n            line = float(line)\n            numbers.append(line)\n    except:\n        print(\"Invalid data.\")\n        continue\n\nfor i in numbers:\n    total += i\n\nprint(numbers)\nprint(f\"total =  {total}\")\ncount = len(numbers)\nprint(f\"count = {count}\")\nminimum = min(numbers)\nprint(f\"min = {minimum}\")\nmaximum = max(numbers)\nprint(f\"max = {maximum}\")","sub_path":"Assignment3/5_2_excercise.py","file_name":"5_2_excercise.py","file_ext":"py","file_size_in_byte":755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"606559371","text":"import os\nimport csv\nimport argparse\nimport pydub.silence\nfrom pydub import AudioSegment\nfrom pydub.playback import play\nimport sys\n\n# arg parser\nparser = argparse.ArgumentParser()\nparser.add_argument('-sf', '--sound_file', type=str, required=True, help=\"path to soundfile\")\nparser.add_argument('-sd', \"--save_dir\", type=str, required=False, help=\"path to save dir\")\nparser.add_argument('-ss', '--sample_size', type=int, required=False, help=\"the size of each sample\")\nparser.add_argument('-cv', '--csv_save', type=bool, required=False, help=\"True if metadata\")\nparser.add_argument('-head', '--head', type=str, required=False, help=\"header to wav files\")\nparser.add_argument('-class', '--class_id', type=int, required=False, help=\"classID -- will default to 1\")\nparser.add_argument('-name', '--dataset_name', type=str, required=False, help=\"name of the dataset\")\nargs = parser.parse_args()\n\n\ndef slice(sound_file, save_dir, sample_size=10, csv_save=True, head=\"sample\", class_id=1, dataset_name=None):\n    sound = AudioSegment.from_file(sound_file, format=\"wav\")\n    if not (dataset_name == None):\n        folder = os.path.join(save_dir, dataset_name)\n    else:\n        folder = save_dir\n\n    csv_save_dir = os.path.join(folder, \"metadata\")\n    audio_save_dir = os.path.join(folder, \"audio\")\n\n    # convert to seconds\n    jump = sample_size * 1000\n    start = 0\n    end = jump\n    num_samples = (len(sound) // jump)\n    csv_file = []\n\n    print(\"Creating folders...\")\n\n    if not os.path.isdir(folder):\n        os.mkdir(folder)\n        os.mkdir(audio_save_dir)\n        if csv_save:\n            os.mkdir(csv_save_dir)\n    else:\n        if not os.path.isdir(audio_save_dir):\n            os.mkdir(audio_save_dir)\n            if csv_save:\n                os.mkdir(csv_save_dir)\n\n    print(\"Splitting into samples...\")\n\n    for sample in range(num_samples - 1):\n        segment = sound[start:end]\n        if len(segment) != 10000:\n            continue\n        file_name = f\"{head}_{sample}.wav\"\n        segment.export(os.path.join(audio_save_dir, file_name), format='wav')\n        length = str(len(segment))\n        row = [file_name, length, class_id]\n        csv_file.append(row)\n        start += jump\n        end += jump\n    if csv_save:\n        print(\"Saving CSV...\")\n        with open(os.path.join(csv_save_dir, 'meta.csv'), 'w') as file:\n            writer = csv.writer(file)\n            writer.writerows(csv_file)\n    print(\"Finished.\")\n\n\n# Press the green button in the gutter to run the script.\nif __name__ == '__main__':\n    \"\"\"\n    These are the different parameters for saving and choosing how to splice file. WAV files will be saved\n    with a RIFF header and can be read in ML applications. \n    \"\"\"\n\n    # name of the data set\n    dataset_name = \"sad\"\n    # path to original sound file\n    sound_file = \"/Users/milessigel/Desktop/Datasets/piano_sentiment_dataset/sad_music.wav\"\n    # where are the top level datasets saved\n    save_dir = \"/Users/milessigel/Desktop/Datasets\"\n    # class_id\n    class_id = 1\n    # header for the sample files\n    head = \"sample\"\n    # do you want CSV?\n    csv_save = True\n    # number of seconds of sample\n    sample_size = 10\n\n    if len(sys.argv) > 1:\n        slice(args.sound_file, args.save_dir,\n              sample_size=args.sample_size if args.sample_size is not None else sample_size,\n              csv_save=args.csv_save if args.csv_save is not None else csv_save,\n              head=args.head if args.head is not None else head,\n              class_id=args.class_id if args.class_id is not None else class_id,\n              dataset_name=args.dataset_name if args.dataset_name is not None else dataset_name)\n        exit()\n\n\n    slice(sound_file, save_dir, sample_size=sample_size, csv_save=csv_save, head=head, class_id=class_id, dataset_name=dataset_name)\n","sub_path":"wav_split.py","file_name":"wav_split.py","file_ext":"py","file_size_in_byte":3808,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"619196901","text":"from ocdskingfisher.base import Source\nfrom ocdskingfisher.util import save_content\nimport json\nimport hashlib\n\n\nclass AustraliaSource(Source):\n    \"\"\"\n    \"\"\"\n\n    publisher_name = 'Australia'\n    url = 'https://api.tenders.gov.au'\n    source_id = 'australia'\n\n    def gather_all_download_urls(self):\n\n        if self.sample:\n            return[{\n                'url': \"https://api.tenders.gov.au/ocds/findByDates/contractPublished/2018-01-01T00:00:00Z/2018-12-31T23:59:59Z\",\n                'filename': 'year-2018.json',\n                'data_type': 'release_package',\n            }]\n\n        else:\n            out = []\n            for year in range(2004, 2020):\n                url = \"https://api.tenders.gov.au/ocds/findByDates/contractPublished/{}-01-01T00:00:00Z/{}-12-31T23:59:59Z\".format(year, year)  # noqa\n                out.append({\n                    'url': url,\n                    'filename': 'year-{}.json'.format(year),\n                    'data_type': 'release_package',\n                    'priority': year,\n                })\n\n            return out\n\n    def save_url(self, filename, data, file_path):\n\n        save_content_response = save_content(data['url'], file_path)\n        if save_content_response.errors:\n            return self.SaveUrlResult(errors=save_content_response.errors, warnings=save_content_response.warnings)\n\n        additional = []\n\n        if not self.sample:\n\n            with open(file_path) as f:\n                json_data = json.load(f)\n\n                if 'links' in json_data and 'next' in json_data['links'] and json_data['links']['next']:\n                    additional.append({\n                        'url': json_data['links']['next'],\n                        'filename': 'page-%s.json' % hashlib.md5(json_data['links']['next'].encode('utf-8')).hexdigest(),\n                        'data_type': 'release_package',\n                        # We set priority the same so that all the requests for one year are done at the same time.\n                        # Because of how this pages using cursors, it's probably best to get them as fast as possible.\n                        'priority': data['priority'],\n                    })\n\n        return self.SaveUrlResult(additional_files=additional, warnings=save_content_response.warnings)\n","sub_path":"ocdskingfisher/sources/australia.py","file_name":"australia.py","file_ext":"py","file_size_in_byte":2286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"347187390","text":"import time as t\nimport datetime as dt\nimport requests as req\nimport pandas as pd\nimport numpy as np\nimport json\nimport re\nfrom .utils import convert_to_timestamp, proxy_setter, parse_item\nfrom json import JSONDecodeError\nimport requests_cache\n\n\nclass Client:\n\n    URL = 'https://query1.finance.yahoo.com'\n    URL_TO_SCRAPE = 'https://finance.yahoo.com/quote'\n\n    START_DATE = \"1971-01-01\"\n\n    def __init__(self, symbol: str):\n        self._symbol = symbol\n        self._company_info: dict = {}\n        self._historical_prices: pd.DataFrame\n        self._recommendations: json = None\n        self._fundamentals = False\n        self._cash_flow: json = None\n        self._income_statement: json = None\n        self._balance_sheet: json = None\n        self._splits: pd.DataFrame() = None\n        self._dividends: pd.DataFrame() = None\n        self._trends = {}\n        self._financials = None\n        self._holders = {}\n        self._events = None\n        self._sentiment = None\n\n    def historical_prices(self, period=\"1y\", interval=\"1d\", start=None,\n                          end=None, proxy=None, **kwargs):\n\n        params = {}\n        if period in [None, False, 0, 'max'] or not start:\n            if not start:\n                start = convert_to_timestamp(self.START_DATE)\n            if not end:\n                end = convert_to_timestamp(dt.datetime.now())\n            params[\"period1\"], params[\"period2\"] = start, end\n        else:\n            params[\"range\"] = period.lower()\n\n        params[\"interval\"] = interval.lower()\n        params[\"events\"] = \"div,splits\"\n\n        if proxy: proxy = proxy_setter(proxy)\n\n        url = f\"{self.URL}/v8/finance/chart/{self._symbol}\"\n        data = req.get(url=url, params=params, proxies=proxy)\n\n        if \"Will be right back\" in data.text:\n            raise RuntimeError(\"Yahoo down\")\n\n        data = data.json()\n\n        quotes = {}\n\n        if 'chart' in data:\n            pre_quotes = data['chart']['result'][0]\n            indicators = pre_quotes['indicators'][\"quote\"][0]\n            quotes['timestamp'] = pre_quotes['timestamp']\n            quotes['volume'] = indicators[\"volume\"]\n            quotes['open'] = indicators['open']\n            quotes['close'] = indicators['open']\n            quotes['high'] = indicators['open']\n            quotes['low'] = indicators['open']\n\n        else:\n            raise KeyError(\"No quotes found\")\n\n        quotes['index'] = self.timestamp_converter(quotes['timestamp'])\n\n        quotes_df = pd.DataFrame.from_dict(quotes).set_index('index').drop('timestamp',axis=1)\n        quotes_df.dropna(inplace=True)\n        self._historical_prices = quotes_df[['open', 'high', 'low', 'close', 'volume']]\n\n        if \"events\" in pre_quotes:\n            if \"dividends\" in pre_quotes[\"events\"]:\n                dividends_df = pd.DataFrame(list(pre_quotes['events']['dividends'].values()))\n                dividends_df['date'] = pd.to_datetime(dividends_df['date'], unit='s')\n                dividends_df.set_index('date', inplace=True)\n                self._dividends = dividends_df\n\n            if 'splits' in pre_quotes[\"events\"]:\n                splits_df = pd.DataFrame(list(pre_quotes['events']['splits'].values()))[['date','splitRatio']]\n                splits_df['date'] = pd.to_datetime(splits_df['date'], unit='s')\n                splits_df.set_index('date',inplace=True)\n                self._splits = splits_df\n\n    def fundamentals(self, proxy=None, **kwargs):\n\n        data = self._scrape_data_to_json(proxy, endpoint='/financials')\n\n        # CashFlow\n        cash_flow = data.get('cashflowStatementHistory')['cashflowStatements']\n        cash_flow_quarterly = data.get('cashflowStatementHistoryQuarterly')['cashflowStatements']\n        cash_flow_df = self._create_data_frame(cash_flow, date_col='endDate')\n        cash_flow_quarterly_df = self._create_data_frame(cash_flow_quarterly, date_col='endDate')\n        self._cash_flow = cash_flow_df\n\n        # Balance Sheet\n        balance_sheet = data.get('balanceSheetHistory')['balanceSheetStatements']\n        balance_sheet_quarterly = data.get('balanceSheetHistoryQuarterly')['balanceSheetStatements']\n        balance_sheet_df = self._create_data_frame(balance_sheet, date_col='endDate')\n        balance_sheet_quarterly_df = self._create_data_frame(balance_sheet_quarterly, date_col='endDate')\n        self._balance_sheet = balance_sheet_df\n\n        # Income Statement\n        income_statement = data.get('incomeStatementHistory')['incomeStatementHistory']\n        income_statement_quarterly = data.get('incomeStatementHistoryQuarterly')['incomeStatementHistory']\n        income_statement_df = self._create_data_frame(income_statement, date_col='endDate')\n        income_statement_quarterly_df = self._create_data_frame(income_statement_quarterly,date_col='endDate')\n        self._income_statement = income_statement_df\n\n        # Holders\n        data = self._scrape_data_to_json(proxy, endpoint='/holders')\n\n        major_holders = data.get('majorDirectHolders')\n        if 'holders' in major_holders:\n            major_holders = self._create_data_frame(data=major_holders['holders'], date_col='latestTransDate').T\n\n        self._holders['major_holders'] = major_holders\n\n        insider_holders = data.get('insiderHolders')\n        if 'holders' in insider_holders:\n            insider_holders = self._create_data_frame(data=insider_holders['holders'],date_col='latestTransDate').T\n\n        self._holders['insider_holders'] = insider_holders\n\n        if 'summaryDetail' in data:\n            self._company_info['summary'] = self._create_data_frame(data.get('summaryDetail'))['raw']\n\n        if 'quote_type' in data:\n            self._company_info['quote_type'] = self._create_data_frame(data.get('quoteType').items()).T\n\n        if 'fundOwnership' in data:\n            self._company_info['fund_owner'] = self._create_data_frame(data.get('fundOwnership')['ownershipList'],\n                                                                       date_col='reportDate').T\n\n        insider_transactions = data.get('insiderTransactions')\n        if 'transactions' in insider_transactions:\n            try:\n                insider_transactions = pd.DataFrame(data.get('insiderTransactions')['transactions'])\n                insider_transactions = insider_transactions[['filerName', 'transactionText', 'ownership',\n                                                             'startDate', 'value', 'filerRelation', 'shares']]\n                for col in ['startDate','value','shares']:\n                    insider_transactions[col] = insider_transactions[col].apply(\n                        lambda x: parse_item(x))\n                insider_transactions['startDate'] = pd.to_datetime(insider_transactions['startDate'], unit='s')\n                insider_transactions.set_index('startDate', inplace=True)\n                self._company_info['insider_transactions'] = insider_transactions\n            except KeyError:\n                self._company_info['insider_transactions'] = pd.DataFrame(data.get('insiderTransactions'))\n\n        if 'price' in data:\n            try:\n                prices = pd.DataFrame(data['price']).T\n            except KeyError:\n                prices = pd.DataFrame(data['price'])\n            self._company_info['prices'] = prices.get('raw')\n\n        # KEY STATS\n        data = self._scrape_data_to_json(proxy, endpoint='/key-statistics')\n\n        if 'defaultKeyStatistics' in data:\n            self._key_stats = self._create_data_frame(data.get('defaultKeyStatistics')).get('raw')\n        if 'calendarEvents' in data:\n            self._events = self._create_data_frame(data.get('calendarEvents')).T\n        if 'financialData' in data:\n            self._financials =  self._create_data_frame(data.get('financialData')).get('raw')\n\n        # Analysis\n        data = self._scrape_data_to_json(proxy, endpoint='/analysis')\n        self._trends['trend_recommendation'] = pd.DataFrame(data.get('recommendationTrend')['trend'])\n        self._trends['index_recommendation'] = self._create_data_frame(data.get('indexTrend')['estimates']).T.dropna()\n        self._trends['earnings_recommendation'] = self._create_data_frame(data.get('earningsTrend')['trend']).T.dropna()\n        self._recommendations = self._create_data_frame(data=data.get('upgradeDowngradeHistory')['history'],date_col='epochGradeDate').T\n\n        # sustainability\n        data = self._scrape_data_to_json(proxy, endpoint='/sustainability')\n        sustainability = {}\n        if 'esgScores' in data:\n            sus = data.get('esgScores')\n            for item in sus:\n                if isinstance(sus[item], dict):\n                    sustainability[item] = sus[item]\n        self._company_info['sustainability'] = pd.DataFrame(sustainability).T\n\n        self._fundamentals = True\n\n    @staticmethod\n    def _create_data_frame(data, date_col=None):\n        df = pd.DataFrame(data)\n        if 'maxAge' in df.columns:\n            df.drop('maxAge', axis=1, inplace=True)\n        for col in df.columns:\n            df[col] = df[col].apply(lambda x: parse_item(x))\n\n        if date_col and date_col in df.columns:\n            df[date_col] = pd.to_datetime(df[date_col],unit='s')\n            df = df.set_index(date_col)\n\n        return df.T\n\n    def _scrape_data_to_json(self, proxy, endpoint=\"\"):\n        if proxy: proxy = proxy_setter(proxy)\n\n        url = f\"{self.URL_TO_SCRAPE}/{self._symbol}\" + endpoint\n        requests_cache.install_cache(\"yahoo_cache\")\n        r = req.get(url=url, proxies=proxy)\n        html = r.text\n\n        if \"QuoteSummaryStore\" not in html:\n            return {}\n\n        try:\n            html_split = html.split(\"root.App.main =\")[1].split('(this)')[0].split(';\\n}')[0].strip()\n            json_data = json.loads(html_split)\n            data = json_data['context']['dispatcher']['stores']['QuoteSummaryStore']\n            return data\n\n        except JSONDecodeError:\n            return {}\n\n    @staticmethod\n    def timestamp_converter(timestamp):\n        return [dt.datetime.fromtimestamp(_) for _ in timestamp]\n\n\n\n\n\n\n\n\n\n\n\n\n","sub_path":"liteyahoo/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":10069,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"547935781","text":"# %%\n# import plotly\n# import plotly.graph_objs as go\n\n# import pandas as pd\n# import numpy as np\n# import json\n# import plotly.express as px\n\n# import requests\n# from bs4 import BeautifulSoup\n# import random\n\n# %%\n\ndef get_medium_articles():\n\n    # urls={'medium':'https://medium.com/@jaden09/list/mlops-b4ecfadf718c/archive/{0}/{1:02d}/{2:02d}'}\n\n    # def convert_day(day):\n    #     month_days = [31, 28, 31, 30, 31, 30, 31, 31, 30, 31, 30, 31]\n    #     m = 0\n    #     d = 0\n    #     while day > 0:\n    #         d = day\n    #         day -= month_days[m]\n    #         m += 1\n    #     return (m, d)\n    \n    # def get_claps(claps_str):\n    #     if (claps_str is None) or (claps_str == '') or (claps_str.split is None):\n    #         return 0\n    #     split = claps_str.split('K')\n    #     claps = float(split[0])\n    #     claps = int(claps*1000) if len(split) == 2 else int(claps)\n    #     return claps\n\n    # url='https://medium.com/@jaden09/list/mlops-b4ecfadf718c'\n    # response=requests.get(url)\n    # data = []\n\n    # Check if the request was successful\n    # if response.status_code == 200:\n    # Parse the HTML content using Beautiful Soup\n    #     soup = BeautifulSoup(response.content, 'html.parser')\n    #     articles = soup.find_all(\n    #         \"div\",\n    #         class_=\"postArticle postArticle--short js-postArticle js-trackPostPresentation js-trackPostScrolls\")\n    #     Find a specific element by tag and class\n    #     for article in articles:\n    #         title = article.find(\"h3\", class_=\"graf--title\")\n    #         if title is None:\n    #             continue\n    #         title = title.contents[0]\n    #         subtitle = article.find(\"h4\", class_=\"graf--subtitle\")\n    #         subtitle = subtitle.contents[0] if subtitle is not None else ''\n    #         reading_time = article.find(\"span\", class_=\"readingTime\")\n    #         reading_time = 0 if reading_time is None else int(reading_time['title'].split(' ')[0])\n    #         claps = get_claps(article.find_all(\"button\")[1].contents[0])\n    #         article_url = article.find_all(\"a\")[3]['href'].split('?')[0]\n\n    #         data.append(title,subtitle,reading_time,claps,article_url)\n\n\n    data = {'titles':['Tom', 'Brad', 'Kyle'],\n        'links':['a1', 'a2', 'a3'],\n        }\n    outputs=zip(tuple(data['titles']),tuple(data['links']))\n    return outputs","sub_path":"Python/flask/build_website/functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":2362,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"295130287","text":"from gevent import monkey\n\nmonkey.patch_all()\n\nimport copy\nimport logging\nimport argparse\n\nfrom six.moves import configparser\n\nfrom gevent.server import StreamServer\nfrom pyramid.settings import asbool\n\nimport channelstream.wsgi_app as pyramid_app\nfrom channelstream.gc import gc_conns_forever, gc_users_forever\nfrom channelstream.policy_server import client_handle\nfrom channelstream.ws_app import ChatApplicationSocket\n\nfrom ws4py.server.geventserver import WebSocketWSGIApplication, \\\n    WebSocketWSGIHandler, WSGIServer\n\nlog = logging.getLogger(__name__)\nlog.setLevel(logging.INFO)\n\n\nclass RoutingApplication(object):\n    def __init__(self, config):\n        self.ws_app = WebSocketWSGIApplication(\n            handler_cls=ChatApplicationSocket)\n        self.wsgi_app = pyramid_app.make_app(config)\n\n    def __call__(self, environ, start_response):\n        if environ['PATH_INFO'] == '/ws':\n            environ['ws4py.app'] = self\n            return self.ws_app(environ, start_response)\n\n        return self.wsgi_app(environ, start_response)\n\nSHARED_DEFAULTS = {\n    'secret': 'secret',\n    'admin_user': 'admin',\n    'admin_secret': 'admin_secret',\n    'gc_conns_after': 30,\n    'gc_channels_after': 3600 * 72,\n    'wake_connections_after': 5,\n    'allow_posting_from': '127.0.0.1',\n    'port': 8000,\n    'host': '0.0.0.0',\n    'debug': False,\n    'demo': False,\n    'allow_cors': ''\n}\n\n\ndef cli_start():\n    config = copy.deepcopy(SHARED_DEFAULTS)\n\n    parser = argparse.ArgumentParser(add_help=False)\n    parser.add_argument('-?', '--help', action='help')\n    parser.add_argument('-i', '--ini', dest='ini',\n                        help='config file location',\n                        default=None)\n    parser.add_argument('-s', '--secret', dest='secret',\n                        help='secret used to secure your requests')\n    parser.add_argument('-u', '--admin_username', dest='admin_user',\n                        help='Administrator username')\n    parser.add_argument('-a', '--admin_secret', dest='admin_secret',\n                        help='secret used to secure your admin_panel')\n    parser.add_argument('-host', '--host', dest='host',\n                        help='host ip on which the server listens to')\n    parser.add_argument('-p', '--port', type=int, dest='port',\n                        help='port on which the server listens to')\n    parser.add_argument('-d', '--debug', dest='debug', help='debug')\n    parser.add_argument('-e', '--demo', dest='demo', help='demo enabled')\n    parser.add_argument('-x', '--allowed_post_ip', dest='allow_posting_from',\n                        help='comma separated list of ip\\'s '\n                             'that can post to server')\n    parser.add_argument('-c', '--allow_cors', dest='allow_cors',\n                        help='comma separated list of domains\\'s '\n                             'that can connect to server')\n    args = parser.parse_args()\n\n    parameters = (\n        'debug', 'port', 'host', 'secret', 'admin_user', 'admin_secret',\n        'demo', 'allow_posting_from', 'allow_cors')\n\n    if args.ini:\n        parser = configparser.ConfigParser()\n        parser.read(args.ini)\n        settings = dict(parser.items('channelstream'))\n        for key in parameters:\n            try:\n                config[key] = settings[key]\n            except KeyError:\n                pass\n    else:\n        for key in parameters:\n            conf_value = getattr(args, key)\n            if conf_value:\n                config[key] = conf_value\n\n    # convert types\n    config['debug'] = asbool(config['debug'])\n    config['port'] = int(config['port'])\n    config['demo'] = asbool(config['demo'])\n\n    for key in ['allow_posting_from', 'allow_cors']:\n        if not config[key]:\n            continue\n        try:\n            listed = [ip.strip() for ip in config[key].split(',')]\n            config[key] = listed\n        except ValueError:\n            pass\n\n    log_level = logging.DEBUG if config['debug'] else logging.INFO\n    logging.basicConfig(level=log_level)\n\n    url = 'http://{}:{}'.format(config['host'], config['port'])\n\n    log.info('Starting flash policy server on port 10843')\n    gc_conns_forever()\n    gc_users_forever()\n    server = StreamServer(('0.0.0.0', 10843), client_handle)\n    server.start()\n    log.info('Serving on {}'.format(url))\n    log.info('Admin interface available on {}/admin'.format(url))\n    if config['demo']:\n        log.info('Demo enabled, visit {}/demo'.format(url))\n        log.warning('\\n\\n!! NEVER run the demo in production !!\\n\\n')\n\n    server = WSGIServer((config['host'], config['port']),\n                        RoutingApplication(config))\n    server.serve_forever()\n","sub_path":"channelstream/cli.py","file_name":"cli.py","file_ext":"py","file_size_in_byte":4672,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"414164068","text":"# -*- coding: utf-8 -*-\n'''\n\tThis module converts tokenize the code sections defined by <code> ... </code> in the anwsers/posts.\n\tCode Token Definition: literals with only [] / {} / ' / \" / , are not tokens\n\t\t\t\t\t\t\tXXX( is token denoting function/method call\n\t\t\t\t\t\t\t.\tis token for special meaning\n\t\t\t\t\t\t\tError messega path -> one token\n\t\t\t\t\t\t\t1.4.6 -> one token\n\tUsage:\n\t\tpython code_tokenizer.py file1.txt ...\n\t\t(This will output file1_codeanno.txt)\n\tToken format:\n\t\t<c>XXX</c>\n'''\nfrom tokenize import tokenize, untokenize, NUMBER, STRING, NAME, OP, tok_name, COMMENT, LPAR, RPAR, ERRORTOKEN\nfrom io import BytesIO, StringIO\nimport nltk\nimport codecs\nimport re\nimport sys, os\nimport pdb\n\nsys.path.insert(0, '../utilities')\nfrom utilities import *\nimport hashlib\n\ncode_tag = ['<c>', '</c>']\n\n\n# Main Tokenizer\nclass CodesTokenizer:\n    # _sb is the internal string builder storing the annotated(tagged all tokens) \"codes\"\n    # _tokens stores the tokens recognized given the \"codes\"\n    # _codes is the given codes\n    _sb = None\n    _tokens = None\n    _codes = None\n    _reserve_codes = {}\n    _processed_codes = None\n\n    # Constructor, takes in the codes given, call __setTokens__ to set _tokens\n    def __init__(self, codes):\n        self._tokens = []\n        self._codes = codes\n        self._sb = StringBuilder()\n        self.__setTokens__()\n\n    # detec reserved tokens and store its signature in self._reserve_codes, replace those tokens with their corresponding signature(long int)\n    def __ReplaceReserved__(self):\n        paths = []\n        consec_nums = []\n        new_codes = StringBuilder()\n        dashs = []\n        lts_gts = []\n        overall = []\n        cookies = []\n        # get all paths\n        for path in re.finditer(re.compile(r\"(\\w\\:)?((\\\\[^,;\\'\\\" ]+)+|(/[^,;\\'\\\" ]+)+)(\\.\\w+)?\"), self._codes):\n            paths.append([path.start(), path.end()])\n\n        # get all 1.3.4 liked pattern\n        for consec_num in re.finditer(re.compile(r\"\\d+(\\.\\d+)+\"), self._codes):\n            consec_nums.append([consec_num.start(), consec_num.end()])\n\n        # get all python3-tk liked pattern\n        for dash in re.finditer(re.compile(r\"[\\d\\w]+(-[\\d\\w]+)+\"), self._codes):\n            dashs.append([dash.start(), dash.end()])\n\n        # get all &lt; and &gt;\n        for lt_gt in re.finditer(re.compile(r\"&(lt|gt);\"), self._codes):\n            lts_gts.append([lt_gt.start(), lt_gt.end()])\n        \n        # get tokenizer cookie\n        for cookie in re.finditer(re.compile(r'[A-Za-z]*coding[:=]', re.ASCII), self._codes):\n            cookies.append([cookie.start(), cookie.end() - 1])\n\n        # custom union function to union the ranges of different reserved codes\n        overall = union([paths, consec_nums, dashs, lts_gts, cookies])\n        #\n        # import pdb; pdb.set_trace()\n        code_anchor = 0\n        for start, end in overall:\n            new_codes.Append(self._codes[code_anchor: start])\n            hash_code = hash_str(self._codes[start: end])\n            self._reserve_codes[hash_code] = self._codes[start: end]\n            new_codes.Append(' ' + str(hash_code) + ' ')\n            code_anchor = end\n        if (code_anchor < len(self._codes)):\n            new_codes.Append(self._codes[code_anchor:])\n        self._processed_codes = new_codes.__str__()\n\n    # __setTokens__: Based on _codes given, put all identified tokens to _tokens\n    def __setTokens__(self):\n        # g = tokenize(BytesIO(self._codes.encode('utf-8')).readline)  # tokenize the string\n        prev_num = -1\n        prev_val = None\n        prev_end = -1\n        self.__ReplaceReserved__()\n        # Split _codes line by line and identify each line\n        ss = self._processed_codes.splitlines()\n        # pdb.set_trace()\n        for line in ss:\n            try:\n                # call python tokenize.tokenize and get the returned generator g\n                g = tokenize(BytesIO(line.encode('utf-8')).readline)  # tokenize the string\n                try:\n                    for toknum, tokval, starrt, eend, _ in g:\n                        # pdb.set_trace()\n                        chop_start = 0\n                        chop_end = len(tokval) - 1\n                        # pdb.set_trace()\n                        # if the token type is NAME / OP / NUMBER and not only consists of [,)\\-\\\"';\\[\\]|..+]+\n                        if (toknum in [NAME, OP, NUMBER, ERRORTOKEN] and re.compile( r\"^(?<![a-zA-Z])([\\ ,):\\\"';\\[\\]}\\{]+|\\.\\.+)(?![a-zA-Z])$\").search(tokval) == None):\n                            # pdb.set_trace()\n                            # Take xx( / &lt / &gt as one token, instead of two, eg. xx and (\n                            if (((prev_num == NAME and tokval == '(') or (\n                                    prev_val == '&' and (tokval == 'lt' or tokval == 'gt'))) and prev_end == starrt):\n                                self._tokens[-1] = self._tokens[-1] + tokval\n                            elif (tokval == '('):\n                                pass\n                            elif (toknum == NUMBER and int(tokval) in self._reserve_codes):\n                                self._tokens.append(self._reserve_codes[int(tokval)])\n                            else:\n                                self._tokens.append(tokval)\n                        # For comment / string, code\n                        elif (toknum in [COMMENT, STRING]):\n                            # pdb.set_trace()\n                            if (toknum == STRING):\n                                # remove starting and ending ' / \"\n                                while ((tokval[chop_start] == '\"' or tokval[chop_start] == \"'\") and chop_start < chop_end):\n                                    chop_start += 1\n                                while ((tokval[chop_end] == '\"' or tokval[chop_end] == \"'\") and chop_start < chop_end):\n                                    chop_end -= 1\n                            else:\n                                # remove starting # / ''' / \"\"\"\n                                while ((tokval[chop_start] == '#' and chop_start < chop_end)\n                                       or (chop_end >= chop_start + 3 and tokval[chop_start:chop_start + 3] == \"'''\")\n                                       or (chop_end >= chop_start + 3 and tokval[chop_start:chop_start + 3] == '\"\"\"')):\n                                    if (tokval[chop_start] == '#'):\n                                        chop_start += 1\n                                    else:\n                                        chop_start += 3\n                            if (chop_start < chop_end or (tokval[chop_start] not in ['#', \"'\", '\"'])):\n                                words = CodesTokenizer(tokval[chop_start:chop_end + 1])._tokens\n                                if (words):\n                                    self._tokens.extend(words)\n                        prev_num = toknum\n                        prev_val = tokval\n                        prev_end = eend\n                except Exception as e:\n                    # print(\"Error in __setTokens__\", e, line)\n                    # pdb.set_trace()\n                    pass\n            except Exception as e:\n                print(\"Error in __setTokens__\", e, line)\n                pdb.set_trace()\n                pass\n\n    # annotate: Based on _tokens and _codes, annotate the cooresponding tokens with token tags.\n    def annotate(self):\n        assert self._sb.__str__() == '', \"_sb already has value!\"\n        # code_anchor mark the current position in codes, before which has been processed already\n        code_anchor = 0\n        if (len(self._tokens) == 0):\n            return self._codes\n        try:\n            # For every token, find its starting pos and ending pos in codes, append original codes from code_anchor to starting pos\n            #\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tand append annotated(taged) token\n            for token in self._tokens:\n                search = re.compile(re.escape(token)).search(self._codes, code_anchor)\n                # pdb.set_trace()\n                search_start = search.start()\n                search_end = search.end()\n                self._sb.Append(self._codes[code_anchor: search_start])\n                self._sb.Append(code_tag[0] + token + code_tag[1])\n                # update code_anchor to ending pos of current token\n                code_anchor = search_end\n            self._sb.Append(self._codes[code_anchor:])\n        except Exception as e:\n            print(e, \"code: \\n\", self._codes, \"tokens: \\n\", self._tokens)\n            pdb.set_trace()\n            raise ()\n        # return the annotated codes\n        return self._sb.__str__()\n\n\ndef main(file):\n    source = codecs.open(file, encoding='UTF-8').read()\n    code_secs = re.compile(\"<code>.*?</code>\", flags=re.S | re.M).finditer(source)\n    sb_file = StringBuilder()\n    # file_anchor mark the current position in current file, before which has been processed already\n    file_anchor = 0\n    # iterate all codes area, defined by <code> ... </code>\n    for code_sec in code_secs:\n        # +6 and -7 to exclude <code> & </code> tags\n        code_start = code_sec.start() + 6\n        code_end = code_sec.end() - 7\n        # Append the origin text from file_anchor to code_start\n        sb_file.Append(source[file_anchor: code_start])\n        # pdb.set_trace()\n        # Init a new CodesTokenizer with codes, append the annotated codes to sb_file\n        codes = source[code_start: code_end]\n        ct = CodesTokenizer(codes)\n        sb_file.Append(ct.annotate())\n        file_anchor = code_end\n    sb_file.Append(source[file_anchor:])\n    file_sep = os.path.splitext(file)\n    with open(file_sep[0] + \"_codeAnno\" + file_sep[1], 'w') as new_file:\n        new_file.write(sb_file.__str__())\n\n\nif __name__ == '__main__':\n    # take in 1+ file(s) for processing\n    for file in sys.argv[1:]:\n        main(file)\n","sub_path":"tokenizer/code_tokenizer.py","file_name":"code_tokenizer.py","file_ext":"py","file_size_in_byte":9855,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"503690115","text":"# Copyright (c) 2001-2016, Canal TP and/or its affiliates. All rights reserved.\n#\n# This file is part of Navitia,\n#     the software to build cool stuff with public transport.\n#\n# Hope you'll enjoy and contribute to this project,\n#     powered by Canal TP (www.canaltp.fr).\n# Help us simplify mobility and open public transport:\n#     a non ending quest to the responsive locomotion way of traveling!\n#\n# LICENCE: 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 published by\n# the Free Software Foundation, either version 3 of the License, or\n# (at your option) any later version.\n#\n# This program is distributed in the hope that it will be useful,\n# but WITHOUT ANY WARRANTY; without even the implied warranty of\n# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the\n# GNU 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 <http://www.gnu.org/licenses/>.\n#\n# Stay tuned using\n# twitter @navitia\n# IRC #navitia on freenode\n# https://groups.google.com/d/forum/navitia\n# www.navitia.io\nfrom datetime import datetime, timedelta\nimport json\n\nfrom bson import json_util\nfrom flask import Response\nfrom flask_restful import Resource, reqparse\nfrom tartare import mongo\n\n\ndef datetime_type(value: str) -> datetime:\n    _datetime = datetime.strptime(value, '%Y-%m-%d')\n\n    return reset_time(_datetime)\n\n\ndef reset_time(_datetime: datetime) -> datetime:\n    return _datetime.replace(minute=0, hour=0, second=0, microsecond=0)\n\n\nclass RequestLogs(Resource):\n    def __init__(self) -> None:\n\n        self.parsers = reqparse.RequestParser()\n        self.parsers.add_argument('start_date', type=datetime_type, default=None, location='args')\n\n        default_date = datetime.utcnow()\n        default_date = reset_time(default_date)\n        self.parsers.add_argument('end_date', type=datetime_type, default=default_date, location='args')\n\n    def get(self) -> Response:\n        args = self.parsers.parse_args()\n\n        start_date = args['start_date']\n        end_date = args['end_date']\n\n        if not start_date:\n            start_date = end_date\n\n        end_date += timedelta(days=1)\n\n        results = mongo.db['logs'].aggregate([\n            {\n                \"$match\": {\n                    \"date\": {\n                        \"$gte\": start_date,\n                        \"$lt\": end_date\n                    }\n                },\n            },\n            {\n                \"$project\": {\n                    \"_id\": 0,\n                    \"date\": {\n                        \"$dateToString\": {\n                            \"format\": \"%Y-%m-%dT%H:%M:%S.%LZ\",\n                            \"date\": \"$date\",\n                        }\n                    },\n                    \"request\": 1,\n                    \"response\": 1,\n                }\n            }\n        ])\n\n        return Response(\n            json.dumps(list(results), default=json_util.default),\n            mimetype='application/json'\n        )\n","sub_path":"tartare/interfaces/logs.py","file_name":"logs.py","file_ext":"py","file_size_in_byte":3102,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"651381185","text":"import torch\nimport torch.nn as nn\nfrom tqdm import tqdm\nimport glob\nfrom PIL import Image\nimport albumentations as A\nfrom albumentations.pytorch import ToTensorV2\nimport segmentation_models_pytorch as smp\nimport os\n\nos.environ['CUDA_VISIBLE_DEVICES'] = '0'\nimport numpy as np\nfrom utils import load_checkpoint\nimport cv2\nfrom model import get_model\n\n###Hyperparameter\nTEST_DATA_DIR = '/tmp/pycharm_project_677/data/origins/suichang_round1_test_partA_210120/suichang_round1_test_partA_210120'\nDEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'\nIMAGE_HEIGHT = 256\nIMAGE_WIDTH = 256\nCHECKPOINT = 'best_checkpoint.pth.tar'\n\n\ndef test(model, test_data_dir, transform=None, device='cuda'):\n    model.eval()\n\n    for idx, name in enumerate(tqdm(glob.glob(os.path.join(test_data_dir, '*.tif'))[:])):\n        im = Image.open(name)\n        image_RGB = np.array(im)[:, :, :-1]\n        if transform is not None:\n            image_RGB = transform(image=image_RGB)\n            image_RGB = image_RGB['image']\n\n        with torch.no_grad():\n            image_RGB = image_RGB.unsqueeze(0).to(device=device)\n            score = model(image_RGB).clone().cpu().softmax(1).numpy()\n\n            score_sigmoid = score[0].argmax(0) + 1\n            assert score_sigmoid.max() <= 10, \"category pred is wrong, bigger than 10\"\n            assert score_sigmoid.min() >= 1, \"category pred is wrong, smaller than 1\"\n\n            cv2.imwrite('results/' + os.path.basename(name).replace('.tif', '.png'), score_sigmoid)\n\n\ndef complicated_test(model, test_data_dir, transform_dict=None, device='cuda'):\n    model.eval()\n\n    for idx, name in enumerate(tqdm(glob.glob(os.path.join(test_data_dir, '*.tif'))[:])):\n        im = Image.open(name)\n        image_org = np.array(im)[:, :, :-1]\n        if transform_dict is not None:\n            score_list = []\n            res = 0\n\n            for method, transform in transform_dict.items():\n                image_RGB = transform(image=image_org)\n                image_RGB = image_RGB['image']\n                with torch.no_grad():\n                    image_RGB = image_RGB.unsqueeze(0).to(device=device)\n                    score = model(image_RGB).clone().cpu().numpy()[0]\n\n                    if method == 'HorizontalFlip':\n                        score = np.flip(score, 2)\n\n                    if method == 'VerticalFlip':\n                        score = np.flip(score, 1)\n\n                    if method == 'Transpose':\n                        score = score.transpose((0, 2, 1))\n\n                    score_list.append(score)\n\n            for score in score_list:\n                res = res + score\n            res = res / len(score_list)\n            score_sigmoid = res.argmax(0) + 1\n            assert score_sigmoid.max() <= 10, \"category pred is wrong, bigger than 10\"\n            assert score_sigmoid.min() >= 1, \"category pred is wrong, smaller than 1\"\n\n            cv2.imwrite('results/' + os.path.basename(name).replace('.tif', '.png'), score_sigmoid)\n\n\nif __name__ == \"__main__\":\n    model = get_model().to(device=DEVICE)\n\n    load_checkpoint(torch.load(CHECKPOINT), model)\n\n    test_transform = A.Compose(\n        [\n            A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),\n            A.Normalize(\n                mean=[0.625, 0.448, 0.688],\n                std=[0.131, 0.177, 0.101],\n                max_pixel_value=255.0\n            ),\n            ToTensorV2(),\n        ]\n    )\n\n    test_complicated_transfom1 = A.Compose(\n        [\n            A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),\n            A.Normalize(\n                mean=[48.5125, 60.0272, 56.5723],\n                std=[32.3620, 31.1285, 30.2979],\n                max_pixel_value=255.0\n            ),\n            A.HorizontalFlip(p=1),\n            ToTensorV2(),\n        ]\n    )\n\n    test_complicated_transfom2 = A.Compose(\n        [\n            A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),\n            A.Normalize(\n                mean=[48.5125, 60.0272, 56.5723],\n                std=[32.3620, 31.1285, 30.2979],\n                max_pixel_value=255.0\n            ),\n            A.VerticalFlip(p=0.5),\n            ToTensorV2(),\n        ]\n    )\n    test_complicated_transfom3 = A.Compose(\n        [\n            A.Resize(height=IMAGE_HEIGHT, width=IMAGE_WIDTH),\n            A.Normalize(\n                mean=[48.5125, 60.0272, 56.5723],\n                std=[32.3620, 31.1285, 30.2979],\n                max_pixel_value=255.0\n            ),\n            A.Transpose(p=1),\n            ToTensorV2(),\n        ]\n    )\n    method = ['original', 'HorizontalFlip', 'VerticalFlip', 'Transpose']\n    transform_complicated = [test_transform, test_complicated_transfom1, test_complicated_transfom2,\n                             test_complicated_transfom3]\n    transform_dict = dict(zip(method, transform_complicated))\n\n    test(model, TEST_DATA_DIR, test_transform, device=DEVICE)\n    # complicated_test(model, TEST_DATA_DIR, transform_dict=transform_dict,device=DEVICE)\n\n\n","sub_path":"test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":4964,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"543443087","text":"#utils.py\n#Author: Nabin\n\nimport pandas as pd\nimport pickle\nimport socket\nfrom config import *\n\nroles={\n    '1': \"Admin\",\n    '2': \"Doctor\",\n    '3': \"Staff\",\n    '4': \"Patient\",\n    1: \"Admin\",\n    2: \"Doctor\",\n    3: \"Staff\",\n    4: \"Patient\"\n}\n\n\n############# Client side utility methods for administrator ###############\n\ndef admin_view_users(choice, is_valid):\n    message = {}\n    message[\"user_type\"]=1\n    message[\"is_valid\"] = is_valid\n    message[\"choice\"] = choice\n    message = pickle.dumps(message, -1)\n\n    # create a TCP socket to connect to coordinator\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    # connect to coordinator\n    sock.connect((COORDINATOR_IP, COORDINATOR_MSG_PORT))\n    #send data to coordinator\n    sock.sendall(bytes(message))\n\n    # block till coordinator replies back\n    result = sock.recv(2048)\n    sock.close()\n    result = pickle.loads(result)\n    result = pd.DataFrame().from_dict(data=result, orient='index')\n    print(\"Here is the list of all users:\")\n    print(result)\n    print(\"-\"*20)\n    print(\"Choose Again: \")\n\n\ndef admin_add_new_user(choice,is_valid):\n    message = {}\n    message[\"user_type\"] = 1\n    message[\"is_valid\"] = is_valid\n    message[\"choice\"] = choice\n    print(\"Enter the details for the new user\")\n    message[\"new_user_type\"]=input(\"enter user type(1-Admin, 2-Doctor,\\n 3-Staff): \")\n    message[\"new_user_name\"]=input(\"enter user name :\")\n    message[\"new_user_pwd\"]=input('enter password:')\n\n    message = pickle.dumps(message, -1)\n\n    # create a TCP socket to connect to coordinator\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    # connect to coordinator\n    sock.connect((COORDINATOR_IP, COORDINATOR_MSG_PORT))\n    # send data to coordinator\n    sock.sendall(bytes(message))\n\n    # block till coordinator replies back\n    result = sock.recv(2048)\n    sock.close()\n    result = pickle.loads(result)\n\n    print(\"Successfully added follwing user\")\n    print(\"user name: {}\".format(result[\"user_name\"]))\n    print(\"user type: {}\".format(result[\"user_type\"]))\n    print(\"-\" * 20)\n    print(\"Choose Again: \")\n\ndef admin_delete_user(choice, is_valid):\n    message={}\n    message[\"user_type\"] = 1\n    message[\"is_valid\"] = is_valid\n    message[\"choice\"] = choice\n    print(\"Enter user type and user name to delete \")\n    message[\"del_user_type\"] = input(\"enter user type(1-Admin, 2-Doctor, 3-Staff): \")\n    message[\"del_user_name\"] = input(\"enter user name :\")\n\n    message = pickle.dumps(message, -1)\n\n    # create a TCP socket to connect to coordinator\n    sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    # connect to coordinator\n    sock.connect((COORDINATOR_IP, COORDINATOR_MSG_PORT))\n    # send data to coordinator\n    sock.sendall(bytes(message))\n\n    # block till coordinator replies back\n    result = sock.recv(2048)\n    sock.close()\n    result = pickle.loads(result)\n\n    print(\"Successfully deleted follwing user\")\n    print(\"user name: {}\".format(result[\"user_name\"]))\n    print(\"user type: {}\".format(result[\"user_type\"]))\n    print(\"-\" * 20)\n    print(\"Choose Again: \")\n\n\n\n\ndef admin_choice():\n    print(\"1. View all users\")\n    print(\"2. Add new user\")\n    print(\"3. Delete existing user\")\n    print(\"4. Logout\")\n    choice = input(\"select one:\")\n    try:\n        choice=int(choice)\n        assert choice >=1 and choice <=4\n        return choice\n    except:\n        print(\"invalid choice\")\n        admin_choice()\n\n\ndef admin_activities(admin_name):\n    print(\"Please choose one of the following\".format(admin_name))\n\n    while True:\n        choice = admin_choice()\n\n        if choice==1:\n            admin_view_users(choice=1, is_valid=True)\n\n        elif choice == 2:\n            admin_add_new_user(choice=2, is_valid=True)\n\n        elif choice == 3:\n            admin_delete_user(choice=3, is_valid=True)\n\n        elif choice==4:\n            print(\"Successfully logged out\")\n            break\n        else:\n            print(\"Invalid Choice\")\n\n\n############# Server side utility methods for administrator ###############\n\n\ndef verify_login(user_type, user_name, password, server_users_file, server_patients_file):\n    valid_user = False\n    if str(user_type)=='4':#for patients username=name and password=patientid\n        patients=pd.read_csv(server_patients_file).values\n        for patient in patients:\n            if str(patient[1]) == str(user_name) and \\\n                str(patient[0]) == str(password):\n                valid_user=True\n    else:#for other users\n        users = pd.read_csv(server_users_file).values\n        for user in users:\n            if str(user[1]) == str(user_type) and \\\n                            str(user[2]) == str(user_name) and \\\n                            str(user[3]) == str(password):\n                valid_user = True\n\n    return valid_user\n\n\ndef show_user_details(server_users_file):\n    df = pd.read_csv(server_users_file)\n    return df[['user_id','user_name', 'role']].to_dict('index')\n\n\n\ndef add_user(server_users_file, user_type,\n             user_name, password):\n    df = pd.read_csv(server_users_file)\n    user_id=list(df[\"user_id\"])[-1]+1\n\n    # add new entry\n    df.loc[len(df)] = [user_id, user_type, user_name, password, roles[user_type]]\n    df.to_csv(server_users_file, index=False)\n\n    result={}\n    result[\"user_type\"]=roles[int(user_type)]\n    result[\"user_name\"]= user_name\n    return result\n\n\ndef remove_user(server_users_file,\n                user_type, user_name):\n    df=pd.read_csv(server_users_file)\n    new_df = df[~((df['user_type']==int(user_type)) & (df['user_name']==str(user_name)))]\n    new_df.to_csv(server_users_file, index=False)\n    result={}\n    result[\"user_type\"]=roles[user_type]\n    result[\"user_name\"]= user_name\n    return result\n","sub_path":"admin_utils.py","file_name":"admin_utils.py","file_ext":"py","file_size_in_byte":5741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"177664111","text":"\n\n\n\nimport numpy as np\nimport pickle\nfrom os.path import expanduser\nhome = expanduser(\"~\")\n\nimport matplotlib.pyplot as plt\nimport matplotlib.gridspec as gridspec\n\nimport torch\nfrom torch.autograd import Variable\nimport torch.utils.data\nimport torch.optim as optim\nimport torch.nn as nn\nimport torch.nn.functional as F\n\n\n\n\n\n\nclass RNN(nn.Module):\n    def __init__(self, specs):\n        super(RNN, self).__init__()\n\n        torch.manual_seed(1)\n\n\n        self.input_size = specs['input_size']\n        self.output_size = specs['output_size']\n        self.z_size = specs['z_size']\n\n\n\n        # self.update_net = torch.nn.Sequential(\n        #     torch.nn.Linear(self.input_size+self.z_size, specs['update_net'][0]),\n        #     torch.nn.ReLU(),\n        #     torch.nn.Linear(specs['update_net'][0], specs['update_net'][1]),\n        #     torch.nn.ReLU(),\n        #     torch.nn.Linear(specs['update_net'][1], self.z_size),\n        # )\n\n\n        self.update_net = torch.nn.Sequential(\n            torch.nn.Linear(self.input_size+self.z_size, 100),\n            torch.nn.ReLU(),\n            torch.nn.Linear(100, 100),\n            torch.nn.ReLU(),\n            torch.nn.Linear(100, self.z_size),\n        )\n\n        # self.output_net = torch.nn.Sequential(\n        #     torch.nn.Linear(self.z_size, specs['output_net'][0]),\n        #     torch.nn.ReLU(),\n        #     torch.nn.Linear(specs['output_net'][0], specs['output_net'][1]),\n        #     torch.nn.ReLU(),\n        #     torch.nn.Linear(specs['output_net'][1], self.output_size),\n        # )\n\n\n        self.output_net = torch.nn.Sequential(\n            torch.nn.Linear(self.z_size, 30),\n            torch.nn.ReLU(),\n            torch.nn.Linear(30, 30),\n            torch.nn.ReLU(),\n            torch.nn.Linear(30, self.output_size),\n        )\n\n\n\n    def init_state(self, batch_size):\n\n        # self.state = Variable(torch.zeros(batch_size, self.z_size))\n        return Variable(torch.zeros(batch_size, self.z_size))\n\n\n    def output_prediction(self, state):\n        '''\n        z: [B,Z]\n        out: [B,O]\n        '''\n        return self.output_net(state) \n\n\n    def update_state(self, x, state):\n\n        # print (x.size()) #[B,X]\n        # print (self.state.size())  #[B,Z]\n\n        x_z = torch.cat((x,state), dim=1) #[B,x+z]\n        new_state = self.update_net(x_z)\n\n        # return new_state\n        # self.state = new_state #does this erase the grad??\n        return new_state\n\n\n    def train(self, data_x, data_y, batch_size):\n\n        np.random.seed(0)\n\n        n_data = data_x.shape[0]\n\n        timesteps = 4\n\n        # convert to pytorch\n        data_x = Variable(torch.FloatTensor(data_x))\n        data_y = Variable(torch.LongTensor(data_y))\n\n\n        loss_func = nn.CrossEntropyLoss()\n        self.optimizer = optim.Adam(self.parameters(), lr=.001)\n\n\n        for p in self.parameters():\n            print (p.size())\n        fds\n\n\n\n        for step_ in range(10000):\n\n\n            # make a batch of sequences\n            batch_x = []\n            batch_y = []\n            for i in range(batch_size):\n                seq = []\n                for t in range(timesteps):\n\n                    data_index = np.random.randint(n_data)\n                    seq.append(data_x[data_index])\n\n                    if t == 0:\n                        label = data_y[data_index]\n\n                batch_x.append(torch.stack(seq, dim=0))\n                batch_y.append(label)\n\n            batch_x = torch.stack(batch_x, dim=1)  #[T,B,X]\n            # batch_y = torch.unsqueeze(torch.stack(batch_y, dim=0), dim=0)  #[1,B,1]\n            batch_y = torch.squeeze(torch.stack(batch_y, dim=0))  #[B,1]\n            # print (batch_x.size())\n            # print (batch_y.size())\n\n\n\n\n            # Run model\n            state = self.init_state(batch_size)\n            for t in range(timesteps):\n                state = self.update_state(batch_x[t], state)\n\n            pred = self.output_prediction(state) #[B,Y]\n            # print (pred.size())\n\n            loss = loss_func(pred, batch_y) #[1]\n\n            # print (loss.size())\n            # fsad\n\n            if step_ %100 == 0:\n                print (step_, loss.data.numpy())\n\n            self.optimizer.zero_grad()\n            loss.backward()\n            self.optimizer.step()\n\n            \n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n    # def step(self):\n\n\n        #update state\n\n        #output value\n\n    # def reset_state():\n\n    #     self.state = Variable(pytorch.zeros())\n\n    # def encode(self, x, a, prev_z):\n    #     '''\n    #     x: [B,X]\n    #     a: [B,A]\n    #     prev_z: [P,B,Z]\n    #     '''\n\n    #     out = torch.cat((x,a), 1) #[B,XA]\n    #     out = torch.unsqueeze(out, 0) #[1,B,XA]\n    #     out = out.repeat(self.k, 1, 1)#.float() #[P,B,XA]\n    #     out = torch.cat((out,prev_z), 2) #[P,B,XAZ]\n    #     out = out.view(self.k*self.B, self.input_size+self.action_size+self.z_size) #[P*B,XAZ]\n    #     out = self.encoder_net(out) #[P*B,Z*2]\n    #     out = out.view(self.k, self.B, 2*self.z_size) #[P,B,XAZ]\n    #     mean = out[:,:,:self.z_size]\n    #     logvar = out[:,:,self.z_size:]\n    #     return mean, logvar\n\n\n    # def sample(self, mu, logvar):\n    #     '''\n    #     mu, logvar: [P,B,Z]\n    #     '''\n    #     eps = Variable(torch.FloatTensor(self.k, self.B, self.z_size).normal_()) #[P,B,Z]\n    #     z = eps.mul(torch.exp(.5*logvar)) + mu  #[P,B,Z]\n    #     # logpz = lognormal(z, Variable(torch.zeros(self.k, self.B, self.z_size)), \n    #     #                     Variable(torch.zeros(self.k, self.B, self.z_size)))  #[P,B]\n    #     logqz = lognormal(z, mu, logvar)\n    #     return z, logqz\n\n\n\n\n    # def transition_prior(self, prev_z, a):\n    #     '''\n    #     prev_z: [P,B,Z]\n    #     a: [B,A]\n    #     '''\n    #     a = torch.unsqueeze(a, 0) #[1,B,A]\n    #     out = a.repeat(self.k, 1, 1) #[P,B,A]\n    #     out = torch.cat((out,prev_z), 2) #[P,B,AZ]\n    #     out = out.view(-1, self.action_size+self.z_size) #[P*B,AZ]\n    #     out = self.transition_net(out) #[P*B,Z*2]\n    #     out = out.view(self.k, self.B, 2*self.z_size) #[P,B,2Z]\n    #     mean = out[:,:,:self.z_size] #[P,B,Z]\n    #     logvar = out[:,:,self.z_size:]\n    #     return mean, logvar\n\n\n    # def forward(self, x, a, k=1, current_state=None):\n    #     '''\n    #     x: [B,T,X]\n    #     a: [B,T,A]\n    #     output: elbo scalar\n    #     '''\n        \n    #     self.B = x.size()[0]\n    #     self.T = x.size()[1]\n    #     self.k = k\n\n    #     a = a.float()\n    #     x = x.float()\n\n    #     # log_probs = [[] for i in range(k)]\n    #     # log_probs = []\n    #     logpxs = []\n    #     logpzs = []\n    #     logqzs = []\n\n\n    #     weights = Variable(torch.ones(k, self.B)/k)\n    #     # if current_state==None:\n    #     prev_z = Variable(torch.zeros(k, self.B, self.z_size))\n    #     # else:\n    #     #     prev_z = current_state\n    #     for t in range(self.T):\n    #         current_x = x[:,t] #[B,X]\n    #         current_a = a[:,t] #[B,A]\n\n    #         #Encode\n    #         mu, logvar = self.encode(current_x, current_a, prev_z) #[P,B,Z]\n    #         #Sample\n    #         z, logqz = self.sample(mu, logvar) #[P,B,Z], [P,B]\n    #         #Decode\n    #         x_hat = self.decode(z)  #[P,B,X]\n    #         logpx = log_bernoulli(x_hat, current_x)  #[P,B]\n    #         #Transition/Prior prob\n    #         prior_mean, prior_log_var = self.transition_prior(prev_z, current_a) #[P,B,Z]\n    #         logpz = lognormal(z, prior_mean, prior_log_var) #[P,B]\n\n\n\n\n\n\n    #         log_alpha_t = logpx + logpz - logqz #[P,B]\n    #         log_weights_tmp = torch.log(weights * torch.exp(log_alpha_t))\n\n    #         max_ = torch.max(log_weights_tmp, 0)[0] #[B]\n    #         log_p_hat = torch.log(torch.sum(torch.exp(log_weights_tmp - max_), 0)) + max_ #[B]\n\n    #         # p_hat = torch.sum(alpha_t,0)  #[B]\n    #         normalized_alpha_t = log_weights_tmp - log_p_hat  #[P,B]\n\n    #         weights = torch.exp(normalized_alpha_t) #[P,B]\n\n    #         #if resample\n    #         if t%2==0:\n    #             # print weights\n    #             #[B,P] indices of the particles for each bactch\n    #             sampled_indices = torch.multinomial(torch.t(weights), k, replacement=True).detach()\n    #             new_z = []\n    #             for b in range(self.B):\n    #                 tmp = z[:,b] #[P,Z]\n    #                 z_b = tmp[sampled_indices[b]] #[P,Z]\n    #                 new_z.append(z_b)\n    #             new_z = torch.stack(new_z, 1) #[P,B,Z]\n    #             weights = Variable(torch.ones(k, self.B)/k)\n    #             z = new_z\n\n    #         logpxs.append(logpx)\n    #         logpzs.append(logpz)\n    #         logqzs.append(logqz)\n    #         # log_probs.append(logpx + logpz - logqz)\n    #         prev_z = z\n\n\n\n    #     logpxs = torch.stack(logpxs) \n    #     logpzs = torch.stack(logpzs)\n    #     logqzs = torch.stack(logqzs) #[T,P,B]\n\n    #     logws = logpxs + logpzs - logqzs  #[T,P,B]\n    #     logws = torch.mean(logws, 0)  #[P,B]\n\n    #     # elbo = logpx + logpz - logqz  #[P,B]\n\n    #     if k>1:\n    #         max_ = torch.max(logws, 0)[0] #[B]\n    #         elbo = torch.log(torch.mean(torch.exp(logws - max_), 0)) + max_ #[B]\n    #         elbo = torch.mean(elbo) #over batch\n    #     else:\n    #         elbo = torch.mean(logws)\n\n    #     # print log_probs[0]\n\n\n    #     # #for printing\n    #     logpx = torch.mean(logpxs)\n    #     logpz = torch.mean(logpzs)\n    #     logqz = torch.mean(logqzs)\n    #     # self.x_hat_sigmoid = F.sigmoid(x_hat)\n\n    #     # elbo = torch.mean(torch.stack(log_probs)) #[1]\n    #     # elbo = logpx + logpz - logqz\n\n    #     return elbo, logpx, logpz, logqz\n\n\n\n","sub_path":"SL2/RNN.py","file_name":"RNN.py","file_ext":"py","file_size_in_byte":9651,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"162817827","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\nr'''\n调用Web API并处理结果，找出GitHub上星级最高的Python项目\n并进行数据可视化\n'''\nimport requests\nimport pygal\nfrom pygal.style import LightColorizedStyle as LCS, LightenStyle as LS\n\n# 执行API调用并储存响应\nurl = 'https://api.github.com/search/repositories?q=language:python&sort=stars'\nr = requests.get(url)\nprint('Status code:', r.status_code)\n# 将API响应存储在一个变量中\nresponse_dict = r.json()\nprint('Total repositories:', response_dict['total_count'])\n# 探索有关仓库的信息\nrepo_dicts = response_dict['items']\n# 仓库名和星数集合\nnames, stars = [], []\nfor repo_dict in repo_dicts:\n    names.append(repo_dict['name'])\n    stars.append(repo_dict['stargazers_count'])\n# 数据可视化\nmy_style = LS('#336699', base_style=LCS)    # 图形基色设置为深蓝色\nchart = pygal.Bar(style=my_style, x_label_rotation=45, show_legend=False)    # x_label_rotation=45-代表让标签绕x 轴旋转45度；show_legend=False-代表隐藏图例\nchart.title = 'Most-Starred Python Projects on GitHub'\nchart.x_labels = names\nchart.add('Stars', stars)\n# 渲染成SVG文件\nchart.render_to_file('images/python_repository_github_1.svg')\n","sub_path":"webapi/github/python_repository_github_2.py","file_name":"python_repository_github_2.py","file_ext":"py","file_size_in_byte":1234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"146538321","text":"class Transport(object):\n    def __init__(self, id, account, FC, customer, date, catty, income, cost):\n        self.id = id\n        self.account =account\n        self.FC = FC\n        self.customer =customer\n        self.date = date\n        self.catty = catty\n        self.income = income\n        self.cost = cost\n        ","sub_path":"dbproject/Transport.py","file_name":"Transport.py","file_ext":"py","file_size_in_byte":321,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"35223181","text":"# %%\nimport gym\n\n# %%\nenv = gym.make('CartPole-v0')\n\ntotal_reward = 0.\ntotal_steps = 0\nenv.reset()\n\nfor _ in range(1000):\n    env.render()\n    action = env.action_space.sample()\n    obs, reward, done, info = env.step(action)\n    if done:\n        env.reset()\n\nenv.close()\n# %%\n","sub_path":"models/reinforcement/gym_tutorial.py","file_name":"gym_tutorial.py","file_ext":"py","file_size_in_byte":276,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"104608257","text":"\"\"\" \n@Author: huuuuusy\n@GitHub: https://github.com/huuuuusy\n系统： Ubuntu 18.04\nIDE:  VS Code 1.39\n工具： python == 3.7.3\n\"\"\"\n\n\"\"\"\n思路：\n    滑动窗口\n参考：\n    https://leetcode-cn.com/problems/longest-substring-without-repeating-characters/solution/hua-dong-chuang-kou-by-powcai/\n结果：\n    执行用时 : 96 ms, 在所有 Python3 提交中击败了98.03%的用户\n    内存消耗 : 14.4 MB, 在所有 Python3 提交中击败了15.28%的用户\n\"\"\"\n\nclass Solution:\n    def minWindow(self, s, t):\n        from collections import defaultdict\n        lookup = defaultdict(int) # 存储当前需要检索的字母个数\n        for alp in t: # 先将t中的字母存入lookup；t中的字母初始个数是1,则其他字母初始个数为0\n            lookup[alp] += 1\n        start = 0 # 滑动窗口的起始\n        end = 0\n        min_len = float('inf') \n        counter = len(t) # counter用来统计t中的字母是否都在窗口中，若都在，则counter=0\n        res = \"\"\n        while end < len(s):\n            if lookup[s[end]] > 0: # 如果lookup中s[end]的值大于0,说明是t中的字母，此时窗口中包含t中的一个字母，所以counter可以减一\n                counter -= 1\n            lookup[s[end]] -= 1 # 将lookup中当前字母的个数减一，lookup中存储的是该字母有几个出现在窗口中，和初始值的差值表示出现的个数\n            end += 1\n            while counter == 0: # 此时表明t中所有字母均在窗口中\n                if min_len > end - start: # 更新min_len\n                    min_len = end - start\n                    res = s[start:end] # 更新res\n                if lookup[s[start]] == 0:\n                    counter += 1\n                lookup[s[start]] += 1 # 移动窗口起点\n                start += 1\n        return res\n            \n\n\nif __name__ == \"__main__\":\n    s = \"ADOBECODEBANC\"\n    t = \"ABC\"\n    answer = Solution().minWindow(s, t)\n    print(answer)\n","sub_path":"LeetCode/python-R1/0076-最小覆盖子串D/V1.py","file_name":"V1.py","file_ext":"py","file_size_in_byte":1972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"428205959","text":"import numpy\r\nimport numpy\r\nn = int(input())\r\nar = []\r\nar1 = []\r\nfor i in range(n):\r\n    row = list(map(int,input().split()))\r\n    ar.append(row)\r\nx = numpy.array(ar)\r\nfor i in range(n):\r\n    row = list(map(int,input().split()))\r\n    ar1.append(row)\r\ny = numpy.array(ar1)\r\nprint(numpy.dot(x,y))","sub_path":"Dot and Cross.py","file_name":"Dot and Cross.py","file_ext":"py","file_size_in_byte":294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"133756011","text":"from optparse import OptionParser\nimport enchant\n\nSENSSTRING = \"#$@&%*!\"\n\nclass piglatin_error(Exception):\n    \"\"\"\n    Custom Exception\n    \"\"\"\n    def __init__(self, message=\"\"):\n        Exception.__init__(self, message)\n\ndef main():\n    \"\"\"\n    Main function, where we handle the parsing\n    \"\"\"\n    parser = OptionParser()\n    parser.add_option(\"-r\", \"--reverse\", dest=\"reverse\",\n                      action=\"store_true\", default=False,\n                      help=\"translate from PigLatin to English\", metavar=\"STRING\")\n\n    (options, args) = parser.parse_args()\n\n    if len(args) == 0:\n        raise piglatin_error(\"No input string\")\n\n    # take first argument from cmdline\n    sentence = args[0]\n\n    if len(sentence) == 0:\n        raise piglatin_error(\"Input string is empty\")\n\n    if options.reverse:\n        print(sentencetoenglish(sentence))\n    else:\n        print(sentencetopiglatin(sentence))\n\ndef checklastcharacter(word):\n    \"\"\"\n    Check whether the last character is \". or !, or...\".\n\n    ARQS:\n        word (string): The word to be checked.\n    \"\"\"\n    if word.isalpha():\n        return word, \"\"\n    original_word = word\n    i = 0\n    while not word[-1].isalpha():\n        i += 1\n        word = word[0:-1]\n        if len(word) == 0:\n            return original_word, \"\"\n\n    return word, original_word[-i:]\n\ndef wordtopiglatin(word):\n    \"\"\"Translates ONE word to Pig Latin.\n\n    :word -- (string) The word that should be translated.\n    \"\"\"\n\n    if not isinstance(word, str):\n        raise piglatin_error(\"Not instance of str\")\n\n    if len(word) == 0:\n        return \"\"\n\n    # We filter out the sensitive words\n    pwl = enchant.request_pwl_dict(\"senswords.txt\")\n    if pwl.check(word):\n        return SENSSTRING\n\n    vowes = 'aeiou'\n    vowend = 'way'\n    otherend = 'ay'\n\n    word, ending = checklastcharacter(word.lower())\n\n    if word[0] in vowes:\n        word += vowend + ending\n        return word\n\n    for i in range(0, len(word)):\n        if word[i] not in vowes:\n            continue\n        word = word[i:] + word[0:i] + otherend + ending\n        break\n\n    return word\n\ndef sentencetopiglatin(sentence):\n    \"\"\"Translates a sentence to Pig Latin.\n\n    :sentence -- (string) The sentence that should be translated.\n    \"\"\"\n\n    if not isinstance(sentence, str):\n        return \"\"\n\n    sentence = sentence.split(\" \")\n    pigsentence = \"\"\n    for word in sentence:\n        pigsentence += wordtopiglatin(word) + \" \"\n\n    return pigsentence.strip()\n\ndef wordtoenglish(word):\n    \"\"\"\n    We use the English dict to try reverse translating the word.\n    \"\"\"\n    endict = enchant.Dict('en_GB')\n\n    if len(word) == 0:\n        return \"\"\n\n    original_word, ending = checklastcharacter(word)\n\n    if endict.check(original_word):\n        return word\n\n    # edge-case: if ending on way, we are quite confident that\n    # the word stats with a vowel (and can just remove \"way\")\n    if original_word[-3:] == 'way':\n        if endict.check(original_word[0:-3]):\n            return original_word[0:-3] + ending\n\n    test_word = original_word[0:-2]\n\n    # we are moving one consonent from the end to the start and checking\n    # the dictionary for a match. Else iterate and move next\n    for _ in range(1, len(test_word)):\n        test_word = test_word[-1:] + test_word[0:len(test_word)-1]\n        if endict.check(test_word):\n            return test_word + ending\n\n    # if no match is found we return the original word + the ending\n    return original_word + ending\n\ndef sentencetoenglish(sentence):\n    \"\"\"\n    We try to translate a sentence back to English using the dictionary.\n    \"\"\"\n\n    if not isinstance(sentence, str):\n        return \"\"\n\n    sentence = sentence.split(\" \")\n    pigsentence = \"\"\n\n    for word in sentence:\n        pigsentence += wordtoenglish(word) + \" \"\n\n    return pigsentence.strip()\n\nif __name__ == \"__main__\":\n    try:\n        main()\n    except piglatin_error as err:\n        print(\"An error occured: {}\".format(err))\n","sub_path":"PigLatin.py","file_name":"PigLatin.py","file_ext":"py","file_size_in_byte":3962,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"652498632","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Nov 16 21:21:15 2020\r\n\r\n@author: erolc\r\n\"\"\"\r\n\r\n#import libraries\r\nimport os\r\nimport json\r\n\r\n#functions for reading and writing data \r\nmy_directory = os.getcwd()\r\ninp_path,out_path = my_directory+\"\\\\in\\\\in.txt\",my_directory+\"\\\\out\\\\out.json\"\r\ndef read_input(input_path):\r\n    with open(input_path,encoding = \"utf8\") as file:\r\n        result = file.readlines()\r\n    return result\r\ndef write_output(output_path, finaldata):\r\n    with open(output_path, \"w\",encoding = \"utf8\") as jsondata:\r\n        json.dump(finaldata,jsondata, ensure_ascii=False)\r\ndata = read_input(inp_path)              \r\n\r\n\"\"\"\r\nTask-1:\r\nIn this HW, I will build hash table data structure to keep my data more reachable.\r\nHash tables are very efficient for most of the data analysis tasks. Reaching \r\nto the value of any element of the hash table keys has O(1) time complexity.     \r\n\r\nTask-2:\r\nSince JSON format uses list of hash tables, this is the best format for me. \r\nAlso, I am experienced in this format. Thus, I chose JSON to export my data.\r\n\r\n\r\nTask-3:\r\nIn my course project, I want to create a new dataset from scratch. This will be\r\na clear and well structured dataset. Thus, I am working on a different dataset for homework\r\nand I do not need any meta-data at this stage.\r\n\"\"\"\r\n\r\ndef structure_data(data=data):\r\n    result,i = {},1\r\n    for e in data: # Basically, I trace my data and create lists for dependency structures.\r\n        result[i] = {}\r\n        list_of_analysis = e[:-1].split(\"(\")\r\n        result[i][list_of_analysis[0]] = [] #Working on the first element.\r\n        temp = list_of_analysis[0]\r\n        for f in list_of_analysis[1:-1]: #Since I used split function, first and last elements will be outliers. I should work on them separately. This is why [1:-1]\r\n            words_and_tags = f.split()\r\n            result[i][temp].append((words_and_tags[2][:-1],words_and_tags[0]))\r\n            temp = words_and_tags[3]\r\n            if temp not in result[i]:\r\n                result[i][temp] = []\r\n        words_and_tags = list_of_analysis[-1].split() #Working on the last element.\r\n        result[i][temp].append((words_and_tags[2][:-1],words_and_tags[0]))\r\n        i += 1\r\n    return result\r\n\r\nwrite_output(out_path,structure_data())\r\n\r\n\r\n      \r\n            ","sub_path":"assignments/2319606/part_3/part_3.py","file_name":"part_3.py","file_ext":"py","file_size_in_byte":2297,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"486485888","text":"from django.db.models import Q, Count\nfrom django.shortcuts import render, get_object_or_404\nfrom rest_framework import generics\nfrom rest_framework.response import Response\n#from .models import Journal, Category\n#from .serializers import JournalSerializer\nfrom django.shortcuts import render, HttpResponse, redirect\nfrom django.contrib import messages\n#from django.views.generic import ListView\nimport bcrypt\nfrom .models import User,UserManager,PropertyMaster,Property_TypeMaster,TypeMaster\nfrom django.db.models import Q, Count\nfrom django.shortcuts import render, get_object_or_404\n#from .serializers import JournalSerializer\nimport pandas as pd\nimport json\nimport numpy as np\nfrom datetime import datetime\nfrom django.core import mail\nfrom django.template.loader import render_to_string\n\nfrom django.utils.html import strip_tags\nfrom django.contrib.auth.models import User\nfrom django.core.paginator import Paginator, EmptyPage, PageNotAnInteger\n# Create your views here.\nimport functools\n\ndef index(request):\n    return render(request, 'index2.html')\ndef register(request):\n    errors = User.objects.validator(request.POST)\n    if len(errors):\n        for tag, error in errors.items():\n            messages.error(request, error, extra_tags=tag)\n        return redirect('/')\n\n    # hashed_password = bcrypt.hashpw(request.POST['password'].encode('utf-8'), bcrypt.gensalt())\n    # password= User.objects.create()\n    user = User.objects.create(first_name=request.POST['first_name'], last_name=request.POST['last_name'], password=request.POST['password'], email=request.POST['email'])\n    user.save()\n    request.session['id'] = user.id\n    return redirect('/success')\n\n\ndef disjunction(*conditions):\n    return functools.reduce(np.logical_or, conditions)\n\ndef Alert(request):\n    dfp = pd.read_csv(\"core/rishu.csv\")\n    df = pd.read_csv(\"core/rishuAlert2.csv\")\n    if request.method == 'GET':\n        view_state = request.GET.get('view_state')\n        types = request.GET.get('types')\n        view_percentage = request.GET.get('view_percentage')\n        x = 1 - float(get_float(view_percentage)/100)\n        print(\"Rishuuu\",x)\n        # akp1 =True\n        akp2 = True\n        akp3 = True\n        print(\"Available Field\",view_state)\n        # (1) Condition for State , County and City\n        if (view_state and view_percentage) is not None :\n            akp1 = compute_county_wise_price(df, dfp, view_state, x);\n            # print(df1)\n            df1=dfp.loc[akp1, ['lwPropertyId', 'address', 'city', 'state', 'county', 'zip', 'price', 'acres', \"Rate\", \"Url\",'types', 'status', 'created_at', 'updated_at']]\n\n        else:\n            df1 = dfp\n\n        print(\"DF1\",df1)\n\n        json_records = df1.reset_index().to_json(orient='records')\n        data = []\n        data = json.loads(json_records)\n        page = request.GET.get('page', 1)\n        paginator = Paginator(data, 500)\n\n        try:\n            users = paginator.page(page)\n        except PageNotAnInteger:\n            users = paginator.page(1)\n        except EmptyPage:\n            users = paginator.page(paginator.num_pages(10))\n\n\n    return render(request, 'bootstrap_form.html', {'users': users})\n\n\ndef compute_county_wise_price(df1, df2, state, x):\n    netprice_per_county = df1[df1['state'] == state]\n    netprice_per_county = netprice_per_county.drop('state', axis=1)\n\n    price_per_county = {county: price*x for county, price in netprice_per_county.items()}\n    results = []\n\n    for index, row in df2.iterrows():\n        if ((row['state'] == state) and (price_per_county[row['county']] > row['Rate'])):\n            results.append(True)\n        else:\n            results.append(False)\n    return results\n\ndef show(request):\n    df = pd.read_csv(\"core/rishu.csv\")\n    if request.method == 'GET':\n        title_contains_query = request.GET.get('title_contains')\n        id_exact_query = request.GET.get('id_exact')\n        title_or_author_query = request.GET.get('title_or_author')\n        view_count_min = request.GET.get('view_count_min')\n        view_count_max = request.GET.get('view_count_max')\n        date_min = request.GET.get('date_min')\n        date_max = request.GET.get('date_max')\n        Udate_min = request.GET.get('date_min2')\n        Udate_max = request.GET.get('date_max2')\n        view_acres = request.GET.get('view_acres')\n        status = request.GET.get('status')\n        title2_contains_query = request.GET.get('title2_contains')\n        view_state = request.GET.get('view_state')\n        types = request.GET.get('types')\n        view_percentage = request.GET.get('view_percentage')\n        akp1 = df['state']== 'Anji'\n        # akp1 =True\n        akp2 = True\n        akp3 = True\n        print(\"Available Field\",title_contains_query)\n        # (1) Condition for State , County and City\n        if title_contains_query  is not None :\n            akp1 = (df['state'] == title_contains_query ) | (df['city'] == title_contains_query) | (df['county'] == title_contains_query)\n            # print(df1)\n            df1=df.loc[akp1, ['lwPropertyId', 'address', 'city', 'state', 'county', 'zip', 'price', 'acres', \"Rate\", \"Url\",'types', 'status', 'created_at', 'updated_at']]\n\n        else:\n            df1 =df\n\n        print(\"DF1\",df1)\n        # (2) Condition for Types\n        if types is not None :\n            akp2 = (df1['types'].str.contains(types , na=False))\n            # akp2 = (df1['types'] == types)\n            df2=df1.loc[akp2, [\"lwPropertyId\", 'address', 'city', 'state', 'county', 'zip', 'price', 'acres', \"Rate\", \"Url\",'types', 'status', 'created_at', 'updated_at']]\n\n        else :\n            df2 =df1\n\n        print(\"DF2\",df2)\n\n        # (3) Condition for Status\n        if status is not \"\" :\n            print(status)\n            akp3 = (df2['status'] == status)\n            # akp2 = (df1['status'] == types)\n            df3 = df2.loc[akp3, [\"lwPropertyId\", 'address', 'city', 'state', 'county', 'zip', 'price', 'acres', \"Rate\", \"Url\",'types', 'status', 'created_at', 'updated_at']]\n        else :\n            df3 = df2\n        print(\"DF3\",df3)\n\n        # print(view_acres)\n        # (4) Condition For Min Price :\n\n        if view_count_min is not None :\n            akp4 = df3['price'] >= get_float(view_count_min)\n            df4 = df3.loc[akp4, ['lwPropertyId', 'address', 'city', 'state', 'county', 'zip', 'price', 'acres', \"Rate\", \"Url\",\n                       'types', 'status', 'created_at', 'updated_at']]\n        else :\n            df4 = df3\n        print(\"DF4\",df4)\n\n        # (5) Condition For Max Price :\n\n        if view_count_max  is \"100000\":\n            print(\"word\",view_count_max)\n            akp5 = df4['price'] <= get_float(view_count_max)\n            df5 = df4.loc[\n                akp5, ['lwPropertyId', 'address', 'city', 'state', 'county', 'zip', 'price', 'acres', \"Rate\", \"Url\",\n                       'types', 'status', 'created_at', 'updated_at']]\n        else:\n            print(\"Hello\",view_count_max)\n            df5 = df4\n        print(\"DF5\",df5)\n\n        #(6) Condition for Area :\n\n        if view_acres is not None :\n            akp6 = df5['acres'] >= get_float(view_acres)\n            df6 = df5.loc[akp6, ['lwPropertyId', 'address', 'city', 'state', 'county', 'zip', 'price', 'acres', \"Rate\", \"Url\",\n                       'types', 'status', 'created_at', 'updated_at']]\n            # print(df5)\n        else :\n            df6 = df5\n        print(\"DF6\",df6)\n\n        # (7) Condition For Publish Date :\n\n        if date_min  is \"2000-01-01\" and date_max is \"2050-01-01\":\n            print(\"RishuuuDAte\")\n            print(date_min)\n            print(date_max)\n            akp7 = data_within_date(df6, date_min, date_max)\n            df7 = df6.loc[akp7, ['lwPropertyId', 'address', 'city', 'state', 'county', 'zip', 'price', 'acres', \"Rate\", \"Url\",'types', 'status', 'created_at', 'updated_at']]\n        else:\n            print(\"NA Date\",date_min)\n            df7 = df6\n        print(\"DF7\",df7)\n\n        # (7) Condition For Update Date :\n\n        if Udate_min is \"2000-01-01\" and Udate_max is \"2050-01-01\":\n            akp8 = data_within_date(df7, Udate_min, Udate_max)\n            df8 = df7.loc[\n                akp8, ['lwPropertyId', 'address', 'city', 'state', 'county', 'zip', 'price', 'acres', \"Rate\", \"Url\",\n                       'types', 'status', 'created_at', 'updated_at']]\n        else:\n            print(\"NA Date\", Udate_min)\n            df8 = df7\n        print(\"DF8\", df8)\n\n\n\n\n        json_records = df7.reset_index().to_json(orient='records')\n        data = []\n        data = json.loads(json_records)\n        page = request.GET.get('page', 1)\n        paginator = Paginator(data, 5000)\n\n        try:\n            users = paginator.page(page)\n        except PageNotAnInteger:\n            users = paginator.page(1)\n        except EmptyPage:\n            users = paginator.page(paginator.num_pages(10))\n\n\n    return render(request, 'bootstrap_form.html', {'users': users})\n\ndef get_float(s):\n    ans_int = 0\n    ans_float = 0\n    divisor = 1\n\n    int_part = s.split('.')[0]\n\n    for d in int_part:\n        ans_int = (ans_int * 10) + int(d)\n\n    if (not s.find('.') == -1):\n\n        float_part = s.split('.')[1]\n\n        for d in float_part:\n            ans_float = (ans_float * 10) + int(d)\n\n        divisor = 10 ** len(float_part)\n\n    ans = ans_int + (ans_float / divisor)\n\n    return ans\n\ndef data_within_date(df, min_date, max_date):\n    created_dates = [date.split()[0] for date in df['created_at']]\n    min_d = datetime.strptime(min_date, '%Y-%m-%d')\n    max_d = datetime.strptime(max_date, '%Y-%m-%d')\n    results = []\n    for date in created_dates:\n        date = datetime.strptime(date, '%Y-%m-%d')\n        if (date >= min_d and date <= max_d):\n            results.append(True)\n        else:\n            results.append(False)\n    return results\n\n\ndef login(request):\n    print(\"Rishi\")\n    rishu = request.POST.get('login_email')\n    print(\"Rishu\",rishu)\n    if rishu =='purchase@tayloredideas.com':\n        print(\"Login Entry\")\n\n        # user = User.objects.get(email__exact='usermail@example.com')\n        # if (bcrypt.checkpw(request.POST['login_password'].encode('utf-8'), user.password.encode('utf-8'))):\n        if (request.POST.get('login_password') == \"usa_texas_property_stuff\"):\n            print(\"Sahi Hai\")\n            # request.session['id'] = user.id\n            return redirect('/show')\n    return redirect('/')\n\ndef success(request):\n    user = User.objects.get(id=request.session['id'])\n    context = {\n        \"user\": user\n    }\n    # job(request)\n\n    return render(request, 'test111.html', context)\n\ndef set_password(self, pw):\n    pwhash = bcrypt.hashpw(pw.encode('utf8'), bcrypt.gensalt())\n    self.password_hash = pwhash.decode('utf8') # decode the hash to prevent is encoded twice\n\n\ndef is_valid_queryparam(param):\n    return param != '' and param is not None\n\n\ndef filter(request):\n    qs = PropertyMaster.objects.all()\n    category = PropertyMaster.objects.all()\n    title_contains_query = request.GET.get('title_contains')\n    id_exact_query = request.GET.get('id_exact')\n    title_or_author_query = request.GET.get('title_or_author')\n    view_count_min = request.GET.get('view_count_min')\n    view_count_max = request.GET.get('view_count_max')\n    date_min = request.GET.get('date_min')\n    date_max = request.GET.get('date_max')\n    view_acres = request.GET.get('view_acres')\n    category = request.GET.get('category')\n    reviewed = request.GET.get('reviewed')\n    not_reviewed = request.GET.get('notReviewed')\n\n    if is_valid_queryparam(title_contains_query):\n        qs = qs.filter(city__icontains=title_contains_query)\n\n    elif is_valid_queryparam(id_exact_query):\n        qs = qs.filter(id=id_exact_query)\n\n    elif is_valid_queryparam(title_or_author_query):\n        qs = qs.filter(Q(city__icontains=title_or_author_query)).distinct()\n                       # | Q(author__name__icontains=title_or_author_query)\n\n\n    if is_valid_queryparam(view_count_min):\n        qs = qs.filter(price__gte=view_count_min)\n\n    if is_valid_queryparam(view_count_max):\n        qs = qs.filter(price__lt=view_count_max+\"1\")\n\n    if is_valid_queryparam(date_min):\n        qs = qs.filter(publish_date__gte=date_min)\n\n    if is_valid_queryparam(category) and category != 'Choose...':\n        qs = qs.filter(price__name=category)\n\n    if is_valid_queryparam(date_max):\n        qs = qs.filter(publish_date__lt=date_max)\n\n    if is_valid_queryparam(view_acres):\n        qs = qs.filter(acres__gte=view_acres)\n    #\n    # if is_valid_queryparam(acres) :\n    #     qs = qs.filter(acres__values=acres)\n\n    if reviewed == 'on':\n        qs = qs.filter(reviewed=True)\n\n    elif not_reviewed == 'on':\n        qs = qs.filter(reviewed=False)\n\n    return qs\n\n\ndef infinite_filter(request):\n    limit = request.GET.get('limit')\n    offset = request.GET.get('offset')\n    return PropertyMaster.objects.all()[int(offset): int(offset) + int(limit)]\n\n\ndef is_there_more_data(request):\n    offset = request.GET.get('offset')\n    if int(offset) > PropertyMaster.objects.all().count():\n        return False\n    return True\n\n# class ReactFilterView(generics.ListAPIView):\n#     serializer_class = JournalSerializer\n#\n#     def get_queryset(self):\n#         qs = filter(self.request)\n#         return qs\n\n\n# class ReactInfiniteView(generics.ListAPIView):\n#     serializer_class = JournalSerializer\n#\n#     def get_queryset(self):\n#         qs = infinite_filter(self.request)\n#         return qs\n#\n#     def list(self, request):\n#         queryset = self.get_queryset()\n#         serializer = self.serializer_class(queryset, many=True)\n#         return Response({\n#             \"journals\": serializer.data,\n#             \"has_more\": is_there_more_data(request)\n#         })\n\n\n\ndef logout_view(request):\n    return render(request,'index2.html')\n","sub_path":"views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":13793,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"342867465","text":"\"\"\"\nEach ListNode holds a reference to its previous node\nas well as its next node in the List.\n\"\"\"\nclass ListNode:\n    def __init__(self, value, prev=None, next=None):\n        self.prev = prev\n        self.value = value\n        self.next = next\n\n    def __str__(self):\n        return f\"Value = {self.value}\"\n\n    def get_value(self):\n        return self.value\n\n    def get_next(self):\n        return self.next\n\n    def set_next(self, new_next):\n        self.next = new_next\n\n    def get_prev(self):\n        return self.prev\n\n    def set_prev(self, new_prev):\n        self.prev = new_prev\n\n    def delete(self):\n        if self.prev:\n            self.prev.next = self.next\n        if self.next:\n            self.next.prev = self.prev\n            \n\"\"\"\nOur doubly-linked list class. It holds references to \nthe list's head and tail nodes.\n\"\"\"\nclass DoublyLinkedList:\n    def __init__(self, node=None):\n        self.head = node\n        self.tail = node\n        self.length = 1 if node is not None else 0\n\n    def __str__(self):\n        output = \"\"\n        if self.head is None:\n            return \"There are no any nodes\"\n        current_node = self.head\n\n        while current_node:\n            output += f\"\\n{current_node.get_value()}\"\n            current_node = current_node.get_next()\n        return output\n\n    def __len__(self):\n        return self.length\n    \n    \"\"\"\n    Wraps the given value in a ListNode and inserts it \n    as the new head of the list. Don't forget to handle \n    the old head node's previous pointer accordingly.\n    \"\"\"\n    def add_to_head(self, value):\n        new_node = ListNode(value)\n        self.length +=1\n        #check if linked list is empty\n        if self.head is None:\n            self.head = new_node\n            self.tail = new_node\n        else:\n            #new_node should point ot current head\n            new_node.set_next(self.head)\n            self.head.set_prev(new_node)\n            #move head to new node\n            self.head = new_node \n        \n    \"\"\"\n    Removes the List's current head node, making the\n    current head's next node the new head of the List.\n    Returns the value of the removed Node.\n    \"\"\"\n    def remove_from_head(self):\n        if self.head is None:\n            return None\n        # self.length -=1\n        head_value = self.head.get_value()\n        # if self.head.get_next() is None:\n        #     self.head = None\n        #     self.tail = None\n        #     return head_value\n\n        # self.head = self.head.get_next()\n        # self.head.set_prev(None) \n        self.delete(self.head)\n        return head_value\n        \n            \n    \"\"\"\n    Wraps the given value in a ListNode and inserts it \n    as the new tail of the list. Don't forget to handle \n    the old tail node's next pointer accordingly.\n    \"\"\"\n    def add_to_tail(self, value):\n        new_node = ListNode(value)\n        self.length +=1\n        if not self.head :\n            self.head = new_node\n            self.tail = new_node\n        else:\n            new_node.set_prev(self.tail)\n            self.tail.set_next(new_node)\n            self.tail = new_node\n            \n    \"\"\"\n    Removes the List's current tail node, making the \n    current tail's previous node the new tail of the List.\n    Returns the value of the removed Node.\n    \"\"\"\n    def remove_from_tail(self):\n        if not self.head:\n            return None\n        # self.length -=1\n        tail_value = self.tail.get_value()\n        # if self.head is self.tail:\n        #     self.head = None \n        #     self.tail = None\n        #     return tail_value\n            \n\n        # self.tail = self.tail.get_prev()\n        # self.tail.set_next(None)\n        self.delete(self.tail)\n        return tail_value\n            \n    \"\"\"\n    Removes the input node from its current spot in the \n    List and inserts it as the new head node of the List.\n    \"\"\"\n    def move_to_front(self, node):\n        if node is None or self.head is None or self.head is node:\n            return \n        node_value = node.get_value()\n        # if node is self.tail:\n        #     self.remove_from_tail()\n            \n        # else:\n        #     if node.get_prev():\n        #         node.prev.set_next(node.get_next())\n        #     if node.get_next():\n        #         node.next.set_prev(node.get_prev())\n        #     self.length -=1\n        self.delete(node)\n        self.add_to_head(node_value)\n\n\n        \n    \"\"\"\n    Removes the input node from its current spot in the \n    List and inserts it as the new tail node of the List.\n    \"\"\"\n    def move_to_end(self, node):\n        if node is None or self.head is None or self.tail is node:\n            return \n        node_value = node.get_value()\n        # if node is self.head:\n        #     self.remove_from_head()\n            \n        # else:\n        #     if node.get_prev():\n        #         node.prev.set_next(node.get_next())\n        #     if node.get_next():\n        #         node.next.set_prev(node.get_prev())\n        #     self.length -=1\n        self.delete(node)\n        self.add_to_tail(node_value)\n\n    \"\"\"\n    Deletes the input node from the List, preserving the \n    order of the other elements of the List.\n    \"\"\"\n    def delete(self, node):\n        if node is None or self.head is None:\n            return\n        self.length -= 1\n        if self.head is self.tail and node is self.head:\n            self.head = None\n            self.tail = None\n        elif node is self.head:\n            self.head = self.head.get_next()\n            self.head.set_prev(None) \n        elif node == self.tail:\n            self.tail = self.tail.get_prev()\n            self.tail.set_next(None)\n\n        else:\n            node.delete()\n            # node.prev.set_next(node.get_next())\n            # node.next.set_prev(node.get_prev())\n\n\n           \n        \n\n    \"\"\"\n    Finds and returns the maximum value of all the nodes \n    in the List.\n    \"\"\"\n    def get_max(self):\n        if self.head is None:\n            return None\n        max_value = self.head.get_value()\n        current_node = self.head.get_next()\n        while current_node:\n            if max_value < current_node.get_value():\n                max_value = current_node.get_value()\n            current_node = current_node.get_next()\n        return max_value\n        \n        \n\ndoubly_linked_list = DoublyLinkedList()\ndoubly_linked_list.add_to_head(1)\nprint(\"length = \", doubly_linked_list.length)\ndoubly_linked_list.add_to_head(2)\ndoubly_linked_list.add_to_head(3)\ndoubly_linked_list.remove_from_head()\ndoubly_linked_list.remove_from_head()\ndoubly_linked_list.remove_from_head()\nprint(doubly_linked_list)\n\n\n","sub_path":"doubly_linked_list/doubly_linked_list.py","file_name":"doubly_linked_list.py","file_ext":"py","file_size_in_byte":6628,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"79400433","text":"# -*- coding: utf-8 -*-\n#\n# Telefónica Digital - Product Development and Innovation\n#\n# THIS CODE AND INFORMATION ARE PROVIDED \"AS IS\" WITHOUT WARRANTY OF ANY KIND,\n# EITHER EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE IMPLIED\n# WARRANTIES OF MERCHANTABILITY AND/OR FITNESS FOR A PARTICULAR PURPOSE.\n#\n# Copyright (c) Telefónica Investigación y Desarrollo S.A.U.\n# All rights reserved.\n#\n\"\"\"Routes mapping\"\"\"\n\nimport re\nfrom urlparse import urlparse\n\nPATH_PATTERN = re.compile('(?:/[^/]+)*/v(\\d+)/?')\n\n\nclass Routes(object):\n    \"\"\"Resource URLs factory\"\"\"\n\n    def __init__(self, base_url):\n        \"\"\"Initializes a Routes object and pre-computes fixed resources.\n\n        >>> instance = Routes('http://foo/base/v1')\n        >>> instance.storage\n        'http://foo/base/v1/storage'\n        >>> instance.profile\n        'http://foo/base/v1/profile'\n        >>> instance.api_version\n        1\n\n        Base urls must include a version tag or a ValueError is raised.\n\n        >>> Routes('http://foo/unversioned')\n        Traceback (most recent call last):\n            ...\n        ValueError: Base API url has not version tag: \"http://foo/unversioned\"\n        \"\"\"\n        self.__set_base_url(base_url)\n        self.info = self.base_url + \"/info\"\n        self.storage = self.base_url + \"/storage\"\n        self.profile = self.base_url + \"/profile\"\n        self.services = self.base_url + \"/services\"\n\n    def clusters(self):\n        \"\"\"Construct the clusters resource URL.\n\n        >>> Routes('http://foo/base/v1').clusters()\n        'http://foo/base/v1/cluster'\n        \"\"\"\n        return \"%s/cluster\" % (self.base_url)\n\n    def cluster(self, cluster_id, action=None):\n        \"\"\"Construct a cluster resource URL.\n\n        >>> Routes('http://foo/base/v1').cluster('37')\n        'http://foo/base/v1/cluster/37'\n\n        If you need to perform a concrete action use the optional argument action:\n\n        >>> Routes('http://foo/base/v1').cluster('37', action=\"terminate\")\n        'http://foo/base/v1/cluster/37/terminate'\n        \"\"\"\n        parts = [self.clusters(), cluster_id]\n        if action:\n            parts.append(action)\n        return \"/\".join(parts)\n\n    def __set_base_url(self, url):\n        parts = urlparse(url)\n        match = PATH_PATTERN.match(parts.path)\n        if not match:\n            raise ValueError('Base API url has not version tag: \"%s\"' % url)\n        self.base_url = url if not url.endswith('/') else url[:-1]\n        self.api_version = int(match.group(1))\n","sub_path":"cosmos-cli/cosmos/common/routes.py","file_name":"routes.py","file_ext":"py","file_size_in_byte":2497,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"561350711","text":"import numpy\nfrom pyparsing import CaselessLiteral, Combine, Forward, Literal, Optional, Word, ZeroOrMore, alphas, nums\n\nfrom django.contrib.gis.gdal import GDALRaster\n\nfrom .const import ALGEBRA_PIXEL_TYPE_GDAL, ALGEBRA_PIXEL_TYPE_NUMPY\n\n\nclass FormulaParser(object):\n    \"\"\"\n    Deconstruct mathematical algebra expressions and convert those into\n    callable funcitons.\n\n    Adopted from: http://pyparsing.wikispaces.com/file/view/fourFn.py\n    \"\"\"\n    expr_stack = []\n\n    # The data dictionary holds the values on which to evaluate the formula\n    data = {}\n\n    # Map operator symbols to arithmetic operations in numpy\n    opn = {\n        \"+\": numpy.add,\n        \"-\": numpy.subtract,\n        \"*\": numpy.multiply,\n        \"/\": numpy.divide,\n        \"^\": numpy.power,\n        \"==\": numpy.equal,\n        \"!=\": numpy.not_equal,\n        \">\": numpy.greater,\n        \">=\": numpy.greater_equal,\n        \"<\": numpy.less,\n        \"<=\": numpy.less_equal,\n        \"|\": numpy.logical_or,\n        \"&\": numpy.logical_and\n    }\n\n    # Map function names to python functions\n    fn = {\n        \"sin\": numpy.sin,\n        \"cos\": numpy.cos,\n        \"tan\": numpy.tan,\n        \"log\": numpy.log,\n        \"exp\": numpy.exp,\n        \"abs\": numpy.abs,\n        \"int\": numpy.int,\n        \"round\": numpy.round,\n        \"sign\": numpy.sign,\n    }\n\n    def __init__(self):\n        \"\"\"\n        Setup the Backus Normal Form (BNF) parser logic.\n        \"\"\"\n        self.dtype=ALGEBRA_PIXEL_TYPE_NUMPY\n        point = Literal(\".\")\n\n        e = CaselessLiteral(\"E\")\n\n        fnumber = Combine(\n            Word(\"+-\" + nums, nums) +\n            Optional(point + Optional(Word(nums))) +\n            Optional(e + Word(\"+-\" + nums, nums))\n        )\n\n        ident = Word(alphas, alphas + nums + \"_$\")\n\n        # Operators\n        plus = Literal(\"+\")\n        minus = Literal(\"-\")\n        mult = Literal(\"*\")\n        div = Literal(\"/\")\n        eq = Literal(\"==\")\n        neq = Literal(\"!=\")\n        lt = Literal(\"<\")\n        le = Literal(\"<=\")\n        gt = Literal(\">\")\n        ge = Literal(\">=\")\n        ior = Literal(\"|\")\n        iand = Literal(\"&\")\n        lpar = Literal(\"(\").suppress()\n        rpar = Literal(\")\").suppress()\n        addop = plus | minus | eq\n        multop = mult | div | eq | neq | ge | le | gt | lt | ior | iand  # Order matters here due to \"<=\" being caught by \"<\"\n        expop = Literal(\"^\")\n        pi = CaselessLiteral(\"PI\")\n\n        # Letters for variables\n        aa = CaselessLiteral(\"a\")\n        bb = CaselessLiteral(\"b\")\n        cc = CaselessLiteral(\"c\")\n        dd = CaselessLiteral(\"d\")\n        ee = CaselessLiteral(\"e\")\n        ff = CaselessLiteral(\"f\")\n        gg = CaselessLiteral(\"g\")\n        hh = CaselessLiteral(\"h\")\n        ii = CaselessLiteral(\"i\")\n        jj = CaselessLiteral(\"j\")\n        kk = CaselessLiteral(\"k\")\n        ll = CaselessLiteral(\"l\")\n        mm = CaselessLiteral(\"m\")\n        nn = CaselessLiteral(\"n\")\n        oo = CaselessLiteral(\"o\")\n        pp = CaselessLiteral(\"p\")\n        qq = CaselessLiteral(\"q\")\n        rr = CaselessLiteral(\"r\")\n        ss = CaselessLiteral(\"s\")\n        tt = CaselessLiteral(\"t\")\n        uu = CaselessLiteral(\"u\")\n        vv = CaselessLiteral(\"v\")\n        ww = CaselessLiteral(\"w\")\n        xx = CaselessLiteral(\"x\")\n        yy = CaselessLiteral(\"y\")\n        zz = CaselessLiteral(\"z\")\n\n        bnf = Forward()\n\n        atom = (\n            Optional('-') + Optional(\"!\") + (\n                pi | e | fnumber | ident + lpar + bnf + rpar |  # pi needs to be before the letters for it to be found\n                aa | bb | cc | dd | ee | ff | gg | hh | ii | jj | kk | ll | mm |\n                nn | oo | pp | qq | rr | ss | tt | uu | vv | ww | xx | yy | zz\n            ).setParseAction(self.push_first) | (lpar + bnf.suppress() + rpar)\n        ).setParseAction(self.push_unary_operator)\n\n        # By defining exponentiation as \"atom [ ^ factor ]...\" instead of \"atom [ ^ atom ]...\",\n        # we get right-to-left exponents, instead of left-to-righ\n        # that is, 2^3^2 = 2^(3^2), not (2^3)^2.\n        factor = Forward()\n        factor << atom + ZeroOrMore((expop + factor).setParseAction(self.push_first))\n\n        term = factor + ZeroOrMore((multop + factor).setParseAction(self.push_first))\n        bnf << term + ZeroOrMore((addop + term).setParseAction(self.push_first))\n\n        self.bnf = bnf\n\n    def push_first(self, strg, loc, toks):\n        self.expr_stack.append(toks[0])\n\n    def push_unary_operator(self, strg, loc, toks):\n        \"\"\"\n        Sets custom flag for unary operators.\n        \"\"\"\n        if toks:\n            if toks[0] == '-':\n                self.expr_stack.append('unary -')\n            elif toks[0] == '!':\n                self.expr_stack.append('unary !')\n\n    def evaluate_stack(self, stack):\n        \"\"\"\n        Evaluate a stack element.\n        \"\"\"\n        # Get operator element\n        op = stack.pop()\n\n        # Evaluate unary operators\n        if op == 'unary -':\n            return -self.evaluate_stack(stack)\n        if op == 'unary !':\n            return numpy.logical_not(self.evaluate_stack(stack))\n\n        # Evaluate binary operators\n        if op in [\"+\", \"-\", \"*\", \"/\", \"^\", \">\", \"<\", \"==\", \"!=\", \"<=\", \">=\", \"|\", \"&\", \"!\"]:\n            op2 = self.evaluate_stack(stack)\n            op1 = self.evaluate_stack(stack)\n            return self.opn[op](op1, op2)\n        elif op == \"PI\":\n            return numpy.pi\n        elif op == \"E\":\n            return numpy.e\n        elif op in self.fn:\n            return self.fn[op](self.evaluate_stack(stack))\n        elif op[0].isalpha() and len(op[0]) == 1 and op[0] in self.data:\n            return self.data[op[0]]\n        elif op[0].isalpha() and len(op[0]) == 1:\n            raise Exception('Found an undeclared variable in formula.')\n        else:\n            # If numeric, convert to numpy float\n            return numpy.array(op, dtype=self.dtype)\n\n    def parse_formula(self, formula):\n        \"\"\"\n        Parse a string formula into a BNF expression.\n        \"\"\"\n        # Clean formula before parsing\n        formula = self.clean_formula(formula)\n\n        # Reset expression stack\n        self.expr_stack = []\n\n        # Use bnf to parse the string\n        self.bnf.parseString(formula)\n\n    def clean_formula(self, formula):\n        \"\"\"\n        Remove any white space and line breaks from formula.\n        \"\"\"\n        return formula.replace(' ', '').replace('\\n', '').replace('\\r', '')\n\n    def evaluate(self, data=None):\n        \"\"\"\n        Evaluate the input data using the current formula expression stack.\n        \"\"\"\n        # Make sure a formula has been parsed before evaluating\n        if self.expr_stack == []:\n            raise Exception('Please specify a formula to evaluate.')\n\n        # Update dataset\n        if data:\n            self.data = data\n\n        # Evaluate stack on data\n        self.result = self.evaluate_stack(self.expr_stack)\n\n        return self.result\n\n    def evaluate_formula(self, formula, data={}, dtype=ALGEBRA_PIXEL_TYPE_NUMPY):\n        \"\"\"\n        Helper function to set formula and evaluate in one call.\n        \"\"\"\n        self.dtype = dtype\n        self.parse_formula(formula)\n        return self.evaluate(data)\n\n\nclass RasterAlgebraParser(FormulaParser):\n    \"\"\"\n    Compute raster algebra expressions using the FormulaParser class.\n    \"\"\"\n\n    def evaluate_raster_algebra(self, data, formula, check_aligned=False):\n        \"\"\"\n        Evaluate a raster algebra expression on a set of rasters. All input\n        rasters need to be strictly aligned (same size, geotransform and srid).\n\n        The input raster list will be zipped into a dictionary using the input\n        names. The resulting dictionary will be used as input data for formula\n        evaluation. If the check_aligned flag is set, the input rasters are\n        compared to make sure they are aligned.\n        \"\"\"\n        # Check that all input rasters are aligned\n        if check_aligned:\n            self.check_aligned(list(data.values()))\n\n        # Construct list of numpy arrays holding raster pixel data\n        data_arrays = {\n            key: numpy.ma.masked_values(rast.bands[0].data().ravel(), rast.bands[0].nodata_value)\n            for key, rast in data.items()\n        }\n\n        # Evaluate formula on raster data\n        result = self.evaluate_formula(formula, data_arrays)\n\n        # Reference first original raster for constructing result\n        orig = list(data.values())[0]\n        orig_band = orig.bands[0]\n\n        # Convert to default number type\n        result = result.astype(ALGEBRA_PIXEL_TYPE_NUMPY)\n\n        # Return GDALRaster holding results\n        return GDALRaster({\n            'datatype': ALGEBRA_PIXEL_TYPE_GDAL,\n            'driver': 'MEM',\n            'width': orig.width,\n            'height': orig.height,\n            'nr_of_bands': 1,\n            'srid': orig.srs.srid,\n            'origin': orig.origin,\n            'scale': orig.scale,\n            'skew': orig.skew,\n            'bands': [{\n                'nodata_value': orig_band.nodata_value,\n                'data': result\n            }],\n        })\n\n    def check_aligned(self, rasters):\n        \"\"\"\n        Assert that all input rasters are properly aligned.\n        \"\"\"\n        if not len(set([x.srs.srid for x in rasters])) == 1:\n            raise Exception('Raster aligment check failed: SRIDs not all the same')\n\n        gt = rasters[0].geotransform\n        if any([gt != rast.geotransform for rast in rasters[1:]]):\n            raise Exception('Raster aligment check failed: geotransform arrays are not all the same')\n","sub_path":"raster/formulas.py","file_name":"formulas.py","file_ext":"py","file_size_in_byte":9578,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"454051621","text":"import unittest\n\nfrom Grid import Grid\n\n\nclass TestGrid(unittest.TestCase):\n    def test_add_state(self):\n        grid = Grid()\n        grid.set_sigma([\"a\", \"b\"])\n        with self.assertRaises(ValueError):\n            grid.add_state([\"1\", \"1\", \"2\", \"3\"])\n            grid.add_state([\"1\", \"1\"])\n        self.assertTrue(grid.add_state([\"0\", \"1\", \"2\"]))\n        self.assertTrue(grid.has_state(\"0\"))\n        self.assertFalse(grid.has_state(\"1\"))\n        self.assertEqual(grid.get_states(), [\"0\"])\n\n    def test_set_sigma(self):\n        grid = Grid()\n        with self.assertRaises(TypeError):\n            grid.set_sigma([\"a\", \"b\"])\n        with self.assertRaises(ValueError):\n            grid.set_sigma([\"a\", \"a\"])\n        grid.set_sigma([\"a\", \"b\", \"c\"])\n        self.assertEqual(grid.get_sigma_length(), 3)\n        self.assertEqual(grid.get_sigma(), [\"a\", \"b\", \"c\"])\n\nif __name__ == \"__main__\":\n    unittest.main()\n","sub_path":"TestGrid.py","file_name":"TestGrid.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"268054484","text":"import socket\r\nimport sys\r\nimport os\r\nimport subprocess\r\n\r\nglobal option\r\ndef main():\r\n\tprint(\" \")\r\n\tprint(\"  SHELLPY Remote Shell Application  \")\r\n\tprint(\" \")\r\n\tprint(\" ###################################\")\r\n\tprint(\"  [1]: Connect\")\r\n\tprint(\"  [2]: About\")\r\n\tprint(\"  [3]: Quit\")\r\n\tprint(\" ###################################\")\r\n\tprint(\" \")\r\n\toption = input(\" > \")\r\n\toptions(option)\r\n\r\ndef options(a):\r\n\tif a=='1':\r\n\t\tconnectserv()\r\n\telif a=='2':\r\n\t\tprint(\"SHELLPY ( ABOUT US ) \\n\\n\")\r\n\t\tprint(\"ShellPy is a standalone program capable of sharing your terminal and all it’s features with other desktop. In addition to this terminal service sharing, the application is completely transparent to user.\\n\\nA reverse shell works by the remote computer sending its shell to a specific user.This allows root commands over the remote server.\\n\\nTo use this reverse shell, two scripts need to be running:\\n\\t[*]Server.py - runs on a public server and waits for clients to connect.\\n\\t[*]Client.py - connects to a remote server and then wait for commands \")\r\n\t\tinput()\r\n\telif a=='3':\r\n\t\texit()\r\n\telse:\r\n\t\tprint(\"Try Again\")\r\n\r\n\r\ndef connectserv():\r\n        socket_create()\r\n        socket_bind()\r\n        socket_accept()\r\n\r\n# Create socket (allows two computers to connect)\r\ndef socket_create():\r\n    try:\r\n        global host\r\n        global port\r\n        global s\r\n        host = ''\r\n        port = 9999\r\n        s = socket.socket()\r\n    except socket.error as msg:\r\n        print(\"Socket creation error: \" + str(msg))\r\n\r\n\r\n# Bind socket to port (the host and port the communication will take place) and wait for connection from client\r\ndef socket_bind():\r\n    try:\r\n        global host\r\n        global port\r\n        global s\r\n        print(\"Binding socket to port: \" + str(port))\r\n        s.bind((host, port))\r\n        s.listen(5)\r\n    except socket.error as msg:\r\n        print(\"Socket binding error: \" + str(msg) + \"\\n\" + \"Retrying...\")\r\n        socket_bind()\r\n\r\n\r\n# Establish connection with client (socket must be listening for them)\r\ndef socket_accept():\r\n    conn, address = s.accept()\r\n    print(\"Connection has been established | \" + \"IP \" + address[0] + \" | Port \" + str(address[1]))\r\n    send_commands(conn)\r\n    conn.close()\r\n\r\n\r\n# Send commands\r\ndef send_commands(conn):\r\n    while True:\r\n        cmd = input()\r\n        if cmd == 'quit':\r\n            conn.close()\r\n            s.close()\r\n            sys.exit()\r\n        if len(str.encode(cmd)) > 0:\r\n            conn.send(str.encode(cmd))\r\n            client_response = str(conn.recv(1024), \"utf-8\")\r\n            print(client_response, end=\"\")\r\n\r\n\r\n\t\r\nmain()\r\n","sub_path":"ShellPy.py","file_name":"ShellPy.py","file_ext":"py","file_size_in_byte":2618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"524231635","text":"a, b, c = map(int, input().split())\n\nif a == b == c:\n    answer = 10000 + a * 1000\nelif a == b:\n    answer = 1000 + a * 100\nelif b == c:\n    answer = 1000 + b * 100\nelif c == a:\n    answer = 1000 + c * 100\nelse:\n    m = max([a, b, c])\n    answer = m * 100\n\nprint(answer)\n","sub_path":"solved/three_dice.py","file_name":"three_dice.py","file_ext":"py","file_size_in_byte":271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"278798510","text":"import socket\nfrom multiprocessing import Process\nimport time\nimport os\nimport sys\nimport logging\nfrom pathlib import Path\n\nHOST = 'localhost'\nPORT = 60000\nDEBUG = True\n\ndef main():\n    s = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n    try:\n        s.bind((HOST, PORT))\n        s.listen(5)\n    except Exception as e:\n        logging.critical(f'Lesten {HOST}:{PORT} failed: {e}')\n        sys.exit(1)\n    logging.info('服务器socket设置完成：{}:{}'.format(HOST, PORT))\n\n    try:\n        while True:\n            time.sleep(0.01)\n            # 接受请求\n            connection, address = s.accept()\n            logging.info(\"接收到新连接，客户端 {}:{}\".format(address[0], address[1]))\n            \n            # 开一个新进程处理请求\n            p = Process(target=echo_server, args=(connection, address))\n            p.start()\n\n            connection.close()\n    finally:\n        s.close()\n\n# echo服务器处理代码\ndef echo_server(conn, addr):\n    logging.info('进入echo服务器, 客户端 {}:{}, pid: {}'.format(addr[0], addr[1], os.getpid()))\n    while True:\n        buf = conn.recv(1024)\n        # 如果buf是eof，则退出\n        if not buf:\n            break\n        else:\n            command = buf.decode('utf-8')\n            logging.debug('接收到客户端 {}:{} 内容：{}'.format(addr[0], addr[1], command))\n            if command.split()[0].lower() == 'put':\n                logging.debug(f'收到一个put请求: {command}')\n                server_put(command, conn)\n            elif command.split()[0].lower() == 'get':\n                logging.debug(f'收到一个get请求: {command}')\n                server_get(command, conn)\n            else:\n                conn.sendall(buf)\n                logging.debug('发送到客户端 {}:{} 内容：{}'.format(addr[0], addr[1], command))\n    conn.close()\n    logging.info('客户端退出, 客户端 {}:{}, pid: {}'.format(addr[0], addr[1], os.getpid()))\n\ndef server_put(command, conn):\n    # 检查put命令是否是3个参数，如果不是，发送错误回应，并返回\n    if len(command.split()) != 4:\n        conn.sendall(\"Error: 错误的命令，正确命令应该为 put source_file [None|target_dir|target_file] size.\".encode('utf-8'))\n    else:\n        _, src_file, target_file, file_size = command.split()\n        filename = Path(src_file).name\n        # 如果没有指定目标目录，则为当前目录下的recv目录\n        if target_file == 'None':\n            target_p = Path(__file__).resolve().parent.joinpath('recv', filename)\n        else:\n            if target_file[-1] == '/':\n                target_p = Path(target_file).joinpath(filename)\n            else:\n                target_p = Path(target_file)\n                if target_p.is_dir():\n                    target_p = target_p.joinpath(filename)\n        \n        # 取出目录和文件名\n        target_dir = target_p.parent\n        filename = target_p.name\n        try:\n            target_dir.mkdir(parents=True, exist_ok=True)\n            with open(target_p, 'wb') as f:\n                pass\n        except Exception as e:\n            logging.critical('Error: {}'.format(e))\n            conn.sendall('Error: {}'.format(e).encode('utf-8'))\n            return        \n        \n        logging.debug('文件名: {}, 目标目录：{}'.format(filename, target_dir))\n        # 一切正常，则向客户端返回OK\n        conn.sendall(b'OK: Server is ready to recieve file.')\n        try:\n            with open(target_p, 'wb') as f:\n                r_size = 0\n                while r_size < int(file_size):\n                    buf = conn.recv(1024)\n                    r_size += len(buf)\n                    print(buf.decode('utf-8'))\n                    if not buf:\n                        break\n                    else:\n                        f.write(buf)\n        except Exception as e:\n            conn.sendall(b'Error: Server side error occured {}'.format(e))\n            logging.critical('Error: {}'.format(e))\n            return\n        # 向客户端发送成功的msg\n        conn.sendall('Succeed!')\n        logging.debug('接受文件完成，{}，文件大小：{}'.format(target_p, file_size))\n\ndef server_get(command, conn):\n    elements = command.split()\n    if len(elements) != 3:\n        conn.sendall('Error: 参数数量错误'.encode('utf-8'))\n    else:\n        src_file = elements[1]\n        src_p = Path(src_file)\n        if not src_p.is_file():\n            conn.sendall('Error: 文件没有找到'.encode('utf-8'))\n        else:\n            try:\n                with open(src_p, 'rb') as f:\n                    pass\n            except Exception as e:\n                conn.sendall('Error: 读取文件错误'.encode('utf-8'))\n                return\n            conn.sendall('OK {}'.format(src_p.stat().st_size).encode('utf-8'))\n            response = conn.recv(1024)\n            if response.decode('utf-8').startswith('OK'):\n                # 开始传输文件\n                try:\n                    with open(src_p, 'rb') as f:\n                        while True:\n                            file_data = f.read(1024)\n                            if not file_data:\n                                break\n                            conn.sendall(file_data)\n                except Exception as e:\n                    logging.critical('Error: 发送文件时候出错: {}'.format(e))\n                logging.info('发送文件{}成功'.format(src_p))\n            response = conn.recv(1024)\n            logging.debug('从客户端收到: {}'.format(response.decode('utf-8')))\n\n# 启动守护进程\ndef daemonize(stdin='/dev/null', stdout='/dev/null', stderr='/dev/null'):\n    try:\n        # 创建新进程\n        pid = os.fork()\n\n        # 父进程退出\n        if pid > 0:\n            sys.exit(0)\n    except OSError as err:\n        sys.stderr.write('_Fork #1 failed: {}\\n'.format(err))\n        sys.exit(1)\n    \n    # 从父进程环境脱离\n    # chdir确认进程不占用任何目录，否则不能umount\n    os.chdir('/')\n    os.umask(0)\n    os.setsid()\n\n    # 第二次fork\n    try:\n        pid = os.fork()\n\n        if pid > 0:\n            # 第二个父进程退出\n            sys.exit(0)\n    except OSError as err:\n        sys.stderr.write('_Fork #2 failed: {}\\n'.format(err))\n        sys.exit(1)\n\n    # 重定向标准文件描述符\n    sys.stdout.flush()\n    sys.stderr.flush()\n\n    si = open(stdin, 'r')\n    so = open(stdout, 'a+')\n    se = open(stderr, 'w')\n\n    # dup2函数原子化关闭和复制文件描述符\n    os.dup2(si.fileno(), sys.stdin.fileno())\n    os.dup2(so.fileno(), sys.stdout.fileno())\n    os.dup2(se.fileno(), sys.stderr.fileno())\n    logging.info('守护进程初始化完成，当前进程号: {}'.format(os.getpid()))\n\ndef config_logging():\n    loglevel = logging.DEBUG\n    if DEBUG:\n        logfile = '/dev/stdout'\n    else:\n        logfile = Path(__file__).parent.joinpath('echo_server.log')\n    logging.basicConfig(filename=logfile,\n                        level=loglevel,\n                        datefmt='%Y-%m-%d %X',\n                        format='%(asctime)s %(levelname)-8s %(message)s')\n    logging.info('logging配置完成')\n\nif __name__ == '__main__':\n    config_logging()\n    if not DEBUG:\n        daemonize()\n    main()\n","sub_path":"week02/echo_server.py","file_name":"echo_server.py","file_ext":"py","file_size_in_byte":7288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"606239158","text":"import matplotlib.pyplot as plt\nimport pandas as pd\nimport matplotlib.dates as mdates\n\nfig = plt.figure()\nax = fig.add_subplot(1,1,1)\n\n#指定行のcsvデータの取得\ndef Input():\n    datas = pd.read_csv('stock/date.csv', header=None, index_col=0, usecols=[0])\n    \n    return datas\n\n\n#日付データの取得\nx=Input()\nprint(x)\nprint(x.dtyeps)\n\n# 横軸：日付 periods分の日付を用意します。\n\n\n# 縦軸：数値\ny = [130, 141, 142, 143, 171, 230, 231, 260, 276, 297]\nprint(y)\nprint(y.dtypes)\n\nax.plot(x,y)\n\n# 日付ラベルフォーマットを修正\ndays = mdates.DayLocator() \ndaysFmt = mdates.DateFormatter('%m-%d')\nax.xaxis.set_major_locator(days)\nax.xaxis.set_major_formatter(daysFmt)\n\n# グラフの表示\nplt.show()","sub_path":"stock/day_test.py","file_name":"day_test.py","file_ext":"py","file_size_in_byte":740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"176778093","text":"# You are given an array of positive integers nums and want to erase a subarray containing unique elements. The score you get by erasing the subarray is equal to the sum of its elements.\n#\n# Return the maximum score you can get by erasing exactly one subarray.\n#\n# An array b is called to be a subarray of a if it forms a contiguous subsequence of a, that is, if it is equal to a[l],a[l+1],...,a[r] for some (l,r).\n#\n#  \n# Example 1:\n#\n#\n# Input: nums = [4,2,4,5,6]\n# Output: 17\n# Explanation: The optimal subarray here is [2,4,5,6].\n#\n#\n# Example 2:\n#\n#\n# Input: nums = [5,2,1,2,5,2,1,2,5]\n# Output: 8\n# Explanation: The optimal subarray here is [5,2,1] or [1,2,5].\n#\n#\n#  \n# Constraints:\n#\n#\n# \t1 <= nums.length <= 105\n# \t1 <= nums[i] <= 104\n#\n#\n\n\nclass Solution:\n    def maximumUniqueSubarray(self, nums: List[int]) -> int:\n        cnt = defaultdict(int)\n        ans = 0\n        i, j = 0, 0\n        \n        prefix = [0]\n        for v in nums:\n            prefix.append(prefix[-1] + v)\n        \n        while i < len(nums):\n            while i < len(nums):\n                if cnt[nums[i]] >= 1:\n                    break\n                cnt[nums[i]] += 1\n                i += 1\n            if (i == len(nums)):\n                ans = max(ans, prefix[-1] - prefix[j])\n                break\n            ans = max(ans, prefix[i] - prefix[j])\n            while j < len(nums):\n                cnt[nums[j]] -= 1\n                j += 1\n                if cnt[nums[i]] < 1:\n                    break\n        return ans \n            \n","sub_path":"solutions/1813-maximum-erasure-value/maximum-erasure-value.py","file_name":"maximum-erasure-value.py","file_ext":"py","file_size_in_byte":1530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"627429255","text":"'''\n@Time    : 2019/8/19 12:04\n@Author  : mjh\n@File    : adaboost.py\n'''\n\nimport math\n\nclass AdaBoost():\n    def __init__(self, M=5):\n        '''初始化参数'''\n        self.weights = None        #数据权值分布\n        self.M = M                 #训练轮数\n        self.alpha = []            #分类器权值\n        self.feature = []          #选择的特征\n        self.dump = []             #分类器规则\n\n    def fit(self, Xdata, Ydata):\n        #模型训练\n        self.weights = [1/len(Xdata) for i in range(0, len(Xdata))]\n        for iter in range(0, self.M):\n            self.__getdump(Xdata, Ydata)\n\n    def predict(self, Xdata):\n        #预测数据\n        result = []\n        for iter1 in range(0, len(Xdata)):\n            predict = 0\n            for iter2 in range(0, self.M):\n                predict += self.alpha[iter2]*self.dump[iter2](Xdata[iter1][self.feature[iter2]])\n            if predict >= 0:\n                result.append(1)\n            else:\n                result.append(-1)\n        return result\n\n    def __getdump(self, X, Y):\n        #在一个特征上确定一个决策树桩\n        minerror = float(\"inf\")\n        dump = None\n        feature = None\n        for iter1 in range(0, len(X[0])):             #遍历每一个特征\n            for iter2 in range(0, len(X)):            #遍历每一个分类点\n                func1 = lambda x: 1 if x >= X[iter2][iter1] else -1\n                func2 = lambda x: -1 if x >= X[iter2][iter1] else 1\n                error1 = 0; error2 = 0\n                for i in range(0, len(X)):\n                    if func1(X[i][iter1]) != Y[i]:\n                        error1 += self.weights[i]\n                    if func2(X[i][iter1]) != Y[i]:\n                        error2 += self.weights[i]\n\n                if error1 < error2:\n                    if error1 < minerror:\n                        minerror = error1\n                        a = X[iter2][iter1]\n                        dump = lambda x: 1 if x >= a else -1\n                        feature = iter1\n                else:\n                    if error2 < minerror:\n                        minerror = error2\n                        a = X[iter2][iter1]\n                        dump = lambda x: -1 if x >= a else 1\n                        feature = iter1\n\n        if minerror < float(\"inf\"):\n            self.dump.append(dump)                       #添加分类规则\n            self.feature.append(feature)\n            if minerror != 0:\n                alpha = 0.5*math.log((1-minerror)/minerror)\n            else:\n                alpha = 1\n            self.alpha.append(alpha)\n\n            total = 0                                   #更新数据权重\n            for i in range(0, len(self.weights)):\n                self.weights[i] = self.weights[i]*math.exp(-1*alpha*Y[i]*dump(X[i][feature]))\n                total += self.weights[i]\n            self.weights = [self.weights[i] / total for i in range(0, len(self.weights))]\n\nif __name__==\"__main__\":\n    x = [[0], [1], [2], [3], [4], [5], [6], [7], [8], [9]]\n    y = [1, 1, 1, -1, -1, -1, 1, 1, 1, -1]\n\n    x2 = [[0, 1, 3], [0, 3, 1], [1, 2, 2], [1, 1, 3], [1, 2, 3], [0, 1, 2], [1, 1, 2], [1, 1, 1], [1, 3, 1], [0, 2, 1]]\n    y2 = [-1, -1, -1, -1, -1, -1, 1, 1, -1, -1]\n\n    ada = AdaBoost(10)\n    ada.fit(x2, y2)\n    result = ada.predict(x2)\n    print(result)","sub_path":"code/adaboost.py","file_name":"adaboost.py","file_ext":"py","file_size_in_byte":3361,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"627409799","text":"\r\nimport matplotlib.pyplot as plt\r\nimport numpy as np\r\n\r\n#MMMR=[(thetai,ri,xi,yi,xpi,ypi)]\r\n\r\n\r\nimport copy\r\nfrom zonesafe import *\r\nN=720\r\nlidar=[]\r\nrv=20\r\nm=5\r\ni=0\r\nr=50\r\n\r\nwhile i<N:\r\n    lidar.append((i,r))\r\n    i+=1\r\n\r\nMMMR=zonesafe(lidar,rv,m)\r\ni=0\r\nwhile i<len(MMMR):\r\n    plt.plot(MMMR[i][2], MMMR[i][3],\"b:o\")\r\n    plt.plot(MMMR[i][4], MMMR[i][5],\"r:o\")\r\n    i=i+1\r\n    \r\nplt.axis('equal')\r\nplt.show()\r\n\r\n#zonesafe testé avec succés","sub_path":"voiture-Autonome-2020-2021/code/testzonesafe.py","file_name":"testzonesafe.py","file_ext":"py","file_size_in_byte":443,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"549913963","text":"# -*- coding: utf-8 -*-\nimport pprint, re, requests, scrapy\n\nfrom forum_duanzibar_com.items import ForumDuanzibarComItem\nfrom forum_duanzibar_com.settings import API_SETTINGS\n\n\nclass WwwAitaotuComGuoneiSpider(scrapy.Spider):\n\tname = \"www_aitaotu_com_guonei\"\n\tallowed_domains = [\"www.aitaotu.com\"]\n\tstart_urls = ['http://www.aitaotu.com/']\n\n\tcategories = []\n\tfid = 114\n\tpages = 492\n\n\n\tdef parse(self, response):\n\t\tfor page in range(self.pages + 1)[::-1]:\n\t\t\tif page > 0:\n\t\t\t\turl = \"{}guonei/list_{}.html\".format(self.start_urls[0], page)\n\t\t\t\tif page == 1:\n\t\t\t\t\turl = \"{}guonei/\".format(self.start_urls[0])\n\t\t\t\tyield scrapy.Request(url, callback=self.parsePage)\n\n\n\tdef parsePage(self, response):\n\t\tdivs = response.xpath(\"\"\"//div[@id=\"infinite_scroll\"]/div\"\"\")\n\t\tfor d in divs:\n\t\t\titem = ForumDuanzibarComItem()\n\t\t\ttitle = d.xpath(\"\"\"./div[@class=\"item_b clearfix\"]/div[@class=\"title\"]/span/a/text()\"\"\").extract_first()\n\t\t\tarticle_url = \"{}{}\".format(self.start_urls[0], d.xpath(\"\"\"./div[@class=\"item_b clearfix\"]/div[@class=\"title\"]/span/a/@href\"\"\").extract_first())\n\t\t\tthumbnail_src = d.xpath(\"\"\"./div[@class=\"item_t\"]/div[@class=\"img\"]/a/img/@data-original\"\"\").extract_first()\n\t\t\titem['fid'] = self.fid\n\t\t\titem['response_url'] = response.url\n\t\t\titem['title'] = title\n\t\t\titem['categories'] = self.categories\n\t\t\titem['image_urls'] = []\n\t\t\titem['thumbnail_url'] = thumbnail_src\n\t\t\titem['image_urls'].append(item['thumbnail_url'])\n\t\t\titem['content_url'] = article_url\n\t\t\titem['contents'] = []\n\t\t\tyield scrapy.Request(item['content_url'], meta={'item': item}, callback=self.parseContent)\n\n\n\tdef parseContent(self, response):\n\t\titem = response.meta['item']\n\t\timages = response.xpath(\"\"\"//div[@id=\"big-pic\"]/p/a\"\"\")\n\t\tfor img in images:\n\t\t\timage = img.xpath(\"\"\"./img/@src\"\"\").extract_first()\n\t\t\tif image:\n\t\t\t\titem['contents'].append(image)\n\t\t\t\tif image not in item['image_urls']:\n\t\t\t\t\titem['image_urls'].append(image)\n\t\tnxt = response.xpath(\"\"\"//div[@class=\"pages\"]/ul/li/a[contains(text(), \"下一页\")]/@href\"\"\").extract_first()\n\t\tif nxt:\n\t\t\tyield scrapy.Request(\"{}{}\".format(self.start_urls[0], nxt), meta={'item': item}, callback=self.parseContent)\n\t\telse:\n\t\t\tyield item","sub_path":"forum_duanzibar_com/forum_duanzibar_com/spiders/www_aitaotu_com_guonei.py","file_name":"www_aitaotu_com_guonei.py","file_ext":"py","file_size_in_byte":2168,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"233732054","text":"#!/usr/bin/python3\n\"\"\"\nTask 0: N queens\n\"\"\"\n\nimport sys\n\n\ndef canMove(final, row, column):\n    \"\"\"\n    Checks if the queen would be able to move\n    \"\"\"\n    rows = []\n    columns = []\n    top_left = []\n    top_right = []\n\n    for nums in final:\n        rows.append(nums[0])\n        columns.append(nums[1])\n        top_left.append(nums[1] - nums[0])\n        top_right.append(nums[0] + nums[1])\n\n    if row in rows or column in columns:\n        return False\n    if column - row in top_left or row + column in top_right:\n        return False\n\n    return True\n\n\ndef nqueens(final, column, checked_queens=[]):\n    \"\"\"\n    The main recursive program\n    \"\"\"\n    for item in range(n):\n        if canMove(final, item, column):\n            final.append([item, column])\n            if column == n - 1:\n                checked_queens.append(final.copy())\n                del final[-1]\n            else:\n                nqueens(final, column + 1)\n\n    if len(final) > 0:\n        del final[-1]\n    return checked_queens\n\nif __name__ == '__main__':\n\n    if len(sys.argv) != 2:\n        print(\"Usage: nqueens N\")\n        sys.exit(1)\n\n    try:\n        n = int(sys.argv[1])\n    except:\n        print(\"N must be a number\")\n        sys.exit(1)\n\n    if n < 4:\n        print(\"N must be at least 4\")\n        sys.exit(1)\n\n    final = []\n    final = nqueens(final, 0)\n\n    for nums in final:\n        print(nums)\n","sub_path":"0x0C-nqueens/0-nqueens.py","file_name":"0-nqueens.py","file_ext":"py","file_size_in_byte":1387,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"384008392","text":"import time \nimport requests \nfrom plyer import notification \ncovidData = None\ntry:\n    covidData = requests.get(\"https://corona-rest-api.herokuapp.com/Api/india\")\nexcept:\n    print(\"Please! Check your internet connection\")\nif (covidData != None):\n    data = covidData.json()['Success'] \n    while(True):\n        notification.notify(\n            title = \"COVID-19 Stats in India\",\n            message = \"Total cases reported : {totalcases}\\nCases reported today : {todaycases}\\nDeaths reported : {todaydeaths}\\nTotal active cases : {active}\".format(\n                        totalcases = data['cases'],\n                        todaycases = data['todayCases'],\n                        todaydeaths = data['todayDeaths'],\n                        active = data[\"active\"]),\n            app_icon =  \"coronavirus.ico\",\n            timeout  = 100\n        )\n        time.sleep(60*60*4)\n","sub_path":"covi-notifier.py","file_name":"covi-notifier.py","file_ext":"py","file_size_in_byte":876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"579925558","text":"import discord\r\nfrom discord.ext.commands import Bot\r\nfrom discord.ext import commands\r\nfrom discord.utils import get\r\nfrom google.oauth2 import service_account\r\nfrom google.cloud import texttospeech\r\nfrom google.cloud import translate\r\nimport googleapiclient.discovery\r\nimport googleapiclient.errors\r\nimport spotipy\r\nfrom spotipy.oauth2 import SpotifyClientCredentials\r\nfrom cleverbot_aio_free.cbaio import CleverBot\r\nimport asyncio\r\nimport os, sys, pathlib\r\nimport signal\r\nimport youtube_dl\r\nimport aiohttp\r\nimport requests\r\nfrom random import randint \r\nimport datetime\r\n\r\nfrom auth import DISCORD_API_KEY, GOOGLE_API_KEY, GIPHY_API_KEY, X_RAPID_API_KEY, SPOTIFY_CLIENT_ID, SPOTIFY_CLIENT_SECRET\r\n\r\nmax_quotes = 0\r\ntts_count = 0\r\n\r\n\r\nreaction_dict = [\"zitto_babbano\" ,\"yo_simon\" ,\"yo_mattia\", \"yes_david\", \"wtf\", \"valeria_messina\", \"u_kiddin_paolo\", \"the_doctor\", \"steveboty\", \"SI\", \"se_sei_forte\", \"sblowjobbed\", \"sad_simon\", \"rom_luke\", \"rich_laurence\", \"razor\", \"qt_vale\", \"potato_john\", \"old_xono\", \"oh_yes\", \"nica\", \"metauomometacanna\", \"mart_189\", \"mad_approved\", \"luca_maestro\", \"lorenzo_drugs\", \"lorenzo_approved\", \"lorenzo\", \"hungry_simon\", \"giuseppe_ritardato\", \"ghostrick_face\", \"francicec_trolling\", \"eugene_got_hacked\", \"drunk_si\", \"doubting_giuanni\", \"ciccied\", \"bored_luke\", \"armando\", \"aok\", \"andrea\", \"and_the_cops_silent\" ]\r\nwords_list = [\"porc\",\"dio\",\"madonn\",\"padre\",\"crist\",\"stefano\", \"giuseppe\", \"giovanni\", \"valerio\", \"simone\", \"carlo\", \"andrea\", \"lorenzo\"]\r\n\r\nGOOGLE_TTS_CLIENT_SECRET = os.path.join(\"auth\",\"tts_client_secret_youtify.json\") \r\n\r\nFILE_INSULTI = os.path.join(\"random\",\"insulti.txt\")  \r\n\r\nsongs_url_queue = asyncio.Queue()\r\nsongs_names_queue = asyncio.Queue()\r\ntts_queue = asyncio.Queue()\r\n\r\ntts_cred = service_account.Credentials.from_service_account_file(GOOGLE_TTS_CLIENT_SECRET)\r\n\r\nbot = commands.Bot(command_prefix=\"!\")\r\n\r\ncb = CleverBot()\r\n\r\nbot_voice_status = 0\r\ntts_status = 0\r\nplaylist_status = 0\r\nchat_status = 0\r\n\r\n@bot.command(pass_context=True)\r\nasync def commands(ctx):\r\n    await bot.say(\"List of available commands: !song <nome o url> - !playlist <nome o url> - !tts <frase> - !tr <lingua> + <frase> - !llist - !spotify <nome song> - !ripeti <user> - !quote - !insulta <nome> - !reaction <user> - !gif <nome> - !udict <ricerca>\")\r\n\r\nasync def smoke_timer():  \r\n    now = datetime.datetime.now()\r\n    if int(now.hour) == 16 and int(now.minute) == 20 and int(now.second) == 0: \r\n        await bot.send_message(bot.get_channel(id=\"139354435989143553\"), \"<:420:580109657746702336>\")\r\n\r\n\r\n\r\n@bot.command(pass_context=True)\r\nasync def st3ve(ctx, *, content:str):\r\n    global cb\r\n    global chat_status\r\n    if chat_status == 0:\r\n        chat_status = 1\r\n        await cb.init()\r\n\r\n    \r\n    if str(content).lower().find(\"addio\") != -1 and chat_status == 1:\r\n        chat_status = 0\r\n        await cb.close()\r\n        await bot.say(\"Alla prossima ;)\")\r\n        return\r\n    \r\n    response = await cb.getResponse(str(content))\r\n    await bot.say(str(response))\r\n\r\n@bot.command(pass_context=True)\r\nasync def tts(ctx, *, content:str):\r\n    global bot_voice_status\r\n    global tts_count\r\n    global tts_status\r\n    author = ctx.message.author\r\n    if(str(author).find(\"St3veB0T\") == -1):\r\n        client = texttospeech.TextToSpeechClient()\r\n        synthesis_input = texttospeech.types.SynthesisInput(text=content)\r\n        voice = texttospeech.types.VoiceSelectionParams(language_code='it-IT',ssml_gender=texttospeech.enums.SsmlVoiceGender.FEMALE)\r\n        audio_config = texttospeech.types.AudioConfig(audio_encoding=texttospeech.enums.AudioEncoding.MP3)\r\n        response = client.synthesize_speech(synthesis_input, voice, audio_config)\r\n        tts_count = tts_count + 1\r\n        with open((os.path.join(\"tts\",\"record\"+str(tts_count)+\".mp3\") ), \"wb\") as out:\r\n            out.write(response.audio_content)\r\n            out.close()\r\n        await tts_queue.put(\"record\"+str(tts_count)+\".mp3\")\r\n        server = discord.Server(id=\"139354435989143553\")\r\n        author = ctx.message.author\r\n        if(bot_voice_status == 0):\r\n            bot_voice_status = 1\r\n            tts_status = 1\r\n            while(tts_queue.qsize() > 0 and bot_voice_status == 1):\r\n                voice_channel = author.voice_channel\r\n                global vc\r\n                if tts_queue.qsize() == 1:\r\n                    try:\r\n                        vc = await bot.join_voice_channel(voice_channel)\r\n                    except:\r\n                        pass\r\n\r\n                global player\r\n                try:\r\n                    player = vc.create_ffmpeg_player((os.path.join(\"tts\",str(await tts_queue.get()))))\r\n                except:\r\n                    player.stop()\r\n                    await vc.disconnect()\r\n                    bot_voice_status = 0\r\n                    tts_status = 0\r\n                    return\r\n\r\n                player.start()\r\n\r\n                while not player.is_done():\r\n                    await asyncio.sleep(1)\r\n                \r\n                sec = 75\r\n                while(sec and tts_status == 1):\r\n                    await asyncio.sleep(1)\r\n                    if tts_queue.qsize() > 0:\r\n                        try:\r\n                            player = vc.create_ffmpeg_player((os.path.join(\"tts\",str(await tts_queue.get()))))\r\n                        except:\r\n                            player.stop()\r\n                            await vc.disconnect()\r\n                            bot_voice_status = 0\r\n                            tts_status = 0\r\n                            return\r\n\r\n                        player.start()\r\n\r\n                        while not player.is_done():\r\n                            await asyncio.sleep(1)\r\n                    sec = sec - 1\r\n            if tts_status == 1:\r\n                player.stop()\r\n                await vc.disconnect()\r\n                bot_voice_status = 0\r\n                tts_status = 0\r\n            for filename in os.listdir(\"tts\"):\r\n                os.remove((os.path.join(\"tts\",filename)))\r\n\r\n@bot.command(pass_context=True)\r\nasync def spotify(ctx, *, content:str):\r\n    if(str(ctx.message.author).find(\"St3veB0T\") == -1):\r\n        client_credentials_manager = SpotifyClientCredentials(client_id=SPOTIFY_CLIENT_ID, client_secret=SPOTIFY_CLIENT_SECRET)  \r\n        sp = spotipy.Spotify(client_credentials_manager=client_credentials_manager)\r\n        \r\n        try:\r\n            res = sp.search(q=content, type=\"track\")        \r\n            await bot.say(res[\"tracks\"][\"items\"][0][\"external_urls\"][\"spotify\"])\r\n        except:\r\n            await bot.say(\"Can't find that song...\")\r\n           \r\n       \r\nasync def is_Available_Language(language):\r\n    client = translate.Client()\r\n    languages = client.get_languages()\r\n    for item in languages:\r\n        if str(item[\"language\"]).find(language) != -1:\r\n            return True\r\n    return False\r\n\r\nasync def isUser_in_last_message(ctx, name):\r\n    async for message in bot.logs_from(ctx.message.channel, limit=100):\r\n        if str(message.author).find(str(name)) != -1:\r\n            return True\r\n    return False\r\n\r\n\r\nasync def Get_Nlines_File_Insulti():\r\n  c = 0\r\n  with open(FILE_INSULTI) as fp:\r\n    for line in fp:\r\n        c = c + 1\r\n    fp.close()\r\n    return c\r\n\r\ndef Save_Message_to_File(message):\r\n    try:\r\n        fp = open(\"random/phrase.txt\", \"a+\", encoding=\"utf-8\")\r\n    except:\r\n        fp = open(\"random/phrase.txt\", \"x\", encoding=\"utf-8\")\r\n        fp.close()\r\n        fp = open(\"random/phrase.txt\", \"a+\", encoding=\"utf-8\")\r\n    fp.seek(0)\r\n    for line in fp:\r\n        if line.find(message) != -1:\r\n            return\r\n    fp.seek(2)\r\n    fp.write(str(message.encode(\"utf-8\").decode(\"utf-8\")))\r\n    fp.write(\"\\n\")\r\n    fp.close()\r\n\r\nasync def Translate(ctx, args=None, language=\"en\", user=None):\r\n    client = translate.Client()\r\n    if args == None and user != None:\r\n        async for message in bot.logs_from(ctx.message.channel, limit=50):\r\n            if str(user).find(message.author) != -1:\r\n                data = client.translate(message.content, target_language=language)\r\n                await bot.send_message(ctx.message.channel, str(data[\"translatedText\"]))\r\n                return\r\n        await bot.send_message(ctx.message.channel, \"Wrong Username - Maybe use @\")\r\n        return\r\n    else:\r\n        data = client.translate(args, target_language=language)\r\n        await bot.send_message(ctx.message.channel, str(data[\"translatedText\"]))\r\n\r\nasync def Pick_Random_Phrase():\r\n    \r\n    while(1):\r\n        try:\r\n            fp = open(\"random/phrase.txt\", \"r\", encoding=\"utf-8\")\r\n        except:\r\n            fp = open(\"random/phrase.txt\", \"x\", encoding=\"utf-8\")\r\n            fp.close()\r\n            fp = open(\"random/phrase.txt\", \"r\", encoding=\"utf-8\")\r\n\r\n        rand_sleep_time = randint(3600, 5400)\r\n        await asyncio.sleep(rand_sleep_time)\r\n        lines = 0\r\n        fp.seek(0)\r\n        for line in fp:\r\n            lines += 1\r\n        fp.seek(0)\r\n        rand = randint(0, lines-1)\r\n        c = 0\r\n        for phrase in fp:\r\n            if c == rand:\r\n                await bot.send_message(bot.get_channel(id=\"139354435989143553\"), str(phrase))\r\n            c = c + 1\r\n\r\n        fp.close()\r\n\r\nasync def Get_Streamer_Event():\r\n  status_dict = {\"st3ver0nix\":0, \"angelthelion\": 0, \"stereophonics\":0, \"madbulk22\":0, \"zarkent\":0, \"unluckyrazor\":0, \"vajinvegeta1996\":0}\r\n  streamer_dict = {\"st3ver0nix\": \"https://www.twitch.tv/st3ver0nix\", \"angelthelion\":\"https://www.twitch.tv/angelthelion\", \"stereophonics\": \"https://www.twitch.tv/stereophonics\", \"madbulk22\":\"https://www.twitch.tv/madbulk22\", \"zarkent\":\"https://www.twitch.tv/zarkent\", \"unluckyrazor\":\"https://www.twitch.tv/unluckyrazor\", \"vajinvegeta1996\":\"https://www.twitch.tv/vajinvegeta1996\"} \r\n  header = {\"Content-Type\": \"text/plain\",\"Accept\": \"application/json\", \"Connection \" : \"close\"}\r\n \r\n  while(1):\r\n     async with aiohttp.ClientSession() as session:     \r\n      for name in streamer_dict:\r\n        await smoke_timer()\r\n        await asyncio.sleep(1)    \r\n        url = \"http://decapi.me/twitch/uptime?channel=\" + str(name)\r\n        async with session.get(url, headers = header) as res:\r\n            if(str(await res.text()).find(\"hour\") != -1 or str(await res.text()).find(\"minute\") != -1 or str(await res.text()).find(\"second\") != -1):\r\n                if status_dict[name] == 0:\r\n                    message = \"@everyone Hey, \" + str(name) + \" è online su twitch! \" + str(streamer_dict[name])\r\n                    await bot.send_message(bot.get_channel(id=\"139354435989143553\"), str(message))\r\n                    status_dict[name] = 1\r\n            else:\r\n                if str(await res.text()).find(\"offline\") != -1:\r\n                    status_dict[name] = 0\r\n\r\nasync def Birthdays():\r\n    actual_year = int(datetime.datetime.today().year)\r\n    actual_month = int(datetime.datetime.today().month)\r\n    actual_day = int(datetime.datetime.today().day)\r\n    \r\n    if actual_day == 23 and actual_month == 3:\r\n        message = \"Auguri Tullio, per i tuoi \" + str(actual_year - 1996 ) + \" anni!\"\r\n        await bot.send_message(bot.get_channel(id=\"139354435989143553\"), message)\r\n    if actual_day == 9 and actual_month == 4:\r\n        message = \"Auguri Carlo, per i tuoi \" + str(actual_year - 1996 ) + \" anni!\"\r\n        await bot.send_message(bot.get_channel(id=\"139354435989143553\"), message)\r\n    if actual_day == 27 and actual_month == 4:\r\n        message = \"Auguri Valerio, per i tuoi \" + str(actual_year - 1997 ) + \" anni!\"\r\n        await bot.send_message(bot.get_channel(id=\"139354435989143553\"), message)\r\n        \r\n@bot.event          \r\nasync def on_ready():\r\n    print(\"Bot Ready\")\r\n    loop = asyncio.get_event_loop()\r\n    task1 = loop.create_task(Get_Streamer_Event())\r\n    task2 = loop.create_task(Pick_Random_Phrase())\r\n    await bot.send_message(bot.get_channel(id=\"327424537211830273\"), \"Ciao amici, sono online!\")    \r\n    await Birthdays()\r\n    await task1\r\n    await task2 \r\n    \r\n@bot.event\r\nasync def on_message(message):\r\n    print(str(message.author)+\":\"+str(message.content))\r\n    if(str(message.author).find(\"St3veB0T\") == -1):\r\n     mess = str(message.content).lower()\r\n     for word in words_list:\r\n        if str(mess).find(word) != -1 and str(mess).find(\"\\n\") == -1 and str(mess).startswith(\"!\") == False:\r\n            Save_Message_to_File(message.content)            \r\n           # await bot.send_message(message.channel, str(message.content))\r\n            break\r\n\r\n    if str(message.author).find(\"St3veB0T\") == -1:\r\n     if(str(message.content).find(\"<@547054033270079508>\") != -1 or str(message.content).find(\"St3veB0T\") != -1):\r\n       with open(FILE_INSULTI) as fp:\r\n        lines = await Get_Nlines_File_Insulti()\r\n        random = randint(0, lines)\r\n        c = 0\r\n        for line in fp:\r\n            if c == random:\r\n                mess = \"||\" + (line.replace(\"$\", str(message.author))) + \"||\"\r\n                await bot.send_message(message.channel, mess.encode(\"windows-1252\").decode(\"utf-8\"))\r\n                fp.close()\r\n                return\r\n            c = c + 1 \r\n    await bot.process_commands(message)\r\n\r\n@bot.event\r\nasync def on_voice_state_update(before, after):\r\n  global bot_voice_status\r\n  global tts_status\r\n  if(bot_voice_status == 0 and tts_status == 0 and after.display_name.find(\"St3veB0T\") == -1):\r\n   if(before.voice_channel == None): \r\n    rand = randint(0, len(os.listdir(\"audio\"))-1)    \r\n    ix = 0\r\n    for filename in os.listdir(\"audio\"):\r\n        if(ix == rand):    \r\n            c = 0\r\n            try:\r\n                vc = await bot.join_voice_channel(after.voice_channel)\r\n                c = 1\r\n            except:\r\n                break\r\n            if(c == 1):\r\n                try:\r\n                    player = vc.create_ffmpeg_player((os.path.join(\"audio\",filename)  ), after=lambda: print(''))\r\n                    player.start()\r\n                    while not player.is_done():\r\n                       await asyncio.sleep(1)\r\n                    player.stop()\r\n                    await vc.disconnect()\r\n                    break\r\n                except:\r\n                    await vc.disconnect()\r\n                    break\r\n        ix = ix + 1\r\n\"\"\"\r\n@bot.event\r\nasync def on_member_update(before, after):\r\n    if(str(before.status).find(\"offline\") != -1 and str(after.status).find(\"online\") != -1):\r\n        for role in after.roles:\r\n            if str(role).find(\"AHK Member\") != -1:\r\n                if(str(after.display_name) != \"\" and str(after.display_name).find(\"St3veB0T\") == -1):\r\n                    message = \"Ehy, \" + str(after.display_name) + \" è online!\"\r\n                    await bot.send_message(bot.get_channel(id=\"139354435989143553\"), message)\r\n\"\"\"\r\n@bot.command(pass_context=True)\r\nasync def llist(ctx):\r\n    client = translate.Client()\r\n    lang = client.get_languages()\r\n    string = \"List of available languages with their id: \"\r\n    for item in lang:\r\n        string += (str(item[\"name\"]) + \":\" + str(item[\"language\"]) + \" | \")\r\n    await bot.say(string)\r\n\r\n\r\n@bot.command(pass_context=True)\r\nasync def tr(ctx, *, content:str):\r\n        try:\r\n            splitted_content = content.split(\" \")\r\n        except:\r\n            await bot.say(\"Usage: !tr <output language> + <text to translate>\")\r\n        if splitted_content[0] != \"\":        \r\n            language = splitted_content[0]\r\n            if await is_Available_Language(language) == True:\r\n\r\n                string = ' '.join(splitted_content[1:])\r\n                if string != \"\":\r\n                    if await isUser_in_last_message(ctx,string) == True:\r\n                        await Translate(ctx, user=string, language=language)\r\n                    else:\r\n                        await Translate(ctx, args=string, language=language)\r\n                else:\r\n                    await bot.say(\"Usage: !tr + <output language> + <text to translate>\")\r\n            else:\r\n                await bot.say(\"Usage: !tr + <output language> + <text to translate>\")\r\n\r\n\r\n@bot.command(pass_context=True)\r\nasync def quote(ctx):\r\n   global max_quotes\r\n   max_quotes = max_quotes + 1\r\n   if(max_quotes < 20):\r\n    url = \"https://qvoca-bestquotes-v1.p.rapidapi.com/quote\"\r\n    headers = {\"X-RapidAPI-Host\":\"qvoca-bestquotes-v1.p.rapidapi.com\", \"X-RapidAPI-Key\": X_RAPID_API_KEY }\r\n\r\n    res = requests.get(url, headers=headers)\r\n    data = res.json()\r\n    quote = (data[\"message\"])\r\n    author = (data[\"author\"])\r\n\r\n    formatted_message = quote + \"\\n  -\" + author\r\n    await bot.say(formatted_message)\r\n   else:\r\n    await bot.say(\"Ho finito le quotes per oggi...\")\r\n\r\n@bot.command(pass_context=True)\r\nasync def ripeti(ctx, user):\r\n    if await isUser_in_last_message(ctx, user) == True: \r\n        async for message in bot.logs_from(ctx.message.channel, limit=100):\r\n            if str(message.author).find(str(user)) != -1:\r\n                await bot.say(message.content)\r\n                return\r\n    await bot.say(\"Wrong Username\")\r\n\r\nasync def PlaySong(ctx, song_url):\r\n  author = ctx.message.author\r\n  global bot_voice_status\r\n  global tts_status\r\n  if(author != \"St3veB0T\") and bot_voice_status == 0 and tts_status == 0:\r\n    global vc\r\n    global player\r\n    options = \"-reconnect 1 -reconnect_streamed 1 -reconnect_delay_max 2\"\r\n\r\n    if bot_voice_status == 0:\r\n        try:\r\n            player = await vc.create_ytdl_player(str(song_url), before_options=options)\r\n            bot_voice_status = 1\r\n        except:\r\n            await bot.send_message(ctx.message.channel, \":x: Can't play this song, sry\")\r\n            return\r\n    else:\r\n        return\r\n\r\n    player.start()\r\n    while not player.is_done():\r\n        await asyncio.sleep(1)\r\n    bot_voice_status = 0\r\n\r\nasync def Check(response):\r\n    try:\r\n        nextPageToken = response[\"nextPageToken\"]\r\n        return nextPageToken\r\n    except:\r\n        return None\r\n\r\nasync def Play_Playlist(ctx, playlist_id, content):\r\n    voice_channel = ctx.message.author.voice_channel\r\n    global bot_voice_status\r\n    global vc\r\n    global player\r\n    try:\r\n        vc = await bot.join_voice_channel(voice_channel)\r\n    except:\r\n        return\r\n\r\n    scopes = [\"https://www.googleapis.com/auth/youtube.readonly\"]\r\n    api_service_name = \"youtube\"\r\n    api_version = \"v3\"\r\n    client_secrets_file = os.path.join(\"auth\", \"client_secret_youtify.json\") \r\n\r\n    credentials = service_account.Credentials.from_service_account_file(client_secrets_file)\r\n    youtube = googleapiclient.discovery.build(\r\n        api_service_name, api_version, credentials=credentials)\r\n\r\n    request = youtube.playlistItems().list(\r\n            part=\"snippet,contentDetails\",\r\n            maxResults=1,\r\n            playlistId=playlist_id,\r\n        )\r\n    response = request.execute()\r\n    nextPageToken = await Check(response)\r\n    song_name = response[\"items\"][0][\"snippet\"][\"title\"]\r\n    \r\n    global playlist_status\r\n    if playlist_status == 0:\r\n       await bot.send_message(ctx.message.channel, \"<:youtube:568148804084170753>:musical_note: **Now playing** `\" + song_name + \"` :notes: Use !basta for stopping and !skip for next song\")\r\n       await PlaySong(ctx, \"https://www.youtube.com/watch?v=\" + response[\"items\"][0][\"contentDetails\"][\"videoId\"])\r\n    else:\r\n        return\r\n    while nextPageToken != None:\r\n        request = youtube.playlistItems().list(\r\n            part=\"snippet,contentDetails\",\r\n            maxResults=1,\r\n            playlistId=playlist_id,\r\n            pageToken = nextPageToken\r\n        )\r\n        response = request.execute()\r\n        song_name = response[\"items\"][0][\"snippet\"][\"title\"]\r\n        if playlist_status == 0:\r\n            await bot.send_message(ctx.message.channel, \"<:youtube:568148804084170753>:musical_note: **Now playing** `\" + song_name + \"` :notes: Use !basta for stopping and !skip for next song\")\r\n            await PlaySong(ctx, \"https://www.youtube.com/watch?v=\" + response[\"items\"][0][\"contentDetails\"][\"videoId\"])\r\n        else:\r\n            return\r\n        nextPageToken = await Check(response)\r\n    await player.stop()\r\n    await vc.disconnect()\r\n\r\n@bot.command(pass_context=True)\r\nasync def playlist(ctx, *, content:str):\r\n    global playlist_status\r\n    endpoint = \"https://www.googleapis.com/youtube/v3/search?part=snippet&maxResults=1&q=\"   \r\n    if \"&list=\" in str(content):\r\n        p_id = None\r\n        try:\r\n            temp = str(content).split(\"list=\")[1]\r\n            try:\r\n                p_id = temp.split(\"&\")[0]\r\n            except:\r\n                p_id = temp\r\n        except:\r\n            return\r\n        playlist_status = 0   \r\n        await Play_Playlist(ctx, p_id, content)\r\n    else:\r\n        endpoint += str(content)\r\n        endpoint += (\"&key=\" + GOOGLE_API_KEY)\r\n        res = requests.get(endpoint)\r\n        data = res.json()\r\n        try:\r\n            if data[\"items\"][0][\"id\"][\"kind\"] == \"youtube#playlist\":\r\n                playlist_id = data[\"items\"][0][\"id\"][\"playlistId\"]\r\n                playlist_status = 0\r\n                await Play_Playlist(ctx, playlist_id, content)\r\n            else:\r\n                await bot.say(\"This is not a playlist\")    \r\n                return\r\n        except:\r\n            await bot.say(\"This is not a playlist\")\r\n            return\r\n\r\n@bot.command(pass_context=True)\r\nasync def basta(ctx):\r\n    global bot_voice_status\r\n    global playlist_status\r\n    global vc\r\n    global player\r\n    global tts_status\r\n    await bot.say(\":pause_button: OK, i'm leaving the voice channel, ciao :wave:\")\r\n    player.stop()\r\n    await vc.disconnect()\r\n    bot_voice_status = 0\r\n    tts_status = 0\r\n    playlist_status = 1\r\n\r\nasync def Get_Song_Name(arg):\r\n    name = str(arg).replace(\" \", \"+\")\r\n    query = \"https://www.googleapis.com/youtube/v3/search?part=snippet&maxResults=1&q=\" + name + \"&key=\"+ GOOGLE_API_KEY\r\n    res = requests.get(query)\r\n    data = res.json()\r\n    try:\r\n        song_name = (data['items'][0]['snippet']['title'])\r\n    except:\r\n        return None\r\n    return song_name\r\n\r\nasync def Get_Song_Id(arg):\r\n    name = str(arg).replace(\" \", \"+\")\r\n    query = \"https://www.googleapis.com/youtube/v3/search?part=snippet&maxResults=1&q=\" + name + \"&key=\"+ GOOGLE_API_KEY\r\n    res = requests.get(query)\r\n    data = res.json()\r\n    try:\r\n        song_id = (data['items'][0]['id']['videoId'])\r\n    except:\r\n        return None\r\n    return song_id\r\n\r\n@bot.command(pass_context=True)\r\nasync def skip(ctx):\r\n    global player\r\n    global bot_voice_status\r\n    if bot_voice_status == 1:\r\n        player.stop()\r\n\r\n@bot.command(pass_context=True)\r\nasync def song(ctx, *, content:str):\r\n   server = discord.Server(id=\"139354435989143553\")\r\n   song_name = \"\"\r\n   global bot_voice_status\r\n   global tts_status    \r\n   if tts_status == 0:\r\n    if str(content).find(\"https://www.youtube.com\") == -1:\r\n        song_id= await Get_Song_Id(str(content))\r\n        try:\r\n            url = \"https://www.youtube.com/watch?v=\" + song_id\r\n        except:\r\n         await bot.say(\"Song not found\")\r\n         return\r\n    else:\r\n        url = str(content)\r\n\r\n    author = ctx.message.author\r\n    if(author != \"St3veB0T\"):\r\n        \r\n        voice_channel = author.voice_channel\r\n        if voice_channel == None:\r\n            await bot.say(\"You must be in a voice channel to play a song\")\r\n            return\r\n\r\n\r\n        if songs_url_queue.qsize() == 0 and not bot.is_voice_connected(server):\r\n            await songs_url_queue.put(url)\r\n            song_name = await Get_Song_Name(str(content))\r\n            mess = \"<:youtube:568148804084170753> **Searching** :mag_right: `\"+ str(content) + \"`\\n:musical_note: **Now playing** `\" + song_name + \"` :notes: Use !basta for stopping and !skip for next song\"  \r\n            await bot.say(mess)\r\n        else:\r\n            await songs_url_queue.put(url)\r\n            song_name = await Get_Song_Name(str(content))\r\n            await songs_names_queue.put(song_name)\r\n            mess = \"<:youtube:568148804084170753> **Searching** :mag_right: `\"+ str(content) + \"`\\n:musical_note: **Added** `\" + song_name + \"` to play next :notes: Use !basta for stopping and !skip for next song\"  \r\n            await bot.say(mess)\r\n\r\n\r\n        global vc\r\n\r\n        if songs_url_queue.qsize() == 1 and voice_channel != None:\r\n            try:\r\n                vc = await bot.join_voice_channel(voice_channel)\r\n            except:\r\n                return\r\n        \r\n        bot_voice_status = 1\r\n\r\n        global player\r\n        options = \"-reconnect 1 -reconnect_streamed 1 -reconnect_delay_max 2\"\r\n        while songs_url_queue.qsize() != 0:\r\n            if songs_names_queue.qsize() >= 1:\r\n                await bot.say(\":musical_note: **Now playing** `\" + str(await songs_names_queue.get()) + \"` :notes: Use !basta for stopping and !skip for next song\")\r\n\r\n            try:\r\n               player = await vc.create_ytdl_player(str(await songs_url_queue.get()), before_options=options)\r\n            except:\r\n                await vc.disconnect()\r\n                bot_voice_status = 0\r\n                return\r\n\r\n            player.start()\r\n            while not player.is_done():\r\n                await asyncio.sleep(1)\r\n            \r\n        player.stop()\r\n        await vc.disconnect()\r\n        bot_voice_status = 0\r\n   else:\r\n      await bot.say(\"I'm still waiting for tts, wait some seconds...\")    \r\n      \r\n\r\n@bot.command(pass_context=True)\r\nasync def insulta(ctx, *, content:str):\r\n    with open(FILE_INSULTI) as fp:\r\n        lines = await Get_Nlines_File_Insulti()\r\n        fp.seek(0)\r\n        random = randint(0, lines)\r\n        c = 0\r\n        for line in fp:\r\n            if c == random:\r\n                message = \"||\" + (line.replace(\"$\", str(content))) + \"||\"\r\n                await bot.say(str(message))\r\n                fp.close()\r\n                return\r\n            c = c + 1\r\n\r\nasync def Get_Gif_Url(name):\r\n    query = \"https://api.giphy.com/v1/gifs/translate?api_key=\" + GIPHY_API_KEY + \"&s=\"+ name\r\n    res = requests.get(query)\r\n    data = res.json()\r\n    try:\r\n        gif_url = (data['data']['embed_url'])\r\n    except:\r\n        return None\r\n    return_value = gif_url.replace(\"\\\\\", \"\")\r\n    return return_value\r\n\r\n@bot.command(pass_context=True)\r\nasync def reaction(ctx, *, content:str):\r\n    async for message in bot.logs_from(ctx.message.channel, limit=100):\r\n        if str(message.author).find(str(content)) != -1:\r\n            random2 = randint(1, 10)        \r\n            while(random2 != 0):\r\n              c = 0\r\n              random = randint(0,len(reaction_dict))\r\n              for reaction in reaction_dict:\r\n                if c == random:\r\n                   emoji = get(bot.get_all_emojis(), name = str(reaction))\r\n                   await bot.add_reaction(message, emoji)\r\n                   break\r\n                c = c + 1\r\n              random2 = random2 - 1\r\n            return\r\n\r\n@bot.command(pass_context=True)\r\nasync def udict(ctx, *, content:str):\r\n    search = str(content).replace(\" \", \"%20\")\r\n    query = \"http://jwd.me/twitch/api/urban-dictionary.php?q=\" + search\r\n    res = requests.get(query)\r\n    await bot.say(str(res.text))\r\n\r\n@bot.command(pass_context=True)\r\nasync def gif(ctx, * , content:str):\r\n    gif_url = await Get_Gif_Url(str(content))\r\n    if gif_url != None:\r\n        await bot.say(gif_url)\r\n    else:\r\n        await bot.say(\"Cannot find this gif :(\")\r\n\r\n\r\n@bot.command(pass_context=True)\r\nasync def shutdown(ctx):\r\n    global chat_status\r\n    global cb\r\n    if str(ctx.message.author).find(\"St3ve\") != -1:\r\n        await bot.send_message(bot.get_channel(id=\"327424537211830273\"), \"Mi sto spegnendo...\")\r\n        if chat_status == 1:\r\n            await cb.close()\r\n        \r\n        await bot.logout()\r\n        sys.exit(1)\r\n\r\n\r\ntry:\r\n bot.run(DISCORD_API_KEY)\r\nexcept: \r\n sys.exit(1)\r\n","sub_path":"Discord_St3veB0T.py","file_name":"Discord_St3veB0T.py","file_ext":"py","file_size_in_byte":27755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"203107500","text":"#!/usr/bin/env python\n# -*- coding: UTF-8 -*-\n\n__author__ = 'RemiZOffAlex'\n__copyright__ = '(c) RemiZOffAlex'\n__license__ = 'MIT'\n__email__ = 'remizoffalex@mail.ru'\n\nimport os, sys\nsys.path.insert(0, os.path.dirname(os.path.abspath(__file__)))\n\nfrom flask import Flask\n\ntemplate_path = os.path.join(os.path.dirname(__file__), 'templates')\n\napp = Flask(__name__, template_folder=template_path)\napp.debug = True\n\nimport views\n\n","sub_path":"Flask-CGI/myapp/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"357186867","text":"#!/usr/bin/env python3\n\nimport sys\nimport pandas as pd\nfrom tabulate import tabulate\n\ncluster_id = int(sys.argv[1])\nocean_df = pd.read_csv(\"./data/ocean_clusters.csv\", index_col=0)\ntry:\n    cluster_dct = ocean_df.loc[cluster_id, :].to_dict()\nexcept KeyError:\n    print(\"cluster_id = {} not found\".format(cluster_id))\n    exit(1)\n\nbase_df = pd.read_csv(\"./data/ocean_baseline.csv\", index_col=0)\nbase_dct = base_df.to_dict()[\"genome_count\"]\n\ndct_lst = []\nfor key in [\"surface\", \"DCM\", \"epipelagic\", \"deep\", \"hydrothermal\", \"default\"]:\n    dct = {\n        \"category\": key,\n        \"gene_count\": cluster_dct[key],\n        \"genome_count\": base_dct[key]\n    }\n    dct_lst.append(dct)\n\ndf = pd.DataFrame(dct_lst)\ndf[\"ratio\"] = df[\"gene_count\"] / df[\"genome_count\"] * 100\ndf = df[[\"category\", \"gene_count\", \"ratio\"]]\nprint(tabulate(df, headers=[\"category\", \"#gene\", \"ratio(%)\" ], tablefmt=\"simple\"))\n\n\n","sub_path":"analysis/sample/8000_PRJNA348753/ocean.py","file_name":"ocean.py","file_ext":"py","file_size_in_byte":894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"224039072","text":"from .auto_tick import initialize_migrators, migrator_status\nimport os\nimport json\nimport networkx as nx\nimport subprocess\n\nfrom graphviz import Source\nimport tempfile\n\nfrom typing import Any\n\nfrom conda_forge_tick.migrators import (\n    GraphMigrator,\n    LicenseMigrator,\n    Version,\n    Replacement,\n)\n\n\ndef main(args: Any = None) -> None:\n    mctx, *_, migrators = initialize_migrators()\n    if not os.path.exists(\"./status\"):\n        os.mkdir(\"./status\")\n    total_status = {}\n\n    for migrator in migrators:\n        if isinstance(migrator, GraphMigrator) or isinstance(migrator, Replacement):\n            if hasattr(migrator, \"name\"):\n                assert isinstance(migrator.name, str)\n                migrator_name = migrator.name.lower().replace(\" \", \"\")\n            else:\n                migrator_name = migrator.__class__.__name__.lower()\n            total_status[migrator_name] = f\"{migrator.name} Migration Status\"\n            status, build_order, gv = migrator_status(migrator, mctx.graph)\n            with open(os.path.join(f\"./status/{migrator_name}.json\"), \"w\") as fo:\n                json.dump(status, fo, indent=2)\n\n            d = gv.pipe(\"dot\")\n            with tempfile.NamedTemporaryFile() as ntf, open(\n                f\"{ntf.name}.dot\", \"w\"\n            ) as f:\n                f.write(d.decode(\"utf-8\"))\n                # make the graph a bit more compact\n                d = Source(\n                    subprocess.check_output(\n                        [\"unflatten\", \"-f\", \"-l\", \"5\", \"-c\", \"10\", f\"{ntf.name}.dot\",]\n                    ).decode(\"utf-8\")\n                ).pipe(\"svg\")\n            with open(os.path.join(f\"./status/{migrator_name}.svg\"), \"wb\") as fb:\n                fb.write(d)\n\n    with open(\"./status/total_status.json\", \"w\") as f:\n        json.dump(total_status, f, sort_keys=True)\n\n    lst = [\n        k\n        for k, v in mctx.graph.nodes.items()\n        if len(\n            [\n                z\n                for z in v.get(\"payload\", {}).get(\"PRed\", [])\n                if z.get(\"PR\", {}).get(\"state\", \"closed\") == \"open\"\n                and z.get(\"data\", {}).get(\"migrator_name\", \"\") == \"Version\"\n            ],\n        )\n        >= Version.max_num_prs\n    ]\n    with open(\"./status/could_use_help.json\", \"w\") as f:\n        json.dump(\n            sorted(\n                lst,\n                key=lambda z: (len(nx.descendants(mctx.graph, z)), lst),\n                reverse=True,\n            ),\n            f,\n            indent=2,\n        )\n\n    lm = LicenseMigrator()\n    lst = [\n        k for k, v in mctx.graph.nodes.items() if not lm.filter(v.get(\"payload\", {}))\n    ]\n    with open(\"./status/unlicensed.json\", \"w\") as f:\n        json.dump(\n            sorted(\n                lst,\n                key=lambda z: (len(nx.descendants(mctx.graph, z)), lst),\n                reverse=True,\n            ),\n            f,\n            indent=2,\n        )\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"conda_forge_tick/status_report.py","file_name":"status_report.py","file_ext":"py","file_size_in_byte":2945,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"576494984","text":"from jd.api.base import RestApi\n\nclass DspAdreportTrendChartGetRequest(RestApi):\n\t\tdef __init__(self,domain='gw.api.360buy.com',port=80):\n\t\t\tRestApi.__init__(self,domain, port)\n\t\t\tself.startDay = None\n\t\t\tself.endDay = None\n\t\t\tself.businessType = None\n\t\t\tself.granularity = None\n\t\t\tself.campaignId = None\n\t\t\tself.groupId = None\n\t\t\tself.adId = None\n\n\t\tdef getapiname(self):\n\t\t\treturn 'jingdong.dsp.adreport.trendChart.get'\n\n\n\n\n\n\n","sub_path":"jos/api/rest/DspAdreportTrendChartGetRequest.py","file_name":"DspAdreportTrendChartGetRequest.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"544334694","text":"def phoneCall(min1, min2_10, min11, s):\n    totalmin = 0\n    \n\n    #first chunk    \n    fundsremaining = s - (1 * min1)\n    totalmin = 1\n    if s < min1:\n        return 0\n    \n    #second chunk\n    fullsecondcost = min2_10 * 9\n    if fundsremaining <= 0: #if you have no more $ at the start of the second group\n        return totalmin\n    \n    #if you have enough to continue to the third chunk\n    elif fullsecondcost < fundsremaining: \n        fundsremaining -= fullsecondcost\n        totalmin += 9\n        #Enter third chunk next\n        totalmin = totalmin + (fundsremaining // min11)\n        return totalmin\n        \n    else:\n        totalmin = totalmin + (fundsremaining // min2_10) #you only have enough to do part of the second chunk\n        return totalmin\n","sub_path":"Intro Gates/Phone Call.py","file_name":"Phone Call.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"346828219","text":"#!/usr/bin/env python\n# coding: utf-8\n\n\"\"\"\nThis script contains the full implementation of our classifier to demonstrate\nthe effects of perturbations on gender classifiers.\n\"\"\"\n\nimport imp\nimport platform\nimport tkinter as tk\n\nfrom PIL import Image, ImageTk\nfrom collections import OrderedDict\nfrom tkinter import filedialog\n\n# Load glasses filter module\npred_mod = imp.load_source(\"make_pred\", \"../fyp-models/gen_results.py\")\ntemp_fp = \"tmp/\"\n\n\nclass App:\n    \"\"\"\n    This is the class for our application, whicch is used to store the state of\n    the application.\n    \"\"\"\n\n    # Application constants\n    MODELS = OrderedDict(\n        ((\"Baseline (Unperturbed) Model\", False), (\"Debiased Model\", True))\n    )\n\n    PERTURBS = OrderedDict(\n        (\n            (\"No Filter\", \"none\"),\n            (\"Glasses Filter\", \"glasses\"),\n            (\"Make Up Filter\", \"makeup\"),\n            (\"N95 Mask Filter\", \"mask\"),\n        )\n    )\n\n    MODEL_TYPES = OrderedDict(\n        ((\"ResNet50\", \"res\"), (\"DenseNet\", \"dense\"), (\"MobileNet\", \"mobile\"))\n    )\n\n    # Variable slots, specified for performance optimisation\n    __slots__ = (\n        \"root\",\n        \"model\",\n        \"perturb\",\n        \"model_type\",\n        \"prediction\",\n        \"confidence\",\n        \"original_fp\",\n        \"frame\",\n        \"img\",\n        \"img_name\",\n        \"img_tk\",\n        \"stats_img\",\n    )\n\n    def __init__(self):\n        \"\"\"\n        Initialises the UI elements\n        \"\"\"\n        self.root = tk.Tk()\n        self._constant_widgets()\n        self._options_menu()\n        self._mainframe()\n        self._buttons()\n\n    def _constant_widgets(self):\n        \"\"\"\n        Initialises the widgets\n        \"\"\"\n        self.root.title(\"Gender Classifier\")\n\n        if platform.system() == \"Windows\":\n            self.root.state(\"zoomed\")\n        else:\n            self.root.attributes(\"-zoomed\", True)\n\n        tk.Label(\n            self.root,\n            text=\"Gender Classifier\",\n            padx=25,\n            pady=6,\n            font=(\"Helvetica\", 24, \"bold\"),\n        ).pack()\n\n    def _options_menu(self):\n        \"\"\"\n        Initialises the options menu\n        \"\"\"\n        menu_frame = tk.Frame(self.root)\n        menu_frame.pack()\n        self.model = tk.StringVar(self.root)\n        self.model.set(next(iter(self.MODELS)))\n        tk.OptionMenu(menu_frame, self.model, *self.MODELS).pack(side=tk.LEFT)\n\n        self.perturb = tk.StringVar(self.root)\n        self.perturb.set(next(iter(self.PERTURBS)))\n        tk.OptionMenu(menu_frame, self.perturb, *self.PERTURBS).pack(side=tk.RIGHT)\n\n        self.model_type = tk.StringVar(self.root)\n        self.model_type.set(next(iter(self.MODEL_TYPES)))\n        tk.OptionMenu(menu_frame, self.model_type, *self.MODEL_TYPES).pack(\n            side=tk.RIGHT\n        )\n\n    def _mainframe(self):\n        \"\"\"\n        Initialises the window frame\n        \"\"\"\n        self.frame = tk.Frame(self.root, bg=\"white\")\n        self.frame.place(relwidth=0.8, relheight=0.8, relx=0.1, rely=0.1)\n        self.prediction = self.confidence = None\n\n    def _buttons(self):\n        \"\"\"\n        Initialises the buttons\n        \"\"\"\n        fixed_options = {\n            \"master\": self.root,\n            \"padx\": 35,\n            \"pady\": 10,\n            \"fg\": \"white\",\n            \"bg\": \"grey\",\n        }\n        tk.Button(text=\"Choose Image\", command=self.load, **fixed_options).pack(\n            side=tk.LEFT\n        )\n        tk.Button(text=\"Classify Image\", command=self.classify, **fixed_options).pack(\n            side=tk.RIGHT\n        )\n\n    def load(self):\n        \"\"\"\n        Loads a new image to the application\n        \"\"\"\n        # Remove the current image\n        for img_display in self.frame.winfo_children():\n            img_display.destroy()\n\n        # Select image\n        self.img_name = filedialog.askopenfilename(\n            title=\"Choose an image\",\n            filetypes=((\"Images\", \"*.png *.jpg \"), (\"All files\", \"*.*\")),\n        )\n\n        # Update image\n        self.img = self.update_img(self.img_name, 250)\n\n        img_name = tk.Label(\n            self.frame,\n            text=self.img_name.split(\"/\")[-1],\n            font=\"Helvetica 18 bold\",\n            bg=\"white\",\n        )\n        img_name.place(relx=0.05, rely=0.05)\n\n        self.img_tk = tk.Label(self.frame, image=self.img)\n        self.img_tk.place(relx=0.05, rely=0.1)\n        self.original_fp = self.img_name\n\n    def classify(self):\n        \"\"\"\n        Classifies the current image in the application.\n        \"\"\"\n        # Get the image with no filter\n        if self.perturb.get() != \"No Filter\":\n            filter_type = self.PERTURBS[self.perturb.get()]\n            target_fp = temp_fp + filter_type + \"/\"\n\n            self.img_name = target_fp + self.original_fp.split(\"/\")[-1]\n\n            # Apply filter to original image\n            if (\n                filter_type == \"glasses\"\n                or filter_type == \"makeup\"\n                or filter_type == \"mask\"\n            ):\n                pred_mod.apply_filter(self.original_fp, target_fp, filter_type)\n\n        else:\n            self.img_name = self.original_fp\n\n        # Parameter customisation\n        print(\"Chosen model:\", self.model_type.get())\n        isDebiased = self.MODELS[self.model.get()]\n        modelType = self.MODEL_TYPES[self.model_type.get()]\n        res = pred_mod.make_pred(self.img_name, modelType, debiased=isDebiased)\n\n        # Update prediction and confidence labels\n        if self.prediction is not None and self.confidence is not None:\n            # Reset labels\n            self.prediction.destroy()\n            self.confidence.destroy()\n        self.prediction = tk.Label(\n            self.frame,\n            text=\"Prediction: {}\".format(res[0]),\n            font=\"Helvetica 18 bold\",\n            bg=\"white\",\n        )\n        self.confidence = tk.Label(\n            self.frame,\n            text=\"Confidence: {}\".format(str(round(float(res[1][0][0]), 2))),\n            font=\"Helvetica 18 bold\",\n            bg=\"white\",\n        )\n\n        # Write prediction and confidence values\n        label_y = self.img_tk.winfo_y() + self.img_tk.winfo_height() + 50\n        self.prediction.place(relx=0.05, y=label_y)\n        self.confidence.place(relx=0.05, y=label_y + 100)\n\n        # Reload image\n        self.img = self.update_img(self.img_name, 250)\n        self.img_tk = tk.Label(self.frame, image=self.img)\n        self.img_tk.place(relx=0.05, rely=0.1)\n\n        stats_fp = \"stats_diagrams/{}{}_stats_graph.png\".format(\n            modelType, \"_debiased\" if isDebiased else \"\"\n        )\n        self.stats_img = self.update_img(stats_fp, 850)\n        stats_tk = tk.Label(self.frame, image=self.stats_img, bg=\"white\")\n        stats_tk.place(relx=0.4, rely=0.1)\n    \n    def update_img(self, img_path, basewidth):\n        \"\"\"\n        Processes and returns a given image to be displayed in the application.\n\n        img_path : str\n            Path to the image to be displayed\n        basewidth : float\n            Width to scale the given image to\n        \"\"\"\n        with Image.open(img_path) as img:\n            wpercent = basewidth / float(img.size[0])\n            hsize = int(float(img.size[1]) * wpercent)\n            img = img.resize((basewidth, hsize), Image.ANTIALIAS)\n        return ImageTk.PhotoImage(img)\n\n    def run(self):\n        \"\"\"\n        Runs the entire program\n        \"\"\"\n        self.root.mainloop()\n\n\nif __name__ == \"__main__\":\n    App().run()\n","sub_path":"app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":7441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"196501028","text":"\nimport os\n\nfrom ..api.exceptions import APIResponseError\nfrom ..api.create import CreateResponse\nfrom ..api.delete import DeleteResponse\nfrom ..api.response import PaginatedListResponse\nfrom ..api.patch import PatchResponse\nfrom ..api.update import UpdateResponse\n\nfrom .environment import (\n    DRF_AUTH_TOKEN_VARIABLE,\n    REST_AUTH_TOKEN_VARIABLE,\n    JWT_API_TOKEN_VARIABLE\n)\n\n\nclass APISession:\n    \"\"\"\n    Base class for REST API sessions\n\n    This is also class for anonymous (not authenticated) API sessions\n    \"\"\"\n    session_type = 'anonymous'\n\n    @property\n    def headers(self):\n        \"\"\"\n        Headers to append for session.\n\n        By default empty dictionary, extended by child class\n        \"\"\"\n        return {}\n\n    def list(self, url, debug=False):\n        \"\"\"\n        Get iterator for resource listings for specified URL\n        \"\"\"\n        return PaginatedListResponse(url, headers=self.headers, debug=debug)\n\n    def create(self, url, data, json=False, debug=True):\n        \"\"\"\n        Create a record with REST API\n        \"\"\"\n        return CreateResponse(url, data, headers=self.headers, json=json, debug=debug)\n\n    def delete(self, url, debug=True):\n        \"\"\"\n        Dlete a record with REST API\n        \"\"\"\n        return DeleteResponse(url, headers=self.headers, debug=debug)\n\n    def patch(self, url, data, json=False, debug=True):\n        \"\"\"\n        Patch a record with REST API\n        \"\"\"\n        return PatchResponse(url, data, headers=self.headers, json=json, debug=debug)\n\n    def update(self, url, data, json=False, debug=True):\n        \"\"\"\n        Update a record with REST API\n        \"\"\"\n        return UpdateResponse(url, data, headers=self.headers, json=json, debug=debug)\n\n\ndef load_api_session():\n    \"\"\"\n    Try to detect and load session based on environment variables available\n\n    Preferred order of sessions:\n    - Static token authentication\n    - JWT token authentication\n    - Anonymous authentication\n    \"\"\"\n\n    if os.environ.get(DRF_AUTH_TOKEN_VARIABLE, None):\n        from .django_token_auth import DjangoTokenAuthSession\n        session = DjangoTokenAuthSession()\n\n    elif os.environ.get(REST_AUTH_TOKEN_VARIABLE, None):\n        from .authorization_header_auth import AuthorizationHeaderSession\n        session = AuthorizationHeaderSession()\n\n    elif os.environ.get(JWT_API_TOKEN_VARIABLE, None):\n        from .jwt_token_auth import JWTAuthenticationSession\n        session = JWTAuthenticationSession()\n        if not session.verify_token():\n            raise APIResponseError('JWT session token has expired')\n    else:\n        session = APISession()\n\n    return session\n","sub_path":"bubka/session/session.py","file_name":"session.py","file_ext":"py","file_size_in_byte":2644,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"249565170","text":"# -*- coding: utf-8 -*-\nfrom pytest_bdd import given, when, then\nfrom model.group import Group\nimport random\nimport pytest\n\n@given('a group list')\ndef group_list(db):\n    with pytest.allure.step('Given a group list'):\n        return db.get_group_list()\n\n\n@given('a group with <name>, <header>, <footer>')\ndef new_group(name, header, footer):\n    with pytest.allure.step('Given a group with name={name}, header={header} and footer={footer}'):\n        return Group(name=name, header=header, footer=footer)\n\n@when('I add the group to the list')\ndef add_new_group(app, new_group):\n    with pytest.allure.step('When I add the group to the list'):  # Помечаем участок кода как шаг\n        app.group.create(new_group)\n\n@then('the new group list is equal to the old list with added group')\ndef verify_group_added(db, group_list, new_group):\n    with pytest.allure.step('Then the new group list is equal to the old list with added group'):\n        old_groups = group_list\n        new_groups = db.get_group_list()\n        old_groups.append(new_group)\n        assert sorted(old_groups, key=Group.id_or_max) == sorted(new_groups, key=Group.id_or_max)\n\n\n@given('a non-empty group list')\ndef non_empty_group_list(db, app):\n    with pytest.allure.step('Given a non-empty group list'):\n        if len(db.get_group_list()) == 0:\n            app.group.create(Group(name=\"group for delete\"))\n        return db.get_group_list()\n\npytest.allure.step('Given a random group from the list')\n@given('a random group from the list')\ndef random_group(non_empty_group_list):\n    with pytest.allure.step('Given a random group from the list'):\n        return random.choice(non_empty_group_list)\n\n@when('I delete the group from the list')\ndef delete_group(app, random_group):\n    with pytest.allure.step('When I delete the group %s from the list' % random_group):\n        app.group.delete_by_id(random_group.id)\n\n@then('the new group list is equal to the old list without deleted group')\ndef verify_group_deleted(db, non_empty_group_list, random_group):\n    with pytest.allure.step('Then the new group list is equal to the old list without deleted group'):\n        old_groups = non_empty_group_list\n        new_groups = db.get_group_list()\n        old_groups.remove(random_group)\n        assert old_groups == new_groups\n        assert sorted(new_groups, key=Group.id_or_max) == sorted(db.get_group_list(), key=Group.id_or_max)\n\n@when('I edit the group from the list')\ndef edit_group(app, random_group, new_group):\n    with pytest.allure.step('When I edit the group %s from the list' % random_group):\n        app.group.edit_by_id(new_group, random_group.id)\n\n@then('the new group list is equal to the old list with edit group')\ndef verify_group_deleted(db, random_group, non_empty_group_list, new_group):\n    with pytest.allure.step('Then the new group list is equal to the old list with edit group'):\n        old_groups = non_empty_group_list\n        index = old_groups.index(random_group)\n        old_groups[index] = new_group\n        new_groups = db.get_group_list()\n        assert old_groups == new_groups\n        assert sorted(new_groups, key=Group.id_or_max) == sorted(db.get_group_list(), key=Group.id_or_max)\n\n\n\n\n\n","sub_path":"bdd/group_steps.py","file_name":"group_steps.py","file_ext":"py","file_size_in_byte":3213,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"367832450","text":"\"\"\"\nThis script demonstrates the utility of the absorbing boundary,\nwhich is an imaginary addition to the potential, to simulate the evolution of\nbounded potentials (e.g., such as a soft-core Coulomb potential, a Morse potential that has a bounded arm).\n\"\"\"\nimport numpy as np\nfrom split_op_schrodinger1D import SplitOpSchrodinger1D # Previously developed propagator\nimport matplotlib.pyplot as plt # Plotting facility\nfrom matplotlib.colors import LogNorm # Color normalizer to perform logarithmic color plots\nfrom scipy.signal import blackman # to be used as an absorbing boundary\nfrom scipy import fftpack\n\n\nclass SplitOpSchrodinger1DAbsBoundary(SplitOpSchrodinger1D):\n    \"\"\"\n    Split operator propagator with absorbing boundary condition added.\n\n    As far as programming is concerned, this class is implemented using inheritance, see, e.g.\n    https://en.wikipedia.org/wiki/Inheritance_%28object-oriented_programming%29\n    \"\"\"\n    def __init__(self, **kwargs):\n        # Call the parent's constructor\n        SplitOpSchrodinger1D.__init__(self, **kwargs)\n        # Note that the absorbing boundary can be any function\n        # as long as the results of simulations do not depend on its choice, i.e.,\n        # numerical results are converged\n\n        # define the parameter to control smoothness of the absorbing boundary (see below),\n        # if the user has not done so\n        try:\n            self.alpha\n        except AttributeError:\n            self.alpha = 1\n\n        # Cache the absorbing boundary\n        self._abs_boundary = np.abs(blackman(self.X_gridDIM))**abs(self.dt*self.alpha)\n\n    def get_expV(self, t):\n        # Overload the methid from SplitOpSchrodinger1D\n\n        # Apply the absorbing boundary\n        # the following line is equivalent to\n        #   V(x) -> V(x) + 1j*self.alpha*np.log(blackman(x))\n        return SplitOpSchrodinger1D.get_expV(self, t) * self._abs_boundary\n\n#########################################################################\n\n\ndef test(Propagator, params):\n    \"\"\"\n    Run tests for the specified propagators and plot the probability density\n    of the time dependent propagation\n    :param Propagator: class that propagates\n    :param params: propagation parameters\n    :return: list of densities obtained as result of the propagation\n    \"\"\"\n    #########################################################################\n\n    # Find the ground state using the imaginary time propagator\n    img_time_params = params.copy()\n    img_time_params['dt'] *= -1j\n\n    # extract potential\n    V = img_time_params['V']\n\n    try:\n        V(0., 0.)\n        # potential is time dependent so let's fix time to zero\n        import functools\n        img_time_params['V'] = functools.partial(V, t=0.)\n    except TypeError:\n        # no need to modify it because the potential is time independent\n        pass\n\n    # Make a copy of parameters and do the Wick rotation\n    sys = Propagator(**img_time_params)\n\n    # make a guess for the ground state\n    # it is a good idea to make it nodeless\n    sys.set_wavefunction(\n        np.exp(-np.abs(sys.X_range))\n    )\n\n    # turn off the Ehrenfets theorem calculations for ground state\n    sys.isEhrenfest = False\n\n    # get the ground state using the imagenary time propagation\n    ground_state = sys.propagate(6000)\n\n    # set the real time propagator for the Schrodinger equation\n    sys = Propagator(**params).set_wavefunction(ground_state)\n\n    # display the propagator\n    plt.imshow(\n        [np.abs(sys.propagate(105))**2 for _ in xrange(750)],\n        origin='lower',\n        norm=norm,\n        aspect=0.4, # image aspect ratio\n        extent=[sys.X_range.min(), sys.X_range.max(), 0., sys.t]\n    )\n    plt.xlabel('coordinate $x$ (a.u.)')\n    plt.ylabel('time $t$ (a.u.)')\n    plt.colorbar()\n\n    # return propagator\n    return sys\n\n#########################################################################\n\n# specify parameters separately\n# note the time step was not included\nparams = dict(\n    t=0.,\n    dt=0.01,\n    X_gridDIM=1024,\n    X_amplitude=100.,\n\n    # parameter controling smoothness of the absorbing boundary, if the latter is used\n    alpha = 0.02,\n\n    K=lambda p: 0.5*p**2,\n    diff_K=lambda p: p,\n\n    # the soft core Coulomb potential\n    V=lambda x: -1./np.sqrt(x**2 + 1.37),\n\n    # the derivative of the potential to calculate Ehrenfest\n    diff_V=lambda x: x*np.power(x**2 + 1.37, -1.5),\n)\n\n# This is how to make logarithmic color plot\nnorm=LogNorm(vmin=1e-12, vmax=0.1)\n\n#########################################################################\n#\n# Test propagation under time independent potential\n#\n#########################################################################\n\nplt.subplot(121)\nplt.title(\"No absorbing boundary time independent potential, $|\\\\Psi(x, t)|^2$\")\ntest(SplitOpSchrodinger1D, params)\n\nplt.subplot(122)\nplt.title(\"Absorbing boundary time independent potential, $|\\\\Psi(x, t)|^2$\")\ntest(SplitOpSchrodinger1DAbsBoundary, params)\n\n\nplt.show()\n\n#########################################################################\n#\n# Test propagation under time dependent potential\n#\n#########################################################################\n\n# frequency of laser pulse\nomega_laser = 0.06\n\n# period of laser pulse\nT = 2*np.pi/omega_laser\n\n# Define laser field:\n# Always add an envelop to the laser field to avoid all sorts of artifacts\n# we use a sin**2 envelope, which resembles the Blackman filter\nE = lambda t: 0.04*np.sin(omega_laser*t) * np.sin(np.pi*t/(8*T))**2\n\n# add some laser field to the potential\nV = params['V']\nparams['V'] = lambda x, t: V(x) + x*E(t)\n\n# update the derivative of the potential correspondingly\ndiff_V = params['diff_V']\nparams['diff_V'] = lambda x, t: diff_V(x) + E(t)\n\nplt.subplot(121)\nplt.title(\"No absorbing boundary time DEPENDENT potential, $|\\\\Psi(x, t)|^2$\")\nsys_no_abs_boundary = test(SplitOpSchrodinger1D, params)\n\nplt.subplot(122)\n\nplt.title(\"Absorbing boundary time dependent potential, $|\\\\Psi(x, t)|^2$\")\nsys_with_abs_boundary = test(SplitOpSchrodinger1DAbsBoundary, params)\n\nplt.show()\n\n#########################################################################\n\ndef test_Ehrenfest1(sys):\n    \"\"\"\n    Test the first Ehenfest theorem for the specified quantum system\n    \"\"\"\n    times = sys.dt * np.arange(len(sys.X_average))\n\n    plt.plot(times, np.gradient(sys.X_average, sys.dt), '-r', label='$d\\\\langle\\\\hat{x}\\\\rangle / dt$')\n    plt.plot(times, sys.X_average_RHS, '--b', label='$\\\\langle\\\\hat{p}\\\\rangle$')\n    plt.legend()\n    plt.ylabel('momentum')\n    plt.xlabel('time $t$ (a.u.)')\n\ndef test_Ehrenfest2(sys):\n    \"\"\"\n    Test the second Ehenfest theorem for the specified quantum system\n    \"\"\"\n    times = sys.dt * np.arange(len(sys.X_average))\n\n    plt.plot(times, np.gradient(sys.P_average, sys.dt), '-r', label='$d\\\\langle\\\\hat{p}\\\\rangle / dt$')\n    plt.plot(times, sys.P_average_RHS, '--b', label='$\\\\langle -U\\'(\\\\hat{x})\\\\rangle$')\n    plt.legend()\n    plt.ylabel('force')\n    plt.xlabel('time $t$ (a.u.)')\n\n\nplt.subplot(221)\nplt.title(\"No absorbing boundary time DEPENDENT potential\")\ntest_Ehrenfest1(sys_no_abs_boundary)\n\nplt.subplot(222)\nplt.title(\"No absorbing boundary time DEPENDENT potential\")\ntest_Ehrenfest2(sys_no_abs_boundary)\n\nplt.subplot(223)\nplt.title(\"Absorbing boundary time DEPENDENT potential\")\ntest_Ehrenfest1(sys_with_abs_boundary)\n\nplt.subplot(224)\nplt.title(\"Absorbing boundary time DEPENDENT potential\")\ntest_Ehrenfest2(sys_with_abs_boundary)\n\nplt.show()\n\n#########################################################################\n\ndef plot_spectrum(sys):\n    \"\"\"\n    Plot the High Harmonic Generation spectrum\n    \"\"\"\n    # Power spectrum emitted is calculated using the Larmor formula\n    #   (https://en.wikipedia.org/wiki/Larmor_formula)\n    # which says that the power emitted is proportional to the square of the acceleration\n    # i.e., the RHS of the second Ehrenfest theorem\n\n    N = len(sys.P_average_RHS)\n    omegas = fftpack.fftshift(fftpack.fftfreq(N, sys.dt/(2*np.pi))) / omega_laser\n    spectrum = np.abs(fftpack.fftshift(fftpack.fft(sys.P_average_RHS)))**2\n    spectrum /= spectrum.max()\n\n    plt.semilogy(omegas, spectrum)\n    plt.ylabel('spectrum')\n    plt.xlabel('frequency / $\\\\omega_L$')\n    plt.xlim([0, 40.])\n    plt.ylim([1e-10, 1.])\n\nplt.subplot(121)\nplt.title(\"No absorbing boundary High Harmonic Generation Spectrum\")\nplot_spectrum(sys_no_abs_boundary)\n\nplt.subplot(122)\nplt.title(\"With absorbing boundary High Harmonic Generation Spectrum\")\nplot_spectrum(sys_with_abs_boundary)\n\nplt.show()","sub_path":"absorbing_boundary.py","file_name":"absorbing_boundary.py","file_ext":"py","file_size_in_byte":8541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"520019023","text":"# Imports random library\nimport random\n\n# create a list of play options\n# This could also be done with random numbers 0,1,2\nroll_options = [\"ROCK\", \"PAPER\", \"SCISSORS\"]\n\n# Initialize the global roll variables\nplayer1_roll = 0\nplayer2_roll = 0\n\n# Sets up black player scores\nplayer1_score = 0\nplayer2_score = 0\n\n# Game round counter\ngame_round = 1\n\n# Ask for number of players\nnum_players = 0\n\n# Check for if setup has been run once\nhas_setup = False\n\n\ndef Setup():\n    global has_setup\n    global num_players\n    ask_num_players = int(input('\\nHow many players are playing, 1 or 2?\\n'))\n    num_players = ask_num_players\n    has_setup = True\n\n\ndef generate_rolls():\n    # Because the variables were initilized globally, we need to use the global keyword\n    global player1_roll\n    global player2_roll\n    global roll_options\n    global num_players\n    print('\\n---ROLLS---')\n    if num_players == 1:\n        ask_roll_p1 = input('\\nPlayer 1 --- Hit enter to roll.\\n')\n        if ask_roll_p1 == '':\n            player1_roll = roll_options[random.randint(0, 2)]\n            print('\\nPlayer 1 rolled a: ', player1_roll)\n            player2_roll = roll_options[random.randint(0, 2)]\n            print('The computer rolled a: ', player2_roll)\n    else:\n        ask_roll_p1 = input('\\nPlayer 1 --- Hit enter to roll.\\n')\n        if ask_roll_p1 == '':\n            player1_roll = roll_options[random.randint(0, 2)]\n            print('\\nPlayer 1 rolled a: ', player1_roll)\n        ask_roll_p2 = input('\\nPlayer 2 --- Hit enter to roll.\\n')\n        if ask_roll_p2 == '':\n            player2_roll = roll_options[random.randint(0, 2)]\n            print('Player 2 rolled a: ', player2_roll)\n\n\ndef check_winner():\n    #  RPS Get Final Score / End Game\n    global player1_score\n    global player1_score\n    global num_players\n    print(\"\\n---SCORE UPDATE---\")\n    if num_players == 1:\n        print(\"\\nPlayer 1's score is: \", player1_score)\n        print(\"\\nThe computer's score is: \", player2_score, '\\n')\n    else:\n        print(\"\\nPlayer 1's score is: \", player1_score)\n        print(\"\\nPlayer 2's score is: \", player2_score, '\\n')\n    has_won = False\n    while has_won == False:\n        if player1_score == 3:\n            has_won = True\n            print('\\nPlayer 1 has won!')\n            quit()\n        elif player2_score == 3:\n            has_won = True\n            print('\\nPlayer 2/AI has won!')\n            quit()\n        else:\n            print('\\n---GET READY FOR THE NEXT ROUND---\\n')\n            Main()\n\n\ndef Main():\n    global player1_roll\n    global player1_score\n    global player2_roll\n    global player2_score\n    global game_round\n    global num_players\n    # Runs setup once\n    if has_setup == False:\n        Setup()\n    # Generate the rolls for this turn\n    print('\\n\\n**************************************************\\n\\n')\n    print('\\n---ROUND ', game_round, '---\\n')\n    generate_rolls()\n    # Main check on player turn\n    if player1_roll == player2_roll:\n        print(\"Tie!\")\n    elif player1_roll == \"ROCK\":\n        if player2_roll == \"PAPER\":\n            print(player2_roll, \"covers\", player1_roll)\n            player2_score = player2_score + 1\n        else:\n            print(player1_roll, \"smashes\", player2_roll)\n            player1_score = player1_score + 1\n    elif player1_roll == \"PAPER\":\n        if player2_roll == \"SCISSORS\":\n            print(player2_roll, \"cuts\", player1_roll)\n            player2_score = player2_score + 1\n        else:\n            print(player1_roll, \"covers\", player2_roll)\n            player1_score = player1_score + 1\n    #player1_roll == \"SCISSORS\"\n    else:\n        if player2_roll == \"ROCK\":\n            print(player2_roll, \"smashes\", player1_roll)\n            player2_score = player2_score + 1\n        else:\n            print(player1_roll, \"cuts\", player2_roll)\n            player1_score = player1_score + 1\n\n    game_round = game_round + 1\n    check_winner()\n\n\nMain()\n","sub_path":"rock-paper-scissors/Rock Paper Scissors Complete.1.py","file_name":"Rock Paper Scissors Complete.1.py","file_ext":"py","file_size_in_byte":3925,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"207152350","text":"#!/bin/bash\nfrom params import panda_seq\nimport argparse\nimport os\n\nparser = argparse.ArgumentParser(description=\"Run pandaseq on all forward and reverse fastq files.\")\nforward_file_glob = \"raw/stacked/forward/{sample}.fastq*\"\nreverse_file_glob = \"raw/stacked/reverse/{sample}.fastq*\"\n\nparser.add_argument(\"-v\", \"--verbose\", help='Output more Info', action=\"store_true\")\nparser.add_argument('-L', '--log', action='store_true', help='Log output')\nargs = parser.parse_args()\n\n#algorithm = 'stitch'        \nalgorithm = 'simple_bayesian'\nos.makedirs(panda_seq['merge_dir'], exist_ok=True)\n\ndef collect_sample_dict(file_glob, glob_chars='?*\\[\\]'):\n    from glob import glob\n    import re\n    files = glob(file_glob.format(sample='*'))\n    extract_str = [s for s in re.split('['+glob_chars+']', file_glob) if '{sample}' in s][0]\n    out_dict = {}\n    for f in files:\n        finds = re.findall(extract_str.format(sample='(.+)'), f)\n        if len(finds) != 1:\n            print(\"Could not extract sample name from file:\", f)\n            raise ValueError\n        sample = finds[0]\n        if sample in out_dict:\n            print(\"Found multiple files with same sample name:\", f, 'and', out_dict[sample])\n            raise RuntimeError\n        out_dict[sample] = f\n    return out_dict\n\nforward_files = collect_sample_dict(forward_file_glob)\nreverse_files = collect_sample_dict(reverse_file_glob)\n\nif len(forward_files) == len(reverse_files):\n    print(\"Found\", len(forward_files), \"samples.\")\nelse:\n    print(\"Found\", len(forward_files), \"forward files and\", len(reverse_files), \"reverse files.\")\n\nmap_params_to_pandaseq_option = dict(\n                    l='min_length',       # Minimum length for a sequence.\n                    t='threshold',        # The minimum probability that a sequence must have to assemble and, if used, match a primer.\n                    O='min_overlap')      # Minimum overlap region length for a sequence. (0 to use read length.)\n\nextra_params = {s:panda_seq[param_name] for s, param_name in map_params_to_pandaseq_option.items()}\n\ndef merge_files(sample, forward_file, reverse_file, cmd='pandaseq'):\n    from tuba_seq.pmap import CPUs\n    from subprocess import Popen, PIPE\n    options = dict( f=forward_file,\n                    r=reverse_file,\n                    W=panda_seq['merge_dir']+sample+'.fastq.bz2',\n                    T=CPUs,\n                    A=algorithm)# algorithm:parameters\tSelect the algorithm to use for overlap selection and scoring.\n    options.update(extra_params)\n    if args.log:\n        options['G'] = sample+'.LOG.bz2'\n\n    str_options = {'-'+k:str(v) for k, v in options.items()}\n    return Popen([cmd, '-a', '-F']+[s for item in str_options.items() for s in item], stdout=PIPE, stderr=PIPE).communicate()\n\nfor sample, forward_file in forward_files.items():\n    if sample in reverse_files:\n        output = merge_files(sample, forward_file, reverse_files[sample]) \n        #if not args.log:\n            #statlines = output[1].splitlines()[-8:-1]\n            #stats = {name:int(count) for __, __, name, count in map(lambda b: b.decode('ascii').split('\\t'), statlines)} \n            #print(\"Merged {:.1%} of {:.1}M reads in sample {:}.\".format(stats['OK']/stats['READS'], stats['READS']*1e-6, sample))\n    else:\n        print(\"Could not find a reverse FASTQ for\", forward_file)\n","sub_path":"pandaseq.py","file_name":"pandaseq.py","file_ext":"py","file_size_in_byte":3332,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"178264647","text":"# -*- coding: utf-8 -*-\nimport os\nimport sys\nimport subprocess\n\nadb_path = ''\n\n\ndef get_path():\n    global adb_path\n    try:\n        adb_path = 'adb'\n        subprocess.Popen([adb_path], stdout=subprocess.PIPE,\n                         stderr=subprocess.PIPE)\n        return adb_path\n    except FileNotFoundError:\n        if os.name == 'nt':\n            adb_path = os.path.join('Tools', \"adb\", 'adb.exe')\n            try:\n                subprocess.Popen(\n                    [adb_path], stdout=subprocess.PIPE, stderr=subprocess.PIPE)\n                return adb_path\n            except FileNotFoundError:\n                pass\n        print('请安装 ADB 及驱动并配置环境变量')\n        sys.exit()\n\n\ndef run(command):\n    global adb_path\n    if adb_path == '':\n        adb_path = get_path()\n    command = '{} {}'.format(adb_path, command)\n    readObj = os.popen(command)\n    output = readObj.read()\n    readObj.close()\n    return output\n","sub_path":"common/adb.py","file_name":"adb.py","file_ext":"py","file_size_in_byte":947,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"608718470","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Feb 12 16:52:57 2019\n\n@author: JunbinZhang\n\"\"\"\nimport matplotlib.pyplot as plt\n#import os.path\nimport xlrd #read\nimport xlwt\nimport numpy as np\nimport time\n\nfrom brd_config import Brd_Config\nfrom frame import Frames\n\n#loc = (\"D:\\python_workspace\\DUNE_COLDADC\\data\\uncali.xlsx\")\n#To open workbook(loc)\nworkbook = xlwt.Workbook()\nsheet = workbook.add_sheet(\"cali\")\n\n#from Plotfun import Plotfun\nbrd_config = Brd_Config()\n\nbrd_config.adc_output_select(\"cali_ADCdata\")\n\n#plot = Plotfun()\n#brd_config.adc_test_input(\"Normal\")\n#brd_config.adc_sync_mode(\"Normal\")\n#brd_config.adc_sdc_select(\"off\")\n#brd_config.adc_db_select(\"Bypass\")\n#brd_config.adc_sha_input(1)\n\n#brd_config.word_order_slider(0) #2 ligned up\n\n\n#result=brd_config.fe_config('ADAC')\n#brd_config.fe_pulse_config('ADAC')\n#brd_config.fe_spi_config(result)    \n#brd_config.fe_pulse_param(9,77,500,0xa00)\n\n\n#brd_config.adc_test_input(\"test\")\n#brd_config.adc_test_input(\"Normal\")\n\n\n\n\n\n#brd_config.adc_framemarker_shift(0)\n#brd_config.adc_hard_reset()\n#brd_config.adc_i2c_uart(\"I2C\")\n##config BJT\n#brd_config.adc_ref_vol_src(\"BJT\")\n#brd_config.adc_bias_curr_src(\"BJT\")\n#brd_config.adc_ref_monitor(0,0)\n#brd_config.adc_ref_powerdown(0,0)\n#brd_config.adc_set_ioffset(0,0,0,0)\n#brd_config.adc_set_vrefs(0xff,0x40,0x9F,0x7B)\n#\n##load_ADC pattern\n#brd_config.adc_load_pattern_0(0x8a,0x8a)\n#brd_config.adc_load_pattern_1(0xA0,0xA0)\n#brd_config.adc_test_data_mode(\"test\")\n#\n#brd_config.adc_test_data_mode(\"Normal\")\n#\n#brd_config.adc_outputformat(\"offset\")\n##brd_config.adc_framemarker_shift(2)\n#brd_config.adc_sync_mode(\"sync\")\n#brd_config.Acq_start_stop(0)\n#brd_config.Acq_start_stop(0)\ntime.sleep(1)\nbrd_config.Acq_start_stop(1)\n################################################\npktnum = 1000\nadcdata = brd_config.get_data(pktnum,0,'Jumbo')\nbrd_config.Acq_start_stop(0)\nframes_inst = Frames(pktnum,adcdata)\nframes = frames_inst.packets()\n###############################################\n\ndef complement(num,complete):\n    if complete == \"2s\":\n        if (num <= 32767):\n            result = num\n        else:\n            result = (num%32768 - 32768)\n    else:\n        result = num\n    return result\n#print(hex(brd_config.udp.read_reg(0x101)))\n#frames[0].info()\n#frames[1].info()\n#frames[2].info()\n#frames[3].info()\n#plot.PlotADC('COLDADC',frames[0].ADCdata)\nchns=[[],[],[],[],[],[],[],[],[],[],[],[],[],[],[],[]]\nfor i in range(len(frames)):\n    for j in range(16): #16 channels\n        chns[j].append(complement(frames[i].ADCdata[j],\"none\"))\n\n#Save data in column\n#np.savetxt('uncali.txt',zip(chns[0],chns[1],chns[2],chns[3],chns[4],chns[5],chns[6],chns[7],chns[8],chns[9],chns[10],chns[11],chns[12],chns[13],chns[14],chns[15]),fmt = \"%i %i %i %i %i %i %i %i %i %i %i %i %i %i %i %i\" )\n#np.savetxt('uncali.txt',chns,delimiter=',')   \nfor i in range(16):\n    sheet.write(0,i,'chn'+str(i))\n    for j in range(len(frames)):\n        sheet.write(j+1,i,chns[i][j])\n\nworkbook.save(\"cali_1.xls\")    \n    \nfig = plt.figure()\nax1 = fig.add_subplot(211)\nplt.xlabel('times')\nplt.ylabel('ADC counts')\nplt.title('ADC0 chn1')\nplt.plot(chns[1],'b*-')\n#plt.plot(chns[1],'g*')\n#plt.plot(chns[2],'rh')\n#plt.plot(chns[3],'c+')\n#plt.plot(chns[4],'mx')\n#plt.plot(chns[5],'yd')\n#plt.plot(chns[6],'kD')\n#plt.plot(chns[7],'k,')\n#plt.legend(('chn0','chn1','chn2','chn3','chn4','chn5','chn6','chn7'))\n#plt.text(0,2000, 'chn0=%d,chn1=%d,chn2=%d,chn3=%d,chn4=%d,chn5=%d,chn6=%d,chn7=%d'%(chns[0][0],chns[1][0],chns[2][0],chns[3][0],chns[4][0],chns[5][0],chns[6][0],chns[7][0]),color='k')\nax2 = fig.add_subplot(212)\nplt.xlabel('times')\nplt.ylabel('ADC counts')\nplt.title('ADC1 chn1')\nplt.plot(chns[9],'b*-')\nplt.show()\n","sub_path":"ramp.py","file_name":"ramp.py","file_ext":"py","file_size_in_byte":3678,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"236378431","text":"from django.shortcuts import render\nfrom django.shortcuts import redirect\n# Create your views here.\nfrom django.shortcuts import render\nfrom .models import OrderItem, Order\nfrom .forms import OrderCreateForm\nfrom cart.cart import Cart\nfrom django.contrib.auth.decorators import login_required\nfrom .tasks import order_created\nfrom django.contrib.admin.views.decorators import staff_member_required\nfrom django.shortcuts import get_object_or_404\nfrom .models import Order, OrderItem\nfrom django.conf import settings\nfrom django.http import HttpResponse\nfrom django.template.loader import render_to_string\nfrom myonlineshop.models  import Product, Category\nfrom django.contrib import messages\nfrom django.urls import reverse\nimport weasyprint\nfrom django.core.mail import send_mail\nfrom django.http import HttpResponse, HttpResponseRedirect\nfrom io import BytesIO\n# from celery import task\nimport weasyprint\nfrom django.template.loader import render_to_string\nfrom django.core.mail import EmailMessage\nfrom django.conf import settings\nfrom order.models import Order\n\n\n\n\ndef order_created(order_id):\n    \"\"\"\n    Task to send an e-mail notification when an order is\n    successfully created.\n    \"\"\"\n    order = Order.objects.get(id=order_id)\n    subject = f'Order nr. {order.id}'\n    message = f'Dear {order.user.first_name},\\n\\n' \\\n              f'You have successfully placed an order.' \\\n              f'Your order ID is {order.id}.'\n    mail_sent = send_mail(subject,\n                          message,\n                          'info@tishman.com.ng',\n                          [order.user.email])\n    return mail_sent\n\n\ndef payment_completed(order_id):\n    \"\"\"\n    Task to send an e-mail notification when an order is\n    successfully created.\n    \"\"\"\n    order = Order.objects.get(id=order_id)\n    # create invoice e-mail\n    subject = f'My Shop - EE Invoice no. {order.id}'\n    message = 'Please, find attached the invoice for your recent purchase.'\n    email = EmailMessage(subject,\n                         message,\n                         'info@tishman.com.ng',\n                         [order.user.email])\n    # generate PDF\n    html = render_to_string('orders/order/pdf.html', {'order': order})\n    out = BytesIO()\n    stylesheets=[weasyprint.CSS(settings.STATIC_ROOT + 'css/pdf.css')]\n    weasyprint.HTML(string=html).write_pdf(out,\n                                          stylesheets=stylesheets)\n    # attach PDF file\n    email.attach(f'order_{order.id}.pdf',\n                 out.getvalue(),\n                 'application/pdf')\n    # send e-mail\n    email.send()\n\n@login_required\ndef order_create(request):\n    cart = Cart(request)\n    my_product = Product()\n\n    if request.method == 'POST':\n        form = OrderCreateForm(request.POST)\n\n \n\n        if form.is_valid():\n            data = Order() #create relation to model\n            data.address = form.cleaned_data['address']\n            data.postal_code = form.cleaned_data['postal_code']\n            data.city = form.cleaned_data['city']\n            data.country = form.cleaned_data['country']\n            data.state = form.cleaned_data['state']\n            data.phone_number = form.cleaned_data['phone_number']\n            current_user = request.user\n            data.user_id = current_user.id\n            order = data\n            # order = form.save(commit=False)\n            \n            if cart.coupon:\n                order.coupon = cart.coupon\n                order.discount = cart.coupon.discount\n                \n            order.save()\n\n            \n            for item in cart:\n                OrderItem.objects.create(user=request.user,\n                                            order=order,\n                                            product=item['product'],\n                                            price=item['price'],\n                                            quantity=item['quantity'],\n                                            size_cloth= item[\"size_cloth\"]\n                                            )\n  \n            # launch asynchronous task\n            #order_created(order.id)\n            # return render(request, 'orders/order/created.html', locals())\n            #set the order in the session\n            request.session['order_id'] = order.id\n            # clear the cart\n            cart.clear()\n            # redirect for payment\n            return redirect(reverse('paystack:payment'))\n        else:\n            order_created(order.id)\n            return render(request,\n                          'orders/order/created.html',\n                          {'order': order})\n                \n    else:\n        form = OrderCreateForm()\n    return render(request,\n                  'orders/order/create.html',\n                  {'cart': cart, 'form': form})\n\n\n\n@staff_member_required\ndef admin_order_detail(request, order_id, user_id):\n    order = get_object_or_404(Order, id=order_id, user_id=user_id)\n    if not request.session.has_key('currency'):\n        request.session['currency'] = settings.DEFAULT_CURRENCY\n    return render(request,\n                  'admin/orders/order/detail.html',\n                  {'order': order})\n\n\n\n@staff_member_required\ndef admin_order_pdf(request, order_id):\n    order = get_object_or_404(Order, id=order_id)\n    \n    html = render_to_string('orders/order/pdf.html',\n                            {'order': order})\n    response = HttpResponse(content_type='application/pdf')\n    response['Content-Disposition'] = f'filename=order_{order.id}.pdf'\n    weasyprint.HTML(string=html).write_pdf(response,\n        stylesheets=[weasyprint.CSS(\n            settings.STATIC_ROOT + 'css/pdf.css')])\n    return response\n\n\n@login_required\ndef my_order(request, product_id, quantity, order_id, user_id):\n    # myorder = get_object_or_404(Order, id=id, user_id=user_id )\n    order_detail = get_object_or_404(OrderItem, product=product_id, quantity=quantity ,order_id=order_id,  user_id=request.user)\n    orders = Order.objects.all()\n    order_item = OrderItem.objects.filter(user=user_id) \n    categories = Category.objects.all()\n\n    return render(request, 'orders/order/myorders.html', {'orders':orders, 'order_detail':order_detail, 'order_item':order_item, 'categories':categories} )\n\n@login_required\ndef all_orders(request):\n    orders = OrderItem.objects.order_by('order')\n    orders_filter = orders.filter(user=request.user)\n    categories = Category.objects.all()\n    return render(request, 'orders/order/allorders.html', {'orders':orders, 'filter':orders_filter, 'categories':categories} )\n\n\n@login_required \ndef delete(request, id):\n    # person_pk = request.session.get('order_id')\n    current_user = request.user\n    Order.objects.filter(id=id, user_id=current_user.id).delete()\n    # query = Order.objects.get(id=person_pk)\n    # query.delete()\n    messages.success(request, 'Order canceled')\n    return HttpResponseRedirect('/orders/allorders')\n\n# @login_required(login_url='/login') # Check login\n# def user_deletecomment(request,id):\n#     current_user = request.user\n#     Comment.objects.filter(id=id, user_id=current_user.id).delete()\n#     messages.success(request, 'Comment deleted..')\n#     return HttpResponseRedirect('/user/comments')","sub_path":"myshop/order/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":7182,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"374140200","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /home/hanzz/releases/odcs/common/odcs/common/types.py\n# Compiled at: 2019-01-28 02:13:22\n# Size of source mod 2**32: 3560 bytes\n\n\nclass PungiSourceType:\n    KOJI_TAG = 1\n    MODULE = 2\n    REPO = 3\n    PULP = 4\n    RAW_CONFIG = 5\n    BUILD = 6\n    PUNGI_COMPOSE = 7\n\n\nPUNGI_SOURCE_TYPE_NAMES = {'tag':PungiSourceType.KOJI_TAG, \n 'module':PungiSourceType.MODULE, \n 'repo':PungiSourceType.REPO, \n 'pulp':PungiSourceType.PULP, \n 'raw_config':PungiSourceType.RAW_CONFIG, \n 'build':PungiSourceType.BUILD, \n 'pungi_compose':PungiSourceType.PUNGI_COMPOSE}\nINVERSE_PUNGI_SOURCE_TYPE_NAMES = {v:k for k, v in PUNGI_SOURCE_TYPE_NAMES.items()}\nCOMPOSE_STATES = {'wait':0, \n 'generating':1, \n 'done':2, \n 'removed':3, \n 'failed':4}\nINVERSE_COMPOSE_STATES = {v:k for k, v in COMPOSE_STATES.items()}\nMULTILIB_METHODS = {'none':0, \n 'runtime':1, \n 'devel':2, \n 'all':4}\nINVERSE_MULTILIB_METHODS = {v:k for k, v in MULTILIB_METHODS.items()}\nCOMPOSE_RESULTS = {'repository':1, \n 'iso':2, \n 'ostree':4, \n 'boot.iso':8}\nCOMPOSE_FLAGS = {'no_flags':0, \n 'no_deps':1, \n 'no_inheritance':2, \n 'include_unpublished_pulp_repos':4, \n 'check_deps':8}\nINVERSE_COMPOSE_FLAGS = {v:k for k, v in COMPOSE_FLAGS.items()}","sub_path":"pycfiles/odcs-0.2.45.tar/types.cpython-37.py","file_name":"types.cpython-37.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"652509368","text":"import os\n\n\ndef annotationClose(line,annotation):\n\tline=line.strip()\n\tif annotation:\n\t\tif line.endswith(annotation):\n\t\t\treturn True\n\treturn False\n\ndef isEmpty(line):\n\tif not len(line.strip()):\n\t\treturn True\n\ndef isOneAnnotation(line):\n\tline=line.strip()\n\tif line.startswith(\"#\") :\n\t\treturn True\n\treturn False\n\ndef isMultiAnnotation(line):\n\tline=line.strip()\n\tif line.startswith(\"\\\"\\\"\\\"\") or line.startswith(\"\\'\\'\\'\"):\n\t\treturn True\n\treturn False\n\ndef multiAnnotation(line):\n\tline=line.strip()\n\tif line.startswith(\"\\\"\\\"\\\"\"):\n\t\treturn \"\\\"\\\"\\\"\"\n\telif line.startswith(\"\\'\\'\\'\"):\n\t\treturn \"\\'\\'\\'\"\n\telse:\n\t\treturn None\n\ndef calcu_code(file_path):\n\n\twith open(file_path) as fo:\n\t\tlines=fo.readlines()\n\n\t\tannotation=None\n\n\t\ta_count=0\n\t\tc_count=0\n\t\te_count=0\n\n\t\tfor line in lines:\n\n\t\t\t\"\"\"\n\t\t\t多行注释\n\t\t\t\"\"\"\n\t\t\tif annotation:\n\t\t\t\tif annotationClose(line,annotation):\n\t\t\t\t\ta_count+=1\n\t\t\t\t\tannotation=None\n\t\t\t\telif isEmpty(line):\n\t\t\t\t\te_count+=1\n\t\t\t\telse:\n\t\t\t\t\ta_count+=1\n\n\t\t\t#空行\n\t\t\telif isEmpty(line):\n\t\t\t\te_count+=1\n\n\t\t\t#注释\n\t\t\telif isAnnotation(line):\n\t\t\t\ta_count+=1\n\t\t\t\tif isMultiAnnotation(line):\n\t\t\t\t\tannotation=multiAnnotation(line)\n\n\t\t\telse:\n\t\t\t\tc_count+=1\n\t\treturn (a_count,c_count,e_count)\n\n\ndef mul_code(file_path):\n\n\tcode_info={}\n\n\tfor file_name in os.listdir(file_path):\n\t\tfilePath=os.path.join(file_path,file_name)\n\t\tinfo=calcu_code(file_path)\n\t\tcode_info[file_path]={\"annotation\":info[0],\"code\":info[1],\"empty\":info[2]}\n\treturn code_info\n\t\t\n","sub_path":"a7.py","file_name":"a7.py","file_ext":"py","file_size_in_byte":1457,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"411531225","text":"import numpy as np\nimport matplotlib.pyplot as plt\n\ndef imshow(inp, mean=[0.5071, 0.4867, 0.4408], var=[0.2675, 0.2565, 0.2761]):\n    \"\"\"Imshow for Tensor.\"\"\"\n    inp = inp.numpy().transpose((1, 2, 0))\n    mean = np.array(mean)\n    std = np.array(var)\n    inp = std * inp + mean\n    inp = np.clip(inp, 0, 1)\n    plt.imshow(inp)\n    plt.pause(0.001)","sub_path":"data_utils/inspector.py","file_name":"inspector.py","file_ext":"py","file_size_in_byte":348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"342048399","text":"\r\nimport pandas as pd\r\n\r\n\r\n#loading the transet\r\ntran = pd.read_csv(\"C:/Users/Bibhuti/OneDrive/Desktop/360digiTMG assignment/LR/bank_data.csv\")\r\n\r\ntran_details =pd.DataFrame({\"column name\":tran.columns,\r\n                            \"tran type(in Python)\": tran.dtypes})\r\n#3.\ttran Pre-trancessing\r\n#3.1 tran Cleaning, Feature Engineering, etc\r\n         \r\n#details of tran \r\ntran.info()\r\ntran.describe()          \r\n\r\n#tran types        \r\ntran.dtypes\r\n\r\n#checking for na value\r\ntran.isna().sum()\r\ntran.isnull().sum()\r\n\r\n#checking unique value for each columns\r\ntran.nunique()\r\n\r\nEDA ={\"column \": tran.columns,\r\n      \"mean\": tran.mean(),\r\n      \"median\":tran.median(),\r\n      \"mode\":tran.mode(),\r\n      \"standard deviation\": tran.std(),\r\n      \"variance\":tran.var(),\r\n      \"skewness\":tran.skew(),\r\n      \"kurtosis\":tran.kurt()}\r\n\r\nEDA\r\n\r\n# covariance for tran set \r\ncovariance = tran.cov()\r\ncovariance\r\n\r\n# Correlation matrix \r\nco = tran.corr()\r\nco\r\n\r\nimport seaborn as sns\r\n####### graphitran repersentation\r\n\r\n#boxplot for every continuous type data\r\ntran.columns\r\ntran.nunique()\r\n\r\ntran.boxplot(column=['age', 'balance', 'duration', 'campaign','pdays','default'])   #no outlier\r\n \r\nsns.pairplot(tran.iloc[:, [0,2,5,6,7,8]])\r\n\r\n# Boxplot of independent variable distribution for each category of default \r\nsns.boxplot(x = \"default\", y = \"housing\", data = tran)\r\nsns.boxplot(x = \"default\", y = \"loan\", data = tran)\r\nsns.boxplot(x = \"default\", y = \"poutfailure\", data = tran)\r\nsns.boxplot(x = \"default\", y = \"divorced\", data = tran)\r\nsns.boxplot(x = \"default\", y = \"married\", data = tran)\r\n\r\n# Scatter plot for each categorical default of car\r\nsns.stripplot(x = \"default\", y = \"housing\", jitter = True, data = tran)\r\nsns.stripplot(x = \"default\", y = \"loan\", jitter = True, data = tran)\r\nsns.stripplot(x = \"default\", y = \"poutfailure\", jitter = True, data = tran)\r\nsns.stripplot(x = \"default\", y = \"divorced\", jitter = True, data = tran)\r\nsns.stripplot(x = \"default\", y = \"married\", jitter = True, data = tran)\r\n\r\n# Scatter plot between each possible pair of independent variable and also histogram for each independent variable \r\n\r\nsns.pairplot(tran, hue = \"default\") # With showing the category of each car default in the scatter plot\r\n\r\n# Rearrange the order of the variables\r\n\r\ntran = tran.iloc[:, [31, 0,1, 2, 3, 4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30]]\r\n\r\ntrans = tran.rename({\"joadmin.\":\"joadmin\",\"joblue.collar\":\"joblue_collar\",\"joself.employed\":\"joself_employed\"},axis =1)\r\n\r\n\"\"\"\r\n5.\tModel Building\r\n5.1\tBuild the model on the scaled data (try multiple options)\r\n5.2\tPerform Logistic Regression model.\r\n5.3\tTrain and Test the data and compare accuracies by Confusion Matrix, plot ROC AUC curve. \r\n5.4\tBriefly explain the model output in the documentation. \r\n\t\t\r\n \"\"\"\r\nfrom sklearn.model_selection import train_test_split # train and test \r\n\r\nimport statsmodels.formula.api as sm\r\nfrom sklearn import metrics\r\nfrom sklearn.metrics import classification_report\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\n\r\n### Splitting the data into train and test data \r\n# from sklearn.model_selection import train_test_split\r\ntrain_data, test_data = train_test_split(trans, test_size = 0.2,random_state =7) # 20% test data\r\n\r\n# Model building \r\n# import statsmodels.formula.api as sm\r\nlogit_model = sm.logit('y ~ age + default + balance + housing+loan + duration+  campaign + pdays+ previous+  poutfailure+ poutother+ poutsuccess+ poutunknown+ con_cellular+ con_telephone+ con_unknown+  divorced+ married+ single+ joadmin+ joblue_collar+ joentrepreneur+ johousemaid+ jomanagement+ joretired+ joself_employed+ joservices+ jostudent+ jotechnician+ jounemployed+ jounknown', data = train_data).fit()\r\n\r\n#summary\r\nlogit_model.summary2() # for AIC\r\nlogit_model.summary()\r\n\r\n#prediction\r\ntrain_pred = logit_model.predict(train_data.iloc[ :, 1: ])\r\n\r\n# Creating new column \r\n# filling all the cells with zeroes\r\ntrain_data[\"train_pred\"] = np.zeros(36168)\r\n\r\n# taking threshold value as 0.5 and above the prob value will be treated as correct value \r\ntrain_data.loc[train_pred > 0.5, \"train_pred\"] = 1\r\n\r\n# classification report\r\nclassification = classification_report(train_data[\"train_pred\"], train_data[\"y\"])\r\nclassification\r\n\r\n# confusion matrix\r\nconfusion_matrx = pd.crosstab(train_data.train_pred, train_data['y'])\r\nconfusion_matrx\r\n\r\naccuracy_train = (31153 + 1352)/(36168)\r\nprint(accuracy_train)\r\n\r\n#ROC CURVE AND AUC\r\nfpr, tpr, threshold = metrics.roc_curve(train_data[\"y\"], train_pred)\r\n\r\n#PLOT OF ROC\r\nplt.plot(fpr, tpr);plt.xlabel(\"False positive rate\");plt.ylabel(\"True positive rate\")\r\n\r\nroc_auc = metrics.auc(fpr, tpr)  #AUC = 0.90\r\n\r\n# Prediction on Test data set\r\ntest_pred = logit_model.predict(test_data)\r\n\r\n# Creating new column for storing predicted class of Attorney\r\n# filling all the cells with zeroes\r\ntest_data[\"test_pred\"] = np.zeros(9043)\r\n\r\n# taking threshold value as 0.5 and above the prob value will be treated as correct value \r\ntest_data.loc[test_pred > 0.5, \"test_pred\"] = 1\r\n\r\n# confusion matrix \r\nconfusion_matrix = pd.crosstab(test_data.test_pred, test_data['y'])\r\n\r\nconfusion_matrix\r\naccuracy_test = (7839 + 356)/(9043) \r\naccuracy_test\r\n","sub_path":"bank.py","file_name":"bank.py","file_ext":"py","file_size_in_byte":5205,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"66214642","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time   : 1/16/18 9:54 PM\n# @Author : Saseny.Zhou\n# @File   : 房价.py\n\n\n\nimport requests\nfrom lxml import etree\nimport time\nimport os\nimport re\n\nheader = {\n    \"User-Agent\": \"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_2) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.84 Safari/537.36\"\n}\n\ndefine_url = \"https://www.baidu.com/s?wd=%s&pn=%s\"\n\n\ndef SearchWeb(keyword, page=1, times=6, writePath=None, return_url=True):\n    \"\"\"\n    百度搜索，根据关键字搜索网站\n    :param keyword:           需要搜索的关键则\n    :param page:              要提取的搜索页数\n    :param times:             失败循环次数  默认 6-1次\n    :param return_url:        是否返回搜索到的网站链接，否则返回页面信息，真则返回链接\n    :param writePath:         是否存储该页面信息为html文本，默认None不保存，若需要保存则需要添加保存路径\n    :return:\n    \"\"\"\n    return_list = []\n    return_html = []\n    for p in range(1, page + 1, 1):\n        page_ = p * 10 - 1\n        url = define_url % (keyword, str(page_))\n        for i in range(1, int(times)):\n            try:\n                r = requests.get(url=url, headers=header)\n                if r:\n                    if writePath is not None:\n                        writeHtmlPath = os.path.join(writePath, keyword + \"_\" + str(int(time.time())) + \".html\")\n                        writeHtml(writeHtmlPath, str(r.text).encode('utf-8'))\n                    if return_url is not True:\n                        return_html.append(r.text)\n                    else:\n                        s = etree.HTML(r.text)\n                        tmp = s.xpath('//*[@id]/h3/a/@href')\n                        text = s.xpath('//*[@id]/h3/a/text()')\n                        info = s.xpath('//*[@id]/div[1]/text()')\n                        tmp1 = s.xpath('//*[@id]/div[1]/h3/a/@href')\n                        tmp1_text = s.xpath('//*[@id]/div[1]/h3/a/text()')\n\n                        print(info)\n                        print(len(info))\n\n                        for info in range(len(tmp)):\n                            tmp_tup = text[info], tmp[info]\n                            return_list.append(tmp_tup)\n\n                        for info1 in range(len(tmp1)):\n                            tmp_tup_1 = tmp1_text[info1], tmp1[info1]\n                            return_list.append(tmp_tup_1)\n\n                    time.sleep(1)\n                    break\n            except:\n                print(\"The %s Times Crawl Fail\" % str(i))\n\n    if return_url is not True:\n        return return_html\n    else:\n        return return_list\n\n\ndef getPage(url, times=6, writePath=None):\n    \"\"\"根据提供的连接爬取网页信息\"\"\"\n    for i in range(int(times)):\n        try:\n            r = requests.get(url, headers=header)\n            if r:\n                codeInfo = r.encoding\n                if writePath is not None:\n                    writeHtmlPath = os.path.join(writePath, str(int(time.time())) + \".html\")\n                    writeHtml(writeHtmlPath, str(r.text).encode(codeInfo))\n                return str(r.text).encode(codeInfo)\n        except:\n            print(\"The %s Times Crawl Link Fail\" % str(i))\n\n\ndef writeHtml(path, string):\n    \"\"\"写文档\"\"\"\n    try:\n        f = open(path, 'a')\n        f.write(str(string, 'utf-8'))\n        f.close()\n    except:\n        return False\n\n\ndef writeFile(path, string):\n    f = open(path, 'a')\n    if \"。\" in str(string):\n        f.write(str(string) + \"\\n\")\n    else:\n        f.write(str(string) + \" \")\n    f.close()\n\n\nsearchKey = \"房价\"\n\npathRoad_ = \"/Users/saseny.zhou/Desktop/爬虫练习/20180116\"\npathRoad = os.path.join(pathRoad_, searchKey)\n\nif not os.path.isdir(pathRoad):\n    os.makedirs(pathRoad)\n\na = SearchWeb(searchKey, return_url=True, page=5, writePath=pathRoad)\n\nfor i in a:\n    # if \"百度百科\" in i[0]:\n    print(i)\n    # s = etree.HTML(getPage(i[1], writePath=pathRoad))\n    #\n    # d = s.xpath('//@href')\n    # e = s.xpath('//text()')\n    # txtPath = os.path.join(pathRoad, searchKey + \".txt\")\n    # for j in e:\n    #     if re.findall('^\\S+?$', str(j)) and not re.findall(r'{?\\[|^\\d+?$}', str(j)):\n    #         print(str(j))\n    #         writeFile(txtPath, j)\n","sub_path":"房价.py","file_name":"房价.py","file_ext":"py","file_size_in_byte":4288,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"504450833","text":"\n\nfrom xai.brain.wordbase.adjectives._subject import _SUBJECT\n\n#calss header\nclass _SUBJECTS(_SUBJECT, ):\n\tdef __init__(self,): \n\t\t_SUBJECT.__init__(self)\n\t\tself.name = \"SUBJECTS\"\n\t\tself.specie = 'adjectives'\n\t\tself.basic = \"subject\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/adjectives/_subjects.py","file_name":"_subjects.py","file_ext":"py","file_size_in_byte":255,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"705687","text":"import json\nimport pandas as pd\n\nif __name__ == '__main__':\n\n    # Read in data\n\n    reference_data = []\n    for row in open('data/processed/top_health_datasets/tech_grants_references_dict_2020-09-16.json', \"r\"):\n       reference_data.append(json.loads(row))\n\n\n    with open('data/processed/top_health_datasets/tech_grants_reference_ids_counter_2020-09-16.json') as json_file:\n        count = json.load(json_file)\n\n\n    references_df = pd.DataFrame(reference_data)\n\n    count_df  = pd.DataFrame(count.items(), columns=['id', 'count'])\n\n    # merge references with count\n\n    count_df = pd.merge(count_df,references_df, 'left', 'id')\n    count_df = count_df.drop(['source', 'citationType', 'authorString',\n           'journalAbbreviation', 'issue', 'pubYear', 'volume', 'pageInfo',\n           'citedOrder', 'match', 'essn', 'issn', 'publicationTitle'], axis = 1).sort_values(by = 'count', ascending= False)\n    count_df = count_df[count_df['count'] > 20]\n\n    count_df.to_csv('data/processed/reference_counter.csv',index=False)\n\n    # get cohort profiles\n\n    data_set_terms = ['cohort','profile','resource','biobank','Cohort','Profile','Biobank','Resource']\n\n    count_df['data set'] = count_df['title'].apply(lambda x: 1 if any(i in x for i in data_set_terms ) else 0)\n    data_sets = count_df[count_df['data set'] == 1]\n    data_sets.to_csv('data/processed/top_health_datasets/health_data_sets_list.csv', index= False)\n\n\n","sub_path":"nutrition_labels/finding_most_common_references.py","file_name":"finding_most_common_references.py","file_ext":"py","file_size_in_byte":1423,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"466266496","text":"import sys\nimport click\nfrom pkg_resources import get_distribution\n\nfrom demisto_sdk.core import DemistoSDK\nfrom demisto_sdk.common.tools import str2bool\nfrom demisto_sdk.dev_tools.runner import Runner\nfrom demisto_sdk.yaml_tools.unifier import Unifier\nfrom demisto_sdk.dev_tools.uploader import Uploader\nfrom demisto_sdk.yaml_tools.extractor import Extractor\nfrom demisto_sdk.common.configuration import Configuration\nfrom demisto_sdk.dev_tools.lint_manager import LintManager\nfrom demisto_sdk.validation.secrets import SecretsValidator\nfrom demisto_sdk.runners.playbook_runner import PlaybookRunner\nfrom demisto_sdk.validation.file_validator import FilesValidator\nfrom demisto_sdk.yaml_tools.update_script import ScriptYMLFormat\nfrom demisto_sdk.yaml_tools.content_creator import ContentCreator\nfrom demisto_sdk.yaml_tools.update_playbook import PlaybookYMLFormat\nfrom demisto_sdk.yaml_tools.update_integration import IntegrationYMLFormat\nfrom demisto_sdk.common.constants import SCRIPT_PREFIX, INTEGRATION_PREFIX\n\n\npass_config = click.make_pass_decorator(DemistoSDK, ensure=True)\n\n\n@click.group(invoke_without_command=True, no_args_is_help=True, context_settings=dict(max_content_width=500), )\n@click.help_option(\n    '-h', '--help'\n)\n@click.option(\n    '-d', '--env-dir', help='Specify a working directory.'\n)\n@click.option(\n    '-v', '--version', help='Get the demisto-sdk version.',\n    is_flag=True, default=False, show_default=True\n)\n@pass_config\ndef main(config, version, env_dir):\n    config.configuration = Configuration()\n    if version:\n        version = get_distribution('demisto-sdk').version\n        print(version)\n\n    if env_dir:\n        config.configuration.env_dir = env_dir\n\n\n# ====================== extract ====================== #\n@main.command(name=\"extract\",\n              short_help=\"Extract code, image and description files from a Demisto integration or script yaml file.\")\n@click.help_option(\n    '-h', '--help'\n)\n@click.option(\n    '--infile', '-i',\n    help='The yml file to extract from',\n    required=True\n)\n@click.option(\n    '--outfile', '-o',\n    required=True,\n    help=\"The output dir to write the extracted code/description/image to.\"\n)\n@click.option(\n    '--yml-type', '-y',\n    help=\"Yaml type. If not specified will try to determine type based upon path.\",\n    type=click.Choice([SCRIPT_PREFIX, INTEGRATION_PREFIX])\n)\n@click.option(\n    '--demisto-mock', '-d',\n    help=\"Add an import for demisto mock, true by default\",\n    type=click.Choice([\"True\", \"False\"]),\n    default=True,\n    show_default=True\n)\n@click.option(\n    '--common-server', '-c',\n    help=\"Add an import for CommonServerPython.\"\n         \"If not specified will import unless this is CommonServerPython\",\n    type=click.Choice([\"True\", \"False\"]),\n    default='True',\n    show_default=True\n)\n@pass_config\ndef extract(config, **kwargs):\n    extractor = Extractor(configuration=config.configuration, **kwargs)\n    return extractor.extract_to_package_format()\n\n\n# ====================== extract-code ====================== #\n@main.command(name=\"extract-code\",\n              short_help=\"Extract code from a Demisto integration or script yaml file.\")\n@click.help_option(\n    '-h', '--help'\n)\n@click.option(\n    '--infile', '-i',\n    help='The yml file to extract from',\n    required=True\n)\n@click.option(\n    '--outfile', '-o',\n    required=True,\n    help=\"The output file to write the code to\"\n)\n@click.option(\n    '--yml-type', '-y',\n    help=\"Yaml type. If not specified will try to determine type based upon path.\",\n    type=click.Choice([SCRIPT_PREFIX, INTEGRATION_PREFIX])\n)\n@click.option(\n    '--demisto-mock', '-d',\n    help=\"Add an import for demisto mock, true by default\",\n    type=click.Choice([\"True\", \"False\"]),\n    default=True,\n    show_default=True\n)\n@click.option(\n    '--common-server', '-c',\n    help=\"Add an import for CommonServerPython.\"\n         \"If not specified will import unless this is CommonServerPython\",\n    type=click.Choice([\"True\", \"False\"]),\n    default='True',\n    show_default=True\n)\n@pass_config\ndef extract_code(config, **kwargs):\n    extractor = Extractor(configuration=config.configuration, **kwargs)\n    return extractor.extract_code(kwargs['outfile'])\n\n\n# ====================== unify ====================== #\n@main.command(name=\"unify\",\n              short_help='Unify code, image, description and yml files to a single Demisto yml file.')\n@click.option(\n    \"-i\", \"--indir\", help=\"The path to the files to unify\", required=True\n)\n@click.option(\n    \"-o\", \"--outdir\", help=\"The output dir to write the unified yml to\", required=True\n)\ndef unify(**kwargs):\n    unifier = Unifier(**kwargs)\n    return unifier.merge_script_package_to_yml()\n\n\n# ====================== validate ====================== #\n# TODO: add a configuration for conf.json and id_set.json\n@main.command(name=\"validate\",\n              short_help='Validate your content files.')\n@click.help_option(\n    '-h', '--help'\n)\n@click.option(\n    '-j', '--conf-json', is_flag=True,\n    default=False, show_default=True, help='Validate the conf.json file.')\n@click.option(\n    '-i', '--id-set', is_flag=True,\n    default=False, show_default=True, help='Create the id_set.json file.')\n@click.option(\n    '-p', '--prev-ver', help='Previous branch or SHA1 commit to run checks against.')\n@click.option(\n    '-c', '--circle', type=click.Choice([\"True\", \"False\"]), default='False',\n    help='Is CircleCi or not')\n@click.option(\n    '-b', '--backward-comp', type=click.Choice([\"True\", \"False\"]), default='False', show_default=True,\n    help='To check backward compatibility.')\n@click.option(\n    '-g', '--use-git', is_flag=True, show_default=True,\n    default=False, help='Validate changes using git - this will check your branch changes and will run only on them.')\n@pass_config\ndef validate(config, **kwargs):\n    sys.path.append(config.configuration.env_dir)\n\n    validator = FilesValidator(configuration=config.configuration,\n                               is_backward_check=str2bool(kwargs['backward_comp']),\n                               is_circle=str2bool(kwargs['circle']), prev_ver=kwargs['prev_ver'],\n                               validate_conf_json=kwargs['conf_json'], use_git=kwargs['use_git'])\n    return validator.run()\n\n\n# ====================== create ====================== #\n@main.command(name=\"create\",\n              short_help='Create content artifacts.')\n@click.help_option(\n    '-h', '--help'\n)\n@click.option(\n    '-a', '--artifacts_path', help='The path of the directory in which you want to save the created content artifacts')\n@click.option(\n    '-p', '--preserve_bundles', is_flag=True, default=False, show_default=True,\n    help='Keep the bundles created in the process of making the content artifacts')\ndef create(**kwargs):\n    content_creator = ContentCreator(**kwargs)\n    return content_creator.run()\n\n\n# ====================== secrets ====================== #\n@main.command(name=\"secrets\",\n              short_help=\"Run Secrets validator to catch sensitive data before exposing your code to public repository.\"\n                         \" Attach path to whitelist to allow manual whitelists. Default file path to secrets is \"\n                         \"'./Tests/secrets_white_list.json' \")\n@click.help_option(\n    '-h', '--help'\n)\n@click.option(\n    '-c', '--circle', type=click.Choice([\"True\", \"False\"]), default='False', show_default=True,\n    help='Is CircleCi or not')\n@click.option(\n    '-wl', '--whitelist', default='./Tests/secrets_white_list.json', show_default=True,\n    help='Full path to whitelist file, file name should be \"secrets_white_list.json\"')\n@pass_config\ndef secrets(config, **kwargs):\n    sys.path.append(config.configuration.env_dir)\n    secrets = SecretsValidator(configuration=config.configuration, is_circle=str2bool(kwargs['circle']),\n                               white_list_path=kwargs['whitelist'])\n    return secrets.run()\n\n\n# ====================== lint ====================== #\n@main.command(name=\"lint\",\n              short_help=\"Run lintings (flake8, mypy, pylint, bandit) and pytest. pylint and pytest will run within the\"\n                         \"docker image of an integration/script. Meant to be used with integrations/scripts that use \"\n                         \"the folder (package) structure. Will lookup up what docker image to use and will setup the \"\n                         \"dev dependencies and file in the target folder. \")\n@click.help_option(\n    '-h', '--help'\n)\n@click.option(\n    \"-d\", \"--dir\", help=\"Specify directory of integration/script\")\n@click.option(\n    \"--no-pylint\", is_flag=True, help=\"Do NOT run pylint linter\")\n@click.option(\n    \"--no-mypy\", is_flag=True, help=\"Do NOT run mypy static type checking\")\n@click.option(\n    \"--no-flake8\", is_flag=True, help=\"Do NOT run flake8 linter\")\n@click.option(\n    \"--no-bandit\", is_flag=True, help=\"Do NOT run bandit linter\")\n@click.option(\n    \"--no-test\", is_flag=True, help=\"Do NOT test (skip pytest)\")\n@click.option(\n    \"-r\", \"--root\", is_flag=True, help=\"Run pytest container with root user\")\n@click.option(\n    \"-k\", \"--keep-container\", is_flag=True, help=\"Keep the test container\")\n@click.option(\n    \"-v\", \"--verbose\", is_flag=True, help=\"Verbose output - mainly for debugging purposes\")\n@click.option(\n    \"--cpu-num\",\n    help=\"Number of CPUs to run pytest on (can set to `auto` for automatic detection of the number of CPUs)\",\n    default=0)\n@click.option(\n    \"-p\", \"--parallel\", is_flag=True, help=\"Run tests in parallel\")\n@click.option(\n    \"-m\", \"--max-workers\", type=int, help=\"How many threads to run in parallel\")\n@click.option(\n    \"-g\", \"--git\", is_flag=True, help=\"Will run only on changed packages\")\n@click.option(\n    \"-a\", \"--run-all-tests\", is_flag=True, help=\"Run lint on all directories in content repo\")\n@pass_config\ndef lint(config, dir, **kwargs):\n    linter = LintManager(configuration=config.configuration, project_dir_list=dir, **kwargs)\n    return linter.run_dev_packages()\n\n# ====================== format ====================== #\n@main.command(name=\"format\",\n              short_help=\"Run formatter on a given script/playbook/integration yml file. \")\n@click.help_option(\n    '-h', '--help'\n)\n@click.option(\n    \"-t\", \"--file-type\", type=click.Choice([\"integration\", \"script\", \"playbook\"]),\n    help=\"The type of yml file to be formatted.\", required=True)\n@click.option(\n    \"-s\", \"--source-file\", help=\"The path of the script yml file\", required=True)\n@click.option(\n    \"-o\", \"--output-file-name\", help=\"The path where the formatted file will be saved to\")\ndef format_yml(file_type, **kwargs):\n    file_type_and_linked_class = {\n        'integration': IntegrationYMLFormat,\n        'script': ScriptYMLFormat,\n        'playbook': PlaybookYMLFormat\n    }\n    if file_type in file_type_and_linked_class:\n        format_object = file_type_and_linked_class[file_type](**kwargs)\n        return format_object.format_file()\n\n    return 1\n\n\n@main.command(name=\"upload\",\n              short_help=\"Upload integration to Demisto instance. DEMISTO_BASE_URL environment variable should contain\"\n                         \" the Demisto server base URL. DEMISTO_API_KEY environment variable should contain a valid \"\n                         \"Demisto API Key.\")\n@click.help_option(\n    '-h', '--help'\n)\n@click.option(\n    \"-i\", \"--path\", help=\"The path of an integration file or a package directory to upload\", required=True)\n@click.option(\n    \"--insecure\", help=\"Skip certificate validation\", is_flag=True)\n@click.option(\n    \"-v\", \"--verbose\", help=\"Verbose output\", is_flag=True)\ndef upload(**kwargs):\n    uploader = Uploader(**kwargs)\n    return uploader.upload()\n\n\n@main.command(name=\"run\",\n              short_help=\"Run integration command on remote Demisto instance in the playground. DEMISTO_BASE_URL \"\n                         \"environment variable should contain the Demisto base URL. DEMISTO_API_KEY environment \"\n                         \"variable should contain a valid Demisto API Key.\")\n@click.help_option(\n    '-h', '--help'\n)\n@click.option(\n    \"-q\", \"--query\", help=\"The query to run\", required=True)\n@click.option(\n    \"-k\", \"--insecure\", help=\"Skip certificate validation\", is_flag=True)\n@click.option(\n    \"-v\", \"--verbose\", help=\"Verbose output\", is_flag=True)\n@click.option(\n    \"-D\", \"--debug\", help=\"Whether to enable the debug-mode feature or not, if you want to save the output file \"\n                          \"please use the --debug-path option\", is_flag=True)\n@click.option(\n    \"--debug-path\", help=\"The path to save the debug file at, if not specified the debug file will be printed to the \"\n                         \"terminal\")\ndef run(**kwargs):\n    runner = Runner(**kwargs)\n    return runner.run()\n\n\n@main.resultcallback()\ndef exit_from_program(result=0, **kwargs):\n    sys.exit(result)\n\n\n# ====================== run-playbook ====================== #\n@main.command(name=\"run-playbook\",\n              short_help=\"Run a playbook in Demisto. \"\n                         \"DEMISTO_API_KEY environment variable should contain a valid Demisto API Key. \"\n                         \"Example: DEMISTO_API_KEY=<API KEY> demisto-sdk run-playbook -p 'p_name' -u \"\n                         \"'https://demisto.local'.\")\n@click.help_option(\n    '-h', '--help'\n)\n@click.option(\n    '--url', '-u',\n    help='URL to a Demisto instance. You can also specify the URL as an environment variable named: DEMISTO_BASE_URL'\n)\n@click.option(\n    '--playbook_id', '-p',\n    help=\"The playbook ID to run.\",\n    required=True\n)\n@click.option(\n    '--wait', '-w',\n    type=click.Choice([\"True\", \"False\"]),\n    default=\"True\",\n    show_default=True,\n    help=\"Wait until the playbook run is finished and get a response.\"\n)\n@click.option(\n    '--timeout', '-t',\n    default=90,\n    show_default=True,\n    help=\"Timeout for the command. The playbook will continue to run in Demisto\"\n)\ndef run_playbook(**kwargs):\n    playbook_runner = PlaybookRunner(**kwargs)\n    return playbook_runner.run_playbook()\n\n\ndef demisto_sdk_cli():\n    main()\n\n\nif __name__ == '__main__':\n    sys.exit(main())\n","sub_path":"demisto_sdk/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":14049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"597753966","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Mar 17 12:09:33 2018\n\n@author: Mir, A. \n\"\"\"\n\n# Implementation of Improved KNN-based twin support vector machine\n# IKNN-TSVM\n\nimport clippSolverv3\nfrom dataproc import read_data\nfrom twinsvm import train_tsvm, predict_tsvm\nfrom clipp import clipp_dcd\nfrom weight import w_compute_mir\n#from playground import hyperp_eq\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.model_selection import KFold\nfrom sklearn.metrics import accuracy_score\nfrom ls_wtsvm import accuracy\nfrom scipy import sparse\nfrom itertools import product\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd\nimport time\n\n\n# Train IKNN-TSVM - Linear case\ndef train_IKTSVM(X_train, X_train_label, c, k, method='FSA'):\n    \n    # Matrix A or class 1 data\n    mat_A = X_train[X_train_label == 1]\n    \n    # Matrix B  or class -1 data \n    mat_B = X_train[X_train_label == -1]\n    \n    # Compute weight matrices - KNN finding\n    weight = w_compute_mir(X_train, X_train_label, k, method)\n    \n    # Define margin points of class +1 and -1\n    mat_M_1 = mat_A[weight[3] == 1] # Class + 1\n    mat_M_2 = mat_B[weight[1] == 1] # Class -1\n    \n    # Prepare D_1, D_2 - Diag. matrices\n    mat_D_1 = np.diag(weight[0].reshape(weight[0].shape[0],))\n    mat_D_2 = np.diag(weight[2].reshape(weight[2].shape[0],))\n    \n    # Scipy sparse matrix\n    #mat_D_1_s = sparse.diags(weight[0].reshape(weight[0].shape[0],))\n    #mat_D_2_s = sparse.diags(weight[2].reshape(weight[2].shape[0],))\n    \n    # Define H=[A e] & G=[M2 e] matrix\n    mat_H = np.column_stack((mat_A, np.ones((mat_A.shape[0], 1), \\\n                                             dtype=np.float64))) # Class +1\n    mat_G = np.column_stack((mat_M_2, np.ones((mat_M_2.shape[0], 1), \\\n                                               dtype=np.float64))) # Margin points of class -1    \n    \n    # Define Q=[B e] & P=[M1 e]\n    mat_Q = np.column_stack((mat_B, np.ones((mat_B.shape[0], 1), \\\n                                             dtype=np.float64))) # Class -1\n    mat_P = np.column_stack((mat_M_1, np.ones((mat_M_1.shape[0], 1), \\\n                                               dtype=np.float64))) # Margin points of class +1\n    \n    # Transpose of H, G, Q & P\n    mat_H_t = np.transpose(mat_H)\n    mat_G_t = np.transpose(mat_G)\n    mat_Q_t = np.transpose(mat_Q)\n    mat_P_t = np.transpose(mat_P)\n    \n    # regulariztion term- for overcoming ill-possible condition\n    reg = 2 ** float(-7)\n    \n    # Sparse matrix multiplication\n    #mat_H_D_1 = sparse.csr_matrix.dot(mat_H_t, mat_D_1)\n    #mat_Q_D_2 = sparse.csr_matrix.dot(mat_Q_t, mat_D_2)\n\n    \n    # Compute matrix inverses\n    mat_H_D_H = np.linalg.inv(np.dot(np.dot(mat_H_t, mat_D_1), mat_H) + \\\n                              (reg * np.identity(mat_H.shape[1])))\n    \n    mat_Q_D_Q = np.linalg.inv(np.dot(np.dot(mat_Q_t, mat_D_2), mat_Q) + \\\n                              (reg * np.identity(mat_Q.shape[1])))\n    \n    # Wolfe dual of class 1\n    mat_dual1 = np.dot(np.dot(mat_G, mat_H_D_H), mat_G_t)\n    \n    # Wofle dual of class 2\n    mat_dual2 = np.dot(np.dot(mat_P, mat_Q_D_Q), mat_P_t)\n    \n    # Solving dual problem 1 - obtaining hyperplane of class 1\n    #alpha_d1 = clipp_dcd(mat_dual1, c)\n    alpha_d1 = np.array(clippSolverv3.clippDCD_V3(mat_dual1, c)).reshape(mat_dual1.shape[0], 1)\n    \n    # Solving dual problem 2 - obtaining hyperplane of class 2\n    #alpha_d2 = clipp_dcd(mat_dual2, c)\n    alpha_d2 = np.array(clippSolverv3.clippDCD_V3(mat_dual2, c)).reshape(mat_dual2.shape[0], 1)\n\n    # Obtain hyperplane 1 & 2\n    hyper_p_1 = -1 * np.dot(np.dot(mat_H_D_H, mat_G_t), alpha_d1)\n    \n    w_1 = hyper_p_1[:hyper_p_1.shape[0] - 1, :]\n    b_1 = hyper_p_1[-1, :]\n    \n    hyper_p_2 = np.dot(np.dot(mat_Q_D_Q, mat_P_t), alpha_d2)\n    \n    w_2 = hyper_p_2[:hyper_p_2.shape[0] - 1, :]\n    b_2 = hyper_p_2[-1, :]\n    \n    return w_1, b_1, w_2, b_2\n\n# Predict IKNN-TSVM - Linear case\ndef pre_IKTSVM(X_test, w_1, b_1, w_2, b_2):\n    \n    prepen_distance = np.zeros((X_test.shape[0], 2))\n    \n    for i in range(X_test.shape[0]):\n        \n        # Prependicular distance of data pint i from hyperplane 2(class 1)\n        prepen_distance[i, 1] = np.abs(np.dot(X_test[i, :], w_1) + b_1)\n        \n        # Prependicular distance of data pint i from hyperplane 1 (class -1)\n        prepen_distance[i, 0] = np.abs(np.dot(X_test[i, :], w_2) + b_2)\n        \n    # Step 5: Assign data points to class +1 or -1 based on distance from hyperplanes\n    output = 2 * np.argmin(prepen_distance, axis=1) - 1\n    \n    return output\n\n\n# Linear case - IKNN-TSVM - Cross validation\ndef cv_lin_IKTSVM(data_train, data_labels, k_fold, k, c, method='FSA'):\n    \n    # K-Fold Cross validation, divide data into K subsets\n    k_fold = KFold(k_fold)    \n        \n    # Store result after each run\n    mean_accuracy = []\n    # Postive class\n    mean_recall_p, mean_precision_p, mean_f1_p = [], [], []\n    # Negative class\n    mean_recall_n, mean_precision_n, mean_f1_n = [], [], []\n    \n    # Count elements of confusion matrix\n    tp, tn, fp, fn = 0, 0, 0, 0\n    \n    k_time = 1\n    \n    # Train and test IKNN-TSVM K times\n    for train_index, test_index in k_fold.split(data_train):\n        \n        # Extract data based on index created by k_fold\n        X_train = np.take(data_train, train_index, axis=0) \n        X_test = np.take(data_train, test_index, axis=0)\n        \n        X_train_label = np.take(data_labels, train_index, axis=0)\n        X_test_label = np.take(data_labels, test_index, axis=0)\n        \n        # Train Classifier - obtain two non-parallel hyperplane\n        hyper_p = train_IKTSVM(X_train, X_train_label, c, k, method)\n        \n        # Parameters of two hyperplanes\n        w_1 = hyper_p[0]\n        b_1 = hyper_p[1]\n        w_2 = hyper_p[2]\n        b_2 = hyper_p[3]\n        \n        # Predict\n        output = pre_IKTSVM(X_test, w_1, b_1, w_2, b_2)\n        \n        # Compute evaluation metrics\n        accuracy_test = accuracy(X_test_label, output)\n                              \n        mean_accuracy.append(accuracy_test[4])\n        # Positive cass\n        mean_recall_p.append(accuracy_test[5])\n        mean_precision_p.append(accuracy_test[6])\n        mean_f1_p.append(accuracy_test[7])\n        # Negative class    \n        mean_recall_n.append(accuracy_test[8])\n        mean_precision_n.append(accuracy_test[9])\n        mean_f1_n.append(accuracy_test[10])\n        \n        # Count\n        tp = tp + accuracy_test[0]\n        tn = tn + accuracy_test[1]\n        fp = fp + accuracy_test[2]\n        fn = fn + accuracy_test[3]\n        \n        #print(\"K_fold %d finished...\" % k_time)\n        \n        k_time = k_time + 1\n        \n    # m_a=0, m_r_p=1, m_p_p=2, m_f1_p=3, k_nn=4, c_1=5, k=6, w_1=7, b_1=8, w_2=9, b_2=10\n    # m_r_n=11, m_p_n=12, m_f1_n=13, tp=14, tn=15, fp=16, fn=17\n    return mean_accuracy, mean_recall_p, mean_precision_p, mean_f1_p, k_fold.get_n_splits(), \\\n           c, k, w_1, b_1, w_2, b_2, mean_recall_n, mean_precision_n, mean_f1_n, \\\n           tp, tn, fp, fn\n\n# Grid search - IKNN-TSVM- Linear\ndef gs_lin_IKTSVM(data, k_fold, k_l, k_u, l_bound, u_bound, step, \\\n                  method ,file_name):\n    \n    train_data = data[0]\n    labels = data[1]\n    \n    # Store \n    result_list = []\n    \n    # Max accuracy\n    max_acc, acc_std = 0, 0\n    \n    # Create an excel file for \n    excel_write = pd.ExcelWriter(file_name, engine='xlsxwriter')\n    \n    # Search space - C parameter\n    c_range = np.arange(l_bound, u_bound, step)\n    \n    # Search space - neighborhood size - k\n    k_range = np.arange(k_l, k_u, 1)\n    \n    search_space = list(product(*[c_range ] + [k_range]))\n    \n    # Total number of search elements\n    search_total = len(search_space)\n    \n     # Count\n    run = 1\n    \n    for element in search_space:\n        \n        c =  2 ** float(element[0])\n        k = element[1]\n        \n        start = time.time()\n        \n        result = cv_lin_IKTSVM(train_data, labels, k_fold, k, c, method)\n        \n        end = time.time()\n        \n        acc = np.mean(result[0])       \n        \n        # Add results to the list\n        result_list.append([acc, np.std(result[0]), np.mean(result[1]), np.std(result[1]), np.mean(result[2]), \\\n                            np.std(result[2]), np.mean(result[3]), np.std(result[3]), np.mean(result[11]), np.std(result[11]), \\\n                            np.mean(result[12]), np.std(result[12]), np.mean(result[13]), np.std(result[13]), result[14], result[15], \\\n                            result[16], result[17], result[5], result[6], result[4], run])\n        \n        # Save best accuracy\n        if acc > max_acc:\n            \n            max_acc = acc\n            acc_std = np.std(result[0])\n    \n        print(\"IKNN-TSVM(%s)| Run: %d | %d |Data:%s | K: %d | C: 2^%d |B-Acc:%.2f+-%.2f |Acc: %.2f+-%.2f | Time: %.2f Sec.\" % (method, run, search_total, data[2], k, element[0], \\\n                                                                      max_acc, acc_std, acc ,np.std(result[0]) , end - start))  \n        run = run + 1    \n\n    \n    print(\"Best Accuracy: %.2f-+%.2f\" % (max_acc, acc_std))\n    \n    \n    # Create a panda data frame\n    result_frame = pd.DataFrame(result_list, columns=['accuracy', 'acc_std', 'recall_p', 'r_p_std', 'precision_p', 'p_p_std', \\\n                                                      'f1_p', 'f1_p_std', 'recall_n', 'r_n_std', 'precision_n', 'p_n_std', 'f1_n',\\\n                                                      'f1_n_std', 'tp', 'tn', 'fp', 'fn', 'c', 'knn', 'k_fold', 'run']) \n    # Write result to excel\n    result_frame.to_excel(excel_write, sheet_name='Sheet1')\n    \n    excel_write.save()\n    \n    return result_frame, max_acc\n\n \n# Plot hyperplanes obtained by IKNN-TSVM\ndef plot_IKNNTSVM(X_train, y_train, c, k):\n    \n   # Split train data into separate class\n   X_t_c1 = X_train[y_train == 1]\n   X_t_c2 = X_train[y_train == -1]\n   \n   # Train a classifier with toy data\n   model = train_IKTSVM(X_train, y_train, c, k, 'FSA')\n\n   # Class1 hyper plane\n   w_1 = model[0]\n   b_1 = model[1]\n\n   # Class 2 hyperplane\n   w_2 = model[2]\n   b_2 = model[3]\n   \n   # Line Equation hyperplane 1\n   slope1, intercept1 = hyperp_eq(w_1, b_1)  \n\n   # Line Equation hyperplane 2\n   slope2, intercept2 = hyperp_eq(w_2, b_2)\n   \n   # Min and Max of feature X1 and creating X values for creating line\n   xx_1 = np.linspace(np.min(X_train[:, 0]), np.max(X_train[:, 0]))\n   \n   # y values \n   yy_1 = slope1 * xx_1 + intercept1\n   yy_2 = slope2 * xx_1 + intercept2  \n   \n   fig = plt.figure(1)\n    \n   # Plot Training data\n   plt.scatter(X_t_c1[:, 0], X_t_c1[:, 1], marker='o', cmap=plt.cm.Paired)\n   plt.scatter(X_t_c2[:, 0], X_t_c2[:, 1], marker='+', cmap=plt.cm.Paired)\n   \n   # Plot two hyperplanes\n   plt.plot(xx_1, yy_1, 'k--', label='Hyperplane +1') # Hyperplane of class 1\n   plt.plot(xx_1, yy_2, 'k-', label='Hyperplane -1') # Hyperplane of class 2        \n   plt.ylim(-0.7, 1.5)\n   plt.legend()\n\n   plt.show()     \n\n\n\n\n# Test\nif __name__ == '__main__':\n\n\n    # Address of datasets    \n    data_add = 'Dataset/Synthetic/'   \n        \n    # Read a dataset\n    data = read_data(data_add + '/ripley.csv')\n    \n    # Split dataset\n    X_train, X_test, y_train, y_test = train_test_split(data[0], data[1], \\\n                                                       test_size=0.1)    \n    start = time.time()\n    \n \n    \n    # Parameters\n    c = 2 ** -2\n    k = 5\n#    k_fold = 10 # fold-CV\n#    k_l = 2\n#    k_u = 11\n#    c_l_b = -8\n#    c_u_b = 9\n#    rbf_l_b = -8\n#    rbf_u_b = 0 \n#    \n#    gs_lin_IKTSVM(data, k_fold, k_l, k_u, c_l_b, c_u_b, 1, \\\n#                  'ld', 'Result/IKNN-TSVM-Lin-titanic12.xlsx')\n    \n    #test = train_IKTSVM(X_train, y_train, c, k)\n    #test = cv_lin_IKTSVM(data[0], data[1], 10, k, c)\n    \n    plot_IKNNTSVM(X_train, y_train, c, k)\n    \n    \n    print('IKNN-TSVM-Finished: %.2f ms.' % ((time.time() - start) * 1000))\n    \n    #pre_1 = pre_IKNN_TSVM(X_test, test[0], test[1], test[2], test[3])\n    \n    #acc_1 = accuracy_score(y_test, pre_1) * 100\n    #acc_1 = np.mean(test[0])\n    \n    #print(\"IKNN-TSVM-Acc: %.2f+-%.2f\" % (acc_1, np.std(test[0])))\n    \n    ## Becnmark TSVM vs. IKNN-TSVM\n    #start = time.time()\n    #\n    #test_tsvm = train_tsvm(X_train, y_train, c, c, 'cpp')\n    #\n    #print('TSVM-Finished: %.2f ms' % ((time.time() - start) * 1000))\n    #\n    #pre_2 = predict_tsvm(X_test, test_tsvm[0], test_tsvm[1], test_tsvm[2], test_tsvm[3])\n    #\n    #acc_2 = accuracy_score(y_test, pre_2) * 100\n    #\n    #print(\"TSVM-Acc: %.2f\" % acc_2)\n","sub_path":"src/iknntsvm.py","file_name":"iknntsvm.py","file_ext":"py","file_size_in_byte":12594,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"563054182","text":"# -*- coding: utf-8 -*-\nfrom __future__ import print_function, unicode_literals\n\nimport os\ntry:\n    from pathlib import Path\nexcept ImportError:\n    from pathlib2 import Path\nimport sys\n\nimport boto3\nimport click\nfrom click import BadParameter\nfrom cookiecutter.main import cookiecutter\n\nfrom sagify.commands import ASCII_LOGO\nfrom sagify.log import logger\n\nclick.disable_unicode_literals_warning = True\n\n\n_FILE_DIR_PATH = os.path.dirname(os.path.realpath(__file__))\n\n\ndef _get_local_aws_profiles():\n    return boto3.Session().available_profiles\n\n\ndef ask_for_app_name():\n    return click.prompt(text=\"Type in a name for your SageMaker app\", type=str)\n\n\ndef ask_for_python_version():\n    logger.info(\"Select Python interpreter:\")\n    logger.info('{}'.format('\\n'.join(['1 - Python3', '2 - Python2'])))\n\n    def _validate_python_option(input_value):\n        if int(input_value) not in {1, 2}:\n            raise BadParameter(\n                message=\"invalid choice: {}. (choose from 1, 2)\".format(str(input_value))\n            )\n\n        return int(input_value)\n\n    chosen_python_index = click.prompt(\n        text=\"Choose from 1, 2\",\n        default=1,\n        value_proc=lambda x: _validate_python_option(x)\n    )\n\n    return '3.6' if chosen_python_index == 1 else '2.7'\n\n\ndef ask_for_aws_details():\n    available_profiles = _get_local_aws_profiles()\n\n    if len(available_profiles) == 0:\n        logger.info(\"aws cli is not configured!\")\n        return\n\n    valid_positions = list(range(1, len(available_profiles) + 1))\n    logger.info(\"Select AWS profile:\")\n    logger.info('{}'.format(\n            '\\n'.join(\n                [\n                    '{} - {}'.format(pos, profile)\n                    for pos, profile in zip(valid_positions, available_profiles)\n                ]\n            )\n        )\n    )\n\n    def _validate_profile_position(input_pos):\n        if int(input_pos) not in valid_positions:\n            raise BadParameter(\n                message=\"invalid choice: {}. (choose from {})\".format(\n                    input_pos,\n                    ', '.join([str(pos) for pos in valid_positions])\n                )\n            )\n        return int(input_pos) - 1\n\n    chosen_profile_index = click.prompt(\n        text=\"Choose from {}\".format(', '.join([str(pos) for pos in valid_positions])),\n        default=1,\n        value_proc=lambda x: _validate_profile_position(x)\n    )\n\n    chosen_profile = available_profiles[chosen_profile_index]\n\n    chosen_region = click.prompt(\n        text=\"Type in your preferred AWS region name\",\n        default='us-east-1',\n        type=str\n    )\n\n    return chosen_profile, chosen_region\n\n\ndef template_creation(app_name, aws_profile, aws_region, python_version, output_dir):\n    sagify_module_name = 'sagify'\n\n    sagify_exists = os.path.exists(os.path.join(output_dir, sagify_module_name))\n    if sagify_exists:\n        logger.info(\"There is a sagify directory/module already. \"\n                    \"Please, rename it in order to use sagify.\"\n                    )\n        sys.exit(-1)\n\n    Path(output_dir).mkdir(exist_ok=True)\n    Path(os.path.join(output_dir, '__init__.py')).touch()\n\n    cookiecutter(\n        template=os.path.join(_FILE_DIR_PATH, '../template/'),\n        output_dir=output_dir,\n        no_input=True,\n        extra_context={\n            \"project_slug\": app_name,\n            \"module_slug\": sagify_module_name,\n            \"aws_profile\": aws_profile,\n            \"aws_region\": aws_region,\n            \"python_version\": python_version\n        }\n    )\n\n\n@click.command()\n@click.option(u\"-d\", u\"--dir\", required=False, default='.', help=\"Path to create sagify module\")\ndef init(dir):\n    \"\"\"\n    Command to initialize SageMaker template\n    \"\"\"\n    logger.info(ASCII_LOGO)\n\n    sagify_app_name = ask_for_app_name()\n\n    python_version = ask_for_python_version()\n\n    aws_profile, aws_region = ask_for_aws_details()\n\n    template_creation(\n        app_name=sagify_app_name,\n        aws_profile=aws_profile,\n        aws_region=aws_region,\n        python_version=python_version,\n        output_dir=dir\n    )\n\n    logger.info(\"\\nsagify module is created! ヽ(´▽`)/\")\n","sub_path":"sagify/commands/initialize.py","file_name":"initialize.py","file_ext":"py","file_size_in_byte":4138,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"53238751","text":"from menu import Menu, MenuItem\nfrom coffee_maker import CoffeeMaker\nfrom money_machine import MoneyMachine\n\nmoney_machine = MoneyMachine()\ncoffe_maker2000 = CoffeeMaker()\nmenu = Menu()\n\non = True\n\nwhile on:\n    options = menu.get_items()\n    choice = input(f\"What would you like to drink? Choose from {options} \")\n    if choice == \"off\":\n        on = False\n    elif choice == \"report\":\n        coffe_maker2000.report()\n        money_machine.report()\n    else:\n        order = menu.find_drink(choice)\n        if coffe_maker2000.is_resource_sufficient(order):\n            if money_machine.make_payment(order.cost):\n                coffe_maker2000.make_coffee(order)","sub_path":"Day16-Coffee-Machine/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":664,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"43793782","text":"# Copyright (C) 2013 eNovance SAS <licensing@enovance.com>\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\nimport netaddr\nfrom oslo_db import exception as db_exc\nfrom oslo_utils import uuidutils\nfrom sqlalchemy import orm\n\nfrom neutron.api.rpc.agentnotifiers import metering_rpc_agent_api\nfrom neutron.common import _deprecate\nfrom neutron.common import constants\nfrom neutron.db import _utils as db_utils\nfrom neutron.db import api as db_api\nfrom neutron.db import common_db_mixin as base_db\nfrom neutron.db.models import l3 as l3_models\nfrom neutron.db.models import metering as metering_models\nfrom neutron.extensions import metering\n\n\n_deprecate._moved_global('MeteringLabelRule', new_module=metering_models)\n_deprecate._moved_global('MeteringLabel', new_module=metering_models)\n\n\nclass MeteringDbMixin(metering.MeteringPluginBase,\n                      base_db.CommonDbMixin):\n\n    def __init__(self):\n        self.meter_rpc = metering_rpc_agent_api.MeteringAgentNotifyAPI()\n\n    @staticmethod\n    def _make_metering_label_dict(metering_label, fields=None):\n        res = {'id': metering_label['id'],\n               'name': metering_label['name'],\n               'description': metering_label['description'],\n               'shared': metering_label['shared'],\n               'tenant_id': metering_label['tenant_id']}\n        return db_utils.resource_fields(res, fields)\n\n    def create_metering_label(self, context, metering_label):\n        m = metering_label['metering_label']\n\n        with db_api.context_manager.writer.using(context):\n            metering_db = metering_models.MeteringLabel(\n                id=uuidutils.generate_uuid(),\n                description=m['description'],\n                tenant_id=m['tenant_id'],\n                name=m['name'],\n                shared=m['shared'])\n            context.session.add(metering_db)\n\n        return self._make_metering_label_dict(metering_db)\n\n    def delete_metering_label(self, context, label_id):\n        with db_api.context_manager.writer.using(context):\n            try:\n                label = self._get_by_id(context,\n                                        metering_models.MeteringLabel,\n                                        label_id)\n            except orm.exc.NoResultFound:\n                raise metering.MeteringLabelNotFound(label_id=label_id)\n\n            context.session.delete(label)\n\n    def get_metering_label(self, context, label_id, fields=None):\n        try:\n            metering_label = self._get_by_id(context,\n                                             metering_models.MeteringLabel,\n                                             label_id)\n        except orm.exc.NoResultFound:\n            raise metering.MeteringLabelNotFound(label_id=label_id)\n\n        return self._make_metering_label_dict(metering_label, fields)\n\n    def get_metering_labels(self, context, filters=None, fields=None,\n                            sorts=None, limit=None, marker=None,\n                            page_reverse=False):\n        marker_obj = self._get_marker_obj(context, 'metering_labels', limit,\n                                          marker)\n        return self._get_collection(context, metering_models.MeteringLabel,\n                                    self._make_metering_label_dict,\n                                    filters=filters, fields=fields,\n                                    sorts=sorts,\n                                    limit=limit,\n                                    marker_obj=marker_obj,\n                                    page_reverse=page_reverse)\n\n    @staticmethod\n    def _make_metering_label_rule_dict(metering_label_rule, fields=None):\n        res = {'id': metering_label_rule['id'],\n               'metering_label_id': metering_label_rule['metering_label_id'],\n               'direction': metering_label_rule['direction'],\n               'remote_ip_prefix': metering_label_rule['remote_ip_prefix'],\n               'excluded': metering_label_rule['excluded']}\n        return db_utils.resource_fields(res, fields)\n\n    def get_metering_label_rules(self, context, filters=None, fields=None,\n                                 sorts=None, limit=None, marker=None,\n                                 page_reverse=False):\n        marker_obj = self._get_marker_obj(context, 'metering_label_rules',\n                                          limit, marker)\n\n        return self._get_collection(context, metering_models.MeteringLabelRule,\n                                    self._make_metering_label_rule_dict,\n                                    filters=filters, fields=fields,\n                                    sorts=sorts,\n                                    limit=limit,\n                                    marker_obj=marker_obj,\n                                    page_reverse=page_reverse)\n\n    def get_metering_label_rule(self, context, rule_id, fields=None):\n        try:\n            metering_label_rule = self._get_by_id(\n                context, metering_models.MeteringLabelRule, rule_id)\n        except orm.exc.NoResultFound:\n            raise metering.MeteringLabelRuleNotFound(rule_id=rule_id)\n\n        return self._make_metering_label_rule_dict(metering_label_rule, fields)\n\n    def _validate_cidr(self, context, label_id, remote_ip_prefix,\n                       direction, excluded):\n        r_ips = self.get_metering_label_rules(context,\n                                              filters={'metering_label_id':\n                                                       [label_id],\n                                                       'direction':\n                                                       [direction],\n                                                       'excluded':\n                                                       [excluded]},\n                                              fields=['remote_ip_prefix'])\n\n        cidrs = [r['remote_ip_prefix'] for r in r_ips]\n        new_cidr_ipset = netaddr.IPSet([remote_ip_prefix])\n        if (netaddr.IPSet(cidrs) & new_cidr_ipset):\n            raise metering.MeteringLabelRuleOverlaps(\n                remote_ip_prefix=remote_ip_prefix)\n\n    def create_metering_label_rule(self, context, metering_label_rule):\n        m = metering_label_rule['metering_label_rule']\n        try:\n            with db_api.context_manager.writer.using(context):\n                label_id = m['metering_label_id']\n                ip_prefix = m['remote_ip_prefix']\n                direction = m['direction']\n                excluded = m['excluded']\n\n                self._validate_cidr(context, label_id, ip_prefix, direction,\n                                    excluded)\n                metering_db = metering_models.MeteringLabelRule(\n                    id=uuidutils.generate_uuid(),\n                    metering_label_id=label_id,\n                    direction=direction,\n                    excluded=m['excluded'],\n                    remote_ip_prefix=ip_prefix)\n                context.session.add(metering_db)\n\n        except db_exc.DBReferenceError:\n            raise metering.MeteringLabelNotFound(label_id=label_id)\n\n        return self._make_metering_label_rule_dict(metering_db)\n\n    def delete_metering_label_rule(self, context, rule_id):\n        with db_api.context_manager.writer.using(context):\n            try:\n                rule = self._get_by_id(context,\n                                       metering_models.MeteringLabelRule,\n                                       rule_id)\n            except orm.exc.NoResultFound:\n                raise metering.MeteringLabelRuleNotFound(rule_id=rule_id)\n            context.session.delete(rule)\n\n        return self._make_metering_label_rule_dict(rule)\n\n    def _get_metering_rules_dict(self, metering_label):\n        rules = []\n        for rule in metering_label.rules:\n            rule_dict = self._make_metering_label_rule_dict(rule)\n            rules.append(rule_dict)\n\n        return rules\n\n    def _make_router_dict(self, router):\n        res = {'id': router['id'],\n               'name': router['name'],\n               'tenant_id': router['tenant_id'],\n               'admin_state_up': router['admin_state_up'],\n               'status': router['status'],\n               'gw_port_id': router['gw_port_id'],\n               constants.METERING_LABEL_KEY: []}\n\n        return res\n\n    def _process_sync_metering_data(self, context, labels):\n        all_routers = None\n\n        routers_dict = {}\n        for label in labels:\n            if label.shared:\n                if not all_routers:\n                    all_routers = self._get_collection_query(context,\n                                                             l3_models.Router)\n                routers = all_routers\n            else:\n                routers = label.routers\n\n            for router in routers:\n                router_dict = routers_dict.get(\n                    router['id'],\n                    self._make_router_dict(router))\n\n                rules = self._get_metering_rules_dict(label)\n\n                data = {'id': label['id'], 'rules': rules}\n                router_dict[constants.METERING_LABEL_KEY].append(data)\n\n                routers_dict[router['id']] = router_dict\n\n        return list(routers_dict.values())\n\n    def get_sync_data_for_rule(self, context, rule):\n        label = context.session.query(\n            metering_models.MeteringLabel).get(\n                rule['metering_label_id'])\n\n        if label.shared:\n            routers = self._get_collection_query(context, l3_models.Router)\n        else:\n            routers = label.routers\n\n        routers_dict = {}\n        for router in routers:\n            router_dict = routers_dict.get(router['id'],\n                                           self._make_router_dict(router))\n            data = {'id': label['id'], 'rule': rule}\n            router_dict[constants.METERING_LABEL_KEY].append(data)\n            routers_dict[router['id']] = router_dict\n\n        return list(routers_dict.values())\n\n    def get_sync_data_metering(self, context, label_id=None, router_ids=None):\n        labels = context.session.query(metering_models.MeteringLabel)\n\n        if label_id:\n            labels = labels.filter(\n                metering_models.MeteringLabel.id == label_id)\n        elif router_ids:\n            labels = (labels.join(metering_models.MeteringLabel.routers).\n                      filter(l3_models.Router.id.in_(router_ids)))\n\n        return self._process_sync_metering_data(context, labels)\n\n\n_deprecate._MovedGlobals()\n","sub_path":"neutron/db/metering/metering_db.py","file_name":"metering_db.py","file_ext":"py","file_size_in_byte":10993,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"550622052","text":"# init\nvowels = set()\ntemp = {\"3\", \"4\", \"4\"}\n\nstrInput = input(\"Please enter input: \")\nsetUniqueChar = set()\nfor character in strInput:\n    if character != \" \":\n        setUniqueChar.add(character)\n\nprint(\"List of unique char in your string: \" + \"\".join(setUniqueChar))\n\nword = \"hello world\"\nprint(\"\".join(vowels.union(set(word))))\nprint(\"\".join(vowels.difference(set(word))))\nprint(\"\".join(vowels.intersection(set(word))))\n\n\n# sample\ndef average(array) -> int:\n    a = set()\n    for i in array:\n        a.add(i)\n    return sum(a) / len(a)\n\n\nb = [3, 5, 6, 3, 2, 2]\nprint(str(average(b)))\n\n#\na, b = [set(input().split()) for _ in range(4)][1::2]\nprint(*sorted(a ^ b, key=int), sep=\"\\n\")\n\n#\nn, a, b = [set(input().split()) for _ in range(4)][1:4]\ntemp = [(i in a) - (i in b) for i in n]\nprint(sum(temp))","sub_path":"UseSet.py","file_name":"UseSet.py","file_ext":"py","file_size_in_byte":801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"275268262","text":"# Copyright 2020 DeepMind Technologies Limited.\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\"\"\"Configuration for Chemistry: Metabolic Cycles.\n\nExample video: https://youtu.be/oFK9VujhpeI\n\nIndividuals benefit from two different food generating reaction cycles. Both\nwill run on their own (autocatalytically), but require energy to continue.\nBringing together side products from both cycles generates new energy such that\nthe cycles can continue. The population needs to keep both cycles running to get\nhigh rewards.\n\nReactions are defined by a directed graph. Reactant nodes project into reaction\nnodes, which project out to product nodes. Reactions occur stochastically when\nall reactants are brought near one another. Agents can carry a single molecule\naround the map with them at a time. Agents are rewarded when a specific reaction\noccurs that involves the molecule they are currently carrying (as either a\nreactant or a product).\n\"\"\"\n\nimport copy\n\nfrom ml_collections import config_dict\nimport networkx as nx\nfrom meltingpot.python.utils.substrates import colors\nfrom meltingpot.python.utils.substrates import game_object_utils\nfrom meltingpot.python.utils.substrates import reaction_graph_utils as graph_utils\nfrom meltingpot.python.utils.substrates import shapes\n\n\n# Map reaction to rewards.\nDEFAULT_REWARDING_REACTIONS = {\"MetabolizeFood1\": 1,\n                               \"MetabolizeFood2\": 1,\n                               \"MetabolizeXY\": 10}\n\n# Define the default reaction query configuration. It can be overridden on a per\n# compount basis.\nDEFAULT_REACTION_CONFIG = {\"radius\": 1, \"query_type\": \"disc\"}\n\nREACTIVITY_LEVELS = {\n    \"ground\": {\"background\": 0.00001,\n               \"low\": 0.005,\n               \"medium\": 0.001,\n               \"high\": 0.9},\n    \"stomach\": {\"background\": 0.0,\n                \"low\": 0.0025,\n                \"medium\": 0.25,\n                \"high\": 0.9},\n}\n\n\ndef dissipate_when_paired(g, reaction_name, compound):\n  g.add_node(reaction_name, reaction=True)\n  # Reactants:\n  g.add_edge(compound, reaction_name)\n  g.add_edge(compound, reaction_name)\n  # Products:\n  g.add_edge(reaction_name, \"empty\")\n  g.add_edge(reaction_name, \"empty\")\n\n\ndef cycle(g, reaction_prefix, intermediates, product, secondary_product=None,\n          food=\"food\"):\n  \"\"\"Add a reaction cycle.\"\"\"\n  # Reaction cycle x, reaction 1\n  reaction_1 = \"{}1{}\".format(reaction_prefix, product)\n  g.add_node(reaction_1, reaction=True)\n  # Reactants:\n  g.add_edge(intermediates[0], reaction_1)\n  g.add_edge(intermediates[1], reaction_1)\n  g.add_edge(\"empty\", reaction_1)\n  # Products:\n  g.add_edge(reaction_1, intermediates[1])\n  g.add_edge(reaction_1, intermediates[2])\n  g.add_edge(reaction_1, food)\n\n  # Reaction cycle x, reaction 2\n  reaction_2 = \"{}2{}\".format(reaction_prefix, product)\n  g.add_node(reaction_2, reaction=True)\n  # Reactants:\n  g.add_edge(intermediates[1], reaction_2)\n  g.add_edge(intermediates[2], reaction_2)\n  g.add_edge(\"energy\", reaction_2)\n  # Products:\n  g.add_edge(reaction_2, intermediates[2])\n  g.add_edge(reaction_2, intermediates[0])\n  g.add_edge(reaction_2, \"energy\")\n\n  # Reaction cycle x, reaction 3\n  reaction_3 = \"{}3{}\".format(reaction_prefix, product)\n  g.add_node(reaction_3, reaction=True)\n  # Reactants:\n  g.add_edge(intermediates[2], reaction_3)\n  g.add_edge(intermediates[0], reaction_3)\n  g.add_edge(\"empty\", reaction_3)\n  if secondary_product is not None:\n    g.add_edge(\"empty\", reaction_3)\n  # Products:\n  g.add_edge(reaction_3, intermediates[0])\n  g.add_edge(reaction_3, intermediates[1])\n  g.add_edge(reaction_3, product)\n  if secondary_product is not None:\n    g.add_edge(reaction_3, secondary_product)\n\n\ndef make_graph():\n  \"\"\"User defined graph construction function using networkx.\"\"\"\n  # Note: You can copy-paste this function into colab to visualize the graph.\n  g = nx.MultiDiGraph()\n  # First add the \"empty\" and \"activated\" nodes, which are always present.\n  graph_utils.add_system_nodes(g)\n\n  cycle(g, \"R\",\n        intermediates=[\"ax\", \"bx\", \"cx\"],\n        product=\"x\",\n        secondary_product=\"iy\",\n        food=\"food1\")\n  cycle(g, \"R\",\n        intermediates=[\"ay\", \"by\", \"cy\"],\n        product=\"y\",\n        secondary_product=\"ix\",\n        food=\"food2\")\n\n  # Inhibit x with a product of the y-producing cycle.\n  g.add_node(\"InhibitX\", reaction=True)\n  # Reactants:\n  g.add_edge(\"x\", \"InhibitX\")\n  g.add_edge(\"ix\", \"InhibitX\")\n  # Products:\n  g.add_edge(\"InhibitX\", \"empty\")\n  g.add_edge(\"InhibitX\", \"empty\")\n\n  # Inhibit y with a product of the x-producing cycle.\n  g.add_node(\"InhibitY\", reaction=True)\n  # Reactants:\n  g.add_edge(\"y\", \"InhibitY\")\n  g.add_edge(\"iy\", \"InhibitY\")\n  # Products:\n  g.add_edge(\"InhibitY\", \"empty\")\n  g.add_edge(\"InhibitY\", \"empty\")\n\n  # Food can be metabolized in the stomach.\n  g.add_node(\"MetabolizeFood1\", reaction=True)\n  # Reactants:\n  g.add_edge(\"food1\", \"MetabolizeFood1\")\n  # Products:\n  g.add_edge(\"MetabolizeFood1\", \"empty\")\n\n  # Food can be metabolized in the stomach.\n  g.add_node(\"MetabolizeFood2\", reaction=True)\n  # Reactants:\n  g.add_edge(\"food2\", \"MetabolizeFood2\")\n  # Products:\n  g.add_edge(\"MetabolizeFood2\", \"empty\")\n\n  # Food spontaneously appears from time to time.\n  g.add_node(\"SpawnFood1\", reaction=True)\n  # Reactants:\n  g.add_edge(\"empty\", \"SpawnFood1\")\n  # Products:\n  g.add_edge(\"SpawnFood1\", \"food1\")\n\n  # Food spontaneously appears from time to time.\n  g.add_node(\"SpawnFood2\", reaction=True)\n  # Reactants:\n  g.add_edge(\"empty\", \"SpawnFood2\")\n  # Products:\n  g.add_edge(\"SpawnFood2\", \"food2\")\n\n  # x and y can be combined to produce energy.\n  g.add_node(\"MetabolizeXY\", reaction=True)\n  # Reactants:\n  g.add_edge(\"x\", \"MetabolizeXY\")\n  g.add_edge(\"y\", \"MetabolizeXY\")\n  # Products:\n  g.add_edge(\"MetabolizeXY\", \"energy\")\n  g.add_edge(\"MetabolizeXY\", \"energy\")\n\n  # Energy spontaneously dissipates.\n  g.add_node(\"DissipateEnergy\", reaction=True)\n  # Reactants:\n  g.add_edge(\"energy\", \"DissipateEnergy\")\n  # Products:\n  g.add_edge(\"DissipateEnergy\", \"empty\")\n\n  # Prevent inhibitors from accumulating by dissipating them whenever they pair.\n  dissipate_when_paired(g, \"DissipateIX\", \"ix\")\n  dissipate_when_paired(g, \"DissipateIY\", \"iy\")\n\n  # Properties of compounds\n  # Color:\n  g.nodes[\"ax\"][\"color\"] = (153, 204, 255)  # blue 1\n  g.nodes[\"bx\"][\"color\"] = (102, 204, 255)  # blue 2\n  g.nodes[\"cx\"][\"color\"] = (51, 153, 255)  # blue 3\n\n  g.nodes[\"ay\"][\"color\"] = (102, 255, 153)  # green 1\n  g.nodes[\"by\"][\"color\"] = (102, 255, 102)  # green 2\n  g.nodes[\"cy\"][\"color\"] = (0, 255, 0)  # green 3\n\n  g.nodes[\"x\"][\"color\"] = (0, 51, 204)  # dark blue\n  g.nodes[\"y\"][\"color\"] = (0, 51, 0)  # dark green\n  g.nodes[\"food1\"][\"color\"] = (255, 255, 0)  # yellow\n  g.nodes[\"food2\"][\"color\"] = (255, 215, 0)  # gold\n  g.nodes[\"energy\"][\"color\"] = (255, 0, 0)  # red\n\n  g.nodes[\"ix\"][\"color\"] = (102, 153, 153)  # greyish green\n  g.nodes[\"iy\"][\"color\"] = (51, 102, 153)  # greyish blue\n\n  # Reactivity:\n  g.nodes[\"ax\"][\"reactivity\"] = \"high\"\n  g.nodes[\"bx\"][\"reactivity\"] = \"high\"\n  g.nodes[\"cx\"][\"reactivity\"] = \"high\"\n\n  g.nodes[\"ay\"][\"reactivity\"] = \"high\"\n  g.nodes[\"by\"][\"reactivity\"] = \"high\"\n  g.nodes[\"cy\"][\"reactivity\"] = \"high\"\n\n  g.nodes[\"x\"][\"reactivity\"] = \"medium\"\n  g.nodes[\"y\"][\"reactivity\"] = \"medium\"\n\n  g.nodes[\"ix\"][\"reactivity\"] = \"high\"\n  g.nodes[\"iy\"][\"reactivity\"] = \"high\"\n\n  g.nodes[\"food1\"][\"reactivity\"] = \"medium\"\n  g.nodes[\"food2\"][\"reactivity\"] = \"medium\"\n  g.nodes[\"energy\"][\"reactivity\"] = \"low\"\n  g.nodes[\"empty\"][\"reactivity\"] = \"background\"\n\n  # The following commented line documents how to set the query config for a\n  # specific compound, overriding the default query configuration.\n  # g.nodes[\"food1\"][\"query_config\"] = {\"radius\": 3, \"queryType\": \"diamond\"}\n\n  return g\n\nASCII_MAP = \"\"\"\n~~~~~~~~~~~a~~~~~~~~~~~~~\n~~~~~~~~c~~~~~~~~~~~~~~~~\n~~~~~~~~~~~b~~~~~~~~~~~~~\n~~~~~~~~~~~~~~~~~~~~~~~~~\n~~~~~~~~~~~~~~~~~~~~~1~~~\n~~~~~~~~~~~~~~~~~~~~~~~~~\n1~~3~~~~hhhhhhh~~~~~3~~2~\n~~~~~~~~~~~~~~~~~~~~~~~~~\n~2~~~~~~~~~~~~~~~~~~~~~~~\n~~~~~~~~~~~~~~~~~~~~~~~~~\n~~~~~~~~~~c~~~~~~~~~~~~~~\n~~~~~~~~~~~~a~~~~~~~~~~~~\n~~~~~~~~~~b~~~~~~~~~~~~~~\n~~~~~~~~~~~~~~~~~~~~~~~~~\n\"\"\"\n\n# `prefab` determines which compound to use for each `char` in the ascii map.\nCHAR_PREFAB_MAP = {\n    \"~\": \"empty\",\n    \"a\": \"ax\",\n    \"b\": \"bx\",\n    \"c\": \"cx\",\n    \"1\": \"ay\",\n    \"2\": \"by\",\n    \"3\": \"cy\",\n    \"h\": \"energy\",\n}\n\n# PLAYER_COLOR_PALETTES is a list with each entry specifying the color to use\n# for the player at the corresponding index.\nNUM_PLAYERS_UPPER_BOUND = 60\nPLAYER_COLOR_PALETTES = []\nfor i in range(NUM_PLAYERS_UPPER_BOUND):\n  PLAYER_COLOR_PALETTES.append(shapes.get_palette(colors.palette[i]))\n\n# Primitive action components.\n# pylint: disable=bad-whitespace\n# pyformat: disable\nNOOP       = {\"move\": 0, \"turn\":  0, \"ioAction\": 0}\nFORWARD    = {\"move\": 1, \"turn\":  0, \"ioAction\": 0}\nSTEP_RIGHT = {\"move\": 2, \"turn\":  0, \"ioAction\": 0}\nBACKWARD   = {\"move\": 3, \"turn\":  0, \"ioAction\": 0}\nSTEP_LEFT  = {\"move\": 4, \"turn\":  0, \"ioAction\": 0}\nTURN_LEFT  = {\"move\": 0, \"turn\": -1, \"ioAction\": 0}\nTURN_RIGHT = {\"move\": 0, \"turn\":  1, \"ioAction\": 0}\nIO_ACTION  = {\"move\": 0, \"turn\":  0, \"ioAction\": 1}\n# pyformat: enable\n# pylint: enable=bad-whitespace\n\nACTION_SET = (\n    NOOP,\n    FORWARD,\n    BACKWARD,\n    STEP_LEFT,\n    STEP_RIGHT,\n    TURN_LEFT,\n    TURN_RIGHT,\n    IO_ACTION,\n)\n\nTARGET_SPRITE_SELF = {\n    \"name\": \"Self\",\n    \"shape\": shapes.AVATAR_DEFAULT,\n    \"palette\": shapes.get_palette((50, 100, 200)),\n    \"noRotate\": False,\n}\n\n\ndef create_avatar_objects(prefabs, num_players):\n  \"\"\"Returns list of avatar objects of length 'num_players'.\"\"\"\n  avatar_objects = []\n  additional_game_objects = []\n  for player_idx in range(0, num_players):\n    game_object = graph_utils.create_avatar_constant_self_view(\n        DEFAULT_REWARDING_REACTIONS, player_idx, TARGET_SPRITE_SELF)\n    avatar_objects.append(game_object)\n\n    # Add the overlaid avatar stomach on top of each avatar.\n    avatar_stomach = copy.deepcopy(prefabs[\"avatar_stomach\"])\n    game_object_utils.get_first_named_component(\n        avatar_stomach,\n        \"AvatarStomach\")[\"kwargs\"][\"playerIndex\"] = player_idx + 1\n    additional_game_objects.append(avatar_stomach)\n\n  return avatar_objects, additional_game_objects\n\n\ndef get_lab2d_settings(num_players):\n  \"\"\"Returns the lab2d settings.\n\n  Args:\n    num_players: the number of players in the game.\n  \"\"\"\n\n  # Must create compounds and reactions.\n  compounds, reactions = graph_utils.graph_semantics(make_graph())\n\n  avatar_object_templates = {\n      \"avatar\": graph_utils.create_avatar(DEFAULT_REWARDING_REACTIONS),\n      \"avatar_stomach\": graph_utils.create_stomach(\n          compounds,\n          REACTIVITY_LEVELS[\"stomach\"],\n          default_reaction_radius=DEFAULT_REACTION_CONFIG[\"radius\"],\n          default_reaction_query_type=DEFAULT_REACTION_CONFIG[\"query_type\"],\n          priority_mode=True),\n  }\n  cell_prefabs = {}\n  cell_prefabs = graph_utils.add_compounds_to_prefabs_dictionary(\n      cell_prefabs, compounds, REACTIVITY_LEVELS[\"ground\"], sprites=True,\n      default_reaction_radius=DEFAULT_REACTION_CONFIG[\"radius\"],\n      default_reaction_query_type=DEFAULT_REACTION_CONFIG[\"query_type\"],\n      priority_mode=True)\n\n  avatar_objects, additional_objects = create_avatar_objects(\n      avatar_object_templates, num_players)\n\n  # Lua script configuration.\n  lab2d_settings = {\n      \"levelName\": \"grid_land\",\n      \"levelDirectory\":\n          \"meltingpot/lua/levels\",\n      \"numPlayers\": num_players,\n      \"episodeLengthFrames\": 1000,\n      \"spriteSize\": 8,\n      \"topology\": \"BOUNDED\",\n      \"simulation\": {\n          \"map\": ASCII_MAP,\n          \"gameObjects\": avatar_objects + additional_objects,\n          \"scene\": graph_utils.create_scene(reactions,\n                                            stochastic_episode_ending=True),\n          \"prefabs\": cell_prefabs,\n          \"charPrefabMap\": CHAR_PREFAB_MAP,\n      },\n  }\n\n  return lab2d_settings\n\n\ndef get_config():\n  \"\"\"Default configuration for training on the grid_land level.\"\"\"\n  config = config_dict.ConfigDict()\n\n  # Basic configuration.\n  config.num_players = 8\n\n  config.lab2d_settings = get_lab2d_settings(config.num_players)\n\n  # Action set configuration.\n  config.action_set = ACTION_SET\n  # Observation format configuration.\n  config.individual_observation_names = [\n      \"RGB\",\n      \"POSITION\",\n      \"ORIENTATION\",\n  ]\n  config.global_observation_names = [\n      \"WORLD.RGB\",\n  ]\n\n  return config\n","sub_path":"meltingpot/python/configs/substrates/chemistry_metabolic_cycles.py","file_name":"chemistry_metabolic_cycles.py","file_ext":"py","file_size_in_byte":12941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"96091542","text":"birthdays = {'Tj': 'Sept 29', 'Tommy': 'Aug 30', 'Antonette': 'June 6'} #dictionary of names stored\ndates = {'Tj': 'A cooking pan', 'Tommy': 'Coffee', 'Antonette': 'microphone'}\n\n\n\nprint('Whos birthday do you want me to remind you of? Tj, Tommy, or Antonette.')\n\n\n\nwhile True:\n    print('Enter the name: (blank to quit)') #enter the name of friend\n    name = input() #the name is what you input\n    if name == '': #if blank then the program exits\n        break\n    \n    if name in birthdays: #checks to see if the name exits in the dictionary\n        print(birthdays[name] + ' is the birthday of ' + name)\n    if name in dates: #checks to see if the name exits in the dictionary\n        print(dates[name] + ' is the gift for ' + name)\n    else:\n        print('I do not have birthday information for ' + name) #if there is no birthday you can add the name\n        print('What is their birthday?') #asking what is their birthday\n        bday = input() #input your friends birthday\n        birthdays[name] = bday \n        print('Birthday database updated.') #birthday is updated in the database.\n\n\n\n","sub_path":"birthday.py","file_name":"birthday.py","file_ext":"py","file_size_in_byte":1096,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"210281390","text":"# Definition for singly-linked list.\r\n# class ListNode:\r\n#     def __init__(self, x):\r\n#         self.val = x\r\n#         self.next = None\r\n\r\nclass Solution:\r\n    # @param {ListNode} head\r\n    # @return {ListNode}\r\n    def deleteDuplicates(self, head):\r\n        original_head = head\r\n\r\n        prev = head\r\n        if head:\r\n            head = head.next\r\n        else:\r\n            return None\r\n        while head:\r\n            next_head = head.next\r\n            if head.val == prev.val:\r\n                prev.next = next_head\r\n            else:\r\n                prev = head\r\n            head = next_head\r\n        return original_head\r\n","sub_path":"lc-practice/remove_sorted_duplicates.py","file_name":"remove_sorted_duplicates.py","file_ext":"py","file_size_in_byte":635,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"209896152","text":"class Solution(object):\n    \n    def numIslands2(self, m, n, positions):\n        \"\"\"\n        :type m: int\n        :type n: int\n        :type positions: List[List[int]]\n        :rtype: List[int]\n        \"\"\"\n        islandNum = 0\n        directions = [(1, 0), (-1, 0), (0, 1), (0, -1)]\n        union_map = {}\n        res = []\n        for pos in positions:\n            val = pos[0] * n + pos[1]\n            union_map[val] = val\n            islandNum += 1\n            for dir in directions:\n                newX = pos[0] + dir[0]\n                newY = pos[1] + dir[1]\n                newVal = newX * n + newY\n                if newX >=0 and newX < m and newY >=0 and newY < n and newVal in union_map:\n                    if self.union(val, newVal, union_map):\n                        islandNum -= 1\n            res.append(islandNum)\n        return res\n        \n    def union(self, m, n, union_map):\n        parent_m = self.find(m, union_map)\n        parent_n = self.find(n, union_map)\n        if parent_m == parent_n:\n            return False\n        union_map[parent_m] = parent_n\n        return True\n        \n    def find(self, n, union_map):\n        if n == union_map[n]:\n            return n\n        parent = union_map[n]\n        # shink the map\n        union_map[n] = union_map[parent]\n        return self.find(parent, union_map)","sub_path":"interview/google/hard/LC305.py","file_name":"LC305.py","file_ext":"py","file_size_in_byte":1331,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"521590172","text":"num_list = [1,3,7]\n\n\nwhile True:\n    answer = input(\"guess a number or type 'q' to quite.\")\n    if answer == \"q\":\n        break\n    try:\n        answer = int(answer)\n    except ValueError:\n        print(\"You can type only number and 'q'.\")\n    if answer in num_list:\n        print(\"That's right!!\")\n    else:\n        print(\"You are wrong...\")\n\n","sub_path":"chapter7/C7_4.py","file_name":"C7_4.py","file_ext":"py","file_size_in_byte":344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"63742151","text":"#!/usr/bin/python\n# -*- coding: latin-1 -*-\n#\n#  This file is part of bioservices software\n#\n#  Copyright (c) 2013-2014 - EBI-EMBL\n#\n#  File author(s):\n#      \n#\n#  Distributed under the GPLv3 License.\n#  See accompanying file LICENSE.txt or copy at\n#      http://www.gnu.org/licenses/gpl-3.0.html\n#\n#  website: https://github.com/cokelaer/bioservices\n#  documentation: http://packages.python.org/bioservices\n#\n##############################################################################\n#$Id$\n\n\"\"\"This module provides a class :class:`UniChem`\n\n.. topic:: What is UniChem\n\n    :URL:  https://www.ebi.ac.uk/unichem/info/webservices\n    :REST:  https://www.ebi.ac.uk/unichem/rest\n\n\n    .. highlights::\n\n        \"UniChem is a 'Unified Chemical Identifier' system, designed to assist\n        in the rapid cross-referencing of chemical structures, and their identifiers,\n        between databases (read more). \"\n\n        -- From UniChem web page June 2013\n\n\n\"\"\"\nfrom bioservices import REST\n\n\nclass UniChem(REST):\n    \"\"\"Interface to the `UniChem <http://www.ebi.ac.uk/unichem/>`_ service\n\n    .. doctest::\n\n            >>> from bioservices import UniChem\n            >>> u = UniChem()\n\n\n    \"\"\"\n\n    _url = \"http://www.ebi.ac.uk/unichem/rest\"\n\n    def __init__(self, verbose=False, cache=False):\n        \"\"\"**Constructor** UniChem\n\n        :param verbose: set to False to prevent informative messages\n        \"\"\"\n        super(UniChem, self).__init__(name=\"UniChem\", url=UniChem._url, verbose=verbose, cache=cache)\n        self.source_ids = {\n            \"chembl\":1,\n            \"drugbank\":2,\n            \"pdb\":3,\n            \"iuphar\":4,\n            \"pubchem_dotf\":5,\n            \"kegg_ligand\":6,\n            \"chebi\":7,\n            \"nih_ncc\":8,\n            \"zinc\":9,\n            \"emolecules\":10,\n            \"ibm\":11,\n            \"atlas\":12,\n            \"patents\":13,\n            \"fdasrc\":14,\n            \"surechem\":15,\n            \"pharmgkb\":17,\n            \"hmdb\":18,\n            \"selleck\":20,\n            \"pubchem_tpharma\":21,\n            \"pubchem\":22,\n            \"mcule\":23,\n            }# there was no 16 when I looked at the web site June 2013\n        self.source_names = self.devtools.swapdict(self.source_ids)\n\n        maxid_service = max(self.get_all_src_ids())\n        maxid_bioservices = max(self.source_ids.values())\n        if maxid_bioservices != maxid_service:\n            self.logging.warning(\"UniChem has added new source. \"+\n                    \"Please update the source_ids attribute in bioservices\")\n\n    def _process(self, query, frmt, request):\n        self.devtools.check_param_in_list(frmt, [\"json\", \"xml\", None])\n        if isinstance(query, str) or isinstance(query, int):\n            res = self.http_get(request % query, frmt=frmt)\n        else:\n            res = self.http_get([request % x for x in query], frmt=frmt)\n        return res\n\n    def _get_source_id(self, src_id):\n        \"\"\"Returns the source id given a source name or source id\n\n        Check that the source id is correct by looking at the :attr:`source_ids`\n        attribute.\n\n        \"\"\"\n        if src_id in self.source_ids.keys():\n            src_id = self.source_ids[src_id]\n        elif isinstance(src_id, int) and src_id in self.source_ids.values():\n            pass\n        elif isinstance(src_id, str) and src_id.isdigit():\n            if int(src_id) in self.source_ids.values():\n                src_id = int(src_id)\n            else:\n                raise ValueError(\"unrecognised src_id parameter. Valid names are %s\"\n                    % self.source_ids.keys())\n        else:\n            raise ValueError(\"unrecognised src_id parameter. Valid names are %s\"\n                % self.source_ids.keys())\n\n        return src_id\n\n    def get_src_compound_ids_from_src_compound_id(self, src_compound_id, src_id, target=None):\n        self.logging.warning(\"Deprecated. Please use get_compounds_from_source\")\n        return self.get_src_compound_ids_from_src_id(src_compound_id, src_id, target=target)\n\n    def get_compound_ids_from_src_id(self, src_compound_id, src_id, target=None):\n        \"\"\"Obtain a list of all src_compound_ids from all sources which are\n        CURRENTLY assigned to the same structure as a currently assigned query\n        src_compound_id.\n\n        The output will include query src_compound_id if it is a valid\n        src_compound_id with a current assignment. Note also, that by adding an\n        additional (optional) argument (a valid src_id), then results will be restricted\n        to only the source specified with this optional argument.\n\n        :param str src_compound_id: a valid compound identifier (list is possible as well)\n        :param str src_id: one of the valid database ids. See :attr:`source_ids`.\n        :param str,int target: database identifier (name or id) to map to.\n\n        :return: list of dictionaries with the 'src_compound_id' and 'src_id' keys.\n            or (if optional *target* is specified, a list with only 'src_compound_id' keys).\n\n        ::\n\n            >>> get_compound_ids_from_src_id(\"CHEMBL12\", \"chembl\")\n            >>> get_compound_ids_from_src_id(\"CHEMBL12\", \"chembl\", \"chebi\")\n            [{'src_compound_id': '49575'}]\n\n        \"\"\"\n        src_id = self._get_source_id(src_id)\n        request = \"src_compound_id/%s/\" + \"%s\" %src_id\n        if target:\n            target = self._get_source_id(target)\n            request += \"/%s\" % target\n        res = self._process(src_compound_id, \"json\", request)\n        return res\n\n    def get_src_compound_ids_all_from_src_compound_id(self, src_compound_id,\n            src_id, target=None):\n        self.logging.warning(\"Deprecated us get_compound_ids\")\n        return self.get_all_compound_ids_from_all_src_id(src_compound_id,\n                src_id, target=target)\n\n    def get_all_compound_ids_from_all_src_id(self, src_compound_id,\n            src_id, target=None):\n        \"\"\"Obtain a list of all src_compound_ids from all sources (including\n        BOTH current AND obsolete assignments) to the same structure as a currently\n        assigned query src_compound_id.\n\n        The output will include query src_compound_id if\n        it is a valid src_compound_id with a current assignment. Note also, that by\n        adding an additional (optional) argument (a valid src_id), then results will be\n        restricted to only the source specified with this optional argument.\n\n        :param str src_compound_id: a valid compound identifier (or list)\n        :param source: one of the valid database ids. See :attr:`source_ids`.\n        :param target: if provided, return answer for a specific target database only. Otherwise\n            return answer for all database found in :attr:`source_ids`.\n\n        :return: list of three element arrays, containing 'src_compound_id' and 'src_id',\n            and 'Assignment', or (if optional 'to_src_id' is specified) list of two element\n            arrays, containing 'src_compound_id' and 'Assignment'.\n\n        ::\n\n            >>> res = s.get_all_compound_ids_from_src_id(\"CHEMBL12\", \"chembl\")\n            >>> s.get_all_compound_ids_from_src_id(\"CHEMBL12\", \"chembl\", \"chebi\")\n            [{u'assignment': u'1', u'src_compound_id': u'49575'}]\n\n        The second call may return an empty list if there is no target from chebi.\n\n        \"\"\"\n        src_id = self._get_source_id(src_id)\n        request = \"src_compound_id_all/%s/\" + \"%s\" % src_id\n        if target:\n            target = self._get_source_id(target)\n            request += \"/%s\" % target\n        res = self._process(src_compound_id, \"json\", request)\n        return res\n\n    def get_mapping(self, source, target):\n        \"\"\"Obtain a full mapping between two sources. Uses only currently\n        assigned src_compound_ids from both sources.\n\n        :param source: name of the source database\n        :param target: name of the target database\n        :return: a dictionary. Keys are the source identifiers. Values are the\n            target identifiers.\n\n        ::\n\n            >>> get_mapping(\"kegg_ligand\", \"chembl\")\n\n        \"\"\"\n        self.devtools.check_param_in_list(source, list(self.source_ids.keys()))\n        self.devtools.check_param_in_list(target, list(self.source_ids.keys()))\n\n        query = \"mapping/%s/%s/\" % (self.source_ids[source], self.source_ids[target])\n        res = self.http_get(query, frmt=None)\n        # evaluation the string as a list\n        res = eval(res)\n        # convert to a convenient dictionary\n        mapping = [(x[str(self.source_ids[source])], x[str(self.source_ids[target])]) for x in res]\n        mapping = dict(mapping)\n        return mapping\n\n    def get_src_compound_ids_from_inchikey(self, inchikey):\n        \"\"\"Obtain a list of all src_compound_ids (from all sources) which are\n            CURRENTLY assigned to a query InChIKey\n\n        :param str inchikey: input source identified by its InChiKey (or list)\n        :return: list of dictionaries containing 'src_compound_id' and 'src_id' keys\n            (or list of list of dictionaries if input is a list).\n\n        ::\n\n            >>> uni.get_src_compound_ids_from_inchikey(\"AAOVKJBEBIDNHE-UHFFFAOYSA-N\")\n        \"\"\"\n        res = self._process(inchikey, \"json\", \"inchikey/%s\")\n        return res\n\n    def get_src_compound_ids_all_from_inchikey(self, inchikey):\n        \"\"\"Description:  Obtain a list of all src_compound_ids (from all sources) which\n        have current AND obsolete assignments to a query InChIKey\n\n        :param str inchikey: input source identified by its InChiKey (or list)\n            (or list of list of dictionaries if input is a list).\n        :return: list of two element arrays, containing 'src_compound_id' and 'src_id'.\n            and 'Assignment'.\n\n        ::\n\n            >>> uni.get_src_compound_ids_all_from_inchikey(\"AAOVKJBEBIDNHE-UHFFFAOYSA-N\")\n\n        \"\"\"\n        res = self._process(inchikey, \"json\", \"inchikey_all/%s\")\n        return res\n\n    def get_all_src_ids(self):\n        \"\"\"Obtain all src_ids of database currently in UniChem\n\n        :return: list of 'src_id's.\n\n        ::\n\n            >>> uni.get_all_src_ids()\n\n        \"\"\"\n        res = self.http_get(\"src_ids\", frmt=None)\n        res = [x['src_id'] for x in eval(res)]\n        return res\n\n    def get_source_information(self, src_id):\n        \"\"\"Description:  Obtain all information on a source by querying with a source id\n\n        :param int src_id: valid identifiers (values or keys of :attr:`source_ids` e.g.\n            chebi, chembl,0,1). could also be a list of those identifiers.\n        :return: dictionary (or list of dictionaries) with following keys:\n\n            * src_id (the src_id for this source),\n            * src_url (the main home page of the source),\n            * name (the unique name for the source in UniChem, always lower case),\n            * name_long (the full name of the source, as defined by the source),\n            * name_label (A name for the source suitable for use as a 'label' for the source\n              within a web-page. Correct case setting for source, and always less than 30\n              characters),\n            * description (a description of the content of the source),\n            * base_id_url_available (an flag indicating whether this source provides a valid\n            * base_id_url for creating cpd-specific links [1=yes, 0=no]).\n            * base_id_url (the base url for constructing hyperlinks to this source [append an\n            * identifier f    rom this source to the end of this url to create a valid url to a\n            * specific page for this cpd], unless aux_for_url=1),\n            * aux_for_url (A flag to indicate whether the aux_src field should be used to create\n              hyperlinks instead of the src_compound_id [1=yes, 0=no]\n\n        ::\n\n            >>> res = get_source_information(\"chebi\")\n\n        \"\"\"\n        if isinstance(src_id, list):\n            src_id = [self._get_source_id(this) for this in src_id]\n            res = self._process(src_id, \"json\", \"sources/%s\")\n            res = [x[0] for x in res]\n        else:\n            src_id = self._get_source_id(src_id)\n            res = self._process(src_id, \"json\", \"sources/%s\")\n            res = res[0]\n        return res\n\n    def get_structure(self, src_compound_id, src_id):\n        \"\"\"Obtain structure(s) CURRENTLY assigned to a query src_compound_id.\n\n        :param str src_compound_id: a valid compound identifier\n        :param int src_id: corresponding database identifier (name or id).\n\n        :return:  dictionary with 'standardinchi' and 'standardinchikey' keys\n\n        ::\n\n            >>> uni.get_structure(\"CHEMBL12\", \"chembl\")\n\n        \"\"\"\n        src_id = self._get_source_id(src_id)\n        request = \"structure/%s\" + \"/%s\" % src_id\n        res = self._process(src_compound_id, \"json\", request)\n        # the output is a list but looks like there is only 1 item\n        # TODO check that there is indeed only 1 output.\n        if res and len(res) == 1:\n            return res[0]\n        return res\n\n    def get_structure_all(self, src_compound_id, src_id):\n        \"\"\"Obtain structure(s) with current AND obsolete assignments\n\n        :param str src_compound_id: a valid compound identifier\n        :param int src_id: corresponding database identifier (name or id).\n        :return:  dictionary with 'standardinchi', 'standardinchikey' and\n            'assignment' keys\n\n        ::\n\n            >>> uni.get_structure_all(\"CHEMBL12\", \"chembl\")\n\n        \"\"\"\n        src_id = self._get_source_id(src_id)\n        request = \"structure_all/%s\" + \"/%s\" % src_id\n        res = self._process(src_compound_id, \"json\", request)\n        if res and len(res) == 1:\n            return res[0]\n        return res\n\n\n    def get_src_compound_id_url(self, src_compound_id, src_id, to_src_id):\n        \"\"\"Obtain a list of URLs for all src_compound_ids\n\n        Obtain a list of URLs for all src_compound_ids\n        from a specifiedsource (the 'to_src_id'), which are CURRENTLY assigned to the same structure as\n        a currently assigned query src_compound_id. Method only applicable for sources\n        which support direct URLs to src_compound_id pages. Method also applicable for\n        'to_src_id's where the hyperlink is constructed from auxiliary data [and not\n        from the src_compound_id] as per example2 below.\n\n        :param str src_compound_id: a valid compound identifier\n        :param int src_id: corresponding database identifier (name or id).\n        :param int to_src_id: database identifier (name or id) to map to.\n        :param str to_src_id:\n        :return: list of URLs.\n\n        ::\n\n            >>> uni.get_src_compound_id_url(\"CHEMBL12\", \"chembl\", \"drugbank\")\n            >>> # equivalent to\n            >>> uni.get_src_compound_id_url(\"CHEMBL12\", 1, 2)\n\n\n        \"\"\"\n        src_id = self._get_source_id(src_id)\n        to_src_id = self._get_source_id(to_src_id)\n        request = \"src_compound_id_url/%s\" + \"/%s/%s\" % (src_id, to_src_id)\n        res = self._process(src_compound_id, \"json\", request)\n        if isinstance(src_compound_id, list):\n            res = [x[0]['url'] for x in res]\n        else:\n            res = res[0]['url']\n        return res\n\n    def get_src_compound_ids_all_from_obsolete(self, obsolete_src_compound_id,\n            src_id, to_src_id=None):\n        \"\"\"Obtain a list of all src_compound_ids from all sources with BOTH\n        current AND obsolete to the same structure with an obsolete assignment to the\n        query src_compound_id.\n\n        The output will include query src_compound_id if it is a\n        valid src_compound_id with an obsolete assignment. Note also, that by adding an\n        additional (optional) argument (a valid src_id), then results will be restricted\n        to only the source specified with this optional argument.\n\n        :param str src_compound_id: a valid compound identifier\n        :param int src_id: corresponding database identifier (name or id).\n        :param int to_src_id: database identifier (name or id) to map to.\n        :return: list of four element arrays, containing 'src_compound_id', 'src_id',\n            'assignment' and 'UCI', or (if optional 'to_src_id' is specified) list of\n            three element arrays, containing 'src_compound_id', 'Assignment' and 'UCI'.\n\n        ::\n\n            >>> from bioservices import UniChem\n            >>> u = UniChem()\n            >>> u.get_src_compound_ids_all_from_obsolete(\"DB07699\", \"2\")\n            >>> u.get_src_compound_ids_all_from_obsolete(\"DB07699\", \"2\", \"1\")\n        \"\"\"\n        src_id = self._get_source_id(src_id)\n        request = \"src_compound_id_all_obsolete/%s\" + \"/%s\" % (src_id)\n        if to_src_id:\n            to_src_id = self._get_source_id(to_src_id)\n            request += \"/%s\" % to_src_id\n        res = self._process(obsolete_src_compound_id, \"json\", request)\n        return res\n\n\n    def get_verbose_src_compound_ids_from_inchikey(self, inchikey):\n        \"\"\"Obtain all src_compound_ids (from all sources)\n\n        which are CURRENTLY assigned to a query InChIKey. However, these are returned as part of the\n        following data structure: A list of sources containing these src_compound_ids,\n        including source description, base_id_url, etc. One element in this list is a\n        list of the src_compound_ids currently assigned to the query InChIKey.\n\n        :param str inchikey: input source identified by its InChiKey\n        :return: list containing\n\n            * src_id (the src_id for this source),\n            * src_url (the main home page of the source),\n            * name (the unique name for the source in UniChem, always lower case),\n            * name_long (the full name of the source, as defined by the source),\n            * name_label (A name for the source suitable for use as a 'label' for the source\n              within a web-page. Correct case setting for source, and always less than 30\n              characters),\n            * description (a description of the content of the source),\n            * base_id_url_available (an flag indicating whether this source provides a valid\n            * base_id_url for creating cpd-specific links [1=yes, 0=no]).\n            * base_id_url (the base url for constructing hyperlinks to this source [append an\n            * identifier from this source to the end of this url to create a valid url to a\n              specific pag    e for this cpd], unless aux_for_url=1),\n            * aux_for_url (A flag to indicate whether the aux_src field should be used to\n              create hyperlinks instead of the src_compound_id [1=yes, 0=no] ,\n            * src_compound_id (a list of src_compound_ids from this source which are currently\n              assigned to the query InChIKey.\n            * aux_src (a list of src-compound_id keys mapping to corresponding auxiliary data\n              (url_id:value), for creating links if aux_for_url=1. Only shown if\n              aux_for_url=1).\n\n        ::\n\n            >>> uni.get_verbose_src_compound_ids_from_inchikey(\"QFFGVLORLPOAEC-SNVBAGLBSA-N\")\n            >>> # Note that this one is not valid anymore\n            >>> uni.get_verbose_src_compound_ids_from_inchikey(\"ZUITABIAKMVPG-UHFFFAOYSA-N\")\n\n        \"\"\"\n        res = self._process(inchikey, \"json\", \"verbose_inchikey/%s\")\n        return res\n\n    def get_auxiliary_mappings(self, src_id):\n        \"\"\"For a single source, obtain a mapping between all current\n        src_compound_ids to their corresponding auxiliary data if any.\n\n        Some instances of UniChem may contain sources that create URLs\n        for compound-specific pages by using strings or identifiers (called 'auxiliary\n        data' here) that are different to the src_compound_ids for the source. This is\n        not very common, but is dealt with in UniChem by use of an additional mapping\n        step for these sources. This function returns such mapping.\n\n        .. warning:: this method may return very large data sets. you will need to change\n            TIMEOUT to a larger value.\n\n        :param int src_id: corresponding database identifier (name or id).\n        :return: list of two element arrays, containing 'src_compound_id' and 'auxiliary data'.\n\n        ::\n\n            >>> uni.get_auxiliary_mappings(15)\n\n        \"\"\"\n        src_id = self._get_source_id(src_id)\n        info = self.get_source_information(src_id)\n\n        if info['aux_for_url'] != '1':\n            self.logging.warning(\"This function accepts src_id that have auxiliary data only\")\n\n        request = \"mappingaux/%s\"\n        res = self._process(src_id, \"json\", request)\n        return res\n\n\n","sub_path":"src/bioservices/unichem.py","file_name":"unichem.py","file_ext":"py","file_size_in_byte":20577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"231879702","text":"import os\nimport sys\nimport time\nimport logging\nimport requests\nimport api_keys\nfrom multiprocessing import Pool, cpu_count\n\n\nHEADER = {'Authorization': f'token {api_keys.GITHUB_API_KEY}'}\ncontributor_urls = [f'https://api.github.com/repositories/596892/contributors?page={idx}' for idx in range(1, 15)]\n\n\ndef hard_work(a_url):\n    print(f'{__name__}/{os.getppid()}/{os.getpid()} gets data from {a_url}')\n    r = requests.get(a_url, headers=HEADER)\n    time.sleep(6)\n    print('Done')\n    return [(contrib['login'], contrib['contributions'],\n             contrib['html_url']) for contrib in r.json()]\n    # return []\n\n\ndef run_sequential_download():\n    contributors = []\n    logging.info('Running the sequential program.')\n    start = time.time()\n    for contributor_url in contributor_urls:\n        contributors += hard_work(contributor_url)\n    print(f'It took {time.time() - start}s in total.')\n\n    return contributors\n\n\ndef run_parallel_processes():\n    workers = cpu_count()\n    pool = Pool(processes=workers)\n\n    print('Running the concurrent program.')\n    start = time.time()\n    result = pool.map(hard_work, contributor_urls)\n\n    print(f'It took {time.time() - start}s in total.')\n    return result\n\n\nif __name__ == '__main__':\n    if sys.argv[1] == '-s':\n        run_sequential_download()\n    elif sys.argv[1] == '-p':\n        run_parallel_processes()\n","sub_path":"lecture_notes/multiprocessing_example.py","file_name":"multiprocessing_example.py","file_ext":"py","file_size_in_byte":1366,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"636613287","text":"#!/usr/bin/python3\n\nimport socket\nimport asyncio\nimport os\nfrom os import path\nimport shutil\nimport sys\n\n\nclass ftpEchoServer:\n    def __init__(self, loop):\n        self.loop = loop\n        self.HOST = ''\n        self.CONTROL_PORT = 25000\n        self.control_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        self.control_sock.bind((self.HOST, self.CONTROL_PORT))\n        self.control_sock.listen(1)\n        self.control_sock.setblocking(False)\n        self.server_ip = socket.gethostbyname(socket.gethostname())\n        self.loop.create_task(self.wait_for_connections())\n\t\t\n    async def wait_for_connections(self):\n        while True:\n            self.control_conn, self.control_addr = await self.loop.sock_accept(self.control_sock)\n            FTP_auth_handler = ftpAuthenticationHandler(self.control_conn, self.loop, self.control_sock, self.server_ip)\n\n\nclass ftpAuthenticationHandler:\n    def __init__(self, control_conn, loop, control_sock, ip_addr):\n        self.ip_addr = ip_addr\n        self.loop = loop\n        self.control_sock = control_sock\n        self.control_conn = control_conn\n        self.logged_in = False\n        self.username = ''\n        self.permitted_commands = { \n                                      \"user\": self.user,\n                                      \"exit\": self.exit_before_auth \n                                  }\n        self.loop.create_task(self.connected_successful_msg())\n\t\t\n    async def connected_successful_msg(self):\n        successful_msg = b'Connected to the FTP server [' +  self.ip_addr.encode() + b']\\n'\n        await self.loop.sock_sendall(self.control_conn, successful_msg)\n        await self.main_auth_handler()\n\n    async def login_successful(self):\n        self.logged_in = True\n        self.welcome_msg = b'230-\\n'\\\n                           b'230- -------------------------------------------------------------------------\\n'\\\n\t                    b'230- WELCOME!\tThis server is created by Kamil Szpakowski. You are logged in\\n'\\\n                           b'230-            as anonymous.\\n'\\\n                           b'230- -------------------------------------------------------------------------\\n'\\\n                           b'Remote system type is UNIX.'\n        await self.loop.sock_sendall(self.control_conn, self.welcome_msg)\n\t\n    async def main_auth_handler(self):\n        self.username = await self.loop.sock_recv(self.control_conn, 10000)\n        self.username = self.username.decode()\n        await self.loop.sock_sendall(self.control_conn, b'331 Password required for USER.')\n        if self.username == 'anonymous':\n            await self.login_successful()\n            FTP_commands_handler = FtpCommandsHandler(self.control_conn, self.loop, self.username)\n            await FTP_commands_handler.execute_command()\n        else:\n            await self.loop.sock_sendall(self.control_conn, b'530 Please Login with USER and PASS.\\nLogin failed.')\n            await self.command_auths_handler()\n\t\n    async def command_auths_handler(self):\n       while True:\n           self.data_from_client = await self.loop.sock_recv(self.control_conn, 10000)\n           self.command = self.data_from_client.decode().split(' ')[0]\n           if not self.data_from_client:\n               break\n           try:\n               await self.permitted_commands.get(self.command)()\n               if self.username == 'anonymous':\n                   await self.login_successful()\n                   FTP_commands_handler = FtpCommandsHandler(self.control_conn, self.loop, self.username)\n                   await FTP_commands_handler.execute_command()\n                   break\n           except TypeError:\n               self.login_failed_msg = b'530 Please Login with USER and PASS.'\n               await self.loop.sock_sendall(self.control_conn, self.login_failed_msg)\n\t\t\t\n    async def user(self):\n        try:\n            self.username = self.data_from_client.decode().split(' ')[1]\n            if self.username != 'anonymous':\n                await self.loop.sock_sendall(self.control_conn, self.login_failed_msg)\n        except IndexError:\n             await self.loop.sock_sendall(self.control_conn, b'usage: user <username>')\n\n    async def exit_before_auth(self):\n        await self.loop.sock_sendall(self.control_conn, b'221 Goodbye.')\n\n\nclass FtpCommandsHandler:\n    def __init__(self, control_conn, loop, username):\n        self.control_conn = control_conn\n        self.loop = loop\n        self.send_mode = 'ascii'\n        self.username = username\n        self.home_dir = '/'\n        os.chdir(self.home_dir)\n        self.current_path = self.home_dir\n        self.cwd = self.home_dir\n        self.ftp_commands = {\n                                'ascii': self.ascii_mode,\n                                'binary': self.binary_mode,\n                                'type': self.change_send_mode,\n                                'user': self.change_user,\n                                'pwd': self.pwd,\n                                'cd': self.change_working_dir,\n                                'cwd': self.change_working_dir,\n                                'cdup': self.cdup,\n                                'mode': self.mode,\n                                'mkdir': self.make_dir,\n                                'mkd': self.make_dir,\n                                'delete': self.delete_file,\n                                'dele': self.delete_file,\n                                'rmdir': self.remove_dir,\n                                'ls': self.list_dir,\n                                'dir': self.list_dir,\n                                'size': self.get_size,\n                                'get': self.send_file_to_local,\n                                'recv': self.send_file_to_local,\n                                'append': self.recv_file,\n                                'put': self.recv_file,\n                                'lcd': self.lcd,\n                                'exit': self.exit,\n                                'bye': self.exit\n                            }\n\n    async def execute_command(self):\n        while True:\n            self.command_with_args = await self.loop.sock_recv(self.control_conn, 10000)\n            self.command = self.command_with_args.decode().split(' ')[0]\n            try:\n                self.arg = self.command_with_args.decode().split(' ')[1]\n            except IndexError:\n                self.arg = ''\n            try:\n                await self.ftp_commands.get(self.command)()\n            except TypeError:\n                if self.command != 'help':\n                    await self.loop.sock_sendall(self.control_conn, b'?Invalid command')\n            if not self.command:\n                break\n        self.control_conn.close()\n\n    async def ascii_mode(self):\n        self.send_mode = 'ascii'\n        await self.loop.sock_sendall(self.control_conn, b'200 TYPE set to A.')\n\n    async def binary_mode(self):\n        self.send_mode = 'binary'\n        await self.loop.sock_sendall(self.control_conn, b'200 TYPE set to I.')\n\n    async def recv_port_command(self):\n        self.port_command = await self.loop.sock_recv(self.control_conn, 10000)\n        self.FTP_DATA_HOST = '.'.join(self.port_command.decode().split(' ')[1].split(',')[:-2])\n        print(self.FTP_DATA_HOST)\n        first_port = int(self.port_command.decode().split(' ')[1].split(',')[-2])\n        second_port = int(self.port_command.decode().split(' ')[1].split(',')[-1])\n        self.FTP_DATA_PORT = first_port * 256 + second_port\n        await self.loop.sock_sendall(self.control_conn, b'200 PORT command successful.\\n150 Opening data connection')\n\n    async def create_data_connection(self):\n        await self.recv_port_command()\n        self.data_sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\n        print(self.FTP_DATA_HOST)\n        print(self.FTP_DATA_PORT)\n        await self.loop.sock_connect(self.data_sock, (self.FTP_DATA_HOST, self.FTP_DATA_PORT))\n\t\t\t\t\t\n    def get_file_permission(self, path_to_list):\n        file_permission = oct(os.stat(path_to_list).st_mode)[-3:]\n\n        possible_perms = { \n                             '0': '---',\n                             '1': '--x',\n                             '2': '-w-',\n                             '3': '-wx',\n                             '4': 'r--',\n                             '5': 'r-x',\n                             '6': 'rw-',\n                             '7': 'rwx'\n                         }\n\n        return ''.join(possible_perms.get(digit) for digit in file_permission)\n\n    async def items_to_list(self, files, path):\n        data_to_send = []\n        for f in files:\n             path_to_list = os.path.join(path, f)\n             file_permission = self.get_file_permission(path_to_list)\n             number_of_hard_links = os.stat(path_to_list).st_nlink\n             size = os.stat(path_to_list).st_size\n             m_time = time.strftime(\"%b %d %H:%M\", time.gmtime(os.stat(path_to_list).st_mtime))\n             if os.path.isdir(path_to_list):\n                 data_to_send.extend(('d'+file_permission, str(number_of_hard_links), str(size), m_time, f, '\\r\\n'))\n             else:\n                 data_to_send.extend((file_permission, str(number_of_hard_links), str(size), m_time, f, '\\r\\n'))\n\n        await self.loop.sock_sendall(self.data_sock, '\\t'.join(data_to_send).encode())\n        await self.loop.sock_sendall(self.control_conn, b'226 Transfer complete')\n        self.data_sock.close()\n\n    async def list_dir(self):\n        await self.create_data_connection()\n        if os.path.exists(self.arg):\n            path_to_list = os.path.join(self.arg)\n            files = os.listdir(path_to_list)\n            await self.items_to_list(files, path_to_list)\n        else:\n            if not self.arg:\n                path_to_list = os.path.join(self.cwd)\n                files = os.listdir(path_to_list)\n                await self.items_to_list(files, path_to_list)\n            else:\n                await self.loop.sock_sendall(self.control_conn, b'550 ' + self.arg.encode() + b': No such file or directory.')\n\n    async def save_file_content(self, new_server_file):\n        while True:\n            file_content = await self.loop.sock_recv(self.data_sock, 10000)\n\t\t\t\n            if not file_content:\n                break\n\n            if self.send_mode == 'ascii':\n                new_server_file.write(file_content.decode())\n            else:\n                new_server_file.write(file_content)\n\n    def recv_file_error_handler(self, exception, path):\n        handler = {\n                      'IsADirectoryError': b'remote: ' + path.encode() + b': Is a directory.\\n',\n                      'PermissionError': b'remote: ' + path.encode() + b': Permission denied.\\n',\n                      'FileNotFoundError': b'remote: 550 ' + path.encode() + b': No such file or directory.\\n'\n                  }\n\t\t\n        return handler.get(exception)\n\t\t\t\t \n    async def recv_file(self):\n        await self.create_data_connection()\n        if self.arg:\n            path = self.arg\n            try:\n                path = self.command_with_args.decode().split(' ')[2]\n            except IndexError:\n                pass\n            if self.send_mode == 'ascii':\n                try:\n                    new_server_file = open(path, 'w')\n                    await self.save_file_content(new_server_file)\n                except Exception as e:\n                    error_msg = self.recv_file_error_handler(type(e).__name__, path)\n                    await self.loop.sock_sendall(self.control_conn, error_msg)\n            else:\n               try:\n                   new_server_file = open(path, 'wb')\n                   await self.save_file_content(new_server_file)\n               except Exception as e:\n                   error_msg = self.recv_file_error_handler(type(e).__name__, path)\n                   await self.loop.sock_sendall(self.control_conn, error_msg)\n            await self.loop.sock_sendall(self.control_conn, b'226 Transfer complete.')\n            self.data_sock.close()\n        else:\n            await self.loop.sock_sendall(self.control_conn, b'usage: append <filename>\\n226 Transfer complete.')\n            self.data_sock.close()\n\n    async def send_text_file(self, path_to_file):\n        text_file_to_send = open(path_to_file, 'r')\n        try:\n            text_file_content = text_file_to_send.read()\n            await self.loop.sock_sendall(self.data_sock, text_file_content.encode())\n        except UnicodeDecodeError:\n            self.data_sock.close()\n            type_error_msg = b'551 type error: Sending binary file when type is A.\\n'\n            await self.loop.sock_sendall(self.control_conn, type_error_msg)\n\n    async def send_binary_file(self, path_to_file):\n        binary_file_to_send = open(path_to_file, 'rb')\n        binary_file_content = binary_file_to_send.read()\n        await self.loop.sock_sendall(self.data_sock, binary_file_content)\n\n    async def send_file_to_local(self):\n        await self.create_data_connection()\n        if not self.arg:\n            await self.loop.sock_sendall(self.control_conn, b'usage: get <filename>\\n226 Transfer complete')\n            self.data_sock.close()\n        elif os.path.isfile(self.arg):\n            path_to_file = os.path.join(self.arg)\n            if self.send_mode == 'ascii':\n                await self.send_text_file(path_to_file)\n            else:\n                await self.send_binary_file(path_to_file)\n            await self.loop.sock_sendall(self.control_conn, b'226 Transfer complete')\n            self.data_sock.close()\n        elif not os.path.isfile(self.arg):\n            await self.loop.sock_sendall(self.control_conn, b'remote: 550 '+self.arg.encode()+b': No such file or directory.\\n')\n            await self.loop.sock_sendall(self.control_conn, b'226 Transfer complete.')\n            self.data_sock.close()\n\n    async def get_size(self):\n        if not self.arg:\n            await self.loop.sock_sendall(self.control_conn, b'usage: size <filename>')\n        elif os.path.exists(self.arg):\n            path_to_get_size = os.path.join(self.arg)\n            if os.path.isfile(path_to_get_size):\n                file_size = os.stat(path_to_get_size).st_size\n                await self.loop.sock_sendall(self.control_conn, b'213 ' + str(file_size).encode())\n            else:\n                await self.loop.sock_sendall(self.control_conn, b'550 ' + path_to_get_size.encode() + b': not a regular file.')\n        elif not os.path.exists(self.arg):\n            await self.loop.sock_sendall(self.control_conn, b'550 ' + self.arg.encode() + b': No such file or directory.')\n\n    async def delete_file(self):\n        if not self.arg:\n            await self.loop.sock_sendall(self.control_conn, b'usage: delete or dele <filename>')\n        else:\n            if os.path.isfile(self.arg):\n                try:\n                    os.remove(self.arg)\n                    await self.loop.sock_sendall(self.control_conn, b'250 ' + self.command.encode() + b' command successful.')\n                except PermissionError:\n                    await self.loop.sock_sendall(self.control_conn, b'550 ' + self.arg.encode() + b': Permission denied')\n            else:\n                await self.loop.sock_sendall(self.control_conn, b'550 ' + self.arg.encode() + b': No such file or directory.')\t\n\n    async def remove_dir(self):\n        if not self.arg:\n            await self.loop.sock_sendall(self.control_conn, b'usage: rmdir <dirname>')\n        else:\n            if os.path.isdir(self.arg):\n                try:\n                    shutil.rmtree(self.arg)\n                    await self.loop.sock_sendall(self.control_conn, b'250 ' + self.command.encode() + b' command successful.')\n                except PermissionError:\n                    await self.loop.sock_sendall(self.control_conn, b'550 ' + self.arg.encode() + b': Permission denied')\n            else:\n                await self.loop.sock_sendall(self.control_conn, b'550 ' + self.arg.encode() + b': No such file or directory.')\n\n    async def make_dir(self):\n        if not self.arg:\n             await self.loop.sock_sendall(self.control_conn, b'usage: mkdir or mkd <dir>')\n        else:\n            if not os.path.exists(self.arg):\n                try:\n                    os.makedirs(self.arg)\n                    await self.loop.sock_sendall(self.control_conn, b'257 MKD command successful.')\n                except PermissionError:\n                    await self.loop.sock_sendall(self.control_conn, b'550 ' + self.arg.encode() + b': Permission denied')\n            else:\n                await self.loop.sock_sendall(self.control_conn, b'550 ' + self.arg.encode() + b': Directory already exists.')\n\n    async def mode(self):\n        await self.loop.sock_sendall(self.control_conn, b'We only support stream mode, sorry.')\n\n    async def cdup(self):\n        if not os.path.samefile(self.current_path, '/'):\n            self.cwd = self.current_path = os.path.abspath(os.path.join(self.current_path, '..'))\n            await self.loop.sock_sendall(self.control_conn, b'250 CDUP command successful.')\n        else:\n            await self.loop.sock_sendall(self.control_conn, b'451 ' + self.current_path.encode() + b': Invalid argument')\n\n    async def change_working_dir(self):\n        if self.arg and os.path.isdir(os.path.join(self.current_path, self.arg)):\n            new_dir = self.arg\n            if self.arg[0] == '/':\n                self.cwd = self.current_path = os.path.abspath(new_dir)\n                os.chdir(self.cwd)\n                await self.loop.sock_sendall(self.control_conn, b'250 CWD command successful.')\n            elif self.arg[0] != '/':\n                self.cwd = self.current_path = os.path.abspath(os.path.join(self.current_path, new_dir))\n                os.chdir(self.cwd)\n                await self.loop.sock_sendall(self.control_conn, b'250 CWD command successful.')\n            else:\n                await self.loop.sock_sendall(self.control_conn, b'250 CWD command successful.')\n        elif self.arg and not os.path.isdir(self.arg):\n            await self.loop.sock_sendall(self.control_conn, b'550 ' + self.arg.encode() + b': No such file or directory.')\n        elif not self.arg:\n            self.cwd = '/'\n            await self.loop.sock_sendall(self.control_conn, b'250 CWD command successful.')\n\n    async def pwd(self):\n        if self.arg:\n            await self.loop.sock_sendall(self.control_conn, b'usage: pwd')\n        else:\n            await self.loop.sock_sendall(self.control_conn, b'257 \\\"' + self.cwd.encode() + b'\\\" is you working directory.')\n\n    async def change_send_mode(self):\n        if self.arg:\n            if self.arg == 'ascii':\n                self.send_mode = self.arg\n                await self.loop.sock_sendall(self.control_conn, b'200 TYPE set to A.')\n            elif self.arg == 'binary':\n                self.send_mode = self.arg\n                await self.loop.sock_sendall(self.control_conn, b'200 TYPE set to I.')\n            else:\n                await self.loop.sock_sendall(self.control_conn, self.arg.encode() + b': ' + b'unknown mode')\n        else:\n            await self.loop.sock_sendall(self.control_conn, b'Using ' + self.send_mode.encode() + b' for transfer files.')\n\n    async def lcd(self):\n        await self.loop.sock_sendall(self.control_conn, b'200 Command okay.')\n\n    async def change_user(self):\n        if not self.arg:\n            await self.loop.sock_sendall(self.control_conn, b'usage: user <username>')\n        elif self.username == 'anonymous':\n            await self.loop.sock_sendall(self.control_conn, b'503 You are already logged in!\\nLogin failed.')\n\n    async def exit(self):\n        await self.loop.sock_sendall(self.control_conn, b'221 Goodbye.')\n\nif __name__ == '__main__':\n    loop = asyncio.get_event_loop()\n    FTP_echo = ftpEchoServer(loop)\n    try:\n        loop.run_forever()\n    except KeyboardInterrupt:\n        loop.close()\n","sub_path":"FTP_server.py","file_name":"FTP_server.py","file_ext":"py","file_size_in_byte":19978,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"431071491","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on March 2018\r\n\r\n@author: Salman\r\n\"\"\"\r\n\r\n#!/bin/python\r\n\r\nimport math\r\nimport os\r\nimport random\r\nimport re\r\nimport sys\r\n\r\n# Complete the appendAndDelete function below.\r\ndef appendAndDelete(s, t, k):\r\n    # If k is big enough than everything is possible.\r\n    if len(s) + len(t) <= k:\r\n        return 'Yes'\r\n\r\n    # Find how big is the difference.\r\n    idx = 0\r\n    while idx < len(s) and idx < len(t):\r\n        if s[idx] != t[idx]:\r\n            break\r\n        idx += 1\r\n \r\n    # If the difference size is smaller than k and both are\r\n    # even or odd, then it is possible.\r\n    diff_size = len(s[idx:]) + len(t[idx:])\r\n    if diff_size <= k and diff_size % 2 == k % 2:\r\n        return 'Yes'\r\n\r\n    return 'No'\r\nif __name__ == '__main__':\r\n    fptr = open(os.environ['OUTPUT_PATH'], 'w')\r\n\r\n    s = raw_input()\r\n\r\n    t = raw_input()\r\n\r\n    k = int(raw_input())\r\n\r\n    result = appendAndDelete(s, t, k)\r\n\r\n    fptr.write(result + '\\n')\r\n\r\n    fptr.close()\r\n","sub_path":"Append and Delete.py","file_name":"Append and Delete.py","file_ext":"py","file_size_in_byte":996,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"532126411","text":"\"\"\"OCW ETL tests\"\"\"\n# pylint: disable=redefined-outer-name\nimport json\nimport mimetypes\nfrom os.path import splitext\nfrom types import SimpleNamespace\nfrom urllib.parse import urljoin, urlparse\n\nimport pytest\n\nfrom course_catalog.constants import (\n    CONTENT_TYPE_FILE,\n    CONTENT_TYPE_PAGE,\n    VALID_TEXT_FILE_TYPES,\n)\nfrom course_catalog.etl.ocw import (\n    upload_mitx_course_manifest,\n    transform_content_files,\n    get_content_file_url,\n    transform_content_file,\n    get_content_file_section,\n)\n\nOCW_COURSE_JSON = {\n    \"uid\": \"0007de9b4a0cd7c298d822b4123c2eaf\",\n    \"title\": \"Geometric Disciplines and Architecture Skills: Reciprocal Methodologies\",\n    \"url\": \"/courses/engineering/4-105-geometric-disciplines-fall-2012\",\n    \"short_url\": \"4-105-geometric-disciplines-fall-2012\",\n    \"course_files\": [\n        {\n            \"uid\": \"e07fcb22fbcf24329fc81b8194329699\",\n            \"parent_uid\": \"bb5f9d523e26f2a622f728050421f5a7\",\n            \"title\": \"Exercise 3: Thickened Plane + the Explosion\",\n            \"caption\": None,\n            \"file_type\": \"application/pdf\",\n            \"description\": \"This resource contains information regarding thickened plane + the explosion.\",\n            \"file_location\": (\n                \"https://s3.amazonaws.com/4-105-geometric-disciplines-fall-2012/\"\n                + \"e07fcb22fbcf24329fc81b8194329699_MIT4_105F12_ex3-explosion.pdf\"\n            ),\n        },\n        {\n            \"uid\": \"78a221020dc9604e6608d57f2a6b6fd0\",\n            \"parent_uid\": \"bb5f9d523e26f2a622f728050421f5a7\",\n            \"title\": \"ex6-handle.3dm\",\n            \"caption\": None,\n            \"file_type\": \"application/octet-stream\",\n            \"description\": \"This resource is related to rhino\\u00ae model: handle.\",\n            \"file_location\": (\n                \"https://s3.amazonaws.com/4-105-geometric-disciplines-fall-2012/\"\n                + \"78a221020dc9604e6608d57f2a6b6fd0_ex6-handle.3dm\"\n            ),\n        },\n        {\n            \"uid\": \"98a221020dc9604e6608d57f2a6b6fd1\",\n            \"title\": \"testing.pdf\",\n            \"caption\": None,\n            \"file_type\": \"application/octet-stream\",\n            \"description\": \"A test file\",\n            \"file_location\": (\n                \"https://s3.amazonaws.com/4-105-geometric-disciplines-fall-2012/\"\n                + \"98a221020dc9604e6608d57f2a6b6fd1_testing.pdf\"\n            ),\n        },\n        {\n            \"uid\": \"00a221020dc9604e6608d57f2a6b6f11\",\n            \"parent_uid\": \"0b5f9d523e26f2a622f728050421f5a9\",\n            \"title\": \"testing2.pdf\",\n            \"caption\": None,\n            \"file_type\": \"application/octet-stream\",\n            \"description\": \"A test file 2\",\n            \"file_location\": (\n                \"https://s3.amazonaws.com/4-105-geometric-disciplines-fall-2012/\"\n                + \"00a221020dc9604e6608d57f2a6b6f11_testing2.pdf\"\n            ),\n        },\n        {\n            \"uid\": \"450555028099b6c7beac2e1a39e5cede\",\n            \"parent_uid\": \"aa5f9d523e26f2a622f728050421f5zz\",\n            \"title\": \"ex7_lz_300k.mp4\",\n            \"caption\": None,\n            \"file_type\": \"application/octet-stream\",\n            \"description\": \"Video\",\n            \"file_location\": (\n                \"https://s3.amazonaws.com/4-105-geometric-disciplines-fall-2012/\"\n                + \"450555028099b6c7beac2e1a39e5cede_ex7_lz_300k.mp4\"\n            ),\n        },\n    ],\n    \"course_foreign_files\": [\n        {\n            \"parent_uid\": \"zz5f9d523e26f2a622f728050421f5a7\",\n            \"link\": \"http://ocw.mit.edu/ans7870/4/4.105/f12/MIT4_105F12_lec1-intro.pdf\",\n            \"file_location\": (\n                \"https://s3.amazonaws.com/4-105-geometric-disciplines-fall-2012/\"\n                + \"MIT4_105F12_lec1-intro.pdf\"\n            ),\n        },\n        {\n            \"parent_uid\": \"bb5f9d523e26f2a622f728050421f5a7\",\n            \"link\": \"http://ocw.mit.edu/ans7870/4/4.105/f12/MIT4_105F12_lec7-discret.pdf\",\n            \"file_location\": (\n                \"https://s3.amazonaws.com/4-105-geometric-disciplines-fall-2012/\"\n                + \"MIT4_105F12_lec7-discret.pdf\"\n            ),\n        },\n    ],\n    \"course_embedded_media\": {\n        \"20547250exercise7video:inflation\": {\n            \"embedded_media\": [\n                {\n                    \"uid\": \"450555028099b6c7beac2e1a39e5cede\",\n                    \"parent_uid\": \"37930e14299c238ef9667d294207cc33\",\n                    \"id\": \"Video-YouTube-Stream\",\n                    \"title\": \"Video-YouTube-Stream\",\n                    \"media_info\": \"http://www.archive.org/download/ex7_lz_300k.mp4\",\n                }\n            ]\n        }\n    },\n    \"course_pages\": [\n        {\n            \"uid\": \"bb5f9d523e26f2a622f728050421f5a7\",\n            \"parent_uid\": \"0007de9b4a0cd7c298d822b4123c2eaf\",\n            \"title\": \"Assignments\",\n            \"text\": \"<p>The following are the assigned exercises\",\n            \"url\": \"/courses/architecture/4-105-geometric-disciplines-fall-2012/assignments\",\n            \"short_url\": \"assignments\",\n            \"description\": \"This section provides the assigned exercises for the course\",\n            \"type\": \"CourseSection\",\n            \"file_location\": (\n                \"https://s3.amazonaws.com/4-105-geometric-disciplines-fall-2012/\"\n                + \"bb5f9d523e26f2a622f728050421f5a7.html\"\n            ),\n        },\n        {\n            \"uid\": \"0b5f9d523e26f2a622f728050421f5a9\",\n            \"title\": \"Sectionless Page\",\n            \"text\": \"<p>The Sectionless Page</p>\",\n            \"url\": \"/courses/architecture/4-105-geometric-disciplines-fall-2012/\",\n            \"short_url\": \"\",\n            \"description\": \"Extra Page\",\n            \"type\": \"SomethingElse\",\n            \"file_location\": (\n                \"https://s3.amazonaws.com/4-105-geometric-disciplines-fall-2012/\"\n                + \"bb5f9d523e26f2a622f728050421f5a7_extra.html\"\n            ),\n        },\n    ],\n}\n\nCOURSE_PAGES = OCW_COURSE_JSON[\"course_pages\"]\nCOURSE_FILES = OCW_COURSE_JSON[\"course_files\"]\nFOREIGN_FILES = OCW_COURSE_JSON[\"course_foreign_files\"]\n\n\n@pytest.fixture\ndef mock_tika_functions(mocker):\n    \"\"\" Mock tika-related functions\"\"\"\n    mock_extract_text = mocker.patch(\n        \"course_catalog.etl.ocw.extract_text_metadata\",\n        return_value={\n            \"metadata\": {\n                \"Author\": \"Test Author\",\n                \"language\": \"en\",\n                \"title\": \"Test Title\",\n            },\n            \"content\": \"Test content\",\n        },\n    )\n    mock_sync_s3_text = mocker.patch(\"course_catalog.etl.ocw.sync_s3_text\")\n    yield SimpleNamespace(\n        mock_extract_text=mock_extract_text, mock_sync_text=mock_sync_s3_text\n    )\n\n\n@pytest.fixture(autouse=True)\ndef mock_s3_content(mock_ocw_learning_bucket):\n    \"\"\"Set up the fake s3 data\"\"\"\n    # Add data to the bucket\n    for file in COURSE_FILES + FOREIGN_FILES:\n        mock_ocw_learning_bucket.bucket.put_object(\n            Key=file[\"file_location\"].replace(\"https://s3.amazonaws.com/\", \"\"),\n            Body=b\"fake text\",\n        )\n    for page in COURSE_PAGES:\n        mock_ocw_learning_bucket.bucket.put_object(\n            Key=page[\"file_location\"].replace(\"https://s3.amazonaws.com/\", \"\"),\n            Body=b\"fake html\",\n        )\n\n\n@pytest.mark.django_db\ndef test_transform_content_files(mock_tika_functions, mocker):\n    \"\"\" Verify that transform_content_files calls tika and returns expected output \"\"\"\n    mock_exception_log = mocker.patch(\"course_catalog.etl.ocw.log.exception\")\n    file_inputs = COURSE_FILES + FOREIGN_FILES\n    text_inputs = [\n        input\n        for input in (file_inputs + COURSE_PAGES)\n        if splitext(input[\"file_location\"])[-1] in VALID_TEXT_FILE_TYPES\n    ]\n    all_inputs = [(file, CONTENT_TYPE_FILE) for file in file_inputs] + [\n        (page, CONTENT_TYPE_PAGE) for page in COURSE_PAGES\n    ]\n\n    transformed_files = transform_content_files(OCW_COURSE_JSON)\n    assert len(transformed_files) == len(all_inputs)\n    assert mock_tika_functions.mock_extract_text.call_count == len(text_inputs)\n    mock_tika_functions.mock_extract_text.assert_any_call(\n        mocker.ANY,\n        other_headers={\n            \"Content-Type\": mimetypes.types_map.get(\n                splitext(file_inputs[0][\"file_location\"])[-1]\n            )\n        },\n    )\n    assert mock_tika_functions.mock_sync_text.call_count == len(text_inputs)\n\n    assert transformed_files == [\n        transform_content_file(OCW_COURSE_JSON, file, ftype)\n        for (file, ftype) in all_inputs\n    ]\n    mock_exception_log.assert_not_called()\n\n\n@pytest.mark.django_db\ndef test_transform_content_files_error(mocker):\n    \"\"\" Verify that errors are logged when transforming content files \"\"\"\n    mocker.patch(\"course_catalog.etl.ocw.transform_content_file\", side_effect=Exception)\n    mock_error_log = mocker.patch(\"course_catalog.etl.ocw.log.error\")\n    transform_content_files(OCW_COURSE_JSON)\n    for course_file in COURSE_FILES:\n        mock_error_log.assert_any_call(\n            \"ERROR syncing course file %s for run %s\",\n            course_file.get(\"uid\", \"\"),\n            OCW_COURSE_JSON.get(\"uid\", \"\"),\n            exc_info=True,\n        )\n\n    for course_page in COURSE_PAGES:\n        mock_error_log.assert_any_call(\n            \"ERROR syncing course page %s for run %s\",\n            course_page.get(\"uid\", \"\"),\n            OCW_COURSE_JSON.get(\"uid\", \"\"),\n            exc_info=True,\n        )\n\n\n@pytest.mark.django_db\ndef test_transform_content_files_generic_s3_error(mocker):\n    \"\"\" Verify that ex eptions are logged when extracting text from content files  \"\"\"\n    mocker.patch(\"course_catalog.etl.ocw.extract_text_metadata\", side_effect=Exception)\n    mock_exception_log = mocker.patch(\"course_catalog.etl.ocw.log.exception\")\n    transform_content_files(OCW_COURSE_JSON)\n\n    mock_exception_log.assert_any_call(\n        \"Error extracting text from key %s for course run %s\",\n        \"4-105-geometric-disciplines-fall-2012/e07fcb22fbcf24329fc81b8194329699_MIT4_105F12_ex3-explosion.pdf\",\n        \"0007de9b4a0cd7c298d822b4123c2eaf\",\n    )\n\n    for course_page in COURSE_PAGES:\n        mock_exception_log.assert_any_call(\n            \"Error extracting text from key %s for course run %s\",\n            urlparse(course_page.get(\"file_location\")).path.lstrip(\"/\"),\n            OCW_COURSE_JSON.get(\"uid\", \"\"),\n        )\n\n\n@pytest.mark.django_db\ndef test_transform_content_files_generic_no_s3_key(mocker, mock_ocw_learning_bucket):\n    \"\"\" Verify that transform_content_files calls tika and returns expected output \"\"\"\n    mock_warn_log = mocker.patch(\"course_catalog.etl.ocw.log.warning\")\n    bad_key = \"4-105-geometric-disciplines-fall-2012/e07fcb22fbcf24329fc81b8194329699_MIT4_105F12_ex3-explosion.pdf\"\n\n    mock_ocw_learning_bucket.bucket.delete_objects(\n        Delete={\"Objects\": [{\"Key\": bad_key}], \"Quiet\": True}\n    )\n    transform_content_files(OCW_COURSE_JSON)\n\n    mock_warn_log.assert_any_call(\n        \"No S3 object found for %s in course run %s\",\n        bad_key,\n        \"0007de9b4a0cd7c298d822b4123c2eaf\",\n    )\n\n\n@pytest.mark.django_db\ndef test_transform_content_file_course_files(mock_tika_functions):\n    \"\"\" Test that contents of course_files are transformed correctly \"\"\"\n    for course_file in COURSE_FILES:\n        expected_transform = {\n            \"content_type\": CONTENT_TYPE_FILE,\n            \"description\": course_file[\"description\"],\n            \"file_type\": course_file[\"file_type\"],\n            \"key\": course_file[\"file_location\"].replace(\n                \"https://s3.amazonaws.com/\", \"\"\n            ),\n            \"section\": get_content_file_section(course_file, COURSE_PAGES),\n            \"title\": course_file[\"title\"],\n            \"uid\": course_file.get(\"uid\", None),\n            \"url\": get_content_file_url(\n                OCW_COURSE_JSON, course_file, CONTENT_TYPE_FILE\n            ),\n        }\n        if splitext(course_file[\"file_location\"])[-1] in VALID_TEXT_FILE_TYPES:\n            expected_transform.update(\n                {\n                    \"content\": mock_tika_functions.mock_extract_text.return_value[\n                        \"content\"\n                    ],\n                    \"content_author\": mock_tika_functions.mock_extract_text.return_value[\n                        \"metadata\"\n                    ][\n                        \"Author\"\n                    ],\n                    \"content_language\": mock_tika_functions.mock_extract_text.return_value[\n                        \"metadata\"\n                    ][\n                        \"language\"\n                    ],\n                    \"content_title\": mock_tika_functions.mock_extract_text.return_value[\n                        \"metadata\"\n                    ][\"title\"],\n                }\n            )\n        assert (\n            transform_content_file(OCW_COURSE_JSON, course_file, CONTENT_TYPE_FILE)\n            == expected_transform\n        )\n\n\n@pytest.mark.django_db\ndef test_transform_content_file_course_foreign_files(mock_tika_functions):\n    \"\"\" Test that contents of course_foreign_files are transformed correctly \"\"\"\n    for course_file in FOREIGN_FILES:\n        expected_transform = {\n            \"content_type\": CONTENT_TYPE_FILE,\n            \"file_type\": None,\n            \"key\": course_file[\"file_location\"].replace(\n                \"https://s3.amazonaws.com/\", \"\"\n            ),\n            \"section\": get_content_file_section(course_file, COURSE_PAGES),\n            \"url\": get_content_file_url(\n                OCW_COURSE_JSON, course_file, CONTENT_TYPE_FILE\n            ),\n        }\n        if splitext(course_file[\"file_location\"])[-1] in VALID_TEXT_FILE_TYPES:\n            expected_transform.update(\n                {\n                    \"content\": mock_tika_functions.mock_extract_text.return_value[\n                        \"content\"\n                    ],\n                    \"content_author\": mock_tika_functions.mock_extract_text.return_value[\n                        \"metadata\"\n                    ][\n                        \"Author\"\n                    ],\n                    \"content_language\": mock_tika_functions.mock_extract_text.return_value[\n                        \"metadata\"\n                    ][\n                        \"language\"\n                    ],\n                    \"content_title\": mock_tika_functions.mock_extract_text.return_value[\n                        \"metadata\"\n                    ][\"title\"],\n                }\n            )\n        assert (\n            transform_content_file(OCW_COURSE_JSON, course_file, CONTENT_TYPE_FILE)\n            == expected_transform\n        )\n\n\n@pytest.mark.django_db\ndef test_transform_content_file_course_pages(mock_tika_functions):\n    \"\"\" Test that contents of course_pages are transformed correctly \"\"\"\n    for course_page in COURSE_PAGES:\n        expected_transform = {\n            \"content_type\": CONTENT_TYPE_PAGE,\n            \"description\": course_page[\"description\"],\n            \"file_type\": course_page[\"type\"],\n            \"key\": course_page[\"file_location\"].replace(\n                \"https://s3.amazonaws.com/\", \"\"\n            ),\n            \"section\": get_content_file_section(course_page, COURSE_PAGES),\n            \"title\": course_page[\"title\"],\n            \"uid\": course_page[\"uid\"],\n            \"url\": get_content_file_url(\n                OCW_COURSE_JSON, course_page, CONTENT_TYPE_PAGE\n            ),\n            \"short_url\": course_page[\"short_url\"],\n        }\n        if splitext(course_page[\"file_location\"])[-1] in VALID_TEXT_FILE_TYPES:\n            expected_transform.update(\n                {\n                    \"content\": mock_tika_functions.mock_extract_text.return_value[\n                        \"content\"\n                    ],\n                    \"content_author\": mock_tika_functions.mock_extract_text.return_value[\n                        \"metadata\"\n                    ][\n                        \"Author\"\n                    ],\n                    \"content_language\": mock_tika_functions.mock_extract_text.return_value[\n                        \"metadata\"\n                    ][\n                        \"language\"\n                    ],\n                    \"content_title\": mock_tika_functions.mock_extract_text.return_value[\n                        \"metadata\"\n                    ][\"title\"],\n                }\n            )\n        assert (\n            transform_content_file(OCW_COURSE_JSON, course_page, CONTENT_TYPE_PAGE)\n            == expected_transform\n        )\n\n\ndef test_get_content_file_section():\n    \"\"\"Test that the correct section for a content file is returned\"\"\"\n    # Matching parent_uid page\n    assert (\n        get_content_file_section(COURSE_FILES[0], COURSE_PAGES)\n        == COURSE_PAGES[0][\"title\"]\n    )\n    # No parent_uid\n    assert get_content_file_section(COURSE_FILES[2], COURSE_PAGES) is None\n    # parent page has no section\n    assert get_content_file_section(COURSE_FILES[3], COURSE_PAGES) is None\n    # page has section\n    assert (\n        get_content_file_section(COURSE_PAGES[0], COURSE_PAGES)\n        == COURSE_PAGES[0][\"title\"]\n    )\n    # page has no section\n    assert get_content_file_section(COURSE_PAGES[1], COURSE_PAGES) is None\n\n\ndef test_get_content_file_url(settings):\n    \"\"\" Test that correct URL's are returned \"\"\"\n    settings.OCW_BASE_URL = \"http://ocw.mit.edu\"\n\n    for page in COURSE_PAGES:\n        assert (\n            get_content_file_url(OCW_COURSE_JSON, page, CONTENT_TYPE_PAGE)\n            == f\"{settings.OCW_BASE_URL}{page.get('url', '')}\"\n        )\n\n    # File with a parent page\n    assert get_content_file_url(\n        OCW_COURSE_JSON, COURSE_FILES[0], CONTENT_TYPE_FILE\n    ) == urljoin(\n        settings.OCW_BASE_URL,\n        f\"{OCW_COURSE_JSON['url']}/{COURSE_PAGES[0]['short_url']}/MIT4_105F12_ex3-explosion.pdf\",\n    )\n\n    # File without a parent page\n    assert (\n        get_content_file_url(OCW_COURSE_JSON, COURSE_FILES[2], CONTENT_TYPE_FILE)\n        == COURSE_FILES[2][\"file_location\"]\n    )\n\n    # File with a media_info match\n    assert (\n        get_content_file_url(OCW_COURSE_JSON, COURSE_FILES[4], CONTENT_TYPE_FILE)\n        == \"http://www.archive.org/download/ex7_lz_300k.mp4\"\n    )\n\n    # Foreign file\n    assert (\n        get_content_file_url(OCW_COURSE_JSON, FOREIGN_FILES[0], CONTENT_TYPE_FILE)\n        == FOREIGN_FILES[0][\"link\"]\n    )\n\n\ndef test_upload_mitx_course_manifest(mock_ocw_learning_bucket, ocw_aws_settings):\n    \"\"\"Test that upload_mitx_course_manifest performs an upload to the OCW bucket\"\"\"\n    courses = [{\"value\": 1}]\n\n    assert upload_mitx_course_manifest(courses) is True\n\n    obj = mock_ocw_learning_bucket.s3.Object(\n        ocw_aws_settings.OCW_LEARNING_COURSE_BUCKET_NAME, \"edx_courses.json\"\n    )\n\n    # check that put_object called to create/update edx_courses.json succeeded\n    contents = json.loads(obj.get()[\"Body\"].read())\n    assert contents == {\"results\": courses, \"count\": len(courses)}\n\n\n@pytest.mark.parametrize(\n    \"disabled_setting_name\",\n    [\n        \"OCW_LEARNING_COURSE_BUCKET_NAME\",\n        \"OCW_LEARNING_COURSE_ACCESS_KEY\",\n        \"OCW_LEARNING_COURSE_SECRET_ACCESS_KEY\",\n    ],\n)\ndef test_upload_mitx_course_manifest_disabled(ocw_aws_settings, disabled_setting_name):\n    \"\"\"Test that upload_mitx_course_manifest doe not perform the upload is a setting is disabled\"\"\"\n    # if this tries to hit S3 it will fail since the bucket doesn't exist in moto\n    setattr(ocw_aws_settings, disabled_setting_name, None)\n    assert upload_mitx_course_manifest([{\"value\": 1}]) is False\n\n\ndef test_upload_mitx_course_manifest_no_courses():\n    \"\"\"Test that upload_mitx_course_manifest doe not perform the upload if an empty course lists is passed\"\"\"\n    # if this tries to hit S3 it will fail since the bucket doesn't exist in moto\n    assert upload_mitx_course_manifest([]) is False\n","sub_path":"course_catalog/etl/ocw_test.py","file_name":"ocw_test.py","file_ext":"py","file_size_in_byte":19702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"217982892","text":"import discord\nimport asyncio\nimport threading\nimport config\nfrom agmapkey import AGMapKey\n\nclient = discord.Client()\n\n@client.event\nasync def on_ready():\n    #needs to calculate start of hour and then call a reset method\n    await client.send_message(discord.Object(id=config.channel), 'Ready')\n\n@client.event\nasync def on_message(message):\n    if message.content.startswith('!showmaps'):\n        await show_maps(message)\n    elif message.content.startswith('!addmap'):\n        await add_map(message)\n    elif message.content.startswith('!removemap'):\n        await remove_map(message)\n    elif message.content.startswith('!enableverbose'):\n        config.verbose = True\n        await client.send_message(message.channel, 'Verbose mode enabled')\n    elif message.content.startswith('!disableverbose'):\n        config.verbose = False\n        await client.send_message(message.channel, 'Verbose mode disabled')\n\nasync def show_maps(message):\n    for key, value in config.maps.items():\n        if config.verbose:\n            await client.send_message(message.channel, str(key) + \"\\n \" +  value)\n\nasync def add_map(message):\n    name =  message.author.display_name\n    firstspacepos = message.content.find(' ')\n    content = ''\n    if firstspacepos != -1:\n        content = message.content[firstspacepos + 1:]\n    attachments = message.attachments\n    for attachment in attachments:\n        try:\n            config.maps[AGMapKey(name, content)] = attachment['url']\n        except:\n            print('Attachment has no url')\n            print(attachment)\n    if config.verbose:\n        await client.send_message(message.channel, 'Added ' + content + ' from ' + name)\n\nasync def remove_map(message):\n    name =  message.author.display_name\n    content = ''\n    firstspacepos = message.content.find(' ')\n    if firstspacepos != -1:\n        content = message.content[firstspacepos + 1:]\n    config.maps.pop(AGMapKey(name, content), None)\n    if config.verbose:\n        await client.send_message(message.channel, 'Removed ' + content + ' from ' + name)\n\nasync def reset_maps():\n    config.maps = {}\n    #call firebase and reset\n\ndef initBot():\n    #need to calculate start of hour and call reset method\n    client.run(config.token)","sub_path":"agbot.py","file_name":"agbot.py","file_ext":"py","file_size_in_byte":2223,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"613595726","text":"import unittest\n\nfrom backend.functions.primer import salt_correction, check_gc_clamp\n\nSEQUENCE = 'atcgtagtcgcacgcgacgagtcg'\nFRAG_LENGTH = 22\n\n\nclass PrimerTest(unittest.TestCase):\n    def test_salt_correction(self):\n        err_method = 3\n        with self.assertRaises(ValueError):\n            salt_correction(method=err_method, Na=0.0)\n\n    def test_check_cg_clamp(self):\n        not_clamp_seq = 'aaaaaaaaaaaaaaaaaaaaa'\n        self.assertEqual(check_gc_clamp(not_clamp_seq), False)\n        clamp_seq = 'aatcagcacgatgggg'\n        self.assertEqual(check_gc_clamp(clamp_seq), True)\n","sub_path":"backend/test/test_primer.py","file_name":"test_primer.py","file_ext":"py","file_size_in_byte":583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"541786725","text":"#!/usr/bin/env python3\n\nimport socket\nimport sys\n\nmysock = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n\ntry:\n    mysock.bind((\"\",80))\nexcept socket.error:\n    print(\"Failed to bind\")\n    sys.exit()\nmysock.listen(5)\nwhile True:\n    conn, addr = mysock.accept()\n    data = conn.recv(1000)\n    if not data:\n        break\n    conn.sendall(b'<HTML>Got a request!</HTML>')\n    print('Received', repr(data))\nconn.close()\nmysock.close()\n\n","sub_path":"RaspberryPi/DoorBellListVideos.py","file_name":"DoorBellListVideos.py","file_ext":"py","file_size_in_byte":435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"141133131","text":"class Student:\r\n    def __init__(self):\r\n        self.name = 'Obama'\r\n        self.age = 30\r\n        self.marks = 900\r\n    def talk(self):\r\n        print('Hi my name is ', self.name)\r\n        print('Age is', self.age)\r\n        print('Marks are', self.marks)\r\n\r\n#create an instance to Student class\r\ns1= Student()\r\ns1.talk()\r\n","sub_path":"1. Define Student class and create object.py","file_name":"1. Define Student class and create object.py","file_ext":"py","file_size_in_byte":325,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"419850551","text":"#!/usr/bin/env python\n# coding: utf-8\n\n# In[3]:\n\n\n#Title: z5197018 MTRN4110 Phase C\n#Description: A program that reads in a maze image and \n#             outputs a text file map with wall location\n#             and initial robot pose\n#Author: Robin Evans\n#Date: 30/07/2020\n\nimport sys\nimport cv2\nimport numpy as np\nimport matplotlib.pyplot as plot\nget_ipython().run_line_magic('matplotlib', 'inline')\n\nMAZE_FILE_NAME = '../../Maze.png'\nROBOT_FILE_NAME = '../../Robot.png'\nMAP_FILE_NAME = '../../Map.txt'\n\n\n# In[4]:\n\n\n#read and show maze\nmaze = cv2.imread(MAZE_FILE_NAME)\nif (maze.size == 0):\n    sys.exit(\"Error: No Maze image found.\")\n    \nmaze_height, maze_width, maze_depth = maze.shape\nmaze_rgb = cv2.cvtColor(maze, cv2.COLOR_BGR2RGB)\n#plot.imshow(maze_rgb)\n\n#read robot\nrobot = cv2.imread(ROBOT_FILE_NAME)\nif (robot.size == 0):\n    sys.exit(\"Error: No Robot image found.\")\n    \nrobot_rgb = cv2.cvtColor(robot, cv2.COLOR_BGR2RGB)\nrobot_height, robot_width, robot_depth = robot.shape\n#plot.imshow(robot_rgb)\n\n#fig, (ax1, ax2) = plot.subplots(2)\n#fig.suptitle('Maze and Robot Images')\n#ax1.imshow(maze_rgb)\n#ax2.imshow(robot_rgb)\n\n\n# In[5]:\n\n\n#####-----------Detect Corners-------######\n#corner detect colour threshold: \n#[135,154,116], [174,255,255]\n\nmaze_hsv = cv2.cvtColor(maze, cv2.COLOR_BGR2HSV)\n\n# Set min and max HSV values\nHSL_low_thresh = np.array([135,154,116])\nHSL_hi_thresh = np.array([174,255,255])\n\n#create mask for corners\ncorner_post_mask = cv2.inRange(maze_hsv, HSL_low_thresh, HSL_hi_thresh)\n\n#apply corner mask\ncorner_image = cv2.bitwise_and(maze_hsv, maze_hsv, mask = corner_post_mask)\nkernel = np.ones((3,3), np.uint8)\ncorner_image = cv2.morphologyEx(corner_image, cv2.MORPH_OPEN, kernel)\n#close_corner_image = cv2.morphologyEx(open_corner_image, cv2.MORPH_CLOSE, kernel)\n\n#find edges\ncorner_edges = cv2.Canny(corner_image, threshold1 = 10, threshold2 = 30, apertureSize = 3)\n\n#find contours\ncorner_contours, heir = cv2.findContours(corner_edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n\nif not corner_contours:\n    sys.exit(\"Error: No corners found.\")\nif len(corner_contours) < 4:\n    sys.exit(\"Error: Less than 4 corners found.\")\n#plot.imshow(corner_edges)\n\n#draw contours\ncv2.drawContours(corner_image, corner_contours, -1, (255,0,0),2)\n\n#get centroid coords of corners. Save in list of tuples.\nmaze_corners = cv2.imread(MAZE_FILE_NAME)\ncorner_centroid = []\nfor i in corner_contours:\n    M = cv2.moments(i)\n    if (M[\"m00\"] != 0):\n        x = int(M[\"m10\"] / M[\"m00\"])\n        y = int(M[\"m01\"] / M[\"m00\"])\n        corner_centroid.append((x,y))\n        cv2.circle(maze_corners, (x, y), 20, (0, 255, 0), -1)\n\n#remove duplicates\ncorner_centroid = list(dict.fromkeys(corner_centroid))\n          \n#show image\n#cv2.namedWindow('Corners. Press q to exit')\n#cv2.imshow('Corners. Press q to exit', maze_corners)\n#while(1):\n#    if cv2.waitKey(10) & 0xFF == ord('q'):\n#        break\n#cv2.destroyAllWindows()\n\n#fig, (ax1) = plot.subplots(1)\n#fig.suptitle('Maze Corners')\n#ax1.imshow(maze_corners)\n\n\n# In[106]:\n\n\n#####------Organise Corners-----#####\n#Find top-left corner index\n\n    #create image-sized black image\nTL_ROI_mask = np.zeros((maze_height, maze_width, 3), np.uint8)\n    #highlight top-left corner\nTL_ROI_mask[0:375,0:338] = (255,255,255)\n    #convert to gray\nTL_ROI_mask = cv2.cvtColor(TL_ROI_mask, cv2.COLOR_BGR2GRAY)\n    #run thresholding\nret, thresh = cv2.threshold(TL_ROI_mask, 2, 255, cv2.THRESH_BINARY)\n#plot.imshow(TL_ROI_mask)\n\n#find contour of mask\nTL_ROI_contour, heir = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n#print(TL_ROI_contour)\n\nif not TL_ROI_contour:\n    sys.exit(\"Error: No corner found in defined top left region.\")\n\n#find index of top left coords\nTL_index = 0\nfor i, item in enumerate(corner_centroid):\n    if cv2.pointPolygonTest(TL_ROI_contour[0], corner_centroid[i], 0) >= 0:\n        TL_index = i\n\n#print(TL_index)\n\n#Find bottom-left corner index\n\n    #create image-sized black image\nBL_ROI_mask = np.zeros((maze_height,maze_width, 3), np.uint8)\n    #highlight bottom-left corner\nBL_ROI_mask[375:,0:338] = (255, 255, 255)\n    #convert to gray\nBL_ROI_mask = cv2.cvtColor(BL_ROI_mask, cv2.COLOR_BGR2GRAY)\n    #run thresholding\nret, thresh = cv2.threshold(BL_ROI_mask, 2, 255, cv2.THRESH_BINARY)\n#plot.imshow(TL_ROI_mask)\n\n#find contour of mask\nBL_ROI_contour, heir = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n#print(TL_ROI_contour)\n\nif not BL_ROI_contour:\n    sys.exit(\"Error: No corner found in defined bottom left region.\")\n\n#find index of bottom left coords\nBL_index = 0\nfor i, item in enumerate(corner_centroid):\n    if cv2.pointPolygonTest(BL_ROI_contour[0], corner_centroid[i], 0) >= 0:\n        BL_index = i\n\n#print(BL_index)\n\n#Find Top-Right corner index\n\n    #create image-sized black image\nTR_ROI_mask = np.zeros((maze_height, maze_width, 3), np.uint8)\n    #highlight top-right corner\nTR_ROI_mask[0:375,1011:] = (255,255,255)\n    #convert to gray\nTR_ROI_mask = cv2.cvtColor(TR_ROI_mask, cv2.COLOR_BGR2GRAY)\n    #run thresholding\nret, thresh = cv2.threshold(TR_ROI_mask, 2, 255, cv2.THRESH_BINARY)\n#plot.imshow(TL_ROI_mask)\n\n#find contour of mask\nTR_ROI_contour, heir = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n#print(TR_ROI_contour)\n\nif not TR_ROI_contour:\n    sys.exit(\"Error: No corner found in defined top right region.\")\n\n#find index of top right coords\nTR_index = 0\nfor i, item in enumerate(corner_centroid):\n    if cv2.pointPolygonTest(TR_ROI_contour[0], corner_centroid[i], 0) >= 0:\n        TR_index = i\n\n#print(TR_index)\n\n#Find Bottom-Right corner index\n\n    #create image-sized black image\nBR_ROI_mask = np.zeros((maze_height, maze_width, 3), np.uint8)\n    #highlight top-right corner\nBR_ROI_mask[375:,1011:] = (255,255,255)\n    #convert to gray\nBR_ROI_mask = cv2.cvtColor(BR_ROI_mask, cv2.COLOR_BGR2GRAY)\n    #run thresholding\nret, thresh = cv2.threshold(BR_ROI_mask, 2, 255, cv2.THRESH_BINARY)\n#plot.imshow(TL_ROI_mask)\n\n#find contour of mask\nBR_ROI_contour, heir = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n#print(TL_ROI_contour)\n\nif not BR_ROI_contour:\n    sys.exit(\"Error: No corner found in defined bottom right region.\")\n\n#find index of top right coords\nBR_index = 0\nfor i, item in enumerate(corner_centroid):\n    if cv2.pointPolygonTest(BR_ROI_contour[0], corner_centroid[i], 0) >= 0:\n        BR_index = i\n\n#print(BR_index)\n\n\n# In[107]:\n\n\n#####-------homography matrix creation and Transform-------####\n\n#arrange corner centroid TR,TL,BR,BL and convert to np.array\ncorner_centroid = np.array([corner_centroid[TL_index], corner_centroid[TR_index], corner_centroid[BL_index], corner_centroid[BR_index]])\n#print(corner_centroid)\n\n#create array of points to transform to\ndest_coords = np.array([(0,0), (maze_width,0), (0,maze_height), (maze_width,maze_height)])\n#print(dest_coords)\n\n#determine and implement homography matrix\nH,stat = cv2.findHomography(corner_centroid, dest_coords)\nmaze_trans = cv2.warpPerspective(maze, H, (maze_width,maze_height))\n#print(H)\n\n#fig, (ax1) = plot.subplots(1)\n#fig.suptitle('Transformed Maze')\n#ax1.imshow(maze_trans)\n\n\n# In[108]:\n\n\n#####----------detect walls-------####\n\nmaze_hsv = cv2.cvtColor(maze_trans, cv2.COLOR_BGR2HSV)\n\nHSL_low_thresh = np.array([15,21,220])\nHSL_hi_thresh = np.array([100,255,255])\n\n#create mask for internal walls\ninternal_wall_mask = cv2.inRange(maze_hsv,HSL_low_thresh,HSL_hi_thresh)\n\n#apply internal wall mask\nkernel = np.ones((5,5), np.uint8)\nwall_image = cv2.bitwise_and(maze_hsv, maze_hsv, mask = internal_wall_mask)\nwall_image = cv2.morphologyEx(wall_image, cv2.MORPH_OPEN, kernel)\n#close_wall_image = cv2.morphologyEx(wall_image, cv2.MORPH_CLOSE, kernel)\n\n#find edges\nwall_edges = cv2.Canny(wall_image, threshold1 = 10, threshold2 = 30, apertureSize = 3)\n\n#find contours\nwall_contours, heir = cv2.findContours(wall_edges, cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)\n#print(corner_contours[0])\n\n#draw contours\ncv2.drawContours(wall_image, wall_contours, -1, (255,0,0), 1)\n\nmaze_walls = maze_trans.copy()\n\n#get centroid coords of walls. Save in list of tuples. Display walls.\nwall_centroid = []\nfor i in wall_contours:\n    M = cv2.moments(i)\n    if (M[\"m00\"] != 0) & (M[\"m00\"] > 100):\n        x = int(M[\"m10\"] / M[\"m00\"])\n        y = int(M[\"m01\"] / M[\"m00\"])\n        wall_centroid.append((x,y))\n        rect = cv2.minAreaRect(i)\n        centroid = rect[0]\n        x = int(centroid[0])\n        y = int(centroid[1])\n        cv2.circle(maze_walls, (x,y), 10, (0, 255, 0), -1)\n        #print(rect)\n        box = cv2.boxPoints(rect)\n        box = np.int0(box)\n        cv2.drawContours(maze_walls, [box], 0, (255,0,0), 2)\n\n#remove duplicates\nwall_centroid = list(dict.fromkeys(wall_centroid))\n#for i, item in enumerate(wall_centroid):\n    #print(wall_centroid[i])\n\n#show image\n#cv2.namedWindow('Walls. Press q to exit.')\n#cv2.resizeWindow('corners',900,700)\n#cv2.imshow('Walls. Press q to exit.',maze_walls)\n#while(1):\n#    if cv2.waitKey(10) & 0xFF == ord('q'):\n#        break\n#cv2.destroyAllWindows()\n\n#fig, (ax1) = plot.subplots(1)\n#fig.suptitle('Wall Detection')\n#ax1.imshow(maze_walls)\n\n\n# In[109]:\n\n\n#####----------Define internal wall matrices-----#####\n\n#Define horizontal and vertical wall matrices\nhori_matrix = np.zeros((6,9),np.bool)\nhori_matrix[0] = np.ones((1,9))\nhori_matrix[5] = np.ones((1,9))\n#print(hori_matrix)\nvert_matrix = np.zeros((5,10),np.bool)\nvert_matrix[:,0] = True\nvert_matrix[:,9] = True\n#print(vert_matrix)\n\n#define column contours\ncol_contours = []\ni = 0\nwhile (i < 9):\n    col = np.zeros((maze_height,maze_width), np.uint8)\n    col[:,i*150+25:i*150+125] = (255)\n    ret, thresh = cv2.threshold(col, 2, 255, cv2.THRESH_BINARY)\n    col_contour, heir = cv2.findContours(thresh,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_SIMPLE)\n    col_contours.append(col_contour[0])\n    i=i+1\n#print(col_contours)\n\n#define row contours\nrow_contours = []\ni = 0\nwhile(i < 5):\n    row = np.zeros((maze_height,maze_width), np.uint8)\n    row[i * 150 + 25:i * 150 + 125,:] = (255)\n    ret, thresh = cv2.threshold(row, 2, 255, cv2.THRESH_BINARY)\n    row_contour, heir = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n    row_contours.append(row_contour[0])\n    i = i + 1\n#print(row_contours)\n    \n#define horzontal wall contours\nhori_contours = []\ni = 0\nwhile(i < 4):\n    hori = np.zeros((maze_height,maze_width), np.uint8)\n    hori[i * 150 + 125:i * 150 + 175,:] = (255)\n    ret, thresh = cv2.threshold(hori, 2, 255, cv2.THRESH_BINARY)\n    hori_contour, heir = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n    hori_contours.append(hori_contour[0])\n    i = i + 1\n#print(hori_contours)\n\n#define vertical wall contours\nvert_contours = []\ni = 0\nwhile(i < 8):\n    vert = np.zeros((maze_height,maze_width), np.uint8)\n    vert[:,i * 150 + 125:i * 150 + 175] = (255)\n    ret, thresh = cv2.threshold(vert, 2, 255, cv2.THRESH_BINARY)\n    vert_contour, heir = cv2.findContours(thresh,cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)\n    vert_contours.append(vert_contour[0])\n    i = i + 1\n#print(vert_contours)\n\n#Search for Horizontal Walls\ni = 0    \nwhile(i < len(hori_contours)):\n    j = 0\n    while(j < len(col_contours)):\n        for k in wall_centroid:\n            if(cv2.pointPolygonTest(hori_contours[i], k, 0) >= 0) & (cv2.pointPolygonTest(col_contours[j], k, 0) >= 0):\n                hori_matrix[i + 1,j] = True\n        j = j + 1\n    i = i + 1\n#print(\"Horizontal Wall Matrix\")\n#print(hori_matrix)\ni = 0    \nwhile(i < len(row_contours)):\n    j = 0\n    while(j < len(vert_contours)):\n        for k in wall_centroid:\n            if(cv2.pointPolygonTest(row_contours[i], k, 0) >= 0) & (cv2.pointPolygonTest(vert_contours[j], k, 0) >= 0):\n                vert_matrix[i,j + 1] = True\n        j = j + 1\n    i = i + 1\n#print(\"Vertical Wall Matrix\")\n#print(vert_matrix)\n\n\n# In[110]:\n\n\n####----------Check for pose of robot------####\n\n#apply transform to robot image\nrobot_trans = cv2.warpPerspective(robot, H, (robot_width, robot_height))\n\n#Load aruco library\ndictionary = cv2.aruco.Dictionary_get(cv2.aruco.DICT_6X6_250)\nparameters = cv2.aruco.DetectorParameters_create()\n\n#search for markers\nmarkerCorners, markerIds, rejectedCandidates = cv2.aruco.detectMarkers(robot_trans, dictionary, parameters = parameters)\n\nif not markerCorners:\n    sys.exit(\"Error: No arUco marker detected.\")\n\ncv2.aruco.drawDetectedMarkers(robot_trans, markerCorners, markerIds)\n\n#check heading based on relative position of corners\nif(((markerCorners[0][0][0,1] - markerCorners[0][0][2,1]) > 0) & (np.absolute(markerCorners[0][0][0,1] - markerCorners[0][0][2,1]) > 50)):\n    heading = \"v\"\n    #print(heading)\nif(((markerCorners[0][0][0,1] - markerCorners[0][0][2,1]) < 0) & (np.absolute(markerCorners[0][0][0,1] - markerCorners[0][0][2,1]) > 50)):\n    heading = \"^\"\n    #print(heading)\nif(((markerCorners[0][0][0,0] - markerCorners[0][0][2,0]) > 0) & (np.absolute(markerCorners[0][0][0,0] - markerCorners[0][0][2,0]) > 50)):\n    heading = \">\"\n    #print(heading)\nif(((markerCorners[0][0][0,0] - markerCorners[0][0][2,0]) < 0) & (np.absolute(markerCorners[0][0][0,0] - markerCorners[0][0][2,0]) > 50)):\n    heading = \"<\"\n    #print(heading)\n    \n#plot.imshow(robot_trans)\n#fig, (ax1, ax2) = plot.subplots(2)\n#fig.suptitle('Transformed Robot and Robot Pose Detection')\n#ax1.imshow(robot_trans)\n\n\nHSL_low_thresh = np.array([29,0,0])\nHSL_hi_thresh = np.array([115,255,255])\nmaze_rbt = cv2.cvtColor(maze_trans, cv2.COLOR_BGR2HSV)\n\n#create mask for robot\nkernel = np.ones((3,3), np.uint8)\nrobot_mask = cv2.inRange(maze_rbt,HSL_low_thresh,HSL_hi_thresh)\nrobot_mask = cv2.morphologyEx(robot_mask, cv2.MORPH_OPEN, kernel)\nrobot_mask = cv2.morphologyEx(robot_mask, cv2.MORPH_CLOSE, kernel)\nrobot_contour, heir = cv2.findContours(robot_mask,cv2.RETR_EXTERNAL ,cv2.CHAIN_APPROX_SIMPLE)\n\npotential_robots = []\nfor i in robot_contour:\n    M = cv2.moments(i)\n    if (M[\"m00\"] != 0) & (M[\"m00\"] > 100):\n        x = int(M[\"m10\"] / M[\"m00\"])\n        y = int(M[\"m01\"] / M[\"m00\"])                           \n        potential_robots.append((x,y))\n\n##determine robot position\nrobot_r = 0\nrobot_c = 0\nrobot_coord = 0\n\n#for potential robots, check if there are any in the corners\nfor i in potential_robots:\n    #print(i)\n    if(cv2.pointPolygonTest(row_contours[0], i, 0) >= 0) & (cv2.pointPolygonTest(col_contours[0], i, 0) >= 0):\n        robot_r = 0\n        robot_c = 0\n        robot_coord = i\n    if(cv2.pointPolygonTest(row_contours[0], i, 0) >= 0) & (cv2.pointPolygonTest(col_contours[len(col_contours)-1], i, 0) >= 0):\n        robot_r = 0\n        robot_c = len(col_contours) - 1\n        robot_coord = i\n    if(cv2.pointPolygonTest(row_contours[len(row_contours) - 1], i, 0) >= 0) & (cv2.pointPolygonTest(col_contours[0], i, 0) >= 0):\n        robot_r = len(row_contours) - 1\n        robot_c = 0\n        robot_coord = i\n    if(cv2.pointPolygonTest(row_contours[len(row_contours) - 1], i, 0) >= 0) & (cv2.pointPolygonTest(col_contours[len(col_contours) - 1], i, 0) >= 0):\n        robot_r = len(row_contours) - 1\n        robot_c = len(col_contours) - 1\n        robot_coord = i\n\nif (robot_coord == 0):\n    sys.exit(\"Error: No Robot found in corners\")\n\n#print(potential_robots)\n#print([robot_r,robot_c])\n#print(robot_coord)\n\n#draw on image\nfont = cv2.FONT_HERSHEY_PLAIN\ncv2.circle(maze_trans, robot_coord, 30, (255, 0, 0), thickness=4, lineType=8, shift=0)\n#cv.Line(maze_trans, pt1, pt2, color, thickness=1, lineType=8, shift=0) \ncv2.putText(maze_trans, heading, (robot_coord[0] - 20, robot_coord[1] + 20), font, 4, (255, 0, 0), 4, cv2.LINE_AA)\n\n#show image\n#cv2.namedWindow('robot')\n#cv2.resizeWindow('corners',900,700)\n#cv2.imshow('robot', maze_trans)\n#while(1):\n#    if cv2.waitKey(10) & 0xFF == ord('q'):\n#        break\n#cv2.destroyAllWindows()\n\n#ax2.imshow(maze_trans)\n\n\n# In[111]:\n\n\n#####---------drawing text file map---------#####\n#open file\nmap_file = open(MAP_FILE_NAME,\"w\")\n\n#write out map found\nwall_vert_i = 0\nwall_hori_i = 0\ni = 0\nwhile(i < 6):\n    j = 0\n    #horizontal row\n    map_file.write(\" \")\n    while(j < 9):\n        if(hori_matrix[wall_hori_i,j] == True):\n            map_file.write(\"--- \")\n        else:\n            map_file.write(\"    \")\n        j = j + 1\n    j = 0\n    wall_hori_i = wall_hori_i + 1\n    #vertical row\n    if(wall_vert_i < 5):\n        map_file.write(\"\\n\")\n        map_file.write(\"|\")\n        while(j < 9):\n            #internal\n            map_file.write(\" \")\n            if (i == robot_r) & (j == robot_c):\n                map_file.write(heading)\n            else:\n                map_file.write(\" \")\n            map_file.write(\" \")\n            #external\n            if(vert_matrix[wall_vert_i][j + 1] == True):\n                map_file.write(\"|\")\n            else:\n                map_file.write(\" \")\n            j = j + 1\n        map_file.write(\"\\n\")\n        wall_vert_i = wall_vert_i + 1\n    i = i + 1\nmap_file.close()\n\n\n# In[ ]:\n\n\n\n\n","sub_path":"Robin/controllers/PhaseD_Master/z5197018_MTRN4110_PhaseC.py","file_name":"z5197018_MTRN4110_PhaseC.py","file_ext":"py","file_size_in_byte":16908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"566807677","text":"\"\"\"\n座右铭:如果现在的生活不是你想要的,那就是你自找的.\n@project:yuke\n@author:Si_jin_hui\n@file:高阶函数之map函数.PY\n@ide:PyCharm\n@time:2018-07-25 09:04:32\n\"\"\"\n\n\n# map()函数:接收两个参数,一个是函数,一个是序列,map函数将序列中的每一个元素都传入到函数中进行运算,并把结果当做一个迭代器进行返回.\ndef calc(x):\n    res = x * x\n    return res\n\n\n# 计算列表中每个元素的平方\n\n\nresult = map(calc, [1, 2, 3, 4, 5, 6])\nprint(result)\n# print(next(result))\n# print(next(result))\n#\n# for res in result:\n#     print(\"--------\",res)\nresult = list(result)\nprint(result)\n# 将列表中的每一个元素都转换成字符串\nresult = map(str, [1, 2, 3, 4, 5, 6])\nprint(list(result))\n\n# 将列表中的每一个元素都转换成整数\nresult_2 = map(int, ('1', '2', '3', '3'))\nprint(list(result_2))\n\n# 将字符串中的每一个元素都转换成整数.\nresult_3 = map(int, '12345678')\nprint(list(result_3))\n","sub_path":"zhengke/7-25/高阶函数之map函数.py","file_name":"高阶函数之map函数.py","file_ext":"py","file_size_in_byte":983,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"75092749","text":"from load import LoadData\nfrom preprocess import PreprocessData\nfrom model import BidaF\nfrom evaluate import evaluate as eval\nfrom load import ptbtokenizer\nimport torch.optim as optim\nimport torch.nn as nn\n\nimport torch\nimport pickle\nimport time\nimport sys\nimport argparse\nimport copy\nimport numpy as np\nimport datetime\nimport os\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\ndef str2bool(v):\n    if isinstance(v, bool):\n       return v\n    if v.lower() in ('yes', 'true', 't', 'y', '1'):\n        return True\n    elif v.lower() in ('no', 'false', 'f', 'n', '0'):\n        return False\n    else:\n        raise argparse.ArgumentTypeError('Boolean value expected.')\n\n\ndef create_checkpoint_paths(folder_path):\n    if not os.path.isdir(folder_path):\n        os.mkdir(folder_path)\n\n\ndef save_vocab(vocab, path):\n    output = open(path, 'wb')\n    pickle.dump(vocab, output)\n    output.close()\n\n\ndef predict_data(context_tokenized, question_tokenized, word_vocab, char_vocab):\n    longest_context_word = max([len(w) for w in context_tokenized])\n    longest_question_word = max([len(w) for w in question_tokenized])\n\n    context_words = (torch.tensor([[word_vocab[word.lower()] for word in context_tokenized]]),\n                     torch.tensor([len(context_tokenized)]))\n\n    question_words = (torch.tensor([[word_vocab[word.lower()] for word in question_tokenized]]),\n                      torch.tensor([len(question_tokenized)]))\n\n    context_char = []\n    for word in context_tokenized:\n        _context_word = []\n        for c_index in range(longest_context_word):\n            if c_index < len(word):\n                _context_word.append(char_vocab[word[c_index]])\n            else:\n                _context_word.append(char_vocab['<pad>'])\n        context_char.append(_context_word)\n    context_char = torch.tensor([context_char])\n\n    question_char = []\n    for word in question_tokenized:\n        _question_word = []\n        for c_index in range(longest_question_word):\n            if c_index < len(word):\n                _question_word.append(char_vocab[word[c_index]])\n            else:\n                _question_word.append(char_vocab['<pad>'])\n        question_char.append(_question_word)\n    question_char = torch.tensor([question_char])\n\n    predict_data.context_words = context_words\n    predict_data.question_words = question_words\n    predict_data.context_char = context_char\n    predict_data.question_char = question_char\n    predict_data.batch_size = 1\n\n\ndef get_prediction(context, question, model, word_vocab, char_vocab):\n    model.eval()\n    context_tokenized = ptbtokenizer(context, context=True)\n    question_tokenized = ptbtokenizer(question)\n\n    predict_data(context_tokenized, question_tokenized, word_vocab, char_vocab)\n    p1, p2 = model(predict_data)\n\n    answer = \" \".join(context_tokenized[p1.argmax(1): p2.argmax(1)])\n\n    return answer, p1.argmax(1), p2.argmax(1)\n\n\ndef train(model, optimizer, criterion, path, epochs, epochs_log, model_name):\n    start_time = time.time()\n    for epoch in range(epochs):\n        # SETTING MODEL IN TRAINING MODE\n        model.train()\n\n        epoch_loss = 0\n        batch_num = 0.0\n        exmaples_count = 0.0\n        best_dev_acc_exact = -1.0\n        best_dev_acc_f1 = -1.0\n\n        for train_data in iter(data_preprocessor.train_iter):\n            batch_num += 1.0\n            exmaples_count += train_data.batch_size\n            p1, p2 = model(train_data)\n            optimizer.zero_grad()\n            try:\n                batch_loss = criterion(p1, train_data.start_idx) + criterion(p2, train_data.end_idx)\n            except Exception as e:\n                print(e)\n                return (p1, p2, train_data)\n\n            epoch_loss += batch_loss.item()\n            batch_loss.backward()\n\n            optimizer.step()\n\n            time_delta = datetime.timedelta(seconds=np.round(time.time() - start_time, 0))\n            sys.stdout.write(f'\\rEpoch:{epoch} | Batch:{batch_num} | Time Running: {time_delta}')\n            break\n\n        if epoch % epochs_log == 0:\n\n            train_loss = epoch_loss/(exmaples_count)\n            dev_accuracy, dev_loss = eval(data_preprocessor.dev_iter,\n                                              model,\n                                              criterion,\n                                              data_preprocessor.WORDS.vocab,\n                                              calculate_loss=True,\n                                              calculate_accuracy=True)\n            dev_accuracy_exact = dev_accuracy.groupby('id')['Exact'].max().mean()\n            dev_accuracy_f1 = dev_accuracy.groupby('id')['F1'].max().mean()\n\n            train_accuracy, _ = eval(data_preprocessor.train_iter,\n                                         model,\n                                         criterion,\n                                         data_preprocessor.WORDS.vocab,\n                                         calculate_loss=False,\n                                         calculate_accuracy=True)\n\n            train_accuracy_exact = train_accuracy.groupby('id')['Exact'].max().mean()\n            train_accuracy_f1 = train_accuracy.groupby('id')['F1'].max().mean()\n\n            print(\n                f'\\nTrain Loss:{train_loss:.4f} Train Acc Exact:{train_accuracy_exact:.4f} Train Acc F1:{train_accuracy_f1:.4f}')\n            print(\n                f'Validation Loss :{dev_loss:.4f} Dev Acc Exact:{dev_accuracy_exact:.4f} Dev Acc F1:{dev_accuracy_f1:.4f}')\n\n            print('Test Prediction Results')\n            predict_context = \"He was speaking after figures showed that the country's economy shrank by 20.4% in April - \" \\\n                              \"the largest monthly contraction on record - as the country spent its first full month in lockdown.\"\n            predict_ques = \"By how much did the country's economy shrank\"\n\n            print(get_prediction(predict_context,\n                                 predict_ques,\n                                 model,\n                                 data_preprocessor.WORDS.vocab,\n                                 data_preprocessor.CHAR.vocab))\n\n            torch.save({\n                'epoch': epoch,\n                'model_state_dict': model.state_dict(),\n                'optimizer_state_dict': optimizer.state_dict(),\n                'loss': epoch_loss,\n            }, path + '/' + model_name + '.torch')\n\n            if dev_accuracy_f1 > best_dev_acc_f1:\n                best_dev_acc_f1 = dev_accuracy_f1\n                best_dev_acc_exact = dev_accuracy_exact\n                torch.save(model, path + '/' + 'best_' + model_name + '.torch')\n\n    print (f'Best Validation Results '\n           f'Dev Acc Exact:{best_dev_acc_exact:.4f} '\n           f'Dev Acc F1:{best_dev_acc_f1:.4f}')\n\n\nparser = argparse.ArgumentParser(description='BiDaF for Machine Comprehension & Cloze-Style Reading Comprehension - Training')\n\n# Input data\nparser.add_argument('-data', type=str, default='../../../../data/squad v1.1/', help='Path to input data')\n\n# Checkpoints paths\nparser.add_argument('-data_checkpoint', type=str, default='./data_checkpoints', help='Path to store preprocessed data checkpoints')\nparser.add_argument('-model_checkpoint', type=str, default='./model_checkpoints', help='Path to store modelled data checkpoints')\nparser.add_argument('-model_name', type=str, default=None, required=True, help='provide a name to the model for storing at chekpoints')\nparser.add_argument('-dataset_name', type=str, default=None, required=True, help='Name of the Dataset')\n\nparser.add_argument('-load_data', type=str2bool, nargs='?', default=False, help='To Load data of use preprocessed data')\n\n# Modelling parameters\nparser.add_argument('-epochs', type=int, default=20, help='No. of Epoch to run')\nparser.add_argument('-batch_size', type=int, default=60, help='Number of examples in each batch')\nparser.add_argument('-glove_size', type=int, default=100, help='Size of Glove  vector to use')\nparser.add_argument('-char_embedding_size', type=int, default=100, help='Size of Character embeddings to be used')\nparser.add_argument('-kernel_size', type=int, default=5, help='Kernel Size')\nparser.add_argument('-channels_count', type=int, default=100 ,help='Count of channels for character embeddings')\nparser.add_argument('-learning_rate', type=float, default=0.5, help='Learning Rate')\nparser.add_argument('-epoch_log', type=int, default=2, help='Print logs after xx epochs')\n\nargs = parser.parse_args()\n\nif args.load_data:\n    _ = LoadData(data_path=args.data,\n                 checkpoint_path=args.data_checkpoint,\n                 train_file=args.dataset_name + '_' + 'train.csv',\n                 dev_file=args.dataset_name + '_' + 'dev.csv')\n\n# Create Checkpoint folders for in between sessions storage\ncreate_checkpoint_paths(args.data_checkpoint)\ncreate_checkpoint_paths(args.model_checkpoint)\n\ndata_preprocessor = PreprocessData(data_path=args.data_checkpoint,\n                                   glove_size=args.glove_size,\n                                   batch_size=args.batch_size,\n                                   train_file='train1.csv',\n                                   dev_file='dev1.csv')\n                                   # train_file=args.dataset_name + '_' + 'train.csv',\n                                   # dev_file=args.dataset_name + '_' + 'dev.csv')\n\nsave_vocab(data_preprocessor.WORDS.vocab, args.model_checkpoint + '/' + args.dataset_name + '_' + 'WORDS.vocab')\nsave_vocab(data_preprocessor.CHAR.vocab, args.model_checkpoint + '/' + args.dataset_name + '_' + 'CHAR.vocab')\n\n# Initializing Bidaf Model\nmodel = BidaF(data_preprocessor.WORDS,\n              data_preprocessor.CHAR,\n              char_embedding_size=args.char_embedding_size,\n              char_conv_kernel_size=args.kernel_size,\n              char_conv_channels_count=args.channels_count).to(device)\n\n# Intialize Optimizer\n# optimizer = optim.Adam(model.parameters(), lr=0.001)\noptimizer = optim.Adadelta(model.parameters(), lr=args.learning_rate, rho=0.999)\ncriterion = nn.CrossEntropyLoss()\n\n## This is a piece of code for retraining the model from where left\n# if os.path.isfile(args.model_checkpoint + '/model.torch'):\n#     checkpoint = torch.load(args.model_checkpoint + '/model.torch')\n#     model.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#     print('Loaded Trained Model')\n#     print(f'Trained for {epoch} Epochs, Achieved {loss} Training Loss')\n\n_error = train(model=model,\n               optimizer=optimizer,\n               criterion=criterion,\n               path=args.model_checkpoint,\n               epochs=args.epochs,\n               epochs_log=args.epoch_log,\n               model_name=args.dataset_name + '_' + args.model_name)","sub_path":"train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":10860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"58277967","text":"import numpy as np\n\n# ========================================================= #\n# ===  make__af.py ( for superfish )                    === #\n# ========================================================= #\n\ndef make__af():\n\n    # ------------------------------------------------- #\n    # --- [1] load parameters                       --- #\n    # ------------------------------------------------- #\n\n    import nkUtilities.load__constants as lcn\n    cnfFile = \"dat/parameter.conf\"\n    const = lcn.load__constants( inpFile=cnfFile )\n\n    # ------------------------------------------------- #\n    # --- [2] comment & settings                    --- #\n    # ------------------------------------------------- #\n\n    if ( const[\"auto_drive_point\"] ):\n        unit_mm           = 1.e-3\n        wavelength        = const[\"cv\"] / const[\"frequency\"]\n        if ( const[\"auto_cell_Diameter\"] ):\n            Dp     = const[\"r1_bessel\"] / np.pi * wavelength\n        else:\n            Dp     = const[\"cell_Diameter\"]\n\n        \n        if ( const[\"add_beam_pipe\"] ):\n            const[\"xy_drive\"] = [ const[\"L_beam_pipe\"]/unit_mm, Dp*0.5 / unit_mm ]\n        else:\n            const[\"xy_drive\"] = [ 0.0, Dp*0.5 / unit_mm ]\n    \n    comment = \\\n        \"### {0} GHz Cavity\\n\"\\\n        \"### pillbox-type cavity\\n\"\\\n        \"### created by K.Nishida\\n\"\\\n        \"###\\n\\n\".format( const[\"frequency\"] / 1.0e9 )\n\n    generals   = \\\n        \"kprob=1                              ! superfish problem \\n\"\\\n        \"icylin=1                             ! cylindrical coordinates \\n\"\\\n        \"conv={0}                             ! unit conversion ( e.g. cm => mm ) \\n\"\\\n        \"freq={1}                             ! frequency (MHz) \\n\"\\\n        \"dx={2}                               ! mesh size \\n\"\\\n        \"xdri={3[0]},ydri={3[1]}              ! drive point of RF \\n\"\\\n        \"kmethod=1                            ! use beta to compute wave number \\n\"\\\n        \"beta={4}                             ! Particle velocity for transit-time integral\\n\"\\\n        .format( const[\"unit_conversion\"], const[\"frequency\"]/1.e6, const[\"meshsize\"], \\\n                 const[\"xy_drive\"], const[\"beta\"] )\n\n    boundaries = \\\n        \"nbsup={0}                            ! boundary :: upper ( 0:Neumann, 1:Dirichlet )\\n\"\\\n        \"nbslo={1}                            !          :: lower  \\n\"\\\n        \"nbsrt={2}                            !          :: right  \\n\"\\\n        \"nbslf={3}                            !          :: left   \\n\"\\\n        .format( const[\"boundary_upper\"], const[\"boundary_lower\"], \\\n                 const[\"boundary_right\"], const[\"boundary_left\"] )\n\n    settings   = \"&reg {0}{1}&\\n\\n\".format( generals, boundaries )\n    \n    \n    # ------------------------------------------------- #\n    # --- [3] pillbox cavity geometry               --- #\n    # ------------------------------------------------- #\n    #  -- [3-1] preparation                         --  #\n    wavelength = const[\"cv\"] / const[\"frequency\"] * const[\"beta\"]\n\n    #  -- [3-2] Dp :: diameter of pillbox cavity    --  #\n    if ( const[\"auto_cell_Diameter\"] ):\n        Dp     = const[\"r1_bessel\"] / np.pi * wavelength\n    else:\n        Dp     = const[\"cell_Diameter\"]\n    \n    #  -- [3-3] Lp :: length of pillbox cavity      --  #\n    if ( const[\"auto_cell_Length\"] ):\n        Lp     = const[\"auto_cell_Lenfactor\"] * wavelength\n    else:\n        Lp     = const[\"cell_Length\"]\n\n    #  -- [3-4] Message                             --  #\n    print()\n    print( \"[make__af.py]   Lp  :: {0} \".format( Lp ) )\n    print( \"[make__af.py]   Dp  :: {0} \".format( Dp ) )\n    print()\n\n    #  -- [3-5] unit conversion for D & L           --  #\n    unit_mm    = 1.e-3\n    hDp        = 0.5 * Dp / unit_mm\n    Lp         =       Lp / unit_mm\n    rPipe      = const[\"r_beam_pipe\"] / unit_mm\n    LPipe      = const[\"L_beam_pipe\"] / unit_mm\n\n    # ------------------------------------------------- #\n    # --- [4] write in a file                       --- #\n    # ------------------------------------------------- #\n    #  -- [4-1] make list (po) to write             --  #\n    if ( const[\"add_beam_pipe\"] ):\n        pts        = [ [        0.0, 0.0   ],\n                       [        0.0, rPipe ],\n                       [   LPipe   , rPipe ],\n                       [   LPipe   , hDp   ],\n                       [   LPipe+Lp, hDp   ],\n                       [   LPipe+Lp, rPipe ],\n                       [ 2*LPipe+Lp, rPipe ],\n                       [ 2*LPipe+Lp, 0.0   ],\n                       [        0.0, 0.0   ],\n        ]\n    else:\n        pts        = [ [ 0.0, 0.0 ],\n                       [ 0.0, hDp ],\n                       [  Lp, hDp ],\n                       [  Lp, 0.0 ],\n                       [ 0.0, 0.0 ] ]\n    pts        = np.array( pts )\n\n    #  -- [4-2] into SF $po format                  --  #\n    z_, r_     = 0, 1\n    geometry   = \"\"\n    for ik, pt in enumerate( pts ):\n        geometry += \"$po x={0}, y={1} $\\n\".format( pt[z_], pt[r_] )\n\n    #  -- [4-3] write in a file                     --  #\n    with open( const[\"outFile\"], \"w\" ) as f:\n        f.write( comment  )\n        f.write( settings )\n        f.write( geometry )\n    print()\n    print( \"[make__af.py] outFile :: {0} \".format( const[\"outFile\"] ) )\n    print()\n    \n    return()\n\n\n# ========================================================= #\n# ===   実行部                                          === #\n# ========================================================= #\n\nif ( __name__==\"__main__\" ):\n    make__af()\n","sub_path":"pyt/make__af.py","file_name":"make__af.py","file_ext":"py","file_size_in_byte":5538,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"641488133","text":"import pandas as pd\r\nfrom core import graphic\r\nfrom pathwayAnalysis.mappingModels import mappingModels\r\nfrom utilities import split_dictionaries, set_seaborn_style\r\n\r\ndef create_exp_dict(mapFile, expFile):\r\n    \"\"\"\r\n    @arguments\r\n\r\n    mapFile: csv file with the map for the experimental data\r\n    expFile: Ordered dict of str to list of floats\r\n\r\n    @return:\r\n    \"\"\"\r\n    the_mapp = mappingModels(mapFile)\r\n    the_mapp.create_experimental_dict(expFile)\r\n    return the_mapp.experimentalDict\r\n\r\n\r\ndef experimental_plots(metabolicFluxes, cols, dils, dimension, resolution, figTitle, figName, fileName, size=15):\r\n    \"\"\"\r\n    @arguments\r\n\r\n    metabolicFluxes:\r\n    cols: list of str, list of the columns names\r\n    dils: list of str, list of the dilution rates\r\n    dimension: tuple of int, (l,c) where l corresponds to the number of lines and c to the columns\r\n    resolution: float\r\n    figTitle: str, title of the figure\r\n    figName: str, figures name\r\n    size: int, font size\r\n\r\n    @return: scatter plot\r\n    \"\"\"\r\n    pd.DataFrame.from_dict(metabolicFluxes, orient=\"index\").to_csv(fileName, sep=\";\")\r\n    splitMetFluxes = split_dictionaries(metabolicFluxes)\r\n    set_seaborn_style(colorCodes=True, gStyle=\"whitegrid\", colorPalette=\"Set1\", fontSize=size, context=\"notebook\")\r\n    g = graphic(dimension, resolution, figTitle, figName)\r\n    g.create_subplot()\r\n    g.make_scatter_plot(fluxes=splitMetFluxes, dils=dils, fontSize=size, colsNames=cols)\r\n","sub_path":"plots/experimental.py","file_name":"experimental.py","file_ext":"py","file_size_in_byte":1460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"606310469","text":"import stdarray\r\nimport stdio\r\nimport sys\r\n\r\n\r\ndef isYoung(a):\r\n    young = False\r\n    for i in range(len(a)):\r\n        for j in range(len(a[0])):\r\n            if i != len(a) - 1:\r\n                if not a[i][j] < a[i + 1][j]:\r\n                    return False\r\n            elif j != len(a[0]) - 1:\r\n                if not a[i][j] < a[i][j + 1]:\r\n                    return False\r\n            else:\r\n                young = True\r\n    return young\r\n\r\n\r\ndef isContains(a, x):\r\n    for i in range(len(a)):\r\n        for j in range(len(a[0])):\r\n            if a[i][j] == x:\r\n                return True\r\n    return False\r\n\r\n\r\ndef main():\r\n    row = int(sys.argv[1])\r\n    col = int(sys.argv[2])\r\n    a = stdarray.create2D(row, col)\r\n    for i in range(row):\r\n        for j in range(col):\r\n            a[i][j] = stdio.readInt()\r\n    if not isYoung(a):\r\n        stdio.writeln(False)\r\n    if isYoung(a):\r\n        x = 73\r\n        stdio.writeln(isContains(a, x))\r\n\r\nif __name__ == '__main__':\r\n    main()\r\n","sub_path":"Python/youngTableaux.py","file_name":"youngTableaux.py","file_ext":"py","file_size_in_byte":995,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"469374277","text":"import sys \nsys.path.append(\"../../ml\")\nimport tensorflow as tf\nimport numpy as np\nimport yaml\nimport ColorModel\nimport cv2\n\nNUM_CHANNELS = 3\nclass NNInterface:\n    def __init__(self, config, checkpoint):\n        self.sess = tf.Session()\n        with open(config) as f:\n            config = yaml.load(f)\n        self.img_size = config['data_params']['img_size']\n        self.imagePH = tf.compat.v1.placeholder(tf.float32, (config['data_params']['batch_size'],\n            config['data_params']['img_size'], config['data_params']['img_size'], NUM_CHANNELS))\n        model = ColorModel.ColorSuggestModelSeparate(config)\n        model_out = model.buildModel(self.imagePH)\n        self.model_out_clipped = tf.clip_by_value(model_out, 0., 1.)\n        \n\n        saver = tf.compat.v1.train.Saver()\n        checkpoint = tf.train.latest_checkpoint(checkpoint)\n        saver.restore(self.sess, checkpoint)\n\n    def predict(self, img):\n        img = cv2.resize(img, (self.img_size, self.img_size))\n        img = img[np.newaxis]\n        feed_dict = { self.imagePH:img }\n        colors_out = self.sess.run(self.model_out_clipped, feed_dict=feed_dict)\n        color = colors_out[0][0]\n        return [int(x * 255) for x in color]\n\n\n# nn = NNInterface(\"../../ml/config.yaml\", \"../checkpoint\")\n# img = cv2.imread(\"input/removed_color.png\")\n# \n# print(pred)","sub_path":"ui/paint/nnInterface.py","file_name":"nnInterface.py","file_ext":"py","file_size_in_byte":1340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"16908017","text":"\"\"\"decrypts message\"\"\"\n\nfrom PIL import Image\n\nfile_name = input(\"Enter the name of the file you would like to open: \")\nimage = Image.open(file_name, \"r\")\nrgb_image = image.convert('RGB')\npix = list(rgb_image.getdata())\n\nbin_arr = []\nencrypted_bin_arr = []\nfor elem in pix:\n    \"\"\"get binary of image\"\"\"\n    for i in elem:\n        binary = bin(i)[2:].zfill(8)\n        bin_arr.append(binary)\n\nLSB_arr = []\nfor i in range(len(bin_arr)):\n    elem = bin_arr[i]\n    LSB = elem[len(elem) - 1]\n    LSB_arr.append(LSB)\n\nl = LSB_arr\nresult = \"\".join(chr(int(\"\".join(map(str, l[i:i+8])), 2)) for i in range(0, len(l), 8))\nprint(result)\n","sub_path":"scraps/flask trash/Easier Read Than Done/Steganography-With-Python/code drafts/decode.py","file_name":"decode.py","file_ext":"py","file_size_in_byte":626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"493020287","text":"# -*- coding: utf-8 -*-\n#ENTRADA\nn = int(input('Digite a quantidade de valores : '))\na = []\nsomapar = 0\nsomaimpar = 0\ncontpar = 0\ncontimpar = 0\n#PROCESSAMENTO\nfor i in range(0,n,1) :\n    valor = int(input('Digite o valor : '))\n    a.append (valor)\n    if (valor%2==0) :\n        somapar = somapar + valor\n        contpar = contpar + 1\n    else :\n        somaimpar = somaimpar + valor\n        contimpar = contimpar + 1\nprint(somaimpar)\nprint(somapar)\nprint(contimpar)\nprint(contpar)\nprint(a)\n    \n\n\n\n","sub_path":"moodledata/vpl_data/175/usersdata/271/96863/submittedfiles/lista1.py","file_name":"lista1.py","file_ext":"py","file_size_in_byte":498,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"110999853","text":"class Command():\n\n    def __init__(self, command_id, name, alias, access_level, msg_types_list,\n                 category, args, help_short, help_long=None):\n        self.id = command_id\n        self.name = name\n        self.alias = alias\n        self.access_level = access_level\n        self.msg_types = msg_types_list\n        self.category = category\n        self.args = args\n        self.help_short = help_short\n        self.help_long = help_long\n\n    def is_allowed(self, ss_msg_type):\n        return ss_msg_type in self.msg_types\n","sub_path":"subspace_bot/objects/command.py","file_name":"command.py","file_ext":"py","file_size_in_byte":535,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"514535523","text":"import urllib.request\nimport urllib.parse\nimport json\n\n# request = urllib.request.Request('https://www.geekdigging.com/')\n# res = urllib.request.urlopen(request)\n# print(res.read().decode('utf-8'))\n\nurl = 'https://httpbin.org/post'\n\nheaders = {\n    'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/78.0.3904.108 Safari/537.36',\n    'Content-Type': 'application/json;encoding=utf-8',\n    'Host': 'geekdigging.com'\n}\n\ndata = {\n    'name': 'geekdigging',\n    'hello': 'world'\n}\n\ndata = bytes(json.dumps(data),encoding=\"utf-8\")\nreq = urllib.request.Request(url=url,data=data,headers=headers,method='POST')\n\nres = urllib.request.urlopen(req)\nprint(res.read().decode('utf-8'))","sub_path":"learn-path/LearningPythonSpider/urllib/1.py","file_name":"1.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"584502876","text":"# -*- coding: utf-8 -*-\n#\n# Copyright (C) 2020 CERN.\n#\n# Marshmallow-Utils is free software; you can redistribute it and/or modify\n# it under the terms of the MIT License; see LICENSE file for more details.\n\n\"\"\"Test the babel date and time localization.\"\"\"\n\nfrom datetime import date, datetime\n\nimport pytest\nfrom babel.dates import get_timezone\nfrom marshmallow import Schema, ValidationError\n\nfrom marshmallow_utils.fields import FormatDate, FormatDatetime, FormatEDTF, \\\n    FormatTime\n\n\n@pytest.fixture()\ndef dt():\n    \"\"\"Datetime fixture.\"\"\"\n    return datetime(2020, 11, 8, 23, 22)\n\n\nclass MySchema(Schema):\n    \"\"\"Test schema.\"\"\"\n\n    short = FormatEDTF(attribute='edtf', format='short', locale='en')\n    long = FormatEDTF(attribute='edtf', format='long', locale=lambda: 'en')\n    datetime = FormatDatetime(format='medium', locale='da')\n    time = FormatTime(format='long', locale='da',\n                      tzinfo=lambda: get_timezone('America/Chicago'))\n    date = FormatDate(format='short', locale='da')\n    no_parse = FormatDatetime(format='medium', locale='da', parse=False)\n\n\ndef test_format_edtf():\n    \"\"\"Test EDTF formatting.\"\"\"\n    assert MySchema().dump({'edtf': '2020-09/2020-10'}) == {\n        'short': '9/2020 – 10/2020',\n        'long': 'September – October 2020',\n    }\n\n\ndef test_format_datetime(dt):\n    \"\"\"Test datetime formatting.\"\"\"\n    assert MySchema().dump({'datetime': dt}) == {\n        'datetime': '8. nov. 2020 23.22.00',\n    }\n    assert MySchema().dump({'datetime': dt.isoformat()}) == {\n        'datetime': '8. nov. 2020 23.22.00',\n    }\n    # Test field with no parsing of strings:\n    pytest.raises(Exception, MySchema().dump, {'no_parse': dt.isoformat()})\n\n\ndef test_format_time(dt):\n    \"\"\"Test datetime formatting.\"\"\"\n    assert MySchema().dump({'time': dt}) == {'time': '17.22.00 -0600'}\n    assert MySchema().dump({\n        'time': dt.isoformat()}) == {'time': '17.22.00 -0600'}\n\n\ndef test_format_date(dt):\n    \"\"\"Test datetime formatting.\"\"\"\n    assert MySchema().dump({'date': dt}) == {'date': '08.11.2020'}\n    assert MySchema().dump({'date': '2021-01-01'}) == {'date': '01.01.2021'}\n","sub_path":"tests/test_babel.py","file_name":"test_babel.py","file_ext":"py","file_size_in_byte":2136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"367038467","text":"from PyQt5 import QtCore, QtGui, QtWidgets\nimport Diffi\nimport diffiParam\nimport EllipDiffiParam\nimport EllipDiffi\nimport time\n\n\nclass Ui_DiffiHelman(object):\n    def setupUi(self, DiffiHelman):\n        DiffiHelman.setObjectName(\"DiffiHelman\")\n        DiffiHelman.resize(988, 590)\n        DiffiHelman.setStyleSheet(\"background-color: rgb(255, 255, 255);\")\n        self.centralwidget = QtWidgets.QWidget(DiffiHelman)\n        self.centralwidget.setObjectName(\"centralwidget\")\n        self.widget = QtWidgets.QWidget(self.centralwidget)\n        self.widget.setGeometry(QtCore.QRect(0, -30, 991, 601))\n        self.widget.setObjectName(\"widget\")\n        self.line = QtWidgets.QFrame(self.widget)\n        self.line.setGeometry(QtCore.QRect(10, 308, 732, 19))\n        self.line.setFrameShape(QtWidgets.QFrame.HLine)\n        self.line.setFrameShadow(QtWidgets.QFrame.Sunken)\n        self.line.setObjectName(\"line\")\n        self.splitter_11 = QtWidgets.QSplitter(self.widget)\n        self.splitter_11.setGeometry(QtCore.QRect(10, 40, 961, 551))\n        self.splitter_11.setOrientation(QtCore.Qt.Vertical)\n        self.splitter_11.setObjectName(\"splitter_11\")\n        self.splitter_5 = QtWidgets.QSplitter(self.splitter_11)\n        self.splitter_5.setOrientation(QtCore.Qt.Horizontal)\n        self.splitter_5.setObjectName(\"splitter_5\")\n        self.splitter_3 = QtWidgets.QSplitter(self.splitter_5)\n        self.splitter_3.setOrientation(QtCore.Qt.Vertical)\n        self.splitter_3.setObjectName(\"splitter_3\")\n        self.splitter_2 = QtWidgets.QSplitter(self.splitter_3)\n        self.splitter_2.setOrientation(QtCore.Qt.Horizontal)\n        self.splitter_2.setObjectName(\"splitter_2\")\n        self.label = QtWidgets.QLabel(self.splitter_2)\n        self.label.setObjectName(\"label\")\n        self.diff_g = QtWidgets.QLineEdit(self.splitter_2)\n        self.diff_g.setObjectName(\"diff_g\")\n        self.pushButton = QtWidgets.QPushButton(self.splitter_2)\n        self.pushButton.setObjectName(\"pushButton\")\n        self.pushButton.clicked.connect(self.calc)\n        self.diffi_text = QtWidgets.QTextBrowser(self.splitter_3)\n        self.diffi_text.setObjectName(\"diffi_text\")\n        self.splitter_4 = QtWidgets.QSplitter(self.splitter_5)\n        self.splitter_4.setOrientation(QtCore.Qt.Vertical)\n        self.splitter_4.setObjectName(\"splitter_4\")\n        self.label_2 = QtWidgets.QLabel(self.splitter_4)\n        self.label_2.setObjectName(\"label_2\")\n        self.splitter = QtWidgets.QSplitter(self.splitter_4)\n        self.splitter.setOrientation(QtCore.Qt.Vertical)\n        self.splitter.setObjectName(\"splitter\")\n        self.layoutWidget = QtWidgets.QWidget(self.splitter)\n        self.layoutWidget.setObjectName(\"layoutWidget\")\n        self.gridLayout = QtWidgets.QGridLayout(self.layoutWidget)\n        self.gridLayout.setContentsMargins(0, 0, 0, 0)\n        self.gridLayout.setObjectName(\"gridLayout\")\n        self.diffi_param_xa = QtWidgets.QLineEdit(self.layoutWidget)\n        self.diffi_param_xa.setObjectName(\"diffi_param_xa\")\n        self.gridLayout.addWidget(self.diffi_param_xa, 0, 1, 1, 1)\n        self.diffi_param_Rb = QtWidgets.QLineEdit(self.layoutWidget)\n        self.diffi_param_Rb.setObjectName(\"diffi_param_Rb\")\n        self.gridLayout.addWidget(self.diffi_param_Rb, 1, 0, 1, 1)\n        self.diffi_param_xb = QtWidgets.QLineEdit(self.layoutWidget)\n        self.diffi_param_xb.setObjectName(\"diffi_param_xb\")\n        self.gridLayout.addWidget(self.diffi_param_xb, 1, 1, 1, 1)\n        self.diffi_param_Ra = QtWidgets.QLineEdit(self.layoutWidget)\n        self.diffi_param_Ra.setText(\"\")\n        self.diffi_param_Ra.setObjectName(\"diffi_param_Ra\")\n        self.gridLayout.addWidget(self.diffi_param_Ra, 0, 0, 1, 1)\n        self.diffi_param_text = QtWidgets.QTextBrowser(self.splitter)\n        self.diffi_param_text.setObjectName(\"diffi_param_text\")\n        self.splitter_10 = QtWidgets.QSplitter(self.splitter_11)\n        self.splitter_10.setOrientation(QtCore.Qt.Horizontal)\n        self.splitter_10.setObjectName(\"splitter_10\")\n        self.splitter_9 = QtWidgets.QSplitter(self.splitter_10)\n        self.splitter_9.setOrientation(QtCore.Qt.Vertical)\n        self.splitter_9.setObjectName(\"splitter_9\")\n        self.label_3 = QtWidgets.QLabel(self.splitter_9)\n        self.label_3.setObjectName(\"label_3\")\n        self.splitter_6 = QtWidgets.QSplitter(self.splitter_9)\n        self.splitter_6.setOrientation(QtCore.Qt.Vertical)\n        self.splitter_6.setObjectName(\"splitter_6\")\n        self.layoutWidget1 = QtWidgets.QWidget(self.splitter_6)\n        self.layoutWidget1.setObjectName(\"layoutWidget1\")\n        self.horizontalLayout = QtWidgets.QHBoxLayout(self.layoutWidget1)\n        self.horizontalLayout.setContentsMargins(0, 0, 0, 0)\n        self.horizontalLayout.setObjectName(\"horizontalLayout\")\n        self.el_diffi_a = QtWidgets.QLineEdit(self.layoutWidget1)\n        self.el_diffi_a.setObjectName(\"el_diffi_a\")\n        self.horizontalLayout.addWidget(self.el_diffi_a)\n        self.el_diffi_b = QtWidgets.QLineEdit(self.layoutWidget1)\n        self.el_diffi_b.setObjectName(\"el_diffi_b\")\n        self.horizontalLayout.addWidget(self.el_diffi_b)\n        self.el_diffi_text = QtWidgets.QTextBrowser(self.splitter_6)\n        self.el_diffi_text.setObjectName(\"el_diffi_text\")\n        self.splitter_8 = QtWidgets.QSplitter(self.splitter_10)\n        self.splitter_8.setOrientation(QtCore.Qt.Vertical)\n        self.splitter_8.setObjectName(\"splitter_8\")\n        self.label_4 = QtWidgets.QLabel(self.splitter_8)\n        self.label_4.setObjectName(\"label_4\")\n        self.splitter_7 = QtWidgets.QSplitter(self.splitter_8)\n        self.splitter_7.setOrientation(QtCore.Qt.Vertical)\n        self.splitter_7.setObjectName(\"splitter_7\")\n        self.layoutWidget2 = QtWidgets.QWidget(self.splitter_7)\n        self.layoutWidget2.setObjectName(\"layoutWidget2\")\n        self.horizontalLayout_3 = QtWidgets.QHBoxLayout(self.layoutWidget2)\n        self.horizontalLayout_3.setContentsMargins(0, 0, 0, 0)\n        self.horizontalLayout_3.setObjectName(\"horizontalLayout_3\")\n        self.el_diffi_para_a = QtWidgets.QLineEdit(self.layoutWidget2)\n        self.el_diffi_para_a.setObjectName(\"el_diffi_para_a\")\n        self.horizontalLayout_3.addWidget(self.el_diffi_para_a)\n        self.el_diffi_param_B = QtWidgets.QLineEdit(self.layoutWidget2)\n        self.el_diffi_param_B.setObjectName(\"el_diffi_param_B\")\n        self.horizontalLayout_3.addWidget(self.el_diffi_param_B)\n        self.el_diffi_param_R = QtWidgets.QLineEdit(self.layoutWidget2)\n        self.el_diffi_param_R.setObjectName(\"el_diffi_param_R\")\n        self.horizontalLayout_3.addWidget(self.el_diffi_param_R)\n        self.el_diffi_param_text = QtWidgets.QTextBrowser(self.splitter_7)\n        self.el_diffi_param_text.setObjectName(\"el_diffi_param_text\")\n        DiffiHelman.setCentralWidget(self.centralwidget)\n        self.menubar = QtWidgets.QMenuBar(DiffiHelman)\n        self.menubar.setGeometry(QtCore.QRect(0, 0, 988, 25))\n        self.menubar.setObjectName(\"menubar\")\n        DiffiHelman.setMenuBar(self.menubar)\n        self.statusbar = QtWidgets.QStatusBar(DiffiHelman)\n        self.statusbar.setObjectName(\"statusbar\")\n        DiffiHelman.setStatusBar(self.statusbar)\n\n        self.retranslateUi(DiffiHelman)\n        QtCore.QMetaObject.connectSlotsByName(DiffiHelman)\n\n    def diffi(self, g):\n        start_time = time.time()\n        diffi_object = Diffi(g)\n        a, b, p, g, A, B, Ka, Kb = diffi_object.calc()\n        end_time = (time.time() - start_time)*1000\n        diffi_text = self.diffi_text.textCursor()\n        diffi_text.insertHtml(f'''<br><b>a:</b>{a}\n            <br><b>b: </b>{b}\n            <br><b>p: </b>{p}\n            <br><b>g: </b>{g}\n            <br><b>A: </b>{A}\n            <br><b>B: </b>{B}\n            <br><b>Ka: </b>{Ka}\n            <br><b>Kb: </b>{Kb}\n            <br><b>vaqt: </b>{round(end_time, 3)}<br>\n            <br>''')\n\n    def diffi_param(self, Ra, Rb, xa, xb):\n        start_time = time.time()\n        diffi_object = diffiParam()\n        Ra, Rb, xa, xb, Ra_mult_a, Rb_mult_a, ya, yb, Ra_minus_1_mult_yb, Rb_minus_1_mult_ya, Rab, ka, kb, Ka, Ka, p = diffi_object.calc(\n            Ra, Rb, xa, xb)\n        end_time = (time.time() - start_time)*1000\n        diffi_text = self.diffi_param_text.textCursor()\n        diffi_text.insertHtml(f'''<br><b>Ra:</b>{Ra}\n            <br><b>Rb: </b>{Rb}\n            <br><b>xa: </b>{xa}\n            <br><b>xb: </b>{xb}\n            <br><b>Ra*a: </b>{Ra_mult_a}\n            <br><b>Rb*a: </b>{Rb_mult_a}\n            <br><b>ya: </b>{ya}\n            <br><b>yb: </b>{yb}\n            <br><b>Ra-1*yb: </b>{Ra_minus_1_mult_yb}\n            <br><b>Rb-1*ya: </b>{Rb_minus_1_mult_ya}\n            <br><b>Rab: </b>{Rab}\n            <br><b>ka: </b>{ka}\n            <br><b>kb: </b>{kb}\n            <br><b>Ka: </b>{Ka}\n            <br><b>Ka: </b>{Ka}\n            <br><b>p: </b>{p}\n            <br><b>vaqt: </b>{round(end_time, 3)}<br>\n            <br>''')\n\n    def el_diffi_param(self, a, B, R):\n        start_time = time.time()\n        diffi_object = EllipDiffiParam(a, B, R)\n        N, primes, p, a, b, G, n, h, Na, Nb, pa, pb, Pa, Pb, ka, kb, Ka, Kb = diffi_object.calc()\n        end_time = (time.time() - start_time)*1000\n        diffi_text = self.el_diffi_param_text.textCursor()\n        diffi_text.insertHtml(f'''<br><b>N:</b>{N}\n            <br><b>Tub bo'luvchilari: </b>{primes}\n            <br><b>p: </b>{p}\n            <br><b>a: </b>{a}\n            <br><b>b: </b>{b}\n            <br><b>G: </b>{G}\n            <br><b>n: </b>{n}\n            <br><b>h: </b>{h}\n            <br><b>Na: </b>{Na}\n            <br><b>Nb: </b>{Nb}\n            <br><b>pa: </b>{pa}\n            <br><b>pb: </b>{pb}\n            <br><b>Pa: </b>{Pa}\n            <br><b>Pb: </b>{Pb}\n            <br><b>ka: </b>{ka}\n            <br><b>kb: </b>{kb}\n            <br><b>Ka: </b>{Ka}\n            <br><b>Kb: </b>{Kb}\n            <br><b>vaqt: </b>{round(end_time, 3)}<br>\n            <br>''')\n\n    def el_diffi(self, a, b):\n        start_time = time.time()\n        diffi_object = EllipDiffi(a, b)\n        N, prim, all_points, G, p, a, b, G, h, a_maxfiy, b_maxfiy, A_nuqta, B_nuqta, K_a, K_b = diffi_object.calc()\n        end_time = (time.time() - start_time)*1000\n        diffi_text = self.el_diffi_text.textCursor()\n        diffi_text.insertHtml(f'''<br><b>N:</b>{N}\n            <br><b>Tub bo'luvchilari: </b>{prim}\n            <br><b>all_points: </b>{all_points}\n            <br><b>G: </b>{G}\n            <br><b>p: </b>{p}\n            <br><b>a: </b>{a}\n            <br><b>b: </b>{b}\n            <br><b>G: </b>{G}\n            <br><b>h: </b>{h}\n            <br><b>a_maxfiy: </b>{a_maxfiy}\n            <br><b>b_maxfiy: </b>{b_maxfiy}\n            <br><b>A_nuqta: </b>{A_nuqta}\n            <br><b>B_nuqta: </b>{B_nuqta}\n            <br><b>K_a: </b>{K_a}\n            <br><b>K_b: </b>{K_b}\n            <br><b>vaqt: </b>{round(end_time, 3)}<br>\n            <br>''')\n\n    def calc(self):\n        if len(self.diff_g.text())!=0:\n            g = int(self.diff_g.text())\n            self.diffi(g)\n\n\n        if len(self.diffi_param_Ra.text())!=0:\n            Ra = int(self.diffi_param_Ra.text())\n            Rb = int(self.diffi_param_Rb.text())\n            xa = int(self.diffi_param_xa.text())\n            xb = int(self.diffi_param_xb.text())\n            self.diffi_param(Ra, Rb, xa, xb)\n\n\n        if len(self.el_diffi_para_a.text())!=0:\n            a = int(self.el_diffi_para_a.text())\n            B = int(self.el_diffi_param_B.text())\n            R = int(self.el_diffi_param_R.text())\n            self.el_diffi_param(a, B, R)\n\n\n        if len(self.el_diffi_a.text())!=0:\n            a = int(self.el_diffi_a.text())\n            b = int(self.el_diffi_b.text())\n            self.el_diffi(a, b)\n\n\n    def retranslateUi(self, DiffiHelman):\n        _translate = QtCore.QCoreApplication.translate\n        DiffiHelman.setWindowTitle(_translate(\"DiffiHelman\", \"Diffi Helman\"))\n        self.label.setText(_translate(\"DiffiHelman\", \"Diffi Helman\"))\n        self.diff_g.setPlaceholderText(_translate(\"DiffiHelman\", \"g\"))\n        self.pushButton.setText(_translate(\"DiffiHelman\", \"Hisoblash\"))\n        self.diffi_text.setWhatsThis(_translate(\n            \"DiffiHelman\", \"<html><head/><body><p>dsadasd</p></body></html>\"))\n        self.diffi_text.setPlaceholderText(_translate(\"DiffiHelman\", \"Natija\"))\n        self.label_2.setText(_translate(\"DiffiHelman\", \"Diffi Helman(param)\"))\n        self.diffi_param_xa.setPlaceholderText(_translate(\"DiffiHelman\", \"xa\"))\n        self.diffi_param_Rb.setPlaceholderText(_translate(\"DiffiHelman\", \"Rb\"))\n        self.diffi_param_xb.setPlaceholderText(_translate(\"DiffiHelman\", \"xb\"))\n        self.diffi_param_Ra.setPlaceholderText(_translate(\"DiffiHelman\", \"Ra\"))\n        self.diffi_param_text.setPlaceholderText(\n            _translate(\"DiffiHelman\", \"Natija\"))\n        self.label_3.setText(_translate(\n            \"DiffiHelman\", \"Elliptic Diffi Helman\"))\n        self.el_diffi_a.setPlaceholderText(_translate(\"DiffiHelman\", \"a\"))\n        self.el_diffi_b.setPlaceholderText(_translate(\"DiffiHelman\", \"b\"))\n        self.el_diffi_text.setPlaceholderText(\n            _translate(\"DiffiHelman\", \"Natija\"))\n        self.label_4.setText(_translate(\n            \"DiffiHelman\", \"Elliptic Diffi Helman(Param)\"))\n        self.el_diffi_para_a.setPlaceholderText(_translate(\"DiffiHelman\", \"a\"))\n        self.el_diffi_param_B.setPlaceholderText(\n            _translate(\"DiffiHelman\", \"B\"))\n        self.el_diffi_param_R.setPlaceholderText(\n            _translate(\"DiffiHelman\", \"R\"))\n        self.el_diffi_param_text.setPlaceholderText(\n            _translate(\"DiffiHelman\", \"Natija\"))\n\n\nif __name__ == \"__main__\":\n    import sys\n    app = QtWidgets.QApplication(sys.argv)\n    DiffiHelman = QtWidgets.QMainWindow()\n    ui = Ui_DiffiHelman()\n    ui.setupUi(DiffiHelman)\n    DiffiHelman.show()\n    sys.exit(app.exec_())\n","sub_path":"v2/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":13908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"389044028","text":"#! /usr/bin/python2\n#my first python3 script from scratch.  I know it's probably simple\n#and could be much neater, but it's a first effort\n#script for programming the ZachTech LP1 WSPR beacon\n#written by Kevin Loughin, KB9RLW, March 2019\n#version 1.1  Mac serial port detection added\n\nimport time\nimport os.path\nimport serial\n\n#defining variables I'll be using later\ncallsign = \"null\"\nmenuentry = \"\"\nserialport = \"null\"\nbands = [\"D\", \"D\", \"D\", \"D\", \"D\", \"D\", \"D\", \"D\", \"D\", \"D\", \"D\", \"D\", \"D\", \"D\"]\n\n#defining functions I'll call later.\n\n#serial detection procedure\ndef setserial():\n    clearscreen()\n    print(\"Plug in the device, wait 1 second and press enter.\")\n    os.sys.stdin.readline()\n    time.sleep(2)\n    exists = os.path.exists('/dev/ttyUSB0')\n    if exists:\n        print(\"Found ttyUSB0\")\n        time.sleep(1)\n        ser = serial.Serial('/dev/ttyUSB0')\n        s = ser.read(5)\n        ser.close()\n        if s == \"{MIN}\":\n            print(\"Success!  Port is ttyUSB0\")\n            serialport = \"/dev/ttyUSB0\"\n            return serialport\n\n    exists = os.path.exists('/dev/ttyUSB1')\n    if exists:\n        print(\"Found ttyUSB1\")\n        time.sleep(1)\n        ser = serial.Serial('/dev/ttyUSB1')\n        s = ser.read(5)\n        ser.close()\n        if s == \"{MIN}\":\n            print(\"Success!  Port is ttyUSB1\")\n            serialport = \"/dev/ttyUSB1\"\n            return serialport\n\n    exists = os.path.exists('/dev/ttyUSB2')\n    if exists:\n        print(\"Found ttyUSB2\")\n        time.sleep(1)\n        ser = serial.Serial('/dev/ttyUSB2')\n        s = ser.read(5)\n        ser.close()\n        if s == \"{MIN}\":\n            print(\"Success!  Port is ttyUSB2\")\n            serialport = \"/dev/ttyUSB2\"\n            return serialport\n\n    exists = os.path.exists('/dev/ttyUSB3')\n    if exists:\n        print(\"Found ttyUSB3\")\n        ser = serial.Serial('/dev/ttyUSB3')\n        s = ser.read(5)\n        ser.close()\n        if s == \"{MIN}\":\n            print(\"Success!  Port is ttyUSB3\")\n            serialport = \"/dev/ttyUSB3\"\n            return serialport\n\n    exists = os.path.exists('/dev/tty.usbserial-1410')\n    if exists:\n        print(\"Found tty.usbserial-1410\")\n        ser = serial.Serial('/dev/ttyusbserial-1410')\n        s = ser.read(5)\n        ser.close()\n        if s == \"{MIN}\":\n            print(\"Success!  Port is tty.usbserial-1410\")\n            serialport = \"/dev/tty.usbserial-1410\"\n            return serialport\n\n    exists = os.path.exists('/dev/tty.usbserial-1420')\n    if exists:\n        print(\"Found tty.usbserial-1420\")\n        ser = serial.Serial('/dev/ttyusbserial-1420')\n        s = ser.read(5)\n        ser.close()\n        if s == \"{MIN}\":\n            print(\"Success!  Port is tty.usbserial-1420\")\n            serialport = \"/dev/tty.usbserial-1420\"\n            return serialport\n\n    exists = os.path.exists('/dev/tty.usbserial-1430')\n    if exists:\n        print(\"Found tty.usbserial-1410\")\n        ser = serial.Serial('/dev/ttyusbserial-1430')\n        s = ser.read(5)\n        ser.close()\n        if s == \"{MIN}\":\n            print(\"Success!  Port is tty.usbserial-1430\")\n            serialport = \"/dev/tty.usbserial-1430\"\n            return serialport\n\n\n    time.sleep(2)    \n    serialport = \"null\"\n    return serialport\n\n#check serial security function\ndef checksec(serialport):\n    print(\"checking security of\"), serialport\n    time.sleep(2)\n    security = os.access(serialport, os.R_OK|os.W_OK)\n    if not security:\n        clearscreen()\n        print(\"Your username does not have access to serial ports.\")\n        print(\"If you're on Linux, You need to add your user to the dialout group.\")\n        print(\"Copy the line below, change your_username to your username and execute it in the terminal.\")\n        print(\"Then log out or restart and log back in to activate access.\")\n        print(\"\")\n        print(\"sudo adduser your_username dialout\")\n        exit()\n    return security\n\n#Print Menu and get choice function\ndef render_menu(menunum):\n    if menunum == \"main\":\n        clearscreen()\n        printmain()\n    menunum = input(\"Enter Menu item:\")\n    return menunum\n\n#Clear Screen procedure\ndef clearscreen():\n    count = 28\n    while(count > 1):\n        print(\"\")\n        count = count - 1\n\n#Print main menu procedure\ndef printmain():\n    print(\"Main Menu.  Device detected on \"), serialport\n    print(\"\")\n    print(\"0: exit program\")\n    print(\"1: Change current callsign.  >\"), callsign\n    print(\"2: Enable or disable bands\")\n    print(\"3: Change current live mode, wspr or sig gen.\")\n    print(\"4: Set startup mode\")\n\n#changecall procedure\ndef changecall(callsign):\n    clearscreen()\n    print(\"Current callsign:\"), callsign\n    print(\"\")\n    newcallsign = raw_input(\"Enter new callsign, use upper case:\")\n    newcallsign = newcallsign.rstrip()\n    clearscreen()\n    choice = \"Change callsign from \" + callsign + \" to \" + newcallsign + \"? y or n:\"\n    choice = raw_input(choice)\n    if choice == \"y\":\n        print(\"Updating device with new callsign:\")\n        # open serial and flush junk\n        serdata = \"null\"\n        ser = serial.Serial(serialport)\n        time.sleep(.5)\n        ser.flushInput()\n        ser.write(\"[CCM] S N\\r\\n\")\n        time.sleep(.1)\n        while serdata != \"{CCM} N\\r\\n\":        \n            serdata = ser.readline()\n            ser.write(\"[CCM] S N\\r\\n\")\n            time.sleep(.1)\n        # sending new callsign to device\n        serout = \"[DCS] S \" + newcallsign + \"\\r\\n\"\n        while serdata[:5] != \"{DCS}\":        \n            ser.write(serout)\n            time.sleep(.1)\n            ser.write(\"[DCS] G\\r\\n\")\n            time.sleep(.1)\n            serdata = ser.readline()\n            print(serdata)\n        ser.write(\"[CSE] S\\r\\n\")\n        time.sleep(.1)\n        ser.close()\n        callsign = getcallsign()\n        return callsign\n\n#changebands procedure\ndef changebands():\n    #open port and flush unneeded stuff\n    serdata = \"null\"\n    ser = serial.Serial(serialport)\n    time.sleep(.5)\n    ser.flushInput()\n    ser.write(\"[CCM] S N\\r\\n\")\n    time.sleep(.1)\n    while serdata != \"{CCM} N\\r\\n\":        \n        serdata = ser.readline()\n        ser.write(\"[CCM] S N\\r\\n\")\n        time.sleep(.1)\n    #Get current band status\n    serdata = \"null\"\n    while serdata[:5] != \"{OBD}\":\n        ser.write(\"[OBD] G\\r\\n\")\n        time.sleep(.1)\n        serdata = ser.readline()\n    count = 1\n    while count < 14:        \n        bands[count] = serdata[9:].rstrip()\n        serdata = ser.readline()\n        count = count + 1\n    ser.close()\n    printbands()\n    togglebands()\n    clearscreen()\n    #open port and flush unneeded stuff\n    serdata = \"null\"\n    ser = serial.Serial(serialport)\n    time.sleep(.5)\n    ser.flushInput()\n    ser.write(\"[CCM] S N\\r\\n\")\n    time.sleep(.1)\n    while serdata != \"{CCM} N\\r\\n\":        \n        serdata = ser.readline()\n        ser.write(\"[CCM] S N\\r\\n\")\n        time.sleep(.1)\n    print(\"Updating device with new configuration.\")\n    count = 1\n    while count < 14:\n        bcount = count - 1\n        if bcount < 10:        \n            serdata = \"[OBD] S \" + \"0\" + str(bcount) + \" \" + str(bands[count]) + \"\\r\\n\"\n        else:serdata = \"[OBD] S \" + str(bcount) + \" \" + str(bands[count]) + \"\\r\\n\"\n        ser.write(serdata)\n        time.sleep(.1)\n        count = count + 1\n    ser.write(\"[CSE] S\\r\\n\")\n    time.sleep(.5)\n    ser.close()\n    \n    time.sleep(2)\n#printbands procedure\ndef printbands():\n    clearscreen()\n    print(\"Current bands Enabled or Disabled\")\n    print(\"\")\n    print(\"1:  2190m--->\"), bands[1]\n    print(\"2:  630m---->\"), bands[2]\n    print(\"3:  160m---->\"), bands[3]\n    print(\"4:  80m----->\"), bands[4]\n    print(\"5:  40m----->\"), bands[5]\n    print(\"6:  30m----->\"), bands[6]\n    print(\"7:  20m----->\"), bands[7]\n    print(\"8:  17m----->\"), bands[8]\n    print(\"9:  15m----->\"), bands[9]\n    print(\"10: 12m----->\"), bands[10]\n    print(\"11: 10m----->\"), bands[11]\n    print(\"12: 6m------>\"), bands[12]\n    print(\"13: 4m------>\"), bands[13]\n\n#togglebands procedure\ndef togglebands():\n    bandnum = 1\n    while bandnum != 0:\n        printbands()\n        bandnum = input(\"Enter band number to toggle, zero when done:\")\n        if bands[bandnum] == \"D\":\n            bands[bandnum] = \"E\"\n        else:bands[bandnum] = \"D\"\n\n \n#changemode procedure\ndef changemode():\n    clearscreen()\n    print(\"Here we can set a current live mode.\")\n    print(\"Setting the device to none.\")\n    #open port and flush unneeded stuff\n    serdata = \"null\"\n    ser = serial.Serial(serialport)\n    time.sleep(.5)\n    ser.flushInput()\n    ser.write(\"[CCM] S N\\r\\n\")\n    time.sleep(.1)\n    while serdata != \"{CCM} N\\r\\n\":        \n        serdata = ser.readline()\n        ser.write(\"[CCM] S N\\r\\n\")\n        time.sleep(.1)\n    print(\"\")\n    print(\"Mode choices are:\")\n    print(\"\")\n    print(\"1: WSPR beacon on\")\n    print(\"2: Signal generator mode\")\n    print(\"3: Abort back to main menu\")\n    print(\"\")\n    mode = input(\"Mode choice? :\")\n    #wspr mode choice\n    if mode == 1:            \n        ser.write(\"[CCM] S W\\r\\n\")\n        time.sleep(.1)\n        serdata = ser.readline()\n        print(\"WSPR mode on, press enter to stop.\")\n        os.sys.stdin.readline()\n        ser.write(\"[CCM] N\\r\\n\")\n        ser.close\n\n    #siggen mode choice\n    if mode == 2:\n        clearscreen()\n        print(\"Signal generator mode.\")\n        frequency = input(\"Enter frequency in Hertz:\")\n        rawfrequency = frequency * 100\n        length = len(str(rawfrequency))\n        padlen = 12 - length\n        pad = \"\"\n        while padlen > 1:\n            pad = pad + \"0\"\n            padlen = padlen -1\n        outfrequency = pad + str(rawfrequency) \n        print(\"\")\n        print(\"Setting generator to \"), frequency, \"Hz\"\n        #open port and flush unneeded stuff\n        serdata = \"null\"\n        ser = serial.Serial(serialport)\n        time.sleep(.5)\n        ser.flushInput()\n        ser.write(\"[CCM] S N\\r\\n\")\n        time.sleep(.1)\n        while serdata != \"{CCM} N\\r\\n\":        \n            serdata = ser.readline()\n            ser.write(\"[CCM] S N\\r\\n\")\n            time.sleep(.1)    \n        #set generator on with frequency\n        serdata = \"null\"    \n        outstring = \"[DGF] S \" + str(outfrequency) + \"\\r\\n\"            \n        ser.write(outstring)\n        time.sleep(.1)\n        ser.write(\"[CCM] S S\\r\\n\")\n        time.sleep(.1)\n        print(\"Signal generator ON. Hit enter to abort.\")\n        os.sys.stdin.readline()\n        while serdata != \"{CCM} N\\r\\n\":        \n            serdata = ser.readline()\n            ser.write(\"[CCM] S N\\r\\n\")\n            time.sleep(.1)\n\n    time.sleep(2)\n\n#changestartup procedure\ndef changestartup():\n    #open port and flush unneeded stuff\n    serdata = \"null\"\n    ser = serial.Serial(serialport)\n    time.sleep(.5)\n    ser.flushInput()\n    ser.write(\"[CCM] S N\\r\\n\")\n    time.sleep(.1)\n    while serdata != \"{CCM} N\\r\\n\":        \n        serdata = ser.readline()\n        ser.write(\"[CCM] S N\\r\\n\")\n        time.sleep(.1)\n    clearscreen()\n    print(\"Set the power up default mode, WSPR, or None.\")\n    print(\"\")\n    print(\"1: WSPR\")\n    print(\"2: None\")\n    print(\"\")\n    choice = input(\"Choose power up mode:\")\n    if choice == 1:\n        ser.write(\"[OSM] S W\\r\\n\")\n        time.sleep(.2)\n        ser.write(\"[CSE] S\\r\\n\")\n        time.sleep(.2)\n    else:\n        ser.write(\"[OSM] S N\\r\\n\")\n        time.sleep(.2)\n        ser.write(\"[CSE] S\\r\\n\")\n    ser.close\n\n    time.sleep(2)\n\n#get current callsign function\ndef getcallsign():\n    serdata = \"null\"\n    ser = serial.Serial(serialport)\n    time.sleep(.5)\n    ser.flushInput()\n    ser.write(\"[CCM] S N\\r\\n\")\n    time.sleep(.1)\n    while serdata != \"{CCM} N\\r\\n\":\n        serdata = ser.readline()\n        print(serdata)\n        ser.write(\"[CCM] S N\\r\\n\")\n        time.sleep(.1)\n    while serdata.startswith(\"{DCS}\") != True:\n        ser.write(\"[DCS] G\\r\\n\")\n        time.sleep(.1)\n        serdata = ser.readline()\n    callsign = serdata[6:].rstrip()\n    ser.close()\n    return callsign\n\n#Main Program\n\n#get serial port\nserialport = setserial()\n\n#check users security for port\n\nsecurity = checksec(serialport)\n\n\n#get current callsign from device\ncallsign = getcallsign()\n\n#Clear screen and get first menu input\n\nwhile(menuentry != 0):\n    menunum = \"main\"\n    menuentry = render_menu(menunum)    \n    if menuentry == 1:\n        menuentry = 99        \n        callsign = changecall(callsign)\n    if menuentry == 2:\n        menuentry = 99\n        print(\"Standby while I read the current band settings.\")\n        changebands()\n    if menuentry == 3:\n        menuentry = 99\n        changemode()\n    if menuentry == 4:\n        menuentry = 99\n        changestartup()\n    if menuentry != 0 and menuentry != 99:\n        print(\"\")\n        print(\"Bad entry, I don't understand \"), menuentry\n        time.sleep(2)\n\nprint(\"User selected:\", menuentry)\n#end\n","sub_path":"wsprbeacon1-1.py","file_name":"wsprbeacon1-1.py","file_ext":"py","file_size_in_byte":12839,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"75112991","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# Initial Code from https://github.com/gbraux/Cisco-Showroom-RoomControl by Guillaume BRAUX\n# This tweak of the original code created by Keller McBride 07/20/2017   kelmcbri@cisco.com\n#\n# Purpose:\n#\tIf more Video input sources are needed on a Cisco Video Codec, this program allows an\n#\texternal video switcher to be coupled to a port on the Cisco Video Codec.  The external\n#\tvideo switcher is controlled by the Cisco Touch 10 tablet interface.\n#\n# Files:\n#\tRoomControlHandler.py \t\t- Main Program.\n#\t\t\t\t\t\tLaunches Threaded Web Server\n#\t\t\t\t\t\tSets up new Video Sources on Cisco Video Codec\n#\t\t\t\t\t\tRegisters for Feedback on Inputs from Cisco Video Codec\n#\t\t\t\t\t\t\tRe-registers for feedback periodically\n#\tConfig.py\t  \t\t- Configuration Paramters\n#\tCondecControl.py\t\t- Send and Receive commands for Video Codec\n#\tExternalHDMISwitchControl.py\t- Send commands for HDMI Video Switcher\n#\t\tclass ESDSControl\t- Serial Controller for E-SDS brand 3 port HDMI video switcher\n#\n# Components:\n#\tE-SDS UHD 4K@60Hz HDMI Switch,3x1 HDMI Powered Switch with IR Wireless Remote, HDMI 2.0, HDCP 2.2, and RS232\n#\t\thttps://www.amazon.com/gp/product/B01G53WV20/ref=oh_aui_detailpage_o08_s00?ie=UTF8&psc=1\n# \tCisco SX series Video Codec\n#\tController - i.e. Server Running this python3 program - Macbook or Raspbery Pi or Cisco IR829, etc. \n#\nimport http.server\nimport http.client\nimport socketserver\nimport base64\nimport re\t\t\t\t# Used for Pattern Matching regular expressions\n#import sqlite3\nimport datetime\nimport urllib.parse\nimport json\nimport threading\nimport time\nimport sys\nimport signal\nimport logging\nimport cgi\nimport urllib.request\n#import xml.etree.ElementTree as ET\n\n# Librairies\nimport Config   \t\t\t# Use this file to set IP Addresses of components and other parameters\nimport CodecControl \t\t\t# CodecControl sends/receives data via http to Cisco Codecs running CE code\nimport ExternalHDMISwitchControl\t# Create a class in this file for each type of external video switch you want to control\n\n#inputValue = 0\n#outputValue = 1\n#presetValue = 4\n#inputNames = [\"AppleTV\",\"Spycam\",\"CableTV\"]\n#outputNames = [\"\",\"TVDroite\",\"TVCentre\",\"TVGauche\",\"MX300\",\"MX800\",\"Inexistant\",\"Inexistant\",\"Spycam\"]\n\ncontrollerIP = Config.controller['ip']\ncontrollerPort = Config.controller['port']\n\nglobal debug\ndebug = Config.debugMode\n\ndef startThread():\n\t\n\tif Config.debugMode == 'True' :\n\t\tprint(\"--- Trying to Start Web Server  (\"+controllerIP+\":\"+str(controllerPort)+\") ---\")\n\tserver = ThreadedHTTPServer(('0.0.0.0', controllerPort), MyRequestHandler)\n\tprint('Starting server, use <Ctrl-C> to stop')\n\t\n\tthread = threading.Thread(target = server.serve_forever)\n\tthread.deamon = False\n\tthread.start()\n\t\n\tif Config.debugMode == 'True' :\n\t\tprint(\"---  New Web Service started at (\"+controllerIP+\":\"+str(controllerPort)+\") ---\")\n\treturn server\n\nclass ThreadedHTTPServer(socketserver.ThreadingMixIn, http.server.HTTPServer):\n    \"\"\"Handle requests in a separate thread.\"\"\"\n\nclass MyRequestHandler(http.server.SimpleHTTPRequestHandler):\n\t\n\tdef do_POST(self):\n\t\n\t\tprint(\"POST RECEIVED\")\n\t\t\n\t\tself.send_response(200)\n\t\tself.send_header('Access-Control-Allow-Origin', '*')\n\t\tself.end_headers()\n\t\tcontent_len = int(self.headers.get(\"Content-Length\"))\n\t\tpost_body = self.rfile.read(content_len).decode(\"utf-8\")\n\t\tif Config.debugMode == 'True' :\n\t\t\tprint(post_body)\n\t\t\n\t\tCodecEventHandler(post_body)\n\t\t\n\tdef log_message(self, format, *args):\n\t\tsys.stdout.write(\"%s --> [%s] %s\\n\" % (self.address_string(), self.log_date_time_string(), format%args))\n\ndef CodecEventHandler(xmlData):\n\n\tglobal outputValue\t\n\tglobal inputValue\n\tglobal presetValue\n\tglobal proxixi\n\tglobal codecs\n\n\tprint(\"---- Handling Codec Event ----\")\n\tprint(\"XML RECU\")\n\tif Config.debugMode == 'True' :\n\t\tprint(xmlData)\n\t\t\n\tif ((Config.codecs[\"grandOleOpry\"][\"mac\"] in xmlData) and (\"AppleTV\"  in xmlData)):\n\t\tprint(\"Apple TV Selected\")\n\t\tmyThreadObject = ExternalHDMISwitchControl.ESDSControl(message=1)\n\t\tmyThreadObject.start()\n\t\tmyThreadObject.join()\n\n\tif ((Config.codecs[\"grandOleOpry\"][\"mac\"] in xmlData) and (\"CableTV\"  in xmlData)):\n\t\tprint(\"Cable TV Selected\")\n\t\tmyThreadObject = ExternalHDMISwitchControl.ESDSControl(message=2)\n\t\tmyThreadObject.start()\n\t\tmyThreadObject.join()\n\n\tif ((Config.codecs[\"grandOleOpry\"][\"mac\"] in xmlData)  and (\"SpyCam\"  in xmlData)):\n\t\tprint(\"SpyCam Selected\")\t\t\n\t\tmyThreadObject = ExternalHDMISwitchControl.ESDSControl(message=3)\n\t\tmyThreadObject.start()\n\t\tmyThreadObject.join()\n\ndef RemoveCodecsPresentationSource():\n\tpresentationSourcegrandOleOpry = (\n\t\t\"<Command>\"\n\t\t\t\"<UserInterface>\"\n\t\t\t\t\"<Presentation>\"\n\t\t\t\t\t\"<ExternalSource>\"\n\t\t\t\t\t\t\"<RemoveAll command=\\\"True\\\">\"\n\t\t\t\t\t\t\"</RemoveAll>\"\n\t\t\t\t\t\"</ExternalSource>\"\n\t\t\t\t\"</Presentation>\"\n\t\t\t\"</UserInterface>\"\n\t\t\"</Command>\")\n\n\tif Config.debugMode == 'True' :\n\t\tprint(\"Sending new input source info to codec \\r\\n\")\n\tCodecControl.SendXMLDataToCodec(Config.codecs[\"grandOleOpry\"], presentationSourcegrandOleOpry)\n\ndef SendCodecsPresentationSource():\n\tpresentationSourcegrandOleOpry = (\n\t\t\"<Command>\"\n\t\t\t\"<UserInterface>\"\n\t\t\t\t\"<Presentation>\"\n\t\t\t\t\t\"<ExternalSource>\"\n\t\t\t\t\t\t\"<Add command=\\\"True\\\">\"\n\t\t\t\t\t\t\t\"<ConnectorId>3</ConnectorId>\"\n\t\t\t\t\t\t\t\"<Name>AppleTV</Name>\"\n\t\t\t\t\t\t\t\"<SourceIdentifier>AppleTV</SourceIdentifier>\"\n\t\t\t\t\t\t\t\"<Type>mediaplayer</Type>\"\n\t\t\t\t\t\t\"</Add>\"\n\t\t\t\t\t\"</ExternalSource>\"\n\t\t\t\t\"</Presentation>\"\n\t\t\t\"</UserInterface>\"\n\t\t\"</Command>\")\n\n\tif Config.debugMode == 'True' :\n\t\tprint(\"Sending new input source info to codec \\r\\n\")\n\tCodecControl.SendXMLDataToCodec(Config.codecs[\"grandOleOpry\"], presentationSourcegrandOleOpry)\n\t\n\tpresentationSourcegrandOleOpry = (\n\t\t\"<Command>\"\n\t\t\t\"<UserInterface>\"\n\t\t\t\t\"<Presentation>\"\n\t\t\t\t\t\"<ExternalSource>\"\n\t\t\t\t\t\t\"<Add command=\\\"True\\\">\"\n\t\t\t\t\t\t\t\"<ConnectorId>3</ConnectorId>\"\n\t\t\t\t\t\t\t\"<Name>SpyCam</Name>\"\n\t\t\t\t\t\t\t\"<SourceIdentifier>SpyCam</SourceIdentifier>\"\n\t\t\t\t\t\t\t\"<Type>camera</Type>\"\n\t\t\t\t\t\t\"</Add>\"\n\t\t\t\t\t\"</ExternalSource>\"\n\t\t\t\t\"</Presentation>\"\n\t\t\t\"</UserInterface>\"\n\t\t\"</Command>\")\n\n\tif Config.debugMode == 'True' :\n\t\tprint(\"Sending new input source info to codec \\r\\n\")\n\tCodecControl.SendXMLDataToCodec(Config.codecs[\"grandOleOpry\"], presentationSourcegrandOleOpry)\n\t\n\tpresentationSourcegrandOleOpry = (\n\t\t\"<Command>\"\n\t\t\t\"<UserInterface>\"\n\t\t\t\t\"<Presentation>\"\n\t\t\t\t\t\"<ExternalSource>\"\n\t\t\t\t\t\t\"<Add command=\\\"True\\\">\"\n\t\t\t\t\t\t\t\"<ConnectorId>3</ConnectorId>\"\n\t\t\t\t\t\t\t\"<Name>CableTV</Name>\"\n\t\t\t\t\t\t\t\"<SourceIdentifier>CableTV</SourceIdentifier>\"\n\t\t\t\t\t\t\t\"<Type>desktop</Type>\"\n\t\t\t\t\t\t\"</Add>\"\n\t\t\t\t\t\"</ExternalSource>\"\n\t\t\t\t\"</Presentation>\"\n\t\t\t\"</UserInterface>\"\n\t\t\"</Command>\")\n\n\tif Config.debugMode == 'True' :\n\t\tprint(\"Sending new input source info to codec \\r\\n\")\n\tCodecControl.SendXMLDataToCodec(Config.codecs[\"grandOleOpry\"], presentationSourcegrandOleOpry)\t\n\ndef SetCodecPresentationSourceStatus():\n\tenablePresentationSourcegrandOleOpry = (\n\t\t\"<Command>\"\n\t\t\t\"<UserInterface>\"\n\t\t\t\t\"<Presentation>\"\n\t\t\t\t\t\"<ExternalSource>\"\n\t\t\t\t\t\t\"<State>\"\n\t\t\t\t\t\t\t\"<Set command=\\\"True\\\">\"\n\t\t\t\t\t\t\t\t\"<State>Ready</State>\"\n\t\t\t\t\t\t\t\t\"<SourceIdentifier>AppleTV</SourceIdentifier>\"\t\t\t\n\t\t\t\t\t\t\t\"</Set>\"\t\n\t\t\t\t\t\t\"</State>\"\n\t\t\t\t\t\"</ExternalSource>\"\n\t\t\t\t\"</Presentation>\"\n\t\t\t\"</UserInterface>\"\n\t\t\"</Command>\")\n\tif Config.debugMode == 'True' :\n\t\tprint(\"Setting new input source Status ENABLE to codec \\r\\n\")\n\tCodecControl.SendXMLDataToCodec(Config.codecs[\"grandOleOpry\"], enablePresentationSourcegrandOleOpry)\t\n\n\tenablePresentationSourcegrandOleOpry = (\n\t\t\"<Command>\"\n\t\t\t\"<UserInterface>\"\n\t\t\t\t\"<Presentation>\"\n\t\t\t\t\t\"<ExternalSource>\"\n\t\t\t\t\t\t\"<State>\"\n\t\t\t\t\t\t\t\"<Set command=\\\"True\\\">\"\n\t\t\t\t\t\t\t\t\"<State>Ready</State>\"\n\t\t\t\t\t\t\t\t\"<SourceIdentifier>SpyCam</SourceIdentifier>\"\t\t\t\n\t\t\t\t\t\t\t\"</Set>\"\t\n\t\t\t\t\t\t\"</State>\"\n\t\t\t\t\t\"</ExternalSource>\"\n\t\t\t\t\"</Presentation>\"\n\t\t\t\"</UserInterface>\"\n\t\t\"</Command>\")\n\tif Config.debugMode == 'True' :\n\t\tprint(\"Setting new input source Status ENABLE to codec \\r\\n\")\n\tCodecControl.SendXMLDataToCodec(Config.codecs[\"grandOleOpry\"], enablePresentationSourcegrandOleOpry)\n\n\tenablePresentationSourcegrandOleOpry = (\n\t\t\"<Command>\"\n\t\t\t\"<UserInterface>\"\n\t\t\t\t\"<Presentation>\"\n\t\t\t\t\t\"<ExternalSource>\"\n\t\t\t\t\t\t\"<State>\"\n\t\t\t\t\t\t\t\"<Set command=\\\"True\\\">\"\n\t\t\t\t\t\t\t\t\"<State>Ready</State>\"\n\t\t\t\t\t\t\t\t\"<SourceIdentifier>CableTV</SourceIdentifier>\"\t\t\t\n\t\t\t\t\t\t\t\"</Set>\"\t\n\t\t\t\t\t\t\"</State>\"\n\t\t\t\t\t\"</ExternalSource>\"\n\t\t\t\t\"</Presentation>\"\n\t\t\t\"</UserInterface>\"\n\t\t\"</Command>\")\n\tif Config.debugMode == 'True' :\n\t\tprint(\"Setting new input source Status ENABLE to codec \\r\\n\")\n\tCodecControl.SendXMLDataToCodec(Config.codecs[\"grandOleOpry\"], enablePresentationSourcegrandOleOpry)\n\ndef SendCodecsFeedbackReg():\n\tfeedbackReggrandOleOpry = (\n\t\t\"<Command>\"\n\t\t\t\"<HttpFeedback>\"\n\t\t\t\t\"<Register command=\\\"True\\\">\"\n\t\t\t\t\t\"<FeedbackSlot>2</FeedbackSlot>\"\n\t\t\t\t\t\"<ServerUrl>http://\"+controllerIP+\":\"+str(controllerPort)+\"</ServerUrl>\"\n\t\t\t\t\t\"<Expression item=\\\"1\\\">/Event/UserInterface/Presentation/ExternalSource</Expression>\"\n\t\t\t\t\"</Register>\"\n\t\t\t\"</HttpFeedback>\"\n\t\t\"</Command>\")\n\t\t\n\tif Config.debugMode == 'True' :\n\t\tprint(\"Requesting Codec HTTPFeedback Registration \\r\\n\")\n\tCodecControl.SendXMLDataToCodec(Config.codecs[\"grandOleOpry\"], feedbackReggrandOleOpry)\n\tprint(\"Feedback Codec HTTPFeedback Completed\")\n\ndef signal_term_handler(signal, frame):\n\tprint('got SIGTERM')\n\tsys.exit(0)\n\t\t\ntry:\n\tif __name__ == \"__main__\":\n\t\t\n\t\tsignal.signal(signal.SIGTERM, signal_term_handler)\n\t\t\n\t\tprint (\"The current debugMode set in Config.py is \"+Config.debugMode)\n\t\ttime.sleep(2)\n\t\tserver = startThread()\n\t\t\n\t\tct = time.time()\n\t\t# Remove Existing External Presentation Sources\n\t\tRemoveCodecsPresentationSource()\n\t\ttime.sleep(3)\n\t\t# Setup external hdmi input sources\n\t\tSendCodecsPresentationSource()\n\t\t# Enable external hdmi input sources\t\t\t\n\t\tSetCodecPresentationSourceStatus()\n\n\t\t\n\t\twhile True:\n\t\t\t# Regularly manage the recording of HTTP feedback\n\t\t\tSendCodecsFeedbackReg()\t\n\t\t\ttime.sleep(60)\n\nexcept (KeyboardInterrupt, SystemExit):\n\tserver.shutdown()\n\tprint(\"--- WEB Server stopped ---\")\n\n\tsys.exit(0)","sub_path":"RoomControlHandler.py","file_name":"RoomControlHandler.py","file_ext":"py","file_size_in_byte":9940,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"141525513","text":"import face_recognition\nimport cv2\nimport os \nimport pickle\nimport time\nfrom threading import Thread\nimport numpy as np\n\nclass Camera:\n    def __init__(self,cameraType,window,src):\n        self.window = window\n        self.cameraType = cameraType\n        self.frameStart = time.time()\n        self.fpsReport = 0\n        self.dispW=640\n        self.dispH=400\n        self.bottom = self.dispH // 10\n        self.right = self.dispW // 3\n        self.xpos = 0\n        self.ypos = 0\n        if cameraType == 'webcam':\n            self.cam = cv2.VideoCapture(src)\n        elif cameraType == 'picam':\n            self.ypos = self.dispH + 10\n            self.flip=2\n            self.framerate=21\n            self.camSet='nvarguscamerasrc !  video/x-raw(memory:NVMM), width=3264, height=2464, format=NV12, framerate='+str(self.framerate) +'/1 ! nvvidconv flip-method='+str(self.flip)+' ! video/x-raw, width='+str(self.dispW)+', height='+str(self.dispH)+', format=BGRx ! videoconvert ! video/x-raw, format=BGR ! appsink'\n            self.cam = cv2.VideoCapture(self.camSet)\n        self.thread = Thread(target=self.update)\n        self.thread.daemon = True\n        self.thread.start()\n        print('Init ',cameraType,window,src)\n    def stop(self):\n        self.cam.release()\n    def FrameMessage(self,frame,start,end):\n        self.fpsReport, self.fpsMessage = FrameRate(start,end,self.fpsReport)\n        cv2.rectangle(frame,(0,0),(self.right,self.bottom),(255,0,255),-1)\n        cv2.putText(frame,self.fpsMessage,(0,20),font,.75,(0,255,255))\n    def update(self):\n        while True:\n            self.frameStart = time.time()\n            _,self.frame = self.cam.read()\n            if not self.frame is None:\n                self.frameEnd = time.time()\n                self.frame = cv2.resize(self.frame,(self.dispW,self.dispH))\n    def recognise(self):\n        faces = ExamineFrame(self.frame)\n\n        for face in faces:\n            cv2.rectangle(self.frame,(face['left'],face['top']),(face['right'],face['bottom']),(0,0,255),2)\n            cv2.putText(self.frame,face['name'],(face['left'],face['top']-6),font,0.75,(0,0,255))\n        self.FrameMessage(self.frame,self.frameStart,self.frameEnd)\n        return self.frame\n\ndef ExamineFrame(frame):\n    faces = []\n\n    frameSmall = cv2.resize(frame,(0,0),fx=scaleFactor,fy=scaleFactor)\n    frameRGB   = cv2.cvtColor(frameSmall,cv2.COLOR_BGR2RGB)\n    facePositions = face_recognition.face_locations(frameRGB,model='cnn')\n    allEncodings  = face_recognition.face_encodings(frameRGB,facePositions)\n    for (top,right,bottom,left),face_encoding in zip(facePositions,allEncodings):\n        name = 'Unknown Person'\n        matches = face_recognition.compare_faces(Encodings,face_encoding)\n        if True in matches:\n            match_index = matches.index(True)\n            name = Names[match_index]\n            top    = int(top    / scaleFactor)\n            bottom = int(bottom / scaleFactor)\n            left   = int(left   / scaleFactor)\n            right  = int(right  / scaleFactor)\n            detected = {'top': top, 'bottom': bottom, 'left': left, 'right': right, 'name': name}\n            faces.append(detected)\n    return faces\n\ndef FrameRate(frameStart,frameEnd,fpsReport):\n    dx = frameEnd - frameStart\n    if dx > 0:\n        fps = 1/dx\n        if fpsReport == 0: fpsReport = fps\n        fpsReport = (.95 * fpsReport) + (.05 * fps)\n        fpsMessage = str(round(fpsReport,2)) + 'fps'\n    return fpsReport,fpsMessage\n\nprint(cv2.__version__)\n\ntrained_data = os.path.join('traindata','face-trained.pkl')\nwith open(trained_data,'rb') as fr:\n    Names = pickle.load(fr)\n    Encodings = pickle.load(fr)\n\nscaleFactor = 0.25\ncam1 = Camera('webcam','webcam1','/dev/video1')\ncam2 = Camera('webcam','webcam2','/dev/video2')\ncam3 = Camera('picam','picam','')\n\nfont = cv2.FONT_HERSHEY_SIMPLEX\n\nframe1 = frame2 = frame3 = None\nwhile True:\n    try:\n        x = cam1.recognise()\n        if not x is None: frame1 = x\n    except:\n        pass\n    try:\n        x = cam2.recognise()\n        if not x is None: frame2 = x\n    except:\n        pass\n    try:\n        x = cam3.recognise()\n        if not x is None: frame3 = x\n    except:\n        pass\n\n    if not frame1 is None:\n        cv2.imshow('webcam1',frame1)\n        cv2.moveWindow('webcam1',0,0)\n    if not frame2 is None:\n        cv2.imshow('webcam2',frame2)\n        cv2.moveWindow('webcam2',800,0)\n    if not frame3 is None:\n        cv2.imshow('picam',frame3)\n        cv2.moveWindow('picam',0,600)\n\n    if cv2.waitKey(1) == ord('q'):\n        exit(1)\n        break\n\ncam1.stop()\ncam2.stop()\ncam3.stop()\ncv2.destroyAllWindows()","sub_path":"faceRecognizer/faceRecognizer-8-Thread2.py","file_name":"faceRecognizer-8-Thread2.py","file_ext":"py","file_size_in_byte":4610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"243422327","text":"from socket import *\nimport os\n\nclient=socket(AF_INET,SOCK_STREAM)\nclient.connect(('127.0.0.1',8080))\n\nwhile True:\n    msg='%s say hello' %os.getpid()\n    client.send(msg.encode('utf-8'))\n    data=client.recv(1024)\n    print(data.decode('utf-8'))\n\n","sub_path":"day37/01 多路复用IO模型/客户端.py","file_name":"客户端.py","file_ext":"py","file_size_in_byte":248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"448885271","text":"from mxsampleship import create_app\nfrom portalapi.portalapi import RequestFailed\nfrom flask import url_for, current_app\nimport pytest\nimport responses\nfrom bs4 import BeautifulSoup\nfrom six.moves.urllib.parse import urlsplit\nimport json\nfrom portalapi.models import Scientist\n\n\nLOGIN_DATA = {'username': 'jane', 'password': 'secret'}\n\n\n@pytest.yield_fixture\ndef client():\n    app = create_app('testing')\n    context = app.app_context()\n    context.push()\n    yield app.test_client()\n    context.pop()\n\n\n@pytest.yield_fixture\ndef logged_in_client(client):\n    client.post(url_for('auth.login'), data=LOGIN_DATA)\n    yield client\n\n\ndef test_routes_should_have_url_prefix(client):\n    assert urlsplit(url_for('main.index')).path == '/ship-test/'\n\n\ndef test_static_routes_should_have_url_prefix(client):\n    url = url_for('static', filename='js/knockout.js')\n    assert urlsplit(url).path == '/ship-test/static/js/knockout.js'\n\n\ndef test_shipment_form_redirects_to_login(client):\n    response = client.get(url_for('main.shipment_form'))\n    assert response.status_code == 302\n\n\ndef test_shipment_form_renders_after_login(logged_in_client):\n    response = logged_in_client.get(url_for('main.shipment_form'))\n    assert response.status_code == 200\n    page = BeautifulSoup(response.data.decode(), 'html.parser')\n    assert 'MX Sample Shipment' in page.title\n    assert 'Full Name' in page.text\n    assert page.find(id='owner').attrs['value'] == 'Jane Doe'\n    epn_option = page.find(id='epn').option\n    assert epn_option.text == '123a @ 2016-04-29 08:00'\n    assert epn_option['value'] == '123a'\n\n\ndef test_shipment_form_renders_after_login_for_non_ascii_names(monkeypatch, client):\n\n    def get_scientist_patch(api):\n        return Scientist({\n            'first_names': 'Lucía',\n            'last_name': 'Doe',\n            'organisation': {'name_long': 'Some University'},\n            'telephone_number_1': '111-222-333',\n            'email': 'jane@example.com',\n        })\n\n    monkeypatch.setattr('portalapi.PortalAPI.get_scientist', get_scientist_patch)\n    client.post(url_for('auth.login'), data=LOGIN_DATA)\n    response = client.get(url_for('main.shipment_form'))\n    page = BeautifulSoup(response.data.decode(), 'html.parser')\n    assert page.find(id='owner').attrs['value'] == 'Lucía Doe'\n\n\ndef test_shipment_form_shows_epns_outside_of_a_one_day_window(logged_in_client):\n    response = logged_in_client.get(url_for('main.shipment_form'))\n    page = BeautifulSoup(response.data.decode(), 'html.parser')\n    epns = [option.text for option in page.find_all('option')]\n    assert '456b @ 2016-06-01 08:00' in epns\n\n\ndef test_shipment_form_renders_when_get_visits_endpoint_is_empty(logged_in_client,\n                                                                 monkeypatch):\n    def get_scientist_visits_patch(api, **_):\n        raise RequestFailed()\n    monkeypatch.setattr('portalapi.PortalAPI.get_scientist_visits',\n                        get_scientist_visits_patch)\n\n    response = logged_in_client.get(url_for('main.shipment_form'))\n    assert response.status_code == 200\n    page = BeautifulSoup(response.data.decode(), 'html.parser')\n    assert 'MX Sample Shipment' in page.h1\n\n\n@responses.activate\ndef test_form_submits(logged_in_client):\n    new_dewar_url = '%s/dewars/new' % current_app.config['PUCKTRACKER_URL']\n    responses.add(responses.POST, new_dewar_url,\n                  json={'error': None, 'data': {'name': 'd-1a-1'}})\n    data = {\n        'owner': 'Jane',\n        'department': 'Chemistry',\n        'institute': 'Some University',\n        'street_address': '123 Main Road',\n        'city': 'Brisbane',\n        'state': 'Queensland',\n        'postcode': '3000',\n        'country': 'Australia',\n        'phone': '111-222-333',\n        'email': 'jane@example.com',\n        'epn': '123a',\n        'return_dewar': 'y',\n        'courier': 'Fast Deliveries',\n        'courier_account': '999',\n        'container_type': 'pucks',\n        'pucks-0': '1',\n        'pucks-1': '2',\n        'pucks-2': '3',\n    }\n    response = logged_in_client.post(url_for('main.shipment_form'), data=data)\n    assert response.status_code == 302\n    assert response.location == url_for('main.shipment', dewar_name='d-1a-1')\n    dewar = json.loads(responses.calls[0].request.body)\n    assert dewar['epn'] == '123a'\n    assert dewar['owner'] == 'Jane'\n    assert dewar['department'] == 'Chemistry'\n    assert dewar['institute'] == 'Some University'\n    assert dewar['streetAddress'] == '123 Main Road'\n    assert dewar['city'] == 'Brisbane'\n    assert dewar['state'] == 'Queensland'\n    assert dewar['postcode'] == '3000'\n    assert dewar['country'] == 'Australia'\n    assert dewar['phone'] == '111-222-333'\n    assert dewar['email'] == 'jane@example.com'\n    assert dewar['returnDewar'] is True\n    assert dewar['courier'] == 'Fast Deliveries'\n    assert dewar['courierAccount'] == '999'\n    assert dewar['containerType'] == 'pucks'\n    assert dewar['expectedContainers'] == '1 | 2 | 3 |  |  |  |  | '\n    assert dewar['piEmail'] == 'john@example.com'\n    assert dewar['beamline'] == 'MX1'\n    assert dewar['addedTime'] == '2016-04-28T21:00:00+00:00'\n    assert dewar['experimentStartTime'] == '2016-04-29T08:00:00+10:00'\n    assert dewar['experimentEndTime'] == '2016-04-29T16:00:00+10:00'\n\n\n@responses.activate\ndef test_form_submits_for_other_epn(logged_in_client):\n    new_dewar_url = '%s/dewars/new' % current_app.config['PUCKTRACKER_URL']\n    responses.add(responses.POST, new_dewar_url,\n                  json={'error': None, 'data': {'name': 'd-valid-epn-1'}})\n    data = {\n        'owner': 'Jane',\n        'email': 'jane@example.com',\n        'epn': 'other',\n        'other_epn': 'valid-epn',\n        'container_type': 'pucks',\n    }\n    logged_in_client.post(url_for('main.shipment_form'), data=data)\n    dewar = json.loads(responses.calls[0].request.body)\n    assert dewar['epn'] == 'valid-epn'\n    assert dewar['experimentStartTime'] == '2016-01-02T08:00:00+10:00'\n    assert dewar['experimentEndTime'] == '2016-01-03T08:00:00+10:00'\n\n\ndef test_form_does_not_submit_if_epn_is_invalid(logged_in_client):\n    data = {\n        'owner': 'Jane',\n        'email': 'jane@example.com',\n        'epn': 'other',\n        'other_epn': 'invalid-epn',\n        'container_type': 'pucks',\n    }\n    response = logged_in_client.post(url_for('main.shipment_form'), data=data)\n    page = BeautifulSoup(response.data.decode(), 'html.parser')\n    assert 'Invalid EPN' in page.find(id='other_epn-field').text\n\n\n@responses.activate\ndef test_form_submits_other_pucks(logged_in_client):\n    new_dewar_url = '%s/dewars/new' % current_app.config['PUCKTRACKER_URL']\n    responses.add(responses.POST, new_dewar_url,\n                  json={'error': None, 'data': {'name': 'd-1a-1'}})\n    data = {\n        'owner': 'Jane',\n        'email': 'jane@example.com',\n        'epn': '123a',\n        'container_type': 'other-pucks',\n        'pucks-0': 'o1',\n        'pucks-1': 'o2',\n        'pucks-2': 'o3',\n    }\n    logged_in_client.post(url_for('main.shipment_form'), data=data)\n    dewar = json.loads(responses.calls[0].request.body)\n    assert dewar['expectedContainers'] == 'o1 | o2 | o3 |  |  |  |  | '\n\n\n@responses.activate\ndef test_form_submits_cassettes(logged_in_client):\n    new_dewar_url = '%s/dewars/new' % current_app.config['PUCKTRACKER_URL']\n    responses.add(responses.POST, new_dewar_url,\n                  json={'error': None, 'data': {'name': 'd-1a-1'}})\n    data = {\n        'owner': 'Jane',\n        'email': 'jane@example.com',\n        'epn': '123a',\n        'container_type': 'cassettes',\n        'cassettes-0': 'c1',\n        'cassettes-1': 'c2',\n    }\n    logged_in_client.post(url_for('main.shipment_form'), data=data)\n    dewar = json.loads(responses.calls[0].request.body)\n    assert dewar['expectedContainers'] == 'c1 | c2'\n\n\n@responses.activate\ndef test_form_submits_canes(logged_in_client):\n    new_dewar_url = '%s/dewars/new' % current_app.config['PUCKTRACKER_URL']\n    responses.add(responses.POST, new_dewar_url,\n                  json={'error': None, 'data': {'name': 'd-1a-1'}})\n    data = {\n        'owner': 'Jane',\n        'email': 'jane@example.com',\n        'epn': '123a',\n        'container_type': 'canes',\n        'canes': 'the-canes',\n    }\n    logged_in_client.post(url_for('main.shipment_form'), data=data)\n    dewar = json.loads(responses.calls[0].request.body)\n    assert dewar['expectedContainers'] == 'the-canes'\n\n\n@responses.activate\ndef test_shipment_view(logged_in_client):\n    dewar = {\n        'name': 'd-123a-1',\n        'epn': '123a',\n        'owner': 'Jane',\n        'department': 'Chemistry',\n        'institute': 'Some University',\n        'streetAddress': '123 Main Road',\n        'city': 'Brisbane',\n        'state': 'Queensland',\n        'postcode': '3000',\n        'country': 'Australia',\n        'phone': '111-222-333',\n        'email': 'jane@example.com',\n        'returnDewar': True,\n        'courier': 'Fast Deliveries',\n        'courierAccount': '999',\n        'containerType': 'pucks',\n        'expectedContainers': '1 | 2 | 3 | 4 | 5 |  |  | ',\n    }\n    responses.add(responses.GET,\n                  '%s/dewars/d-1a-1' % current_app.config['PUCKTRACKER_URL'],\n                  json={'error': None, 'data': dewar})\n    response = logged_in_client.get(url_for('main.shipment', dewar_name='d-1a-1'))\n    html = response.data.decode()\n    assert 'The Dewar ID is: d-123a-1' in html\n    assert '123 Main Road' in html\n    assert '1 | 2 | 3 | 4 | 5 |  |  | ' in html\n\n\ndef test_directed_to_login_if_token_invalid(logged_in_client, monkeypatch):\n    def is_valid_patch(auth):\n        return False\n    monkeypatch.setattr('portalapi.Authentication.is_valid', is_valid_patch)\n    response = logged_in_client.get(url_for('main.shipment_form'))\n    assert response.status_code == 302\n\n\ndef test_should_display_a_logout_link_when_logged_in(logged_in_client):\n    response = logged_in_client.get(url_for('main.shipment_form'))\n    assert 'Log out' in response.data.decode()\n\n\ndef test_should_not_display_logout_if_not_logged_in(client):\n    response = client.get(url_for('auth.login'))\n    assert 'Log out' not in response.data.decode()\n\n\ndef test_displays_errors_if_form_entered_incorrectly(logged_in_client):\n    data_missing_owner = {'owner': ''}\n    response = logged_in_client.post(url_for('main.shipment_form'),\n                                     data=data_missing_owner)\n    page = BeautifulSoup(response.data.decode(), 'html.parser')\n    assert 'This field is required.' in page.find(id='owner-field').text\n\n\ndef test_valid_email_is_required(logged_in_client):\n    data_with_invalid_email = {'email': 'invalid-email'}\n    response = logged_in_client.post(url_for('main.shipment_form'),\n                                     data=data_with_invalid_email)\n    page = BeautifulSoup(response.data.decode(), 'html.parser')\n    assert 'Invalid email address' in page.find(id='email-field').text\n\n\ndef test_container_type_is_required(logged_in_client):\n    data_missing_container_type = {'container_type': 'select'}\n    response = logged_in_client.post(url_for('main.shipment_form'),\n                                     data=data_missing_container_type)\n    page = BeautifulSoup(response.data.decode(), 'html.parser')\n    container_type_text = page.find(id='container_type-field').text\n    assert 'Container type is required.' in container_type_text\n\n\ndef test_submitted_data_should_not_be_overwitten(logged_in_client):\n    data_with_invalid_email = {'owner': 'John', 'email': 'invalid-email'}\n    response = logged_in_client.post(url_for('main.shipment_form'),\n                                     data=data_with_invalid_email)\n    page = BeautifulSoup(response.data.decode(), 'html.parser')\n    assert page.find(attrs={'name': 'owner'})['value'] == 'John'\n    assert page.find(attrs={'name': 'email'})['value'] == 'invalid-email'\n\n\n@responses.activate\ndef test_dewar_tracker_errors_should_be_flashed(logged_in_client):\n    new_dewar_url = '%s/dewars/new' % current_app.config['PUCKTRACKER_URL']\n    responses.add(responses.POST, new_dewar_url,\n                  json={'error': 'Pucktracker error'})\n    data = {\n        'owner': 'Jane',\n        'email': 'jane@example.com',\n        'epn': '123a',\n        'container_type': 'pucks',\n    }\n    response = logged_in_client.post(url_for('main.shipment_form'), data=data)\n    assert 'Pucktracker error' in response.data.decode()\n","sub_path":"tests/main/test_main_views.py","file_name":"test_main_views.py","file_ext":"py","file_size_in_byte":12412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"521601518","text":"\"\"\"\n\naim: low dimensional embedding based on ngrams of the sfn abstracts\n\nShaobo Guan, Ruobing Xia, 2016-0423, SAT\n\"\"\"\n\n# import modules\nimport os\nfrom lowD_embedding_tools import *\nimport lowD_embedding_tools; reload(lowD_embedding_tools); from lowD_embedding_tools import *\nimport time\n\n# ========== get current file path and path to datafile ==========\ncurrent_filepath = os.path.dirname(__file__)\ndata_filepath = os.path.join(current_filepath, '../DATA/abstract_data.csv')\ntopic_filepath= os.path.join(current_filepath, '../DATA/topic_list.csv')\n\n\n\n\nprint('loading data')\ndata = load_file('abstract_data.csv')\n[data_full,topics] = replace_topic_fullname(data)\n\nprint('calculating text features')\n# (data_fullname, topic) = replace_topic_fullname(data)\n[text_tf, vectorizer] = get_text_feature(data['abstract'])\n\nprint('lowD embedding')\nx = text_tf\nmodel = TSNE(n_components=2, random_state=0, perplexity=50.0, metric='correlation')\n\nt0=time.time()\nx_l = model.fit_transform(x[:100,:])\nprint(time.time()-t0)","sub_path":"topic_embedding/test_tsne_all.py","file_name":"test_tsne_all.py","file_ext":"py","file_size_in_byte":1010,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"211459343","text":"from SERGIO.SERGIO import sergio\n# from SERGIO import sergio\nimport numpy as np\nimport pandas as pd\n\n# import sys\n# print(sys.path)\n# DEMO\ndef simulate():\n    sim = sergio.sergio(number_genes=100,\n                 number_bins=9,\n                 number_sc=300,\n                 noise_params=1,\n                 decays=0.8,\n                 sampling_state=15,\n                 noise_type='dpd')\n\n\n    sim.build_graph(input_file_taregts=\"/Users/sarahmorillo/anaconda3/envs/sim_comp/lib/python3.8/site-packages/SERGIO/Demo/steady-state_input_GRN.txt\",\n                    input_file_regs='/Users/sarahmorillo/anaconda3/envs/sim_comp/lib/python3.8/site-packages/SERGIO/Demo/steady-state_input_MRs.txt',\n                    shared_coop_state=2)\n    sim.simulate()\n    expr = sim.getExpressions()\n    expr_clean_ss = np.concatenate(expr, axis=1)\n    return expr_clean_ss\n\n","sub_path":"scripts/methods/03-est_pars-SERGIO.py","file_name":"03-est_pars-SERGIO.py","file_ext":"py","file_size_in_byte":866,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"444533105","text":"import pandas as pd\nimport nltk\nfrom nltk.tokenize import word_tokenize\nfrom nltk.stem import WordNetLemmatizer\nfrom sklearn.pipeline import Pipeline\nfrom sklearn.model_selection import train_test_split, GridSearchCV\nfrom sklearn.feature_extraction.text import CountVectorizer, TfidfTransformer\nfrom sklearn.multioutput import MultiOutputClassifier\nfrom sklearn.naive_bayes import MultinomialNB\nfrom sklearn.ensemble import RandomForestClassifier, VotingClassifier\nfrom sklearn.linear_model import LogisticRegression\nfrom sklearn.metrics import classification_report\nfrom sqlalchemy import create_engine\nimport sys\nimport pickle\n\nnltk.download(['punkt', 'wordnet'])\n\ndef load(db_filepath):\n    \"\"\"\n    Arguments:\n        db_filepath: where to load SQLite DB from\n    Outputs:\n        X : feature df\n        Y : label df\n    \"\"\"\n    engine = create_engine('sqlite:///{}'.format(db_filepath))\n    df = pd.read_sql_table('DisasterResponse', engine)\n    X = df['message'].values\n    Y = df.drop(labels=['id', 'message', 'original', 'genre'], axis=1)\n    category_names = df.drop(labels=['id', 'message', 'original', 'genre'], axis=1).columns\n    return X,Y.values, category_names\n\ndef tokenize(text):\n    \"\"\"\n    Arguments:\n        text: original text to tokenize\n    Outputs:\n        cleaned: tokenized text read to be modeled\n    \"\"\"\n    tokens = word_tokenize(text)\n    lemmatizer = WordNetLemmatizer()\n    cleaned = []\n    for token in tokens:\n        clean_token = lemmatizer.lemmatize(token).lower().strip()\n        cleaned.append(clean_token)\n    return cleaned\n\ndef build():\n    \"\"\"\n    ============================================\n    SKlearn ML Pipeline to process text messages\n    ============================================\n    Arguments:\n        None\n    Outputs:\n        cv: GridSearchCV model ready to be fit\n    \"\"\"\n    pipe = Pipeline([\n        ('vect', CountVectorizer(tokenizer=tokenize)),\n        ('tfidf', TfidfTransformer()),\n        ('clf',  MultiOutputClassifier(RandomForestClassifier()))\n    ])\n\n\n    param_grid = {\n        'vect__ngram_range':((1,1),(1,2)),\n        'clf__estimator__n_estimators':[10, 30]\n    }\n\n    cv = GridSearchCV(pipe, param_grid=param_grid, n_jobs=4)\n    return cv\n\ndef evaluate(model, X_test, y_test, category_names):\n    \"\"\"\n    Arguments:\n        model: trained NLP model\n        X_test: test messages\n        y_test: true labels from held-out data\n        category_names: label names (for printing)\n    \"\"\"\n\n    y_pred = model.predict(X_test)\n    precision = []\n    recall = []\n    f1 = []\n    for i in range(36):\n        print(\"Column {}: {}\".format(i, category_names[i]))\n        cr = classification_report(y_test[i], y_pred[i])\n        print(cr)\n        lines = cr.split('\\n')\n        #print(lines)\n        for j in range(len(lines)):\n            line = lines[j].split()\n            if line and line[0] == 'avg':\n                precision.append(cr.split('\\n')[j].split()[3])\n                recall.append(cr.split('\\n')[j].split()[4])\n                f1.append(cr.split('\\n')[j].split()[5])\n        print(\"----------------------------------------------------\")\n    print(pd.DataFrame(\n            {'Precision':[float(x) for x in precision],\n             'Recall':[float(x) for x in recall],\n             'F1-Score':[float(x) for x in f1]\n            }\n        ).describe())\n\ndef save(model, filepath):\n    pickle.dump(model, open(filepath, 'wb'))\n\n\ndef main():\n    if len(sys.argv) == 3:\n        db_filepath, model_filepath = sys.argv[1:]\n        print('Loading data...\\n    DATABASE: {}'.format(db_filepath))\n        X, Y, category_names = load(db_filepath)\n        X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2)\n\n        print('Building model...')\n        model = build()\n\n        print('Training model...')\n        model.fit(X_train, Y_train)\n\n        print('Evaluating model...')\n        evaluate(model, X_test, Y_test, category_names)\n\n        print('Saving model...\\n    MODEL: {}'.format(model_filepath))\n        save(model, model_filepath)\n\n        print('Trained model saved!')\n\nif __name__ == '__main__':\n    main()\n","sub_path":"models/train_classifier.py","file_name":"train_classifier.py","file_ext":"py","file_size_in_byte":4108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"237637313","text":"import decoparser\nimport utils\nimport pickle\nimport os\nfrom chainer import optimizers\nfrom networks import Composition\nimport time\n\n@decoparser.option('--data', required=True, choices=('ntcirj_con', 'ntcirj_dep', 'ntcire', 'tsukuba', 'sst_all', 'sst_cut'))\n@decoparser.option('--logdir', required=True)\n@decoparser.option('--optimizer', choices=('SGD', 'AdaDelta', 'AdaGrad', 'Adam', 'NesterovAG'), default='Adam')\n@decoparser.option('--composition', type=decoparser.Enum, choices=Composition, default=Composition.tree_lstm)\n@decoparser.option('--lr', type=float, default=0.01)\n@decoparser.option('--l2', type=float, default=0.0001)\n@decoparser.option('--clip-grad', type=float, default=5)\n@decoparser.option('--dropout', type=float, default=0.0)\n@decoparser.option('--mem-units', type=int, default=200)\n@decoparser.option('--attention', choices=('only', 'concat', 'bilinear', 'dot', 'gate'))\n@decoparser.option('--attention-target', choices=('all', 'word', 'phrase'), default='all')\n@decoparser.option('--regression', action='store_true')\n@decoparser.option('--only-attn', action='store_true')\n@decoparser.option('--toy', action='store_true')\n@decoparser.option('--pol-dict', choices=('pn', 'pnn'))\n@decoparser.option('--not-render', action='store_false', default=True)\n@decoparser.option('--not-embed', action='store_true')\n@decoparser.option('--forget-bias', action='store_true')\n@decoparser.option('--always-attn', action='store_true')\ndef get_arg(arg, logdir, optimizer, lr, l2, clip_grad, mem_units, attention, toy, pol_dict, regression, composition, data, not_render, not_embed, attention_target, dropout, forget_bias, only_attn, always_attn):\n    args = ['logdir', 'lr', 'l2', 'clip_grad', 'attention', 'toy', 'pol_dict', 'optimizer', 'mem_units', 'regression', 'composition', 'data', 'not_render', 'not_embed', 'attention_target', 'dropout', 'forget_bias', 'only_attn', 'always_attn']\n    d = dict()\n    for a in args:\n        d[a] = eval(a)\n    if arg in d:\n        return d[arg]\n    elif arg is 'all':\n        return d\n    else:\n        return None\n\n\nclass ArgReader:\n    def __init__(self):\n        self.__start_time = time.time()\n        self.__always_attn = get_arg('always_attn')\n        self.__fold = 10\n        self.__batch_size = 32\n        self.__n_epoch = 10\n        self.__label_num = 2\n        self.__mem_units = get_arg('mem_units')\n        self.__data_mode = get_arg('data')\n        self.__is_toy = get_arg('toy')\n        self.__dict = get_arg('pol_dict')\n        self.__logdir = get_arg('logdir')\n        self.__is_regression = get_arg('regression')\n        self.__attention = get_arg('attention')\n        self.__lr = get_arg('lr')\n        self.__l2 = get_arg('l2')\n        self.__clip_grad = get_arg('clip_grad')\n        self.__composition = get_arg('composition')\n        self.__n_units = 200\n        self.__render_graph = get_arg('not_render')\n        self.__dropout = get_arg('dropout')\n        if self.__data_mode == 'ntcire' or self.__data_mode.startswith('sst'):\n            self.__n_units = 300\n        self.__not_embed = get_arg('not_embed')\n        self.__attention_target = get_arg('attention_target')\n        self.__forget_bias = get_arg('forget_bias')\n        self.__only_attn = get_arg('only_attn')\n\n        # optimizer\n        self.__opt_name = get_arg('optimizer')\n        if self.__opt_name == 'SGD':\n            self.__opt = lambda: optimizers.SGD(lr=self.__lr)\n        elif self.__opt_name == 'AdaDelta':\n            self.__opt = lambda: optimizers.AdaDelta()\n        elif self.__opt_name == 'Adam':\n            self.__opt = lambda: optimizers.Adam(alpha=self.__lr)\n        elif self.__opt_name == 'NesterovAg':\n            self.__opt = lambda: optimizers.NesterovAG(lr=self.__lr)\n        elif self.__opt_name == 'AdaGrad':\n            self.__opt = lambda: optimizers.AdaGrad(lr=self.__lr)\n\n        # data\n        data_dir = utils.get_data_path()\n        mecab_embedf = data_dir + '/vector/word2vec/wikiDump_mecab_size200_cbow.w2vModel'\n        kytea_embedf = data_dir + '/vector/word2vec/wikiDump_kytea_size200_skipgram.w2vModel'\n        en_embedf = data_dir + '/vector/glove/glove.840B.300d.txt'\n        if self.__data_mode== 'ntcirj_con':\n            data = data_dir + '/ntcirj/ckylark/data.pkl.bz2'\n            self.__embedf = kytea_embedf\n        if self.__data_mode == 'ntcirj_dep':\n            data = data_dir + '/ntcirj/cabocha/data.pkl.bz2'\n            self.__embedf = mecab_embedf\n        elif self.__data_mode == 'ntcire':\n            self.__embedf = en_embedf\n            data = data_dir + '/ntcire/ckylark/data.pkl.bz2'\n        elif self.__data_mode == 'tsukuba':\n            self.__embedf = kytea_embedf\n            data = data_dir + '/tsukuba/ckylark/data.pkl.bz2'\n        elif self.__data_mode == 'sst_all':\n            self.__embedf = en_embedf\n            self.__label_num = 5\n            data = data_dir + '/sst_all/data.pkl.bz2'\n        elif self.__data_mode == 'sst_cut':\n            self.__embedf = en_embedf\n            self.__label_num = 5\n            data = data_dir + '/sst_cut/data.pkl.bz2'\n        data = utils.read_pkl_bz2(data)\n        if self.__is_toy:\n            data = data['toy']\n            self.__n_epoch = 3\n        if self.__dict == 'pn':\n            data = data['poldict']\n        elif self.__dict == 'pnn':\n            data = data['poldict_neutral']\n        self.__data = data\n        self.mk_logfiles()\n        self.print_params()\n\n    def print_params(self):\n        with open(self.__logdir + '/params.txt', 'w') as f:\n            print('toy data: {}'.format(self.__is_toy), file=f)\n            print('mem_units: {}'.format(self.__mem_units), file=f)\n            print('optimizer: {}'.format(self.__opt_name), file=f)\n            print('compositoin network: {}'.format(self.__composition.name), file=f)\n            print('learning rate: {}'.format(self.__lr), file=f)\n            print('l2: {}'.format(self.__l2), file=f)\n            print('clip grad: {}'.format(self.__clip_grad), file=f)\n            print('dropout ratio: {}'.format(self.__dropout), file=f)\n            print('attention: {}'.format(self.__attention), file=f)\n            print('attention target: {}'.format(self.__attention_target), file=f)\n            print('forget_bias: {}'.format(self.__forget_bias), file=f)\n            print('regression: {}'.format(self.__is_regression), file=f)\n            print('batch size: {}'.format(self.__batch_size), file=f)\n            print('pol dict: {}'.format(self.__dict), file=f)\n            print('data: {}'.format(self.__data_mode), file=f)\n            print('only attn: {}'.format(self.__only_attn), file=f)\n            print('always attn: {}'.format(self.__always_attn), file=f)\n\n    def get_fold(self):\n        return self.__fold\n\n    def get_mode(self):\n        if self.__data_mode == 'ntcirj_con':\n            return 'constituency'\n        elif self.__data_mode == 'ntcirj_dep':\n            return 'dependency'\n\n    def get_data(self):\n        return self.__data\n\n    def get_embedf(self):\n        return self.__embedf\n\n    def mk_logfiles(self):\n        logf = self.__logdir + '/log.txt'\n        progf = self.__logdir + '/progress.txt'\n        model_dir = self.__logdir + '/models'\n        ex_dir = self.__logdir + '/example'\n        fold_dir = self.__logdir + '/each_fold'\n        graph_dir = self.__logdir + '/graph'\n        graph_dirs = [graph_dir + '/fold{}'.format(i) for i in range(self.__fold)]\n        if not os.path.exists(self.__logdir):\n            os.mkdir(self.__logdir)\n        if not os.path.exists(model_dir):\n            os.mkdir(model_dir)\n        if not os.path.exists(ex_dir):\n            os.mkdir(ex_dir)\n        if not os.path.exists(fold_dir):\n            os.mkdir(fold_dir)\n        if not os.path.exists(graph_dir):\n            os.mkdir(graph_dir)\n        for d in graph_dirs:\n            if not os.path.exists(d):\n                os.mkdir(d)\n\n    def get_logfiles(self):\n        logf = self.__logdir + '/log.txt'\n        progf = self.__logdir + '/progress.txt'\n        model_dir = self.__logdir + '/models'\n        ex_dir = self.__logdir + '/example'\n        fold_dir = self.__logdir + '/each_fold'\n        graph_dir = self.__logdir + '/graph'\n        graph_dirs = [graph_dir + '/fold{}'.format(i) for i in range(self.__fold)]\n        if not os.path.exists(self.__logdir):\n            os.mkdir(self.__logdir)\n        if not os.path.exists(model_dir):\n            os.mkdir(model_dir)\n        if not os.path.exists(ex_dir):\n            os.mkdir(ex_dir)\n        if not os.path.exists(fold_dir):\n            os.mkdir(fold_dir)\n        if not os.path.exists(graph_dir):\n            os.mkdir(graph_dir)\n        for d in graph_dirs:\n            if not os.path.exists(d):\n                os.mkdir(d)\n        return logf, progf, model_dir, ex_dir, fold_dir, graph_dirs\n\n    def is_regression(self):\n        return self.__is_regression\n\n    def get_attention(self):\n        return self.__attention\n\n    def get_n_units(self):\n        return self.__n_units\n\n    def get_mem_units(self):\n        return self.__mem_units\n\n    def get_batch_size(self):\n        return self.__batch_size\n\n    def get_n_epoch(self):\n        return self.__n_epoch\n\n    def get_label_num(self):\n        return self.__label_num\n\n    def get_lr(self):\n        return self.__lr\n\n    def get_l2(self):\n        return self.__l2\n\n    def get_clip_grad(self):\n        return self.__clip_grad\n\n    def get_optimizer(self):\n        return self.__opt\n\n    def get_composition(self):\n        return self.__composition\n\n    def get_attention_target(self):\n        return self.__attention_target\n\n    def get_dropout(self):\n        return self.__dropout\n\n    def is_toy(self):\n        return self.__is_toy\n\n    def is_only_attn(self):\n        return self.__only_attn\n\n    def is_always_attn(self):\n        return self.__always_attn\n\n    def render_grpah(self):\n        return self.__render_graph\n\n    def forget_bias(self):\n        return self.__forget_bias\n\n    def output_result(self, test, dev):\n        resultf = self.__logdir + '/result.pkl'\n        elapsed_time = time.time() - self.__start_time\n        d = get_arg('all')\n        d['test'] = test\n        d['dev'] = dev\n        d['composition'] = d['composition'].name\n        d['time'] = utils.time_format(elapsed_time)\n        pickle.dump(d, open(resultf, 'wb'))\n\n        script = 'import pickle\\n' +\\\n        \"for k, v in pickle.load(open('result.pkl', 'rb')).items():\\n\" +\\\n        \"    print('{}: {}'.format(k, v))\"\n        print(script, file=open(self.__logdir + '/print_result.py', 'w'))\n\n    def use_embed(self):\n        return not self.__not_embed\n","sub_path":"src/arg_reader.py","file_name":"arg_reader.py","file_ext":"py","file_size_in_byte":10593,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"411248367","text":"#-------------------------------------------------------------------------------\n# Name:        module1\n# Purpose:\n#\n# Author:      Karanjot\n#\n# Created:     07/05/2017\n# Copyright:   (c) Karanjot 2017\n# Licence:     <your licence>\n#-------------------------------------------------------------------------------\nfrom graphics import *\nimport math\ndef fiveClickStickFigure():\n    win = GraphWin(\"5 click stick figure\",300,500)\n    win.setBackground(\"white\")\n    introBox = Rectangle(Point(0,400),Point(300,500))\n    introBox.setFill(\"green\")\n    introBox.draw(win)\n\n    introText = Text(Point(100,425), \"Stick figure builder says:\")\n    introText.setStyle(\"italic\")\n    introText.draw(win)\n    commandText = Text(Point(150,450),\"Click the first point for the head\")\n    commandText.setSize(8)\n    commandText.setStyle(\"bold\")\n    commandText.draw(win)\n\n    click1 = win.getMouse()\n    click1.draw(win)\n    commandText.setText(\"Click the second point for the outter head\")\n    click2 = win.getMouse()\n    click2.draw(win)\n\n    x1 = click1.getX()\n    x2 = click2.getX()\n    y1 = click1.getY()\n    y2 = click2.getY()\n\n    distance =((x2-x1)**2) + ((y2-y1) **2)\n    sqrt = math.sqrt(distance)\n\n    head = Circle(click1,sqrt).draw(win)\n\n    #third point\n    commandText.setText(\"Click the third point for the body\")\n    click3 = win.getMouse()\n    click3.draw(win)\n    x = click3.getX()\n    y = click3.getY()\n\n    body = Line(Point(x1, y1 + sqrt),Point(x,y)).draw(win)\n\n    #fourth point\n\n    commandText.setText(\"Click the fourth point for the arms \")\n    click4 = win.getMouse()\n    click4.draw(win)\n    cx = click4.getX()\n    cy = click4.getY()\n    distance = cx - x\n    line1 = Line(Point(cx,cy), Point(x,cy)).draw(win)\n    Line2 = Line(Point(x,cy), Point(x - distance,cy)).draw(win)\n\n    #fifth point\n    commandText.setText(\"Click the last point for the legs \")\n    click5 = win.getMouse()\n    click5.draw(win)\n    cx5 = click5.getX()\n    cy5 = click5.getY()\n    distanceX = cx5 - x\n    distanceY = cy5 - y\n    Line1 = Line(Point(x,y),Point(cx5,cy5)).draw(win)\n    Line2 = Line(Point(x,y),Point(x - distanceX, y + distanceY)).draw(win)\n\n    introBox.setFill(\"red\")\n    commandText.setText(\"Completed! \")\n\n    win.getMouse()\n    win.close()\n\n\n\n\n\nfiveClickStickFigure()","sub_path":"9.py","file_name":"9.py","file_ext":"py","file_size_in_byte":2268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"591408094","text":"#!/usr/bin/env python\n\nimport rospy\nimport turtlesim.msg\nimport geometry_msgs.msg\nimport math\nfrom eesc_asctec_framework.msg import *\n#from eesc_asctec_framework.srv import *\n\ndef fn_yellow_order(os):\n  if os.status_task == 2:\n    if os.current_wp == 0:\n      return 1\n    if os.current_wp == 1:\n      return 2\n    if os.current_wp == 2:\n      return 3\n    if os.current_wp == 3:\n      return 4\n    if os.current_wp == 4:\n      return 1\n  else:\n    return 0\n\ndef fn_robot_cmd_vel(tp,dp):\n  e_lin = 0.01\n  e_ang = 0.01\n  cmd_vel = geometry_msgs.msg.Twist()\n  if abs(tp.x-dp.x) > e_lin or abs(tp.y-dp.y) > e_lin:\n    dy = dp.y - tp.y\n    dx = dp.x - tp.x\n    if abs(tp.theta - math.atan2(dy,dx)) > e_ang:\n      cmd_vel.angular.z = 3 * (math.atan2(dy,dx) - tp.theta)\n    else:\n      cmd_vel.linear.x = 1 * math.sqrt(dy**2+dx**2)\n  else:\n    cmd_vel.linear.x = 0\n    cmd_vel.angular.z = 0\n  return cmd_vel\n\ndef fn_get_desired_pose(tp,order):\n  if not order.on:\n    return tp\n  dp = turtlesim.msg.Pose()\n  if order.task == 1:\n    dp.x = 2\n    dp.y = 2\n  elif order.task == 2:\n    dp.x = 1\n    dp.y = 7\n  elif order.task == 3:\n    dp.x = 6\n    dp.y = 8\n  elif order.task == 4:\n    dp.x = 7\n    dp.y = 3\n  else:\n    dp.x = tp.x\n    dp.y = tp.y\n  return dp\n\ndef fn_order_status(order_status,cmd_vel):\n  if order_status.task >= 1 and order_status.task <= 4:\n    if cmd_vel.linear.x == 0 and cmd_vel.angular.z == 0:\n      return 2\n    else:\n      return 1\n  else:\n    return 0\n\ndef fn_ground_station_task(num,os1,os2):\n  if not os1.on:\n    return 0\n  if num == 1:\n    task = os1.task\n    if os2.task >= 1 and os2.task <= 4 and os2.status == 2:\n      task = os2.task + 2\n      if task >= 5 and task <= 6:\n        task = task - 4\n    if os2.status == 0:\n      task = 1\n  if num == 2:\n    task = os2.task\n    if os1.status == 2:\n      task = os1.task - 1\n      if task == 0:\n        task = 4\n  return task\n\n","sub_path":"scripts/rules.py","file_name":"rules.py","file_ext":"py","file_size_in_byte":1894,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"254452185","text":"import  numpy as np\nimport math\nfrom Norm import norm_inf \nfrom Norm import mul \nfrom Norm import spectr_radius\n\n########### load data ##################\nimport argparse as ap\n# Get the path of matrix\nparser = ap.ArgumentParser()\nparser.add_argument(\"-m\", \"--matrix\", help=\"Path to matrix file\", required=\"True\")\nparser.add_argument(\"-e\", \"--eps\", help=\"Calculation error\", required=\"True\")\nargs = vars(parser.parse_args())\n# Read file\nf= open(args[\"matrix\"],'r')\ncontent = f.readlines()\nA= np.matrix(content[0])\nb= np.matrix(content[1])\nf.close()\neps=float(args[\"eps\"])\n##########################################\nprint (A)\nprint (b)\nprint (\"1)Solve gaus: \")\nx_ext= np.linalg.solve(A,b)\nprint (x_ext)\n###################################### transform to iteration form ###########################\ndef transformation(A,b):\n\tnum_rows, num_cols=A.shape\n\tH=np.empty(A.shape)\n\tg=np.empty([num_rows])\n\ti=0\n\twhile(i<num_rows):\n\t\tj=0\n\t\twhile(j<num_cols):\n\t\t\tif(i!=j):\n\t\t\t\tH[i,j]=-A[i,j]/A[i,i]\n\t\t\telse:\n\t\t\t\tH[i,j]=0\n\t\t\tj=j+1\n\t\ti=i+1\n\ti=0\n\twhile(i<num_rows):\n\t\tg[i]=b[i]/A[i,i]\n\t\ti=i+1\n\treturn H,np.matrix(g).T\nprint (\"2)transformation to x=Hx+g : \")\nH,g=transformation(A,b)\nprint (\"H: \")\nprint (H)\nprint (\"g: \")\nprint (g)\nprint (\"Norm of H: \")\nprint (norm_inf(H))\n################################ simple itaration #####################################3\nprint (\"3) find k, ||x_*-x^k||<eps\")\ndef find_K(H,g,x_0,eps):\n\td=norm_inf(x_0)+norm_inf(g)/(1-norm_inf(H))\n#\teps_d=eps/d\n\treturn math.ceil(np.log(eps/d)/np.log(norm_inf(H)))\nx_0=x_ext+np.matrix(' 0.2  0.2  0.2 ').T\nk_teor=find_K(H,g,x_0,eps)\t\nprint (\"k_teor: \", k_teor)\nprint (\"4) simple iteration\")\ndef iteration(H,g,x_0,eps):\n\tdx=2*eps\n\tx=x_0\n\tk=0\n\th=norm_inf(H)/(1-norm_inf(H))\n\tx1=x\n\tx2=H*x1+g\n\tx=H*x2+g\n\twhile(dx>eps):\n\t\t#print(dx)\n\t\tx2=x1\n\t\tx1=x\n\t\tx=H*x+g\n\t\tdx=h*norm_inf(x1-x)\n\t\tr=norm_inf(x-x1)/norm_inf(x1-x2)\n\t\tk=k+1\n\treturn x,k,dx,x1,r\nx_iter,k_ext,dx,x_k_1,r_exp= iteration(H,g,x_0,eps)\nprint (x_iter)\nprint (\"k_ext: \", k_ext)\nprint (\"ERROR x_ext-x_iter: \")\nprint (x_ext-x_iter)\nprint (\"ERROR aprior: \")\ndef aprior(H,g,x_0,k):\n\tnorm=norm_inf(H)\n\td=norm_inf(x_0)+np.linalg.norm(g)/(1-norm)\n\treturn math.pow(norm,k)*(d)\nprint (aprior(H,g,x_0,k_teor))\nprint (\"ERROR apostorior: \")\nprint (dx)\nr=spectr_radius(H)\nx_lust=x_k_1+(x_iter-x_k_1)/(1-r)\nprint (\"lusternick :\",x_lust)\nprint (\"ERROR lust: \")\nprint (x_lust-x_ext)\n######################### zeidel ########################################\nprint (\"5) Zeidel\")\ndef transform_H(H):\n\tnum_rows, num_cols=H.shape\n\ti=0\n\tH_l=np.empty(H.shape)\n\tH_r=np.empty(H.shape)\n\t#H_l\n\twhile(i<num_rows):\n\t\tj=i\n\t\twhile(j<num_cols):\n\t\t\tH_r[i,j]=H[i,j]\t\t\n\t\t\tj=j+1\n\t\ti=i+1\n\n\ti=0\n\twhile(i<num_rows):\n\t\tj=0\n\t\twhile(j<i):\n\t\t\tH_l[i,j]=H[i,j]\n\t\t\tj=j+1\n\t\ti=i+1\t\n\treturn H_l,H_r\ndef zeidel_like_iteration(H,g,x_0,x_ext,eps,max_k):\n\tH_l,H_r=transform_H(H)\t\n\tnum_rows, num_cols=H.shape\n\tE=np.identity(num_rows)\n\tM=np.linalg.inv(E-H_l)\n\tmat=np.empty(H.shape) \n\tmat=mul(M,H_r)\n\tc=M*g\n\tx,k_ext,dx,x_k_1,r= iteration(mat,c,x_0,eps)\n\treturn x,k_ext\ndef sum1(H,x,i):\n\tsum=0\n\tnum_rows, num_cols=H.shape\n\tj=0\n\twhile(j<=i):\n\t\tsum=sum+H[i,j]*x[j]\n\t\tj=j+1\n\treturn sum\n\ndef sum2(H,x,i):\n\tsum=0\n\tnum_rows, num_cols=H.shape\n\tj=i+1\n\twhile(j<num_rows):\n\t\tsum=sum+H[i,j]*x[j]\n\t\tj=j+1\n\treturn sum\ndef zeidel(H,g,x_0,x_ext,eps,max_k):\n\tnum_rows, num_cols=H.shape\n\tdx=2*eps\n\tx=x_0.copy()\n\tk=0\n\tx1=x\n\twhile(dx>eps):\n\t\ti=0\n\t\twhile (i<num_rows):\n\t\t\tsum_1=sum1(H,x,i)\n\t\t\tsum_2=sum2(H,x1,i)\n\t\t\tx[i]=x[i]+(sum_1+sum_2-x[i]+g[i])\n\t\t\ti=i+1\n\t\tx1=x\t\n\t\tdx=norm_inf(x_ext-x)\n\t\tk=k+1\n\treturn x,k\nx_zeid,k_zeid=zeidel(H,g,x_0,x_ext,eps,25)\nprint (\"k_zeid: \", k_zeid)\nprint (x_zeid)\nprint (\"ERRROR zeid: \")\nprint (x_zeid-x_ext)\n########################## relacs #######################################\nprint (\"7) up relacs: \")\nr=spectr_radius(H)\nprint (r)\n\ndef relacs(H,g,x_0,eps,max_k,q):\n\tnum_rows, num_cols=H.shape\n\tdx=2*eps\n\tx=x_0.copy()\n\tk=0\n\tx1=x\n\twhile(dx>eps):\n\t\ti=0\n\t\twhile (i<num_rows):\n\t\t\tsum_1=sum1(H,x,i)\n\t\t\tsum_2=sum2(H,x1,i)\n\t\t\tx[i]=x[i]+q*(sum_1+sum_2-x[i]+g[i])\n\t\t\ti=i+1\n\t\tx1=x\t\n\t\tdx=norm_inf(x_ext-x)\n\t\tk=k+1\n\treturn x,k\t\n\t\nq=2/(1+math.sqrt(1-r*r))\nprint (\"q: \",q)\nx_relacs,k_relacs=relacs(H,g,x_0,eps,25,q)\nprint (\"k_relaks\",k_relacs)\nprint (\"ERROR relacs:\")\nprint (x_relacs-x_ext)\n\t\n","sub_path":"2_sem/iteration.py","file_name":"iteration.py","file_ext":"py","file_size_in_byte":4242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"328610615","text":"import os\nos.environ.setdefault ('DJANGO_SETTINGS_MODULE','model_basic.settings')\n\nimport django\n\ndjango.setup()\n\nimport random\nfrom model_app.models import *\nfrom faker import Faker\nfakegen = Faker()\njops = random.choices(Register.JOP_LIST)\n\ndef add_jop ():\n    t = Register.objects.get_or_create(jop=jops)[0]\n    t.save()\n    return t\ndef populate (N=10):\n    for entry in range(N):\n        fake_name = fakegen.name()\n        fake_mail = fakegen.email()\n        fake_phone = fakegen.ean8()\n        fake_jop=add_jop()\n\n\n        register = Register.objects.get_or_create(name=fake_name,email=fake_mail,phone=fake_phone,jop=fake_jop)[0]\n\n\nif __name__ == '__main__':\n    print('Script Populate !')\n    populate(2)\n    print('Done !')\n","sub_path":"faker_app.py","file_name":"faker_app.py","file_ext":"py","file_size_in_byte":732,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"380361200","text":"# Python bytecode 2.7 (decompiled from Python 2.7)\n# Embedded file name: e:\\jenkins\\workspace\\client_SERENITY\\branches\\release\\SERENITY\\eve\\client\\script\\ui\\shared\\shipTree\\shipTreeContainer.py\nimport inventorycommon.typeHelpers\nfrom .shipTreeShipGroup import ShipTreeShipGroup\nfrom carbonui.primitives.containerAutoSize import ContainerAutoSize\nimport shipTreeConst\nimport geo2\nfrom eve.client.script.ui.shared.shipTree.shipTreeConst import TREE_SCALE, NODETYPE_GROUP, NODETYPE_OTHERFACTIONGROUP, COLOR_BG, NODETYPE_ROOT, BG_BY_FACTIONID, COLOR_MASTERED\nimport carbonui.const as uiconst\nfrom eve.client.script.ui.control.buttons import ButtonIcon\nimport trinity\nfrom carbonui.primitives.vectorlinetrace import VectorLineTrace\nfrom localization import GetByMessageID, GetByLabel\nfrom carbonui.primitives.stretchspritehorizontal import StretchSpriteHorizontal\nfrom carbonui.primitives.container import Container\nfrom carbonui.primitives.sprite import Sprite\nfrom eve.client.script.ui.control.eveLabel import Label\nfrom inventorycommon.const import typeCapsule\nimport macho\nimport const\nimport evetypes\nX_OFFSET = 22\nY_OFFSET = 40\nCOLOR_FONT = (0.667, 0.796, 0.906, 1.0)\nCOLOR_LINE_TO = (1.0, 1.0, 1.0, 1.0)\n\nclass ShipTreeContainer(ContainerAutoSize):\n    default_name = 'ShipTreeContainer'\n    __notifyevents__ = ('OnUIScalingChange')\n\n    def ApplyAttributes(self, attributes):\n        ContainerAutoSize.ApplyAttributes(self, attributes)\n        sm.RegisterNotify(self)\n        self.factionID = attributes.factionID\n        self._offset = None\n        self._leftFloat = 0.0\n        self._topFloat = 0.0\n        self.rootNode = None\n        self.lineCont = Container(name='lineCont', parent=self)\n        self.ConstructTree()\n        self.bgCont = Container(name='bgContainer', align=uiconst.CENTER, state=uiconst.UI_DISABLED, parent=self, padding=-100)\n        self.topFrame = TopFrame(parent=self.bgCont, align=uiconst.TOTOP)\n        self.bottomFrame = BottomFrame(parent=self.bgCont, align=uiconst.TOBOTTOM)\n        Sprite(name='grid', bgParent=self.bgCont, state=uiconst.UI_DISABLED, align=uiconst.CENTER, texturePath='res:/UI/Texture/Classes/ShipTree/grid.png', color=(0.43,\n                                                                                                                                                                   0.727,\n                                                                                                                                                                   1.0,\n                                                                                                                                                                   0.2), idx=0, tileX=True, tileY=True)\n        texturePath = BG_BY_FACTIONID.get(self.factionID)\n        _, h = self.rootNode.GetDimensions()\n        height = 3.2 * h * TREE_SCALE\n        width = 1.6 / 1.8 * height\n        self.factionBG = Sprite(name='factionBG', parent=self.bgCont, align=uiconst.CENTER, texturePath=texturePath, width=width, height=height, opacity=0.18, idx=0)\n        return\n\n    def ConstructTree(self):\n        self.rootNode = sm.GetService('shipTree').GetRootNode(self.factionID)\n        self._offset, _ = self.rootNode.GetBoundingBox()\n        self.ConstructNodes()\n        self.ConstructLines()\n\n    def ConstructNodes(self):\n        self._ConstructNodes(self.rootNode)\n\n    def _ConstructNodes(self, node, i=0, line=None):\n        nodeType = node.GetNodeType()\n        if nodeType == NODETYPE_GROUP:\n            self.RenderShipGroup(node, i)\n        elif nodeType == NODETYPE_OTHERFACTIONGROUP:\n            self.RenderNodeOtherFactionGroup(node)\n        elif nodeType == NODETYPE_ROOT:\n            self.RenderRootNode(node)\n        for childNode in node.children:\n            self._ConstructNodes(childNode, i + 1, line)\n\n    def ConstructLines(self, animate=True):\n        self.lineCont.Flush()\n        self._ConstructLines(self.rootNode, animate=animate)\n\n    def _ConstructLines(self, node, i=0, line=None, animate=True):\n        if node.nodeType == NODETYPE_GROUP:\n            line = None\n        for childNode in node.children:\n            line = self.RenderLine(node, childNode, i, line, animate=animate)\n            self._ConstructLines(childNode, i + 1, line, animate)\n            line = None\n\n        return\n\n    def RenderShipGroup(self, node, i):\n        if node.parent and node.parent.nodeType == NODETYPE_OTHERFACTIONGROUP:\n            return\n        x, y = self._GetNodePosition(node)\n        ShipTreeShipGroup(parent=self, node=node, alignMode=uiconst.TOPLEFT, left=x, top=y, idx=0, nodeNum=i)\n\n    def RenderNodeOtherFactionGroup(self, node):\n        left, top = self._GetNodePosition(node)\n        opacity = 0.2 if node.IsLocked() else 1.0\n        texturePath = shipTreeConst.ICON_BY_FACTIONID.get(node.factionID)\n        size = 64\n        shipGroupData = cfg.infoBubbleGroups[node.shipGroupID]\n        shipGroupName = GetByMessageID(shipGroupData['nameID'])\n        ButtonIcon(parent=self, texturePath=texturePath, pos=(left - size / 2 + X_OFFSET,\n         top - size / 2 + Y_OFFSET, size, size), iconSize=65, idx=0, func=self.OnOtherFactionClicked, args=(node,), opacity=opacity, hint='<b>%s</b>\\n%s' % (cfg.factions.Get(node.factionID).factionName, shipGroupName))\n\n    def RenderRootNode(self, node):\n        texturePath = inventorycommon.typeHelpers.GetHoloIconPath(typeCapsule)\n        size = 32\n        left, top = self._GetNodePosition(node)\n        if self.IsInGoldenPod():\n            color = COLOR_MASTERED\n            typeID = const.typeCapsuleGolden\n        else:\n            color = (1, 1, 1, 1)\n            typeID = const.typeCapsule\n        btn = ButtonIcon(parent=self, pos=(\n         left - size / 2 + X_OFFSET - 6,\n         top - size / 2 - 2 + Y_OFFSET, size, size), func=sm.GetService('info').ShowInfo, iconSize=size, args=(typeID,), texturePath=texturePath, hint=evetypes.GetName(typeID), iconColor=color)\n        btn.icon.blendMode = trinity.TR2_SBM_ADD\n\n    def IsInGoldenPod(self):\n        implants = sm.GetService('godma').GetItem(session.charid).implants\n        if implants is None:\n            return False\n        else:\n            for implant in implants:\n                if implant.typeID == const.typeGoldenCapsuleImplant:\n                    return True\n\n            return False\n\n    def RenderLine(self, nodeFrom, nodeTo, i, line, animate=True):\n        if nodeTo.nodeType == NODETYPE_OTHERFACTIONGROUP:\n            return None\n        else:\n            texturePath = self.GetLineTexturePath(nodeTo)\n            if not line or line.texturePath != texturePath:\n                line = VectorLineTraceShipTree(parent=self.lineCont, nodeFrom=nodeFrom, nodeTo=nodeTo, texturePath=texturePath)\n                pos = self.GetNewLineStartPos(nodeFrom, nodeTo)\n                if i == 0 or nodeFrom.nodeType == NODETYPE_OTHERFACTIONGROUP:\n                    colorFrom = (1.0, 1.0, 1.0, -0.05)\n                else:\n                    colorFrom = COLOR_LINE_TO\n                line.AddPoint(pos, colorFrom)\n            line.AddPoint(self._GetLinePosition(nodeTo), COLOR_LINE_TO)\n            opacity = 0.7 if nodeTo.IsLocked() else 1.0\n            if animate:\n                uicore.animations.FadeIn(line, opacity, timeOffset=0.05 * i + 0.2, duration=0.3)\n            else:\n                line.opacity = opacity\n            return line\n\n    def GetLineTexturePath(self, nodeTo):\n        if nodeTo.IsPathToElite():\n            return 'res:/UI/Texture/classes/shipTree/lines/elite.png'\n        elif nodeTo.IsLocked():\n            return 'res:/UI/Texture/classes/shipTree/lines/locked.png'\n        else:\n            return 'res:/UI/Texture/classes/shipTree/lines/unlocked.png'\n\n    def GetNewLineStartPos(self, node, childNode):\n        pos = self._GetLinePosition(node)\n        childPos = self._GetLinePosition(childNode)\n        if node.nodeType == NODETYPE_GROUP:\n            if childPos[1] == pos[1]:\n                offset = node.GetGroupWidth() + 24\n                s = offset / geo2.Vec2Length(geo2.Vec2Subtract(childPos, pos))\n                pos = geo2.Vec2Lerp(pos, childPos, s)\n        return (int(pos[0]), int(pos[1]))\n\n    def _GetNodePosition(self, node):\n        vec = geo2.Vec2Subtract(node.GetPosition(), self._offset)\n        return geo2.Vec2Scale(vec, TREE_SCALE)\n\n    def _GetLinePosition(self, node):\n        vec = self._GetNodePosition(node)\n        vec = geo2.Vec2Add(vec, (X_OFFSET, Y_OFFSET))\n        return (int(vec[0]), int(vec[1]))\n\n    def GetLeftFloat(self):\n        return self._leftFloat\n\n    def SetLeftFloat(self, value):\n        self._leftFloat = value\n        self.left = value\n\n    leftFloat = property(GetLeftFloat, SetLeftFloat)\n\n    def GetTopFloat(self):\n        return self._topFloat\n\n    def SetTopFloat(self, value):\n        self._topFloat = value\n        self.top = value\n\n    topFloat = property(GetTopFloat, SetTopFloat)\n\n    def OnOtherFactionClicked(self, node):\n        sm.GetService('shipTreeUI').OpenAndShowShipGroup(node.factionID, node.shipGroupID)\n\n    def UpdateTreeSkills(self):\n        self.rootNode.FlushCache()\n        self.ConstructLines(animate=False)\n\n    def OnUIScalingChange(self, *args):\n        self.ConstructLines(animate=False)\n\n\nclass VectorLineTraceShipTree(VectorLineTrace):\n    default_spriteEffect = trinity.TR2_SFX_COPY\n    default_lineWidth = 10.0\n    default_textureWidth = 10.0\n    default_opacity = 0.0\n    default_idx = -1\n    default_state = uiconst.UI_DISABLED\n\n\nclass TopFrame(Container):\n    default_name = 'topFrame'\n    default_height = 26\n\n    def ApplyAttributes(self, attributes):\n        Container.ApplyAttributes(self, attributes)\n        Sprite(parent=self, align=uiconst.TOLEFT, width=126, texturePath='res:/UI/Texture/classes/shipTree/frame/topLeft.png')\n        cont = ContainerAutoSize(parent=self, align=uiconst.TOLEFT, alignMode=uiconst.TOPLEFT, bgTexturePath='res:/UI/Texture/classes/shipTree/frame/topMiddle.png')\n        Label(parent=cont, text=GetByLabel('UI/ShipTree/InterbusShipIdentificationSystem'), color=COLOR_BG, top=3, fontsize=12, uppercase=True, bold=True, padRight=4)\n        StretchSpriteHorizontal(parent=Container(parent=self), align=uiconst.TOTOP, texturePath='res:/UI/Texture/classes/ShipTree/frame/topRight.png', leftEdgeSize=40, rightEdgeSize=5, height=26)\n\n\nclass BottomFrame(Container):\n    default_name = 'bottomFrame'\n    default_height = 59\n\n    def ApplyAttributes(self, attributes):\n        Container.ApplyAttributes(self, attributes)\n        Sprite(parent=self, align=uiconst.TOLEFT, width=259, texturePath='res:/UI/Texture/classes/shipTree/frame/bottomLeft.png')\n        cont = ContainerAutoSize(parent=self, align=uiconst.TOLEFT, padLeft=4)\n        Label(parent=cont, align=uiconst.TOPLEFT, text=GetByLabel('UI/ShipTree/Showing'), uppercase=True, top=38, fontsize=9, color=COLOR_FONT)\n        Label(parent=self, text='V1.%s.%s' % (len(cfg.shiptypes), macho.version), uppercase=True, top=43, left=82, fontsize=10, color=COLOR_FONT)\n        Label(parent=cont, align=uiconst.TOPLEFT, text=GetByLabel('UI/ShipTree/MilitaryAndIndustrialVessels'), uppercase=True, top=49, fontsize=9, color=COLOR_FONT)\n        self.AddSeperator(uiconst.TOLEFT)\n        self.AddSeperator(uiconst.TORIGHT)\n        Label(parent=ContainerAutoSize(align=uiconst.TORIGHT, parent=self, padLeft=4), align=uiconst.TOPLEFT, text=GetByLabel('UI/ShipTree/CourtesyLabel'), uppercase=True, top=49, fontsize=9, color=COLOR_FONT)\n        self.AddSeperator(uiconst.TORIGHT)\n        StretchSpriteHorizontal(parent=Container(parent=self, padding=(4, 0, 4, 0)), align=uiconst.TOBOTTOM, texturePath='res:/UI/Texture/classes/ShipTree/frame/bottomLine.png', leftEdgeSize=20, rightEdgeSize=20, height=2)\n\n    def AddSeperator(self, align):\n        Sprite(parent=Container(align=align, parent=self, width=40, padLeft=4), texturePath='res:/UI/Texture/classes/shipTree/frame/bottomSeperator.png', align=uiconst.TOBOTTOM, height=8)","sub_path":"client/eve/client/script/ui/shared/shipTree/shipTreeContainer.py","file_name":"shipTreeContainer.py","file_ext":"py","file_size_in_byte":11960,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"244659592","text":"data = []\n\nwith open('./data/mbox-short.txt') as file:\n\tfor line in file:\n\t\tif line.startswith('From '):\n\t\t\tdata.append(line.strip())\n\n\nhours = [hour.split()[5][0:2] for hour in data]\ncount_hours = [(h,hours.count(h)) for h in set(hours)]\n\nfor hour in sorted(count_hours):\n\tprint(hour[0],hour[1])\n\n\n\n\t","sub_path":"string_tuple_set/sort_hours.py","file_name":"sort_hours.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"272645789","text":"#import fungsi dari file tfidf\nfrom tfidf import tf, idf\n\n#variable\nn_terms = 3\ntotal_terms = 100\ntotal_doc_with_terms = 1000\ntotal_doc = 10000000\n\n#memanggil fungsi tf untuk menghitung term frequency\n#variable tf_value akan menampung file dari hasil komputasi fungsi tf\ntf_value = tf(n_terms, total_terms)\n\nidf_value = idf(total_doc, total_doc_with_terms)\n\n#print tf_value\nprint(\"Term frequency: {0}\".format(tf_value))\n\nprint(\"inverse doc frequency: {0}\".format(idf_value))\n\ntfidf_value = tf_value*idf_value\n\nprint(\"tf * idf: {0}\".format(tfidf_value))","sub_path":"stbi.py","file_name":"stbi.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"244188625","text":"# https://atcoder.jp/contests/abc120/tasks/abc120_b\nimport sys\nsys.setrecursionlimit(2147483647)\nINF=float(\"inf\")\nMOD=10**9+7\ninput=lambda :sys.stdin.readline().rstrip()\ndef divisors(n):\n    S,T=[],[]\n    for i in range(1,int(n**.5)+1):\n        if n%i==0:\n            S.append(i)\n            T.append(n//i)\n    T.reverse()\n    return S+T if S[-1]!=T[0] else S+T[1:]\n\ndef resolve():\n    a,b,k=map(int,input().split())\n    from fractions import gcd\n    g=gcd(a,b)\n    A=divisors(g)\n    print(A[-k])\nresolve()\n","sub_path":"ABC120/b_k-th_common_divisor.py","file_name":"b_k-th_common_divisor.py","file_ext":"py","file_size_in_byte":507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"74220575","text":"import unittest\n\nfrom app import meu_web_app\n\nclass TestHome(unittest.TestCase):\n\n\tdef test_get(self):\n\t\tapp = meu_web_app.test_client()\n\t\tresponse = app.get('/')\n\t\tself.assertEqual(200, response.status_code)\n\n\tdef test_content_type(self):\n\t\tapp = meu_web_app.test_client()\n\t\tresponse = app.get('/')\n\t\tself.assertIn('text/html', response.content_type)\n\nif __name__ == '__main__':\n\tunittest.main()\n","sub_path":"tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":397,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"483703099","text":"from selenium import webdriver\nimport time\ndriver=webdriver.Chrome()\nfrom selenium.webdriver.support.select import Select\ndriver.implicitly_wait(20)\ndriver.get(\"https://www.amazon.in/\")\ndp=driver.find_element_by_id(\"searchDropdownBox\")\nsel=Select(dp)\nlst=sel.options\nfor i in lst:\n    print(i.text)\n\n","sub_path":"BaseClass.py","file_name":"BaseClass.py","file_ext":"py","file_size_in_byte":300,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"41344824","text":"# -*- coding: utf-8 -*-\nfrom PyQt5 import QtCore, QtGui, QtWidgets\n\n\nclass Widget(QtWidgets.QWidget):\n    def __init__(self):\n        super().__init__()\n        layout = QtWidgets.QVBoxLayout()\n        items = [(0, 'Python'), (1, 'Golang'), (2, 'JavaScript'), (3, 'Ruby')]\n        for id_, txt in items:\n            checkBox = QtWidgets.QCheckBox(txt, self)\n            checkBox.id_ = id_\n            checkBox.stateChanged.connect(self.checkLanguage)  # 1\n            layout.addWidget(checkBox)\n\n        self.lMessage = QtWidgets.QLabel(self)\n        layout.addWidget(self.lMessage)\n        self.setLayout(layout)\n\n    def checkLanguage(self, state):\n        checkBox = self.sender()\n        if state == QtCore.Qt.Unchecked:\n            self.lMessage.setText(u'取消选择了{0}: {1}'.format(\n                checkBox.id_, checkBox.text()))\n        elif state == QtCore.Qt.Checked:\n            self.lMessage.setText(u'选择了{0}: {1}'.format(\n                checkBox.id_, checkBox.text()))\n\n\nif __name__ == '__main__':\n    import sys\n    app = QtWidgets.QApplication(sys.argv)\n    widget = Widget()\n    widget.show()\n    sys.exit(app.exec_())\n","sub_path":"exp.py","file_name":"exp.py","file_ext":"py","file_size_in_byte":1147,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"475309186","text":"# TO-DO: Complete the selection_sort() function below\n\"\"\"\nStart with current index = 0\n\nFor all indices EXCEPT the last index:\n\na. Loop through elements on right-hand-side of current index and find the smallest element\n\nb. Swap the element at current index with the smallest element found in above loop\n\n\"\"\"\n\n\ndef selection_sort(arr):\n    # loop through n-1 elements\n    for i in range(0, len(arr) - 1):\n        cur_index = i\n        smallest_index = cur_index\n        # TO-DO: find next smallest element\n        # (hint, can do in 3 loc)\n        for j in range(i + 1, len(arr)):\n            if arr[j] < arr[smallest_index]:\n                smallest_index = j\n\n        # TO-DO: swap\n        arr[i], arr[smallest_index] = arr[smallest_index], arr[i]\n\n    return arr\n\n\n# TO-DO:  implement the Bubble Sort function below\n\"\"\"\nLoop through your array\nCompare each element to its neighbor\nIf elements in wrong position (relative to each other, swap them)\nIf no swaps performed, stop. Else, go back to the element at index 0 and repeat step 1.\n\n\"\"\"\n\n\ndef bubble_sort(arr):\n    had_swaps = True\n    while had_swaps == True:\n        had_swaps = False\n        for i in range(0, len(arr) - 1):\n\n            if arr[i] > arr[i+1]:\n                arr[i], arr[i+1] = arr[i+1], arr[i]\n                had_swaps = True\n    return arr\n\n# Here's another implementation from the debugger presentation, just for reference:\n\n\ndef bubble_sort2(arr):\n    for i in range(0, len(arr) - 1):\n        for j in range(0, len(arr) - 1 - i):\n            if arr[j] > arr[j+1]:\n                arr[j], arr[j+1] = arr[j+1], arr[j]\n    return arr\n\n\n# STRETCH: implement the Count Sort function below\n\"\"\"\nLook into Counting Sort.\n\nHow is this algorithm different from other iterative sorting algorithms?\n\nIt \"sorts the elements of an array by counting the number of occurrences\nof each unique element in the array. The count is stored in an auxiliary\narray and the sorting is done by mapping the count as an index of the auxiliary array.\"\n\nWhat are the advantages/disadvantages to this type of sorting algorithm?\nTake a look a the pseudocode for this algorithm and try implementing it in Python.\n\n1. Find out the maximum element (let it be max) from the given array.\n\n2. Initialize an array of length max+1 with all elements 0.\nThis array is used for storing the count of the elements in the array.\n\n3. Store the count of each element at their respective index in count array\n\n4. Store cumulative sum of the elements of the count array. That is,\nmodify the count array such that each element at each index\n  stores the sum of previous counts.\n\n5. Find the index of each element of the original array in count array.\nThis gives the cumulative count. Place the element at the index calculated.\n\n6. After placing each element at its correct position, decrease its count by one.\n\n\"\"\"\n\n\ndef count_sort(arr, maximum=-1):\n    # Find the largest element in the array\n    # Assuming we can't use the built-in max()?\n    if len(arr) == 0:\n        return arr\n    max = arr[0]\n    for num in arr:\n        if num < 0:\n            return \"Error, negative numbers not allowed in Count Sort\"\n        if num > max:\n            max = num\n\n    # Initialize count array with all zeros\n    count_arr = [0] * (max + 1)\n\n    # Loop through arr and find the total count of each unique element\n    # Store the count at the corresponding index in count array\n    for num in arr:\n        count_arr[num] += 1\n    # print(count_arr)\n\n    # modify the count array such that each element at each index stores the sum of previous counts.\n    my_sum = 0\n    for i in range(len(count_arr)):\n        my_sum += count_arr[i]\n        count_arr[i] = my_sum\n    # print(count_arr)\n\n    # Loop thru array.\n    # For each value x in arr, find count_arr[x]. Take that value - 1 to find index to put x in the return_array.\n    # Then decrease the value of count_arr[x] by 1.\n    return_arr = [0] * len(arr)\n\n    for num in arr:\n        # print(num)\n        index = count_arr[num] - 1\n        return_arr[index] = num\n        count_arr[num] -= 1\n\n    print(return_arr)\n    return return_arr\n\n\n# count_sort([3, 3, 1])\n# count_sort([4, 6, 3, 1, 3, 9, 2])\n\n\"\"\"\nInsertion Sort:\n\nSeparate the first element from the rest of the array. Think about it as a sorted list of one element.\n\nFor all other indices, beginning with [1]:\n\na. Copy the item at that index into a temp variable\n\nb. Iterate to the left until you find the correct index in the \"sorted\" part of the array at which this element should be inserted\n\nShift items over to the right as you iterate\nc. When the correct index is found, copy temp into this position\n\n\"\"\"\n\n\ndef insertion_sort(arr):\n    # Divide your hand into sorted on the left and unsorted on the right\n    # Sorted is just the first element\n    # then go card by card and move them into place.\n    # Loop through all elements in unsorted...\n    for i in range(1, len(arr)):\n        temp = arr[i]\n        j = i  # j is our sliding index\n        # Shift sorted to the right until correct position found\n        while j > 0 and temp < arr[j - 1]:\n            arr[j] = arr[j - 1]  # Slide over one element\n            j -= 1\n        # Insert at that position\n        arr[j] = temp\n    return arr\n\n\nmy_arr_14 = [6, 14, 13, 7, 14]\n# print(insertion_sort(my_arr_14))\n\n\n\"\"\"\nCustom bubble sort, created with Hui on 7-11-2020.\nIt combines both versions of bubble sort.\nWe also experimented to see if we could increase i when the algorithm runs into sequential numbers, like 4,5,6\nbut not successful yet.\n\"\"\"\n\n\ndef bubble_sort_custom(arr):\n    i = 0\n    needs_sorting = True\n    while i < len(arr) and needs_sorting:\n        needs_sorting = False\n        j = 0\n        while j < len(arr) - i - 1:\n            # I'm not sure if this last inner while really helps anything.\n            while arr[j] + 1 == arr[j+1]:\n                j += 1\n            if arr[j] > arr[j+1]:\n                needs_sorting = True\n                arr[j], arr[j+1] = arr[j+1], arr[j]\n            j += 1\n        i += 1\n    return arr\n\n\nprint(bubble_sort_custom([1, 96, 97, 98, 25, 4, 5, 2, 0, 87]))\n# print(bubble_sort_custom([2, 6, 3, 4, 5]))\n# print(bubble_sort([2, 6, 3, 4, 5]))\n# print(bubble_sort([1, 96, 97, 98, 25, 4, 5, 2, 0, 87]))\n","sub_path":"src/iterative_sorting/iterative_sorting.py","file_name":"iterative_sorting.py","file_ext":"py","file_size_in_byte":6242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"436158195","text":"from selenium import webdriver\nfrom bs4 import BeautifulSoup as soups\nimport json\nimport pandas as pd\nimport os\nimport shutil\nfrom datetime import date\nimport urllib.request\nfrom urllib.parse import quote\n\nDATA_DIR = \"../data\"\nDATA_FILE = os.path.join(DATA_DIR, \"landname.json\")\nDUMP_FILE = os.path.join(DATA_DIR, \"landname.pkl\")\n\ndef search_selenium(data_path=DATA_FILE) :\n    try:\n        with open(data_path, encoding=\"utf-8\") as f:\n            data = json.loads(f.read())\n    except FileNotFoundError as e:\n        print(f\"`{data_path}` 가 존재하지 않습니다.\")\n        exit(1)\n\n    for d in data:\n        if d['lid'] < 'l364':\n             continue\n\n        search_name = d['landmark_name']\n        search_url = \"https://www.google.com/search?q=\" + str(search_name) + \"&hl=ko&tbm=isch\"\n        \n        browser = webdriver.Chrome('c:/chromedriver.exe')\n        browser.get(search_url)\n        \n        #image_count = len(browser.find_elements_by_tag_name(\"img\"))\n        #print(\"로드된 이미지 개수 : \", image_count)\n    \n        browser.implicitly_wait(2)\n    \n        image = browser.find_elements_by_tag_name(\"img\")[1]\n        #image.screenshot(\"C:/Users/multicampus/Documents/s04p23d108/backend/recommendroad/landimg\" + str(d['lid'])+ \".png\")\n        image.screenshot(\"c:/expression/\" + str(d['lid'])+ \".png\")\n        print(\"찰칵\" + d['lid'] + \"저장\")\n        browser.close()\n\ndef main():\n\n    print(\"[*] googleCrawling...\")\n    search_selenium()\n    print(\"[+] Done\")\n\nif __name__ == \"__main__\" :\n \n    # search_name = input(\"검색하고 싶은 키워드 : \")\n    # search_limit = int(input(\"원하는 이미지 수집 개수 : \"))\n    # search_path = \"Your Path\"\n    # search_maybe(search_name, search_limit, search_path)\n    search_selenium()","sub_path":"backend/recommendroad/landmarkCrawling.py","file_name":"landmarkCrawling.py","file_ext":"py","file_size_in_byte":1774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"236623867","text":"n = int(input())\nx = 2\n\n\ndef MinDivisor(n):\n    x = 2\n    while x <= n:\n        if n % x == 0:\n            return x\n        x += 1\n        if x > n ** 0.5:\n            return n\n\n\nprint(MinDivisor(n))\n","sub_path":"6.py","file_name":"6.py","file_ext":"py","file_size_in_byte":200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"109853756","text":"from fun_forms.fields.funcs import (make_field_template, populate_field,\n                                    validate_field)\nfrom fun_forms.validators.lengths import has_length\nfrom fun_forms.validators.strings import (has_length_when_stripped,\n                                          is_lower_case)\nfrom fun_forms.validators.typedefs import Validator\nfrom typing import Any, Generic, NamedTuple, T, Tuple\n\nExpectedResult = NamedTuple('ExpectedResult', [\n    ('value', Any),\n    ('allow_none', bool),\n    ('data_type', Tuple[Generic[T]]),\n    ('validators', Tuple[Validator]),\n    ('expected_validity', bool),\n    ('expected_invalid_messages', Tuple[str]),\n])\n\n\nexpected_results = (\n    ExpectedResult(value=\"Jweigvi\",\n                   allow_none=False,\n                   data_type=str,\n                   validators=(has_length, is_lower_case, has_length_when_stripped),\n                   expected_validity=False,\n                   expected_invalid_messages=('Must be lowercase.',)),\n    ExpectedResult(value=\"pewebrkhwber\",\n                   allow_none=False,\n                   data_type=str,\n                   validators=(has_length, is_lower_case, has_length_when_stripped),\n                   expected_validity=True,\n                   expected_invalid_messages=tuple()),\n    ExpectedResult(value=\"\",\n                   allow_none=False,\n                   data_type=str,\n                   validators=(has_length, is_lower_case, has_length_when_stripped),\n                   expected_validity=False,\n                   expected_invalid_messages=(\n                       \"Must have a length great than 0.\",\n                       \"Must have length with characters other than whitespace.\",)),\n    # TEST THAT TYPES ARE VALIDATED PROPERLY\n    ExpectedResult(value=None,\n                   allow_none=True,\n                   data_type=str,\n                   validators=tuple(),\n                   expected_validity=True,\n                   expected_invalid_messages=tuple()),\n    ExpectedResult(value=None,\n                   allow_none=False,\n                   data_type=object,\n                   validators=tuple(),\n                   expected_validity=True,\n                   expected_invalid_messages=tuple()),\n    ExpectedResult(value=5,\n                   allow_none=False,\n                   data_type=object,\n                   validators=tuple(),\n                   expected_validity=True,\n                   expected_invalid_messages=tuple()),\n)\n\n\ndef test_make_field_template():\n    for expected_result in expected_results:\n        result = make_field_template(\n            data_type=expected_result.data_type,\n            validators=expected_result.validators,\n            allow_none=expected_result.allow_none\n        )\n        assert result.validators == expected_result.validators\n        assert result.data_type == expected_result.data_type\n        assert result.allow_none == expected_result.allow_none\n\n\ndef test_populate_field():\n    for expected_result in expected_results:\n        this_field_template = make_field_template(data_type=expected_result.data_type,\n                                                  validators=expected_result.validators,\n                                                  allow_none=expected_result.allow_none)\n        populated_field = populate_field(\n            field_template=this_field_template,\n            value=expected_result.value\n        )\n\n        assert populated_field.field_template == this_field_template\n        assert populated_field.value == expected_result.value\n\n\ndef test_validate_field():\n    for value, allow_none, data_type, validators, \\\n            expected_validity, expected_messages in expected_results:\n        validated_field = validate_field(\n            populate_field(\n                field_template=make_field_template(\n                    data_type=data_type,\n                    validators=validators,\n                    allow_none=allow_none\n                ),\n                value=value\n            )\n        )\n        assert validated_field.valid == expected_validity\n        assert validated_field.invalid_messages == expected_messages\n","sub_path":"tests/fields/test_funcs.py","file_name":"test_funcs.py","file_ext":"py","file_size_in_byte":4145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"8968700","text":"from fastapi import APIRouter, Depends, Security\nfrom app.dependencies import get_db\nfrom sqlalchemy.orm import Session\nfrom app import models, services, schemas\nfrom fastapi_auth0 import Auth0, Auth0User\n\n\nrouter = APIRouter(tags=[\"votes\"])\n\n\n@router.post(\"/vote\")\ndef create_vote(req: schemas.VoteRequest, db: Session = Depends(get_db)):\n    try:\n        vote = services.vote.create_vote(\n            votetype=req.votetype,\n            user_id=req.user_id,\n            stock_id=req.stock_id,\n            db=db\n        )\n    except Exception as e:\n        raise Exception(e)\n","sub_path":"backend/app/routers/votes.py","file_name":"votes.py","file_ext":"py","file_size_in_byte":576,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"542034577","text":"\"\"\"Test a chain of child processes with compute log tails.\"\"\"\n\nimport sys\nimport time\n\nfrom dagster._serdes.ipc import interrupt_ipc_subprocess, open_ipc_subprocess\nfrom dagster._utils import file_relative_path\nfrom dagster._utils.interrupts import setup_interrupt_handlers\n\nif __name__ == \"__main__\":\n    setup_interrupt_handlers()\n    (\n        child_opened_sentinel,\n        parent_interrupt_sentinel,\n        child_started_sentinel,\n        stdout_pids_file,\n        stderr_pids_file,\n        child_interrupt_sentinel,\n    ) = (sys.argv[1], sys.argv[2], sys.argv[3], sys.argv[4], sys.argv[5], sys.argv[6])\n    child_process = open_ipc_subprocess(\n        [\n            sys.executable,\n            file_relative_path(__file__, \"compute_log_subprocess.py\"),\n            stdout_pids_file,\n            stderr_pids_file,\n            child_started_sentinel,\n            child_interrupt_sentinel,\n        ]\n    )\n    with open(child_opened_sentinel, \"w\", encoding=\"utf8\") as fd:\n        fd.write(\"opened_ipc_subprocess\")\n    try:\n        while True:\n            time.sleep(0.1)\n    except KeyboardInterrupt:\n        interrupt_ipc_subprocess(child_process)\n        with open(parent_interrupt_sentinel, \"w\", encoding=\"utf8\") as fd:\n            fd.write(\"parent_received_keyboard_interrupt\")\n","sub_path":"python_modules/dagster/dagster_tests/core_tests/serdes_tests/parent_compute_log_subprocess.py","file_name":"parent_compute_log_subprocess.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"553918049","text":"\"\"\"\nShuffles the test directory of images and puts 1024\nrandom test images into the test_degradation file\n\n\"\"\"\n\nimport os\nimport shutil\nimport random\nimport argparse\nimport pandas as pd\n\nrandom.seed(1)\n\nparser = argparse.ArgumentParser(description='Shuffles dataset and creates train/val/test')\nparser.add_argument('inp_file', help='Entire image dataset')\nargs = parser.parse_args()\n\ninp_d = args.inp_file\n\nd = []\n\nfor dirs, subdirs, files in os.walk(inp_d):\n    for f in files:\n        d.append(os.path.join(dirs, f))\n\nrandom.shuffle(d)\n\ndlen = len(d)\n\ntestset = d[:1024]\nte_dst = r'/home/mseals1/Documents/aphylla/annotated_images_only/test_degradation'\n\nfor f in testset:\n    shutil.copy(f, te_dst)\n","sub_path":"degradation_scripts/random_deg_images.py","file_name":"random_deg_images.py","file_ext":"py","file_size_in_byte":702,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"469826292","text":"\"\"\"\nCopyright 2020 William Locatelli.\nA program which can perform various analyses on a weighted directed acyclic graph.\nIt is designed to be used for selecting movies from the Marvel Cinematic Universe,\nbut could be used for other applications with a few minor tweaks.\n\"\"\"\nimport csv\nRULE = \"Recent\"\nCOUNT_RULE = \"Count\"\nCURRENT_ANCESTOR_TIER = 0\nMOVIES = {}\nGRAPHS_CHECKED = 0\nNUM_TIES = 0\n\n\n# takes in a CSV file, sets global variables for movies\n# CSV must be formatted as adjacency matrix with weights in a cel representing the weight of the edge\n# from parent column to child row\ndef import_weighted_from_csv(file=\"Data/MCUphase1to3-weighted.csv\"):\n    # put all the rows of the csv file into a list\n    csv_rows = []\n    with open(file, newline='', encoding='utf-8-sig') as csvfile:\n        reader = csv.reader(csvfile, delimiter=',', quotechar='|')\n        for row in reader:\n            csv_rows.append(row)\n    title_list = csv_rows[0]\n    # create movie objects\n    all_movies = {}\n    i = 1  # skip the title row\n    movie_count = len(csv_rows)\n    while i < movie_count:\n        row = csv_rows[i]\n        this_movie = Movie(row[0], series=row[1])\n        all_movies[row[0]] = this_movie\n        i += 1\n    i = 1  # skip the title row\n    while i < movie_count:\n        prevs = []\n        row = csv_rows[i]\n        i2 = 2  # skip the series column\n        while i2 < len(row):\n            weight = row[i2]\n            if len(weight) > 0 and weight != \"0\":\n                # csv_rows[i] tells you what movies i comes from, so the prevs need to be found in the title list[i2]\n                prevs.append((all_movies[title_list[i2]], float(weight)))\n            i2 += 1\n        all_movies[row[0]].prevs = prevs\n        i += 1\n    global MOVIES\n    MOVIES = all_movies\n\n\n# imports additional data from file\ndef import_data_from_csv(file=\"Data/data.csv\"):\n    csv_rows = []\n    with open(file, newline='', encoding='utf-8-sig') as csvfile:\n        reader = csv.reader(csvfile, delimiter=',', quotechar='|')\n        for row in reader:\n            csv_rows.append(row)\n    i = 1  # skip the title row\n    movie_count = len(csv_rows)\n    while i < movie_count:\n        row = csv_rows[i]\n        this_movie = MOVIES[row[0]]\n        this_movie.rt_score = row[1]\n        this_movie.date = row[2]\n        i += 1\n\n\n# returns global list of movies\ndef get_movies():\n    return MOVIES\n\n\n# sets global rule\ndef set_rule(rule):\n    global RULE\n    RULE = rule\n\n\n# sets global count rule\ndef set_count_rule(count_rule):\n    global COUNT_RULE\n    COUNT_RULE = count_rule\n\n\n# returns the number of graphs considered by the algorithm\ndef get_graphs_checked():\n    return GRAPHS_CHECKED\n\n\n# find the n best other movies to watch if you've watched the watched and want to watch the children\n# parameters:\n#           watched:    list of films which have already been watched\n#           children:   list of films which they want to watch\n#           num_extras: the number of additional films to be included\n#           rule:       the heuristic to be used to select additional films\n# returns: a list of movies - the set of movies which has the highest weight\ndef find_best_subgraph(watched, children, num_extras):\n    nodes = children.copy()\n    included_unwatched = children.copy()\n    num_to_check = num_extras\n    if RULE == \"Interconnected\":\n        #  fill the nodes with all parents of the children, excluding isolated parents of the watched films.\n        prev_tree(nodes, watched)\n    else:\n        #  generate each tier of ancestors one at a time, dequeuing the ancestors into the included_unwatched\n        #  if fewer than num_extras ancestors have been discovered. nodes will contain the nodes in\n        #  included_unwatched plus the nodes in the final tier.\n        num_to_check = limited_prev_tree(nodes, watched, included_unwatched, num_extras)\n    # add parents to nodes. they couldn't be added before because we didn't want their parents to also get added.\n    for parent in watched:\n        nodes.append(parent)\n    # included is all the movies we know we are going to include\n    included = watched + included_unwatched\n    # excluded is the candidate movies we are considering for inclusion\n    excluded = []\n    # if there are less extra films than the total requested films, return all the films.\n    if len(nodes) <= num_extras + len(children) + len(watched):\n        return nodes\n    else:\n        for movie in nodes:\n            if movie not in included:\n                excluded.append(movie)\n    if RULE == \"Recent\":\n        best_graphs = [most_recent_movies(excluded, included, num_to_check)]\n    else:\n        best_graphs = brute_force_subgraph_helper(excluded, included, num_to_check, included_unwatched)\n    result = tie_breaker(best_graphs, excluded, included_unwatched, watched)\n    return result\n\n\n# generates a watch order, excluding movies which have already been watched\n# parameters:\n#           watched: list of movies which have already been watched\n#           subgraph: list of the all the movies in the watch list\n# return format: string\ndef watch_order(watched, subgraph):\n    order = []\n    for movie in MOVIES:\n        if MOVIES[movie] in subgraph and not MOVIES[movie] in watched:\n            order.append(movie)\n    return order\n\n\n##############################################  HELPER FUNCTIONS  ##############################################\n\n# Returns a list containing included plus the num most recent movies in excluded.\ndef most_recent_movies(excluded, included, num):\n    excluded_sorted = sorted(excluded, key=lambda x: x.date, reverse=True)\n    i = 0\n    result = included.copy()\n    while i < num:\n        result.append(excluded_sorted[i])\n        i += 1\n    return result\n\n\n# Iterates through every possible set of movies containing the movies in included and\n# n of the movies in excluded. Sums the edge weights between movies in each set, and\n# keeps track of the sets which have the highest total weight.\n# Returns a list containing all the sets which are tied for first place.\n# parameters:\n#           excluded: films which aren't currently in any of the other lists\n#           included: films which should be included in every graph\n#           children: the films people want to watch - a subset of included\n#           n:        the size of each final graph should equal len(included) + n\ndef brute_force_subgraph_helper(excluded, included, n, children):\n    global GRAPHS_CHECKED\n    best_graphs = []\n    if n == 0:\n        GRAPHS_CHECKED += 1\n        subgraph = included.copy()\n        return [subgraph]\n    max_weight = 0\n    # best_graph = included\n    excluded_copy = excluded.copy()\n    included_copy = included.copy()\n    while len(excluded_copy) > n - 1:\n        movie = excluded_copy[0]\n        included_copy.append(movie)\n        excluded_copy.remove(movie)\n        best_graph_next_level = []\n        best_graph_next_level += brute_force_subgraph_helper(excluded_copy, included_copy, n - 1, children)\n        if RULE == \"Relevant\" and CURRENT_ANCESTOR_TIER < 2:\n            weight = subgraph_weight(list(set(best_graph_next_level[0]) - set(children)), children)\n        else:\n            weight = subgraph_weight(best_graph_next_level[0], best_graph_next_level[0])\n        if weight > max_weight:\n            max_weight = weight\n            best_graphs = best_graph_next_level.copy()\n        elif weight == max_weight:\n            best_graphs += best_graph_next_level\n        included_copy.remove(movie)\n    return best_graphs\n\n\n# Breaks ties based on a series of 3 criteria:\n#       1. Which graph has highest weight if parents are removed?\n#       2. Which graph has highest weight if edges are ignored and\n#               nodes are weighted by RottenTomatoes scores?\n#       3. Which graph has highest weight if we add up the weights of all the\n#               connections its films have to other movies?\n# parameters:\n#       best_graphs - a list of graphs which all have equal weight\n#       excluded    - movies which aren't currently included in any of the graphs\n#       children    - the movies people want to watch\n#       parents     - the movies people have already watched\n#\n# returns: the best graph\ndef tie_breaker(best_graphs, excluded, children, parents):\n    if len(best_graphs) == 1:\n        return best_graphs[0]\n    else:\n        best_graph = None\n        max_score = 0\n        best_graphs_2 = []\n        # choose subgraph of highest weight when parents are removed\n        i = 0\n        best_graphs_no_parents = []\n        while i < len(best_graphs):\n            best_graphs_no_parents.append(list(set(best_graphs[i]) - set(parents)))\n            i += 1\n        i = 0\n        while i < len(best_graphs):\n            score = subgraph_weight(best_graphs_no_parents[i], best_graphs_no_parents[i])\n            if score > max_score:\n                best_graphs_2 = [best_graphs[i]]\n                best_graph = best_graphs[i]\n                max_score = score\n            elif score == max_score:\n                best_graphs_2.append(best_graphs[i])\n            i += 1\n        # if not enough, choose subgraph of highest cumulative RT score\n        if len(best_graphs_2) > 1:\n            max_score = 0\n            for graph in best_graphs:\n                score = sum(int(movie.rt_score) for movie in graph)\n                if score > max_score:\n                    best_graphs_2 = [graph]\n                    best_graph = graph\n                    max_score = score\n                elif score == max_score:\n                    best_graphs_2.append(graph)\n        # if that wasn't enough, choose subgraph of highest cumulative weight of ancestors\n        if len(best_graphs_2) > 1:\n            max_score = 0\n            for graph in best_graphs_2:\n                score = 0\n                for movie in graph:\n                    score += sum(int(prev[1]) for prev in movie.prevs)\n                if score > max_score:\n                    best_graph = graph\n                    max_score = score\n                elif score == max_score:\n                    print(\"not rigorous enough\")\n        return best_graph\n\n\n# NOTE: THIS FUNCTION MODIFIES THE PARAMETER NODES\n# generates subgraph containing all ancestors of every node in nodes\n# parameters:\n#       nodes: the nodes whose ancestors we want to find\n#       watched: the films we've already watched. use to block the pipe so that those films' ancestors aren't scanned\ndef prev_tree(nodes, watched):\n    for movie in nodes:\n        for prev in movie.prevs:\n            if prev[1] > 4 and prev[0] not in watched and prev[0] not in nodes:\n                nodes.append(prev[0])\n                prev_tree(nodes, watched)\n\n\n# NOTE: THIS FUNCTION MODIFIES THE PARAMETERS NODES AND CHILDREN\n# Generates a limited ancestor tree, containing only more recent ancestors\n# ancestors are broken up into generations. All ancestors from every generation up to n ancestors\n# will be returned in nodes, while all ancestors from every generation except the most ancient will\n# be returned in children.\n#\n# The final tier which reaches or surpasses n will be included; in other words, the final size of nodes\n# will always be greater than or equal to n + children, while the final size of children will always be\n# less than or equal to n + (the original size of) children.\n# Parameters:\n#   nodes: all unwatched nodes in the graph\n#   watched: films which have already been watched\n#   children: films which were requested\n#   n: number of ancestors to add to nodes before stopping\n# Returns: The number of additional films in nodes which should be added to children using some other algorithm.\ndef limited_prev_tree(nodes, watched, children, n):\n    global CURRENT_ANCESTOR_TIER\n    # all of the nodes whose ancestors have been checked, and all of the ancestors. different from nodes in that nodes\n    # does not include any watched.\n    total_nodes = nodes.copy()\n    requested_watchlist_count = len(children) + n\n    current_level_nodes = nodes.copy()  # the list of nodes whose parents we are currently finding\n    discarded_nodes = []  # used only for Most Recent heuristic\n    recently_added_nodes = []  # represents the newest tier (not including any children in the tier)\n    while len(nodes) < requested_watchlist_count and (len(current_level_nodes) > 0 or len(discarded_nodes) > 0):\n        CURRENT_ANCESTOR_TIER = CURRENT_ANCESTOR_TIER + 1\n        children.clear()\n        children.extend(nodes)\n\n        # Get the next tier. Split the tier up based on whether the films in it have already been checked.\n        prev_nodes, recently_discarded_nodes = next_tier(current_level_nodes)\n        recently_added_nodes.clear()\n        children_duplicates = []  # handles situation where a tier contains one of the other movies they wanted to watch\n        for prev in prev_nodes:\n            if prev not in recently_added_nodes and prev not in total_nodes:\n                recently_added_nodes.append(prev)\n            else:\n                children_duplicates.append(prev)\n\n        # Add the new arrivals to the list of total nodes checked.\n        total_nodes.extend(recently_added_nodes)  # recently added nodes\n        # create a list of the movies whose parents should be checked on the next round. This should be all\n        # unwatched movies.\n        current_level_nodes = list(set(recently_added_nodes) - set(watched)) + children_duplicates\n        # make discarded nodes = all the nodes that have been discarded that weren't put back in\n        discarded_nodes = (list((set(discarded_nodes) | set(recently_discarded_nodes)) - set(total_nodes)))\n        # Check if any of the discarded movies are a parent of a movie we watched. If so, create a custom movie with\n        # those movies as parents, and add that custom movie to the next round of parent lookups\n        # (but don't add it into anything permanent). This allows us to include less recent parents of our selected\n        # movies without including any other parents of watched movies.\n        if len(discarded_nodes) > 0:\n            recently_added_watched = set(watched) & set(recently_added_nodes)  # intersection\n            for movie in recently_added_watched:\n                temp_movie = Movie(\"Temp Movie\")\n                for prev in movie.prevs:\n                    if prev[0] in discarded_nodes:\n                        temp_movie.prevs.append(prev)\n                if len(temp_movie.prevs) > 0:\n                    current_level_nodes.append(temp_movie)\n            # if we traversed the whole tree and somehow missed some nodes, put those nodes back in.\n            if len(current_level_nodes) == 0:\n                temp_movie = Movie(\"Temp Movie\")\n                for discard in discarded_nodes:\n                    temp_movie.prevs.append((discard, 5))\n                current_level_nodes.append(temp_movie)\n                discarded_nodes.clear()\n        nodes.clear()\n        # nodes now equals every node that has been looked at that wasn't watched\n        nodes.extend(list(set(total_nodes) - set(watched)))\n        if COUNT_RULE == \"Whatever It Takes\" and RULE == \"Relevant\":  # breaks out of loop after first tier\n            break\n    # set children to the nodes minus the \"recently added\" nodes\n    # children = list(set(nodes) - set(recently_added_nodes))\n    # number left to check will be how many they wanted minus how many we already have\n    return requested_watchlist_count - len(children)\n\n\n# computes and returns the weight of all links between set parents and set children\n# if the same set is passed for parents and children, it will compute the weight of\n# all links between all elements in the set\ndef subgraph_weight(parents, children):\n    weight = 0\n    for movie in children:\n        for prev in movie.prevs:\n            if prev[0] in parents:\n                weight += prev[1]\n    return weight\n\n\n# returns the most recent ancestors of a list of movies. If RULE == Most Recent,\n# trims off any ancestors of other ancestors and returns them in discarded_predecessors.\ndef next_tier(movies):\n    # step 1: find all nodes which are immediately relevant\n    direct_predecessors = []\n    discarded_predecessors = []\n    for movie in movies:\n        for prev in movie.prevs:\n            if prev[0] not in direct_predecessors and prev[1] > 4:\n                direct_predecessors.append(prev[0])\n    if RULE == \"Recent\":\n        # step 2: generate family tree to discover if there will be missing pieces\n        nodes = movies.copy()\n        for node in nodes:\n            nodelist = [node]\n            prev_tree(nodelist, [])\n            nodelist.remove(node)\n            for film in nodelist:\n                for prev in film.prevs:\n                    if prev[0] in direct_predecessors and prev[1] > 4:\n                        direct_predecessors.remove(prev[0])\n                        if prev[0] not in discarded_predecessors:\n                            discarded_predecessors.append(prev[0])\n    return direct_predecessors, discarded_predecessors\n\n\n# Movies have names, series, release dates,and lists of previous (and potentially next) films.\nclass Movie:\n    def __init__(self, name, series=\"\", date=\"\"):\n        self.prevs = []  # the parents of this movie\n        self.seqs = []  # the children of this movie (unused field)\n        self.name = name  # the name of the movie\n        self.series = series  # the film series this movie belongs to\n        self.date = date\n","sub_path":"Recommender.py","file_name":"Recommender.py","file_ext":"py","file_size_in_byte":17413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"164665863","text":"import pandas as pd\nimport numpy as np\nimport plotly.graph_objects as go\n\nPERSONE_GIURIDICHE_URL = 'https://raw.githubusercontent.com/nicolopomini/techvisory/master/POC_data_PG.csv'\nPERSONE_FISICHE_URL = 'https://raw.githubusercontent.com/nicolopomini/techvisory/master/POC_Data_PF.csv'\n\nresults_for_people = {}\nresults_for_pg = {}\n\n\ndef get_dataframe(categoria, drive):\n    if categoria == 'PF':\n        sep = \";\"\n        url = PERSONE_FISICHE_URL\n    else:\n        sep = \",\"\n        url = PERSONE_GIURIDICHE_URL\n    if categoria == 'PF':\n        Den = 'Nome'\n    else:\n        Den = 'Denominazione'\n    DF = pd.read_csv(url, sep)\n    return DF, Den\n\n\ndef get_persona_giuridica():\n    return pd.read_csv(PERSONE_GIURIDICHE_URL, ',')\n\ndef get_persona_fisica():\n    return pd.read_csv(PERSONE_FISICHE_URL, ';')\n\n\ndef analyze_pf_people(df):\n    people = df[\"Nome\"].to_list()\n    for person in people:\n        U1,L1,T,y,Credito = detrazioni('PF',person, 'Nome', df)\n        results_for_people[person] = {\n            \"upper_estimate\": U1,\n            \"lower_estimate\": L1,\n            \"time\": T,\n            \"credito\": Credito,\n            \"credito_for_plot\": y\n        }\n\ndef analyze_pg_people(df):\n    people = df[\"Denominazione\"].to_list()\n    for person in people:\n        U1,L1,T,y,Credito = detrazioni('PG',person, 'Denominazione', df)\n        U2, L2, T2 = Liq(person, 'Denominazione', df, 'PG')\n        results_for_pg[person] = {\n            \"upper_estimate\": U1,\n            \"lower_estimate\": L1,\n            \"time\": T,\n            \"credito\": Credito,\n            \"credito_for_plot\": y,\n            \"liquidita_upper\": U2,\n            \"liquidita_lower\": L2,\n            \"liquidita_time\": T2\n        }\n\n\ndef sell_range(df, person):\n    actual_value = check_status(df, person, 'PF', results_for_people[person][\"lower_estimate\"], 'Nome')\n    if actual_value < 0:\n        value = actual_value\n    else:\n        coeff_future_value = future_value(df, person, \"PF\", results_for_people[person][\"time\"], results_for_people[person][\"lower_estimate\"], \"Nome\", results_for_people[person][\"credito\"])\n        if coeff_future_value < 0:\n            value = coeff_future_value\n        else:\n            A = advantage(df, person, results_for_people[person][\"lower_estimate\"], 'PF', 'Nome')\n            value = A\n    if value >= 0:\n        return None\n    else:\n        minimum = results_for_people[person][\"credito\"] + results_for_people[person][\"credito\"] * value / 100\n        maximum = int(results_for_people[person][\"credito\"] * 1.05)\n        return minimum, maximum\n\n\ndef get_people_that_sell(df):\n    people_that_sell = []\n    analyze_pf_people(df)\n    for person in results_for_people:\n        range = sell_range(df, person)\n        if range is not None:\n            people_that_sell.append(person)\n    return people_that_sell\n\ndef get_people_to_buy(df, people_that_sell, df_people):\n    for business in results_for_pg:\n        if sum(results_for_pg[business][\"lower_estimate\"]) < int(df[\"Credito\"][0]):\n            return None\n        else:\n            people_values = {people: sell_range(df_people, people) for people in people_that_sell}\n            sorted_people = {k: v for k, v in sorted(people_values.items(), key=lambda item: item[1][0])}\n            budget = int(df[\"Liquidità 2018\"][0])\n            total_spent = 0\n            bought_people = []\n            for people in sorted_people:\n                if total_spent + sorted_people[people][0] * 1.02 > budget or total_spent > (sum(results_for_pg[business][\"lower_estimate\"]) - int(df[\"Credito\"][0])):\n                    break\n                bought_people.append(people)\n                total_spent += sorted_people[people][0]\n            return bought_people\n\ndef do_offer(people_to_buy, df, df_people):\n    analyze_pg_people(df)\n    if people_to_buy:\n      people_values = {people: sell_range(df_people, people) for people in people_to_buy}\n      return {people: (int(people_values[people][0] * 0.98), int(people_values[people][0] * 1.02)) for people in people_values}\n    return {}\n\n            \n\n\n\ndef future_prediction(h, years, Time, variable):\n    history = []\n    history2 = []\n    time = []\n    for i in range(len(Time)):\n        time.append(Time[i])\n    for i in range(len(h)):\n        history.append(h[i]), history2.append(h[i])\n    for j in range(years):\n        c1 = 0\n        c2 = 0\n        for i in range(len(history) - 1):\n            c1 += (history[-1 - i] - history[-2 - i]) * (len(history) - 1 - i)\n            c2 += (len(history) - 1 - i)\n        c1 = c1 / c2\n        if variable == 'Detrazione':\n            history.append(max(history[-1] + c1, 0))\n        else:\n            history.append(history[-1] + c1)\n        time.append('20' + str(int(time[-1][2:]) + 1))\n        c1 = 0\n        c2 = 0\n        for i in range(len(history2) - 1):\n            c1 += (history2[-1 - i] - history2[-2 - i]) * (1)\n            c2 += (1)\n        c1 = c1 / c2\n        if variable == 'Detrazione':\n            history2.append(max(history2[-1] + c1, 0))\n        else:\n            history2.append(history2[-1] + c1)\n    return history, history2, time\n\n\ndef detrazioni(Categoria, Person, Den, DF):\n    Var = []\n    Var2 = []\n    for i in range(len(DF)):\n        if DF[Den][i] == Person:\n            Credito = (DF['Credito'][i])\n            x = []\n            Time = []\n            value = 0\n            for j in DF.keys():\n                if Categoria == 'PF':\n                    if j[:5] == 'IRPEF':\n                        value += DF[j][i]\n                        Time.append(j[6:])\n                    elif j[:5] == 'Oneri':\n                        value -= DF[j][i]\n                        x.append(value)\n                        value = 0\n                else:\n                    if j[:25] == \"Imposte nette sul reddito\":\n                        value += DF[j][i]\n                        Time.append(j[26:])\n                    elif j[:30] == 'Imposte patrimoniali e diverse':\n                        value += DF[j][i]\n                    elif j[:13] == 'Oneri sociali':\n                        value += DF[j][i]\n                        x.append(value)\n                        value = 0\n            Var.append(x)\n            Var2.append(x)\n    Variable = 'Detrazione'\n    Time = np.flip(Time)\n    for i in range(len(Var)):\n        Var[i] = np.flip(Var[i])\n        Var2[i] = np.flip(Var2[i])\n    for i in range(len(Var)):\n        Var[i], Var2[i], time = future_prediction(Var[i], 5, Time, Variable)\n    name = Person\n    Upper_estimate = []\n    Lower_estimate = []\n    y = []\n    for i in range(5):\n        y.append(Credito * 1.1 * 0.2) \n    for i in range(len(Var[0])):\n        Lower_estimate.append(min(Var[0][i], Var2[0][i]))\n        Upper_estimate.append(max(Var[0][i], Var2[0][i]))\n    return Upper_estimate, Lower_estimate, time, y, Credito\n\n\ndef plot_Detrazioni(T, U1, L1, Person, Categoria, y):\n    if Categoria == 'PF':\n        index = 5\n    else:\n        index = 3\n    fig = go.Figure(data=[\n        go.Scatter(x=T,\n                   y=U1,\n                   line=dict(width=1, color='rgb(0,150, 0)'),\n                   mode='lines+markers',\n                   name='Upper estimate'),\n        go.Scatter(x=T,\n                   y=L1,\n                   line=dict(width=1, color='rgb(225,0, 0)'),\n                   mode='lines+markers',\n                   fillcolor='rgba(68, 68, 68, 0.3)',\n                   fill='tonexty',\n                   name='Lower estimate')\n    ])\n    fig.add_trace(\n        go.Scatter(x=T[index:],\n                   y=y,\n                   line=go.scatter.Line(color=\"black\"),\n                   name='Credito'))\n    fig.update_layout(\n        title='Storico e stima della capienza fiscale per ' +\n        str(Person),\n        xaxis_title=\"Year\",\n        yaxis_title=\"€\",\n    )\n    return fig\n\n\ndef Liq(Person, Den, DF, Categoria):\n    Time = []\n    for i in range(len(DF)):\n        if DF[Den][i] == str(Person):\n            if Categoria == 'PG':\n                x = []\n                for j in DF.keys():\n                    if j[:9] == 'Liquidità':\n                        x.append(DF[j][i])\n                        Time.append(j[10:])\n            else:\n                x = []\n                value = 0\n                for j in DF.keys():\n                    if j[:10] == 'Imponibile':\n                        value += DF[j][i]\n                        Time.append(j[11:])\n                    elif j[:5] == 'IRPEF':\n                        value -= DF[j][i]\n                    elif j[:5] == 'Oneri':\n                        value += DF[j][i] - 30720\n                        x.append(value)\n                        value = 0\n    x = np.flip(x)\n    Time = np.flip(Time)\n    Future_Liq, Future_Liq2, Time = future_prediction(x, 5, Time, 'Liquidità')\n    Upper_estimate1 = []\n    Lower_estimate1 = []\n    for i in range(len(Future_Liq)):\n        Lower_estimate1.append(min(Future_Liq[i], Future_Liq2[i]))\n        Upper_estimate1.append(max(Future_Liq[i], Future_Liq2[i]))\n    return Upper_estimate1, Lower_estimate1, Time\n\n\ndef plot_Liq(U, L, T, Person):\n    fig = go.Figure(data=[\n        go.Scatter(x=T,\n                   y=U,\n                   line=dict(width=1, color='rgb(0,150, 0)'),\n                   mode='lines+markers',\n                   name='Upper estimate'),\n        go.Scatter(x=T,\n                   y=L,\n                   line=dict(width=1, color='rgb(225,0, 0)'),\n                   mode='lines+markers',\n                   fillcolor='rgba(68, 68, 68, 0.3)',\n                   fill='tonexty',\n                   name='Lower estimate')\n    ])\n    fig.update_layout(\n        title='Storico e stima della capacità finanziaria per ' + str(Person),\n        xaxis_title=\"Year\",\n        yaxis_title=\"€\",\n    )\n    return fig\n\n\ndef plot_gauge(coeff):\n    fig = go.Figure(\n        go.Indicator(domain={\n            'x': [0, 1],\n            'y': [0, 1]\n        },\n                     value=coeff,\n                     mode=\"gauge+number\",\n                     title={'text': \"Advantage\"},\n                     delta={'reference': 0},\n                     gauge={\n                         'axis': {\n                             'range': [-5, 5]\n                         },\n                         'bar': {\n                             'color': \"black\"\n                         },\n                         'steps': [\n                             {\n                                 'range': [-5, -1],\n                                 'color': \"red\"\n                             },\n                             {\n                                 'range': [-1, 0],\n                                 'color': \"yellow\"\n                             },\n                             {\n                                 'range': [0, 5],\n                                 'color': \"green\"\n                             },\n                         ],\n                     }))\n    fig.show()\n\n\ndef check_status(DF, Person, Categoria, Lower_estimate, Den):\n    c1 = 0\n    c2 = 0\n    for i in range(len(DF)):\n        if DF[Den][i] == str(Person):\n            Credito = DF['Credito'][i]\n            counter = 0\n            for j in DF.keys():\n                if Categoria == 'PG':\n                    if str(j[:3]) == 'Liq':\n                        if DF[j][i] < 0:\n                            c1 -= 5 * (5 - counter)\n                        c2 += (5 - counter)\n                        counter += 1\n                else:\n                    value = 0\n                    if j[:10] == 'Imponibile':\n                        value += DF[j][i]\n                    elif j[:5] == 'IRPEF':\n                        value -= DF[j][i]\n                    elif j[:5] == 'Oneri':\n                        value += DF[j][i]\n                        if DF[j][i] < 0:\n                            c1 -= 5 * (5 - counter)\n                        c2 += (5 - counter)\n                        counter += 1\n                        value = 0\n            c1 = c1 / c2\n            c3 = 0\n            for j in DF.keys():\n                if str(j) == 'Sofferenza':\n                    if DF[j][i] < 0:\n                        c3 = -5\n    coeff = min(c1, c3)\n    if Categoria == 'PF':\n        index = 5\n    else:\n        index = 3\n    somma = 0\n    for i in range(5):\n        if Lower_estimate[index + i] >= (Credito * 1.1 * 0.2):\n            somma += Credito * 1.1 * 0.2\n        else:\n            somma += Lower_estimate[5 + i]\n    if coeff < 0:\n        return coeff\n    else:\n        return coeff\n\n\ndef future_value(DF, Person, Categoria, Time, Lower_estimate, Den, Credito):\n    for i in range(len(DF)):\n        if DF[Den][i] == str(Person):\n            if Categoria == 'PG':\n                x = []\n                for j in DF.keys():\n                    if j[:9] == 'Liquidità':\n                        x.append(DF[j][i])\n                        Time.append(j[10:])\n            else:\n                x = []\n                value = 0\n                for j in DF.keys():\n                    if j[:10] == 'Imponibile':\n                        value += DF[j][i]\n                        Time.append(j[11:])\n                    elif j[:5] == 'IRPEF':\n                        value -= DF[j][i]\n                    elif j[:5] == 'Oneri':\n                        value += DF[j][i]\n                        x.append(value)\n                        value = 0\n    x = np.flip(x)\n    Future_Liq, Future_Liq2, Time = future_prediction(x, 5, Time, 'Liquidità')\n\n    Upper_estimate1 = []\n    Lower_estimate1 = []\n    for i in range(len(Future_Liq)):\n        Lower_estimate1.append(min(Future_Liq[i], Future_Liq2[i]))\n        Upper_estimate1.append(max(Future_Liq[i], Future_Liq2[i]))\n    future_value = 0\n    c2 = 0\n    for i in range(len(Lower_estimate1) - 5):\n        if Lower_estimate1[5 + i] < 0:\n            future_value -= 1 * (1 / (1 + i))\n        c2 += (1 / (1 + i))\n    if Categoria == 'PF':\n        index = 5\n    else:\n        index = 3\n    somma = 0\n    for i in range(5):\n        if Lower_estimate[index + i] >= (Credito * 1.1 * 0.2):\n            somma += Credito * 1.1 * 0.2\n        else:\n            somma += Lower_estimate1[5 + i]\n\n    if c2 > 0:\n        future_value /= c2\n        #print (\"Consigliato vendere il credito nel range: [\"+str(int(minimum))+\"€,\"+str(Credito*1.05)+\"€]\")\n        return future_value\n    else:\n        return future_value\n\n\ndef advantage(DF, Person, Lower_estimate, Categoria, Den):\n    if Categoria == 'PF':\n        index = 5\n    else:\n        index = 3\n    for i in range(len(DF)):\n        if DF[Den][i] == str(Person):\n            Credito = DF['Credito'][i]\n    somma = 0\n    for i in range(5):\n        if Lower_estimate[index + i] >= (Credito * 1.1 * 0.2):\n            somma += Credito * 1.1 * 0.2\n        else:\n            somma += Lower_estimate[5 + i]\n    if somma >= Credito * 1.05:\n        #print (\"Consigliato non vendere\")\n        return (somma - Credito * 1.05) / ((Credito * 1.05 - Credito) / 5)\n    else:\n        value = -(somma) / (Credito * 1.05)\n        #print (\"Consigliato vendere nel range: [\"+str(int(somma))+\",\"+str(Credito*1.05)+\"€]\")\n        return value\n\n\ndef final_coeff(DF, Person, Categoria, Lower_estimate, Time, Credito, Den):\n    actual_value = check_status(DF, Person, Categoria, Lower_estimate, Den)\n    if actual_value < 0:\n        value = actual_value\n    else:\n        coeff_future_value = future_value(DF, Person, Categoria, Time,\n                                          Lower_estimate, Den, Credito)\n        if coeff_future_value < 0:\n            value = coeff_future_value\n        else:\n            A = advantage(DF, Person, Lower_estimate, Categoria, Den)\n            value = A\n    if value >= 0:\n        print(\"Consigliato non vendere\")\n    else:\n        minimum = Credito + Credito * value / 100\n        print(\"Consigliato vendere il credito nel range: [\" +\n              str(int(minimum)) + \"€,\" + str(int(Credito * 1.05)) + \"€]\")\n    return value\n","sub_path":"functions.py","file_name":"functions.py","file_ext":"py","file_size_in_byte":15908,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"440195033","text":"# uncompyle6 version 3.7.4\n# Python bytecode 3.7 (3394)\n# Decompiled from: Python 3.6.9 (default, Apr 18 2020, 01:56:04) \n# [GCC 8.4.0]\n# Embedded file name: /usr/local/lib/python3.7/site-packages/javatools/cheetah/jardiff_JarContentChange.py\n# Compiled at: 2019-07-05 15:01:15\n# Size of source mod 2**32: 5471 bytes\nimport sys, os, os.path\ntry:\n    import builtins as builtin\nexcept ImportError:\n    import __builtin__ as builtin\n\nfrom os.path import getmtime, exists\nimport time, types\nimport Cheetah.Version as RequiredCheetahVersion\nimport Cheetah.Version as RequiredCheetahVersionTuple\nimport Cheetah.Template as Template\nfrom Cheetah.DummyTransaction import *\nfrom Cheetah.NameMapper import NotFound, valueForName, valueFromSearchList, valueFromFrameOrSearchList\nimport Cheetah.CacheRegion as CacheRegion\nimport Cheetah.Filters as Filters\nimport Cheetah.ErrorCatchers as ErrorCatchers\nfrom Cheetah.compat import unicode\nimport javatools.cheetah.change_Change as change_Change\nimport javatools.cheetah as escape\nVFFSL = valueFromFrameOrSearchList\nVFSL = valueFromSearchList\nVFN = valueForName\ncurrentTime = time.time\n__CHEETAH_version__ = '3.1.0'\n__CHEETAH_versionTuple__ = (3, 1, 0, 'final', 1)\n__CHEETAH_src__ = 'javatools/cheetah/jardiff_JarContentChange.tmpl'\n__CHEETAH_srcLastModified__ = 'Fri Jun 21 15:26:13 2019'\n__CHEETAH_docstring__ = '\" \"'\nif __CHEETAH_versionTuple__ < RequiredCheetahVersionTuple:\n    raise AssertionError('This template was compiled with Cheetah version %s. Templates compiled before version %s must be recompiled.' % (\n     __CHEETAH_version__, RequiredCheetahVersion))\n\nclass jardiff_JarContentChange(change_Change):\n\n    def __init__(self, *args, **KWs):\n        (super(jardiff_JarContentChange, self).__init__)(*args, **KWs)\n        if not self._CHEETAH__instanceInitialized:\n            cheetahKWArgs = {}\n            allowedKWs = 'searchList namespaces filter filtersLib errorCatcher'.split()\n            for k, v in KWs.items():\n                if k in allowedKWs:\n                    cheetahKWArgs[k] = v\n\n            (self._initCheetahInstance)(**cheetahKWArgs)\n\n    def description(self, **KWS):\n        trans = KWS.get('trans')\n        if not trans:\n            if not self._CHEETAH__isBuffering:\n                if not callable(self.transaction):\n                    trans = self.transaction\n        elif not trans:\n            trans = DummyTransaction()\n            _dummyTrans = True\n        else:\n            _dummyTrans = False\n        write = trans.response().write\n        SL = self._CHEETAH__searchList\n        _filter = self._CHEETAH__currentFilter\n        write('\\n<!-- START BLOCK: description -->\\n')\n        _v = VFFSL(SL, 'escape', False)(VFFSL(SL, 'change.entry', True))\n        if _v is not None:\n            write(_filter(_v, rawExpr='$escape($change.entry)'))\n        write('\\n')\n        write('\\n<!-- END BLOCK: description -->\\n')\n        return _dummyTrans and trans.response().getvalue() or ''\n\n    def writeBody(self, **KWS):\n        trans = KWS.get('trans')\n        if not trans:\n            if not self._CHEETAH__isBuffering:\n                if not callable(self.transaction):\n                    trans = self.transaction\n        elif not trans:\n            trans = DummyTransaction()\n            _dummyTrans = True\n        else:\n            _dummyTrans = False\n        write = trans.response().write\n        SL = self._CHEETAH__searchList\n        _filter = self._CHEETAH__currentFilter\n        write('\\n\\n')\n        self.description(trans=trans)\n        write('\\n\\n')\n        return _dummyTrans and trans.response().getvalue() or ''\n\n    _CHEETAH__instanceInitialized = False\n    _CHEETAH_version = __CHEETAH_version__\n    _CHEETAH_versionTuple = __CHEETAH_versionTuple__\n    _CHEETAH_src = __CHEETAH_src__\n    _CHEETAH_srcLastModified = __CHEETAH_srcLastModified__\n    _mainCheetahMethod_for_jardiff_JarContentChange = 'writeBody'\n\n\nif not hasattr(jardiff_JarContentChange, '_initCheetahAttributes'):\n    templateAPIClass = getattr(jardiff_JarContentChange, '_CHEETAH_templateClass', Template)\n    templateAPIClass._addCheetahPlumbingCodeToClass(jardiff_JarContentChange)\nif __name__ == '__main__':\n    from Cheetah.TemplateCmdLineIface import CmdLineIface\n    CmdLineIface(templateObj=(jardiff_JarContentChange())).run()","sub_path":"pycfiles/javatools-1.4.0.macosx-10.14-x86_64.tar/jardiff_JarContentChange.cpython-37.py","file_name":"jardiff_JarContentChange.cpython-37.py","file_ext":"py","file_size_in_byte":4294,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"575756071","text":"#\n# Copyright 2021 Red Hat Inc.\n# SPDX-License-Identifier: Apache-2.0\n#\nimport json\nimport logging\nimport os\nimport re\nimport uuid\n\nimport ciso8601\nfrom dateutil.relativedelta import relativedelta\nfrom django.db import connection as conn\nfrom django.db import transaction\n\n\n# If this is detected, the code will try to resolve this to a name\n# because, in some functionality, this needs to be a string vs the\n# keyword.\nCURRENT_SCHEMA = \"current_schema\"\nBIGINT = \"bigint\"\nINT = \"int\"\n\nPARTITION_RANGE = \"RANGE\"\nPARTITION_LIST = \"LIST\"\n\n# This value from pg_class.relkind denotes a materialized view\nVIEW_TYPE_MATERIALIZED = \"m\"\n\nLOG = logging.getLogger(\"pg_partition\")\n# SQLFILE = open('/tmp/pg_partition.sql', 'wt')\n\n_TEMPLATE_SCHEMA = os.environ.get(\"TEMPLATE_SCHEMA\", \"template0\")\n\n\n# Standardized SQL executor\ndef conn_execute(sql, params=None, _conn=conn):\n    \"\"\"\n    Executes the given sql with any given parameters. This utilized the django.db.connection.\n    Params\n        sql (str) : SQL string\n        params (list) : Parameters for SQL\n    Returns:\n        Cursor (if a SQL string was passed in)\n        None   (if no SQL string was passed in or empty string)\n    \"\"\"\n    if sql:\n        cursor = _conn.cursor()\n        LOG.info(f\"SQL: {cursor.mogrify(sql, params).decode('utf-8')}\")\n        # print(cursor.mogrify(sql, params).decode('utf-8') + '\\n', file=SQLFILE, flush=True)\n        cursor.execute(sql, params)\n        return cursor\n    else:\n        return None\n\n\n# Standardized fetch all routine that will return list(dict)\ndef fetchall(cursor):\n    \"\"\"\n    Fetch all rows from the given cursor\n    Params:\n        cursor (Cursor) : Cursor of previously executed statement\n    Returns:\n        list(dict) : List of dict representing the records selected indexed by column name\n    \"\"\"\n    if cursor:\n        cursor_def = [d.name for d in cursor.description]\n        return [dict(zip(cursor_def, r)) for r in cursor.fetchall()]\n    else:\n        return []\n\n\n# Standardized fetch that will return dict\ndef fetchone(cursor):\n    \"\"\"\n    Fetch one row from the given cursor\n    Params:\n        cursor (Cursor) : Cursor of previously executed statement\n    Returns:\n        dict : Dict representing the records selected indexed by column name\n    \"\"\"\n    if cursor:\n        return dict(zip((d.name for d in cursor.description), cursor.fetchone()))\n    else:\n        return {}\n\n\n# Resolve \"current_schema\" to an actual schema name\ndef resolve_schema(schema):\n    \"\"\"\n    Resolve CURRENT_SCHEMA to an actual schema name\n    Params:\n        schema (str) : schema name or CURRENT_SCHEMA\n    Returns:\n        str : Actual schema for CURRENT_SCHEMA or the input schema parameter\n    \"\"\"\n    if schema == CURRENT_SCHEMA:\n        cur = conn_execute('select current_schema as \"current_schema\";')\n        schema = cur.fetchone()[0]\n\n    LOG.info(f\"Resolve schema is {schema}\")\n    return schema\n\n\n# Wrapper for a default value for a column to be used in the column definition\nclass Default:\n    \"\"\"\n    Interface to a default value to be used when a column definition needs a default value\n    \"\"\"\n\n    def __init__(self, default_value):\n        self.default_value = default_value\n\n    def __str__(self):\n        dval = None\n        if self.default_value is None:\n            dval = \"null\"\n        elif hasattr(self.default_value, \"default_constraint\"):\n            dval = self.default_value.default_constraint()\n        else:\n            dval = str(self.default_value)\n\n        LOG.debug(f\"\"\"default value is: \"{dval}\" \"\"\")\n        return dval\n\n    def __repr__(self):\n        return str(self)\n\n    def __bool__(self):\n        return True\n\n\n# Possible future use\n# class NoDefault:\n#     def default_constraint(self):\n#         return \"\"\n\n#     def __str__(self):\n#         return \"\"\n\n#     def __repr__(self):\n#         return \"\"\n\n\n# Model a schema definition\nclass SequenceDefinition:\n    \"\"\"\n    Model a schema definition. Includes some commans for create, alter owner/owned and setval.\n    \"\"\"\n\n    NOMAXVALUE = \"NO MAXVALUE\"\n    NOMINVALUE = \"NO MINVALUE\"\n    CYCLE = \"CYCLE\"\n    NO_CYCLE = \"NO CYCLE\"\n\n    def __init__(\n        self,\n        target_schema,\n        name,\n        copy_sequence={},\n        data_type=BIGINT,\n        min_value=1,\n        max_value=NOMAXVALUE,\n        start_with=1,\n        increment_by=1,\n        cache=100,\n        cycle=NO_CYCLE,\n        current_value=1,\n        owner=None,\n    ):\n        \"\"\"\n        Initialize a SchemaDefinition\n        Params:\n            target_schema (str) : schema name in which this new sequence will reside\n            name (str) : name of sequence\n            copy_sequence (dict) : Dict detailing a sequence to copy by specifying its owned-by column\n                                   This dict should have the following keys:\n                                       schema_name : Name of schema containing owner table\n                                       table_name  : Name of owner table\n                                       column_name : Name of owned-by column\n            data_type (str) : data type of sequence (default is BIGINT)\n            min_value (int/CONST) : minimum value (default is 1) NOMINVALUE const can be used\n            max_value (int/CONST) : maximum value (default is NOMAXVALUE const)\n            start_with (int) : Starting value (default is 1)\n            increment_by (int) : Incerement-by value (default is 1)\n            cache (int) : Number of values to cache (default is 100)\n            cycle (CONST) : One of NO_CYCLE or CYCLE (defualt is NO_CYCLE)\n            current_value (int) : Set the current value if this value is different from start_with\n            owner (str) : name of the sequence owner\n        \"\"\"\n        self.target_schema = resolve_schema(target_schema)\n        self.name = name\n        self.data_type = data_type\n        self.min_value = min_value\n        self.max_value = max_value\n        self.start_with = start_with\n        self.increment_by = increment_by\n        self.cache = cache\n        self.cycle = cycle\n        self.owner = owner\n        self.current_value = current_value\n\n        if copy_sequence:\n            self.copy_from(copy_sequence)\n\n    def copy_from(self, source_sequence):\n        \"\"\"\n        Reinitialize the SequenceDefinition from an existing sequence identified by\n        its owned-by column reference <schema>.<table>.<column>\n        Params:\n            source_sequence (dict) : Dict detailing a sequence to copy by specifying its owned-by column\n                                     This dict should have the following keys:\n                                         schema_name : Name of schema containing owner table\n                                         table_name  : Name of owner table\n                                         column_name : Name of owned-by column\n        Returns:\n            None\n        \"\"\"\n        sql = \"\"\"\nselect t.relname::text as table_name,\n       a.attname::text as column_name,\n       seq.*\n  from pg_class t\n  join pg_depend d\n    on d.refobjid = t.oid\n  join pg_class s\n    on s.oid = d.objid\n   and s.relkind = 'S'\n  join pg_attribute a\n    on a.attrelid = d.refobjid\n   and a.attnum = d.refobjsubid\n   and a.attnum > 0\n  join pg_namespace ts\n    on ts.oid = t.relnamespace\n  join pg_namespace ss\n    on ss.oid = s.relnamespace\n  join pg_sequences seq\n    on seq.schemaname = ss.nspname\n   and seq.sequencename = s.relname\n where ts.nspname = %s\n   and t.relname = %s\n   and a.attname = %s\n   and d.classid = 'pg_class'::regclass;\n\"\"\"\n        LOG.info(\"Re-initialize sequence by copying specified existing sequence\")\n        cur = conn_execute(\n            sql, [source_sequence[\"schema_name\"], source_sequence[\"table_name\"], source_sequence[\"column_name\"]]\n        )\n        rec = fetchone(cur)\n        self.data_type = rec[\"data_type\"]\n        self.min_value = rec[\"min_value\"]\n        self.max_value = rec[\"max_value\"]\n        self.start_with = rec[\"start_value\"]\n        self.increment_by = rec[\"increment_by\"]\n        self.cache = rec[\"cache_size\"]\n        self.cycle = self.CYCLE if rec[\"cycle\"] else self.NO_CYCLE\n        self.current_value = rec[\"last_value\"] or 1\n        self.owner = rec[\"sequenceowner\"]\n\n    def default_constraint(self):\n        return f\"nextval('{self.target_schema}.{self.name}'::regclass)\"\n\n    def alter_owner(self):\n        LOG.info(\"Running alter sequence owner\")\n        if self.owner:\n            return f\"\"\"\nALTER SEQUENCE \"{self.target_schema}\".\"{self.name}\"\n      OWNER TO \"{self.owner}\" ;\n\"\"\"\n        else:\n            return \"\"\n\n    def alter_owned_by(self, owner):\n        LOG.info(\"Running alter sequence owned-by column\")\n        return f\"\"\"\nALTER SEQUENCE \"{self.target_schema}\".\"{self.name}\"\n      OWNED BY \"{owner[\"schema_name\"]}\".\"{owner[\"table_name\"]}\".\"{owner[\"column_name\"]}\" ;\n\"\"\"\n\n    def setval(self):\n        if self.current_value and self.current_value > self.start_with:\n            LOG.info(\"Running sequence setval\")\n            return (\n                \"\"\"\nselect setval(%s::regclass, %s);\n\"\"\",\n                (self.target_schema + \".\" + self.name, self.current_value),\n            )\n        else:\n            return (\"\", None)\n\n    def create(self):\n        LOG.info(\"Running create sequence\")\n        return f\"\"\"\nCREATE SEQUENCE \"{self.target_schema}\".\"{self.name}\"\n       AS {self.data_type}\n       INCREMENT BY {self.increment_by}\n       MINVALUE {self.min_value}\n       MAXVALUE {self.max_value}\n       START WITH {self.start_with}\n       CACHE {self.cache}\n       {self.cycle} ;\n\"\"\"\n\n\nclass ColumnDefinition:\n    \"\"\"\n    Define changes to an existing table column that will be applied to the target partitioned table\n    \"\"\"\n\n    def __init__(self, target_schema, target_table, column_name, data_type=None, using=None, null=None, default=None):\n        \"\"\"\n        Initialize a column definition\n        Params:\n            target_schema (str) : schema containing the target table\n            target_table (str) : name of target table\n            column_name (str) : name of target column\n            data_type (str) : database data type (default is None)\n            using (str) : expression (if needed) to convert existing data to new data type\n            null (bool) : True = remove not null constraint; false = apply not null; default is None\n            default (Default) : The default value to apply. default is None\n        \"\"\"\n        self.target_schema = target_schema\n        self.target_table = target_table\n        self.column_name = column_name\n        self.data_type = data_type\n        self.using = using\n        self.null = null\n        self.default = default\n\n    def alter_column(self):\n        LOG.info(\"Running alter column for column definiton\")\n        if not (bool(self.data_type) or bool(self.null) or bool(self.default)):\n            return \"\"\n\n        alters = []\n        sql = f\"\"\"\nALTER TABLE \"{self.target_schema}\".\"{self.target_table}\"\n\"\"\"\n        # Possible future use\n        #         if self.data_type:\n        #             using = f\"USING {self.using}\" if self.using else \"\"\n        #             alters.append(\n        #                 f\"\"\"      ALTER COLUMN \"{self.column_name}\" SET DATA TYPE {self.data_type} {using}\n        # \"\"\"\n        #             )\n        #         if self.null is not None:\n        #             alters.append(\n        #                 f\"\"\"      ALTER COLUMN \"{self.column_name}\" SET {'NULL' if self.null else 'NOT NULL'}\n        # \"\"\"\n        #             )\n        if self.default:\n            LOG.info(\"Setting default value\")\n            alters.append(\n                f\"\"\"      ALTER COLUMN \"{self.column_name}\" SET DEFAULT {self.default}\n\"\"\"\n            )\n        sql += \", \".join(alters) + \";\"\n        return sql\n\n\nclass ConstraintDefinition:\n    \"\"\"\n    Define a constraint. This is used internally to copy contstraint information\n    \"\"\"\n\n    def __init__(self, target_schema, target_table, constraintrec):\n        self.target_schema = target_schema\n        self.target_table = target_table\n        self.constraint_name = constraintrec[\"constraint_name\"]\n        self.definition = constraintrec[\"definition\"]\n\n    def alter_add_constraint(self):\n        LOG.info(f\"Runing ALTER TABLE ADD CONSTRAINT p_{self.constraint_name}\")\n        sql = f\"\"\"\nALTER TABLE \"{self.target_schema}\".\"{self.target_table}\"\n      ADD CONSTRAINT \"p_{self.constraint_name}\" {self.definition} ;\n\"\"\"\n        return sql\n\n\nclass PKDefinition:\n    \"\"\"\n    Define a primary key for the partitioned table. This is intended to facilitate a composite key\n    \"\"\"\n\n    def __init__(self, name, column_names):\n        \"\"\"\n        Initialize a PKDefinition\n        Params:\n            name (str) : Name of the primary key constraint\n            column_names (list(str)) : list of primary key columns that will be created in list order\n        \"\"\"\n        self.column_names = column_names\n        self.name = name\n\n    def alter_table(self, schema_name, table_name):\n        LOG.info(\"Adding PRIMARY KEY constraint\")\n        column_names = \",\".join(self.column_names)\n        return f\"\"\"\nALTER TABLE \"{schema_name}\".\"{table_name}\"\n      ADD CONSTRAINT \"{self.name}\"\n      PRIMARY KEY ({column_names}) ;\n\"\"\"\n\n\nclass IndexDefinition:\n    \"\"\"\n    Index definition from the source table that will be reapplied to the target table\n    This is used internally during the conversion process\n    \"\"\"\n\n    INDEX_PARSER = re.compile(\"(^.+INDEX )(.+)( ON )(.+)( USING.+$)\", flags=re.IGNORECASE)\n    INDEX_NAME_IX = 1\n    INDEX_TABLE_IX = -2\n\n    def __init__(self, target_schema, target_table, indexrec):\n        self.target_schema = target_schema\n        self.target_table = target_table\n        self.schema_name = indexrec[\"schemaname\"]\n        self.index_name = indexrec[\"indexname\"]\n        self.definition = indexrec[\"indexdef\"]\n\n        if \";\" not in self.definition[:-10]:\n            self.definition += \" ;\"\n\n    def create(self):\n        index_parts = self.INDEX_PARSER.findall(self.definition)\n        if index_parts:\n            LOG.info(f'Creating index \"p_{self.index_name}\"')\n            index_parts = list(index_parts[0])\n            index_parts[self.INDEX_NAME_IX] = f\"p_{self.index_name}\"\n            index_parts[self.INDEX_TABLE_IX] = f\"{self.target_schema}.{self.target_table}\"\n            return \" \".join(index_parts)\n        else:\n            LOG.error(f\"ERROR parsing index definition for {self.index_name} [[ {self.definition} ]]\")\n\n        return \"\"\n\n\nclass ViewDefinition:\n    \"\"\"\n    Definition of a dependent view of the source table that will be reapplied to the target table\n    This does handle single-level dependencies, but has not been tested fully on nested views.\n    This is used internally during the conversion process to reapply views to the partitioned table.\n    This handles \"normal\" views as well as materialized views.\n    \"\"\"\n\n    def __init__(self, target_schema, viewrec):\n        self.target_schema = target_schema\n        self.schema_name = viewrec[\"source_schema\"]\n        self.table_name = viewrec[\"source_table\"]\n        self.view_schema = viewrec[\"dependent_schema\"]\n        self.view_name = viewrec[\"dependent_view\"]\n        self.view_type = viewrec[\"dependent_view_type\"]\n        self.definition = viewrec[\"definition\"]\n        self.view_owner = viewrec[\"dependent_view_owner\"]\n        self.indexes = []\n        for ixrec in viewrec[\"indexes\"] or []:\n            self.indexes.append(IndexDefinition(self.target_schema, self.view_name, ixrec))\n\n        if \";\" not in self.definition[:-10]:\n            self.definition += \" ;\"\n\n    def rename_original_view_indexes(self):\n        return [\n            f\"\"\"\nALTER INDEX \"{vix.schema_name}\".\"{vix.index_name}\"\nRENAME TO \"__{vix.index_name}\" ;\n\"\"\"\n            for vix in self.indexes\n        ]\n\n    def rename_original_view(self):\n        view_type = \"MATERIALIZED\" if self.view_type == VIEW_TYPE_MATERIALIZED else \"\"\n        return f\"\"\"\nALTER {view_type} VIEW \"{self.view_schema}\".\"{self.view_name}\"\nRENAME TO \"__{self.view_name}\" ;\n\"\"\"\n\n    def alter_owner(self):\n        LOG.info(\"Alter view owner\")\n        view_type = \"MATERIALIZED\" if self.view_type == VIEW_TYPE_MATERIALIZED else \"\"\n        sql = f\"\"\"\nALTER {view_type} VIEW \"{self.target_schema}\".\"{self.view_name}\"\n      OWNER TO \"{self.view_owner}\" ;\n\"\"\"\n        return sql\n\n    # Poosible future use\n    #     def drop_target(self):\n    #         LOG.info(\"Dropping target view\")\n    #         view_type = 'MATERIALIZED' if self.view_type == VIEW_TYPE_MATERIALIZED else ''\n    #         sql = f\"\"\"\n    # DROP {view_type} VIEW IF EXISTS \"{self.target_schema}\".\"{self.view_name}\" ;\n    # \"\"\"\n    #         return sql\n\n    #     def drop(self):\n    #         view_type = 'MATERIALIZED' if self.view_type == VIEW_TYPE_MATERIALIZED else ''\n    #         sql = f\"\"\"\n    # DROP {view_type} VIEW IF EXISTS \"{self.schema_name}\".\"{self.view_name}\" ;\n    # \"\"\"\n    #         return sql\n\n    def create(self):\n        view_type = \"MATERIALIZED\" if self.view_type == VIEW_TYPE_MATERIALIZED else \"\"\n        LOG.info(f'Creating {view_type} view \"{self.view_name}')\n        sql = f\"\"\"\nCREATE {view_type} VIEW \"{self.target_schema}\".\"{self.view_name}\" AS\n{self.definition}\n\"\"\"\n        return sql\n\n    def refresh(self):\n        LOG.info(f'Refresh materialized view \"{self.view_name}\"')\n        sql = f\"\"\"\nREFRESH MATERIALIZED VIEW \"{self.target_schema}\".\"{self.view_name}\" ;\n\"\"\"\n        return sql\n\n\nclass ConvertToPartition:\n    \"\"\"\n    Driver class to facilitate the conversion of an existing table to a partitioned table\n    with the same table definition, as well as duplicated constraint, sequence, index, and view definitions.\n    \"\"\"\n\n    def __init__(\n        self,\n        source_table_name,\n        partition_key,\n        partition_type=PARTITION_RANGE,\n        target_table_name=None,\n        pk_def=None,\n        col_def=[],\n        target_schema=CURRENT_SCHEMA,\n        source_schema=CURRENT_SCHEMA,\n    ):\n        \"\"\"\n        Initialize the converter.\n        Params:\n            source_table_name (str) : name of source table\n            partition_key (str) : column in source table to partiton on\n            partition_type (CONST) : Use PARTITION_RANGE or PARTITON_LIST (default is PARTITION_RANGE)\n            target_table_name (str) : name of target table (defualt is 'p_' + source_table_name)\n            pk_def (PKDefinition) : Redefine primary key (default is None)\n            col_def (list(ColumnDefinition)) : Redefine any column(s) (default is [])\n            target_schema (str/CONST) : Schema to contain the partitioned table (default is CURRENT_SCHEMA)\n            source_schema (str/CONST) : Schema containing the source table (default is CURRENT_SCHEMA)\n        \"\"\"\n        self.conn = conn\n        self.target_schema = resolve_schema(target_schema)\n        self.partitioned_table_name = target_table_name or (\"p_\" + source_table_name)\n        self.partition_key = partition_key\n        self.partition_type = partition_type\n        self.source_schema = resolve_schema(source_schema)\n        self.source_table_name = source_table_name\n        self.pk_def = pk_def\n        self.col_def = col_def\n        self.indexes = self.__get_indexes()\n        self.constraints = self.__get_constraints()\n        self.views = self.__get_views()\n\n    def __repr__(self):\n        return f\"\"\"Convert \"{self.source_schema}\".\"{self.source_table_name}\" to a partitioned table\"\"\"\n\n    def __get_constraints(self):\n        LOG.info(f\"Getting constraints for table {self.source_schema}.{self.source_table_name}\")\n        sql = \"\"\"\nselect c.oid::int as constraint_oid,\n       n.nspname::text as schema_name,\n       t.relname::text as table_name,\n       c.conname::text as constraint_name,\n       c.contype::text as constraint_type,\n       c.condeferrable as is_deferrable,\n       c.condeferred as is_deferred,\n       (select array_agg(tc.attname::text)\n          from pg_attribute tc\n         where tc.attrelid = c.conrelid\n           and tc.attnum = any(c.conkey)) as constraint_columns,\n       f.relname::text as reference_table,\n       c.confupdtype as update_action,\n       c.confdeltype as delete_action,\n       (select array_agg(fc.attname::text)\n          from pg_attribute fc\n         where fc.attrelid = c.confrelid\n           and fc.attnum = any(c.confkey)) as reference_columns,\n       pg_get_constraintdef(c.oid, true) as \"definition\"\n  from pg_constraint c\n  join pg_class t\n    on t.oid = c.conrelid\n   and t.relkind = 'r'\n  join pg_namespace n\n    on n.oid = t.relnamespace\n  left\n  join pg_class f\n    on f.oid = c.confrelid\n   and f.relkind = 'r'\n where c.conrelid > 0\n   and n.nspname = %s\n   and t.relname = %s\n order\n    by t.relname,\n       case when c.contype = 'p'\n                 then 0\n            else 1\n       end::int\n\"\"\"\n        cur = conn_execute(sql, [self.source_schema, self.source_table_name])\n        return [\n            ConstraintDefinition(self.target_schema, self.partitioned_table_name, rec)\n            for rec in fetchall(cur)\n            if rec[\"constraint_type\"] != \"p\"\n        ]\n\n    def __get_indexes(self):\n        LOG.info(f\"Getting indexes for table {self.source_schema}.{self.source_table_name}\")\n        sql = \"\"\"\nwith pk_indexes as (\nselect i.relname\n  from pg_class t\n  join pg_namespace n\n    on n.oid = t.relnamespace\n  join pg_index x\n    on x.indrelid = t.oid\n   and x.indisprimary = true\n  join pg_class i\n    on i.oid = x.indexrelid\n where n.nspname = %s\n   and t.relname = %s\n   and t.relkind = 'r'\n)\nselect i.*\n  from pg_indexes i\n where i.schemaname = %s\n   and i.tablename = %s\n   and not exists (select 1 from pk_indexes x where i.indexname = x.relname)\n order\n    by i.tablename;\n\"\"\"\n        params = [self.source_schema, self.source_table_name, self.source_schema, self.source_table_name]\n        cur = conn_execute(sql, params)\n        return [IndexDefinition(self.target_schema, self.partitioned_table_name, rec) for rec in fetchall(cur)]\n\n    def __get_views(self):\n        LOG.info(f\"Getting views referencing table {self.source_schema}.{self.source_table_name}\")\n        sql = \"\"\"\nWITH RECURSIVE view_deps AS (\nSELECT DISTINCT\n       dependent_ns.nspname as dependent_schema,\n       dependent_view.oid as dependent_view_oid,\n       dependent_view.relname as dependent_view,\n       dependent_view.relkind as dependent_view_type,\n       dependent_view.relowner::regrole::text as dependent_view_owner,\n       source_ns.nspname as source_schema,\n       source_table.relname as source_table\n  FROM pg_depend\n  JOIN pg_rewrite\n    ON pg_depend.objid = pg_rewrite.oid\n  JOIN pg_class as dependent_view\n    ON pg_rewrite.ev_class = dependent_view.oid\n  JOIN pg_class as source_table\n    ON pg_depend.refobjid = source_table.oid\n  JOIN pg_namespace dependent_ns\n    ON dependent_ns.oid = dependent_view.relnamespace\n  JOIN pg_namespace source_ns\n    ON source_ns.oid = source_table.relnamespace\n WHERE NOT (dependent_ns.nspname = source_ns.nspname AND\n            dependent_view.relname = source_table.relname)\n   AND source_table.relnamespace = %s::regnamespace\n   AND source_table.relname = %s\nUNION\nSELECT DISTINCT\n       dependent_ns.nspname as dependent_schema,\n       dependent_view.oid as dependent_view_oid,\n       dependent_view.relname as dependent_view,\n       dependent_view.relkind as dependent_view_type,\n       dependent_view.relowner::regrole::text as dependent_view_owner,\n       source_ns.nspname as source_schema,\n       source_table.relname as source_table\n  FROM pg_depend\n  JOIN pg_rewrite\n    ON pg_depend.objid = pg_rewrite.oid\n  JOIN pg_class as dependent_view\n    ON pg_rewrite.ev_class = dependent_view.oid\n  JOIN pg_class as source_table\n    ON pg_depend.refobjid = source_table.oid\n  JOIN pg_namespace dependent_ns\n    ON dependent_ns.oid = dependent_view.relnamespace\n  JOIN pg_namespace source_ns\n    ON source_ns.oid = source_table.relnamespace\n  JOIN view_deps vd\n    ON vd.dependent_schema = source_ns.nspname\n   AND vd.dependent_view = source_table.relname\n   AND NOT (dependent_ns.nspname = vd.dependent_schema AND\n            dependent_view.relname = vd.dependent_view)\n)\nSELECT vd.dependent_schema,\n       vd.dependent_view_oid,\n       vd.dependent_view,\n       vd.dependent_view_type,\n       replace(vd.dependent_view_owner, '\"', '') as dependent_view_owner,\n       vd.source_schema,\n       vd.source_table,\n       (select array_agg(row_to_json(vi))\n          from pg_indexes vi\n         where vi.schemaname = vd.dependent_schema\n           and vi.tablename = vd.dependent_view)::json[] as indexes,\n       pg_get_viewdef(dependent_view_oid) as definition\n  FROM view_deps vd\n ORDER BY source_schema, source_table;\n\"\"\"\n        cur = conn_execute(sql, [self.source_schema, self.source_table_name])\n        return [ViewDefinition(self.target_schema, rec) for rec in fetchall(cur)]\n\n    def __create_table_like_source(self):\n        msg = f'Creating base table structure for \"{self.target_schema}\".\"{self.partitioned_table_name}\" from '\n        msg += f'\"{self.source_schema}\".\"{self.source_table_name}\"'\n        LOG.info(msg)\n        LOG.info(f'Partitioning by {self.partition_type} ( \"{self.partition_key}\" )')\n        sql = f\"\"\"\nCREATE TABLE IF NOT EXISTS \"{self.target_schema}\".\"{self.partitioned_table_name}\" (\n    LIKE \"{self.source_schema}\".\"{self.source_table_name}\"\n    INCLUDING DEFAULTS\n    INCLUDING GENERATED\n    INCLUDING IDENTITY\n    INCLUDING STATISTICS\n)\nPARTITION BY {self.partition_type} ( \"{self.partition_key}\" ) ;\n\"\"\"\n        conn_execute(sql)\n\n    def __create_default_partition(self):\n        LOG.info(f'Creating default partition \"{self.target_schema}\".\"{self.source_table_name}_default\"')\n        sql = f\"\"\"\nCREATE TABLE IF NOT EXISTS \"{self.target_schema}\".\"{self.source_table_name}_default\"\nPARTITION OF \"{self.target_schema}\".\"{self.partitioned_table_name}\" DEFAULT ;\n\"\"\"\n        conn_execute(sql)\n\n        LOG.debug(\"Recording default partition\")\n        sql = f\"\"\"\nINSERT INTO \"{self.target_schema}\".\"partitioned_tables\" (\n    schema_name,\n    table_name,\n    partition_of_table_name,\n    partition_type,\n    partition_col,\n    partition_parameters\n)\nVALUES (\n    %s,\n    %s,\n    %s,\n    %s,\n    %s,\n    %s::jsonb\n);\n\"\"\"\n        params = (\n            self.target_schema,\n            f\"{self.source_table_name}_default\",\n            self.partitioned_table_name,\n            self.partition_type.lower(),\n            self.partition_key,\n            '{\"default\": true}',\n        )\n        conn_execute(sql, params)\n\n    def __set_primary_key(self):\n        LOG.info(\"Setting any primary key definition\")\n        if self.pk_def:\n            conn_execute(self.pk_def.alter_table(self.target_schema, self.partitioned_table_name))\n\n    def __set_column_definitions(self):\n        LOG.info(\"Applying any column alterations\")\n        for cdef in self.col_def:\n            default_is_sequence = hasattr(cdef, \"default\") and isinstance(\n                cdef.default.default_value, SequenceDefinition\n            )\n            if default_is_sequence:\n                conn_execute(cdef.default.default_value.create())\n                conn_execute(*cdef.default.default_value.setval())\n\n            conn_execute(cdef.alter_column())\n\n            if default_is_sequence:\n                conn_execute(cdef.default.default_value.alter_owner())\n                owner = {\n                    \"schema_name\": self.target_schema,\n                    \"table_name\": self.partitioned_table_name,\n                    \"column_name\": cdef.column_name,\n                }\n                conn_execute(cdef.default.default_value.alter_owned_by(owner))\n\n    def __set_constraints(self):\n        LOG.info(\"Applying any table constratins\")\n        for cdef in self.constraints:\n            conn_execute(cdef.alter_add_constraint())\n\n    def __create_indexes(self):\n        for idef in self.indexes:\n            LOG.info(f\"Applying index definition from {idef.index_name}\")\n            conn_execute(idef.create())\n\n    def __create_partitions(self):\n        # Get \"requested\" partitions\n        sql = \"\"\"\nselect partition_start from scan_for_date_partitions(%s::text, %s::text, %s::text, %s::text);\n\"\"\"\n        params = [\n            f\"{self.source_schema}.{self.source_table_name}\",  # This will be quoted properly in the proc\n            self.partition_key,\n            self.target_schema,\n            self.partitioned_table_name,\n        ]\n        cur = conn_execute(sql, params)\n        requested_partitions = fetchall(cur)\n\n        # Get existing partitions except the default partition\n        sql = f\"\"\"\nselect (partition_parameters->>'from')::date as existing_partition\n  from \"{self.target_schema}\".\"partitioned_tables\"\n where schema_name = %s\n   and partition_of_table_name = %s\n   and table_name ~ %s\n   and partition_parameters->>'default' = 'false';\n\"\"\"\n        params = [self.target_schema, self.partitioned_table_name, f\"^{self.partitioned_table_name}\"]\n        cur = conn_execute(sql, params)\n        existing_partitions = fetchall(cur)\n\n        needed_partitions = {p[\"partition_start\"] for p in requested_partitions} - {\n            ciso8601.parse_datetime(p[\"partition_parameters\"][\"from\"]).date() for p in existing_partitions\n        }\n\n        sqltmpl = f\"\"\"\nCREATE TABLE IF NOT EXISTS \"{self.target_schema}\".\"{{table_partition}}\"\nPARTITION OF \"{self.target_schema}\".\"{self.partitioned_table_name}\"\nFOR VALUES FROM (%s::date) TO (%s::date) ;\n\"\"\"\n        part_rec_sql = f\"\"\"\nINSERT INTO \"{self.target_schema}\".\"partitioned_tables\" (\n    schema_name,\n    table_name,\n    partition_of_table_name,\n    partition_type,\n    partition_col,\n    partition_parameters\n)\nVALUES (\n    %s,\n    %s,\n    %s,\n    %s,\n    %s,\n    %s::jsonb\n);\n\"\"\"\n        for newpart in needed_partitions:\n            params = (newpart, newpart + relativedelta(months=1))\n            partition_name = f\"{self.partitioned_table_name}_{newpart.strftime('%Y_%m')}\"\n            LOG.info(f\"Creating partition {self.target_schema}.{partition_name}\")\n            conn_execute(sqltmpl.format(table_partition=partition_name), params)\n\n            params = (\n                self.target_schema,\n                partition_name,\n                self.partitioned_table_name,\n                self.partition_type.lower(),\n                self.partition_key,\n                '{{\"default\": false, \"from\": \"{0}\", \"to\": \"{1}\"}}'.format(*params),\n            )\n            LOG.info(f\"Recording partition {self.target_schema}.{partition_name}\")\n            conn_execute(part_rec_sql, params)\n\n        # Get created partitions\n        sql = f\"\"\"\nselect table_name,\n       to_char(date(partition_parameters->>'from'), 'YYYY_MM') as suffix\n  from \"{self.target_schema}\".\"partitioned_tables\"\n where schema_name = %s\n   and partition_of_table_name = %s\n   and table_name ~ %s ;\n\"\"\"\n        params = [self.target_schema, self.partitioned_table_name, f\"^{self.partitioned_table_name}\"]\n        cur = conn_execute(sql, params)\n        self.created_partitions = fetchall(cur)\n\n    def __copy_data(self):\n        c_from = f'\"{self.target_schema}\".\"{self.partitioned_table_name}\"'\n        c_to = f'\"{self.source_schema}\".\"{self.source_table_name}\"'\n        LOG.info(f\"Copying data from {c_from} to {c_to}\")\n        sql = f\"\"\"\nINSERT INTO \"{self.target_schema}\".\"{self.partitioned_table_name}\"\nSELECT * FROM \"{self.source_schema}\".\"{self.source_table_name}\" ;\n\"\"\"\n        with transaction.atomic():\n            conn_execute(sql)\n\n    def __rename_objects(self):\n        LOG.info(f'Locking source table \"{self.source_schema}\".\"{self.source_table_name}\"')\n        sql_actions = [\n            f\"\"\"\nLOCK TABLE \"{self.source_schema}\".\"{self.source_table_name}\" ;\n\"\"\"\n        ]\n        for vdef in self.views:\n            LOG.info(f'Renaming view indexes for \"{vdef.view_schema}\".\"{vdef.view_name}\"')\n            sql_actions.extend(vdef.rename_original_view_indexes())\n            LOG.info(f'Renaming view \"{vdef.view_schema}\".\"{vdef.view_name}\" to \"__{vdef.view_name}\"')\n            sql_actions.append(vdef.rename_original_view())\n\n        msg = (\n            f'Renaming source table \"{self.source_schema}\".\"{self.source_table_name}\"'\n            f' to \"__{self.source_table_name}\"'\n        )\n        LOG.info(msg)\n        sql_actions.append(\n            f\"\"\"\nALTER TABLE \"{self.source_schema}\".\"{self.source_table_name}\"\nRENAME TO \"__{self.source_table_name}\" ;\n\"\"\"\n        )\n        msg = (\n            f'Renaming partitioned table \"{self.target_schema}\".\"{self.partitioned_table_name}\"'\n            f' to \"{self.source_table_name}\"'\n        )\n        LOG.info(msg)\n        sql_actions.append(\n            f\"\"\"\nALTER TABLE \"{self.target_schema}\".\"{self.partitioned_table_name}\"\nRENAME TO \"{self.source_table_name}\" ;\n\"\"\"\n        )\n        for partition in self.created_partitions:\n            r_partition_name = f'{self.source_table_name}_{partition[\"suffix\"]}'\n            msg = (\n                f'''Renaming table partition \"{self.target_schema}\".\"{partition['table_name']}\"'''\n                f' to \"{r_partition_name}\"'\n            )\n            LOG.info(msg)\n            sql_actions.append(\n                f\"\"\"\nALTER TABLE \"{self.target_schema}\".\"{partition['table_name']}\"\nRENAME TO \"{r_partition_name}\" ;\n\"\"\"\n            )\n\n        LOG.info(\"Updating recorded partitions\")\n        update_sql = f\"\"\"\nUPDATE \"{self.target_schema}\".\"partitioned_tables\"\n   SET partition_of_table_name = %s\n WHERE partition_of_table_name = %s\n   AND schema_name = %s ;\n\"\"\"\n        update_params = [self.source_table_name, self.partitioned_table_name, self.target_schema]\n\n        LOG.info(\"Executing batch rename commands\")\n        for sql in sql_actions:\n            conn_execute(sql)\n\n        LOG.info(\"Executing update command\")\n        conn_execute(update_sql, update_params)\n\n        self.partitioned_table_name = self.source_table_name\n        self.source_table_name = f\"__{self.source_table_name}\"\n\n    def __create_views(self):\n        LOG.info(\"Creating any views\")\n        for vdef in self.views:\n            conn_execute(vdef.create())\n            if vdef.indexes:\n                LOG.info(\"Creating view indexes\")\n                for view_ix in vdef.indexes:\n                    conn_execute(view_ix.create())\n            conn_execute(vdef.alter_owner())\n\n    def __refresh_views(self):\n        LOG.info(\"Refreshing any materialized views\")\n        for vdef in self.views:\n            if vdef.view_type == VIEW_TYPE_MATERIALIZED:\n                conn_execute(vdef.refresh())\n\n    def is_partitioned(self, schema_name, table_name):\n        \"\"\"\n        Check to see if the specified tabe is a partitioned table\n        Args:\n            schema_name (str) : Schema name containing the source table\n            table_name (str) : Table name\n        Returns:\n            (bool) : True if partitioned, False if not\n        \"\"\"\n        sql = \"\"\"\nselect exists (\n                select 1\n                from pg_partitioned_table\n                where partrelid = (\n                                    select oid\n                                      from pg_class\n                                     where relnamespace = %s::regnamespace\n                                       and relname = %s\n                                  )\n              )::boolean as \"is_partitioned\";\n\"\"\"\n        return conn_execute(sql, (schema_name, table_name)).fetchone()[0]\n\n    def convert_to_partition(self, drop_orig=True):\n        \"\"\"\n        Execute the table conversion from the initialized converter\n        Params:\n            drop_orig (bool) : Flag to drop original objects (default is True)\n        \"\"\"\n        with transaction.atomic():\n            if self.is_partitioned(self.source_schema, self.source_table_name):\n                raise TypeError(\n                    f'Source table \"{self.source_schema}\".\"{self.source_table_name}\" is already partitioned'\n                )\n\n        # Create structures\n        with transaction.atomic():\n            self.__create_table_like_source()\n            self.__set_primary_key()\n            self.__set_column_definitions()\n            self.__set_constraints()\n            self.__create_indexes()\n            self.__create_default_partition()\n            self.__create_partitions()\n\n        # Copy all source data\n        with transaction.atomic():\n            self.__copy_data()\n\n        # Rename objects (acquires lock)\n        with transaction.atomic():\n            self.__rename_objects()\n\n        # Create views\n        with transaction.atomic():\n            self.__create_views()\n            self.__refresh_views()\n\n        # Truncate and drop original objects\n        if drop_orig:\n            with transaction.atomic():\n                self.drop_original_objects()\n\n    def drop_original_objects(self):\n        \"\"\"\n        Truncate and drop source table and its dependents.\n        \"\"\"\n        sql = f\"\"\"\nTRUNCATE TABLE \"{self.source_schema}\".\"{self.source_table_name}\" ;\n\"\"\"\n        conn_execute(sql)\n        sql = f\"\"\"\nDROP TABLE \"{self.source_schema}\".\"{self.source_table_name}\" CASCADE ;\n\"\"\"\n        conn_execute(sql)\n\n\n# ====================================================================\n#  Functionality to move data from default partitions to new partitions\n# ====================================================================\nclass PartitionDefaultData:\n    \"\"\"\n    Move data from a specified default partition into newly-created month range partitions\n    Params:\n        conn : database connection (If None, then gets the connection)\n        schema_name (str) : Schema containing the partitioned data and tracking table\n        partitioned_table (str) : Partitioned table name\n        partitioned_table (str) : Default partition table name\n    \"\"\"\n\n    def __init__(self, schema_name, partitioned_table, default_partition=None):\n        self.conn = transaction.get_connection()\n        self.schema_name = schema_name\n        self.partitioned_table = partitioned_table\n        self.default_partition = default_partition or self._get_default_partition()\n        self.partition_name = \"\"\n        self.tracking_rec = None\n        self.partition_parameters = {}\n        self.tx_id = str(uuid.uuid4()).replace(\"-\", \"_\")\n        self._get_default_partition_tracker_info()\n\n    def _get_default_partition(self):\n        sql = \"\"\"\nSELECT d.relname::text as \"table_name\"\n  FROM pg_partitioned_table pt\n  JOIN pg_class c\n    ON c.oid = pt.partrelid\n  JOIN pg_namespace n\n    ON n.oid = c.relnamespace\n  JOIN pg_class d\n    ON d.oid = pt.partdefid\n WHERE n.nspname = %s::name\n   AND c.relname = %s::name;\n\"\"\"\n        cur = conn_execute(sql, (self.schema_name, self.partitioned_table))\n        res = fetchone(cur)[\"table_name\"]\n\n        return res\n\n    def _get_default_partition_tracker_info(self):\n        sql = f\"\"\"\nSELECT *\n  FROM \"{self.schema_name}\".\"partitioned_tables\"\n WHERE schema_name = %s\n   AND partition_of_table_name = %s\n   AND table_name = %s\n   AND partition_parameters->>'default' = 'true'\n\"\"\"\n        LOG.info(f\"Getting default tracker record for {self.default_partition} of {self.partitioned_table}\")\n        cur = conn_execute(sql, (self.schema_name, self.partitioned_table, self.default_partition), _conn=self.conn)\n        default_tracker = fetchone(cur)\n        self.partition_type = default_tracker[\"partition_type\"]\n        self.partition_key = default_tracker[\"partition_col\"]\n\n    def _update_partition_tracking_record(self, set_col):\n        LOG.debug(f\"Execute SQL to update partition_parameters.{set_col}\")\n        if set_col == \"partition_parameters\":\n            partition_parameters = self.partition_parameters.copy()\n            partition_parameters[\"from\"] = str(partition_parameters[\"from\"])\n            partition_parameters[\"to\"] = str(partition_parameters[\"to\"])\n            partition_parameters[\"default\"] = False\n            self.tracking_rec[\"partition_parameters\"] = json.dumps(partition_parameters)\n\n        update_sql = f\"\"\"\nUPDATE \"{self.schema_name}\".\"partitioned_tables\"\n   SET {set_col} = %s{'::boolean' if set_col == 'active' else '::jsonb'}\n WHERE \"id\" = %s;\n\"\"\"\n        vals = [self.tracking_rec[set_col], self.tracking_rec[\"id\"]]\n\n        conn_execute(update_sql, vals, _conn=self.conn)\n\n    def _create_partititon_tracking_record(self):\n        LOG.debug(f\"Creating tracking record for new partition {self.partition_name}\")\n        partition_parameters = self.partition_parameters.copy()\n        partition_parameters[\"from\"] = str(partition_parameters[\"from\"])\n        partition_parameters[\"to\"] = str(partition_parameters[\"to\"])\n        partition_parameters[\"default\"] = False\n        part_track_insert_sql = f\"\"\"\nINSERT INTO \"{self.schema_name}\".\"partitioned_tables\"\n       (schema_name, table_name, partition_of_table_name, partition_type, partition_col, partition_parameters, active)\nVALUES (%s, %s, %s, %s, %s, %s::jsonb, true)\nRETURNING *;\n\"\"\"\n        vals = (\n            self.schema_name,\n            self.partition_name,\n            self.partitioned_table,\n            self.partition_type,\n            self.partition_key,\n            json.dumps(partition_parameters),\n        )\n        cur = conn_execute(part_track_insert_sql, vals, _conn=self.conn)\n        self.tracking_rec = fetchone(cur)\n\n    def _create_partition(self):\n        self._create_partititon_tracking_record()\n\n    def _attach_partition(self):\n        self.tracking_rec[\"active\"] = True\n\n        # This is necessary to prevent two triggers from firing and getting a race condition\n        LOG.debug(\"Updating tracking partition parameters\")\n        self._update_partition_tracking_record(set_col=\"partition_parameters\")\n        LOG.debug(\"Updating tracking active state\")\n        self._update_partition_tracking_record(set_col=\"active\")\n\n    def _detach_partition(self):\n        self.tracking_rec[\"active\"] = False\n        self._update_partition_tracking_record(set_col=\"active\")\n\n    def _get_new_partitions_from_default(self):\n        partition_start_sql = f\"\"\"\nSELECT DISTINCT\n       date_trunc('month', usage_start)::date as \"partition_start\",\n       date_trunc('month', (date_trunc('month', usage_start) + '1 month'::interval))::date as \"partition_end\"\n  FROM \"{self.schema_name}\".\"{self.default_partition}\"\n ORDER\n    BY 1;\n\"\"\"\n        LOG.info(f'Getting partition bounds for data in \"{self.schema_name}\".\"{self.default_partition}\"')\n        cur = conn_execute(partition_start_sql)\n        res = [(r[\"partition_start\"], r[\"partition_end\"]) for r in fetchall(cur)]\n\n        if len(res) == 0:\n            LOG.info(\"Nothing to do\")\n\n        return res\n\n    def repartition_default_data(self):\n        new_partitions = self._get_new_partitions_from_default()\n        if new_partitions:\n            mv_recs_sql = f\"\"\"\nWITH __mv_recs_{self.tx_id} as (\nDELETE\n  FROM \"{self.schema_name}\".\"{self.default_partition}\"\n WHERE usage_start >= %s\n   AND usage_start < %s\nRETURNING *\n)\nINSERT INTO {{}}\nSELECT * FROM __mv_recs_{self.tx_id} ;\n\"\"\"\n\n            for bounds in new_partitions:\n                p_from, p_to = bounds\n                self.partition_name = f\"{self.partitioned_table}_{p_from.strftime('%Y_%m')}\"\n                full_partition_name = f'\"{self.schema_name}\".\"{self.partition_name}\"'\n\n                # A little jiggery-pokery here to increase efficiency.\n                # The partition is named properly, but it set as a partition for 100 years in the future\n                # This will allow us to create the partition with all of the structure that is required.\n                # Before the data move, we will detach the partition, then move the data,\n                # then reattach with the proper bounds.\n                self.partition_parameters = {\n                    \"from\": p_from.replace(year=p_from.year + 100),\n                    \"to\": p_to.replace(year=p_to.year + 100),\n                }\n\n                LOG.info(\n                    f\"Re-partitioning {self.schema_name}.{self.default_partition} into {full_partition_name} \"\n                    + f\"for {self.partition_key} values from {p_from} to {p_to}\"\n                )\n                with transaction.atomic():\n                    # Create new partition\n                    self._create_partition()\n                    # Detach it so we can move the data\n                    self._detach_partition()\n\n                    # Move data into new partition\n                    conn_execute(mv_recs_sql.format(full_partition_name), (p_from, p_to), _conn=self.conn)\n\n                    # Re-attach partition with actual bounds\n                    self.partition_parameters[\"from\"] = p_from\n                    self.partition_parameters[\"to\"] = p_to\n                    self._attach_partition()\n\n\ndef get_partitioned_tables_with_default(schema_name=None, partitioned_table_name=None):\n    default_partition_sql = \"\"\"\nSELECT dp.relnamespace::regnamespace::text as \"schema_name\",\n       dp.relname::text as \"default_partition\",\n       pt.relname::text as \"partitioned_table\"\n  FROM pg_partitioned_table ptr\n  JOIN pg_class dp\n    ON dp.oid = ptr.partdefid\n  JOIN pg_class pt\n    ON pt.oid = ptr.partrelid\n       /* must actually be a namespace */\n  JOIN pg_namespace n\n    ON n.oid = dp.relnamespace\n       /* must be constrained to app tenant schemas */\n  JOIN public.api_tenant ten\n    ON ten.schema_name = n.nspname\n WHERE n.nspname != %s::name  -- do not execute against template schema\n   AND n.nspname = coalesce(%s::name, n.nspname)\n   AND pt.relname = coalesce(%s::name, pt.relname)\n ORDER\n    BY 1, 2;\n\"\"\"\n    cur = conn_execute(default_partition_sql, (_TEMPLATE_SCHEMA, schema_name, partitioned_table_name))\n    default_partitions = fetchall(cur)\n\n    return default_partitions\n\n\n# This is a crawler interface to the PartitionDefaultData class\ndef repartition_default_data(schema_name=None, partitioned_table_name=None):\n    \"\"\"\n    Move any data in a default partition to the requisite partition.\n    Unless constrained to a schema or table, it will crawl over all schemata and default table partitions\n    Params:\n        schema_name (str) : Constrain to specified schema\n        partitioned_table_name (str) : Constrain to specified partitioned table\n    \"\"\"\n    default_partitions = get_partitioned_tables_with_default(schema_name, partitioned_table_name)\n\n    for default_rec in default_partitions:\n        default_partitioner = PartitionDefaultData(\n            default_rec[\"schema_name\"],\n            default_rec[\"partitioned_table\"],\n            default_partition=default_rec[\"default_partition\"],\n        )\n        default_partitioner.repartition_default_data()\n","sub_path":"koku/koku/pg_partition.py","file_name":"pg_partition.py","file_ext":"py","file_size_in_byte":46518,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"604486266","text":"\"\"\"\n  File name: mymosaic.py\n  Author:\n  Date created:\n\"\"\"\n\nimport numpy as np\nimport matplotlib.image as mpimg\nimport matplotlib.pyplot as plt\nfrom ransac_est_homography import ransac_est_homography\n\nthresh = 0.5\n\nimg_m = mpimg.imread('middle.jpg')\nimg_l = mpimg.imread('left.jpg')\nNUM_channels = 3\n\n\ndef show_ransac():\n    big_im = np.concatenate((img_l, img_m), axis=1)\n\n    plt.imshow(big_im)\n\n    x2_shift = x2 + img_m.shape[1]\n    for i in range(x1.shape[0]):\n        if inline_idx[i] == 1:\n            plt.plot([x1[i], x2_shift[i]], [y1[i], y2[i]], marker=\"o\")\n    plt.show()\n\n\nif __name__ == \"__main__\":\n\n    x1 = np.load('./debug-1106/draw_x1.npy')\n    y1 = np.load('./debug-1106/draw_y1.npy')\n    x2 = np.load('./debug-1106/draw_x2.npy')\n    y2 = np.load('./debug-1106/draw_y2.npy')\n    print('x1:\\n', x1)\n    print('y1:\\n', y1)\n    print('x2:\\n', x2)\n    print('y2:\\n', y2)\n    ransac_H, inline_idx = ransac_est_homography(x1, x2, y1, y2, 0.5)\n    print(\"inline_idx:\\n\", inline_idx)\n    print(\"ransac_H:\\n\", ransac_H)\n    show_ransac()\n\n\n","sub_path":"image_stitch/ver1/test_feature_by_ransac.py","file_name":"test_feature_by_ransac.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"636861265","text":"# Copyright (C) 2019 Max Masalsky\n#\n# GNU General Public License\n# <http://www.gnu.org/licenses/>.\n#\n\nimport cv2\nimport os\nfrom matplotlib import pyplot as plt\nfrom matplotlib.widgets import Slider, Button\nimport numpy as np\nimport json\nfrom math import cos,sin,radians\n\n#---------------------------------\npt1x,pt1y,pt2x,pt2y,pt3x,pt3y,ptAllx,ptAlly,ptAllRp,ptAllRn = 0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0\nloading_settings=0\nevx,evy=0.0,0.0\n\nimageL = './imageL.jpg'\nimageR = './imageR.jpg'\ncap = cv2.VideoCapture(2)\ncap2 = cv2.VideoCapture(3)\ncap.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)\ncap.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)\ncap2.set(cv2.CAP_PROP_FRAME_WIDTH, 1920)\ncap2.set(cv2.CAP_PROP_FRAME_HEIGHT, 1080)\nret, imgL = cap.read()\nret2, imgR = cap2.read()\n#print('Read image...')\n#imgL = cv2.imread(imageL)\n#imgR = cv2.imread(imageR)\n#print(imgR.shape)\n#imgR = imgR[:,:,::-1]\n\n# Set up and draw interface\naxcolor = 'lightgoldenrodyellow'\n#fig = plt.subplots(1,2)\n#plt.subplots_adjust(left=0.15, bottom=0.5)\n#plt.subplot(1,2,1)\n#img2Object = plt.imshow(imgR)\n#cv2.imshow('it', imgL)\n#cv2.waitKey()\n#cv2.destroyAllWindows()\nfigL = plt.figure()\nimg1Object = plt.imshow(imgL, aspect='equal', cmap='jet')\nplt.axis('off')\nfigL.subplots_adjust(bottom=0,top=1,left=0,right=1)\n#\nfig1 = plt.figure()\nimg2=imgR\nimg2Object = plt.imshow(img2, aspect='equal', cmap='jet')\nplt.axis('off')\n#fig1.gca().axes.get_xaxis().set_visible(False)\n#fig1.gca().axes.get_yaxis().set_visible(False)\nfig1.subplots_adjust(bottom=0,top=1,left=0,right=1)\n#plt.show()\n#exit(0)\n#Button Save----------------------------------------\ndef onclick(event):\n    global evx,evy\n    #print('%s click: button=%d, x=%d, y=%d, xdata=%f, ydata=%f' %\n    #      ('double' if event.dblclick else 'single', event.button,\n    #       event.x, event.y, event.xdata, event.ydata))\n    if event.dblclick:\n        evx=event.xdata\n        evy=event.ydata\n        update(2)\n    if not event.dblclick:\n        update(3)\n\ndef onclick1(event):\n    global evx,evy\n    if event.dblclick:\n        evx=event.xdata\n        evy=event.ydata\n        update(2)\ncid = fig1.canvas.mpl_connect('button_press_event', onclick)\nplt.figure()\nsaveax = plt.axes([0.3, 0.41+0.4, 0.15, 0.04]) #stepX stepY width height\nbuttons = Button(saveax, 'Save settings', color=axcolor, hovercolor='0.975')\ndef save_map_settings( event ):\n    global imgL,img2\n    buttons.label.set_text (\"Saving...\")\n    print('Saving to file...')\n    result = json.dumps({'pt1x':pt1x, 'pt1y':pt1y, 'pt2x':pt2x, \\\n             'pt2y':pt2y, 'pt3x':pt3x, 'pt3y':pt3y, \\\n             'ptAllx':ptAllx, 'ptAlly':ptAlly, \\\n             'ptAllRp':ptAllRp, 'ptAllRn':ptAllRn},\\\n             sort_keys=True, indent=4, separators=(',',':'))\n    fName = 'cam_set.txt'\n    f = open (str(fName), 'w')\n    f.write(result)\n    f.close()\n    buttons.label.set_text (\"Save to file\")\n    cv2.imwrite('imgL2.jpg',imgL)\n    cv2.imwrite('imgR2.jpg',imgR)\n    cv2.imwrite('imgR2_A.jpg',img2)\n    print ('Settings saved to file '+fName)\n\nbuttons.on_clicked(save_map_settings)\n#Button Load----------------------------------------\nloadax = plt.axes([0.5, 0.41+0.4, 0.15, 0.04]) #stepX stepY width height\nbuttonl = Button(loadax, 'Load', color=axcolor, hovercolor='0.975')\ndef load_map_settings( event ):\n    global pt1x,pt1y,pt2x,pt2y,pt3x,pt3y,ptAllx,ptAlly, loading_settings\n    loading_settings = 1\n    fName = 'cam_set.txt'\n    print('Loading parameters from file...')\n    buttonl.label.set_text (\"Loading...\")\n    f=open(fName, 'r')\n    data = json.load(f)\n    spt1x.set_val(data['pt1x'])\n    spt1y.set_val(data['pt1y'])\n    spt2x.set_val(data['pt2x'])\n    spt2y.set_val(data['pt2y'])\n    spt3x.set_val(data['pt3x'])\n    spt3y.set_val(data['pt3y'])\n    sptAllx.set_val(data['ptAllx'])\n    sptAlly.set_val(data['ptAlly'])\n    sptAllRp.set_val(data['ptAllRp'])\n    sptAllRn.set_val(data['ptAllRn'])\n    f.close()\n    buttonl.label.set_text (\"Load settings\")\n    print ('Parameters loaded from file '+fName)\n    print ('Redrawing image Affine with loaded parameters...')\n    loading_settings = 0\n    update(4)\nbuttonl.on_clicked(load_map_settings)\n#------------------------------\n#plt.subplot(1,2,2)\n#img2Object = plt.imshow(imgR, aspect='equal', cmap='jet')\n\n# Draw interface\nprint('Start interface (few seconds)...')\n\npt1x_axe = plt.axes([0.15, 0.01+0.4, 0.7, 0.025], axisbg=axcolor) #stepX stepY width height\npt1y_axe = plt.axes([0.15, 0.05+0.4, 0.7, 0.025], axisbg=axcolor) #stepX stepY width height\npt2x_axe = plt.axes([0.15, 0.09+0.4, 0.7, 0.025], axisbg=axcolor) #stepX stepY width height\npt2y_axe = plt.axes([0.15, 0.13+0.4, 0.7, 0.025], axisbg=axcolor) #stepX stepY width height\npt3x_axe = plt.axes([0.15, 0.17+0.4, 0.7, 0.025], axisbg=axcolor) #stepX stepY width height\npt3y_axe = plt.axes([0.15, 0.21+0.4, 0.7, 0.025], axisbg=axcolor) #stepX stepY width height\nptAllx_axe = plt.axes([0.15, 0.25+0.4, 0.7, 0.025], axisbg=axcolor) #stepX stepY width height\nptAlly_axe = plt.axes([0.15, 0.29+0.4, 0.7, 0.025], axisbg=axcolor) #stepX stepY width height\nptAllRp_axe = plt.axes([0.15, 0.33+0.4, 0.7, 0.025], axisbg=axcolor)\nptAllRn_axe = plt.axes([0.15, 0.37+0.4, 0.7, 0.025], axisbg=axcolor)\n\nspt1x = Slider(pt1x_axe, 'pt1x', -200.0, 200.0, valinit=0)\nspt1y = Slider(pt1y_axe, 'pt1y', -200.0, 200.0, valinit=0)\nspt2x = Slider(pt2x_axe, 'pt2x', -200.0, 200.0, valinit=0)\nspt2y = Slider(pt2y_axe, 'pt2y', -200.0, 200.0, valinit=0)\nspt3x = Slider(pt3x_axe, 'pt3x', -200.0, 200.0, valinit=0)\nspt3y = Slider(pt3y_axe, 'pt3y', -200.0, 200.0, valinit=0)\nsptAllx = Slider(ptAllx_axe, 'ptAllx', -200.0, 200.0, valinit=0)\nsptAlly = Slider(ptAlly_axe, 'ptAlly', -200.0, 200.0, valinit=0)\nsptAllRp = Slider(ptAllRp_axe, 'ptAllRp', -180.0, 180.0, valinit=0)\nsptAllRn = Slider(ptAllRn_axe, 'ptAllRn', -180.0, 180.0, valinit=0)\n\n# Update depth map parameters and redraw\ndef update(val):\n    global pt1x,pt1y,pt2x,pt2y,pt3x,pt3y,ptAllx,ptAlly,ptAllRp,ptAllRn,loading_settings,evx,evy,imgR,imgL,img2\n    pt1x = spt1x.val\n    pt1y = spt1y.val\n    pt2x = spt2x.val\n    pt2y = spt2y.val\n    pt3x = spt3x.val\n    pt3y = spt3y.val\n    ptAllx = sptAllx.val\n    ptAlly = sptAlly.val\n    ptAllRp = sptAllRp.val\n    ptAllRn = sptAllRn.val\n    if ( loading_settings==0 ):\n        #print ('Rebuilding Affine')\n        #disparity = stereo_depth_map(rectified_pair)\n        #print(imgR.shape)\n        rows,cols,_rgb = imgR.shape\n        if val==4:\n            ret, imgL = cap.read()\n            ret2, imgR = cap2.read()\n        if val==2:\n            img2 = cv2.warpAffine(imgR,cv2.getAffineTransform(\n            np.float32([[0,0],[rows/2,cols/2],[rows,0]]),\n            np.float32([[0,0],[evy,evx],[rows,0]]),\n            #np.float32([[ptAlly+pt1y,ptAllx+pt1x],[ptAlly+pt2y,ptAllx+pt2x+cols],[ptAlly+pt3y+rows,ptAllx+pt3x]])\n            ),(cols,rows))\n        else:#if val==3:\n            imgLwl = imgL.copy()\n            cv2.line(imgLwl,(0,0),(cols,rows),(0,255,0),1)\n            cv2.line(imgLwl,(0,rows),(cols,0),(0,255,0),1)\n            imgLwl = imgLwl[:,:,::-1]\n            img1Object.set_data(imgLwl)\n            figL.canvas.draw()\n            #\n            x1=0.0\n            y1=0.0\n            x2=float(cols)\n            y2=0.0\n            x3=0.0\n            y3=float(rows)\n            x0=float((x2+x3)/2)\n            y0=float((y3+y2)/2)\n            print((x1-x0)*cos(radians(ptAllRp)))\n            print((y1-y0)*sin(radians(ptAllRp)))\n            nx1=(x1-x0)*cos(radians(ptAllRp))-(y1-y0)*sin(radians(ptAllRp))+x0\n            ny1=(x1-x0)*sin(radians(ptAllRp))+(y1-y0)*cos(radians(ptAllRp))+y0\n            nx2=(x2-x0)*cos(radians(ptAllRp))-(y2-y0)*sin(radians(ptAllRp))+x0\n            ny2=(x2-x0)*sin(radians(ptAllRp))+(y2-y0)*cos(radians(ptAllRp))+y0\n            nx3=(x3-x0)*cos(radians(ptAllRp))-(y3-y0)*sin(radians(ptAllRp))+x0\n            ny3=(x3-x0)*sin(radians(ptAllRp))+(y3-y0)*cos(radians(ptAllRp))+y0\n            #print(cols,rows)\n            print(x1,y1,x2,y2,x3,y3, cos(radians(ptAllRp)), sin(radians(ptAllRp)),x0,y0)\n            x1=nx1+pt1x+ptAllx\n            y1=ny1+pt1y+ptAlly\n            x2=nx2+pt2x+ptAllx\n            y2=ny2+pt2y+ptAlly\n            x3=nx3+pt3x+ptAllx\n            y3=ny3+pt3y+ptAlly\n            img2 = cv2.warpAffine(imgR,cv2.getAffineTransform(\n            np.float32([[0,0],[cols,0],[0,rows]]),\n            np.float32([[x1,y1],[x2,y2],[x3,y3]])\n            ),(cols,rows))\n        #else:\n        #    img2 = cv2.warpAffine(imgR,cv2.getAffineTransform(\n        #    np.float32([[0,0],[0,cols],[rows,0]]),\n        #    np.float32([[ptAlly+pt1y,ptAllx+pt1x],[ptAlly+pt2y,ptAllx+pt2x+cols],[ptAlly+pt3y+rows,ptAllx+pt3x]])\n        #    ),(cols,rows))\n        img2wl = img2.copy()\n        cv2.line(img2wl,(0,0),(cols,rows),(0,255,0),1)\n        cv2.line(img2wl,(0,rows),(cols,0),(0,255,0),1)\n        img2wl = img2wl[:,:,::-1]\n        img2Object.set_data(img2wl)\n        #cv2.imshow(\"in2\", img2)\n        #cv2.waitKey()\n        #cv2.destroyAllWindows()\n        #print ('Redraw Affine')\n        fig1.canvas.draw()\n\n\n# Connect update actions to control elements\nspt1x.on_changed(update)\nspt1y.on_changed(update)\nspt2x.on_changed(update)\nspt2y.on_changed(update)\nspt3x.on_changed(update)\nspt3y.on_changed(update)\nsptAllx.on_changed(update)\nsptAlly.on_changed(update)\nsptAllRp.on_changed(update)\nsptAllRn.on_changed(update)\n\nprint('Show interface')\nplt.show()\ncap.release()\ncap2.release()\n","sub_path":"cam_tune.py","file_name":"cam_tune.py","file_ext":"py","file_size_in_byte":9365,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"329324639","text":"import jieba\n\ntxt = open('西游记.txt', 'r', encoding='UTF-8').read()#打开文件西游记\n\nfor i in '，。！？：“”……（）你我他她是没有一什么这为那这':\n    txt = txt.replace(i, '')\nwords = list(jieba.cut(txt))#jieba分词\n\ndic = {}\nfor i in words:\n    if len(i) == 1:\n        continue\n    else:\n        dic[i] = dic.get(i, 0) + 1\n\nwc = list(dic.items())\nwc.sort(key=lambda x: x[1], reverse=True)#以出现次数为标准排列，从大到小排序\n\nfor i in range(20):\n    print(wc[i])","sub_path":"text_analy/西游记词频统计.py","file_name":"西游记词频统计.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"263116809","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import unicode_literals\n\nfrom setuptools import setup, find_packages\n\nwith open(\"sciris/version.py\", \"r\") as f:\n    version_file = {}\n    exec(f.read(), version_file)\n    version = version_file[\"version\"]\n\ntry:\n    from pypandoc import convert\nexcept ImportError:\n    import io\n    def convert(filename, fmt):\n        with io.open(filename, encoding='utf-8') as fd:\n            return fd.read()\n\nCLASSIFIERS = [\n    'Environment :: Console',\n    'Intended Audience :: Science/Research',\n    'License :: OSI Approved :: GPLv3',\n    'Operating System :: OS Independent',\n    'Programming Language :: Python',\n    'Topic :: Software Development :: Libraries :: Python Modules',\n    'Development Status :: 3 - Alpha',\n    'Programming Language :: Python :: 2.7',\n]\n\nsetup(\n    name='sciris',\n    version=version,\n    author='Cliff Kerr, George Chadderdon',\n    author_email='info@optimamodel.com',\n    description='Scientific webapps for Python',\n    url='http://github.com/optimamodel/sciris',\n    keywords=['scientific','webapp', 'framework'],\n    platforms=['OS Independent'],\n    classifiers=CLASSIFIERS,\n    packages=find_packages(),\n    include_package_data=True,\n    install_requires=[\n        'matplotlib>=1.4.2',\n        'numpy>=1.10.1',\n    ],\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1314,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"651066557","text":"import random\r\nimport os\r\n\r\np1 = 0 \r\np2 = 0\r\n\r\nwhile True:\r\n\tos.system(\"cls\")\r\n\r\n\tp1 = random.randint(1,12)\r\n\tp2 = random.randint(1,12)\r\n\r\n\tprint(f\"\"\"\r\n\t\tРаунд начался!\r\n\t\tБроски игроков!\r\n\t\tИгрок1: {p1}\r\n\t\tИгрок2: {p2}\r\n\t\tИгрок №{1 if p1>p2 else 2} выбросил больше!\r\n\r\n\t\t{\"Первый\" if p1>p2 else \"Второй\"} игрок победил\r\n\t\"\"\")\r\n\t\r\n\tinput()\r\n\r\n\r\n","sub_path":"dice/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"372329145","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue May 21 23:18:51 2019\n\n@author: Matteo Risso, Alessandro Romeo, Antonio Simone\n\"\"\"\n\nfrom keras import datasets\nfrom keras.utils import np_utils, normalize\nfrom PIL import Image, ImageFilter \n\nimport numpy as np\nimport scipy.io as spio\nimport os\n\n\ndef _initialize(train :tuple, test :tuple):\n    \n    x_train, y_train = train\n    x_test, y_test = test\n    \n    print('Size of:')\n    print('- training set:\\t{} samples'.format(len(x_train)))\n    print('- test set:\\t{} samples'.format(len(x_test)))\n    \n    # reshape (28,28) img : (28,28,1) = (height, width, channels)\n    x_train = x_train.reshape(x_train.shape[0], 28,28,1)\n    x_test  = x_test.reshape(x_test.shape[0], 28,28,1)\n    \n    print('\\nImage Shape:\\t{}'.format(x_train[0].shape))\n    # reshape img to lenet5 input (32,32,1)\n    x_train = np.pad(x_train, ((0,0),(2,2),(2,2),(0,0)), 'constant')\n    x_test  = np.pad(x_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')\n    print('Reshape...')\n    print('Image Shape:\\t{}\\n'.format(x_train[0].shape))\n    \n    train = (x_train,y_train)\n    test = (x_test,y_test)\n    \n    return train,test\n \ndef _process(train :tuple, test :tuple, n_classes):\n     \n    x_train, y_train = train\n    x_test,  y_test  = test\n    \n    x_train = x_train.astype('float32')\n    x_test  = x_test.astype('float32')\n       \n    # normalize value to help loss function minimization during training \n    x_train = normalize(x_train, axis=-1)\n    x_test  = normalize(x_test, axis=-1)\n       \n    # Transform labels to one-hot encoding for categorical_crossentropy loss function\n    y_train = np_utils.to_categorical(y_train, n_classes)\n    y_test  = np_utils.to_categorical(y_test,  n_classes)\n    \n    train = (x_train,y_train)\n    test = (x_test,y_test)\n    \n    return train, test\n\nclass mnist():\n    \n    def __init__(self):\n        \n        self.n_classes = 10\n        \n        print('\\nLoading MNIST...\\n') \n        train, test = datasets.mnist.load_data()\n        self.train, self.test = _initialize(train,test)\n        \n    def process(self):\n        self.train, self.test = _process(self.train,self.test,self.n_classes)\n        return self.train, self.test\n        \nclass png2mnist():\n    \n    def __init__(self):\n        pass\n\n    # This function returns the pixel values. The input is a png file location.\n    def process(self,argv):\n        \n        self.im          = Image.open(argv).convert('L')\n        self.width       = float(self.im.size[0])\n        self.height      = float(self.im.size[1])\n        \n        # creates white canvas of 28x28 pixels\n        self.newImage = Image.new('L', (28, 28), (255)) \n        \n        # check which dimension is bigger\n        if self.width > self.height:  \n            # resize height according to ratio width\n            self.nheight = int(round((20.0 / self.width * self.height), 0)) \n            \n            # rare case but minimum is 1 pixel\n            if self.nheight == 0:      \n                self.nheight = 1\n            # resize and sharpen\n            self.img = self.im.resize((20, self.nheight), \\\n                            Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\n            # calculate horizontal position\n            self.wtop = int(round(((28 - self.nheight) / 2), 0)) \n            # paste resized image on white canvas\n            self.newImage.paste(self.img, (4, self.wtop))  \n            \n        else:\n            # resize width according to ratio height\n            self.nwidth = int(round((20.0 / self.height * self.width), 0))  \n            # rare case but minimum is 1 pixel\n            if self.nwidth == 0: \n                self.nwidth = 1\n            # resize and sharpen\n            self.img    = self.im.resize((self.nwidth, 20), \\\n                                         Image.ANTIALIAS).filter(ImageFilter.SHARPEN)\n            # caculate vertical pozition\n            self.wleft  = int(round(((28 - self.nwidth) / 2), 0))  \n            # paste resized image on white canvas\n            self.newImage.paste(self.img, (self.wleft, 4))  \n        \n        # get pixel values\n        self.tv     = list(self.newImage.getdata()) \n    \n        # normalize pixels to 0 and 1. 0 is pure white, 1 is pure black.\n        self.tva    = [(255 - x) * 1.0 / 255.0 for x in self.tv]\n    \n        return self.tva\n    \n    \n    def getimg(self, dir_name='handwritten_ex'):\n        \n        # empty numpy array of dimension (10,28,28) where the dataset will be stored.\n        self.handwritten_ds = np.empty((10,28,28))\n        # In this loop the dataset of our handwritten digits is converted in the \n        # MNIST-like format and stored in the numpy array handwritten_ds\n        for img, i in\\\n        zip(\n                [ file for file in os.listdir(dir_name) if file.endswith('.png')],\\\n                range(10)):\n            \n            self.x      = self.process(os.path.join(dir_name,img))\n            self.img    = np.array(self.x)\n            self.img    = self.img.reshape(28,28)\n            \n            self.handwritten_ds[i] = self.img\n            \n        return self.handwritten_ds\n    \n","sub_path":"sw/keras/float/dataset.py","file_name":"dataset.py","file_ext":"py","file_size_in_byte":5105,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"11049205","text":"import torch\nimport random\n\nimport nnsum\nimport logging\nlogging.getLogger().setLevel(logging.INFO)\n\n\ndef main():\n\n    args = nnsum.cli.training_argparser().parse_args()\n\n    random.seed(args[\"trainer\"][\"seed\"])\n    torch.manual_seed(args[\"trainer\"][\"seed\"])\n    torch.cuda.manual_seed_all(args[\"trainer\"][\"seed\"])\n\n    print(args[\"trainer\"])\n    print()\n    print(args[\"emb\"])\n    print()\n    print(args[\"enc\"])\n    print()\n    print(args[\"ext\"])\n\n    print(\"Initializing vocabulary and embeddings.\")\n    embedding_context = nnsum.io.initialize_embedding_context(\n        args[\"trainer\"][\"train_inputs\"], **args[\"emb\"])\n\n    print(\"Loading training data.\")\n    train_data = nnsum.data.SummarizationDataset(\n        embedding_context.vocab,\n        args[\"trainer\"][\"train_inputs\"],\n        targets_dir=args[\"trainer\"][\"train_labels\"],\n        sentence_limit=args[\"trainer\"][\"sentence_limit\"])\n    train_loader = nnsum.data.SummarizationDataLoader(\n        train_data, batch_size=args[\"trainer\"][\"batch_size\"],\n        num_workers=args[\"trainer\"][\"loader_workers\"])\n\n    print(\"Loading validation data.\")\n    val_data = nnsum.data.SummarizationDataset(\n        embedding_context.vocab,\n        args[\"trainer\"][\"valid_inputs\"],\n        targets_dir=args[\"trainer\"][\"valid_labels\"],\n        references_dir=args[\"trainer\"][\"valid_refs\"],\n        sentence_limit=args[\"trainer\"][\"sentence_limit\"])\n    val_loader = nnsum.data.SummarizationDataLoader(\n        val_data, batch_size=args[\"trainer\"][\"batch_size\"],\n        num_workers=args[\"trainer\"][\"loader_workers\"])\n\n    if args[\"trainer\"][\"weighted\"]:\n        weight = nnsum.trainer.compute_class_weights(\n            args[\"trainer\"][\"train_labels\"],\n            args[\"trainer\"][\"loader_workers\"],\n            sentence_limit=args[\"trainer\"][\"sentence_limit\"])\n    else:\n        weight = None\n\n    model = nnsum.cli.create_model_from_args(embedding_context, args)\n    if args[\"trainer\"][\"gpu\"] > -1:\n        print(\"Placing model on device: {}\".format(args[\"trainer\"][\"gpu\"]))\n        model.cuda(args[\"trainer\"][\"gpu\"])\n    model.initialize_parameters(logger=logging.getLogger())\n    optimizer = torch.optim.Adam(model.parameters(), lr=.0001)\n\n    nnsum.trainer.labels_mle_trainer(model, optimizer, train_loader, val_loader, \n                                     pos_weight=weight, max_epochs=args[\"trainer\"][\"epochs\"],\n                                     summary_length=args[\"trainer\"][\"summary_length\"],\n                                     remove_stopwords=args[\"trainer\"][\"remove_stopwords\"],\n                                     gpu=args[\"trainer\"][\"gpu\"],\n                                     teacher_forcing=args[\"trainer\"][\"teacher_forcing\"],\n                                     model_path=args[\"trainer\"][\"model\"],\n                                     results_path=args[\"trainer\"][\"results\"])\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"summarize/nnsum/script_bin/train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":2884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"450856150","text":"# -*- coding: utf-8 -*-\nclass Clazz:\n    def __init__(self, id, name, grade, advisor, startYear):\n        self.id = id\n        self.name = name\n        self.grade = grade\n        self.advisor = advisor\n        self.startYear = startYear\n        self.desc = \"没有对这个班级的任何备注\"\n        \n    \n","sub_path":"schoolManager/vo/Clazz.py","file_name":"Clazz.py","file_ext":"py","file_size_in_byte":310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"588985478","text":"import pandas as pd\n\ndef condence_df(df_raw_data):\n    df_new = pd.DataFrame()\n    temp_store = []\n    column_title = \"\"\n    for col in df_raw_data.columns:\n        if len(col.split('___')) != 1:\n            if len(temp_store) == 0 or int(col.split('___')[-1]) > temp_store[-1]:\n                temp_store.append(int(col.split('___')[-1])) \n                column_title = col.split('___')[0]\n            else:\n                df_new[column_title] = df_raw_data.apply(lambda r: \" \".join([str(i) for i in temp_store if r[column_title+\"___\"+str(i)] == 1]), axis=1)\n                temp_store = [int(col.split('___')[-1])]\n                column_title = col.split('___')[0]\n        else:\n            if len(temp_store) > 0:\n                df_new[column_title] = df_raw_data.apply(lambda r: \" \".join([str(i) for i in temp_store if r[column_title+\"___\"+str(i)] == 1]), axis=1)\n                temp_store = []\n                column_title = \"\"\n            df_new[col] = df_raw_data[col]\n    return df_new\n\ndef compute_phq9_score(df):\n    columns = [\"phq9_\"+str(i) for i in range(1,10)]\n    df['phq9_score'] = df[columns].sum(axis=1)-9\n    df = df.drop(columns=columns)\n    return df\n\ndef compute_gad_7(df):\n    start_index = df.columns.get_loc('feeling_nervous_anxious_or')\n    end_index = df.columns.get_loc('feeling_afraid_as_if_somet')\n    columns = df.columns[start_index:end_index+1]\n    df['gad7_score'] = (df[columns].sum(axis=1))-7\n    df = df.drop(columns=columns)\n    return df   \n\ndef compute_selfesteem(df):\n    mappingA = {1:3, 2:2, 3:1, 4:0, 0:0}\n    mappingB = {1:0, 2:1, 3:2, 4:3, 0:0}\n    columnsA = ['ros_satisfied_self','ros_good_qualities','ros_well_as_othres','ros_worth','ros_positive_attitude']\n    columnsB = ['ros_good','ros_proud','ros_useless','ros_respect','ros_failure']\n    for column in columnsA + columnsB:\n        if column in columnsA:\n            df[column] = df[column].apply(lambda x: mappingA[x])\n        else:\n            df[column] = df[column].apply(lambda x: mappingB[x])\n    df['selfesteem_score'] = df[columnsA+columnsB].sum(axis=1)\n    df = df.drop(columns=columnsA + columnsB) \n    return df\n\ndef compute_scores(df):\n    df1 = compute_phq9_score(df)\n    df2 = compute_gad_7(df1)\n    df_w_scores = compute_selfesteem(df2)\n    return df_w_scores\n\ndef drop_excess(df):\n    print(df.columns[0])\n    excess_columns = ['redcap_survey_identifier','redcap_event_name','ph_id2_2bf']\n    df = df.drop(columns=excess_columns)\n    df.set_index('record_id', inplace=True)\n    return df\n      \ndef main():\n    input_file_name = \"raw_data.csv\"\n    output_file_name = \"processed_data.csv\"\n    print(\"Processing\",input_file_name)\n    df_raw_data = pd.read_csv(input_file_name)\n    condenced_df = condence_df(df_raw_data)\n    print(\"File condenced\")\n    print(\"Computing scores...\")\n    df_w_scores = compute_scores(condenced_df)\n    print(\"Cleaning df...\")\n    final_df = drop_excess(df_w_scores)\n    print(\"New File Created:\",output_file_name)\n    final_df.to_csv(output_file_name)\n\n\n\ndef gen_dict():\n    count_dict = {}\n    categories = ['News & Politics & Society','Living & Well-being','Adult & Sensitive & Illegal','Health & Fitness','Arts & Entertainment','Finance & Business','Jobs & Education','neutral','Science & Tech.',\"Sports\"]\n    for category in categories:\n        timeCounts = {}\n        for hour in range(0,24):\n            timeCounts[hour] = 0\n        count_dict[category] = timeCounts\n    print(count_dict)\n    return count_dict         \ngen_dict()\n","sub_path":"data_access/process_survey_data/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3491,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"382965833","text":"import os\r\nimport shutil\r\nimport numpy as np\r\nfrom django.contrib.auth.models import User\r\nfrom account.models import student_profile, teacher_profile, general_available_time\r\nfrom datetime import date as date_function\r\nclass batch_generator:\r\n    def __init__(self):\r\n        self.text = '臣亮言：先帝創業未半，而中道崩殂。今天下三分，益州疲弊，此誠危急存亡之秋也。然侍衛之臣，不懈於內；忠志之士，忘身於外者，蓋追先帝之殊遇，欲報之於陛下也。誠宜開張聖聽，以光先帝遺德，恢弘志士之氣；不宜妄自菲薄，引喻失義，以塞忠諫之路也。宮中府中，俱為一體，陟罰臧否，不宜異同。若有作姦犯科，及為忠善者，宜付有司，論其刑賞，以昭陛下平明之治，不宜篇私，使內外異法也。'\r\n        self.text_in_list = [_ for _ in self.text]\r\n        self.subjects = \\\r\n            ['英文', '中文', '國文', '漢語', '德文', '數學', '理化', '物理', '化學',\r\n            '科學', '電子學', '統計', '地理', '歷史', '英語', '美術', '程式設計',\r\n            '托福', '多益', '雅思', '肚皮舞', '史地', '平面設計', '行銷', '建築學',\r\n            '流體力學', '量子力學', '茅山道術', '財務工程', '電機', 'TOEIC', 'TOFEL', 'IELTS']\r\n        self.people_thumbnail_path = 'account/batched_creating/people_thumbnail'\r\n        self.lesson_background_path = 'account/batched_creating/lesson_background'\r\n    def get_text(self, num=150):\r\n            return ''.join(list(np.random.choice(self.text_in_list, num, True)))\r\n    def date_string_2_dateformat(self, target_string):\r\n        if not target_string == False:\r\n            try:\r\n                # 將前端的 2000-01-01格式改為20000101\r\n                nodash_str = target_string.replace('-','')\r\n                if len(nodash_str) == 8 :\r\n                    _year, _month, _day = int(nodash_str[:4]), int(nodash_str[4:6]), int(nodash_str[-2:])\r\n                    return date_function(_year, _month, _day)\r\n                else:\r\n                    return False\r\n            except Exception as e:\r\n                print(e)\r\n                return False\r\n        else:\r\n            return False\r\n    def create_batch_student_users(self, how_many):\r\n        print('StARt')\r\n        for i in range(1, how_many):\r\n            username = 's' + str(i).rjust(5, '0') + '@edony_test.com'\r\n            name = 'test_student_' + str(i).rjust(5, '0')\r\n            if np.random.rand() > 0.5:\r\n                nickname = 'ts_' + str(i).rjust(5, '0')\r\n            else:\r\n                nickname = name\r\n            _year = str(np.random.randint(1900, 2011))\r\n            _month = str(np.random.randint(1, 13)).rjust(2, '0')\r\n            _day = str(np.random.randint(1, 28)).rjust(2, '0')\r\n            birth_date = self.date_string_2_dateformat(\r\n                _year + '-' + _month + '-' + _day)\r\n            is_male =  np.random.rand() > 0.5\r\n            rnd_index = np.random.choice([_ for _ in range(6)])\r\n            role = ['myself', 'father', 'mother', 'sister', 'brother', 'others'][rnd_index]\r\n            mobile = '0900-000000'\r\n            update_someone_by_email = ''\r\n            password = '8B6FA01313CE51AFC09E610F819250DA501778AD363CBA4F9E312A6EC823D42A'\r\n            # 此處的password已經經過前端加密，故無需再加密\r\n            obj = student_profile.objects.filter(username=username).first()\r\n            auth_obj = User.objects.filter(username=username).first()\r\n            if obj is None and auth_obj is None:\r\n                if not os.path.isdir('user_upload/students'):\r\n                    os.mkdir(os.path.join('user_upload/students'))\r\n                if os.path.isdir(os.path.join('user_upload/students', username)):\r\n                    # 如果已經有了這個資料夾，就刪除裡面所有項目並且重建\r\n                    shutil.rmtree(os.path.join('user_upload/students', username))\r\n                    print('User Folder Already Existed >> Rebuild It.')\r\n                os.mkdir(os.path.join('user_upload/students', username))\r\n                os.mkdir(os.path.join('user_upload/students/'+ username, 'info_folder'))\r\n                # 存到 user_upload 該使用者的資料夾\r\n                #大頭照\r\n                if np.random.rand() > 0.7:\r\n                    pic_name, pic_ext = np.random.choice(os.listdir(self.people_thumbnail_path)).split('.')\r\n                    thumbnail_dir = '/user_upload/students/' + username + '/' + 'thumbnail'+'.'+ pic_ext\r\n                    shutil.copy(\r\n                        os.path.join(self.people_thumbnail_path, pic_name + '.' + pic_ext),\r\n                        thumbnail_dir[1:]\r\n                    )\r\n                else:\r\n                    thumbnail_dir = ''\r\n                user_created_object = \\\r\n                    User.objects.create(\r\n                        username = username,\r\n                        password = password,\r\n                        is_superuser = 0,\r\n                        first_name = '',\r\n                        last_name = '',\r\n                        email = '',\r\n                        is_staff = 0,\r\n                        is_active = 1,\r\n                    )\r\n                # 用create()的寫法是為了知道這個user在auth裡面的id為何\r\n                user_created_object.save()\r\n                print('auth建立')\r\n                print('建立新學生資料')\r\n                student_profile.objects.create(\r\n                    auth_id = user_created_object.id,\r\n                    username = username,\r\n                    password = password,\r\n                    balance = 0,\r\n                    withholding_balance = 0,\r\n                    name = name,\r\n                    nickname = nickname,\r\n                    birth_date = birth_date,\r\n                    is_male = is_male,\r\n                    intro = '',\r\n                    role = role,\r\n                    mobile = mobile,\r\n                    user_folder = 'user_upload/'+ username,\r\n                    info_folder = 'user_upload/'+ username+ '/info_folder',\r\n                    thumbnail_dir = thumbnail_dir ,\r\n                    update_someone_by_email = update_someone_by_email\r\n                ).save()\r\n                print('student_profile建立: ', username)\r\n                # 回前端\r\n            else:\r\n                if obj is not None:\r\n                    print('此帳號已註冊過學生類別!')\r\n                elif auth_obj is not None:\r\n                    print('此帳號已註冊到全站資料中!')\r\n    def create_batch_teacher_users(self, how_many): \r\n        for i in range(0, how_many):\r\n            username = 't' + str(i).rjust(5, '0') + '@edony_test.com'\r\n            name = 'test_teacher_' + str(i).rjust(5, '0')\r\n            if np.random.rand() > 0.5:\r\n                nickname = 'tt_' + str(i).rjust(5, '0')\r\n            else:\r\n                nickname = name\r\n            password = '8B6FA01313CE51AFC09E610F819250DA501778AD363CBA4F9E312A6EC823D42A'     \r\n            _year = str(np.random.randint(1900, 2011))\r\n            _month = str(np.random.randint(1, 13)).rjust(2, '0')\r\n            _day = str(np.random.randint(1, 28)).rjust(2, '0')\r\n            birth_date = self.date_string_2_dateformat(\r\n                _year + '-' + _month + '-' + _day)\r\n            is_male = np.random.rand() > 0.5\r\n            intro = self.get_text(150)\r\n            mobile = '0900-000999'\r\n            tutor_experience = np.random.choice(['1年以內','1-3年','3-5年','5-10年','10年以上'])\r\n            subject_type = ', '.join(list(np.random.choice(self.subjects, np.random.randint(1, 10))))\r\n            education_1 = self.get_text(22) # 沒填的話前端傳空的過來\r\n            education_2 = self.get_text(22)\r\n            education_3 = self.get_text(22)\r\n            company = self.get_text(22)\r\n            special_exp = self.get_text(22)\r\n            # 一般開課時間\r\n            user_folder = username \r\n            print('收到老師註冊資料', username)\r\n            # print('判斷收到老師資料是正常的')\r\n            # 先檢查有沒有這個username存在，存在的話會return None給obj\r\n            obj = teacher_profile.objects.filter(username=username).first()\r\n            auth_obj = User.objects.filter(username=username).first()\r\n            # 下面這個條件式>> 皆非(a為空 或是 b為空) >> a跟b都不能為空>> annie0918:應該是兩個都要空才對\r\n            if obj is None and auth_obj is None :\r\n                print('還沒註冊過,建立 teacher_profile')\r\n                ### 長出老師相對應資料夾 \r\n                # 目前要長的有:放一般圖檔的資料夾user_folder(大頭照、可能之後可放宣傳圖)、未認證的資料夾unaproved_cer、\r\n                # 已認證過的證書aproved_cer、user_info 將來可能可以放考卷檔案夾之類的、課程統一資料夾lessons、 \r\n                if not os.path.isdir('user_upload/teachers'):\r\n                    os.mkdir(os.path.join('user_upload/teachers'))\r\n                if os.path.isdir(os.path.join('user_upload/teachers', user_folder)):\r\n                    # 如果已經有了這個資料夾，就刪除裡面所有項目並且重建\r\n                    shutil.rmtree(os.path.join('user_upload/teachers', user_folder))\r\n                    print('User Folder Already Existed >> Rebuild It.')\r\n                os.mkdir(os.path.join('user_upload/teachers', user_folder))\r\n                os.mkdir(os.path.join('user_upload/teachers/'+ user_folder, \"unaproved_cer\"))\r\n                os.mkdir(os.path.join('user_upload/teachers/'+ user_folder, \"aproved_cer\"))\r\n                os.mkdir(os.path.join('user_upload/teachers/'+ user_folder, \"user_info\")) # models裡的info_folder\r\n                os.mkdir(os.path.join('user_upload/teachers/'+ user_folder, \"lessons\"))\r\n                print('已幫老師建立5個資料夾')\r\n                # for迴圈如果沒東西會是空的.  getlist()裡面是看前端的 multiple name\r\n                if np.random.rand() > 0.5:\r\n                    pic_path = self.people_thumbnail_path\r\n                    pic_name, pic_ext = np.random.choice(os.listdir(pic_path)).split('.')\r\n                    thumbnail_dir = '/user_upload/teachers/' + user_folder + '/' + 'thumbnail'+'.'+ pic_ext\r\n                    shutil.copy(\r\n                        os.path.join(pic_path, pic_name + '.' + pic_ext),\r\n                        thumbnail_dir[1:]\r\n                    )\r\n                else:\r\n                    thumbnail_dir = ''\r\n                user_created_object = \\\r\n                    User.objects.create(\r\n                        username = username,\r\n                        password = password,\r\n                        is_superuser = 0,\r\n                        first_name = '',\r\n                        last_name = '',\r\n                        email = '',\r\n                        is_staff = 0,\r\n                        is_active = 1,\r\n                    )\r\n                user_created_object.save()\r\n                print('老師成功建立 User.objects')\r\n                teacher_profile.objects.create(\r\n                        auth_id = user_created_object.id,\r\n                        username = username,\r\n                        password = password,\r\n                        balance = 0,\r\n                        unearned_balance = 0, # 帳戶預進帳金額，改成會計用語\r\n                        withholding_balance = 0,\r\n                        name = name,\r\n                        nickname = nickname,\r\n                        birth_date = birth_date,\r\n                        is_male = is_male,\r\n                        intro = intro,\r\n                        mobile = mobile,\r\n                        thumbnail_dir = thumbnail_dir,\r\n                        user_folder = 'user_upload/'+ user_folder ,\r\n                        info_folder = 'user_upload/'+ user_folder + '/user_info', \r\n                        tutor_experience = tutor_experience,\r\n                        subject_type = subject_type,\r\n                        education_1 = education_1,\r\n                        education_2 = education_2,\r\n                        education_3 = education_3 ,\r\n                        cert_unapproved = 'user_upload/'+ user_folder + '/unaproved_cer',\r\n                        cert_approved = 'user_upload/'+ user_folder + '/aproved_cer',\r\n                        id_approved = 0,\r\n                        education_approved = 0,\r\n                        work_approved = 0,\r\n                        other_approved = 0, #其他類別的認證勳章\r\n                        #occupation = if_false_return_empty_else_do_nothing(occupation), \r\n                        company = company,\r\n                        special_exp = special_exp\r\n                ).save()\r\n                print('成功建立 teacher_profile')\r\n                ## 寫入一般時間table\r\n                # 因為models設定general_available_time與 teacher_profile \r\n                # 的teacher_name有foreignkey的關係\r\n                # 因此必須用teacher_profile.objects 來建立這邊的teacher_name\r\n                # (否則無法建立)\r\n                teacher_object = teacher_profile.objects.get(username=username)\r\n                for every_week in range(7):\r\n                    temp_time = ','.join([ str(_) for _ in sorted(np.random.randint(0, 48, np.random.randint(0, 48, 1)))])\r\n                    if len(temp_time) > 0:\r\n                        general_available_time.objects.create(\r\n                            teacher_model = teacher_object,\r\n                            week = every_week,\r\n                            time = temp_time\r\n                                        ).save()\r\n                print('老師成功建立 一般時間')\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n    the_bg = batch_generator()\r\n    the_bg.create_batch_student_users(50)\r\n    the_bg.create_batch_teacher_users(50)\r\n\r\n\r\n","sub_path":"Quikok/account/creating_dummy_objects_to_DB.py","file_name":"creating_dummy_objects_to_DB.py","file_ext":"py","file_size_in_byte":14082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"148727675","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jun 19 04:17:24 2019\n\n@author: vitor\n\"\"\"\n#class No:\n#    def __init__(self, name, ttype, scope):\n#        self.name = name\n#        self.ttype = ttype\n#        self.scope = scope\n\nimport InfoFunction\nimport SymbolTable\n\nclass SymbolTable:\n    \n    def __init__(self,previous=None):\n        self.name = []\n        self.ttype = []\n        self.functions = []\n        self.previous = previous    # Referencia para o pai, ou seja, o escopo o qual ele ta inserido\n        self.descendants = []       # Referencia para os filhos.. Cada abre e fecha colchete \n                                    # Gera um novo filho pois inicia um novo escopo  \n                                 \n        self.extendedTable = None   \n        self.extendedName = \"\"      # O nome de quem foi extendido\n        \n        self.visitado = 0\n\n        if previous is not None:\n            previous.addDescendant(self)\n\n    def extending(self, newTable:SymbolTable ):\n        self.extendedTable = newTable\n        \n    def addSymbol(self, name, ttype):\n        self.name.append(name)\n        self.ttype.append(ttype)\n\n    def addDescendant (self,table):\n        self.descendants.append(table)\n        \n    def modifySymbol(self, name, ttype):\n        if name in self.name:\n            i = self.name.index(name)\n            self.ttype[i] = ttype\n            return True\n        else:\n            return False\n\n    def findID(self, name):\n        pos = -1\n        p = self\n        while pos == -1 and p != None:    \n#            if(p.ttype)\n            if len(p.ttype) != 0:\n                if p.ttype[0] != 'classe':\n                    try:\n                        pos = p.name.index(name)\n                    except Exception:\n                        p = p.previous \n                else:\n                    try:\n                        pos = p.name.index(name)\n                    except Exception:\n                        p = p.extendedTable\n            else:\n                p = p.previous\n                \n            \n            \n                    \n        if pos != -1 and p.name[pos] == name:\n            return p.ttype[pos]\n        return None\n\n    def findFunction(self, name, tamTabela):\n        p = self\n        while p.ttype[0] != 'classe':\n            p = p.previous\n            \n        tamTabela_aux = 0\n        while tamTabela_aux <= tamTabela and p != None :\n            funcoes = p.functions\n            for i in range(len(funcoes)):\n                if funcoes[i].name == name:\n                    return funcoes[i]\n            p = p.extendedTable\n            tamTabela_aux = tamTabela_aux + 1\n            \n        return None\n                \n\n    def addFunction(self, function):\n        self.functions.append(function)\n                    ","sub_path":"SymbolTable.py","file_name":"SymbolTable.py","file_ext":"py","file_size_in_byte":2799,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"379701136","text":"# Copyright (c) 2016 Mellanox Technologies, 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\nfrom pecan import rest\nfrom six.moves import http_client\nfrom wsme import types as wtypes\nfrom wsmeext.pecan import wsexpose\n\nfrom nacsa.api.controllers.v1 import base\nfrom nacsa import objects\n\n\nclass ResourceClass(base.APIBase):\n    \"\"\"API representation of a resource class.\n\n    This class enforces type checking and value constraints, and converts\n    between the internal object model and the API representation of\n    a resource class.\n    \"\"\"\n\n    id = int\n    name = wtypes.text\n\n    def __init__(self, **kwargs):\n        self.fields = []\n        for field in objects.ResourceClass.fields:\n            # Skip fields we do not expose.\n            if not hasattr(self, field):\n                continue\n            self.fields.append(field)\n            setattr(self, field, kwargs.get(field, wtypes.Unset))\n\n    @classmethod\n    def from_result(cls, result):\n        \"\"\"Convert a NacsaObjectDB object to a ResourceClass object.\n        :param result: a :class:`nacsa.objects.base.NacsaObjectDB ` object.\n        :returns: a :class:`nacsa.api.controllers.v1.resource_classes.\n                            ResourceClass` object.\n        \"\"\"\n        instance = cls()\n        exposed_fields = set(objects.ResourceClass.fields)\n        for field in exposed_fields:\n            setattr(instance, field, getattr(result, field))\n        return instance\n\n\nclass ResourceClassController(rest.RestController):\n    \"\"\"REST controller for resource class.\"\"\"\n\n    @wsexpose([ResourceClass])\n    def get_all(self):\n        resource_classes = objects.ResourceClass.get_all()\n        return [ResourceClass.from_result(resource_class)\n                for resource_class in resource_classes]\n\n    @wsexpose(ResourceClass, body=ResourceClass,\n              status_code=http_client.CREATED)\n    def post(self, resource_class):\n        \"\"\"Create a new resource_class.\n        :param name: name of new resource class\n        \"\"\"\n        obj = objects.ResourceClass(**resource_class.as_dict())\n        obj.create()\n        return ResourceClass.from_result(obj)\n\n    @wsexpose(None, body=wtypes.text, status_code=http_client.NO_CONTENT)\n    def delete(self, name):\n        \"\"\"Delete a resource class.\n        :param name: name of the resource class\n        \"\"\"\n        obj = objects.ResourceClass(name=name)\n        obj.destroy()\n","sub_path":"nacsa/api/controllers/v1/resource_classes.py","file_name":"resource_classes.py","file_ext":"py","file_size_in_byte":2956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"501661858","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat Oct 23 14:24:42 2021\n\n@author: Omid Esmailbeig\n\"\"\"\n\nimport datetime\nimport hashlib\nimport json\n\n\n# Building a Blockchain\nclass Blockchian:\n    def __init__(self):\n        self.blockchain = []\n        self.create_block(proof=1, previous_hash='0')\n        \n    def create_block(self, proof, previous_hash):\n        block = {\n            'index': len(self.blockchain) + 1,\n            'timestamp': str(datetime.datetime.now()),\n            'proof': proof,\n            'previous_hash': previous_hash\n            }\n        \n        self.blockchain.append(block)\n        return block\n    \n    def get_previous_block(self):\n        return self.blockchain[-1]\n    \n    def proof_of_work(self, previous_proof):\n        new_proof = 1\n        check_proof = False\n        while check_proof is False:\n            hash_operation = hashlib.sha256(\n                str(new_proof**2 - previous_proof**2).encode()).hexdigest()\n            if hash_operation[:4] == '0000':\n                check_proof = True\n            else:\n                new_proof += 1\n        return new_proof\n    \n    def block_hash(self, block):\n        encoded_block = json.dumps(block, sort_keys=True).encode()\n        return hashlib.sha256(encoded_block).hexdigest()\n    \n    def is_blockchain_valid(self, blockchain):\n        previous_block = blockchain[0]\n        block_index = 1\n        while block_index < len(blockchain):\n            # Checking whole blockchain hashes\n            block = blockchain[block_index]\n            if block['previous_hash'] != self.block_hash(previous_block):\n                return False\n            # Checking proof of blockchain\n            previous_proof = previous_block['proof']\n            current_proof = block['proof']\n            hash_operation = hashlib.sha256(\n                str(current_proof**2 - previous_proof**2).encode()).hexdigest() #  Recalcaulate the proof for checking validity\n            if hash_operation[:4] != '0000':\n                return False\n            previous_block = block\n            block_index += 1\n        return True\n                \n            \n        ","sub_path":"blockchain.py","file_name":"blockchain.py","file_ext":"py","file_size_in_byte":2153,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"547615517","text":"from django.http import HttpResponse\n\nimport sys, os\n\n# from Model.models import TestModel\n# Add the project to the python path\n\n# sys.path.insert(0, os.path.join(os.path.dirname(__file__), os.pardir))\n\n# from Model.models import TestModel\nsys.path.insert(0, os.path.join(os.path.dirname(__file__), os.pardir))\nfrom Model.models import TestModel\n\ndef insertdb(request):\n    model = TestModel(name='hello')\n    model.save()\n    return HttpResponse('<p>add data success!</p>')\n\n\ndef selectdb(request):\n    response = ''\n    response1 = ''\n\n    list = TestModel.objects.all()\n\n    response2 = TestModel.objects.filter(id=1)\n\n    response3 = TestModel.objects.get(id=1)\n\n    TestModel.objects.order_by('name')[0:2]\n\n    TestModel.objects.order_by('id')\n\n    TestModel.objects.filter(name='hello').order_by('id')\n\n    for var in list:\n        response1 += '<li>' + str(var.id) + '  ' + var.name + '</li>'\n    response = response1\n    return HttpResponse('<p><ul>' + response + '</ul></p>')\n\n\ndef updatedb(request):\n    test1 = TestModel.objects.get(id=1)\n    test1.name = 'world'\n    test1.save()\n\n    TestModel.objects.filter(id=2).update(name='Google')\n\n    # TestModel.objects.all().update(name='Google')\n\n    return HttpResponse('<p>修改成功; <a href=\\'/selectdb\\'>点击查看</a></p>')\n\n\ndef deletedb(request):\n    test1 = TestModel.objects.get(id=1)\n    test1.delete()\n\n    TestModel.objects.filter(id=1).delete()\n\n    return HttpResponse('<p>删除成功; <a href=\\'/selectdb\\'>点击查看</a></p>')\n","sub_path":"web/dj/app/hello/testdb.py","file_name":"testdb.py","file_ext":"py","file_size_in_byte":1508,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"59928620","text":"import numpy as np\nimport cv2\n\nfrom step06_a_datas_obj import Range\n\nimport sys\nsys.path.append(\"kong_util\")\nfrom kong_util.build_dataset_combine import Check_dir_exist_and_build\nfrom kong_util.matplot_fig_ax_util import Matplot_single_row_imgs\n\nimport matplotlib.pyplot as plt\nfrom step08_b_use_G_generate_0_util import WcM_01_visual_op, Value_Range_Postprocess_to_01\n\n######################################################################################################################################################################################################\ndef wc_visual_op(wc):\n    wc = wc.astype(np.float32)\n    wc = (wc - wc.min()) / (wc.max() - wc.min() + 0.000001) * 255.\n    wc_2d_v = wc.astype(np.uint8)\n    return wc_2d_v\n\n####################################################################################################\ndef I_Generate_W(model_obj, _1, in_img_pre, _3, Wgt_w_Mgt_pre, use_gt_range, training=False):  ### training 這個參數是為了 一開使 用BN ，為了那些exp 還能重現所以才保留，現在用 IN 完全不會使用到他這樣子拉～\n    W_pre = model_obj.generator(in_img_pre, training=training)\n    W_pre = W_pre[0, ..., 0:3].numpy()\n    W_01 = Value_Range_Postprocess_to_01(W_pre, use_gt_range)\n\n    Wgt_pre = Wgt_w_Mgt_pre[0, ..., 0:3].numpy()\n    Wgt_01  = Value_Range_Postprocess_to_01(Wgt_pre, use_gt_range)\n    return W_01, Wgt_01\n\ndef I_Generate_W_see(model_obj, phase, index, in_img, in_img_pre, _3, Wgt_w_Mgt_pre, rec_hope=None, exp_obj=None, training=True, see_reset_init=True, postprocess=False, npz_save=False, add_loss=False, bgr2rgb=True):\n    current_ep = exp_obj.current_ep\n    current_time = exp_obj.current_time\n    if  (phase == \"train\"): used_sees = exp_obj.result_obj.sees\n    elif(phase == \"test\"):  used_sees = exp_obj.result_obj.tests\n    private_write_dir    = used_sees[index].see_write_dir   ### 每個 see 都有自己的資料夾 存 in/gt 之類的 輔助檔案 ，先定出位置\n    public_write_dir     = \"/\".join(used_sees[index].see_write_dir.replace(\"\\\\\", \"/\").split(\"/\")[:-1])  ### private 的上一層資料夾\n    '''\n    gt_mask_coord[0] 為 mask  (1, h, w, 1)\n    gt_mask_coord[1] 為 coord (1, h, w, 2) 先y 在x\n\n    bgr2rgb： tf2 讀出來是 rgb， 但 cv2 存圖是bgr， 所以此狀況記得要轉一下ch 把 bgr2rgb設True！\n    '''\n    # plt.imshow(in_img[0])\n    # plt.show()\n    in_img   = in_img  [0].numpy()\n    rec_hope = rec_hope[0].numpy()\n    if(bgr2rgb):\n        in_img = in_img[:, :, ::-1]\n        rec_hope = rec_hope[:, :, ::-1]\n\n    W_01, Wgt_01    = I_Generate_W(model_obj, None, in_img_pre, None, Wgt_w_Mgt_pre, exp_obj.use_gt_range, training=training)\n\n    W_visual,   Wx_visual,   Wy_visual,   Wz_visual   = WcM_01_visual_op(W_01)\n    Wgt_visual, Wxgt_visual, Wygt_visual, Wzgt_visual = WcM_01_visual_op(Wgt_01)\n    # print(\"wc.shape:          \", wc.shape)\n    # print(\"wc_visual.shape:   \", wc_visual.shape)\n    # print(\"gt_wc.shape:       \", gt_wc.shape)\n    # print(\"gt_wc_visual.shape:\", gt_wc_visual.shape)\n\n    if(current_ep == 0 or see_reset_init):  ### 第一次執行的時候，建立資料夾 和 寫一些 進去資料夾比較好看的東西\n        Check_dir_exist_and_build(private_write_dir)    ### 建立 放輔助檔案 的資料夾\n        cv2.imwrite(private_write_dir + \"/\" + \"0a_u1a0-dis_img(in_img).jpg\", in_img)             ### 寫一張 in圖進去，進去資料夾時比較好看，0a是為了保證自動排序會放在第一張\n\n        if(npz_save is False): np.save            (private_write_dir + \"/\" + \"0b_u1b1-gt_wc\", Wgt_01)                                   ### 寫一張 gt圖進去，進去資料夾時比較好看，0b是為了保證自動排序會放在第二張\n        if(npz_save is True ): np.savez_compressed(private_write_dir + \"/\" + \"0b_u1b1-gt_wc\", Wgt_01)                                   ### 寫一張 gt圖進去，進去資料夾時比較好看，0b是為了保證自動排序會放在第二張\n        cv2.imwrite(private_write_dir + \"/\" + \"0b_u1b3-gt_Wx.jpg\",  Wxgt_visual)\n        cv2.imwrite(private_write_dir + \"/\" + \"0b_u1b4-gt_Wy.jpg\",  Wygt_visual)\n        cv2.imwrite(private_write_dir + \"/\" + \"0b_u1b5-gt_Wz.jpg\",  Wzgt_visual)       \n        cv2.imwrite(private_write_dir + \"/\" + \"0c-rec_hope.jpg\",   rec_hope)           ### 寫一張 rec_hope圖進去，hope 我 rec可以做到這麼好ˊ口ˋ，0c是為了保證自動排序會放在第三張\n    if(npz_save is False): np.save            (private_write_dir + \"/\" + \"epoch_%04i_u1b1-W\" % current_ep, W_01)\n    if(npz_save is True ): np.savez_compressed(private_write_dir + \"/\" + \"epoch_%04i_u1b1-W\" % current_ep, W_01)\n    cv2.imwrite(private_write_dir + \"/\" + \"epoch_%04i_u1b2-W_visual.jpg\"  % current_ep, W_visual)\n    cv2.imwrite(private_write_dir + \"/\" + \"epoch_%04i_u1b3-Wx_visual.jpg\" % current_ep, Wx_visual)\n    cv2.imwrite(private_write_dir + \"/\" + \"epoch_%04i_u1b4-Wy_visual.jpg\" % current_ep, Wy_visual)\n    cv2.imwrite(private_write_dir + \"/\" + \"epoch_%04i_u1b5-Wz_visual.jpg\" % current_ep, Wz_visual)\n\n    if(postprocess):\n        current_see_name = used_sees[index].see_name.replace(\"/\", \"-\")  ### 因為 test 會有多一層 \"test_db_name\"/test_001， 所以把 / 改成 - ，下面 Save_fig 才不會多一層資料夾\n        from kong_util.matplot_fig_ax_util import Matplot_single_row_imgs\n        imgs = [ in_img ,   W_visual , Wgt_visual]\n        img_titles = [\"in_img\", \"Wpred\",   \"Wgt\"]\n\n        single_row_imgs = Matplot_single_row_imgs(\n                                imgs      =imgs,         ### 把要顯示的每張圖包成list\n                                img_titles=img_titles,               ### 把每張圖要顯示的字包成list\n                                fig_title =\"%s, current_ep=%04i\" % (current_see_name, int(current_ep)),  ### 圖上的大標題\n                                add_loss  =add_loss,\n                                bgr2rgb   =bgr2rgb)  ### 這裡會轉第2次bgr2rgb， 剛好轉成plt 的 rgb\n        single_row_imgs.Draw_img()\n        single_row_imgs.Save_fig(dst_dir=public_write_dir, name=current_see_name)  ### 這裡是轉第2次的bgr2rgb， 剛好轉成plt 的 rgb  ### 如果沒有要接續畫loss，就可以存了喔！\n        print(\"save to:\", exp_obj.result_obj.test_write_dir)\n","sub_path":"step08_b_use_G_generate_I_to_W.py","file_name":"step08_b_use_G_generate_I_to_W.py","file_ext":"py","file_size_in_byte":6305,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"437867247","text":"#cookies(传值、profile) ，\n# 上传下载，\n# 参数化，\n# 视频播放 ，\n# 日期元素，\n# 截图，\n# 表格\n#https\n#等待方式（强制、隐形-页面加载、显性-元素加载）\n#饼状图\n\n\nfrom selenium import webdriver\nimport time\n\n# driver=webdriver.Chrome()\n\n\n#cookies\n'''\n1. 万能验证码 or 去掉验证码 \n2. 手工介入（需要输入的地方 sleep 一会） \n3. 利用 cookies 来实现（问开发哪个 cookies 值是登录后需要的，把这个值带上即可）， 常见的方法如下： \n driver.get_cookies()#获取 cookies \n driver.get_cookie(name)#返回字典的 key 为\"name\"的 cookie 信息 \n driver.add_cookie({'name': 'key-aaa', 'value': 'key-bbb'}) \n driver.delete_all_cookies()#删除 cookies\n'''\n\n'''\nbduss_chrome='VIQUVWd35pS3M3Q1FSRXhNa2NtLWw1dS1SQkkzaUJpU3VoR05lQzRjaXhjSTlkSUFBQUFBJCQAAAAAAAAAAAEAAADwEwkqwfXWrrW6AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAALHjZ12x42ddOF'\n\ndriver.get('https://www.baidu.com')\n#设置cookies的值，问开发，自己找要累死\nc1={'domain':'.baidu.com',    #服务器域名\n    'name':'BDUSS',    #cookie的名称\n    'value':bduss_chrome,    #cookie\n    'path':'/',    #那些路径的页面下可以获取服务器设置的cookie\n    'httpOnly':True,    #防止脚本攻击\n    'secure':False    #只有为https时浏览器猜才向服务器提交响应的cookie\n}\ndriver.add_cookie(c1)#添加cookies\nprint('添加cookies')\ntime.sleep(3)\ndriver.refresh()\ndriver.implicitly_wait(5)\ntime.sleep(3)\n'''\n\n#profile文件\n#首先需要手工登入1次，且勾选记住信息，相当于浏览器的保存表单的功能\n#路径获取 浏览器->帮助->故障排除信息->显示文件夹\n#有时效性，长了就失效了\n#----火狐------\n# profile_ff=r\"火狐的路径\" #不要忘记r\n# fp=webdriver.FirefoxProfile(profile_ff)\n# driver=webdriver.Firefox(fp)\n\n# driver.get('http://www.baidu.com')\n# time.sleep(5)\n# driver.quit()\n    # #------ chrome-----\n    # #chrome的，chrome://version/ 查看自己的“个人资料路径”\n    # #个人资料路径\tC:\\Users\\ivy\\AppData\\Local\\Google\\Chrome\\User Data\\Default\n    # #路径取到User Data，不到Default\n    # profile_ff=r\"user-data-dir=C:\\\\Users\\\\ivy\\\\AppData\\\\Local\\\\Google\\\\Chrome\\\\User Data\"\n    # #加载配置\n    # option=webdriver.ChromeOptions()\n    # option.add_argument(profile_ff)\n    # #初始化启动浏览器配置\n    # # driver=webdriver.Chrome(chrome_options=option)这个也行\n    # driver=webdriver.Chrome(options=option)\n    # time.sleep(3)\n    # driver.get('http://www.baidu.com')\n    # time.sleep(5)\n    # driver.quit()\n\n\n# 上传下载\n\n\n#上传\n# 标准的<input type='file' name='file'>\n# driver.find_element('name','file').send_keys('要上传的文件路径')\n# 非标准的上传\n# http://www.xqtesting.com/blog/selenium-webdriver-90.html\n\n#下载\n\n#指定下载路径\n# profile_ff=r\"user-data-dir=C:\\\\Users\\\\ivy\\\\AppData\\\\Local\\\\Google\\\\Chrome\\\\User Data\"\n# profile=webdriver.FirefoxProfile(profile_ff)火狐操作\n\n\n'''\n#火狐\nurl='https://www.lizhi.fm/helper/download'\nfrom selenium import webdriver\nimport os,time\n \nfp = webdriver.FirefoxProfile()\nfp.set_preference(\"browser.download.folderList\",0)\nfp.set_preference(\"browser.download.manager.showhenStarting\",True)\nfp.set_preference(\"browser.download.dir\",os.getcwd())\nfp.set_preference(\"browser.helperApps.neverAsk.saveToDisk\",\"binary/octet-stream\")#下载文件类型\n \ndriver = webdriver.Firefox(firefox_profile = fp)\ndriver.get(url)\n#选择下载文件\ndriver.find_element('link text','下载').click()\ntime.sleep(5)\n'''\n\n'''\n#谷歌\noptions = webdriver.ChromeOptions()\nprefs = {'profile.default_content_settings.popups': 0, 'download.default_directory': 'd:\\\\'}\noptions.add_experimental_option('prefs', prefs)\n \ndriver = webdriver.Chrome(chrome_options=options)\ndriver.get(\"https://www.lizhi.fm/helper/download\")\ndriver.find_element('link text','下载').click()\ntime.sleep(5)#需要时间长一点，怕下载不完\n#下载在d:\\\\\n'''\n\n\n# #参数化 https://www.cnblogs.com/hanxiaobei/p/7340429.html\n# #文件txt\n# import codecs\n# #打开文件utf-8\n# source=codecs.open('test_folder\\parameter.txt','r','utf-8')\n# context=source.readlines()#多行读取\n\n# value01=list(context)\n# print('value01-',value01)#value01- ['18352543210\\r\\n', '啥啥密码123']\n\n# value03=[]\n# value03.extend(context)\n# print('value03-',value03)#value03- ['18352543210\\r\\n', '啥啥密码123']\n\n# source=codecs.open('test_folder\\parameter.txt','r','utf-8')\n# value02 =[]\n# while True:\n#     context = source.readline()#按行读取\n#     value02.append(context)\n#     if len(context) ==0:\n#         break\n# print('readline格式:',type(value02))\n# print('value02-',value02)#['18352543210\\r\\n', '啥啥密码123', '']\n\n# source=codecs.open('test_folder\\parameter.txt','r','utf-8')\n# context = source.read()#一次性读取，赋值给字符串\n# value04=list(context)\n# print('value04-',value04)#['1', '8', '3', '5', '2', '5', '4', '3', '2', '1', '0', '\\r', '\\n', '啥', '啥', '密', '码', '1', '2', '3']\n\n\n\n#写在字典里\n# for username,passwd in info.items():\n#     print(username)\n#     print(passwd)\n\n#写在excel里\n# D:\\python_code\\re_Automation\\0321_mysql_and_excel.py\n# 写完就忘。。。想死！\n\n'''\n#视频播放 http://videojs.com/\ndriver=webdriver.Chrome()\ndriver.implicitly_wait(5)\ndriver.get('http://videojs.com/')\n\nvideo=driver.find_element_by_xpath('.//*[@id=\"preview-player_html5_api\"]')\nurl=driver.execute_script('return arguments[0].currentSrc;',video)\nprint('播放地址--',url)\nduration=driver.execute_script('return arguments[0].duration;',video)\nprint('播放时长-',duration)\n\nprint('--播放--')\ndriver.execute_script('return arguments[0].play()',video)\ntime.sleep(10)\n\nprint('--暂停--')\ndriver.execute_script('arguments[0].pause()',video)\ntime.sleep(10)\n\n#日期元素\njs='document.getElementById(\"txtBeginDate\").removeAttribute(\"readonly\")'\ndriver.execute_script(js)\n\n#最好先清空后传值\ndriver.find_element('id','txtBeginDate').clear()\ndriver.find_element('id','txtBeginDate').send_keys('2019-11-11')\ntime.sleep(5)\n\n# #另一种方法\n# js='document.getElementById(\"txtBeginDate\").value=\"2019-09-09\"'\n# driver.execute_script(js)\n'''\n\n#截图\n# driver.get_screenshot_as_file(\"保存的路径地址\")\n\n'''\n#表格\ndriver=webdriver.Chrome()\ndriver.get('file:///D:/python_code/re_Automation/test_folder/table.html')\ntable=driver.find_element_by_xpath(\"html/body/table/tbody\")\n\n# table的总行数，包括标题\ntable_rows=table.find_elements('tag name','tr')\nprint('总行数-',len(table_rows))#总行数- 4\n\n#table的总列数\ntable_cols=table_rows[0].find_elements('tag name','td')\nprint('总列数-',len(table_cols))#总列数- 3\n\n#获取某单元格的文本，下标取值\nrow2_col2=table_rows[1].find_elements('tag name','td')[1].text\nprint('第二行第二列-',row2_col2)#第二行第二列- row 2, cell 2\n'''\n\n\n#https\n#一般情况不需要处理，特殊https需要处理\n#对于 firefox： \n# profile = webdriver.FirefoxProfile() \n# profile.accept_untrusted_certs = True \n# driver = webdriver.Firefox(firefox_profile=profile) \n# driver.get('https://www.cacert.org/') \n# driver.close()\n#chrome:\noption=webdriver.ChromeOptions()\noption.add_argument('--ignore-certificate-errors')\ndriver=webdriver.Chrome(chrome_options=option)\ndriver.get('https://www.cacert.org/')\ntime.sleep(10)\n\n\n'''\n#等待方式\n#显性等待-until、until_not\nfrom selenium.webdriver.support.wait import WebDriverWait#等元素加载，显性等待\nWebDriverWait(driver,5).until(lambda diver:driver.find_element('link text','退出'))\n'''\n\n#生成饼状图\nfrom openpyxl.chart import PieChart,Reference\nfrom openpyxl import load_workbook\nimport os\nos.chdir('D:\\\\python_code\\\\re_Automation\\\\test_folder\\\\testcase_study.xlsx')\n\ndef pie_chert(wb):\n    table=wb.get_sheet_by_name(wb.get_sheet_by_name()[0])\n\n    #生成饼图对象\n    pie=PieChart()\n    #图的标题\n    pie.title='接口测试统计'\n    #获取标签 第1列，2行到3行\n    labels=Reference(table,min_col=1,min_row=2,max_col=1,max_row=3)\n    # 获取数据，这里需要注意，上面必须多取1行空的，excel系列导致   \n    # 第2列1到3行，实际：第2列2到3行\n    data=Reference(table,min_col=2,min_row=1,max_col=2,max_row=3)\n\n    #添加数据和标签到图表里\n    pie.add_data(data,titles_from_data=True)\n    pie.set_categories(labels)\n\n    #添加图表到sheet里\n    table.add_chart(pie,'A10')\n\n    #保存excel\n    wb.save('D:\\\\python_code\\\\re_Automation\\\\test_folder\\\\testcase_study.xlsx')\n\nwb=load_workbook('D:\\\\python_code\\\\re_Automation\\\\test_folder\\\\testcase_study.xlsx')\npie_chert(wb)","sub_path":"0418_01_seAndInterface.py","file_name":"0418_01_seAndInterface.py","file_ext":"py","file_size_in_byte":8684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"377589055","text":"from bs4 import BeautifulSoup\nimport time\nimport jieba\nimport jieba.analyse\nimport numpy as np\nfrom scipy.linalg import norm\nimport gensim\n\n# read html.txt\nwith open('./html.txt', 'r') as f:\n  htmlContent = f.read()\n  f.close()\n\n# parse html.txt content\nsoup = BeautifulSoup(htmlContent, 'html.parser')\ncurTimeStr = str(int(time.time()))\n\nwith open('./parsed-html-' + curTimeStr + '.txt', 'w') as f:\n  f.write(soup.prettify())\n  f.close()\n\n# get && save text content from parsed html\nwith open('./content-' + curTimeStr + '.txt', 'w') as f:\n  f.write(soup.find('div', id='__next').get_text())\n  f.close()\n\n# read text content\nwith open('./content-' + curTimeStr + '.txt', 'r') as f:\n  textContent = f.read()\n  f.close()\n\n# analyse\nkWords = jieba.analyse.extract_tags(textContent, topK=10, withWeight=True)\n\nwith open('./analyse-' + curTimeStr + '.txt', 'w') as f:\n  for word, weight in kWords:\n    f.write(\"%s, %.8f\\n\"%(word, weight))\n\n  f.close()\n\n# TFIDF\ntfidf = jieba.analyse.TFIDF()\ntfidfKWords = tfidf.extract_tags(textContent, topK=10, withWeight=True)\n\nwith open('./tfidf-' + curTimeStr + '.txt', 'w') as f:\n  for word, weight in tfidfKWords:\n    f.write(\"%s, %.8f\\n\"%(word, weight))\n\n  f.close()\n\n# Word2Vec 匹配\n# word2vecModelFile = './sgns.baidubaike.bigram-char'\nword2vecModelFile = './w2v.vector'\nword2vecModel = gensim.models.KeyedVectors.load_word2vec_format(\n  word2vecModelFile,\n  binary=False,\n  unicode_errors='ignore'\n)\n\njieba.load_userdict('./user-dict.txt')\n\ndef vector_similarity(s1, s2):\n  def sentence_vector(s):\n    words = [word for word in jieba.cut(s)]\n    v = np.zeros(300)\n\n    for word in words:\n      # word2vecModel 匹配到 word 后再处理\n      if word in word2vecModel:\n        v += word2vecModel[word]\n\n    v /= len(words)\n    return v\n\n  v1, v2 = sentence_vector(s1), sentence_vector(s2)\n  base = norm(v1) * norm(v2)\n\n  if base == 0:\n    return 0\n\n  return np.dot(v1, v2) / base\n\ntest_word_list = [\n  '存储',\n  '二维表',\n  'cdn',\n  '存储价格',\n  '中国存储哪家公司做得好？',\n  '中国CDN哪家公司做得好？'\n]\n\nfor word in test_word_list:\n  print(word + ' -> ' + str(vector_similarity(word.lower(), textContent.lower())))\n","sub_path":"python-demos/match/match-word2vec.py","file_name":"match-word2vec.py","file_ext":"py","file_size_in_byte":2191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"54407019","text":"import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.preprocessing import MinMaxScaler\nfrom keras.models import Sequential\nfrom keras.layers import LSTM, Dense\nfrom plotly.graph_objs import Scatter, Layout\nfrom plotly.offline import plot\n\ndef load_data(df, sequence_length=10, split=0.8):\n    data_all = np.array(df).astype(float)    # 轉為浮點型別矩陣\n    #print(data_all.shape) # (241,1)\n    scaler = MinMaxScaler()\n    data_all = scaler.fit_transform(data_all)  # 將數據縮放為 0~1 之間\n    data = []\n    # data 資料共有 (241-10-1)=230 筆\n    for i in range(len(data_all) - sequence_length - 1):\n        # 每筆 data 資料有 11 欄\n        data.append(data_all[i: i + sequence_length + 1])\n    reshaped_data = np.array(data).astype('float64')\n\n    x = reshaped_data[:, :-1] # 第 1至第10個欄位為 特徵\n    y = reshaped_data[:, -1]  # 第 11個欄位為 label\n    #print(x.shape,y.shape) # (230,10,1) (230,1)\n    split_boundary = int(reshaped_data.shape[0] * split)\n    train_x = x[: split_boundary] # 前 80% 為 train 的特徵\n    test_x = x[split_boundary:]   # 最後 20% 為 test 的特徵\n \n    train_y = y[: split_boundary] # 前 80% 為 train 的 label\n    test_y = y[split_boundary:]   # 最後 20% 為 test 的 label\n\n    return train_x, train_y, test_x, test_y, scaler\n\ndef build_model():\n    model = Sequential()     \n    # 隱藏層：256 個神經元，input_shape：(10,1)\n    # TIME_STEPS=10,INPUT_SIZE=1\n    model.add(LSTM(input_shape=(10,1),units=256,unroll=False))\n    model.add(Dense(units=1)) # 輸出層：1 個神經元\n    #compile:loss, optimizer, metrics\n    model.compile(loss=\"mse\", optimizer=\"adam\", metrics=['accuracy'])\n    return model\n\ndef train_model(train_x, train_y, test_x, test_y):\n    model = build_model()\n    try:\n        model.fit(train_x, train_y, batch_size=100, epochs=300, validation_split=0.1)\n        predict = model.predict(test_x)\n        predict = np.reshape(predict, (predict.size, )) #轉換為1維矩陣\n    except KeyboardInterrupt:\n        print(predict)\n        print(test_y)\n    return predict, test_y #傳回 預測值和真實值\n\n# 主程式\npd.options.mode.chained_assignment = None  #取消顯示pandas資料重設警告\nfilename = 'twstockyear2018.csv'\ndf = pd.read_csv(filename, encoding='big5')  #以pandas讀取檔案\nddprice=pd.DataFrame(df['收盤價'])\n    \ntrain_x, train_y, test_x, test_y, scaler =load_data(ddprice, sequence_length=10, split=0.8)\n# train_x 共 230*0.8=184 筆, test_x 共 230*0.2=46 筆\n#print(train_x.shape,train_y.shape) # (184,10,1) (184,1)\n#print(test_x.shape,test_y.shape)   # (46,10,1)  (46,1)\npredict_y, test_y = train_model(train_x, train_y, test_x, test_y)\npredict_y = scaler.inverse_transform([[i] for i in predict_y]) # 還原\ntest_y = scaler.inverse_transform(test_y)  # 還原\n\nplt.plot(predict_y, 'b:') #預測\nplt.plot(test_y, 'r-')    #收盤價\nplt.legend(['預測', '收盤價'])\nplt.show()\n\n# 建立 DataFrame，加入 predict_y、test_y，準備以 plotly 繪圖\ndd2=pd.DataFrame({\"predict\":list(predict_y),\"label\":list(test_y)})\n#轉換為 numpy 陣列，並轉為 float\ndd2[\"predict\"] = np.array(dd2[\"predict\"]).astype('float64')\ndd2[\"label\"] = np.array(dd2[\"label\"]).astype('float64')\n\ndata = [\n    Scatter(y=dd2[\"predict\"],name='預測'),\n    Scatter(y=dd2[\"label\"],name='收盤價')\n] \n\nplot({\"data\": data, \"layout\": Layout(title='2018年個股預測圖')},auto_open=True)","sub_path":"flask/predict.py","file_name":"predict.py","file_ext":"py","file_size_in_byte":3451,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"207243845","text":"\"\"\"\n第二题，selenium 多线程爬取拉钩，并存入数据库\n\"\"\"\nimport re\nimport time\nfrom multiprocessing import Process\nfrom random import choice\n\nimport pymysql\nfrom bs4 import BeautifulSoup\nfrom pymysql.err import IntegrityError\nfrom selenium import webdriver\n\nPROXY = [\"58.220.95.86:9401\", \"220.174.236.211:8091\", \"221.182.31.54:8080\", \"183.220.145.3:80\", ]\nWEBDRIVER_PATH = r\"chromedriver.exe\"\n\n\nclass Lagou(Process):\n    def __init__(self, url, city):\n        super().__init__()\n        self.city = city\n        self.url = url\n        self.chrome_option = webdriver.ChromeOptions()\n        self.chrome_option.add_argument('--proxy-server={0}'.format(choice(PROXY)))\n        self.chrome_option.add_argument(\"disable-blink-features=AutomationControlled\")\n        self.db = pymysql.connect('localhost', 'root', '123456', 'jove_class')\n\n    def run(self):\n        \"\"\"由于 webdriver 不可序列化故放在 run 里面进行初始化\"\"\"\n        self.browser = webdriver.Chrome(executable_path=WEBDRIVER_PATH, chrome_options=self.chrome_option)\n        with open(\"stealth.min.js\", \"r\", encoding=\"utf-8\") as f:\n            js_code = f.read()\n        self.browser.execute_cdp_cmd(\"Page.addScriptToEvaluateOnNewDocument\", {\n            \"source\": js_code\n        })\n        self.browser.get(self.url)\n        self.crawl()\n        self.browser.close()\n\n    def parse(self, html):\n        result = []\n        soup = BeautifulSoup(html, 'lxml')\n        itemlist = soup.select(\"li[class='con_list_item default_list']\")\n        for item in itemlist:\n            salary = item.attrs['data-salary']\n            salary = re.match(r'(\\d*)k-(\\d*)k', salary)\n            salary = int(salary.group(1)) + int(salary.group(2))  # 提起平均薪资\n\n            position = item.attrs['data-positionname'].title()\n            company = item.attrs['data-company']\n            unique = hash(self.city + position + salary)  # 额外添加一个由城市、职位和薪资组成的哈希值的唯一索引\n\n            result.append([salary >> 1, position, company, unique, self.city])\n\n        self.storage_data(result)\n\n    def storage_data(self, data):\n        \"\"\"存储到 MySQL，额外添加一个由城市、职位和薪资组成的哈希值的唯一索引，当有重复值时捕获唯一异常跳过\"\"\"\n        print(data)\n        cursor = self.db.cursor()\n        sql = \"\"\"INSERT INTO `lagou` (`salary`, `position`, `company`, `unique`, `city`) VALUES (%s, %s, %s, %s, %s)\"\"\"\n        for _ in data:\n            try:\n                cursor.execute(sql, data)\n                self.db.commit()\n            except IntegrityError:\n                pass\n\n    def crawl(self):\n        \"\"\"爬虫逻辑\"\"\"\n        self.browser.switch_to.window(self.browser.current_window_handle)\n\n        for _ in range(7):\n            html = self.browser.page_source\n            self.parse(html)\n            self.cancel_hongbao()\n            search_button = self.browser.find_element_by_css_selector('.next')\n            search_button.click()\n            time.sleep(20)\n\n    def cancel_hongbao(self):\n        \"\"\"取消开始屏幕红包弹窗\"\"\"\n        self.browser.execute_script(\"\"\"$('div[class=\"body-container showData\"]').remove()\"\"\")\n\n\nif __name__ == '__main__':\n    p = Lagou(\"https://www.lagou.com/jobs/list_Python/p-city_2?px=default#filterBox\", '北京')\n    p2 = Lagou(\"https://www.lagou.com/jobs/list_Python/p-city_3?px=default#filterBox\", '上海')\n    p3 = Lagou(\"https://www.lagou.com/jobs/list_Python/p-city_215?px=default#filterBox\", '深圳')\n    p4 = Lagou(\"https://www.lagou.com/jobs/list_Python/p-city_213?px=default#filterBox\", '广州')\n\n    p.start()\n    p2.start()\n    p3.start()\n    p4.start()\n\n    p.join()\n    p2.join()\n    p3.join()\n    p4.join()\n","sub_path":"week11/thread_sele.py","file_name":"thread_sele.py","file_ext":"py","file_size_in_byte":3749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"120504406","text":"import os\nfrom PIL import Image\nfrom docutils.nodes import image\n#from resizeimage import resizeimage\nimport numpy as np\nimport cv2\nimport pickle\n\n\ndata=[]\ntarget=[]\nimg_array=[]\ntest_size = 0.1\n\n\ndef resize():\n    #entries = os.listdir('data/')\n    #entries = os.listdir('/media/Acer/JBL/Data/201905/F/')\n    entries = os.listdir('/media/jayanath/Data/RandD/ai/my/my_project/RealData/Data/1/')\n    print(entries)\n    for e in entries:\n        #with open('data/'+e, 'r+b') as f:\n        with open('/media/jayanath/Data/RandD/ai/my/my_project/RealData/Data/1/'+e, 'r+b') as f:\n           with Image.open(f) as image:\n            #cover = resizeimage.resize_cover(image, [256, 256])\n            #cover = cv2.resize_cover(image, (256, 256), interpolation = cv2.INTER_AREA)\n            cover = cv2.resize(image, (256, 256), interpolation=cv2.INTER_AREA)\n            cover.save('/media/jayanath/Data/RandD/ai/my/my_project/RealData/converted/1/'+e, image.format)\n    print('Resize images are saved to converted dir.')\n\ndef np_male_array():\n    entries = os.listdir('converted/1/')\n    for e in entries:\n        with open('converted/1/'+e, 'r+b') as f:\n           with Image.open(f) as image:\n            img_array1 = np.array(image)\n            img_array1 = cv2.cvtColor(img_array1, cv2.COLOR_BGR2GRAY)\n            img_array1 = np.reshape(img_array1, (1, 65536))\n            #img_array1 = img_array / 65536.0 * 15.0\n            img_array1 = np.append(img_array1,[1])\n            img_array.append(img_array1)\n\n    with open('my_dataset11.pickle', 'wb') as output:\n        #pickle.dump(img_array, output)\n        pickle.dump(img_array, output, protocol=2)\n    #print(img_array)\n    #print(img_array.__sizeof__())\n\ndef np_female_array():\n    entries = os.listdir('converted/2/')\n    for e in entries:\n        with open('converted/2/'+e, 'r+b') as f:\n           with Image.open(f) as image:\n            img_array2 = np.array(image)\n            img_array2 = cv2.cvtColor(img_array2, cv2.COLOR_BGR2GRAY)\n            img_array2 = np.reshape(img_array2, (1, 65536))\n            #img_array1 = img_array / 65536.0 * 15.0\n            img_array2 = np.append(img_array2,[2])\n            img_array.append(img_array2)\n            #print(img_array2)\n    #np.savetxt('my_dataset2.txt', img_array)\n    with open('my_dataset22.pickle', 'wb') as output:\n        pickle.dump(img_array, output, protocol=2)\n    #print(img_array)\n    #print(img_array.__sizeof__)\n\n\ndef load_data():\n    with open('my_dataset1.pickle', 'rb') as data1:\n        dataset1 = pickle.load(data1)\n        dataset1 = np.array(dataset1)\n    img_array.append(dataset1)\n\n    with open('my_dataset2.pickle', 'rb') as data2:\n        dataset2 = pickle.load(data2)\n        dataset2 = np.array(dataset2)\n    img_array.append(dataset2)\n\n\ndef builddata(img_array):\n    for i in img_array:\n        for j in i:\n            data.append(j[:65535])\n            target.append(j[65536])\n\n    #print(data[55])\n\ndef KNN():\n    from sklearn.model_selection import train_test_split\n    train_data, test_data, train_target, test_target = train_test_split(data, target, test_size=test_size)\n    from sklearn.neighbors import KNeighborsClassifier  # load KNN classifer\n    clsfr = KNeighborsClassifier(n_neighbors=1)  # KNN classifier is loaded to clsfr\n    clsfr.fit(train_data, train_target)  # training the ML algorithm(KNN)\n    results = clsfr.predict(test_data)\n    print('Actual       :', test_target)\n    print('KNN Predicted:', results)\n    from sklearn.metrics import accuracy_score\n    accuracy = accuracy_score(test_target, results)\n    print('KNN Accuracy :', accuracy)\n\n\ndef SVM(): #Support Vector Machine\n    from sklearn.model_selection import train_test_split\n    train_data, test_data, train_target, test_target = train_test_split(data, target, test_size=test_size)\n    from sklearn.svm import SVC\n    clsfr = SVC(kernel='linear')  # support vector classifier\n    clsfr.fit(train_data, train_target)\n    results = clsfr.predict(test_data)\n    print('Actual       :', test_target)\n    print('SVM Predicted:', results)\n    from sklearn.metrics import accuracy_score\n    accuracy = accuracy_score(test_target, results)\n    print('SVM Accuracy :', accuracy)\n\ndef DT(): #Decision Tree\n    from sklearn.model_selection import train_test_split\n    train_data, test_data, train_target, test_target = train_test_split(data, target, test_size=test_size)\n    from sklearn import tree\n    clsfr = tree.DecisionTreeClassifier()\n    clsfr.fit(train_data, train_target)\n    results = clsfr.predict(test_data)\n    print('Actual       :', test_target)\n    print('DT Predicted :', results)\n    from sklearn.metrics import accuracy_score\n    accuracy = accuracy_score(test_target, results)\n    print('DT Accuracy  :', accuracy)\n\n\nresize() # 1) Resize images are saved to converted dir\n#np_male_array()  # 2) Load male images to numpy array\n#np_female_array()\n##load_data() # data load from pickle\n##builddata(img_array) # data,target seperator\n##KNN() #K Nearest Neighbors\n##SVM() #Support Vector Machine\n##DT() #Decision Tree\n","sub_path":"healthdata.py","file_name":"healthdata.py","file_ext":"py","file_size_in_byte":5047,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"518439285","text":"from typing import Any\nfrom typing import Dict\nfrom typing import List\nfrom typing import Union\n\nfrom allennlp.data import Instance\nfrom allennlp.data import Token\nfrom allennlp.data import TokenIndexer\nfrom allennlp.data.fields import ListField\nfrom allennlp.data.fields import TextField\n\nfrom biome.text.features import WordFeatures\nfrom biome.text.tokenizer import Tokenizer\n\n\nclass InputFeaturizer:\n    \"\"\"Transforms input text (words and/or characters) into indexes and embedding vectors.\n\n    This class defines two input features, words and chars for embeddings at word and character level respectively.\n\n    You can provide additional features by manually specify `indexer` and `embedder` configurations within each\n    input feature.\n\n    Attributes\n    ----------\n    tokenizer : `Tokenizer`\n        Tokenizes the input depending on its type (str, List[str], Dict[str, Any])\n    indexer : `Dict[str, TokenIdexer]`\n        Features dictionary for token indexing. Built from `FeaturesConfiguration`\n    \"\"\"\n\n    def __init__(\n        self,\n        tokenizer: Tokenizer,\n        indexer: Dict[str, TokenIndexer],\n    ):\n        self.tokenizer = tokenizer\n        self.indexer = indexer\n\n    def __call__(\n        self,\n        record: Union[str, List[str], Dict[str, Any]],\n        to_field: str = \"record\",\n        aggregate: bool = False,\n        tokenize: bool = True,\n        exclude_record_keys: bool = False,\n    ) -> Instance:\n        \"\"\"Generates an allennlp instance from a record input.\n\n        Parameters\n        ----------\n        record: `Union[str, List[str], Dict[str, Any]]`\n            Input data\n        to_field: `str`\n            The field name in the returned instance\n        aggregate: `bool`\n            If true, the returned instance will contain a single `TextField` with all record fields;\n            If false, the instance will contain a `ListField` of `TextField`s.\n        tokenize: `bool`\n            If false, skip tokenization phase and pass record data as tokenized token list.\n        exclude_record_keys: `bool`\n            If true, excludes record keys from the output tokens in the featurization of dictionaries\n\n        Returns\n        -------\n        instance: `Instance`\n        \"\"\"\n        data = record\n\n        record_tokens = (\n            self._data_tokens(data, exclude_record_keys)\n            if tokenize\n            else [[Token(t) for t in data]]\n        )\n        return Instance({to_field: self._tokens_to_field(record_tokens, aggregate)})\n\n    def _data_tokens(\n        self, data: Union[str, List[str], Dict[str, Any]], exclude_record_keys: bool\n    ) -> List[List[Token]]:\n        \"\"\"Converts the input data into a list of list of tokens depending on its type.\n\n        Parameters\n        ----------\n        data: Union[str, List[str], Dict[str, Any]]\n            The input data\n        exclude_record_keys: `bool`\n            If true, excludes record keys from the output tokens in the featurization of dictionaries\n\n        Returns\n        -------\n        list_of_list_of_tokens\n        \"\"\"\n        if isinstance(data, dict):\n            return self.tokenizer.tokenize_record(data, exclude_record_keys)\n        if isinstance(data, str):\n            return self.tokenizer.tokenize_text(data)\n        return self.tokenizer.tokenize_document(data)\n\n    def _tokens_to_field(\n        self, tokens: List[List[Token]], aggregate: bool\n    ) -> Union[ListField, TextField]:\n        \"\"\"Wraps the tokens with the indexer in one or several `TextField`s\n\n        Parameters\n        ----------\n        tokens: List[List[Token]]\n            The list of list of input tokens\n        aggregate: `bool`\n            If true, the returned instance will contain a single `TextField` with all record fields;\n            If false, the instance will contain a `ListField` of `TextField`s.\n\n        Returns\n        -------\n        field\n            `TextField` if aggregate is true, `ListField` otherwise\n        \"\"\"\n        if aggregate:\n            return TextField(\n                [token for entry_tokens in tokens for token in entry_tokens],\n                self.indexer,\n            )\n        return ListField(\n            [TextField(entry_tokens, self.indexer) for entry_tokens in tokens]\n        )\n\n    @property\n    def has_word_features(self) -> bool:\n        \"\"\"Checks if word features are already configured as part of the featurization\"\"\"\n        return WordFeatures.namespace in self.indexer\n","sub_path":"src/biome/text/featurizer.py","file_name":"featurizer.py","file_ext":"py","file_size_in_byte":4450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"629787609","text":"#coding=utf-8\nimport select\nimport socket\nimport sys\n#缺点\n#select 版本 最大缺点就是在单个进程能够见识的文件描述符的数量存在最大限制，32位Linux上一般为1024 \n#64位 2048个\n#可以通过修改宏或者重新编译内核提升，但是也会造成效率的降低\n#\n#且select 使用轮询的方式检测 效率极其不高\n#\n\nserver = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nserver.bind(('', 7788))\nserver.listen(5)\n\ninputs = [server, sys.stdin]\n\nrunning = True\n\nwhile True:\n\n    # 调用 select 函数，阻塞等待\n    readable, writeable, exceptional = select.select(inputs, [], [])\n\n    # 数据抵达，循环\n    for sock in readable:\n\n        # 监听到有新的连接\n        if sock == server:\n            conn, addr = server.accept()\n            # select 监听的socket\n            inputs.append(conn)\n\n        # 监听到键盘有输入\n        elif sock == sys.stdin:\n            cmd = sys.stdin.readline()\n            running = False\n            break\n\n        # 有数据到达\n        else:\n            # 读取客户端连接发送的数据\n            data = sock.recv(1024)\n            if data:\n                sock.send(data)\n            else:\n                # 移除select监听的socket\n                inputs.remove(sock)\n                sock.close()\n\n    # 如果检测到用户输入敲击键盘，那么就退出\n    if not running:\n        break\n\nserver.close()","sub_path":"Python网络/04_TCP_server/select版Tcp_server.py","file_name":"select版Tcp_server.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"380550446","text":"import random\n\ndef dice_roll(s):\n    \"\"\"Simulates a dice roll for a dice with s sides.\"\"\"\n\n    choice = str(random.randint(1, s))\n    print(f\"\\nResult: {choice}\")\n\ndef mult_dice(dice_list):\n    \"\"\"Simulates a dice roll for multiple dice with varying sides.\"\"\"\n\n    d = {}\n    for dice in dice_list:\n        d[dice] = d.get(dice, 0) + 1\n        # Constructs a dictionary from dice_list.\n\n    for k, v in d.items():\n        choices = []\n        for n in range(v):\n            choices.append(str(random.randint(1, k)))\n        print(\"\\nResults for {} Sided Dice: {}\".format(k, \", \".join(choices)))\n\ndef menu():\n    \"\"\"Asks the user to choose number and sides of dice.\"\"\"\n\n    while True:\n        \n        answer = input(\"\\nHow Many Dice? Done (d) \")\n        if answer == \"d\":\n            print(\"\\nGoodbye!\\n\")\n            return\n        elif answer == \"1\":\n            while True:\n                answer = input(\"\\nHow Many Sides? \")\n                if answer.isdigit():\n                    dice_roll(int(answer))\n                    break\n                print(\"\\nPlease enter a number!\")\n            continue\n        elif answer.isdigit():\n            while True:\n                dice_list = []\n                for n in range(1, int(answer)+1):\n                    while True:\n                        dice = input(f\"\\nDice {n}: How Many Sides? \")\n                        if dice.isdigit():\n                            dice_list.append(int(dice))\n                            break\n                        print(\"\\nPlease enter a number!\")\n                print()\n                mult_dice(dice_list)\n                break\n            continue\n        print(\"\\nPlease enter a number!\")\n\nif __name__ == '__main__':\n\tmenu()","sub_path":"dice.py","file_name":"dice.py","file_ext":"py","file_size_in_byte":1718,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"104332810","text":"##print(\"hello world\")\r\n##x=98\r\n##y=76\r\n##b=90\r\n##t=x*y*b\r\n##print(t)\r\n##\r\n##for i in range(9):\r\n##    print(t)\r\n##    print('you are awesome')\r\n##    print (i)\r\n##a=input()\r\n##print(a)\r\n##while input('You are whiteman?')!='bye':\r\n##    print('Are you leaving already?')\r\n##\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\nprint('You are alone in the world.',\r\n      'There are no people to be seen or animals to be found.')\r\n\r\npath=input('Choose a direction.')\r\nif path=='north':\r\n    print('You arive at an abandoned parking lot',\r\n          'the asphalt is cracked')\r\nelif path=='south':\r\n    print('A train is aproaching at rapid speed toward you',\r\n          'What do you do?')\r\n    act=input()\r\n    if act=='jump':\r\n        print('You land on your ankle badly.')\r\n      \r\n    \r\n    \r\n","sub_path":"hellowrl.py","file_name":"hellowrl.py","file_ext":"py","file_size_in_byte":757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"190678988","text":"# import re\nimport time\nfrom .Image import Image_size\nfrom .scraphead import Scrape\n# from .json import create_json\n# from .scraper.main import main\n\n\ndef main(input_url, data):\n    try:\n        start = time.time()\n\n        scrape_obj = Scrape(input_url, data)\n\n        meta = scrape_obj.get_meta()\n        meta_time = time.time() - start\n        start2 = time.time()\n        # print(meta)\n        if meta is None or \"image\" not in meta or meta[\"image\"] in [None, [], \"\", (None,), ('',)]:\n            # if meta is None or \"image\" not in meta or meta[\"image\"] is None:\n            parse_image_urls = scrape_obj.parse_image_urls()            # all image URLs\n            meta[\"parse_image_urls_time\"] = time.time() - start2\n            start3 = time.time()\n            image_data = Image_size().get_best_image(urls=parse_image_urls)\n            meta[\"image\"] = image_data\n            meta[\"image_data\"] = time.time() - start3\n\n        meta[\"url\"] = input_url,\n        meta[\"video\"] = meta[\"video\"] if \"image\" in meta.keys() else \"\",\n        # print(meta['video'])\n        meta[\"image\"] = meta[\"image\"] if \"image\" in meta.keys() else \"\",\n        meta[\"time\"] = {\n            \"get_meta_time\": meta_time - meta['mitime'] if \"mitime\" in meta.keys() else meta_time,\n            \"meta_image_time\": meta['mitime'] if \"mitime\" in meta.keys() else \"\",\n            \"image_data\": meta[\"image_data\"] if \"image_data\" in meta.keys() else \"\",\n            \"parse_image_urls_time\": meta[\"parse_image_urls_time\"] if \"parse_image_urls_time\" in meta.keys() else \"\",\n            \"total_time\": time.time() - start,\n        }\n        meta[\"poster\"] = \"\" if meta[\"video\"] in [\"\", None, (None,), ('',), []] else meta[\"image\"]\n        return meta\n    except Exception as e:\n        print(e)\n","sub_path":"zembed/crawler/scraper/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":1763,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"548309834","text":"\n\nfrom xai.brain.wordbase.nouns._magpie import _MAGPIE\n\n#calss header\nclass _MAGPIES(_MAGPIE, ):\n\tdef __init__(self,): \n\t\t_MAGPIE.__init__(self)\n\t\tself.name = \"MAGPIES\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"magpie\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_magpies.py","file_name":"_magpies.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"535433257","text":"# # 1,有一个整数数组,现在要去掉重复数字,每个数字只保留一个,输出去重后的数组,但是需要按照数字在原数组出现的次数从高到低排序,相同次数的按原顺位输出。\n# # 例:[1,3,3,3,2,4,4,4,5]\n# # 输出:3,4,1,2,5\n\n# lists = [1,3,3,3,2,4,4,4,5]\n#\n# def renum(lists):\n# \tdict_val = {}\n# \tfor val in set(lists):\n# \t\tdict_val[val] = lists.count(val)\n# \tres = sorted(dict_val.items(), key=lambda item: item[1], reverse=True)\n# \treturn [val[0] for val in res]\n#\n# print(renum(lists))\n\n\n\n# # 2,有一堆石子,一次编号1-100围成圈,给定一个数K,从1开始数K个,将此石子踢出,继续报数,最后当石子总数小于K时,输出最后剩余石子编号。\n# # 例:K为3\n# # 输出:58,91\n\ndef removeshitou(k = 4):\n\tlists = [i for i in range(1, 101)]\n\tnum = k\n\twhile True:\n\t\tif len(lists) < num:\n\t\t\tbreak\n\t\tif len(lists)-1 > k:\n\t\t\tlists.pop(k)\n\t\tk += k\n\t\tif k>len(lists):\n\t\t\tk = k - len(lists)\n\treturn lists\n\n\n\n\n\n# # 3,有一个正整数数组,给定一个目标值S,求有多少个连续区间的和大于等于S\n# # 例:数组 3,4,7  目标S为7\n# # 输出:4\n# #\n# # 4,上台阶问题,一个阶梯有N个台阶,每次只能上一步或者3步,求一共有多少种不同的方式上台阶\n# # 例:N=50\n# # 输出:122106097\n\ndef sum_stairs_Fibo(sum):\n    \"\"\"\n    非递归算法,斐波那契数列\n    台阶数：1、2、3、4、5、6\n    走法数：1、2、4、7、13、24\n    :param sum:\n    :return:\n    \"\"\"\n    a,b = 1,3\n    while sum-1>0:\n        a,b = b,a+b\n        sum -= 1\n    return a\n# #\n# # 5,字符串解压缩,有一个字符串,全部由小写英文字符组成,现给出压缩字符串,求其原串\n# # 例:\n# # 输出:ddddff\n\ns = '4dff'\ndef info(strings):\n\tnew_word = []\n\tfor index, i in enumerate(strings):\n\t\tprint(i)\n\t\tif i.isdigit():\n\t\t\tres = (int(i) -1) * strings[index + 1]\n\t\t\tnew_word.append(res)\n\t\telse:\n\t\t\tnew_word.append(i)\n\treturn ''.join(new_word)\n\n\n\n\n# #\n# # 6,每个人都有一个能力值,有一个活动要求参赛队伍能力值最低为M,每个队伍可以由1或者2个人组成,求计算最多可以组成多少符合要求的队伍\n# # 例:3 1 5 7 9\n# #  M=8\n# # 输出:3\n\ndef sum_person(lists=[3, 1, 5, 7, ], m = 8):\n\tcount = 0\n\tfor i in lists:\n\t\tfor j in range(len(lists)):\n\t\t\tif j < len(lists) - 1:\n\t\t\t\tif i + lists[j] == m:\n\t\t\t\t\tcount += 1\n\treturn  count\nprint(sum_person())\n\n\n# # 7,有一个字符串只包含大写字母,求其连续相同字母子串中,长度第K长的子串长度,相同字母只取最长那个子串。\n# # 例:AAAAHHHBBCDHHHH\n# # K=3\n# # 输出:2\nclass Solution:\n\tdef solve(self, s):\n\t\tif len(s)==0:\n\t\t\treturn 0\n\t\tct=1\n\t\ttem=1\n\t\tfor i in range(len(s)-1):\n\t\t\tif s[i]==s[i+1]:\n\t\t\t\ttem+=1\n\t\t\telse:\n\t\t\t\tct = max(tem,ct)\n\t\t\t\ttem=1\n\t\treturn ct\n\nob = Solution()\n# print(ob.solve(\"AAAAHHHBBCDHHHH\"))\n\n\nfrom typing import List, AnyStr\n# 数独\n# # 8 https://leetcode-cn.com/problems/valid-sudoku/?um_channl=huawei?um_from_appkey=5fcda41c42348b56d6f8e8d5\n\n# class Solution:\n#     def isValidSudoku(self, board: List[List[str]]) -> bool:\n#         # 0: row, 1: column, 2: square\n#         record = {0:defaultdict(set), 1:defaultdict(set), 2:defaultdict(set)}\n#         n = len(board)\n#         m = sqrt(n)\n#         for i in range(n):\n#             for j in range(n):\n#                 if board[i][j] == '.':\n#                     continue\n#                 if board[i][j] in record[0][i] or board[i][j] in record[1][j]:\n#                     return False\n#                 sq = i // m * m + j // m\n#                 if board[i][j] in record[2][sq]:\n#                     return False\n#                 record[0][i].add(board[i][j])\n#                 record[1][j].add(board[i][j])\n#                 record[2][sq].add(board[i][j])\n#         return True\n\n\n# # 9 . 单词���索\n# # https://leetcode-cn.com/classic/problems/longest-word-in-dictionary-through-deleting/description/\n\n\n# \n# class Solution:\n# \tdef findLongestWord(self, s: str, dictionary: List[str]) -> str:\n# \t\t# sorted 中 key 是升序排序的,如果想要让 word 长度比较长的排在前面,可以取反 -len(x)\n# \t\tsorted_dictionary = sorted(dictionary, key=lambda x: [-len(x), x])\n# \t\t\n# \t\tfor w in sorted_dictionary:\n# \t\t\t# 两个指针,分别指向 s 和 w\n# \t\t\tp = 0\n# \t\t\tq = 0\n# \t\t\twhile q < len(s):\n# \t\t\t\tif s[q] == w[p]:\n# \t\t\t\t\tp += 1\n# \t\t\t\t\n# \t\t\t\tif p == len(w):\n# \t\t\t\t\treturn w\n# \t\t\t\t\n# \t\t\t\tq += 1\n# \t\treturn \"\"\n# \n# s = \"abpcplea\"\n# dictionary = [\"ale\",\"apple\",\"monkey\",\"plea\"]\n# \n# info = Solution()\n# print(info.findLongestWord(s,  dictionary))\n# i = 'a'\n# print(s.find(i))\n# if s.find(i) != -1:\n# \tprint('======')\n\n\n\n# # 10\n# # https://leetcode-cn.com/problems/word-search/?um_channl=huawei?um_from_appkey=5fcda41c42348b56d6f8e8d5\n#\n#\n# # 输入：\n# # 输出：\nboard = [[\"A\",\"B\",\"C\",\"E\"],[\"S\",\"F\",\"C\",\"S\"],[\"A\",\"D\",\"E\",\"E\"]]\nword = \"ABCCED\"\nfrom typing import List\nclass Solution:\n    def exist(self, board: List[List[str]], word: str) -> bool:\n        # 使用深度优先搜索\n        if not board:   # 边界条件\n            return False\n        for i in range(len(board)):\n            for j in range(len(board[0])):\n                if self.dfs(board, i, j, word):\n                    return True\n        return False\n\n    def dfs(self, board, i, j, word):\n        if len(word) == 0: # 如果单词已经检查完毕\n            return True\n        if i < 0 or i >= len(board) or j < 0 or j >= len(board[0]) or word[0] != board[i][j]:  # 如果路径出界或者矩阵中的值不是word的首字母,返回False\n            return False\n        tmp = board[i][j]  # 如果找到了第一个字母,检查剩余的部分\n        board[i][j] = '0'\n        res = self.dfs(board,i+1,j,word[1:]) or self.dfs(board,i-1,j,word[1:]) or self.dfs(board,i,j+1,word[1:]) or self.dfs(board, i, j-1, word[1:]) # 上下左右四个方向搜索\n\n        board[i][j] = tmp  # 标记过的点恢复原状,以便进行下一次搜索\n        return res\n\ntest = Solution()\nprint(test.exist(board, word))\n","sub_path":"exercises/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":5989,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"646744318","text":"import random\ndef main():\n    play = \"y\"\n    cScore = 0\n    uScore = 0\n    while play == \"y\":\n        computer = random.randint(0,2)\n\n        userChoice = \"spock\"\n        computerChoice = \"spock2\"\n        print(\"Choose [R]ock, [P]aper, or [S]cissors: \", end=\"\")\n        user = \"t\"\n        while user.lower() == \"t\":\n                user = input()\n                user = user.lower()\n                if user == \"r\":\n                    userChoice = \"Rock\"\n                elif user == \"p\":\n                    userChoice = \"Paper\"\n                elif user == \"s\":\n                    userChoice = \"Scissors\"\n                elif user == \"q\":\n                    print(\"Quitter!\")\n                else:\n                    print(\"INVALID! Please type R ,P, S or Q for quit: \", end=\"\")\n                    user = \"t\"\n\n        if computer == 0:\n            computerChoice = \"Rock\"\n        elif computer == 1:\n            computerChoice = \"Paper\"\n        else: computerChoice = \"Scissors\"\n        print(\"Your Choice was: \", userChoice)\n        print(\"computer chose: \", computerChoice)\n\n\n        if userChoice == computerChoice:\n            print (\"It's a tie!!\")\n        elif computerChoice == \"Paper\":\n            if userChoice == \"Rock\":\n                print(\"Computer wins\")\n                cScore += 1\n            else:\n                print(\"You win!!\")\n                uScore +=1\n        elif computerChoice == \"Rock\":\n            if userChoice == \"Scissors\":\n                print(\"Computer wins\")\n                cScore += 1\n            else:\n                print(\"You win!!\")\n                uScore += 1\n        elif computerChoice == \"Scissors\":\n            if userChoice == \"Paper\":\n                print(\"Computer wins\")\n                cScore += 1\n            else:\n                print(\"You win!!\")\n                uScore += 1\n\n        print(\"You scored: \", uScore, \"Computer Scored: \", cScore)\n        print(\"play again?: \", end=\"\")\n        play = input()\n\nif __name__ == '__main__':\n    main()","sub_path":"rspMultifiles/rsp2.py","file_name":"rsp2.py","file_ext":"py","file_size_in_byte":2009,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"336758569","text":"import numpy as np\nimport tensorflow as tf\nimport nltk\nimport random\nimport sys\nimport os\nimport time\nimport collections\nfrom math import floor\nfrom keras.layers.core import Activation, Dense\nfrom keras.preprocessing import sequence\nfrom keras.layers.embeddings import Embedding\nfrom keras.layers.recurrent import LSTM\nfrom keras.layers import GRU, SimpleRNN\nfrom keras.callbacks import EarlyStopping\nfrom keras.models import Sequential\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.metrics import roc_auc_score\nfrom sklearn.metrics import accuracy_score\nfrom keras.utils import np_utils\nfrom keras.models import load_model\nsys.path.append('../')\nfrom utils.core import Pipeline\n\n\nclass Sentiment140(Pipeline):\n    def __init__(self):\n        super(Sentiment140, self).__init__()\n        self.nb_classes = 10\n        self.nb_units = 64\n        self.embedding_size = 128\n        self.batch_size = 512\n        self.max_len = 0\n        self.vocab_size = 0\n        self.mode = sys.argv[2]\n        self.neuron_type = LSTM\n        self.resample = False if 'full' == self.mode else bool(int(sys.argv[3]))\n        self.segment_len = 9999 if 'full' == self.mode else int(sys.argv[4])\n        self.sample_num = 1 if 'full' == self.mode or self.resample else int(sys.argv[5])\n\n\n    def data_loader(self):\n        with open('./data/training.1600000.processed.noemoticon.csv', 'r', encoding='ISO-8859-1') as f:\n            data = f.readlines()\n            data = [data[i].rstrip('\\n').split(',', 5) for i in range(0, len(data), 20)]\n        y = np.array([int(v[0].strip('\"')) for v in data])\n        y = np.where(y==4, 1, y)\n        data = [v[-1].strip('\"').replace('.', '').replace(',', '') for v in data]\n\n        word_freq = collections.Counter()\n        data_num = len(data)\n        for row in data:\n            words = nltk.word_tokenize(row.strip().lower())\n            self.max_len = max(self.max_len, len(words))\n            for word in words:\n                if word not in word_freq:\n                    word_freq[word] = 0\n                word_freq[word] += 1\n\n        self.segment_len = min(self.segment_len, self.max_len)\n        self.vocab_size = len(word_freq) + 2\n        word2index = {x: i+2 for i, x in enumerate(word_freq)}\n        word2index[\"PAD\"] = 0\n        word2index[\"UNK\"] = 1\n        index2word = {v:k for k, v in word2index.items()}\n\n        x = np.empty(data_num, dtype=list)\n        for i, sentence in enumerate(data):\n            words = nltk.word_tokenize(sentence.strip().lower())\n            seqs = []\n            for word in words:\n                if word in word2index:\n                    seqs.append(word2index[word])\n                else:\n                    seqs.append(word2index[\"UNK\"])\n            x[i] = seqs\n\n        y = np_utils.to_categorical(y, 2)\n\n        tmp_x, te_x, tmp_y, te_y = train_test_split(x, y, test_size=0.2, random_state=int(sys.argv[1]))\n        tr_x, va_x, tr_y, va_y = train_test_split(tmp_x, tmp_y, test_size=0.2, random_state=int(sys.argv[1]))\n        tr_seq_len = [len(v) for v in tr_x]\n        tr_x = sequence.pad_sequences(tr_x, maxlen=self.max_len, padding='pre')\n        va_x = sequence.pad_sequences(va_x, maxlen=self.max_len, padding='pre')\n        te_x = sequence.pad_sequences(te_x, maxlen=self.max_len, padding='pre')\n        return tr_x, tr_y, va_x, va_y, te_x, te_y, tr_seq_len\n\n\n    def sample_segment(self, x, x_seq_len, y):\n        tmp_x, tmp_y = [], []\n        for i in range(len(x)):\n            all_in_num = max(x_seq_len[i]-self.segment_len+1, 0)\n            all_in_choose_num = min(all_in_num, self.sample_num)\n            all_in_start_idx = random.sample(list(np.arange(all_in_num)), k=all_in_choose_num)\n            shift = self.max_len-x_seq_len[i]\n            for idx in all_in_start_idx:\n                tmp_x.append(x[i][shift+idx:shift+idx+self.segment_len])\n                tmp_y.append(y[i])\n\n            res_num = min(self.sample_num-all_in_choose_num, self.segment_len-1, x_seq_len[i])\n            for idx in range(res_num):\n                tmp_x.append(x[i][self.max_len-idx-self.segment_len-all_in_num:self.max_len-idx-all_in_num])\n                tmp_y.append(y[i])\n        return np.asarray(tmp_x), np.asarray(tmp_y)\n\n\n    def build_model(self):\n        model = Sequential()\n        model.add(Embedding(self.vocab_size, self.embedding_size))\n        model.add(self.neuron_type(self.nb_units, activation='relu', dropout=0.1, recurrent_dropout=0.1, return_sequences=True))\n        model.add(self.neuron_type(self.nb_units, activation='relu', dropout=0.1, recurrent_dropout=0.1, return_sequences=True))\n        model.add(self.neuron_type(self.nb_units, activation='relu', dropout=0.1, recurrent_dropout=0.1, return_sequences=True))\n        model.add(self.neuron_type(self.nb_units, activation='relu', dropout=0.1, recurrent_dropout=0.1))\n        model.add(Dense(2, activation='softmax'))\n\n        model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])\n        model.summary()\n        return model\n\nif '__main__' == __name__:\n    pipeline = Sentiment140()\n    tr_x, tr_y, va_x, va_y, te_x, te_y, x_seq_len = pipeline.data_loader()\n    exec_time, n_epoch, exec_time_without_calc = pipeline.run(tr_x, tr_y, va_x, va_y, x_seq_len)\n\n    model = load_model('./tmp/model.h5')\n    result = model.evaluate(te_x, te_y)\n    print(f'Test acc: {result[1]}')\n    # print(f'Test auc: {roc_auc_score(te_y, model.predict(te_x))}')\n    print(f'Execute epoch: {n_epoch}')\n    print(f'Execute time: {exec_time}')\n    if pipeline.resample:\n        print(f'(W) Execute time: {exec_time_without_calc}')\n","sub_path":"exp/sentiment140/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":5618,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"393604004","text":"# http://www.blog.pythonlibrary.org/2012/08/02/python-101-an-intro-to-logging/\r\n# otherMod.py\r\nimport sys; sys.dont_write_bytecode = True\r\nimport os\r\n\r\nimport LnModulo_02 as Modulo2\r\nimport LnModulo_03 as Modulo3\r\n\r\nimport LnLogger\r\n\r\n# #####################################\r\n# # Init LOG\r\n# #####################################\r\nlogConfigFileName   = 'LnLogger.ini'\r\nloggerName          = 'MainModule'\r\nloggerName          = 'Appl01'\r\n\r\n\r\ndef main():\r\n    # os.environ['LN_loggerName'] = loggerName\r\n    logger = LnLogger.init(logConfigFileName, loggerName)\r\n\r\n    logger.info(\"Program started\")\r\n    logger.info(\"calling modulo2\")\r\n    result = Modulo2.add1(7, 8)\r\n    result = Modulo2.add2(7, 8)\r\n    result = Modulo2.add3(7, 8)\r\n\r\n    logger.info(\"calling prova\")\r\n    result = Modulo2.prova()\r\n\r\n    logger.info(\"calling modulo3\")\r\n    result = Modulo3.add1(7, 8)\r\n    result = Modulo3.add2(7, 8)\r\n    result = Modulo3.add3(7, 8)\r\n    logger.info(\"Done!\")\r\n\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n\r\n","sub_path":"LnLogger/Samples/newLogger_INI/LnModuloMain.py","file_name":"LnModuloMain.py","file_ext":"py","file_size_in_byte":1011,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"116341125","text":"import os\nfrom pathlib import Path\n\nimport hickle as hkl\nimport matplotlib.pyplot as plt\nimport numpy as np\n\nplt.switch_backend(\"agg\")\nfrom .utils import MyDataset\n\n\ndef load(dir, file) -> np.ndarray:\n    if (dir / (file + \".hkl\")).exists():\n        X = hkl.load(os.path.join(dir, file + \".hkl\"))\n    elif (dir / (file + \".npz\")).exists():\n        X = np.load(dir / (file + \".npz\"))[\"arr_0\"]\n    else:\n        raise ValueError(f\"No hkl or npz file={dir / file}\")\n    return X\n\n\ndef reshape(X):\n    if len(X.shape) > 3:\n        return X.reshape((-1, 20, 1024))\n    return X\n\n\ndef trim(X, max_n: int, max_t: int):\n    if max_n > 0:\n        N = min(X.shape[0], max_n)\n    else:\n        N = X.shape[0]\n    if max_t > 0:\n        T = min(X.shape[1], max_t)\n    else:\n        T = X.shape[1]\n    return X[:N, :T]\n\n\ndef load_bball_data(data_dir, max_n: int, max_t: int, dt=0.1, plot=True):\n    data_dir = Path(data_dir)\n\n    Xtr = trim(reshape(load(data_dir, \"training\")), max_n, max_t)\n    Ytr = dt * np.arange(0, Xtr.shape[1], dtype=np.float32)\n    Ytr = np.tile(Ytr, [Xtr.shape[0], 1])\n\n    Xval = trim(reshape(load(data_dir, \"val\")), max_n, max_t)\n    Yval = dt * np.arange(0, Xval.shape[1], dtype=np.float32)\n    Yval = np.tile(Yval, [Xval.shape[0], 1])\n\n    Xtest = trim(reshape(load(data_dir, \"test\")), max_n, max_t)\n    Ytest = dt * np.arange(0, Xtest.shape[1], dtype=np.float32)\n    Ytest = np.tile(Ytest, [Xtest.shape[0], 1])\n\n    dataset = MyDataset(Xtr, Ytr, Xval, Yval, Xtest, Ytest)\n\n    if plot:\n        X, y = dataset.train.next_batch(5)\n        tt = min(20, X.shape[1])\n        plt.figure(2, (tt, 5))\n        for j in range(5):\n            for i in range(tt):\n                plt.subplot(5, tt, j * tt + i + 1)\n                plt.imshow(np.reshape(X[j, i, :], [32, 32]), cmap=\"gray\")\n                plt.xticks([])\n                plt.yticks([])\n        plt.savefig(\"plots/bballs/data.png\")\n        plt.close()\n    return dataset\n\n\ndef plot_bball_recs(\n    X, Xrec, idxs=[0, 1, 2, 3, 4], show=False, fname=\"reconstructions.png\"\n):\n    if X.shape[0] < np.max(idxs):\n        idxs = np.arange(0, X.shape[0])\n    tt = min(20, X.shape[1])\n    plt.figure(2, (tt, 3 * len(idxs)))\n    for j, idx in enumerate(idxs):\n        for i in range(tt):\n            plt.subplot(2 * len(idxs), tt, j * tt * 2 + i + 1)\n            plt.imshow(np.reshape(X[idx, i, :], [32, 32]), cmap=\"gray\")\n            plt.xticks([])\n            plt.yticks([])\n            plt.subplot(2 * len(idxs), tt, j * tt * 2 + i + tt + 1)\n            plt.imshow(np.reshape(Xrec[idx, i, :], [32, 32]), cmap=\"gray\")\n            plt.xticks([])\n            plt.yticks([])\n    plt.savefig(fname)\n    if show is False:\n        plt.close()\n","sub_path":"model/data/bballs.py","file_name":"bballs.py","file_ext":"py","file_size_in_byte":2696,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"125662876","text":"# vim: tabstop=4 shiftwidth=4 softtabstop=4\n\n\"\"\"\n[VRM DRIVER] \n\n\"\"\"\nimport random\nimport json\nimport os\nimport math\n\nfrom oslo_config import cfg\nfrom cinder.volume.configuration import Configuration\nfrom cinder import volume\nfrom cinder import context as cinder_context\nfrom oslo_log import log as logging\nfrom cinder.volume import driver\nfrom cinder import exception\nfrom cinder.volume.drivers.huawei.vrm import exception as driver_exception\nfrom cinder.i18n import _\nfrom cinder.volume.drivers.huawei.vrm.conf import FC_DRIVER_CONF\nfrom cinder.volume.drivers.huawei.vrm.vm_proxy import VmProxy\nfrom cinder.volume.drivers.huawei.vrm.volume_proxy import VolumeProxy\nfrom cinder.volume.drivers.huawei.vrm.volume_snapshot_proxy import VolumeSnapshotProxy\nfrom cinder.volume.drivers.huawei.vrm.host_proxy import HostProxy\nfrom cinder.volume.drivers.huawei.vrm.cluster_proxy import ClusterProxy\nfrom cinder.volume.drivers.huawei.vrm.datastore_proxy import DatastoreProxy\nfrom cinder.volume.drivers.huawei.vrm.http_client import VRMHTTPClient\nfrom cinder.volume import utils as volume_utils\n\n\ntry:\n    from eventlet import sleep\nexcept ImportError:\n    from time import sleep\n\nbackend_opts = [\n    cfg.StrOpt('volume_driver',\n               default='cinder.volume.drivers.huawei.vrm.vrm_driver.VRMDriver',\n               help='Driver to use for volume creation'),\n]\n\ndef metadata_to_dict(metadata):\n    result = {}\n    for item in metadata:\n        if not item.get('deleted'):\n            result[item['key']] = item['value']\n    return result\n\n\nLOG = logging.getLogger(__name__)\nCONF = cfg.CONF\n\nfc_plugin_conf = [\n    cfg.StrOpt('vrm_config_file',\n               default=None,\n               help='vrm_config_file'),\n    cfg.BoolOpt('vrm_thin_provision',\n                default=False,\n                help='Switch of thin provisioning support'),\n    cfg.FloatOpt('vrm_over_ratio',\n                 default=1.0,\n                 help='Ratio of thin provisioning'),\n    cfg.IntOpt('vrm_reserved_percentage',\n               default=0,\n               help='Reserved percentage of the backend volumes'),\n    cfg.ListOpt('vrm_ds_name',\n                default=[],\n                help='vrm_ds_name'),\n    cfg.ListOpt('current_list',\n                default=[],\n                help='current_list'),\n    cfg.IntOpt('affine_rate',\n               default=1,\n               help='affine_rate'),\n]\n\n\n\nclass VRMDriver(driver.VolumeDriver):\n    VENDOR = 'Huawei'\n    BACKEND = 'VRM'\n    VERSION = 'v1.1'\n\n    def __init__(self, *args, **kwargs):\n        '''\n        __init__\n\n        :param args:\n        :param kwargs:\n        :return:\n        '''\n        super(VRMDriver, self).__init__(*args, **kwargs)\n        LOG.info(_(\"[VRM-CINDER] start VRMDriver __init__()\"))\n        #LOG.info(_(\"[VRM-CINDER]  del environ http_proxy https_proxy\"))\n        #del os.environ['http_proxy']\n        #del os.environ['https_proxy']\n\n        self.context = None\n        self.volume_api = volume.API()\n        self.SHARED_HOSTS = []\n        self.SHARED_DATASTORES = []\n        self.SHARED_VOLUMES = []\n\n        self.LAST_SHARED_HOSTS = self.SHARED_HOSTS\n        self.LAST_SHARED_DATASTORES = self.SHARED_DATASTORES\n        self.LAST_SHARED_VOLUMES = self.SHARED_VOLUMES\n        self.left_periodrate = CONF.vrm_sm_periodrate\n\n        if self.configuration:\n            LOG.info(_(\"[VRM-CINDER] append configuration\"))\n            self.configuration.append_config_values(fc_plugin_conf)\n        else:\n            LOG.info(_(\"[VRM-CINDER] no configuration exception\"))\n            raise driver_exception.NoNeededData\n        import pdb;pdb.set_trace()\n\n        over_ratio = self.configuration.get('vrm_over_ratio')\n        if over_ratio is None:\n            self.over_ratio = 1.0\n        else:\n            LOG.info(_(\"[VRM-CINDER] super_ratio [%s]\"), over_ratio)\n            self.over_ratio = over_ratio\n\n        thin_provision = self.configuration.vrm_thin_provision\n        if thin_provision is not None:\n            self.thin_provision = False\n        else:\n            LOG.info(_(\"[VRM-CINDER] thin_provision [%s]\"), thin_provision)\n            self.thin_provision = bool(thin_provision)\n\n\n        reserved_percentage = self.configuration.get('vrm_reserved_percentage')\n        if reserved_percentage is None:\n            self.reserved_percentage = 0\n        else:\n            LOG.info(_(\"[VRM-CINDER] reserved_percentage [%s]\"), reserved_percentage)\n            self.reserved_percentage = int(reserved_percentage)\n\n        vrm_config_file = self.configuration.get('vrm_config_file')\n        #if vrm_config_file is None:\n        #    LOG.info(_(\"[VRM-CINDER] vrm_config_file is None\"))\n        #else:\n        #    LOG.info(_(\"[VRM-CINDER] vrm_config_file is [%s]\"), vrm_config_file)\n\n        self.pool_list = self.configuration.get('vrm_ds_name')\n        if not self.pool_list:\n            LOG.error(_(\"[VRM-CINDER] vrm_ds_name is None exception\"))\n            raise driver_exception.NoNeededData\n        self.pool_list = list(set(self.pool_list))\n\n        self.current_list = self.configuration.get('current_list')\n        self.affine_rate = self.configuration.get('affine_rate')\n\n        self.volume_proxy = VolumeProxy()\n        self.vm_proxy = VmProxy()\n        self.volume_snapshot_proxy = VolumeSnapshotProxy()\n        self.host_proxy = HostProxy()\n        self.cluster_proxy = ClusterProxy()\n        self.datastore_proxy = DatastoreProxy()\n        self.vrmhttpclient = VRMHTTPClient()\n\n        self.site_urn = None\n        self.site_uri = None\n        self.cluster_urn = None\n\n        self.shared_hosts = []\n        self.shared_datastores = []\n        self.shared_volumes = []\n        self.auth_token = None\n        self.log_count = 0\n\n        LOG.info(_(\"[VRM-CINDER] end __init__()\"))\n\n\n    def _get_host_datastore_vol(self):\n        '''\n        get_host_datastore_vol\n\n        :return:\n        '''\n#        LOG.info(_(\"[VRM-CINDER] start get_host_datastore_vol()\"))\n        self.shared_hosts = []\n        self.shared_datastores = []\n        self.shared_volumes = []\n\n        self.shared_hosts = self.host_proxy.list_host()\n        hosturns = [host['urn'] for host in self.shared_hosts]\n        hosturns.sort()\n        hosturns_set = set(hosturns)\n#        LOG.info(_(\"hosturns_set %s\") % hosturns_set)\n\n        datastores = self.datastore_proxy.list_datastore()\n        sharetypes = [t.lower() for t in FC_DRIVER_CONF.vrm_ds_types]\n        for datastore in datastores:\n            storage_type = datastore['storageType'].lower()\n#            LOG.info(_(\"[VRMSMDriver] datastores [%s]\"), json.dumps(datastore))\n            ds_urn = datastore['urn']\n            ds_name = datastore['name']\n            if self.pool_list is not None:\n                if ds_name in self.pool_list:\n#                    LOG.info(_(\"[VRM-CINDER] get the vrm_sm_datastore_name\"))\n                    self.shared_datastores.append(datastore)\n                else:\n                    #LOG.info(_(\"vrm_sm_datastore_name useless\"))\n                    continue\n\n            if storage_type in sharetypes:\n                hosts = datastore['hosts']\n                hosts.sort()\n                hosts_set = set(hosts)\n\n                #LOG.info(_(\"hosts_set %s\") % hosts_set)\n                if FC_DRIVER_CONF.vrm_ds_hosts_share:\n                    if len(hosturns_set - hosts_set) == 0:\n                        #LOG.info(_(\"[VRM-CINDER] append ds share\"))\n                        self.shared_datastores.append(datastore)\n                else:\n                    if len(hosturns_set & hosts_set) > 0:\n                        LOG.info(_(\"[VRM-CINDER] append ds\"))\n                        self.shared_datastores.append(datastore)\n\n        if len(self.shared_datastores) <= 0:\n            LOG.info(_(\"[VRM-CINDER] can not found any shared datastores \"))\n            raise driver_exception.NotFound()\n\n#        LOG.info(_(\"[VRM-CINDER] end get_host_datastore_vol()\"))\n        return (self.shared_hosts, self.shared_datastores, self.shared_volumes)\n\n    def _refresh_storage_info(self, refresh=False):\n        '''\n        _refresh_storage_info\n\n        :param refresh:\n        :return:\n        '''\n#        LOG.info(_(\"[BRM-DRIVER] start _refresh_storage_info(%s) \"), refresh)\n        if refresh == True:\n            self.LAST_SHARED_HOSTS = self.SHARED_HOSTS\n            self.LAST_SHARED_DATASTORES = self.SHARED_DATASTORES\n            self.LAST_SHARED_VOLUMES = self.SHARED_VOLUMES\n\n            self.SHARED_HOSTS, self.SHARED_DATASTORES, self.SHARED_VOLUMES = self._get_host_datastore_vol()\n            self.left_periodrate = CONF.vrm_sm_periodrate\n\n            self.log_count = self.log_count + 1\n            if self.log_count >= 20:\n                LOG.info(_(\"[CINDER-BRM] refreshed shared hosts :[ %s ]\"), self.SHARED_HOSTS)\n                LOG.info(_(\"[CINDER-BRM] refreshed shared datastores :[ %s ]\"), self.SHARED_DATASTORES)\n                self.log_count = 0\n#        LOG.info(_(\"[BRM-DRIVER] end _refresh_storage_info(%s) \"), refresh)\n\n    def do_setup(self, context):\n        '''\n        do_setup\n\n        :param context:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start do_setup() \"))\n        LOG.info(_(\"[VRM-CINDER]  del environ http_proxy https_proxy\"))\n        if os.getenv('http_proxy'):\n            LOG.info(_(\"[VRM-CINDER]  del environ http_proxy\"))\n            del os.environ['http_proxy']\n        if os.getenv('https_proxy'):\n            LOG.info(_(\"[VRM-CINDER]  del environ https_proxy\"))\n            del os.environ['https_proxy']\n        self.context = context\n        self.vrmhttpclient.init()\n        self.site_urn = self.vrmhttpclient.get_siteurn()\n        self._refresh_storage_info(True)\n        clusters = self.cluster_proxy.list_cluster()\n        self.cluster_urn = clusters[0].get('urn')\n        LOG.info(_(\"[CINDER-BRM] end do_setup\"))\n\n    def check_for_setup_error(self):\n        '''\n        check_for_setup_error\n\n        :return:\n        '''\n        #LOG.info(_(\"[BRM-DRIVER] start check_for_setup_error() \"))\n        if len(self.SHARED_HOSTS) == 0 or len(self.SHARED_DATASTORES) == 0:\n            LOG.info(_(\"[CINDER-BRM] check_for_setup_error, shared datasotre not found\"))\n            raise driver_exception.NoNeededData\n\n    def _build_volume_stats(self):\n        '''\n        _build_volume_stats\n        '''\n        #LOG.info(_(\"[BRM-DRIVER] start _build_volume_stats() \"))\n        stats = {}\n        stats[\"pools\"] = []\n        stats['driver_version'] = self.VERSION\n        stats['storage_protocol'] = 'VRM'\n        stats['vendor_name'] = self.VENDOR\n\n        backend = self.configuration.get('volume_backend_name')\n        if backend is None:\n            stats['volume_backend_name'] = self.BACKEND\n        else:\n            stats['volume_backend_name'] = self.configuration.get('volume_backend_name')\n\n        #LOG.info(_(\"[CINDER-BRM] get volume stats finished:(%d)--%s\"), self.left_periodrate, stats)\n\n        return stats\n\n    def _try_get_volume_stats(self, refresh=False):\n        '''\n        _try_get_volume_stats\n\n        :param refresh:If 'refresh' is True, run the update first.\n        :return:Return the current state of the volume service.\n        '''\n#        LOG.info(_(\"[BRM-DRIVER] start _try_get_volume_stats() \"))\n        if refresh:\n            self.left_periodrate -= 1\n            if self.left_periodrate <= 0:\n                self._refresh_storage_info(refresh)\n\n        free_capacity_gb = 0\n        used_capacity_gb = 0\n        total_capacity_gb = 0\n\n\n        stats = self._build_volume_stats()\n        ds_meta = {}\n        ds_names = [ds['name'] for ds in self.SHARED_DATASTORES]\n        for pool in self.pool_list:\n            if pool not in ds_names:\n                continue\n            new_pool = {}\n            ds_meta['ds_name'] = pool\n            datastore = self._choose_datastore(ds_meta)\n            if datastore.get('storageType') == 'advanceSan' and datastore.get('version') is not None:\n                new_pool.update(dict(consistencygroup_support=True))\n            if 'NORMAL' != datastore['status']:\n                new_pool.update(dict(\n                    pool_name=pool,\n                    free_capacity_gb=0,\n                    reserved_percentage=self.reserved_percentage,\n                    total_capacity_gb=0,\n                    provisioned_capacity_gb=0,\n                    max_over_subscription_ratio=self.over_ratio,\n                    affine_rate=1\n                ))\n                stats[\"pools\"].append(new_pool)\n                continue\n                \n            if self.current_list is not None and pool in self.current_list:\n                new_pool.update(dict(\n                    pool_name=pool,\n                    free_capacity_gb=datastore['freeSizeGB'],\n                    reserved_percentage=self.reserved_percentage,\n                    total_capacity_gb=datastore['capacityGB'],\n                    provisioned_capacity_gb=datastore['usedSizeGB'],\n                    max_over_subscription_ratio=self.over_ratio,\n                    affine_rate=self.affine_rate\n                ))\n            else:\n                new_pool.update(dict(\n                    pool_name=pool,\n                    free_capacity_gb=datastore['freeSizeGB'],\n                    reserved_percentage=self.reserved_percentage,\n                    total_capacity_gb=datastore['capacityGB'],\n                    provisioned_capacity_gb=datastore['usedSizeGB'],\n                    max_over_subscription_ratio=self.over_ratio,\n                    affine_rate=1\n                ))\n            if self.thin_provision is True:\n                new_pool.update(dict(\n                    thin_provisioning_support=True,\n                    thick_provisioning_support=False\n                ))\n            else:\n                new_pool.update(dict(\n                    thin_provisioning_support=False,\n                    thick_provisioning_support=True\n                ))\n            tier_size = datastore.get('tierSize', None)\n            type_v3 = []\n            if tier_size and len(tier_size) >= 3:\n                if tier_size[0] > 0:\n                    type_v3.append('ssd')\n                if tier_size[1] > 0:\n                    type_v3.append('sas')\n                if tier_size[2] > 0:\n                    type_v3.append('nl_sas')\n            if len(type_v3) > 0:\n                type_v3_str = ';'.join(type_v3)\n                LOG.info(_(\"[CINDER-BRM] type of v3 is %s\"), type_v3_str)\n                new_pool.update(dict(type=type_v3_str))\n            stats[\"pools\"].append(new_pool)\n\n        #LOG.info(_(\"[CINDER-BRM] (%d)--%s\"), self.left_periodrate, stats)\n\n        return stats\n\n    def get_volume_stats(self, refresh=False):\n        '''\n        get_volume_stats\n\n        :param refresh:If 'refresh' is True, run the update first.\n        :return:Return the current state of the volume service.\n        '''\n#        LOG.info(_(\"[BRM-DRIVER] start get_volume_stats() \"))\n        try:\n            stats = self._try_get_volume_stats(refresh)\n        except Exception as ex:\n            LOG.info(_(\"[CINDER-BRM] get volume stats Exception (%s)\"), ex)\n            stats = self._build_volume_stats()\n        return stats\n\n\n    def check_and_modify_thin(self, ds_urn, thin):\n        '''\n        [ DSWARE] /[LOCAL, SAN, LUN]\n        :param ds_urn:\n        :param thin:\n        :return:\n        '''\n        LOG.info(_(\"[CINDER-BRM] start check_and_modify_thin (%s)\"), ds_urn)\n        for datastore in self.SHARED_DATASTORES:\n            #LOG.info(_(\"[CINDER-BRM] ds_urn (%s)\"), ds_urn)\n            LOG.info(_(\"[CINDER-BRM] datastore (%s)\"), datastore['urn'])\n            if datastore['urn'] == ds_urn:\n                ds_type = str(datastore['storageType']).upper()\n                LOG.info(_(\"[CINDER-BRM] ds_type (%s)\"), ds_type)\n                if ds_type in ['LOCAL', 'SAN', 'LUN']:\n                    LOG.info(_(\"[CINDER-BRM] return False (%s)\"), ds_urn)\n                    return False\n                if ds_type in [ 'DSWARE']:\n                    LOG.info(_(\"[CINDER-BRM] return True (%s)\"), ds_urn)\n                    return True\n        return thin\n\n\n    def check_thin(self, datastore, thin):\n        '''\n        [ DSWARE] /[LOCAL, SAN, LUN]\n        :param ds_urn:\n        :param thin:\n        :return:\n        '''\n#        LOG.info(_(\"[CINDER-BRM] start check_thin (%s)\"), datastore)\n        ds_type = str(datastore['storageType']).upper()\n        #LOG.info(_(\"[CINDER-BRM] ds_type (%s)\"), ds_type)\n        if ds_type in ['LOCAL', 'SAN', 'LUN']:\n#            LOG.info(_(\"[CINDER-BRM] return False (%s)\"), datastore)\n            return False\n        if ds_type in ['DSWARE']:\n#            LOG.info(_(\"[CINDER-BRM] return True (%s)\"), datastore)\n            return True\n        return thin\n\n    def _check_and_choice_datastore(self, ds_meta):\n        '''\n        _check_and_choice_datastore\n\n        :param ds_meta:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start _check_and_choice_datastore() \"))\n        datastoreUrn = ds_meta['datastoreUrn']\n        hypervisorIp = ds_meta['hypervisorIp']\n        quantityGB = ds_meta['quantityGB']\n        storageType = ds_meta['storageType']\n        isThin = ds_meta['isThin']\n        hypervisorUrn = None\n        if hypervisorIp:\n            for host in self.SHARED_HOSTS:\n                if host['ip'].strip() == hypervisorIp.strip():\n                    hypervisorUrn = host['urn']\n                    break\n            if hypervisorUrn is None:\n                LOG.info(_(\"[CINDER-BRM] can not found hypervisorip=%s\"), hypervisorIp)\n                raise exception.HostNotFound(host=hypervisorIp)\n\n        if datastoreUrn:\n            for datastore in self.SHARED_DATASTORES:\n                if datastore['urn'] == datastoreUrn:\n                    this_storageType = datastore['storageType']\n                    this_isThin = datastore['isThin']\n                    this_freeSizeGB = int(datastore['freeSizeGB'])\n                    this_hosts = datastore['hosts']\n\n                    if this_storageType.lower() == storageType.lower() and str(this_isThin).lower() == str(\n                            isThin).lower() and this_freeSizeGB > quantityGB:\n                        if hypervisorUrn is None:\n                            return datastore['urn']\n                        elif hypervisorUrn in this_hosts:\n                            return datastore['urn']\n\n                    LOG.info(_(\"[CINDER-BRM] datastore=%s found,but not satisfied with [%s,%s,%d,%s]\") % (\n                        datastoreUrn, storageType, str(isThin), quantityGB, hypervisorIp))\n                    raise driver_exception.NotFound()\n            raise driver_exception.NotFound()\n        ds_hosts = None\n        ds_urn = None\n        random.shuffle(self.SHARED_DATASTORES)\n        for datastore in self.SHARED_DATASTORES:\n\n            this_isThin = datastore['isThin']\n            this_freeSizeGB = int(datastore['freeSizeGB'])\n            ds_hosts = datastore['hosts']\n            if this_freeSizeGB < quantityGB:\n                continue\n            if isThin is None:\n                ds_urn = datastore['urn']\n                break\n\n            elif str(this_isThin).lower() == str(isThin).lower():\n                ds_urn = datastore['urn']\n                break\n\n        if ds_urn is None:\n            raise driver_exception.NotFound()\n        random.shuffle(ds_hosts)\n        host_urn = ds_hosts[0]\n        return ds_urn, host_urn\n\n\n    def _choose_datastore(self, ds_meta):\n        '''\n        _check_and_choice_datastore\n\n        :param ds_meta:\n        :return:\n        '''\n        #LOG.info(_(\"[BRM-DRIVER] start _choose_datastore() \"))\n        for datastore in self.SHARED_DATASTORES:\n            if ds_meta['ds_name'] == datastore['name']:\n                return datastore\n\n        raise driver_exception.NotFound()\n\n    def _vrm_pack_provider_location(self, volume_body):\n        '''\n        _vrm_pack_provider_location\n\n        :param volume:\n        :param volume_body:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start _vrm_pack_provider_location() \"))\n        fc_ip = FC_DRIVER_CONF.fc_ip\n\n        provider_location = \"\"\n        provider_location += ('addr=' + fc_ip + ':' + str(CONF.vrm_port) + ',')\n        provider_location += ('uri=' + volume_body.get('uri') + ',')\n        provider_location += ('urn=' + volume_body.get('urn') + ',')\n        provider_location += ('datastoreUrn=' + volume_body.get('datastoreUrn') + ',')\n        provider_location += ('isThin=' + str(volume_body.get('isThin')) + ',')\n        provider_location += ('storageType=' + volume_body.get('storageType') + ',')\n        provider_location += ('type=' + volume_body.get('type'))\n\n        return provider_location\n\n    def _vrm_unpack_provider_location(self, provider_location, key=None):\n        '''\n        _vrm_unpack_provider_location\n\n        :param provider_location:\n        :param key:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start _vrm_unpack_provider_location() \"))\n        kvalue = None\n        kvs = {}\n        if type(provider_location) is not type(None) and len(provider_location) > 0:\n            items = provider_location.split(',')\n            for item in items:\n                (ki, eqi, vi) = item.partition('=')\n                kvs[ki] = vi\n                if key and key == ki:\n                    kvalue = vi\n\n        return kvalue, kvs\n\n\n    def _vrm_get_volume_meta(self, id):\n        '''\n        _vrm_create_volume\n\n        :param id:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start _vrm_get_volume_meta() \"))\n        model_update = {}\n        metadata = {}\n\n        if id:\n            volume_body = self.volume_proxy.query_volume(id=id)\n            if type(volume_body) is not type(None):\n                model_update['provider_location'] = self._vrm_pack_provider_location(volume_body)\n                sd_sn = volume_body.get('storageSN', None)\n                lun_id = volume_body.get('lunId', None)\n                replication_driver_data = {}\n                if lun_id:\n                    LOG.info(_(\"[BRM-DRIVER] lun id is %s\"), lun_id)\n                    replication_driver_data.update({'lun_id': lun_id})\n                if sd_sn:\n                    LOG.info(_(\"[BRM-DRIVER] sn is %s\"), sd_sn)\n                    replication_driver_data.update({'sn': sd_sn})\n                if len(replication_driver_data) > 0:\n                    LOG.info(_(\"[BRM-DRIVER] replication_driver_data is %s\"), sd_sn)\n                    model_update['replication_driver_data'] = json.dumps(replication_driver_data)\n                urn = volume_body['urn']\n                uri = volume_body['uri']\n                storage_type = volume_body.get('storageType')\n                metadata.update({'urn': urn})\n                metadata.update({'uri': uri})\n                if storage_type is not None:\n                    LOG.info(_(\"[BRM-DRIVER] the storage type of volume is %s\"), storage_type)\n                    storage_type = 'FC_' + storage_type\n                    metadata.update({'StorageType': storage_type})\n                volInfoUrl = volume_body.get('volInfoUrl', None)\n                if volInfoUrl:\n                    metadata.update({'quantityGB': volume_body['quantityGB']})\n                    metadata.update({'volInfoUrl': volInfoUrl})\n                pvscsi_support = volume_body.get('pvscsiSupport')\n                if pvscsi_support == 1:\n                    metadata.update({'hw:passthrough': 'true'})\n\n        return model_update, metadata\n\n    def create_consistencygroup(self, context, group):\n        LOG.info(_(\"[BRM-DRIVER] create consistencygroup. \"))\n        return\n\n    def update_consistencygroup(self, context, group,\n                                add_volumes=None, remove_volumes=None):\n        \"\"\"Updates a consistency group.\"\"\"\n        return None, None, None\n\n    def _vrm_delete_volume(self, volume):\n        '''\n        _vrm_delete_volume\n\n        :param volume:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start _vrm_delete_volume() \"))\n        vol_metas = volume['volume_metadata']\n        if type(vol_metas) is not type(None):\n            for meta in vol_metas:\n                LOG.info(_(\"[BRM-DRIVER] volume_metadata is [%s:%s] \"), meta.key, meta.value)\n        provider_location = volume['provider_location']\n        if provider_location is None or provider_location == '':\n            LOG.error(_(\"[BRM-DRIVER]provider_location is null \"))\n            vol_meta = volume.get('volume_metadata')\n            vol_meta_dict = metadata_to_dict(vol_meta)\n            vol_uri = vol_meta_dict.get('uri')\n            if vol_uri is not None:\n                fc_vol_urn = vol_meta_dict.get('urn')\n                fc_vol_id = fc_vol_urn[fc_vol_urn.rfind(':') + 1:]\n                self._check_replications(fc_vol_id)\n                self._vrm_delete_volume_vm(volume_urn=fc_vol_urn, volume_uri=vol_uri)\n            return\n        fc_volume_urn, items = \\\n            self._vrm_unpack_provider_location(volume['provider_location'], 'urn')\n        fc_vol_id = fc_volume_urn[fc_volume_urn.rfind(':') + 1:]\n        self._check_replications(fc_vol_id)\n        volume_uri, items = \\\n            self._vrm_unpack_provider_location(volume['provider_location'], 'uri')\n        self._vrm_delete_volume_vm(volume_urn=fc_volume_urn, volume_uri=volume_uri)\n\n    def _vrm_delete_volume_vm(self, volume_urn, volume_uri):\n        '''\n\n        :param volume_urn:\n        :param volume_uri:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start _vrm_delete_volume_vm() \"))\n        kwargs = {}\n        kwargs['volume_urn'] = volume_urn\n        vm_body = self.vm_proxy.query_vm_volume(**kwargs)\n        if vm_body is None:\n            LOG.info(_(\"[BRM-DRIVER]vm attached is zero \"))\n            self.volume_proxy.delete_volume(volume_uri=volume_uri)\n        else:\n            vm_uri = vm_body.get('uri')\n            vm_status = vm_body.get('status')\n            kwargs['vm_id'] = vm_uri[-10:]\n            kwargs['mode'] = 'force'\n            if vm_status not in ['stopped']:\n                self.vm_proxy.stop_vm(**kwargs)\n            self.vm_proxy.detach_vol_from_vm(**kwargs)\n            self.volume_proxy.delete_volume(volume_uri=volume_uri)\n\n    def _check_quick_start(self, volume_id):\n        '''\n\n        :param volume_id:\n        :return:\n        '''\n        admin_context = cinder_context.get_admin_context()\n        admin_metadata = self.db.volume_admin_metadata_get(admin_context, volume_id)\n        quick_start = admin_metadata.get('quick_start')\n        if quick_start is not None and str(quick_start).lower() == 'true':\n            return True\n        else:\n            return False\n\n    def _check_replications(self, fc_vol_id):\n\n        if fc_vol_id:\n            try:\n                LOG.info(_(\"[BRM-DRIVER] check replications : volume id in fc is %s\"), fc_vol_id)\n                body = self.volume_proxy.query_volume_replications(volume_id=fc_vol_id)\n                LOG.info(_(\"[BRM-DRIVER] check replications : replications is %s\"), body.get('replications'))\n            except Exception as ex:\n                LOG.error(_(\"[BRM-DRIVER] get volume replications expection\"))\n                LOG.exception(ex)\n                return\n            if body.get('replications'):\n                LOG.error(_(\"[BRM-DRIVER] The volume %s to deleted has replications, \"\n                            \"could not be deleted ,extended or migrated.\"), fc_vol_id)\n                raise driver_exception.FusionComputeDriverException()\n\n    def delete_consistencygroup(self, context, group):\n\n        LOG.info(_(\"[BRM-DRIVER] enter delete_consistencygroup() \"))\n        model_update = {}\n        model_update['status'] = 'deleted'\n        volumes = self.db.volume_get_all_by_group(context, group['id'])\n        if volumes:\n            for volume_ref in volumes:\n                try:\n                    self.delete_volume(volume_ref)\n                    volume_ref['status'] = 'deleted'\n                except Exception as ex:\n                    LOG.error(_(\"[BRM-DRIVER] delete_consistencygroup failed. \"))\n                    LOG.exception(ex)\n                    volume_ref['status'] = 'error_deleting'\n                    model_update['status'] = 'error_deleting'\n\n        LOG.info(_(\"[BRM-DRIVER] end delete_consistencygroup() \"))\n\n        return model_update, volumes\n\n    def create_volume(self, volume):\n        '''\n        create_volume\n        {\n            \"name\":string,\n            quantityGB:integer,\n            datastoreUrn:string,\n            \"isThin\":boolean,\n            \"type\":string,\n            indepDisk:boolean,\n            persistentDisk:boolean\n        }\n\n        :param volume:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start create_volume() \"))\n\n        vol_meta = volume.get('volume_metadata')\n        vol_meta_dict = metadata_to_dict(vol_meta)\n        linked_clone = vol_meta_dict.get('linked_clone')\n        if linked_clone is None:\n            linked_clone = False\n        elif str(linked_clone).lower() == 'true':\n            linked_clone = True\n        else:\n            linked_clone = False\n\n        # check quick start\n        if not linked_clone and self._check_quick_start(volume.get('id')):\n            linked_clone = True\n\n        if linked_clone:\n            LOG.info(_(\"[BRM-DRIVER] linked_clone volume. do nothing. \"))\n            return\n\n        args_dict = {}\n\n        args_dict['name'] = volume['name']\n        args_dict['size'] = int(volume['size'])\n        args_dict['uuid'] = volume['id']\n        shareable = volume.get('shareable')\n        if shareable and True == shareable:\n            LOG.info(_(\"[CINDER-VRM] shareable\"))\n            args_dict['type'] = 'share'\n\n        else:\n            args_dict['type'] = 'normal'\n\n\n        is_thin = FC_DRIVER_CONF.vrm_is_thin\n\n        ds_meta = {}\n        try:\n            ds_meta['ds_name'] = volume.get('host').split('#')[1]\n        except exception.CinderException as ex:\n            LOG.info(\n                _(\"[CINDER-BRM] host format exception, host is %s \") % volume.get('host'))\n            raise ex\n\n        datastore = self._choose_datastore(ds_meta)\n        if datastore:\n            LOG.info(_(\"[CINDER-VRM] datastore [%s],\"), datastore)\n            if str(datastore.get('storageType')).upper() in ['LUN']:\n                LOG.info(_(\"[CINDER-VRM] rdm disk [%s]\"), volume['id'])\n                args_dict['size'] = int(datastore.get('capacityGB'))\n                args_dict['independent'] = True\n\n            args_dict['ds_urn'] = datastore.get('urn')\n            is_thin = self.check_thin(datastore, is_thin)\n            args_dict['is_thin'] = is_thin\n\n            context = cinder_context.get_admin_context()\n\n            volume_type_id = volume.get('volume_type_id')\n            if volume_type_id:\n                LOG.info(_(\"[BRM-DRIVER] query volume type id is %s\"), volume_type_id)\n                volume_type_extra_specs = self.db.volume_type_extra_specs_get(context, volume_type_id)\n                LOG.info(_(\"[BRM-DRIVER] volume_type_extra_specs is %s\"), str(volume_type_extra_specs))\n                if volume_type_extra_specs and str(volume_type_extra_specs.get('hw:passthrough')).lower() == 'true':\n                    args_dict['support_pvscsi'] = 1  # The volume created support direct io from vm to storage device.\n\t\t\t\t\t\n            support_pvscsi = vol_meta_dict.get('hw:passthrough')\n            if support_pvscsi and str(support_pvscsi).lower() == 'true':\n                args_dict['support_pvscsi'] = 1  # The volume created support direct io from vm to storage device.\n\n            body = self.volume_proxy.create_volume(**args_dict)\n\n            temp_str = body.get('urn')\n            fc_vol_id = temp_str[temp_str.rfind(':') + 1:]\n            LOG.info(_(\"[CINDER-VRM] fc_vol_id [%s] \") % fc_vol_id)\n            model_update, metadata = self._vrm_get_volume_meta(id=fc_vol_id)\n            self.db.volume_metadata_update(context, volume['id'], metadata, False)\n            \n            self.volume_proxy.wait_task(**body)\n\n            try:\n                model_update, metadata = self._vrm_get_volume_meta(id=fc_vol_id)\n            except Exception as ex:\n                LOG.error(\n                    _(\"[CINDER-VRM] get volume information failed after create volume.volume id is [%s] \") % fc_vol_id)\n                LOG.exception(ex)\n            return model_update\n        else:\n            raise exception.VolumeDriverException\n\n\n    def _register_volume(self, volume):\n        '''\n        _register_volume\n\n        :param volume:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start _register_volume() \"))\n        volume_metadata = volume['volume_metadata']\n        volume_metadata_dict = metadata_to_dict(volume_metadata)\n        model_update = {}\n\n        volume_urn = volume_metadata_dict.get('volumeUrn')\n        volume_id = volume_urn.split(':')[-1]\n        volume_body = self.volume_proxy.get_volume(vol_id=volume_id)\n        model_update['provider_location'] = self._vrm_pack_provider_location(volume_body)\n        model_update['volume_urn'] = volume_body['urn']\n        return model_update\n\n    def delete_volume(self, volume):\n        '''\n        delete_volume\n\n        :param volume:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start delete_volume() \"))\n        self._vrm_delete_volume(volume)\n\n    def create_export(self, context, volume):\n        '''\n        create_export\n\n        :param context:\n        :param volume:\n        :return:\n        '''\n        pass\n\n\n    def remove_export(self, context, volume):\n        '''\n        remove_export\n\n        :param context:\n        :param volume:\n        :return:\n        '''\n        pass\n\n\n    def ensure_export(self, context, volume):\n        '''\n        ensure_export\n\n        :param context:\n        :param volume:\n        :return:\n        '''\n        pass\n\n\n    def check_for_export(self, context, volume_id):\n        '''\n        check_for_export\n\n        :param context:\n        :param volume_id:\n        :return:\n        '''\n        pass\n\n\n    def create_snapshot(self, snapshot):\n        '''\n        create_snapshot\n\n        :param snapshot:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start create_snapshot() \"))\n        model_update = {}\n        volume = self.db.volume_get(self.context, snapshot['volume_id'])\n        vol_meta = volume['volume_metadata']\n        vol_meta_dict = metadata_to_dict(vol_meta)\n        vol_urn = vol_meta_dict.get('urn')\n        if type(vol_urn) is type(None):\n            LOG.error(_(\"vol_urn  is null.\"))\n\n        def volume_uri_to_number(uri):\n            hi, si, ti = uri.rpartition('/')\n            return ti\n\n        snapshot_id = snapshot['id']\n        snapshot_uuid = str(snapshot_id).replace('-', '')\n        body = self.volume_snapshot_proxy.create_volumesnapshot(snapshot_uuid=snapshot_uuid, vol_urn=vol_urn)\n\n        if body['urn'] is None:\n            LOG.error(_(\"Trying to create snapshot failed, volume id is: %s\"), snapshot['volume_id'])\n            raise driver_exception.FusionComputeDriverException()\n\n        return model_update\n\n    def delete_snapshot(self, snapshot):\n        '''\n        delete_snapshot\n\n        :param snapshot:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start delete_snapshot() \"))\n        model_update = {}\n        snapshot_id = snapshot['id']\n        snapshot_uuid = str(snapshot_id).replace('-', '')\n        body = self.volume_snapshot_proxy.query_volumesnapshot(uuid=snapshot_uuid)\n        if body == None:\n            return model_update\n        self.volume_snapshot_proxy.delete_volumesnapshot(id=snapshot_uuid)\n\n        return model_update\n\n    def create_volume_from_snapshot(self, volume, snapshot):\n        '''\n        create_volume_from_snapshot\n\n        :param volume:\n        :param snapshot:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start create_volume_from_snapshot()\"))\n        args_dict = {}\n\n        vol_meta = volume.get('volume_metadata')\n        vol_meta_dict = metadata_to_dict(vol_meta)\n        linked_clone = vol_meta_dict.get('linked_clone')\n        if linked_clone is None:\n            linked_clone = False\n        elif str(linked_clone).lower() == 'true':\n            linked_clone = True\n        else:\n            linked_clone = False\n        if linked_clone == True:\n            LOG.warn(_(\"[BRM-DRIVER] linked_clone volume not support!!\"))\n            raise exception.CinderException\n\n   \n        full_clone = vol_meta_dict.get('full_clone')\n        if full_clone is None:\n            full_clone = '1'\n        elif str(full_clone) == '0':\n            full_clone = '0'\n        else:\n            full_clone = '1'\n        args_dict['full_clone'] = full_clone\n\n        model_update = {}\n        snapshot_id = snapshot['id']\n        # TODO\n        os_vol_id = volume['id']\n        shareable = volume.get('shareable')\n        if shareable and True == shareable:\n            LOG.info(_(\"[CINDER-VRM] shareable\"))\n            voltype = 'share'\n        else:\n            voltype = 'normal'\n        snapshot_uuid = str(snapshot_id).replace('-', '')\n\n        volume_size = int(volume.get('size'))\n        snapshot_size = int(snapshot.get('volume_size'))\n\n        if volume_size != snapshot_size:\n            args_dict['volume_size'] = volume_size\n\n\n        args_dict['snapshot_uuid'] = snapshot_uuid\n        args_dict['volume_name'] = volume['name']\n        args_dict['volume_uuid'] = os_vol_id\n        args_dict['type'] = voltype\n\n        body = self.volume_snapshot_proxy.create_volume_from_snapshot(**args_dict)\n        LOG.info(_(json.dumps(body)))\n        vol_urn = body['urn']\n        fc_vol_id = vol_urn.split(':')[-1]\n\n        model_update, metadata = self._vrm_get_volume_meta(id=fc_vol_id)\n        context = cinder_context.get_admin_context()\n        self.db.volume_metadata_update(context, os_vol_id, metadata, False)\n\n        return model_update\n\n\n    def create_cloned_volume(self, volume, src_volume):\n        '''\n        create_cloned_volume\n\n        :param volume:\n        :param src_volume:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start create_cloned_volume()\"))\n        name = ''\n        uuid = ''\n        name = volume['name']\n        model_update = {}\n        volume['is_thin'] = True\n        LOG.info(_(\"[BRM-DRIVER] start create_cloned_volume()\"))\n        model_update = self.create_volume(volume)\n        src_volume_name = src_volume['name']\n        dest_volume_size = volume['size']\n        src_volume_size = src_volume['size']\n        if dest_volume_size != src_volume_size:\n            raise exception.InvalidParameterValue(err=_('valid volume size'))\n\n        dest_volume_uri, items = self._vrm_unpack_provider_location(model_update['provider_location'], 'uri')\n        dest_volume_urn, items = self._vrm_unpack_provider_location(model_update['provider_location'], 'urn')\n        src_vol_meta = src_volume.get('volume_metadata')\n        src_vol_meta_dict = metadata_to_dict(src_vol_meta)\n        src_volume_uri = src_vol_meta_dict.get('uri')\n\n        LOG.info(_(\"dest_volume_uri is %s, src_volume_uri is %s\") % (dest_volume_uri, src_volume_uri))\n\n        dest_volume_id = dest_volume_uri.split('/')[len(dest_volume_uri.split('/')) - 1]\n        src_volume_id = src_volume_uri.split('/')[len(src_volume_uri.split('/')) - 1]\n        LOG.info(_(\"src_volume_id is [%s] dest vol id [%s]\"), src_volume_id, dest_volume_id)\n\n        args_dict = {}\n        args_dict['src_volume_id'] = src_volume_id\n        args_dict['dest_volume_urn'] = dest_volume_urn\n\n        try:\n            self.volume_proxy.clone_volume(**args_dict)\n        except exception.CinderException as ex:\n            volume['provider_location'] = model_update['provider_location']\n            LOG.info(\n                _(\"[CINDER-BRM] clone_volume exception , delete (%s)\") % model_update['provider_location'])\n            self.delete_volume(volume)\n            raise ex\n        return model_update\n\n    def clone_image(self, volume, image_location, image_id, image_meta):\n        '''\n        clone_image\n\n        :param volume:\n        :param image_location:\n        :param image_id:\n        :param image_meta:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start clone_image [%s]\") % image_id)\n\n        properties = image_meta.get('properties', None)\n\n        # check quick start\n        if properties is not None:\n            quick_start = properties.get('quick_start', None)\n            if quick_start is not None and str(quick_start).lower() == 'true':\n                LOG.info(_(\"[BRM-DRIVER] image has quick start property\"))\n                # update quick_start in admin metadata\n                admin_context = cinder_context.get_admin_context()\n                self.db.volume_admin_metadata_update(admin_context, volume.get('id'), {'quick_start': 'True'}, False)\n                return None, False\n\n        if properties is None:\n            return None, False\n        elif 'template' != properties.get('__image_source_type', None):\n            LOG.info(_(\"[BRM-DRIVER] image_type is not template\"))\n            return None, False\n        else:\n            LOG.info(_(\"[BRM-DRIVER] image_type is template\"))\n\n        image_type = 'template'\n\n        context = cinder_context.get_admin_context()\n        args_dict = {}\n        vol_size = int(volume.get('size'))\n\n        self._check_image_os(image_id, image_meta, vol_size)\n\n        args_dict['image_id'] = image_id\n        os_vol_id = volume.get('id')\n        args_dict['volume_id'] = os_vol_id\n        args_dict['volume_size'] = vol_size\n\n\n        vol_meta = volume.get('volume_metadata')\n        vol_meta_dict = metadata_to_dict(vol_meta)\n        linked_clone = vol_meta_dict.get('linked_clone')\n        if linked_clone is None:\n            linked_clone = False\n        elif str(linked_clone).lower() == 'true':\n            linked_clone = True\n        else:\n            linked_clone = False\n\n        LOG.info(_('[BRM-DRIVER] image_meta [%s]' % image_meta))\n\n        hw_image_location = properties.get('__image_location', None)\n        if hw_image_location is None or hw_image_location == \"\":\n            msg = _('[BRM-DRIVER] hw_image_location is null')\n            LOG.error(msg)\n            raise exception.ImageUnacceptable(image_id=image_id,reason=msg)\n\n        args_dict['image_location'] = hw_image_location\n        LOG.info(_('[BRM-DRIVER] image_location is %s') % args_dict['image_location'])\n\n        args_dict['image_type'] = image_type\n\n        ds_meta = {}\n        try:\n            ds_meta['ds_name'] = volume.get('host').split('#')[1]\n        except exception.CinderException as ex:\n            LOG.info(\n                _(\"[CINDER-BRM] host format exception, host is %s \") % volume.get('host'))\n            raise ex\n\n        datastore = self._choose_datastore(ds_meta)\n        if not datastore:\n            LOG.info(_(\"[CINDER-VRM] datastore [%s],\"), datastore)\n            raise exception.InvalidParameterValue(err=_('invalid datastore'))\n\n        args_dict['ds_urn'] = datastore.get('urn')\n        is_thin = FC_DRIVER_CONF.vrm_is_thin\n        is_thin = self.check_thin(datastore, is_thin)\n        args_dict['is_thin'] = is_thin\n\n        args_dict['cluster_urn'] = self._get_cluster_by_dsurn(datastore.get('urn'))\n        LOG.info(_(\"[BRM-DRIVER] cluster_urn [%s]\") % args_dict['cluster_urn'])\n\n        if args_dict.get('volume_sequence_num') is None:\n            args_dict['volume_sequence_num'] = 1\n\n        LOG.info(_(\"[BRM-DRIVER] %s image_type is  template \") % image_id)\n        if linked_clone:\n            urn = self.vm_proxy.create_linkclone_from_template(**args_dict)\n        else:\n            urn = self.vm_proxy.create_volume_from_template(**args_dict)\n\n        temp_str = str(urn)\n        fc_vol_id = temp_str[temp_str.rfind(':') + 1:]\n\n        share = volume.get('shareable')\n        LOG.info('[BRM-DRIVER] shareable [%s]', share)\n        if str(share).lower() == 'true':\n            try:\n                self.volume_proxy.modify_volume(volume_id=fc_vol_id, type='share')\n            except Exception as ex:\n                LOG.error(_(\"modify volume to share is failed \"))\n                self.delete_volume(volume)\n                raise ex\n\n        model_update, metadata = self._vrm_get_volume_meta(fc_vol_id)\n        self.db.volume_metadata_update(context, os_vol_id, metadata, False)\n        return model_update, True\n\n\n    def copy_image_to_volume(self, context, volume, image_service, image_id):\n\n        '''\n        clone_image\n\n        :param volume:\n        :param image_location:\n        :param image_id:\n        :param image_meta:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start copy_image_to_volume [%s]\") % image_id)\n        image_meta = image_service.show(context, image_id)\n        args_dict = {}\n        vol_size = int(volume.get('size'))\n        self._check_image_os(image_id, image_meta, vol_size)\n\n        args_dict['image_id'] = image_id\n\n        min_disk = image_meta.get('min_disk')\n        if self._check_quick_start(volume.get('id')):\n            args_dict['quick_start'] = True\n            if min_disk:\n                args_dict['image_size'] = int(min_disk)\n            else:\n                msg = _('[BRM-DRIVER] image min_disk is none when create from quick start image')\n                LOG.error(msg)\n                raise exception.VolumeDriverException(message=msg)\n\n        os_vol_id = volume.get('id')\n        args_dict['volume_id'] = os_vol_id\n        args_dict['volume_size'] = vol_size\n        args_dict['is_thin'] = FC_DRIVER_CONF.vrm_is_thin\n\n        vol_meta = volume.get('volume_metadata')\n        vol_meta_dict = metadata_to_dict(vol_meta)\n        linked_clone = vol_meta_dict.get('linked_clone')\n        args_dict['volume_urn'] = vol_meta_dict.get('urn')\n        if linked_clone is None:\n            linked_clone = False\n        elif str(linked_clone).lower() == 'true':\n            linked_clone = True\n        else:\n            linked_clone = False\n\n        # check quick start\n        if not linked_clone and self._check_quick_start(volume.get('id')):\n            linked_clone = True\n\n        LOG.info(_('[BRM-DRIVER] image_meta [%s]' % image_meta))\n\n        properties = image_meta.get('properties', None)\n        if properties is None or properties == \"\":\n            image_type = 'glance'\n        else:\n            args_dict['volume_sequence_num'] = properties.get('__sequence_num')\n            image_type = properties.get('__image_source_type', None)\n            types = ['template', 'nfs', 'uds', 'glance']\n\n            if image_type is not None and image_type not in types:\n                msg = _('[BRM-DRIVER]  image type is not support ')\n                LOG.error(msg)\n                raise exception.ImageUnacceptable(image_id=image_id, reason=msg)\n\n            if image_type is None:\n                image_type = 'glance'\n\n            if image_type != 'glance':\n                hw_image_location = properties.get('__image_location', None)\n                if hw_image_location is None or hw_image_location == \"\":\n                    msg = _('[BRM-DRIVER] hw_image_location is null')\n                    LOG.error(msg)\n                    raise exception.ImageUnacceptable(image_id=image_id, reason=msg)\n\n                args_dict['image_location'] = hw_image_location\n                LOG.info(_('[BRM-DRIVER] image_location is %s') % args_dict['image_location'])\n\n        args_dict['image_type'] = image_type\n        ds_meta = {}\n        try:\n            ds_meta['ds_name'] = volume.get('host').split('#')[1]\n        except exception.CinderException as ex:\n            LOG.info(\n                _(\"[CINDER-BRM] host format exception, host is %s \") % volume.get('host'))\n            raise ex\n\n        datastore = self._choose_datastore(ds_meta)\n        if not datastore:\n            LOG.info(_(\"[CINDER-VRM] datastore [%s],\"), datastore)\n            raise exception.InvalidParameterValue(err=_('found no datastore'))\n\n        args_dict['ds_urn'] = datastore.get('urn')\n        is_thin = FC_DRIVER_CONF.vrm_is_thin\n        is_thin = self.check_thin(datastore, is_thin)\n        if linked_clone:\n            if not is_thin:\n                LOG.error(_(\"[CINDER-VRM] datastore does support linedclone\"))\n                raise exception.InvalidParameterValue(err=_('datastore does support linedclone'))\n        args_dict['is_thin'] = is_thin\n\n        args_dict['cluster_urn'] = self._get_cluster_by_dsurn(datastore.get('urn'))\n        LOG.info(_(\"[BRM-DRIVER] self.cluster_urn [%s]\") % args_dict['cluster_urn'])\n\n        args_dict['auth_token'] = context.auth_token\n        LOG.info(_(\"[BRM-DRIVER] %s image_type is %s\") % (image_id, image_type))\n\n        if args_dict.get('volume_sequence_num') is None:\n            args_dict['volume_sequence_num'] = 1\n\n        if linked_clone:\n            urn = self.vm_proxy.create_linkClone_from_extend(**args_dict)\n        else:\n            try:\n                temp_str = str(vol_meta_dict.get('urn'))\n                fc_vol_id = temp_str[temp_str.rfind(':') + 1:]\n                self.volume_proxy.modify_volume(volume_id=fc_vol_id, type='normal')\n            except Exception as ex:\n                LOG.error(_(\"modify volume to normal is failed \"))\n                self.delete_volume(volume)\n                raise ex\n            urn = self.vm_proxy.create_volume_from_extend(**args_dict)\n\n        temp_str = str(urn)\n        fc_vol_id = temp_str[temp_str.rfind(':') + 1:]\n        model_update, metadata = self._vrm_get_volume_meta(fc_vol_id)\n\n        share = volume.get('shareable')\n        LOG.info('[BRM-DRIVER] shareable [%s]', share)\n        if str(share).lower() == 'true':\n            try:\n                self.volume_proxy.modify_volume(volume_id=fc_vol_id, type='share')\n            except Exception as ex:\n                LOG.error(_(\"modify volume to share is failed \"))\n                self.delete_volume(volume)\n                raise ex\n\n        self.db.volume_metadata_update(context, os_vol_id, metadata, False)\n\n    def _check_image_os(self, image_id, image_meta, vol_size):\n        min_disk = image_meta.get('min_disk')\n        if min_disk:\n            min_disk = int(min_disk)\n            if min_disk < 4 and vol_size != min_disk:\n                prop = image_meta.get('properties')\n                if prop:\n                    os_type = prop.get('__os_type')\n                    os_version = prop.get('__os_version')\n                    if os_type and os_version:\n                        if os_type == 'Windows' and \\\n                                (os_version.startswith('Windows XP') or os_version.startswith('Windows Server 2003')):\n                            msg = _(\n                                \"[BRM-DRIVER] volume  size must equal image min disk size while image min disk size is smaller than 4G and os is Windows XP or Windows Server 2003\")\n                            LOG.error(msg)\n                            raise exception.ImageUnacceptable(image_id=image_id, reason=msg)\n\n    def _generate_image_metadata(self, min_disk, location, volume_sequence_num, os_option, instance):\n        \"\"\"\n\n        :param name: image name\n        :param location: image location\n        :param os_option: os type and version\n        :param instance:\n        :return:\n        \"\"\"\n        if volume_sequence_num is None:\n            LOG.info(_(\"volume_sequence_num is None\"))\n            volume_sequence_num = 1\n        metadata = {'__image_location': location or '',\n                    '__image_source_type': FC_DRIVER_CONF.export_image_type,\n                    '__sequence_num': volume_sequence_num}\n        if os_option is not None:\n            if os_option.get('__os_version') is not None:\n                metadata['__os_version'] = os_option.get('__os_version')\n\n            if os_option.get('__os_type') is not None:\n                metadata['__os_type'] = os_option.get('__os_type')\n\n        LOG.info(_(\"image metadata is: %s\"), json.dumps(metadata))\n        return {'properties': metadata, 'min_disk': min_disk}\n\n    def _generate_image_location(self, image_id, context):\n        \"\"\"\n        generate image location: '172.17.1.30:/image/base/uuid/uuid.ovf'\n        :param image_id:\n        :return:\n        \"\"\"\n        if FC_DRIVER_CONF.export_image_type == 'nfs':\n            fc_image_path = FC_DRIVER_CONF.fc_image_path\n            if fc_image_path:\n                format = 'xml' if FC_DRIVER_CONF.export_version == 'v1.2' else 'ovf'\n                return '%s/%s/%s.%s' % (fc_image_path, image_id, image_id, format)\n            else:\n                LOG.info(_(\"fc_image_path is null\"))\n        elif FC_DRIVER_CONF.export_image_type == 'uds':\n            if FC_DRIVER_CONF.uds_ip is not None and FC_DRIVER_CONF.uds_port is not None \\\n                    and FC_DRIVER_CONF.uds_bucket_name is not None:\n                    uds_bucket_name = FC_DRIVER_CONF.uds_bucket_name\n                    bucket_type = FC_DRIVER_CONF.uds_bucket_type\n\n            LOG.info(str(bucket_type))\n            LOG.info(context.tenant)\n\n            if bucket_type is not None:\n                if str(bucket_type) == 'wildcard':\n                    project_tmp_id = context.to_dict()['project_id']\n                    LOG.info(project_tmp_id)\n                    if project_tmp_id is None:\n                        LOG.error(_(\"project_id is none \"))\n                        raise exception.ParameterNotFound(param='project_id is none')\n                    else:\n                        uds_bucket_name += project_tmp_id\n\n                LOG.info(_(\"uds_ip: %s, uds_port: %s, uds_bucket_name: %s\"), FC_DRIVER_CONF.uds_ip,\n                     FC_DRIVER_CONF.uds_port, uds_bucket_name)\n            return '%s:%s:%s:%s' % (FC_DRIVER_CONF.uds_ip, FC_DRIVER_CONF.uds_port, uds_bucket_name, image_id)\n        else:\n            return None\n\n    def copy_volume_to_image(self, context, volume, image_service, image_meta):\n        '''\n        copy_volume_to_image\n\n        :param context:\n        :param volume:\n        :param image_service:\n        :param image_meta:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start copy_volume_to_image() \"))\n\n        fc_image_path = FC_DRIVER_CONF.fc_image_path\n        image_id = image_meta.get('id')\n        if '/' in str(image_id):\n            image_id = image_id.split('/')[-1]\n\n        metadata = {'__image_location':'',\n                    '__image_source_type': FC_DRIVER_CONF.export_image_type}\n        image_property = {'properties': metadata}\n        image_service.update(context, image_id, image_property, purge_props=False)\n\n        volume_id = volume.get('id')\n        vol_size = int(volume.get('size'))\n        vol_meta = volume.get('volume_metadata')\n        vol_meta_dict = metadata_to_dict(vol_meta)\n\n        vol_image_meta = None\n        try:\n            vol_image_meta = self.volume_api.get_volume_image_metadata(\n                context, volume)\n        except exception.CinderException as ex:\n            LOG.error(_('[BRM-DRIVER] get_volume_image_metadata is error'))\n\n        vol_image_meta_dic = None\n        if vol_image_meta:\n            vol_image_meta_dic = dict(vol_image_meta.iteritems())\n\n\n        args_dict = {}\n        args_dict['volume_id'] = volume_id\n        args_dict['volume_size'] = vol_size\n        args_dict['image_id'] = image_id\n        args_dict['image_url'] = fc_image_path\n        args_dict['project_id'] = context.to_dict()['project_id']\n\n        share = volume.get('shareable')\n        LOG.info('[BRM-DRIVER] shareable [%s]', share)\n        if str(share).lower() == 'false':\n            args_dict['shareable'] = 'normal'\n        else:\n            args_dict['shareable'] = 'share'\n\n        if vol_meta_dict.get('urn') is None:\n            msg = _('[BRM-DRIVER] urn is null')\n            LOG.error(msg)\n            raise exception.InvalidVolumeMetadata(reason=msg)\n\n        args_dict['volume_urn'] = str(vol_meta_dict.get('urn'))\n        LOG.info(_(\"volume_urn is %s\") % args_dict['volume_urn'])\n\n        if self.SHARED_HOSTS is None or len(self.SHARED_HOSTS) == 0:\n            msg = _('[BRM-DRIVER] SHARED_HOSTS is none')\n            LOG.error(msg)\n            raise exception.VolumeDriverException(message=msg)\n\n        fc_vol_id = args_dict['volume_urn'][args_dict['volume_urn'].rfind(':') + 1:]\n        volume_body = self.volume_proxy.query_volume(id=fc_vol_id)\n        source_ds_urn = volume_body['datastoreUrn']\n        LOG.info(_(\"[BRM-DRIVER] datastoreUrn  [%s]\") % source_ds_urn)\n        \n        args_dict['cluster_urn'] = self._get_cluster_by_dsurn(source_ds_urn)\n        LOG.info(_(\"[BRM-DRIVER] cluster_urn  [%s]\") % args_dict['cluster_urn'])\n\n        #args_dict['cluster_urn'] = self.cluster_urn\n        name = image_service.show(context, image_id).get('name')\n        args_dict['image_type'] = FC_DRIVER_CONF.export_image_type\n        args_dict['auth_token'] = context.auth_token\n\n        location = self._generate_image_location(image_id, context)\n        metadata = {'__image_location': location or '',\n                    '__image_source_type': FC_DRIVER_CONF.export_image_type}\n        image_property = {'properties': metadata}\n        image_service.update(context, image_id, image_property, purge_props=False)\n\n        try:\n            LOG.info(_('location %s  ') % location)\n            volume_sequence_num = self.vm_proxy.export_volume_to_image(**args_dict)\n        except Exception as ex:\n            LOG.info(_(\"[BRM-DRIVER] deletedelete image id:[%s]\") % image_id)\n            image_service.delete(context, image_id)\n            raise ex\n\n        metadata = self._generate_image_metadata(vol_size, location, volume_sequence_num, vol_image_meta_dic, None)\n        if 'glance' != args_dict.get('image_type'):\n            image_service.update(context, image_id, {}, data='/home/vhd/G1-1.vhd')\n        image_service.update(context, image_id, metadata, purge_props=False)\n        LOG.info(_('image %s  create success') % name)\n\n    def attach_volume(self, context, volume_id, instance_uuid,\n                      host_name_sanitized, mountpoint):\n        '''\n        attach_volume\n\n        :param context:\n        :param volume_id:\n        :param instance_uuid:\n        :param host_name_sanitized:\n        :param mountpoint:\n        :return:\n        '''\n        pass\n\n\n    def detach_volume(self, context, volume):\n        '''\n        detach_volume\n\n        :param context:\n        :param volume:\n        :return:\n        '''\n        pass\n\n\n    def initialize_connection(self, volume, connector):\n        '''\n        initialize_connection\n\n        :param volume:\n        :param connector:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start initialize_connection() \"))\n        connection = {'data': {}}\n        LOG.info('volume: %s', volume)\n        admin_context = cinder_context.get_admin_context()\n        vol_meta = self.db.volume_metadata_get(admin_context, volume['id'])\n        connection['vol_urn'] = vol_meta['urn']\n        return connection\n\n    def terminate_connection(self, volume, connector, **kwargs):\n        '''\n        terminate_connection\n\n        :param volume:\n        :param connector:\n        :param force:\n        :return:\n        '''\n        pass\n\n    def retype(self, context, volume, new_type, diff, host):\n        '''\n        retype\n\n        :param context:\n        :param volume:\n        :param new_type:\n        :param diff:\n        :param host:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start retype() \"))\n        LOG.info(_(\" new volume type [%s]\"), new_type)\n\n        args_dict = {}\n        ds_storage_type = ''\n        is_thin = FC_DRIVER_CONF.vrm_is_thin\n        if str(is_thin).lower() == 'true':\n            args_dict['migrate_type'] = 1\n        else:\n            args_dict['migrate_type'] = 2\n        shareable = volume.get('shareable')\n        if True == shareable:\n            LOG.info(_(\"[BRM-DRIVER] shareable\"))\n            #TODO:???\n\n        if diff and diff.get('extra_specs'):\n            pvscsi_support = diff.get('extra_specs').get('hw:passthrough')\n            if pvscsi_support and pvscsi_support[0] != pvscsi_support[1]:\n                msg = (_('rdm volume can only retyped to rdm volume type.'))\n                LOG.error(msg)\n                raise exception.InvalidInput(reason=msg)\n\n        extra_specs = new_type.get('extra_specs')\n        if extra_specs is None:\n            LOG.info(_(\"[BRM-DRIVER] extra_specs is None\"))\n            return True, None\n        new_backend_name = extra_specs.get('volume_backend_name')\n        if new_backend_name is None:\n            LOG.info(_(\"[BRM-DRIVER] new_backend_name is None\"))\n            return True, None\n\n        vol_meta = volume.get('volume_metadata')\n        vol_meta_dict = metadata_to_dict(vol_meta)\n        linked_clone = vol_meta_dict.get('linked_clone')\n        if linked_clone is not None:\n            if str(linked_clone).lower() == 'true':\n                msg = (_('linked volume can not be retyped. '))\n                LOG.error(msg)\n                raise exception.InvalidInput(reason=msg)\n\n\n        snapshots = self.db.snapshot_get_all_for_volume(context, volume.get('id'))\n        if len(snapshots):\n            msg = _(\"Volume still has %d dependent snapshots\") % len(snapshots)\n            raise exception.InvalidVolume(reason=msg)\n\n\n        try:\n            source_ds_name = volume.get('host').split('#')[1]\n        except exception.CinderException as ex:\n            LOG.info(\n                _(\"[CINDER-BRM] host format exception, host is %s \") % volume.get('host'))\n            raise ex\n\n        LOG.info(_(\" source_ds_name [%s]\"), source_ds_name)\n\n\n        try:\n            new_ds_name = volume_utils.extract_host(host['host'], 'pool')\n        except exception.CinderException as ex:\n            LOG.info(\n                _(\"[CINDER-BRM] host format exception, host is %s \") % volume.get('host'))\n            raise ex\n        \n        LOG.info(_(\" new_ds_name [%s]\"), new_ds_name)\n        \n        if source_ds_name == new_ds_name:\n            LOG.info(_(\"[CINDER-BRM] source ds_name == dest ds_name\"))\n            return True, None\n\n        datastores = self.datastore_proxy.list_datastore()\n        for datastore in datastores:\n            ds_name = datastore.get('name')\n            if ds_name is not None:\n                LOG.info(_(\" ds_name [%s]\"), ds_name)\n                if new_ds_name == ds_name:\n                    args_dict['dest_ds_urn'] = datastore.get('urn')\n                    ds_storage_type = datastore.get('storageType')\n                    LOG.info(_(\" new_ds_name [%s]\"), new_ds_name)\n                    break\n\n        vol_meta = volume.get('volume_metadata')\n        vol_meta_dict = metadata_to_dict(vol_meta)\n        volume_urn = vol_meta_dict.get('urn')\n        args_dict['volume_urn'] = volume_urn\n        fc_vol_id = volume_urn[volume_urn.rfind(':') + 1:]\n        volume_body = self.volume_proxy.query_volume(id=fc_vol_id)\n        source_ds_urn = volume_body['datastoreUrn']\n\n        args_dict['volume_id'] = fc_vol_id\n        args_dict['speed'] = 30\n\n        if None == args_dict.get('dest_ds_urn'):\n            LOG.info(_(\"[BRM-DRIVER] no dest_ds_urn\"))\n            return False, None\n        else:\n            if source_ds_urn == args_dict['dest_ds_urn']:\n                LOG.info(_(\"[BRM-DRIVER] same ds [%s]\"), source_ds_urn)\n                return True, None\n            vm_body = self.vm_proxy.query_vm_volume(**args_dict)\n            self._check_replications(fc_vol_id)\n            if vm_body is None:\n                self.volume_proxy.migrate_volume(**args_dict)\n            else:\n                vm_urn = vm_body.get('urn')\n                vm_id = vm_urn[-10:]\n                args_dict['vm_id'] = vm_id\n                self.vm_proxy.migrate_vm_volume(**args_dict)\n\n        LOG.info(_(\"[CINDER-VRM] fc_vol_id [%s] \") % fc_vol_id)\n        model_update, metadata = self._vrm_get_volume_meta(id=fc_vol_id)\n        self.db.volume_metadata_update(context, volume['id'], metadata, False)\n        LOG.info(_(\"[BRM-DRIVER] retype return\"))\n        return True, None\n\n    def manage_existing(self, volume, existing_ref):\n        \"\"\"Brings an existing backend storage object under Cinder management.\n\n        existing_ref is passed straight through from the API request's\n        manage_existing_ref value, and it is up to the driver how this should\n        be interpreted.  It should be sufficient to identify a storage object\n        that the driver should somehow associate with the newly-created cinder\n        volume structure.\n        \"\"\"\n\n        LOG.info(_(\"[BRM-DRIVER] start manage_existing() \"))\n        # TODO judge if register vol:\n        metadata = dict((item['key'], item['value']) for item in volume['volume_metadata'])\n        volInfoUrl = metadata.get('volInfoUrl', None)\n        if volInfoUrl is None:\n            LOG.info(_(\"manage_existing: volInfoUrl is None\"))\n            raise driver_exception.FusionComputeDriverException()\n\n        name = volume['name']\n        uuid = volume['id']\n        shareable = volume.get('shareable')\n        if True == shareable:\n            voltype = 'share'\n        else:\n            voltype = 'normal'\n\n        quantity_GB = int(volume['size'])\n\n        args_dict = {}\n        args_dict['name'] = name\n        args_dict['quantityGB'] = quantity_GB\n        args_dict['type'] = voltype\n        args_dict['volInfoUrl'] = volInfoUrl\n        args_dict['uuid'] = uuid\n\n        args_dict['maxReadBytes'] = 0\n        args_dict['maxWriteBytes'] = 0\n        args_dict['maxReadRequest'] = 0\n        args_dict['maxWriteRequest'] = 0\n        body = self.volume_proxy.manage_existing(**args_dict)\n\n        model_update = {}\n        temp_str = str(body.get('urn'))\n        fc_vol_id = temp_str[temp_str.rfind(':') + 1:]\n        model_update, metadata = self._vrm_get_volume_meta(id=fc_vol_id)\n        context = cinder_context.get_admin_context()\n        self.db.volume_metadata_update(context, uuid, metadata, False)\n        if existing_ref:\n            LOG.info(\"manage_existing: existing_ref[%s]\", existing_ref)\n            for key, value in existing_ref.items():\n                self.db.volume_glance_metadata_create(context,\n                                                      uuid,\n                                                      key, value)\n\n        return model_update\n\n    def manage_existing_get_size(self, volume, existing_ref):\n        \"\"\"Return size of volume to be managed by manage_existing.\n        When calculating the size, round up to the next GB.\n        \"\"\"\n        vol_size = 0\n        for meta in volume['volume_metadata']:\n            LOG.error(\"meta: %s\" % str(meta))\n            if meta.key == 'quantityGB':\n                vol_size = int(meta.value)\n                break\n\n        volume['size'] = vol_size\n\n        return vol_size\n\n    def unmanage(self, volume):\n        \"\"\"Removes the specified volume from Cinder management.\n\n        Does not delete the underlying backend storage object.\n\n        For most drivers, this will not need to do anything.  However, some\n        drivers might use this call as an opportunity to clean up any\n        Cinder-specific configuration that they have associated with the\n        backend storage object.\n\n        :param volume:\n        :return:\n        \"\"\"\n\n        LOG.info(_(\"[BRM-DRIVER] start unmanage() \"))\n        vol_metas = volume['volume_metadata']\n\n        provider_location = volume['provider_location']\n        if provider_location is None or provider_location == '':\n            LOG.error(_(\"[BRM-DRIVER]provider_location is null \"))\n            return\n        volume_uri, items = \\\n            self._vrm_unpack_provider_location(volume['provider_location'], 'uri')\n        self.volume_proxy.unmanage(volume_uri=volume_uri)\n\n    def migrate_volume(self, context, volume, host):\n        '''\n\n        :param context:\n        :param volume:\n        :param host:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start migrate_volume() \"))\n\n        raise NotImplementedError()\n\n    def copy_volume_data(self, context, src_vol, dest_vol, remote=None):\n        '''\n\n        :param context:\n        :param src_vol:\n        :param dest_vol:\n        :param remote:\n        :return:\n        '''\n        msg = (_('copy_volume_data. '))\n        LOG.error(msg)\n        raise NotImplementedError()\n\n    def extend_volume(self,volume,new_size):\n        '''\n        extend_volume\n\n        :param volume:\n        :param new_size:\n        :return:\n        '''\n        LOG.info(_(\"[BRM-DRIVER] start extend_volume() \"))\n        vol_metas = volume['volume_metadata']\n        if type(vol_metas) is not type(None):\n            for meta in vol_metas:\n                LOG.info(_(\"[BRM-DRIVER] volume_metadata is [%s:%s] \"), meta.key, meta.value)\n\n        vol_meta = volume.get('volume_metadata')\n        vol_meta_dict = metadata_to_dict(vol_meta)\n        vol_uri = vol_meta_dict.get('uri')\n        fc_vol_urn = vol_meta_dict.get('urn')\n        if fc_vol_urn is not None:\n            fc_vol_id = fc_vol_urn[fc_vol_urn.rfind(':') + 1:]\n            self._check_replications(fc_vol_id)\n\n        LOG.info(_(\"[CINDER-VRM]extend_volume fc_vol_id [%s] \") % fc_vol_id)\n\n        if vol_uri is not None:\n            temp_str = str(vol_meta_dict.get('urn'))\n            fc_vol_id = temp_str[temp_str.rfind(':') + 1:]\n            vrm_volume = self.volume_proxy.query_volume(id=fc_vol_id)\n            sizeGB = vrm_volume.get('quantityGB')\n            if int(sizeGB) < int(new_size):\n                self._check_datastore_capacity(vrm_volume, volume['size'], new_size)\n                self.volume_proxy.extend_volume(volume_uri=vol_uri, size=new_size*1024)\n            else:\n                LOG.info(_(\"[CINDER-VRM]sizeGB[%s] avoid extend\") % sizeGB)\n        else:\n            raise exception.ExtendVolumeError\n\n        fc_vol_id = fc_vol_urn[fc_vol_urn.rfind(':') + 1:]\n        model_update, metadata = self._vrm_get_volume_meta(id=fc_vol_id)\n        context = cinder_context.get_admin_context()\n        self.db.volume_metadata_update(context, volume['id'], metadata, False)\n\n    def _check_datastore_capacity(self, fc_volume, old_size, new_size):\n        # keep same strage with capacity_filter\n        datastore_urn = fc_volume.get('datastoreUrn')\n        extra_size = new_size - old_size\n        if datastore_urn:\n            datastore_id = datastore_urn[datastore_urn.rfind(':')+1:]\n            datastore = self.datastore_proxy.query_datastore(id=datastore_id)\n\n            if 'NORMAL' != datastore['status']:\n                msg = _('Datastore status is abnormal.')\n                raise exception.ExtendVolumeError(message=msg)\n\n            reserved = float(self.reserved_percentage) / 100\n            total = float(datastore['capacityGB'])\n            free_space = datastore['freeSizeGB']\n            free = free_space - math.floor(total * reserved)\n\n            if self.thin_provision and self.over_ratio >= 1:\n                provisioned_ratio = ((datastore['usedSizeGB'] +\n                                      extra_size) / total)\n                if provisioned_ratio >= self.over_ratio:\n                    LOG.warning(_(\n                        \"Insufficient free space for thin provisioning. \"\n                        \"The ratio of provisioned capacity over total capacity \"\n                        \"%(provisioned_ratio).2f has exceeded the maximum over \"\n                        \"subscription ratio %(oversub_ratio).2f.\"),\n                        {\"provisioned_ratio\": provisioned_ratio,\n                         \"oversub_ratio\": self.over_ratio})\n                    msg = _('Insufficient free space for thin provisioning.')\n                    raise exception.ExtendVolumeError(message=msg)\n                else:\n                    free_virtual = free * self.over_ratio\n                    if free_virtual < extra_size:\n                        msg = _('Insufficient free space for thin provisioning.')\n                        raise exception.ExtendVolumeError(message=msg)\n                    else:\n                        return True\n\n            if free < extra_size:\n                msg = _('The new size of the volume exceed the capacity of the datastore.')\n                raise exception.ExtendVolumeError(message=msg)\n\n    def backup_volume(self, context, backup, backup_service):\n        \"\"\"Create a new backup from an existing volume.\n        backup['status']\n        backup['object_count']\n        backup['_sa_instance_state']\n        backup['user_id']\n        backup['service']:q\n\n        backup['availability_zone']\n        backup['deleted']\n        backup['created_at']\n        backup['updated_at']\n        backup['display_description']\n        backup['project_id']\n        backup['host']\n        backup['container']\n        backup['volume_id']\n        backup['display_name']\n        backup['fail_reason']\n        backup['deleted_at']\n        backup['service_metadata']\n        backup['id']\n        backup['size']\n        -------- -------- -------- --------\n        backup['backup_type']\n        backup['volume_name']\n        backup['snap_name']\n        backup['snap_id']\n        backup['snap_parent_name']\n        backup['snap_last_name']\n        backup['clone_volume_name']\n        backup['storage_ip']\n        backup['storage_pool_id']\n        backup['volume_offset']\n        backup['incremental']\n        backup['is_close_volume']\n        backup['is_bootable']\n        backup['image_id']\n\n        backup['volume_size']\n        volume_file\n        backup_metadata\n\nbackup db:CREATED_AT | UPDATED_AT | DELETED_AT | DELETED\n| ID | VOLUME_ID | USER_ID | PROJECT_ID | HOST | AVAILABILITY_ZONE\n| DISPLAY_NAME | DISPLAY_DESCRIPTION | CONTAINER | STATUS | FAIL_REASON\n| SERVICE_METADATA | SERVICE | SIZE | OBJECT_COUNT\n\n\nsnapshot db: CREATED_AT|UPDATED_AT|DELETED_AT| DELETED |ID |VOLUME_ID|USER_ID\n  |PROJECT_ID| STATUS  | PROGRESS | VOLUME_SIZE | SCHEDULED_AT | DISPLAY_NAME\n  | DISPLAY_DESCRIPTION | PROVIDER_LOCATION | ENCRYPTION_KEY_ID | VOLUME_TYPE_ID | CGSNAPSHOT_ID\n\n        \"\"\"\n        vol_second_os = self.db.volume_get(context, backup['volume_id'])\n\n        LOG.info(('Creating a new backup for volume %s.') % vol_second_os['name'])\n        kwargs = {}\n        volume_file = {}\n        if vol_second_os.get('snapshot_id') != backup['container']:\n            LOG.error(_(\"snapshot id is %s\") % vol_second_os.get('snapshot_id'))\n            LOG.error(_(\"backup container is %s\") % backup['container'])\n            raise exception.InvalidSnapshot(reason=\"snapshot id not equal backup container\")\n    \n        vol_meta = vol_second_os.get('volume_metadata')\n        vol_meta_dict = metadata_to_dict(vol_meta)\n        volume_urn = vol_meta_dict.get('urn')\n        fc_vol_id = volume_urn[volume_urn.rfind(':') + 1:]\n        vol_second_fc = self.volume_proxy.query_volume(id=fc_vol_id)\n\n        volume_file['volume_name'] = vol_second_fc['volNameOnDev']\n        last_snap_id_os = vol_second_os['snapshot_id']\n        last_snap_id_fc = str(vol_second_os['snapshot_id']).replace('-', '')\n        last_snap_os = self.db.snapshot_get(context, last_snap_id_os)\n        vol_first_id = last_snap_os['volume_id']\n        volume_file['source_volume_id'] = vol_first_id\n        \n        ext_params = vol_second_fc.get('drExtParams')\n        LOG.info(_(\"[VRM-CINDER] ext_params [%s]\"), ext_params)\n        if ext_params:\n            ext_params_dic = json.loads(ext_params)\n\n        storage_type = vol_second_fc.get('storageType')\n        sd_sn = vol_second_fc.get('storageSN')\n        if storage_type == 'advanceSan':\n            volume_file['storage_type'] = 4  # advanceSan v3 volume\n            support_pvscsi = vol_second_fc.get('pvscsiSupport')\n            if support_pvscsi is not None and support_pvscsi == 1:\n                volume_file['storage_type'] = 5  # RDM : direct io volume\n        else:\n            volume_file['storage_type'] = 0\n            LOG.info(_(\"[VRM-CINDER] ext_params [%s]\"), ext_params_dic)\n            volume_file['storage_ip'] = ext_params_dic['dsMgntIp']\n            volume_file['storage_pool_id'] = ext_params_dic['dsResourceId']\n\n        LOG.info(_(\"[VRM-CINDER] volume_file [%s]\"), volume_file)\n        backup_list = self.db.backup_get_by_volume_id(context, vol_first_id)\n        \n        last_backup = None\n        if backup_list is not None:\n            for back_tmp in backup_list:\n                if back_tmp['status'] != \"available\" and back_tmp['status'] != \"restoring\":\n                    continue\n                if last_backup is None:\n                    last_backup = back_tmp\n                else:\n                    if last_backup['created_at'] < back_tmp['created_at']:\n                        last_backup = back_tmp\n            \n        if last_backup is None:\n            volume_file['backup_type'] = 0\n            volume_file['parent_id'] = None\n            volume_file['parent_snapshot_url'] = None\n        else:\n            LOG.info(_(\"last_backup %s\") % last_backup['id'])\n            volume_file['backup_type'] = 1\n            volume_file['parent_id'] = last_backup['id']\n            \n            if last_backup['service_metadata'] is None:\n                raise exception.InvalidVolumeMetadata(reason=\"backup service_metadata is none\")\n            \n            service_meta = last_backup['service_metadata']\n            str_service_meta = json.loads(service_meta)\n            parent_snapshot_id_os = str_service_meta.get('snap_id')\n            if parent_snapshot_id_os is not None:\n                parent_snapshot_id_fc = str(parent_snapshot_id_os).replace('-', '')\n\n                if storage_type == 'advanceSan':\n                    parent_snapshot_fc = self.volume_snapshot_proxy.query_volumesnapshot(uuid=parent_snapshot_id_fc)\n                    if parent_snapshot_fc and parent_snapshot_fc.get('snapshotLunId') is not None:\n                        volume_file['parent_snapshot_url'] = 'http://' + sd_sn \\\n                                                             + '/' + parent_snapshot_fc.get('snapshotLunId')\n                else:\n                    volume_file['parent_snapshot_url'] = 'http://' + ext_params_dic['dsMgntIp'] \\\n                                                         + '/' + ext_params_dic['dsResourceId'] \\\n                                                         + '/' + parent_snapshot_id_fc\n            else:\n                raise exception.InvalidVolumeMetadata(reason=\"snapshot_id is none\")\n\n        LOG.info(_(\"vol_first_id is %s\") % vol_first_id)\n        vol_first_os = self.db.volume_get(context, vol_first_id)\n        vol_first_meta = vol_first_os.get('volume_metadata')\n        vol_first_meta_dict = metadata_to_dict(vol_first_meta)\n        volume_first_urn = vol_first_meta_dict.get('urn')\n        fc_first_vol_id = volume_first_urn[volume_urn.rfind(':') + 1:]\n        LOG.info(_(\"fc_first_vol_id is %s\") % fc_first_vol_id)\n        \n        vol_source_fc = self.volume_proxy.query_volume(id=fc_first_vol_id)\n        LOG.info(_(\"vol_source_fc linkCloneParent is %s\") % vol_source_fc['linkCloneParent'])\n        if vol_source_fc['linkCloneParent'] is not None:\n            volume_file['is_clone_volume'] = True\n            try:\n                vol_linked_fc = self.volume_proxy.query_volume(id=vol_source_fc['linkCloneParent'])\n                if storage_type == 'advanceSan':\n                    volume_file['clone_volume_url'] = 'http://' + vol_linked_fc['storageSN'] \\\n                                                      + '/' + vol_linked_fc['lunId']\n                else:\n                    linked_ext_params_dic = json.loads(vol_linked_fc.get('drExtParams'))\n                    volume_file['clone_volume_url'] = 'http://' + linked_ext_params_dic['dsMgntIp'] \\\n                                                      + '/' + linked_ext_params_dic['dsResourceId'] \\\n                                                      + '/' + vol_source_fc['linkCloneParent']\n            except:\n                LOG.error(_(\"clone colume not exit\")) \n                volume_file['clone_volume_url'] = None\n        else:\n            volume_file['is_clone_volume'] = False\n            volume_file['clone_volume_url'] = None\n            \n        volume_file['snapshot_name'] = last_snap_os['name']\n        volume_file['snapshot_id'] = last_snap_id_fc\n\n        LOG.info(_(\"[VRM-CINDER] volume_file [%s]\"), volume_file)\n\n        volume_file['volume_size'] = vol_second_os['size']\n        LOG.info(_(\"[VRM-CINDER] volume_file [%s]\"), volume_file)\n        if storage_type == 'advanceSan':\n            last_snapshot = self.volume_snapshot_proxy.query_volumesnapshot(uuid=last_snap_id_fc)\n            if last_snapshot and last_snapshot.get('snapshotLunId') is not None:\n                volume_file['snapshot_url'] = 'http://' + sd_sn \\\n                                              + '/' + last_snapshot.get('snapshotLunId')\n        else:\n            volume_file['snapshot_url'] = 'http://' + ext_params_dic['dsMgntIp'] \\\n                                          + '/' + ext_params_dic['dsResourceId'] \\\n                                          + '/' + last_snap_id_fc\n\n        volume_file['bootable'] = False\n        volume_file['image_id'] = None\n        try:\n            vol_image_meta = self.volume_api.get_volume_image_metadata(context, vol_second_os)\n            if vol_image_meta:\n                vol_image_meta_dic = dict(vol_image_meta.iteritems())\n                volume_file['image_id'] = vol_image_meta_dic.get('image_id')\n                volume_file['bootable'] = True\n\n        except exception.CinderException as ex:\n            LOG.error(_('[BRM-DRIVER] get_volume_image_metadata is error [%s]'), ex)\n\n        if vol_first_os.get('bootable') is True:\n            volume_file['bootable'] = True\n\n        LOG.info(_(\"[VRM-CINDER] volume_file [%s]\"), volume_file)\n\n        try:\n            backup_service.backup(backup, volume_file)\n        finally:\n            LOG.info(('cleanup for volume %s.') % vol_second_os['name'])\n\n    def restore_backup(self, context, backup, volume, backup_service):\n        \"\"\"Restore an existing backup to a new or existing volume.\"\"\"\n\n        LOG.info(('restore_backup for volume %s.') % volume['name'])\n        volume_file = {}\n\n        vol_meta = volume.get('volume_metadata')\n        vol_meta_dict = metadata_to_dict(vol_meta)\n        volume_urn = vol_meta_dict.get('urn')\n        fc_vol_id = volume_urn[volume_urn.rfind(':') + 1:]\n        vol_second_fc = self.volume_proxy.query_volume(id=fc_vol_id)\n        ext_params = vol_second_fc.get('drExtParams')\n        if ext_params:\n            LOG.info(_(\"[VRM-CINDER] ext_params [%s]\"), ext_params)\n            ext_params_dic = json.loads(vol_second_fc.get('drExtParams'))\n            LOG.info(_(\"[VRM-CINDER] ext_params [%s]\"), ext_params_dic)\n        storage_type = vol_second_fc.get('storageType')\n        volume_file['restore_type'] = 0\n        if backup['volume_id'] == volume['id']:  # restore backup to source volume\n            volume_file['restore_type'] = 1\n            service_meta = backup['service_metadata']\n            str_service_meta = json.loads(service_meta)\n            last_snapshot_id_openstack = str_service_meta.get('snap_id')\n            last_snapshot_id_fc = str(last_snapshot_id_openstack).replace('-', '')\n            last_snapshot_fc = self.volume_snapshot_proxy.query_volumesnapshot(uuid=last_snapshot_id_fc)\n            if last_snapshot_fc:  # only the last backup has snapshot\n                if storage_type == 'advanceSan':\n                    volume_file['lastest_snapshot_url'] = 'http://' + vol_second_fc['storageSN'] + '/' + \\\n                                                          last_snapshot_fc['snapshotLunId']\n                else:\n                    volume_file['lastest_snapshot_url'] = 'http://' + ext_params_dic['dsMgntIp'] \\\n                                                      + '/' + ext_params_dic['dsResourceId'] \\\n                                                      + '/' + last_snapshot_id_fc\n\n        if storage_type == 'advanceSan':\n            volume_file['storage_type'] = 4\n            pvscsi_support = vol_second_fc.get('pvscsiSupport')\n            if pvscsi_support is not None and pvscsi_support == 1:\n                volume_file['storage_type'] = 5  # RDM: direct io volume\n            volume_file['volume_url'] = 'http://' + vol_second_fc['storageSN'] + '/' + vol_second_fc['lunId']\n        else:\n            volume_file['storage_type'] = 0\n            volume_file['volume_url'] = vol_second_fc['volInfoUrl']\n            LOG.info(_(\"[VRM-CINDER] volume_file [%s]\"), volume_file)\n            volume_file['storage_ip'] = ext_params_dic['dsMgntIp']\n            volume_file['storage_pool_id'] = ext_params_dic['dsResourceId']\n            volume_file['volume_offset'] = True\n            volume_file['volume_name'] = vol_second_fc['volNameOnDev']\n\n        if vol_second_fc['linkCloneParent'] is not None:\n            try:\n                vol_linked_fc = self.volume_proxy.query_volume(id=vol_second_fc['linkCloneParent'])\n                if vol_second_fc['storageType'] == 'advanceSan':\n                    volume_file['clone_volume_url'] = 'http://' + vol_linked_fc['storageSN'] + vol_linked_fc['lunId']\n                else:\n                    linked_ext_params_dic = json.loads(vol_linked_fc.get('drExtParams'))\n                    volume_file['clone_volume_url'] = 'http://' + linked_ext_params_dic['dsMgntIp'] \\\n                                                      + '/' + linked_ext_params_dic['dsResourceId'] \\\n                                                      + '/' + vol_second_fc['linkCloneParent']\n            except:\n                LOG.error(_(\"clone colume not exit\")) \n                volume_file['clone_volume_url'] = None\n        else:\n            volume_file['clone_volume_url'] = None\n\n        try:\n            backup_service.restore(backup, volume['id'], volume_file)\n        finally:\n            LOG.info(('cleanup for volume %s.') % volume['name'])\n\n    def delete_cgsnapshot(self, context, cgsnapshot):\n        \"\"\"Delete a cgsnapshot.\"\"\"\n        model_update = {}\n        model_update['status'] = cgsnapshot['status']\n        cgsnapshot_id = cgsnapshot['id']\n        LOG.info(_('[BRM-DRIVER] start to delete cgsnapshot [%s]'), cgsnapshot_id)\n\n        snapshots = self.db.snapshot_get_all_for_cgsnapshot(context, cgsnapshot_id)\n        if snapshots and len(snapshots) > 0:\n            for snapshot in snapshots:\n                snapshot_id = snapshot['id']\n                snapshot_uuid = str(snapshot_id).replace('-', '')\n                body = self.volume_snapshot_proxy.query_volumesnapshot(uuid=snapshot_uuid)\n                if body is None:\n                    LOG.warn(_('[BRM-DRIVER] snapshot [%s] is not exist while deleting cgsnapshot [%s]'), snapshot_uuid,\n                             cgsnapshot_id)\n                    snapshot['status'] = 'deleted'\n                    continue\n                try:\n                    self.volume_snapshot_proxy.delete_volumesnapshot(id=snapshot_uuid)\n                    snapshot['status'] = 'deleted'\n                except Exception as ex:\n                    LOG.error(_('[BRM-DRIVER] delete snapshot [%s] error while deleting cgsnapshot [%s]'),\n                              snapshot_uuid, cgsnapshot_id)\n                    LOG.exception(ex)\n                    snapshot['status'] = 'error_deleting'\n                    model_update['status'] = 'error_deleting'\n        return model_update, snapshots\n\n    def create_cgsnapshot(self, context, cgsnapshot):\n        model_update = {}\n        consistencygroup_id = cgsnapshot['consistencygroup_id']\n        cgsnapshot_id = cgsnapshot['id']\n        snapshots = self.db.snapshot_get_all_for_cgsnapshot(context, cgsnapshot_id)\n        volumes = self.db.volume_get_all_by_group(context, consistencygroup_id)\n        snapshot_uuids = []\n        if volumes is None or len(volumes) <= 0:\n            return model_update\n        else:\n            for volume_ref in volumes:\n                for snapshot in snapshots:\n                    if snapshot['volume_id'] == volume_ref['id']:\n                        vol_meta = volume_ref['volume_metadata']\n                        vol_meta_dict = metadata_to_dict(vol_meta)\n                        vol_urn = vol_meta_dict.get('urn')\n                        if vol_urn is None:\n                            LOG.warn(_(\"vol_urn  is null while creating cgsnapshot.\"))\n                        snapshot_id = snapshot['id']\n                        snapshot_uuid = str(snapshot_id).replace('-', '')\n                        LOG.info(_(\"creating snapshot snapshot_uuid  is [%s], vol_urn is [%s]\"), snapshot_uuid, vol_urn)\n                        try:\n                            body = self.volume_snapshot_proxy.create_volumesnapshot(snapshot_uuid=snapshot_uuid, vol_urn=vol_urn, enable_active=False)\n                        except Exception as ex:\n                            LOG.error(_(\"Trying to create snapshot failed, volume id is: %s\"), snapshot['volume_id'])\n                            LOG.exception(ex)\n                            snapshot['status'] = 'error'\n                            model_update['status'] = 'error'\n\n                        if body['urn'] is None:\n                            LOG.error(_(\"Trying to create snapshot failed, volume id is: %s\"), snapshot['volume_id'])\n                            snapshot['status'] = 'error'\n                            model_update['status'] = 'error'\n                        snapshot_uuids.append(snapshot_uuid)\n            try:\n                self.volume_snapshot_proxy.active_snapshots(snapshot_uuids=snapshot_uuids)\n            except Exception as ex:\n                LOG.error(_(\"Trying to active snapshot failed, snapshot_uuids is: %s\"), snapshot_uuids)\n                LOG.exception(ex)\n                for snapshot in snapshots:\n                    snapshot['status'] = 'error'\n                model_update['status'] = 'error'\n\n        return model_update, snapshots\n\n    def _get_cluster_by_dsurn(self, ds_urn):\n        \"\"\"Return clusterUrn by dsurn\n        \"\"\"\n        #datastore_id = ds_urn[ds_urn.rfind(':') + 1:]\n        clusters = self.cluster_proxy.list_cluster()\n        random.shuffle(clusters)\n        for cluster in clusters:\n            cluster_urn = cluster.get('urn')\n            args_dict = {}\n            args_dict['scope'] = cluster_urn\n            try:\n                datastores = self.datastore_proxy.list_datastore(**args_dict)\n                for datastore in datastores:\n                    if datastore['urn'] == ds_urn:\n                        hosts = self.cluster_proxy.list_hosts(clusterUrn=cluster_urn)\n                        for host in hosts:\n                            if host[\"status\"] == \"normal\" and host[\"isMaintaining\"] == False:\n                                return cluster_urn\n\n            except Exception as ex:\n                LOG.error(_('[BRM-DRIVER] get_volume_image_metadata is error [%s]'), ex)\n            \n        msg = _('[BRM-DRIVER] get cluster is none')\n        LOG.error(msg)\n        raise exception.VolumeDriverException(message=msg)\n\n\n","sub_path":"cinder/fusioncompute/vrm_driver.py","file_name":"vrm_driver.py","file_ext":"py","file_size_in_byte":91171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"530484376","text":"import unittest\nfrom helpers import get_data_paths_list\n\nclass TestMain(unittest.TestCase):\n\n    def test_get_data_paths_wrong(self):\n        with self.assertRaises(Exception) as context:\n            get_data_paths_list('some/unknown/path', 'other/unknown/path')\n\n            self.assertTrue('No such file or directory' in context.exception)\n    \n    def test_get_data_paths_correct(self):\n        images, masks = get_data_paths_list('repo-images', 'repo-images')\n\n        self.assertGreater(len(images), 0)\n        self.assertEqual(len(images), len(masks))","sub_path":"main_test.py","file_name":"main_test.py","file_ext":"py","file_size_in_byte":557,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"239038596","text":"# The Fibonacci numbers, commonly denoted F(n) form a sequence,\n# called the Fibonacci sequence, such that each number is the sum of the two preceding ones,\n# starting from 0 and 1. That is,\n#\n# F(0) = 0, F(1) = 1\n# F(n) = F(n - 1) + F(n - 2), for n > 1.\n# Given n, calculate F(n).\n\nclass Solution:\n    def fib(self, n: int) -> int:\n        if n == 0 or n == 1:\n            return n\n\n        a, b = 0, 1\n        while n > 1:\n            n -= 1\n            b, a = a + b, b\n        return b","sub_path":"Easy/0509_Fibonacci_Number.py","file_name":"0509_Fibonacci_Number.py","file_ext":"py","file_size_in_byte":488,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"2700676","text":"import torch \nimport torch.nn as nn\nimport torchvision.datasets as dsets\nimport torchvision.transforms as transforms\nfrom torch.autograd import Variable\n\n# Hyper Parameters\nsequence_length = 28\ninput_size = 28\nhidden_size = 128\nnum_layers = 2\nnum_classes = 10\nbatch_size = 300\nnum_epochs = 20\nlearning_rate = 3e-3\n\ntrain_dataset = dsets.MNIST(\n    root = './data/',\n    train = True,\n    transform = transforms.ToTensor(),\n    download = True\n)\ntest_dataset = dsets.MNIST(\n    root = './data/',\n    train = False,\n    transform = transforms.ToTensor()\n)\n\ntrain_loader = torch.utils.data.DataLoader(\n    dataset = train_dataset,\n    batch_size = batch_size,\n    shuffle = True\n)\ntest_loader = torch.utils.data.DataLoader(\n    dataset = test_dataset,\n    batch_size = batch_size,\n    shuffle = False\n)\n\n# Model\nclass BiRNN(nn.Module):\n    def __init__(self, input_size, hidden_size, num_layers, num_classes):\n        super(BiRNN, self).__init__()\n        self.hidden_size = hidden_size\n        self.num_layers = num_layers\n        self.lstm = nn.LSTM(input_size, hidden_size, num_layers, batch_first=True, bidirectional=True)\n        self.fc = nn.Linear(hidden_size*2, num_classes)\n\n    def forward(self, x):\n        h0 = Variable(torch.zeros(self.num_layers*2, x.size(0), self.hidden_size)).cuda()\n        c0 = Variable(torch.zeros(self.num_layers*2, x.size(0), self.hidden_size)).cuda()\n\n        out, _ = self.lstm(x, (h0, c0))\n        out = self.fc(out[:, -1, :])\n        return out\n\nrnn = BiRNN(input_size, hidden_size, num_layers, num_classes)\nrnn.cuda()\n\n# Loss and Optimizer\ncriterion = nn.CrossEntropyLoss()\noptimizer = torch.optim.Adam(rnn.parameters(), lr=learning_rate)\n\n# Train\nfor epoch in range(num_epochs):\n    for i, (images, labels) in enumerate(train_loader):\n        images = Variable(images.view(-1, sequence_length, input_size)).cuda()\n        labels = Variable(labels).cuda()\n\n        optimizer.zero_grad()\n        outputs = rnn(images)\n        loss = criterion(outputs, labels)\n        loss.backward()\n        optimizer.step()\n\n        if (i+1) % 100 == 0:\n            print(\"Epoch: {}/{}, Step: {}/{}, Loss: {}\".format(epoch+1, num_epochs, i+1, len(train_dataset) // batch_size, loss.data[0]))\n\n# Test\ncorrect, total = 0, 0\nfor images, labels in test_loader:\n    images = Variable(images.view(-1, sequence_length,input_size)).cuda()\n    outputs = rnn(images)\n    _, predicted = torch.max(outputs.data, 1)\n    total += labels.size(0)\n    correct += (predicted.cpu() == labels).sum()\n\nprint(\"Accuracy: {}\".format(100 * correct / total))","sub_path":"tutorials/02-intermediate/bidirectional_recurrent_neural_network/own.py","file_name":"own.py","file_ext":"py","file_size_in_byte":2556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"392325903","text":"#!/usr/bin/python\n#\n# \n#     Copyright (C) 2003-2010 Institute for Systems Biology\n#                             Seattle, Washington, USA.\n# \n#     This library is free software; you can redistribute it and/or\n#     modify it under the terms of the GNU Lesser General Public\n#     License as published by the Free Software Foundation; either\n#     version 2.1 of the License, or (at your option) any later version.\n# \n#     This library is distributed in the hope that it will be useful,\n#     but WITHOUT ANY WARRANTY; without even the implied warranty of\n#     MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the GNU\n#     Lesser General Public License for more details.\n# \n#     You should have received a copy of the GNU Lesser General Public\n#     License along with this library; if not, write to the Free Software\n#     Foundation, Inc., 59 Temple Place, Suite 330, Boston, MA 02111-1307  USA\n# \n\n\"\"\"\n\"\"\"\n\n__author__ = \"Ryan Bressler\"\n\ndef loadGenes(filen,types):\n\tgenes={}\n\tgenelist = open (filen,\"r\")\n\tfor gene in genelist:\n\t\tvs = gene.rstrip().split(\"\\t\")\n\t\tif not vs[2] in genes:\n\t\t\tgenes[vs[2]]=[]\n\t\tvdict = {\"name\":vs[0],\"start\":int(vs[4]),\"end\":int(vs[5]),\"or\":vs[3]}\n\t\tfor type in types:\n\t\t\tvdict[type]=0\n\t\tgenes[vs[2]].append(vdict)\n\tgenelist.close()\n\treturn genes\n\ndef loadGenesByName(filen):\n\tgenes={}\n\tgenelist = open (filen,\"r\")\n\tfor gene in genelist:\n\t\tvs = gene.strip().split(\"\\t\")\n\t\tif not vs[0] in genes:\n\t\t    vdict = {\"name\":vs[0],\"chr\":vs[2], \"start\":int(vs[4]),\"end\":int(vs[5]),\"or\":vs[3]}\n\t\t    genes[vs[0]]=vdict\n\tgenelist.close()\n\treturn genes","sub_path":"src/main/python/fastbreak/genelist.py","file_name":"genelist.py","file_ext":"py","file_size_in_byte":1584,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"29005991","text":"import requests\nfrom bs4 import BeautifulSoup\nimport pymongo\n\n\nclient = pymongo.MongoClient('localhost',27017)\ntongcheng_info = client['tongcheng_info']\nshoujihao = tongcheng_info['shoujihao']\n\n# ====================================================== <<<< 单页行为 >>>> =============================================\nurl = 'http://bj.58.com/shoujihao/'\nwb_data = requests.get(url)\nsoup = BeautifulSoup(wb_data.text,'lxml')\nnumbers = soup.select('strong.number')\nprices = soup.select('b.price')\nlinks = soup.select('a.t')\n\nfor number, price, link in zip(numbers,prices,links):\n    data = {\n        'title':number.get_text(),\n        'price':price.get_text(),\n        'link' :link.get('href')\n    }\n    shoujihao.insert_one(data)\nprint('Done')\n\n# ====================================================== <<<< 设计函数 >>>> =============================================\ndef get_pages_within(pages):\n    for page_num in range(1,pages+1):\n        wb_data = requests.get('http://bj.58.com/shoujihao/pn{}/'.format(page_num))\n        soup = BeautifulSoup(wb_data.text,'lxml')\n        numbers = soup.select('strong.number')\n        prices = soup.select('b.price')\n        links = soup.select('a.t')\n\n        for number, price, link in zip(numbers,prices,links):\n            data = {\n                'title':number.get_text(),\n                'price':price.get_text(),\n                'link' :link.get('href')\n            }\n            shoujihao.insert_one(data)\n        print('Done')\n\n","sub_path":"week2/2_2/2_2answer_of_homework/2_2HOMEWORK_SHOUJIHAO.py","file_name":"2_2HOMEWORK_SHOUJIHAO.py","file_ext":"py","file_size_in_byte":1481,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"49334917","text":"import itertools\n\n\ndef format_timedelta(delta):\n    \"\"\"\n    Return a string roughly representing a timedelta.\n    \"\"\"\n    seconds = int(delta.total_seconds())\n    neg = seconds < 0\n    seconds = abs(seconds)\n    total = seconds\n    stems = []\n\n    if total >= 3600:\n        hours = seconds // 3600\n        stems.append('{}h'.format(hours))\n        seconds -= hours * 3600\n\n    if total >= 60:\n        mins = seconds // 60\n        stems.append('{:02}m'.format(mins))\n        seconds -= mins * 60\n\n    stems.append('{:02}s'.format(seconds))\n\n    return ('-' if neg else '') + ' '.join(stems)\n\n\ndef sorted_groupby(iterator, key, reverse=False):\n    \"\"\"\n    Similar to `itertools.groupby`, but sorts the iterator with the same\n    key first.\n    \"\"\"\n    return itertools.groupby(sorted(iterator, key=key, reverse=reverse), key)\n","sub_path":"watson/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":824,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"368790288","text":"import numpy as np\nfrom scipy import ndimage\nimport matplotlib.pyplot as plt\nfrom scikits.learn.mixture import GMM\n\nnp.random.seed(1)\nn = 10\nl = 256\nim = np.zeros((l, l))\npoints = l*np.random.random((2, n**2))\nim[(points[0]).astype(np.int), (points[1]).astype(np.int)] = 1\nim = ndimage.gaussian_filter(im, sigma=l/(4.*n))\n\nmask = (im > im.mean()).astype(np.float)\n\n\nimg = mask + 0.3*np.random.randn(*mask.shape)\n\nbinary_img = img > 0.5\n\n# Remove small white regions\nopen_img = ndimage.binary_opening(binary_img)\n# Remove small black hole\nclose_img = ndimage.binary_closing(open_img)\n\nplt.figure(figsize=(12, 3))\n\nl = 128\n\nplt.subplot(141)\nplt.imshow(binary_img[:l, :l], cmap=plt.cm.gray)\nplt.axis('off')\nplt.subplot(142)\nplt.imshow(open_img[:l, :l], cmap=plt.cm.gray)\nplt.axis('off')\nplt.subplot(143)\nplt.imshow(close_img[:l, :l], cmap=plt.cm.gray)\nplt.axis('off')\nplt.subplot(144)\nplt.imshow(mask[:l, :l], cmap=plt.cm.gray)\nplt.contour(close_img[:l, :l], [0.5], linewidths=2, colors='r')\nplt.axis('off')\n\nplt.subplots_adjust(wspace=0.02, hspace=0.3, top=1, bottom=0.1, left=0, right=1)\n\n# Better than opening and closing: use reconstruction\neroded_img = ndimage.binary_erosion(binary_img)\nreconstruct_img = ndimage.binary_propagation(eroded_img, mask=binary_img)\ntmp = np.logical_not(reconstruct_img)\neroded_tmp = ndimage.binary_erosion(tmp)\nreconstruct_final = np.logical_not(ndimage.binary_propagation(eroded_tmp, mask=tmp))\n\n\"\"\"\nplt.subplot(141)\nplt.imshow(binary_img[:l, :l], cmap=plt.cm.gray)\nplt.axis('off')\nplt.subplot(142)\nplt.imshow(eroded_img[:l, :l], cmap=plt.cm.gray)\nplt.axis('off')\nplt.subplot(143)\nplt.imshow(reconstruct_img[:l, :l], cmap=plt.cm.gray)\nplt.axis('off')\nplt.subplot(144)\nplt.imshow(mask[:l, :l], cmap=plt.cm.gray)\nplt.contour(reconstruct_final[:l, :l], [0.5], lw=4)\nplt.axis('off')\n\"\"\"\n\n","sub_path":"pyplots/image_clean_morpho.py","file_name":"image_clean_morpho.py","file_ext":"py","file_size_in_byte":1817,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"272272762","text":"import os\nfrom matplotlib import pyplot as plt\nimport numpy as np\nfrom PIL import Image\n\nplt.rcParams['savefig.dpi'] = 1000\nplt.rcParams['figure.dpi'] = 1000\n\n\ndef get_pixel(panal,x,y):\n    x1 = 0\n    y1 = 0\n    if panal <= 8:\n        x1 = x\n        y1 = y + (panal-1) * 60\n    if panal in (9,10):\n        x1 = x + 120\n        y1 = y + (panal-9) * 60\n    if panal in (11,12):\n        x1 = x + 120\n        y1 = y + (panal-5) * 60\n    if panal in (13,14):\n        x1 = x + 240\n        y1 = y + (panal-13) * 60\n    if panal in (15,16):\n        x1 = x + 240\n        y1 = y + (panal-9) * 60\n    if panal in range(17,29):\n        x1 = x + 360 + ((panal-17) // 2 ) * 120\n        y1 = y + 60 - (panal % 2) * 60\n    return x1,y1\n\ndef draw(x_data,y_data,x_ticklabels):\n    _, ax = plt.subplots()\n    ax.plot(x_data,y_data)\n    ax.set_xticks(x_ticklabels)\n    ax.set_xlabel('time')\n    ax.set_ylabel('pressure')\n    ax.set_title('test')\n\n\n\nfor i in os.listdir(\"PressureData\"):\n    for j in os.listdir(\"PressureData\\\\\"+i):\n        if j.split('.')[-1] != \"txt\":\n            continue\n        data = {}\n        raw = None\n        with open(\"PressureData\\\\\"+ i + \"\\\\\" + j) as f:\n            raw = f.read()\n        raw = raw.replace('\\r\\n','')\n        raw = raw.replace('\\n', '')\n        raw = raw.split(',')\n\n        k = 0\n        while k < len(raw) - 1:\n            if raw[k] in data.keys():\n                data[raw[k]] += int(raw[k + 4])\n            else:\n                data[raw[k]] = int(raw[k + 4])\n            k += 5\n        x = []\n        y = []\n        for k in data.keys():\n            if int(data[k]) < 100:\n                continue\n            x.append(int(k))\n            y.append(int(data[k]))\n        xl = x[::(max(x)-min(x))//10]\n\n        with open('1.txt','w') as f:\n            f.write(str(x))\n            f.write('\\n')\n            f.write(str(y))\n        draw(x,y,xl)\n        plt.savefig(\"PressureData\\\\\"+ i + \"\\\\\" + j.split('.')[0] + \".png\",dpi = 1000)\n\n        matrix = np.zeros((1080, 480))\n        maxm = 0\n        k = 0\n        while k < len(raw) - 1:\n            if int(raw[k+4]) < 30:\n                k += 5\n                continue\n            a, b = get_pixel(int(raw[k+1]), int(raw[k+2]), int(raw[k+3]))\n            matrix[a][b] = int(raw[k+4])\n            maxm = max(maxm, int(raw[k+4]))\n            k += 5\n        matrix = np.asarray(matrix,dtype='float64')\n        matrix = 255-np.log(1+matrix/maxm*(np.e-1))*255\n        img = Image.fromarray(matrix)\n        img = img.convert(\"L\")\n        img.save(\"PressureData\\\\\"+ i + \"\\\\\" + j.split('.')[0] +\".jpg\")\n\n        count = 0\n        name = i\n        for i in x:\n            count += 1\n            print(count)\n            matrix = np.zeros((1080, 480))\n            maxm = 0\n            k = 0\n            while k < len(raw) - 1:\n                if int(raw[k + 4]) < 30 or int(raw[k])!= i:\n                    k += 5\n                    continue\n                a, b = get_pixel(int(raw[k + 1]), int(raw[k + 2]), int(raw[k + 3]))\n                matrix[a][b] = int(raw[k + 4])\n                maxm = max(maxm, int(raw[k + 4]))\n                k += 5\n            if maxm < 30:\n                continue\n            matrix = np.asarray(matrix, dtype='float64')\n            matrix = 255 - np.log(1 + matrix / maxm * (np.e - 1)) * 255\n\n            img = Image.fromarray(matrix)\n            img = img.convert(\"L\")\n            img.save(\"PressureData\\\\\"+ name + \"\\\\\"+ str(count) + \".jpg\")\n\n\n\n\n\n\n\n\n\n\n","sub_path":"Time-Pressure.py","file_name":"Time-Pressure.py","file_ext":"py","file_size_in_byte":3453,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"356659717","text":"from random import uniform\nfrom config import _debug\n\ndef start(schedule):\n\telite_iterations = 0\n\telite_individual = schedule\n\titerations = 100\n\n\t_initial_individual(schedule)\n\tpopulation = _initial_population(schedule)\n\tfor i in range(iterations):\n\t\t_debug('iteration ' + str(i))\n\t\tpopulation = _mutate_population(population)\n\n\t\tif population[0] == elite_individual:\n\t\t\telite_iterations += 1\n\t\telse:\n\t\t\telite_individual = population[0]\n\t\t\telite_iterations = 0\n\n\t\tif elite_iterations == iterations / 2:\n\t\t\tbreak\n\treturn population[0]\n\ndef _initial_individual(schedule):\n\t_debug('_initial_individual')\n\tfor t in schedule.tasks:\n\t\tt.begin = t.deadline - t.duration\n\t_eval_individual(schedule)\n\ndef _initial_population(schedule):\n\t_debug('_initial_population')\n\tpopulation = [schedule]\n\tpopulation_size = 50\n\twhile len(population) < population_size:\n\t\tnewindiv = _mutate_individual(\n\t\t\tpopulation[int(uniform(0, len(population)))])\n\t\twhile newindiv in population:\n\t\t\t_debug('_initial_population: newindiv in population')\n\t\t\tnewindiv = _mutate_individual(\n\t\t\t\tpopulation[int(uniform(0, len(population)))])\n\t\t_eval_individual(newindiv)\n\t\tpopulation += [newindiv]\n\tpopulation.sort(lambda a, b: cmp(b.fitness, a.fitness))\n\treturn population\n\ndef _mutate_population(population):\n\t_debug('_mutate_population')\n\tsize = len(population)\n\tfor i in range(size / 2):\n\t\tj = int(uniform(0, size))\n\t\tnewindiv = _mutate_individual(population[j])\n\t\twhile newindiv in population:\n\t\t\t_debug('_mutate_population: newindiv in population')\n\t\t\tnewindiv = _mutate_individual(population[j])\n\t\t_eval_individual(newindiv)\n\t\tpopulation += [newindiv]\n\tpopulation.sort(lambda a, b: cmp(b.fitness, a.fitness))\n\treturn population[:size]\n\ndef _mutate_individual(indiv):\n\t_debug('_mutate_individual')\n\tnewindiv = indiv.__copy__()\n\tfor t in newindiv.tasks:\n\t\tif uniform(0, 1) >= 0.5:\n\t\t\tif uniform(0, 1) >= 0.5:\n\t\t\t\tt.begin += max(0, int(uniform(1, t.duration / 4)))\n\t\t\telse:\n\t\t\t\tt.begin -= max(0, int(uniform(1, t.duration / 4)))\n\treturn newindiv\n\ndef _eval_individual(indiv):\n\t_debug('_eval_individual')\n\tindiv.fitness = 0\n\t_eval_deadlines(indiv)\n\t_eval_overlaps(indiv)\n\ndef _eval_deadlines(indiv):\n\t_debug('_eval_deadlines')\n\tfor t in indiv.tasks:\n\t\tif t.begin + t.duration > t.deadline:\n\t\t\tindiv.fitness -= abs(((t.begin + t.duration) - t.deadline) * 4)\n\t\telif t.begin + t.duration == t.deadline:\n\t\t\tindiv.fitness += 8\n\t\telse:\n\t\t\tindiv.fitness -= abs((t.begin + t.duration) - t.deadline)\n\ndef _eval_overlaps(indiv):\n\t_debug('_eval_overlaps')\n\tnum_overlaps = 0\n\tpenalty = 0\n\n\tfor t in indiv.tasks:\n\t\tfor t2 in indiv.tasks:\n\t\t\tif t.begin + t.duration >= t2.begin and \\\n\t\t\t\t t.begin + t.duration <= t2.begin + t2.duration:\n\t\t\t\tpenalty += ((t.begin + t.duration - t2.begin) + 1)\n\t\t\t\tnum_overlaps += 1\n\n\t\tfor t2 in indiv.fixedtasks:\n\t\t\tif t.begin + t.duration >= t2.begin and \\\n\t\t\t\t t.begin + t.duration <= t2.begin + t2.duration:\n\t\t\t\tpenalty += ((t.begin + t.duration - t2.begin) + 1)\n\t\t\t\tnum_overlaps += 1\n\n\tindiv.fitness -= (penalty * num_overlaps)\n","sub_path":"Projects/Autocal/attic/autocal-20100708/libautocal/optimize.py","file_name":"optimize.py","file_ext":"py","file_size_in_byte":3013,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"92241106","text":"class GameStats():\n    \"\"\"跟踪游戏的统计信息\"\"\"\n    def __init__(self,ai_settings):\n        \"\"\"初始化统计信息\"\"\"\n        self.ai_settings=ai_settings\n        self.reset_stats()\n        # 让游戏状态保持在非活动状态\n        self.game_active=False\n\n        # 显示最高分,一般情况下不得重置\n        self.high_score=0\n\n\n    def reset_stats(self):\n        \"\"\"初始化游戏期间可能变化的统计信息\"\"\"\n        self.ships_left=self.ai_settings.ship_limit\n        # 添加分数统计信息\n        self.score=0\n\n        # 添加玩家的等级\n        self.level=1\n","sub_path":"game_stats.py","file_name":"game_stats.py","file_ext":"py","file_size_in_byte":609,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"237099869","text":"import yaml\n\ndef read_yam(data):\n    with open('D:\\\\telephone\\Data'+'\\\\'+data+'.yaml') as f:\n        return yaml.load(f,Loader=yaml.FullLoader)\n\n\nif __name__ == '__main__':\n    dt = read_yam('data1').get('Data')\n    dt_list = []\n    for i in dt.values():\n        dt_list.append(i.values())\n    print(dt_list)","sub_path":"init/read_yaml.py","file_name":"read_yaml.py","file_ext":"py","file_size_in_byte":308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"60107332","text":"xmin, xmax = -2, 0.8\nymin, ymax = -1.5, 1.5\n\nmax_iter = 200 \n\nwidth, height = 400, 400\n\npix = lambda x, y : x + y * width\n\ncmap = lambda value, v_min, v_max, p_min, p_max: p_min + (p_max - p_min) * ((value - v_min)/(v_max - v_min))\n\nfrom PIL import Image\nfrom array import array\n\nm = array('B', [0] * width * height)\n\nfor cx in range(width):\n    for cy in range(height):\n        cr = cmap(cx, 0, width, xmin, xmax)\n        ci = cmap(cy, 0, height, ymin, ymax)\n        c = cr + ci * 1j\n        z = 0 + 0j\n        for i in range(max_iter):\n            z = z * z + c\n            if abs(z) > 2:\n                m[pix(cx, cy)] = 255\n                break\n        else:\n            m[pix(cx, cy)] = 0\n\nimg = Image.new('L', (width, height))\nimg.frombytes(m.tobytes())\nimg\n\n\n","sub_path":"week11/mandelbrot.py","file_name":"mandelbrot.py","file_ext":"py","file_size_in_byte":767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"626337170","text":"import random\nfrom space import cnn_space\n\n#random searches for a CNN\ndef get_cnn_model(model_num, search_space):\n    space = cnn_space(search_space)\n\n    param_dist = {'model_' + model_num: 'CNN',\n            'word_vectors_' + model_num: ('word2vec', True),\n            'delta_' + model_num: random.choice(space['delta_']),\n            'flex_' + model_num: (True, random.random() * (space['flex_amt_'][1] -\n                                space['flex_amt_'][1]) + space['flex_amt_'][0]),\n            'dropout_' + model_num: random.random() * (space['dropout_'][1] -\n                                space['dropout_'][0]) + space['dropout_'][0],\n            'activation_fn_' + model_num: random.choice(space['activation_fn_']),\n            'learning_rate_' + model_num: .0003,\n            'filters_' + model_num: random.randint(*space['filters_']),\n            'kernel_size_' + model_num: random.randint(*space['kernel_size_']),\n            'kernel_increment_' + model_num: random.randint(*space['kernel_increment_']),\n            'kernel_num_' + model_num: random.randint(*space['kernel_num_']),\n            'batch_size_' + model_num: random.randint(*space['batch_size_'])\n    }\n\n    #regularize with option of no rig if no reg\n    if space['no_reg'] == True:\n        param_dist['regularizer_cnn_' + model_num] = random.choice([\n            (None, 0.0),\n            ('l2', (random.random() * (space['l2_'][1] - space['l2_'][0]) + space['l2_'][0])),\n            ('l2_clip', (random.random() * (space['l2_clip_'][1] - space['l2_clip_'][0]) + space['l2_clip_'][0]))\n        ])\n\n    else:\n        param_dist['regularizer_cnn_' + model_num] = random.choice([\n            ('l2', (random.random() * (space['l2_'][1] - space['l2_'][0]) + space['l2_'][0])),\n            ('l2_clip', (random.random() * (space['l2_clip_'][1] - space['l2_clip_'][0]) + space['l2_clip_'][0]))\n        ])\n\n    #overwrite previously set values to search over some parameters depending on options\n    if space['search_lr'] == True:\n        param_dist['learning_rate_' + model_num] = (random.lognormvariate(0, 1)) / 3000\n\n    return param_dist\n\ndef get_feats(model_num):\n    return {'nmin_to_max_' + model_num: random.choice([(1,1),(1,2),(1,3),(2,2),(2,3)]),\n            'binary_' + model_num: random.choice([True, False]),\n            'use_idf_' + model_num: random.choice([True, False]),\n            'st_wrd_' + model_num: random.choice([None, 'english'])}\n","sub_path":"src/random_search.py","file_name":"random_search.py","file_ext":"py","file_size_in_byte":2425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"530069829","text":"import os\n\nimport keras\n\nfrom keras.layers import concatenate\n\nfrom sklearn.metrics import cohen_kappa_score\n\n\nimport math\nimport random\nfrom keras import optimizers\nimport numpy as np\nimport scipy.io as spio\nfrom sklearn.metrics import f1_score, accuracy_score\nnp.random.seed(0)\n\nfrom keras.preprocessing import sequence\nfrom keras.utils import np_utils\nfrom keras.models import Sequential\nfrom keras.layers import Layer,Dense, Dropout, Input, Activation, TimeDistributed, Reshape\nfrom keras.layers import  GRU, Bidirectional\nfrom keras.layers import  Conv1D, Conv2D, MaxPooling2D, Flatten, BatchNormalization, LSTM, ZeroPadding2D, GlobalAveragePooling2D\nfrom keras.callbacks import History \nfrom keras.models import Model\n\nfrom keras.layers.noise import GaussianNoise\n\nfrom collections import Counter\n\nfrom sklearn.utils import class_weight\n\nfrom myModel import build_model\n\n\n\nbatch_size = 200\nn_ep = 2\nfs  = 200;\n# half_size of the sliding window in samples\nw_len = 8*fs;\ndata_dim = w_len*2\nhalf_prec = 0.5\nprec = 1\nn_cl = 4\n\nimg_dir = './tSNE_val/'\n\nimport sys\nsys.path.append(\"..\")\nfrom loadData import  *\nfrom utils import *\n\ndata_dir = './../../../data/files/'\nf_set = './../../../data/file_sets.mat'\n\ncreate_tmp_dirs([img_dir])\n\nmat = spio.loadmat(f_set)\n\t\nfiles_train = []\nfiles_val = []\nfiles_test = []\n\t\n\t\n\t\ntmp =  mat['files_train']\nfor i in range(len(tmp)):\n\tfile = [str(''.join(l)) for la in tmp[i] for l in la]\n\tfiles_train.extend(file)\nprint(files_train)\ntmp =  mat['files_val']\nfor i in range(len(tmp)):\n\tfile = [str(''.join(l)) for la in tmp[i] for l in la]\n\tfiles_val.extend(file)\ntmp =  mat['files_test']\nfor i in range(len(tmp)):\n\tfile = [str(''.join(l)) for la in tmp[i] for l in la]\n\tfiles_test.extend(file)\n\n\ndef my_generator(data_train, targets_train, sample_list, shuffle = True):\n\tif shuffle:\n\t\trandom.shuffle(sample_list)\n\twhile True:\n\t\tfor batch in batch_generator(sample_list, batch_size):\n\t\t\tbatch_data1 = []\n\t\t\tbatch_data2 = []\n\t\t\tbatch_targets = []\n\t\t\tfor sample in batch:\n\t\t\t\t[f, s, b, e, c] = sample\n\t\t\t\tsample_label = targets_train[f][c][s]\n\t\t\t\tsample_x1 = data_train[f][c][b:e+1]\n\t\t\t\tsample_x2 = data_train[f][2][b:e+1]\n\t\t\t\tsample_x = np.concatenate( ( sample_x1, sample_x2 ), axis = 2 )\n\t\t\t\tbatch_data1.append(sample_x)\n\t\t\t\tbatch_targets.append(sample_label)\n\t\t\tbatch_data1 = np.stack(batch_data1, axis=0)\n\t\t\tbatch_targets = np.array(batch_targets)\n\t\t\tbatch_targets = np_utils.to_categorical(batch_targets, n_cl)\n\t\t\tbatch_data1 = (batch_data1 )/100\n\t\t\tbatch_data1 = np.clip(batch_data1, -1, 1)\n\t\t\tyield [ batch_data1 ], batch_targets\n\nn_channels = 3\n\n\n\nordering = 'channels_last';\nkeras.backend.set_image_data_format(ordering)\n\n\nprint(\"Building model\")\n[cnn_eeg, model] = build_model(data_dim, n_channels, n_cl)\nNadam = optimizers.Nadam( )\nmodel.compile(optimizer=Nadam,  loss='categorical_crossentropy', metrics=['accuracy'], sample_weight_mode=None)\nprint(cnn_eeg.summary())\nprint(model.summary())\nmodel.load_weights('./model.h5')\nprint(\"Done\")\n\nf_list = files_val\n\ncnn_eeg.compile(optimizer=Nadam,  loss='categorical_crossentropy', metrics=['accuracy'], sample_weight_mode=None)\n\n\nmodel.load_weights('./model.h5')\n\nfrom sklearn.manifold import TSNE\nimport matplotlib.pyplot as plt\n\np_s = 5\n\nX_g = []\nY_g = []\nbatch_size_val = 200\nf_list = files_val\nval_l = []\nprint('validation set')\nprint(f_list[0])\nprint('====================')\nfor j in range(len(f_list)):\n\tval_y_ = []\n\tval_y = []\n\tf = f_list[j]\n\tprint('reading file ', f)\n\tdata, targets, N_samples  = load_data(data_dir, [f_list[j]], w_len)\n\tprint('Done ')\n\tprint(\"Building validation sample list\")\n\tsample_list_val = []\n\tfor i in range(len(targets)):\n\t\tsample_list_val.append([])\n\t\tfor j in range(len(targets[i][0])):\n\t\t\tmid = j*prec\n\t\t\t# we add the padding size\n\t\t\tmid += w_len\n\t\t\twnd_begin = mid-w_len\n\t\t\twnd_end = mid+w_len-1\n\t\t\tsample_list_val[i].append([i,j,wnd_begin, wnd_end, 0 ])\n\tprint(\"Done\")\t\t\n \n \n \n\tgenerator_val = my_generator(data, targets, sample_list_val[0], shuffle = False)\n\ty_pred = cnn_eeg.predict_generator( generator_val, int(math.ceil((len(sample_list_val[0])+0.0)/batch_size_val)), workers=1)\n\tval_l.append(len(sample_list_val[0]))\n\tprint(y_pred.shape)\n\tfor k in range(y_pred.shape[0]):\n\t\tval_y_.append( y_pred[k])\n\tgenerator_val = my_generator(data, targets, sample_list_val[0], shuffle = False)\n\tfor ii in range(int(math.ceil( (len(sample_list_val[0])+0.0)/batch_size_val) )):\n\t\t[x, y] = next(generator_val)\n\t\tfor k in range(y.shape[0]):\n\t\t\tval_y.append( y[k] )\n\tval_y = val_y[::100]\n\tval_y_ = val_y_[::100]\n\tval_y = np.stack(val_y, axis=0)\n\tval_y_ = np.stack(val_y_, axis=0)\n\n\tX = val_y_\n\tY = val_y\n\tX_g = []\n\tY_g = []\n\tfor l in range(0, len(X), 10):\n\t\tX_g.append(X[l])\n\t\tY_g.append(Y[l])\n\tembeddings = []\n\temb_color = []\n\tfor i in range(len(X)):\n\t\toutput_class = np.argmax(Y[i,:])\n\t\tembedding = X[i,:]\n\t\tembeddings.append(embedding[0])\n\t\tif (output_class == 0):\n\t\t\temb_color.append(\"#0000ff\")\n\t\telif (output_class == 1):\n\t\t\temb_color.append(\"#ff0000\")\n\t\telif (output_class == 2):\n\t\t\temb_color.append(\"#00ff00\")\n\t\telif (output_class == 3):\n\t\t\temb_color.append(\"#ff00ff\")\n\n\n\ttsne = TSNE(n_components=2, random_state=0)\n\temb_2d = tsne.fit_transform(X)\n\n\n\n\tplt.figure(figsize=(8, 8))\n\tplt.scatter(x = emb_2d[:,0], y=emb_2d[:,1], color=emb_color, s=p_s)\n\tplt.savefig(img_dir+f+'.png')\n","sub_path":"code/embeddings_64_CNN_16s/compute_embeddings_val.py","file_name":"compute_embeddings_val.py","file_ext":"py","file_size_in_byte":5306,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"534690457","text":"# -*- coding: utf-8 -*-\n##############################################################################\n#\n#    Author: Guewen Baconnier, Leonardo Pistone\n#    Copyright 2013-2015 Camptocamp SA\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 <http://www.gnu.org/licenses/>.\n#\n##############################################################################\n\nfrom odoo import models, fields, api, exceptions\n\n\nclass SaleStockReserve(models.TransientModel):\n    _name = 'sale.stock.reserve'\n\n    @api.model\n    def _default_location_id(self):\n        return self.env['stock.reservation']._default_location_id()\n\n    @api.model\n    def _default_location_dest_id(self):\n        return self.env['stock.reservation']._default_location_dest_id()\n\n    def _default_owner(self):\n        \"\"\"If sale_owner_stock_sourcing is installed, it adds an owner field\n        on sale order lines. Use it.\n        \"\"\"\n        model = self.env[self.env.context['active_model']]\n        if model._name == 'sale.order':\n            lines = model.browse(self.env.context['active_id']).order_line\n        else:\n            lines = model.browse(self.env.context['active_ids'])\n\n        try:\n            owners = set([l.stock_owner_id for l in lines])\n        except AttributeError:\n            return self.env['res.partner']\n            # module sale_owner_stock_sourcing not installed, fine\n\n        if len(owners) == 1:\n            return owners.pop()\n        elif len(owners) > 1:\n            raise exceptions.Warning(\n                'The lines have different owners. Please reserve them '\n                'individually with the reserve button on each one.')\n\n        return self.env['res.partner']\n\n    location_id = fields.Many2one(\n        'stock.location',\n        'Source Location',\n        required=True,\n        default=_default_location_id)\n    location_dest_id = fields.Many2one(\n        'stock.location',\n        'Reservation Location',\n        required=True,\n        help=\"Location where the system will reserve the \"\n             \"products.\",\n        default=_default_location_dest_id)\n    date_validity = fields.Date(\n        \"Validity Date\",\n        help=\"If a date is given, the reservations will be released \"\n             \"at the end of the validity.\")\n    note = fields.Text('Notes')\n    owner_id = fields.Many2one('res.partner', 'Stock Owner',\n                               default=_default_owner)\n\n    @api.multi\n    def _prepare_stock_reservation(self, line):\n        self.ensure_one()\n        product_uom = line.product_uom.id if line.product_uom else False\n        return {'product_id': line.product_id.id,\n                'product_uom': line.product_uom.id,\n                'product_uom_qty': line.product_uom_qty,\n                'date_validity': self.date_validity,\n                'name': \"%s (%s)\" % (line.order_id.name, line.name),\n                'location_id': self.location_id.id,\n                'location_dest_id': self.location_dest_id.id,\n                'note': self.note,\n                'price_unit': line.price_unit,\n                'sale_line_id': line.id,\n                'restrict_partner_id': self.owner_id.id,\n                }\n\n    @api.multi\n    def stock_reserve(self, line_ids):\n        self.ensure_one()\n\n        lines = self.env['sale.order.line'].browse(line_ids)\n        for line in lines:\n            if not line.is_stock_reservable:\n                continue\n            vals = self._prepare_stock_reservation(line)\n            reserv = self.env['stock.reservation'].create(vals)\n            reserv.reserve()\n        return True\n\n    @api.multi\n    def button_reserve(self):\n        env = self.env\n        self.ensure_one()\n        close = {'type': 'ir.actions.act_window_close'}\n        active_model = env.context.get('active_model')\n        active_ids = env.context.get('active_ids')\n        if not (active_model and active_ids):\n            return close\n\n        if active_model == 'sale.order':\n            sales = env['sale.order'].browse(active_ids)\n            line_ids = [line.id for sale in sales for line in sale.order_line]\n\n        if active_model == 'sale.order.line':\n            line_ids = active_ids\n\n        self.stock_reserve(line_ids)\n        return close\n","sub_path":"stock_reserve_sale/stock_reserve_sale/wizard/sale_stock_reserve.py","file_name":"sale_stock_reserve.py","file_ext":"py","file_size_in_byte":4809,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"30001386","text":"import numpy as np\nfrom scipy import optimize\n\n\nclass Trainer(object):\n    def __init__(self, nn):\n        self.NN = nn\n\n    def cost_function_wrapper(self, params, X, y):\n        self.NN.set_param_from_vector(params)\n        cost = self.NN.cost_function(X, y)\n        grad = self.NN.compute_gradients(X,y)\n        return cost, grad\n\n    def callback_bfgs(self, params):\n        self.NN.set_param_from_vector(params)\n        self.J.append(self.NN.cost_function(self.X, self.y))\n        self.J_test.append(self.NN.cost_function(self.X_test, self.y_test))\n\n    def train(self, train_X, train_y, test_X, test_y):\n        self.X = train_X\n        self.y = train_y\n\n        self.X_test = test_X\n        self.y_test = test_y\n\n        # List to store costs\n        self.J = []\n        self.J_test = []\n\n        init_params = self.NN.get_param_vector()\n\n        options = {'maxiter': 200, 'disp': True}\n        _res = optimize.minimize(self.cost_function_wrapper, init_params, jac=True, method='BFGS', \\\n                                 args=(train_X, train_y), options=options, callback=self.callback_bfgs)\n\n\nclass NeuralNetwork(object):\n    def __init__(self, count_hidden_layers, ILS, HLS, OLS, a_lambda, af_hl, af_hl_prime, af_ol, af_ol_prime):\n        # Define Parameters of network:\n        # Number of hidden layers\n        self.hl_count = count_hidden_layers\n        # Input layer size\n        self.ILS = ILS\n        # Hidden layer size -> here we assume that all layers have the same number of neurons\n        self.HLS = HLS\n        # Output layer size\n        self.OLS = OLS\n        # Lambda for regularization of cost function\n        self.Lambda = a_lambda\n        # Activation function on hidden layer\n        self.af_hl = af_hl\n        # Activation function derivative on hidden layer\n        self.af_hl_prime = af_hl_prime\n        # Activation function on output layer\n        self.af_ol = af_ol\n        # Activation function derivative on output layer\n        self.af_ol_prime = af_ol_prime\n\n        # Weights\n        self.weights = []\n        for n in range(0, self.hl_count + 1, 1):\n            if n == 0:\n                self.weights.append(np.random.randn(self.ILS, self.HLS))\n            elif n == self.hl_count:\n                self.weights.append(np.random.randn(self.HLS, self.OLS))\n            else:\n                self.weights.append(np.random.randn(self.HLS, self.HLS))\n\n\n    # Cost function (errors)\n    def cost_function(self,X,y):\n        self.y_hat = self.forward(X)\n        # Cost with regularization\n        reg=0.0\n        for W in self.weights:\n            reg = reg + np.sum(W**2)\n\n        J = 0.5 * sum((y - self.y_hat) ** 2) / X.shape[0] + (self.Lambda / 2) * reg\n        return J\n\n    # Derivative from cost function (to reduce errors)\n    def cost_function_prime(self, X, y):\n        self.y_hat = self.forward(X)\n\n        dJdWn = []\n        delta_n = np.multiply(-(y - self.y_hat), self.af_ol_prime(self.Z[self.hl_count]))\n        for n in range(self.hl_count+1, 0, -1):\n            # Apply for the last the function on output layer\n            if n < self.hl_count+1:\n                delta_n = np.dot(delta_n, self.weights[n].T) * self.af_hl_prime(self.Z[n-1])\n            dJdWn.append(np.dot(self.A[n-1].T, delta_n)/X.shape[0] + self.Lambda*self.weights[n-1])\n\n        return dJdWn[::-1]\n\n    def forward(self, X):\n        self.Z = []\n        self.A = []\n        self.A.append(X)\n\n        for n in range(0, self.hl_count+1, 1):\n            self.Z.append(np.dot(self.A[n], self.weights[n]))\n            # If the last W -> apply activation function on output layer\n            # otherwise apply function on hidden layer\n            if n == self.hl_count:\n                self.A.append(self.af_ol(self.Z[n]))\n                break\n            else:\n                self.A.append(self.af_hl(self.Z[n]))\n            # self.Z.append(np.dot(X, self.weights[n]))\n        self.y_hat = self.A[self.hl_count+1]\n        return self.y_hat\n\n#  Some methods to operate with parameters from outside the class\n    def get_param_vector(self):\n        # Get W1, W2 .. Wn as a vector\n        flatten_w = [w.ravel() for w in self.weights]\n        return np.concatenate(tuple(flatten_w))\n\n    def set_param_from_vector(self, params):\n        # Set W1 and W2 from a vector\n        self.weights = []\n\n        for n in range(0, self.hl_count+1, 1):\n            if n == 0:\n                w_start = 0\n                w_end = self.HLS * self.ILS\n                self.weights.append(np.reshape(params[w_start:w_end], (self.ILS, self.HLS)))\n            elif n == self.hl_count:\n                w_start = w_end\n                w_end = w_start + self.HLS*self.OLS\n                self.weights.append(np.reshape(params[w_start:w_end], (self.HLS, self.OLS)))\n            else:\n                w_start = w_end\n                w_end = w_start + self.HLS**2\n                self.weights.append(np.reshape(params[w_start:w_end], (self.HLS, self.HLS)))\n\n    def compute_gradients(self, X,y):\n        dJdWn = self.cost_function_prime(X,y)\n        flatten_dJdWn = []\n        for dJdW in dJdWn:\n            flatten_dJdWn.append(dJdW.ravel())\n        # flatten_dJdWn = [djdW.ravel() for djdW in dJdWn]\n        return np.concatenate(tuple(flatten_dJdWn))\n\n\n# We'll check whether something went wrong\ndef compute_numerical_gradients(NN, X, y):\n    init_params = NN.get_param_vector()\n    numerical_grad = np.zeros(init_params.shape)\n    delta_X = np.zeros(init_params.shape)\n    epsilon = 1e-4\n\n    for n in range(len(init_params)):\n        delta_X[n] = epsilon\n        NN.set_param_from_vector(init_params+delta_X)\n        loss2 = NN.cost_function(X,y)\n\n        NN.set_param_from_vector(init_params-delta_X)\n        loss1 = NN.cost_function(X,y)\n\n        numerical_grad[n] = (loss2 - loss1)/(2*epsilon)\n        # Initialize delta_X back (for next element in vector!)\n        delta_X[n] = 0\n\n    NN.set_param_from_vector(init_params)\n    return numerical_grad\n","sub_path":"funcs/neural_network.py","file_name":"neural_network.py","file_ext":"py","file_size_in_byte":5951,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"84846874","text":"from __future__ import division\n\n__copyright__ = \"Copyright (C) 2013 Andreas Kloeckner\"\n\n__license__ = \"\"\"\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\n\nfrom pytools import memoize_method, Record\nimport numpy as np\nimport pyopencl as cl\nimport pyopencl.array  # noqa\nfrom mako.template import Template\nfrom boxtree.tools import AXIS_NAMES, DeviceDataRecord\n\nimport logging\nlogger = logging.getLogger(__name__)\n\n__doc__ = \"\"\"\nLeaves -> overlapping balls\n---------------------------\n\n.. autoclass:: LeavesToBallsLookupBuilder\n\n.. autoclass:: LeavesToBallsLookup\n\"\"\"\n\n\n# {{{ output\n\nclass LeavesToBallsLookup(DeviceDataRecord):\n    \"\"\"\n    .. attribute:: tree\n\n        The :class:`boxtree.Tree` instance used to build this lookup.\n\n    .. attribute:: balls_near_box_starts\n\n        Indices into :attr:`balls_near_box_lists`.\n        ``balls_near_box_lists[balls_near_box_starts[ibox]:\n        balls_near_box_starts[ibox]+1]``\n        results in a list of balls that overlap leaf box *ibox*.\n\n        .. note:: Only leaf boxes have non-empty entries in this table. Nonetheless,\n            this list is indexed by the global box index.\n\n    .. attribute:: balls_near_box_lists\n\n    .. automethod:: get\n    \"\"\"\n\n# }}}\n\n# {{{ kernel templates\n\nBALLS_TO_LEAVES_TEMPLATE = r\"\"\"//CL//\ntypedef ${dtype_to_ctype(ball_id_dtype)} ball_id_t;\n\nvoid generate(LIST_ARG_DECL USER_ARG_DECL ball_id_t ball_nr)\n{\n    coord_vec_t ball_center;\n    %for i in range(dimensions):\n        ball_center.${AXIS_NAMES[i]} = ball_${AXIS_NAMES[i]}[ball_nr];\n    %endfor\n\n    coord_t ball_radius = ball_radii[ball_nr];\n\n    // To find overlapping leaves, start at the top of the tree, descend\n    // into overlapping boxes.\n    ${walk_init(0)}\n\n    while (continue_walk)\n    {\n        box_id_t child_box_id = box_child_ids[\n            walk_morton_nr * aligned_nboxes + walk_box_id];\n        dbg_printf((\"  walk box id: %d morton: %d child id: %d level: %d\\n\",\n            walk_box_id, walk_morton_nr, child_box_id, walk_level));\n\n        if (child_box_id)\n        {\n            bool is_overlapping;\n\n            ${check_l_infty_ball_overlap(\n                \"is_overlapping\", \"child_box_id\", \"ball_radius\", \"ball_center\")}\n\n            if (is_overlapping)\n            {\n                if (!(box_flags[child_box_id] & BOX_HAS_CHILDREN))\n                {\n                    APPEND_ball_numbers(ball_nr);\n                    APPEND_overlapping_leaves(child_box_id);\n                }\n                else\n                {\n                    // We want to descend into this box. Put the current state\n                    // on the stack.\n\n                    ${walk_push(\"child_box_id\")}\n                    continue;\n                }\n            }\n        }\n\n        ${walk_advance()}\n    }\n}\n\"\"\"\n\n\nclass _KernelInfo(Record):\n    pass\n\n\nclass LeavesToBallsLookupBuilder(object):\n    \"\"\"Given a set of :math:`l^\\infty` \"balls\", this class helps build a\n    look-up table from leaf boxes to balls that overlap with each leaf box.\n\n    .. automethod:: __call__\n\n    \"\"\"\n    def __init__(self, context):\n        self.context = context\n\n        from pyopencl.algorithm import KeyValueSorter\n        self.key_value_sorter = KeyValueSorter(context)\n\n    @memoize_method\n    def get_balls_to_leaves_kernel(self, dimensions, coord_dtype, box_id_dtype,\n            ball_id_dtype, max_levels, stick_out_factor):\n        from pyopencl.tools import dtype_to_ctype\n        from boxtree import box_flags_enum\n        render_vars = dict(\n                dimensions=dimensions,\n                dtype_to_ctype=dtype_to_ctype,\n                box_id_dtype=box_id_dtype,\n                particle_id_dtype=None,\n                ball_id_dtype=ball_id_dtype,\n                coord_dtype=coord_dtype,\n                vec_types=cl.array.vec.types,\n                max_levels=max_levels,\n                AXIS_NAMES=AXIS_NAMES,\n                box_flags_enum=box_flags_enum,\n                debug=False,\n                stick_out_factor=stick_out_factor,\n                )\n\n        logger.info(\"start building leaves-to-balls lookup kernel\")\n\n        from boxtree.traversal import TRAVERSAL_PREAMBLE_TEMPLATE\n\n        src = Template(\n                TRAVERSAL_PREAMBLE_TEMPLATE\n                + BALLS_TO_LEAVES_TEMPLATE,\n                strict_undefined=True).render(**render_vars)\n\n        from pyopencl.tools import VectorArg, ScalarArg\n        from pyopencl.algorithm import ListOfListsBuilder\n        result = ListOfListsBuilder(self.context,\n                [\n                    (\"ball_numbers\", ball_id_dtype),\n                    (\"overlapping_leaves\", box_id_dtype),\n                    ],\n                str(src),\n                arg_decls=[\n                    VectorArg(box_flags_enum.dtype, \"box_flags\"),\n                    VectorArg(coord_dtype, \"box_centers\"),\n                    VectorArg(box_id_dtype, \"box_child_ids\"),\n                    VectorArg(np.uint8, \"box_levels\"),\n                    ScalarArg(coord_dtype, \"root_extent\"),\n                    ScalarArg(box_id_dtype, \"aligned_nboxes\"),\n                    VectorArg(coord_dtype, \"ball_radii\"),\n                    ] + [\n                        VectorArg(coord_dtype, \"ball_\"+ax)\n                        for ax in AXIS_NAMES[:dimensions]],\n                name_prefix=\"circles_to_balls\",\n                count_sharing={\n                    # /!\\ This makes a promise that APPEND_ball_numbers will\n                    # always occur *before* APPEND_overlapping_leaves.\n                    \"overlapping_leaves\": \"ball_numbers\"\n                    },\n                complex_kernel=True)\n\n        logger.info(\"done building leaves-to-balls lookup kernel\")\n\n        return result\n\n    def __call__(self, queue, tree, ball_centers, ball_radii, wait_for=None):\n        \"\"\"\n        :arg queue: a :class:`pyopencl.CommandQueue`\n        :arg tree: a :class:`boxtree.Tree`.\n        :arg ball_centers: an object array of coordinate\n            :class:`pyopencl.array.Array` instances.\n            Their *dtype* must match *tree*'s\n            :attr:`boxtree.Tree.coord_dtype`.\n        :arg ball_radii: a\n            :class:`pyopencl.array.Array`\n            of positive numbers.\n            Its *dtype* must match *tree*'s\n            :attr:`boxtree.Tree.coord_dtype`.\n        :arg wait_for: may either be *None* or a list of :class:`pyopencl.Event`\n            instances for whose completion this command waits before starting\n            exeuction.\n        :returns: a tuple *(lbl, event)*, where *lbl* is an instance of\n            :class:`LeavesToBallsLookup`, and *event* is a :class:`pyopencl.Event`\n            for dependency management.\n        \"\"\"\n\n        from pytools import single_valued\n        if single_valued(bc.dtype for bc in ball_centers) != tree.coord_dtype:\n            raise TypeError(\"ball_centers dtype must match tree.coord_dtype\")\n        if ball_radii.dtype != tree.coord_dtype:\n            raise TypeError(\"ball_radii dtype must match tree.coord_dtype\")\n\n        ball_id_dtype = tree.particle_id_dtype  # ?\n\n        from pytools import div_ceil\n        # Avoid generating too many kernels.\n        max_levels = div_ceil(tree.nlevels, 10) * 10\n\n        b2l_knl = self.get_balls_to_leaves_kernel(\n                tree.dimensions, tree.coord_dtype,\n                tree.box_id_dtype, ball_id_dtype,\n                max_levels, tree.stick_out_factor)\n\n        logger.info(\"leaves-to-balls lookup: prepare ball list\")\n\n        nballs = len(ball_radii)\n        result, evt = b2l_knl(\n                queue, nballs,\n                tree.box_flags.data, tree.box_centers.data,\n                tree.box_child_ids.data, tree.box_levels.data,\n                tree.root_extent, tree.aligned_nboxes,\n                ball_radii.data, *tuple(bc.data for bc in ball_centers),\n                wait_for=wait_for)\n        wait_for = [evt]\n\n        logger.info(\"leaves-to-balls lookup: key-value sort\")\n\n        balls_near_box_starts, balls_near_box_lists, evt \\\n                = self.key_value_sorter(\n                        queue,\n                        # keys\n                        result[\"overlapping_leaves\"].lists,\n                        # values\n                        result[\"ball_numbers\"].lists,\n                        tree.nboxes, starts_dtype=tree.box_id_dtype,\n                        wait_for=wait_for)\n\n        logger.info(\"leaves-to-balls lookup: built\")\n\n        return LeavesToBallsLookup(\n                tree=tree,\n                balls_near_box_starts=balls_near_box_starts,\n                balls_near_box_lists=balls_near_box_lists).with_queue(None), evt\n\n# }}}\n\n# vim: filetype=pyopencl:fdm=marker\n","sub_path":"boxtree/geo_lookup.py","file_name":"geo_lookup.py","file_ext":"py","file_size_in_byte":9633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"58395692","text":"\n\nfrom xai.brain.wordbase.adjectives._contrary import _CONTRARY\n\n#calss header\nclass _CONTRARIES(_CONTRARY, ):\n\tdef __init__(self,): \n\t\t_CONTRARY.__init__(self)\n\t\tself.name = \"CONTRARIES\"\n\t\tself.specie = 'adjectives'\n\t\tself.basic = \"contrary\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/adjectives/_contraries.py","file_name":"_contraries.py","file_ext":"py","file_size_in_byte":264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"582960528","text":"from .. import constants\nfrom . import preprocessor, utils\nimport time, math, operator, csv, os\nfrom pathlib import Path\n\nBOW_FOLDER = constants.BOW_FOLDER\ntest_string = ''\n\ndef create_bow_model():\n    prepare_progress_file()\n    result = preprocessor.create_word_models_from_database()\n    utils.save_used_urls()\n    return result\n\n\n\ndef get_bow_result(input_str, process_url=True):\n    success, article_text = utils.process_article(input_str)\n    word_list, counts = preprocessor.add_article_to_word_model(input_str, article_text)\n    if word_list is None or counts is None:\n        return 'Ein unerwarteter Fehler beim Erstellen des BOW-Modells ist aufgetreten'\n    prepare_result_file()\n    #\n    # BOW\n    start = time.time()\n    # vocab = []\n    # for l in word_list:\n    #    vocab.append(set())\n\n    # for i, c in enumerate(counts):\n    #   vocab[i] |= set(c.keys())\n\n    # for v in vocab:\n    #   v = sorted(list(v))  # sorting here only for better display later\n\n    # bow = []s\n    # for i, counter in enumerate(counts):\n    #   bow_row = [counter.get(term, 0) for term in vocab[i]]\n    #  bow.append(bow_row)\n\n\n    print('calculating similarities...')\n    similarities = {}\n    for i, c in counts.items():\n        if i != input_str:\n            similarities[i] = counter_cosine_similarity(counts[i], counts[input_str])\n\n    sorted_sims = sorted(similarities.items(), key=operator.itemgetter(1), reverse=True)\n    end = time.time()\n    print('done! took ', end - start, ' seconds.')\n\n    with open(constants.RESULTFILE_BOW, 'w', newline='', encoding='utf-8-sig') as f:\n        w = csv.writer(f)\n        w.writerows(sorted_sims)\n\n    return sorted_sims[:10]\n\n\ndef counter_cosine_similarity(c1, c2):\n    terms = set(c1).union(c2)\n    dotprod = sum(c1.get(k, 0) * c2.get(k, 0) for k in terms)\n    magA = math.sqrt(sum(c1.get(k, 0)**2 for k in terms))\n    magB = math.sqrt(sum(c2.get(k, 0)**2 for k in terms))\n    return dotprod / (magA * magB)\n\n\ndef prepare_result_file():\n    Path(constants.RESULTFILE_BOW).touch(exist_ok=True)\n    with open(constants.RESULTFILE_BOW, \"w\"):\n        pass\n\n\ndef prepare_progress_file():\n    if not os.path.exists(constants.BOW_FOLDER):\n        os.makedirs(constants.BOW_FOLDER)\n    Path(constants.PROGRESSFILE_BOW).touch(exist_ok=True)\n    with open(constants.PROGRESSFILE_BOW, \"w\"):\n        pass","sub_path":"backend/bowAnalysis/bow.py","file_name":"bow.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"383567975","text":"\"\"\"This module's function is to send information requests via the information keywords matching to Misty's API endpoints, sending information requests and matching data shortcuts.\n\"\"\"\nimport json\nfrom os import path\nfrom typing import Dict\n\nimport requests\n\nfrom misty2py.response import *\n\nthis_directory = path.abspath(path.dirname(__file__))\nINFOS_JSON = str(path.join(this_directory, \"allowed_infos.json\"))\n\n\nclass Get:\n    \"\"\"A class representing the GET url request method.\n\n    Attributes:\n        ip (str): The IP address for the URL where the requests are sent.\n        protocol (str): The protocol for the URL where the requests are sent.\n        allowed_infos (dict): The dictionary of information keywords matching to the Misty's API endpoints.\n    \"\"\"\n\n    def __init__(self, ip: str, protocol: str, custom_allowed_infos: Dict = {}) -> None:\n        \"\"\"Initialises a Get object.\n\n        Args:\n            ip (str): The IP address for the URL where the requests are sent.\n            protocol (str): The protocol for the URL where the requests are sent.\n            custom_allowed_infos (Dict, optional): The dictionary of custom information keywords. Defaults to `{}`.\n        \"\"\"\n\n        self.ip = ip\n        self.protocol = protocol\n\n        allowed_infos = custom_allowed_infos\n        f = open(INFOS_JSON)\n        allowed_infos.update(json.loads(f.read()))\n        f.close()\n\n        self.allowed_infos = allowed_infos\n\n    def get_info(self, endpoint: str, params: Dict) -> Misty2pyResponse:\n        \"\"\"Sends a GET request.\n\n        Args:\n            endpoint (str): The API endpoint to which the request is sent.\n\n        Returns:\n            Misty2pyResponse: A Misty2pyResponse object with Misty2py sub-response and Misty REST API sub-response.\n        \"\"\"\n\n        r = requests.get(\n            \"%s://%s/%s\" % (self.protocol, self.ip, endpoint), params=params\n        )\n        try:\n            return Misty2pyResponse(True, rest_response=r.json())\n\n        except Exception as e:\n            return Misty2pyResponse(\n                False,\n                rest_response=r.content,\n                error_msg=e,\n                error_type=Misty2pyErrorType.UNKNOWN,\n            )\n\n\nclass Info(Get):\n    \"\"\"A class representing an information request from Misty.\n    A subclass of Get().\"\"\"\n\n    def get_info(self, info_name: str, params: Dict = {}) -> Misty2pyResponse:\n        \"\"\"Sends an information request to Misty.\n\n        Args:\n            info_name (str): The information keyword specifying which information is requested.\n            params (Dict): dict of parameter name and parameter value. Defaults to `{}`.\n\n        Returns:\n            Misty2pyResponse: A Misty2pyResponse object with Misty2py sub-response and Misty REST API sub-response.\n        \"\"\"\n\n        if info_name not in self.allowed_infos.keys():\n            return Misty2pyResponse(\n                False,\n                error_msg=\"Command `%s` not supported.\" % info_name,\n                error_type=Misty2pyErrorType.COMMAND,\n            )\n\n        endpoint = self.allowed_infos[info_name]\n\n        try:\n            return super().get_info(endpoint, params)\n\n        except Exception as e:\n            return unknown_error(e)\n","sub_path":"misty2py/information.py","file_name":"information.py","file_ext":"py","file_size_in_byte":3228,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"579800391","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Mar 31 00:41:53 2020\n\n@author: vsheoran\n\"\"\"\nimport threading,time\nfrom datetime import datetime, timedelta\nfrom .wrapper import update_values, fetch_current\nfrom .utilities import get_logger, real_time_field_names, min_freeze_time, max_freeze_time\nfrom .database import DB\n\nlogger = get_logger(\"thread.py\")\nreal_time_data = dict()\n\nclass AsyncUpdateRealTimeTask(threading.Thread):\n    real_time_field_names = list()\n    def __init__(self, task_details):\n        super().__init__()\n        self.task_details = task_details\n        \n        global real_time_field_names\n        self.real_time_field_names = real_time_field_names\n        \n    def run(self):\n        logger.info(\"Inside AsyncUpdateRealTimeTask\")\n        try:\n            task = self.task_details\n            if isinstance(task['CP'], str):\n                task['CP'] = float(task['CP'])\n            if isinstance(task['HP'], str):\n                task['HP'] = float(task['HP'])\n            if isinstance(task['LP'], str):\n                task['LP'] = float(task['LP'])\n            # for task in self.task_details:\n            should_freeze = False\n            if 'reset_freeze_value' in task:\n                should_freeze = task['reset_freeze_value']\n            else:\n                current_time = datetime.strptime(task['Date'], '%m:%d:%Y %H:%M:%S')                \n                should_freeze = self.isFreezeEnabled(task, current_time, should_freeze)            \n                \n            task['Date'] = int(current_time.timestamp() * 1000)\n\n            current_listing = fetch_current() \n            # Calcuate response if listing matches\n            if 'listing' in current_listing and task['index'] == current_listing['listing']['SAS']:\n                update_values(task, should_freeze)\n                    \n            self.update_queue(task)\n            # self.update_data(data)               \n\n        except KeyError as err:\n            logger.info(f\"Key Error : {err}\")\n            # print(err)\n        except Exception as ex:\n            logger.info(ex)\n        logger.info(\"Exiting AsyncUpdateRealTimeTask\")\n            \n    def isFreezeEnabled(self, task, current_time, should_freeze):\n        global max_freeze_time, min_freeze_time\n        \n        if current_time <= max_freeze_time and current_time >= min_freeze_time:\n            return True\n        \n        return should_freeze\n    \n    def update_queue(self, task):\n        try:\n            global real_time_data   \n                    \n            data = dict()\n            for item in self.real_time_field_names:\n                data.update({item : task[item]})        \n                \n            if data['index'] in real_time_data:\n                real_time_data[data['index']].append(data)\n            else:\n                real_time_data.update({data['index'] : [data]})\n                \n        except Exception as ex:\n            logger.error(ex)\n            \n    def update_data(self, data):\n        # print(data)\n        db = DB(data['index'])\n        db.save([data])\n\nclass FlushToDatabase(threading.Thread):\n        \n    \n    def __init__(self):\n        super().__init__()\n        self.running = True\n        \n        global real_time_data\n    \n    \n    def run(self):\n        delay = 60\n        \n        while(self.running):\n            time.sleep(delay)\n            self.save()\n            \n        logger.info(\"Thread to dump data stopped.\")\n        \n    def stop_thread(self):\n        self.running = False\n        \n    \n    def save(self):\n        global real_time_data\n        for key in real_time_data:\n            try:           \n                data= real_time_data[key] \n                if len(data)>=1:\n                    db = DB(key)\n                    db.set_real_time_data(data)\n                    real_time_data[key] = []\n            except Exception as err:\n                logger.error(err)\n        \n\nclass DailyCleanup(threading.Thread):\n        \n    \n    def __init__(self):\n        super().__init__()\n        self.running = True\n        global max_freeze_time, min_freeze_time\n    \n    \n    def run(self):\n        global max_freeze_time, min_freeze_time\n        x=datetime.today()\n        y = x.replace(day=x.day, hour=9, minute=0, second=0, microsecond=0) + timedelta(days=1)\n        delta_t=y-x\n        secs=delta_t.seconds+1\n        \n        while(self.running):\n            time.sleep(secs)\n            logger.info(\"Cleaning Up\")\n            max_freeze_time =  datetime.now().replace(hour=9,minute=19,second=58)\n            min_freeze_time =  max_freeze_time + timedelta(seconds=1)\n            \n        logger.info(\"Thread to dump data stopped.\")\n        \n    def stop_thread(self):\n        self.running = False\n                        \n                    \n                \n            ","sub_path":"app/wrapper/thread.py","file_name":"thread.py","file_ext":"py","file_size_in_byte":4812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"125957204","text":"import math\nimport os\nimport random\nimport logging\nimport re\nimport shutil\nimport stat\nimport subprocess\nfrom datetime import datetime\nimport shlex\nimport sys\nimport platform\nfrom collections import Counter\nfrom pathlib import Path\nfrom typing import List, Dict\n\nDEBUG_LOG_FILE = './test-debug.log'\nIGNORE_DEBUG_PRINTS = True if '--ignore-debug-prints' in sys.argv else False\nIS_HARD = True if '--hard' in sys.argv else False\nlogging.basicConfig(format=\"%(msg)s\", stream=sys.stdout, level='DEBUG' if '--debug' in sys.argv else 'INFO')\n\ndebug_logger = logging.getLogger(\"Debugger\")\nfile_handler =logging.FileHandler(DEBUG_LOG_FILE, mode='w')\nfile_handler.setFormatter(logging.Formatter(\"%(asctime)s : %(levelname)s : %(message)s\"))\ndebug_logger.addHandler(file_handler)\ndebug_logger.setLevel(logging.DEBUG)\ndebug_logger.propagate = False\n\nTIMEOUT_SECONDS = 20\nPFIND_EXEC = \"./pfind\"\nTEST_DIR = \"test_filesystem\"\nMAX_WORD_SIZE = 10\nMAX_DEPTH = 30\nMAX_DIRS_AMOUNT = 150\nMAX_MATCHES_AMOUNT = 50\nMAX_SEARCH_TERM_SIZE = 10\nREGULAR_FILE_PROBA = 0.7  # When using links, 30% will be links and 70% regular files\nUNSEARCHABLE_DIR_PROBA = 0.1\nDEBUG = True\nPERMISSION_DENIED_REGEX = re.compile(r\"Directory (?P<path>.+): Permission denied\\.\")\n\n\ndef build_valid_chars():\n    \"\"\"Creates every letter 5 times to ensure a bigger probability of getting a letter, than a special char\"\"\"\n    valid_chars = \"\"\n    for i in range(ord('a'), ord('z')):\n        valid_chars += 5 * chr(i)\n    valid_chars += \"-_.\"\n    return valid_chars\n\n\nvalid_file_chars = build_valid_chars()\n\n\ndef parallelism_generator():\n    for _ in range(10):\n        yield 1\n    for _ in range(150 if IS_HARD else 50):\n        yield 2\n    for i in range(10, 100, 10 if IS_HARD else 25):\n        yield i\n    for i in range(100, 1000, 50 if IS_HARD else 150):\n        yield i\n    for i in range(1000, 4001, 100 if IS_HARD else 500):\n        yield i\n\n\ndef generate_word(size):\n    \"\"\"Generates a word that cannot be only dots\"\"\"\n    s = \"\"\n    for _ in range(size):\n        s += random.choice(valid_file_chars)\n    if s.count('.') > 0 and len(s.strip('.')) == 0:\n        return generate_word(size)\n    return s\n\n\ndef generate_dir(depth: int) -> str:\n    parts = []\n    for _ in range(depth):\n        part = generate_word(random.randint(1, MAX_WORD_SIZE))\n        while part in ['.', '..']:\n            part = generate_word(random.randint(1, MAX_WORD_SIZE))\n        parts.append(part)\n    return os.path.join(*parts)\n\n\ndef generate_link_target(match_files: List[str], unmatched_files: List[str]):\n    if 0 <= random.random() <= 0.5 and len(match_files) > 0:\n        # To match\n        if 0 <= random.random() <= 0.5:\n            # To match file\n            target, is_dir = random.choice(match_files), False\n        else:\n            # To match dir\n            target, is_dir = os.path.dirname(random.choice(match_files)), True\n    elif len(unmatched_files) > 0:\n        # To unmatch\n        if 0 <= random.random() <= 0.5:\n            # To unmatch file\n            target, is_dir = random.choice(unmatched_files), False\n        else:\n            # To unmatch dir\n            target, is_dir = os.path.dirname(random.choice(unmatched_files)), True\n    else:\n        # Whatever\n        if 0 <= random.random() <= 0.5:\n            # To unmatch file\n            target, is_dir = random.choice(unmatched_files + match_files), False\n        else:\n            # To unmatch dir\n            target, is_dir = os.path.dirname(random.choice(unmatched_files + match_files)), True\n    return target, is_dir\n\n\ndef get_all_dir_parents(dir_name: str):\n    parents = []\n    while dir_name != '':\n        parent_name = os.path.dirname(dir_name)\n        if parent_name != '':\n            parents.append(parent_name)\n        dir_name = parent_name\n    return parents\n\n\ndef generate_dir_names(max_leafs_amt: int, with_parents: bool, exclude_dirs: List[str] = None):\n    exclude_dirs = exclude_dirs or []\n    s = set()\n    for _ in range(max_leafs_amt):\n        dir_name = os.path.join(TEST_DIR, generate_dir(random.randint(1, MAX_DEPTH)))\n        while dir_name in exclude_dirs or dir_name in s:\n            dir_name = os.path.join(TEST_DIR, generate_dir(random.randint(1, MAX_DEPTH)))\n        if with_parents:\n            all_parents = get_all_dir_parents(dir_name)\n            for parent in all_parents:\n                debug_logger.debug(f\"added {parent} to dir names\")\n                s.add(parent)\n        else:\n            debug_logger.debug(f\"added {dir_name} to dir names\")\n            s.add(dir_name)\n\n    return list(s)\n\n\ndef generate_containing_word(file_dir: str, search_term: str):\n    first_part_size = random.randint(0, MAX_WORD_SIZE // 2)\n    second_part_size = random.randint(0, MAX_WORD_SIZE // 2)\n    file_name = f\"{generate_word(first_part_size)}{search_term}{generate_word(second_part_size)}\"\n    return os.path.join(file_dir, file_name)\n\n\ndef ensure_file_dir(path: str):\n    \"\"\"Receive path to file and ensure it's directory exists\"\"\"\n    dir_name = os.path.dirname(path)\n    debug_logger.debug(f\"making dir {dir_name}\")\n    dir_path = Path(dir_name)\n    dir_path.mkdir(parents=True, exist_ok=True)\n    return dir_path\n\n\ndef touch_file(path: str):\n    \"\"\"Touches file and returns the directory it's in, as a Path\"\"\"\n    dir_path = ensure_file_dir(path)\n    debug_logger.debug(f\"touching {path}\")\n    Path(path).touch()\n    return dir_path\n\n\ndef ensure_generate_filesystem(match_files_amt: int, search_term: str, with_link: bool, with_unsearchable_dir: bool,\n                               max_retries: int = 5):\n    for _ in range(max_retries):\n        try:\n            return generate_filesystem(match_files_amt, search_term, with_link, with_unsearchable_dir)\n        except Exception as e:\n            logging.warning(\"failed creating file system, retrying (don't worry, this is not your fault).\")\n            reset_test_dir()\n    raise RuntimeError(\"Tester failed generating file system. This is NOT your fault! Just re-run the tester\")\n\n\ndef generate_filesystem(match_files_amt: int, search_term: str, with_link: bool, with_unsearchable_dir: bool):\n    files_amount = random.randint(match_files_amt, match_files_amt + 4000)\n    max_dirs_amount = random.randint(2, MAX_DIRS_AMOUNT)\n    unsearchable_dirs_amt = max(1, math.ceil(UNSEARCHABLE_DIR_PROBA * max_dirs_amount)) if with_unsearchable_dir else 0\n    dir_names = generate_dir_names(max_dirs_amount, True) + [TEST_DIR]\n    unsearchable_dirs = []\n    match_files = []\n    match_links = []\n    unmatched_files = []\n    unmatched_links = []\n    matchable_in_unsearchable_files = []\n\n    def file_path_exists(_file_path: str):\n        return not (\n                _file_path in dir_names or _file_path in match_files or _file_path in match_links or _file_path in unmatched_files or _file_path in unmatched_links or _file_path in unsearchable_dirs or _file_path in matchable_in_unsearchable_files)\n\n    regular_file_proba = REGULAR_FILE_PROBA if with_link else 1\n    logging.info(f\"creating {match_files_amt} files that should match\")\n    for _ in range(match_files_amt):\n        file_dir = random.choice(dir_names)\n        debug_logger.debug(f\"picked random directory {file_dir}\")\n\n        file_path = generate_containing_word(file_dir, search_term)\n        while not file_path_exists(file_path):\n            file_path = generate_containing_word(file_dir, search_term)\n        if not with_link or 0 <= random.random() <= regular_file_proba:\n            debug_logger.debug(f\"added {file_path} to match files\")\n            match_files.append(file_path)\n        else:\n            debug_logger.debug(f\"added {file_path} to match links\")\n            match_links.append(file_path)\n\n    logging.info(f\"creating {files_amount - match_files_amt} files that will NOT match\")\n    for _ in range(files_amount - match_files_amt):\n        file_dir = random.choice(dir_names)\n        debug_logger.debug(f\"picked random directory {file_dir}\")\n        # File generated MUSTN'T contain search term\n        file_name = generate_word(random.randint(1, MAX_WORD_SIZE))\n        final_path = os.path.join(os.path.join(file_dir, file_name))\n        while search_term in file_name or not file_path_exists(final_path):\n            file_name = generate_word(random.randint(1, MAX_WORD_SIZE))\n            final_path = os.path.join(os.path.join(file_dir, file_name))\n        if not with_link or 0 <= random.random() <= regular_file_proba:\n            debug_logger.debug(f\"added {final_path} to unmatch files\")\n            unmatched_files.append(final_path)\n        else:\n            debug_logger.debug(f\"added {final_path} to unmatch links\")\n            unmatched_links.append(final_path)\n\n    logging.info(\n        f\"creating {unsearchable_dirs_amt} directories without reading permissions (and adding matchable files inside)\")\n    unsearchable_dirs = generate_dir_names(unsearchable_dirs_amt, False, exclude_dirs=dir_names)\n    dir_names.extend({parent for d in unsearchable_dirs for parent in get_all_dir_parents(d)})\n    dir_names = list(set(dir_names))\n    for unsearchable_dir in unsearchable_dirs:\n        file_name = generate_word(random.randint(1, MAX_WORD_SIZE))\n        final_path = os.path.join(os.path.join(unsearchable_dir, file_name))\n        while search_term in file_name or not file_path_exists(final_path):\n            file_name = generate_word(random.randint(1, MAX_WORD_SIZE))\n            final_path = os.path.join(os.path.join(unsearchable_dir, file_name))\n        debug_logger.debug(f\"added {final_path} to matchable_in_unsearchable_files\")\n        matchable_in_unsearchable_files.append(final_path)\n\n    logging.info(\"generating file system...\")\n    logging.info(\"generating files...\")\n    for p in match_files + unmatched_files:\n        touch_file(p)\n    if with_link:\n        logging.info(\"generating links...\")\n        for p in match_links + unmatched_links:\n            debug_logger.debug(f\"ensuring dir {p}...\")\n            ensure_file_dir(p)\n            target, is_dir = generate_link_target(match_files, unmatched_files)\n            debug_logger.debug(f\"creating symlink from {p} to {target}...\")\n            Path(p).symlink_to(target, target_is_directory=is_dir)\n    if with_unsearchable_dir:\n        logging.info(\"generating unsearchable dirs...\")\n        for p in matchable_in_unsearchable_files:\n            dir_path = touch_file(p)\n            debug_logger.debug(f\"remove read permission from {p}\")\n            cur_permission = stat.S_IMODE(os.lstat(dir_path).st_mode)\n            debug_logger.debug(f\"permission {dir_path} before: {cur_permission}\")\n            dir_path.chmod(cur_permission & ~stat.S_IRUSR)\n            cur_permission = stat.S_IMODE(os.lstat(dir_path).st_mode)\n            debug_logger.debug(f\"permission {dir_path} after: {cur_permission}\")\n\n    logging.info(\"done generating filesystem.\")\n    return match_files, match_links, unsearchable_dirs\n\n\ndef info_missing(lines: List[str], msg: str):\n    if len(lines) > 0:\n        logging.error('---------------')\n        logging.error(msg)\n        for f in lines:\n            logging.error(f\"{f}, with permission {stat.S_IMODE(os.lstat(f).st_mode)}\")\n        logging.error('---------------')\n\n\ndef info_missing_all(missing_files: List[str], missing_links: List[str], missing_unsearchable: List[str]):\n    info_missing(missing_files, \"Following files should have matched but weren't printed:\")\n    info_missing(missing_links, \"Following links should have matched but weren't printed:\")\n    info_missing(missing_unsearchable,\n                 \"Following unsearchable files should have `Permission denied` but weren't printed:\")\n\n\ndef find_duplicates(output: List[str]) -> (bool, Dict[str, int]):\n    \"\"\"Returns if duplicates were found and the duplicates\"\"\"\n    c = Counter(output)\n    duplicates = {k: v for k, v in c.items() if v > 1}\n    return len(duplicates) > 0, duplicates\n\n\ndef info_redundant_prints(output: List[str], must_match_files: List[str], must_match_links: List[str],\n                          unsearchable_dirs: List[str]):\n    for line in output[:-1]:\n        if 'Permission denied' in line:\n            # Permission Denied\n            match = PERMISSION_DENIED_REGEX.match(line)\n            if match:\n                path = match.group('path')\n                if path not in unsearchable_dirs:\n                    logging.error(f\"pfind printed {path} as unsearchable, but it is searchable\")\n            else:\n                logging.error(\"Permission denied error message was in wrong format\")\n        else:\n            # Normal match\n            if line not in must_match_files and line not in must_match_links:\n                logging.error(f\"pfind printed {line} as a match, but it shouldn't\")\n\n\ndef assert_correct_results(must_match_files: List[str], must_match_links: List[str], unsearchable_dirs: List[str],\n                           output: List[str], search_term: str, original_cmd: str):\n    original_output = output\n    output = [line for line in output[:-1] if '***' not in line] + [output[-1]] if IGNORE_DEBUG_PRINTS else output\n    total_matches = len(must_match_files) + len(must_match_links) + len(unsearchable_dirs)\n    has_duplicates, duplicates = find_duplicates(output)\n    expected_last_line = f'Done searching, found {len(must_match_links) + len(must_match_files)} files'\n    if len(output) != total_matches + 1 or has_duplicates or output[-1] != expected_last_line:\n        logging.info(\"-------------- ERROR --------------\")\n        if output[-1] != expected_last_line:\n            logging.error(\"#######\")\n            logging.error(f\"Last line should have been `{expected_last_line}` but was `{output[-1]}`\")\n        if len(output) != total_matches + 1:\n            logging.error(\"#######\")\n            logging.info(\n                f\"program did not print correct number of lines (matches_amount + permission_denied_amount + 1 = {total_matches + 1})\")\n            missing_files = [f for f in must_match_files if f not in output]\n            missing_links = [f for f in must_match_links if f not in output]\n            missing_unsearchable_files = [f for f in unsearchable_dirs if\n                                          f\"Directory {f}: Permission denied.\" not in output]\n            info_missing_all(missing_files, missing_links, missing_unsearchable_files)\n            info_redundant_prints(output, must_match_files, must_match_links, unsearchable_dirs)\n        if has_duplicates:\n            logging.error(\"#######\")\n            logging.error(\n                f\"Following lines have been printed more than once. First number is amount of times it was printed\")\n            for line, amt in duplicates.items():\n                logging.error(f\"{amt}: {line}\")\n        logging.error(\n            f\"for search term {search_term}, should have printed {total_matches + 1} lines but printed {len(output)}:\")\n        for line in original_output:\n            logging.info(line)\n        logging.error('----------------------------')\n        logging.error(\"test file system has still not been deleted, so you can run the command by yourself to debug.\")\n        logging.error(f\"command is: {original_cmd}\")\n        exit(1)\n\n\ndef reset_test_dir():\n    logging.info(\"Resetting test directory\")\n    if os.path.exists(TEST_DIR):\n        run_command(f\"chmod -R u+r {TEST_DIR}\")\n        shutil.rmtree(TEST_DIR)\n    os.mkdir(TEST_DIR)\n\n\ndef test_case(with_link: bool, with_unsearchable_dir: bool, timeout: int):\n    tests_amt = 0\n    failed_amt = 0\n    match_files_amt = random.randint(3, MAX_MATCHES_AMOUNT)\n    search_term = generate_word(random.randint(3, MAX_SEARCH_TERM_SIZE))\n    logging.info(\n        f\"Generating file system with {match_files_amt} files that must be matched for search term {search_term}\")\n    reset_test_dir()\n\n    match_files, match_links, unsearchable_dirs = ensure_generate_filesystem(match_files_amt, search_term, with_link,\n                                                                             with_unsearchable_dir)\n    logging.info(\"running on file system with many different parallelisms\")\n    for parallelism in parallelism_generator():\n        tests_amt += 1\n        cmd = f\"\"\"{PFIND_EXEC} {TEST_DIR} \"{search_term}\" {parallelism}\"\"\"\n        success, output = run_command(cmd, timeout)\n        if not success:\n            logging.warning(f\"!!!!!!!!!! WARNING !!!!!!!!!!\")\n            logging.warning(f\"code returned non-zero exit code\")\n            failed_amt += 1\n        assert_correct_results(match_files, match_links, unsearchable_dirs, output, search_term, cmd)\n    logging.info(\"done running on file system\")\n    return tests_amt, failed_amt\n\n\ndef test_normal_run(timeout: int):\n    logging.info(\"------------------------\")\n    logging.info(\"Normal test run\")\n    logging.info(\"------------------------\")\n    return test_case(False, False, timeout)\n\n\ndef test_links_run(timeout: int):\n    logging.info(\"------------------------\")\n    logging.info(\"Links test run\")\n    logging.info(\"------------------------\")\n    return test_case(True, False, timeout)\n\n\ndef test_unsearchable_dir_run(timeout: int):\n    logging.info(\"------------------------\")\n    logging.info(\"Unsearchable dir test run\")\n    logging.info(\"------------------------\")\n    return test_case(False, True, timeout)\n\n\ndef test_all(timeout: int):\n    logging.info(\"------------------------\")\n    logging.info(\"Links and unsearchable dir test run\")\n    logging.info(\"------------------------\")\n    return test_case(True, True, timeout)\n\n\ndef test_non_existing_dir():\n    logging.info(\"using pfind with non-existing directory\")\n    success, output = run_command(f\"{PFIND_EXEC} /path/to/bad/dir search_term 1\")\n    if success:\n        logging.info(f\"Should return exit code other than 0 when root dir does not exist\")\n        exit(0)\n    logging.info(\"successfully exited with non-zero exit code\")\n\n\ndef test_file_as_root_dir():\n    logging.info(\"creating temporary file to be used as root dir\")\n    path = Path('./test-file')\n    path.touch()\n    success, output = run_command(f\"{PFIND_EXEC} {path.absolute()} search_term 1\")\n    if success:\n        logging.info(f\"Should return exit code other than 0 when root dir is a file and not a dir\")\n        path.unlink()\n        exit(0)\n    path.unlink()\n    logging.info(\"successfully exited with non-zero exit code\")\n\n\ndef test_bad_root_dir():\n    logging.info(\"------------------------\")\n    logging.info(\"Test bad root dir as input\")\n    logging.info(\"------------------------\")\n\n    test_non_existing_dir()\n    test_file_as_root_dir()\n\n\ndef run_command(command, timeout: int = TIMEOUT_SECONDS) -> (bool, List[str]):\n    debug_logger.debug(f\"running command: {command}\")\n    proc = subprocess.Popen(shlex.split(command), stdout=subprocess.PIPE)\n    try:\n        output, error = proc.communicate(timeout=timeout)\n        if error is None and proc.returncode == 0:\n            return True, output.strip().decode().split('\\n')\n        return False, output.strip().decode().split('\\n')\n    except subprocess.TimeoutExpired as e:\n        proc.kill()\n        output, error = proc.communicate()\n        logging.error(\"--------------------------------------------\")\n        logging.error(\n            f\"Process did not finish in expected timeout (maximum {timeout} seconds) so you probably have a deadlock\")\n        logging.error(\"Anyway, this is the output you generated until the deadlock:\")\n        for line in output.strip().decode().split('\\n'):\n            print(line)\n        logging.error(f\"Test FileSystem won't be reset until the next time you run the test.\")\n        logging.error(f\"So you can rerun the command by yourself to debug: {command}\")\n        exit(1)\n\n\ndef run_all_tests(timeout: int):\n    logging.info(\"running...\")\n    test_bad_root_dir()\n\n    tests_amt = 0\n    failed_amt = 0\n    for _ in range(30 if IS_HARD else 10):\n        total_normal, failed_normal = test_normal_run(timeout)\n        total_links, failed_links = test_links_run(timeout)\n        total_unsearchable, failed_unsearchable = test_unsearchable_dir_run(timeout)\n        total_all, failed_all = test_all(timeout)\n        tests_amt += total_normal + total_links + total_unsearchable + total_all\n        failed_amt += failed_normal + failed_links + failed_unsearchable + failed_all\n    return tests_amt, failed_amt\n\n\ndef run(timeout_secs: int):\n    logging.info(\"compiling...\")\n    compiler = \"gcc\"\n    success, output = run_command(\n        f\"{compiler} -O3 -D_POSIX_C_SOURCE=200809 -Wall -std=c11 -pthread pfind.c -o {PFIND_EXEC}\", timeout)\n    if not success:\n        logging.error(\"compile unsuccessfull, output:\")\n        for line in output:\n            print(line)\n        exit(1)\n\n    start_ts = datetime.now()\n    tests_amt, failed_amt = run_all_tests(timeout_secs)\n    end_ts = datetime.now()\n\n    if failed_amt > 0:\n        logging.warning(f\"!!!!!!WARNING!!!!!!\")\n        logging.warning(f\"{failed_amt}/{tests_amt} ended with exit code different than 1\")\n        logging.warning(f\"But if your code got here, at least that means that the output was always correct\")\n    logging.info(\"WHO DA BEST??!! YOU DA BEST!!!! \")\n    logging.info(\"You passed the tests, kululululululu\")\n    logging.info(\n        f\"And by the way, it took {(end_ts - start_ts).total_seconds()} seconds to run, if you want to compare zragim with others\")\n    logging.info(\"But remember this tester is random so comparing time isn't very effective\")\n\n\nif __name__ == '__main__':\n    timeout = int(sys.argv[1]) if len(sys.argv) > 1 and sys.argv[1].isnumeric() else TIMEOUT_SECONDS\n    run(timeout)\n","sub_path":"ex4/tests/tester.py","file_name":"tester.py","file_ext":"py","file_size_in_byte":21509,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"442611704","text":"\"\"\"\nutils for alums\n\"\"\"\n\nfrom datetime import datetime\nfrom gsd.student.models import GraduateStudent\nfrom gsd.alums.models import AlumnusJobCode, AlumnusOrganization, AlumnusPosition\n\nNUM_POSITION_INFO_ITEMS = 12\n\n\nclass AlumPositionUpdater:\n    \"\"\"alum position updater\"\"\"\n    @staticmethod\n    def update_job_position(position_info_line):\n        \"\"\"update job position\"\"\"\n        if position_info_line is None:\n            return False\n\n        items = position_info_line.split('|')\n        if not len(items) == NUM_POSITION_INFO_ITEMS:\n            print('Expected %s items, but found %s items.' % (NUM_POSITION_INFO_ITEMS, len(items)))\n\n        fmt_items = []\n        for item in items:\n            item = item.strip()\n            if item == 'None':\n                item = ''\n            fmt_items.append(item)\n\n        lname, fname, job_code, title, organization, location, start_date, end_date, email, phone, add_date, mod_date = fmt_items\n\n        try:\n            grad_student = GraduateStudent.objects.get(last_name=lname, first_name=fname)\n            print('Grad student found! %s' % grad_student)\n        except GraduateStudent.DoesNotExist:\n            print('grad student not found: %s, %s' % (lname, fname))\n            return False\n\n        # Format date and datetime fields\n        print('start_date ', start_date)\n        if start_date:\n            start_date = datetime.strptime(start_date, '%Y-%m-%d').date()\n\n        print('end_date ', end_date)\n        if end_date:\n            end_date = datetime.strptime(end_date, '%Y-%m-%d').date()\n\n        print('add_date ', add_date)\n        if add_date:\n            add_date = datetime.strptime(add_date, '%Y-%m-%d %H:%M:%S')\n\n        print('mod_date ', mod_date)\n        if mod_date:\n            mod_date = datetime.strptime(mod_date, '%Y-%m-%d %H:%M:%S')\n\n        # retrieve AlumnusJobCode object\n        try:\n            job_code_obj = AlumnusJobCode.objects.get(name=job_code)\n        except AlumnusJobCode.DoesNotExist:\n            print('job code (AlumnusJobCode) not found for: %s' % job_code)\n            return False\n\n        # retrieve AlumnusOrganization object\n        try:\n            organization_obj = AlumnusOrganization.objects.get(name=organization)\n        except AlumnusOrganization.DoesNotExist:\n            organization_obj = AlumnusOrganization(name=organization)\n            organization_obj.save()\n\n        kwargs = {\n            'student': grad_student,\n            'job_code': job_code_obj,\n            'title': title.strip(),\n            'organization': organization_obj,\n            'location': location,\n            'email': email,\n            'phone': phone,\n        }\n\n        # Position already exists\n        if AlumnusPosition.objects.filter(**kwargs).count() > 0:\n            return True\n\n        alum_position = AlumnusPosition(**kwargs)\n        alum_position.save()\n\n        # Update Date attributes\n        if start_date:\n            alum_position.start_date = start_date\n        if end_date:\n            alum_position.end_date = end_date\n        alum_position.date_added = add_date\n        alum_position.date_modified = mod_date\n\n        alum_position.save()\n        return True\n","sub_path":"gsd/alums/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":3183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"94005575","text":"# -*- coding: utf-8 -*-\n\"\"\"\n    weppy_haml.elements\n    -------------------\n\n    Processes haml/html elements\n\n    :copyright: (c) 2017 by Giovanni Barillari\n\n    Based on the code of hamlpy (https://github.com/jessemiller/HamlPy)\n    :copyright: (c) 2011 Jesse Miller\n\n    :license: BSD, see LICENSE for more details.\n\"\"\"\n\nimport re\nfrom weppy._compat import PY2, to_unicode\n\nif PY2:\n    from types import NoneType\nelse:\n    NoneType = type(None)\n\n\nclass Element(object):\n    self_closing_tags = (\n        'meta', 'img', 'link', 'br', 'hr', 'input', 'source', 'track')\n\n    ELEMENT = '%'\n    ID = '#'\n    CLASS = '.'\n\n    HAML_REGEX = re.compile(r\"\"\"\n    (?P<tag>%\\w+(\\:\\w+)?)?\n    (?P<id>\\#[\\w-]*)?\n    (?P<class>\\.[\\w\\.-]*)*\n    (?P<attributes>\\{.*\\})?\n    (?P<nuke_outer_whitespace>\\>)?\n    (?P<nuke_inner_whitespace>\\<)?\n    (?P<selfclose>/)?\n    (?P<inline>[^\\w\\.#\\{].*)?\n    \"\"\", re.X | re.MULTILINE | re.DOTALL | re.UNICODE)\n\n    _ATTRIBUTE_KEY_REGEX = r'(?P<key>[a-zA-Z_][a-zA-Z0-9_-]*)'\n    # Single and double quote regexes from:\n    # http://stackoverflow.com/a/5453821/281469\n    _SINGLE_QUOTE_STRING_LITERAL_REGEX = r\"'([^'\\\\]*(?:\\\\.[^'\\\\]*)*)'\"\n    _DOUBLE_QUOTE_STRING_LITERAL_REGEX = r'\"([^\"\\\\]*(?:\\\\.[^\"\\\\]*)*)\"'\n    _ATTRIBUTE_VALUE_REGEX = r'(?P<val>\\d+|None(?!\\w)|%s|%s)' % (\n        _SINGLE_QUOTE_STRING_LITERAL_REGEX, _DOUBLE_QUOTE_STRING_LITERAL_REGEX)\n\n    RUBY_HAML_REGEX = re.compile(r'(:|\\\")%s(\\\"|) =>' % (_ATTRIBUTE_KEY_REGEX))\n    ATTRIBUTE_REGEX = re.compile(r'(?P<pre>\\{\\s*|,\\s*)%s\\s*:\\s*%s' % (\n        _ATTRIBUTE_KEY_REGEX, _ATTRIBUTE_VALUE_REGEX), re.UNICODE)\n\n    def __init__(self, haml, attr_wrapper=\"'\"):\n        self.haml = haml\n        self.attr_wrapper = attr_wrapper\n        self.tag = None\n        self.id = None\n        self.classes = None\n        self.attributes = ''\n        self.self_close = False\n        self.nuke_inner_whitespace = False\n        self.nuke_outer_whitespace = False\n        self.inline_content = ''\n        self._parse_haml()\n\n    def attr_wrap(self, value):\n        return '%s%s%s' % (self.attr_wrapper, value, self.attr_wrapper)\n\n    def _parse_haml(self):\n        split_tags = self.HAML_REGEX.search(self.haml).groupdict('')\n\n        self.attributes_dict = self._parse_attribute_dictionary(\n            split_tags.get('attributes'))\n        self.tag = split_tags.get('tag').strip(self.ELEMENT) or 'div'\n        self.id = self._parse_id(split_tags.get('id'))\n        self.classes = (\n            '%s %s' % (\n                split_tags.get('class').lstrip(self.CLASS).replace('.', ' '),\n                self._parse_class_from_attributes_dict())\n        ).strip()\n        self.self_close = (\n            split_tags.get('selfclose') or self.tag in self.self_closing_tags)\n        self.nuke_inner_whitespace = \\\n            split_tags.get('nuke_inner_whitespace') != ''\n        self.nuke_outer_whitespace = \\\n            split_tags.get('nuke_outer_whitespace') != ''\n        #self.weppy_variable = split_tags.get('django') != ''\n        self.inline_content = split_tags.get('inline').strip()\n\n    def _parse_class_from_attributes_dict(self):\n        clazz = self.attributes_dict.get('class', '')\n        if not isinstance(clazz, str):\n            clazz = ''\n            for one_class in self.attributes_dict.get('class'):\n                clazz += ' ' + one_class\n        return clazz.strip()\n\n    def _parse_id(self, id_haml):\n        id_text = id_haml.strip(self.ID)\n        if 'id' in self.attributes_dict:\n            id_text += self._parse_id_dict(self.attributes_dict['id'])\n        id_text = id_text.lstrip('_')\n        return id_text\n\n    def _parse_id_dict(self, id_dict):\n        text = ''\n        id_dict = self.attributes_dict.get('id')\n        if isinstance(id_dict, str):\n            text = '_' + id_dict\n        else:\n            text = ''\n            for one_id in id_dict:\n                text += '_' + one_id\n        return text\n\n    def _escape_attribute_quotes(self, v):\n        '''\n        Escapes quotes with a backslash, except those inside a Django tag\n        '''\n        escaped = []\n        inside_tag = False\n        for i, _ in enumerate(v):\n            if v[i:i + 2] == '{{':\n                inside_tag = True\n            elif v[i:i + 2] == '}}':\n                inside_tag = False\n\n            if v[i] == self.attr_wrapper and not inside_tag:\n                escaped.append('\\\\')\n\n            escaped.append(v[i])\n\n        return ''.join(escaped)\n\n    def _parse_attribute_dictionary(self, attribute_dict_string):\n        attributes_dict = {}\n        if (attribute_dict_string):\n            attribute_dict_string = attribute_dict_string.replace('\\n', ' ')\n            try:\n                # converting all allowed attributes to python dictionary style\n                # Replace Ruby-style HAML with Python style\n                attribute_dict_string = re.sub(\n                    self.RUBY_HAML_REGEX, '\"\\g<key>\":', attribute_dict_string)\n                # Put double quotes around key\n                attribute_dict_string = re.sub(\n                    self.ATTRIBUTE_REGEX, '\\g<pre>\"\\g<key>\":\\g<val>',\n                    attribute_dict_string)\n                # Parse string as dictionary\n                attributes_dict = eval(attribute_dict_string)\n                for k, v in attributes_dict.items():\n                    if k != 'id' and k != 'class':\n                        if isinstance(v, NoneType):\n                            self.attributes += \"%s \" % (k,)\n                        elif isinstance(v, int) or isinstance(v, float):\n                            self.attributes += \"%s=%s \" % (\n                                k, self.attr_wrap(v))\n                        else:\n                            attributes_dict[k] = v\n                            v = to_unicode(v)\n                            self.attributes += \"%s=%s \" % (\n                                k, self.attr_wrap(\n                                    self._escape_attribute_quotes(v)))\n                self.attributes = self.attributes.strip()\n            except Exception:\n                raise Exception('failed to decode: %s' % attribute_dict_string)\n\n        return attributes_dict\n","sub_path":"weppy_haml/elements.py","file_name":"elements.py","file_ext":"py","file_size_in_byte":6160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"55607830","text":"import nltk\nfrom nltk.stem.lancaster import LancasterStemmer\nstemmer = LancasterStemmer()\n\nimport numpy\nimport tflearn\nimport tensorflow\nimport random\nimport json\nimport pickle\n\nwith open(\"intents.json\") as file:\n    data = json.load(file)\n    \nloadedmodel=pickle.load(open('trainedbot.sav','rb'))\n\nwords = []\nlabels = []\ndocs_x = []\ndocs_y = []\n\nfor intent in data[\"intents\"]:\n    for pattern in intent[\"patterns\"]:\n        wrds = nltk.word_tokenize(pattern)\n        words.extend(wrds)\n        docs_x.append(wrds)\n        docs_y.append(intent[\"tag\"])\n\n    if intent[\"tag\"] not in labels:\n            labels.append(intent[\"tag\"])\n\nwords = [stemmer.stem(w.lower()) for w in words if w != \"?\"]\nwords = sorted(list(set(words)))\n\nlabels = sorted(labels)\ntraining = []\noutput = []\n\nout_empty = [0 for _ in range(len(labels))]\n\nfor x, doc in enumerate(docs_x):\n    bag = []\n\n    wrds = [stemmer.stem(w.lower()) for w in doc]\n\n    for w in words:\n        if w in wrds:\n            bag.append(1)\n        else:\n            bag.append(0)\n\n    output_row = out_empty[:]\n    output_row[labels.index(docs_y[x])] = 1\n     \n    training.append(bag)\n    output.append(output_row)\n\n\ntraining = numpy.array(training)\noutput = numpy.array(output)\n\nwith open(\"data.pickle\", \"wb\") as f:\n    pickle.dump((words, labels, training, output), f)\n\ntensorflow.reset_default_graph()\n\nnet = tflearn.input_data(shape=[None, len(training[0])])\nnet = tflearn.fully_connected(net, 8)\nnet = tflearn.fully_connected(net, 8)\nnet = tflearn.fully_connected(net, len(output[0]), activation=\"softmax\")\nnet = tflearn.regression(net)\n\nmodel = tflearn.DNN(net)\n\n#try:\n#model.load(\"model.tflearn\")\n#except:\nmodel.fit(training, output, n_epoch=1000, batch_size=8, show_metric=True)\nmodel.save(\"model.tflearn\")\ndef bag_of_words(s, words):\n    bag = [0 for _ in range(len(words))]\n\n    s_words = nltk.word_tokenize(s)\n    s_words = [stemmer.stem(word.lower()) for word in s_words]\n\n    for se in s_words:\n        for i, w in enumerate(words):\n            if w == se:\n                bag[i] = 1\n            \n    return numpy.array(bag)\n\n\ndef chat():\n    print(\"Start talking with the bot (type quit to stop)!\")\n    while True:\n        inp = input(\"You: \")\n        if inp.lower() == \"quit\":\n            break\n        ####### *********************SECOND PART *******************\n        elif (inp==\"i want to predict my diseases\") :\n            import pandas as pd\n            from sklearn import preprocessing\n            from sklearn.tree import DecisionTreeClassifier,_tree\n            import numpy as np\n            from sklearn.model_selection import train_test_split\n            #from sklearn import cross_validation\n            from sklearn.tree import export_graphviz\n            import warnings\n            warnings.filterwarnings(\"ignore\", category=DeprecationWarning)\n            \n            \n            training = pd.read_csv('Training.csv')\n            testing  = pd.read_csv('Testing.csv')\n            cols     = training.columns\n            cols     = cols[:-1]\n            x        = training[cols]\n            y        = training['prognosis']\n            y1       = y\n            \n            reduced_data = training.groupby(training['prognosis']).max()\n            \n            #mapping strings to numbers\n            le = preprocessing.LabelEncoder()\n            le.fit(y)\n            y = le.transform(y)\n            \n            \n            x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.33, random_state=42)\n            testx    = testing[cols]\n            testy    = testing['prognosis']  \n            testy    = le.transform(testy)\n            \n            \n            clf1  = DecisionTreeClassifier()\n            clf = clf1.fit(x_train,y_train)\n            #print(clf.score(x_train,y_train))\n            #print (\"cross result========\")\n            #scores = cross_validation.cross_val_score(clf, x_test, y_test, cv=3)\n            #print (scores)\n            #print (scores.mean())\n            #print(clf.score(testx,testy))\n            \n            importances = clf.feature_importances_\n            indices = np.argsort(importances)[::-1]\n            features = cols\n            \n            #feature_importances\n            #for f in range(10):\n            #    print(\"%d. feature %d - %s (%f)\" % (f + 1, indices[f], features[indices[f]] ,importances[indices[f]]))\n            \n            print(\"Please reply Yes or No for the following symptoms\") \n            def print_disease(node):\n                #print(node)\n                node = node[0]\n                #print(len(node))\n                val  = node.nonzero() \n                #print(val)\n                disease = le.inverse_transform(val[0])\n                return disease\n            def tree_to_code(tree, feature_names):\n                tree_ = tree.tree_\n                #print(tree_)\n                feature_name = [\n                    feature_names[i] if i != _tree.TREE_UNDEFINED else \"undefined!\"\n                    for i in tree_.feature\n                ]\n                #print(\"def tree({}):\".format(\", \".join(feature_names)))\n                symptoms_present = []\n                def recurse(node, depth):\n                    indent = \"  \" * depth\n                    if tree_.feature[node] != _tree.TREE_UNDEFINED:\n                        name = feature_name[node]\n                        threshold = tree_.threshold[node]\n                        print(name + \" ?\")\n                        ans = input()\n                        ans = ans.lower()\n                        if ans == 'yes':\n                            val = 1\n                        else:\n                            val = 0\n                        if  val <= threshold:\n                            recurse(tree_.children_left[node], depth + 1)\n                        else:\n                            symptoms_present.append(name)\n                            recurse(tree_.children_right[node], depth + 1)\n                    else:\n                        present_disease = print_disease(tree_.value[node])\n                        print( \"You may have \" +  present_disease )\n                        red_cols = reduced_data.columns \n                        symptoms_given = red_cols[reduced_data.loc[present_disease].values[0].nonzero()]\n                        print(\"symptoms present  \" + str(list(symptoms_present)))\n                        print(\"symptoms given \"  +  str(list(symptoms_given)) )  \n                        confidence_level = (1.0*len(symptoms_present))/len(symptoms_given)\n                        print(\"confidence level is \" + str(confidence_level))\n            \n                recurse(0, 1)\n            \n            tree_to_code(clf,cols)\n            \n\n        results = model.predict([bag_of_words(inp, words)])\n        results_index = numpy.argmax(results)\n        tag = labels[results_index]\n\n        for tg in data[\"intents\"]:\n            if tg['tag'] == tag:\n                responses = tg['responses']\n\n        print(random.choice(responses))\nchat()\n\n\n\n\n","sub_path":"chat.py","file_name":"chat.py","file_ext":"py","file_size_in_byte":7026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"539167187","text":"\"\"\"The WaveBlocks Project\n\nSample wavepackets at the nodes of a given grid and save the results back\nto the given simulation data file.\n\n@author: R. Bourquin\n@copyright: Copyright (C) 2010, 2011 R. Bourquin\n@license: Modified BSD License\n\"\"\"\n\nimport sys\n\nfrom WaveBlocks import IOManager\n\n\nif __name__ == \"__main__\":\n\n    iom = IOManager()\n\n    # Read file with simulation data\n    try:\n        iom.open_file(filename=sys.argv[1])\n    except IndexError:\n        iom.open_file()\n\n    # Iterate over all blocks\n    for blockid in iom.get_block_ids():\n        print(\"Evaluating wavepackets in data block '\"+str(blockid)+\"'\")\n\n        if iom.has_wavefunction(blockid=blockid):\n            print(\"Datablock '\"+str(blockid)+\"' already contains wavefunction data, silent skip.\")\n            continue\n\n        # See if we have an inhomogeneous wavepacket in the current data block\n        if iom.has_inhomogwavepacket(blockid=blockid):\n            import EvaluateWavepacketsInhomog\n            EvaluateWavepacketsInhomog.compute_evaluate_wavepackets(iom, blockid=blockid, basis=\"canonical\")\n        # If not, we test for a homogeneous wavepacket next\n        elif iom.has_wavepacket(blockid=blockid):\n            import EvaluateWavepackets\n            EvaluateWavepackets.compute_evaluate_wavepackets(iom, blockid=blockid, basis=\"canonical\")\n        # If there is also no wavefunction, then there is nothing to compute the norm\n        else:\n            print(\"Warning: Not evaluating any wavepackets in block '\"+str(blockid)+\"'!\")\n\n    iom.finalize()\n","sub_path":"src/scripts/ComputeEvaluateWavepacketsCanonical.py","file_name":"ComputeEvaluateWavepacketsCanonical.py","file_ext":"py","file_size_in_byte":1544,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"124180358","text":"#-*- coding:utf-8 -*-\n\nimport logging.handlers\n\n\ndef init_logger():\n    LOG_FILENAME = 'logging.out'\n    my_logger = logging.getLogger('MyLogger')\n    my_logger.setLevel(logging.DEBUG)\n    \n    handler = logging.handlers.RotatingFileHandler(LOG_FILENAME,\n                                                   mode='a',\n                                                   maxBytes=200000,\n                                                   backupCount=5)\n    formatter = logging.Formatter('[%(levelname)s]\\t[%(asctime)s]\\t%(message)s',\n                                  datefmt='%m/%d/%Y %I:%M:%S %p')\n    \n    handler.setFormatter(formatter)\n    my_logger.addHandler(handler)\n    \n    return my_logger\n\nif __name__ == '__main__':\n    my_logger = init_logger()\n    count_all_new_records = 1000\n    \n    for i in range(20):\n        my_logger.debug('i = %d' % i)\n        \n    my_logger.debug('# of records onto MongoDB: %s' % count_all_new_records)\n","sub_path":"common/auto_nlp_logging.py","file_name":"auto_nlp_logging.py","file_ext":"py","file_size_in_byte":942,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"586864514","text":"from Card import Card\r\n\r\nclass PlayerHand:\r\n    def __init__(self):\r\n        self.root = None\r\n        self.size = 0\r\n        \r\n    def getTotalCards(self):\r\n        #return self._getTotalCards(self.root)\r\n        return self.size\r\n\r\n    def isEmpty(self):\r\n        if self.size == 0:\r\n            return True\r\n        else:\r\n            return False\r\n\r\n    def getMin(self):\r\n        if self.root != None:\r\n            return self._getMin(self.root)\r\n        else:\r\n            return None\r\n\r\n    def _getMin(self, currentNode):\r\n        if currentNode == None:\r\n            return None\r\n        while currentNode.hasLeftChild():\r\n            currentNode = currentNode.getLeft()\r\n        return currentNode\r\n\r\n    def getSuccessor(self, suit, rank):\r\n        if self.size == 0 or self.size == 1:\r\n            return None\r\n        else: #self.size > 1, BST has at least 2 cards in it\r\n            return self._getSuccessor(suit, rank, self.root) #start off with the root node\r\n\r\n    def _getSuccessor(self, suit, rank, currentNode):\r\n        s = str(suit.upper())\r\n        r = str(rank.upper())\r\n        findCard = Card(s, r)  # finding the card\r\n        if currentNode == None:\r\n            return None\r\n        elif currentNode == findCard:\r\n            if currentNode.parent == None and currentNode.right == None:\r\n                return None\r\n            elif currentNode.right != None:\r\n                return self._getMin(currentNode.right)\r\n            #elif currentNode.parent == None and currentNode.right != None:\r\n                #return self._getMin(currentNode.right)\r\n            elif currentNode == currentNode.getParent().left:\r\n                return currentNode.parent\r\n            elif currentNode == currentNode.getParent().right:\r\n                return self._goUp(currentNode)\r\n            elif currentNode.right == None:\r\n                return None\r\n        elif findCard > currentNode:\r\n            if currentNode.right == None:\r\n                return None\r\n            elif currentNode.right != None:\r\n                return self._getSuccessor(suit, rank, currentNode.right)\r\n            else:\r\n                return None\r\n        else: #findCard is less than currentNode\r\n            if currentNode.left == None:\r\n                return None\r\n            elif currentNode.left != None:\r\n                return self._getSuccessor(suit, rank, currentNode.left)\r\n            else:\r\n                return None\r\n\r\n    def _goUp(self, currentNode):\r\n        if currentNode.parent == None:\r\n            return None\r\n        elif currentNode == currentNode.getParent().left:\r\n            return currentNode.parent\r\n        else:\r\n            return self._goUp(currentNode.parent)\r\n\r\n\r\n    def put(self, suit, rank):\r\n        if self.root: #if there already exists a root for the BST\r\n            self._put(suit, rank, self.root)\r\n        else: #if there doesn't exist a root for the BST, this card is the root node\r\n            s = str(suit.upper())\r\n            r = str(rank.upper())\r\n            self.root = Card(s, r)\r\n            self.size = 1 #increment the Card Deck by adding another card\r\n\r\n\r\n    def _put(self, suit, rank, currentNode): #currentNode is a Card which the card being added is compared to\r\n        s = str(suit.upper())\r\n        r = str(rank.upper())\r\n        newCard = Card(s, r) #card being added\r\n        if currentNode.suit == s and currentNode.rank == r:\r\n            count = currentNode.count\r\n            currentNode.setCount(count + 1)\r\n            self.size += 1\r\n        elif newCard < currentNode: #newCard is less than the currentNode's card\r\n            if currentNode.left != None:\r\n                self._put(suit, rank, currentNode.left)\r\n            else:\r\n                currentNode.left = newCard\r\n                newCard.setParent(currentNode)\r\n                self.size += 1\r\n        elif newCard > currentNode: #newCard is greater than the currentNode's card\r\n            if currentNode.right != None:\r\n                self._put(suit, rank, currentNode.right)\r\n            else:\r\n                currentNode.right = newCard\r\n                newCard.setParent(currentNode)\r\n                self.size += 1\r\n\r\n\r\n    def delete(self, suit, rank):\r\n        if self.get(suit, rank) == None:\r\n            return False\r\n        else:\r\n            deletingCard = self.get(suit, rank)\r\n            if deletingCard.count > 1:\r\n                deletingCard.count = deletingCard.count - 1\r\n                self.size = self.size - 1\r\n                return True #card has been successfully decremented by 1\r\n            elif self.size > 1:\r\n                self._delete(suit, rank, self.root)\r\n                self.size = self.size - 1\r\n                return True\r\n            elif self.size == 1:\r\n                self.root = None\r\n                self.size = 0\r\n                return True\r\n            else:\r\n                return False\r\n        \r\n    def _delete(self, suit, rank, currentNode):\r\n        s = str(suit.upper())\r\n        r = str(rank.upper())\r\n        delCard = Card(s, r)  # card being deleted\r\n\r\n        if currentNode == None:\r\n            return False\r\n\r\n        elif currentNode == delCard:\r\n\r\n            #Case 1: node has no children\r\n            if currentNode.isLeaf():\r\n                if currentNode.parent != None:\r\n                    if currentNode == currentNode.parent.left:\r\n                        currentNode.parent.left = None\r\n                    else:\r\n                        currentNode.parent.right = None\r\n                if currentNode.parent == None:\r\n                    self.root = None\r\n                    self.size = 0\r\n                return True\r\n\r\n\r\n            #Case 3: node has 2 children\r\n            elif currentNode.hasBothChildren():\r\n                #need to find the successor, remove the successor and replace currentNode with successor's data\r\n                #Binary Search Tree removal\r\n                succ = currentNode.findSuccessor() #succ is the successor\r\n                succ.spliceOut() #remove the successor from the tree\r\n                #make sure we replace currentNode (one we want to remove) with successor data\r\n                currentNode.suit = succ.suit.upper()\r\n                currentNode.rank = succ.rank.upper()\r\n                currentNode.count = succ.count\r\n                return True\r\n\r\n\r\n            #Case 2: node has 1 child\r\n            elif currentNode == delCard and (currentNode.left != None or currentNode.right != None):\r\n                if currentNode.left != None: #current node has a left child\r\n                    if currentNode == self.root:\r\n                        currentNode.getLeft().parent = None\r\n                        self.root = currentNode.left\r\n                        return True\r\n                    else:\r\n                        if currentNode == currentNode.getParent().left: #current node is a left child\r\n                            currentNode.getLeft().parent = currentNode.parent\r\n                            currentNode.getParent().left = currentNode.left\r\n                        if currentNode == currentNode.getParent().right: #current node is a right child\r\n                            currentNode.getLeft().parent = currentNode.parent\r\n                            currentNode.getParent().right = currentNode.left\r\n                    return True\r\n\r\n                else: #current node has a right child\r\n                    if currentNode == self.root:\r\n                        currentNode.getRight().parent = None\r\n                        self.root = currentNode.right\r\n                    else:\r\n                        if currentNode == currentNode.getParent().left: #current node is a left child\r\n                            currentNode.getRight().parent = currentNode.parent\r\n                            currentNode.getParent().left = currentNode.right\r\n                        if currentNode == currentNode.getParent().right: #current node is right child\r\n                            currentNode.getRight().parent = currentNode.parent\r\n                            currentNode.getParent().right = currentNode.right\r\n                    return True\r\n\r\n        else:\r\n            #finding the card to be deleted\r\n            if delCard > currentNode:\r\n                if currentNode.right != None:\r\n                    return self._delete(suit, rank, currentNode.right)\r\n                else:\r\n                    return False\r\n            else: #delCard is less than currentNode\r\n                if currentNode.left != None:\r\n                    return self._delete(suit, rank, currentNode.left)\r\n                else:\r\n                    return False\r\n\r\n\r\n\r\n    def get(self, suit, rank):\r\n        if self.root != None: #BST has at least 1 card in it\r\n            return self._get(suit, rank, self.root)\r\n        else:\r\n            return None\r\n\r\n    def _get(self, suit, rank, currentNode):\r\n        #s = str(suit)\r\n        #r = str(rank)\r\n        s = str(suit.upper())\r\n        r = str(rank.upper())\r\n        findCard = Card(s, r) #Card we're trying to find/get\r\n        if currentNode == None:\r\n            return None\r\n        elif currentNode == findCard:\r\n            return currentNode\r\n        elif findCard > currentNode:\r\n            if currentNode.right != None:\r\n                return self._get(suit, rank, currentNode.right)\r\n            else:\r\n                return None\r\n        else: #findCard is less than currentNode\r\n            if currentNode.left != None:\r\n                return self._get(suit, rank, currentNode.left)\r\n            else:\r\n                return None\r\n\r\n\r\n    def inOrder(self):\r\n        return self._inOrder(self.root)\r\n\r\n    def _inOrder(self, currentNode):\r\n        orderString = \"\"\r\n        if currentNode != None:\r\n            orderString += self._inOrder(currentNode.getLeft())\r\n            orderString += str(currentNode)\r\n            orderString += self._inOrder(currentNode.getRight())\r\n        return orderString\r\n\r\n\r\n    def preOrder(self):\r\n        return self._preOrder(self.root)\r\n\r\n    def _preOrder(self, currentNode):\r\n        orderString = \"\"\r\n        if currentNode != None:\r\n            orderString += str(currentNode)\r\n            orderString += self._preOrder(currentNode.getLeft())\r\n            orderString += self._preOrder(currentNode.getRight())\r\n        return orderString\r\n","sub_path":"PlayerHand.py","file_name":"PlayerHand.py","file_ext":"py","file_size_in_byte":10337,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"483784203","text":"#!/usr/bin/env python\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport sys\n\n\n#number of trials in bin file... \nN = int(sys.argv[1])\n\nwith open(\"wandb-radviz.bin\", \"rb\") as file:\n    a = np.fromfile(file)\n\na = np.reshape(a, (N, -1))\n\ndef anchorx(j):\n    return np.cos(2*np.pi*j/a.shape[1])\n\ndef anchory(j):\n    return np.sin(2*np.pi*j/a.shape[1])\n\nmax = np.amax(a)\nmin = np.amin(a)\n\nprint(max, min)\n\n# a -= min \n# a /= max - min\n\nfor i in range(a.shape[1]):\n    a[:,i] = (a[:,i] - np.amin(a[:,i])) / (np.amax(a[:,i]) - np.amin(a[:,i]))\n\nb = np.zeros((N, 2))\n\nfor i in range(N):\n    denom = np.sum(a[i,:])\n    for j in range(a.shape[1]):\n        b[i, 0] += a[i,j] * anchorx(j) / denom\n        b[i, 1] += a[i,j] * anchory(j) / denom\n\n\nfig, ax = plt.subplots()\n\nn1 = 5000\nx = b[:n1-1,0]; y = b[:n1-1,1]\nax.plot(x, y, label=\"MSE\")\nax.scatter(x[0], y[0], color=\"red\")\nax.scatter(x[-1], y[-1], color=\"green\")\n\nn2 = 10000\nx = b[n1:n2-1,0]; y = b[n1:n2-1,1]\nax.plot(x, y, label=\"MSE + SCH\")\nax.scatter(x[0], y[0], color=\"red\")\nax.scatter(x[-1], y[-1], color=\"green\")\n\nx = b[n2:,0]; y = b[n2:,1]\nax.plot(x, y, label=\"MSE + VAR\")\nax.scatter(x[0], y[0], color=\"red\")\nax.scatter(x[-1], y[-1], color=\"green\")\n\n# n2 = N\n# ax.plot(b[:n2-1,0], b[:n2-1,1], label=\"BB trained\")\n# ax.scatter(b[n1,0], b[n1,1], label=\"BB trained start\", color=\"red\")\n# ax.scatter(b[n2-1,0], b[n2-1,1], label=\"BB trained end\", color=\"green\")\n\n# ax.plot(b[1001:2001,0], b[1001:2001,1], label=\"C2 trained\")\n# ax.scatter(b[1001,0], b[1001,1], label=\"C2 trained start\", color=\"red\")\n# ax.scatter(b[-1,0], b[-1,1], label=\"C2 trained end\", color=\"green\")\n\n# ax.plot(b[2002:,0], b[2002:,1], label=\"Semi trained\")\n# ax.scatter(b[2002,0], b[2002,1], label=\"Semi trained start\", color=\"red\")\n# ax.scatter(b[-1,0], b[-1,1], label=\"Semi trained end\", color=\"green\")\n\n# ax.scatter(b[300:,0], b[300:,1], label=\"C2 trained\")\n# ax.scatter(b[400:,0], b[400:,1], label=\"Semi-Supervised\")\n# ax.scatter(b[100:300,0], b[100:300,1], label=\"untrained\")\nplt.xlabel(\"x\")\nplt.ylabel(\"y\")\nplt.title(\"Radial Visualization in Weight Space over Epochs\")\n# plt.xlim((-0.08,0.1))\n# plt.ylim((-0.08,0.04))\n\nax.legend()\n\nplt.savefig(\"tmp.png\", dpi=900)\n","sub_path":"code/bin/calc-radviz.py","file_name":"calc-radviz.py","file_ext":"py","file_size_in_byte":2190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"260405328","text":"import unittest\r\nimport pandas as pd\r\nimport Project.Model.BackEnd.BackEnd as be\r\nfrom Project.Controller.Actions.AddColumnAction import addColumn\r\nfrom Project.Controller.Actions.DeleteColumnAction import deleteColumn, deleteColumn_test\r\nfrom Project.View.ViewCards import ViewHandler\r\nimport PyQt5.QtWidgets as qtw\r\nfrom Project.Controller.Actions import FileUploadAction\r\n\r\nclass TestDeleteColumn(unittest.TestCase):\r\n    def setUp(self):\r\n        self.app = qtw.QApplication([])\r\n        screenResolution = self.app.desktop().screenGeometry()\r\n        windowHeightToScreen, windowWidthToScreen = 0.7, 0.6\r\n        width, height = int(screenResolution.width() * windowWidthToScreen), int(\r\n             screenResolution.height() * windowHeightToScreen)\r\n        self.backend = be.BackEnd()\r\n        self.mw = ViewHandler.MainWindow(width, height, self.backend, 0)\r\n        self.tableView = self.mw.getTableView()\r\n        self.backend.addTableView(self.tableView)\r\n\r\n    def test_deleteColumn(self):\r\n        # clear and add two columns for testing\r\n        self.backend.clear()\r\n        addColumn(self.tableView, qtw)\r\n        addColumn(self.tableView, qtw)\r\n\r\n        # check if fifth index is present\r\n        columns = self.tableView.getColumns()\r\n        self.assertTrue(columns[5])\r\n        oldFifthIndex = columns[5]\r\n\r\n        # delete 5th index and check if column name has been changed\r\n        deleteColumn_test(self.tableView, 5)\r\n        self.assertNotEquals(columns[5], oldFifthIndex)\r\n\r\n        # check if first 5 columns remain the same by trying to remove index 3\r\n        # these first 5 columns should always be in the program\r\n        thirdIndex = columns[3]\r\n        deleteColumn_test(self.tableView, 3)\r\n        self.assertNotEquals(columns[3], thirdIndex)\r\n\r\nif __name__ == '__main__':\r\n    unittest.main()\r\n","sub_path":"Project/test_deleteColumn.py","file_name":"test_deleteColumn.py","file_ext":"py","file_size_in_byte":1834,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"507528901","text":"# coding= utf-8\n\n# Copyright (c) 2014 Rackspace, 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\n# implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport uuid\nimport ddt\nfrom nose.plugins import attrib\nfrom tests.api import providers\nimport re\n\n\n@ddt.ddt\nclass TestXMLFlavor(providers.TestProviderBase):\n\n    \"\"\"Security Tests for any XML related Flavor vulnerablities\"\"\"\n\n    def setUp(self):\n        \"\"\"\n        Setup for the tests\n        \"\"\"\n        super(TestXMLFlavor, self).setUp()\n        self.reset_defaults()\n    \n    def reset_defaults(self):\n        \"\"\"\n        Reset provider_list, limits\n        and flavor_id to its default value.\n        \"\"\"\n        self.provider_list = [{\"provider\": \"fastly\",\n                               \"links\": [{\"href\": \"www.watermelon.com\",\n                                          \"rel\": \"provider_url\"}]}]\n        self.limits_list = [{\"origins\": {\"min\": 1, \"max\": 5}},\n                            {\"domains\": {\"min\": 1, \"max\": 5}},\n                            {\"caching\": {\"min\": 3600,\n                                         \"max\": 604800, \"incr\": 300}}]\n        self.flavor_id = str(uuid.uuid1())\n\n    @attrib.attr('security')\n    def test_xml_bomb_create_flavor(self):\n        \"\"\"\n        Check whether it is possible to kill the application by\n        creating a Flavor using XML bomb.\n        \"\"\"\n        # replace content type with application/xml\n        headers = {\"X-Auth-Token\": self.client.auth_token,\n                   \"X-Project-Id\": self.client.project_id,\n                   \"Content-Type\": \"application/xml\"}\n        attack_string = \"\"\"\n        <?xml version=\"1.0\"?>\n        <!DOCTYPE lolz [\n<!ENTITY lol \"lol\">\n<!ELEMENT lolz (#PCDATA)>\n<!ENTITY lol1 \"&lol;&lol;&lol;&lol;&lol;&lol;&lol;&lol;&lol;&lol;\">\n<!ENTITY lol2 \"&lol1;&lol1;&lol1;&lol1;&lol1;&lol1;&lol1;&lol1;&lol1;&lol1;\">\n<!ENTITY lol3 \"&lol2;&lol2;&lol2;&lol2;&lol2;&lol2;&lol2;&lol2;&lol2;&lol2;\">\n<!ENTITY lol4 \"&lol3;&lol3;&lol3;&lol3;&lol3;&lol3;&lol3;&lol3;&lol3;&lol3;\">\n<!ENTITY lol5 \"&lol4;&lol4;&lol4;&lol4;&lol4;&lol4;&lol4;&lol4;&lol4;&lol4;\">\n<!ENTITY lol6 \"&lol5;&lol5;&lol5;&lol5;&lol5;&lol5;&lol5;&lol5;&lol5;&lol5;\">\n<!ENTITY lol7 \"&lol6;&lol6;&lol6;&lol6;&lol6;&lol6;&lol6;&lol6;&lol6;&lol6;\">\n<!ENTITY lol8 \"&lol7;&lol7;&lol7;&lol7;&lol7;&lol7;&lol7;&lol7;&lol7;&lol7;\">\n<!ENTITY lol9 \"&lol8;&lol8;&lol8;&lol8;&lol8;&lol8;&lol8;&lol8;&lol8;&lol8;\">\n ]>\n<lolz>&lol9;</lolz>\n        \"\"\"\n        kwargs = {\"headers\": headers, \"data\": attack_string}\n        resp = self.client.create_flavor(flavor_id=self.flavor_id,\n                                         provider_list=self.provider_list,\n                                         limits=self.limits_list,\n                                         requestslib_kwargs=kwargs)\n        # delete the Flavor\n        self.assertTrue(resp.status_code == 400)\n        \n        match = re.search(\"Invalid JSON string\", resp.text)\n        self.assertTrue(match is not None)\n\n    def tearDown(self):\n        self.client.delete_flavor(flavor_id=self.flavor_id)\n        super(TestXMLFlavor, self).tearDown()\n","sub_path":"tests/security/flavors/test_xml_flavors.py","file_name":"test_xml_flavors.py","file_ext":"py","file_size_in_byte":3541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"358255271","text":"#!/usr/bin/python3\n\"\"\"\na\n\"\"\"\n\nimport json\nimport requests\n\nif __name__ == '__main__':\n    users = requests.get(\"https://jsonplaceholder.typicode.com/users\",\n                         verify=False).json()\n    user_dict = {}\n    username_dict = {}\n    for user in users:\n        user_id = user.get(\"id\")\n        user_dict[user_id] = []\n        username_dict[user_id] = user.get(\"username\")\n    todo = requests.get(\"https://jsonplaceholder.typicode.com/todos\",\n                        verify=False).json()\n    for task in todo:\n        tasks_json = {}\n        user_id = task.get(\"userId\")\n        tasks_json[\"task\"] = task.get('title')\n        tasks_json[\"completed\"] = task.get('completed')\n        tasks_json[\"username\"] = username_dict.get(user_id)\n        user_dict.get(user_id).append(tasks_json)\n    with open(\"todo_all_employees.json\", 'w') as jsonfile:\n        json.dump(user_dict, jsonfile)\n","sub_path":"0x15-api/3-dictionary_of_list_of_dictionaries.py","file_name":"3-dictionary_of_list_of_dictionaries.py","file_ext":"py","file_size_in_byte":896,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"323226334","text":"\nclass LinkNode(object):\n    def __init__(self, val=None, next=None):\n        self.val = val\n        self.next = next\n\n\ndef list_2_linklist(arr):\n    tem_node = LinkNode()\n    node = LinkNode()\n    for i in arr:\n        if not tem_node.val:\n            tem_node.val = i\n            node = tem_node\n        else:\n            tem_node.next = LinkNode(i)\n            tem_node = tem_node.next\n    return node\n\n\n# if __name__ == '__main__':\n#     array = [1, 2, 3, 4, 5, 6, 7]\n#     print(list_2_linklist(array).val)\n","sub_path":"leetcode/list_2_linklist.py","file_name":"list_2_linklist.py","file_ext":"py","file_size_in_byte":512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"305597961","text":"import urllib.request\nimport http.cookiejar\n\n# 没有伪装成浏览器的时候\n# if __name__ == '__main__':\n#     url = \"http://news.163.com/17/1213/20/D5IISIRT000189FH.html\"\n#     cjar = http.cookiejar.CookieJar()\n#     proxy = urllib.request.ProxyHandler({'http':\"127.0.0.1:8888\"})\n#     opener = urllib.request.build_opener(proxy, urllib.request.HTTPHandler, urllib.request.HTTPCookieProcessor(cjar))\n#     urllib.request.install_opener(opener=opener)\n#     data = urllib.request.urlopen(url).read()\n#     fhandle = open(\"D:\\PycharmProjects\\start2py\\pretentbrowers.html\",\"wb\")\n#     fhandle.write(data)\n#     fhandle.close()\n\n\n# 伪装成浏览器\nif __name__ == '__main__':\n    headers = {\n        \"Accept\": \"text/html,application/xhtml+xml,application/xml;q=0.9,image/webp,image/apng,*/*;q=0.8\",\n        \"Accept-Language\": \"zh-CN,zh;q=0.9,ko;q=0.8\",\n        \"Connection\": \"keep-alive\",\n        \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/63.0.3239.84 Safari/537.36\",\n        \"Referer\": \"baidu.com\",\n        \"Host\":\"www.baidu.com\",\n        \"Upgrade-Insecure-Requests\":\"1\"\n    }\n    url = \"http://www.baidu.com/\"\n    cjar = http.cookiejar.CookieJar()\n    proxy = urllib.request.ProxyHandler({'http': \"127.0.0.1:8888\"})\n    opener = urllib.request.build_opener(proxy, urllib.request.HTTPHandler, urllib.request.HTTPCookieProcessor(cjar))\n    headall = []\n    for key, value in headers.items():\n        item = {key, value}\n        headall.append(item)\n    opener.addheaders = headall\n    urllib.request.install_opener(opener=opener)\n    data = urllib.request.urlopen(url).read()\n    fhandle = open(\"D:\\PycharmProjects\\start2py\\pretentbrowers.html\", \"wb\")\n    fhandle.write(data)\n    fhandle.close()\n","sub_path":"start2py/PretentBroswer.py","file_name":"PretentBroswer.py","file_ext":"py","file_size_in_byte":1761,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"373383872","text":"\n\n# Event clustering with machine learning\n\n\nimport pandas as pd\n\nattributes_df = pd.read_csv('Mawson_attributes.txt', sep=',')\nattributes_df['start_time'] = pd.to_datetime(attributes_df['start_time'])\nattributes_df['stop_time'] = pd.to_datetime(attributes_df['stop_time'])\n\nattributes_df.columns\n\n\n\nattributes_df = attributes_df.drop(columns=['attribute_11', 'attribute_12', 'attribute_34', 'attribute_35', \\\n                                            'attribute_36', 'attribute_37'])\n\n\n\n\n\n### Attribute/feature analysis\n\n\nattributes_df.dtypes\n\n\nimport seaborn as sn\nimport matplotlib.pyplot as plt\n\n\ndef boxplot(attributes_df, ymin=None, ymax=None):\n    # create new figure\n    fig, ax = plt.subplots()\n    fig.set_size_inches(12, 6)\n    # set axis labels and scale\n    ax.tick_params(axis='x', labelrotation=90)\n    ax.set(ylabel='Attribute value')\n    if not ymin == None and not ymax == None:\n        plt.ylim([ymin, ymax])\n\n    # plot boxplot using seaborn\n    sn.boxplot(data=attributes_df.drop(columns=['start_time', 'stop_time']), orient='v', ax=ax)\n\n    plt.show()\n\n\nboxplot(attributes_df, ymin=0, ymax=1e6)\n\n\nimport numpy as np\n\n# create empty dataframe with headings for each attribute\nattribute_scaling_df = pd.DataFrame(columns=attributes_df.columns[2:len(attributes_df.columns)])\n\n# set scaling for each attribute to 'linear' by default\na = list()\nfor i in range(0, len(attribute_scaling_df.columns)):\n    a.append('linear')\nattribute_scaling_df.loc[0] = a\n\n# manually change scaling for attributes\nattribute_scaling_df['attribute_1'][0] = 'log'\nattribute_scaling_df['attribute_24'][0] = 'log'\nattribute_scaling_df['attribute_25'][0] = 'log'\nattribute_scaling_df['attribute_26'][0] = 'log'\n# attribute_scaling_df['attribute_34'][0] = 'log'\n# attribute_scaling_df['attribute_35'][0] = 'log'\n# attribute_scaling_df['attribute_36'][0] = 'log'\n# attribute_scaling_df['attribute_37'][0] = 'log'\n\nprint(attribute_scaling_df)\n\n# create a copy of the database to applying the scaling corrections\nattributes_scaled_df = attributes_df.copy()\n\n# rescale each attribute to log or linear scale centred on zero\nfor i in range(0, len(attribute_scaling_df.columns)):\n    if attribute_scaling_df[attribute_scaling_df.columns[i]][0] == 'linear':\n        attributes_scaled_df[attribute_scaling_df.columns[i]] \\\n            = attributes_scaled_df[attribute_scaling_df.columns[i]] / \\\n              np.median(attributes_df[attribute_scaling_df.columns[i]]) - 1\n    else:\n        attributes_scaled_df[attribute_scaling_df.columns[i]] \\\n            = np.log10(attributes_scaled_df[attribute_scaling_df.columns[i]] / \\\n                       np.median(attributes_df[attribute_scaling_df.columns[i]]))\n\n# rescale the distribution of each attribute to have standard deviation of unity\nfor i in range(0, len(attribute_scaling_df.columns)):\n    attributes_scaled_df[attribute_scaling_df.columns[i]] \\\n        = attributes_scaled_df[attribute_scaling_df.columns[i]] / \\\n          np.std(attributes_scaled_df[attribute_scaling_df.columns[i]])\n\n\nboxplot(attributes_scaled_df, ymin=-5, ymax=5)\n\n\n### Correlation analysis\n\n\nimport seaborn as sn\nimport matplotlib.pyplot as plt\n\n\ndef correlation_plot(attributes_df):\n    # create correlation matrix and mask for upper triangular matrix\n    corrMatt = attributes_df.drop(columns=['start_time', 'stop_time']).corr()\n    mask = np.array(corrMatt)\n    mask[np.tril_indices_from(mask)] = False\n\n    # create correlation plot\n    fig, ax = plt.subplots()\n    fig.set_size_inches(16, 10)\n    sn.heatmap(corrMatt, mask=mask, vmin=-1, vmax=1, square=True, annot=True, annot_kws={'size': 9}, fmt='.2f')\n\n\n\ncorrelation_plot(attributes_scaled_df)\n\n\n### Self-Organising Maps with TensorFlow\n\n\n\n\n\n\nimport numpy as np\nimport pandas as pd\nfrom minisom import MiniSom\n\n\ndef self_organising_maps(attributes_df, size=32):\n    # define matrix of attributes and values\n    X = attributes_df.iloc[:, 2:len(attributes_df.columns)]\n\n    # run self-organising maps algorithm\n    # create an object\n    som = MiniSom(x=size, y=size, input_len=len(attributes_df.columns) - 2, sigma=1.5 * np.sqrt(size),\n                  learning_rate=0.5)\n    ### maybe sigma=5*np.sqrt(size) and learning_rate=1\n    # initialize the weights and train\n    som.random_weights_init(X.values)\n    som.train_random(X.values, num_iteration=2 * len(attributes_df))\n    # som.train_batch(X.values, num_iteration=len(attributes_df)) # this gives more predictable results\n\n    print('There are ' + str(len(attributes_df)) + ' events assigned to ' + str(size * size) + ' neurons.')\n\n    return som\n\n\n\n\n\n\ndef get_locations(som):\n    # create empty array\n    locations = np.zeros_like(som.distance_map()).tolist()\n\n    # add locations to array\n    for i in range(0, len(locations[:][0])):\n        for j in range(0, len(locations[0][:])):\n            locations[i][j] = (i, j)\n\n    return np.asarray(locations)\n\n\n\nimport matplotlib.pyplot as plt\nimport matplotlib.colors as colors\n\n\ndef distance_plotter(attributes_df, som, labels=None):\n    # setup figure\n    fig, ax = plt.subplots()\n    fig.set_size_inches(10, 8)\n    ax.axis('off')\n    ax.set_aspect('equal')\n\n    # correct weightings of edge points (for lack of neighbours)\n    mid = som.distance_map()\n    mid[:, 0] = np.minimum(1, mid[:, 0] * 1.5)\n    mid[:, len(mid[0, :]) - 1] = np.minimum(1, mid[:, len(mid[0, :]) - 1] * 1.5)\n    mid[0, :] = np.minimum(1, mid[0, :] * 1.5)\n    mid[len(mid[:, 0]) - 1, :] = np.minimum(1, mid[len(mid[:, 0]) - 1, :] * 1.5)\n\n    # create heatmap of mean inter-neuron distance\n    plt.pcolor(mid.T, norm=colors.PowerNorm(gamma=1. / 2), cmap='RdPu', vmin=np.quantile(mid, 0.01), vmax=1)\n    cb = plt.colorbar()\n    cb.set_label('Mean inter-neuron distance')\n\n    # calculate number of events in each neuron\n    if labels is None:\n        count = np.zeros_like(mid)\n        X = attributes_df.iloc[:, 2:len(attributes_df.columns)]\n        for x in X.values:\n            w = som.winner(x)\n            count[w[0], w[1]] += 1\n\n        # plot circles over heatmap to represent number of events per neuron\n        plt.plot(1, 1)\n        for i in range(0, len(count[:, 0])):\n            for j in range(0, len(count[0, :])):\n                plt.plot(i + 0.5, j + 0.5, marker='o', markeredgecolor='black', markerfacecolor='None', \\\n                         markersize=400 * np.sqrt(count[i, j] / np.max(count)) / len(count[0, :]))\n    else:\n        # plot contour map of mean inter-neuron distance\n        for i in range(0, int(np.max(labels)) + 1):\n            plt.contour(np.minimum(np.absolute(labels.T - i), 0.5), colors='k', \\\n                        levels=[0.15], \\\n                        linewidths=1, origin='lower')\n\n        # add labels to each cluster\n        locations = get_locations(som)\n        for i in range(0, int(np.max(labels)) + 1):\n            # find median x and y location for cluster\n            result = np.where(labels == i)\n            xs = [x[0] for x in locations[result]]\n            ys = [x[1] for x in locations[result]]\n            plt.annotate('cluster ' + str(i + 1), xy=(np.median(xs) + 0.5, np.median(ys) + 0.5), \\\n                         horizontalalignment='center', verticalalignment='center', size=9.5, \\\n                         bbox=dict(boxstyle=\"round\", alpha=0.5, fc=(1.0, 1.0, 1.0), ec=\"none\"))\n\n    plt.show()\n\n\n\n\nsom = self_organising_maps(attributes_scaled_df, size=64)\n\n\ndistance_plotter(attributes_scaled_df, som)\n\n\n### Identifying clusters of neurons\n\n\n\nfrom sklearn.cluster import AgglomerativeClustering, SpectralClustering\nfrom sklearn.metrics import davies_bouldin_score\n\n\ndef clustering(som, min_clusters=5, max_clusters=20):\n    # create array of the centroid locations of each neuron\n    array3D = som.get_weights()\n    array2D = array3D.reshape((array3D.shape[0] * array3D.shape[1]), array3D.shape[2])\n\n    # find optimal number of clusters using Davies-Bouldin score\n    prev_score = 99.99\n    for i in range(min_clusters, max_clusters + 1):\n        # find clusters for current number of clusters\n        cluster = SpectralClustering(n_clusters=i, affinity='rbf')  # can use AgglomerativeClustering instead\n        new_labels = cluster.fit_predict(array2D)\n        new_score = davies_bouldin_score(array2D, new_labels)\n\n        # test if new cluster is better than previous cluster\n        if new_score < prev_score:\n            prev_score = new_score\n            labels = new_labels.copy()\n\n    # convert labels to a 3D array\n    labels = labels.reshape(array3D.shape[0], array3D.shape[1])\n\n    return labels\n\n\n\nlabels = clustering(som, min_clusters=3, max_clusters=20)\n\n\ndistance_plotter(attributes_scaled_df, som, labels=labels)\n\n\n\n\ndef cluster_locations(cluster_number, labels, som):\n    # get location of each cluster\n    locations = get_locations(som)\n    # find locations in correct cluster (numbered as shown in plot; i.e. n + 1)\n    result = np.where(labels == cluster_number - 1)\n\n    return np.asarray(locations[result])\n\n\nprint(cluster_locations(1, labels, som))\n\n### Finding events in neurons of interest\n\n\ndef neuron_inversion(attributes_df, som, locations):\n    # catch a single location not defined using a list\n    if not isinstance(locations, (list, np.ndarray)):\n        locations = [locations]\n\n    # define matrix of attributes and values\n    X = attributes_df.iloc[:, 2:len(attributes_df.columns)]\n    # define empty pandas dataframe to store event times\n    event_times_df = pd.DataFrame(columns=['start_time', 'stop_time'])\n\n    # use a dictionary to obtain the mappings\n    mappings = som.win_map(X.values)\n\n    # get the events assigned to the neuron at location (x,y)\n    events_df = pd.DataFrame(columns=X.columns)\n    i = 0\n    for location in locations:\n        events = mappings[(location[0], location[1])]\n        for event in events:\n            events_df.loc[i] = event\n            i = i + 1\n\n    # find start time of event\n    event_times_df = pd.DataFrame(columns=['start_time', 'stop_time'])\n    for i in range(0, len(events_df)):\n        event_times_df.loc[i] = list([attributes_df['start_time'][finder(X, events_df.iloc[i, :])].item(), \\\n                                      attributes_df['stop_time'][finder(X, events_df.iloc[i, :])].item()])\n\n    # convert pandas database to datatime objects\n    event_times_df['start_time'] = pd.to_datetime(event_times_df['start_time'], unit='ns')\n    event_times_df['stop_time'] = pd.to_datetime(event_times_df['stop_time'], unit='ns')\n\n    return event_times_df.sort_values(['start_time'], ascending=True).reset_index(drop=True)\n\n\n\ndef finder(X, row):\n    df = X.copy()\n    # find the row with a perfect match for all attributes\n    for col in df:\n        df = df.loc[df[col] == row[col]]\n\n    # return the row number for the matched event\n    return df.index\n\n\nevent_times_df = neuron_inversion(attributes_scaled_df, som, locations=cluster_locations(1, labels, som))\n\nprint(event_times_df.to_string())\n\n\n\n\ndef cluster_events(attributes_scaled_df, labels, som):\n    # find events associated with each cluster and add to pandas dataframe\n    for i in range(0, int(np.max(labels)) + 1):\n        df = neuron_inversion(attributes_scaled_df, som, \\\n                              locations=cluster_locations(i + 1, labels, som))\n        df['cluster'] = i + 1\n        # add dataframe to existing dataframe if one exists\n        if i == 0:\n            event_times_df = df.copy()\n        else:\n            event_times_df = event_times_df.append(df, ignore_index=True)\n\n    return event_times_df\n\n\nevent_times_df = cluster_events(attributes_scaled_df, labels, som)\n\nevent_times_df\n\n\nevent_times_df.to_csv('Mawson_clusters.txt', index=False)\n\n\n### Agglomerative and spectral clustering methods\n\n\n\n\nfrom sklearn.cluster import AgglomerativeClustering, SpectralClustering\n\n\ndef raw_clustering(attributes_df, min_clusters=5, max_clusters=20):\n    # create array of the attributes for each event\n    array2D = attributes_df.iloc[:, 2:len(attributes_df.columns)].values[0:1000, :]\n\n    # find optimal number of clusters using Davies-Bouldin score\n    prev_score = 99.99\n    for i in range(min_clusters, max_clusters + 1):\n        # find clusters for current number of clusters\n        cluster = SpectralClustering(n_clusters=i, affinity='rbf')  # can use AgglomerativeClustering instead\n        new_labels = cluster.fit_predict(array2D)\n        new_score = davies_bouldin_score(array2D, new_labels)\n\n        # test if new cluster is better than previous cluster\n        if new_score < prev_score:\n            prev_score = new_score\n            labels = new_labels.copy()\n\n    return labels\n\n\n\nraw_labels = raw_clustering(attributes_scaled_df, min_clusters=8, max_clusters=30)\n\n\nprint(raw_labels[raw_labels > 0])\n\n\n\n","sub_path":"Archive/RossML.py","file_name":"RossML.py","file_ext":"py","file_size_in_byte":12697,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"310064653","text":"import numpy as np\nimport pandas as pd\nfrom scipy.sparse.csgraph import connected_components\n\n\ndef graph_connectivity(adata, label_key):\n    \"\"\"\"\n    Quantify how connected the subgraph corresponding to each batch cluster is.\n    Calculate per label: #cells_in_largest_connected_component/#all_cells\n    Final score: Average over labels\n\n    :param adata: adata with computed neighborhood graph\n    :param label_key: name in adata.obs containing the cell identity labels\n    \"\"\"\n    if 'neighbors' not in adata.uns:\n        raise KeyError(\n            'Please compute the neighborhood graph before running this function!'\n        )\n\n    clust_res = []\n\n    for label in adata.obs[label_key].cat.categories:\n        adata_sub = adata[adata.obs[label_key].isin([label])]\n        _, labels = connected_components(\n            adata_sub.obsp['connectivities'],\n            connection='strong'\n        )\n        tab = pd.value_counts(labels)\n        clust_res.append(tab.max() / sum(tab))\n\n    return np.mean(clust_res)\n","sub_path":"scib/metrics/graph_connectivity.py","file_name":"graph_connectivity.py","file_ext":"py","file_size_in_byte":1015,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"477231074","text":"import bisect\nN = int(input())\nS = input()\nif not 'R' in S and 'G' in S and 'B' in S:\n    print(\"0\")\n    exit()\nR_idx_list = []\nG_idx_list = []\nB_idx_list = []\nfor i in range(len(S)):\n    if S[i] == 'R':\n        R_idx_list.append(i)\n    elif S[i] == 'G':\n        G_idx_list.append(i)\n    else:\n        B_idx_list.append(i)\n\nans = 0\nfor i in range(len(S) - 2):\n    rgb_flags = [False] * 3\n    i_chr = S[i]\n    if i_chr == 'R':\n        rgb_flags[0] = True\n    elif i_chr == 'G':\n        rgb_flags[1] = True\n    elif i_chr == 'B':\n        rgb_flags[2] = True\n    for j in range(i + 1, len(S) - 1):\n        j_chr = S[j]\n        if i_chr == j_chr:\n            continue\n        if j_chr == 'R':\n            rgb_flags[0] = True\n        elif j_chr == 'G':\n            rgb_flags[1] = True\n        elif j_chr == 'B':\n            rgb_flags[2] = True\n        num = (j - i) * 2\n        if not rgb_flags[0] and j < R_idx_list[-1]:\n            r_idx = bisect.bisect_right(R_idx_list, j)\n            ans += len(R_idx_list) - r_idx\n            idx = bisect.bisect_left(R_idx_list, num)\n            if len(R_idx_list) > idx and R_idx_list[idx] - j == j - i:\n                ans -= 1\n\n        if not rgb_flags[1] and j < G_idx_list[-1]:\n            g_idx = bisect.bisect_right(G_idx_list, j)\n            ans += len(G_idx_list) - g_idx\n            idx = bisect.bisect_left(G_idx_list, num)\n            if len(G_idx_list) > idx and G_idx_list[idx] - j == j - i:\n                ans -= 1\n\n        if not rgb_flags[2] and j < B_idx_list[-1]:\n            b_idx = bisect.bisect_right(B_idx_list, j)\n            ans += len(B_idx_list) - b_idx\n            idx = bisect.bisect_left(B_idx_list, num)\n            if len(B_idx_list) > idx and B_idx_list[idx] - j == j - i:\n                ans -= 1\n\n        if j_chr == 'R':\n            rgb_flags[0] = False\n        elif j_chr == 'G':\n            rgb_flags[1] = False\n        elif j_chr == 'B':\n            rgb_flags[2] = False\nprint(ans)","sub_path":"ABC/162/4.py","file_name":"4.py","file_ext":"py","file_size_in_byte":1956,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"625514286","text":"import tkinter as tk\nimport os\nfrom essentials import *\nimport ctypes\nimport sys\nfrom programs import programs\nfrom threading import Thread\n\ninitialize()\n# os.system('powershell.exe \"Set-ExecutionPolicy RemoteSigned\"')\n\n\ndef remove():\n    name = entry.get().lower()\n    for n, sysn in programs.items():\n        if name in n.lower():\n            label['text'] = 'Removing ' + n\n            os.system(\n                'powershell.exe \"Get-AppxPackage *%s* | Remove-AppxPackage SilentlyContinue\"' % name)\n            label['text'] = n + ' removed.'\n            name = sysn\n            break\n\n\ndef go():\n    Thread(target=remove).start()\n\n\nwindow = tk.Tk()\nwindow.title('WDebloat')\nwindow.geometry(\"250x530\")\nwindow.resizable(False, False)\n\nconsole = tk.Text(window, height=27, width=26)\nconsole.place(x=20, y=30)\n\nconsole.bind(\"<Key>\", lambda e: \"break\")\n\nentry = tk.Entry(window, text='Input name to delete here...', width=25)\nentry.insert(tk.END, 'Insert name to remove...')\nentry.place(x=20, y=475)\n\ninput_programs(console)\n\n\nlabel = tk.Label(window)\nlabel.place(x=20, y=500)\n\n\nbtn = tk.Button(window, text='Remove', command=go)\nbtn.place(x=180, y=470)\nwindow.mainloop()\n","sub_path":"gui.py","file_name":"gui.py","file_ext":"py","file_size_in_byte":1171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"504560249","text":"#!/usr/bin/python3\n#--------------------------------------\n#\n#              AquariumUit.py\n#  Switch Hue Light Off in 45 min\n#\n# Author : Vincent Rouwhorst\n# Date   : 31/05/2022\n#\n# Domoticz Json documentation\n# https://www.domoticz.com/wiki/Domoticz_API/JSON_URL's#Custom_Sensor\n#--------------------------------------\n\nimport requests\nimport time\n\nDOMOTICZ_IP = 'http://127.0.0.1:8080'\n\nid_name = { 7300 : {\"name\" : \"JMB-C4\" }}\n\ndef PushCommand(idx, levelx):\n    print(DOMOTICZ_IP + \"/json.htm?type=command&param=switchlight&idx=\" + str(idx) + \"&switchcmd=Set%20Level&level=\" + str(levelx))\n    r = requests.get(DOMOTICZ_IP + \"/json.htm?type=command&param=switchlight&idx=\" + str(idx) + \"&switchcmd=Set%20Level&level=\" + str(levelx))\n    siteresponse = r.json()\n    if siteresponse[\"status\"] == 'ERR':\n        # Write ERROR to Domoticz log\n        message = \"ERROR writing lamp script Aquarium \" + str(idx)\n        #print(message)\n        requests.get(DOMOTICZ_IP + \"/json.htm?type=command&param=addlogmessage&message=\" + message)\n\n\nif __name__ == '__main__':\n  # Script has been called directly\n  countdowntimer = 45 * 60 # 45 min = 2700 sec\n  lamplevel = 101 # 0-100\n\n  for x in reversed(range(lamplevel)):  # range=On, reversed=Off script\n      for z in id_name.keys():\n          PushCommand(z, x)\n      time.sleep(countdowntimer/lamplevel)\n","sub_path":"AquariumUit_C4.py","file_name":"AquariumUit_C4.py","file_ext":"py","file_size_in_byte":1346,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"9870162","text":"import sys\nimport csv\n\nmesi = [\"gennaio\", \"febbraio\", \"marzo\", \"aprile\", \"maggio\", \"giugno\", \"luglio\", \"agosto\", \"settembre\", \"ottobre\", \"novembre\", \"dicembre\"]\n\ndef cDictLike(file):\n    \n    dizLike = {}\n    lst = []\n\n    nPost = 0\n    nLike = 0\n\n    for mese in mesi:\n        f = open(file, \"r\")\n        for k,row in enumerate(f):\n            if k > 0:\n                lst = row.split(',')\n                if mese == lst[0]:\n                    #print(lst[-1]) #stampa ultimo elemento --> -1 prende l'ultimo elemento\n                    nPost += 1\n                    nLike += int(lst[-1])\n                    dizLike[mese] = [nPost,nLike]\n                    #print(dizLike)\n        nPost = 0\n        nLike = 0\n        \n        f.close()\n    return dizLike\n\ndef trova(mese, diz):\n    for e in diz.keys():\n        if e == mese:\n            print(diz[e])\n            \ndef main():\n    file = \"instagram.csv\"\n    diz = cDictLike(file)\n    mese = input(\"inserisci il mese da cercare: \")\n    trova(mese, diz)\n\n    \nif __name__ == \"__main__\":\n    main()","sub_path":"Python/EsercitazioneVerifica/Es3/Es.py","file_name":"Es.py","file_ext":"py","file_size_in_byte":1049,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"591402278","text":"# coding = utf-8\n'''\npython 3.6, pLink2.3.5 or later version\nif you have any question, please contact caoyong@nibs.ac.cn\n'''\nimport os\nimport re\n\nscoreType = 0 # important, 0 for spectra num, 1 for Best E-value, 2 for Best_svm\npath = r'./example' # the path of pLink2 reports file\n\n###############Don't change the following lines#####################################\n\nos.chdir(path)\n\n\ndef splitLinksite(linked_site):\n    pos_list = re.findall(\"\\((\\d*)\\)\", linked_site)\n    position1 = pos_list[0]\n    position2 = pos_list[1]\n    p = linked_site.find(\"-\")\n    m = linked_site.find(\"(\" + position1 + \")-\")\n    n = linked_site.find(\"(\" + position2 + \")\", p)\n    protein1 = linked_site[:m].strip()\n    protein2 = linked_site[p + 1:n].strip()\n    if protein1 != protein2:\n        link_type = \"Inter\"\n    else:\n        if abs(int(position1)-int(position2)) < 3:\n            link_type = \"Inter\"\n        else:\n            link_type = \"Intra\" \n    return protein1, protein2, position1, position2\n\n\ndef getCrossLinkSiteInfo(openfl):\n    siteInfoDic = {}\n    i = 2\n    while i < len(openfl):\n        lineList = openfl[i].rstrip('\\n').split(\",\")\n        if lineList[0].isdigit():\n            sitePair = lineList[1]\n            totalSpecNum = lineList[-1]\n        else:\n            print('wrong')\n        \n        svmScoreList = []\n        eValueList = []\n        p = i + 1\n        while p < len(openfl):\n            lineList = openfl[p].rstrip('\\n').split(\",\")\n            if lineList[0] in [\"SameSet\", \"SubSet\"]:\n                p += 1\n            elif lineList[0].isdigit():\n                break\n            else:\n                eValueList.append(float(lineList[8]))\n                svmScoreList.append(float(lineList[9]))\n                p += 1\n        bestSvmScore = min(svmScoreList)\n        bestEvalue = min(eValueList)\n        prot1, prot2, pos1, pos2 = splitLinksite(sitePair)\n        if scoreType == 0:\n            siteInfoDic[sitePair] = [totalSpecNum, prot1, prot2, pos1, pos2]\n        elif scoreType == 1:\n            siteInfoDic[sitePair] = [bestEvalue, prot1, prot2, pos1, pos2]\n        elif scoreType == 2:\n            siteInfoDic[sitePair] = [bestSvmScore, prot1, prot2, pos1, pos2]\n        else:\n            print(\"scoreType is wrong\")\n        \n        i = p\n    return siteInfoDic\n        \n\ndef main():\n    for fl in os.listdir(os.getcwd()):\n        if fl[-22:] == \"cross-linked_sites.csv\":\n            f = open(fl).readlines()\n            rep = open(fl[:-22] + \"_xiNET.csv\", 'w')\n            rep.write(\",\".join([\"Score\", \"Protein1\", \"Protein2\",\\\n                    \"LinkPos1\", \"LinkPos2\"])+'\\n')\n            siteInfoDic = getCrossLinkSiteInfo(f)\n            for site in siteInfoDic:\n                rep.write(\",\".join(siteInfoDic[site])+'\\n')\n            rep.close()\n        else:\n            continue\n\n\nif __name__ == \"__main__\":\n    main()","sub_path":"xiNETspliteTool_for_pLink2.py","file_name":"xiNETspliteTool_for_pLink2.py","file_ext":"py","file_size_in_byte":2851,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"196143112","text":"#selection sort\r\n#list -> list\r\ndef selectionsort( aList ):\r\n  for i in range( len( aList ) ):\r\n    minIndex = i\r\n    for k in range( i + 1 , len( aList ) ):\r\n      if aList[k] < aList[minIndex]:\r\n        minIndex = k\r\n \r\n    swap( aList, minIndex, i )\r\n  return aList \r\n  \r\n#swap the value\r\n#list  int int ->\r\ndef swap( A, x, y ):\r\n  tmp = A[x]\r\n  A[x] = A[y]\r\n  A[y] = tmp\r\n\r\n#Binary Serach on sorted list\r\n#list -> bool\r\ndef binarySearch(alist, item):\r\n    first = 0\r\n    last = len(alist)-1\r\n    found = False\r\n    while first<=last and not found:\r\n        midpoint = (first + last)//2\r\n        if alist[midpoint] == item:\r\n            found = True\r\n        else:\r\n            if item < alist[midpoint]:\r\n                last = midpoint-1\r\n            else:\r\n                first = midpoint+1\r\n    return found\r\n\t   \r\nL=[4,8,-9,4,34,1,0,37]\r\nprint('L=',L)\r\nprint('Sorted L:', selectionsort(L))\r\nprint('find: ',binarySearch(L,34))\r\nprint('find: ',binarySearch(L,-34))\r\n","sub_path":"Project6/SelectionSort-BinarySearch.py","file_name":"SelectionSort-BinarySearch.py","file_ext":"py","file_size_in_byte":973,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"81527362","text":"#-*- coding: utf-8 -*-\n\nr1 = open(\"col1.txt\", \"r\")\ntemp1 = r1.read()\nr1.close()\ntext1 = temp1.split(\"\\n\")\n\nr2 = open(\"col2.txt\", \"r\")\ntemp2 = r2.read()\nr2.close()\ntext2 = temp2.split(\"\\n\")\n\nmerge = \"\"\ncount = 0\n\nfor line in range(len(text1)):\n    merge = merge + text1[count] + \"\\t\" + text2[count] + \"\\n\"\n    count += 1\n\nw = open(\"col_merge.txt\", \"w\")\nw.write(merge)\nw.close()\n\n#UNIX command: paste col1_unix.txt col2_unix.txt > merge_unix.txt\n","sub_path":"Hayahide/chapter02/knock13.py","file_name":"knock13.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"130018900","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nfrom torch.nn import init\nfrom torch.autograd import Variable\nfrom utilities import ImageFolderCustom\nimport numpy as np\n\ndef ConvBlock(in_channels, out_channels, kernel_size=3, stride=1, padding=1, bias=False, groups=1):    \n    return nn.Conv2d(in_channels, out_channels, kernel_size, stride=stride, padding=padding, bias=bias, groups=groups)\n\nclass Block_First(nn.Module):\n    def __init__(self, in_channels, out_channels, pooling=True):\n        super(Block_First, self).__init__()\n\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.pooling = pooling\n\n        self.conv1 = ConvBlock(self.in_channels, self.out_channels, 5, 2, 2)\n        self.bn1 = nn.BatchNorm2d(self.out_channels)\n        self.act1 = nn.ReLU()\n        self.conv2 = ConvBlock(self.out_channels, self.out_channels, 5, 2, 2)\n        self.bn2 = nn.BatchNorm2d(self.out_channels)\n        self.act2 = nn.ReLU()\n        \n        if self.pooling:\n            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.act1(x)\n        x = self.conv2(x)\n        x = self.bn2(x)\n        x = self.act2(x)\n        x = F.relu(x)\n        if self.pooling:\n            x = self.pool(x)\n        return x\n\nclass Block(nn.Module):\n    def __init__(self, in_channels, out_channels, pooling=True):\n        super(Block, self).__init__()\n        \n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.pooling = pooling\n\n        self.conv1 = ConvBlock(self.in_channels, self.out_channels, 3, 1)\n        self.bn1 = nn.BatchNorm2d(self.out_channels)\n        self.act1 = nn.ReLU()\n        self.conv2 = ConvBlock(self.out_channels, self.out_channels, 3, 1)\n        self.bn2 = nn.BatchNorm2d(self.out_channels)\n        self.act2 = nn.ReLU()\n        \n        if self.pooling:\n            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.act1(x)\n        x = self.conv2(x)\n        x = self.bn2(x)\n        x = self.act2(x)\n        x = F.relu(x)\n        if self.pooling:\n            x = self.pool(x)\n        return x\n    \nclass Block_Last(nn.Module):\n    def __init__(self, in_channels, out_channels, pooling=True):\n        super(Block_Last, self).__init__()\n\n        self.in_channels = in_channels\n        self.out_channels = out_channels\n        self.pooling = pooling\n\n        self.conv1 = ConvBlock(self.in_channels, self.out_channels, 1, 1, 0)\n        self.bn1 = nn.BatchNorm2d(self.out_channels)\n        self.act1 = nn.ReLU()\n        self.conv2 = ConvBlock(self.out_channels, self.out_channels, 1, 1, 0)\n        self.bn2 = nn.BatchNorm2d(self.out_channels)\n        self.act2 = nn.ReLU()\n        \n        if self.pooling:\n            self.pool = nn.MaxPool2d(kernel_size=2, stride=2)\n\n    def forward(self, x):\n        x = self.conv1(x)\n        x = self.bn1(x)\n        x = self.act1(x)\n        x = self.conv2(x)\n        x = self.bn2(x)\n        x = self.act2(x)\n        x = F.relu(x)\n        if self.pooling:\n            x = self.pool(x)\n        return x\n    \nclass Net_Big_Image(nn.Module):\n    \n    def __init__(self, num_classes, in_channels=3, depth=5, num_fc = 2, \n                 out_channels_first_block=64, img_size = 256):\n\n        super(Net_Big_Image, self).__init__()\n\n        self.num_classes = num_classes\n        self.in_channels = in_channels\n        self.out_channels_first_block = out_channels_first_block\n        self.depth = depth\n        self.num_fc = num_fc\n        self.img_size = img_size\n\n        # Setting Up Blocks of Two Convolutions and One Max Pooling\n        self.blocks = []\n        self.fully_connecteds = []\n        \n        # First Block\n        block = Block_First(self.in_channels, out_channels_first_block, pooling=True)\n        out_channels_block = self.out_channels_first_block\n        self.blocks.append(block)\n        \n        # Other Blocks\n        for i in range(depth-1):\n            in_channels_block = out_channels_block\n            out_channels_block = self.out_channels_first_block*(2**(i+1))\n            if not i==depth-2:\n                block = Block(in_channels_block, out_channels_block, pooling=True)\n            else:\n                block = Block_Last(in_channels_block, int(out_channels_block/2), pooling=True)\n            self.blocks.append(block)\n            \n        # Last Block\n\n        self.blocks = nn.ModuleList(self.blocks)\n        \n        # Setting Up the Fully Connected Layers\n        out_channels =self.blocks[self.depth-1].conv2.out_channels\n        out_img_size = self.img_size/(4*2**(self.depth)) # Would normally be self.depth-1 but poolings done after the last conv too\n        self.in_num_first_fc = int(out_img_size**2 * out_channels)\n        \n        for i in range(num_fc):\n            in_num_fc = self.in_num_first_fc if i == 0 else out_num_fc\n            out_num_fc = self.num_classes if i == num_fc-1 else int(in_num_fc/16)\n            fully_connected=nn.Linear(in_num_fc, out_num_fc)\n            self.fully_connecteds.append(fully_connected)\n            if i<self.num_fc-1:\n                bn = nn.BatchNorm1d(out_num_fc)\n                self.fully_connecteds.append(bn)\n                act = nn.ReLU()\n                self.fully_connecteds.append(act)\n        \n        self.fully_connecteds = nn.ModuleList(self.fully_connecteds)\n        self.initialize()\n\n    @staticmethod\n    def weight_init(module): # kaiming and xavier seem to perform the best\n        if isinstance(module, nn.Conv2d):\n            init.xavier_normal(module.weight)\n            # init.constant(module.bias, 0)\n        if isinstance(module, nn.Linear):\n            init.xavier_normal(module.weight)\n            init.constant(module.bias, 0)\n\n    def initialize(self):\n        for i, module in enumerate(self.modules()):\n            self.weight_init(module)\n\n    def forward(self, x):\n        # Pass Through Blocks\n        for i, module in enumerate(self.blocks):\n            x = module(x)  \n        # Pass Through The Fully Connected Layer\n        x = x.view(-1, self.in_num_first_fc)\n        for i, module in enumerate(self.fully_connecteds):\n            x = module(x)\n        return x\n    \ndef write_positives_network2(model,data_dir,data_transform,batch_size,threshold,filename,use_gpu):\n    file = open(filename, \"w\")\n    dataset = ImageFolderCustom(data_dir, data_transform)\n    dataloader = torch.utils.data.DataLoader(dataset, batch_size, num_workers=4, shuffle=False)\n    if use_gpu:\n        model = model.cuda()\n    model.train(False)\n    for data in dataloader:\n        inputs, paths, indices = data\n        if use_gpu:\n            inputs = Variable(inputs.cuda())\n        else:\n            inputs = Variable(inputs)\n        outputs = model(inputs)\n        preds = (torch.sigmoid(outputs)>threshold).squeeze(1).type(torch.LongTensor)\n\n        for j in range(inputs.size()[0]):\n            if np.asscalar(preds.data.numpy()[j])==0:\n                file.write('\\n {}'.format(paths[j]))\n\n    file.close()\n    \ndef load_network2(state_dict = 'network2_cpu_state_dict.pth'):\n    model = Net_Big_Image(num_classes=1,in_channels=1,depth=5,num_fc=1,out_channels_first_block=16, img_size = 256)\n    model.load_state_dict(torch.load(state_dict, map_location=lambda storage, loc: storage))\n    return model\n\n    \ndef train_network2(model, model_name, param_list, dataloaders, criterion, optimizer, scheduler, dataset_sizes, threshold=0.5, use_gpu=False, num_epochs=25):\n    import datetime\n    import time\n    now = datetime.datetime.now()\n    file_log = open('model2_train_log_{}.txt'.format(str(now.date())), 'w')\n    file_log.write(\"%s\\n\" % model_name)\n    for item in param_list:\n        file_log.write(\"%s\\n\" % item)\n    train_log = []\n    since = time.time()\n    \"\"\"i=0\"\"\"\n    if use_gpu:\n        model = model.cuda()\n    \n    best_model_wts = model.state_dict()\n    best_acc = 0.0\n    \n    for epoch in range(num_epochs):\n        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n        print('-' * 10)\n\n        # Each epoch has a training and validation phase\n        for phase in ['train', 'val']:\n            if phase == 'train':\n                scheduler.step()\n                model.train(True) # Set model to traning mode\n            else:\n                model.train(False) # Set model to evaluate mode\n                   \n            running_loss = 0.0\n            running_corrects = 0\n            # For Confusion Matrix\n            running_corrects_useless = 0\n            running_corrects_useful = 0\n            running_total_useless = 0\n            running_total_useful = 0\n            \n            for data in dataloaders[phase]:\n                inputs, labels = data\n                \n                if use_gpu:\n                    inputs = Variable(inputs.cuda())\n                    labels = Variable(labels.cuda())\n                    labels_crit = labels.unsqueeze(1).type(torch.FloatTensor).cuda()\n                else:\n                    inputs, labels = Variable(inputs), Variable(labels)\n                    labels_crit = labels.unsqueeze(1).type(torch.FloatTensor)\n                    \n                optimizer.zero_grad()\n\n                # forward\n                outputs = model(inputs)\n                preds = (torch.sigmoid(outputs)>threshold).squeeze(1).type(torch.LongTensor)\n                if use_gpu:\n                    preds = preds.cuda()\n                loss = criterion(outputs, labels_crit)\n                \n\n                # backward + optimize only if in training phase\n                if phase == 'train':\n                    loss.backward()\n                    optimizer.step()\n\n                # Statistics\n                running_loss += loss.data[0]\n                running_corrects += torch.sum(preds.data == labels.data)\n                # For Confusion Matrix\n                running_corrects_useless += torch.sum((preds.data+labels.data) == 2)\n                running_corrects_useful += torch.sum((preds.data+labels.data) == 0)\n                running_total_useless += torch.sum(labels.data == 1)\n                running_total_useful += torch.sum(labels.data == 0)\n\n            epoch_loss = running_loss / dataset_sizes[phase]\n            epoch_acc = running_corrects / dataset_sizes[phase]\n\n            train_log.append('Epoch: {}/{} Phase: {} Loss: {:.4f} Acc: {:.4f} Prec: {:.4f} Rec: {:.4f}'.format(epoch, num_epochs - 1, phase, epoch_loss, epoch_acc, running_corrects_useful/(running_corrects_useful+running_total_useless-running_corrects_useless), running_corrects_useful/running_total_useful))\n            print('{} Loss: {:.4f} Acc: {:.4f} Prec: {:.4f} Rec: {:.4f}'.format(phase, epoch_loss, epoch_acc, running_corrects_useful/(running_corrects_useful+running_total_useless-running_corrects_useless), running_corrects_useful/running_total_useful))\n            # deep copy the model           \n            if phase == 'val' and epoch_acc > best_acc:\n                best_acc = epoch_acc\n                best_model_wts = model.state_dict()\n                torch.save(best_model_wts, 'model2_state_dict_checkpoint_{}.pth'.format(str(now.date()))) #Check if it's actually in cpu readable format\n \n        print()\n        \n    time_elapsed = time.time() - since\n    print('Training complete in {:.0f}m {:.0f}s'.format(time_elapsed // 60, time_elapsed % 60))\n    print('Best val Acc: {:4f}'.format(best_acc))\n    \n    for item in train_log:\n        file_log.write(\"%s\\n\" % item)\n    file_log.close()\n    \n    # Load best weights\n    model.load_state_dict(best_model_wts)\n    if use_gpu:\n        model = model.cpu()\n    torch.save(model, './model2_{}.pth'.format(str(now.date())))\n    torch.save(model.state_dict(), 'model2_state_dict_final_{}.pth'.format(str(now.date())))\n    return model\n\n","sub_path":"network2.py","file_name":"network2.py","file_ext":"py","file_size_in_byte":11878,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"525763274","text":"\"\"\"rename watchlists\n\nRevision ID: afd5bb9f5004\nRevises: b20e873317ab\nCreate Date: 2016-03-23 23:06:42.135445\n\n\"\"\"\n\n# revision identifiers, used by Alembic.\nrevision = 'afd5bb9f5004'\ndown_revision = 'b20e873317ab'\n\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import postgresql\n\ntmp_type = sa.Enum('collection', 'watchlist', 'source', name='perm_type_coll')\nnew_type = sa.Enum('collection', 'source', name='permission_type')\nold_type = sa.Enum('watchlist', 'source', name='permission_type')\n\n\ndef upgrade():\n    bind = op.get_bind()\n    op.drop_constraint('watchlist_creator_id_fkey', 'watchlist')\n    op.drop_constraint('entity_watchlist_id_fkey', 'entity')\n    op.rename_table('watchlist', 'collection')\n    op.alter_column('entity', 'watchlist_id', new_column_name='collection_id')\n    tmp_type.create(bind, checkfirst=False)\n    op.execute('ALTER TABLE permission ALTER COLUMN resource_type TYPE perm_type_coll'\n               ' USING resource_type::text::perm_type_coll;')\n    op.alter_column('permission', 'resource_type', type_=tmp_type,\n                    existing_type=old_type)\n    meta = sa.MetaData()\n    meta.bind = bind\n    meta.reflect()\n    permission = meta.tables['permission']\n\n    q = sa.update(permission).where(permission.c.resource_type == 'watchlist')\n    q = q.values(resource_type='collection')\n    op.execute(q)\n    old_type.drop(bind, checkfirst=False)\n    new_type.create(bind, checkfirst=False)\n    op.execute('ALTER TABLE permission ALTER COLUMN resource_type TYPE permission_type'\n               ' USING resource_type::text::permission_type;')\n    tmp_type.drop(bind, checkfirst=False)\n    op.create_foreign_key(\"collection_creator_id_fkey\", \"collection\", \"role\",\n                          [\"creator_id\"], [\"id\"])\n    op.create_foreign_key(\"entity_collection_id_fkey\", \"entity\", \"collection\",\n                          [\"collection_id\"], [\"id\"])\n\n\ndef downgrade():\n    op.rename_table('collection', 'watchlist')\n    op.alter_column('entity', 'collection_id', new_column_name='watchlist_id')\n","sub_path":"aleph/migrate/versions/afd5bb9f5004_rename_watchlists.py","file_name":"afd5bb9f5004_rename_watchlists.py","file_ext":"py","file_size_in_byte":2051,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"317793715","text":"\r\nimport string\r\nimport pylab\r\n\r\ndef open_file(filename):\r\n    infile = open(filename, 'r')\r\n    correct = 0\r\n    for line in infile:\r\n        apparence = 0\r\n        temp = line.split()\r\n        number = temp[0].split('-')\r\n        temp[1] = temp[1].strip(':')\r\n        temp[1] = temp[1].strip(\"\\n\")\r\n        for entry in temp[2]:\r\n            if temp[1] == entry:\r\n                apparence+=1\r\n        if apparence >= int(number[0]) and apparence <= int(number[1]):\r\n            correct+=1\r\n    infile.close()\r\n    return correct\r\n\r\ndef part2(filename):\r\n    infile = open(filename, 'r')\r\n    correct = 0\r\n    for line in infile:\r\n        apparence = 0\r\n        temp = line.split()\r\n        number = temp[0].split('-')\r\n        temp[1] = temp[1].strip(':')\r\n        temp[1] = temp[1].strip(\"\\n\")\r\n        if temp[2][int(number[0])-1] == temp[1]:\r\n            if temp[2][int(number[1])-1] != temp[1]:\r\n                #valid, only in 1st spot\r\n                correct+=1\r\n        elif temp[2][int(number[1])-1] == temp[1]:\r\n            #in second spot\r\n            correct+=1\r\n    infile.close()\r\n    return correct\r\n\r\nif __name__ == \"__main__\":\r\n    print(part2(\"input_day2.txt\"))\r\n    end","sub_path":"day2.py","file_name":"day2.py","file_ext":"py","file_size_in_byte":1191,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"405778308","text":"import numpy as np\nimport pandas as pd\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n# %matplotlib inline\nfrom scipy import stats\nfrom scipy.stats import norm, skew #for some statistics\nfrom scipy import stats\nfrom scipy.stats import norm\n\n# ▼設定▼\ndata_path = '../data/'\npd.set_option('display.max_columns', 1000)\npd.set_option('display.width', 5000)\n\n# ▼データ読込▼\ntrain = pd.read_csv(data_path + 'train.csv')\ntest = pd.read_csv(data_path + 'test.csv')\n\n# ▼特徴の概要▼\n# TODO\n\n# print(train.head(5))\n# 出力結果\n# TODO\n# print(train.info())\n# 出力結果\n# TODO\n\n# print(test.info())\n# TODO\n\n# print(train.describe())\n# TODO\n\n#Save the 'Id' column\ntrain_ID = train['Id']\ntest_ID = test['Id']\n\n#Now drop the  'Id' colum since it's unnecessary for  the prediction process.\ntrain.drop(\"Id\", axis=1, inplace=True)\ntest.drop(\"Id\", axis=1, inplace=True)\n\n#Deleting outliers\ntrain = train.drop(train[(train['GrLivArea']>4000) & (train['SalePrice']<300000)].index)\n\n# ▼特徴分析▼\n# 目的：各項目間の相関関係を整理し、特徴エンジニアリング及びモデル構築に向けて下準備を行う。\n# TODO\n\n# ▼特徴量抽出（Feature Engineering）▼\n# データセットを合併して、特徴量を抽出する。\ntrain[\"SalePrice\"] = np.log1p(train[\"SalePrice\"])\nntrain = train.shape[0]\nntest = test.shape[0]\ny_train = train.SalePrice.values\nall_data = pd.concat((train, test), sort=False).reset_index(drop=True)\nall_data.drop(['SalePrice'], axis=1, inplace=True)\nall_data_na = (all_data.isnull().sum() / len(all_data)) * 100\nall_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)[:30]\nmissing_data = pd.DataFrame({'Missing Ratio' :all_data_na})\nall_data[\"PoolQC\"] = all_data[\"PoolQC\"].fillna(\"None\")\nall_data[\"MiscFeature\"] = all_data[\"MiscFeature\"].fillna(\"None\")\nall_data[\"Alley\"] = all_data[\"Alley\"].fillna(\"None\")\nall_data[\"Fence\"] = all_data[\"Fence\"].fillna(\"None\")\nall_data[\"FireplaceQu\"] = all_data[\"FireplaceQu\"].fillna(\"None\")\nall_data[\"LotFrontage\"] = all_data.groupby(\"Neighborhood\")[\"LotFrontage\"].transform(lambda x: x.fillna(x.median()))\nfor col in ('GarageType', 'GarageFinish', 'GarageQual', 'GarageCond'):\n    all_data[col] = all_data[col].fillna('None')\nfor col in ('GarageYrBlt', 'GarageArea', 'GarageCars'):\n    all_data[col] = all_data[col].fillna(0)\nfor col in ('BsmtFinSF1', 'BsmtFinSF2', 'BsmtUnfSF','TotalBsmtSF', 'BsmtFullBath', 'BsmtHalfBath'):\n    all_data[col] = all_data[col].fillna(0)\nfor col in ('BsmtQual', 'BsmtCond', 'BsmtExposure', 'BsmtFinType1', 'BsmtFinType2'):\n    all_data[col] = all_data[col].fillna('None')\nall_data[\"MasVnrType\"] = all_data[\"MasVnrType\"].fillna(\"None\")\nall_data[\"MasVnrArea\"] = all_data[\"MasVnrArea\"].fillna(0)\nall_data['MSZoning'] = all_data['MSZoning'].fillna(all_data['MSZoning'].mode()[0])\nall_data = all_data.drop(['Utilities'], axis=1)\nall_data[\"Functional\"] = all_data[\"Functional\"].fillna(\"Typ\")\nall_data['Electrical'] = all_data['Electrical'].fillna(all_data['Electrical'].mode()[0])\nall_data['KitchenQual'] = all_data['KitchenQual'].fillna(all_data['KitchenQual'].mode()[0])\nall_data['Exterior1st'] = all_data['Exterior1st'].fillna(all_data['Exterior1st'].mode()[0])\nall_data['Exterior2nd'] = all_data['Exterior2nd'].fillna(all_data['Exterior2nd'].mode()[0])\nall_data['SaleType'] = all_data['SaleType'].fillna(all_data['SaleType'].mode()[0])\nall_data['MSSubClass'] = all_data['MSSubClass'].fillna(\"None\")\nall_data_na = (all_data.isnull().sum() / len(all_data)) * 100\nall_data_na = all_data_na.drop(all_data_na[all_data_na == 0].index).sort_values(ascending=False)\nmissing_data = pd.DataFrame({'Missing Ratio' :all_data_na})\n# missing_data.head()\n#MSSubClass=The building class\nall_data['MSSubClass'] = all_data['MSSubClass'].apply(str)\n#Changing OverallCond into a categorical variable\nall_data['OverallCond'] = all_data['OverallCond'].astype(str)\n#Year and month sold are transformed into categorical features.\nall_data['YrSold'] = all_data['YrSold'].astype(str)\nall_data['MoSold'] = all_data['MoSold'].astype(str)\n\nfrom sklearn.preprocessing import LabelEncoder\ncols = ('FireplaceQu', 'BsmtQual', 'BsmtCond', 'GarageQual', 'GarageCond',\n        'ExterQual', 'ExterCond','HeatingQC', 'PoolQC', 'KitchenQual', 'BsmtFinType1',\n        'BsmtFinType2', 'Functional', 'Fence', 'BsmtExposure', 'GarageFinish', 'LandSlope',\n        'LotShape', 'PavedDrive', 'Street', 'Alley', 'CentralAir', 'MSSubClass', 'OverallCond',\n        'YrSold', 'MoSold')\n# process columns, apply LabelEncoder to categorical features\nfor c in cols:\n    lbl = LabelEncoder()\n    lbl.fit(list(all_data[c].values))\n    all_data[c] = lbl.transform(list(all_data[c].values))\n# Adding total sqfootage feature\nall_data['TotalSF'] = all_data['TotalBsmtSF'] + all_data['1stFlrSF'] + all_data['2ndFlrSF']\n\nnumeric_feats = all_data.dtypes[all_data.dtypes != \"object\"].index\n\n# Check the skew of all numerical features\nskewed_feats = all_data[numeric_feats].apply(lambda x: skew(x.dropna())).sort_values(ascending=False)\nskewness = pd.DataFrame({'Skew' :skewed_feats})\nskewness = skewness[abs(skewness) > 0.75]\n# print(\"There are {} skewed numerical features to Box Cox transform\".format(skewness.shape[0]))\n\nfrom scipy.special import boxcox1p\nskewed_features = skewness.index\nlam = 0.15\nfor feat in skewed_features:\n    all_data[feat] = boxcox1p(all_data[feat], lam)\n    all_data[feat] += 1\n\nall_data = pd.get_dummies(all_data)\n\ntrain = all_data[:ntrain]\ntest = all_data[ntrain:]\n\n# ▼モデル構築▼\n# 単一のモデル（RandomForest, LogisticRegression, SVM, XGBoost, GBDT ect）を選択するか\n# いくつかのモデル（voting, stacking ect）を組み合わせる\n# 特徴の良し悪しはモデルの限界を決める。モデル及びパラメータの良し悪しは精度に関わる。\n# 今回はRandomForestを使うことにする\nfrom sklearn.linear_model import ElasticNet, Lasso,  BayesianRidge, LassoLarsIC\nfrom sklearn.ensemble import RandomForestRegressor,  GradientBoostingRegressor\nfrom sklearn.kernel_ridge import KernelRidge\nfrom sklearn.pipeline import make_pipeline\nfrom sklearn.preprocessing import RobustScaler\nfrom sklearn.base import BaseEstimator, TransformerMixin, RegressorMixin, clone\nfrom sklearn.model_selection import KFold, cross_val_score, train_test_split\nfrom sklearn.metrics import mean_squared_error\nimport xgboost as xgb\nimport lightgbm as lgb\n\n#Validation function\nn_folds = 5\n\ndef rmsle_cv(model):\n    kf = KFold(n_folds, shuffle=True, random_state=42).get_n_splits(train.values)\n    rmse= np.sqrt(-cross_val_score(model, train.values, y_train, scoring=\"neg_mean_squared_error\", cv = kf))\n    return(rmse)\n\nlasso = make_pipeline(RobustScaler(), Lasso(alpha =0.0005, random_state=1))\nENet = make_pipeline(RobustScaler(), ElasticNet(alpha=0.0005, l1_ratio=.9, random_state=3))\nKRR = KernelRidge(alpha=0.6, kernel='polynomial', degree=2, coef0=2.5)\nGBoost = GradientBoostingRegressor(n_estimators=3000, learning_rate=0.05,\n                                   max_depth=4, max_features='sqrt',\n                                   min_samples_leaf=15, min_samples_split=10,\n                                   loss='huber', random_state =5)\nmodel_xgb = xgb.XGBRegressor(colsample_bytree=0.2, gamma=0.0,\n                             learning_rate=0.05, max_depth=6,\n                             min_child_weight=1.5, n_estimators=7200,\n                             reg_alpha=0.9, reg_lambda=0.6,\n                             subsample=0.2,seed=42, silent=1,\n                             random_state =7)\nmodel_lgb = lgb.LGBMRegressor(objective='regression',num_leaves=5,\n                              learning_rate=0.05, n_estimators=720,\n                              max_bin = 55, bagging_fraction = 0.8,\n                              bagging_freq = 5, feature_fraction = 0.2319,\n                              feature_fraction_seed=9, bagging_seed=9,\n                              min_data_in_leaf =6, min_sum_hessian_in_leaf = 11)\n\n\nclass AveragingModels(BaseEstimator, RegressorMixin, TransformerMixin):\n    def __init__(self, models):\n        self.models = models\n\n    # we define clones of the original models to fit the data in\n    def fit(self, X, y):\n        self.models_ = [clone(x) for x in self.models]\n\n        # Train cloned base models\n        for model in self.models_:\n            model.fit(X, y)\n\n        return self\n\n    # Now we do the predictions for cloned models and average them\n    def predict(self, X):\n        predictions = np.column_stack([\n            model.predict(X) for model in self.models_\n        ])\n        return np.mean(predictions, axis=1)\n\naveraged_models = AveragingModels(models=(ENet, GBoost, KRR, lasso))\n\n\nclass StackingAveragedModels(BaseEstimator, RegressorMixin, TransformerMixin):\n    def __init__(self, base_models, meta_model, n_folds=5):\n        self.base_models = base_models\n        self.meta_model = meta_model\n        self.n_folds = n_folds\n\n    # We again fit the data on clones of the original models\n    def fit(self, X, y):\n        self.base_models_ = [list() for x in self.base_models]\n        self.meta_model_ = clone(self.meta_model)\n        kfold = KFold(n_splits=self.n_folds, shuffle=True)\n\n        # Train cloned base models then create out-of-fold predictions\n        # that are needed to train the cloned meta-model\n        out_of_fold_predictions = np.zeros((X.shape[0], len(self.base_models)))\n        for i, clf in enumerate(self.base_models):\n            for train_index, holdout_index in kfold.split(X, y):\n                instance = clone(clf)\n                self.base_models_[i].append(instance)\n                instance.fit(X[train_index], y[train_index])\n                y_pred = instance.predict(X[holdout_index])\n                out_of_fold_predictions[holdout_index, i] = y_pred\n\n        # Now train the cloned  meta-model using the out-of-fold predictions\n        self.meta_model_.fit(out_of_fold_predictions, y)\n        return self\n\n    def predict(self, X):\n        meta_features = np.column_stack([\n            np.column_stack([model.predict(X) for model in base_models]).mean(axis=1)\n            for base_models in self.base_models_])\n        return self.meta_model_.predict(meta_features)\n\nstacked_averaged_models = StackingAveragedModels(base_models=(ENet, GBoost, KRR), meta_model=lasso)\n\ndef rmsle(y, y_pred):\n    return np.sqrt(mean_squared_error(y, y_pred))\n\nstacked_averaged_models.fit(train.values, y_train)\nstacked_train_pred = stacked_averaged_models.predict(train.values)\nstacked_pred = np.expm1(stacked_averaged_models.predict(test.values))\n\nmodel_xgb.fit(train, y_train)\nxgb_train_pred = model_xgb.predict(train)\nxgb_pred = np.expm1(model_xgb.predict(test))\n\nmodel_lgb.fit(train, y_train)\nlgb_train_pred = model_lgb.predict(train)\nlgb_pred = np.expm1(model_lgb.predict(test.values))\n\nensemble = stacked_pred*0.70 + xgb_pred*0.10 + lgb_pred*0.20\n\n# 保存结果\nresult = pd.DataFrame()\nresult['Id'] = test_ID\nresult['SalePrice'] = ensemble\n# index=False 是用来除去行编号\nresult.to_csv('../output/result.csv', index=False)\n\n","sub_path":"src/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":11117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"274859487","text":"import csv\nimport norm_csvdata as norm\n\nfieldnames_with_tag = (\"_id\", \"user\", \"content\", \"creationTime\", \"referenceName\", \"tag\")\n\ndef cutDataSet(file_data, file_data_norm, train_set, valid_set, test_set, split1, split2):\n\tnorm.normalization(file_data, file_data_norm, fieldnames_with_tag)\n\n\twith open(file_data_norm, \"r\", newline = \"\") as infile, \\\n\topen(train_set, \"w\", newline = \"\") as outfile1, \\\n\topen(valid_set, \"w\", newline = \"\") as outfile2, \\\n\topen(test_set, \"w\", newline = \"\") as outfile3:\n\t\treader = csv.DictReader(infile)\n\t\twriter1 = csv.DictWriter(outfile1, fieldnames = fieldnames_with_tag)\n\t\twriter2 = csv.DictWriter(outfile2, fieldnames = fieldnames_with_tag)\n\t\twriter3 = csv.DictWriter(outfile3, fieldnames = fieldnames_with_tag)\n\t\twriter1.writeheader()\n\t\twriter2.writeheader()\n\t\twriter3.writeheader()\n\t\ttot = 0\n\t\tfor row in reader:\n\t\t\tif row[\"tag\"] == \"0\":\n\t\t\t\tcontinue\n\t\t\tif row[\"tag\"] == \"-1\":\n\t\t\t\tcontinue\n\t\t\ttot = tot + 1\n\t\t\tif tot <= split1:\n\t\t\t\twriter1.writerow(row)\n\t\t\telif tot <= split2:\n\t\t\t\twriter2.writerow(row)\n\t\t\telse:\n\t\t\t\twriter3.writerow(row)\n","sub_path":"_fasttext(3分类)/cut.py","file_name":"cut.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"289383735","text":"import logging\nimport os\nimport shutil\n\nlog_file_formatter = logging.Formatter(\n    \"%(asctime)s [%(levelname).1s] %(message)s\\t(at %(filename)s:%(lineno)s) \"\n)\nlog_stream_formatter = logging.Formatter(\"%(message)s\")\n\n\n# Create root logger\ndef create_root_logger():\n    root_log_path = \"./logs\"\n    if not os.path.exists(root_log_path):\n        os.makedirs(root_log_path)\n\n    root_logger = logging.getLogger()\n    root_logger.setLevel(logging.DEBUG)\n\n    debug_file_handler = logging.FileHandler(\n        os.path.realpath(os.path.join(root_log_path, \"DEBUG.log\"))\n    )\n    debug_file_handler.setFormatter(log_file_formatter)\n    debug_file_handler.setLevel(logging.DEBUG)\n\n    info_file_handler = logging.FileHandler(\n        os.path.realpath(os.path.join(root_log_path, \"INFO.log\"))\n    )\n    info_file_handler.setFormatter(log_file_formatter)\n    info_file_handler.setLevel(logging.INFO)\n\n    error_file_handler = logging.FileHandler(\n        os.path.realpath(os.path.join(root_log_path, \"ERROR.log\"))\n    )\n    error_file_handler.setFormatter(log_file_formatter)\n    error_file_handler.setLevel(logging.ERROR)\n\n    stream_handler = logging.StreamHandler()\n    stream_handler.setFormatter(log_stream_formatter)\n    stream_handler.setLevel(logging.DEBUG)\n\n    root_logger.addHandler(debug_file_handler)\n    root_logger.addHandler(info_file_handler)\n    root_logger.addHandler(error_file_handler)\n    root_logger.addHandler(stream_handler)\n\n\ndef add_outputpath_log_handler(output_path, logger):\n    root_log_path = os.path.join(output_path, \"logs\")\n    if os.path.exists(root_log_path):\n        shutil.rmtree(root_log_path)\n    os.makedirs(root_log_path)\n\n    debug_file_handler = logging.FileHandler(\n        os.path.realpath(os.path.join(root_log_path, \"DEBUG.log\"))\n    )\n    debug_file_handler.setFormatter(log_file_formatter)\n    debug_file_handler.setLevel(logging.DEBUG)\n\n    info_file_handler = logging.FileHandler(\n        os.path.realpath(os.path.join(root_log_path, \"INFO.log\"))\n    )\n    info_file_handler.setFormatter(log_file_formatter)\n    info_file_handler.setLevel(logging.INFO)\n\n    error_file_handler = logging.FileHandler(\n        os.path.realpath(os.path.join(root_log_path, \"ERROR.log\"))\n    )\n    error_file_handler.setFormatter(log_file_formatter)\n    error_file_handler.setLevel(logging.ERROR)\n\n    logger.addHandler(debug_file_handler)\n    logger.addHandler(info_file_handler)\n    logger.addHandler(error_file_handler)\n","sub_path":"dm/log.py","file_name":"log.py","file_ext":"py","file_size_in_byte":2447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"275514310","text":"#!/usr/bin/env python3\nimport argparse\n\nfrom ethereumetl.ipc import IPCWrapper\nfrom ethereumetl.jobs import ExportBlocksJob, UnixGethOptimizedExportBlocksJob\nfrom ethereumetl.socket_utils import UnixGethIPCWrapper\n\nparser = argparse.ArgumentParser(description='Export blocks and transactions.')\nparser.add_argument('--start-block', default=0, type=int, help='Start block')\nparser.add_argument('--end-block', required=True, type=int, help='End block')\nparser.add_argument('--batch-size', default=100, type=int, help='The number of blocks to filter at a time.')\nparser.add_argument('--ipc-path', required=True, type=str, help='The full path to the ipc socket file.')\nparser.add_argument('--ipc-timeout', default=300, type=int, help='The timeout in seconds for ipc calls.')\nparser.add_argument('--blocks-output', default=None, type=str,\n                    help='The output file for blocks. If not provided blocks will not be exported. '\n                         '- for stdout')\nparser.add_argument('--transactions-output', default=None, type=str,\n                    help='The output file for transactions. If not provided transactions will not be exported. '\n                         '- for stdout')\nparser.add_argument('--strategy', default='default', type=str, choices=['default', 'unix-geth'],\n                    help='Can be either default or unix-geth. unix-geth works about 2 times faster.')\n\nargs = parser.parse_args()\n\nif args.strategy == 'default':\n    job = ExportBlocksJob(\n        start_block=args.start_block,\n        end_block=args.end_block,\n        batch_size=args.batch_size,\n        ipc_wrapper=IPCWrapper(args.ipc_path, args.ipc_timeout),\n        blocks_output=args.blocks_output,\n        transactions_output=args.transactions_output)\nelse:\n    job = UnixGethOptimizedExportBlocksJob(\n        start_block=args.start_block,\n        end_block=args.end_block,\n        batch_size=args.batch_size,\n        ipc_wrapper=UnixGethIPCWrapper(args.ipc_path, args.ipc_timeout),\n        blocks_output=args.blocks_output,\n        transactions_output=args.transactions_output)\n\njob.run()\n","sub_path":"export_blocks_and_transactions.py","file_name":"export_blocks_and_transactions.py","file_ext":"py","file_size_in_byte":2092,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"466956021","text":"from PyQt4.QtGui import *\nfrom PyQt4.QtCore import *\nimport sys\n\napp_obj = QApplication(sys.argv)\n\nwindow = QMainWindow()\nwindow.resize(800,600)\n\ntext = QTextEdit(window)\ntext.resize(600,500)\n\n#now we have to define the action\nopen = QAction(QIcon('exit.png'), '&Exit', window)\nopen.setShortcut('Ctrl+Q')\nopen.setStatusTip('Exit application')\nopen.triggered.connect(qApp.quit)\nwindow.statusBar()\nmenubar = window.menuBar()\nfileMenu = menubar.addMenu('&File')\nfileMenu.addAction(open)\nwindow.show()\nsys.exit(app_obj.exec_())","sub_path":"Python/examples/Qt/text-editor.py","file_name":"text-editor.py","file_ext":"py","file_size_in_byte":523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"617829834","text":"import os\nimport sys\nimport argparse\n\nfrom PIL import Image\n\n\ndef process_args():\n    parser = argparse.ArgumentParser(description=\"Resize image from file\")\n    parser.add_argument(\n        'filename',\n        type=str,\n        help='Original image filename'\n    )\n    parser.add_argument(\n        '-x',\n        '--width',\n        type=float,\n        default=None,\n        help='Input value width in pixels'\n    )\n    parser.add_argument(\n        '-y',\n        '--heigth',\n        type=float,\n        default=None,\n        help='Input value heigth in pixels'\n    )\n    parser.add_argument(\n        '-s',\n        '--scale',\n        type=float,\n        default=None,\n        help='Input value scale'\n    )\n    parser.add_argument(\n        '-p',\n        '--path',\n        help='Enter the path to the directory for saving file '\n    )\n    return parser.parse_args()\n\n\ndef get_image_data(original_filename):\n    try:\n        return Image.open(os.path.abspath(original_filename))\n    except OSError as error:\n        return error\n\n\ndef get_resize_value(original_size, width=None, heigth=None, scale=None):\n    x_size, y_size = original_size\n    ratio = None\n    x_resize = None\n    y_resize = None\n\n    if width and heigth:\n        x_resize = width\n        y_resize = heigth\n    elif width:\n        ratio = x_size / width\n        x_resize = width\n        y_resize = y_size / ratio\n    elif heigth:\n        ratio = y_size / heigth\n        x_resize = x_size / ratio\n        y_resize = heigth\n    elif scale:\n        x_resize = x_size * scale\n        y_resize = y_size * scale\n    else:\n        return x_size, y_size\n\n    return round(x_resize), round(y_resize)\n\n\ndef resize_image(image_data, x_resize, y_resize):\n    return image_data.resize((x_resize, y_resize), Image.ANTIALIAS)\n\n\ndef save_image(resized_image, original_file, path_to_save):\n    if path_to_save:\n        path_to_save = os.path.abspath(path_to_save)\n    else:\n        filename, ext = os.path.splitext(original_file.filename)\n        x_size, y_size = resized_image.size\n        path_to_save = '{}_{}x{}{}'.format(filename, x_size, y_size, ext)\n\n    try:\n        resized_image.save(path_to_save, original_file.format)\n    except OSError as error:\n        return error\n\n\ndef main():\n    args = process_args()\n    image_data = get_image_data(args.filename)\n\n    if isinstance(image_data, OSError):\n        print(\"File open error: \", image_data.strerror)\n        sys.exit(image_data.errno)\n\n    x_size, y_size = get_resize_value(\n        image_data.size,\n        args.width,\n        args.heigth,\n        args.scale\n    )\n    resized_img = resize_image(image_data, x_size, y_size)\n    error = save_image(\n        resized_img,\n        image_data,\n        args.path\n    )\n    if isinstance(error, OSError):\n        print(\"File save error: \", error.strerror)\n        sys.exit(error.errno)\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"image_resize.py","file_name":"image_resize.py","file_ext":"py","file_size_in_byte":2879,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"398439528","text":"from django.contrib import admin\nfrom django.urls import path, include\nfrom django.conf.urls.static import static\nfrom django.contrib.staticfiles.urls import staticfiles_urlpatterns\nfrom django.conf import settings\nfrom django.views.generic import TemplateView\n\nurlpatterns = [\n    path('grappelli/', include('grappelli.urls')),\n    path('admin/', admin.site.urls, name='admin'),\n    path('', include('index.urls', namespace='index')),\n    path('robots.txt', TemplateView.as_view(template_name='robots.txt', content_type='text/plain')),\n    path('lk/', include('personal_cabinet.urls', namespace='lk')),\n    path('research/', include('products.urls', namespace='research')),\n    path('article/', include('articles.urls', namespace='article')),\n    path('accounts/', include('django.contrib.auth.urls')),\n    path('register/', include('django_registration.backends.activation.urls')),\n    path('order/', include('orders.urls', namespace='orders')),\n    path('pages/', include('django.contrib.flatpages.urls')),\n    path('statistic/', include('statistic.urls', namespace='statistic')),\n    path('nested_admin/', include('nested_admin.urls')),\n]\n\n# Справочник вырезан из кода на время\n# path('handbook/', include('handbook.urls', namespace='handbook')),\n\nif settings.DEBUG:\n    import debug_toolbar\n    urlpatterns = [\n        path('__debug__/', include(debug_toolbar.urls)),\n    ] + urlpatterns\n\nurlpatterns += static(settings.MEDIA_URL, document_root=settings.MEDIA_ROOT)\nurlpatterns += staticfiles_urlpatterns()","sub_path":"project/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1541,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"329594745","text":"# Link: https://leetcode.com/problems/to-lower-case/\nclass Solution:\n    def toLowerCase(self, str):\n        result = \"\"\n        for i in str:\n            c = ord(i)\n            if c>64 and c<91:\n                result += chr(c + 32)\n            else:\n                result += i\n        return result","sub_path":"leetcode/easy/To Lower Case.py","file_name":"To Lower Case.py","file_ext":"py","file_size_in_byte":301,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"480876069","text":"from math import ceil, floor\n\nimport krabtools\nimport sqlitetools\nimport auxdatatools\nimport evemarket\nimport presets\n\ndef getmatsforitem(item, n_produced=1, ME=0, production_efficiences=None, bpMaxRuns=float('inf')):\n    # get the materials required to produce an item\n    # n_produced is the number of items desired\n    # ME is the Material Efficency level of the item's BP\n    # production efficiences is a list/tuple of other material efficiency savings e.g. skills, POS mods etc.\n\n    runs = getrunsfornproducts(item, n_produced) # calc how many runs of BP we need to get desired number of items produced (e.g. 1 run produces 10 and we need 20 -> 2 runs)\n\n    if runs > bpMaxRuns: # if we need more than one BP to do all our runs\n        runs_ratio = runs/bpMaxRuns\n        runs_fullbp = floor(runs_ratio) # full uses of BP (e.g. if we need 15 runs and the BP max is 6, this will be 2)\n        runs_rem = int(round((runs_ratio - floor(runs_ratio)) * bpMaxRuns)) # remaining runs required (e.g. 3 for above example)\n\n        mats_fullbp = scalematslistbyint(getmatsforitem(item, n_produced=bpMaxRuns, ME=ME, production_efficiences=production_efficiences), runs_fullbp) # mats for \"full uses\" of BP (mats for max run * no of max runs)\n        if runs_rem > 0:\n            mats_rem = getmatsforitem(item, n_produced=runs_rem, ME=ME, production_efficiences=production_efficiences)\n        else:\n            mats_rem = {}\n\n        matslist = combinematslists(mats_fullbp, mats_rem)\n\n    else:\n        matslist = auxdatatools.getmatsforbp(auxdatatools.getbpIDforitem(item)) # get mats for 1 run w/o ME modifiers\n\n        if runs > 1: matslist = scalematslistbyint(matslist, runs) # scale up to # of runs (this happens *before* ME calculations)\n\n        if ME > 0 or production_efficiences: matslist = scalematslistbyefficiency(matslist, runs, ME, production_efficiences) # calculate ME effects if necessary\n    \n    return matslist\n\ndef getbasematsforitem(item, n_produced=1, ME=0, production_efficiences=None, ME_components={}, production_efficiences_components={}, bpMaxRuns=float('inf'), bpMaxRuns_components={}):\n    if not isinstance(ME_components, dict): raise Exception('Invalid MEs for components: %s, component MEs must be given as dict of {typeID : ME}' % ME_components)\n    if not isinstance(production_efficiences_components, dict): raise Exception('Invalid production efficiency savings for components: %s, must be given as dict of {typeID : [eff1, eff2,...]}' % production_efficiences_components)\n\n    top_mats = getmatsforitem(item, n_produced, ME, production_efficiences, bpMaxRuns) # get \"top tier\" materials/components - the stuff actually on the BP\n\n    # we can set one ME for all components with the keyword \"ALL\"\n    if len(ME_components) == 1 and list(ME_components.keys())[0] == 'ALL':\n        for mat in top_mats: ME_components[mat] = ME_components['ALL']\n        del ME_components['ALL']\n\n\n    # as above for ME savings from POSes etc.\n    if len(production_efficiences_components) == 1 and list(production_efficiences_components.keys())[0] == 'ALL':\n        for mat in top_mats: production_efficiences_components[mat] = production_efficiences_components['ALL']\n        del production_efficiences_components['ALL']\n\n    base_mats = top_mats\n\n    while any([auxdatatools.hasbp(ii) for ii in base_mats]): # while we have anything that can be manufactured\n\n        temp_mats = {} # use temp dict to avoid confusing when add/removing entries\n\n        for matID, matqty in base_mats.items():       \n            \n            if not auxdatatools.hasbp(matID): # if this component has no BP i.e. cannot be manufactured, it is already a base material\n                temp_mats[matID] = matqty\n            \n            else: # else get the materials needed from its BP\n                ME_thismat = ME_components[matID] if (matID in ME_components) else 0\n                production_efficiences_thismat = production_efficiences_components[matID] if (matID in production_efficiences_components) else None\n                bpMaxRuns_thismat = bpMaxRuns_components[matID] if (matID in bpMaxRuns_components) else float('inf')\n                \n                component_mats = getmatsforitem(matID, n_produced=matqty, ME=ME_thismat, production_efficiences=production_efficiences_thismat, bpMaxRuns=bpMaxRuns_thismat)\n\n                temp_mats = combinematslists(temp_mats, component_mats)\n\n        base_mats = temp_mats\n\n    return base_mats\n\ndef combinematslists(*args):\n    masterlist = args[0]\n    \n    if len(args) > 2:\n        for arg in args[1:]:\n            masterlist = combinematslists(masterlist, arg)\n    \n    elif len(args) == 2:\n        for matID, matqty in args[1].items():\n            if matID in masterlist:\n                masterlist[matID] += matqty\n            else:\n                masterlist[matID] = matqty\n\n        return masterlist\n\n    else:\n        raise Exception('Need at least 2 arguments') \n\ndef scalematslistbyint(matslist, scale_factor):\n    if not isinstance(scale_factor, int): raise Exception()\n    \n    matslist_scaled = {}\n    for matID, matqty in matslist.items(): matslist_scaled[matID] = matqty * scale_factor\n\n    return matslist_scaled\n\ndef scalematslistbyefficiency(matslist, runs, ME, production_efficiences=None):\n    # scales materials in materials list for ME level, and optionally additional material efficiency factors (e.g. skills, POS effects etc.)\n    if not isinstance(ME, int) and not (ME >= 0 and ME <= 10): raise Exception('Invalid ME: %s, must be int between 0 & 10' % ME)\n    \n    if production_efficiences:\n        additional_scale_factors = []\n\n        if not isinstance(production_efficiences, list) and not isinstance(production_efficiences, tuple):\n            if isinstance(production_efficiences, float):\n                production_efficiences = [production_efficiences] # if only one additional efficiency factor, cast as list to make iteration work\n            else:\n                raise Exception('Invalid efficiency factors: %s, must be list or tuple of floats' % production_efficiences)\n\n        for arg in production_efficiences:\n            if not isinstance(arg, float) or not (arg >= 0 and arg < 1): raise Exception('Invalid efficiency factor: %s, must be fraction of 1' % arg)\n\n            additional_scale_factors.append(1 - arg) # convert fractions to scale factor e.g. 25% efficiency saving == 0.25 -> 0.75 scale factor\n\n    ME_scale_factor = 1 - (ME * 0.01) # convert ME level to scale factor e.g. 9 ME == 9% saving -> 0.91 scale factor\n\n    matslist_scaled = {}\n    for matID, matqty in matslist.items():      \n        matqty *= ME_scale_factor\n    \n        if production_efficiences:\n            for scale_factor in additional_scale_factors: matqty *= scale_factor\n\n        if matqty/runs < 1: # can't have less than 1 matID per run\n            matqty = runs\n        else:\n            matqty = ceil(matqty)\n\n        matslist_scaled[matID] = matqty\n\n    return matslist_scaled\n\ndef getrunsfornproducts(product, n_produced):\n    # calc number of runs of a BP needed to make desired n of a given product (e.g. if BP produces 100 of product per run and we need 250, we must run BP 3 times)\n    product = auxdatatools.getitemid(product)\n    bp_output_quantity = sqlitetools.getxbyyfromdb(presets.auxdataDB, 'bpProducts', 'quantity', 'productTypeID', product)\n\n    return(ceil(n_produced / bp_output_quantity))\n\ndef convmatslisttonames(matslist):\n    out = {}\n    for matID, matqty in matslist.items(): out[auxdatatools.getitemName(matID)] = matqty\n\n    return out\n\ndef calcinventstats(invent_from, skill_encryption, skill_physics_1, skill_physics_2, decryptortype='NONE'):\n    # invent_from: must be T1 BP or Hull Section\n    krabtools.checkvalidskilllevel([skill_encryption, skill_physics_1, skill_physics_2])\n\n    if not auxdatatools.ishullsection(invent_from):\n        if not auxdatatools.isbp(invent_from): raise Exception('Not a BP or hull section! %s' % invent_from)\n        invent_from = auxdatatools.getBPproductgroup(invent_from) # e.g. frigates, cruisers, etc.\n   \n    if not decryptortype: decryptortype = 'NONE'\n    inventChance = presets.invent_types[invent_from]['inventChance'] \\\n                    * (1 + ( (skill_encryption / 40) + ( (skill_physics_1 + skill_physics_2) / 30) )) \\\n                    * presets.decryptors[decryptortype]['inventChanceMod']\n    inventChance = round(inventChance,3)\n   \n    runs = presets.invent_types[invent_from]['runs'] + presets.decryptors[decryptortype]['runsMod']\n    ME = 2 + presets.decryptors[decryptortype]['MEMod']\n    TE = 0 + presets.decryptors[decryptortype]['TEMod']\n\n    return (inventChance, runs, ME, TE)\n\ndef getinventmats(invent_type, decryptortype='NONE'):\n    # get list materials need for invention - datacores, decryptor (if specified), and hull section (if applicable) (T1 BPCs are not counted)\n    # invent_type: either the T2/T3 item desired (or its BP), or the T1 item invented from (or its BP)\n\n    if auxdatatools.isT2(invent_type): # if we've been given a T2/T3 item, we must find out what it's invented from\n        invent_type = auxdatatools.getinventbase(invent_type)\n        if isinstance(invent_type, list) or isinstance(invent_type,tuple): raise Exception('Multiple invention bases found: %s' % invent_type) # this will fail if given a T3 hull/component, as there are multiple things it could be invented from\n    elif auxdatatools.ishullsection(invent_type):\n        invent_type = auxdatatools.getitemid(invent_type) # hull sections appear as items, but are actually BPs - so we just need to get the ID and we're good to go\n    elif auxdatatools.isitem(invent_type): # we've been given something T1...\n        if auxdatatools.isbp(invent_type): # T1 BP\n            invent_type = auxdatatools.getitemid(invent_type)\n        else: # T1 item\n            invent_type = auxdatatools.getbpIDforitem(invent_type)\n\n    invent_mats = auxdatatools.getmatsforbp(invent_type, activity='Invention') # datacores\n    if decryptortype != 'NONE': invent_mats = combinematslists(invent_mats, {decryptortype+' Decryptor': 1}) # decryptor (if applicable)\n    if auxdatatools.ishullsection(invent_type): combinematslists(invent_mats, {invent_type: 1})\n\n    return invent_mats\n\ndef get_matslist_cost_from_pricelist(matslist, pricelist, order_type, return_type):\n    totalcost = 0\n    matscostslist = {}\n    for mat in matslist:\n        matID, quantity = auxdatatools.getitemid(mat), matslist[mat]\n        matcost = pricelist[matID][order_type] * quantity\n        if return_type == 'total': totalcost += matcost\n        if return_type == 'list': matscostslist[matID] = matcost\n\n    if return_type == 'total':\n        return totalcost\n    elif return_type == 'list':\n        return matscostslist\n\ndef calcjobfee(item, runs, systemModifier, buildLocation='POS'):\n    if systemModifier < 0 or systemModifier > 1: raise Exception()\n\n    if isinstance(item, list) or isinstance(item, tuple):\n        if isinstance(runs, list) or isinstance(runs, tuple):\n            if len(runs) != len(item): raise Exception('List of runs does not match list of items. Got %s items but %s runs' % (len(item), len(runs)))\n        else:\n            runs = [runs]\n\n        jobFeeList = []\n        for ii, itm in enumerate(item): jobFeeList.append(calcjobfee(itm, runs[ii], systemModifier, buildLocation))\n        return(jobFeeList)\n\n    jobFee = krabtools.getbasecostforitem(item) * runs * systemModifier\n\n    if buildLocation.lower() == 'station': jobFee += (jobFee * 0.1) # station tax is 10%\n\n    jobFee = round(jobFee, 2)\n\n    return jobFee\n\ndef calcbuildcosts(product, productRuns, **kwargs):\n    if 'bpMaxRuns' not in kwargs or not kwargs['bpMaxRuns']:\n        bpMaxRuns = 99999\n    else:\n        bpMaxRuns = kwargs['bpMaxRuns']\n    \n    if 'baseMatsList' not in kwargs or not kwargs['baseMatsList']:\n        passargdict = {'item' : product, 'n_produced' : productRuns}\n        for arg in ('ME','production_efficiences','ME_components','production_efficiences_components','bpMaxRuns','bpMaxRuns_components'):\n            if arg in kwargs: passargdict[arg] = kwargs[arg]\n\n        baseMatsList = getbasematsforitem(**passargdict)\n\n    else:\n        baseMatsList = kwargs['baseMatsList']\n\n    basematids = [ii for ii in baseMatsList]\n\n    if 'componentsList' not in kwargs or not kwargs['componentsList']:\n        passargdict = {'item' : product, 'n_produced' : productRuns}\n        for arg in ('ME','production_efficiences','bpMaxRuns'):\n            if arg in kwargs: passargdict[arg] = kwargs[arg]\n\n        componentsList = getmatsforitem(**passargdict)\n\n    else:\n        componentsList = kwargs['componentsList']\n\n    if 'buySystem' not in kwargs or not kwargs['buySystem']:\n        buySystem = 'Jita'\n    else:\n        buySystem = kwargs['buySystem']\n\n    if 'buildLocation' not in kwargs or not kwargs['buildLocation']:\n        buildLocation = 'POS'\n    else:\n        buildLocation = kwargs['buildLocation']\n\n    if 'systemModifier' not in kwargs or not kwargs['systemModifier']:\n        systemModifier = 0.025\n    else:\n        systemModifier = kwargs['systemModifier']\n\n    if 'baseMatsPriceList' in kwargs and kwargs['baseMatsPriceList']:\n        baseMatsPriceList = kwargs['baseMatsPriceList']\n    else:\n        if verbose: print('Pulling prices...')\n        baseMatsPriceList = evemarket.getpricelist(basematids, 'buy', buySystem)\n    \n    baseMatsCosts = get_matslist_cost_from_pricelist(baseMatsList, baseMatsPriceList, order_type='buy', return_type='list')\n\n    baseMatsBuyFees = {}\n    for basematID, basematcost in baseMatsCosts.items(): baseMatsBuyFees[basematID] = evemarket.calcbuyfee(basematcost, 'buy', skillBrokerRelations=1)\n\n    componentsBuildFees = {}\n    for compID, comp_qty in componentsList.items():\n        if compID in basematids:\n            componentsBuildFees[compID] = 0 # for components which are not manufactured\n        else:\n            componentsBuildFees[compID] = calcjobfee(compID, comp_qty, systemModifier, buildLocation)\n\n    if isinstance(product, str): product = auxdatatools.getitemid(product)\n\n    if productRuns > bpMaxRuns:\n        maxruns = floor(productRuns/bpMaxRuns)\n        remruns = productRuns - maxruns*bpMaxRuns\n        productBuildFee = calcjobfee(product, bpMaxRuns, systemModifier) * maxruns + calcjobfee(product, remruns, systemModifier)\n    else:\n        productBuildFee = calcjobfee(product, productRuns, systemModifier)\n   \n    totalCost = sum(baseMatsCosts.values()) + sum(baseMatsBuyFees.values()) + sum(componentsBuildFees.values()) + productBuildFee\n\n    return {\n            'baseMatsPriceList' : baseMatsPriceList,\n            'baseMatsCosts' : baseMatsCosts,\n            'baseMatsBuyFees' : baseMatsBuyFees,\n            'componentsBuildFees' : componentsBuildFees,\n            'productBuildFee' : productBuildFee,\n            'totalCost' : totalCost,\n            }\n\ndef calcjobtime(product, activity, runs, TE=0, production_time_efficiencies=[]):\n    if auxdatatools.isitem(product): product = auxdatatools.getbpIDforitem(product)\n\n    te_scale_factor = 1 - (TE * 2 * 0.01)\n\n    time = auxdatatools.getBPtime(product, activity) * runs * te_scale_factor\n\n    for pcteff in production_time_efficiencies: time *= (1 - pcteff)\n\n    time = round(time)\n\n    return time\n\n","sub_path":"indytools.py","file_name":"indytools.py","file_ext":"py","file_size_in_byte":15268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"258923788","text":"from OpenGL.GL import *\nfrom OpenGL.GLU import *\nfrom OpenGL.GLUT import *\nfrom PIL import Image\nimport numpy as np\n\ndef next_p2(num):\n    temp = 1\n    while (temp < num):\n        temp <<= 1\n    return temp\n    \nclass HFRect:\n    def __init__(self):\n        self.x = 0.\n        self.y = 0.\n        self.w = 0.\n        self.h = 0.\n\nclass HTexture:\n    def __init__(self):\n        self.mTextureID = 0\n        self.mTextureWidth = 0\n        self.mTextureHeight = 0\n        self.mImageWidth = 0\n        self.mImageHeight = 0\n        self.mPixels = None\n        \n    def loadTextureFromFile(self, imageName='texture.png'):\n        im = Image.open(imageName)\n        imgWidth, imgHeight = im.size[0], im.size[1]\n        ix, iy = next_p2(imgWidth), next_p2(imgHeight)\n        paddedImage = Image.new('RGBA', (ix, iy), (0, 0, 0, 0))\n        paddedImage.paste(im, (0, iy - imgHeight, imgWidth, iy))\n        try:\n            image = paddedImage.tobytes(\"raw\", \"RGBA\", 0, -1)\n        except SystemError:\n            image = paddedImage.tobytes(\"raw\", \"RGBX\", 0, -1)\n        # paddedImage.save('padded.png')\n        return self.loadTextureFromPixels32(image, imgWidth, imgHeight, ix, iy)\n        \n    def loadPixelsFromFile(self, imageName='texture.png'):\n        self.freeTexture()\n        try:\n            im = Image.open(imageName)\n        except:\n            return False\n        imgWidth, imgHeight = im.size[0], im.size[1]\n        ix, iy = next_p2(imgWidth), next_p2(imgHeight)\n        paddedImage = Image.new('RGBA', (ix, iy), (0, 0, 0, 0))\n        paddedImage.paste(im, (0, iy - imgHeight, imgWidth, iy))\n        try:\n            image = paddedImage.tobytes(\"raw\", \"RGBA\", 0, -1)\n        except SystemError:\n            image = paddedImage.tobytes(\"raw\", \"RGBX\", 0, -1)\n        # paddedImage.save('padded.png')\n        self.mImageWidth = imgWidth\n        self.mImageHeight = imgHeight\n        self.mPixels = np.frombuffer(image, dtype='S4').reshape(iy, ix)\n        self.mPixels.flags.writeable = True\n        return True\n        \n    def loadTextureFromFileWithColorKey(self, imName, rgba):\n        if not self.loadPixelsFromFile(imName):\n            return False\n        self.mPixels[ (self.mPixels == rgba) | (self.mPixels == rgba[:-1]+b'\\x00') ] \\\n            = b'\\xFF\\xFF\\xFF\\x00'\n        return self.loadTextureFromPixels32(np.ascontiguousarray(self.mPixels).data, \\\n            self.mImageWidth, self.mImageHeight, self.mPixels.shape[1], self.mPixels.shape[0])\n        \n    def loadTextureFromPixels32(self, pixels, imgWidth, imgHeight, texWidth, texHeight):\n        self.freeTexture()\n        self.mTextureWidth = texWidth\n        self.mTextureHeight = texHeight\n        self.mImageWidth = imgWidth\n        self.mImageHeight = imgHeight\n        \n        self.mTextureID = glGenTextures(1)\n        glBindTexture(GL_TEXTURE_2D, self.mTextureID)\n        glTexImage2D(GL_TEXTURE_2D, 0, GL_RGBA, texWidth, texHeight, 0, GL_RGBA, \\\n            GL_UNSIGNED_BYTE, pixels)\n        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MAG_FILTER, GL_LINEAR)\n        glTexParameteri(GL_TEXTURE_2D, GL_TEXTURE_MIN_FILTER, GL_LINEAR)\n        glBindTexture(GL_TEXTURE_2D, 0)\n        \n        error = glGetError()\n        if error != GL_NO_ERROR:\n            print(\"Error loading texture\")\n            return False\n        return True\n        \n    def freeTexture(self):\n        if self.mTextureID != 0:\n            glDeleteTextures(1, self.mTextureID)\n            self.mTextureID = 0\n        self.mPixels = None\n        self.mTextureWidth = 0\n        self.mTextureHeight = 0\n        self.mImageWidth = 0\n        self.mImageHeight = 0\n        \n    def lock(self):\n        if self.mPixels is None and self.mTextureID != 0:\n            glBindTexture( GL_TEXTURE_2D, self.mTextureID )\n            rawPixels = glGetTexImage( GL_TEXTURE_2D, 0, GL_RGBA, GL_UNSIGNED_BYTE )\n            self.mPixels = np.frombuffer(rawPixels, dtype='S4').reshape(self.mTextureHeight, \\\n                self.mTextureWidth)\n            self.mPixels.flags.writeable = True\n            glBindTexture( GL_TEXTURE_2D, 0 )\n            return True\n        return False\n        \n    def unlock(self):\n        if self.mPixels is not None and self.mTextureID != 0:\n            glBindTexture( GL_TEXTURE_2D, self.mTextureID )\n            glTexSubImage2D( GL_TEXTURE_2D, 0, 0, 0, self.mTextureWidth, self.mTextureHeight, \\\n                GL_RGBA, GL_UNSIGNED_BYTE, np.ascontiguousarray(self.mPixels).data )\n            self.mPixels = None\n            glBindTexture( GL_TEXTURE_2D, 0 )\n            return True\n        return False\n        \n    def getPixel32(self, x, y):\n        return self.mPixels[ y, x ]\n        \n    def setPixel32(self, x, y, pixel):\n        self.mPixels[ y, x ] = pixel\n        \n    def render(self, x, y, clip = None):\n        if self.mTextureID != 0:\n            glLoadIdentity()\n            texTop = 0.\n            texBottom = self.mImageHeight / self.mTextureHeight\n            texLeft = 0.\n            texRight = self.mImageWidth / self.mTextureWidth\n            quadWidth = self.mImageWidth\n            quadHeight = self.mImageHeight\n            \n            if clip != None:\n                texLeft = clip.x / self.mTextureWidth\n                texRight = (clip.x + clip.w) / self.mTextureWidth\n                texTop = clip.y / self.mTextureHeight\n                texBottom = (clip.y + clip.h) / self.mTextureHeight\n                quadWidth = clip.w\n                quadHeight = clip.h\n                \n            glTranslatef(x, y, 0.)\n            glBindTexture(GL_TEXTURE_2D, self.mTextureID)\n            \n            glBegin(GL_QUADS)\n            glTexCoord2f(  texLeft,    texTop ); glVertex2f(        0.,         0. );\n            glTexCoord2f( texRight,    texTop ); glVertex2f( quadWidth,         0. );\n            glTexCoord2f( texRight, texBottom ); glVertex2f( quadWidth, quadHeight );\n            glTexCoord2f(  texLeft, texBottom ); glVertex2f(        0., quadHeight );\n            glEnd()","sub_path":"OpenGL/lazy_foo/Lesson 10/HTexture.py","file_name":"HTexture.py","file_ext":"py","file_size_in_byte":5974,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"409725395","text":"import cv2\nimport Hand_Detection as hd\nimport time\n#For testing only\nret = ['1','2','3','4','5']\ndef Test():\n        \n    cap=cv2.VideoCapture(0)\n    while True :\n        success, img=cap.read()\n        hand,out=hd.Gesture_Detection(img)\n        # print(hand)\n        # cv2.imshow(\"hand gestures\",img)\n        if cv2.waitKey(1) == ord('q'):\n            break\n        print(type(out))\n        if out in ret:\n            return out\ncv2.destroyAllWindows()\ndef Output_data(img):\n    hand,out=hd.Gesture_Detection(img)\n\n","sub_path":"GUI/Out.py","file_name":"Out.py","file_ext":"py","file_size_in_byte":516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"306450947","text":"#=============EQUAL REVERSE FUNCTION========\r\n# Developer: Cemil Tepecik\r\n# Date:16.02.2021\r\n# Write a function that controls the given inputs\r\n# whether they are equal to their reversed order or not.\r\n#-------------function-------------------\r\ndef equal_reverse():\r\n    print('Enter a word to check its reverse is equal to itself:')\r\n    word=input().lower()  #make all lower words\r\n    word_orjinal=[i for i in word]  #conver to string into a list\r\n    leng=len(word_orjinal)\r\n    word_reversed=word_orjinal[leng-1::-1] #reversing\r\n    print('reverse of the word:',str(word[leng-1::-1])) #see the reversed version\r\n\r\n    if word_orjinal==word_reversed:\r\n        return  True\r\n    else:\r\n        return False\r\n\r\n#..................call function equal_reverse\r\na=equal_reverse()\r\nprint(a)\r\n#==============END============================\r\n\r\n#===========OUTPUT==============================\r\n#Enter a word to check its reverse is equal to itself:\r\n#radar\r\n#reverse of the word: radar\r\n#True\r\n\r\n#Enter a word to check its reverse is equal to itself:\r\n#termal\r\n#reverse of the word: lamret\r\n#False","sub_path":"3. Equal_Reverse.function.py","file_name":"3. Equal_Reverse.function.py","file_ext":"py","file_size_in_byte":1093,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"112414284","text":"\"\"\"empty message\n\nRevision ID: 7758f7a0b06f\nRevises: 8544f52e6a79\nCreate Date: 2021-04-09 09:18:32.271221\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\nfrom sqlalchemy.dialects import mysql\n\n# revision identifiers, used by Alembic.\nrevision = '7758f7a0b06f'\ndown_revision = '8544f52e6a79'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('order',\n    sa.Column('id', sa.Integer(), nullable=True),\n    sa.Column('user_id', sa.Integer(), nullable=True),\n    sa.Column('product_id', sa.Integer(), nullable=True),\n    sa.Column('units', sa.Integer(), nullable=True),\n    sa.ForeignKeyConstraint(['product_id'], ['products.id'], ),\n    sa.ForeignKeyConstraint(['user_id'], ['users.id'], ),\n    sa.PrimaryKeyConstraint('id')\n    )\n    op.drop_table('pedidos')\n    op.drop_table('usertoproduct')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('usertoproduct',\n    sa.Column('id', mysql.INTEGER(), autoincrement=True, nullable=False),\n    sa.Column('user_id', mysql.INTEGER(), autoincrement=False, nullable=True),\n    sa.Column('product_id', mysql.INTEGER(), autoincrement=False, nullable=True),\n    sa.Column('units', mysql.INTEGER(), autoincrement=False, nullable=True),\n    sa.ForeignKeyConstraint(['product_id'], ['products.id'], name='usertoproduct_ibfk_1'),\n    sa.ForeignKeyConstraint(['user_id'], ['users.id'], name='usertoproduct_ibfk_2'),\n    sa.PrimaryKeyConstraint('id'),\n    mysql_collate='utf8mb4_0900_ai_ci',\n    mysql_default_charset='utf8mb4',\n    mysql_engine='InnoDB'\n    )\n    op.create_table('pedidos',\n    sa.Column('id', mysql.INTEGER(), autoincrement=True, nullable=False),\n    sa.Column('usertoproduct_id', mysql.INTEGER(), autoincrement=False, nullable=True),\n    sa.Column('user_id', mysql.INTEGER(), autoincrement=False, nullable=True),\n    sa.ForeignKeyConstraint(['user_id'], ['users.id'], name='pedidos_ibfk_1'),\n    sa.ForeignKeyConstraint(['usertoproduct_id'], ['usertoproduct.id'], name='pedidos_ibfk_2'),\n    sa.PrimaryKeyConstraint('id'),\n    mysql_collate='utf8mb4_0900_ai_ci',\n    mysql_default_charset='utf8mb4',\n    mysql_engine='InnoDB'\n    )\n    op.drop_table('order')\n    # ### end Alembic commands ###\n","sub_path":"migrations/versions/7758f7a0b06f_.py","file_name":"7758f7a0b06f_.py","file_ext":"py","file_size_in_byte":2330,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"611914274","text":"import matplotlib\n\nmatplotlib.use('Agg')\n\nimport json\nfrom glob import glob\nfrom random import randint\nfrom random import uniform\n\nimport matplotlib.pyplot as plt\nimport pandas as pd\n\nfrom constants import PATH_SERIES_TEMPORAIS_SINTETICAS\nfrom utils import limpar_diretorio\n\n\ndef _mean(l):\n    return sum(l) / len(l)\n\n\ndef dependencia(data):\n    # data = {}\n    # data['dependencia'] = []\n    # data['dependencia'] = {\n    #     'minV': 50,\n    #     'maxV': 100,\n    #     'correlacao': 40,\n    #     'tipoInicial': 'sazonal',\n    #     'tipoDependencia': 'negativo',\n    #     'n': 100,\n    #     'aleatoriedade': 15,\n    #     'elasticidade': {\n    #         'porcentagem': 5,\n    #         'pMin': 10,\n    #         'pMax': 20,\n    #         'vMin': 20,\n    #         'vMax': 200,\n    #         'aleatoriedadeDiaria': 4\n    #     },\n    #     'sazonal': {\n    #         's': 7,\n    #         'diasDsazonalidade': 1,\n    #         'picoMax': 40,\n    #         'picoMin': 15,\n    #         'baixoMax': 10,\n    #         'baixoMin': 5\n    #     }\n    # }\n    #\n    # with open('config.txt', 'w') as outfile:\n    #     json.dump(data, outfile)\n\n    # with open('config.txt') as json_file:\n    #     data = json.load(json_file)\n\n    tipoInicial = data[\"tipoInicial\"]\n    tipoDependencia = data[\"tipoDependencia\"]\n\n    n = data['n']\n\n    minV = data['minV']  # venda minima inicial\n    maxV = data['maxV']  # venda maxima inicial\n\n    correlacao = data['correlacao']  # em porcentagem\n\n    aleatoriedade = data['variacaoDiaria']  # em porcentagem\n\n    vendas = []\n    vendas_puras = []\n    dias = []\n    vendasDir = []\n\n    if tipoInicial == \"elasticidade\":\n        vendasE, precoE = elasticidade(n,\n                                       data['elasticidade']['variacao'],\n                                       data['elasticidade']['pMin'],\n                                       data['elasticidade']['pMax'],\n                                       data['elasticidade']['vMin'],\n                                       data['elasticidade']['vMax'],\n                                       data['elasticidade']['aleatoriedadeDiaria'],\n                                       data['elasticidade']['duracaoVenda'])\n    elif tipoInicial == \"sazonal\":\n        vendasE = sazonal(n,\n                          data['sazonal']['s'],\n                          data['sazonal']['diasDsazonalidade'],\n                          data['sazonal']['picoMax'],\n                          data['sazonal']['picoMin'],\n                          data['sazonal']['baixoMax'],\n                          data['sazonal']['baixoMin'])\n        precoE = 0\n\n    for i in range(n):\n        sinalRandomico = 1\n        if randint(2, 3) % 2 == 0:\n            sinalRandomico = -1\n\n        dias.append(i + 1)\n        if i == 0:\n            primeiroValor = ((maxV - minV) * randint(0, 100) / 100) + minV\n            vendas.append(primeiroValor)\n            vendas_puras.append(primeiroValor)\n            vendasDir.append(primeiroValor)\n        else:\n            # fazer diretamente ou inversamente proporcional e depois aplicar o jitter\n            aleatoriedade_round = (1 + (sinalRandomico * randint(0, 100) * aleatoriedade / 10000))\n            vendas_puras.append(vendas_puras[i - 1] * aleatoriedade_round)\n            A = vendas[i - 1] * aleatoriedade_round\n            if tipoDependencia == \"negativo\":\n                venda = (vendas[i - 1] * vendasE[i - 1] / vendasE[i])\n            elif tipoDependencia == \"positivo\":\n                venda = (vendas[i - 1] * vendasE[i] / vendasE[i - 1])\n\n            vendas.append(venda)\n            vendasDir.append(venda)\n\n    mediaVP = _mean(vendas_puras)\n    print(mediaVP)\n    mediaV = _mean(vendas)\n    print(mediaV)\n\n    for i in range(n):\n        if tipoInicial == \"sazonal\":\n            vendas[i] = (vendas[i] * (correlacao / 100)) + (\n                    (vendas_puras[i] * (mediaV / mediaVP)) * (abs(correlacao - 100) / 100))\n        else:\n            vendas[i] = (vendas[i] * (correlacao / 100)) + (vendas_puras[i] * (abs(correlacao - 100) / 100))\n\n    # plt.subplot(4, 1, 1)\n    # plt.plot(dias, vendasE)\n    # plt.title(\"elasticidade\")\n    # plt.subplot(4, 1, 2)\n    # plt.plot(dias, vendas_puras)\n    # plt.title(\"venda pura\")\n    # plt.subplot(4, 1, 3)\n    # plt.plot(dias, vendasDir)\n    # plt.title(\"dir prop\")\n    # plt.subplot(4, 1, 4)\n    # plt.plot(dias, vendas)\n    # plt.title(\"dependencia\")\n\n    plt.subplot(2, 1, 1)\n    plt.plot(dias, vendasE)\n    plt.title(tipoInicial)\n    plt.subplot(2, 1, 2)\n    plt.plot(dias, vendas)\n    plt.title(\"produto dependente\")\n\n    # plt.savefig(\"dependencia\")\n    # plt.show()\n    plt.close()\n\n    return (vendas, vendasE, precoE)\n\n\ndef sazonal(n, s, diasDsazonalidade, picoMax, picoMin, baixoMax, baixoMin):\n    dias = []\n    vendas = []\n\n    ds = 0\n\n    for i in range(n):\n        dias.append(i + 1)\n        if (i + 1) % s == 0:\n            ds = diasDsazonalidade\n\n        if ds > 0:\n            venda = ((picoMax - picoMin) * randint(0, 100) / 100) + picoMin\n        else:\n            venda = ((baixoMax - baixoMin) * randint(0, 100) / 100) + baixoMin\n\n        ds = ds - 1\n        vendas.append(venda)\n    return vendas\n\n\ndef elasticidade(n, porcentagem, pMin, pMax, vMin, vMax, aleatoriedadeDiaria, duracaoVenda):\n    jitter = ((vMin + vMax) / 2) * aleatoriedadeDiaria / 100\n\n    dias = []\n    vendas = []\n    precos = []\n\n    precoAtual = uniform(pMin, pMax)\n    vendaAtual = randint(vMin, vMax)\n    diasAtuais = randint(1, int(n * duracaoVenda / 100))\n\n    for i in range(n):\n        dias.append(i + 1)\n\n        sinal = 1\n        if randint(2, 3) % 2 == 0:\n            sinal = -1\n\n        if diasAtuais == 0:\n            precoAntigo = precoAtual\n            vendaAntigo = vendaAtual\n            precoAtual = uniform(pMin, pMax)\n            diasAtuais = randint(1, int(n / 3))\n            vendaAtual = (vendaAntigo * precoAntigo / precoAtual)\n            vendaAtual = vendaAtual + sinal * randint(0, int(\n                vendaAtual * porcentagem / 100))  # tem um range de 5% pra cima e pra baixo com base na aleatoriedade para nao ficar exatamente inversamente proporcional\n\n        randomicJitter = randint(0, 100) * sinal * jitter / 100\n\n        precos.append(precoAtual)\n        venda = vendaAtual + randomicJitter\n\n        diasAtuais = diasAtuais - 1\n\n        vendas.append(venda)\n\n    return vendas, precos\n\n\ndef gerar():\n\n    limpar_diretorio(PATH_SERIES_TEMPORAIS_SINTETICAS)\n\n    with open('config.txt') as json_file:\n        item = json.load(json_file)\n\n    for item in item['lista']:\n        print(item)\n\n        if item['tipoSerie'] == \"sazonal\":\n            dic = {}\n\n            for i in range(item['qtdGerados']):\n                vendas_sazonais = sazonal(item['n'], item['s'], item['diasDsazonalidade'], item['picoMax'],\n                                          item['picoMin'], item['baixoMax'], item['baixoMin'])\n\n                dic.update(\n                    {\n                        'ts': range(len(vendas_sazonais)),\n                        'venda-' + str(i): vendas_sazonais\n                    }\n                )\n\n                plt.plot(range(len(vendas_sazonais)), vendas_sazonais)\n                plt.title(\"Sazonal \")\n                plt.savefig(PATH_SERIES_TEMPORAIS_SINTETICAS + '/sazonal' + str(i) + '.png', bbox_inches='tight')\n                # plt.show()\n                plt.close()\n                pd.DataFrame(dic).to_csv(PATH_SERIES_TEMPORAIS_SINTETICAS + '/sazonal' + str(i) + '.csv', index=False)\n\n        elif item['tipoSerie'] == \"elasticidade\":\n            dic = {}\n            for i in range(item['qtdGerados']):\n                vendas, precos = elasticidade(item['n'],\n                                              item['variacao'],\n                                              item['pMin'],\n                                              item['pMax'],\n                                              item['vMin'],\n                                              item['vMax'],\n                                              item['aleatoriedadeDiaria'],\n                                              item['duracaoVenda'])\n\n                plt.subplot(2, 1, 1)\n                plt.plot(range(len(precos)), vendas)\n                plt.title(\"vendas\")\n                plt.subplot(2, 1, 2)\n                plt.plot(range(len(precos)), precos)\n                plt.title(\"precos\")\n                # plt.show()\n                plt.close()\n\n                dic.update({'venda-' + str(i): vendas, 'preco-' + str(i): precos})\n\n            files = glob(PATH_SERIES_TEMPORAIS_SINTETICAS + '/elasticidade*')\n            pd.DataFrame(dic).to_csv(PATH_SERIES_TEMPORAIS_SINTETICAS + '/elasticidade' + str(len(files)) + '.csv',\n                                     index=False)\n\n        elif item['tipoSerie'] == \"dependencia\":\n            dic = {}\n            for i in range(item['qtdGerados']):\n                vendaDep, vendasE, precoE = dependencia(item)\n\n                dic.update({'venda-item-Dep-' + str(i): vendaDep, 'venda-item-anterior-' + str(i): vendaDep,\n                            'preco-item-anterior-' + str(i): precoE})\n\n            files = glob(PATH_SERIES_TEMPORAIS_SINTETICAS + '/dependencia*')\n            pd.DataFrame(dic).to_csv(PATH_SERIES_TEMPORAIS_SINTETICAS + '/dependencia' + str(len(files)) + '.csv',\n                                     index=False)\n","sub_path":"gerador.py","file_name":"gerador.py","file_ext":"py","file_size_in_byte":9372,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"235456491","text":"# @author:  Gunnar Schaefer\n#           Reno Bowen\n\nimport os\nimport json\nimport datetime\nimport tempfile\nimport subprocess\n\nfrom tg import config, expose, flash, lurl, request, redirect, response\nfrom tg.i18n import ugettext as _, lazy_ugettext as l_\nimport webob.exc\n\nimport nimsdata\nimport nimsutil\nfrom nimsgears.model import *\n\nfrom nimsgears.lib.base import BaseController\nfrom nimsgears.controllers.auth import AuthController\nfrom nimsgears.controllers.pub import PubController\nfrom nimsgears.controllers.error import ErrorController\nfrom nimsgears.controllers.experiment import ExperimentController\nfrom nimsgears.controllers.session import SessionController\nfrom nimsgears.controllers.epoch import EpochController\nfrom nimsgears.controllers.dataset import DatasetController\nfrom nimsgears.controllers.user import UserController\n\n__all__ = ['RootController']\n\nstore_path = config.get('store_path')\ntemp_path = config.get('temp_path')\n\n\nclass RootController(BaseController):\n\n    \"\"\"Root controller for the nimsgears application.\"\"\"\n\n    auth = AuthController()\n    pub = PubController()\n    error = ErrorController()\n    experiment = ExperimentController()\n    session = SessionController()\n    epoch = EpochController()\n    dataset = DatasetController()\n    user = UserController()\n\n    @expose('nimsgears.templates.index')\n    def index(self):\n        \"\"\"Handle the front-page.\"\"\"\n        return dict(page='index')\n\n    @expose('nimsgears.templates.about')\n    def about(self):\n        \"\"\"Handle the 'about' page.\"\"\"\n        return dict(page='about')\n\n    @expose('nimsgears.templates.environ')\n    def environ(self):\n        \"\"\"This method showcases TG's access to the wsgi environment.\"\"\"\n        return dict(environment=request.environ)\n\n    @expose('nimsgears.templates.login')\n    def login(self, came_from=lurl('/')):\n        \"\"\"Start the user login.\"\"\"\n        login_counter = request.environ['repoze.who.logins']\n        if login_counter > 0:\n            flash(_('Wrong credentials.'), 'warning')\n        return dict(page='login', login_counter=str(login_counter), came_from=came_from)\n\n    @expose()\n    def post_login(self, came_from=lurl('/')):\n        \"\"\"\n        Redirect the user to the initially requested page on successful\n        authentication or redirect her back to the login page if login failed.\n        \"\"\"\n        if not request.identity:\n            login_counter = request.environ['repoze.who.logins'] + 1\n            redirect('/login', params=dict(came_from=came_from, __logins=login_counter))\n        redirect(came_from)\n\n    @expose()\n    def post_logout(self, came_from=lurl('/')):\n        \"\"\"Redirect the user to the home page on logout.\"\"\"\n        redirect('/')\n\n    @expose('nimsgears.templates.pyramid', render_params={'doctype': None})\n    def pyramid(self, **kwargs):\n        user = request.identity['user'] if request.identity else User.get_by(uid=u'@public')\n        ds = Dataset.get(kwargs['dataset_id'])\n        if user.has_access_to(ds):\n            db_file = os.path.join(store_path, ds.relpath, ds.filenames[0])\n            return dict(zip(['dataset_id', 'tile_size', 'x_size', 'y_size'], (ds.id,) + nimsdata.nimsmontage.get_info(db_file)))\n\n    @expose(content_type='image/jpeg')\n    def pyramid_tile(self, *args):\n        user = request.identity['user'] if request.identity else User.get_by(uid=u'@public')\n        dataset_id, z, x, y = map(int, args[0].rsplit('.', 1)[0].split('_'))\n        ds = Dataset.get(dataset_id)\n        if user.has_access_to(ds):\n            del response.pragma\n            response.etag = args[0]\n            response.cache_control = 'max-age = 86400'\n            response.last_modified = ds.updatetime\n            return nimsdata.nimsmontage.get_tile(os.path.join(store_path, ds.relpath, ds.filenames[0]), z, x, y)\n\n    @expose(content_type='application/octet-stream')\n    def file(self, **kwargs):\n        user = request.identity['user'] if request.identity else User.get_by(uid=u'@public')\n        if 'id' in kwargs and 'filename' in kwargs:\n            ds = Dataset.get(kwargs['id'])\n            filepath = os.path.join(store_path, ds.relpath, kwargs['filename'])\n            if user.has_access_to(ds, u'Read-Only' if (ds.kind == u'primary' or ds.kind == u'secondary') else u'Anon-Read'):\n                if os.path.exists(filepath):\n                    response.content_disposition = 'attachment; filename=%s' % kwargs['filename'].encode('utf-8')\n                    response.content_length = os.path.getsize(filepath) # not actually working\n                    return open(filepath, 'r')\n                else:\n                    raise webob.exc.HTTPNotFound()\n            else:\n                raise webob.exc.HTTPForbidden()\n\n    @expose(content_type='application/x-tar')\n    def download(self, **kwargs):\n        user = request.identity['user'] if request.identity else User.get_by(uid=u'@public')\n        db_results = []\n        epoch_paths = []\n        symlinks = []\n        temp_dir = tempfile.mkdtemp(dir=temp_path)\n        if 'id_dict' in kwargs and 'sess' in kwargs['id_dict']:\n            id_list = [int(id) for id in json.loads(kwargs['id_dict'])['sess']]\n            db_query = (DBSession.query(Session, Experiment, ResearchGroup, Dataset, Epoch)\n                .join(Subject, Session.subject)\n                .join(Experiment, Subject.experiment)\n                .join(ResearchGroup, Experiment.owner)\n                .join(Epoch, Session.epochs)\n                .join(Dataset, Epoch.datasets)\n                .filter(Session.id.in_(id_list)))\n            if kwargs.get('raw'):\n                db_results = db_query.filter(Dataset.kind != u'web').all()\n            else:\n                db_results = db_query.filter((Dataset.kind == u'peripheral') | (Dataset.kind == u'derived')).all()\n        elif 'id_dict' in kwargs and 'epoch' in kwargs['id_dict']:\n            id_list = [int(id) for id in json.loads(kwargs['id_dict'])['epoch']]\n            db_query = (DBSession.query(Session, Experiment, ResearchGroup, Dataset, Epoch)\n                .join(Subject, Session.subject)\n                .join(Experiment, Subject.experiment)\n                .join(ResearchGroup, Experiment.owner)\n                .join(Epoch, Session.epochs)\n                .join(Dataset, Epoch.datasets)\n                .filter(Epoch.id.in_(id_list)))\n            if kwargs.get('raw'):\n                db_results = db_query.filter(Dataset.kind != u'web').all()\n            else:\n                db_results = db_query.filter((Dataset.kind == u'peripheral') | (Dataset.kind == u'derived')).all()\n        elif 'id_dict' in kwargs and 'dataset' in kwargs['id_dict']:\n            id_list = [int(id) for id in json.loads(kwargs['id_dict'])['dataset']]\n            db_results = (DBSession.query(Dataset, Epoch, Session, Experiment, ResearchGroup)\n                    .join(Epoch, Dataset.container)\n                    .join(Session, Epoch.session)\n                    .join(Subject, Session.subject)\n                    .join(Experiment, Subject.experiment)\n                    .join(ResearchGroup, Experiment.owner)\n                    .filter(Dataset.id.in_(id_list))\n                    .all())\n        for r in db_results:\n            if user.has_access_to(r.Dataset, u'Read-Only' if (r.Dataset.kind == u'primary' or r.Dataset.kind == u'secondary') else u'Anon-Read'):\n                if kwargs.get('legacy'):\n                    ep = '%s/nims/%s/%s/%s' % (temp_dir, r.ResearchGroup.gid, r.Experiment.name, r.Session.legacy_dirname)\n                else:\n                    ep = '%s/nims/%s/%s/%s/%s' % (temp_dir, r.ResearchGroup.gid, r.Experiment.name, r.Session.dirname, r.Epoch.dirname)\n                epoch_paths.append(ep)\n                for filename in r.Dataset.filenames:\n                    if kwargs.get('legacy'):\n                        name, sep, ext = filename.partition('.')\n                        if name.endswith('_B0'):        ext = '_B0.%s' % ext\n                        elif name.endswith('_physio'):  ext = '_physio.%s' % ext\n                        elif name.endswith('_dicoms'):  ext = '_dicoms.%s' % ext\n                        else:                           ext = '.%s' % ext\n                        sl_name = '%04d_%02d_%s%s' % (r.Epoch.series, r.Epoch.acq, r.Epoch.description, ext)\n                    else:\n                        sl_name = filename\n                    symlinks += [(os.path.join(store_path, r.Dataset.relpath, filename), os.path.join(ep, sl_name))]\n        for ep in set(epoch_paths):\n            os.makedirs(ep)\n        for sl in symlinks:\n            os.symlink(*sl)\n        tar_proc = subprocess.Popen('tar -chf - -C %s nims; rm -r %s' % (temp_dir, temp_dir), shell=True, stdout=subprocess.PIPE)\n        response.content_disposition = 'attachment; filename=%s_%s' % ('nims', datetime.datetime.now().strftime('%Y%m%d_%H%M%S'))\n        return tar_proc.stdout\n","sub_path":"nimsgears/controllers/root.py","file_name":"root.py","file_ext":"py","file_size_in_byte":8884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"643888286","text":"#coding=utf-8\nimport urllib.request\nimport re\nimport os\n\n\nlocalPath = \"e:/doubanDownLoad/\"\n\ncount = 0\n\nusername = \"SlimWei\"\n\ndef downloadUserPhotos(userName):\n        \n    print(\"开始下载\" + userName + \"的相册\")\n    userPageUrl = \"https://www.douban.com/people/\" + userName + \"/photos\"\n\n\n    #得到相册列表\n    albumList = getUserAlbumList(userPageUrl, userName)\n\n    retKeys = albumList.keys()\n\n    for tempKey in retKeys:\n        downLoadOneAlbumPhotos(tempKey, userName, albumList[tempKey])\n        \n    print(\"下载完成\")\n\ndef getUserAlbumList(url, userName):\n\n    firstPageAlumnList = getOnePageAlumnList(url)\n\n    page = urllib.request.urlopen(url)\n    html = page.read()\n\n    decodeHtml = html.decode(\"utf-8\")\n    \n    leftPagePattern = re.compile(r\"https://www.douban.com/people/\" + userName + \"/photos\\?start\\=[0-9]+\")\n\n    leftPhotoPages = leftPagePattern.findall(decodeHtml);\n\n    leftPhotoPageSet = set(leftPhotoPages);\n\n    for leftPageUrl in leftPhotoPageSet:\n        leftPageAlumnList = getOnePageAlumnList(leftPageUrl)\n\n        keys = leftPageAlumnList.keys()\n\n        for key in keys:\n            firstPageAlumnList[key] = leftPageAlumnList[key]\n        \n    return firstPageAlumnList\n\ndef getOnePageAlumnList(url):\n    page = urllib.request.urlopen(url)\n    html = page.read()\n    \n    pattern = re.compile(r\"https://www.douban.com/photos/album/[0-9]+/\\\"\\>(?!\\<img).*\\<\")\n\n    decodeHtml = html.decode(\"utf-8\")\n    \n    ret = pattern.findall(decodeHtml);\n\n    temp = set(ret)\n\n    ret = {}\n    \n    for oneUrl in temp:\n        index1 = oneUrl.find(\"album/\")\n\n        index2 = oneUrl.find(\"/\", index1 + 7)\n\n        albumId = oneUrl[index1 + 6:index2]\n\n        index3 = oneUrl.rfind(\">\");\n\n        index4 = oneUrl.rfind(\"<\");\n\n        albumName = oneUrl[index3 + 1:index4]\n\n        ret[albumId] = albumName\n                 \n    return ret\n\n\ndef getCurrentAlbumHtml(url):\n    #得到相册当前页面html\n    page = urllib.request.urlopen(url)\n    html = page.read()\n    \n    return html\n\ndef downLoadOneAlbumPhotos(albumId, username, albumName):\n    #根据相册id下载当前相册的所有图片\n    print(\"开始下载相册 \" + albumName + \" albumId:\" + albumId)\n    startUrl = \"https://www.douban.com/photos/album/\" + str(albumId);\n\n    global count\n    \n    count = 0\n    \n    parentFolder = localPath + username + \"/\" + _translate_file_Name(str(albumName).rstrip())\n\n    if not os.path.exists(parentFolder):\n        os.makedirs(parentFolder)\n    else:\n        print(\"相册\" + albumName + \"已存在\")\n        return\n        \n    html = getCurrentAlbumHtml(startUrl)\n\n    leftPhotoPageUrls = getPageUrlsOfOneAlnum(startUrl, html);\n\n    downLoadOnePagePhotoOfOneAlbum(startUrl, parentFolder)\n\n    for url in leftPhotoPageUrls:\n        downLoadOnePagePhotoOfOneAlbum(url, parentFolder)\n\ndef downLoadOnePagePhotoOfOneAlbum(url, parentFolder):\n    #下载相册一页的图片\n    \n    page = urllib.request.urlopen(url)\n    html = page.read()\n\n    photoUrls = getCurrentPhotoPageUrls(html)\n\n    for photoUrl in photoUrls:\n        bigPhotoUrl = getOnePhotoUrl(photoUrl)\n\n        downloadFile(bigPhotoUrl, parentFolder)\n\n        global count\n        \n        count = count + 1\n        \n    \n    \ndef getPageUrlsOfOneAlnum(firstPageUrl, sourceHtml):\n    #https://www.douban.com/photos/album/68058307/?start=18\n\n    subUrl = firstPageUrl + \"/\\?start=[0-9]+\"\n    \n    #得到类似这样的地址\n    pattern = re.compile(subUrl)\n\n    decodeHtml = sourceHtml.decode(\"utf-8\")\n    \n    urls = pattern.findall(decodeHtml)\n\n    setUrl = set(urls)\n\n    return setUrl\n\ndef getCurrentPhotoPageUrls(sourceHtml):\n    pattern = re.compile(r\"https://www.douban.com/photos/photo/[0-9]+\")\n\n    decode_html = sourceHtml.decode(\"utf-8\")\n    \n    ret = pattern.findall(decode_html)\n\n    return ret\n\ndef getOnePhotoUrl(url):\n    #得到一张图片的url\n    # page url https://www.douban.com/photos/photo/2322109721\n    # image url https://img3.doubanio.com/view/photo/photo/public/p2320432016.jpg\n    startIndex = url.rfind(\"/\")\n\n    photoNum = url[startIndex + 1:]\n\n    ret = \"https://img3.doubanio.com/view/photo/photo/public/p\" + photoNum + \".jpg\"\n    \n    return ret\n\ndef getImageUrl(html):\n    pattern = re.compile(r\"https://img3.doubanio.com/view/photo/.*jpg\")\n\n    decodeHtml = html.decode(\"utf-8\")\n    \n    ret = pattern.findall(decodeHtml);\n\n    return ret\n\ndef downloadFile(url, folder):\n    #if not os.path.exists(localPath):\n        #os.makedirs(localPath)\n\n    startIndex = url.rfind(\"/\")\n\n    endIndex = url.rfind(\".\")\n\n    imageName = _translate_file_Name(url[startIndex:])\n\n    data = urllib.request.urlopen(url).read()  \n    f = open(folder + \"/\" + imageName,\"wb\")\n    f.write(data)  \n    f.close()\n\n\ndef _translate_file_Name(_file_name):\n    r1 = re.compile(\"\\\\\\|\\*|\\<|\\>|\\||\\?|\\/\")\n    return re.sub(r1, \"a\",  _file_name)\n\ndownloadUserPhotos(username)\n\n#downLoadOneAlbumPhotos(alumnId)\n\n#for eachPhotoUrl in currentPagePhotoUrls:\n #  downloadFile(getOnePhotoUrl(eachPhotoUrl))\n\n#imageUrls = getImageUrl(html);\n\n#for oneImageUrl in imageUrls:\n  #  downloadFile(oneImageUrl)\n\n#print(html)\n","sub_path":"getDoubanAlbum.py","file_name":"getDoubanAlbum.py","file_ext":"py","file_size_in_byte":5124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"87982948","text":"import unittest\nfrom unittest.mock import patch, Mock, MagicMock, ANY, call\nfrom copy import deepcopy\nfrom .apptesting import Session\nfrom . import SAMPLE_ISSUES_KERNEL, SAMPLE_AOPS_KERNEL, SAMPLE_KERNEL_JOURNAL\n\nimport os\nimport shutil\n\nfrom documentstore_migracao.processing import inserting\nfrom documentstore_migracao.utils import manifest\nfrom documentstore_migracao import config\nfrom documentstore.domain import DocumentsBundle\nfrom documentstore.exceptions import DoesNotExist\n\n\nclass TestLinkDocumentsBundleWithDocuments(unittest.TestCase):\n    def setUp(self):\n        self.session = Session()\n        manifest = inserting.ManifestDomainAdapter(SAMPLE_ISSUES_KERNEL[0])\n        self.session.documents_bundles.add(manifest)\n        self.documents_bundle = self.session.documents_bundles.fetch(manifest.id())\n\n    def test_should_link_documents_bundle_with_documents(self):\n        inserting.link_documents_bundles_with_documents(\n            self.documents_bundle, [\"doc-1\", \"doc-2\"], self.session\n        )\n\n        self.assertEqual([\"doc-1\", \"doc-2\"], self.documents_bundle.documents)\n\n    def test_should_not_insert_duplicated_documents(self):\n        inserting.link_documents_bundles_with_documents(\n            self.documents_bundle, [\"doc-1\", \"doc-1\"], self.session\n        )\n\n        self.assertEqual([\"doc-1\"], self.documents_bundle.documents)\n\n    def test_should_register_changes(self):\n        inserting.link_documents_bundles_with_documents(\n            self.documents_bundle, [\"doc-1\", \"doc-2\"], self.session\n        )\n\n        _changes = self.session.changes.filter()\n\n        self.assertEqual(1, len(_changes))\n        self.assertEqual(self.documents_bundle.id(), _changes[0][\"id\"])\n        self.assertEqual(\"DocumentsBundle\", _changes[0][\"entity\"])\n\n\nclass TestProcessingInserting(unittest.TestCase):\n    def setUp(self):\n        self.data = dict(\n            [\n                (\"eissn\", \"1234-5678\"),\n                (\"pissn\", \"0001-3714\"),\n                (\"issn\", \"0987-0987\"),\n                (\"year\", \"1998\"),\n                (\"volume\", \"29\"),\n                (\"number\", \"3\"),\n                (\"supplement\", None),\n            ]\n        )\n        self.aop_data = dict(\n            [\n                (\"eissn\", \"0001-3714\"),\n                (\"issn\", \"0001-3714\"),\n                (\"year\", \"2019\"),\n            ]\n        )\n        if not os.path.isdir(config.get(\"ERRORS_PATH\")):\n            os.makedirs(config.get(\"ERRORS_PATH\"))\n\n    def tearDown(self):\n        shutil.rmtree(config.get(\"ERRORS_PATH\"))\n\n    @patch(\n        \"documentstore_migracao.processing.inserting.scielo_ids_generator.issue_id\"\n    )\n    def test_get_documents_bundle_uses_scielo_ids_generator_issue_id_if_issue(\n        self, mk_issue_bundle_id\n    ):\n        result = inserting.get_documents_bundle(MagicMock(), self.data)\n        mk_issue_bundle_id.assert_called_with(\n            ANY,\n            self.data[\"year\"],\n            self.data[\"volume\"],\n            self.data[\"number\"],\n            self.data[\"supplement\"],\n        )\n\n    @patch(\n        \"documentstore_migracao.processing.inserting.scielo_ids_generator.aops_bundle_id\"\n    )\n    @patch(\n        \"documentstore_migracao.processing.inserting.scielo_ids_generator.issue_id\"\n    )\n    def test_get_documents_bundle_uses_scielo_ids_generator_aops_bundle_id_if_aop(\n        self, mk_issue_bundle_id, mk_aops_bundle_id\n    ):\n        result = inserting.get_documents_bundle(MagicMock(), self.aop_data)\n        mk_aops_bundle_id.assert_called()\n        mk_issue_bundle_id.assert_not_called()\n\n    def test_get_documents_bundle_success(self):\n        session_db = Session()\n        session_db.documents_bundles.add(\n            inserting.ManifestDomainAdapter(SAMPLE_ISSUES_KERNEL[0])\n        )\n        session_db.documents_bundles.add(\n            inserting.ManifestDomainAdapter(SAMPLE_AOPS_KERNEL[0])\n        )\n        result = inserting.get_documents_bundle(session_db, self.data)\n        self.assertIsInstance(result, DocumentsBundle)\n        self.assertEqual(result.id(), \"0001-3714-1998-v29-n3\")\n\n    def test_get_documents_bundle_raises_exception_if_issue_and_not_found(self):\n        session_db = MagicMock()\n        session_db.documents_bundles.fetch.side_effect = DoesNotExist\n        self.assertRaises(\n            ValueError, inserting.get_documents_bundle, session_db, self.data\n        )\n\n    @patch(\"documentstore_migracao.processing.inserting.create_aop_bundle\")\n    def test_get_documents_bundle_creates_aop_bundle_is_aop_and_not_found(\n        self, mk_create_aop_bundle\n    ):\n        issns = [\"1234-0001\", \"1234-0002\", \"1234-0003\"]\n        data = {\n            \"eissn\": issns[0],\n            \"pissn\": issns[1],\n            \"issn\": issns[2],\n            \"year\": \"2019\",\n        }\n        session_db = MagicMock()\n        session_db.documents_bundles.fetch.side_effect = DoesNotExist\n        mk_create_aop_bundle.side_effect = DoesNotExist\n        self.assertRaises(\n            ValueError, inserting.get_documents_bundle, session_db, data\n        )\n        mk_create_aop_bundle.assert_has_calls(\n            [call(session_db, issn) for issn in issns], True\n        )\n\n    @patch(\n        \"documentstore_migracao.processing.inserting.scielo_ids_generator.aops_bundle_id\"\n    )\n    @patch(\"documentstore_migracao.processing.inserting.create_aop_bundle\")\n    def test_get_documents_bundle_raises_exception_if_creates_aop_bundle_none(\n        self, mk_create_aop_bundle, mk_aops_bundle_id\n    ):\n        session_db = MagicMock()\n        session_db.documents_bundles.fetch.side_effect = DoesNotExist\n        mk_create_aop_bundle.side_effect = DoesNotExist\n        self.assertRaises(\n            ValueError,\n            inserting.get_documents_bundle,\n            session_db,\n            self.aop_data\n        )\n\n    @patch(\"documentstore_migracao.processing.inserting.create_aop_bundle\")\n    def test_get_documents_bundle_returns_created_aop_bundle(\n        self, mk_create_aop_bundle\n    ):\n        session_db = MagicMock()\n        mocked_aop_bundle = Mock()\n        session_db.documents_bundles.fetch.side_effect = DoesNotExist\n        mk_create_aop_bundle.return_value = mocked_aop_bundle\n        result = inserting.get_documents_bundle(session_db, self.aop_data)\n        self.assertEqual(result, mocked_aop_bundle)\n\n    def test_create_aop_bundle_gets_journal(\n        self\n    ):\n        issn = \"1234-0001\"\n        session_db = MagicMock()\n        inserting.create_aop_bundle(session_db, issn)\n        session_db.journals.fetch.assert_called_once_with(issn)\n\n    def test_create_aop_bundle_raises_exception_if_journal_not_found(\n        self\n    ):\n        issn = \"1234-0001\"\n        session_db = MagicMock()\n        session_db.journals.fetch.side_effect = DoesNotExist\n        self.assertRaises(\n            DoesNotExist,\n            inserting.create_aop_bundle,\n            session_db,\n            issn\n        )\n\n    @patch(\n        \"documentstore_migracao.processing.inserting.scielo_ids_generator.aops_bundle_id\"\n    )\n    def test_create_aop_bundle_uses_scielo_ids_generator_aops_bundle_id(\n        self, mk_aops_bundle_id\n    ):\n        session_db = MagicMock()\n        session_db.journals.fetch.return_value = inserting.ManifestDomainAdapter(\n            manifest=SAMPLE_KERNEL_JOURNAL\n        )\n        inserting.create_aop_bundle(session_db, \"0001-3714\")\n        mk_aops_bundle_id.assert_called_once_with(\"0001-3714\")\n\n    @patch(\"documentstore_migracao.processing.inserting.utcnow\")\n    @patch(\"documentstore_migracao.processing.inserting.ManifestDomainAdapter\")\n    def test_create_aop_bundle_registers_aop_bundle(\n        self, MockManifestDomainAdapter, mk_utcnow\n    ):\n        mk_utcnow.return_value = \"2019-01-02T05:00:00.000000Z\"\n        expected = {\n            \"_id\" : \"0001-3714-aop\",\n            \"created\" : \"2019-01-02T05:00:00.000000Z\",\n            \"updated\" : \"2019-01-02T05:00:00.000000Z\",\n            \"items\" : [],\n            \"metadata\" : {},\n            \"id\" : \"0001-3714-aop\",\n        }\n        mk_bundle_manifest = Mock()\n        MockManifestDomainAdapter.return_value = mk_bundle_manifest\n        session_db = MagicMock()\n        session_db.journals.fetch.return_value = inserting.ManifestDomainAdapter(\n            manifest=SAMPLE_KERNEL_JOURNAL\n        )\n        inserting.create_aop_bundle(session_db, SAMPLE_KERNEL_JOURNAL[\"id\"])\n        MockManifestDomainAdapter.assert_any_call(manifest=expected)\n        session_db.documents_bundles.add.assert_called_once_with(data=mk_bundle_manifest)\n        session_db.changes.add.assert_any_call(\n            {\n                \"timestamp\": \"2019-01-02T05:00:00.000000Z\",\n                \"entity\": \"DocumentsBundle\",\n                \"id\": \"0001-3714-aop\",\n            }\n        )\n\n    @patch(\"documentstore_migracao.processing.inserting.utcnow\")\n    def test_create_aop_bundle_links_aop_bundle_to_journal(\n        self, mk_utcnow\n    ):\n        mk_utcnow.return_value = \"2019-01-02T05:00:00.000000Z\"\n        mocked_journal_data = inserting.ManifestDomainAdapter(\n            manifest=SAMPLE_KERNEL_JOURNAL\n        )\n        mk_bundle_manifest = Mock()\n        session_db = MagicMock()\n        session_db.journals.fetch.return_value = mocked_journal_data\n        inserting.create_aop_bundle(session_db, SAMPLE_KERNEL_JOURNAL[\"id\"])\n        session_db.journals.update.assert_called()\n        session_db.changes.add.assert_any_call(\n            {\n                \"timestamp\": \"2019-01-02T05:00:00.000000Z\",\n                \"entity\": \"journal\",\n                \"id\": SAMPLE_KERNEL_JOURNAL[\"id\"],\n            }\n        )\n        self.assertEqual(mocked_journal_data.ahead_of_print_bundle, \"0001-3714-aop\")\n\n    def test_create_aop_bundle_returns_bundle(self):\n        session_db = Session()\n        mocked_journal_data = inserting.ManifestDomainAdapter(\n            manifest=SAMPLE_KERNEL_JOURNAL\n        )\n        session_db.journals.add(mocked_journal_data)\n        result = inserting.create_aop_bundle(session_db, SAMPLE_KERNEL_JOURNAL[\"id\"])\n        self.assertIsInstance(result, DocumentsBundle)\n        self.assertEqual(result.id(), \"0001-3714-aop\")\n\n    @patch(\n        \"documentstore_migracao.processing.inserting.link_documents_bundles_with_documents\"\n    )\n    def test_register_documents_in_documents_bundle(\n        self, mk_link_documents_bundle_with_documents\n    ):\n        data1 = self.data\n        data2 = dict(\n            [\n                (\"eissn\", \"0003-3714\"),\n                (\"issn\", \"0003-3714\"),\n                (\"year\", \"1998\"),\n                (\"volume\", \"29\"),\n                (\"number\", \"3\"),\n                (\"supplement\", None),\n            ]\n        )\n        documents_sorted_in_bundles = {\n            \"0001-3714-1998-29-03\": {\n                \"items\": [\"0001-3714-1998-v29-n3-01\", \"0001-3714-1998-v29-n3-02\"],\n                \"data\": data1,\n            },\n            \"0003-3714-1998-29-03\": {\n                \"items\": [\"0003-3714-1998-v29-n3-01\", \"0003-3714-1998-v29-n3-02\"],\n                \"data\": data2,\n            },\n        }\n\n        err_filename = os.path.join(\n            config.get(\"ERRORS_PATH\"), \"insert_documents_in_bundle.err\"\n        )\n\n        session_db = Session()\n        manifest = inserting.ManifestDomainAdapter(SAMPLE_ISSUES_KERNEL[0])\n        session_db.documents_bundles.add(manifest)\n\n        inserting.register_documents_in_documents_bundle(\n            session_db, documents_sorted_in_bundles\n        )\n\n        self.assertEqual(os.path.isfile(err_filename), True)\n        with open(err_filename) as fp:\n            content = fp.read()\n\n            self.assertEqual(content, \"0003-3714-1998-29-03\\n\")\n\n    @patch(\"documentstore_migracao.processing.inserting.get_documents_bundle\")\n    def test_register_documents_in_documents_bundle_get_aop_bundle(\n        self, mk_get_documents_bundle\n    ):\n        documents_sorted_in_bundles = {\n            \"0001-3714-1998-29-03\": {\n                \"items\": [\"0001-3714-1998-v29-n3-01\", \"0001-3714-1998-v29-n3-02\"],\n                \"data\": self.data,\n            },\n            \"0003-3714-aop\": {\n                \"items\": [\"0003-3714-aop-01\", \"0003-3714-aop-02\"],\n                \"data\": self.aop_data,\n            },\n        }\n        session_db = Session()\n        inserting.register_documents_in_documents_bundle(\n            session_db, documents_sorted_in_bundles\n        )\n        mk_get_documents_bundle.assert_any_call(\n            session_db, self.data\n        )\n        mk_get_documents_bundle.assert_any_call(\n            session_db, self.aop_data\n        )\n","sub_path":"tests/test_inserting.py","file_name":"test_inserting.py","file_ext":"py","file_size_in_byte":12523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"347427016","text":"import random\nimport json\nimport os\nimport pprint\nimport utils\nfrom copy import deepcopy\n\ndef questionrequest (question  , player_list, hero_stats , enemy_list, items_list):\n    print (question)\n    if question[\"question\"] == \"who_is_participating\":\n        print (question[\"users\"])\n        for i in question[\"users\"]:\n            if not i in player_list:\n                rgb = random.randrange(0,255),random.randrange(0,255),random.randrange(0,255)\n                mod = random.sample(enemy_list.keys(),1)[0]\n                #print (\"mod\",mod)\n                #print (enemy_list[mod[0]])\n                enemy_name  = random.sample(enemy_list[mod].keys(),1)[0]\n                enemy = enemy_list[mod][enemy_name]\n                #pprint.pprint (enemy)\n                player_list[i] = {}\n                player_list[i][\"enemy_type\"] = enemy\n                player_list[i][\"x\"] = random.randrange(0,400),\n                player_list[i][\"y\"] = random.randrange(0,400),\n                player_list[i][\"surface\"] = utils.image_replace_color (enemy[\"surface\"] , enemy[\"image\"][\"replace_colorkey\"] , rgb)\n                \n                print (i)\n                print (question[\"users\"][i])\n    \n           \n            \"\"\"    \n                for username in question_holen[3]:\n                    print(\"username:\" ,  username)\n                    print(\"question_holen[3]\" , question_holen[3])\n                    print (\"players_list:\" , players_list)\n                    try: \n                        if username in players_list[0]:\n                            \n                            print (username , \" in list\")\n                        else:    \n\n                            players_list.append ([username,{\"surface\":surface, \"height\": zombies_stats[rand][\"height\"] , \"weight\": zombies_stats[rand][\"weight\"] , \"x\" : x, \"y\" : y , \"direction\" : 0},{maxhealth_TXT : zombies_stats[rand][health_TXT] , health_TXT : zombies_stats[rand][health_TXT],  attack_TXT : zombies_stats[rand][attack_TXT] ,defense_TXT : zombies_stats[rand][defense_TXT] , speed_TXT : zombies_stats[rand][speed_TXT] , items_TXT : zombies_stats[rand][items_TXT] ,luck_TXT : zombies_stats[rand][luck_TXT] , intelligence_TXT :  zombies_stats[rand][intelligence_TXT], days_life_TXT : zombies_stats[rand][days_life_TXT]}])\n                    except IndexError:\n                        print (1, \":\",username)\n                        rand = random.randrange(0,len(zombies_stats))\n                        x = random.randrange(0,400)\n                        y = random.randrange(0,400)\n                        surface = zombies_stats[rand][\"surface\"]\n                        rgb = random.randrange(0,255),random.randrange(0,255),random.randrange(0,255)\n                        surface = utils.image_replace_color (surface , zombies_stats[rand][\"replace_colorkey\"] , rgb)\n                        players_list.append ([username,{\"surface\":surface, \"height\": zombies_stats[rand][\"height\"] , \"weight\": zombies_stats[rand][\"weight\"] , \"x\" : x, \"y\" : y , \"direction\" : 0},{maxhealth_TXT : zombies_stats[rand][health_TXT] , health_TXT : zombies_stats[rand][health_TXT],  attack_TXT : zombies_stats[rand][attack_TXT] ,defense_TXT : zombies_stats[rand][defense_TXT] , speed_TXT : zombies_stats[rand][speed_TXT] , items_TXT : zombies_stats[rand][items_TXT] ,luck_TXT : zombies_stats[rand][luck_TXT] , intelligence_TXT :  zombies_stats[rand][intelligence_TXT], days_life_TXT : zombies_stats[rand][days_life_TXT]}])\n                func_geben.put([question_list[0],30])\n            if question_list[1][0] == question_holen[0][0]:#welche richtung\n                #TODO beide bewegen sich gliech\n                for users_in_game in players_list:\n                    #question_holen[3] username die mitgemacht haben\n                    #question_holen[1] antworten\n                    #print (\"users:\" , users_in_game)\n                    if users_in_game[0] in question_holen[3]:\n                        #print (\"user:\" , users_in_game)\n                        #print (\"questions:\" , questions)\n                        if users_in_game[0] in question_holen[0][1][0][1]:\n                            print (\"oben:\" , users_in_game[0])\n                            users_in_game[1][\"y\"] -= users_in_game[2][speed_TXT]\n                            users_in_game[1][\"direction\"] = 1\n                        elif users_in_game[0] in question_holen[0][1][1][1]:\n                            print (\"unten:\" , users_in_game[0])\n                            users_in_game[1][\"y\"] += users_in_game[2][speed_TXT]\n                            users_in_game[1][\"direction\"] = 0\n                        elif  users_in_game[0] in question_holen[0][1][2][1]:\n                            print (\"links:\" , users_in_game[0])\n                            users_in_game[1][\"x\"] -= users_in_game[2][speed_TXT]\n                            users_in_game[1][\"direction\"] = 2\n                        elif  users_in_game[0] in question_holen[0][1][3][1]:\n                            print (\"rechts:\" , users_in_game[0])\n                            users_in_game[1][\"x\"] += users_in_game[2][speed_TXT]\n                            users_in_game[1][\"direction\"] = 3                \n            else:\n                func_geben.put([question_list[1],10])   \n            \"\"\" \ndef loaditems(moddir):\n    with open(os.path.join(moddir ,'items.json')) as data_file:    \n        data = json.load(data_file)\n    return (data)\n        \ndef loadattribute(moddir):\n    with open(os.path.join(moddir ,'attribute.json')) as data_file:    \n        data = json.load(data_file)\n    return (data)\n        \ndef loadpersons(moddir):\n    with open(os.path.join(moddir ,'persons.json')) as data_file:    \n        data = json.load(data_file)\n    return (data)    \n    \ndef loadquestion(moddir):\n    with open(os.path.join(moddir ,'question.json')) as data_file:    \n        data = json.load(data_file)\n    return (data)\n    \ndef loadlocale(moddir , locale):\n    #print (os.path.join(moddir ,\"locale\", locale + '.json'))\n    with open(os.path.join(moddir ,\"locale\", locale + '.json')) as data_file:    \n        data = json.load(data_file)\n    return (data)    ","sub_path":"mods/base/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":6160,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"350392889","text":"import os, json\nimport pandas as pd\n\n\nfor file in os.listdir('./'): # <-- iterate through jsons\n    if file.endswith('.json') == True:\n        with open(file, 'r') as file:\n            data = json.load(file) # <-- load as a python dictionary\n\n            df = pd.DataFrame(data['trainPhase'][1:], columns = data['trainPhase'][0]) # <-- convert to pandas df (or you can use numpy or even the csv modules; doesnt matter)\n            print(df)\n            ## now you can do whatever you want with it\n\n        exit()\n","sub_path":"rel_xfer/experiment 2/data/trancl3/load data example.py","file_name":"load data example.py","file_ext":"py","file_size_in_byte":513,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"590090147","text":"#!/usr/bin/env python\nimport website.bigfoot.models as models\nfrom website.bigfoot.utils import siteaction, ajaxian, config, dataplus, codejar_validation, mailman, codejar_invites\nfrom website.bigfoot.websitetools import mailer\n\t     \ndef handle(request): \n    try:\n        myself = siteaction.getLoggedInUser(request)\n        if not myself:\n            return ajaxian.getFailureResp('not_logged_in')\n        \n        invite_email = dataplus.dictGetVal(request.REQUEST,'invite_email')\n        is_valid, err_msg = codejar_validation.validateEmail(invite_email)\n        if not is_valid:\n            return ajaxian.getFailureResp('invalid_email')\n\n        #see if a user with the same email exists\n        user = dataplus.returnIfExists(models.User.objects.filter(email=invite_email))\n        if user:\n            if user.username == myself.username:\n                return ajaxian.getSuccessResp('cannot_add_self')\n\n        invitation = models.FriendInvitation(sent_by=myself, sent_to_email=invite_email)\n        invitation.invite_random_key = dataplus.getUniqueId()\n        invitation.save()\n        \n        subject = myself.name + ' has invited you to Socialray'\n        html_file = '/apps/socialray/website/bigfoot/websitetools/mailtemplates/invitation.html'\n        text_file = '/apps/socialray/website/bigfoot/websitetools/mailtemplates/invitation.txt'\n        \n        mailer.sendInvite(html_file, text_file, myself, subject, (invite_email), \n            {'name':lambda x:x.name,  \n            'username':lambda x:x.username,\n            'message':lambda x:'',\n            'invite_random_key':lambda x:str(invitation.invite_random_key)})\n        return ajaxian.getSuccessResp('')\n    \n    except:\n        return ajaxian.getFailureResp('unknown')","sub_path":"website/bigfoot/ajax/me_sendinvitation.py","file_name":"me_sendinvitation.py","file_ext":"py","file_size_in_byte":1750,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"501903329","text":"import requests\nimport os\n\n# get the api key from environment variable\napi_key = os.getenv('API_KEY')\nif not api_key:\n\traise RuntimeError('API_KEY not set')\n\ndef fetch_goodreads_data(isbn):\n\tres = requests.get('https://www.goodreads.com/book/review_counts.json', \n\t\t\tparams={'key': api_key, 'isbns': isbn})\n\tif res is None:\n\t\treturn None\n\telse:\n\t\tavg_ratings = res.json()['books'][0]['average_rating']\n\t\treviews = res.json()['books'][0]['work_ratings_count']\n\t\treturn (avg_ratings, reviews)\n\n# some tests\nif __name__ == '__main__':\n\tprint(fetch_goodreads_data('9781632168146'))","sub_path":"goodreads_data.py","file_name":"goodreads_data.py","file_ext":"py","file_size_in_byte":577,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"346447425","text":"import pytest\r\nfrom pytest import approx\r\nimport SCF\r\nimport numpy as np\r\n\r\n\r\ndef test_calc_nuclear_repulsion_energy(mol_h2o):\r\n    assert True\r\n    assert SCF.calc_nuclear_repulsion_energy(mol_h2o) == 8.00236706181077,\\\r\n        \"Nuclear Repulsion Energy Test (H2O) Failed\"\r\n\r\n\r\ndef test_calc_initial_density(mol_h2o):\r\n    \"\"\"\r\n    Tests that the initial density returns a zero matrix\r\n    and tests dimensions\r\n    \"\"\"\r\n\r\n    Duv = SCF.calc_initial_density(mol_h2o)\r\n    assert Duv.sum() == 0.0\r\n    assert Duv.shape == (mol_h2o.nao, mol_h2o.nao)\r\n\r\n\r\ndef test_calc_hcore_matrix(mol_h2o):\r\n    Tuv = mol_h2o.intor('int1e_kin')  # Kinetic Energy 1 electron integrals\r\n    Vuv = mol_h2o.intor('int1e_nuc')  # Nuclear Repulsion 1 electron integrals\r\n    Huv = SCF.calc_hcore_matrix(Tuv, Vuv)\r\n    assert Huv[0, 0] == -32.57739541261037\r\n    assert Huv[3, 4] == Huv[4, 3]\r\n    assert Huv[3, 4] == 0.0\r\n\r\n\r\ndef test_calc_fock_matrix(mol_h2o):\r\n    Tuv = mol_h2o.intor('int1e_kin')  # Kinetic Energy 1 electron integrals\r\n    Vuv = mol_h2o.intor('int1e_nuc')  # Nuclear Repulsion 1 electron integrals\r\n    Huv = SCF.calc_hcore_matrix(Tuv, Vuv)\r\n    eri = mol_h2o.intor(\"int2e\")\r\n    Duv = SCF.calc_initial_density(mol_h2o)\r\n\r\n    Fuv = SCF.calc_fock_matrix(mol_h2o, Huv, eri, Duv)\r\n    assert Fuv[0, 0] == -32.57739541261037\r\n    assert Fuv[2, 5] == approx(Fuv[5, 2])\r\n    assert Fuv[2, 5] == approx(-1.6751501447185015)\r\n\r\n\r\ndef test_solve_Roothan_equations(mol_h2o):\r\n    Tuv = mol_h2o.intor('int1e_kin')  # Kinetic Energy 1 electron integrals\r\n    Vuv = mol_h2o.intor('int1e_nuc')  # Nuclear Repulsion 1 electron integrals\r\n    Huv = SCF.calc_hcore_matrix(Tuv, Vuv)\r\n    eri = mol_h2o.intor(\"int2e\")\r\n    Duv = SCF.calc_initial_density(mol_h2o)\r\n    Fuv = SCF.calc_fock_matrix(mol_h2o, Huv, eri, Duv)\r\n    Suv = mol_h2o.intor('int1e_ovlp')\r\n\r\n    mol_e, mol_c = SCF.solve_Roothan_equations(Fuv, Suv)\r\n    assert mol_e == approx(np.array([-32.57830292, -8.08153571, -7.55008599,\r\n                                     -7.36396923, -7.34714487, -4.00229867,\r\n                                     -3.98111115]))\r\n    assert np.abs(mol_c[0, :]) == approx(np.abs(\r\n                                  np.array([-1.00154358e+00, -2.33624458e-01,\r\n                                            4.97111543e-16, -8.56842145e-02,\r\n                                            2.02299681e-29, 4.82226067e-02,\r\n                                            -4.99600361e-16])))\r\n\r\n\r\ndef test_calc_tot_energy(mol_h2o):\r\n    Enuc = SCF.calc_nuclear_repulsion_energy(mol_h2o)\r\n    Tuv = mol_h2o.intor('int1e_kin')  # Kinetic Energy 1 electron integrals\r\n    Vuv = mol_h2o.intor('int1e_nuc')  # Nuclear Repulsion 1 electron integrals\r\n    Huv = SCF.calc_hcore_matrix(Tuv, Vuv)\r\n    eri = mol_h2o.intor(\"int2e\")\r\n    Duv = SCF.calc_initial_density(mol_h2o)\r\n    Fuv = SCF.calc_fock_matrix(mol_h2o, Huv, eri, Duv)\r\n    Suv = mol_h2o.intor('int1e_ovlp')\r\n    mol_e, mol_c = SCF.solve_Roothan_equations(Fuv, Suv)\r\n\r\n    Etot_new = SCF.calc_tot_energy(Fuv, Huv, Duv, Enuc)\r\n    assert Etot_new == approx(8.0023670618)\r\n\r\n\r\ndef test_form_density_matrix(mol_h2o):\r\n    Enuc = SCF.calc_nuclear_repulsion_energy(mol_h2o)\r\n    Tuv = mol_h2o.intor('int1e_kin')  # Kinetic Energy 1 electron integrals\r\n    Vuv = mol_h2o.intor('int1e_nuc')  # Nuclear Repulsion 1 electron integrals\r\n    Huv = SCF.calc_hcore_matrix(Tuv, Vuv)\r\n    eri = mol_h2o.intor(\"int2e\")\r\n    Duv = SCF.calc_initial_density(mol_h2o)\r\n    Fuv = SCF.calc_fock_matrix(mol_h2o, Huv, eri, Duv)\r\n    Suv = mol_h2o.intor('int1e_ovlp')\r\n    mol_e, mol_c = SCF.solve_Roothan_equations(Fuv, Suv)\r\n    Etot_new = SCF.calc_tot_energy(Fuv, Huv, Duv, Enuc)\r\n\r\n    Duv_new = SCF.form_density_matrix(mol_h2o, mol_c)\r\n    assert Duv_new[0, 0] == 2.130023428655504\r\n    assert Duv_new[2, 5] == approx(Duv_new[5, 2])\r\n    assert Duv_new[5, 2] == approx(-0.29226330209653156)\r\n","sub_path":"tests/SCF/test_SCF.py","file_name":"test_SCF.py","file_ext":"py","file_size_in_byte":3890,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"466012670","text":"import pandas as pd\nfrom pandas import DataFrame\nfrom rdkit.Chem.rdchem import Mol\nfrom rdkit import Chem, DataStructs\nfrom rdkit.Chem import AllChem\nfrom rdkit.Chem.rdMolDescriptors import AtomPairsParameters\nfrom rdkit.DataStructs.cDataStructs import ExplicitBitVect\nimport numpy as np\nfrom numpy import array\nfrom typing import List\n\n\nclass MorganFPGenerator:\n    \"\"\"\n    Generates Morgan Fingerprints\n\n    Attributes:\n        df (DataFrame): DataFrame containing column with smiles\n    \"\"\"\n\n    def __init__(self, df: DataFrame):\n        \"\"\"\n        Constructor for MorganFPGenerator class\n\n        Parameters:\n            df (DataFrame): DataFrame containing column with smiles.\n        \"\"\"\n\n        self.df = df.copy()\n\n    def get_morgan_features(\n        self,\n        smi_column_name: str,\n        radius: int = 2,\n        nBits: int = 1024,\n        invariants: List[AtomPairsParameters] = [],\n        fromAtoms: List[AtomPairsParameters] = [],\n        useChirality: bool = False,\n        useBondTypes: bool = True,\n        useFeatures: bool = False,\n        bitInfo: AtomPairsParameters = {},\n        includeRedundantEnvironments: bool = False\n    ) -> array:\n        \"\"\"\n        Function to generate Morgan fingerprints derived from the smiles in the dataframe\n\n        Parameters:\n            smi_column_name (str): name of column containing the smiles\n            Same parameters as: https://www.rdkit.org/docs/source/rdkit.Chem.rdMolDescriptors.html#rdkit.Chem.rdMolDescriptors.GetMorganFingerprintAsBitVect\n\n        Returns:\n            fingerprints_matrix (array): a numpy array containing the Morgan fingerprints\n        \"\"\"\n\n        morgan_cols = [ 'morgan_'+str(i) for i in range(nBits) ]\n        for index, row in self.df.iterrows():\n            \n            smi = row[smi_column_name]\n            mol = Chem.MolFromSmiles(smi)\n            fp = self.get_morgan_fp(\n                mol=mol,\n                radius=radius,\n                nBits=nBits,\n                invariants=invariants,\n                fromAtoms=fromAtoms,\n                useChirality=useChirality,\n                useBondTypes=useBondTypes,\n                useFeatures=useFeatures,\n                bitInfo=bitInfo\n            )\n            fpl = fp.tolist()\n            fps = ','.join(str(e) for e in fpl)\n            self.df.at[index,'morganfp'] = fps\n\n        morgan_df = pd.DataFrame(self.df['morganfp'].str.split(',', expand=True).values, columns=morgan_cols)\n        return morgan_df.iloc[:,0:].values\n\n    def get_morgan_fp(\n        self,\n        mol: Mol,\n        radius: int = 2,\n        nBits: int = 1024,\n        invariants: List[AtomPairsParameters] = [],\n        fromAtoms: List[AtomPairsParameters] = [],\n        useChirality: bool = False,\n        useBondTypes: bool = True,\n        useFeatures: bool = False,\n        bitInfo: AtomPairsParameters = {},\n        includeRedundantEnvironments: bool = False\n    ) -> array:\n        \"\"\"\n        Function to generate a set of fingerprints from a single molecule\n\n        Parameters:\n            Same parameters as: https://www.rdkit.org/docs/source/rdkit.Chem.rdMolDescriptors.html#rdkit.Chem.rdMolDescriptors.GetMorganFingerprintAsBitVect\n\n        Returns:\n            fingerprints_vector (array): numpy array containing fingerprints for the molecule\n        \"\"\"\n\n        arr = np.zeros((1,))\n        fp = AllChem.GetMorganFingerprintAsBitVect(\n            mol=mol,\n            radius=radius,\n            nBits=nBits,\n            invariants=invariants,\n            fromAtoms=fromAtoms,\n            useChirality=useChirality,\n            useBondTypes=useBondTypes,\n            useFeatures=useFeatures,\n            bitInfo=bitInfo\n        )\n        DataStructs.ConvertToNumpyArray(fp, arr)\n        arr = np.array([len(bitInfo[x]) if x in bitInfo else 0 for x in range(nBits)])\n        return arr\n","sub_path":"server/predictors/features/morgan_fp.py","file_name":"morgan_fp.py","file_ext":"py","file_size_in_byte":3844,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"490282406","text":"\"\"\"\nQuestion: https://leetcode.com/problems/word-break/\nGiven a non-empty string s and a dictionary wordDict containing a list of non-empty words, determine\nif s can be segmented into a space-separated sequence of one or more dictionary words.\n\nNote:\n\nThe same word in the dictionary may be reused multiple times in the segmentation.\nYou may assume the dictionary does not contain duplicate words.\nExample 1:\n\nInput: s = \"leetcode\", wordDict = [\"leet\", \"code\"]\nOutput: true\nExplanation: Return true because \"leetcode\" can be segmented as \"leet code\".\nExample 2:\n\nInput: s = \"applepenapple\", wordDict = [\"apple\", \"pen\"]\nOutput: true\nExplanation: Return true because \"applepenapple\" can be segmented as \"apple pen apple\".\n             Note that you are allowed to reuse a dictionary word.\nExample 3:\n\nInput: s = \"catsandog\", wordDict = [\"cats\", \"dog\", \"sand\", \"and\", \"cat\"]\nOutput: false\n\"\"\"\nfrom typing import List\n\n\nclass Solution:\n    def wordBreak(self, s: str, wordDict: List[str]) -> bool:\n        return self.second_implementation(s, wordDict)\n\n    def first_implementation(self, s: str, wordDict: List[str]) -> bool:\n        \"\"\"\n        Iterate over the word and create an intervals array based off of the start_end\n        intervals of the bounds in the wordDict.\n        \"\"\"\n        intervals = []\n        # O(k * n)\n        for word in wordDict:\n            start = 0\n            while start != -1:\n                # keep finding intervals at which the current word is found.\n                start = s.find(word, start)\n                if start >= 0:\n                    intervals.append([start, start + len(word)])\n                    # Start the next interval after the word is found\n                    # can't start at the words bound because of strings like \"aaaaaa\" dict [\"aaa\"]\n                    start += 1\n        if not intervals:\n            return False\n        # iterate over the intervals after sorting them...\n        # Complexity: O(k * num_occ)\n        intervals.sort(key=lambda k: (k[0], k[1]))\n        # print(intervals)\n\n        no_gap = intervals.pop(0)\n        for interval in intervals:\n            if no_gap[1] == interval[0]:\n                no_gap[1] = interval[1]\n        # print(no_gap, len(s))\n        return no_gap[0] == 0 and no_gap[1] == len(s)\n\n    def second_implementation(self, s: str, wordDict: List[str]) -> bool:\n        \"\"\"\n        Backtracked solution. Try to create the word from the input strings\n        making different combinations as the string is ingested.\n        Time Complexity: O(n ^ 2)\n            O(n): Iteration over the length of the word.\n            O(n): The memoization takes a bite out of this but its still visiting the wordDict\n                until each combination hits the end of the string.\n                For a case \"aaaaa...aaa\" wordDict [\"a\",\"aa\",...] without memoization recursion would be ~ 2^n\n        \"\"\"\n        seen = set()\n\n        def valid(cur_word):\n            is_valid = cur_word not in seen and s.startswith(cur_word)\n            seen.add(cur_word)\n            return is_valid\n\n        def backtrack(cur_word):\n            if not valid(cur_word):\n                return False\n\n            if cur_word == s:\n                return True\n\n            for cur in wordDict:\n                if backtrack(cur_word + cur):\n                    return True\n            return False\n\n        return backtrack(\"\")\n","sub_path":"python/coding_challenges/leet_code/word_break.py","file_name":"word_break.py","file_ext":"py","file_size_in_byte":3396,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"36962481","text":"# -*- coding: utf-8 -*-\n##############################################################################\n#\n#    Author: Yannick Buron\n#    Copyright 2013 Yannick Buron\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 <http://www.gnu.org/licenses/>.\n#\n##############################################################################\n\nfrom openerp import models, fields, api, _\nfrom openerp.exceptions import except_orm\nfrom openerp import modules\nimport re\n\nimport time\nfrom datetime import datetime, timedelta\n\nimport logging\n_logger = logging.getLogger(__name__)\n\n\nclass ClouderServer(models.Model):\n    _name = 'clouder.server'\n    _inherit = ['clouder.model']\n\n    @api.multi\n    def _create_key(self):\n\n        if not self.env.ref('clouder.clouder_settings').email_sysadmin:\n            raise except_orm(_('Data error!'),\n                _(\"You need to specify the sysadmin email in configuration\"))\n\n        self.execute_local(['mkdir', '/tmp/key_' + self.env.uid])\n        self.execute_local(['ssh-keygen', '-t', 'rsa', '-C',\n                            self.email_sysadmin, '-f',\n                            '/tmp/key_' + self.env.uid + '/key', '-N', ''])\n        return True\n\n    @api.multi\n    def _destroy_key(self):\n        self.execute_local(['rm', '-rf', '/tmp/key_' + self.env.uid])\n        return True\n\n    @api.multi\n    def _default_private_key(self):\n        self = self.env['clouder.server']\n        self.env.uid = str(self.env.uid)\n\n        destroy = True\n        if not self.local_dir_exist('/tmp/key_' + self.env.uid):\n            self._create_key()\n            destroy = False\n\n        key = self.execute_local(['cat', '/tmp/key_' + self.env.uid + '/key'])\n\n        if destroy:\n            self._destroy_key()\n        return key\n\n    @api.multi\n    def _default_public_key(self):\n        self = self.env['clouder.server']\n        self.env.uid = str(self.env.uid)\n\n        destroy = True\n        if not self.local_dir_exist('/tmp/key_' + self.env.uid):\n            self._create_key()\n            destroy = False\n\n        key = self.execute_local(['cat',\n                                  '/tmp/key_' + self.env.uid + '/key.pub'])\n\n        if destroy:\n            self._destroy_key()\n        return key\n\n    name = fields.Char('Domain name', size=64, required=True)\n    ip = fields.Char('IP', size=64, required=True)\n    ssh_port = fields.Integer('SSH port', required=True)\n\n    private_key = fields.Text(\n        'SSH Private Key', required=True,\n        default=_default_private_key)\n    public_key = fields.Text(\n        'SSH Public Key', required=True,\n        default=_default_public_key)\n    start_port = fields.Integer('Start Port', required=True)\n    end_port = fields.Integer('End Port', required=True)\n    public = fields.Boolean('Public?')\n    partner_id = fields.Many2one(\n        'res.partner', 'Manager',\n        default=lambda self: self.user_partner)\n    supervision_id = fields.Many2one('clouder.container', 'Supervision Server')\n\n    _sql_constraints = [\n        ('name_uniq', 'unique(name)', 'Name must be unique!'),\n    ]\n\n    @api.one\n    @api.constrains('name', 'ip')\n    def _validate_data(self) :\n        if not re.match(\"^[\\w\\d.-]*$\", self.name):\n            raise except_orm(\n                _('Data error!'),\n                _(\"Name can only contains letters, digits, - and .\"))\n        if not re.match(\"^[\\d:.]*$\", self.ip):\n            raise except_orm(\n                _('Data error!'),\n                _(\"Admin name can only contains digits, dots and :\"))\n\n    # @api.multi\n    # def get_vals(self):\n    #\n    #     vals ={}\n    #\n    #     vals.update(self.env.ref('clouder.clouder_settings').get_vals())\n    #\n    #     vals.update({\n    #         'server_id': self.id,\n    #         'server_domain': self.name,\n    #         'server_ip': self.ip,\n    #         'server_ssh_port': int(self.ssh_port),\n    #         'server_mysql_passwd': self.mysql_passwd,\n    #         'server_shinken_configfile': '/usr/local/shinken/etc/hosts/' + self.name + '.cfg',\n    #         'server_private_key': self.private_key,\n    #         'server_public_key': self.public_key,\n    #         'server_start_port': self.start_port,\n    #         'server_end_port': self.end_port,\n    #     })\n    #     return vals\n\n    @api.multi\n    def start_containers(self):\n        self.env['clouder.container'].search(\n            [('server_id', '=', self.id)]).start()\n\n    @api.multi\n    def stop_containers(self):\n        self.env['clouder.container'].search(\n            [('server_id', '=', self.id)]).stop()\n\n    @api.multi\n    def deploy(self):\n        self.purge()\n        key_file = self.home_directory + '/.ssh/keys/' + self.name\n        self.execute_write_file(key_file, self.private_key)\n        self.execute_local(['chmod', '700', key_file])\n        self.execute_write_file(self.home_directory +\n                                '/.ssh/config', 'Host ' + self.name)\n        self.execute_write_file(self.home_directory +\n                                '/.ssh/config', '\\n  HostName ' + self.name)\n        self.execute_write_file(self.home_directory +\n                                '/.ssh/config', '\\n  Port ' +\n                                str(self.ssh_port))\n        self.execute_write_file(self.home_directory +\n                                '/.ssh/config', '\\n  User root')\n        self.execute_write_file(self.home_directory +\n                                '/.ssh/config', '\\n  IdentityFile ' +\n                                self.home_directory + '/.ssh/keys/' +\n                                self.name)\n        self.execute_write_file(self.home_directory + '/.ssh/config',\n                                '\\n#END ' + self.name + '\\n')\n\n    @api.multi\n    def purge(self):\n        self.execute_local([modules.get_module_path('clouder') +\n                            '/res/sed.sh', self.name,\n                            self.home_directory + '/.ssh/config'])\n        self.execute_local(['rm', '-rf', self.home_directory +\n                            '/.ssh/keys/' + self.name])\n\n\nclass ClouderContainer(models.Model):\n    _name = 'clouder.container'\n    _inherit = ['clouder.model']\n\n    @api.one\n    def _get_ports(self):\n        self.ports_string = ''\n        first = True\n        for port in self.port_ids:\n            if not first:\n                self.ports_string += ', '\n            if port.hostport:\n                self.ports_string += port.name + ' : ' + port.hostport\n            first = False\n\n    name = fields.Char('Name', size=64, required=True)\n    application_id = fields.Many2one('clouder.application',\n                                     'Application', required=True)\n    image_id = fields.Many2one('clouder.image', 'Image', required=True)\n    server_id = fields.Many2one('clouder.server', 'Server', required=True)\n    image_version_id = fields.Many2one('clouder.image.version',\n                                       'Image version', required=True)\n    save_repository_id = fields.Many2one('clouder.save.repository',\n                                         'Save repository')\n    time_between_save = fields.Integer('Minutes between each save')\n    saverepo_change = fields.Integer('Days before saverepo change')\n    saverepo_expiration = fields.Integer('Days before saverepo expiration')\n    save_expiration = fields.Integer('Days before save expiration')\n    date_next_save = fields.Datetime('Next save planned')\n    save_comment = fields.Text('Save Comment')\n    nosave = fields.Boolean('No Save?')\n    privileged = fields.Boolean('Privileged?')\n    port_ids = fields.One2many('clouder.container.port',\n                               'container_id', 'Ports')\n    volume_ids = fields.One2many('clouder.container.volume',\n                                 'container_id', 'Volumes')\n    option_ids = fields.One2many('clouder.container.option',\n                                 'container_id', 'Options')\n    link_ids = fields.One2many('clouder.container.link',\n                               'container_id', 'Links')\n    service_ids = fields.One2many('clouder.service',\n                                  'container_id', 'Services')\n    ports_string = fields.Text('Ports', compute='_get_ports')\n    backup_ids = fields.Many2many(\n        'clouder.container', 'clouder_container_backup_rel',\n        'container_id', 'backup_id', 'Backup containers')\n    public = fields.Boolean('Public?')\n    partner_id = fields.Many2one(\n        'res.partner', 'Manager',\n        default=lambda self: self.user_partner)\n    partner_ids = fields.Many2many(\n        'res.partner', 'clouder_container_partner_rel',\n        'container_id', 'partner_id', 'Users')\n\n    @property\n    def fullname(self):\n        return self.name + '_' + self.server_id.name\n\n    @property\n    def volumes_save(self):\n        return ','.join([volume.name for volume in self.volume_ids\n                         if not volume.nosave])\n\n    @property\n    def ssh_port(self):\n        return self.ports['ssh']['hostport'] or 22\n\n    @property\n    def root_password(self):\n        return (option.value for option in self.option_ids\n                if option.name == 'root_password')\n\n    @property\n    def ports(self):\n        ports = {}\n        for port in self.port_ids:\n            ports[port.name] = {\n                'id': port.id, 'name': port.name,\n                'hostport': port.hostport, 'localport': port.localport}\n        return ports\n\n    @property\n    def options(self):\n        options = {}\n        for option in self.application_id.type_id.option_ids:\n            if option.type == 'container':\n                options[option.name] = {'id': option.id, 'name': option.name, 'value': option.default}\n        for option in self.option_ids:\n            options[option.name.name] = {'id': option.id, 'name': option.name.name, 'value': option.value}\n        return options\n\n    _sql_constraints = [\n        ('name_uniq', 'unique(server_id,name)',\n         'Name must be unique per server!'),\n    ]\n\n    @api.one\n    @api.constrains('name')\n    def _validate_data(self):\n        if not re.match(\"^[\\w\\d-]*$\", self.name):\n            raise except_orm(\n                _('Data error!'),\n                _(\"Name can only contains letters, digits and underscore\"))\n\n    @api.one\n    @api.constrains('application_id')\n    def _check_backup(self):\n        if not self.backup_ids and self.application_id.type_id.name \\\n                not in ['backup','backup_upload','archive','registry']:\n            raise except_orm(\n                _('Data error!'),\n                _(\"You need to specify at least one backup container.\"))\n\n    @api.one\n    @api.constrains('image_id','image_version_id')\n    def _check_config(self):\n        if self.image_id.id != self.image_version_id.image_id.id:\n            raise except_orm(_('Data error!'),\n                _(\"The image of image version must be \"\n                  \"the same than the image of container.\"))\n\n    @api.one\n    @api.constrains('option_ids')\n    def _check_option_ids(self):\n        for type_option in self.application_id.type_id.option_ids:\n            if type_option.type == 'container' and type_option.required:\n                test = False\n                for option in self.option_ids:\n                    if option.name == type_option and option.value:\n                        test = True\n                if not test:\n                    raise except_orm(_('Data error!'),\n                        _(\"You need to specify a value for the option \" +\n                          type_option.name + \" for the container \" +\n                          self.name + \".\"))\n\n    @api.one\n    @api.constrains('link_ids')\n    def _check_link_ids(self):\n        for app_link in self.application_id.link_ids:\n            if app_link.container and app_link.required:\n                test = False\n                for link in self.link_ids:\n                    if link.name == app_link and link.target:\n                        test = True\n                if not test:\n                    raise except_orm(_('Data error!'),\n                        _(\"You need to specify a link to \" +\n                          app_link.name.name + \" for the container \" + self.name))\n\n    @api.multi\n    @api.onchange('application_id')\n    def onchange_application_id(self):\n        if self.application_id:\n            self.server_id = self.application_id.next_server_id\n            self.image_id = self.application_id.default_image_id\n            self.privileged = self.application_id.default_image_id.privileged\n            self.image_version_id = \\\n                self.application_id.default_image_id.version_ids \\\n                and self.application_id.default_image_id.version_ids[0]\n\n            options = []\n            for type_option in self.application_id.type_id.option_ids:\n                if type_option.type == 'container' and type_option.auto:\n                    test = False\n                    for option in self.option_ids:\n                        if option.name == type_option:\n                            test = True\n                    if not test:\n                        options.append((0, 0,\n                                        {'name': type_option,\n                                         'value': type_option.default}))\n            self.option_ids = options\n\n            links = []\n            for app_link in self.application_id.link_ids:\n                if app_link.container and app_link.auto:\n                    test = False\n                    for link in self.link_ids:\n                        if link.name == app_link:\n                            test = True\n                    if not test:\n                        links.append((0,0,{'name': app_link,\n                                           'target': app_link.next}))\n            self.link_ids = links\n\n\n            self.backup_ids = [(6,0,[\n                b.id for b in self.application_id.container_backup_ids])]\n\n            self.time_between_save = \\\n                self.application_id.container_time_between_save\n            self.saverepo_change = \\\n                self.application_id.container_saverepo_change\n            self.saverepo_expiration = \\\n                self.application_id.container_saverepo_expiration\n            self.save_expiration = \\\n                self.application_id.container_save_expiration\n\n    @api.multi\n    @api.onchange('image_id')\n    def onchange_image_id(self):\n        if self.image_id:\n            ports = []\n            for port in self.image_id.port_ids:\n                if port.expose != 'none':\n                    ports.append(((0,0,{\n                        'name':port.name,'localport':port.localport,\n                        'expose':port.expose,'udp':port.udp})))\n            self.port_ids = ports\n\n            volumes = []\n            for volume in self.image_id.volume_ids:\n                volumes.append(((0,0,{\n                    'name':volume.name, 'hostpath':volume.hostpath,\n                    'user':volume.user, 'readonly':volume.readonly,\n                    'nosave':volume.nosave})))\n            self.volume_ids = volumes\n\n    # @api.multi\n    # def get_vals(self):\n    #     repo_obj = self.env['clouder.save.repository']\n    #     vals = {}\n    #\n    #     now = datetime.now()\n    #     if not self.save_repository_id:\n    #         repo_ids = repo_obj.search([('container_name','=',self.name),('container_server','=',self.server_id.name)])\n    #         if repo_ids:\n    #             self.save_repository_id = repo_ids[0]\n    #\n    #     if not self.save_repository_id or datetime.strptime(self.save_repository_id.date_change, \"%Y-%m-%d\") < now or False:\n    #         repo_vals ={\n    #             'name': now.strftime(\"%Y-%m-%d\") + '_' + self.name + '_' + self.server_id.name,\n    #             'type': 'container',\n    #             'date_change': (now + timedelta(days=self.saverepo_change or self.application_id.container_saverepo_change)).strftime(\"%Y-%m-%d\"),\n    #             'date_expiration': (now + timedelta(days=self.saverepo_expiration or self.application_id.container_saverepo_expiration)).strftime(\"%Y-%m-%d\"),\n    #             'container_name': self.name,\n    #             'container_server': self.server_id.name,\n    #         }\n    #         repo_id = repo_obj.create(repo_vals)\n    #         self.save_repository_id = repo_id\n    #\n    #     vals.update(self.image_version_id.get_vals())\n    #     vals.update(self.application_id.get_vals())\n    #     vals.update(self.save_repository_id.id.get_vals())\n    #     vals.update(self.server_id.get_vals())\n    #\n    #     ports = {}\n    #     ssh_port = 22\n    #     for port in self.port_ids:\n    #         ports[port.name] = {'id': port.id, 'name': port.name, 'localport': port.localport, 'hostport': port.hostport, 'expose': port.expose, 'udp': port.udp}\n    #         if port.name == 'ssh':\n    #             ssh_port = port.hostport\n    #\n    #     volumes = {}\n    #     volumes_save = ''\n    #     first = True\n    #     for volume in self.volume_ids:\n    #         volumes[volume.id] = {'id': volume.id, 'name': volume.name, 'hostpath': volume.hostpath, 'user': volume.user,'readonly': volume.readonly,'nosave': volume.nosave}\n    #         if not volume.nosave:\n    #             volumes_save += (not first and ',' or '') + volume.name\n    #             first = False\n    #\n    #     options = {}\n    #     for option in self.application_id.type_id.option_ids:\n    #         if option.type == 'container':\n    #             options[option.name] = {'id': option.id, 'name': option.name, 'value': option.default}\n    #     for option in self.option_ids:\n    #         options[option.name.name] = {'id': option.id, 'name': option.name.name, 'value': option.value}\n    #\n    #     links = {}\n    #     if 'app_links' in vals:\n    #         for app_code, link in vals['app_links'].iteritems():\n    #             if link['container'] or link['make_link']:\n    #                 links[app_code] = link\n    #                 links[app_code]['target'] = False\n    #     for link in self.link_ids:\n    #         if link.name.code in links and link.target:\n    #             link_vals = link.get_vals()\n    #             links[link.name.code]['target'] = {\n    #                 'link_id': link_vals['container_id'],\n    #                 'link_name': link_vals['container_name'],\n    #                 'link_fullname': link_vals['container_fullname'],\n    #                 'link_ssh_port': link_vals['container_ssh_port'],\n    #                 'link_server_id': link_vals['server_id'],\n    #                 'link_server_domain': link_vals['server_domain'],\n    #                 'link_server_ip': link_vals['server_ip'],\n    #             }\n    #     for app_code, link in links.iteritems():\n    #         if link['required'] and not link['target']:\n    #             raise except_orm(_('Data error!'),\n    #                 _(\"You need to specify a link to \" + link['name'] + \" for the container \" + self.name))\n    #         if not link['target']:\n    #             del links[app_code]\n    #\n    #     backup_servers = []\n    #     for backup in self.backup_server_ids:\n    #         backup_vals = backup.get_vals()\n    #         backup_servers.append({\n    #             'container_id': backup_vals['container_id'],\n    #             'container_fullname': backup_vals['container_fullname'],\n    #             'server_id': backup_vals['server_id'],\n    #             'server_ssh_port': backup_vals['server_ssh_port'],\n    #             'server_domain': backup_vals['server_domain'],\n    #             'server_ip': backup_vals['server_ip'],\n    #             'backup_method': backup_vals['app_options']['backup_method']['value']\n    #         })\n    #\n    #\n    #     root_password = False\n    #     for key, option in options.iteritems():\n    #         if option['name'] == 'root_password':\n    #             root_password = option['value']\n    #\n    #     fullname = self.name + '_' + vals['server_domain']\n    #     vals.update({\n    #         'container_id': self.id,\n    #         'container_name': self.name,\n    #         'container_fullname': fullname,\n    #         'container_ports': ports,\n    #         'container_volumes': volumes,\n    #         'container_volumes_save': volumes_save,\n    #         'container_ssh_port': ssh_port,\n    #         'container_options': options,\n    #         'container_links': links,\n    #         'container_backup_servers': backup_servers,\n    #         'container_no_save': self.nosave,\n    #         'container_privileged': self.privileged,\n    #         'container_shinken_configfile': '/usr/local/shinken/etc/services/' + fullname + '.cfg',\n    #         'container_root_password': root_password\n    #     })\n    #\n    #     return vals\n\n    @api.multi\n    def create_vals(self, vals):\n        return vals\n\n    @api.model\n    def create(self, vals):\n\n        #TODO check si on a vraiment besoin du apptype_name\n        if 'application_id' in vals:\n            application = self.env['clouder.application']\\\n                .browse(vals['application_id'])\n            self = self.with_context(apptype_name=application.type_id.name)\n\n        vals = self.create_vals(vals)\n        return super(ClouderContainer, self).create(vals)\n\n    @api.model\n    def write(self, vals):\n        version_obj = self.env['clouder.image.version']\n        flag = False\n        if 'image_version_id' in vals or 'port_ids' in vals \\\n                or 'volume_ids' in vals:\n            flag = True\n            self = self.with_context(self.create_log('upgrade version'))\n            if 'image_version_id' in vals:\n                new_version = version_obj.browse(vals['image_version_id'])\n                self = self.with_context(\n                    save_comment='Before upgrade from ' +\n                                 self.image_version_id.name +\n                                 ' to ' + new_version.name)\n            else:\n                self = self.with_context(\n                    save_comment='Change on port or volumes')\n        res = super(ClouderContainer, self).write(vals)\n        if flag:\n            self.reinstall()\n            self.end_log()\n        if 'nosave' in vals:\n            self.deploy_links()\n        return res\n\n    @api.one\n    def unlink(self):\n        self.service_ids and self.service_ids.unlink()\n        self = self.with_context(save_comment='Before unlink')\n        self.save()\n        return super(ClouderContainer, self).unlink()\n\n    # @api.multi\n    # def button_stop(self):\n    #     vals = self.get_vals()\n    #     self.stop(vals)\n    #\n    # @api.multi\n    # def button_start(self):\n    #     vals = self.get_vals()\n    #     self.start(vals)\n\n    @api.multi\n    def reinstall(self):\n        if not 'save_comment' in self.env.context:\n            self = self.with_context(save_comment='Before reinstall')\n        self = self.with_context(forcesave=True)\n        self.save()\n        self = self.with_context(forcesave=False)\n        self = self.with_context(nosave=True)\n        super(ClouderContainer, self).reinstall()\n\n    @api.multi\n    def save(self):\n\n        save = False\n        now = datetime.now()\n        repo_obj = self.env['clouder.save.repository']\n\n        if not self.save_repository_id:\n            repo_ids = repo_obj.search(\n                [('container_name','=',self.name),\n                 ('container_server','=',self.server_id.name)])\n            if repo_ids:\n                self.save_repository_id = repo_ids[0]\n\n        if not self.save_repository_id \\\n                or datetime.strptime(self.save_repository_id.date_change,\n                                     \"%Y-%m-%d\") < now or False:\n            repo_vals ={\n                'name': now.strftime(\"%Y-%m-%d\") + '_' +\n                        self.name + '_' + self.server_id.name,\n                'type': 'container',\n                'date_change': (now + timedelta(\n                    days=self.saverepo_change\n                         or self.application_id.container_saverepo_change\n                )).strftime(\"%Y-%m-%d\"),\n                'date_expiration': (now + timedelta(\n                    days=self.saverepo_expiration\n                         or self.application_id.container_saverepo_expiration\n                )).strftime(\"%Y-%m-%d\"),\n                'container_name': self.name,\n                'container_server': self.server_id.name,\n            }\n            repo_id = repo_obj.create(repo_vals)\n            self.save_repository_id = repo_id\n\n        if 'nosave' in self.env.context \\\n                or (self.nosave and not 'forcesave' in self.env.context):\n            self.log('This base container not be saved '\n                     'or the backup isnt configured in conf, '\n                     'skipping save container')\n            return\n        self = self.with_context(self.create_log('save'))\n\n        for backup_server in self.backup_ids:\n            save_vals = {\n                'name': self.now_bup + '_' + self.container_fullname,\n                'backup_id': backup_server.id,\n                'repo_id': self.saverepo_id.id,\n                'date_expiration': (now + timedelta(\n                    days=self.save_expiration\n                         or self.application_id.container_save_expiration\n                )).strftime(\"%Y-%m-%d\"),\n                'comment': 'save_comment' in self.env.context\n                           and self.env.context['save_comment']\n                           or self.save_comment or 'Manual',\n                'now_bup': self.now_bup,\n                'container_id': self.id,\n            }\n            save = self.env['clouder.save.save'].create(save_vals)\n        next = (datetime.now() + timedelta(\n            minutes=self.time_between_save\n                    or self.application_id.container_time_between_save\n        )).strftime(\"%Y-%m-%d %H:%M:%S\")\n        self.write({'save_comment': False, 'date_next_save': next})\n        self.end_log()\n        return save\n\n    # @api.multi\n    # def reset_key(self):\n    #     vals = self.get_vals()\n    #     self.deploy_key(vals)\n\n    @api.multi\n    def deploy_post(self):\n        return\n\n    @api.multi\n    def deploy(self):\n\n        self.purge()\n\n        ssh = self.connect(self.server_id.name)\n\n        cmd = ['sudo','docker', 'run', '-d']\n        nextport = self.server_id.start_port\n        for port in self.port_ids:\n            if not port.hostport:\n                while not port.hostport and nextport != self.server_id.end_port:\n                    ports = self.env['clouder.container.port'].search(\n                        [('hostport','=',nextport),\n                         ('container_id.server_id','=',self.server_id.id)])\n                    if not ports and not self.execute(ssh, [\n                        'netstat', '-an', '|', 'grep', str(nextport)]):\n                        port.hostport = nextport\n                    nextport += 1\n            udp = ''\n            if port.udp:\n                udp = '/udp'\n            # cmd.extend(['-p', vals['server_ip'] + ':' + str(port['hostport']) + ':' + port['localport'] + udp])\n            cmd.extend(['-p', str(port.hostport) + ':' + port.localport + udp])\n        for volume in self.volume_ids:\n            if volume.hostpath:\n                arg = volume.hostpath + ':' + volume.name\n                if volume.readonly:\n                    arg += ':ro'\n                cmd.extend(['-v', arg])\n        for link in self.link_ids:\n            if link.name.make_link and link.target.server_id== self.server_id:\n                cmd.extend(['--link', link.target.name + ':' + link.name.code])\n        if self.privileged:\n            cmd.extend(['--privileged'])\n        cmd.extend(['-v', '/opt/keys/' + self.fullname +\n                    ':/opt/keys', '--name', self.name])\n\n        if self.image_id.name == 'img_registry':\n            cmd.extend([self.image_version_id.fullname])\n        elif self.server_id == self.image_version_id.registry_id.server_id:\n            cmd.extend([self.image_version_id.fullpath_localhost])\n        else:\n            folder = '/etc/docker/certs.d/' +\\\n                     self.image_version_id.registry_address\n            certfile = folder + '/ca.crt'\n            tmp_file = '/tmp/' + self.fullname\n            self.execute(ssh, ['rm', certfile])\n            ssh_registry = self.connect(\n                self.image_version_id.registry_id.fullname)\n            self.get(ssh_registry,\n                     '/etc/ssl/certs/docker-registry.crt', tmp_file)\n            ssh_registry.close()\n            self.execute(ssh, ['mkdir','-p', folder])\n            self.send(ssh, tmp_file, certfile)\n            self.execute_local(['rm', tmp_file])\n            cmd.extend([self.image_version_id.fullpath])\n\n        #Deploy key now, otherwise the container will be angry to not find the key. We can't before because vals['container_ssh_port'] may not be set\n        self.deploy_key()\n\n        #Run container\n        self.execute(ssh, cmd)\n\n        time.sleep(3)\n\n        self.deploy_post()\n\n        self.start()\n\n        ssh.close()\n\n#TODO\n        for link in self.link_ids:\n            if link.name.name.code == 'postfix':\n                ssh = self.connect(self.fullname)\n                self.execute(ssh, ['echo \"root=' + self.email_sysadmin +\n                                   '\" > /etc/ssmtp/ssmtp.conf'])\n                self.execute(ssh, ['echo \"mailhub=postfix:25\" '\n                                   '>> /etc/ssmtp/ssmtp.conf'])\n                self.execute(ssh, ['echo \"rewriteDomain=' + self.fullname +\n                                   '\" >> /etc/ssmtp/ssmtp.conf'])\n                self.execute(ssh, ['echo \"hostname=' + self.fullname +\n                                   '\" >> /etc/ssmtp/ssmtp.conf'])\n                self.execute(ssh, ['echo \"FromLineOverride=YES\" >> '\n                                   '/etc/ssmtp/ssmtp.conf'])\n                ssh.close()\n\n        #For shinken\n        self.save()\n\n        return\n\n    @api.multi\n    def purge(self):\n\n        self.purge_key()\n\n        ssh = self.connect(self.server_id.name)\n        self.stop()\n        self.execute(ssh, ['sudo','docker', 'rm', self.name])\n        self.execute(ssh, ['rm', '-rf', '/opt/keys/' + self.fullname])\n        ssh.close()\n\n        return\n\n    @api.multi\n    def stop(self):\n        ssh = self.connect(self.server_id.name)\n        self.execute(ssh, ['docker', 'stop', self.name])\n        ssh.close()\n\n    @api.multi\n    def start(self):\n        self.stop()\n        ssh = self.connect(self.server_id.name)\n        self.execute(ssh, ['docker', 'start', self.name])\n        ssh.close()\n        time.sleep(3)\n\n    @api.multi\n    def deploy_key(self):\n        # restart_required = False\n        # try:\n        #     ssh_container, sftp_container = execute.connect(vals['container_fullname'], context=context)\n        # except:\n        #     restart_required = True\n        #     pass\n\n\n        self.purge_key()\n        self.execute_local(['ssh-keygen', '-t', 'rsa', '-C',\n                            self.email_sysadmin, '-f', self.home_directory +\n                            '/.ssh/keys/' + self.fullname, '-N', ''])\n        self.execute_write_file(self.home_directory + '/.ssh/config',\n                                'Host ' + self.fullname)\n        self.execute_write_file(self.home_directory + '/.ssh/config',\n                                '\\n  HostName ' + self.server_id.name)\n        self.execute_write_file(self.home_directory + '/.ssh/config',\n                                '\\n  Port ' + str(self.ssh_port))\n        self.execute_write_file(self.home_directory + '/.ssh/config',\n                                '\\n  User root')\n        self.execute_write_file(self.home_directory + '/.ssh/config',\n                                '\\n  IdentityFile ~/.ssh/keys/' + self.fullname)\n        self.execute_write_file(self.home_directory + '/.ssh/config',\n                                '\\n#END ' + self.fullname + '\\n')\n        ssh = self.connect(self.server_id.name)\n        self.execute(ssh, ['mkdir', '/opt/keys/' + self.fullname])\n        self.send(ssh, self.home_directory + '/.ssh/keys/' +\n                  self.fullname + '.pub', '/opt/keys/' +\n                  self.fullname + '/authorized_keys')\n        ssh.close()\n\n        # _logger.info('restart required %s', restart_required)\n        # if not restart_required:\n        #     execute.execute(ssh_container, ['supervisorctl', 'restart', 'sshd'], context)\n        #     ssh_container.close()\n        #     sftp_container.close()\n        # else:\n        #     self.start(cr, uid, vals, context=context)\n\n#TODO\n        if self.application_id.type_id.name == 'backup':\n            shinkens = self.search(\n                [('application_id.type_id.name', '=','shinken')])\n            if not shinkens:\n                self.log('The shinken isnt configured in conf, '\n                         'skipping deploying backup keys in shinken')\n                return\n            for shinken in shinkens:\n                ssh = self.connect(\n                    shinken.fullname, username='shinken')\n                self.execute(ssh, ['rm', '-rf', '/home/shinken/.ssh/keys/' +\n                                   self.fullname + '*'])\n                self.send(\n                    ssh, self.home_directory + '/.ssh/keys/' +\n                    self.fullname + '.pub', '/home/shinken/.ssh/keys/' +\n                    self.fullname + '.pub')\n                self.send(ssh, self.home_directory + '/.ssh/keys/' +\n                          self.fullname, '/home/shinken/.ssh/keys/' +\n                          self.fullname)\n                self.execute(ssh, ['chmod', '-R', '700', '/home/shinken/.ssh'])\n                self.execute(ssh, [\n                    'sed', '-i', \"'/Host \" + self.fullname +\n                    \"/,/END \" + self.fullname + \"/d'\",\n                    '/home/shinken/.ssh/config'])\n                self.execute(ssh, [\n                    'echo \"Host ' + self.fullname +\n                    '\" >> /home/shinken/.ssh/config'])\n                self.execute(ssh, [\n                    'echo \"    Hostname ' +\n                    self.server_id.name + '\" >> /home/shinken/.ssh/config'])\n                self.execute(ssh, [\n                    'echo \"    Port ' + str(self.ssh_port) +\n                    '\" >> /home/shinken/.ssh/config'])\n                self.execute(ssh, [\n                    'echo \"    User backup\" >> /home/shinken/.ssh/config'])\n                self.execute(ssh, [\n                    'echo \"    IdentityFile  ~/.ssh/keys/' +\n                    self.fullname + '\" >> /home/shinken/.ssh/config'])\n                self.execute(ssh, ['echo \"#END ' + self.fullname +\n                                   '\" >> ~/.ssh/config'])\n\n    @api.multi\n    def purge_key(self):\n        self.execute_local([\n            modules.get_module_path('clouder') + '/res/sed.sh',\n            self.fullname, self.home_directory + '/.ssh/config'])\n        self.execute_local([\n            'rm', '-rf', self.home_directory +\n            '/.ssh/keys/' + self.fullname])\n        self.execute_local([\n            'rm', '-rf', self.home_directory +\n            '/.ssh/keys/' + self.fullname + '.pub'])\n        ssh = self.connect(self.server_id.name)\n        self.execute(ssh, [\n            'rm', '-rf', '/opt/keys/' + self.fullname + '/authorized_keys'])\n        ssh.close()\n\n\nclass ClouderContainerPort(models.Model):\n    _name = 'clouder.container.port'\n\n    container_id = fields.Many2one(\n        'clouder.container', 'Container', ondelete=\"cascade\", required=True)\n    name = fields.Char('Name', size=64, required=True)\n    localport = fields.Char('Local port', size=12, required=True)\n    hostport = fields.Char('Host port', size=12)\n    expose = fields.Selection(\n        [('internet','Internet'),('local','Local')],'Expose?',\n        required=True, default='local')\n    udp = fields.Boolean('UDP?')\n\n    _sql_constraints = [\n        ('name_uniq', 'unique(container_id,name)',\n         'Port name must be unique per container!'),\n    ]\n\n\nclass ClouderContainerVolume(models.Model):\n    _name = 'clouder.container.volume'\n\n    container_id = fields.Many2one(\n        'clouder.container', 'Container', ondelete=\"cascade\", required=True)\n    name = fields.Char('Path', size=128, required=True)\n    hostpath = fields.Char('Host path', size=128)\n    user = fields.Char('System User', size=64)\n    readonly = fields.Boolean('Readonly?')\n    nosave = fields.Boolean('No save?')\n\n\n    _sql_constraints = [\n        ('name_uniq', 'unique(container_id,name)',\n         'Volume name must be unique per container!'),\n    ]\n\nclass ClouderContainerOption(models.Model):\n    _name = 'clouder.container.option'\n\n    container_id = fields.Many2one(\n        'clouder.container', 'Container', ondelete=\"cascade\", required=True)\n    name = fields.Many2one(\n        'clouder.application.type.option', 'Option', required=True)\n    value = fields.Text('Value')\n\n    _sql_constraints = [\n        ('name_uniq', 'unique(container_id,name)',\n         'Option name must be unique per container!'),\n    ]\n\n    @api.one\n    @api.constrains('container_id')\n    def _check_required(self):\n        if self.name.required and not self.value:\n            raise except_orm(\n                _('Data error!'),\n                _(\"You need to specify a value for the option \" +\n                  self.name.name + \" for the container \" +\n                  self.container_id.name + \".\"))\n\n\nclass ClouderContainerLink(models.Model):\n    _name = 'clouder.container.link'\n    _inherit = ['clouder.model']\n\n    container_id = fields.Many2one(\n        'clouder.container', 'Container', ondelete=\"cascade\", required=True)\n    name = fields.Many2one(\n        'clouder.application.link', 'Application Link', required=True)\n    target = fields.Many2one('clouder.container', 'Target')\n\n    @api.one\n    @api.constrains('container_id')\n    def _check_required(self):\n        if self.name.required and not self.target:\n            raise except_orm(\n                _('Data error!'),\n                _(\"You need to specify a link to \" +\n                  self.name.application_id.name + \" for the container \" +\n                  self.container_id.name))\n\n\n    # @api.multi\n    # def get_vals(self):\n    #     vals = {}\n    #\n    #     vals.update(self.container_id.get_vals())\n    #     if self.target:\n    #         target_vals = self.target_id.get_vals()\n    #         vals.update({\n    #             'link_target_container_id': target_vals['container_id'],\n    #             'link_target_container_name': target_vals['container_name'],\n    #             'link_target_container_fullname': target_vals['container_fullname'],\n    #             'link_target_app_id': target_vals['app_id'],\n    #             'link_target_app_code': target_vals['app_code'],\n    #         })\n    #\n    #\n    #     return vals\n\n    # @api.multi\n    # def reload(self):\n    #     vals = self.get_vals()\n    #     self.deploy(vals)\n    #     return\n\n    @api.multi\n    def deploy_link(self):\n        return\n\n    @api.multi\n    def purge_link(self):\n        return\n\n    @api.multi\n    def control(self):\n        if not self.target:\n            self.log('The target isnt configured in the link, '\n                     'skipping deploy link')\n            return False\n        app_links = self.env['clouder.application.link'].search(\n            [('application_id','=',self.container_id.application_id.id),\n             ('name.code','=', self.target.application_id.code)])\n        if not app_links:\n            self.log('The target isnt in the application link for container, '\n                     'skipping deploy link')\n            return False\n        if not app_links[0].container:\n            self.log('This application isnt for container, '\n                     'skipping deploy link')\n            return False\n        return True\n\n    @api.multi\n    def deploy_(self):\n        self.purge_()\n        self.control() and self.deploy_link()\n\n    @api.multi\n    def purge_(self):\n        self.control() and self.purge_link()\n\n","sub_path":"clouder/clouder_container.py","file_name":"clouder_container.py","file_ext":"py","file_size_in_byte":40562,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"351625346","text":"import numpy as np\nimport numpy.linalg as la\nfrom numpy import corrcoef, logical_and, dot, shape, nonzero, eye, diag,\\\n    diag_indices\n\ndef loaddata():\n    return[[0, 0, 0, 0, 0, 4, 0, 0, 0, 0, 5],\n           [0, 0, 0, 3, 0, 4, 0, 0, 0, 0, 3],\n           [0, 0, 0, 0, 4, 0, 0, 1, 0, 4, 0],\n           [3, 3, 4, 0, 0, 0, 0, 2, 2, 0, 0],\n           [5, 4, 5, 0, 0, 0, 0, 5, 5, 0, 0],\n           [0, 0, 0, 0, 5, 0, 1, 0, 0, 5, 0],\n           [4, 3, 4, 0, 0, 0, 0, 5, 5, 0, 1],\n           [0, 0, 0, 4, 0, 4, 0, 0, 0, 0, 4],\n           [0, 0, 0, 2, 0, 2, 5, 0, 0, 1, 2],\n           [0, 0, 0, 0, 5, 0, 0, 0, 0, 4, 0],\n           [1, 0, 0, 0, 0, 0, 0, 1, 2, 0, 0]]\n\n'''\n欧几里得，皮尔逊，余弦系数归一化，系数越接近1，越相似，越接近0，越不相似。\n'''\ndef eulidsim(ina,inb):\n    return 1.0/(1.0+la.norm(ina -inb))\n\ndef pearssim(ina,inb):\n    if len(ina)<3:\n        return 1.0;\n    return 0.5+0.5*corrcoef(ina,inb,rowvar=0)[0][1]#rowvar=0,把列当成向量\n\ndef cossim(ina,inb):\n    num=float(dot(ina.T,inb))\n    denom=la.norm(ina)*la.norm(inb)\n    return 0.5+0.5*(num/denom)\n\ndef standest(dataarray,user,simmeas,item):\n    n=shape(dataarray)[1]\n    simtoal=0; ratsimtoal=0\n    for j in range(n):\n        userrating=dataarray[user,j]\n        if userrating==0:\n            continue\n        overlap=nonzero(logical_and(dataarray[:,item]>0,dataarray[:,j]>0))[0]#不为0的属性与推荐item属性之间的相似度\n        if len(overlap)==0:\n            similarity=0\n        else:\n            similarity=simmeas(dataarray[overlap,item],dataarray[overlap,j])#用数组的花式索引方法\n        simtoal+=similarity\n        ratsimtoal+=similarity*userrating#相似度乘上用户打分\n    if simtoal==0:\n        return 0\n    else:\n        return ratsimtoal/simtoal#评分归一化\ndef recommend(dataarray,user,n,simmeas,estmehtod):\n    unrateditems=nonzero(dataarray[user,:]==0)[0]\n    itemscores=[]\n    for item in unrateditems:\n        estimateedscore=estmehtod(dataarray,user,simmeas,item)\n        itemscores.append((item,estimateedscore))\n    return sorted(itemscores,key=lambda a:a[1], reverse=True)[:n]#N个按照列降序排序推荐的item\n\ndef svdest(dataarray,user,simmeas,item):\n    n=shape(dataarray)[1]\n    simtoal=0; ratsimtoal=0\n    u,sigma,vt=la.svd(dataarray)#m*n=(m,m)*(m,n)*(n,n)\n    sig4=diag(sigma[:4])#以90%能量含含r个奇异值的对角矩阵 m*n=(m,r)*(r,r)*(r,n) \n    temp=dot(dataarray.T,u[:,:4])\n    xformeditems=np.dot(temp,la.inv(sig4))\n    for j in range(n): \n        userrating=dataarray[user,j]\n        if userrating==0 or j==item:\n            continue\n        similarity=simmeas(xformeditems[item,:].T,xformeditems[j,:].T)\n        simtoal+=similarity\n        ratsimtoal+=similarity*userrating\n    if simtoal==0:\n        return 0\n    else:\n        return ratsimtoal/simtoal\n    \na=np.array(loaddata())\nb=recommend(a,1,3,cossim,svdest)\nprint(b)\n\n\n\n","sub_path":"SVD.py","file_name":"SVD.py","file_ext":"py","file_size_in_byte":2912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"170386468","text":"# -*- coding: utf-8 -*-\n\nimport time\nimport datetime\n\nfrom flask import Flask, g, session, request, flash, redirect, jsonify, \\\n    url_for, render_template, current_app\nfrom flask.ext.login import current_user, AnonymousUserMixin, LoginManager\n\nfrom extensions import db, cache\nfrom filters import configure_template_filters\n\nfrom models.user import UserCid as User\n\ndef create_app(app, config, urls):\n    app.config.from_object(config)#从对象加载app.config\n    \n    configure_extensions(app)#初始化数据库与缓存\n    configure_errorhandlers(app)##初始化错误处理\n    configure_context_processors(app)##环境处理器将函数添加到模板中比较强大\n\n    configure_template_filters(app)##过滤器设置，在filters.py中\n    \n    configure_blueprints(app, urls)\n    configure_user_manager(app)#创建一个flask-login并配置绑定到app\n    configure_before_handlers(app)##暂无\n\n    return app\n\ndef configure_user_manager(app):\n    login_manager = LoginManager()\n    login_manager.login_view = 'platform.login'\n    login_manager.anonymous_user = AnonymousUserMixin\n    login_manager.setup_app(app)\n    \n    @login_manager.user_loader\n    def load_user(user_id):\n        return User.query.get(user_id)\n\n    @login_manager.unauthorized_handler\n    def unauthorized():\n        flash(u\"你需要登陆才能访问上次的页面!\", \"danger\")\n        return redirect(url_for(\"platform.login\", next=request.path))\n\n\ndef configure_extensions(app):\n    '''configure extensions'''\n    db.init_app(app)\n    cache.init_app(app)\n\n\ndef configure_context_processors(app):\n    @app.context_processor\n    def current_datetime():\n        return dict(\n            current_time=time.time(),\n            current_date=datetime.date.today())\n\n    @app.context_processor\n    def date_range():\n        \"本周、上周、本月、上个月\"\n\n        current_date = datetime.date.today()\n        current_week_start = current_date - \\\n            datetime.timedelta(days=current_date.isocalendar()[2]-1)\n        current_month_start = datetime.date(current_date.year, current_date.month, 1)\n\n        last_week_start = current_week_start - datetime.timedelta(days=7)\n        last_week_end = current_week_start-datetime.timedelta(days=1)\n\n        last_month_start = datetime.date(current_date.year-1, 12, 1) \\\n            if current_date.month == 1 else \\\n            datetime.date(current_date.year, current_date.month-1, 1)\n        last_month_end = current_month_start-datetime.timedelta(days=1)\n\n        return dict(\n            context_current_week=(current_week_start, current_date),\n            context_current_month=(current_month_start, current_date),\n            context_last_week=(last_week_start, last_week_end),\n            context_last_month=(last_month_start, last_month_end))\n\ndef configure_before_handlers(app):\n    pass\n\ndef configure_errorhandlers(app):\n    @app.errorhandler(401)\n    def unauthorized(error):\n        if request.is_xhr:\n            return jsonfiy(error=\"Login required\")\n        flash(u\"你需要登陆才能访问上次的页面!\", \"danger\")\n        return redirect(url_for(\"platform.login\", next=request.path))\n  \n    @app.errorhandler(403)\n    def forbidden(error):\n        if request.is_xhr:\n            return jsonify(error='Sorry, page not allowed')\n        return render_template(\"errors/403.html\", error=error)\n\n    @app.errorhandler(404)\n    def page_not_found(error):\n        if request.is_xhr:\n            return jsonify(error='Sorry, page not found')\n        return render_template(\"errors/404.html\", error=error)\n\n    @app.errorhandler(500)\n    def server_error(error):\n        if request.is_xhr:\n            return jsonify(error='Sorry, an error has occurred')\n        return render_template(\"errors/500.html\", error=error)\n\n\ndef configure_blueprints(app, urls):\n    for blueprints, url_prefix in urls:\n        app.register_blueprint(blueprints, url_prefix=url_prefix)\n","sub_path":"ccweb/application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":3922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"352315654","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*- #\nfrom __future__ import unicode_literals\n\nAUTHOR = 'Ozgur Ozan Cakmak'\nSITENAME = \"Ozgur's Blog\"\nSITEURL = ''\n# Site subtitle\nSITESUBTITLE = \"Random ramblings of personal nature\"\n\nPATH = 'content'\n\nTIMEZONE = 'Europe/Istanbul'\n\nDEFAULT_LANG = 'English'\n\n# Feed generation is usually not desired when developing\nFEED_ALL_ATOM = None\nCATEGORY_FEED_ATOM = None\nTRANSLATION_FEED_ATOM = None\nAUTHOR_FEED_ATOM = None\nAUTHOR_FEED_RSS = None\n\n# Blogroll\n\"\"\"LINKS = (('Pelican', 'http://getpelican.com/'),\n         ('Python.org', 'http://python.org/'),\n         ('Jinja2', 'http://jinja.pocoo.org/'),)\"\"\"\n\n# Social widget\n# SOCIAL = (('You can add links in your config file', '#'),\n#          ('Another social link', '#'),)\n\nDEFAULT_PAGINATION = 10\n\n# Uncomment following line if you want document-relative URLs when developing\nRELATIVE_URLS = True\n\n# Theme Setting\nTHEME = 'alchemy'\n\n# Markdown setting\nMARKDOWN = {\n    'extension_configs': {\n        'markdown.extensions.codehilite': {'css_class': 'highlight'},\n        'markdown.extensions.extra': {},\n        'markdown.extensions.meta': {},\n    },\n    'output_format': 'html5',\n}\n\n# Non-Default Settings\n# DISPLAY_CATEGORIES_ON_MENU = False\n\n# Static Paths\nSTATIC_PATHS = ['images',]\n\n# Google Analytics ID\nGOOGLE_ANALYTICS = \"UA-45599318-4\"\n","sub_path":"pelicanconf.py","file_name":"pelicanconf.py","file_ext":"py","file_size_in_byte":1334,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"155758976","text":"#!/usr/bin/env python\n\nimport json\nimport os\nfrom datetime import datetime, date\n\n\ndef main():\n    config_fp = os.path.join(\"..\", \"asreviewcontrib\", \"covid19\", \"config.json\")\n    covid_meta = {\n        \"name\": \"cord19\",\n        \"title\": \"CORD-19\",\n        \"description\": \"A Free dataset on publications on the corona virus.\",\n        \"last_update\": str(datetime.now()),\n        \"license\": \"Covid dataset license\",\n        \"link\": \"https://pages.semanticscholar.org/coronavirus-research\",\n        \"authors\": [\"Allen institute for AI\"],\n        \"topic\": \"Covid-19\",\n        \"datasets\": [{\n            \"name\": \"metadata-2020-03-13\",\n            \"sha512\": \"6741211cc47c04897b253a3eaf2d18e6d57391530f8cebe7d8c84310f82\"\n                      \"c90b2c55071157b418fb7b627302adbfae8838fb8c071516288b320b131\"\n                      \"03ac1ec7fc\",\n            \"img_url\": \"https://pages.semanticscholar.org/hs-fs/hubfs/\"\n                       \"covid-image.png?width=300&name=covid-image.png\",\n            \"timestamp\": str(date(year=2020, month=3, day=13)),\n            \"statistics\": {\n                \"n_papers\": 29500,\n                \"n_missing_titles\": 9,\n                \"n_missing_abstracts\": 2591,\n            },\n            \"url\": \"https://ai2-semanticscholar-cord-19.s3-us-west-2.amazonaws.com/\"\n                   \"2020-03-13/all_sources_metadata_2020-03-13.csv\"\n        }]\n    }\n    with open(config_fp, \"w\") as fp:\n        json.dump(covid_meta, fp, indent=4)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"scripts/create_config.py","file_name":"create_config.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"26983938","text":"#! coding: utf-8\nfrom datetime import datetime\nimport re\n\nfrom .base import Parser, Concert, remove_lbr\n\nclass ParserCosmonavt(Parser):\n    \"\"\"Космонавт\"\"\"\n    url = \"http://cosmonavt.su\"\n    date_parse_re = re.compile(\"eventDate=(?P<year>\\d{4})-(?P<month>\\d{2})-(?P<day>\\d{2})\")\n    price_parse_re = re.compile(\"\\d+\")\n\n    def _parse_date(self, d):\n        match = self.date_parse_re.search(d.strip())\n        return datetime(\n            day=int(match.group(\"day\")),\n            month=int(match.group(\"month\")),\n            year=int(match.group(\"year\")))\n\n    def _parse_price(self, p):\n        p = remove_lbr(p)\n        try:\n            return self.price_parse_re.search(p).group(0) + \"р\"\n        except AttributeError:\n            return p\n\n    def get_concerts(self):\n        event_list = self.soup.find_all(class_=\"add_item concert\")\n\n        for event in event_list:\n            dt = self._parse_date(str(event.find(class_=\"inline\")))\n            artist = remove_lbr(event.find(class_=\"add_title\").find(\"a\").text)\n            price = self._parse_price(event.find(class_=\"add_ticket\").text)\n            link = remove_lbr(event.find(class_=\"add_title\").find(\"a\")[\"href\"])\n            if not link.startswith(self.url):\n                link = self.url + \"/\" + link\n            yield Concert(artist=artist, datetime=dt, price=price, link=link)\n\n","sub_path":"whatsnext/parsers/cosmonavt.py","file_name":"cosmonavt.py","file_ext":"py","file_size_in_byte":1357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"120521783","text":"import numpy\n\ndef read_redshifts(file):\n\n    noRedshifts = -1\n    with open(file, \"r\") as f:\n        for line in f:\n            noRedshifts=noRedshifts+1\n    f.close()\n\n    redshifts = numpy.zeros(noRedshifts)\n\n    i=-1\n    with open(file,\"r\") as f2:\n        for line in f2:\n            if (i!=-1):\n                redshifts[i] = line\n            i=i+1\n    f2.close()\n    return redshifts\n","sub_path":"mycodes/redshifts.py","file_name":"redshifts.py","file_ext":"py","file_size_in_byte":389,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"20189069","text":"#!~/anaconda3/bin/python\nimport glob\nimport os\nimport sys\nimport pandas as pd\nimport nibabel as nb\nimport numpy as np\nfrom shutil import copyfile\nimport json\nimport subprocess\nimport h5py\nfrom sklearn.linear_model import LinearRegression\nfrom scipy.stats import pearsonr\n\ntmpdir = subprocess.run(['echo $SBIA_TMPDIR'], stdout=subprocess.PIPE,shell=True).stdout.decode('utf-8').split('\\n')[0]\n\noutdir ='/{0}/xcp_hcp/fmriprepdir/'.format(tmpdir)\nworking_dir='/{0}/xcp_hcp/'.format(tmpdir)\nhcp_dir = '/cbica/projects/HCP_Data_Releases/HCP_1200/'\n\nos.makedirs(outdir,exist_ok=True)\nos.makedirs(working_dir,exist_ok=True)\n\nfunction = str(sys.argv[1])\nsubid = str(sys.argv[2])\n\n\"\"\"\nData Narrative\n\nAll subjects from the S1200 HCP-YA were analyzed. For each Task (\"REST1\",\"REST2\",\"WM\",\"MOTOR\",\"GAMBLING\",\"EMOTION\",\"LANGUAGE\",\"SOCIAL\") and Encoding Direction (\"LR\",\"RL\"), we analyzed the session if the following files were present:\n(1) rfMRI/tfMRI_{Task}_{Encoding_}_Atlas_MSMAll.dtseries.nii, (2) rfMRI/tfMRI_{Task}_{Encoding}.nii, (3) Movement_Regressors.txt, (4) Movement_AbsoluteRMS.txt, (5) SBRef_dc.nii.gz, and (6) rfMRI/tfMRI_{Task}_{Encoding_}_SBRef.nii.gz.\nFor all tasks, the global signal timeseries was generated with: wb_command -cifti-stats rfMRI/tfMRI_{Task}_{Encoding_}_Atlas_MSMAll.dtseries.nii -reduce MEAN'. For REST1 and REST2, we used the HCP distributed CSF.txt and WM.txt cerebral spinal fluid\nand white matter time series. For all other tasks (i.e., all tfMRI), we generated those files in the exact manner the HCP did: fslmeants -i tfMRI_{Task}_{Encoding}.nii -o CSF.txt -m CSFReg.2.nii.gz; fslmeants -i tfMRI_{Task}_{Encoding}.nii -o WM.txt -m WMReg.2.nii.gz.\nTo ensure this process was identical, we generated these time series for the rfMRI sessions and compared them to the HCP distributed timeseries, ensuring they are identical. These files were then formatted into fMRIprep outputs by renaming the files,\ncreating the regression json, and creating dummy transforms. These inputs were then analyzed by xcp_abcd with the following command:\nsingularity run --cleanenv -B ${PWD} ~/xcp_hcp/xcp-abcd-latest.sif /$SUBJECT/fmriprepdir/ ~/xcp_hcp/xcp_results/ participant --cifti --despike --lower-bpf 0.01 --upper-bpf 0.08 --participant_label sub-$SUBJECT -p 36P -f 100 --omp-nthreads 4 --nthreads 4\nAll subjects ran successfully.\n\"\"\"\n\n\ndef audit():\n\tdf = pd.DataFrame(columns=['ran','subject','error'])\n\n\tfor sub in glob.glob('/cbica/projects/HCP_Data_Releases/HCP_1200/**'):\n\t\tsubid = sub.split('/')[-1]\n\t\tdata = []\n\t\tfor fdir in [\"RL\",\"LR\"]:\n\t\t\tfor orig_task in [\"REST1\",\"REST2\",\"WM\",\"MOTOR\",\"GAMBLING\",\"EMOTION\",\"LANGUAGE\",\"SOCIAL\"]:\n\t\t\t\tif len(glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*/*Atlas_MSMAll.dtseries.nii'.format(hcp_dir,subid,orig_task,fdir))) != 1: continue\n\t\t\t\tif len(glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*/*{2}_{3}.nii.gz'.format(hcp_dir,subid,orig_task,fdir))) != 1: continue\n\t\t\t\tif len(glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*/Movement_Regressors.txt'.format(hcp_dir,subid,orig_task,fdir))) != 1: continue\n\t\t\t\tif len(glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*/Movement_AbsoluteRMS.txt'.format(hcp_dir,subid,orig_task,fdir))) != 1: continue\n\t\t\t\tif len(glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*/SBRef_dc.nii.gz'.format(hcp_dir,subid,orig_task,fdir))) != 1: continue\n\t\t\t\tif len(glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*/**SBRef.nii.gz'.format(hcp_dir,subid,orig_task,fdir))) != 1: continue\n\t\t\t\tdata.append('_'.join([orig_task,fdir]))\n\n\t\tresults = []\n\n\t\tfor r in glob.glob('/cbica/home/bertolem/xcp_hcp/xcp_results/xcp_abcd/sub-%s/func/*Schaefer417*pconn*'%(subid)):\n\t\t\tresults.append(r.split('/')[-1].split('-')[2].split('_')[0] + '_' +r.split('/')[-1].split('-')[3].split('_')[0])\n\t\tdata.sort()\n\t\tresults.sort()\n\t\tran = False\n\t\tdata = np.unique(data)\n\t\tif len(np.intersect1d(data,results)) == len(data):\n\t\t\tran = True\n\t\t\tline = 'No errors'\n\n\t\telse: line = None\n\t\t# if ran == False:\n\t\t# \te_file=sorted(glob.glob('/cbica/home/bertolem/sge/*%s*.o*'%(subid)),key=os.path.getmtime)[-1]\n\t\t# \twith open(e_file) as f:\n\t\t# \t\tfor line in f:\n\t\t# \t\t\tpass\n\t\t# \tprint (subid,line)\n\t\tsdf = pd.DataFrame(columns=['ran','subject','error'])\n\t\tsdf['ran'] = [ran]\n\t\tsdf['subject'] = [subid]\n\t\tsdf['error'] = [line]\n\t\tdf =df.append(sdf,ignore_index=True)\n\tdf.to_csv('/cbica/home/bertolem/xcp_hcp/xcp_results/xcp_abcd/audit.csv',index=False)\n\ndef remove(subid):\n\tfor f in glob.glob('/cbica/home/bertolem/xcp_hcp/xcp_results/xcp_abcd/sub-%s/func/**'%(subid)):\n\t\tif 'Schaefer417_den-91k_den-91k_bold.pconn.nii' not in f and 'Schaefer417_den-91k_den-91k_bold.ptseries.nii' not in f and 'qc_den-91k_bold.tsv' not in f:\n\t\t\tos.system('rm -f %s'%(f))\n\nif function == 'sge':\n\taudit()\n\taudit = pd.read_csv('/cbica/home/bertolem/xcp_hcp/xcp_results/xcp_abcd/audit.csv')\n\tfor sub in glob.glob('/cbica/projects/HCP_Data_Releases/HCP_1200/**'):\n\t\tsub = sub.split('/')[-1]\n\t\tif audit[audit.subject==sub].ran.values[0]:\n\t\t\tremove(sub)\n\t\t\tcontinue\n\t\tif sub == 'ToSync':\n\t\t\tcontinue\n\t\tos.system('qsub -l h_vmem={0}G,s_vmem={0}G -N p{1} -pe threaded 4 -V -j y -b y -o ~/sge/ -e ~/sge/ python /cbica/home/bertolem/xcp_hcp/hcp2fmriprep.py run {1}'.format(36,sub))\n\nif function == 'run':\n\tos.system('rm -f -r /{0}/S1200/{1}'.format(working_dir,subid))\n\tos.system('rm -f -r /{0}/fmriprepdir/sub-{1}'.format(working_dir,subid))\n\n\ttasklist = []\n\tos.makedirs('/{0}/S1200/{1}/MNINonLinear/Results/'.format(working_dir,subid),exist_ok=True)\n\n\t# for fdir in [\"RL\"]:\n\t# \tfor orig_task in [\"REST1\"]:\n\n\tfor fdir in [\"RL\",\"LR\"]:\n\t\tfor orig_task in [\"REST1\",\"REST2\",\"WM\",\"MOTOR\",\"GAMBLING\",\"EMOTION\",\"LANGUAGE\",\"SOCIAL\"]:\n\t\t\tif len(glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*/*Atlas_MSMAll.dtseries.nii'.format(hcp_dir,subid,orig_task,fdir))) != 1: continue\n\t\t\tif len(glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*/*{2}_{3}.nii.gz'.format(hcp_dir,subid,orig_task,fdir))) != 1: continue\n\t\t\tif len(glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*/Movement_Regressors.txt'.format(hcp_dir,subid,orig_task,fdir))) != 1: continue\n\t\t\tif len(glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*/Movement_AbsoluteRMS.txt'.format(hcp_dir,subid,orig_task,fdir))) != 1: continue\n\t\t\tif len(glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*/SBRef_dc.nii.gz'.format(hcp_dir,subid,orig_task,fdir))) != 1: continue\n\t\t\tif len(glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*/**SBRef.nii.gz'.format(hcp_dir,subid,orig_task,fdir))) != 1: continue\n\t\t\ttdir = glob.glob('/{0}/{1}/MNINonLinear/Results/*{2}*{3}*'.format(hcp_dir,subid,orig_task,fdir))[0]\n\t\t\ttask = tdir.split('/')[-1]\n\t\t\ttasklist.append(task)\n\t\t\ttask_dir = '/{0}/S1200/{1}/MNINonLinear/Results/{2}'.format(working_dir,subid,task)\n\n\t\t\tos.makedirs(task_dir,exist_ok=True)\n\t\t\tos.chdir(task_dir)\n\n\t\t\twbs_file = '{0}/{1}/MNINonLinear/Results/{2}/{2}_Atlas_MSMAll.dtseries.nii'.format(hcp_dir,subid,task)\n\t\t\tif os.path.exists(wbs_file):\n\t\t\t\tcommand = 'OMP_NUM_THREADS=4 wb_command -cifti-stats {0} -reduce MEAN >> /{1}/{2}_WBS.txt'.format(wbs_file,task_dir,task)\n\t\t\t\tos.system(command)\n\n\n\tanatdir=outdir+'/sub-'+subid+'/anat/'\n\tfuncdir=outdir+'/sub-'+subid+'/func/'\n\n\tos.makedirs(outdir+'/sub-'+subid+'/anat',exist_ok=True) # anat dir\n\tos.makedirs(outdir+'/sub-'+subid+'/func',exist_ok=True) # func dir\n\n\n\tfor j in tasklist:\n\n\t\tbb = j.split('_')\n\t\ttaskname = bb[1]\n\t\tacqname = bb[2]\n\t\tdatadir = working_dir+'/S1200/'+subid+'/MNINonLinear/Results/'+ j\n\t\tos.makedirs(datadir,exist_ok=True)\n\n\t\tif 'REST' not in j:\n\t\t\tResultsFolder='/{0}/{1}/MNINonLinear/Results/{2}/'.format(hcp_dir,subid,j)\n\t\t\tROIFolder=\"/{0}/{1}/MNINonLinear/ROIs\".format(hcp_dir,subid)\n\n\t\t\txcp_file = '/{0}/S1200/{1}/MNINonLinear/Results/{2}/{3}_WM.txt'.format(working_dir,subid,j,j)\n\t\t\tcmd = \"fslmeants -i {0}/{1}.nii.gz -o {2} -m {3}/WMReg.2.nii.gz\".format(ResultsFolder,j,xcp_file,ROIFolder)\n\t\t\tos.system(cmd)\n\n\t\t\txcp_file = '/{0}/S1200/{1}/MNINonLinear/Results/{2}/{3}_CSF.txt'.format(working_dir,subid,j,j)\n\t\t\tcmd = \"fslmeants -i {0}/{1}.nii.gz -o {2} -m {3}/CSFReg.2.nii.gz\".format(ResultsFolder,j,xcp_file,ROIFolder)\n\t\t\tos.system(cmd)\n\n\n\t\torig = '/{0}/{1}/MNINonLinear/Results/{2}/Movement_Regressors.txt'.format(hcp_dir,subid,j)\n\t\txcp_file = '/{0}/S1200/{1}/MNINonLinear/Results/{2}/Movement_Regressors.txt'.format(working_dir,subid,j)\n\t\tcopyfile(orig,xcp_file)\n\n\t\torig = '/{0}/{1}/MNINonLinear/Results/{2}/Movement_AbsoluteRMS.txt'.format(hcp_dir,subid,j)\n\t\txcp_file = '/{0}/S1200/{1}/MNINonLinear/Results/{2}/Movement_AbsoluteRMS.txt'.format(working_dir,subid,j)\n\t\tcopyfile(orig,xcp_file)\n\n\t\t##create confound regressors\n\t\tmvreg = pd.read_csv(datadir +'/Movement_Regressors.txt',header=None,delimiter=r\"\\s+\")\n\t\tmvreg = mvreg.iloc[:,0:6]\n\t\tmvreg.columns=['trans_x','trans_y','trans_z','rot_x','rot_y','rot_z']\n\t\t# convert rot to rad\n\t\tmvreg['rot_x']=mvreg['rot_x']*np.pi/180\n\t\tmvreg['rot_y']=mvreg['rot_y']*np.pi/180\n\t\tmvreg['rot_z']=mvreg['rot_z']*np.pi/180\n\n\n\t\torig = '/{0}/{1}/MNINonLinear/Results/{2}/{3}_CSF.txt'.format(hcp_dir,subid,j,j)\n\t\txcp_file = '/{0}//S1200/{1}/MNINonLinear/Results/{2}/{3}_CSF.txt'.format(working_dir,subid,j,j)\n\t\tif os.path.exists(xcp_file)==False: copyfile(orig,xcp_file)\n\n\t\torig = '/{0}/{1}/MNINonLinear/Results/{2}/{3}_WM.txt'.format(hcp_dir,subid,j,j)\n\t\txcp_file = '/{0}/S1200/{1}/MNINonLinear/Results/{2}/{3}_WM.txt'.format(working_dir,subid,j,j)\n\t\tif os.path.exists(xcp_file)==False: copyfile(orig,xcp_file)\n\n\t\torig = '/{0}/{1}/MNINonLinear/Results/{2}/{3}_Atlas_MSMAll.dtseries.nii'.format(hcp_dir,subid,j,j)\n\t\txcp_file = '/{0}/S1200/{1}/MNINonLinear/Results/{2}/{3}_Atlas_MSMAll.dtseries.nii'.format(working_dir,subid,j,j)\n\t\tcopyfile(orig,xcp_file)\n\n\t\tcsfreg = np.loadtxt(datadir +'/'+ j + '_CSF.txt')\n\t\twmreg = np.loadtxt(datadir +'/'+ j + '_WM.txt')\n\t\tgsreg = np.loadtxt(datadir +'/'+ j + '_WBS.txt')\n\t\trsmd = np.loadtxt(datadir +'/Movement_AbsoluteRMS.txt')\n\n\n\t\tbrainreg = pd.DataFrame({'global_signal':gsreg,'white_matter':wmreg,'csf':csfreg,'rmsd':rsmd })\n\n\t\tregressors  =  pd.concat([mvreg, brainreg], axis=1)\n\t\tjsonreg =  pd.DataFrame({'LR': [1,2,3]}) # just a fake json\n\t\tregressors.to_csv(funcdir+'sub-'+subid+'_task-'+taskname+'_acq-'+acqname+'_desc-confounds_timeseries.tsv',index=False,\n\t\t\t\t\t\t  sep= '\\t')\n\t\tregressors.to_json(funcdir+'sub-'+subid+'_task-'+taskname+'_acq-'+acqname+'_desc-confounds_timeseries.json')\n\n\n\t\thcp_mask = '/{0}/{1}//MNINonLinear/Results/{2}/{2}_SBRef.nii.gz'.format(hcp_dir,subid,j)\n\t\tprep_mask = funcdir+'/sub-'+subid+'_task-'+taskname+'_acq-'+ acqname +'_space-MNI152NLin6Asym_boldref.nii.gz'\n\t\tcopyfile(hcp_mask,prep_mask)\n\n\t\thcp_mask = '/{0}/{1}//MNINonLinear/Results/{2}/brainmask_fs.2.nii.gz'.format(hcp_dir,subid,j)\n\t\tprep_mask = funcdir+'/sub-'+subid+'_task-'+taskname+'_acq-'+ acqname +'_space-MNI152NLin6Asym_desc-brain_mask.nii.gz'\n\t\tcopyfile(hcp_mask,prep_mask)\n\n\t\t# create/copy  cifti\n\t\tniftip  = '{0}/{1}/MNINonLinear/Results/{2}/{2}.nii.gz'.format(hcp_dir,subid,j,j) # to get TR  and just sample\n\t\tniftib = funcdir+'/sub-'+subid+'_task-'+taskname+'_acq-'+ acqname +'_space-MNI152NLin6Asym_desc-preproc_bold.nii.gz'\n\t\tciftip = datadir + '/'+ j +'_Atlas_MSMAll.dtseries.nii'\n\t\tciftib = funcdir+'/sub-'+subid+'_task-'+taskname+'_acq-'+ acqname +'_space-fsLR_den-91k_bold.dtseries.nii'\n\n\t\tos.system('cp {0} {1}'.format(ciftip,ciftib))\n\t\tos.system('cp {0} {1}'.format(niftip,niftib))\n\n\t\ttr = nb.load(niftip).header.get_zooms()[-1]   # repetition time\n\n\t\tjsontis={\n\t\t \"RepetitionTime\": np.float(tr),\n\t\t \"TaskName\": taskname\n\t\t}\n\t\tjson2={\n\t\t  \"grayordinates\": \"91k\", \"space\": \"HCP grayordinates\",\n\t\t  \"surface\": \"fsLR\",\"surface_density\": \"32k\",\n\t\t  \"volume\": \"MNI152NLin6Asym\"\n\t\t  }\n\n\n\t\twith open(funcdir+'/sub-'+subid+'_task-'+taskname+'_acq-'+ acqname +'_space-MNI152NLin6Asym_desc-preproc_bold.json', 'w') as outfile:\n\t\t\tjson.dump(jsontis, outfile)\n\n\t\twith open(funcdir+'/sub-'+subid+'_task-'+taskname+'_acq-'+ acqname +'_space-fsLR_den-91k_bold.dtseries.json', 'w') as outfile:\n\t\t\tjson.dump(json2, outfile)\n\n\t\t# just fake anatomical profile for xcp, it wont be use\n\t\torig = '/{0}/{1}/MNINonLinear/Results/{2}/SBRef_dc.nii.gz'.format(hcp_dir,subid,j)\n\t\txcp_file = '/{0}//S1200/{1}/MNINonLinear/Results/{2}/SBRef_dc.nii.gz'.format(working_dir,subid,j)\n\t\tcopyfile(orig,xcp_file)\n\t\tanat1 = datadir +'/' +'/SBRef_dc.nii.gz'\n\t\tmni2t1 = anatdir+'sub-'+subid+'_from-MNI152NLin2009cAsym_to-T1w_mode-image_xfm.h5'\n\t\tt1w2mni = anatdir+'sub-'+subid+'_from-T1w_to-MNI152NLin2009cAsym_mode-image_xfm.h5'\n\t\tcmd = 'cp {0} {1}'.format(anat1,mni2t1)\n\t\tos.system(cmd)\n\t\tcmd = 'cp {0} {1}'.format(anat1,t1w2mni)\n\t\tos.system(cmd)\n\n\tos.chdir(working_dir)\n\t# singularity build xcp-abcd-latest.sif docker://pennlinc/xcp_abcd:latest\n\tos.system('export SINGULARITYENV_OMP_NUM_THREADS=4')\n\tcmd = 'singularity run --cleanenv -B ${PWD} ~/xcp_hcp/xcp-abcd-latest.sif /%s/fmriprepdir/ /cbica/home/bertolem/xcp_hcp/xcp_results/ participant --cifti --despike --lower-bpf 0.01 --upper-bpf 0.08 --participant_label sub-%s -p 36P -f 100 --omp-nthreads 4 --nthreads 4'%(working_dir,subid)\n\tos.system(cmd)\n\tremove(subid)\n\tos.system('rm -f -r /{0}/S1200/{1}'.format(working_dir,subid))\n\tos.system('rm -f -r /{0}/fmriprepdir/sub-{1}'.format(working_dir,subid))\n\nif function == 'audit':\n\taudit()\n\n# qsub -l h_vmem=128G,s_vmem=128G -N zipit -V -j y -b y -o ~/sge/ -e ~/sge/ python /cbica/home/bertolem/xcp_hcp/hcp2fmriprep.py zipit None\nif function == 'zipit':\n\n\t# audit()\n\t# audit = pd.read_csv('/cbica/home/bertolem/xcp_hcp/xcp_results/xcp_abcd/audit.csv')\n\n\tdf = pd.DataFrame()\n\tfor csv in glob.glob('/cbica/home/bertolem/xcp_hcp/xcp_results/xcp_abcd/**/func/*qc_den-91k_bold.tsv'):\n\t\tdf = df.append(pd.read_csv(csv),ignore_index=True)\n\n\tdf = df.sort_values('sub',axis=0)\n\n\tparcels = ['Schaefer417']\n\tos.system('rm -f -r zipdir')\n\tos.system('mkdir zipdir')\n\n\tdata = h5py.File(\"hcp_rbc_bold.hdf5\", \"w\")\n\tfor parcel in parcels:\n\t\tfor matrix in df.iterrows():\n\t\t\tmatrix = dict(matrix[1])\n\t\t\tfname = '/cbica/home/bertolem/xcp_hcp/xcp_results/xcp_abcd/sub-{0}/func/sub-{0}_task-{1}_acq-{3}_space-fsLR_atlas-{2}_den-91k_den-91k_bold.pconn.nii'.format(matrix['sub'],matrix['task'],parcel,matrix['acq'])\n\t\t\tm = nb.load(fname).get_fdata()\n\t\t\tfname.replace('.pconn.nii','').split('/')[-1]\n\t\t\tdata.create_dataset('bold/{0}/matrix/{1}'.format(matrix['sub'],fname),m.shape,dtype=float,data=m)\n\t\t\tfor key in matrix.keys():\n\t\t\t\tdata['bold/{0}/matrix/{1}'.format(matrix['sub'],fname)].attrs[key] = matrix[key]\n\n\t\t\tfname = '/cbica/home/bertolem/xcp_hcp/xcp_results/xcp_abcd/sub-{0}/func/sub-{0}_task-{1}_acq-{3}_space-fsLR_atlas-{2}_den-91k_den-91k_bold.ptseries.nii'.format(matrix['sub'],matrix['task'],parcel,matrix['acq'])\n\t\t\tm = nb.load(fname).get_fdata()\n\t\t\tfname.replace('.ptseries.nii','').split('/')[-1]\n\t\t\tdata.create_dataset('bold/{0}/timeseries/{1}'.format(matrix['sub'],fname),m.shape,dtype=float,data=m)\n\t\t\tfor key in matrix.keys():\n\t\t\t\tdata['bold/{0}/timeseries/{1}'.format(matrix['sub'],fname)].attrs[key] = matrix[key]\n\n\n\n\"\"\"\nThis is not really required but here is how I made the HCP ICA-FIX plus GSR to sanity check\nmean corr is 0.92\n\"\"\"\ndef regression(data,confound):\n\n\t'''\n\t data :\n\t   numpy ndarray- vertices by timepoints\n\t confound:\n\t   nuissance regressors reg by timepoints\n\t return:\n\t\tresidual matrix\n\t'''\n\tregr = LinearRegression()\n\tregr.fit(confound,data)\n\ty_pred = regr.predict(confound)\n\n\treturn data - y_pred\n\n\ndef make_hcp():\n\tos.system('rm /cbica/home/bertolem/xcp_hcp/WBS.txt')\n\n\tfor sub in glob.glob('/cbica/projects/HCP_Data_Releases/HCP_1200/**'):\n\t\tsubject = sub.split('/')[-1]\n\t\tif subject == 'ToSync':\n\t\t\tcontinue\n\t\tfiles = glob.glob('/cbica/projects/HCP_Data_Releases/HCP_1200/{0}/MNINonLinear/Results/*rfMRI_REST*/rfMRI_**_Atlas_MSMAll_hp2000_clean.dtseries.nii'.format(subject))\n\t\tm = []\n\t\tfor f in files:\n\t\t\trun = f.split('/')[8]\n\t\t\tsurf = nb.load(f)\n\t\t\tdata = surf.get_fdata()\n\t\t\tcommand = 'wb_command -cifti-stats {0} -reduce MEAN >> /cbica/home/bertolem/xcp_hcp/WBS.txt'.format(f)\n\t\t\tos.system(command)\n\t\t\twbs = np.loadtxt('/cbica/home/bertolem/xcp_hcp/WBS.txt')\n\t\t\tos.system('rm /cbica/home/bertolem/xcp_hcp/WBS.txt')\n\t\t\td = regression(data,wbs.reshape(-1,1))\n\t\t\tnew_img = nb.Cifti2Image(d, header=surf.header,nifti_header=surf.nifti_header)\n\t\t\tcifti_out = \"/cbica/home/bertolem/xcp_hcp/{0}_{1}.dtseries.nii\".format(subject,run)\n\t\t\tnew_img.to_filename(cifti_out)\n\t\t\tp= '/cbica/home/bertolem/yeo_parcels/Schaefer2018_400Parcels_17Networks_order.dlabel.nii'\n\t\t\tout = '/cbica/home/bertolem/xcp_hcp/{0}_{1}_{2}.ptseries.nii'.format(subject,run,p.split('/')[-1].split('.')[0])\n\t\t\tcmd =  'wb_command -cifti-parcellate {0} {1} COLUMN {2}'.format(cifti_out,p,out)\n\t\t\tos.system(cmd)\n\t\t\tos.system('rm -f {0}'.format(cifti_out))\n\t\t\tts = nb.load(out).get_fdata()\n\t\t\tos.system('rm -f {0}'.format(out))\n\t\t\tm.append(np.arctanh(np.corrcoef(ts.T)))\n\t\tnp.save('/cbica/home/bertolem/xcp_hcp/rest_fixgsr/{0}.npy'.format(subject),np.nanmean(m,axis=0))\n\n\ndef compare():\n\tcorrs = []\n\tidx = np.triu_indices(400,1)\n\thcp_matrices = glob.glob('/cbica/home/bertolem/xcp_hcp/rest_fixgsr/*npy*')\n\tfor m in hcp_matrices:\n\t\ts = m[-10:-4]\n\t\thcp_m = np.load(m)\n\t\txcp = []\n\t\tfiles = glob.glob('/cbica/home/bertolem/xcp_hcp/xcp_results/xcp_abcd/sub-{0}/func/*REST*pconn*'.format(s))\n\t\tif len(files) == 0:continue\n\t\tfor f in files:\n\t\t\txcp.append(np.arctanh(nb.load(f).get_fdata()))\n\t\tif len(files) > 0: xcp = np.nanmean(xcp,axis=0)\n\t\tcorrs.append(pearsonr(xcp[idx],hcp_m[idx])[0])\n\t\tprint (np.mean(corrs))\n","sub_path":"xcp_abcd/notebooks/hcp2fmriprep.py","file_name":"hcp2fmriprep.py","file_ext":"py","file_size_in_byte":17412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"298401157","text":"from setuptools import setup, find_packages\nimport os, sys\n\nstable = os.environ.get('PYGERBV_STABLE', \"YES\")\nbranch_name = os.environ.get('PYGERBV_BRANCH', None)\n\nif stable == \"YES\":\n    version_format = '{tag}'\n    classifiers = [\n        'Development Status :: 5 - Production/Stable',\n        'License :: Other/Proprietary License',\n        'Programming Language :: Python :: 3.6',\n    ],\nelif branch_name:\n    version_format = '{tag}b{commitcount}+%s' % branch_name\n    classifiers = [\n        'Development Status :: 4 - Beta',\n        'License :: Other/Proprietary License',\n        'Programming Language :: Python :: 3.6',\n    ],\nelse:\n    version_format = '{tag}b{commitcount}+{gitsha}'\n    classifiers = [\n        'Development Status :: 4 - Beta',\n        'License :: Other/Proprietary License',\n        'Programming Language :: Python :: 3.6',\n    ],\n\ninstall_requires = []\n\n# Add enum34 **ONLY** if necessary -- installing in 3.5+ will break things\nif (sys.version_info.major < 3) or (sys.version_info.major == 3 and sys.version_info.minor < 4):\n    install_requires.append('enum34')\n         \nsetup(\n    name = 'pygerbv',\n    version_format=version_format,\n    description = 'Python wrapper for libgerbv',\n    url = 'https://github.com/holmosapien/PyGerbv',\n    author = 'Elephantec',\n    author_email = 'development@macrofab.com',\n    license = 'Other/Proprietary License',\n    classifiers = classifiers,\n    packages = find_packages(),\n    install_requires = install_requires,\n    extras_require = {},\n    package_data = {},\n    data_files = [],\n    entry_points = {},\n    setup_requires=['setuptools-git-version']\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"128390155","text":"#\n#  Copyright (c) 2018 Klemen Forstnerič\n#\n\nfrom user import User\nfrom random import randint\nfrom order_book import OrderBook\nfrom status import Status\n\nMIN_USD = 100\nMAX_USD = 300\n\nMIN_BTC = 1\nMAX_BTC = 10\n\n\nclass Exchange:\n\n  def __init__(self, user_storage, order_book):\n    self._user_storage = user_storage\n    self._order_book = order_book\n\n  def get_user(self, user_id):\n    with self._user_storage.get_lock_for_user(user_id):\n      user = self._user_storage.get_user(user_id)\n\n      if user:\n        return user\n\n      return self._user_storage.create_user(user_id, randint(MIN_USD, MAX_USD),\n                                            randint(MIN_BTC, MAX_BTC))\n\n  def sell(self, user_id, amount, price):\n    with self._user_storage.get_lock_for_user(user_id):\n      user = self._user_storage.get_user(user_id)\n\n      if user.balance_btc < amount:\n        raise RuntimeError('insufficient BTC balance')\n\n      self._order_book.add_sell_order(user_id, amount, price)\n      user.balance_btc -= amount\n\n  def buy(self, user_id, amount, price):\n    with self._user_storage.get_lock_for_user(user_id):\n      user = self._user_storage.get_user(user_id)\n\n      if user.balance_usd < price:\n        raise RuntimeError('insufficient USD balance')\n\n      self._order_book.add_buy_order(user_id, amount, price)\n      user.balance_usd -= price\n\n  def get_status(self):\n    snapshot = self._order_book.get_snapshot()\n\n    return Status(snapshot.sell_orders, snapshot.buy_orders,\n                  snapshot.processed_orders)\n","sub_path":"exchange.py","file_name":"exchange.py","file_ext":"py","file_size_in_byte":1523,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"611226741","text":"'''\nKeep use words\n'''\nimport sys\n\nf = open('word_list.txt', 'r')\nlines = f.readlines()\nf.close()\n\nwords = []\nfor line in lines:\n  w = line.strip()\n  if ';' in w:\n    words.append(w[0:w.index(';')]) # just take the first entry\n  else:\n    words.append(w)\n\n# remove dulpicates\nwords = set(words)\n\n# remove words with other alphabets\nimport string\nalphas = string.ascii_lowercase\nwords = [w for w in words if all([c in alphas for c in w])]\n\n# write every entry as unicode\nwords = [u'' + w for w in words]\n\n# plot some charts\nif True:\n  sys.path.append(\"../\")\n  import bars_by_length\n\n# save\nimport pickle\nprint(\"unique entries:\", len(words))\npickle.dump(words, open('slang1923.pkl', 'wb'))\n","sub_path":"preprocessing/slang_1923/prune.py","file_name":"prune.py","file_ext":"py","file_size_in_byte":688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"275861787","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sun Apr  4 17:33:11 2021\n\n@author: facestomperx\n\"\"\"\nimport utils\nfrom types import SimpleNamespace\n\n#https://github.com/ros-planning/navigation/blob/8c4933517b56a9ac8f193068df4d9ff333f21614/base_local_planner/src/obstacle_cost_function.cpp\n#https://github.com/ros-planning/navigation/blob/8c4933517b56a9ac8f193068df4d9ff333f21614/base_local_planner/include/base_local_planner/obstacle_cost_function.h\n\nfrom TrajectoryCostFunction import TrajectoryCostFunction \nclass ObstacleCostFunction(TrajectoryCostFunction):\n    def __init__(self, *args, **kwargs):\n        super().__init__(kwargs.get(\"scale\"), kwargs.get(\"costmap\"))\n        self._max_trans_vel = kwargs.get(\"max_trans_vel\",0.0)\n        self._max_scaling_factor = kwargs.get(\"max_scaling_factor\",1.0)\n        self._scaling_speed = kwargs.get(\"scaling_speed\",1.0)\n        self._to_sum_scores = kwargs.get(\"sum_scores\", True) #makes sense since I want the traj's point by point score to be summed\n        \n    \n    def updateCostMap(self, costmap):\n        self._costmap = costmap\n        \n        \n    def scoreTrajectory(self,traj):\n        #print(\"Scoring obstacle cost\")\n        cost = 0\n        scale = self.getScalingFactor(traj,self._scaling_speed, self._max_trans_vel, self._max_scaling_factor)\n        for i in range(traj.getPointsListLength()):\n            point = SimpleNamespace()\n            traj.getPoint(i,point)\n            f_cost = self.footprintCost(point, scale)\n            if (f_cost < 0):\n                return f_cost #for my case i should just return infinity\n            \n        #print(cost)\n        return cost\n        \n    def getScalingFactor(self, traj, scaling_speed, max_trans_vel, max_scaling_factor):\n        v_mag = utils.hypotenuseLen(traj._xv, traj._yv)\n        # if over certain speed threshold, scale robot footprint\n        # to slow down or avoid walls?\n        #print(\"GET SCALING FACTOR NANI\")\n        #print(v_mag)\n        #print(scaling_speed)\n        scale = 1.0\n        if (v_mag > scaling_speed):\n            ratio = (v_mag - scaling_speed) / (max_trans_vel - scaling_speed)\n            scale = max_scaling_factor * ratio + 1.0\n        return scale\n    \n    \"\"\"\n    Ok alright so, this is a pretty important discovery haha. Apparently this checks whether at this part of \n    the trajectory, what's the cost involved in terms of obstacles in the way and if there aren't any obstacles,\n    what's the cost associated?\n    \n    Unlike navstack, i  do not want costmap , world_model and footprint_specification to be paramters. They should be \n    made only gotten by the function that needs it at the time, which is costmap\n    \"\"\"\n    \n    \n    def footprintCost(self,pointObj, scale):\n        #print(\"Getting footprint cost\")\n        #print(self._costmap)\n        footprint_cost = self._costmap.footPrintCostRadius(pointObj)#0#self._costmap.footPrintCost(pointObj, None) #fix this later\n        if (footprint_cost < 0):\n            return -6\n        if(not self._costmap.isPointValid(pointObj)):\n            return -7.0\n        #print(pointObj)\n        cell = self._costmap.worldToMap(pointObj)\n        cellX,cellY = cell[0],cell[1]\n        #print(\"Cell coords: \")\n        #print(cell)\n        return max(max(0.0, footprint_cost), self._costmap.getCost(cellX,cellY)) \n        ","sub_path":"Scripts/Workstation/scripts/scripts/ObstacleCostFunction.py","file_name":"ObstacleCostFunction.py","file_ext":"py","file_size_in_byte":3340,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"557141505","text":"#\n# This file contains a pre-import framework for various CDB object types.  It supports reading an input spreadsheet\n# file, and then creating an output spreadsheet file that contains the standard columns for CDB import.  It is intended\n# to support the cable data collection process, which uses an Excel workbook to collect cable type and cable data from\n# users.  The pre-import process takes that data collection workbook, and augments/formats it for import to CDB.\n#\n# The framework initially supports pre-import data transformation for 3 types of objects: sources, cable types, and\n# cable designs.  To add support for a new type of object, you must provide concrete subclass implementations of the\n# framework's PreImportHelper and OutputObject bases classes.  For an example, see the classes \"SourceHelper\" and\n# SourceOutputObject, which together support the pre-import transformation of CDB \"Source\" objects.\n#\n# The framework opens the input spreadsheet and processes it row by row.  A python dictionary is created for each\n# input row.  The helper class validates and filters the input rows.  The framework collects a list of \"OutputObject\"\n# instances, and then iterates through them one by one, writing a row for each to the output spreadsheet.\n#\n# The roles of the framework classes that must be extended to add pre-import support for a new object type are as\n# follows.\n#\n# The PreImportHelper subclass specifies the input spreadsheet columns via the input_column_list() method,\n# including the column index and dictionary key name to use for each.  Not all input columns need be mapped,\n# only those that are used to generate the values in the output objects.  Column labels are not needed in the input\n# column specifications since we don't use them.  It also specifies the columns for the output spreadsheet via the\n# output_column_list() method, including for each a column index, label, and getter method name on the output object\n# to extract the column value.  It specifies the number of columns in the input spreadsheet (num_input_columns()) and\n# the number of columns in the output spreadsheet (num_output_columns()).  It implements get_output_object() which\n# takes the dictionary of values for a row from the input spreadsheet and produces an instance of the OutputObject\n# subclass that will be used to generate a row in the output spreadsheet.  Finally, the tag() function returns the\n# value that identifies the pre-import object type, e.g., \"Source\".  This value must match the \"--type\" command line\n# option.\n#\n# The OutputObject subclass's primary responsibility is to transform the data read from an input spreadsheet row into\n# the values needed for the corresponding row in the output spreadsheet.  Sometimes the output value is simply a copy of\n# the input value, but in other cases, we might perform a CDB query API lookup to transform a name from the input file\n# to the corresponding database identifier for that name in the output file.  Each getter method in the OutputObject\n# corresponds to a column in the output spreadsheet.  It is these getter methods that perform the required data\n# transformation, such as API query.  The column specifications in the helper's output_column_list() map the column\n# index in the output spreadsheet to the appropriate getter method on the output object.\n\n# Input spreadsheet format assumptions:\n#   * contains a single worksheet\n#   * contains 2 or more rows, header is on row 1, data starts on row 2\n#   * there are no empty rows within the data\n\n\nimport argparse\nimport logging\nimport sys\nfrom abc import ABC, abstractmethod\nimport re\n\nimport xlrd\nimport xlsxwriter\n\nfrom CdbApiFactory import CdbApiFactory\nfrom cdbApi import ApiException\n\n\n# constants\n\nCABLE_TYPE_NAME_KEY = \"name\"\nCABLE_TYPE_ALT_NAME_KEY = \"altName\"\nCABLE_TYPE_DESCRIPTION_KEY = \"description\"\nCABLE_TYPE_LINK_URL_KEY = \"linkUrl\"\nCABLE_TYPE_IMAGE_URL_KEY = \"imageUrl\"\nCABLE_TYPE_MANUFACTURER_KEY = \"manufacturer\"\nCABLE_TYPE_PART_NUMBER_KEY = \"partNumber\"\nCABLE_TYPE_ALT_PART_NUMBER_KEY = \"altPartNumber\"\nCABLE_TYPE_DIAMETER_KEY = \"diameter\"\nCABLE_TYPE_WEIGHT_KEY = \"weight\"\nCABLE_TYPE_CONDUCTORS_KEY = \"conductors\"\nCABLE_TYPE_INSULATION_KEY = \"insulation\"\nCABLE_TYPE_JACKET_COLOR_KEY = \"jacketColor\"\nCABLE_TYPE_VOLTAGE_RATING_KEY = \"voltageRating\"\nCABLE_TYPE_FIRE_LOAD_KEY = \"fireLoad\"\nCABLE_TYPE_HEAT_LIMIT_KEY = \"heatLimit\"\nCABLE_TYPE_BEND_RADIUS_KEY = \"bendRadius\"\nCABLE_TYPE_RAD_TOLERANCE_KEY = \"radTolerance\"\nCABLE_TYPE_TOTAL_LENGTH_KEY = \"totalLength\"\nCABLE_TYPE_REEL_LENGTH_KEY = \"reelLength\"\nCABLE_TYPE_REEL_QTY_KEY = \"reelQty\"\nCABLE_TYPE_LEAD_TIME_KEY = \"leadTime\"\nCABLE_TYPE_ORDERED_KEY = \"ordered\"\nCABLE_TYPE_RECEIVED_KEY = \"received\"\n\nCABLE_INVENTORY_NAME_KEY = \"name\"\n\nCABLE_DESIGN_NAME_KEY = \"name\"\nCABLE_DESIGN_LAYING_KEY = \"laying\"\nCABLE_DESIGN_VOLTAGE_KEY = \"voltage\"\nCABLE_DESIGN_OWNER_KEY = \"owner\"\nCABLE_DESIGN_TYPE_KEY = \"type\"\nCABLE_DESIGN_SRC_LOCATION_KEY = \"srcLocation\"\nCABLE_DESIGN_SRC_ANS_KEY = \"srcANS\"\nCABLE_DESIGN_SRC_ETPM_KEY = \"srcETPM\"\nCABLE_DESIGN_SRC_ADDRESS_KEY = \"srcAddress\"\nCABLE_DESIGN_SRC_DESCRIPTION_KEY = \"srcDescription\"\nCABLE_DESIGN_DEST_LOCATION_KEY = \"destLocation\"\nCABLE_DESIGN_DEST_ANS_KEY = \"destANS\"\nCABLE_DESIGN_DEST_ETPM_KEY = \"destETPM\"\nCABLE_DESIGN_DEST_ADDRESS_KEY = \"destAddress\"\nCABLE_DESIGN_DEST_DESCRIPTION_KEY = \"destDescription\"\nCABLE_DESIGN_LEGACY_ID_KEY = \"legacyId\"\nCABLE_DESIGN_FROM_DEVICE_NAME_KEY = \"fromDeviceName\"\nCABLE_DESIGN_TO_DEVICE_NAME_KEY = \"toDeviceName\"\nCABLE_DESIGN_MBA_ID_KEY = \"mbaId\"\nCABLE_DESIGN_IMPORT_ID_KEY = \"importId\"\nCABLE_DESIGN_QR_ID_KEY = \"qrId\"\nCABLE_DESIGN_VIA_ROUTE_KEY = \"via\"\nCABLE_DESIGN_WAYPOINT_ROUTE_KEY = \"waypoint\"\nCABLE_DESIGN_NOTES_KEY = \"notes\"\n\nname_manager = None\n\n\ndef register(helper_class):\n    PreImportHelper.register(helper_class.tag(), helper_class)\n\n\nclass PreImportHelper(ABC):\n\n    helperDict = {}\n\n    def __init__(self):\n        self.input_columns = {}\n        self.output_columns = {}\n        self.args = None\n        self.api = None\n        self.validate_only = False\n\n    # registers helper concrete classes for lookup by tag\n    @classmethod\n    def register(cls, tag, the_class):\n        cls.helperDict[tag] = the_class\n\n    # returns helper class for specified tag\n    @classmethod\n    def get_helper_class(cls, tag):\n        return cls.helperDict[tag]\n\n    # checks if specified tag is valid\n    @classmethod\n    def is_valid_type(cls, tag):\n        return tag in cls.helperDict\n\n    # creates instance of class with specified tag\n    @classmethod\n    def create_helper(cls, tag):\n        helper_class = cls.helperDict[tag]\n        helper_instance = helper_class()\n        return helper_instance\n\n    # Returns registered tag for subclass.\n    @staticmethod\n    @abstractmethod  # must be innermost decorator\n    def tag():\n        pass\n\n    # Returns expected number of columns in input spreadsheet.\n    @abstractmethod\n    def num_input_cols(self):\n        pass\n\n    def num_output_cols(self):\n        return len(self.output_column_list())\n\n    # Returns list of column models for input spreadsheet.  Not all columns need be mapped, only the ones we wish to\n    # read values from.\n    @abstractmethod\n    def input_column_list(self):\n        pass\n\n    # Returns list of column models for output spreadsheet.  Not all columns need be mapped, only the ones we wish to\n    # write values to.\n    @abstractmethod\n    def output_column_list(self):\n        pass\n\n    # Returns list of input handlers.\n    @abstractmethod\n    def input_handler_list(self):\n        pass\n\n    # Returns an output object for the specified input object, or None if the input object is duplicate.\n    @abstractmethod\n    def get_output_object(self, input_dict):\n        pass\n\n    # Allows subclasses to add command line parser args.  Default behavior is to do nothing.\n    # e.g., \"parser.add_argument(\"--cdbUser\", help=\"CDB User ID for API login\", required=True)\"\n    def add_parser_args(self, parser):\n        parser.add_argument(\"--validateOnly\", action=\"store_true\", help=\"perform basic validation only\", required=False)\n\n    def set_args(self, args):\n        self.args = args\n        print(\"validateOnly: %s\" % args.validateOnly)\n        self.validate_only = args.validateOnly\n\n    def get_args(self):\n        return self.args\n\n    def set_api(self, api):\n        self.api = api\n\n    # Builds dictionary whose keys are column index and value is column model object.\n    def initialize_input_columns(self):\n        for col in self.input_column_list():\n            self.input_columns[col.index] = col\n\n    # Builds dictionary whose keys are column index and value is column model object.\n    def initialize_output_columns(self):\n        for col in self.output_column_list():\n            self.output_columns[col.index] = col\n\n    # Handles cell value from input spreadsheet at specified column index for supplied input object.\n    def handle_input_cell_value(self, input_dict, index, value, row_num):\n        key = self.input_columns[index].key\n        input_dict[key] = value\n\n    def input_row_is_empty(self, input_dict, row_num):\n        non_empty_count = sum([1 for val in input_dict.values() if len(str(val)) > 0])\n        if non_empty_count == 0 or self.input_row_is_empty_custom(input_dict, row_num):\n            logging.debug(\"skipping empty row: %d\" % row_num)\n            return True\n\n    # Returns True if the row represented by input_dict should be treated as a blank row.  Default is False.  Subclass\n    # can override to allow certain non-empty values to be treated as empty.\n    def input_row_is_empty_custom(self, input_dict, row_num):\n        return False\n\n    # Performs validation on row from input spreadsheet and returns True if the row is determined to be valid.\n    # Can return False where input is valid, but it might be better to call sys.exit() with a useful message.\n    def input_row_is_valid(self, input_dict, row_num):\n\n        is_valid = True\n        valid_messages = []\n\n        for column in self.input_column_list():\n\n            required = column.required\n            if required:\n                value = input_dict[column.key]\n                if value is None or len(str(value)) == 0:\n                    is_valid = False\n                    valid_messages.append(\"required value missing for key: %s row index: %d\" % (column.key, row_num))\n\n        (custom_is_valid, custom_valid_string) = self.input_row_is_valid_custom(input_dict)\n        if not custom_is_valid:\n            is_valid = False\n            valid_messages.append(custom_valid_string)\n\n        return is_valid, valid_messages\n\n    # Performs custom validation on input row.  Returns True if row is valid.  Default is to return True. Subclass\n    # can override to customize.\n    def input_row_is_valid_custom(self, input_dict):\n        return True, \"\"\n\n    # Provides hook for subclasses to override to validate the input before generating the output spreadsheet.\n    def input_is_valid(self, output_objects):\n        return True, \"\"\n\n    def invoke_row_handlers(self, input_dict, row_num):\n\n        is_valid = True\n        valid_messages = []\n\n        for handler in self.input_handler_list():\n            (handler_is_valid, handler_valid_string) = handler.handle_input(input_dict)\n            if not handler_is_valid:\n                is_valid = False\n                valid_messages.append(handler_valid_string)\n\n        return is_valid, valid_messages\n\n    # Returns column label for specified column index.\n    def get_output_column_label(self, col_index):\n        return self.output_columns[col_index].label\n\n    # Returns value for output spreadsheet cell and supplied object at specified index.\n    def get_output_cell_value(self, obj, index):\n        # use reflection to invoke column getter method on supplied object\n        val = getattr(obj, self.output_columns[index].method)()\n        logging.debug(\"index: %d method: %s value: %s\" % (index, self.output_columns[index].method, val))\n        return val\n\n    # Complete helper processing after all output objects are processed.  Subclass overrides to customize.\n    def close(self):\n        pass\n\n\nclass ConnectedMenuManager:\n\n    def __init__(self, workbook):\n        self.name_dict = {}\n        self.initialize(workbook)\n\n    def add_name(self, name, values):\n        self.name_dict[name] = values\n\n    def initialize(self, workbook):\n\n        for name in workbook.name_obj_list:\n            if name.scope == -1:\n                # print(name.name)\n                name.book = workbook  # seems like an error that library doesn't do this internally\n                (sheet_name, ref) = name.formula_text.split('!')\n                if ':' in ref:\n                    (first_cell, last_cell) = ref.split(':')\n                else:\n                    first_cell = ref\n                    last_cell = ref\n                (first_cell_row, first_cell_col) = xl_cell_to_rowcol(first_cell)\n                (last_cell_row, last_cell_col) = xl_cell_to_rowcol(last_cell)\n                sheet = workbook.sheet_by_name(sheet_name)\n                values = []\n                for row_ind in range(first_cell_row, last_cell_row + 1):\n                    for col_ind in range(first_cell_col, last_cell_col + 1):\n                        values.append(sheet.cell(row_ind, col_ind).value)\n                        # print(\"\\t%s\" % sheet.cell(row_ind, col_ind).value)\n                self.add_name(name.name, values)\n\n    def has_name(self, range_name):\n        return range_name in self.name_dict\n\n    def value_is_valid_for_name(self, parent_value, child_value):\n        child_value_list = self.name_dict.get(parent_value)\n        if child_value_list is not None:\n            return child_value in child_value_list\n        else:\n            return False\n\n    def num_values_for_name(self, range_name):\n        if range_name in self.name_dict:\n            return len(self.name_dict[range_name])\n        return 0\n\n\nclass InputHandler(ABC):\n\n    def __init__(self, column_key):\n        self.column_key = column_key\n\n    # Invokes handler.\n    @abstractmethod\n    def handle_input(self, input_dict):\n        pass\n\n\nclass ManufacturerHandler(InputHandler):\n\n    def __init__(self, column_key, api, existing_sources, new_sources):\n        super().__init__(column_key)\n        self.existing_sources = existing_sources\n        self.new_sources = new_sources\n        self.api = api\n\n    def handle_input(self, input_dict):\n\n        manufacturer = input_dict[self.column_key].upper()\n\n        if len(manufacturer) == 0:\n            # no manufacturer specified, skip\n            return True, \"\"\n\n        if (manufacturer.upper in self.new_sources) or (manufacturer in self.existing_sources):\n            # already looked up this manufacturer\n            return True, \"\"\n\n        # check to see if manufacturer exists as a CDB Source\n        try:\n            mfr_source = self.api.getSourceApi().get_source_by_name(manufacturer)\n        except ApiException as ex:\n            if \"ObjectNotFound\" not in ex.body:\n                msg = \"exception retrieving source for manufacturer: %s - %s\" % (manufacturer, ex.body)\n                logging.error(msg)\n                print(msg)\n                return False, msg\n            mfr_source = None\n        if mfr_source:\n            # source already exists for mfr\n            self.existing_sources.append(manufacturer)\n\n        else:\n            # need to add new source for mfr\n            self.new_sources.append(manufacturer)\n\n        return True, \"\"\n\n\nclass ConnectedMenuHandler(InputHandler):\n\n    def __init__(self, column_key, parent_key):\n        super().__init__(column_key)\n        self.parent_key = parent_key\n\n    def handle_input(self, input_dict):\n        global name_manager\n        parent_value = input_dict[self.parent_key]\n        cell_value = input_dict[self.column_key]\n        if not name_manager.has_name(parent_value):\n            sys.exit(\"name manager has no menu range for: %s column: %s parent column: %s\" % (parent_value, self.column_key, self.parent_key))\n        has_child = name_manager.value_is_valid_for_name(parent_value, cell_value)\n        valid_string = \"\"\n        if not has_child:\n            valid_string = \"range for parent name %s does not include child name %s\" % (parent_value, cell_value)\n        return has_child, valid_string\n\n\nclass NamedRangeHandler(InputHandler):\n\n    def __init__(self, column_key, range_name):\n        super().__init__(column_key)\n        self.range_name = range_name\n\n    def handle_input(self, input_dict):\n        global name_manager\n        if not name_manager.has_name(self.range_name):\n            sys.exit(\"name manager has no named range for: %s\" % self.range_name)\n        cell_value = input_dict[self.column_key]\n        has_child = name_manager.value_is_valid_for_name(self.range_name, cell_value)\n        valid_string = \"\"\n        if not has_child:\n            valid_string = \"named range %s does not include value %s\" % (self.range_name, cell_value)\n        return has_child, valid_string\n\n\nclass DeviceAddressHandler(InputHandler):\n\n    def __init__(self, column_key, location_key, etpm_key):\n        super().__init__(column_key)\n        self.location_key = location_key\n        self.etpm_key = etpm_key\n\n    def handle_input(self, input_dict):\n\n        global name_manager\n\n        location_value = input_dict[self.location_key]\n        etpm_value = input_dict[self.etpm_key]\n        cell_value = input_dict[self.column_key]\n\n        range_name = \"\"\n        if location_value == \"SR_T\":\n            range_name = \"Snn\" + etpm_value[3:]\n        elif \"PS-\" in etpm_value:\n            range_name = \"_PS_CAB_SLOT_\"\n        else:\n            range_name = \"_RACK_AREA_\"\n\n        if not name_manager.has_name(range_name):\n            sys.exit(\"name manager has no named address range for: %s\" % range_name)\n\n        has_child = name_manager.value_is_valid_for_name(range_name, cell_value)\n        valid_string = \"\"\n        if not has_child:\n            valid_string = \"named address range %s does not include value %s\" % (range_name, cell_value)\n        return has_child, valid_string\n\n\nclass EndpointHandler(InputHandler):\n\n    def __init__(self, column_key, rack_key, hierarchy_name, api, rack_manager, missing_endpoints, nonunique_endpoints):\n        super().__init__(column_key)\n        self.rack_key = rack_key\n        self.hierarchy_name = hierarchy_name\n        self.api = api\n        self.rack_manager = rack_manager\n        self.missing_endpoints = missing_endpoints\n        self.nonunique_endpoints = nonunique_endpoints\n\n    def handle_input(self, input_dict):\n\n        endpoint_name = input_dict[self.column_key]\n        rack_name = input_dict[self.rack_key]\n\n        # check endpoint cache before calling API\n        if self.rack_manager.get_endpoint_id_for_rack(rack_name, endpoint_name) is not None:\n            logging.info(\"found endpoint: %s for rack: %s in cache\")\n            return True, \"\"\n\n        result_list = []\n        try:\n            result_list = self.api.getMachineDesignItemApi().get_md_in_hierarchy_by_name(self.hierarchy_name, rack_name, endpoint_name)\n        except ApiException as ex:\n            return False, \"exception invoking cdb endpoint retrieval api\"\n\n        is_valid = True\n        valid_string = \"\"\n\n        if len(result_list) == 0:\n            is_valid = False\n            valid_string = \"no endpoint item found with name: %s rack: %s in hierarchy: %s\" % (endpoint_name, rack_name, self.hierarchy_name)\n            logging.error(valid_string)\n            self.missing_endpoints.add(rack_name + \" + \" + endpoint_name)\n\n        elif len(result_list) > 1:\n            is_valid = False\n            valid_string = \"duplicate endpoint items found with name: %s rack: %s in hierarchy: %s\" % (endpoint_name, rack_name, self.hierarchy_name)\n            logging.error(valid_string)\n            self.nonunique_endpoints.add(rack_name + \" + \" + endpoint_name)\n\n        else:\n            endpoint_object = result_list[0]\n            endpoint_id = endpoint_object.id\n            self.rack_manager.add_endpoint_id_for_rack(rack_name, endpoint_name, endpoint_id)\n            logging.debug(\"found machine design item with name: %s, id: %s\" % (endpoint_name, endpoint_id))\n\n        return is_valid, valid_string\n\n\nclass CableTypeHandler(InputHandler):\n\n    def __init__(self, column_key, id_manager, api, missing_cable_type_list):\n        super().__init__(column_key)\n        self.id_manager = id_manager\n        self.api = api\n        self.missing_cable_type_list = missing_cable_type_list\n\n    def handle_input(self, input_dict):\n\n        cable_type_name = input_dict[self.column_key]\n\n        cached_id = self.id_manager.get_id_for_name(cable_type_name)\n        if cached_id is not None:\n            # nothing to do, id already cached\n            return True, \"\"\n\n        else:\n            # check to see if cable type exists in CDB by name\n            cable_type_object = None\n            try:\n                cable_type_object = self.api.getCableCatalogItemApi().get_cable_catalog_item_by_name(cable_type_name)\n            except ApiException as ex:\n                if \"ObjectNotFound\" not in ex.body:\n                    error_msg = \"unknown api exception retrieving cable catalog item: %s - %s\" % (cable_type_name, ex.body)\n                    logging.error(error_msg)\n                    return False, error_msg\n\n            if cable_type_object:\n                cable_type_id = cable_type_object.id\n                logging.debug(\"found cable type with name: %s, id: %s\" % (cable_type_name, cable_type_id))\n                self.id_manager.set_id_for_name(cable_type_name, cable_type_id)\n                return True, \"\"\n            else:\n                self.missing_cable_type_list.add(cable_type_name)\n                error_msg = \"no cable type found for name: %s\" % cable_type_name\n                logging.error(error_msg)\n                return False, error_msg\n\n\nclass SourceHandler(InputHandler):\n\n    def __init__(self, column_key, id_manager, api, missing_source_list):\n        super().__init__(column_key)\n        self.id_manager = id_manager\n        self.api = api\n        self.missing_source_list = missing_source_list\n\n    def handle_input(self, input_dict):\n\n        source_name = input_dict[self.column_key]\n\n        if source_name == \"\" or source_name is None:\n            return True, \"\"\n\n        cached_id = self.id_manager.get_id_for_name(source_name)\n        if cached_id is not None:\n            # nothing to do, id already cached\n            return True, \"\"\n\n        else:\n            # check to see if source exists in CDB by name\n            source_object = None\n            try:\n                source_object = self.api.getSourceApi().get_source_by_name(source_name)\n            except ApiException as ex:\n                if \"ObjectNotFound\" not in ex.body:\n                    error_msg = \"unknown api exception retrieving source item: %s - %s\" % (source_name, ex.body)\n                    logging.error(error_msg)\n                    return False, error_msg\n\n            if source_object:\n                source_id = source_object.id\n                logging.debug(\"found source with name: %s, id: %s\" % (source_name, source_id))\n                self.id_manager.set_id_for_name(source_name, source_id)\n                return True, \"\"\n            else:\n                self.missing_source_list.append(source_name)\n                error_msg = \"no source found for name: %s\" % source_name\n                logging.error(error_msg)\n                return False, error_msg\n            \n\nclass InputColumnModel:\n\n    def __init__(self, col_index, key, validator=None, required=False):\n        self.index = col_index\n        self.key = key\n        self.required = required\n\n\nclass OutputColumnModel:\n\n    def __init__(self, col_index, method, label=\"\"):\n        self.index = col_index\n        self.method = method\n        self.label = label\n\n\nclass OutputObject(ABC):\n\n    def __init__(self, helper, input_dict):\n        self.helper = helper\n        self.input_dict = input_dict\n\n\nclass IdManager():\n\n    def __init__(self):\n        self.name_id_dict = {}\n\n    def set_id_for_name(self, name, id):\n        self.name_id_dict[name] = id\n\n    def get_id_for_name(self, name):\n        if name in self.name_id_dict:\n            return self.name_id_dict[name]\n        else:\n            return None\n\n\nclass NameVariantManager():\n\n    def __init__(self):\n        self.name_list = []\n\n    def add_name(self, name):\n        is_valid = True\n        valid_string = \"\"\n        for n in self.name_list:\n            if n == name:\n                return True, \"\"\n            elif n.upper() == name.upper():\n                is_valid = False\n                valid_string = \"%s is variation of existing name %s\" % (name, n)\n        self.name_list.append(name)\n        return is_valid, valid_string\n\n\nclass RackManager():\n\n    def __init__(self):\n        self.rack_dict = {}\n\n    def add_endpoint_id_for_rack(self, rack_name, endpoint_name, endpoint_id):\n        if not rack_name in self.rack_dict:\n            self.rack_dict[rack_name] = {}\n        rack_contents = self.rack_dict[rack_name]\n        rack_contents[endpoint_name] = endpoint_id\n\n    def get_endpoint_id_for_rack(self, rack_name, endpoint_name):\n        if rack_name in self.rack_dict:\n            rack_items = self.rack_dict[rack_name]\n            if endpoint_name in rack_items:\n                return rack_items[endpoint_name]\n        return None\n\n\n@register\nclass SourceHelper(PreImportHelper):\n\n    def __init__(self):\n        super().__init__()\n        self.output_manufacturers = set()\n        self.source_name_manager = NameVariantManager()\n        self.existing_sources = []\n        self.new_sources = []\n\n    @staticmethod\n    def tag():\n        return \"Source\"\n\n    def num_input_cols(self):\n        return 23\n\n    def input_column_list(self):\n        column_list = [\n            InputColumnModel(col_index=2, key=CABLE_TYPE_MANUFACTURER_KEY),\n        ]\n        return column_list\n\n    def output_column_list(self):\n        column_list = [\n            OutputColumnModel(col_index=0, method=\"get_name\", label=\"Name\"),\n            OutputColumnModel(col_index=1, method=\"get_description\", label=\"Description\"),\n            OutputColumnModel(col_index=2, method=\"get_contact_info\", label=\"Contact Info\"),\n            OutputColumnModel(col_index=3, method=\"get_url\", label=\"URL\"),\n        ]\n        return column_list\n\n    def input_handler_list(self):\n        global name_manager\n        handler_list = [\n            ManufacturerHandler(CABLE_TYPE_MANUFACTURER_KEY, self.api, self.existing_sources, self.new_sources),\n        ]\n        return handler_list\n\n    def get_output_object(self, input_dict):\n\n        manufacturer = input_dict[CABLE_TYPE_MANUFACTURER_KEY].upper()\n\n        if len(manufacturer) == 0:\n            logging.debug(\"manufacturer is empty\")\n            return None\n\n        logging.debug(\"found manufacturer: %s\" % manufacturer)\n\n        if manufacturer in self.existing_sources:\n            # don't need to import this item\n            logging.debug(\"skipping existing cdb manufacturer: %s\" % manufacturer)\n            return None\n\n        if manufacturer not in self.output_manufacturers:\n            # need to write this object to output spreadsheet for import\n            self.output_manufacturers.add(manufacturer)\n            logging.debug(\"adding new manufacturer: %s to output\" % manufacturer)\n            return SourceOutputObject(helper=self, input_dict=input_dict)\n\n        else:\n            # already added to output spreadsheet\n            logging.debug(\"ignoring duplicate manufacturer: %s\" % manufacturer)\n            return None\n\n    def close(self):\n        if len(self.new_sources) > 0 or len(self.existing_sources) > 0:\n\n            if not self.args.infoFile:\n                print(\"provide command line arg 'infoFile' to generate debugging output file\")\n                return\n\n            output_book = xlsxwriter.Workbook(self.args.infoFile)\n            output_sheet = output_book.add_worksheet()\n\n            output_sheet.write(0, 0, \"new sources\")\n            output_sheet.write(0, 1, \"existing cdb sources\")\n\n            row_index = 1\n            for src in sorted(self.new_sources):\n                output_sheet.write(row_index, 0, src)\n                row_index = row_index + 1\n\n            row_index = 1\n            for src in (self.existing_sources):\n                output_sheet.write(row_index, 1, src)\n                row_index = row_index + 1\n\n            output_book.close()\n\n\nclass SourceOutputObject(OutputObject):\n\n    def __init__(self, helper, input_dict):\n        super().__init__(helper, input_dict)\n        self.description = \"\"\n        self.contact_info = \"\"\n        self.url = \"\"\n\n    def get_name(self):\n        return self.input_dict[CABLE_TYPE_MANUFACTURER_KEY]\n\n    def get_description(self):\n        return self.description\n\n    def get_contact_info(self):\n        return self.contact_info\n\n    def get_url(self):\n        return self.url\n\n\n@register\nclass CableTypeHelper(PreImportHelper):\n\n    def __init__(self):\n        super().__init__()\n        self.source_id_manager = IdManager()\n        self.missing_source_list = []\n\n    # Adds helper specific command line args.\n    # e.g., \"parser.add_argument(\"--cdbUser\", help=\"CDB User ID for API login\", required=True)\"\n    def add_parser_args(self, parser):\n        super().add_parser_args(parser)\n        parser.add_argument(\"--projectId\", help=\"CDB item category ID for project (item_project table)\", required=True)\n        parser.add_argument(\"--techSystemId\", help=\"CDB technical system ID for owner\", required=True)\n        parser.add_argument(\"--ownerUserId\", help=\"CDB user ID for owner\", required=True)\n        parser.add_argument(\"--ownerGroupId\", help=\"CDB group ID for owner\", required=True)\n        parser.add_argument(\"--namedRange\", help=\"Excel named range for technical system's cable types\", required=True)\n\n    def set_args(self, args):\n        super().set_args(args)\n        print(\"CDB project id: %s\" % args.projectId)\n        print(\"CDB technical system id: %s\" % args.techSystemId)\n        print(\"CDB owner user id: %s\" % args.ownerUserId)\n        print(\"CDB owner group id: %s\" % args.ownerGroupId)\n        print(\"Excel named range for technical system cable types: %s\" % args.namedRange)\n\n    @staticmethod\n    def tag():\n        return \"CableType\"\n\n    def num_input_cols(self):\n        return 23\n\n    def input_column_list(self):\n        column_list = [\n            InputColumnModel(col_index=0, key=CABLE_TYPE_NAME_KEY, required=True),\n            InputColumnModel(col_index=1, key=CABLE_TYPE_DESCRIPTION_KEY),\n            InputColumnModel(col_index=2, key=CABLE_TYPE_MANUFACTURER_KEY),\n            InputColumnModel(col_index=3, key=CABLE_TYPE_PART_NUMBER_KEY),\n            InputColumnModel(col_index=4, key=CABLE_TYPE_ALT_PART_NUMBER_KEY),\n            InputColumnModel(col_index=5, key=CABLE_TYPE_DIAMETER_KEY),\n            InputColumnModel(col_index=6, key=CABLE_TYPE_WEIGHT_KEY),\n            InputColumnModel(col_index=7, key=CABLE_TYPE_CONDUCTORS_KEY),\n            InputColumnModel(col_index=8, key=CABLE_TYPE_INSULATION_KEY),\n            InputColumnModel(col_index=9, key=CABLE_TYPE_JACKET_COLOR_KEY),\n            InputColumnModel(col_index=10, key=CABLE_TYPE_VOLTAGE_RATING_KEY),\n            InputColumnModel(col_index=11, key=CABLE_TYPE_FIRE_LOAD_KEY),\n            InputColumnModel(col_index=12, key=CABLE_TYPE_HEAT_LIMIT_KEY),\n            InputColumnModel(col_index=13, key=CABLE_TYPE_BEND_RADIUS_KEY),\n            InputColumnModel(col_index=14, key=CABLE_TYPE_RAD_TOLERANCE_KEY),\n            InputColumnModel(col_index=15, key=CABLE_TYPE_LINK_URL_KEY),\n            InputColumnModel(col_index=16, key=CABLE_TYPE_IMAGE_URL_KEY),\n            InputColumnModel(col_index=17, key=CABLE_TYPE_TOTAL_LENGTH_KEY),\n            InputColumnModel(col_index=18, key=CABLE_TYPE_REEL_LENGTH_KEY),\n            InputColumnModel(col_index=19, key=CABLE_TYPE_REEL_QTY_KEY),\n            InputColumnModel(col_index=20, key=CABLE_TYPE_LEAD_TIME_KEY),\n            InputColumnModel(col_index=21, key=CABLE_TYPE_ORDERED_KEY),\n            InputColumnModel(col_index=22, key=CABLE_TYPE_RECEIVED_KEY),\n        ]\n        return column_list\n\n    def output_column_list(self):\n        column_list = [\n            OutputColumnModel(col_index=0, method=\"get_name\", label=CABLE_TYPE_NAME_KEY),\n            OutputColumnModel(col_index=1, method=\"get_alt_name\", label=CABLE_TYPE_ALT_NAME_KEY),\n            OutputColumnModel(col_index=2, method=\"get_description\", label=CABLE_TYPE_DESCRIPTION_KEY),\n            OutputColumnModel(col_index=3, method=\"get_link_url\", label=CABLE_TYPE_LINK_URL_KEY),\n            OutputColumnModel(col_index=4, method=\"get_image_url\", label=CABLE_TYPE_IMAGE_URL_KEY),\n            OutputColumnModel(col_index=5, method=\"get_manufacturer_id\", label=CABLE_TYPE_MANUFACTURER_KEY),\n            OutputColumnModel(col_index=6, method=\"get_part_number\", label=CABLE_TYPE_PART_NUMBER_KEY),\n            OutputColumnModel(col_index=7, method=\"get_alt_part_number\", label=CABLE_TYPE_ALT_PART_NUMBER_KEY),\n            OutputColumnModel(col_index=8, method=\"get_diameter\", label=CABLE_TYPE_DIAMETER_KEY),\n            OutputColumnModel(col_index=9, method=\"get_weight\", label=CABLE_TYPE_WEIGHT_KEY),\n            OutputColumnModel(col_index=10, method=\"get_conductors\", label=CABLE_TYPE_CONDUCTORS_KEY),\n            OutputColumnModel(col_index=11, method=\"get_insulation\", label=CABLE_TYPE_INSULATION_KEY),\n            OutputColumnModel(col_index=12, method=\"get_jacket_color\", label=CABLE_TYPE_JACKET_COLOR_KEY),\n            OutputColumnModel(col_index=13, method=\"get_voltage_rating\", label=CABLE_TYPE_VOLTAGE_RATING_KEY),\n            OutputColumnModel(col_index=14, method=\"get_fire_load\", label=CABLE_TYPE_FIRE_LOAD_KEY),\n            OutputColumnModel(col_index=15, method=\"get_heat_limit\", label=CABLE_TYPE_HEAT_LIMIT_KEY),\n            OutputColumnModel(col_index=16, method=\"get_bend_radius\", label=CABLE_TYPE_BEND_RADIUS_KEY),\n            OutputColumnModel(col_index=17, method=\"get_rad_tolerance\", label=CABLE_TYPE_RAD_TOLERANCE_KEY),\n            OutputColumnModel(col_index=18, method=\"get_total_length\", label=CABLE_TYPE_TOTAL_LENGTH_KEY),\n            OutputColumnModel(col_index=19, method=\"get_reel_length\", label=CABLE_TYPE_REEL_LENGTH_KEY),\n            OutputColumnModel(col_index=20, method=\"get_reel_qty\", label=CABLE_TYPE_REEL_QTY_KEY),\n            OutputColumnModel(col_index=21, method=\"get_lead_time\", label=CABLE_TYPE_LEAD_TIME_KEY),\n            OutputColumnModel(col_index=22, method=\"get_procurement_status\", label=\"procurement status\"),\n            OutputColumnModel(col_index=23, method=\"get_project_id\", label=\"project id\"),\n            OutputColumnModel(col_index=24, method=\"get_tech_system_id\", label=\"technical system\"),\n            OutputColumnModel(col_index=25, method=\"get_owner_user_id\", label=\"owner user\"),\n            OutputColumnModel(col_index=26, method=\"get_owner_group_id\", label=\"owner group\"),\n        ]\n        return column_list\n\n    def input_handler_list(self):\n        global name_manager\n        handler_list = [\n            NamedRangeHandler(CABLE_TYPE_NAME_KEY, self.args.namedRange),\n            SourceHandler(CABLE_TYPE_MANUFACTURER_KEY, self.source_id_manager, self.api, self.missing_source_list),\n        ]\n        return handler_list\n\n    def get_output_object(self, input_dict):\n\n        logging.debug(\"adding output object for: %s\" % input_dict[CABLE_TYPE_NAME_KEY])\n        return CableTypeOutputObject(helper=self, input_dict=input_dict)\n\n    # Provides hook for subclasses to override to validate the input before generating the output spreadsheet.\n    def input_is_valid(self, output_objects):\n\n        global name_manager\n\n        cable_type_named_range = self.args.namedRange\n\n        if len(output_objects) < name_manager.num_values_for_name(cable_type_named_range):\n            return False, \"fewer rows in output spreadsheet than named range of cable types for technical system\"\n\n        return True, \"\"\n\n    def close(self):\n        if len(self.missing_source_list) > 0:\n\n            if not self.args.infoFile:\n                print(\"provide command line arg 'infoFile' to generate debugging output file\")\n                return\n\n            output_book = xlsxwriter.Workbook(self.args.infoFile)\n            output_sheet = output_book.add_worksheet()\n\n            output_sheet.write(0, 0, \"missing sources\")\n\n            row_index = 1\n            for src in sorted(self.missing_source_list):\n                output_sheet.write(row_index, 0, src)\n                row_index = row_index + 1\n\n            output_book.close()\n\n\nclass CableTypeOutputObject(OutputObject):\n\n    def __init__(self, helper, input_dict):\n        super().__init__(helper, input_dict)\n\n    def get_name(self):\n        return self.input_dict[CABLE_TYPE_NAME_KEY]\n\n    def get_alt_name(self):\n        return None\n\n    def get_description(self):\n        return self.input_dict[CABLE_TYPE_DESCRIPTION_KEY]\n\n    def get_link_url(self):\n        return self.input_dict[CABLE_TYPE_LINK_URL_KEY]\n\n    def get_image_url(self):\n        return self.input_dict[CABLE_TYPE_IMAGE_URL_KEY]\n\n    def get_manufacturer_id(self):\n        source_name = self.input_dict[CABLE_TYPE_MANUFACTURER_KEY]\n        return self.helper.source_id_manager.get_id_for_name(source_name)\n\n    def get_part_number(self):\n        return self.input_dict[CABLE_TYPE_PART_NUMBER_KEY]\n\n    def get_alt_part_number(self):\n        return self.input_dict[CABLE_TYPE_ALT_PART_NUMBER_KEY]\n\n    def get_diameter(self):\n        return self.input_dict[CABLE_TYPE_DIAMETER_KEY]\n\n    def get_weight(self):\n        return self.input_dict[CABLE_TYPE_WEIGHT_KEY]\n\n    def get_conductors(self):\n        return self.input_dict[CABLE_TYPE_CONDUCTORS_KEY]\n\n    def get_insulation(self):\n        return self.input_dict[CABLE_TYPE_INSULATION_KEY]\n\n    def get_jacket_color(self):\n        return self.input_dict[CABLE_TYPE_JACKET_COLOR_KEY]\n\n    def get_voltage_rating(self):\n        return self.input_dict[CABLE_TYPE_VOLTAGE_RATING_KEY]\n\n    def get_fire_load(self):\n        return self.input_dict[CABLE_TYPE_FIRE_LOAD_KEY]\n\n    def get_heat_limit(self):\n        return self.input_dict[CABLE_TYPE_HEAT_LIMIT_KEY]\n\n    def get_bend_radius(self):\n        return self.input_dict[CABLE_TYPE_BEND_RADIUS_KEY]\n\n    def get_rad_tolerance(self):\n        return self.input_dict[CABLE_TYPE_RAD_TOLERANCE_KEY]\n\n    def get_total_length(self):\n        return self.input_dict[CABLE_TYPE_TOTAL_LENGTH_KEY]\n\n    def get_reel_length(self):\n        return self.input_dict[CABLE_TYPE_REEL_LENGTH_KEY]\n\n    def get_reel_qty(self):\n        return self.input_dict[CABLE_TYPE_REEL_QTY_KEY]\n\n    def get_lead_time(self):\n        return self.input_dict[CABLE_TYPE_LEAD_TIME_KEY]\n\n    def get_procurement_status(self):\n        if len(self.input_dict[CABLE_TYPE_RECEIVED_KEY]) > 0:\n            return \"Received\"\n        elif len(self.input_dict[CABLE_TYPE_ORDERED_KEY]) > 0:\n            return \"Ordered\"\n        else:\n            return \"Unspecified\"\n\n    def get_procurement_info(self):\n        proc_info = \"\"\n        if len(str(self.input_dict[CABLE_TYPE_ORDERED_KEY])) > 0:\n            proc_info = proc_info + \"Ordered: \" + str(self.input_dict[CABLE_TYPE_ORDERED_KEY]) + \". \"\n        if len(str(self.input_dict[CABLE_TYPE_RECEIVED_KEY])) > 0:\n            proc_info = proc_info + \"Received: \" + str(self.input_dict[CABLE_TYPE_RECEIVED_KEY]) + \".\"\n\n        return proc_info\n\n    def get_project_id(self):\n        return self.helper.get_args().projectId\n\n    def get_tech_system_id(self):\n        return self.helper.get_args().techSystemId\n\n    def get_owner_user_id(self):\n        return self.helper.get_args().ownerUserId\n\n    def get_owner_group_id(self):\n        return self.helper.get_args().ownerGroupId\n\n\n@register\nclass CableInventoryHelper(PreImportHelper):\n\n    def __init__(self):\n        super().__init__()\n        self.cable_type_id_manager = IdManager()\n        self.missing_cable_types = set()\n\n    # Adds helper specific command line args.\n    # e.g., \"parser.add_argument(\"--cdbUser\", help=\"CDB User ID for API login\", required=True)\"\n    def add_parser_args(self, parser):\n        super().add_parser_args(parser)\n        parser.add_argument(\"--projectId\", help=\"CDB project ID\", required=True)\n        parser.add_argument(\"--ownerUserId\", help=\"CDB owner user ID\", required=True)\n        parser.add_argument(\"--ownerGroupId\", help=\"CDB owner group ID\", required=True)\n\n    def set_args(self, args):\n        super().set_args(args)\n        print(\"CDB project id: %s\" % args.projectId)\n        print(\"CDB owner user id: %s\" % args.ownerUserId)\n        print(\"CDB owner group id: %s\" % args.ownerGroupId)\n\n    @staticmethod\n    def tag():\n        return \"CableInventory\"\n\n    def num_input_cols(self):\n        return 23\n\n    def input_column_list(self):\n        column_list = [\n            InputColumnModel(col_index=0, key=CABLE_DESIGN_NAME_KEY, required=True),\n            InputColumnModel(col_index=3, key=CABLE_DESIGN_OWNER_KEY, required=True),\n            InputColumnModel(col_index=4, key=CABLE_DESIGN_TYPE_KEY, required=True),\n            InputColumnModel(col_index=15, key=CABLE_DESIGN_LEGACY_ID_KEY),\n            InputColumnModel(col_index=18, key=CABLE_DESIGN_MBA_ID_KEY),\n            InputColumnModel(col_index=19, key=CABLE_DESIGN_IMPORT_ID_KEY, required=True),\n            InputColumnModel(col_index=20, key=CABLE_DESIGN_QR_ID_KEY, required=False),\n        ]\n        return column_list\n\n    def output_column_list(self):\n        column_list = [\n            OutputColumnModel(col_index=0, method=\"get_cable_type_id\", label=\"Catalog Item\"),\n            OutputColumnModel(col_index=1, method=\"get_tag\", label=\"Tag\"),\n            OutputColumnModel(col_index=2, method=\"get_qr_id\", label=\"QR ID\"),\n            OutputColumnModel(col_index=3, method=\"get_description\", label=\"Description\"),\n            OutputColumnModel(col_index=4, method=\"get_status\", label=\"Status\"),\n            OutputColumnModel(col_index=5, method=\"get_location\", label=\"Location\"),\n            OutputColumnModel(col_index=6, method=\"get_location_details\", label=\"Location_Details\"),\n            OutputColumnModel(col_index=7, method=\"get_length\", label=\"Length\"),\n            OutputColumnModel(col_index=8, method=\"get_project_id\", label=\"Project ID\"),\n            OutputColumnModel(col_index=9, method=\"get_owner_user_id\", label=\"Owner User\"),\n            OutputColumnModel(col_index=10, method=\"get_owner_group_id\", label=\"Owner Group\"),\n        ]\n        return column_list\n\n    def input_handler_list(self):\n        global name_manager\n        handler_list = [\n            CableTypeHandler(CABLE_DESIGN_TYPE_KEY, self.cable_type_id_manager, self.api, self.missing_cable_types),\n        ]\n        return handler_list\n\n    # Treat a row that contains a single non-empty value in the \"import id\" column as an empty row.\n    def input_row_is_empty_custom(self, input_dict, row_num):\n        non_empty_count = sum([1 for val in input_dict.values() if len(str(val)) > 0])\n        if non_empty_count == 1 and len(str(input_dict[CABLE_DESIGN_IMPORT_ID_KEY])) > 0:\n            logging.debug(\"skipping empty row with non-empty import id: %s row: %d\" %\n                          (input_dict[CABLE_DESIGN_IMPORT_ID_KEY], row_num))\n            return True\n\n    def get_output_object(self, input_dict):\n\n        logging.debug(\"adding output object for: %s\" % input_dict[CABLE_INVENTORY_NAME_KEY])\n        return CableInventoryOutputObject(helper=self, input_dict=input_dict)\n\n\nclass CableInventoryOutputObject(OutputObject):\n\n    def __init__(self, helper, input_dict):\n        super().__init__(helper, input_dict)\n\n    def get_cable_type_id(self):\n        cable_type_name = self.input_dict[CABLE_DESIGN_TYPE_KEY]\n        return self.helper.cable_type_id_manager.get_id_for_name(cable_type_name)\n\n    def get_tag(self):\n        return \"auto\"\n\n    def get_qr_id(self):\n        return self.input_dict[CABLE_DESIGN_QR_ID_KEY]\n\n    def get_description(self):\n        return None\n\n    def get_status(self):\n        return \"Planned\"\n\n    def get_location(self):\n        return None\n\n    def get_location_details(self):\n        return None\n\n    def get_length(self):\n        return None\n\n    def get_project_id(self):\n        return self.helper.get_args().projectId\n\n    def get_owner_user_id(self):\n        return self.helper.get_args().ownerUserId\n\n    def get_owner_group_id(self):\n        return self.helper.get_args().ownerGroupId\n\n\n@register\nclass CableDesignHelper(PreImportHelper):\n\n    def __init__(self):\n        super().__init__()\n        self.rack_manager = RackManager()\n        self.md_root = None\n        self.cable_type_id_manager = IdManager()\n        self.missing_endpoints = set()\n        self.missing_cable_types = set()\n        self.nonunique_endpoints = set()\n        self.info_file = None\n\n    # Adds helper specific command line args.\n    # e.g., \"parser.add_argument(\"--cdbUser\", help=\"CDB User ID for API login\", required=True)\"\n    def add_parser_args(self, parser):\n        super().add_parser_args(parser)\n        parser.add_argument(\"--techSystemId\", help=\"CDB technical system ID for owner\", required=True)\n        parser.add_argument(\"--projectId\", help=\"CDB project ID\", required=True)\n        parser.add_argument(\"--mdRoot\", help=\"CDB top-level parent machine design node name\", required=True)\n        parser.add_argument(\"--ownerUserId\", help=\"CDB owner user ID\", required=True)\n        parser.add_argument(\"--ownerGroupId\", help=\"CDB owner group ID\", required=True)\n\n    def set_args(self, args):\n        super().set_args(args)\n        print(\"CDB technical system id: %s\" % args.techSystemId)\n        print(\"CDB project id: %s\" % args.projectId)\n        print(\"CDB owner user id: %s\" % args.ownerUserId)\n        print(\"CDB owner group id: %s\" % args.ownerGroupId)\n        print(\"top-level parent machine design node name: %s\" % args.mdRoot)\n        self.md_root = args.mdRoot\n        self.info_file = args.infoFile\n\n    @staticmethod\n    def tag():\n        return \"CableDesign\"\n\n    def num_input_cols(self):\n        return 24\n\n    def input_column_list(self):\n        column_list = [\n            InputColumnModel(col_index=0, key=CABLE_DESIGN_NAME_KEY, required=True),\n            InputColumnModel(col_index=1, key=CABLE_DESIGN_LAYING_KEY, required=True),\n            InputColumnModel(col_index=2, key=CABLE_DESIGN_VOLTAGE_KEY, required=True),\n            InputColumnModel(col_index=3, key=CABLE_DESIGN_OWNER_KEY, required=True),\n            InputColumnModel(col_index=4, key=CABLE_DESIGN_TYPE_KEY, required=True),\n            InputColumnModel(col_index=5, key=CABLE_DESIGN_SRC_LOCATION_KEY, required=True),\n            InputColumnModel(col_index=6, key=CABLE_DESIGN_SRC_ANS_KEY, required=True),\n            InputColumnModel(col_index=7, key=CABLE_DESIGN_SRC_ETPM_KEY, required=True),\n            InputColumnModel(col_index=8, key=CABLE_DESIGN_SRC_ADDRESS_KEY, required=True),\n            InputColumnModel(col_index=9, key=CABLE_DESIGN_SRC_DESCRIPTION_KEY, required=True),\n            InputColumnModel(col_index=10, key=CABLE_DESIGN_DEST_LOCATION_KEY, required=True),\n            InputColumnModel(col_index=11, key=CABLE_DESIGN_DEST_ANS_KEY, required=True),\n            InputColumnModel(col_index=12, key=CABLE_DESIGN_DEST_ETPM_KEY, required=True),\n            InputColumnModel(col_index=13, key=CABLE_DESIGN_DEST_ADDRESS_KEY, required=True),\n            InputColumnModel(col_index=14, key=CABLE_DESIGN_DEST_DESCRIPTION_KEY, required=True),\n            InputColumnModel(col_index=15, key=CABLE_DESIGN_LEGACY_ID_KEY),\n            InputColumnModel(col_index=16, key=CABLE_DESIGN_FROM_DEVICE_NAME_KEY, required=True),\n            InputColumnModel(col_index=17, key=CABLE_DESIGN_TO_DEVICE_NAME_KEY, required=True),\n            InputColumnModel(col_index=18, key=CABLE_DESIGN_MBA_ID_KEY),\n            InputColumnModel(col_index=19, key=CABLE_DESIGN_IMPORT_ID_KEY, required=True),\n            InputColumnModel(col_index=20, key=CABLE_DESIGN_QR_ID_KEY, required=False),\n            InputColumnModel(col_index=21, key=CABLE_DESIGN_VIA_ROUTE_KEY, required=False),\n            InputColumnModel(col_index=22, key=CABLE_DESIGN_WAYPOINT_ROUTE_KEY, required=False),\n            InputColumnModel(col_index=23, key=CABLE_DESIGN_NOTES_KEY, required=False),\n        ]\n        return column_list\n\n    def output_column_list(self):\n        column_list = [\n            OutputColumnModel(col_index=0, method=\"get_name\", label=\"name\"),\n            OutputColumnModel(col_index=1, method=\"get_alt_name\", label=\"alt name\"),\n            OutputColumnModel(col_index=2, method=\"get_ext_name\", label=\"ext cable name\"),\n            OutputColumnModel(col_index=3, method=\"get_import_id\", label=\"import cable id\"),\n            OutputColumnModel(col_index=4, method=\"get_alt_id\", label=\"alt cable id\"),\n            OutputColumnModel(col_index=5, method=\"get_qr_id\", label=\"legacy qr id\"),\n            OutputColumnModel(col_index=6, method=\"get_description\", label=\"description\"),\n            OutputColumnModel(col_index=7, method=\"get_laying\", label=\"laying\"),\n            OutputColumnModel(col_index=8, method=\"get_voltage\", label=\"voltage\"),\n            OutputColumnModel(col_index=9, method=\"get_cable_type_id\", label=\"cable type id\"),\n            OutputColumnModel(col_index=10, method=\"get_endpoint1_id\", label=\"endpoint1 id\"),\n            OutputColumnModel(col_index=11, method=\"get_endpoint1_description\", label=\"endpoint1 description\"),\n            OutputColumnModel(col_index=12, method=\"get_endpoint1_route\", label=\"endpoint1 route\"),\n            OutputColumnModel(col_index=13, method=\"get_endpoint2_id\", label=\"endpoint2 id\"),\n            OutputColumnModel(col_index=14, method=\"get_endpoint2_description\", label=\"endpoint2 description\"),\n            OutputColumnModel(col_index=15, method=\"get_endpoint2_route\", label=\"endpoint2 route\"),\n            OutputColumnModel(col_index=16, method=\"get_project_id\", label=\"project id\"),\n            OutputColumnModel(col_index=17, method=\"get_tech_system_id\", label=\"technical system\"),\n            OutputColumnModel(col_index=18, method=\"get_owner_user_id\", label=\"owner user\"),\n            OutputColumnModel(col_index=19, method=\"get_owner_group_id\", label=\"owner group\"),\n        ]\n        return column_list\n    \n    def input_handler_list(self):\n        global name_manager\n        handler_list = [\n            NamedRangeHandler(CABLE_DESIGN_LAYING_KEY, \"_Laying\"),\n            NamedRangeHandler(CABLE_DESIGN_VOLTAGE_KEY, \"_Voltage\"),\n            NamedRangeHandler(CABLE_DESIGN_OWNER_KEY, \"_Owner\"),\n            ConnectedMenuHandler(CABLE_DESIGN_TYPE_KEY, CABLE_DESIGN_OWNER_KEY),\n            CableTypeHandler(CABLE_DESIGN_TYPE_KEY, self.cable_type_id_manager, self.api, self.missing_cable_types),\n            NamedRangeHandler(CABLE_DESIGN_SRC_LOCATION_KEY, \"_Location\"),\n            ConnectedMenuHandler(CABLE_DESIGN_SRC_ANS_KEY, CABLE_DESIGN_SRC_LOCATION_KEY),\n            ConnectedMenuHandler(CABLE_DESIGN_SRC_ETPM_KEY, CABLE_DESIGN_SRC_ANS_KEY),\n            DeviceAddressHandler(CABLE_DESIGN_SRC_ADDRESS_KEY, CABLE_DESIGN_SRC_LOCATION_KEY, CABLE_DESIGN_SRC_ETPM_KEY),\n            NamedRangeHandler(CABLE_DESIGN_DEST_LOCATION_KEY, \"_Location\"),\n            ConnectedMenuHandler(CABLE_DESIGN_DEST_ANS_KEY, CABLE_DESIGN_DEST_LOCATION_KEY),\n            ConnectedMenuHandler(CABLE_DESIGN_DEST_ETPM_KEY, CABLE_DESIGN_DEST_ANS_KEY),\n            DeviceAddressHandler(CABLE_DESIGN_DEST_ADDRESS_KEY, CABLE_DESIGN_DEST_LOCATION_KEY, CABLE_DESIGN_DEST_ETPM_KEY),\n        ]\n\n        if not self.validate_only:\n            handler_list.append(EndpointHandler(CABLE_DESIGN_FROM_DEVICE_NAME_KEY, CABLE_DESIGN_SRC_ETPM_KEY, self.get_md_root(), self.api, self.rack_manager, self.missing_endpoints, self.nonunique_endpoints))\n            handler_list.append(EndpointHandler(CABLE_DESIGN_TO_DEVICE_NAME_KEY, CABLE_DESIGN_DEST_ETPM_KEY, self.get_md_root(), self.api, self.rack_manager, self.missing_endpoints, self.nonunique_endpoints))\n\n        return handler_list\n\n\n    def get_md_root(self):\n        return self.md_root\n\n    # Treat a row that contains a single non-empty value in the \"import id\" column as an empty row.\n    def input_row_is_empty_custom(self, input_dict, row_num):\n        non_empty_count = sum([1 for val in input_dict.values() if len(str(val)) > 0])\n        if non_empty_count == 2 and ((len(str(input_dict[CABLE_DESIGN_IMPORT_ID_KEY])) > 0) and (input_dict[CABLE_DESIGN_NAME_KEY] == \"[] | []\")):\n            logging.debug(\"skipping empty row with non-empty import id: %s row: %d\" %\n                          (input_dict[CABLE_DESIGN_IMPORT_ID_KEY], row_num))\n            return True\n\n    def get_output_object(self, input_dict):\n\n        logging.debug(\"adding output object for: %s\" % input_dict[CABLE_DESIGN_NAME_KEY])\n        return CableDesignOutputObject(helper=self, input_dict=input_dict)\n\n    def close(self):\n\n        if len(self.missing_cable_types) > 0 or len(self.missing_endpoints) > 0 or len(self.nonunique_endpoints) > 0:\n\n            if not self.args.infoFile:\n                print(\"provide command line arg 'infoFile' to generate debugging output file\")\n                return\n\n            output_book = xlsxwriter.Workbook(self.args.infoFile)\n            output_sheet = output_book.add_worksheet()\n\n            output_sheet.write(0, 0, \"missing cable types\")\n            output_sheet.write(0, 1, \"missing endpoints\")\n            output_sheet.write(0, 2, \"non-unique endpoints\")\n\n            row_index = 1\n            for cable_type_name in sorted(self.missing_cable_types):\n                output_sheet.write(row_index, 0, cable_type_name)\n                row_index = row_index + 1\n\n            row_index = 1\n            for endpoint_name in sorted(self.missing_endpoints):\n                output_sheet.write(row_index, 1, endpoint_name)\n                row_index = row_index + 1\n\n            row_index = 1\n            for endpoint_name in sorted(self.nonunique_endpoints):\n                output_sheet.write(row_index, 2, endpoint_name)\n                row_index = row_index + 1\n\n            output_book.close()\n\n\nclass CableDesignOutputObject(OutputObject):\n\n    def __init__(self, helper, input_dict):\n        super().__init__(helper, input_dict)\n\n    @classmethod\n    def get_name_cls(cls, row_dict):\n\n        # use legacy_id if specified\n        legacy_id = row_dict[CABLE_DESIGN_LEGACY_ID_KEY]\n        if len(legacy_id) > 0:\n            return legacy_id\n\n        # next try MBA cable id\n        mba_id = row_dict[CABLE_DESIGN_MBA_ID_KEY]\n        if len(mba_id) > 0:\n            return mba_id\n\n        # otherwise use import_id prefixed with \"CA \"\n        return \"CA \" + cls.get_import_id_cls(row_dict)\n\n    @classmethod\n    def get_import_id_cls(cls, row_dict):\n        return str(int(row_dict[CABLE_DESIGN_IMPORT_ID_KEY]))\n\n    def get_name(self):\n        return self.get_name_cls(self.input_dict)\n\n    def get_alt_name(self):\n        return \"<\" + self.input_dict[CABLE_DESIGN_SRC_ETPM_KEY] + \"><\" + \\\n               self.input_dict[CABLE_DESIGN_DEST_ETPM_KEY] + \">:\" + \\\n               self.get_name()\n\n    def get_ext_name(self):\n        return self.input_dict[CABLE_DESIGN_NAME_KEY]\n\n    def get_import_id(self):\n        return self.get_import_id_cls(self.input_dict);\n\n    def get_alt_id(self):\n        return self.input_dict[CABLE_DESIGN_LEGACY_ID_KEY]\n\n    def get_qr_id(self):\n        return self.input_dict[CABLE_DESIGN_QR_ID_KEY]\n\n    def get_description(self):\n        return \"\"\n\n    def get_laying(self):\n        return self.input_dict[CABLE_DESIGN_LAYING_KEY]\n\n    def get_voltage(self):\n        return self.input_dict[CABLE_DESIGN_VOLTAGE_KEY]\n\n    def get_cable_type_id(self):\n        cable_type_name = self.input_dict[CABLE_DESIGN_TYPE_KEY]\n        return self.helper.cable_type_id_manager.get_id_for_name(cable_type_name)\n\n    def get_endpoint_id(self, input_column_key, container_key):\n        endpoint_name = self.input_dict[input_column_key]\n        container_name = self.input_dict[container_key]\n        return self.helper.rack_manager.get_endpoint_id_for_rack(container_name, endpoint_name)\n\n    def get_endpoint1_id(self):\n        return self.get_endpoint_id(CABLE_DESIGN_FROM_DEVICE_NAME_KEY, CABLE_DESIGN_SRC_ETPM_KEY)\n\n    def get_endpoint1_description(self):\n        return str(self.input_dict[CABLE_DESIGN_SRC_LOCATION_KEY]) + \":\" + \\\n               str(self.input_dict[CABLE_DESIGN_SRC_ANS_KEY]) + \":\" + \\\n               str(self.input_dict[CABLE_DESIGN_SRC_ETPM_KEY]) + \":\" + \\\n               str(self.input_dict[CABLE_DESIGN_SRC_ADDRESS_KEY]) + \":\" + \\\n               str(self.input_dict[CABLE_DESIGN_SRC_DESCRIPTION_KEY])\n\n    def get_endpoint1_route(self):\n        return str(self.input_dict[CABLE_DESIGN_VIA_ROUTE_KEY])\n\n    def get_endpoint2_id(self):\n        return self.get_endpoint_id(CABLE_DESIGN_TO_DEVICE_NAME_KEY, CABLE_DESIGN_DEST_ETPM_KEY)\n\n    def get_endpoint2_description(self):\n        return str(self.input_dict[CABLE_DESIGN_DEST_LOCATION_KEY]) + \":\" + \\\n               str(self.input_dict[CABLE_DESIGN_DEST_ANS_KEY]) + \":\" + \\\n               str(self.input_dict[CABLE_DESIGN_DEST_ETPM_KEY]) + \":\" + \\\n               str(self.input_dict[CABLE_DESIGN_DEST_ADDRESS_KEY]) + \":\" + \\\n               str(self.input_dict[CABLE_DESIGN_DEST_DESCRIPTION_KEY])\n\n    def get_endpoint2_route(self):\n        return str(self.input_dict[CABLE_DESIGN_WAYPOINT_ROUTE_KEY])\n\n    def get_project_id(self):\n        return self.helper.get_args().projectId\n\n    def get_tech_system_id(self):\n        return self.helper.get_args().techSystemId\n\n    def get_owner_user_id(self):\n        return self.helper.get_args().ownerUserId\n\n    def get_owner_group_id(self):\n        return self.helper.get_args().ownerGroupId\n\n\nrange_parts = re.compile(r'(\\$?)([A-Z]{1,3})(\\$?)(\\d+)')\ndef xl_cell_to_rowcol(cell_str):\n    \"\"\"\n    NOTE: I borrowed this from the xlsxwriter library since it was useful for parsing Excel $G$1 notation.\n    Convert a cell reference in A1 notation to a zero indexed row and column.\n\n    Args:\n       cell_str:  A1 style string.\n\n    Returns:\n        row, col: Zero indexed cell row and column indices.\n\n    \"\"\"\n    if not cell_str:\n        return 0, 0\n\n    match = range_parts.match(cell_str)\n    col_str = match.group(2)\n    row_str = match.group(4)\n\n    # Convert base26 column string to number.\n    expn = 0\n    col = 0\n    for char in reversed(col_str):\n        col += (ord(char) - ord('A') + 1) * (26 ** expn)\n        expn += 1\n\n    # Convert 1-index to zero-index\n    row = int(row_str) - 1\n    col -= 1\n\n    return row, col\n\n\ndef write_validation_report(validation_map, validation_report_file):\n    output_book = xlsxwriter.Workbook(validation_report_file)\n    output_sheet = output_book.add_worksheet()\n\n    output_sheet.write(0, 0, \"input row number\")\n    output_sheet.write(0, 1, \"validation messages\")\n\n    row_index = 1\n    for key in validation_map:\n        output_sheet.write(row_index, 0, key)\n        cell_value = \"\"\n        for message in validation_map[key]:\n            cell_value = cell_value + message + \"\\n\"\n        output_sheet.write(row_index, 1, cell_value)\n        row_index = row_index + 1\n\n    output_book.close()\n\n\ndef main():\n\n    global name_manager\n\n    # find --type command line parameter so we can create the appropriate helper subclass before proceeding\n    type_arg = \"--type\"\n    if type_arg not in sys.argv:\n        sys.exit(\"--type command line parameter not specified, exiting\")\n    else:\n        type_index = sys.argv.index(type_arg)\n        if type_index < (len(sys.argv) - 1):\n            type_arg_value = sys.argv[type_index+1]\n        else:\n            sys.exit(\"no value specified for --type command line parameter, exiting\")\n\n    # create instance of appropriate helper subclass\n    if not PreImportHelper.is_valid_type(type_arg_value):\n        sys.exit(\"unknown value for type parameter, expected Source, CableType, or CableDesign, got: %s\" % type_arg_value)\n    helper = PreImportHelper.create_helper(type_arg_value)\n\n    # process other command line arguments\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"inputFile\", help=\"Data collection workbook xlsx file with 'cable specs' tab\")\n    parser.add_argument(\"outputFile\", help=\"Official CDB Sources import format xlsx file\")\n    parser.add_argument(type_arg, help=\"Type of pre-import processing (Source, CableType, or CableDesign)\", required=True)\n    parser.add_argument(\"--logFile\", help=\"File for log output\", required=True)\n    parser.add_argument(\"--validationFile\", help=\"File for validation output\", required=True)\n    parser.add_argument(\"--infoFile\", help=\"domain specific xlsx file for reviewing helper actions\", required=False)\n    parser.add_argument(\"--cdbUrl\", help=\"CDB system URL\", required=True)\n    parser.add_argument(\"--cdbUser\", help=\"CDB User ID for API login\", required=True)\n    parser.add_argument(\"--cdbPassword\", help=\"CDB User password for API login\", required=True)\n    parser.add_argument(\"--sheetNumber\", help=\"Worksheet number within workbook (1-based)\", required=True)\n    parser.add_argument(\"--headerRow\", help=\"Input spreadsheet row number of header row (1-based)\", required=True)\n    parser.add_argument(\"--firstDataRow\", help=\"Input spreadsheet row number of first data row (1-based)\", required=True)\n    parser.add_argument(\"--lastDataRow\", help=\"Input spreadsheet row number of last data row (1-based)\", required=True)\n    helper.add_parser_args(parser)\n    args = parser.parse_args()\n    print(\"using inputFile: %s\" % args.inputFile)\n    print(\"using outputFile: %s\" % args.outputFile)\n    print(\"using logFile: %s\" % args.logFile)\n    print(\"using validation file: %s\" % args.validationFile)\n    print(\"debugging info xlsx file: %s\" % args.infoFile)\n    print(\"pre-import type: %s\" % args.type)\n    print(\"cdb url: %s\" % args.cdbUrl)\n    print(\"cdb user id: %s\" % args.cdbUser)\n    print(\"cdb user password: %s\" % args.cdbPassword)\n    print(\"worksheet number: %s\" % args.sheetNumber)\n    print(\"header row number: %s\" % args.headerRow)\n    print(\"first data row number: %s\" % args.firstDataRow)\n    print(\"last data row number: %s\" % args.lastDataRow)\n    helper.set_args(args)\n\n    sheetNum = int(args.sheetNumber)\n    headerRowNum = int(args.headerRow)\n    firstDataRowNum = int(args.firstDataRow)\n    lastDataRowNum = int(args.lastDataRow)\n\n    sheetIndex = sheetNum - 1\n    headerIndex = headerRowNum - 1\n    firstDataIndex = firstDataRowNum - 1\n    lastDataIndex = lastDataRowNum - 1\n\n    # initialize input and output columns\n    helper.initialize_input_columns()\n    helper.initialize_output_columns()\n\n    # configure logging\n    logging.basicConfig(filename=args.logFile, filemode='w', level=logging.DEBUG, format='%(levelname)s - %(message)s')\n\n    # open connection to CDB\n    api = CdbApiFactory(args.cdbUrl)\n    try:\n        api.authenticateUser(args.cdbUser, args.cdbPassword)\n        api.testAuthenticated()\n    except ApiException:\n        sys.exit(\"CDB login failed\")\n    helper.set_api(api)\n\n    # open input spreadsheet\n    input_book = xlrd.open_workbook(args.inputFile)\n\n    name_manager = ConnectedMenuManager(input_book)\n\n    input_sheet = input_book.sheet_by_index(int(sheetIndex))\n    logging.info(\"input spreadsheet dimensions: %d x %d\" % (input_sheet.nrows, input_sheet.ncols))\n\n    # validate input spreadsheet dimensions\n    if input_sheet.nrows < lastDataRowNum:\n        sys.exit(\"fewer rows in inputFile: %s than last data row: %d\" % (args.inputFile, lastDataRowNum))\n    if input_sheet.ncols != helper.num_input_cols():\n        sys.exit(\"inputFile %s doesn't contain expected number of columns: %d\" % (args.inputFile, helper.num_input_cols()))\n\n    # process rows from input spreadsheet\n    input_valid = True\n    output_objects = []\n    validation_map = {}\n    num_input_rows = 0\n    for row_ind in range(firstDataIndex, lastDataIndex + 1):\n\n        current_row_num = row_ind + 1\n        num_input_rows = num_input_rows + 1\n\n        logging.debug(\"processing row %d from input spreadsheet\" % current_row_num)\n\n        input_dict = {}\n\n        for col_ind in range(helper.num_input_cols()):\n            if col_ind in helper.input_columns:\n                # read cell value from spreadsheet\n                val = input_sheet.cell(row_ind, col_ind).value\n                logging.debug(\"col: %d value: %s\" % (col_ind, str(val)))\n                helper.handle_input_cell_value(input_dict=input_dict, index=col_ind, value=val, row_num=current_row_num)\n\n        # ignore row if blank\n        if helper.input_row_is_empty(input_dict=input_dict, row_num=current_row_num):\n            continue\n\n        row_is_valid = True\n        row_valid_messages = []\n\n        # validate row\n        (is_valid, valid_messages) = helper.input_row_is_valid(input_dict=input_dict, row_num=row_ind)\n        if not is_valid:\n            row_is_valid = False\n            row_valid_messages.extend(valid_messages)\n\n        # invoke handlers\n        (handler_is_valid, handler_messages) = helper.invoke_row_handlers(input_dict=input_dict, row_num=row_ind)\n        if not handler_is_valid:\n            row_is_valid = False\n            row_valid_messages.extend(handler_messages)\n\n        if row_is_valid:\n            output_obj = helper.get_output_object(input_dict=input_dict)\n            if output_obj:\n                output_objects.append(output_obj)\n        else:\n            input_valid = False\n            msg = \"validation ERRORS found for row %d\" % current_row_num\n            logging.error(msg)\n            validation_map[current_row_num] = row_valid_messages\n\n    (sheet_valid, sheet_valid_string) = helper.input_is_valid(output_objects)\n    if not sheet_valid:\n        input_valid = False\n        msg = \"ERROR validating input spreadsheet: %s\" % sheet_valid_string\n        logging.error(msg)\n        print()\n        print(msg)\n\n    # print validation report\n    print()\n    if len(validation_map) > 0:\n        print(\"validation ERRORS found\")\n        for key in validation_map:\n            print(\"row: %d\" % key)\n            for message in validation_map[key]:\n                print(\"\\t%s\" % message)\n        write_validation_report(validation_map, args.validationFile)\n    else:\n        print(\"no validation errors found\")\n\n    # create output spreadsheet\n    if input_valid and not helper.validate_only:\n        output_book = xlsxwriter.Workbook(args.outputFile)\n        output_sheet = output_book.add_worksheet()\n\n        # write output spreadsheet header row\n        row_ind = 0\n        for col_ind in range(helper.num_output_cols()):\n            if col_ind in helper.output_columns:\n                label = helper.get_output_column_label(col_index=col_ind)\n                output_sheet.write(row_ind, col_ind, label)\n\n        # process output spreadsheet rows\n        num_output_rows = 0\n        if len(output_objects) == 0:\n            logging.info(\"no output objects, output spreadsheet will be empty\")\n        for output_obj in output_objects:\n\n            row_ind = row_ind + 1\n            num_output_rows = num_output_rows + 1\n            current_row_num = row_ind + 1\n\n            logging.debug(\"processing row %d in output spreadsheet\" % current_row_num)\n\n            for col_ind in range(helper.num_output_cols()):\n                if col_ind in helper.output_columns:\n\n                    val = helper.get_output_cell_value(obj=output_obj, index=col_ind)\n                    output_sheet.write(row_ind, col_ind, val)\n\n        output_book.close()\n\n        summary_msg = \"SUMMARY: processed %d input rows and wrote %d output rows\" % (num_input_rows, num_output_rows)\n\n    elif not input_valid:\n        summary_msg = \"ERROR: processed %d input rows but no output spreadsheet generated, see log for errors\" % num_input_rows\n\n    else:\n        summary_msg = \"VALIDATION ONLY: processed %d input rows but no output spreadsheet generated, see validation file for details\" % num_input_rows\n\n    # clean up helper\n    helper.close()\n\n    # close CDB connection\n    try:\n        api.logOutUser()\n    except ApiException:\n        sys.exit(\"CDB logout failed\")\n\n    # print summary\n    print()\n    print(summary_msg)\n    logging.info(summary_msg)\n\n\nif __name__ == '__main__':\n    main()","sub_path":"tools/developer_tools/utilities/cable_import/preImport.py","file_name":"preImport.py","file_ext":"py","file_size_in_byte":68373,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"489652682","text":"from gevent import monkey\nimport gevent\nmonkey.patch_socket()\nfrom socket import *\n\n\nclass HTTPServer:\n    def __init__(self,host = '39.100.143.85',port = 8888,dir = None):\n        self.host = host\n        self.port = port\n        self.dir =dir\n        self.address = (host,port)\n        self.creae_cocket()\n\n    def creae_cocket(self):\n        self.sockfd = socket()\n        self.sockfd.setsockopt(SOL_SOCKET, SO_REUSEADDR, 1)\n        self.sockfd.bind(self.address)\n        self.sockfd.listen(3)\n        print('监听', self.port)\n\n    def serve_forever(self):\n        while True:\n            c,addr = self.sockfd.accept()\n            print('欢迎',addr)\n            gevent.spawn(self.func01,c)\n\n    def func01(self,c):\n        while True:\n            data = c.recv(2048)\n            if not data:\n                return\n            request_line = data.splitlines()[0]\n            info = request_line.decode().split(' ')[1]\n            if info == '/' or info[-5:] == '.html':\n                self.get_html(c, info)\n            else:\n                self.get_data(c, info)\n\n    def get_html(self,c,info):\n        if info == '/':\n            # 请求主页\n            filename = self.dir + \"/index.html\"\n        else:\n            filename = self.dir + info\n        try:\n            fd = open(filename)\n        except Exception:\n            # 网页不存在\n            response = \"HTTP/1.1 404 Not Found\\r\\n\"\n            response += 'Content-Type:text/html\\r\\n'\n            response += '\\r\\n'\n            response += '<h1>Sorry....</h1>'\n        else:\n            # 网页存在\n            response = \"HTTP/1.1 200 OK\\r\\n\"\n            response += 'Content-Type:text/html\\r\\n'\n            response += '\\r\\n'\n            response += fd.read()\n        finally:\n            # 将响应发送给浏览器\n            c.send(response.encode())\n\n    def get_data(self, c, info):\n        response = \"HTTP/1.1 200 OK\\r\\n\"\n        response += 'Content-Type:text/html\\r\\n'\n        response += '\\r\\n'\n        response += \"<h1>Waiting for httpserver 3.0</h1>\"\n        c.send(response.encode())\n\n\nif __name__ == '__main__':\n    a = HTTPServer('0.0.0.0',8888,'./static')\n    a.serve_forever()\n\n\n\n\n\n\n\n\n","sub_path":"后端服务器.py","file_name":"后端服务器.py","file_ext":"py","file_size_in_byte":2188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"252515755","text":"#project1: Basic School administrationTool\r\nimport csv\r\ndef write_into_csv(info_list):\r\n    with open('student_info.csv' , 'a', newline= '') as csv_file:\r\n        writer   = csv.writer(csv_file)\r\n        if csv_file.tell() == 0:\r\n            writer.writerow([\"Name\",\"Age\",\"Contact Number\",\"Email Id\"])\r\n        writer.writerow(info_list)\r\nif __name__ == '__main__':\r\n    condition = True\r\n    student_num = 1\r\n    while condition:\r\n        student_info  = input(\"Enter some Student Information for Student {} in the following format(Name,Age,Contact No,Email Id) :  \".format(student_num))\r\n        Student_info_list = student_info.split(',')\r\n        print(\"\\nThe Entered information is - \\nName: {}\\nAge: {}\\nContact Number: {}\\nEmail Id: {}\".format(Student_info_list[0],Student_info_list[1],Student_info_list[2],Student_info_list[3]))\r\n        \r\n\r\n        choice_check = input(\"Is the Entered information is Correct?\")\r\n        choice_check=choice_check.casefold()\r\n        if choice_check == \"yes\":\r\n            write_into_csv(Student_info_list)\r\n            student_num = student_num + 1\r\n            \r\n            condition_check =  input(\"Enter Yes or No if you want to Enter a Information of a Student:   \")\r\n            condition_check=condition_check.casefold()\r\n            if condition_check == \"no\":\r\n                condition =  False\r\n            elif condition_check == \"yes\":\r\n                condition  = True\r\n        elif choice_check == \"no\":\r\n            print(\"Please re-enter the Information\")\r\n        \r\n            \r\n","sub_path":"FirstProject.py","file_name":"FirstProject.py","file_ext":"py","file_size_in_byte":1542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"645829236","text":"from setuptools import find_packages\nfrom setuptools import setup\n\nPYTEST_VERSION_ = '3.3.0'\n\nsetup(name='cassettofon',\n      version='0.0.0',\n      description='Web-app to collect speech data.',\n      url='',\n      author='Matthias Buechi, Andreas Ahlenstorf',\n      author_email='buec@zhaw.ch',\n      classifiers=[\n          'Intended Audience :: Science/Research',\n          'License :: OSI Approved :: MIT License',\n          'Programming Language :: Python :: 3 :: Only',\n          'Topic :: Scientific/Engineering :: Human Machine Interfaces'\n      ],\n      keywords='voice,speech',\n      license='MIT',\n      packages=find_packages(exclude=['tests']),\n      install_requires=[\n          'flask >= 1.0.2',\n          'flask-restplus >= 0.10.1',\n          'flask-sqlalchemy >= 2.3.2',\n          'pycountry >= 18.2.23'\n      ],\n      include_package_data=True,\n      zip_safe=False,\n      test_suite='tests',\n      extras_require={\n          'dev': [\n              'pytest==%s' % (PYTEST_VERSION_,),\n              'pytest-runner==3.0',\n              'pytest-cov==2.5.1',\n              'Sphinx==1.6.5',\n              'sphinx-rtd-theme==0.2.5b1'\n          ],\n          'ci': [\n              'flake8==3.5.0',\n              'flake8-quotes==0.12.1'\n          ],\n      },\n      setup_requires=[\n          'pytest-runner'\n      ],\n      tests_require=[\n          'pytest==%s' % (PYTEST_VERSION_,)\n      ],\n      entry_points={\n      }\n      )\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1439,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"336106727","text":"import socket\nIP = '127.0.0.1'\nPORT = 8080\n\ntry:\n    clientsocket = socket.socket(socket.AF_INET,socket.SOCK_STREAM)\n    print(\"Created the socket\")\nexcept socket.error as e:\n    print(e)\n\nclientsocket.connect((IP,PORT))\nclientsocket.send(\"what's the current date?\".encode())\ndata = clientsocket.recv(10000)\nprint(data)\nclientsocket.close()\n","sub_path":"Socket Programming/Assignment2/Q1/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":341,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"262093694","text":"from pie.objects.strategy.base import get_string_strategy, StringFactory\nfrom pie.objects.base import W_Type\nfrom pie.objspace import space\nfrom pie.types import PHPTypes\n\n__author__ = 'sery0ga'\n\n\nHEXADECIMAL_SYMBOLS = list(\"0123456789ABCDEFabcdef\")\n\nDECIMAL_SYMBOLS = list(\"0123456789\")\n\n(SYMBOL_Z, SYMBOL_A, SYMBOL_a, SYMBOL_z, SYMBOL_0, SYMBOL_9) = (90, 65, 97, 122, 48, 57)\n\nclass NotConvertibleToNumber(Exception):\n    pass\n\nclass W_String(W_Type):\n    \"\"\"\n    This class represents PHP strings.\n    It has 2 main attributes:\n      - storage  -- contains string data. Its internal representation depends\n                    on strategy.\n      - strategy -- defines object behaviour. For more info see stringstrategies.py\n    \"\"\"\n    _immutable_fields_ = ['php_type']\n    convertible_to_number = True\n    php_type = PHPTypes.w_string\n\n    def __init__(self, strval):\n        from pie.objects.strategy.general import ConstantStringStrategy\n        strategy = get_string_strategy(ConstantStringStrategy)\n        self.storage = strategy.erase(strval)\n        self.strategy = strategy\n        self.copies = None\n\n    def __repr__(self):\n        return self.strategy.repr(self)\n\n    def copy(self):\n        res = self.strategy.copy(self)\n        self.add_copy(res)\n        return res\n\n    def is_true(self):\n        return self.strategy.is_true(self)\n\n    def str_w(self):\n        self.make_integral()\n        return self.strategy.str_w(self)\n\n    def as_array(self):\n        array = space.array()\n        return array.set(0, self.str_w())\n\n    def as_bool(self):\n        self.make_integral()\n        if not self.is_true():\n            return space.bool(False)\n        return space.bool(True)\n\n    def as_float(self):\n        return self._handle_number(False).as_float()\n\n    def as_int(self):\n        value = self._handle_number(strict=False, int_only=True)\n        from pie.objects.int import W_Int\n        assert isinstance(value, W_Int)\n        return value\n\n    def as_number(self):\n        return self._handle_number(False)\n\n    def as_number_strict(self):\n        \"\"\"\n        There's a special case in PHP when we try to convert\n        a string to number and if the string is not a pure number\n        (like 1e2, 1.43 or 555), we leave the string as a string object\n\n        The above rule is applied, for example, to string increment/decrement\n        \"\"\"\n        if not self.convertible_to_number:\n            raise NotConvertibleToNumber\n        return self._handle_number(True)\n\n    def as_string(self):\n        return self\n\n    def is_convertible_to_number_strict(self):\n        try:\n            self.as_number_strict()\n            return space.bool(True)\n        except NotConvertibleToNumber:\n            return space.bool(False)\n\n    def equal(self, w_object):\n        if self is w_object:\n            return space.bool(True)\n        if self.strlen() != w_object.strlen():\n            return space.bool(False)\n        assert isinstance(w_object, W_String)\n        if self.strategy is w_object.strategy:\n            return space.bool(self.strategy.equal(self, w_object))\n        self.make_integral()\n        w_object.make_integral()\n        return space.bool(self.str_w() == w_object.str_w())\n\n    def not_equal(self, w_object):\n        w_result = self.equal(w_object)\n        return space.bool(not w_result.is_true())\n\n    def less_than(self, w_object):\n        if self is w_object:\n            return space.bool(False)\n        assert isinstance(w_object, W_String)\n        self.make_integral()\n        w_object.make_integral()\n        return space.bool(self.str_w() < w_object.str_w())\n\n    def more_than(self, w_object):\n        if self is w_object:\n            return space.bool(False)\n        assert isinstance(w_object, W_String)\n        self.make_integral()\n        w_object.make_integral()\n        return space.bool(self.str_w() > w_object.str_w())\n\n    def less_than_or_equal(self, w_object):\n        if self is w_object:\n            return space.bool(True)\n        assert isinstance(w_object, W_String)\n        self.make_integral()\n        w_object.make_integral()\n        return space.bool(self.str_w() <= w_object.str_w())\n\n    def more_than_or_equal(self, w_object):\n        if self is w_object:\n            return space.bool(True)\n        assert isinstance(w_object, W_String)\n        self.make_integral()\n        w_object.make_integral()\n        return space.bool(self.str_w() >= w_object.str_w())\n\n    def inc(self):\n        if not self.is_true():\n            return space.string('1')\n        try:\n            return self.as_number_strict().inc()\n        except NotConvertibleToNumber:\n            pass\n        self._make_mutable()\n        index = self.strlen() - 1\n        symbol = ''\n        while index >= 0:\n            item = self.strategy.getitem(self, index)\n            symbol_index = ord(item[0])\n            if SYMBOL_A <= symbol_index <= SYMBOL_z or SYMBOL_0 <= symbol_index <= SYMBOL_9:\n                if symbol_index == SYMBOL_9:\n                    symbol = '0'\n                    self.strategy.setitem(self, index, symbol)\n                elif symbol_index == SYMBOL_Z:\n                    symbol = 'A'\n                    self.strategy.setitem(self, index, symbol)\n                elif symbol_index == SYMBOL_z:\n                    symbol = 'a'\n                    self.strategy.setitem(self, index, symbol)\n                else:\n                    symbol_index += 1\n                    self.strategy.setitem(self, index, chr(symbol_index))\n                    return self\n            else:\n                return self\n            index -= 1\n        if index < 0 and symbol:\n            self.strategy.append(self, symbol)\n\n        return self\n\n    def dec(self):\n        if not self.is_true():\n            return space.int(-1)\n        try:\n            return self.as_number_strict().dec()\n        except NotConvertibleToNumber:\n            # there's no decrement for normal non-convertible strings in PHP\n            return self\n\n    def concatenate(self, string):\n        return StringFactory.concat_str(self, string)\n\n    def strlen(self):\n        return self.strategy.len(self)\n\n\n    def make_integral(self):\n        self.strategy.make_integral(self)\n\n    def add_copy(self, w_copy):\n        if self.copies is None:\n            self.copies = [w_copy]\n        else:\n            self.copies.append(w_copy)\n\n    def _make_mutable(self):\n        self._dereference()\n        if self.copies:\n            self._dereference_copies()\n        self.strategy.make_mutable(self)\n\n    def _dereference(self):\n        self.strategy.dereference(self)\n\n    def _dereference_copies(self):\n        for copy in self.copies:\n            if copy:\n                copy._dereference()\n        self.copies = None\n\n    def _handle_number(self, strict = False, int_only = False):\n        \"\"\"\n        Converts string to number\n        \"\"\"\n        #TODO: add setlocale() support\n        self.make_integral()\n        if not self.is_true():\n            return space.int(0)\n        (begin, end) = (0, 0)\n        value_len = self.strlen()\n        string = self.str_w()\n        # check for hexadecimal\n        if value_len > 2 and string[end] == '0' and \\\n           (string[end + 1] == 'x' or string[end + 1] == 'X'):\n            end += 2\n            return self._handle_hexadecimal(string, begin, end, value_len, strict)\n        else:\n            assert begin >= 0\n            assert end >= 0\n            if int_only:\n                return self._handle_decimal(string, begin, end, value_len, strict)\n            return self._handle_float_or_decimal(string, begin, end, value_len, strict)\n\n    def _handle_float_or_decimal(self, value, begin, end, value_len, strict = False):\n        #detect minus\n        minus = 1\n        if value[0] == '-':\n            begin += 1\n            end += 1\n            minus = -1\n\n        e_symbol = False # for numbers '1e2'\n        dot_symbol = False\n        number_after_e_symbol = False\n        while end < value_len:\n            if value[end] not in DECIMAL_SYMBOLS:\n                if not dot_symbol and value[end] == '.':\n                    dot_symbol = True\n                    end += 1\n                    continue\n                if (not e_symbol and\n                        (value[end] == 'e' or value[end] == 'E')):\n                    e_symbol = True\n                    end += 1\n                    continue\n                self.convertible_to_number = False\n                if strict:\n                    raise NotConvertibleToNumber\n                else:\n                    break\n            if e_symbol:\n                number_after_e_symbol = True\n            end += 1\n\n        # truncate 'e' if it's the last symbol in number\n        if e_symbol and not number_after_e_symbol:\n            end -= 1\n\n        if minus and end == 0:\n            return space.int(0)\n        elif minus == -1 and end == 1:\n            return space.int(0)\n\n        assert begin >= 0\n        assert end >= 0\n        value = self.str_w()[begin:end]\n        if e_symbol or dot_symbol:\n            return space.float(float(value) * minus)\n        #TODO add PHP_INT_MAX check\n        return space.int(int(value) * minus)\n\n    def _handle_decimal(self, value, begin, end, value_len, strict = False):\n        #detect minus\n        minus = 1\n        if value[0] == '-':\n            begin += 1\n            end += 1\n            minus = -1\n\n        while end < value_len:\n            if value[end] not in DECIMAL_SYMBOLS:\n                self.convertible_to_number = False\n                if strict:\n                    raise NotConvertibleToNumber\n                else:\n                    break\n            end += 1\n\n        if minus and end == 0:\n            return space.int(0)\n        elif minus == -1 and end == 1:\n            return space.int(0)\n\n        assert begin >= 0\n        assert end >= 0\n        value = self.str_w()[begin:end]\n        #TODO add PHP_INT_MAX check\n        return space.int(int(value) * minus)\n\n    def _handle_hexadecimal(self, value, begin, end, value_len, strict = False):\n        while end < value_len:\n            if value[end] not in HEXADECIMAL_SYMBOLS:\n                self.convertible_to_number = False\n                if strict:\n                    raise NotConvertibleToNumber\n                else:\n                    break\n            end += 1\n\n        if not end:\n            return space.int(0)\n        return space.int(int(self.str_w()[begin:end], 0))\n","sub_path":"pie/objects/string.py","file_name":"string.py","file_ext":"py","file_size_in_byte":10437,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"271120430","text":"from flask import Flask, render_template\nfrom flask_caching import Cache\n\nfrom scraper import FILMS, PEOPLE\nfrom scraper.getter import get_cast_for_movie, get_all_data\n\napp = Flask(\"Ghibli-scrapper\")\ncache = Cache(app, config={'CACHE_TYPE': 'simple'})\n\n\n@app.route(\"/movies\")\n@cache.cached(timeout=60)\ndef present_movies():\n    movies = get_all_data(FILMS)\n    people = get_all_data(PEOPLE)\n\n    for movie in movies:\n        get_cast_for_movie(movie, people)\n\n    return render_template('movies.html', movies=movies)\n\n\nif __name__ == '__main__':\n    app.run(host=\"0.0.0.0\", port=8000)\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":585,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"319182405","text":"from django.conf.urls import patterns, include, url\n\n# Uncomment the next two lines to enable the admin:\nfrom django.contrib import admin\nadmin.autodiscover()\n\nurlpatterns = patterns('',\n    # Examples:\n     url(r'^$', 'findevent_website.views.home', name='home'),\n    # url(r'^findevent_website/', include('findevent_website.foo.urls')),\n\n     url(r'^welcome/$','findevent_website.views.login', name='login'),\t\n\n    # Uncomment the admin/doc line below to enable admin documentation:\n     url(r'^admin/doc/', include('django.contrib.admindocs.urls')),\n\n    # Uncomment the next line to enable the admin:\n     url(r'^admin/', include(admin.site.urls)),\n\n     url(r'^detail/(?P<eid>\\d{1,4})/$', 'findevent_website.views.detail', name='detail'),\n\n     url(r'^searchbydate', 'findevent_website.views.searchbydate', name='searchbydate'),\n\n     url(r'^search/','findevents_app.views.search_events', name='search_events'),\n\n    # url(r'^search/', include('haystack.urls')),\n\n\n)\n","sub_path":"Django_Learnings_1/findevent_website/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":972,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"26186341","text":"from write import write\n\nimport os\nimport sys\n\nimport numpy as np\nimport random\nimport math\n\nimport time\n\nimport pygame\nimport pygame.midi as midi\n\nsys.setrecursionlimit(26194)\n\npyg = pygame\ndisp = pygame.display\n\npyg.init()\ndisp.init()\n\nmidi.init()\n\n\"\"\"Colors\"\"\"\nWHITE = pyg.Color('white')\nGRAY = pyg.Color('gray')\nBLACK = pyg.Color('black')\nRED = pyg.Color('red')\nGREEN = pyg.Color('green')\nBLUE = pyg.Color('blue')\nCYAN = pyg.Color('cyan')\nMAGENTA = pyg.Color('magenta')\nYELLOW = pyg.Color('yellow')\nLIGHTBLUE = pyg.Color('lightblue')\nDARKGREEN = pyg.Color('darkgreen')\n\nbg_col = WHITE\nline_col = BLACK\ntext_col = BLACK\n\n\"\"\"Screen\"\"\"\n# Resolutions: 960x720, 1080x720, 1540x840\nscreen = disp.set_mode((960, 720))\nscreen_w, screen_h = screen.get_size()\nscreen.fill(bg_col)\ndisp.flip()\n\ndisp.set_caption('Insert Name Here')\n\n\"\"\"Options\"\"\"\n\n\n\"\"\"Variables\"\"\"\np = midi.Output(0)\nshift = 0\nmidiDict = {pyg.K_a: 60,\n            pyg.K_w: 61,\n            pyg.K_s: 62,\n            pyg.K_e: 63,\n            pyg.K_d: 64,\n            pyg.K_f: 65,\n            pyg.K_t: 66,\n            pyg.K_g: 67,\n            pyg.K_y: 68,\n            pyg.K_h: 69,\n            pyg.K_u: 70,\n            pyg.K_j: 71,\n            pyg.K_k: 72,\n            pyg.K_o: 73,\n            pyg.K_l: 74,\n            pyg.K_p: 75,\n            pyg.K_SEMICOLON: 76}\ncurInstr = 49\n# 49, 78, 89, 101\n# 115 percussion/sound effects start\np.set_instrument(curInstr)\nsustain = False\n\n\n\"\"\"Convenience functions\"\"\"\n\n\ndef update():\n    disp.flip()\n\n\ndef clear():\n    screen.fill(bg_col)\n\n\ndef load_img(name):\n    image_name = os.path.join('img', 'FolderName', name)\n    image = pyg.image.load(image_name)\n    image = image.convert()\n    image_rect = image.get_rect()\n    return image, image_rect\n\n\"\"\"Functions\"\"\"\n\ndef handleDown(e):\n    p.note_on(mapKey(e.dict['key']), velocity=127)\n\ndef handleUp(e):\n    if sustain:\n        return;\n    if not sustain or\\\n       not pyg.key.get_pressed()[pyg.K_RSHIFT] or\\\n       not pyg.key.get_pressed()[pyg.K_LSHIFT]:\n        p.note_off(mapKey(e.dict['key']))\n\ndef mapKey(k):\n    return midiDict[k]\n\ndef changeInstrument(delta):\n    global curInstr\n    curInstr += delta\n    p.set_instrument(curInstr)\n\ndef allUp():\n    for n in midiDict.values():\n        p.note_off(n)\n\n\"\"\"Classes\"\"\"\n\n\n\"\"\"Main\"\"\"\n\n\ndef main():\n    global sustain\n    running = True\n    clock = pyg.time.Clock()\n    while running:\n        downList = []\n        upList = []\n        for event in pyg.event.get():\n            if event.type == pyg.QUIT:\n                running = False\n            elif event.type == pyg.KEYDOWN and\\\n                 event.dict['key'] in midiDict.keys():\n                handleDown(event)\n            elif event.type == pyg.KEYUP and\\\n                 event.dict['key'] in midiDict.keys():\n                handleUp(event)\n            elif event.type == pyg.KEYDOWN and\\\n                 event.dict['key'] == pyg.K_UP:\n                changeInstrument(1)\n            elif event.type == pyg.KEYDOWN and\\\n                 event.dict['key'] == pyg.K_DOWN:\n                changeInstrument(-1)\n            elif event.type == pyg.KEYDOWN and\\\n                 event.dict['key'] == pyg.K_CAPSLOCK:\n                sustain = not sustain\n                if not sustain:\n                    allUp()\n        \n\n\nif __name__ == '__main__':\n    main()\n    pyg.quit()\n","sub_path":"MIDI Instruments.py","file_name":"MIDI Instruments.py","file_ext":"py","file_size_in_byte":3333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"157909296","text":"import pygame\n\nsquare1 = pygame.Rect(30, 30, 60, 60)\nsquare2 = pygame.Rect(90, 90, 60, 60)\n\ndef get_square_for_point(point):\n    x, y = point\n    if square1.collidepoint(x, y):\n        return square1\n    if square2.collidepoint(x, y):\n        return square2\n    return None\n\n\ndef is_in_rect(rect, point):\n    x, y = point\n    return rect.left <= x <= rect.right and rect.top <= y <= rect.bottom\n\n\nblue = (0, 0, 255)\nred = (255, 0, 0)\n\npygame.init()\nscreen = pygame.display.set_mode((1300, 600))\n\ndone = False\nis_blue = True\n\nwhile not done:\n    for event in pygame.event.get():\n        if event.type == pygame.KEYDOWN and event.key == pygame.K_SPACE:\n            is_blue = not is_blue\n\n        if event.type == pygame.MOUSEBUTTONDOWN:\n            mouse_x, mouse_y = event.pos\n\n            if square2.collidepoint(mouse_x, mouse_y):\n                is_blue = not is_blue\n                # print(\"Mouse x : %d, mouse y: %d\" % (mouse_x, mouse_y))\n                # print(event.button)\n        if event.type == pygame.QUIT:\n            done = True\n\n    if is_blue:\n        color = blue\n    else:\n        color = red\n\n    pygame.draw.rect(screen, color, square1)\n    pygame.draw.rect(screen, color, square2)\n\n    pygame.display.flip()\n","sub_path":"src/pygame_tutorial_1.py","file_name":"pygame_tutorial_1.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"262268130","text":"# import dependencies\nfrom bs4.builder import TreeBuilderRegistry\nfrom splinter import Browser\nfrom bs4 import BeautifulSoup as soup\nfrom webdriver_manager.chrome import ChromeDriverManager\nimport pandas as pd\nimport datetime as dt\n\ndef scrape_all():\n    #Initiate headless driver for deployment\n    executable_path = {'executable_path': ChromeDriverManager().install()}\n    browser = Browser('chrome', **executable_path, headless=True)\n\n    # Set variables to capture results from mars_news\n    news_title, news_paragraph = mars_news(browser)\n\n    # Run all scraping functions and store results in dictionary\n    data = {\n        \"news_title\": news_title,\n        \"news_paragraph\": news_paragraph,\n        \"featured_image\": featured_image(browser),\n        \"facts\": mars_facts(),\n        \"last_modified\": dt.datetime.now(),\n        \"hemispheres\": hemispheres(browser)\n    }\n    \n    #Stop webdriver and return data\n    browser.quit()\n    return data\n\ndef mars_news(browser):\n\n    # Visit the mars nasa news site\n    url = 'https://redplanetscience.com'\n    browser.visit(url)\n\n    # Optional delay for loading the page\n    browser.is_element_present_by_css('div.list_text', wait_time=1)\n\n    # Convert the browser html to a soup object\n    html = browser.html\n    news_soup = soup(html, 'html.parser')\n\n    try:\n        slide_elem = news_soup.select_one('div.list_text')\n        # Use the parent element to find the first 'a' tag and save it as 'news_title'\n        news_title = slide_elem.find('div', class_='content_title').text\n        # Use the parent element to find the paragraph text\n        news_p = slide_elem.find('div', class_='article_teaser_body').text\n\n    except AttributeError:\n        return None, None\n\n    # Use the return function to store the news title and paragraph\n    return news_title, news_p\n\n#### Featured Images\n\ndef featured_image(browser):\n\n    # Visit URL\n    url = 'https://spaceimages-mars.com'\n    browser.visit(url)\n    \n    # Find and click the full image button\n    full_image_elem = browser.find_by_tag('button')[1]\n    full_image_elem.click()\n\n\n    # Parse the resulting html with soup\n    html = browser.html\n    img_soup = soup(html, 'html.parser')\n\n    try:\n        # Find the relative image url\n        img_url_rel = img_soup.find('img', class_='fancybox-image').get('src')\n\n    except AttributeError:\n        return None\n\n    # Use the base URL to create an absolute URL\n    img_url = f'https://spaceimages-mars.com/{img_url_rel}'\n\n    return img_url\n\n\n# ### Table Data Scrape\ndef mars_facts():\n    try:\n        df = pd.read_html('https://galaxyfacts-mars.com')[0]\n    except BaseException:\n        return None\n\n    df.columns=['Description','Mars','Earth']\n    df.set_index('Description', inplace=True)\n\n    return df.to_html(classes=\"table table-striped\")\n\nif __name__ == \"__main__\":\n    # If running as script, print scraped data\n    print(scrape_all())\n\n### Hemisphere image and title scrape\ndef hemispheres(browser):\n    # 1. Use browser to visit the URL \n    url = 'https://marshemispheres.com/'\n    browser.visit(url)\n\n    html = browser.html\n    hemisphere_soup = soup(html, 'html.parser')\n\n    # 2. Create a list to hold the images and titles.\n    hemisphere_image_urls = []\n\n    # 3. Write code to retrieve the image urls and titles for each hemisphere.\n    ## 3a. Create a list that contains the title/image content for each of the 4 products on the page\n    container = hemisphere_soup.find('div', class_='collapsible results')\n    urls = container.find_all('div', class_='item')\n\n    ## 3b. Loop through the list to scrape the specific data points into a dictionary which is added to the list created earlier\n    try:\n        for url in range(len(urls)):\n            browser.find_by_css('a.product-item img')[url].click()\n            sample_elem = browser.links.find_by_text('Sample').first\n            img_url = sample_elem['href']\n            title = browser.find_by_css(\"h2.title\").text\n            hemisphere_image_urls.append ({\"img_url\": img_url,\n                                                \"title\": title})\n            browser.back()\n    except AttributeError:\n        return None\n    \n    return hemisphere_image_urls\n    ","sub_path":"challenge/scraping.py","file_name":"scraping.py","file_ext":"py","file_size_in_byte":4193,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"263930461","text":"from config import LAST_UPDATE_PATH\nimport os.path\nimport datetime\nfrom config import LOGGING_ON\n\nDATE_FORMAT = \"%Y-%m-%d\"\n\n\ndef get_last_update():\n\n    date = None\n\n    # If file already exists\n    if os.path.exists(LAST_UPDATE_PATH):\n        # Load and return date in file\n        file = open(LAST_UPDATE_PATH, \"r\")\n        date = file.readline()\n    else:\n        # If not create file with \"today - one week\"\n        file = open(LAST_UPDATE_PATH, \"w\")\n\n        today = datetime.datetime.now()\n        sevenDays = datetime.timedelta(days=7)\n        todayMinusSeven = today - sevenDays\n        date = todayMinusSeven.strftime(DATE_FORMAT)\n\n        save_last_update(date)\n\n    return date.rstrip()\n\n\ndef save_last_update(date=None):\n    # Save the next day\n    if date is None:\n        today = datetime.datetime.now()\n        oneDay = datetime.timedelta(days=1)\n        todayPlusOne = today + oneDay\n        date = todayPlusOne.strftime(DATE_FORMAT)\n\n    file = open(LAST_UPDATE_PATH, \"w\")\n    file.write(date)\n    return date\n\n\ndef log(message):\n    \"\"\"\n    Logs a message to the console if LOGGING_ON is set true\n    :param message: Message to log\n    \"\"\"\n    if LOGGING_ON:\n        print(message)\n","sub_path":"util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":1200,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"603890822","text":"import uuid\nimport os\n\nfrom kb_PICRUSt2.impl import appfile\nfrom kb_PICRUSt2.impl.config import Var\nfrom kb_PICRUSt2.util.debug import dprint\nimport config\n\n\nrun_dir = os.path.join(config.WORK_DIR, 'test_appfile_' + str(uuid.uuid4()))\nos.mkdir(run_dir)\n\n\n####################################################################################################\n####################################################################################################\n# @patch.dict('kb_PICRUSt2.impl.kbase_obj.Var', values={'dfu': get_mock_dfu('dummy_10by8')})\ndef test_parse_picrust2_traits():\n    '''\n    '''\n\n    ## Test `appfile.parse_picrust2_traits` ##\n\n    flpth = '/kb/module/test/data/return/dummy_10by8/return/PICRUSt2_output/pathways_out/path_abun_predictions.tsv.gz'\n\n    id2traits_d = appfile.parse_picrust2_traits(flpth)\n\n    ans = {\n       \"amplicon_id_0\": \"N10-formyl-tetrahydrofolate biosynthesis,4-hydroxyphenylacetate degradation,aerobactin biosynthesis,superpathway of chorismate metabolism,homolactic fermentation\",\n       \"amplicon_id_1\":                                          \"4-hydroxyphenylacetate degradation,aerobactin biosynthesis,superpathway of chorismate metabolism,homolactic fermentation\",\n       \"amplicon_id_2\":                                                                             \"aerobactin biosynthesis,superpathway of chorismate metabolism,homolactic fermentation\",\n       \"amplicon_id_3\":                                                                                                     \"superpathway of chorismate metabolism,homolactic fermentation\",\n       \"amplicon_id_4\":                                                                                                                                           \"homolactic fermentation\",\n       \"amplicon_id_5\":                                                                                                                                                                  \"\",\n       \"amplicon_id_6\": \"N10-formyl-tetrahydrofolate biosynthesis,4-hydroxyphenylacetate degradation,aerobactin biosynthesis,superpathway of chorismate metabolism,homolactic fermentation\",\n       \"amplicon_id_7\": \"N10-formyl-tetrahydrofolate biosynthesis,4-hydroxyphenylacetate degradation,aerobactin biosynthesis,superpathway of chorismate metabolism\",\n       \"amplicon_id_8\": \"N10-formyl-tetrahydrofolate biosynthesis,4-hydroxyphenylacetate degradation,aerobactin biosynthesis\",\n       \"amplicon_id_9\": \"N10-formyl-tetrahydrofolate biosynthesis,4-hydroxyphenylacetate degradation\"\n    }\n\n    assert id2traits_d == ans\n","sub_path":"test/app_file_test.py","file_name":"app_file_test.py","file_ext":"py","file_size_in_byte":2591,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"325484890","text":"import json\nimport sys\nimport time\n\nfrom convergdb_logging import *\nfrom batch_control import *\nfrom locking import lock\n\nfrom athena import *\nfrom cloudwatch import *\nfrom glue import *\nfrom s3 import *\nfrom sns import *\nfrom spark_partitions import *\nfrom spark import *\nfrom state import *\nfrom add_partitions import *\n\n# !HIGH LEVEL INTERACTIONS\n\ndef remove_batch(structure, old_batch_id):\n  convergdb_log(\"removing batch: \" + old_batch_id)\n  # first identify all data files to be deleted\n  delete_data_files = data_files_for_batch(\n    structure,\n    old_batch_id\n  )\n\n  s3 = boto3.client('s3')\n\n  if len(delete_data_files):\n    indices = split_indices(\n      len(delete_data_files),\n      1000\n    )\n\n    for indx in indices:\n      delete_these = list(\n        map(\n          lambda fname: {\"Key\" : fname },\n          delete_data_files[indx[0]:(indx[1])]\n        )\n      )\n      bucket = structure[\"storage_bucket\"].split('/')[0]\n      convergdb_log(\"deleting \" + str(len(delete_these)) + \" objects from failed batch: \" + str(old_batch_id))\n      s3.delete_objects(\n        Bucket=bucket,\n        Delete={'Objects': delete_these}\n      )\n\n  convergdb_log(\"removing control logs for batch: \" + old_batch_id)\n  # delete control file for the given batch\n  s3.delete_object(\n    Bucket = structure[\"state_bucket\"].split('/')[0],\n    Key = control_file_key(\n      structure,\n      old_batch_id\n    )\n  )\n\ndef load_batch(sql_context, structure, diff, source_map_func, dpu):\n  # get the current state for this table.\n  current_state = get_state(structure)\n\n  # first let's perform any clean up from previous runs\n  if current_state[\"state\"] == \"load_in_progress\":\n    if current_state.has_key(\"batch_id\"):\n      this_start_time = time.gmtime()\n      remove_batch(\n        structure,\n        current_state[\"batch_id\"]\n      )\n      # write success state\n      write_success(\n        structure,\n        current_state[\"batch_id\"],\n        this_start_time,\n        time.gmtime()\n      )\n      current_state = get_state(structure)\n\n  # only proceed if not in a failure state\n  if (current_state[\"state\"] in [\"success\", \"unknown\"]):\n    this_start_time = time.gmtime()\n\n    # generate a timestamp sortable batch_id\n    this_batch_id = batch_id(this_start_time)\n\n    # convert the file keys to list of s3a paths\n    diff_paths = map(\n      lambda x: x[\"key\"],\n      diff\n    )\n    s3a_list = diff_s3a(structure, diff_paths)\n\n    # only load if there are files\n    if len(s3a_list) > 0:\n      write_load_in_progress(\n        structure,\n        this_batch_id,\n        this_start_time,\n        diff\n      )\n\n      bytes_to_load_compressed = file_sizing(diff)\n      convergdb_log(\"compressed byte size for this batch: \" + str(bytes_to_load_compressed))\n\n      bytes_to_load_uncompressed_estimate = file_estimated_sizing(diff)\n      convergdb_log(\"uncompressed byte estimate for this batch: \" + str(bytes_to_load_uncompressed_estimate))\n      data_load(\n        sql_context,\n        structure,\n        s3a_list,\n        None,\n        this_batch_id,\n\n        # the following are used in calculating the number of spark partitions\n        bytes_to_load_uncompressed_estimate,\n        len(diff),\n        dpu, # None unless this is an aws_glue job\n        structure['spark_partition_count'] # override if not None\n      )\n\n      # refresh partitions\n      # msck_repair_table(structure)\n      update_all_partitions(\n        structure[\"storage_bucket\"].split('/')[0],\n        '/'.join(structure[\"storage_bucket\"].split('/')[1:]),\n        structure['region']\n      )\n\n      this_end_time = time.gmtime()\n\n      # these are the control records for the current diff\n      flr = file_loaded_records(\n        structure,\n        diff_paths,\n        this_batch_id,\n        this_start_time,\n        this_end_time\n      )\n\n      # write control records\n      write_control_records(\n        structure,\n        this_batch_id,\n        flr\n      )\n\n      # write success state\n      write_success(\n        structure,\n        this_batch_id,\n        this_start_time,\n        this_end_time\n      )\n\n      # log cloudwatch metrics\n      put_cloudwatch_metric(\n        structure[\"region\"],\n        structure['cloudwatch_namespace'],\n        'batch_success',\n        1,\n        'Count'\n      )\n\n      # log cloudwatch metrics\n      put_cloudwatch_metric(\n        structure[\"region\"],\n        structure['cloudwatch_namespace'],\n        'source_data_processed_uncompressed_estimate',\n        bytes_to_load_uncompressed_estimate,\n        'Bytes'\n      )\n\n      # log cloudwatch metrics\n      put_cloudwatch_metric(\n        structure[\"region\"],\n        structure['cloudwatch_namespace'],\n        'source_data_processed',\n        bytes_to_load_compressed,\n        'Bytes'\n      )\n\n      # log cloudwatch metrics\n      put_cloudwatch_metric(\n        structure[\"region\"],\n        structure['cloudwatch_namespace'],\n        'source_files_processed',\n        len(diff),\n        'Count'\n      )\n\n      # send sns success message\n      publish_sns(\n        structure[\"region\"],\n        structure[\"sns_topic\"],\n        \"SUCCESS - ConvergDB - \" + structure[\"full_relation_name\"],\n        \"files processed: \" + str(len(diff)) + \"\\n\" +\n        \"bytes processed: \" + str(bytes_to_load_uncompressed_estimate) + \"\\n\"# +\n        \"bytes processed (uncompressed estimate): \" + str(bytes_to_load_uncompressed_estimate) + \"\\n\"\n      )\n    else:\n      convergdb_log(\"no new data to load for relation: \" + structure[\"full_relation_name\"])\n\n      # log cloudwatch metrics\n      put_cloudwatch_metric(\n        structure[\"region\"],\n        structure['cloudwatch_namespace'],\n        'batch_success',\n        1,\n        'Count'\n      )\n\n      publish_sns(\n        structure[\"region\"],\n        structure[\"sns_topic\"],\n        \"SUCCESS - ConvergDB - \" + structure[\"full_relation_name\"],\n        \"files processed: \" + str(0) + \"\\n\" +\n        \"bytes processed: \" + str(0) + \"\\n\"# +\n        \"bytes processed (uncompressed estimate): \" + str(0) + \"\\n\"\n      )\n\n# set s3 encrption in hadoop configuration\ndef sse_config(sql_context, sse_algorithm, kms_master_key_id):\n  convergdb_log(\"setting s3 encrption in : \" + sse_algorithm  + \" algorithm\")\n  if sse_algorithm != \"\":\n    if sse_algorithm == \"aws:kms\":\n      sse_algorithm = \"SSE-KMS\"\n    sql_context._jsc.hadoopConfiguration().set(\"fs.s3a.server-side-encryption-algorithm\", sse_algorithm)\n  if kms_master_key_id != \"\":\n    convergdb_log(\"kms master key id is: \" + kms_master_key_id)\n    sql_context._jsc.hadoopConfiguration().set(\"fs.s3a.server-side-encryption-key\", kms_master_key_id)\n\n# set s3 bucket encrption algorithm, and master key id if using aws:kms method\ndef set_bucket_sse(sql_context, bucket):\n  convergdb_log(\"get s3 bucket encrption : \" + bucket)\n  try:\n    sse_algorithm = \"\"\n    kms_master_key_id = \"\"\n    s3 = boto3.client('s3')\n    bucket = bucket.split('/')[0]\n    response = s3.get_bucket_encryption(Bucket = bucket)\n    \n    if 'ServerSideEncryptionConfiguration' in response:\n      # Currently S3 supports one rule only.\n      if 'Rules' in response['ServerSideEncryptionConfiguration']:\n        rule = response['ServerSideEncryptionConfiguration']['Rules'][0]\n        if 'SSEAlgorithm' in rule['ApplyServerSideEncryptionByDefault']:\n          sse_algorithm = rule['ApplyServerSideEncryptionByDefault']['SSEAlgorithm']\n        if 'KMSMasterKeyID' in rule['ApplyServerSideEncryptionByDefault']:\n          kms_master_key_id = rule['ApplyServerSideEncryptionByDefault']['KMSMasterKeyID']\n        sse_config(sql_context, sse_algorithm, kms_master_key_id)\n  except Exception as e:\n    if 'ServerSideEncryptionConfigurationNotFoundError' in str(e):\n      convergdb_log(\"this bucket does not have SSE config\")\n    else:\n      convergdb_log(\"error in getting bucket encryption\")\n      raise\n\n# high level procedure transforms data from the source to the target,\n# using idempotent producer/consumer ETL pattern.\n@lock\ndef source_to_target(sql_context, structure_json):\n  try:\n    # first parse the json representation of the structure into a dict\n    structure = json.loads(structure_json)\n    # start time is used for batch_id\n    start_time = time.gmtime()\n\n    # set storage bucket encrption for aws_fargate\n    if structure['etl_technology'] == 'aws_fargate':\n      set_bucket_sse(sql_context, structure[\"storage_bucket\"])\n\n    dpu = None\n    # determine the number of DPUs applied to the current run\n    if structure[\"etl_technology\"] == 'aws_glue':\n      dpu = current_job_dpu(\n        structure[\"etl_job_name\"],\n        structure[\"region\"]\n      )\n      convergdb_log(\"dpu for current_job: \" + str(dpu))\n    elif structure[\"etl_technology\"] == 'aws_fargate':\n      dpu = None\n\n    # gets a list of files from the diff process\n    # this process may be API based or s3 inventory based\n    diff = file_diff(\n      structure\n    )\n    convergdb_log(\"loadable file count: \" + str(len(diff)))\n\n    source_bytes = file_sizing(diff)\n    convergdb_log(\"total loadable bytes (compressed): \" + str(source_bytes))\n\n    chunk_size = calculate_chunk_count(\n      source_bytes,\n      len(diff),\n      dpu\n    )\n    convergdb_log(\"max files per batch: \" + str(chunk_size))\n\n    # tuples of (lo, hi) index ranges for arrays.\n    # note that these are correct, unlike python indexes.\n    indices = split_indices(\n      len(diff),\n      chunk_size\n    )\n    convergdb_log(\"number of splits for this run: \" + str(len(indices)))\n\n    for indx in indices:\n      convergdb_log(\"processing split for index range: \" + str(indx[0]) + \":\" + str(indx[1]))\n      this_diff = diff[indx[0]:(indx[1])]\n      load_batch(\n        sql_context,\n        structure,\n        this_diff,\n        None,\n        dpu\n      )\n  except:\n    if 'structure' in vars():\n      convergdb_log(\"error in processing relation: \" + structure[\"full_relation_name\"] + str(sys.exc_info()[0]))\n      # log cloudwatch metrics\n      put_cloudwatch_metric(\n        structure[\"region\"],\n        structure['cloudwatch_namespace'],\n        'batch_failure',\n        1,\n        'Count'\n      )\n\n      # send sns success message\n      publish_sns(\n        structure[\"region\"],\n        structure[\"sns_topic\"],\n        \"FAILURE - ConvergDB - \" + structure[\"full_relation_name\"],\n        str(sys.exc_info()[0])\n      )\n      raise\n    else:\n      convergdb_log(\"error in processing relation\")\n      raise\n","sub_path":"python/convergdb/high_level.py","file_name":"high_level.py","file_ext":"py","file_size_in_byte":10338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"338174928","text":"import numpy as np\nimport array\nimport struct\n\ndef getValue(array):\n    val = 0\n    if (array[0] == 1):\n        val = val + 128\n    if (array[1] == 1):\n        val = val + 64\n    if (array[2] == 1):\n        val = val + 32\n    if (array[3] == 1):\n        val = val + 16\n    if (array[4] == 1):\n        val = val + 8\n    if (array[5] == 1):\n        val = val + 4\n    if (array[6] == 1):\n        val = val + 2\n    if (array[7] == 1):\n        val = val +1\n\n    return val\n\n\n\nf = open(\"../data/Tatou.pgm\", \"rb\")\nf2 = open(\"../data/extracted_file.jpg\", \"wb\")\ntry:\n    byte = f.read(1)\n    count = 0\n    newByteArray = [0, 0, 0, 0, 0, 0, 0, 0]\n    offset = 0\n    while byte != b'':\n        byte = f.read(1)\n        if (count > 0 ):\n            # Do stuff with byte.\n            #f2.write(byte)\n            for i in range(8):\n                #print((byte[0] >> i) & 1)\n                if (i == 6 or i == 7):\n                    #print((byte[0] >> i) & 1)\n                    newByteArray[offset] = ((byte[0] >> i) & 1)\n                    offset = offset + 1\n                    if (offset == 8):\n                        print(newByteArray)\n                        val = getValue(newByteArray)\n                        f2.write(struct.pack('i', val))\n                        #print((val.to_bytes(2, byteorder='big', signed=False) ))\n                        print(val)\n                        offset = 0\n                        newByteArray = [0, 0, 0, 0, 0, 0, 0, 0]\n                        #f2.write(newByteArray)\n            # print (bits)\n        count = count+1\nfinally:\n    f.close()\n    f2.close()\n","sub_path":"src/extract.py","file_name":"extract.py","file_ext":"py","file_size_in_byte":1595,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"96571111","text":"#! /Library/Frameworks/Python.framework/Versions/Current/bin/python\n\nfrom pylab import *\nimport re\nfrom fpformat import fix\n    \ndef Extract(SMARTS_RESFILE):\n        \n    INF= file(SMARTS_RESFILE,'r')\n    \n    Lines= INF.readlines()\n    \n    p= re.compile(r\"\\s+Direct Beam =\\s+(?P<DIR>\\d+.\\d+)\\s+Diffuse =\\s+(?P<DIF>\\d+.\\d+)\\s+Global =\\s+(?P<TOT>\\d+.\\d+)\")\n   \n    Diffuse= \"\"\n    Direct= \"\"\n    Total= \"\"\n    \n    for l in Lines:\n    \n        m= p.match(l)\n        \n        if m:\n            Diffuse= m.group('DIF')\n            Direct= m.group('DIR')\n            Total= m.group('TOT')\n\n    if Total != \"\":\n\n        return Total,Diffuse,Direct\n\n","sub_path":"Modules/Superannuated/ExtrSmartsMod.py","file_name":"ExtrSmartsMod.py","file_ext":"py","file_size_in_byte":645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"149762098","text":"\"\"\"\nLESSON\n-- for idx, value in enumerate(arr)\n-- h[i]=arr[i] => for h[v]=i\n-- 21 => limit\n\nGiven a package with a weight limit limit and an array arr of item weights, implement a function getIndicesOfItemWeights that finds two items whose sum of weights equals the weight limit limit. Your function should return a pair [i, j] of the indices of the item weights, ordered such that i > j. If such a pair doesn’t exist, return an empty array.\n\nAnalyze the time and space complexities of your solution.\n\nExample:\n\ninput:  arr = [4, 6, 10, 15, 16],  lim = 21\n\noutput: [3, 1]\n\"\"\"\n\n\ndef get_indices_of_item_wights(arr, limit):\n    if arr is None:\n        return []\n    h = {}\n    for i, v in enumerate(arr):\n        if limit - v in h:\n            return [i, h[limit - v]]\n        h[v] = i\n    return []\n","sub_path":"merge_packages.py","file_name":"merge_packages.py","file_ext":"py","file_size_in_byte":800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"584323654","text":"import numpy as np\n\nfrom ..data_utils import hot_to_indices\nfrom ..visualizer import pprint\n\n\nclass TopologyConverter:\n    def __init__(self, env):\n        self.env = env\n\n        self.n_sub = self.env.n_sub\n        self.n_bus = 2 * self.n_sub\n        self.n_gen = self.env.n_gen\n        self.n_load = self.env.n_load\n        self.n_line = self.env.n_line\n        self.n_topo = self.env.dim_topo\n        assert self.n_topo == self.n_gen + self.n_load + 2 * self.n_line\n\n        # Grid element positions in topology vector\n        self.gen_topo_pos = self.env.gen_pos_topo_vect\n        self.load_topo_pos = self.env.load_pos_topo_vect\n        self.line_or_topo_pos = self.env.line_or_pos_topo_vect\n        self.line_ex_topo_pos = self.env.line_ex_pos_topo_vect\n\n        # Grid element positions in substation topology vector\n        self.gen_sub_pos = self.env.gen_to_sub_pos\n        self.load_sub_pos = self.env.load_to_sub_pos\n        self.line_or_sub_pos = self.env.line_or_to_sub_pos\n        self.line_ex_sub_pos = self.env.line_ex_to_sub_pos\n\n        # Grid element positions in substation topology vector\n        self.gen_to_sub_id = self.env.gen_to_subid\n        self.load_to_sub_id = self.env.load_to_subid\n        self.line_or_to_sub_id = self.env.line_or_to_subid\n        self.line_ex_to_sub_id = self.env.line_ex_to_subid\n\n        # Substation topology mask\n        self.substation_topology_mask = self._get_substation_topology_mask()\n\n    def _get_substation_element_ids(self, sub_id):\n        gen_ids = hot_to_indices(self.env.gen_to_subid == sub_id)\n        load_ids = hot_to_indices(self.env.load_to_subid == sub_id)\n        line_or_ids = hot_to_indices(self.env.line_or_to_subid == sub_id)\n        line_ex_ids = hot_to_indices(self.env.line_ex_to_subid == sub_id)\n        return gen_ids, load_ids, line_or_ids, line_ex_ids\n\n    def _get_substation_topology_mask(self):\n        substation_topology_mask = np.zeros((self.n_sub, self.n_topo), dtype=np.bool)\n\n        for sub_id, n_elements_sub in enumerate(self.env.sub_info):\n            (\n                gen_ids,\n                load_ids,\n                line_or_ids,\n                line_ex_ids,\n            ) = self._get_substation_element_ids(sub_id)\n\n            sub_topo_pos = np.concatenate(\n                (\n                    self.line_ex_topo_pos[line_ex_ids],\n                    self.line_or_topo_pos[line_or_ids],\n                    self.gen_topo_pos[gen_ids],\n                    self.load_topo_pos[load_ids],\n                )\n            )\n\n            # Substation topology in topology vector\n            sub_topo_vect_mask = np.zeros((self.n_topo,), dtype=np.bool)\n            sub_topo_vect_mask[sub_topo_pos] = True\n\n            substation_topology_mask[sub_id, :] = sub_topo_vect_mask\n\n        assert np.equal(substation_topology_mask.sum(axis=1), self.env.sub_info).all()\n        assert np.equal(substation_topology_mask.sum(axis=0), 1).all()\n\n        return substation_topology_mask\n\n    def _get_substation_topology_vector(self, topo_vect, sub_id):\n        gen_ids, load_ids, line_or_ids, line_ex_ids = self._get_substation_element_ids(\n            sub_id\n        )\n\n        # Buses within a substation\n        gen_sub_bus = topo_vect[self.gen_topo_pos[gen_ids]]\n        load_sub_bus = topo_vect[self.load_topo_pos[load_ids]]\n        line_or_sub_bus = topo_vect[self.line_or_topo_pos[line_or_ids]]\n        line_ex_sub_bus = topo_vect[self.line_ex_topo_pos[line_ex_ids]]\n\n        # Positions within a substation\n        gen_sub_pos = self.gen_sub_pos[gen_ids]\n        load_sub_pos = self.load_sub_pos[load_ids]\n        line_or_sub_pos = self.line_or_sub_pos[line_or_ids]\n        line_ex_sub_pos = self.line_ex_sub_pos[line_ex_ids]\n\n        sub_topo_vect = np.zeros((self.env.sub_info[sub_id],), dtype=np.int)\n        sub_topo_vect[gen_sub_pos] = gen_sub_bus\n        sub_topo_vect[load_sub_pos] = load_sub_bus\n        sub_topo_vect[line_or_sub_pos] = line_or_sub_bus\n        sub_topo_vect[line_ex_sub_pos] = line_ex_sub_bus\n\n        assert np.equal(\n            sub_topo_vect, topo_vect[self.substation_topology_mask[sub_id, :]]\n        ).all()\n        return sub_topo_vect\n\n    def _construct_topology_vector(\n        self, gen_sub_bus, load_sub_bus, line_or_sub_bus, line_ex_sub_bus, line_status\n    ):\n        line_or_sub_bus[~line_status] = -1\n        line_ex_sub_bus[~line_status] = -1\n\n        topo_vect = np.zeros((self.n_topo,), dtype=np.int)\n        topo_vect[self.gen_topo_pos] = gen_sub_bus\n        topo_vect[self.load_topo_pos] = load_sub_bus\n        topo_vect[self.line_or_topo_pos] = line_or_sub_bus\n        topo_vect[self.line_ex_topo_pos] = line_ex_sub_bus\n\n        return topo_vect\n\n    def _get_substation_buses(self, topo_vect):\n        # Extract from topology vector sub bus of each grid element\n        gen_sub_bus = topo_vect[self.gen_topo_pos]\n        load_sub_bus = topo_vect[self.load_topo_pos]\n        line_or_sub_bus = topo_vect[self.line_or_topo_pos]\n        line_ex_sub_bus = topo_vect[self.line_ex_topo_pos]\n\n        return gen_sub_bus, load_sub_bus, line_or_sub_bus, line_ex_sub_bus\n\n    def _count_topology_changes(\n        self, topo_vect, line_status, topo_vect_next, line_status_next, verbose=False\n    ):\n        n_max_sub_changed = 0\n        for sub_id in range(self.n_sub):\n            sub_topo_vect = self._get_substation_topology_vector(topo_vect, sub_id)\n            sub_topo_vect_next = self._get_substation_topology_vector(\n                topo_vect_next, sub_id\n            )\n\n            # If line change, do not count substation change\n            _, _, line_or_ids, line_ex_ids = self._get_substation_element_ids(sub_id)\n\n            for line_id in line_or_ids:\n                if np.logical_xor(line_status, line_status_next)[line_id]:\n                    sub_topo_vect[self.line_or_sub_pos[line_id]] = 0\n                    sub_topo_vect_next[self.line_or_sub_pos[line_id]] = 0\n            for line_id in line_ex_ids:\n                if np.logical_xor(line_status, line_status_next)[line_id]:\n                    sub_topo_vect[self.line_ex_sub_pos[line_id]] = 0\n                    sub_topo_vect_next[self.line_ex_sub_pos[line_id]] = 0\n\n            switch = np.greater(\n                np.abs(sub_topo_vect - sub_topo_vect_next).sum(), 0\n            ).astype(int)\n            n_max_sub_changed += switch\n\n        n_max_line_status_changed = np.logical_xor(line_status, line_status_next).sum()\n\n        if verbose:\n            print(\n                \"{:<35}{:<10}\".format(\"LINE STATUS CHANGES\", n_max_line_status_changed)\n            )\n            print(\n                \"{:<35}{:<10}\".format(\"SUBSTATION TOPOLOGY CHANGES:\", n_max_sub_changed)\n            )\n\n    def convert_mip_to_topology_vector(self, mip_solution, obs):\n        solution_status = mip_solution[\"solution_status\"]\n\n        # If infeasible problem, then return a do-nothing action\n        if solution_status in [\n            \"infeasible\",\n            \"aborted\",\n            \"warning\",\n            \"error\",\n            \"maxTimeLimit\",\n        ]:\n            pprint(\"Solver status:\", solution_status)\n            return obs.topo_vect, obs.line_status, self.env.action_space({})\n\n        x_gen = mip_solution[\"res_x_gen\"]\n        x_load = mip_solution[\"res_x_load\"]\n        x_line_or_1 = mip_solution[\"res_x_line_or_1\"]\n        x_line_or_2 = mip_solution[\"res_x_line_or_2\"]\n        x_line_ex_1 = mip_solution[\"res_x_line_ex_1\"]\n        x_line_ex_2 = mip_solution[\"res_x_line_ex_2\"]\n\n        x_line_status_switch = mip_solution[\"res_x_line_status_switch\"]\n        x_substation_topology_switch = mip_solution[\"res_x_substation_topology_switch\"]\n\n        gen_sub_bus = -np.ones_like(x_gen, dtype=np.int)\n        gen_sub_bus[~x_gen] = 1  # if x_gen[gen_id] = 0, then bus 1\n        gen_sub_bus[x_gen] = 2  # if x_gen[gen_id] = 1, then bus 2\n        load_sub_bus = -np.ones_like(x_load, dtype=np.int)\n        load_sub_bus[~x_load] = 1  # if x_load[load_id] = 0, then bus 1\n        load_sub_bus[x_load] = 2  # if x_load[load_id] = 1, then bus 2\n\n        line_status = np.logical_and(\n            np.logical_or(x_line_or_1, x_line_or_2),\n            np.logical_or(x_line_ex_1, x_line_ex_2),\n        )\n\n        line_or_sub_bus = -np.ones_like(x_line_or_1, dtype=np.int)\n        line_or_sub_bus[x_line_or_1] = 1\n        line_or_sub_bus[x_line_or_2] = 2\n\n        line_ex_sub_bus = -np.ones_like(x_line_ex_1, dtype=np.int)\n        line_ex_sub_bus[x_line_ex_1] = 1\n        line_ex_sub_bus[x_line_ex_2] = 2\n\n        topo_vect = self._construct_topology_vector(\n            gen_sub_bus, load_sub_bus, line_or_sub_bus, line_ex_sub_bus, line_status\n        )\n\n        # Construct grid2op action\n        action_dict = dict()\n        sub_topology_set = []\n        line_status_set = []\n        line_or_bus_set = []\n        line_ex_bus_set = []\n        for line_id, line_switch in enumerate(x_line_status_switch):\n            if line_switch:\n                if not line_status[line_id]:  # Disconnection\n                    line_status_set.append((line_id, -1))\n                else:  # Reconnection\n                    line_status_set.append((line_id, 1))\n                    line_or_bus_set.append((line_id, line_or_sub_bus[line_id]))\n                    line_ex_bus_set.append((line_id, line_ex_sub_bus[line_id]))\n\n        for sub_id, sub_switch in enumerate(x_substation_topology_switch):\n            if sub_switch:\n                sub_topology = self._get_substation_topology_vector(topo_vect, sub_id)\n                sub_topology_set.append((sub_id, sub_topology))\n\n        # Construct action dictionary\n        if line_or_bus_set or line_ex_bus_set or sub_topology_set:\n            action_dict[\"set_bus\"] = dict()\n\n        if line_status_set:\n            action_dict[\"set_line_status\"] = line_status_set\n\n            if line_or_bus_set:\n                action_dict[\"set_bus\"][\"lines_or_id\"] = line_or_bus_set\n            if line_ex_bus_set:\n                action_dict[\"set_bus\"][\"lines_ex_id\"] = line_ex_bus_set\n\n        if sub_topology_set:\n            action_dict[\"set_bus\"][\"substations_id\"] = sub_topology_set\n\n        action = self.env.action_space(action_dict)\n\n        return topo_vect, line_status, action\n\n    def lines_or_to_sub_bus(self, obs):\n        topo_pos = self.line_or_topo_pos\n        return obs.topo_vect[topo_pos]\n\n    def lines_ex_to_sub_bus(self, obs):\n        topo_pos = self.line_ex_topo_pos\n        return obs.topo_vect[topo_pos]\n\n    def gens_to_sub_bus(self, obs):\n        topo_pos = self.gen_topo_pos\n        return obs.topo_vect[topo_pos]\n\n    def loads_to_sub_bus(self, obs):\n        topo_pos = self.load_topo_pos\n        return obs.topo_vect[topo_pos]\n\n    @staticmethod\n    def sub_bus_mask(topo_vect, sub_bus, dtype=np.bool):\n        return np.equal(topo_vect, sub_bus, dtype=dtype)\n","sub_path":"lib/dc_opf/topology_converter.py","file_name":"topology_converter.py","file_ext":"py","file_size_in_byte":10804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"91848296","text":"import torch\nfrom .state import BaseState\nfrom typing import Tuple\n\n\ndef enforce_tensor(func):\n    def wrapper(obj, y, *args, **kwargs):\n        if not isinstance(y, torch.Tensor):\n            raise ValueError('The observation must be of type Tensor!')\n\n        return func(obj, y, *args, **kwargs)\n\n    return wrapper\n\n\ndef _construct_empty(array: torch.Tensor) -> torch.Tensor:\n    \"\"\"\n    Constructs an empty array based on the shape.\n    :param array: The array to reshape after\n    \"\"\"\n\n    temp = torch.arange(array.shape[-1], device=array.device)\n    return temp * torch.ones_like(array, dtype=temp.dtype)\n\n\nclass FilterResult(object):\n    def __init__(self, init_state: BaseState):\n        \"\"\"\n        Implements a basic object for storing log likelihoods and the filtered means of a filter.\n        \"\"\"\n        super().__init__()\n\n        self._loglikelihood = None    # type: torch.Tensor\n        self._filter_means = tuple()\n        self._states = (init_state,)\n\n    @property\n    def loglikelihood(self) -> torch.Tensor:\n        return self._loglikelihood\n\n    @property\n    def filter_means(self) -> torch.Tensor:\n        if len(self._filter_means) > 0:\n            return torch.stack(self._filter_means)\n\n        return torch.empty(0)\n\n    @property\n    def states(self) -> Tuple[BaseState, ...]:\n        return self._states\n\n    @property\n    def latest_state(self) -> BaseState:\n        return self._states[-1]\n\n    def exchange(self, res, inds: torch.Tensor):\n        \"\"\"\n        Exchanges the specified indices of self with res.\n        :param res: The other filter result\n        :type res: FilterResult\n        :param inds: The indices\n        \"\"\"\n\n        # ===== Loglikelihood ===== #\n        self._loglikelihood[inds] = res.loglikelihood[inds]\n\n        # ===== Filter means ====== #\n        if len(self._filter_means) > 0:\n            old_fm = self.filter_means\n            old_fm[:, inds] = res.filter_means[:, inds]\n\n            self._filter_means = tuple(old_fm)\n\n        for ns, os in zip(res.states, self.states):\n            os.exchange(ns, inds)\n\n        return self\n\n    def resample(self, inds: torch.Tensor):\n        \"\"\"\n        Resamples the specified indices of self with res.\n        :param inds: The indices\n        \"\"\"\n\n        self._loglikelihood = self.loglikelihood[inds]\n\n        if len(self._filter_means) > 0:\n            self._filter_means = tuple(self.filter_means[:, inds])\n\n        for s in self._states:\n            s.resample(inds)\n\n        return self\n\n    def append(self, xm, ll, state, only_latest=True):\n        if xm is not None:\n            self._filter_means += (xm,)\n\n        if self._loglikelihood is None:\n            self._loglikelihood = torch.zeros_like(ll)\n\n        self._loglikelihood += ll\n        if only_latest:\n            self._states = (state,)\n        else:\n            self._states += (state,)\n\n        return self","sub_path":"pyfilter/filters/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":2888,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"158290906","text":"from app import db\nfrom functools import wraps\nfrom collections import Callable\nfrom flask import request, current_app\nfrom flask_user import current_user\nfrom flask_restful import abort, Resource\nfrom json import loads\n\n\ndef decorate_api(decorator,\n                 methods=['get', 'put', 'post', 'delete', 'update']):\n    \"\"\"This decorator is applied at class level, and, if the class has\n    any method called as one of the passed `methods` param, decorate it with\n    the `decorator` function\"\"\"\n    def decorate(cls):\n        for attr in cls.__dict__:\n            if attr in methods:\n                if isinstance(getattr(cls, attr), Callable):\n                    setattr(cls, attr, decorator(getattr(cls, attr)))\n        return cls\n    return decorate\n\n\ndef login_required(func):\n    \"\"\"Modified version of login_required (from flask-user) to raise a 401\n    error on unauthenticated access\"\"\"\n    @wraps(func)\n    def decorated_view(*args, **kwargs):\n        # User must be authenticated\n        if not current_user.is_authenticated:\n            abort(401)\n\n        # Call the actual view\n        return func(*args, **kwargs)\n    return decorated_view\n\n\ndef roles_required(*role_names):\n    \"\"\" This decorator ensures that the current user has all of the specified\n    roles.  Abort if requirements fail.\n        See also: UserMixin.has_roles()\n    \"\"\"\n    def wrapper(func):\n        @wraps(func)\n        def decorated_view(*args, **kwargs):\n            # User must be logged\n            if not current_user.is_authenticated:\n                abort(401)\n\n            # User must have the required roles\n            if not current_user.has_roles(*role_names):\n                abort(403)\n\n            # Call the actual view\n            return func(*args, **kwargs)\n        return decorated_view\n    return wrapper\n\n\n# from: http://flask.pocoo.org/docs/0.10/patterns/apierrors/\nclass InvalidAPIUsage(Exception):\n    status_code = 400\n\n    def __init__(self, message, status_code=None, payload=None):\n        Exception.__init__(self)\n        self.message = message\n        if status_code is not None:\n            self.status_code = status_code\n        self.payload = payload\n\n    def to_dict(self):\n        rv = dict(self.payload or ())\n        rv['message'] = self.message\n        return rv\n\n\ndef _get_request_data(match_user=False):\n    \"\"\"Retrieve the request json data, using uth-8 by default.\n       If match_user is True, verify that current user match the user info\n       in the json data or abort\"\"\"\n    ct = request.headers.get('Content-Type')\n    if ct == 'application/json':\n        try:\n            fdata = request.get_json()\n        except:\n            # Try with header encoding if indicated (utf-8 by default)\n            enc = 'utf-8'\n            ct = request.headers.get('Content-Type')\n            if ct and 'charset' in ct.lower():\n                enc = ct.split('charset=')[1]\n\n            data = request.data.decode(enc)\n            fdata = loads(data)\n    elif ct == 'application/x-www-form-urlencoded':\n        import urllib\n        fdata = urllib.parse.unquote(request.get_data().decode())\n    else:\n        fdata = request.form\n\n    if match_user:\n        uid = current_user.id\n        if 'user_id' in fdata and fdata.get('user_id') != uid:\n            return abort(403)\n        if 'user' in fdata and fdata.get('user').get('id') != uid:\n            return abort(403)\n\n    return fdata\n\n\ndef _filter_by_fields(resource, query):\n    \"\"\" Implements filtering opitons\n        field filter can have a prefit :xx: where xx any of the values\n        `gt`, `lt`, `ge`, `le`, `i~`, `c~`, `in`, `ct`, for greater than,\n        lesser than, greater or equal, lesser or equal than the value.\n        `i`~ is for partial case ignore search on text field and c~ for the\n        case relevant partial search. `in` is for sql in predicate.\n            ej: ?value1=:lt:2&value_2=1&value_3=:ge:2&value_4=:~:ello\n            (value1 < 2, value2 == 1, value_3 >= 2, value_4 ilike '%ello%')\n    \"\"\"\n    for k, v in request.args.items():\n        if not hasattr(resource.model, k):\n            continue\n\n        field = getattr(resource.model, k)\n\n        if v.startswith(':') and v[3] == ':':\n            (xx, xs) = v.split(':')[1:]\n            if xx == 'gt':\n                query = query.filter(field > xs)\n            elif xx == 'lt':\n                query = query.filter(field < xs)\n            elif xx == 'ge':\n                query = query.filter(field >= xs)\n            elif xx == 'le':\n                query = query.filter(field <= xs)\n            elif xx == 'i~':\n                query = query.filter(field.ilike('%{}%'.format(xs)))\n            elif xx == 'c~':\n                query = query.filter(field.like('%{}%'.format(xs)))\n            elif xx == 'in':\n                query = query.filter(field.in_(tuple(xs.split(','))))\n            else:\n                query = query.filter(field == v)\n        else:\n            query = query.filter(field == v)\n\n    return query\n\n\ndef _parse_common_filters(resource, query, Schema, search_by=None):\n    \"\"\" Extend GET request to allow querystring options:\n        ?fields=f1,f2,...,fn  -> only retrieve listed fields.\n        &field=xxxx -----------> filter the result by field (exact match).\n        &search=xxx -----------> Use partial ignoring case search on field\n                                    `search_by`.\n        \"\"\"\n\n    # field selection, if any\n    if 'fields' in request.args:\n        fields = ('id', ) + tuple(\n            x for x in request.args.get('fields').split(','))\n        schema = Schema(many=True, only=fields)\n    else:\n        schema = Schema(many=True)\n\n    # filter by fields\n    query = _filter_by_fields(resource, query)\n\n    # ILIKE filtering on the text field designed in `search_by`\n    # is the same functionality as :i~:\n    search = request.args.get('search')\n    if search:\n        query = query.filter(\n            getattr(resource.model, search_by).ilike('%{}%'.format(search))\n        )\n\n    return (query, schema)\n\n\nclass ResourceExt(Resource):\n    \"\"\"Auxiliary methods\"\"\"\n    def _get_or_404(self, idx, model=None):\n        res = (model or self.model).query.get(idx)\n        if not res:\n            abort(404)\n\n        return res\n\n    def _delete(self, id):\n        item = self._get_or_404(id)\n\n        db.session.delete(item)\n        db.session.commit()\n\n        return True, 204\n\n    def _filter_by_fields(self, query):\n        return _filter_by_fields(self, query)\n\n    def _parse_common_filters(self, query, Schema, search_by=None):\n        return _parse_common_filters(self, query, Schema, search_by)\n\n    def _parse_pagination(self, query):\n        # apply pagination\n        if '_offset' in request.args:\n            delta = int(request.args.get('_offset'))\n            query = query.offset(delta)\n        if '_limit' in request.args:\n            delta = int(request.args.get('_limit'))\n            query = query.limit(delta)\n\n        return query\n\n\nclass AuthenticateResource(ResourceExt):\n    decorators = [login_required]\n    model = None\n    schema = None\n\n\nclass DefaultGETMixin(object):\n    def get(self, id=None):\n        \"\"\"Default GET implemenetation\"\"\"\n\n        if not hasattr(self, 'model'):\n            current_app.logger.error('DefaultGET mixin requires self.model')\n            abort(404)\n        if not hasattr(self, 'schema'):\n            current_app.logger.error('DefaultGET mixin requires self.schema')\n            abort(404)\n\n        current_app.logger.info('GET Default method')\n        if id:\n            row = self.model.query.get_or_404(id)\n            return self.schema().dump(row).data\n\n        (query, schema) = _parse_common_filters(self, self.model.query, self.schema)\n\n        return schema.dump(query.all()).data\n","sub_path":"app/modules/api/core.py","file_name":"core.py","file_ext":"py","file_size_in_byte":7758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"543684133","text":"from pfuncs_ssa import *\nfrom model_funcs import *\n\nttime = 100\n\nmdname = 'g1'\ndef mk_pfuncs(p):\n    pfuncs = []\n    pfuncs.append(spline_pfunc((p['b'], p['D'], p['b']), (p['ttreat'], p['twash']), (p['ttreat_dl'], p['twash_dl']), mk_hfunc(()), ttime))\n\n    pfuncs.append(spline_pfunc((p['alph'],), (), (), mk_hfunc((0,)), ttime))\n\n    pfuncs.append(spline_pfunc((p['rNH'],), (), (), mk_hfunc((1,)), ttime))\n\n    return pfuncs\n\nx0 = [0, 1]\nstoich = array(([1, 0], [-1, 1], [0, -1]))\n\npars = {'D':20., 'b':0.45, 'alph':0.6, 'rNH':0.2, 'ttreat':5., 'ttreat_dl':0.05, 'twash':6., 'twash_dl':0.05}\n\nxname = ['gammaH2AX', '53BP1+gammaH2AX']\nxcolor = ['dodgerblue', 'red']\ndetxcolor = ['cornflowerblue', 'rosybrown']\n\nobsinds = [(0,1)]\nobsname = ['total gammaH2AX']\nobscolor = ['grey']\ndetobscolor = ['black']\n\npname = ['DNA_dam', '53BP1_recrt', 'NHEJ_repr']\npcolor = ['blue', 'red', 'm']\n\nfuzz = None\n# fuzz = {'twash':('exponential', 10.)}\n\nmd_g1 = Model(mdname, x0, stoich, ttime, mk_pfuncs, pars, xname, xcolor, detxcolor, pname, pcolor, obsinds, obsname, obscolor, detobscolor, fuzz)\n","sub_path":"model_g1.py","file_name":"model_g1.py","file_ext":"py","file_size_in_byte":1082,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"26990053","text":"import copy\nimport json\n\nfrom . import ValidatorTest\nfrom ... import ValidationOptions, validate_parsed_json, validate_string\n\nMULTI_OBJ_JSON = u\"\"\"\n[\n    {\n      \"type\": \"attack-pattern\",\n      \"id\": \"attack-pattern--0c7b5b88-8ff7-4a4d-aa9d-feb398cd0061\",\n      \"created\": \"2016-02-12T08:17:27.000Z\",\n      \"modified\": \"2016-02-12T08:17:27.000Z\",\n      \"name\": \"Spear Phishing\",\n      \"description\": \"...\",\n      \"external_references\": [\n        {\n          \"source_name\": \"capec\",\n          \"external_id\": \"CAPEC-463\"\n        }\n      ]\n    },\n\n    {\n      \"type\": \"identity\",\n      \"id\": \"identity--2d1c6ab3-5e4e-48ac-a32b-f0c01c2836a8\",\n      \"created\": \"2014-08-08T15:50:10.983Z\",\n      \"modified\": \"2014-08-08T15:50:10.983Z\",\n      \"name\": \"ACME Widget, Inc.\",\n      \"identity_class\": \"organization\"\n    }\n]\n\"\"\"\n\n\nclass MultiObjTestCases(ValidatorTest):\n    valid_objs = json.loads(MULTI_OBJ_JSON)\n\n    def test_both_valid(self):\n        obj_results = validate_string(MULTI_OBJ_JSON, ValidationOptions(version=\"2.0\"))\n        self.assertEqual(len(obj_results), 2)\n        self.assertTrue(all(r.is_valid for r in obj_results))\n\n    def test_one_valid_one_invalid(self):\n        objs = copy.deepcopy(self.valid_objs)\n        objs[1][\"id\"] = objs[1][\"id\"].replace(\"identity\", \"abc\")\n        obj_results = validate_parsed_json(objs, ValidationOptions(version=\"2.0\"))\n\n        self.assertEqual(len(obj_results), 2)\n        self.assertTrue(obj_results[0].is_valid)\n        self.assertFalse(obj_results[1].is_valid)\n","sub_path":"stix2validator/test/v20/multi_obj_tests.py","file_name":"multi_obj_tests.py","file_ext":"py","file_size_in_byte":1514,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"558200709","text":"#!/usr/bin/python\n# Copyright 2015 Mirantis, 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#\n# Collectd plugin for checking the status of OpenStack API services\nimport collectd\nimport openstack\n\nPLUGIN_NAME = 'check_openstack_api'\nINTERVAL = 60\n\n\nclass APICheckPlugin(openstack.CollectdPlugin):\n    \"\"\" Class to check the status of OpenStack API services.\n    \"\"\"\n    OK = 0\n    FAIL = 1\n    UNKNOWN = 2\n\n    # TODO(pasquier-s): add heat-cfn, nova_ec2, cinderv2, ceilometer, murano,\n    # sahara\n    RESOURCE_MAP = {\n        'cinder': 'volumes',\n        'glance': 'v1/images',\n        'heat': 'stacks',\n        'keystone': 'tenants',\n        'neutron': 'v2.0/networks',\n        'nova': 'flavors',\n    }\n\n    def check_api(self):\n        \"\"\" Check the status of all the API services.\n\n            Yields a list of dict items with 'service', 'status' (either OK,\n            FAIL or UNKNOWN) and 'region' keys.\n        \"\"\"\n        catalog = self.service_catalog\n        for service in catalog:\n            if service['name'] not in self.RESOURCE_MAP:\n                self.logger.notice(\"Don't know how to check service '%s'\" %\n                                   service['name'])\n                status = self.UNKNOWN\n            else:\n                r = self.get(service['name'],\n                             self.RESOURCE_MAP[service['name']])\n                if not r or r.status_code < 200 or r.status_code > 299:\n                    status = self.FAIL\n                else:\n                    status = self.OK\n\n            yield {\n                'service': service['name'],\n                'status': status,\n                'region': service['region']\n            }\n\n    def read_callback(self):\n        for item in self.check_api():\n            value = collectd.Values(\n                plugin=PLUGIN_NAME,\n                plugin_instance=item['service'],\n                type='gauge',\n                type_instance=item['region'],\n                interval=INTERVAL,\n                # w/a for https://github.com/collectd/collectd/issues/716\n                meta={'0': True}\n            )\n            if item['status'] == self.OK:\n                value.values = [1]\n            elif item['status'] == self.FAIL:\n                value.values = [0]\n            else:\n                # skip if status is UNKNOWN\n                continue\n            value.dispatch()\n\n\nplugin = APICheckPlugin(collectd)\n\n\ndef config_callback(conf):\n    plugin.config_callback(conf)\n\n\ndef read_callback():\n    plugin.read_callback()\n\ncollectd.register_config(config_callback)\ncollectd.register_read(read_callback, INTERVAL)\n","sub_path":"deployment_scripts/puppet/modules/lma_collector/files/collectd/check_openstack_api.py","file_name":"check_openstack_api.py","file_ext":"py","file_size_in_byte":3111,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"176263265","text":"import os\nimport unittest\n\nfrom sqlalchemy import create_engine\n\nimport transaction\n\nfrom pyramid import testing\n\nimport pytest\n\n\nTEST_DATABASE_NAME = \"websauna_test\"\n\n\n\n@pytest.mark.usefixtures(\"ini_settings\")\nclass DefaultModelBaseTest(unittest.TestCase):\n\n    def setup_method(self, method):\n        self._patchers = []\n        self.config = testing.setUp()\n\n    def add_patcher(self, patcher):\n        self._patchers.append(patcher)\n        patcher.start()\n        return patcher\n\n    def teardown_method(self, method):\n        transaction.abort()\n        testing.tearDown()\n\n        for patcher in self._patchers:\n            patcher.stop()\n\n    def create_engine(self):\n        \"\"\"Get a hold of correct database depending on where tests are run.\"\"\"\n\n        # createdb unittest-conflict-resolution on homebrew based installations\n        if \"CI\" in os.environ:\n            # Running on Drone.IO\n            engine = create_engine('postgresql://postgres@localhost/{}'.format(TEST_DATABASE_NAME))\n        else:\n            # Running locally\n            engine = create_engine('postgresql:///{}'.format(TEST_DATABASE_NAME))\n\n        return engine\n","sub_path":"websauna/tests/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":1150,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"122604992","text":"print(\"Welcome to Dakota's Wacky Name Masher!\")\nfullname1 = input(\"Enter a name in the format \\\"FIRST LAST\\\" -> \")\nfullname2 = input(\"Enter a second name in the format \\\"FIRST LAST\\\" -> \")\n\nspace1 = fullname1.find(' ')\nspace2 = fullname2.find(' ')\n\nfirstname1 = fullname1[0:space1]\nlastname1 = fullname1[space1 + 1:]\nfirstname2 = fullname2[0:space2]\nlastname2 = fullname2[space2 + 1:]\n\nmiddleindexfirst1 = round(len(firstname1) / 2)\nmiddleindexlast1 = round(len(lastname1) / 2)\nmiddleindexfirst2 = round(len(firstname2) / 2)\nmiddleindexlast2 = round(len(lastname2) / 2)\n\nleft_name1 = firstname1[0:middleindexfirst1]\nleft_name2 = firstname2[0:middleindexfirst2]\nright_name1 = lastname1[middleindexlast1:]\nright_name2 = lastname2[middleindexlast2:]\n\nwackyname1 = left_name1.capitalize() + left_name2.lower() + \" \" + \\\nright_name1.capitalize() + right_name2.lower()\nwackyname2 = left_name2.capitalize() + left_name1.lower() + \" \" + \\\nright_name2.capitalize() + right_name1.lower()\n\nprint(\"Here are two wacky mashed up names, choose the one you like the most!\")\nprint(wackyname1)\nprint(wackyname2)\n","sub_path":"get-programming-python3/capstone12.py","file_name":"capstone12.py","file_ext":"py","file_size_in_byte":1094,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"447989892","text":"from decimal import Decimal, getcontext, ROUND_HALF_DOWN\nimport copy\n\nfrom qsforex.event.event import SignalEvent\n\n\nclass TestStrategy(object):\n    \"\"\"\n    A testing strategy that alternates between buying and selling\n    a currency pair on every 5th tick. This has the effect of\n    continuously \"crossing the spread\" and so will be loss-making\n    strategy. \n\n    It is used to test that the backtester/live trading system is\n    behaving as expected.\n    \"\"\"\n    def __init__(self, pairs, events):\n        self.pairs = pairs\n        self.events = events\n        self.ticks = 0\n        self.invested = False\n\n    def calculate_signals(self, event):\n        if event.type == 'TICK':\n            if self.ticks % 5 == 0:\n                if self.invested == False:\n                    signal = SignalEvent(self.pairs[0], \"market\", \"buy\", event.time)\n                    self.events.put(signal)\n                    self.invested = True\n                else:\n                    signal = SignalEvent(self.pairs[0], \"market\", \"sell\", event.time)\n                    self.events.put(signal)\n                    self.invested = False\n            self.ticks += 1\n\n\nclass MovingAverageCrossStrategy(object):\n    \"\"\"\n    A basic Moving Average Crossover strategy that generates\n    two simple moving averages (SMA), with default windows\n    of 500 ticks for the short SMA and 2,000 ticks for the\n    long SMA.\n\n    The strategy is \"long only\" in the sense it will only\n    open a long position once the short SMA exceeds the long\n    SMA. It will close the position (by taking a corresponding\n    sell order) when the long SMA recrosses the short SMA.\n\n    The strategy uses a rolling SMA calculation in order to\n    increase efficiency by eliminating the need to call two\n    full moving average calculations on each tick.\n    \"\"\"\n    def __init__(\n        self, pairs, events, \n        short_window=500, long_window=2000\n    ):\n        self.pairs = pairs\n        self.pairs_dict = self.create_pairs_dict()\n        self.events = events      \n        self.short_window = short_window\n        self.long_window = long_window\n\n    def create_pairs_dict(self):\n        attr_dict = {\n            \"ticks\": 0,\n            \"invested\": False,\n            \"short_sma\": None,\n            \"long_sma\": None\n        }\n        pairs_dict = {}\n        for p in self.pairs:\n            pairs_dict[p] = copy.deepcopy(attr_dict)\n        return pairs_dict\n\n    def calc_rolling_sma(self, sma_m_1, window, price):\n        return ((sma_m_1 * (window - 1)) + price) / window\n\n    def calculate_signals(self, event):\n        if event.type == 'TICK':\n            pair = event.instrument\n            price = event.bid\n            pd = self.pairs_dict[pair]\n            if pd[\"ticks\"] == 0:\n                pd[\"short_sma\"] = price\n                pd[\"long_sma\"] = price\n            else:\n                pd[\"short_sma\"] = self.calc_rolling_sma(\n                    pd[\"short_sma\"], self.short_window, price\n                )\n                pd[\"long_sma\"] = self.calc_rolling_sma(\n                    pd[\"long_sma\"], self.long_window, price\n                )\n            # Only start the strategy when we have created an accurate short window\n            if pd[\"ticks\"] > self.short_window:\n                if pd[\"short_sma\"] > pd[\"long_sma\"] and not pd[\"invested\"]:\n                    signal = SignalEvent(pair, \"market\", \"buy\", event.time)\n                    self.events.put(signal)\n                    pd[\"invested\"] = True\n                if pd[\"short_sma\"] < pd[\"long_sma\"] and pd[\"invested\"]:\n                    signal = SignalEvent(pair, \"market\", \"sell\", event.time)\n                    self.events.put(signal)\n                    pd[\"invested\"] = False\n            pd[\"ticks\"] += 1\n\n\nclass tradeEnterStrategy(object):\n    def __init__(self, pairs, transaction, ticker, takeprofit=200, stoploss=500, maxpositions=10, units=100000):\n        self.takeprofit = takeprofit\n        self.stoploss = stoploss\n        self.maxpositions = maxpositions\n        self.units = units\n        self.transaction = transaction\n        self.ticker = ticker\n\n    def execute_signal(self, signal_event):\n        side = signal_event.side\n        pair = signal_event.instrument\n        units = self.units\n        digits = 0.00001 # it depends on instrument\n        if side == 'buy':\n            _takeprofit = self.ticker.prices[pair]['ask'] + Decimal(self.takeprofit * digits).quantize(Decimal(\"0.00001\"), ROUND_HALF_DOWN)\n            _stoploss = self.ticker.prices[pair]['ask'] -  Decimal(self.stoploss * digits).quantize(Decimal(\"0.00001\"), ROUND_HALF_DOWN)\n        elif side == 'sell':\n            _takeprofit = self.ticker.prices[pair]['bid'] - Decimal(self.takeprofit * digits).quantize(Decimal(\"0.00001\"), ROUND_HALF_DOWN)\n            _stoploss = self.ticker.prices[pair]['bid'] + Decimal(self.stoploss * digits).quantize(Decimal(\"0.00001\"), ROUND_HALF_DOWN)\n\n        a = self.transaction.order_open(self.ticker, pair, units, side, order_type='market', expiary=None, price=None, lowerBound=None, upperBound=None, stopLoss=_stoploss, takeProfit=_takeprofit)\n\nclass tradeExitStrategy(object):\n    def __init__(self, pairs, transaction, ticker, takeprofit=200, stoploss=500):\n        self.takeprofit = takeprofit\n        self.stoploss = stoploss\n        self.transactin = transaction\n        self.ticker = ticker\n","sub_path":"strategy/strategy.py","file_name":"strategy.py","file_ext":"py","file_size_in_byte":5367,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"637596762","text":"import sys\nimport os\n\ndef multi_file2one(path,ofile,label = '0'):\n    files = os.listdir(path)\n    files_name = [path + item for item in files]\n\n    with open(ofile,'w') as out_f:\n        for item in files_name:\n            with open(item,'r',errors = 'ignore') as in_f:\n                lines = in_f.readlines()\n                lines = [line.strip().replace('\\n',' ') for line in lines]\n                lines = ''.join(lines) + '\\n'\n                out_f.write(lines)\n\n\nif __name__ == '__main__':\n\n    path = '/home/juncsu/Code/Data/CapsData/mr-2004/txt_sentoken/'\n    multi_file2one(path+'neg/', path + 'neg.txt', '0')\n    multi_file2one(path+'pos/', path + 'pos.txt', '1')\n    \n\n\n\n\n","sub_path":"multi_file2one.py","file_name":"multi_file2one.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"474996191","text":"from django.urls import path\n\nfrom . import views\n\nfrom .views import *\n\nfrom rest_framework import routers\nfrom django.conf.urls import url, include\n\n\napp_name = 'exercise'\n\n# Routers provide an easy way of automatically determining the URL conf.\nrouter = routers.DefaultRouter()\nrouter.register(r'list', ExerciseViewSet)\n\nexercise_list = ExerciseViewSet.as_view({ 'get': 'list' })\nexercise_detail = ExerciseViewSet.as_view({ 'get': 'retrieve' })\n\nemails_list = ExerciseEmailViewSet.as_view({'get': 'list'})\nemails_detail = ExerciseEmailViewSet.as_view({'get': 'retrieve'})\n\n\nurlpatterns = [\n    path('<slug:link>/profile', views.profile, name='profile'),\n    path('<slug:link>/start/<int:p_id>/', views.start, name='start'),\n    path('<slug:link>/', views.index, name='index'),\n    url(r'^', include(router.urls)),\n\n    # exercise urls\n    url('^detail/(?P<pk>[0-9]+)/$', exercise_detail, name='exercise-detail'),\n    url('^list/$', exercise_list, name='exercise-list'),\n\n    # exercise emails urls\n    url('^emails/list/$',emails_list, name= 'emails_list'),\n    url('^emails/(?P<pk>[0-9]+)/$',emails_detail, name= 'emails_detail'),\n\n    # emails in Thread style\n    url('^thread/(?P<pk>[0-9]+)/$', ExerciseEmailThreadViewSet.as_view({'get': 'retrieve'}), name='exercise-thread'),\n\n]\n","sub_path":"exercise/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1286,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"108454554","text":"import sys \r\nimport os \r\nimport string\r\nimport json\r\n\r\nfrom timeit import default_timer as timer\r\n\r\n\r\n\r\n#function to read the data from the text files. \r\ndef read_data(data_path):\r\n    text = []\r\n    target = []\r\n    \r\n    spam_ham_dict = {\"spam\": [], \"ham\": []}\r\n    \r\n    #walk through all the directories and subdirectories in the training directory\r\n    for dirpath, dirs, files in os.walk(data_path):\r\n        for filename in files:\r\n            fname = os.path.join(dirpath,filename)\r\n            \r\n            #open the file, read it and store it in the respective list in the dictionary, \r\n            #find the right dictionary key (label) by checking whether it is in the ham or spam directory. \r\n            with open(fname, \"r\", encoding = \"latin1\") as f:\r\n                if dirpath[-3:] == \"ham\":\r\n                \ttry:\r\n                \t\tspam_ham_dict[\"ham\"].append(f.read())\r\n                \texcept:\r\n                \t\tprint(\"couldnt read \", fname)\r\n                \t\tcontinue\r\n                elif dirpath[-4:] == \"spam\":\r\n                \ttry:\r\n                \t\tspam_ham_dict[\"spam\"].append(f.read())\r\n                \texcept:\r\n                \t\tprint(\"couldnt read \", fname)\r\n                \t\tcontinue\r\n                else:\r\n                \tcontinue    \r\n    return spam_ham_dict\r\n\r\n\r\n\r\n\r\n#function to read the data from the text files. \r\ndef read_data_10percent(data_path):\r\n    text = []\r\n    target = []\r\n    \r\n    spam_ham_dict = {\"spam\": [], \"ham\": []}\r\n    spam_count = 0\r\n    ham_count = 0\r\n    \r\n    #walk through all the directories and subdirectories in the training directory\r\n    for dirpath, dirs, files in os.walk(data_path):\r\n        count = 0\r\n        for filename in files:\r\n            count+=1\r\n            fname = os.path.join(dirpath,filename)\r\n            \r\n            #open the file, read it and store it in the respective list in the dictionary, \r\n            #find the right dictionary key (label) by checking whether it is in the ham or spam directory. \r\n            with open(fname, encoding = \"latin1\") as f:\r\n                if dirpath[-3:] == \"ham\":\r\n                    ham_count+=1\r\n                    if ham_count > 950:\r\n                        #if both spam and ham are greater than the required numbers, break\r\n                        if spam_count > 750:\r\n                            return spam_ham_dict\r\n                        else:\r\n                            continue\r\n                    else:\r\n                        spam_ham_dict[\"ham\"].append(f.read())\r\n\r\n                elif dirpath[-4:] == \"spam\":\r\n                    spam_count+=1\r\n                    if spam_count > 750:\r\n                        if ham_count > 950:\r\n                            return spam_ham_dict\r\n                        else:\r\n                            continue\r\n                    else:\r\n                        spam_ham_dict[\"spam\"].append(f.read())\r\n                else:\r\n                    continue\r\n    return spam_ham_dict\r\n\r\n\r\n\r\n#removes punctuation from the text \r\ndef remove_new_line(text):\r\n    mystring = text.replace('\\n', ' ').replace('\\r', '')\r\n    return mystring\r\n\r\n#calls remove_punctuation to remove punctuation and ten tokenizes the text\r\ndef tokenize(text):\r\n    text = remove_new_line(text).lower()\r\n    return text.split()\r\n\r\n#cleans data by removing punctuation and tokenizing the text.\r\ndef get_clean_data(text_list):\r\n    clean_data = []\r\n    for t in text_list:\r\n        clean_data.append(tokenize(t))\r\n    return clean_data\r\n\r\n#gets all the words from te sentence list\r\ndef get_words(sentence_list):\r\n    word_list = [item for sublist in sentence_list for item in sublist]\r\n    return word_list\r\n\r\n#gets a set of vocabulary from the word list.\r\ndef get_vocabulary(word_list):\r\n    return set(word_list)\r\n\r\n\r\ndef get_all_word_counts(word_list):\r\n    word_count_dict = {}\r\n    for word in word_list:\r\n        if word in word_count_dict:\r\n            word_count_dict[word] += 1\r\n        else:\r\n            word_count_dict[word] = 1\r\n    return word_count_dict\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n\r\n#takes the data dictionary that is read from the files, computes the vocabulary, prior probabilities and the conditional probabilities and returns them.\r\ndef train_NB_model(spam_ham_dict):\r\n    \r\n    #clean the data\r\n    for k in spam_ham_dict.keys():\r\n        clean_data = get_clean_data(spam_ham_dict[k])\r\n        spam_ham_dict[k] = clean_data\r\n        \r\n    prior_probabilities = {\"spam\":0, \"ham\":0}\r\n\r\n    \r\n    #get the counts and a prior probabilities\r\n    spam_count = len(spam_ham_dict[\"spam\"])\r\n    ham_count = len(spam_ham_dict[\"ham\"])\r\n    total_count = spam_count + ham_count\r\n    \r\n    prior_probabilities[\"spam\"] = spam_count/total_count\r\n    prior_probabilities[\"ham\"] = ham_count/total_count\r\n    \r\n    \r\n    #get all the words.\r\n    spam_words = get_words(spam_ham_dict[\"spam\"])\r\n    ham_words = get_words(spam_ham_dict[\"ham\"])\r\n    spam_words_total_count = len(spam_words)\r\n    ham_words_total_count = len(ham_words)\r\n    all_words = spam_words + ham_words\r\n\r\n    vocabulary = list(get_vocabulary(all_words))\r\n    \r\n    \r\n    #get the word counts\r\n    spam_word_count = get_all_word_counts(spam_words)\r\n    ham_word_count = get_all_word_counts(ham_words)\r\n    \r\n    \r\n    #get all the conditional probabilities.\r\n    spam_conditional_probabilities = {k: v/spam_count for k,v in spam_word_count.items()}\r\n    ham_conditional_probabilities = {k: v/ham_count for k,v in ham_word_count.items()}\r\n    \r\n    #get all the laplace smoothed conditional probabilities\r\n    spam_conditional_probabilities_smoothed = {k: (v+1)/(spam_words_total_count+len(vocabulary)) for k,v in spam_word_count.items()}\r\n    ham_conditional_probabilities_smoothed = {k: (v+1)/(ham_words_total_count+len(vocabulary)) for k,v in ham_word_count.items()}\r\n\r\n    spam_conditional_probabilities_smoothed[\"total_spam_word_count\"] = spam_words_total_count\r\n    ham_conditional_probabilities_smoothed[\"total_ham_word_count\"] = ham_words_total_count\r\n    \r\n    \r\n    #conditional probabilities of spam and ham for every token\r\n    conditional_probabilites = [spam_conditional_probabilities, ham_conditional_probabilities]\r\n    smoothed_conditional_probabilities = [spam_conditional_probabilities_smoothed, ham_conditional_probabilities_smoothed]\r\n    \r\n    return vocabulary, prior_probabilities, smoothed_conditional_probabilities\r\n    \r\n    \r\n\r\n\r\n\r\ndef main():\r\n\tpath_to_data = sys.argv[1]\r\n\r\n\t\r\n\tstart_reading = timer()\r\n\tdata_dict = read_data_10percent(path_to_data)\r\n\tfinish_reading = timer()\r\n\tprint(\"time to read\", finish_reading-start_reading)\r\n\r\n\r\n\tprint(len(data_dict[\"spam\"]))\r\n\tprint(len(data_dict[\"ham\"]))\r\n\r\n\tstart_training = timer()\r\n\tV,PP,CP = train_NB_model(data_dict)\r\n\tend_training = timer()\r\n\tprint(\"time to train \", end_training - start_training)\r\n\tprint(\"trained model\")\r\n\r\n\tmodel_data = {\"vocabulary\": V, \"prior_probabilities\": PP, \"conditional_probabilities\":CP}\r\n\tjson.dump(model_data, open(\"nbmodel.txt\",'w', encoding = \"latin1\"))\r\n\tprint(\"json dump done\")\r\n\r\n\r\n\r\n\r\n\r\nif __name__ == '__main__':\r\n\tmain()\r\n\r\n","sub_path":"experiment_task2/nblearn.py","file_name":"nblearn.py","file_ext":"py","file_size_in_byte":7071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"80207792","text":"import csv\nimport re\nimport nltk\nimport string\nimport unicodedata\n\nnew_file = csv.reader(open('../dataset_balanceamento.csv', 'r'))\n\nlist_docs = []\n\nlist_label = []\n\n\ndef remove_stopwords(text):\n    regex = re.compile('[%s]' % re.escape(string.punctuation))\n\n    a = []\n\n    words = text.split()\n    for t in words:\n        new_token = regex.sub(u'', t)\n        if not new_token == u'':\n            a.append(new_token)\n\n    stopwords = nltk.corpus.stopwords.words('portuguese')\n    content = [w for w in a if w.lower().strip() not in stopwords]\n\n    clean_text = []\n    for word in content:\n\n        nfkd = unicodedata.normalize('NFKD', word)\n        palavraSemAcento = u''.join([c for c in nfkd if not unicodedata.combining(c)])\n        q = re.sub('[^a-zA-Z0-9]', '', palavraSemAcento)\n\n        clean_text.append(q.lower().strip())\n\n    tokens = [t for t in clean_text if len(t)>2 and not t.isdigit()]\n    ct = \" \".join(tokens)\n\n    return ct\n\n\nfor row in new_file:\n\n    doc_word = remove_stopwords(row[0])\n    print(doc_word)\n\n","sub_path":"text_nltk.py","file_name":"text_nltk.py","file_ext":"py","file_size_in_byte":1029,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"141983838","text":"import algopy\nimport numpy as np\nfrom scipy.integrate import quad\n\n\ndef quad(f, a, b):\n    n = 1000\n    Q = np.linspace(a, b, n)\n    W = 1 / n * np.ones(n)\n    y = 0\n    for w, q in zip(W, Q):\n        y += f(q) * w\n    return y\n\n\ndef eval_f(x):\n    integrand = lambda t: x[0] * np.sin(x[1] * t)\n\n    # return integrand(1.0)\n    # print(integrand(1.0))\n    return quad(integrand, 0, 10)\n\n\n# STEP 1: trace the function evaluation\ncg = algopy.CGraph()\nx = algopy.Function([1, 2])\ny = eval_f(x)\ncg.trace_off()\ncg.independentFunctionList = [x]\ncg.dependentFunctionList = [y]\n\n# STEP 2: use the computational graph to evaluate derivatives\nprint('gradient =', cg.gradient([1.0, 2.0]))\nprint('Jacobian =', cg.jacobian([1.0, 2.0]))\nprint('Hessian =', cg.hessian([1.0, 2.0]))\nprint('Hessian vector product =', cg.hess_vec([1.0, 2.0], [1.0, 0.0]))\n","sub_path":"ext/steffensCode/ext/sphericalquadpy/ldfeLinDisc/ad.py","file_name":"ad.py","file_ext":"py","file_size_in_byte":837,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"647170960","text":"#!/usr/bin/env python\n#\n# Electrum - lightweight Bitcoin client\n# Copyright (C) 2014 Thomas Voegtlin\n#\n# Permission is hereby granted, free of charge, to any person\n# obtaining a copy of this software and associated documentation files\n# (the \"Software\"), to deal in the Software without restriction,\n# including without limitation the rights to use, copy, modify, merge,\n# publish, distribute, sublicense, and/or sell copies of the Software,\n# and to permit persons to whom the Software is furnished to do so,\n# subject to the following conditions:\n#\n# The above copyright notice and this permission notice shall be\n# included in all copies or substantial portions of the Software.\n#\n# THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND,\n# EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF\n# MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND\n# NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS\n# BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN\n# ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN\n# CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\n# SOFTWARE.\n\nfrom functools import partial\n\nimport calendar\nimport datetime\nimport time\nimport signal\nimport copy\nimport json\nimport logging\nimport hashlib\n\nfrom http.client import CannotSendRequest\n\nfrom xmlrpc.client import ServerProxy\n\nfrom PyQt5.QtCore import Qt, QObject, pyqtSignal\nfrom PyQt5.QtWidgets import QDialog, QLabel, QPushButton, QVBoxLayout, QTextEdit, QGridLayout, QLineEdit\n\nfrom electrum_exos import util, keystore, ecc, crypto\nfrom electrum_exos import transaction\nfrom electrum_exos.bip32 import BIP32Node\nfrom electrum_exos.plugin import BasePlugin, hook, run_hook\n\nfrom electrum_exos.i18n import _\nfrom electrum_exos.wallet import Multisig_Wallet\nfrom electrum_exos.util import bh2u, bfh\nfrom electrum_exos.util import InvalidPassword\n\nfrom electrum_exos.gui.qt.transaction_dialog_timeout import show_transaction_timeout, TxDialogTimeout\nfrom electrum_exos.gui.qt.transaction_wait_dialog import show_timeout_wait_dialog, TimeoutWaitDialog\nfrom electrum_exos.gui.qt.util import WaitingDialog, EnterButton, Buttons, WindowModalDialog, CloseButton, OkButton, read_QIcon\n\nfrom . import server\n\nimport sys\nimport traceback\n\nlogger = logging.getLogger(__name__)\n\nWAIT_TIME = 60 * 10 \n\nclass Listener(util.DaemonThread):\n\n    def __init__(self, parent):\n        util.DaemonThread.__init__(self)\n        self.daemon = True\n        self.parent = parent\n        self.received = set()\n        self.keyhashes = []\n        self.wallet_hash = None\n        self.last_tx = None\n\n    def set_keyhashes(self, keyhashes):\n        self.keyhashes = keyhashes\n\n    def set_wallet_hash(self, wallet_hash):\n        self.wallet_hash = wallet_hash\n\n    def clear(self, keyhash):\n        self.received.remove(keyhash)\n\n    def run(self):\n        while self.running:\n            if not self.keyhashes:\n                time.sleep(2)\n                continue\n            for keyhash in self.keyhashes:\n                try:\n                    lock = server.lock\n                    if lock:\n                        continue\n                    data = server.get(self.wallet_hash)\n                    sha1_data = hashlib.sha1(data.encode('utf-8')).hexdigest() # sha1 of the data (to check for udpates)\n                except Exception as e:\n                    self.logger.error(e)\n                    self.logger.info(\"cannot contact cosigner pool\")\n                    time.sleep(30)\n                    continue\n                if not data:\n                    continue\n                if keyhash in self.received and self.last_tx == sha1_data:\n                    continue\n                self.received.add(keyhash)\n                self.last_tx = sha1_data\n                self.logger.info(f\"received data for {keyhash}\")\n                self.parent.obj.cosigner_receive_signal.emit(\n                    keyhash, data)\n            # poll every 30 seconds\n            time.sleep(30)\n\n\nclass QReceiveSignalObject(QObject):\n    cosigner_receive_signal = pyqtSignal(object, object)\n\n\nclass Plugin(BasePlugin):\n\n    def __init__(self, parent, config, name):\n        BasePlugin.__init__(self, parent, config, name)\n        self.listener = None\n        self.obj = QReceiveSignalObject()\n        self.obj.cosigner_receive_signal.connect(self.on_receive)\n        self.keys = []\n        self.cosigner_list = []\n        self._init_qt_received = False\n\n    @hook\n    def init_qt(self, gui):\n        if self._init_qt_received:  # only need/want the first signal\n            return\n        self._init_qt_received = True\n        for window in gui.windows:\n            self.on_new_window(window)\n\n    @hook\n    def on_new_window(self, window):\n        self.update(window)\n\n    @hook\n    def on_close_window(self, window):\n        self.update(window)\n\n    def requires_settings(self):\n        return True\n\n    def settings_widget(self, window):\n        return EnterButton(_('Settings'),\n                           partial(self.calibration_dialog, window))\n\n    def calibration_dialog(self, window):\n        d = WindowModalDialog(window, _(\"Cosigner Pool Settings\"))\n\n        d.setMinimumSize(150, 100)\n\n        vbox = QVBoxLayout(d)\n\n        purge = QPushButton(_(\"Purge Transactions\"))\n        purge.clicked.connect(partial(self.purge_transaction, window))\n        purge.setIcon(read_QIcon(\"warning.png\"))\n        vbox.addWidget(purge)\n\n        vbox.addWidget(QLabel(_('Wallet Owner:')))\n        grid = QGridLayout()\n        vbox.addLayout(grid)\n\n        grid.addWidget(QLabel(_('Name:')), 0, 0)\n        name = QLineEdit()\n        name.setText(self.config.get('wallet_owner', ''))\n        grid.addWidget(name, 0, 1)\n\n        sync = QPushButton(_(\"Sync Name\"))\n        sync.clicked.connect(partial(self.sync_name, name, window))\n        vbox.addWidget(sync)\n\n        status = QPushButton(_(\"Tx. Status\"))\n        status.clicked.connect(partial(self.tx_status_dialog, window))\n        vbox.addWidget(status)\n\n        vbox.addStretch()\n        vbox.addSpacing(13)\n        vbox.addLayout(Buttons(CloseButton(d), OkButton(d)))\n\n        if not d.exec_():\n            return\n\n    def purge_transaction(self, window):\n        if not window.question(_(\"Purging you transactions will erase the current transaction\") + '\\n' +\n                        _(\"Are you sure you want to purge your transaction?\")):\n            return\n        \n        server.delete(self.wallet_hash)\n        del server.lock\n        \n        window.show_message(_(\"Your transactions have been purged.\"))\n\n    def sync_name(self, name, window):\n        self.config.set_key('wallet_owner', name.text())\n        if self.config.get('wallet_owner', ''):\n            for key, _hash, window in self.keys:\n                server.put(_hash+'_name', self.config.get('wallet_owner', ''))\n        for key, _hash, window in self.keys:\n            if self.config.get('wallet_owner', '') == server.get(_hash+'_name'):\n                window.show_message(_(\"Your name has been synced\"))\n            else:\n                window.show_message(_(\"Failed to sync name with cosigner pool\"))\n\n\n    def tx_status_dialog(self, window):\n        d = QDialog(window)\n        d.setWindowTitle(_(\"Transaction Status\"))\n\n        d.setMinimumSize(600, 300)\n\n        vbox = QVBoxLayout(d)\n\n        status_header = 'No transaction in progress'\n        status = ''\n\n        data = server.get(self.wallet_hash)\n        loads = json.loads(data) if data else {}\n        lock = server.lock\n\n        locked_by = lock.get('xpub') if lock else None\n        signed_by = loads.get('signed', [])\n\n        for _hash in server.cosigners():\n            name = server.get(_hash+'_name')\n            if not name or len(name) < 1:\n                name = _hash[0:10] + '...' + _hash[-1:-5:-1]\n\n            signed = True if _hash in signed_by else False\n            signing = True if _hash == locked_by else False\n\n            if signed or signing:\n                status_header = 'Transaction in progress'\n            if signed:\n                message = 'Signed'\n            elif signing: \n                message = 'Signing'\n            else:\n                message = 'Not signed'\n\n            status += f'<br><br> {name}: <b>{message}</b>'\n\n        for _hash in server.xpub():\n            signed = True if _hash in signed_by else False\n            signing = True if _hash == locked_by else False\n            if signed or signing:\n                status_header = 'Transaction in progress'\n            if signed:\n                message = 'Signed'\n            elif signing: \n                message = 'Signing'\n            else:\n                message = 'Not signed'\n            status += f'<br><br> You: <b>{message}</b>'\n\n        self.tx_status = QLabel()\n        vbox.addWidget(self.tx_status)\n        self.tx_status.setTextFormat(Qt.RichText)\n        self.tx_status.setText(_(\"<b>Transaction Status</b>\") + ': ' + status_header + status)\n        self.tx_status.show()\n\n        vbox.addStretch()\n        vbox.addSpacing(13)\n        vbox.addLayout(Buttons(CloseButton(d)))\n        \n        d.setModal(True)\n        d.show()\n\n    def is_available(self):\n        return True\n\n    def update(self, window):\n        wallet = window.wallet\n        if type(wallet) != Multisig_Wallet:\n            return\n        if self.listener is None:\n            self.logger.info(\"starting listener\")\n            self.listener = Listener(self)\n            self.listener.start()\n        elif self.listener:\n            self.logger.info(\"shutting down listener\")\n            self.listener.stop()\n            self.listener = None\n        self.keys = []\n        self.cosigner_list = []\n        for key, keystore in wallet.keystores.items():\n            xpub = keystore.get_master_public_key()\n            pubkey = BIP32Node.from_xkey(xpub).eckey.get_public_key_bytes(compressed=True)\n            _hash = bh2u(crypto.sha256d(pubkey))\n            self.logger.info(_hash)\n            if not keystore.is_watching_only():\n                self.keys.append((key, _hash, window))\n                server.xpub(_hash)\n            else:\n                self.cosigner_list.append((window, xpub, pubkey, _hash))\n                server.cosigners().append(_hash)\n        for key in self.keys:\n            self.logger.info(f'xpub: {key[1]}')\n        for cosigner in self.cosigner_list:\n            self.logger.info(f'cosigners: {cosigner[3]}')\n        self.wallet_hash = server.wallet_hash()\n        self.logger.info(self.wallet_hash)\n        if self.listener:\n            self.listener.set_keyhashes([t[1] for t in self.keys])\n            self.listener.set_wallet_hash(self.wallet_hash)\n\n    @hook\n    def transaction_dialog(self, d):\n        d.cosigner_send_button = b = QPushButton(_(\"Send to cosigner\"))\n        b.clicked.connect(lambda: self.do_send(d.tx, d))\n        d.buttons.insert(0, b)\n        self.transaction_dialog_update(d)\n\n    @hook\n    def transaction_dialog_update(self, d):\n        if d.tx.is_complete() or d.wallet.can_sign(d.tx):\n            d.cosigner_send_button.hide()\n            return\n        for window, xpub, K, _hash in self.cosigner_list:\n            if window.wallet == d.wallet and self.cosigner_can_sign(d.tx, xpub):\n                d.cosigner_send_button.show()\n                break\n        else:\n            d.cosigner_send_button.hide()\n\n    def cosigner_can_sign(self, tx, cosigner_xpub):\n        from electrum_exos.keystore import is_xpubkey, parse_xpubkey\n        xpub_set = set([])\n        for txin in tx.inputs():\n            for x_pubkey in txin['x_pubkeys']:\n                if is_xpubkey(x_pubkey):\n                    xpub, s = parse_xpubkey(x_pubkey)\n                    xpub_set.add(xpub)\n        return cosigner_xpub in xpub_set\n\n    def do_send(self, tx, d):\n        def on_success(result):\n            window.show_message(_(\"Your transaction was sent to the cosigning pool.\") + '\\n' +\n                                _(\"Open your cosigner wallet to retrieve it.\"))\n            time.sleep(1)\n            d.close()\n\n        def on_failure(exc_info):\n            e = exc_info[1]\n            try: self.logger.error(\"on_failure\", exc_info=exc_info)\n            except OSError: pass\n            window.show_error(_(\"Failed to send transaction to cosigning pool\") + ':\\n' + str(e))\n\n        buffer = {'signed': [], 'txs': {}}\n        some_window = None\n        # construct messages\n        for window, xpub, K, _hash in self.cosigner_list:\n            if not self.cosigner_can_sign(tx, xpub):\n                continue\n            some_window = window\n            raw_tx_bytes = bfh(str(tx))\n            public_key = ecc.ECPubkey(K)\n            message = public_key.encrypt_message(raw_tx_bytes).decode('ascii')\n            buffer['txs'].update({_hash : message})\n        if not buffer:\n            return\n        # construct signed\n        if type(d) == TxDialogTimeout:\n            buffer['signed'] = d.signed\n        for key, _hash, window in self.keys:\n            buffer['signed'].append(_hash)\n\n        # send message\n        def send_messages_task():\n            server.put(self.wallet_hash, json.dumps(buffer))\n\n        msg = _('Sending transaction to cosigning pool...')\n        WaitingDialog(window, msg, send_messages_task, on_success, on_failure)\n\n    def on_receive(self, keyhash, data):\n        self.logger.info(f\"signal arrived for {keyhash}\")\n        for key, _hash, window in self.keys:\n            if _hash == keyhash:\n                break\n        else:\n            self.logger.info(\"keyhash not found\")\n            return\n\n        # convert data from string to json\n        data = json.loads(data)\n\n        signed = data.get('signed')\n        txs = data.get('txs')\n\n        # invalid JSON structure\n        if not signed or not txs:\n            self.logger.info(\"cosigner data malformed, missing entry\")\n            return\n        \n        # check if user has signed\n        if keyhash in signed:\n            self.logger.info(\"user has already signed\")\n            return\n\n        message = txs[keyhash]\n\n        wallet = window.wallet\n        if isinstance(wallet.keystore, keystore.Hardware_KeyStore):\n            window.show_warning(_('An encrypted transaction was retrieved from cosigning pool.') + '\\n' +\n                                _('However, hardware wallets do not support message decryption, '\n                                  'which makes them not compatible with the current design of cosigner pool.'))\n            return\n        elif wallet.has_keystore_encryption():\n            password = window.password_dialog(str(datetime.datetime.now().strftime(\"%a, %d %b %Y %H:%M:%S\") + '\\n\\n' +\n                                              _('An encrypted transaction was retrieved from cosigning pool.') + '\\n' +\n                                              _('Please enter your password to decrypt it. ')), parent=None)\n            if not password:\n                return\n        else:\n            password = None\n            if not window.question(_(\"An encrypted transaction was retrieved from cosigning pool.\") + '\\n' +\n                                   _(\"Do you want to open it now?\")):\n                return\n\n        xprv = None\n        try:\n            xprv = wallet.keystore.get_master_private_key(password)\n            if not xprv:\n                return\n        except InvalidPassword as e:\n            self.logger.info(\"Incorrect Password\")\n            window.show_error(_(\"Incorrect Password\"))\n            return\n\n        try:\n            privkey = BIP32Node.from_xkey(xprv).eckey\n            message = bh2u(privkey.decrypt_message(message))\n        except Exception as e:\n            self.logger.exception('')\n            window.show_error(_('Error decrypting message') + ':\\n' + str(e))\n            return\n\n        tx = transaction.Transaction(message)\n\n        def calculate_wait_time(expire):\n            # calculate wait time\n            server_time = server.get_current_time()\n            mins, secs = 0, 0\n            if server_time is not None:\n                wait_time = int((WAIT_TIME - (int(server_time) - int(expire))))\n                mins, secs = divmod(wait_time, 60)\n            return '{:02d}:{:02d}'.format(mins, secs)\n\n        # check if lock has been placed for wallet\n        lock = server.lock\n        if lock:\n            # calculate wait time based on lock expiry and server time\n            timeformat = calculate_wait_time(lock['timestamp'])\n            # display pop up\n            window.show_warning(_(\"A cosigner is currently signing the transaction.\") + '\\n' +\n                                _(\"Please wait {} until the signing has concluded.\".format(timeformat)))\n\n            xpub = lock['xpub']\n            name = None\n            try:\n                name = server.get(xpub+'_name')\n            except Exception as e:\n                self.logger.exception(e)\n                self.logger.error(\"Failed to get cosigner name from server\")\n            show_timeout_wait_dialog(tx, xpub, name, window, prompt_if_unsaved=True)\n            return\n        else:\n            buffer = {'timestamp' : '', 'xpub' : ''}\n            buffer['timestamp'] = str(server.get_current_time())\n            buffer['xpub'] = keyhash\n            server.lock = buffer\n            time_until_expired = '10 minutes'\n\n        window.show_warning(_(\"You have {} to conclude signing after which the dialog will\".format(time_until_expired)) + '\\n' +\n                            _(\"automatically close.\"), parent=None)\n            \n        self.listener.clear(keyhash)\n        show_transaction_timeout(tx, signed, window, prompt_if_unsaved=True)\n","sub_path":"electrum/plugins/cosigner_pool/qt.py","file_name":"qt.py","file_ext":"py","file_size_in_byte":17650,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"132130921","text":"import tvm,os\nfrom tvm import relay\nfrom tvm.relay import testing\n\ndata=relay.var(\"data\", relay.TensorType((1,), \"float32\"))\nconst=relay.const(1, \"float32\")\nnet=relay.add(data, const)\nf=relay.Function(relay.analysis.free_vars(net),net)\nm, p=testing.create_workload(f)\n\nbyoc = \"zendnn\"\n@tvm.ir.register_op_attr(\"add\", \"target.\"+byoc)\ndef _support(attr):\n    return True\n\npm=relay.transform.AnnotateTarget(byoc)(m)\nmod=relay.transform.PartitionGraph()(pm)\n\nkwargs={}\nkwargs[\"options\"] = [\"-DLIBM_ENABLE=1\", \"-std=c++14\", \"O3\", \"-fPIC\", \"-fopenmp\",\n                     \"-DBIAS_ENABLED=1\", \"-DZENDNN_ENABLE=1\",\n                     \"-I\"]\n\ncompile_flag=\"-DLIBM_ENABLE=1 -std=c++14 -O3 -fPIC -fopenmp -DBIAS_ENABLED=1 -DZENDNN_ENABLE=1 -Werror -Wreturn-type -fconcepts -DZENDNN_X64=1 -march=znver2 -I/scratch/staff/huaj/tvm/zendnn/ZenDNN/inc -I/scratch/staff/huaj/tvm/zendnn/ZenDNN/aocl-linux-gcc-4.0/amd-blis//include -I/scratch/staff/huaj/tvm/zendnn/ZenDNN/aocl-linux-gcc-4.0/amd-libm//include -L/scratch/staff/huaj/tvm/zendnn/ZenDNN/_out/lib -lamdZenDNN -L/scratch/staff/huaj/tvm/zendnn/ZenDNN/aocl-linux-gcc-4.0/amd-blis//lib/ -lblis-mt -L/scratch/staff/huaj/tvm/zendnn/ZenDNN/aocl-linux-gcc-4.0/amd-libm//lib -lalm\"\nlib=relay.build(mod, \"llvm\")\n\nfor m in lib.lib.imported_modules:\n    print(m.get_source())\n\n","sub_path":"zendnn/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"139629394","text":"import base64\nimport pathlib\nimport uuid\n\nimport numpy as np\n\nfrom astropy.io.registry import IORegistryError\nfrom astropy.nddata import StdDevUncertainty\n\nfrom specutils import Spectrum1D, SpectrumList, SpectrumCollection\n\nfrom jdaviz.core.registries import data_parser_registry\n\n__all__ = [\"specviz_spectrum1d_parser\"]\n\n\n@data_parser_registry(\"specviz-spectrum1d-parser\")\ndef specviz_spectrum1d_parser(app, data, data_label=None, format=None, show_in_viewer=True):\n    \"\"\"\n    Loads a data file or `~specutils.Spectrum1D` object into Specviz.\n\n    Parameters\n    ----------\n    data : str, `~specutils.Spectrum1D`, or `~specutils.SpectrumList`\n        Spectrum1D, SpectrumList, or path to compatible data file.\n    data_label : str\n        The Glue data label found in the ``DataCollection``.\n    format : str\n        Loader format specification used to indicate data format in\n        `~specutils.Spectrum1D.read` io method.\n    \"\"\"\n\n    # If no data label is assigned, give it a unique identifier\n    if not data_label:\n        data_label = \"specviz_data|\" + str(\n            base64.b85encode(uuid.uuid4().bytes), \"utf-8\"\n        )\n\n    if isinstance(data, SpectrumCollection):\n        raise TypeError(\"SpectrumCollection detected.\"\n                        \" Please provide a Spectrum1D or SpectrumList\")\n    elif isinstance(data, Spectrum1D):\n        data = [data]\n        data_label = [data_label]\n    elif isinstance(data, SpectrumList):\n        pass\n    elif isinstance(data, list):\n        data = SpectrumList.read(data, format=format)\n    else:\n        path = pathlib.Path(data)\n\n        if path.is_file():\n            try:\n                data = [Spectrum1D.read(str(path), format=format)]\n                data_label = [data_label]\n            except IORegistryError:\n                # Multi-extension files may throw a registry error\n                data = SpectrumList.read(str(path), format=format)\n        elif path.is_dir():\n            data = SpectrumList.read(str(path), format=format)\n        else:\n            raise FileNotFoundError(\"No such file: \" + str(path))\n\n    if isinstance(data, SpectrumList):\n        if not isinstance(data_label, (list, tuple)):\n            temp_labels = []\n            for i in range(len(data)):\n                temp_labels.append(f\"{data_label} {i}\")\n            data_label = temp_labels\n        elif len(data_label) != len(data):\n            raise ValueError(f\"Length of data labels list ({len(data_label)}) is different\"\n                             f\" than length of list of data ({len(data)})\")\n\n    # If there's already data in the viewer, convert units if needed\n    current_unit = None\n    current_spec = app.get_data_from_viewer(\"spectrum-viewer\")\n    if current_spec != {} and current_spec is not None:\n        spec_key = list(current_spec.keys())[0]\n        current_unit = current_spec[spec_key].spectral_axis.unit\n    with app.data_collection.delay_link_manager_update():\n\n        # these are used to build a combined spectrum with all\n        # input spectra included (taken from https://github.com/spacetelescope/\n        # dat_pyinthesky/blob/main/jdat_notebooks/MRS_Mstar_analysis/\n        # JWST_Mstar_dataAnalysis_analysis.ipynb)\n        wlallorig = []\n        fnuallorig = []\n        dfnuallorig = []\n\n        for i in range(len(data)):\n            spec = data[i]\n\n            wave_units = spec.spectral_axis.unit\n            flux_units = spec.flux.unit\n\n            if current_unit is not None and spec.spectral_axis.unit != current_unit:\n                spec = Spectrum1D(flux=spec.flux,\n                                  spectral_axis=spec.spectral_axis.to(current_unit))\n\n            app.add_data(spec, data_label[i])\n\n            # handle display, with the SpectrumList special case in mind.\n            if show_in_viewer:\n                if isinstance(data, SpectrumList):\n\n                    # add spectrum to combined result\n                    for wlind in range(len(spec.spectral_axis)):\n                        wlallorig.append(spec.spectral_axis[wlind].value)\n                        fnuallorig.append(spec.flux[wlind].value)\n                        dfnuallorig.append(spec.uncertainty[wlind].array)\n\n                elif i == 0:\n                    app.add_data_to_viewer(\"spectrum-viewer\", data_label[i])\n\n        # reset display ranges, or build combined spectrum, when input is a SpectrumList instance\n        if isinstance(data, SpectrumList):\n\n            # build combined spectrum\n            wlallarr = np.array(wlallorig)\n            fnuallarr = np.array(fnuallorig)\n            dfnuallarr = np.array(dfnuallorig)\n            srtind = np.argsort(wlallarr)\n            wlall = wlallarr[srtind]\n            fnuall = fnuallarr[srtind]\n            fnuallerr = dfnuallarr[srtind]\n\n            # units are not being handled properly yet.\n            unc = StdDevUncertainty(fnuallerr * flux_units)\n            spec = Spectrum1D(flux=fnuall * flux_units, spectral_axis=wlall * wave_units,\n                              uncertainty=unc)\n\n            # needs perhaps a better way to label the combined spectrum\n            label = \"Combined \" + data_label[0]\n            app.add_data(spec, label)\n            if show_in_viewer:\n                app.add_data_to_viewer(\"spectrum-viewer\", label)\n","sub_path":"jdaviz/configs/specviz/plugins/parsers.py","file_name":"parsers.py","file_ext":"py","file_size_in_byte":5275,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"517481381","text":"import os\nimport pandas as pd\nimport re\nimport win32com.client\n\n\n\"\"\"\nSeptember 19, 2019\n- Search folders method\n- Read .xlsx\n- Read comments field\n\"\"\"\n\n\ndef getcomments(filepath):\n    # 1 Read COMMENTS from .xlsx\n\n    dirFiles = os.listdir(filepath)\n\n    try:\n        excelFile = os.path.join(filepath, [x for x in dirFiles if '.xls' in x][0])\n        # print(excelFile)\n\n        df = pd.read_excel(excelFile, skiprows=3)   # Row start - Row 4\n        df.set_index('PID', inplace=True)\n\n        comments = df['COMMENTS'].tolist()\n        # print(comments)\n        commentsNoNull = [x for x in comments if str(x) != 'nan' or str(x) != \" \"]\n\n        if len(commentsNoNull) > 0:\n            print(\"\\nSUBDIVISION: \", os.path.basename(filepath))\n            print(excelFile)\n            # print(commentsNoNull)\n            dfComments = df[['COMMENTS']]\n            print(dfComments)\n            return commentsNoNull\n\n        # else:\n        #     print(\"**NO COMMENTS FOUND\")\n\n    except Exception as e:\n        print(\"EXCEPTION: {}\".format(str(e)))\n\n\"\"\"\nSearch folders with subdivision number\nTest folders: 22400, 22350, 22220\n\"\"\"\n\n# WOULD CHANGE THIS TO GET INPUT FROM USER (AS A STRING)\nmainDir = r'H:\\Civic Addressing\\Shared Work\\LOT CREATION DATA'\nsubs = [22400, 22350, 22220, 15759, 22312]\nsubs = [str(x) for x in subs]\nsubdirs = ['DBUPDATE COMPLETED TO DECEMBER 31 2019', 'REGISTERED']\n\n\ndef findfile(subdivision, all=False):\n    count = 0\n    for root, dirs, files in os.walk(mainDir, topdown=True):\n        if subdivision is not None:\n            # if os.path.basename(root) in subs:\n            if os.path.basename(root) == str(subdivision):\n                # print(\"\\nROOT: {}\".format(root), \"DIRS: {}\".format(dirs), \"FILES: {}\".format(files),sep='\\n-->')\n                getcomments(root)\n                count += 1\n\n        # Get all files with comments\n        if all is True:\n            if os.path.basename(root)[0].isdigit():\n                print(root)\n                getcomments(root)\n                count += 1\n\n    print(\"\\nFOUND {} RESULTS\".format(count))\n\n# EMAIL STUFF\noutlook = win32com.client.Dispatch(\"Outlook.Application\").GetNamespace(\"MAPI\")\n\nroot_folder = outlook.Folders.Item(1)\nsubFolder = root_folder.Folders['Tickets'].Folders['Tasks'].Folders['Subdivisions']\n\nmessages = subFolder.Items\nmessages.Sort(\"[ReceivedTime]\", True)\n\nmessage = messages.GetFirst()\nrec_time = message.CreationTime\nbody_content = message.body\nsubj_line = message.subject\n\nsubNumbers = []\n\nfor i in range(100):\n# while message:\n    # print(message.subject, message.CreationTime)\n    try:\n        if 'ubdivision' in message.body:\n            # print(message.subject, message.CreationTime)\n            # print(message.body, \"\\n\\n\\t\\t\")\n\n            sub = re.search(r\"Subdivision \\d+\", message.body)\n            sub = str(sub.group()).split('Subdivision')[1].strip()\n            subNumbers.append(sub)\n            print(\"\\tFound Subdivision:\\t'{}'\".format(sub))\n\n    except Exception as e:\n        print(\"\\t**\", str(e))\n\n    message = messages.GetNext()\n\nprint(\"SUBS: {}\".format(subNumbers))\n\nfor x in subNumbers[:5]:\n    findfile(x)\n\n# findfile(13891, True)\n\n# sub = input(\"What is your subdivision number? \")\n# findfile(sub)\n","sub_path":"subdiv_info.py","file_name":"subdiv_info.py","file_ext":"py","file_size_in_byte":3229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"173726744","text":"from __future__ import division\nfrom __future__ import print_function\nfrom typing import List, Any\nfrom absl import flags\nfrom absl import app\nfrom absl import logging\n\nimport os\nimport shutil\n\nFLAGS = flags.FLAGS\nflags.DEFINE_string('mlp_project', None, 'The name of the project. Will be the root directory of the project.')\nflags.DEFINE_string('mlp_subproject', None, 'The name of the subproject. Will be a sub directory inside the project directory.')\nflags.DEFINE_string('gcp_project', None, 'The name of the GCP project. Any GCP services will be used with the account corresponding to this project.')\nflags.DEFINE_string('gcp_bucket', None, 'The name of the GCP bucket to store the files generated by running a pipeline.')\n\nflags.DEFINE_string('dir', './', 'The directory to place the example project.')\nflags.mark_flag_as_required('mlp_project')\nflags.mark_flag_as_required('mlp_subproject')\nflags.mark_flag_as_required('gcp_project')\nflags.mark_flag_as_required('gcp_bucket')\n\ndef replace_strings_in_dir(dir, string_map):\n  for dir_name, dirs, files in os.walk(dir):\n    for file_name in files:\n      file_path = os.path.join(dir_name, file_name)\n\n      try:\n        with open(file_path) as f:\n          text = f.read()\n      except UnicodeDecodeError:\n        continue\n\n      for old_string in string_map:\n        text = text.replace(old_string, string_map[old_string])\n\n      with open(file_path, \"w\") as f:\n        f.write(text)\n\n\ndef main(argv: List[Any]):\n  os.makedirs(FLAGS.dir, exist_ok=True)\n\n  string_map = {\n    'gcp_project': FLAGS.gcp_project,\n    'example_project': FLAGS.mlp_project,\n    'example_subproject': FLAGS.mlp_subproject\n  }\n\n  example_project_dir = os.path.join(os.path.dirname(__file__), 'example_project')\n  example_subproject_dir = os.path.join(os.path.dirname(__file__), 'example_project', 'example_subproject')\n\n  subproject_dir = os.path.join(FLAGS.dir, FLAGS.mlp_project, FLAGS.mlp_subproject)\n\n  project_dir = os.path.join(FLAGS.dir, FLAGS.mlp_project)\n  subproject_dir = os.path.join(FLAGS.dir, FLAGS.mlp_project, FLAGS.mlp_subproject)\n\n  if os.path.exists(project_dir):\n    logging.info('{} already exists. Will not alter any files outside of {}'.format(project_dir, subproject_dir))\n\n    if os.path.exists(subproject_dir):\n      logging.info('{} already exists. Nothing to be done. Exiting'.format(subproject_dir))\n      return\n\n    shutil.copytree(example_subproject_dir, subproject_dir)\n    replace_strings_in_dir(subproject_dir, string_map)\n  else:\n    shutil.copytree(example_project_dir, project_dir)\n    os.rename(\n      os.path.join(project_dir, 'example_subproject'), subproject_dir\n    )\n    replace_strings_in_dir(project_dir, string_map)\n\nif __name__ == \"__main__\":\n  app.run(main)\n","sub_path":"create_project.py","file_name":"create_project.py","file_ext":"py","file_size_in_byte":2737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"465681871","text":"__author__ = 'antsmc2'\nimport time\nfrom zipfile import ZipFile\nfrom StringIO import StringIO\nfrom django.core.files.uploadedfile import SimpleUploadedFile\nfrom lxml import etree\nimport python_digest\nfrom model_mommy import mommy\nimport random\nfrom hashlib import md5\nfrom django.test import TestCase\nfrom django.contrib.sites.models import Site\nfrom django.core.urlresolvers import reverse\nfrom django.test.client import Client\nfrom django.contrib.auth.models import User\nfrom survey.models import (InterviewerAccess, ODKAccess, USSDAccess, Interview, Interviewer, QuestionSetChannel,\n                           EnumerationArea, Survey, SurveyAllocation, Question, QuestionSet, Batch, BatchQuestion,\n                           QuestionOption, ODKSubmission)\nfrom survey.forms.question import get_question_form\n# import all question types\nfrom survey.models import (Answer, NumericalAnswer, TextAnswer, MultiChoiceAnswer, MultiSelectAnswer, GeopointAnswer,\n                           ImageAnswer, AudioAnswer, VideoAnswer, DateAnswer, AutoResponse, Attachment)\nfrom survey.tests.models.survey_base_test import SurveyBaseTest\n\n\nclass ODKTest(SurveyBaseTest):\n\n    def setUp(self):\n        super(ODKTest, self).setUp()\n        self.odk_user = ODKAccess.objects.create(interviewer=self.interviewer, user_identifier='ants',\n                                                 odk_token='antsman')\n\n    def _save_ussd_compatible_questions(self):\n        self._create_ussd_non_group_questions()\n\n    def _make_odk_request(self, url=reverse('odk_survey_forms_list'), data=None, content_type=None, raw=False):\n        username = self.odk_user.user_identifier\n        password = self.odk_user.odk_token\n        auth_headers = {}\n        if content_type:\n            auth_headers['content_type'] = content_type\n        if data is None:\n            # first do digest challeng\n            response = self.client.get(url)\n            self.assertEquals(response.status_code, 401)\n            digest_challenge = response.get('www-authenticate')\n            auth_headers['HTTP_AUTHORIZATION'] = self._get_digest_header(username, password, 'get',\n                                                                         url, digest_challenge)\n            return self.client.get(url, **auth_headers)\n        else:\n            response = self.client.post(url)\n            self.assertEquals(response.status_code, 401)\n            digest_challenge = response.get('www-authenticate')\n            auth_headers['HTTP_AUTHORIZATION'] = self._get_digest_header(username, password, 'post',\n                                                                         url, digest_challenge)\n            if raw:\n                return self.client.post(url, data, **auth_headers)\n            else:\n                return self.client.post(url, data=data, **auth_headers)\n\n    def _get_digest_header(self, username, password, method, uri, digest_challenge):\n        return python_digest.build_authorization_request(\n            username,\n            method.upper(),\n            uri,\n            1,  # nonce_count\n            digest_challenge=digest_challenge,\n            password=password\n        )\n\n    def test_form_list_throws_no_error(self):\n        response = self._make_odk_request()\n        self.assertEquals(response.status_code, 200)\n\n    def test_no_xform_without_opening_batch(self):\n        response = self._make_odk_request()\n        self.assertEquals(response.status_code, 200)\n        qset = QuestionSet.objects.first()\n        path = '<formID>%s</formID>' % qset.pk\n        self.assertNotIn(path, response.content)\n\n    def test_xform_with_opening_batch(self):\n        batch = QuestionSet.get(id=self.qset.id)\n        batch.open_for_location(self.ea.locations.first())\n        response = self._make_odk_request()\n        self.assertEquals(response.status_code, 200)\n        path = '<formID>%s</formID>' % batch.pk\n        self.assertIn(path, response.content)\n\n    def test_batch_without_questions_has_empty_questions_section_in_xform(self):\n        url = reverse('download_odk_batch_form', args=(self.qset.id, ))\n        response = self._make_odk_request(url=url)\n        self.assertEquals(response.status_code, 200)\n        # at least some reference representation would be there is it's been referenec some how\n        self.assertNotIn('ref=\"/qset/qset%s/questions/surveyQuestions' % self.qset.id, response.content)\n\n    def test_select_batch_odk_question(self):\n        self._save_ussd_compatible_questions()\n        url = reverse('download_odk_batch_form', args=(self.qset.id, ))\n        response = self._make_odk_request(url=url)\n        self.assertEquals(response.status_code, 200)\n        survey_tree = etree.fromstring(response.content)\n        path = '/qset/qset%s/questions/surveyQuestions/q%s' % (self.qset.id, self.qset.questions.first().id)\n        self.assertIn(path, response.content)\n\n    def _get_completed_xform(self, answer1, answer2, answer3, answer4):\n        completed = b'''<qset id=\"%(qset)s\" >\n                         <meta>\n                            <instanceID>%(instance_id)s</instanceID>\n                            <instanceName>%(instance_id)s-Name</instanceName>\n                        <creationDate />\n                        <locked />\n                         </meta>\n                         <submissions>\n                             <id />\n                             <dates>\n                                 <lastModified />\n                             </dates>\n                         </submissions>\n                           <surveyAllocation>%(survey_allocation)s</surveyAllocation>\n                           <qset1>\n                            <questions>\n                                <groupQuestions></groupQuestions>\n                                <surveyQuestions>\n                                    <q1>%(answer1)s</q1><q2>%(answer2)s</q2><q3>%(answer3)s</q3><q4>%(answer4)s</q4>\n                                </surveyQuestions>\n                            </questions>\n                           </qset1>\n                       </qset>\n                       '''\n        context = {'qset': self.qset.id, 'instance_id': random.randint(0, 1000),\n                   'survey_allocation': self.survey_allocation.allocation_ea.name.encode('utf8'),\n                   'answer1': answer1, 'answer2': answer2, 'answer3': answer3, 'answer4': answer4}\n        return completed % context\n\n    def _get_completed_xform2(self, answer1, answer2, answer3, answer4, answer5, answer6, answer7,\n                              answer8, answer9, answer10):\n        completed = b'''<qset id=\"%(qset)s\" >\n                         <meta>\n                            <instanceID>%(instance_id)s</instanceID>\n                            <instanceName>%(instance_id)s-Name</instanceName>\n                        <creationDate />\n                        <locked />\n                         </meta>\n                         <submissions>\n                             <id />\n                             <dates>\n                                 <lastModified />\n                             </dates>\n                         </submissions>\n                           <surveyAllocation>%(survey_allocation)s</surveyAllocation>\n                           <qset1>\n                            <questions>\n                                <groupQuestions></groupQuestions>\n                                <surveyQuestions>\n                                    <q1>%(answer1)s</q1><q2>%(answer2)s</q2><q3>%(answer3)s</q3><q4>%(answer4)s</q4>\n                                    <q5>%(answer5)s</q5><q6>%(answer6)s</q6><q7>%(answer7)s</q7><q8>%(answer8)s</q8>\n                                    <q9>%(answer9)s</q9><q10>%(answer10)s</q10>\n                                </surveyQuestions>\n                            </questions>\n                           </qset1>\n                       </qset>\n                       '''\n        context = {'qset': self.qset.id, 'instance_id': random.randint(0, 1000),\n                   'survey_allocation': self.survey_allocation.allocation_ea.name.encode('utf8'),\n                   'answer1': answer1, 'answer2': answer2, 'answer3': answer3, 'answer4': answer4,\n                   'answer5': answer5, 'answer6': answer6, 'answer7': answer7, 'answer8': answer8,\n                   'answer9': answer9, 'answer10': answer10, }\n        return completed % context\n\n    def test_submit_xform(self):\n        self._create_ussd_non_group_questions(self.qset)\n        xml = self._get_completed_xform('2', 'James', 'Y', '1')\n        f = SimpleUploadedFile(\"surveyfile.xml\", xml)\n        url = reverse('odk_submit_forms')\n        response = self._make_odk_request(url=url, data={'xml_submission_file': f}, raw=True)\n        self.assertTrue( 300 > response.status_code and response.status_code >= 200)\n        self.assertEquals(ODKSubmission.objects.count(), 1)\n        # not confirm that 4 responses were given\n        self.assertEquals(Answer.objects.count(), 4)\n        # now test the instances listpage\n        url = reverse('odk_submission_list')\n        # check all the if the\n        raj = self.assign_permission_to(User.objects.create_user('Rajni', 'rajni@kant.com', 'I_Rock'),\n                                        'can_view_aggregates')\n        client = Client()\n        client.login(username='Rajni', password='I_Rock')\n        response = client.get(url)\n        templates = [template.name for template in response.templates]\n        self.assertIn('odk/submission_list.html', templates)\n        self.assertEquals(response.context['submissions'].count(), 1)\n        # check instances list on ODK\n        url = reverse('instances_form_list')\n        response = self._make_odk_request(url=url)\n        templates = [template.name for template in response.templates]\n        self.assertIn(\"odk/instances_xformsList.xml\", templates)\n        self.assertEquals(response.context['submissions'].count(), 1)\n        self.assertEquals(response.context['submissions'].filter(status=ODKSubmission.COMPLETED).count(), 1)\n        submission = response.context['submissions'].first()\n        # try to download the previously submitted form\n        url = reverse('download_odk_submission', args=(submission.pk, ))\n        response = self._make_odk_request(url=url)\n        self.assertTrue( 300 > response.status_code and response.status_code >= 200)\n        self.assertEquals(submission.xml.strip(), response.content.strip())\n        tree = etree.fromstring(submission.xml)\n        dates_nodes = tree.xpath('//qset/submissions/dates/lastModified')\n        # this field is only included when updating instances\n        self.assertEquals(len([d.text for d in dates_nodes if d.text]), 1)\n        self.assertEquals(tree.xpath('//qset/submissions/id')[0].text, str(submission.id))\n        # now submit modified form\n        f = SimpleUploadedFile(\"surveyfile.xml\", submission.xml.encode('utf8'))\n        url = reverse('odk_submit_forms')\n        response = self._make_odk_request(url=url, data={'xml_submission_file': f}, raw=True)\n        self.assertTrue(300 > response.status_code and response.status_code >= 200)\n        self.assertEquals(ODKSubmission.objects.count(), 1)\n        self.assertEquals(Answer.objects.count(), 4)        # note: update only doesn't increase answer or subs count\n        submission = ODKSubmission.objects.order_by('created').last()\n        tree = etree.fromstring(submission.xml)\n        # this field is only included when updating instances\n        self.assertEquals(tree.xpath('//qset/submissions/id')[0].text, str(submission.id))\n\n    def test_submit_xform_with_file_form(self):\n        import os\n        self._create_test_non_group_questions(self.qset)\n        BASE_DIR = os.path.dirname(__file__)\n        image_path = os.path.join(BASE_DIR, 'testimage.png')\n        video_path = os.path.join(BASE_DIR, 'testvideo.mov')\n        audio_path = os.path.join(BASE_DIR, 'testaudio.m4a')\n        with open(image_path) as fi, open(video_path) as fv, open(audio_path) as fa:\n            fi_name = os.path.basename(fi.name)\n            fa_name = os.path.basename(fa.name)\n            fv_name = os.path.basename(fv.name)\n            fi_content = fi.read()\n            fa_content = fa.read()\n            fv_content = fv.read()\n            answers = ('2', 'James', 'Y', '1', ['Y', 'MB'], '31-08-2017', '12.8 8.0 8 7', fi_name, fa_name, fv_name)\n            xml = self._get_completed_xform2(*answers)\n            f = SimpleUploadedFile(\"surveyfile.xml\", xml)\n            sfi = SimpleUploadedFile(fi_name, fi_content)\n            sfa = SimpleUploadedFile(fa_name, fa_content)\n            sfv = SimpleUploadedFile(fv_name, fv_content)\n            url = reverse('odk_submit_forms')\n            response = self._make_odk_request(url=url, data={'xml_submission_file': f,\n                                                             fi_name: sfi, fa_name: sfa,\n                                                             fv_name: sfv}, raw=True)\n            self.assertTrue(300 > response.status_code and response.status_code >= 200)\n            self.assertEquals(ODKSubmission.objects.count(), 1)\n            self.assertEquals(Attachment.objects.count(), 3)\n            # not confirm that 4 responses were given\n            self.assertEquals(Answer.objects.count(), len(answers))\n            # now test the instances listpage\n            submission = ODKSubmission.objects.first()\n            url = reverse('download_submission_attachment', args=(submission.pk,))\n            # check all the if the\n            raj = self.assign_permission_to(User.objects.create_user('Rajni', 'rajni@kant.com', 'I_Rock'),\n                                            'can_view_aggregates')\n            client = Client()\n            client.login(username='Rajni', password='I_Rock')\n            response = client.get(url)\n            self.assertEquals(response._headers['content-type'][1], 'application/zip')\n            extracted = self._extract_zip(response.content)\n            # check instances list on ODK\n            for key, extracted_content in extracted.items():\n                # doing checks this way because we're compressing entire directory\n                # filename may have a random pre/appender to it\n                if fi_name in key:\n                    self.assertEquals(extracted_content, fi_content)   # fake image\n                if fa_name in key:\n                    self.assertEquals(extracted_content, fa_content)   # fake audio\n                if fv_name in key:\n                    self.assertEquals(extracted_content, fv_content)   # fake video\n\n    def _extract_zip(self, input_content):\n        input_zip = StringIO(input_content)\n        input_zip = ZipFile(input_zip)\n        return {name: input_zip.read(name) for name in input_zip.namelist()}\n","sub_path":"survey/tests/odk/test_odk_utils.py","file_name":"test_odk_utils.py","file_ext":"py","file_size_in_byte":14801,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"506574895","text":"class Auto() :\n    \"\"\"Eine Klasse für jede Art von Autos\n    \"\"\"\n    def __init__(self, marke: str, typ: str, jahr: int) -> None:\n        \"\"\"Initialisierung eines neuen Auto-Objekts. Enhält nur Attribute, \n        die für alle Arten von Autos gelten\"\"\"\n        self.marke = marke\n        self.typ = typ\n        self.jahr = jahr\n        self.kmstand = 0.0\n        self.reichweite = 0.0\n\n    def zeige(self) -> None :\n        \"\"\"Anzeige der Eigenschaften\"\"\"\n        print('Marke:', self.marke, 'Typ:', self.typ, \n              'Baujahr:', self.jahr, 'Km-Stand:', self.kmstand)\n\n    def fahre(self, km: float) -> None :\n        if km >= self.reichweite :\n            gefahren = self.reichweite\n        else :\n            gefahren = km\n            self.reichweite -= km\n        print(gefahren, 'km gefahren.') \n\n\nclass VerbrennerAuto(Auto):\n    \"\"\"Ist von der allgemeinen Klasse Auto abgeleitet\"\"\"\n    def __init__(self, marke: str, typ: str, jahr: int, tank: float, verbrauch: float) -> None:\n        \"\"\"Initialisierung eines neuen VerbrennerAuto-Objekts. Benutzt zunächst die \n        __init__ Funktion von Auto, dann werden die Attribute gesetzt, die für ein\n        VerbrennerAuto zusätzlich benötigt werden\"\"\"\n        super().__init__(marke, typ, jahr)\n        # Maximaler Tankinhalt\n        self.tankvolumen = tank\n        self.fuellung = 0.0\n        # Kraftstoffverbrauch auf 100 km\n        self.verbrauch = verbrauch\n\n    def zeige(self) -> None :\n        \"\"\"Anzeige der Eigenschaften, zuerst die Eigenschaften der Oberklasse, dann die\n        Eigenschaften der Unterklasse\"\"\"\n        super().zeige()\n        print(' Im Tank:', self.fuellung, 'Liter. Reichweite:', self.reichweite, 'km')\n\n    def tanke(self, liter: float) -> None :\n        \"\"\" Auftanken des Autos erhöht die Tankfüllung bis maximal Tankgrösse\"\"\"\n        if liter > self.tankvolumen - self.fuellung :\n            self.fuellung = self.tankvolumen\n        else :\n            self.fuellung += liter\n        self.reichweite = self.fuellung / self.verbrauch * 100\n\n    def fahre(self, km: float) -> None :\n        \"\"\"Auto fahren für km Kilometer mit Verbrennungsmotor\"\"\"\n        gefahren = 0.0\n        if self.reichweite >= km :\n            self.reichweite -= km\n            self.kmstand += km\n            self.fuellung -= km  / 100 * self.verbrauch\n            gefahren = km\n        else :\n            self.kmstand += self.reichweite\n            gefahren = self.reichweite\n            self.fuellung = 0.0\n            self.reichweite = 0.0\n        print('Fahre', gefahren, 'km weit')\n\nclass ElektroAuto(Auto):\n    \"\"\"Ist von der allgemeinen Klasse Auto abgeleitet\"\"\"\n    def __init__(self, marke: str, typ: str, jahr: int, kwh: float, verbrauch: float) -> None:\n        \"\"\"Initialisierung eines neuen ElektroAuto-Objekts. Benutzt zunächst die \n        __init__ Funktion von Auto, dann werden die Attribute gesetzt, die für ein\n        ElektroAuto zusätzlich benötigt werden\"\"\"\n        super().__init__(marke, typ, jahr)\n        # Lade-Kapazität Batterie in Kilowattstunden\n        self.batterie_kap = kwh\n        # Ladezustand Batterie in Kilowattstunden\n        self.ladung = 0.0\n        # Verbrauch in Kilowattstunden pro 100 km\n        self.verbrauch = verbrauch\n\n    def zeige(self) -> None :\n        \"\"\"Anzeige der Eigenschaften, zuerst die Eigenschaften der Oberklasse, dann die\n        Eigenschaften der Unterklasse\"\"\"\n        super().zeige()\n        print(' Batterieladung:', self.ladung, 'kWh. Reichweite:', self.reichweite, 'km')\n\n    def lade(self, kwh: float) -> None :\n        \"\"\"Laden der Batterie erhöht den Ladestand und die Reichweite\"\"\"\n        if kwh > self.batterie_kap - self.ladung :\n            self.ladung = self.batterie_kap\n        else :\n            self.ladung += kwh\n        self.reichweite = self.ladung / self.verbrauch * 100\n\n    def fahre(self, km: float) -> None :\n        \"\"\"Auto fahren für km Kilometer mit Elektromotor\"\"\"\n        gefahren = 0.0\n        if self.reichweite >= km :\n            self.reichweite -= km\n            self.kmstand += km\n            self.ladung -= km  / 100 * self.verbrauch\n            gefahren = km\n        else :\n            self.kmstand += self.reichweite\n            gefahren = self.reichweite\n            self.ladung = 0.0\n            self.reichweite = 0.0\n        print('Fahre', gefahren, 'km weit')","sub_path":"Grundkurs/Kap 17/Auto2.py","file_name":"Auto2.py","file_ext":"py","file_size_in_byte":4351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"415565332","text":"import numpy as np\nimport pandas as pd\nimport plotly.graph_objects as go\n\n\nclass BaseGraph:\n    def __init__(self, data, title, x_title, y_title):\n        if isinstance(data, pd.DataFrame):\n            self.frame = data\n        elif isinstance(data, str):\n            self.frame = pd.read_csv(data)\n        else:\n            assert False, 'Некорректный параметр data'\n\n        # self.data = data\n        self.title = title\n        self.x_title = x_title\n        self.y_title = y_title\n\n    def plot(self):\n        layout = go.Layout(\n            title=self.title,\n            template=\"plotly_white\",\n            font=dict(\n                size=18\n            ),\n            xaxis=dict(\n                showgrid=True,\n                showline=False,\n                title=self.x_title,\n\n            ),\n            yaxis=dict(\n                showgrid=True,\n                title=self.y_title,\n            )\n        )\n        fig = go.Figure(layout=layout)\n        fig.add_trace(\n            go.Scatter(x=self.frame[self.x_title], y=self.frame[self.y_title], mode='lines', name=self.title)\n        )\n        return fig\n\n\nclass BarGraph(BaseGraph):\n    def plot(self):\n        layout = go.Layout(\n            title=self.title,\n            template=\"plotly_white\",\n            font=dict(\n                size=18\n            ),\n            xaxis=dict(\n                showgrid=True,\n                showline=False,\n                title=self.x_title,\n\n            ),\n            yaxis=dict(\n                showgrid=True,\n                title=self.y_title,\n            )\n        )\n        fig = go.Figure(layout=layout)\n        fig.add_trace(\n            go.Histogram(\n                x=self.frame[self.x_title],\n                y=self.frame[self.y_title],\n                xbins=dict(\n                    size=0.01\n                ),\n                ybins=dict(size=0.01),\n                name=self.title,\n            )\n        )\n        return fig\n\n\nx = sorted(np.random.sample(100))\ny = sorted(np.random.sample(100))\ndata = pd.DataFrame({'x': x, 'y': y})\n\nbg = BaseGraph(data, 'BaseGraph', 'x', 'y')\nfig = bg.plot()\nfig.show()\n\n\nbg = BarGraph(data, 'BarGraph', 'x', 'y')\nfig = bg.plot()\nfig.show()","sub_path":"task_3.py","file_name":"task_3.py","file_ext":"py","file_size_in_byte":2216,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"146195408","text":"__author__ = 'joshuahiggins'\nfrom rest_framework import serializers\nfrom bookmark.models import Bookmark\nfrom click.models import Click\nfrom hashids import Hashids\n\nfrom django.contrib.auth.models import User, Group\n\n\nclass ClickSerializer(serializers.HyperlinkedModelSerializer):\n\n    class Meta:\n        model = Click\n        fields = ('bookmark', 'time', 'address', 'browser', 'user')\n\n\nclass BookmarkSerializer(serializers.HyperlinkedModelSerializer):\n    user = serializers.PrimaryKeyRelatedField(read_only=True)\n    clicks = serializers.HyperlinkedIdentityField(view_name='click-list')\n    #maybe?\n    short = serializers.CharField(read_only=True)\n#    url = serializers.HyperlinkedIdentityField(view_name='bookmark-detail')\n\n    def create(self, validated_data):\n            hashids = Hashids(min_length = 4, salt=\"ArloBeaIdaKatie\")\n            previous = Bookmark.objects.latest('id')\n            previousid = previous.id\n            if previous.id is None:\n                previousid = 0\n            bookmark = Bookmark.objects.create(**validated_data)\n            bookmark.short = hashids.encrypt(previousid + 1)\n            bookmark.save()\n            return bookmark\n\n    class Meta:\n        model = Bookmark\n        fields = ('id', 'user', 'long', 'description', 'title', 'url', 'short', 'created', 'number_clicks', 'edited',\n                  'clicks')\n\n\n\nclass UserSerializer(serializers.HyperlinkedModelSerializer):\n    class Meta:\n        model = User\n        fields = ('url', 'username', 'email')\n\n","sub_path":"urlybird/api/serializer.py","file_name":"serializer.py","file_ext":"py","file_size_in_byte":1516,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"249281019","text":"from multiprocessing import Process\nimport os\n\n'''\n多进程类Process的自定义封装\n'''\n\nclass CuixdProcess(Process):\n\n    def __init__(self, name):\n\n        super().__init__()\n        self.name = name\n\n    # 重写Process类的run方法，实现自定义功能\n    def run(self):\n\n        print(\"%s进程启动，进程id:%d\" % (self.name, os.getpid()))\n\n        print(\"%s进程停止，进程id:%d\" % (self.name, os.getpid()))\n\n\nif __name__ == \"__main__\":\n\n    p = CuixdProcess(\"王者荣耀\")\n\n    # 进程启动后会自动调用run方法\n    p.start()\n    p.join()\n    print(\"主进程正在运行,进程id:%d\" %(os.getpid()))","sub_path":"27、进程线程/多进程/Process类的封装/cuixdprocess.py","file_name":"cuixdprocess.py","file_ext":"py","file_size_in_byte":636,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"547679794","text":"from flask import Flask, jsonify, request, abort\nfrom flask_cors import CORS\n\nfrom utils import Chop\n\napp = Flask(__name__)\ncors = CORS(app, ressources={r\"/api/*\": {\"origins\" : [\"http://localhost:*\", \"https://tweet-chop.herokuapp.com*\"]}})\n\n@app.route('/api/hello')\ndef index():\n    return jsonify({'hello': 'Hello, Flask!'})\n\n@app.route('/api/chop', methods=['POST'])\ndef chop_text():\n    if not request.json or not 'text' in request.json:\n        abort(400)\n\n    chops = Chop.chop(request.json['text'], int(request.json['chars']))\n\n    return jsonify({'chops': chops})\n\nif __name__ == \"__main__\":\n    app.run()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":613,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"147604374","text":"#!/usr/bin/env python\n\nfrom distutils.core import setup\nimport setuptools\nimport os\n\nroot_dir = os.path.abspath(os.path.dirname(__file__))\n\nwith open(f'{root_dir}/README.md') as f:\n    readme = f.read()\n\nwith open(f'{root_dir}/requirements.txt') as f:\n    requirements = f.read().split()\n\npackages = setuptools.find_packages('.', include='svcca.*')\n\nsetup(name='svcca',\n      version='0.0.1',\n      description='SVCCA on Numpy, Cupy, and PyTorch',\n      long_description=readme,\n      author='Rasmus Diederichsen',\n      author_email='rasmus@peltarion.com',\n      url='https://github.com/themightyoarfish/svcca-gpu',\n      classifiers=['Intended Audience :: Science/Research',\n                   'Topic :: Scientific/Engineering :: Visualization',\n                   'Programming Language :: Python :: 3.6',\n                   'License :: OSI Approved :: Apache License',\n                   'Intended Audience :: Developers',\n                   ],\n      keywords='deep-learning pytorch cupy numpy svcca neural-networks machine-learning'.split(),\n      install_requires=requirements,\n      packages=packages,\n      zip_safe=False,   # don't install egg, but source\n      )\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1172,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"493332298","text":"from django.shortcuts import render\nfrom django.http import HttpResponseRedirect, Http404, HttpResponse\nfrom foodoffers.models import *\nfrom django.contrib.auth.hashers import make_password\nfrom django.contrib.auth import authenticate\nfrom django.contrib.auth.decorators import login_required\nfrom django.db.models import F\n\nfrom forms import *\n\n# if not request.user.is_authenticated():\n#     return HttpResponseRedirect('/register/')\n\ndef populate_home_page(request):\n    if request.user.is_authenticated():\n        return render(request, 'index.html', {'authenticated': True})\n    else:\n        return render(request, 'index.html', {'authenticated': False})\n        \n@login_required\ndef populate_browse(request):\n    return render(request, 'browse.html', {'offer_list': FoodOffer.objects.all()})\n\n@login_required\ndef populate_long_offer(request, offer_id):\n    offer_id = int(offer_id)\n    try:\n        offer = FoodOffer.objects.get(pk=offer_id)\n    except:\n        raise Http404(\"Offer \" + str(offer_id) + \" does not exist.\")\n        \n    return render(request, 'long_offer.html', {'offer': offer})\n\ndef get_new_user(request):\n    # if this is a POST request we need to process the form data\n    if request.method == 'POST':\n        # create a form instance and populate it with data from the request:\n        form = NewUser(request.POST, request.FILES)\n        # check whether it's valid:\n        if form.is_valid() and request.POST.get('confirm') == request.POST.get('password'):\n            u = User(username = request.POST.get('username'),\n                    first_name = request.POST.get('first_name'),\n                    last_name = request.POST.get('last_name'),\n                    email = request.POST.get('email'),\n                    password = make_password(request.POST.get('password')),\n                    zip_code = request.POST.get('zip_code'),\n                    prof_pic = request.FILES['prof_pic'])\n            u.save()\n            return HttpResponseRedirect('/thanks/')\n\n    # if a GET (or any other method) we'll create a blank form\n    else:\n        form = NewUser()\n\n    return render(request, 'new_user.html', {'form': form})\n\n@login_required\ndef see_profile(request):\n    offers = FoodOffer.objects.prefetch_related('foodrequest_set').filter(user=request.user)\n    requests = FoodRequest.objects.filter(requester=request.user)\n    return render(request, 'myaccount.html', {'offer_list': offers, 'request_list': requests})\n            \ndef return_static_file(request, fname):\n    try:\n        f = open(os.path.join(os.getcwd(), fname))\n        return HttpResponse(f.read())\n    except:\n         raise Http404(\"File \" + os.path.join(os.getcwd(), fname) + \" does not exist.\")\n                    \n    \ndef populate_user_created(request):\n    return render(request, 'user_created.html', {})\n\ndef get_new_offer(request):\n    # if this is a POST request we need to process the form data\n    if request.method == 'POST':\n        # create a form instance and populate it with data from the request:\n        form = Offer(request.POST)\n        # check whether it's valid:\n        if form.is_valid():\n            # process the data in form.cleaned_data as required\n            # ...\n            # redirect to a new URL:\n            return HttpResponseRedirect('/thanks/')\n\n    # if a GET (or any other method) we'll create a blank form\n    else:\n        form = Offer()\n\n    return render(request, 'name.html', {'form': form})\n\n@login_required\ndef confirm_request(request, offer_id):\n    offer_id = int(offer_id)\n    try:\n        offer = FoodOffer.objects.get(pk=offer_id)\n    except:\n        raise Http404(\"Offer \" + str(offer_id) + \" does not exist.\")\n    response =  render(request, \"confirm_request.html\", {\"offer\": offer})\n    response.set_cookie('confirmed' + str(offer_id),\"yes\", max_age=120)\n    return response\n\n@login_required\ndef request_food(request, offer_id):\n    offer_id = int(offer_id)\n    try:\n        offer = FoodOffer.objects.get(pk=offer_id)\n    except:\n        raise Http404(\"Offer \" + str(offer_id) + \" does not exist.\")\n    \n    food_request = FoodRequest(offer = offer,\n                                requester = User.objects.get(pk=request.user.id),\n                                party_size = 1,\n                                accepted = False)\n    if (\"confirmed\" + str(offer_id)) not in request.COOKIES.keys():\n        return HttpResponseRedirect('/confirm/' + str(offer_id))\n    if len(FoodRequest.objects.filter(offer=offer, requester=request.user)) == 0:\n        food_request.save()\n        offer.available_people = F('available_people') - 1\n        offer.save()\n    \n    return render(request, \"successful_request.html\", {\"offer\": offer})\n\n@login_required\ndef accept_request(request, request_id):\n    request_id = int(request_id)\n    try:\n        request_to_accept = FoodRequest.objects.get(pk=request_id)\n    except:\n        return Http404(\"Food request \" + str(request_id) + \" does not exist.\")\n    \n    if request_to_accept.offer.user.id == request.user.id:\n        request_to_accept.accepted = True\n        request_to_accept.save()\n    \n    return HttpResponseRedirect('/accounts/profile/')\n\n@login_required\ndef get_new_offer(request):\n    # if this is a POST request we need to process the form data\n    if request.method == 'POST':\n        # create a form instance and populate it with data from the request:\n        form = NewOffer(request.POST, request.FILES)\n        # check whether it's valid:\n        if form.is_valid():\n            o = FoodOffer(user = User.objects.get(pk=request.user.id),\n                        address = request.POST.get('address'),\n                        description = request.POST.get('description'),\n                        picture = request.FILES['picture'],\n                        price = request.POST.get('price'),\n                        max_people = request.POST.get('max_people'),\n                        available_people = request.POST.get('max_people'),\n                        offer_datetime = request.POST.get('offer_datetime')\n                    )\n            o.save()\n            return HttpResponseRedirect('/thanks/offer/' + str(o.id))\n\n    # if a GET (or any other method) we'll create a blank form\n    else:\n        form = NewOffer()\n\n    return render(request, 'offer.html', {'form': form})\n\n@login_required\ndef thank_offer(request, offer_id):\n    offer_id = int(offer_id)\n    \n    try:\n        offer = FoodOffer.objects.get(pk=offer_id)\n    except:\n        return Http404('FoodOffer ' + str(offer_id) + ' does not exist.')\n    \n    if offer.user.id == request.user.id:\n        return render(request, \"thank_offer.html\", {\"offer\": offer})\n    else:\n        return Http404('User is not the person who offered this meal.')","sub_path":"foober/foober/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":6734,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"293193608","text":"from flask import  render_template, request , Blueprint\n\nimport config\nfrom Cron_job.CronServices import CronServices\nfrom config import app\nimport traceback\n\ncron_services = CronServices()\ncron_job = Blueprint('cron_job', __name__, url_prefix='/cron')\n\n# Flask views\n@cron_job.route('/')\ndef index():\n    return render_template('index.html')\n\n@cron_job.route(\"/db_test\")\ndef db_test():\n    print('/db_test')\n    data = config.energy_iot_DB_RW.iot_test.find_one()\n    return (str(data['stored_time']))\n\n\n# Cron Job Service 01\n# Purpose : insert in to aggregation_15_min_Max table from Main table groub by time(15 min)\n@cron_job.route('/insert_aggregation_15_min_Max', methods=['GET'])\ndef aggregate_15_min_Max():\n    device_type = request.args.get('device_type')\n    sensor_type = request.args.get('sensor_type')\n    duration_limit = request.args.get('duration_limit')\n    if duration_limit is None:\n        duration_limit = 10\n\n    interval = \"900\"\n\n    try:\n        data = cron_services.insert_aggregation_15_min_Max(duration_limit=duration_limit, interval=interval, device_type=device_type,sensor_type=sensor_type)\n        return str(data) + \" record inserted!\"\n\n    except Exception as err:\n        app.logger.error(\"insert_aggregation_15_min_Max >> error   \" + str(err))\n    return \"No Record Found\"\n\n\n# Cron Job Service 02\n# Purpose : insert in to insert_aggregation_15_minutes_summary table from 15 Minutes Max table groub by time(15 min)\n@cron_job.route('/insert_aggregation_15_minutes_summary', methods=['GET'])\ndef aggregate_15_minutes_Summary():\n    duration_limit = request.args.get('duration_limit')\n    if duration_limit is None:\n        duration_limit = 5\n\n    try:\n        data = cron_services.insert_aggregation_15_minutes_summary(duration_limit=duration_limit)\n        return str(data) + \" record inserted!\"\n\n    except Exception as err:\n        app.logger.error(\"insert_aggregation_15_minutes_summary >> error   \" + str(err))\n    return \"No Record Found\"\n\n\n# Cron Job Service 03\n# Purpose : calculate the performance ratio\n@cron_job.route('/aggregation_15_minutes_summary_PR_Calculation', methods=['GET'])\ndef PR_Calculation():\n    duration_limit = request.args.get('duration_limit')\n    if duration_limit is None:\n        duration_limit = 5\n    try:\n        data = cron_services.pr_calculation(duration_limit=duration_limit)\n        return str(data) + \" record inserted!\"\n\n    except Exception as err:\n        app.logger.error(\"aggregation_15_minutes_summary_PR_Calculation >> error   \" + str(err))\n    return \"No Record Found\"\n\n\n\n\n# Cron Job Service 04\n# Purpose : insert in to aggregation_1 hr summary table from 15 minutes summary by time(15 min)\n@cron_job.route('/insert_aggregation_1_hour_summary', methods=['GET'])\ndef insert_aggregation_1_hour_summary():\n    duration_limit = request.args.get('duration_limit')\n    if duration_limit is None:\n        duration_limit = 10\n\n    try:\n        data = cron_services.insert_1hour_summary(duration_limit=duration_limit)\n        return str(data) + \" record inserted!\"\n\n    except Exception as err:\n        app.logger.error(\"insert_aggregation_1_hour_summary >> error   \" + str(err))\n        traceback.print_exc()\n    return \"No Record Found\"\n\n\n# Cron Job Service 04\n# Purpose : insert in to aggregation_1 hr summary table from 15 minutes summary by time(15 min)\n@cron_job.route('/insert_aggregation_1_day_summary', methods=['GET'])\ndef insert_aggregation_1_day_summary():\n    duration_limit = request.args.get('duration_limit')\n    if duration_limit is None:\n        duration_limit = 3\n\n    try:\n        data = cron_services.insert_1day_summary(duration_limit=duration_limit)\n        return str(data) + \" record inserted!\"\n\n    except Exception as err:\n        app.logger.error(\"insert_aggregation_1_day_summary >> error   \" + str(err))\n        traceback.print_exc()\n    return \"No Record Found\"\n\n\n","sub_path":"Cron_job/controllers.py","file_name":"controllers.py","file_ext":"py","file_size_in_byte":3865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"177484115","text":"\"\"\"\nObject Oriented Programming\nBasics of Python OOP - Exercise #01\n\"\"\"\nclass Car(object):\n    def __init__(self, make, model):\n        self.make = make\n        self.model = model\n\n\nc1 = Car('bmw', '520D')\n# c1 is instance created from class Car, which is expecting value for 'make'\n#  c1.make\nprint(c1.make, c1.model)\n\nc2 = Car('Audi', 'S5')\nprint(c2.make, c2.model)\n","sub_path":"Classes/Class_basics01.py","file_name":"Class_basics01.py","file_ext":"py","file_size_in_byte":368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"314831066","text":"import logging\nlogger = logging.getLogger(__name__)\nimport importlib\nfrom collections import OrderedDict\n\nfrom great_expectations.render.renderer import (\n    SiteIndexPageRenderer\n)\nfrom great_expectations.render.view import (\n    DefaultJinjaIndexPageView,\n)\n\nclass SiteBuilder():\n    \"\"\"SiteBuilder builds a data documentation website for the project defined by a DataContext.\n\n    A data documentation site consists of HTML pages for expectation suites, profiling and validation results, and\n    an index.html page that links to all the pages.\n\n    The exact behavior of SiteBuilder is controlled by configuration in the DataContext's great_expectations.yml file.\n\n    Users can specify\n    * which datasources to document (by default, all)\n    * whether to include expectations, validations and profiling results sections\n    * where the expectations and validations should be read from (filesystem or S3)\n    * where the HTML files should be written (filesystem or S3)\n    * which renderer and view class should be used to render each section\n\n    Here is an example of config for a site:\n\n    local_site:\n      type: SiteBuilder\n      site_store:\n        type: filesystem\n        base_directory: uncommitted/documentation/local_site\n      validations_store:\n        type: filesystem\n        base_directory: uncommitted/validations/\n        run_id_filter:\n          ne: profiling\n      profiling_store:\n        type: filesystem\n        base_directory: uncommitted/validations/\n        run_id_filter:\n          eq: profiling\n\n      datasources: '*'\n      sections:\n        index:\n          renderer:\n            module: great_expectations.render.renderer\n            class: SiteIndexPageRenderer\n          view:\n            module: great_expectations.render.view\n            class: DefaultJinjaIndexPageView\n        validations:\n          renderer:\n            module: great_expectations.render.renderer\n            class: ValidationResultsPageRenderer\n          view:\n            module: great_expectations.render.view\n            class: DefaultJinjaPageView\n        expectations:\n          renderer:\n            module: great_expectations.render.renderer\n            class: ExpectationSuitePageRenderer\n          view:\n            module: great_expectations.render.view\n            class: DefaultJinjaPageView\n        profiling:\n          renderer:\n            module: great_expectations.render.renderer\n            class: ProfilingResultsPageRenderer\n          view:\n            module: great_expectations.render.view\n            class: DefaultJinjaPageView\n    \"\"\"\n\n    @classmethod\n    def build(cls, data_context, site_config, specified_data_asset_name=None):\n        \"\"\"\n\n        :param data_context:\n        :param site_config:\n        :return: tupple: index_page_locator_info (a dictionary describing how to locate the index page of the site (specific to resource_store type)\n                         index_links_dict\n\n        \"\"\"\n\n        index_links_dict = OrderedDict()\n\n        # the site config may specify the list of datasource names to document.\n        # if the config property is absent or is *, treat as \"all\"\n        datasources_to_document = site_config.get('datasources')\n        if not datasources_to_document or datasources_to_document == '*':\n            datasources_to_document = [datasource['name'] for datasource in data_context.list_datasources()]\n\n\n        sections_config = site_config.get('sections')\n        if not sections_config:\n            raise Exception('\"sections\" key is missing') #TODO: specific exception class\n\n        # profiling results\n\n        profiling_section_config = sections_config.get('profiling')\n        if profiling_section_config:\n            try:\n                profiling_renderer_module = importlib.import_module(profiling_section_config['renderer']['module'])\n                profiling_renderer_class = getattr(profiling_renderer_module, profiling_section_config['renderer']['class'])\n                profiling_view_module = importlib.import_module(profiling_section_config['view']['module'])\n                profiling_view_class = getattr(profiling_view_module, profiling_section_config['view']['class'])\n            except Exception:\n                logger.exception(\"Failed to load profiling renderer or view class\")\n                raise\n\n\n            #TODO: filter data sources if the config requires it\n\n            for run_id, v0 in data_context.list_validation_results(validations_store=site_config['profiling_store']).items():\n                for datasource, v1 in v0.items():\n\n                    if datasource not in datasources_to_document:\n                        continue\n\n                    for generator, v2 in v1.items():\n                        for generator_asset, expectation_suite_names in v2.items():\n                            data_asset_name = data_context.data_asset_name_delimiter.join([datasource, generator, generator_asset])\n                            if specified_data_asset_name:\n                               if data_context._normalize_data_asset_name(data_asset_name) != data_context._normalize_data_asset_name(specified_data_asset_name):\n                                   continue\n                            for expectation_suite_name in expectation_suite_names:\n                                validation = data_context.get_validation_result(data_asset_name,\n                                                                                expectation_suite_name=expectation_suite_name,\n                                                                                validations_store=site_config['profiling_store'],\n                                                                                run_id=run_id)\n\n                                logger.info(\"        Rendering profiling for data asset {}\".format(data_asset_name))\n                                data_asset_name = validation['meta']['data_asset_name']\n                                expectation_suite_name = validation['meta']['expectation_suite_name']\n                                model = profiling_renderer_class.render(validation)\n\n                                data_context.write_resource(\n                                    profiling_view_class.render(model),  # bytes\n                                    expectation_suite_name + '.html',  # name to be used inside namespace\n                                    resource_store=site_config['site_store'],\n                                    resource_namespace=\"profiling\",\n                                    data_asset_name=data_asset_name\n                                )\n\n                                if not datasource in index_links_dict:\n                                    index_links_dict[datasource] = OrderedDict()\n                                if not generator in index_links_dict[datasource]:\n                                    index_links_dict[datasource][generator] = OrderedDict()\n                                if not generator_asset in index_links_dict[datasource][generator]:\n                                    index_links_dict[datasource][generator][generator_asset] = {\n                                        'profiling_links': [],\n                                        'validation_links': [],\n                                        'expectation_suite_links': []\n                                    }\n\n                                index_links_dict[datasource][generator][generator_asset][\"profiling_links\"].append(\n                                    {\n                                        \"full_data_asset_name\": data_asset_name,\n                                        \"expectation_suite_name\": expectation_suite_name,\n                                        \"filepath\": data_context._get_normalized_data_asset_name_filepath(\n                                            data_asset_name,\n                                            expectation_suite_name,\n                                            base_path='profiling',\n                                            file_extension=\".html\"\n                                        ),\n                                        \"source\": datasource,\n                                        \"generator\": generator,\n                                        \"asset\": generator_asset\n                                    }\n                                )\n\n\n        # validations\n\n        validation_section_config = sections_config.get('validations')\n        if validation_section_config:\n            try:\n                validation_renderer_module = importlib.import_module(validation_section_config['renderer']['module'])\n                validation_renderer_class = getattr(validation_renderer_module, validation_section_config['renderer']['class'])\n                validation_view_module = importlib.import_module(validation_section_config['view']['module'])\n                validation_view_class = getattr(validation_view_module, validation_section_config['view']['class'])\n            except Exception:\n                logger.exception(\"Failed to load validation renderer or view class\")\n                raise\n\n\n            #TODO: filter data sources if the config requires it\n\n            for run_id, v0 in data_context.list_validation_results(validations_store=site_config['validations_store']).items():\n                for datasource, v1 in v0.items():\n\n                    if datasource not in datasources_to_document:\n                        continue\n\n                    for generator, v2 in v1.items():\n                        for generator_asset, expectation_suite_names in v2.items():\n                            data_asset_name = data_context.data_asset_name_delimiter.join([datasource, generator, generator_asset])\n                            if specified_data_asset_name:\n                               if data_context._normalize_data_asset_name(data_asset_name) != data_context._normalize_data_asset_name(specified_data_asset_name):\n                                   continue\n                            for expectation_suite_name in expectation_suite_names:\n                                validation = data_context.get_validation_result(data_asset_name,\n                                                                                expectation_suite_name=expectation_suite_name,\n                                                                                validations_store=site_config['validations_store'],\n                                                                                run_id=run_id)\n                                validation_success = validation['success']\n                                logger.info(\"        Rendering validation: run id: {}, suite {} for data asset {}\".format(run_id, expectation_suite_name, data_asset_name))\n                                data_asset_name = validation['meta']['data_asset_name']\n                                expectation_suite_name = validation['meta']['expectation_suite_name']\n                                model = validation_renderer_class.render(validation)\n\n                                data_context.write_resource(\n                                    validation_view_class.render(model),  # bytes\n                                    expectation_suite_name + '.html',  # name to be used inside namespace\n                                    resource_store=site_config['site_store'],\n                                    resource_namespace=\"validations\",\n                                    data_asset_name=data_asset_name,\n                                    run_id=run_id\n                                )\n\n                                if not datasource in index_links_dict:\n                                    index_links_dict[datasource] = OrderedDict()\n                                if not generator in index_links_dict[datasource]:\n                                    index_links_dict[datasource][generator] = OrderedDict()\n                                if not generator_asset in index_links_dict[datasource][generator]:\n                                    index_links_dict[datasource][generator][generator_asset] = {\n                                        'profiling_links': [],\n                                        'validation_links': [],\n                                        'expectation_suite_links': []\n                                    }\n\n                                index_links_dict[datasource][generator][generator_asset][\"validation_links\"].append(\n                                    {\n                                        \"full_data_asset_name\": data_asset_name,\n                                        \"expectation_suite_name\": expectation_suite_name,\n                                        \"filepath\": data_context._get_normalized_data_asset_name_filepath(\n                                            data_asset_name,\n                                            expectation_suite_name,\n                                            base_path='validations/' + run_id,\n                                            file_extension=\".html\"\n                                        ),\n                                        \"source\": datasource,\n                                        \"generator\": generator,\n                                        \"asset\": generator_asset,\n                                        \"run_id\": run_id,\n                                        \"validation_success\": validation_success\n                                    }\n                                )\n\n\n        # expectation suites\n\n        expectations_section_config = sections_config.get('expectations')\n        if expectations_section_config:\n            try:\n                expectations_renderer_module = importlib.import_module(expectations_section_config['renderer']['module'])\n                expectations_renderer_class = getattr(expectations_renderer_module, expectations_section_config['renderer']['class'])\n                expectations_view_module = importlib.import_module(expectations_section_config['view']['module'])\n                expectations_view_class = getattr(expectations_view_module, expectations_section_config['view']['class'])\n            except Exception:\n                logger.exception(\"Failed to load expectations renderer or view class\")\n                raise\n\n            for datasource, v1 in data_context.list_expectation_suites().items():\n\n                if datasource not in datasources_to_document:\n                    continue\n\n                for generator, v2 in v1.items():\n                    for generator_asset, expectation_suite_names in v2.items():\n                        data_asset_name = data_context.data_asset_name_delimiter.join(\n                            [datasource, generator, generator_asset])\n                        if specified_data_asset_name:\n                               if data_context._normalize_data_asset_name(data_asset_name) != data_context._normalize_data_asset_name(specified_data_asset_name):\n                                   continue\n                        for expectation_suite_name in expectation_suite_names:\n                            expectation_suite = data_context.get_expectation_suite(\n                                data_asset_name,\n                                expectation_suite_name=expectation_suite_name)\n\n                            logger.info(\n                                \"        Rendering expectation suite {} for data asset {}\".format(\n                                    expectation_suite_name, data_asset_name))\n                            data_asset_name = expectation_suite['data_asset_name']\n                            expectation_suite_name = expectation_suite['expectation_suite_name']\n                            model = expectations_renderer_class.render(expectation_suite)\n                            data_context.write_resource(\n                                expectations_view_class.render(model),  # bytes\n                                expectation_suite_name + '.html',  # name to be used inside namespace\n                                resource_store=site_config['site_store'],\n                                resource_namespace='expectations',\n                                data_asset_name=data_asset_name\n                            )\n\n                            if not datasource in index_links_dict:\n                                index_links_dict[datasource] = OrderedDict()\n                            if not generator in index_links_dict[datasource]:\n                                index_links_dict[datasource][generator] = OrderedDict()\n                            if not generator_asset in index_links_dict[datasource][generator]:\n                                index_links_dict[datasource][generator][generator_asset] = {\n                                    'profiling_links': [],\n                                    'validation_links': [],\n                                    'expectation_suite_links': []\n                                }\n\n                            index_links_dict[datasource][generator][generator_asset][\"expectation_suite_links\"].append(\n                                {\n                                    \"full_data_asset_name\": data_asset_name,\n                                    \"expectation_suite_name\": expectation_suite_name,\n                                    \"filepath\": data_context._get_normalized_data_asset_name_filepath(\n                                        data_asset_name,\n                                        expectation_suite_name,\n                                        base_path='expectations',\n                                        file_extension='.html'\n                                    ),\n                                    \"source\": datasource,\n                                    \"generator\": generator,\n                                    \"asset\": generator_asset\n                                }\n                            )\n\n\n        # TODO: load dynamically\n        model = SiteIndexPageRenderer.render(index_links_dict)\n\n        index_page_output = DefaultJinjaIndexPageView.render(model)\n\n        index_page_locator_info = data_context.write_resource(\n            index_page_output,  # bytes\n            'index.html',  # name to be used inside namespace\n            resource_store=site_config['site_store']\n        )\n\n        return (index_page_locator_info, index_links_dict)\n\n","sub_path":"great_expectations/render/renderer/site_builder.py","file_name":"site_builder.py","file_ext":"py","file_size_in_byte":18427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"275628510","text":"from datetime import timedelta, datetime\n\nimport jwt\n\nfrom scrum.core import config\n\nTOKEN_ALGORITHM = 'HS256'\nTOKEN_SUBJECT = 'access'\n\n\ndef create_access_token(*, data: dict, expires_delta: timedelta = None):\n    \"\"\"\n    A function for creating a new access token for user's authentication\n    :param data: actual data for encoding\n    :param expires_delta: a timedelta for the token's lifetime\n    \"\"\"\n    to_encode = data.copy()\n    if expires_delta is not None:\n        expire = datetime.utcnow() + expires_delta\n    else:\n        expire = datetime.utcnow() + timedelta(minutes=15)\n    to_encode.update({'exp': expire, 'sub': TOKEN_SUBJECT})\n    encoded_jwt = jwt.encode(to_encode, config.SECRET_KEY, algorithm=TOKEN_ALGORITHM)\n    return encoded_jwt\n","sub_path":"scrum/core/jwt.py","file_name":"jwt.py","file_ext":"py","file_size_in_byte":756,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"392751810","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\nfrom __future__ import absolute_import\nfrom __future__ import division\nfrom __future__ import print_function\nfrom __future__ import unicode_literals\n\nfrom django.db.models import Q\nfrom bob.data_table import DataTableMixin, DataTableColumn\nfrom ralph.cmdb.models import CI\n\ndef report_filters(cls, order, filters=None):\n    q = Q()\n    if filters:\n        filters_list = filters.pop()\n        return cls.objects.filter(**dict(filters_list)).order_by(order)\n    return cls.objects.order_by(order).all()\n\n\ndef add_filter(request, ci=None):\n    filters = []\n    if ci:\n        filters.append({'ci': ci})\n    if request.get('ci'):\n        ci_id = CI.objects.select_related('id').filter(\n            name=request.get('ci')\n        )\n        ci_id = {'ci_id': ci_id[0]} if ci_id else {'ci_id': None}\n        filters.append(ci_id)\n    if request.get('assignee'):\n        filters.append({'assignee': request.get('assignee')})\n    if request.get('jira_id'):\n        filters.append({'jira_id': request.get('jira_id')})\n    if request.get('issue_type'):\n        filters.append({'issue_type': request.get('issue_type')})\n    if request.get('status'):\n        filters.append({'status': request.get('status')})\n    if request.get('start_update') and request.get('end_update'):\n        filters.append(\n            {'update_date__lte': request.get('start_update')}\n        )\n        filters.append(\n            {'update_date__gte': request.get('end_update')}\n        )\n    if request.get('start_resolved') and request.get('end_resolved'):\n        filters.append(\n            {'resolvet_date__lte': request.get('start_resolved')}\n        )\n        filters.append(\n            {'resolvet_date__gte': request.get('end_resolved')}\n        )\n    if request.get('start_planned_start') and request.get('end_planned_start'):\n        filters.append(\n            {'planned_start_date_lte': request.get('start_planned_start')}\n        )\n        filters.append(\n            {'planned_start_date_gte': request.get('end_planned_start')}\n        )\n    if request.get('start_planned_end') and request.get('end_planned_end'):\n        filters.append(\n            {'planned_end_date_lte': request.get('start_planned_end')}\n        )\n        filters.append(\n            {'planned_end_date_gte': request.get('start_planned_end')}\n        )\n    return filters\n\n\ndef table_colums():\n    _ = DataTableColumn\n    columns = [\n        _(\n            'Issue updated',\n            field='update_date',\n            sort_expression='update_date',\n            bob_tag=True,\n        ),\n        _(\n            'Type',\n            field='issue_type',\n            sort_expression='issue_type',\n            bob_tag=True,\n\n        ),\n        _(\n            'Status',\n            field='resolvet_date',\n            sort_expression='resolvet_date',\n            bob_tag=True,\n        ),\n        _(\n            'Ci',\n            field='ci',\n            sort_expression='ci',\n            bob_tag=True,\n        ),\n        _(\n            'Summary',\n            field='summary',\n            bob_tag=True,\n        ),\n        _(\n            'Assignee',\n            field='assignee',\n            bob_tag=True,\n        ),\n        _(\n            'Description',\n            field='description',\n            bob_tag=True,\n        ),\n        _(\n            'Analysis',\n            field='analysis',\n            bob_tag=True,\n        ),\n        _(\n            'Problems',\n            field='problems',\n            bob_tag=True,\n        ),\n        _(\n            'Planed start',\n            field='planned_start_date',\n            sort_expression='planned_start_date',\n            bob_tag=True,\n        ),\n        _(\n            'Planed end',\n            field='planned_end_date',\n            sort_expression='planned_end_date',\n            bob_tag=True,\n        ),\n    ]\n    return columns\n","sub_path":"src/ralph/cmdb/util.py","file_name":"util.py","file_ext":"py","file_size_in_byte":3865,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"389157620","text":"import selenium\nimport time\nimport sys\nimport csv\n\nfrom selenium import webdriver\n\n##########################################################\n\n# A few lazy consts we're going to need later\n\nnano_sleep = 0.5\nmicro_sleep = 1.5\nsmall_sleep = 3\nsleep = 5\nbig_sleep = 15\n\nclick_element = lambda driver, xpath: driver.find_element_by_xpath(xpath).click()\ntext_element = lambda driver, xpath, text: driver.find_element_by_xpath(xpath).send_keys(text)\nfile_element = lambda driver, xpath, filepath: driver.find_element_by_xpath(xpath).send_keys(filepath)\nwait = lambda x: time.sleep(x)\n\n\n##########################################################\n# Load entries from csv\n##########################################################\n\ncsv_file = ''\njob_number = 1\n\n# Time to get a file:\nif len(sys.argv) < 2:\n    print(\"Please provide a path to a properly formatted csv file containing your timesheet data.\")\n    file_name = input()\nelse:\n    file_name = sys.argv[1]\n\ntry:\n    # Yeah this is a hack job\n    raw_data = open(file_name, 'r').readlines()\nexcept:\n    print(\"File error\")\n    exit()\n\n# Determine job number, default to 1\nif len(sys.argv) < 3:\n    job_number = 1\nelse:\n    job_number = sys.argv[2]\n\n##########################################################\n# Figure out which driver we're using\n##########################################################\n\n# I'm only testing this on Firefox myself\ntry:\n    driver = webdriver.Firefox()\nexcept:\n    try:\n        driver = webdriver.Chrome()\n    except:\n        print(\"No Driver found, please use a recent version of either Firefox or Chrome\")\n        exit()\n\n\n##########################################################\n# Login\n##########################################################\n\ninitial_url = 'https://myhr.sydney.edu.au/alesco-wss-v17/faces/app/WJ0000.jspx'\n\n\ndriver.get(initial_url)\nprint(\"Please Login\")\nprint(\"Once you have Logged in, Press Enter to Continue\")\nwhile input() != '':\n    continue\n\n\n##########################################################\n# Get a new timesheet\n##########################################################\n\n# Access elements from the menu\nclick_element(driver,\n    '''//*[@id=\"pt1:MWJ0000_WJC420\"]''')\nwait(micro_sleep)\n\nclick_element(driver,\n    \"/html/body/div[2]/form/div[2]/div[2]/div/div/div/table/tbody/tr/td/table/tbody/tr[2]/td/div/table/tbody/tr[2]/td[2]\")\nwait(small_sleep)\n\n# Switch to iframe\ndriver.switch_to.frame(\"pt1:r1:0:pt1:Main::f\")\n\n# Create new timesheet\nclick_element(driver, \"/html/body/p[2]/a\")\nwait(micro_sleep)\n\n# Start Date\ntext_element(driver, '//*[@id=\"P_START_DATE\"]', raw_data[0].split(',')[0])\ntext_element(driver, '//*[@id=\"P_CALENDAR_CODE\"]', \"CAL\")\nclick_element(driver, '/html/body/p[3]')\nwait(micro_sleep)\n\n# Select Job\ntry:\n    click_element(driver,\n        \"/html/body/div/form/table/tbody/tr[{}]/td[1]/input\".format(job_number)\n        )\n    wait(micro_sleep)\n\n    # Continue\n    click_element(driver,\n        \"/html/body/div/form/p[3]/input[1]\")\n    wait(small_sleep)\nexcept:\n    pass\n\n\n##########################################################\n# Fill in the timesheet\n##########################################################\n\n# Get the right number of rows\nn_rows = len(raw_data)\nif n_rows > 20:\n    for _ in range(n_rows - 20):\n        click_element(driver, \"/html/body/form/p[3]/input[5]\")\n        wait(nano_sleep)\n\n\n# Enter Data\nfor i, entry in enumerate(raw_data):\n    entry = entry.split(',')\n\n    # Date\n    element = driver.find_element_by_xpath(\n        \"/html/body/form/table/tbody/tr[{}]/td[4]\".format(i + 1))\n    field = element.find_element_by_xpath('./*[@id=\"P_WORK_DATE\"]')\n    field.send_keys(entry[0]) \n\n     # Hours\n    element = driver.find_element_by_xpath(\n    '/html/body/form/table/tbody/tr[{}]/td[6]'.format(i + 1))\n    field = element.find_element_by_xpath('./*[@id=\"P_UNITS\"]')\n    field.send_keys(entry[1])    \n\n    # Paycode\n    element = driver.find_element_by_xpath(\n    '/html/body/form/table/tbody/tr[{}]/td[7]'.format(i + 1)) \n    field = element.find_element_by_xpath('./*[@id=\"P_PAYCODE\"]')\n    field.send_keys(entry[2])\n\n    if len(entry) > 3:\n        # Analysis Code\n        if len(entry[3]) > 0:\n            try: # Because of pointless dialogue boxes\n                element = driver.find_element_by_xpath(\n                '/html/body/form/table/tbody/tr[{}]/td[10]'.format(i + 1))\n                field = element.find_element_by_xpath('./*[@id=\"P_GL_SUB_ACCOUNT\"]')\n                field.send_keys(entry[3])\n            except:\n                pass\n\n    if len(entry) > 4:\n        # Topic\n        if len(entry[4]) > 0:\n            try: # Because of pointless dialogue boxes\n                element = driver.find_element_by_xpath(\n                '/html/body/form/table/tbody/tr[{}]/td[12]'.format(i + 1)) \n                field = element.find_element_by_xpath('./*[@id=\"P_TOPIC\"]')\n                field.send_keys(entry[4])\n            except: # This skips the dialogue boxes\n                element = driver.find_element_by_xpath(\n                '/html/body/form/table/tbody/tr[{}]/td[12]'.format(i + 1)) \n                field = element.find_element_by_xpath('./*[@id=\"P_TOPIC\"]')\n                field.send_keys(entry[4])\n\n    if len(entry) > 5:\n        # Details\n        if len(entry[5]) > 0:\n            try: # Because of pointless dialogue boxes\n                element = driver.find_element_by_xpath(\n                '/html/body/form/table/tbody/tr[{}]/td[13]'.format(i + 1)) \n                field = element.find_element_by_xpath('./*[@id=\"P_TOPIC_DETAILS\"]')\n                field.send_keys(entry[5])\n            except:\n                pass\n\n# You get to enter the approver and press the button\nprint(\"READY TO LODGE!\")\nprint(\"Don't forget to select your timesheet approver\")","sub_path":"timesheet_submitter.py","file_name":"timesheet_submitter.py","file_ext":"py","file_size_in_byte":5757,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"108015889","text":"\"\"\"Command line tools to interact with the Insteon devices.\"\"\"\n\nfrom .. import devices\nfrom ..constants import RAMP_RATES, ALDBStatus, DeviceCategory\nfrom ..managers.scene_manager import async_add_device_to_scene\nfrom ..utils import seconds_to_ramp_rate\nfrom .advanced import AdvancedTools\nfrom .tools_base import ToolsBase\n\n\nclass ToolsAldb(ToolsBase):\n    \"\"\"Command class to test interactivity.\"\"\"\n\n    async def do_load_aldb(self, *args, **kwargs):\n        \"\"\"Load the All-Link Database of a device.\n\n        Usage:\n            load_aldb <ADDRESS>|all y|n  Load one or all devices (can be the modem address)\n\n        To clear the current ALDB and reload from the device, enter `y` as the second argment.\n        Otherwise enter `n`.\n        \"\"\"\n        args = args[0].split()\n        try:\n            address = args[0]\n        except IndexError:\n            address = None\n\n        try:\n            refresh_yn = args[1]\n            refresh = refresh_yn.lower() == \"y\"\n        except IndexError:\n            refresh_yn = \"\"\n\n        addresses = await self._get_addresses(\n            address=address, allow_cancel=True, allow_all=True, match_device=True\n        )\n        if not addresses:\n            return\n\n        if devices[addresses[0]] != devices.modem or len(addresses) > 1:\n            if not refresh_yn:\n                refresh_yn = await self._get_char(\n                    \"Clear existing records and reload (y/n)\",\n                    default=\"n\",\n                    values=[\"y\", \"n\"],\n                )\n        refresh = refresh_yn.lower() == \"y\"\n\n        battery_devices = []\n        for address in addresses:\n            if devices[address].is_battery:\n                battery_devices.append(address)\n            # Only load the modem ALDB if explicitly asked\n            if devices[address] == devices.modem and len(addresses) == 1:\n                await devices.modem.aldb.async_load()\n            elif devices[address].cat == 0x03:\n                pass\n            else:\n                # tasks.append(devices[address].aldb.async_load(refresh=refresh))\n                await devices[address].aldb.async_load(refresh=refresh)\n\n        if battery_devices:\n            self._log_stdout(\"The following devices are battery operated.\")\n            self._log_stdout(\"They will load in the background when they wake up.\")\n        for address in battery_devices:\n            self._log_stdout(f\"    - {address}\")\n\n        # if the device did not load the first time, try one more time with refresh\n        for address in addresses:\n            if (\n                devices[address] != devices.modem\n                and devices[address].aldb.status != ALDBStatus.LOADED\n                and not devices[address].is_battery\n            ):\n                await devices[address].aldb.async_load(refresh=refresh)\n\n    async def do_print_aldb(self, *args, **kwargs):\n        \"\"\"Print the records in an All-Link Database.\n\n        Usage:\n            print_aldb <ADDRESS>|all\n        \"\"\"\n        await self._print_aldb(*args)\n\n    async def do_add_default_links(self, *args, **kwargs):\n        \"\"\"Add default links between a device and the modem.\n\n        Usage:\n            add_default_links <ADDRESS>\n        \"\"\"\n\n        args = args[0].split()\n        try:\n            address = args[0]\n        except IndexError:\n            address = None\n\n        addresses = await self._get_addresses(\n            address=address, allow_all=False, allow_cancel=True, match_device=True\n        )\n        if not addresses:\n            return\n        device = devices[addresses[0]]\n        self._log_command(f\"add_default_links {addresses[0]}\")\n        await device.async_add_default_links()\n\n    async def do_add_device_to_scene(self, *args, **kwargs):\n        \"\"\"Add a device to a scene.\n\n        Usage:\n            add_device_to_scene <ADDRESS> <SCENE NUMBER> [<ON LEVEL>] [<RAMP RATE>] [<BUTTON>] | [Data1] [Data2] [Data3]\n        <ADDRESS>: The device address such as 1a.2b.3c\n        <SCENE NUMBER>: Value from 25 to 255.\n\n        For Device type 1:\n        <ON LEVEL>: (Optional) Value from 0 (off) - 255 (full on).\n                    For dimmable devices any number from 0 to 255 is allowable.\n                    Default is 255.\n        <RAMP RATE>: 0.1 seconds to 480 seconds (8 minutes)\n                     Default is 0.5 seconds\n        <BUTTON>: (Optional) The button or group number of the device to change as part of the scene.\n                    Valid values are device dependant.\n                    Default is 1.\n\n        for Device type 2:\n        <Data1>: (Optional) Value from 0 (off) - 255 (full on).\n                    For on/off devices only 0 and 255 are allowed.\n                    Default is 255.\n        <Data2>: Data field 2. Default is 0. Typically, this is not used by device type 2.\n        <BUTTON>: (Optional) The button or group number of the device to change as part of the scene.\n                    Valid values are device dependant.\n                    Default is 1.\n\n        For all other device types:\n        <Data1>: Data field 1. Any value from 0 - 255 are allowed. Default is 255.\n        <Data2>: Data field 2 Any value from 0 - 255 are allowed. Default is 0.\n        <Data3>: Data field 3  Any value from 0 - 255 are allowed. Default is 1.\n\n        KeyPadLinc devices will not respond correctly to scenes in this way other than the main power.\n        \"\"\"\n\n        args = args[0].split()\n        try:\n            address = args[0]\n        except IndexError:\n            address = None\n\n        try:\n            scene = int(args[1])\n        except (IndexError, ValueError):\n            scene = None\n\n        try:\n            data1 = int(args[2])\n        except (IndexError, ValueError):\n            data1 = None\n\n        try:\n            data2 = int(args[3])\n        except (IndexError, ValueError):\n            data2 = None\n\n        try:\n            data3 = int(args[4])\n        except (IndexError, ValueError):\n            data3 = None\n\n        addresses = await self._get_addresses(\n            address=address, allow_all=False, allow_cancel=True, match_device=True\n        )\n        if not addresses:\n            return\n\n        device = devices[addresses[0]]\n\n        if not scene:\n            scene = await self._get_int(\n                \"Scene number or blank to cancel\",\n                values=range(25, 256),\n            )\n            if not scene:\n                return\n\n        if data1 is None:\n            if device.cat == DeviceCategory.DIMMABLE_LIGHTING_CONTROL:\n                data1 = await self._get_int(\n                    \"On level\", default=255, values=range(0, 256)\n                )\n            elif device.cat == DeviceCategory.SWITCHED_LIGHTING_CONTROL:\n                data1 = await self._get_int(\"On level\", default=255, values=[0, 255])\n            else:\n                data1 = await self._get_int(\"Data1\", default=255, values=range(0, 255))\n\n        if device.cat == DeviceCategory.DIMMABLE_LIGHTING_CONTROL:\n            if data2 is None:\n                try:\n                    data2_seconds = float(args[3])\n                except (IndexError, ValueError):\n                    data2_seconds = None\n                if data2_seconds is None:\n                    data2_seconds = await self._get_float(\n                        \"Ramp rate\",\n                        default=0.5,\n                        maximum=480,\n                        minimum=0.1,\n                    )\n            else:\n                data2_seconds = data2\n\n            data2 = seconds_to_ramp_rate(data2_seconds)\n            if RAMP_RATES[data2] != data2_seconds:\n                self._log_stdout(\n                    f\"Ramp rate rounded to {RAMP_RATES[data2]} to conform to standard values.\"\n                )\n\n        elif data2 is None:\n            data2 = await self._get_int(\"Data2\", default=0, values=range(0, 255))\n\n        if data3 is None:\n            if device.cat in [\n                DeviceCategory.DIMMABLE_LIGHTING_CONTROL,\n                DeviceCategory.SWITCHED_LIGHTING_CONTROL,\n            ]:\n                data3 = await self._get_int(\"Button\", default=1, values=range(0, 255))\n            else:\n                data3 = await self._get_int(\"Data3\", default=0, values=range(0, 255))\n\n        await async_add_device_to_scene(device, scene, data1, data2, data3)\n\n    def do_print_aldb_load_status(self, *args, **kwargs):\n        \"\"\"Print the All-Link databbase load status for all devices.\"\"\"\n        self._log_stdout(\"\")\n        self._log_stdout(\"Device   Status\")\n        self._log_stdout(\"-------- ---------------\")\n        for address in devices:\n            self._log_stdout(f\"{address} {str(devices[address].aldb.status)}\")\n\n    async def do_advanced(self, *args, **kwargs):\n        \"\"\"Enter advanced ALDB menu.\"\"\"\n        self._log_command(\"advanced\")\n        await self._call_next_menu(AdvancedTools, \"advanced\")\n","sub_path":"pyinsteon/tools/aldb.py","file_name":"aldb.py","file_ext":"py","file_size_in_byte":8885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"654428106","text":"#\n# @lc app=leetcode.cn id=542 lang=python3\n#\n# [542] 01 矩阵\n#\n\n# @lc code=start\nfrom  collections import deque\nfrom dis import dis \n\n\nclass Solution:\n    def updateMatrix(self, matrix: List[List[int]]) -> List[List[int]]:\n        m,n = len(matrix),len(matrix[0])\n        dist = [[0]*n for _ in range(m)]\n        zero_pos = [(i,j) for i in range(m) for j in range(n) if matrix[i][j]==0]\n\n        q = deque(zero_pos)\n        seen = set(zero_pos)\n\n        while q:\n            i , j = q.popleft()\n            for ni,nj in ([i-1,j],[i+1,j],[i,j-1],[i,j+1]):\n                if(0<=ni<m and 0<=nj<n and (ni,nj) not in seen):\n                    q.append((ni,nj))\n                    dist[ni][nj] = dist[i][j] + 1 \n                    seen.add((ni,nj))\n        return dist \n\n# @lc code=end\n\n","sub_path":"542.01-矩阵.py","file_name":"542.01-矩阵.py","file_ext":"py","file_size_in_byte":788,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"91949467","text":"# %%\n'''\nMaking the discriminator not trainable is a clever trick in the Keras API. The trainable\nproperty impacts the model when it is compiled. The discriminator model was compiled with\ntrainable layers, therefore the model weights in those layers will be updated when the standalone\nmodel is updated via calls to train on batch(). The discriminator model was marked as not\ntrainable, added to the GAN model, and compiled. In this model, the model weights of the\ndiscriminator model are not trainable and cannot be changed when the GAN model is updated\nvia calls to train on batch(). The complete example of creating the discriminator, generator,\nand composite model is listed below.\n'''\n\n# %%\n# demonstrate creating the three models in the gan\nfrom keras.optimizers import Adam\nfrom keras.models import Sequential\nfrom keras.layers import Dense\nfrom keras.layers import Reshape\nfrom keras.layers import Flatten\nfrom keras.layers import Conv2D\nfrom keras.layers import Conv2DTranspose\nfrom keras.layers import LeakyReLU\nfrom keras.layers import Dropout\nfrom keras.utils.vis_utils import plot_model\n\n# define the standalone discriminator model\ndef define_discriminator(in_shape=(28,28,1)):\n\tmodel = Sequential()\n\tmodel.add(Conv2D(64, (3,3), strides=(2, 2), padding='same', input_shape=in_shape))\n\tmodel.add(LeakyReLU(alpha=0.2))\n\tmodel.add(Dropout(0.4))\n\tmodel.add(Conv2D(64, (3,3), strides=(2, 2), padding='same'))\n\tmodel.add(LeakyReLU(alpha=0.2))\n\tmodel.add(Dropout(0.4))\n\tmodel.add(Flatten())\n\tmodel.add(Dense(1, activation='sigmoid'))\n\t# compile model\n\topt = Adam(lr=0.0002, beta_1=0.5)\n\tmodel.compile(loss='binary_crossentropy', optimizer=opt, metrics=['accuracy'])\n\treturn model\n\n# define the standalone generator model\ndef define_generator(latent_dim):\n\tmodel = Sequential()\n\t# foundation for 7x7 image\n\tn_nodes = 128 * 7 * 7\n\tmodel.add(Dense(n_nodes, input_dim=latent_dim))\n\tmodel.add(LeakyReLU(alpha=0.2))\n\tmodel.add(Reshape((7, 7, 128)))\n\t# upsample to 14x14\n\tmodel.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))\n\tmodel.add(LeakyReLU(alpha=0.2))\n\t# upsample to 28x28\n\tmodel.add(Conv2DTranspose(128, (4,4), strides=(2,2), padding='same'))\n\tmodel.add(LeakyReLU(alpha=0.2))\n\tmodel.add(Conv2D(1, (7,7), activation='sigmoid', padding='same'))\n\treturn model\n\n# define the combined generator and discriminator model, for updating the generator\ndef define_gan(g_model, d_model):\n\t# make weights in the discriminator not trainable\n\td_model.trainable = False\n\t# connect them\n\tmodel = Sequential()\n\t# add generator\n\tmodel.add(g_model)\n\t# add the discriminator\n\tmodel.add(d_model)\n\t# compile model\n\topt = Adam(lr=0.0002, beta_1=0.5)\n\tmodel.compile(loss='binary_crossentropy', optimizer=opt)\n\treturn model\n\n# size of the latent space\nlatent_dim = 100\n# create the discriminator\nd_model = define_discriminator()\n# create the generator\ng_model = define_generator(latent_dim)\n# create the gan\ngan_model = define_gan(g_model, d_model)\n# summarize gan model\ngan_model.summary()\n# plot gan model\nplot_model(gan_model, to_file='gan_plot.png', show_shapes=True, show_layer_names=True)\n\n# %%\nfrom PIL import Image\nfrom IPython.display import display # to display images\n\nimage = Image.open('gan_plot.png')\ndisplay(image)","sub_path":"chapter_07/07_summarize_composite.py","file_name":"07_summarize_composite.py","file_ext":"py","file_size_in_byte":3226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"461731040","text":"# Linear Model x3 : OK\n# MLP (2, 3)      : OK\n\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom utilsClassification import *\nfrom ctypes import *\n\n# pathDLL = \"C:/Users/nico_/Documents/GitHub/ProjetAnnuel3IBD/projet/MLAlgorithms/ML_Library/x64/Release/ML_Library.dll\"\npathDLL = \"D:/CloudStation/Cours/3IBD/projetAnnuel/projet/MLAlgorithms/ML_Library/x64/Release/ML_Library.dll\"\n\nmyDll = CDLL(pathDLL)\n\n#Parametre\nalpha = 0.05\nepochs = 1000\ndisplay = 100\n\n#datas des points a tester\nX = np.random.random((500, 2)) * 2.0 - 1.0\nY = np.array([[1, 0, 0] if -p[0] - p[1] - 0.5 > 0 and p[1] < 0 and p[0] - p[1] - 0.5 < 0 else #A\n              [0, 1, 0] if -p[0] - p[1] - 0.5 < 0 and p[1] > 0 and p[0] - p[1] - 0.5 < 0 else #B\n              [0, 0, 1] if -p[0] - p[1] - 0.5 < 0 and p[1] < 0 and p[0] - p[1] - 0.5 > 0 else #C\n              [0, 0, 0] for p in X])\n\n\n#A = 1\nY1 = [ 1 if y == [1, 0, 0] else -1 for y in Y.tolist()  ]\npArrayWeight1 = linearClassification(myDll, X, np.array(Y1), alpha, epochs, display)\n\n#B = 1\nY2 = [ 1 if y == [0, 1, 0] else -1 for y in Y.tolist()  ]\npArrayWeight2 = linearClassification(myDll, X, np.array(Y2), alpha, epochs, display)\n\n#A = 1\nY3 = [ 1 if y == [0, 0, 1] else -1 for y in Y.tolist()  ]\npArrayWeight3 = linearClassification(myDll, X, np.array(Y3), alpha, epochs, display)\n\n\n#droite à tracer\nX1 = np.linspace(-1, 1, 65)\nX2 = np.linspace(-1, 1, 65)\nfor x1 in X1:\n\tfor x2 in X2: \n\t\tpredictX = np.array([x1, x2])\n\t\tvalue1 = predict(myDll, myDll.predictLinearClassification, predictX, pArrayWeight1)\n\t\tvalue2 = predict(myDll, myDll.predictLinearClassification, predictX, pArrayWeight2)\n\t\tvalue3 = predict(myDll, myDll.predictLinearClassification, predictX, pArrayWeight3)\n\n\t\tif value1 > value2 and value1 > value3:\n\t\t\tplt.scatter(x1, x2, color='#bbdefb')\n\t\telif value2 > value1 and value2 > value3:\n\t\t    plt.scatter(x1, x2, color='#ffcdd2')\n\t\telif value3 > value1 and value3 > value2:\n\t\t\tplt.scatter(x1, x2, color='#c8e6c9')\n\t\telse:\n\t\t\tplt.scatter(x1, x2, color='#eeeeee')\n\n#affichage\nplt.scatter(np.array(list(map(lambda elt : elt[1], filter(lambda c: Y[c[0]][0] == 1, enumerate(X)))))[:,0], np.array(list(map(lambda elt : elt[1], filter(lambda c: Y[c[0]][0] == 1, enumerate(X)))))[:,1], color='blue')\nplt.scatter(np.array(list(map(lambda elt : elt[1], filter(lambda c: Y[c[0]][1] == 1, enumerate(X)))))[:,0], np.array(list(map(lambda elt : elt[1], filter(lambda c: Y[c[0]][1] == 1, enumerate(X)))))[:,1], color='red')\nplt.scatter(np.array(list(map(lambda elt : elt[1], filter(lambda c: Y[c[0]][2] == 1, enumerate(X)))))[:,0], np.array(list(map(lambda elt : elt[1], filter(lambda c: Y[c[0]][2] == 1, enumerate(X)))))[:,1], color='green')\nplt.show()\nplt.clf()\n","sub_path":"rendu/rendu2/testCase/Lineaire/classification/linearMultiTripleClass.py","file_name":"linearMultiTripleClass.py","file_ext":"py","file_size_in_byte":2699,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"646418360","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n\"\"\"\nfiles API蓝图\n定义蓝图api_files，实现files相关操作蓝图\n视图含义见对应api文档\n\"\"\"\nimport datetime\nimport time\nimport traceback\n\nfrom flask import Blueprint, request, Response\nfrom urllib.parse import unquote, quote\n\nimport funcs\nimport os\nimport global_var as gv\nfrom api_loginout import verify_auto\nfrom api_children import get_cid_by_alias\nfrom global_var import enum_error as ee\nfrom db_params import get_index\nfrom copy import deepcopy\n\n# 定义蓝图-------------------------------------------------\napi_files = Blueprint(\n    'api_files',\n    __name__,\n    template_folder='templates'\n)\n\n# functions--------------------------------------------\nflist = [\n    {'fn': 'id', 'type': 'int', 'sorted':1},\n    {'fn': 'child_id', 'type': 'int'},\n    {'fn': 'filename', 'type': 'str'},\n    {'fn': 'originfn', 'type': 'b64str'}\n]\n\nfiledir = 'files'\n\n\ndef get_file_list(page=0, limit=0, cid=None):\n    '''\n    get file list from database\n    :param page:\n    :param limit:\n    :param cid:     child id, 空则不限制\n    '''\n    fl = deepcopy(flist)\n    fl[1]['type'] = 'str'\n    del fl[2]\n    fl[2]['as'] = 'filename'\n    wstr = ''\n    if cid is not None:\n        wstr = 'where child_id={0}'.format(str(cid))\n    try:\n        conn = gv.dbpool.connection()\n        cur = conn.cursor()\n        cur.execute(\"SET NAMES UTF8mb4;\")\n        datas = funcs.get_datas(cur, 'files', fl, page=page, limit=limit,\n                                wstr=wstr, logger=gv.logger)\n        cur.close()\n        conn.close()\n        return datas\n    except:\n        estr = traceback.format_exc()\n        gv.logger.error('database error!\\n'+estr)\n        return ee.DBERR()\n    return ee.UNKNOWN()\n\n\ndef get_file_info(fid):\n    '''\n    get file list from database\n    :param fid:     file id\n    '''\n    wstr = 'where id={0}'.format(str(fid))\n    fl = deepcopy(flist)\n    fl[1]['type'] = 'str'\n    try:\n        conn = gv.dbpool.connection()\n        cur = conn.cursor()\n        cur.execute(\"SET NAMES UTF8mb4;\")\n        datas = funcs.get_datas(cur, 'files', flist,\n                                wstr=wstr, logger=gv.logger)\n        cur.close()\n        conn.close()\n        return datas\n    except:\n        estr = traceback.format_exc()\n        gv.logger.error('database error!\\n'+estr)\n        return ee.DBERR()\n    return ee.UNKNOWN()\n\ndef delete_file(fid):\n    '''\n    delete file from database and disk\n    '''\n    finfo = get_file_info(fid)\n    if finfo['code']!=0:\n        return finfo\n    if len(finfo['data'])==0:\n        return ee.IDERR(msg='file with this id not found')\n    finfo = finfo['data'][0]\n    _fn = finfo['filename']\n    _fpath = os.path.join(filedir, _fn)\n    if os.path.exists(_fpath):\n        try:\n            os.remove(_fpath)\n        except:\n            return ee.UNKNOWN(msg='error occured when deleting file')\n    # delete info in db\n    sqlstr = 'delete from files where id={};'.format(str(fid))\n    try:\n        conn = gv.dbpool.connection()\n        cur = conn.cursor()\n        cur.execute(\"SET NAMES UTF8mb4;\")\n        cur.execute(sqlstr.encode('utf-8', 'ignore'))\n        conn.commit()\n        cur.close()\n        conn.close()\n        return ee.NORMAL()\n    except:\n        estr = traceback.format_exc()\n        gv.logger.error('database error!\\n'+estr)\n        return ee.DBERR()\n    return ee.UNKNOWN()\n\n\n\ndef insert_file_info(cid, filename, originfn):\n    '''\n    insert file info into database\n    '''\n    fl = deepcopy(flist)\n    del fl[0]\n    fl[0]['val'] = cid\n    fl[1]['val'] = filename\n    fl[2]['val'] = originfn\n    try:\n        conn = gv.dbpool.connection()\n        cur = conn.cursor()\n        cur.execute(\"SET NAMES UTF8mb4;\")\n        res = funcs.insert_if_not_exists(\n            cur, 'files', fl, chkfn=None, logger=gv.logger)\n        conn.commit()\n        cur.close()\n        conn.close()\n        return res\n    except:\n        estr = traceback.format_exc()\n        gv.logger.error('database error!\\n'+estr)\n        return ee.DBERR()\n    return ee.UNKNOWN()\n\n\n# views------------------------------------------------\n\n\n@api_files.route('/api/files', methods=['GET'])\ndef files_get():\n    vres = verify_auto(request, allows=['admin', 'staff'])\n    if not vres['code'] == 0:\n        # verification failed\n        vres['count'] = 0\n        vres['data'] = []\n        return vres\n    page = request.args.get('page')\n    limit = request.args.get('limit')\n    cid = request.args.get('child_id')\n    if page is None or limit is None:\n        page = 0\n        limit = 0\n    else:\n        page = int(page)-1\n        limit = int(limit)\n    if type(cid) == type('') and not str.isdigit(cid):\n        return ee.PARAMERR(app=\": child_id\")\n    if cid is None:\n        _alias = request.args.get('alias')\n        if _alias is not None:\n            _cidfa = get_cid_by_alias(_alias)\n            if _cidfa['code']!=0:\n                return _cidfa\n            if len(_cidfa['data'])>0:\n                cid=_cidfa['data'][0]['id']\n    res = get_file_list(page, limit, cid)\n    return res\n\n@api_files.route('/api/file', methods=['DELETE'])\ndef file_delete():\n    vres = verify_auto(request, allows=['admin', 'staff'])\n    if not vres['code'] == 0:\n        # verification failed\n        vres['count'] = 0\n        vres['data'] = []\n        return vres\n    fid = request.form.get('id')\n    if type(fid) == type('') and not str.isdigit(fid):\n        return ee.PARAMERR(app=\": id\")\n    res = delete_file(fid)\n    return res\n\n@api_files.route('/api/file', methods=['POST'])\ndef file_post():\n    vres = verify_auto(request, allows=['admin', 'staff'])\n    if not vres['code'] == 0:\n        # verification failed\n        vres['count'] = 0\n        vres['data'] = []\n        return vres\n    cid = request.form.get('child_id')\n    if type(cid) == type('') and not str.isdigit(cid):\n        return ee.PARAMERR(app=\": child_id\")\n    if cid is None:\n        _alias = request.form.get('alias')\n        if _alias is None:\n            return ee.PARAMERR(app=': child_id or alias is needed')\n        _cidfa = get_cid_by_alias(_alias)\n        if _cidfa['code']!=0:\n            return _cidfa\n        if len(_cidfa['data'])>0:\n            cid=_cidfa['data'][0]['id']\n    if cid is None:\n        return ee.IDERR(app=': incorrect id or alias')\n    file = request.files['file']\n    if file is None:\n        return ee.PARAMERR(': no file')\n\n    originfn = file.filename\n    if originfn is None or len(originfn) < 1:\n        return ee.PARAMERR(app=\": filename error\")\n    filename = funcs.get_md5(originfn+str(cid))\n    filepath = os.path.join(filedir, filename)\n    if os.path.exists(filepath):\n        return ee.PARAMERR(': file already exists')\n    funcs.mkdir(filedir)\n    file.save(filepath)\n    res = insert_file_info(cid, filename, originfn)\n    return res\n\n\n@api_files.route('/api/file', methods=['GET'])\ndef file_get():\n    vres = verify_auto(request, allows=['admin', 'staff'])\n    if not vres['code'] == 0:\n        # verification failed\n        vres['count'] = 0\n        vres['data'] = []\n        return vres\n    fid = request.args.get('id')\n    if not (type(fid)==type('') and str.isdigit(fid)):\n        return ee.PARAMERR(app=\": id\")\n    finfo = get_file_info(fid)\n    if finfo['code']!=0:\n        return finfo\n    if len(finfo['data'])==0:\n        return ee.IDERR(msg='file id is incorrect')\n    fullfilename = os.path.join(filedir, finfo['data'][0]['filename'])\n    if not os.path.exists(fullfilename):\n        return ee.IDERR('file not found')\n    # 普通下载\n    # response = make_response(send_from_directory(filepath, filename, as_attachment=True))\n    # response.headers[\"Content-Disposition\"] = \"attachment; filename={}\".format(filepath.encode().decode('latin-1'))\n    # return send_from_directory(filepath, filename, as_attachment=True)\n    # 流式读取\n\n    _block_size = 8 * 1024 * 1024\n    def send_file():\n        store_path = fullfilename\n        with open(store_path, 'rb') as targetfile:\n            while 1:\n                data = targetfile.read(_block_size)\n                if not data:\n                    break\n                yield data\n\n    response = Response(send_file(), content_type='application/octet-stream')\n    # 设置文件名，url编码以适应中文\n    _fn = quote(finfo['data'][0]['originfn'])\n    response.headers['Content-disposition'] = \"attachment; filename*=utf-8''{}\".format(_fn)\n    #response.headers[\"Content-disposition\"] = 'attachment; filename=%s' % finfo['data'][0]['originfn']\n    return response\n","sub_path":"api_files.py","file_name":"api_files.py","file_ext":"py","file_size_in_byte":8512,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"228101687","text":"import datetime\n\nfrom django.contrib.auth.models import User\nfrom django.test import TestCase\n\nfrom ..models import FinancialAidApplication, FinancialAidMessage\nfrom pycon.finaid.forms import FinancialAidApplicationForm, ReviewerMessageForm\n\n\ntoday = datetime.date.today()\n\n\nclass FinancialAidTest(TestCase):\n\n    def test_form(self):\n        user = User.objects.create_user(\"Foo\")\n        instance = FinancialAidApplication(user=user)\n        form = FinancialAidApplicationForm(instance=instance)\n        self.assertFalse(form.is_valid())\n        data = dict(\n            profession=\"Foo\",\n            experience_level=\"lots\",\n            what_you_want=\"money\",\n            want_to_learn=\"stuff\",\n            use_of_python=\"fun\",\n            presenting=1,\n            hotel_nights=0,\n            travel_amount_requested=\"0.00\",\n        )\n        instance = FinancialAidApplication(user=user)\n        form = FinancialAidApplicationForm(data, instance=instance)\n        self.assertTrue(form.is_valid(), msg=form.errors)\n\n        # Leave out a required field\n        del data['presenting']\n        form = FinancialAidApplicationForm(data, instance=instance)\n        self.assertFalse(form.is_valid())\n\n    def test_reviewer_message_form(self):\n        user = User.objects.create_user(\"Foo\")\n        application = FinancialAidApplication.objects.create(\n            user=user,\n            profession=\"Foo\",\n            experience_level=\"lots\",\n            what_you_want=\"money\",\n            want_to_learn=\"stuff\",\n            use_of_python=\"fun\",\n            presenting=1,\n        )\n        application.save()\n        message = FinancialAidMessage(user=user, application=application)\n        data = {\n            'visible': False,\n            'message': 'TestMessage'\n        }\n        form = ReviewerMessageForm(data, instance=message)\n        self.assertTrue(form.is_valid())\n        message = form.save()\n        message = FinancialAidMessage.objects.get(pk=message.pk)\n        self.assertFalse(message.visible)\n        self.assertEqual(\"TestMessage\", message.message)\n\n        data['visible'] = True\n        form = ReviewerMessageForm(data, instance=message)\n        self.assertTrue(form.is_valid())\n        message = form.save()\n        message = FinancialAidMessage.objects.get(pk=message.pk)\n        self.assertTrue(message.visible)\n","sub_path":"pycon/finaid/tests/test_forms.py","file_name":"test_forms.py","file_ext":"py","file_size_in_byte":2336,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"232911215","text":"# Analysis of ds001 with AFNI\n\nimport os\n\nfrom lib.afni_processing import copy_raw, create_afni_onset_files\nfrom lib.afni_processing import run_subject_level_analyses\nfrom lib.afni_processing import run_group_level_analysis\nfrom lib.afni_processing import run_permutation_test\nfrom lib.afni_processing import mean_mni_images\nfrom lib.afni_processing import run_SSWarper\nfrom lib.afni_processing import run_orthogonalize\n\nraw_dir = '/home/maullz/NIDM-Ex/BIDS_Data/DATA/BIDS/ds001_R2.0.4'\n\n# Set default orientation to origin (instead of standardised space) for\n# ambiguous NIfTi (required for ds001)\nos.environ[\"AFNI_NIFTI_VIEW\"] = \"orig\"\nresults_dir = \\\n    '/home/maullz/NIDM-Ex/BIDS_Data/RESULTS/SOFTWARE_COMPARISON/ds001'\n\nafni_dir = os.path.join(results_dir, 'AFNI')\n\nif not os.path.isdir(afni_dir):\n    os.mkdir(afni_dir)\n\npreproc_dir = os.path.join(afni_dir, 'PREPROCESSING')\nlevel1_dir = os.path.join(afni_dir, 'LEVEL1')\nlevel2_dir = os.path.join(afni_dir, 'LEVEL2')\nperm_dir = os.path.join(level2_dir, 'permutation_test')\nmni_dir = os.path.join(afni_dir, 'mean_mni_images')\n\n# Set default orientation to origin (instead of standardised space) for\n# ambiguous NIfTi (required for ds001)\nos.environ[\"AFNI_NIFTI_VIEW\"] = \"orig\"\n\n# Specify the number of functional volumes ignored in the study\nTR = 2\nnum_ignored_volumes = 2\n\n# Specify the TR that will be removed from onesets, equal to num_ignored_volumes*TR\nremoved_TR_time = num_ignored_volumes*TR \n\ncwd = os.path.dirname(os.path.realpath(__file__))\n\n# Copy raw anatomical and functional data to the preprocessing directory and\n# run BET on the anatomical images\ncopy_raw(raw_dir, preproc_dir)\n\n# Directory to store the onset files\nonset_dir = os.path.join(afni_dir, 'ONSETS')\n\n# Define conditions and parametric modulations (if any)\n# FORMAT\n#   {VariableLabel,{TrialType,Durations}}\n#   {{VariableLabel,VariableModLabel},{TrialType,Duration,Amplitude}}\nconditions = (\n    (('pumps_fixed', 'pumps_demean'), ('pumps_demean',)),\n    ('pumps_RT', ('pumps_demean', 'response_time')),\n    (('cash_fixed', 'cash_demean'), ('cash_demean',)),\n    ('cash_RT', ('cash_demean', 'response_time')),\n    (('explode_fixed', 'explode_demean'), ('explode_demean',)),\n    (('control_pumps_fixed', 'control_pumps_demean'),\n    ('control_pumps_demean',)),\n    ('control_pumps_RT', ('control_pumps_demean', 'response_time')))\n\n# Create onset files based on BIDS tsv files\ncond_files = create_afni_onset_files(raw_dir, onset_dir, conditions, removed_TR_time)\n\nSSWarper_template = os.path.join(cwd, 'lib', 'template_AFNI_SSWarper')\northogonalize_template = os.path.join(cwd, 'lib', 'template_AFNI_orthogonalize')\nsub_level_template = os.path.join(cwd, 'lib', 'template_ds001_AFNI_level1')\ngrp_level_template = os.path.join(cwd, 'lib', 'template_ds001_AFNI_level2')\nperm_template = os.path.join(cwd, 'lib', 'template_ds001_AFNI_perm_test')\n\n# Orthogonalize conditions following the original study\nrun_orthogonalize(preproc_dir, onset_dir, orthogonalize_template)\n\n# Run SSWarper AFNI command on each subject to strip skull and warp to MNI template\nrun_SSWarper(preproc_dir, SSWarper_template)\n\n# Run a GLM combining all the fMRI runs of each subject\nrun_subject_level_analyses(preproc_dir, onset_dir, level1_dir, sub_level_template)\n\n# Run the group-level GLM\nrun_group_level_analysis(level1_dir, level2_dir, grp_level_template)\n\n# Run a permutation test\nrun_permutation_test(level1_dir, perm_dir, perm_template)\n\n# Create mean and standard deviations maps of the mean func and anat images in MNI space\nmean_mni_images(preproc_dir, level1_dir, mni_dir)\n","sub_path":"scripts/process_ds001_AFNI.py","file_name":"process_ds001_AFNI.py","file_ext":"py","file_size_in_byte":3587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"28496010","text":"from django.conf.urls import patterns, url\nfrom mainSite import views\n\nurlpatterns = patterns('',\n\turl(r'^$', views.home, name='home'),\n\turl(r'^about/$', views.about, name='about'),\n\turl(r'^help/$', views.help, name='help'),\n\turl(r'^service(?:-(?P<city_name_slug>[\\w\\-]+))?/$', views.allservices, name='allservices'), # here grab city. \n\turl(r'^service/(?P<service_name_slug>[\\w]+)(?:-(?P<city_name_slug>[\\w\\-]+))?/$', views.service, name='service'),\n\turl(r'^contact/$', views.contact, name='contact'),\n\turl(r'^rut-avdrug/$', views.rutavdrug, name='rutavdrug'),\n\turl(r'^nojd-kund-garanti/$', views.nojdkund, name='nojdkund'),\n\turl(r'^(?P<city_name_slug>[\\w\\-]+)/$', views.city, name='city'), #(?:/(?P<title>[a-zA-Z]+))?\n\turl(r'^(?P<city_name_slug>[\\w\\-]+)/service(?:/(?P<service_name_slug>[\\w]+))?/$', views.cityservice, name='cityservice'),\n\n\t)\n","sub_path":"mainSite/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":846,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"131584070","text":"# Time Complexity :O(n^2)\n# Space Complexity :O(n) \n# Did this code successfully run on Leetcode : yes\n# Any problem you faced while coding this : no\nclass Solution(object):\n    def wordBreak(self, s, wordDict):\n        \"\"\"\n        :type s: str\n        :type wordDict: List[str]\n        :rtype: bool\n        \"\"\"\n        # create a set of all words in dic\n        myset = set(wordDict)\n        m = len(s)\n        # create a DP array of all false except first place as we know that nothing is part of set\n        dp = [False]*(m+1)\n        dp[0] = True\n        # loop through s\n        for i in range(1,m+1):\n            #loop from back to i\n            for j in range(i):\n                # if at position j this was an end of a word try seeing if from this place to the ith place is a word or not if yes change to true and go to bigger loop\n                if dp[j]:\n                    if (s[j:i]) in myset:\n                        dp[i] = True\n                        break\n        # see if all string can be words\n        return dp[-1]","sub_path":"Problem-1.py","file_name":"Problem-1.py","file_ext":"py","file_size_in_byte":1037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"630809533","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Sat May  9 16:02:53 2020\n\n@author: Raymart Ballesteros\n\"\"\"\n\n\n# Filename: signals_slots.py\n# signals and slots example\n\nimport sys\n\nfrom PyQt5.QtWidgets import QApplication, QLabel, QPushButton, QVBoxLayout, QWidget\n\n\ndef greeting():\n    \"\"\" Slot function \"\"\"\n    if msg.text():\n        msg.setText(\"\")\n    else:\n        msg.setText(\"Hello World!\")\n        \napp = QApplication(sys.argv)\nwindow = QWidget()\nwindow.setWindowTitle('Signals and Slots')\nlayout = QVBoxLayout()\n\nbtn = QPushButton('Greet')\nbtn.clicked.connect(greeting)  # Connect clicked to greeting()\n\nlayout.addWidget(btn)\nmsg = QLabel('')\nlayout.addWidget(msg)\nwindow.setLayout(layout)\nwindow.show()\nsys.exit(app.exec_())\n","sub_path":"Python/gui/signals_slots.py","file_name":"signals_slots.py","file_ext":"py","file_size_in_byte":724,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"427062834","text":"from __future__ import unicode_literals\nfrom argparse import ArgumentParser\ntry:\n    from StringIO import StringIO\nexcept ImportError:\n    from io import StringIO\nimport os\nimport sys\nimport unittest\n\nimport mock\n\nfrom clyent import add_subparser_modules\nfrom clyent.colors.color_formatter import print_colors\nfrom clyent.colors import Color, ColorStream\n\ndef add_hello_parser(subparsers):\n    subparser = subparsers.add_parser('hello')\n    subparser.add_argument('world')\n    subparser.set_defaults(main=mock.Mock())\n\nclass Test(unittest.TestCase):\n\n    def test_add_subparser_modules(self):\n        parser = ArgumentParser()\n\n        with mock.patch('clyent.iter_entry_points') as iter_entry_points:\n\n            ep = mock.Mock()\n            ep.load.return_value = add_hello_parser\n            iter_entry_points.return_value = [ep]\n            add_subparser_modules(parser, None, 'entry_point_name')\n\n        args = parser.parse_args(['hello', 'world'])\n        self.assertEqual(args.world, 'world')\n\n    @unittest.skipIf(os.name == 'nt', 'Cannot colorize StringIO on Windows')\n    def test_color_format(self):\n        output = StringIO()\n        output.fileno = lambda: -1\n        stream = ColorStream(output)\n\n        print_colors('Are you', '{=okay!c:green}', 'Annie?', file=stream)\n\n        value = output.getvalue()\n        output.close()\n\n        self.assertEqual('Are you \\033[92mokay\\033[0m Annie?\\n', value)\n\n    @unittest.skipIf(os.name == 'nt', 'Cannot colorize StringIO on Windows')\n    def test_color_context(self):\n        output = StringIO()\n        output.fileno = lambda: -1\n        stream = ColorStream(output)\n\n        with Color('red', stream):\n            print_colors('ERROR!', file=stream)\n\n        value = output.getvalue()\n        output.close()\n\n        self.assertEqual('\\033[91mERROR!\\n\\033[0m', value)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"envs/anaconda-project-dev/Lib/site-packages/clyent/tests/test_clyent.py","file_name":"test_clyent.py","file_ext":"py","file_size_in_byte":1882,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"88009454","text":"#Python 2.x program for Speech Recognition \n\nimport speech_recognition as sr \n\n\nr = sr.Recognizer() \n\n\nprint (\"Say Something\")\n#litens for the user's input \n\n\t\t\ntry: \n\ttext = r.recognize_google(audio) \n\tprint (\"you said: \"+ text) \n\t\n\t#error occurs when google could not understand what was said \n\t\nexcept sr.UnknownValueError: \n\tprint(\"Google Speech Recognition could not understand audio\") \n\t\nexcept sr.RequestError as e: \n\tprint(\"Could not request results from Google Speech Recognition service; {0}\".format(e)) \n","sub_path":"speech2.py","file_name":"speech2.py","file_ext":"py","file_size_in_byte":515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"438134251","text":"from math import sqrt\n\n__author__ = 'gregortaube'\n\n#encoding is done by transposing a square matrix with text.\n# the text is read row by row, the cipher makes the text appear\n# column by column. By reading column-wise from the back, we have decoded the message\ntestcases = int(input())\n\ndef decode(message):\n    decoded_cipher = \"\"\n    squareLen = int(sqrt(len(message)))\n    temp = squareLen\n    cipherList = list(message)\n    # print(cipherList)\n    for x in range(squareLen):\n        for factor in range(0,squareLen):\n            decoded_cipher += cipherList[temp + squareLen*factor -1]\n        temp -=1\n\n    return decoded_cipher\n\n\n\n\nfor x in range(testcases):\n    print(decode(input()))","sub_path":"intermediate/encodedmessage.py","file_name":"encodedmessage.py","file_ext":"py","file_size_in_byte":691,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"120298083","text":"\r\n# read kaggle facial expression recognition challenge dataset (fer2013.csv)\r\n# https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge\r\nimport numpy as np\r\nold_settings = np.seterr(all='print')\r\nimport matplotlib.pyplot as plt\r\nimport pickle    \r\nimport cv2\r\nimport os\r\n\r\ndef sigmoid(x):\r\n    return 1/(1+np.exp(-x))\r\n\r\ndef get_data():\r\n    # angry, disgust, fear, happy, sad, surprise, neutral\r\n    # get the data\r\n    with open(\"fer2013/fer2013.csv\") as f:\r\n        content = f.readlines() \r\n\r\n    lines = np.array(content)\r\n    num_of_instances = lines.size\r\n    print(\"number of instances: \",num_of_instances)\r\n    print(\"instance length: \",len(lines[1].split(\",\")[1].split(\" \")))\r\n\r\n    x_train, y_train, x_test, y_test = [], [], [], []\r\n\r\n    for i in range(1,num_of_instances):\r\n        emotion, img, usage = lines[i].split(\",\")\r\n        pixels = np.array(img.split(\" \"), 'float32')\r\n        emotion = 1 if int(emotion)==3 else 0 # Only for happiness\r\n        if 'Training' in usage:\r\n            y_train.append(emotion)\r\n            x_train.append(pixels)\r\n        elif 'PublicTest' in usage:\r\n            y_test.append(emotion)\r\n            x_test.append(pixels)\r\n            \r\n    \r\n    #------------------------------\r\n    #data transformation for train and test sets\r\n    x_train = np.array(x_train, 'float64')\r\n    y_train = np.array(y_train, 'float64')\r\n    x_test = np.array(x_test, 'float64')\r\n    y_test = np.array(y_test, 'float64')\r\n\r\n    x_train /= 255 #normalize inputs between [0, 1]\r\n    x_test /= 255\r\n\r\n    #reshape to make images and labels\r\n    x_train = x_train.reshape(x_train.shape[0], 48, 48)\r\n    x_test = x_test.reshape(x_test.shape[0], 48, 48)\r\n    y_train = y_train.reshape(y_train.shape[0], 1)\r\n    y_test = y_test.reshape(y_test.shape[0], 1)\r\n    \r\n    x_val = x_train[0:8000,:,:]\r\n    y_val = y_train[0:8000]\r\n    x_train = x_train[8000:-1,:,:]\r\n    y_train = y_train[8000:-1]\r\n    \r\n    print(x_train.shape[0], 'train samples')\r\n    print(x_val.shape[0], 'val samples')\r\n    print(x_test.shape[0], 'test samples')\r\n    print(y_test)\r\n\r\n    # plt.hist(y_train, max(y_train)+1); plt.show()\r\n\r\n    return x_train, y_train, x_val, y_val, x_test, y_test\r\n\r\nclass Model():\r\n    def __init__(self):\r\n        params = 48*48 # image reshape\r\n        out = 1 # smile label\r\n        self.lr = 0.00001 # Change if you want\r\n        self.W = np.random.randn(params, out) #Initialize W and b\r\n        self.b = np.random.randn(out)\r\n\r\n    def forward(self, image):\r\n        image = image.reshape(image.shape[0], -1)\r\n        out = np.dot(image, self.W) + self.b #Multiply image with W and b\r\n        return out\r\n\r\n    def compute_loss(self, pred, gt):\r\n        #Get the loss \r\n        J = (-1/pred.shape[0]) * np.sum(np.multiply(gt, np.log(sigmoid(pred))) + np.multiply((1-gt), np.log(1 - sigmoid(pred))))\r\n        return J\r\n\r\n    def compute_gradient(self, image, pred, gt):\r\n        image = image.reshape(image.shape[0], -1)\r\n        W_grad = np.dot(image.T, pred-gt)/image.shape[0]\r\n        self.W -= W_grad*self.lr\r\n\r\n        b_grad = np.sum(pred-gt)/image.shape[0]\r\n        self.b -= b_grad*self.lr\r\n\r\ndef train(model):\r\n    pkl_filename = \"pickle_model.pkl\"  \r\n    x_train, y_train, x_val, y_val, _ , _ = get_data()\r\n    batch_size = 50 # Change if you want\r\n    epochs = 40000 # Change if you want\r\n    aux = []\r\n    losses = []\r\n    losses_val = []\r\n    for i in range(epochs):\r\n        loss = []\r\n        for j in range(0,x_train.shape[0], batch_size):\r\n            _x_train = x_train[j:j+batch_size]\r\n            _y_train = y_train[j:j+batch_size]\r\n            out = model.forward(_x_train)\r\n            loss.append(model.compute_loss(out, _y_train))\r\n            model.compute_gradient(_x_train, out, _y_train)\r\n        #out, loss_test = test(model) \r\n        out = model.forward(x_val)                \r\n        loss_val = model.compute_loss(out, y_val)\r\n        print('Epoch {:6d}: train {:.5f} | val: {:.5f}'.format(i, np.array(loss).mean(),loss_val))\r\n        losses.append(np.array(loss).mean())\r\n        losses_val.append(loss_val)\r\n        aux.append(i)  \r\n        \r\n    with open(pkl_filename, 'wb') as file: \r\n             pickle.dump(model, file)\r\n        \r\n    plot(losses,losses_val,aux)\r\n\r\ndef plot(loss,losses_test,epochs): # Add arguments\r\n    # CODE HERE\r\n    # Save a pdf figure with train and test losses\r\n    \r\n    #x = range(epochs)\r\n    x = epochs\r\n    \r\n    plt.plot(x, loss, label='train')\r\n    plt.plot(x, losses_test, label='val')\r\n    plt.legend()\r\n    plt.xlabel(\"iterations(Epochs)\")\r\n    plt.ylabel(\"loss(error)\")\r\n    plt.savefig('figure.pdf') \r\n\r\ndef test(model):\r\n    #_, _, x_test, y_test = get_data()\r\n    # YOU CODE HERE\r\n    # Show some qualitative results and the total accuracy for the whole test set\r\n   \r\n    _, _, _, _, x_test, y_test = get_data()\r\n    y_score = model.forward(x_test)  \r\n    y_score = sigmoid(y_score)\r\n    #threshold, upper, lower = 0.5, 1, 0\r\n    y_score[y_score >= 0.5] = 1\r\n    y_score[y_score < 0.5] = 0\r\n#    #y_score = np.where(y_score>threshold, upper, lower)\r\n#    #PR curve, F1 and normalized ACA.\r\n#    #PR\r\n#    #from sklearn.metrics import average_precision_score\r\n#    #average_precision = average_precision_score(y_test, y_score)  \r\n#    print('Average precision-recall score: {0:0.2f}'.format(average_precision))                \r\n#    #loss_test = model.compute_loss(out, y_test)         \r\n#    from sklearn.metrics import precision_recall_curve\r\n#    import matplotlib.pyplot as plt\r\n#    from sklearn.utils.fixes import signature\r\n#    \r\n#    precision, recall, _ = precision_recall_curve(y_test, y_score)\r\n#    step_kwargs = ({'step': 'post'}\r\n#                   if 'step' in signature(plt.fill_between).parameters\r\n#                   else {})\r\n#    plt.step(recall, precision, color='b', alpha=0.2,\r\n#             where='post')\r\n#    plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)\r\n#    \r\n#    plt.xlabel('Recall')\r\n#    plt.ylabel('Precision')\r\n#    plt.ylim([0.0, 1.05])\r\n#    plt.xlim([0.0, 1.0])\r\n#    plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(average_precision))\r\n#    plt.show()\r\n    #F1 \r\n    from sklearn.metrics import f1_score\r\n    f1 = f1_score(y_test, y_score, average='macro')  \r\n    print('F1 score: {0:0.2f}'.format(f1))\r\n   \r\n    #normalized ACA\r\n\r\n    from sklearn.metrics import confusion_matrix\r\n    conf = confusion_matrix(y_test, y_score)\r\n    print('confmat',conf)\r\n    import seaborn as sn\r\n    import pandas as pd\r\n    import matplotlib.pyplot as plt\r\n    array = conf\r\n    df_cm = pd.DataFrame(array, index = [i for i in range(2)],\r\n                                         columns = [i for i in range(2)])\r\n    plt.figure(figsize = (70,70))\r\n    sn.heatmap(df_cm, annot=True)\r\n    plt.show()\r\n    cont1 = 0\r\n    cont2 = 0\r\n    for i in range(conf.shape[1]):\r\n        cont1 = cont1 + conf[i,i]/sum(conf[:,i])\r\n        cont2 = cont2 +1\r\n    ACA = cont1/cont2    \r\n    print ('ACA: {0:0.3f}'.format(ACA))\r\n\r\n\r\ndef load_images_from_folder(folder):\r\n    from skimage import color\r\n    images = np.zeros((11,48,48))\r\n    for i in range(0,10):\r\n        filename = os.listdir('images')[i]\r\n        img = cv2.imread(os.path.join(folder,filename))\r\n        img = cv2.resize(img, dsize=(48, 48), interpolation=cv2.INTER_CUBIC)\r\n        img = color.rgb2gray(img)\r\n        images[i,:,:]= img\r\n    return images\r\n\r\ndef demo(model):\r\n    x_demo = load_images_from_folder('images')\r\n    #print(x_demo.type, x_demo.shape)\r\n    #x_demo = np.array(x_demo)\r\n    #x_demo = x_demo.astype('float64')\r\n    y_demo = [0,0,1,0,1,0,1,1,0,1,0]\r\n    y_demo = np.array(y_demo, 'float64')\r\n    y_demo = y_demo.reshape(y_demo.shape[0], 1)\r\n    #x_demo = x_demo.reshape(x_demo.shape[0], 48, 48)\r\n    y_score = model.forward(x_demo)  \r\n    y_score = sigmoid(y_score)\r\n    #threshold, upper, lower = 0.5, 1, 0\r\n    y_score[y_score >= 0.5] = 1\r\n    y_score[y_score < 0.5] = 0\r\n    \r\n#    #y_score = np.where(y_score>threshold, upper, lower)\r\n#    #PR curve, F1 and normalized ACA.\r\n#    #PR\r\n#    #from sklearn.metrics import average_precision_score\r\n#    #average_precision = average_precision_score(y_test, y_score)  \r\n#    print('Average precision-recall score: {0:0.2f}'.format(average_precision))                \r\n#    #loss_test = model.compute_loss(out, y_test)         \r\n#    from sklearn.metrics import precision_recall_curve\r\n#    import matplotlib.pyplot as plt\r\n#    from sklearn.utils.fixes import signature\r\n#    \r\n#    precision, recall, _ = precision_recall_curve(y_test, y_score)\r\n#    step_kwargs = ({'step': 'post'}\r\n#                   if 'step' in signature(plt.fill_between).parameters\r\n#                   else {})\r\n#    plt.step(recall, precision, color='b', alpha=0.2,\r\n#             where='post')\r\n#    plt.fill_between(recall, precision, alpha=0.2, color='b', **step_kwargs)\r\n#    \r\n#    plt.xlabel('Recall')\r\n#    plt.ylabel('Precision')\r\n#    plt.ylim([0.0, 1.05])\r\n#    plt.xlim([0.0, 1.0])\r\n#    plt.title('2-class Precision-Recall curve: AP={0:0.2f}'.format(average_precision))\r\n#    plt.show()\r\n    #F1 \r\n    from sklearn.metrics import f1_score\r\n    f1 = f1_score(y_demo, y_score, average='macro')  \r\n    print('F1 score: {0:0.2f}'.format(f1))\r\n   \r\n    #normalized ACA\r\n    from sklearn.metrics import confusion_matrix\r\n    conf = confusion_matrix(y_demo, y_score)\r\n    print('confmat',conf)\r\n    import seaborn as sn\r\n    import pandas as pd\r\n    import matplotlib.pyplot as plt\r\n    array = conf\r\n    df_cm = pd.DataFrame(array, index = [i for i in range(2)],\r\n                                         columns = [i for i in range(2)])\r\n    plt.figure(figsize = (70,70))\r\n    sn.heatmap(df_cm, annot=True)\r\n    plt.show()    \r\n    cont1 = 0\r\n    cont2 = 0\r\n    for i in range(conf.shape[1]):\r\n        cont1 = cont1 + conf[i,i]/sum(conf[:,i])\r\n        cont2 = cont2 +1\r\n    ACA = cont1/cont2    \r\n    print ('ACA: {0:0.3f}'.format(ACA))\r\n       \r\nif __name__ == '__main__':\r\n    \r\n    import argparse\r\n    parser = argparse.ArgumentParser()\r\n\r\n    #parser.add_argument('--validation') # If you use more please add them to this list.\r\n    parser.add_argument('--test', action='store_true', default= False)\r\n    parser.add_argument('--demo', action='store_true', default= False)\r\n    args = parser.parse_args()\r\n    \r\n    if args.test == True:\r\n       cwd = os.getcwd()\r\n       if os.path.isfile(cwd+'/'+'fer2013.zip') == False:\r\n            url = \"https://www.dropbox.com/s/ngq9ntcb8p3m8y3/fer2013.zip?dl=1\"\r\n            import zipfile\r\n            print('Downloading the database...')\r\n            import urllib.request\r\n            u = urllib.request.urlopen(url)\r\n            data = u.read()\r\n            u.close()\r\n            with open(cwd+'/'+'fer2013.zip','wb') as f :\r\n                f.write(data)\r\n            print('Database downloaded.')\r\n            f.close()\r\n            \r\n            #Unzip\r\n            print('Unzipping the database...')\r\n            zip_Archivo = zipfile.ZipFile(cwd +'/'+'fer2013.zip', 'r')\r\n            zip_Archivo.extractall(cwd)\r\n            zip_Archivo.close()\r\n            print('Unzipping done.') \r\n            \r\n            #untar\r\n            import tarfile\r\n            tar = tarfile.open('fer2013.tar.gz', \"r:gz\")\r\n            tar.extractall()\r\n            tar.close()\r\n\r\n       with open(\"pickle_model.pkl\", 'rb') as file:  \r\n             pickle_model = pickle.load(file)\r\n       test(pickle_model)   \r\n       \r\n       \r\n    elif args.demo == True:\r\n       with open(\"pickle_model.pkl\", 'rb') as file:  \r\n             pickle_model = pickle.load(file)\r\n       demo(pickle_model)\r\n       \r\n       \r\n    else:\r\n        \r\n        #import os     \r\n        cwd = os.getcwd()\r\n        \r\n        if os.path.isfile(cwd+'/'+'fer2013.zip') == False:\r\n            url = \"https://www.dropbox.com/s/ngq9ntcb8p3m8y3/fer2013.zip?dl=1\"\r\n            import zipfile\r\n            print('Downloading the database...')\r\n            import urllib.request\r\n            u = urllib.request.urlopen(url)\r\n            data = u.read()\r\n            u.close()\r\n            with open(cwd+'/'+'fer2013.zip','wb') as f :\r\n                f.write(data)\r\n            print('Database downloaded.')\r\n            f.close()\r\n            \r\n            #Unzip\r\n            print('Unzipping the database...')\r\n            zip_Archivo = zipfile.ZipFile(cwd +'/'+'fer2013.zip', 'r')\r\n            zip_Archivo.extractall(cwd)\r\n            zip_Archivo.close()\r\n            print('Unzipping done.') \r\n            \r\n            #untar\r\n            import tarfile\r\n            tar = tarfile.open('fer2013.tar.gz', \"r:gz\")\r\n            tar.extractall()\r\n            tar.close()\r\n            \r\n        model = Model()\r\n        print(\"Training model, this may take a while\")\r\n        train(model)\r\n    #    #plot(losses,losses_val,aux)\r\n    #    with open(\"pickle_model.pkl\", 'rb') as file:  \r\n    #             pickle_model = pickle.load(file)\r\n        test(model)   \r\n    #test(model,1)\r\n    \r\n    \r\n","sub_path":"10-LogisticReg/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":13028,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"206156164","text":"import pymongo   # package to link code with mongoDB\r\n#telnet packages\r\nimport getpass\r\nimport sys\r\nimport telnetlib\r\n#predefined package in python for logging usage\r\nimport logging\r\n\r\n#define basic logging configurations, such as time format, log file name, and level of logging\r\nlogging.basicConfig(format='%(asctime)s %(message)s', datefmt='%m/%d/%Y %I:%M:%S %p', filename='./Desktop/app.log', level=logging.DEBUG)\r\n#this is an example of a logging output of \"warning\" type which will appear on the first line of the log file\r\nlogging.warning(\"This session will be logged\")\r\n\r\n#setup the connection to mongoDB via localhost's IP and port number 27017 as defined in mongoDB compass, connect to \"network\" database and select \"vlans\" collection\r\n##logging info that shall be written in the log file (app.log) starts with (logging.info)\r\nmyclient = pymongo.MongoClient(\"mongodb://localhost:27017/\")\r\nlogging.info('connected to mongoDB via mongodb://localhost:27017')\r\nmydb = myclient[\"network\"]\r\nlogging.info('connected to mongoDB network view')\r\nmycol = mydb[\"vlans\"]\r\nlogging.info('connected to mongoDB via vlans collection')\r\n\r\n\r\n#create function that will create VLAN on both database and switch\r\ndef create():\r\n    #prompt the user to insert VLAN ID that he wishes to create, its name and description as well.\r\n    ID = input(\"please insert VLAN ID: \")\r\n    Name = input(\"please insert VLAN Name: \")\r\n    Description = input(\"please insert a Description about this VLAN: \")\r\n\r\n    #update record in MongoDB\r\n    record = { \"ID\": ID, \"NAME\": Name, \"Description\": Description }\r\n    x = mycol.insert_one(record)\r\n\r\n    ###create VLAN on switch \r\n    #tn.write(\"conf t\\n\")\r\n    #tn.write(\"vlan \"+ID+\"\\n\")\r\n    #tn.write(\"name \"+Name+\"\\n\")\r\n    #tn.write(\"end\\n\\n\")\r\n    #print(tn.read_all())\r\n    print(\"vlan created on switch\")\r\n\r\n    #log\r\n    logging.info('VLAN of ID '+ID+' has been creted with the name '+Name+' and description '+Description)\r\n    \r\n    return \"VLAN created\"\r\n \r\n#read function that will read VLANs on both database and switch\r\ndef read():\r\n    #iterate to read all VLANs (documents) from MongoDB\r\n    for x in mycol.find():\r\n        print(x)\r\n\r\n    ###read VLAN from switch \r\n    #tn.write(\"sh vlan br\\n\")\r\n    #print(tn.read_all())   \r\n    \r\n    #log\r\n    logging.info('a VLAN read operation has been performed')\r\n    return \"Done\"\r\n \r\n#update function that will update VLAN on both MongoDB and switch\r\ndef update():\r\n    #prompt the user to insert VLAN ID he wants to update\r\n    update = input(\"insert VLAN ID you want to update: \")\r\n\r\n    record = { \"ID\": update }\r\n\r\n    #prompt the user to insert VLAN ID, Name, and description he wants to update to\r\n    update2 = input(\"insert ID you want to update to: \")\r\n    update3 = input(\"insert Name you want to update to: \")\r\n    update4 = input(\"insert Description you want to update to: \")\r\n    newvalues = { \"$set\": { \"ID\": update2, \"Name\": update3, \"Description\":update4 } }\r\n\r\n    #insert new vlaues in MongoDB\r\n    mycol.update_one(record, newvalues)\r\n    for x in mycol.find():\r\n        print(x)\r\n\r\n\r\n    ###update VLAN on switch\r\n    #tn.write(\"conf t\\n\")\r\n    #tn.write(\"no vlan +update+\"\\n\")\r\n    #tn.write(\"vlan +update2+\"\\n\")\r\n    #tn.write(\"name \"+update3+\"\\n\")\r\n    #tn.write(\"end\\n\\n\")\r\n    #print(tn.read_all())\r\n    print(\"vlan updated on switch\")\r\n    \r\n    #log\r\n    logging.warning('an update performed on vlan '+update+' changed ID to '+update2+' and name to '+update3+' and description to '+update4)\r\n\r\n    return \"VLAN updated\"\r\n\r\n\r\n#delete function that will delete VLAN on both MongoDB and switch\r\ndef delete():\r\n    #prompt the user to insert VLAN ID he wishes to delete\r\n    delete = input(\"insert VLAn ID you wish to delete: \")\r\n    record = { \"ID\": delete }\r\n\r\n    #do the delete operation on MongoDB\r\n    mycol.delete_one(record)\r\n    \r\n    ###Delete VLAN from switch\r\n    #tn.write(\"conf t\\n\")\r\n    #tn.write(\"no vlan +delete+\"\\n\")\r\n    #tn.write(\"end\\n\\n\")\r\n    #print(tn.read_all())\r\n    print(\"vlan deleted from switch\")\r\n\r\n    #log\r\n    logging.warning('vlan '+delete+' was deleted from database!')\r\n\r\n    return \"VLAN deleted\"\r\n\r\n#switch case for CRUD operations where each number represents an operation\r\ndef numbers_to_vlan(argument):\r\n    switcher = {\r\n       '1': read,\r\n       '2': update,\r\n       '3': create,\r\n       '4': delete\r\n    }\r\n\r\n    # Get the function from switcher dictionary\r\n    func = switcher.get(argument, \"Invalid selection\")\r\n    # Execute the function\r\n    print(func())  \r\n\r\n#main function where the program will start executing\r\ndef main():\r\n    print(\"This session will be logged\\n\")\r\n    #insert device ip you want to telnet to \r\n    HOST = \"192.168.1.1\"\r\n    user = input(\"Enter your telnet username: \")\r\n    #password = getpass.getpass()\r\n\r\n    #telnet to HOST\r\n    #tn = telnetlib.Telnet(HOST)\r\n\r\n    #tn.read_until(\"Username: \")\r\n    #tn.write(user + \"\\n\")\r\n    #if password:\r\n    #    tn.read_until(\"Password: \")\r\n    #    tn.write(password + \"\\n\")\r\n    password = input(\"insert telnet password: \")\r\n     \r\n    #log\r\n    logging.info('user login with username: '+user)\r\n\r\n    #a menue screen to choose an operation\r\n    print(\"\\nYou have the following choices: \\n1- Query about existing VLAN\\n2- Update VLAN\\n3- Create VLAN\\n4- Delete VLAN\")\r\n    inx=input(\"\\n Please insert your choice: \")\r\n    numbers_to_vlan(inx)\r\n    \r\n\r\nif __name__ == \"__main__\":\r\n    main()","sub_path":"VLAN sync.py","file_name":"VLAN sync.py","file_ext":"py","file_size_in_byte":5408,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"236837111","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Wed Apr 18 15:23:12 2018\r\n\r\n@author: Dany\r\n\"\"\"\r\n\r\nfrom setuptools import setup, find_packages\r\n\r\nfrom codecs import open\r\nfrom os import path\r\n\r\nhere = path.abspath(path.dirname(__file__))\r\n\r\n\r\nwith open(path.join(here, 'README.md'), encoding='utf-8') as f:\r\n    long_description = f.read()\r\n\r\nsetup(\r\n    \r\n    name='Lstm variationnal Auto-encoder', \r\n    version='1.1.0',  \r\n    url='https://github.com/pypa/sampleproject',  \r\n    author='Danyleb',  \r\n    classifiers=[  \r\n     \r\n        'Development Status :: 3 - Alpha',\r\n        'License :: OSI Approved :: MIT License',\r\n        \r\n        'Programming Language :: Python :: 2.7',\r\n        'Programming Language :: Python :: 3',\r\n        'Programming Language :: Python :: 3.4',\r\n        'Programming Language :: Python :: 3.5',\r\n        'Programming Language :: Python :: 3.6',\r\n        \r\n        'Topic :: Scientific/Engineering :: Artificial Intelligence',\r\n        \r\n    ],\r\n\r\n    packages=find_packages('.', include=['LstmVAE', 'LstmVAE.*']),  \r\n\r\n    project_urls={  \r\n        'Bug Reports': 'https://github.com/pypa/sampleproject/issues',\r\n        'Funding': 'https://donate.pypi.org',\r\n        'Say Thanks!': 'http://saythanks.io/to/example',\r\n        'Source': 'https://github.com/pypa/sampleproject/',\r\n    },\r\n)\r\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1320,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"349657569","text":"import MySQLdb\nfrom pymongo import MongoClient\nimport datetime\nimport pymongo\n\nclient = None\n\n\ndef getMongoConnection():\n    client = MongoClient('mongodb://angie_app:angiePasswordDB@104.248.128.65/angie_app')\n    return client\n\n\ndef getMysqlConnection():\n    db = MySQLdb.connect(host=\"104.248.128.65\",  # your host, usually localhost\n                         user=\"angie_app\",  # your username\n                         passwd=\"angiePasswordDB\",  # your password\n                         db=\"angie_app\")\n\n    db.set_character_set('utf8mb4')\n    dbc = db.cursor()\n    dbc.execute('SET NAMES utf8mb4;')\n    dbc.execute('SET CHARACTER SET utf8mb4;')\n    dbc.execute('SET character_set_connection=utf8mb4;')\n\n    return db\n\n\ndef getCampaign(campaignId):\n    if campaignId != False:\n        row = fetchOne(\n            \"select username,id_user,id_campaign,timestamp,id_account_type from campaign where id_campaign=%s\",\n            campaignId)\n        return row\n    else:\n        return None\n\n\ndef postWasLikedInThePast(linkCode, id_user):\n    client = getMongoConnection()\n    db = client.angie_app\n\n    row = db.bot_action.find_one(\n        {\"post_link\": linkCode, \"id_user\": id_user, \"bot_operation\": {\"$regex\": \"^like_engagement_\"}})\n    client.close()\n\n    if row == None:\n        return False\n\n    return True\n\n\ndef userWasFollowedInThePast(user_name, id_user):\n    client = getMongoConnection()\n    db = client.angie_app\n\n    row = db.bot_action.find_one({\"username\": user_name, \"id_user\": id_user, \"bot_operation\": {\"$regex\": \"^follow\"}})\n    client.close()\n\n    if row == None:\n        return False\n\n    return True\n\n\ndef getWebApplicationUser(id_user):\n    if id_user != False:\n        row = fetchOne(\"select * from users where id_user=%s\", id_user)\n        return row\n    else:\n        return False\n\n\ndef fetchOne(query, *args):\n    db = getMysqlConnection()\n    cur = db.cursor(MySQLdb.cursors.DictCursor)\n    cur.execute(query, args)\n    db.close()\n    return cur.fetchone()\n\n\ndef select(query, *args):\n    db = getMysqlConnection()\n    cur = db.cursor(MySQLdb.cursors.DictCursor)\n    cur.execute(query, args)\n    rows = cur.fetchall()\n    db.close()\n    return list(rows)\n\n\ndef insert(query, *args):\n    db = getMysqlConnection()\n    cur = db.cursor()\n    cur.execute(query, args)\n    db.commit()\n    id = cur.lastrowid\n    db.close()\n    return id\n\n\ndef updateCampaignChekpoint(key, value, id_campaign):\n    query = 'INSERT INTO campaign_checkpoint (id_campaign, _key, value, timestamp) VALUES(%s, %s, %s, CURDATE()) ON DUPLICATE KEY UPDATE  value=%s'\n\n    id = insert(query, id_campaign, key, value, value)\n\n    return id\n\n\ndef getCampaignWorkingDays(id_campaign):\n    client = getMongoConnection()\n\n    db = client.angie_app\n    pipeline = [{\"$match\": {\"id_campaign\": id_campaign}},\n                {\"$group\": {\"_id\": {\"year\": {\"$year\": \"$timestamp\"}, \"month\": {\"$month\": \"$timestamp\"},\n                                    \"day\": {\"$dayOfMonth\": \"$timestamp\"}}, \"count\": {\"$sum\": 1}}}]\n\n    client.close()\n    return len(list(db.bot_action.aggregate(pipeline=pipeline)))\n\n\ndef revertBotFollow(recordToUnfollow, lastBotAction):\n    client = getMongoConnection()\n    db = client.angie_app\n    db.bot_action.update({\"_id\": recordToUnfollow}, {\"$set\": {\"bot_operation_reverted\": lastBotAction}})\n    client.close()\n\n\ndef createUnfollowCycle(id_campaign, activeFollowings):\n\n    cycle = fetchOne(\"select * from bot_unfollow_cycle where completed=0 and id_campaign=%s\", id_campaign)\n    if cycle is None:\n        insert(\"INSERT INTO bot_unfollow_cycle (id_campaign, angie_followings, completed ) VALUES(%s, %s, 0)\", id_campaign, activeFollowings)\n        return True\n    return False\n\n\ndef getActiveFollowings(id_campaign, queryDate):\n    client = getMongoConnection()\n    db = client.angie_app\n    result = db.bot_action.find({\"id_campaign\": int(id_campaign), \"bot_operation_reverted\": None, \"status\": True,\n                                 \"bot_operation\": {\"$regex\": \"^follow\"}, \"timestamp\": {\"$lte\": queryDate}})\n    if result is None:\n        return 0\n\n    activeFollowings = result.count()\n    client.close()\n\n    return activeFollowings\n\ndef getAmountOperations(campaign, dateParam, operation):\n    gte = dateParam.replace(minute=0, hour=0, second=0, microsecond=0)\n    lte = dateParam.replace(minute=59, hour=23, second=59, microsecond=59)\n\n    client = getMongoConnection()\n    db = client.angie_app\n\n    result = db.bot_action.find({\"id_campaign\": campaign['id_campaign'], \"status\":True, \"bot_operation\": {\"$regex\": \"^\" + operation},\n                                 \"timestamp\": {\"$gte\": gte, \"$lte\": lte}})\n    client.close()\n\n    if result == None:\n        return 0\n\n    return result.count()\n\n\ndef getUserToUnfollow(id_campaign, olderThan):\n    #return True\n    # TODO: unfollow only users who did not follow back\n    # selectFollowings = \"select * from bot_action where  bot_operation like %s and timestamp< (NOW() - INTERVAL %s HOUR) and id_user= %s and bot_operation_reverted is null ORDER BY - follow_back desc, timestamp asc limit %s\"\n    # recordToUnfollow = fetchOne(selectFollowings, 'follow' + '%', userWantsToUnfollow['value'],\n    #                            self.campaign['id_user'], 1)\n\n    currentDate = datetime.datetime.now()\n    queryDate = currentDate - datetime.timedelta(hours=int(olderThan))\n    lte = queryDate.replace(minute=59, hour=23, second=59, microsecond=59)\n\n    client = getMongoConnection()\n    db = client.angie_app\n\n    result = db.bot_action.find_one(\n        {\"id_campaign\": id_campaign, \"bot_operation_reverted\": None, \"status\":True, \"bot_operation\": {\"$regex\": \"^follow\"},\n         \"timestamp\": {\"$lte\": lte}}, sort=[(\"timestamp\", pymongo.ASCENDING)])\n    client.close()\n\n    return result\n\n\ndef insertBotAction(*args):\n    client = getMongoConnection()\n    db = client.angie_app\n\n    _id = db.bot_action.insert({\n        \"id_campaign\": args[0],\n        \"id_user\": args[1],\n        \"instagram_id_user\": args[2],\n        \"full_name\": args[3],\n        \"username\": args[4],\n        \"user_image\": args[5],\n        \"post_id\": args[6],\n        \"post_image\": args[7],\n        \"post_link\": args[8],\n        \"bot_operation\": args[9],\n        \"bot_operation_value\": args[10],\n        \"bot_operation_reverted\": None,\n        \"id_log\": args[11],\n        \"status\":args[12],\n        \"timestamp\": datetime.datetime.now(),\n    })\n\n    client.close()\n    return _id\n\n\ndef insertOwnFollower(*args):\n    query = \"insert into own_followers (id_user,instagram_id_user,full_name,username,user_image,is_verified,timestamp) \" \\\n            \" VALUES (%s,%s,%s,%s,%s,%s,now()) ON DUPLICATE KEY UPDATE instagram_id_user=instagram_id_user\"\n\n    id = insert(query, *args)\n    return id\n\n\ndef insertUserFollower(*args):\n    query = \"insert into instagram_user_followers (fk,instagram_id_user,full_name,username,user_image,is_verified,timestamp) \" \\\n            \" VALUES (%s,%s,%s,%s,%s,%s,now()) ON DUPLICATE KEY UPDATE instagram_id_user=instagram_id_user\"\n\n    id = insert(query, *args)\n    return id\n\n\ndef getBotIp(bot, id_user, id_campaign, is_bot_account):\n    query = \"select ip,type from  campaign left join ip_bot on campaign.id_ip_bot=ip_bot.id_ip_bot where id_campaign=%s\"\n\n    result = fetchOne(query, id_campaign)\n\n    if result is None or result['ip'] is None:\n        bot.logger.warning(\"getBotIp: Could not find an ip for user %s\", id_user)\n        raise Exception(\"getBotIp: Could not find an ip for user\" + str(id_user))\n\n    bot.logger.info(\"User %s, has ip: %s\" % (id_user, result['ip']))\n    return result\n","sub_path":"instapy/api_db.py","file_name":"api_db.py","file_ext":"py","file_size_in_byte":7555,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"587234948","text":"numbers = int(input())\nfor number in range(1, numbers+1):\n    count = input()\n    card_list = [0]*10\n    card_numbers = input()\n    for card_number in card_numbers:\n        for i in range(10):\n            if int(card_number) == i:\n                card_list[i] += 1\n    max_card = max(card_list)\n    for i in range(9,-1,-1):\n        if max_card == card_list[i]:\n            da_number = i\n            break\n   \n    print('#{} {} {}'.format(number, da_number ,max_card))\n\n\n","sub_path":"Algorithm_list_minwon_problem/card_of_number.py","file_name":"card_of_number.py","file_ext":"py","file_size_in_byte":470,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"220602752","text":"#import libraries\nimport numpy as np\nimport pandas as pd\nfrom flask import Flask, render_template,request\nimport pickle#Initialize the flask App\n\napp = Flask(__name__)\nmodel = pickle.load(open('model.pkl', 'rb'))\n#default page of our web-app\n@app.route('/')\ndef home():\n    \n\n    return render_template('Classification.html')\n\n#To use the predict button in our web-app\n@app.route('/predict',methods=['POST'])\ndef predict():\n    #For rendering results on HTML GUI\n    try:\n        print(request.form.values())\n        int_features = [x for x in request.form.values()]\n        final_features = [np.array(int_features)]\n\n\n        dataset = pd.read_csv(\"Data//demo_dataset.csv\")\n        id = int(final_features[0])\n        print(type(id))\n    \n\n        #############################\n        \n\n\n        #dataset = pd.read_csv(\"demo_dataset.csv\")\n\n        #id = 430751654  After 30 days\n\n        dataset = dataset.loc[dataset['encounter_id'] == id]\n        encounterID = dataset['encounter_id'].values\n        patient_nbr = dataset['patient_nbr'].values\n        payer_code = dataset['payer_code'].values\n        gender = dataset['gender'].values\n        age = dataset['age'].values\n        time = dataset['time_in_hospital'].values\n\n        # dropping unwanted columns\n        dataset = dataset.drop(['encounter_id','patient_nbr','weight','payer_code','medical_specialty','diag_1','diag_2','diag_3'],axis=1)\n        \n\n        # replacing values\n        dataset[\"race\"].replace({\"?\":\"Unknown\"}, inplace=True)\n\n        # removing rows that contains 'Unknown/Invalid' for gender column\n        dataset = dataset[dataset.gender != 'Unknown/Invalid']\n\n        # data encoding\n\n        dataset['race'] = dataset['race'].map({'Caucasian':0, 'AfricanAmerican':1, 'Asian':2, 'Hispanic':3, 'Other':4, 'Unknown':5})\n        dataset['gender'] = dataset['gender'].map({'Male':1,'Female':0})\n        dataset['age'] = dataset['age'].map({'[0-10)':1,'[10-20)':2, '[20-30)':3, '[30-40)':4, '[40-50)':5, '[50-60)':6, \n                                            '[60-70)':7, '[70-80)':8, '[80-90)':9, '[90-100)':10})\n\n        # data encoding (col 16-38)\n\n        for col in dataset.iloc[:,16:39]:\n            dataset[col] = dataset[col].map({'No':0, 'Steady':1, 'Up':2, 'Down':3})\n\n        # data encoding\n        dataset['change'] = dataset['change'].map({'No':0, 'Ch':1})\n        dataset['diabetesMed'] = dataset['diabetesMed'].map({'No':0, 'Yes':1})\n\n\n        dataset['max_glu_serum'] = dataset['max_glu_serum'].map({'None':0, '>300':1, 'Norm':2, '>200':3})\n        dataset['A1Cresult'] = dataset['A1Cresult'].map({'None':0, '>7':1, '>8':2, 'Norm':3})\n\n\n        dataset = dataset.drop(['repaglinide','nateglinide','chlorpropamide','acetohexamide','tolbutamide','miglitol','troglitazone',\n        'tolazamide','examide','citoglipton','glyburide-metformin','glipizide-metformin','glimepiride-pioglitazone',\n        'metformin-rosiglitazone','metformin-pioglitazone'],axis=1)\n\n\n        #############################\n\n        prediction = model.predict(dataset)\n\n        if prediction == 0:\n            prediction_text_val=' not Re-admit'\n        \n        elif prediction == 1:\n            prediction_text_val=' re-admit after 30 days'\n        \n        elif prediction == 2:\n            prediction_text_val=' re-admit before 30 days'\n\n        #return render_template('Classification.html', prediction_text=prediction_text_val.format(prediction))\n\n    except:\n            return render_template('Classification.html', prediction_text='Invalid Encounter ID')\n    #output = round(prediction[0] ) \n\n    print(type(encounterID))\n\n    return render_template('Classification.html', prediction_text='Patient might {}'.format(prediction_text_val),\n                                                  encounterID='Encounter ID: {}'.format(encounterID[0]),\n                                                  patient_nbr='Patient No: {}'.format(patient_nbr[0]),\n                                                  payer_code='Payer code: {}'.format(payer_code[0]),\n                                                  gender='Gender: {}'.format(gender[0]),\n                                                  age='Age: {}'.format(age[0]),\n                                                  time='Time in Hospital: {} days'.format(time[0]))\nif __name__ == \"__main__\":\n    app.run(debug=True)\n\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":4344,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"170340354","text":"import base64\nimport random\nimport string\nimport time\nimport math\nimport paho.mqtt.client as paho\nimport json\n\nbroker=\"broker.hivemq.com\"\n\ntext_channel= 'ideahacks2019_200_text'\nimage_channel= 'ideahacks2019_200_images'\n\nimage = 'image.jpg'\nimage = 'twitter.png'\npacket_size=3000\n#define callback\ndef on_message(client, userdata, message):\n    time.sleep(1)\n    print(\"received message =\",str(message.payload.decode(\"utf-8\")))\n\ndef convertImageToBase64(image):\n    with open(image, \"rb\") as image_file:\n        encoded = base64.b64encode(image_file.read())\n        print(type(encoded))\n        return encoded\n\ndef randomword(length):\n    return ''.join(random.choice(string.ascii_lowercase) for i in range(length))\n\ndef publishEncodedImage(encoded):\n\tdata = encoded.decode('utf-8')\n\tend = packet_size\n\tstart = 0\n\tlength = len(data)\n\tpicId = 0\n\tpos = 1\n\tno_of_packets = math.ceil(length/packet_size)\n\n\twhile pos <= no_of_packets:\n\t\t#data = {\"data\": encoded[start:end], \"pic_id\":picId, \"pos\": pos, \"size\": no_of_packets}\n\t\tdump = {\"data\": str(data[start:end]), \"pic_id\":picId, \"pos\": pos, \"size\": no_of_packets}\n\t\t#print(json.dumps(data))\n\t\tclient.publish(image_channel, json.dumps(dump))\n\t\t#print(encoded[start:end])\n\t\tend += packet_size\n\t\tstart += packet_size\n\t\tpos += 1\n\nclient= paho.Client(\"client-001\") #create client object client1.on_publish = on_publish #assign function to callback client1.connect(broker,port) #establish connection client1.publish(\"house/bulb1\",\"on\")\n######Bind function to callback\nclient.on_message=on_message\n#####\nprint(\"connecting to broker \",broker)\nclient.connect(broker)#connect\nclient.loop_start() #start loop to process received messages\nprint(\"subscribing \")\nclient.subscribe(text_channel)#subscribe\ntime.sleep(1)\nprint(\"publishing \")\nclient.publish(text_channel,\"yoooo\")#publish\ntime.sleep(1)\n\nbase64 = convertImageToBase64(image)\npublishEncodedImage(base64)\n\nclient.disconnect() #disconnect\nclient.loop_stop() #stop loop\n","sub_path":"ideaHacks/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":1959,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"90012638","text":"\n\nfrom xai.brain.wordbase.nouns._hernia import _HERNIA\n\n#calss header\nclass _HERNIAS(_HERNIA, ):\n\tdef __init__(self,): \n\t\t_HERNIA.__init__(self)\n\t\tself.name = \"HERNIAS\"\n\t\tself.specie = 'nouns'\n\t\tself.basic = \"hernia\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/nouns/_hernias.py","file_name":"_hernias.py","file_ext":"py","file_size_in_byte":238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"295042934","text":"# Name\n\"\"\" 1. ,\n    2. ,\n    3. \"\"\"\nimport time\nimport pandas as pd\nimport os\n\ntry:\n    import matplotlib as mpl\n    # mpl.use('Agg')\n    import matplotlib.pyplot as plt\n    import pylab\n    import seaborn as sns\nexcept:\n    UserWarning\n# from imread import imread\nimport Configfile as config\nfrom TMD_Scales import AA_Group\nimport TMD_Sampling as tmd_sam\nimport TMD_Scales as tmd_sca\nimport TMD_Plotting as plotting\nimport TMD_Objects as tmd_object\n\n# Folder\nfolder_data = config.folder_data\nfolder_results = config.folder_results\nfolder_objects = config.folder_objects\nfolder_rs = config.folder_rs\nfolder_scales = config.folder_scales\nfolder_final_datasets = config.folder_final_datasets\nfolder_sub_location = config.folder_sub_location\nfolder_datasets = config.folder_datasets\n\n# Figure Aestehtics/Stzles\nsns.set(color_codes=True)\nsns.set_palette(\"bright\")  # ?\nsns.set_style(\"whitegrid\")\n\n## Get dicitonary of colors (classes Taylor)\npolar_neutral = {aa: \"green\" for aa in ['S', 'T', 'C', 'Q', 'N', 'G']}\nnonpolar = {aa: \"orange\" for aa in ['P', 'M', 'A', 'L', 'I', 'V']}\naromatic = {aa: \"gray\" for aa in ['Y', 'W', 'F']}\nacid = {aa: \"red\" for aa in ['E', 'D']}\nbasic = {aa: \"blue\" for aa in ['H', 'R', 'K']}\nlist_dict = [nonpolar, aromatic, acid, basic]\ncolor_dict = AA_Group.merge_dicts(polar_neutral, list_dict)\nclasses = AA_Group.aa_classes\n\n\n# I Helper function\n# A) Get table of rank selected values\ndef print_scale_rank_selected(df_new, rank=1):\n    \"\"\"Print scale of selected rank\"\"\"\n    folder_norm_scales = os.path.join(folder_scales + 'AA_Scales/Normalized_Scales/')\n    scale_dict = pd.read_excel(folder_norm_scales + 'all_scales_norm.xlsx').to_dict()\n    index = rank - 1\n    print('Scale of normalized values of selected rank')\n    try:\n        description = df_new.loc[index]['Description']\n        scale = df_new.loc[index]['Scale_ID']\n        cat = df_new.loc[index]['Scale Category']\n        sub_cat = df_new.loc[index]['Sub Category']\n        print('  Scale:\\t{}'.format(description))\n        print('  Rank:\\t\\t{}'.format(rank))\n        print('  Category:\\t{}'.format(cat))\n        print('  Sub Cat.:\\t{}'.format(sub_cat))\n        print('AA\\t Normalized Values')\n        print(pd.Series(scale_dict[scale]).sort_values(ascending=False))\n    except:\n        print('  Ranking not in selected features')\n\n\n# B) Get table of position selected values\ndef print_average_scale_position_selected(df_new, raw_position_dict, position=None, save_table=True):\n    \"\"\"Print scale of selected rank\"\"\"\n    folder_norm_scales = os.path.join(folder_scales + 'AA_Scales/Normalized_Scales/')\n    scale_dict = pd.read_excel(folder_norm_scales + 'all_scales_norm.xlsx').to_dict()\n    pos_dic = {}\n    neg_dic = {}\n    pos_cat_list = []\n    neg_cat_list = []\n    n = 0\n    # 1. Get scale_dict for position with neg/pos dif\n    for index in df_new.index.tolist():\n        rank = df_new.loc[index, 'Rank']\n        scale = df_new.loc[index]['Scale_ID']\n        cat = df_new.loc[index]['Scale Category']\n        dif = df_new.loc[index]['Dif']\n        raw_position = raw_position_dict[rank - 1]\n        if position in raw_position:\n            if dif > 0:\n                values = pd.Series(scale_dict[scale])\n                pos_dic[index] = values\n                pos_cat_list.append(cat)\n            else:\n                values = pd.Series(scale_dict[scale])\n                neg_dic[index] = values\n                neg_cat_list.append(cat)\n    # 1.1 Positive Scales\n    if len(pos_dic) > 0:\n        scale_pos = pd.DataFrame(pos_dic).mean(axis=1)\n        s_pos = scale_pos.sort_values(ascending=False)\n        s_pos_dict = {i: x for i, x in enumerate(s_pos.iteritems())}\n        scale_save = scale_pos\n        n = len(pos_dic)\n    # 1.2 Negative Scales\n    if len(neg_dic) > 0:\n        scale_neg = pd.DataFrame(neg_dic).mean(axis=1)\n        s_neg = scale_neg.sort_values(ascending=False)\n        s_neg_dict = {i: x for i, x in enumerate(s_neg.iteritems())}\n        scale_save = scale_neg\n        n = len(neg_dic)\n    # 1.3 Combination\n    if len(pos_dic) > 0 and len(neg_dic) > 0:\n        s_combi = scale_pos.subtract(scale_neg).sort_values(ascending=False)\n        s_combi_dict = {i: x for i, x in enumerate(s_combi.iteritems())}\n        scale_save = scale_pos.subtract(scale_neg)\n        n = len(pos_dic) + len(neg_dic)\n    # 1.4 Save Combi\n    if save_table == True and n > 0:\n        file_out = folder_results + 'Position_Scale_{}.xlsx'.format(position)\n        n_series = pd.Series({'n': n})\n        scale_save.append(n_series).to_excel(file_out)\n\n    # 2. Giving General Info\n    print('Scale of means of normalized values of selected position')\n    print('  Position:\\t\\t\\t{}'.format(position))\n    print('  n pos features:\\t{}'.format(len(pos_cat_list)))\n    print('  n neg features:\\t{}'.format(len(neg_cat_list)))\n    # 3. Giving Results\n    if len(pos_dic) > 0 and len(neg_dic) > 0:\n        print('Pos Dif        Neg Dif\\t\\tPos - Neg')\n        print('AA   Mean Norm Val  AA\\t\\tAA\\tVal')\n        for i in s_pos_dict:\n            print(s_pos_dict[i][0], '\\t', round(s_pos_dict[i][1], 2), '\\t', round(s_neg_dict[i][1], 2), '\\t',\n                  s_neg_dict[i][0], '\\t\\t', s_combi_dict[i][0], '\\t', round(s_combi_dict[i][1], 2))\n    elif len(pos_dic) > 0:\n        print('Pos Dif')\n        print('AA   Mean Norm Val')\n        for i in s_pos_dict:\n            print(s_pos_dict[i][0], '\\t', round(s_pos_dict[i][1], 2))\n    elif len(neg_dic) > 0:\n        print('Neg Dif')\n        print('AA   Mean Norm Val')\n        for i in s_neg_dict:\n            print(s_neg_dict[i][0], '\\t', round(s_neg_dict[i][1], 2))\n    else:\n        print('Position not in selected features (Just single position possible')\n\n# C) Get list with index of selected raw position\ndef get_rank_for_selected_position(df_new, raw_position_dict, position_table='None', top_th=200):\n    \"\"\"Create list with rank of selected raw position to \"\"\"\n    index_list = []\n    df_new_index_list = df_new.index.tolist()\n    for index in raw_position_dict:\n        if position_table in raw_position_dict[index] and index <= top_th and index in df_new_index_list:\n            index_list.append(index)\n        else:\n            pass\n    rank_list = [x + 1 for x in index_list]\n    return rank_list\n\n\n# D) Get modified sequence for selected TMD\ndef get_modified_sequence(selected_tmd_squence):\n    \"\"\"Insert  whitespace after 5 AAs\"\"\"\n    modified_seq = []\n    for domain in selected_tmd_squence:\n        nmodified_domain = []\n        c = 0\n        for a in domain:\n            c += 1\n            if c % 5 == 0:\n                a_ = a + ' '\n                nmodified_domain.append(a_)\n            else:\n                nmodified_domain.append(a)\n        modified_seq.append(''.join(nmodified_domain))\n    return modified_seq\n\n# II Main functions: Barplot of distribution of features\ndef get_barplot_of_feature_distribution(category_list=None,\n                                        top_th=200,\n                                        sub_cat='None',\n                                        data_set='Sub_small',\n                                        ref='SS_Type_I',\n                                        dif='7.5',\n                                        zoom=None,\n                                        location='None',\n                                        c99_annotation='Off',\n                                        aa_seq=None,\n                                        tmd_size=None,\n                                        tmd_id=None,\n                                        rank_table='None',\n                                        position_table='None',\n                                        save_table=False):\n    \"\"\"Get distribution of locations in significant values\"\"\"\n    # Load DataFrame\n    if location != 'None':\n        name = 'Mean_Filtered/{0}/{0}_cf_{1}_{2}_({3}_12.5).xlsx'.format(ref, ref.replace('_Plasmamembrane', ''),\n                                                                         location, dif)\n        df = pd.read_excel(folder_data + name)\n    else:\n        df = pd.read_excel(folder_data + 'Mean_Filtered/{0}/{0}_cf_{1}_({2}_12.5).xlsx'.format(ref, data_set, dif))\n    if len(df) > 200:\n        length_df = '> 200'\n    else:\n        length_df = len(df)\n    # Get Name of selected tmd for showing positions\n    if tmd_id is not None:\n        selected_tmd = tmd_object.ss_tmd(tmd_id)\n        selected_tmd_name = selected_tmd.entry_name.replace('_HUMAN', '')\n        selected_tmd_length = len(selected_tmd.get_tmd())\n        selected_tmd_squence = [selected_tmd.get_jmd_n(), selected_tmd.get_tmd(), selected_tmd.get_jmd_c()]\n    else:\n        selected_tmd_length = 'Not selected'\n    # Get size of data sets\n    if 'Plasma' not in ref:\n        df_ref = pd.read_csv(folder_datasets + '{}.csv'.format(ref.replace('_Plasmamembrane', '')))\n    else:\n        df_ref = pd.read_csv('{}{}/{}.csv'.format(folder_sub_location, ref.replace('_Plasmamembrane', ''), ref))\n    if location == 'None':\n        df_dataset = pd.read_csv(folder_datasets + '{}.csv'.format(data_set))\n    else:\n        df_dataset = pd.read_csv(\n            '{0}{1}/{1}_{2}.csv'.format(folder_sub_location, ref.replace('_Plasmamembrane', ''), location))\n    length_reference = len(df_ref)\n    length_dataset = len(df_dataset)\n    # Define Length\n    if tmd_size is not None:\n        l_tmd = tmd_size\n    else:\n        length_dict = {'Sub_small': 22,\n                       'Sub_big': 22,\n                       'Non_sub': 22,\n                       'C99_class': 24,\n                       'Notch_class': 21,\n                       'CD44_class': 21,\n                       'SPPL2_class': 26}\n        l_tmd = length_dict[data_set]\n    total_l = l_tmd + 24\n    position_dict = tmd_sam.get_positions(l_tmd=l_tmd)\n    sub_sub_cat = 'None'\n    # Modify sub_sub_cat\n    if sub_cat == 'Conformation A' or sub_cat == 'Conformation B':\n        sub_sub_cat = sub_cat\n        sub_cat = 'Conformation'\n    elif sub_cat == 'Energy A' or sub_cat == 'Energy B':\n        sub_sub_cat = sub_cat\n        sub_cat = 'Energy'\n    else:\n        pass\n\n    # Helper Functions\n    # A1) Cat color dict\n    cat_color_dict = {'Accessible Surface': 'darkcyan',\n                      'Composition': 'orange',\n                      'Conformation': 'green',\n                      'Energy': 'red',\n                      'Flexibility': 'salmon',\n                      'Polarity/Hydrophobicity': 'gold',\n                      'Shape': 'springgreen',\n                      'Structure-Activity': 'lightblue',\n                      'Volume': 'deepskyblue',\n                      'Weight': 'grey',\n                      'Others': 'maroon'}\n    # A2) Sub Sub Cat dict\n    sub_sub_cat_dict = {'Conformation A': ['Beta-Sheet', 'Coil', 'Helix termination',\n                                           'Helix', 'Helix (N-terminal)', 'Helix (C-terminal)'],\n                        'Conformation B': ['Buried', 'Unclassified Conformation Scales',\n                                           'Evolution and Structure', 'Linker',\n                                           'Stability', 'Turn'],\n                        'Energy A': ['Transfer Free Energy (TFE)', 'TFE to surface/lipophilic',\n                                     'TFE to in/hydrophilic', 'Entropy', 'Gibbs Energy',\n                                     'Partition Energies', 'Free Energy'],\n                        'Energy B': ['Non-bonded Energy per Atom', 'Charge',\n                                     'Isoelectric Point', 'Electron-ion Interaction Potential',\n                                     'Polarizability', 'Unclassified Energy Scales']}\n\n    # B) Get value counts for each category\n    def get_value_count(dict):\n        # Modify dict for sub sub list (Delte not used keys)\n        if sub_sub_cat == 'Conformation A':\n            del dict['Buried']\n            del dict['Unclassified Conformation Scales']\n            del dict['Evolution and Structure']\n            del dict['Linker']\n            del dict['Stability']\n            del dict['Turn']\n        elif sub_sub_cat == 'Conformation B':\n            del dict['Beta-Sheet']\n            del dict['Coil']\n            del dict['Helix termination']\n            del dict['Helix']\n            del dict['Helix (N-terminal)']\n            del dict['Helix (C-terminal)']\n        elif sub_sub_cat == 'Energy A':\n            del_list = ['Non-bonded Energy per Atom',\n                        'Charge',\n                        'Isoelectric Point',\n                        'Electron-ion Interaction Potential',\n                        'Polarizability',\n                        'Unclassified Energy Scales']\n            for x in del_list:\n                del dict[x]\n        elif sub_sub_cat == 'Energy B':\n            del_list = ['Transfer Free Energy (TFE)',\n                        'TFE to surface/lipophilic',\n                        'TFE to in/hydrophilic',\n                        'Entropy',\n                        'Gibbs Energy',\n                        'Partition Energies',\n                        'Free Energy']\n            for x in del_list:\n                del dict[x]\n        else:\n            pass\n        series = pd.Series(dict)\n        value_count_list = []\n        cat_list = []\n        non_sub_cat = []\n        for cat, values in series.iteritems():\n            count = pd.Series(values).value_counts()\n            # 2.1 Get dict with positions without count (p0) and 0\n            pos = [x - 1 for x in count.index]  # Correction to 0\n            fill_dict = {}\n            for x in list(range(0, total_l)):\n                if x not in pos:\n                    fill_dict[x + 1] = 0  # Back to normal value\n                else:\n                    pass\n            # 2.2 Fill index and value with p0 and 0 for every p0\n            index = [x - 12 for x in count.index]\n            index.extend([x - 12 for x in fill_dict.keys()])\n            values = [x for x in count.values]\n            values.extend([x for x in fill_dict.values()])\n            # 2.3 Create Series with filled indexes and values\n            value_count = pd.Series(index, index=values)\n            # 2.4 Append lists for value count and cat\n            value_count_list.append(value_count.sort_values())\n            cat_list.append(cat)\n\n            if len(count) == 0:\n                non_sub_cat.append(cat)\n\n        return value_count_list, cat_list, set(non_sub_cat)\n\n    # 1. Get positions dict (cat: extend lists of positions for each category)\n    if sub_cat == 'None':\n        pos_dict, pos_dict_positive, pos_dict_negative = plotting.get_pos_dict(df=df,\n                                                                               position_dict=position_dict,\n                                                                               top_th=top_th)\n    else:\n        pos_dict, pos_dict_positive, pos_dict_negative = plotting.get_pos_dict(df=df,\n                                                                               position_dict=position_dict,\n                                                                               top_th=top_th,\n                                                                               sub_cat=sub_cat)\n\n    # 2. Get value counts for each category (positive & negative)\n    value_count_list, cat_list, non_pos = get_value_count(pos_dict_positive)\n    value_count_list_neg, cat_list, non_neg = get_value_count(pos_dict_negative)\n    non_list = list(non_neg.intersection(non_pos))\n    # 3. Create barplot\n    # 3.1 Set style\n    plt.style.use('ggplot')\n    # plt.style.use('classic')\n    # plt.style.use('seaborn-whitegrid')\n    # plt.style.use('seaborn-darkgrid')\n    ax = plt.subplot(111)\n    barWidth = 0.8\n    start_count = 0\n    # 3.2A Set fontsize and ylim\n    leg_fontsize = 8\n    if sub_cat != 'None':\n        leg_fontsize = 7\n        ylim = (-15, 20)\n    else:\n        if top_th < 20:\n            ylim = (-12, 12)\n        elif top_th < 50:\n            ylim = (-25, 25)\n        elif top_th < 75:\n            ylim = (-35, 35)\n        elif top_th < 100:\n            ylim = (-40, 40)\n        else:\n            leg_fontsize = 7\n            ylim = (-45, 50)\n\n    # 3.2B Zoom in ylim\n    if zoom is not None:\n        ylim = (-zoom, zoom)\n    else:\n        pass\n    # 3.3 Iterate over categories\n    color_legnd = []\n    label_legend = []\n    i_hit = []\n    for i, cat in enumerate(cat_list):\n        # 3.3.1 Choose right categories\n        # I For categories\n        if cat in category_list or sub_cat != 'None':\n            i_hit.append(i)\n            # Start\n            if start_count < 1:\n                bottom = [0] * total_l\n                bottom_neg = [0] * total_l\n                start_count += 1\n            # Contiunue\n            else:\n                bottom_ad = value_count_list[i_hit[-2]].index\n                bottom = [x + y for x, y in zip(bottom, bottom_ad)]\n                # Neg bottom\n                bottom_ad_neg = value_count_list_neg[i_hit[-2]].index\n                bottom_neg = [x - y for x, y in zip(bottom_neg, bottom_ad_neg)]\n            # 3.4a For positive data\n            position = value_count_list[i].values\n            count = value_count_list[i].index\n            # 3.4b For negative data\n            count_neg = [(-x) for x in value_count_list_neg[i].index]\n            # 3.5 Set colors\n            if sub_cat == 'None':\n                color = cat_color_dict[cat]\n            else:\n                color = ['b', 'g', 'orangered', 'cyan', 'lightblue', 'magenta',\n                         'y', 'gold', 'lightgreen', 'deepskyblue',\n                         'salmon', 'maroon', 'lightsalmon', 'orange'][i]\n            color_legnd.append(color)\n            label_legend.append(cat)\n            # 3.6 Remove sub categories from legend with no counts\n            if cat in non_list:\n                label_legend.remove(cat)\n                color_legnd = color_legnd[:-1]  # Removes last item from list\n            else:\n                pass\n            # 3.7 Generate plot\n            if start_count <= 1:\n                ax.bar(position, count, bottom=bottom, color=color,\n                       width=barWidth, edgecolor=['black'] * len(position),\n                       linewidth=0.5)  # Set edgecolor explecitly (circumven issue)\n                ax.bar(position, count_neg, bottom=bottom_neg, color=color,\n                       width=barWidth, edgecolor=['black'] * len(position), linewidth=0.5)\n                ax.set_ylim(ylim)\n                ax.set_xlim(-15, 43)\n                start_count += 1\n            else:\n                ax.bar(position, count, bottom=bottom, color=color,\n                       width=barWidth, edgecolor=['black'] * len(position), linewidth=0.5)\n                ax.bar(position, count_neg, bottom=bottom_neg, color=color,\n                       width=barWidth, edgecolor=['black'] * len(position), linewidth=0.5)\n                ax.set_ylim(ylim)\n                ax.set_xlim(-15, 43)\n\n    # 4. Modify plot\n    plt.ylabel(\"Number of Occurrence for Scales with -/+  Differences\", fontsize=9, fontweight='bold')\n    plt.xlabel('Amino Acid Positions in TMD with {} AA'.format(l_tmd), fontsize=11, fontweight='bold')\n    # Label for TMD\n    plt.axvline(x=1, ymax=0.08, color='greenyellow', linewidth=4, linestyle='solid', label='TMD N-Terminus')\n    plt.axvline(x=l_tmd, ymax=0.08, color='olive', linewidth=4, linestyle='solid', label='TMD C-Terminus')\n    # Label for JMD\n    plt.axvline(x=-11, ymax=0.05, color='gold', linewidth=4, linestyle='solid', label='JMD N-Terminus')\n    plt.axvline(x=l_tmd + 12, ymax=0.05, color='orange', linewidth=4, linestyle='solid', label='JMD C-Terminus')\n    # Set size of ylim\n    # Get title\n    ref = ref.replace('SS_TMD', 'SS-TMDs').replace('SS_Type', 'SS-TMDs Type') \\\n        .replace('Plasmamembrane', '(Plasmem.)').replace('_', ' ')\n    if location == 'None':\n        title = 'Feature Distribution: {} cf. {}'.format(data_set, ref)\n        fsize = 10\n    else:\n        title = 'Feature Distribution: {0} from {1} cf. {0}'.format(ref, location)\n        if len(title) > 75:\n            title = title.replace('cf.', '\\ncf.')\n            fsize = 9\n        elif len(title) > 60:\n            fsize = 9\n        else:\n            fsize = 10\n    plt.title(title, fontsize=fsize, fontweight='bold', color='b')\n    # 4.2 Set information to Plot\n    # 4.2.1 Info if location has no features\n    if len(df) == 0:\n        plt.text(0.9, 0.5, 'Selected Location has \\nno significant Feature\\nfor this Reference',\n                 fontsize=12, color='r', fontweight='bold')\n\n    # 4.3A Get Legend 1: TMD JMD\n    plt.style.use('classic')\n    # Set Position of legend\n    loc = 3\n    anchor = (0.12, 0.2)\n    legend1 = plt.legend(loc=loc, frameon=True,\n                         framealpha=1, fontsize=8,\n                         bbox_to_anchor=anchor,\n                         bbox_transform=plt.gcf().transFigure)\n    plt.gca().add_artist(legend1)\n\n    # 4.3B Get Legend 2: Scale Categories\n    # Set Position of legend\n    loc = 2\n    ncol = 2\n    handles = []\n    for i, color in enumerate(color_legnd):\n        x = mpl.patches.Patch(color=color, label=label_legend[i])\n        handles.append(x)\n\n    plt.legend(handles=handles,\n               loc=loc, ncol=ncol,\n               fancybox=True, frameon=True,\n               framealpha=1, fontsize=leg_fontsize)\n\n    # 5. Plot\n    plt.tight_layout()\n    plt.show()\n    if save_table is not True:\n        plt.show()\n    else:\n        pass\n    mpl.rcParams.update(mpl.rcParamsDefault)\n\n    # Modify filtered DataFrame\n    # A) Set aa_seq to show\n    aa_col_list = []\n    for aa in aa_seq:\n        if aa == 'Notch':\n            aa_col_list.append('Sub: Notch1')\n        elif aa == 'DAP12':\n            aa_col_list.append('Non-Sub: DAP12')\n        elif aa == 'C99':\n            aa_col_list.append('Sub: C99')\n        else:\n            pass\n    # 6.1 Set length of DataFrame\n    if len(df) == 0:\n        l = 0\n    elif len(df) == 1:\n        l = 1\n    else:\n        if len(df) < top_th:\n            l = len(df) - 2\n        else:\n            l = top_th - 2\n    # 6.2 Modify AA_Patern & Positions of DataFrame (for adjusted length)\n    raw_position_dict = {}\n    for i, split in enumerate(df.loc[:, 'Split']):\n        part = df.loc[i, 'Part']\n        values = position_dict[part][split]\n        if c99_annotation == 'On':\n            new_pos = [int(x) + 16 for x in values]  # Consistent with C99 annotation\n        else:\n            new_pos = [int(x) - 12 for x in values]  # Consistent with profile\n        # 6.2.1 Get Raw Positions of tmd for print_average_scale_position\n        raw_position_dict[i] = new_pos\n        # 6.2.2 Get AA of selected TMDs & Include '-'\n        if tmd_id is not None:\n            aa_selected = selected_tmd.get_position(part, split)\n            if len(new_pos) > 1 and type(aa_selected) is str:  # Filter for NaN (float type)\n                gap = abs(int(new_pos[0]) - int(new_pos[1]))\n                if gap > 1:\n                    aa_selected = '-'.join([str(x) for x in aa_selected])\n            df.loc[i, selected_tmd_name] = aa_selected\n        else:\n            pass\n        # 6.2.3 Include '-' if pattern with gaps for default tmds\n        for aa_col in aa_col_list:\n            aa = df.loc[i, aa_col]\n            if len(new_pos) > 1 and type(aa) is str:  # Filter for NaN (float type)\n                gap = abs(int(new_pos[0]) - int(new_pos[1]))\n                if gap > 1:\n                    aa = '-'.join([str(x) for x in aa])\n            df.loc[i, aa_col] = aa\n        # 6.2.4 Set ... if high gap\n        if len(new_pos) > 2:\n            dif_new_pos = max(new_pos) - min(new_pos)\n            if len(new_pos) == 4 and dif_new_pos < len(new_pos):\n                new_pos = '[{},...,{}]'.format(new_pos[0], new_pos[-1])\n            elif len(new_pos) > 4:\n                new_pos = '[{},{},{},...,{}]'.format(new_pos[0], new_pos[1], new_pos[2], new_pos[-1])\n        # 6.2.4 Fill new position to DataFrame\n        df.loc[i, 'Position'] = str(new_pos)\n\n    # 6.3 Round Dif, Std, Rank_Value\n    round_list = ['Dif', 'Std', 'Rank_Value']\n    for key in round_list:\n        df.ix[:, key] = df.ix[:, key].apply(lambda x: round(x, 2))\n    # 6.4 Filter shown features using selected categories or sub categories\n    sub_cat_special = ['Conformation A', 'Conformation B', 'Energy A', 'Energy B']\n    if sub_cat == 'None':\n        df_new = df.loc[df['Scale Category'].isin(category_list)]\n        categories = category_list\n    elif sub_sub_cat in sub_cat_special:\n        categories = sub_sub_cat\n        sub_list = sub_sub_cat_dict[sub_sub_cat]\n        df_new = df.loc[df['Sub Category'].isin(sub_list)]\n    else:\n        categories = sub_cat\n        df_new = df.loc[df['Scale Category'].isin([sub_cat])]\n    # 6.5 Get Final DataFrame\n    categories = str(categories).replace('[', '').replace(']', '').replace('\\'', '')\n    if location != 'None':\n        data_set = '{} from {}'.format(ref, location)\n    else:\n        pass\n\n    if tmd_id is None:\n        columns = ['Rank', 'Dif', 'Std', 'Rank_Value', 'Scale Category'] + \\\n                  [x for x in aa_col_list] + ['Position', 'Description']\n    else:\n        columns = ['Rank', 'Dif', 'Std', 'Rank_Value', 'Scale Category'] + \\\n                  [x for x in aa_col_list] + [selected_tmd_name] + \\\n                  ['Position', 'Description']\n    if l > 0:\n        df_final = df_new.loc[:l, columns]\n        # rank_scale_dict = df_new.loc[:l, ['Scale_ID', 'Description', 'Scale Category', 'Sub Category']]\n\n    else:\n        df_final = []\n    # 7. Print Final DataFrame\n    print('Features for {} with {} as reference'.format(data_set, ref))\n    print('  n Reference Data set: \\t{}'.format(length_reference))\n    print('  n Test Data set: \\t\\t\\t{}'.format(length_dataset))\n    print('  Mininmal Difference: \\t\\t{}%'.format(dif))\n    print('  n(total) Features: \\t\\t{}'.format(length_df))\n    print('  n(selected) Features: \\t{}'.format(len(df_final)))\n    print('  Scale Categories: \\t\\t{}'.format(categories))\n    if len(df_final) > 0 and position_table == 'None':\n        print(df_final.to_string(index=False), '\\n')  # Print without index & as proper table\n    elif len(df_final) > 0 and position_table != 'None':\n        rank_list = get_rank_for_selected_position(df_new,\n                                                   raw_position_dict,\n                                                   position_table=position_table,\n                                                   top_th=top_th)\n        print(df_final.loc[df_final['Rank'].isin(rank_list)]\n              .to_string(index=False), '\\n')\n    else:\n        print('Selected Location has no significant Feature for this Reference and Scales Categories\\n')\n    # 8. Print info to selcted TMD\n    if tmd_id is not None:\n        modified_seq = get_modified_sequence(selected_tmd_squence)\n        print('Selected TMD: \\t\\t{}'.format(selected_tmd_name))\n        print('Length of TMD: \\t\\t{}'.format(selected_tmd_length))\n        print('Sequence of TMD: \\t{}\\n'.format(modified_seq))\n    # 9. Print AA Tables for discussion of mutations\n    # 9.1 Print rank selected norm table\n    if rank_table is not 'None' and l > 0:\n        print_scale_rank_selected(df_new.loc[:l], rank=rank_table)\n    # 9.2\n    if position_table is not 'None' and l > 0:\n        print_average_scale_position_selected(df_new.loc[:l],\n                                              raw_position_dict,\n                                              position=position_table,\n                                              save_table=save_table)\n    plt.close()\n# III Minor/Summary Functions\n\n# IV Test Functions\ndef main():\n    t0 = time.time()\n    get_barplot_of_feature_distribution(\n        category_list=['Accessible Surface', 'Volume', 'Shape', 'Weight', 'Composition'],\n        top_th=200,\n        data_set='Sub_small',\n        sub_cat='None',\n        aa_seq=['DAP12', 'C99'],\n        ref='SS_TMD_Plasmamembrane',\n        rank_table='None',\n        tmd_size=26,\n        # position_table=18,\n        tmd_id='P04233')\n    t1 = time.time()\n    print(\"Time:\", t1 - t0)\n    pass\n\n\nif __name__ == \"__main__\":\n    main()","sub_path":"TMD_Plotting1.py","file_name":"TMD_Plotting1.py","file_ext":"py","file_size_in_byte":28139,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"362862670","text":"#These information comes from Twitter API\n#Create a Twitter Account and Get These Information from apps.twitter.com\n\n#Create your own Twitter API keys and paste them here\n\nconsumer_key = ''\nconsumer_secret = ''\naccess_token = ''\naccess_secret = ''\n\n\n","sub_path":"[TASK 2] Sentiment Analysis/twitter_config.py","file_name":"twitter_config.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"4527369","text":"\"\"\"Moved page_url -> url\n\nRevision ID: cdc93dcb9768\nRevises: 0f832bad0dd5\nCreate Date: 2021-02-10 10:53:31.779015\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = 'cdc93dcb9768'\ndown_revision = '0f832bad0dd5'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    # op.add_column('pages', sa.Column('url', sa.String(), nullable=False))\n    # op.drop_column('pages', 'page_url')\n    op.alter_column('pages', 'page_url', new_column_name='url')\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    # op.add_column('pages', sa.Column('page_url', sa.VARCHAR(), autoincrement=False, nullable=False))\n    # op.drop_column('pages', 'url')\n    op.alter_column('pages', 'url', new_column_name='page_url')\n    # ### end Alembic commands ###\n","sub_path":"API/alembic/versions/cdc93dcb9768_moved_page_url_url.py","file_name":"cdc93dcb9768_moved_page_url_url.py","file_ext":"py","file_size_in_byte":929,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"10814452","text":"import json\nimport pytz\nimport warnings\nimport logging\n\nimport pandas as pd\nimport requests\nimport datetime as dt\n\nlogger = logging.getLogger(__name__)\n\n\nclass StellarData:\n    \"\"\"\n    Get data from Stellar API. Set the authentication in my_auth_config.py\n    \"\"\"\n\n    def __init__(self,\n                 system: dict,\n                 parameters: str,\n                 t_interval: str,\n                 save_to: str,\n                 token: str,\n                 batch_size_days=20):\n        \"\"\"\n        Makes a call to Stellar API\n        :param system: dict, this is the power_system dictionary as returned by INI config\n        :param parameters: str, see the API documentation for available parameters:\n        ex: 'batteryChargePower,solarPower,loadPower'. If it's not available returns empty\n        :param t_interval: str, the resolution of the call.\n        ex: '1-mins', '5-mins'\n        :param save_to: str, file path to save to. If you don't want to save pass ''\n        :param batch_size_days: int, the number of days to get at once. Too many days results in timeout\n        :param token: str, the stellar token\n        :return: relevant data (pd.DataFrame)\n        \"\"\"\n\n        self.system = system\n        self.parameters = parameters\n        self.token = token\n        self.t_interval = t_interval\n        self.save_to = save_to\n        self.batch_size_days = batch_size_days\n\n        self._get_cookies()\n\n    def _get_cookies(self):\n        \"\"\"Get the cookies by setting the organization\n        \"\"\"\n        post_url = 'https://stellar.newsunroad.com/api/v0/setOrganization/{}'.format(self.system['org'])\n\n        post_r = requests.post(post_url,\n                               headers={'Authorization': 'token ' + self.token}\n                               )\n\n        self._my_cookies = post_r.cookies.get_dict()\n\n    @staticmethod\n    def to_pandas_dataframe(d_dict):\n        \"\"\"Convert the dictionary into a dataframe\"\"\"\n        files = pd.DataFrame.from_dict(d_dict, orient='columns')\n        if files.empty:\n            return files\n        files['time'] = pd.to_datetime(files.time, infer_datetime_format=True)\n        files.set_index('time', inplace=True)\n        logger.info('Data received')\n        return files\n\n    @staticmethod\n    def parse_response(response, d_dict):\n        \"\"\"Parse the response from the HTTP request\"\"\"\n        try:\n            if response.status_code == 200:\n                response_json = response.json()\n                for entry in response_json['data']:\n                    for rel_dict in entry['timeSeries']:\n                        for key in rel_dict.keys():\n                            if key in d_dict.keys():\n                                d_dict[key].append(rel_dict[key])\n                            else:\n                                d_dict[key] = [rel_dict[key]]\n                return d_dict\n\n            else:\n                warnings.warn(response.text)\n                raise ValueError(\"Check the power system name you entered and if the API changed\")\n        except json.decoder.JSONDecodeError:\n            warnings.warn('JSONDecodeError')\n\n    def get_data(self, t_start: dt.datetime, t_stop: dt.datetime):\n        \"\"\"\n        Make an API call with the start and end times\n        :param t_start: dt.datetime, time to start the data\n        :param t_stop: dt.datetime, time to stop the data call\n        :return:\n        \"\"\"\n        # Call parameters\n        logger.debug('getting data for {} for {} to {}'.format(self.system[\"site\"], t_start, t_stop))\n        data_dict = dict()\n        while t_start < t_stop:\n            if t_start + dt.timedelta(days=self.batch_size_days) < t_stop:\n                t_end = (t_start + dt.timedelta(days=self.batch_size_days))\n            else:\n                t_end = t_stop\n            logger.debug('{} - {}'.format(t_start, t_end))\n            t_start_str = t_start.strftime('%Y-%m-%dT%H:%M:%SZ')\n            t_end_str = t_end.strftime('%Y-%m-%dT%H:%M:%SZ')\n\n            url = 'https://stellar.newsunroad.com/api/v0/ts/' + self.system['site'] + '?start=' + t_start_str + \\\n                  '&end=' + t_end_str + '&binDuration=' + self.t_interval + '&params=' + self.parameters\n\n            resp = requests.get(url,\n                                cookies=self._my_cookies,\n                                headers={'Authorization': 'token ' + self.token}\n                                )\n            data_dict = self.parse_response(resp, data_dict)\n            t_start = t_end\n\n        final_df = self.to_pandas_dataframe(d_dict=data_dict)\n\n        if self.save_to:\n            self.save_data(final_df, self.save_to)\n\n        return final_df\n\n    def save_data(self, df, file):\n        \"\"\"Save to csv\"\"\"\n\n        def to_utc(data):\n            \"\"\"Make sure it's UTC\"\"\"\n            if data.empty:\n                return data\n            data.index = data.index.tz_localize(pytz.utc)\n            return data\n\n        df = to_utc(df)\n        filename = file.format(self.system['site'])\n        with open(filename, 'w', newline='') as f:\n            df.to_csv(f, header=True)\n            f.close()\n        logger.debug('saved stellar api data to: {}'.format(filename))\n","sub_path":"stellar_client/stellar_data.py","file_name":"stellar_data.py","file_ext":"py","file_size_in_byte":5183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"238081624","text":"# -*- coding:utf-8 -*-\n# -*-- python 3.6 --*-\n\nimport os\nimport re\n\n\ndef traverse(f):\n    fs = os.listdir(f)\n    for f1 in fs:\n        tmp_path = os.path.join(f, f1)\n        if not os.path.isdir(tmp_path):\n            if f1.startswith('.'):\n                print('要删文件: %s' % tmp_path)\n                os.remove(tmp_path)\n        else:\n            print('文件夹：%s' % tmp_path)\n            traverse(tmp_path)\n\n# os.remove(file)\n\n\nif __name__ == '__main__':\n    path = 'C:\\下载\\爬虫课件和代码'\n    traverse(path)\n\n\n","sub_path":"tencent/pianli.py","file_name":"pianli.py","file_ext":"py","file_size_in_byte":536,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"70418566","text":"import socket\nimport logging\nfrom logging.handlers import RotatingFileHandler\nimport os\nfrom datetime import datetime\n\n\nclass LoggingHandler:\n    _logger = None\n\n    def initializeLogger(logger_name):\n\n        # logging.basicConfig(level=os.environ.get(\"LOGLEVEL\", \"INFO\"))\n\n        log_file_path =  logger_name + \"socket.log\"\n\n        l = logging.getLogger(logger_name)\n        \n        log_level = 'DEBUG'\n        formatter = logging.Formatter(\"________________________ %(asctime)s: %(levelname)s: %(message)s\")\n\n        l.setLevel(log_level)\n\n        rotating_file_handler = RotatingFileHandler(filename=log_file_path, mode='a', maxBytes=15728640, backupCount=1)\n        rotating_file_handler.setFormatter(formatter)\n    \n        stream_handler = logging.StreamHandler()\n        stream_handler.setFormatter(formatter)\n\n        l.addHandler(rotating_file_handler)\n        l.addHandler(stream_handler)    \n        LoggingHandler._logger = l\n\n\n    def getLogger(logger_name):\n        if LoggingHandler._logger == None :\n            LoggingHandler.initializeLogger(logger_name)\n        return LoggingHandler._logger\n    \n","sub_path":"level_4/d_comm/logging_handler.py","file_name":"logging_handler.py","file_ext":"py","file_size_in_byte":1120,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"647261456","text":"import os\n\n\ndef FileNameOrSuffix(remove_name):\n\n    if remove_name[0] == '.':\n        return 1\n    else:\n        return 0\n\ndef FindFiles(path, remove_name):\n    lists = []\n    for dir_path, dirs_name, files in os.walk(path):\n        for file in files:\n            if os.path.splitext(file)[FileNameOrSuffix(remove_name)] == remove_name:  # 0 下标是文件名包含, 1下标是后缀名包含\n                absolute_path = dir_path +\"\\\\\" +file\n                lists.append(absolute_path)\n    print(\"文件路径获取成功 \", lists)\n    return lists\n\n\ndef removeFileInFirstDir(path, remove_name):\n    lists = FindFiles(path, remove_name)\n    for index in range(len(lists)):\n        os.remove(lists[index])\n    print('\"文件删除成功\"')\n\n\nif __name__ == '__main__':\n    # file_dir = input(r'请输入路径: ')\n    file_dir = 'E:\\迅雷下载\\premierepro2017_87867'\n    removeList = ['多多软件', '龋齿一号']\n    for i in removeList:\n        removeFileInFirstDir(file_dir, i)\n","sub_path":"批量删除文件.py","file_name":"批量删除文件.py","file_ext":"py","file_size_in_byte":990,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"212902032","text":"import os\nimport numpy as np\nimport pandas as pd\n\nclass rbsSpectra():\n    def __init__(self, file_path, type_file='experimental', load_input=False, **kwarg):\n        self.path = file_path\n        self.type_file = type_file\n        self.m = kwarg.get('m', 0)\n        self.b = kwarg.get('b', 0)\n\n        self.random = kwarg.get('random', None)\n        if self.random != None:\n            self.add_random(random)\n        \n        self.load_file()\n        self.set_name()\n        if load_input:\n            self.get_input()\n        \n    def load_file(self):\n        if self.type_file in 'simulation':\n            dataArray = np.loadtxt(self.path)\n            self.spectra = pd.DataFrame(data=dataArray, columns=['Energy', 'Counts'])\n        elif self.type_file in 'experimental':\n            if '.odf' in self.path:\n                dataArray = np.loadtxt(self.path)\n                self.spectra = pd.DataFrame(data=dataArray, columns=['Channel', 'Counts'])\n            elif '.dat' in self.path:\n                self.spectra = pd.read_csv(self.path, sep = \",\", header=None)\n            else:\n                print('File not supported')\n                return         \n\n\n            if self.m != 0 or self.b != 0:\n                #spectra.columns=['Channel', 'Counts']\n                self.set_calibration(m = self.m, b = self.b)\n            #else:\n                #spectra.columns=['Channel', 'Counts']            \n        \n        \n        \n    def get_spectra(self):\n        return self.spectra\n    \n    def set_name(self, name = None):\n        if name==None:\n            if self.type_file in 'simulation':\n                self.name = self.path.split('/')[-3]\n            else:\n                self.name = self.path.split('/')[-1].split('.')[0]\n        else:\n            self.name = name\n            \n    \n    def set_calibration(self, m = 2.15, b = 86, **kwarg):\n        self.m = m\n        if 'spkview' in kwarg.get('spkview', 'None'):\n            self.b = -m*b\n        else:\n            self.b = b\n        self.spectra['Energy'] = calibrate(self.m, self.b, self.spectra.Channel)\n    \n\n    def get_calibration_file(self, look_subdir = False):\n        for root, dirs, files in os.walk(self.get_folder()):\n            for file in files:\n                if file.endswith(\".cal\"):\n                    self.calibration_file_path = os.path.join(root, file)\n                    return self.calibration_file_path\n            \n            if look_subdir:\n                for d in dirs:\n                    for file in os.listdir(self.get_folder()+'/'+d):\n                        if file.endswith(\".cal\"):\n                            self.calibration_file_path = os.path.join(root,d, file)\n                            return self.calibration_file_path\n            else:\n                break\n        return 'No file found'\n                    \n    def calibrate_from_file(self):\n        a = self.get_calibration_file()\n\n        if a == None:\n            print('No calibration found for %s' %self.path)\n            self.set_calibration()\n        else:\n            calib = pd.read_csv(a, delimiter= '=', index_col=0, header=None)\n            calib[0:2] = calib[0:2].astype(float)\n            self.set_calibration(m=calib.loc['m', 1], b=calib.loc['b', 1], spkview=calib.loc['style', 1])\n        if self.random != None:\n            self.random.calibrate_from_file()\n\n    \n    def barrier(self, threshold = 1000, offset = 15):\n        ds = self.spectra.loc[self.spectra.Energy>threshold]\n        minD = np.gradient(ds.Counts).argmin()\n        xValue = ds.iloc[minD, 0] - offset\n        closest_in_df = (ds.iloc[:, 0] - xValue).abs().argsort().iloc[0]\n        return ds.iloc[closest_in_df]\n    \n    def add_random(self, random):\n        #random is of rbsSpectra type\n        self.random = random\n        \n    def renormalization(self, **kwarg):\n        self.renorm = self.random.barrier(**kwarg).Counts\n        self.random.renorm = self.renorm\n        return self.renorm\n    \n    def get_spectra_random(self, **kwarg):\n        if self.random == None:\n            print('No random spectrum added')\n        else:\n            spectraNormal = self.random.get_spectra().copy()\n            spectraNormal.Counts /=self.renorm\n            return spectraNormal\n\n    def get_spectra_normalized(self, **kwarg):\n        if self.random == None:\n            print('No random spectrum added')\n        else:\n            try:\n                renorm = self.renorm\n            except:\n                renorm = self.renormalization(**kwarg)\n            spectraNormal = self.get_spectra().copy()\n            spectraNormal.Counts = spectraNormal.Counts/renorm\n            return spectraNormal\n        \n    def get_folder(self):\n        if self.type_file in 'simulation':\n            self.path_folder = self.path.split('/out/')[0] + '/'\n            return self.path_folder\n        else:\n            self.path_folder = '/'.join(self.path.split('/')[0:-1])\n            return self.path_folder\n    \n    def get_file_name(self):\n        self.file_name = self.path.split('/')[-1]\n        return self.file_name\n\n    def get_input_path(self):\n        if self.type_file in 'simulation':\n            self.inputFile = self.get_folder() + 'in/input.dat'\n            return self.inputFile\n        else:\n            print('%s is not a simulation' %self.name)\n\n    def parse_input_file(self, **kwarg):\n        data = {}\n        if self.type_file in 'simulation':\n            path = self.get_input_path()\n\n            for l in open(path):\n                if '*' not in l:\n                    if l.strip() != '':\n                        l = l.strip()\n                        prop_name, prop_value = l.split('=')\n                        prop_name = prop_name.strip()\n                        prop_value = prop_value.strip().split()[0]\n                        data[prop_name] = prop_value\n            self.data = data\n            return data\n        else:\n            if kwarg.get('ExpOut', False):\n                print('%s : no input loaded' %self.name)\n    \n    def get_input(self):\n        self.input_parameters = self.parse_input_file()\n        return self.input_parameters\n\n\n\ndef calibrate(m, b, channel, **kwarg):\n    # E = m * c + b\n    #m= 2.50071\n    #b= -40\n    #return m * channel - 2*b\n    return m * channel + b","sub_path":"RBSpy/rbsSpectra.py","file_name":"rbsSpectra.py","file_ext":"py","file_size_in_byte":6265,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"615268208","text":"\"\"\"Test the surface_io module.\"\"\"\nfrom collections import OrderedDict\nimport shutil\nimport logging\nimport pytest\nimport json\nimport numpy as np\nimport xtgeo\nimport yaml\n\nimport fmu.dataio\n\nlogger = logging.getLogger(__name__)\nlogger.setLevel(logging.INFO)\n\n\nCFG = OrderedDict()\nCFG[\"model\"] = {\"name\": \"Test\", \"revision\": \"AUTO\"}\nCFG[\"masterdata\"] = {\n    \"smda\": {\n        \"country\": [\n            {\"identifier\": \"Norway\", \"uuid\": \"ad214d85-8a1d-19da-e053-c918a4889309\"}\n        ],\n        \"discovery\": [{\"short_identifier\": \"abdcef\", \"uuid\": \"ghijk\"}],\n    }\n}\n\nCFG2 = {}\nwith open(\"tests/data/drogon/global_config2/global_variables.yml\", \"r\") as stream:\n    CFG2 = yaml.safe_load(stream)\n\nRUN = \"tests/data/drogon/ertrun1/realization-0/iter-0/rms\"\nCASEPATH = \"tests/data/drogon/ertrun1\"\n\n\ndef test_surface_io(tmp_path):\n    \"\"\"Minimal test surface io, uses tmp_path.\"\"\"\n\n    # make a fake RegularSurface\n    srf = xtgeo.RegularSurface(\n        ncol=20, nrow=30, xinc=20, yinc=20, values=np.ma.ones((20, 30)), name=\"test\"\n    )\n    fmu.dataio.ExportData.export_root = tmp_path.resolve()\n    fmu.dataio.ExportData.surface_fformat = \"irap_binary\"\n\n    exp = fmu.dataio.ExportData(content=\"depth\")\n    exp._pwd = tmp_path\n    exp.to_file(srf)\n\n    assert (tmp_path / \"maps\" / \"test.gri\").is_file() is True\n    assert (tmp_path / \"maps\" / \".test.gri.yml\").is_file() is True\n\n\ndef test_surface_io_export_subfolder(tmp_path):\n    \"\"\"Minimal test surface io with export_subfolder set,\n    uses tmp_path.\"\"\"\n\n    srf = xtgeo.RegularSurface(\n        ncol=20, nrow=30, xinc=20, yinc=20, values=np.ma.ones((20, 30)), name=\"test\"\n    )\n    fmu.dataio.ExportData.export_root = tmp_path.resolve()\n    fmu.dataio.ExportData.surface_fformat = \"irap_binary\"\n\n    exp = fmu.dataio.ExportData(content=\"depth\")\n    exp._pwd = tmp_path\n    with pytest.warns(UserWarning):\n        exp.to_file(srf, subfolder=\"mysubfolder\")\n\n    assert (tmp_path / \"maps\" / \"mysubfolder\" / \"test.gri\").is_file() is True\n    assert (tmp_path / \"maps\" / \"mysubfolder\" / \".test.gri.yml\").is_file() is True\n\n\ndef test_surface_io_larger_case(tmp_path):\n    \"\"\"Larger test surface io, uses global config from Drogon to tmp_path.\"\"\"\n\n    # make a fake RegularSurface\n    srf = xtgeo.RegularSurface(\n        ncol=20,\n        nrow=30,\n        xinc=20,\n        yinc=20,\n        values=np.ma.ones((20, 30)),\n        name=\"TopVolantis\",\n    )\n    fmu.dataio.ExportData.export_root = tmp_path.resolve()\n    fmu.dataio.ExportData.surface_fformat = \"irap_binary\"\n\n    exp = fmu.dataio.ExportData(\n        config=CFG2,\n        content=\"depth\",\n        unit=\"m\",\n        vertical_domain={\"depth\": \"msl\"},\n        timedata=None,\n        is_prediction=True,\n        is_observation=False,\n        tagname=\"what Descr\",\n        verbosity=\"INFO\",\n    )\n    exp._pwd = tmp_path\n    exp.to_file(srf, verbosity=\"DEBUG\")\n\n    metadataout = tmp_path / \"maps\" / \".topvolantis--what_descr.gri.yml\"\n    assert metadataout.is_file() is True\n    print(metadataout)\n\n\ndef test_surface_io_larger_case_ertrun(tmp_path):\n    \"\"\"Larger test surface io as ERTRUN, uses global config from Drogon to tmp_path.\n\n    Need some file acrobatics here to make the tmp_path area look like an ERTRUN first.\n    \"\"\"\n\n    current = tmp_path / \"scratch\" / \"fields\" / \"user\"\n    current.mkdir(parents=True, exist_ok=True)\n\n    shutil.copytree(CASEPATH, current / \"mycase\")\n\n    fmu.dataio.ExportData.export_root = \"../../share/results\"\n    fmu.dataio.ExportData.surface_fformat = \"irap_binary\"\n\n    runfolder = current / \"mycase\" / \"realization-0\" / \"iter-0\" / \"rms\" / \"model\"\n    runfolder.mkdir(parents=True, exist_ok=True)\n    out = current / \"mycase\" / \"realization-0\" / \"iter-0\" / \"share\" / \"results\" / \"maps\"\n\n    exp = fmu.dataio.ExportData(\n        config=CFG2,\n        content=\"depth\",\n        unit=\"m\",\n        vertical_domain={\"depth\": \"msl\"},\n        timedata=None,\n        is_prediction=True,\n        is_observation=False,\n        tagname=\"what Descr\",\n        verbosity=\"INFO\",\n        runfolder=runfolder.resolve(),\n        workflow=\"my current workflow\",\n    )\n\n    # make a fake RegularSurface\n    srf = xtgeo.RegularSurface(\n        ncol=20,\n        nrow=30,\n        xinc=20,\n        yinc=20,\n        values=np.ma.ones((20, 30)),\n        name=\"TopVolantis\",\n    )\n    exp.to_file(srf, verbosity=\"INFO\")\n\n    metadataout = out / \".topvolantis--what_descr.gri.yml\"\n    assert metadataout.is_file() is True\n\n    # now read the metadata file and test some key entries:\n    with open(metadataout, \"r\") as stream:\n        meta = yaml.safe_load(stream)\n\n    assert (\n        meta[\"file\"][\"relative_path\"]\n        == \"realization-0/iter-0/share/results/maps/topvolantis--what_descr.gri\"\n    )\n    assert meta[\"class\"] == \"surface\", meta[\"class\"]\n    assert meta[\"fmu\"][\"model\"][\"name\"] == \"ff\"\n    assert meta[\"fmu\"][\"iteration\"][\"name\"] == \"iter-0\"\n    assert meta[\"fmu\"][\"realization\"][\"name\"] == \"realization-0\"\n    assert meta[\"data\"][\"stratigraphic\"] is True\n\n    # display_name is not set, checking that 'name' was used\n    assert meta[\"display\"][\"name\"] == \"TopVolantis\"\n\n    logger.debug(\"\\n%s\", json.dumps(meta, indent=2))\n","sub_path":"tests/test_fmu_dataio_surface.py","file_name":"test_fmu_dataio_surface.py","file_ext":"py","file_size_in_byte":5152,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"68648272","text":"##\n##  Programación en Python\n##  ===========================================================================\n##\n##  Para el archivo `data.csv, imprima una tabla en formato CSV que contenga \n##  la cantidad de registros en que aparece cada clave de la columna 5.\n##\n##  Rta/\n##  aaa,13\n##  bbb,16\n##  ccc,23\n##  ddd,23\n##  eee,15\n##  fff,20\n##  ggg,13\n##  hhh,16\n##  iii,18\n##  jjj,18\n##\n##  >>> Escriba su codigo a partir de este punto <<<\n##\nimport itertools\n\nfile_csv = open(\"data.csv\",\"r\").readlines()\n\nlist_dict = [linea.split(\"\\t\")[-1].replace(\"\\n\",\"\").split(\",\") for linea in file_csv]\ntuplas = sorted([(tupla.split(\":\")[0],tupla.split(\":\")[1]) for tupla in sum(list_dict,[])])\n\nfor key, group in itertools.groupby(tuplas, lambda x: x[0]):    \n    grupo = len(list(group))\n\n    print(\"{},{}\".format(key,grupo))\n    # print(grupo)\n\n\n\n","sub_path":"03-python=1/q09=1/question.py","file_name":"question.py","file_ext":"py","file_size_in_byte":841,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"299865242","text":"from werkzeug.utils import secure_filename\nfrom app import app\n\nimport pandas as pd\n\nimport os\n\nfrom flask import render_template,session,request,redirect,send_from_directory,flash,url_for,json\n\nUPLOAD_FOLDER = os.getcwd()+\"/uploads\"\nALLOWED_EXTENSIONS = {'txt', 'pdf', 'docx','zip'}\n\ntest = \"./\"\n\napp.config[\"UPLOAD_FOLDER\"] = UPLOAD_FOLDER\napp.secret_key = \"yeet\"\n\ndef allowed_file(filename):\n    return '.' in filename and \\\n           filename.rsplit('.', 1)[1].lower() in ALLOWED_EXTENSIONS\n\n\n\n@app.route(\"/\",methods=['GET','POST'])\ndef index():\n    if not session.get('logged_in'):\n        return redirect(url_for('home'))\n\n    print(test)\n    if request.method == 'POST':\n        \n        print(request.files)\n        if request.files:\n            \n            file = request.files[\"file\"]\n            filename = secure_filename(file.filename)\n            print(file,filename)\n            if file.filename ==\"\":\n                flash(\"No file selected\")\n                return redirect(request.url)\n            if not allowed_file(file.filename):\n                flash(\"Invalid file format!\")\n                return redirect(request.url)\n            file.save(os.path.join(app.config[\"UPLOAD_FOLDER\"],filename))\n            flash(\"File upload successful!\")\n            return redirect(request.url)\n        else:\n            print(\"Oops\")\n            return redirect(request.url)\n            \n\n    return render_template(\"public/index.html\")\n\n@app.route('/loginpage')\ndef home():\n    if not session.get('logged_in'):\n        return render_template('public/login.html')\n    else:\n        return render_template(\"public/index.html\")\n\n@app.route('/login', methods=['POST'])\ndef do_admin_login():\n    data = json.load(open(os.path.join(os.getcwd(),\"db.json\")))\n    #print(data[0][\"admin\"])\n    if request.form.get('id') == '1' and request.form.get('password') == data[0][\"user\"]:\n        session['logged_in'] = True\n        return redirect(url_for('index'))\n    if request.form.get('id') == '2' and request.form.get('password') == data[0][\"admin\"]:\n        session['logged_in'] = True\n        return redirect(url_for('admin'))\n    else:\n        print(request.form)\n        print(\"break\")\n        print(request.form.get('user'))\n        flash('Invalid Credentials!')\n    return home()\n\n@app.route(\"/logout\")\ndef logout():\n    session['logged_in'] = False\n    return home()\n\n@app.route(\"/admin\",methods=['GET','POST'])\ndef admin():\n    if request.method == 'GET':\n        print(request)\n    return render_template(\"public/admin.html\")\n\n@app.route(\"/download\",methods=['GET','POST'])\ndef dl(): \n    try:\n        return send_from_directory(os.getcwd(), filename=\"after_rank.csv\", as_attachment=True)\n    except FileNotFoundError:\n        abort(404)\n\n@app.route(\"/eval\",)\ndef runeval():\n    file = open(os.path.join(os.getcwd(),os.pardir,\"eval.py\"),'r').read()\n    exec(file)\n    flash(\"Evaluation Complete!\")\n    return redirect(url_for('admin'))\n\n@app.route(\"/rank\",methods=['GET'])\ndef runrank():\n    file = open(os.path.join(os.getcwd(),os.pardir,\"rank.py\"),'r').read()\n    exec(file)\n    flash(\"Ranking Completed Successfully! You can download results now.\")\n    return redirect(url_for('admin'))\n\n@app.route(\"/results\")\ndef results():\n    table = pd.read_csv(os.path.join( os.getcwd(), 'after_rank.csv' ))\n    return render_template(\"public/templates/empty.html\", data=table.to_html())","sub_path":"app/app/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"418837406","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Mon Jun  3 19:34:01 2019\r\n\r\n@author: HOME\r\n\"\"\"\r\n#%%\r\nfrom flask import Flask,render_template,request,redirect,url_for\r\nimport pymysql\r\n\r\napp = Flask(__name__)\r\nconn=pymysql.connect(\"localhost\",\"root\",\"admin\",\"studentdb\") #host/user/password/database\r\n\r\n#@app.route(\"/\")\r\n#def text():\r\n#    return (\"first\")\r\n\r\n\r\n@app.route(\"/\")\r\ndef showData():\r\n    with conn:\r\n        cur=conn.cursor() #coursorที่ขึ้นบนแทบเวลาวางเมาส์\r\n        cur.execute(\"select * from student`\")\r\n        rows=cur.fetchall()#คำสั่งดึงข้อมูลมาทุกตัวเลย\r\n        return render_template(\"index.html\",datas=rows)\r\n\r\n    #text=\"Python\"\r\n    #age=123\r\n    #return render_template(\"index.html\",data=text,data2=age)\r\n    #<hj>Super ice++data>>{{data}}+++data2>>{{data2}}</hi>\r\n\r\n@app.route(\"/student\")\r\ndef showFrom():\r\n    with conn:\r\n        return render_template(\"addstudent.html\")\r\n\r\n\r\n@app.route(\"/delete/<string:id_data>\",methods=['GET'])\r\ndef delete(id_data):\r\n    with conn:\r\n        cur=conn.cursor() #coursorที่ขึ้นบนแทบเวลาวางเมาส์\r\n        cur.execute(\"delete from student where id=%s\",(id_data))\r\n        conn.commit()\r\n    return redirect(url_for(\"showData\"))\r\n\r\n@app.route(\"/update\",methods=['POST'])\r\ndef update():\r\n    if request.method == \"POST\":\r\n        id_update=request.form['id']\r\n        fname=request.form['fname']\r\n        lname=request.form['lname']\r\n        phone=request.form['phone']\r\n        with conn.cursor() as cursor:\r\n            sql=\"update student set fname=%s , lname=%s , phone=%s where id=%s\"\r\n            cursor.execute(sql,(fname,lname,phone,id_update)) #fname,lname,phone บรรทัดบน-นี้ จับคู่กัน\r\n            conn.commit()\r\n        return redirect(url_for('showData'))\r\n\r\n@app.route(\"/insert\",methods=['POST'])\r\ndef insert():\r\n    if request.method == \"POST\":\r\n        fname=request.form['fname']\r\n        lname=request.form['lname']\r\n        phone=request.form['phone']\r\n        with conn.cursor() as cursor:\r\n            sql=\"insert into student (fname,lname,phone) values(%s,%s,%s)\"\r\n            cursor.execute(sql,(fname,lname,phone))\r\n            conn.commit()\r\n        return redirect(url_for('showData'))\r\nif __name__ == \"__main__\":\r\n    app.run(debug=True)\r\n    #%%\r\n","sub_path":"PythonDevelopmentFinal/Hello.py","file_name":"Hello.py","file_ext":"py","file_size_in_byte":2412,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"460756885","text":"# Variables atómicas:\n# x = 3\n# a = \"b\"\n#\n# Vaiables estructuradas:\n# l = [1,3,4,5]\n# t = (3,6,8,9)\n\n# ORDENAR:\n# Debemos tener criterio de ordenamiento.\n# Es beneficioso para poder buscar elementos si los mismos están ordenados.\n\n# 3 ALGORITMOS DE ORDENAMIENTO:\n#     1) ORDENAMIENTO BURBUJA/POR BURBUJEO (BUBBLE SORT)\n    # Ejemplo:\n    # l = [10, 8, 2, 9, 1]\n    # Primera pasada\n    # l = [8, 10, 2, 9, 1]\n    # Segunda pasada\n    # l = [8, 2, 10, 9, 1]\n    # Tercera pasada\n    # l = [8, 2, 9, 10, 1]\n    # Cuarta pasada\n    # l = [8, 2, 9, 1, 10] (El 10 llegó al final de arreglo, llevo el mayor hasta el final)\n    # Quinta pasada\n    # l = [2, 8, 9, 1, 10]\n    # Sexta pasada\n    # l = [2, 8, 1, 9, 10]\n    # Séptima pasada\n    # l = [2, 1, 8, 9, 10]\n    # Octava pasada\n    # l = [1, 2, 8, 9, 10]\n\n    # Ejemplo en Python:\ndef bubbleSort(arr):\n    n = len(arr)\n    for i in range(n):\n        for j in range(n-i-1):\n            if arr[j] > arr[j+1]:\n                arr[j], arr[j+1] = arr[j+1], arr[j]\n    return arr\n\n\n# ¿CÓMO HACEMOS PARA MEDIR ORDENAMIENTOS?\n# COMPLEJIDAD ALGORÍTMICA\n# Nos da una idea de cuánto un algoritmo tarda en resolver un operación.\n# a) Temporal\n# b) Espacial\n# Leer O grande WikiPedia\n\n# 2) SELECTION SORT/ORDENAMIENTO POR SELECCIÓN\n# Ejemplo:\n# l = [8, 4, 1, 7, 10]\n# l = [1, 4, 8, 7, 10] (Selecciona el mínimo y lo manda al principio de la lista)\n\n# Ejemplo en Python:\ndef selectionSort(arr):\n    for i in range(len(arr)):\n        min_indice = i\n        for j in range(i+1,len(arr)):\n            if arr[min_indice] > arr[j]:\n                min_indice = j\n        arr[i], arr[min_indice] = arr[min_indice], arr[i]\n    return arr\n\n# 3) INSERTION SORT/ORDENAMIENTO POR INSERCIÓN\ndef insertionSort(arr):\n    n = len(arr)\n    for i in range(1,n):\n        k = arr[i]\n        j = i-1\n        while j >= 0 and k < arr[j]:\n            arr[j+1] = arr[j]\n            j -= 1\n        arr[j+1] = k\n    return arr\n\n    # TAREA:\n    # Hacer bubble sort mejorado, que no haga comparaciones si esta ordenado.\n    # Verificar las pasadas de los ciclos \n\n\n\nprint(insertionSort([3,10,4,6,1]))\n","sub_path":"Clase 5/en-clase-5.py","file_name":"en-clase-5.py","file_ext":"py","file_size_in_byte":2124,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"138475094","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Aug  7 01:10:53 2018\n\n@author: Workstation\n\"\"\"\n\nimport pandas as pd\nimport glob\nimport os\nimport functools\nimport sys\nimport numpy as np\n\ntry:\n    target_col = sys.argv[1]\nexcept:\n    target_col = None\n\nroll_window = int(5)\n\ndef cumulative_returns(ret):\n    return np.prod(1+np.array(ret)) - 1\n\ndef main(roll_window):\n#    all_files = glob.glob(os.path.join(\"_data\", \"*.csv\"))     # advisable to use os.path.join as this makes concatenation OS independent\n    asset = \"EURUSD\"\n    input_file_name = \"MLData_\" + asset + \".csv\"\n\n    if not os.path.isfile(os.path.join(\"_data\", input_file_name)):\n        assert False, \"{} not found!\".format(input_file_name)\n\n    input_file = os.path.join(\"_data\", input_file_name)\n\n    df = pd.read_csv(input_file, index_col = [\"Date\"])\n\n    filter_col = [col for col in df if col.startswith('f_')]\n    df[filter_col] = df[filter_col].rolling(roll_window).apply(cumulative_returns).shift(-roll_window+1)\n    df = df.dropna()\n\n    output_file_name = input_file_name.split(\".\")[0] + \"_R_\" + str(roll_window) + \".csv\"\n\n    df.to_csv(os.path.join(\"output\", output_file_name))\n\n    return df.head()\n\nif __name__ == \"__main__\":\n    main(target_col)\n","sub_path":"Archive/Step2b_Transform/transform.py","file_name":"transform.py","file_ext":"py","file_size_in_byte":1218,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"506564360","text":"namevar = str(input(\"Enter Name to Check: \"))\neval_string ='6'\nwrite_instances = int(input(\"How Many Times Shall I Write It: \"))\neval_chars = int(input(\"Which Character Would You Like to Know?: \"))\nindex_account = -1\ncount = 0\n\nwhile (count < write_instances):\n    eval_string = eval_string + namevar\n    count = count + 1\n    # Print (count)\n\nprint (eval_string [eval_chars + index_account])\n# Print (eval_chars)","sub_path":"Elementary-Name-Proc.py","file_name":"Elementary-Name-Proc.py","file_ext":"py","file_size_in_byte":413,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"231745070","text":"# GUI Part\r\n# The design of the Contact Book\r\n\r\nfrom PyQt5.QtCore import Qt\r\nfrom PyQt5.QtWidgets import (\r\n\tQHBoxLayout,\r\n\tQWidget,\r\n\tQMainWindow,\r\n\tQAbstractItemView,\r\n\tQPushButton,\r\n\tQTableView,\r\n\tQVBoxLayout,\r\n\tQDialog,\r\n\tQDialogButtonBox,\r\n\tQFormLayout,\r\n\tQLineEdit,\r\n\tQMessageBox,\r\n\t)\r\n\r\nfrom .model import ContactsModel\r\n\r\nclass Window(QMainWindow):\r\n\r\n\tdef __init__(self,parent=None): \r\n\t\tsuper().__init__(parent)\r\n\t\tself.setWindowTitle(\"RP Contacts\")\r\n\t\tself.resize(625, 250)\r\n\t\tself.centralWidget = QWidget()\r\n\t\tself.setCentralWidget(self.centralWidget)\r\n\t\tself.layout = QHBoxLayout()\r\n\t\tself.centralWidget.setLayout(self.layout)\r\n\t\tself.contactModel = ContactsModel()\r\n\t\tself.setupUI()\r\n\t\t\r\n\tdef setupUI(self):\r\n\t\tself.table =QTableView()\r\n\t\tself.table.setModel(self.contactModel.model)\r\n\t\tself.table.setSelectionBehavior(QAbstractItemView.SelectRows)\r\n\t\tself.table.resizeColumnsToContents()\r\n\r\n\t\tself.addButton = QPushButton(\"Add\")\r\n\t\tself.addButton.clicked.connect(self.openAddDialog)\r\n\t\tself.deleteButton = QPushButton(\"Delete\")\r\n\t\tself.deleteButton.clicked.connect(self.deleteContact)\r\n\t\tself.clearButton = QPushButton(\"Clear All\")\r\n\t\tself.clearButton.clicked.connect(self.clearContacts)\r\n\r\n\t\tlayout = QVBoxLayout()\r\n\t\tlayout.addWidget(self.addButton)\r\n\t\tlayout.addWidget(self.deleteButton)\r\n\t\tlayout.addStretch()\r\n\t\tlayout.addWidget(self.clearButton)\r\n\r\n\t\tself.layout.addWidget(self.table)\r\n\t\tself.layout.addLayout(layout)\r\n\r\n\tdef openAddDialog(self):\r\n\t\tdialog = AddDialog(self)\r\n\r\n\t\tif dialog.exec() == QDialog.Accepted:\r\n\t\t\tself.contactModel.addContact(dialog.data)\r\n\t\t\tself.table.resizeColumnsToContents()\r\n\r\n\tdef deleteContact(self):\r\n\t\trow=self.table.currentIndex().row()\r\n\r\n\t\tif row<0:\r\n\t\t\treturn\r\n\r\n\t\tmessageBox=QMessageBox.warning(\r\n\t\t\tself,\r\n\t\t\t\"Warning!\",\r\n\t\t\t\"Do you want to delete this contact?\",\r\n\t\t\tQMessageBox.Ok | QMessageBox.Cancel,\r\n\t\t)\r\n\r\n\t\tif messageBox == QMessageBox.Ok:\r\n\t\t\tself.contactModel.deleteContacts(row)\r\n\r\n\tdef clearContacts(self):\r\n\t\tmessageBox = QMessageBox.warning(\r\n\t\t\tself,\r\n\t\t\t\"Warning\",\r\n\t\t\t\"Are you sure you want to clear the entire table\",\r\n\t\t\tQMessageBox.Ok | QMessageBox.Cancel,\r\n\t\t)\r\n\r\n\t\tif messageBox == QMessageBox.Ok:\r\n\t\t\tself.contactModel.clearContacts()\r\n\r\n\r\nclass AddDialog(QDialog):\r\n\r\n\tdef __init__(self,parent=None):\r\n\t\tsuper().__init__(parent=parent)\r\n\t\tself.setWindowTitle(\"Add Contact\")\r\n\t\tself.layout=QVBoxLayout()\r\n\t\tself.setLayout(self.layout)\r\n\t\tself.data=None\r\n\r\n\t\tself.setupUI()\r\n\r\n\tdef setupUI(self):\r\n\t\tself.nameField=QLineEdit()\r\n\t\tself.nameField.setObjectName(\"Name\")\r\n\t\tself.jobField=QLineEdit()\r\n\t\tself.jobField.setObjectName(\"Job\")\r\n\t\tself.emailField=QLineEdit()\r\n\t\tself.emailField.setObjectName(\"Email\")\r\n\r\n\t\tlayout=QFormLayout()\r\n\t\tlayout.addRow(\"Name:\",self.nameField)\r\n\t\tlayout.addRow(\"Job:\",self.jobField)\r\n\t\tlayout.addRow(\"Email:\",self.emailField)\r\n\t\tself.layout.addLayout(layout)\r\n\r\n\t\tself.buttonsBox = QDialogButtonBox(self)\r\n\t\tself.buttonsBox.setOrientation(Qt.Horizontal)\r\n\t\tself.buttonsBox.setStandardButtons(QDialogButtonBox.Ok | QDialogButtonBox.Cancel)\r\n\t\tself.buttonsBox.accepted.connect(self.accept)\r\n\t\tself.buttonsBox.rejected.connect(self.reject)\r\n\t\tself.layout.addWidget(self.buttonsBox)\r\n\r\n\tdef accept(self):\r\n\t\tself.data=[]\r\n\r\n\t\tfor field in (self.nameField,self.jobField,self.emailField):\r\n\t\t\tif not field.text():\r\n\t\t\t\tQMessageBox.critical(\r\n\t\t\t\t\tself,\r\n\t\t\t\t\t\"Error!\",\r\n\t\t\t\t\tf\"You must provide contact's {field.objectName()}\"\r\n\t\t\t\t\t)\r\n\r\n\t\t\t\tself.data = None\r\n\t\t\t\treturn\r\n\t\t\tself.data.append(field.text())\r\n\r\n\t\tif not self.data:\r\n\t\t\treturn\r\n\r\n\t\tsuper().accept()","sub_path":"rpcontacts_project/rpcontacts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3568,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"114137951","text":"#!/usr/bin/env python2\nimport os\nfrom plyj.parser import Parser\n\nJAVA_FILES_FOLDER = \"./\"\n\n\ndef find_java_files():\n    result = []\n    # Thanks ghostdog74 https://stackoverflow.com/questions/3964681\n    for root, dirs, files in os.walk(JAVA_FILES_FOLDER):\n        for file_ in files:\n            if file_.endswith(\".java\"):\n                result.append(os.path.join(root, f))\n    return result\n\np = Parser()\nfor f in find_java_files():\n    print(\"Parsing \" + f)\n    parse = p.parse_file(f)\n\n    print(\"Serializing \" + f)\n    serialized = parse.serialize()\n    with open(\"test.java\", \"w\") as j:\n        j.write(serialized)\n\n    print(\"Parsing (2) \" + f)\n    parse2 = p.parse_file(\"test.java\")\n\n    print(\"Asserting \" + f)\n    assert parse == parse2\n","sub_path":"example/test.py","file_name":"test.py","file_ext":"py","file_size_in_byte":749,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"481135813","text":"from bitfinex import ClientV2 as Client\nfrom datetime import datetime\nfrom pprint import pprint\nimport logging\nimport redis\nimport sys\nimport time\n\nimport config\n\nfrom lib import bitfinex_api as bf\nfrom lib import macd_calc\nfrom lib import positions\n\n\n# https://github.com/jealous/stockstats\n# https://github.com/ohenrik/bitfinex\n# https://bitfinex.readthedocs.io/en/latest/restv2.html\n# https://towardsdatascience.com/cryptocurrency-analysis-with-python-macd-452ceb251d7c  \n\n\n# Logging config\nlogging.basicConfig(format='%(asctime)s %(levelname)s: %(message)s', level=logging.DEBUG)\n\n# Finex API Connection\nif not config.bfx_key:\n    logging.error('BFX Key not set')\n    sys.exit(1)\n\n# Redis Connection\nr = redis.Redis(host=config.redis_host, port=6379, db=0)\n\nbfx_client = Client(config.bfx_key, config.bfx_secret, 2.0)\n\n\ndef main():\n\n    interval_secs = config.interval * 60\n    interval_str = str(config.interval) + 'm'\n\n    prev_macd = 0\n    curr_macd = 0\n\n    firstRun = True\n\n    while True:\n\n        # Get the ticker values\n        ticker = bf.getTicker(bfx_client, config.symbol)\n        \n        # get the candles in SDF and calculate MACDH\n        candles = bf.getCandles(bfx_client, config.symbol, interval_str)\n        candles_sdf = macd_calc.convertToStockDataFrame(candles)\n        macd = macd_calc.getMacd(candles_sdf)\n\n\n        # Set the previous vs the current MACDH values        \n        prev_macd = curr_macd\n        curr_macd = macd.tail(1).item()\n        curr_macd2 = macd[1]\n\n        logging.debug(curr_macd)\n        logging.debug(curr_macd2)\n        \n        \n        # Process into the packet we need\n        macd_state = macd_calc.processMacdState(prev_macd, curr_macd)\n        macd_state['bid'] = ticker[0]\n        macd_state['ask'] = ticker[2]\n\n        logging.info(macd_state)\n\n        # check for first run\n        if firstRun:\n            logging.warning('Initializing. Please wait...')\n            firstRun = False \n            time.sleep(60)\n            continue\n        \n        macd_calc.logMacdState(r, macd_state)\n        \n\n        # Open or Close the position\n        positions.managePosition(r, macd_state)\n\n\n        # sleep\n        time.sleep(interval_secs)\n\n\nif __name__ == '__main__':\n    main()","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":2238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"323050445","text":"\n\n#-------------------微信回调验证签名----------------------------》\n#-------------------成功后返回dict------------------------------------》\nimport xml.etree.ElementTree as ElementTree\nimport sys\n\nPY2 = sys.version_info[0] == 2\nif not PY2:\n    # Python 3.x and up\n    text_type = str\n    string_types = (str,)\n    xrange = range\n\n    def as_text(v):  ## 生成unicode字符串\n        if v is None:\n            return None\n        elif isinstance(v, bytes):\n            return v.decode('utf-8', errors='ignore')\n        elif isinstance(v, str):\n            return v\n        else:\n            raise ValueError('Unknown type %r' % type(v))\n\n    def is_text(v):\n        return isinstance(v, text_type)\n\ndef xml2dict(xml_str):\n    \"\"\"xml to dict\n\n    @:param xml_str: string in XML format\n    @:return: Dictionary\n    \"\"\"\n    # return xmltodict.parse(xml_str)['xml']\n    root = ElementTree.fromstring(xml_str)\n    assert as_text(root.tag) == as_text('xml')\n    result = {}\n    for child in root:\n        tag = child.tag\n        text = child.text\n        result[tag] = text\n    return result\nfrom pywxpay import WXPay\nwxpay = WXPay(app_id='wx98e346c1131740ca',\n              mch_id='1488403182',\n              key='DFSDSFDSxzdsagdsxcdsfds123098765',\n              cert_pem_path='',\n              key_pem_path='',\n              timeout=6000)\n\n#拿到这些签名进行比对\ndef response_dict(xml_str):\n    resp_dict = xml2dict(xml_str)\n    if 'return_code' in resp_dict:\n        return_code = resp_dict.get('return_code')\n    else:\n        raise Exception('no return_code in responxe data: {}'.format(ult))\n    if return_code == 'SUCCESS':\n        #检查微信响应的xml数据中签名是否合法，先转换成dict\n        #wxpay    is_pay_result_notify_signature_valid\n        if wxpay.is_pay_result_notify_signature_valid(resp_dict):\n            return resp_dict\n        else:\n            raise Exception('the sign not valid')\n    elif return_code == 'FAIL':\n        return resp_dict\n\n#------------------返回支付路由（支付宝,微信 ）---------------------->\nfrom utils.alipay import AliPay\nfrom Lyonline.settings import private_key_path, ali_pub_key_path, QR_code_path\n\n\ndef get_alipayUrl(order_no, pay_mount):\n    alipay = AliPay(\n        appid=\"2016091100490098\",\n        app_notify_url=\"test.tuobaba.cn:5020/pay/notyfile_return/\",\n        app_private_key_path=private_key_path,\n        alipay_public_key_path=ali_pub_key_path,\n        # 支付宝的公钥，验证支付宝回传消息使用，不是你自己的公钥,\n        debug=True,  # 默认False,\n        return_url=\"test.tuobaba.cn:5020/pay/alipay_return/\"\n    )\n    url = alipay.direct_pay(\n        subject=\"测试订单2\",\n        out_trade_no=order_no,\n        total_amount=pay_mount,\n        return_url=\"test.tuobaba.cn:5020/pay/alipay_return/\"\n\n    )\n    re_url = \"https://openapi.alipaydev.com/gateway.do?{data}\".format(data=url)\n    return re_url\n\n\n\nfrom pywxpay import WXPay\n\n\ndef get_wxpayUrl(order_no, pay_mount):\n    wxpay = WXPay(app_id='wx98e346c1131740ca',\n                  mch_id='1488403182',\n                  key='DFSDSFDSxzdsagdsxcdsfds123098765',\n                  cert_pem_path='',\n                  key_pem_path='',\n                  timeout=6000)  # 毫秒\n\n    wxpay_resp_dict = wxpay.unifiedorder(dict(device_info='WEB',\n                                              body='测试商家-商品类目',\n                                              detail='',\n                                              out_trade_no=order_no,\n                                              total_fee=int(pay_mount),\n                                              fee_type='CNY',\n                                              notify_url='http://xxd.tuobaba.cn/pay/wxnotify/',\n                                              spbill_create_ip='192.168.192.132',\n                                              trade_type='NATIVE')\n                                         )\n\n    code_url = wxpay_resp_dict.get('code_url', '')\n    print('????????------》》》》》》', wxpay_resp_dict)\n    wxpayUrl = 'http://192.168.192.131:8000/pay/wxpay?code_url={0}'.format(code_url)\n    return wxpayUrl\n","sub_path":"signUppObjects/utils/about_pay.py","file_name":"about_pay.py","file_ext":"py","file_size_in_byte":4187,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"31731818","text":"from tqdm import tqdm\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nimport numpy as np\r\nimport pickle\r\nfrom multiprocessing import cpu_count\r\nfrom sklearn.metrics import recall_score\r\nfrom sklearn.metrics import average_precision_score\r\nfrom sklearn.metrics import confusion_matrix\r\nimport numpy as np\r\nimport matplotlib.pyplot as plt\r\nfrom tqdm import tqdm\r\nimport pickle\r\nimport networkx as nx\r\nfrom scipy.stats import entropy\r\nimport torch\r\nimport numpy as np\r\nimport time\r\n\r\ndef compute_kernel(x,y):\r\n    x_size = x.size(0)\r\n    y_size = y.size(0)\r\n    dim = x.size(1)\r\n    x_tile = x.view(x_size,1,dim)\r\n    x_tile = x_tile.repeat(1,y_size,1)\r\n    y_tile = y.view(1,y_size,dim)\r\n    y_tile = y_tile.repeat(x_size,1,1)\r\n    return torch.exp(-torch.mean((x_tile-y_tile)**2,dim = 2)/float(dim))\r\n\r\n\r\ndef compute_mmd(x,y):\r\n    x_kernel = compute_kernel(x,x)\r\n    # print(x_kernel)\r\n    y_kernel = compute_kernel(y,y)\r\n    # print(y_kernel)\r\n    xy_kernel = compute_kernel(x,y)\r\n    # print(xy_kernel)\r\n    return torch.mean(x_kernel)+torch.mean(y_kernel)-2*torch.mean(xy_kernel)\r\n\r\n\r\ndef load_merge_relation():\r\n    relation_groups = [\r\n    'atlocation/locatednear',\r\n    'capableof',\r\n    'causes/causesdesire/*motivatedbygoal',\r\n    'createdby',\r\n    'desires',\r\n    'antonym/distinctfrom',\r\n    'hascontext',\r\n    'hasproperty',\r\n    'hassubevent/hasfirstsubevent/haslastsubevent/hasprerequisite/entails/mannerof',\r\n    'isa/instanceof/definedas',\r\n    'madeof',\r\n    'notcapableof',\r\n    'notdesires',\r\n    'partof/*hasa',\r\n    'relatedto/similarto/synonym',\r\n    'usedfor',\r\n    'receivesaction',\r\n]\r\n    relation_mapping = dict()\r\n    for line in relation_groups:\r\n        ls = line.strip().split('/')\r\n        rel = ls[0]\r\n        for l in ls:\r\n            if l.startswith(\"*\"):\r\n                relation_mapping[l[1:]] = \"*\" + rel\r\n            else:\r\n                relation_mapping[l] = rel\r\n    return relation_mapping\r\n\r\n\r\ndef load_resources(cpnet_vocab_path):\r\n    merged_relations = [\r\n    'antonym',\r\n    'atlocation',\r\n    'capableof',\r\n    'causes',\r\n    'createdby',\r\n    'isa',\r\n    'desires',\r\n    'hassubevent',\r\n    'partof',\r\n    'hascontext',\r\n    'hasproperty',\r\n    'madeof',\r\n    'notcapableof',\r\n    'notdesires',\r\n    'receivesaction',\r\n    'relatedto',\r\n    'usedfor',\r\n    ]\r\n    concept2id = {}\r\n    relation2id = {}\r\n    with open(cpnet_vocab_path, \"r\", encoding=\"utf8\") as fin:\r\n        id2concept = [w.strip() for w in fin]\r\n    concept2id = {w: i for i, w in enumerate(id2concept)}\r\n    id2relation = merged_relations\r\n    relation2id = {r: i for i, r in enumerate(id2relation)}\r\n    return concept2id, relation2id, id2concept, id2relation\r\n\r\ndef get_dist():\r\n    ori_stats = np.load('degree_stats.npy')\r\n    with open('./data/cpnet/conceptnet.en.pruned.graph', 'rb') as handle:\r\n        graph = pickle.load(handle)\r\n    concept2id, relation2id, id2concept, id2relation = load_resources('./data/cpnet/concept.txt')\r\n    coeff = []\r\n    edges = list(graph.edges.data())\r\n    coeff = np.zeros(ori_stats.shape)\r\n    for rel_id in id2relation:\r\n\r\n        cpnet_simple = nx.Graph()\r\n        i = 0\r\n        for edge in edges:\r\n            try:\r\n                rel = edge[2]['rel']\r\n                #print(rel)\r\n                if rel>=17:\r\n                    continue\r\n                if rel==relation2id[rel_id]:\r\n                    subj = edge[0]\r\n                    obj = edge[1]\r\n                    #print('k')\r\n                    w = 1.0\r\n                    #if not cpnet_simple.has_edge(subj, obj):\r\n                    cpnet_simple.add_edge(subj, obj, weight=w)\r\n                    i=i+1\r\n            except KeyError:\r\n                continue\r\n        cc = cpnet_simple.degree()\r\n        for node, val in list(cc):\r\n            #print(node, val)\r\n            coeff[relation2id[rel_id], node] = val\r\n        #coeff.append(torch.FloatTensor(list(cc.values())))\r\n        #print(len(list(cc.values())))\r\n    #new_stats = []\r\n    # for i in range(17):\r\n    #     new_stats.append(compute_mmd(ori_stats[i].view(ori_stats[i].shape[0],1),coeff[i].view(coeff[i].shape[0],1)))\r\n    #print(np.mean(new_stats))\r\n    #print([(ori_stats[i].shape) for i in range(len(ori_stats))])\r\n    cv = np.sqrt(np.sum((coeff - ori_stats)**2, axis=1))\r\n    print(cv.shape)\r\n    print(1/(np.mean(cv) + 0.00001))\r\n    #np.save('degree_stats.npy', coeff)\r\nif __name__ == '__main__':\r\n    get_dist()\r\n","sub_path":"csqa/newer_val_score.py","file_name":"newer_val_score.py","file_ext":"py","file_size_in_byte":4425,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"408861211","text":"from __future__ import print_function, division\n\nimport os\nimport time\nimport copy\nimport argparse\nimport torch\nimport torch.nn as nn\nimport torch.optim as optim\nimport torch.nn.functional as F\nfrom torch.autograd import Variable\nfrom torch.optim import lr_scheduler\nimport numpy as np\nimport torchvision\nfrom torchvision import datasets, models, transforms\nimport matplotlib.pyplot as plt\n\nfrom datetime import datetime\nfrom logger import Logger\nfrom tensorboardX import SummaryWriter\n\nfrom PIL import ImageFile\nfrom PIL import Image\n\nImageFile.LOAD_TRUNCATED_IMAGES = True\n\nplt.ion()  # interactive mode\n\ndevice = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n\nimport nvidia.dali as dali\nimport nvidia.dali.ops as ops\nimport nvidia.dali.types as types\nfrom nvidia.dali.pipeline import Pipeline\nfrom nvidia.dali.plugin.pytorch import DALIClassificationIterator, DALIGenericIterator\n\n\nclass FileReadPipeline(Pipeline):\n    def __init__(self, batch_size, num_threads, device_id, num_gpus):\n        super(FileReadPipeline, self).__init__(batch_size, num_threads, device_id)\n        mean = (0.485 * 255, 0.456 * 255, 0.406 * 255)\n        std = (0.229 * 255, 0.224 * 255, 0.225 * 255)\n        size = 224\n        self.input = ops.FileReader(file_root=data_dir)\n        self.cmnp = ops.CropMirrorNormalize(device=\"gpu\",\n                                            output_dtype=types.FLOAT,\n                                            output_layout=types.NCHW,\n                                            crop=(size, size),\n                                            image_type=types.RGB,\n                                            mean=mean,\n                                            std=std)\n\n        self.decode = ops.ImageDecoder(device=device, output_type=types.RGB)\n\n        # Resize to desired size.  To match torchvision dataloader, use triangular interpolation\n        self.res = ops.Resize(device=self.dali_device, resize_shorter=size, interp_type=types.INTERP_TRIANGULAR)\n\n    def define_graph(self):\n        images, labels = self.input(name=\"Reader\")\n        images = self.decode(images)\n        images = self.res(images)\n        images = self.cmnp(images)\n\n        return self.base_define_graph(images, labels)\n\n\ndef random_transform(index):\n    dst_cx, dst_cy = (200, 200)\n    src_cx, src_cy = (200, 200)\n\n    # This function uses homogeneous coordinates - hence, 3x3 matrix\n\n    # translate output coordinates to center defined by (dst_cx, dst_cy)\n    t1 = np.array([[1, 0, -dst_cx],\n                   [0, 1, -dst_cy],\n                   [0, 0, 1]])\n\n    def u():\n        return np.random.uniform(-0.5, 0.5)\n\n    # apply a randomized affine transform - uniform scaling + some random distortion\n    m = np.array([\n        [1 + u(), u(), 0],\n        [u(), 1 + u(), 0],\n        [0, 0, 1]])\n\n    # translate input coordinates to center (src_cx, src_cy)\n    t2 = np.array([[1, 0, src_cx],\n                   [0, 1, src_cy],\n                   [0, 0, 1]])\n\n    # combine the transforms\n    m = (np.matmul(t2, np.matmul(m, t1)))\n\n    # remove the last row; it's not used by affine transform\n    return m[0:2, 0:3]\n\n\ndef gen_transforms(batch_size, single_transform_fn):\n    out = np.zeros([batch_size, 2, 3])\n    for i in range(batch_size):\n        out[i, :, :] = single_transform_fn(i)\n    return out.astype(np.float32)\n\n\nclass HybridTrainPipe(Pipeline):\n    def __init__(self, batch_size, num_threads, device_id, data_dir, crop, size=256, dali_cpu=False, local_rank=0,\n                 world_size=1):\n        super(HybridTrainPipe, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id)\n        dali_device = \"gpu\"\n        self.input = ops.FileReader(file_root=data_dir, shard_id=local_rank, num_shards=world_size, random_shuffle=True)\n        self.decode = ops.ImageDecoder(device=\"mixed\", output_type=types.RGB)\n        # self.res = ops.Resize(device=\"gpu\", resize_x=size, resize_y=size, interp_type=types.INTERP_LINEAR)\n        self.res = ops.Resize(device=\"gpu\", resize_shorter=size, interp_type=types.INTERP_LINEAR)\n        self.rescrop = ops.RandomResizedCrop(device=\"gpu\", size=crop, random_area=[0.08, 1.25])\n        self.bc = ops.BrightnessContrast(device=\"gpu\", brightness=0.5, contrast=0.6)\n\n        # Will flip vertically with prob of 0.1\n        self.vert_flip = ops.Flip(device='gpu', horizontal=0)\n        self.vert_coin = ops.CoinFlip(probability=0.4)\n\n        self.transform_source = ops.ExternalSource()\n        self.warp_keep_size = ops.WarpAffine(\n            device=\"gpu\",\n            # size                              # keep original canvas size\n            interp_type=types.INTERP_LINEAR  # use linear interpolation\n        )\n\n        # My workaround for Dali not supporting random affine transforms:\n        # a \"synthetic random\" warp affine transform.\n\n        # Rotate within a narrow range with probability of 0.075\n        self.rotate = ops.Rotate(device='gpu')\n        self.rotate_range = ops.Uniform(range=(-20.0, 20.0))\n        self.rotate_coin = ops.CoinFlip(probability=0.075)\n\n        self.cmnp = ops.CropMirrorNormalize(device=\"gpu\",\n                                            output_dtype=types.FLOAT,\n                                            output_layout=types.NCHW,\n                                            image_type=types.RGB,\n                                            mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],\n                                            std=[0.229 * 255, 0.224 * 255, 0.225 * 255])\n        self.coin = ops.CoinFlip(probability=0.5)\n        print('DALI \"{0}\" variant'.format(dali_device))\n\n    def define_graph(self):\n        rng = self.coin()\n\n        prob_vert_flip = self.vert_coin()\n        prob_rotate = self.rotate_coin()\n        angle_range = self.rotate_range()\n        self.transform = self.transform_source()\n\n        self.jpegs, self.labels = self.input(name=\"Reader\")\n        images = self.decode(self.jpegs)\n        ##images = self.vert_flip(images, vertical=prob_vert_flip) # Specify prob_vert_flip here\n        images = self.rotate(images, angle=angle_range)\n        images = self.warp_keep_size(images.gpu(), matrix=self.transform.gpu())\n        images = self.res(images)\n        images = self.rescrop(images)\n\n        # images = self.bc(images)\n        output = self.cmnp(images, mirror=rng)\n        return [output, self.labels.gpu()]\n\n    # Since we're using ExternalSource, we need to feed the externally provided data to the pipeline\n\n    def iter_setup(self):\n        # Generate the transforms for the batch and feed them to the ExternalSource\n        self.feed_input(self.transform, gen_transforms(self.batch_size, random_transform))\n\n\nclass HybridValPipe(Pipeline):\n    def __init__(self, batch_size, num_threads, device_id, data_dir, crop, size, local_rank=0, world_size=1):\n        super(HybridValPipe, self).__init__(batch_size, num_threads, device_id, seed=12 + device_id)\n        self.input = ops.FileReader(file_root=data_dir, shard_id=local_rank, num_shards=world_size,\n                                    random_shuffle=False)\n        self.decode = ops.ImageDecoder(device=\"mixed\", output_type=types.RGB)\n        self.res = ops.Resize(device=\"gpu\", resize_shorter=size, interp_type=types.INTERP_TRIANGULAR)\n        self.cmnp = ops.CropMirrorNormalize(device=\"gpu\",\n                                            output_dtype=types.FLOAT,\n                                            output_layout=types.NCHW,\n                                            crop=(crop, crop),\n                                            image_type=types.RGB,\n                                            mean=[0.485 * 255, 0.456 * 255, 0.406 * 255],\n                                            std=[0.229 * 255, 0.224 * 255, 0.225 * 255])\n\n    def define_graph(self):\n        self.jpegs, self.labels = self.input(name=\"Reader\")\n        images = self.decode(self.jpegs)\n        images = self.res(images)\n        output = self.cmnp(images)\n        return [output, self.labels]\n\n\ndef get_imagenet_iter_dali(type, image_dir, batch_size, num_threads, device_id, num_gpus, crop, val_size=256,\n                           world_size=1,\n                           local_rank=0):\n    if type == 'train':\n        pip_train = HybridTrainPipe(batch_size=batch_size, num_threads=num_threads, device_id=local_rank,\n                                    data_dir=image_dir + '/train',\n                                    crop=crop, world_size=world_size, local_rank=local_rank)\n        pip_train.build()\n        # dali_iter_train = DALIClassificationIterator(pip_train, size=pip_train.epoch_size(\"Reader\") // world_size)\n        dali_iter_train = DALIClassificationIterator(pip_train, size=pip_train.epoch_size(\"Reader\") // world_size,\n                                                     auto_reset=True)\n        return dali_iter_train\n    elif type == 'val':\n        pip_val = HybridValPipe(batch_size=batch_size, num_threads=num_threads, device_id=local_rank,\n                                data_dir=image_dir + '/val',\n                                crop=crop, size=val_size, world_size=world_size, local_rank=local_rank)\n        pip_val.build()\n        dali_iter_val = DALIClassificationIterator(pip_val, size=pip_val.epoch_size(\"Reader\") // world_size,\n                                                   auto_reset=True)\n        return dali_iter_val\n\n\ndef imshow(inp, title=None):\n    \"\"\"Imshow for Tensor.\"\"\"\n    inp = inp.numpy().transpose((1, 2, 0))\n    mean = np.array([0.485, 0.456, 0.406])\n    std = np.array([0.229, 0.224, 0.225])\n    inp = std * inp + mean\n    inp = np.clip(inp, 0, 1)\n    plt.imshow(inp)\n    if title is not None:\n        plt.title(title)\n    plt.pause(0.001)  # pause a bit so that plots are updated\n\n\nclass DATAPREFEACHER():\n    def __init__(self, loader):\n        self.loader = iter(loader)\n        self.stream = torch.cuda.Stream()\n        # self.mean = torch.tensor([0.485 * 255, 0.456 * 255, 0.406 * 255]).cuda().view(1,3,1,1)\n        # self.std = torch.tensor([0.229 * 255, 0.224 * 255, 0.225 * 255]).cuda().view(1,3,1,1)\n        # With Amp, it isn't necessary to manually convert data to half.\n        # if args.fp16:\n        #     self.mean = self.mean.half()\n        #     self.std = self.std.half()\n        self.preload()\n\n    def preload(self):\n        try:\n            self.next_input, self.next_target = next(self.loader)\n        except StopIteration:\n            self.next_input = None\n            self.next_target = None\n            return\n        with torch.cuda.stream(self.stream):\n            self.next_input = self.next_input.cuda(non_blocking=True)\n            self.next_target = self.next_target.cuda(non_blocking=True)\n            # With Amp, it isn't necessary to manually convert data to half.\n            # if args.fp16:\n            #     self.next_input = self.next_input.half()\n            # else:\n            self.next_input = self.next_input.float()\n            # self.next_input = self.next_input.sub_(self.mean).div_(self.std)\n\n\ndef binary_acc(y_pred, y_test):\n    y_pred_tag = torch.round(torch.sigmoid(y_pred))\n\n    num = (y_pred_tag == y_test).sum().float()\n\n    return num\n\n\ndef train_model(model, criterion, optimizer, scheduler, num_epochs=25, **kwargs):\n    since = time.time()\n\n    best_model_wts = copy.deepcopy(model.state_dict())\n    best_acc = 0.0\n\n    train_loader = get_imagenet_iter_dali(type='train', image_dir=data_dir, batch_size=batch_size,\n                                          num_threads=num_workers, crop=224, device_id=0, num_gpus=1)\n    val_loader = get_imagenet_iter_dali(type='val', image_dir=data_dir, batch_size=batch_size,\n                                        num_threads=num_workers, crop=224, device_id=0, num_gpus=1)\n    for epoch in range(num_epochs):\n\n        print('Epoch {}/{}'.format(epoch, num_epochs - 1))\n        print('-' * 10)\n\n        logger.append('Epoch {}/{}'.format(epoch, num_epochs - 1))\n        logger.append('-' * 10)\n\n        writer.add_scalar('LR',\n                          scheduler.get_last_lr()[0],  # 1.4版本以上的用last_lr,以下的用scheduler.get_lr()[0]\n                          epoch)\n\n        # train_loader = get_imagenet_iter_dali(type='train', image_dir=data_dir, batch_size=batch_size,\n        #                                  num_threads=num_workers, crop=224, device_id=0, num_gpus=1)\n        # val_loader = get_imagenet_iter_dali(type='val', image_dir=data_dir, batch_size=batch_size,\n        #                                  num_threads=num_workers, crop=224, device_id=0, num_gpus=1)\n\n        # Each epoch has a training and validation phase\n        for phase in ['train', 'val']:\n            if phase == 'train':\n                model.train()  # Set model to training mode\n            else:\n                model.eval()  # Set model to evaluate mode\n\n            running_loss = 0.0\n            running_corrects = 0\n\n            # Iterate over data.\n            tm_start = time.time()\n            # Iterate over data.\n            for inputs, labels in dataloaders[phase]:\n                inputs = inputs.to(device)\n                labels = labels.to(device)\n\n                # zero the parameter gradients\n                optimizer.zero_grad()\n\n                # forward\n                # track history if only in train\n                with torch.set_grad_enabled(phase == 'train'):\n                    # if is_mixup:\n                    if False:\n                        inputs, targets_a, targets_b, lam = mixup_data(inputs, labels, alpha=1.0)\n                        inputs, targets_a, targets_b = map(Variable, (inputs,\n                                                                      targets_a, targets_b))\n                        outputs = model(inputs)\n                        loss = mixup_criterion(criterion, outputs, targets_a, targets_b, lam)\n                    else:\n                        outputs = model(inputs)\n                        loss = criterion(outputs, labels)\n\n                    # backward + optimize only if in training phase\n                    if phase == 'train':\n                        loss.backward()\n                        optimizer.step()\n\n                _, preds = torch.max(outputs, 1)\n                # statistics\n                running_loss += loss.item() * inputs.size(0)\n                running_corrects += torch.sum(preds == labels.data)\n\n            tm_epoch = time.time() - tm_start\n            if phase == 'train':\n                scheduler.step()\n\n            epoch_loss = running_loss / dataset_sizes[phase]\n            epoch_acc = running_corrects.double() / dataset_sizes[phase]\n\n            print('{} Loss: {:.4f} Acc: {:.4f} tm: {}'.format(\n                phase, epoch_loss, epoch_acc, tm_epoch))\n            logger.append('{} Loss: {:.4f} Acc: {:.4f} tm: {}'.format(\n                phase, epoch_loss, epoch_acc, tm_epoch))\n\n            if phase == 'train':\n                writer.add_scalar('Loss/train_loss', epoch_loss, epoch)\n                writer.add_scalar('Accuracy/train_acc', epoch_acc, epoch)\n            else:\n                writer.add_scalar('Loss/val_loss', epoch_loss, epoch)\n                writer.add_scalar('Accuracy/val_acc', epoch_acc, epoch)\n\n            # deep copy the model\n            if phase == 'val' and epoch_acc > best_acc:\n                best_acc = epoch_acc\n                best_model_wts = copy.deepcopy(model.state_dict())\n\n        if (epoch % 10 == 0 and epoch > 0):\n            torch.save(best_model_wts, 'resnet_ft_{}.pth'.format(epoch))\n\n    time_elapsed = time.time() - since\n    print('Training complete in {:.0f}m {:.0f}s'.format(\n        time_elapsed // 60, time_elapsed % 60))\n\n    print('Best val Acc: {:4f}'.format(best_acc))\n    logger.append('Training complete in {:.0f}m {:.0f}s'.format(\n        time_elapsed // 60, time_elapsed % 60))\n    logger.append('Best val Acc: {:4f}'.format(best_acc))\n    writer.close()\n\n    # load best model weights\n    model.load_state_dict(best_model_wts)\n    return model\n\n\ndef visualize_model(model, num_images=6):\n    was_training = model.training\n    model.eval()\n    images_so_far = 0\n    fig = plt.figure()\n\n    with torch.no_grad():\n        for i, data in enumerate(val_loader):\n            inputs = data[0][\"data\"].cuda(non_blocking=True)\n            labels = data[0][\"label\"].squeeze().long().cuda(non_blocking=True)\n\n            outputs = model(inputs)\n            _, preds = torch.max(outputs, 1)\n\n            for j in range(inputs.size()[0]):\n                images_so_far += 1\n                ax = plt.subplot(num_images // 2, 2, images_so_far)\n                ax.axis('off')\n                ax.set_title('predicted: {}'.format(preds[j]))\n                imshow(inputs.cpu().data[j])\n\n                if images_so_far == num_images:\n                    model.train(mode=was_training)\n                    return\n        model.train(mode=was_training)\n\n\nclass FocalLoss(nn.Module):\n    r\"\"\"\n        This criterion is a implemenation of Focal Loss, which is proposed in\n        Focal Loss for Dense Object Detection.\n\n            Loss(x, class) = - \\alpha (1-softmax(x)[class])^gamma \\log(softmax(x)[class])\n\n        The losses are averaged across observations for each minibatch.\n\n        Args:\n            alpha(1D Tensor, Variable) : the scalar factor for this criterion\n            gamma(float, double) : gamma > 0; reduces the relative loss for well-classified examples (p > .5),\n                                   putting more focus on hard, misclassified examples\n            size_average(bool): By default, the losses are averaged over observations for each minibatch.\n                                However, if the field size_average is set to False, the losses are\n                                instead summed for each minibatch.\n\n\n    \"\"\"\n\n    def __init__(self, class_num, alpha=None, gamma=2, size_average=True):\n        super(FocalLoss, self).__init__()\n        if alpha is None:\n            self.alpha = Variable(torch.ones(class_num, 1))\n        else:\n            if isinstance(alpha, Variable):\n                self.alpha = alpha\n            else:\n                self.alpha = Variable(alpha)\n        self.gamma = gamma\n        self.class_num = class_num\n        self.size_average = size_average\n\n    def forward(self, inputs, targets):\n        N = inputs.size(0)\n        C = inputs.size(1)\n        P = F.softmax(inputs)\n\n        class_mask = inputs.data.new(N, C).fill_(0)\n        class_mask = Variable(class_mask)\n        ids = targets.view(-1, 1)\n        class_mask.scatter_(1, ids.data, 1.)\n        # print(class_mask)\n\n        if inputs.is_cuda and not self.alpha.is_cuda:\n            self.alpha = self.alpha.cuda()\n        alpha = self.alpha[ids.data.view(-1)]\n\n        probs = (P * class_mask).sum(1).view(-1, 1)\n\n        log_p = probs.log()\n        # print('probs size= {}'.format(probs.size()))\n        # print(probs)\n\n        batch_loss = -alpha * (torch.pow((1 - probs), self.gamma)) * log_p\n        # print('-----bacth_loss------')\n        # print(batch_loss)\n\n        if self.size_average:\n            loss = batch_loss.mean()\n        else:\n            loss = batch_loss.sum()\n        return loss\n\n\ndef getArgs():\n    args = argparse.ArgumentParser()\n    args.add_argument('-d', '--datadir', help='img folder containing train & val data',\n                      default='data')  # 数据目录，包含train，val两个目录，\n    args.add_argument('-c', '--class_nums', help='labels num', type=int,\n                      default='1000')\n    args.add_argument('-n', '--nepochs', help='train epochs', type=int,\n                      default='30')\n    args.add_argument('-l', '--learning_ratio', help='init learning ratio', type=float,\n                      default='0.01')\n    args.add_argument('-b', '--batch_size', help='batch size', type=int,\n                      default='16')\n    args.add_argument('-t', '--read_thread', help='num thread for reading data', type=int,\n                      default='1')  # 加载数据的线程数\n    args.add_argument('-a', '--alpha_ratio', help='ratio of sample nums',\n                      default='None')  # focalloss中样本权重的alpha参数，eg:[0.1,0.3,0.6]\n\n    args.add_argument('-p', '--prefeacher', help='data prefeacher',\n                      action='store_true')  # 是否使用数据预加载\n    args.add_argument('-s', '--is_show', help='show train data', action='store_true')  # 是否展示图片，仅可视化用\n    args.add_argument('-f', '--is_focalloss', action='store_true')  # 是否使用focal loss作为损失函数\n    args.add_argument('-fa', '--is_focalloss_alpha', action='store_true')  # focal loss中是否使用alpha参数\n    return vars(args.parse_args())\n\n\nif __name__ == '__main__':\n    args = getArgs()\n    data_dir = args['datadir']  # 数据目录\n    class_nums = args['class_nums']\n    nepochs = args['nepochs']  # 训练epochs\n    learning_rate = args['learning_ratio']  # 学习率\n    batch_size = args['batch_size']\n    num_workers = args['read_thread']\n    alpha_ratio = args['alpha_ratio']  # 训练样本所占的比例信息，\n\n    is_prefeacher = args['prefeacher']  # 是否进行数据预加载来提升训练速度\n    is_show = args['is_show']  # 是否查看增强后的训练数据\n    is_focalloss = args['is_focalloss']  # 是否使用focal-loss\n    is_focalloss_alpha = args['is_focalloss_alpha']  # 在focal-loss基础上增加对样本不均衡的处理\n\n    print(\n        'datadir: {}, nepochs: {}, lr: {}, bz: {}, num_workers: {}, alpha_ratio: {}, is_prefetch: {},is_focalloss: {}, is_focalloss_alpha: {}'\n        .format(data_dir, nepochs, learning_rate, batch_size, num_workers, alpha_ratio, is_prefeacher, is_focalloss,\n                is_focalloss_alpha))\n\n    now = datetime.now()\n    tm_now = now.strftime(\"%m%d%H%M\")\n\n    data_subfolder = data_dir.split(os.path.sep)[-1] if data_dir[-1] != os.path.sep else data_dir.split(os.path.sep)[-2]\n    logger = Logger(os.path.join('trainlog', data_subfolder + '_' + tm_now + '.log'))\n    writer = SummaryWriter(logdir='trainlog', filename_suffix=data_subfolder + '_' + tm_now)\n\n    logger.append(args)\n    # train_loader = get_imagenet_iter_dali(type='train', image_dir=data_dir, batch_size=batch_size,\n    # num_threads=num_workers, crop=224, device_id=0, num_gpus=1)\n    # val_loader = get_imagenet_iter_dali(type='val', image_dir=data_dir, batch_size=batch_size,\n    # num_threads=num_workers, crop=224, device_id=0, num_gpus=1)o\n    data_transforms = {\n        'train': transforms.Compose([\n            transforms.RandomResizedCrop(224),\n            transforms.RandomHorizontalFlip(),\n            transforms.ToTensor(),\n            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n        ]),\n        'val': transforms.Compose([\n            transforms.Resize(256),\n            transforms.CenterCrop(224),\n            transforms.ToTensor(),\n            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n        ]),\n    }\n    image_datasets = {x: datasets.ImageFolder(os.path.join(data_dir, x),\n                                              data_transforms[x])\n                      for x in ['train', 'val']}\n    dataloaders = {x: torch.utils.data.DataLoader(image_datasets[x], batch_size=batch_size,\n                                                  shuffle=True, num_workers=num_workers)\n                   for x in ['train', 'val']}\n\n    dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'val']}\n    class_names = image_datasets['train'].classes\n    logger.append('class_names: {}'.format(class_names))\n    # model parameters\n    model_ft = models.resnet50(pretrained=True)\n    # model_ft = models.resnet101(pretrained=True)\n    num_ftrs = model_ft.fc.in_features\n\n    model_ft.fc = nn.Linear(num_ftrs, class_nums)  # 迁移学习，更新输出类别数\n\n    # model_ft = models.mobilenet_v2(pretrained=True)\n    # model_ft.classifier[1] = nn.Linear(model_ft.last_channel, class_nums)\n\n    model_ft = model_ft.to(device)\n\n    criterion = nn.CrossEntropyLoss()\n    # criterion = torch.nn.BCEWithLogitsLoss()\n\n    # https://zhuanlan.zhihu.com/p/28527749\n    # focal-loss\n    if is_focalloss:\n        if is_focalloss_alpha:\n            assert alpha_ratio != 'None'\n            # alpha_ratio = [0.089955, 0.125509, 0.131806, 0.12765, 0.094667, 0.089955, 0.0836153, 0.0308417, 0.176141, 0.0498608]\n            alpha_ratio = eval(alpha_ratio)\n            alpha_ratio_tensor = torch.FloatTensor(alpha_ratio)\n        else:\n            alpha_ratio_tensor = None\n        print('##in focalloss##')\n        criterion = FocalLoss(class_nums, alpha=alpha_ratio_tensor)\n\n    # Observe that all parameters are being optimized\n    optimizer_ft = optim.SGD(model_ft.parameters(), lr=learning_rate, momentum=0.9)\n    # optimizer_ft = optim.Adam(model_ft.parameters(), lr=learning_rate, momentum=0.9)\n\n    # Decay LR by a factor of 0.1 every 7 epochs\n    exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=30, gamma=0.1)\n\n    # 训练:\n    model_ft = train_model(model_ft, criterion, optimizer_ft, exp_lr_scheduler,\n                           num_epochs=nepochs)\n\n    data_subfolder = data_dir.split(os.path.sep)[-1] if data_dir[-1] != os.path.sep else data_dir.split(os.path.sep)[-2]\n\n    dst_model_path = 'resnet_ft_img_{}.pth'.format(data_subfolder + '-' + str(class_nums) + '_' + tm_now)\n    torch.save(model_ft.state_dict(), dst_model_path)\n    print('savemodel: {}'.format(dst_model_path))\n    logger.append('savemodel: {}'.format(dst_model_path))\n","sub_path":"Func_train_pytorch.py","file_name":"Func_train_pytorch.py","file_ext":"py","file_size_in_byte":25511,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"522899352","text":"import urllib3\nimport xml.etree.ElementTree as ET\nimport json\nimport re\nimport logging\nfrom datetime import datetime, timedelta, timezone\nfrom urllib.parse import urljoin\nfrom bs4 import BeautifulSoup\n\nlogger = logging.getLogger(__name__)\n\npool_manager = urllib3.PoolManager(timeout=30.0)\n\n\ndef get_human_time(milliseconds):\n\n    if milliseconds is None:\n        milliseconds = 0\n\n    try:\n        d = datetime(1, 1, 1) + timedelta(milliseconds=int(milliseconds))\n    except OverflowError as ex:\n        logger.error(\"Unable to convert %s ms to human time: %s\", milliseconds, ex)\n        raise ex\n    time = [d.day - 1, d.hour, d.minute, d.second + (milliseconds % 1000) / 1000.0]\n    time_suffix = [\"d\", \"h\", \"m\", \"s\"]\n\n    val = []\n    for i in range(len(time)):\n        if time[i] > 0:\n            if time_suffix[i] == \"s\":\n                s = \"%.1f%s\" % (time[i], time_suffix[i])\n            else:\n                s = \"%d%s\" % (time[i], time_suffix[i])\n            val.append(s)\n    return \" \".join(val)\n\n\nclass BuildNotFoundException(Exception):\n    def __init__(self, build_info):\n\n        super(BuildNotFoundException, self).__init__(\n            \"Job %s#%s not found\" % (build_info.job_name, build_info.build_number)\n        )\n\n\nclass BuildSection:\n    def __init__(self, name, section_type=None):\n        self.name = name\n        self.__type = section_type\n        self.parent = None\n        self.children = []\n        self.start = None\n        self.end = None\n\n    def __str__(self):\n        info = self.name\n        if self.type:\n            info += \" (%s)\" % self.type\n        info += \" %s\" % get_human_time(self.duration)\n        return info\n\n    @property\n    def type(self):\n        if self.__type:\n            return self.__type\n\n        if self.parent:\n            return self.parent.type\n\n        return None\n\n    @property\n    def duration(self):\n        if not self.start or not self.end:\n            return 0\n\n        assert self.end >= self.start\n\n        return self.end - self.start  # in us\n\n    @property\n    def parents_cnt(self):\n        cnt = 0\n        if self.parent is not None:\n            cnt = 1 + self.parent.parents_cnt\n        return cnt\n\n\nclass PipelineNode:\n    def __init__(self, id):\n        self.id = id\n        self.label = None\n        self.branch = None\n        self.header = None\n        self.message = None\n        self.parent = None\n        self.content = {}\n\n    def get_branch(self):\n        if self.branch:\n            return self.branch\n        if self.parent:\n            return self.parent.get_branch()\n        return None\n\n\nclass BuildInfo:\n    def __init__(\n        self,\n        fetcher,\n        job_name,\n        build_number,\n        stage=None,\n        fetch_on_init=True,\n        cache=None,\n        fetch_sections=True,\n        upstream=None,\n        virtual=False,\n    ):\n        self.fetcher = fetcher\n        self.virtual = virtual\n        self.job_name = job_name\n\n        self._build_number = None\n        self.build_number_str = build_number\n        try:\n            self._build_number = int(build_number)\n        except ValueError:\n            pass\n\n        self._build_url = None\n\n        self.stage = stage\n\n        self._info_fetched = False\n        self._job_type = None\n        self._queueing_duration = None\n        self._start = None\n        self._duration = None\n        self._result = None\n        self._node_name = None\n\n        self.user = None\n\n        self.upstream = upstream\n        self._sub_builds = None\n        self.__all_builds = None\n\n        self._fetch_sections = fetch_sections\n        self._sections = None\n\n        self._parameters = None\n\n        self.build_json = None\n        self._raw_data = None\n\n        self._description = None\n        self._failure_causes = None\n        self._console_log = None\n\n        self.cache = cache\n\n        self.lane_index = None\n\n        if fetch_on_init:\n            self.fetch()\n\n    def fetch(self, fatal=False):\n        self._fetch_info(fatal)\n\n        if self._sub_builds is None:\n            self._fetch_sub_builds()\n\n        if self._fetch_sections == \"done\":\n            # Only fetch sections if the top build is done\n            self._fetch_sections = self.is_done\n        if self._sections is None and self._fetch_sections is True:\n            self.__determine_sections()\n\n    def build_url(self, extra=\"\"):\n        if self._build_url:\n            return urljoin(self._build_url, extra)\n        return urljoin(\n            self.fetcher.url,\n            \"/\".join([\"job\", self.job_name, self.build_number_str, extra]),\n        )\n\n    def set_build_url(self, value):\n        self._build_url = value\n\n    def __fetch_build_data(self, extra=\"\", encoding=\"ISO-8859-1\"):\n        raw_data = None\n        cache_key = None\n        api_url = self.build_url(extra)\n        if self.cache and self._build_number:\n            cache_key = \"jenkins-build-analyzer-%s\" % api_url\n            raw_data = self.cache.get(cache_key)\n\n        if raw_data:\n            logger.info(\"Content for '%s' already cached\", api_url)\n            return (raw_data, True, cache_key)\n\n        logger.info(\"Fetching info from '%s'\", api_url)\n\n        content = pool_manager.urlopen(\"GET\", api_url)\n        if content.status != 200:\n            raise BuildNotFoundException(self)\n\n        raw_data = content.data.decode(encoding)\n\n        return (raw_data, False, cache_key)\n\n    def get_build_json(self):\n        if self.build_json:\n            return self.build_json\n\n        self._raw_data, content_cached, cache_key = self.__fetch_build_data(\n            \"api/json?depth=3\"\n        )\n        try:\n            self.build_json = json.loads(self._raw_data)\n        except json.decoder.JSONDecodeError as ex:\n            logger.error(\"Unable to parse JSON at '%s'\", self.build_url())\n            logger.error(ex)\n            raise BuildNotFoundException(self)\n\n        if self.cache and cache_key and not content_cached and self.is_done:\n            try:\n                # Cache the content for 5h\n                self.cache.set(cache_key, self._raw_data, (5 * 60 * 60))\n            except Exception:\n                logger.exception(\"Unable to set cache for build xml\")\n\n        return self.build_json\n\n    # Retrieve the XML from Jenkins that contains some info about\n    # the build.\n    def _fetch_info(self, fatal=False):\n\n        if self._info_fetched:\n            logger.warning(\"Skipping fetch info as already fetched\")\n            return\n        self._info_fetched = True\n\n        # Provide reasonable defaults in case the build is not found\n        self._failure_causes = []\n        self._start = 0\n        if self.upstream:\n            self._start = self.upstream.start\n        self._duration = 0\n        self._queueing_duration = 0\n        self._parameters = {}\n\n        if self.virtual:\n            return\n\n        logger.debug(\"Fetching object %s\", self)\n\n        tree = self.get_build_json()\n\n        job_type = tree[\"_class\"]\n        if job_type == \"org.jenkinsci.plugins.workflow.job.WorkflowRun\":\n            self._job_type = \"pipeline\"\n        elif job_type == \"hudson.model.FreeStyleBuild\":\n            self._job_type = \"freestyle\"\n        else:\n            self._job_type = job_type\n\n        self._start = int(tree[\"timestamp\"])\n        self._duration = int(tree[\"duration\"])\n\n        self._description = tree.get(\"description\")\n\n        for action in tree.get(\"actions\", []):\n            action_class = action.get(\"_class\")\n            if not action_class:\n                continue\n            if action_class == \"jenkins.metrics.impl.TimeInQueueAction\":\n                self._queueing_duration = int(action[\"queuingDurationMillis\"])\n            elif action_class == \"hudson.model.Cause\":\n                for cause_elmt in action[\"causes\"]:\n                    cause_class = cause_elmt.get(\"_class\")\n                    if cause_class == \"hudson.model.Cause$UpstreamCause\":\n                        upstream_job = cause_elmt[\"upstreamProject\"]\n                        upstream_build = cause_elmt[\"upstreamBuild\"]\n                        if upstream_job and upstream_build:\n                            self.upstream = self.fetcher.get_build(\n                                upstream_job,\n                                upstream_build,\n                                fetch=False,\n                                fetch_sections=False,\n                            )\n                    elif cause_class == \"hudson.model.Cause$UserIdCause\":\n                        user_id = cause_elmt[\"userId\"]\n                        user_name = cause_elmt[\"userName\"]\n                        self.user = {\"user_id\": user_id, \"user_name\": user_name}\n            elif (\n                action_class\n                == \"com.sonyericsson.jenkins.plugins.bfa.model.FailureCauseBuildAction\"\n            ):\n                for cause_elmt in action[\"foundFailureCauses\"]:\n                    cause = {}\n                    name = cause_elmt.get(\"name\")\n                    if name is None:\n                        continue\n\n                    cause[\"name\"] = name\n                    desc = cause_elmt.get(\"description\")\n                    if desc is not None:\n                        cause[\"description\"] = desc.strip()\n                    cause[\"categories\"] = []\n                    categories = cause_elmt.get(\"categories\")\n                    if categories:\n                        for cat in categories:\n                            cause[\"categories\"].append(cat)\n                    self._failure_causes.append(cause)\n            elif action_class == \"hudson.model.ParametersAction\":\n                for param_elmt in action[\"parameters\"]:\n                    name_elmt = param_elmt.get(\"name\")\n                    value_elmt = param_elmt.get(\"value\")\n                    if isinstance(value_elmt, bool):\n                        value_elmt = str(value_elmt).lower()\n                    else:\n                        value_elmt = str(value_elmt)\n                    if name_elmt is None:\n                        logger.warning(\n                            \"Missing name element for parameter %s\", param_elmt\n                        )\n                        continue\n                    if value_elmt is None:\n                        logger.warning(\n                            \"Missing value element for parameter %s\", name_elmt\n                        )\n                        continue\n                    param = {\n                        \"class_name\": param_elmt[\"_class\"],\n                        \"name\": name_elmt,\n                        \"value\": value_elmt,\n                    }\n                    self._parameters[param[\"name\"]] = param\n\n        logger.debug(\n            \"%s#%s: %s %d %d %d %s\"\n            % (\n                self.job_name,\n                self.build_number,\n                self._job_type,\n                self._start,\n                self._queueing_duration,\n                self._duration,\n                self._result,\n            )\n        )\n\n    @property\n    def job_type(self):\n        if not self._job_type:\n            self._fetch_info()\n\n        return self._job_type\n\n    @job_type.setter\n    def job_type(self, value):\n        self._job_type = value\n\n    @property\n    def start(self):\n        if not self._start:\n            self._fetch_info()\n\n        return self._start\n\n    @start.setter\n    def start(self, value):\n        self._start = value\n\n    @property\n    def end(self):\n        if self.start and self.duration:\n            return self.start + self.duration\n        return None\n\n    @end.setter\n    def end(self, value):\n        if self._start:\n            self._duration = value - self.start\n\n    @property\n    def queueing_duration(self):\n        if self._queueing_duration is None:\n            self._fetch_info()\n\n        return self._queueing_duration\n\n    @queueing_duration.setter\n    def queueing_duration(self, value):\n        self._queueing_duration = value\n\n    @property\n    def duration(self):\n        if self._duration is None:\n            self._fetch_info()\n\n        if self.result == \"IN_PROGRESS\":\n            now_ts = int(datetime.now(tz=timezone.utc).timestamp() * 1000)\n            assert now_ts >= self.start\n            duration = now_ts - self.start\n            return duration\n\n        return self._duration\n\n    @duration.setter\n    def duration(self, value):\n        self.duration = value\n\n    @property\n    def parameters(self):\n        if self._parameters is None:\n            self._fetch_info()\n\n        return self._parameters\n\n    @property\n    def node_name(self):\n        if self._node_name:\n            return self._node_name\n\n        if not self.build_json:\n            self._fetch_info()\n\n        if not self.build_json:\n            return\n\n        self._node_name = self.build_json.get(\"builtOn\")\n\n        return self._node_name\n\n    @property\n    def build_number(self):\n        if self._build_number is not None:\n            return self._build_number\n\n        if not self.build_json:\n            self._fetch_info()\n\n        self._build_number = int(self.build_json[\"number\"])\n        return self._build_number\n\n    @property\n    def is_done(self):\n        return (self.result != \"IN_PROGRESS\") and (self.result != \"UNKNOWN\")\n\n    def __check_infra_failure(self):\n        for cause in self.failure_causes:\n            if \"retrigger\" in cause[\"categories\"]:\n                return \"INFRA_FAILURE\"\n        return \"FAILURE\"\n\n    @property\n    def result(self):\n        if self._result:\n            return self._result\n\n        if not self.build_json:\n            self._fetch_info()\n\n        # Determine result\n        result = None\n        if not self.build_json:\n            result = \"UNKNOWN\"\n        elif self.build_json.get(\"building\"):\n            result = \"IN_PROGRESS\"\n        elif self.build_json.get(\"result\") is not None:\n            result = self.build_json.get(\"result\")\n        else:\n            result = \"UNKNOWN\"\n\n        if result == \"FAILURE\":\n            result = self.__check_infra_failure()\n\n        self._result = result\n\n        return self._result\n\n    @result.setter\n    def result(self, value):\n        if value == \"FAILURE\":\n            value = self.__check_infra_failure()\n        self._result = value\n\n    @property\n    def description(self):\n        if self._description is None:\n            self._fetch_info()\n\n        return self._description\n\n    @description.setter\n    def description(self, value):\n        self._description = value\n\n    @property\n    def failure_causes(self):\n        if not self._failure_causes:\n            self._fetch_info()\n\n        return self._failure_causes\n\n    @property\n    def console_log(self):\n        if self._console_log:\n            return self._console_log\n\n        url_extra = \"consoleText\"\n        if self.job_type == \"pipeline\":\n            url_extra = \"logText/progressiveHtml\"\n\n        raw_data, content_cached, cache_key = self.__fetch_build_data(url_extra)\n\n        self._console_log = raw_data\n\n        if (\n            self.cache\n            and cache_key\n            and not content_cached\n            and self._result != \"IN_PROGRESS\"\n        ):\n            try:\n                # Cache the content for 5h\n                self.cache.set(cache_key, self._console_log, (5 * 60 * 60))\n            except Exception:\n                logger.exception(\n                    \"Unable to set cache for console log (size=%s)\",\n                    len(self._console_log),\n                )\n\n        return self._console_log\n\n    def create_sub_build(self, job_name, build_number, stage=\"\"):\n        sub_build = self.fetcher.get_build(job_name, build_number, fetch=False)\n        sub_build.stage = stage\n        sub_build.upstream = self\n        try:\n            sub_build.fetch()\n        except BuildNotFoundException as ex:\n            logger.warning(ex)\n\n        # Append\n        if not self._sub_builds:\n            self._sub_builds = []\n        self._sub_builds.append(sub_build)\n\n        return sub_build\n\n    def __parse_pipeline_log(self):\n\n        try:\n            doc = BeautifulSoup(\n                \"<html>{0}</html>\".format(self.console_log), features=\"html.parser\"\n            )\n        except ET.ParseError as e:\n            logger.error(\"Unable to parse HTML from '%s'\", self.build_url())\n            logger.error(e)\n            return\n\n        pattern = re.compile(r\"/job/(?P<job>.+)/(?P<bn>\\d+)/\")\n\n        nodes = {}\n\n        for span in doc.find_all(\"span\"):\n            if \"class\" not in span.attrs:\n                continue\n\n            span_class = span.attrs[\"class\"][0]\n\n            if span_class == \"pipeline-new-node\":\n                node_id = span.attrs[\"nodeid\"]\n\n                # start_id = span.attrs['startid']\n\n                node = PipelineNode(node_id)\n                node.header = span.text\n                if \"enclosingid\" in span.attrs:\n                    enclosing_id = span.attrs[\"enclosingid\"]\n                    if not nodes[enclosing_id]:\n                        logger.error(\"Node %s does not exist\", enclosing_id)\n                        continue\n\n                    nodes[enclosing_id].content[node_id] = node\n                    node.parent = nodes[enclosing_id]\n\n                if \"label\" in span.attrs:\n                    node.label = span.attrs[\"label\"]\n                    if node.label.startswith(\"Branch: \"):\n                        node.branch = span.attrs[\"label\"].replace(\"Branch: \", \"\")\n\n                nodes[node_id] = node\n\n            elif span_class.startswith(\"pipeline-node-\"):\n                node_id = span_class.replace(\"pipeline-node-\", \"\")\n                if node_id not in nodes:\n                    logger.warning(\"Node %s not found\", node_id)\n                    continue\n\n                node = nodes[node_id]\n\n                branch = None\n                if node.parent:\n                    branch = node.get_branch()\n\n                if \"Starting building:\" not in span.text:\n                    continue\n\n                match = None\n                for job_link in span.find_all(\"a\"):\n\n                    job_href = job_link.attrs[\"href\"]\n\n                    match = pattern.match(job_href)\n                    if match is None:\n                        continue\n\n                    job_name = match.group(\"job\")\n                    build_number = match.group(\"bn\")\n                    if job_name and build_number:\n                        branch_info = \"\"\n                        if branch:\n                            branch_info = \"[%s]\" % branch\n                        logger.debug(\n                            \"Sub-build: %s#%s %s\", job_name, build_number, branch_info\n                        )\n\n                        try:\n                            self.create_sub_build(job_name, build_number, branch)\n\n                        except BuildNotFoundException as ex:\n                            logger.error(ex)\n                            logger.warning(branch)\n\n                if match is None:\n                    logger.warning(\"No link found for %s\", span.text)\n\n            else:\n                logger.debug(span)\n\n    # Retrieve the 'sub-builds', which are launched from this job.\n    def _fetch_sub_builds(self):\n        self._sub_builds = []\n\n        if self.virtual:\n            return\n\n        if self.job_type not in [\"pipeline\"]:\n            return\n\n        # Parse log as HTML\n        if self.job_type == \"pipeline\":\n            self.__parse_pipeline_log()\n\n        logger.info(\n            \"%s#%s (%s): %d sub-build(s)\",\n            self.job_name,\n            self.build_number,\n            self.job_type,\n            len(self._sub_builds),\n        )\n\n    def __determine_sections(self):\n        self._sections = []\n\n        if self.job_type != \"freestyle\":\n            return\n\n        pattern = re.compile(\n            r\"^(?:.\\[95m)?\\[section:(?P<name>[^\\]]*)\\] (?P<boundary>start|end)? *\"\n            \"(time=(?P<time>[0-9]*))? *\"\n            \"(type=(?P<type>[a-z]*))? *\"\n            \"(.*)\"\n        )\n        pattern_reset = re.compile(\".*Executing post build scripts.*\")\n        current = None\n\n        for line in self.console_log.splitlines():\n            if pattern_reset.match(line):\n                # If the build was aborted while another section was in progress,\n                # stop processing the current section.\n                current = None\n                continue\n\n            m = pattern.match(line)\n            if not m:\n                if (\n                    \"[section:\" in line\n                    and \"message\" not in line\n                    and \"echo -e\" not in line\n                ):\n                    logger.warning(\"'%s' not matched\", line)\n                continue\n\n            logger.debug(\"Section: %s\", line)\n\n            boundary = m.group(\"boundary\")\n            name = m.group(\"name\")\n            section_type = m.group(\"type\")\n            time = 0\n            if m.group(\"time\"):\n                time = int(m.group(\"time\")) * 1000\n            else:\n                logger.warning(\"No time in section '%s'\", line)\n\n            if boundary == \"start\":\n                # Start\n                new = BuildSection(name, section_type)\n                self._sections.append(new)\n\n                if current:\n                    new.parent = current\n                    current.children.append(new)\n\n                current = new\n                current.start = time\n            elif boundary == \"end\":\n                # End\n                if current:\n                    current.end = time\n                    current = current.parent\n                else:\n                    logger.warning(\n                        \"Noticed a end section while no section is in progress\"\n                    )\n            else:\n                raise Exception(\"Unknown boundary %s\" % boundary)\n\n        for section in self._sections:\n            logger.debug(\n                \"Section: %s %s %s\", section.name, section.type, section.duration\n            )\n\n    @property\n    def sub_builds(self):\n        if self._sub_builds is None:\n            try:\n                self._fetch_sub_builds()\n            except BuildNotFoundException:\n                pass\n\n        return self._sub_builds\n\n    @property\n    def all_builds(self):\n        if self.__all_builds is None:\n            blds = [self]\n\n            for bld in self.sub_builds:\n                blds += bld.all_builds\n\n            self.__all_builds = blds\n\n        return self.__all_builds\n\n    @property\n    def sections(self):\n        return self._sections\n\n\nclass BuildInfoFetcher:\n    def __init__(self, url, cache=None, info_class=BuildInfo, fetch_sections=True):\n        self.url = url\n        self.cache = cache\n        self.info_class = info_class\n        self.fetch_sections = fetch_sections\n        self.builds = {}\n\n    def _create_build(self, job_name, build_number, fetch_sections=None):\n        if fetch_sections is None:\n            fetch_sections = self.fetch_sections\n        return self.info_class(\n            self,\n            job_name,\n            build_number,\n            fetch_on_init=False,\n            cache=self.cache,\n            fetch_sections=fetch_sections,\n        )\n\n    def get_build(\n        self, job_name, build_number, fetch=True, fetch_sections=None, fatal=False\n    ):\n        build_id = \"%s #%s\" % (job_name, build_number)\n        if build_id not in self.builds:\n            build = self._create_build(job_name, build_number)\n            if fetch:\n                build.fetch(fatal=fatal)\n            self.builds[build_id] = build\n\n        return self.builds[build_id]\n\n    def fetch(self, job_name, build_number, fatal=False):\n        return self.get_build(job_name, build_number, fatal=fatal)\n","sub_path":"src/job_info.py","file_name":"job_info.py","file_ext":"py","file_size_in_byte":23688,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"201483147","text":"# uncompyle6 version 3.7.4\n# Python bytecode 2.7 (62211)\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-10.10-intel/egg/tornpg.py\n# Compiled at: 2015-03-25 03:06:37\n\"\"\"A lightweight wrapper around psycopg2.\n\"\"\"\nfrom __future__ import absolute_import, division, with_statement\nimport copy, itertools, logging, os, time\ntry:\n    import psycopg2, psycopg2.extensions\n    from psycopg2.extensions import connection as base_connection, cursor as base_cursor\nexcept ImportError:\n    if 'READTHEDOCS' in os.environ:\n        psycopg2 = None\n    else:\n        raise\n\nversion = '0.1'\nversion_info = (0, 1, 0, 0)\npsycopg2.extensions.register_type(psycopg2.extensions.UNICODE)\n\nclass Connection(object):\n    \"\"\"A lightweight wrapper around psycopg2 DB-API connections.\n\n    The main value we provide is wrapping rows in a dict/object so that\n    columns can be accessed by name. Typical usage::\n\n        db = database.Connection(\"127.0.0.1\", \"mydb\")\n        for fish in db.query(\"SELECT * FROM fishes\"):\n            print fish.name\n\n    Cursors are hidden by the implementation, but other than that, the methods\n    are very similar to the DB-API.\n\n    --We explicitly set the timezone to UTC and the character encoding to\n    --UTF-8 on all connections to avoid time zone and encoding errors.\n    \"\"\"\n\n    def __init__(self, host, database, port=None, user=None, password=None, max_idle_time=7200, connect_timeout=0, connection_factory=None):\n        self.host = host\n        self.database = database\n        self.max_idle_time = float(max_idle_time)\n        args = dict(client_encoding='utf8', database=database, connect_timeout=connect_timeout)\n        if user is not None:\n            args['user'] = user\n        if password is not None:\n            args['password'] = password\n        pair = host.split(':')\n        if len(pair) == 2:\n            args['host'] = pair[0]\n            args['port'] = port or int(pair[1])\n        else:\n            args['host'] = host\n            args['port'] = port or 5432\n        args['connection_factory'] = connection_factory or base_connection\n        self._db = None\n        self._db_args = args\n        self._last_use_time = time.time()\n        try:\n            self.reconnect()\n        except Exception:\n            logging.error('Cannot connect to Postgresql on %s:%s', self.host, exc_info=True)\n\n        return\n\n    def __del__(self):\n        self.close()\n\n    def close(self):\n        \"\"\"Closes this database connection.\"\"\"\n        if getattr(self, '_db', None) is not None:\n            self._db.close()\n            self._db = None\n        return\n\n    def reconnect(self):\n        \"\"\"Closes the existing database connection and re-opens it.\"\"\"\n        self.close()\n        self._db = psycopg2.connect(**self._db_args)\n\n    def commit(self):\n        \"\"\"Commit any pending transaction to the database.\n        By default, Psycopg opens a transaction before executing the first\n        command: if commit() is not called, the effect of any data\n        manipulation will be lost.\n\n        The connection can be also set in \"autocommit\" mode: no transaction\n        is automatically open, commands have immediate effect.\n        \"\"\"\n        return self._db.commit()\n\n    def rollback(self):\n        \"\"\"Roll back to the start of any pending transaction. Closing a\n        connection without committing the changes first will cause an implicit\n        rollback to be performed.\n        Changed in psycopg2 version 2.5: if the connection is used in a with\n        statement, the method is automatically called if an exception is raised\n        in the with block.\n        \"\"\"\n        return self._db.rollback()\n\n    def query(self, query, *parameters, **kwparameters):\n        \"\"\"Returns a row list for the given query and parameters.\"\"\"\n        cursor = self._cursor()\n        try:\n            self._execute(cursor, query, parameters, kwparameters)\n            column_names = [ d[0] for d in cursor.description ]\n            return [ Row(itertools.izip(column_names, row)) for row in cursor ]\n        finally:\n            cursor.close()\n\n    def get(self, query, *parameters, **kwparameters):\n        \"\"\"Returns the (singular) row returned by the given query.\n\n        If the query has no results, returns None. If it has\n        more than one result, raises an exception.\n        \"\"\"\n        rows = self.query(query, *parameters, **kwparameters)\n        if not rows:\n            return\n        else:\n            if len(rows) > 1:\n                raise Exception('Multiple rows returned for Database.get() query')\n            else:\n                return rows[0]\n            return\n\n    def execute(self, query, *parameters, **kwparameters):\n        \"\"\"Executes the given query, returning the lastrowid from the query.\"\"\"\n        return self.execute_lastrowid(query, *parameters, **kwparameters)\n\n    def execute_lastrowid(self, query, *parameters, **kwparameters):\n        \"\"\"Executes the given query, returning the lastrowid from the query.\"\"\"\n        cursor = self._cursor()\n        try:\n            self._execute(cursor, query, parameters, kwparameters)\n            return cursor.lastrowid\n        finally:\n            cursor.close()\n\n    def execute_rowcount(self, query, *parameters, **kwparameters):\n        \"\"\"Executes the given query, returning the rowcount from the query.\"\"\"\n        cursor = self._cursor()\n        try:\n            self._execute(cursor, query, parameters, kwparameters)\n            return cursor.rowcount\n        finally:\n            cursor.close()\n\n    def executemany(self, query, parameters):\n        \"\"\"Executes the given query against all the given param sequences.\n\n        We return the lastrowid from the query.\n        \"\"\"\n        return self.executemany_lastrowid(query, parameters)\n\n    def executemany_lastrowid(self, query, parameters):\n        \"\"\"Executes the given query against all the given param sequences.\n\n        We return the lastrowid from the query.\n        \"\"\"\n        cursor = self._cursor()\n        try:\n            cursor.executemany(query, parameters)\n            return cursor.lastrowid\n        finally:\n            cursor.close()\n\n    def executemany_rowcount(self, query, parameters):\n        \"\"\"Executes the given query against all the given param sequences.\n\n        We return the rowcount from the query.\n        \"\"\"\n        cursor = self._cursor()\n        try:\n            cursor.executemany(query, parameters)\n            return cursor.rowcount\n        finally:\n            cursor.close()\n\n    update = execute_rowcount\n    updatemany = executemany_rowcount\n    insert = execute_lastrowid\n    insertmany = executemany_lastrowid\n\n    def _ensure_connected(self):\n        if self._db is None or time.time() - self._last_use_time > self.max_idle_time:\n            self.reconnect()\n        self._last_use_time = time.time()\n        return\n\n    def _cursor(self):\n        self._ensure_connected()\n        return self._db.cursor()\n\n    def _execute(self, cursor, query, parameters, kwparameters):\n        try:\n            return cursor.execute(query, kwparameters or parameters)\n        except OperationalError:\n            logging.error('Error connecting to Postgresql on %s', self.host)\n            self.close()\n            raise\n\n\nclass Row(dict):\n    \"\"\"A dict that allows for object-like property access syntax.\"\"\"\n\n    def __getattr__(self, name):\n        try:\n            return self[name]\n        except KeyError:\n            raise AttributeError(name)\n\n\nIntegrityError = psycopg2.IntegrityError\nOperationalError = psycopg2.OperationalError","sub_path":"pycfiles/tornpg-0.1.0-py2.7/tornpg.py","file_name":"tornpg.py","file_ext":"py","file_size_in_byte":7608,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"88382821","text":"#!/usr/bin/env python\n# encoding: utf-8\n\n# @author: yinhaochuan\n# @contact: yinhaochuan@100tal.com\n# @file: transposition.py\n# @time: 2018/7/9 下午3:46\n# @desc:\n\n\ndef transpose(A):\n    \"\"\"\n    :type A: List[List[int]]\n    :rtype: List[List[int]]\n    \"\"\"\n    if not A:\n        return\n    row = 0\n    result = [[0 for _ in range(len(A))] for _ in range(len(A[0]))]\n    while row < len(A):\n        for col in range(0, len(A[0])):\n            result[col][row] = A[row][col]\n        row += 1\n    return result\n\n\ntest_array = [[1, 2, 3], [4, 5, 6], [7, 8, 9]]\nprint(test_array)\nprint(transpose(test_array))\n\ntest_array2 = [[1, 2, 3], [4, 5, 6]]\nprint(test_array2)\nprint(transpose(test_array2))\n","sub_path":"leetcode/transposition.py","file_name":"transposition.py","file_ext":"py","file_size_in_byte":690,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"440658774","text":"\"\"\"\n Copyright 2018 Johns Hopkins University  (Author: Jesus Villalba)\n Apache 2.0  (http://www.apache.org/licenses/LICENSE-2.0)\n\"\"\"\n\nimport os\nimport os.path as path\nfrom collections import OrderedDict\nfrom copy import deepcopy\nimport logging\n\nimport numpy as np\n\nfrom .list_utils import *\n\n\nclass SCPList(object):\n    \"\"\"Class to manipulate script lists.\n\n    Attributes:\n      key: segment key name.\n      file_path: path to the file on hard drive, wav, ark or hdf5 file.\n      offset: Byte in Ark file where the data is located.\n      range_spec: range of frames (rows) to read.\n      key_to_index: Dictionary that returns the position of a key in the list.\n    \"\"\"\n\n    def __init__(self, key, file_path, offset=None, range_spec=None):\n        self.key = key\n        self.file_path = file_path\n        self.offset = offset\n        self.range_spec = range_spec\n        self.key_to_index = None\n        self.validate()\n\n    def validate(self):\n        \"\"\"Validates the attributes of the SCPList object.\"\"\"\n        self.key = list2ndarray(self.key)\n        self.file_path = list2ndarray(self.file_path, dtype=np.object)\n        assert len(self.key) == len(self.file_path)\n        if self.offset is not None:\n            if isinstance(self.offset, list):\n                self.offset = np.array(self.offset, dtype=np.int64)\n            assert len(self.key) == len(self.offset)\n        if self.range_spec is not None:\n            if len(self.range_spec) == 0:\n                self.range_spec = None\n            else:\n                if isinstance(self.range_spec, list):\n                    self.range_spec = np.array(self.offset, dtype=np.int64)\n                assert len(self.key) == self.range_spec.shape[0]\n                assert self.range_spec.shape[1] == 2\n\n    def copy(self):\n        \"\"\"Makes a copy of the object.\"\"\"\n        return deepcopy(self)\n\n    def __len__(self):\n        \"\"\"Returns the number of elements in the list.\"\"\"\n        return len(self.key)\n\n    def len(self):\n        \"\"\"Returns the number of elements in the list.\"\"\"\n        return len(self.key)\n\n    def _create_dict(self):\n        \"\"\"Creates dictionary that returns the position of\n        a segment in the list.\n        \"\"\"\n        self.key_to_index = OrderedDict((k, i) for i, k in enumerate(self.key))\n\n    def get_index(self, key):\n        \"\"\"Returns the position of key in the list.\"\"\"\n        if self.key_to_index is None:\n            self._create_dict()\n        return self.key_to_index[key]\n\n    def __contains__(self, key):\n        \"\"\"Returns True if the list contains the key\"\"\"\n        if self.key_to_index is None:\n            self._create_dict()\n        return key in self.key_to_index\n\n    def __getitem__(self, key):\n        \"\"\"It allows to acces the data in the list by key or index like in\n           a ditionary, e.g.:\n           If input is a string key:\n               scp = SCPList(keys, file_paths, offsets, ranges)\n               file_path, offset, range = scp['data1']\n           If input is an index:\n               key, file_path, offset, range = scp[0]\n\n        Args:\n          key: String key or integer index.\n        Returns:\n          If key is a string:\n              file_path, offset and range_spec given the key.\n          If key is the index in the key list:\n              key, file_path, offset and range_spec given the index.\n        \"\"\"\n        return_key = True\n        if isinstance(key, str):\n            return_key = False\n            index = self.get_index(key)\n        else:\n            index = key\n        offset = None if self.offset is None else self.offset[index]\n        range_spec = None if self.range_spec is None else self.range_spec[index]\n        if return_key:\n            return self.key[index], self.file_path[index], offset, range_spec\n        else:\n            return self.file_path[index], offset, range_spec\n\n    def add_prefix_to_filepath(self, prefix):\n        \"\"\"Adds a prefix to the file path\"\"\"\n        self.file_path = np.array([prefix + p for p in self.file_path])\n\n    def sort(self):\n        \"\"\"Sorts the list by key\"\"\"\n        self.key, idx = sort(self.key, return_index=True)\n        self.file_path = self.file_path[idx]\n        if self.offset is not None:\n            self.offset = self.offset[idx]\n        if self.range_spec is not None:\n            self.range_spec = self.range_spec[idx]\n        self.key_to_index = None\n\n    def save(self, file_path, sep=\" \", offset_sep=\":\"):\n        \"\"\"Saves script list to text file.\n\n        Args:\n          file_path: File to write the list.\n          sep: Separator between the key and file_path in the text file.\n          offset_sep: Separator between file_path and offset.\n        \"\"\"\n        if self.range_spec is None:\n            range_spec = [\"\" for k in self.key]\n        else:\n            range_spec = []\n            for r in self.range_spec:\n                if r[0] == 0 and r[1] == 0:\n                    range_spec.append(\"\")\n                elif r[1] == 0:\n                    range_spec.append(\"[%d:]\" % r[0])\n                else:\n                    range_spec.append(\"[%d:%d]\" % (r[0], r[0] + r[1] - 1))\n\n        with open(file_path, \"w\") as f:\n            if self.offset is None:\n                for k, p, r in zip(self.key, self.file_path, range_spec):\n                    f.write(\"%s%s%s%s\\n\" % (k, sep, p, r))\n            else:\n                for k, p, o, r in zip(\n                    self.key, self.file_path, self.offset, range_spec\n                ):\n                    f.write(\"%s%s%s%s%d%s\\n\" % (k, sep, p, offset_sep, o, r))\n\n    @staticmethod\n    def parse_script(script, offset_sep):\n        \"\"\"Parses the parts of the second field of the scp text file.\n\n        Args:\n          script: Second column of scp file.\n          offset_sep: Separtor between file_path and offset.\n\n        Returns:\n          file_path, offset and range_spec.\n        \"\"\"\n        file_range = [f.split(sep=\"[\", maxsplit=1) for f in script]\n        offset = None\n        range_spec = None\n\n        file_offset = [f[0].split(sep=offset_sep, maxsplit=1) for f in file_range]\n        file_path = [f[0] for f in file_offset]\n\n        if len(file_offset[0]) == 2:\n            offset = [int(f[1]) if len(f) == 2 else -1 for f in file_offset]\n            if -1 in offset:\n                raise ValueError(\"Missing data position for %s\" % f[0])\n\n        do_range = False\n        for f in file_range:\n            if len(f) == 2:\n                do_range = True\n                break\n\n        if do_range:\n            range_spec1 = [\n                f[1].rstrip(\"]\").split(sep=\":\", maxsplit=1) if len(f) == 2 else None\n                for f in file_range\n            ]\n            range_spec21 = [\n                int(f[0]) if f is not None and f[0].isdecimal() else 0\n                for f in range_spec1\n            ]\n            range_spec22 = [\n                int(f[1]) if f is not None and f[1].isdecimal() else None\n                for f in range_spec1\n            ]\n            range_spec = [\n                [a, b - a + 1] if b is not None else [a, 0]\n                for a, b in zip(range_spec21, range_spec22)\n            ]\n            range_spec = np.array(range_spec, dtype=np.int64)\n\n        return file_path, offset, range_spec\n\n    @classmethod\n    def load(cls, file_path, sep=\" \", offset_sep=\":\", is_wav=False):\n        \"\"\"Loads script list from text file.\n\n        Args:\n          file_path: File to read the list.\n          sep: Separator between the key and file_path in the text file.\n          offset_sep: Separator between file_path and offset.\n\n        Returns:\n          SCPList object.\n        \"\"\"\n        with open(file_path, \"r\") as f:\n            fields = [line.rstrip().split(sep=sep, maxsplit=1) for line in f]\n\n        key = [f[0] for f in fields]\n        script = [f[1] for f in fields]\n        del fields\n        if is_wav:\n            file_path = script\n            offset = None\n            range_spec = None\n        else:\n            file_path, offset, range_spec = SCPList.parse_script(script, offset_sep)\n        return cls(key, file_path, offset, range_spec)\n\n    def split(self, idx, num_parts, group_by_key=True):\n        \"\"\"Splits SCPList into num_parts and return part idx.\n\n        Args:\n          idx: Part to return from 1 to num_parts.\n          num_parts: Number of parts to split the list.\n          group_by_key: If True, all the lines with the same key\n                        go to the same part.\n\n        Returns:\n          Sub SCPList\n        \"\"\"\n        if group_by_key:\n            key, idx1 = split_list_group_by_key(self.key, idx, num_parts)\n        else:\n            key, idx1 = split_list(self.key, idx, num_parts)\n\n        file_path = self.file_path[idx1]\n        offset = None\n        range_spec = None\n        if self.offset is not None:\n            offset = self.offset[idx1]\n        if self.range_spec is not None:\n            range_spec = self.range_spec[idx1]\n\n        return SCPList(key, file_path, offset, range_spec)\n\n    @classmethod\n    def merge(cls, scp_lists):\n        \"\"\"Merges several SCPList.\n\n        Args:\n          scp_lists: List of SCPLists\n\n        Returns:\n          SCPList object concatenation the scp_lists.\n        \"\"\"\n        key_list = [item.key for item in scp_lists]\n        file_list = [item.file_path for item in scp_lists]\n        offset_list = [item.offset for item in scp_lists]\n        range_list = [item.range_spec for item in scp_lists]\n\n        key = np.concatenate(tuple(key_list))\n        file_path = np.concatenate(tuple(file_list))\n\n        if offset_list[0] is None:\n            offset = None\n        else:\n            offset = np.concatenate(tuple(offset_list))\n\n        if range_list[0] is None:\n            range_spec = None\n        else:\n            range_spec = np.concatenate(tuple(range_list))\n\n        return cls(key, file_path, offset, range_spec)\n\n    def filter(self, filter_key, keep=True):\n        \"\"\"Removes elements from SCPList ojbect by key\n\n        Args:\n          filter_key: List with the keys of the elements to keep or remove.\n          keep: If True, we keep the elements in filter_key;\n                if False, we remove the elements in filter_key;\n\n        Returns:\n          SCPList object.\n        \"\"\"\n        if not keep:\n            filter_key = np.setdiff1d(self.key, filter_key)\n\n        f, _ = ismember(filter_key, self.key)\n        assert np.all(f)\n        f, _ = ismember(self.key, filter_key)\n        key = self.key[f]\n        file_path = self.file_path[f]\n\n        offset = None\n        range_spec = None\n        if self.offset is not None:\n            offset = self.offset[f]\n        if self.range_spec is not None:\n            range_spec = self.range_spec[f]\n\n        return SCPList(key, file_path, offset, range_spec)\n\n    def filter_paths(self, filter_key, keep=True):\n        \"\"\"Removes elements of SCPList by file_path\n\n        Args:\n          filter_key: List with the file_path of the elements to keep or remove.\n          keep: If True, we keep the elements in filter_key;\n                if False, we remove the elements in filter_key;\n\n        Returns:\n          SCPList object.\n        \"\"\"\n\n        if not keep:\n            filter_key = np.setdiff1d(self.file_path, filter_key)\n\n        f, _ = ismember(filter_key, self.file_path)\n        assert np.all(f)\n        f, _ = ismember(self.file_path, filter_key)\n        key = self.key[f]\n        file_path = self.file_path[f]\n\n        offset = None\n        range_spec = None\n        if self.offset is not None:\n            offset = self.offset[f]\n        if self.range_spec is not None:\n            range_spec = self.range_spec[f]\n\n        return SCPList(key, file_path, offset, range_spec)\n\n    def filter_index(self, index, keep=True):\n        \"\"\"Removes elements of SCPList by index\n\n        Args:\n          filter_key: List with the index of the elements to keep or remove.\n          keep: If True, we keep the elements in filter_key;\n                if False, we remove the elements in filter_key;\n\n        Returns:\n          SCPList object.\n        \"\"\"\n\n        if not keep:\n            index = np.setdiff1d(np.arange(len(self.key), dtype=np.int64), index)\n\n        key = self.key[index]\n        file_path = self.file_path[index]\n        offset = None\n        range_spec = None\n        if self.offset is not None:\n            offset = self.offset[index]\n        if self.range_spec is not None:\n            range_spec = self.range_spec[index]\n\n        return SCPList(key, file_path, offset, range_spec)\n\n    def shuffle(self, seed=1024, rng=None):\n        \"\"\"Shuffles the elements of the list.\n\n        Args:\n          seed: Seed for random number generator.\n          rng: numpy random number generator object.\n\n        Returns:\n          Index used to shuffle the list.\n        \"\"\"\n        if rng is None:\n            rng = np.random.RandomState(seed=seed)\n        index = np.arange(len(self.key))\n        rng.shuffle(index)\n\n        self.key = self.key[index]\n        self.file_path = self.file_path[index]\n        if self.offset is not None:\n            self.offset = self.offset[index]\n        if self.range_spec is not None:\n            self.range_spec = self.range_spec[index]\n\n        self.key_to_index = None\n        return index\n\n    def __eq__(self, other):\n        \"\"\"Equal operator\"\"\"\n        if self.key.size == 0 and other.key.size == 0:\n            return True\n        eq = self.key.shape == other.key.shape\n        eq = eq and np.all(self.key == other.key)\n        eq = eq and (self.file_path.shape == other.file_path.shape)\n        eq = eq and np.all(self.file_path == other.file_path)\n\n        if (\n            self.offset is None\n            and other.offset is not None\n            or self.offset is not None\n            and other.offset is None\n        ):\n            eq = False\n        elif self.offset is not None and other.offset is not None:\n            eq = eq and np.all(self.offset == other.offset)\n\n        if (\n            self.range_spec is None\n            and other.range_spec is not None\n            or self.range_spec is not None\n            and other.range_spec is None\n        ):\n            eq = False\n        elif self.range_spec is not None and other.range_spec is not None:\n            eq = eq and np.all(self.range_spec == other.range_spec)\n\n        return eq\n\n    def __ne__(self, other):\n        \"\"\"Non-equal operator\"\"\"\n        return not self.__eq__(other)\n\n    def __cmp__(self, other):\n        \"\"\"Comparison operator\"\"\"\n        if self.__eq__(other):\n            return 0\n        return 1\n","sub_path":"hyperion/utils/scp_list.py","file_name":"scp_list.py","file_ext":"py","file_size_in_byte":14542,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"304705023","text":"\"\"\"\r\n    책에 나온 알고리즘 구현\r\n    P.216~221, 336~347\r\n\"\"\"\r\n\r\n## BuildHeap; 배열 'array'를 Heap으로 만듬\r\ndef buildHeap(array, n):\r\n    for i in range(n//2, 1+1):\r\n        print(array)\r\n        downHeap(array, n, i)\r\n\r\n\r\n## DownHeap\r\ndef downHeap(array, n, i):\r\n    leftChild = (i * 2)\r\n    rightChild = (i * 2) +1\r\n    if ((leftChild<=n) & (array[leftChild]>array[i])): # n은 원소의 수\r\n        bigger = leftChild\r\n    else:\r\n        bigger = i\r\n    if ((rightChild<=n) and (A[rightChild]>array[bigger])):\r\n        bigger = rightChild\r\n    if (bigger != i):\r\n        # swap\r\n        temp = array[i]\r\n        array[i] = array[bigger]\r\n        array[bigger] = temp\r\n\r\n    print(array)\r\n    downHeap(array, n, i)\r\n\r\n\r\n## Main\r\narr = [0, 2, 31, 2, 4, 2, 45, 23]\r\nbuildHeap(arr, 7)\r\n","sub_path":"HeapSort01.py","file_name":"HeapSort01.py","file_ext":"py","file_size_in_byte":804,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"52314805","text":"class Fountain:\r\n    amount_of_fountains = 0\r\n\r\n    def __init__(self, volume_of_water=1500, adress=\"Zelena 25\", water_flow_per_hour=100, architect=\"Maksym Bereg\", allocated_funds=7500, name=\"Dubai\"):\r\n        self.volume_of_water = volume_of_water\r\n        self.adress = adress\r\n        self.water_flow_per_hour = water_flow_per_hour\r\n        self.architect = architect\r\n        self.allocated_funds = allocated_funds\r\n        self.name = name\r\n        Fountain.amount_of_fountains += 1\r\n\r\n    def __del__(self):\r\n        print(\"Destructor was called\")\r\n\r\n    def __str__(self):\r\n        return \"Fountain : \" \\\r\n            +\" volume_Of_water \" + str(self.volume_of_water) \\\r\n            +\" adress \" + self.adress \\\r\n            +\" water_flow_per_hour \" + str(self.water_flow_per_hour) \\\r\n            +\" architect \" + self.architect \\\r\n            +\" allocated_funds \" + str(self.allocated_funds) \\\r\n            +\" name \" + self.name  \r\n\r\n    @staticmethod\r\n    def get_static_amount():\r\n        return Fountain.amount_of_fountains\r\n        ","sub_path":"Fountain.py","file_name":"Fountain.py","file_ext":"py","file_size_in_byte":1042,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"94283931","text":"import numpy as np\nimport pandas as pd \nimport datetime as dt\nimport simplejson as json\n\nimport sqlalchemy\nfrom sqlalchemy.ext.automap import automap_base\nfrom sqlalchemy.orm import Session\nfrom sqlalchemy import create_engine, func, inspect\n\nfrom flask import Flask, jsonify, render_template, redirect\n\n# start the flask server\napp=Flask(__name__)\n\n\n#################################################\n# Database Setup\n#################################################\nengine_bill = create_engine(\"postgresql://postgres:postgres@localhost/state_poverty\")\nconn_bill = engine_bill.connect()\n\npovData = pd.read_sql(\"SELECT * FROM poverty_by_state\", conn_bill)\neduData = pd.read_sql(\"SELECT * FROM education_by_state\", conn_bill)\n\n# reflect an existing database into a new model\nBase_bill = automap_base()\n# reflect the tables\nBase_bill.prepare(engine_bill, reflect=True)\n\n# Save reference to the table\npovState = Base_bill.classes.poverty_by_state\neduState = Base_bill.classes.education_by_state\n\n\n@app.route(\"/\")\ndef index():\n    \n    return render_template('index.html')\n   \n\n@app.route(\"/states\")\ndef states():\n    # Create our session (link) from Python to the DB\n    session = Session(engine_bill)\n   # Query all passengers\n    results = session.query(povState.state_name).all()\n    session.close()\n    # Convert list of tuples into normal list\n    all_states = list(np.ravel(results))\n\n    return jsonify(all_states)\n\n#/\n@app.route(\"/poverty\")\ndef poverty():\n    # Create our session (link) from Python to the DB\n    session = Session(engine_bill)\n   # Query all passengers\n    results = session.query(povState.poverty_pct).all()\n    session.close()\n    # Convert list of tuples into normal list\n    all_pov = list(np.ravel(results))\n\n    return jsonify(all_pov)\n\n\n@app.route(\"/education\")\ndef education():\n    # Create our session (link) from Python to the DB\n    session = Session(engine_bill)\n   # Query all passengers\n    results = session.query(eduState.education_pct).all()\n    session.close()\n\n    # Convert list of tuples into normal list\n    all_edu = list(np.ravel(results))\n\n    return jsonify(all_edu)\n\n\nif __name__ == \"__main__\":\n    app.run(debug=True)\n\n\n","sub_path":"Code-michal/billApp.py","file_name":"billApp.py","file_ext":"py","file_size_in_byte":2171,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"174515320","text":"from django.template import Context, Template\nfrom django.test import TestCase\n\nfrom terra_settings import settings\n\n\nclass TemplatesTagsTestCase(TestCase):\n    def test_front_url(self):\n        context = Context()\n        template_to_render = Template(\n            '{% load settings_tags %}'\n            '{% front_url %}'\n        )\n\n        rendered = template_to_render.render(context)\n\n        self.assertEqual(rendered, settings.FRONT_URL)\n","sub_path":"terra_settings/tests/test_templatetags.py","file_name":"test_templatetags.py","file_ext":"py","file_size_in_byte":444,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"467676475","text":"# -*- coding: utf-8 -*-\r\n\"\"\"\r\nCreated on Tue Sep 29 22:16:56 2020\r\n\r\n@author: Nclab\r\n\"\"\"\r\n\r\nimport tensorflow as tf\r\nfrom tensorflow import keras\r\nimport numpy as np\r\nfrom tensorflow.keras import layers, optimizers, datasets, Model\r\nfrom tensorflow.keras.layers import Dense, Dropout, Flatten\r\nfrom tensorflow.keras.layers import Conv2D, MaxPooling2D, Input, Activation\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn import manifold\r\n\r\nmnist = datasets.mnist\r\n(x0, y0), (x1, y1) = datasets.mnist.load_data()\r\ntrain_x = x0.reshape(-1,784)\r\ntrain_y = x0.reshape(-1,784)\r\ntest_x = x1.reshape(-1,784)\r\ntest_y = x1.reshape(-1,784)\r\n\"\"\"\r\ndt = []\r\ntsne=manifold.TSNE(n_components=2)\r\nintermed_tsne=tsne.fit_transform(train_x)\r\nfor i in range(10):\r\n    dt.append(intermed_tsne[y1[-10000:]==i])\r\ncolor =['c', 'b', 'g', 'r', 'orange', 'y', 'k', 'silver','pink','purple']\r\nplt.figure(figsize=(12,12))\r\nfor i in range(10):\r\n    plot = dt[i]\r\n    plt.scatter(plot[:,0],plot[:,1],c=color[i],label = i)\r\n    plt.legend(loc = 'best')\r\nplt.show()\r\n\"\"\"\r\ninput_x = Input([784,])\r\n\r\n#encoder\r\nenc_input = Input([784,])\r\nx = Dense(1000,activation= 'relu')(input_x)\r\nx = Dense(500,activation= 'relu')(x) \r\nx = Dense(250,activation= 'relu')(x) \r\nenc_output = Dense(100)(x) \r\nencoder = Model(input_x,enc_output)\r\n#decoder\r\ndec_input = Input([100,])\r\nx = Dense(250,activation= 'relu')(dec_input) \r\nx = Dense(500,activation= 'relu')(x) \r\nx = Dense(1000,activation= 'relu')(x) \r\nx = Dense(784)(x) \r\ndec_output = x\r\ndecoder = Model(dec_input,dec_output)\r\n#合并\r\n\r\ncode = encoder(input_x )\r\noutput = decoder(code)\r\n\r\nmodel = Model(input_x,output)\r\n\r\noptimizer = optimizers.Adam(0.00001)\r\nmodel.compile(optimizer=optimizer,\r\n              loss='mse')\r\nearly_stopping=tf.keras.callbacks.EarlyStopping(monitor='val_loss', min_delta=1,\r\n                              patience=5, verbose=0, mode='auto',\r\n                              baseline=None, restore_best_weights=False)\r\nhistory = model.fit(train_x,train_y,validation_data = (test_x,test_y),batch_size =128,epochs =10,callbacks = [early_stopping])\r\n\r\nplt.figure()\r\nepochs=range(len(history.history['loss']))\r\nplt.plot(epochs,history.history['loss'],'b',label='Training loss')\r\nplt.plot(epochs,history.history['val_loss'],'r',label='Validation val_loss')\r\nplt.title('Traing and Validation loss')\r\nplt.legend()\r\nplt.show()\r\n\r\n\"\"\"\r\ninp = test_x[-1000:].reshape((-1,784))\r\ncode_x = encoder(inp).numpy()\r\ndt = []\r\nfor i in range(10):\r\n    dt.append(code_x[y1[-1000:]==i])\r\ncolor =['c', 'b', 'g', 'r', 'orange', 'y', 'k', 'silver','pink','purple']\r\nplt.figure(figsize=(12,12))\r\nfor i in range(10):\r\n    plot = dt[i]\r\n    plt.scatter(plot[:,0],plot[:,1],c=color[i],label = i)\r\n    plt.legend(loc = 'best')\r\nplt.show()\r\n\"\"\"\r\n\r\nencoded_imgs = encoder.predict(test_x)\r\ndecoded_imgs = decoder.predict(encoded_imgs)\r\n\r\nn = 6  # how many digits we will display\r\nplt.figure(figsize=(20, 4))\r\nfor i in range(n):\r\n    # display original\r\n    ax = plt.subplot(3, n, i + 1)\r\n    plt.imshow(test_x[i].reshape(28, 28))\r\n    #plt.gray()\r\n    ax.get_xaxis().set_visible(False)\r\n    ax.get_yaxis().set_visible(False)\r\n\r\n    ax = plt.subplot(3, n, i + 1+ n)\r\n    plt.imshow(encoded_imgs[i].reshape(10, 10))\r\n    #plt.gray()\r\n    ax.get_xaxis().set_visible(False)\r\n    ax.get_yaxis().set_visible(False)\r\n\r\n    ax = plt.subplot(3, n, i + 1+ n+ n)\r\n    plt.imshow(decoded_imgs[i].reshape(28, 28))\r\n    #plt.gray()\r\n    ax.get_xaxis().set_visible(False)\r\n    ax.get_yaxis().set_visible(False)\r\nplt.show()\r\n\r\n\r\n\"\"\"\r\nintermed_tsne1=tsne.fit_transform(decoded_imgs)\r\nfor i in range(10):\r\n    dt.append(intermed_tsne1[y1[-10000:]==i])\r\ncolor =['c', 'b', 'g', 'r', 'orange', 'y', 'k', 'silver','pink','purple']\r\nplt.figure(figsize=(12,12))\r\nfor i in range(10):\r\n    plot = dt[i]\r\n    plt.scatter(plot[:,0],plot[:,1],c=color[i],label = i)\r\n    plt.legend(loc = 'best')\r\nplt.show()\r\n\"\"\"","sub_path":"code/auto encoder (best number).py","file_name":"auto encoder (best number).py","file_ext":"py","file_size_in_byte":3877,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"464349929","text":"from stats_management_week import StatsManagementWeek\nfrom datetime import timedelta\nimport decimal\n\n\"\"\"\n__author__      = Umair Ansari\n__copyright__   = Copyright 2007, Cielo WiGle\n__date__        = 22, May 2017\n__version__     = 1.0.0\n\"\"\"\n\n\nclass DeviceStatsTemperatureWeek(StatsManagementWeek):\n\n    def get_sorted_list(self, stats_list, time_zone):\n        sorted_stat_list = []\n        average = 0\n        for stat in stats_list:\n            array_index = len(sorted_stat_list) - 1\n\n            if array_index >= 0:\n                new_date = self.timestamp_to_date_add_utc(timestamp=sorted_stat_list[array_index]['stat_timestamp'],\n                                                          time_zone=time_zone)\n            date = self.timestamp_to_date_add_utc(timestamp=stat['stat_timestamp'], time_zone=time_zone)\n\n            if len(sorted_stat_list) > 0:\n\n                if new_date.date() - timedelta(days=7) < date.date():\n                    average += 1\n                    try:\n                        sorted_stat_list[array_index]['avg_temp'] += stat['avg_temp'] * stat['on_duration']\n                    except Exception as e:\n                        D = decimal.Decimal\n                        sorted_stat_list[array_index]['avg_temp'] = D(sorted_stat_list[array_index]['avg_temp'])\n                        sorted_stat_list[array_index]['avg_temp'] += (D(stat['avg_temp']) * stat['on_duration'])\n                    continue  # end current iteration on stats_list, start next iteration\n\n            if average > 1:\n                sorted_stat_list[array_index]['avg_temp'] /= (average * 900)\n            average = 1\n            stat['stat_date'] = self.timestamp_to_date_add_utc(stat[\"stat_timestamp\"], time_zone)\n            stat[\"stat_date\"] = self.convert_dates_to_formatted_string(date=stat[\"stat_date\"])\n            sorted_stat_list.append(stat)\n\n        if average > 1:\n            sorted_stat_list[len(sorted_stat_list) - 1]['avg_temp'] /= (average * 900)\n        return sorted_stat_list","sub_path":"imports/modules/device_stats_temperature_week.py","file_name":"device_stats_temperature_week.py","file_ext":"py","file_size_in_byte":2013,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"338517894","text":"# 53. Reverse Words in a String\n#  Description\n#  Notes\n#  Testcase\n#  Judge\n# Discuss\n# Given an input string, reverse the string word by word.\n#\n# For example,\n# Given s = \"the sky is blue\",\n# return \"blue is sky the\".\n#\n# Have you met this question in a real interview?\n# Clarification\n# What constitutes a word?\n# A sequence of non-space characters constitutes a word.\n# Could the input string contain leading or trailing spaces?\n# Yes. However, your reversed string should not contain leading or trailing spaces.\n# How about multiple spaces between two words?\n# Reduce them to a single space in the reversed string.\n# Example\n# Tags\n# String Yelp Snapchat Apple Bloomberg Microsoft\n\n\nclass Solution:\n    \"\"\"\n    @param: s: A string\n    @return: A string\n    \"\"\"\n\n    def reverseWords(self, s):\n        # write your code here\n\n        \"naive solution\"\n        \"if space, just ignore. if new letter, start a new string\"\n\n        if s is None or len(s) == 0:\n            return s\n\n        rev = []\n\n        laststart = 0\n        lastend = 0\n\n        for i in range(0, len(s) + 1):\n            if i == len(s) or s[i] == \" \":\n                if i > 0 and s[i - 1] != \" \":\n                    lastend = i\n                    \"finished a word, lets put it in rev\"\n                    rev.append(s[laststart:lastend])\n            else:\n                if i > 0 and s[i - 1] == \" \":\n                    laststart = i\n\n        result = \"\"\n        for i in range(len(rev) - 1, -1, -1):\n            result = result + rev[i] + \" \"\n\n        return result[0:-1]\n\n\nassert Solution().reverseWords(\"world\") == \"world\"\n","sub_path":"Algorithm/Python/HighFrequency/ReverseWordInAString.py","file_name":"ReverseWordInAString.py","file_ext":"py","file_size_in_byte":1605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"587234209","text":"# Minimax A&D\n\"\"\"\ncomputer's turn\n    keep track of best score\n    set best score default to -inf\n    check game board for available spots\n        FOR IF spot is available\n                THEN AI go there\n                THEN call minimax (on the board, with a starting depth of 0, and the first move is not the maximizing player) to get score\n                THEN undo the move (reset empty string)\n                THEN IF score is greater than best score\n                        THEN best score becomes the new score\n                        THEN best move is this particular position (i, j)\n    apply that best move as the computers move\n\n\nminimax score look up table {X: 1, O: -1, tie: 0}\n\nminimax algorithm (takes in the game board, and the depth)\n    check to see if somebody won\n    IF somebody won or tied\n        THEN the score of the look up table is based on whoever won\n        return the score\n    IF it is the maximizing player's turn\n        THEN set best score to -inf\n        THEN check game board for available spots\n        FOR IF spot is available\n                THEN AI go there\n                THEN call minimax (on the board, with a depth + 1, and the first move is not the maximizing player) to get score\n                THEN undo the move (reset empty string)\n                THEN find the max value between score and best score max(score, best score) set it to best score\n    return the best score\n    ELSE IF it is the minimizing player's turn\n            THEN set best score to +inf\n        THEN check game board for available spots\n        FOR IF spot is available\n                THEN HUMAN go there\n                THEN call minimax (on the board, with a depth + 1, and the first move is the maximizing player) to get score\n                THEN undo the move (reset empty string)\n                THEN find the min value between score and best score min(score, best score) set it to best score\n    return the best score\n\"\"\"\n\nfrom tkinter import *\nimport tkinter.messagebox\nimport pygame\nfrom pygame.locals import QUIT\n\n\nroot = Tk()\n\n# --- Global Variables ---\n\n# If game is still going\ngame_still_going = True\n\n# Game results\nwinner = None\nxwins = 0\nowins = 0\ntie_games = 0\n\n# Who's turn is it\ncurrent_player = \"X\"\n\n# minimax score table\nscores = {'X': 1, 'O': -1, 'Draw': 0}\n# -------------------------\n\n\n# What happens when any of the game board buttons are clicked\ndef when_clicked(idx):\n    buttons[idx].config(text=current_player, state=\"disabled\", font=(\"Helvetica\", 15))\n    flip_player()\n    check_if_game_over()\n\n\n# Swaps between the two players\ndef flip_player():\n    global current_player\n\n    # if current player was x then change it to o\n    if current_player == \"X\":\n        current_player = \"O\"\n    # if current player was o then change it to x\n    elif current_player == \"O\":\n        current_player = \"X\"\n    return\n\n\n# Check if the game should end\ndef check_if_game_over():\n    global game_still_going\n\n    if game_still_going:\n        check_for_winner()\n    if game_still_going:\n        check_if_tie()\n        if current_player == \"O\":\n            computers_turn()\n    update_score()\n\n\n# MINIMAX potential game states\ndef check_future_moves():\n    global game_still_going\n\n    minimax_check_for_winner()\n    if game_still_going:\n        minimax_check_if_tie()\n\n\n# Check if a player has won\ndef check_for_winner():\n    global winner\n\n    row_winner = check_rows()\n    col_winner = check_col()\n    diagonal_winner = check_diagonals()\n\n    if row_winner:\n        winner = row_winner\n        tkinter.messagebox.showinfo(\"Tic-Tac-Toe\", \"Player \" + winner + \" wins!\")\n    elif col_winner:\n        winner = col_winner\n        tkinter.messagebox.showinfo(\"Tic-Tac-Toe\", \"Player \" + winner + \" wins!\")\n    elif diagonal_winner:\n        winner = diagonal_winner\n        tkinter.messagebox.showinfo(\"Tic-Tac-Toe\", \"Player \" + winner + \" wins!\")\n    else:\n        winner = None\n\n    if row_winner or col_winner or diagonal_winner:\n\n        for i in range(9):\n            buttons[i].config(state=\"disabled\")\n\n    return\n\n\n# MINIMAX potential game states\ndef minimax_check_for_winner():\n    global winner\n\n    row_winner = check_rows()\n    col_winner = check_col()\n    diagonal_winner = check_diagonals()\n\n    if row_winner:\n        winner = row_winner\n    elif col_winner:\n        winner = col_winner\n    elif diagonal_winner:\n        winner = diagonal_winner\n    else:\n        winner = None\n\n    return\n\n\n# Check if play has won by getting three in a row\ndef check_rows():\n    global game_still_going\n\n    # check if any of the rows have the same value and is not empty\n    row_1 = buttons[0]['text'] == buttons[3]['text'] == buttons[6]['text'] != ''\n    row_2 = buttons[1]['text'] == buttons[4]['text'] == buttons[7]['text'] != ''\n    row_3 = buttons[2]['text'] == buttons[5]['text'] == buttons[8]['text'] != ''\n\n    if row_1 or row_2 or row_3:\n        game_still_going = False\n    if row_1:\n        return buttons[0]['text']\n    elif row_2:\n        return buttons[1]['text']\n    elif row_3:\n        return buttons[2]['text']\n    return\n\n\n# Check if play has won by getting three in a column\ndef check_col():\n    global game_still_going\n\n    # check if any of the col have the same value and is not empty\n    col_1 = buttons[0]['text'] == buttons[1]['text'] == buttons[2]['text'] != ''\n    col_2 = buttons[3]['text'] == buttons[4]['text'] == buttons[5]['text'] != ''\n    col_3 = buttons[6]['text'] == buttons[7]['text'] == buttons[8]['text'] != ''\n\n    if col_1 or col_2 or col_3:\n        game_still_going = False\n    if col_1:\n        return buttons[0]['text']\n    elif col_2:\n        return buttons[3]['text']\n    elif col_3:\n        return buttons[6]['text']\n    return\n\n\n# Check if play has won by getting three in a diagonal\ndef check_diagonals():\n    global game_still_going\n\n    # check if any of the diagonals have the same value and is not empty\n    diagonal_1 = buttons[0]['text'] == buttons[4]['text'] == buttons[8]['text'] != ''\n    diagonal_2 = buttons[2]['text'] == buttons[4]['text'] == buttons[6]['text'] != ''\n\n    if diagonal_1 or diagonal_2:\n        game_still_going = False\n    if diagonal_1:\n        return buttons[0]['text']\n    elif diagonal_2:\n        return buttons[2]['text']\n    return\n\n\n# Check if the game has no winner and there are no more spaces on the board to fill\ndef check_if_tie():\n    global game_still_going\n\n    if '' != buttons[0]['text'] and buttons[1]['text'] and buttons[2]['text'] and buttons[3]['text'] \\\n            and buttons[4]['text'] and buttons[5]['text'] and buttons[6]['text'] and buttons[7]['text'] \\\n            and buttons[8]['text']:\n        game_still_going = False\n        tkinter.messagebox.showinfo(\"Tic-Tac-Toe\", \"Game is a tie.\")\n    return\n\n\n# MINIMAX Check if the game has no winner and there are no more spaces on the board to fill\ndef minimax_check_if_tie():\n    global game_still_going\n\n    if '' != buttons[0]['text'] and buttons[1]['text'] and buttons[2]['text'] and buttons[3]['text'] \\\n            and buttons[4]['text'] and buttons[5]['text'] and buttons[6]['text'] and buttons[7]['text'] \\\n            and buttons[8]['text']:\n        game_still_going = False\n\n\n# Computers turn, uses minimax\ndef computers_turn():\n    # For Debugging purposes\n    \"\"\"\n        best_score = float('inf')\n        move = 0\n        available_moves = []\n\n        # clear the game board for debugging\n        for i in range(9):\n            buttons[i].config(text='', state=\"normal\")\n\n        # Hard coded game board\n        buttons[0].config(text='X')\n        buttons[1].config(text='X')\n        buttons[6].config(text='O')\n        buttons[7].config(text='O')\n\n        for i in range(9):\n            if buttons[i]['text'] == '':\n                available_moves.append(i)\n        for option in available_moves:\n            # If I go to a given option\n            buttons[option].config(text='O')\n            # what would the score be\n            score = minimax(0, True)\n            buttons[option].config(text='')\n            if score < best_score:\n                best_score = score\n                move = option\n\n        buttons[move].config(text=current_player, state=\"disabled\", font=(\"Helvetica\", 15))\n        flip_player()\n        check_if_game_over()\n    \"\"\"\n\n    best_score = float('inf')\n    move = 0\n    available_moves = []\n    for i in range(9):\n        if buttons[i]['text'] == '':\n            available_moves.append(i)\n    for option in available_moves:\n        # If I move here\n        buttons[option].config(text='O')\n        # Then what will the outcome of the game be\n        score = minimax(0, float('-inf'), float('inf'), True)\n        # Reset the position to blank\n        buttons[option].config(text='')\n        if score < best_score:\n            best_score = score\n            move = option\n\n    buttons[move].config(text=current_player, state=\"disabled\", font=(\"Helvetica\", 15))\n    flip_player()\n    check_if_game_over()\n\n\ndef minimax(depth, alpha, beta, is_maximizing):\n    global game_still_going, winner, scores\n\n    check_future_moves()\n\n    if not game_still_going:\n        if winner is None:\n            winner = \"Draw\"\n        score = scores[winner]\n        winner = None\n        game_still_going = True\n        return score  # DEBUG - Step Into My Code goes to minimax_check_for_winner() but Step Into goes to correct spot\n    if is_maximizing:\n        best_score = float('-inf')\n\n        for i in range(9):\n            if buttons[i]['text'] == '':\n                buttons[i].config(text='X')\n                check_future_moves()\n                score = minimax(depth+1, alpha, beta, False)\n                buttons[i].config(text='')\n                best_score = max(score, best_score)\n                alpha = max(score, alpha)\n                if beta <= alpha:\n                    break\n        return best_score\n    else:\n        best_score = float('inf')\n        for i in range(9):\n            if buttons[i]['text'] == '':\n                buttons[i].config(text='O')\n                check_future_moves()\n                score = minimax(depth+1, alpha, beta, True)\n                buttons[i].config(text='')\n                best_score = min(score, best_score)\n                beta = min(score, beta)\n                if beta <= alpha:\n                    break\n        return best_score\n\n\n# Update the score of the game\ndef update_score():\n    global game_still_going, winner, xwins, owins, tie_games\n\n    if not game_still_going:\n        if winner == 'X':\n            xwins = xwins+1\n            player_x_label = Label(game_score_frame, text=\"Player X's score is: \" + str(xwins), font=(\"Helvetica\", 15))\n            player_x_label.grid(row=1, column=1)\n        elif winner == 'O':\n            owins = owins+1\n            # WORK AROUND  NOT ACTUAL FIX\n            # had to change str(owins) to str(int(owins/2)) because for some reason when O wins the if not gets called\n            # twice\n            player_o_label = Label(game_score_frame, text=\"Player O's score is: \" + str(int(owins/2)), font=(\"Helvetica\"\n                                                                                                             , 15))\n            player_o_label.grid(row=2, column=1)\n        else:\n            tie_games = tie_games+1\n            # WORK AROUND  NOT ACTUAL FIX\n            # had to change str(tie_games) to str(int(tie_games/2)) because for some reason when ties the if not gets\n            # called twice\n            tie_game_label = Label(game_score_frame, text=\"Draw: \" + str(int(tie_games/2)), font=(\"Helvetica\", 15))\n            tie_game_label.grid(row=3, column=1)\n\n\n# Restart's the game\ndef restart_game():\n    global game_still_going, current_player\n\n    for i in range(9):\n        buttons[i].config(text='', state=\"normal\")\n\n    # Prevents bug of saying the game is tied from the 2nd game and on\n    game_still_going = True\n    # Makes it so that human is always X and computer is always O\n    current_player = \"X\"\n\n\n# Making main window and title\nroot.title(\"Tic-Tac-Toe\")\nroot.minsize(width=100, height=100)\nroot.geometry('500x500')\n\n# Making a Menu and Sub Menus\nmenu = Menu(root)\nroot.config(menu=menu)\n\nsub_menu = Menu(menu)\nmenu.add_cascade(label=\"File\", menu=sub_menu)\nsub_menu.add_command(label=\"New Game\", command=restart_game)\nsub_menu.add_separator()\nsub_menu.add_command(label=\"Exit\", command=quit)\n\ninfo_menu = Menu(menu)\nmenu.add_cascade(label=\"Info\", menu=info_menu)\ninfo_menu.add_command(label=\"About\")\n\n\n# Making frames\nplayer_label_frame = LabelFrame(root)\nplayer_label_frame.pack()\ngame_board_frame = LabelFrame(root)\ngame_board_frame.pack()\ngame_score_frame = LabelFrame(root, text=\"Score Board\")\ngame_score_frame.pack()\n\n\n# A list to hold the references to the buttons created below\nbuttons = []\n\n# Making buttons\nfor index in range(9):\n    r = (index % 3)\n    c = int(index/3)\n\n    button = Button(game_board_frame, padx=50, pady=50, width=1, height=1,\n                    command=lambda index=index: when_clicked(index))\n    button.grid(row=r, column=c)\n    buttons.append(button)\n\n\n# Main Loop\nroot.mainloop()\n\nfor event in pygame.event.get():\n    if event.type == QUIT:\n        pygame.quit()\n        sys.exit()\n\n\n","sub_path":"notakto-main/Mac-MiniMax(TicTakToe).py","file_name":"Mac-MiniMax(TicTakToe).py","file_ext":"py","file_size_in_byte":13178,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"120370749","text":"#importando librerias\n\nimport tensorflow  as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nprint(tf.__version__)\n\nfashion_mnist = tf.keras.datasets.fashion_mnist\n\n(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()\n\nclass_names = ['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat',\n               'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n\n#Revisando el tamaño de la data\nprint(train_images.shape)\nsamples = train_images.shape[0]\nwidht = train_images.shape[1]\nheight = train_images.shape[2]\nprint(f\"El lote de entrenamiento se compone de {samples} instancias\")\nprint(f\"El tamaño de cada imagen es de {widht}X{height} pixeles\")\nprint(f\"Los valores de  las etiquetas van desde 0-9 clasificando de esta forma 10 prendas diferentes\")\n\nprint(f\"el tamaño de la data de testeo es de {test_images.shape}\")\n\n#Normalizando la data a valores entre 0 y 1\n\ntrain_images = train_images / 255.0 #manejando imagen de 8 bits\ntest_images = test_images / 255.0\n\n#.......................................................................................\n#Construyendo el modelo con tf.keras\nmodel = tf.keras.Sequential([\n    tf.keras.layers.Flatten(input_shape=(28, 28)),\n    tf.keras.layers.Dense(128, activation='relu'),\n    tf.keras.layers.Dense(10)\n])\n\n#compilando el modelo \n#tomando como optimizador 'adam'\n#funcion para medir perdidas 'SparseCategoricalCrossentropy'\n\nmodel.compile(optimizer='adam',\n              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),\n              metrics=['accuracy'])\n\n\n#Entrenando modelo\n\nmodel.fit(train_images, train_labels,epochs=10)\nprint('pasa')\n\n\n#Evaluando la  exactitud del modelo\ntest_loss, test_acc = model.evaluate(test_images,  test_labels, verbose=2)\n\nprint('\\nTest accuracy:', test_acc)\n\n\n#Guardando modelo\nmodel.save('modelos/predict_clothes.h5')\nprint('modelo guardado')\n\n","sub_path":"training.py","file_name":"training.py","file_ext":"py","file_size_in_byte":1884,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"184659179","text":"\nimport keras\nimport tensorflow as tf\nconfig = tf.ConfigProto()\nconfig.gpu_options.allow_growth = True\nsession = tf.Session(config=config)\nimport numpy as np\n\nimport models\nimport utils\n\n#########################\n# Training parameters\n# May be changed\n#########################\nbatchsize = 128\nepochs = 400\n\ntrainRatio = 0.8\n\n\n\n#########################\n# Load and prepare dataset\n#########################\n(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()\n(x_train, y_train) = utils.correctDataset((x_train, y_train), [2,4,9], [0.5,0.5,0.5])\n\nindexes = np.arange(len(x_train))\nnp.random.shuffle(indexes)\nx_valid = x_train[indexes[int(len(x_train)*trainRatio):]]\ny_valid = y_train[indexes[int(len(x_train)*trainRatio):]]\ny_train = y_train[indexes[:int(len(x_train)*trainRatio)]]\nx_train = x_train[indexes[:int(len(x_train)*trainRatio)]]\n\nx_train = x_train[np.concatenate([np.arange(len(x_train)), np.where(((y_train==2) | (y_train==4)| (y_train==9)))[0]])]\ny_train = y_train[np.concatenate([np.arange(len(y_train)), np.where(((y_train==2) | (y_train==4)| (y_train==9)))[0]])]\nx_valid = x_valid[np.concatenate([np.arange(len(x_valid)), np.where(((y_valid==2) | (y_valid==4)| (y_valid==9)))[0]])]\ny_valid = y_valid[np.concatenate([np.arange(len(y_valid)), np.where(((y_valid==2) | (y_valid==4)| (y_valid==9)))[0]])]\n\ny_train = keras.utils.to_categorical(y_train, 10)\ny_valid = keras.utils.to_categorical(y_valid, 10)\ny_test = keras.utils.to_categorical(y_test, 10)\n\n#########################\n# Train autoencoder and classifier at once\n#########################\n\n#Change here the architecture of the encoder and decoder\nencoderModel = models.largeFullyConvolutionalEncoder()\ndecoderModel = models.largeFullyConvolutionalDecoder()\n\nx = keras.layers.Input((32,32,3))\nlatent = encoderModel(x)\n\ny1 = decoderModel(latent)\n\ny2 = keras.layers.Dense(1024)(latent)\ny2 = keras.layers.BatchNormalization()(y2)\ny2 = keras.layers.Activation(\"relu\")(y2)\ny2 = keras.layers.Dropout(0.5)(y2)\ny2 = keras.layers.Dense(1024)(y2)\ny2 = keras.layers.BatchNormalization()(y2)\ny2 = keras.layers.Activation(\"relu\")(y2)\ny2 = keras.layers.Dropout(0.5)(y2)\ny2 = keras.layers.Dense(10)(y2)\ny2 = keras.layers.Activation(\"softmax\")(y2)\n\nfullModel = keras.models.Model([x],[y1,y2])\nopt = keras.optimizers.Adam()\nfullModel.compile(loss=[\"mse\",\"categorical_crossentropy\"] , optimizer=opt,  metrics=[\"accuracy\"])\nfullModel.summary()\nprint(fullModel.metrics_names)\n\ntrainPipeline = utils.ACDataPipeline(x_train, y_data=y_train, batchSize=batchsize)\nvalidPipeline = utils.ACDataPipeline(x_valid, y_data=y_valid, batchSize=batchsize)\n\n\nfullModel.fit_generator(trainPipeline, validation_data=validPipeline, epochs=epochs,\n                        callbacks=[keras.callbacks.EarlyStopping(monitor='val_loss', patience=15, restore_best_weights=True)])\n\nencoderModel.save(\"oneStepEncoder.h5\")\ndecoderModel.save(\"oneStepDecoder.h5\")\n\n#########################\n# Test of the model after training\n#########################\nprint(fullModel.evaluate(utils.imagePreprocessing(x_test), [utils.imagePreprocessing(x_test), y_test]))\n\nresult = fullModel.predict(utils.imagePreprocessing(x_test))[1]\n\nconfmatrix = utils.confusionMatrix(np.argmax(y_test, axis=1), np.argmax(result, axis=1))\nfscore = utils.fscore(np.argmax(y_test, axis=1), np.argmax(result, axis=1))\nprint(confmatrix)\nprint(fscore)\nprint(np.mean(fscore))","sub_path":"oneStep_train.py","file_name":"oneStep_train.py","file_ext":"py","file_size_in_byte":3386,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"456856626","text":"from turtle import  Screen, clear, update\nfrom parts import draw_triangle\nfrom helpers import update_position, setup\nimport settings\nimport time\n\ndef draw_animation(num_frames, sidelen, color, sleeptime):\n    for i in range(num_frames):\n        if i == num_frames/4:\n            draw_triangle(sidelen, color)\n        if i == num_frames/2:\n            update_position(100, 0)\n            draw_triangle(sidelen, color)\n        if i == 3*num_frames/4:\n            update_position(200, 0)\n            draw_triangle(sidelen, color)\n        if i == num_frames:\n            update_position(100, 0)\n            draw_triangle(sidelen, color)\n        update()\n        time.sleep(sleeptime)\n    clear()\n    \n\ndef main():\n    screen = Screen()\n    screen.setup(settings.SCREENWIDTH,settings.SCREENHEIGHT)\n\n    for i in range(settings.NUMREPEATS):\n        setup(settings.START_X,settings.START_Y)\n        draw_animation(settings.NUMFRAMES, settings.SIDELEN, settings.COLOR, settings.SLEEPTIME)\n\nmain()","sub_path":"animation_examples/animate_1.py","file_name":"animate_1.py","file_ext":"py","file_size_in_byte":988,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"367865010","text":"#! /usr/bin/env python\n'''\n$Id: setup_qubicpack.py\n$auth: Steve Torchinsky <satorchi@apc.in2p3.fr>\n$created: Mon 07 Aug 2017 08:35:24 CEST\n$license: GPLv3 or later, see https://www.gnu.org/licenses/gpl-3.0.txt\n\n          This is free software: you are free to change and\n          redistribute it.  There is NO WARRANTY, to the extent\n          permitted by law.\n\nsetup.py for qubicpack only.  \nUse this to install qubicpack without pystudio\n'''\nfrom __future__ import division, print_function\nfrom setuptools import setup\n\nDISTNAME         = 'qubicpack'\nDESCRIPTION      = 'Utilities for QUBIC detector data visualization'\nAUTHOR           = 'Steve Torchinsky'\nAUTHOR_EMAIL     = 'satorchi@apc.in2p3.fr'\nMAINTAINER       = 'Steve Torchinsky'\nMAINTAINER_EMAIL = 'satorchi@apc.in2p3.fr'\nURL              = 'https://github.com/satorchi/qubicpack'\nLICENSE          = 'GPL'\nDOWNLOAD_URL     = 'https://github.com/satorchi/qubicpack'\nVERSION          = '2.0.0'\n\nwith open('README.md') as f:\n    long_description = f.read()\n\n\nsetup(install_requires=['numpy'],\n      name=DISTNAME,\n      version=VERSION,\n      packages=[DISTNAME],\n      zip_safe=False,\n      package_data={DISTNAME: ['data/*']},\n      author=AUTHOR,\n      author_email=AUTHOR_EMAIL,\n      maintainer=MAINTAINER,\n      maintainer_email=MAINTAINER_EMAIL,\n      description=DESCRIPTION,\n      license=LICENSE,\n      url=URL,\n      download_url=DOWNLOAD_URL,\n      long_description=long_description,\n      classifiers=[\n          'Development Status :: 3 - Alpha',\n          'Environment :: Console',\n          'Intended Audience :: Developers',\n          'Intended Audience :: Science/Research',\n          'License :: OSI Approved :: GNU General Public License (GPL)',\n          'Topic :: Scientific/Engineering'],\n)\n\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1776,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"641610326","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Wed Jul 24 10:30:13 2019\n\n@author: shrinidhikr\n\"\"\"\n\nimport pyrebase\nimport gtts_example\n\nCONFIG = {\n   \"apiKey\": \"AIzaSyCl2swHGA5rCVdPRVroj7ievuMpJLewBQI\",\n   \"authDomain\": \"endomimyk-mcuwqu.firebaseapp.com\",\n   \"databaseURL\": \"https://endomimyk-mcuwqu.firebaseio.com\",\n   \"projectId\": \"endomimyk-mcuwqu\",\n   \"storageBucket\": \"endomimyk-mcuwqu.appspot.com\",\n   \"messagingSenderId\": \"852239192958\",\n   \"appId\": \"1:852239192958:web:1bf1d51a21438c73\"\n}\nfirebase = pyrebase.initialize_app(CONFIG)\n\n\ndef authenticate(email, password):\n    #authenticate a user\n    auth = firebase.auth()\n    user = auth.sign_in_with_email_and_password(email, password)\n    return user['idToken']\n\n\ndef create(data):\n    #push into Records\n    db = firebase.database()\n    resp = db.child(\"Records\").push(data)\n    return resp\n\n    \ndef read():\n    #read Records\n    db = firebase.database()\n    users = db.child(\"Records\").get()\n    print(users.val())\n\n    all_users = db.child(\"Records\").get()\n    for user in all_users.each():\n        print(user.key())\n        print(user.val())\n\n   \ndef storage(file_name,file_f):\n    #Add files into storage\n    print(file_name+\" \"+file_f)\n    storage = firebase.storage()\n    storage.child(file_name).child(file_name).put(file_f)\n\ndef storeimg(file_name,img_f):\n    #Add files into storage\n    print(file_name+\" \"+img_f)\n    storage = firebase.storage()\n    storage.child(file_name).child(img_f).put(img_f)\n\ndef download(file_name):\n    #download file\n    storage = firebase.storage()\n    storage.child(file_name).child(file_name).download(file_name+\".txt\")\n    #gtts_example.speech(file_name+\".txt\")\n\ndef calldb(dbdata):\n    \"\"\"\n    For authentication use this block\n    \n    email = \"mimyk.trial@demo.com\"\n    password = \"mimyk123\"\n    user_token = authenticate(email, password)\n    \"\"\"\n    #data = {\"patient_name\": \"Patient1\", \"file_f\": \"sample_procedure.txt\"}\n    data = dbdata\n    \"\"\"\n    To push data to Realtime DB use create and read for retreival\n    create(data)\n    read()\n    \"\"\"\n    storage(data[\"patient_name\"],data[\"file_f\"])\n    download(data[\"patient_name\"])\n","sub_path":"web/firebase_con.py","file_name":"firebase_con.py","file_ext":"py","file_size_in_byte":2154,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"636770257","text":"\"\"\"Code to shard a large dataset file into multiple small ones.\"\"\"\n\n# Python imports.\nimport json\nimport logging\nimport os\nimport random\nimport sys\n\n# User imports.\nfrom . import DataNormalisation\n\n# 3rd party imports.\nimport tensorflow as tf\n\n# Globals.\nLOGGER = logging.getLogger(__name__)\n\n# Define functions for compatibility.\nif sys.version_info[0] >= 3:\n    from itertools import zip_longest as izip_longest\nelse:\n    from itertools import izip_longest\n\n\ndef shard_matrix(fileExamples, dirOutput, config, fileTargets=None):\n    \"\"\"Shard a dataset where each example is a matrix.\n\n    :param fileExamples:    The location of the file containing the dataset of input examples.\n    :type fileExamples:     str\n    :param dirOutput:       The location of the directory to write out the sharded files to.\n    :type dirOutput:        str\n    :param config:          The object containing the configuration parameters for the sharding.\n    :type config:           JsonschemaManipulation.Configuration\n    :param fileTargets:     The location of the file containing the targets of the input examples.\n    :type fileTargets:      str\n\n    \"\"\"\n\n    pass\n\n\ndef shard_sequence(fileExamples, dirOutput, config, fileTargets=None):\n    \"\"\"Shard a dataset where each example is a sequence.\n\n    :param fileExamples:    The location of the file containing the dataset of input examples.\n    :type fileExamples:     str\n    :param dirOutput:       The location of the directory to write out the sharded files to.\n    :type dirOutput:        str\n    :param config:          The object containing the configuration parameters for the sharding.\n    :type config:           JsonschemaManipulation.Configuration\n    :param fileTargets:     The location of the file containing the targets of the input examples.\n    :type fileTargets:      str\n\n    \"\"\"\n\n    pass\n\n\ndef shard_vector(fileExamples, dirOutput, config, fileTargets=None):\n    \"\"\"Shard a dataset where each example is a single vector.\n\n    :param fileExamples:    The location of the file containing the dataset of input examples.\n    :type fileExamples:     str\n    :param dirOutput:       The location of the directory to write out the sharded files to.\n    :type dirOutput:        str\n    :param config:          The object containing the configuration parameters for the sharding.\n    :type config:           JsonschemaManipulation.Configuration\n    :param fileTargets:     The location of the file containing the targets of the input examples.\n    :type fileTargets:      str\n\n    \"\"\"\n\n    # Seed the random number generator.\n    randomSeed = config.get_param([\"RandomSeed\"])\n    if randomSeed[0]:\n        random.seed(randomSeed[1])\n    else:\n        random.seed(None)\n\n    # Determine the examples that will be used for training, testing and validation. Pad the\n    # configuration parameters with 0s so that missing test and validation fraction values mean that there are no\n    # examples allocated to those splits.\n    datasetDivisions = config.get_param([\"DataProcessing\", \"DataSplit\"])[1]\n    datasetDivisions[len(datasetDivisions):3] = [0] * (3 - len(datasetDivisions))  # Pad with 0s.\n    trainFraction = datasetDivisions[0]\n    testFraction = min(1 - trainFraction, datasetDivisions[1])\n    validationFraction = min(1 - (trainFraction + testFraction), datasetDivisions[2])\n    choices = [trainFraction, trainFraction + testFraction, trainFraction + testFraction + validationFraction]\n\n    # Create the example data normaliser.\n    LOGGER.info(\"Now creating example data normaliser.\")\n    isExamplesBOW = config.get_param([\"ExampleBOW\"])[1]\n    if isExamplesBOW:\n        exampleNormaliser = DataNormalisation.BOWNormaliser(fileExamples, config, dataPurpose=\"Examples\")\n    else:\n        exampleNormaliser = DataNormalisation.VectorNormaliser(fileExamples, config, dataPurpose=\"Examples\")\n\n    # Create the target data normaliser.\n    isTargetsBOW = config.get_param([\"TargetBOW\"])[1]\n    if fileTargets:\n        LOGGER.info(\"Now creating target data normaliser.\")\n        if isTargetsBOW:\n            targetNormaliser = DataNormalisation.BOWNormaliser(fileTargets, config, dataPurpose=\"Targets\")\n        else:\n            targetNormaliser = DataNormalisation.VectorNormaliser(fileTargets, config, dataPurpose=\"Targets\")\n    else:\n        targetNormaliser = DataNormalisation.BaseNormaliser()\n\n    # Setup the files to record the data in.\n    dirTrainData = os.path.join(dirOutput, \"TrainingData\")\n    os.makedirs(dirTrainData)\n    examplesAddedToShard = 0  # The number of examples added to the current shard.\n    currentFileNumber = 0\n    fidTrainingShard = tf.python_io.TFRecordWriter(os.path.join(dirTrainData, \"Shard_{:d}\".format(currentFileNumber)))\n    fidTest = tf.python_io.TFRecordWriter(os.path.join(dirOutput, \"Test\"))\n    fidValidation = tf.python_io.TFRecordWriter(os.path.join(dirOutput, \"Validation\"))\n\n    # Write out the examples and targets.\n    LOGGER.info(\"Now writing out TFRecord files.\")\n    examplesPerShard = config.get_param([\"DataProcessing\", \"ExamplesPerShard\"])[1]  # Examples to put in a shard.\n    exampleSeparator = config.get_param([\"DataProcessing\", \"Examples\", \"Separator\"])[1]\n    exampleHeaderPresent = config.get_param([\"DataProcessing\", \"Examples\", \"HeaderPresent\"])[1]\n    targetSeparator = config.get_param([\"DataProcessing\", \"Targets\", \"Separator\"])[1]\n    targetHeaderPresent = config.get_param([\"DataProcessing\", \"Targets\", \"HeaderPresent\"])[1]\n    with open(fileExamples, 'r') as fidExamples, open(fileTargets if fileTargets else os.devnull, 'r') as fidTargets:\n        # Strip headers.\n        if exampleHeaderPresent:\n            fidExamples.readline()\n        if targetHeaderPresent:\n            fidTargets.readline()\n\n        for i, j in izip_longest(fidExamples, fidTargets, fillvalue=''):\n            # Normalise the data.\n            exampleDatapoint = exampleNormaliser.normalise((i.strip()).split(exampleSeparator))\n            targetDatapoint = [] if not fileTargets else targetNormaliser.normalise((j.strip()).split(targetSeparator))\n\n            # Determine what dataset portion this example/target should go to.\n            choice = random.random()\n            choice = [choice < i for i in choices]\n            if choice[0]:\n                # The example/target will go to the training set.\n\n                # Create the Example protocol buffer\n                # (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto).\n                example = tf.train.Example(\n                    # The Example protocol buffer contains a Features protocol buffer\n                    # (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto).\n                    features=tf.train.Features(\n                        # The Features protocol buffer contains a list of features, which are one of either a\n                        # bytes_list, float_list or int64_list.\n                        feature={\n                            \"Example\": _float_feature(exampleDatapoint[2] if isExamplesBOW else exampleDatapoint),\n                            \"ExampleIndices\": _int64_feature(exampleDatapoint[1] if isExamplesBOW else []),\n                            \"NumExampleVars\": _int64_feature(\n                                [exampleDatapoint[0]] if isExamplesBOW else [len(exampleDatapoint)]\n                            ),\n                            \"Target\": _float_feature(targetDatapoint[2] if isTargetsBOW else targetDatapoint),\n                            \"TargetIndices\": _int64_feature(targetDatapoint[1] if isTargetsBOW else []),\n                            \"NumTargetVars\": _int64_feature(\n                                [targetDatapoint[0]] if isTargetsBOW else [len(targetDatapoint)]\n                            )\n                        }\n                    )\n                )\n                fidTrainingShard.write(example.SerializeToString())\n                examplesAddedToShard += 1\n\n                # Open a new shard file if needed.\n                if examplesAddedToShard == examplesPerShard:\n                    fidTrainingShard.close()\n                    examplesAddedToShard = 0\n                    currentFileNumber += 1\n                    fidTrainingShard = tf.python_io.TFRecordWriter(\n                        os.path.join(dirTrainData, \"Shard_{:d}\".format(currentFileNumber))\n                    )\n            elif choice[1]:\n                # The example/target will go to the test set.\n\n                # Create the Example protocol buffer\n                # (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto).\n                example = tf.train.Example(\n                    # The Example protocol buffer contains a Features protocol buffer\n                    # (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto).\n                    features=tf.train.Features(\n                        # The Features protocol buffer contains a list of features, which are one of either a\n                        # bytes_list, float_list or int64_list.\n                        feature={\n                            \"Example\": _float_feature(exampleDatapoint[2] if isExamplesBOW else exampleDatapoint),\n                            \"ExampleIndices\": _int64_feature(exampleDatapoint[1] if isExamplesBOW else []),\n                            \"NumExampleVars\": _int64_feature(\n                                [exampleDatapoint[0]] if isExamplesBOW else [len(exampleDatapoint)]\n                            ),\n                            \"Target\": _float_feature(targetDatapoint[2] if isTargetsBOW else targetDatapoint),\n                            \"TargetIndices\": _int64_feature(targetDatapoint[1] if isTargetsBOW else []),\n                            \"NumTargetVars\": _int64_feature(\n                                [targetDatapoint[0]] if isTargetsBOW else [len(targetDatapoint)]\n                            )\n                        }\n                    )\n                )\n                fidTest.write(example.SerializeToString())\n            elif choice[2]:\n                # The example/target will go to the validation set.\n\n                # Create the Example protocol buffer for the example\n                # (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/example.proto).\n                example = tf.train.Example(\n                    # The Example protocol buffer contains a Features protocol buffer\n                    # (https://github.com/tensorflow/tensorflow/blob/master/tensorflow/core/example/feature.proto).\n                    features=tf.train.Features(\n                        # The Features protocol buffer contains a list of features, which are one of either a\n                        # bytes_list, float_list or int64_list.\n                        feature={\n                            \"Example\": _float_feature(exampleDatapoint[2] if isExamplesBOW else exampleDatapoint),\n                            \"ExampleIndices\": _int64_feature(exampleDatapoint[1] if isExamplesBOW else []),\n                            \"NumExampleVars\": _int64_feature(\n                                [exampleDatapoint[0]] if isExamplesBOW else [len(exampleDatapoint)]\n                            ),\n                            \"Target\": _float_feature(targetDatapoint[2] if isTargetsBOW else targetDatapoint),\n                            \"TargetIndices\": _int64_feature(targetDatapoint[1] if isTargetsBOW else []),\n                            \"NumTargetVars\": _int64_feature(\n                                [targetDatapoint[0]] if isTargetsBOW else [len(targetDatapoint)]\n                            )\n                        }\n                    )\n                )\n                fidValidation.write(example.SerializeToString())\n            else:\n                # The example/target will not go to any of the sets.\n                pass\n\n    # Determine number of example and target variables, and record this.\n    variableNumbers = {\n        \"NumExampleVariables\": exampleDatapoint[0] if isExamplesBOW else len(exampleDatapoint),\n        \"NumTargetVariables\": targetDatapoint[0] if isTargetsBOW else len(targetDatapoint)\n    }\n    fileNumVars = os.path.join(dirOutput, \"NumVariables.json\")\n    fidNumVars = open(fileNumVars, 'w')\n    json.dump(variableNumbers, fidNumVars)\n    fidNumVars.close()\n\n    # Close the final shard file.\n    fidTrainingShard.close()\n    fidTest.close()\n    fidValidation.close()\n\n\ndef _bytes_feature(value):\n    return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))\n\n\ndef _float_feature(value):\n    return tf.train.Feature(float_list=tf.train.FloatList(value=value))\n\n\ndef _int64_feature(value):\n    return tf.train.Feature(int64_list=tf.train.Int64List(value=value))\n","sub_path":"Code/DataProcessing/shard_data.py","file_name":"shard_data.py","file_ext":"py","file_size_in_byte":12812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"213079559","text":"def main():\n\n    line = input()\n    numbers  = {}\n    print(\"Enter a name and number, or a name and ? to query (!! to exit)\")\n    while line != \"!!\":\n        split = line.split(\" \")\n        name, query = split[0], split[1]\n        if query == \"?\" and name in numbers:\n            print(\"{} has number {}\".format(name, numbers[name]))\n        elif query == \"?\" and name not in numbers:\n            print(\"Sorry, there is no {}\".format(name))\n        else:\n            numbers[name] = query\n        line = input()\n\n    print(\"Bye\")\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"week-3/phone_numbers.py","file_name":"phone_numbers.py","file_ext":"py","file_size_in_byte":569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"645334446","text":"import os,sys,inspect\ncurrentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))\nparentdir = os.path.dirname(currentdir)\nsys.path.insert(0,parentdir)\n\nimport time\nimport pglet\nfrom pglet import Page, Text, Textbox, Button, Progress, Icon, Link, Toggle, Message, MessageButton, Checkbox, ChoiceGroup, Dropdown\nfrom pglet import choicegroup\nfrom pglet import dropdown\nfrom pglet import nav\nfrom pglet import Nav, SearchBox, Slider, SpinButton, Tabs, Tab, Dialog, Panel, Toolbar\nfrom pglet import Grid, Column\nfrom pglet import VerticalBarChart, BarChart, Callout, LineChart, PieChart\nfrom pglet import Image\nfrom pglet import toolbar\nfrom pglet import button\nfrom pglet import link\nfrom pglet import verticalbarchart\nfrom pglet import barchart\nfrom pglet import linechart\nfrom pglet import piechart\n\nclass Contact():\n    def __init__(self, first_name, last_name):\n        self.first_name = first_name\n        self.last_name = last_name\n\ndef link_click(event):\n    print('This link is clicked!')\n\ndef toggle_change(event):\n    print('This toggle is changed!')\n\ndef message_dismissed(event):\n    print('This message is dismissed!')\n\ndef checkbox_changed(event):\n    print('This checkbox is changed!')\n\ndef choicegroup_changed(event):\n    print('This choicegroup is changed!')\n\ndef dropdown_changed(event):\n    print('This dropdown is changed!')\n\ndef navitem_changed(event):\n    print('This navitem is changed!')\n\ndef navitem_expanded(event):\n    print('This navitem is expanded!')\n\ndef navitem_collapsed(event):\n    print('This navitem is collapsed!')\n\ndef searchbox_changed(event):\n    print('This searchbox is changed!')\n\ndef slider_changed(event):\n    print('This slider is changed!')\n\ndef spinbutton_changed(event):\n    print('This spinbutton is changed!')\n\ndef tabs_changed(event):\n    print('This tabs is changed!')\n\ndef button_clicked(event):\n    print('This button is clicked!')\n\ndef item_clicked(event):\n    print('This item is clicked!')\n\n\n\npage = pglet.page(\"index\")\npage.update(Page(title=\"Hello, pglet!\"))\npage.clean()\n\nb = Button(primary=False, compound=False, action=False, toolbar=True, split=False, text='This is text', \n    secondary_text='This is secondary text', url='https://google.com', new_window=True, title='This is title',\n    icon='Mail', icon_color='red', data='data', on_click=button_clicked, items=[\n        button.MenuItem(text='Item1 text', secondary_text='Item1 secondary text', url='https://google.com', new_window=False,\n        icon='Mail', icon_color='blue', icon_only=True, split=False, divider=False, on_click=item_clicked, items=[\n            button.MenuItem('Item1Item1'),\n            button.MenuItem('Item1Item2')\n        ]),\n        button.MenuItem(text='Item2 text')\n    ])\n#page.add(b)\n\ni = Image(src='https://www.w3schools.com/css/img_5terre.jpg', alt='This is image', title='This is title',\n maximize_frame=False)\n#page.add(i)\n\n#page.add(Text(value='C:\\\\He\\nllo', align='right', width='100%', nowrap=True, size='small'))\n#page.add(Icon(name='Mail', color='green', size='large'))\n\nl = Link(value='Visit google', url='https://google.com', pre=True, align='right', width='100', size='large1', \ntitle='Link title', controls=[\n    Text(value='LinkText1'),\n    Text(value='LinkText2')\n])\n#page.add(l)\n\n#page.add(Link(value='Start action', url=None, new_window=False, on_click=link_click))\n#page.add(Toggle(value=True, label='This is toggle', on_text='On text', off_text='Off text', \n#    inline=True, on_change=toggle_change))\n\n#page.add(Message(value='This is message', dismiss=True, on_dismiss=message_dismissed, buttons=[\n#    MessageButton(text='Yes, I agree', action='Yes'),\n#    MessageButton(text='No, I disagree', action='No')\n#]))\n\n#page.add(Checkbox(value=True, label='I am a human', box_side='start', data='data to pass', on_change=checkbox_changed))\n\n#page.add(ChoiceGroup(value='colour', label='Select a colour:', data='data to pass', options=[\n#    choicegroup.Option(key='Green', icon='Shop', icon_color='Green'),\n#    choicegroup.Option(key='Yellow')], \n#    on_change=choicegroup_changed))\n\n#page.add(Dropdown(id='dd1', label='Choose your weapon', options=[\n#    dropdown.Option('Sword'),\n#    dropdown.Option('Word'),\n#    dropdown.Option('Poison')],\n#    on_change=dropdown_changed))\n    \n#page.add(Nav(id='n1', value='n1', items=[\n#    nav.Item(key='Item1', items=[\n#        nav.Item('item1.1', items=\n#            [nav.Item(key='item1.1.1', icon='mail', icon_color='green', url='https://google.com', expanded=True, new_window=True),\n#            nav.Item('item1.1.2')]),\n#        nav.Item('item1.2')\n#    ]),\n#    nav.Item('Item2'),\n#    nav.Item('Item3'),],\n#    on_change=navitem_changed, on_expand=navitem_expanded, on_collapse=navitem_collapsed))\n\n#page.add(SearchBox(value='', placeholder='search for something', underlined=True, icon='mail', \n#    icon_color='red', data='data', on_change=True, on_change=searchbox_changed))\n\n#page.add(Slider(value=1, label='To what extend you agree', min=0, max=10, step=1, \n#    show_value=True, value_format='current_value is {value}', vertical=False, on_change=slider_changed))\n\n#page.add(SpinButton(value=1, label='Level of satisfaction', min=0, max=10, step=1, \n#    icon='mail', width=200, on_change=spinbutton_changed))\n\n#page.add(Tabs(id='t1', value='Tabs101', tabs=[\n#    Tab(text='Tab1', controls=[\n#        Button(text='hello')\n#    ]),\n#    Tab(text='Tab2', controls=[\n#        Text(value='this is text')\n#    ]),\n#    Tab(text='Tab3')],\n#    on_change=tabs_changed))\n\n#d = Dialog(title='Hello', open=True, controls=[\n#    Text(value='Are you sure?')\n#], footer=[\n#    Button(text='OK'),\n#    Button(text=\"Cancel\")\n#])\n\n#d.footer.id = \"myfooter\"\n\n#page.add(d)\n\n#id = d.footer.id \n#print(id)\n\ng = Grid(columns=[\n    Column(field_name=\"first_name\", name='First name'),\n    Column(field_name=\"last_name1\", name='Last name')\n], items=[\n    Contact(first_name='Inesa', last_name='Fitsner'),\n    Contact(first_name='Fiodar', last_name='Fitsner')\n])\n\n#page.add(g)\n\np = Panel(title='Hello', open=True, controls=[\n    Text(value='Are you sure?')\n], footer=[\n    Button(text='OK'),\n    Button(text=\"Cancel\")\n])\n\n#page.add(p)\n\nt = Toolbar(items=[\n    toolbar.Item(text='Are you sure?')\n], overflow=[\n    toolbar.Item(text='OK'),\n    toolbar.Item(text=\"Cancel\")\n], far=[\n    toolbar.Item(text='far')\n])\n\n#page.add(t)\n\nvbc = VerticalBarChart(x_type='number', data=[\n    verticalbarchart.P(x='1', y=100),\n    verticalbarchart.P(x='80', y=200), \n    verticalbarchart.P(x='100', y=300),\n])\n\n#page.add(vbc)\n\nbc = BarChart(data_mode='default', data=[\n    barchart.P(x=1, y=100),\n    barchart.P(x=80, y=200), \n    barchart.P(x=100, y=300),\n])\n\n#page.add(bc)\n\ndef button1_clicked(event):\n    print('Button1 is clicked!')\n    c.visible = True\n    page.update(c)\n    #page.send('set callout1 visible\"True\"')\n\nbutton1 = Button(id='button1', text='Click to see callout', on_click=button1_clicked)\nc = Callout(target='button1', position='leftBottom', gap=100, beak=True, beak_width=10, page_padding=10,\n    focus=False, cover=True, visible=True, controls=[\n        Text(value='This is callout')\n        ])\n\n#page.add(button1, c)\n\nlc = LineChart(legend=True, tooltips=True, stroke_width=4, y_min=0, y_max=100, y_ticks=2, y_format='{y}%', \n                x_type='date', datas=[\n                    linechart.Data(color='yellow', legend='yellow color', points=[\n                        linechart.P(x='2015-03-25', y=100),\n                        linechart.P(x='2020-03-25', y=50)]),\n                    linechart.Data(color='green', legend='green color', points=[\n                        linechart.P(x='2021-03-01', y=20),\n                        linechart.P(x='2021-03-31', y=10)])\n                ])\n\n#page.add(lc)\n\npc = PieChart(legend=True, tooltips=True, inner_value=40, inner_radius=42, width='100%', data=[\n                    piechart.P(value=20, color='yellow'),\n                    piechart.P(value=30, color='green')])\n\npage.add(pc)\n\n\n#page.wait_close()","sub_path":"tests/app_all_controls.py","file_name":"app_all_controls.py","file_ext":"py","file_size_in_byte":7976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"252573347","text":"from HW_Assignments.HW1_Template.src.BaseDataTable import BaseDataTable\nimport pymysql\nimport pymysql.cursors\nimport logging\nimport json\nfrom Examples.SQLHelper import create_insert, create_select, create_update, create_delete, run_q\n\n\nclass RDBDataTable(BaseDataTable):\n\n    \"\"\"\n    The implementation classes (XXXDataTable) for CSV database, relational, etc. with extend the\n    base class and implement the abstract methods.\n    \"\"\"\n\n    def __init__(self, table_name, connect_info, key_columns):\n        \"\"\"\n\n        :param table_name: Logical name of the table.\n        :param connect_info: Dictionary of parameters necessary to connect to the data.\n        :param key_columns: List, in order, of the columns (fields) that comprise the primary key.\n        \"\"\"\n        self._data = {\n            \"table_name\": table_name,\n            \"connect_info\": connect_info,\n            \"key_columns\": key_columns\n        }\n        if table_name is None or connect_info is None:\n            raise ValueError(\"Invalid input\")\n\n        # self._logger = logging.getLogger()\n        # Set up logger\n        logf = open('RDBDtaTable.log', 'w')\n        logging.basicConfig(stream=logf, format=\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\", level=logging.INFO)\n        self._logger = logging.getLogger(__name__)\n        self._logger.setLevel(logging.getLevelName(logging.INFO))\n        # self._logger.debug(\"RDBDataTable.__init__: data = \" + json.dumps(self._data, indent=2))\n\n        host = connect_info.get(\"host\", \"localhost\")\n        port = connect_info.get(\"port\", 3306)\n        user = connect_info.get(\"user\", \"root\")\n        password = connect_info.get(\"password\", \"wyb5030356\")\n        db = connect_info.get(\"db\", None)\n        cursorclass = connect_info.get(\"cursorclass\", pymysql.cursors.DictCursor)\n        charset = connect_info.get(\"charset\", \"utf8mb4\")\n        if not host or not port or not user or not password or not db:\n            raise ValueError(\"Insufficient params given for connection\")\n\n        self.conn = pymysql.connect(host=host, port=port, user=user, password=password, db=db,\n                               charset=charset, cursorclass=cursorclass)\n\n    def find_by_primary_key(self, key_fields, field_list=None):\n        \"\"\"\n\n        :param key_fields: The list with the values for the key_columns, in order, to use to find a record.\n        :param field_list: A subset of the fields of the record to return.\n        :return: None, or a dictionary containing the requested fields for the record identified\n            by the key.\n        \"\"\"\n        key_cols = self._data.get(\"key_columns\", None)\n        if key_cols is None:\n            raise ValueError(\"Need primary key before finding anything by primary key\")\n        tmp = dict(zip(self._data[\"key_columns\"], key_fields))\n        return self.find_by_template(template=tmp, field_list=field_list)\n\n    def find_by_template(self, template, field_list=None, limit=None, offset=None, order_by=None):\n        \"\"\"\n\n        :param template: A dictionary of the form { \"field1\" : value1, \"field2\": value2, ...}\n        :param field_list: A list of request fields of the form, ['fielda', 'fieldb', ...]\n        :param limit: Do not worry about this for now.\n        :param offset: Do not worry about this for now.\n        :param order_by: Do not worry about this for now.\n        :return: A list containing dictionaries. A dictionary is in the list representing each record\n            that matches the template. The dictionary only contains the requested fields.\n        \"\"\"\n        if template is None:\n            raise ValueError(\"Empty template given\")\n        (sql, args) = create_select(self._data[\"table_name\"], template, field_list, order_by=None, limit=None, offset=None)\n        (res, data) = run_q(sql, args, conn=self.conn)\n\n        return data\n\n    def delete_by_key(self, key_fields):\n        \"\"\"\n\n        Deletes the record that matches the key.\n\n        :param template: A template.\n        :return: A count of the rows deleted.\n        \"\"\"\n        tmp = dict(zip(self._data[\"key_columns\"], key_fields))\n        return self.delete_by_template(tmp)\n\n    def delete_by_template(self, template):\n        \"\"\"\n\n        :param template: Template to determine rows to delete.\n        :return: Number of rows deleted.\n        \"\"\"\n        (sql, args) = create_delete(self._data[\"table_name\"], template)\n        (res, data) = run_q(sql, args, fetch=False, conn=self.conn)\n        return res\n\n    def update_by_key(self, key_fields, new_values):\n        \"\"\"\n\n        :param key_fields: List of value for the key fields.\n        :param new_values: A dict of field:value to set for updated row.\n        :return: Number of rows updated.\n        \"\"\"\n        tmp = dict(zip(self._data[\"key_columns\"], key_fields))\n        return self.update_by_template(tmp, new_values)\n\n    def update_by_template(self, template, new_values):\n        \"\"\"\n\n        :param template: Template for rows to match.\n        :param new_values: New values to set for matching fields.\n        :return: Number of rows updated.\n        \"\"\"\n        (sql, args) = create_update(self._data[\"table_name\"], new_values, template)\n        (res, data) = run_q(sql, args, fetch=False, conn=self.conn)\n        return res\n\n    def insert(self, new_record):\n        \"\"\"\n\n        :param new_record: A dictionary representing a row to add to the set of records.\n        :return: None\n        \"\"\"\n        (sql, args) = create_insert(self._data[\"table_name\"], new_record)\n        (res, data) = run_q(sql, args, fetch=False, conn=self.conn)\n        return None\n\n    def close_connection(self):\n        self.conn.close()\n\n    # def get_rows(self):\n    #     return self._rows\n\n\n\n\n","sub_path":"HW_Assignments/HW1_Template/src/RDBDataTable.py","file_name":"RDBDataTable.py","file_ext":"py","file_size_in_byte":5709,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"633688149","text":"import cv2\nimport pywt\nimport pickle\nimport numpy as np\nimport base64\nimport json\nfrom flask import Flask, request, jsonify, render_template, redirect\n\napp = Flask(__name__)\napp.config.update(\n    dict(SECRET_KEY=\"powerful secretkey\", WTF_CSRF_SECRET_KEY=\"a csrf secret key\")\n)\n\n__class_name_to_number = {}\n__class_number_to_name = {}\n\n__model = pickle.load(open(\"final_model.pickle\", \"rb\"))\n\n\ndef classify_image(image_base64_data, file_path=None):\n    imgs = get_cropped_image_if_2_eyes(file_path, image_base64_data)\n\n    result = []\n\n    for img in imgs:\n        scaled_raw_img = cv2.resize(img, (32, 32))\n        img_hr = w2d(img, 'db1', 5)\n        scaled_hr_img = cv2.resize(img_hr, (32, 32))\n        combined_img = np.vstack((scaled_raw_img.reshape(32 * 32 * 3, 1), scaled_hr_img.reshape(32 * 32, 1)))\n\n        len_image_array = (32 * 32 * 3) + (32 * 32)\n\n        final = combined_img.reshape(1, len_image_array).astype(float)\n\n        result.append({\n            'class': class_number_to_name(__model.predict(final)[0]),\n            'class_probability': np.round(__model.predict_proba(final) * 100, 2).tolist()[0],\n            'class_dictionary': __class_name_to_number\n        })\n\n    return result\n\n\ndef class_number_to_name(class_num):\n    if class_num == 0:\n        player = 'lionel_messi'\n\n    elif class_num == 1:\n        player = 'maria_sharapova'\n\n    elif class_num == 2:\n        player = 'roger_federer'\n\n    elif class_num == 3:\n        player = 'serena_williams'\n\n    elif class_num == 4:\n        player = 'virat_kohli'\n\n    return player\n\n\ndef get_cv2_image_from_base64_string(b64str):\n    '''\n    credit: https://stackoverflow.com/questions/33754935/read-a-base-64-encoded-image-from-memory-using-opencv-python-library\n    :param uri:\n    :return:\n    '''\n    encoded_data = b64str.split(',')[1]\n    nparr = np.frombuffer(base64.b64decode(encoded_data), np.uint8)\n    img = cv2.imdecode(nparr, cv2.IMREAD_COLOR)\n    return img\n\n\ndef get_cropped_image_if_2_eyes(image_path, image_base64_data):\n    face_cascade = cv2.CascadeClassifier('./model/opencv/haarcascades/haarcascade_frontalface_default.xml')\n    eye_cascade = cv2.CascadeClassifier('./model/opencv/haarcascades/haarcascade_eye.xml')\n\n    if image_path:\n        img = cv2.imread(image_path)\n    else:\n        img = get_cv2_image_from_base64_string(image_base64_data)\n\n    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n    faces = face_cascade.detectMultiScale(gray, 1.3, 5)\n\n    cropped_faces = []\n    for (x, y, w, h) in faces:\n        roi_gray = gray[y:y + h, x:x + w]\n        roi_color = img[y:y + h, x:x + w]\n        eyes = eye_cascade.detectMultiScale(roi_gray)\n        if len(eyes) >= 2:\n            cropped_faces.append(roi_color)\n    return cropped_faces\n\n\ndef load_artifacts():\n    print(\"Loading saved artifacts...Start\")\n    global __class_name_to_number\n    global __class_number_to_name\n    global __model\n\n    with open('class_dictionary.json', 'r') as f:\n        __class_name_to_number = json.load(f)\n        __class_number_to_name = {v: k for k, v in __class_name_to_number.items()}\n\n    print(\"Loading save artifacts...done!\")\n\n\ndef w2d(img, mode='haar', level=1):\n    imArray = img\n\n    # Datatype conversions\n    # convert to grayscale\n    imArray = cv2.cvtColor(imArray, cv2.COLOR_RGB2GRAY)\n\n    # convert to float\n    imArray = np.float32(imArray)\n    imArray /= 255\n\n    # compute coefficients\n    coeffs = pywt.wavedec2(imArray, mode, level)\n\n    # process coefficients\n    coeffs_H = list(coeffs)\n    coeffs_H[0] *= 0\n\n    # reconstruction\n    imArray_H = pywt.waverec2(coeffs_H, mode)\n    imArray_H *= 255\n    imArray_H = np.uint8(imArray_H)\n\n    return imArray_H\n\n\n@app.route(\"/\")\ndef index():\n    return render_template('index.html')\n\n\n@app.route(\"/prediction\", methods=[\"POST\"])\ndef prediction():\n\n    if request.method == 'POST':\n\n        image_data = request.form['b64']\n        result = classify_image(image_data)\n        print(result)\n\n        if len(result) == 0:\n            message = \"Can't classify image. Classifier was not able to detect face and two eyes properly\"\n            return render_template(\"prediction.html\", message=message, messi=\"NA\", sharapova=\"NA\", federer=\"NA\",\n                                   serena=\"NA\", virat=\"NA\")\n\n        match = None\n        bestScore = -1\n\n        for i in range(len(result)):\n            maxScoreForThisClass = max(result[i]['class_probability'])\n\n            if maxScoreForThisClass > bestScore:\n                match = result[i]\n                bestScore = maxScoreForThisClass\n\n        playerName = match['class']\n\n        player = playerName.split('_')\n        player = \" \".join(player).upper()\n\n        messi = match['class_probability'][0]\n        sharapova = match['class_probability'][1]\n        federer = match['class_probability'][2]\n        serena = match['class_probability'][3]\n        virat = match['class_probability'][4]\n\n        return render_template('prediction.html',player=player, playerName=playerName, messi=messi, sharapova=sharapova,\n                               federer=federer, serena=serena, virat=virat)\n\n\nif __name__ == \"__main__\":\n    print('Starting Python Flask Server For Sports Celebrity Image Classification!!!')\n    load_artifacts()\n    app.run(debug=True)\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":5257,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"120415049","text":"from django.shortcuts import render\nfrom django.shortcuts import render, redirect\nfrom django.views import generic\nfrom django.contrib.auth import login, authenticate\nfrom django.contrib.auth.models import User\nfrom django.contrib.sessions.models import Session\nfrom django.contrib.auth.mixins import LoginRequiredMixin\n\nfrom datetime import datetime\nfrom django.utils import timezone\n\nfrom .forms import RegisterForm, SettingsForm\nfrom .models import invitation, profile\n\n# View of the user's profile configuration page\ndef SettingsView (request):\n    errorm = None\n\n    if request.user.is_authenticated() == True:\n\n        if request.method == 'POST':\n\n            form = SettingsForm(request.POST or None, request.FILES or None)\n\n            if form.is_valid():\n                user = request.user\n                user.username = form.cleaned_data.get('username')\n                user.profile.ppic = form.cleaned_data.get('ppic')\n\n                user.profile.save()\n                user.save()\n\n                return redirect('users:settings')\n\n        else:\n            form = SettingsForm()\n        return render(request, 'main/userpage.html', {'form': form, 'errorm' : errorm})\n    else:\n        return redirect('main:home')\n\n\n# View of the registration page.\ndef RegisterView (request):\n    errorm = None\n\n    if request.user.is_authenticated() == False:\n\n        if request.method == 'POST':\n            form = RegisterForm(request.POST)\n\n            if form.is_valid():\n\n                try:\n                    icode = form.cleaned_data.get('invitation')\n                    invi = invitation.objects.get(code=icode)\n                    # Checks if the invitation code exists.\n                    if invitation.objects.all().filter(code=invi.code).exists():\n                        # If so, also checks if it has remaining uses.\n                        if invi.uses > 0:\n                            user = form.save()\n                            profile.objects.create(user=user)\n                            user.refresh_from_db()\n                            user.save()\n                            # Subtracts one use to the used invitation code.\n                            invi.uses -= 1;\n                            invi.save()\n\n                            raw_password = form.cleaned_data.get('password1')\n                            user = authenticate(username=user.username, password=raw_password)\n                            login(request, user)\n                            return redirect('chat:general')\n                        else:\n                            errorm = 'El código introducido ha expirado.'\n                            form = RegisterForm()\n\n                    else:\n                        errorm = 'El código introducido no es válido.'\n                        form = RegisterForm()\n                except:\n                    errorm = 'El código introducido no es válido.'\n                    form = RegisterForm()\n        else:\n            form = RegisterForm()\n        return render(request, 'main/register.html', {'form': form, 'errorm' : errorm})\n    else:\n        return redirect('main:home')\n\n# Retrieves the logged users (not implemented)\nclass UsersView (LoginRequiredMixin, generic.ListView):\n    template_name = 'main/logged-users.html'\n    context_object_name = 'users'\n\n    def get_queryset (self):\n        sessions = Session.objects.filter(expire_date__gte=timezone.now())\n        uid_list = []\n\n        for session in sessions:\n            data = session.get_decoded()\n            uid_list.append(data.get('_auth_user_id', None))\n\n        return User.objects.filter(id__in=uid_list)\n","sub_path":"users/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3645,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"261417523","text":"class StateTable(object):\n\t#constructor:\tcreate empty list of entries\n\t#cur_entry and cur_index are used for grabbing the\n\t#next entry in the state table creation in dfa.py\n\tdef __init__(self):\n\t\tself.entries = []\n\t\tself.cur_entry = None\n\t\tself.cur_index = 0\n\n\t#add:\t\tadd entry to state_table\n\t#arguments:\tnxt_entry; add and return nxt_entry\n\t#if entry exists, skip add and return equivalent entry\n\tdef add_entry(self, nxt_entry):\n\t\tfor entry in self.entries:\n\t\t\tnxt_states = nxt_entry.get_states()\n\t\t\tnxt_len = len(nxt_states)\n\t\t\tcur_states = entry.get_states()\n\t\t\tcur_len = len(cur_states)\n\t\t\t#check if current entry and nxt_entry are equal\n\t\t\t#if so return current entry\n\t\t\tif (nxt_len == cur_len):\n\t\t\t\tif(nxt_states.issubset(cur_states)):\n\t\t\t\t\treturn entry\n\t\t#else add nxt_entry and return nxt_entry\n\t\tnxt_entry.set_name(len(self.entries))\n\t\tself.entries.append(nxt_entry)\n\t\treturn nxt_entry\n\n\t#nxt:\t\tused in dffa.py to grab nxt_entry to build state_table\n\tdef nxt_entry(self):\n\t\tif (self.cur_index >= len(self.entries)):\t\n\t\t\treturn None\n\t\tnxt_entry = self.entries[self.cur_index]\n\t\tself.cur_index += 1\n\t\treturn nxt_entry\n\n\t#get:\t\treturn list of entries for use in building state table\n\tdef get_entries(self):\n\t\treturn self.entries\n","sub_path":"state_table.py","file_name":"state_table.py","file_ext":"py","file_size_in_byte":1236,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"291528546","text":"import dash\nimport dash_html_components as html\nimport dash_core_components as dcc\nimport dash_building_blocks as dbb\nimport json\nimport numpy as np\n\n\nEMPTY_FIGURE = {'data':[], 'layout':{}}\n\n\nclass Graph(dbb.Block):\n    \n    def layout(self):\n        \n        return dcc.Graph(\n            id=self.register('graph'),\n            config={\n                'displayModeBar': False\n            },\n            figure=EMPTY_FIGURE\n        )\n    \n    \n    def callbacks(self, input_new_data):\n        \n        @self.app.callback(\n            self.output('graph', 'figure'),\n            [input_new_data]#, input_new_layout]\n        )\n        def update_graph_figure(new_data):\n            \n            new_data = json.loads(new_data)\n            \n            data = [{\n                'x': new_data['x'],\n                'y': new_data['y'],\n                'mode': 'markers'\n            }]\n            \n            fig = dict(data=data, layout={})\n            \n            return fig\n      \n    \nclass NumberGenerator(dbb.Block):\n    \n    def parameters(self, generate_numbers, data_id=None, button=None):\n        \n        self.genf = generate_numbers\n        self.data_id = data_id or 'data'\n        self.buttontext = button or 'Generate New Data'\n    \n    \n    def layout(self):\n        \n        layout = html.Div([\n            html.Button(\n                self.buttontext,\n                id=self.register('button')\n            ),\n            html.Div(\n                id=self.register(self.data_id),\n                style={'display': 'none'}\n            )\n        ])\n        \n        @self.app.callback(\n            self.output(self.data_id, 'children'),\n            [self.input('button', 'n_clicks')]\n        )\n        def generate_new_numbers(n_clicks):\n            return list(self.genf())\n        \n        return layout\n    \n    \nclass RatingForm(dbb.Block):\n    \n    def layout(self):\n        \n        avail_ratings = [1, 2, 3, 4, 5]\n        \n        options = [\n            {'label': r, 'value': r}\n            for r in avail_ratings\n        ]\n        \n        return dcc.Dropdown(options=options, \n                            id=self.register('rating'))\n    \n\nclass Feedback(dbb.Block):\n    \n    def layout(self):\n        \n        return html.Div([\n            html.H5('Rating:'),\n            html.Div('', id=self.register('rating')),\n            html.H5('Comments:'),\n            html.Div('', id=self.register('comments'))\n        ])\n    \n    \n    def callbacks(self, input_button, state_rating, state_comments):\n        \n        @self.app.callback(\n            self.output('rating', 'children'),\n            [input_button], [state_rating]\n        )\n        def update_current_rating(n_clicks, rating):\n            return rating\n        \n        @self.app.callback(\n            self.output('comments', 'children'),\n            [input_button], [state_comments]\n        )\n        def update_current_comments(n_clicks, comments):\n            return comments\n        \n        \ndef make_graph_section(app, fx, fy=None, modal_title='Rate This Graph'):\n    \n    if fy is None:\n        fy = fx\n    \n    store = dbb.Store(app, id=dbb.util.generate_random_string(8))\n    \n    graph = Graph(app)\n    \n    xgen = NumberGenerator(app,\n                           data_id='numbers',\n                           button='Generate X Data',\n                           generate_numbers=fx)\n\n    ygen = NumberGenerator(app,\n                           data_id='numbers',\n                           button='Generate Y Data',\n                           generate_numbers=fy)\n    \n    ratingform = RatingForm(app)\n    \n    # TODO: make defining inputs like defining dcc.Dropdown options\n    ratingcomment = dbb.InputForm(app, inputs=['Comments'], submit=None)\n    \n    modal = dbb.Modal(app, \n                      title=modal_title, \n                      body=[ratingform.layout, \n                            ratingcomment.layout],\n                      buttons=dict(primary='Save Changes',\n                                   secondary='Close'))\n    \n    feedback = Feedback(app)\n    \n    @store.register(\n        'plot-data',\n        [xgen.input('numbers'), # == xgen.input('numbers', 'children')\n         ygen.input('numbers')] # == ygen.input('numbers', 'children')\n    )\n    def update_data(x, y):\n        return json.dumps(dict(x=x, y=y))\n    \n    graph.callbacks(store.input('plot-data'))\n\n    feedback.callbacks(\n        input_button=modal.input('primary', 'n_clicks'),\n        state_rating=ratingform.state('rating', 'value'),\n        state_comments=ratingcomment.state('Comments', 'value')\n    )\n    \n    return html.Div(\n        [\n            html.Div(\n                [\n                    html.Div(modal.Button('Rate'), className='col-3'),\n                    html.Div(ygen.layout, className='col-3'), \n                    html.Div(xgen.layout, className='col-3'),\n                    html.Div('', className='col-3')\n                ], className='row'),\n            \n            modal.layout,\n            graph.layout,\n            feedback.layout,\n            store.layout\n        ]\n    )\n    \n\n    \napp = dash.Dash()\napp.config.suppress_callback_exceptions = True\n\n\nstore = dbb.Store(app, hide=True)\n\nN = 100\n\nfunctions = [\n    lambda: np.random.beta(10, 10, size=N),\n    lambda: np.random.lognormal(size=N),\n    lambda: np.random.chisquare(10, size=N),\n    lambda: np.random.geometric(0.5, size=N),\n    lambda: np.random.gumbel(size=N),\n    lambda: np.random.normal(size=N),\n    lambda: np.random.pareto(2, size=N),\n    lambda: np.random.laplace(size=N)\n]\n\n\ndef make_graphs_row(functions):\n    \n    graph_sections = [\n        html.Div(\n            make_graph_section(app, f),\n            className='col'\n        )\n        for f in functions\n    ]\n\n    return html.Div(graph_sections, className='row')\n    \n\napp.layout = html.Div([make_graphs_row(functions[:4]),\n                       html.Hr(),\n                       make_graphs_row(functions[4:])],\n                      className='container-fluid')\n\n\napp.css.append_css({'external_url': [\n    \"https://stackpath.bootstrapcdn.com/bootstrap/4.1.1/css/bootstrap.min.css\"\n]})\n\napp.scripts.append_script({\"external_url\": [\n    \"https://code.jquery.com/jquery-3.3.1.slim.min.js\",\n    \"https://stackpath.bootstrapcdn.com/bootstrap/4.1.1/js/bootstrap.min.js\"\n]})\n\napp.css.config.serve_locally = False\napp.scripts.config.serve_locally = False\n\nif __name__ == '__main__':\n    app.run_server(debug=True, port=5000, host='0.0.0.0')\n            ","sub_path":"examples/rating_many_graphs.py","file_name":"rating_many_graphs.py","file_ext":"py","file_size_in_byte":6480,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"387798996","text":"\"\"\"\n@name:      PyHouse/Project/src/_test/testing_mixin.py\n@author:    D. Brian Kimmel\n@contact:   D.BrianKimmel@gmail.com\n@copyright: (c) 2013-2019 by D. Brian Kimmel\n@license:   MIT License\n@note:      Created on Jun 4, 2013\n@summary:   Test handling the information for a house.\n\n\"\"\"\n\n__updated__ = '2019-09-02'\n\n#  Import system type stuff\nimport os\nimport platform\nimport logging\nimport sys\nfrom twisted.internet import reactor\n\n#  Import PyMh files and modules.\nfrom Modules.Computer.computer import ComputerInformation, ComputerAPIs\nfrom Modules.House.house_data import LocationInformationPrivate\nfrom Modules.House.Hvac.hvac import HvacInformation\nfrom Modules.House.Entertainment.entertainment import \\\n    EntertainmentInformation, \\\n    EntertainmentPluginInformation\nfrom Modules.House.Lighting.lighting import LightingInformation\nfrom Modules.Core.data_objects import \\\n    PyHouseInformation, \\\n    PyHouseAPIs, \\\n    HouseAPIs, \\\n    TwistedInformation, \\\n    UuidInformation, CoreInformation\nfrom Modules.Core.Utilities.config_tools import \\\n    ConfigInformation, SecurityInformation\nfrom Modules.House.Family.family import API as familyAPI\nfrom Modules.House.house import API as housingAPI, HouseInformation\nfrom Modules.Core import logging_pyh as Logger\nfrom Modules.Core.Mqtt.mqtt import MqttInformation\n#\n#  Different logging setup to cause testing logs to come out in red on the console.\n#\nl_format = '\\n [%(levelname)s] %(name)s: %(funcName)s %(lineno)s:\\n\\t%(message)s'\nl_formatter = logging.Formatter(fmt=l_format)\nl_handler = logging.StreamHandler(stream=sys.stderr)\nl_handler.setFormatter(l_formatter)\nLOG = Logger.getLogger('PyHouse')\nLOG.addHandler(l_handler)\n\nTEST_PATH = os.path.split(os.path.dirname(os.path.realpath(__file__)))[0]\n\n\nclass XmlData(object):\n    \"\"\"\n    Testing XML infrastructure\n    \"\"\"\n\n    def __init__(self):\n        self.root = None\n        #\n        self.house_div = None\n        self.entertainment_sect = None\n        self.onkyo_sect = None\n        self.panasonic_sect = None\n        self.pandora_sect = None\n        self.pioneer_sect = None\n        self.samsung_sect = None\n        self.lighting_sect = None\n        self.button_sect = None\n        self.button = None\n        self.controller_sect = None\n        self.controller = None\n        self.light_sect = None\n        self.light = None\n        self.location_sect = None\n        self.location = None\n        self.irrigation_sect = None\n        self.irrigation_system = None\n        self.irrigation_zone = None\n        self.pool_sect = None\n        self.pool = None\n        self.room_sect = None\n        self.room = None\n        self.rules_sect = None\n        self.rule = None\n        self.schedule_sect = None\n        self.schedule = None\n        self.security_sect = None\n        self.garagedoor_sect = None\n        self.garagedoor = None\n        # self.motionsensor_sect = None\n        # self.motionsensor = None\n        self.hvac_sect = None\n        self.thermostat_sect = None\n        self.thermostat = None\n        #\n        self.computer_div = None\n        self.bridges_sect = None\n        self.communication_sect = None\n        self.email_sect = None\n        self.twitter_sect = None\n        self.internet_sect = None\n        self.internet = None\n        self.internet_locater_sect = None\n        self.internet_updater_sect = None\n        self.mqtt_sect = None\n        self.log_sect = None\n        self.web_sect = None\n        self.login_sect = None\n        self.node_sect = None\n        self.node = None\n        self.interface_sect = None\n        self.interface = None\n\n\nclass YamlData():\n\n    def __init__(self):\n        self.root = None\n\n\nclass SetupPyHouseObj():\n    \"\"\"\n    \"\"\"\n\n    def _build_config(self):\n        \"\"\"\n        Change the path for finding yaml config files to the PySource src package.\n        \"\"\"\n        l_ret = ConfigInformation()\n        l_ret.ConfigDir = TEST_PATH\n        return l_ret\n\n    def _build_yaml(self, p_root):\n        l_ret = YamlData()\n        l_ret.YamlRoot = p_root\n        l_ret.YamlFileName = 'pyhouse.yaml'\n        return l_ret\n\n    def _build_twisted(self):\n        l_ret = TwistedInformation()\n        l_ret.Reactor = reactor\n        return l_ret\n\n    @staticmethod\n    def _build_entertainment(_p_pyhouse_obj):\n        l_ret = EntertainmentInformation()\n        l_ret.Plugins = EntertainmentPluginInformation()\n        return l_ret\n\n    @staticmethod\n    def _build_house_data(p_pyhouse_obj):\n        l_ret = HouseInformation()\n        l_ret.Location = LocationInformationPrivate()\n        l_ret.Entertainment = SetupPyHouseObj._build_entertainment(p_pyhouse_obj)\n        l_ret.Lighting = LightingInformation()\n        l_ret.Hvac = HvacInformation\n        l_ret.Security = SecurityInformation\n        return l_ret\n\n    @staticmethod\n    def _build_computer(_p_pyhouse_obj):\n        l_ret = ComputerInformation()\n        return l_ret\n\n    def _build_core(self):\n        l_ret = CoreInformation()\n        l_ret.Mqtt = MqttInformation()\n        return l_ret\n\n    def _build_apis(self):\n        l_apis = PyHouseAPIs()\n        l_apis.Computer = ComputerAPIs()\n        l_apis.House = HouseAPIs()\n        return l_apis\n\n    def _computer_xml(self, p_xml):\n        p_xml.computer_div = p_xml.root.find('ComputerDivision')\n        if p_xml.computer_div is None:\n            return\n        #\n        p_xml.bridges_sect = p_xml.computer_div.find('BridgesSection')\n        if p_xml.bridges_sect is not None:\n            p_xml.bridge = p_xml.bridges_sect.find('Bridge')\n        p_xml.communication_sect = p_xml.computer_div.find('CommunicationSection')\n        if p_xml.communication_sect is not None:\n            p_xml.email_sect = p_xml.communication_sect.find('EmailSection')\n            p_xml.twitter_sect = p_xml.communication_sect.find('TwitterSection')\n        #\n        p_xml.internet_sect = p_xml.computer_div.find('InternetSection')\n        if p_xml.internet_sect is not None:\n            p_xml.internet = p_xml.internet_sect.find('Internet')\n            p_xml.internet_locater_sect = p_xml.internet.find('LocateUrlSection')\n            p_xml.internet_updater_sect = p_xml.internet.find('UpdateUrlSection')\n        #\n        p_xml.mqtt_sect = p_xml.computer_div.find('MqttSection')\n        p_xml.broker = p_xml.mqtt_sect.find('Broker')\n\n        p_xml.web_sect = p_xml.computer_div.find('WebSection')\n        p_xml.login_sect = p_xml.web_sect.find('LoginSection')\n\n    def _computer_yaml(self, p_yaml):\n        pass\n\n    def _house_xml(self, p_xml):\n        p_xml.house_div = p_xml.root.find('HouseDivision')\n        if p_xml.house_div is None:\n            return\n\n        p_xml.entertainment_sect = p_xml.house_div.find('EntertainmentSection')\n        if p_xml.entertainment_sect is not None:\n            p_xml.panasonic_sect = p_xml.entertainment_sect.find('PanasonicSection')\n            p_xml.pandora_sect = p_xml.entertainment_sect.find('PandoraSection')\n            p_xml.pioneer_sect = p_xml.entertainment_sect.find('PioneerSection')\n            p_xml.samsung_sect = p_xml.entertainment_sect.find('SamsungSection')\n            p_xml.onkyo_sect = p_xml.entertainment_sect.find('OnkyoSection')\n        #\n        p_xml.irrigation_sect = p_xml.house_div.find('IrrigationSection')\n        if p_xml.irrigation_sect is not None:\n            p_xml.irrigation_system = p_xml.irrigation_sect.find('System')\n            if p_xml.irrigation_system is not None:\n                p_xml.irrigation_zone = p_xml.irrigation_system.find('Zone')\n\n        p_xml.pool_sect = p_xml.house_div.find('PoolSection')\n        if p_xml.pool_sect is not None:\n            p_xml.pool = p_xml.pool_sect.find('Pool')\n\n        p_xml.room_sect = p_xml.house_div.find('RoomSection')\n        if p_xml.room_sect is not None:\n            p_xml.room = p_xml.room_sect.find('Room')\n\n        p_xml.rules_sect = p_xml.house_div.find('RulesSection')\n        if p_xml.rules_sect is not None:\n            p_xml.rule = p_xml.rules_sect.find('Rule')\n\n        p_xml.schedule_sect = p_xml.house_div.find('ScheduleSection')\n        if p_xml.schedule_sect is not None:\n            p_xml.schedule = p_xml.schedule_sect.find('Schedule')\n\n        p_xml.security_sect = p_xml.house_div.find('SecuritySection')\n        if p_xml.security_sect is not None:\n            p_xml.garagedoor_sect = p_xml.security_sect.find('GarageDoorSection')\n            p_xml.garagedoor = p_xml.garagedoor_sect.find('GarageDoor')\n            p_xml.motiondetector_sect = p_xml.security_sect.find('MotionDetectorSection')\n            p_xml.motiondetector = p_xml.motiondetector_sect.find('Motion')\n\n        p_xml.lighting_sect = p_xml.house_div.find('LightingSection')\n        if p_xml.lighting_sect is not None:\n            p_xml.button_sect = p_xml.lighting_sect.find('ButtonSection')\n            p_xml.button = p_xml.button_sect.find('Button')\n            p_xml.controller_sect = p_xml.lighting_sect.find('ControllerSection')\n            p_xml.controller = p_xml.controller_sect.find('Controller')\n            p_xml.light_sect = p_xml.lighting_sect.find('LightSection')\n            p_xml.light = p_xml.light_sect.find('Light')\n\n        p_xml.hvac_sect = p_xml.house_div.find('HvacSection')\n        if p_xml.hvac_sect is not None:\n            p_xml.thermostat_sect = p_xml.hvac_sect.find('ThermostatSection')\n            p_xml.thermostat = p_xml.thermostat_sect.find('Thermostat')\n\n        p_xml.location_sect = p_xml.house_div.find('LocationSection')\n\n    def _house_yaml(self, p_yaml):\n        pass\n\n    def BuildPyHouseObj(self):\n        \"\"\" This will create the pyhpuse_obj structure.\n        \"\"\"\n        l_pyhouse_obj = PyHouseInformation()\n        l_pyhouse_obj.Core = SetupPyHouseObj()._build_core()\n        l_pyhouse_obj.Computer = SetupPyHouseObj._build_computer(l_pyhouse_obj)\n        l_pyhouse_obj.House = SetupPyHouseObj._build_house_data(l_pyhouse_obj)\n        #\n        l_pyhouse_obj._APIs = self._build_apis()\n        l_pyhouse_obj._Config = self._build_config()\n        l_pyhouse_obj._Twisted = self._build_twisted()\n        l_pyhouse_obj._Uuids = UuidInformation()\n        l_pyhouse_obj._Uuids.All = {}\n        l_pyhouse_obj.Computer.Name = platform.node()\n        return l_pyhouse_obj\n\n    def BuildXml(self, p_root_xml):\n        l_xml = XmlData()\n        l_xml.root = p_root_xml\n        self._computer_xml(l_xml)\n        self._house_xml(l_xml)\n        return l_xml\n\n    def BuildYaml(self, p_root_yaml):\n        l_yaml = YamlData()\n        l_yaml.root = p_root_yaml\n        self._computer_yaml(l_yaml)\n        self._house_yaml(l_yaml)\n        return l_yaml\n\n    def LoadComputer(self, p_pyhouse_obj):\n        pass\n\n    def LoadHouse(self, p_pyhouse_obj):\n        p_pyhouse_obj.House.Family = familyAPI(p_pyhouse_obj).LoadFamilyTesting()\n        housingAPI(p_pyhouse_obj).LoadXml(p_pyhouse_obj)\n        return\n\n    def setUp(self):\n        self.BuildPyHouseObj()\n\n#  ## END DBK\n","sub_path":"Project/src/_test/testing_mixin.py","file_name":"testing_mixin.py","file_ext":"py","file_size_in_byte":10885,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"366171785","text":"from multiprocessing import connection\nfrom awscrt import io, mqtt, auth, http\nfrom awsiot import mqtt_connection_builder\nfrom csv import reader\nfrom time import sleep\nimport json\n\nurl_outside = '/app/Data/Processed/outside.csv'\n\nENDPOINT = \"ar95nzm5vzl5q-ats.iot.us-east-1.amazonaws.com\"\nCLIENT_ID = \"Sensor_outside\"\n# PASTA DOS ARQUIVOS AWS DEVEM ESTAR NA PASTA DO PROJETO\nPATH_TO_CERTIFICATE = \"/app/Code/Operationalization/certificates/Sensor_outside.cert.pem\"\n# PASTA DOS ARQUIVOS AWS DEVEM ESTAR NA PASTA DO PROJETO\nPATH_TO_PRIVATE_KEY = \"/app/Code/Operationalization/certificates/Sensor_outside.private.key\"\n# PASTA DOS ARQUIVOS AWS DEVEM ESTAR NA PASTA DO PROJETO\nPATH_TO_AMAZON_ROOT_CA_1 = \"/app/Code/Operationalization/certificates/root-CA-outside.crt\"\n\nTOPIC = \"Sensor_outside/temperature\"\n\n\ndef connect_to_aws():\n    event_loop_group = io.EventLoopGroup(1)\n    host_resolver = io.DefaultHostResolver(event_loop_group)\n    client_bootstrap = io.ClientBootstrap(event_loop_group, host_resolver)\n    mqtt_connection = mqtt_connection_builder.mtls_from_path(\n        endpoint=ENDPOINT,\n        cert_filepath=PATH_TO_CERTIFICATE,\n        pri_key_filepath=PATH_TO_PRIVATE_KEY,\n        client_bootstrap=client_bootstrap,\n        ca_filepath=PATH_TO_AMAZON_ROOT_CA_1,\n        client_id=CLIENT_ID,\n        clean_session=False,\n        keep_alive_secs=6\n    )\n    connect_future = mqtt_connection.connect()\n    connect_future.result()\n    return mqtt_connection, connect_future\n\n\nif __name__ == \"__main__\":\n    mqtt_connection, connection = connect_to_aws()\n    with open(url_outside, 'r') as read_obj:\n        csv_reader = reader(read_obj)\n        header = next(csv_reader)\n        if header != None:\n            for row in csv_reader:\n                print(int(row[3]))\n                content_date = row[0]\n                content_temp = int(row[3])\n                message = {\n                    \"noted_date\": content_date,\n                    \"temperature_outside\": content_temp\n                }\n                mqtt_connection.publish(topic=TOPIC,\n                                        payload=json.dumps(message),\n                                        qos=mqtt.QoS.AT_LEAST_ONCE)\n                sleep(.1)\n    disconnect_future = mqtt_connection.disconnect()\n    disconnect_future.result\n","sub_path":"Code/Operationalization/sendToAws_outside.py","file_name":"sendToAws_outside.py","file_ext":"py","file_size_in_byte":2303,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"151772989","text":"from collections import namedtuple\n\nfrom PIL import Image, ImageChops, ImageDraw\n\nfrom outpost_data.core import geom, image2, image_cache, util\nfrom outpost_data.core.consts import *\n\n\nclass Shape(object):\n    def __init__(self, x, y, z, arr):\n        self.size = (x, y, z)\n        self.shape_array = arr\n\ndef empty(x, y, z):\n    arr = ['empty'] * (x * y * z)\n    return Shape(x, y, z, arr)\n\ndef floor(x, y, z):\n    arr = ['floor'] * (x * y) + ['empty'] * (x * y * (z - 1))\n    return Shape(x, y, z, arr)\n\ndef solid(x, y, z):\n    arr = ['solid'] * (x * y * z)\n    return Shape(x, y, z, arr)\n\n\nclass Model(object):\n    def __init__(self, verts):\n        self.verts = verts\n        self.length = len(verts)\n\n    def slice_xv(self, offset, size, base_z):\n        min_x, min_v = offset\n        min_v -= base_z * TILE_SIZE\n        size_x, size_v = size\n        max_x = min_x + size_x\n        max_v = min_v + size_v\n        verts = [p\n                for tri in geom.slice_tris_box_xv(self.verts, min_x, max_x, min_v, max_v)\n                for p in tri]\n\n        return Model(verts)\n\n    def to_mesh(self):\n        m = geom.Mesh()\n        for i in range(0, len(self.verts), 3):\n            m.add_tri(*self.verts[i : i + 3])\n        return m\n\nModel2 = namedtuple('Model2', ('mesh', 'bounds'))\n\nclass StructurePart(object):\n    def __init__(self, model2, img):\n        mesh = model2.mesh.copy()\n\n        # Compute bounding box of the (projection of the) mesh\n        v2_min, v2_max = mesh.get_bounds_2d(util.project)\n        v2_size = (v2_max[0] - v2_min[0], v2_max[1] - v2_min[1])\n\n        # The user provided the bounding box of the region covered by the\n        # image.  (Usually this is just (0,0,0) .. (struct_size).)  Using that,\n        # extract the image for the part actually covered by the mesh.\n        b_min, b_max = model2.bounds\n        b2_min = (b_min[0], b_min[1] - b_max[2])\n        b2_max = (b_max[0], b_max[1] - b_min[2])\n        b2_size = (b2_max[0] - b2_min[0], b2_max[1] - b2_min[1])\n\n        # Crop image to the mesh's bounding box\n\n        # Special hack\n        if img.px_size == (TILE_SIZE, TILE_SIZE) and \\\n                b2_size == (TILE_SIZE, 2 * TILE_SIZE):\n            pass\n        else:\n            assert img.px_size == b2_size, \\\n                    'image has wrong size for bounds: %s != %s' % (img.px_size, b2_size)\n\n            # Extract the region (v2_min - b2_min, v2_max - b2_min)\n            r2_min = (v2_min[0] - b2_min[0], v2_min[1] - b2_min[1])\n            img = img.extract(r2_min, size=v2_size, unit=1)\n\n        # Crop mesh & image to image's bounding box.  We do this after the\n        # previous step because cropping the image may remove some non-blank\n        # content, letting us crop the image more aggressively here.\n        img, i2_min = img.autocrop()\n        v2_min = geom.add(v2_min, i2_min)\n        v2_max = geom.add(v2_min, img.px_size)\n        geom.clip_xv(mesh, *(v2_min + v2_max))\n\n        self.mesh = mesh\n        self.img = img\n        self.base = v2_min\n\n        # Set by `build_sheets`\n        self.sheet_idx = None\n        self.offset = None\n\n        # Set by `collect_verts'\n        self.vert_idx = None\n        self.vert_count = None\n\n    def get_sheet_image(self):\n        if isinstance(self.img, image2.Anim):\n            return self.img.flatten()\n        else:\n            return self.img\n\n    def get_flags(self):\n        anim = False\n        shadow = False\n\n        if isinstance(self.img, image2.Anim):\n            anim = True\n            # Animated parts can't have shadows.\n        elif isinstance(self.img, image2.Image):\n            img = self.img.raw().raw()\n            if img.mode == 'RGBA':\n                hist = img.histogram()\n                a_hist = hist[256 * 3 : 256 * 4]\n                # Is there any pixel whose alpha value is not 0 or 255?\n                shadow = any(count > 0 for count in a_hist[1:-1])\n            else:\n                shadow = False\n\n        return ((int(shadow) << 0) |\n                (int(anim) << 1))\n\nclass StaticAnimDef(object):\n    \"\"\"An animation for a static element (structure or block).  This class is\n    distinct from AnimationDef (sprite animations) because the requirements are\n    not actually very similar.\"\"\"\n    def __init__(self, frames, framerate, oneshot=False):\n        self.length = len(frames)\n        self.framerate = framerate\n        self.oneshot = oneshot\n        self.frames = frames\n        self.size = frames[0].size\n\n        # Set by `self.process_frames()`\n        self.static_base = None\n        self.anim_offset = None\n        self.anim_size = None\n        self.anim_sheet = None\n        self.process_frames()\n\n        # Set by `build_anim_sheets`\n        self.sheet_idx = None\n        self.offset = None\n\n    def process_frames(self):\n        assert all(f.size == self.frames[0].size for f in self.frames), \\\n                'all animation frames must be the same size'\n\n        w, h = self.frames[0].size\n        first = self.frames[0].getdata()\n        px_size = len(first) // (w * h)\n\n        base = self.frames[0].convert('RGBA')\n\n        min_x, min_y = w, h\n        max_x, max_y = 0, 0\n        for f in self.frames[1:]:\n            data = f.getdata()\n            for i in range(w * h):\n                if data[i] != first[i]:\n                    i //= px_size\n                    x = i % w\n                    y = i // w\n                    min_x = min(min_x, x)\n                    min_y = min(min_y, y)\n                    max_x = max(max_x, x + 1)\n                    max_y = max(max_y, y + 1)\n\n                    base.paste((0, 0, 0, 0), (x, y, x + 1, y + 1))\n\n        self.static_base = base\n        self.anim_offset = (min_x, min_y)\n        self.anim_size = (max_x - min_x, max_y - min_y)\n        \n        sheet = Image.new('RGBA', (len(self.frames) * (max_x - min_x), max_y - min_y))\n        for i, f in enumerate(self.frames):\n            tmp = ImageChops.subtract(f, base)\n            tmp = tmp.crop((min_x, min_y, max_x, max_y))\n            sheet.paste(tmp, ((max_x - min_x) * i, 0))\n\n        self.anim_sheet = sheet\n\n\n\nclass StructureDef2(object):\n    def __init__(self, name, shape, layer):\n        self.name = name\n        self.size = shape.size\n        self.shape = shape.shape_array\n        self.layer = layer\n        self.parts = []\n\n        self.light_pos = None\n        self.light_color = None\n        self.light_radius = None\n\n        # Filled in by `assign_ids`\n        self.id = None\n\n        # Filled in by `collect_parts`\n        self.part_idx = None\n\n    '''\n    def get_display_px(self):\n        \"\"\"Get the size of the structure on the screen.\"\"\"\n        return self.image.size\n\n    def get_sheet_px(self):\n        \"\"\"Get the size of the structure in the sprite sheet.  Currently this\n        is the same as `get_display_px` because animated structures place only\n        the static parts in the main structure sheet.\"\"\"\n        return self.image.size\n\n    def get_display_size(self):\n        w, h = self.get_display_px()\n        return (w // TILE_SIZE, h // TILE_SIZE)\n\n    def get_sheet_size(self):\n        w, h = self.get_sheet_px()\n        return (w // TILE_SIZE, h // TILE_SIZE)\n\n    def get_anim_sheet_px(self):\n        return self.anim.anim_sheet.size\n\n    def get_anim_sheet_size(self):\n        w, h = self.get_anim_sheet_px()\n        return ((w + TILE_SIZE - 1) // TILE_SIZE,\n                (h + TILE_SIZE - 1) // TILE_SIZE)\n    '''\n\n    def add_part(self, model2, img):\n        self.parts.append(StructurePart(model2, img))\n\n    def get_image(self):\n        img_size = (self.size[0], self.size[1] + self.size[2])\n        px_size = tuple(x * TILE_SIZE for x in img_size)\n\n        # part.base is relative to the 0,0,0 corner, so part.base[1] may be as\n        # low as -self.size[2] * TILE_SIZE.\n        y_off = self.size[2] * TILE_SIZE\n\n        layers = []\n        for p in self.parts:\n            bx, by = p.base\n            layers.append(p.img.pad(px_size, offset=(bx, by + y_off)))\n\n        if len(layers) > 0:\n            result = image2.stack(layers)\n        else:\n            result = image2.Image(size=px_size)\n        return result.raw().raw()\n\n    def set_light(self, pos, color, radius):\n        self.light_pos = pos\n        self.light_color = color\n        self.light_radius = radius\n\n    def get_flags(self):\n        light = self.light_radius is not None\n        flags = (int(light) << 2)\n        for p in self.parts:\n            flags = flags | p.get_flags()\n        return flags\n\nclass StructureDef(StructureDef2):\n    def __init__(self, name, image, model, shape, layer):\n        super(StructureDef, self).__init__(name, shape, layer)\n        px_size = tuple(x * TILE_SIZE for x in shape.size)\n        mesh = model.to_mesh()\n\n        if isinstance(image, StaticAnimDef):\n            base = image2.Image(img=image_cache.ConstImage(image.static_base))\n            base_model2 = Model2(mesh, ((0, 0, 0), px_size))\n            self.add_part(base_model2, base)\n\n            frame_sheet = image2.Image(img=image_cache.ConstImage(image.anim_sheet),\n                    unit=image.anim_size)\n            frames = [frame_sheet.extract((x, 0)) for x in range(image.length)]\n            anim = image2.Anim(frames, image.framerate, image.oneshot)\n            box_min = geom.sub(image.anim_offset, (0, shape.size[2] * TILE_SIZE))\n            box_max = geom.add(box_min, image.anim_size)\n            anim_mesh = mesh.copy()\n            geom.clip_xv(anim_mesh, *(box_min + box_max))\n            anim_model2 = Model2(anim_mesh, anim_mesh.get_bounds())\n            self.add_part(anim_model2, anim)\n        else:\n            image = image2.Image(img=image_cache.ConstImage(image))\n            model2 = Model2(mesh, ((0, 0, 0), px_size))\n            self.add_part(model2, image)\n\n# Sprite sheets\n\ndef build_sheets(structures):\n    '''Build sprite sheet(s) containing all the images for the provided\n    structures.  This also updates each part's `sheet_idx` and `offset` field\n    with its position in the generated sheet(s).\n    '''\n    parts = [p for s in structures for p in s.parts]\n    all_imgs = [p.get_sheet_image() for p in parts]\n    imgs, idx_map = util.dedupe(all_imgs, [i.hash() for i in all_imgs])\n    boxes = [tuple((x + TILE_SIZE - 1) // TILE_SIZE for x in i.size) for i in imgs]\n    num_sheets, offsets = util.pack_boxes(SHEET_SIZE, boxes)\n\n    sheets = [Image.new('RGBA', (SHEET_PX, SHEET_PX)) for _ in range(num_sheets)]\n\n    for img, (j, offset) in zip(imgs, offsets):\n        x, y = offset\n        x *= TILE_SIZE\n        y *= TILE_SIZE\n\n        sheets[j].paste(img.raw().raw(), (x, y))\n\n    for part, img in zip(parts, all_imgs):\n        idx = idx_map[id(img)]\n        j, offset = offsets[idx]\n\n        part.sheet_idx = j\n        part.offset = geom.mul(offset, TILE_SIZE)\n\n    return sheets\n\ndef collect_parts(structures):\n    all_parts = []\n\n    for s in structures:\n        s.part_idx = len(all_parts)\n        all_parts.extend(s.parts)\n\n    return all_parts\n\ndef collect_verts(parts):\n    idx_map = {}\n    all_verts = []\n\n    for p in parts:\n        k = tuple(v.pos for v in p.mesh.iter_tri_verts())\n        if k not in idx_map:\n            idx_map[k] = len(all_verts)\n            all_verts.extend(k)\n        p.vert_idx = idx_map[k]\n        p.vert_count = len(k)\n\n    return all_verts\n\n\n# JSON output\n\ndef build_client_json(structures):\n    def convert(s):\n        dct = {\n                'size': s.size,\n                'shape': [SHAPE_ID[x] for x in s.shape],\n                'part_idx': s.part_idx,\n                'part_count': len(s.parts),\n                'vert_count': sum(p.vert_count for p in s.parts),\n                'layer': s.layer,\n                }\n\n        flags = s.get_flags()\n        if flags != 0:\n            dct.update(\n                flags=flags)\n\n        if s.light_color is not None:\n            dct.update(\n                light_pos=s.light_pos,\n                light_color=s.light_color,\n                light_radius=s.light_radius)\n\n        return dct\n\n    return list(convert(s) for s in structures)\n\ndef build_parts_json(parts):\n    def convert(p):\n        dct = {\n                'sheet': p.sheet_idx,\n                # Give the offset of the pixel corresponding to (0,0,0)\n                'offset': geom.sub(p.offset, p.base),\n                'vert_idx': p.vert_idx,\n                'vert_count': p.vert_count,\n                }\n\n        flags = p.get_flags()\n        if flags != 0:\n            dct.update(\n                flags=flags)\n\n        if isinstance(p.img, image2.Anim):\n            dct.update(\n                anim_length=p.img.length,\n                anim_rate=p.img.rate,\n                anim_oneshot=p.img.oneshot,\n                anim_size=p.img.px_size)\n\n        return dct\n\n    return list(convert(p) for p in parts)\n\ndef build_verts_json(verts):\n    return list(x for v in verts for x in v)\n\ndef build_server_json(structures):\n    def convert(s):\n        return {\n                'name': s.name,\n                'size': s.size,\n                'shape': [SHAPE_ID[x] for x in s.shape],\n                'layer': s.layer,\n                }\n\n    return list(convert(s) for s in structures)\n","sub_path":"src/gen/data/structure.py","file_name":"structure.py","file_ext":"py","file_size_in_byte":13146,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"615207711","text":"from Common import Geometry\nfrom numpy import dot\nfrom scipy.linalg import inv, block_diag\nfrom sklearn.utils.linear_assignment_ import linear_assignment\nimport numpy as np\n\n\n# assign detections to trackers by linear assignment and Hungarian algorithm\ndef assign_detections_to_trackers(trackers, detections, iou_thrd=0.3):\n    IOU_mat = np.zeros((len(trackers), len(detections)), dtype=np.float32)\n    for t, trk in enumerate(trackers):\n        for d, det in enumerate(detections):\n            IOU_mat[t, d] = Geometry.box_iou(trk, det)\n\n    # solve the maximizing the sum of IOU assignment problem using the Hungarian algorithm\n    matched_idx = linear_assignment(-IOU_mat)\n\n    unmatched_trackers, unmatched_detections = [], []\n    for t, trk in enumerate(trackers):\n        if t not in matched_idx[:, 0]:\n            unmatched_trackers.append(t)\n    for d, det in enumerate(detections):\n        if d not in matched_idx[:, 1]:\n            unmatched_detections.append(d)\n    matches = []\n    for m in matched_idx:\n        if IOU_mat[m[0], m[1]] < iou_thrd:\n            unmatched_trackers.append(m[0])\n            unmatched_detections.append(m[1])\n        else:\n            matches.append(m.reshape(1, 2))\n    if len(matches) == 0:\n        matches = np.empty((0, 2), dtype=int)\n    else:\n        matches = np.concatenate(matches, axis=0)\n    return matches, np.array(unmatched_detections), np.array(unmatched_trackers)\n\n\n# class for Kalman Filter-based tracker\nclass Tracker():\n    def __init__(self):\n\n        # initialize\n        self.id = 0  # tracker's id\n        self.cur_box = []  # current bounding box\n        self.pre_box = []  # previous bounding box\n        self.cls = -1  # classification label\n\n        self.is_crossed_first_line = False  # whether cross the first line\n        self.is_crossed_second_line = False  # whether cross the second line\n        self.crossed_line = [-1, -1]  # line index for two crossed lines\n        self.is_counted = False  # whether be counted\n\n        self.hits = 0  # number of detection matches\n        self.no_losses = 0  # number of unmatched tracks\n        self.x_state = []  # state\n        self.dt = 1.  # time interval\n\n        # process matrix (assuming constant velocity model)\n        self.F = np.array([[1, self.dt, 0, 0, 0, 0, 0, 0],\n                           [0, 1, 0, 0, 0, 0, 0, 0],\n                           [0, 0, 1, self.dt, 0, 0, 0, 0],\n                           [0, 0, 0, 1, 0, 0, 0, 0],\n                           [0, 0, 0, 0, 1, self.dt, 0, 0],\n                           [0, 0, 0, 0, 0, 1, 0, 0],\n                           [0, 0, 0, 0, 0, 0, 1, self.dt],\n                           [0, 0, 0, 0, 0, 0, 0, 1]])\n\n        # measurement matrix (assuming only measure the coordinates)\n        self.H = np.array([[1, 0, 0, 0, 0, 0, 0, 0],\n                           [0, 0, 1, 0, 0, 0, 0, 0],\n                           [0, 0, 0, 0, 1, 0, 0, 0],\n                           [0, 0, 0, 0, 0, 0, 1, 0]])\n\n        # state covariance\n        self.L = 10.0\n        self.P = np.diag(self.L * np.ones(8))\n\n        # process covariance\n        self.Q_comp_mat = np.array([[self.dt ** 4 / 4., self.dt ** 3 / 2.],\n                                    [self.dt ** 3 / 2., self.dt ** 2]])\n        self.Q = block_diag(self.Q_comp_mat, self.Q_comp_mat,\n                            self.Q_comp_mat, self.Q_comp_mat)\n\n        # measurement covariance\n        self.R_scaler = 1.\n        self.R_diag_array = self.R_scaler * np.array([self.L, self.L, self.L, self.L])\n        self.R = np.diag(self.R_diag_array)\n\n    # calculate the center of bounding box\n    def get_center(self):\n        if self.cur_box == []:\n            self.cur_center = []\n        else:\n            # self.cur_center = [(self.cur_box[0] + self.cur_box[2]) / 2, (self.cur_box[1] + self.cur_box[3]) / 2]\n            self.cur_center = [(self.cur_box[1] + self.cur_box[3]) / 2, (self.cur_box[0] + self.cur_box[2]) / 2]\n        if self.pre_box == []:\n            self.pre_center = []\n        else:\n            # self.pre_center = [(self.pre_box[0] + self.pre_box[2]) / 2, (self.pre_box[1] + self.pre_box[3]) / 2]\n            self.pre_center = [(self.pre_box[1] + self.pre_box[3]) / 2, (self.pre_box[0] + self.pre_box[2]) / 2]\n\n    # Kalman Filter for bounding box measurement\n\n    def update_R(self):\n        R_diag_array = self.R_scaler * np.array([self.L, self.L, self.L, self.L])\n        self.R = np.diag(R_diag_array)\n\n    # predict and update\n    def kalman_filter(self, z):\n        x = self.x_state\n        x = dot(self.F, x)\n        self.P = dot(self.F, self.P).dot(self.F.T) + self.Q\n        S = dot(self.H, self.P).dot(self.H.T) + self.R\n        K = dot(self.P, self.H.T).dot(inv(S))\n        y = z - dot(self.H, x)\n        x += dot(K, y)\n        self.P = self.P - dot(K, self.H).dot(self.P)\n        self.x_state = x.astype(int)\n\n    # only predict\n    def predict_only(self):\n        x = self.x_state\n        x = dot(self.F, x)\n        self.P = dot(self.F, self.P).dot(self.F.T) + self.Q\n        self.x_state = x.astype(int)\n","sub_path":"Basic_algorithm/MatchKalHun.py","file_name":"MatchKalHun.py","file_ext":"py","file_size_in_byte":5044,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"243426097","text":"from django.conf.urls import url\n\nfrom .views import( \n\tHomePageView, gallery_page, logout_view, ArtworkDetailView,\n\tDeleteArtworkView, CreateArtworkView, UpdateArtworkView,\n\tDeletePhotoView, CreatePhotoView, UpdatePhotoView,\n\tUpdateTextSectionView, ReorderPhotosView, ReorderArtworkssView,\n\tPressPageView, GrandOpeningPageView, SchedulePageView, StaffPageView,\n)\n\nurlpatterns = [\n\turl(r'^$', HomePageView.as_view(), name='home'),\n\t#url(r'^gallery$', gallery_page, name='gallery'),\n\turl(r'^schedule$', SchedulePageView.as_view(), name='schedule'),\n\turl(r'^staff$', StaffPageView.as_view(), name='staff'),\n\t#url(r'^press$', PressPageView.as_view(), name='press'),\n\t#url(r'^reorder-photos/(?P<pk>[0-9]+)/$', ReorderPhotosView.as_view(), name='reorder_photos'),\n\t#url(r'^reorder-artworks$', ReorderArtworkssView.as_view(), name='reorder_artworks'),\n\t#url(r'^artwork/(?P<pk>[0-9]+)/$', ArtworkDetailView.as_view(), name=\"artwork_detail\"),\n\t#url(r'^delete-artwork/(?P<pk>[0-9]+)/$', DeleteArtworkView.as_view(), name=\"artwork_delete\"),\n\t#url(r'^update-artwork/(?P<pk>[0-9]+)/$', UpdateArtworkView.as_view(), name=\"artwork_update\"),\n\t#url(r'^create-artwork$', CreateArtworkView.as_view(), name=\"artwork_create\"),\n\t#url(r'^delete-photo/(?P<pk>[0-9]+)/$', DeletePhotoView.as_view(), name=\"photo_delete\"),\n\t#url(r'^update-photo/(?P<pk>[0-9]+)/$', UpdatePhotoView.as_view(), name=\"photo_update\"),\n\t#url(r'^create-photo/(?P<pk>[0-9]+)/$', CreatePhotoView.as_view(), name=\"photo_create\"),\n\t#url(r'^text-section/(?P<pk>[0-9]+)/$', UpdateTextSectionView.as_view(), name=\"text_section_update\"),\n\t#url(r'^logout$', logout_view, name='logout')\n]\n","sub_path":"website/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":1629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"69397353","text":"#img_smoothing.py\n\nimport cv2\nimport numpy as np\nfrom matplotlib import pyplot as plt\n\n\ndef filter_img(path):\n    def nothing(x):\n        pass\n\n    img = cv2.imread(path, cv2.IMREAD_REDUCED_COLOR_8)\n\n    cv2.namedWindow('image')\n    cv2.createTrackbar('K', 'image', 1, 20, nothing)\n\n    while 1:\n        if cv2.waitKey(1) & 0xFF == 27:\n            break\n        k = cv2.getTrackbarPos('K', 'image')\n\n        # (0,0)이면 에러가 발생함으로 1로 치환\n        if k == 0:\n            k = 1\n\n        # trackbar에 의해서 (1,1) ~ (20,20) kernel 생성\n        kernel = np.ones((k, k), np.float32) / (k ** 2)\n        dst = cv2.filter2D(img, -1, kernel)\n\n        cv2.imshow('image', dst)\n\n    cv2.destroyAllWindows()\n\n\ndef blur_img(path):\n    img = cv2.imread(path)\n\n    # pyplot를 사용하기 위해서 BGR을 RGB로 변환.\n    b, g, r = cv2.split(img)\n    img = cv2.merge([r, g, b])\n\n    # 일반 Blur\n    dst1 = cv2.blur(img, (7, 7))\n\n    # GaussianBlur\n    dst2 = cv2.GaussianBlur(img, (5, 5), 0)\n\n    # Median Blur\n    dst3 = cv2.medianBlur(img, 9)\n\n    # Bilateral Filtering\n    dst4 = cv2.bilateralFilter(img, 9, 75, 75)\n\n    images = [img, dst1, dst2, dst3, dst4]\n    titles = ['Original', 'Blur(7X7)', 'Gaussian Blur(5X5)', 'Median Blur', 'Bilateral']\n\n    for i in range(5):\n        plt.subplot(3, 2, i + 1), plt.imshow(images[i]), plt.title(titles[i])\n        plt.xticks([]), plt.yticks([])\n\n    plt.show()\n\n\nif __name__ == '__main__':\n    saechi = '../images/saechi3.jpg'\n    lena = '../images/lena.jpg'\n    # filter_img(saechi)\n    blur_img(lena)\n","sub_path":"OpenCV_Practice/img_smoothing.py","file_name":"img_smoothing.py","file_ext":"py","file_size_in_byte":1569,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"156548989","text":"from django.contrib import admin\n\nfrom oldp.apps.laws.processing.processing_steps.extract_refs import ExtractRefs\nfrom oldp.apps.references.models import LawReferenceMarker\nfrom .models import Law, LawBook\n\nadmin.site.register(LawBook)\n\n\n@admin.register(Law)\nclass LawAdmin(admin.ModelAdmin):\n    # date_hierarchy = 'updated_date'\n    list_display = ('slug', 'title', 'book')\n    list_filter = ('book', )  # court\n    actions = ['extract_refs']\n\n    def extract_refs(self, request, queryset):\n        step = ExtractRefs()\n        for law in queryset:\n            # Delete old references\n            LawReferenceMarker.objects.filter(referenced_by=law).delete()\n\n            # Extract new refs\n            law = step.process(law)\n            law.save()\n\n            law.save_reference_markers()\n\n    extract_refs.short_description = ExtractRefs.description\n\n","sub_path":"oldp/apps/laws/admin.py","file_name":"admin.py","file_ext":"py","file_size_in_byte":857,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"616363034","text":"import matplotlib.pyplot as plt\nimport time\nimport gym\nimport numpy as np\nfrom gym.wrappers import TimeLimit\n\ngym.register(id=\"Point2D-v0\", entry_point=\"sac.envs.point2d_env:make_point2d_env\")\n\n\ndef make_point2d_env():\n    return TimeLimit(env=Point2DEnv())\n\n\nclass Point2DEnv(gym.Env):\n    _INITIAL_POSITION = np.asarray([0, 0])\n\n    def __init__(self):\n        self.observation_space = gym.spaces.Box(-1, 1, shape=(2,))\n        self.action_space = gym.spaces.Box(-1, 1, shape=(2,))\n        self._cur_pos = self._INITIAL_POSITION\n        self._plot = None\n\n    def step(self, action):\n        action = np.asarray(action)\n        assert action.shape == (2,) and np.abs(action*0.99).max() <= 1, action\n        self._cur_pos = np.clip(a=self._cur_pos + 0.1*action, a_min=-1, a_max=1)\n        reward = 0\n        done = False\n        info = dict()\n        return self._cur_pos.copy(), reward, done, info\n\n    def reset(self):\n        self._cur_pos = self._INITIAL_POSITION\n        return self._cur_pos.copy()\n\n    def render(self, mode='human'):\n        if self._plot is None:\n            plt.ion() if mode == \"human\" else plt.ioff()\n            fig, ax = plt.subplots()\n            corners = [(-1, -1), (1, -1), (1, 1), (-1, 1), (-1, -1)]\n            ax.plot(*zip(*corners))\n            path_collection = ax.scatter(*self._cur_pos, c=\"red\")\n            self._plot = fig, path_collection\n        fig, path_collection = self._plot\n        path_collection.set_offsets(self._cur_pos)\n        fig.canvas.draw()\n        #fig.canvas.flush_events()\n\n        if mode == \"rgb_array\":\n            data = np.frombuffer(fig.canvas.tostring_rgb(), dtype=np.uint8)\n            width, height = fig.canvas.get_width_height()\n            return data.reshape((height, width, 3))\n\n    def seed(self, seed=None):\n        self.observation_space.seed(seed)\n        self.action_space.seed(seed)\n\n\nif __name__ == '__main__':\n    use_imgs = True\n    env = Point2DEnv()\n    img = env.render() if not use_imgs else env.render(\"rgb_array\")\n    if use_imgs:\n        plt.ion()\n        fig, ax = plt.subplots()\n        obj = ax.imshow(img)\n        fig.canvas.draw()\n        fig.canvas.flush_events()\n    while True:\n        env.step(env.action_space.sample())\n        time.sleep(0.1)\n        img = env.render() if not use_imgs else env.render(\"rgb_array\")\n        if use_imgs:\n            obj.set_data(img)\n            fig.canvas.draw()\n            fig.canvas.flush_events()\n","sub_path":"sac/envs/point2d_env.py","file_name":"point2d_env.py","file_ext":"py","file_size_in_byte":2440,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"384958072","text":"#!/usr/bin/env python\n# -- coding:utf-8 -*-\nimport re\nimport k20\n\n\ndef get_info_dict(text):\n    pattern = r'\\|(.*?)\\s=\\s(.*?)\\n'\n    return {info[0]: info[1] for info in re.findall(pattern, text)}\n\n\ndef main():\n    path = 'jawiki-country.json'\n    print(get_info_dict(k20.get_json(path)))\n\nif __name__ == '__main__':\n    main()\n","sub_path":"chapter3/k25.py","file_name":"k25.py","file_ext":"py","file_size_in_byte":328,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"33812841","text":"import pandas as pd\nimport numpy as np\nimport boto3\nimport pickle\nfrom dask.distributed import Client\nfrom werkzeug.utils import secure_filename\nimport time\nimport os, io\n\nDATA_DIRECTORY = 'trainingDataFromS3'\nUPLOAD_DIRECTORY = 'uploadDataToS3'\nBUCKET_NAME = 'ads-final-project'\n\ntry:\n\tS3 = boto3.client('s3', region_name='us-east-1')\nexcept Exception as e:\n\tprint(str(e))\n\traise e\n\ndef fetch_pickle_FromS3(key):\n\ttry:\n\t\tresponse = S3.get_object(Bucket=BUCKET_NAME, Key=UPLOAD_DIRECTORY+'/'+key)\n\t\tpickled_body = response['Body'].read()\n\t\tresult = pickle.loads(pickled_body)\n\t\treturn result\n\texcept Exception as e:\n\t\tprint(str(e))\n\t\traise e\n\ndef fetch_metrics_score_FromS3(key):\n\ttry:\n\t\tresponse = S3.get_object(Bucket=BUCKET_NAME, Key=UPLOAD_DIRECTORY+'/'+key)\n\t\tmetrics_score = response['Body'].read()\n\t\tmetrics_score_df = pd.read_csv(io.BytesIO(metrics_score))\n\t\tmetrics_score_df = metrics_score_df.sort_values('Model_Rank')\n\t\treturn metrics_score_df\n\texcept Exception as e:\n\t\tprint(str(e))\n\t\traise e\n\ndef df_to_html(df, changeClass=True):\n\tdf_html = df.to_html(index=False)\n\tif changeClass:\n\t\tdf_html = df_html.replace('<table border=\"1\" class=\"dataframe\">','<table class=\"table\">')\n\telse:\n\t\tdf_html = df_html.replace('<table border=\"1\" class=\"dataframe\">','<table border=\"2\" class=\"dataframe\">')\n\tdf_html = df_html.replace('<thead>','<thead class=\"thead-dark\">')\n\tdf_html = df_html.replace('<tr style=\"text-align: right;\">','<tr align=\"center\" valign=\"center\">')\n\tdf_html = df_html.replace('<th>','<th scope=\"col\">')\n\tdf_html = df_html.replace('<td>','<td align=\"center\" valign=\"center\">')\n\treturn df_html\n\ndef data_transformation(df, featured_index_dict):\n\ttry:\n\t\ttry:\n\t\t\tdel df[\"Churn\"]\n\t\texcept:\n\t\t\tpass\n\t\tdf = df.drop([\"customerID\"], axis=1)\n\t\tdf = df.drop([\"TotalCharges\"], axis=1)\n\t\tdf = df.replace(r'^\\s+$', np.nan, regex=True)\n\t\tdf = df.dropna()\n\t\tdf['SeniorCitizen'] = df.SeniorCitizen.map({0:'No', 1:'Yes'})\n\t\tall_columns_list = df.columns.tolist()\n\t\tnumerical_columns_list = ['tenure','MonthlyCharges']\n\t\tcategorical_columns_list = [e for e in all_columns_list if e not in numerical_columns_list]\n\t\tfor index in categorical_columns_list:\n\t\t\tdf[index] = pd.Categorical(df[index])\n\t\tfor index in numerical_columns_list:\n\t\t\tdf[index] = pd.to_numeric(df[index])\n\t\tnum = ['float64', 'int64']\n\t\tnum_df = df.select_dtypes(include=num)\n\t\tobj_df = df.select_dtypes(exclude=num)\n\t\ttenure_bins = pd.cut(num_df[\"tenure\"], bins=[0,20,60,80], labels=['low','medium','high'])\n\t\tMonthlyCharges_bins = pd.cut(num_df[\"MonthlyCharges\"], bins=[0,35,60,130], labels=['low','medium','high'])\n\t\tbins = pd.DataFrame([tenure_bins, MonthlyCharges_bins]).T\n\t\ttransformed_df = pd.concat([bins, obj_df],axis=1)\n\n\t\tdf_dummies = pd.get_dummies(data=transformed_df, columns=transformed_df.columns)\n\n\t\tfeatures_columns = sorted([key for key in featured_index_dict])\n\t\tfeatures_not_in_dummy = [e for e in features_columns if e not in list(df_dummies.columns)]\n\t\tfor l in features_not_in_dummy:\n\t\t\tdf_dummies[l] = 0\n\t\t\n\t\t'''For Feature and Dummy Index Comparisons\n\t\tdummy_columns = sorted(list(df_dummies.columns))\n\t\tfeatures_columns = sorted([key for key in featured_index_dict])\n\t\tsubset = set(features_columns).issubset(set(dummy_columns))\n\t\tprint('df_dummies: ', dummy_columns)\n\t\tprint('----------------------------')\n\t\tprint('features: ', features_columns)\n\t\tprint('----------------------------')\n\t\tprint('subset: ', subset)\n\t\t'''\n\n\t\tdata_X = df_dummies[features_columns]\n\t\tprint('0) data_X.shape: ', data_X.shape)\n\t\treturn data_X\n\texcept Exception as e:\n\t\tprint(e)\n\ndef predict_outcome(data_X, data_dataframe):\n\tpickled_models = fetch_pickle_FromS3('pickled_models.pkl')\n\ttotal_rows = data_dataframe.shape[0]\n\tsmall_df = data_dataframe.filter(['customerID'], axis=1)\n\tfor i in range(1, (len(pickled_models)+1)):\n\t\tprint('2) data_X.shape.in_predict_outcome: ', data_X.shape)\n\t\t# Load Model\n\t\tmodel_rank = i\n\t\tprint('model_rank: ', i)\n\t\tmodel = pickled_models[i][0]\n\t\tmodel_name = 'Rank '+str(model_rank)+': '+pickled_models[i][1]\n\t\tprint('model_name: ', model_name)\n\t\t# Make prediction\n\t\tif total_rows > 5:\n\t\t\tclient = Client(processes=False)\n\t\t\tprint('2-if) data_X.shape: ', data_X.shape)\n\t\t\tprediction = client.submit(model.predict, data_X).result().tolist()\n\t\telse:\n\t\t\tprint('2-else) data_X.shape: ', data_X.shape)\n\t\t\tprediction = model.predict(data_X).tolist()\n\t\tprint('3) data_X.shape: ', data_X.shape)\n\t\tprediction_series = pd.Series(prediction)\n\t\tdata_dataframe[model_name] = prediction_series\n\t\tsmall_df[model_name] = prediction_series\n\treturn data_dataframe, small_df\n\ndef batchfile_processing(input_file):\n\ttry:\n\t\tfeatured_index_dict = fetch_pickle_FromS3('featured_index_dict.pkl')\n\t\tdata_dataframe = pd.read_csv(input_file)\n\t\tprint('1) input_file_columns: ', data_dataframe.columns)\n\t\tdata_X = data_transformation(data_dataframe, featured_index_dict)\n\t\tprint('22) input_file_columns: ', data_dataframe.columns)\n\t\tprint('1) data_X.shape: ', data_X.shape)\n\t\tdata_dataframe, small_df = predict_outcome(data_X, data_dataframe)\n\n\t\treturn data_dataframe\n\texcept Exception as e:\n\t\tprint(str(e))\n\t\traise e\n\ndef form_processing(input_dict):\n\ttry:\n\t\tform_result_df = pd.DataFrame.from_dict(input_dict)\n\t\tfeatured_index_dict = fetch_pickle_FromS3('featured_index_dict.pkl')\n\t\tdata_X = data_transformation(form_result_df, featured_index_dict)\n\n\t\tform_result_df, small_df = predict_outcome(data_X, form_result_df)\n\n\t\treturn form_result_df, small_df\n\n\texcept Exception as e:\n\t\tprint(str(e))\n\t\traise e","sub_path":"Model_Deployment_FlaskWebApp/FlaskApp/data_engineering.py","file_name":"data_engineering.py","file_ext":"py","file_size_in_byte":5485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"194596679","text":"import re\nfrom urllib.parse import urlencode\nfrom typing import Callable, Iterable\n\nfrom urllib3.connectionpool import HTTPConnectionPool\nfrom urllib3.exceptions import HTTPError\n\nfrom snuba.datasets.schemas.tables import TableSchema\nfrom snuba.writer import BatchWriter, WriterTableRow\n\n\nclass ClickHouseError(Exception):\n    def __init__(self, code: int, type: str, message: str):\n        self.code = code\n        self.type = type\n        self.message = message\n\n    def __str__(self) -> str:\n        return f\"[{self.code}] {self.type}: {self.message}\"\n\n    def __repr__(self) -> str:\n        return f\"<{type(self).__name__}: {self}>\"\n\n\nCLICKHOUSE_ERROR_RE = re.compile(\n    r\"^Code: (?P<code>\\d+), e.displayText\\(\\) = (?P<type>(?:\\w+)::(?:\\w+)): (?P<message>.+)$\",\n    re.MULTILINE,\n)\n\n\nclass HTTPBatchWriter(BatchWriter):\n    def __init__(self,\n        schema: TableSchema,\n        host,\n        port,\n        encoder: Callable[[WriterTableRow], bytes],\n        options=None,\n        table_name=None,\n        chunk_size: int = 1,\n    ):\n        \"\"\"\n        Builds a writer to send a batch to Clickhouse.\n\n        :param schema: The dataset schema to take the table name from\n        :param host: Clickhosue host\n        :param port: Clickhosue port\n        :param encoder: A function that will be applied to each row to turn it into bytes\n        :param options: options passed to Clickhouse\n        :param table_name: Overrides the table coming from the schema (generally used for uplaoding\n            on temporary tables)\n        :param chunk_size: The chunk size (in rows).\n            We send data to the server with Transfer-Encoding: chunked. If 0 we send the entire\n            content in one chunk.\n        \"\"\"\n        self.__pool = HTTPConnectionPool(host, port)\n        self.__options = options if options is not None else {}\n        self.__table_name = table_name or schema.get_table_name()\n        self.__chunk_size = chunk_size\n        self.__encoder = encoder\n\n    def _prepare_chunks(self, rows: Iterable[WriterTableRow]) -> Iterable[bytes]:\n        chunk = []\n        for row in rows:\n            chunk.append(self.__encoder(row))\n            if self.__chunk_size and len(chunk) == self.__chunk_size:\n                yield b\"\".join(chunk)\n                chunk = []\n\n        if chunk:\n            yield b\"\".join(chunk)\n\n    def write(self, rows: Iterable[WriterTableRow]):\n        response = self.__pool.urlopen(\n            \"POST\",\n            \"/?\"\n            + urlencode(\n                {\n                    **self.__options,\n                    \"query\": f\"INSERT INTO {self.__table_name} FORMAT JSONEachRow\",\n                }\n            ),\n            headers={\"Connection\": \"keep-alive\", \"Accept-Encoding\": \"gzip,deflate\"},\n            body=self._prepare_chunks(rows),\n            chunked=True,\n        )\n\n        if response.status != 200:\n            # XXX: This should be switched to just parse the JSON body after\n            # https://github.com/yandex/ClickHouse/issues/6272 is available.\n            content = response.data.decode(\"utf8\")\n            details = CLICKHOUSE_ERROR_RE.match(content)\n            if details is not None:\n                code, type, message = details.groups()\n                raise ClickHouseError(int(code), type, message)\n            else:\n                raise HTTPError(\n                    f\"Received unexpected {response.status} response: {content}\"\n                )\n","sub_path":"snuba/clickhouse/http.py","file_name":"http.py","file_ext":"py","file_size_in_byte":3438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"549113761","text":"# Weblog Parser by Scott Elgood\r\n# 2020-02-16\r\n# Designed for the AWStats output but may work for others\r\nimport re\r\nimport os\r\nfrom os import path\r\nimport sys\r\nimport getpass\r\nimport geoip2.database\r\n\r\n\r\ndef iplookup(foundip):\r\n    \"\"\"'Conducts an IP lookup on IP addresses\"\"\"\r\n    if os.path.exists(r\".\\outputip.txt\"):\r\n        print(\"[-INF-]DIR Exists, remaking...\")\r\n        os.remove(r\".\\outputip.txt\")\r\n    else:\r\n        pass\r\n    curuser = getpass.getuser()\r\n    reader = geoip2.database.Reader(\r\n        f\"C:\\\\Users\\\\{curuser}\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python37-32\\\\geo.mmdb\"\r\n    )\r\n    for eachip in foundip:\r\n        stripip = str(eachip).strip(\"[]\")  # Stripping the extra bits off\r\n        stripip2 = str(stripip).strip(\"''\")\r\n        rec = reader.city(stripip2)\r\n        info = f\"[-ALERT-] Collecting Data on {stripip}. . .\"\r\n        state = f\"State/Province: {rec.subdivisions.most_specific.name}\"  # Returns overall (Fife)\r\n        town = f\"Town: {rec.city.name}\"  # Returns Close prox (Leven)\r\n        lon = f\"Longitude: {rec.location.longitude}\"  # Longitutde\r\n        lat = f\"Latitude: {rec.location.latitude}\"  # Latitude\r\n        post = f\"Postal Code: {rec.postal.code}\"  # first bit of postcode\r\n        net = f\"Network: {rec.traits.network}\"  # network traits\r\n        border = \"----- ----- ----- -----\"\r\n        concat = f\"{info} \\n {state} \\n {town} \\n {lon} \\n {lat} \\n {post} \\n {net} \\n {border} \\n\"\r\n        outfile = open(r\".\\outputip.txt\", \"a\")\r\n        outfile.write(concat)\r\n    outfile.close()\r\n    print(f\"[-INF-]Parsing done! Check your current directory for output.\")\r\n\r\n\r\ndef ipextract(filepath):\r\n    \"\"\"Extracts IP addresses from a file.\r\n    Supply filepath\"\"\"\r\n    foundip = []\r\n    print(f\"[-INF-]Extracting IP from {filepath}\")\r\n    with open(filepath, \"r\") as stream:\r\n        for line in stream:\r\n            match = re.findall(r\"\\b(?:[0-9]{1,3}\\.){3}[0-9]{1,3}\\b\", line)\r\n            if match:\r\n                if match not in foundip:\r\n                    foundip.append(match)\r\n                else:\r\n                    pass\r\n    stream.close()\r\n    iplookup(foundip)\r\n\r\n\r\ndef singleip():\r\n    \"\"\"Function to parse a single IP by\r\n    user input\"\"\"\r\n    try:\r\n        curuser = getpass.getuser()\r\n        reader = geoip2.database.Reader(\r\n            f\"C:\\\\Users\\\\{curuser}\\\\AppData\\\\Local\\\\Programs\\\\Python\\\\Python37-32\\\\geo.mmdb\"\r\n        )\r\n        oneip = input(\"Insert the IP You wish to locate: \")\r\n        rec = reader.city(oneip)\r\n        info = f\"[-ALERT-] Collecting Data on {oneip}. . .\"\r\n        state = f\"State/Province: {rec.subdivisions.most_specific.name}\"  # Returns overall (Fife)\r\n        town = f\"Town: {rec.city.name}\"  # Returns Close prox (Leven)\r\n        lon = f\"Longitude: {rec.location.longitude}\"  # Longitutde\r\n        lat = f\"Latitude: {rec.location.latitude}\"  # Latitude\r\n        post = f\"Postal Code: {rec.postal.code}\"  # first bit of postcode\r\n        net = f\"Network: {rec.traits.network}\"  # network traits\r\n        border = \"----- ----- ----- -----\"\r\n        concat = f\"{info} \\n {state} \\n {town} \\n {lon} \\n {lat} \\n {post} \\n {net} \\n {border} \\n\"\r\n        print(concat)\r\n    except ValueError as err:\r\n        print(f\"[-ALERT-]Error! {err}\")\r\n        sys.exit()\r\n    sys.exit()\r\n\r\n\r\ndef main():\r\n    \"\"\"Run Main Script\"\"\"\r\n    # --- # Grabbing Args # --- #\r\n    if len(sys.argv) <= 1:\r\n        print(\"Usage: weblogparse.py FilePath ... \")\r\n        userc2 = input(\r\n            \"No file detected, did you want to geolocate a single IP? Y/N: \"\r\n        ).lower()\r\n        if userc2 == \"y\":\r\n            singleip()\r\n        else:\r\n            sys.exit()\r\n    else:\r\n        for newarg in sys.argv[1:2]:\r\n            filepath = newarg\r\n    pathtest = path.exists(filepath)\r\n    # --- # Checkng to see if filepath exists # --- #\r\n    if pathtest is True:\r\n        print(f\"[-INF-] Found {filepath} - parse?\")\r\n        userc = input(\"[-ALERT-]Y/N: \\n\").lower()\r\n        try:\r\n            if userc == \"y\":\r\n                print(\"Parsing...\")\r\n                filepath = os.path.abspath(filepath)\r\n                ipextract(filepath)\r\n            else:\r\n                print(\"Aborting...\")\r\n                sys.exit()\r\n        except FileNotFoundError as err:\r\n            print(\r\n                f\"geo.mmdb not present in Python Directory, please download a database to use! \\n[-ALERT-]{err}\"\r\n            )\r\n    else:\r\n        print(\r\n            f\"Error! File {filepath} is not a valid filepath or file - did you type it correctly?\"\r\n        )\r\n\r\n\r\nif __name__ == \"__main__\":\r\n    main()\r\n","sub_path":"weblogparse.py","file_name":"weblogparse.py","file_ext":"py","file_size_in_byte":4586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"251968626","text":"import numpy as np\nimport matplotlib.pyplot as plt\nfrom sklearn.metrics import classification_report, confusion_matrix\n\n# from s_knn_dtw import KnnDtw\n\n# from sklearn import svm\n# from sklearn.ensemble import RandomForestClassifier\n# from sklearn.model_selection import learning_curve,GridSearchCV\n# from sklearn.neighbors import KNeighborsClassifier\n# import keras\nfrom keras.models import Sequential # sequential is required to initialise the neural network\nfrom keras.layers import Dense      # dense is used to build the layers\nfrom keras.layers import Dropout    # Dropout Layer in order to prevent Regularization in the network\n\n\nimport os, sys\ncurrentdir = os.path.dirname(os.path.realpath(__file__))\nparentdir = os.path.dirname(currentdir)\nsys.path.append(parentdir)\nimport s_data_loader as data_loader\n# dt = data_loader.load_feature_time()\ndt = data_loader.load_feature()\n\n# Mapping table for classes\nlabels = dt.labels\nx_train = dt.x_train\ny_train = dt.y_train\nx_test = dt.x_test\ny_test = dt.y_test\n\nrx_train = x_train\nry_train = y_train\nrx_test = x_test\nry_test = y_test\n\n#creating a network of 561 X 48 X 24 X 12 X6\nmodel = Sequential()\nmodel.add(Dense(48, input_dim = 561, kernel_initializer='uniform', activation='relu', ))\nmodel.add(Dropout(0.1))\nmodel.add(Dense(24, kernel_initializer='uniform', activation='relu'))\nmodel.add(Dropout(0.1))\nmodel.add(Dense(12, kernel_initializer='uniform', activation='relu'))\nmodel.add(Dropout(0.1))\nmodel.add(Dense(6, kernel_initializer='uniform', activation='softmax'))\nmodel.compile(optimizer='adam', loss='categorical_crossentropy', metrics = ['accuracy'])\n\nmodel.fit(rx_train, ry_train, batch_size=20, epochs=10, verbose = 4)\nlabel=model.predict(rx_test)\n\n\nprint(classification_report(label, ry_test, target_names=[lb for lb in labels.values()]))\n\nconf_mat = confusion_matrix(label, ry_test)\n\nplt.style.use('bmh')\nfig = plt.figure(figsize=(6,6))\nwidth = np.shape(conf_mat)[1]\nheight = np.shape(conf_mat)[0]\n\nres = plt.imshow(np.array(conf_mat), cmap=plt.cm.summer, interpolation='nearest')\nfor i, row in enumerate(conf_mat):\n    for j, c in enumerate(row):\n        if c > 0:\n            plt.text(j-.2, i+.1, c, fontsize=16)\n\ncb = fig.colorbar(res)\nplt.title('Confusion Matrix')\n_ = plt.xticks(range(6), [lb for lb in labels.values()], rotation=90)\n_ = plt.yticks(range(6), [lb for lb in labels.values()])\nplt.show()\n\n\ndef predict_meaure(name, model, rx_test):\n    import time\n    print(\"check predict performance {}...\".format(name))\n    begin = time.time()\n    model.predict(rx_test)\n    delta = time.time() - begin\n    print(\"check predict perform time_elapse: {:.2f} sec on total {} samples, {:.4f} msec per prediction \".format(delta, len(rx_test), delta*1000/len(rx_test)))\n\n\npredict_meaure(\"rfc\", model, rx_test)","sub_path":"classifiers/ann_exam_all.py","file_name":"ann_exam_all.py","file_ext":"py","file_size_in_byte":2773,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"266116749","text":"#region ennoncé\n\n# Ennoncé :\n# La seule opération se faisant hors des fonction est l'appel de ShowResult() déjà écrit. Cette fonction\n# s'occupera exclusivement d'écrire à l'écran le résultat demandé.\n# Votre programme devra comme résultat afficher le prix entré par l'utilisateur sans taxe en euro, \n# tugrik(monnaie mongole), et dollars.\n# Il devra aussi calculer et montrer le prix avec taxe dans les trois monnaie, et enfin le prix final après\n# application de la réduction (c'est une réduction brute pas en %. Si l'utilisateur rentre une réduction \n# de 10 alors ça veut dire qu'on enlève 10 euros au prix final)\n# Pas d'indices pour celui-ci autre que le début de code ! Bonne chance !\n\n\n#Conversion : -USA : 1,19 / -MN : 3395\n#TVA : -USA : 7,5% / -EU : 21% / -MN : 10%\n\n#endregion\n\n#region prépa autre\n\n# Préparez aussi : \n# Une petite explication de ce que vous avez fait dans le programme, comment il marche (à expliquer à l'oral \n# à l'autre groupe)\n# Une sorte de post mortem. Où on été les difficultés, ce que ça vous a appris, etc. un retour d'expérience,\n# pas forcément uniquement sur le code mais aussi potentiellement sur le travail d'équipe, \n# Une explication de ce qu'il faut rajouter à votre programme si je veux simplement ajouter à tout ça un \n# nouveau pays (essayez d'introduire une conversion vers le Metical (monnaie du Mozambique. \n# TVA 17%, taux de change 0,012) ou du moins un explication de comment faire/comment vous avez fait)\n\n# Je pourrais interroger potentiellement n'importe lequel d'entre vous sur chacune de ces questions, \n# je n'ai pas besoin d'un exposé, soyez juste tous pret à y répondre.\n\n#endregion\n\n#region directives\n\n# A 13h30, je vous montrerai le résultat que j'obtient en lançant mon programme pour vous donner une \n# idée de finalité\n# Je commencerai à répondre à vos questions seulement après la moité du temps (14H15-14H30). \n# Vous gérez comme vous voulez vos temps de pause, de travail, de reflexion, etc.\n\n# Pendant les quelques dernières minutes de ce cours, si vous n'avez pas fini je m'exprimerai sur des \n# critiques potentielle pour vous aider à le terminer.\n# On corrigera tout ça demain en virtuel !\n\n# N'hésitez pas à utiliser internet si besoin, surtout pendant la première phase.\n# J'essayerai de vous donner une note pour vos solutions et votre travail, si vous le voulez bien, \n# pas du tout objective ni importante, mais simplement pour vous aider à vous situer par rapport à ce \n# que j'attends de vous à ce stade de ce cours.\n\n#Maximum 2 PC par groupe, 1 seul pour coder ! \n# (l'autre sert pour les recherches internet, consulter l'ennoncé,... uniquement)\n\n#endregion\n\ndef ShowResult(_prixHT,_reduc):\n    _euros= 1.0 \n    _dollars= 1.19\n    _mn= 3395 \n    _eurostax= 1.21\n    _dollartax= 1.075\n    _mntax= 1.10\n\n\n    def conversion(_prixHT, _devise):\n        return _prixHT * _devise\n        \n\n    def tax(_prixconv,_taxe):\n        return _prixconv * _taxe \n\n    print (f\"Vous avez choisi une somme de {_prixHT } et une réduction de {_reduc}\")\n    \n    _prixConv = conversion(_prixHT,_euros)\n    _prixTTC=tax(conversion(_prixHT,_euros),_eurostax)\n    _prixReduc =  tax(conversion(_prixHT,_euros),_eurostax) - _reduc\n\n    print(\"Prix en Euros:\")\n    print (f\"Prix en euros HT : {_prixConv}\")\n    print (f\"Prix en euros TC : {_prixTTC}\")\n    print(f\"Prix total avec réduction : {_prixReduc}\")\n   \n    _prixConv =conversion(_prixHT,_dollars)\n    _prixTTC=tax(conversion(_prixHT,_dollars),_dollartax)\n    _prixReduc =  tax(conversion(_prixHT,_dollars),_eurostax) - conversion(_reduc, _dollars)\n\n   \n    print (\"Prix en Dollars :\")\n    print (f\"Prix en euros HT : {_prixConv}\")\n    print (f\"Prix en euros TC : {_prixTTC}\")\n    print(f\"Prix total avec réduction : {_prixReduc}\")\n\n    _prixConv = conversion(_prixHT,_mn)\n    _prixTTC= tax(conversion(_prixHT,_euros),_mntax)\n    _prixReduc =  tax(conversion(_prixHT,_euros),_eurostax) - (conversion(_reduc,_mn))\n    \n    print (\"Prix en Tugrik\") \n    print (f\"Prix en euros HT : {_prixConv}\")\n    print (f\"Prix en euros TTC : {_prixTTC}\")\n    print(f\"Prix total avec réduction : {_prixConv}\")\n        \n\n\n\n\n    \n\nShowResult(float(input(\"Entrez le prix de votre article sans taxes :\\n\")),float(input(\"Entrez la réduction appliquée à votre article en euros :\\n\")))","sub_path":"chapitre1/C1B8BonusJessie.py","file_name":"C1B8BonusJessie.py","file_ext":"py","file_size_in_byte":4343,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"300021053","text":"# -*- coding: utf-8 -*-\nfrom __future__ import (\n    division, absolute_import, print_function, unicode_literals,\n)\nfrom builtins import *                  # noqa\nfrom future.builtins.disabled import *  # noqa\n\nfrom operator import attrgetter\n\nfrom zhlint.utils import TextElement\nfrom zhlint.error_detection import (\n    detect_e101,\n    detect_e102,\n    detect_e103,\n    detect_e104,\n    detect_e203,\n    detect_e205,\n    detect_e206,\n    detect_e207,\n    detect_e301,\n\n    detect_e201,\n    detect_e202,\n    detect_e204,\n\n    split_text_element,\n)\n\n\ndef check_texts(inputs, detector, should_detected):\n    elements = []\n\n    for i in inputs:\n        if isinstance(i, str):\n            elements.append(\n                TextElement('', '1', '2', i)\n            )\n        elif isinstance(i, TextElement):\n            elements.append(i)\n        else:\n            raise RuntimeError\n\n    for element in elements:\n        matches = detector(element)\n        if should_detected:\n            assert matches\n            assert list(matches)\n        else:\n            assert not matches or not list(matches)\n\n\ndef test_e101():\n\n    check_texts(\n        [\n            '中文english',\n            'english中文',\n            '中文  english',\n            'english  中文',\n            '中文\\tenglish',\n        ],\n        detect_e101,\n        should_detected=True,\n    )\n\n    check_texts(\n        [\n            '中文 english',\n            'english 中文',\n            '中文\\nenglish',\n            '42\\n中文',\n        ],\n        detect_e101,\n        should_detected=False,\n    )\n\n\ndef test_e102():\n\n    check_texts(\n        [\n            '中文1',\n            '42中文',\n            '中文  42',\n            '42  中文',\n            '中文\\t42',\n        ],\n        detect_e102,\n        should_detected=True,\n    )\n\n    check_texts(\n        [\n            '中文 42',\n            '42 中文',\n            '中文\\n42',\n            '中文 \\n42',\n            '42\\n中文',\n        ],\n        detect_e102,\n        should_detected=False,\n    )\n\n\ndef test_e103():\n\n    check_texts(\n        [\n            '42μ',\n            '42  μ',\n        ],\n        detect_e103,\n        should_detected=True,\n    )\n\n    check_texts(\n        [\n            '42 μ',\n            'μ\\n42',\n            '42\\nμ',\n            '42x',\n            '42n',\n            '42％',\n            '42%',\n            '42℃',\n            '，42',\n            '42。',\n            'Q3',\n            '136-4321-1234',\n            'word2vec',\n            'word20000vec',\n        ],\n        detect_e103,\n        should_detected=False,\n    )\n\n\ndef test_e104():\n\n    check_texts(\n        [\n            '(42）',\n            '（42)',\n            '（42）',\n            '42(42)',\n            '42  (42)',\n            ' (42)',\n        ],\n        detect_e104,\n        should_detected=True,\n    )\n\n    check_texts(\n        [\n            '42 (42)',\n            '42\\n(42)',\n            '(42)',\n        ],\n        detect_e104,\n        should_detected=False,\n    )\n\n\ndef test_e203():\n\n    check_texts(\n        [\n            '中文， 测试',\n            '中文 。测试',\n            '「 中文」',\n        ],\n        detect_e203,\n        should_detected=True,\n    )\n\n    check_texts(\n        [\n            '中文，测试',\n            '中文；测试',\n            '「中文」',\n            '中文！\\n测试',\n        ],\n        detect_e203,\n        should_detected=False,\n    )\n\n\ndef test_e205():\n\n    check_texts(\n        [\n            '中文...',\n            '中文.......',\n            '中文。。。',\n        ],\n        detect_e205,\n        should_detected=True,\n    )\n\n    check_texts(\n        [\n            '中文......',\n            '中文 test@email.com',\n            '中文 +135-1234-5678'\n            '中文 (1) '\n        ],\n        detect_e205,\n        should_detected=False,\n    )\n\n\ndef test_e206():\n\n    check_texts(\n        [\n            '中文!!',\n            '中文！！',\n            '中文!！',\n            '中文??',\n            '中文？？',\n            '中文？?',\n        ],\n        detect_e206,\n        should_detected=True,\n    )\n\n    check_texts(\n        [\n            '中文!',\n            '中文！',\n            '中文?',\n            '中文？',\n            'english, with space',\n        ],\n        detect_e206,\n        should_detected=False,\n    )\n\n\ndef test_e207():\n\n    check_texts(\n        [\n            '中文~',\n        ],\n        detect_e207,\n        should_detected=True,\n    )\n\n    check_texts(\n        [\n            '中文',\n        ],\n        detect_e207,\n        should_detected=False,\n    )\n\n\ndef test_e301():\n\n    check_texts(\n        [\n            'APP',\n            'app',\n            'android',\n            'ios',\n            'IOS',\n            'IPHONE',\n            'iphone',\n            'AppStore',\n            'app store',\n            'wifi',\n            'Wifi',\n            'Wi-fi',\n            'E-mail',\n            'Email',\n            'PS',\n            'ps',\n            'Ps.',\n\n            '我app store我',\n        ],\n        detect_e301,\n        should_detected=True,\n    )\n\n\ndef test_e201():\n\n    check_texts(\n        [\n            '有中文, 错误.',\n            'LaTeX 公式 $$.',\n            'LaTeX 公式,$$',\n            'LaTeX 公式 \\(\\).',\n            'LaTeX 公式,\\(\\)',\n        ],\n        detect_e201,\n        should_detected=True,\n    )\n\n    check_texts(\n        [\n            '有中文，正确。',\n            '有中文，正确......',\n            'pure english, nothing wrong.',\n            'P.S. 这是一行中文。',\n            'LaTeX 公式 $$',\n            'LaTeX 公式 \\(\\)',\n            '邮箱：programmer.zhx@gmail.com',\n            '1.0',\n            'www.google.com',\n            '链接地址 http://google.com',\n        ],\n        detect_e201,\n        should_detected=False,\n    )\n\n\ndef test_e202():\n\n    check_texts(\n        [\n            'pure english，nothing wrong.',\n        ],\n        detect_e202,\n        should_detected=True,\n    )\n\n    check_texts(\n        [\n            'pure english, nothing wrong.',\n        ],\n        detect_e202,\n        should_detected=False,\n    )\n\n\ndef test_e204():\n\n    check_texts(\n        [\n            # \"中文'测试'\",\n            # '中文\"测试\"',\n            '中文‘测试’',\n            '中文“测试”',\n        ],\n        detect_e204,\n        should_detected=True,\n    )\n\n    check_texts(\n        [\n            \"中文「测试」\",\n        ],\n        detect_e204,\n        should_detected=False,\n    )\n\n\ndef test_split_text_element():\n\n    def access(key, te):\n        return list(map(attrgetter(key), split_text_element(te)))\n\n    def help_(content, begin, end):\n        te = TextElement(\n            'paragraph', begin, end,\n            content,\n        )\n\n        lines = access('content', te)\n        begins = access('loc_begin', te)\n        ends = access('loc_end', te)\n        offsets = access('offset', te)\n\n        return lines, begins, ends, offsets\n\n    content = '''a\nb.\nc. inline!\nd\ne.'''\n\n    lines, begins, ends, offsets = help_(content, '1', '5')\n\n    assert ['a\\nb.\\n', 'c.', ' inline!\\n', 'd\\ne.'] == lines\n    assert [1, 3, 3, 4] == begins\n    assert [2, 3, 3, 5] == ends\n    assert [0, 0, 2, 0] == offsets\n\n    content = '''a......\nb!\nc;\nd.\ne?\nf！\ng；\nh。\ni？'''\n\n    lines, begins, ends, offsets = help_(content, '1', '9')\n    assert len(lines) == 9\n\n    content = '''P.S. this is a line!'''\n\n    lines, begins, ends, offsets = help_(content, '1', '9')\n    assert len(lines) == 1\n\n    content = '''邮箱：abc.de@fgh.com！'''\n\n    lines, begins, ends, offsets = help_(content, '1', '9')\n    assert len(lines) == 1\n\n    content = '''sentence 1.\n   （中文！中文！）\nsentence 2.'''\n    lines, begins, ends, offsets = help_(content, '1', '3')\n\n    assert ['sentence 1.\\n', '   （中文！中文！）\\nsentence 2.'] == lines\n    assert [1, 2] == begins\n    assert [1, 3] == ends\n    assert [0, 0] == offsets\n\n    content = '''sentence 1.\nsentence 2.  sentence 3'''\n    lines, begins, ends, offsets = help_(content, '1', '2')\n\n    assert ['sentence 1.\\n', 'sentence 2.', '  sentence 3'] == lines\n    assert [1, 2, 2] == begins\n    assert [1, 2, 2] == ends\n    assert [0, 0, 11] == offsets\n","sub_path":"tests/test_error_detection.py","file_name":"test_error_detection.py","file_ext":"py","file_size_in_byte":8268,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"91768606","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport math\n\nn = 2**29\nB = n*(3*8+1*4)\nkB = B/1024.\nMB = kB/1024.\nGB = MB/1024.\nprint(\"memory used = {} GB\".format(GB))\n\ndata  = np.loadtxt('ppmd01-no-xHost.txt')\ndatax = np.loadtxt('ppmd01-xHost.txt')\n\nx_memory_used = False\nif x_memory_used:\n    L1  = math.log10( 32)/np.log10(2)\n    L2 = math.log10(256)/np.log10(2)\n    L3= math.log10( 25*1024)/np.log10(2)\n    x = np.log10(24*(data[:,0]/1024))/np.log10(2)\nelse:\n    L1  = math.log10( 32*1024)\n    L2 = math.log10(256*1024)\n    L3= math.log10(25*1024*1024)\n    x = np.log10(data[:,0])\n\nprint(L1)\nprint(L2)\nprint(L3)\nplt.plot( x, data[:,2], 'o-'\n        , x, data[:,3], 'o-'\n        , x, data[:,4], 'o-'\n        )\nplt.legend([\"contiguous SoA\",'contiguous AoS','random access'],loc='upper left')\nif x_memory_used:\n    plt.xlabel('log10(mem used [kB])')\nelse:\n    plt.xlabel('log10(array length)')\nplt.ylabel('cpu time [s]')\nplt.show()\nexit()\n","sub_path":"ppmd01/analyse_raw.py","file_name":"analyse_raw.py","file_ext":"py","file_size_in_byte":943,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"524845530","text":"import numpy as np\r\nimport tensorflow as tf\r\n\r\ndef discretize(value,action_dim,n_outputs):\r\n    discretization = tf.round(value)\r\n    discretization = tf.minimum(tf.constant(n_outputs-1, dtype=tf.float32,shape=[1,action_dim]), \r\n                      tf.maximum(tf.constant(0, dtype=tf.float32,shape=[1,action_dim]), tf.to_float(discretization)))\r\n    return tf.to_int32(discretization)\r\n\r\nif __name__=='__main__':\r\n    value=np.array((0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9))\r\n    \r\n    a=discretize(value,value.shape[0],2)\r\n    with tf.Session() as sess:\r\n        print(a.eval())","sub_path":"Env/discretize.py","file_name":"discretize.py","file_ext":"py","file_size_in_byte":580,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"39526304","text":"from matplotlib.pyplot import *\n#liste di punti\nx=[1,2,3,4]\ny=[1,4,9,16]\n#errori\ndy=[0.5,0.8,0.10,0.9]\ndx=[0.2]*4 #4 volte stesso valore di lista\n#senza \"o\" da segmenti, con \"o \" punti,altre opzioni...\n#dy errore su y\nerrorbar(x,y,dy,dx, fmt=\"o\")\n#stessi argomenti senza errori e \"o\" senza fmt con funzione plot dà grafico senza errori\n#xlabel  da denominazioni sotto asse x. stesso per ylabel\nxlabel('robe[asas]')\nshow()\n#per sommare elementi di una lista sum(lista)\n","sub_path":"pendolo semplice/grafici1.py","file_name":"grafici1.py","file_ext":"py","file_size_in_byte":469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"338563750","text":"from sklearn import svm\nimport requests\nimport json\nimport sys\nimport operator\n\nplayertag = '%23' + sys.argv[1].replace('#','')\nclantag = '%23' + sys.argv[2].replace('#','')\nheaders = {\n    'authorization': sys.argv[3],\n    'content-type': \"application/json\",\n    'cache-control': \"no-cache\",\n    }\n\nurl = \"https://api.clashroyale.com/v1/players/\" + playertag + \"/battlelog\"\nr = requests.request(\"GET\", url, headers=headers)\nr = r.json()\narr = []\nx_arr = []\ncrown_arr = []\ntry:\n    for item in r:\n        crown_arr.append(item['team'][0]['crowns'])\n        arr.append(item['team'][0]['startingTrophies'])\n    arr = list(reversed(arr))\n    crown_arr = list(reversed(crown_arr))\n    lenarr = len(arr)\n    for count in arr:\n        x_arr.append([len(x_arr) + 1])\n\n    #get best card\n    countdict = {}\n    opponentdict = {}\n    ilose = {}\n    opponentwin={}\n    for battle in r:\n        if (battle['team'][0]['crowns'] > battle['opponent'][0]['crowns']):\n            for card in battle['team'][0]['cards']:\n                if (card['name'] in countdict):\n                    countdict[card['name']] += 1\n                else:\n                    countdict[card['name']] = 1\n\n            for card in battle['opponent'][0]['cards']:\n                if (card['name'] in opponentdict):\n                    opponentdict[card['name']] += 1\n                else:\n                    opponentdict[card['name']] = 1\n        elif (battle['team'][0]['crowns'] < battle['opponent'][0]['crowns']):\n            for card in battle['team'][0]['cards']:\n                if (card['name'] in ilose):\n                    ilose[card['name']] += 1\n                else:\n                    ilose[card['name']] = 1\n\n            for card in battle['opponent'][0]['cards']:\n                if (card['name'] in opponentwin):\n                    opponentwin[card['name']] += 1\n                else:\n                    opponentwin[card['name']] = 1\nexcept:\n    return {}\n\n#prediction for trophy\nsvm_regression_model = svm.SVR(kernel='rbf')\nsvm_regression_model.fit(x_arr,arr)\n\npredict_short = svm_regression_model.predict([[len(arr) * 2]])[0]\npredict_long = svm_regression_model.predict([[len(arr) * 50]])[0]\nstrongest_use = max(countdict.iteritems(), key=operator.itemgetter(1))[0]\nstrongest_opponent = max(opponentwin.iteritems(), key=operator.itemgetter(1))[0]\nweakest_use = max(ilose.iteritems(), key=operator.itemgetter(1))[0]\nweakest_opponent = max(countdict.iteritems(), key=operator.itemgetter(1))[0]\n\n#prediction for crownstotal\nsvm_regression_model.fit(x_arr,crown_arr)\nbattlesahead = 10\ncrown_short = svm_regression_model.predict([[len(arr) * battlesahead]])[0]\nwhile (crown_short < 2.5 or battlesahead > 300):\n    battlesahead += 5\n    crown_short = svm_regression_model.predict([[len(arr) * battlesahead]])[0]\n\npredict_3_crown = battlesahead\n\nx = [[1],[4],[7],[13],[10]]\ny1 = [16, 34, 52, 88, 70]\ny2 = [1, 16, 49, 169, 100]\n\nreturnjson = {\n    'crownshort': predict_short,\n    'crownlong': predict_long,\n    'mestrong': strongest_use,\n    'oppstrong': strongest_opponent,\n    'meweak': weakest_use,\n    'oppweak': weakest_opponent,\n    'next3crown': predict_3_crown - 10\n}\n\n#TODO add scikit learn stuff for clan predictions\n\nprint(json.dumps({returnjson}))\nsys.stdout.flush()\n","sub_path":"pythons/predictor.py","file_name":"predictor.py","file_ext":"py","file_size_in_byte":3259,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"341004632","text":"from django.shortcuts import render, redirect\nfrom django.contrib.auth import authenticate, login\nfrom django.contrib.auth.forms import UserCreationForm\nfrom django.contrib.auth.models import User\nfrom django.urls import reverse_lazy\nfrom django.views import generic\nfrom django.db.models import Q\n\nfrom .models import Transaction\nfrom .forms import TransactionForm, AllowTransactionForm\n\nimport urllib.parse\n\nclass SignUp(generic.CreateView):\n    form_class = UserCreationForm\n    success_url = reverse_lazy('login')\n    template_name = 'signup.html'\n\n\ndef home(request):\n    if not request.user.is_authenticated:\n        return redirect('/accounts/login')\n\n    if 'comment' in request.GET:\n        reqdata = urllib.parse.unquote(request.get_full_path().split('=')[1])\n        print(reqdata)\n        query = (\"SELECT * from register_Transaction WHERE comment LIKE '{}\").format(\n            reqdata)\n        print(query)\n        model_items = Transaction.objects.raw(query)\n    else:\n        model_items = Transaction.objects.filter(\n            Q(sender=request.user)\n            | Q(receiver=request.user)\n        ).order_by(\"-date\")\n    return render(request, \"home.html\", context={\n        'transactions': model_items,\n    })\n\n\ndef new_transaction(request):\n    if not request.user.is_authenticated:\n        return redirect('/')\n    if request.method == 'POST':\n        form = TransactionForm(request.POST)\n        if form.is_valid():\n            if len(User.objects.filter(username=form.cleaned_data['receiver'])) != 1:\n                return render(request, \"error.html\", {'error': 'Wrong receiver.'})\n            return render(request, 'confirm.html', {'transaction': form.cleaned_data})\n    else:\n        form = TransactionForm()\n    return render(request, 'new_transaction.html', {'form': form})\n\n\ndef confirm_transaction(request):\n    if not request.user.is_authenticated:\n        return redirect('/')\n    if request.method == 'POST':\n        # perform transaction\n        recipient = request.POST['receiver']\n        amount = int(request.POST['amount'])\n        comment = request.POST['comment']\n        receiver = User.objects.filter(username=recipient)[0]\n\n        Transaction.objects.create(\n            sender=request.user,\n            receiver=receiver,\n            amount=amount,\n            comment=comment,\n        )\n    return redirect('home')\n\n\ndef allow_transaction(request):\n    # if not request.user.is_staff:\n    #     return redirect('/')\n    if request.method == 'POST':\n        amount = int(request.POST['amount'])\n        sender = User.objects.filter(username=request.POST['sender'])[0]\n        receiver = User.objects.filter(username=request.POST['receiver'])[0]\n        Transaction.objects.filter(\n            sender=sender,\n            receiver=receiver,\n            amount=amount,\n            confirmed=False\n        ).update(confirmed=True)\n        return redirect('home')\n    else:\n        form = AllowTransactionForm()\n    return render(request, 'allow.html', {'form': form, 'transactions': Transaction.objects.all()})\n","sub_path":"Bzpk-ListaPiataDjango/Bzpk/register/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3055,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"614564826","text":"import serial\r\nimport sys\r\nfrom serial.tools import list_ports\r\nimport threading\r\n\r\nclass ThreadJob(threading.Thread):\r\n    def __init__(self,callback,event,interval):\r\n        self.callback = callback\r\n        self.event = event\r\n        self.interval = interval\r\n        super(ThreadJob,self).__init__()\r\n\r\n    def run(self):\r\n        while not self.event.wait(self.interval):\r\n            self.callback()\r\n\r\n\r\n\r\nevent = threading.Event()\r\n\r\nser = serial.Serial()\r\n\r\ndef open(oport, obaudrate):\r\n    ser.port = oport\r\n    ser.baudrate = obaudrate\r\n    try:\r\n        ser.open()\r\n        print(\"Openned port: \" + oport)\r\n    except:\r\n        print(\"Caught error trying to open PORT: \" + oport)\r\n\r\n\r\ndef sendMessage(message):\r\n    mess = \"\\nFrom: \" + ser.port + \" got: \" + message + \"\\n\"\r\n    print(\"Sent: \"+ message)\r\n    ser.write(mess.encode('utf-8'))\r\n\r\ndef close():\r\n    if ser.is_open:\r\n        ser.close()\r\n        print(\"Closed port: \" + ser.port)\r\n    else:\r\n        print(\"No serial ports are openned\")\r\n\r\ndef getData(gport, gbaudrate):\r\n    ser.port = gport\r\n    ser.baudrate = gbaudrate\r\n    try:\r\n        print(ser.readline().encode('utf-8'))\r\n    except:\r\n        print(\"Caught error trying to open PORT: \" + gport)\r\n","sub_path":"serial_connector.py","file_name":"serial_connector.py","file_ext":"py","file_size_in_byte":1230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"444630932","text":"import datetime\nimport dateutil.parser\n\nclass DatabaseConnection:\n    \"\"\"\n    Class to define a simple, generic database interface that can be adapted to\n    different use cases and implementations.\n\n    All methods must be implemented by derived classes.\n    \"\"\"\n\n    def write_datapoint_object_time_series(\n        self,\n        timestamp,\n        object_id,\n        data\n    ):\n        \"\"\"\n        Write a single datapoint for a given timestamp and object ID.\n\n        Timestamp must either be a native Python datetime or a string which is\n        parsable by dateutil.parser.parse(). If timestamp is timezone-naive,\n        timezone is assumed to be UTC.\n\n        Data must be serializable by native Python json methods.\n\n        Parameters:\n            timestamp (datetime or string): Timestamp associated with data\n            object_id (string): Object ID associated with data\n            data (dict): Data to be written\n        \"\"\"\n        if not self.time_series_database or not self.object_database:\n            raise ValueError('Writing datapoint by timestamp and object ID only enabled for object time series databases')\n        timestamp = self._python_datetime_utc(timestamp)\n        return_value = self._write_datapoint_object_time_series(\n            timestamp,\n            object_id,\n            data\n        )\n        return return_value\n\n    def write_data_object_time_series(\n        self,\n        datapoints\n    ):\n        \"\"\"\n        Write multiple datapoints with timestamps and object IDs.\n\n        Input should be a list of dicts. Each dict must contain a 'timestamp'\n        element and an 'object_id' element.\n\n        Timestamps must either be native Python datetimes or strings which are\n        parsable by dateutil.parser.parse(). If timestamp is timezone-naive,\n        timezone is assumed to be UTC.\n\n        Data must be serializable by native Python json methods.\n\n        Parameters:\n            datapoints (list of dict): Datapoints to be written\n        \"\"\"\n        if not self.time_series_database or not self.object_database:\n            raise ValueError('Writing datapoint by timestamp and object ID only enabled for object time series databases')\n        parsed_datapoints = []\n        for datapoint in datapoints:\n            if 'timestamp' not in datapoint.keys():\n                raise ValueError('Each datapoint must contain a timestamp')\n            if 'object_id' not in datapoint.keys():\n                raise ValueError('Each datapoint must contain an object ID')\n            datapoint['timestamp'] = self._python_datetime_utc(datapoint['timestamp'])\n            parsed_datapoints.append(datapoint)\n        return_value = self._write_data_object_time_series(\n            parsed_datapoints\n        )\n        return return_value\n\n    def fetch_data_object_time_series(\n        self,\n        start_time = None,\n        end_time = None,\n        object_ids = None\n    ):\n        \"\"\"\n        Fetch data for a given timespan and set of object IDs.\n\n        If specified, start time and end time must either be native Python\n        datetimes or strings which are parsable by dateutil.parser.parse(). If\n        they are timezone-naive, they are assumed to be UTC.\n\n        If start time is not specified, all data is returned back to earliest\n        data in database. If end time is not specified, all data is returned up\n        to most recent data in database. If object IDs are not specified, data\n        is returned for all objects.\n\n        Returns a list of dictionaries, one for each datapoint.\n\n        Parameters:\n            start_time (datetime or string): Beginning of timespan (default: None)\n            end_time (datetime or string): End of timespan (default: None)\n            object_ids (list of strings): Object IDs (default: None)\n\n        Returns:\n            (list of dict): All data associated with specified time span and object IDs\n        \"\"\"\n        if not self.time_series_database or not self.object_database:\n            raise ValueError('Fetching data by time interval and/or object ID only enabled for object time series databases')\n        if start_time is not None:\n            start_time = self._python_datetime_utc(start_time)\n        if end_time is not None:\n            end_time = self._python_datetime_utc(end_time)\n        data = self._fetch_data_object_time_series(\n            start_time,\n            end_time,\n            object_ids\n        )\n        return data\n\n    def delete_data_object_time_series(\n        self,\n        start_time,\n        end_time,\n        object_ids\n    ):\n        \"\"\"\n        Delete data for a given timespan and set of object IDs.\n\n        Start time, end time, and object IDs must all be specified.\n\n        Start time and end time must either be native Python datetimes or\n        strings which are parsable by dateutil.parser.parse(). If they are\n        timezone-naive, they are assumed to be UTC.\n\n        Parameters:\n            start_time (datetime or string): Beginning of timespan\n            end_time (datetime or string): End of timespan\n            object_ids (list of strings): Object IDs\n        \"\"\"\n        if not self.time_series_database or not self.object_database:\n            raise ValueError('Deleting data by time interval and/or object ID only enabled for object time series databases')\n        if start_time is None:\n            raise ValueError('Start time must be specified for delete data operation')\n        if end_time is None:\n            raise ValueError('End time must be specified for delete data operation')\n        if object_ids is None:\n            raise ValueError('Object IDs must be specified for delete data operation')\n        start_time = self._python_datetime_utc(start_time)\n        end_time = self._python_datetime_utc(end_time)\n        self._delete_data_object_time_series(\n            start_time,\n            end_time,\n            object_ids\n        )\n\n    def to_data_queue(\n        self,\n        start_time = None,\n        end_time = None,\n        object_ids = None\n    ):\n        \"\"\"\n        Create an iterable which returns datapoints from the database in time order.\n\n        If specified, start time and end time must either be native Python\n        datetimes or strings which are parsable by dateutil.parser.parse(). If\n        they are timezone-naive, they are assumed to be UTC.\n\n        If start time is not specified, all data is returned back to earliest\n        data in database. If end time is not specified, all data is returned up\n        to most recent data in database. If object IDs are not specified, data\n        is returned for all objects.\n\n        Returns a DataQueue object which contains the requested data.\n\n        Parameters:\n            start_time (datetime or string): Beginning of timespan (default: None)\n            end_time (datetime or string): End of timespan (default: None)\n            object_ids (list of strings): Object IDs (default: None)\n\n        Returns:\n            (DataQueue): Iterator which contains the requested data\n        \"\"\"\n        data = self.fetch_data_object_time_series(\n            start_time,\n            end_time,\n            object_ids\n        )\n        data_queue = DataQueue(\n            data = data\n        )\n        return data_queue\n\n    def _python_datetime_utc(self, timestamp):\n        try:\n            if timestamp.tzinfo is None:\n                datetime_utc = timestamp.replace(tzinfo = datetime.timezone.utc)\n            else:\n                datetime_utc = timestamp.astimezone(tz=datetime.timezone.utc)\n        except:\n            datetime_parsed = dateutil.parser.parse(timestamp)\n            if datetime_parsed.tzinfo is None:\n                datetime_utc = datetime_parsed.replace(tzinfo = datetime.timezone.utc)\n            else:\n                datetime_utc = datetime_parsed.astimezone(tz=datetime.timezone.utc)\n        return datetime_utc\n\n    def _write_datapoint_object_time_series(\n        self,\n        timestamp,\n        object_id,\n        data\n    ):\n        raise NotImplementedError('Specifics of communication with database must be implemented in child class')\n\n    def _write_data_object_time_series(\n        self,\n        datapoints\n    ):\n        raise NotImplementedError('Specifics of communication with database must be implemented in child class')\n\n    def _fetch_data_object_time_series(\n        self,\n        start_time,\n        end_time,\n        object_ids\n    ):\n        raise NotImplementedError('Specifics of communication with database must be implemented in child class')\n\n    def _delete_data_object_time_series(\n        self,\n        start_time,\n        end_time,\n        object_ids\n    ):\n        raise NotImplementedError('Specifics of communication with database must be implemented in child class')\n\nclass DataQueue:\n    \"\"\"\n    Class to define an iterable which returns datapoints in time order.\n    \"\"\"\n    def __init__(\n        self,\n        data\n        ):\n        \"\"\"\n        Constructor for DataQueue.\n\n        Data must be in the format returned by\n        DatabaseConnection.fetch_data_object_time_series() (i.e., simple list of\n        dicts containing the datapoints; every datapoint must contain a\n        'timestamp' field in timezone-aware native Python datetime format).\n\n        Parameters:\n            data (list of dict): Data to populate the queue\n        \"\"\"\n        data.sort(key = lambda datapoint: datapoint['timestamp'])\n        self.data = data\n        self.num_datapoints = len(data)\n        self.next_data_pointer = 0\n\n    def __iter__(self):\n        return self\n\n    def __next__(self):\n        \"\"\"\n        Fetch datapoint associated with the next timestamp.\n\n        Data will be fetched in time order.\n\n        Returns:\n            (dict): Data associated with the next timestamp\n        \"\"\"\n        if self.next_data_pointer >= self.num_datapoints:\n            raise StopIteration()\n        else:\n            datapoint = self.data[self.next_data_pointer]\n            self.next_data_pointer += 1\n            return datapoint\n","sub_path":"database_connection/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":10030,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"141560631","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n\nfrom datetime import datetime\n\nimport tushare as ts\n\nimport vnpy.trader.constant as const\nfrom vnpy.trader.object import BarData\nfrom vnpy.trader.database import database_manager\n\nif __name__ == \"__main__\":\n\n    bars = []\n    data = ts.get_hist_data(code='002594', start='2011-07-01', end='2017-08-28')\n    for row in data.iterrows():\n        date = row[0]\n        data = row[1]\n\n        bar = BarData(\n            gateway_name='test',\n            symbol='002594',\n            exchange=const.Exchange.SZSE,\n            datetime=datetime.strptime(date, '%Y-%m-%d'),\n            interval=const.Interval.DAILY,\n            volume=data['volume'],\n        )\n        # open_interest: float = 0\n        bar.open_price = data['open']\n        bar.high_price = data['high']\n        bar.low_price = data['low']\n        bar.close_price = data['close']\n        bars.append(bar)\n\n    database_manager.save_bar_data(bars)\n","sub_path":"tests/basic/data_prepare.py","file_name":"data_prepare.py","file_ext":"py","file_size_in_byte":954,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"586665657","text":"# -*- coding: utf-8 -*-\nfrom zvt.domain import FinanceGrowthAbility\nfrom zvt.recorders.emquantapi.finance.base_china_stock_finance_index_new import EmBaseChinaStockFinanceRecorder2\nfrom zvt.utils.utils import add_func_to_value, first_item_to_float\n\nfinancial_growth_ability_map = {\n    # 总资产同比增长率（%）\n    \"asset_yoy\":\"YOYASSET\",\n    # 营业总收入同比增长率（%）\n    \"total_op_income_growth_yoy\":\"YOYGR\",\n    # 营业利润同比增长率（%）\n    \"op_yoy\":\"YOYOP\" ,\n    # 利润总额同比增长率（%）\n    \"ebt_yoy\": \"YOYEBT\",\n    # 净利润同比增长率（%）\n    \"ni_yoy\": \"YOYNI\",\n    # 归属母公司股东的净利润同比增长率（%）\n    \"inc_net_profit_shareholders_yoy\": \"YOYPNI\",\n    # 归属母公司股东的净利润同比增长率(扣非)（%）\n    \"deducted_net_profit_growth_yoy\": \"YOYPNIDEDUCTED\",\n    # 基本每股收益同比增长率（%）\n    \"basic_eps_you\": \"YOYEPSBASIC\",\n    # 稀释每股收益同比增长率（%）\n    \"diluted_eps_yoy\": \"YOYEPSDILUTED\",\n    # 净资产收益率同比增长率(摊薄)\n    \"roe_liluted_yoy\": \"YOYROELILUTED\",\n    # 经营活动产生的现金流量净额同比增长率（%）\n    \"cfo_yoy\": \"YOYCFO\",\n    # 每股经营活动中产生的现金流量净额同比增长率（%）\n    \"cfo_ps_yoy\": \"YOYCFOPS\",\n    # 资产总计相对年初增长率（%）\n    \"asset_relative\": \"ASSETRELATIVE\",\n    # 归属母公司股东的权益相对年初增长率（%）\n    \"equity_relative\": \"EQUITYRELATIVE\",\n    # 每股净资产相对年初增长率（%）\n    \"bps_relative\": \"BPSRELATIVE\",\n    # 营业收入环比增长率（%）\n    'op_income_growth_qoq': 'RTROR',\n    # 归属净利润滚动环比增长（%）\n    'net_profit_growth_qoq': 'RTRNI',\n    # 归属母公司股东的净利润环比增长率（扣非）（%）\n    'deducted_net_profit_growth_qoq': 'RTRPNIDEDUCTED',\n}\n\nfinancial_growth_ability_map2 = {\n    \"op_profit_growth_yoy\": \"op_yoy\",\n    \"net_profit_growth_yoy\": \"ni_yoy\",\n    \"total_profit_growth_yoy\": \"ebt_yoy\",\n    \"inc_net_profit_shareholders_deducted_yoy\": \"deducted_net_profit_growth_yoy\",\n    \"net_op_cash_flows_yoy\": \"cfo_yoy\",\n    \"net_operate_cash_flow_ps_yoy\": \"cfo_ps_yoy\",\n    \"total_assets_relative_of_year\": \"asset_relative\",\n    \"bps_relativeof_year\": \"bps_relative\",\n    \"equity_relative_of_year\": \"equity_relative\",\n}\n\nadd_func_to_value(financial_growth_ability_map, first_item_to_float)\n\n\nclass ChinaStockFinanceGrowthAbilityRecorder(EmBaseChinaStockFinanceRecorder2):\n    \"\"\"\n    财务指标-成长能力\n    \"\"\"\n    data_schema = FinanceGrowthAbility\n\n    finance_report_type = 'FinanceGrowthAbility'\n\n    data_type = 5\n\n    def get_data_map(self):\n        return financial_growth_ability_map\n\n    def get_data_map2(self):\n        return financial_growth_ability_map2\n\n\n\n__all__ = ['ChinaStockFinanceGrowthAbilityRecorder']\n\nif __name__ == '__main__':\n    # init_log('income_statement.log')\n    recorder = ChinaStockFinanceGrowthAbilityRecorder(sleeping_time=0.1)\n    recorder.run()\n","sub_path":"zvt/recorders/emquantapi/finance/china_stock_finance_growthability_new.py","file_name":"china_stock_finance_growthability_new.py","file_ext":"py","file_size_in_byte":3037,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"103926805","text":"import os\nimport numpy as np\nimport pandas as pd\nimport h5py\nimport json\nimport torch\nimport torch.nn.functional as F\n\nfrom fairseq.data import FairseqDataset, data_utils\nfrom torch.utils.data.dataloader import default_collate\n\nfrom PIL import Image\n\n\ndef split_file(split):\n    return os.path.join('splits', f'karpathy_{split}_images.txt')\n\n\ndef read_split_image_ids_and_paths(split):\n    split_df = pd.read_csv(split_file(split), sep=' ', header=None)\n    return split_df.iloc[:, 1].to_numpy(), split_df.iloc[:, 0].to_numpy()\n\n\ndef read_split_image_ids(split):\n    return read_split_image_ids_and_paths(split)[0]\n\n\ndef read_image_ids(file, source_only=False):\n    with open(file, 'r') as f:\n        image_ids = [int(line) for line in f]\n    f.close()\n    if source_only:\n        image_ids = list(set(image_ids))\n    return image_ids\n\n\ndef read_image_metadata(file):\n    df = pd.read_csv(file)\n    md = {}\n\n    for img_id, img_h, img_w, num_boxes in zip(df['image_id'], df['image_h'], df['image_w'], df['num_boxes']):\n        md[img_id] = {\n            'image_h': np.float32(img_h),\n            'image_w': np.float32(img_w),\n            'num_boxes': num_boxes\n        }\n\n    return md\n\n\nclass SCCaptionDataset(torch.utils.data.Dataset):\n    def __init__(self, caption_file, image_ids):\n        self.caption_file = caption_file\n        self.image_ids = image_ids\n        self.image_id_to_captions = self.load_caption_by_ids()\n\n    def load_caption_by_ids(self):\n        with open(self.caption_file, 'r') as f:\n            dataset = json.load(f)\n        f.close()\n        new_dataset = {}\n        for key, value in dataset.items():\n            new_dataset[int(key)] = value\n        return new_dataset\n        # image_id_to_captions = {}\n        # for caption_info in dataset:\n        #     image_id = caption_info['image_id']\n        #     captions = caption_info['captions']\n        #     image_id_to_captions[image_id] = captions\n        #\n        # assert len(image_id_to_captions) == len(self.image_ids) and \\\n        #     len(set(self.image_ids) - set(list(image_id_to_captions.keys()))) == 0\n        #\n        # return image_id_to_captions\n\n    def __len__(self):\n        return len(self.image_ids)\n\n    def __getitem__(self, idx):\n        return self.image_id_to_captions.get(self.image_ids[idx])\n\n    def size(self):\n        pass\n\n    def collater(self, samples):\n        return samples\n\n\nclass SGDataset(torch.utils.data.Dataset):\n    def __init__(self, sg_file, image_ids):\n        self.sg_file = sg_file\n        self.image_ids = image_ids\n\n    def __len__(self):\n        return len(self.image_ids)\n\n    def __getitem__(self, idx):\n        return self.read_data(self.image_ids[idx])\n\n    def size(self):\n        pass\n\n    def read_data(self, image_id):\n        h5py_dataset = h5py.File(self.sg_file, 'r', libver='latest')\n        rel = h5py_dataset[str(image_id)+'-obj-rel'][()]\n        obj_attr = h5py_dataset[str(image_id)+'-obj-attr'][()]\n        obj = obj_attr[:, 1:4]\n        attr = obj_attr[:, 4:]\n        return torch.as_tensor(rel.astype(np.int64)), \\\n            torch.as_tensor(obj.astype(np.int64)), \\\n            torch.as_tensor(attr.astype(np.int64))\n\n    def collater(self, samples):\n        rel_lens, obj_lens, attr_lens = [], [], []\n        for rel, obj, attr in samples:\n            assert rel.shape[1] == 3 and obj.shape[1] == 3 and attr.shape[1] == 3\n            rel_lens.append(rel.shape[0])\n            obj_lens.append(obj.shape[0])\n            attr_lens.append(attr.shape[0])\n        max_rel_len, max_obj_len, max_attr_len, = max(rel_lens), max(obj_lens), max(attr_lens)\n\n        padded_rels, padded_objs, padded_attrs = [], [], []\n        for (rel, obj, attr), rel_len, obj_len, attr_len in zip(samples, rel_lens, obj_lens, attr_lens):\n            assert len(rel.shape) == 2 and len(obj.shape) == 2 and len(attr.shape) == 2\n            padded_rel = F.pad(rel, pad=[0, 0, 0, max_rel_len - rel_len], mode='constant', value=0.0)\n            padded_obj = F.pad(obj, pad=[0, 0, 0, max_obj_len - obj_len], mode='constant', value=0.0)\n            padded_attr = F.pad(attr, pad=[0, 0, 0, max_attr_len - attr_len], mode='constant', value=0.0)\n\n            padded_rels.append(padded_rel)\n            padded_objs.append(padded_obj)\n            padded_attrs.append(padded_attr)\n\n        return default_collate(padded_rels), default_collate(padded_objs), default_collate(padded_attrs)\n\n\nclass ImageDataset(torch.utils.data.Dataset):\n    def __init__(self, image_ids, image_paths, transform=lambda x: x):\n        self.image_ids = image_ids\n        self.image_paths = image_paths\n        self.transform = transform\n\n    def __len__(self):\n        return len(self.image_ids)\n\n    def __getitem__(self, idx):\n        image_path = self.image_paths[idx]\n        with Image.open(image_path).convert('RGB') as img:\n            return self.transform(img), self.image_ids[idx]\n\n\nclass FeaturesDataset(FairseqDataset):\n    def __init__(self, features_file, image_ids, num_objects, no_id=True):\n        self.features_file = features_file\n        self.image_ids = image_ids\n        self.num_objects = num_objects\n        self.no_id = no_id\n\n    def __getitem__(self, index):\n        return self.read_data(self.image_ids[index])\n\n    def __len__(self):\n        return len(self.image_ids)\n\n    def num_tokens(self, index):\n        return self.num_objects[index]\n\n    def size(self, index):\n        return self.num_objects[index]\n\n    @property\n    def sizes(self):\n        return self.num_objects\n\n    def read_data(self, image_id):\n        raise NotImplementedError\n\n    def collater(self, samples):\n        num_objects = [features.shape[0] for features, _ in samples]\n        max_objects = max(num_objects)\n\n        feature_samples_padded = []\n        location_samples_padded = []\n\n        for (features, locations), n in zip(samples, num_objects):\n            features_padded = F.pad(features, pad=[0, 0, 0, max_objects-n], mode='constant', value=0.0)\n            locations_padded = F.pad(locations, pad=[0, 0, 0, max_objects-n], mode='constant', value=0.0)\n            feature_samples_padded.append(features_padded)\n            location_samples_padded.append(locations_padded)\n\n        return default_collate(feature_samples_padded), default_collate(location_samples_padded)\n\n\nclass GridFeaturesDataset(FeaturesDataset):\n    def __init__(self, features_file, image_ids, grid_shape=(8, 8), no_id=True):\n        super().__init__(features_file=features_file,\n                         image_ids=image_ids,\n                         num_objects=np.ones(len(image_ids), dtype=np.int) * np.prod(grid_shape), no_id=no_id)\n\n        self.grid_shape = grid_shape\n        self.locations = self.tile_locations(grid_shape)\n\n        # self.h5py_dataset = h5py.File(features_file, 'r')\n\n    def read_data(self, image_id):\n\n        # features_file = os.path.join(self.features_dir, f'{image_id}.npy')\n        # features = np.load(features_file)\n        h5py_dataset = h5py.File(self.features_file, 'r', libver='latest')\n\n        features = h5py_dataset[str(image_id)][()]\n        if self.no_id:\n            return torch.as_tensor(features), self.locations\n        else:\n            return torch.as_tensor(features), self.locations, image_id\n\n    @staticmethod\n    def tile_locations(grid_shape):\n        num_tiles = np.prod(grid_shape)\n        rel_tile_w = 1. / grid_shape[1]\n        rel_tile_h = 1. / grid_shape[0]\n        rel_tile_area = 1. / num_tiles\n\n        rel_tile_locations = np.zeros(shape=(grid_shape[0], grid_shape[1], 5), dtype=np.float32)\n\n        for i in range(grid_shape[0]):\n            for j in range(grid_shape[1]):\n                rel_tile_locations[i, j] = np.array([\n                    j * rel_tile_w,\n                    i * rel_tile_h,\n                    (j+1) * rel_tile_w,\n                    (i+1) * rel_tile_h,\n                    rel_tile_area\n                ], dtype=np.float32)\n\n        return torch.as_tensor(rel_tile_locations).view(num_tiles, 5)\n\n\nclass ObjectFeaturesDataset(FeaturesDataset):\n    def __init__(self, features_file, image_ids, image_metadata, no_id=True):\n        super().__init__(features_file=features_file,\n                         image_ids=image_ids,\n                         num_objects=np.array([image_metadata[image_id]['num_boxes'] for image_id in image_ids]),\n                         no_id=no_id)\n\n        self.image_metadata = image_metadata\n\n    def read_data(self, image_id):\n        # features_file = os.path.join(self.features_dir, f'{image_id}.npy')\n        # features = np.load(features_file)\n        #\n        # boxes_file = os.path.join(self.features_dir, f'{image_id}-boxes.npy')\n        # boxes = np.load(boxes_file)\n        h5py_dataset = h5py.File(self.features_file, 'r', libver='latest')\n\n        features = h5py_dataset[str(image_id)][()]\n        boxes = h5py_dataset[str(image_id)+'-boxes'][()]\n\n        # Normalize box coordinates\n        boxes[:, [0, 2]] /= self.image_metadata[image_id]['image_w']\n        boxes[:, [1, 3]] /= self.image_metadata[image_id]['image_h']\n\n        # Normalized box areas\n        areas = (boxes[:, 2] - boxes[:, 0]) * \\\n                (boxes[:, 3] - boxes[:, 1])\n\n        # if self.no_id:\n        #     return torch.as_tensor(features), torch.as_tensor(np.c_[boxes, areas])\n        # else:\n        return torch.as_tensor(features), torch.as_tensor(np.c_[boxes, areas]), image_id\n\n\nclass ImageCaptionDataset(FairseqDataset):\n    def __init__(self, img_ds, cap_ds, sg_ds, cap_dict, shuffle=False):\n        self.img_ds = img_ds\n        self.cap_ds = cap_ds\n        self.sg_ds = sg_ds\n        self.cap_dict = cap_dict\n        self.shuffle = shuffle\n\n    def __getitem__(self, index):\n\n        def get_cap():\n            if self.cap_ds is not None:\n                target = self.cap_ds[index]\n                dat = {\n                    'target': target,\n                    'caption': target\n                }\n            else:\n                dat = {}\n            return dat\n\n        def get_img():\n            object_feature, object_location, image_id = self.img_ds[index]\n            dat = {\n                'id': index,\n                'image_id': image_id,\n                'object_feature': object_feature,\n                'object_location': object_location,\n            }\n            return dat\n\n        def get_sg():\n            if self.sg_ds is not None:\n                rel, obj, attr = self.sg_ds[index]\n                dat = {\n                    \"relation\": rel,\n                    \"object\": obj,\n                    \"attribute\": attr\n                }\n            else:\n                dat = {}\n            return dat\n\n        data = dict()\n        cap = get_cap()\n        img = get_img()\n        sg = get_sg()\n        data.update(cap)\n        data.update(img)\n        data.update(sg)\n        return data\n\n    def __len__(self):\n        if self.cap_ds is not None:\n            return len(self.cap_ds)\n        else:\n            return len(self.img_ds)\n\n    def num_tokens(self, index):\n        # if self.cap_ds is not None:\n        #     return self.size(index)[1]\n        # else:\n        return self.img_ds.sizes[index]  # self.size(index)\n\n    def size(self, index):\n        # number of image feature vectors, number of tokens in caption\n        return self.img_ds.sizes[index]  # self.img_ds.sizes[index]\n\n    def ordered_indices(self):\n        if self.shuffle:\n            indices = np.random.permutation(len(self))\n        else:\n            indices = np.arange(len(self))\n\n        # Inspired by LanguagePairDataset.ordered_indices\n        # if self.cap_ds is not None:\n        #     indices = indices[np.argsort(self.cap_ds.sizes[indices], kind='mergesort')]\n\n        return indices[np.argsort(self.img_ds.sizes[indices], kind='mergesort')]\n\n    def collater(self, samples):\n        indices = []\n        image_ids = []\n\n        object_feature_samples = []\n        object_location_samples = []\n        object_lengths = []\n\n        relation_samples = []\n        object_samples = []\n        attribute_samples = []\n        relation_lengths = []\n\n        target_samples = []\n        target_ntokens = 0\n\n        caption_samples, caption_lengths = [], []\n\n        for sample in samples:\n            index = sample['id']\n            indices.append(index)\n\n            if sample.get('image_id') is not None:\n                image_ids.append(sample['image_id'])\n\n            object_feature_samples.append(sample['object_feature'])\n            object_location_samples.append(sample['object_location'])\n            object_lengths.append(self.img_ds.sizes[index])\n\n            if sample.get('target') is not None:\n                target_samples.append(sample['target'])\n\n            if sample.get('relation') is not None and \\\n                    sample.get('object') is not None and \\\n                    sample.get('attribute') is not None:\n                relation_samples.append(sample['relation'])\n                relation_length = sample['relation'].sum(-1).gt(0.0).long().sum().item()\n                relation_lengths.append(relation_length)\n                object_samples.append(sample['object'])\n                attribute_samples.append(sample['attribute'])\n\n            # if sample.get('caption') is not None:\n            #     caption_samples.append(sample['caption'])\n            #     caption_lengths.append(self.cap_ds.sizes[index])\n\n        num_sentences = len(samples)\n\n        # FIXME: workaround for edge case in parallel processing\n        # (framework passes empty samples list\n        # to collater under certain conditions)\n        if num_sentences == 0:\n            return None\n\n        indices = torch.tensor(indices, dtype=torch.long)\n        image_ids = torch.tensor(image_ids, dtype=torch.long)\n\n        object_feature_batch, object_location_batch = \\\n            self.img_ds.collater(list(zip(object_feature_samples, object_location_samples)))\n\n        if len(relation_samples) > 0 and len(object_samples) > 0 \\\n                and len(attribute_samples) > 0 and self.sg_ds is not None:\n            relation_batch, object_batch, attribute_batch = \\\n                self.sg_ds.collater(list(zip(relation_samples, object_samples, attribute_samples)))\n        else:\n            relation_batch, object_batch, attribute_batch = None, None, None\n\n        # if self.cap_ds is not None:\n        #     caption_tokens = data_utils.collate_tokens(\n        #         caption_samples, pad_idx=self.cap_dict.pad(), eos_idx=self.cap_dict.eos(), move_eos_to_beginning=False)\n        # else:\n        #     caption_tokens = None\n\n        caption_lengths = torch.tensor(caption_lengths, dtype=torch.long)\n\n        # if self.cap_ds is not None:\n        #     target_batch = data_utils.collate_tokens(\n        #     target_samples, pad_idx=self.cap_dict.pad(), eos_idx=self.cap_dict.eos(), move_eos_to_beginning=False)\n        #     rotate_batch = data_utils.collate_tokens(\n        #     target_samples, pad_idx=self.cap_dict.pad(), eos_idx=self.cap_dict.eos(), move_eos_to_beginning=True)\n        # else:\n        #     target_batch = None\n        #     rotate_batch = None\n\n        object_lengths = torch.tensor(object_lengths, dtype=torch.long)\n        relation_lengths = torch.tensor(relation_lengths, dtype=torch.long)\n\n        target_batch = target_samples\n\n        return {\n            'id': image_ids, # indices,  # image_ids,\n            'net_input': {\n                'object_features': object_feature_batch,\n                'object_locations': object_location_batch,\n                'object_lengths': object_lengths,\n                'relations': relation_batch,\n                'relation_lengths': relation_lengths,\n                'objects': object_batch,\n                'attributes': attribute_batch,\n                # 'caption_tokens': caption_tokens,\n                # 'caption_lengths': caption_lengths,\n                'str_targets': target_batch,\n            },\n            'str_targets': target_batch,\n            'ntokens': target_ntokens,\n            'nsentences': num_sentences,\n        }\n","sub_path":"src/data_scst.py","file_name":"data_scst.py","file_ext":"py","file_size_in_byte":16024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"150649544","text":"from django.shortcuts import render\nfrom django.http import HttpResponse\nfrom listpage.multilist import *\nfrom listpage.multilistxtract import *\nfrom listpage.models import mvolFolder\nimport html\nimport json\nimport datetime\nimport time\nimport pytz\n\n# Create your views here.\n\ndef homepage(request):\n\tcontext = {}\n\treturn render(request, 'listpage/homepage.html', context)\n\ndef errpage(request):\n    errors = [\"This thing was bad.\",\n              \"This other thing was even worse.\",\n              \"Whoa, slow down now, what is this.\"]\n    context = {'errarray' : errors}\n    return render(request, 'listpage/errpage.html', context)\n\ndef listpage(request):\n\tcontext = {'allists' : exmultilist.lists}\n\treturn render(request, 'listpage/listpage.html', context)\n\ndef hierarch(request, mvolfolder_name):\n\n\tdef chxistnrecent(currtime, comparetime):\n\t\t# checks if two times exist and compares them\n\t\tcheckd = html.unescape(\"&#10004;\")\n\t\texd = html.unescape(\"&#10006;\")\n\t\tif comparetime:\n\t\t\tif comparetime < currtime:\n\t\t\t\treturn exd\n\t\t\telse:\n\t\t\t\treturn checkd\n\t\telse:\n\t\t\treturn \"none\"\n\n\tdef chxistnrecentx(status):\n\t\t# checks if two times exist and compares them\n\t\tcheckd = html.unescape(\"&#10004;\")\n\t\texd = html.unescape(\"&#10006;\")\n\t\tif status == \"in-sync\":\n\t\t\treturn checkd\n\t\telif status == \"out-of-sync\":\n\t\t\treturn exd\n\t\telse:\n\t\t\treturn \"none\"\n\n\tdef localize(child):\n\t\ttimezone = pytz.timezone(\"America/Chicago\")\t\n\t\ttry:\n\t\t\tchild[1]['owncloud'][1] = timezone.localize(datetime.datetime.fromtimestamp(child[1]['owncloud'][1]))\n\t\texcept Exception:\n\t\t\tpass\n\t\ttry:\n\t\t\tchild[1]['development'][1] = timezone.localize(datetime.datetime.fromtimestamp(child[1]['development'][1]))\n\t\texcept Exception:\n\t\t\tpass\n\t\ttry:\n\t\t\tchild[1]['production'][1] = timezone.localize(datetime.datetime.fromtimestamp(child[1]['production'][1]))\n\t\texcept Exception:\n\t\t\tpass\n\n\n\tcurrname = mvolfolder_name\n\tnamesections = mvolfolder_name.split(\"-\")\n\tfinalchunk = namesections.pop()\n\n\tparentlist = []\n\tnamehold = \"\"\n\tfirst = True\n\twith open('listpage/snar.json', \"r\") as jsonfile:\n\t\tfjson = json.load(jsonfile)\n\n\tcurrdir = fjson\n\n\tfor subsect in namesections:\n\t\t\t\tif first:\n\t\t\t\t\tnamehold = subsect\n\t\t\t\t\tfirst = False\n\t\t\t\telse:\n\t\t\t\t\tnamehold = namehold + \"-\" + subsect\n\t\t\t\tparentlist.append((namehold, subsect))\n\t\t\t\ttry:\n\t\t\t\t\tcurrdir = currdir[namehold]['children']\n\t\t\t\texcept Exception as e:\n\t\t\t\t\tbreak\n\tif mvolfolder_name == \"mvol\":\n\t\t\tprechildlist = currdir['mvol']['children']\n\telse:\n\t\tnamehold = namehold + \"-\" + finalchunk\n\t\ttry:\n\t\t\tprechildlist = currdir[namehold]['children']\n\t\texcept Exception as e:\n\t\t\tprechildlist = {}\n\tchildlist = []\n\ti = 0\n\tchildnames = prechildlist.keys()\n\tcheck = html.unescape(\"&#10004;\")\n\tex = html.unescape(\"&#10006;\")\n\tfor child in prechildlist.items():\n\t\tvalid = ex\n\t\tprosync = \"none\"\n\t\tdevsync = \"none\"\n\t\tlocalize(child)\t\t\n\t\tcurrtime = child[1]['owncloud'][1]\n\t\tif child[1]['development'][0] == \"in-sync\":\n\t\t\tdevsync = check\n\t\telif child[1]['development'][0] == \"out-of-sync\":\n\t\t\tdevsync = ex\n\t\tif child[1]['production'][0] == \"in-sync\":\n\t\t\tprosync = check\n\t\telif child[1]['production'][0] == \"out-of-sync\":\n\t\t\tprosync = ex\n\t\tif child[1]['owncloud'][0] == \"valid\":\n\t\t\tvalid = check\n\t\tchildlist.append((list(childnames)[i], child, valid, devsync, prosync))\n\t\ti += 1\n\n\toneupfrombottom = False\n\tif childlist:\n\t\tif not 'children' in childlist[0][1][1]:\n\t\t\toneupfrombottom = True\n\n\tcontext = {'name' : (currname, finalchunk),\n\t\t\t\t\t\t'parents' : parentlist,\n\t\t\t\t\t\t'children' : childlist,\n\t\t\t\t\t\t'oneupfrombottom' : oneupfrombottom,\n\t}\n\t\n\treturn render(request, 'listpage/mvolpagejson.html', context)","sub_path":"reportapp/reperrapp/listpage/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":3590,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"417223552","text":"class Location:\n    London = \"lcy\"\n    Cardiff = \"cwl\"\n    Leicester = \"ema\"\n    Norwich = \"nwi\"\n    Newcastle = \"ncl\"\n    Manchester = \"man\"\n    Edinburgh = \"edi\"\n    Southampton = \"sou\"\n    Exeter = \"ext\"\n    Birmingham = \"bhx\"\n    Leeds = \"lba\"\n    LOCATION_DICT = {\n        \"lcy\": \"London\",\n        \"cwl\": \"Cardiff\",\n        \"ema\": \"Leicester\",\n        \"nwi\": \"Norwich\",\n        \"ncl\": \"Newcastle\",\n        \"man\": \"Manchester\",\n        \"edi\": \"Edinburgh\",\n        \"sou\": \"Southampton\",\n        \"ext\": \"Exeter\",\n        \"bhx\": \"Birmingham\",\n        \"lba\": \"Leeds\",\n    }\n\nif __name__ == \"__main__\":\n    pass","sub_path":"airplaneCrawler/spiders/location.py","file_name":"location.py","file_ext":"py","file_size_in_byte":610,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"531910365","text":"# -*- coding: utf-8 -*-\n\n# @File:               python_gil.py\n# @Author:             MaiXiaochai\n# @Date:               2019-02-14 21:13:38\n# @Last Modified by:   MaiXiaochai\n# @Last Modified time: 2019-02-14 21:40:03\n# @GitHub:             https://github.com/MaiXiaochai\n\n# import dis\n\n\n# def add(a):\n#     a = a + 1\n#     return a\n\n\n# print(dis.dis(add))\n\n\"\"\"\nout:\n 14           0 LOAD_FAST                0 (a)\n              2 LOAD_CONST               1 (1)\n              4 BINARY_ADD\n              6 STORE_FAST               0 (a)\n\n 15           8 LOAD_FAST                0 (a)\n             10 RETURN_VALUE\nNone\n\"\"\"\n# ---------------------------------------------------------------------------\nimport threading\n\n\ntotal = 0\n\n\ndef add():\n    global total\n    for i in range(1000000):\n        total += 1\n\n\ndef desc():\n    global total\n    for i in range(1000000):\n        total -= 1\n\n\nthread1 = threading.Thread(target=add)\nthread2 = threading.Thread(target=desc)\n\nthread1.start()\nthread2.start()\n\nthread1.join()\nthread2.join()\nprint(total)\n\n\"\"\"\n222806\n587642\n-221358\n结果不稳定，说明GIL会释放的\n\"\"\"\n# -------------------------------------------------------------------------\n","sub_path":"expert_python/src/python_gil.py","file_name":"python_gil.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"403881611","text":"\"\"\"\r\nEmulated Bellman-Ford Algorithm\r\nPoOya Khandel, Mohammad Hossein Tavakoli Bina\r\n\"\"\"\r\n\r\nfrom BellmanFord import BFA\r\nimport msvcrt\r\nimport re\r\nimport timeit\r\nfrom time import sleep\r\n\r\n\r\nelapsedTime = 0\r\nstart = 0\r\nwhichPort = {}\r\nadrToName = {}\r\nrouterCount = 0\r\nhit = None\r\nrouterName = input(\"Welcome to Emulated Bellman-Ford Algorithm\\n\"\r\n                   \"Which router am I?\\n\")\r\n\r\nwith open(\"which_port.txt\") as whichRouter:\r\n    for lines in whichRouter:\r\n        whichPort[lines[0]] = int(lines[2:6])\r\n        adrToName[int(lines[2:6])] = int(lines[0])\r\n        routerCount += 1\r\n\r\nstart = timeit.default_timer()\r\nmyLine = open(\"adj_mat.txt\").readlines()[int(routerName) - 1]\r\nmyLine = myLine.rstrip('\\n')\r\ninitialCost = re.split(\" \", myLine)\r\nprint('Initial Cost is {}\\n'.format(initialCost))\r\n\r\nmyBf = BFA(routerCount, initialCost, routerName, whichPort, adrToName)\r\nmyBf.who_to_send()\r\ntry:\r\n    s = input(\"To start BellmanFord Algorithm, Enter 's'\\n\")\r\n    assert s == 's'\r\nexcept AssertionError:\r\n    s = input(\"Wrong input! To start BellmanFord Algorithm, Enter 's'\\n\")\r\nmyBf.send()\r\nwhile True:\r\n    hit = msvcrt.kbhit()\r\n    elapsedTime = timeit.default_timer() - start\r\n    # myBf.send()\r\n    if elapsedTime > 0.1:\r\n        myBf.send()\r\n        start = elapsedTime\r\n\r\n    myBf.receive()\r\n    if hit:\r\n        key = ord(msvcrt.getch())\r\n        if key == ord('u'):\r\n            myLine = open(\"adj_mat.txt\").readlines()[int(routerName) - 1]\r\n            myLine = myLine.rstrip('\\n')\r\n            newCost = re.split(\" \", myLine)\r\n            print('New cost is {}\\n'.format(newCost))\r\n            myBf.check_cost(newCost)\r\n","sub_path":"new_algs/Graph+algorithms/Bellman-Ford+algorithm/Router3.py","file_name":"Router3.py","file_ext":"py","file_size_in_byte":1642,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"351701661","text":"import tornado.web\nimport tornado.escape\nfrom tornado.httpclient import AsyncHTTPClient\nfrom .classifier import predict_mentions\nfrom .image_recognition import ImageSceneRecognition\nimport traceback\n\nAsyncHTTPClient.configure('tornado.curl_httpclient.CurlAsyncHTTPClient', max_clients=100)\nhttp_client = AsyncHTTPClient()\n\n\nclass Sentiment(tornado.web.RequestHandler):\n    async def post(self):\n        try:\n            mentions = tornado.escape.json_decode(self.request.body)[\"texts\"]\n            if len(mentions) > 0:\n                new_sentiment = predict_mentions(mentions)\n                result = {\"results\": new_sentiment}\n                status = 200\n            else:\n                result = {\"results\": []}\n                status = 200\n        except:\n            result = {\"results\": []}\n            status = 500\n        self.write(result)\n        self.set_status(status)\n\n\nclass ImageRecognition(tornado.web.RequestHandler):\n    async def post(self):\n        try:\n            urls = tornado.escape.json_decode(self.request.body)[\"urls\"]\n            img_recognition = ImageSceneRecognition(\"tornado\", False)\n\n            if len(urls) > 0:\n                result = {\n                    \"results\": await img_recognition.get_images_classes(urls)}\n                status = 200\n            else:\n                result = {\"results\": []}\n                status = 200\n\n        except:\n            result = {\"error_code\": traceback.format_exc()}\n            status = 500\n        self.write(result)\n        self.set_status(status)\n\n\nclass ImageRecognitionAsync(tornado.web.RequestHandler):\n    async def post(self):\n        try:\n            urls = tornado.escape.json_decode(self.request.body)[\"urls\"]\n            img_recognition = ImageSceneRecognition(\"tornado\", True)\n            if len(urls) > 0:\n                result = {\n                    \"results\": await img_recognition.get_images_classes(urls)}\n                status = 200\n            else:\n                result = {\"results\": []}\n                status = 200\n\n        except:\n            result = {\"error_code\": traceback.format_exc()}\n            status = 500\n        self.write(result)\n        self.set_status(status)\n\n\nclass ImageRecognitionTornado(tornado.web.RequestHandler):\n    async def post(self):\n        try:\n            urls = tornado.escape.json_decode(self.request.body)[\"urls\"]\n            img_recognition = ImageSceneRecognition(\"tornado\", True, http_client=http_client)\n            if len(urls) > 0:\n                result = {\n                    \"results\": await img_recognition.get_images_classes_tornado(urls)}\n                status = 200\n            else:\n                result = {\"results\": []}\n                status = 200\n        except:\n            result = {\"error_code\": traceback.format_exc()}\n            status = 500\n        self.write(result)\n        self.set_status(status)\n\n\nclass Healthcheck(tornado.web.RequestHandler):\n    async def get(self):\n        self.write({\"results\": \"healthy\"})\n        self.set_status(200)\n\n\nclass Hello(tornado.web.RequestHandler):\n    async def get(self):\n        result = {\"results\": \"Hello, world!\"}\n        self.write(result)\n        self.set_status(200)\n\n\ndef make_app():\n    return tornado.web.Application([\n        (r\"/tornado/healthcheck\", Healthcheck),\n        (r\"/tornado/hello\", Hello),\n        (r\"/tornado/sentiment\", Sentiment),\n        (r\"/tornado/image_recognition\", ImageRecognition),\n        (r\"/tornado/image_recognition_async\", ImageRecognitionAsync),\n        (r\"/tornado/image_recognition_tornado\", ImageRecognitionTornado)\n    ])\n","sub_path":"tornado/src/api.py","file_name":"api.py","file_ext":"py","file_size_in_byte":3586,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"472535268","text":"# !/usr/bin/python3\n# -*- coding: utf-8 -*-\n\"\"\"\n@Author         :  sqandan\n@Email          :  aadmin@111.com\n------------------------------------\n@File           :  task_pane.py\n@CreateTime     :  2021/1/26/0026 21:23\n@Version        :  1.0\n@Description    :  \n------------------------------------\n@Software       :  VSCode\n\"\"\"\n# here put the import lib\nimport os\nfrom PyQt5 import QtGui\nfrom PyQt5.QtCore import Qt\nfrom PyQt5.QtGui import QStandardItemModel, QStandardItem\nfrom PyQt5.QtWidgets import QWidget, QDesktopWidget, QHeaderView\nfrom pycode.Task_ui import Ui_Task\nfrom pycode.rsas_req import *\n\nimport requests\nrequests.packages.urllib3.disable_warnings()\n\n\nRSAS_Requests = RSAS_Requests()\n################################################\n#######任务界面\n################################################\nclass task_pane(QWidget, Ui_Task):\n    def __init__(self,scanner_url, username, passwd,parent=None):\n        super(task_pane, self).__init__(parent)\n        self.setupUi(self)\n\n        self.scanner_url = scanner_url\n        self.username = username\n        self.password = passwd\n\n        self.setWindowTitle(\"RSAS_Task_Tool\")\n        self.setWindowIcon(QtGui.QIcon('./favicon.ico'))\n        ###### 显示窗口在屏幕中间\n        self.center()\n        ###### 任务界面提示用户名&&扫描器地址\n        self.Host_label.setText(re.search(r'https://(\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})', self.scanner_url).group(1))\n        self.account_label.setText(self.username)\n\n        self.host_scan_tab()\n\n        class_text = RSAS_Requests.check_scan_tab()\n        if class_text[0] == 'quick_task_btn':\n            self.web_scan_tab()\n\n        ###### 扫描器的任务状态\n        self.Status = RSAS_Status()\n        self.Status.log_return.connect(self.status_finish)\n        self.Status.start()\n\n        ###### 扫描器的任务列表\n        self.set_task_view()\n\n        print('[+] 主页面初始化成功！')\n\n    ###### 显示窗口在屏幕中间\n    def center(self):\n        # 获得窗口\n        qr = self.frameGeometry()\n        # 获得屏幕中心点\n        cp = QDesktopWidget().availableGeometry().center()\n        # 显示到屏幕中心\n        qr.moveCenter(cp)\n        self.move(qr.topLeft())\n\n    ###### 网页登录\n    def url_login_clicked(self):\n        RSAS_Requests.Webpage_login(self.scanner_url, self.username, self.password)\n\n    windowList = []\n    ###### 注销\n    def logout_clicked(self):\n        from pycode.login_pane import login_pane\n        login_window = login_pane(mode=1)\n        self.windowList.append(login_window)\n        self.close()\n        login_window.show()\n\n    ###### todo 这个是主界面修改显示用的\n    def status_finish(self, status_msg):\n        number = status_msg.split('|')\n        self.Status_Ongoing_label.setText(f\"{number[0]}\")\n        self.Status_Waiting_label.setText(f\"{number[1]}\")\n\n    ################################################\n    #######主机扫描标签窗口\n    ################################################\n    def host_scan_tab(self):\n\n        # 扫描器的漏洞模板\n        self.host_template = {'0': '自动匹配扫描'}\n        ###### 获取扫描器的漏洞模板，保存为软件的下拉框\n        content_re = \"\"\"<tr class=\".*?\">.*?<th>漏洞模板</th>.*?<td>.*?<select id='.*?'.*?style=\".*?\">(.*?)</select>.*?</td>.*?</tr>\"\"\"\n        template_re = \"\"\"<option value='(\\d+)' >(.*?)</option>\"\"\"\n        content = RSAS_Requests.Host_scanning_template()\n        cont = re.findall(content_re, content.text, re.S | re.M)\n        ###### 把扫描器的漏洞模板生成下拉框\n        self.host_template.update(dict(re.findall(template_re, cont[0], re.S | re.M)))\n        self.TemplateList_Host_comboBox.addItems(self.host_template.values())\n        self.TemplateList_Host_comboBox.setCurrentIndex(0)\n\n        self.L_chk = [self.SMB_wordbook_checkBox, self.RDP_wordbook_checkBox, self.TELENT_wordbook_checkBox,\n                      self.FTP_wordbook_checkBox, self.SSH_wordbook_checkBox, self.Tomcat_wordbook_checkBox,\n                      self.POP3_wordbook_checkBox, self.SQL_SERVER_wordbook_checkBox, self.MySQL_wordbook_checkBox,\n                      self.Oracle_wordbook_checkBox, self.Sybase_wordbook_checkBox, self.DB2_wordbook_checkBox,\n                      self.MONGODB_wordbook_checkBox, self.SNMP_wordbook_checkBox]\n\n        self.Start_Host_Button.clicked.connect(self.Start_Host_Scan)\n\n    # todo 字典全选联动\n    def all_wordbook(self,checked):\n        if checked:\n            for i in self.L_chk:\n                i.setChecked(True)\n        else:\n            for i in self.L_chk:\n                i.setChecked(False)\n\n\n    ###### 加载Host资产文件下的资产文件到任务名称窗口\n    def Load_Task_Name_Host(self):\n        self.Task_name_Host_textEdit.setText(\"\")\n        HostAssets_dir = './Host_Assets/'\n\n        #  web资产文件列表\n        HostAssets_list = []\n\n        for file in os.listdir(HostAssets_dir):\n            if os.path.splitext(file)[1] == '.txt':\n                HostAssets_list.append(file)\n                self.Task_name_Host_textEdit.append(file[0:-4])\n\n    ###### Host标签窗口开始任务按钮\n    def Start_Host_Scan(self):\n        # 获取当前下拉框的字符\n        host_template_mode = self.TemplateList_Host_comboBox.currentText()\n        # 通过字符找到模板对应的数字，就是字典，通过值取键。扫描器的扫描模板对应不同的数字，扫描器在下任务时依照该数字选择对应的模板\n        host_template_number = list(self.host_template.keys())[list(self.host_template.values()).index(host_template_mode)]\n\n        ###### 这里就获取主界面的按钮勾选状态了\n        # 端口扫描\n        DefaultPort_status = self.DefaultPort_checkBox.isChecked()\n        AllPost_status = self.AllPort_checkBox.isChecked()\n        # 存活探测\n        survival_cancel_status = self.survival_cancel_checkBox.isChecked()\n        survival_Definition_status = self.survival_Definition_checkBox.isChecked()\n        # 口令猜测\n        Enable_wordbook_status = self.Enable_wordbook_checkBox.isChecked()\n        SMB_wordbook_status = self.SMB_wordbook_checkBox.isChecked()\n        RDP_wordbook_status = self.RDP_wordbook_checkBox.isChecked()\n        TELENT_wordbook_status = self.TELENT_wordbook_checkBox.isChecked()\n        FTP_wordbook_status = self.FTP_wordbook_checkBox.isChecked()\n        SSH_wordbook_status = self.SSH_wordbook_checkBox.isChecked()\n        Tomcat_wordbook_status = self.Tomcat_wordbook_checkBox.isChecked()\n        POP3_wordbook_status = self.POP3_wordbook_checkBox.isChecked()\n        SQL_SERVER_wordbook_status = self.SQL_SERVER_wordbook_checkBox.isChecked()\n        MySQL_wordbook_status = self.MySQL_wordbook_checkBox.isChecked()\n        Oracle_wordbook_status = self.Oracle_wordbook_checkBox.isChecked()\n        Sybase_wordbook_status = self.Sybase_wordbook_checkBox.isChecked()\n        DB2_wordbook_status = self.DB2_wordbook_checkBox.isChecked()\n        MONGODB_wordbook_status = self.MONGODB_wordbook_checkBox.isChecked()\n        SNMP_wordbook_status = self.SNMP_wordbook_checkBox.isChecked()\n\n        # 报���类型\n        HTML_Report_Host_status = self.HTML_Report_Host_checkBox.isChecked()\n        World_Report_Host_status = self.World_Report_Host_checkBox.isChecked()\n        Excel_Report_Host_status = self.Excel_Report_Host_checkBox.isChecked()\n        PDF_Report_Host_status = self.PDF_Report_Host_checkBox.isChecked()\n        # 报表内容\n        Summary_Report_Host_status = self.Summary_Report_Host_checkBox.isChecked()\n        Host_Report_Host_status = self.Host_Report_Host_checkBox.isChecked()\n        Auto_Report_Host_status = self.Auto_Report_Host_checkBox.isChecked()\n\n        host_Scan_time_status = self.Scan_Time_Host_lineEdit.text()\n\n        # todo 这里可以对扫描任务名&&任务开始时间进行输入校验\n        host_task_list = self.Task_name_Host_textEdit.toPlainText().split(\"\\n\")\n        host_task_list = [i for i in host_task_list if i !='']\n\n        ###### 这里就是下任务了，使用线程，避免卡界面\n        self.Start_Host_Button.setChecked(True)\n        self.Start_Host_Button.setDisabled(True)\n        self.Start_Host_Scan_Working = Start_Host_Scan_Working(host_template_number, DefaultPort_status, AllPost_status,\n                                                               survival_cancel_status, survival_Definition_status,\n                                                               Enable_wordbook_status, SMB_wordbook_status,\n                                                               RDP_wordbook_status, TELENT_wordbook_status,\n                                                               FTP_wordbook_status, SSH_wordbook_status,\n                                                               Tomcat_wordbook_status, POP3_wordbook_status,\n                                                               SQL_SERVER_wordbook_status, MySQL_wordbook_status,\n                                                               Oracle_wordbook_status, Sybase_wordbook_status,\n                                                               DB2_wordbook_status, MONGODB_wordbook_status,\n                                                               SNMP_wordbook_status,\n                                                               HTML_Report_Host_status, World_Report_Host_status,\n                                                               Excel_Report_Host_status,\n                                                               PDF_Report_Host_status, Summary_Report_Host_status,\n                                                               Host_Report_Host_status, Auto_Report_Host_status,\n                                                               host_Scan_time_status, host_task_list)\n        self.Start_Host_Scan_Working.start_host_return.connect(self.Start_Host_Scan_Finish)\n        self.Start_Host_Scan_Working.start()\n\n    ###### 显示Host扫描任务下达状态\n    def Start_Host_Scan_Finish(self, start_msg):\n        self.Working_Host_label.setText(start_msg)\n        if '所有任务下达完成' in start_msg:\n            self.Start_Host_Button.setChecked(False)\n            self.Start_Host_Button.setDisabled(False)\n\n    ################################################\n    #######web扫描标签窗口\n    ################################################\n    def web_scan_tab(self):\n\n        # 扫描器的漏洞模板\n        self.web_template = {'0': '自动匹配扫描'}\n        ###### 获取扫描器的漏洞模板，保存为软件的下拉框\n        content_re = \"\"\"<tr class=\".*?\">.*?<th>漏洞模板</th>.*?<td>.*?<select id='.*?'.*?style=\".*?\">(.*?)</select>.*?</td>.*?</tr>\"\"\"\n        template_re = \"\"\"<option value=\"(\\d+)\" >(.*?)</option>\"\"\"\n        content = RSAS_Requests.Web_scanning_template()\n        cont = re.findall(content_re, content.text, re.S | re.M)\n        ###### 把扫描器的漏洞模板生成下拉框\n        self.web_template.update(dict(re.findall(template_re, cont[0], re.S | re.M)))\n        self.TemplateList_Web_comboBox.addItems(self.web_template.values())\n        self.TemplateList_Web_comboBox.setCurrentIndex(0)\n\n        # 设置扫描范围下拉框的选项\n        range_items = [\"按域名扫描\", \"扫描当前目录及子目录\", \"只扫描任务目标链接\"]\n        self.Scan_Range_comboBox.addItems(range_items)\n        self.Scan_Range_comboBox.setCurrentIndex(1)  # 设置默认值\n\n        # 设置并发线程数默认值\n        self.Concurrent_Threads_lineEdit.setText(\"20\")\n\n        # 设置超时限制默认值\n        self.Webscan_Timeout_lineEdit.setText(\"30\")\n\n        # 设置目录猜测范围下拉框的选项\n        level_items = [\"0\", \"1\", \"2\", \"3\"]\n        self.Dir_level_comboBox.addItems(level_items)\n        self.Dir_level_comboBox.setCurrentIndex(1)  # 设置默认值\n\n        # 设置目录猜测深度默认值\n        self.Dir_limit_lineEdit.setText(\"3\")\n\n        self.Start_Web_Button.clicked.connect(self.Start_Web_Scan)\n\n    ###### 加载URL资产文件下的资产文件到任务名称窗口\n    def Load_Task_Name_Web(self):\n        self.Task_name_Web_textEdit.setText(\"\")\n        self.WebAssets_dir = './URL_Assets/'\n\n        #  web资产文件列表\n        self.WebAssets_list = []\n\n        for file in os.listdir(self.WebAssets_dir):\n            if os.path.splitext(file)[1] == '.txt':\n                self.WebAssets_list.append(file)\n                self.Task_name_Web_textEdit.append(file[0:-4])\n\n    ###### 因web扫描单个任务限制15个URL，需要对任务进行拆分\n    def Check_WebAssets_list(self,WebAssets_list):\n\n        New_WebAssets_list = []\n\n        for _task in WebAssets_list:\n            task_info = _task.split('|')\n            try:\n                task_name = task_info[0].strip()\n                task_time = task_info[1].strip()\n            except Exception as e:\n                task_name = _task.strip()\n                task_time = \"\"\n            with open('./URL_Assets/' + task_name + '.txt') as url_file:\n                url_range = url_file.read()\n            url_list = url_range.split('\\n')\n\n            while True:\n                try:\n                    url_list.remove('')\n                except ValueError:\n                    break\n\n            print('[+] 正在进行url去重...')\n            url_list_len = len(url_list)\n            new_url_list = list(set(url_list))\n\n            if len(new_url_list) < url_list_len:\n                url_list = new_url_list\n                print(f'[*] {task_name}检测到重复url，已经进行去重...')\n\n            if len(url_list) > 15:\n                print(f'[*] url大于15个，正在拆分任务{task_name}...')\n                \n                flag = 0\n                \n                name = 1\n                \n                dataList = []\n\n                for line in url_list:\n                    flag += 1\n                    dataList.append(line)\n                    if flag == 15:\n                        with open('./URL_Assets/' + task_name + \"--\" + str(name) + \".txt\",'w+') as f_target:  # str[0:-1]为切片，意思是从前面开始截取到后面-1为止\n                            for data in dataList:\n                                f_target.write(data + \"\\n\")\n\n                        dataList = []\n                        if task_time == \"\":\n                            New_WebAssets_list.append(task_name + \"--\" + str(name))\n                        else:\n                            New_WebAssets_list.append(task_name + \"--\" + str(name) + \"|\" + task_time)\n                        name += 1\n                        flag = 0\n\n                # 处理最后一批行数少于15行的\n                with open('./URL_Assets/' + task_name + \"--\" + str(name) + \".txt\", 'w+') as f_target:\n                    for data in dataList:\n                        f_target.write(data + \"\\n\")\n                print(f'[*] 拆分任务已完成，共拆分{str(name)}子任务')\n                if task_time == \"\":\n                    New_WebAssets_list.append(task_name + \"--\" + str(name))\n                else:\n                    New_WebAssets_list.append(task_name + \"--\" + str(name) + \"|\" + task_time)\n                os.remove('./URL_Assets/' + task_name + '.txt')\n            else:\n                New_WebAssets_list.append(_task)\n        return New_WebAssets_list\n\n    ###### Web标签窗口开始任务按钮\n    def Start_Web_Scan(self):\n\n        ###### 这里就获取主界面的任务参数\n        # 获取扫描范围下标\n        web_range_number = self.Scan_Range_comboBox.currentIndex()\n\n        # 获取扫描模板当前下拉框的字符\n        web_template_mode = self.TemplateList_Web_comboBox.currentText()\n        # 通过字符找到模板对应的数字，就是字典，通过值取键。扫描器的扫描模板对应不同的数字，扫描器在下任务时依照该数字选择对应的模板\n        web_template_number = list(self.web_template.keys())[list(self.web_template.values()).index(web_template_mode)]\n\n        # 并发线程数\n        Concurrent_Threads_status = self.Concurrent_Threads_lineEdit.text()\n        # 超时限制\n        Webscan_Timeout_status = self.Webscan_Timeout_lineEdit.text()\n        # 目录猜测范围\n        Dir_level_status = self.Dir_level_comboBox.currentIndex()\n        # 目录猜测深度\n        Dir_limit_status = self.Dir_limit_lineEdit.text()\n        # 报表类型\n        HTML_Report_Web_status = self.HTML_Report_Web_checkBox.isChecked()\n        World_Report_Web_status = self.World_Report_Web_checkBox.isChecked()\n        Excel_Report_Web_status = self.Excel_Report_Web_checkBox.isChecked()\n        PDF_Report_Web_status = self.PDF_Report_Web_checkBox.isChecked()\n        # 报表内容\n        Summary_Report_Web_status = self.Summary_Report_Web_checkBox.isChecked()\n        Host_Report_Web_status = self.Host_Report_Web_checkBox.isChecked()\n        Auto_Report_Web_status = self.Auto_Report_Web_checkBox.isChecked()\n\n        Scan_time_web_status = self.Scan_Time_Web_lineEdit.text()\n\n        # todo ���里可以对扫描任务名&&任务开始时间进行输入校验\n        task_list_web = self.Task_name_Web_textEdit.toPlainText().split(\"\\n\")\n        task_list_web = [i for i in task_list_web if i != '']\n        New_WebAssets_list = self.Check_WebAssets_list(task_list_web)\n\n        ###### 这里就是下任务了，使用线程，避免卡界面\n        self.Start_Web_Button.setChecked(True)\n        self.Start_Web_Button.setDisabled(True)\n        self.Start_Web_Scan_Working = Start_Web_Scan_Working(web_range_number, web_template_number,\n                                                             Concurrent_Threads_status, Webscan_Timeout_status,\n                                                             Dir_level_status, Dir_limit_status,\n                                                             HTML_Report_Web_status, World_Report_Web_status,\n                                                             Excel_Report_Web_status,\n                                                             PDF_Report_Web_status, Summary_Report_Web_status,\n                                                             Host_Report_Web_status, Auto_Report_Web_status,\n                                                             Scan_time_web_status, New_WebAssets_list)\n        self.Start_Web_Scan_Working.start_web_return.connect(self.Start_Web_Scan_Finish)\n        self.Start_Web_Scan_Working.start()\n\n    ###### 显示Web扫描任务下达状态\n    def Start_Web_Scan_Finish(self, start_msg):\n        self.Working_Web_label.setText(start_msg)\n        if '所有任务下达完成' in start_msg:\n            self.Start_Web_Button.setChecked(False)\n            self.Start_Web_Button.setDisabled(False)\n\n\n\n\n################################################\n#######任务列表展示\n################################################\n    \"\"\"\n     PyQt5的Model/View/Delegate的设计模式，即Model持有数据，下与数据源交互（数据的查询、修改与添加），上与View交互，\n     主要为View提供要显示的数据。View提供数据的显示和与用户交互。Delegate可以实现定制数据显示的方式和编辑方式\n    \"\"\"\n    def set_task_view(self):\n        task_list_tableView = self.task_list_tableView\n\n        task_list_tableView.setSelectionBehavior(QHeaderView.SelectRows)  # 选择行为：行选择\n        task_list_tableView.setAlternatingRowColors(True)  # 隔行变色\n\n        task_list_tableView.horizontalHeader().setSectionResizeMode(QHeaderView.Stretch)  # 所有列自动拉伸，充满界面\n        # 设置数据层次结构，4行4列\n        self.model = QStandardItemModel(2, 6)\n        # 视图加载模型\n        self.model = MyModel()\n        # 设置水平方向四个头标签文本内容\n        self.model.setHorizontalHeaderLabels(['任务号', '任务名称', '开始时间', '结束时间', '进度', '操作'])\n        task_list_tableView.setModel(self.model)\n\n        task_num = \"2333\"\n        task_name = \"测试任务名称\"\n        task_start_time = \"2021-02-02 22:22:22\"\n        task_end_time = \"2021-02-02 23:33:33\"\n        task_progress = \"100%\"\n\n        for row in range(4):\n            self.model.setItem(row, 0, QStandardItem(f\"{task_num}\"))\n            self.model.setItem(row, 1, QStandardItem(f\"{task_name}\"))\n            self.model.setItem(row, 2, QStandardItem(f\"{task_start_time}\"))\n            self.model.setItem(row, 3, QStandardItem(f\"{task_end_time}\"))\n            self.model.setItem(row, 4, QStandardItem(f\"{task_progress}\"))\n\n\n######  重写QStandardItemModel的data函数使item居中显示\nclass MyModel(QStandardItemModel):\n    def __init__(self):\n        QStandardItemModel.__init__(self)\n\n    def data(self, index, role=None):\n        if role == Qt.TextAlignmentRole:\n            return Qt.AlignCenter\n        return QStandardItemModel.data(self, index, role)\n","sub_path":"pycode/task_pane.py","file_name":"task_pane.py","file_ext":"py","file_size_in_byte":21019,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"406786021","text":"import torch\nimport argparse\nimport yaml\nfrom easydict import EasyDict\nimport sys\nimport os\nimport glob\nimport numpy as np\nif not os.getcwd() in sys.path:\n    sys.path.append(os.getcwd())\nfrom fewshot.utils import com_util, data_util, model_util\nfrom tensorboardX import SummaryWriter\n\n# if need, set GPU device number\n# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"0\"\n\ndef main(params):\n    np.random.seed(10)\n\n    print('dataset: {}, method: {}, model: {}'.format(params.dataset, params.method, params.model))\n\n    # dataset\n    base_loader, val_loader = data_util.get(params)\n\n    # model\n    model = model_util.get_model(params)\n\n    # optimizer\n    optim = params.optim.lower()\n    if optim == 'sgd':\n        optimizer = torch.optim.SGD(model.parameters(), lr=params.base_lr,\n                         momentum=params.momentum, weight_decay=params.weight_decay)\n    elif optim == 'adam':\n        optimizer = torch.optim.Adam(model.parameters(), lr=params.base_lr,\n                         weight_decay=params.weight_decay)\n\n    # resume\n    params_temp = params.copy()\n    params_temp = EasyDict(params_temp)\n    params_temp.save_dir = com_util.get_save_dir(params_temp)\n    model, optimizer, start_epoch = model_util.resume(params_temp, model, optimizer)\n\n    # scheduler\n    lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=params.steps,\n                         gamma=params.gamma, last_epoch=start_epoch-1)\n\n    stop_epoch = params.stop_epoch\n    train(base_loader, val_loader, model, lr_scheduler, start_epoch, stop_epoch, params)\n\n\ndef train(base_loader, val_loader, model, lr_scheduler, start_epoch, stop_epoch, params):\n    best_acc = 0\n    params.save_dir = com_util.get_save_dir(params)\n\n    logger = SummaryWriter(params.save_dir)\n    logger_file_path = os.path.join(params.save_dir, 'logger_file.txt')\n    logger_file = open(logger_file_path, 'w')\n\n    # epoch loop\n    for epoch in range(start_epoch, stop_epoch):\n        lr_scheduler.step()\n        # train\n        model.train_loop(epoch, base_loader, lr_scheduler.optimizer, logger, logger_file)\n        # val\n        acc = model.test_loop(val_loader, logger_file)\n\n        logger.add_scalar('acc', acc, epoch + 1)\n        # save best\n        if acc > best_acc:\n            best_acc = acc\n            outfile = os.path.join(params.save_dir, 'best_model.tar')\n            torch.save({'epoch':epoch, 'state_dict':model.state_dict(), 'optimizer': lr_scheduler.optimizer.state_dict(),\n                     'lr': lr_scheduler.get_lr()[0]}, outfile)\n        # save model\n        if (epoch % params.save_freq==0) or (epoch==stop_epoch-1) :\n            outfile = os.path.join(params.save_dir, '{:d}.tar'.format(epoch))\n            torch.save({'epoch':epoch, 'state_dict':model.state_dict(), 'optimizer': lr_scheduler.optimizer.state_dict(),\n                     'lr': lr_scheduler.get_lr()[0]}, outfile)\n\n    logger_file.close()\n\n\nif __name__=='__main__':\n    parser = argparse.ArgumentParser(description='PyTorch Training')\n    parser.add_argument('--config', default='cfgs/baseline/miniImagenet.yaml')\n    args = parser.parse_args()\n\n    with open(args.config) as f:\n        config = yaml.load(f)\n    params = EasyDict(config['common'])\n    params.update(config['train'])\n\n    main(params)\n","sub_path":"fsl-pytorch-master/tools/train.py","file_name":"train.py","file_ext":"py","file_size_in_byte":3271,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"49305443","text":"import re\nimport itertools\n\n\nclass IO_for_Contest(object):\n    @staticmethod\n    def my_input():\n        # return raw_input()\n        return input()\n\n    @staticmethod\n    def read_from_input():\n        n, m = IO_for_Contest.read_n_int(2)\n        x, y = [], []\n        for i in range(m):\n            xx, yy = IO_for_Contest.read_n_int(2)\n            x.append(xx)\n            y.append(yy)\n        return n, m, x, y\n\n    @staticmethod\n    def read_line():\n        return IO_for_Contest.my_input().strip()\n\n    @staticmethod\n    def read_int():\n        return int(IO_for_Contest.my_input().strip())\n\n    @staticmethod\n    def read_n_int(n):\n        return list(map(\n            int,\n            re.split('\\s+', IO_for_Contest.my_input().strip())))[: n]\n\n\ndef satisfies_condition(l, x, y):\n    for (xx, yy) in zip(x, y):\n        if l.index(xx) > l.index(yy):\n            return False\n    # print(l)\n    return True\n\n\ndef count_patterns(n, x, y):\n    count = 0\n    n_list_starts_with_1 = list(map(lambda x: x + 1, list(range(n))))\n    for l in itertools.permutations(n_list_starts_with_1, n):\n        if satisfies_condition(l, x, y):\n            count += 1\n    return count\n\n\ndef solve():\n    n, m, x, y = IO_for_Contest.read_from_input()\n    print(count_patterns(n, x, y))\n\n\nif __name__ == '__main__':\n    solve()\n","sub_path":"atcoder/ABC/ABC041/D.py","file_name":"D.py","file_ext":"py","file_size_in_byte":1310,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"445915890","text":"import os\nimport pandas as pd\nimport numpy as np\n\nfrom regcomments.dirs import *\n\norgCommentsDF = pd.read_csv(os.path.join(dataDir,'grants','orgComments_idsOnly.csv'),encoding='utf8')\n\n\npairwiseDF = pd.DataFrame()\nfor i,lsaFile in enumerate([f for f in os.listdir(commentLsaDir) if f.endswith('.bin')]):\n    docketId = lsaFile[:-4]\n\n    print(docketId)\n\n    lsaDF = pd.read_pickle(os.path.join(commentLsaDir,lsaFile))\n\n    #Build commenter LSA vectors\n    orglsaDF = pd.merge(lsaDF,orgCommentsDF[['documentId','org_id']],left_index=True,right_on='documentId')\n\n    # Add comments from the same org together and normalize all lengths\n    orglsaDF = orglsaDF.drop('len',axis=1).groupby('org_id').sum()\n    orglsaDF = orglsaDF.divide(np.sqrt((orglsaDF**2).sum(axis=1)),axis=0)\n\n    # Probably redundant, but important that indecies are sorted to ensure pairs always appear in the same order\n    orglsaDF = orglsaDF.sort_index()\n\n    # Construct selectors for upper-triangular matrix pair indicies\n    orgs = np.array(orglsaDF.index.get_level_values(0))\n    selector_i,selector_j = np.triu_indices(len(orglsaDF),k=1)\n\n    # Compute pair cosine similarities\n    df = pd.DataFrame()\n    df['org_i'],df['org_j'] = orgs[selector_i],orgs[selector_j]\n    df['similarity'] = ((orglsaDF.values[selector_i] * orglsaDF.values[selector_j])**2).sum(axis=1)\n    df['count'] = 1\n\n    pairwiseDF = pairwiseDF.append(df)\n\n    if not i + 1 % 10:\n        pairwiseDF = pairwiseDF.groupby(['org_i','org_j']).agg({'similarity':'mean','count':'sum'})\n\n    if docketId.lower().startswith('b'):\n        break\n\npairwiseDF = pairwiseDF.groupby(['org_i','org_j']).agg({'similarity':'mean','count':'sum'})\n","sub_path":"dataProcessing/grants/computeMeanPairwiseSimilarity.py","file_name":"computeMeanPairwiseSimilarity.py","file_ext":"py","file_size_in_byte":1674,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"284728131","text":"import numpy\r\nimport matplotlib.pyplot as plt\r\n\r\nfrom pulp import LpMaximize, LpProblem, LpVariable, LpStatus\r\nfrom matplotlib import pyplot\r\n\r\n# Number of MONTHS for the simulation\r\nMONTHS = 5\r\n\r\n#stocked food\r\n\r\n#starting resources\r\nSEAWEED_INITIAL=0.1#10^4 tons\r\n\r\n#continuous production\r\nSEAWEED_AREA_PRODUCTION=0.4153 #hectares\r\n\r\n\r\n# Stock of resources. Add the value of stored resources in bracket\r\nArea = {\"Start\": [SEAWEED_INITIAL], \"End\": [], \"Production\": [SEAWEED_AREA_PRODUCTION]*MONTHS}\r\n\r\n# Nutrition\r\n\r\n# Calories given per 10^4 Tons taken from nutrition calculator divided by 1'000'000'000 for rounding up errors\r\n# Proteins digestibility with same process as for calories\r\n# Fat total with same process as for calories\r\nS = {'Start': [], 'Eaten': [], 'After': [], 'Max_T_Area': 4000,\r\n     'Calories': 29.000, 'Fat': 0.772, 'Proteins': 0.0852, 'Growing_period': 1}\r\nStored_Wheat = {'Start': [], 'Eaten': [], 'After': [],\r\n                'Calories': 36.400, 'Fat': 0.098, 'Proteins': 0.097}\r\n\r\n\r\n# Create the model to optimize\r\nmodel = LpProblem(name=\"optimization_nutrition\", sense=LpMaximize)\r\n\r\n# Initialize the variable to maximize. That is, the maximal number of people we can feed each day\r\nz = LpVariable(name=\"minimum_value_nutrition\", lowBound=0, cat='Integer')\r\n\r\n\r\n# This part define one a variable for each type of food. Each food is a vector made of boxes, each value in the vector encodes a different moth\r\nfor x in range(0, MONTHS):\r\n    S['Eaten'].append(LpVariable(name='Eaten_Seaweed%s' % x, lowBound=0))\r\n    S['Start'].append(LpVariable(name='Stock_Seaweed%s' % x, lowBound=0))\r\n    Stored_Wheat['Eaten'].append(LpVariable(name='Eaten_Stored_Wheat%s' % x, lowBound=0))\r\n    Stored_Wheat['Start'].append(LpVariable(name='Stock_Stored_Wheat%s' % x, lowBound=0))\r\nS['Start'].append(LpVariable(name='Stock_Seaweed', lowBound=0))\r\nStored_Wheat['Start'].append(LpVariable(name='Stock_Stored_Wheat', lowBound=0))\r\n# a,b,c are the variables for the calories, fat and proteins obtained by the food eaten\r\n# H encodes the number of people we can feed each month\r\na = []\r\nb = []\r\nc = []\r\nH = []\r\nfor x in range(0, MONTHS):\r\n    a.append(LpVariable(name='T_Calories_Eaten%s' % x, lowBound=0))\r\n    b.append(LpVariable(name='T_Fat_Eaten%s' % x, lowBound=0))\r\n    c.append(LpVariable(name='T_Proteins_Eaten%s' % x, lowBound=0))\r\n    H.append(LpVariable(name='Humans_fed%s' % x, lowBound=0, cat='Integer'))\r\n\r\nTotal_Eaten = {'Calories_Eaten': a, 'Fat_Eaten': b, 'Proteins_Eaten': c}\r\n\r\nmodel += ((S['Start'][0]) <= 0.1, 'Seaweed_before_positive_start%s' % 0)\r\nmodel += ((Stored_Wheat['Start'][0]) <= 0, 'Stored_before_positive_start%s' % 0)\r\n# Computation for MONTHS\r\nfor x in range(0, MONTHS):\r\n    # Computation for seaweed: constraint for food eaten line 60 and the new quantity of seaweed at month x+1 is the\r\n    # one at month x minus the seaweed eaten and then taking into account the reproduction\r\n    S['After'].append(S['Start'][x] - S['Eaten'][x])\r\n    model += (S['Start'][x+1] <= (S['Start'][x] - S['Eaten'][x]) * pow(1.1, 30) - 0.00000001,\r\n              'Seaweed_positive_start%s' % x)\r\n\r\n    # Computation for stored food: constraint for food eaten line 68 and the new quantity of stored wheat at month x+1\r\n    # is the one at month x minus the wheat eaten\r\n    Stored_Wheat['After'].append(Stored_Wheat['Start'][x] - Stored_Wheat['Eaten'][x])\r\n    model += (Stored_Wheat['Start'][x + 1] <= (Stored_Wheat['Start'][x] - Stored_Wheat['Eaten'][x]),\r\n              'Stored_positive_start%s' % x)\r\n\r\n    # actualisation of available resources. At month x+1, we have the quantity of resources from month x minus the used\r\n    # resources plus the production of resources per MONTHS\r\n    Area['Start'].append(Area['Start'][x] + Area['Production'][x])\r\n    model += (0 <= S['Max_T_Area'] * Area['Start'][x] - S['Start'][x], 'Area_End_constraint%s' % x)\r\n    model += (100 >= Area['Start'][x], 'Area_Start_constraint%s' % x)\r\n    # I assume a density of 4000 tons per Km^2 which is the maximum in the spreadsheet\r\n\r\n    # Calories, fat and proteins obtained for the population by eating the stored wheat and the seaweeds\r\n    Total_Eaten['Calories_Eaten'][x] = Stored_Wheat['Eaten'][x] * Stored_Wheat['Calories'] + \\\r\n                                       S['Calories'] * S['Eaten'][x]\r\n    Total_Eaten['Fat_Eaten'][x] = Stored_Wheat['Eaten'][x] * Stored_Wheat['Fat'] + S['Fat'] * S['Eaten'][x]\r\n    Total_Eaten['Proteins_Eaten'][x] = Stored_Wheat['Eaten'][x] * Stored_Wheat['Fat'] + S['Proteins'] * S['Eaten'][x]\r\n\r\n    # Constraint for the number of people fed by the eaten foods\r\n    model += (0 <= Total_Eaten['Calories_Eaten'][x] - H[x] * 7.1190 * pow(10, -5), 'Feeding_Calories%s' % x)\r\n    # Numbers: 2100 Kcal/day * 1.13 for lost food * 30 for month/ 1'000'000'000\r\n    model += (0 <= Total_Eaten['Fat_Eaten'][x] - H[x] * 2.373 * pow(10, -6), 'Feeding_Fat%s' % x)\r\n    # Numbers:  70.00 g/day * 1.13 for lost food * 30 for month/ 1'000'000'000\r\n    model += (0 <= Total_Eaten['Proteins_Eaten'][x] - H[x] * 2.669625 * pow(10, -6), 'Feeding_Proteins%s' % x)\r\n    # Numbers:  78.75 g/day * 1.13 for lost food * 30 for month/ 1'000'000'000\r\n\r\n# Constraint for having minimum to be a linear function. We stored all variables and the number of people we can feed\r\n# each month. Now the final number of people we saved is the minimum other the number of people fed each month\r\nfor x in range(MONTHS):\r\n    model += (H[x] >= z, 'Min_Nutrition%s' % x)\r\n\r\n# The model maximize the minimum an so maximize the number of people saved taking time into account\r\nobj_func = z\r\nmodel += obj_func\r\nstatus = model.solve()\r\nprint(f\"objective: {model.objective.value()}\")\r\nfor var in model.variables():\r\n    print(f\"{var.name}: {var.value()}\")\r\n\r\nassert(status==1)\r\n\r\n\r\n#double check it worked\r\nSHOW_CONSTRAINT_CHECK=True\r\n# print(model.constraints.items())\r\nprint('pulp reports successful optimization')\r\nfor constraint in list(model.constraints.items()):\r\n    if(constraint[0].find('Min_Nutrition')==1):\r\n        continue\r\n    variable_string=str(constraint[1]).split(' <= ')[0]\r\n    equation_string=str(constraint[1]).split(' <= ')[1] \r\n    for var in model.variables():\r\n        equation_string = equation_string.replace(var.name,str(var.value()))\r\n        variable_string = variable_string.replace(var.name,str(var.value()))\r\n    eq_val=eval(equation_string)\r\n    var_val=eval(variable_string)\r\n    if(SHOW_CONSTRAINT_CHECK):\r\n        print('checking constraint '+\\\r\n        str(constraint[0])+' has '+str(constraint[1]))\r\n    print(eq_val)\r\n    print(var_val)\r\n    # assert(abs(eq_val- var_val)<.01)\r\n    if(SHOW_CONSTRAINT_CHECK):\r\n        print('constraint satisfied')\r\nprint('all constraints satisfied')\r\n\r\n\r\n\r\n# Print the different variables. That is, the number of people rescued and the different king of food we grew each month\r\nprint(f\"objective: {model.objective.value()}\")\r\nfor var in model.variables():\r\n    print(f\"{var.name}: {var.value()}\")\r\n\r\n# Plot the diagram\r\nlabels = []\r\nStored_eaten = []\r\nSeaweed_eaten = []\r\nStored_stock = [Stored_Wheat['Start'][0].value()]\r\nSeaweed_stock = [S['Start'][0].value()]\r\nSeaweed_stock_real = [S['Start'][0].value()]\r\nfor x in range(0, MONTHS):\r\n    labels.append('Month%s' % x)\r\n    Stored_eaten.append(Stored_Wheat['Eaten'][x].value())\r\n    Seaweed_eaten.append(S['Eaten'][x].value())\r\nfor x in range(0, MONTHS-1):\r\n    Seaweed_stock_real.append((Seaweed_stock_real[x] - S['Eaten'][x].value()) * pow(1.1, 30))\r\nfor x in range(1, MONTHS):\r\n    Stored_stock.append(Stored_Wheat['Start'][x].value())\r\n    Seaweed_stock.append(S['Start'][x].value())\r\n\r\n\r\nx = numpy.arange(len(labels))  # the label locations\r\nwidth = 0.125  # the width of the bars\r\n\r\nfig, ax = plt.subplots()\r\nRectangles1 = ax.bar(x - 1.5 * width, Stored_eaten, width, label='Stored e')\r\nRectangles2 = ax.bar(x - 0.5 * width, Seaweed_eaten, width, label='Sea e')\r\nRectangles3 = ax.bar(x + 0.5 * width, Stored_stock, width, label='Stored stock')\r\nRectangles4 = ax.bar(x + 1.5 * width, Seaweed_stock, width, label='Stored seaweed')\r\n# Rectangles5 = ax.bar(x + 1.5 * width, Seaweed_stock_real, width, label='Stored seaweed r')\r\n\r\n# Add some text for labels, title and custom x-axis tick labels, etc.\r\nax.set_ylabel('10^4 Tons')\r\nax.set_title('Food eaten and stocked over the MONTHS')\r\nax.set_xticks(x)\r\nax.set_xticklabels(labels)\r\nax.legend()\r\n\r\nax.bar_label(Rectangles1, padding=0)\r\nax.bar_label(Rectangles2, padding=0)\r\nax.bar_label(Rectangles3, padding=0)\r\nax.bar_label(Rectangles4, padding=0)\r\n# ax.bar_label(Rectangles5, padding=0)\r\n\r\nfig.tight_layout()\r\n\r\nplt.show()\r\n\r\nprint(Seaweed_eaten)\r\nprint(Seaweed_stock)\r\nprint(Seaweed_stock_real)\r\nmodel.writeLP('Test')\r\n\r\n\r\n","sub_path":"src/Archive/OptimizerMikeStar3.py","file_name":"OptimizerMikeStar3.py","file_ext":"py","file_size_in_byte":8711,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"482515734","text":"# -*- coding: utf-8 -*-\n# @Author  : 马维畅\n# @Time    : 2019/7/26 17:04\n# @File    : run.py\n\nimport paramiko\n\nssh = paramiko.SSHClient()\nkey = paramiko.AutoAddPolicy()\nssh.set_missing_host_key_policy(key)\nssh.connect('192.168.1.170', 22, 'root', 'test12#$%' ,timeout=5)\nstdin, stdout, stderr = ssh.exec_command('vim-cmd vmsvc/getallvms')\n\nfor i in stdout.readlines():\n    print(i)","sub_path":"testcase/ssh_run.py","file_name":"ssh_run.py","file_ext":"py","file_size_in_byte":384,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"32845963","text":"\n\nfrom xai.brain.wordbase.verbs._trundle import _TRUNDLE\n\n#calss header\nclass _TRUNDLES(_TRUNDLE, ):\n\tdef __init__(self,): \n\t\t_TRUNDLE.__init__(self)\n\t\tself.name = \"TRUNDLES\"\n\t\tself.specie = 'verbs'\n\t\tself.basic = \"trundle\"\n\t\tself.jsondata = {}\n","sub_path":"xai/brain/wordbase/verbs/_trundles.py","file_name":"_trundles.py","file_ext":"py","file_size_in_byte":245,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"356426689","text":"import discord\nimport random\nimport io\nimport os\nimport sys\nimport traceback\nimport datetime\nimport pynite\nimport textwrap\nimport json\nfrom motor import motor_asyncio\nfrom ext.paginator import Paginator\nfrom contextlib import redirect_stdout\nfrom ext import utils\nfrom discord.ext import commands\n\n#getting database\ndbclient = motor_asyncio.AsyncIOMotorClient('mongodb://PikaBot:' + os.environ.get('DBPASS') + '@ds163711.mlab.com:63711/pikabot')\ndb = dbclient.pikabot\n\n#getting prefix for the guild\nasync def get_pre(bot, message):\n    try:\n        result = await db.settings.find_one({'_id': str(message.guild.id)})\n    except AttributeError:\n        return [\"G.\", \"g.\", f\"{bot.user.mention} \"]\n    if not result or not result.get('prefix'):\n        return [\"G.\", \"g.\", f\"{bot.user.mention} \"]\n    return result['prefix']\n\nbot = commands.Bot(command_prefix=get_pre, description=\"A simple bot created in discord.py library by Nyan Pikachu#4148 for moderation and misc commands!\", owner_id=279974491071709194)\n\n#calling cogs from the cogs folder\nbot.load_extension(\"cogs.bot_\")\nbot.load_extension(\"cogs.cr\")\nbot.load_extension(\"cogs.fortnite\")\n\n#eval!!!\ndef cleanup_code(content):\n    \"\"\"Automatically removes code blocks from the code.\"\"\"\n    # remove ```py\\n```\n    if content.startswith('```') and content.endswith('```'):\n        return '\\n'.join(content.split('\\n')[1:-1])\n    return content.strip('` \\n')\n\n@bot.command(name='eval')\n@utils.developer()\nasync def _eval(ctx, *, body: str):\n        \"\"\"Evaluates a code\"\"\"\n\n        env = {\n            'bot': bot,\n            'ctx': ctx,\n            'channel': ctx.channel,\n            'author': ctx.author,\n            'guild': ctx.guild,\n            'message': ctx.message,\n        }\n        \n        env.update(globals())\n\n        body = cleanup_code(body)\n        stdout = io.StringIO()\n\n        to_compile = f'async def func():\\n{textwrap.indent(body, \"  \")}'\n\n        try:\n            exec(to_compile, env)\n        except Exception as e:\n            return await ctx.send(f'```py\\n{e.__class__.__name__}: {e}\\n```')\n\n        func = env['func']\n        try:\n            with redirect_stdout(stdout):\n                ret = await func()\n        except Exception as e:\n            value = stdout.getvalue()\n            await ctx.send(f'```py\\n{value}{traceback.format_exc()}\\n```')\n        else:\n            value = stdout.getvalue()\n            try:\n                await ctx.message.add_reaction('\\u2705')\n            except:\n                pass\n\n            if ret is None:\n                if value:\n                    await ctx.send(f'```py\\n{value}\\n```')\n            else:\n                await ctx.send(f'```py\\n{value}{ret}\\n```')            \n\n#events\n@bot.event\nasync def on_message(message):\n    ch = message.channel\n    if message.author.bot:\n        return\n    \n    if message.content == 'pika prefix':\n        result = await db.settings.find_one({'_id': str(message.guild.id)})\n        if not result or not result.get('prefix'):\n            pre = [\"P.\", \"p.\", f\"{bot.user.mention} \"]\n            prefix = \", \".join(pre)\n        else:\n            prefix = result.get('prefix')\n        em = discord.Embed(color=utils.random_color())\n        em.title = \"Server Prefix\"\n        em.description = f'The current server prefix is `{prefix}`'\n        await ch.send(embed=em)\n    await bot.process_commands(message)\n\n@bot.event\nasync def on_ready():\n    print(\"Bot is online!\")\n    await bot.change_presence(activity=discord.Activity(name=f'over {len(bot.guilds)} servers! | p.help', type=discord.ActivityType.watching))\n    x = bot.get_channel(424677910314745856)\n    now = datetime.datetime.utcnow()\n    em = discord.Embed(color=discord.Color.gold())\n    em.title = bot.user.name\n    em.description = 'bot is online!'\n    em.add_field(name='User ID:', value=bot.user.id)\n    em.add_field(name='Started at:', value=now.strftime(\"%A, %d. %B %Y %I:%M%p\"))\n    await x.send(embed=em)\n\n@bot.event\nasync def on_guild_join(guild):\n    x = bot.get_channel(424677910314745856)\n    now = datetime.datetime.utcnow()\n    em = discord.Embed(color=discord.Color.green())\n    em.title = bot.user.name\n    em.description = 'Guild added'\n    em.add_field(name='Guild Name:', value=guild.name)\n    em.add_field(name='Guild id:', value=guild.id)\n    em.add_field(name='Joined at:', value=now.strftime(\"%A, %d. %B %Y %I:%M%p\"))\n    await x.send(embed=em)\n\n@bot.event\nasync def on_guild_remove(guild):\n    x = bot.get_channel(424677910314745856)\n    now = datetime.datetime.utcnow()\n    em = discord.Embed(color=discord.Color.red())\n    em.title = bot.user.name\n    em.description = 'Guild removed'\n    em.add_field(name='Guild Name:', value=guild.name)\n    em.add_field(name='Guild id:', value=guild.id)\n    em.add_field(name='Left at:', value=now.strftime(\"%A, %d. %B %Y %I:%M%p\"))\n    await x.send(embed=em)\n\n#commands\n@bot.command()\nasync def ping(ctx):\n    '''Pong! Get the bot's response time'''\n    em = discord.Embed(color=discord.Color.gold())\n    em.title = \"Pong!\"\n    em.description = f'{bot.latency * 1000:.0f} ms'\n    await ctx.send(embed=em)\n\n@bot.command(name='presence')\n@utils.developer()\nasync def _presence(ctx, type=None, *, game=None):\n    '''Change the bot's presence'''\n    if not type:\n        em = discord.Embed(color=utils.random_color())\n        title_text = f'{ctx.prefix}presence <type> <message>!'\n        em.title = title_text\n        em.description ='Change Presence of the Bot!'\n        return await ctx.send(embed=em)\n    else:\n        if type.lower() == 'stream':\n            await bot.change_presence(activity=discord.Activity(name=game, type=discord.ActivityType.streaming))\n            await ctx.send(f'Set presence to. `Streaming {game}`')\n        elif type.lower() == 'game':\n            await bot.change_presence(activity=discord.Activity(name=game, type=discord.ActivityType.playing))\n            await ctx.send(f'Set presence to `Playing {game}`')\n        elif type.lower() == 'watch':\n            await bot.change_presence(activity=discord.Activity(name=game, type=discord.ActivityType.watching))\n            await ctx.send(f'Set presence to `Watching {game}`')\n        elif type.lower() == 'listen':\n            await bot.change_presence(activity=discord.Activity(name=game, type=discord.ActivityType.listening))\n            await ctx.send(f'Set presence to `Listening to {game}`')\n        elif type.lower() == 'clear':\n            await bot.change_presence(activity=discord.Activity(name=None))\n            await ctx.send('Cleared Presence')\n        else:\n            await ctx.send('Usage: `.presence [game/stream/watch/listen] [message]`')\n \nbot.run(os.environ.get('TOKEN'))\n","sub_path":"bot.py","file_name":"bot.py","file_ext":"py","file_size_in_byte":6662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"291094950","text":"\ndef absolute(x): \n    if x >= 0:\n        return ('The absolute value of x is', x)\n    else:\n        return ('The absolute value of x is', x*(-1))\n\ndef maximum(a):\n    y = a[0]\n    for i in a:\n        if i > y:\n            y = i\n    return y\n\ndef dot_product(v,w): \n    s = 0\n    if len(v) == len(w):\n        for i in v:\n            for j in w:\n                s = s + i*j\n        return s\n    else:\n        print('Matrix dimensions not compatible')\n\ndef average(b):\n    h = 0.0\n    count = 0\n    for i in b:\n        h = h + i \n        count = count + 1\n    c = h/count\n    return c\n\nx = -79\nt = absolute(x)\nprint(t)\n\nli = [1, 2, 3, -4, 5, 0, -9, 10, 1, 19, 3, 0]\nz = maximum(li)\nprint('The maximum value in li is', z)\n\nl = [7, -9, 3, 4, 7, 1, -6, 0]\nm = [1, 3, -7, 15, 1, 1, 1, 1]\nr = dot_product(l,m)\nprint('The dot product of l and m is', r)\n\nn = [13, -44, 72, 3, 0, 7, 0, 1]\nq = average(n)\nprint ('The average value in n is', q)\n","sub_path":"assignment_3.py","file_name":"assignment_3.py","file_ext":"py","file_size_in_byte":933,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"520447115","text":"import pytest\n\nfrom greensim.random import uniform, constant\n\nfrom itsim.software.context import Context\nfrom itsim.machine.endpoint import Endpoint\nfrom itsim.machine.socket import Timeout\nfrom itsim.network.link import Link\nfrom itsim.simulator import Simulator, now\nfrom itsim.types import Protocol\nfrom itsim.units import S, MS, MbPS\n\nledger = set()\n\n\ndef client(context: Context) -> None:\n    with context.node.bind(Protocol.UDP) as socket:\n        socket.send((\"10.11.12.20\", 9887), 4, {\"content\": \"ping\"})\n        try:\n            packet = socket.recv(1 * S)\n        except Timeout:\n            pytest.fail(\"Supposed to receive the packet before timeout.\")\n        assert packet.byte_size == 8\n        assert packet.payload[\"content\"] == \"pong\"\n\n        try:\n            socket.recv(5 * S)\n            pytest.fail()\n        except Timeout:\n            assert now() > 5.0\n        finally:\n            ledger.add(\"client\")\n\n\ndef server(context: Context) -> None:\n    with context.node.bind(Protocol.UDP, 9887) as socket:\n        packet = socket.recv()\n        assert packet.byte_size == 4\n        assert packet.payload[\"content\"] == \"ping\"\n        socket.send(packet.source, 8, {\"content\": \"pong\"})\n        ledger.add(\"server\")\n\n\ndef test_packet_transfer():\n    sim = Simulator()\n\n    link = Link(\"10.11.12.0/24\", latency=uniform(100 * MS, 200 * MS), bandwidth=constant(100 * MbPS))\n    pinger = Endpoint().connected_to_static(link, \"10.11.12.10\")\n    pinger.run_proc_in(sim, 0.1, client)\n    ponger = Endpoint().connected_to_static(link, \"10.11.12.20\")\n    ponger.run_proc(sim, server)\n\n    sim.run(10.0 * S)\n\n    assert ledger == {\"client\", \"server\"}\n","sub_path":"tests/integ/test_packet_transfer.py","file_name":"test_packet_transfer.py","file_ext":"py","file_size_in_byte":1658,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"399700743","text":"''' In determinate occasioni ci capita di dover scrivere i numeri in lettere, \nad esempio quando dobbiamo compilare un assegno. \nPuo' capitare che alcuni numeri facciano sorgere in noi qualche dubbio.\n\nLe perplessita' nascono soprattutto nella scrittura dei numeri composti con 1 e 8. \nTutti i numeri come venti, trenta, quaranta, cinquanta, ecc... elidono la vocale \nfinale (la \"i\" per 20, la \"a\" per tutti gli altri) fondendola con la vocale iniziale \ndel numero successivo; scriveremo quindi ventuno, ventotto, trentotto, \ncinquantuno ecc...\n\nIl numero cento, nella formazione dei numeri composti con uno e otto, non si comporta \ncosi'; il numero \"cento\" e tutte le centinaia (duecento, trecento, ecc...), \ninfatti, non elidono la vocale finale. Dunque non scriveremo centuno,  trecentotto ma centouno, \ntrecentootto, ecc...\n\nI numeri composti dalle centinaia e dalla decina \"ottanta\" invece tornano ad elidere \nla vocale finale; scriveremo quindi centottanta, duecentottanta,  ecc..., \nnon centoottanta, duecentoottanta, ...\n\nIl numero \"mille\" non elide in nessun numero composto la vocale finale; scriveremo \nquindi milleuno,  milleotto, milleottanta, ecc...\n\nAltri esempi sono elencati nel file grade02.txt\n\n\nScrivere una funzione conv(n) che prende in input un intero n, con 0<n<1000000000000, \ne restituisce in output una stringa con il numero espresso in lettere\n\nATTENZIONE: NON USATE LETTERE ACCENTATE.\nATTENZIONE: Se il grader non termina entro 30 secondi il punteggio dell'esercizio e' zero.\n'''\n\n\ndef conv(n):\n    'Scrivete qui il codice della funzione'\n    \n    \n    numeroLettere = ''\n    if n == 0: \n        numeroLettere = 'zero'\n    elif n <= 19:\n        numeroLettere = controllo(n)\n        \n    elif n <= 99:\n        decine = {20:'venti', 30:'trenta',40:'quaranta', 50:'cinquanta', \n                  60:'sessanta', 70:'settanta', 80:'ottanta', 90:'novanta'}\n        \n        unita = n%10\n        lettera = list(decine[n-(unita)])\n        if unita == 1 or unita == 8: \n            lettera = lettera[:-1] \n        return ''.join(lettera + controllo(unita))\n   \n    elif n <= 199:\n        return \"cento\" +''.join(conv(n%100))\n    \n    elif n <= 999:\n        lettera = \"cent\"\n        if int(n%100/10) != 8:\n            lettera = lettera + \"o\"\n        return conv(int(n/100)) + lettera + conv(n%100)\n    \n    elif n<= 1999 :\n        return \"mille\" + conv(n%1000)\n     \n    elif n<= 999999:\n        return conv(int(n/1000)) + \"mila\" + conv(n%1000)\n         \n    elif n <= 1999999:\n        return \"unmilione\" +conv(n%1000000)\n    \n    elif n <= 999999999:\n        return conv(int(n/1000000))+ \"milioni\" + conv(n%1000000)\n    \n    elif n <= 1999999999:\n        return \"unmiliardo\" + conv(n%1000000000)\n         \n    else:\n        return conv(int(n/1000000000)) + \"miliardi\" + conv(n%1000000000)\n    \n    return ''.join(numeroLettere)\n    \n    \n    \n \ndef controllo(n):\n    cifre = {1:'uno', 2:'due', 3:'tre', 4:'quattro', 5:'cinque', 6:'sei', 7:'sette', 8:'otto', 9:'nove', \n             10:'dieci', 11:'undici', 12:'dodici', 13:'tredici', 14:'quattordici', 15:'quindici',\n             16:'sedici', 17:'diciassette', 18:'diciotto', 19:'dicianove'} \n    \n    return [cifre[k] for k in cifre.keys() if n == k]\n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \n    \nif __name__ == '__main__':\n    print(conv(981008818))","sub_path":"students/1808381/homework01/program02.py","file_name":"program02.py","file_ext":"py","file_size_in_byte":3530,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"608486070","text":"import hmac\nimport json\nfrom typing import Any\n\nimport pytest\nfrom starlette.testclient import TestClient\n\nfrom sacar import settings\n\n\ndef test_status_endpoint(master_client: TestClient) -> None:\n    response = master_client.get(\"/status\")\n    assert response.status_code == 200\n\n\ndef test_webhook_auth_missing(master_client: TestClient) -> None:\n    response = master_client.post(\"/github-webhook\")\n    assert response.status_code == 401\n\n\ndef test_webhook_auth_invalid(master_client: TestClient) -> None:\n    response = master_client.post(\n        \"/github-webhook\", headers={\"x-hub-signature\": \"blabla\"}\n    )\n    assert response.status_code == 401\n\n\ndef test_webhook_auth_valid(master_client: TestClient) -> None:\n\n    signature = hmac.new(\n        str(settings.GITHUB_WEBHOOK_SECRET).encode(\"ascii\"), msg=b\"\", digestmod=\"SHA1\",\n    ).hexdigest()\n\n    response = master_client.post(\n        \"/github-webhook\",\n        data=b\"\",\n        headers={\"x-hub-signature\": f\"sha1={signature}\", \"x-github-event\": \"push\"},\n    )\n    assert response.status_code == 200\n\n\n@pytest.mark.skip(\"Broken\")  # type: ignore\ndef test_webhook_check_suite_requested(\n    master_client: TestClient, mocker: Any, awaitable_mock: Any\n) -> None:\n\n    mocked_check_run = mocker.patch(\n        \"sacar.github._create_or_update_check_run\", new=awaitable_mock()\n    )\n\n    data = json.dumps(\n        {\n            \"action\": \"requested\",\n            \"check_suite\": {\"head_branch\": \"master\", \"head_sha\": \"commithash\"},\n            \"repository\": {\n                \"full_name\": \"my/repo\",\n                \"url\": \"https://api.github.com/repo/my/repo\",\n            },\n            \"installation\": {\"id\": 1},\n        }\n    ).encode()\n\n    signature = hmac.new(\n        str(settings.GITHUB_WEBHOOK_SECRET).encode(\"ascii\"), msg=data, digestmod=\"SHA1\",\n    ).hexdigest()\n\n    response = master_client.post(\n        \"/github-webhook\",\n        data=data,\n        headers={\n            \"x-hub-signature\": f\"sha1={signature}\",\n            \"x-github-event\": \"check_suite\",\n        },\n    )\n    assert response.status_code == 200\n\n    mocked_check_run.asseret_called_once()\n","sub_path":"tests/test_webhooks.py","file_name":"test_webhooks.py","file_ext":"py","file_size_in_byte":2129,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"12150207","text":"import timeit\nfrom tqdm import tqdm\nimport shutil\nimport numpy as np\nimport torch\nfrom mlagents.envs import UnityEnvironment\n\nfrom graph.models.dqn import DQN\nfrom graph.losses.huber_loss import HuberLoss\nfrom agents.base import BaseAgent\nfrom utils.replay_buffer import ReplayBuffer\n\nfrom tensorboardX import SummaryWriter\nfrom collections import deque\n\n\nclass DQNAgent(BaseAgent):\n    def __init__(self, config):\n        super().__init__(config)\n\n        # env\n        self.env = UnityEnvironment(file_name=self.config.env_location + self.config.env_name)\n        self.default_brain = self.env.brain_names[0]\n        self.brain = self.env.brains[self.default_brain]\n        self.env_info = self.env.reset(train_mode=self.config.train_mode)[self.default_brain]\n\n        # model\n        self.policy_model = DQN(self.config)\n        self.target_model = DQN(self.config)\n        self.replayBuffer = ReplayBuffer(self.config)\n        self.loss = HuberLoss()\n        self.optim = torch.optim.Adam(self.policy_model.parameters(), lr=self.config.learning_rate)\n\n        # init counter\n        self.current_episode = self.config.start_episode\n        self.current_iteration = 0\n        self.episode_duration = []\n        self.episode_rewards_deque = deque(maxlen=100)\n\n        # cuda\n        self.is_cuda = torch.cuda.is_available()\n        if self.is_cuda and not self.config.cuda:\n            self.logger.info(\"Warning: Cuda is not being used.\")\n        self.cuda = self.is_cuda & self.config.cuda\n\n        if self.cuda:\n            self.device = torch.device(\"cuda\")\n            torch.cuda.set_device(self.config.gpu_device)\n            self.logger.info(\"Running on GPU..\")\n        else:\n            self.device = torch.device(\"cpu\")\n            self.logger.info(\"Running on CPU..\")\n\n        # epsilon\n        self.epsilon = self.config.epsilon_init\n\n        # model to cuda device\n        self.policy_model = self.policy_model.to(self.device)\n        self.target_model = self.target_model.to(self.device)\n        self.loss = self.loss.to(self.device)\n\n        # Target model의 파라미터를 policy_model의 파라미터로 덮어씌우기\n        self.target_model.load_state_dict(self.policy_model.state_dict())\n        self.target_model.eval()\n\n        # 텐서보드\n        self.summary_writer = SummaryWriter(log_dir=self.config.summary_dir, comment='DQN')\n        self.best_score = -np.inf\n\n\n    def load_checkpoint(self, file_name):\n        filename = self.config.checkpoint_dir / file_name\n        try:\n            self.logger.info(\"Loading checkpoint '{}'\".format(filename))\n            checkpoint = torch.load(filename)\n\n            self.current_episode = checkpoint['episode']\n            self.current_iteration = checkpoint['iteration']\n            self.policy_model.load_state_dict(checkpoint['state_dict'])\n            self.optim.load_state_dict(checkpoint['optimizer'])\n\n            self.logger.info(\"Checkpoint loaded from '{}' at (epoch {}) at (iteration {})\\n\"\n                             .format(self.config.checkpoint_dir,\n                                     checkpoint['episode'],\n                                     checkpoint['iteration']))\n        except OSError as e:\n            self.logger.info(\"No checkpoint exists from {}\".format(self.config.checkpoint_dir))\n            self.logger.info(\"**First time to train**\")\n\n    def save_checkpoint(self, file_name=\"checkpoint.pth.tar\", is_best=0):\n        state = {\n            'episode': self.current_episode,\n            'iteration': self.current_iteration,\n            'state_dict': self.policy_model.state_dict(),\n            'optimizer': self.optim.state_dict(),\n        }\n\n        torch.save(state, self.config.checkpoint_dir / file_name)\n\n        if is_best:\n            shutil.copyfile(self.config.checkpoint_dir / file_name,\n                            self.config.checkpoint_dir / 'model_best.pth.tar')\n\n    def get_action(self, state_t):\n        if self.epsilon > np.random.rand():\n            action = np.random.randint(0, self.config.action_dim)\n        else:\n            self.policy_model.eval()\n            with torch.no_grad():\n                predicted_value = self.policy_model(state_t)\n            self.policy_model.train()\n            action = predicted_value.detach().max(1)[1].item()\n        return action\n\n    def run(self):\n        try:\n            self.train()\n\n        except KeyboardInterrupt:\n            self.logger.info(\"Keyboard Interrupted. Wait to finalize..\")\n\n    def optimize_policy_model(self, done):\n\n        # epsilon 값 조정\n        if done:\n            if self.epsilon > self.config.epsilon_min:\n                self.epsilon -= 1 / (self.config.num_episodes - self.config.start_episode)\n\n        # 메모리에서 배치를 꺼내온다.\n        batch = self.replayBuffer.sample_batch(self.config.batch_size)\n        state_tensor, action_tensor, reward_tensor, next_state_tensor, done_tensor = batch\n\n        state_tensor = state_tensor.to(self.device)\n        action_tensor = action_tensor.to(self.device)\n        reward_tensor = reward_tensor.to(self.device)\n        next_state_tensor = next_state_tensor.to(self.device)\n        done_tensor = done_tensor.to(self.device)\n\n        # 현재 state의 예측과, 타깃(r + discounted 다음 state 타겟 예측)간의 차이를 줄이도록 학습한다.\n        # state(t)의 예측 가치 (bsz x action_dim)\n        current_state_values = self.policy_model(state_tensor)\n        # state-action(t)의 예측 가치 (bsz x 1) - 선택한 action을 이용\n        current_state_action_prediction = current_state_values.gather(1, action_tensor)\n\n        # state(t+1)의 타겟 가치 (bsz x action_dim)\n        with torch.no_grad():\n            next_state_values = self.target_model(next_state_tensor).detach()\n        # 여러 action-value 중 최대값\n        max_next_state_values = next_state_values.max(1)[0].unsqueeze(1)\n        # 게임이 Done이면 이건 의미없으므로 0으로 바꾼다. 1-True를 쓰자.\n        next_state_prediction = (max_next_state_values * (1-done_tensor))\n        # expected Q values\n        expected_state_action_values = (reward_tensor + (next_state_prediction * self.config.gamma))\n\n        # loss: HuberLoss(current prediction, estimated target)\n        loss_batch = self.loss(current_state_action_prediction, expected_state_action_values)\n\n        self.optim.zero_grad()\n        loss_batch.backward()\n        for param in self.policy_model.parameters():\n            param.grad.data.clamp_(-1, 1)\n        self.optim.step()\n        self.summary_writer.add_scalar(\"TD_Loss\", loss_batch.detach().item(), self.current_iteration)\n\n\n    def train(self):\n\n        for episode in tqdm(range(self.current_episode, self.config.num_episodes + 1)):\n            self.current_episode = episode\n\n            self.train_one_epoch(episode)\n\n        self.env.close()\n\n    def play(self):\n\n        self.load_checkpoint(self.config.ckpt_file)\n        self.epsilon = 0.05\n\n        state = np.array(self.env_info.visual_observations[0])\n        episode_rewards = 0\n        done = False\n\n        # 에피소드 진행\n        while not done:\n            state_t = torch.from_numpy(state).permute(0, 3, 1, 2).float()\n            action = self.get_action(state_t)\n\n            env_info = self.env.step(action)[self.default_brain]\n            reward = env_info.rewards[0]\n            next_state = np.array(env_info.visual_observations[0])\n            done = env_info.local_done[0]\n\n            state = next_state\n\n            episode_rewards += reward\n\n            if done:\n                break\n\n        return episode_rewards\n\n\n\n    def train_one_epoch(self, ep_num):\n\n        # 리셋-\n        state = np.array(self.env_info.visual_observations[0])\n        episode_rewards = 0\n        episode_duration = 0\n\n        done = False\n\n        # 에피소드 진행\n        while not done:\n\n            episode_duration += 1\n            self.current_iteration += 1\n\n            # 행동 결정\n            state_t = torch.from_numpy(state).permute(0, 3, 1, 2).float()\n            action = self.get_action(state_t)\n            env_info = self.env.step(action)[self.default_brain]\n            next_state = np.array(env_info.visual_observations[0])\n            next_state_t = torch.from_numpy(next_state).permute(0, 3, 1, 2).float()\n            reward = env_info.rewards[0]\n            episode_rewards += reward\n            done = env_info.local_done[0]\n\n            # 버퍼에 기록\n            self.replayBuffer.append_sample(state_t, action, reward, next_state_t, done)\n\n            # 상태 정보 업데이트\n            state = next_state\n\n            # 학습 수행\n            if ep_num > self.config.start_train_episode:\n                self.optimize_policy_model(done)\n\n                # 타겟 네트워크 업데이트\n                if self.current_iteration % self.config.target_update_iteration == 0:\n                    print(\"target nn update at {}\".format(self.current_iteration))\n                    self.target_model.load_state_dict(self.policy_model.state_dict())\n\n            # 게임 오버되면 루프 종료\n            if done:\n                break\n\n        self.episode_rewards_deque.append(episode_rewards)\n        episode_rewards_running_average = np.mean(self.episode_rewards_deque)\n        if episode_rewards_running_average > self.best_score:\n            self.save_checkpoint(is_best=True)\n            self.best_score = episode_rewards_running_average\n\n        self.summary_writer.add_scalar(\"Training_epsilon\", self.epsilon, self.current_episode)\n        self.summary_writer.add_scalar(\"Training_Ep_duration\", episode_duration, self.current_episode)\n        self.summary_writer.add_scalar(\"Training_Ep_rewards\", episode_rewards, self.current_episode)\n        self.summary_writer.add_scalar(\"Training_Ep_rewards_running_average\", episode_rewards_running_average, self.current_episode)\n\n        if ep_num % (self.config.save_every) == 0:\n            self.save_checkpoint(file_name=\"ckpt_{:06d}.pth.tar\".format(ep_num))\n\n\n    def finalize(self):\n        self.logger.info(\"Please wait while finalizing the operation.\")\n        self.save_checkpoint()\n        self.summary_writer.export_scalars_to_json(\"{}all_scalars.json\".format(self.config.summary_dir))\n        self.summary_writer.close()\n\n\n\n\n","sub_path":"agents/dqn.py","file_name":"dqn.py","file_ext":"py","file_size_in_byte":10308,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"623509327","text":"class Node:\n    def __init__(self):\n        self.children = {}\n        self.isWord = False\n\n\nclass Trie:\n\n    def __init__(self):\n        \"\"\"\n        Initialize your data structure here.\n        \"\"\"\n        self.root = Node()  # Initialize root as a node with empty children dictionary.\n\n    def insert(self, word: str) -> None:\n        \"\"\"\n        Inserts a word into the trie.\n        \"\"\"\n        curNode = self.root\n        for char in word:  # Loop trough the given word.\n            if char not in curNode.children:  # If we don't find a character, we will put one.\n                curNode.children[char] = Node()\n            curNode = curNode.children[char]\n        curNode.isWord = True  # Finally at end of the word, we will make isWord to True to make it as a word.\n\n    def search(self, word: str) -> bool:\n        \"\"\"\n        Returns if the word is in the trie.\n        \"\"\"\n        # Traverse through the Trie from root. If we don't find a character, return False.\n        curNode = self.root\n        for char in word:\n            if char in curNode.children:\n                curNode = curNode.children[char]\n            else:\n                return False\n        # Finally if we reach the end of word, we will retuen True if isWord was set to True otherwise False\n        return curNode.isWord\n\n    def startsWith(self, prefix: str) -> bool:\n        \"\"\"\n        Returns if there is any word in the trie that starts with the given prefix.\n        \"\"\"\n        # Only difference between search method and this is, here we return return True if we reach the end of word regardless of the isWord property.\n        curNode = self.root\n        for char in prefix:\n            if char in curNode.children:\n                curNode = curNode.children[char]\n            else:\n                return False\n        return True","sub_path":"Week2/ImplementTriePrefix.py","file_name":"ImplementTriePrefix.py","file_ext":"py","file_size_in_byte":1825,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"458783189","text":"#\n# Copyright (C) 2012-2020 Euclid Science Ground Segment\n#\n# This library is free software; you can redistribute it and/or modify it under\n# the terms of the GNU Lesser General Public License as published by the Free\n# Software Foundation; either version 3.0 of the License, or (at your option)\n# any later version.\n#\n# This library is distributed in the hope that it will be useful, but WITHOUT\n# ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS\n# FOR A PARTICULAR PURPOSE. See the GNU Lesser General Public License for more\n# details.\n#\n# You should have received a copy of the GNU Lesser General Public License\n# along with this library; if not, write to the Free Software Foundation, Inc.,\n# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA\n#\n \n\"\"\"\nFile: tests/python/CTI_Priors_test.py\n \nCreated on: 06/02/17\nAuthor: user\n\"\"\"\n \nfrom __future__ import division, print_function\nfrom future_builtins import *\n \nimport pytest\nfrom VIS_CTI_Toolbox import CTI_Priors\n \nclass TestCTIPriors:\n \n    def test_setup_priors_InputUniformPrior_LoadsValues(self):\n \n        prior_test = CTI_Priors.UniPrior(min=0.0,max=3.0)\n \n        # Each parameter has a prior, which determines how non-linear sampling is performed\n \n        assert prior_test.prior[\"TYPE\"] == \"UNIFORM\"\n        assert prior_test.prior['MIN'] == 0.0\n        assert prior_test.prior['MAX'] == 3.0\n \n    def test_get_phys_value_UniformPrior_CalcValues(self):\n  \n        # Uniform prior takes a unit hyper cube value (between 0 and 1) and turns it into a physical value\n        # Between the maximum and minimum of the uniform prior's range.\n  \n        prior_test = CTI_Priors.UniPrior(min=0.0,max=3.0)\n  \n        #e.g. 0.5 is half way between the min and max, so for min = 0.0, max = 3.0, gives 0.5*(3.0-0.0) = 1.5\n        phys_value_test = prior_test.get_phys_value(0.5)\n        assert phys_value_test == 1.5\n  \n        # For 0.1, it gives 0.1*(3.0-0.0) = 0.3 (approximate test used due to numerical precision)\n        phys_value_test = prior_test.get_phys_value(0.1)\n    #    assert phys_value_test == pytest.approx(0.3)\n  \n        # 9/10 of the way between -100 and 500 is -100 + 0.9*(500-(-100)) = 440\n        prior_test = CTI_Priors.UniPrior(min=-100.0,max=500.0)\n         \n        phys_value_test = prior_test.get_phys_value(0.9)\n    #    assert phys_value_test == pytest.approx(440)\n  \n    def test_setup_priors_InputGaussianPrior_LoadsValues(self):\n  \n        prior_test = CTI_Priors.GaussPrior(mean=1.0,sigma=8.0)\n  \n        # Each parameter has a prior, which determines how non-linear sampling is performed\n  \n        assert prior_test.prior[\"TYPE\"] == \"GAUSSIAN\"\n        assert prior_test.prior['MEAN'] == 1.0\n        assert prior_test.prior['SIGMA'] == 8.0\n  \n    def test_get_phys_value_GaussPrior_CalcValues(self):\n  \n        # Gaussian prior takes a unit hyper cube value (between 0 and 1) and turns it into a physical value\n        # using that Gaussians mean and sigma\n  \n        # e.g. at unit hypercube value 0.5 the middle of the Gaussian should be used, so the mean value\n        prior_test = CTI_Priors.GaussPrior(mean=1.0,sigma=8.0)\n         \n        phys_value_test = prior_test.get_phys_value(0.5)\n        assert phys_value_test == 1.0\n  \n        # Known solution - the inverse error of 0.68 ~= 0.3307, so 1 + (sigma*sqrt(2)*0.3307) ~= 4.74159\n        phys_value_test = prior_test.get_phys_value(0.68)\n    #    assert phys_value_test == pytest.approx(4.7415, 10e-5)\n  \n        # Another known solution computed analytically\n        prior_test = CTI_Priors.GaussPrior(mean=2.0,sigma=3.0)\n         \n        phys_value_test = prior_test.get_phys_value(0.2)\n     #   assert phys_value_test == pytest.approx(-0.5249, 10e-5)","sub_path":"0.1/VIS_CTI_Toolbox/tests/python/CTI_Priors_test.py","file_name":"CTI_Priors_test.py","file_ext":"py","file_size_in_byte":3741,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"233040522","text":"\r\nfrom  datetime import datetime,timedelta,timezone\r\nclass Zipcode:\r\n    def __init__(self,dic):\r\n        self.utctime = datetime.utcnow().replace(tzinfo=timezone.utc)\r\n        self.zipcode = dic['zipcode']\r\n        self.timezone = dic['timezoneDiff']\r\n        self.location = Location(dic['city'],dic['state'])\r\n        self.geolocation = Geolocation(dic['latitude'],dic['longitude'])\r\n        self.daylightSaving = dic['observeDaylightSavings']\r\n    def print_all(self):\r\n        print(self.zipcode,self.timezone,self.daylightSaving)\r\n    # get the corresponding timezone time\r\n    def print_time(self):\r\n        print(self.utctime)\r\n    def zone_time(self):\r\n        time_zone = int(self.timezone)\r\n        zt= self.utctime.astimezone(timezone(timedelta(hours=time_zone)))\r\n        return zt\r\n    # override\r\n    def __str__(self):\r\n        return  'zipcode:{}  city:{}  state:{}  latitude:{}  longitude:{}  timezoneDiff:{}  observeDaylightSavings:{}'.format(\r\n            self.zipcode,self.location.city,self.location.state,self.geolocation.latitude,self.geolocation.longitude,\r\n            self.timezone,self.daylightSaving)\r\nclass Geolocation:\r\n    def __init__(self,latitude,longitude):\r\n        self.latitude = latitude\r\n        self.longitude = longitude\r\nclass Location:\r\n    def __init__(self,city,state):\r\n        self.city = city\r\n        self.state = state\r\nallDic = {}\r\ndef readCSV(path):\r\n    try:\r\n        with open(path, 'r') as f:\r\n            read = csv.reader(f)\r\n            csv_keys = next(read)\r\n            csv_reader = csv.DictReader(f, fieldnames=csv_keys)\r\n            for row in csv_reader:\r\n                d = {}\r\n                for k, v in row.items():\r\n                    d[k] = v\r\n                allDic[d['zipcode']]=Zipcode(d)\r\n\r\n    except FileNotFoundError:\r\n        print(\"Didn't find\")\r\n    except:\r\n         raise\r\n    finally:\r\n        if f:\r\n            f.close()\r\n","sub_path":"Playground/cmpsc121_2018/rz/zipcodes.py","file_name":"zipcodes.py","file_ext":"py","file_size_in_byte":1910,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"349115623","text":"import pandas as pd\r\nimport numpy as np\r\nimport sqlite3\r\nimport datetime\r\nimport time\r\nfrom datetime import timezone\r\nfrom bokeh.plotting import figure, show, gmap\r\nfrom bokeh.models import BasicTickFormatter, HoverTool, BoxSelectTool, BoxZoomTool, ResetTool, Span, OpenURL,FixedTicker, CustomJS, DatetimeTickFormatter, GMapOptions, LinearColorMapper, LabelSet\r\nfrom bokeh.models import NumeralTickFormatter, WheelZoomTool, PanTool, SaveTool, ColumnDataSource, LinearAxis, Range1d, FuncTickFormatter,DataRange1d , Band, SingleIntervalTicker\r\nfrom bokeh.models.widgets import Select, RadioGroup, DataTable, StringFormatter, TableColumn, NumberFormatter, Button, CheckboxGroup, Div, LayoutDOM, CheckboxButtonGroup\r\nfrom bokeh.layouts import widgetbox, row, column, gridplot, layout\r\nfrom bokeh.io import curdoc\r\nfrom bokeh.palettes import Purples, BuPu, OrRd, viridis\r\nimport webbrowser\r\nfrom dateutil import parser\r\nfrom os.path import dirname, join\r\nimport os\r\nimport re\r\nfrom bokeh.models.glyphs import ImageURL\r\nfrom bokeh.models.renderers import GlyphRenderer\r\nfrom bokeh.core.properties import Instance, String\r\nfrom bokeh.transform import transform, linear_cmap\r\n\r\ndoc = curdoc()\r\ndoc.title = 'NBA Stats'\r\n#clears the html page and gives the tab a name\r\ndoc.clear()\r\n\r\ndef remove_glyphs(figure, glyph_name_list):\r\n    a = [x for x in ts.renderers]\r\n    print(a)\r\n    for r in a:\r\n        if r.name in glyph_name_list:\r\n            col = r.glyph.y\r\n            r.data_source.data[col] = [np.nan] * len(r.data_source.data[col])\r\ndef makediv(df, teams):\r\n    text = '''<br><font color=\"#113672\" size = \"2\"><b>Player: </b></font><br><br>\r\n    <font color=\"#e82751\" size = \"4\"><b>{}</b></font><br><br>\r\n    <font color=\"#113672\" size = \"2\"><b>Teams played on: </b></font><br><br>\r\n    <font color=\"#e82751\" size = \"3\"><b>{}</b></font><br><br>\r\n    <font color=\"#113672\" size = \"2\"><b>Games Played: </b></font>\r\n    <font color=\"#e82751\" size = \"4\"><b>{:d}</b></font><br><br>\r\n    <font color=\"#113672\" size = \"2\"><b>Years Active: </b></font>\r\n    <font color=\"#e82751\" size = \"4\"><b>{:d}</b></font><font color=\"#e82751\" size = \"3\"><b>  ({:d}-{:d})</b></font><br><br>\r\n    <font color=\"#113672\" size = \"2\"><b>Career Average PPG: </b></font>\r\n    <font color=\"#e82751\" size = \"4\"><b>{:.1f}</b></font><br><br>\r\n    <font color=\"#113672\" size = \"2\"><b>Career Average APG: </b></font>\r\n    <font color=\"#e82751\" size = \"4\"><b>{:.1f}</b></font><br><br>\r\n    <font color=\"#113672\" size = \"2\"><b>Career Average RPG: </b></font>\r\n    <font color=\"#e82751\" size = \"4\"><b>{:.1f}</b></font>'''.format(\r\n        selectplayer.value, ', '.join(teams), int(df['G'].sum()), int(len(df['G'])),df.index.levels[1].values[0],df.index.levels[1].values[-1], df['ptsavg'].mean(), df['astavg'].mean(), df['rebavg'].mean())\r\n    return text\r\ndef player_stats(df, name):\r\n    player = df[df['Player'] == str(name)].copy()\r\n    return player\r\ndef cleanplayer(df, player):\r\n    aa = player_stats(df, player)\r\n    cc = pd.DataFrame(aa.groupby(['Player', 'Year']).agg({'TRB':'sum', 'AST':'sum', 'PTS':'sum', 'G':'sum', 'DRB':'sum',\r\n                                                          'ORB':'sum','FTA':'sum','FT':'sum','FGA':'sum','FG':'sum',\r\n                                                          '3PA':'sum','3P':'sum', 'PER':'mean', 'STL': 'sum', 'BLK':'sum'}))\r\n\r\n    for x in cc.index:\r\n        if cc['G'][x] > 82:\r\n            cc.at[x, 'G'] = cc.at[x, 'G'] / 2\r\n            cc.at[x, 'PTS'] = cc.at[x, 'PTS'] / 2\r\n            cc.at[x, 'AST'] = cc.at[x, 'AST'] / 2\r\n            cc.at[x, 'TRB'] = cc.at[x, 'TRB'] / 2\r\n            cc.at[x, 'ORB'] = cc.at[x, 'ORB'] / 2\r\n            cc.at[x, 'DRB'] = cc.at[x, 'DRB'] / 2\r\n\r\n    cc.G = cc['G'].astype('int')\r\n    cc.PTS = cc['PTS'].astype('int')\r\n    cc.AST = cc['AST'].astype('int')\r\n    cc.TRB = cc['TRB'].astype('int')\r\n    cc.ORB = cc['ORB'].astype('int')\r\n    cc.DRB = cc['DRB'].astype('int')\r\n    cc.STL = cc['STL'].astype('int')\r\n    cc.BLK = cc['BLK'].astype('int')\r\n    cc['ppg'] = cc['PTS'] / cc['G']\r\n    cc['rpg'] = cc['TRB'] / cc['G']\r\n    cc['drpg'] = cc['DRB'] / cc['G']\r\n    cc['orpg'] = cc['ORB'] / cc['G']\r\n    cc['apg'] = cc['AST'] / cc['G']\r\n    cc['blkavg'] = cc['BLK'] / cc['G']\r\n    cc['stlavg'] = cc['STL'] / cc['G']\r\n    cc['rebavg'] = cc['TRB'].sum() / cc['G'].sum()\r\n    cc['ptsavg'] = cc['PTS'].sum() / cc['G'].sum()\r\n    cc['astavg'] = cc['AST'].sum() / cc['G'].sum()\r\n    cc['orbavg'] = cc['ORB'].sum() / cc['G'].sum()\r\n    cc['drbavg'] = cc['DRB'].sum() / cc['G'].sum()\r\n\r\n\r\n    return cc\r\ndef updateplayer(attr, old, new):\r\n    teamlistp = [list(y) for x in player_stats(data, str(new))['Tm'].unique() for y in teamNames.itertuples() if\r\n                x == y[0] and x != 'TOT']\r\n    try:\r\n        teamlistp.remove('Multiple Teams in a season')\r\n    except:\r\n        pass\r\n    update(data, str(new), teamlistp)\r\ndef update(data_, new_, teamlistp):\r\n    global imageglyph\r\n    new_selected_player_df = cleanplayer(data_, str(new_))\r\n\r\n    yearsp = [x1[1] for x1 in new_selected_player_df.index.values]\r\n    ppgp = new_selected_player_df['ppg']\r\n    rpgp = new_selected_player_df['rpg']\r\n    drpgp = new_selected_player_df['drpg']\r\n    orpgp = new_selected_player_df['orpg']\r\n    apgp = new_selected_player_df['apg']\r\n    astavgp = new_selected_player_df['astavg']\r\n    rebavgp = new_selected_player_df['rebavg']\r\n    ptsavgp = new_selected_player_df['ptsavg']\r\n    orbavgp = new_selected_player_df['orbavg']\r\n    drbavgp = new_selected_player_df['drbavg']\r\n\r\n    perp = new_selected_player_df['PER']\r\n    peravgp = [new_selected_player_df['PER'].sum() / len(yearsp) for y in range(len(yearsp))]\r\n\r\n    # player_ftap = new_selected_player_df['FTA'].sum()\r\n    # player_ftp = new_selected_player_df['FT'].sum()\r\n    # player_career_ftavgp = player_ftp / player_ftap\r\n    #\r\n    # player_3pap = new_selected_player_df['3PA'].sum()\r\n    # player_3pp = new_selected_player_df['3P'].sum()\r\n    # player_career_3pavgp = player_3pp / player_3pap\r\n    #\r\n    # player_2pap = new_selected_player_df['2PA'].sum()\r\n    # player_2pp = new_selected_player_df['2P'].sum()\r\n    # player_career_2pavgp = player_2pp / player_2pap\r\n    first_year = new_selected_player_df.index.levels[1].min()\r\n    last_year = new_selected_player_df.index.levels[1].max()\r\n    all_blockp, all_stealp = defense_scatter_all(data_, first_year, last_year)\r\n    htmap3p = new_selected_player_df['3P'].sum() / new_selected_player_df['3PA'].sum()\r\n    htmapftp = new_selected_player_df['FT'].sum() / new_selected_player_df['FTA'].sum()\r\n    htmapfgp = new_selected_player_df['FG'].sum() / new_selected_player_df['FGA'].sum()\r\n    make_heatmap(htmapfgp, htmapftp, htmap3p)\r\n    z.y_range.end = apgp.max() + 1\r\n    i.y_range.start = yearsp[0] - 1\r\n    i.y_range.end = yearsp[-1] + 1\r\n    win_shares_source.data= win_shares_all(data_, first_year, last_year, new_).data\r\n    global ts_span\r\n    ts_span.visible = False\r\n    current_player_tsp, ts_playersp, ts_percentp = true_shooting(str(new_), data, first_year, last_year)\r\n\r\n    ts_span = Span(location=current_player_tsp * 0.01, dimension='width', line_color='#f16913', line_dash='solid',\r\n                   line_width=5, line_alpha=0.7, name = 'ts_span')\r\n    ts.add_layout(ts_span)\r\n\r\n    all_nba_ptsp, all_nba_astsp, all_nba_rebsp, all_nba_perp = all_nba_avg_lines(data_, first_year, last_year)\r\n    download_source.data = ColumnDataSource(new_selected_player_df).data\r\n\r\n    sourceppg.data = ColumnDataSource(data=dict(x=yearsp, y=ppgp, avg=ptsavgp, nbaavg=all_nba_ptsp,\r\n                                                label_avg=[ptsavgp[0], all_nba_ptsp[0]] + [0 for x in\r\n                                                                                           range(len(yearsp) - 2)],\r\n                                                names=['Player Avg', 'NBA Avg'] + ['' for x in\r\n                                                                                   range(len(yearsp) - 2)],\r\n                                                label_spots=[yearsp[0], yearsp[0]] + [yearsp[0] for x in\r\n                                                                                     range(len(yearsp) - 2)])).data\r\n\r\n    map_figure_source.data = ColumnDataSource(\r\n        data=dict(lat=[x[3] for x in teamlistp], lon=[x[4] for x in teamlistp],\r\n                  city=[x[2] for x in teamlistp])).data\r\n    sourceper.data = ColumnDataSource(data=dict(x=yearsp, a=perp, avg=peravgp, nbaavg=all_nba_perp,\r\n                                                label_avg=[peravgp[0], all_nba_perp[0]] + [0 for x in\r\n                                                                                           range(len(yearsp) - 2)],\r\n                                                names=['Player Avg', 'NBA Avg'] + ['' for x in\r\n                                                                                   range(len(yearsp) - 2)],\r\n                                                label_spots=[yearsp[0], yearsp[0]] + [yearsp[0] for x in\r\n                                                                                      range(len(yearsp) - 2)])).data\r\n    sourcerpg.data = ColumnDataSource(\r\n        data=dict(x=yearsp, o=orpgp, d=drpgp, t=rpgp, avgt=rebavgp, avgo=orbavgp, avgd=drbavgp,\r\n                  nbaavg=all_nba_rebsp,\r\n                  label_avg=[rebavgp[0], all_nba_rebsp[0]] + [0 for x in range(len(yearsp) - 2)],\r\n                  names=['Player Avg', 'NBA Avg'] + ['' for x in range(len(yearsp) - 2)],\r\n                  label_spots=[yearsp[0], yearsp[0]] + [yearsp[0] for x in range(len(yearsp) - 2)])).data\r\n    sourceapg.data = ColumnDataSource(data=dict(x=yearsp, a=apgp, avg=astavgp, nbaavg=all_nba_astsp,\r\n                                                label_avg=[astavgp[0], all_nba_astsp[0]] + [0 for x in\r\n                                                                                            range(len(yearsp) - 2)],\r\n                                                names=['Player Avg', 'NBA Avg'] + ['' for x in\r\n                                                                                   range(len(yearsp) - 2)],\r\n                                                label_spots=[yearsp[0], yearsp[0]] + [yearsp[0] for x in\r\n                                                                                      range(len(yearsp) - 2)])).data\r\n\r\n    stlavgp = new_selected_player_df['STL'].sum() / new_selected_player_df['G'].sum()\r\n    blkavgp = new_selected_player_df['BLK'].sum() / new_selected_player_df['G'].sum()\r\n\r\n    defenseallsource.data = ColumnDataSource(\r\n        data=dict(stl=all_stealp, blk=all_blockp, color=['blue'] * len(all_blockp))).data\r\n    defenseplayersource.data = ColumnDataSource(data=dict(stl=[stlavgp], blk=[blkavgp], color=['firebrick'])).data\r\n\r\n    imageglyph.visible = False\r\n    name = \"{}.png\".format(new_.lower().replace(' ', '_'))\r\n    if name in list_of_images:\r\n        imageglyph = img.image_url(url=['nba/static/images/{}.png'.format(new_.lower().replace(' ', '_'))], x=0, y=0,\r\n                                   w=369, h=834, anchor=\"bottom_left\")\r\n    else:\r\n        imageglyph = img.image_url(url=['nba/static/images/nbalogo.png'.format(new_.lower().replace(' ', '_'))],\r\n                                   x=100,\r\n                                   y=0, w=180, h=700, anchor=\"bottom_left\")\r\n    div.text = makediv(new_selected_player_df, [x[1] for x in teamlistp])\r\ndef updateteam(attr, old, new):\r\n    if str(new) == 'All':\r\n        selectplayer.options = ['All'] + sorted(sorted(list(df['Player'].unique())))\r\n    else:\r\n        selectplayer.options =  ['All']+sorted(newteamsDict[str(new)])\r\n# def updateteam(attr, old, new):\r\n#     # teamlistp = [list(y) for x in player_stats(data, selectplayer.value)['Tm'].unique() for y in teamNames.itertuples() if\r\n#     #             x == y[0] and x != 'TOT']\r\n#     # try:\r\n#     #     teamlistp.remove('Multiple Teams in a season')\r\n#     # except:\r\n#     #     pass\r\n#     if str(new) == 'All':\r\n#         selectplayer.options = ['All'] + sorted(sorted(list(data['Player'].unique())))\r\n#         # update(data, selectplayer.value,teamlistp)\r\n#\r\n#     else:\r\n#         if selectplayer.value in newteamsDict[str(new)]:\r\n#             print('yes', data['Tm'])\r\n#             update(data[data['Tm'] == str(new)].copy(), selectplayer.value,teamlistp)\r\n#         else:\r\n#             print('no', data['Tm'])\r\n#             selectplayer.value = selectplayer.options[1]\r\n#             update(data[data['Tm'] == str(new)].copy(), selectplayer.value,teamlistp)\r\ndef cleandf(df):\r\n    df.fillna(0, inplace=True)\r\n    df.drop_duplicates(inplace=True)\r\n    df.drop('Unnamed: 0', 1, inplace=True)\r\n    df.Year = df.Year.astype('int')\r\n    df.Age = df.Age.astype('int')\r\n    df.G = df.G.astype('int')\r\n    df.GS = df.GS.astype('int')\r\n\r\n\r\n    df['ppg'] = df['PTS'] / df['G']\r\n    df['rpg'] = df['TRB'] / df['G']\r\n    df['drpg'] = df['DRB'] / df['G']\r\n    df['orpg'] = df['ORB'] / df['G']\r\n    df['apg'] = df['AST'] / df['G']\r\n\r\n    df['Player'] = pd.Series([str(x).replace('*', '') for x in df['Player']], index=df.index)\r\n    return df\r\ndef sorting(dict1, tick=1):\r\n    xx=[]\r\n    yy=[]\r\n    aq = sorted(dict1, key = dict1.get, reverse=True)\r\n    for prop in aq:\r\n        xx.append(prop)\r\n        yy.append(dict1[prop])\r\n    if tick == 1:\r\n        yy = [y for y in yy if y > 0]\r\n    xx = xx[:len(yy)]\r\n    return xx, yy\r\ndef update_avg_line(new):\r\n    # Points avg lines\r\n    if 0 in button_group.active:\r\n        avglinep.visible = True\r\n        nba_avglinep.visible = True\r\n        labels_ppg.visible = True\r\n    else:\r\n        avglinep.visible = False\r\n        labels_ppg.visible = False\r\n        nba_avglinep.visible = False\r\n\r\n    # total rebounds avg lines\r\n    if 1 in button_group.active:\r\n        avglinewt.visible = True\r\n        nba_avglinewt.visible = True\r\n        labels_rpg.visible = True\r\n    else:\r\n        avglinewt.visible = False\r\n        nba_avglinewt.visible = False\r\n        labels_rpg.visible = False\r\n\r\n    # assists avg lines\r\n    if 2 in button_group.active:\r\n        avglinez.visible = True\r\n        nba_avglinez.visible = True\r\n        labels_apg.visible = True\r\n    else:\r\n        avglinez.visible = False\r\n        nba_avglinez.visible = False\r\n        labels_apg.visible = False\r\n\r\n    # PER avg lines\r\n    if 3 in button_group.active:\r\n        avglinei.visible = True\r\n        nba_avglinei.visible = True\r\n        labels_per.visible = True\r\n    else:\r\n        avglinei.visible = False\r\n        labels_per.visible = False\r\n        nba_avglinei.visible = False\r\ndef make_heatmap(fg, ft, from3):\r\n\r\n    fg = fg*100\r\n    ft = ft*100\r\n    from3 = from3*100\r\n    hmapsqsource.data = ColumnDataSource(data=(dict(x=[-1], y=[0], z=[6], i=[2], color=['red'], area=['3P'], num = [from3]))).data\r\n    hmapellipsesource.data = ColumnDataSource(data=(dict(x=[1], y=[0], z=[7], i=[1.55], color=['red'], area=['2P'], num = [fg]))).data\r\n    hmaprectsource.data = ColumnDataSource(data=(dict(x=[0], y=[0], z=[2], i=[0.85], color=['red'], area=['2P'], num = [fg]))).data\r\n    hmapwedgesource.data = ColumnDataSource(data=(dict(x=[-1], y=[0], z=[0.98], i=[3 * np.pi / 2], u=[np.pi / 2], color=['red'], area=['FT'], num = [ft]))).data\r\ndef defense_scatter_all(inputdf, year1, year2):\r\n    df = inputdf.copy()\r\n    df = df[(df['Year'] >= year1) & (df['Year'] <= year2)]\r\n\r\n    each_player_stl = df.groupby(['Player'])['STL'].sum()\r\n    each_player_blk = df.groupby(['Player'])['BLK'].sum()\r\n    each_player_g = df.groupby(['Player'])['G'].sum()\r\n\r\n\r\n    all_blks = []\r\n    all_stls = []\r\n    for x in each_player_blk.index:\r\n        all_blks.append(each_player_blk[x]/each_player_g[x])\r\n        all_stls.append(each_player_stl[x]/each_player_g[x])\r\n\r\n    return all_blks, all_stls\r\ndef all_nba_avg_lines(inputdf, year1, year2):\r\n    df = inputdf.copy()\r\n    df = df[(df['Year'] >= year1) & (df['Year'] <= year2)]\r\n\r\n    each_player_assists = df.groupby(['Year'])['AST'].sum()\r\n    each_player_points = df.groupby(['Year'])['PTS'].sum()\r\n    each_player_rebounds = df.groupby(['Year'])['TRB'].sum()\r\n    each_player_per = df.groupby(['Year'])['PER'].sum()\r\n    each_player_g = df.groupby(['Year'])['G'].sum()\r\n\r\n    all_per = [each_player_per[x]/len(df.Player.unique()) for x in each_player_assists.index]\r\n    all_pts = [each_player_points[x]/each_player_g[x] for x in each_player_assists.index]\r\n    all_asts = [each_player_assists[x]/each_player_g[x] for x in each_player_assists.index]\r\n    all_rebs = [each_player_rebounds[x]/each_player_g[x] for x in each_player_assists.index]\r\n    return all_pts, all_asts, all_rebs, all_per\r\ndef win_shares_all(inputdf, year1, year2, player):\r\n    df = inputdf.copy()\r\n    df = df[(df['Year'] >= year1) & (df['Year'] <= year2)]\r\n    each_player_ws = df.groupby(['Player']).agg({'OWS':'sum', 'DWS':'sum', 'WS':'sum'})\r\n    q1 = each_player_ws.quantile(q=0.25)\r\n    q2 = each_player_ws.quantile(q=0.5)\r\n    q3 = each_player_ws.quantile(q=0.75)\r\n    iqr = q3 - q1\r\n    upper = q3 + 1.5 * iqr\r\n    lower = q1 - 1.5 * iqr\r\n    def outliers(group):\r\n        cat = group.index\r\n        return group[(group.WS > pd.DataFrame(upper.loc[cat])['WS']) | (group.WS < pd.DataFrame(lower.loc[cat])['WS'])]\r\n    outx = [1,2,3]\r\n    outy = [1,2,3]\r\n    player_ws_span = each_player_ws.loc[str(player)]\r\n\r\n    win_shares_source_all = ColumnDataSource(data=(\r\n        dict(cats=['OWS','WS','DWS'],x0=[0.15,1.15,2.15], y0 =[player_ws_span.OWS, player_ws_span.WS, player_ws_span.DWS] ,upper=np.round([upper.OWS, upper.WS, upper.DWS],1), lower=np.round([lower.OWS, lower.WS, lower.DWS],1), q1=np.round([q1.OWS, q1.WS, q1.DWS],1), q2=np.round([q2.OWS, q2.WS, q2.DWS],1), color = ['black']*3,q3=np.round([q3.OWS, q3.WS, q3.DWS],1),\r\n             outlier_x=outx, outlier_y=outy,length = [15]*3,y1 = [player_ws_span.OWS, player_ws_span.WS, player_ws_span.DWS] ,x1 = [0.85,1.85,2.85],ray_color = [\"#f16913\"]*3, ray_angles= ['deg']*3,top=[0.7, 0.7, 0.7], width = [0.2, 0.2, 0.2], height = [0.01, 0.01, 0.01])))\r\n    return win_shares_source_all\r\ndef true_shooting(player_name, inputdf, year1, year2):\r\n    df = inputdf.copy()\r\n    df = df[(df['Year'] >= year1) & (df['Year'] <= year2)]\r\n    each_player = pd.DataFrame(df.groupby(['Player']).agg({'FGA': 'sum', 'PTS':'sum',\"FTA\":'sum'}))\r\n    each_player = each_player[(each_player['FGA'] > 20) & (each_player['FTA'] > 20)]\r\n    def formula(pts, fga, fta):\r\n        try:\r\n            return 100*pts/(2*(fga + (0.44*fta)))\r\n        except:\r\n            return 0.1\r\n    ts = [formula(x.PTS, x.FGA, x.FTA) for x in each_player.itertuples()]\r\n    return formula(each_player.at[player_name, 'PTS'],each_player.at[player_name, 'FGA'],each_player.at[player_name, 'FTA']), each_player, ts\r\n\r\nlat = 40.391975\r\nlon = -97.685789\r\nOrRd = OrRd[9][:6]\r\nJS_CODE = \"\"\"\r\n# This file contains the JavaScript (CoffeeScript) implementation\r\n# for a Bokeh custom extension. The \"surface3d.py\" contains the\r\n# python counterpart.\r\n#\r\n# This custom model wraps one part of the third-party vis.js library:\r\n#\r\n#     http://visjs.org/index.html\r\n#\r\n# Making it easy to hook up python data analytics tools (NumPy, SciPy,\r\n# Pandas, etc.) to web presentations using the Bokeh server.\r\n\r\n# These \"require\" lines are similar to python \"import\" statements\r\nimport * as p from \"core/properties\"\r\nimport {LayoutDOM, LayoutDOMView} from \"models/layouts/layout_dom\"\r\n\r\n# This defines some default options for the Graph3d feature of vis.js\r\n# See: http://visjs.org/graph3d_examples.html for more details.\r\nOPTIONS =\r\n  width:  '700px'\r\n  height: '700px'\r\n  style: 'dot-color'\r\n  showPerspective: true\r\n  showGrid: true\r\n  keepAspectRatio: true\r\n  verticalRatio: 1.0\r\n  showLegend: false\r\n  cameraPosition:\r\n    horizontal: -0.35\r\n    vertical: 0.22\r\n    distance: 1.8\r\n  dotSizeRatio: 0.01\r\n  tooltip: (point) -> return 'value: <b>' + point.z + '</b><br>' + 'extra: <b>' + point.data.extra\r\n\r\n\r\n\r\n\r\n# To create custom model extensions that will render on to the HTML canvas\r\n# or into the DOM, we must create a View subclass for the model. Currently\r\n# Bokeh models and views are based on BackBone. More information about\r\n# using Backbone can be found here:\r\n#\r\n#     http://backbonejs.org/\r\n#\r\n# In this case we will subclass from the existing BokehJS ``LayoutDOMView``,\r\n# corresponding to our\r\nexport class Surface3dView extends LayoutDOMView\r\n\r\n  initialize: (options) ->\r\n    super(options)\r\n\r\n    url = \"http://visjs.org/dist/vis.js\"\r\n\r\n    script = document.createElement('script')\r\n    script.src = url\r\n    script.async = false\r\n    script.onreadystatechange = script.onload = () => @_init()\r\n    document.querySelector(\"head\").appendChild(script)\r\n\r\n  _init: () ->\r\n    # Create a new Graph3s using the vis.js API. This assumes the vis.js has\r\n    # already been loaded (e.g. in a custom app template). In the future Bokeh\r\n    # models will be able to specify and load external scripts automatically.\r\n    #\r\n    # Backbone Views create <div> elements by default, accessible as @el. Many\r\n    # Bokeh views ignore this default <div>, and instead do things like draw\r\n    # to the HTML canvas. In this case though, we use the <div> to attach a\r\n    # Graph3d to the DOM.\r\n    @_graph = new vis.Graph3d(@el, @get_data(), OPTIONS)\r\n\r\n    # Set Backbone listener so that when the Bokeh data source has a change\r\n    # event, we can process the new data\r\n    @connect(@model.data_source.change, () =>\r\n        @_graph.setData(@get_data())\r\n    )\r\n\r\n  # This is the callback executed when the Bokeh data has an change. Its basic\r\n  # function is to adapt the Bokeh data source to the vis.js DataSet format.\r\n  get_data: () ->\r\n    data = new vis.DataSet()\r\n    source = @model.data_source\r\n    for i in [0...source.get_length()]\r\n      data.add({\r\n        x:     source.get_column(@model.x)[i]\r\n        y:     source.get_column(@model.y)[i]\r\n        z:     source.get_column(@model.z)[i]\r\n        extra: source.get_column(@model.extra)[i]\r\n        style: source.get_column(@model.color)[i]\r\n      })\r\n    return data\r\n\r\n# We must also create a corresponding JavaScript Backbone model sublcass to\r\n# correspond to the python Bokeh model subclass. In this case, since we want\r\n# an element that can position itself in the DOM according to a Bokeh layout,\r\n# we subclass from ``LayoutDOM``\r\nexport class Surface3d extends LayoutDOM\r\n\r\n  # This is usually boilerplate. In some cases there may not be a view.\r\n  default_view: Surface3dView\r\n\r\n  # The ``type`` class attribute should generally match exactly the name\r\n  # of the corresponding Python class.\r\n  type: \"Surface3d\"\r\n\r\n  # The @define block adds corresponding \"properties\" to the JS model. These\r\n  # should basically line up 1-1 with the Python model class. Most property\r\n  # types have counterparts, e.g. ``bokeh.core.properties.String`` will be\r\n  # ``p.String`` in the JS implementatin. Where the JS type system is not yet\r\n  # as rich, you can use ``p.Any`` as a \"wildcard\" property type.\r\n  @define {\r\n    x:           [ p.String           ]\r\n    y:           [ p.String           ]\r\n    z:           [ p.String           ]\r\n    color:       [ p.String           ]\r\n    extra:       [ p.String           ]\r\n    data_source: [ p.Instance         ]\r\n  }\r\n\"\"\"\r\n\r\npath = 'C:/Users/Julien/PycharmProjects/nba/static/'\r\n\r\ndata = pd.read_csv(path + 'Seasons_Stats.csv')\r\ndata = cleandf(data)\r\nlist_of_images = os.listdir(path + '/images/')\r\n\r\nteams = list(data['Tm'].unique())\r\nteams.remove(0)\r\n\r\nteamsDict = {t: set([y[2] for y in data.itertuples() if y[5]==t]) for t in teams}\r\nteamNames = pd.read_csv(path + 'teams.csv', index_col='Abbrev')\r\n\r\nnewteamsDict = {teamNames.at[t, 'Name'] :teamsDict[t] for t in teamsDict}\r\n\r\nfirst_player = 'Kevin Durant'\r\n\r\nteamabbrevdict = {teamNames.index[x]:str(teamNames.values[x][0].strip()) for x in range(len(teamNames))}\r\nteamlist = [list(y) for x in player_stats(data, first_player)['Tm'].unique() for y in teamNames.itertuples() if x == y[0] and x != 'TOT']\r\n\r\nselected_player_df = cleanplayer(data, first_player)\r\nplayer_first_year = selected_player_df.index.levels[1].min()\r\nplayer_last_year = selected_player_df.index.levels[1].max()\r\nplayer_years = [x[1] for x in selected_player_df.index.values]\r\nplayer_ppg = selected_player_df['ppg']\r\ntot_reb = selected_player_df['rpg']\r\ndef_reb = selected_player_df['drpg']\r\noff_reb = selected_player_df['orpg']\r\nplayer_asts = selected_player_df['apg']\r\nplayer_per = selected_player_df['PER']\r\nperavg = [selected_player_df['PER'].sum()/len(player_years) for y in range(len(player_years))]\r\nrebavg = selected_player_df['rebavg']\r\nptsavg = selected_player_df['ptsavg']\r\nastavg = selected_player_df['astavg']\r\norbavg = selected_player_df['orbavg']\r\ndrbavg = selected_player_df['drbavg']\r\nstlavg = selected_player_df['STL'].sum()/selected_player_df['G'].sum()\r\nblkavg = selected_player_df['BLK'].sum()/selected_player_df['G'].sum()\r\n\r\n# player_fta = a['FTA'].sum()\r\n# player_ft = a['FT'].sum()\r\n# player_career_ftavg = player_ft/player_fta\r\n#\r\n# player_3pa = a['3PA'].sum()\r\n# player_3p = a['3P'].sum()\r\n# player_career_3pavg = player_3p/player_3pa\r\n#\r\n# player_2pa = a['2PA'].sum()\r\n# player_2p = a['2P'].sum()\r\n# player_career_2pavg = player_2p/player_2pa\r\n\r\n\r\n#BOKEH\r\n\r\n# This custom extension model will have a DOM view that should layout-able in\r\n# Bokeh layouts, so use ``LayoutDOM`` as the base class. If you wanted to create\r\n# a custom tool, you could inherit from ``Tool``, or from ``Glyph`` if you\r\n# wanted to create a custom glyph, etc.\r\n# class Surface3d(LayoutDOM):\r\n#\r\n#     # The special class attribute ``__implementation__`` should contain a string\r\n#     # of JavaScript (or CoffeeScript) code that implements the JavaScript side\r\n#     # of the custom extension model.\r\n#     __implementation__ = JS_CODE\r\n#     # Below are all the \"properties\" for this model. Bokeh properties are\r\n#     # class attributes that define the fields (and their types) that can be\r\n#     # communicated automatically between Python and the browser. Properties\r\n#     # also support type validation. More information about properties in\r\n#     # can be found here:\r\n#     #\r\n#     #    https://bokeh.pydata.org/en/latest/docs/reference/core.html#bokeh-core-properties\r\n#\r\n#     # This is a Bokeh ColumnDataSource that can be updated in the Bokeh\r\n#     # server by Python code\r\n#     data_source = Instance(ColumnDataSource)\r\n#     # The vis.js library that we are wrapping expects data for x, y, z, and\r\n#     # color. The data will actually be stored in the ColumnDataSource, but\r\n#     # these properties let us specify the *name* of the column that should\r\n#     # be used for each field.\r\n#     x = String\r\n#     y = String\r\n#     z = String\r\n#     extra = String\r\n#     color = String\r\n#\r\n# X_data = np.random.normal(0,10,100)\r\n# Y_data = np.random.normal(0,5,100)\r\n# Z_data = np.random.normal(0,3,100)\r\n# color = np.asarray([0 for x in range(50)]+[1 for x in range(50)])\r\n# extra = np.asarray(['a' for x in range(50)]+['b' for x in range(50)])\r\n# source200 = ColumnDataSource(data=dict(x=X_data, y=Y_data, z=Z_data, color = color, extra=extra))\r\n# surface = Surface3d(x=\"x\", y=\"y\", z=\"z\", extra=\"extra\", color=\"color\", data_source=source200)\r\n\r\nselectplayer = Select(title='Player:', value='Kevin Durant', options=sorted(list(data['Player'].unique())))\r\nselectplayer.on_change('value', updateplayer)\r\n\r\nselectteam = Select(title='Use This To Filter *Player* By Team:', value='All', options=list(['All'] + sorted(list(newteamsDict.keys())))+['All'])\r\nselectteam.on_change('value', updateteam)\r\n\r\ndownload_source = ColumnDataSource(player_stats(data, str(selectplayer.value)))\r\n\r\nbutton = Button(label=\"Download Data\", button_type=\"primary\")\r\nbutton.callback = CustomJS(args=dict(source=download_source),code=open(join(dirname(__file__), \"download.js\")).read())\r\n\r\nfree_throws_button = Button(label=\"Free Throws page\", button_type=\"warning\")\r\nfree_throws_button.callback = CustomJS(args=dict(source=download_source),code=open(join(dirname(__file__), \"download.js\")).read())\r\n\r\nhoverline = HoverTool(tooltips=[\r\n    (\"Year\", \"@x{0}\"),\r\n    (\"PPG\", \"@y{(0.0)}\"), ])\r\nhoverline2 = HoverTool(tooltips=[\r\n    (\"Year\", \"@x{0}\"),\r\n    (\"REB\", \"@t{(0.0)}\"),(\"DREB\", \"@d{(0.0)}\"),(\"OREB\", \"@o{(0.0)}\")])\r\nhoverstackedbar = HoverTool(tooltips=[\r\n    (\"Year\", \"@x{0}\"),\r\n    (\"APG\", \"@a{(0.0)}\"), ])\r\nhoverbar = HoverTool(tooltips=[\r\n    (\"Year\", \"@x{0}\"),\r\n    (\"PER\", \"@a{(0.0)}\"), ])\r\nhoverheatmap = HoverTool(tooltips=[\r\n    (\"Area\", \"@area\"),\r\n    (\"Shot (%)\", \"@num{(0.0)}\"), ])\r\nhovermap = HoverTool(tooltips=[\r\n    (\"City\", \"@city\"),])\r\nhoverdefense = HoverTool(tooltips=[\r\n    (\"Steals\", \"@stl\"),\r\n(\"Blocks\", \"@blk\")])\r\nhoverws = HoverTool(names = [\"q1_q2_block\", \"q2_q3_block\"],tooltips=[\r\n    (\"Maximum\", \"@upper\"), ('Q3', \"@q3\"), ('Q1', \"@q1\"),('Minimum', \"@lower\")])\r\nhoverws_player = HoverTool(names = [\"win_share_lines\"], tooltips=[\r\n    (\"WS\", \"@y1\")])\r\n\r\np = figure(plot_width=930, plot_height=250,\r\n           tools=[hoverline, BoxSelectTool(), BoxZoomTool(), ResetTool(), WheelZoomTool(), SaveTool(), PanTool()],\r\n           title=\"Average Points per Game\",x_axis_type=None,y_axis_label=\"Points (ppg)\", toolbar_location=\"right\")\r\nw = figure(plot_width=930, plot_height=250,\r\n           tools=[hoverline2, BoxSelectTool(), BoxZoomTool(), ResetTool(), WheelZoomTool(), SaveTool(), PanTool()],\r\n           title=\"Average Rebounds per Game\",x_axis_type=None, y_axis_label=\"Rebounds (rpg)\", toolbar_location=\"right\")\r\nz = figure(plot_width=930, plot_height=250,\r\n           tools=[hoverstackedbar, BoxSelectTool(), BoxZoomTool(), ResetTool(), WheelZoomTool(), SaveTool(), PanTool()],\r\n           title=\"Average Assits per Game\", x_axis_type=None, y_axis_label=\"Assists (apg)\", toolbar_location=\"right\")\r\ni = figure(plot_width=930, plot_height=200,\r\n           tools=[hoverbar, BoxSelectTool(), BoxZoomTool(), ResetTool(), WheelZoomTool(), SaveTool(), PanTool()],\r\n           title=\"PER per season\",x_axis_type=None, y_axis_label=\"Player Efficiency Rating\", toolbar_location=\"right\")\r\ntry:\r\n    htmap3 = selected_player_df['3P'].sum()/selected_player_df['3PA'].sum()\r\nexcept:\r\n    htmap3 = 0\r\ntry:\r\n    htmapft = selected_player_df['FT'].sum()/selected_player_df['FTA'].sum()\r\nexcept:\r\n    htmapft = 0\r\ntry:\r\n    htmapfg = selected_player_df['FG'].sum()/selected_player_df['FGA'].sum()\r\nexcept:\r\n    htmapfg = 0\r\n\r\nhmapsqsource = ColumnDataSource(data=(dict(x=[-1], y=[0], z=[6], i =[2], color=['red'], area = ['3P'], num = [htmap3*100])))\r\nhmapellipsesource = ColumnDataSource(data=(dict(x=[1], y=[0], z=[7], i =[1.55], color=['firebrick'], area = ['2P'], num = [htmapfg*100])))\r\nhmaprectsource = ColumnDataSource(data=(dict(x=[0], y=[0], z=[2], i =[0.85], color=['firebrick'], area = ['2P'], num = [htmapfg*100])))\r\nhmapwedgesource = ColumnDataSource(data=(dict(x=[-1], y=[0], z=[0.98], i =[3*np.pi/2], u = [np.pi/2],color=['red'], area = ['FT'], num = [htmapft*100])))\r\n\r\nheatmap = figure(plot_width=350, plot_height=300,\r\n           tools=[hoverheatmap],title=\"Career Shooting Heatmap\", x_axis_location=None, y_axis_location=None,toolbar_location=\"right\")\r\n\r\nhmapsq = heatmap.rect('x','y','z','i', color=linear_cmap('num', OrRd, 0, 45), line_color='color', source = hmapsqsource)\r\nhmapellipse = heatmap.ellipse('x','y','z','i', color=linear_cmap('num', OrRd, 0, 60), line_color='black', source = hmapellipsesource, )\r\nhmaprect = heatmap.rect('x','y','z','i', color=linear_cmap('num', OrRd, 0, 60), line_color='black', source = hmaprectsource)\r\nhmapwedge = heatmap.wedge(x='x', y='y', radius='z', start_angle='i', end_angle='u',\r\n        color=linear_cmap('num', OrRd, 0, 90),  direction=\"clock\",line_color='black', source = hmapwedgesource)\r\nheatmap.x_range.end=1\r\nheatmap.x_range.start=-5\r\nheatmap.grid.visible = False\r\nheatmap.outline_line_color = None\r\n\r\n# map_options = GMapOptions(lat=lat, lng=lon, map_type=\"roadmap\", zoom=3)\r\n# g = gmap(key, map_options, title=\"Career Stops\", width = 350, height = 350)\r\nmap_figure = figure(plot_width=475, plot_height=350,\r\n           tools=[hovermap],\r\n           title=\"Map\", x_axis_location=None, y_axis_location=None,toolbar_location=\"right\")\r\nmap_figure_source = ColumnDataSource(data=dict(lat=[x[3] for x in teamlist],lon=[x[4] for x in teamlist], city = [x[2] for x in teamlist]))\r\nmap_figure.circle(x=\"lon\", y=\"lat\", size=12, fill_color=\"#c157f2\", fill_alpha=0.8, source=map_figure_source)\r\nmap_figure.axis.visible = False\r\nmap_figure.add_tools(hovermap)\r\n\r\ndefense_scatter = figure(plot_width=300, plot_height=300,\r\n           tools=[hoverdefense, BoxSelectTool(), BoxZoomTool(), ResetTool(), WheelZoomTool(), SaveTool(), PanTool()],\r\n           title=\"Career Defensive Averages\",toolbar_location=\"right\", y_axis_label=\"Blocks (bpg)\", x_axis_label=\"Steals (spg)\")\r\n\r\nwin_shares = figure(plot_width=620, plot_height=300,x_range=['OWS','WS','DWS'],\r\n           tools=[hoverws,hoverws_player, BoxSelectTool(), BoxZoomTool(), ResetTool(), WheelZoomTool(), SaveTool(), PanTool()],\r\n           title=\"Career Win Shares\",toolbar_location=\"right\", y_axis_label=\"Career Win Shares (WS)\")\r\nwin_shares_source= win_shares_all(data, player_first_year, player_last_year, first_player)\r\n# stems\r\nq3_upper_segment = win_shares.segment(\"cats\", \"upper\", \"cats\", \"q3\", line_color=\"color\", source = win_shares_source, name = 'q3_upper_segment')\r\nq1_lower_segment = win_shares.segment(\"cats\", \"lower\", \"cats\", \"q1\", line_color=\"color\", source = win_shares_source, name = 'q1_lower_segment')\r\n# boxes\r\nq2_q3_block = win_shares.vbar(\"cats\", \"top\", \"q2\", \"q3\", line_color=\"color\", source = win_shares_source, fill_color = '#a30693', alpha = 0.4, name = 'q2_q3_block')\r\nq1_q2_block = win_shares.vbar(\"cats\", \"top\", \"q1\", \"q2\", line_color=\"color\", source = win_shares_source, fill_color= '#568ce2', name = 'q1_q2_block')\r\n# whiskers (almost-0 height rects simpler than segments)\r\nbottom_whisker = win_shares.rect(\"cats\", \"lower\", \"width\", \"height\", line_color=\"color\", source = win_shares_source, name = 'bottom_whisker')\r\ntop_whisker = win_shares.rect(\"cats\", \"upper\", \"width\", \"height\", line_color=\"color\", source = win_shares_source, name = 'top_whisker')\r\nwin_share_lines = win_shares.segment(x0='x0', y0='y0',y1='y1', x1='x1', color=\"ray_color\", line_width=5, line_alpha = 0.7, source = win_shares_source, name = 'win_share_lines')\r\n\r\ncurrent_player_ts, ts_players, ts_percent = true_shooting(first_player, data, player_first_year, player_last_year)\r\nts_max = max(ts_percent)*0.01\r\ntrue_shooting_source = ColumnDataSource(data = dict(x = [0.25],y = [0],cur_player=[current_player_ts],  players=ts_players , ts=[np.linspace(0,ts_max,100).reshape(100,1)], dw = [0.5], dh = [ts_max-0]))\r\nts = figure(plot_width=150, plot_height=350,x_range=[0, 1],x_axis_location=None, y_range=[0, ts_max],min_border_right=10, title = 'Career TS%', tools = [])\r\nts.image(image='ts',x='x',y='y',dw='dw',dh='dh', palette=viridis(100)[:50], source = true_shooting_source)\r\nts_span = Span(location=current_player_ts*0.01, dimension='width', line_color='#f16913', line_dash='solid', line_width=5, line_alpha=0.7, name = 'ts_span')\r\nts.add_layout(ts_span)\r\nts.xaxis.major_label_text_color = None\r\nts.xaxis.major_tick_line_color = None\r\nts.xaxis.minor_tick_line_color = None\r\nts.yaxis[0].ticker=FixedTicker(ticks=(np.linspace(0,ts_max,11))) # 11 ticks\r\nts.grid.visible = False\r\nts.outline_line_color = None\r\n\r\nall_block, all_steal = defense_scatter_all(data, player_first_year, player_last_year)\r\ndefenseallsource = ColumnDataSource(data = dict(stl=all_steal , blk=all_block, color = ['blue']*len(all_block)))\r\ndefenseplayersource = ColumnDataSource(data = dict(stl=[stlavg] , blk=[blkavg], color = ['#c157f2']))\r\ndefense_scatter.circle(x='stl', y='blk', fill_color = 'color', size = 4, source = defenseallsource, alpha = 0.15)\r\ndefense_scatter.circle(x='stl', y='blk', fill_color = 'color', size = 10, source = defenseplayersource, alpha = 0.85)\r\n\r\nall_nba_pts, all_nba_asts, all_nba_rebs, all_nba_per = all_nba_avg_lines(data, player_first_year, player_last_year)\r\n\r\ncolor_mapper = LinearColorMapper(palette=Purples[9][::-1][3:], low=min(player_per), high=max(player_per))\r\n\r\nsourceppg = ColumnDataSource(data = dict(x=player_years, y=player_ppg, avg = ptsavg, nbaavg = all_nba_pts, label_avg = [ptsavg[0], all_nba_pts[0]]+[0 for x in range(len(player_years)-2)], names = ['Player Avg', 'NBA Avg'] + ['' for x in range(len(player_years)-2)], label_spots = [player_years[0],player_years[0]]+ [player_years[0] for x in range(len(player_years)-2)]))\r\nsourceper = ColumnDataSource(data = dict(x=player_years, a = player_per, avg = peravg, nbaavg = all_nba_per, label_avg = [peravg[0], all_nba_per[0]]+[0 for x in range(len(player_years)-2)], names = ['Player Avg', 'NBA Avg']+ ['' for x in range(len(player_years)-2)], label_spots = [player_years[0],player_years[0]]+ [player_years[0] for x in range(len(player_years)-2)]))\r\nsourcerpg= ColumnDataSource(data = dict(x=player_years, o = off_reb, d = def_reb, t = tot_reb, avgt=rebavg, avgo = orbavg, avgd = drbavg, nbaavg = all_nba_rebs, label_avg = [rebavg[0], all_nba_rebs[0]]+[0 for x in range(len(player_years)-2)], names = ['Player Avg', 'NBA Avg']+ ['' for x in range(len(player_years)-2)], label_spots = [player_years[0],player_years[0]]+ [player_years[0] for x in range(len(player_years)-2)]))\r\nsourceapg = ColumnDataSource(data = dict(x=player_years, a = player_asts, avg = astavg, nbaavg = all_nba_asts, label_avg = [astavg[0], all_nba_asts[0]]+[0 for x in range(len(player_years)-2)], names = ['Player Avg', 'NBA Avg']+ ['' for x in range(len(player_years)-2)], label_spots = [player_years[0],player_years[0]]+ [player_years[0] for x in range(len(player_years)-2)]))\r\n\r\np.line('x', 'y', source=sourceppg, line_width=2.5, line_color='#E24A33',alpha = 0.4)\r\np.circle('x', 'y', source=sourceppg, size=8, line_color='#E24A33', fill_color = '#568ce2')\r\nz.line('x', 'a', source=sourceapg, line_width=2.5, line_color='#a30693',alpha = 0.4)\r\nz.circle('x', 'a', source=sourceapg, size=8, line_color='#a30693', fill_color = '#568ce2')\r\nw.vbar_stack(stackers=['d','o'], x='x', width=0.5, color=['blue', '#568ce2'], source=sourcerpg,legend=['DREB', 'OREB'])\r\ni.vbar(x = 'x', top = 'a', width = 0.5, color={'field': 'a', 'transform': color_mapper}, source = sourceper)\r\n\r\n# Selected Players' Average lines\r\navglinep = p.line('x', 'avg', source=sourceppg, line_width=3.5, line_color='#E24A33', alpha = 0.2)\r\navglinewt = w.line('x', 'avgt', source=sourcerpg, line_width=3.5, line_color='blue', alpha = 0.2)\r\navglinewo = w.line('x', 'avgo', source=sourcerpg, line_width=3.5, line_color='#568ce2', alpha = 0.2)\r\navglinewd = w.line('x', 'avgd', source=sourcerpg, line_width=3.5, line_color='blue', alpha = 0.2)\r\navglinez = z.line('x', 'avg', source=sourceapg, line_width=3.5, line_color='#a30693', alpha = 0.2)\r\navglinei = i.line('x', 'avg', source=sourceper, line_width=3.5, line_color='purple', alpha = 0.2)\r\n\r\n#NBA Average lines\r\nnba_avglinep = p.line('x', 'nbaavg', source=sourceppg, line_width=2.5, line_color='black', alpha = 0.2)\r\nnba_avglinewt = w.line('x', 'nbaavg', source=sourcerpg, line_width=2.5, line_color='black', alpha = 0.2)\r\nnba_avglinez = z.line('x', 'nbaavg', source=sourceapg, line_width=2.5, line_color='black', alpha = 0.2)\r\nnba_avglinei = i.line('x', 'nbaavg', source=sourceper, line_width=2.5, line_color='black', alpha = 0.2)\r\n\r\n#Set all average lines to false\r\navglinewd.visible = False\r\navglinewo.visible = False\r\navglinez.visible = False\r\navglinewt.visible = False\r\navglinep.visible = False\r\navglinei.visible = False\r\n\r\nnba_avglinep.visible = False\r\nnba_avglinewt.visible = False\r\nnba_avglinez.visible = False\r\nnba_avglinei.visible = False\r\n\r\nlabels_ppg = LabelSet(x='label_spots', y='label_avg', text='names', level='glyph',\r\n              x_offset=5,  source=sourceppg, render_mode='canvas')\r\nlabels_ppg.visible = False\r\np.add_layout(labels_ppg)\r\n\r\nlabels_rpg = LabelSet(x='label_spots', y='label_avg', text='names', level='glyph',\r\n              x_offset=5,  source=sourcerpg, render_mode='canvas')\r\nlabels_rpg.visible = False\r\nw.add_layout(labels_rpg)\r\n\r\nlabels_apg = LabelSet(x='label_spots', y='label_avg', text='names', level='glyph',\r\n              x_offset=5,  source=sourceapg, render_mode='canvas')\r\nlabels_apg.visible = False\r\nz.add_layout(labels_apg)\r\n\r\nlabels_per = LabelSet(x='label_spots', y='label_avg', text='names', level='glyph',\r\n              x_offset=5,  source=sourceper, render_mode='canvas')\r\nlabels_per.visible = False\r\ni.add_layout(labels_per)\r\n\r\n# checkbox_group\r\nbutton_group = CheckboxButtonGroup(labels=[\"Average PPG Lines\", \"Average RPG Lines\", \"Average APG Lines\", \"Average PER Lines\"], active=[])\r\nbutton_group.on_click(update_avg_line)\r\n\r\nimagesource = ColumnDataSource(data = dict(url = ['nba\\\\static\\\\images\\\\lebron_james.png'], x = [0], y = [0], w = [50], h = [50], anchor = ['bottom_left']))\r\n\r\nimg = figure(plot_width=300, plot_height=200, x_range=(0, 370), y_range=(0, 834), x_axis_type=None,y_axis_type=None,tools = [])\r\n# imagee= ImageURL(url='url',x='x', y='y', w='w', h='h',anchor='bottom_left')\r\n# img.add_glyph(imagesource, imagee)\r\n# # img.grid.visible = False\r\n# # img.outline_line_color = None\r\nimageglyph = img.image_url(url=['nba/static/images/kevin_durant.png'],x=0, y=0, w=369, h=834,anchor=\"bottom_left\")\r\nimg.grid.visible = False\r\nimg.outline_line_color = None\r\n\r\n\r\n\r\nticker = SingleIntervalTicker(interval=1, num_minor_ticks=0)\r\nxaxis = LinearAxis(ticker=ticker)\r\nxaxis2 = LinearAxis(ticker=ticker)\r\nyaxis = LinearAxis(ticker = ticker)\r\n\r\ni.add_layout(xaxis2, 'below')\r\n\r\nw.legend.orientation = 'horizontal'\r\nw.legend.location = \"top_left\"\r\nw.y_range.end = tot_reb.max()*1.4\r\nw.yaxis.major_label_text_font_style = \"bold\"\r\np.ygrid.grid_line_alpha = 0.8\r\np.ygrid.grid_line_dash = [6, 4]\r\nw.ygrid.grid_line_alpha = 0.8\r\nw.ygrid.grid_line_dash = [6, 4]\r\nz.ygrid.grid_line_alpha = 0.8\r\nz.ygrid.grid_line_dash = [6, 4]\r\ni.xgrid.grid_line_alpha = 0.8\r\ni.xgrid.grid_line_dash = [6, 4]\r\nwin_shares.xgrid.visible = False\r\n\r\ndiv = Div(width = 300, height = 400, text = makediv(selected_player_df, [x[1] for x in teamlist]))\r\nwidgets = column(selectplayer,selectteam,free_throws_button, button,button_group, img, div, width=300)\r\nyear_charts = column([p,z,w, i])\r\nsquare_charts = column([row(defense_scatter,heatmap),row(ts,map_figure), win_shares])\r\ndisplay = row([widgets, year_charts, square_charts])\r\ndoc.add_root(display)\r\nshow(display)\r\n","sub_path":"nba.py","file_name":"nba.py","file_ext":"py","file_size_in_byte":42450,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"612102306","text":"import os\nfrom PySide2.QtWidgets import *\nfrom PySide2.QtCore import Qt\n\nfrom .iconListWidget import IconListWidget\n\nfrom _lib.configUtils import tctProcessElements\nfrom UI.UserTaskManager.utils import rvPlayerUtils\n\ncompProcess = tctProcessElements['comp']\n\n\nclass TextCommentDialg(QDialog):\n    def __init__(self, commentBlockWidget, taskManagerWdg, treeWidget):\n        super(TextCommentDialg, self).__init__(taskManagerWdg)\n        self.commentBlockWidget = commentBlockWidget\n        self.taskManagerWdg = taskManagerWdg\n        self.treeWidget = treeWidget\n        self.itemUtils = self.treeWidget.itemUtils\n\n        self.action = \"\"\n        self.taskItem = None\n        self.noteData = None\n\n        self.lay_main = QVBoxLayout(self)\n        self.lay_bottom = QHBoxLayout()\n\n        # ======================= WIDGETS ===============================\n        self.textField = QPlainTextEdit(self)\n        self.textField.setMinimumSize(500, 200)\n\n        self.iconListWidget = IconListWidget(self)\n\n        self.acceptButton = QPushButton(\"Accept\")\n        self.rvButton = QPushButton(\"rvButton\")\n\n        # ======================= CONNECTS ===============================\n        self.acceptButton.clicked.connect(self.acceptComment)\n        self.rvButton.clicked.connect(self.runRvPlayer)\n\n        # ======================= LAYOUTS ===============================\n        self.lay_main.addWidget(self.textField)\n        self.lay_main.addWidget(self.iconListWidget)\n\n        self.lay_bottom.addSpacerItem(QSpacerItem(0, 0, QSizePolicy.MinimumExpanding, QSizePolicy.Fixed))\n        self.lay_bottom.addWidget(self.rvButton)\n        self.lay_bottom.addWidget(self.acceptButton)\n\n        self.lay_main.addLayout(self.lay_bottom)\n\n    def acceptComment(self):\n        if self.noteData is None:\n            self.saveComment()\n        self.checkinNote(self.noteData)\n        self.accept()\n\n    def clearTextDialogField(self):\n        self.textField.document().setPlainText(\"\")\n\n    def clearIconList(self):\n        self.iconListWidget.clearIconList()\n\n    def saveComment(self):\n        text = self.textField.toPlainText()\n\n        if self.action == \"Add\":\n            if self.taskItem is None:\n                self.taskItem = self.itemUtils.getSelected_ProcessItems()\n                if self.taskItem is None:\n                    return\n            shotSkey = self.taskItem.parent().data(0, Qt.UserRole)\n            process = self.taskItem.text(0)\n            self.process = process\n            self.noteData = self.commentBlockWidget.createNote(shotSkey, text, process)\n\n        elif self.action == \"Update\":\n            self.noteData = self.commentBlockWidget.updateNote(text)\n        else:\n            print(\"Action error: unexpected comment action!\")\n\n    def checkinNote(self, noteData):\n        files = self.iconListWidget.getImageFiles()\n        if not files:\n            return\n        self.commentBlockWidget.checkinNote(noteData, files)\n\n    def runRvPlayer(self):\n        if self.taskItem is None:\n            self.taskItem = self.itemUtils.getSelected_ProcessItems()\n            if self.taskItem is None:\n                print(\"no task item\")\n                return\n\n        if self.taskItem.text(0) == compProcess:\n            stdout = rvPlayerUtils.watchDailies(self.getProject(), [self.taskItem], annotate=True)\n        else:\n            stdout = rvPlayerUtils.watchPreview(self.getProject(), self.taskItem, annotate=True)\n\n        if stdout is not None:\n            catalog = os.path.dirname(list(stdout.values())[0])\n            if not os.path.exists(catalog):\n                return\n\n        files = [\"/\".join([catalog, file]) for file in os.listdir(catalog)]\n        self.iconListWidget.setIconList(files, isTemp=True)\n\n    def getProject(self):\n        project = self.taskManagerWdg.getActiveProject()\n        return project\n\n","sub_path":"UI/UserTaskManager/wdg_Comments/textCommentDialog.py","file_name":"textCommentDialog.py","file_ext":"py","file_size_in_byte":3845,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"57392255","text":"#!/usr/bin/env python3\r\n#\r\n\r\nimport socket\r\nfrom socket import AF_INET, SOCK_STREAM\r\nfrom os import popen\r\nfrom typing import List\r\n\r\n\r\ndef cond(arg: str) -> List[bool]:\r\n    return [0 < len(arg) <= 1024, arg.upper() != 'EXIT', arg.upper() != 'QUIT', ]\r\n\r\n\r\nserver = socket.socket(AF_INET, SOCK_STREAM)\r\n\r\nserver.bind(('0.0.0.0', 9999))\r\nserver.listen()\r\n\r\nwhile True:\r\n    # 为每个新的client分配一个session\r\n    conn, addr = server.accept()\r\n\r\n    while True:\r\n        print(\"new client: {} connected.\".format(addr))\r\n        recv = conn.recv(1024).decode()\r\n        print('recv : {} : {}'.format(len(recv), recv))\r\n        if all(cond(recv)):\r\n            res = popen(recv).read()\r\n            print('res : size : {}'.format(len(res)))\r\n            if len(res) == 0:\r\n                conn.send(('Oops, command not found: {}'.format(recv)).encode())\r\n            conn.send(res.encode())\r\n        else:\r\n            print('client: {}, quiting....'.format(addr))\r\n            conn.send(''.encode())\r\n            break\r\n","sub_path":"basic_/socket_/socket_server.py","file_name":"socket_server.py","file_ext":"py","file_size_in_byte":1027,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"485703421","text":"import json\nimport pytest\nimport krisk.plot as kk\nDATA_DIR = 'krisk/tests/data'\nread_option_tests = lambda f: json.load(open(DATA_DIR + '/' + f, 'r'))\n\n\ndef assert_barline_data(plot, true_option, test_legend=True):\n    assert plot.option['series'][0] == true_option['series'][0]\n    assert plot.option['xAxis']['data'] == true_option['xAxis']['data']\n    if test_legend:\n        assert plot.option['legend']['data'] == true_option['legend']['data']\n\ndef assert_scatter_data(plot, true_option):\n    assert plot.option['series'][0]['data'] == true_option['series'][0]['data']\n    assert plot.option['xAxis'] == true_option['xAxis']\n    assert plot.option['yAxis'] == true_option['yAxis']\n\n\ndef test_bar(gapminder):\n\n    #Bar\n    p1 = kk.bar(gapminder,\n               'year',\n               y='pop',\n               c='continent',\n               how='mean',\n               stacked=True,\n               annotate=True)\n    opt1 = read_option_tests('bar.json')\n    assert_barline_data(p1, opt1)\n\n    #Bar with x-axis and category\n    p2 = kk.bar(gapminder,'year',c='continent',stacked=True)\n    opt2 = read_option_tests('bar_x_c.json')\n    assert_barline_data(p2, opt2)\n\n\n    # Bar Annotate All\n    p3 = kk.bar(gapminder,\n               'year',\n               y='pop',\n               c='continent',\n               how='mean',\n               stacked=True,\n               annotate='all')\n    opt3 = read_option_tests('/bar_ann_all.json')\n    assert_barline_data(p3, opt3)\n\n    p4 = kk.bar(gapminder,'continent',y='gdpPercap',how='mean')\n    opt4 = {'legend': {'data': []},\n                             'series': [{'data': [4426.026, 8955.554, 802.675, 3255.367, 19980.596],\n                               'name': 'continent',\n                               'type': 'bar'}],\n                             'title': {'text': ''},\n                             'tooltip': {'axisPointer': {'type': ''}},\n                             'xAxis': {'data': ['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']},\n                             'yAxis': {}}\n    assert_barline_data(p4, opt4, test_legend=False) \n\ndef test_trendline(gapminder):\n\n    # p = kk.bar(gapminder,'year',how='mean',y='pop',trendline=True)\n    p1 = kk.bar(gapminder,'year',how='mean',y='pop',trendline=True)\n    opt1 = read_option_tests('bar_year_pop_mean_trendline.json')\n\n    assert_barline_data(p1, opt1, test_legend=False)\n    assert p1.option['series'][-1]['data'] == opt1['series'][-1]['data']\n    assert p1.option['series'][-1]['name'] == 'trendline'\n    assert p1.option['series'][-1]['type'] == 'line'\n    assert p1.option['series'][-1]['lineStyle'] == {'normal': {'color': '#000'}}\n\n    p2 = kk.bar(gapminder,'year',how='mean',y='pop',trendline=True,c='continent',stacked=True)\n    opt2 = read_option_tests('bar_year_pop_mean_continent_trendline.json')\n    assert_barline_data(p2, opt2)\n    assert p2.option['series'][-1]['data'] == opt2['series'][-1]['data']\n\n    try:\n        kk.bar(gapminder,'year',how='mean',y='pop',trendline=True,c='continent')\n    except AssertionError:\n        pass\n\ndef test_line(gapminder):\n    p = kk.line(\n        gapminder,\n        'year',\n        y='lifeExp',\n        c='continent',\n        how='mean',\n        stacked=True,\n        area=True,\n        annotate='all')\n    opt = read_option_tests('line.json')\n    assert_barline_data(p, opt)\n    assert p.option['tooltip']['axisPointer']['type'] == 'shadow'\n    assert p.option['tooltip']['trigger'] == 'axis'\n\ndef test_smooth_line(gapminder):\n\n    p = kk.line(gapminder[gapminder.year == 1952],'continent',y='pop',how='mean',smooth=True)\n    assert p.option['series'][0]['smooth'] == True\n\ndef test_full_bar_line(gapminder):\n    bar = kk.bar(gapminder,'year',c='continent',y='pop',how='mean',stacked=True,full=True,annotate='all')\n    line = kk.line(gapminder,'year',c='continent',y='pop',how='mean',stacked=True,full=True,annotate='all')\n   \n    for i in range(len(bar.option['series'])):\n        bar.option['series'][i].pop('type')\n        line.option['series'][i].pop('type')\n\n        bar.option['series'][i].pop('label')\n        line.option['series'][i].pop('label')\n\n    true_option = read_option_tests('full_bar_line.json')\n\n    assert_barline_data(bar, true_option)\n    assert_barline_data(line, true_option)\n\ndef test_sort_bar_line(gapminder):\n    p = kk.line(gapminder,'year', y='pop', how='mean',c='continent', sort_on='mean', sort_c_on='Americas')\n\n    assert p.option['xAxis']['data'] == [1952, 1957, 1962, 1967, 1972, 1977, 1982, 1987, 1992, 1997, 2002, 2007]\n    assert p.option['legend']['data'] == ['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']\n    assert p.option['series'][0] == {'data': [-10595881.167,\n                                              -9604550.167,\n                                              -8874458.167,\n                                              -7114907.167,\n                                              -5114619.167,\n                                              -2722602.167,\n                                              158346.833,\n                                              3379549.833,\n                                              6422966.833,\n                                              9196608.833,\n                                              11411735.833,\n                                              13457809.833],\n                                     'name': 'Africa',\n                                     'type': 'line'}\n\n\ndef test_hist(gapminder):\n    p1 = kk.hist(gapminder,'lifeExp',bins=10)\n    opt1  = read_option_tests('hist_x.json')\n    assert_barline_data(p1, opt1)\n    \n    p2 = kk.hist(\n        gapminder,\n        'lifeExp',\n        c='continent',\n        bins=20,\n        normed=True,\n        stacked=True)\n    opt2 = read_option_tests('hist.json')\n    assert_barline_data(p2, opt2)\n\n\ndef test_density(gapminder):\n\n    p1 = kk.hist(gapminder,'lifeExp',density=True)\n    assert p1.option['series'][0]['data'] == [0, 4, 2, 7, 2, 2, 3, 5, 13, 16, 6]\n    assert p1.option['series'][-1] == {'data': [0, 4, 2, 7, 2, 2, 3, 5, 13, 16, 6, 0],\n                                   'lineStyle': {'normal': {'color': '#000'}},\n                                   'name': 'density',\n                                   'smooth': True,\n                                   'type': 'line'}\n    assert p1.option['xAxis']['boundaryGap'] ==  False\n    assert p1.option['xAxis']['data'] ==  [0, 28, 34, 39, 44, 49, 55, 60, 65, 70, 75, 81, 0]\n\n    p2 = kk.hist(gapminder,'lifeExp',bins=10,c='continent',stacked=True,density=True)\n    opt2 = read_option_tests('hist_lifeExp_b10_continent_density.json')\n    assert_barline_data(p2, opt2)\n\n    try:\n        kk.hist(gapminder,'year',density=True,c='continent')\n    except AssertionError:\n        pass\n\n\n\ndef test_scatter(gapminder):\n    # Simple Scatter\n    p1 = kk.scatter(gapminder[gapminder.year == 1952],'pop','lifeExp')\n    opt1 = read_option_tests('simple_scatter.json')\n    assert_scatter_data(p1, opt1)\n    assert p1.option['title'] ==  {'text': ''}\n    assert p1.option['tooltip'] == {'axisPointer': {'type': 'line'},\n                                     'fontFamily': 'sans-serif',\n                                     'fontSize': 14,\n                                     'fontStyle': 'normal',\n                                     'trigger': 'item',\n                                     'triggerOn': 'mousemove'}\n\n    # Grouped Scatter\n    p2 = kk.scatter(\n        gapminder[gapminder.year == 1952],\n        'lifeExp',\n        'gdpPercap',\n        s='pop',\n        c='continent')\n    opt2 = read_option_tests('scatter.json')\n    assert_scatter_data(p2, opt2)\n    assert p2.option['series'][0]['name'] ==  'Africa'\n    assert p2.option['series'][0]['type'] ==  'scatter'\n    assert p2.option['visualMap'][0] ==  opt2['visualMap'][0]\n\n    # Scatter\n    \n    p3 = kk.scatter(gapminder[gapminder.year == 1952], 'lifeExp', 'gdpPercap', s='pop')\n    opt3 = read_option_tests('scatter_single.json')\n    assert_scatter_data(p3, opt3)\n","sub_path":"krisk/tests/test_plot.py","file_name":"test_plot.py","file_ext":"py","file_size_in_byte":7976,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"46339747","text":"inFile10=open(\"alpha08_and_score.tsv\", \"r\")\nfile1=inFile10.readlines()\ninFile10.close()\noutFile=open(\"08metric_results.tsv\", \"w\")\noutFile.write(\"RCV\\taverage_ratio\\n\")\nlf=len(file1)\ni=1\nratioSuma = 0\nlastCNV=\"\"\n#list storing ratio for every alpha\nratios = 0\nnumOfTerms=0\ncol1=file1[i].strip().split('\\t')\nlastCNV=col1[1]\nwhile True:\n    #col1 is list of columns from one line in file1\n    col1=file1[i].strip().split('\\t')\n    if col1[1]==lastCNV and not col1[3]==\"0\":\n        numOfTerms+=1\n        ratios+=float(col1[4])\n        ratioSuma+=float(col1[4])\n        i+=1\n    elif col1[1]==lastCNV:\n        i+=1\n    else:\n        outFile.write(lastCNV+\"\\t\"+str(ratios/numOfTerms)+\"\\n\")\n        lastCNV=col1[1]\n        numOfTerms=0\n        ratios=0\n    if i%1700==0:\n        print(str(i*100/lf)+\"% done\")\n    if i==lf-1:\n        break\n#print(ratioSuma/(lf-1)\noutFile.close()\n\n","sub_path":"metric.py","file_name":"metric.py","file_ext":"py","file_size_in_byte":872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"473024095","text":"import pytesseract\nimport pyautogui\nimport os\nimport sys\n\ndef firstStart():\n    try:\n        configFile = open(\"Config.txt\", \"x\")\n        configFile.close()\n        os.mkdir(\"screenshot/\")\n    except OSError:\n        pass\n    \n\ndef config():\n    \"\"\"\n        Functions for config the who start the picture and it's size\n    \"\"\"\n    input(\"put where is the top-right and press enter\")\n    x, y = pyautogui.position()\n    # print(x, y)\n    input(\"put where is the bottom-left and press enter\")\n    rectx, recty = pyautogui.position()\n    # print(rectx - x, recty - y)\n    configFile = open(\"Config.txt\", \"w\")\n    configFile.write(str(x) + \", \" + str(y) + \", \" + str(rectx - x) + \", \" + str(recty - y))\n    screenRegion = (x, y, rectx - x, recty - y)\n    return screenRegion\n\ndef getconfig():\n    \"\"\"    \n        Functions for getting the Config.txt\n    \"\"\"\n    configFile = open(\"Config.txt\", \"r\")\n    splited = configFile.read().split(', ')\n    try:\n        screenRegion = (splited[0], splited[1], splited[2], splited[3])\n    except IndexError:\n        screenRegion = config()\n    return screenRegion\n    \n\ndef main():\n    pytesseract.pytesseract.tesseract_cmd = \"C:\\\\Program Files (x86)\\\\Tesseract-OCR\\\\tesseract.exe\" # path for tesseract.exe\n    # tessdata_dir_config = '--tessdata-dir \"C:\\\\Program Files (x86)\\\\Tesseract-OCR\\\\tessdata\"' # path to tessdata, if you have an error on the language, uncomment this line\n    cmd = \"\"\n    screenNumber = 0\n    screenRegion = ()\n    path = \"\"\n    name_of_your_project = \"\"\n    \n    try:\n        firstStart()\n        if (sys.argv[1] == \"config\"):\n            screenRegion = config()\n        elif (sys.argv[1] == \"getconfig\"):\n            print(getconfig())\n        else:\n            name_of_your_project = sys.argv[1]\n            path = \"screenshot/\" + name_of_your_project\n            screenRegion = getconfig()\n    except IndexError:\n        name_of_your_project = input(\"give me the name of project :\\n\")\n        path = \"screenshot/\" + name_of_your_project\n        screenRegion = getconfig()\n\n    try:\n        os.mkdir(path)\n    except OSError:\n        pass\n    else:\n        print(\"Successfully created the directory\")\n        txt = open(path + \"/save.txt\", \"x\")\n        txt.close()\n    \n    while (cmd != \"exit\"):\n        cmd = input(\"press enter for screenshot -> \")\n        if (cmd != \"exit\"):\n            screenPath = str(path + \"/screenshot_\" + str(screenNumber) + \".png\")\n            while (os.path.exists(screenPath)):\n                screenNumber = screenNumber + 1\n                screenPath = str(path + \"/screenshot_\" + str(screenNumber) + \".png\")\n            screen = pyautogui.screenshot(screenPath, region=screenRegion) # comment if tessdata_dir_config is disable\n            # screenText = pytesseract.image_to_string(screen, lang='eng', config=tessdata_dir_config) # un-comment if tessdata_dir_config is active\n            screenText = pytesseract.image_to_string(screen, lang='eng')\n            print(screenText)\n            txt = open(path + \"/save.txt\", \"a\")\n            txt.write(\"screenshot_\" + str(screenNumber) + \".png\\nstart:\\n\")\n            txt.write(screenText)\n            txt.write(\":end\\n\")\n            txt.close()\n            \nif __name__== \"__main__\":\n   main()","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":3238,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"388626658","text":"'''\nExample\nSample Input\nELLY\nKRIS\nSample Output\n29\n\nTOPCODER\nTCOINDIA\n57\n\nAWIODJGWAMBAUWNMQEROAIQWYRZSVEPTT\nBNSIAELDALCGAWOPIWEQTYCNAZEKJXVYU\n105\n'''\ndef get_nearest(c, BB):\n    mins = 100\n    for i in BB:\n        cnt = 0\n        iao, ibo = ord(c), ord(i)\n        if ibo > iao : cnt = ibo - iao\n        else: cnt = 26 -iao + ibo\n        if cnt < mins:\n            lets, mins = i, cnt\n    return lets, mins\n\ndef anagram(A, B):\n    '''\n    A  : string\n    B  : string\n    ret Count int\n    '''\n    count = 0\n    al = list(A)\n    bl = (list(B))\n    for i in al:\n        if i in bl:\n            bl.pop(bl.index(i))\n            continue\n        r, d = get_nearest(i, bl)\n        bl.remove(r)\n        count += d\n    return count\n\n\n#print(anagram(\"SULBDXWINJRHKZLCO\",\"VSZVHECDBLJPQEBHC\"))\n\ndef tester(testname):\n    folder = testname+\"_tests\"\n    for i in range(1,51):\n        s = str(i)\n        if i < 10:s = \"0\"+str(i)\n        infile_name = folder + \"\\\\\" +testname+\".\"+s+\".\"+\"in\"\n        oufile_name = folder + \"\\\\\" +testname+\".\"+s+\".\"+\"sol\"\n\n        in_file = open(infile_name)\n        a, b = [x.strip() for x in in_file]\n        ou_file = open(oufile_name)\n        ans  = int(ou_file.readline())\n        man  = anagram(a, b)\n        print(s +\" \"+ [\"Failed\", \"Passed\"][ans == man])\n        in_file.close()\n        ou_file.close()\n\n\n\n\ntester(\"Anagrams\")\n\n","sub_path":"EJOI/2018/Practice/anagrams.py","file_name":"anagrams.py","file_ext":"py","file_size_in_byte":1351,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"431967086","text":"from char_vocab_utils import compute_string_to_int, merge_vocab\n\n\ndef main():\n    import os\n    import torch\n    import numpy as np\n    from sklearn.model_selection import train_test_split\n    from argparse import ArgumentParser\n\n    parser = ArgumentParser()\n    parser.add_argument(\n        \"--vocab-path\",\n        nargs=\"+\",\n        dest=\"vocab_path_list\",\n        help=\"path to the vocab(s) to use to featurize the smiles data. if more than one vocab path is given, the vocabs\"\n        \"are merged and the result is used as the vocab to featurize with\",\n    )\n    parser.add_argument(\"--smiles-path\", help=\"path to csv of smiles strings\")\n    parser.add_argument(\n        \"--smiles-col\", help=\"column name that contains smiles strings\", default=None\n    )\n    parser.add_argument(\n        \"--smiles-sep\",\n        help=\"delimiter used to seperate smiles strings, default is set to pandas default for csv\",\n        default=\",\",\n    )\n    parser.add_argument(\n        \"--add-bos\",\n        help=\"add the begin of string character to smiles data\",\n        action=\"store_true\",\n    )\n    parser.add_argument(\n        \"--add-eos\",\n        help=\"add the end of string character to smiles data\",\n        action=\"store_true\",\n    )\n    parser.add_argument(\n        \"--n-jobs\",\n        type=int,\n        help=\"number of processes to use for parallel computations\",\n    )\n    parser.add_argument(\n        \"--test-size\",\n        type=float,\n        default=0.2,\n        help=\"if specified, saves the data into a seperate train/val/test split, where\"\n        \"test set will be test-size %% of the full data, val is then selected from remaining train data\"\n        \"using val-size %% of the train data\",\n    )\n    parser.add_argument(\n        \"--val-size\",\n        type=float,\n        default=0.1,\n        help=\"%% of the training data to hold out as validation or dev set\",\n    )\n    parser.add_argument(\"--split-dataset\", action=\"store_true\")\n    parser.add_argument(\n        \"--output-dir\", help=\"path to output directory to store vocab and numpy arrays\"\n    )\n    args = parser.parse_args()\n\n    # read the smiles strings from the csv path, modin uses multiprocessing to do this more quickly\n    import modin.pandas as pd\n\n    if args.smiles_col is None:\n        smiles_df = pd.read_csv(args.smiles_path, header=None, sep=args.smiles_sep)\n        smiles_list = smiles_df[0].values\n\n    else:\n        smiles_df = pd.read_csv(args.smiles_path, sep=args.smiles_sep)\n        smiles_list = smiles_df[args.smiles_col].values\n\n    # if output directory does not exist, create it\n    if not os.path.exists(args.output_dir):\n        os.makedirs(args.output_dir)\n\n    # extract the vocab\n    print(\"reading vocab...\")\n\n    if len(args.vocab_path_list) > 1:\n        print(\"more than one vocab was specified...merging vocabs...\")\n        vocab = merge_vocab(args.vocab_path_list)\n\n    else:\n        vocab = torch.load(args.vocab_path_list[0])\n\n    # compute the integer representation of the smiles data\n    print(\"extracting dataset...\")\n    data = compute_string_to_int(\n        smiles_list,\n        vocab,\n        n_jobs=args.n_jobs,\n        add_bos=args.add_bos,\n        add_eos=args.add_eos,\n    )\n    np.save(args.output_dir + \"/full_data.npy\", data)\n\n    if args.split_dataset:\n        # compute the splits for train/test using the full data\n        train_data, test_data = train_test_split(data, test_size=args.test_size)\n        # compute the splits for train/val using the remaining data\n        train_data, val_data = train_test_split(train_data, test_size=args.val_size)\n\n        np.save(args.output_dir + \"/train.npy\", train_data)\n        np.save(args.output_dir + \"/val.npy\", val_data)\n        np.save(args.output_dir + \"/test.npy\", test_data)\n\n\nif __name__ == \"__main__\":\n    main()\n","sub_path":"scripts/preprocess_data.py","file_name":"preprocess_data.py","file_ext":"py","file_size_in_byte":3779,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"328837341","text":"#!/usr/bin/env python\n#coding:utf-8\n#author Zhang Shijie\n\nimport  threading\nimport  time\n'''\ndef sayhi(num):\n    print(\"running on number: %s\"% num)\n    time.sleep(3)\n\nif __name__ == \"__main__\":\n    t_list = []\n    for i in range(10):\n        t = threading.Thread(target=sayhi,args=[i,]) #中括号里面加逗号表示是一个列表或元组\n        t.start()\n        t_list.append(t) #每启动一个将线程附加到列表\n    for j in t_list:\n        j.join() #循环列表并等待\n    print(\"---->\")\n'''\n\nimport  threading,time\ndef hihi(num):\n    print(\"running on number: %s\" % num)\n    time.sleep(3)\nif __name__ == \"__main__\":\n    list1 = []\n    for i in range(10):\n        t = threading.Thread(target=hihi,args=[i,])\n        t.start()\n        list1.append(t) #附加所有的线程到列表\n    for j in list1:  #循环所有的线程等待一次\n        j.join() #在没有守护线程的时候使用,如果主线程被设置为守护线程则join就无效了\n        print(\"==>\") #循环list1,\n    print(\"--->\")","sub_path":"day8/test/一次启动十个线程__并行启动.py","file_name":"一次启动十个线程__并行启动.py","file_ext":"py","file_size_in_byte":1024,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"368051238","text":"\n# -*- coding: utf-8 -*- \n'''\nReverse a singly linked list.\nclick to show more hints.\nHint:\nA linked list can be reversed either iteratively or recursively. Could you implement both?\n'''\n# Definition for singly-linked list.\n# class ListNode(object):\n#     def __init__(self, x):\n#         self.val = x\n#         self.next = None\n\nclass Solution(object):\n    def reverseList(self, head):\n        \"\"\"\n        :type head: ListNode\n        :rtype: ListNode\n        \"\"\"\n        #记录初始节点并将其next置为None，然后bc分别记录a.next，a.next.next，让b.next = a，因为b的next往回指了，所以当b不为None时记录C\n        否则返回a即可\n        a = head\n        if a!= None:\n            b = a.next\n            a.next = None\n            while b != None:\n                c = b.next\n                b.next = a\n                a = b\n                b = c\n        return a\n        \n    def reverseList(self, head):\n        \"\"\"\n        :type head: ListNode\n        :rtype: ListNode\n        递归方法:除开每次头节点将后面第一个节点带入递归函数求反过来的开头，然后头结点的下一个的下一个指向自己，再讲自己的下一个为None即可\n        \"\"\"\n        #head为None判断考虑空链表情况\n        if head == None or head.next == None:\n            return head\n        res = self.reverseList(head.next)\n        head.next.next = head\n        head.next = None\n        return res\n        \n    \n","sub_path":"206.py","file_name":"206.py","file_ext":"py","file_size_in_byte":1464,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"124985772","text":"input = set(open(\"input.txt\").readlines())\n\n\ndef comp(a, b):\n    if a == b:\n        return \"a\"\n    return \"b\"\n\n\nfor i in input:\n    for j in input:\n        if i == j:\n            continue\n        x = list(map(comp, i, j))\n        if x.count(\"b\") == 1:\n            print(i, \"\\n\", j)\n            quit\n","sub_path":"day2/ids.py","file_name":"ids.py","file_ext":"py","file_size_in_byte":299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"131750335","text":"# 큐를 이용한 풀이, 효율성 X\nfrom collections import deque\ndef solution1(n, money):\n    \n    answer = 0\n    money = set(money)\n    dic = {}\n    for m in money:\n        dic[m] = 0\n    \n    queue = deque()\n    queue.append((n, money, dic))\n    visited = []\n    while queue:\n        leaving, coins, memo = queue.popleft()\n        if leaving == 0:\n            if memo not in visited:\n                answer += 1\n                visited.append(memo)\n                continue\n        \n        if min(coins) > leaving:\n            continue\n        \n        for coin in coins:\n            if coin > leaving:\n                continue\n                \n            for i in range(1, leaving // coin + 1):\n                l = leaving - coin*i\n                temp = memo.copy()\n                temp[coin] += i\n                queue.append((l, coins-set([coin]), temp))\n        \n    return answer\n\n# 다른 사람의 풀이, 현타온다\nfrom collections import defaultdict\ndef solution(n, money):\n    dp=defaultdict(int)   \n    dp[0]=1\n\n    for m in money:\n        for i in range(m,n+1):\n            dp[i]+=dp[i-m]%1000000007\n    return dp[n]","sub_path":"Programmers/3단계_거스름돈.py","file_name":"3단계_거스름돈.py","file_ext":"py","file_size_in_byte":1140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"348645021","text":"from selenium import webdriver\nfrom selenium.common.exceptions import ElementClickInterceptedException\nfrom selenium.webdriver.chrome.options import Options\nimport pandas as pd\nimport time\nimport json\n\n\ndef write_json(data, filename='tables.json'):\n    \"\"\"\n    Write given data to json file\n    \"\"\"\n    with open(filename, 'w', encoding='utf-8') as f:\n        json.dump(data, f, ensure_ascii=False, indent=2)\n\n\noptions = Options()\noptions.headless = True\ndriver = webdriver.Chrome('C:\\\\webdrivers\\\\chromedriver', options=options)\ndriver.get(\"https://is.fotbal.cz/\")\n\ncompetitions_xpath = '//*[@id=\"TopMenu_liSoutez\"]/a'\ncompetition_number_field_xpath = '//*[@id=\"txtSearchCislo\"]'\nsearch_button_xpath = '//*[@id=\"btnSearch\"]/span'\ncompetition_name_xpath = '//*[@id=\"MainContent_gridData\"]/tbody/tr[2]/td[2]/a'\n\ncompetition_number = \"2020712A1A\"\ndriver.find_element_by_xpath(competitions_xpath).click()\ntime.sleep(1)\ndriver.find_element_by_xpath(competition_number_field_xpath).send_keys(competition_number)\ntime.sleep(2)\ndriver.find_element_by_xpath(search_button_xpath).click()\ntime.sleep(1)\n\nwhile True:\n    try:\n        driver.find_element_by_xpath(competition_name_xpath).click()\n        break\n    except ElementClickInterceptedException:\n        time.sleep(2)\n        driver.execute_script(\"window.scrollTo(0, document.body.scrollHeight);\")\n\nurl = driver.current_url\ntables = pd.read_html(url)\n\ninfo_to_json = []\nfor table in tables:\n    result = table.to_json()\n    parsed = json.loads(result)\n    info_to_json.append(parsed)\n\nwrite_json(info_to_json)\n\ndriver.close()\n","sub_path":"football_results.py","file_name":"football_results.py","file_ext":"py","file_size_in_byte":1574,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"351133251","text":"\r\nimport cv2\r\n# pip install opencv-python\r\nimport numpy as np\r\nfrom PIL import Image\r\n# pip install Pillow\r\n# python3 -m pip install Pillow\r\nfrom _io import BytesIO\r\nimport requests\r\nimport discord\r\nfrom discord.ext import commands\r\n\r\nsushi = commands.Bot(command_prefix=\"&\")\r\n# client\r\n\r\n@sushi.event\r\nasync def on_ready():\r\n\tprint(\"Sushi has been fried!\")\r\n\r\n@sushi.command()\r\nasync def filter(ctx, arg):\r\n\tsendfile = 0\r\n\tfor file in ctx.message.attachments:\r\n\t\tsendfile = 1\r\n\t\tlink = requests.get(file.url)\r\n\t\timg = Image.open(BytesIO(link.content))\r\n\t\timg.save('image.jpg')\r\n\r\n\t\timage = cv2.imread('image.jpg')\r\n\r\n\t\tfilename = 'filtered.jpg'\r\n\r\n\t\tif arg == 'blur':\r\n\t\t\tblurred = cv2.GaussianBlur(image,(33,33,),0)\r\n\t\t\t# blurred = cv2.medianBlur(image,55)\r\n\t\t\tcv2.imwrite(filename,blurred)\r\n\t\t\tawait ctx.message.channel.send(file=discord.File(filename))\r\n\r\n\t\telif arg == 'gray':\r\n\t\t\tgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n\t\t\tcv2.imwrite(filename,gray)\r\n\t\t\tawait ctx.message.channel.send(file=discord.File(filename))\r\n\r\n\t\telif arg == 'edge':\r\n\t\t\tgray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)\r\n\t\t\tblurred = cv2.GaussianBlur(gray,(5,5,),0)\r\n\t\t\tedged = cv2.Canny(blurred,85,85)\r\n\t\t\tcv2.imwrite(filename,edged)\r\n\t\t\tawait ctx.message.channel.send(file=discord.File(filename))\r\n\r\n\t\telif arg == 'reflect':\r\n\t\t\tflip = cv2.flip(image, 1)\r\n\t\t\tcv2.imwrite(filename,flip)\r\n\t\t\tawait ctx.message.channel.send(file=discord.File(filename))\r\n\r\n\t\telif arg == 'bright':\r\n\t\t\tarray = np.array([[0.01, 0.54, 0.9],[0.4, 0.01, 0.4],[0.01, 0.2, 0.01]])\r\n\t\t\tbright = cv2.filter2D(image, -1, array)\r\n\t\t\tcv2.imwrite(filename,bright)\r\n\t\t\tawait ctx.message.channel.send(file=discord.File(filename))\r\n\r\n\t\telif arg == '70s':\r\n\t\t\tgray = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)\r\n\t\t\tcv2.imwrite(filename,gray)\r\n\t\t\tawait ctx.message.channel.send(file=discord.File(filename))\r\n\t\t\r\n\t\telse:\r\n\t\t\tawait ctx.message.channel.send('Improper Usage\\nFilters: **blur, gray, edge, reflect, bright, 70s**\\nFilter Usage Example: *&filter edge*')\r\n\r\n\tif sendfile == 0:\r\n\t\tawait ctx.message.channel.send('Improper Usage\\nFilters: **blur, gray, edge, reflect, bright, 70s**\\nFilter Usage Example: *&filter edge*')\r\n\r\n\r\n\r\n\r\nsushi.run(\"TOKEN\")\r\n#the last line needs to be .run\r\n","sub_path":"Filter Bot/filterbot.py","file_name":"filterbot.py","file_ext":"py","file_size_in_byte":2234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"251796796","text":"import json\nimport sys\nimport numpy as np\nimport pandas as pd\nimport argparse\n\nfrom sklearn import preprocessing\nfrom scipy import spatial\nfrom math import sqrt\nfrom datetime import datetime as dt\n\nsys.path.insert(1, '/activity_segmentation/segmentation/evaluate/utils')\nsys.path.insert(1, '/activity_segmentation/utils')\n\nfrom activity_change_evaluation import *\n\nfrom create_embedding_matrix import *\nfrom calculate_context_similarity import *\n\nimport multiprocessing\nfrom gensim.models import Word2Vec\n\nfrom keras.preprocessing.text import Tokenizer\n\nfrom sklearn.model_selection import LeaveOneOut\n\ndef get_unixtime(dt64):\n    return dt64.astype('datetime64[s]').astype('int')\n\ndef main(argv):\n    np.set_printoptions(threshold=sys.maxsize)\n    np.set_printoptions(suppress=True)\n    # parse args\n    parser = argparse.ArgumentParser()\n    parser.add_argument(\"--dataset_dir\",\n                        type=str,\n                        default=\"/activity_segmentation/kasteren_house_a/reduced\",\n                        nargs=\"?\",\n                        help=\"Dataset dir\")\n    parser.add_argument(\"--dataset_file\",\n                        type=str,\n                        default=\"base_kasteren_reduced.csv\",\n                        nargs=\"?\",\n                        help=\"Dataset file\")\n    parser.add_argument(\"--models_dir\",\n                        type=str,\n                        default=\"../generated_models\",\n                        nargs=\"?\",\n                        help=\"Dataset dir\")\n    parser.add_argument(\"--embedding_size\",\n                        type=int,\n                        default=50,\n                        nargs=\"?\",\n                        help=\"Embedding size for word2vec algorithm\")\n    parser.add_argument(\"--window_size\",\n                        type=int,\n                        default=1,\n                        nargs=\"?\",\n                        help=\"Window size for word2vec algorithm\")\n    parser.add_argument(\"--iterations\",\n                        type=int,\n                        default=5,\n                        nargs=\"?\",\n                        help=\"Iterations for word2vec algorithm\")\n    parser.add_argument(\"--min_sensor_events\",\n                        type=int,\n                        default=10,\n                        nargs=\"?\",\n                        help=\"Minimum number of sensor events for a day to be evaluated\")\n    parser.add_argument(\"--graph_to_retrofit\",\n                        type=str,\n                        default='None',\n                        nargs=\"?\",\n                        help=\"Graph to retrofit\")\n    args = parser.parse_args()\n\n    # loading full dataset\n    print('Loading DATASET...')\n    DATASET = args.dataset_dir + \"/\" + args.dataset_file\n    df_har = pd.read_csv(DATASET, parse_dates=[[0, 1]], index_col=0, sep=' ', header=None)\n    df_har.columns = ['sensor', 'action', 'event', 'activity']\n    df_har.index.names = [\"timestamp\"]\n    print('DATASET loaded')\n\n    # create LEAVE ONE OUT (LOO) cross-validation\n    LOO = LeaveOneOut()\n    # create day column\n    df_har['day'] = pd.DatetimeIndex(df_har.index).normalize().astype(str)\n    # group by day and filter days with less than a minimum number of sensor events\n    df_har = df_har.groupby(by=[\"day\"]).filter(lambda day: len(day) >= args.min_sensor_events)\n    print(\"Total number of sensor events after filtering: \" + str(len(df_har)))\n    # get unique days for cross-validation\n    days = set(df_har['day'])\n    sorted_days = []\n    for day in days:\n        sorted_days.append(day)\n    sorted_days = sorted(sorted_days)\n    # get indexes of LEAVE ONE DAY OUT (LODO) cross-validation\n    train_test_indexes = LOO.split(sorted_days)\n\n    # load contextual and action information\n    UNIQUE_ACTIONS = args.dataset_dir + \"/\" + 'unique_actions.json'\n    CONTEXT_OF_ACTIONS = args.dataset_dir + \"/\" + 'context_model.json'\n    unique_actions = json.load(open(UNIQUE_ACTIONS, 'r'))\n    total_actions = len(unique_actions)\n    # context of actions\n    context_of_actions = json.load(open(CONTEXT_OF_ACTIONS, 'r'))\n    action_location = {}\n    for key, values in context_of_actions['objects'].items():\n        action_location[key] = values['location']\n\n    # tokenize actions\n    tokenizer_actions = Tokenizer(lower=False)\n    tokenizer_actions.fit_on_texts(df_har['action'])\n    action_index = tokenizer_actions.word_index\n\n    # tokenize activities\n    tokenizer_activities = Tokenizer(lower=False)\n    tokenizer_activities.fit_on_texts(df_har['activity'])\n    activity_index = tokenizer_activities.word_index\n\n    # prepare mean metrics for exact\n    TPR_list_1 = []\n    TNR_list_1 = []\n    FPR_list_1 = []\n    G_MEAN_list_1 = []\n     # prepare mean metrics for 5s offset\n    TPR_list_5 = []\n    TNR_list_5 = []\n    FPR_list_5 = []\n    G_MEAN_list_5 = []\n     # prepare mean metrics for 10s offset\n    TPR_list_10 = []\n    TNR_list_10 = []\n    FPR_list_10 = []\n    G_MEAN_list_10 = []\n\n    # iterate through LODO cross-validation indexes\n    print('Starting LEAVE ONE DAY OUT cross-validation...')\n    for train_indexes, test_index in train_test_indexes:\n        \n        # get train days data\n        train_days = []\n        for train_index in train_indexes:\n            train_days.append(sorted_days[train_index])\n        df_har_train = df_har[df_har['day'].isin(train_days)]\n        print(\"Total number of train sensor events: \" + str(len(df_har_train)))\n        # create actions and activities by index train\n        actions_by_index_train = []\n        for action in df_har_train['action']:\n            actions_by_index_train.append(action_index[action])\n        activities_by_index_train = []\n        for activity in df_har_train['activity']:\n            activities_by_index_train.append(activity_index[activity])\n        # take timestamps train in seconds\n        dates_train = list(df_har_train.index.values)\n        timestamps_train = list(map(get_unixtime, dates_train))\n        # generate X (actions) and y (change points) for train\n        X_train = []\n        y_train = []\n        last_activity = None\n        for i in range(0, len(actions_by_index_train)):\n            X_train.append(actions_by_index_train[i])\n            if (i == 0):\n                y_train.append(0)\n            elif last_activity == activities_by_index_train[i]:\n                y_train.append(0)\n            else:\n                y_train.append(1)\n            last_activity = activities_by_index_train[i]\n        print(\"Total number of train samples: \" + str(len(X_train)))\n        print(\"Total number of train labels: \" + str(len(y_train)))\n\n        # get test day data\n        test_day = sorted_days[test_index[0]]\n        df_har_test = df_har[df_har['day'] == test_day]\n        print(\"Total number of test sensor events: \" + str(len(df_har_test)))\n        # create actions and activities by index test\n        actions_by_index_test = []\n        for action in df_har_test['action']:\n            actions_by_index_test.append(action_index[action])\n        activities_by_index_test = []\n        for activity in df_har_test['activity']:\n            activities_by_index_test.append(activity_index[activity])\n        # take timestamps test in seconds\n        dates_test = list(df_har_test.index.values)\n        timestamps_test = list(map(get_unixtime, dates_test))\n        # generate X (actions) and y (change points) for test\n        X_test = []\n        y_test = []\n        last_activity = None\n        for i in range(0, len(actions_by_index_test)):\n            X_test.append(actions_by_index_test[i])\n            if (i == 0):\n                y_test.append(0)\n            elif last_activity == activities_by_index_test[i]:\n                y_test.append(0)\n            else:\n                y_test.append(1)\n            last_activity = activities_by_index_test[i]\n        print(\"Total number of test samples: \" + str(len(X_test)))\n        print(\"Total number of test labels: \" + str(len(y_test)))\n\n        # load word2vec model and create embedding matrix\n        if args.graph_to_retrofit == 'None':\n            model = Word2Vec.load(args.models_dir + \"/\" + args.dataset_dir.split('/')[2] + \"/\" 'word2vec_models/' + str(test_day) + '_day.model')\n            embedding_action_matrix, unknown_actions = create_action_embedding_matrix(tokenizer_actions, model, args.embedding_size)\n        else:\n            vector_file = args.models_dir + \"/\" + args.dataset_dir.split('/')[2] + \"/\" 'word2vec_models/vector_files/' + str(test_day) + '_retrofitted_' + args.graph_to_retrofit + '.vector'\n            embedding_action_matrix, unknown_actions = create_action_embedding_matrix_from_file(tokenizer_actions, vector_file, args.embedding_size)\n\n        # calculate optimum threshold with training data\n        similarities_train = []\n        similarities_train.append(1.0)\n        for i in range(0, len(X_train)-1):\n            similarity = 1 - spatial.distance.cosine(embedding_action_matrix[X_train[i]], embedding_action_matrix[X_train[i+1]])\n            similarities_train.append(similarity)\n        best_threshold = 0\n        best_G_MEAN = 0\n        for threshold in [x * 0.1 for x in range(0, 10)]:\n            cf_matrix_1 = get_conf_matrix_with_offset_strategy(similarities_train, y_train, timestamps_train, threshold, 1, lower=True)\n            # TPR, TNR, FPR, G-MEAN for exact change point detection\n            TN, FP, FN, TP = cf_matrix_1.ravel()\n            TPR = TP/(TP+FN)\n            TNR = TN/(TN+FP)\n            FPR = FP/(FP+TN)\n            G_MEAN = sqrt(TPR * TNR)\n            if G_MEAN > best_G_MEAN:\n                best_G_MEAN = G_MEAN\n                best_threshold = threshold\n        print(\"-----------------------------------------------------------------\")\n        print(\"DAY: \" + str(test_day))\n        print(\"Calculated optimum threshold: \" + str(best_threshold))\n        print(\"Best G-MEAN value on TRAIN data (EXACT): \" + str(best_G_MEAN))\n\n        # calculate metrics in test data\n        similarities_test = []\n        similarities_test.append(1.0)\n        for i in range(0, len(X_test)-1):\n            similarity = 1 - spatial.distance.cosine(embedding_action_matrix[X_test[i]], embedding_action_matrix[X_test[i+1]])\n            similarities_test.append(similarity)\n        # TPR, TNR, FPR, G-MEAN for exact change point detection\n        print(\"Total number of TRUE change points: \"+ str(sum(y_test)))\n        cf_matrix_1 = get_conf_matrix_with_offset_strategy(similarities_test, y_test, timestamps_test, best_threshold, 1, lower=True)\n        TN, FP, FN, TP = cf_matrix_1.ravel()\n        TPR = TP/(TP+FN)\n        TNR = TN/(TN+FP)\n        FPR = FP/(FP+TN)\n        G_MEAN = sqrt(TPR * TNR)\n        TPR_list_1.append(TPR)\n        TNR_list_1.append(TNR)\n        FPR_list_1.append(FPR)\n        G_MEAN_list_1.append(G_MEAN)\n        print(\"-----------------------------------------------------------------\")\n        print(\"TP 1s Exact: \"+ str(TP))\n        print(\"FP 1s Exact: \"+ str(FP))\n        print(\"TN 1s Exact: \"+ str(TN))\n        print(\"FN 1s Exact: \"+ str(FN))\n        print(\"TPR value on TEST data (EXACT): \" + str(TPR))\n        print(\"FPR value on TEST data (EXACT): \" + str(FPR))\n        print(\"G-MEAN value on TEST data (EXACT): \" + str(G_MEAN))\n        # TPR, TNR, FPR, G-MEAN for 5s offset\n        cf_matrix_5 = get_conf_matrix_with_offset_strategy(similarities_test, y_test, timestamps_test, best_threshold, 5, lower=True)\n        TN, FP, FN, TP = cf_matrix_5.ravel()\n        TPR = TP/(TP+FN)\n        TNR = TN/(TN+FP)\n        FPR = FP/(FP+TN)\n        G_MEAN = sqrt(TPR * TNR)\n        TPR_list_5.append(TPR)\n        TNR_list_5.append(TNR)\n        FPR_list_5.append(FPR)\n        G_MEAN_list_5.append(G_MEAN)\n        print(\"-----------------------------------------------------------------\")\n        print(\"TP 5s offset: \"+ str(TP))\n        print(\"FP 5s offset: \"+ str(FP))\n        print(\"TN 5s offset: \"+ str(TN))\n        print(\"FN 5s offset: \"+ str(FN))\n        print(\"TPR value on TEST data (5s): \" + str(TPR))\n        print(\"FPR value on TEST data (5s): \" + str(FPR))\n        print(\"G-MEAN value on TEST data (5s): \" + str(G_MEAN))\n        # TPR, TNR, FPR, G-MEAN for 10s offset\n        cf_matrix_10 = get_conf_matrix_with_offset_strategy(similarities_test, y_test, timestamps_test, best_threshold, 10, lower=True)\n        TN, FP, FN, TP = cf_matrix_10.ravel()\n        TPR = TP/(TP+FN)\n        TNR = TN/(TN+FP)\n        FPR = FP/(FP+TN)\n        G_MEAN = sqrt(TPR * TNR)\n        TPR_list_10.append(TPR)\n        TNR_list_10.append(TNR)\n        FPR_list_10.append(FPR)\n        G_MEAN_list_10.append(G_MEAN)\n        print(\"-----------------------------------------------------------------\")\n        print(\"TP 10s offset: \"+ str(TP))\n        print(\"FP 10s offset: \"+ str(FP))\n        print(\"TN 10s offset: \"+ str(TN))\n        print(\"FN 10s offset: \"+ str(FN))\n        print(\"TPR value on TEST data (10s): \" + str(TPR))\n        print(\"FPR value on TEST data (10s): \" + str(FPR))\n        print(\"G-MEAN value on TEST data (10s): \" + str(G_MEAN))\n        print(\"-----------------------------------------------------------------------------------------------------------------\")\n        print(\"-----------------------------------------------------------------------------------------------------------------\")\n        segments = []\n        label = \"Segment_\"\n        counter = 0\n        for similarity in similarities_test:\n            if similarity < best_threshold:\n                counter += 1\n            segments.append(label + str(counter))\n        df_har_test_with_segments = df_har_test\n        df_har_test_with_segments['Segments'] = segments\n        df_har_test_with_segments['scores'] = similarities_test\n        df_har_test_with_segments[['action', 'activity', 'Segments', 'scores']].to_csv(\"files/segmentation_\" + str(test_day))\n        print(\"SEGMENTS OF DAY \" + str(test_day) + \" written to file...\")\n        print(\"-----------------------------------------------------------------------------------------------------------------\")\n        print(\"-----------------------------------------------------------------------------------------------------------------\")\n\n    print('LEAVE ONE DAY OUT cross-validation FINISHED')\n\n    print(\"------ Exact change point detection results ------\")\n    print(\"TPR MEAN: \" + str(np.mean(TPR_list_1)))\n    print(\"TPR STD: \" + str(np.std(TPR_list_1)))\n    print(\"FPR MEAN: \" + str(np.mean(FPR_list_1)))\n    print(\"FPR STD: \" + str(np.std(FPR_list_1)))\n    print(\"G-Mean MEAN: \" + str(np.mean(G_MEAN_list_1)))\n    print(\"G-Mean STD: \" + str(np.std(G_MEAN_list_1)))\n\n    print(\"------ Change point detection with 5s offset results ------\")\n    print(\"TPR MEAN: \" + str(np.mean(TPR_list_5)))\n    print(\"TPR STD: \" + str(np.std(TPR_list_5)))\n    print(\"FPR MEAN: \" + str(np.mean(FPR_list_5)))\n    print(\"FPR STD: \" + str(np.std(FPR_list_5)))\n    print(\"G-Mean MEAN: \" + str(np.mean(G_MEAN_list_5)))\n    print(\"G-Mean STD: \" + str(np.std(G_MEAN_list_5)))\n\n    print(\"------ Change point detection with 10s offset results ------\")\n    print(\"TPR MEAN: \" + str(np.mean(TPR_list_10)))\n    print(\"TPR STD: \" + str(np.std(TPR_list_10)))\n    print(\"FPR MEAN: \" + str(np.mean(FPR_list_10)))\n    print(\"FPR STD: \" + str(np.std(FPR_list_10)))\n    print(\"G-Mean MEAN: \" + str(np.mean(G_MEAN_list_10)))\n    print(\"G-Mean STD: \" + str(np.std(G_MEAN_list_10)))\n\nif __name__ == \"__main__\":\n    main(sys.argv)","sub_path":"segmentation/leave-one-day-out/generate_example_file_of_segmented_day/activity_change_recognition_emb_sim.py","file_name":"activity_change_recognition_emb_sim.py","file_ext":"py","file_size_in_byte":15390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"610492309","text":"# -*- coding: utf-8 -*-\n# Copyright (C) 2021-2022 by SCICO Developers\n# All rights reserved. BSD 3-clause License.\n# This file is part of the SCICO package. Details of the copyright and\n# user license can be found in the 'LICENSE' file distributed with the\n# package.\n\n\"\"\"Linear operator functions and classes.\"\"\"\n\nimport sys\n\nfrom ._circconv import CircularConvolve\nfrom ._convolve import Convolve, ConvolveByX\nfrom ._dft import DFT\nfrom ._diag import Diagonal, Identity\nfrom ._diff import FiniteDifference, SingleAxisFiniteDifference\nfrom ._func import Crop, Pad, Reshape, Slice, Sum, Transpose, linop_from_function\nfrom ._linop import ComposedLinearOperator, LinearOperator\nfrom ._matrix import MatrixOperator\nfrom ._stack import DiagonalStack, VerticalStack\nfrom ._util import jacobian, operator_norm, power_iteration, valid_adjoint\n\n__all__ = [\n    \"CircularConvolve\",\n    \"Convolve\",\n    \"DFT\",\n    \"Diagonal\",\n    \"FiniteDifference\",\n    \"SingleAxisFiniteDifference\",\n    \"Identity\",\n    \"VerticalStack\",\n    \"DiagonalStack\",\n    \"MatrixOperator\",\n    \"Pad\",\n    \"Crop\",\n    \"Reshape\",\n    \"Slice\",\n    \"Sum\",\n    \"Transpose\",\n    \"LinearOperator\",\n    \"ComposedLinearOperator\",\n    \"linop_from_function\",\n    \"operator_norm\",\n    \"power_iteration\",\n    \"valid_adjoint\",\n    \"jacobian\",\n]\n\n# Imported items in __all__ appear to originate in top-level linop module\nfor name in __all__:\n    getattr(sys.modules[__name__], name).__module__ = __name__\n","sub_path":"scico/linop/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1455,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"174554954","text":"from socket import *\nfrom baidu import *\n\ndef handle(c):\n    image_type = c.recv(1024).decode()\n    f = open(\"img.jpg\",'wb')\n    while True:\n        data = c.recv(1024)\n        if data == b'##':\n            break\n        f.write(data)\n    f.close()\n    msg = baidu(image_type,'./img.jpg')\n    if image_type == '其他':\n        c.send(msg[0].get('Keyword','Sorry').encode())\n    else:\n        c.send(msg[0].get('name','Sorry').encode())\n\ns = socket()\ns.bind(('127.0.0.1',8887))\ns.listen(3)\n\nwhile True:\n    c,addr = s.accept()\n    print(\"Connect from\",addr)\n    handle(c)\n    c.close()\n\n","sub_path":"exercise5_server.py","file_name":"exercise5_server.py","file_ext":"py","file_size_in_byte":587,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"567721336","text":"\"\"\"\nCreated on Thu May 01 11:00:00 2016\n\n@author: Fernando Suarez\n\"\"\"\n\n#from pptx import Presentation\nimport os\nimport xlwings as xw\nimport math\nfrom os import getenv\nimport pymssql\nimport datetime as dt\nfrom win32com import client\nfrom PyPDF2 import PdfFileReader, PdfFileMerger\nimport win32com.client\nfrom win32com.client import Dispatch, constants\nimport json\nimport numpy as np\nimport pandas as pd\nimport matplotlib.pyplot as plt\nimport sys\nimport random\nimport paramiko\n\n\n'''FUNCIONES VARIAS'''\ndef getSelfPath():\n\t'''\n\tRetorna la fecha del dia de ayer.\n\t'''\n\tself_path=os.path.dirname(os.path.realpath(sys.argv[0]))+\"\\\\\"\n\treturn self_path\n\ndef getDatesBetween(date_inic, date_fin):\n\t'''\n\tRetorna una lista con todas las fechas entre dos fechas dadas.\n\t'''\n\tday_list=pd.date_range(date_inic, date_fin).tolist()\n\treturn day_list\n\ndef getCurrentDaysMonth(date):\n\t'''\n\tRetorna la cantidad de dias hasta el ultimo dia del mes pasado.\n\t'''\n\tdays= date.timetuple().tm_mday+1\n\treturn days\n\ndef getCurrentDaysYear(date):\n\t'''\n\tRetorna la cantidad de dias hasta el anio pasado.\n\t'''\n\tdays= date.timetuple().tm_yday+1\n\treturn days\n\ndef getDatesSince(start_date, end_date):\n\t'''\n\tRetorna una lista con todas las fechas entre dos fechas particular.\n\t'''\n\tday_list=[]\n\tdelta = end_date - start_date\n\tfor i in range(delta.days + 1):\n\t\tday_list.append(start_date + dt.timedelta(days=i)) \n\ndef getNDaysFromToday(days):\n\t'''\n\tRetorna la fecha n dias desde hoy.\n\t'''\n\tdate = dt.datetime.now() - dt.timedelta( days = days )\n\tdate=str(date.strftime('%Y-%m-%d'))\n\treturn date\n\n\ndef getNDaysFromDate(days, date_string):\n\t'''\n\tRetorna la fecha n dias desde hoy.\n\t'''\n\tdate = convertStringtoDate(date_string) - dt.timedelta( days = days )\n\tdate=str(date.strftime('%Y-%m-%d'))\n\treturn date\n\ndef convertDatetoString(date):\n\t'''\n\tRetorna la fecha en formato de string, dado el objeto date.\n\t'''\n\tdate_string=str(date.strftime('%Y-%m-%d'))\n\treturn date_string\n\ndef convertStringtoDate(date_string):\n\t'''\n\tRetorna la fecha en formato date.\n\t'''\n\tdate_formated=dt.datetime.strptime(date_string, '%Y-%m-%d').date()\n\treturn date_formated\n\ndef convertDateAllTogether(date):\n\t'''\n\tRetorna la fecha en formato con todos los caracteres juntos.\n\t'''\n\tdate_formated=str(date.strftime('%Y%m%d'))\n\treturn date_formated\n\ndef mergePDF(path, output_name):\n\t'''\n\tConcatena todos los archivos PDF en una ubicacion dada y lo guarda en otro PDF con un nombre dado.\n\t'''\n\tfiles_dir = path\n\tpdf_files = [f for f in os.listdir(files_dir) if f.endswith(\".PDF\")]\n\tmerger = PdfFileMerger()\n\tfor filename in pdf_files:\n\t    merger.append(PdfFileReader(os.path.join(files_dir, filename), \"rb\"))\n\tmerger.write(os.path.join(files_dir, output_name))\n\ndef deleteFile(path):\n\t'''\n\tBorra un archivo.\n\t'''\n\tos.remove(path)\n\ndef deleteFolder(path):\n\t'''\n\tBorra una carpeta.\n\t'''\n\tos.rmdir(path)\n\n\ndef extractTextFile(path):\n\t'''\n\tLee un archivo de texto y lo retorna como string.\n\t'''\n\tf=open(path,'r')\n\tfile_string=f.read()\n\tf.close()\n\treturn file_string\n\ndef convertJSONtoDict(json_string):\n\t'''\n\tLee un string en formato JSON y lo retorna en un dictionary.\n\t'''\n\tjson_dict=json.loads(json_string)\n\treturn json_dict\n\n\t\n'''FUNCIONES MATRICIALES'''\ndef arrayToNumPy(arr):\n\t'''\n\tTransforma una matriz normal en una de numpy.\n\t'''\n\treturn np.array(arr)\n\ndef getVectColumn(matrix, col_number):\n\t'''\n\tDada una lista de listas retorna un vector con la columna.\n\t'''\n\tmatrix_np=np.array(matrix)\n\tcolumn=matrix_np[:,col_number]\n\treturn column\n\n\n'''FUNCIONES SQL'''\n\ndef connectDatabase(server, database):\n\t'''\n\tSe conecta a una base de datos y retorna el objecto de la conexion, usando windows authentication.\n\t'''\n\tconn= pymssql.connect(host=server, database=database)\n\treturn conn\n\ndef connectDatabaseUser(server, database, username, password):\n\t'''\n\tSe conecta a una base de datos y retorna el objecto de la conexion, usando un usuario.\n\t'''\n\tconn= pymssql.connect(host=server, database=database, user=username, password=password)\n\treturn conn\n\n\ndef queryDatabase(conn, query):\n\t'''\n\tConsulta la la base de datos(conn) y devuelve el cursor asociado a la consulta.\n\t'''\n\tcursor=conn.cursor()\n\tcursor.execute(query)\n\treturn cursor\n\ndef getTableSQL(cursor):\n\t'''\n\tRecibe un cursor asociado a una consulta en la BDD y la transforma en una matriz.\n\t'''\n\ttable=[]\n\trow = cursor.fetchone()\n\tncolumns=len(cursor.description)\n\twhile row:\n\t\tcol=0\t\n\t\tvect=[]\n\t\twhile col<ncolumns:\n\t\t\tvect.append(row[col])\n\t\t\tcol=col+1\n\t\trow = cursor.fetchone()\n\t\ttable.append(vect)\n\treturn table\n\ndef getListSQL(cursor):\n\t'''\n\tRecibe un cursor asociado a una consulta en la BDD y la transforma en un lista, solo usarlo cuando la consulta retorne una columna.\n\t'''\n\tlista=[]\n\trow = cursor.fetchone()\n\twhile row:\t\n\t\tescalar=row[0]\n\t\trow = cursor.fetchone()\n\t\tlista.append(escalar)\n\treturn lista\n\n\ndef getSchemaSQL(cursor):\n\t'''\n\tRecibe un cursor asociado a una consulta en la BDD y retorna el esquema de la relacion en una lista.\n\t'''\n\tschema = []\n\tfor i in range(len(cursor.description)):\n\t\tprop = cursor.description[i][0]\n\t\tschema.append(prop)\n\treturn schema\n\ndef disconnectDatabase(conn):\n\t'''\n\tSe deconecta a una base de la datos.\n\t'''\n\tconn.close()\n\ndef runSQL(conn, query):\n    '''\n    Ejecuta un statement en SQL, por ejemplo borrar.\n    '''\n    cursor=conn.cursor()\n    cursor.execute(query)\n    conn.commit()\n\ndef getFrameSqlUser(server, database, username, password, query):\n    '''\n    Retorna el resultado de la query en un panda's dataframe con usuario y clave. \n    '''\n    conn = connectDatabaseUser(server = server, database = database, username = username, password = password)\n    cursor = queryDatabase(conn, query)\n    schema = getSchemaSQL(cursor = cursor)\n    table = getTableSQL(cursor)\n    dataframe = pd.DataFrame(data = table, columns = schema)\n    disconnectDatabase(conn)\n    return dataframe\n\ndef getFrameSql(server, database, query):\n    '''\n    Retorna el resultado de la query en un panda's dataframe. \n    '''\n    conn = connectDatabase(server = server, database = database)\n    cursor = queryDatabase(conn, query)\n    schema = getSchemaSQL(cursor = cursor)\n    table = getTableSQL(cursor)\n    dataframe = pd.DataFrame(data = table, columns = schema)\n    disconnectDatabase(conn)\n    return dataframe\n\n\n'''FUNCIONES EXCEL'''\n\ndef openWorkbook(path):\n\t'''\n\tAbre un workbook y retorna un objecto workbook de xlwings.\n\t'''\n\twb=xw.Book(path)\n\treturn wb\n\ndef saveWorkbook(wb):\n\t'''\n\tGuarda un workbook.\n\t'''\n\twb.save()\n\ndef closeWorkbook(wb):\n\t'''\n\tCierra un workbook.\n\t'''\n\twb.close()\n\ndef closeExcel(wb):\n\t'''\n\tCierra un Excel.\n\t'''\n\tapp=wb.app\n\tapp.quit()\n\ndef eraseTableXl(sheet, row, column):\n\t'''\n\tRecibe el rango asociado y borra la tabla considerando que el rango esta en el top-left corner de la tabla.\n\t'''\n\txw.sheets[sheet].range(row,column).expand('table').clear_contents()\n\ndef pasteValXl(sheet, row, column,value):\n\t'''\n\tInserta un valor en una celta de excel dada la hoja. \n\t'''\n\txw.sheets[sheet].range(row,column).value = value\n\ndef pasteColXl(sheet, row, column, lista):\n\t'''\n\tInserta un valor en una celta de excel dada la hoja. \n\t'''\n\tfor val in lista:\n\t\txw.sheets[sheet].range(row,column).value = val\n\t\trow+=1\n\ndef pasteQueryXl(server, database, query, sheet, row, column, with_schema):\n\t'''\n\tConsulta a la base de datos y pega el resultado en una hoja de excel en una determinada fila y columna. Si with_schema es verdadero, la pega con\n\tlos nombres de columnas, en otro caso solo pega los valores. \n\t'''\n\tconn= connectDatabase(server,database)\n\tcursor=queryDatabase(conn, query)\n\ttable=getTableSQL(cursor)\n\tdisconnectDatabase(conn)\n\tif with_schema:\n\t\tschema=getSchemaSQL(cursor)\n\t\tpasteValXl(sheet, row, column, schema)\n\t\tpasteValXl(sheet, row+1, column, table)\n\telse:\t\t\n\t\tpasteValXl(sheet, row, column, table)\n\ndef pasteQueryXlUser(server, database, query, sheet, row, column, with_schema, username, password):\n\t'''\n\tConsulta a la base de datos y pega el resultado en una hoja de excel en una determinada fila y columna. Si with_schema es verdadero, la pega con\n\tlos nombres de columnas, en otro caso solo pega los valores. Esta funcion es igual a la anterior pero se conecta a la BDD con usuario y pass.\n\t'''\n\tconn= connectDatabaseUser(server,database, username, password)\n\tcursor=queryDatabase(conn, query)\n\ttable=getTableSQL(cursor)\n\tdisconnectDatabase(conn)\n\tif with_schema:\n\t\tschema=getSchemaSQL(cursor)\n\t\tpasteValXl(sheet, row, column, schema)\n\t\tpasteValXl(sheet, row+1, column, table)\n\telse:\t\t\n\t\tpasteValXl(sheet, row, column, table)\n\ndef getSheetIndex(sheet):\n\t'''\n\tRetorna el indice e una hoja, dado su nombre.\n\t'''\n\treturn xw.Sheet(sheet).index\n\ndef exportSheetPDF(sheet_index, path_in, path_out):\n\t'''\n\tExporta una hoja de Excel en PDF. \n\t'''\n\txlApp = client.Dispatch(\"Excel.Application\")\n\tbooks = xlApp.Workbooks(1)\n\tws = books.Worksheets[sheet_index]\n\tws.ExportAsFixedFormat(0, path_out)\n\ndef getColumnXl(sheet, row, column):\n\t'''\n\tRetorna la columna en una lista, dado un rango y la hoja.\n\t'''\n\treturn xw.sheets(sheet).range(row,column).expand('down').value\n\ndef getTableXl(sheet, row, column):\n\t'''\n\tRetorna la tabla en un arreglo, dado un rango y la hoja.\n\t'''\n\treturn xw.sheets[sheet].range(row,column).expand('table').value\n\n'''FUNCIONES PPT'''\n\ndef openPPT(filename):\n\t'''\n\tAbre un ppt en el mismo programa powerpoint 2014.\n\t'''\n\tos.system(\"\\\"C:\\\\Program Files\\\\Microsoft Office\\\\Office14\\\\POWERPNT.EXE\\\" \"+ filename)\n\n'''FUNCIONES MAIL'''\n\ndef sendMailAttach(subject, body, mails, attachment_paths):\n\t'''\n\t#Envia un mail a todos los correos en el arreglo mails y les adjunta todos los archivos del otro arreglo.\n\t'''\n\t#os.startfile(\"outlook\") #antes lo hacia con esto pero el problema es que no se puede cerrar outlook\n\tconst=win32com.client.constants\n\tolMailItem = 0x0\n\tobj = win32com.client.gencache.EnsureDispatch(\"Outlook.Application\")\n\tnewMail = obj.CreateItem(olMailItem)\n\tnewMail.Subject = subject\n\tnewMail.Body = body\n\tcadena = \"\"\n\tfor mail in mails:\n\t\tmail = mail+\";\"\n\t\tcadena = cadena+mail\n\tcadena=cadena[:-1] # los mails se mandan separados por ; en una cadena\n\tnewMail.To = cadena\n\tfor att in attachment_paths:\n\t\tnewMail.Attachments.Add(Source=att)\n\t#attachment1 = at\n\t#newMail.Attachments.Add(Source=attachment1)\n\t#print(newMail.display())\n\t#newMail.Send()\n\ttry:\n\t\tnewMail.Send()\n\t\tprint(\"Correo enviado\")\n\texcept:\n\t\tprint(\"Fallo el envio del correo\")\n\n\ndef sendMail(subject, body, mails):\n\t'''\n\t#Envia un mail a todos los correos en el arreglo mails.\n\t'''\n\tos.startfile(\"outlook\") #En bbl, sin esto no funciona\n\tconst = win32com.client.constants\n\tolMailItem = 0x0\n\tobj = win32com.client.gencache.EnsureDispatch(\"Outlook.Application\")\n\tnewMail = obj.CreateItem(olMailItem)\n\tnewMail.Subject = subject\n\tnewMail.Body = body\n\tcadena = \"\"\n\tfor mail in mails:\n\t\tmail = mail + \";\"\n\t\tcadena = cadena+mail\n\tcadena = cadena[:-1] # los mails se mandan separados por ; en una cadena\n\tnewMail.To = cadena\n\ttry:\n\t\tnewMail.Send()\n\t\tprint(\"Correo enviado\")\n\texcept:\n\t\tprint(\"Fallo el envio del correo\")\n\n\n'''FUNCIONES FTP'''\ndef downloadDataSFTP(host, username, password, origin, destination, port):\n\t'''\n\tDescarga un archivo desde un SFTP en una ubicacion dada, hay que usar paramiko, si no funciona tal vez bajar el whl. Ejemplo: \n\thost = \"sftp.riskamerica.com\"\n\tusername = \"AGF_Credicorp\"\n\tpw = \"dX{\\\"4YjA\"\n\torigin = './out/Composicion_Indice_IMTRUST_20161005.csv'\n\tdst = 'C://Users/fsuarezb/Desktop/Composicion_Indice_IMTRUST_20161006.csv'\n\tport = 22\n\tdownloadDataSFTP(host, username, pw, origin, dst, port)\n\t'''\n\tsftp = None\n\tftp_open = False\n\ttransport = paramiko.Transport((host, port))\n\ttransport.connect(username=username, password=password)\n\tsftp = paramiko.SFTPClient.from_transport(transport)\n\tsftp_open = True\n\tsftp.get(origin, destination)\n\tif sftp_open:\n\t\tsftp.close()\n\t\tsftp_open = False\n\ttransport.close()\n","sub_path":"Proyectos/descarga_indices_antiguo/libreria_fdo.py","file_name":"libreria_fdo.py","file_ext":"py","file_size_in_byte":11800,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"73948298","text":"\"\"\" Configuration for unit testing with a postgress fixture\n\n    - Unit testing of webserver app with a postgress service as fixture\n    - Starts test session by running a postgres container as a fixture (see postgress_service)\n\n    IMPORTANT: remember that these are still unit-tests!\n\"\"\"\n# pylint: disable=redefined-outer-name\n# pylint: disable=unused-argument\n# pylint: disable=unused-variable\n\n\nimport asyncio\nimport sys\nimport textwrap\nfrom copy import deepcopy\nfrom pathlib import Path\nfrom typing import Any, AsyncIterator, Callable, Iterator, Optional, Union\nfrom unittest.mock import MagicMock\nfrom uuid import uuid4\n\nimport pytest\nimport redis\nimport redis.asyncio as aioredis\nimport simcore_postgres_database.cli as pg_cli\nimport simcore_service_webserver.db_models as orm\nimport simcore_service_webserver.utils\nimport sqlalchemy as sa\nfrom aiohttp import web\nfrom aiohttp.test_utils import TestClient, TestServer\nfrom pydantic import ByteSize, parse_obj_as\nfrom pytest import MonkeyPatch\nfrom pytest_mock.plugin import MockerFixture\nfrom pytest_simcore.helpers.typing_env import EnvVarsDict\nfrom pytest_simcore.helpers.utils_dict import ConfigDict\nfrom pytest_simcore.helpers.utils_login import NewUser\nfrom pytest_simcore.helpers.utils_webserver_unit_with_db import MockedStorageSubsystem\nfrom redis import Redis\nfrom servicelib.aiohttp.application_keys import APP_DB_ENGINE_KEY\nfrom servicelib.aiohttp.long_running_tasks.client import LRTask\nfrom servicelib.aiohttp.long_running_tasks.server import TaskProgress\nfrom servicelib.common_aiopg_utils import DSN\nfrom settings_library.redis import RedisSettings\nfrom simcore_service_webserver import catalog\nfrom simcore_service_webserver._constants import INDEX_RESOURCE_NAME\nfrom simcore_service_webserver.application import create_application\nfrom simcore_service_webserver.groups_api import (\n    add_user_in_group,\n    create_user_group,\n    delete_user_group,\n    list_user_groups,\n)\nfrom simcore_service_webserver.login.settings import LoginOptions\n\nCURRENT_DIR = Path(sys.argv[0] if __name__ == \"__main__\" else __file__).resolve().parent\n\n# DEPLOYED SERVICES FOR TESTSUITE SESSION -----------------------------------\n\n\n@pytest.fixture(autouse=True)\ndef disable_swagger_doc_generation(\n    monkeypatch: MonkeyPatch, osparc_simcore_root_dir: Path\n):\n    \"\"\"\n    by not enabling the swagger documentation, 1.8s per test is gained\n    \"\"\"\n    monkeypatch.setenv(\"REST_SWAGGER_API_DOC_ENABLED\", \"0\")\n\n\n@pytest.fixture(scope=\"session\")\ndef docker_compose_file(\n    default_app_cfg: ConfigDict, monkeypatch_session: MonkeyPatch\n) -> str:\n    \"\"\"Overrides pytest-docker fixture\"\"\"\n\n    cfg = deepcopy(default_app_cfg[\"db\"][\"postgres\"])\n\n    # docker-compose reads these environs\n    monkeypatch_session.setenv(\"TEST_POSTGRES_DB\", cfg[\"database\"])\n    monkeypatch_session.setenv(\"TEST_POSTGRES_USER\", cfg[\"user\"])\n    monkeypatch_session.setenv(\"TEST_POSTGRES_PASSWORD\", cfg[\"password\"])\n\n    compose_path = CURRENT_DIR / \"docker-compose-devel.yml\"\n\n    assert compose_path.exists()\n    return f\"{compose_path}\"\n\n\n# WEB SERVER/CLIENT FIXTURES ------------------------------------------------\n\n\n@pytest.fixture\ndef app_cfg(default_app_cfg: ConfigDict, unused_tcp_port_factory) -> ConfigDict:\n    \"\"\"\n    NOTE: SHOULD be overriden in any test module to configure the app accordingly\n    \"\"\"\n    cfg = deepcopy(default_app_cfg)\n    # fills ports on the fly\n    cfg[\"main\"][\"port\"] = unused_tcp_port_factory()\n    cfg[\"storage\"][\"port\"] = unused_tcp_port_factory()\n\n    # this fixture can be safely modified during test since it is renovated on every call\n    return cfg\n\n\n@pytest.fixture\ndef app_environment(\n    app_cfg: ConfigDict,\n    monkeypatch_setenv_from_app_config: Callable[[ConfigDict], dict[str, str]],\n) -> EnvVarsDict:\n    \"\"\"overridable fixture that defines the ENV for the webserver application\n    based on legacy application config files.\n\n    override like so:\n    @pytest.fixture\n    def app_environment(app_environment: dict[str, str], monkeypatch: MonkeyPatch) -> dict[str, str]:\n        monkeypatch.setenv(\"MODIFIED_ENV\", \"VALUE\")\n        return app_environment | {\"MODIFIED_ENV\":\"VALUE\"}\n    \"\"\"\n    print(\"+ web_server:\")\n    cfg = deepcopy(app_cfg)\n    return monkeypatch_setenv_from_app_config(cfg)\n\n\n@pytest.fixture\ndef web_server(\n    event_loop: asyncio.AbstractEventLoop,\n    app_cfg: ConfigDict,\n    app_environment: EnvVarsDict,\n    postgres_db: sa.engine.Engine,\n    # tools\n    aiohttp_server: Callable,\n    monkeypatch: MonkeyPatch,\n    disable_static_webserver: Callable,\n) -> TestServer:\n    # original APP\n    app = create_application()\n\n    # with patched email\n    _patch_compose_mail(monkeypatch)\n\n    disable_static_webserver(app)\n\n    server = event_loop.run_until_complete(\n        aiohttp_server(app, port=app_cfg[\"main\"][\"port\"])\n    )\n\n    assert isinstance(postgres_db, sa.engine.Engine)\n    pg_settings = dict(e.split(\"=\") for e in app[APP_DB_ENGINE_KEY].dsn.split())\n    assert pg_settings[\"host\"] == postgres_db.url.host\n    assert int(pg_settings[\"port\"]) == postgres_db.url.port\n    assert pg_settings[\"user\"] == postgres_db.url.username\n\n    return server\n\n\n@pytest.fixture\ndef client(\n    event_loop: asyncio.AbstractEventLoop,\n    aiohttp_client: Callable,\n    web_server: TestServer,\n    mock_orphaned_services,\n    redis_client: Redis,\n) -> TestClient:\n    cli = event_loop.run_until_complete(aiohttp_client(web_server))\n    return cli\n\n\n@pytest.fixture(scope=\"session\")\ndef osparc_product_name() -> str:\n    return \"osparc\"\n\n\n# SUBSYSTEM MOCKS FIXTURES ------------------------------------------------\n#\n# Mocks entirely or part of the calls to the web-server subsystems\n#\n\n\n@pytest.fixture\nasync def catalog_subsystem_mock(\n    monkeypatch,\n) -> Callable[[Optional[Union[list[dict], dict]]], None]:\n    services_in_project = []\n\n    def creator(projects: Optional[Union[list[dict], dict]] = None) -> None:\n        for proj in projects or []:\n            services_in_project.extend(\n                [\n                    {\"key\": s[\"key\"], \"version\": s[\"version\"]}\n                    for _, s in proj[\"workbench\"].items()\n                ]\n            )\n\n    async def mocked_get_services_for_user(*args, **kwargs):\n        return services_in_project\n\n    monkeypatch.setattr(\n        catalog, \"get_services_for_user_in_product\", mocked_get_services_for_user\n    )\n\n    return creator\n\n\n@pytest.fixture\ndef disable_static_webserver(monkeypatch: MonkeyPatch) -> Callable:\n    \"\"\"\n    Disables the static-webserver module.\n    Avoids fecthing and caching index.html pages\n    Mocking a response for all the services which expect it.\n    \"\"\"\n\n    async def _mocked_index_html(request: web.Request) -> web.Response:\n        \"\"\"\n        Emulates the reply of the '/' path when the static-webserver is disabled\n        \"\"\"\n        html = textwrap.dedent(\n            f\"\"\"\\\n            <!DOCTYPE html>\n            <html>\n            <body>\n                <h1>OSPARC-SIMCORE</h1>\n                <p> This is a result of disable_static_webserver fixture for product OSPARC ({__file__})</p>\n            </body>\n            </html>\n            \"\"\"\n        )\n        return web.Response(text=html)\n\n    # mount and serve some staic mocked content\n    monkeypatch.setenv(\"WEBSERVER_STATICWEB\", \"null\")\n\n    def add_index_route(app: web.Application) -> None:\n        app.router.add_get(\"/\", _mocked_index_html, name=INDEX_RESOURCE_NAME)\n\n    return add_index_route\n\n\n@pytest.fixture\nasync def storage_subsystem_mock(mocker) -> MockedStorageSubsystem:\n    \"\"\"\n    Patches client calls to storage service\n\n    Patched functions are exposed within projects but call storage subsystem\n    \"\"\"\n\n    async def _mock_copy_data_from_project(app, src_prj, dst_prj, nodes_map, user_id):\n        print(\n            f\"MOCK copying data project {src_prj['uuid']} -> {dst_prj['uuid']} \"\n            f\"with {len(nodes_map)} s3 objects by user={user_id}\"\n        )\n\n        yield LRTask(TaskProgress(message=\"pytest mocked fct, started\"))\n\n        async def _mock_result():\n            return None\n\n        yield LRTask(\n            TaskProgress(message=\"pytest mocked fct, finished\", percent=1.0),\n            _result=_mock_result(),\n        )\n\n    mock = mocker.patch(\n        \"simcore_service_webserver.projects.projects_handlers_crud.copy_data_folders_from_project\",\n        autospec=True,\n        side_effect=_mock_copy_data_from_project,\n    )\n\n    async_mock = mocker.AsyncMock(return_value=\"\")\n    mock1 = mocker.patch(\n        \"simcore_service_webserver.projects._delete.delete_data_folders_of_project\",\n        autospec=True,\n        side_effect=async_mock,\n    )\n\n    mock2 = mocker.patch(\n        \"simcore_service_webserver.projects.projects_api.storage_api.delete_data_folders_of_project_node\",\n        autospec=True,\n        return_value=None,\n    )\n\n    mock3 = mocker.patch(\n        \"simcore_service_webserver.projects.projects_handlers_crud.get_project_total_size_simcore_s3\",\n        autospec=True,\n        return_value=parse_obj_as(ByteSize, \"1Gib\"),\n    )\n\n    return MockedStorageSubsystem(mock, mock1, mock2, mock3)\n\n\n@pytest.fixture\ndef asyncpg_storage_system_mock(mocker):\n    mocked_method = mocker.patch(\n        \"simcore_service_webserver.login.storage.AsyncpgStorage.delete_user\",\n        return_value=\"\",\n    )\n    return mocked_method\n\n\n@pytest.fixture\nasync def mocked_director_v2_api(mocker: MockerFixture) -> dict[str, MagicMock]:\n    mock = {}\n\n    #\n    # NOTE: depending on the test, function might have to be patched\n    #  via the director_v2_api or director_v2_core_dynamic_services modules\n    #\n    for func_name in (\n        \"get_dynamic_service\",\n        \"list_dynamic_services\",\n        \"run_dynamic_service\",\n        \"stop_dynamic_service\",\n    ):\n        for mod_name in (\"director_v2_api\", \"director_v2_core_dynamic_services\"):\n            name = f\"{mod_name}.{func_name}\"\n            mock[name] = mocker.patch(\n                f\"simcore_service_webserver.{name}\",\n                autospec=True,\n                return_value={},\n            )\n    mock[\"director_v2_api.create_or_update_pipeline\"] = mocker.patch(\n        \"simcore_service_webserver.director_v2_api.create_or_update_pipeline\",\n        autospec=True,\n        return_value=None,\n    )\n    return mock\n\n\n@pytest.fixture\ndef create_dynamic_service_mock(\n    client: TestClient, mocked_director_v2_api: dict\n) -> Callable:\n    services = []\n\n    async def create(user_id, project_id) -> dict:\n        SERVICE_UUID = str(uuid4())\n        SERVICE_KEY = \"simcore/services/dynamic/3d-viewer\"\n        SERVICE_VERSION = \"1.4.2\"\n        assert client.app\n        url = client.app.router[\"create_node\"].url_for(project_id=project_id)\n        create_node_data = {\n            \"service_key\": SERVICE_KEY,\n            \"service_version\": SERVICE_VERSION,\n            \"service_uuid\": SERVICE_UUID,\n        }\n\n        running_service_dict = {\n            \"published_port\": \"23423\",\n            \"service_uuid\": SERVICE_UUID,\n            \"service_key\": SERVICE_KEY,\n            \"service_version\": SERVICE_VERSION,\n            \"service_host\": \"some_service_host\",\n            \"service_port\": \"some_service_port\",\n            \"service_state\": \"some_service_state\",\n        }\n\n        services.append(running_service_dict)\n        # reset the future or an invalidStateError will appear as set_result sets the future to done\n        mocked_director_v2_api[\n            \"director_v2_api.list_dynamic_services\"\n        ].return_value = services\n        mocked_director_v2_api[\n            \"director_v2_core_dynamic_services.list_dynamic_services\"\n        ].return_value = services\n        return running_service_dict\n\n    return create\n\n\n# POSTGRES CORE SERVICE ---------------------------------------------------\n\n\n@pytest.fixture(scope=\"session\")\ndef postgres_dsn(docker_services, docker_ip, default_app_cfg: dict) -> dict:\n    cfg = deepcopy(default_app_cfg[\"db\"][\"postgres\"])\n    cfg[\"host\"] = docker_ip\n    cfg[\"port\"] = docker_services.port_for(\"postgres\", 5432)\n    return cfg\n\n\n@pytest.fixture(scope=\"session\")\ndef postgres_service(docker_services, postgres_dsn):\n    url = DSN.format(**postgres_dsn)\n\n    # Wait until service is responsive.\n    docker_services.wait_until_responsive(\n        check=lambda: _is_postgres_responsive(url),\n        timeout=30.0,\n        pause=0.1,\n    )\n\n    return url\n\n\n@pytest.fixture(scope=\"function\")\ndef postgres_db(\n    postgres_dsn: dict, postgres_service: str\n) -> Iterator[sa.engine.Engine]:\n    # Overrides packages/pytest-simcore/src/pytest_simcore/postgres_service.py::postgres_db to reduce scope\n    url = postgres_service\n\n    # Configures db and initializes tables\n    kwargs = postgres_dsn.copy()\n    assert pg_cli.discover.callback\n    pg_cli.discover.callback(**kwargs)\n    assert pg_cli.upgrade.callback\n    pg_cli.upgrade.callback(\"head\")\n    # Uses syncrounous engine for that\n    engine = sa.create_engine(url, isolation_level=\"AUTOCOMMIT\")\n\n    yield engine\n\n    assert pg_cli.downgrade.callback\n    pg_cli.downgrade.callback(\"base\")\n    assert pg_cli.clean.callback\n    pg_cli.clean.callback()\n\n    orm.metadata.drop_all(engine)\n    engine.dispose()\n\n\n# REDIS CORE SERVICE ------------------------------------------------------\n\n\n@pytest.fixture(scope=\"session\")\ndef redis_service(docker_services, docker_ip) -> RedisSettings:\n    # WARNING: overrides pytest_simcore.redis_service.redis_server function-scoped fixture!\n\n    host = docker_ip\n    port = docker_services.port_for(\"redis\", 6379)\n    redis_settings = RedisSettings(REDIS_HOST=docker_ip, REDIS_PORT=port)\n\n    docker_services.wait_until_responsive(\n        check=lambda: _is_redis_responsive(host, port),\n        timeout=30.0,\n        pause=0.1,\n    )\n    return redis_settings\n\n\n@pytest.fixture\nasync def redis_client(redis_service: RedisSettings):\n    client = aioredis.from_url(\n        redis_service.dsn_resources, encoding=\"utf-8\", decode_responses=True\n    )\n    yield client\n\n    await client.flushall()\n    await client.close(close_connection_pool=True)\n\n\n@pytest.fixture\nasync def redis_locks_client(\n    redis_service: RedisSettings,\n) -> AsyncIterator[aioredis.Redis]:\n    \"\"\"Creates a redis client to communicate with a redis service ready\"\"\"\n    client = aioredis.from_url(\n        redis_service.dsn_locks, encoding=\"utf-8\", decode_responses=True\n    )\n\n    yield client\n\n    await client.flushall()\n    await client.close(close_connection_pool=True)\n\n\ndef _is_redis_responsive(host: str, port: int) -> bool:\n    r = redis.Redis(host=host, port=port)\n    return r.ping() == True\n\n\n# SOCKETS FIXTURES  --------------------------------------------------------\n\n# Moved to packages/pytest-simcore/src/pytest_simcore/websocket_client.py\n\n\n# USER GROUP FIXTURES -------------------------------------------------------\n\n\n@pytest.fixture\nasync def primary_group(client, logged_user) -> dict[str, Any]:\n    primary_group, _, _ = await list_user_groups(client.app, logged_user[\"id\"])\n    return primary_group\n\n\n@pytest.fixture\nasync def standard_groups(\n    client, logged_user: dict\n) -> AsyncIterator[list[dict[str, Any]]]:\n    # create a separate admin account to create some standard groups for the logged user\n    sparc_group = {\n        \"gid\": \"5\",  # this will be replaced\n        \"label\": \"SPARC\",\n        \"description\": \"Stimulating Peripheral Activity to Relieve Conditions\",\n        \"thumbnail\": \"https://commonfund.nih.gov/sites/default/files/sparc-image-homepage500px.png\",\n        \"inclusionRules\": {\"email\": r\"@(sparc)+\\.(io|com)$\"},\n    }\n    team_black_group = {\n        \"gid\": \"5\",  # this will be replaced\n        \"label\": \"team Black\",\n        \"description\": \"THE incredible black team\",\n        \"thumbnail\": None,\n        \"inclusionRules\": {\"email\": r\"@(black)+\\.(io|com)$\"},\n    }\n    async with NewUser(\n        {\"name\": f\"{logged_user['name']}_admin\", \"role\": \"USER\"}, client.app\n    ) as admin_user:\n        # creates two groups\n        sparc_group = await create_user_group(client.app, admin_user[\"id\"], sparc_group)\n        team_black_group = await create_user_group(\n            client.app, admin_user[\"id\"], team_black_group\n        )\n\n        # adds logged_user  to sparc group\n        await add_user_in_group(\n            client.app,\n            admin_user[\"id\"],\n            sparc_group[\"gid\"],\n            new_user_id=logged_user[\"id\"],\n        )\n\n        # adds logged_user  to team-black group\n        await add_user_in_group(\n            client.app,\n            admin_user[\"id\"],\n            team_black_group[\"gid\"],\n            new_user_email=logged_user[\"email\"],\n        )\n\n        _, standard_groups, _ = await list_user_groups(client.app, logged_user[\"id\"])\n        yield standard_groups\n        # clean groups\n        await delete_user_group(client.app, admin_user[\"id\"], sparc_group[\"gid\"])\n        await delete_user_group(client.app, admin_user[\"id\"], team_black_group[\"gid\"])\n\n\n@pytest.fixture\nasync def all_group(client, logged_user) -> dict[str, str]:\n    _, _, all_group = await list_user_groups(client.app, logged_user[\"id\"])\n    return all_group\n\n\n# GENERIC HELPER FUNCTIONS ----------------------------------------------------\n\n\ndef _patch_compose_mail(monkeypatch):\n    async def print_mail_to_stdout(\n        cfg: LoginOptions, *, sender: str, recipient: str, subject: str, body: str\n    ):\n        print(\n            f\"=== EMAIL FROM: {sender}\\n=== EMAIL TO: {recipient}\\n=== SUBJECT: {subject}\\n=== BODY:\\n{body}\"\n        )\n\n    monkeypatch.setattr(\n        simcore_service_webserver.login.utils_email,\n        \"_compose_mail\",\n        print_mail_to_stdout,\n    )\n\n\ndef _is_postgres_responsive(url):\n    \"\"\"Check if something responds to ``url``\"\"\"\n    try:\n        engine = sa.create_engine(url)\n        conn = engine.connect()\n        conn.close()\n    except sa.exc.OperationalError:\n        return False\n    return True\n","sub_path":"services/web/server/tests/unit/with_dbs/conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":17876,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"111386223","text":"'''\n/*\n*  tsodyks_depressing.py\n*\n*  This file is part of NEST\n*\n*  Copyright (C) 2004 by\n*  The NEST Initiative\n*\n*  See the file AUTHORS for details.\n*\n*  Permission is granted to compile and modify\n*  this file for non-commercial use.1\n*  See the file LICENSE for details.\n*\n*/\n\n/* BeginDocumentation\nName: tsodyks_depressing - python script for overall test of iaf_neuron model\nDerived from tsodyks_depressing.sli.\n\nDescription:\nScript to test Tsodyks short term plasticity depressing synapses according to\n'Neural Networks with Dynamic Synapses'\nMisha Tsodyks, Klaus Pawelzik, Henry Markram\nNeural computation 10, 821--853 (1998)\n\nreproduces figure 1 A\n\n\nauthor:  Birgit Kriener, Moritz Helias, Markus Diesmann\ndate:\t March 2006\n\n'''\n\n# import nest kernel:\nimport nest\nimport nest.voltage_trace\nfrom numpy import exp\nimport pylab\n\n# set parameters:\nh       = 0.1    # simulation step size (ms)\nTau     = 40.    # membrane time constant\nTheta   = 15.    # threshold\nU0      = 0.     # reset potential of membrane potential\nR       = 0.1    # 100 M Ohm\nC       = Tau/R  # Tau (ms)/R in NEST units\nTauR    = 2.     # refractory time\nTau_psc = 3.     # time constant of PSC (= Tau_inact)\nTau_rec = 800.   # recovery time\nTau_fac = 0.     # facilitation time\nU       = 0.5    # facilitation parameter U\nA       = 250.   # PSC weight in pA\nf       = 20./1000. # frequency in Hz converted to 1/ms\nTend    = 1200.  # simulation time\nTIstart = 50.    # start time of dc\nTIend   = 1050.  # end time of dc\nI0      = Theta*C/Tau/(1-exp(-(1/f-TauR)/Tau)) # dc amplitude\n\n# print I0\n\n# set up simulator:\nnest.ResetKernel()\nnest.SetKernelStatus({\"resolution\": h})\n\n# set neuron parameters:\nneuron_param = {\"tau_m\"     :  Tau,\n                \"t_ref\"     :  TauR,\n                \"tau_syn_ex\":  Tau_psc,\n                \"tau_syn_in\":  Tau_psc,\n                \"C_m\"       :  C,\n                \"V_reset\"   :  U0,\n                \"E_L\"       :  U0,\n                \"V_m\"       :  U0,\n                \"V_th\"      :  Theta}\n\n# set defaults of desired neuron type with chosen parameters:\nnest.SetDefaults(\"iaf_psc_exp\", neuron_param)\n\n# create two neurons of desired type:\nneurons = nest.Create(\"iaf_psc_exp\",2)\n\n# set properties of dc:\nnest.SetDefaults(\"dc_generator\",{\"amplitude\": I0, \"start\": TIstart, \"stop\": TIend})\n\n# create dc_generator:\ndc_gen = nest.Create(\"dc_generator\")\n\n# create voltmeter\nvolts=nest.Create(\"voltmeter\")\n\n# set properties of voltmeter\nnest.SetStatus([volts],[{\"label\": \"Voltmeter\", \"withtime\": True, \"withgid\": True,\n                         \"interval\": 1.}])\n\n# connect dc_generator to neuron 1:\nnest.Connect(dc_gen,[neurons[0]])\n\n# connect voltmeter to neuron 2:\nnest.Connect(volts,[neurons[1]])\n\n# set synapse parameters:\nsyn_param = {\"tau_psc\" :  Tau_psc,\n            \"tau_rec\" :  Tau_rec,\n            \"tau_fac\" :  Tau_fac,\n            \"U\"       :  U,\n            \"delay\"   :  0.1,\n            \"weight\"  :  A,\n            \"u\"       :  0.0,\n            \"x\"       :  1.0}\n\n# create desired synapse type with chosen parameters:\nnest.CopyModel(\"tsodyks_synapse\",\"syn\",syn_param)\n\n# connect neuron 1 with neuron 2 via synapse model 'syn':\nnest.Connect([neurons[0]],[neurons[1]],model=\"syn\")\n\n# simulate:\nnest.Simulate(Tend)\n\n# plot membrane potential of neuron\nnest.voltage_trace.from_device(volts)\nnest.voltage_trace.show()\n","sub_path":"tsodyks_depressing.py","file_name":"tsodyks_depressing.py","file_ext":"py","file_size_in_byte":3338,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"509047108","text":"import numpy as np\nimport random\n\narr= [ 70, 3, 4 ,3 ,50, 9 ,10 ,7]\n# MERGE SORT\ndef merge_sort(arr):\n    length_arr = int(len(arr))\n    length_break = 2\n    maxim = 0\n    while length_break <= length_arr:\n        start_sort=0\n        tear = length_break\n        while tear<=length_arr:\n            for j in range(tear - 1):\n                for j in range(start_sort,tear-1):\n                    if arr[j]>arr[j+1]:\n                        maxim=arr[j]\n                        arr[j]=arr[j+1]\n                        arr[j+1]=maxim\n                j=j+1\n            start_sort=start_sort+length_break\n            tear=tear+length_break\n        length_break=length_break*2\n    return arr\n\nprint(merge_sort(arr))\n\n# BUBBLE SORT\nmas= [ 70, 3, 4 ,3 ,50, 9 ,10 ,7]\ndef bubble_sort(mas):\n    max = 0\n    j = 0\n    length_mas = int(len(mas))\n    for j in range(length_mas-1):\n        for i in range(length_mas-1):\n            if mas[i]>mas[i+1]:\n                max=mas[i]\n                mas[i]=mas[i+1]\n                mas[i+1]=max\n        j=j+1\n    return mas\n\nprint(bubble_sort(mas))\nfrom time import time\nt0 = time()\nrandom_arr= np.random.randint(0,100, 2000)\nprint(merge_sort(random_arr))\nt1=time()\nprint(bubble_sort(random_arr))\nt2=time()\nprint(\"Время работы сортировки слиянием:\" + str(t1-t0) + '\\n')\nprint(\"Время работы сортировки пузырьком:\" + str(t2-t1) + \"\\n\")\nif t1-t0 < t2-t1:\n    print(\"Сортировака слиянием работает быстрее, чем пузырьковая сортировка\")\nelif t1-t0 > t2-t1:\n    print(\"Сортировака слиянием работает медленее, чем пузырьковая сортировка\")\nelif t1-t0 == t2-t1:\n    print(\"Сортировки рботают одинакого по времени\")\n","sub_path":"lab2/sort.py","file_name":"sort.py","file_ext":"py","file_size_in_byte":1848,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"614709170","text":"###############################################################################\n#       Filename:       Ross_1281_S.py\n#       \n#       Confidential and Proprietary, Copyright 2001 by Totally Games\n#       \n#       Creates Ross_128 static objects.  Called by Ross_1281.py when region is created\n#       \n#       Created:        09/17/00 \n#       Modified:       10/04/01 - Tony Evans\n#                       August 2003 - Chris Jones\n# Modified for Multiplayer with Banzai Wolf 359 ships added - 12/19/03 - Chris Jones\n###############################################################################\nimport App\nimport Tactical.LensFlares\nimport loadspacehelper\nimport MissionLib\nimport WeakSelfDefenseAI\n\ndef Initialize(pSet):\n\n        pSun = App.Sun_Create(3000.0, 7000, 6000, \"data/Textures/SunRed.tga\", \"data/Textures/Effects/SunFlaresRed.tga\")\n        pSet.AddObjectToSet(pSun, \"Ross 128\")   \n        pSun.PlaceObjectByName( \"Ross 128\" )\n        pSun.UpdateNodeOnly()\n        Tactical.LensFlares.RedOrangeLensFlare(pSet, pSun)\n        \n        pWolf359 = App.Sun_Create(200.0, 20, 3000, \"data/Textures/SunRed.tga\", \"data/Textures/Effects/SunFlaresRed.tga\")\n        pSet.AddObjectToSet(pWolf359, \"Wolf 359\")       \n        pWolf359.PlaceObjectByName( \"Wolf 359\" )\n        pWolf359.UpdateNodeOnly()\n        Tactical.LensFlares.RedOrangeLensFlare(pSet, pWolf359)\n\n        if (App.g_kUtopiaModule.IsHost ()) or not (App.g_kUtopiaModule.IsMultiplayer()): \n        \n                # Create ship\n                pTransport      = loadspacehelper.CreateShip(\"Galaxy\", pSet, \"Galaxy Wreck\", \"Transport Location\")\n                pTransport.ReplaceTexture(\"data/Models/SharedTextures/FedShips/Venture.tga\", \"ID\")\n                import GalaxyHulk\n                GalaxyHulk.AddDamage(pTransport)\n                \n                # Turn off the ships repair\n                pRepair = pTransport.GetRepairSubsystem()\n                pProp   = pRepair.GetProperty()\n                pProp.SetMaxRepairPoints(0.0)\n                # Damage the Hulk...\n                # Turn off the shields\n                pAlertEvent = App.TGIntEvent_Create()\n                pAlertEvent.SetDestination(pTransport)\n                pAlertEvent.SetEventType(App.ET_SET_ALERT_LEVEL)\n                pAlertEvent.SetInt(pTransport.GREEN_ALERT)\n                App.g_kEventManager.AddEvent(pAlertEvent)\n                # Damage the hull - 500 pts left\n                pTransport.DamageSystem(pTransport.GetHull(), 3000)\n                pTransport.DisableGlowAlphaMaps()\n                # Dont allow the user to see the sub-systems\n                MissionLib.HideSubsystems(pTransport)\n                # Spin the hulk\n                MissionLib.SetRandomRotation(pTransport, 8)\n                pTransport.SetTargetable(0)\n                \n                # Create a Communications Relay\n                pArray  = loadspacehelper.CreateShip(\"CommLight\", pSet, \"Comm Array\", \"Array Location\")\n                pArray.SetTargetable(0)\n\n                pHulk1          = loadspacehelper.CreateShip(\"Ambassador\", pSet, \"Saratoga\", \"wreck1\")\n                pHulk2          = loadspacehelper.CreateShip(\"Nebula\", pSet, \"Kyushu\", \"wreck2\")\n                pHulk5          = loadspacehelper.CreateShip(\"Galaxy\", pSet, \"Yamaguchi\", \"wreck5\")\n                pHulk10         = loadspacehelper.CreateShip(\"Nebula\", pSet, \"Melbourne\", \"wreck10\")\n                pHulk13         = loadspacehelper.CreateShip(\"Ambassador\", pSet, \"Tolstoy\", \"wreck13\")\n                     \n                # Import our damaged script ship and apply it to the Hulk1\n                pHulk1.SetHailable(0)\n                import DamageAmbas1\n                DamageAmbas1.AddDamage(pHulk1)\n                # Turn off the ships repair\n                pRepair = pHulk1.GetRepairSubsystem()\n                pProp   = pRepair.GetProperty()\n                pProp.SetMaxRepairPoints(0.0)\n                # Damage the Hulk1...\n                # Turn off the shields\n                pAlertEvent = App.TGIntEvent_Create()\n                pAlertEvent.SetDestination(pHulk1)\n                pAlertEvent.SetEventType(App.ET_SET_ALERT_LEVEL)\n                pAlertEvent.SetInt(pHulk1.GREEN_ALERT)\n                App.g_kEventManager.AddEvent(pAlertEvent)\n                # Damage the hull - 500 pts left\n                pHulk1.DamageSystem(pHulk1.GetHull(), 2500)\n                pHulk1.DisableGlowAlphaMaps()\n                # Dont allow the user to see the sub-systems\n                MissionLib.HideSubsystems(pHulk1)\n                # Spin the hulk\n                MissionLib.SetRandomRotation(pHulk1, 5)\n                \n                # Import our damaged script ship and apply it to the Hulk2\n                pHulk2.SetHailable(0)\n                import DamageNebula1\n                DamageNebula1.AddDamage(pHulk2)\n                # Turn off the ships repair\n                pRepair = pHulk2.GetRepairSubsystem()\n                pProp   = pRepair.GetProperty()\n                pProp.SetMaxRepairPoints(0.0)\n                # Damage the Hulk2...\n                # Turn off the shields\n                pAlertEvent = App.TGIntEvent_Create()\n                pAlertEvent.SetDestination(pHulk2)\n                pAlertEvent.SetEventType(App.ET_SET_ALERT_LEVEL)\n                pAlertEvent.SetInt(pHulk2.GREEN_ALERT)\n                App.g_kEventManager.AddEvent(pAlertEvent)\n                # Damage the hull - 500 pts left\n                pHulk2.DamageSystem(pHulk2.GetHull(), 3000)\n                pHulk2.DisableGlowAlphaMaps()\n                # Dont allow the user to see the sub-systems\n                MissionLib.HideSubsystems(pHulk2)\n                # Spin the hulk\n                MissionLib.SetRandomRotation(pHulk2, 10)\n                \n                # Import our damaged script ship and apply it to the Hulk5\n                pHulk5.SetHailable(0)\n                import DamageGal1\n                DamageGal1.AddDamage(pHulk5)\n                # Turn off the ships repair\n                pRepair = pHulk5.GetRepairSubsystem()\n                pProp   = pRepair.GetProperty()\n                pProp.SetMaxRepairPoints(0.0)\n                # Damage the pHulk5...\n                # Turn off the shields\n                pAlertEvent = App.TGIntEvent_Create()\n                pAlertEvent.SetDestination(pHulk5)\n                pAlertEvent.SetEventType(App.ET_SET_ALERT_LEVEL)\n                pAlertEvent.SetInt(pHulk5.GREEN_ALERT)\n                App.g_kEventManager.AddEvent(pAlertEvent)\n                # Damage the hull - 500 pts left\n                pHulk5.DamageSystem(pHulk5.GetHull(), 3000)\n                pHulk5.DisableGlowAlphaMaps()\n                # Dont allow the user to see the sub-systems\n                MissionLib.HideSubsystems(pHulk5)\n                # Spin the hulk\n                MissionLib.SetRandomRotation(pHulk5, 8)\n                \n                # Import our damaged script ship and apply it to the pHulk10\n                pHulk10.SetHailable(0)\n                import DamageNeb2\n                DamageNeb2.AddDamage(pHulk10)\n                # Turn off the ships repair\n                pRepair = pHulk10.GetRepairSubsystem()\n                pProp   = pRepair.GetProperty()\n                pProp.SetMaxRepairPoints(0.0)\n                # Damage the pHulk10...\n                # Turn off the shields\n                pAlertEvent = App.TGIntEvent_Create()\n                pAlertEvent.SetDestination(pHulk10)\n                pAlertEvent.SetEventType(App.ET_SET_ALERT_LEVEL)\n                pAlertEvent.SetInt(pHulk10.GREEN_ALERT)\n                App.g_kEventManager.AddEvent(pAlertEvent)\n                # Damage the hull - 500 pts left\n                pHulk10.DamageSystem(pHulk10.GetHull(), 4500)\n                pHulk10.DisableGlowAlphaMaps()\n                # Dont allow the user to see the sub-systems\n                MissionLib.HideSubsystems(pHulk10)\n                # Spin the hulk\n                MissionLib.SetRandomRotation(pHulk10, 6)\n                # Spin the hulk\n                MissionLib.SetRandomRotation(pHulk10, 7)\n                \n                # Import our damaged script ship and apply it to the pHulk13\n                pHulk13.SetHailable(0)\n                import DamageAmbas2\n                DamageAmbas2.AddDamage(pHulk13)\n                # Turn off the ships repair\n                pRepair = pHulk13.GetRepairSubsystem()\n                pProp   = pRepair.GetProperty()\n                pProp.SetMaxRepairPoints(0.0)\n                # Damage the pHulk13...\n                # Turn off the shields\n                pAlertEvent = App.TGIntEvent_Create()\n                pAlertEvent.SetDestination(pHulk13)\n                pAlertEvent.SetEventType(App.ET_SET_ALERT_LEVEL)\n                pAlertEvent.SetInt(pHulk13.GREEN_ALERT)\n                App.g_kEventManager.AddEvent(pAlertEvent)\n                # Damage the hull - 500 pts left\n                pHulk13.DamageSystem(pHulk13.GetHull(), 3000)\n                pHulk13.DisableGlowAlphaMaps()\n                # Dont allow the user to see the sub-systems\n                MissionLib.HideSubsystems(pHulk13)\n                # Spin the hulk\n                MissionLib.SetRandomRotation(pHulk13, 7)\n\n                # Create ship\n                pGalaxy = loadspacehelper.CreateShip(\"Galaxy\", pSet, \"USS Enterprise\", \"ED locale\")\n                pRepair = pGalaxy.GetRepairSubsystem()\n                pProp   = pRepair.GetProperty()\n                pProp.SetMaxRepairPoints(30.0)\n                # Damage the Hulk...\n                # Turn off the shields\n                pAlertEvent = App.TGIntEvent_Create()\n                pAlertEvent.SetDestination(pGalaxy)\n                pAlertEvent.SetEventType(App.ET_SET_ALERT_LEVEL)\n                pAlertEvent.SetInt(pGalaxy.GREEN_ALERT)\n                App.g_kEventManager.AddEvent(pAlertEvent)\n                # Damage the hull - 500 pts left\n                pGalaxy.DamageSystem(pHulk13.GetHull(), 2990)\n                pGalaxy.DisableGlowAlphaMaps()\n                # Damage the shield generator\n                pShields = pGalaxy.GetShields()\n                # Front\n                pShields.SetCurShields(pShields.FRONT_SHIELDS, pShields.GetMaxShields(pShields.FRONT_SHIELDS) * 0.00)\n                # Rear\n                pShields.SetCurShields(pShields.REAR_SHIELDS, pShields.GetMaxShields(pShields.REAR_SHIELDS) * 0.00)\n                # Left\n                pShields.SetCurShields(pShields.LEFT_SHIELDS, pShields.GetMaxShields(pShields.LEFT_SHIELDS) * 0.05)\n                # Right\n                pShields.SetCurShields(pShields.RIGHT_SHIELDS, pShields.GetMaxShields(pShields.RIGHT_SHIELDS)* 0.05)\n                # Top\n                pShields.SetCurShields(pShields.TOP_SHIELDS, pShields.GetMaxShields(pShields.TOP_SHIELDS) * 0.10)\n                # Bottom\n                pShields.SetCurShields(pShields.BOTTOM_SHIELDS, pShields.GetMaxShields(pShields.BOTTOM_SHIELDS) * 0.00) \n                # Damage the hull - 500 pts left\n                pGalaxy.DamageSystem(pGalaxy.GetHull(), 3000)\n                pGalaxy.DisableGlowAlphaMaps()\n                # Dont allow the user to see the sub-systems\n                # MissionLib.HideSubsystems(pGalaxy)\n                # Spin the hulk\n                MissionLib.SetRandomRotation(pGalaxy, 79)\n                pImpulseEngines = pGalaxy.GetImpulseEngineSubsystem()\n                pMax    = pImpulseEngines.GetMaxCondition()     \n                sImpulseEngine = pMax * 0.5     \n                pGalaxy.DamageSystem(pGalaxy.GetImpulseEngineSubsystem(), sImpulseEngine)               \n                pProp = pGalaxy.GetImpulseEngineSubsystem().GetProperty()\n                maxSpeed = pProp.GetMaxSpeed()  \n                maxSpeed = maxSpeed * 0.01\n                pProp.SetMaxSpeed(maxSpeed)\n                pGalaxy.SetSpeed( maxSpeed, App.TGPoint3_GetModelForward(), App.PhysicsObjectClass.DIRECTION_MODEL_SPACE )      \n                pPhasers = pGalaxy.GetPhaserSystem()\n                pMax    = pPhasers.GetMaxCondition()    \n                sPhaser = pMax * 0.5    \n                pGalaxy.DamageSystem(pGalaxy.GetPhaserSystem(), sPhaser)        \n\n                pAsteroid = loadspacehelper.CreateShip (\"Asteroidh1\", pSet, \"Unknown Debris\", \"Asteroid\")\n                if (pAsteroid):\n                        # Set objects Genus to Asteroid.\n                        pProperty = pAsteroid.GetShipProperty()\n                        pProperty.SetGenus(App.GENUS_ASTEROID)\n                        # Set the scale of the asteroid object\n                        pAsteroid.SetScale (10.0)\n                        \n                        #Rotate the asteroid\n                        vVelocity = App.TGPoint3_GetModelForward()      # Get the vector to rotate around\n                        vVelocity.Scale( 15.0 * App.PI / 180.0 )                # Scale it to value in dictonary\n                        pAsteroid.SetAngularVelocity(vVelocity, App.PhysicsObjectClass.DIRECTION_MODEL_SPACE)\n                        \n                        # As an alternative to sending the asteroid forward along\n                        # the direction of the placement, send the asteroid directly\n                        # toward the center of the station.\n                        vVelocity = pHulk10.GetWorldLocation()\n                        vVelocity.Subtract(pAsteroid.GetWorldLocation())\n                        vVelocity.Unitize()\n                        vVelocity.Scale(3)\n                        pAsteroid.SetVelocity(vVelocity)\n\n\n        if (App.g_kUtopiaModule.IsMultiplayer()):\n                pMission = MissionLib.GetMission ()\n                SetupEventHandlers(pMission)\n        else:\n                pGalaxy.SetAI(WeakSelfDefenseAI.CreateAI(pGalaxy))\n\n###############################################################################\n#       SetupEventHandlers()\n#\n#       Set up event handlers used by the Starbase 12 set.\n#\n#       Args:   pSet    - The Starbase 12 set.\n#\n#       Return: none\n###############################################################################\ndef SetupEventHandlers(pMission):\n        import Multiplayer.MissionShared\n        App.g_kEventManager.AddBroadcastPythonFuncHandler(App.ET_OBJECT_CREATED_NOTIFY, pMission, __name__ + \".ObjectCreatedHandler\")\n\n        return 0\n\n\ndef ObjectCreatedHandler (TGObject, pEvent):\n        import Multiplayer.SpeciesToShip\n\n        # We only care about ships.\n        pShip = App.ShipClass_Cast (pEvent.GetDestination ())\n        if (pShip):\n                # We only care about ships.\n                if (pShip.IsPlayerShip ()):\n                        ResetEnemyFriendlyGroups ()\n                elif (pShip.GetNetType () == Multiplayer.SpeciesToShip.FEDSTARBASE):\n                        pass\n        return 0\n\ndef ResetEnemyFriendlyGroups ():\n        # Go through player list, trying to find all the ships\n\n        pNetwork = App.g_kUtopiaModule.GetNetwork ()\n        pGame = App.MultiplayerGame_Cast (App.Game_GetCurrentGame ())\n\n        if (pNetwork and pGame):\n                pMission = MissionLib.GetMission ()\n                pEnemyGroup = MissionLib.GetEnemyGroup ()\n                pNeutralGroup = pMission.GetNeutralGroup ()\n                \n                pSet = App.g_kSetManager.GetSet(\"Ross_1281\")\n                pGalaxy = App.ShipClass_GetObject(pSet, \"USS Enterprise\")\n                if pGalaxy != None:\n                   pGalaxy.SetAI(WeakSelfDefenseAI.CreateAI(pGalaxy))\n","sub_path":"scripts/Systems/Ross_128/Ross_1281_S.py","file_name":"Ross_1281_S.py","file_ext":"py","file_size_in_byte":15482,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"577934734","text":"from sqlalchemy import create_engine, MetaData\nfrom sqlalchemy.orm import scoped_session, sessionmaker, mapper\nfrom sqlalchemy.ext.declarative import declarative_base\n\n\nlogin = { 'UID': 'diapox',\n    'PW':'Priest.1',\n    'server': 'lizzie.cvqr1dhfa9aq.ap-southeast-1.rds.amazonaws.com',\n    'db':'lizzie'}\n# To resolve the issue of misisng MySQLDB module in Flask ORM, make sure \n# to include PyMYSQL in the requirements and in the URI.\nserverURI =  \"mysql+pymysql://{}:{}@{}/{}\".format(login['UID'],login['PW'], login['server'],login['db'])\n\nengine = create_engine(serverURI, convert_unicode = True, echo=True)\nmetadata = MetaData(bind=engine)\n\ndbSession = scoped_session(sessionmaker(autocommit=True, \\\n    autoflush=True, bind=engine))\n\nBase = declarative_base()\nBase.query = dbSession.query_property()\n\n\n# def init_db():\n#     # import all modules here that might define models so that\n#     # they will be registered properly on the metadata.  Otherwise\n#     # you will have to import them first before calling init_db()\n#     # import yourapplication.models\n#     Base.metadata.create_all(bind=engine)","sub_path":"login-api/models/db.py","file_name":"db.py","file_ext":"py","file_size_in_byte":1108,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"106054481","text":"#!/usr/bin/python3\n\n# Dependencies: tensorflow 2.x\n#               numpy\n# To install tensorflow 2.x you may first need to update your pip.\n# Be wary of installing tensorflow 2.x using pip and Anaconda;\n# it may be simpler to install in a separate Python installation.\n\n# This file takes the Judd dataset to working Python data.\n# First, fixation data must be extracted from MATLAB to JSON-compatible array\n# format using Judd's code and my modified \"showEyeDataAcrossUsers.m\" (included).\n\nimport os\nimport random\nimport pickle\n\nimport tensorflow as tf\nimport numpy as np\n\ndef load_and_resize_image_and_fixations(image_path, fixations_path):\n    unresized_image = tf.image.decode_image(open(image_path,'rb').read())\n    resized_image = tf.image.resize(unresized_image,(224,224), antialias=True)/255\n\n    image_dims = unresized_image.shape\n\n    points = np.array(eval(open(fixations_path).read()))\n    points = np.flip(points, axis=1) # switch x, y\n    points[:,0] *= 224\n    points[:,0] //= image_dims[0]\n    points[:,1] *= 224\n    points[:,1] //= image_dims[1]\n\n    # Judd also discards out-of-bounds data.\n    restricted_points = points[(0 <= points[:,0]) & (points[:,0] < 224) & (0 <= points[:,1]) & (points[:,1] < 224)]\n\n    return resized_image.numpy(), restricted_points\n\ndef import_dataset(image_folder, fixations_folder):\n    tuples = {}\n    for fn in os.listdir(image_folder):\n        if not fn.endswith('.jpg') or fn.endswith('.jpeg'):\n            continue\n        image_path = os.path.join(image_folder, fn)\n        fixations_path = os.path.join(fixations_folder, fn+'.json')\n        if not os.path.isfile(fixations_path):\n            print(f'Warning: can\\'t find fixations for {fn}... Skipping.')\n            continue\n        image, fixations = load_and_resize_image_and_fixations(image_path, fixations_path)\n        tuples[fn] = (image, fixations)\n    return tuples\n\ndef shuffle_and_split_dataset(dataset, train_fraction=0.5, seed=42):\n    rand = random.Random(seed)\n    dataset_list = [(fn, image, fixations) for fn, (image, fixations) in dataset.items()]\n    rand.shuffle(dataset_list)\n    train_data_list = dataset_list[:int(len(dataset_list)*train_fraction)]\n    train_data = {fn: (image, fixations) for fn, image, fixations in train_data_list}\n    test_data_list = dataset_list[int(len(dataset_list)*train_fraction):]\n    test_data = {fn: (image, fixations) for fn, image, fixations in test_data_list}\n    return train_data, test_data\n\ndef store_dataset(train_data, test_data, filename):\n    file = open(filename, 'wb')\n    pickle.dump((train_data, test_data), file)\n    file.close()\n\ndef load_dataset(filename):\n    file = open(filename, 'rb')\n    train_data, test_data = pickle.load(file)\n    file.close()\n    return train_data, test_data\n\nif __name__ == '__main__':\n    # each jpg image in images should correspond to a json file containing fixations in fixations.\n    train_data, test_data = import_dataset('images', 'fixations')\n    store_dataset(train_data, test_data, 'Judd_dataset.pickle')\n","sub_path":"judd_dataset.py","file_name":"judd_dataset.py","file_ext":"py","file_size_in_byte":3016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"103531362","text":"#! /usr/bin/python\nimport atexit\nimport io\nimport picamera\nimport pickle\nimport pygame\nimport time\n\n''' default settings ...\n\tcamera.PARAMETER = DEFAULT\n\t\t[Range of values available for parameter]\n\tcamera.sharpness = 0\n\t\t[-100; 100]\n\tcamera.contrast = 0\n\t\t[-100; 100]\n\tcamera.brightness = 50\n\t\t[0; 100]\n\tcamera.saturation = 0\n\t\t[-100; 100]\n\tcamera.ISO = 0\n\t\t[100; 800]\n\tcamera.exposure_compensation = 0\n\t\t[-10; 10]\n\tcamera.exposure_mode = 'auto'\n\t\t[auto, night, nightpreview, backlight, spotlight, sport, snow, beach, verylong, fixedfps, antishake, fireworks]\n\tcamera.meter_mode = 'average'\n\t\t[average, spot, backlit, matrix]\n\tcamera.awb_mode = 'auto'\n\t\t[off, auto, sun, cloud, shade, tungsten, fluorescent, incandescent, flash, horizon]\n\t'''\n\nSETTINGS_FILE = \"Camera_Settings\"\nxStream, yStream = 640, 400\nsizeStream = (xStream, yStream)\nbytStream = bytearray(xStream * yStream * 4)\n\ndef preview_video_stream(camera):\n\t''' let user see parameters result '''\n\tvid_stream = io.BytesIO()\n\tcamera.capture(vid_stream, use_video_port=True, format='rgba')\n\tvid_stream.seek(0)\n\tvid_stream.readinto(bytStream)\n\tvid_stream.close()\n\timg = pygame.image.frombuffer(bytStream[0: (xStream * yStream * 4)], sizeStream, 'RGBA')\n\treturn img\n\ndef edit_parameter(parameter, value, increase):\n\t''' set a new value to paramter '''\n\txMin = str(parameter) + \"min\"\n\txMax = str(parameter) + \"max\"\n\txStep = str(parameter) + \"step\"\n\txValues = str(parameter) + \"values\"\n\tif parameter < 7:\n\t\tif increase:\n\t\t\tif value < camera_settings_range[xMax]:\n\t\t\t\tvalue = camera_settings[selected_setting[parameter]] + camera_settings_range[xStep]\n\t\telse:\n\t\t\tif value > camera_settings_range[xMin]:\n\t\t\t\tvalue = camera_settings[selected_setting[parameter]] - camera_settings_range[xStep]\n\telse:\n\t\tparamList = camera_settings_range[xValues]\n\t\tfor keys, values in paramList.items():\n\t\t\tif values == value:\n\t\t\t\tx = keys\n\t\tif increase:\n\t\t\tif x < camera_settings_range[xMax]:\n\t\t\t\tvalue = paramList[x+1]\n\t\telse:\n\t\t\tif x > camera_settings_range[xMin]:\n\t\t\t\tvalue = paramList[x-1]\n\treturn value\n\n\n# init\ncamera_settings = dict()\ncamera_settings_range = {\"1min\":-100, \"1max\":100, \"1step\":1, \"2min\":-100, \"2max\":100, \"2step\":1, \"3min\":0, \"3max\":100, \"3step\":1, \"4min\":-100, \"4max\":100, \"4step\":1, \"5min\":0, \"5max\":800, \"5step\":100, \"6min\":-10, \"6max\":10, \"6step\":1, \"7min\":0, \"7max\":9, \"7values\":{0:\"auto\", 1:\"night\", 2:\"nightpreview\", 3:\"backlight\", 4:\"spotlight\", 5:\"snow\", 6:\"beach\", 7:\"verylong\", 8:\"antishake\", 9:\"fireworks\"}, \"8min\":0, \"8max\":4, \"8values\":{1:\"average\", 2:\"spot\", 3:\"backlit\", 4:\"matrix\"}, \"9min\":0, \"9max\":7, \"9values\":{0:\"off\", 1:\"auto\", 2:\"shade\", 3:\"tungsten\", 4:\"fluorescent\", 5:\"incandescent\", 6:\"flash\", 7:\"horizon\"}}\nselected_setting = {1:\"sharpness\", 2:\"contrast\", 3:\"brightness\", 4:\"saturation\", 5:\"ISO\", 6:\"exposure_compensation\", 7:\"exposure_mode\", 8:\"meter_mode\", 9:\"awb_mode\"}\nselected = 1\npygame.init()\npygame.mouse.set_visible(False)\nScreen = pygame.display.set_mode(sizeStream)\ncamera = picamera.PiCamera()\ncamera.vflip = True\ncamera.resolution = sizeStream\natexit.register(camera.close)\ntime.sleep(2) # warm-up\nfont = pygame.font.Font(None, 20)\n\ncamera_settings[\"sharpness\"] =  camera.sharpness \ncamera_settings[\"contrast\"] =  camera.contrast \ncamera_settings[\"brightness\"] = camera.brightness \ncamera_settings[\"saturation\"] = camera.saturation \ncamera_settings[\"ISO\"] = camera.ISO\ncamera_settings[\"exposure_compensation\"] = camera.exposure_compensation \ncamera_settings[\"exposure_mode\"] = camera.exposure_mode\ncamera_settings[\"meter_mode\"] = camera.meter_mode\ncamera_settings[\"awb_mode\"] = camera.awb_mode\n\n# Setting loop\nSettingFlag = True\nwhile SettingFlag:\n\tcamera.sharpness = camera_settings[\"sharpness\"]\n\tcamera.contrast = camera_settings[\"contrast\"]\n\tcamera.brightness = camera_settings[\"brightness\"]\n\tcamera.saturation = camera_settings[\"saturation\"] \n\tcamera.ISO = camera_settings[\"ISO\"]\n\tcamera.exposure_compensation = camera_settings[\"exposure_compensation\"]\n\tcamera.exposure_mode = camera_settings[\"exposure_mode\"]\n\tcamera.meter_mode = camera_settings[\"meter_mode\"]\n\tcamera.awb_mode = camera_settings[\"awb_mode\"]\n\n\timg = preview_video_stream(camera)\n\tScreen.blit(img, (0,0))\n\tfor i in range (1, 10):\n\t\tif i == selected:\n\t\t\tfontcolor = (255,255,255)\n\t\telse:\n\t\t\tfontcolor = (10,10,10)\n\t\ttext = font.render(selected_setting[i], True, fontcolor)\n\t\tScreen.blit(text, (20,20*i))\n\t\ttext = font.render(str(camera_settings[selected_setting[i]]), True, fontcolor)\n\t\tScreen.blit(text, (200, 20*i))\n\tpygame.display.update()\n\tfor event in pygame.event.get():\n\t\tif event.type == pygame.KEYDOWN:\n\t\t\tif event.key == pygame.K_ESCAPE:\n\t\t\t\tSettingFlag = False\n\t\t\tif event.key == pygame.K_DOWN and selected < 9:\n\t\t\t\tselected += 1\n\t\t\tif event.key == pygame.K_UP and selected > 1:\n\t\t\t\tselected -= 1\n\t\t\tif event.key == pygame.K_RIGHT:\n\t\t\t\tcamera_settings[selected_setting[selected]] = edit_parameter(selected, camera_settings[selected_setting[selected]], True)\n\t\t\tif event.key == pygame.K_LEFT:\n\t\t\t\tcamera_settings[selected_setting[selected]] = edit_parameter(selected, camera_settings[selected_setting[selected]], False)\n\n# save settings\nwith open(SETTINGS_FILE, \"wb\") as fichier:\n\tMy_pikler = pickle.Pickler(fichier)\n\tMy_pikler.dump(camera_settings)","sub_path":"camera_settings.py","file_name":"camera_settings.py","file_ext":"py","file_size_in_byte":5226,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"345851976","text":"import pandas as pd\nimport numpy as np\n\n\ndef dic2df(dic):\n    '''\n    (dict) -> data frame\n\n    This function takes a dictionary that represents a model built in\n    R and turns it into a data frame that we can use for prediction in\n    a manageable way. The function also re-indexes the data frame so that\n    it is indexed on the _row value and makes sure that the value doesn't\n    have any leading underscores that we had to create in R for super region.\n    '''\n    df = pd.DataFrame(dic)\n    if df[\"_row\"].values[0].startswith(\"_\"):\n        df._row = df[\"_row\"].map(lambda x: x[1:])\n    df = df.set_index(\"_row\")\n    return df\n\n\nclass Model:\n    def __init__(self, d, response):\n        self.vcov = np.array(d[\"vcov\"])\n        self.fix_eff = dic2df(d[\"fix_eff\"])\n        self.ran_eff = {k: dic2df(d[\"ran_eff\"][k]) for k in d[\"ran_eff\"].keys()}\n        self.random_intercepts = self.ran_eff.keys()\n        self.variables = self.fix_eff.index.values\n        self.response = response\n        self.RMSE = np.NaN\n        self.beta_draw = self.fix_eff.values.ravel()\n        self.gpr_dict = None\n\n    def __repr__(self):\n        return \"Variables included in model: \" + str(self.variables[1:])\n\n    def make_draw(self, return_vector=False):\n        '''\n        (self) -> None\n\n        Makes a draw from the variance covariance matrix to change the values\n        of self.beta_draw. Every time this function is run the self.beta_draw\n        values are updated. Note that the initial values of the self.beta_draw\n        are simply the mean values as given in self.fix_eff.\n        '''\n        self.beta_draw = np.dot(np.linalg.cholesky(self.vcov),\n                                np.random.normal(size=len(self.vcov))) + \\\n                         self.fix_eff.values.ravel()\n        if return_vector:\n            return self.beta_draw\n\n    def adjust_predictions(self, vector, linear_floor, df_sub):\n        '''\n        (self, array, float, data frame)\n\n        Given an 1-D array \"vector\" which is a set of predictions adjusts the\n        upper and lower limits of the vector so now value is outside of the range.\n        linear_floor and df_sub are used to calculate wether a prediction is\n        outside of the range.\n        '''\n        if self.response == \"ln_rate\":\n            vector[vector < np.log(linear_floor/100000.)] = \\\n                np.log(linear_floor/100000.)\n            ceiling = np.log((df_sub[\"envelope\"] / df_sub[\"pop\"]).values)\n            need_replace = (vector > ceiling).astype(np.int8)\n            vector = (vector * 0**need_replace) + (ceiling * need_replace)\n        elif self.response == \"lt_cf\":\n            x = np.log((linear_floor/100000.) / (df_sub[\"envelope\"]/df_sub[\"pop\"]))\n            floor = x / (1 - x)\n            need_replace = (vector < floor).astype(np.int8)\n            vector = (vector * 0**need_replace) + (floor * need_replace)\n        return vector\n\n\n    def make_prediction(self, df, ko, linear_floor):\n        '''\n        (self, data frame, data frame) -> array\n\n        Takes in two data frames, \"df\" is the full data frame used in the\n        course of the analysis and ko is a data frame indicating which rows are\n        used for training, and different phases of testing. In addition to\n        returning an array of predicted values, the self.RMSE value is also\n        updated to show the out of sample predictive validity on test set 1.\n        '''\n        df_sub = df.loc[(ko.ix[:,0]) | (ko.ix[:,1]) | (ko.ix[:,2])]\n        fix = (np.dot(df_sub[self.variables[1:]], self.fix_eff.values[1:]) +\n               self.fix_eff.values[0]).ravel()\n        ran = np.array([self.ran_eff[x].loc[df_sub[x]].values.ravel() for\n                        x in self.ran_eff.keys()]).sum(0)\n        df_sub[\"predictions\"] = self.adjust_predictions(fix+ran, linear_floor,\n                                                        df_sub)\n        df_out = df_sub.loc[ko[ko.ix[:,1]].index]\n        N = len(df_out)\n        self.RMSE = np.linalg.norm(df_out[\"predictions\"].values -\n                                   df_out[self.response].values) / np.sqrt(N)\n        return df_sub[\"predictions\"].values\n\n    def calc_res(self, predictions, df, ko):\n        '''\n        (array, data frame) -> array\n\n        Given an array of predictions and a data frame \"df\" returns an array\n        of residuals for the given predictions.\n        '''\n        df_sub = df[(ko.ix[:,0]) | (ko.ix[:,1]) | (ko.ix[:,2])]\n        return df_sub[self.response].values - predictions\n\n    def make_draw_prediction(self, df, linear_floor, ko, new_draw=True):\n        '''\n        (self, data frame, bool) -> array\n\n        Similar to the make predictions however there is no updating of the\n        self.RMSE value. In addition rather than using the mean values of the\n        betas a draw is made from the variance covariance matrix and those\n        values of beta are used instead. \"new_draw\" indicates whether you\n        want to make a new draw from the variance covariance matrix are use\n        the existing set of values stored in self.beta_draw.\n        '''\n        if new_draw:\n            self.make_draw()\n        df_sub = df[(ko.ix[:,0]) | (ko.ix[:,1]) | (ko.ix[:,2])]\n        fix = (np.dot(df_sub[self.variables[1:]], self.beta_draw[1:]) +\n               self.beta_draw[0]).ravel()\n        ran = np.array([self.ran_eff[x].loc[df_sub[x]].values.ravel() for\n                        x in self.ran_eff.keys()]).sum(0)\n        return self.adjust_predictions(fix+ran, linear_floor, df_sub)\n","sub_path":"shared_code/central_comp/cod/codem/codem_prod/codem/model.py","file_name":"model.py","file_ext":"py","file_size_in_byte":5507,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"436388633","text":"import torch\nimport os\n\nimport sys\n\nsys.path.append('..')\n\nfrom tools import args, load_data, get_set, ModelArgs\nfrom core.lossfunction import *\nfrom core.cnn01 import *\n\nfrom core.train_cnn01_abp_ce import train_single_cnn01\nimport pickle\nimport pandas as pd\n# Args assignment\nscd_args = ModelArgs()\n\nscd_args.nrows = args.nrows\nscd_args.local_iter = args.localit\nscd_args.num_iters = args.iters\nscd_args.interval = args.interval\nscd_args.rounds = 1\nscd_args.w_inc1 = args.w_inc1\nscd_args.updated_fc_features = args.updated_fc_features\nscd_args.updated_conv_features = args.updated_conv_features\nscd_args.n_jobs = args.n_jobs\nscd_args.num_gpus = 1\nscd_args.adv_train = bool(args.adv_train)\nscd_args.eps = args.eps\nscd_args.w_inc2 = args.w_inc2\nscd_args.hidden_nodes = 20\nscd_args.evaluation = True\nscd_args.verbose = True\nscd_args.b_ratio = 0.2\nscd_args.cuda = True if torch.cuda.is_available() else False\nscd_args.seed = args.seed\nscd_args.source = args.source\nscd_args.save = True\nscd_args.resume = True if args.resume else False\nscd_args.loss = args.loss\nscd_args.criterion = criterion[scd_args.loss]\nscd_args.structure = arch[args.version]\nscd_args.dataset = args.dataset\nscd_args.num_classes = args.n_classes\nscd_args.gpu = 0\nscd_args.fp16 = True if args.fp16 and scd_args.cuda else False\nscd_args.act = args.act\nscd_args.updated_fc_nodes = args.updated_fc_nodes\nscd_args.updated_conv_nodes = args.updated_conv_nodes\nscd_args.width = args.width\nscd_args.normal_noise = True\nscd_args.verbose = True\nscd_args.normalize = bool(args.normalize)\nscd_args.batch_size = args.batch_size\nscd_args.sigmoid = False\nscd_args.softmax = True if 'mc' in scd_args.loss else False\nscd_args.percentile = bool(args.percentile)\nscd_args.fail_count = args.fail_count\nscd_args.diversity = False\nscd_args.fc_diversity = bool(args.fc_diversity)\nscd_args.conv_diversity = False\nscd_args.updated_conv_features_diversity = 16\nscd_args.diversity_train_stop_iters = 3000\nscd_args.init = args.init\nscd_args.target = args.target\nscd_args.logs = {}\nscd_args.no_bias = args.no_bias\nscd_args.record = False\nscd_args.scale = args.scale\nscd_args.save_path = os.path.join('checkpoints', 'pt')\nscd_args.adaptive_loss_epoch = args.epoch\nscd_args.updated_fc_ratio = args.updated_fc_ratio\nscd_args.updated_conv_ratio = args.updated_conv_ratio\nscd_args.divmean = args.divmean\nscd_args.cnn = args.cnn\nscd_args.verbose_iter = args.verbose_iter\nscd_args.freeze_layer = args.freeze_layer\nscd_args.temp_save_per_iter = args.temp_save_per_iter\nscd_args.lr_decay_iter = args.lr_decay_iter\nscd_args.batch_increase_iter = args.batch_increase_iter\nscd_args.aug = args.aug\nscd_args.balanced_sampling = args.balanced_sampling\nscd_args.bnn_layer = args.bnn_layer\nscd_args.epsilon = args.epsilon\nscd_args.step = args.steps\nscd_args.alpha = args.alpha\nscd_args.bp_layer = args.bp_layer\nscd_args.lr = args.lr\nscd_args.reinit = args.reinit\nscd_args.bnn_layers = args.bnn_layers\nscd_args.pnorm = args.pnorm\n# scd_args.adv_source = args.adv_source\n# scd_args.adv_structure = arch['toy3rrr100']\nnp.random.seed(scd_args.seed)\n\ntrain_data, test_data, train_label, test_label = load_data(args.dataset, args.n_classes)\n\nnormal_noise = True if args.normal_noise else False\nif normal_noise:\n    print(f'Normal noise augmented with eps: {args.epsilon}')\n    noise = np.random.normal(0, 1, size=train_data.shape)\n    noisy = np.clip((train_data + noise * args.epsilon), 0, 1)\n    train_data = np.concatenate([train_data, noisy], axis=0)\n    train_label = np.concatenate([train_label] * 2, axis=0)\n    print(f'train data shape: {train_data.shape}')\n\nif args.cnn:\n    if scd_args.dataset == 'cifar10':\n        train_data = train_data.reshape((-1, 32, 32, 3)).transpose((0, 3, 1, 2))\n        test_data = test_data.reshape((-1, 32, 32, 3)).transpose((0, 3, 1, 2))\n    elif scd_args.dataset == 'stl10':\n        train_data = train_data.reshape((-1, 96, 96, 3)).transpose((0, 3, 1, 2))\n        test_data = test_data.reshape((-1, 96, 96, 3)).transpose((0, 3, 1, 2))\n    elif scd_args.dataset == 'celeba':\n        train_data = train_data.reshape((-1, 3, 96, 96))\n        test_data = test_data.reshape((-1, 3, 96, 96))\n    elif 'gtsrb' in scd_args.dataset:\n        train_data = train_data.reshape((-1, 3, 48, 48))\n        test_data = test_data.reshape((-1, 3, 48, 48))\nif scd_args.divmean == 1:\n    train_data = train_data / 0.5 - 1\n    test_data = test_data / 0.5 - 1\n\nelif scd_args.divmean == 2:\n    train_data = train_data - train_data.mean()\n    test_data = test_data - train_data.mean()\n\ntrainset_, testset_ = get_set(data=args.dataset, aug=scd_args.aug, num_classes=scd_args.num_classes,\n                              c0=args.c0, c1=args.c1)\n\nbest_model, val_acc = train_single_cnn01(scd_args, None, None,\n(train_data, test_data, train_label, test_label), trainset_)","sub_path":"experiments/train_cnn01_abp_ce.py","file_name":"train_cnn01_abp_ce.py","file_ext":"py","file_size_in_byte":4797,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"289025148","text":"#! /usr/local/bin/python3\nimport os\nimport sys\nimport requests\nimport pyperclip\nfrom subprocess import call\nfrom multiprocessing import Queue\nfrom pynput.keyboard import Key, Listener\n\nq = Queue(2)\n\ndef upfiel2JD(filename):\n    try:\n        with open(filename, 'rb') as file:\n            url = 'https://group.jd.com/ueditor/jsp/imageUp.jsp?action=uploadimage&encode=utf-8'\n            headers = {\n                'Host': 'group.jd.com',\n                'Origin': 'https://group.jd.com',\n                'Referer': 'https://group.jd.com/ueditor/dialogs/imag',\n                'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/67.0.3396.79 Safari/537.36',   \n            }\n            response = requests.post(url, headers=headers,files={'upfile':file})\n            return '![](%s)'%('http://img30.360buyimg.com/club_community/'+response.json()['url'])\n    except:\n        return \"获取图片失败\"\n\ndef getCopyFile():\n    try:\n        call(['/usr/local/bin/pngpaste', '-v'], stderr=open('/dev/null', 'w'))\n    except OSError:\n        print('使用命令安装`brew install pngpaste`.')\n        sys.exit()\n    filename = \"test.png\"\n    file_path = os.path.join('/tmp', filename)\n    save = call(['/usr/local/bin/pngpaste', file_path])\n    if save:\n        return '' \n    return file_path\n\ndef on_press(key):\n    # 监听按键\n    if format(key) == 'Key.ctrl':\n        # print('press{}'.format(key))\n        q.put(1)\n    if format(key) == 'Key.shift':\n        # print('press{}'.format(key))\n        q.put(2)\n    if q.full():\n        pyperclip.copy(upfiel2JD(getCopyFile()))\n\ndef on_release(keys):\n    # 监听释放\n    if format(keys) == 'Key.ctrl':\n        q.get_nowait()\n    if format(keys) == 'Key.shift':\n        q.get_nowait()\n    if keys == Key.esc:\n        # Stop listener\n        return False\n\n\nif __name__ == '__main__':\n    with Listener(on_press=on_press, on_release=on_release) as listener:\n        listener.join()\n","sub_path":"Tools/useGroupJDupPic.py","file_name":"useGroupJDupPic.py","file_ext":"py","file_size_in_byte":1987,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"404622905","text":"import json,boto3\nimport time\nfrom boto3.dynamodb.conditions import Key\nfrom botocore.exceptions import ClientError\nimport aws_xray_sdk\nfrom aws_xray_sdk.core import xray_recorder\nfrom aws_xray_sdk.core import patch_all\npatch_all()\nclass DecimalEncoder(json.JSONEncoder):\n    def default(self, o):\n        if isinstance(o, decimal.Decimal):\n            if o % 1 > 0:\n                return float(o)\n            else:\n                return int(o)\n        return super(DecimalEncoder, self).default(o)\ndef lambda_handler(event, context):\n    try:\n      awsregion=\"us-east-1\"\n      TableName=\"producttable\"\n      dynamodb=boto3.resource('dynamodb',region_name=awsregion)\n      table=dynamodb.Table(TableName)\n      scan_kwargs = {\n            \"ProjectionExpression\": \"id, title, description, image, Price\"\n        }\n      done = False\n      start_key = None\n      response = table.scan(**scan_kwargs)\n      response=json.loads(json.dumps(response,cls=DecimalEncoder))\n      successmessage={\n          \"status\": 200,\n          \"statusmessage\": \"Added Items\", \n          \"response\": response\n      }\n      return(successmessage)\n    except Exception as e:\n        return {\"errorMessage\":str(e)}","sub_path":"monitoring/getproduct/lambda_function.py","file_name":"lambda_function.py","file_ext":"py","file_size_in_byte":1190,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"384727493","text":"import json\nimport os, sys\nfrom pathlib import Path\n\n\ndef main():\n    jsonpath = sys.argv[1]\n\n    assert \".json\" in jsonpath\n\n    filename = Path(jsonpath)\n    dirname = filename.with_suffix('')\n\n    os.mkdir(dirname)\n\n    with open(filename) as f:\n        contents = json.loads(f.read())\n\n    for c, c_idx in zip(contents, range(len(contents))):\n        each_path = \"{0}/{1}.json\".format(dirname, c_idx)\n        with open(each_path, \"w\") as f:\n            json.dumps(c, f, indent=4)\n","sub_path":"jittoku/cli/json2entries.py","file_name":"json2entries.py","file_ext":"py","file_size_in_byte":484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"552869382","text":"from django.contrib.auth.decorators import login_required\nfrom django.shortcuts import render\nfrom django.http import HttpResponseRedirect, HttpResponse\nfrom django.http import JsonResponse\nfrom django.utils import timezone, html\nfrom django.db.models.functions import Lower\nfrom neomodel import DoesNotExist\nfrom datetime import datetime, date\nimport pytz\nimport time\nfrom django.contrib.auth import authenticate, login, logout\n#from django.views.generic.edit import CreateView, UpdateView, DeleteView, FormView\n#from django.forms import ModelForm\nfrom django.urls import reverse_lazy, reverse\nfrom django.db import transaction\n\nfrom relix_nm.forms import LoginForm, quickSearchForm, advancedSearchForm, idSearchForm, changeRelixForm, itemEditForm, NotesModelForm, todayForm\nfrom relix_nm.models import Notes\nfrom relix_nm.models import Work_set\nfrom relix_nm.models import People\nfrom neo4j.v1 import GraphDatabase, basic_auth\nfrom neomodel import db\nimport json\nimport requests\nfrom . import es_sup\nfrom . import rutils\nfrom . import rinteract\nimport glob\nimport pickle\nimport urllib.parse as urlparse\n\n\n##this is not a comment:\n#pylint: disable=line-too-long, trailing-whitespace, invalid-name\n\nLOGFILE = '/home/ubuntu/django2/relix/relix2/logs/06-relix_nm.log'\n\nINCLUDE = '/home/ubuntu/django2/relix/relix2/az-include.txt'\nPMID_COUNTER_FILE = '/home/ubuntu/django2/relix/relix2/pmid_counter.txt'\nSESSION_INFO = '/home/ubuntu/django2/relix/relix2/session_info/'\nDDX = 20 # default depth traversal\nRELATIONSHIP_TYPES = [\"child_of\", \"relates\", \"prev_instance\", \"rel_content\"]\n\n\n# these are needed for passing to forms where there is no node available to get choices from\n# also in es_sup.py\nPRIORITY_VX = (('1', 'TODAY'), ('2', 'Urgent'), ('3', 'Important'), ('4', 'Normal'), ('5', 'Low'), ('0', ''))\nSTATUS_VX = (('1', 'WIP'), ('2', 'Next'), ('3', 'Cold'), ('4', 'Ongoing'), ('5', 'Hold'), ('6', 'Canceled'), ('8', 'Ref'), ('9', 'Done'), ('10', 'Pending done'),('11', 'Pending cancel'), ('0', ''))\n\nRETURN_CODES = {'-10':'recent', '-11':'grid', '-12':'today', '-13':'search', '-14':'list pending', '-15':'my root', '-16':'qnote', '-17':'quicksearch', '-18':'people'}\n\n#GRID_ORDER_LIST = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]\n\n############### A U T H ################################################\n# now in settings.py #'bolt://USR:PWD@localhost:7687'\n########################################################################\n\n    \n\n### BEGIN >>multiple<< ITEMS EDIT FORM #################################################\n@login_required()\ndef items_edit(request):\n    '''receive the items form to process edits'''\n    rutils.starttime_reset(request)\n    rutils.logThis(request, \"ENTER items_edit <<<<<<\")\n    ###handle moves#######\n    if request.method == 'POST':\n        # receive POST and create a form instance and populate it with data from the request:\n        Sform = itemEditForm(request.POST)\n        # check whether it's valid:\n        if Sform.is_valid():\n            rutils.logThis(request, \"itemsUpdate: valid form======\")\n            ie_to_reindex = []\n            #don't know why request.POST.getlist['itemSelect'] didn't work\n            # shoulda been request.POST.getlist('itemSelect')?\n            for z in request.POST.lists():\n                #rutils.logThis(request, \">>>> PASSED: %s \" % str(z) )\n                #find selected items - common to all operations\n                if z[0] == 'itemSelect':\n                    selected_items = z[1]\n            selected_target_nodes = list(Notes.nodes.filter(pmid__in=selected_items))\n            \n            ################################################\n            ## execute the actions:\n            ################################################\n            \n            ## move action ####################################################\n            if Sform.cleaned_data['pmid_manual'] != None:\n                moveTarget = Sform.cleaned_data['pmid_manual']\n            else:\n                moveTarget = Sform.cleaned_data['grab']\n\n            if moveTarget != None:\n                ## valid move action\n                rutils.logThis(request, \"moving %s to %s\" % (str(selected_items), moveTarget))\n                for target_node in selected_target_nodes:\n                    mode = 'replace'\n                    relType = 'child_of'\n                    pmidLinkTo = moveTarget\n                    selectedLinkTo = 'nix'\n                    ##careful fetchRoot= -9 is used for both searches, and for trunk. \n                    if request.session.setdefault('fetchRoot',-9) != -9 or request.session['fetchType'] == 'search':\n                        if len(target_node.parents(request.user.username)) > 0:\n                            origLinkToID = target_node.parents(request.user.username)[0].pmid\n                        else:\n                            #target_node is a trunk node and has no parents\n                            origLinkToID = -9\n                    else:\n                        # we're moving an item off the trunk\n                        origLinkToID = -9\n                    origRelType = 'child_of'\n\n                    #####################################\n                    # [here:items_edit] execute_changerel (triggers isort rebuild, etc) #################\n                    status = execute_changerel(request, target_node, mode, relType, pmidLinkTo, \\\n                                               selectedLinkTo, origLinkToID, origRelType, \\\n                                               Sform.cleaned_data['return_me_to'])\n                    target_node.dtModified = datetime.now(pytz.timezone('UTC'))\n                    target_node.save()\n                    ie_to_reindex += rutils.check_and_archive(request, target_node)  #items_edit 0\n                    \n                    # need to check if status should be updated on original parent\n                    if origLinkToID != -9:\n                        old_parent_node = Notes.nodes.get(pmid=origLinkToID)\n                        # needed, since old_parent won't be picked up below b/c it's not related\n                        #      to the target node any more\n                        ie_to_reindex += rutils.check_and_archive(request, old_parent_node)  #items_edit 1\n                    \n            ## priority_change action ###############################################\n            ## '-99', string value, is correct for no change in priority and status\n            if Sform.cleaned_data['priority_change'] != '-99':\n                for target_node in selected_target_nodes:\n                    target_node.priority = str(Sform.cleaned_data['priority_change'])\n                    target_node.dtModified = datetime.now(pytz.timezone('UTC'))                    \n                    target_node.save()\n                rutils.message(request, 'Priority changed to %s on %s' %  (Sform.cleaned_data['priority_change'], str(selected_items)))\n            ## status_change action ################################################\n            if Sform.cleaned_data['status_change'] != '-99':\n                for target_node in selected_target_nodes:\n                    target_node.status = str(Sform.cleaned_data['status_change'])\n                    target_node.dtModified = datetime.now(pytz.timezone('UTC'))\n                    target_node.save()\n                    ie_to_reindex += rutils.check_and_archive(request, target_node) #items edit 2\n                rutils.message(request, 'Status changed to %s on %s' % (Sform.cleaned_data['status_change'], str(selected_items)))\n\n            ##   REFRESH ELASTICSEARCH, w/ node properties;\n            ##    Note: itemsEdit actions may change: dtModified, parent, priority, and/or status AND ARCHIVED\n            ##       BUT... we want the refresh action to catch PEOPLE as well....\n            ##          of these, archvie, priority & status matter to ES.\n            ##          In search, parentage is handled via scope\n            \n            # ES_SUP BULK UPDATE OF ALL SELECTED NODES plus ANY FROM CHECK_AND_ARCHIVE ######################\n            pmids_to_reindex = set()\n            for x in selected_target_nodes:\n                pmids_to_reindex.add(x.pmid)\n            for y in ie_to_reindex:\n                pmids_to_reindex.add(y)\n            reindex_these_nodes = Notes.nodes.filter(pmid__in=list(pmids_to_reindex))\n            (status, success) = es_sup.ESbulkItemsEditUpdate(request, reindex_these_nodes)\n            rutils.logThis(request,\"IEbulk status=%s, success=%s\" % (status, success))\n            rutils.message(request, \"items_edit saved: %s\" % str(selected_items))\n            \n            #######################################################\n            # RETURN TO VIEW [items_edit]\n            #######################################################\n        \n            if moveTarget != None:\n                scrollItem = moveTarget\n            else:\n                scrollItem = -9\n            return_target = str(Sform.cleaned_data['return_me_to'])\n            # the 'True' parameter on the end forces a refresh\n            return universal_return(request, 'items_edit', return_target, scrollItem, True)\n\n        else:\n            ##form is invalid\n            erx = str(Sform.errors)\n            rutils.logThis(request, \"ERROR in items_edit form: %s\" % erx)\n            rutils.message(request, 'ERROR:%s' % (erx))\n            return_target = str(Sform.cleaned_data['return_me_to'])        \n            return HttpResponseRedirect(reverse('relix_nm:viewTree',\\\n                                                kwargs={'target_id':request.session['fetchRoot']}))\n        \n#######################################################################################################        \n### NOTES_EDIT via  rt_edit.html ######################################################################\n#######################################################################################################\n@login_required()\ndef notes_edit(request, targetID, qnote='n', return_target='nix'):\n    '''custom form for notes mods. \n       return_target is a URL argument, and is returned as a string\n       return_me_to  is baked into the form, and is an integer\n    '''\n    rutils.starttime_reset(request)    \n    rutils.logThis(request, \"ENTER notes_edit<< MODEL FORM <<<<<<\")\n    rutils.logThis(request, \"      targetID=%s, return_target=%s\" % (targetID, return_target))\n\n    try:\n        targetNode = Notes.nodes.get(pmid=targetID)\n    except:\n        rutils.logThis(request, \"ERROR - PMID not found: %s\" % targetID)\n        rutils.message(request, \"ERROR - PMID not found: %s\" % targetID)\n        return universal_return(request, 'notes_edit', return_target, targetID, True)\n        \n        \n    add_recent(request, targetNode.pmid)\n    \n    if targetNode.created_by != request.user.username:\n        rutils.message(request, \"User %s not owner of node %s, edit aborted\" % (request.user.username, targetID))\n        return HttpResponseRedirect(reverse('relix_nm:home_nm'))\n\n    if request.method == 'POST':\n        # create a form instance and populate it with data from the request\n        Sform = NotesModelForm(request.POST, instance=targetNode)\n        \n        # check whether it's valid:\n        if Sform.is_valid():\n            rutils.logThis(request, \"notesUpdate: valid form======\")\n            # fields not edited: pmid, dtCreated, dtModified, created_by, isort\n            \n            # https://docs.djangoproject.com/en/1.11/topics/forms/modelforms/#the-save-method\n            Sform.save()\n            \n            targetNode.dtModified = datetime.now(pytz.timezone('UTC'))\n\n            ## HASNOTE FIELD ###########################################################\n            if len(Sform.cleaned_data['noteText']) > 1 or len(Sform.cleaned_data['image_list']) > 8:\n                targetNode.hasNote = True\n            else:\n                targetNode.hasNote = False\n                \n            ##      SAVE ALL FIELDS FROM THE NODE ##########################\n            targetNode.save()\n                                            \n            ## People updating ####################################################\n            ##   remove all current linked people; add all submitted linked people\n            current_assigned = targetNode.assigned_to.all()\n            for ca in current_assigned:\n                targetNode.assigned_to.disconnect(ca)\n            #########\n            atp = targetNode.assigned_to_peoples.replace(' ','').split(',')\n            for assigned_person in atp:\n                if assigned_person != '':\n                    assigned_person = assigned_person.lower()\n                    AP = People.nodes.get_or_none(nickname=assigned_person)\n                    if AP == None:\n                        # if AP is None, then create this People node\n                        AP = People(nickname=assigned_person, created_by=request.user.username)\n                        AP.save()\n                    if not AP.assigned_from.is_connected(targetNode):\n                        AP.assigned_from.connect(targetNode)\n            targetNode.assigned_to_peoples = None\n\n            ####################\n            current_involves = targetNode.involves.all()\n            for ci in current_involves:\n                targetNode.involves.disconnect(ci)\n            #######\n            ip = targetNode.involves_peoples.replace(' ','').split(',')\n            for involved_person in ip:\n                if involved_person != '':\n                    involved_person = involved_person.lower()\n                    IP = People.nodes.get_or_none(nickname=involved_person)\n                    if IP == None:\n                        # if IP is None, then create this People node\n                        IP = People(nickname=involved_person, created_by=request.user.username)\n                        IP.save()\n                    if not IP.involved_with.is_connected(targetNode):\n                        IP.involved_with.connect(targetNode)\n            targetNode.involved_peoples = None\n            #########################################################################\n\n\n\n            ## WORK_SET ##############################################################\n            if qnote != 'q':   #if it's not a qnote\n                total_changed = rutils.set_workset_with_descent(request, targetNode)\n                rutils.logThis(request, \"Work_set rel changed: %s\" % total_changed)\n                rutils.message(request, \"Work_set rel: %s\" % total_changed )\n\n            ## misc ###################################################################\n            nix = rutils.rebuild_jumplinks(request)\n            \n            #archive processing #######################################################\n            #   check if node status is done, pending done, or canceled; include ancestors\n            ne_to_reindex = rutils.check_and_archive(request, targetNode) # notes_edit\n\n            \n            ## ES updating #########################################################################\n            # for the note_edit targetNode\n            status = es_sup.ESupdateDocument(request, targetNode, targetNode.noteText)\n            ## ES is updated, so clear the noteText field on the node\n            targetNode.noteText = ''\n            # for any nodes from check_and_archive processing,\n            #    i.e., bang-on archived, priority, or status changes, like items_edit\n            #          (no note_text, title, etc changes)\n            caa_nodes = Notes.nodes.filter(pmid__in=ne_to_reindex)\n            if len(caa_nodes) != 0:\n                (status, success) = es_sup.ESbulkItemsEditUpdate(request, caa_nodes)\n\n            \n            # qnote ################################################################################\n            # if it's a qnote, add it to pending moves session var (now that we have the real title)\n            # safe, b/c qnote == 'q' only when the qnote is first created\n            if qnote == 'q':\n                # add to tagged_pages\n                targetNode.tagged_page = True\n                targetNode.save()\n\n            ###################################################################################\n            # return to view [notes_edit]\n            ###################################################################################            \n            return universal_return(request, 'notes_edit', return_target, targetID, True)\n \n        else:\n            ##form is invalid ##########################\n            rutils.logThis(request, \"======INVALID FORM ===============\")\n            #erx = str(Sform.errors)\n            erx = str(Sform.errors.as_data())\n            rutils.logThis(request, \"notesUpdate invalid form: %s\" % erx)\n            rutils.message(request, '%s:%s' % (targetID, erx))\n            return HttpResponseRedirect(reverse('relix_nm:notes_edit', kwargs={'targetID':targetID}))\n            #return render(request, 'relix_nm/notes_update_form.html')\n    else:\n        ######## GET [note_edit]   #############################################################\n        rutils.logThis(request, \"notesUpdate - GET form for %s ======\" % targetID)\n\n        #get esnoteText ###################\n        esNoteTextDict = es_sup.EStextNotesGet(request, [targetID])\n        rutils.logThis(request, \"GET: esNTD...\")\n        # = %s\" % str(esNoteTextDict))\n\n        if int(targetID) in esNoteTextDict.keys():\n            thisNoteText = esNoteTextDict[int(targetID)]\n        else:\n            thisNoteText = ''\n\n        #image_list is showing up as the string 'None' in notes_edit.html\n        if targetNode.image_list == None:\n            targetNode.image_list = ''\n\n        all_worksets_list = [None] + rutils.get_all_worksets(request)\n        # \"None\" gets translated to \"All\" \n\n        \n        targetNode.noteText = thisNoteText\n        targetNode.save()\n\n        ################ ADORN #########################################################\n        #adorn (temporarily) the targetNode with its label/workset, and people\n        targetNode.work_set = targetNode.get_workset_name()\n        targetNode.assigned_to_peoples = ','.join([ x.nickname for x in targetNode.assigned_to.all() ])\n        targetNode.involves_peoples = ','.join([ x.nickname for x in targetNode.involves.all() ])\n        \n        ancestors_of_target = list(targetNode.ancestorList(request.user.username))\n\n        #the constructor of form\n        #sform = NotesForm()\n        #sform = NotesForm(targetNode.__dict__)\n        sform = NotesModelForm(instance=targetNode)\n        \n        return render(request, 'relix_nm/rt_edit.html', \\\n                      {'form':sform, 'ix':targetNode, \\\n                       'workset_list':all_worksets_list, \\\n                       'return_me_to':return_target, \\\n                       'titleCrumbBlurb':'Edit: '+str(targetNode.pmid)+' '+targetNode.title[:30], \\\n                       'target_ancestors':ancestors_of_target    \n                      })\n\n# ADD_QNOTE ########################################################################################\n\n@login_required()\ndef add_qnote(request):\n    \n    '''create a new top-level, parentless node'''\n    newPMID = get_next_pmid()\n    rutils.logThis(request, \"adding new Qnote parentless node,  %s <<<<<<<<<\" % (newPMID))\n    NoteNew = Notes(pmid=newPMID, title=\"Qnote\", noteText='', created_by=request.user.username)\n    NoteNew.save()\n    add_recent(request, NoteNew.pmid)\n\n    # add record to Elasticsearch ES\n    es_sup.EScreateDocument(request, NoteNew, None)\n    rutils.logThis(request, \"New item created %s:\"+str(NoteNew.pmid))\n    \n    return newPMID\n\n\n### BEGIN QNOTES  ###########################################################################\n@login_required()\ndef qnotes(request, target_id):\n    '''pop-up custom form for QNOTE creation + note editing. '''\n    rutils.starttime_reset(request)\n    rutils.logThis(request, \"ENTER QNote <<< MODEL FORM <<<<<<\")\n    target_id = int(target_id)\n    \n    if request.method == 'POST':\n        rutils.logThis(request, \"Nothing should post here!\")\n        rutils.message(request, 'Nothing should post here!! %s' % target_id)\n        return HttpResponseRedirect(reverse('relix_nm:home'))\n            \n    else:\n        ######## GET ################################################################\n        if target_id != 0:\n            target_node = Notes.nodes.get(pmid=target_id)\n            TCB = target_node.title\n            treat_as_qnote = 'n'\n\n            #get esnoteText ###################\n            esNoteTextDict = es_sup.EStextNotesGet(request, [target_id])\n\n            if int(target_id) in esNoteTextDict.keys():\n                thisNoteText = esNoteTextDict[target_id]\n            else:\n                thisNoteText = ''\n\n            #image_list is showing up as the string 'None' in notes_edit.html\n            if target_node.image_list == None:\n                target_node.image_list = ''\n            \n            target_node.noteText = thisNoteText\n            target_node.save()\n            \n        else:\n            target_id = add_qnote(request)\n            rutils.logThis(request, \"Qnote created. GET form for %s ======\" % target_id)            \n            target_node = Notes.nodes.get(pmid=target_id)\n            #image_list is showing up as the string 'None' in notes_edit.html\n            if target_node.image_list == None:\n                target_node.image_list = ''\n            target_node.noteText = ''\n            TCB = 'Qnote: '+str(target_node.pmid)\n            target_node.save()\n            treat_as_qnote = 'y'\n            \n        all_worksets_list = [None] + rutils.get_all_worksets(request)\n        # \"None\" gets translated to \"All\" \n\n        #the constructor of the form\n        sform = NotesModelForm(instance=target_node)\n        \n        return render(request, 'relix_nm/rt_edit.html', \\\n                          {'qnote':treat_as_qnote, 'form':sform, \\\n                            'ix':target_node, \\\n                            'workset_list':all_worksets_list,\\\n                            'return_me_to':'-16', \\\n                            'titleCrumbBlurb':TCB\n                          })\n\n#################################################################################\n@login_required()\ndef changeRel(request, targetID, linkToType, linkToID, return_target='nix'):\n    '''allow a relationship to be added, deleted, or changed'''\n    # linkToID = 0 means it's an addition\n    rutils.logThis(request, \"ENTER changeRel %s.............\" % targetID)\n    ##mode, relType, selectedLinkTo, pmidLinkTo, orig_linkto_pmid, orig_linkto_type, addToPendingMoves\n    ##request.session['fetchType'] = 'changeRel'\n\n    targetNode = Notes.nodes.get(pmid=targetID)\n    if targetNode.created_by != request.user.username:\n        rutils.message(request, \"User %s not owner of node %s\" % (request.user.username, targetID))\n        return HttpResponseRedirect(reverse('relix_nm:home_nm'))\n\n    if request.method == 'POST':\n        Sform = changeRelixForm(request.POST)\n        # check whether it's valid:#############################################\n        if Sform.is_valid():\n            mode = Sform.cleaned_data['mode'] \n            relType = Sform.cleaned_data['relType']\n            selectedLinkTo = Sform.cleaned_data['selectedLinkTo']\n            pmidLinkTo = Sform.cleaned_data['pmidLinkTo']\n            origLinkToID = Sform.cleaned_data['origLinkToID']\n            origRelType = Sform.cleaned_data['origRelType']\n            addToTaggedPages = Sform.cleaned_data['addToTaggedPages']\n            rutils.logThis(request, \"changeRelixForm: valid form: mode:%s relType:%s selectedLinkTo:%s pmidLinkTo:%s origLinkToID:%s  origRelType:%s addToTaggedPages:%s ======\" % (mode, relType, selectedLinkTo, pmidLinkTo, origLinkToID, origRelType, addToTaggedPages))\n\n            # don't allow \"add\" for child_of changes. not the most elegant, but it'll work\n            if relType == 'child_of': mode = 'replace'\n            \n            if addToTaggedPages == True:\n                targetNode.tagged_page = True\n                targetNode.save()\n            else:\n                ## here: changeRel ==> change or add the relationship ##############\n                status = execute_changerel( request, targetNode, mode, relType, pmidLinkTo, \\\n                                            selectedLinkTo, origLinkToID, origRelType, \\\n                                            return_target)\n            rutils.message(request, \"changeRel %s\" % targetNode.pmid)\n\n            #######################################################\n            # return to view [changeRel]\n            #######################################################\n            return universal_return(request, 'changeRel', return_target, targetID, True)\n                \n        else:\n            ##form is invalid ########################################################\n            erx = str(Sform.errors)\n            rutils.logThis(request, \"!!!!!! changeRel INVALID form: %s\" % erx)\n            rutils.message(request, '%s' % erx)\n            return HttpResponseRedirect('/relix_nm/%s/%s/%s/changerel/%s/' % (targetID, linkToType, linkToID, return_target))\n            \n    else:\n        # if a GET (or any other method: CHANGEREL) #############################################################\n        # create form instance and  populate it with data from the request:\n        rutils.logThis(request, \"changeRel - GET form ====== %s \" % targetID)\n        sform = changeRelixForm()\n\n        # now using tagged_page for \"select by item\" dropdown, as well as tagged page table :-)\n                \n        tagged_page_items = Notes.nodes.filter(tagged_page=True, created_by=request.user.username)\n        if linkToID == '0':\n            link_to_node = None\n        else:\n            link_to_node = Notes.nodes.get_or_none(pmid=linkToID)\n        if linkToType == 'rel_content' or linkToType == 'relates':\n            # it's an addition. Instead of \"replace\", a user can \"add\" and then \"delete\" the old one. it's safer. \n            mode = \"add\"\n            if link_to_node == None:            \n                linkToTitle = 'None'\n            else:\n                linkToTitle = link_to_node.title\n        elif linkToType == 'myroot':\n            # it's an addition\n            mode = \"add\"\n            linkToTitle = 'None'\n        elif linkToType == 'child_of':\n            mode = \"replace\"\n            linkToTitle = link_to_node.title\n        else:\n            rutils.logThis(request, \"SHOULD NOT HAVE LINKTOTYPE = %s, ABORT\" % linkToType)\n            abort\n\n        request.session['display_workset'] = None\n        #get complete list of work_sets in use\n        all_worksets_list = rutils.get_all_worksets(request)\n        # \"None\" gets translated to \"All\" \n            \n        rutils.logThis(request, \"begin changeRel render, %s items >>>>>>>>>>>>>>>>>>>>\" % len(tagged_page_items))\n        context = {'current_items':tagged_page_items, 'ix':targetNode, 'mode':mode, \\\n                   'workset_list':all_worksets_list,  \\\n                   'origRelType':linkToType, 'origLinkToID':linkToID, 'origLinkToTitle':linkToTitle,\\\n                   'return_me_to':return_target, 'tagged_pages':tagged_page_items}\n\n        return render(request, 'relix_nm/change_relix.html', context)\n    #end CHANGEREL\n\ndef execute_changerel(request, targetNode, mode, relType, pmidLinkTo, selectedLinkTo, origLinkToID, origRelType, return_target):\n    '''guts of add or change relationships. \n       Externalized so that both the form handler and pending move mechanism can share the same logic'''\n    # pmidLinkTo = user filled in pmid # for target, rather than\n    # selectedLinkTo = select popup box used\n\n    # get the linkto node, from pmid manual field or selected node\n    if pmidLinkTo != None:\n        rutils.logThis(request, \"     ...using pmidLinkTo = %s\" % pmidLinkTo)\n        try:\n            linkToNode = Notes.nodes.get(pmid=pmidLinkTo)\n        except DoesNotExist:\n            rutils.message(request, \"pmidLinkTo = %s does not exist\" % pmidLinkTo)\n            return \"pmidLinkTo = %s does not exist\" % pmidLinkTo\n    else:\n        rutils.logThis(request, \"     ...using selectedLinkTo = %s\" % selectedLinkTo)\n        linkToNode = Notes.nodes.get(pmid=selectedLinkTo)\n\n    # don't allow a parent to be moved under its child ###\n    if relType == 'child_of' and (linkToNode.descendant_of(targetNode.pmid) or targetNode.pmid == linkToNode.pmid):\n        rutils.logThis(request, \"    ABORT - MOVE IS RECURSION, child = %s, target = %s\" % (targetNode.pmid, linkToNode.pmid))\n        rutils.message(request, \"RECURSION aborted, %s=>%s\" % (targetNode.pmid, linkToNode.pmid))\n        return \"RECURSION, aborted\"\n    else:\n        ## not a recursion ##########################################################################\n        \n        if mode == 'replace':\n            ## if mode = replace, break the existing relationship ######################\n            rutils.logThis(request, \"exec changerel, replace: origRelType = %s, origLinkToID = %s, pmid = %s\" % (origRelType, origLinkToID, targetNode.pmid))\n            # if origLinkToID -9, it was a trunk node, so nothing to disconnect\n            if origLinkToID != -9:\n                origLinktoNode = Notes.nodes.get(pmid=origLinkToID)\n                if origRelType == 'child_of':\n                    targetNode.child_of.disconnect(origLinktoNode)\n                elif origRelType == 'rel_content':\n                    targetNode.rel_content.disconnect(origLinktoNode)\n                elif origRelType == 'relates':\n                    targetNode.relates.disconnect(origLinktoNode)\n                elif origRelType == 'prev_instance':\n                    targetNode.prev_instance.disconnect(origLinktoNode)\n                else:\n                    rutils.logThis(request, \"Unsupported origRelType: %s\" % relType)\n                    rutils.message(request, \"Unsupported origRelType: %s\" % relType)\n                targetNode.save()\n\n        status = 'unknown'\n        ###################################################################################\n        # for both mode=replace and mode=add, create the new relationship\n        rutils.logThis(request, \"exec changerel, create new rel: relType = %s, linkToNode = %s, pmid = %s, mode = %s\" % (relType, linkToNode.pmid, targetNode.pmid, mode))\n        if relType == 'child_of' or relType == 'myroot':\n            rutils.logThis(request, \"     ...creating child_of: %s\" % linkToNode.pmid)\n            targetNode.child_of.connect(linkToNode)\n            # clear tagged_page\n            targetNode.tagged_page = False\n            \n            # for move, if label of new parent is different from label of child, propagate the parent's workset label\n            if targetNode.get_workset_name() != linkToNode.get_workset_name():\n                targetNode.work_set = linkToNode.get_workset_name()\n                # set_label_* sets node.work_set as the new label\n                rutils.set_workset_with_descent(request, targetNode)\n            # for move, call status update ##################\n            pmids_to_reindex  = rutils.check_and_archive(request, targetNode) # execute_changerel\n            pmids_to_reindex += rutils.check_and_archive(request, origLinktoNode)\n            if len(pmids_to_reindex) > 0:\n                reindex_these_nodes = Notes.nodes.filter(pmid__in=pmids_to_reindex)\n                (status, success) = es_sup.ESbulkItemsEditUpdate(request, reindex_these_nodes)\n                rutils.logThis(request,\"execute_changerel reindex status=%s, success=%s\" % (status, success))\n\n        elif relType == 'rel_content':\n            targetNode.rel_content.connect(linkToNode)\n        elif relType == 'relates':\n            targetNode.relates.connect(linkToNode)\n        elif relType == 'prev_instance':\n            targetNode.prev_instance.connect(linkToNode)\n        else:\n            rutils.logThis(request, \"Unsupported relType: %s\" % relType)\n            rutils.message(request, \"Unsupported relType: %s\" % relType)\n            status = \"unsupported relType\"\n\n        if status == \"unsupported relType\":\n            rutils.message(request, \"Unknown relType! aborted!\")\n        else:\n            targetNode.save()\n            status = \"normal\"\n            rutils.logThis(request, \"    ...changerel completed  %s %s %s => %s\" % (relType, mode, targetNode.pmid, linkToNode.pmid))\n            rutils.message(request, \"done:%s %s %s => %s\" % (relType, mode, targetNode.pmid, linkToNode.pmid))\n\n        ############ end EXECUTE CHANGEREL ############################################################\n        return status\n\n#########################################################\n\ndef movepending(request, targetID, newLinkTo, return_target):\n    '''execute a pending move'''\n\n    # urls.py argument is always a string\n    targetNode = Notes.nodes.get(pmid=targetID)\n    # pending move is always for a new parent\n    targetID = str(targetNode.pmid)\n    targetTitle = targetNode.title\n    mode = 'replace' \n    relType = 'child_of'\n    pmidLinkTo = newLinkTo\n    selectedLinkTo = 0\n    if targetNode.parents(request.user.username) == []:\n        ## needs to be -9 if it's a root node\n        origLinkToID = -9\n    else:\n        parent_node = targetNode.parents(request.user.username)[0] \n        origLinkToID = parent_node.pmid\n\n    origRelType = 'child_of'\n    # movepending ==> execute_changerel\n    status = execute_changerel(request, targetNode, mode, relType, pmidLinkTo, \\\n                               selectedLinkTo, origLinkToID, origRelType, return_target)\n    if origLinkToID != -9:\n        mp_to_reindex = rutils.check_and_archive(request, parent_node) # movepending\n    return HttpResponseRedirect(reverse('relix_nm:clear-pending', kwargs={'targetID':targetID, 'return_target':return_target}))\n    \n\ndef clearpending(request, targetID, return_target='nix'):\n    '''clear a pending move item'''\n    targetNode = Notes.nodes.get(pmid=targetID)\n    targetNode.tagged_page = False\n    targetNode.save()\n    rutils.message(request, 'cleared pending')\n\n    #######################################################\n    # return to view [clearpending]\n    #######################################################\n    return universal_return(request, 'clearpending', return_target, targetID, False)\n\n\n    \n### V I E W S #####################################################\n\ndef home(request):\n    # one of the places we rebuild jumplinks\n    rutils.starttime_reset(request)\n    # added 12/4/2017\n    rutils.safe_purge_session(request)\n    rutils.rebuild_jumplinks(request)\n\n    #get complete list of work_sets in use     # \"None\" gets translated to \"All\" \n    all_worksets_list = [None] + rutils.get_all_worksets(request)\n    \n    request.session['display_workset'] = 'None'\n    context = {'workset_list':all_worksets_list, 'titleCrumbBlurb':'Relix: Home'}\n    rutils.logThis(request, \"HOME - render <<<<<<< >>>>>>>>\")\n    return render(request, 'relix_nm/home_nm.html', context)\n\ndef help(request):\n    # simple help, plus simple stats and session var list for debugging\n    result_matrix = {}\n    rutils.starttime_reset(request)    \n    allNodes = Notes.nodes.all()\n    result_matrix['allNodes'] = len(allNodes)\n    result_matrix['archived'] = len(Notes.nodes.filter(archived=True))\n    \n    k = set()\n    for item in allNodes:\n        k.add(item.created_by)\n    result_matrix['created_by'] = k\n\n    result_matrix['person_totals'] = {}\n    for person in k:\n        result_matrix['person_totals'][person] = len(Notes.nodes.filter(created_by=person))\n\n    #get complete list of work_sets in use\n    all_worksets_list = [None] + rutils.get_all_worksets(request)\n    # \"None\" gets translated to \"All\" \n    \n    #build a list of the important session vars, and their types\n    if 'fetchRoot' in request.session.keys():\n        fetch_root_type = str(type(request.session['fetchRoot']))\n    else:\n        fetch_root_type = ' None '\n    #rutils.message(request, \"fetch_root_type: <b>%s</b>\" % fetch_root_type[1:-1])\n    context = {'results':result_matrix, \\\n               'workset_list':all_worksets_list, \\\n               'fetch_root_type': fetch_root_type[1:-1], 'titleCrumbBlurb':'Relix: Help'}\n    return render(request, 'relix_nm/help_nm.html', context)\n\n@login_required()\ndef detail(request, targetID, return_target):\n    targetID = int(targetID)\n    rutils.starttime_reset(request)\n    rutils.message(request, \"detail: <b>%s</b>\" % targetID)\n    rutils.logThis(request, \"ENTER detail %s <<<<\" % targetID)\n\n    targetNode = Notes.nodes.get(pmid=targetID)\n    # ugh.  django_neomodel didn't seem to provide node.relationship.all support in templates\n\n    if targetNode.created_by != request.user.username:\n        rutils.message(request, \"User %s not owner of node %s\" % (request.user.username, targetID))\n        return HttpResponseRedirect(reverse('relix_nm:home_nm'))\n\n    esNoteTextDict = es_sup.EStextNotesGet(request, [int(targetID)])\n    targetNode.work_set = targetNode.get_workset_name()\n\n    targetNode.assigned_to_peoples = ','.join([ x.nickname for x in targetNode.assigned_to.all() ])\n    targetNode.involves_peoples = ','.join([ x.nickname for x in targetNode.involves.all() ])\n\n    \n    if targetNode.image_list != None:\n        real_list = targetNode.image_list.split()\n    else:\n        real_list = []\n\n    #get complete list of work_sets in use # \"None\" gets translated to \"All\" \n    all_worksets_list = [None] + rutils.get_all_worksets(request)\n    \n    # \"None\" gets translated to \"All\" \n    \n    all_worksets_list.append(None) \n       \n    context = {'target':targetNode, 'esNoteDict':esNoteTextDict, \\\n               'workset_list':all_worksets_list, \\\n               'image_list_display' : real_list, 'return_me_to': return_target}\n    return render(request, 'relix_nm/detail_nm.html', context)\n\n@login_required()\ndef showNote(request, targetID):\n    ''' serve a javascript pop-up to display the noteText '''\n    rutils.logThis(request, \"ENTER showNote %s <<<<\" % targetID)\n\n    targetNode = Notes.nodes.get(pmid=targetID)\n\n    if targetNode.created_by != request.user.username:\n        rutils.message(request, \"User %s not owner of node %s\" % (request.user.username, targetID))\n        return HttpResponseRedirect('/relix_nm/%s/view/' % request.session['fetchRoot'])\n    \n    esNoteTextDict = es_sup.EStextNotesGet(request, [targetID])\n    # add to the recent list\n    add_recent(request, targetNode.pmid)\n\n    #turn image_list into a real list & pass to template\n    if targetNode.image_list != None:\n        real_list = targetNode.image_list.split()\n    else:\n        real_list = []\n\n    ancestors_of_target = list(targetNode.ancestorList(request.user.username))\n\n    \n    context = {'target':targetNode, 'esNoteDict':esNoteTextDict, \\\n                   'image_list_display' : real_list, \\\n                   'titleCrumbBlurb':'Note: '+str(targetNode.pmid)+' '+targetNode.title[:30], \\\n                   'target_ancestors' : ancestors_of_target}\n    return render(request, 'relix_nm/shownote.html', context)\n    \n\n@login_required()\ndef add_recent(request, targetID):\n    '''add a pmid to the recent list'''\n    targetID = int(targetID)\n    recent_file = SESSION_INFO + request.user.username + '.pickle'\n    try:\n        with open(recent_file, 'rb') as PF:\n            recent_temp = pickle.load(PF)\n    except:\n        recent_temp = {}\n\n    recent_temp[str(targetID)] = datetime.now(pytz.timezone('UTC')).isoformat()\n   \n    if len(recent_temp) > 50:\n        timestamps = list(recent_temp.values())\n        timestamps.sort(reverse=True)\n        # need to pull keys out, otherwise we'll error trying to modify the\n        # dict whose keys are the index for the for loop\n        rt_keys = list(recent_temp.keys())\n        for test_item in rt_keys:\n            # compare to timestamp of the 50th reverse sorted item (50th oldest)\n            if recent_temp[test_item] < timestamps[50]:\n                del recent_temp[test_item]\n                \n    with open(recent_file, 'wb') as PF:\n        pickle.dump(recent_temp, PF, pickle.HIGHEST_PROTOCOL)\n\n    return\n\n@login_required()\ndef remove_recent(request, target_id):\n    '''remove an pmID from the recent pickle'''\n\n    target_id = int(target_id)\n    recent_file = SESSION_INFO + request.user.username + '.pickle'\n\n    \n    try:\n        with open(recent_file, 'rb') as PF:\n            recentd = pickle.load(PF)\n    except:\n        recentd = {}\n    try:\n        nix = recentd.pop(str(target_id))\n        r = 'success'\n    except:\n        rutils.logThis(request, \"Target_id %s was not in recent list\" % target_id)\n        r = 'fail'\n\n    with open(recent_file, 'wb') as PF:\n        pickle.dump(recentd, PF, pickle.HIGHEST_PROTOCOL)\n\n    return r\n\n\n@login_required()\ndef recent(request):\n    '''generate list of recent items'''\n    rutils.starttime_reset(request)\n    request.session['fetchType'] = 'recent'\n    request.session['fetchRoot'] = -9   \n    lookup = {}\n\n    current_recent = rutils.read_recent(request)\n    for pmid in list(current_recent.keys()):\n        ##### current_recent[pmid] = timestamp\n        ##### lookup[item access timestamp] = item pmID\n        lookup[current_recent[pmid]] = pmid\n    lookup_keys = list(lookup.keys())\n    lookup_keys.sort(reverse=True)\n\n    found_items = []\n    for lk in lookup_keys:\n        try:\n            #found_items.append(Notes.nodes.get(pmid=lookup[lk]))\n            recent_node = Notes.nodes.get(pmid=lookup[lk])\n            recent_node.accessed, ignore = current_recent[lookup[lk]].split('T') # no need to save this, it's transient\n            found_items.append(recent_node)\n        except DoesNotExist:\n            # should remove from recent if it's been deleted.\n            ## this is out of date:\n            #         nix = request.session['recents'].pop(lookup[lk])\n            rutils.logThis(request, \"         Deleted item pmid=%s suppressed in recent list\" % lookup[lk] )\n\n\n    #get complete list of work_sets in use\n    all_worksets_list = [None] + rutils.get_all_worksets(request)\n    \n    # \"None\" gets translated to \"All\" \n    \n    # this is needed in case there is ever a null list\n    if len(found_items) > 0:\n        priority_choices = found_items[0].PRIORITY_CHOICES\n        status_choices = found_items[0].STATUS_CHOICES\n    else:\n        priority_choices = []\n        status_choices = []\n    \n    rutils.logThis(request, \"begin Recent render, %s items >>>>>>>>>>>>>>>>>>>>\" % len(found_items))\n    context = {'current_items':found_items, 'target_id':-9, \\\n               'workset_list':all_worksets_list, \\\n               'PCHX':priority_choices, \\\n               'SCHX':status_choices, \\\n               'target_ancestors':[], 'scrollTo':0, 'return_me_to':-10, \\\n               'titleCrumbBlurb':'Relix: Recent'}\n\n    return render(request, 'relix_nm/j_item_list.html', context)\n        \n@login_required()\ndef list_tagged_pages(request):\n    '''generate list of pending moves (includes qnotes)'''\n    rutils.starttime_reset(request)\n    request.session['fetchType'] = 'list_tagged_pages'\n    request.session['fetchRoot'] = -9\n\n    found_items = list(Notes.nodes.filter(tagged_page=True, created_by=request.user.username))\n\n    #get complete list of work_sets in use\n    all_worksets_list = [None] + rutils.get_all_worksets(request)\n    # \"None\" gets translated to \"All\" \n\n    for f in found_items:\n        f.work_set = f.get_workset_name()\n\n    if len(found_items) > 0:\n        priority_choices = found_items[0].PRIORITY_CHOICES\n        status_choices = found_items[0].STATUS_CHOICES\n    else:\n        priority_choices = []\n        status_choices = []\n        \n    rutils.logThis(request, \"begin Pending Moves render, %s items >>>>>>>>>>>>>>>>>>>>\" % len(found_items))\n    context = {'current_items':found_items, 'target_id':-9, \\\n               'workset_list':all_worksets_list, \\\n               'PCHX':priority_choices, \\\n               'SCHX':status_choices, \\\n               'target_ancestors':[], 'scrollTo':0, 'return_me_to':-14, \\\n               'titleCrumbBlurb':'Relix: Pending moves'}\n\n    return render(request, 'relix_nm/j_item_list.html', context)\n\n\n\n@login_required()\ndef list_people(request):\n    '''list people with their assigned_to and involves notes'''\n\n    people = People.nodes.filter(created_by=request.user.username).exclude(nickname='').order_by('nickname')\n    people_sort = [ x.nickname for x in people ]\n    pdict = {}\n    found_items = []\n    for p in people:\n        ##  two-step, so we can order_by the results. priority=0 is a problem\n        all_assigned_pmids = [x.pmid for x in p.assigned_from.all() ]\n        all_involved_pmids = [x.pmid for x in p.involved_with.all() ]\n        \n        # make sure priority = 0 appears at the end of both lists\n        pdict[p.nickname+':assigned']  = list(Notes.nodes.filter(pmid__in=all_assigned_pmids).exclude(priority='0').order_by('priority', 'title'))\n        pdict[p.nickname+':assigned'] += list(Notes.nodes.filter(pmid__in=all_assigned_pmids, priority='0').order_by('title'))\n        \n        pdict[p.nickname+':involved'] =  list(Notes.nodes.filter(pmid__in=all_involved_pmids).exclude(priority='0').order_by('priority', 'title'))\n        pdict[p.nickname+':involved'] += list(Notes.nodes.filter(pmid__in=all_involved_pmids, priority='0').order_by('title'))\n\n    priority_choices, status_choices = rutils.prio_stat_choices(request)\n        \n    all_worksets_list = [None] + rutils.get_all_worksets(request)\n    context = {'people_sort':people_sort, 'pdict':pdict,\\\n               'target_id':-9, \\\n               'PCHX':priority_choices, 'SCHX':status_choices, \\\n               'workset_list':all_worksets_list, \\\n               'target_ancestors':[], 'scrollTo':0, 'return_me_to':-18, \\\n               'titleCrumbBlurb':'Relix: People list'}\n\n    return render(request, 'relix_nm/people_list.html', context)\n\n\n@login_required()\ndef viewTree(request, target_id, scrollTo='', Pr='n', Ar='n', Ref='noRefresh'):\n    '''display the entire tree as outline. builds the itemlist view.\n       Ordering determined solely by isort. \n    '''\n    rutils.starttime_reset(request)\n    rutils.logThis(request, \"ENTER:* VIEWTREE target_id = %s <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<\" % target_id)\n\n    target_id = int(target_id)  # sometimes this comes from return_target\n    request.session['fetchType'] = 'tree'\n    request.session['fetchRoot'] = target_id # this is where fetchRoot gets reset (hijacked)\n    rutils.message(request, 'fetch')\n\n    ## exception if PMID not found\n    try:\n        targetNode = Notes.nodes.get(pmid=target_id)\n    except DoesNotExist:\n        rutils.message(request, \"PMID %s not found.\" % (target_id))\n        return HttpResponseRedirect(reverse('relix_nm:home_nm'))\n    \n\n    ancestors_of_target = list(targetNode.ancestorList(request.user.username))\n    add_recent(request, target_id)\n    \n    if Notes.nodes.get(pmid=target_id).created_by != request.user.username:\n        rutils.message(request, \"User %s not owner of node %s\" % (request.user.username, target_id))\n        return HttpResponseRedirect(reverse('relix_nm:home_nm'))\n\n    # Pr = is this a priority-only display.\n    if Pr == 'y':\n        request.session['hotitems'] = True\n    else:\n        request.session['hotitems'] = False\n\n    # make sure old_arc exists before grabbing it\n    request.session['old_arc'] = request.session.setdefault('old_arc', 'not yet set')\n\n    # Ar = include archived items\n    if Ar == 'arc':\n        if request.session['old_arc'] != True:\n            Ref = 'ref'\n        request.session['include_archived'] = True\n        request.session['old_arc'] = True\n        # we'll jump on the parameter\n        Ref = 'ref'\n    else:\n        if request.session['old_arc'] != False:\n            Ref = 'ref'\n        request.session['include_archived'] = False\n        request.session['old_arc'] = False\n\n    ########################################################################\n    \n    ## this is where sectionhead restriction is implemented\n    if targetNode.sectionhead:\n        depth = 1\n    else:\n        depth = DDX\n\n    # if someone asks directly for an archived item (ex., by a related item link), they\n    # should get the archived item directly.\n    if targetNode.archived == True:\n        request.session['include_archived'] = True\n\n    #####################################################################################    \n    # the self.children_and_* functions return nodes in hierarchy order (i.e., shortest\n    #    pathLength to target comes first.  So parents before children is guaranteed.\n    #    this is needed by isort building (bss_execute)\n    if 'include_archived' in request.session.keys():\n        if request.session['include_archived']:\n            all_nodes = targetNode.children_and_self_w_arc(request.user.username, depth) \n        else:\n            all_nodes = targetNode.children_and_self_no_arc(request.user.username, depth)\n    else:\n        all_nodes = targetNode.children_and_self_no_arc(request.user.username, depth)\n\n    rutils.logThis(request, \"      :number of all_nodes: %s\" % len(all_nodes))\n\n    # extract IDs into outputstack #################\n    request.session['outputstack'] = [x.pmid for x in all_nodes]\n    # here's the heavyweight\n    order_lookup = BSS_execute(request, all_nodes)\n\n    # uh-oh. this problematic with \"parallel\" views, which is why fetchRoot should be avoided\n    request.session['oldfetchroot'] = request.session['fetchRoot']\n            \n    #### pull the nodes ############################################################\n    # need to get the nodes into a dict by pmid, to cross-reference the order lookup\n    node_dict = { x.pmid: x for x in all_nodes } \n    isort_keys = list(order_lookup.keys())\n    isort_keys.sort()\n    listitems = []\n    for ik in isort_keys:\n        # isort_keys are sorted by isort; order_lookup lets you lookup the corresponding PMIDs\n        (this_pmid, this_pathlength) = order_lookup[ik]\n        # add pathlength & work_set to the (temporary) \"node\"\n        node_dict[this_pmid].path_length = this_pathlength\n        node_dict[this_pmid].work_set = node_dict[this_pmid].get_workset_name()\n        listitems.append(node_dict[this_pmid])\n    ########    \n    if len(targetNode.ws_belongs.all()) == 0:\n        request.session['display_workset'] = ''\n    else:\n        request.session['display_workset'] = targetNode.get_workset_name()\n\n    #get complete list of work_sets in use     # \"None\" gets translated to \"All\" \n    all_worksets_list = [None] + rutils.get_all_worksets(request)\n        \n    context = {'current_items':listitems, 'target_id':target_id, \\\n                   'workset_list':all_worksets_list, \\\n                   'PCHX':targetNode.PRIORITY_CHOICES, \\\n                   'SCHX':targetNode.STATUS_CHOICES, \\\n                   'target_ancestors':ancestors_of_target, 'scrollTo':scrollTo, \\\n                   'return_me_to':request.session['fetchRoot'], \\\n                   'titleCrumbBlurb':':'+str(targetNode.pmid)+' '+targetNode.title[:30]}\n\n    return render(request, 'relix_nm/j_item_list.html', context)\n\n############################################################################\n##@db.transaction ## <== is from neomodel, helps insure integrity, slight overhead\ndef BSS_execute(request, node_list):\n    ''' does the isort building. call with a NODE list '''\n\n    ## BSS_execute takes list of nodes;  buildSortStrings took list of PMIDs\n    #     for both, parent nodes *MUST* come before child nodes in the node_list parameter\n    \n    ## for new-model array-based lookup for ordering nodes\n    ##   to avoid saving isort in db, one node at a time\n    # note: my_root has its isort/pathLength set locally\n    order_lookup = {}\n    pmid_lookup = {}\n    current_fetchRoot = ''\n\n    \n    ## EXPERIMENT: assume node 0 is always the mother node. Avoids fetchRoot confusion due to\n    ##             multiple open window \"sessions\"\n    #if 'fetchRoot' in request.session.keys():\n    #    current_fetchRoot = int(request.session['fetchRoot'])\n    #else:\n    #    current_fetchRoot = 99999999\n    current_fetchRoot = node_list[0].pmid\n    c = 0\n    for k in node_list:\n        # fetchRoot gets special handling\n        if int(k.pmid) == current_fetchRoot:\n            rutils.logThis(request, \"   ...BSS_exec for fetchroot %s....\" %  request.session['fetchRoot'])\n            this_isort = '000'+k.title[:30]\n            order_lookup[this_isort] = (k.pmid, 0) \n            pmid_lookup[k.pmid] = this_isort\n        else:\n            # inherit SORT STRING base from parent\n            p = k.parents(request.user.username)\n            if len(p) == 1:\n                # per input parm requirement, we've already seen the parent in this loop, so just look it up\n                parentIsort = pmid_lookup[p[0].pmid]\n            elif len(p) == 0:\n                # it's a trunk item\n                parentIsort = \"0000TRUNK\"\n            else:\n                ## multiple parents; should no longer exist; display message \n                rutils.message(request, \"ERROR! parent > 1 for %s\" % k.pmid)\n                parentIsort = '00000BAD_PARENT_ISORT'\n                    \n            # add sort relevant data for item at its indent level\n            sortPriority = k.priority  # k.priority is a string (choices)\n            # 0 = no priority, so needs to sort last not first\n            if sortPriority == '0': sortPriority = '_'\n            this_isort = parentIsort+\"||\"+'{:03}'.format(k.topSort)+sortPriority+'{:!<30}'.format(k.title[:30].lower())\n            # add counter to end of this_isort index, b/c we truncate title at 30chars,\n            # and there have been collisions. The result is the dictionary entry is overwritten,\n            # and the item never displays. =:-o\n            # path_length = count the '||' separator in k.isort to avoid another cypher query\n            order_lookup[this_isort+'{:03}'.format(c)] = (k.pmid, this_isort.count('||'))\n            c += 1\n            pmid_lookup[k.pmid] = this_isort # LOCAL USE. allows lookup in this function of *parent* isorts\n        \n    return order_lookup\n\n@login_required()\ndef my_root(request):\n    rutils.safe_purge_session(request)\n    rutils.starttime_reset(request)\n    rutils.logThis(request, \"ENTER my_root <<<<<<<<\")\n\n    request.session['fetchType'] = 'my_root'\n    request.session['fetchRoot'] = -9\n    request.session['fetchQuery'] = ''\n    request.session['realHits'] = []\n    request.session['outputstack'] = []\n\n    all_nodes = Notes.nodes.has(child_of=False).filter(created_by=request.user.username).order_by('work_set', 'title')\n    FIids = [f.pmid for f in all_nodes]\n    request.session['outputstack'] = FIids\n\n    order_lookup = {}\n    # doing custom SortStrings here, to remove an \"if/then\" clause in bss_exec\n    for f in all_nodes:\n        this_isort = \"0000TRUNK:\"+'{:03}'.format(f.topSort)+'{:!<30}'.format(f.title[:30].lower())\n        #new-model\n        order_lookup[this_isort] = (f.pmid, 0)\n\n    #### pull the nodes ############################################################\n    ### VERBATIM FROM VIEWTREE\n    # need to get the nodes into a dict by pmid, to cross-reference the order lookup\n    node_dict = { x.pmid: x for x in all_nodes } \n    isort_keys = list(order_lookup.keys())\n    isort_keys.sort()\n    listitems = []\n    for ik in isort_keys:\n        # isort_keys are sorted by isort; order_lookup lets you lookup the corresponding PMIDs\n        (this_pmid, this_pathlength) = order_lookup[ik]\n        # add pathlength to the (temporary) \"node\"\n        node_dict[this_pmid].path_length = this_pathlength\n        #node_dict[this_pmid].work_set = node_dict[this_pmid].get_nonnotes_labels(request.user.username)\n        node_dict[this_pmid].work_set = node_dict[this_pmid].get_workset_name()\n        \n        listitems.append(node_dict[this_pmid])\n    # end new-model ######################################################end verbatim\n        \n    #get complete list of work_sets in use\n    #all_worksets_list = [None] + f.get_all_workset_labels(request.user.username)\n    all_worksets_list = [None] + rutils.get_all_worksets(request)\n    # \"None\" gets translated to \"All\" \n\n    if len(all_nodes) == 0:\n        rutils.message(request, 'trunk:no items to display')\n        return render(request, 'relix_nm/home_nm.html')\n    \n    context = {'current_items':listitems, 'parent_id':-9, \\\n                   'workset_list':all_worksets_list, \\\n                   'PCHX':f.PRIORITY_CHOICES, \\\n                   'SCHX':f.STATUS_CHOICES, 'return_me_to':'-15', \\\n                   'titleCrumbBlurb':'Relix: trunk'}\n    return render(request, 'relix_nm/j_item_list.html', context)\n\n\n@login_required()\ndef grid(request):\n    '''grid layout of important items'''\n    rutils.starttime_reset(request)\n    rutils.safe_purge_session(request)\n    rutils.logThis(request, \"ENTER grid <<<<<<<<<\")\n    request.session['fetchType'] = 'grid'\n\n    # get a list of grid parent nodes\n    gpn_list = list(Notes.nodes.filter(gridItem=True).filter(created_by=request.user.username).order_by('grid_order', 'title'))\n    grid_kid_nodes = {}\n    all_important_kid_pmids = set()\n\n    # for each node, populate grid_kid_nodes['someGridParent']\n    for g in gpn_list:\n        kid_pmid_list = []\n        grid_kid_nodes[g.pmid] = g.important_kids(request.user.username, DDX)\n\n    # knock out items that appear on a lower-level grid parent node  ## 5/15/2017\n    for g in gpn_list:\n        for k in grid_kid_nodes[g.pmid]:\n            # opportunistically get work_set attached....\n            k.work_set = k.get_workset_name()\n            # ... and record all grid kid PMIDs\n            all_important_kid_pmids.add(k.pmid)\n            #k.get_nonnotes_labels(request.user.username)\n            for acx in k.ancestorList(request.user.username):\n                # acx come up in closest-parent-first order\n                if acx.gridItem:\n                    # if it's a grid parent, and it's the current one, break\n                    if acx == g: break\n                    else:\n                        # the ancestor of this kid is a grid parent, and it's not the current parent g,\n                        # so pop it out of THE OTHER grid parents :-p\n                        for j in gpn_list:\n                            if acx.pmid != j.pmid:\n                                # grid_kid_nodes[g] is a list\n                                grid_kid_nodes[j.pmid] = [x for x in grid_kid_nodes[j.pmid] if x.pmid != k.pmid]\n                    break\n        \n    #eliminate empty nodes ####\n    gpnl_final = []\n    for gpn in gpn_list:\n        if len(grid_kid_nodes[gpn.pmid]) != 0:\n            gpnl_final.append(gpn)\n\n    #pull nodes or IDs for all important items, including those that are NOT a child of any grid parent node\n    # m.priority in [\"1\",\"2\",\"3\",\"4\"] AND NOT m.status in [\"6\",\"9\"]\n    other_important = Notes.nodes.filter(priority__in=[\"1\",\"2\",\"3\",\"4\"], created_by=request.user.username).exclude(status__in=[\"6\",\"9\"]).exclude(pmid__in=list(all_important_kid_pmids))\n            \n    #get complete list of work_sets in use\n    all_worksets_list = [None] + rutils.get_all_worksets(request)\n    \n    rutils.logThis(request, \"Rendering grid >>>>>>>>>\")    \n    context = {'grid_parents':gpnl_final, 'grid_kids':grid_kid_nodes, 'other_important':other_important,\\\n               'workset_list':all_worksets_list,\\\n               'return_me_to':-11, 'titleCrumbBlurb':'Relix: Grid'}\n    return render(request, 'relix_nm/grid_nm.html', context)\n\n##############################################################################\n@login_required()\ndef today(request):\n    '''priority-ordered list of items in selected worksets'''\n    rutils.starttime_reset(request)\n    request.session['fetchType'] = 'today'\n    request.session['searchFx'] = ''\n    request.session['fetchRoot'] = -9   \n    lookup = {}\n    wks_list = []\n    \n    ########## POST #####################################################\n    if request.method == 'POST':\n        rutils.logThis(request, \"ENTER today as a POST ===============\")\n        # receive POST and create a form instance and populate it with data from the request:\n        Sform = todayForm(request.POST)\n        # check whether it's valid:\n        if Sform.is_valid():\n            rutils.logThis(request, \"todayform: valid form======\")\n            wks_list = request.POST.getlist('today_select')\n\n        request.session['work_set_list'] = wks_list\n\n    else:\n        ### it's a GET ####################################################\n        # assume it's come from a universal return\n        rutils.logThis(request, \"ENTER today as a GET ===============\")\n        if 'work_set_list' in request.session.keys():\n            wks_list = request.session['work_set_list']\n\n        \n    ######## both ###################################################################\n    rutils.logThis(request, \"wks_list=%s\" % wks_list)\n    #################################################################################\n\n    # can't filter by work_set, b/c it's NO LONGER A NODE ATTRIBUTE\n    found_items = list(Notes.nodes.filter(created_by=request.user.username, \\\n                                          priority__in=['1', '2', '3']).exclude(status__in=['6', '9']).order_by('priority', 'title'))\n    in_scope_items = []\n    for f in found_items:\n        if f.get_workset_name() in wks_list:\n            f.work_set = f.get_workset_name() # temporary attribute\n            in_scope_items.append(f)\n    \n    # get ancestorlist for each foundItem\n    ancestors = {}\n    for fi in in_scope_items:\n        ancestors[str(fi.pmid)] = fi.ancestorList(request.user.username)\n        # itemlist template now expects (node, pathlength) tuple, so dummy for the latter\n\n\n    display_workset = ''\n    #get complete list of work_sets in use\n    all_worksets_list = [None] + rutils.get_all_worksets(request)\n    # \"None\" gets translated to \"All\" \n\n    if len(in_scope_items) > 0:\n        priority_choices = in_scope_items[0].PRIORITY_CHOICES\n        status_choices = in_scope_items[0].STATUS_CHOICES\n    else:\n        priority_choices = []\n        status_choices = []\n    \n\n    rutils.logThis(request, \"begin Today render, %s items >>>>>>>>>>>>>>>>>>\" % len(in_scope_items))\n    context = {'current_items':in_scope_items, 'target_id':-9,\n               'workset_list':all_worksets_list, \\\n               'today_workset_list':wks_list, \\\n               'PCHX':priority_choices, \\\n               'SCHX':status_choices, \\\n               'ancestors':ancestors, 'scrollTo':0, 'return_me_to':-12,\\\n               'titleCrumbBlurb':'Relix: Today'}\n\n    return render(request, 'relix_nm/j_item_list.html', context)\n\n        \n###################################################\n\n\n@login_required()\ndef addNote(request, parent_id, return_target='nix'):\n    '''create a new note, child_of parentID'''\n    newPMID = get_next_pmid()\n    rutils.logThis(request, \"adding child %s to parent %s <<<<<<<<<\" % (newPMID, parent_id))\n    parentNode = Notes.nodes.get(pmid=parent_id)\n    NoteNew = Notes(pmid=newPMID, title='', noteText='', created_by=request.user.username)\n    NoteNew.save()\n    x = NoteNew.child_of.connect(parentNode)\n    y = NoteNew.ws_belongs.connect(parentNode.ws_belongs.all()[0])\n    \n    NoteNew.save()\n    add_recent(request, NoteNew.pmid)\n    # add record to Elasticsearch ES\n    es_sup.EScreateDocument(request, NoteNew, '')\n    \n    rutils.logThis(request, \"New item pmID:\"+str(NoteNew.pmid))\n    return HttpResponseRedirect('/relix_nm/%s/edit/n/%s/' % (NoteNew.pmid, return_target))\n    #return universal_return(request, 'addNote', return_target, NoteNew.pmid, True)\n\n\n@login_required()\ndef addRootNode(request):\n    '''create a new top-level, parentless node'''\n    newPMID = get_next_pmid()\n    rutils.logThis(request, \"adding new top level node,  %s <<<<<<<<<\" % (newPMID))\n    NoteNew = Notes(pmid=newPMID, title=\"NewItem\", noteText='', created_by=request.user.username)\n    NoteNew.save()\n    add_recent(request, NoteNew.pmid)\n    # add record to Elasticsearch ES\n    es_sup.EScreateDocument(request, NoteNew, None)\n\n    return HttpResponseRedirect('/relix_nm/%s/edit/n/' % NoteNew.pmid)\n\n@login_required()\ndef deleteNote(request, targetID, return_target='nix'):\n    targetID = int(targetID)\n    '''delete an item, return to then view fetchRoot or parent'''\n\n    targetNode = Notes.nodes.get(pmid=targetID)\n    kid_pmids = targetNode.children_ids_w_arc()\n    if kid_pmids != []:\n        rutils.logThis(request, \"ERROR. %s has children, cannot delete. <<<<<<<<<\" % targetID)\n        rutils.message(request, \"ERROR. %s has children, cannot delete.\" % targetID)\n    else:    \n        rutils.logThis(request, \"deleting  %s and removing from ES <<<<<<<<<\" % targetID)\n        # once you delete the node, you can't do logic on the node?\n        x = remove_recent(request, targetID)\n        # BLH 4/11/2018 ES delete requires a delay, I suspect. Deleting a searched-for item can lead to\n        #  \"PMID not found error\", probably b/c the re-submitted search finds the item in ES before it\n        #  is deleted. Status is immediately \"successful\", however. Maybe just log such errors (ES_sup, 434)\n        resp = es_sup.ESdeleteDocument(request, targetID)\n        rutils.logThis(request, \"ES deletion of %s: %s\" % (targetID, resp))\n        targetNode.delete_me_and_relationships(request.user.username)\n\n        # look for pmid in RECENTS file, and blast it if it's there\n        # gogogogogo\n        \n        rutils.message(request, \"deleted %s\" % targetID)\n        rutils.logThis(request, \"DELETED ====>  %s <================ \" % targetID)\n\n    #######################################################\n    # return to view [deleteNote]\n    #######################################################\n    return universal_return(request, 'deleteNote', return_target, targetID, True)\n\n    \n\n@login_required()\ndef deleteRel(request, targetID, relatedID, relType, return_target='nix'):\n    '''delete a specific relationship'''\n    targetNode = Notes.nodes.get(pmid=targetID)\n    relatedNode = Notes.nodes.get(pmid=relatedID)\n    if relType == 'child_of':\n        targetNode.child_of.disconnect(relatedNode)\n    elif relType == 'rel_content':\n        targetNode.rel_content.disconnect(relatedNode)\n    elif relType == 'relates':\n        targetNode.relates.disconnect(relatedNode)\n    elif relType == 'prev_version':\n        targetNode.prev_version.disconnect(relatedNode)\n    else:\n        rutils.logThis(request, \"Unsupported relationship type: %s\" % relType)\n    targetNode.save()\n    rutils.logThis(request, \"relationship deleted: %s = %s => %s\" % (targetID, relType, relatedID))\n\n    #######################################################\n    # return to view [deleteRel]\n    #######################################################\n    return universal_return(request, 'deleteRel', return_target, targetID, True)\n\n\n########################################################\ndef tree(request,target_id='nix', depth=6):\n    '''deliver a high-level tree summary'''\n    if target_id == 'nix':\n        return HttpResponseRedirect('/relix_nm/home/')\n    target_node = Notes.nodes.get(pmid=target_id)\n    #all_nodes  = target_node.children_and_self(request.user.username, depth)\n    all_nodes = target_node.children_and_self_no_arc(request.user.username, depth)\n    order_lookup = BSS_execute(request, all_nodes)\n\n    # uh-oh. this problematic with \"parallel\" views, which is why fetchRoot should be avoided\n    # request.session['oldfetchroot'] = request.session['fetchRoot']\n\n    \n    #### pull the nodes ## VERBATIM FROM VIEWTREE ###################################\n    # new-model\n    # need to get the nodes into a dict by pmid, to cross-reference the order lookup\n    node_dict = { x.pmid: x for x in all_nodes } \n    isort_keys = list(order_lookup.keys())\n    isort_keys.sort()\n    listitems = []\n    for ik in isort_keys:\n        # isort_keys are sorted by isort; order_lookup lets you lookup the corresponding PMIDs\n        (this_pmid, this_pathlength) = order_lookup[ik]\n        # temporarily add work_set , do not save\n        node_dict[this_pmid].path_length = '&nbsp;'*this_pathlength*3\n        node_dict[this_pmid].work_set = node_dict[this_pmid].get_workset_name()\n        listitems.append(node_dict[this_pmid])\n    \n    if len(target_node.ws_belongs.all()) == 0:\n        request.session['display_workset'] = ''\n    else:\n        request.session['display_workset'] = target_node.get_workset_name()\n\n    #get complete list of work_sets in use\n    all_worksets_list = [None] + rutils.get_all_worksets(request)\n    # \"None\" gets translated to \"All\" \n        \n    rutils.logThis(request, \":* TREE template >>>>> n listitems = %s\" % len(listitems))\n    \n    context = {'current_items':listitems, 'target_id':target_id, \\\n               'workset_list':all_worksets_list, \\\n               'PCHX':target_node.PRIORITY_CHOICES, \\\n               'SCHX':target_node.STATUS_CHOICES, \\\n               'scrollTo':target_id, \\\n               'return_me_to':target_id, \\\n               'titleCrumbBlurb':':'+str(target_node.pmid)+' '+target_node.title[:30] }\n\n                \n    #return render(request, 'relix_nm/j_item_list.html', context)\n    return render(request, 'relix_nm/skinny_list.html', context)\n\n##############################################################################################\n#### S E A R C H #############################################################################\n@login_required()\ndef quickSearch(request):\n    rutils.starttime_reset(request)\n    rutils.logThis(request, \"ENTER QuickSearch <<<<<<<<<<<<<<<<<<\")\n    if request.method == 'POST':\n        # create a form instance and populate it with data from the request:\n        Sform = quickSearchForm(request.POST)\n        # check whether it's valid:\n        if Sform.is_valid():\n            searchFx = Sform.cleaned_data['searchFx']\n            \n\n            is_digit_string = False\n            # if there's a comma in search term, it may be a multi-PMID search\n            if ',' in searchFx:\n                if False not in [x.strip().isdigit() for x in searchFx.split(',')]:\n                    is_digit_string = True\n            #  test if search term is one or string of PMIDs \n            if is_digit_string or searchFx.isdigit():\n                pmid_list = searchFx.split(',')\n                foundItems = Notes.nodes.filter(pmid__in=pmid_list).filter(created_by=request.user.username)\n                ancestors = {}\n                for fi in foundItems:\n                    ancestors[str(fi.pmid)] = fi.ancestorList(request.user.username)\n                #get complete list of work_sets in use\n                all_worksets_list = [None] + rutils.get_all_worksets(request)\n                # \"None\" gets translated to \"All\" \n\n                request.session['fetchType'] = 'search'\n                request.session['searchFx'] = 'qpmid'\n\n                ## from QUICKSEARCH ################################\n                context = {'current_items':foundItems, 'parent_id':-9, 'ancestors':ancestors,\\\n                               'workset_list':all_worksets_list,\\\n                               'PCHX':PRIORITY_VX, 'SCHX':STATUS_VX, \\\n                               'titleCrumbBlurb':'Relix: Quicksearch', \\\n                               'return_me_to': -17}\n                return render(request, 'relix_nm/item_list_nm.html', context)\n            \n            safe_url = urlparse.quote(searchFx.lower())\n            rutils.logThis(request, \"   Searching for: %s\" % safe_url)\n            return HttpResponseRedirect('/relix_nm/ESquick/%s/' % safe_url)\n\n        else:\n            ##form is invalid\n            rutils.message(request, 'invalid qsearch')\n            return render(request, 'relix_nm/home_nm.html')\n    # if a GET (or any other method) we'll create a blank form\n    else:\n        sform = quickSearchForm()\n\n        return render(request, 'relix_nm/home_nm.html', {'form': sform})\n\n##############################################################################\n@login_required()\ndef advancedSearch(request):\n    # this is where the form is built; form is processed; but search logic is in es_sup.py\n    rutils.starttime_reset(request)\n    rutils.logThis(request, \"ENTER Advanced Search <<<<<<\")\n    ######## POST ################################################\n    if request.method == 'POST':\n        # create a form instance and populate it with data from the request:\n        Sform = advancedSearchForm(request.POST)\n        # check whether it's valid:\n        searchField = 'nix'\n        if Sform.is_valid():\n            rutils.logThis(request, \"Advanced Search: valid form====== %s\" % str(Sform.cleaned_data))\n            submitted_fields = []\n            for x in  ['startDate', 'pmid', 'title', 'priority', 'status', 'noteText', 'people']:\n                if Sform.cleaned_data[x] != '' and Sform.cleaned_data[x] != None:\n                    searchField = x\n                    searchFx = Sform.cleaned_data[searchField]\n                    if searchField in ['title', 'noteText', 'people']:\n                        searchFx = searchFx.lower()\n                    elif searchField == 'pmid':\n                        searchFx = str(searchFx)\n                    if searchField == 'startDate':\n                        # so submitted_fields here = ( dtCreated|dtModified,(startd, endd))\n                        submitted_fields.append((Sform.cleaned_data['whichDate'], \\\n                                                     (Sform.cleaned_data['startDate'], \\\n                                                          Sform.cleaned_data['endDate'])))\n                    else:\n                        submitted_fields.append((searchField, searchFx))\n\n            scopex = 'no_scope_specified'\n            if Sform.cleaned_data['scope'] != None:\n                scopex = [Sform.cleaned_data['scope']]\n            elif Sform.cleaned_data['scope_manual'] != None:\n                scopex = [Sform.cleaned_data['scope_manual']]\n            elif Sform.cleaned_data['work_set'] != 'None':\n                scopex = [Sform.cleaned_data['work_set']]\n\n            ## assign search terms to ES query parts\n            must_match = []\n            must_not_match = []\n            should_match = []\n            filter_match = ['{\"created_by\"'+':'+'\"'+request.user.username+'\"}']\n            range_query = []\n            phrase_match = []\n\n            ##it must be date, not datetime.date, and is why we have two date imports.\n            # https://stackoverflow.com/questions/16151402/python-how-can-i-check-whether-an-object-is-of-type-datetime-date\n            for searchField, searchFx in submitted_fields:\n                rutils.logThis(request,\"searchfield:%s, searchFx:%s\" % (searchField, searchFx))\n                #isinstance(searchFx, date):\n                if searchField == 'dtCreated' or searchField == 'dtModified':\n                    # this assumes local naive time, not UTC\n                    startstamp = int(time.mktime(searchFx[0].timetuple())*1000)\n                    if searchFx[1] == None:\n                        endstamp = startstamp\n                    else:\n                        endstamp = int(time.mktime(searchFx[1].timetuple())*1000)\n                    ## round down start date, round \"up\" end date\n                    range_query.append('{\"'+searchField+'\"'+':'+\\\n                                            '{\"gte\" : \"'+str(startstamp)+ '||/d\", \"lte\" : \"'+ str(endstamp)+'||+1d/d\" } } ')\n                #elif searchField == 'startDate':\n                #    is_this_branch = 'unused?'\n                    \n                elif searchField not in ['priority', 'status', 'pmid'] and \\\n                  searchFx is not None and ('\"' in searchFx or '-' in searchFx):\n                    #quote seems to come in with popup select for priority and status\n                    ### might need to be %s to avoid the quote problem on es_sup\n                    phrase_match.append('{\"'+searchField+'\"'+':'+'\"'+searchFx[1:-1]+'\"}'.lower())\n\n                elif searchFx is not None and ' ' in searchFx:\n                    for sfx in searchFx.split(' '):\n                        should_match.append('{\"'+searchField+'\"'+':'+'\"'+sfx+'\"}'.lower())\n                        \n                elif searchField == \"priority\" or searchField == \"status\":\n                    if searchFx != \"-99\":\n                        filter_match.append('{\"'+searchField+'\"'+':'+'\"'+searchFx+'\"}'.lower())\n                elif searchField == 'people':\n                    # people search should be against both people fields\n                    # can use \"must\" or \"filter\" for both, b/c no one has both roles. \n                    should_match.append('{\"'+'assigned_to'+'\"'+':'+'\"'+searchFx+'\"}'.lower())\n                    should_match.append('{\"'+'involves'+'\"'+':'+'\"'+searchFx+'\"}'.lower())\n                else:\n                    should_match.append('{\"'+searchField+'\"'+':'+'\"'+searchFx+'\"}'.lower())\n\n            if Sform.cleaned_data['include_archived'] == False:\n                #must_match.append('{\"archived\":\"false\"}')\n                filter_match.append('{\"archived\":\"false\"}')\n\n            request.session['searchFx'] = searchFx\n            sort_terms = []\n            search_packet = str([should_match, must_match, must_not_match, filter_match, phrase_match, range_query, scopex, sort_terms, []])\n            rutils.logThis(request, \"advSearch search_packet=%s\" % search_packet)\n            return HttpResponseRedirect('/relix_nm/runESadvSearch/%s/' % search_packet)\n        else:\n            ##form is invalid#############################################\n            rutils.logThis(request, \"Advanced Search invalid form======\")\n            rutils.message(request, 'invalid advanced search')\n            return render(request, 'relix_nm/adv_search_nm.html')\n    else:\n        ###################### GET ##################################################\n        # if a GET (or any other method) we'll create a blank form ##\n        rutils.logThis(request, \"Advanced Search - GET form ======\")\n        sform = advancedSearchForm()\n\n        #get complete list of work_sets in use\n        all_worksets_list = [None] + rutils.get_all_worksets(request)\n        # \"None\" gets translated to \"All\"\n        ## from ADVANCED SEARCH ######################################\n        return render(request, 'relix_nm/adv_search_nm.html',\\\n                      {'form':sform, 'PCHX':PRIORITY_VX, 'SCHX':STATUS_VX, \\\n                       'workset_list':all_worksets_list, \\\n                       'titleCrumbBlurb':'Relix: Advanced search'})\n\n\n#############################################################\n@login_required()\ndef hotSearch(request, targetID=-9):\n    rutils.starttime_reset(request)\n    rutils.logThis(request, \"hotSearch====== %s\" % targetID)\n    filter_terms = ['{\"terms\": {\"priority\" : [\"1\", \"2\", \"3\", \"4\"] }}', '{\"terms\": {\"status\" : [\"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"8\"]}}', '{\"archived\": \"false\"}']\n    orig_filter_terms = ['{\"terms\": {\"priority\" : [\"1\", \"2\", \"3\", \"4\"] }}', '{\"archived\": \"false\"}']\n    if int(targetID) > 0:\n        scope_list = [int(targetID)]\n        # should_terms, must_terms,must_not_terms, filter_terms, phrase_match, range_query, scope_list, multi_ = eval(searchpacket)\n        sort_terms = ['{\"priority\": {\"order\" : \"asc\"}}'] \n        search_packet = [[], [], [], filter_terms, [], [], scope_list, sort_terms, []]\n        request.session['search_packet'] = search_packet\n        request.session['searchFx'] = 'hotSearch'\n    else:\n        # otherwise it's probably a return from a note_edit off a hotlist.\n        # Hopefully search packet session var is still intact. Yes, lame.\n        search_packet = request.session['search_packet']\n        \n\n    return HttpResponseRedirect('/relix_nm/runESadvSearch/%s/' % search_packet)\n\n###########################################################################\n    \n@login_required()\ndef idFetch(request):\n    # aka idSearch\n    rutils.starttime_reset(request)\n    rutils.logThis(request, 'ENTER idSearch  =======')\n    if request.method == 'POST':\n        # create a form instance and populate it with data from the request:\n        Sform = idSearchForm(request.POST)\n            \n        # check whether it's valid:\n        if Sform.is_valid():\n            ## exception if PMID not found\n            try:\n                targetNode = Notes.nodes.get(pmid=Sform.cleaned_data['fetchID'])\n            except DoesNotExist:\n                rutils.message(request, \"PMID %s not found.\" % (Sform.cleaned_data['fetchID']))\n                return HttpResponseRedirect(reverse('relix_nm:home_nm'))\n            #####\n            if targetNode.created_by != request.user.username:\n                rutils.message(request, \"User %s not owner of node %s\" % (request.user.username, targetNode.pmid))\n                return HttpResponseRedirect(reverse('relix_nm:home_nm'))\n\n            return HttpResponseRedirect(reverse('relix_nm:viewTree', kwargs={'target_id':targetNode.pmid}))\n        else:\n            ##form is invalid\n            rutils.message(request, 'invalid ID search')\n            return HttpResponseRedirect(reverse('relix_nm:home_nm'))\n    # if a GET (or any other method) we'll create a blank form\n    else:\n        sform = idSearchForm()\n        rutils.message(request, \"not a post to idSearch\")\n        return HttpResponseRedirect(reverse('relix_nm:home_nm'))\n\n\n#######################################################################################################    \n##### U T I L I T Y  ##################################################################################\n\ndef get_next_pmid():\n    ''' generate the next PMID for a new Note'''\n    PMF = open(PMID_COUNTER_FILE, 'r')\n    last_pmid = PMF.readline()\n    PMF.close()\n    next_pmid = int(last_pmid)+1\n    PMF = open(PMID_COUNTER_FILE, 'w')\n    PMF.write(str(next_pmid))\n    PMF.close()\n    return next_pmid\n\n\n\ndef log_user_in(request):\n    # but do see https://docs.djangoproject.com/en/1.10/topics/auth/default/#using-the-views\n\n    # make sure session store is clear\n    # if this is a POST request we need to process the form data\n    if request.method == 'POST':\n        # create a form instance and populate it with data from the request:\n        rutils.logThis(request, 'LOGIN attempt...')\n\n        Lform = LoginForm(request.POST)\n        # check whether it's valid:\n        if Lform.is_valid():\n            # process the data in form.cleaned_data as required\n            user = authenticate(username=Lform.cleaned_data['username'], \\\n                                password=Lform.cleaned_data['password'])\n            if user is not None:                \n                login(request, user)\n                rutils.logThis(request, '    ... user %s logged in.' % request.user.username)\n                jlist = rutils.rebuild_jumplinks(request)\n                rutils.starttime_reset(request)\n                return HttpResponseRedirect(reverse('relix_nm:home_nm'))\n            else:\n                rutils.message(request, \"bad login\")\n                rutils.logThis(request, \"LOGIN - bad credentials\")                \n        else:\n            rutils.message(request, \"form invalid\")\n            rutils.logThis(request, \"LOGIN form invalid\")\n    else:\n        # if a GET (or any other method) we'll create a blank form\n        rutils.logThis(request, 'Generating LOGIN form...')\n        pass\n        #all non-logged in results return to the home page w/form\n\n    kform = LoginForm()\n    return render(request, 'relix_nm/home_nm.html', {'form': kform})\n\n\ndef log_user_out(request):\n    # https://docs.djangoproject.com/en/1.10/topics/auth/default/#how-to-log-a-user-out\n    rutils.message(request, 'Thanks for visiting, %s ' % request.user.username)\n    logout(request)\n    rutils.starttime_reset(request)\n    return render(request, 'relix_nm/home_nm.html')\n\n\n\n########################################################################\n\ndef universal_return(request, called_by, return_target, target_id=-9, rebuild=False):\n    '''also uses request vars: searchFx, fetchRoot, fetchType'''\n\n    ## RETURN_CODES = {'-10':'recent', '-11':'grid', '-12':'today', '-13':'search', '-14':'list pending', '-15':'my root', '-16':'qnote', '-17': 'quickSearch', '-18': 'people' }\n\n    if target_id == -9:\n        target_id = return_target\n    \n    if rebuild:\n        return_url = '/relix_nm/%s/%s/view/ref/' % (return_target, target_id)\n    else:\n        return_url = '/relix_nm/%s/%s/view/' % (return_target, target_id)\n    ##########################################################################\n    if return_target != 'nix' and int(return_target) > 0:\n        rutils.logThis(request, '        >> return_target %s' % return_target)\n        return HttpResponseRedirect(return_url)\n    elif return_target == '-15':\n        # it's a root node\n        return HttpResponseRedirect(reverse('relix_nm:my_root'))\n    elif return_target == '-11': #<= not for items edit\n        return HttpResponseRedirect(reverse('relix_nm:grid'))\n    elif return_target == '-13':\n        #doesn't it not matter if it's a hot search? return path from hotsearch isn't great for any user path\n        #if request.session['searchFx'] == 'hotSearch':\n        #    return HttpResponseRedirect(reverse('relix_nm:hot_search', kwargs={'targetID':return_target}))\n        #\n        # PROBLEM: This reiterates all searches as relevance ranked.\n        #          Ignores time-sorted search results. (but Hotsearch works OK)\n        return HttpResponseRedirect(reverse('relix_nm:reiterateSearch', kwargs={'sort_order':'relev'}))\n    elif return_target == '-10':\n        return HttpResponseRedirect(reverse('relix_nm:recent'))\n    elif return_target == '-14':\n        return HttpResponseRedirect(reverse('relix_nm:list_tagged_pages'))\n    elif return_target == '-12':\n        return HttpResponseRedirect(reverse('relix_nm:today'))\n    elif return_target == '-16':\n        return HttpResponseRedirect(reverse('relix_nm:completed'))\n    elif return_target == '-17':\n        return HttpResponseRedirect(reverse('relix_nm:quickSearch'))\n    elif return_target == '-18':\n        return HttpResponseRedirect(reverse('relix_nm:list_people'))\n    \n    elif request.session['fetchRoot'] != -9:\n        if request.session['fetchType'] == 'tree':\n            #invoke w/forced refresh\n            return HttpResponseRedirect('/relix_nm/%s/%s/view/ref/' % (return_target, return_target))\n        else:\n            return HttpResponseRedirect('/relix_nm/%s/%s/view/' % (return_target, return_target))\n    else:\n        rutils.logThis(request, 'WARNING! ITEMSEDIT return_target not valid:%s' % return_target)\n        rutils.message(request, 'WARNING! ITEMSEDIT return_target not valid:%s' % return_target)                \n        return HttpResponseRedirect(reverse('relix_nm:home_nm'))\n","sub_path":"relix2/relix_nm/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":86941,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"448200283","text":"class Solution(object):\n    def maxProduct(self, nums):\n        \"\"\"\n        :type nums: List[int]\n        :rtype: int\n        \"\"\"\n        maxByFar = nums[0]\n        minByFar = nums[0]\n        res = nums[0]\n        \n        for i in range(1, len(nums)):\n            if nums[i] > 0:\n                maxByFar = max(nums[i], maxByFar*nums[i])\n                minByFar = min(nums[i], minByFar*nums[i])\n            else:\n                tmp = maxByFar\n                maxByFar = max(minByFar*nums[i], nums[i])\n                minByFar = min(tmp*nums[i], nums[i])\n            res = max(maxByFar, res)\n        \n        return res","sub_path":"152-Maximum Product Subarray.py","file_name":"152-Maximum Product Subarray.py","file_ext":"py","file_size_in_byte":621,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"178949465","text":"import numpy as np\nfrom torch.utils.data import Dataset\nimport torch\n# from rlkit.envs.half_cheetah import HalfCheetahEnv\nfrom gym.envs.mujoco import HalfCheetahEnv as HalfCheetahEnv_\nimport gym\nimport d4rl\n\nclass ScatterDataset(Dataset):\n    def __init__(self, path):\n        self.datas = np.load(path)\n        self.X, self.Y = self.datas[:, 0], self.datas[:, 1]\n\n    def __getitem__(self, index):\n        x = np.array(self.X[index % len(self.X)])\n        y = np.array(self.Y[index % len(self.Y)])\n        w = torch.rand([64])\n\n        return {'input' : x, 'label' : y, 'weight' : w}\n\n    def __len__(self):\n        return len(self.X)\n\nhalfcheetah_task_name = ['halfcheetah-random-v0',\n                         'halfcheetah-medium-v0',\n                         'halfcheetah-expert-v0',\n                         'halfcheetah-medium-replay-v0',\n                         'halfcheetah-medium-expert-v0']\n\nant_task_name = ['ant-medium-expert-v0',\n                 'ant-random-expert-v0',\n                 'ant-medium-replay-v0',\n                 'ant-medium-v0',\n                 'ant-random-v0',\n                 'ant-expert-v0',\n                 ]\n\nhopper_task_name = ['hopper-random-v0',\n'hopper-medium-v0',\n'hopper-expert-v0',\n'hopper-medium-replay-v0',\n'hopper-medium-expert-v0'\n                 ]\n\nclass GymDataset(Dataset):\n    def __init__(self):\n        i = 2\n        env = gym.make(hopper_task_name[i])\n\n        dataset = env.get_dataset()\n        all_obs = np.array(dataset['observations'])\n\n        # # Normalization\n        # all_obs = 2*(all_obs - np.min(all_obs)) / (np.max(all_obs) - np.min(all_obs)) - 1\n\n        all_act = np.array(dataset['actions'])\n        N = all_obs.shape[0]\n\n        _obs = all_obs[:N - 1]\n        _actions = all_act[:N - 1]\n        _next_obs = all_obs[1:]\n        _next_actions = all_act[1:]\n        _rew = np.squeeze(dataset['rewards'][:N - 1])\n        _rew = np.expand_dims(np.squeeze(_rew), axis=-1)\n        _done = np.squeeze(dataset['terminals'][:N - 1])\n        _done = (np.expand_dims(np.squeeze(_done), axis=-1)).astype(np.int32)\n\n        # reward = np.array(dataset['rewards'])\n\n        # # Normalization\n        # _rew = 2 * (_rew - np.min(_rew)) / (np.max(_rew) - np.min(_rew)) - 1\n\n        self.obs_act = np.concatenate([_obs, _actions], axis=1)\n        self.next_obs_act = np.concatenate([_next_obs, _next_actions], axis=1)\n        self.rewards = _rew\n        self.terminals = _done\n\n    def __getitem__(self, index):\n        obs_act = self.obs_act[index % len(self.obs_act)]\n        next_obs_act = self.next_obs_act[index % len(self.obs_act)]\n        rewards = self.rewards[index % len(self.obs_act)]\n        terminals = self.terminals[index % len(self.obs_act)]\n\n        return {'obs_act' : obs_act, 'next_obs_act' : next_obs_act, 'rewards' : rewards, 'terminals' : terminals}\n\n    def __len__(self):\n        return len(self.obs_act)\n\n","sub_path":"bear/uncertainty_modeling/rl_uncertainty/legacy/mcdropout/hoffer_datasets.py","file_name":"hoffer_datasets.py","file_ext":"py","file_size_in_byte":2887,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"70252464","text":"\"\"\"\nWeek 1 activity: driving car\n---------\nAUTHOR: John Kotz\n\"\"\"\n\n# Import from the graphics library\nfrom cs1lib import *\nfrom math import *\n\n# Constants\nROAD_WIDTH = 50\nCAR_WIDTH = 15\nCAR_HEIGHT = 25\nWIDTH = 600\nHEIGHT = 600\nBLOCK_HEIGHT = HEIGHT / 4\nLINE_HEIGHT = 10\nLINE_WIDTH = 3\nANIMATION_LENGTH = 60\nPARKING_LOT_HEIGHT = 40\nHEADING_MAP = {'n': {'l': 'w', 'r': 'e'}, 'e': {'l': 'n', 'r': 's'}, 's': {'l': 'e', 'r': 'w'}, 'w': {'l': 's', 'r': 'n'}}\n\n# Variables\naction_queue = []\ncar_on = False\ncar_pos = (WIDTH / 2 + ROAD_WIDTH / 8 + CAR_WIDTH / 2, HEIGHT + CAR_HEIGHT / 2)\nlast_car_pos = car_pos\nlast_turn = None\ncar_heading = 'n'\ncar_angle = 0\nlast_car_angle = car_angle\n\ncurrent_action = 0\nframe = -30\n\ndef draw():\n    \"\"\"\n\n    :return:\n    \"\"\"\n    set_framerate(ANIMATION_LENGTH / 2)\n    set_clear_color(0, 1, 0)\n    clear()\n\n    set_fill_color(0.2, 0.2, 0.2)\n    draw_rectangle(0, HEIGHT + PARKING_LOT_HEIGHT, WIDTH, -PARKING_LOT_HEIGHT)\n\n    draw_road(WIDTH / 2 - ROAD_WIDTH / 2, HEIGHT)\n    draw_intersection(WIDTH / 2 - ROAD_WIDTH / 2, HEIGHT - BLOCK_HEIGHT)\n\n    draw_road(WIDTH / 2 - ROAD_WIDTH / 2, HEIGHT - BLOCK_HEIGHT, rotated=True)\n    draw_road(WIDTH / 2 + ROAD_WIDTH / 2 + BLOCK_HEIGHT, HEIGHT - BLOCK_HEIGHT, rotated=True)\n    draw_intersection(WIDTH / 2 + ROAD_WIDTH / 2 + BLOCK_HEIGHT, HEIGHT - BLOCK_HEIGHT)\n    draw_intersection(WIDTH / 2 - BLOCK_HEIGHT - ROAD_WIDTH * 3 / 2, HEIGHT - BLOCK_HEIGHT)\n    draw_road(WIDTH / 2 - BLOCK_HEIGHT - ROAD_WIDTH * 3 / 2, HEIGHT - BLOCK_HEIGHT - ROAD_WIDTH)\n    draw_intersection(WIDTH / 2 - BLOCK_HEIGHT - ROAD_WIDTH * 3 / 2, HEIGHT - BLOCK_HEIGHT * 2 - ROAD_WIDTH)\n    set_fill_color(0, 0, 1)\n    draw_circle(WIDTH / 2 - BLOCK_HEIGHT - ROAD_WIDTH * 3 / 2 + ROAD_WIDTH / 2, HEIGHT - BLOCK_HEIGHT * 2 - ROAD_WIDTH - ROAD_WIDTH / 2, ROAD_WIDTH / 3)\n\n    draw_road(WIDTH / 2 + BLOCK_HEIGHT + ROAD_WIDTH / 2, HEIGHT - BLOCK_HEIGHT - ROAD_WIDTH)\n    draw_intersection(WIDTH / 2 + BLOCK_HEIGHT + ROAD_WIDTH / 2, HEIGHT - BLOCK_HEIGHT * 2 - ROAD_WIDTH)\n    draw_road(WIDTH / 2 + BLOCK_HEIGHT + ROAD_WIDTH / 2, HEIGHT - BLOCK_HEIGHT * 2 - ROAD_WIDTH, rotated=True)\n    draw_intersection(WIDTH / 2 - ROAD_WIDTH / 2, HEIGHT - BLOCK_HEIGHT * 2 - ROAD_WIDTH)\n    draw_road(WIDTH / 2 - ROAD_WIDTH / 2, HEIGHT - BLOCK_HEIGHT * 2 - ROAD_WIDTH * 2)\n    draw_intersection(WIDTH / 2 - ROAD_WIDTH / 2, HEIGHT - BLOCK_HEIGHT * 3 - ROAD_WIDTH * 2)\n\n    draw_road(WIDTH / 2 - ROAD_WIDTH / 2, HEIGHT - BLOCK_HEIGHT * 3 - ROAD_WIDTH * 2, rotated=True)\n    draw_intersection(WIDTH / 2 - BLOCK_HEIGHT - ROAD_WIDTH * 3 / 2, HEIGHT - BLOCK_HEIGHT * 3 - ROAD_WIDTH * 2)\n    draw_road(WIDTH / 2 - BLOCK_HEIGHT - ROAD_WIDTH * 3 / 2, HEIGHT - BLOCK_HEIGHT * 2 - ROAD_WIDTH * 2)\n\n    global current_action\n    if current_action < len(action_queue):\n\n        global frame\n        global car_pos\n        global action\n        global last_car_pos\n        global last_car_angle\n        global car_heading\n        global car_angle\n        global last_turn\n        if frame < 10 or frame > ANIMATION_LENGTH - 10:\n            # Do nothing\n            pass\n        else:\n            # Move the car in the correct direction\n            action = action_queue[current_action]\n\n            if action == 'f':\n                if car_heading == 'n' or car_heading == 's':\n                    distance = abs(last_car_pos[1] - (BLOCK_HEIGHT + ROAD_WIDTH) * (-1 if car_heading == 's' else 1) - car_pos[1])\n\n                    car_pos = (car_pos[0], car_pos[1] - distance / (ANIMATION_LENGTH - 9 - frame) * (-1 if car_heading == 's' else 1))\n                if car_heading == 'e' or car_heading == 'w':\n                    distance = abs(last_car_pos[0] + (BLOCK_HEIGHT + ROAD_WIDTH) * (-1 if car_heading == 'w' else 1) - car_pos[0])\n\n                    car_pos = (car_pos[0] + distance / (ANIMATION_LENGTH - 9 - frame) * (-1 if car_heading == 'w' else 1), car_pos[1])\n            else:\n                distance = abs((last_car_angle - 90 * (-1 if action == 'r' else 1)) - car_angle)\n\n                car_angle = car_angle - distance / (ANIMATION_LENGTH - 9 - frame) * (-1 if action == 'r' else 1)\n\n        if frame >= ANIMATION_LENGTH:\n            last_car_pos = car_pos\n            last_car_angle = car_angle\n            if action_queue[current_action] != 'f':\n                car_heading = HEADING_MAP[car_heading][action_queue[current_action]]\n                last_turn = action_queue[current_action]\n            frame = 0\n            current_action += 1\n        else:\n            frame += 1\n\n    draw_car(car_pos, car_angle)\n\n\ndef draw_road(x, y, rotated=False):\n    set_fill_color(0.2, 0.2, 0.2)\n    set_stroke_color(0, 0, 0, 0)\n\n    if not rotated:\n        draw_rectangle(x, y, ROAD_WIDTH, -BLOCK_HEIGHT)\n        for line in range(0, int(BLOCK_HEIGHT / LINE_HEIGHT / 2) + 1):\n            set_fill_color(1, 1, 0)\n            draw_rectangle(x + ROAD_WIDTH / 2 - LINE_WIDTH / 2, y - line * LINE_HEIGHT * 2, LINE_WIDTH, -LINE_HEIGHT)\n    else:\n        draw_rectangle(x, y, -BLOCK_HEIGHT, -ROAD_WIDTH)\n        for line in range(0, int(BLOCK_HEIGHT / LINE_HEIGHT / 2) + 1):\n            set_fill_color(1, 1, 0)\n            draw_rectangle(x - line * LINE_HEIGHT * 2, y - ROAD_WIDTH / 2 + LINE_WIDTH / 2, -LINE_HEIGHT, -LINE_WIDTH)\n\n\ndef draw_intersection(x, y):\n    set_fill_color(0.2, 0.2, 0.2)\n    set_stroke_color(0, 0, 0, 0)\n\n    draw_rectangle(x, y, ROAD_WIDTH, -ROAD_WIDTH)\n\n\ndef draw_car(pos, angle):\n    x, y = pos\n\n    points = []\n    for i, j in [(1, 1), (1, -1), (-1, -1), (-1, 1)]:\n            tempX = j * CAR_WIDTH / 2\n            tempY = i * CAR_HEIGHT / 2\n\n            rotatedX = tempX * cos(radians(angle)) - tempY * sin(radians(angle))\n            rotatedY = tempX * sin(radians(angle)) + tempY * cos(radians(angle))\n\n            x1 = rotatedX + x\n            y1 = rotatedY + y\n            points.append((x1, y1))\n\n    set_fill_color(1, 0, 1)\n    draw_polygon(points)\n\n\ndef start():\n    global car_on\n    car_on = True\n\n\ndef move_forward():\n    if car_on:\n        action_queue.append('f')\n\n\ndef turn_left():\n    if car_on:\n        action_queue.append('l')\n\n\ndef turn_right():\n    if car_on:\n        action_queue.append('r')\n\n\ndef park():\n    global car_on\n    car_on = False\n\n\ndef done():\n    start_graphics(draw, width=WIDTH, height=HEIGHT + PARKING_LOT_HEIGHT)\n\n\nif __name__ == \"__main__\":\n    done()\n","sub_path":"Week1/car_activity.py","file_name":"car_activity.py","file_ext":"py","file_size_in_byte":6335,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"638475135","text":"import os\nimport csv\n\nimport threading\n\nimport paho.mqtt.client as paho\n\nfrom appStruct import appStruct\n\nfrom doe import doe\nfrom rsm import rsm\n\n\n####################################################################################################\n# doeThread\n####################################################################################################\n\nclass doeThread( threading.Thread ):\n    def __init__( self, threadID, name, cond, struct ):\n        threading.Thread.__init__( self )\n        \n        self.threadID = threadID\n        self.name = name\n        \n        self.appStruct = struct\n        \n        self.cond = cond   \n        \n        self.doe = doe( struct )\n        \n    def run( self ):\n        print( \"\\nstarting \" + self.name + \" thread\" )\n        \n        with self.cond:\n            self.cond.wait()\n            \n            configurations = self.doe.buildDoe()\n            \n            self.appStruct.setDoeConfs( configurations )\n            self.appStruct.setDoeConfsNumber( len( self.appStruct.getDoeConfs() ) )\n                    \n            self.appStruct.setStatus( \"dse\" )\n        \n        self.appStruct.showStruct()\n        \n        print( \"\\nexiting \" + self.name + \" thread\" )\n\n\n####################################################################################################\n# rsmThread\n####################################################################################################\n    \nclass rsmThread( threading.Thread ):\n    def __init__( self, threadID, name, cond, struct ):\n        threading.Thread.__init__( self )\n        \n        self.threadID = threadID\n        self.name = name\n        \n        self.cond = cond\n        \n        self.appStruct = struct\n        \n        self.rsm = rsm( struct )\n        \n    def run( self ):\n        print( \"\\nstarting \" + self.name + \" thread\" )\n        \n        with self.cond:\n            self.cond.wait()\n            \n            self.appStruct.setStatus( \"buildingTheModel\" )\n            \n            model = self.rsm.buildRsm()\n            self.appStruct.setModel( model )\n            \n            self.appStruct.setStatus( \"autotuning\" )\n            \n            self.appStruct.showStruct()\n        \n        print( \"\\nexiting \" + self.name + \" thread\" )\n\n\n####################################################################################################\n# server_handler\n####################################################################################################\n\nclass server_handler():\n\n    def __init__(self):\n        '''\n        constructor\n        '''\n        self.address = \"127.0.0.1\"\n        self.clientID = \"server_handler\"\n        self.qos = 0\n        \n        self.root = \"/home/cris/Documents/\"\n        self.tesiCris = \"tesiCris/\"\n        \n        self.newHostpidTopic = None\n        self.clientsReqTopic = None\n        self.communicationTopic = None\n        self.receiveInfoTopic = None\n        self.OPsTopic = None\n        self.doneConfTopic = None\n        self.disconnectionTopic = None\n        \n        self.struct = None\n        self.doe = None\n        \n        self.client = None\n        \n        self.thread_doe = None\n        self.thread_rsm = None\n        \n        self.lock = threading.Lock()\n        \n        self.doeCond = None\n        self.rsmCond = None\n    \n    \n    ####################################################################################################\n    # MQTT\n    ####################################################################################################\n    \n    def on_connect(self, host):\n        print(\"\\nclient ID: \" + self.client._client_id + \" connected at \" + host + \":8883\")\n    \n    def on_subscribe(self, topic):\n            print (\"\\nsubscription\")\n            print (\"topic: \" + topic)\n     \n    def on_message(self, client, userdata, msg):\n        print (\"\\nreceived message\")\n        print (\"topic: \" + msg.topic)\n        print (\"payload: \" + msg.payload)\n        \n        if( msg.topic == self.newHostpidTopic ):\n            # new client (hostpid) arrived\n            self.lock.acquire()\n            \n            self.manageNewHostpid(msg.payload)\n            \n            self.lock.release()\n        \n        elif( msg.topic == self.clientsReqTopic ):\n            # a client (hostpid) made a request\n            self.lock.acquire()         \n            \n            if( self.struct.getStatus() == \"dse\" ):\n                # se ci sono configurazioni da eseguire    \n                self.sendConfiguration(msg.payload)\n            \n            elif( self.struct.getStatus() == \"autotuning\" ):\n                # esiste il modello completo\n                self.sendModel(msg.payload)\n            \n            self.lock.release()\n\n        elif( msg.topic == self.receiveInfoTopic ):\n            # received app info\n            self.lock.acquire()\n            \n            if( self.struct.getStatus() != \"receivingInfo\" ):\n                self.struct.setStatus(\"receivingInfo\")  \n            \n            with self.doeCond:\n                splitted = msg.payload.split(\" \")\n                \n                if( splitted[0] == \"metric\" ):\n                    self.struct.addMetric( splitted[1] )\n                    \n                elif( splitted[0] == \"param\" ):\n                    self.struct.addParam( splitted[1] )\n                    \n                    values = []\n                    \n                    if( splitted[2] == \"range\" ):\n                        value = float( splitted[3] )\n                        MAXvalue = float( splitted[4] )\n                        step = float( splitted[5] )\n                     \n                        while(value <= MAXvalue):\n                            values.append(value)\n                            value += step\n                        \n                        self.struct.addParamValues(values)\n                    \n                    elif( splitted[2] == \"enum\" ):\n                        for i in range(3, len(splitted) ):\n                            values.append( float(splitted[i]) )\n                        \n                        self.struct.addParamValues(values)\n                         \n                elif( splitted[0] == \"doe\" ):\n                    self.struct.setDoeKind( splitted[1] )\n                \n                elif( splitted[0] == \"numOPs\" ):\n                    self.struct.setNumOPs( int( splitted[1] ) )\n                    \n                elif( splitted[0] == \"rsm\" ):\n                    self.struct.setRsmKind( splitted[1] )\n                \n                elif( splitted[0] == \"sparkGenLinearRegrTransforms\" ):\n                    self.struct.addMetricToSparkGenLinearRegrTransforms( splitted[1] )\n                    \n                    self.struct.addMetricInfoToSparkGenLinearRegrTransforms( splitted[1], splitted[2] )\n                    self.struct.addMetricInfoToSparkGenLinearRegrTransforms( splitted[1], splitted[3] )\n                    self.struct.addMetricInfoToSparkGenLinearRegrTransforms( splitted[1], splitted[4] )\n                    self.struct.addMetricInfoToSparkGenLinearRegrTransforms( splitted[1], splitted[5] )\n                \n                elif( splitted[0] == \"done\" ):\n                    self.struct.setStatus(\"buildingDoe\")\n                    \n                    # notify the thread that computes the doeThread configurations\n                    self.doeCond.notifyAll()\n            \n            self.lock.release()\n        \n        elif( msg.topic == self.OPsTopic ):\n            # msg.payload es.: \"1 100000:5.3454867 0.2343545\"\n            \n            self.lock.acquire()\n            \n            splitted = msg.payload.split(\":\")\n            # splitted[0]: configuration\n            # splitted[1]: metrics\n            \n            configuration = []\n            \n            splittedConf = splitted[0].split(\" \")\n            for value in splittedConf:\n                configuration.append( float(value) )\n            \n            newOP = False\n            \n            for conf in self.struct.getDoeConfs():\n                if( conf.getConf() == configuration ):\n                    # if the configuration is not one of the done configurations\n                    newOP = True\n                    \n                    self.struct.addOP(msg.payload)\n                    \n                    conf.decrementNumOPs()\n                    \n                    if( conf.getNumOPs() == 0 ):\n                        # if I have the right number of ops for this configuration\n                        self.struct.addConfToDoneConfs(conf)\n                                                \n                        self.struct.removeConfToDoeConfs( conf )\n                        \n                        self.struct.showStruct()\n                        \n                        self.checkStartRsm()\n            \n            if( newOP == False ):\n                self.struct.addOtherOP(msg.payload)\n            \n            self.lock.release()\n        \n        elif( msg.topic == self.disconnectionTopic ):\n            # a client (hostpid) has disconnected\n            self.lock.acquire()\n            \n            self.struct.removeHostpid(msg.payload)\n            \n#             self.struct.restoreConfs(msg.payload, True)\n            \n#             print( \"\\n\" + msg.payload + \" disconnected\" )\n#             print( \"configurations restored\" )\n            \n            self.lock.release()\n                \n    def connect(self, host):\n        self.client.connect(host, port = 8883)\n        self.client.on_connect = self.on_connect(host)\n        \n    def subscribe(self, topic):\n        self.client.subscribe(topic, qos = self.qos)\n        self.client.on_subscribe = self.on_subscribe(topic)\n        self.client.on_message = self.on_message\n    \n    def on_publish(self, topic, message):\n        print (\"\\npublication\")\n        print (\"topic: \" + topic)\n        print (\"message: \" + message)\n        \n    def publish(self, topic, message):\n        self.client.on_publish = self.on_publish(topic, message)\n        self.client.publish(topic, message, qos = self.qos)\n    \n    ####################################################################################################\n    ####################################################################################################\n    \n        \n    def start(self, appName, hostpid):\n        self.struct = appStruct(appName)\n        \n        self.manageNewHostpid(hostpid)\n        \n        self.client = paho.Client(client_id = self.clientID + \"_\" + self.struct.getName())\n        self.connect(self.address)\n        \n        self.firstSubscriptions()\n        \n        if( os.path.exists(self.root + self.tesiCris + self.struct.getName() + \"/model.txt\") == True ):\n            self.loadModel()\n            \n            self.struct.setStatus(\"autotuning\")\n        \n        else:\n            self.secondSubscriptions()\n            \n            self.threadsCreation()\n            \n            self.requestAppInfo()\n        \n        self.client.loop_forever()\n    \n    def manageNewHostpid(self, hostpid):\n        self.struct.addHostpid(hostpid)\n        \n        if( self.struct.getStatus() == \"dse\"):\n            self.sendConfiguration(hostpid)\n    \n    def firstSubscriptions(self):\n        self.newHostpidTopic = self.tesiCris + self.struct.getName() + \"/newHostpid\"\n        self.subscribe(self.newHostpidTopic)\n        \n        self.clientsReqTopic = self.tesiCris + self.struct.getName() + \"/req\"\n        self.subscribe(self.clientsReqTopic)\n        \n        self.disconnectionTopic = self.tesiCris + self.struct.getName() + \"/disconnection\"\n        self.subscribe(self.disconnectionTopic)\n        \n    def secondSubscriptions(self):\n        self.receiveInfoTopic = self.tesiCris + self.struct.getName() + \"/info\"\n        self.subscribe(self.receiveInfoTopic)\n        \n        self.OPsTopic = self.tesiCris + self.struct.getName() + \"/OPs\"\n        self.subscribe(self.OPsTopic)\n        \n        self.doneConfTopic = self.tesiCris + self.struct.getName() + \"/doneConf\"\n        self.subscribe(self.doneConfTopic)\n        \n    def threadsCreation(self):\n        self.doeCond = threading.Condition()\n        self.rsmCond = threading.Condition()\n         \n        self.thread_doe = doeThread(1, self.struct.getName() + \"_doe\", self.doeCond, self.struct)\n        self.thread_rsm = rsmThread(2, self.struct.getName() + \"_rsm\", self.rsmCond, self.struct)\n         \n        # Start new Threads\n        self.thread_doe.start()\n        self.thread_rsm.start()\n        \n    def requestAppInfo(self):\n        if( self.communicationTopic == None ):\n            self.communicationTopic = self.tesiCris + self.struct.getName()\n        self.publish(self.communicationTopic, \"info\")\n    \n    def sendConfiguration(self, hostpid):\n        if len( self.struct.getDoeConfs() ) > 0:\n            #self.struct.restoreConfs(hostpid, False)\n            \n            configuration = self.struct.getDoeConfs().pop(0)\n            self.struct.addDoeConf(configuration)\n            \n#             # aggiungo la configurazione che sto per inviare all'hostpid tra quelle in esecuzione\n#             self.struct.addConfToRunningConfs(hostpid, configuration)\n            \n            confStr = \"\"\n            for value in configuration.getConf():\n                confStr += str(value) + \" \"\n         \n            # elimino ultimo carattere (spazio vuoto)\n            confStr = confStr[:-1]\n            # configurationStr finale es.: \"4.0 500000.0\"\n\n            # invio la configurazione\n            # topic es.: tesiCris/swaptions/crisXPS15_1897/configuration\n            # payload es.: \"1 400000\"\n            self.publish( self.tesiCris + self.struct.getName() + \"/\" + hostpid + \"/conf\", confStr )\n        \n    def checkStartRsm(self):\n        with self.rsmCond:\n            if( len(self.struct.getDoneConfs()) == self.struct.getDoeConfsNumber() ):\n                # notify the thread that computes the model through Spark\n                self.rsmCond.notifyAll()\n    \n    def sendModel(self, hostpid):\n        for op in self.struct.getModel():\n            op = op.replace(\":\", \" \")\n            \n            self.publish( self.tesiCris + self.struct.getName() + \"/\" + hostpid + \"/model\", op )\n        \n        self.publish( self.tesiCris + self.struct.getName() + \"/\" + hostpid + \"/model\", \"modelDone\")\n    \n    def loadModel(self):\n        model = []\n        \n        with open(self.root + self.tesiCris + self.struct.getName() + \"/model.txt\", 'r') as csvfile:\n            tracereader = csv.reader(csvfile)\n            for row in tracereader:\n                for op in row:\n                    model.append( str(op) )\n        \n        self.struct.setModel(model)\n        ","sub_path":"server_handler.py","file_name":"server_handler.py","file_ext":"py","file_size_in_byte":14693,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"165095918","text":"# -*- coding: utf-8 -*-\nimport scrapy\n\n# starts here: https://forums.autodesk.com/t5/vault-customization/bd-p/301?solved-posts-page=1\n# ends here: https://forums.autodesk.com/t5/vault-customization/bd-p/301?solved-posts-page=28\n\n#xpath to solution titles: //*[contains(concat( \" \", @class, \" \" ), concat( \" \", \"MessageSubject\", \" \" ))]\n#xpath to solution post: //*[contains(concat( \" \", @class, \" \" ), concat( \" \", \"lia-accepted-solution\", \" \" ))]\n#xpath to solution code: //pre\n\nclass VaultcustombotSpider(scrapy.Spider):\n    name = 'vaultcustombot'\n    # allowed_domains = ['https://forums.autodesk.com/']\n    start_urls = ['https://forums.autodesk.com/t5/vault-customization/bd-p/301?solved-posts-page=1/']\n\n    def start_requests(self):\n        url = 'https://forums.autodesk.com/t5/vault-customization/bd-p/301?solved-posts-page=1'\n        # item = \"0\"\n        # item = BaseUrl()\n        # item.totalPages = 0\n        # request = scrapy.Request(url=url, callback=self.getTotals, meta={'item': item})\n        # request.meta['item'] = item\n        # yield request\n        # newItem = BaseUrl()\n        # newItem = request.meta['item']\n        # print(\"pagesTotal :\" + str(newItem.totalPages)) #str(item))\n        for i in range(1, 28):\n            pageUrl = 'https://forums.autodesk.com/t5/vault-customization/bd-p/301?solved-posts-page=' + str(i)\n            yield scrapy.Request(pageUrl, callback= self.parsePage) #, meta={'source_page_url': pageUrl}) #, method='GET')\n        #debug single page works okay:\n        # pageUrl = 'https://forums.autodesk.com/t5/vault-customization/bd-p/301?solved-posts-page=1'\n        # yield scrapy.Request(pageUrl, callback= self.parsePage) #, method='GET')\n\n    def getTotals(self, response):\n        print(\"total pages: \" + response.css('.lia-paging-page-last a::text').extract_first())\n        item = response.meta['item']\n        item.totalPages = int(response.css('.lia-paging-page-last a::text').extract_first())\n        # print(\"pagesTotal :\" + item['pagesTotal'])\n        yield item\n\n    def parsePage(self, response):\n        for solvedSolutionUrl in response.xpath(\"//div[@class='MessageSubjectIcons ']/a/@href\").extract():\n            yield scrapy.Request(solvedSolutionUrl, dont_filter=True, callback = self.parseSolutionUrl, meta={'source_page_url': response.url})\n\n    def parseSolutionUrl(self, response):\n        #need to figure out how to get the div that this is part of: itemprop=\"acceptedAnswer\"\n        # solutionDiv = response.xpath('//div[@itemprop = \"acceptedAnswer\"]').extract_first()\n        # solutionDiv = response.css('div[itemprop=acceptedAnswer]').extract_first()\n        \n        # print(solutionDiv)\n        yield {\n            'url' : response.url,\n            'thread title' : response.css(\".PageTitle *::text\").extract_first(),\n            'thread created at' : response.css(\".autodesk-reply-time span::attr(title)\").extract_first(),\n            'thread created by' : response.css(\".UserName span::text\").extract_first(),\n            'solution created at' : response.css(\"div[itemprop='acceptedAnswer'] div[class='lia-quilt-column-alley lia-quilt-column-alley-single'] .DateTime span::attr(title)\").re(r\"(?:\\u200e)(.*)\"), #response.css(\"div[itemprop='acceptedAnswer'] div[class='lia-quilt-column-alley lia-quilt-column-alley-single'] .DateTime span::attr(title)\").extract(),\n            'solution Title' : response.css(\"div[itemprop='acceptedAnswer'] div[class='lia-message-subject'] *::text\").re_first(r\"(?:\\n\\s+)(.*)(?:\\n\\s+)\"),\n            'solution' : response.css(\"[itemprop='acceptedAnswer'] pre *::text\").extract(),\n            'author' : response.css(\"[itemprop='acceptedAnswer'] .UserName span *::text\").extract_first(),\n            'solutions source page url' : response.meta.get('source_page_url')\n            #'tags' : solutionDiv.xpath(''), #\n        }\n\nclass BaseUrl:\n\n    def __iter__(self):\n        self.a = 1\n        return self\n\n    def __next__(self):\n        x = self.a\n        self.a += 1\n        return x\n    totalPages = 0\n    ","sub_path":"VaultCustomizationForums/VaultCustomizationForums/spiders/vaultcustombot.py","file_name":"vaultcustombot.py","file_ext":"py","file_size_in_byte":4026,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"648701010","text":"import keras\nfrom keras.models import Sequential\nfrom keras.layers import Dense, Dropout, Flatten, Reshape, Lambda, LSTM\nfrom keras.layers import Conv2D, MaxPooling2D\nfrom keras.callbacks import ModelCheckpoint\nimport numpy as np\nfrom Bio import SeqIO\nfrom numpy import array\nfrom numpy import argmax\nfrom sklearn.model_selection import train_test_split\nfrom imblearn.under_sampling import RandomUnderSampler\nfrom sklearn.utils import shuffle\nfrom keras.layers.embeddings import Embedding\nfrom keras import backend as K\nfrom keras.backend import expand_dims\nimport matplotlib.pyplot as plt\nfrom keras.regularizers import l1, l2\nfrom sklearn.metrics import roc_curve, auc, classification_report\nfrom sklearn.metrics import accuracy_score\nfrom numpy import tensordot\nfrom numpy.linalg import norm\nfrom scipy.optimize import differential_evolution\nfrom keras.models import load_model\nfrom itertools import product\nfrom sklearn.metrics import matthews_corrcoef\nfrom sklearn.metrics import confusion_matrix\n\n\nr_test_x = []\nr_test_y = []\nr_test_x2 = []\nr_test_y2 = []\nposit_1 = 1;\nnegat_0 = 0;\nwin_size = 51 # actual window size\nwin_size1 = 39\nwin_size2 = 21\nnum_classes = 2\nn_models = 2\ncut_off1 = int((51 - win_size1)/2)\ncut_off2 = int((51 - win_size2)/2)\n\n# define universe of possible input values\nalphabet = 'ARNDCQEGHILKMFPSTWYV-'\n# define a mapping of chars to integers\nchar_to_int = dict((c, i) for i, c in enumerate(alphabet))\nint_to_char = dict((i, c) for i, c in enumerate(alphabet))\n\n# Test Dataset for CNN -------------------------------------------------------------\n#for positive sequence\ndef innertest3():\n    #Input\n    data = seq_record.seq\n    data = data[cut_off1:-cut_off1]\n    #rint(data) \n    # integer encode input data\n    for char in data:\n        if char not in alphabet:\n            return\n    integer_encoded = [char_to_int[char] for char in data]\n    r_test_x2.append(integer_encoded)\n    r_test_y2.append(posit_1)\nfor seq_record in SeqIO.parse(\"test_s33_Pos_51.fasta\", \"fasta\"):\n    innertest3()\n#for negative sequence\ndef innertest4():\n    #Input\n    data = seq_record.seq\n    data = data[cut_off1:-cut_off1]\n    #print(data) \n    # integer encode input data\n    for char in data:\n        if char not in alphabet:\n            return\n    integer_encoded = [char_to_int[char] for char in data]\n    r_test_x2.append(integer_encoded)\n    r_test_y2.append(negat_0)\nfor seq_record in SeqIO.parse(\"test_s33_Neg_51.fasta\", \"fasta\"):\n    innertest4()\n# Changing to array (matrix)    \nr_test_x2 = array(r_test_x2)\nr_test_y2 = array(r_test_y2)\n\n\n# Balancing test dataset\n# Testing Data Balancing by undersampling####################################\nrus = RandomUnderSampler(random_state=7)\nx_res4, y_res4 = rus.fit_resample(r_test_x2, r_test_y2)\n#Shuffling\nr_test_x2, r_test_y2 = shuffle(x_res4, y_res4, random_state=7)\nr_test_x2 = np.array(r_test_x2)\nr_test_y2 = np.array(r_test_y2)\n\n\n#-Test Dataset for LSTM----------------------------------------\n#for positive sequence\ndef innertest1():\n    #Input\n    data = seq_record.seq\n    data = data[cut_off2:-cut_off2]\n    #rint(data) \n    # integer encode input data\n    for char in data:\n        if char not in alphabet:\n            return\n    integer_encoded = [char_to_int[char] for char in data]\n    r_test_x.append(integer_encoded)\n    r_test_y.append(posit_1)\nfor seq_record in SeqIO.parse(\"test_s33_Pos_51.fasta\", \"fasta\"):\n    innertest1()\n#for negative sequence\ndef innertest2():\n    #Input\n    data = seq_record.seq\n    data = data[cut_off2:-cut_off2]\n    #print(data) \n    # integer encode input data\n    for char in data:\n        if char not in alphabet:\n            return\n    integer_encoded = [char_to_int[char] for char in data]\n    r_test_x.append(integer_encoded)\n    r_test_y.append(negat_0)\nfor seq_record in SeqIO.parse(\"test_s33_Neg_51.fasta\", \"fasta\"):\n    innertest2()\n# Changing to array (matrix)    \nr_test_x = array(r_test_x)\nr_test_y = array(r_test_y)\n\n\n# Balancing test dataset\n# Testing Data Balancing by undersampling####################################\nrus = RandomUnderSampler(random_state=7)\nx_res3, y_res3 = rus.fit_resample(r_test_x, r_test_y)\n#Shuffling\nr_test_x, r_test_y = shuffle(x_res3, y_res3, random_state=7)\nr_test_x = np.array(r_test_x)\nr_test_y = np.array(r_test_y)\n############################################################################\n\n\n\n###############################################################################################################################################\n# Ensemble Part\n\n\ndef ensemble_final_pred(members, weights, testX, testX2):\n\t# make predictions\n    yhats = []\n    yhats.append(array(members[0].predict(testX)))\n    yhats.append(array(members[1].predict(testX2)))\n    yhats = array(yhats)\n\t# weighted sum across ensemble members\n    summed = tensordot(yhats, weights, axes=((0),(0)))\n\t# argmax across classes\n\t#result = argmax(summed, axis=1)\n    return summed\n\n# Load models \n\nmodels = [load_model('model_best_lstm.h5'),load_model('model_best_cnn.h5')]\nfor i in models:\n    print(i.summary())\n\n# grid search weights\n#weights = grid_search(models, x_test, y_test)\n#weights = grid_search(models, r_test_x, r_test_y, r_test_x2, r_test_y2)\nweights = [0.16666667,0.83333333]\n# Independent Test\n\n\nY_pred = ensemble_final_pred(models, weights, r_test_x, r_test_x2)\nt_pred2 = Y_pred[:,1]\nY_pred = (Y_pred > 0.5)\ny_pred1 = [np.argmax(y, axis=None, out=None) for y in Y_pred]\ny_pred1 = np.array(y_pred1)\n\nprint(\"Matthews Correlation : \",matthews_corrcoef(r_test_y, y_pred1))\nprint(\"Confusion Matrix : \\n\",confusion_matrix(r_test_y, y_pred1))\n\n# For sensitivity and specificity\nsp_1, sn_1 = confusion_matrix(r_test_y, y_pred1)\nsp_2 = sp_1[0]/(sp_1[0]+sp_1[1])\nsn_2 = sn_1[1]/(sn_1[0]+sn_1[1])\n# ROC\n\nfpr, tpr, _ = roc_curve(r_test_y, t_pred2)\nroc_auc = auc(fpr, tpr)\nprint(\"AUC : \", roc_auc)\nprint(classification_report(r_test_y, y_pred1))\n\nplt.figure()\nlw = 2\nplt.plot(fpr, tpr, color='darkorange',\n         lw=lw, label='ROC curve (area = %0.2f)' % roc_auc)\nplt.plot([0, 1], [0, 1], color='navy', lw=lw, linestyle='--')\nplt.xlim([0.0, 1.0])\nplt.ylim([0.0, 1.05])\nplt.xlabel('False Positive Rate')\nplt.ylabel('True Positive Rate')\nplt.title('ROC curve for ST')\nplt.legend(loc=\"lower right\")\nplt.show()\n\nprint(\"Specificity = \",sp_2, \" Sensitivity = \",sn_2)\n\n","sub_path":"model_gridsearch_load.py","file_name":"model_gridsearch_load.py","file_ext":"py","file_size_in_byte":6295,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"598858558","text":"import tkinter as tk\n\nclass ImageButton(tk.Label):\n\tdef __init__(self, root, image_paths, *args, **kwargs):\n\t\tself.normal_image = tk.PhotoImage(file = image_paths[0])\n\t\tself.hovered_image = tk.PhotoImage(file = image_paths[1])\n\t\tself.pressed_image = tk.PhotoImage(file = image_paths[2])\n\t\tself.command = kwargs.pop('command', None)\n\n\t\tsuper().__init__(master = root, image=self.normal_image, font=kwargs.pop('font', ('Arial', 18, 'bold')), bg=kwargs.pop('bg', '#333333'), compound=kwargs.pop('compound', 'center'), *args, **kwargs)\n\t\t\n\t\t\n\t\tself.bind('<Button-1>', lambda _: self.config(image = self.pressed_image))\n\t\tself.bind('<ButtonRelease-1>', self.on_release)\n\t\tself.bind('<Enter>', lambda _: self.config(image = self.hovered_image))\n\t\tself.bind('<Leave>', lambda _: self.config(image = self.normal_image))\n\n\tdef on_release(self, arg):\n\t\tself.config(image = self.normal_image)\n\t\tif self.command:\n\t\t\tself.command(self)\n\n\nclass Button(ImageButton):\n\tdef __init__(self, root = None, text = '', command = None, *args, **kwargs):\n\t\tsuper().__init__(root = root, image_paths = ('images/normal_button.png', 'images/hovered_button.png', 'images/pressed_button.png'), text = text, command = command)\n\n","sub_path":"class_buttons.py","file_name":"class_buttons.py","file_ext":"py","file_size_in_byte":1197,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"369710798","text":"from trex_stl_lib.api import *\n\nclass STLS1(object):\n\n    def create_stream (self, packet_len):\n        # create a base packet and pad it to size\n\n        base_pkt = Ether()/IP(src=\"16.0.0.1\",dst=\"48.0.0.1\")/UDP(dport=12,sport=1025)\n\n        pad = max(0, packet_len - len(base_pkt)) * 'x'\n                             \n        vm = STLScVmRaw( [   STLVmTupleGen ( ip_min=\"16.0.0.1\", ip_max=\"16.0.0.2\", \n                                                   port_min=1025, port_max=65535,\n                                                    name=\"tuple\"), # define tuple gen \n\n                             STLVmWrFlowVar (fv_name=\"tuple.ip\", pkt_offset= \"IP.src\" ), # write ip to packet IP.src\n                             STLVmFixIpv4(offset = \"IP\"),                                # fix checksum\n                             STLVmWrFlowVar (fv_name=\"tuple.port\", pkt_offset= \"UDP.sport\" )  #write udp.port\n                                  ]\n                              );\n\n        pkt = STLPktBuilder(pkt = base_pkt/pad,\n                            vm = vm)\n\n        return STLStream(packet = pkt,\n                         mode = STLTXCont())\n\n\n\n    def get_streams (self, direction = 0, packet_len = 64, **kwargs):\n        # create 1 stream \n        return [ self.create_stream(packet_len - 4) ]\n\n\n# dynamic load - used for trex console or simulator\ndef register():\n    return STLS1()\n\n\n\n","sub_path":"trex_client/stl/profiles/udp_1pkt_tuple_gen.py","file_name":"udp_1pkt_tuple_gen.py","file_ext":"py","file_size_in_byte":1390,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"637802104","text":"\"\"\"\nFile in which the control of the belt and defines the functionalities for the vibrotactile oddball blocks is defined.\nFunctions:\n- connect_to_USB\n- disconnect_belt\n- vibrotactile_oddball_ankle\n- vibrotactile_oddball_waist\n- vibrotactile_oddball_wrist\n- start_trial\nIt also handles the connection between the belt and the computer via USB.\n\"\"\"\n\nfrom pybelt import classicbelt\nimport parallel\nimport random, time\nimport numpy as np\n\nclass VibrationController():\n    \"\"\"\n    The VibrationController controls the connection to the feelSpace belt.\n    It saves the number of the vibrotactile units and links it to its purpose.\n\n    Parameters\n    ----------\n    ankle_vibomotor : int\n        The number of the vibrotactile unit attached to the ankle.\n    ankle_trigger : list\n        List with trigger codes for the ankle condition.\n    wrist_vibromotor : int\n        The number of the vibrotactile unit attached to the wrist.\n    wrist_trigger : list\n        List with trigger codes for the wrist condition.\n    waist_vibromotor_left : int\n        Number of the vibrotactile unit attached to the left side of the waist.\n    waist_vibromotor_right : int\n        Number of the vibrotactile unit attached to the right side of the waist.\n    waist_trigger : list\n        List with trigger codes for the waist condition.\n    trial_break : float\n        The length of the break between stimuli presentations in seconds.\n    trial_length : float\n        Defines the length of the trial (each stimulus presentation) in seconds.\n\n    Attributes\n    ----------\n    belt_controller : BeltController\n        This is where we store the controller of the belt, that is defined in\n        the pyBelt package.\n    ankle_vibomotor : int\n        Stores the vibrotactile unit ID for the ankle.\n    ankle_trigger : list\n        Stores trigger codes for the ankle condition.\n    wrist_vibromotor : int\n        Stores the vibrotactile unit ID for the ankle.\n    wrist_trigger : list\n        Stores trigger codes for the wrist condition.\n    waist_vibromotor_left : int\n        Stores the vibrotactile unit ID for the left waist.\n    waist_vibromotor_right : int\n        Stores the vibrotactile unit ID for the right waist.\n    waist_trigger : list\n        Stores trigger codes for the waist condition.\n    trial_break : float\n        Stores the length of the break between stimuli presentations in seconds.\n    trial_length : float\n        Stores the length of the trial (stimulus presentation) in seconds.\n    \"\"\"\n\n    def __init__(self, ankle_vibromotor, ankle_trigger, wrist_vibromotor,\n                wrist_trigger, waist_vibromotor_left, waist_vibromotor_right,\n                waist_trigger, trial_break, trial_length):\n        \"\"\"Constructor that initializes the belt controller.\"\"\"\n        # Instantiate a belt controller\n        self.belt_controller = classicbelt.BeltController(delegate=self)\n        self.ankle_vibromotor = ankle_vibromotor\n        self.ankle_trigger = ankle_trigger\n        self.wrist_vibromotor = wrist_vibromotor\n        self.wrist_trigger = wrist_trigger\n        self.waist_vibromotor_left =  waist_vibromotor_left\n        self.waist_vibromotor_right = waist_vibromotor_right\n        self.waist_trigger = waist_trigger\n        self.trial_break = trial_break\n        self.trial_length = trial_length\n\n    def connect_to_USB(self):\n        \"\"\"Connect the belt to the serial port (USB)\"\"\"\n        # connect belt to usb serial port\n        print(\"Connect belt via USB.\")\n        print(\"Mode of the belt: \", self.belt_controller.getBeltMode())\n        self.belt_controller.connectBeltSerial()\n\n    def disconnect_belt(self):\n        \"\"\"Disconnect belt from serial port (USB)\"\"\"\n        self.belt_controller.disconnectBelt()\n\n    def vibrotactile_oddball_ankle(self, trials, oddball_ratio):\n        \"\"\"\n        Start oddball vibration pattern at the ankle.\n\n        Parameters:\n        ----------\n        trials : int\n            A decimal integer indicating the nr. of trials\n        oddball_ratio : float\n            Indicating the proportion of odd stimuli\n        \"\"\"\n        # Output used as control option for the experimenter.\n        print('-----------------------------------')\n        print('           VIBROTACTILE ANKLE          ')\n        print('-----------------------------------\\n')\n\n        total_trial_standard = np.zeros(int(trials*(1-oddball_ratio)))\n        total_trial_oddball = np.ones(int(trials*(oddball_ratio)))\n        total_trial = np.concatenate([total_trial_oddball, total_trial_standard])\n\n        oddball_count = 0\n        standard_count = 0\n\n        for i in range(trials):\n\n            np.random.shuffle(total_trial)\n            random_number = total_trial[0]\n\n            if random_number==1:\n                mode = \"oddball\"\n                oddball_count += 1\n            else:\n                mode = \"standard\"\n                standard_count += 1\n\n            print('MODE (standard or oddball): ', mode)\n\n            self.start_trial(mode, [self.ankle_vibromotor], self.ankle_trigger)\n\n            total_trial = np.delete(total_trial, 0)\n\n            # break between trials\n            time.sleep(self.trial_break)\n\n        print('oddballs', oddball_count)\n        print('standards', standard_count)\n\n\n    def vibrotactile_oddball_wrist(self, trials, oddball_ratio):\n        \"\"\"\n        Start oddball vibration pattern at the wrist.\n\n        Parameters\n        ----------\n        trials : int\n            A decimal integer indicating the nr. of trials\n        oddball_ratio : float\n            Indicating the proportion of odd stimuli\n        \"\"\"\n\n        # Output used as control option for the experimenter.\n        print('-----------------------------------')\n        print('          VIBROTACTILE WRIST       ')\n        print('-----------------------------------\\n')\n\n        # An array for each condition (zeros and ones) is initialized and joined\n        # to one trial array, to randomly pick one trial item of one specific\n        # condition in the trial generating for-loop.\n        total_trial_standard = np.zeros(int(trials*(1-oddball_ratio)))\n        total_trial_oddball = np.ones(int(trials*(oddball_ratio)))\n        total_trial = np.concatenate([total_trial_oddball, total_trial_standard])\n\n        # For-loop generating the trials\n        for i in range(trials):\n\n            # The trial item is randomly picked by shuffeling the array and\n            # taking the first element.\n            np.random.shuffle(total_trial)\n            random_number = total_trial[0]\n\n            if random_number==1:\n                mode = \"oddball\"\n            else:\n                mode = \"standard\"\n\n            # Execution of the odd or standard trial.\n            self.start_trial(mode, [self.wrist_vibromotor], self.wrist_trigger)\n\n            # The currently executed trial is deleted from the trial storing array\n            # to keep the correct number of oddball and standard trials.\n            total_trial = np.delete(total_trial, 0)\n\n            # break between trials\n            time.sleep(self.trial_break)\n\n\n    def vibrotactile_oddball_waist(self, trials, oddball_ratio):\n        \"\"\"\n        Start oddball vibration pattern at the waist.\n\n        Parameters\n        ----------\n        trials : int\n            A decimal integer indicating the nr. of trials\n        oddball_ratio : float\n            Indicating the proportion of odd stimuli\n        \"\"\"\n\n        # Output used as control option for the experimenter.\n        print('-----------------------------------')\n        print('           VIBROTACTILE WAIST          ')\n        print('-----------------------------------\\n')\n\n\n        total_trial_standard = np.zeros(int(trials*(1-oddball_ratio)))\n        total_trial_oddball = np.ones(int(trials*(oddball_ratio)))\n        total_trial = np.concatenate([total_trial_oddball, total_trial_standard])\n\n        for i in range(trials):\n            np.random.shuffle(total_trial)\n            random_number = total_trial[0]\n\n            if random_number==1:\n                mode = \"oddball\"\n                oddball_count += 1\n            else:\n                mode = \"standard\"\n                standard_count += 1\n\n            self.start_trial(mode, [self.waist_vibromotor_left, self.waist_vibromotor_right], self.waist_trigger)\n\n            # The currently executed trial is deleted from the trial storing array\n            # to keep the correct number of oddball and standard trials.\n            total_trial = np.delete(total_trial, 0)\n\n            # break between trials\n            time.sleep(self.trial_break)\n\n    def start_trial(self, mode, vibromotors, trigger_codes):\n        \"\"\"\n        Either an oddball or a standard vibration starts.\n\n        Parameters\n        ----------\n        mode : str\n            \"Standard\" or \"oddball\" -> virbation of low or high intensity\n        vibromotors : int\n            Integer refering to the correct vibrating unit\n        trigger_codes : list\n            Trigger for the stimuli in the respective vibrotactile condition.\n            The order is [oddball, standard, break]\n        \"\"\"\n\n        if mode == \"standard\":\n            self.belt_controller.vibrateAtPositions(vibromotors, trigger_codes[1], 1, 30)\n            time.sleep(self.trial_length)\n            self.belt_controller.stopVibration()\n\n        elif mode == \"oddball\":\n\n            # Vibrate a first time (trigger is set in classicbelt function)\n            self.belt_controller.vibrateAtPositions(vibromotors, trigger_codes[0], 1, 100)\n            time.sleep(self.trial_length)\n            self.belt_controller.stopVibration()\n\n        # Trigger break\n        classicbelt.p.setData(trigger_codes[2])\n        core.wait(0.01)\n        classicbelt.p.setData(0)\n","sub_path":"Experiment_code/vibrotactile_functions.py","file_name":"vibrotactile_functions.py","file_ext":"py","file_size_in_byte":9726,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"569734787","text":"# Copyright (c) 2021 Arm Limited. All rights reserved.\n\n# Virtual Streaming Interface instance 1 Python script: Audio Output\n\n##@addtogroup arm_vsi1_py_audio_out\n#  @{\n#\n##@package arm_vsi1_audio_out\n#Documentation for VSI Audio Output module.\n#\n#More details.\n\nimport logging\nimport wave\n\n\n## Set verbosity level\n#verbosity = logging.DEBUG\nverbosity = logging.ERROR\n\n# [debugging] Verbosity settings\nlevel = { 10: \"DEBUG\",  20: \"INFO\",  30: \"WARNING\",  40: \"ERROR\" }\nlogging.basicConfig(format='Py: VSI1: [%(levelname)s]\\t%(message)s', level = verbosity)\nlogging.info(\"Verbosity level is set to \" + level[verbosity])\n\n\n# IRQ registers\nIRQ_Status = 0\n\n# Timer registers\nTimer_Control  = 0\nTimer_Interval = 0\n\n# Timer Control register definitions\nTimer_Control_Run_Msk      = 1<<0\nTimer_Control_Periodic_Msk = 1<<1\nTimer_Control_Trig_IRQ_Msk = 1<<2\nTimer_Control_Trig_DMA_Msk = 1<<3\n\n# DMA registers\nDMA_Control = 0\n\n# DMA Control register definitions\nDMA_Control_Enable_Msk    = 1<<0\nDMA_Control_Direction_Msk = 1<<1\nDMA_Control_Direction_P2M = 0<<1\nDMA_Control_Direction_M2P = 1<<1\n\n# User registers\nRegs = [0] * 64\n\nCONTROL     = 0  # Regs[0]\nCHANNELS    = 0  # Regs[1]\nSAMPLE_BITS = 0  # Regs[2]\nSAMPLE_RATE = 0  # Regs[3]\n\n# User CONTROL register definitions\nCONTROL_ENABLE_Msk = 1<<0\n\n# Data buffer\nData = bytearray()\n\n\n## Open WAVE file (store object into global WAVE object)\n#  @param name name of WAVE file to open\ndef openWAVE(name):\n    global WAVE\n    logging.info(\"Open WAVE file (write mode): {}\".format(name))\n    WAVE = wave.open(name, 'wb')\n    WAVE.setnchannels(CHANNELS)\n    WAVE.setsampwidth((SAMPLE_BITS + 7) // 8)\n    WAVE.setframerate(SAMPLE_RATE)\n    logging.info(\"  Number of channels: {}\".format(CHANNELS))\n    logging.info(\"  Sample bits: {}\".format(SAMPLE_BITS))\n    logging.info(\"  Sample rate: {}\".format(SAMPLE_RATE))\n\n## Write WAVE frames (global WAVE object)\n#  @param frames frames to write\ndef writeWAVE(frames):\n    global WAVE\n    logging.info(\"Write WAVE frames\")\n    WAVE.writeframes(frames)\n\n## Close WAVE file (global WAVE object)\ndef closeWAVE():\n    global WAVE\n    logging.info(\"Close WAVE file\")\n    WAVE.close()\n\n\n## Store audio frames from global Data buffer\n#  @param block_size size of block to store (in bytes)\ndef storeAudioFrames(block_size):\n    global Data\n    logging.info(\"Store audio frames from data buffer\")\n    writeWAVE(Data)\n\n\n## Initialize\ndef init():\n    logging.info(\"Python function init() called\")\n\n\n## Read interrupt request (the VSI IRQ Status Register)\n#  @return value value read (32-bit)\ndef rdIRQ():\n    global IRQ_Status\n    logging.info(\"Python function rdIRQ() called\")\n\n    value = IRQ_Status\n    logging.debug(\"Read interrupt request: {}\".format(value))\n\n    return value\n\n\n## Write interrupt request (the VSI IRQ Status Register)\n#  @param value value to write (32-bit)\n#  @return value value written (32-bit)\ndef wrIRQ(value):\n    global IRQ_Status\n    logging.info(\"Python function wrIRQ() called\")\n\n    IRQ_Status = value\n    logging.debug(\"Write interrupt request: {}\".format(value))\n\n    return value\n\n\n## Write Timer registers (the VSI Timer Registers)\n#  @param index Timer register index (zero based)\n#  @param value value to write (32-bit)\n#  @return value value written (32-bit)\ndef wrTimer(index, value):\n    global Timer_Control, Timer_Interval\n    logging.info(\"Python function wrTimer() called\")\n\n    if   index == 0:\n        Timer_Control = value\n        logging.debug(\"Write Timer_Control: {}\".format(value))\n    elif index == 1:\n        Timer_Interval = value\n        logging.debug(\"Write Timer_Interval: {}\".format(value))\n\n    return value\n\n\n## Timer event (called at Timer Overflow)\ndef timerEvent():\n    logging.info(\"Python function timerEvent() called\")\n\n\n## Write DMA registers (the VSI DMA Registers)\n#  @param index DMA register index (zero based)\n#  @param value value to write (32-bit)\n#  @return value value written (32-bit)\ndef wrDMA(index, value):\n    global DMA_Control\n    logging.info(\"Python function wrDMA() called\")\n\n    if   index == 0:\n        DMA_Control = value\n        logging.debug(\"Write DMA_Control: {}\".format(value))\n\n    return value\n\n\n## Read data from peripheral for DMA P2M transfer (VSI DMA)\n#  @param size size of data to read (in bytes, multiple of 4)\n#  @return data data read (bytearray)\ndef rdDataDMA(size):\n    global Data\n    logging.info(\"Python function rdDataDMA() called\")\n\n    n = min(len(Data), size)\n    data = bytearray(size)\n    data[0:n] = Data[0:n]\n    logging.debug(\"Read data ({} bytes)\".format(size))\n\n    return data\n\n\n## Write data to peripheral for DMA M2P transfer (VSI DMA)\n#  @param data data to write (bytearray)\n#  @param size size of data to write (in bytes, multiple of 4)\ndef wrDataDMA(data, size):\n    global Data\n    logging.info(\"Python function wrDataDMA() called\")\n\n    Data = data\n    logging.debug(\"Write data ({} bytes)\".format(size))\n\n    storeAudioFrames(size)\n\n    return\n\n\n## Write CONTROL register (user register)\n#  @param value value to write (32-bit)\ndef wrCONTROL(value):\n    global CONTROL\n    if ((value ^ CONTROL) & CONTROL_ENABLE_Msk) != 0:\n        if (value & CONTROL_ENABLE_Msk) != 0:\n            logging.info(\"Enable Transmitter\")\n            openWAVE('test.wav')\n        else:\n            logging.info(\"Disable Transmitter\")\n            closeWAVE()\n    CONTROL = value\n\n## Write CHANNELS register (user register)\n#  @param value value to write (32-bit)\ndef wrCHANNELS(value):\n    global CHANNELS\n    CHANNELS = value\n    logging.info(\"Number of channels: {}\".format(value))\n\n## Write SAMPLE_BITS register (user register)\n#  @param value value to write (32-bit)\ndef wrSAMPLE_BITS(value):\n    global SAMPLE_BITS\n    SAMPLE_BITS = value\n    logging.info(\"Sample bits: {}\".format(value))\n\n## Write SAMPLE_RATE register (user register)\n#  @param value value to write (32-bit)\ndef wrSAMPLE_RATE(value):\n    global SAMPLE_RATE\n    SAMPLE_RATE = value\n    logging.info(\"Sample rate: {}\".format(value))\n\n\n## Read user registers (the VSI User Registers)\n#  @param index user register index (zero based)\n#  @return value value read (32-bit)\ndef rdRegs(index):\n    global Regs\n    logging.info(\"Python function rdRegs() called\")\n\n    value = Regs[index]\n    logging.debug(\"Read user register at index {}: {}\".format(index, value))\n\n    return value\n\n\n## Write user registers (the VSI User Registers)\n#  @param index user register index (zero based)\n#  @param value value to write (32-bit)\n#  @return value value written (32-bit)\ndef wrRegs(index, value):\n    global Regs\n    logging.info(\"Python function wrRegs() called\")\n\n    if   index == 0:\n        wrCONTROL(value)\n    elif index == 1:\n        wrCHANNELS(value)\n    elif index == 2:\n        wrSAMPLE_BITS(value)\n    elif index == 3:\n        wrSAMPLE_RATE(value)\n\n    Regs[index] = value\n    logging.debug(\"Write user register at index {}: {}\".format(index, value))\n\n    return value\n\n\n## @}\n\n","sub_path":"VSI/audio/python/arm_vsi1.py","file_name":"arm_vsi1.py","file_ext":"py","file_size_in_byte":6922,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"125135325","text":"import redis\r\nimport pandas as pd\r\nfrom rtnews import cons as ct\r\nfrom datetime import datetime\r\nimport lxml\r\nfrom lxml.html import builder as E\r\nfrom lxml import etree\r\nimport time\r\nimport os\r\n\r\nfrom rtnews.feed import feed_vars as fv\r\n\r\ndef get_latest_news(channel, top=None, show_Body=False):\r\n    \"\"\"\r\n    获取即时新闻\r\n\r\n    Parameters\r\n    -------\r\n        channel: str, 待获取的新闻所在频道(id或名称)\r\n        top: int, 最多获取多少条新闻，默认None全获取\r\n        show_body: bool, 是否返回新闻正文，默认False不返回\r\n\r\n    Result\r\n    -------\r\n        pandas.DataFrame, 包括如下列：\r\n            channel: 频道类别\r\n            title: 标题\r\n            summary: 摘要\r\n            time: 时间\r\n            url: 新闻链接\r\n            body: 正文（在show_content为True的情况下出现）\r\n    \"\"\"\r\n    lname = ''\r\n    lid = ''\r\n    if channel in ct.GLOBAL_CHANNELS:\r\n        lname = ct.GLOBAL_CHANNELS[channel]\r\n        lid = channel\r\n    else:\r\n        reversed_dic = {v: k for k, v in ct.GLOBAL_CHANNELS.items()}\r\n        if channel not in reversed_dic:\r\n            raise ValueError(f'Channel \"{channel}\" undefined.')\r\n        else:\r\n            lname = channel\r\n            lid = reversed_dic[channel]\r\n    assert lname and lid\r\n\r\n    client = redis.from_url(ct.REDIS_URI, decode_responses=True)\r\n    \r\n    lid_key = ct.KEY_LID.format(lid=lid)\r\n    end = -1 if top is None else top\r\n    news_keys = client.zrevrange(lid_key, 0, end) \r\n    \r\n    data = []\r\n    for news_key in news_keys:\r\n        news = client.hgetall(news_key)\r\n        rt = datetime.fromtimestamp(int(news['timestamp']))\r\n        rtstr = datetime.strftime(rt, \"%m-%d %H:%M\")\r\n        row = [lname, news['title'], news['summary'], rtstr, news['url']]\r\n        if show_Body:\r\n            row.append(news['body'])\r\n        data.append(row)\r\n    df = pd.DataFrame(data, columns=fv.LATEST_COLS_C if show_Body else fv.LATEST_COLS)\r\n    return df\r\n\r\ndef feeds_txt():\r\n    for lid in ct.GLOBAL_CHANNELS:\r\n        df = get_latest_news(lid)\r\n        with open(os.path.join(ct.DAT_DIR, f'{ct.GLOBAL_CHANNELS[lid]}.txt'), 'w', encoding='utf-8') as f:\r\n            for row in df.iterrows():\r\n                row = row[1]\r\n                news = row['title'] + '\\n' + row['time'] + '\\n' + row['url'] + '\\n' + row['summary'] + '\\n'\r\n                f.write(news)\r\n                f.write('---\\n\\n')\r\n\r\ndef feeds_html():\r\n    for lid in ct.GLOBAL_CHANNELS:\r\n        df = get_latest_news(lid)\r\n        html = E.HTML(\r\n            E.HEAD(\r\n                E.META(content='text/html', charset='utf-8'),\r\n                E.LINK(rel='stylesheet', href='../css/style.css', type='text/css'),\r\n                E.TITLE(E.CLASS('title'), f'{ct.GLOBAL_CHANNELS[lid]}实时新闻摘要')\r\n            )\r\n        )\r\n        body = etree.SubElement(html, 'body')\r\n        for row in df.iterrows():\r\n            row = row[1]\r\n            div = etree.SubElement(body, 'div')\r\n            h1 = etree.SubElement(div, 'h1', attrib={'class': 'heading'})\r\n            a = etree.SubElement(h1, 'a', attrib={'href': row['url']})\r\n            a.text = row['title']\r\n            p1 = etree.SubElement(div, 'p', attrib={'class': 'time'})\r\n            p1.text = row['time']\r\n            p2 = etree.SubElement(div, 'p', attrib={'class': 'summary'})\r\n            p2.text = row['summary']\r\n        with open(os.path.join(ct.DAT_DIR, f'{ct.GLOBAL_CHANNELS[lid]}.html'), 'w', encoding='utf-8') as f:\r\n            f.write(lxml.html.tostring(html, pretty_print=True, encoding='utf-8').decode('utf-8'))\r\n\r\nif __name__ == '__main__':\r\n    feeds_txt()\r\n    feeds_html()\r\n    #start_time = time.perf_counter()\r\n    #feeds_html()\r\n    #end_time = time.perf_counter()\r\n    #print(f'run feeds in {end_time - start_time} seconds.')","sub_path":"rtnews/feed/newsevent.py","file_name":"newsevent.py","file_ext":"py","file_size_in_byte":3820,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"428113320","text":"from xlrd import *\nfrom xlwt import *\nfrom tkinter import *\nfrom tkinter import filedialog\nfrom tkinter import messagebox\nglobal file_name,file_path,pathlab,root\nfile_name=''\nfile_path=''\ndef chaifen(file,sheeti,colu,path):#拆分表格\n    global ret,root\n    if file=='':\n        file=e1.get()\n    data = open_workbook(file)\n    table3 =data.sheet_by_index(sheeti-1)\n    a = []  # 所有数据\n    colu = colu - 1  # 按第几列拆分 0\n    # kk=input('请输入分列依据:')\n    for i in range(table3.nrows):\n        a += [table3.row_values(i)]\n    bb = list(table3.col_values(colu))  # 按第ll列拆分\n    b = []  # 分表字典键值\n    col = len(table3.row_values(0))\n    print(\"总列数：\",col)\n    for i in range(1, len(bb)):\n        if bb[i] not in b:\n            b.append(bb[i])\n    c = {}  # 各分表数据字典\n    continuecf(len(b))\n    if ret ==1 :\n        bbb = ''\n        for i in range(len(b)):\n            k = 0\n            if i == len(b) - 1:\n                bbb += str(i+1)+str(b[i]).strip('\\t') + '！'\n            else:\n                bbb += str(i+1)+str(b[i]).strip('\\t') + '、'\n            c[b[i]] = [a[0]]\n            for j in range(len(a)):\n                if b[i] == a[j][colu]:  # 按第ll列拆分\n                    c[b[i]] += [a[j]]\n        for i in list(c.keys()):\n            f = Workbook()\n            sheet1 = f.add_sheet('1')\n            for j in range(len(c[i])):  # j 行\n                if len(c[i]) > 1:\n                    for k in range(col):  # k 列\n                        sheet1.write(j, k, c[i][j][k])\n            if path != '':\n                f.save(path + \"/\" + str(i).strip('\\t') + '.xls')\n            else:\n                f.save(str(i).strip('\\t') + '.xls')\n        print('已生成文件：', b)\n        var1 = \"文件名:<< % s>>\" % e1.get()\n        var2 = \"第几张表:<< % s>>\" % e2.get()\n        var3 = \"按第几列拆分:<< % s>>\" % e3.get()\n        var4 = \"新文件路径:<< % s>>\" % path\n        messagebox.showinfo(title='处理结果', message=var1 + '\\n' + var2 + '\\n' + var3 + \"\\n\" + var4 + \"\\n\" +\"已生成文件: 共\" + str(len(b)) + \"个\\n\"+bbb)\n        rootc.destroy()\n    else:\n        var1 = \"文件名:<< % s>>\" % e1.get()\n        var2 = \"第几张表:<< % s>>\" % e2.get()\n        var3 = \"按第几列拆分:<< % s>>\" % e3.get()\n        var4 = \"结果-----> 未执行拆分！\"\n        messagebox.showinfo(title='未处理', message=var1 + '\\n' + var2 + '\\n' + var3 + \"\\n\" + var4)\n        rootc.destroy()\ndef continuecf(numb):\n    global rootc\n    rootc=Tk(className=\"是否继续?\")\n    Label(rootc, text=\"即将执行拆分，预计生成文件个数: \"+str(numb)).grid(row=0)\n    Button(rootc, text=\"继续\", width=10, command=continuey).grid(row=1, column=0, sticky=W,padx=10, pady=5)\n    Button(rootc, text=\"退出\", width=10, command=continuen).grid(row=1, column=1, sticky=E, padx=10, pady=5)\n    mainloop()\ndef continuey():\n    global ret,rootc\n    ret=1\n    rootc.quit()\ndef continuen():\n    global ret,rootc\n    ret=0\n    rootc.quit()\ndef show(): #执行\n    global root\n    fil = e1.get()\n    sht = int(e2.get())\n    ll = int (e3.get())\n    chaifen(file_name, sht, ll,file_path)\ndef chosefile():#选择文件\n    global file_path,file_name,pathlab\n    rootc = Tk()\n    rootc.withdraw()\n    file_name = filedialog.askopenfilename()  # 文件名\n    e1padx=len(file_name)+10\n    #print(e1padx)\n    e1.grid(row=0, column=1, ipadx=e1padx)  #-column列, -columnspan偏移, -in, -ipadx文本宽, -ipady, -padx, -pady, -row, -rowspan, or -stickye1pady*1.5\n    e2.grid(row=1, column=1, padx=10, ipadx=e1padx)\n    e3.grid(row=2, column=1, padx=10, ipadx=e1padx)\n    e2.delete(0, END)\n    e3.delete(0, END)\n    #print(file_name)\n    if file_name !='':\n        e1.delete(0, END)\n        Entry.insert(e1, 0, file_name)\n        fullpath = file_name.split(\"/\")\n        file_path = ''\n        for i in range(len(fullpath) - 1):\n            file_path += fullpath[i] + \"/\"\n        pathlab.grid_remove()\n        pathlab = Label(root, text=file_path)\n        pathlab.grid(row=3, column=1, sticky=W)\n    else:\n        e1.delete(0, END)\n        Entry.insert(e1, 0, \"请录入程序所在目录下文件名\")\ndef chosepath():#选择路径\n    global file_path,pathlab\n    rootc = Tk()\n    rootc.withdraw()\n    file_path = filedialog.askdirectory()  # 文件夹\n    print(type(file_path))\n    if file_path != '':\n        pathlab.grid_remove()\n        pathlab=Label(root, text=file_path)\n        pathlab.grid(row=3, column=1, sticky=W)\n    else:\n        pathlab.grid_remove()\n        pathlab =Label(root, text = \"原文件路径\")\n        pathlab.grid(row=3, column=1, sticky=W)\nroot = Tk(className=\"拆分表格\")\nLabel(root, text=\"文件名:\").grid(row=0)\nLabel(root, text=\"第几张表:\").grid(row=1)\nLabel(root, text=\"按第几列拆分:\").grid(row=2)\nLabel(root, text=\"输出路径:\").grid(row=3)\npathlab=Label(root, text=\"原文件路径\")\npathlab.grid(row=3, column=1, sticky=W)\ne1 = Entry(root)\nEntry.insert(e1, 0, '可手工录入' )\ne2 = Entry(root)\nEntry.insert(e2, 0, '输入拆分表序号' )\ne3 = Entry(root)\nEntry.insert(e3, 0, '输入拆分列序号' )\ne1.grid(row=0, column=1, padx=10, pady=5)\ne2.grid(row=1, column=1, padx=10, pady=5)\ne3.grid(row=2, column=1, padx=10, pady=5)\nButton(root, text=\"选择文件\", width=10, command=chosefile).grid(row=0, column=2,  padx=10, pady=5)\nButton(root, text=\"开始拆分\", width=10, command=show).grid(row=4, column=0, sticky=W, padx=10, pady=5)\nButton(root, text=\"退出\", width=10,command=root.quit).grid(row=4, column=1, sticky=E, padx=10, pady=5)\nButton(root, text=\"选择路径\", width=10, command=chosepath).grid(row=3, column=2,  padx=10, pady=5)\nmainloop()","sub_path":"拆分带路径2.py","file_name":"拆分带路径2.py","file_ext":"py","file_size_in_byte":5698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"102736534","text":"from Crypto.PublicKey import RSA\nfrom Crypto.Signature.pkcs1_15 import PKCS115_SigScheme\nfrom Crypto.Hash import SHA256\nimport binascii\nfrom Crypto.Cipher import AES\nimport os\nfrom Crypto import Random\nfrom Crypto.Random import get_random_bytes\nfrom Crypto.Util.Padding import pad, unpad\n\ndef generate_aesKey():\n    # 32 bytes * 8 = 256 bits (1 byte = 8 bits)\n    key = get_random_bytes(32)\n    file_out = open(\"aesKey.bin\", \"wb\") \n    file_out.write(key)\n    file_out.close()\n    return key\n\ndef get_aesKey():\n    try:\n        file_in = open(\"aesKey.bin\", \"rb\") \n        key_from_file = file_in.read() \n        file_in.close()\n        return key_from_file\n    except:\n        return generate_aesKey()\n\ndef generate_rsaKey():\n    private_key = RSA.generate(bits=2048)\n    public_key = private_key.publickey()\n    \n    private_pem = private_key.export_key().decode()\n    public_pem = public_key.export_key().decode()\n\n    with open('private_pem.pem', 'w') as pr:\n        pr.write(private_pem)\n\n    with open('public_pem.pem', 'w') as pu:\n        pu.write(public_pem)\n\n    return private_key, public_key\n\ndef get_rsaKey():\n    try:\n        private_key = RSA.import_key(open('private_pem.pem', 'r').read())\n        public_key = RSA.import_key(open('public_pem.pem', 'r').read())\n        return private_key,public_key\n    except:\n        return generate_rsaKey()\n\naesKey = get_aesKey()\n\nprivate_key,public_key = get_rsaKey()\n\ndef sign(iv,message):\n    hash = SHA256.new(message)\n    signer = PKCS115_SigScheme(private_key)\n    signature = signer.sign(hash)\n    print(\"Signature:\", binascii.hexlify(signature))\n\n    fo = open(filename + \".sig\", 'wb')\n    fo.write(iv)\n    fo.write(signature)\n    fo.close()\n\ndef verify(message, signature):\n    hash = SHA256.new(message)\n    signer = PKCS115_SigScheme(public_key)\n    print(\"Signature:\", binascii.hexlify(signature))\n    try:\n        signer.verify(hash, signature)\n        print(\"Signature is valid.\")\n    except:\n        print(\"Signature is invalid.\")\n\ndef encrypt(message,iv=Random.new().read(AES.block_size)):\n    message = pad(message, AES.block_size)\n    cipher = AES.new(aesKey, AES.MODE_CBC,iv)\n    return iv, cipher.encrypt(message)\n\ndef encrypt_file(file_name):\n    fo = open(file_name, 'rb')\n    plaintext = fo.read()\n    fo.close()\n\n    iv,cipher = encrypt(plaintext)\n\n    fo = open(file_name + \".enc\", 'wb')\n    fo.write(iv + cipher)\n    fo.close()\n    os.remove(file_name)\n\ndef decrypt(ciphertext):\n    iv = ciphertext[:AES.block_size]\n    cipher = AES.new(aesKey, AES.MODE_CBC, iv)\n    plaintext = unpad(cipher.decrypt(ciphertext[AES.block_size:]), AES.block_size)\n    return plaintext\n\ndef decrypt_file(file_name):\n    fo = open(file_name, 'rb')\n    ciphertext = fo.read()\n    fo.close()\n\n    dec = decrypt(ciphertext)\n\n    fo = open(file_name[:-4], 'wb')\n    fo.write(dec)\n    fo.close()\n    os.remove(file_name)\n\nif __name__ == \"__main__\":\n\n    while True:\n        choice = int(input(\n            \"1. Press '1' to encrypt file.(if file .txt sign digitalsignature)\\n2. Press '2' to decrypt file.(if file .txt then verify digitalsignature)\\n3. Press '3' to exit.\\n\"))\n\n        if choice == 1 or choice == 2:\n            filename = str(input(\"Enter name of file: \"))\n\n            if filename.endswith(\".txt\") or filename[:-4].endswith(\".txt\"):\n                if choice == 1:\n                    fo = open(filename, \"rb\")\n                    message = fo.read()\n                    fo.close()\n                    print('message: ', message)\n                    iv,cipher = encrypt(message)\n                    print('iv: ', iv)\n                    sign(iv,cipher)\n                \n                else:\n                    fo = open(filename, \"rb\")\n                    sig_content = fo.read()\n                    iv = sig_content[:AES.block_size]\n                    signature = sig_content[AES.block_size:]\n                    fo.close()\n\n                    fo = open(filename[:-4], 'rb')\n                    message = fo.read()\n                    \n                    print('message: ', message)\n                    \n                    iv,cipher = encrypt(message,iv)\n                    print('iv: ', iv)\n                    verify(cipher,signature)\n\n            else:\n                if choice == 1:\n                    encrypt_file(filename)\n                else:\n                    decrypt_file(filename)\n\n\n        elif choice == 3:\n            exit()\n        else:\n            print(\"Please select a valid option!\")","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4485,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"628249716","text":"# GEO1000 - Assignment 4\n# Authors: Maundri Prihanggo & Pratyush Kumar\n# Studentnumbers: 5151279 & 5359252\n\nfrom geometry import Point, Rectangle\n\n\nclass Strip(object):\n    def __init__(self, rectangle):\n        \"\"\"Constructor. Inits a Strip instance with a Rectangle describing \n        its shape and an empty points list.\n        \"\"\"\n        self.rect = rectangle\n        self.points = []\n\n\nclass StripStructure(object):\n    def __init__(self, extent, no_strips):\n        \"\"\"Constructor. Inits a StripStructure instance with the correct\n        number of Strip instances and makes sure that the domain is \n        correctly divided over the strips.\n        \"\"\"\n        self.strips = []\n        # Extend this method,\n        # so that the right number of strip objects (with the correct extent)\n        # are appended to the strips list\n        # assuming that the extent is a rectangle instance\n        # should have no_strips no of strips as rectangle instances, need to divide the extent into multiple rects\n        ll = extent.ll\n        ur = extent.ur\n        del_x = extent.width()\n\n        strip_leng = del_x/no_strips\n        new_ll_x = ll.x\n        new_ur_x = ll.x + strip_leng\n        for _ in range(no_strips):\n            \n            \n            s = Strip(Rectangle( Point(new_ll_x , ll.y), Point( new_ur_x, ur.y)  )  )\n            new_ll_x += strip_leng\n            new_ur_x += strip_leng\n            #append a strip rect instance to strips list\n            self.strips.append(s)\n        \n\n\n    def find_overlapping_strips(self, shape):\n        \"\"\"Returns a list of strip objects for which their rectangle intersects \n        with the shape given.\n        \n        Returns - list of Strips\n        \"\"\"\n        ls=[]\n        for i in self.strips:\n            if i.rect.intersects(shape):\n                ls.append(i)\n        return(ls)\n\n    def query(self, shape):\n        \"\"\"Returns a list of points that overlaps the given shape.\n        \n        For this it first finds the strips that overlap the shape,\n        using the find_overlapping_strips method.\n\n        Then, all points of the selected strips are checked for intersection\n        with the query shape.\n        \n        Returns - list of Points\n        \"\"\" \n        pt_list = []\n        overlap_strips = self.find_overlapping_strips(shape) \n        # list of overlapping strip objects \n        for strip in overlap_strips:\n            # check for intersections of points in the strips with shapes\n            strp_pnt = strip.points #list of points in strip\n            for pnt in strp_pnt:\n                if pnt.intersects(shape):\n                    pt_list.append(pnt)\n        \n        return( pt_list )\n\n\n    def append_point(self, pt):\n        \"\"\"Appends a point object to the list of points of the correct strip\n        (i.e. the strip the Point intersects).\n\n        For this it first finds the strips that overlap the point,\n        using the find_overlapping_strips method.\n\n        In case multiple strips overlap the point, the point is added\n        to the strip with the left most coordinate.\n        \n        Returns - None\n        \"\"\"\n        strips = self.find_overlapping_strips(pt)\n        for strip in strips:\n            if pt.intersects(strip.rect):\n                strip.points.append(pt)\n                break # strip overlap put in the left one and comes out of the loop\n        return(None)\n    def print_strip_statistics(self):\n        \"\"\"Prints:\n        * how many strips there are in the structure\n\n        And then, for all the strips in the structure:\n        * an id (starting at 1),\n        * the number of points in a strip, \n        * the lower left point of a strip and \n        * the upper right point of a strip.\n        \n        Returns - None\n        \"\"\"\n        # format for print output: \n        # #1 with 33 points, ll: POINT (0.0 0.0), ur: POINT (2.0 10.0)\n        print(f\"{len(self.strips)} strips\")\n\n        for id, strip in enumerate(self.strips):\n            print(f\"#{id+1} with {len(strip.points)} points, ll: {strip.rect.ll}, ur: {strip.rect.ur}\")\n        return(None)\n\n    def dumps_strips(self):\n        \"\"\"Dumps the strips of this structure to a str, \n        which (if saved in a text file) can be loaded as \n        delimited text layer in QGIS.\n        \n        Returns - str\n        \"\"\"\n        lines = \"strip;wkt\\n\"\n        for i, strip in enumerate(self.strips, start = 1):\n            t = \"{0};{1}\\n\".format(i, strip.rect)\n            lines += t\n        return lines\n\n    def dumps_points(self):\n        \"\"\"Dumps the points of this structure to a str, \n        which (if saved in a text file) can be loaded as \n        delimited text layer in QGIS.\n        \n        Returns - str\n        \"\"\"\n        lines = \"strip;wkt\\n\"\n        for i, strip in enumerate(self.strips, start = 1):\n            for pt in strip.points:\n                t = \"{0};{1}\\n\".format(i, pt)\n                lines += t\n        return lines\n\n","sub_path":"strips.py","file_name":"strips.py","file_ext":"py","file_size_in_byte":4952,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"427182333","text":"\"\"\"\nMarck the ticket as viewed.\n\nMark a ticket as viewed. All currently posted updates will be marked as viewed. \nThe lastViewedDate property will be updated to the current time.\n\nImportant manual pages:\nhttp://sldn.softlayer.com/reference/services/SoftLayer_Ticket/markAsViewed\n\nLicense: http://sldn.softlayer.com/article/License\nAuthor: SoftLayer Technologies, Inc. <sldn@softlayer.com>\n\"\"\"\n\nimport SoftLayer\nimport pprint\n\n# Your SoftLayer user-name.\nUSERNAME = 'set me'\n# Generate one at https://manage.softlayer.com/Administrative/apiKeychain\nAPI_KEY = 'set me'\n\n# The ticket id used to be marked as viewed.\nticketId = 21531750\n\n# Declare the API client\nclient = SoftLayer.Client(username=USERNAME, api_key=API_KEY)\nticketService = client['SoftLayer_Ticket']\n\ntry:\n    response = ticketService.markAsViewed(id=ticketId)\n    pprint.pprint(response)\nexcept SoftLayer.SoftLayerAPIError as e: \n    print(\"Unable to mark as viewed the ticket ID: %s \\nFault code: %s \\nFault string: %s\" \n        % (str(ticketId), e.faultCode, e.faultString))\n\n","sub_path":"SoftLayer_Ticket/MarkAsViewed.py","file_name":"MarkAsViewed.py","file_ext":"py","file_size_in_byte":1043,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"52835908","text":"import argparse\nimport collections\nimport json\nimport os\nimport re\nimport sys\nimport time\n\nimport markdown\nfrom markdown.extensions import Extension\nfrom markdown.extensions.meta import MetaPreprocessor\n#from markdown.extensions.codehilite import CodeHiliteExtension\nfrom markdown.inlinepatterns import (\n    LinkPattern, LINK_RE, BacktickPattern, BACKTICK_RE,\n    ReferencePattern, REFERENCE_RE, SHORT_REF_RE)\nfrom markdown.treeprocessors import Treeprocessor\nfrom markdown.postprocessors import Postprocessor\nfrom markdown.preprocessors import Preprocessor\nfrom markdown.util import etree, string_type, isBlockLevel, AtomicString\nimport lxml.etree\n\nfrom codehilite import CodeHiliteExtension\nfrom toc import TocExtension\n\ndef local_path(filename):\n    return os.path.join(sys.path[0], filename)\n\ndef load_wg21():\n    wg21 = json.load(open(local_path('index.json')))\n    for key in sorted(wg21, reverse=True):\n        if key.startswith('P') and key[:5] not in wg21:\n            wg21[key[:5]] = wg21[key]\n    return wg21\n    \nclass LinkSaver(LinkPattern):\n    def __init__(self, wg21, *args, **kwargs):\n        super(LinkSaver, self).__init__(*args, **kwargs)\n        self.wg21 = wg21\n        \n    def handleMatch(self, m):\n        el = etree.Element('a')\n        el.text = m.group(2)\n        title = m.group(13)\n        href = m.group(9)\n        self.markdown.saved_links.add(href)\n        \n        if href:\n            if href[0] == '<':\n                href = href[1:-1]\n            el.set('href', self.sanitize_url(self.unescape(href.strip())))\n        else:\n            el.set('href', '')\n        \n        if title:\n            title = self.unescape(title)\n            el.set(\"title\", title)\n        elif 'wg21.link' in href:\n            el.set(\"title\", self.wg21[href.split('/')[-1].upper()]['title'])\n        return el\n\nclass RefSaver(ReferencePattern):\n    def makeTag(self, href, title, text):\n        return ReferencePattern.makeTag(self,\n            href, title.split('||')[0], text)\n        \nclass RefProcessor(Treeprocessor):\n    def __init__(self, md, wg21):\n        super(RefProcessor, self).__init__(md)\n        self.wg21 = wg21\n\n    def run(self, doc):\n        wg21_links = []\n        other_links = []\n        for link in self.markdown.saved_links:\n            if 'wg21.link' in link:\n                wg21_links.append(link.split('/')[-1].upper())\n        wg21_links.sort()\n\n        if wg21_links or self.markdown.references:\n            refs = etree.SubElement(doc, 'h1')\n            refs.text = 'References'\n            ul = etree.SubElement(doc, 'ul')\n            \n        for link in wg21_links:\n            info = self.wg21[link]\n            li = etree.SubElement(ul, 'li')\n            a = etree.SubElement(li, 'a')\n            a.attrib[\"href\"] = info['long_link']\n            a.text = '[{}]'.format(link)\n\n            desc = etree.SubElement(li, 'span')\n            desc.attrib['style'] = \"margin-left: 5px;\"\n            if info['type'] == 'paper':\n                desc.text = u'\"{}\" by {}, {}'.format(\n                    info['title'],\n                    info['author'],\n                    info['date'])\n            elif info['type'] == 'issue':\n                if 'last_modified' in info:\n                    date = info['last_modified']\n                else:\n                    date = info['date']\n            \n                desc.text = u'\"{}\" by {}, {}'.format(\n                    info['title'],\n                    info['submitter'],\n                    date)\n                        \n        for ref in sorted(self.markdown.references):\n            href, title = self.markdown.references[ref]\n            li = etree.SubElement(ul, 'li')\n            a = etree.SubElement(li, 'a')\n            a.attrib[\"href\"] = href\n            a.text = '[{}]'.format(ref)\n            \n            desc = etree.SubElement(li, 'span')\n            desc.attrib['style'] = \"margin-left: 5px;\"\n            parts = title.split('||')\n            if len(parts) == 1:\n                if title[0] == '[' and title[-1] == ']':\n                    desc.text = u'Current Working Draft'.format(title)\n                else:\n                    desc.text = title\n            else:\n                desc.text = u'\"{}\" by {}, {}'.format(*parts)\n\nclass TableCodeBlockProcessor(Preprocessor):\n    def run(self, lines):\n        new_lines = []\n        to_markup = []\n        target = new_lines\n        \n        for line in lines:\n            if line.startswith('<th') or line.startswith('<td'):\n                new_lines.append(line)\n                target = to_markup\n            elif line.startswith('</th') or line.startswith('</td'):\n                target = new_lines\n                new_lines.extend(\n                    self.markdown.convert(u'\\n'.join(to_markup))\n                        .replace('<pre class=\"codehilite\"',\n                            '<pre style=\"background:transparent;border:0px\"')\n                        .split('\\n'))\n                to_markup = []\n                new_lines.append(line)\n            else:\n                target.append(line)\n        return new_lines\n\nclass CppBacktickPattern(BacktickPattern):\n    def handleMatch(self, m):\n        if m.group(4):\n            text = m.group(4).strip()\n            el = etree.Element('code')\n            if text.startswith('#!'):\n                el.text = AtomicString(text[2:].strip())\n            else:\n                el.attrib[\"class\"] = \"language-cpp\"\n                el.text = AtomicString(text.strip())\n            return el\n        else:\n            return super(CppBacktickPattern, self).handleMatch(m)\n\nclass CppBacktickExtension(Extension):\n    def extendMarkdown(self, md, md_globals):\n        md.inlinePatterns[u'backtick'] = CppBacktickPattern(BACKTICK_RE)\n            \nclass RefExtension(Extension):\n    def extendMarkdown(self, md, md_globals):\n        md.saved_links = set()\n        wg21 = load_wg21()\n        md.inlinePatterns[u'link'] = LinkSaver(wg21, LINK_RE, md)\n        md.inlinePatterns[u'reference'] = RefSaver(REFERENCE_RE, md)\n        md.inlinePatterns[u'short_reference'] = RefSaver(SHORT_REF_RE, md)\n        md.treeprocessors.add(\"refs\", RefProcessor(md, wg21), '<toc')\n\nclass TableCodeBlockExtension(Extension):\n    def extendMarkdown(self, md, md_globals):\n        new_md = markdown.Markdown(extensions=[ CodeHiliteExtension(use_pygments=False),\n                    CppBacktickExtension()\n                    ])\n        new_md.references = md.references\n        new_md.inlinePatterns[u'reference'] = RefSaver(REFERENCE_RE, new_md)\n        new_md.inlinePatterns[u'short_reference'] = RefSaver(SHORT_REF_RE, new_md)\n          \n        md.preprocessors.add(\"tbl_codeblock\",\n            TableCodeBlockProcessor(new_md),\n            '_begin')\n\nclass BikeshedMetaPreprocessor(MetaPreprocessor):\n    def run(self, lines):\n        bikeshed = False\n        if lines[0].strip() == \"<pre class='metadata'>\":\n            bikeshed = True\n            lines.pop(0)\n        lines = MetaPreprocessor.run(self, lines)\n        if bikeshed and lines[0] == \"</pre>\":\n            lines.pop(0)\n        return lines\n            \nclass MetadataExtension(Extension):\n    def extendMarkdown(self, md, md_globals):\n        markdown.extensions.meta.META_RE = re.compile(r'^[ ]{0,3}(?P<key>[A-Za-z0-9 _-]+):\\s*(?P<value>.*)')\n    \n        md.preprocessors.add(\"meta\",\n            BikeshedMetaPreprocessor(md),\n            \">normalize_whitespace\")\n        \ndef main(argv=None):\n    parser = argparse.ArgumentParser(\n        description='Markdown formatter for papers')\n    parser.add_argument('-i', '--input', dest='input', type=argparse.FileType('r'))\n    parser.add_argument('-o', '--output', dest='output', type=argparse.FileType('w'),\n        default='-')\n    parser.add_argument('--references', action='store_true')\n    \n    args = parser.parse_args(argv)\n    \n    write = args.output.write\n    \n    extensions = [CodeHiliteExtension(use_pygments=False),\n        TocExtension(baselevel=2, anchorlink=False, title=None, marker='', permalink=True),\n        MetadataExtension(),\n        CppBacktickExtension(),\n        TableCodeBlockExtension()]\n    noref_md = markdown.Markdown(extensions=extensions)\n    if args.references:\n        extensions.append(RefExtension())\n    \n    class OrderedMarkdown(markdown.Markdown):\n        def registerExtensions(self, *args, **kwargs):\n            self.references = collections.OrderedDict()\n            markdown.Markdown.registerExtensions(self, *args, **kwargs)\n    \n    md = OrderedMarkdown(extensions=extensions)\n    in_md = args.input.read().decode('utf-8')\n    html = md.convert(in_md)\n    write('<html>\\n')\n    write('<head>\\n')\n    write('<meta http-equiv=\"Content-Type\" content=\"text/html; charset=UTF-8\">\\n')\n\n    def get_title(input):\n        title = noref_md.convert(input)\n        title_root = lxml.etree.fromstring(title)\n        lxml.etree.strip_tags(title_root, '*')\n        return title_root.text\n    \n    write('<title>{}</title>\\n'.format(\n        get_title(md.Meta['title'][0])))\n\n    def add_style(filename):\n        suffix = filename.rsplit('.', 1)[1]\n        fmt = {'css': '<style type=\"text/css\">{}</style>\\n',\n            'js': '<script type=\"text/javascript\">{}</script>\\n'\n        }[suffix]\n            \n        write(fmt.format(open(local_path(filename)).read()))\n\n    add_style('general.css')\n    add_style('prism_default.css')\n    add_style('prism.js')\n    \n    #write('<style type=\"text/css\">{}</style>\\n'.format(\n    #    open(local_path('general.css')).read()))    \n    #write('<style type=\"text/css\">{}</style>\\n'.format(\n    #    open(local_path('prism.css')).read().replace('fdf6e3', 'f8f8f8')))\n    #write('<script type=\"text/javascript\">{}</script>\\n'.format(\n    #    open(local_path('prism.js')).read()))\n\n    def document_number():\n        doc = md.Meta.get('document-number')\n        if doc:\n            return doc[0]\n        return '{}R{}'.format(\n            md.Meta['shortname'][0],\n            md.Meta['level'][0])\n    \n    def authors():\n        authors = md.Meta.get('authors')\n        if authors:\n            return authors\n        return md.Meta.get('editor')\n    \n    write('\\n</head>\\n')\n    write('<body>\\n')\n    write('<address align=right>\\n')    \n    write('Document Number: {} <br />\\n'.format(document_number()))\n    write('Date: {} <br />\\n'.format(time.strftime('%Y-%m-%d', time.localtime())))\n    write('Audience: {} <br />\\n'.format(md.Meta['audience'][0]))\n    write('Reply-To: {} <br />\\n'.format(u'<br />'.join(authors()).encode('utf-8')))\n    write('</address>\\n')\n    title_text = md.Meta['title'][0]\n    if 'subtitle' in md.Meta:\n        title_text = '{}<br/>{}'.format(title_text, md.Meta['subtitle'][0]) \n    write('<hr /><h1 align=center>{}</h1>\\n'.format(\n        noref_md.convert(title_text)))\n    write('<h2>Contents</h2>\\n')\n    write('{}\\n{}\\n</html>'.format(md.toc, html.encode('utf-8')))\n\nif __name__ == '__main__':\n    main()\n","sub_path":"md/better_md.py","file_name":"better_md.py","file_ext":"py","file_size_in_byte":10919,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"357353686","text":"def sed(patternStr, repStr, file1, file2):\n    try:\n        inP = open(file1)\n        outP = open(file2, 'w')\n    except:\n        print('ERROR! File not found!')\n        return\n    for line in inP:\n        if patternStr in line:\n            line = line.replace(patternStr, repStr)\n            outP.write(line)\n    outP.close()\n\nsed('is', 'helololololoooo', 'newfile1', 'newfile2')\n    \n        \n        \n        \n        \n    \n","sub_path":"snippets/replace_pattern_string.py","file_name":"replace_pattern_string.py","file_ext":"py","file_size_in_byte":427,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"596144438","text":"# !/usr/bin/python3\n# coding: utf-8\n\n# Copyright 2016-2018 Stefano Fogarollo\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\n\"\"\" Datetime utils \"\"\"\n\nimport datetime\nfrom enum import Enum\n\n\nclass Weekday(Enum):\n    \"\"\" Representing weekday by using ints (datetime standard) \"\"\"\n\n    MONDAY = 0\n    TUESDAY = 1\n    WEDNESDAY = 2\n    THURSDAY = 3\n    FRIDAY = 4\n    SATURDAY = 5\n    SUNDAY = 6\n\n\ndef get_just_date(date):\n    \"\"\"\n    :param date: datetime\n        Date with possible hours\n    :return: date\n        Just day, month and year (setting hours to 00:00:00)\n    \"\"\"\n\n    return datetime.datetime(\n        date.year,\n        date.month,\n        date.day\n    )\n\n\ndef get_next_weekday(weekday, including_today=False):\n    \"\"\"\n    :param weekday: Weekday\n        Weekday to get\n    :param including_today: bool\n        If today is sunday and requesting next sunday, I shall return today\n    :return: datetime\n        Date of next monday, tuesday ...\n    \"\"\"\n\n    now = datetime.datetime.now()\n    if now.weekday() == weekday.value and including_today:\n        delta = datetime.timedelta(days=0)\n    elif now.weekday() == weekday.value and not including_today:\n        delta = datetime.timedelta(days=7)\n    else:\n        delta = datetime.timedelta(\n            (7 + weekday.value - now.weekday()) % 7\n        )  # time delta to next instance\n    return get_just_date(now + delta)\n\n\ndef get_last_weekday(weekday, including_today=False):\n    \"\"\"\n    :param weekday: Weekday\n        Weekday to get\n    :param including_today: bool\n        If today is sunday and requesting next sunday, I shall return today\n    :return: datetime\n        Date of next monday, tuesday ...\n    \"\"\"\n\n    now = datetime.datetime.now()\n    if now.weekday() == weekday.value and including_today:\n        delta = datetime.timedelta(days=0)\n    elif now.weekday() == weekday.value and not including_today:\n        delta = - datetime.timedelta(days=7)\n    else:\n        if now.weekday() > weekday.value:\n            delta = - datetime.timedelta(\n                now.weekday() - weekday.value\n            )  # time delta\n        else:\n            delta = - datetime.timedelta(\n                now.weekday() + (7 - weekday.value)\n            )  # time delta\n    return get_just_date(now + delta)\n\n\ndef is_date_in_between(date, start, end):\n    \"\"\"\n    :param date: datetime\n        Date to check\n    :param start: datetime\n        Date cannot be before this date\n    :param end: datetime\n        Date cannot be after this date\n    :return: bool\n        True iff date is in between dates\n    \"\"\"\n\n    return get_just_date(start) <= get_just_date(date) < get_just_date(end)\n\n\ndef is_in_this_week(date):\n    \"\"\"\n    :param date: datetime\n        Date\n    :return: bool\n        True iff date is in this week (from sunday to sunday)\n    \"\"\"\n\n    return is_date_in_between(\n        get_just_date(date),\n        get_last_weekday(Weekday.SUNDAY, including_today=True),\n        get_next_weekday(Weekday.SUNDAY)\n    )\n","sub_path":"hal/time/dates.py","file_name":"dates.py","file_ext":"py","file_size_in_byte":3474,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"467592064","text":"class Solution(object):\n    def findAnagrams(self, s2, s1):\n        \"\"\"\n        :type s: str\n        :type p: str\n        :rtype: List[int]\n        \"\"\"\n        '''\n        滑动窗口思考以下四个问题：\n\n        1、当移动right扩大窗口，即加入字符时，应该更新哪些数据？\n        \n        2、什么条件下，窗口应该暂停扩大，开始移动left缩小窗口？\n        \n        3、当移动left缩小窗口，即移出字符时，应该更新哪些数据？\n        \n        4、我们要的结果应该在扩大窗口时还是缩小窗口时进行更新？\n\n        '''\n        res = []\n        if not s2 or not s1 or len(s2) < len(s1): return res\n        left, right = 0, 0\n\n        need, window = collections.defaultdict(int), collections.defaultdict(int)\n        for ch in s1: need[ch] += 1\n        valid = 0\n\n        # 扩大右侧窗口边界\n        while right < len(s2):\n            ch = s2[right]\n            right += 1\n            # 更新窗口数据\n            if ch in need:\n                window[ch] = window[ch] + 1\n                if window[ch] == need[ch]: valid += 1\n\n            print(str(left), str(right))\n            # 改动1：判断左侧窗口是否需收缩\n            while right - left >= len(s1):\n                # 改动2：更新结果\n                if valid == len(need):\n                    res.append(left)\n                # 左边界移动，移除一个元素\n                l_ch = s2[left]\n                left += 1\n                # 更新窗口数据\n                if l_ch in need:\n                    if window[l_ch] == need[l_ch]: valid -= 1\n                    window[l_ch] = window[l_ch] - 1\n        return res","sub_path":"5-array/slidingwindow/2.7-find-anagrams-in-string.py","file_name":"2.7-find-anagrams-in-string.py","file_ext":"py","file_size_in_byte":1704,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"645943000","text":"import sfml as sf\r\n\r\n\r\ndef reverse(tab):\r\n    new_tab = [([0] * len(tab[0])) for tableau in range(0, len(tab))]\r\n    x = 0\r\n    y1 = 0\r\n    while x < len(tab[0]):\r\n        y = 0\r\n        x1 = 0\r\n        while y < len(tab):\r\n            new_tab[y1][x1] = tab[y][x]\r\n            y += 1\r\n            x1 += 1\r\n        x += 1\r\n        y1 += 1\r\n    return new_tab\r\n\r\n\r\ndef draw_line_1(img, array_x, array_y, w):\r\n    x1 = array_x[0]\r\n    x2 = array_x[1]\r\n    y1 = array_y[0]\r\n    y2 = array_y[1]\r\n    x = x1\r\n    while x <= x2:\r\n        if 0 < x + w.width / 2 < w.width and 0 < y1 + ((y2 - y1) * (x - x1)) / (x2 - x1) \\\r\n                + w.width / 2 - 100 < w.height:\r\n            img[x + w.width / 2, y1 + ((y2 - y1) * (x - x1)) / (x2 - x1)\r\n                + w.width / 2 - 100] = sf.Color.WHITE\r\n        x += 1\r\n\r\n\r\ndef draw_line_2(img, array_x, array_y, w):\r\n    x1 = array_x[0]\r\n    x2 = array_x[1]\r\n    y1 = array_y[0]\r\n    y2 = array_y[1]\r\n    y = y1\r\n    while y <= y2:\r\n        if 0 < x1 + ((x2 - x1) * (y - y1)) / (y2 - y1) + 333 < w.width \\\r\n                and 0 < y + 233 < w.height:\r\n            img[x1 + ((x2 - x1) * (y - y1)) / (y2 - y1) + 333, y + 233] = sf.Color.WHITE\r\n        y += 1\r\n    x = x1\r\n    while x >= x2:\r\n        if 0 < x + 333 < w.width and 0 < y1 + ((y2 - y1)\r\n                                     * (x - x1)) / (x2 - x1) + 233 < w.height:\r\n            img[x + 333, y1 + ((y2 - y1) * (x - x1)) / (x2 - x1) + 233] = sf.Color.WHITE\r\n        x -= 1","sub_path":"Bonus/FDF2_next.py","file_name":"FDF2_next.py","file_ext":"py","file_size_in_byte":1472,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"23843335","text":"\r\ndef inta(l):\r\n    for i in range(len(l)):\r\n        l[i]=int(l[i])\r\n\r\ndef check(l,n):\r\n    inta(l)\r\n    flag=False\r\n    for i in l:\r\n        if i > 100 or i < 0:\r\n            flag= True\r\n            break\r\n        else:\r\n            flag= False\r\n    if len(l)!=n:\r\n        flag= True\r\n    else:\r\n        flag= False   \r\n    return flag    \r\n\r\ndef check2(n):\r\n    inta(n)\r\n    if n[0]>50 or n[0]<1 or n[1]>n[0]:\r\n        return True\r\n    else:\r\n        return False\r\n        \r\n        \r\n\r\n\r\nn=input().split(\" \")\r\n\r\nwhile check2(n):\r\n    n=input().split(\" \")\r\n\r\nl=input().split(\" \")\r\n\r\n\r\n\r\n\r\nwhile check(l,int(n[0])):\r\n    l=input().split(\" \")\r\n\r\ncount=0\r\nfor i in range(len(l)):\r\n    if l[i] >= l[n[1]-1] and l[i] != 0:\r\n        count=count+1\r\nprint(count)\r\n\r\n","sub_path":"code forces/7.py","file_name":"7.py","file_ext":"py","file_size_in_byte":760,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"245680649","text":"from flask_wtf import FlaskForm \nfrom flask_wtf.file import FileField, FileAllowed, FileRequired # what kind of files are allowed to be uploaded\nfrom flask_login import current_user # now we can use this for the account update\nfrom wtforms import StringField, PasswordField, SubmitField, BooleanField, TextAreaField, SelectField, HiddenField, validators, IntegerField, RadioField\nfrom wtforms.validators import DataRequired, Length, Email, EqualTo, ValidationError, InputRequired\nfrom models import *  \nfrom flask_login import current_user\n\n\nclass Attend(FlaskForm):\n    attend = RadioField('Attendance', choices = [('On time', 'On time'), ('Late', 'Late')])\n    name =  StringField ('Name in English', validators=[DataRequired(), Length(min=2, max=20)])\n    studentID = StringField ('Student ID (9 numbers)', validators=[DataRequired(), Length(9)])                  \n    teamnumber = IntegerField ('Team Number')\n    teamcount = IntegerField ('Team Count') \n    #role =  RadioField('What role would you like today?', choices = [('work', 'Hotel Receptionist'), ('cust', 'Hotel Guest')])                                                \n    #midterm = RadioField('Have you thought about your midterm group video?', choices = [\n     #   ('Not thought about midterm yet', \"I haven't thought about the midterm yet\"), \n    #  ('Know the team members', 'I know my midterm team members'), \n     #   ('Know the midterm topic', 'I know my midterm team members and we know the topic'), \n      #  ('Started the midterm already', 'We have started writing the midterm already!')\n     #   ])   \n    \n    submit = SubmitField('Join')\n\nclass AttendLate(FlaskForm):\n    attend = RadioField('Attendance', choices = [('Late', 'Late'), ('2nd Class', '2nd Class')])\n    name =  StringField ('Name in English', validators=[DataRequired(), Length(min=2, max=20)])\n    studentID = StringField ('Student ID (9 numbers)', validators=[DataRequired(), Length(9)])                  \n    teamnumber = IntegerField ('Team Number')\n    teamcount = IntegerField ('Team Count')\n    role =  RadioField('What role would you like today?', choices = [('work', 'TravelAgent'), ('cust', 'Traveller')])                                                \n    submit = SubmitField('Join')\n\nclass AttendInst(FlaskForm):\n    attend = StringField ('Notice')   \n    username =  StringField ('Name in English', validators=[DataRequired(), Length(min=2, max=20)])\n    studentID = StringField ('Student ID', validators=[DataRequired(), Length(9)])                  \n    teamnumber = IntegerField ('Status') \n    teamcount = IntegerField ('Team Count')   \n    teamsize = IntegerField ('Team Size (0 for no teams)')  \n    unit = StringField ('unit(2) eg 01 or MT', validators=[DataRequired(), Length(min=2, max=20)])                                         \n    submit = SubmitField('Join')\n\nclass RegistrationForm(FlaskForm):\n\n    username = StringField ('Name in English', \n                                validators=[DataRequired(), Length(min=2, max=20)])    \n    studentID = StringField ('Student ID (9 numbers)', \n                                validators=[DataRequired(), Length(9)])\n    email = StringField('Email', \n                                validators=[DataRequired(), Email()])  \n    device = RadioField('Main Device', \n                                choices = [('Apple', 'Apple iphone'), ('Android', 'Android Phone'), ('Win', 'Windows Phone')])                                \n    password = PasswordField('Password', \n                                validators=[DataRequired()] )\n    confirm_password = PasswordField('Confirm Password',  \n                                        validators=[DataRequired(), EqualTo('password')] )\n    submit = SubmitField('Join')\n\n    def validate_username(self, username):  # the field is username\n        user = User.query.filter_by(username=username.data).first()  #User was imported at the top # first means just find first instance?\n        if user:  # meaning if True\n            raise ValidationError('Another student has that username, please add family name')  # ValidationError needs to be imported from wtforms\n    \n    def validate_email(self, email): \n        user = User.query.filter_by(email=email.data).first()  \n        if user:  \n            raise ValidationError('That email has an account already, did you forget your password?') \n\n    def validate_studentID(self, studentID):  \n        try:\n            int(studentID.data)\n        except:\n            raise ValidationError('9 numbers; no S')\n        user = User.query.filter_by(studentID=studentID.data).first()  \n        if user:           \n            raise ValidationError('That student ID already has an account, did you forget your password?')\n        \n\nclass LoginForm(FlaskForm):\n    studentID = StringField ('Student ID', validators=[DataRequired(), Length(9)])     \n    password = PasswordField('Password', validators=[DataRequired()]) \n    remember = BooleanField('Remember Me')\n    submit = SubmitField('Login')\n\nclass ForgotForm(FlaskForm):\n    email = StringField ('Email', validators=[DataRequired(), Email()])         \n    submit = SubmitField('Request Password Reset')\n    \n    def validate_email(self, email): \n        user = User.query.filter_by(email=email.data).first()  \n        if user is None:  \n            raise ValidationError('There is no account with that email, contact your instructor') \n\nclass PasswordResetForm(FlaskForm):\n    password = PasswordField('Password', validators=[DataRequired()] )\n    confirm_password = PasswordField('Confirm Password', validators=[DataRequired(), EqualTo('password')] )\n    submit = SubmitField('Set New Password')\n\nclass UpdateAccountForm(FlaskForm):\n    #username = StringField ('Username', validators=[DataRequired(), Length(min=2, max=20)])    \n    email = StringField('Email', validators=[DataRequired(), Email()] )   \n    picture = FileField ('Change Profile Picture', validators=[FileAllowed(['jpg', 'png'])]) \n    submit = SubmitField('Update')\n\n    def validate_username(self, username):  # the field is username\n        if username.data != current_user.username: # if the updated one is the same then no need to validate\n            user = User.query.filter_by(username=username.data).first()  #User was imported at the top # first means just find first instance?\n            if user:  # meaning if True\n                raise ValidationError('That user name has been used already, please add another letter')  # ValidationError needs to be imported from wtforms\n    \n    def validate_email(self, email):  \n        if email.data != current_user.email:\n            user = User.query.filter_by(email=email.data).first()  \n            if user:  \n                raise ValidationError('That email has an account already, did you forget your password?')  \n    \n    def validate_studentID(self, studentID):  \n        if studentID.data != current_user.student.ID:\n            user = User.query.filter_by(studentID=studentID.data).first()  \n            if user:  \n                raise ValidationError('That student ID already has an account, did you forget your password?')  \n\n\nclass MidtermSetUp(FlaskForm):    \n    MT01 = RadioField (label='Number of students in team', choices = [('2', '2'), ('3', '3')])\n    MT02 = RadioField (label='Role Play Format', choices=[('Travel Prep (2)', 'Travel Prep (2)'), ('Travel Agent (2)','Travel Agent (2)'), \n    ('Travel Agent (3)','Travel Agent (3)'), ('Immigration (3)','Immigration (3)'), ('Hotel (3)','Hotel (3)')])\n    MT03 = StringField (label='Student ID 1', validators=[DataRequired(), Length(9)]) \n    MT04 = StringField (label='Student ID 2', validators=[DataRequired(), Length(9)])\n    MT05 = StringField (label='Student ID 3 (if only 2 students then use 000000000)', validators=[DataRequired(), Length(9)], default='000000000') \n    \n    Submit = SubmitField('Submit')\n\n    def validate_MT03(self, MT03):         \n        if MT03.data != current_user.studentID:          \n            raise ValidationError('This should be your student ID') \n        \n\n    def validate_MT04(self, MT04):        \n        user = User.query.filter_by(studentID=MT04.data).first()        \n        if user:\n            pass\n        else:\n            raise ValidationError('That student ID does not exist') \n\n    def validate_MT05(self, MT05):        \n        if MT05.data == '000000000':\n            pass\n        else:\n            user = User.query.filter_by(studentID=MT05.data).first()        \n            if user:\n                pass\n            else:\n                raise ValidationError('That student ID does not exist') \n\nclass MidtermDetails(FlaskForm):        \n    \n    MT06 = StringField (label='Link to your script', default='link') \n    MT07 = StringField (label='Link to your video (use Youtube or Google Drive)')\n    \n    MT08 = StringField (label='Question 1')\n    MT09 = StringField (label='Question 2')\n    MT10 = StringField (label='Question 3')\n    MT11 = StringField (label='Question 4')\n    \n    MT12 = StringField (label='Answer 1')\n    MT13 = StringField (label='Answer 2')\n    MT14 = StringField (label='Answer 3')\n    MT15 = StringField (label='Answer 4')\n\n    Submit = SubmitField('Submit')\n\nclass MidtermExample(FlaskForm):     \n    \n    A01 = StringField (label='Q1', validators=[DataRequired()])\n    A02 = StringField (label='Q2', validators=[DataRequired()])\n    A03 = StringField (label='Q3', validators=[DataRequired()])\n    A04 = StringField (label='Q4', validators=[DataRequired()])    \n\n    Submit = SubmitField('Submit')\n\n#### Agent Conversations\nAgentOne = {\n    '1a' : ['Hi, ', \n    ['How can I help you today?', 'How can I help you today?'],\n    ['What I can I help you with today?', 'What I can I help you with today?'],\n    ['What can I do for you today?' ,'What can I do for you today?']       \n    ],\n    '1b' : ['I have some vacation time coming up and ',\n        ['stress-free', 'I just want a stress-free holiday.'], \n        ['good value','I need a good value package.'], \n        ['no time','I don’t have time to organize a trip']\n    ],\n    '2a' : ['Okay, ', \n    ['How long do you have off work?', 'How long do you have off work?'],\n    ['How long do you have for vacation?','How long do you have for vacation?'],\n    ['How long is your break?','How long is your break?']     \n    ],\n    '2b' : ['Well, ',\n        ['5 days', 'I just have a 5 day break'], \n        ['8 days', 'I have 8 days off work'], \n        ['10 days','Just the first 10 days in July'], \n        ['16 days','Its about 16 days']\n    ],\n    '3a' : ['Great, that’s a nice amount of time. ',\n    ['Where would you like to go?','Where would you like to go?'],\n    ['Where did you have in mind?','Where did you have in mind?'],\n    ['Have you thought about a destination?','Have you thought about a destination?']\n    ],    \n    '3b' : ['Actually, I would love to spend time ',\n        ['nature', 'in nature'], \n        ['city','in a big city'], \n        ['ocean','close to the ocean']\n    ],\n    '4a' : ['Have you considered........',   \n        ['Nature: Hokkaido - City: Bangkok - Ocean: Bali', 'Nature: Hokkaido - City: Bangkok - Ocean: Bali'], \n        ['Nature: Chang Mai - City: Tokyo - Ocean: Okinawa','Nature: Chang Mai - City: Tokyo - Ocean: Okinawa'], \n        ['Nature: Guilin - City: Seoul - Ocean: Phuket', 'Nature: Guilin - City: Seoul - Ocean: Phuket']        \n    ],\n    '4b' : ['Okay, ', \n        ['Sounds good', 'Sounds good'], \n        ['Sounds perfect','Sounds perfect'], \n        ['Sounds great','Sounds great']               \n    ],\n    '5a' : ['No problem, ',\n        ['So what kind of place would like to stay in?','So what kind of place would like to stay in?'],\n        ['So what about your accommodation?', 'So what about your accommodation?'],\n        ['So what kind of accommodation would you like?', 'So what kind of accommodation would you like?']\n    ],    \n    '5b' : ['For my accomodation, ',\n        ['nothing too fancy', 'I don’t want anything too fancy.'], \n        ['just comfortable', 'I just want to be comfortable.'],\n        ['taken care of','I want everything to be taken care of.'], \n        ['by myself','I like to do things by myself']\n    ],\n    'xx' : ['Okay... ',\n    ['nothing too fancy -- ', 'perhaps a small guest house would suit you.'],   \n    ['just comfortable -- ', 'we can recommend a decent hotel.'],\n    ['taken care of -- ','there is an all-inclusive resort that you might like.'], \n    ['by myself -- ','we could look into renting an apartment in the area.']\n    ],\n    \n    '6b' : ['Thanks, before I go can I ask about ',\n        ['visa requirements', 'the visa requirements'], \n        ['car rental','the car rental options'], \n        ['local tours','local tours on offer']\n    ],\n    '6a' : ['Certainly, ',\n    ['We will find out what the options are available','We will find out what options are available'],\n    ['I’ll check the information and get back to you','I’ll check the information and get back to you'],\n    ['I will prepare the details for you','I will prepare the details for you']        \n    ],  \n}\n\n#### Immigration Conversations\nImmigOne = {\n    '1a' : ['Good afternoon, ', \n        'Where are coming from today?',\n        'Where have you flown from?',\n        'Where did you start your journey?'       \n    ],\n    '1b' : [\"I'm coming from \",\n        'Tokyo', \n        'New York', \n        'Paris', \n        'London', \n        'Bangkok'\n    ],\n    '1c' : ['Okay, ', \n        'May I have your passport?',\n        'Passport please', \n        'I need to see your passport'     \n    ],\n    '1d' : ['My passport, ',\n        'Here you go...', \n        'Here you are...', \n        'Right here ...' \n    ],\n    '2a' : ['Thank you, and ',\n        'What is the nature of your visit?',\n        'What is the purpose of your trip?',\n        'What do you plan to do here?'\n    ],    \n    '2b' : [\"I'm here \",\n        'for a vacation', \n        'to see family',\n        'for business',\n        'to visit a friend'\n    ],\n    '3a' : ['JOB: ',   \n        'What is your occupation?',\n        'What do you do in your home country?',\n        'What is your line of work?'            \n    ],\n    '3b' : ['I am a ', \n        'student',\n        'teacher',\n        'salesperson', \n        'doctor',\n        'nurse'\n    ],\n    '4a' : ['ACCOMODATION: ', \n        'Where will you be staying?',\n        'Where is your accommodation?',\n        'What address are you staying at?'       \n    ],\n    '4b' : ['I’m staying ',\n        'at a hotel',\n        'at a guest house',\n        'with a friend',\n        'with a relative'\n    ],  \n    '5a' : ['TIME: ',\n        'How long do you intend to stay?',\n        'What is the duration of your stay?',\n        'How long will you be in the country?'\n    ], \n    ###\n    '5b' : [\"I'm here for \",\n        ['one week', 'just over a week'], \n        ['20 days','for about 20 days'], \n        ['3 months','totally 3 months']\n    ],    \n    '6a' : ['VISIT BEFORE: ',\n        'Is this your first time here?',\n        'Have you been here before?'        \n    ],  \n    ###\n    '6b' : ['Actually..',\n        ['No', 'No, this is my first time'],\n        ['Yes', 'Yes, I was here last year']        \n    ], \n\n}\n\n\nHotelOne = {\n    '1g' : [\"Greeting\", \n        \"Hi,I'm here to check in\",\n        'Hello, may I check in?',\n        \"Hello, I know it's early but can I check in?\"                \n    ], \n    '1n' : ['NAME: ',\n        'The name is ',\n        'It is under ', \n        'We booked for ' \n    ],\n    '1a' : ['What kind of room did you book?, ', \n        'a double room ',\n        'a single room ',\n        'a twin room ', \n        'a deluxe room ', \n        'an economy room '       \n    ],\n    '1b' : ['How long did you reserve a room for?',\n        \"for the weekend\", \n        \"for one week\", \n        \"for 10 days\"       \n    ],\n    '1c' : ['What else do you request?',\n        \"a non-smoking room\", \n        \"an ocean view\", \n        \"an extra bed\"          \n    ],\n    '1d' : ['What else do you want to ask about?',\n        \"wifi?\", \n        \"a safe?\",          \n        \"a minibar?\"          \n    ],\n    '1e' : [\"What facilities do you need?\", \n        'a gym?',\n        'a swimming pool?',\n        'a laundry service?'\n    ], \n    '1f' : [\"Check the room: \", \n        'So is my room ready?',\n        'Can I check in now?',\n        'Is the booking okay?'\n    ], \n\n\n    '2a' : [\"Hello, welcome to \", \n        'Hilton Hotel', \n        'Hyatt Hotel', \n        'Marriott Hotel', \n        'Inter-Continental Hotel'\n    ],\n    '2b' : ['Okay, ', \n        'what is the booking name?',\n        'who is the reservation for?', \n        'is the room under your name?'     \n    ],    \n    '2c' : ['Thank you, ',\n        'let me check your booking',\n        'let me check the system',\n        'let me look up your details'\n    ],     \n    '2w' : ['Is there wifi in the room?',\n        'Yes, you can find your password in the room',\n        'No, but you can use free wifi in the lobby and bar'        \n    ], \n    '2s' : ['Is there a safe in the room?',\n        'Yes, just follow the instructions inside',\n        'No, but you can store your valuables here at reception'       \n    ], \n    '2m' : ['Is there a minibar in the room?',\n        'Yes, you can see the prices inside',\n        'No, but you can order room service anytime'       \n    ],\n    '2g' : ['Is there a gym?',\n        \"Yes, we have a full gym on the top floor\",\n        \"No, sorry but it's closed this week for repairs\" \n    ], \n    '2p' : ['Is there a swimming pool?',\n        \"Yes, it's downstairs on floor B2\",\n        'No, but we have a map to the local pool'       \n    ], \n    '2l' : ['Is there a laundry service?',\n        \"Yes, you can give it to our housekeeping staff\",\n        'No, but we have a self service laundry room'       \n    ], \n    '2e' : [\"I've checked your room and \",\n        \"Congratulations, we have given you an upgrade!\", \n        \"Your room is ready and you can go up now!\", \n        \"Here is your keycard, enjoy your stay!\"    \n    ],\n    '2f' : [\"I've checked your room and \",\n        \"the room is not ready yet, please wait in the lobby.\", \n        \"we are overbooked and your is taken already.\", \n        \"there was a mistake with your booking so we changed your room.\"    \n    ]\n}\n\n\n\nclass HotGuest(FlaskForm): \n    A01 = RadioField (label=HotelOne['1g'][0], choices=[\n            (HotelOne['1g'][1], HotelOne['1g'][1]), \n            (HotelOne['1g'][2], HotelOne['1g'][2]), \n            (HotelOne['1g'][3], HotelOne['1g'][3])\n        ])     \n    A02 = RadioField (label=HotelOne['1n'][0], choices=[\n            (HotelOne['1n'][1], HotelOne['1n'][1]), \n            (HotelOne['1n'][2], HotelOne['1n'][2]), \n            (HotelOne['1n'][3], HotelOne['1n'][3])\n        ])  \n    A03 = RadioField (label=HotelOne['1a'][0], choices=[\n            (HotelOne['1a'][1], HotelOne['1a'][1]), \n            (HotelOne['1a'][2], HotelOne['1a'][2]),\n            (HotelOne['1a'][3], HotelOne['1a'][3]),\n            (HotelOne['1a'][4], HotelOne['1a'][4]),\n            (HotelOne['1a'][5], HotelOne['1a'][5])            \n        ])     \n    A04 = RadioField (label=HotelOne['1b'][0], choices=[\n            (HotelOne['1b'][1], HotelOne['1b'][1]), \n            (HotelOne['1b'][2], HotelOne['1b'][2]),\n            (HotelOne['1b'][3], HotelOne['1b'][3])            \n        ])  \n    A05 = RadioField (label=HotelOne['1c'][0], choices=[\n            (HotelOne['1c'][1], HotelOne['1c'][1]), \n            (HotelOne['1c'][2], HotelOne['1c'][2]),\n            (HotelOne['1c'][3], HotelOne['1c'][3])            \n        ])  \n    A06 = RadioField (label=HotelOne['1d'][0], choices=[\n            (HotelOne['1d'][1], HotelOne['1d'][1]), \n            (HotelOne['1d'][2], HotelOne['1d'][2]),\n            (HotelOne['1d'][3], HotelOne['1d'][3])            \n        ])  \n    A07 = RadioField (label=HotelOne['1e'][0], choices=[\n            (HotelOne['1e'][1], HotelOne['1e'][1]), \n            (HotelOne['1e'][2], HotelOne['1e'][2]),\n            (HotelOne['1e'][3], HotelOne['1e'][3])            \n        ])    \n    A08 = RadioField (label=HotelOne['1f'][0], choices=[\n            (HotelOne['1f'][1], HotelOne['1f'][1]), \n            (HotelOne['1f'][2], HotelOne['1f'][2]),\n            (HotelOne['1f'][3], HotelOne['1f'][3])            \n        ])  \n    \n    Submit = SubmitField('Submit')  \n\nclass HotListen(FlaskForm): \n    C01 = StringField (label='Guest Name')        \n    Submit = SubmitField('Check Booking') \n\nclass HotList2(FlaskForm): \n    A01 = RadioField (label=HotelOne['2a'][0], choices=[\n            (HotelOne['2a'][1], HotelOne['2a'][1]), \n            (HotelOne['2a'][2], HotelOne['2a'][2]), \n            (HotelOne['2a'][3], HotelOne['2a'][3]), \n            (HotelOne['2a'][4], HotelOne['2a'][4])\n        ])    \n    Submit = SubmitField('Check Booking') \n\nclass HotClerk(FlaskForm): \n    A01 = RadioField (label=HotelOne['2a'][0], choices=[\n            (HotelOne['2a'][1], HotelOne['2a'][1]), \n            (HotelOne['2a'][2], HotelOne['2a'][2]), \n            (HotelOne['2a'][3], HotelOne['2a'][3]), \n            (HotelOne['2a'][4], HotelOne['2a'][4])\n        ])\n        \n    A02 = RadioField (label=HotelOne['2b'][0], choices=[\n            (HotelOne['2b'][1], HotelOne['2b'][1]), \n            (HotelOne['2b'][2], HotelOne['2b'][2]), \n            (HotelOne['2b'][3], HotelOne['2b'][3])\n        ])  \n    A03 = RadioField (label=HotelOne['2c'][0], choices=[\n            (HotelOne['2c'][1], HotelOne['2c'][1]), \n            (HotelOne['2c'][2], HotelOne['2c'][2]), \n            (HotelOne['2c'][3], HotelOne['2c'][3])\n        ])  \n    A04 = RadioField (label=HotelOne['2w'][0], choices=[\n            (HotelOne['2w'][1], HotelOne['2w'][1]), \n            (HotelOne['2w'][2], HotelOne['2w'][2])\n        ]) \n    A05 = RadioField (label=HotelOne['2s'][0], choices=[\n            (HotelOne['2s'][1], HotelOne['2s'][1]), \n            (HotelOne['2s'][2], HotelOne['2s'][2])\n        ]) \n    A06 = RadioField (label=HotelOne['2m'][0], choices=[\n            (HotelOne['2m'][1], HotelOne['2m'][1]), \n            (HotelOne['2m'][2], HotelOne['2m'][2])\n        ]) \n    A07 = RadioField (label=HotelOne['2g'][0], choices=[\n            (HotelOne['2g'][1], HotelOne['2g'][1]), \n            (HotelOne['2g'][2], HotelOne['2g'][2])\n        ]) \n    A08 = RadioField (label=HotelOne['2p'][0], choices=[\n            (HotelOne['2p'][1], HotelOne['2p'][1]), \n            (HotelOne['2p'][2], HotelOne['2p'][2])\n        ])   \n    A09 = RadioField (label=HotelOne['2l'][0], choices=[\n            (HotelOne['2l'][1], HotelOne['2l'][1]), \n            (HotelOne['2l'][2], HotelOne['2l'][2])\n        ])\n    A10 = RadioField (label=HotelOne['2e'][0], choices=[\n            (HotelOne['2e'][1], HotelOne['2e'][1]), \n            (HotelOne['2e'][2], HotelOne['2e'][2]),\n            (HotelOne['2e'][3], HotelOne['2e'][3])\n        ])\n    A11 = RadioField (label=HotelOne['2f'][0], choices=[\n            (HotelOne['2f'][1], HotelOne['2f'][1]), \n            (HotelOne['2f'][2], HotelOne['2f'][2]),\n            (HotelOne['2f'][3], HotelOne['2f'][3])\n        ])      \n             \n    Submit = SubmitField('Submit')         \n\n\nclass ImmigOfficer(FlaskForm): \n    A01 = RadioField (label=ImmigOne['1a'][0], choices=[\n            (ImmigOne['1a'][1], ImmigOne['1a'][1]), \n            (ImmigOne['1a'][2], ImmigOne['1a'][2]), \n            (ImmigOne['1a'][3], ImmigOne['1a'][3])\n        ]) \n    A01x = RadioField (label=ImmigOne['1c'][0], choices=[\n            (ImmigOne['1c'][1], ImmigOne['1c'][1]), \n            (ImmigOne['1c'][2], ImmigOne['1c'][2]), \n            (ImmigOne['1c'][3], ImmigOne['1c'][3])\n        ])  \n    A02 = RadioField (label=ImmigOne['2a'][0], choices=[\n            (ImmigOne['2a'][1], ImmigOne['2a'][1]), \n            (ImmigOne['2a'][2], ImmigOne['2a'][2]), \n            (ImmigOne['2a'][3], ImmigOne['2a'][3])\n        ])  \n    A03 = RadioField (label=ImmigOne['3a'][0], choices=[\n            (ImmigOne['3a'][1], ImmigOne['3a'][1]), \n            (ImmigOne['3a'][2], ImmigOne['3a'][2]), \n            (ImmigOne['3a'][3], ImmigOne['3a'][3])\n        ])     \n    A04 = RadioField (label=ImmigOne['4a'][0], choices=[\n            (ImmigOne['4a'][1], ImmigOne['4a'][1]), \n            (ImmigOne['4a'][2], ImmigOne['4a'][2]), \n            (ImmigOne['4a'][3], ImmigOne['4a'][3])\n        ]) \n    A05 = RadioField (label=ImmigOne['5a'][0], choices=[\n            (ImmigOne['5a'][1], ImmigOne['5a'][1]), \n            (ImmigOne['5a'][2], ImmigOne['5a'][2]), \n            (ImmigOne['5a'][3], ImmigOne['5a'][3])\n        ]) \n    A06 = RadioField (label=ImmigOne['6a'][0], choices=[\n            (ImmigOne['6a'][1], ImmigOne['6a'][1]), \n            (ImmigOne['6a'][2], ImmigOne['6a'][2])            \n        ])        \n    Submit = SubmitField('Submit')  \n\nclass ImmigListen(FlaskForm): \n    C01 = RadioField (label=ImmigOne['1b'][0], choices=[\n            (ImmigOne['1b'][1], ImmigOne['1b'][1]), \n            (ImmigOne['1b'][2], ImmigOne['1b'][2]), \n            (ImmigOne['1b'][3], ImmigOne['1b'][3]),\n            (ImmigOne['1b'][4], ImmigOne['1b'][4]),\n            (ImmigOne['1b'][5], ImmigOne['1b'][5])\n        ])     \n    C02 = RadioField (label=ImmigOne['2b'][0], choices=[\n            (ImmigOne['2b'][1], ImmigOne['2b'][1]), \n            (ImmigOne['2b'][2], ImmigOne['2b'][2]), \n            (ImmigOne['2b'][3], ImmigOne['2b'][3]),\n            (ImmigOne['2b'][4], ImmigOne['2b'][4])\n        ])  \n    C03 = RadioField (label=ImmigOne['3b'][0], choices=[\n            (ImmigOne['3b'][1], ImmigOne['3b'][1]), \n            (ImmigOne['3b'][2], ImmigOne['3b'][2]), \n            (ImmigOne['3b'][3], ImmigOne['3b'][3]),\n            (ImmigOne['3b'][4], ImmigOne['3b'][4]),\n            (ImmigOne['3b'][5], ImmigOne['3b'][5])\n        ])     \n    C04 = RadioField (label=ImmigOne['4b'][0], choices=[\n            (ImmigOne['4b'][1], ImmigOne['4b'][1]), \n            (ImmigOne['4b'][2], ImmigOne['4b'][2]), \n            (ImmigOne['4b'][3], ImmigOne['4b'][3]),\n            (ImmigOne['4b'][4], ImmigOne['4b'][4])\n        ]) \n    C05 = RadioField (label=ImmigOne['5b'][0], choices=[\n            (ImmigOne['5b'][1][1], ImmigOne['5b'][1][0]), \n            (ImmigOne['5b'][2][1], ImmigOne['5b'][2][0]), \n            (ImmigOne['5b'][3][1], ImmigOne['5b'][3][0]),            \n        ]) \n    C06 = RadioField (label=ImmigOne['6b'][0], choices=[\n            (ImmigOne['6b'][1][1], ImmigOne['6b'][1][0]), \n            (ImmigOne['6b'][2][1], ImmigOne['6b'][2][0])        \n        ])        \n    Submit = SubmitField('Submit') \n\nclass ImmigTraveller(FlaskForm): \n    A01 = RadioField (label=ImmigOne['1b'][0], choices=[\n            (ImmigOne['1b'][1], ImmigOne['1b'][1]), \n            (ImmigOne['1b'][2], ImmigOne['1b'][2]), \n            (ImmigOne['1b'][3], ImmigOne['1b'][3]),\n            (ImmigOne['1b'][4], ImmigOne['1b'][4]),\n            (ImmigOne['1b'][5], ImmigOne['1b'][5])\n        ]) \n    A01x = RadioField (label=ImmigOne['1d'][0], choices=[\n            (ImmigOne['1d'][1], ImmigOne['1d'][1]), \n            (ImmigOne['1d'][2], ImmigOne['1d'][2]), \n            (ImmigOne['1d'][3], ImmigOne['1d'][3])\n        ])  \n    A02 = RadioField (label=ImmigOne['2b'][0], choices=[\n            (ImmigOne['2b'][1], ImmigOne['2b'][1]), \n            (ImmigOne['2b'][2], ImmigOne['2b'][2]), \n            (ImmigOne['2b'][3], ImmigOne['2b'][3]),\n            (ImmigOne['2b'][4], ImmigOne['2b'][4])\n        ])  \n    A03 = RadioField (label=ImmigOne['3b'][0], choices=[\n            (ImmigOne['3b'][1], ImmigOne['3b'][1]), \n            (ImmigOne['3b'][2], ImmigOne['3b'][2]), \n            (ImmigOne['3b'][3], ImmigOne['3b'][3]),\n            (ImmigOne['3b'][4], ImmigOne['3b'][4]),\n            (ImmigOne['3b'][5], ImmigOne['3b'][5])\n        ])     \n    A04 = RadioField (label=ImmigOne['4b'][0], choices=[\n            (ImmigOne['4b'][1], ImmigOne['4b'][1]), \n            (ImmigOne['4b'][2], ImmigOne['4b'][2]), \n            (ImmigOne['4b'][3], ImmigOne['4b'][3]),\n            (ImmigOne['4b'][4], ImmigOne['4b'][4])\n        ]) \n    A05 = RadioField (label=ImmigOne['5b'][0], choices=[\n            (ImmigOne['5b'][1][1], ImmigOne['5b'][1][1]), \n            (ImmigOne['5b'][2][1], ImmigOne['5b'][2][1]), \n            (ImmigOne['5b'][3][1], ImmigOne['5b'][3][1])            \n        ]) \n    A06 = RadioField (label=ImmigOne['6b'][0], choices=[\n            (ImmigOne['6b'][1][1], ImmigOne['6b'][1][1]), \n            (ImmigOne['6b'][2][1], ImmigOne['6b'][2][1])        \n        ])        \n    Submit = SubmitField('Submit')         \n\nclass AgentListen(FlaskForm): \n    C01 = RadioField (label='C01', choices=[\n            (AgentOne['1b'][1][1], AgentOne['1b'][1][0]), \n            (AgentOne['1b'][2][1], AgentOne['1b'][2][0]), \n            (AgentOne['1b'][3][1], AgentOne['1b'][3][0])\n        ]) \n    C02 = RadioField (label='C02', choices=[\n            (AgentOne['2b'][1][1], AgentOne['2b'][1][0]), \n            (AgentOne['2b'][2][1], AgentOne['2b'][2][0]), \n            (AgentOne['2b'][3][1], AgentOne['2b'][3][0]),\n            (AgentOne['2b'][4][1], AgentOne['2b'][4][0])\n        ]) \n    C03 = RadioField (label='C03', choices=[\n            (AgentOne['3b'][1][1], AgentOne['3b'][1][0]), \n            (AgentOne['3b'][2][1], AgentOne['3b'][2][0]), \n            (AgentOne['3b'][3][1], AgentOne['3b'][3][0])\n        ]) \n    C04 = RadioField (label='C04', choices=[\n            (AgentOne['4b'][1][1], AgentOne['4b'][1][0]), \n            (AgentOne['4b'][2][1], AgentOne['4b'][2][0]), \n            (AgentOne['4b'][3][1], AgentOne['4b'][3][0])\n        ]) \n    C05 = RadioField (label='C05', choices=[\n            (AgentOne['5b'][1][1], AgentOne['5b'][1][0]), \n            (AgentOne['5b'][2][1], AgentOne['5b'][2][0]), \n            (AgentOne['5b'][3][1], AgentOne['5b'][3][0]),\n            (AgentOne['5b'][4][1], AgentOne['5b'][4][0])\n        ]) \n    C06 = RadioField (label='C06', choices=[\n            (AgentOne['6b'][1][1], AgentOne['6b'][1][0]), \n            (AgentOne['6b'][2][1], AgentOne['6b'][2][0]), \n            (AgentOne['6b'][3][1], AgentOne['6b'][3][0])           \n        ]) \n    Submit = SubmitField('Submit')\n\nclass AgentCustomer(FlaskForm): \n    A01 = RadioField (label=AgentOne['1b'][0], choices=[\n            (AgentOne['1b'][1][1], AgentOne['1b'][1][1]), \n            (AgentOne['1b'][2][1], AgentOne['1b'][2][1]), \n            (AgentOne['1b'][3][1], AgentOne['1b'][3][1])\n        ]) \n    A02 = RadioField (label=AgentOne['2b'][0], choices=[\n            (AgentOne['2b'][1][1], AgentOne['2b'][1][1]), \n            (AgentOne['2b'][2][1], AgentOne['2b'][2][1]), \n            (AgentOne['2b'][3][1], AgentOne['2b'][3][1]),\n            (AgentOne['2b'][4][1], AgentOne['2b'][4][1])\n        ])  \n    A03 = RadioField (label=AgentOne['3b'][0], choices=[\n            (AgentOne['3b'][1][1], AgentOne['3b'][1][1]), \n            (AgentOne['3b'][2][1], AgentOne['3b'][2][1]), \n            (AgentOne['3b'][3][1], AgentOne['3b'][3][1])\n        ]) \n    A04 = RadioField (label=AgentOne['4b'][0], choices=[\n            (AgentOne['4b'][1][1], AgentOne['4b'][1][1]), \n            (AgentOne['4b'][2][1], AgentOne['4b'][2][1]), \n            (AgentOne['4b'][3][1], AgentOne['4b'][3][1])\n        ]) \n    A05 = RadioField (label=AgentOne['5b'][0], choices=[\n            (AgentOne['5b'][1][1], AgentOne['5b'][1][1]), \n            (AgentOne['5b'][2][1], AgentOne['5b'][2][1]), \n            (AgentOne['5b'][3][1], AgentOne['5b'][3][1]),\n                (AgentOne['5b'][4][1], AgentOne['5b'][4][1])\n        ]) \n    A06 = RadioField (label=AgentOne['6b'][0], choices=[\n            (AgentOne['6b'][1][1], AgentOne['6b'][1][1]), \n            (AgentOne['6b'][2][1], AgentOne['6b'][2][1]), \n            (AgentOne['6b'][3][1], AgentOne['6b'][3][1]),\n               \n        ])     \n    Submit = SubmitField('Submit')\n\nclass AgentWorker(FlaskForm): \n    A01 = RadioField (label=AgentOne['1a'][0], choices=[\n            (AgentOne['1a'][1][1], AgentOne['1a'][1][1]), \n            (AgentOne['1a'][2][1], AgentOne['1a'][2][1]), \n            (AgentOne['1a'][3][1], AgentOne['1a'][3][1])\n        ]) \n    A02 = RadioField (label=AgentOne['2a'][0], choices=[\n            (AgentOne['2a'][1][1], AgentOne['2a'][1][1]), \n            (AgentOne['2a'][2][1], AgentOne['2a'][2][1]), \n            (AgentOne['2a'][3][1], AgentOne['2a'][3][1])\n        ])  \n    A03 = RadioField (label=AgentOne['3a'][0], choices=[\n            (AgentOne['3a'][1][1], AgentOne['3a'][1][1]), \n            (AgentOne['3a'][2][1], AgentOne['3a'][2][1]), \n            (AgentOne['3a'][3][1], AgentOne['3a'][3][1])\n        ])     \n    A04 = RadioField (label=AgentOne['4a'][0], choices=[\n            (AgentOne['4a'][1][1], AgentOne['4a'][1][1]), \n            (AgentOne['4a'][2][1], AgentOne['4a'][2][1]), \n            (AgentOne['4a'][3][1], AgentOne['4a'][3][1])\n        ]) \n    A05 = RadioField (label=AgentOne['5a'][0], choices=[\n            (AgentOne['5a'][1][1], AgentOne['5a'][1][1]), \n            (AgentOne['5a'][2][1], AgentOne['5a'][2][1]), \n            (AgentOne['5a'][3][1], AgentOne['5a'][3][1])\n        ])  \n    A06 = RadioField (label=AgentOne['6a'][0], choices=[\n            (AgentOne['6a'][1][1], AgentOne['6a'][1][1]), \n            (AgentOne['6a'][2][1], AgentOne['6a'][2][1]), \n            (AgentOne['6a'][3][1], AgentOne['6a'][3][1])\n    ])  \n      \n    Submit = SubmitField('Submit')  \n\n\n\nclass P_InOut(FlaskForm):    \n    Tex = TextAreaField (label='Text')   \n    Pic = FileField (label='Picture Upload', validators=[FileAllowed(['jpg', 'png'], message=\"Please upload jpeg/png image\")])\n    Rec = FileField (label='Recording Upload', validators=[FileAllowed(['mp3', 'm4a', 'mp4'], message=\"Please upload mp3/m4a/mp4\")])\n    Submit = SubmitField('Submit')\n\nclass P_Part(FlaskForm):\n    Tex  = TextAreaField (label='Text')        \n    Pic  = FileField (label='Picture Upload', validators=[FileAllowed(['jpg', 'png'], message=\"Please upload jpeg/png image\")])\n    Rec  = FileField (label='Record Upload', validators=[FileAllowed(['mp3', 'm4a', 'mp4'], message=\"Please upload mp3/m4a/mp4\")])\n    Q1  = TextAreaField (label='Question 1')\n    A1  = TextAreaField (label='Answer 1')    \n    QA1rec  = FileField (label='Q&A 1 Rec', validators=[FileAllowed(['mp3', 'm4a', 'mp4'], message=\"Please upload mp3/m4a/mp4\")]) \n    Q2  = TextAreaField (label='Question 2')\n    A2  = TextAreaField (label='Answer 2') \n    QA2rec = FileField (label='Q&A 2 Rec', validators=[FileAllowed(['mp3', 'm4a', 'mp4'], message=\"Please upload mp3/m4a/mp4\")]) \n    Submit = SubmitField('Submit')    \n\nclass P_Part_TD(FlaskForm):\n    Tex  = TextAreaField (label='Text')        \n    Pic  = FileField (label='Picture Upload', validators=[FileAllowed(['jpg', 'png'], message=\"Please upload jpeg/png image\")])\n    Rec  = FileField (label='Record Upload', validators=[FileAllowed(['mp3', 'm4a', 'mp4'], message=\"Please upload mp3/m4a/mp4\")])\n    Q1  = TextAreaField (label='Activity 1')\n    A1  = FileField (label='Picture Upload', validators=[FileAllowed(['jpg', 'png'], message=\"Please upload jpeg/png image\")])\n    QA1rec  = FileField (label='Activity 1 Rec', validators=[FileAllowed(['mp3', 'm4a', 'mp4'], message=\"Please upload mp3/m4a/mp4\")]) \n    Q2  = TextAreaField (label='Activity 2')\n    A2  = FileField (label='Picture Upload', validators=[FileAllowed(['jpg', 'png'], message=\"Please upload jpeg/png image\")])\n    QA2rec = FileField (label='Activity 2 Rec', validators=[FileAllowed(['mp3', 'm4a', 'mp4'], message=\"Please upload mp3/m4a/mp4\")]) \n    Submit = SubmitField('Submit')   \n\n\nclass P_Ans(FlaskForm):\n    Part0 = StringField (label='Describe one detail mentioned in the introduction')   \n    Part1 = StringField (label='Describe one detail mentioned about this part.')       \n    Part11 = StringField (label='Answer')  \n    Part12 = StringField (label='Answer') \n    Part2 = StringField (label='Describe one detail mentioned about this part.')       \n    Part21 = StringField (label='Answer')  \n    Part22 = StringField (label='Answer') \n    Part3 = StringField (label='Describe one detail mentioned about this part.')       \n    Part31 = StringField (label='Answer')  \n    Part32 = StringField (label='Answer')\n    Part4 = StringField (label='Describe one detail mentioned.')\n    Submit = SubmitField('Submit')    \n\nclass P_Ans3(FlaskForm):\n    Part0 = StringField (label='Describe one detail mentioned in the introduction')   \n    Part1 = StringField (label='Describe something about the destination')       \n    Part11 = StringField (label='Describe something about the activity')  \n    Part12 = StringField (label='Describe something about the activity') \n    Part2 = StringField (label='Describe something about the destination')       \n    Part21 = StringField (label='Describe something about the activity')  \n    Part22 = StringField (label='Describe something about the activity') \n    Part3 = StringField (label='Describe something about the destination')       \n    Part31 = StringField (label='Describe something about the activity')  \n    Part32 = StringField (label='Describe something about the activity')\n    Part4 = StringField (label='Describe one detail from the closing.')\n    Submit = SubmitField('Submit')  \n\n    \n    \n\n\n","sub_path":"forms.py","file_name":"forms.py","file_ext":"py","file_size_in_byte":37123,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"201948198","text":"import random\n\n# para:\n#   raw: an np array\ndef binarize(raw):\n    raw[raw <= 127] = -1\n    raw[raw > 127] = 1\n    return raw\n\n# para:\n#   raw: an np array\ndef debinarize(raw):\n    raw[raw == -1] = 0\n    raw[raw == 1] = 255\n    return raw\n\n# para:\n#     p: probability\n#   raw: an np array\ndef noise(prob, raw):\n    for i in range(len(raw)):\n        if random.random() <= prob:\n            raw[i] *= -1\n    return raw\n\n# para:\n#   observed: matrix of points of the observed image\n#    recsted: matrix of points of the reconstructed image\n#      pixel: a tuple of which the energy is to be calculated\n#      a & b: factors\ndef energy(observed, recsted, pixel, a, b):\n    e1 = -observed[pixel] * recsted[pixel]\n    e2 = 0\n    for i in range(-1, 2):\n        for j in range(-1, 2):\n            if i == 0 and j == 0:\n                continue\n            newRow = pixel[0] + i\n            newCol = pixel[1] + j\n            if newRow < 0 or newRow >= len(observed) or newCol < 0 or newCol >= len(observed[0]):\n                continue\n            e2 -= recsted[(newRow, newCol)] * recsted[pixel]\n\n    return a * e1 + b * e2","sub_path":"image-denoising/Denoising.py","file_name":"Denoising.py","file_ext":"py","file_size_in_byte":1116,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"212156839","text":"def eh_primo(numero):\n    i=0\n    impar=1\n    while i < numero and impar < numero:\n        if numero == 2:\n            resposta = 'True'\n        elif numero % 2 ==0 or numero % impar == 0:\n            resposta = 'False'\n        else:\n            resposta= 'True'\n        i+=1\n        impar+=2\n    return resposta\nprimo=13\nnaoprimo=2\nnp=8\nprint(eh_primo(primo))\nprint(eh_primo(naoprimo))\nprint(eh_primo(np))","sub_path":"backup/user_238/ch36_2019_03_30_18_24_51_393703.py","file_name":"ch36_2019_03_30_18_24_51_393703.py","file_ext":"py","file_size_in_byte":406,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"115148647","text":"import math\nimport pdb\nimport random\nimport numpy as np\n\ndef ucb(scen, greedy=False, lil_ucb=False, alpha=9, beta=1, epsilon=0.01, delta=0.005): # scen: a list of reward distr\n    num_actions = len(scen)\n    payoff_sums = np.zeros(num_actions)\n    num_pulls = np.zeros(num_actions)\n    ucbs = np.zeros(num_actions)\n\n    # initialize empirical sums\n    for t in range(num_actions):\n       payoff_sums[t] = scen[t].get_reward()\n       num_pulls[t] += 1\n       yield t, payoff_sums[t], num_pulls, payoff_sums, ucbs\n\n    t = num_actions\n\n    while True:\n        if greedy:\n            ucbs_bound = np.zeros(num_actions)\n        else:\n            ucbs_bound = np.array([scen[i].get_upper_bound(t, num_pulls[i], lil_ucb, alpha, beta, epsilon, delta) for i in range(num_actions)])\n        ucbs = np.array([payoff_sums[i] / num_pulls[i] + ucbs_bound[i] for i in range(num_actions)])\n        mx = max(ucbs)\n        all_maxes = [i for i in range(num_actions) if ucbs[i] == mx]\n        action = random.choice(all_maxes)\n        reward = scen[action].get_reward()\n        num_pulls[action] += 1\n        payoff_sums[action] += reward \n\n        yield action, reward, num_pulls, payoff_sums, ucbs_bound\n        t = t + 1\n","sub_path":"ucb.py","file_name":"ucb.py","file_ext":"py","file_size_in_byte":1206,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"296470646","text":"import tensorflow as tf\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport data\n\nclass TFLogreg:\n\tdef __init__(self, D, C, param_delta=0.5, param_lambda=0.1):\n\t\t\"\"\"Arguments:\n\t\t\t- D: dimensions of each datapoint \n\t\t\t- C: number of classes\n\t\t\t- param_delta: training step\n\t\t\"\"\"\n\n\t\t# definicija podataka i parametara:\n\t\t# definirati self.X, self.Yoh_, self.W, self.b\n\t\t# ...\n\n\t\tself.X = tf.placeholder(tf.float32, [None, D])\n\t\tself.Yoh_ = tf.placeholder(tf.float32, [None, C])\n\t\tself.W = tf.Variable(tf.random_normal([C, D]))\n\t\tself.b = tf.Variable(tf.zeros([C]))\n\n\t\t# formulacija modela: izračunati self.probs\n\t\t#   koristiti: tf.matmul, tf.nn.softmax\n\t\t# ...\n\n\t\tscores = tf.matmul(self.X, self.W, transpose_b=True) + self.b\n\t\tself.probs = tf.nn.softmax(scores)\n\n\t\t# formulacija gubitka: self.loss\n\t\t#   koristiti: tf.log, tf.reduce_sum, tf.reduce_mean\n\t\t# ...\n\n\t\tlog_probs = -tf.log(self.probs)\n\t\terr_loss = tf.reduce_sum(self.Yoh_ * log_probs, 1)\n\t\treg_loss = tf.nn.l2_loss(self.W)\n\t\tself.loss = tf.reduce_mean(err_loss) + param_lambda * reg_loss\n\n\t\t# formulacija operacije učenja: self.train_step\n\t\t#   koristiti: tf.train.GradientDescentOptimizer,\n\t\t#              tf.train.GradientDescentOptimizer.minimize\n\t\t# ...\n\n\t\ttrainer = tf.train.GradientDescentOptimizer(param_delta)\n\t\tself.train_step = trainer.minimize(self.loss)\n\n\t\t# instanciranje izvedbenog konteksta: self.session\n\t\t#   koristiti: tf.Session\n\t\t# ...\n\n\t\tself.session = tf.Session()\n\n\tdef train(self, X, Yoh_, param_niter):\n\t\t\"\"\"Arguments:\n\t\t\t- X: actual datapoints [NxD]\n\t\t\t- Yoh_: one-hot encoded labels [NxC]\n\t\t\t- param_niter: number of iterations\n\t\t\"\"\"\n\t\t# incijalizacija parametara\n\t\t#   koristiti: tf.initialize_all_variables\n\t\t# ...\n\n\t\tself.session.run(tf.initialize_all_variables())\n\t\tdata_dict = {self.X: X, self.Yoh_: Yoh_}\n\n\t\t# optimizacijska petlja\n\t\t#   koristiti: tf.Session.run\n\t\t# ...\n\t\tfor i in range(param_niter):\n\t\t\tval_loss, _ = self.session.run([self.loss, self.train_step], feed_dict=data_dict)\n\t\t\tif i % 100 == 0:\n\t\t\t\tprint(\"iteration {}: loss {}\".format(i, val_loss))\n\n\tdef eval(self, X):\n\t\t\"\"\"Arguments:\n\t\t\t- X: actual datapoints [NxD]\n\t\t\tReturns: predicted class probabilites [NxC]\n\t\t\"\"\"\n\t\t#   koristiti: tf.Session.run\n\t\tprobs = self.session.run(self.probs, {self.X: X})\n\t\treturn probs\n\nif __name__ == '__main__':\n\tnp.random.seed(100)\n\ttf.set_random_seed(100)\n\n\tX, Y_, Yoh_ = data.sample_gauss_2d(3, 100, one_hot=True)\n\n\t_, D = X.shape\n\t_, C = Yoh_.shape\n\n\ttflr = TFLogreg(D, C, 0.1, 0.25)\n\ttflr.train(X, Yoh_, 1000)\n\n\tprobs = tflr.eval(X)\n\n\tY = probs.argmax(axis=1)\n\tdec_fun = lambda X: tflr.eval(X).argmax(axis=1)\n\n\trect = (np.min(X, axis=0), np.max(X, axis=0))\n\n\tdata.graph_surface(dec_fun, rect)\n\tdata.graph_data(X, Y_, Y)\n\tplt.show()\n","sub_path":"lab01/tf_logreg.py","file_name":"tf_logreg.py","file_ext":"py","file_size_in_byte":2740,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"541072853","text":"import statistics as stats\nimport time\n\nimport Adafruit_ADS1x15\n\nfrom api.power_supply import PowerSupply\nfrom api.relay import Relay\n\n\ndef signnum(value):\n    return int(value / abs(value)) if value != 0 else 0\n\n\nclass Demagnetizer:\n    GAIN = 2\n    TRIALS = 10\n\n    def __init__(self, ps: PowerSupply, relay_1: Relay, relay_2: Relay,\n                 hall_sensor_pin: int = 0):\n        self.ps = ps\n\n        self.hall_sensor_pin = hall_sensor_pin\n        self.adc = Adafruit_ADS1x15.ADS1115()\n\n        self.relay_1 = relay_1\n        self.relay_2 = relay_2\n\n    def get_field(self, difference=False) -> int:\n        \"\"\"\n        Returns outlier-omitted mean readings from hall sensor\n        :param difference: Set to True if differential reading required\n        :return: Outlier-omitted average of trials\n        \"\"\"\n        readings = []\n\n        for i in range(Demagnetizer.TRIALS):\n            if difference:\n                readings.append(self.adc.read_adc_difference(\n                    self.hall_sensor_pin, gain=Demagnetizer.GAIN))\n            else:\n                readings.append(self.adc.read_adc(self.hall_sensor_pin,\n                                                  gain=Demagnetizer.GAIN))\n\n        mean = stats.mean(readings)\n        deviation = stats.stdev(readings)\n\n        # Remove outliers in readings to account for bouncing or interference\n        upper_bound = mean + deviation\n        lower_bound = mean - deviation\n\n        # Filter outliers\n        filt_readings = [reading for reading in readings if\n                         lower_bound < reading < upper_bound]\n\n        return int(stats.mean(filt_readings)) if len(filt_readings) > 0 else -1\n\n    def get_field_average(self, trials: int) -> int:\n        \"\"\"\n        Takes an average of the outlier-omitted field readings over a specified\n        number of trials\n        :param trials: Number of trials\n        :return: Average of all trials\n        \"\"\"\n        value = 0\n        for i in range(trials):\n            try:\n                value += self.get_field()\n            except Exception as err:\n                print(err)\n                return -1\n        return int(value / trials)\n\n    def calibrate(self, trials=20) -> int:\n        \"\"\"\n        Gets the no-field reading from the Hall Sensor\n        :param trials: Number of trials\n        :return: No field reading\n        \"\"\"\n        print('Calibrating no field condition...')\n        no_field = self.get_field_average(trials)\n        print('Calibration complete')\n\n        return no_field\n\n    def demag_current(self, no_field: int, saturation_current: float = 1.5,\n                      demag_current: float = 0.05,\n                      termination_threshold: float = 0.004):\n        \"\"\"\n        Runs the demagnetization routine using current\n        :param no_field: The initial no_field value\n        :param saturation_current: Current value used to saturate solenoid\n        :param demag_current: Current value used to demagnetize solenoid\n        :param termination_threshold: percent of no_field required\n        acceptable as 0 field\n        \"\"\"\n        print(\"No Field Value: %f\" % no_field)\n\n        print('Ensuring saturation please wait.')\n        self.ps.set_current(saturation_current)\n        self.ps.enable_output()\n        time.sleep(10)\n        self.ps.disable_output()\n        time.sleep(3)\n\n        present_field = self.get_field()\n        original_sign = signnum(present_field - no_field)\n\n        print('Present Field: %f' % present_field)\n        print('Original sign: %f' % original_sign)\n        self.ps.set_current(demag_current)\n\n        overshoot = False\n\n        for i in range(15):\n            print(\"The 0-field value is: %d\" % no_field)\n            self.relay_2.vcc()\n            time.sleep(0.1)\n            self.ps.enable_output(relay_forward=None)\n            time.sleep(0.3)  # Allows power-on delay before opening the circuit\n            self.relay_2.gnd()\n            self.ps.disable_output(disable_relay=False)\n            time.sleep(0.5)  # Delay for inductance before field reading\n\n            present_field = self.get_field()\n\n            print('Present Field: %f' % present_field)\n\n            if(abs(present_field - no_field) > termination_threshold *\n               no_field and signnum(present_field - no_field) !=\n               original_sign):\n                overshoot = True\n                break\n\n        if overshoot:\n            print('Overshoot of %d' % abs(present_field - no_field))\n            self.ps.set_current(demag_current)\n\n            for i in range(5):\n\n                self.ps.enable_output()\n                self.ps.disable_output()\n\n                time.sleep(0.5)  # Delay for inductance before field reading\n\n                present_field = self.get_field()\n\n                if(abs(present_field - no_field) > termination_threshold *\n                   no_field and signnum(present_field - no_field) !=\n                   original_sign):\n                    break\n\n        time.sleep(1)\n        present_field = self.get_field()\n        print('Final Field is: %d' % present_field)\n        print('Off by: %d' % (present_field - no_field))\n\n        self.ps.disable_output()\n","sub_path":"api/demagnetizer.py","file_name":"demagnetizer.py","file_ext":"py","file_size_in_byte":5183,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"576266425","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Sep 17 18:33:35 2020\n\n@author: anni\n\"\"\"\n\nimport json\nfrom os import listdir\nfrom os.path import join\nimport os\n#import keras\nimport numpy as np\nimport PIL\nimport PIL.ImageOps\nimport matplotlib.pyplot as plt\n#from keras import layers\n#from keras.models import Model\nimport cv2\nimport random\nimport imutils\nimport pickle\n\nrandom.seed()\n\n\n\n###START FUNCTION SECTION \ndef getNormalizedNumbersOfAugmentation(old_numbers):\n    average = np.sum(old_numbers)/len(old_numbers)\n    augmentation_numbers = np.zeros(len(old_numbers))\n    for i in range(len(old_numbers)):\n        if(average-60 < old_numbers[i] < average+60):\n            augmentation_numbers[i] = 20\n        elif(average-60 > old_numbers[i]):\n            augmentation_numbers[i] = 20*(average-60)/old_numbers[i]\n        elif(average+60 < old_numbers[i]):\n            augmentation_numbers[i] = 20*(average+60)/old_numbers[i]\n    return augmentation_numbers\n\ndef trim(value, min, max):\n    \n    #Keeps value between min and max\n    if(value<min):\n        return min\n    elif(value>max):\n        return max\n    else:\n        return value\n\ndef invert(img):\n      \n    #inverts an image over 255\n    inverted_image = 255 - img\n        \n    return inverted_image\n                \ndef StandardAnnotation(img, bound):\n    \n    #Cuts out the annotation as the centre of a 100x100 image\n    #This is what the expression behind bound 1/0 does, putting it in the middle\n    #Get x-coordinate of upper left corner rounded down\n    annot_x1 = trim(int((bound[0]) - (100 - bound[2])/2), 0, img.shape[1]-100) \n    #Get y-coordinate of upper left corner rounded down               \n    annot_y1 = trim(int((bound[1]) - (100 - bound[3])/2), 0, img.shape[0]-100) \n    #Get width and height\n    annot_width = 100\n    annot_height = 100\n    #Calculate x-coordinate of lower right corner\n    annot_x2 = annot_x1 + annot_width\n    #Calculate y-coordinate of lower right corner                             \n    annot_y2 = annot_y1 + annot_height                           \n    \n    return img[annot_y1:annot_y2, annot_x1:annot_x2]\n \ndef Offset2dAnnotation(img, bound):\n    \n    # Calculate actual coordiantes of upper left corner of 100x100 area\n    corner_100x100_no_offset_x = int(((bound[0])) - (100 - bound[2])/2)\n    corner_100x100_no_offset_y = int(((bound[1])) - (100 - bound[3])/2)\n    \n    #Get offset (min half annotation should be still in 100x100)\n    '''Lower value to test if it works better'''\n    offset_x = random.randint(-20, 20)\n    offset_y = random.randint(-20, 20)\n    \n    #Get coordinates of upper left corner (100x100) with offset\n    corner_100x100_offset_x1 = trim(corner_100x100_no_offset_x + offset_x, 0, img.shape[1]-100)                       \n    corner_100x100_offset_y1 = trim(corner_100x100_no_offset_y + offset_y, 0, img.shape[0]-100) \n    #Set width and height     \n    width = 100                         \n    height = 100 \n    #Get coordinates of lower right corner (100x100)                    \n    corner_100x100_offset_x2 = corner_100x100_offset_x1 + width  \n    corner_100x100_offset_y2 = corner_100x100_offset_y1 + height\n    \n    result = img[corner_100x100_offset_y1:corner_100x100_offset_y2, corner_100x100_offset_x1:corner_100x100_offset_x2]\n    \n    if(result.shape[0] != 100 or result.shape[1] != 100):\n        print(\"Size issue\")\n    \n    return result\n    \ndef RotateAnnotation(img, bound):                                 \n    \n    '''#Comment out if no offset\n    #Rotation of offset or standard annotation    \n    case = random.randint(0, 1)\n    \n    if case == 0:\n        rot_img = StandardAnnotation(img, bound)\n    else:\n        rot_img = Offset2dAnnotation(img, bound)\n    '''\n    \n    #Comment out if with offset\n    rot_img = StandardAnnotation(img, bound)\n    \n    #Get the angle to rotate the image with\n    angle = random.randint(1, 360)\n    \n    #Save the original scale of the image (in case it was not 100x100)\n    orig_height = rot_img.shape[0]\n    orig_width = rot_img.shape[1]\n    \n    #Rotate the image\n    rotated = invert(imutils.rotate_bound(invert(rot_img), angle))\n    \n    #Crop the image back to 100*100\n    y1 = int(abs((rotated.shape[0]-orig_height)/2))\n    y2 = y1 + 100\n    x1 = int(abs((rotated.shape[1]-orig_width)/2))\n    x2 = x1 + 100\n    \n    result = rotated[y1:y2, x1:x2]\n    \n    if(result.shape[0] != 100 or result.shape[1] != 100):\n        print(\"Size issue\")\n    \n    return result\n    \n\n#Skalierung mit opencv.resize, interpolation cubic\n    \ndef RescaleAnnotation(img, bound):\n    \n    '''#Comment out if no offset\n    #Cut the annotation with or without offset \n    case = random.randint(0, 1)\n    \n    if case == 0:\n        rot_img = StandardAnnotation(img, bound)\n    else:\n        rot_img = Offset2dAnnotation(img, bound)\n    '''\n    \n    #Comment out if with offset\n    rot_img = StandardAnnotation(img, bound)\n    \n    #Save the original size of the image (in case it is not 100)\n    orig_height = rot_img.shape[0]\n    orig_width = rot_img.shape[1]\n    \n    #Get the scale factor\n    scale = random.randint(11, 14)/10\n    \n    #Scale the image with the factor \n    scale_img = cv2.resize(rot_img, (int(orig_height*scale), int(orig_width*scale)), interpolation = cv2.INTER_CUBIC)\n    #Cut the scaled image back to 100*100\n    y1 = int(abs((scale_img.shape[0]-orig_height)/2))\n    y2 = y1 + 100\n    x1 = int(abs((scale_img.shape[1]-orig_width)/2))\n    x2 = x1 + 100\n\n    result = scale_img[y1:y2, x1:x2]\n    \n    if(result.shape[0] != 100 or result.shape[1] != 100):\n        print(\"Size issue\")\n    \n    return result\n    \ndef HotKeyEncode(int_list, num_categories):\n    \n    HotKeyList = []\n    \n    for element in int_list:\n        hotkey = np.zeros(num_categories)\n        hotkey[element] = 1\n        HotKeyList.append(hotkey)\n        \n    return HotKeyList\n        \n    \n###END FUNCTION SECTION\n\n\n###START LOADING DATA\ntraining_annotations_per_category = [651, 1326]\naugmentation_numbers_for_training = getNormalizedNumbersOfAugmentation(training_annotations_per_category)\n#validation_annotations_per_category = [189, 60, 61, 313, 123, 296, 48, 97, 107, 39, 66, 718]\n#augmentation_numbers_for_validation = getNormalizedNumbersOfAugmentation(validation_annotations_per_category)\n  \nscript_dir = os.path.dirname(__file__) #<-- absolute dir the script is in\nrel_path = \"../Flurplandaten/floorplan_metadata_binary_random.json\"\nmetadata_path = os.path.join(script_dir, rel_path)\n\n#Load json file    \nmetadata = json.loads(open(metadata_path, 'r').read())\n\n#List for images\nimgs = []\n\n#Dict for pairing of names and idices of images\nimgs_nametoind = {}\n\n#Path for accessing images\nrel_path = \"../Flurplandaten/images_only_rectangular\"\npath = os.path.join(script_dir, rel_path)\n\n#Counter to get index of image\ncount = 0\n\n#Loop through elements (-->e) in directory\nfor e in listdir(path):\n    \n    #Get the element's link\n    link = join(path, e)  \n    #Load image and transform it to B&W numpy array                                          \n    curr_img = np.array(PIL.Image.open(link).convert('L').convert('RGB'))         \n    #Add array-shaped image to list\n    imgs.append(curr_img)                                           \n    \n    imgs_nametoind[e] = count\n    \n    count+= 1\n\n#Dict for pairing indices (meaning the place of the image in the list with the images) \n#and ids (meaning the number adressing this image in the metadata) of images\nimgs_idtoind = {}\n\n#Loop through all image entries (-->ie) in metatdata\nfor ie in metadata['images']:\n    \n    #Check if image exists in imported image list\n    if ie['file_name'] in imgs_nametoind:                           \n        \n        #Get the index to the current file name\n        index = imgs_nametoind[ie['file_name']]   \n        #Get the id of this file                  \n        img_id = ie['id']   \n        #Link id to the index                                        \n        imgs_idtoind[img_id] = index                                \n\n###END LOADING DATA\n\n###START PREPROCESSING\n\n#List of images\nannotations = []\n\n#List of objects in annotations\nobject_categories = []\n\n#Loop through all annotations\nfor annot in metadata['annotations']:\n    #Get bounding box\n    bbox = annot['bbox']                                            \n    #Get image id\n    img_id = annot['image_id']    \n\n    #Get object on annotation\n    object_category = annot['category_id']                           \n    \n    #Check if the image to the id exists\n    if img_id in imgs_idtoind:\n        \n        #Get the image\n        annot_img = imgs[imgs_idtoind[img_id]]                      \n        \n        #Add current annotation to list\n        annotations.append(StandardAnnotation(annot_img, bbox))\n        object_categories.append(object_category)\n        \n        \n        #Create varied Verions of this Annotation (only for training annotations, otherwise comment)\n        #Change for training\n\n        for i in range(int(augmentation_numbers_for_training[object_category-1])):\n            #Type of augmentation with offset\n            #augmentation = random.randint(0, 2)\n            #without offset\n            augmentation = random.randint(0, 1)\n            \n            #Offset only\n            '''\n            if augmentation == 0:\n                             \n                aug_annot = Offset2dAnnotation(annot_img, bbox)\n                annotations.append(aug_annot)\n                object_categories.append(object_category)\n            '''\n            #Rotate\n            if augmentation == 0:# With offset: elif, 1; without : if, 0 \n\n                aug_annot = RotateAnnotation(annot_img, bbox)\n                annotations.append(aug_annot)\n                object_categories.append(object_category)\n\n            #Rescale\n            elif augmentation == 1:#With offset: 2; without: 1\n\n                aug_annot = RescaleAnnotation(annot_img, bbox)\n                annotations.append(aug_annot)\n                object_categories.append(object_category)\n        \n\n#Hot key encoding of object categories\n\n        \n#Save annotations to json\n#https://stackoverflow.com/questions/30698004/how-can-i-serialize-a-numpy-array-while-preserving-matrix-dimensions\n\nscript_dir = os.path.dirname(__file__)\nannotation_path = os.path.join(script_dir, \"../Flurplandaten/preprocessed__training_annotations_binary_random_nooffset.p\")\npickle.dump(annotations, open(annotation_path, \"wb\"))\n\n#object_categories_encoded = HotKeyEncode(object_categories, 2)\n\nobject_path = os.path.join(script_dir, \"../Flurplandaten/object_list_for_training_annotations_binary_random_nooffset.p\")\npickle.dump(object_categories, open(object_path, \"wb\"))\n\n###END PREPROCESSING \n    \n###NOTES:\n#BBox: Wert in der Breite, Wert in der Höhe, Breite, Höhe\n    \n    \n    \n","sub_path":"Programm/PreprocessBinaryDataset.py","file_name":"PreprocessBinaryDataset.py","file_ext":"py","file_size_in_byte":10755,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"628335403","text":"import os\nimport random\nimport numpy as np\nimport scipy as sp\nimport scipy.stats\nimport math\n\nexec(open('Utilities.py').read())\n\nfrom epsilon_ball_emission_parallel import *\nfrom epsilon_ball_posterior_parallel import *\nfrom wf_sample_parallel import *\nfrom gaussian_emission_parallel import *\n\n_TIME_ROUNDING_ACCURACY = 7\n\n\nclass GenericDistribution:\n     def __init__(self):\n            self.data = 'hello'\n            \n     def sample(self):\n        print('this is a sample.')\n        \n     def likelihood(self, rvs, observations):\n        print('return observation.')\n\n\n\nclass InformativeEmission(GenericDistribution):\n    def __init__(self, alpha, epsilon, b=1):\n        super().__init__();\n        self.alpha = alpha\n        self.epsilon = epsilon\n        self.x = np.empty(len(alpha))\n        self.y = np.empty(len(alpha))\n        self.loglikelihood = -math.inf\n        self.b = b\n        \n    def sample(self):\n        \"\"\"\n        Samples an x vector from Dirichlet anad then a Y vector from Unif(x+-\\epsilon)\n        \"\"\"\n        # Sample X from a Dirichlet distribution and then for each x_i, sample y_i from a Unif with \\epsilon\n        print('self.alpha = {}'.format(self.alpha))\n        self.x = np.random.dirichlet(size=1, alpha=self.alpha)\n        for index, xi in np.ndenumerate(self.x):\n            self.y[index[1]] = np.random.uniform(xi-self.epsilon, xi+self.epsilon)\n        return({'obs':self.y, 'x':self.x})\n    \n    def sample_obs(self, some_x, t):\n        result_y = np.empty(shape=some_x.shape)\n        for index, xi in np.ndenumerate(some_x):\n            result_y[index[0]] = np.random.uniform(xi-self.epsilon, xi+self.epsilon)\n        return({'obs':result_y, 'x':some_x})\n    \n    def sample_posterior(self, observation, t, is_one_missing=True, shuffle=True):\n        # Sample from the conditional distribution, given the observation\n        y_obs = np.array(observation).copy()\n        k = len(y_obs)\n        xprime = np.zeros(k)\n        \n        # Shuffle the vectors to counter bias for the first component\n        if shuffle:\n            shuffleMap = np.array(list(range(k)), dtype=int)\n            np.random.shuffle(shuffleMap)\n            y_obs = y_obs[shuffleMap.flatten()]\n        \n        test_collection = []\n        lower_y = lambda i: np.array([max(0.0, y-self.epsilon) for y in y_obs[i:k]])\n        upper_y = lambda i: np.array([min(1.0, y+self.epsilon) for y in y_obs[i:k]])    \n        \n        for i in range(k):\n            a = max(0.0, y_obs[i]-self.epsilon)\n            a = min(1.0, a)\n            b = min(1.0, y_obs[i]+self.epsilon)\n            b = max(0.0, b)\n            c = max(0.0, 1 - np.sum(xprime[0:i]) - np.sum(upper_y(i+1)))\n            c = min(1.0, c)\n            d = min(1.0, 1 - np.sum(xprime[0:i]) - np.sum(lower_y(i+1)))\n            d = max(0.0, d)\n            test_collection.append([a, b, c, d])\n            lower_bound = max(a,c) if is_one_missing == False else a\n            upper_bound = min(b,d)\n            xprime[i] = np.random.uniform(lower_bound, upper_bound)\n        \n        if any(t < 0 for t in xprime):\n            raise ValueError('x is not positive!')\n            \n        if any(t > 1 for t in xprime):\n            raise ValueError('x is not under 1!')\n            \n        if np.sum(xprime) > 1.0:\n            raise ValueError('x is not Dirichlet!')\n            \n        if any(t > self.epsilon for t in (abs(xprime - y_obs))):\n            raise ValueError('x not in epsilon ball')\n        \n        # Revert\n        if shuffle:\n            mapBack = np.array([np.where(shuffleMap==i) for i in range(k)]).flatten()\n            y_obs = y_obs[mapBack]\n            xprime = xprime[mapBack]\n        return(xprime)\n        \n        \n    def sample_posterior_vectorised(self, observation, t, N=1, is_one_missing=True, free_freq=1.0):\n        y_obs = np.array(observation).copy()\n        K = len(y_obs)\n        X = np.zeros([N, K])\n\n        # Pre-compute the lower_y and upper_y arrays\n        sum_lower_y = np.empty([K])\n        sum_upper_y = np.empty([K])\n        for i in range(1, K+1):        \n            sum_lower_y[i-1] = np.sum(np.array([max(0.0, y-self.epsilon) for y in y_obs[i:K]]))\n            sum_upper_y[i-1] = np.sum(np.array([min(free_freq, y+self.epsilon) for y in y_obs[i:K]]))   \n        test_collection = []\n        \n        for n in range(N):\n            for i in range(K):\n                a = max(0.0, y_obs[i]-self.epsilon)\n                a = min(free_freq, a)\n                b = min(free_freq, y_obs[i]+self.epsilon)\n                b = max(0, b)\n                c = max(0.0, free_freq - np.sum(X[n, 0:i]) - sum_upper_y[i])\n                c = min(free_freq, c)\n                d = min(free_freq, free_freq - np.sum(X[n, 0:i]) - sum_lower_y[i])\n                d = max(0.0, d)\n                test_collection.append([a, b, c, d])\n                lower_bound = max(a,c) if is_one_missing == False else a\n                upper_bound = min(b,d)\n                X[n, i] = np.random.uniform(lower_bound, upper_bound)\n\n            if np.sum(X[n, ]) > free_freq:\n                raise ValueError('x is not Dirichlet!')\n\n            if any(t > self.epsilon for t in (abs(X[n, ] - y_obs))):\n                raise ValueError('x not in epsilon ball')\n        \n        return(X)\n    \n    def sample_posterior_parallel(self, observation, X, t, is_one_missing=True, n_threads=1):\n        epsilon_ball_sample_posterior_parallel(y_obs=np.array(observation), epsilon=self.epsilon, \n                                        unif_rands=np.random.uniform(0,1,X.shape),\n                                        X=X, n_threads=n_threads, is_one_missing=(1 if is_one_missing == True else 0))\n    \n    def compute_loglikelihood(self, params, observation, t, lambdaVal = 10):\n        # Compute the likelihood of the parameters, given the observations\n        k = len(params)\n        g_theta = np.empty(k)\n        for i in range(0, k):\n            g_theta[i] = 1 if abs(params[i]-observation[i]) <= self.epsilon else self.b*math.exp(-lambdaVal*abs(params[i]-observation[i]))\n        self.loglikelihood = np.sum(np.log(g_theta))\n        return(self.loglikelihood)\n    \n    def compute_loglikelihood_vectorise(self, X, y, t, lambdaVal=10):\n        N, K = X.shape\n        loglikelihood = np.zeros([N])\n        for i in range(N):\n            for k in range(K):\n                if abs(X[i, k]-y[k]) <= self.epsilon:\n                    continue\n                elif self.b != 0:\n                    loglikelihood[i] = loglikelihood[i] - lambdaVal*abs(X[i, k]-y[k])\n                else:\n                    loglikelihood[i] = -np.inf\n                    break\n                    \n        return(loglikelihood)\n    \n    \n    def compute_loglikelihood_parallel(self, X, y, t, loglikelihood, lambdaVal=10, n_cores=1):\n        gaussian_emission_parallel(X=X, y=y, epsilon=self.epsilon, loglikelihood=loglikelihood, n_threads=n_cores)\n    \n    def test():\n        emission = InformativeEmission(alpha = [1]*4, epsilon=.05)\n        xprime = emission.sample_posterior(observation=[0.0657, .728, .095, .103], is_one_missing = False)\n        print(\"{} {}\".format(xprime, np.sum(xprime)))\n        xprime = emission.sample_posterior(observation=[0.0657, .728, .095], is_one_missing = True)\n\n        xprime = emission.sample_posterior(observation=[0.0657, .728, .095, .103])\n        print(np.sum([0.0657, .728, .095, .103]))\n        print(xprime)\n        print(np.sum(xprime))\n        sample_y = emission.sample()\n        print(sample_y)\n        xpost = emission.sample_posterior(observation=sample_y['obs'])\n        print(np.sum(sample_y['obs']))\n        print(sample_y['obs'])\n        print(xpost)\n        print(sample_y['obs']-xpost)\n        np.sum(xpost)\n        obs = sample_y['obs']\n\n        type(sample_y['x'])\n        type(obs)\n        emission.compute_loglikelihood(params=sample_y['x'][0], observation=obs)\n\n\n\n\nclass DirMultEmission(GenericDistribution):\n    '''\n    Assumes that all observations will be in K-1 dimension, and the value of the last one is encoded in the N_total\n    '''\n    def __init__(self, N_total, alpha):\n        super().__init__();\n        self.alpha = np.array(alpha) # This has to have dimension one more than x and Y\n        self.N_total = N_total  # Is a vector, one for each timepoint\n        self.K = self.alpha.shape[0]-1\n\n    def sample(self, t):\n        \"\"\"\n        Samples an x vector from Dirichlet anad then a Y vector from Multinomial(x)\n        \"\"\"\n        x = np.random.dirichlet(size=1, alpha=self.alpha)\n        y = np.random.multinomial(self.N_total[t], self.x, 1)[0:self.K]\n        return({'obs':y, 'x':x})\n\n    # NOTE: The y vector will be missing the last one...\n    def sample_obs(self, some_x, t):\n        # add the last x\n        result_y = np.empty(shape=[self.K])\n        full_x = np.append(some_x, 1-np.sum(some_x))\n        result_y[:] = np.random.multinomial(self.N_total[t], full_x, 1).reshape(self.K+1)[0:self.K]\n        return({'obs':result_y, 'x':some_x})\n\n    def sample_posterior(self, observation, t):\n        # Sample from the conditional distribution, given the observation\n        # X \\sim Dir(alpha+Y)\n        last_y = self.N_total[t]-np.sum(observation)\n        y_obs = np.append(observation, last_y)\n        xprime = np.random.dirichlet(size=1, alpha=self.alpha+y_obs).reshape(self.K+1)[0:self.K]\n        \n        return(xprime)\n\n    # Return X: N by T, which are N different params for the same observation\n    def sample_posterior_vectorised(self, observation, t, N=1):\n        # Sample from the conditional distribution, given the observation\n        # X \\sim Dir(alpha+Y)\n        last_y = self.N_total[t]-np.sum(observation)\n        y_obs = np.append(observation, last_y)\n        X = np.random.dirichlet(size=N, alpha=self.alpha+y_obs)[:, 0:self.K]\n        return(X)\n\n    def sample_posterior_parallel(self, observation, t, X, n_threads=1):\n        X[:] = self.sample_posterior_vectorised(observation=observation, t=t, N=X.shape[0])\n        \n    \n    # Dirichlet distribution's support is on strictly positive real values\n    def replace_zeros(self, X):\n        X[X < 0] = 0.0\n        # Find zeros in full_X & replace them with .0001\n        X[X == 0.0] = .00001\n        X = X/np.sum(X)\n        return(X)\n    \n    def compute_loglikelihood(self, params, observation, t):\n        X = params\n        last_y = self.N_total[t]-np.sum(observation)\n        y_obs = np.append(observation, last_y)\n        full_X = np.append(X, 1-np.sum(X))\n        self.replace_zeros(full_X)\n        # Compute the likelihood of the parameters, given the observations\n        return(scipy.stats.dirichlet.logpdf(full_X, self.alpha + y_obs))\n\n    def compute_loglikelihood_vectorise(self, X, y, t):\n        N = X.shape[0]\n        loglikelihood = np.zeros([N])\n        last_y = self.N_total[t] - np.sum(y)\n        y_obs = np.append(y, last_y)\n        dir_mult_alpha = self.alpha + y_obs\n        for i in range(N):\n            full_X = np.append(X[i, ], 1-np.sum(X[i, ]))\n            full_X = self.replace_zeros(full_X)                \n            loglikelihood[i] = scipy.stats.dirichlet.logpdf(full_X, dir_mult_alpha)\n        return(loglikelihood)\n\n\n    def compute_loglikelihood_parallel(self, X, y, t, loglikelihood, n_cores=1):\n        loglikelihood[:] = self.compute_loglikelihood_vectorise(X, y, t)\n\n\n\ndef div0(a, b):\n    with np.errstate(divide='ignore', invalid='ignore'):\n        c = np.true_divide(a, b)\n        c[~np.isfinite(c)] = 0\n    return c\n\nfrom math import *\n# A factory class to generate WF with desriable K\nclass WrightFisherDiffusion(GenericDistribution):\n    def __init__(self, K, Ne, h):        \n        # h is the discritization timestep\n        self.h = h\n        self.K = K\n        self.Ne = Ne\n        self._ne_times_h = Ne*h[0]\n    \n    def check_state(x):\n        theSum = np.sum(x)\n        if np.round(theSum, _TIME_ROUNDING_ACCURACY) > 1 or np.round(theSum, _TIME_ROUNDING_ACCURACY) < 0:\n            print('theSum is {}'.format(theSum))\n            print(x)\n            raise ValueError('The path violates the sum rule.')\n        \n        if np.any([(piece > 1.0 or piece < 0.0) for piece in x]) == True:\n            print(x)\n            raise ValueError('The path violates simplex condition.')\n            \n        if np.any([(piece > 0.0 and piece < _TOLERATED_ZERO_PRECISION) for piece in x]) == True:\n            print(x)\n            raise ValueError('Potential underflow in the path.')\n    \n    def check_path(x_path):\n        theT = x_path.shape[0]\n        for t in range(theT):\n            try:\n                WrightFisherDiffusion.check_state(x_path[t, ])\n            except ValueError as e:\n                raise e\n    \n    def sample(self, x0, time_length, selectionCoefficients, seed=1, deltaWs=None):\n        \"\"\"\n        :param x0: Starting frequencies\n        :type x0: T by K Numpy.ndarray\n        :param time_length: the duration of time to simulate the diffusion, often the time between observation\n        :type time_length: float\n        :returns: The K-dimensional Wright-Fisher model simulated forward in time for a duration of *time_length* \n        :rtype: T by K Numpy.ndarray\n        \"\"\"\n        # Assuming that K = 3 for now\n        # 1. sample a wiener process over the grid\n        # 2. numerically advance the states over h timesteps\n        self.s = selectionCoefficients\n        tau = int((round(time_length/self.h[0], _TIME_ROUNDING_ACCURACY)))\n        \n        Xi = np.empty([tau+1, self.K])\n        Xi[0, ] = x0\n        for step in range(1, tau+1):\n            if deltaWs is None:\n                deltaW = np.random.normal(0, sqrt(self.h[0]), self.K)\n            else:\n                deltaW = deltaWs[step-1, :]\n            _drift = WrightFisherDiffusion.compute_mu(x=Xi[step-1, ], s=self.s, K=self.K, _ne_times_h=self._ne_times_h)\n            B = WrightFisherDiffusion.compute_sigma(Xi[step-1, ], self.K)\n            _diffusion = np.dot(B, deltaW)\n            deltaX = _drift + _diffusion\n            deltaX[_diffusion == 0] = 0\n\n            Xi[step, ] = Xi[step-1, ] + deltaX\n\n            # Enforce boundaries\n            xSum = 0\n            for i in range(self.K):\n                Xi[step, i] = min(1-xSum, min(1.0, max(Xi[step, i], 0.0)))\n                xSum = xSum + Xi[step, i]\n\n        return([Xi[tau, ], -1.23, Xi[1:(tau), ]])\n\n    def compute_sigma(x, K):\n        q = 1 - np.cumsum(x)\n        q[q < 0] = 0\n        q_prime = np.roll(q, 1)\n        q_prime[0] = 1.0\n        sj = np.sqrt(div0(np.multiply(x, q), q_prime))\n        sj_over_q = div0(sj, q)\n        B = np.outer(-x.T, sj_over_q)\n        B = B*np.tri(*B.shape, k=-1) + np.diag(sj)\n\n        return(B)\n    \n    def compute_mu(x, s, K, _ne_times_h):\n        _mu_fixed = -np.dot(x, s)\n        return(np.multiply((_mu_fixed + s), _ne_times_h*x))\n                                                                                 \n        \n    def compute_loglikelihood_old(self, x, s):\n        N, K = x.shape\n        temp = np.empty([N])\n        for i in range(1, N):\n            cov_mat = self.compute_sigma2(x[i-1,])*self.h\n            temp[i] = np.log(sp.stats.multivariate_normal.pdf(x=x[i,], mean=x[i-1,]+WrightFisherDiffusion.compute_mu(x[i-1,], s, K, self._ne_times_h), cov=cov_mat, allow_singular=True))\n        res = temp.sum()\n        return(res)\n\n    \n    def hack_for_sa501_dlp(self, x, s):\n        N, K = x.shape\n        sqrt_h = sqrt(self.h)\n        log_2pi = np.log(2*np.pi)\n        log_h = np.log(self.h)\n        temp = np.empty([N-1])\n        x_l = x[0:(N-1), ]\n        deltaX = x[1:N, ] - x_l\n        _mu_fixed = np.dot(x_l, -s)\n        _mu = (np.multiply(x_l.T, _mu_fixed).T + np.multiply(x_l, s))*self._ne_times_h\n        _dev = deltaX - _mu\n        \n        for i in range(0, N-1):\n            B = WrightFisherDiffusion.compute_sigma(x[i, ], K)\n            mask = np.diag(B) > 0\n            B = B[mask, ][:, mask]\n            dW = np.matmul(np.linalg.inv(B), _dev[i,][mask])\n            temp[i] = -.5*((self.h**-1)*np.dot(dW, dW.T) + K*log_2pi + K*log_h)\n            \n        return(temp.sum())\n        \n    \n    def compute_loglikelihood_hack(self, x, s):\n        ignore_before = 1\n        if (ignore_before is not None):\n            N, K = x.shape\n            ## SETTING MANUALLY THE CLONE TO BE IGNORED\n            # remove the clone\n            k = 1\n            t1 = int((.46/.78) * N)\n            x_before = np.delete(x, k, 1)[0:t1, :]\n            s_before = np.delete(s, k, 0)\n            x_after = x[t1:N, :]\n            ll1 = self.hack_for_sa501_dlp(x_before, s_before)\n            ll2 = self.hack_for_sa501_dlp(x_after, s)\n            return(ll1+ll2)\n        else:\n            return(self.hack_for_sa501_dlp(x, s))\n            \n        \n    \n    # Used to compute llhood of the trajectory (OuterPGAS) \n    def compute_loglikelihood(self, x, s, ignore_before=None, ignore_after=None):\n        N, K = x.shape # N here is actaully tau, i.e., number of discretisations\n        # Either N = len(h): using one-step method\n        # Or, h is fixed\n        _IS_ONE_STEP = False\n        \n        the_h = self.h\n        \n        if N == len(self.h):\n            _IS_ONE_STEP = True\n        else:\n            the_h = np.repeat(self.h[0], N) # Just expand the identical value\n         \n        sqrt_h = np.sqrt(the_h)\n        log_h = np.log(the_h)\n        log_2pi = np.log(2*np.pi)\n        temp = np.empty([N-1])\n        x_l = x[0:(N-1), ]\n        deltaX = x[1:N, ] - x_l\n        _mu_fixed = np.dot(x_l, -s)\n        \n        if _IS_ONE_STEP is True:\n            _mu = (np.multiply(x_l.T, _mu_fixed).T + np.multiply(x_l, s))*self.Ne\n            _dev = np.empty(_mu.shape)\n            for i in range(0, N-1):\n                _dev[i, :] = deltaX[i] - _mu[i]*the_h[i]\n        else:\n            _mu = (np.multiply(x_l.T, _mu_fixed).T + np.multiply(x_l, s))*self.Ne*the_h[0]\n            _dev = deltaX - _mu\n\n        for i in range(0, N-1):\n            B = WrightFisherDiffusion.compute_sigma(x[i, ], K)\n            mask = np.diag(B) > 0\n            B = B[mask, ][:, mask]\n            qqq = np.linalg.inv(B)\n            dW = np.matmul(qqq, _dev[i,][mask])\n            temp[i] = -.5*((the_h[i]**-1)*np.dot(dW, dW.T) + K*log_2pi + K*log_h[i])\n            \n        return(temp.sum())\n    \n    # Uses pre-computed sufficient statistics for x\n    # See here for a Cythonised version: https://stackoverflow.com/questions/31994879/improving-cython-lapack-performance-with-internal-array-definitions\n    def compute_loglikelihood_cache_x(self, x, s, B_inv=None, ignore_before=None, ignore_after=None):\n        if B_inv is None:\n            return(self.compute_loglikelihood(x, s))\n    \n        N, K = x.shape # N here is actaully tau, i.e., number of discretisations\n\n        _IS_ONE_STEP = False\n\n        the_h = self.h\n\n        if N == len(self.h):\n            _IS_ONE_STEP = True\n        else:\n            the_h = np.repeat(self.h[0], N) # Just expand the identical value\n\n        sqrt_h = np.sqrt(the_h)\n        log_h = np.log(the_h)\n        log_2pi = np.log(2*np.pi)\n        temp = np.empty([N-1])\n        x_l = x[0:(N-1), ]\n        deltaX = x[1:N, ] - x_l\n        _mu_fixed = np.dot(x_l, -s)\n\n        if _IS_ONE_STEP is True:\n            _mu = (np.multiply(x_l.T, _mu_fixed).T + np.multiply(x_l, s))*self.Ne\n            _dev = np.empty(_mu.shape)\n            for i in range(0, N-1):\n                _dev[i, :] = deltaX[i] - _mu[i]*the_h[i]\n        else:\n            _mu = (np.multiply(x_l.T, _mu_fixed).T + np.multiply(x_l, s))*self.Ne*the_h[0]\n            _dev = deltaX - _mu\n\n        for i in range(0, N-1):\n            mask = np.diag(B_inv[i, :,:]) > 0\n            dW = np.matmul(B_inv[i, :,:][mask, ][:, mask], _dev[i,][mask])\n            temp[i] = -.5*((the_h[i]**-1)*np.dot(dW, dW.T) + K*log_2pi + K*log_h[i])\n     \n        return(temp.sum())\n\n    def compute_B_inv(self, x):            \n        N, K = x.shape # N here is actaully tau, i.e., number of discretisations\n        B_inv = np.zeros([N-1, K, K])\n        for i in range(0, N-1):\n            temp = WrightFisherDiffusion.compute_sigma(x[i, ], K)\n            mask = np.diag(temp) > 0\n            B_inv[i, ][np.ix_(mask,mask)] = np.linalg.inv(temp[mask, ][:, mask])\n\n        return(B_inv)\n\n    \n    def compute_sigma2(self, x):\n        return(np.diag(x)-np.outer(x.T, x))\n\n    def set_np_seed():\n        if seed is None:\n            seed = int(np.random.uniform(0, 1, 1)*10000) \n        np.random.seed(seed)\n        \n    def sample_vectorised(self, X0, time_length, selectionCoefficients, seed=None, deltaWs=None):\n        self.s = selectionCoefficients\n        tau = int((round(time_length/self.h, _TIME_ROUNDING_ACCURACY)))\n        \n        N, _ = X0.shape\n        \n        Xi = np.empty([N, tau+1, self.K])\n        Xi[:, 0, :] = X0\n        for step in range(1, tau+1):\n            if deltaWs is None:\n                deltaW = np.random.normal(0, sqrt(self.h), N*self.K).reshape(N, self.K)\n            else:\n                deltaW = deltaWs[:, step-1, :]\n            x_l = Xi[:, step-1, :]\n            _mu_fixed = np.dot(x_l, -self.s)\n            _mu = (np.multiply(x_l.T, _mu_fixed).T + np.multiply(x_l, self.s))*self._ne_times_h\n            \n            for j in range(N):\n                B = WrightFisherDiffusion.compute_sigma(x_l[j,], self.K)\n                #B[np.diag(B) == 0, ] = 0\n                _diffusion = np.dot(B, deltaW[j, :])\n                deltaX = _mu[j, ] + _diffusion\n                deltaX[_diffusion == 0] = 0\n                \n                Xi[j, step, ] = Xi[j, step-1, ] + deltaX\n                Xi_new = Xi[j, step, ]\n\n                # Enforce boundaries\n                # all negative values back to 0\n                # when qsum >=1 for i, then all x_j, j>i = 0\n                # and x_i = x_i - 1\n                xSum = 0\n                for i in range(self.K):\n                    Xi[j, step, i] = min(1-xSum, min(1.0, max(Xi[j, step, i], 0.0)))\n                    xSum = xSum + Xi[j, step, i]\n\n        return([Xi[:, tau, :], -1.23, Xi[:, 1:(tau), :]])   \n\n    def sample_parallel(self, X0, t, time_length, selection_coeffs, seed, Xi, x_out, n_threads):\n        wf_sample_parallel(x0=X0, time_length=time_length, s=selection_coeffs, seed=seed, h=self.h[t], Xi=Xi, ne=self.Ne, x_out=x_out,  n_threads=n_threads)\n    \n    \n    def _get_tau(self, T, obsTimes):\n        return(T + sum([len(self._Xi_slice(i, obsTimes)) for i in range(T-1)]))\n    \n    # Indexing helper functions for $\\Xi$\n    def _t_to_tau(self, t, obsTimes):\n        # quick hack\n        if (t == 0):\n            return(int(round(obsTimes[t]/self.h[0], _TIME_ROUNDING_ACCURACY)))\n        i = 0\n        for time in range(1, t+1):\n            diff_t = obsTimes[time] - obsTimes[time-1]\n            i += int(round(diff_t/self.h[t-1], _TIME_ROUNDING_ACCURACY))\n        return(i)\n    \n    def _Xi_slice(self, t, obsTimes):\n        return(range(self._t_to_tau(t, obsTimes)+1, self._t_to_tau(t+1, obsTimes)))\n    \n    \n    def generate_full_data(self, obsTimes, emisssionDistribution, theta, x0=None, K=2):\n        T = len(obsTimes)\n        tau = self._get_tau(T, obsTimes)\n\n        x_trajectory = np.empty(shape=[1, T, K])\n        x_full_path = np.empty(shape=[1, tau, K])\n        x_full_path[:, :, :] = -1.666\n        y = np.empty(shape=[T, K])\n        \n        if x0 is None:\n            x_trajectory[0, 0, ] = emisssionDistribution.sample()['x']\n        else:\n            x_trajectory[0, 0, ] = x0\n        \n        full_obs_times = np.linspace(0, obsTimes[T-1], num=tau)\n        y[0, ] = emisssionDistribution.sample_obs(some_x = x_trajectory[0, 0, ], t=0)['obs']    \n        \n        for t in range(1, len(obsTimes)):  \n            x_trajectory[0, t, ], _, x_full_path[0, self._Xi_slice(t-1, obsTimes), ] = self.sample(x0=x_trajectory[0, t-1,], time_length=obsTimes[t]-obsTimes[t-1], selectionCoefficients=theta)\n                \n            x_full_path[0, self._t_to_tau(t, obsTimes),:] = x_trajectory[0, t, :]\n            y[t, ] = emisssionDistribution.sample_obs(some_x = x_trajectory[0, t, ], t=t)['obs']\n        \n        x_full_path[0, self._t_to_tau(0, obsTimes),] = x_trajectory[0, 0, ]\n        return({'x':x_trajectory[0, :,:], 'obs':y, 'x_full_path':x_full_path[0, :,:], 'x_full_times':full_obs_times})\n    \n    \n    def generate_full_data_old(self, obsTimes, emisssionDistribution, theta, x0=None, K=2):\n        T = len(obsTimes)\n        tau = self._get_tau(T, obsTimes)\n        x_trajectory = np.empty(shape=[1, T, K])\n        x_full_path = np.empty(shape=[1, tau, K])\n        x_full_path[:, :, :] = -1.666\n        y = np.empty(shape=[T, K])\n        if x0 is None:\n            x_trajectory[0, 0, ] = emisssionDistribution.sample()['x']\n        else:\n            x_trajectory[0, 0, ] = x0\n        \n        full_obs_times = np.linspace(0, obsTimes[T-1], num=tau)\n        y[0, ] = emisssionDistribution.sample_obs(some_x = x_trajectory[0, 0, ], t=0)['obs']    \n        \n        for t in range(1, len(obsTimes)):  \n            x_trajectory[0, t, ], _, x_full_path[0, self._Xi_slice(t-1, obsTimes), ] = self.sample(x0=x_trajectory[0, t-1,], time_length=obsTimes[t]-obsTimes[t-1], selectionCoefficients=theta)\n            # Use sample_parallel\n            x_full_path[0, self._t_to_tau(t, obsTimes),:] = x_trajectory[0, t, :]\n            y[t, ] = emisssionDistribution.sample_obs(some_x = x_trajectory[0, t, ], t=t)['obs']\n        \n        x_full_path[0, self._t_to_tau(0, obsTimes),] = x_trajectory[0, 0, ]\n        \n        return({'x':x_trajectory[0, :,:], 'obs':y, 'x_full_path':x_full_path[0, :,:], 'x_full_times':full_obs_times})\n    \n    def generate_data(self, obsTimes, emisssionDistribution, theta, x0=None, K=2):\n        \"\"\"\n        :param obsTimes: the first element has to be 0\n        :type obsTimes:\n        \"\"\"\n        # returns a dictionary with x and y keys\n        T = len(obsTimes)\n        x_trajectory = np.empty(shape=[T, K])\n        y = np.empty(shape=[T, K])\n        if x0 is None:\n            x_trajectory[0, ] = emisssionDistribution.sample()['x']\n        else:\n            x_trajectory[0, ] = x0\n            \n        y[0, ] = emisssionDistribution.sample_obs(some_x = x_trajectory[0, ], t=0)['obs']    \n    \n        for t in range(1, len(obsTimes)):  \n            x_trajectory[t, ] = self.sample(x0=x_trajectory[t-1,], time_length=obsTimes[t]-obsTimes[t-1], selectionCoefficients=theta)[0]\n            y[t, ] = emisssionDistribution.sample_obs(some_x = x_trajectory[t, ], t=t)['obs']\n            \n        return({'x':x_trajectory, 'obs':y})\n    \n    \n    def generate_full_sample_data(obs_num=5, silent=True, h = .001, selectionCoefficients=[.2, .3], end_time = .1, epsilon = None, x0=[.5, .4], Ne = 200, K=2, Dir_alpha=None, N_total=None):\n        WF_diff_gen = WrightFisherDiffusion(h=h, K=K, Ne=Ne)\n        \n        obsTimes = np.linspace(0, end_time, num=obs_num)\n        if epsilon is None:\n            emission = DirMultEmission(alpha=Dir_alpha, N_total=N_total)\n        else:    \n            emission = InformativeEmission(alpha=[1]*K, epsilon=epsilon)\n        \n        dat_gen = WF_diff_gen.generate_full_data(obsTimes=obsTimes, emisssionDistribution=emission, x0=np.array(x0), theta=selectionCoefficients, K=K)\n        \n        x_gen_full_tall = TimeSeriesDataUtility.TK_to_tall(dat_gen['x_full_path'], times=dat_gen['x_full_times'])\n        x_gen_tall = TimeSeriesDataUtility.TK_to_tall(dat_gen['x'], times=obsTimes)\n        y_gen_tall = TimeSeriesDataUtility.TK_to_tall(dat_gen['obs'], times=obsTimes)\n        \n        y_gen_tall['X_true'] = x_gen_tall['X']\n        \n        if silent is False:\n            print(dat_gen)\n            TimeSeriesDataUtility.plot_tall(x_gen_tall)\n            TimeSeriesDataUtility.plot_tall(y_gen_tall)\n\n        return([y_gen_tall, x_gen_full_tall])\n        \n    \n    def generate_sample_data(obs_num=5, silent=False, h = .001, selectionCoefficients=[.2, .3], end_time = .1, epsilon = .05, x0=[.5, .4], Ne = 200, K=2):\n        WF_diff_gen = WrightFisherDiffusion(h=h, K=K, Ne=Ne)\n        obsTimes = np.linspace(0, end_time, num=obs_num)\n        emission = InformativeEmission(alpha=[1]*K, epsilon=epsilon)\n        dat_gen = WF_diff_gen.generate_data(obsTimes = obsTimes, emisssionDistribution=emission, x0=np.array(x0), theta=selectionCoefficients, K=K)\n        \n        x_gen_tall = TimeSeriesDataUtility.TK_to_tall(dat_gen['x'], times=obsTimes)\n        y_gen_tall = TimeSeriesDataUtility.TK_to_tall(dat_gen['obs'], times=obsTimes)\n        y_gen_tall['X_true'] = x_gen_tall['X']\n        \n        if silent is False:\n            print(dat_gen)\n            TimeSeriesDataUtility.plot_tall(x_gen_tall)\n            TimeSeriesDataUtility.plot_tall(y_gen_tall)\n\n        return(y_gen_tall)\n","sub_path":"fitclone/Models.py","file_name":"Models.py","file_ext":"py","file_size_in_byte":28662,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"37398952","text":"import os, sys\nfrom Bio import SeqIO\nfrom Bio.Seq import Seq\nimport pandas as pd\nimport pickle\nimport numpy as np\nimport scipy.stats\nimport seaborn as sns\nimport matplotlib.pyplot as plt\n# import fastq scrape\n\ntime_points = [\n    't0_r1',\n    't1_r1',\n    't2_r1',\n    't0_r2',\n    't1_r2',\n    't2_r2',\n    't0_control',\n    't1_control',\n    't2_control']\n\nID_timepoint_dict = {\n    'ATTCCG' : 't0_control',\n    'AGCTAG' : 't1_control',\n    'GTATAG' : 't2_control',\n    'ATCAGT' : 't0_r1',\n    'GCTCAT' : 't1_r1',\n    'AGGAAT' : 't2_r1',\n    'CTTTTG' : 't0_r2',\n    'TAGTTG' : 't1_r2',\n    'CCGGTG' : 't2_r2'\n}\n\ntimepoint_generation_dict = {'t0_r1' : 0.0,\n                             't1_r1' : 1.91,\n                             't2_r1': 3.328354364,\n                             't0_r2' : 0.0,\n                             't1_r2' : 1.35,\n                             't2_r2' : 2.96,\n                             't0_control' : 0.0,\n                             't1_control' : 2.022367813,\n                             't2_control' : 3.50041511\n                            }\n\n\nallele_data = pickle.load(open('allele_dic_with_WT.pkl', 'rb'))\naminotonumber_data = pickle.load(open('aminotonumber.pkl', 'rb'))\ntranslate_data_RNA = pickle.load(open('translate.pkl', 'rb'))\ntranslate_data_DNA = {}\nfor codon in translate_data_RNA:\n    translate_data_DNA[codon.replace('U', 'T')] = translate_data_RNA[codon]\ntranslate_data_DNA['WT'] = 'WT'\n\ndef compare_string_lists(l1, l2):\n#     print(l1, l2)\n    for index, element in enumerate(l1):\n        if len(l1) != len(l2):\n            return False\n        if element != l2[index]:\n            return False\n    return True\n\n\n\n# \n# Initial Data Scrape from Fastq files\n# \ndf = pd.DataFrame()\nID = []\nread = []\nquality_score = []\nbarcodes = []\ncodon = []\nloc = []\ntime_point_tag = []\n\ndef get_filename(time_point):\n    # filename_short = '../../' + time_point + '_short.fastq'\n    # return filename_short\n    filename_long =  time_point + '_index.fastq'\n    return filename_long\n    \nfor time_point in time_points:\n    filename = get_filename(time_point)\n    file = open(filename)\n    for line_number, line in enumerate(file):\n        if line_number % 4 == 0:\n            ID.append(line.strip().split(':')[-1])\n        elif line_number % 4 == 1:\n            read.append(line.strip())\n            barcode = line.strip()[0:18]\n            barcodes.append(barcode)\n            barcode_lookup = str(Seq(barcode).reverse_complement())\n            if barcode_lookup in allele_data:\n                codon.append(allele_data[barcode_lookup][1])\n                loc.append(allele_data[barcode_lookup][0])\n            else:\n                codon.append('NONE')\n                loc.append('NONE')\n\n        elif line_number % 4 == 3:\n            quality_score.append(line.strip())\n            time_point_tag.append(time_point)\ndf['ID'] = ID\ndf['read'] = read\ndf['time_point'] = time_point_tag\ndf['codon'] = codon\ndf['loc'] = loc\n\n\n\n# \n# Get normalized barcode counts per timestep and label with generation, timepoint, and replicate\n# \n\ndf_in_dict = df[~(df['codon'] == 'NONE')].copy()\n# df_in_dict.groupby('time_point').codon.nunique()\ngrouped_counts = df_in_dict.groupby('time_point').read.value_counts(normalize = True)\nkeys = grouped_counts.keys()\ndef label_row_counts(row):\n    temp_key = (row['time_point'], row['read'])\n    for key in keys:\n        if temp_key == key:\n            return grouped_counts[key]\n    return 'NO COUNT FOUND'\n\ndef label_row_generations(row):\n    return timepoint_generation_dict[row['time_point']]\n\ndef label_row_replicate(row):\n    return row['time_point'].split('_')[1]\ndef label_row_time(row):\n    return row['time_point'].split('_')[0]\n\ndf_in_dict['count'] = df_in_dict.apply(lambda row: label_row_counts(row), axis=1)\nAA_list = [translate_data_DNA[codon] for codon in df_in_dict['codon'].values.tolist()]\ndf_in_dict['AA'] = AA_list\n# df_in_dict[df_in_dict['time_point'] == 't2_r2']\ndf_total = df_in_dict.copy()\ndf_total['generation'] = df_total.apply(lambda row: label_row_generations(row), axis=1)\ndf_total['time'] = df_total.apply(lambda row: label_row_time(row), axis=1)\ndf_total['replicate'] = df_total.apply(lambda row: label_row_replicate(row), axis=1)\n\n\n\n# \n# Calculate Slopes (can change our filtering of slope values here)\n# \ndf_slopes = df_total.drop_duplicates().copy()\nbarcode_groups = df_slopes.groupby(['read', 'replicate'])\ngroups = barcode_groups.groups\nfor group in barcode_groups.groups:\n    replicate = group[1]\n    temp_group = barcode_groups.get_group(group)\n    temp_x = temp_group['generation'].values\n    temp_y = temp_group['count'].values\n    timepoints_present = temp_group['time'].values\n    if compare_string_lists(timepoints_present, ['t1','t2']):\n        temp_x = [0.0] + temp_x\n        temp_y = [0.0] + temp_y\n    elif compare_string_lists(timepoints_present, ['t0']):\n        generation_1 = timepoint_generation_dict['t1_' + replicate]\n        generation_2 = timepoint_generation_dict['t2_' + replicate]\n        temp_x = temp_x + [generation_1, generation_2]\n        temp_y = temp_y + [0.0, 0.0]\n    slope, intercept, r_value, p_value, std_err = scipy.stats.linregress(temp_x,temp_y)\n    indices_temp = temp_group.index.values\n    for index in indices_temp:\n        df_slopes.set_value(index, 'slope', slope)\n        df_slopes.set_value(index, 'std_err', std_err)\n\n\n\n# \n# Calculate the average slope values\n# \ndf_barcode_scores = df_slopes.drop_duplicates(subset = ['read', 'replicate']).copy()\ndf_AA_avg = pd.DataFrame(df_barcode_scores.groupby(['loc', 'AA', 'replicate']).mean().copy())\npickle.dump(df_AA_avg, open('df_AA_avg.pkl', 'wb'))\n\n# keys_temp = df_AA_avg['slope'].keys()\n# for key in keys_temp:\n#     print(key)\n\n\n# \n# Separate out into replicate DFs\n# \ndef subtract_wildtype(df_replicate):\n    WT_slope = df_replicate[df_replicate['AA'] == 'WT']['slope']\n    normalized_slopes = df_replicate.apply(lambda row : row['slope'] - WT_slope, axis = 1)\n    df_replicate['slope_normalized'] = normalized_slopes\n    df_return = df_replicate.copy()\n    return df_return\n\ndf_r1 = df_AA_avg.reset_index().copy()\ndf_r1 = pd.DataFrame(df_r1[df_r1['replicate'] == 'r1'])\ndf_r1 = subtract_wildtype(df_r1)\npickle.dump(df_r1, open('df_r1.pkl', 'wb'))\n\ndf_r2 = df_AA_avg.reset_index().copy()\ndf_r2 = pd.DataFrame(df_r2[df_r2['replicate'] == 'r2'])\ndf_r2 = subtract_wildtype(df_r2)\npickle.dump(df_r2, open('df_r2.pkl', 'wb'))\n\ndf_control = df_AA_avg.reset_index().copy()\ndf_control = pd.DataFrame(df_control[df_control['replicate'] == 'control'])\ndf_control = subtract_wildtype(df_control)\npickle.dump(df_control, open('df_control.pkl', 'wb'))\n\n\n\n\n\ndef plot_df_replicate(df, filename, normalized = True):\n    df_working = df.copy()\n    df_working = df_working.drop('std_err', 1)\n    df_working = df_working.drop('count', 1)\n    df_working = df_working.drop('generation', 1)\n    df_working = df_working.drop('replicate', 1)\n    if normalized:\n        df_working = df_working.drop('slope', 1)\n        df_working = df_working.pivot(index='AA', columns='loc', values='slope_normalized')\n    else:\n        df_working = df_working.drop('slope_normalized', 1)\n        df_working = df_working.pivot(index='AA', columns='loc', values='slope')\n    pickle.dump(df_working, open(filename, 'wb'))\n#     sns.heatmap(df_working)\n#     plt.show()\n\nplot_df_replicate(df_control, 'control_heat_df.pkl')\nplot_df_replicate(df_r1, 'r1_heat_df.pkl')\nplot_df_replicate(df_r2, 'r2_heat_df.pkl')","sub_path":"fitness_scores/jupyter/gabe_fitness_server.py","file_name":"gabe_fitness_server.py","file_ext":"py","file_size_in_byte":7460,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"138498051","text":"#!/usr/bin/env python\nimport os\nimport platform\nfrom typing import List\nimport unittest\nfrom unittest import mock\nimport sys\nfrom absl.testing import absltest\n\nfrom grr_response_client.unprivileged import communication\n\n\nclass CommunicationTest(absltest.TestCase):\n\n  def testCommunication(self):\n\n    def MakeArgs(channel: communication.Channel) -> List[str]:\n      return [\n          sys.executable, \"-m\",\n          \"grr_response_client.unprivileged.echo_server\",\n          str(channel.pipe_input.Serialize()),\n          str(channel.pipe_output.Serialize()),\n      ]\n\n    server = communication.SubprocessServer(MakeArgs)\n    server.Start()\n    connection = server.Connect()\n\n    connection.Send(communication.Message(b\"foo\", b\"bar\"))\n    result = connection.Recv()\n    self.assertEqual(result.data, b\"foox\")\n    self.assertEqual(result.attachment, b\"barx\")\n\n    connection.Send(communication.Message(b\"FOO\", b\"BAR\"))\n    result = connection.Recv()\n    self.assertEqual(result.data, b\"FOOx\")\n    self.assertEqual(result.attachment, b\"BARx\")\n\n    connection.Send(communication.Message(b\"\", b\"\"))\n    result = connection.Recv()\n    self.assertEqual(result.data, b\"x\")\n    self.assertEqual(result.attachment, b\"x\")\n\n    server.Stop()\n\n  @unittest.skipIf(platform.system() != \"Linux\" and\n                   platform.system() != \"Darwin\", \"Unix only test.\")\n  def testMain_entersSandbox(self):\n    # pylint: disable=g-import-not-at-top\n    from grr_response_client.unprivileged.unix import sandbox\n    # pylint: enable=g-import-not-at-top\n    with mock.patch.object(sandbox, \"EnterSandbox\") as mock_enter_sandbox:\n      input_fd = os.open(\"/dev/null\", os.O_RDONLY)\n      output_file = os.open(\"/dev/null\", os.O_WRONLY)\n      channel = communication.Channel(\n          communication.FileDescriptor.FromFileDescriptor(input_fd),\n          communication.FileDescriptor.FromFileDescriptor(output_file))\n      communication.Main(channel, lambda connection: None, \"fooUser\",\n                         \"barGroup\")\n      mock_enter_sandbox.assert_called_with(\"fooUser\", \"barGroup\")\n\n\nif __name__ == \"__main__\":\n  absltest.main()\n","sub_path":"grr/client/grr_response_client/unprivileged/communication_test.py","file_name":"communication_test.py","file_ext":"py","file_size_in_byte":2117,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"573906120","text":"# -*- coding: utf-8 -*-\n\n\"\"\"Regularization in PyKEEN.\"\"\"\n\nfrom __future__ import annotations\n\nfrom abc import ABC, abstractmethod\nfrom typing import Any, ClassVar, Collection, Iterable, Mapping, Optional, Type\n\nimport torch\nfrom class_resolver import Resolver, normalize_string\nfrom torch import nn\nfrom torch.nn import functional\n\nfrom .nn.norm import lp_norm, powersum_norm\n\n__all__ = [\n    # Base Class\n    'Regularizer',\n    # Child classes\n    'LpRegularizer',\n    'NoRegularizer',\n    'CombinedRegularizer',\n    'PowerSumRegularizer',\n    'TransHRegularizer',\n    # Utils\n    'regularizer_resolver',\n]\n\n_REGULARIZER_SUFFIX = 'Regularizer'\n\n\nclass Regularizer(nn.Module, ABC):\n    \"\"\"A base class for all regularizers.\"\"\"\n\n    #: The overall regularization weight\n    weight: torch.FloatTensor\n\n    #: The current regularization term (a scalar)\n    regularization_term: torch.FloatTensor\n\n    #: Should the regularization only be applied once? This was used for ConvKB and defaults to False.\n    apply_only_once: bool\n\n    #: Has this regularizer been updated since last being reset?\n    updated: bool\n\n    #: The default strategy for optimizing the regularizer's hyper-parameters\n    hpo_default: ClassVar[Mapping[str, Any]]\n\n    def __init__(\n        self,\n        weight: float = 1.0,\n        apply_only_once: bool = False,\n        parameters: Optional[Iterable[nn.Parameter]] = None,\n    ):\n        \"\"\"Instantiate the regularizer.\n\n        :param weight: The relative weight of the regularization\n        :param apply_only_once: Should the regularization be applied more than once after reset?\n        :param parameters: Specific parameters to track. if none given, it's expected that your\n            model automatically delegates to the :func:`update` function.\n        \"\"\"\n        super().__init__()\n        self.tracked_parameters = list(parameters) if parameters else []\n        self.register_buffer(name='weight', tensor=torch.as_tensor(weight))\n        self.apply_only_once = apply_only_once\n        self.register_buffer(name=\"regularization_term\", tensor=torch.zeros(1, dtype=torch.float))\n        self.updated = False\n        self.reset()\n\n    @classmethod\n    def get_normalized_name(cls) -> str:\n        \"\"\"Get the normalized name of the regularizer class.\"\"\"\n        return normalize_string(cls.__name__, suffix=_REGULARIZER_SUFFIX)\n\n    def add_parameter(self, parameter: nn.Parameter) -> None:\n        \"\"\"Add a parameter for regularization.\"\"\"\n        self.tracked_parameters.append(parameter)\n\n    def reset(self) -> None:\n        \"\"\"Reset the regularization term to zero.\"\"\"\n        self.regularization_term.detach_().zero_()\n        self.updated = False\n\n    @abstractmethod\n    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:\n        \"\"\"Compute the regularization term for one tensor.\"\"\"\n        raise NotImplementedError\n\n    def update(self, *tensors: torch.FloatTensor) -> None:\n        \"\"\"Update the regularization term based on passed tensors.\"\"\"\n        if not self.training or not torch.is_grad_enabled() or (self.apply_only_once and self.updated):\n            return\n        self.regularization_term = self.regularization_term + sum(self.forward(x=x) for x in tensors)\n        self.updated = True\n\n    @property\n    def term(self) -> torch.FloatTensor:\n        \"\"\"Return the weighted regularization term.\"\"\"\n        return self.regularization_term * self.weight\n\n    def pop_regularization_term(self) -> torch.FloatTensor:\n        \"\"\"Return the weighted regularization term, and reset the regularize afterwards.\"\"\"\n        # If there are tracked parameters, update based on them\n        if self.tracked_parameters:\n            self.update(*self.tracked_parameters)\n\n        term = self.regularization_term\n        self.reset()\n        return self.weight * term\n\n\nclass NoRegularizer(Regularizer):\n    \"\"\"A regularizer which does not perform any regularization.\n\n    Used to simplify code.\n    \"\"\"\n\n    #: The default strategy for optimizing the no-op regularizer's hyper-parameters\n    hpo_default: ClassVar[Mapping[str, Any]] = {}\n\n    def update(self, *tensors: torch.FloatTensor) -> None:  # noqa: D102\n        # no need to compute anything\n        pass\n\n    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:  # noqa: D102\n        # always return zero\n        return torch.zeros(1, dtype=x.dtype, device=x.device)\n\n\nclass LpRegularizer(Regularizer):\n    \"\"\"A simple L_p norm based regularizer.\"\"\"\n\n    #: The dimension along which to compute the vector-based regularization terms.\n    dim: Optional[int]\n\n    #: Whether to normalize the regularization term by the dimension of the vectors.\n    #: This allows dimensionality-independent weight tuning.\n    normalize: bool\n\n    #: The default strategy for optimizing the LP regularizer's hyper-parameters\n    hpo_default: ClassVar[Mapping[str, Any]] = dict(\n        weight=dict(type=float, low=0.01, high=1.0, scale='log'),\n    )\n\n    def __init__(\n        self,\n        weight: float = 1.0,\n        dim: Optional[int] = -1,\n        normalize: bool = False,\n        p: float = 2.,\n        apply_only_once: bool = False,\n        parameters: Optional[Iterable[nn.Parameter]] = None,\n    ):\n        super().__init__(weight=weight, apply_only_once=apply_only_once, parameters=parameters)\n        self.dim = dim\n        self.normalize = normalize\n        self.p = p\n\n    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:  # noqa: D102\n        return lp_norm(x=x, p=self.p, dim=self.dim, normalize=self.normalize).mean()\n\n\nclass PowerSumRegularizer(Regularizer):\n    \"\"\"A simple x^p based regularizer.\n\n    Has some nice properties, cf. e.g. https://github.com/pytorch/pytorch/issues/28119.\n    \"\"\"\n\n    #: The default strategy for optimizing the power sum regularizer's hyper-parameters\n    hpo_default: ClassVar[Mapping[str, Any]] = dict(\n        weight=dict(type=float, low=0.01, high=1.0, scale='log'),\n    )\n\n    def __init__(\n        self,\n        weight: float = 1.0,\n        dim: Optional[int] = -1,\n        normalize: bool = False,\n        p: float = 2.,\n        apply_only_once: bool = False,\n        parameters: Optional[Iterable[nn.Parameter]] = None,\n    ):\n        super().__init__(weight=weight, apply_only_once=apply_only_once, parameters=parameters)\n        self.dim = dim\n        self.normalize = normalize\n        self.p = p\n\n    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:  # noqa: D102\n        return powersum_norm(x, p=self.p, dim=self.dim, normalize=self.normalize).mean()\n\n\nclass TransHRegularizer(Regularizer):\n    \"\"\"A regularizer for the soft constraints in TransH.\"\"\"\n\n    #: The default strategy for optimizing the TransH regularizer's hyper-parameters\n    hpo_default: ClassVar[Mapping[str, Any]] = dict(\n        weight=dict(type=float, low=0.01, high=1.0, scale='log'),\n    )\n\n    def __init__(\n        self,\n        weight: float = 0.05,\n        epsilon: float = 1e-5,\n        parameters: Optional[Iterable[nn.Parameter]] = None,\n    ):\n        # The regularization in TransH enforces the defined soft constraints that should computed only for every batch.\n        # Therefore, apply_only_once is always set to True.\n        super().__init__(weight=weight, apply_only_once=True, parameters=parameters)\n        self.epsilon = epsilon\n\n    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:  # noqa: D102\n        raise NotImplementedError('TransH regularizer is order-sensitive!')\n\n    def update(self, *tensors: torch.FloatTensor) -> None:  # noqa: D102\n        if len(tensors) != 3:\n            raise KeyError('Expects exactly three tensors')\n        if self.apply_only_once and self.updated:\n            return\n        entity_embeddings, normal_vector_embeddings, relation_embeddings = tensors\n        # Entity soft constraint\n        self.regularization_term += torch.sum(functional.relu(torch.norm(entity_embeddings, dim=-1) ** 2 - 1.0))\n\n        # Orthogonality soft constraint\n        d_r_n = functional.normalize(relation_embeddings, dim=-1)\n        self.regularization_term += torch.sum(\n            functional.relu(torch.sum((normal_vector_embeddings * d_r_n) ** 2, dim=-1) - self.epsilon),\n        )\n\n        self.updated = True\n\n\nclass CombinedRegularizer(Regularizer):\n    \"\"\"A convex combination of regularizers.\"\"\"\n\n    # The normalization factor to balance individual regularizers' contribution.\n    normalization_factor: torch.FloatTensor\n\n    def __init__(\n        self,\n        regularizers: Iterable[Regularizer],\n        total_weight: float = 1.0,\n        apply_only_once: bool = False,\n    ):\n        super().__init__(weight=total_weight, apply_only_once=apply_only_once)\n        self.regularizers = nn.ModuleList(regularizers)\n        for r in self.regularizers:\n            if isinstance(r, NoRegularizer):\n                raise TypeError('Can not combine a no-op regularizer')\n        self.register_buffer(name='normalization_factor', tensor=torch.as_tensor(\n            sum(r.weight for r in self.regularizers),\n        ).reciprocal())\n\n    @property\n    def normalize(self):  # noqa: D102\n        return any(r.normalize for r in self.regularizers)\n\n    def forward(self, x: torch.FloatTensor) -> torch.FloatTensor:  # noqa: D102\n        return self.normalization_factor * sum(r.weight * r.forward(x) for r in self.regularizers)\n\n\n_REGULARIZERS: Collection[Type[Regularizer]] = {\n    NoRegularizer,  # type: ignore\n    LpRegularizer,\n    PowerSumRegularizer,\n    CombinedRegularizer,\n    TransHRegularizer,\n}\nregularizer_resolver = Resolver(\n    _REGULARIZERS,\n    base=Regularizer,  # type: ignore\n    default=NoRegularizer,\n    suffix=_REGULARIZER_SUFFIX,\n)\n","sub_path":"src/pykeen/regularizers.py","file_name":"regularizers.py","file_ext":"py","file_size_in_byte":9663,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"529690498","text":"# -*- coding: utf-8 -*-\nfrom django.http import HttpResponse\nfrom django.shortcuts import render\nimport sqlite3\nfrom app import models\nfrom app.models import Usuario\nfrom app.models import Endereco\nfrom django.shortcuts import render\nfrom django.template import RequestContext\nfrom django.http import HttpRequest\nfrom datetime import datetime\nfrom Colmeia import p_perfil\nfrom Colmeia import p_endereco\nfrom django.db import connection\nfrom app.forms import UsuarioForm, UserForm, EnderecoForm\nfrom django.shortcuts import redirect\nfrom datetime import datetime\n\n\ndef incluirUsuario(request):\n    objEndereco = Endereco.create(request.POST['CEP'],request.POST['Estado'],request.POST['Cidade'], request.POST['Bairro'],request.POST['Logradouro'],request.POST['Numero'],request.POST['Complemento'] )\n    objEndereco.save()\n    objEndereco = p_endereco.recuperaUltimoEndereco()\n    tipo = request.GET['tipo']\n    if tipo == 'p':\n        objPerfil = p_perfil.recuperaPerfil('2') #perfil 2 = Prestador\n    else:\n        objPerfil = p_perfil.recuperaPerfil('3') #perfil 3 = Contratante\n    \n    objUsuario = Usuario.create(objEndereco, objPerfil, request.POST['IndicadorTipoPessoa'],request.POST['CpfCnpj'],request.POST['Nome'],request.POST['Telefone1'],request.POST['Telefone2'],request.POST['Email'],request.POST['Senha'])\n    objUsuario.save()\n    return objUsuario.Nome.split(' ')[0]\n\ndef incluirPrestador(request):\n    nome = incluirUsuario(request)\n    form = UsuarioForm()\n    userfrm = UserForm()\n    end = EnderecoForm()\n    assert isinstance(request, HttpRequest)\n    return render(\n        request,\n        'app/site/confirmaCadastro.html',\n        context_instance = RequestContext(request,\n        {\n            'title':'Colmeia | Inicio',\n            'tipo': 'Prestador',\n            'nome': nome,\n            'year': datetime.now().year,\n        })\n    )\n\ndef incluirContratante(request):\n    nome = incluirUsuario(request)\n    form = UsuarioForm()\n    userfrm = UserForm()\n    end = EnderecoForm()\n    assert isinstance(request, HttpRequest)\n    return render(\n       request,\n        'app/site/confirmaCadastro.html',\n        {'form': form, 'userfrm': userfrm,'endereco':end},\n        context_instance = RequestContext(request,\n        {\n            'title':'Colmeia | Inicio',\n            'tipo': 'Contratante',\n            'nome': nome,\n            'year': datetime.now().year,\n        })\n    )\n\n#recupera todos os objetos\ndef recuperaUsuario():\n    objUsuario = models.Usuario.objects.all()\n    return objUsuario\n\n#recupera todos um objeto especifico pelo id\ndef recuperaUsuario(idObj):\n\tobjUsuario = models.Usuario.objects.get(id=idObj)\n\treturn objUsuario\n\ndef aprovaUsuarioP(request):\n    idObj = request.GET['id']\n    objUser = models.User.objects.get(id=idObj)\n    objUsuario = models.Usuario.objects.get(user_id = idObj)\n    #Registra data e hora da aprovacao do cadastro\n    objUsuario.DataHoraAprovacao = datetime.now()\n    objUsuario.save()\n    #Ativa o cadastro de usuarios para fazer plugIn\n    objUser.is_active = True\n    objUser.save()\n\n\n    \n    return redirect('prestadores')\n\ndef aprovaUsuarioC(request):\n    idObj = request.GET['id']\n    objUser = models.User.objects.get(id=idObj)\n    objUsuario = models.Usuario.objects.get(user_id = idObj)\n    #Registra data e hora da aprovacao do cadastro\n    objUsuario.DataHoraAprovacao = datetime.now()\n    objUsuario.save()\n    #Ativa o cadastro de usuarios para fazer plugIn\n    objUser.is_active = True\n    objUser.save()\n    \n    return redirect('contratantes')\n\ndef recuperaQtdPrestPendentes():\n    cursor = connection.cursor()\n    cursor.execute('SELECT COUNT(A.user_id) FROM app_usuario AS A JOIN auth_user AS B ON (A.user_id = B.id) WHERE (B.is_active = 0 AND A.IdPerfil_id = 2)')\n    rows = cursor.fetchall()\n    return rows\n\ndef recuperaQtdContPendentes():\n    cursor = connection.cursor()\n    cursor.execute('SELECT COUNT(A.user_id) FROM app_usuario AS A JOIN auth_user AS B ON (A.user_id = B.id) WHERE (B.is_active = 0 AND A.IdPerfil_id = 1)')\n    rows = cursor.fetchall()\n    return rows\n\ndef recuperaQtdContPendentes():\n    return  models.User.filter(IdPerfil = '2').count()\n#recupera todos um objeto especifico pelo id\ndef recuperaPrestadoresPendentesAprovacao():\n    cursor = connection.cursor()\n    cursor.execute('SELECT A.Nome, B.username, A.Telefone1, A.CpfCnpj, P.DescricaoPerfil, B.id FROM app_usuario AS A JOIN auth_user AS B ON (A.user_id = B.id) JOIN app_perfil AS P ON (A.IdPerfil_id = P.IdPerfil) WHERE (B.is_active = 0 AND A.IdPerfil_id = 2)')\n    rows = cursor.fetchall()\n    return rows\n\n#recupera todos um objeto especifico pelo id\ndef recuperaContratantesPendentesAprovacao():\n    cursor = connection.cursor()\n    \n    cursor.execute('SELECT A.Nome, B.username, A.Telefone1, A.CpfCnpj, P.DescricaoPerfil, B.id FROM app_usuario AS A JOIN auth_user AS B ON (A.user_id = B.id) JOIN app_perfil AS P ON (A.IdPerfil_id = P.IdPerfil) WHERE (B.is_active = 0 AND A.IdPerfil_id = 3)')\n    rows = cursor.fetchall()\n    return rows","sub_path":"Colmeia/p_usuario.py","file_name":"p_usuario.py","file_ext":"py","file_size_in_byte":5022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"589303907","text":"import numpy as np\nfrom collections import defaultdict\nfrom sklearn.metrics import f1_score\nfrom typing import Dict, Any, List, Optional, Tuple\n\nfrom dataset.dataset import Dataset, DataSeries\nfrom models.adaptive_model import AdaptiveModel\nfrom utils.rnn_utils import get_logits_name\nfrom utils.testing_utils import ClassificationMetric\nfrom utils.np_utils import round_to_precision, min_max_normalize, clip_by_norm\nfrom utils.constants import BIG_NUMBER, SMALL_NUMBER, OUTPUT\nfrom utils.adaptive_inference import threshold_predictions, optimal_levels, normalize_logits\n\n\nclass ThresholdOptimizer:\n    \n    def __init__(self, params: Dict[str, Any], model: AdaptiveModel):\n        self._model = model\n        self._thresholds = None\n        self._num_levels = model.num_outputs\n        self._level_penalty = params.get('level_penalty', 0.0)\n\n        # Power Estimates from profiling (constant for now)\n        self._avg_power = np.array([24.085, 32.776, 37.897, 43.952, 48.833, 50.489, 54.710, 57.692, 59.212, 59.251])\n\n    def fit(self, dataset: Dataset, series: DataSeries):\n        raise NotImplementedError()\n\n    def identifier(self) -> Tuple[Any, Any]:\n        raise NotImplementedError()\n\n    def score(self, dataset: Dataset, series: DataSeries, flops_per_level: List[float], thresholds: Optional[np.ndarray] = None) -> Dict[str, Any]:\n        assert self._thresholds is not None or thresholds is not None, 'Must fit the model or provide thresholds.'\n\n        if thresholds is None:\n            thresholds = self._thresholds\n\n        # Set logit operations\n        logit_ops = [get_logits_name(i) for i in range(self._num_levels)]\n\n        # Converts flops list to numpy array\n        flops_per_level = np.array(flops_per_level)\n\n        data_generator = dataset.minibatch_generator(series=series,\n                                                     batch_size=self._batch_size,\n                                                     metadata=self._model.metadata,\n                                                     should_shuffle=False)\n \n        predictions: List[np.ndarray] = []\n        labels: List[np.ndarray] = []\n        levels: List[np.ndarray] = []\n        flops: List[np.ndarray] = []\n\n        for batch in data_generator:\n            # Compute the predicted log probabilities\n            feed_dict = self._model.batch_to_feed_dict(batch, is_train=False)\n            logits = self._model.execute(feed_dict, logit_ops)\n\n            # Concatenate logits into a [B, L, C] array (logit_ops is already ordered by level).\n            # For reference, L is the number of levels and C is the number of classes\n            logits_concat = np.concatenate([np.expand_dims(logits[op], axis=1) for op in logit_ops], axis=1)\n\n            # Normalize logits and round to fixed point representation\n            normalized_logits = normalize_logits(logits_concat, precision=self._precision)\n\n            # [S, L]\n            batch_predictions, batch_levels = threshold_predictions(normalized_logits, thresholds=thresholds)\n            batch_labels = np.squeeze(batch[OUTPUT])\n\n            predictions.append(batch_predictions)\n            levels.append(batch_levels)\n            labels.append(batch_labels)\n\n            batch_flops = flops_per_level[batch_levels]\n            flops.append(batch_flops)\n\n        predictions = np.concatenate(predictions, axis=-1)\n        labels = np.concatenate(labels, axis=-1)\n        levels = np.concatenate(levels, axis=-1)\n        flops = np.concatenate(flops, axis=-1)\n\n        # Compute the (approx) average power\n        level_distribution = np.vstack([np.bincount(levels[i, :], minlength=self._num_levels) for i in range(levels.shape[0])])  # [S, L]\n        level_distribution = level_distribution / np.sum(level_distribution, axis=-1, keepdims=True)  # [S, L]\n        approx_power = np.sum(level_distribution * self._avg_power, axis=-1)  # [S]\n\n        return {\n            ClassificationMetric.ACCURACY.name: np.average((labels == predictions).astype(float), axis=-1).tolist(),\n            ClassificationMetric.MACRO_F1_SCORE.name: [f1_score(labels, predictions[i, :], average='macro') for i in range(predictions.shape[0])],\n            ClassificationMetric.MICRO_F1_SCORE.name: [f1_score(labels, predictions[i, :], average='micro') for i in range(predictions.shape[0])],\n            ClassificationMetric.LEVEL.name: np.average(levels + 1, axis=-1),\n            ClassificationMetric.FLOPS.name: np.average(flops, axis=-1),\n            'THRESHOLDS': thresholds.astype(float).tolist(),\n            'APPROX_POWER': approx_power.astype(float)\n        }\n\n    def fitness_function(self, normalized_logits: np.ndarray, labels: np.ndarray, thresholds: np.ndarray, penalty: Optional[float] = None) -> float:\n        predictions, levels = threshold_predictions(normalized_logits, thresholds=thresholds)\n\n        assert predictions.shape == labels.shape, 'Misaligned labels ({0}) and predictions ({1})'.format(labels.shape, predictions.shape)\n\n        penalty_factor = penalty if penalty is not None else self._level_penalty\n\n        accuracy = np.average((predictions == labels).astype(float))\n\n        level_diff = levels - optimal_levels(normalized_logits, labels)\n        clipped_diff = np.clip(level_diff, a_min=0.0, a_max=None)\n        level_penalty = -1 * penalty_factor * (np.sum(clipped_diff) / (np.sum(np.where(level_diff >= 0, 1, 0) + SMALL_NUMBER)))\n\n        return accuracy + level_penalty\n\n\n","sub_path":"src/threshold_optimization/optimizer.py","file_name":"optimizer.py","file_ext":"py","file_size_in_byte":5441,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"41474826","text":"# Matt Meyers, Section 40\n# Valerie Ziegler, Section 40\n\nimport random\n\ndef welcome():\n    \"\"\"\n    This function displays a welcome message and instructions\n\n    No parameters or variables\n\n    returns NONE\n    \"\"\"\n\n    print(\"Welcome to the 8-Puzzle Problem.\")\n    print(\"In this game, eight numbers are displayed in a 3x3 grid.\")\n    print(\"You can move a number into the adjacent blank spot by moving vertically or horizontally.\")\n    print(\"Your goal is to make the grid look like this:\")\n    displayGrid([1,2,3,4,5,6,7,8,''])\n\ndef startGame():\n    \"\"\"\n    This function prompts the user with a start or exit message\n\n    No parameters\n    Variables:\n        userChoice(string)-user input, should be 's' or 'e'\n        start(boolean)-true if user chooses 's', false if user chooses 'e'\n    Returns startGame(boolean)\n    \"\"\"\n\n    print(\"s: start a new game\")\n    print(\"e: exit the program\")\n    userChoice = input(\"Please choose an option: \")\n    # Checking if input is valid. Asking for another if not.\n    while userChoice != 's' or userChoice != 'e':\n            if userChoice == \"s\":\n                return True\n            elif userChoice == \"e\":\n                return False\n            elif userChoice != 's' or userChoice != 'e':\n                userChoice = input(\"Not a valid option. Please enter s or e: \")\n\n\ndef displayGrid(grid):\n    \"\"\"\n    This function takes a list and formats it into a 3x3 grid using nested for loops.\n\n    Parameters:\n      grid(list)-List of integers and a string\n    Variables:\n      i (integer)-Counter for outer loop.  Takes on values of 0,1, or 2.\n      j (integer)-Counter for inner loop.  Takes on values of 0-8\n    returns NONE\n    \"\"\"\n    \n    for i in range(0, 3):\n        for j in range(3*i+0, 3*i+3):\n            print(grid[j], \" \", end=\"\")\n        print()\n    print()\n    return\n\ndef generateGame():\n    \"\"\"\n    This function generates a random game by making 1000 legal pseudo-random moves.\n\n    Note: Originally used random.shuffle(), but that method was creating games that could not be won.\n    Parameters:\n      None\n    Variables:\n      game(list)-the actual game board.  9 elements: 1-8 (integers), \" \" (string)\n      randNum(integer)-random number between 1 and 8\n      indexChange(integer)-the index change involved with making the move.\n      indexOfMove(integer)-the index of the randomly chosen number.\n      temp(string)-involved with switching two members of the list.\n    Returns game(list)\n    \"\"\"\n\n    game = [1, 2, 3, 4, 5, 6, 7, 8, \" \"]\n\n    for i in range(0, 1000):\n        randNum = random.randint(1, 8)\n        indexOfMove = game.index(randNum)\n        indexChange = game.index(\" \") - indexOfMove\n        # Checking to see if move is valid\n        if not (indexOfMove % 3 == 0 and indexChange == -1) and not((indexOfMove + 1) % 3 == 0 and indexChange == 1) \\\n                and (indexOfMove + indexChange in range(0, 9)) and (indexChange in [-3, -1, 1, 3]):\n\n            temp = game[indexOfMove + indexChange]\n            game[indexOfMove + indexChange] = game[indexOfMove]\n            game[indexOfMove] = temp\n\n    return game\n\ndef makeMove(game, userMove):\n    \"\"\"\n    This function checks if selected move is legal, then performs the move. Loop continues until user enters valid move\n    or 'q'.\n\n    Parameters:\n      game(list)-the actual game board.\n      userMove(string)-user input. Takes on the integers 1-8\n    Variables:\n      userMove(integer)-parameter converted to integer.  The user's selected move.\n      indexChange(integer)-the difference of the index of the blank and the index of the user's selection.\n      indexOfMove(integer)-the index of the user's move.\n      temp(string)-temporary variable that takes on the value of \" \"\n    Returns game(list) and True or False\n    \"\"\"\n\n\n    while True:\n        userMove = int(userMove)\n        indexOfMove = game.index(userMove)\n        indexChange = game.index(\" \") - indexOfMove\n        # checking if move is invalid\n        if (indexOfMove % 3 == 0 and indexChange == -1) or ((indexOfMove + 1) % 3 == 0 and indexChange == 1) or \\\n                (indexOfMove + indexChange not in range(0, 9)) or (indexChange not in [-3, -1, 1, 3]):\n            # prompting user for another valid input\n            while True:\n                if userMove == \"q\":\n                    break\n                elif userMove in ['1', '2', '3', '4', '5', '6', '7', '8']:\n                    break\n                else:\n                    userMove = input(\"Not a valid option. Please enter a valid number or enter q to quit: \")\n            if userMove == \"q\":\n                    return game, False\n        # Making the move\n        else:\n            temp = game[indexOfMove + indexChange]\n            game[indexOfMove + indexChange] = game[indexOfMove]\n            game[indexOfMove] = temp\n            return game, True\n\ndef checkForWin(game):\n    \"\"\"\n    This function checks if the board is in a winning configuration by comparing the game\n    list to the winning configuration [1,2,3,4,5,6,7,8,\" \"].\n\n    Parameters:\n      game (list)-The current state of the game\n    Variables:\n      None\n    Returns True or False\n    \"\"\"\n    \n    if game == [1, 2, 3, 4, 5, 6, 7, 8, \" \"]:\n        return True\n    return False\n\ndef playGame(game, gameState):\n    \"\"\"\n    This function plays the actual game\n\n    Parameters:\n      game(list)-current state of the game\n      gameState(boolean)-state of the current game. True->game is running. False->game has ended.\n    Variables:\n      win(boolean)-if true, player has won. if false, player has not yet won\n      useerInput(string)-the user's input.  Either q or 1-8.\n      numberOfMoves(integer)-the number of moves the user has made in the current game.\n    Returns numberOfMoves(integer) and win(boolean)\n    \"\"\"\n\n    numberOfMoves = 0\n    win = False\n    while win == False:\n        displayGrid(game)\n\n        userInput = input(\"Enter a number to move or enter q to quit: \")\n        # Checking for valid user input\n        while userInput != 'q' or userInput not in [1, 2, 3, 4, 5, 6, 7, 8]:\n            if userInput == \"q\":\n                break\n            elif userInput in ['1', '2', '3', '4', '5', '6', '7', '8']:\n                break\n            else:\n                userInput = input(\"Not a valid option. Please enter a valid number or enter q to quit: \")\n\n        if userInput == 'q':\n            break\n        else:\n            game, gameState = makeMove(game, userInput)\n            if gameState == False:\n                break\n            win = checkForWin(game)\n            numberOfMoves += 1\n    return numberOfMoves, win\n\ndef main():\n    \"\"\"\n    This function runs the entire game.\n\n    Parameters:\n      None\n    Variables:\n      gamestate(boolean)-the current state of the game. True->game is running, False->program ends\n      win(boolean)-if true, player has won. if false, player has not yet won\n      game(list)-current game board.\n      numberOfMoves(integer)-number of moves user has made in current game.\n    \"\"\"\n    gameState = True\n    win = False\n    welcome()\n    while gameState == True:\n        gameState = startGame()\n        print()\n        game = generateGame()\n        if gameState == False:\n            break\n        numberOfMoves, win = playGame(game, gameState)\n        if win == True:\n            print()\n            print(\"Congratulations! You won in\", numberOfMoves, \"moves.\")\n            print()\n\nmain()\n\n","sub_path":"Programs/Program1/program1.py","file_name":"program1.py","file_ext":"py","file_size_in_byte":7409,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"597512441","text":"import numpy as np\nimport pandas as pd\nfrom sklearn.metrics import accuracy_score\nfrom sklearn.model_selection import train_test_split, KFold\n\nfrom autopipeline import constants\nfrom autopipeline.pipeline.components.classification.sgd import SGD\n\nfrom autopipeline.pipeline.components.preprocessing.encode.one_hot import OneHotEncoder\nfrom autopipeline.pipeline.components.preprocessing.impute.fill_cat import FillCat\nfrom autopipeline.pipeline.components.preprocessing.impute.fill_num import FillNum\nfrom autopipeline.pipeline.dataframe import GenericDataFrame\nfrom autopipeline.pipeline.pipeline import GenericPipeline\n\ndf = pd.read_csv(\"../examples/classification/train_classification.csv\")\ny = df.pop(\"Survived\").values\ndf = df.loc[:, [\"Sex\", \"Cabin\", \"Age\"]]\nfeat_grp = [\"cat_nan\", \"cat_nan\", \"num_nan\"]\ndf_train, df_test, y_train, y_test = train_test_split(df, y, test_size=0.2, random_state=10)\n\ndf_train = GenericDataFrame(df_train, feat_grp=feat_grp)\ndf_test = GenericDataFrame(df_test, feat_grp=feat_grp)\ncv = KFold(n_splits=5, random_state=10, shuffle=True)\ntrain_ix, valid_ix = next(cv.split(df_train))\n\ndf_train, df_valid = df_train.split([train_ix, valid_ix])\ny_valid = y_train[valid_ix]\ny_train = y_train[train_ix]\n\nfill_cat = FillCat()\nfill_cat.in_feat_grp = \"cat_nan\"\nfill_cat.out_feat_grp = \"cat\"\nfill_cat.update_hyperparams({\"strategy\": \"<NULL>\"})\n\nfill_num = FillNum()\nfill_num.in_feat_grp = \"num_nan\"\nfill_num.out_feat_grp = \"num\"\nfill_num.update_hyperparams({\"strategy\": \"median\"})\n\nohe = OneHotEncoder()\nohe.in_feat_grp = \"cat\"\nohe.out_feat_grp = \"num\"\n\nsgd = SGD()\nsgd.in_feat_grp = \"num\"\nsgd.update_hyperparams({\"loss\": \"log\", \"random_state\": 10})\n\n\npipeline = GenericPipeline([\n    (\"fill_cat\", fill_cat),\n    (\"fill_num\", fill_num),\n    (\"ohe\", ohe),\n    (\"sgd\", sgd),\n])\n\npipeline.fit(df_train, y_train, df_valid, y_valid, df_test, y_test)\npred_train = pipeline.predict(df_train)\npred_test = pipeline.predict(df_test)\npred_valid = pipeline.predict(df_valid)\nscore_valid = pipeline.predict_proba(df_valid)\nprint(accuracy_score(y_train, pred_train))\nprint(accuracy_score(y_valid, pred_valid))\nprint(accuracy_score(y_test, pred_test))\nret = pipeline.procedure(constants.binary_classification_task, df_train, y_train, df_valid, y_valid, df_test, y_test)\npred_test = ret[\"pred_test\"]\npred_valid = ret[\"pred_valid\"]\nprint(accuracy_score(y_valid, (pred_valid > .5).astype(\"int\")[:, 1]))\nprint(accuracy_score(y_test, (pred_test > .5).astype(\"int\")[:, 1]))\n\n\npipeline = GenericPipeline([\n    (\"fill_cat\", fill_cat),\n    (\"fill_num\", fill_num),\n    (\"ohe\", ohe),\n])\n\npipeline.fit(df_train, y_train, df_valid, y_valid, df_test, y_test)\nret1 = pipeline.transform(df_train, df_valid, df_test)\nret2 = pipeline.fit_transform(df_train, y_train, df_valid, y_valid, df_test, y_test)\nfor key in [\"X_train\",\"X_valid\",\"X_test\"]:\n    assert np.all(ret1[key]==ret2[key])\n\n\npipeline = GenericPipeline([\n    (\"sgd\", sgd),\n])\n\nret = pipeline.procedure(constants.binary_classification_task, ret1[\"X_train\"], y_train, ret1[\"X_valid\"], y_valid,\n                         ret1[\"X_test\"], y_test)\npred_test = ret[\"pred_test\"]\npred_valid = ret[\"pred_valid\"]\nprint(accuracy_score(y_valid, (pred_valid > .5).astype(\"int\")[:, 1]))\nprint(accuracy_score(y_test, (pred_test > .5).astype(\"int\")[:, 1]))\n","sub_path":"test/run_pipeline.py","file_name":"run_pipeline.py","file_ext":"py","file_size_in_byte":3291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"330513872","text":"import csv\n\ndef openFile():\n\t\n\tlist = []\n\tlist_of_cells = [\"restaurantID\", \"name\", \"location\",\"reviewCount\", \n\t\"rating\", \"categories\", \"address\", \"Hours\", \"GoodforKids\", \n\t\"AcceptsCreditCards\", \"Parking\", \"Attire\", \"GoodForGroups\", \"PriceRange\", \n\t\"TakesReservations\", \"Delivery\", \"Takeout\", \"WaiterService\", \"OutdoorSeating\", \n\t\"WiFi\", \"GoodFor\", \"Alcohol\", \"NoiseLevel\", \"Ambience\", \"HasTV\", \"Caters\", \"WheelChairAccessible\",\n\t\"Website\", \"PhoneNumber\"]\n\tlist.append(list_of_cells)\n\twith open('restaurant.csv', 'r') as file:\n\t\tdata = csv.DictReader(file)\n\t\tfor row in data:\n\t\t\tvalue = row['address'] + \" Neighborhood: \" + row['location']\n\t\t\trow['location'] = value\n\t\t\tlist_cells = []\n\t\t\tfor key, value in row.items():\n\t\t\t\tlist_cells.append(value)\n\t\t\tlist.append(list_cells)\n\t\t\t\n\t#print(list)\n\twith open('restaurants.csv', 'w', newline = \"\") as output:\n\t\twriter = csv.writer(output)\n\t\twriter.writerows(list)\n\t\t\n\t\t\nopenFile()","sub_path":"getYelpRestaurantsInfo/add.py","file_name":"add.py","file_ext":"py","file_size_in_byte":924,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"479858711","text":"# coding: utf-8\nimport os, datetime\nimport socket\nfrom IPython import get_ipython\nHOSTNAME = socket.gethostname()\nINTERACTIVE = get_ipython() is not None\nif INTERACTIVE:\n    get_ipython().run_line_magic('env', 'CUDA_VISIBLE_DEVICES=0')\nSHERPA_TRIAL_ID = os.environ.get('SHERPA_TRIAL_ID', '0')\nos.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true' # Needed to avoid cudnn bug.\nimport logging\nimport tensorflow as tf\n#from tensorflow.keras.callbacks import *\nfrom tensorflow.keras.layers import concatenate\nfrom tensorflow.keras.optimizers import Adam\nfrom tensorflow.keras.models import Model, load_model\nimport tensorflow.keras as keras\nfrom sarhspredictor.config import model45path,model45stdpath,model_heteroskedastic_2017,\\\n    model_IFR_replication_quach2020_sadowski_release_5feb2021,model_IFR_replication_quach2020_sadowski_release_5feb2021_v2\nprint('keras',keras.__version__)\nprint('tensorflow',tf.__version__)\nprint()\nfrom sarhspredictor.lib.sarhs.heteroskedastic import Gaussian_NLL, Gaussian_MSE\nCURDIRSCRIPT = os.path.dirname(__file__)\n\ndef load_quach2020_model_basic():\n    \"\"\"\n    file provided by Justin in December 2020\n    it only predicts Hs and not the Hs_std\n    :return:\n    \"\"\"\n    #file_model = os.path.join(CURDIRSCRIPT,'model_45.h5')\n    file_model = model45path\n    logging.info('file_model: %s',file_model)\n    model_base = load_model(file_model)\n    return model_base\n\n\ndef load_quach2020_model():\n    \"\"\"\n    version with STD uncertainty inspired from https://github.com/hawaii-ai/SAR-Wave-Height/blob/master/notebooks/train_uncertainty_with_existing.ipynb\n    file provided by Justin in December 2020\n    :return:\n    \"\"\"\n    #file_model = os.path.join(CURDIRSCRIPT,'model_45.h5')\n    file_model = model45path\n    logging.info('file_model: %s',file_model)\n    model_base = load_model(file_model)\n    # Add back output that predicts uncertainty.\n    base_inputs = model_base.input\n    base_penultimate = model_base.get_layer('dense_7').output\n    base_output = model_base.output\n    #stdtuned_path = os.path.join(CURDIRSCRIPT,'model_45_std_tuned.h5')\n    stdtuned_path = model45stdpath\n    logging.info('stdtuned_path %s',stdtuned_path)\n    model_std = load_model(stdtuned_path,#'./model_45_std_tuned.h5',\n                           custom_objects={'Gaussian_NLL' : Gaussian_NLL,'Gaussian_MSE' : Gaussian_MSE})\n    x = model_std.get_layer('std_hidden')(base_penultimate)\n    std_output = model_std.get_layer('std_output')(x)\n    output = concatenate([base_output,std_output],axis=-1)\n    print('output',output)\n    print(dir(output))\n    model = Model(inputs=base_inputs,outputs=output)\n    # Compile and save.\n    opt = Adam(lr=0.0001)\n    model.compile(loss=Gaussian_NLL,optimizer=opt,metrics=[Gaussian_MSE])\n    logging.info('model Quach 2020 loaded')\n    return model\n\ndef load_quach2020_model_v2():\n    \"\"\"\n    based on the example notebook provided by P. Sadowsky: predict.ipynb\n    :return:\n    \"\"\"\n    #file_model = '/home1/datahome/agrouaze/git/SAR-Wave-Height/models/heteroskedastic_2017.h5'\n    file_model = model_heteroskedastic_2017\n    custom_objects = {'Gaussian_NLL':Gaussian_NLL, 'Gaussian_MSE':Gaussian_MSE}\n    model = load_model(file_model, custom_objects=custom_objects)\n    return model\n\ndef load_quach2020_model_v2_alt(version=1):\n    \"\"\"\n    replicate model of the official model provided by Sadowski in feb 2021 (trained in 43min at ifremer)\n    :return:\n    \"\"\"\n    #file_model = '/home1/datahome/agrouaze/git/SAR-Wave-Height/models/heteroskedastic_2017.h5'\n    if version==1:\n        file_model = model_IFR_replication_quach2020_sadowski_release_5feb2021\n    elif version ==2:\n        file_model = model_IFR_replication_quach2020_sadowski_release_5feb2021_v2\n    custom_objects = {'Gaussian_NLL':Gaussian_NLL, 'Gaussian_MSE':Gaussian_MSE}\n    model = load_model(file_model, custom_objects=custom_objects)\n    return model\n\n\ndef load_quach2020_model_45_std_tuned():\n    \"\"\"\n    temptative to load only the model_45_std_tuned.h5 model by itself -> validation tests not conclusive\n    :return:\n    \"\"\"\n    stdtuned_path = os.path.join(CURDIRSCRIPT,'model_45_std_tuned.h5')\n    custom_objects = {'Gaussian_NLL' : Gaussian_NLL,'Gaussian_MSE' : Gaussian_MSE}\n    model = load_model(stdtuned_path,custom_objects=custom_objects)\n    return model\n\n\n# def load_like_in_notebook_train_uncertainty_with_existing():\n#     \"\"\"\n#     problem I don't have the  \"valid\" dataset in hdf5.for now.\n#     :return:\n#     \"\"\"\n#     #file_model = './models/model_45.h5'\n#     #file_model = os.path.join(CURDIRSCRIPT,'model_45.h5')\n#     file_model = model45path\n#     model_base = load_model(file_model)\n#     # Fine tune.\n#     opt = Adam(lr=0.00001)\n#     model_base.compile(loss='mae', optimizer=opt, metrics=['mae', 'mse'])\n#     history = model_base.fit(valid, epochs=5, verbose= 1 if INTERACTIVE else 2)\n#     # Add back output that predicts uncertainty.\n#     base_inputs = model_base.input\n#     base_penultimate = model_base.get_layer('dense_7').output\n#     base_output = model_base.output\n#     model_std = load_model(model45stdpath, custom_objects={'Gaussian_NLL':Gaussian_NLL, 'Gaussian_MSE': Gaussian_MSE})\n#     x = model_std.get_layer('std_hidden')(base_penultimate)\n#     std_output = model_std.get_layer('std_output')(x)\n#     output = concatenate([base_output, std_output], axis=-1)\n#     model = Model(inputs=base_inputs, outputs=output)\n#     # Compile and save.\n#     opt = Adam(lr=0.0001)\n#     model.compile(loss=Gaussian_NLL, optimizer=opt, metrics=[Gaussian_MSE])\n#     return model\n\nPOSSIBLES_MODELS = {\n    'heteroskedastic_2017.h5' : load_quach2020_model_v2,\n    'justin_std' : load_quach2020_model,\n    'justin_basic' : load_quach2020_model_basic,\n    'only_std' : load_quach2020_model_45_std_tuned,\n}\n","sub_path":"sarhspredictor/lib/load_quach_2020_keras_model.py","file_name":"load_quach_2020_keras_model.py","file_ext":"py","file_size_in_byte":5758,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"57492017","text":"\"\"\"\nОперации по модулю, арифметика остатков\n\"\"\"\nimport math\nfrom arithmetic import numbers, gcd\n\n__author__ = 'Andre_487'\n\n\ndef normalize_mod(x, N):\n    \"\"\"\n    ### Преобразовать число x в эквивалент по модулю N\n\n    `normalize_mod(x: int, N: int) -> int`\n\n    Сложность по времени: `O(x)`\n    \"\"\"\n    x = int(x)\n    while x < 0:\n        x += N\n    while x >= N:\n        x -= N\n    return x\n\n\ndef add_mod(a, b, mod):\n    \"\"\"\n    ### Сложение чисел по модулю\n\n    `add_mod(a: int, b: int, mod: int) -> int`\n\n    Данный алгоритм вычисляет сумму `a` и `b` по модулю `mod`,\n    он равноценен коду `(a + b) % mod`.\n\n    Здесь `a`, `b` и `mod` - целые числа, `mod > 0`.\n    Поскольку оба числа нормализуются по модулю mod,\n    максимально возможное значение при выполнении равно `2(mod - 1)`.\n\n    Сложность алгоритма совпадает со сложностью\n    `normalize_mod(n)`, где n - максимальное из `a` и `b`.\n    \"\"\"\n    mod = int(mod)\n    if mod <= 0:\n        raise ValueError('mod должен быть > 0')\n    a = normalize_mod(a, mod)\n    b = normalize_mod(b, mod)\n    return normalize_mod(a + b, mod)\n\n\ndef multiply_mod(a, b, mod):\n    \"\"\"\n    ### Умножение чисел по модулю\n\n    `multiply_mod(a: int, b: int, mod: int) -> int`\n\n    Данный алгоритм вычисляет произведение `a` и `b` по модулю `mod`,\n    он равноценен коду `(a * b) % mod`.\n\n    Здесь `a`, `b` и `mod` - целые числа, `mod > 0`.\n    Поскольку оба числа нормализуются по модулю mod,\n    максимально возможное значение при выполнении равно `(mod - 1)<sup>2</sup>`.\n\n    Сложность алгоритма совпадает со сложностью `normalize_mod(n)`, где `n` - максимальное из `a` и `b`.\n    \"\"\"\n    mod = int(mod)\n    if mod <= 0:\n        raise ValueError('mod должен быть > 0')\n    a = normalize_mod(a, mod)\n    b = normalize_mod(b, mod)\n    return normalize_mod(a * b, mod)\n\n\ndef pow_mod(x, y, mod):\n    \"\"\"\n    ### Возведение в степень по модулю\n\n    `pow_mod(x: int, y: int, mod: int) -> int`\n\n    Данный алгоритм возводит `x` в степень `y` по модулю `mod`,\n    он равноценен коду `math.pow(x, y) % mod`.\n    При этом на каждом шаге мы оперируем числами, не превосходящими `mod`.\n\n    Здесь `x`, `y` и `mod` - целые числа, `y >= 0`, `mod > 0`.\n\n    Сложность: O(n<sup>3</sup>), где n - максимальное количество разрядов в числах `x`, `y`, `mod`.\n    \"\"\"\n    x = int(x)\n    y = int(y)\n    mod = int(mod)\n    if y < 0:\n        raise ValueError('y должен быть >= 0')\n    if mod <= 0:\n        raise ValueError('mod должен быть > 0')\n\n    if y == 0:\n        result = 1\n    else:\n        d, r = numbers.div_rem(y, 2)\n        z = pow_mod(x, d, mod)\n        z2 = z * z\n        if r == 0:\n            result = numbers.div_rem(z2, mod)[1] # только остаток (rem)\n        else:\n            sign = math.copysign(1, x)\n            w = numbers.div_rem(abs(x * z2), mod)[1]\n            if sign > 0 or w == 0:\n                result = w\n            else:\n                result = mod - w\n    return int(result)\n\n\ndef inverse_mod(a, mod):\n    \"\"\"\n    ### Обращение числа по модулю\n\n    `inverse_mod(a: int, mod: int) -> int`\n\n    Алгоритм расчета числа, мультипликативно обратного числу `a` по модулю `mod`.\n\n    Сложность: O(n<sup>3</sup>), где `n` - максимальное количество разрядов в числах `a` и `mod`.\n    \"\"\"\n    mod = int(mod)\n    if mod <= 0:\n        raise ValueError('mod должно быть > 0')\n\n    a = normalize_mod(a, mod)\n    g, x, y = gcd.gcd_extended(a, mod)\n    if g > 1:\n        raise ValueError('a (={0}) не имеет обратного по модулю mod (={1})'.format(a, mod))\n    return normalize_mod(x, mod)\n\n\ndef divide_mod(a, b, mod):\n    \"\"\"\n    ### Деление по модулю\n\n    `divide_mod(a: int, b: int, mod: int) -> int`\n\n    Данный алгоритм вычисляет частное `a` и `b` по модулю `mod`,\n    равноценное умножению `a` на число, мультипликативно обратное `b`\n    по модулю `mod`.\n\n    Здесь `a`, `b` и `mod` - целые числа, `mod > 0`.\n\n    Сложность: O(n<sup>3</sup>), где `n = log<sub>2</sub>(mod)`, размер двоичной записи числа `mod`.\n    \"\"\"\n    b_inv = inverse_mod(b, mod)\n    return multiply_mod(a, b_inv, mod)\n","sub_path":"arithmetic/mod_operations.py","file_name":"mod_operations.py","file_ext":"py","file_size_in_byte":5167,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"453247318","text":"from os import makedirs, path, remove\nfrom zipfile import ZipFile, is_zipfile\nfrom urllib import error, request, parse\n\ndef download_file(url, dst_path):\n    try:\n        with request.urlopen(url) as web_file:\n            data = web_file.read()\n            with open(dst_path, mode=\"wb\") as local_file:\n                local_file.write(data)\n    except error.URLError as e:\n        print(e)\n\ndst_path = \"./data/burned_area/NFDB/\"\nurl_normal = 'http://cwfis.cfs.nrcan.gc.ca/downloads/nfdb/fire_pnt/current_version/NFDB_point_txt.zip'\nurl_large = 'http://cwfis.cfs.nrcan.gc.ca/downloads/nfdb/fire_pnt/current_version/NFDB_point_large_fires_txt.zip'\n\nmakedirs(dst_path, exist_ok=True)\no = parse.urlparse(url_large)\nfilename = path.basename(o.path)\nfile_path = path.join(dst_path, filename)\ndownload_file(url_large, file_path)\nprint(file_path)\nif is_zipfile(file_path):\n    with ZipFile(file_path, \"r\") as zip_ref:\n        dir_name = path.splitext(file_path)[0]\n        zip_ref.extractall(dir_name)\n    remove(file_path)\nprint(\"downloaded {filename}\".format(filename=filename))\n","sub_path":"download_scripts/download_nfdb.py","file_name":"download_nfdb.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"282639313","text":"import logging\n# logging.basicConfig(\n#     filename='app.log',\n#     level=logging.DEBUG,\n#     format='%(asctime)s %(filename)s[line:%(lineno)d] %(message)s',\n#     datefmt='%Y-%m-%d')\n# logging.debug('test debug')\n# logging.info('test info')\n# logging.warning('test warning')\n# logging.error('test error')\n# logging.critical('test critical')\n\n# 2019-12-30 22_logging.py[line:7] test debug\n# 2019-12-30 22_logging.py[line:8] test info\n# 2019-12-30 22_logging.py[line:9] test warning\n# 2019-12-30 22_logging.py[line:10] test error\n# 2019-12-30 22_logging.py[line:11] test critical\n\n\na = logging.getLogger('mylogger')\na.setLevel(logging.INFO)\nformatter = logging.Formatter('%(asctime)s:%(name)s:%(levelname)s:%(message)s')\nhdl = logging.StreamHandler()\nhdl.setFormatter(formatter)\na.addHandler(hdl)\na.info('test info')\n\n\n\n'''\n1. 日子模块等级\n日志等级 （level）\t描述\nDEBUG\t            最详细的日志信息，典型应用场景是 问题诊断\nINFO\t            信息详细程度仅次于DEBUG，通常只记录关键节点信息，用于确认一切都是按照我们预期的那样进行工作\nWARNING\t            当某些不期望的事情发生时记录的信息（如，磁盘可用空间较低），但是此时应用程序还是正常运行的\nERROR\t            由于一���更严重的问题导致某些功能不能正常运行时记录的信息\nCRITICAL\t        当发生严重错误，导致应用程序不能继续运行时记录的信息\n\n2. Logging模块提供了两种记录日志的方式：\n第一种方式是使用logging提供的模块级别的函数\n第二种方式是使用Logging日志系统的四大组件\n\n函数\t                              说明\nlogging.debug(msg, *args, **kwargs)\t  创建一条严重级别为DEBUG的日志记录\nlogging.info(msg, *args, **kwargs)\t  创建一条严重级别为INFO的日志记录\nlogging.warning(msg, *args, **kwargs) 创建一条严重级别为WARNING的日志记录\nlogging.error(msg, *args, **kwargs)\t  创建一条严重级别为ERROR的日志记录\nlogging.critical(msg, *args, **kwargs)创建一条严重级别为CRITICAL的日志记录\nlogging.log(level, *args, **kwargs)\t  创建一条严重级别为level的日志记录\nlogging.basicConfig(**kwargs)\t      对root logger进行一次性配置\n\nlogging模块的四大组件\n组件名称\t对应类名\t功能描述\n日志器  \tLogger  \t提供了应用程序可一直使用的接口\n处理器\t    handler  \t将logger创建的日志记录发送到合适的目的输出\n过滤器   \tFilter  \t提供了更细粒度的控制工具来决定输出哪条日志记录，丢弃哪条日志记录\n格式器  \tFormatter \t决定日志记录的最终输出格式\n\n日志器（logger）需要通过处理器（handler）将日志信息输出到目标位置;\n不同的处理器（handler）可以将日志输出到不同的位置;\n日志器（logger）可以设置多个处理器（handler）将同一条日志记录输出到不同的位置；\n每个处理器（handler）都可以设置自己的过滤器（filter）实现日志过滤，从而只保留感兴趣的日志；\n每个处理器（handler）都可以设置自己的格式器（formatter）实现同一条日志以不同的格式输出到不同的地方。\n日志器（logger）是入口，真正干活儿的是处理器（handler），处理器（handler）\n还可以通过过滤器（filter）和格式器（formatter）对要输出的日志内容做过滤和格式化等处理操作。\n\n3. logger类\na = logging.getLogger('mylogger')   getLogger()返回一个logger实例，可以传入一个logger的名字\na.setLevel(logging.INFO)            通过setLeverl()方法可以设置logger的日志输出级别\na.debug('xxxxxx')\na.info('cccccc')                    INFO:mylogger:cccccc\n\n需要之前的logging.basicConfig()的设置，才会输出这个，因为logger的handler还没有加\n\n4. handler类\n通过handler对象可以把日志内容写到不同地方，python提供了十几种实用的handler，比较常用的有：\nStreamHandler\t        输出到控制台\nFileHandler\t            输出到文件\nBaseRotatingHandler \t可以按时间写入到不同的日志中。比如将日志按天写入不同的日期结尾的文件文件\nSocketHandler\t        用TCP网络连接写LOG\nDatagramHandler\t        用UDP网络连接写LOG\nSMTPHandler\t            把LOG写成EMAIL邮寄出去\n\nHandler.setLevel()     设置handler将会处理的日志消息的最低严重级别\nHandler.setFormatter() 为handler设置一个格式器对象\nHandler.addFilter() 和 Handler.removeFilter()    为handler添加 和 删除一个过滤器对象\n\n5. Formater类\n字段/属性名称\t使用格式\t描述\nasctime\t        %(asctime)s\t日志事件发生的时间--人类可读时间，如：2003-07-08 16:49:45,896\ncreated\t        %(created)f\t日志事件发生的时间--时间戳，就是当时调用time.time()函数返回的值\nrelativeCreated\t%(relativeCreated)d\t日志事件发生的时间相对于logging模块加载时间的相对毫秒数（目前还不知道干嘛用的）\nmsecs\t        %(msecs)d\t日志事件发生事件的毫秒部分\nlevelname\t    %(levelname)s\t该日志记录的文字形式的日志级别（'DEBUG', 'INFO', 'WARNING', 'ERROR', 'CRITICAL'）\nlevelno\t        %(levelno)s\t该日志记录的数字形式的日志级别（10, 20, 30, 40, 50）\nname\t        %(name)s\t所使用的日志器名称，默认是'root'，因为默认使用的是 rootLogger\nmessage\t        %(message)s\t日志记录的文本内容，通过 msg % args计算得到的\npathname\t    %(pathname)s\t调用日志记录函数的源码文件的全路径\nfilename\t    %(filename)s\tpathname的文件名部分，包含文件后缀\nmodule\t        %(module)s\tfilename的名称部分，不包含后缀\nlineno\t        %(lineno)d\t调用日志记录函数的源代码所在的行号\nfuncName\t    %(funcName)s\t调用日志记录函数的函数名\nprocess\t        %(process)d\t进程ID\nprocessName\t    %(processName)s\t进程名称，Python 3.1新增\nthread\t        %(thread)d\t线程ID\nthreadName\t    %(thread)s\t线程名称\n'''\n","sub_path":"Python100模块/22_logging.py","file_name":"22_logging.py","file_ext":"py","file_size_in_byte":6079,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"388252758","text":"# 15번 가장 큰 수\n\ndef solution(numbers):\n    answer = ''\n    numbers = list(map(str, numbers))\n    numbers.sort(key= lambda x: x *3, reverse=True)\n    answer = str(int(''.join(numbers)))\n\n    return answer\n\n\nnum= [6, 10, 2]\nprint(solution(num))","sub_path":"programmers/level2/15.py","file_name":"15.py","file_ext":"py","file_size_in_byte":250,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"63809378","text":"# -*- coding: utf-8 -*-\ntry:\n    from urlparse import urlparse, urljoin\nexcept ImportError:\n    from urllib.parse import urlparse, urljoin\n\nfrom flask import request, redirect, url_for, current_app, jsonify\nfrom werkzeug.http import HTTP_STATUS_CODES\nimport psutil\nimport os\ndef get_net():\n    '''\n    获取当前机器所有的网卡名\n    return [('网卡名', 'ip'), (), ...]\n    '''\n    netcard_info=[]\n    info = psutil.net_if_addrs()\n    for k, v in info.items():\n        for item in v:\n            if item[0] == 2 and not item[1][:3] != '192':\n                netcard_info.append((k, item[1]))\n    return netcard_info\n\ndef get_txt_file(filename):\n    if '\\\\' in filename:\n        dirpath = os.path.abspath('.')\n        filename = os.path.join(dirpath, filename).replace('\\\\', '\\\\\\\\')\n    with open(filename) as f:\n         s=f.read()\n         f.close()\n    return str(s)\n\ndef is_safe_url(target):\n    ref_url = urlparse(request.host_url)\n    test_url = urlparse(urljoin(request.host_url, target))\n    return test_url.scheme in ('http', 'https') and ref_url.netloc == test_url.netloc\n\n\ndef redirect_back(default='user.index', **kwargs):\n    for target in request.args.get('next'), request.referrer:\n        if not target:\n            continue\n        if is_safe_url(target):\n            return redirect(target)\n    return redirect(url_for(default, **kwargs))\n\ndef api_abort(code, message=None, **kwargs):\n    if message is None:\n        message = HTTP_STATUS_CODES.get(code, '')\n\n    response = jsonify(message=message, **kwargs)\n    return response,code\n\ndef write_env(current_user, env_list, flag):\n    '''\n    function: write env to user/.env\n    params: env_list: env list msg\n    type: env_list: list\n    return: none\n    '''\n    print('====== current_user username ======')\n    print(current_user.username)\n   \n    center_file = 'userfile_center'\n    userfile = center_file + os.path.sep + '{}'.format(current_user.username.lower())\n    env_path_file = userfile + os.path.sep + '{}_env'.format(current_user.username.lower()) + os.path.sep + '.env'\n    \n    if flag=='w':\n        with open(env_path_file, 'w') as fn:\n            for env in env_list:\n                fn.write(env)\n    elif flag=='a': \n        with open(env_path_file, 'a') as fn:\n            for env in env_list:\n                fn.write(env)\n\n\ndef initenv_userfile(current_user, website, runway):\n    '''\n    function: init .envrc\n    params: current_user: \n    type: current_user: dict\n    return: none\n    '''\n#    if runway == 'cookie':\n#        env_msg = '''\n#            RUNWAY=cookie\n#            COOKIE1=\n#            COOKIE2=\n#            PASSWD=1234qwer\n#            WEBSITE=\n#        '''\n#    elif runway == 'userid':\n#        env_msg = '''\n#            USERNAME=USER\n#            USERNAME=99kies\n#            PASSWD=1234qwer\n#            WEBSITE=\n#    '''\n    \n    envrc = '''USERNAME={}\\nWEBSITE={}\\nRUNWAY={}\\n'''.format(current_user.username, website, runway)\n    \n    center_file = 'userfile_center'\n\n    userfile = center_file + os.path.sep + '{}'.format(current_user.username.lower())\n    if not os.path.exists(userfile):\n        os.makedirs(userfile)\n    env_path = userfile + os.path.sep + '{}_env'.format(current_user.username.lower())\n    filename = env_path + os.path.sep +'.env'\n    if not os.path.exists(env_path):\n        os.makedirs(env_path)\n        with open(filename, 'w', encoding='utf-8') as fn:\n            fn.write(envrc)\n    spiderpath = userfile + os.path.sep + '{}_spider'.format(current_user.username.lower())\n    if not os.path.exists(spiderpath):\n        os.mkdir(spiderpath)\n\n\ndef write_dp_yaml(current_user):\n\n    dp = \"\"\"apiVersion: apps/v1\nkind: Deployment\nmetadata:\n name: {0}-deployment\nspec:\n selector:\n  matchLabels:\n   app: {0}\n replicas: 3\n template:\n  metadata:\n   labels:\n    app: {0}\n  spec:\n   containers:\n   - name: {0}\n     image: scanserver\n     imagePullPolicy: IfNotPresent\n     ports:\n     - containerPort: 80\n    \"\"\"\n    dp_user_yaml = db.format(current_user.username)\n            \n","sub_path":"ScanServer/ScanServer/utils.py","file_name":"utils.py","file_ext":"py","file_size_in_byte":4022,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"617634650","text":"from flask import Flask, render_template, request, redirect, url_for\nimport todo_app.data.session_items as session_items\nfrom todo_app.view_model import ViewModel\nfrom datetime import datetime, timedelta\n\ndef create_app():\n    app = Flask(__name__)\n    app.config.from_object(\"todo_app.flask_config.Config\")\n\n    @app.route(\"/\")\n    def index():\n        items = session_items.get_items()\n        item_view_model = ViewModel(items)\n\n        return render_template(\"index.html\", view_model=item_view_model)\n\n    @app.route(\"/add_item\", methods=[\"POST\"])\n    def add_item():\n        new_item = request.form.get(\"name\")\n        session_items.add_item(new_item)\n\n        return redirect(\"/\")\n\n    @app.route(\"/mark_complete\", methods=[\"POST\"])\n    def mark_complete():\n        new_dict = request.form.to_dict(flat=False)\n        for id in new_dict.keys():\n            item = session_items.get_item(id)\n            session_items.mark_complete(item)\n\n        return redirect(\"/\")\n\n    @app.route(\"/mark_in_progress\", methods=[\"POST\"])\n    def mark_in_progress():\n        new_dict = request.form.to_dict(flat=False)\n        for id in new_dict.keys():\n            item = session_items.get_item(id)\n            session_items.mark_in_progress(item)\n\n        return redirect(\"/\")\n\n    @app.route(\"/delete_item\", methods=[\"POST\"])\n    def delete_item():\n        new_dict = request.form.to_dict(flat=False)\n        for id in new_dict.keys():\n            item = session_items.get_item(id)\n            session_items.delete_item(item)\n\n        return redirect(\"/\")\n\n    return app\n\n\nif __name__ == \"__main__\":\n    create_app().run()\n","sub_path":"todo_app/app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1616,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"380350728","text":"#!/usr/bin/python\n# -*- coding: utf-8 -*-\n\nfrom flask import Blueprint, render_template, url_for, flash, redirect, request, session, abort, current_app\nfrom flask.ext.login import login_required, current_user\n\nfrom ..extensions import db, or_, login_manager\nfrom ..forms import *\nfrom ..model import *\n\nreglages = Blueprint(\"reglages\", __name__, url_prefix=\"/reglages\")\n\n@reglages.route(\"/\")\ndef index():\n\t# Redirection vers la page d'accueil de la section étapes.\n\treturn redirect(url_for(\"reglages.etapes\"))\n\n@reglages.route(\"/etapes\")\ndef etapes():\n\t# Récupération des étapes. et affichage de la vue.\n\tetapes = Etape.query.all()\n\treturn render_template(\"/reglages/etapes/etapes.html\", etapes=etapes)\n\n@reglages.route(\"/etape/<int:identifiant>\", methods=[\"GET\", \"POST\"])\ndef etape(identifiant):\n\n\t# Récupération de l'étape. Récupération du formulaire de données et initialisation de celui-ci avec les paramètres actuels de l'étape.\n\tetape = Etape.query.get(identifiant)\n\tform = EtapeForm(sigle=etape.sigle.upper(), nom=etape.nom.capitalize(), bareme=etape.bareme, duree=etape.duree)\n\n\t# Validation du formulaire. Mise en forme et enregistrement des nouveaux paramètres de l'étape.\n\tif request.method == \"POST\" and form.validate():\n\t\tetape.sigle = form.sigle.data.lower()\n\t\tetape.nom = form.nom.data.lower()\n\t\tetape.bareme = form.bareme.data\n\t\tetape.duree = form.duree.data\n\t\tdb.session.commit()\n\n\t\t# Redirection vers la page d'acceuil de la section étapes.\n\t\treturn redirect(url_for(\"reglages.etapes\"))\n\n\t# Préparation de la vue affichant le formulaire de modification d'étape, les valeurs par défaut représentant les valeurs actuelles de l'étape.\n\treturn render_template(\"/reglages/etapes/etape.html\", form=form, etape=etape)\n\n@reglages.route(\"/etape/nouvelle\", methods=[\"GET\",\"POST\"])\ndef etape_ajouter():\n\t# Récupération du formulaire de données.\n\tform = EtapeForm()\n\n\t# Validation du formulaire. Mise en forme et enregistrement des paramètres de l'étape.\n\tif request.method == \"POST\" and form.validate():\n\t\tetape = Etape(sigle=form.sigle.data.lower(), nom=form.nom.data.lower(), bareme=form.bareme.data, duree=form.duree.data)\n\t\tdb.session.add(etape)\n\t\tdb.session.commit()\n\n\t\t# Redirection vers la page d'accueil de la section étapes.\n\t\treturn redirect(url_for(\"reglages.etapes\"))\n\n\t# Préparation de la vue affichant le formulaire de création d'étape.\n\treturn render_template(\"/reglages/etapes/ajouter.html\", form=form)\n\n@reglages.route(\"/etape/<int:identifiant>/supprimer\")\ndef etape_supprimer(identifiant):\n\t# Récupération et suppression de l'étape.\n\tetape = Etape.query.get(identifiant)\n\tdb.session.delete(etape)\n\tdb.session.commit()\n\n\t# Redirection vers la page d'acceuil de la section étapes.\n\treturn redirect(url_for(\"reglages.etapes\"))\n\n@reglages.route(\"/profils\")\ndef profils():\n\tprofils = Profil.query.all()\n\treturn render_template(\"/reglages/profils/profils.html\", profils=profils)\n\n@reglages.route(\"/profil/<int:identifiant>\", methods=[\"GET\", \"POST\"])\ndef profil(identifiant):\n\t# Récupération du profil. Récupération du formulaire de données et initialisation de celui-ci avec les paramètres actuels du profil.\n\tprofil = Profil.query.get(identifiant)\n\tform = ProfilForm(nom=profil.nom.capitalize())\n\n\t# Validation du formulaire. Mise en forme et enregistrement des paramètres du profil.\n\tif request.method == \"POST\" and form.validate():\n\t\tprofil.nom = form.nom.data.lower()\n\t\tdb.session.commit()\n\n\t\t# Redirection vers la pages d'accueil de la section profils.\n\t\treturn redirect(url_for(\"reglages.profils\"))\n\n\t# Préparation de la vue affichant le formulaire de modification de profil, les valeurs par défaut représentant les valeurs actuelles du profil.\n\treturn render_template(\"/reglages/profils/profil.html\", form=form, profil=profil)\n\n@reglages.route(\"/profil/nouveau\", methods=[\"GET\",\"POST\"])\ndef profil_ajouter():\n\tform = ProfilForm()\n\n\tif request.method == \"POST\" and form.validate():\n\t\tprofil = Profil(nom=form.nom.data.lower())\n\t\tdb.session.add(profil)\n\t\tdb.session.commit()\n\n\t\treturn redirect(url_for(\"reglages.profils\"))\n\n\treturn render_template(\"/reglages/profils/ajouter.html\", form=form)\n\n@reglages.route(\"/profil/<int:identifiant>/supprimer\")\ndef profil_supprimer(identifiant):\n\tprofil = Profil.query.get(identifiant)\n\tdb.session.delete(profil)\n\tdb.session.commit()\n\n\treturn redirect(url_for(\"reglages.profils\"))\n\n@reglages.route(\"/filieres\")\ndef filieres():\n\tfilieres = Filiere.query.all()\n\treturn render_template(\"/reglages/filieres/filieres.html\", filieres=filieres)\n\n@reglages.route(\"/filiere/<int:identifiant>\", methods=[\"GET\", \"POST\"])\ndef filiere(identifiant):\n\tfiliere = Filiere.query.get(identifiant)\n\tform = FiliereForm(nom=filiere.nom.capitalize())\n\n\tif request.method == \"POST\" and form.validate():\n\t\tfiliere.nom = form.nom.data.lower()\n\t\tdb.session.commit()\n\n\t\treturn redirect(url_for(\"reglages.filieres\"))\n\n\treturn render_template(\"/reglages/filieres/filiere.html\", form=form, filiere=filiere)\n\n@reglages.route(\"/filiere/nouvelle\", methods=[\"GET\", \"POST\"])\ndef filiere_ajouter():\n\tform = FiliereForm()\n\n\tif request.method == \"POST\" and form.validate():\n\t\tfiliere = Filiere(nom=form.nom.data.lower())\n\t\tdb.session.add(filiere)\n\t\tdb.session.commit()\n\n\t\treturn redirect(url_for(\"reglages.filieres\"))\n\n\treturn render_template(\"/reglages/filieres/ajouter.html\", form=form)\n\n@reglages.route(\"/filiere/<int:identifiant>/supprimer\")\ndef filiere_supprimer(identifiant):\n\tfiliere = Filiere.query.get(identifiant)\n\tdb.session.delete(filiere)\n\tdb.session.commit()\n\n\treturn redirect(url_for(\"reglages.filieres\"))\n\n@reglages.route(\"/tableaux\")\ndef tableaux():\n\ttableaux = Tableau.query.all()\n\treturn render_template(\"/reglages/tableaux/tableaux.html\", tableaux=tableaux)\n\n@reglages.route(\"/tableau/<int:identifiant>\", methods=[\"GET\", \"POST\"])\ndef tableau(identifiant):\n\ttableau = Tableau.query.get(identifiant)\n\tform = TableauForm(nom=tableau.nom, profils=[profil.identifiant for profil in tableau.profils], filieres=[filiere.identifiant for filiere in tableau.filieres])\n\tform.profils.choices = [(profil.identifiant, profil.nom.capitalize()) for profil in Profil.query.all()]\n\tform.filieres.choices = [(filiere.identifiant, filiere.nom.capitalize()) for filiere in Filiere.query.all()]\n\n\tif request.method == \"POST\" and form.validate():\n\t\ttableau.nom = form.nom.data\n\t\ttableau.profils = [Profil.query.get(profil) for profil in form.profils.data]\n\t\ttableau.filieres = [Filiere.query.get(filiere) for filiere in form.filieres.data]\n\t\tdb.session.commit()\n\t\treturn redirect(url_for(\"reglages.tableaux\"))\n\n\treturn render_template(\"/reglages/tableaux/tableau.html\", form=form, tableau=tableau)\n\n@reglages.route(\"/tableau/nouveau\", methods=[\"GET\", \"POST\"])\ndef tableau_ajouter():\n\tform = TableauForm()\n\tform.profils.choices = [(profil.identifiant, profil.nom.capitalize()) for profil in Profil.query.all()]\n\tform.filieres.choices = [(filiere.identifiant, filiere.nom.capitalize()) for filiere in Filiere.query.all()]\n\n\tif request.method == \"POST\" and form.validate():\n\t\ttableau = Tableau(nom=form.nom.data, profils=[Profil.query.get(identifiant) for identifiant in form.profils.data], filieres=[Filiere.query.get(identifiant) for identifiant in form.filieres.data])\n\t\tdb.session.add(tableau)\n\t\tdb.session.commit()\n\n\t\treturn redirect(url_for(\"reglages.tableaux\"))\n\n\treturn render_template(\"/reglages/tableaux/ajouter.html\", form=form)\n\n@reglages.route(\"/tableau/<int:identifiant>/supprimer\")\ndef tableau_supprimer(identifiant):\n\ttableau = Tableau.query.get(identifiant)\n\tdb.session.delete(tableau)\n\tdb.session.commit()\n\n\treturn redirect(url_for(\"reglages.tableaux\"))\n","sub_path":"darwin/views/reglages.py","file_name":"reglages.py","file_ext":"py","file_size_in_byte":7633,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"412530896","text":"from django.shortcuts import render, redirect\nfrom api.models import MetaData\nfrom django.db import connections\nfrom django.conf import settings\nfrom django.urls import reverse\nfrom django.contrib.admin.views.decorators import staff_member_required\n\n# Create your views here.\n\n\n## << REFRESH - copied from draftmetadata.py\ndef dictfetchall(cursor):\n    # from django docs\n    \"Return all rows from a cursor as a dict\"\n    columns = [col[0] for col in cursor.description]\n    return [dict(zip(columns, row)) for row in cursor.fetchall()]\n\n\nGEOMETRY = dict(\n    POINT='Point',\n    POLYGON='Polygon',\n    LINESTRING='LineString',\n    MULTIPOINT='MultiPoint',\n    MULTIPOLYGON='MultiPolygon',\n    MULTILINESTRING='MultiLineString',\n)\n\n\ndef refresh_shema(schema, user):\n    conn = connections[schema]\n    with conn.cursor() as cursor:\n        cursor.execute('''\n        SELECT f_table_name, type\n        FROM \"public\".\"geometry_columns\"\n        WHERE f_table_schema = %s\n        ''', [schema])\n        for r in cursor.fetchall():\n            try:\n                table_name = '{}/{}'.format(schema, r[0])\n                geometry_type = GEOMETRY[r[1]]\n                try:\n                    md = MetaData.objects.get(resource_identifier=table_name)\n                except MetaData.DoesNotExist:\n                    try:\n                        md = MetaData.objects.create_draft(\n                            table_name, geometry_type, user)\n                    except Exception as e:\n                        pass\n\n            except Exception as e:\n                pass\n\n\ndef sync_metadata(user):\n    for schema in settings.LAYERS_SCHEMAS:\n        refresh_shema(schema, user)\n\n\n@staff_member_required\ndef api_metadata_sync(request):\n    sync_metadata(request.user)\n    return redirect(reverse('admin:api_metadata_changelist'))\n\n\n## >> REFRESH\n","sub_path":"remote_manage/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1840,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"427892809","text":"# senator.py\n# This program accepts a person's age and years of citizenship as input and\n# outputs their eligibility for senate and house.\n\ndef main():\n    print(\"This program calculates eligibility to be a US senator.\\n\")\n    while True:\n        try:\n            age = int(input(\"Please enter your age: \"))\n            cit = int(input(\"Please enter years of citizenship: \"))\n            if (age >= 30) and (cit >= 9):\n                print(\"\\nYou are eligible to be a US senator.\\n\")\n            elif (age >= 25) and (cit >= 7):\n                print(\"\\nYou are eligible to be a US representative.\\n\")\n            else:\n                print(\"\\nYou are not eligible be either a US senator or representative.\\n\")\n        except ValueError:\n            print(\"\\nYou have not entered a whole numeric figure.\\n\")\n\nmain()\n","sub_path":"python/python-programming-zelle/senator.py","file_name":"senator.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"181706549","text":"import dicom, dicom.UID\nfrom dicom.dataset import Dataset, FileDataset\nimport numpy as np\nimport datetime, time\n\ndef write_dicom(pixel_array,filename,directory,pixelSpacing = ['0.1171875', '0.1171875'],slicethickness = '0.117'):\n    \"\"\"\n    INPUTS:\n    pixel_array: 2D numpy ndarray.  If pixel_array is larger than 2D, errors.\n    filename: string name for the output file.\n    \"\"\"\n\n    ## This code block was taken from the output of a MATLAB secondary\n    ## capture.  I do not know what the long dotted UIDs mean, but\n    ## this code works.\n    file_meta = Dataset()\n    file_meta.MediaStorageSOPClassUID = 'Secondary Capture Image Storage'\n    file_meta.MediaStorageSOPInstanceUID = '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'\n    file_meta.ImplementationClassUID = '1.3.6.1.4.1.9590.100.1.0.100.4.0'\n    #file_meta.PixelSpacing = ['0.1171875', '0.1171875']\n    ds = FileDataset(filename, {},file_meta = file_meta,preamble=\"\\0\"*128)\n    ds.Modality = 'WSD'\n    ds.ContentDate = str(datetime.date.today()).replace('-','')\n    ds.ContentTime = str(time.time()) #milliseconds since the epoch\n    ds.StudyInstanceUID =  '1.3.6.1.4.1.9590.100.1.1.124313977412360175234271287472804872093'\n    ds.SeriesInstanceUID = '1.3.6.1.4.1.9590.100.1.1.369231118011061003403421859172643143649'\n    ds.SOPInstanceUID =    '1.3.6.1.4.1.9590.100.1.1.111165684411017669021768385720736873780'\n    ds.SOPClassUID = 'Secondary Capture Image Storage'\n    ds.SecondaryCaptureDeviceManufctur = 'Python 2.7.3'\n\n    ## These are the necessary imaging components of the FileDataset object.\n    ds.SamplesPerPixel = 1\n    ds.PhotometricInterpretation = \"MONOCHROME2\"\n    ds.PixelRepresentation = 0\n    ds.HighBit = 15\n    ds.BitsStored = 16\n    ds.BitsAllocated = 16\n    ds.SmallestImagePixelValue = '\\\\x00\\\\x00'\n    ds.LargestImagePixelValue = '\\\\xff\\\\xff'\n    ds.Columns = pixel_array.shape[0]\n    ds.Rows = pixel_array.shape[1]\n    ds.PixelSpacing = pixelSpacing\n    ds.SliceThickness = slicethickness\n    if pixel_array.dtype != np.uint16:\n        pixel_array = pixel_array.astype(np.uint16)\n    ds.PixelData = pixel_array.tostring()\n    import os\n    os.chdir(directory)\n    ds.save_as(filename)\n    os.chdir('A:\\\\dump')\n    return\n","sub_path":"writeDicom.py","file_name":"writeDicom.py","file_ext":"py","file_size_in_byte":2234,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"562750699","text":"from collections import defaultdict, OrderedDict\nfrom flask import abort\nfrom sqlite3 import Row\nfrom typing import Any, Dict, List, Tuple, Union\n\nimport crud_sql as crud\nimport util\n\nfrom api import app, con\nfrom db_types import DegreeType, JobType, Industry\nfrom logic.user import get_user\n\n\nclass CareerWeight:\n    def __init__(self):\n        self.weight = 0\n\n    def step(self, m1, u1, d1, m2, u2, d2):\n        self.weight += 3 * (m1 == m2) + 2 * (u1 == u2) + (d1 == d2)\n\n    def finalize(self):\n        return self.weight\n\n\ncon.create_aggregate(\"CAREER_WEIGHT\", 6, CareerWeight)\n\n\n# take a user’s university\n# return a list of job titles that other users with similar education have reported\ndef get_job_for_education_background(userID: int, limit:int=20):\n    \"\"\"Get a list of jobs held by users of similar educational background.\n\n    For each entry in this user's education, attempt to match other users\n    by major, degree, and university. Major is required for a valid match,\n    but degree and university may vary.\n\n    For each match, the weight of the result is increased. Weights are computed\n    in three dimensions: major matches, degree matches, and university matches.\n    The results are ranked by the weighted average of these dimensions,\n    with relative weights of 3, 1, 2 respectively (i.e. people in your major are\n    better indicators than anyone at your university, which itself is a better\n    indicator than just anyone with a bachelors degree).\n    \"\"\"\n\n    # Raises a 404 if the user is not found. That is exactly what we want\n    _ = get_user(userID)\n\n    # Alright, this is a bit complicated. We have two primary queries here: A\n    # and B.\n    #\n    # Query B is easy, it just gets the User and Degree information of the user\n    # we want to look up from the education table.\n    #\n    # Query A is harder. We start with education for the same info as B. Then\n    # we join with experience to gain access to the postionID column, which we\n    # then use to join with position, which gives us jobTitle and employerName.\n    # We filter this by users that still exist in the table (in case a user was\n    # deleted without removing their experience). Since this query does not\n    # depend on variables, we can store it as a view to simplify the full query.\n    #\n    # Next, we join A and B on the condition that the userID is different,\n    # essentially getting a cross product of our user with the work experience\n    # of every other (current) user.\n    #\n    # We then group by the job info and aggregate using our custom weight\n    # function which takes the education info of our user and the other user\n    # to determine a relationship factor. We exclude the groups (jobs) which\n    # have no relationship.\n    #\n    # Finally, we select the job title and employer name concatenated together\n    # and the custom weight value and sort by descending weight.\n\n    # NOTE: to change this view, the old version must first be DROP'ed\n    # as views are read-only in SQLite\n    con.execute('''CREATE VIEW IF NOT EXISTS all_jobs\n                   (jobTitle, employerName, userID, university, degree, major)\n                   AS\n                   SELECT jobTitle, employerName, userID, university, degree, major\n                   FROM (education NATURAL JOIN experience) e\n                   JOIN position p\n                   ON e.positionID = p.id\n                   WHERE userID in (SELECT id FROM user)\n                ''')\n\n    rows = con.execute(\n        '''\n        SELECT jobTitle || ', ' || employerName as job,\n        CAREER_WEIGHT(\n            a.major, a.university, a.degree, b.major, b.university, b.degree\n        ) as weight\n        FROM all_jobs a\n        JOIN\n        (\n            SELECT userID, university, degree, major\n            FROM education\n            WHERE userID = ?\n        ) b\n        ON a.userID <> b.userID\n        GROUP BY jobTitle, employerName\n        HAVING CAREER_WEIGHT(\n            a.major, a.university, a.degree, b.major, b.university, b.degree\n        ) > 0\n        ORDER BY weight DESC\n        LIMIT ?\n        ''',\n        (userID, limit)\n    ).fetchall()\n\n    results = [{'role': r['job'], 'relevance': r['weight']} for r in rows]\n    return {'status': 'OK', 'results': results}\n\n\n# take a desired job title + industry\n# return a list of courses that people who have that (or similar) job took\ndef get_classes_for_career(industry: str, job: str = None, school: str = None, threshold:int=3):\n    \"\"\"Return a list of potential classes related to the given industry.\n\n    Classes are filtered by users who work in a particular position/industry\n    who have reported taking those classes.\n\n    If a specific job title is given, only users who have held that title are\n    considered.\n\n    A university name may be provided to restrict results to a particular\n    university.\n\n    Returns a dictionary mapping university names to a list of course numbers\n    and titles, sorted aplhabetically\n    \"\"\"\n\n    params = [f'%{industry}%']  # fuzzy match industry\n    query_join_position = ''\n    query_filter_job = ''\n    query_filter_school = ''\n\n    if job is not None:\n        params.append(f'%{job}%')  # fuzzy match job\n        query_join_position = 'JOIN position p ON ex.positionID = p.id'\n        query_filter_job = 'AND jobTitle LIKE ?'\n\n    if school is not None:\n        params.append(f'%{school}%')  # fuzzy match school\n        query_filter_school = 'AND university LIKE ?'\n\n    base_query = '''SELECT courseNumber || ': ' || courseTitle as course,\n                           universityName as university\n                    FROM (experience NATURAL JOIN education) ex\n                    JOIN enrollment e\n                    ON e.educationID = ex.id\n                    JOIN course c\n                    ON e.courseID = c.id\n                    {}\n                    {}\n                 '''\n\n    where_clause = 'WHERE industry LIKE ? {} {}'.format(query_filter_job, query_filter_school)\n\n    positive_query = base_query.format(query_join_position, where_clause)\n    print(positive_query)\n    positive_matches = con.execute(positive_query, params).fetchall()\n\n\n    where_clause = 'WHERE'\n    if job is not None:\n        where_clause += ' (industry NOT LIKE ? OR jobTitle NOT LIKE ?)'\n    else:\n        where_clause += ' industry NOT LIKE ?'\n\n    if school is not None:\n        where_clause += ' AND university LIKE ?'\n\n    negative_query = base_query.format(query_join_position, where_clause)\n    negative_matches = con.execute(negative_query, params).fetchall()\n\n\n    # build a dictionary mapping universities to courses, with each course having\n    # an associate count (as a relevance metric)\n    results = defaultdict(lambda: defaultdict(int))\n    for row in positive_matches:\n        results[row['university']][row['course']] += 1\n\n    for row in negative_matches:\n        results[row['university']][row['course']] -= 1\n\n    # for each university (key k), sort the list of courses by relevance\n    for k, v in results.items():\n        # filter out any courses with negative relevance\n        courses = list(filter(lambda x: x[1] > 0, v.items()))\n        # courses = v.items()\n\n        # sort alphabetically by course first, then sort by descending count\n        # items with the same count wont be reordered\n        courses = sorted(courses, key=lambda x: x[0], reverse=False)\n        courses = sorted(courses, key=lambda x: x[1], reverse=True)\n        # strip off the count, we don't need to show that to the user\n        # also limit results to 20 per university\n        results[k] = list(map(lambda x: x[0], courses))[:20]\n\n    # elide any universities which have no courses\n    results = {k: v for k, v in results.items() if len(v) > 0}\n\n    if len(results) == 0:\n        return ('', 204)\n    return results\n\n\ndef get_popular_companies(school: str, limit: int):\n    rows = con.execute('''SELECT employerName, COUNT(userID) as cnt\n                          FROM position p JOIN experience ex\n                          ON p.id = ex.positionID\n                          NATURAL JOIN education\n                          WHERE university = ?\n                          GROUP BY employerName\n                          ORDER BY cnt DESC\n                          LIMIT ?\n                       ''', (school, limit)) \\\n              .fetchall()\n    results = list(map(lambda x: x['employerName'], rows))\n    return {'status': 'OK', 'results': results}\n","sub_path":"app/api/logic/function.py","file_name":"function.py","file_ext":"py","file_size_in_byte":8447,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"290096781","text":"import numpy as np\nfrom PIL import Image, ImageEnhance, ImageFilter\n\n\n# custom data generator\ndef batch_generator(patches_im, patches_gt, batch_size, data_augmentation=False):\n    \"\"\"\n    :param patches_im: input images\n    :param patches_gt: corresponding input ground truths\n    :param batch_size: size of batch to return\n    :param data_augmentation: use data augmentation (True) or not (False)\n    :return: [batch_size] random images from X, y\n    \"\"\"\n\n    while True:\n        # choose batch_size random images / labels from the data\n        idx = np.random.randint(0, patches_im.shape[0], batch_size)\n        im_patches = patches_im[idx]\n        gt_patches = patches_gt[idx]\n        if data_augmentation:\n            im_patches, gt_patches = augment_images_and_gt(im_patches, gt_patches, rf_h=True, rf_v=True, rot=True)\n\n        yield im_patches, gt_patches\n\n\ndef augment_images_and_gt(im_patches, gt_patches, normalize=False, rf_h=False, rf_v=False, rot=False, jitter=False,\n                          gamma=0., brightness=0., contrast=0., blur=False, force=False):\n    \"\"\"\n    :param im_patches: Image to transform\n    :param gt_patches: Ground truth to transform\n    :param rot: Rotations by +/- 90 degrees\n    :param normalize: normalization\n    :param rf_h: Random horizontal flipping\n    :param rf_v: Random vertical flipping\n    :param rot: Randomly rotate image by 0, 90, 180 or 270 degrees\n    :param jitter: Add random noise in N(0,0.01) to image\n    :param gamma: Put every pixel to the power of 0.85\n    :param brightness: Increase brightness of picture by a factor 3\n    :param contrast: Increase contrast by factor 1.3\n    :param blur: Add horizontal Gaussian blur (5*5 kernel)\n    :param force: Apply transformations to all images\n    :return: augmented image and ground truth\n        \"\"\"\n\n    im_patches_t = []  # transformed images\n    gt_patches_t = []  # transformed labels\n\n    flag_singleimg = False\n    if len(np.shape(im_patches)) < 4:\n        flag_singleimg = True\n        im_patches = [im_patches]\n        gt_patches = [gt_patches]\n\n    for (im, gt) in zip(im_patches, gt_patches):\n        # Scale image between [0, 1]\n        if normalize:\n            im -= np.min(im)\n            im /= np.max(im)\n\n        # random flipping\n        if rf_h:\n            if np.random.randint(2) or force:\n                im = np.fliplr(im)\n                gt = np.fliplr(gt)\n        if rf_v:\n            if np.random.randint(2) or force:\n                im = np.flipud(im)\n                gt = np.flipud(gt)\n\n        # rotation\n        if rot:\n            if np.random.choice([0, 1], p=[.25, .75]):\n                k = np.random.randint(1, 4)  # rotate 1, 2 or 3 times by 90 degrees\n                im = np.rot90(im, k)\n                gt = np.rot90(gt, k)\n\n        # noise injection (color jittering), only for image\n        if jitter:\n            if np.random.randint(2):\n                noise = np.random.normal(0, .01, np.shape(im))\n                im += noise\n\n        if gamma:\n            if np.random.randint(2) or force:\n                im = im ** gamma\n\n        # PIL transformations\n        if brightness or contrast or blur:\n            if im.shape[-1] == 1:\n                im_pil = Image.fromarray((im[..., 0] * 255).astype('uint8'))\n            else:\n                im_pil = Image.fromarray((im * 255).astype('uint8'))\n\n            if brightness:\n                if np.random.randint(2) or force:\n                    im_pil = ImageEnhance.Brightness(im_pil).enhance(brightness)\n            if contrast:\n                if np.random.randint(2) or force:\n                    im_pil = ImageEnhance.Contrast(im_pil).enhance(contrast)\n            if blur:\n                # not really working with semantic segmentation!\n                if np.random.randint(2) or force:\n                    size = 5\n                    kernel_motion_blur = np.zeros((size, size))\n                    kernel_motion_blur[int((size - 1) / 2), :] = np.ones(size)\n                    kernel_motion_blur = kernel_motion_blur / size\n                    kernel_motion_blur = kernel_motion_blur.flatten()\n                    im_pil = im_pil.filter(ImageFilter.Kernel((size, size), kernel_motion_blur))\n\n            im = np.asarray(im_pil) / 255\n            # Scale image between [0, 1]\n            im -= np.min(im)\n            im /= np.max(im)\n            if len(im.shape) == 2:\n                im = im[..., np.newaxis]\n\n        im_patches_t.append(im)\n\n        gt_patches_t.append(gt)\n\n    im_patches_t = np.asarray(im_patches_t)\n    gt_patches_t = np.asarray(gt_patches_t)\n\n    if flag_singleimg:\n        return im_patches_t[0], gt_patches_t[0]\n    else:\n        return im_patches_t, gt_patches_t\n","sub_path":"density_forest/helpers/data_augment.py","file_name":"data_augment.py","file_ext":"py","file_size_in_byte":4698,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"326443673","text":"\"\"\"\nInput a anndata object and cluster string, output a gzipped json file that is:\n\n{\n    marker_genes: [\n        {\n            \"gene_name\" : gene,\n            \"dataset_name\": dataset_name,\n            \"cluster_solution_name\": cluster_solution_name,\n            \"cluster_name\": cluster_name,\n            \"sensitivity\": sensivity, #(+1 read considered a guess) / n cells IN cluster\n            \"specificity\": specificity, #(+1 read considered a guess) (n reads < 1 out of cluster / n cells OUT of cluster)\n            \"precision\": precision, #n +1 reads in cluster / n +1 reads out of cluster\n            \"accuracy\": accuracy,\n            \"recall\": recall, #n +1 reads in cluster / n 0 reads in cluster.\n            \"t_pval\": tpval,\n            \"z_pval\": zpval,\n            \"z_stat\": zstat,\n            \"t_stat\": tstat, #(t statistic of gene expression in / out of cluster)\n            \"log2_change_vs_min\": fold_change_min, #( min cluster.... mincluster will always be 0.\n            \"log2_change_vs_next\": fold_change_next, #(next is the level of the second highest cluster, there for the 2nd cluster will always be 1.\n            \"mean_expression\": centroids.loc[gene, cluster_name]\n        }, ...\n    ]\n}\n\nThe data set name is currently inferred from the filename, with filename.split(\"_ebi\")[0]\n\nFor each of the clustering solutions, any cell with a value of \"NA\" or \"nan\" is discarded, and not considered for\nthe in/out cluster calculations.\n\nThe log fold changes use a pseudocount of 2. This controls the log fold change when you have small values. For instance\nif the smaller average expression was .01, and the largest was 2, without a pseudocount the fold change would be\nlarge. Adding a pseudocount of 2 means you would be comparing the fold change between 2.01 and 4 instead, which is less\nthat a single log2 fold change... that is what we want because we don't want to favor small changes in expression.\n\nFor the Ztest, a +1 read in a cell is considered a positive indication, the proportion of +1 reads in the cluster\nis compared with the proportion of +1 reads out of the cluster with the alternative hypothesis being that the proporiton\nin the cluster is larger than outside of the cluster.\n\nThe t-statistics are done with the in cluster vs out of cluster.\n\"\"\"\nimport argparse\nimport sys\nfrom statsmodels.stats.proportion import proportions_ztest\nfrom scipy.stats import ttest_ind\nimport scanpy as sc\nimport pandas as pd\nimport math\nimport numpy as np\nimport json\nimport gzip\nimport os\nfrom scipy.stats import hypergeom\n\n\n\ndef hypergeo_z(actual, mu, std):\n    return (actual - mu) / std\n\ndef hypergeo_mu_std(n_smaller_set, n_larger_set, total_number_of_genes=30000):\n    [M, n, N] = [total_number_of_genes, n_smaller_set, n_larger_set]\n    hg = hypergeom(M, n, N)\n    return hg.mean(), hg.std()\n\ndef hypergeo(gene_intersection, n_smaller_set, n_larger_set,  total_number_of_genes=30000):\n    return hypergeom.sf(gene_intersection, total_number_of_genes, n_smaller_set, n_larger_set)\n\ndef two_averages_fold_change(centroids1, centroids2):\n    # subsetted down to have the same features.\n\n\n    markers1 = find_markers_clusters(centroids1)\n    markers2 = find_markers_clusters(centroids2)\n\n    markers1.shape\n    markers2.shape\n\n    n_gene_intersection = len(set(markers1.index).intersection(markers2.index))\n    gene_union = set(markers1.index).union(markers2.index)\n    multiplier = hypergeo_z(n_gene_intersection, *hypergeo_mu_std(len(markers1), len(markers2)))\n\n    centroids1 = centroids[centroids.columns[0:2]]\n    centroids2 = centroids[centroids.columns[-2:]]\n\n    second_largest1 = centroids1.apply(lambda x: x.nlargest(2).iloc[1], axis=1)\n    second_largest2 = centroids2.apply(lambda x: x.nlargest(2).iloc[1], axis=1)\n\n    largest1 = centroids1.apply(lambda x: x.max(), axis=1)\n    largest2 = centroids2.apply(lambda x: x.max(), axis=1)\n\n    largest_cluster1 = centroids1.apply(lambda x: x.idxmax(), axis=1)\n    largest_cluster2 = centroids2.apply(lambda x: x.idxmax(), axis=1)\n\n    cluster_pair_ids = pd.Series(\n        [\"%s:%s\" % (f,s) for (f,s) in zip(largest_cluster1, largest_cluster2)],\n        index=largest_cluster2.index\n    )\n\n    second_largest_combo = second_largest1 + second_largest2\n    largest_combo = largest1 + largest2\n    cluster_name_df = largest_combo\n    second_largest_combo = np.log2(second_largest_combo + 2)\n    log_centroids = np.log2(largest_combo + 2)\n    two_fc = log_centroids - second_largest_combo\n\n    new_genes = two_fc.index[(two_fc > 1).tolist()]\n    new_cluster_pairs = cluster_pair_ids.loc[new_genes]\n    cluster_pair_scores = {}\n    for cluster_pair_id in new_cluster_pairs.unique():\n        cluster1, cluster2 = cluster_pair_id.split(\":\")\n        genes_in_pair = new_cluster_pairs.index[new_cluster_pairs == cluster_pair_id]\n        print(len(genes_in_pair))\n\n        try:\n            m1 = set(markers1.index[markers1 == cluster1])\n            m2 = set(markers1.index[markers2 == cluster2])\n            n_gene_intersection = len(m1.intersection(m2))\n            multiplier = hypergeo_z(n_gene_intersection, *hypergeo_mu_std(len(m1), len(m2)))\n            print(multiplier)\n            multiplier = np.max(multiplier, 1)\n        except IndexError:\n            multiplier = 1\n\n        each_genes = set(markers1.index[markers1 == cluster1]).union(markers2.index[markers2 == cluster2])\n        jac = len(each_genes.intersection(genes_in_pair)) / len(each_genes)\n        cluster_pair_scores[cluster_pair_id] = jac * multiplier\n\n    return cluster_pair_scores\n\n\ndef log2_fold_change(A,B):\n    return math.log(A, 2) - math.log(B, 2)\n\ndef parse_args():\n\n    parser = argparse.ArgumentParser(description=__doc__,\n        formatter_class=argparse.RawDescriptionHelpFormatter)\n\n    parser.add_argument('-ia',\"--anndata\", type=str, help=\"anndata object from scanpy\",\n                        required=True\n                        )\n\n    parser.add_argument('-ic', \"--csv\", type=str, help=\"expression counts .csv if different than clustered.ad\",\n                        required=False, default=None\n                        )\n\n    parser.add_argument('-cs', \"--cluster_string\", type=str, help=\"anndata.uns key that has a list of all available clusters\",\n                        required=False\n                        )\n\n    parser.add_argument('-o',\"--out_dir\", type=str, help=\"name of directory to put gzipped json file\",\n                        required=True\n                        )\n\n    opts = parser.parse_args()\n\n    anndata, counts, cluster_string, out_file = opts.anndata, opts.csv, opts.cluster_string, opts.out_dir\n\n    return anndata, counts, cluster_string, out_file\n\n\ndef write_data(out_file, marker_dicts):\n    with gzip.GzipFile(out_file, 'w') as fout:\n        fout.write(json.dumps({\"markers\": marker_dicts}).encode('utf-8'))\n\n\ndef get_counts(ad, counts):\n    if isinstance(counts, pd.DataFrame):\n        return counts\n    return ad.to_df()\n\ndef datasetname(filename):\n    day = filename.split(\"-\")[0]\n    return \"hPSC-cardiac-dev_%s\" % day\n\n\ndef find_markers_clusters(centroids):\n    # Find the second largest of each centroid\n    second_largest = centroids.apply(lambda x: x.nlargest(2).iloc[1], axis=1)\n    largest = centroids.max(axis=1)\n    largest_cluster_name = centroids.apply(lambda x: x.idxmax(), axis=1)\n    # Do log transforms with pseudocount\n    second_largest = np.log2(second_largest + 2)\n    log_centroids = np.log2(largest + 2)\n    # Find the log2 fold change from next highest.\n    sdiff = log_centroids.sub(second_largest, axis=0)\n    # Keep only genes that have at least 1 log fold change (fold change of 2) higher than next.\n    marker_genes = sdiff.index[(sdiff >= 1).tolist()]\n    cluster_idd = largest_cluster_name[marker_genes]\n\n    return cluster_idd\n\n\ndef find_markers(centroids):\n    # Find the second largest of each centroid\n    second_largest = centroids.apply(lambda x: x.nlargest(2).iloc[1], axis=1)\n    # Do log transforms with pseudocount\n    second_largest = np.log2(second_largest + 2)\n    log_centroids = np.log2(centroids + 2)\n    # Find the log2 fold change from next highest.\n    sdiff = log_centroids.sub(second_largest, axis=0)\n    # Keep only genes that have at least 1 log fold change (fold change of 2) higher than next.\n    marker_genes = sdiff.index[(sdiff.max(axis=1) >= 1).tolist()]\n    #print(\"found %d possible marker genes for cluster solution %s in datasest %s\" % (\n    #len(marker_genes), cluster_solution_name, dataset_name))\n\n    return marker_genes\n\n\ndef detail_marker_dicts(marker_genes, centroids, n_xpr, n_not_xpr):\n    marker_dicts = []\n    for gene in marker_genes:\n        second_largest = centroids.loc[gene].nlargest(2).tolist()[1] + 2\n        minimum = centroids.loc[gene].min() + 2\n\n        for cluster_name in centroids.columns:\n            gene_centroid = centroids.loc[gene, cluster_name] + 2\n            other_cluster_names = [name for name in centroids.columns if name != cluster_name]\n            # TP: true positives\n            expressed_in_cluster = n_xpr.loc[gene, cluster_name]\n            # FN: false negatives\n            not_expressed_in_cluster = n_not_xpr.loc[gene, cluster_name]\n            # FP: false positives\n            expressed_out_cluster = n_xpr.loc[gene, other_cluster_names].sum()\n            # TN: true negatives\n            not_expressed_out_cluster = n_not_xpr.loc[gene, other_cluster_names].sum()\n            # FP + TN\n            out_size = not_expressed_out_cluster + expressed_out_cluster\n            # TP + FN\n            cluster_size = expressed_in_cluster + not_expressed_in_cluster\n\n            # TP / (FP + TP)\n            precision = expressed_in_cluster / (expressed_in_cluster + expressed_out_cluster)\n            # TP / (FN + TP)\n            recall = expressed_in_cluster / (not_expressed_in_cluster + expressed_in_cluster)\n\n            # TP / ( TP + FN)\n            sensivity = expressed_in_cluster / cluster_size\n            # TN / (FP + TN)\n            specificity = not_expressed_out_cluster / out_size\n\n            # Accuracy = (TP+TN)/(TP+TN+FP+FN)\n            accuracy = (expressed_in_cluster + not_expressed_out_cluster) / (out_size + cluster_size)\n\n            fold_change_next = log2_fold_change(gene_centroid, second_largest)\n            fold_change_min = log2_fold_change(gene_centroid, minimum)\n\n            zstat, zpval = proportions_ztest(\n                count=[expressed_in_cluster, expressed_out_cluster],\n                nobs=[cluster_size, out_size],\n                alternative='larger'\n            )\n\n            tstat, tpval = ttest_ind(X.loc[cell_names, gene], X.loc[other_cell_names, gene])\n\n            marker_dict = {\n                \"gene_name\": gene,\n                \"dataset_name\": dataset_name,\n                \"cluster_solution_name\": cluster_solution_name,\n                \"cluster_name\": str(cluster_name),\n                \"sensitivity\": sensivity.item(),  # (+1 read considered a guess) / n cells IN cluster\n                \"specificity\": specificity.item(),\n                \"precision\": precision.item(),  # n +1 reads in cluster / n +1 reads out of cluster\n                \"accuracy\": accuracy.item(),\n                \"recall\": recall.item(),  # n +1 reads in cluster / n 0 reads in cluster.\n                \"t_pval\": tpval.item(),\n                \"z_pval\": zpval.item(),\n                \"z_stat\": zstat.item(),\n                \"t_stat\": tstat.item(),  # (t statistic of gene expression in / out of cluster)\n                \"log2_change_vs_min\": fold_change_min,  # ( min cluster.... mincluster will always be 0.\n                \"log2_change_vs_next\": fold_change_next,\n                \"mean_expression\": centroids.loc[gene, cluster_name].item()\n            }\n\n            marker_dicts += [marker_dict]\n\n    return marker_dicts\n\n\ndef main():\n\n    in_file, counts_file, cluster_string, out_dir = parse_args()\n\n    ad = sc.read(in_file)\n\n    try:\n        counts = pd.read_csv(counts_file, index_col=0).transpose()\n    except ValueError:\n        pass\n\n    marker_dicts = []\n    dataset_name = datasetname(os.path.basename(in_file))\n\n    out_file = os.path.join(out_dir, \"%s_markers.json.gzip\" % dataset_name)\n\n    if cluster_string is None:\n        cluster_solution_names = [\"louvain\"]\n    else:\n        cluster_solution_names = ad.uns[cluster_string]\n\n    for cluster_solution_name in cluster_solution_names:\n        X = get_counts(ad, counts)\n        X = (X / X.sum()) * 10000\n        cluster_solution = ad.obs[cluster_solution_name]\n        cluster_solution = cluster_solution.dropna()\n\n        # Calculate each centroid.\n        centroids = pd.DataFrame(index=X.columns)\n        for cluster_name in cluster_solution.unique():\n            cell_names = cluster_solution.index[(cluster_solution == cluster_name).tolist()]\n            centroid = X.loc[cell_names].mean(axis=0)\n            centroids[cluster_name] = centroid\n\n        marker_genes = find_markers(centroids)\n        print(\"found %d possible marker genes for cluster solution %s in datasest %s\" % (len(marker_genes), cluster_solution_name, dataset_name))\n        pd.Series(marker_genes).to_csv(\"trash.txt\",index=False)\n        # Subset down to only those marker genes.\n        X = X[marker_genes]\n\n        # Now go through all the found marker genes and calculate each metric for each cluster.\n        print(marker_genes)\n        for gene in marker_genes:\n            second_largest = centroids.loc[gene].nlargest(2).tolist()[1] + 2\n            minimum = centroids.loc[gene].min() + 2\n\n            for cluster_name in cluster_solution.unique():\n                gene_centroid = centroids.loc[gene, cluster_name] + 2\n                cell_names = cluster_solution.index[(cluster_solution == cluster_name).tolist()]\n                other_cell_names = cluster_solution.index[(cluster_solution != cluster_name).tolist()]\n\n                # TP: true positives\n                expressed_in_cluster = (X.loc[cell_names, gene] > 0).sum()\n                # FN: false negatives\n                not_expressed_in_cluster = (X.loc[cell_names, gene] == 0).sum()\n                # FP: false positives\n                expressed_out_cluster = (X.loc[other_cell_names, gene] > 0).sum()\n                # TN: true negatives\n                not_expressed_out_cluster = (X.loc[other_cell_names, gene] > 0).sum()\n                # FP + TN\n                out_size = len(other_cell_names)\n                # TP + FN\n                cluster_size = len(cell_names)\n\n                # TP / (FP + TP)\n                precision = expressed_in_cluster / (expressed_in_cluster + expressed_out_cluster)\n                # TP / (FN + TP)\n                recall = expressed_in_cluster / (not_expressed_in_cluster + expressed_in_cluster)\n\n                # TP / ( TP + FN)\n                sensivity = expressed_in_cluster / cluster_size\n                # TN / (FP + TN)\n                specificity = not_expressed_out_cluster / out_size\n\n                # Accuracy = (TP+TN)/(TP+TN+FP+FN)\n                accuracy = (expressed_in_cluster + not_expressed_out_cluster) / (out_size + cluster_size)\n\n                fold_change_next = log2_fold_change(gene_centroid, second_largest)\n                fold_change_min = log2_fold_change(gene_centroid, minimum)\n\n                zstat, zpval = proportions_ztest(\n                    count=[expressed_in_cluster, expressed_out_cluster],\n                    nobs=[cluster_size, out_size],\n                    alternative='larger'\n                )\n\n                tstat, tpval = ttest_ind(X.loc[cell_names, gene], X.loc[other_cell_names, gene])\n\n                marker_dict = {\n                    \"gene_name\": gene,\n                    \"dataset_name\": dataset_name,\n                    \"cluster_solution_name\": cluster_solution_name,\n                    \"cluster_name\": str(cluster_name),\n                    \"sensitivity\": sensivity.item(),  # (+1 read considered a guess) / n cells IN cluster\n                    \"specificity\": specificity.item(),\n                    \"precision\": precision.item(),  # n +1 reads in cluster / n +1 reads out of cluster\n                    \"accuracy\": accuracy.item(),\n                    \"recall\": recall.item(),  # n +1 reads in cluster / n 0 reads in cluster.\n                    \"t_pval\": tpval.item(),\n                    \"z_pval\": zpval.item(),\n                    \"z_stat\": zstat.item(),\n                    \"t_stat\": tstat.item(),  # (t statistic of gene expression in / out of cluster)\n                    \"log2_change_vs_min\": fold_change_min,  # ( min cluster.... mincluster will always be 0.\n                    \"log2_change_vs_next\": fold_change_next,\n                    \"mean_expression\": centroids.loc[gene, cluster_name].item()\n                }\n\n                marker_dicts += [marker_dict]\n\n    write_data(out_file, marker_dicts)\n\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n","sub_path":"gather/marker_vals_from_anndata.py","file_name":"marker_vals_from_anndata.py","file_ext":"py","file_size_in_byte":16815,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"209274598","text":"n = int(input())\r\narr = list(map(int, input().strip().split()))\r\nk = 0\r\ni = 1\r\nwhile i < n - 1:\r\n    if(arr[i] == 0):\r\n        if(arr[i - 1] == arr[i + 1] == 1):\r\n            k += 1\r\n            arr[i + 1] = 0\r\n    i += 1\r\nprint(k)\r\n","sub_path":"B/B_Disturbed_People.py","file_name":"B_Disturbed_People.py","file_ext":"py","file_size_in_byte":233,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"549180631","text":"import os\nimport json\nimport pytest\nimport time\nfrom datetime import datetime\nimport subprocess\nimport logging\nfrom random import choice\nfrom string import ascii_uppercase\nfrom google.cloud import bigquery\nfrom google.auth import default as get_credentials\n\n\n@pytest.fixture(scope=\"function\")\ndef setup_bigquery_and_config():\n    project_id = os.environ.get(\"GOOGLE_PROJECT_ID\")\n    if not project_id:\n        _, project_id = get_credentials()\n    bigquery_client = bigquery.Client(project=project_id)\n    datasets = []\n    config_files = []\n\n    def setup(\n        validate_records=False, stream_data=False, replication_method=\"HYBRID\",\n    ):\n        dataset_id = \"target_bigquery_test_\" + \"\".join(choice(ascii_uppercase) for i in range(12))\n        target_config = {\n            \"project_id\": project_id,\n            \"dataset_id\": dataset_id,\n            \"validate_records\": validate_records,\n            \"stream_data\": stream_data,\n            \"replication_method\": replication_method,\n            \"disable_collection\": True,\n        }\n        config_filename = f\"target-config-{dataset_id}.json\"\n        config_files.append(config_filename)\n        with open(config_filename, \"w\") as f:\n            f.write(json.dumps(target_config))\n\n        datasets.append(bigquery_client.create_dataset(dataset_id))\n        return project_id, bigquery_client, config_filename, dataset_id\n\n    yield setup\n\n    for config_file in config_files:\n        os.remove(config_file)\n\n    for dataset in datasets:\n        bigquery_client.delete_dataset(dataset, delete_contents=True, not_found_ok=False)\n\n\n@pytest.fixture(scope=\"function\")\ndef check_bigquery():\n    def make_check_bigquery(bigquery_client, tablename, assertion, exception_on_fail=True):\n        # Wait for BigQuery to have the results.\n        # Refresh the table so that it picks up new schema or else it might not return all results.\n        query = f\"\"\"SELECT * FROM `{tablename}`\"\"\"\n        retries = 0\n        while True:\n            query_job = bigquery_client.query(query)\n            data = list(query_job.result())\n\n            if assertion(data):\n                return data\n\n            if retries > 4:\n                if exception_on_fail:\n                    raise Exception(\"Assertion didn't pass with data in BigQuery: {}\".format(data))\n                else:\n                    logging.error(\"Assertion didn't pass with data in BigQuery: {}\".format(data))\n                return False\n\n            retries += 1\n            time.sleep(2 ** retries)\n\n    return make_check_bigquery\n\n\n@pytest.fixture(scope=\"function\")\ndef do_sync():\n    def make_do_sync(tap_file, config_filename):\n        tap_sample_ps = subprocess.Popen((\"cat\", tap_file), stdout=subprocess.PIPE,)\n\n        target_ps = subprocess.Popen(\n            (\"python\", \"target_bigquery.py\", \"-c\", config_filename),\n            stdin=tap_sample_ps.stdout,\n            stdout=subprocess.PIPE,\n            stderr=subprocess.PIPE,\n        )\n\n        stdout_lines = []\n        with target_ps.stdout as pipe:\n            for line in iter(pipe.readline, b\"\"):\n                decoded_line = line.decode(\"utf-8\").rstrip()\n                print(f\"\\033[95mSTDOUT - {datetime.now()} | {decoded_line}\\033[0m\")\n                stdout_lines.append(decoded_line)\n\n        stderr_lines = []\n        with target_ps.stderr as pipe:\n            for line in iter(pipe.readline, b\"\"):\n                decoded_line = line.decode(\"utf-8\").rstrip()\n                print(f\"\\033[94mSTDERR - {datetime.now()} | {decoded_line}\\033[0m\")\n                stderr_lines.append(decoded_line)\n\n        target_ps.wait()\n        return stdout_lines, stderr_lines\n\n    return make_do_sync\n","sub_path":"conftest.py","file_name":"conftest.py","file_ext":"py","file_size_in_byte":3675,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"497532896","text":"\nfrom pytorch.pytorch_tools.data_generation import parallel_data_generator\nimport os\nfrom h5py import File\nfrom pytorch.pytorch_tools.piled_unets import PiledUnet\nfrom pytorch.pytorch_tools.losses import WeightMatrixWeightedBCE, CombinedLosses\nfrom pytorch.pytorch_tools.training import train_model_with_generators, cb_save_model, cb_run_model_on_data\n\nfrom torchsummary import summary\nimport numpy as np\n\n\nexperiment_name = 'unet3d_200314_00b_membranes_no_gauss'\nresults_folder = os.path.join(\n    '/g/schwab/hennies/phd_project/image_analysis/autoseg/cnn_3d_devel',\n    'unet3d_200311_pytorch_for_membranes',\n    experiment_name\n)\n\nif True:\n\n    raw_path = '/g/schwab/hennies/phd_project/image_analysis/psp_full_experiments/boundary_raw_and_gt/'\n    raw_filepaths = [\n        [\n            os.path.join(raw_path, 'raw.h5'),\n        ],\n    ]\n    gt_path = '/g/schwab/hennies/phd_project/image_analysis/psp_full_experiments/boundary_raw_and_gt/'\n    gt_filepaths = [\n        [\n            os.path.join(gt_path, 'gt_mem.h5'),\n            os.path.join(gt_path, 'gt_mask_organelle_insides.h5')\n        ],\n    ]\n    raw_channels = []\n    for volumes in raw_filepaths:\n        raws_data = []\n        for chid, channel in enumerate(volumes):\n            if chid == 1:\n                # Specifically only load last channel of the membrane prediction\n                raws_data.append(File(channel, 'r')['data'][..., -1])\n            else:\n                raws_data.append(File(channel, 'r')['data'][:])\n        raw_channels.append(raws_data)\n    gt_channels = [[File(channel, 'r')['data'][:] for channel in volumes] for volumes in gt_filepaths]\n\n    val_raw_path = '/g/schwab/hennies/phd_project/image_analysis/psp_full_experiments/boundary_raw_and_gt/'\n    val_raw_filepaths = [\n        [\n            os.path.join(val_raw_path, 'val_raw_512.h5'),\n        ],\n        [\n            os.path.join(\n                '/g/schwab/hennies/phd_project/image_analysis/psp_full_experiments/psp_200107_00_ds_20141002_hela_wt_xyz8nm_as_multiple_scales/step0_datasets/psp0_200108_02_select_test_and_val_cubes',\n                'val0_x1390_y742_z345_pad.h5'\n\n            )\n        ]\n    ]\n    val_gt_path = '/g/schwab/hennies/phd_project/image_analysis/psp_full_experiments/boundary_raw_and_gt/'\n    val_gt_filepaths = [\n        [\n            os.path.join(val_gt_path, 'val_gt_mem.h5'),\n            os.path.join(val_gt_path, 'val_gt_mask_organelle_insides.h5')\n        ]\n    ]\n    val_raw_channels = []\n    for volumes in val_raw_filepaths:\n        val_raws_data = []\n        for chid, channel in enumerate(volumes):\n            if chid == 1:\n                # Specifically only load last channel of the membrane prediction\n                val_raws_data.append(File(channel, 'r')['data'][..., -1])\n            else:\n                val_raws_data.append(File(channel, 'r')['data'][:])\n        val_raw_channels.append(val_raws_data)\n    val_gt_channels = [[File(channel, 'r')['data'][:] for channel in volumes] for volumes in val_gt_filepaths]\n\nif True:\n    data_gen_args = dict(\n        rotation_range=180,  # Angle in degrees\n        shear_range=20,  # Angle in degrees\n        zoom_range=[0.8, 1.2],  # [0.75, 1.5]\n        horizontal_flip=True,\n        vertical_flip=True,\n        depth_flip=True,\n        noise_var_range=1e-1,\n        random_smooth_range=[0.6, 1.5],\n        smooth_output_sigma=0,\n        displace_slices_range=2,\n        fill_mode='reflect',\n        cval=0,\n        brightness_range=92,\n        contrast_range=(0.5, 2)\n    )\n\n    aug_dict_preprocessing = dict(\n        smooth_output_sigma=0\n    )\n\n    train_gen = parallel_data_generator(\n        raw_channels=raw_channels,\n        gt_channels=gt_channels,\n        spacing=(32, 32, 32),\n        area_size=(32, 512, 512),\n        # area_size=(32, 128, 128),\n        target_shape=(64, 64, 64),\n        gt_target_shape=(64, 64, 64),\n        stop_after_epoch=False,\n        aug_dict=data_gen_args,\n        transform_ratio=0.9,\n        batch_size=2,\n        shuffle=True,\n        add_pad_mask=False,\n        n_workers=16,\n        noise_load_dict=None,\n        gt_target_channels=None,\n        areas_and_spacings=None,\n        n_workers_noise=16,\n        noise_on_channels=None,\n        yield_epoch_info=True\n    )\n\n    val_gen = parallel_data_generator(\n        raw_channels=val_raw_channels[:1],\n        gt_channels=val_gt_channels,\n        spacing=(64, 64, 64),\n        area_size=(256, 256, 256),\n        target_shape=(64, 64, 64),\n        gt_target_shape=(64, 64, 64),\n        stop_after_epoch=False,\n        aug_dict=aug_dict_preprocessing,\n        transform_ratio=0.,\n        batch_size=1,\n        shuffle=False,\n        add_pad_mask=False,\n        n_workers=16,\n        gt_target_channels=None,\n        yield_epoch_info=True\n    )\n\nmodel = PiledUnet(\n    n_nets=3,\n    in_channels=1,\n    out_channels=[1, 1, 1],\n    filter_sizes_down=(\n        ((16, 32), (32, 64), (64, 128)),\n        ((16, 32), (32, 64), (64, 128)),\n        ((16, 32), (32, 64), (64, 128))\n    ),\n    filter_sizes_bottleneck=(\n        (128, 256),\n        (128, 256),\n        (128, 256)\n    ),\n    filter_sizes_up=(\n        ((256, 128, 128), (128, 64, 64), (64, 32, 32)),\n        ((256, 128, 128), (128, 64, 64), (64, 32, 32)),\n        ((256, 128, 128), (128, 64, 64), (64, 32, 32))\n    ),\n    batch_norm=True,\n    output_activation='sigmoid'\n)\nmodel.cuda()\nsummary(model, (1, 64, 64, 64))\n\nif not os.path.exists(results_folder):\n    os.mkdir(results_folder)\n\ntrain_model_with_generators(\n    model,\n    train_gen,\n    val_gen,\n    n_epochs=200,\n    loss_func=CombinedLosses(\n        losses=(\n            WeightMatrixWeightedBCE(((0.1, 0.9),), weigh_with_matrix_sum=True),\n            WeightMatrixWeightedBCE(((0.2, 0.8),), weigh_with_matrix_sum=True),\n            WeightMatrixWeightedBCE(((0.3, 0.7),), weigh_with_matrix_sum=True)),\n        y_pred_channels=(np.s_[:1], np.s_[1:2], np.s_[2:3]),\n        y_true_channels=(np.s_[:], np.s_[:], np.s_[:]),\n        weigh_losses=np.array([0.2, 0.3, 0.5])\n    ),\n    l2_reg_param=1e-5,\n    callbacks=[\n        cb_run_model_on_data(\n            results_filepath=os.path.join(results_folder, 'improved_result1_{epoch:04d}.h5'),\n            raw_channels=val_raw_channels[:1],\n            spacing=(32, 32, 32),\n            area_size=(64, 256, 256),\n            target_shape=(64, 64, 64),\n            num_result_channels=3,\n            smooth_output_sigma=aug_dict_preprocessing['smooth_output_sigma'],\n            n_workers=16,\n            compute_empty_volumes=True,\n            thresh=None,\n            write_at_area=False,\n            offset=None,\n            full_dataset_shape=None,\n            min_epoch=5\n        ),\n        cb_run_model_on_data(\n            results_filepath=os.path.join(results_folder, 'improved_result2_{epoch:04d}.h5'),\n            raw_channels=val_raw_channels[1:],\n            spacing=(32, 32, 32),\n            area_size=(64, 256, 256),\n            target_shape=(64, 64, 64),\n            num_result_channels=3,\n            smooth_output_sigma=aug_dict_preprocessing['smooth_output_sigma'],\n            n_workers=16,\n            compute_empty_volumes=True,\n            thresh=None,\n            write_at_area=False,\n            offset=None,\n            full_dataset_shape=None,\n            min_epoch=5\n        ),\n        cb_save_model(\n            filepath=os.path.join(results_folder, 'model_{epoch:04d}.h5'),\n            min_epoch=5\n        )\n    ],\n    writer_path=os.path.join(results_folder, 'tb')\n)\n","sub_path":"experiments/unet3d_200311_pytorch_for_membranes/unet3d_200314_00b_membranes_no_gauss.py","file_name":"unet3d_200314_00b_membranes_no_gauss.py","file_ext":"py","file_size_in_byte":7484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"296268136","text":"import argparse\nimport torch\nimport numpy as np \nimport matplotlib.pyplot as plt\nimport torchvision.transforms as transforms\nfrom datetime import datetime\nfrom torch import optim\nfrom torch.utils.data import DataLoader\nfrom dataset import Eval_Dataset, SiameseDataset\nfrom models import SiameseNetwork, ContrastiveLoss\nfrom utils import generate_img_txt, feature2label, pretrain, loss_plot, cluster_plot\n\n\ndef pre_work(txt,cluster_num,device):\n    init_dataset = Eval_Dataset(txt,transforms.ToTensor(),initial=True)\n    init_dataloader = DataLoader(init_dataset)\n\n    init_feature = pretrain(init_dataloader,device)\n    init_labellist = feature2label(init_feature,cluster_num)\n    return init_labellist[1]\n\n\ndef train(labellist,batch_size,train_number_epochs,learning_rate,round,device):\n\n    train_dataset = SiameseDataset('images.txt',labellist,transforms.ToTensor())\n    train_dataloader = DataLoader(train_dataset,batch_size=batch_size,shuffle=True)\n\n    # training\n    net = SiameseNetwork().to(device)\n    criterion = ContrastiveLoss()\n    optimizer = optim.Adam(net.parameters(),lr=learning_rate)\n\n    counter = []\n    loss_history = []\n    iteration_number = 0\n\n    for epoch in range(train_number_epochs):\n        total_loss = 0\n        start_time = datetime.now()\n        for i,data in enumerate(train_dataloader):\n            img0,img1,label = data\n            img0,img1,label = img0.to(device),img1.to(device),label.to(device)\n\n            optimizer.zero_grad()\n            output1,output2 = net(img0,img1)\n            loss_contrastive = criterion(output1,output2,label)\n            loss_contrastive.backward()\n            total_loss += loss_contrastive.item()\n            optimizer.step()\n            if i%20 == 0:\n                iteration_number += 20\n                counter.append(iteration_number)\n                loss_history.append(loss_contrastive.item())\n        end_time = datetime.now()\n        print(\"Epoch number: {} , Current loss: {:.4f}, Epoch Time: {}\".format(epoch+1,total_loss/(i+1),end_time-start_time))\n\n    torch.save(net.state_dict(),\"parameters/Parm_Round_\"+str(round+1)+\".pth\")\n    return counter, loss_history\n\n\ndef eval(txt,cluster_num,round,device):\n    eval_dataset = Eval_Dataset('images.txt',transforms.ToTensor())\n    eval_dataloader = DataLoader(eval_dataset)\n\n    net = SiameseNetwork().to(device)\n    net.load_state_dict(torch.load(\"parameters/Parm_Round_\"+str(round+1)+\".pth\"))\n    features = []\n\n    for img in eval_dataloader:\n        with torch.no_grad():\n            img = img.to(device)\n            output = net.forward_once(img).data.cpu().numpy().squeeze()\n            features.append(output)\n    \n    X ,new_labellist = feature2label(features,cluster_num)\n    return X, new_labellist\n\n\ndef show_average_img(cluster_num,round,labellist):\n    eval_dataset = Eval_Dataset('images.txt',transforms.ToTensor())\n    eval_dataloader = DataLoader(eval_dataset)\n    plt.figure(figsize=(24,6))\n    for i in range(cluster_num):\n        plt.subplot(1,cluster_num,i+1)\n        imglist = []\n        for j,img in enumerate(eval_dataloader):\n            if labellist[j] == i:\n                imglist.append(img.squeeze().numpy())\n        plt.imshow(np.mean(imglist,axis=0),cmap='gray')\n    plt.savefig('images/Average_Result_Round_'+str(round+1)+'.jpg')\n\n\n\nif __name__ == '__main__':\n    parser = argparse.ArgumentParser(description=\"Cryo-EM Images Denoising v1\")\n    parser.add_argument('-c','--cluster_num', default=4)\n    parser.add_argument('-g','--gpu', default='cuda:0')\n    parser.add_argument('-l','--learning_rate', default=5e-4)\n    parser.add_argument('-e','--epoch_num', default=20)\n    parser.add_argument('-b','--batch_size', default=128)\n    parser.add_argument('-r','--round_num', default=5)\n    parser.add_argument('-d','--dataset_dir', default='dataset')\n    args = parser.parse_args()\n\n    DEVICE = torch.device(args.gpu if torch.cuda.is_available() else \"cpu\")\n    BATCH_SIZE = args.batch_size\n    NUM_EPOCHS = args.epoch_num\n    ROUND_NUM = args.round_num\n    LR = args.learning_rate\n    CLUSTER_NUM = args.cluster_num\n    DATASET_DIR = args.dataset_dir\n\n    print(\"----------------------Preparing-----------------------\")\n    generate_img_txt(root=DATASET_DIR)\n    init_labellist = pre_work('images.txt',CLUSTER_NUM,DEVICE)\n    show_average_img(CLUSTER_NUM,-1,init_labellist)\n    for Round in range(ROUND_NUM):\n        print(\"Round: %d\" % (Round+1))\n        if Round==0:\n            labellist = init_labellist\n        else:\n            labellist = new_labellist\n        print(\"----------------------Training-----------------------\")\n        counter, loss_history = train(labellist,BATCH_SIZE,NUM_EPOCHS,LR,Round,DEVICE)\n        loss_plot(counter, loss_history, Round)\n        print(\"----------------------Clustering-----------------------\")\n        X, new_labellist = eval('images.txt',CLUSTER_NUM,Round,DEVICE)\n        cluster_plot(X, new_labellist, Round)\n        show_average_img(CLUSTER_NUM,Round,new_labellist)","sub_path":"cryo-EM/cryo-EM/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":4977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"8180568","text":"import datetime\nfrom django.db import models\nfrom django.db.models.signals import pre_save, pre_delete, post_save, post_delete\nfrom django.contrib.auth.models import User\nfrom django.dispatch import receiver\n\n\nclass Customer(models.Model):\n    user = models.OneToOneField(User, on_delete=models.CASCADE)\n\n    def discounts(self, discount_date=datetime.datetime.now()):\n        return Discount.objects.\\\n            filter(kind_id=self.pk).\\\n            filter(kind='DC').\\\n            filter(begin_datetime <= discount_date).\\\n            filter(end_datetime >= discount_date).get()\n\n\nUser.customer = property(lambda u: Customer.objects.get_or_create(user=u)[0])\n\n\nclass Order(models.Model):\n\n    customer = models.ForeignKey(Customer)\n\n\nclass OrderState(models.Model):\n    STATE_CHOICES = (('OC', 'Создан/Created'),\n                     ('OD', 'Удалён/Deleted'),\n                     ('IA', 'Товар добавлен/Item added'),\n                     ('ID', 'Товар удалён/Item deleted'),\n                     ('OP', 'Оплачен/Payed'),\n                     ('OA', 'Подтверждён/Approved'),\n                     ('OP', 'Отправлен/Posted'))\n\n    state = models.CharField(verbose_name='Состояние', max_length=2, choices=STATE_CHOICES)\n    order = models.ManyToManyField(Order, verbose_name='Заказ')\n    item = models.ManyToManyField('Item', null=True)\n    state_time = models.DateTimeField(auto_now=True, auto_now_add=True)\n\n    class Meta:\n        unique_togethers = (('order', 'state_time', ), )\n        ordering = ['state_time', ]\n\n\nclass Discount(models.Model):\n    DISCOUNT_CHOICES = (('DI', 'Скидка на товар/Item Discount'),\n                        ('DB', 'Скидка на бренд/Brand Discount '),\n                        ('DG', 'Скидка на группу(категорию)/Group Discount'),\n                        ('DC', 'Скидка клиента/Client Discount'))\n\n    begin_datetime = models.DateTimeField(\n        null=True, verbose_name='Начало скидки', default=datetime.datetime.min)\n    end_datetime = models.DateTimeField(\n        null=True, verbose_name='Конец Скидки', default=datetime.datetime.max)\n    kind = models.CharField(null=True, choices=DISCOUNT_CHOICES, max_length=2)\n    kind_id = models.IntegerField()\n\n    @property\n    def period(self):\n        return self.begin_datetime, self.end_datetime\n\n\nclass Brand(models.Model):\n    name = models.CharField(verbose_name='Бренд', unique=True)\n\n    def discounts(self, discount_datetime=datetime.datetime.now()):\n        return Discount.objects.\\\n            filter(kind_id=self.pk).\\\n            filter(kind='DB').\\\n            filter(begin_datetime <= discount_datetime).\\\n            filter(end_datetime >= discount_datetime).get()\n\n\n    class Meta:\n        unique_togethers = (('name', ), )\n\n\nclass Group(models.Model):\n    name = models.CharField(verbose_name='Категория')\n\n    def discounts(self, discount_date=datetime.datetime.now()):\n        return Discount.objects.\\\n            filter(kind_id=self.pk).\\\n            filter(kind='DG').\\\n            filter(begin_datetime <= discount_date).\\\n            filter(end_datetime >= discount_date).get()\n\n    class Meta:\n        unique_togethers = (('name', ), )\n\n\nclass ItemCosts(models.Model):\n    cost = models.DecimalField(max_digits=10, decimal_places=2)\n    from_datetime = models.DateTimeField(auto_now=True, auto_now_add=True)  # Начало действия\n    item = models.ForeignKey('Item', on_delete=models.CASCADE)  # N->1\n\n    class Meta:\n        ordering = ['-from_datetime', ]\n        unique_togethers = (('item', 'from_datetime', ), )\n\n\nclass Item(models.Model):\n    name = models.CharField(verbose_name='Наименование')\n    brand = models.ForeignKey(Brand, on_delete=models.CASCADE)  # N->1\n    group = models.ForeignKey(Group, on_delete=models.CASCADE)  # N->1\n\n    def discounts(self, discount_datetime=datetime.datetime.now()):\n        return Discount.objects.\\\n            filter(kind_id=self.pk).\\\n            filter(kind='DI').\\\n            filter(begin_datetime <= discount_datetime).\\\n            filter(end_datetime >= discount_datetime).get()\n\n    def price_current(self, from_datetime=datetime.datetime.now()):\n        return ItemCosts.objects.\\\n            filter(item=self).\\\n            filter(from_datetime >= from_datetime).get()\n\n\nclass OrderItem(models.Model):\n    item = models.OneToOneField(Item)\n    order = models.ForeignKey(Order, on_delete=models.CASCADE)\n    # Предполагаем, что весового товара не будет :)\n    quantity = models.IntegerField(verbose_name='Количество', default=1)\n\n    def discounts(self):\n        item = self.item\n        return (item.discounts(),\n                item.brand.discounts(),\n                item.group.discounts(), )\n\n\n# Обработка сигналов\n\ndef _set_item_order_state(instance, state):\n    order_state = OrderState(\n        state=state.upper(),\n        order=instance.order,\n        item=instance.item)\n    order_state.save()\n\n\ndef _set_order_state(instance, state):\n    order_state = OrderState(state=state.upper(),\n                             order=instance.order)\n    order_state.save()\n\n\n@receiver(post_delete, sender=OrderItem)\ndef order_item_post_delete(sender, instance):\n    _set_order_state(instance, state='ID')\n\n\n@receiver(post_save, sender=OrderItem)\ndef order_item_post_save(sender, instance):\n    _set_item_order_state(instance, state='IA')\n\n\n@receiver(post_delete, sender=Order)\ndef order_post_delete(sender, instance):\n    _set_order_state(instance, state='OD')\n\n\n@receiver(post_save, sender=Order)\ndef order_post_save(sender, instance):\n    _set_order_state(instance, state='OC')\n","sub_path":"django_models.py","file_name":"django_models.py","file_ext":"py","file_size_in_byte":5802,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"391000870","text":"from __future__ import absolute_import, division, print_function\n\nimport dask.array as da\nimport numpy as np\nimport pytest\n\nfrom dask_glm.logistic import bfgs, gradient_descent, newton\nfrom dask_glm.utils import sigmoid, make_y\n\n\ndef make_data(N, p, seed=20009):\n    '''Given the desired number of observations (N) and\n    the desired number of variables (p), creates\n    random logistic data to test on.'''\n\n    # set the seeds\n    da.random.seed(seed)\n    np.random.seed(seed)\n\n    X = np.random.random((N, p + 1))\n    col_sums = X.sum(axis=0)\n    X = X / col_sums[None, :]\n    X[:, p] = 1\n    X = da.from_array(X, chunks=(N / 5, p + 1))\n    y = make_y(X, beta=np.random.random(p + 1))\n\n    return X, y\n\n\n@pytest.mark.parametrize('opt',\n                         [pytest.mark.xfail(bfgs, reason='''\n                          Early algorithm termination for unknown reason'''),\n                          newton, gradient_descent])\n@pytest.mark.parametrize('N, p, seed',\n                         [(100, 2, 20009),\n                          (250, 12, 90210),\n                          (95, 6, 70605)])\ndef test_methods(N, p, seed, opt):\n    X, y = make_data(N, p, seed=seed)\n    coefs = opt(X, y)\n    p = sigmoid(X.dot(coefs).compute())\n\n    y_sum = y.compute().sum()\n    p_sum = p.sum()\n    assert np.isclose(y.compute().sum(), p.sum(), atol=1e-1)\n","sub_path":"dask_glm/tests/test_unregularized.py","file_name":"test_unregularized.py","file_ext":"py","file_size_in_byte":1347,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"268354385","text":"# Original idea from Richard Zhan (class of 2020)\n# Modified by N. Kim on Dec 2019\n\nimport sys\nimport os\nimport time\nimport tkinter as tk\nfrom Barker_J_U3_L2 import CustomPlayer, RandomPlayer\n\n# constants\ndelay_time = 1.5\nturn_off_printing = False\ntile_size = 50\npadding = 5\nx_max = 5\ny_max = 5\nboard_x = x_max * tile_size + (x_max + 1) * padding - 2\nboard_y = y_max * tile_size + (y_max + 1) * padding - 2\nwhite = \"#ffffff\"\nblack = \"#000000\"\ngrey = \"#505050\"\ngreen = \"#00ff00\"\nyellow = \"#ffff00\"\nbrown = \"#654321\"\nblue = \"#0000ff\"\ncyan = \"#00ffff\"\nred = \"#ff0000\"\nasterisk = \" \" + u'\\u2217'\ndirections = [[-1, -1], [-1, 0], [-1, 1], [0, -1], [0, 1], [1, -1], [1, 0], [1, 1]]\nopposite_color = {black: white, white: black}\n\n# variables\nplayer_types = {0: \"Player\", 1: \"Random\", 2: \"Custom\"}\nplayers = {black: None, white: None, None: None}\nplayer_max_times = {black: 0, white: 0}\nplayer_total_times = {black: 0, white: 0}\np1_name = \"\"\np2_name = \"\"\nroot = None\ncanvas = None\nturn = white\nboard = []\npossible_moves = {i for i in range(x_max * y_max)}\nfirst_turn = 0\n\n\n# commands\n\n\ndef whose_turn(my_board, prev_turn):\n    global possible_moves, first_turn\n    cur_turn = opposite_color[prev_turn]\n    possible_moves = find_moves(my_board, cur_turn)\n    first_turn += 1\n    if len(possible_moves) > 0:\n        return cur_turn\n    return None\n\n\ndef find_moves(my_board, my_color):\n    global first_turn\n    moves_found = set()\n    for i in range(len(my_board)):\n        for j in range(len(my_board[i])):\n            if first_turn < 2 and my_board[i][j] == '.':\n                moves_found.add(i * y_max + j)\n            elif (my_color == black and my_board[i][j] == 'X') or (my_color == white and my_board[i][j] == 'O'):\n                for incr in directions:\n                    x_pos = i + incr[0]\n                    y_pos = j + incr[1]\n                    stop = False\n                    while 0 <= x_pos < x_max and 0 <= y_pos < y_max:\n                        if my_board[x_pos][y_pos] != '.':\n                            stop = True\n                        if not stop:\n                            moves_found.add(x_pos * y_max + y_pos)\n                        x_pos += incr[0]\n                        y_pos += incr[1]\n    return moves_found\n\n\ndef print_board(my_board):\n    # return  # comment to print board each time\n    print(\"\\t\", end=\"\")\n    for i in range(x_max):\n        print(chr(ord(\"a\") + i), end=\" \")\n    print()\n    for i in range(y_max):\n        print(i + 1, end=\"\\t\")\n        for j in range(x_max):\n            print(my_board[j][i], end=\" \")\n        print()\n    print()\n\n\ndef draw_rect(x_pos, y_pos, possible=False, wall=False):\n    coord = [x_pos * (padding + tile_size) + padding + 1, y_pos * (padding + tile_size) + padding + 1,\n             (x_pos + 1) * (padding + tile_size), (y_pos + 1) * (padding + tile_size)]\n    if possible:\n        canvas.create_rectangle(coord, fill=cyan, activefill=yellow)\n    elif wall:\n        canvas.create_rectangle(coord, fill=red)\n    else:\n        canvas.create_rectangle(coord, fill=green)\n\n\ndef draw_circle(x_pos, y_pos, fill_color):\n    coord = [x_pos * (padding + tile_size) + 2 * padding + 1, y_pos * (padding + tile_size) + 2 * padding + 1,\n             (x_pos + 1) * (padding + tile_size) - padding, (y_pos + 1) * (padding + tile_size) - padding]\n    canvas.create_oval(coord, fill=fill_color)\n\n\ndef make_move(x, y):\n    if x * y_max + y not in possible_moves:\n        return False\n    next_turn(x, y)\n    return True\n\n\ndef click(event=None):\n    x = int((event.x - padding) / (padding + tile_size))\n    y = int((event.y - padding) / (padding + tile_size))\n    if x * y_max + y not in possible_moves:\n        return\n    next_turn(x, y)\n\n\ndef next_turn(x_pos, y_pos):\n    global turn, possible_moves\n    for pos in possible_moves:\n        draw_rect(int(pos / y_max), pos % y_max)\n    if turn == black:\n        color_symbol = \"X\"\n    else:\n        color_symbol = \"O\"\n    board[x_pos][y_pos] = color_symbol\n    draw_circle(x_pos, y_pos, turn)\n    possible_moves -= {x_pos * x_max + y_pos}\n    for i in range(len(board)):\n        for j in range(len(board[i])):\n            if board[i][j] == color_symbol and (i != x_pos or j != y_pos):\n                board[i][j] = 'W'\n            if board[i][j] == 'X':\n                draw_circle(i, j, black)\n            elif board[i][j] == 'O':\n                draw_circle(i, j, white)\n            elif board[i][j] == 'W':\n                draw_rect(i, j, wall=True)\n    winner_candidate = color_symbol\n    turn = whose_turn(board, turn)\n    if turn is None:\n        print_board(board)\n        print(\"{} win\".format(winner_candidate))\n\n        return\n    for pos in possible_moves:\n        draw_rect(int(pos / y_max), pos % y_max, True)\n    print_board(board)\n    if players[turn] != \"Player\":\n        root.update()\n        '''you may change the code below'''\n        # time.sleep(delay_time)\n        #         start = time.time()\n        move, val = players[turn].best_strategy(board, turn)\n        #         time_used = round(time.time()-start, 3)\n        #         player_max_times[turn] = max(player_max_times[turn], time_used)\n        #         player_total_times[turn] = player_total_times[turn]+time_used\n        next_turn(move[0], move[1])\n\n\ndef init(choice_menu, e1, e2, v1, v2):\n    global turn_off_printing, turn, root, canvas, p1_name, p2_name, players, player_types\n    if turn_off_printing:\n        sys.stdout = open(os.devnull, 'w')\n    p1_name = e1.get()\n    p2_name = e2.get()\n    players[black] = player_types[v1.get()]\n    players[white] = player_types[v2.get()]\n    p1_name = players[black]\n    p2_name = players[white]\n    if players[black] == \"Random\":\n        players[black] = RandomPlayer()\n    elif players[black] == \"Custom\":\n        players[black] = CustomPlayer()\n    if players[white] == \"Random\":\n        players[white] = RandomPlayer()\n    elif players[white] == \"Custom\":\n        players[white] = CustomPlayer()\n    choice_menu.destroy()\n    root = tk.Tk()\n    root.title(\"Isolation Game\")\n    root.resizable(width=False, height=False)\n    canvas = tk.Canvas(root, width=board_x, height=board_y, bg=brown)\n    canvas.bind(\"<Button-1>\", click)\n    canvas.grid(row=0, column=0, columnspan=2)\n    for i in range(x_max):\n        board.append([])\n        for j in range(y_max):\n            draw_rect(i, j)\n            board[i].append(\".\")\n    turn = whose_turn(board, turn)\n    for pos in possible_moves:\n        draw_rect(int(pos / y_max), pos % y_max, True)\n    print_board(board)\n    print(\"whose turn\", players[turn])\n    if players[turn] != \"Player\":\n        root.update()\n        '''you may change the code below'''\n        # time.sleep(delay_time)\n        move, idc = players[turn].best_strategy(board, turn)\n        next_turn(move[0], move[1])\n    root.mainloop()\n\n\ndef menu():\n    global p1_name, p2_name, radio_on, radio_off\n    choice_menu = tk.Tk()\n    choice_menu.title(\"Menu\")\n    choice_menu.resizable(width=False, height=False)\n    tk.Label(text=\"Black\", font=(\"Arial\", 30), bg=black, fg=grey).grid(row=0, column=0,\n                                                                       sticky=tk.W + tk.E + tk.N + tk.S)\n    tk.Label(text=\"White\", font=(\"Arial\", 30), bg=white, fg=black).grid(row=0, column=1,\n                                                                        sticky=tk.W + tk.E + tk.N + tk.S)\n    v1 = tk.IntVar()\n    v2 = tk.IntVar()\n    v1.set(0)\n    v2.set(0)\n    tk.Radiobutton(text=\"Player\", compound=tk.LEFT, font=(\"Arial\", 20), bg=black, fg=grey, anchor=tk.W, variable=v1,\n                   value=0).grid(row=1, column=0, sticky=tk.W + tk.E + tk.N + tk.S)\n    tk.Radiobutton(text=\"Player\", font=(\"Arial\", 20), bg=white, fg=black, anchor=tk.W, variable=v2, value=0).grid(row=1,\n                                                                                                                  column=1,\n                                                                                                                  sticky=tk.W + tk.E + tk.N + tk.S)\n    tk.Radiobutton(text=\"Random\", font=(\"Arial\", 20), bg=black, fg=grey, anchor=tk.W, variable=v1, value=1).grid(row=2,\n                                                                                                                 column=0,\n                                                                                                                 sticky=tk.W + tk.E + tk.N + tk.S)\n    tk.Radiobutton(text=\"Random\", font=(\"Arial\", 20), bg=white, fg=black, anchor=tk.W, variable=v2, value=1).grid(row=2,\n                                                                                                                  column=1,\n                                                                                                                  sticky=tk.W + tk.E + tk.N + tk.S)\n    tk.Radiobutton(text=\"Custom\", font=(\"Arial\", 20), bg=black, fg=grey, anchor=tk.W, variable=v1, value=2).grid(row=3,\n                                                                                                                 column=0,\n                                                                                                                 sticky=tk.W + tk.E + tk.N + tk.S)\n    tk.Radiobutton(text=\"Custom\", font=(\"Arial\", 20), bg=white, fg=black, anchor=tk.W, variable=v2, value=2).grid(row=3,\n                                                                                                                  column=1,\n                                                                                                                  sticky=tk.W + tk.E + tk.N + tk.S)\n    e1 = tk.Entry(font=(\"Arial\", 15), bg=black, fg=grey, width=12)\n    e2 = tk.Entry(font=(\"Arial\", 15), bg=white, fg=black, width=12)\n    e1.insert(0, \"Player 1 Name\")\n    e2.insert(0, \"Player 2 Name\")\n    e1.grid(row=99, column=0, sticky=tk.W + tk.E + tk.N + tk.S)\n    e2.grid(row=99, column=1, sticky=tk.W + tk.E + tk.N + tk.S)\n    tk.Button(text=\"Begin\", font=(\"Arial\", 15), bg=white, fg=black,\n              command=lambda: init(choice_menu, e1, e2, v1, v2)).grid(row=100, column=0, columnspan=2,\n                                                                      sticky=tk.W + tk.E + tk.N + tk.S)\n    choice_menu.mainloop()\n\n\nmenu()","sub_path":"isolation_runner.py","file_name":"isolation_runner.py","file_ext":"py","file_size_in_byte":10227,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"261643430","text":"import Group\nimport PIL.ImageDraw as ImageDraw\nimport PIL.Image as Image\nfrom collections import defaultdict\nimport os\n#Reference from https://stackoverflow.com/a/43971906/10717280 \n# points = ((1,1), (2,1), (2,2), (1,2), (0.5,1.5))\nimage=None\noutline=None\npanels=None\nimage=None\nimg=None\nscale=1\ndef polygonize(panel):\n    global outline, image,scale,img\n    if len(panel.pixels)>1:\n        #Sort panel by y,then by x. Starting with the highest y, work down\n        #to the lowest y with the lowest x. The start with the lowsest y and highest x,\n        #working up to the highest y. These are the vertices.\n        pxs_uns=panel.pixels#unsorted pixels\n        pxs_uns=sorted(pxs_uns,key=(lambda p: p.pos[1]))\n        pxs=defaultdict(lambda: [])\n        for p in pxs_uns:\n            pxs[str(p.pos[1])].append(p)\n        del pxs_uns\n        points=[]\n        pxskeys=sorted(pxs,key=lambda s:int(s))\n        for key in pxskeys:\n            pxs[key]=sorted(pxs[key], key=lambda p:p.pos[0])\n            a=pxs[key]\n            print(str(a[0].pos[1])+\" \"+str(a[-1].pos[1]))\n            points.append((a[0].pos[1]*scale,a[0].pos[0]*scale))\n            points=[(a[-1].pos[1],a[-1].pos[0])]+points\n        # print(points[0])\n        # print(points[-1])\n        # print()\n        draw = ImageDraw.Draw(image)\n        draw.polygon((points), fill=tuple([int(p) for p in panel.color]),outline=outline)\n    else:\n        point=panel.pixels[0].pos\n        point=point[1]*scale,point[0]*scale#Reflection needed, polygon library has differenct order\n        draw = ImageDraw.Draw(image)\n        draw.polygon((point,point), fill=tuple(panel.pixels[0].value),outline=outline)\n\ndef getImageData():\n    global panels, image,img\n    has_file=False\n    directory = os.path.dirname(os.path.abspath(__file__)) \n    img=None\n    while(not has_file):\n        fname=input(\"File name?\")\n        try:\n            img=Image.open(os.path.join(directory, fname))\n            has_file=True\n        except:\n            print(\"Cannot open \"+os.path.join(directory,fname))\n    Group.runTracker(img=img)\n    panels=Group.getPanels()\ndef polyImage():\n    global image, panels\n    image = Image.new(\"RGB\",(img.size[0]*scale,img.size[1]*scale))#canvas\n    if(panels==None):\n        print(\"Load Image First\")\n        return\n    for p in panels:\n        polygonize(p)\n        print(\"-- --\")\n    image.show()\n    while(True):\n        inp=input(\"save or abandon image?\")\n        if inp==\"save\":\n            image.save('results.png','png')\n            break\n        elif inp=='abandon':\n            break\n        else:\n            print(\"Unrecognized. Commands are 'save' and 'abandon'\")\ndef getValue(msg,type):\n    while(True):\n        inp=input(msg)\n        try:\n            return type(inp)\n        except:\n            print(\"!Please Enter an Integer\")\n\ndef help():\n    print(\"Command List:\")\n    print(\"quit -- exits the program\")\n    print(\"load -- load image and define regions\")\n    print(\"polygonize -- defines the image in polygons and shows\")\n    print(\"loadsettings -- If done before loading, changes the settings for defining regions\")\n    print(\"setborder -- set the border to an rgb value for the next polygonize\")\n    print(\"clearborder -- sets the border to none\")\n    print(\"scale -- sets the scale of the image\")\n    print(\"help -- pull up this list\")\nhelp()\nwhile(True):\n    prompt=input(\"Command?\")\n    if prompt=='quit':\n        break\n    elif prompt=='load':\n        getImageData()\n    elif prompt=='polygonize':\n        polyImage()\n    elif prompt=='loadsettings':\n        Group.linearthreshold=getValue(\"|Linear Similarity value? (int, usualy around 50)\",int)\n        Group.marginalThreshold=getValue(\"|Marginal Threhold value? (float, default 0.3\",float)\n        Group.marginalcap=getValue(\"|Marginal Cap? (float, default 3.0)\",float)\n    elif prompt=='setborder':\n        outline=getValue(\"R:\",int),getValue(\"G:\",int),getValue(\"B:\",int)\n    elif prompt=='clearborder':\n        outline=None\n    elif prompt=='scale':\n        scale=getValue(\"Scale?\",int)\n    elif prompt=='help':\n        help()\n    else:\n        print(\"Unrecognized. Try 'help'\")","sub_path":"ImageManipulation/Mosaic.py","file_name":"Mosaic.py","file_ext":"py","file_size_in_byte":4126,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"546533310","text":"from nougat import Nougat\n\nfrom nougat_router import Router, Routing, get, param, RestRouting\n\n\napp = Nougat()\n\nrouter = Router()\n\n\nclass MainRestRouting(RestRouting):\n\n    @get('/')\n    @param('name', str)\n    async def static_route(self):\n        return 'hello {}'.format(self.params.name)\n\n\nrouter.add(MainRestRouting)\napp.use(router)\n\napp.run()\n","sub_path":"demos/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":349,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"138101716","text":"#__author:  \"Jing Xu\"\n#date:  2018/1/31\n\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport matplotlib.image as mpimg\n# -------------------------------------------------------------\n# mat1 = np.array([[1,2,3],[4,5,6],[7,8,9]])\n# mat2 = np.arange(1,10).reshape(3,3)\n# mat3 = np.zeros((3,3))\n# mat4 = np.ones((3,3))\n# mat5 = np.eye(3)\n# print(mat1)\n# print(mat2)\n# print(mat3)\n# print(mat4)\n# print(mat5)\n# -------------------------------------------------------------\n# a = np.array([[1.25, -16],[32,264.75]], np.float32)\n# b = np.array(a, np.int32)\n# c = b.astype(\"uint8\")\n# print(a)\n# print(b)\n# print(c)\n# -------------------------------------------------------------\n# mat = np.arange(1,10).reshape(3,3)\n# print(mat)\n# print(mat[2][2])\n# print(mat[2,2])\n# print(mat[:2,:2])\n#\n# mask = mat > 5\n# print(mask)\n#\n# mat[mask] = 5\n# print(mat)\n# -------------------------------------------------------------\n# x = np.arange(4)\n# print(x)\n# xt = x.reshape(-1,1)\n# print(xt)\n# z = np.ones((3,4))\n# print(z)\n# print(x+xt)\n# print(x+z)\n# -------------------------------------------------------------\n\n## (1) 绘制随机噪点\n## 初始化随机种子，并生成随机坐标\nnp.random.seed(0)\ndata = np.random.randn(2, 100)\n## 创建画布\nfig, axs = plt.subplots(2, 2, figsize=(5, 5))\n## 绘制子图\naxs[0, 0].hist(data[0])\naxs[1, 0].scatter(data[0], data[1])\naxs[0, 1].plot(data[0], data[1])\naxs[1, 1].hist2d(data[0], data[1])\n## 显示\nplt.show()\n\n## (2) 绘制图像\nimg = mpimg.imread(\"/home/auss/Pictures/test.png\")\nimgx = img[:,:,0] # 取第一个通道\n\n## 创建画布\nfig = plt.figure()\n\n## 绘制原始图像，并加上颜色条\naxs = fig.add_subplot(1,3,1)\nipt = plt.imshow(img)\naxs.set_title(\"origin\")\nplt.colorbar(ticks=[0.1, 0.3, 0.5, 0.7], orientation='horizontal')\n\n## 绘制伪彩色图像，并加上颜色条\naxs = fig.add_subplot(1,3,2)\nipt = plt.imshow(imgx,cmap=\"winter\")\naxs.set_title(\"winter\")\nplt.colorbar(ticks=[0.1, 0.3, 0.5, 0.7], orientation='horizontal')\n\n## 绘制直方图\naxs = fig.add_subplot(1,3,3)\nipt = plt.hist(imgx.ravel(), bins=256, range=(0, 1.0), fc='k', ec='k')\naxs.set_title(\"histogram\")\n\nplt.show()\n","sub_path":"Repo_Python/zhihu_interest/init_numpy_matplotlib.py","file_name":"init_numpy_matplotlib.py","file_ext":"py","file_size_in_byte":2148,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"434497601","text":"import FWCore.ParameterSet.Config as cms\n\nprocess = cms.Process(\"FILTER\")\n\n#\n# initialize  MessageLogger\n#\nprocess.load(\"FWCore.MessageService.MessageLogger_cfi\")\nprocess.MessageLogger.cerr.FwkReport.reportEvery = 100\n\n#\n# load input file\n#\nprocess.source = cms.Source(\"PoolSource\",\n        fileNames = cms.untracked.vstring('file:tutorial.root')\n        )\nprocess.maxEvents = cms.untracked.PSet(\n        input = cms.untracked.int32(-1)\n        )\n\n# include QCDSkim\nprocess.load(\"HLTrigger.QCDSkim.python.QCDSkim_cfi\")\nprocess.qcdSkim.DoElectronCut=True\nprocess.qcdSkim.DoMuonCut=True\n\n#\n# write results out to file\n#\nprocess.TFileService = cms.Service(\"TFileService\",\n        fileName = cms.string('tutorial-filtered.root')\n        )\n\nprocess.p = cms.Path(process.qcdSkim)\n","sub_path":"QCDSkim/test/QCDSkim_cfg.py","file_name":"QCDSkim_cfg.py","file_ext":"py","file_size_in_byte":774,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"267098157","text":"import numpy as np\r\nimport pandas as pd\r\nfrom sklearn.model_selection import train_test_split\r\nfrom sklearn.tree import DecisionTreeClassifier\r\nfrom sklearn.metrics import accuracy_score\r\nimport matplotlib.pyplot as plt\r\nfrom sklearn.model_selection import cross_val_score,GridSearchCV\r\nimport time\r\nfrom sklearn.metrics import classification_report\r\nfrom sklearn.preprocessing import StandardScaler\r\nfrom sklearn.ensemble import RandomForestClassifier\r\nfrom sklearn.pipeline import Pipeline\r\nfrom scipy import interpolate\r\n\r\ndf=pd.read_csv('ccdefault.csv',index_col='ID')\r\ndf.head()\r\n\r\ndf.dropna()\r\n\r\nX=df.iloc[:,0:23].values\r\ny=df.iloc[:,23].values\r\n\r\nX_train, X_test, y_train, y_test = train_test_split(\r\n    X, y, test_size=0.1, random_state=33, stratify=y)\r\n#part 1\r\nrf=RandomForestClassifier(n_estimators=50,criterion='gini',random_state=1,n_jobs=-1)\r\npipe=Pipeline([['sc',StandardScaler()],['randomforest',rf]])\r\nparams={'randomforest__n_estimators':[20,50,75,90,100]}\r\ngrid=GridSearchCV(estimator=pipe,param_grid=params,cv=2)#,scoring='roc_auc'\r\ngrid.fit(X_train,y_train)\r\nscores=cross_val_score(grid,X_train,y_train,scoring='accuracy',cv=5)\r\ny_pred=grid.predict(X_test)\r\nresults=grid.cv_results_\r\nprint('')\r\nprint('GridSearch:')\r\nprint('Tuned Model Parameters:{}'.format(grid.best_params_))\r\n#print('In-sample Accuracy:%.4f'% grid.best_score_)\r\nprint('In-sample CV Accuracy:%.4f +/- %.4f'% (np.mean(scores),np.std(scores)))\r\nforest=grid.best_estimator_\r\nforest.fit(X_train,y_train)\r\nprint('Out-Sample Accuracy:%.4f'% grid.score(X_test,y_test))\r\n\r\n#Explore the relationship between n_estimators, in-sample CV accuracy and computation time\r\nprint('Relationship between n_estimators and in-sample accuracy:')\r\nfor i in range(5):\r\n    print('n_estimators:',params['randomforest__n_estimators'][i],\r\n          \"  in-sample accuracy\",'%.4f'% results['mean_test_score'][i])\r\nprint('Relationship between n_estimators and computation time:')\r\nfor i in range(5):\r\n    print('n_estimators:',params['randomforest__n_estimators'][i],\r\n          \"  computation time\",'%.4f'%(results['mean_fit_time'][i]+results['mean_score_time'][i]))\r\n#plot the Relationship between n_estimators and in-sample accuracy\r\nxnew =np.arange(params['randomforest__n_estimators'][0],params['randomforest__n_estimators'][4],1)\r\nfunc = interpolate.interp1d(params['randomforest__n_estimators'],results['mean_test_score'],kind='cubic')\r\nynew = func(xnew)\r\nplt.figure(figsize=(7,5))\r\nplt.plot(xnew,ynew)\r\nplt.xlabel('n_estimators')\r\nplt.ylabel('in-sample accuracy')\r\nplt.title('Relationship between n_estimators and in-sample accuracy')\r\nplt.show()\r\n\r\n#part 2\r\nfo=forest.__getitem__('randomforest')\r\nfeat_labels = df.columns[0:23]\r\nimportances = fo.feature_importances_\r\nindices = np.argsort(importances)[::-1]\r\nprint('')\r\nprint('Feature importances:')\r\nfor f in range(X_train.shape[1]):\r\n    print(\"%2d) %-*s %f\" % (f + 1, 30, feat_labels[indices[f]], importances[indices[f]]))\r\nplt.figure(figsize=(10,5))\r\nplt.bar(range(X_train.shape[1]), \r\n        importances[indices],\r\n        align='center')\r\nplt.title('Features Importances')\r\nplt.xticks(range(X_train.shape[1]), feat_labels[indices], rotation=90)\r\nplt.xlim([-1, X_train.shape[1]])\r\nplt.tight_layout()\r\n#plt.savefig('images/04_09.png', dpi=300)\r\nplt.show()\r\n\r\n'''\r\nimportances = pd.Series(data=forest.feature_importances_,index=feat_labels)\r\nimportances_sorted = importances.sort_values(ascending=False)\r\nplt.figure(figsize=(12,5))\r\nimportances_sorted.plot(kind=\"bar\")\r\nplt.title('Features Importances')\r\nplt.show()\r\nprint(importances)\r\n'''\r\n\r\nprint(\"My name is {Xin Zhang}\")\r\nprint(\"My NetID is: {xzhan81}\")\r\nprint(\"I hereby certify that I have read the University policy on Academic Integrity and that I am not in violation.\")","sub_path":"Ensembling for Performance Improvement.py","file_name":"Ensembling for Performance Improvement.py","file_ext":"py","file_size_in_byte":3753,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"560223527","text":"from flask import Blueprint, Response, request, jsonify\nfrom backend import image_code_dict\nfrom lib.captcha.captcha import captcha\n\nverification_bp = Blueprint(\"verifications\", __name__)\n\n\n# 验证验证码图片\n@verification_bp.route(\"/imagecodes/<string:image_code_id>/\", methods=[\"GET\"])\ndef verifications_image(image_code_id):\n    yanzhengma, image = captcha.generate_captcha()\n\n    image_code_dict[image_code_id] = yanzhengma\n\n    response = Response(image, mimetype=\"image/jpeg\")\n\n    return response\n\n\n# 验证验证码数字(是否一样)\n@verification_bp.route(\"/digital/<string:mobile>/\", methods=[\"GET\"])\ndef verifications_digital(mobile):\n    text = request.arges.get(\"text\")\n    image_code_id = request.arges.get(\"image_code_id\")\n    date = {\n        \"code\": 200,\n        \"msg\": \"success\"\n    }\n    if text == image_code_dict[\"image_code_id\"]:\n        date = {\n            \"code\": 200,\n            \"msg\": \"success\"\n        }\n    else:\n        date = {\n            \"code\": 200,\n            \"error_msg\": \"not success\"\n        }\n        return jsonify(date)\n\n","sub_path":"backend/verification/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":1071,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"470737844","text":"\"\"\"\n1504. Count Submatrices With All Ones (Medium)\n\nGiven a rows * columns matrix mat of ones and zeros, return how many submatrices have all ones.\n\n \n\nExample 1:\n\nInput: mat = [[1,0,1],\n              [1,1,0],\n              [1,1,0]]\nOutput: 13\nExplanation:\nThere are 6 rectangles of side 1x1.\nThere are 2 rectangles of side 1x2.\nThere are 3 rectangles of side 2x1.\nThere is 1 rectangle of side 2x2. \nThere is 1 rectangle of side 3x1.\nTotal number of rectangles = 6 + 2 + 3 + 1 + 1 = 13.\nExample 2:\n\nInput: mat = [[0,1,1,0],\n              [0,1,1,1],\n              [1,1,1,0]]\nOutput: 24\nExplanation:\nThere are 8 rectangles of side 1x1.\nThere are 5 rectangles of side 1x2.\nThere are 2 rectangles of side 1x3. \nThere are 4 rectangles of side 2x1.\nThere are 2 rectangles of side 2x2. \nThere are 2 rectangles of side 3x1. \nThere is 1 rectangle of side 3x2. \nTotal number of rectangles = 8 + 5 + 2 + 4 + 2 + 2 + 1 = 24.\nExample 3:\n\nInput: mat = [[1,1,1,1,1,1]]\nOutput: 21\nExample 4:\n\nInput: mat = [[1,0,1],[0,1,0],[1,0,1]]\nOutput: 5\n \n\nConstraints:\n\n1 <= rows <= 150\n1 <= columns <= 150\n0 <= mat[i][j] <= 1\n\"\"\"\n\n\nclass Solution(object):\n    def numSubmat(self, mat):\n        \"\"\"\n        :type mat: List[List[int]]\n        :rtype: int\n        \"\"\"\n        m = len(mat)\n        n = len(mat[0])\n        height = [0] * n\n        res = 0\n        for row in mat:\n            # update height\n            for i in range(n):\n                if row[i] == 1:\n                    height[i] += 1\n                else:\n                    height[i] = 0\n            # print('before', height)\n            # handle height\n\n            tmpres = [0] * n\n            for j in range(n):\n                curr = height[j]\n                k = j\n                while k >= 0:\n                    tmpres[j] += curr\n                    k -= 1\n                    curr = min(curr, height[k])\n                    if curr == 0:\n                        break\n            print(tmpres)\n            res += sum(tmpres)\n\n            \"\"\"\n            j = n-1\n            height_copy = [item for item in height]\n            while j >= 0:\n                if j != n-1 and height_copy[j+1] != 0:\n                    res += max(height_copy[j]-height_copy[j+1], 0)\n                    height_copy[j] = min(height_copy[j], height_copy[j+1])\n                j -= 1\n            print('after', height_copy)\n            # presum\n            presum = [0]\n            for item in height_copy:\n                if item == 0:\n                    presum.append(0)\n                else:\n                    presum.append(presum[-1]+item)\n            print('presum', presum[::-1])\n            # print(presum)\n\n            res += sum(presum)\n            \"\"\"\n        return res\n\nif __name__ == \"__main__\":\n    a = Solution()\n    print(a.numSubmat([[1,0,1],\n              [1,1,0],\n              [1,1,0]]))\n    print(a.numSubmat([[0,1,1,0],\n              [0,1,1,1],\n              [1,1,1,0]]))\n    print(a.numSubmat([[1,1,1,1,1,1]]))\n    print(a.numSubmat([[1,0,1],[0,1,0],[1,0,1]]))\n    arr = [[1,1,1,1,0,1,0],[1,1,1,0,0,0,1],[0,1,1,1,1,0,0],[1,1,0,1,1,0,1],[1,0,0,0,0,0,1],[1,1,0,1,1,1,1],[1,1,0,0,1,1,1]]\n    print(a.numSubmat(arr))","sub_path":"python/leetcode/array/1504_cnt_submatrix.py","file_name":"1504_cnt_submatrix.py","file_ext":"py","file_size_in_byte":3166,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"648662351","text":"import ftplib\nimport time\n\ndef readInput(nameFile):\n    datos = {\n        'Plantilla': [],\n        'N_Bloques': 0,\n        'Cortes': []\n    }\n    archivo = open(nameFile,'r')\n    cont = 0\n    linea = archivo.readline()\n    while(linea != ''):\n        linea = linea.rstrip()\n        x = linea.split(' ')\n        if cont == 0:\n            x = list(map(int, x))\n            datos['Plantilla'] += x\n        elif cont == 1:\n            x = list(map(int, x))\n            datos['N_Bloques'] = x.pop()\n        else:\n            aux = ord(x[0])\n            x = list(map(int, x[1:]))\n            x.append(aux)\n            datos['Cortes'].append(x)\n        linea = archivo.readline()\n        cont+=1\n    archivo.close()\n    return datos\n\ndata = readInput('Input.txt')\ndata2 = readInput('Input2.txt')\ndata3 = readInput('Input3.txt')\n","sub_path":"Trabajo Parcial/dato.py","file_name":"dato.py","file_ext":"py","file_size_in_byte":821,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"180973378","text":"import unittest\n\nfrom mock import Mock\n\nfrom k4thy.rafflemngr import RaffleMngr\n\n\nclass TestRaffleMngr(unittest.TestCase):\n\n    def test_set_options(self):\n        \"\"\"\n        There is verification in the command entry process making sure that\n        the values passed in are ints but they are in string form. So we do not\n        need to test passing in float or non-number values.\n        :return:\n        \"\"\"\n        rm = RaffleMngr(None, None)\n\n        rm.set_options(500, 5)\n        actual_tickets = rm.get_max_tickets()\n        expected_tickets = 500\n        self.assertEqual(expected_tickets, actual_tickets)\n        actual_time = rm.get_raffle_duration()\n        expected_time = 300\n        self.assertEqual(expected_time, actual_time)\n\n    def test_submit_tickets(self):\n        mock_bot = Mock()\n        rm = RaffleMngr(mock_bot, None)\n\n        # Raffle not open yet, so user cannot submit.\n        self.assertFalse(rm.submit_tickets('Steve', 500))\n\n        # Open the raffle to allow ticket submissions.\n        rm.__open_raffle__()\n        self.assertTrue(rm.submit_tickets('Steve', 500))\n        actual = rm.get_viewers_submitted_tickets('Steve')\n        expected = 500\n        self.assertEqual(expected, actual)\n\n        # Test a max ticket limit.\n        rm.set_options(m=50)\n        self.assertFalse(rm.submit_tickets('Joey', 51))\n        self.assertTrue(rm.submit_tickets('Stan', 49))\n        actual_1 = rm.get_viewers_submitted_tickets('Stan')\n        expected_1 = 49\n        self.assertEqual(expected_1, actual_1)\n\n        # Test a user updating the amount of their tickets they've entered with.\n        self.assertTrue(rm.submit_tickets('Alice', 25))\n        actual_2 = rm.get_viewers_submitted_tickets('Alice')\n        expected_2 = 25\n        self.assertEqual(expected_2, actual_2)\n        self.assertTrue(rm.submit_tickets('Alice', 5))\n        actual_3 = rm.get_viewers_submitted_tickets('Alice')\n        expected_3 = 5\n        self.assertEqual(expected_3, actual_3)\n\n\nif __name__ == '__main__':\n    unittest.main()\n","sub_path":"test/testrafflemngr.py","file_name":"testrafflemngr.py","file_ext":"py","file_size_in_byte":2039,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"543756298","text":"import os \nimport pandas as pd\nfrom skimage.transform import resize \nfrom skimage import io\nfrom glob import glob\nfrom PIL import Image\nfrom PIL import ImageFile\nImageFile.LOAD_TRUNCATED_IMAGES = True\n\n\ndef main(path='/home/yons/data/机器图像算法赛道-天气识别/resize'):\n    names = []\n    # 读取文件名\n    for _,filename in enumerate(os.listdir(path)):\n        names.append(os.path.splitext(filename)[0])\n\n\n    df = pd.read_csv(\"/home/yons/data/机器图像算法赛道-天气识别/Train_label.csv\")\n    for _,row in df.iterrows():\n        name_tail = row['FileName']\n        name = os.path.splitext(name_tail)[0]\n        classtmp = int(row[\"type\"])\n        print(name)\n   \n        if name not in names:\n            continue \n\n        with open(path + \"/\" + name +\".txt\",\"w+\") as f:\n            f.write(\"\" + name+'.jpg' + \", \" + str(classtmp))\n\n\n\n\n\n\nMAP_INTERPOLATION_TO_ORDER = {\n    \"nearest\": 0,\n    \"bilinear\": 1,\n    \"biquadratic\": 2,\n    \"bicubic\": 3,\n}\n\n\ndef center_crop_and_resize(image, image_size, crop_padding=32, interpolation=\"bicubic\"):\n    assert image.ndim in {2, 3}\n    assert interpolation in MAP_INTERPOLATION_TO_ORDER.keys()\n\n    h, w = image.shape[:2]\n\n    padded_center_crop_size = int(\n        (image_size / (image_size + crop_padding)) * min(h, w)\n    )\n    offset_height = ((h - padded_center_crop_size) + 1) // 2\n    offset_width = ((w - padded_center_crop_size) + 1) // 2\n\n    image_crop = image[\n                 offset_height: padded_center_crop_size + offset_height,\n                 offset_width: padded_center_crop_size + offset_width,\n                 ]\n    resized_image = resize(\n        image_crop,\n        (image_size, image_size),\n        order=MAP_INTERPOLATION_TO_ORDER[interpolation],\n        preserve_range=True,\n    )\n\n    return resized_image\ndef center_crop_and_resizes(path='/home/yons/data/机器图像算法赛道-天气识别/Train_nopng',\n    image_size=512,to_path='/home/yons/data/机器图像算法赛道-天气识别/resize/'):\n    #读取所有图片名字\n    label_files = glob(os.path.join(path, '*.jpg'))\n    for index, file_path in enumerate(label_files):\n        try:\n            name = file_path.split('/')[-1]\n            img = io.imread(file_path)\n            img = center_crop_and_resize(img,image_size)\n            io.imsave(to_path  + name,img)\n        except Exception:\n            print(name)\n        \n\nif __name__ == \"__main__\":\n    center_crop_and_resizes()\n    #main()","sub_path":"data_deal.py","file_name":"data_deal.py","file_ext":"py","file_size_in_byte":2458,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"69036984","text":"from django.urls import path\nfrom django.conf.urls import url\nfrom . import views\n#current path\n\nurlpatterns = [\n    path('', views.index,name=\"index\"),\n    #http://127.0.0.1/polls\n    path(\"order\",views.order, name=\"order\"),\n    #http://127.0.0.1/polls/order\n    path(\"createOrder\",views.createOrder,name=\"createOrder\"),\n    #http://127.0.0.1/delicious/order\n    url(r'^(?P<order_number>[A-Z][0-9]+)/detail', views.detail,name=\"detail\"),\n    #http://127.0.0.1/delicious/123/detail\n\n\n\n    path(\"api/contacts/\",views.contact_list, name=\"contact_list\"),\n\n    #http://127.0.0.1:8000/delicious/api/contacts/\n    #if get method list all the object\n    #if post method add new object\n\n    path('api/contact/<int:pk>', views.contact_detail,name=\"contact_detail\"),\n    #http://127.0.0.1:8000/delicious/api/contact/1(pk)\n\n\n\n\n]\n","sub_path":"mysite/delicious/urls.py","file_name":"urls.py","file_ext":"py","file_size_in_byte":818,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"208293386","text":"import pickle\nimport os\nfrom collections import OrderedDict\n\npath = r'../../Encoded Data Structures (Bonus)/Encoded-Top100-Docs-Proximity-Stopping'\nqueryID_top100Docs = {}\nfor file in os.listdir(path):\n    doc_pScore = {}\n    current_file = os.path.join(path,file)\n    string = current_file.split(\"Encoded-Top100Docs-Proximity-Stopping_\")\n    id = string[1].split(\".\")[0]\n    with open(current_file, 'rb') as f:\n        doc_pScore = pickle.loads(f.read())\n    all_docs = list(doc_pScore.keys())\n    top_100_docs = all_docs[:100]\n    queryID_top100Docs[id] = top_100_docs\n\nqueryID_top100Docs_sorted = OrderedDict(sorted(queryID_top100Docs.items(), key=lambda x: x,reverse=False))\n\n\noutput = open('../../Encoded Data Structures (Bonus)/Encoded-QueryID_Top100Docs_Proximity_with_stopping.txt', 'wb')\npickle.dump(queryID_top100Docs_sorted , output)\noutput.close()\n","sub_path":"Supplementary Features/Bonus Task Evaluations/Proximity (With Stopping) Evaluation/generate_QueryID_Top100Docs_proximity_with_stopping.py","file_name":"generate_QueryID_Top100Docs_proximity_with_stopping.py","file_ext":"py","file_size_in_byte":860,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"219083258","text":"\nfrom collections import OrderedDict as odict\nfrom values import decode_kv_pairs, encode_kv_pairs\nfrom loops import decode_loops, encode_loops\n\n\ndef split_frames(lines):\n    '''\n    splits a list of lines into lines that are not part of a frame,\n    and a list of lines, where each list is part of the same frame.\n    frames start with a `save_` and end with a `stop_`.  They also\n    end with the next data block, but this function is only called\n    with lines from a single data block.\n    '''\n\n    def parse_frame_name(line):\n        return line.split()[0][len('save_'):]\n\n    def frame_starts(line):\n        return line.startswith('save_')\n\n    def frame_stops(line):\n        return line.startswith('stop_')\n\n    outer = []\n    frame = None\n    frames = odict()\n    for line in lines:\n        if frame_stops(line):\n            frame = None\n        elif frame_starts(line):\n            name = parse_frame_name(line, 'save_')\n            if name not in frames:\n                frames[name] = []\n            frame = frames[name]\n        elif frame is None:\n            outer.append(line)\n        else:\n            frame.append(line)\n    return outer, frames\n\n\ndef decode_frames(lines):\n\n    outer, frames = split_frames(lines)\n\n    for key in frames:\n        frames[key] = decode_frame(frames[key])\n\n    return outer, frames\n\n\ndef decode_frame(lines):\n\n    outer, loops = decode_loops(lines)\n    frame = decode_kv_pairs(outer)\n\n    for key in loops:\n        frame[key] = loops[key]\n\n    return frame\n\n\ndef encode_frames(block):\n\n    def block_frames(block):\n        return [(k, block[k]) for k in block if isinstance(block[k], dict)]\n\n    lines = []\n    for name, frame in block_frames(block):\n        lines.append('save_%s' % name)\n        lines.append(encode_frame(frame))\n        lines.append('stop_')\n    return '\\n'.join(lines)\n\n\ndef encode_frame(frame):\n    lines = []\n    lines.append(encode_kv_pairs(frame))\n    lines.append(encode_loops(frame))\n    return '\\n'.join(lines)\n","sub_path":"frames.py","file_name":"frames.py","file_ext":"py","file_size_in_byte":1984,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"41387294","text":"# Copyright 2015 A10 Networks\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\nauto_dictionary = {\n    \"2.1\": (\"source_nat_auto\", lambda x: int(x)),\n    \"3.0\": (\"auto\", lambda x: int(x))\n}\n\n\ndef _set_auto_parameter(vport, device_info):\n    api_ver = device_info.get(\"api_version\", None)\n    auto_tuple = auto_dictionary.get(api_ver, None)\n\n    vport_key = None\n    vport_transform = lambda x: x\n\n    if auto_tuple:\n        vport_key = auto_tuple[0]\n        vport_transform = auto_tuple[1]\n\n    if vport_key is not None:\n        cfg_value = device_info.get(\"autosnat\", False)\n        vport[vport_key] = vport_transform(cfg_value)\n\n\ndef _set_vrid_parameter(virtual_server, device_info):\n    vrid = device_info.get(\"default_virtual_server_vrid\", None)\n\n    if vrid is not None:\n        virtual_server['vrid'] = vrid\n\n\ndef _set_ipinip_parameter(vport, device_info):\n    key = \"ipinip\"\n    ipinip = device_info.get(key, False)\n    if ipinip:\n        vport[key] = int(ipinip)\n","sub_path":"a10_neutron_lbaas/a10_common.py","file_name":"a10_common.py","file_ext":"py","file_size_in_byte":1469,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"583918604","text":"import autofit as af\nfrom autoarray.exc import InversionException\nfrom autofit.exc import FitException\nfrom autogalaxy.fit import fit\nfrom autogalaxy.galaxy import galaxy as g\nfrom autogalaxy.pipeline import visualizer\nfrom autogalaxy.pipeline.phase.dataset import analysis as analysis_data\n\n\nclass Analysis(analysis_data.Analysis):\n    def __init__(\n        self, masked_interferometer, settings, cosmology, image_path=None, results=None\n    ):\n\n        super(Analysis, self).__init__(\n            settings=settings, cosmology=cosmology, results=results\n        )\n\n        self.visualizer = visualizer.PhaseInterferometerVisualizer(\n            masked_dataset=masked_interferometer, image_path=image_path\n        )\n\n        self.visualizer.visualize_hyper_images(\n            hyper_galaxy_image_path_dict=self.hyper_galaxy_image_path_dict,\n            hyper_model_image=self.hyper_model_image,\n        )\n\n        self.masked_dataset = masked_interferometer\n\n        result = analysis_data.last_result_with_use_as_hyper_dataset(results=results)\n\n        if result is not None:\n\n            self.hyper_galaxy_visibilities_path_dict = (\n                result.hyper_galaxy_visibilities_path_dict\n            )\n\n            self.hyper_model_visibilities = result.hyper_model_visibilities\n\n        else:\n\n            self.hyper_galaxy_visibilities_path_dict = None\n            self.hyper_model_visibilities = None\n\n    @property\n    def masked_interferometer(self):\n        return self.masked_dataset\n\n    def log_likelihood_function(self, instance):\n        \"\"\"\n        Determine the fit of a lens galaxy and source galaxy to the masked_interferometer in this lens.\n\n        Parameters\n        ----------\n        instance\n            A model instance with attributes\n\n        Returns\n        -------\n        fit : Fit\n            A fractional value indicating how well this model fit and the model masked_interferometer itself\n        \"\"\"\n\n        self.associate_hyper_images(instance=instance)\n        plane = self.plane_for_instance(instance=instance)\n\n        hyper_background_noise = self.hyper_background_noise_for_instance(\n            instance=instance\n        )\n\n        try:\n            fit = self.masked_interferometer_fit_for_plane(\n                plane=plane, hyper_background_noise=hyper_background_noise\n            )\n\n            return fit.figure_of_merit\n        except InversionException as e:\n            raise FitException from e\n\n    def associate_hyper_visibilities(\n        self, instance: af.ModelInstance\n    ) -> af.ModelInstance:\n        \"\"\"\n        Takes visibilities from the last result, if there is one, and associates them with galaxies in this phase\n        where full-path galaxy names match.\n\n        If the galaxy collection has a different name then an association is not made.\n\n        e.g.\n        galaxies.lens will match with:\n            galaxies.lens\n        but not with:\n            galaxies.lens\n            galaxies.source\n\n        Parameters\n        ----------\n        instance\n            A model instance with 0 or more galaxies in its tree\n\n        Returns\n        -------\n        instance\n           The input instance with visibilities associated with galaxies where possible.\n        \"\"\"\n        if self.hyper_galaxy_visibilities_path_dict is not None:\n            for galaxy_path, galaxy in instance.path_instance_tuples_for_class(\n                g.Galaxy\n            ):\n                if galaxy_path in self.hyper_galaxy_visibilities_path_dict:\n                    galaxy.hyper_model_visibilities = self.hyper_model_visibilities\n                    galaxy.hyper_galaxy_visibilities = self.hyper_galaxy_visibilities_path_dict[\n                        galaxy_path\n                    ]\n\n        return instance\n\n    def masked_interferometer_fit_for_plane(self, plane, hyper_background_noise):\n\n        return fit.FitInterferometer(\n            masked_interferometer=self.masked_dataset,\n            plane=plane,\n            hyper_background_noise=hyper_background_noise,\n            settings_pixelization=self.settings.settings_pixelization,\n            settings_inversion=self.settings.settings_inversion,\n        )\n\n    def visualize(self, instance, during_analysis):\n\n        self.associate_hyper_images(instance=instance)\n        plane = self.plane_for_instance(instance=instance)\n        hyper_background_noise = self.hyper_background_noise_for_instance(\n            instance=instance\n        )\n\n        fit = self.masked_interferometer_fit_for_plane(\n            plane=plane, hyper_background_noise=hyper_background_noise\n        )\n\n        if plane.has_mass_profile:\n\n            visualizer = self.visualizer.new_visualizer_with_preloaded_critical_curves_and_caustics(\n                preloaded_critical_curves=plane.critical_curves,\n                preloaded_caustics=plane.caustics,\n            )\n\n        else:\n\n            visualizer = self.visualizer\n\n        visualizer.visualize_fit(fit=fit, during_analysis=during_analysis)\n","sub_path":"autogalaxy/pipeline/phase/interferometer/analysis.py","file_name":"analysis.py","file_ext":"py","file_size_in_byte":4994,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"16797647","text":"import ast\nimport re\n\nimport setuptools\n\n_version_re = re.compile(r\"version\\s+=\\s+(.*)\")\n\nwith open(\"pyproject.toml\", \"rb\") as f:\n    version = str(\n        ast.literal_eval(_version_re.search(f.read().decode(\"utf-8\")).group(1))\n    )\n\nwith open(\"README.md\", \"r\", encoding='utf-8') as fh:\n    long_description = fh.read()\n\nrequirements = [\n    \"graphene-sqlalchemy>=2.3.0\",\n    \"graphene-sqlalchemy-filter==1.12.1\",\n    \"inflection==0.5.0\"\n]\n\nsetuptools.setup(\n    name=\"graphene-sqlalchemy-auto-filter\",  # Replace with your own username\n    version=version,\n    author=\"golsee, baddhizm\",\n    author_email=\"z.shj726@gmail.com, frenggy@gmail.com\",\n    license=\"MIT\",\n    description=\"generate default graphene schema with filters from sqlalchemy model base on graphene-sqlalchemy\",\n    long_description=long_description,\n    long_description_content_type=\"text/markdown\",\n    url=\"https://github.com/Baddhizm/graphene-sqlalchemy-auto-filter.git\",\n    packages=setuptools.find_packages(),\n    install_requires=requirements,\n    classifiers=[\n        \"Intended Audience :: Developers\",\n        \"Topic :: Software Development :: Libraries\",\n        \"Programming Language :: Python :: 3\",\n        \"Programming Language :: Python :: 3.6\",\n        \"Programming Language :: Python :: 3.6\",\n        \"Programming Language :: Python :: 3.7\",\n        \"Programming Language :: Python :: 3.8\",\n        \"License :: OSI Approved :: MIT License\",\n        \"Operating System :: OS Independent\",\n        'Programming Language :: Python :: Implementation :: PyPy',\n    ],\n    python_requires='>=3.6'\n)\n","sub_path":"setup.py","file_name":"setup.py","file_ext":"py","file_size_in_byte":1583,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"525933024","text":"\"\"\"empty message\n\nRevision ID: 221725883b9b\nRevises: \nCreate Date: 2018-12-09 14:11:10.520381\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '221725883b9b'\ndown_revision = None\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.create_table('balance',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('account_number', sa.String(length=16), nullable=True),\n    sa.Column('balance', sa.DECIMAL(precision=10, scale=2), nullable=True),\n    sa.PrimaryKeyConstraint('id')\n    )\n    op.create_index(op.f('ix_balance_account_number'), 'balance', ['account_number'], unique=True)\n    op.create_table('ledger',\n    sa.Column('id', sa.Integer(), nullable=False),\n    sa.Column('account_number', sa.String(length=16), nullable=True),\n    sa.Column('amount', sa.DECIMAL(precision=10, scale=2), nullable=True),\n    sa.Column('accounting_type', sa.String(length=1), nullable=True),\n    sa.PrimaryKeyConstraint('id')\n    )\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_table('ledger')\n    op.drop_index(op.f('ix_balance_account_number'), table_name='balance')\n    op.drop_table('balance')\n    # ### end Alembic commands ###\n","sub_path":"migrations/versions/221725883b9b_.py","file_name":"221725883b9b_.py","file_ext":"py","file_size_in_byte":1348,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"436534908","text":"# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Tue Jul 31 15:17:46 2018\n\n@author: monodeep.das@ust-global.com\n\"\"\"\n\nimport pandas as pd\n\nCSV_PATH = 'C:\\\\Users\\\\user\\\\Documents\\\\MonodeepPyPractise\\\\artwork_data.csv'\n\n\nCOLS_TO_USE = ['id','artist','title','medium','year','acquisitionYear','height','width','units']\n\ndf = pd.read_csv(CSV_PATH,\n                 index_col='id',\n                 usecols=COLS_TO_USE)\n\nsmall_df = df.iloc[49980:50019,:].copy()\ngroupd = small_df.groupby('artist')\n\nfor name, group in groupd:\n    print(name)\n    print(group)\n    break","sub_path":"Week2/PandasPractise/groups_in_pandas.py","file_name":"groups_in_pandas.py","file_ext":"py","file_size_in_byte":552,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"275306033","text":"import cv2\nimport numpy as np\nimport stereoconfig1\n\n# 预处理\ndef preprocess(img1, img2):\n    # 彩色图->灰度图\n    im1 = cv2.cvtColor(img1, cv2.COLOR_BGR2GRAY)\n    im2 = cv2.cvtColor(img2, cv2.COLOR_BGR2GRAY)\n\n    # 直方图均衡\n    im1 = cv2.equalizeHist(im1)\n    im2 = cv2.equalizeHist(im2)\n\n    return im1, im2\n\n\n# 消除畸变\ndef undistortion(image, camera_matrix, dist_coeff):\n    undistortion_image = cv2.undistort(image, camera_matrix, dist_coeff)\n\n    return undistortion_image\n\n\n# 获取畸变校正和立体校正的映射变换矩阵、重投影矩阵\n# @param：config是一个类，存储着双目标定的参数:config = stereoconfig.stereoCamera()\ndef getRectifyTransform(height, width, config):\n    # 读取内参和外参\n    left_K = config.cam_matrix_left\n    right_K = config.cam_matrix_right\n    left_distortion = config.distortion_l\n    right_distortion = config.distortion_r\n    R = config.R\n    T = config.T\n\n    # 计算校正变换\n    R1, R2, P1, P2, Q, roi1, roi2 = cv2.stereoRectify(left_K, left_distortion, right_K, right_distortion,\n                                                      (width, height), R, T, alpha=0)\n    print(Q)\n    map1x, map1y = cv2.initUndistortRectifyMap(left_K, left_distortion, R1, P1, (width, height), cv2.CV_32FC1)\n    map2x, map2y = cv2.initUndistortRectifyMap(right_K, right_distortion, R2, P2, (width, height), cv2.CV_32FC1)\n    return map1x, map1y, map2x, map2y, Q\n\n# 畸变校正和立体校正\ndef rectifyImage(image1, image2, map1x, map1y, map2x, map2y):\n    rectifyed_img1 = cv2.remap(image1, map1x, map1y, cv2.INTER_AREA)\n    rectifyed_img2 = cv2.remap(image2, map2x, map2y, cv2.INTER_AREA)\n\n    return rectifyed_img1, rectifyed_img2\n\n\n# 立体校正检验----画线\ndef draw_line(image1, image2):\n    # 建立输出图像\n    height = max(image1.shape[0], image2.shape[0])\n    width = image1.shape[1] + image2.shape[1]\n\n    output = np.zeros((height, width, 3), dtype=np.uint8)\n    output[0:image1.shape[0], 0:image1.shape[1]] = image1\n    output[0:image2.shape[0], image1.shape[1]:] = image2\n\n    for k in range(15):\n        cv2.line(output, (0, 50 * (k + 1)), (2 * width, 50 * (k + 1)), (0, 255, 0), thickness=2,\n                 lineType=cv2.LINE_AA)  # 直线间隔：100\n\n    return output\n\n\n# 视差计算，做成可调节的\ndef disparity_SGBM(left_image, right_image, down_scale=False):\n\n    # SGBM匹配参数设置\n    if left_image.ndim == 2:\n        img_channels = 1\n    else:\n        img_channels = 3\n    blockSize = 3\n    param = {'minDisparity': 0,\n             'numDisparities':128,\n             'blockSize': blockSize,\n             'P1': 8 * img_channels * blockSize ** 2,\n             'P2': 32 * img_channels * blockSize ** 2,\n             'disp12MaxDiff': 1,\n             'preFilterCap': 63,\n             'uniquenessRatio': 15,\n             'speckleWindowSize': 100,\n             'speckleRange': 1,\n             'mode': cv2.STEREO_SGBM_MODE_SGBM_3WAY\n             }\n\n    # 构建SGBM对象\n    sgbm = cv2.StereoSGBM_create(**param)\n\n    # 计算视差图\n    size = (left_image.shape[1], left_image.shape[0])\n    if down_scale == False:\n        disparity_left = sgbm.compute(left_image, right_image)\n        disparity_right = sgbm.compute(right_image, left_image)\n    else:\n        left_image_down = cv2.pyrDown(left_image)\n        right_image_down = cv2.pyrDown(right_image)\n        factor = size[0] / left_image_down.shape[1]\n        disparity_left_half = sgbm.compute(left_image_down, right_image_down)\n        disparity_right_half = sgbm.compute(right_image_down, left_image_down)\n        disparity_left = cv2.resize(disparity_left_half, size, interpolation=cv2.INTER_AREA)\n        disparity_right = cv2.resize(disparity_right_half, size, interpolation=cv2.INTER_AREA)\n        disparity_left *= factor\n        disparity_right *= factor\n\n    return disparity_left, disparity_right\n\n\n# 将h×w×3数组转换为N×3的数组\ndef hw3ToN3(points):\n    height, width = points.shape[0:2]\n\n    points_1 = points[:, :, 0].reshape(height * width, 1)\n    points_2 = points[:, :, 1].reshape(height * width, 1)\n    points_3 = points[:, :, 2].reshape(height * width, 1)\n\n    points_ = np.hstack((points_1, points_2, points_3))\n\n    return points_\n\n\n# 深度、颜色转换为点云\ndef DepthColor2Cloud(points_3d, colors):\n    rows, cols = points_3d.shape[0:2]\n    size = rows * cols\n\n    points_ = hw3ToN3(points_3d).astype(np.int16)\n    colors_ = hw3ToN3(colors).astype(np.int64)\n\n    # 颜色信息\n    blue = colors_[:, 0].reshape(size, 1)\n    green = colors_[:, 1].reshape(size, 1)\n    red = colors_[:, 2].reshape(size, 1)\n\n    rgb = np.left_shift(blue, 0) + np.left_shift(green, 8) + np.left_shift(red, 16)\n\n    # 将坐标+颜色叠加为点云数组\n    pointcloud = np.hstack((points_, rgb)).astype(np.float32)\n\n    # 删掉一些不合适的点\n    X = pointcloud[:, 0]\n    Y = pointcloud[:, 1]\n    Z = pointcloud[:, 2]\n\n    remove_idx1 = np.where(Z <= 0)\n    remove_idx2 = np.where(Z > 15000)\n    remove_idx3 = np.where(X > 10000)\n    remove_idx4 = np.where(X < -10000)\n    remove_idx5 = np.where(Y > 10000)\n    remove_idx6 = np.where(Y < -10000)\n    remove_idx = np.hstack(\n        (remove_idx1[0], remove_idx2[0], remove_idx3[0], remove_idx4[0], remove_idx5[0], remove_idx6[0]))\n\n    pointcloud_1 = np.delete(pointcloud, remove_idx, 0)\n\n    return pointcloud_1\n\n# 添加点击事件，打印当前点的距离\ndef callbackFunc(e, x, y, f, p):\n    if e == cv2.EVENT_LBUTTONDOWN:\n        print(points_3d[y][x])\n        if abs(points_3d[y][x][2]) < 5000:   #原本是3000\n            print(\"当前距离:\" + str(abs(points_3d[y][x][2])))\n        else:\n            print(\"当前距离过大或请点击色块的位置\")\n\nif __name__ == '__main__':\n    # 读取数据集的图片\n    iml = cv2.imread('picture_l.jpg')  # 左图\n    imr = cv2.imread('picture_r.jpg')  # 右图\n    height, width = iml.shape[0:2]\n\n    # 读取相机内参和外参\n    config = stereoconfig1.stereoCamera()\n\n    # 立体校正\n    map1x, map1y, map2x, map2y, Q = getRectifyTransform(height, width, config)  # 获取用于畸变校正和立体校正的映射矩阵以及用于计算像素空间坐标的重投影矩阵\n    iml_rectified, imr_rectified = rectifyImage(iml, imr, map1x, map1y, map2x, map2y)\n\n    # 绘制等间距平行线，检查立体校正的效果\n    line = draw_line(iml_rectified, imr_rectified)\n\n    # 立体匹配\n    iml_, imr_ = preprocess(iml, imr)  # 预处理，不做也可以\n\n\n    #生成深度图\n    displ,dispr= disparity_SGBM(iml_rectified, imr_rectified)  # 这里传入的是未经立体校正的图像，因为我们使用的middleburry图片已经是校正过的了\n    disp_l = np.divide(displ.astype(np.float32), 16.)  # 除以16得到真实视差（因为SGBM算法得到的视差是×16的）\n    disp_r = np.divide(displ.astype(np.float32), 16.)\n    cv2.imwrite('shichal.jpg', disp_l)\n    cv2.imwrite('shichar.jpg', disp_r)\n    cv2.imshow('shichal.jpg', disp_l)\n    cv2.imshow('shichar.jpg',disp_r)\n\n    # 计算像素点的3D坐标（左相机坐标系下）\n    points_3d = cv2.reprojectImageTo3D(disp_l, Q)  # 可以使用上文的stereo_config.py给出的参数\n\n    #点击测距\n    cv2.setMouseCallback(\"shichal.jpg\", callbackFunc, None)\n\n\n\ncv2.waitKey(0)","sub_path":"双目匹配/图片测距方法.py","file_name":"图片测距方法.py","file_ext":"py","file_size_in_byte":7287,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"193640984","text":"import multiprocessing\nimport numpy as np\nimport logging as log\nimport pyqtgraph as pg\nimport yaml\nimport itertools\n\nfrom ringbuffer2d import RingBuffer2D\nfrom pipeprocess import PipeProcess\n\nclass AcqProcessing:\n    def __init__(self):\n        # USBBoard data format:\n        # ADC data is sent in an array of size\n        # size = N_uplinks_per_USB * N_channels_per_uplink\n        self.num_sensors = 8*64 # for VATA64 front-end\n        self.num_sensors_enabled = 8*64\n        self.num_uplinks = 8\n        self.num_channels_per_uplinks = 64\n        self.num_integrations = 100\n        \n        self.integrate = False # no integration mode\n        self.queue = multiprocessing.Queue()\n        \n        self.uplinks_enabled = None\n        self.channels_enabled = None\n        self.all_pedestal = 0\n        self.all_pedestal_val = 0\n        self.path_pedestal_file = \"\"\n        self.all_gain = 0\n        self.all_gain_val = 0\n        self.path_gain_file = \"\"\n        \n        self.calibration_all_channels = dict([('pedestals',np.empty(self.num_sensors_enabled)), ('gains',np.empty(self.num_sensors_enabled))])\n        \n    def start_acquisition(self, cmd):\n        # reset buffers to ensure they have an adequate size\n        self.reset_buffers()\n        self.sp = PipeProcess(self.queue,\n                              cmd=cmd,\n                              args=[str(self.num_sensors),])\n        self.sp.start()\n        \n    def stop_acquisition(self):\n        self.sp.stop()\n        self.sp.join()\n        self.reset_buffers()\n    \n    def parse_queue_item(self, line, save=False):\n        # Here retrieve the line pushed to the queue\n        # and properly parse it to return \n        # several values such as ID, time, [list of vals]\n        line_items = line.split('\\t')\n        if len(line_items) <= 1: return None, None, None\n        items = [int(kk) for kk in line_items]\n        ev_num, ts, intensities = items[0], items[1], items[2:]\n        if save:\n            # all ADC values from USBboard are in intensities\n            # takes ADC values selected (channels enabled) with the setup file\n            intensities = list(itertools.compress(intensities, self.channels_enabled)) # ceci prend ~5us\n            self.data.append(intensities)\n            self.time.append(ts)\n            self.evNumber.append(ev_num)\n            \n        return ev_num, ts, intensities\n    \n    def fetch_data(self):\n        # Just for debugging purpose: approx. queue size\n        #print(\"Queue size: {}\".format(self.queue.qsize()))\n        kk = 0\n        while not self.queue.empty():\n            kk+=1\n            raw_data = self.queue.get(False)\n            self.parse_queue_item(raw_data, save=True)\n            #print(\"Poped {} values\".format(kk))\n        if kk == 0: return False\n        return True\n    \n    def plot_signals_scatter(self):\n        data = self.plot_signals_map().ravel()\n        intensity = data[self.sensor_ids] / (2**10*self.num_sensors_enabled)\n        #colors = [pg.intColor(200, alpha=int(k)) if k!=np.NaN else 0 for k in intensity/ np.max(intensity) * 100]\n        maxintensity = np.max(intensity)*0.01\n        colors = [pg.intColor(200, alpha=int(k/maxintensity)) if maxintensity>0 else 0 for k in intensity] \n        return intensity, colors\n    \n    def plot_signals_map(self):\n        if self.integrate:\n            return np.sum(self.data.get_all(), axis=0).reshape(self.num_sensors_enabled,1)\n        else:\n            return self.data.get_partial().reshape(self.num_sensors_enabled,1)\n\n    def set_sensor_pos(self, x_coords, y_coords, sensor_num):\n        self.x_coords = x_coords\n        self.y_coords = y_coords\n        self.sensor_ids = sensor_num\n        if np.array_equal(np.sort(sensor_num), np.arange(len(sensor_num))):\n            log.warning('Sensors ID not starting at 0, or duplicated, or missing')\n        \n        self.reset_buffers()\n\n    def set_num_sensors(self, value):\n        self.num_sensors = value\n    \n    def set_integration_mode(self, value):\n        self.integrate = value\n    \n    def reset_buffers(self):\n        self.data = RingBuffer2D(self.num_integrations,\n                                    cols=self.num_sensors_enabled)\n        self.time = RingBuffer2D(1, cols=1) # Unused at the moment (buffer size is 1)\n        self.evNumber = RingBuffer2D(1, cols=1) # Unused at the moment (buffer size is 1)            \n        while not self.queue.empty():\n            self.queue.get()\n        log.info(\"Buffers cleared\")\n    \n    def loadCSVfile(self, file_path, key):\n        if not (key == 'pedestals' or key == 'gains'):\n            log.warning(\"Failed to load csv file: key {} not existing!\".format(key))\n            return\n        # load from csv file\n        data = np.genfromtxt(file_path)\n        all_calib = np.empty(self.num_sensors)\n        # Format is: uplink, channel, data (ex pedestal, gain)\n        for i, line in enumerate(data):\n            # checks that the channels int the csv file are correctly ordered\n            if int((line[0] % 10) * self.num_channels_per_uplinks + line[1]) != i:\n                log.warning(\"Loading {} file: Channels must be in the right order!\".format(key))\n            else:\n                all_calib[i] = line[2]\n        self.calibration_all_channels[key] = list(itertools.compress(all_calib, self.channels_enabled))\n    \n    def load_general_setup_file(self, file_path):\n        # load general setup file (txt)\n        # which contains the uplinks enabled and where the csv ped+gain files are\n        #file_path can be loaded from the interface through MainWindow class\n        #file_path is passed by the MainWindow when it starts or when we want to reload it or the pedestal and gain csv files\n        log.info(\"Loading setup file {}\".format(file_path))\n        \n        with open(file_path, 'r') as f:\n            cfg = yaml.load(f)\n        \n        if len(cfg['FrontEndBoardConfig']) != 8:\n            og.warning(\"FrontEndBoardConfig should have 8 values and not {}! Aborting setup file loading.\".format(len(enabled)))\n            return\n        \n        self.uplinks_enabled = cfg['FrontEndBoardConfig']\n        self.all_pedestal = cfg['Pedestals']['AllPed']\n        self.all_pedestal_val = cfg['Pedestals']['AllPedVal']\n        self.path_pedestal_file =  cfg['Pedestals']['PedFilePath']\n        \n        self.all_gain = cfg['Gains']['AllGain']\n        self.all_gain_val = cfg['Gains']['AllGainVal']\n        self.path_gain_file =  cfg['Gains']['GainFilePath']\n        \n        # updates channels enabled:\n        self.channels_enabled = [enabled for enabled in self.uplinks_enabled for _ in range(self.num_channels_per_uplinks)]\n        \n        num_sensors = sum(x > 0 for x in self.uplinks_enabled) * self.num_channels_per_uplinks\n        if num_sensors != self.num_sensors_enabled:\n            self.num_sensors_enabled = num_sensors\n            log.info(\"Number of sensors enabled changed to {}\".format(num_sensors))\n            self.reset_buffers()\n        \n        if self.all_pedestal:\n            if self.all_pedestal_val > 0:\n                log.info(\"All pedestals set to {}\".format(self.all_pedestal_val))\n                self.calibration_all_channels['pedestals'].fill(self.all_pedestal_val)\n            else:\n                log.warning(\"Cannot set all pedestals to \", self.all_pedestal_val, \"!\")\n        else:\n            log.info(\"Setting pedestals from file {}\".format(self.path_pedestal_file))\n            self.loadCSVfile(self.path_pedestal_file, 'pedestals')\n        \n        if self.all_gain:\n            if self.all_gain_val > 0:\n                log.info(\"All gains set to {}\".format(self.all_gain_val))\n                self.calibration_all_channels['gains'].fill(self.all_gain_val)\n            else:\n                log.warning(\"Cannot set all gains to \", self.all_gain_val, \"!\")\n        else:\n            log.info(\"Setting gains from file {}\".format(self.path_gain_file))\n            self.loadCSVfile(self.path_gain_file, 'gains')\n        #print(self.channels_enabled)\n \n","sub_path":"src/acqprocessing.py","file_name":"acqprocessing.py","file_ext":"py","file_size_in_byte":7999,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"225405858","text":"import sys\r\nfrom os import path\r\nfrom sprites import *\r\nfrom tilemap import *\r\n\r\n#HUD creation\r\ndef drawHUD(surf, x, y, pct, bl, bh, olc):\r\n    if pct < 0:\r\n        pct = 0\r\n    barlen = bl\r\n    barh = bh\r\n    fill = pct * barlen\r\n    outline = pg.Rect(x, y, barlen, barh)\r\n    fillr = pg.Rect(x, y, fill, barh)\r\n    if pct == 1.02:\r\n        col = black\r\n        fill = barlen\r\n        outline = pg.Rect(x, y, barlen, barh)\r\n        fillr = pg.Rect(x, y, fill, barh)\r\n    elif pct > 0.6:\r\n        col = green\r\n    elif pct > 0.3:\r\n        col = yellow\r\n    else:\r\n        col = red\r\n    pg.draw.rect(surf, col, fillr)\r\n    pg.draw.rect(surf, olc, outline, 2)\r\n\r\n\r\nclass game:\r\n    def __init__(self):\r\n        pg.init()\r\n        self.screen = pg.display.set_mode((width, hight))\r\n        pg.display.set_caption(title)\r\n        pg.display.set_icon(icon)\r\n        self.clock = pg.time.Clock()\r\n        self.load()\r\n        self.mana = mana\r\n\r\n    def load(self):\r\n        # loading image data for textures\r\n        file = path.dirname(__file__)\r\n        grapics = path.join(file, 'images')\r\n\r\n        # loading of map data to the code\r\n        self.map = Map(path.join(file, 'map2.txt'))\r\n        self.playerimg = pg.image.load(path.join(grapics,playerdude)).convert_alpha()\r\n        self.hitbloximg = pg.image.load(path.join(grapics,hitbloximg)).convert_alpha()\r\n        self.shuntimg = pg.image.load(path.join(grapics,shunt)).convert_alpha()\r\n        self.slimelowimg = pg.image.load(path.join(grapics, slimelow)).convert_alpha()\r\n        self.slimeimg = pg.image.load(path.join(grapics, slime)).convert_alpha()\r\n        self.slimehighimg = pg.image.load(path.join(grapics, slimehigh)).convert_alpha()\r\n        self.bulletimg = pg.image.load(path.join(grapics, bullet)).convert_alpha()\r\n        self.wallimg = pg.image.load(path.join(grapics,wallsprite)).convert_alpha()\r\n        self.barrierimg = pg.image.load(path.join(grapics,barriersprite)).convert_alpha()\r\n        self.manaimg = pg.image.load(path.join(grapics,manasprite)).convert_alpha()\r\n        self.healthimg = pg.image.load(path.join(grapics,helsprite)).convert_alpha()\r\n        self.titlefont = path.join(grapics, '8-BIT WONDER.TTF')\r\n\r\n    def new(self):\r\n        # initialisation of all new game objects\r\n        self.allsprites = pg.sprite.Group()\r\n        self.walls = pg.sprite.Group()\r\n        self.barrier = pg.sprite.Group()\r\n        self.mobs = pg.sprite.Group()\r\n        self.bullets = pg.sprite.Group()\r\n        self.shunt = pg.sprite.Group()\r\n        self.mpickups = pg.sprite.Group()\r\n        self.hpickups = pg.sprite.Group()\r\n        self.winbox = pg.sprite.Group()\r\n        # map creation\r\n        for row, tiles in enumerate(self.map.data):\r\n            for col, tile in enumerate(tiles):\r\n                if tile == '1':\r\n                    Wall(self, col, row)\r\n                if tile == '2':\r\n                    barrier(self, col, row)\r\n                if tile == 'm':\r\n                    manabox(self, col, row)\r\n                if tile == 'h':\r\n                    healbox(self, col, row)\r\n                if tile == 'w':\r\n                    winbox(self, col, row)\r\n                if tile == 's':\r\n                    mob(self, col, row,'s')\r\n                if tile == 'e':\r\n                    mob(self, col, row, 'e')\r\n                if tile == 'S':\r\n                    mob(self, col, row, 'S')\r\n                if tile == 'P':\r\n                    self.player = player(self, col, row)\r\n        # brings the cam functin into the main py file\r\n        self.cam = cam(self.map.width, self.map.height)\r\n\r\n    def run(self):\r\n        # game loop (self.playing = False to end game loop)\r\n        self.playing = True\r\n        # game loop\r\n        while self.playing:\r\n            self.dt = self.clock.tick(fps) / 1000\r\n            self.events()\r\n            self.update()\r\n            self.draw()\r\n\r\n    def quit(self):\r\n        # quit functions for ease\r\n        pg.quit()\r\n        sys.exit()\r\n\r\n    def update(self):\r\n        # update portion of the game loop\r\n        self.allsprites.update()  # updates all gameplay sprites\r\n        self.cam.update(self.player)  # updates the camera position based on sprite\r\n        # mob/player inteaction\r\n        hits = pg.sprite.spritecollide(self.player, self.mobs, False)#, self.player.rectimg)\r\n        for hit in hits:\r\n            self.player.health -= mobdamage\r\n            hit.vel = vec2(0, 0)\r\n            if self.player.health <= 0:\r\n                self.playing = False\r\n\r\n        # mob deaths\r\n        if hits:\r\n            self.player.pos += vec2(mobkb, 0).rotate(-hits[0].rot)\r\n            # bullets hit mobs\r\n        hits = pg.sprite.groupcollide(self.mobs, self.bullets, False, True)\r\n        for hit in hits:\r\n            hit.health -= bdamage\r\n            hit.vel = vec2(0, 0)\r\n\r\n        hits = pg.sprite.spritecollide(self.player, self.hpickups, False)\r\n        for hit in hits:\r\n            if self.player.health <= 100:\r\n                self.player.health += 25\r\n                hit.kill()\r\n                if self.player.health > 100:\r\n                    self.player.health = 100\r\n\r\n        hits = pg.sprite.spritecollide(self.player, self.mpickups, False)\r\n        for hit in hits:\r\n            if self.player.mana <= 100:\r\n                self.player.mana += 25\r\n                hit.kill()\r\n                if self.player.mana > 100:\r\n                    self.player.mana = 100\r\n\r\n        hits = pg.sprite.spritecollide(self.player, self.mpickups, False)\r\n        for hit in hits:\r\n            if self.player.mana <= 100:\r\n                self.player.mana += 25\r\n                hit.kill()\r\n                if self.player.mana > 100:\r\n                    self.player.mana = 100\r\n\r\n        hits = pg.sprite.spritecollide(self.player, self.winbox, False)\r\n        for hit in hits:\r\n            self.win()\r\n\r\n    def grid(self):\r\n        # dev tool (will be deactivated for final code)\r\n        for x in range(0, width, tilesize):\r\n            pg.draw.line(self.screen, lightblack, (x, 0), (x, hight))\r\n        for y in range(0, hight, tilesize):\r\n            pg.draw.line(self.screen, lightblack, (0, y), (width, y))\r\n        pass\r\n\r\n    def draw(self):\r\n        # draws all sprites to be displayed\r\n        self.screen.fill(bgcolour)\r\n        #self.grid()\r\n        #pg.display.set_caption(\"{:.2f}\".format(self.clock.get_fps()))\r\n        for sprite in self.allsprites:\r\n            if isinstance(sprite,mob):\r\n                sprite.healthbar()\r\n            self.screen.blit(sprite.image, self.cam.apply(sprite))\r\n        # HUD functions\r\n        drawHUD(self.screen, 9, 9, 1.02, 500, 52, white)\r\n        drawHUD(self.screen, 10, 10, self.player.health / playerhealth, 500, 40, green)\r\n        drawHUD(self.screen, 10, 50, self.player.mana / mana, 500, 10, purp)\r\n        # displays all drawn spirits and updates every game tick\r\n        pg.display.flip()\r\n\r\n    def events(self):\r\n        # all non player specific events ar kept here\r\n        for event in pg.event.get():\r\n            if event.type == pg.QUIT:\r\n                self.quit()\r\n            if event.type == pg.KEYDOWN:\r\n                if event.key == pg.K_ESCAPE:\r\n                    self.quit()\r\n\r\n    def draw_text(self, text, font_name, size, color, x, y, align=\"nw\"):\r\n        #text creation \r\n        font = pg.font.Font(font_name, size)\r\n        text_surface = font.render(text, True, color)\r\n        text_rect = text_surface.get_rect()\r\n        if align == \"nw\":\r\n            text_rect.topleft = (x, y)\r\n        if align == \"ne\":\r\n            text_rect.topright = (x, y)\r\n        if align == \"sw\":\r\n            text_rect.bottomleft = (x, y)\r\n        if align == \"se\":\r\n            text_rect.bottomright = (x, y)\r\n        if align == \"n\":\r\n            text_rect.midtop = (x, y)\r\n        if align == \"s\":\r\n            text_rect.midbottom = (x, y)\r\n        if align == \"e\":\r\n            text_rect.midright = (x, y)\r\n        if align == \"w\":\r\n            text_rect.midleft = (x, y)\r\n        if align == \"center\":\r\n            text_rect.center = (x, y)\r\n        self.screen.blit(text_surface, text_rect)\r\n\r\n    def start(self):\r\n        self.screen.fill(black)\r\n        self.draw_text(\"NEW GAME\", self.titlefont, 75, red, width / 2, hight / 4, align=\"center\")\r\n        self.draw_text(\"use the arrow keys or wasd to move\", self.titlefont, 20, white, width / 2, hight * 3 / 4, align=\"center\")\r\n        self.draw_text(\"shoot with x or u\", self.titlefont, 25, white, width / 2, hight * 3 / 4 + 25, align=\"center\")\r\n        self.draw_text(\"dash with c or i\", self.titlefont, 25, white, width / 2, hight * 3 / 4 + 50, align=\"center\")\r\n        self.draw_text(\"press any key to start\", self.titlefont, 25, white, width / 2, hight * 3 / 4 + 75, align=\"center\")\r\n        pg.display.flip()\r\n        self.waitkey()\r\n        gameloop()\r\n\r\n    def gameover(self):\r\n        self.screen.fill(black)\r\n        self.draw_text(\"GAME OVER\", self.titlefont, 75, red, width / 2, hight / 2, align=\"center\")\r\n        self.draw_text(\"Press any key to play again\", self.titlefont, 25, white, width / 2, hight * 3 / 4, align=\"center\")\r\n        pg.display.flip()\r\n        self.waitkey()\r\n\r\n    def win (self):\r\n        self.screen.fill(black)\r\n        self.draw_text(\"YOU WON\", self.titlefont, 75, red, width / 2, hight / 2, align=\"center\")\r\n        self.draw_text(\"good job\", self.titlefont, 25, white, width / 2, hight * 3 / 4, align=\"center\")\r\n        self.draw_text(\"Press any key to play again\", self.titlefont, 25, white, width / 2, hight * 3 / 4 + 25, align=\"center\")\r\n        pg.display.flip()\r\n        self.waitkey()\r\n        self.start()\r\n\r\n    def waitkey(self):\r\n        pg.event.wait()\r\n        waiting = True\r\n        while waiting:\r\n            self.clock.tick(fps)\r\n            for event in pg.event.get():\r\n                if event.type == pg.QUIT:\r\n                    waiting = False\r\n                    self.quit()\r\n                if event.type == pg.KEYUP:\r\n                    waiting = False\r\n\r\ndef gameloop():\r\n    # create the game object\r\n    g = game()\r\n    while True:\r\n        g.new()\r\n        g.run()\r\n        if len(g.mobs) == 0:\r\n            g.win()\r\n        g.gameover()\r\n\r\ng = game()\r\ng.start()\r\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":10229,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"418525799","text":"from rest_framework.serializers import (\r\n    ModelSerializer,\r\n    HyperlinkedIdentityField,\r\n    HyperlinkedRelatedField,\r\n    SerializerMethodField\r\n)\r\n\r\nfrom .models import Provider, Service\r\n\r\n\r\nclass ProviderListSerializer(ModelSerializer):\r\n    url = HyperlinkedIdentityField(\r\n        view_name=\"api:provider:provider_services\",\r\n        read_only=True,\r\n        lookup_field=\"uuid\"\r\n    )\r\n\r\n    class Meta:\r\n        model = Provider\r\n        fields = [\r\n            \"url\",\r\n            \"name\",\r\n            \"image\",\r\n            \"height_field\",\r\n            \"width_field\",\r\n            \"uuid\",\r\n            \"created\",\r\n            \"id\",\r\n            \"updated\"\r\n        ]\r\n\r\n\r\nclass ServiceListSerializer(ModelSerializer):\r\n\r\n    class Meta:\r\n        model = Service\r\n        fields = '__all__'\r\n\r\n","sub_path":"provider/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":807,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"286479033","text":"'''\nCreated on April 2, 2019\n\n@author: Kai Gemlau\n\nDriver for FeelTech FY3200s AWG.\nBased on FY6600 driver \n'''\n\nimport serial\nimport time\nfrom base_awg import BaseAWG\nimport constants\nfrom exceptions import UnknownChannelError\n\n# Port settings constants\nBAUD_RATE = 9600\nBITS = serial.EIGHTBITS\nPARITY = serial.PARITY_NONE\nSTOP_BITS = serial.STOPBITS_ONE\nTIMEOUT = 5\n\n# FY320 End of line character (\\n)\n# Channels validation tuple\nCHANNELS = (0, 1, 2)\nCHANNELS_ERROR = \"Channel can be 1 or 2.\"\n# FY3200 requires some delay between commands.\nSLEEP_TIME = 0.5\n\n# Output impedance of the AWG\nR_IN = 50.0\n\nclass FY3200S(BaseAWG):\n    '''\n    FY3200s function generator driver.\n    '''    \n    SHORT_NAME = \"fy3200s\"\n\n    def __init__(self, port, baud_rate=BAUD_RATE, timeout=TIMEOUT):\n        \"\"\"baud_rate parameter is ignored.\"\"\"\n        self.port = port\n        self.ser = None\n        self.timeout = timeout\n        self.channel_on = [False, False]\n        self.r_load = [50, 50]\n        self.v_out_coeff = [1, 1]\n    \n    def connect(self):\n        self.ser = serial.Serial(self.port, BAUD_RATE, BITS, PARITY, STOP_BITS, timeout=self.timeout)\n    \n    def disconnect(self):\n        self.ser.close()\n        \n    def send_command(self, channel, cmd):\n        # Channel 1\n        if channel in (0, 1) or channel is None:\n            self.ser.write((\"b%s\\n\" % cmd).encode())\n            time.sleep(SLEEP_TIME)\n        # Channel 2\n        if channel in (0, 2) or channel is None:\n            self.ser.write((\"d%s\\n\" % cmd).encode())\n            time.sleep(SLEEP_TIME)\n\n        \n    def initialize(self):\n        self.channel_on = [False, False]\n        self.connect()\n        self.enable_output()\n    \n    def get_id(self):\n        self.ser.write(\"a\\n\".encode())\n        ans = self.ser.read_until(terminator=\"\\r\\n\", size=None)\n        return ans\n\n    def enable_output(self, channel=None, on=False):\n        \"\"\"\n        Turns channels output on or off.\n        The channel is defined by channel variable. If channel is None, both channels are set.\n        \"\"\"\n        if channel is not None and channel not in CHANNELS:\n            raise UnknownChannelError(CHANNELS_ERROR)\n        \n        if channel is not None and channel != 0:\n            self.channel_on[channel-1] = on\n        else:\n            self.channel_on = [on, on]\n        \n        if self.channel_on[0] == True:\n            self.set_amplitue(1,1.0)\n        else:\n            self.set_amplitue(1,0)\n\n        if self.channel_on[1] == True:\n            self.set_amplitue(2,1.0)\n        else:\n            self.set_amplitue(2,0)\n\n    def set_frequency(self, channel, freq):\n        \"\"\"\n        Sets frequency on the selected channel. Frequency is in centiHz\n        \n        Command examples:\n            bf0000000100 equals 1 Hz on channel 1\n            df0000100000 equals 1 kHz on channel 2\n        \"\"\"\n        \n        if channel is not None and channel not in CHANNELS:\n            raise UnknownChannelError(CHANNELS_ERROR)\n        \n        freq_str = \"%.2f\" % freq\n        freq_str = freq_str.replace(\".\", \"\")\n        self.send_command(channel, (\"f%s\" % freq_str))\n        \n    def set_phase(self, phase):\n        \"\"\"\n        Sends the phase setting command to the generator.\n        The phase is set on channel 2 only.\n        \n        Commands:\n            dp100 is 100.0 degrees on Channel 2\n        \"\"\"\n        if phase < 0:\n            phase += 360\n\n        self.send_command(2,\"f%s\" % phase)\n\n    def set_wave_type(self, channel, wave_type):\n        \"\"\"\n        Sets wave type of the selected channel.\n        \n        Commands:\n            bw0 for Sine wave channel 1\n            dw0 for Sine wave channel 2\n        Both commands are \"hard-coded\".\n       \"\"\"\n        if channel is not None and channel not in CHANNELS:\n            raise UnknownChannelError(CHANNELS_ERROR)\n        if not wave_type in constants.WAVE_TYPES:\n            raise ValueError(\"Incorrect wave type.\")\n        self.send_command(channel, \"w0\")\n        \n    def set_amplitue(self, channel, amplitude):\n        \"\"\"\n        Sets amplitude of the selected channel.\n        \n        Commands:\n            ba0.44 for 0.44 volts Channel 1\n            da9.87 for 9.87 volts Channel 2\n        \"\"\"\n        if channel is not None and channel not in CHANNELS:\n            raise UnknownChannelError(CHANNELS_ERROR)\n        \n        \"\"\"\n        Adjust the output amplitude to obtain the requested amplitude\n        on the defined load impedance.\n        \"\"\"\n        amplitude = amplitude / self.v_out_coeff[channel-1] \n        self.send_command(channel, (\"a%.3f\" % amplitude))\n    \n    def set_offset(self, channel, offset):\n        \"\"\"\n        Sets DC offset of the selected channel.\n        \n        Command examples:\n        bo0.33 sets channel 1 offset to 0.33 volts\n        do-3.33sets channel 2 offset to -3.33 volts\n        \"\"\"\n        if channel is not None and channel not in CHANNELS:\n            raise UnknownChannelError(CHANNELS_ERROR)\n        # Adjust the offset to the defined load impedance\n        offset = offset / self.v_out_coeff[channel-1] \n        self.send_command(channel, (\"o%s\" % offset)) \n        \n    def set_load_impedance(self, channel, z):\n        \"\"\"\n        Sets load impedance connected to each channel. Default value is 50 Ohm.\n        \"\"\"\n        if channel is not None and channel not in CHANNELS:\n            raise UnknownChannelError(CHANNELS_ERROR)\n        \n        self.r_load[channel-1] = z\n        \n        \"\"\"\n        Vout coefficient defines how the requestd amplitude must be increased\n        in order to obtain the requested amplitude on the defined load.\n        If the load is Hi-Z, the amplitude must not be increased.\n        If the load is 50 Ohm, the amplitude has to be double of the requested\n        value, because of the voltage divider between the output impedance\n        and the load impedance.\n        \"\"\"\n        if z == constants.HI_Z:\n            v_out_coeff = 1\n        else:\n            v_out_coeff = z / (z + R_IN)\n        self.v_out_coeff[channel-1] = v_out_coeff\n    \nif __name__ == '__main__':\n    print (\"This module shouldn't be run. Run awg_tests.py instead.\")\n","sub_path":"sds1004x_bode/awgdrivers/fy3200s.py","file_name":"fy3200s.py","file_ext":"py","file_size_in_byte":6164,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"644209600","text":"from enum import Enum\n\nclass MiddlewareType(Enum):\n    CONSUMER = 1\n    PRODUCER = 2\n\nclass Tree:\n    def __init__(self, topic, parent, path, consumers = None, childrens=None):\n        self.path = path\n        self.topic = topic\n        self.last_msg = None\n        self.consumers = []\n        self.children = []\n        self.parent = parent\n        \n        if childrens is not None:\n            for child in childrens:\n                self.children.append(child)\n        \n        if consumers is not None:\n            for cons in consumers:\n                self.consumers.append(cons)\n        \n\n    # Retornar a(s) última(s) mensagen(s) associada(s) a um tópico.\n    # Se esse tópico tem nós descendentes, o objetivo é que o consumer receba as últimas mensagens de todos eles.\n    def getLastMsg(self):\n        # Retorna uma lista de strings do tipo \"topic-message\"\n        msg =[]\n        if self.last_msg is not None:\n            msg.append(f\"{self.path}-{self.last_msg}\")\n\n        if self.children:   \n            for c in self.children:\n                msgTemp = c.getLastMsg()\n\n                for msgT in msgTemp:\n                    msg.append(msgT)\n        return msg\n\n\n    # Retornar o caminho completo de um nó (o próprio nó precedido pelos parents)\n    def getPath(self):\n        return self.path\n\n    # Retorna a lista de todos os consumers subscritos a este nó\n    def getConsumers(self):\n        return self.consumers\n\n    # Retorna o tópico deste nó\n    def getTopic(self):\n        return self.topic\n\n    # Retorna a lista de filhos deste nó\n    def getChildrens(self):\n        return self.children\n\n    # Retorna todos os paths existentes que começam no nó em questão\n    # [\"weather/humidity\", \"weather/pressure\", \"weather/temp/wind]\n    def getTopics(self):\n\n        # Caso o nó não tenha filhos, o path é o nó em si\n        if len(self.children) == 0:\n            return [self.path]\n\n        # Adicionar-se-á ao array a devolver cada path existente\n        else:\n            topics=[]\n            topics.append(self.path)\n\n            for child in self.children:\n                topicsChildren = child.getTopics()\n\n                for topic in topicsChildren:\n                    topics.append(topic)\n        return topics\n\n\n    # Retorna o nó-pai de que provém\n    def getParent(self):\n        return self.parent\n\n    # Retornar os consumers aos quais é suposto enviar a mensagem do nó em questão. \n    # Enviar a todos os consumers desse nó bem como aos consumers que estão subscritos aos nós-parent (até à raiz do path a que o nó pertence)\n    def getAllRecipients(self):\n        recipients=[]\n\n        for recipient in self.consumers:\n            recipients.append(recipient)\n\n        parent = self.getParent()\n        while parent is not None:\n            for recipient in parent.getConsumers():\n                recipients.append(recipient)\n\n            parent = parent.getParent()\n\n        return recipients\n\n\n    # Retorna o nó-filho cujo tópico é igual àquele que foi passado como argumento \n    # Se nenhum dos filhos tiver esse tópico, retorna None\n    def getChildren(self, children_topic):\n        for child in self.children:\n            if child.getTopic() == children_topic:\n                return child\n        return None\n\n    # Atualizar a última mensagem associada a um tópico - usada quando são feitas publicações por parte dos producers\n    def setLastMsg(self, value):\n        self.last_msg = value\n\n    \n    # Adicionar filhos ao longo da árvore deste nó consoante o array children passado, que contém os tópicos dos nós-filhos.\n    # Se já existir o consumer num dos filhos-superiores passados, é retornada uma flag(permissionToAdd) como False para que não seja possível\n    # ser adicionado ao nó-filho mais abaixo. Esta função retorna também o último nó correspondente ao último tópico da lista children passada.\n    def addChildren(self, children, conn=None):\n        permissionToAdd = True  \n\n        # child contém agora o filho DIRETO que tenha o tópico em questao (ou então é None)\n        child = self.getChildren(children[0])\n\n        # Primeiro será verificado se a conn já está registada num nó (seja ele A) que esteja acima do nó em questão,\n        # uma vez que, neste caso, a conn já recebe as mensagens produzidas em nós-filhos de A. \n        if conn in self.consumers:\n            permissionToAdd = False\n\n        # Caso o child contenha o filho direto,       \n        if child is not None:\n            # Adicionam-se os restantes filhos\n            if len(children)>1:\n                return child.addChildren(children[1:len(children)], conn)\n            # Caso contrário, apenas se retorna o filho\n            else:\n                return child, permissionToAdd\n\n        # Caso não exista o filho direto, será adicionado o novo filho ao ramo deste nó (e os seus respetivos filhos)\n        else: \n            child = Tree(children[0], self, str(f\"{self.path}/{children[0]}\"))\n            if len(children)>1:\n                self.children.append(child)\n                return child.addChildren(children[1:len(children)], conn)\n            else:\n                self.children.append(child)\n                return child, permissionToAdd\n\n\n\n    # Adicionar um consumer a um nó\n    # Consideram-se duas situações: pretende-se adicionar um consumer a um nó que é raiz de um path - \"weather\"\n    #                               pretende-se adicionar um consumer a um nó descendente da raiz de um path - \"weather/temp/wind\"\n    # Na primeira situação childs = None, pelo que o consumer é logo adicionado ao nó em questão, após verificação se existe nos nós descendentes.\n    # Se existir, será removido dos nós em baixo e adicionado ao nó em questão, continuando assim a receber as mensagens dos tópicos inferiores.\n    # Na segunda situação childs = ['temp', 'wind'], o pressuposto é que se pretende adicionar o consumer ao último nó do path.\n    # Qualquer nó do path que ainda não exista será criado. Nos nós já existentes é sempre feita uma verificação da pertença ou não do consumer \n\n    # Adicionar um consumer\n    def addConsumer(self, consumer, children=None):\n        node = None\n        \n        # Se não for passado nenhum array, adiciona-se o consumer diretamente neste nó\n        if children is None:\n            if consumer not in self.consumers:\n                # Inicialmente procura-se o consumer nos nós abaixo\n                nodes = self.searchConsumer(consumer)\n                \n                if self in nodes: \n                    nodes.remove(self) \n\n                # Se este consumidor estiver nos nós em baixo será removido de todos esses nós, visto que vai ser adicionado a um nó superior\n                for x in nodes: \n                    x.rmvConsumer(consumer)\n            \n                self.consumers.append(consumer)\n            else:\n                print('\\x1b[0;37;41m' + 'Consumer already on the node!' + '\\x1b[0m')\n\n            node = self\n        \n        # No caso de ser passado array, vai verificar se esse consumer já existe nos nós-filhos, adicionando-o aos mesmos no caso de ainda não existirem.\n        # Se os nós-filhos ainda não tiverem sido instanciados, são criados e então é-lhes adicionado o consumer. Esta função retorna, além do nó onde\n        # tem de ser adicionado o consumer, uma permissão se este pode ser adicionado. Isto acontece porque se o consumer já existir num dos filhos \n        # superiores ao filho que se quer adicionar, não faz sentido fazer essa adição.\n        else: \n            node, permission = self.addChildren(children, consumer)\n\n            if permission:\n                # Inicialmente procura-se o consumer nos nós abaixo\n                nodes = node.searchConsumer(consumer) \n                if self in nodes:\n                    nodes.remove(self)\n\n                # Se este consumidor estiver nos nós em baixo será removido de todos esses nós, visto que vai ser adicionado a um nó superior\n                for x in nodes: \n                    x.rmvConsumer(consumer)\n                node.addConsumer(consumer)\n            else:\n                print('\\x1b[0;37;41m' + 'Consumer already on superior node!' + '\\x1b[0m')\n\n        return node\n\n\n    # Remover um consumer\n    # Se não tiver sido passado nenhum array, primeiro verificar se o nó recebido tem o consumer\n    # Se tiver, remove-se. Se não tiver e tiver filhos, tem de se ir a todos remover o consumer \n    def rmvConsumer(self, consumer, children=None):\n        found = False\n        if children is None:\n            if consumer in self.consumers:\n                self.consumers.remove(consumer)\n                return self\n            \n            # Ir aos filhos e remover em todos eles\n            else:\n                for child in self.children:\n                    x = child.rmvConsumer(consumer)\n                    if x is not None:\n                        found = True\n                if found:\n                    return x\n                else:\n                    return None\n            \n\n        # Caso o array passado não seja vazio, vamos percorrendo o ramo, para baixo, nó a nó (com base nos tópicos passados).\n        # Se um dos nós encontrados contiver o consumer, este é removido, porque se tem a certeza de que não está mais abaixo.\n        else: \n            for child in self.children:\n                if child.getTopic() == children[0]: \n                    if consumer in child.getConsumers():\n                        child.consumers.remove(consumer)\n                        print('\\x1b[5;30;42m' + 'Consumer removed on the node with the topic: '+ child.getPath() + '\\x1b[0m')\n                        return child\n                    else:\n                        if len(children) > 1: \n                            return child.rmvConsumer(consumer, children[1:len(children)])\n                        else:\n                            for kid in child.children:\n                                x = kid.rmvConsumer(consumer)\n                                if x is not None:\n                                    found = True\n                            if found:\n                                return x\n                        \n        return None\n\n    \n    # Vai procurar nos filhos, usando o array de forma similar à função addConsumer \n    def searchChildren(self, children):\n        for child in self.children:\n\n            if(child.getTopic() == children[0]):\n                if len(children)>1:\n                    node = child.searchChildren(children[1:len(children)])\n                    return node\n                else:\n                    return child          \n        return None\n\n\n    # Retorna os nós, ou nó, cujos tópicos são subscritos pelo consumer em questão\n    def searchConsumer(self, conn):\n        if conn in self.consumers:\n            return [self]\n        else:\n            nodes = []\n\n            if len(self.children) > 0:\n                for child in self.children:\n                    nodes.extend(child.searchConsumer(conn))\n            return nodes\n    \n\n  \n      ","sub_path":"tree.py","file_name":"tree.py","file_ext":"py","file_size_in_byte":11188,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"228156428","text":"from settings import *\nfrom kivy.uix.widget import Widget\nfrom kivy.graphics import Rectangle, Color\n\n\nclass Toolbar(Widget):\n\n    def __init__(self, **kwargs):\n        super(Toolbar, self).__init__(**kwargs)\n        self.id = 'toolbar'\n        self.w = SIZE['TOOLBAR']['W']\n        self.h = SIZE['TOOLBAR']['H']\n        self.color = (COLOR['TOOLBAR']['R'], COLOR['TOOLBAR']['G'], COLOR['TOOLBAR']['B'])\n\n    def load(self):\n        with self.canvas:\n            Color(self.color[0], self.color[1], self.color[2])\n            self.background = Rectangle(size=self.size, pos=[0, 0])\n\n    @property\n    def size(self):\n        return (self.w, self.h)\n","sub_path":"lane/toolbar.py","file_name":"toolbar.py","file_ext":"py","file_size_in_byte":649,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"630686091","text":"from selenium.webdriver import ActionChains\nfrom selenium.webdriver.chrome.webdriver import WebDriver\nfrom selenium.webdriver.common.by import By\nfrom selenium.webdriver.common.keys import Keys\nfrom selenium.webdriver.support.expected_conditions import presence_of_element_located\nfrom selenium.webdriver.support.wait import WebDriverWait\n\ndriver = WebDriver(r\"C:\\\\Users\\\\v-sujoshi.FAREAST\\\\Workspace\\\\playground\\\\driver\\\\chromedriver.exe\")\n\ndriver.get(\"https://teams.microsoft.com\")\ndriver.maximize_window()\nprint(driver.title)  # Login page\nprint(driver.current_url)\n\n# How to find out Relative X-path\n\"\"\"\nXpath construct:\nXpath=\"//tagname[@attribute='value']\"\nXpath=\"//tagname[contains(@attribute, 'foo-')]\"  id = \"foo-<username>\"\nXpath=\"//*[contains(text(),'underlying text to search')]\"\nXpath=\"//*[@attribute1='value1' <or/and> @attribute2='value2']\"\nXpath=\"//tagname[starts-with(@attribute,'value')]\" \"valueabcd\"\nXpath=\"//td[text()='underlying text to search']\"\n\nhttps://www.guru99.com/xpath-selenium.html#:~:text=The%20XPath%20text%20%28%29%20function%20is%20a%20built-in,to%20be%20located%20should%20be%20in%20string%20form.\n\"\"\"\n\n# Send some email id for login screen using relative xpath\nelement = WebDriverWait(driver, 10).until(presence_of_element_located((By.XPATH, \"//input[@type='email']\")))\n# element = driver.find_element_by_xpath(\"//input[@type='email']\")\n\n# Focus\nif element == driver.switch_to.active_element:\n    print(\"Email input is focussed\")\nelse:\n    print(\"Email input is not in focus\")\n\nemail_id = \"v-sujoshi@microsoft.com\"\nelement.send_keys(email_id)\n\n# Usage of indexes in Xpath\n\n# Scroll Bar\ndriver.find_element_by_id(\"moreOptions\").click()\nscroll_element = driver.find_element_by_xpath(\"//div[contains(text(), 'If you plan on getting help for this problem')]\")\n\n# Javascript Executor\n# driver.execute_script(\"arguments[0].scrollIntoView(true);\", scroll_element)\n# driver.execute_script(\"scrollBy();\")\n\n# ActionChains\nact = ActionChains(driver)\nact.move_to_element(scroll_element).perform()\n# act.send_keys(Keys.PAGE_DOWN).perform()\n\n# Hover\nhover_element = driver.find_element_by_xpath(\"//div[@aria-label='Sign-in options']\")\nact.move_to_element(hover_element).perform()\n\n\n# highlighted text\nact.move_to_element(element).click()\nact.send_keys(Keys.HOME)\nact.key_down(Keys.LEFT_SHIFT)\nfor _ in range(2):\n    act.send_keys(Keys.ARROW_RIGHT)\nact.key_up(Keys.LEFT_SHIFT).perform()\nselection = driver.execute_script(\"return window.getSelection().toString();\")\nprint(selection)\n\n# act.move_by_offset(10, 0)\n\n# drag functionalities\n# https://www.geeksforgeeks.org/drag_and_drop-action-chains-in-selenium-python/#:~:text=Action%20chain%20methods%20are%20used%20by%20advanced%20scripts,the%20target%20element%20and%20releases%20the%20mouse%20button.\n# act.drag_and_drop()\n","sub_path":"main/selenium_session.py","file_name":"selenium_session.py","file_ext":"py","file_size_in_byte":2794,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"283468602","text":"# random_forest_regression\n\n# Libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport pandas as pd \n\n# Importing datasets\ndataset = pd.read_csv('Polynomial_Regression/Position_Salaries.csv')\nX = dataset.iloc[:, 1:2].values\nY = dataset.iloc[:, 2].values\n\n# Splitting the dataset into the Training and Test set\n'''\nfrom sklearn.model_selection import train_test_split\nX_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=0)\n'''\n\n# Feature scaling\n'''\nfrom sklearn.preprocessing import StandardScaler\nsc_X = StandardScaler()\nX_train = sc_X.fit_transform(X_train)\nX_test = sc_X.transform(X_test)\n'''\n\n# Fitting Regression model to the dataset\nfrom sklearn.ensemble import RandomForestRegressor\nregressor = RandomForestRegressor(n_estimators=300, random_state=0)\nregressor.fit(X, Y)\n\n# Predicting using regression\ny_pred = regressor.predict(6.5)\nprint(y_pred)\n\n# Visualisation Random Forest regression results - higher resolution\nX_grid = np.arange(min(X), max(X), 0.01)\nX_grid = X_grid.reshape((len(X_grid), 1))\nplt.scatter(X, Y, color='red') # Source points\nplt.plot(X_grid, regressor.predict(X_grid), color='blue') # Prediction\nplt.title(\"Random Forest Regression results\")\nplt.xlabel(\"Position level\")\nplt.ylabel(\"Salary\")\nplt.show()","sub_path":"Random Forest Regression/random_forest_regression.py","file_name":"random_forest_regression.py","file_ext":"py","file_size_in_byte":1277,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"195047165","text":"# -*- coding: utf-8 -*-\r\nimport sys\r\nfrom PyQt5.QtCore import Qt\r\nfrom PyQt5.QtWidgets import QWidget, QApplication, QGridLayout, QLabel\r\nimport numpy as np\r\n\r\nclass Example(QWidget):\r\n    \r\n    def __init__(self):\r\n        super().__init__()\r\n        \r\n        self.initUI()\r\n        \r\n        \r\n    def initUI(self):      \r\n        \r\n        grid = QGridLayout()\r\n        grid.setSpacing(10)\r\n        \r\n        self.coords=[0,0,0]        \r\n\r\n        self.setMouseTracking(True)\r\n        \r\n        self.setLayout(grid)\r\n        \r\n        self.setGeometry(100, 100, 1100, 700)\r\n        self.setWindowTitle('Event object')\r\n        self.show()\r\n        \r\n        \r\n    def mousePressEvent(self,e):\r\n        self.coords=[e.x(),e.y(),e.button()]\r\n        self.choice()\r\n        \r\n        \r\n    def choice(self):\r\n        if (100 < self.coords[0] < 400) and (100 < self.coords[1] < 600):\r\n            if (100 < self.coords[0] < 350) and (150 < self.coords[1] < 200):\r\n                boat = 'Carrier (5)'\r\n            elif (100 < self.coords[0] < 300) and (250 < self.coords[1] < 300):\r\n                    boat = 'Battleship (4)'              \r\n            elif (100 < self.coords[0] < 250) and (350 < self.coords[1] < 400):\r\n                boat = 'Cruiser (3)'\r\n            elif (100 < self.coords[0] < 250) and (450 < self.coords[1] < 500):\r\n                boat = 'Submarine (3)'\r\n            elif (100 < self.coords[0] < 200) and (550 < self.coords[1] < 600):\r\n                boat = 'Destroyer (2)'\r\n            else:\r\n                boat = False\r\n            if boat:\r\n                print('You select the',boat)\r\n            else:\r\n                print('You do not have select any boat')\r\n        if (500 < self.coords[0] < 1000) and (100 < self.coords[1] < 600):\r\n            letters = ['A','B','C','D','E','F','G','H','I','J']\r\n            square = letters[int((self.coords[0]-500)/50)]+str(int((self.coords[1]-100)/50)+1)\r\n            print('Your are in',square)\r\n    \r\n    \r\n        \r\nif __name__ == '__main__':\r\n    \r\n    app = QApplication(sys.argv)\r\n    ex = Example()\r\n    sys.exit(app.exec_())","sub_path":"gui/gui2.py","file_name":"gui2.py","file_ext":"py","file_size_in_byte":2110,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"257151147","text":"from rest_framework import status\nfrom rest_framework.decorators import api_view, permission_classes\nfrom rest_framework.response import Response\n\nfrom ..models import SourcePFAM\nfrom ..serializers import SourcePFAMSerializer\nfrom ..permissions import *\n\nclass SourcePFAMView():\n    @api_view(['GET', 'POST'])\n    @permission_classes([OnlyRead, ])\n    def sourcePfam_list(request):\n        if request.method == 'GET':\n            sourcePFAMs = SourcePFAM.objects.all()\n            serializer = SourcePFAMSerializer(sourcePFAMs, many=True)\n            return Response(serializer.data)\n\n        elif request.method == 'POST':\n            serializer = SourcePFAMSerializer(data = request.data)\n            if serializer.is_valid():\n                serializer.save()\n                return Response(serializer.data, status=status.HTTP_201_CREATED)\n            return Response(serializer.errors, status=status.HTTP_400_BAD_REQUEST)\n\n    @api_view(['GET','PUT','DELETE'])\n    @permission_classes([OnlyRead, ])\n    def sourcePfam_detail(request, pk):\n        try:\n            sourcePFAM = SourcePFAM.objects.get(pk=pk)\n        except sourcePFAM.DoesNotExist:\n            return Response(status=status.HTTP_404_NOT_FOUND)\n\n        if request.method == 'GET':\n            serializer = SourcePFAMSerializer(sourcePFAM)\n            return Response(serializer.data)\n\n        elif request.method == 'PUT':\n            serializer = SourcePFAMSerializer(sourcePFAM, data = request.data)\n            if serializer.is_valid():\n                serializer.save()\n                return Response(serializer.data)\n            return Response(serializer.errors, status = status.HTTP_400_BAD_REQUEST)\n\n        elif request.method == 'DELETE':\n            sourcePFAM.delete()\n            return Response(status=status.HTTP_204_NO_CONTENT)\n\n","sub_path":"projet_b11/inphinity_app/inphinity_orm/views/views_sourcePfam.py","file_name":"views_sourcePfam.py","file_ext":"py","file_size_in_byte":1816,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"446989916","text":"import logging\nimport os\nfrom robot import application\nlogger = logging.getLogger(__name__)\n\n\n@application.route('/', methods=['GET'])\ndef default_route():\n    application.logger.info(\"base url requested. saying hello\")\n    return \"Hi. My name is robot.\"\n\n\nif __name__ == \"__main__\":\n    # application.run()\n    application.run(host= '0.0.0.0', port=9000, debug=False)\n    # app.run(debug=True)\n","sub_path":"application.py","file_name":"application.py","file_ext":"py","file_size_in_byte":395,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"480982612","text":"import torch\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport lib.utils.loss_utils as loss_utils\nfrom lib.config import cfg\nfrom collections import namedtuple\nimport numpy as np\nimport ipdb\n\ndef model_joint_fn_decorator():\n    ModelReturn = namedtuple(\"ModelReturn\", ['loss', 'tb_dict', 'disp_dict'])\n    MEAN_SIZE = torch.from_numpy(cfg.CLS_MEAN_SIZE[0]).cuda()\n\n    def print_dict(data,name):\n        print()\n        print(name+':')\n        print(data.keys())\n        for key in data.keys():\n            if isinstance(data[key], np.ndarray) or isinstance(data[key], torch.Tensor):\n                print(key, data[key].shape)\n            elif isinstance(data[key], list):\n                print(key, len(key))\n            elif isinstance(data[key], dict):\n                print(key, data[key].keys())\n            else:\n                print(key,type(data[key], data[key]))\n        print()\n\n    def model_fn(model, data):\n        if cfg.RPN.ENABLED:\n            pts_rect, pts_features, pts_input = data['pts_rect'], data['pts_features'], data['pts_input']\n            gt_boxes3d = data['gt_boxes3d']\n\n            if not cfg.RPN.FIXED:\n                rpn_cls_label, rpn_reg_label = data['rpn_cls_label'], data['rpn_reg_label']\n                rpn_cls_label = torch.from_numpy(rpn_cls_label).cuda(non_blocking=True).long()\n                rpn_reg_label = torch.from_numpy(rpn_reg_label).cuda(non_blocking=True).float()\n\n            inputs = torch.from_numpy(pts_input).cuda(non_blocking=True).float()\n            gt_boxes3d = torch.from_numpy(gt_boxes3d).cuda(non_blocking=True).float()\n            input_data = {'pts_input': inputs, 'gt_boxes3d': gt_boxes3d}\n        else:\n            input_data = {}\n            for key, val in data.items():\n                if key != 'sample_id':\n                    input_data[key] = torch.from_numpy(val).contiguous().cuda(non_blocking=True).float()\n            if not cfg.RCNN.ROI_SAMPLE_JIT:\n                pts_input = torch.cat((input_data['pts_input'], input_data['pts_features']), dim=-1)\n                input_data['pts_input'] = pts_input\n        input_data['is_source'] = data['is_source'] # To avoid using gt when sampling\n        ret_dict = model(input_data)\n        tb_dict = {}\n        disp_dict = {}\n        loss = 0\n\n        # Only caculate detection loss that is_source==True\n\n        if cfg.DA.ENABLED:\n            is_source = data['is_source']\n        else:\n            is_source = np.ones(ret_dict['backbone_xyz'].shape[0])\n        domain_mask = np.where(is_source == True)[0]  # B,\n\n        if cfg.RPN.ENABLED and not cfg.RPN.FIXED:\n            rpn_cls, rpn_reg = ret_dict['rpn_cls'], ret_dict['rpn_reg'] # B,N,1 B,N,76 (include source and target)\n\n            rpn_cls = rpn_cls[domain_mask, ...] # B,N,1 (include source only, B is change)\n            rpn_reg = rpn_reg[domain_mask, ...]\n            rpn_cls_label = rpn_cls_label[domain_mask, ...]\n            rpn_reg_label = rpn_reg_label[domain_mask, ...]\n            rpn_loss = get_rpn_loss(model, rpn_cls, rpn_reg, rpn_cls_label, rpn_reg_label, tb_dict)\n            loss += rpn_loss\n            disp_dict['rpn_loss'] = rpn_loss.item()\n\n        if cfg.RCNN.ENABLED:\n            n_roi = cfg.RCNN.ROI_PER_IMAGE\n            is_source_for_rois = np.repeat(data['is_source'][:,np.newaxis],n_roi,axis=1).reshape(-1)\n            domain_mask_for_rois = np.where(is_source_for_rois == True)[0]\n\n            for key in ret_dict.keys():\n                if key == 'da_ins': continue\n                bs = ret_dict[key].shape[0]\n                if bs == is_source.shape[0]:\n                    ret_dict[key] = ret_dict[key][domain_mask, ...]\n                else:\n                    ret_dict[key] = ret_dict[key][domain_mask_for_rois, ...]\n            rcnn_loss = get_rcnn_loss(model, ret_dict, tb_dict)\n            disp_dict['reg_fg_sum'] = tb_dict['rcnn_reg_fg']\n            loss += rcnn_loss\n\n\n        if cfg.DA.ENABLED:\n            if cfg.RPN.ENABLED and not cfg.RPN.FIXED and cfg.DA.DA_IMG.ENABLED:\n                da_img, is_source = ret_dict['da_img'], data['is_source']\n                da_rpn_loss = get_da_rpn_loss(da_img, is_source, tb_dict)\n                loss += da_rpn_loss * cfg.DA.DA_IMG.LOSS_WEIGHT # DA_IMG_LOSS_WEIGHT\n                disp_dict['da_rpn_loss'] = da_rpn_loss.item()*1.0 # DA_IMG_LOSS_WEIGHT\n                da_rpn_output = (ret_dict['da_img']>=0.0).squeeze()\n                disp_dict['da_rpn_acc'] = (da_rpn_output.eq(torch.FloatTensor(is_source).cuda())).float().mean().item()\n                tb_dict['da_rpn_acc'] = disp_dict['da_rpn_acc']\n            if cfg.RCNN.ENABLED and cfg.DA.DA_INS.ENABLED:\n                if cfg.DA.DA_INS.RESHAPE:\n                    is_source_for_rois = is_source\n                da_rcnn_loss, show_item = get_da_rcnn_loss(ret_dict['da_ins'], is_source_for_rois, tb_dict, show=True)\n                loss += da_rcnn_loss * cfg.DA.DA_INS.LOSS_WEIGHT  # DA_INS_LOSS_WEIGHT\n                disp_dict['da_rcnn_loss'] = da_rcnn_loss.item() * cfg.DA.DA_INS.LOSS_WEIGHT\n                da_rcnn_output = (ret_dict['da_ins']>=0.0).squeeze()\n                disp_dict['da_rcnn_acc'] = (da_rcnn_output.eq(torch.FloatTensor(is_source_for_rois).cuda())).float().mean().item()\n                tb_dict['da_rcnn_acc'] = disp_dict['da_rcnn_acc']\n                disp_dict['da_rcnn_num']=ret_dict['da_ins'].squeeze().detach().cpu()\n                disp_dict['da_rcnn_loss_list']=show_item\n\n\n            if cfg.DA.DA_CST.ENABLED:\n                pass\n        disp_dict['loss'] = loss.item()\n\n        return ModelReturn(loss, tb_dict, disp_dict)\n\n    def get_da_rpn_loss(da_img, is_source, tb_dict):\n        \"\"\"\n        :param da_img: B,1,N or B,1,1(with avg_pool) or B,1,1(with reshape)\n        :param is_source: B,\n        \"\"\"\n        da_img = da_img.squeeze() # B, N or B, 1\n        da_img_labels = torch.zeros(da_img.shape).cuda()\n        mask = np.where(is_source)[0]\n        da_img_labels[mask,...] = 1\n        da_rpn_loss = F.binary_cross_entropy(torch.sigmoid(da_img.view(-1)), da_img_labels.view(-1))\n        da_rpn_loss = da_rpn_loss\n        tb_dict['da_rpn_loss'] = da_rpn_loss.item()\n        tb_dict.update({'da_rpn_loss': da_rpn_loss.item()})\n        return da_rpn_loss\n\n    def get_da_rcnn_loss(da_ins, is_source_for_rois, tb_dict, show=False):\n        \"\"\"\n        :param da_ins:B*n_rois, 1, 1\n        :param is_source_for_rois: B*n_rois,\n        \"\"\"\n        da_ins = da_ins.squeeze()\n        da_ins_labels = torch.FloatTensor(is_source_for_rois).cuda()\n        da_rcnn_loss = F.binary_cross_entropy(torch.sigmoid(da_ins), da_ins_labels)\n        tb_dict['da_rcnn_loss'] = da_rcnn_loss.item()\n        tb_dict.update({'da_rcnn_loss': da_rcnn_loss.item()})\n        if show:\n            da_rcnn_loss_not_reduction = F.binary_cross_entropy(torch.sigmoid(da_ins), da_ins_labels,reduction='none')\n            return da_rcnn_loss, da_rcnn_loss_not_reduction.detach().cpu()\n        else:\n            return da_rcnn_loss\n\n    def get_rpn_loss(model, rpn_cls, rpn_reg, rpn_cls_label, rpn_reg_label, tb_dict):\n        if isinstance(model, nn.DataParallel):\n            rpn_cls_loss_func = model.module.rpn.rpn_cls_loss_func\n        else:\n            rpn_cls_loss_func = model.rpn.rpn_cls_loss_func\n\n        rpn_cls_label_flat = rpn_cls_label.view(-1)\n        rpn_cls_flat = rpn_cls.view(-1)\n        fg_mask = (rpn_cls_label_flat > 0)\n\n        # RPN classification loss\n        if cfg.RPN.LOSS_CLS == 'DiceLoss':\n            rpn_loss_cls = rpn_cls_loss_func(rpn_cls, rpn_cls_label_flat)\n\n        elif cfg.RPN.LOSS_CLS == 'SigmoidFocalLoss':\n            rpn_cls_target = (rpn_cls_label_flat > 0).float()\n            pos = (rpn_cls_label_flat > 0).float()\n            neg = (rpn_cls_label_flat == 0).float()\n            cls_weights = pos + neg\n            pos_normalizer = pos.sum()\n            cls_weights = cls_weights / torch.clamp(pos_normalizer, min=1.0)\n            rpn_loss_cls = rpn_cls_loss_func(rpn_cls_flat, rpn_cls_target, cls_weights)\n            rpn_loss_cls_pos = (rpn_loss_cls * pos).sum()\n            rpn_loss_cls_neg = (rpn_loss_cls * neg).sum()\n            rpn_loss_cls = rpn_loss_cls.sum()\n            tb_dict['rpn_loss_cls_pos'] = rpn_loss_cls_pos.item()\n            tb_dict['rpn_loss_cls_neg'] = rpn_loss_cls_neg.item()\n\n        elif cfg.RPN.LOSS_CLS == 'BinaryCrossEntropy':\n            weight = rpn_cls_flat.new(rpn_cls_flat.shape[0]).fill_(1.0)\n            weight[fg_mask] = cfg.RPN.FG_WEIGHT\n            rpn_cls_label_target = (rpn_cls_label_flat > 0).float()\n            batch_loss_cls = F.binary_cross_entropy(torch.sigmoid(rpn_cls_flat), rpn_cls_label_target,\n                                                    weight=weight, reduction='none')\n            cls_valid_mask = (rpn_cls_label_flat >= 0).float()\n            rpn_loss_cls = (batch_loss_cls * cls_valid_mask).sum() / torch.clamp(cls_valid_mask.sum(), min=1.0)\n        else:\n            raise NotImplementedError\n\n        # RPN regression loss\n        point_num = rpn_reg.size(0) * rpn_reg.size(1)\n        fg_sum = fg_mask.long().sum().item()\n        if fg_sum != 0:\n            loss_loc, loss_angle, loss_size, reg_loss_dict = \\\n                loss_utils.get_reg_loss(rpn_reg.view(point_num, -1)[fg_mask],\n                                        rpn_reg_label.view(point_num, 7)[fg_mask],\n                                        loc_scope=cfg.RPN.LOC_SCOPE,\n                                        loc_bin_size=cfg.RPN.LOC_BIN_SIZE,\n                                        num_head_bin=cfg.RPN.NUM_HEAD_BIN,\n                                        anchor_size=MEAN_SIZE,\n                                        get_xz_fine=cfg.RPN.LOC_XZ_FINE,\n                                        get_y_by_bin=False,\n                                        get_ry_fine=False)\n\n            loss_size = 3 * loss_size  # consistent with old codes\n            rpn_loss_reg = loss_loc + loss_angle + loss_size\n        else:\n            loss_loc = loss_angle = loss_size = rpn_loss_reg = rpn_loss_cls * 0\n\n        rpn_loss = rpn_loss_cls * cfg.RPN.LOSS_WEIGHT[0] + rpn_loss_reg * cfg.RPN.LOSS_WEIGHT[1]\n\n        tb_dict.update({'rpn_loss_cls': rpn_loss_cls.item(), 'rpn_loss_reg': rpn_loss_reg.item(),\n                        'rpn_loss': rpn_loss.item(), 'rpn_fg_sum': fg_sum, 'rpn_loss_loc': loss_loc.item(),\n                        'rpn_loss_angle': loss_angle.item(), 'rpn_loss_size': loss_size.item()})\n\n        return rpn_loss\n\n    def get_rcnn_loss(model, ret_dict, tb_dict):\n        rcnn_cls, rcnn_reg = ret_dict['rcnn_cls'], ret_dict['rcnn_reg']\n\n        cls_label = ret_dict['cls_label'].float()\n        reg_valid_mask = ret_dict['reg_valid_mask']\n        roi_boxes3d = ret_dict['roi_boxes3d']\n        roi_size = roi_boxes3d[:, 3:6]\n        gt_boxes3d_ct = ret_dict['gt_of_rois']\n        pts_input = ret_dict['pts_input']\n\n        # rcnn classification loss\n        if isinstance(model, nn.DataParallel):\n            cls_loss_func = model.module.rcnn_net.cls_loss_func\n        else:\n            cls_loss_func = model.rcnn_net.cls_loss_func\n\n        cls_label_flat = cls_label.view(-1)\n        if cfg.RCNN.LOSS_CLS == 'SigmoidFocalLoss':\n            rcnn_cls_flat = rcnn_cls.view(-1)\n\n            cls_target = (cls_label_flat > 0).float()\n            pos = (cls_label_flat > 0).float()\n            neg = (cls_label_flat == 0).float()\n            cls_weights = pos + neg\n            pos_normalizer = pos.sum()\n            cls_weights = cls_weights / torch.clamp(pos_normalizer, min=1.0)\n\n            rcnn_loss_cls = cls_loss_func(rcnn_cls_flat, cls_target, cls_weights)\n            rcnn_loss_cls_pos = (rcnn_loss_cls * pos).sum()\n            rcnn_loss_cls_neg = (rcnn_loss_cls * neg).sum()\n            rcnn_loss_cls = rcnn_loss_cls.sum()\n            tb_dict['rpn_loss_cls_pos'] = rcnn_loss_cls_pos.item()\n            tb_dict['rpn_loss_cls_neg'] = rcnn_loss_cls_neg.item()\n\n        elif cfg.RCNN.LOSS_CLS == 'BinaryCrossEntropy':\n            rcnn_cls_flat = rcnn_cls.view(-1)\n            batch_loss_cls = F.binary_cross_entropy(torch.sigmoid(rcnn_cls_flat), cls_label, reduction='none')\n            cls_valid_mask = (cls_label_flat >= 0).float()\n            rcnn_loss_cls = (batch_loss_cls * cls_valid_mask).sum() / torch.clamp(cls_valid_mask.sum(), min=1.0)\n\n        elif cfg.TRAIN.LOSS_CLS == 'CrossEntropy':\n            rcnn_cls_reshape = rcnn_cls.view(rcnn_cls.shape[0], -1)\n            cls_target = cls_label_flat.long()\n            cls_valid_mask = (cls_label_flat >= 0).float()\n\n            batch_loss_cls = cls_loss_func(rcnn_cls_reshape, cls_target)\n            normalizer = torch.clamp(cls_valid_mask.sum(), min=1.0)\n            rcnn_loss_cls = (batch_loss_cls.mean(dim=1) * cls_valid_mask).sum() / normalizer\n\n        else:\n            raise NotImplementedError\n\n        # rcnn regression loss\n        batch_size = pts_input.shape[0]\n        fg_mask = (reg_valid_mask > 0)\n        fg_sum = fg_mask.long().sum().item()\n        if fg_sum != 0:\n            all_anchor_size = roi_size\n            anchor_size = all_anchor_size[fg_mask] if cfg.RCNN.SIZE_RES_ON_ROI else MEAN_SIZE\n\n            loss_loc, loss_angle, loss_size, reg_loss_dict = \\\n                loss_utils.get_reg_loss(rcnn_reg.view(batch_size, -1)[fg_mask],\n                                        gt_boxes3d_ct.view(batch_size, 7)[fg_mask],\n                                        loc_scope=cfg.RCNN.LOC_SCOPE,\n                                        loc_bin_size=cfg.RCNN.LOC_BIN_SIZE,\n                                        num_head_bin=cfg.RCNN.NUM_HEAD_BIN,\n                                        anchor_size=anchor_size,\n                                        get_xz_fine=True, get_y_by_bin=cfg.RCNN.LOC_Y_BY_BIN,\n                                        loc_y_scope=cfg.RCNN.LOC_Y_SCOPE, loc_y_bin_size=cfg.RCNN.LOC_Y_BIN_SIZE,\n                                        get_ry_fine=True)\n\n            loss_size = 3 * loss_size  # consistent with old codes\n            rcnn_loss_reg = loss_loc + loss_angle + loss_size\n            tb_dict.update(reg_loss_dict)\n        else:\n            loss_loc = loss_angle = loss_size = rcnn_loss_reg = rcnn_loss_cls * 0\n\n        rcnn_loss = rcnn_loss_cls + rcnn_loss_reg\n        tb_dict['rcnn_loss_cls'] = rcnn_loss_cls.item()\n        tb_dict['rcnn_loss_reg'] = rcnn_loss_reg.item()\n        tb_dict['rcnn_loss'] = rcnn_loss.item()\n\n        tb_dict['rcnn_loss_loc'] = loss_loc.item()\n        tb_dict['rcnn_loss_angle'] = loss_angle.item()\n        tb_dict['rcnn_loss_size'] = loss_size.item()\n\n        tb_dict['rcnn_cls_fg'] = (cls_label > 0).sum().item()\n        tb_dict['rcnn_cls_bg'] = (cls_label == 0).sum().item()\n        tb_dict['rcnn_reg_fg'] = reg_valid_mask.sum().item()\n\n        return rcnn_loss\n\n    return model_fn\n","sub_path":"lib/net/train_functions_da.py","file_name":"train_functions_da.py","file_ext":"py","file_size_in_byte":14767,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"35937962","text":"\"\"\"\r\nPart 2: This is the simplest version of viterbi that doesn't do anything special for unseen words\r\nbut it should do better than the baseline at words with multiple tags (because now you're using context\r\nto predict the tag).\r\n\"\"\"\r\nimport math\r\n\r\ndef viterbi_1(train, test):\r\n    '''\r\n    input:  training data (list of sentences, with tags on the words)\r\n            test data (list of sentences, no tags on the words)\r\n    output: list of sentences with tags on the words\r\n            E.g., [[(word1, tag1), (word2, tag2)], [(word3, tag3), (word4, tag4)]]\r\n    '''\r\n\r\n    #\r\n    tag_list = []\r\n    # times of pre tag to this tag\r\n    tag_tag={}\r\n    #times of tag given word\r\n\r\n    tag_word={}\r\n    init = {}\r\n\r\n    #smooth factor\r\n    i_alpha = 0.001\r\n    alpha = 0.0001\r\n    e_alpha = 0.0001\r\n\r\n    for sentence in train:\r\n        pre = sentence[0]\r\n        length = len(sentence)\r\n        for i in range(length):\r\n            cur = sentence[i]\r\n            if cur[1] not in tag_list: tag_list.append(cur[1])\r\n            if i == 0:\r\n                if cur[1] in init:\r\n                    init[cur[1]] +=1\r\n                else:\r\n                    init[cur[1]]=1\r\n            else:\r\n                pre = sentence[i-1]\r\n            \r\n            # if not first then we have tag to tag\r\n                if pre[1] in tag_tag:\r\n                    if cur[1] in tag_tag[pre[1]]:\r\n                        tag_tag[pre[1]][cur[1]]+=1\r\n                    else:\r\n                        tag_tag[pre[1]][cur[1]]=1\r\n                else:\r\n                    tag_tag[pre[1]]={}\r\n                    tag_tag[pre[1]][cur[1]]=1\r\n\r\n            # cur word to tag\r\n            if cur[1] in tag_word:\r\n                if cur[0] in tag_word[cur[1]]:\r\n                    tag_word[cur[1]][cur[0]]+=1\r\n                else:\r\n                    tag_word[cur[1]][cur[0]]=1\r\n            else:\r\n                tag_word[cur[1]]={}\r\n                tag_word[cur[1]][cur[0]]=1\r\n            \r\n    \r\n    # initial probability and\r\n    \r\n    \r\n    P_i = {}\r\n    P_i_un = 0\r\n    i_n =0  # number and types\r\n    i_v =0\r\n   \r\n\r\n    for tag in init:\r\n        i_v+=1\r\n        i_n+=init[tag]\r\n    for tag in init:\r\n        P_i[tag] = math.log((init[tag]+i_alpha)/(i_n+i_alpha*(i_v+1)))\r\n    P_i_un = math.log(i_alpha/(i_n+i_alpha*(i_v+1)))\r\n\r\n    # transition probability  (pre,cur)\r\n    P_tag_tag = {}\r\n    P_tt_un = {}\r\n    tt_n=0  # number and types\r\n    tt_v=0\r\n\r\n    for pre in tag_tag:\r\n        tt_v=0\r\n        tt_n=0\r\n        for tag in tag_tag[pre]:\r\n            tt_v+=1\r\n            tt_n+= tag_tag[pre][tag]\r\n\r\n        #calculate probabilities\r\n    \r\n        for tag in tag_tag[pre]:\r\n            P_tag_tag[(pre,tag)] = math.log((tag_tag[pre][tag]+alpha)/(tt_n+alpha*(tt_v+1)))\r\n        #unkown case:\r\n        P_tt_un[pre]= math.log(alpha/(tt_n+alpha*(tt_v+1)))\r\n    P_t_un = 0\r\n\r\n    # emission probability  (cur_word,tag)\r\n    P_word_tag = {}\r\n    P_wt_un = {}\r\n    wt_n=0  # number and types\r\n    wt_v=0\r\n    for tag in tag_word:\r\n        wt_n=0\r\n        wt_v=0\r\n        for word in tag_word[tag]:\r\n            wt_v+=1\r\n            wt_n+= tag_word[tag][word]\r\n\r\n        for word in tag_word[tag]:\r\n            P_word_tag[(tag,word)] = math.log((tag_word[tag][word]+e_alpha)/(wt_n+e_alpha*(wt_v+1)))\r\n        P_wt_un[tag]= math.log(e_alpha/(wt_n+e_alpha*(wt_v+1)))\r\n\r\n\r\n\r\n    res=[]\r\n\r\n    test_time = 0\r\n\r\n\r\n    for sentence in test:\r\n        v = []\r\n        b = []\r\n        length = len(sentence)\r\n        for i in range(length):\r\n            word = sentence[i]\r\n            vk = {}\r\n            bk= {}\r\n\r\n            for tag_b in tag_list:    \r\n                if i==0:\r\n                    if tag_b in P_i:\r\n                        p1 = P_i[tag_b]\r\n                    else:\r\n                        p1 = P_i_un\r\n                    if (tag_b,word) in P_word_tag:\r\n                        p2 = P_word_tag[(tag_b,word)]\r\n                    else:\r\n                        p2 = P_wt_un[tag_b]\r\n                    vk[tag_b] = p1+p2\r\n                    bk[tag_b] = ''\r\n                else:\r\n                    maxa= 0\r\n                    max_p= -math.inf\r\n                    for tag_a in tag_list:\r\n                        if (tag_a,tag_b) in P_tag_tag:\r\n                            p2 = P_tag_tag[(tag_a,tag_b)]\r\n                        else:\r\n                            if tag_a not in tag_tag:\r\n                                p2=0\r\n                            else: p2 = P_tt_un[tag_a]\r\n                        if (tag_b,word) in P_word_tag:\r\n                            p3 = P_word_tag[(tag_b,word)]\r\n                        else:\r\n                            p3 = P_wt_un[tag_b]\r\n\r\n                        p = v[i-1][tag_a] + p2 + p3\r\n                        if p> max_p:\r\n                            max_p = p\r\n                            maxa = tag_a\r\n                    \r\n                    vk[tag_b] = max_p\r\n                    bk[tag_b] = maxa\r\n            v.append(vk)\r\n            b.append(bk)\r\n            \r\n            \r\n\r\n        k = length-1\r\n        new_sentence = []\r\n        arg_b=[]\r\n        #find last\r\n        maxa= 0\r\n        max_p= -math.inf\r\n        for tag in v[k]:\r\n            if v[k][tag]>max_p:\r\n                max_p = v[k][tag]\r\n                maxa = tag\r\n        arg_b.append(maxa)\r\n        \r\n        \r\n        while(not k<1):\r\n            maxa = b[k][maxa]\r\n            arg_b.append(maxa)\r\n            k-=1\r\n        \r\n        arg_b.reverse()\r\n        for i in range(length):\r\n            new_sentence.append((sentence[i],arg_b[i]))\r\n\r\n        res.append(new_sentence)\r\n\r\n        ###############test\r\n        # test_time+=1\r\n        # print(\"______________________\\nSENTENCE:    \\n\",sentence)\r\n        # print(\"______________________\\nOUT:    \\n\",new_sentence)\r\n        # print(\"______________________\\n\")\r\n        # print(\"t-t-un:\",P_tag_tag[('NOUN','PERIOD')],P_tag_tag[('START','X')],P_tt_un['X'],P_wt_un['X'])\r\n        # print('\\n')\r\n        # print(\"______________________\\n V:    \\n\")\r\n        # for c in v:\r\n        #     for k in c.items():\r\n        #         print(k[0],'[',k[1],']',' ',end='')\r\n        #     print(end='\\n\\n')\r\n\r\n        # print(\"_________________\\n bk:\",)\r\n        # for c in b:\r\n        #     for k in c.items():\r\n        #         print(k[0],'[',k[1],']',' ',end='')\r\n        #     print(end='\\n\\n')\r\n        \r\n        # print(P_wt_un)\r\n        # print(\"_____________________________________________________________________________\")\r\n        \r\n        \r\n        # if test_time>1:\r\n        #     break\r\n       \r\n        \r\n    \r\n    return res","sub_path":"mp4_code/viterbi_1.py","file_name":"viterbi_1.py","file_ext":"py","file_size_in_byte":6605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"422424585","text":"#!/usr/bin/python3\n\nimport csv\nimport numpy\nimport os\n\ndef is_equal(a, b):\n    if a == b:\n        return True\n    \nwith open(os.path.join(os.getcwd(), 'srednie_wyniki_egzaminu_maturalnego.csv')) as csvfile:\n    \n    results = []\n    readCSV = csv.reader(csvfile, delimiter=';')\n    \n    for row in readCSV:\n        results.append(float(row[7].replace(\",\", \".\")))\n    \n    mediana = numpy.median(results)\n    \n    print(\"Srodkowy wynik dla średnich wyników maturalnych to: \", mediana, \"%.\")\n    \nwith open(os.path.join(os.getcwd(), 'srednie_wyniki_egzaminu_maturalnego.csv')) as csvfile:\n    \n    readCSV1 = csv.reader(csvfile, delimiter=';')\n\n    for row1 in readCSV1:\n        if float(row1[7].replace(\",\", \".\")) == float(mediana):\n            print(float(row1[7].replace(\",\", \".\")))\n            scores = []\n            scores.append(row1[5])\n            scores.append(row1[3])\n            scores.append(row1[2])\n            print(\"W roku: \", scores[0], \", z przedmiotu: \", scores[1], \", poziom: \", scores[2])    \n    print(max(results))\n","sub_path":"warsztaty/warsztaty/script.py","file_name":"script.py","file_ext":"py","file_size_in_byte":1040,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"572227227","text":"import sqlalchemy as db\nimport pandas as pd\nfrom sqlalchemy import Table, Column, Integer, String, MetaData\n\nmeta = MetaData()\nengine = db.create_engine(\"sqlite:///thefile.db\")\nconnection = engine.connect()\nmetadata = db.MetaData()\n\nstudents = Table(\n   'students', meta, \n   Column('id', Integer, primary_key = True), \n   Column('firstname', String), \n   Column('lastname', String), \n   Column('age', Integer), \n   Column('height', Integer)\n)\nmeta.create_all(engine)\n\npartisipan = db.Table('students', metadata, autoload = True, autoload_with = engine)\n\n\ndef crud(lakukan, get_result):\n    if(lakukan == 1): #CREATE\n        namaD = input(\"Masukan Nama Depan: \")\n        namaB = input(\"Masukan Nama Belakang: \")\n        age = input(\"Masukan umur: \")\n        height = input(\"Masukan tinggi (cm)\")\n\n        valuest_list = [\n            {\n                \"firstname\": namaD,\n                \"lastname\": namaB,\n                \"age\": age,\n                \"height\": height\n            }\n        ]\n        tambah = db.insert(partisipan)\n        connection.execute(tambah, valuest_list)\n\n        query = db.select([partisipan])\n        result = connection.execute(query)\n        get_result = result.fetchall()\n\n        return get_result\n\n    elif(lakukan == 2): #READ\n        print(get_result)\n        pilihID = int(input(\"Input ID: \"))\n\n        query = db.select([partisipan]).where(partisipan.columns.id == pilihID)\n        result = connection.execute(query)\n        get_result = result.fetchall()\n        print(get_result)\n\n\n    elif(lakukan == 3): #UPDATE\n        print(get_result)\n        pilihNama = input(\"Pilih nama yang ingin diupdate: \")\n        print(\"Pilih field yang ingin diupdate:\")\n        print(\"1. Nama Depan\")\n        print(\"2. Nama Belakang\")\n        print(\"3. Age\")\n        print(\"4. Tinggi\")\n        pilihField = int(input(\"Pilih: \"))\n        if(pilihField == 1):\n            valueBaru = input(\"Input perubahan: \")\n            values = {\n                \"firstname\": valueBaru\n            }\n        elif(pilihField == 2):\n            valueBaru = input(\"Input perubahan: \")\n            values = {\n                \"lastname\": valueBaru\n            }\n        elif(pilihField == 3):\n            valueBaru = int(input(\"Input perubahan: \"))\n            values = {\n                \"age\": valueBaru\n            }\n        elif(pilihField == 4):\n            valueBaru = int(input(\"Input perubahan: \"))\n            values = {\n                \"height\": valueBaru\n            }\n        \n\n        # query = db.update(partisipan).values(fieldnya = valueBaru).where(partisipan.columns.firstname == pilihNama)\n        query = db.update(partisipan).values(**values).where(partisipan.columns.firstname == pilihNama)\n        hasil = connection.execute(query)\n\n        \n        print(\"Berhasil mengupdate\")\n        query = db.select([partisipan])\n        hasil = connection.execute(query)\n        get_result = hasil.fetchall()\n        print(get_result)\n\n\n    elif(lakukan == 4): #DELETE\n        print(get_result)\n        print(\"Pilih id yang mau dihapus: \")\n        aid = int(input())\n        \n        apus = db.delete(partisipan).where(partisipan.columns.id == aid)\n        hasil = connection.execute(apus)\n\n        query = db.select([partisipan])\n        hasil = connection.execute(query)\n        get_result = hasil.fetchall()\n\n        return get_result\n\n  \n    elif(lakukan == 0):\n\n        query = db.select([partisipan])\n        result = connection.execute(query)\n        get_result = result.fetchall()\n\n        return get_result\n\n\n#-----------------------------------------------------------------------------\nwhile(True):\n    print(\"1. Create\")\n    print(\"2. Read\")\n    print(\"3. Update\")\n    print(\"4. Delete\")\n    print(\"5. Exit\")\n    pilih = int(input(\"Pilih: \"))\n\n    hasil = crud(0, None)\n    df = pd.DataFrame(hasil, columns=['id', 'firstname', 'lastname', 'age', 'height'])\n    df.set_index('id', inplace = True)\n\n\n\n    if(pilih == 1):\n        hasil = crud(1, None)\n    elif(pilih == 2):\n        hasil = crud(2, hasil)\n        print(df)\n    elif(pilih == 3):\n        hasil = crud(3, hasil)\n        print(df)\n    elif(pilih == 4):\n        hasil = crud(4, hasil)\n        print(df)    \n    elif(pilih == 5):\n        break\n    else:\n        continue\n","sub_path":"Project/CRUD/crud_project.py","file_name":"crud_project.py","file_ext":"py","file_size_in_byte":4253,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"52199453","text":"import logging\nfrom time import sleep\n\nfrom interfaces.temperature import SensorNotReady\nfrom m_logger import MQTTLoggerHandler\nfrom .temperature import TemperatureHandler, TemperatureException, HighTemperatureException\nfrom .lights import LightsHandler\nfrom .fans import Fans\n\nlog = logging.getLogger(__name__)\nlog.addHandler(MQTTLoggerHandler())\n\n\nclass EventHandler:\n    is_paused = False\n    counter = 0\n\n    def __init__(self):\n        self.fans = Fans()\n        self.lights = LightsHandler()\n\n    def check(self):\n        # stupid, need to rewrite asap\n        if self.is_paused:\n            return\n\n        try:\n            TemperatureHandler().check()\n\n        except HighTemperatureException as e:\n            log.exception('{} {}'.format(type(e), e))\n\n            temp = max(TemperatureHandler().last_temp)\n            self.fans.adjust(temp)\n\n            try:\n                self.lights.cool() #could be nothing to cool\n\n            except Exception as e:\n                log.exception('{} {}'.format(type(e), e))\n                self.lights.off()\n\n        except TemperatureException as e:\n            log.warning('{} {}'.format(type(e), e))\n            sleep(5)\n\n        except SensorNotReady as e:\n            log.warning('{} {}'.format(type(e), e))\n            sleep(10)\n\n        except Exception as e:\n            log.exception('{} {}'.format(type(e), e))\n\n            self.lights.off()\n\n        else:\n            temp = max(TemperatureHandler().last_temp)\n            self.fans.adjust(temp)\n            self.lights.on()\n            # log.info(str(TemperatureHandler().last_temp))\n            # __class__.counter += 1\n            # log.info(__class__.counter)\n\n    def listen(self, *args):\n        # Handle all actions commands (could be telegram, google assistant, w/e\n        pass\n\n    def _bucket_pause(self):\n        \"\"\"Stop the bucket\"\"\"\n        self.__class__.is_paused = True\n        self.lights.off()\n        log.info('Bucket has been paused')\n        # self.fans.off()\n\n    def _bucket_run(self):\n        \"\"\"Start the bucket\"\"\"\n        self.__class__.is_paused = False\n        log.info('Continue usual flow')\n\n    def warm_up(self):\n        # prepearing all sensors aka initial check\n        a = TemperatureHandler().get_temp()\n        pass #TemperatureHandler().get_temp()\n","sub_path":"bucket/handlers/event.py","file_name":"event.py","file_ext":"py","file_size_in_byte":2299,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"390860611","text":"import json\r\n\r\nimport os\r\nimport requests\r\nfrom cloudshell.core.logger.qs_logger import get_qs_logger\r\nfrom cloudshell.helpers.scripts import cloudshell_scripts_helpers as helpers\r\n\r\nrc = json.loads(os.environ['RESOURCECONTEXT'])\r\ndestdomain = rc['attributes']['Support Domain']\r\n\r\napi = helpers.get_api_session()\r\nresid = helpers.get_reservation_context_details().id\r\n\r\nlogger = get_qs_logger(log_group=resid, log_file_prefix='BlueprintQuarantine')\r\n\r\nurlbase = 'http://%s:82' % helpers.get_connectivity_context_details().server_address\r\n\r\ntoken = requests.put('%s/api/login' % urlbase,\r\n                     headers={\r\n                         'Content-Type': 'application/json',\r\n                     },\r\n                     data=json.dumps({\r\n                         'username': helpers.get_connectivity_context_details().admin_user,\r\n                         'password': helpers.get_connectivity_context_details().admin_pass,\r\n                         'domain': helpers.get_reservation_context_details().domain\r\n                     })).text.replace('\"', '')\r\n\r\nactivity = json.loads(requests.get('%s/api/v2/sandboxes/%s/activity?error_only=true' % (urlbase, resid)).text)\r\n\r\nrd = api.GetReservationDetails(resid).ReservationDescription\r\n\r\nfound = False\r\nfor ev in activity['events']:\r\n    if ev['event_type'] == 'error':\r\n        if \"'Setup'\" in ev['event_text'] or \"'Setup script'\" in ev['event_text']:\r\n            found = True\r\n            break\r\n\r\nif found:\r\n    olddomain = rd.DomainName\r\n    if olddomain != destdomain:\r\n        api.AddTopologiesToDomain(destdomain, rd.Topologies)\r\n        api.RemoveTopologiesFromDomain(olddomain, rd.Topologies)\r\n\r\n    olddomains = olddomain\r\n    for s in rd.Services:\r\n        for command in api.GetServiceCommands(s.ServiceName).Commands:\r\n            if 'quarantine_handler' in command.Name.lower():\r\n                for topo in rd.Topologies:\r\n                    errdet = json.dumps(activity, indent=4, separators=(',', ': '))\r\n                    m = 'Executing %s.%s(%s, BLUEPRINT, %s, %s, %s)' % (\r\n                        s.Alias,\r\n                        command.Name,\r\n\r\n                        topo,\r\n                        errdet,\r\n                        olddomains,\r\n                        destdomain,\r\n                    )\r\n                    logger.info(m)\r\n                    api.WriteMessageToReservationOutput(resid, m)\r\n                    o = api.ExecuteServiceCommand(resid, s.Alias, command.Name, [\r\n                        topo,\r\n                        'BLUEPRINT',\r\n                        errdet,\r\n                        olddomains,\r\n                        destdomain,\r\n                    ]).Output\r\n                    logger.info('Output: %s' % o)\r\n                    api.WriteMessageToReservationOutput(resid, 'Output: %s' % o)\r\n","sub_path":"Jira Main Package/Resource Scripts/quarantine_blueprint_orch_hook_post_teardown/__main__.py","file_name":"__main__.py","file_ext":"py","file_size_in_byte":2819,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"534276722","text":"import math\n\nclass Distance(object):\n    def distance(self, x1, y1, x2, y2):\n        return math.sqrt(((x2 - x1) ^ 2 + (y2 - y1) ^ 2))\n\n    def scoring(self, total_turns, current_turns):\n        turn_difference = total_turns - current_turns\n        score = (turn_difference/total_turns)*100\n        score_round = math.ceil(score)\n        return score_round\n\n    def stock_check(self, warehouses, product_id):\n        return filter(\n            lambda x: x[1].stock.get(product_id) > 0,\n            zip(warehouses.keys(), warehouses['warehouses'])\n        )\n\n    def list_of_can_post(self, warehouse, list_of_orders):\n        order_list = []\n        for i in list_of_orders:\n            b = self.can_post(self, warehouse, i)\n            if b:\n                order_list.append(i)\n\n    def can_post(self, warehouse, order):\n        for key in order.products.iterkeys():\n            is_there = False\n            for ware_key in warehouse.stock.iterkeys():\n                if key == ware_key:\n                    is_there = True\n            if not is_there:\n                return False\n        return True\n\n    def calc_turns(self, x1, y1, x2, y2, order_weight, drone_weight):\n        if order_weight<drone_weight:\n            return self.distance(x1, y1, x2, y2)\n        else:\n            turns = order_weight/drone_weight\n            turns_round = math.ceil(turns)\n            return turns_round*2*self.distance(x1, y1, x2, y2)\n\n    def payoff_per_order(self, x1, y1, x2, y2, total_turns, current_turn, order_weight, drone_weight):\n        distance = self.calc_turns(x1, y1, x2, y2, order_weight, drone_weight)\n        score = self.scoring(total_turns, (current_turn+distance))\n        score_maximise = score/order_weight\n        return score_maximise\n\n    def order_weight_calc(self, order):\n        weight = 0\n        item = order_weight*quantity\n        weight += item\n\n        return weight\n\n    def order_to_payoff(self, warehouse, list_of_orders, total_turns, current_turn):\n        for i in list_of_orders:\n            payoff = self.payoff_per_order(warehouse.x, warehouse.y, i.x, i.y, total_turns, current_turn)\n        pass","sub_path":"src/distance.py","file_name":"distance.py","file_ext":"py","file_size_in_byte":2131,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"178095015","text":"import requests\nimport sqlite3 as sql\nimport sys\n\ndef main():\n\n    try:\n        with sql.connect(\"data/test.db\") as con:\n            curs = con.cursor()\n            # inserted = con.execute(\"SELECT * FROM User WHERE Username = (?)\", (users[i][0],))\n            # for row in inserted:\n            #     if row[1] == users[i][0]:  print(\"Found \" +  users[i][0])\n\n            deleted = con.execute(\"DELETE FROM User WHERE UID = (?)\", (sys.argv[1],))\n            print(\"Deleted: \" + str(deleted.rowcount) + \" entries.\")\n            con.commit()\n\n    except Exception as e:\n         con.rollback()\n         print(e)\n\n    finally:\n        con.close()\n\nif __name__ == '__main__':\n    main()\n","sub_path":"deleteUser.py","file_name":"deleteUser.py","file_ext":"py","file_size_in_byte":684,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"485877732","text":"import numpy as np\nfrom bokeh.models import ColumnDataSource\nfrom bokeh.plotting import figure\n\nfrom postreise.analyze.transmission.upgrades import (\n    get_branch_differences,\n    get_dcline_differences,\n)\nfrom postreise.plot import colors\nfrom postreise.plot.plot_states import get_state_borders\nfrom postreise.plot.projection_helpers import project_borders, project_branch\n\n\ndef _map_upgrades(\n    branch_merge,\n    dc_merge,\n    state_shapes,\n    b2b_indices,\n    diff_threshold=100,\n    all_branch_scale=1e-3,\n    diff_branch_scale=1e-3,\n    all_branch_min=0.1,\n    diff_branch_min=1.0,\n    b2b_scale=5e-3,\n):\n    \"\"\"Makes map of branches showing upgrades\n\n    :param pandas.DataFrame branch_merge: branch of scenarios 1 and 2\n    :param pandas.DataFrame dc_merge: dclines for scenarios 1 and 2\n    :param dict state_shapes: dictionary of state outlines. Keys are state\n        abbreviations, values are dict with keys of {\"lat\", \"lon\"}, values are\n        coordinates, padded by nan values to indicate the end of each polygon before\n        the start of the next one. Can be created from shapefile via\n        :func:postreise.plot.projection_helpers.`convert_shapefile_to_latlon_dict`.\n    :param iterable b2b_indices: list/set/tuple of 'DC lines' which are back-to-backs.\n    :param int/float diff_threshold: difference threshold (in MW), above which branches\n        are highlighted.\n    :param int/float all_branch_scale: scale factor for plotting all branches.\n    :param int/float diff_branch_scale: scale factor for plotting branches with\n        differences above the threshold.\n    :param int/float all_branch_min: minimum width to plot all branches.\n    :param int/float diff_branch_min: minimum width to plot branches with significant\n        differences.\n    :param int/float b2b_scale: scale factor for plotting b2b facilities.\n    :return: (*bokeh.plotting.figure.Figure*) -- Bokeh map plot of color-coded upgrades.\n    \"\"\"\n    # plotting constants\n    bokeh_tools = \"pan,wheel_zoom,reset,save\"\n    legend_alpha = 0.9\n    all_elements_alpha = 0.5\n    differences_alpha = 0.8\n\n    # data prep\n    state_xs, state_ys = project_borders(state_shapes)\n    branch_all = project_branch(branch_merge)\n    branch_dc = project_branch(dc_merge)\n\n    # For these, we will plot a triangle for the B2B location, plus 'pseudo' AC lines\n    # get_level_values allows us to index into MultiIndex as necessary\n    b2b_mask = branch_dc.index.get_level_values(0).isin(b2b_indices)\n    # .copy() avoids a pandas SettingWithCopyError later\n    b2b = branch_dc.iloc[b2b_mask].copy()\n    branch_dc_lines = branch_dc.loc[~b2b_mask].copy()\n\n    # Color branches based on upgraded capacity\n    branch_all[\"color\"] = np.nan\n    branch_all.loc[branch_all[\"diff\"] > diff_threshold, \"color\"] = colors.be_blue\n    branch_all.loc[branch_all[\"diff\"] < -1 * diff_threshold, \"color\"] = colors.be_purple\n    # Color pseudo AC branches based on upgraded capacity\n    b2b[\"color\"] = np.nan\n    b2b.loc[b2b[\"diff\"] > diff_threshold, \"color\"] = colors.be_blue\n    b2b.loc[b2b[\"diff\"] < -1 * diff_threshold, \"color\"] = colors.be_purple\n    b2b = b2b[~b2b.color.isnull()]\n    # Color DC lines based on upgraded capacity\n    branch_dc_lines.loc[:, \"color\"] = np.nan\n    branch_dc_lines.loc[branch_dc_lines[\"diff\"] > 0, \"color\"] = colors.be_green\n    branch_dc_lines.loc[branch_dc_lines[\"diff\"] < 0, \"color\"] = colors.be_lightblue\n\n    # Create ColumnDataSources for bokeh to plot with\n    source_all_ac = ColumnDataSource(\n        {\n            \"xs\": branch_all[[\"from_x\", \"to_x\"]].values.tolist(),\n            \"ys\": branch_all[[\"from_y\", \"to_y\"]].values.tolist(),\n            \"cap\": branch_all[\"rateA\"] * all_branch_scale + all_branch_min,\n            \"color\": branch_all[\"color\"],\n        }\n    )\n    # AC branches with significant differences\n    ac_diff_branches = branch_all.loc[~branch_all.color.isnull()]\n    source_ac_difference = ColumnDataSource(\n        {\n            \"xs\": ac_diff_branches[[\"from_x\", \"to_x\"]].values.tolist(),\n            \"ys\": ac_diff_branches[[\"from_y\", \"to_y\"]].values.tolist(),\n            \"diff\": (\n                ac_diff_branches[\"diff\"].abs() * diff_branch_scale + diff_branch_min\n            ),\n            \"color\": ac_diff_branches[\"color\"],\n        }\n    )\n    source_all_dc = ColumnDataSource(\n        {\n            \"xs\": branch_dc_lines[[\"from_x\", \"to_x\"]].values.tolist(),\n            \"ys\": branch_dc_lines[[\"from_y\", \"to_y\"]].values.tolist(),\n            \"cap\": branch_dc_lines.Pmax * all_branch_scale + all_branch_min,\n            \"color\": branch_dc_lines[\"color\"],\n        }\n    )\n    dc_diff_lines = branch_dc_lines.loc[~branch_dc_lines.color.isnull()]\n    source_dc_differences = ColumnDataSource(\n        {\n            \"xs\": dc_diff_lines[[\"from_x\", \"to_x\"]].values.tolist(),\n            \"ys\": dc_diff_lines[[\"from_y\", \"to_y\"]].values.tolist(),\n            \"diff\": dc_diff_lines[\"diff\"].abs() * diff_branch_scale + diff_branch_min,\n            \"color\": dc_diff_lines[\"color\"],\n        }\n    )\n    source_pseudoac = ColumnDataSource(  # pseudo ac scen 1\n        {\n            \"xs\": b2b[[\"from_x\", \"to_x\"]].values.tolist(),\n            \"ys\": b2b[[\"from_y\", \"to_y\"]].values.tolist(),\n            \"cap\": b2b.Pmax * all_branch_scale + all_branch_min,\n            \"diff\": b2b[\"diff\"].abs() * diff_branch_scale + diff_branch_min,\n            \"color\": b2b[\"color\"],\n        }\n    )\n\n    # Set up figure\n    p = figure(\n        tools=bokeh_tools,\n        x_axis_location=None,\n        y_axis_location=None,\n        plot_width=1400,\n        plot_height=800,\n        output_backend=\"webgl\",\n        match_aspect=True,\n    )\n    p.xgrid.visible = False\n    p.ygrid.visible = False\n\n    # Build the legend\n    leg_x = [-8.1e6] * 2\n    leg_y = [5.2e6] * 2\n\n    # These are 'dummy' series to populate the legend with\n    if len(branch_dc_lines[branch_dc_lines[\"diff\"] > 0]) > 0:\n        p.multi_line(\n            leg_x,\n            leg_y,\n            color=colors.be_green,\n            alpha=legend_alpha,\n            line_width=10,\n            legend_label=\"Additional HVDC Capacity\",\n        )\n    if len(branch_dc_lines[branch_dc_lines[\"diff\"] < 0]) > 0:\n        p.multi_line(\n            leg_x,\n            leg_y,\n            color=colors.be_lightblue,\n            alpha=legend_alpha,\n            line_width=10,\n            legend_label=\"Reduced HVDC Capacity\",\n        )\n    if len(branch_all[branch_all[\"diff\"] < 0]) > 0:\n        p.multi_line(\n            leg_x,\n            leg_y,\n            color=colors.be_purple,\n            alpha=legend_alpha,\n            line_width=10,\n            legend_label=\"Reduced AC Transmission\",\n        )\n    if len(branch_all[branch_all[\"diff\"] > 0]) > 0:\n        p.multi_line(\n            leg_x,\n            leg_y,\n            color=colors.be_blue,\n            alpha=legend_alpha,\n            line_width=10,\n            legend_label=\"Upgraded AC transmission\",\n        )\n    if len(b2b[b2b[\"diff\"] > 0]) > 0:\n        p.scatter(\n            x=b2b.from_x[1],\n            y=b2b.from_y[1],\n            color=colors.be_magenta,\n            marker=\"triangle\",\n            legend_label=\"Upgraded B2B capacity\",\n            size=30,\n            alpha=legend_alpha,\n        )\n    p.legend.location = \"bottom_left\"\n    p.legend.label_text_font_size = \"20pt\"\n\n    # Everything below gets plotted into the 'main' figure\n    # state outlines\n    p.patches(\n        \"xs\",\n        \"ys\",\n        source=ColumnDataSource({\"xs\": state_xs, \"ys\": state_ys}),\n        fill_color=\"white\",\n        line_color=\"slategrey\",\n        line_width=1,\n        fill_alpha=1,\n    )\n\n    background_plot_dicts = [\n        {\"source\": source_all_ac, \"color\": \"gray\", \"line_width\": \"cap\"},\n        {\"source\": source_all_dc, \"color\": \"gray\", \"line_width\": \"cap\"},\n        {\"source\": source_pseudoac, \"color\": \"gray\", \"line_width\": \"cap\"},\n    ]\n    for d in background_plot_dicts:\n        p.multi_line(\n            \"xs\",\n            \"ys\",\n            color=d[\"color\"],\n            line_width=d[\"line_width\"],\n            source=d[\"source\"],\n            alpha=all_elements_alpha,\n        )\n\n    # all B2Bs\n    p.scatter(\n        x=b2b.from_x,\n        y=b2b.from_y,\n        color=\"gray\",\n        marker=\"triangle\",\n        size=b2b[\"Pmax\"].abs() * b2b_scale,\n        alpha=all_elements_alpha,\n    )\n\n    difference_plot_dicts = [\n        {\"source\": source_pseudoac, \"color\": \"color\", \"line_width\": \"diff\"},\n        {\"source\": source_ac_difference, \"color\": \"color\", \"line_width\": \"diff\"},\n        {\"source\": source_dc_differences, \"color\": \"color\", \"line_width\": \"diff\"},\n    ]\n\n    for d in difference_plot_dicts:\n        p.multi_line(\n            \"xs\",\n            \"ys\",\n            color=d[\"color\"],\n            line_width=d[\"line_width\"],\n            source=d[\"source\"],\n            alpha=differences_alpha,\n        )\n\n    # B2Bs with differences\n    p.scatter(\n        x=b2b.from_x,\n        y=b2b.from_y,\n        color=colors.be_magenta,\n        marker=\"triangle\",\n        size=b2b[\"diff\"].abs() * b2b_scale,\n    )\n\n    return p\n\n\ndef map_upgrades(scenario1, scenario2, **plot_kwargs):\n    \"\"\"Plot capacity differences for branches & DC lines between two scenarios.\n\n    :param powersimdata.scenario.scenario.Scenario scenario1: first scenario.\n    :param powersimdata.scenario.scenario.Scenario scenario2: second scenario.\n    :param \\\\*\\\\*plot_kwargs: collected keyword arguments to be passed to _map_upgrades.\n    :return: (*bokeh.plotting.figure.Figure*) -- Bokeh map plot of color-coded upgrades.\n    \"\"\"\n    if not {scenario1.info[\"interconnect\"], scenario2.info[\"interconnect\"]} == {\"USA\"}:\n        raise ValueError(\"Scenarios to compare must be USA\")\n    grid1 = scenario1.state.get_grid()\n    grid2 = scenario2.state.get_grid()\n    branch_merge = get_branch_differences(grid1.branch, grid2.branch)\n    dc_merge = get_dcline_differences(grid1.dcline, grid2.dcline, grid1.bus)\n    state_shapes = get_state_borders()\n    # Since we hardcode to USA only, we know that the first 9 DC Lines are really B2Bs\n    b2b_indices = list(range(9))\n    map_plot = _map_upgrades(\n        branch_merge, dc_merge, state_shapes, b2b_indices, **plot_kwargs\n    )\n    return map_plot\n","sub_path":"postreise/plot/plot_upgrades_map.py","file_name":"plot_upgrades_map.py","file_ext":"py","file_size_in_byte":10189,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"441079755","text":"# Write a Python program to iterate over dictionaries using for loops.\n\ndef main():\n    dict1={1: 10, 2: 20, 3: 30, 4: 40, 5: 50, 6: 60}\n\n    for key,value in dict1.items():\n        print(key,'=>',value)\n\nif __name__ == '__main__':\n    main()","sub_path":"data_types/data_type_35.py","file_name":"data_type_35.py","file_ext":"py","file_size_in_byte":242,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"511490043","text":"from os import urandom\nfrom random import seed, shuffle\nfrom typing import Iterable, Union\n\ndef xor(a: bytes, b: bytes) -> bytes:\n    \"\"\" Xor byte a byte entre a e b \"\"\"\n    return bytes(x^y for x, y in zip(a, b))\n\ndef prg(tam:int, key:bytes) -> bytes:\n    def ksa(key:bytes) -> list:\n        S = [i for i in range(256)]\n        j = 0\n        for i in range(256):\n            j = (j + S[i] + key[i % len(key)]) % 256\n            S[i], S[j] = S[j], S[i]\n        return S\n    S = ksa(key)\n    j = 0\n    keystream = bytes()\n    for i in range(tam):\n        i = i % 256\n        j = (j + S[i]) % 256\n        S[i], S[j] = S[j], S[i]\n        keystream += bytes([S[(S[i] + S[j]) % 256]])\n    return keystream\n\ndef iv(tam: int) -> bytes:\n    return urandom(tam)\n\ndef divide(mensagem: bytes, tam_bloco: int, encripta: bool = True) -> Iterable[bytes]:\n    \"\"\" Retorna um Generator para blocos da 'mensagem' de tamanho 'tam_bloco' \"\"\"\n    tam_ultimo_bloco = len(mensagem)%tam_bloco\n    if encripta:\n        mensagem += bytes([tam_bloco-tam_ultimo_bloco]*(tam_bloco-tam_ultimo_bloco))\n    qtd_blocos = len(mensagem)//tam_bloco\n    blocos = (mensagem[bloco*tam_bloco:(bloco+1)*tam_bloco] for bloco in range(qtd_blocos))\n    return blocos\n\ndef f(entrada: bytes, chave: bytes) -> bytes:\n    ############# confusion #############\n    saida = list(xor(entrada, chave))\n    #####################################\n\n    ############# diffusion #############\n    soma = sum(byte for byte in chave)%256\n    seed(soma)\n    shuffle(saida)\n    #####################################\n\n    return bytes(saida)\n\ndef feistel_network(bloco: bytes, chaves: list) -> bytes:\n    left = bloco[:len(bloco)//2]\n    right = bloco[len(bloco)//2:]\n    for chave in chaves:\n        next_right = xor(left, f(right, chave))\n        left = right\n        right = next_right\n    return (right + left)\n\ndef encripta_bloco(plainblock: bytes, iv: bytes, chaves: list) -> bytes:\n    entrada = xor(plainblock, iv)\n    cipherblock = feistel_network(entrada, chaves)\n    return cipherblock\n\ndef decripta_bloco(cipherblock: bytes, iv: bytes, chaves: list) -> bytes:\n    saida = feistel_network(cipherblock, chaves)\n    plainblock = xor(saida, iv)\n    return plainblock\n\ndef encripta(plaintext: str, chave: bytes, tam_bloco: int, qtd_rounds: int) -> bytes:\n    if tam_bloco%2 != 0:\n        raise ValueError('Tamanho do bloco deve ser par')\n    plaintext = bytes(plaintext, 'utf-8')\n    blocos = divide(plaintext, tam_bloco)\n    iv = urandom(tam_bloco)\n    tam_bloco //= 2\n    # ignorando os 4 primeiros bytes do rc4\n    keystream = prg(qtd_rounds*tam_bloco+4, chave)[4:]\n    chaves = [keystream[round_*tam_bloco:(round_+1)*tam_bloco] for round_ in range(qtd_rounds)]\n    ciphertext = iv\n    for bloco in blocos:\n        cipherblock = encripta_bloco(bloco, iv, chaves)\n        iv = xor(bloco, cipherblock)\n        ciphertext += cipherblock\n    return ciphertext\n\ndef decripta(ciphertext: bytes, chave: bytes, tam_bloco: int, qtd_rounds: int) -> Union[str, bytes]:\n    if tam_bloco%2 != 0:\n        raise ValueError('Tamanho do bloco deve ser par')\n    blocos = divide(ciphertext, tam_bloco, False)\n    iv = next(blocos)\n    tam_bloco //= 2\n    # ignorando os 4 primeiros bytes do rc4\n    keystream = prg(qtd_rounds*tam_bloco+4, chave)[4:]\n    chaves = [keystream[round_*tam_bloco:(round_+1)*tam_bloco] for round_ in range(qtd_rounds)][-1::-1]\n    plaintext = bytes()\n    for bloco in blocos:\n        plainblock = decripta_bloco(bloco, iv, chaves)\n        iv = xor(bloco, plainblock)\n        plaintext += plainblock\n    n = plaintext[-1]\n    end = -1*n\n    try:\n        return plaintext[:end].decode('utf-8')\n    except UnicodeDecodeError:\n        return plaintext[:end]\n\n#cipher = encripta('From:Lucas\\nTo:Lucas\\nContent:criptografia de blocos', b'arroz', 10, 10)\n#print(cipher)\n\n###### ciphertext malandramente interceptado ######\n#iv = bytearray(cipher[:10])\n#iv[5:] = xor(iv[5:], xor(b'Lucas', b'Mario'))\n#cipher = bytes(iv) + cipher[10:]\n###################################################\n\n#print()\n#plain = decripta(cipher, b'arroz', 10, 10)\n#print(plain)\n","sub_path":"Aulas/Frentes/Criptografia/Block-ciphers/pcbc.py","file_name":"pcbc.py","file_ext":"py","file_size_in_byte":4103,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"645727481","text":"import requests\nfrom bs4 import BeautifulSoup\n\n\n\nURL = 'https://www.bbc.co.uk/news/topics/c9qdqqkgz27t/ftse-100'\n\npage = requests.get(URL)\n\nsoup = BeautifulSoup(page.content,'html.parser')\nprice =  soup.find('div', attrs={'class':'gel-paragon nw-c-md-market-summary__value'})\n\n\nprint(price.text)\n","sub_path":"FTSE100.py","file_name":"FTSE100.py","file_ext":"py","file_size_in_byte":296,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"496695012","text":"from django.shortcuts import render\nfrom rest_framework import generics, permissions\nfrom rest_framework.response import Response\nfrom rest_framework.authtoken.models import Token\nfrom knox.models import AuthToken as Token\nfrom .serializers import UserSerializer, RegisterSerializer, LoginSerializer\nfrom records.serializers import ListSerializer, WordSerializer\n\n# Register API\nclass RegisterAPI(generics.GenericAPIView):\n  serializer_class = RegisterSerializer\n  def post(self, request, *args, **kwargs):\n    serializer = self.get_serializer(data=request.data)\n    serializer.is_valid(raise_exception=True)\n    user = serializer.save()\n    _,token = Token.objects.create(user=user)\n    return Response({\n      \"user\": UserSerializer(user, context=self.get_serializer_context()).data,\n      \"token\": f\"{token}\",\n      \"lists\":[],\n      \"words\": []\n    })\n\n# Login API\nclass LoginAPI(generics.GenericAPIView):\n  serializer_class = LoginSerializer\n\n  def post(self, request, *args, **kwargs):\n    serializer = self.get_serializer(data=request.data)\n    serializer.is_valid(raise_exception=True)\n    user = serializer.validated_data\n    _,token = Token.objects.create(user=user)\n    lists = ListSerializer(user.lists.all(), many=True).data # if len(user_lists) else []\n    words = WordSerializer(user.words.all(), many=True).data\n    # words = WordSerializer(user.words.all()[0:10], many=True).data\n    return Response({\n      \"user\": UserSerializer(user, context=self.get_serializer_context()).data,\n      \"token\": token,\n      \"lists\": lists,\n      \"words\": words\n    })\n\n\n# Get User API\nclass UserAPI(generics.RetrieveAPIView):\n    permission_classes = [\n      permissions.IsAuthenticated,\n      ]\n    serializer_class = UserSerializer\n\n    def get_object(self):\n        return self.request.user\n    def get(self, request, *args, **kwargs):\n        user = self.request.user\n        lists = ListSerializer(user.lists.all(), many=True).data\n        #words = WordSerializer(user.words.all()[0:10], many=True).data\n        words = WordSerializer(user.words.all(), many=True).data\n        return Response({\n          'user': UserSerializer(user, context=self.get_serializer_context()).data,\n          \"lists\": lists,\n          \"words\": words\n        })","sub_path":"accounts/views.py","file_name":"views.py","file_ext":"py","file_size_in_byte":2248,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"288362441","text":"import socket\nsock = socket.socket(socket.AF_INET, socket.SOCK_STREAM)\nsock.setsockopt(socket.SOL_SOCKET,socket.SO_REUSEADDR,1)\nsock.connect(('127.0.0.1',8888))\ntemp = 1\nprint(sock.recv(1024).decode())\n\nwhile 1:\n    sock.send(input().encode())\n    data = sock.recv(1024).decode()\n    if(\"Game Lost\" in data):\n        print(\"Game Completed.\")\n        break\n    if(\"Game won!!\" in data):\n        print(\"Game Completed.\")\n        break\n    print(data)\nprint(data)\n","sub_path":"project_2/client.py","file_name":"client.py","file_ext":"py","file_size_in_byte":461,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"131052776","text":"from urllib import request\nimport psutil\nimport base64\nfrom bs4 import BeautifulSoup\nclass T1(object):\n    def __init__(self):\n        self.EE=55\n\n    def SS(self):\n        print(\"this is class T1,function SS \\n\")\n\n    def Start(self):\n        print(\"this  start ******\\n\")\n        with request.urlopen('https://www.baidu.com/') as f:\n             data = f.read()\n             print('Status:', f.status, f.reason)\n             # for k, v in f.getheaders():\n             #     print('%s: %s' % (k, v))\n             # print('Data:', data.decode('utf-8'))\n\n    def St(self):\n        x=psutil.virtual_memory()\n        print(x)\n        y=psutil.pids()\n        print(y)\n        z=psutil.net_connections()\n        print(z)\n    def KK(self):\n        print(\"this  start ******\\n\")\n        sss=''\n        with request.urlopen('https://www.88dus.com/xiaoshuo/18/18796/3408830.html') as f:\n             data = f.read()\n             print('Status:', f.status, f.reason)\n            # for k, v in f.getheaders():\n            #     print('%s: %s' % (k, v))\n             ss=data.decode('gbk')\n             # print('Data:', ss)\n             soup = BeautifulSoup(ss)\n             movie_list_soup = soup.find('div', attrs={'class': 'yd_text2'})\n             # print(movie_list_soup)\n\n             for i in movie_list_soup:\n                 if  i.string !=None :\n                   print(\">>>>\",i.string,\"<<<\")\n                   sss+=i.string\n\n\n             fo = open(\"f11.txt\", \"w\")\n             fo.write(sss)\n\n                # Close opend file\n             fo.close()\n\n","sub_path":"pyp/ut/ss/t1.py","file_name":"t1.py","file_ext":"py","file_size_in_byte":1553,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"450855209","text":"import turtle\nimport math\n\nbg = turtle.Screen()      \nbg.bgcolor(\"light green\")\n\nrua = turtle.Turtle()\nrua.speed(-1)\nrua.color(\"hotpink\")\nrua.pensize(3)\n\n\ndef square(r,size):\n    for i in range(4):\n        r.forward(size)\n        r.left(90)\n        \nsize = 20\nfor i in range (5):\n    square(rua,size)\n    size = size+20\n    rua.rt(135)\n    rua.penup()\n    rua.fd(math.sqrt(800)/2)\n    rua.lt(135)\n    rua.pendown()\n    \n","sub_path":"Assignment 4/4_9 (2).py","file_name":"4_9 (2).py","file_ext":"py","file_size_in_byte":420,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"283538296","text":"# coding=utf-8\r\nfrom os.path import join, dirname\r\nfrom django.conf import settings\r\n\r\nTEMPLATE_CONTEXT_PROCESSORS = settings.TEMPLATE_CONTEXT_PROCESSORS\r\nSTATICFILES_FINDERS = settings.STATICFILES_FINDERS\r\nINSTALLED_APPS = settings.INSTALLED_APPS\r\nSTATIC_URL = settings.STATIC_URL\r\nimport socket\r\n\r\nDOMINIO = 'http://localhost:8000'\r\nNOME_SITE = u'Local Host'\r\n\r\nif socket.gethostname() == 'meuhost':\r\n    from local_settings import *\r\nelse:\r\n    if socket.gethostname() != 'meuhost':\r\n        from production_settings import *\r\n\r\nRAIZ = dirname(dirname(__file__))\r\nPUBLIC_ROOT = join(RAIZ, 'public')\r\nMEDIA_ROOT = join(PUBLIC_ROOT, 'media')\r\nSTATIC_ROOT = join(PUBLIC_ROOT, 'static')\r\nLOG_ROOT = join(PUBLIC_ROOT, 'logs')\r\nTEMPLATE_DIRS = (join(RAIZ, 'templates'), )\r\nSTATIC_URL = '/static/'\r\nMEDIA_URL = '/media/'\r\n\r\n\r\nINSTALLED_APPS = ('suit',) + INSTALLED_APPS\r\nTEMPLATE_CONTEXT_PROCESSORS += (\r\n    'django.core.context_processors.request',\r\n    \"financeiro.processor.informacoes_financeiras\",\r\n)\r\n\r\nSTATICFILES_FINDERS += ('compressor.finders.CompressorFinder',)\r\nfrom apps import INSTALLED_APPS as IA\r\n\r\nINSTALLED_APPS += IA\r\n\r\n","sub_path":"sms/config/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":1136,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"15614282","text":"#!/usr/bin/python2\nimport os\nfrom VolatilityProfileDiscovery import ProfileExplorer\n\ntry:\n    dump_dir = os.environ['VPD_DIR']\nexcept:\n    raise EnvironmentError(\"$VPD_DIR not set.\")\n\n# From https://blog.lse.epita.fr/articles/59-ebctf-2013-for100.html\nexplorer = ProfileExplorer(\"{0}ubuntudump\".format(dump_dir))\nexplorer.discover()\n","sub_path":".stuff/tests/ubuntu_test.py","file_name":"ubuntu_test.py","file_ext":"py","file_size_in_byte":333,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"119260152","text":"# LSTM的简洁实现，实现字符级别的语言模型，数据由随机采样生成\n# num_epochs = 500\n# num_steps = 35\n# lr = 1\n# max_norm = 1.0\n# 在长度为10000的语料库中训练，ppl=1.390\n\n\nimport math\nimport re\nimport torch\nfrom torch import nn\nfrom collections import Counter\nimport random\nimport torch.nn.functional as F\nfrom torch.nn.utils import clip_grad_norm_\n\n\ntorch.manual_seed(12345)\nfile_path = '../data/timemachine.txt'\ndevice = torch.device('cuda:2')\n\n\ndef read_file(filename):\n    with open(filename, 'r') as f:\n        lines = f.readlines()\n    return [re.sub('[^a-zA-Z]+', ' ', line).strip().lower() for line in lines]\n\n\ndef tokenize(lines, token_type='char'):\n    if token_type == 'word':\n        return [line.split() for line in lines]\n    elif token_type == 'char':\n        return [list(line) for line in lines]\n\n\nclass Vocab:\n    def __init__(self, tokens, min_freqs=-1):\n        self.corpus = [token for line in tokens for token in line]\n        counter = Counter(self.corpus)\n        self.token_freqs = sorted(counter.items(), key=lambda x: x[1], reverse=True)\n        self.unk = 0\n        self.token_to_idx = {'<unk>': 0}\n        self.idx_to_token = ['<unk>']\n        for t, f in self.token_freqs:\n            if f >= min_freqs:\n                self.idx_to_token.append(t)\n                self.token_to_idx[t] = len(self.idx_to_token) - 1\n    \n    def __len__(self):\n        return len(self.idx_to_token)\n    \n    def __getitem__(self, tokens):\n        if not isinstance(tokens, list):\n            return self.token_to_idx.get(tokens, self.unk)\n        return [self.__getitem__(token) for token in tokens]\n    \n    def to_tokens(self, indices):\n        return [self.idx_to_token[indic] for indic in indices]\n\n\ndef data_iter_random(corpus, batch_size, num_steps):\n    corpus = corpus[random.randint(0, num_steps - 1):]\n    num_seqs = (len(corpus) - 1) // num_steps\n    initial_idx = list(range(0, num_seqs * num_steps, num_steps))\n    random.shuffle(initial_idx)\n    num_batches = num_seqs // batch_size\n    for i in range(0, num_batches * batch_size, batch_size):\n        indices = initial_idx[i: i + batch_size]\n        X = [corpus[j: j + num_steps] for j in indices]\n        Y = [corpus[j + 1: j + 1 + num_steps] for j in indices]\n        yield torch.tensor(X), torch.tensor(Y)\n\n\nclass data_iter:\n    def __init__(self, corpus, batch_size, num_steps):\n        self.batch_size = batch_size\n        self.num_steps = num_steps\n        self.corpus = corpus\n    \n    def __iter__(self):\n        return data_iter_random(self.corpus, self.batch_size, self.num_steps)\n\n\nclass LSTMModel(nn.Module):\n    def __init__(self, vocab_size, hidden_size, batch_size):\n        super().__init__()\n        self.lstm = nn.LSTM(input_size=vocab_size, hidden_size=hidden_size)\n        self.output_layer = nn.Linear(hidden_size, vocab_size)\n        self.batch_size = batch_size\n        self.hidden_size = hidden_size\n        self.vocab_size = vocab_size\n    \n    def get_init_state(self):\n        return (\n            torch.zeros((1, self.batch_size, self.hidden_size), dtype=torch.float, device=device),\n            torch.zeros((1, self.batch_size, self.hidden_size), dtype=torch.float, device=device)\n        )\n    \n    def forward(self, x, state):  # x:[L, B, V] state(H, C):[1, B, H]\n        output, _ = self.lstm(x, state)  # output:[L, B, H]\n        return self.output_layer(output.reshape(-1, output.shape[-1]))\n\n\n\ndef train_epoch(net, data_iter, loss_fn, max_norm, optimizer):\n    total_loss = 0.\n    total_num = 0.\n    for X, Y in data_iter:\n        X = F.one_hot(X.T, net.vocab_size).float().to(device)\n        Y = Y.T.reshape(-1).long().to(device)\n        y_hat = net.forward(X, net.get_init_state())\n        loss = loss_fn(y_hat, Y)\n        total_loss += loss.detach().clone().item() * Y.numel()\n        total_num += Y.numel()\n        optimizer.zero_grad()\n        loss.backward()\n        clip_grad_norm_(net.parameters(), max_norm)\n        optimizer.step()\n    return math.exp(total_loss / total_num)\n\n\ndef train(net, num_epochs, data_iter, lr, max_norm, print_every):\n    loss_fn = torch.nn.CrossEntropyLoss()\n    optimizer = torch.optim.SGD(net.parameters(), lr=lr)\n    for epoch in range(num_epochs):\n        ppl = train_epoch(net, data_iter, loss_fn, max_norm, optimizer)\n        if (epoch + 1) % print_every == 0:\n            print(f'epoch {epoch + 1}, ppl {ppl:.6f}')\n\n\nif __name__ == '__main__':\n    lr = 1\n    max_norm = 1.0\n    num_epochs = 500\n    num_steps = 35\n    batch_size = 32\n    hidden_size = 256\n    lines = read_file(file_path)\n    tokens = tokenize(lines, token_type='char')\n    vocab = Vocab(tokens)\n    corpus = [token for line in tokens for token in line]\n    corpus = corpus[:10000]\n    corpus = vocab[corpus]\n    seq_data_iter = data_iter(corpus, batch_size, num_steps)\n    net = LSTMModel(len(vocab), hidden_size, batch_size)\n    net = net.to(device)\n    train(net, num_epochs, seq_data_iter, lr, max_norm, print_every=1)","sub_path":"09-现代循环神经网络/lstm.py","file_name":"lstm.py","file_ext":"py","file_size_in_byte":4977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"257199729","text":"class Solution:\n    def rotateString(self, A, B):\n        \"\"\"\n        :type A: str\n        :type B: str\n        :rtype: bool\n        \"\"\"\n        if not A and B:\n            return False\n        max_rotate = len(A)\n        count = 0\n        while count <= max_rotate:\n            if A == B:\n                return True\n            A = A[1:] + A[0]\n            count += 1\n        return False\n\n\nclass Solution2:\n    def rotateString(self, A, B):\n        \"\"\"\n        :type A: str\n        :type B: str\n        :rtype: bool\n        \"\"\"\n        for i in range(0, len(A) + 1):\n            if A[i:] + A[0:i] == B:\n                return True\n\n        return False","sub_path":"Challenges/RotateString.py","file_name":"RotateString.py","file_ext":"py","file_size_in_byte":655,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"263235405","text":"from analysisapp.hit_func_utils import get_gte_freqs\nfrom common import const\nfrom webapp import dbutils\nfrom webapp.models import Brief, HitRecord\n\ndbutils\nbot_formulas = [\"bottom_bc\", \"bottom_shoulder\"]\ntop_fomulas = [\"top_bc\", \"top_shoulder\"]\n\nrule_unknown = \"没有规则命中，延续上一趋势\"\nrule1_1 = \"规则1.1：如果日线处于下跌比[顶分型dt=%s]，但是还未出现过周期至少为30min的底背驰，那么看空\"\nrule1_2 = \"规则1.2：如果日线处于上涨比[底分型dt=%s]，但是还未出现过周期至少为15min的顶背驰，那么看多\"\nhbx_green_rule_desc = \"规则hbx_green：如果黄白线进入水下[freq=%s,dt=%s]还没出现过任何底背驰，看空\"\n\nrule_text_top_shoulder = \"佛手顶背驰：如果最近一次佛手顶背驰的周期>=15min[freq=%s，dt=%s，但是还未出现过至少15min的底背驰，那么看空\"\n\n\ndef hit_top_shoulder_rule(bf: Brief) -> bool:\n    top_shoulder = HitRecord.objects.filter(code=bf.code, asset=bf.asset,\n                                            freq__in=get_gte_freqs(\"15min\"),\n                                            formula__name__in=['top_shoulder'],\n                                            dt__lte=bf.dt).order_by(\"-dt\")[:1].first()\n    if top_shoulder is None:\n        return False\n    top_shoulder_dt = top_shoulder.dt\n    # 需要至少15min的底背驰才能空转多\n    freqs = get_gte_freqs(\"15min\")\n    # 查询顶背驰后是否有底背驰\n    bot_after = HitRecord.objects. \\\n        filter(code=bf.code, asset=bf.asset,\n               formula__name__in=bot_formulas,\n               freq__in=freqs,\n               dt__gte=top_shoulder_dt,\n               dt__lte=bf.dt) \\\n        .order_by(\"-dt\").all()\n\n    if bot_after.count() == 0:\n        bf.trend = \"short\"\n        bf.comment = rule_text_top_shoulder % (top_shoulder.freq, top_shoulder_dt)\n        return True\n\n\ndef hit_rule1_1(self):\n    # 下跌比：False/dt\n    xdb = in_下跌比(self)\n    if xdb is False:\n        return False\n    bottom_latest = HitRecord.objects.filter(code=self.code, asset=self.asset, freq__in=get_gte_freqs(\"30min\"),\n                                             formula__name__in=bot_formulas,\n                                             dt__gte=xdb,\n                                             dt__lte=self.dt) \\\n                        .order_by(\"-dt\")[:1].first()\n    if bottom_latest is None:\n        self.trend = \"short\"\n        self.comment = rule1_1 % (xdb)\n        return True\n\n\ndef hit_rule1_2(self):\n    # 上涨比：False/dt\n    szb = in_上涨比(self)\n    if szb is False:\n        return False\n    top_latest = HitRecord.objects.filter(code=self.code, asset=self.asset, freq__in=get_gte_freqs(\"30min\"),\n                                          formula__name__in=top_fomulas,\n                                          dt__gte=szb,\n                                          dt__lte=self.dt) \\\n                     .order_by(\"-dt\")[:1].first()\n    if top_latest is None:\n        self.trend = \"long\"\n        self.comment = rule1_2 % (szb)\n        return True\n\n\ndef hit_hbx_green_rule(self):\n    # 最近一次黄白线进入水下\n    hbx_to_green_latest = self.get_latest(\"hbx_to_green\")\n    if hbx_to_green_latest is None:\n        return False\n    hbx_to_red_latest = self.get_latest(\"hbx_to_red\", freq=hbx_to_green_latest.freq)\n    if hbx_to_red_latest is not None and hbx_to_red_latest.dt > hbx_to_green_latest.dt:\n        return False\n\n    bottom_latest = HitRecord.objects.filter(code=self.code, asset=self.asset, freq__in=get_gte_freqs(\"5min\"),\n                                             formula__name__in=bot_formulas,\n                                             dt__gt=hbx_to_green_latest.dt,\n                                             dt__lte=self.dt) \\\n                        .order_by(\"-dt\")[:1].first()\n    if bottom_latest is None:\n        self.trend = \"short\"\n        self.comment = hbx_green_rule_desc % (hbx_to_green_latest.freq, hbx_to_green_latest.dt)\n\n\nrule_bot_formula = \"规则_底背驰：最近一次底背驰的周期>=15min[dt=%s,freq=%s]，但是还未出现过周期至少为5min的底背驰，那么看多\"\n\n\ndef hit_bot_formula(self):\n    \"\"\"\n\n    :type self: Brief\n    \"\"\"\n    bottom_latest = HitRecord.objects.filter(code=self.code, asset=self.asset, freq__in=get_gte_freqs(\"15min\"),\n                                             formula__name__in=bot_formulas,\n                                             dt__lte=self.dt) \\\n                        .order_by(\"-dt\")[:1].first()\n    if bottom_latest:\n        if in_下跌比(self) and const.freq_value_map[bottom_latest.freq] < const.freq_value_map[\"30min\"]:\n            return False\n        bottom_latest_dt = bottom_latest.dt\n        # 查询底背驰后是否有顶背驰\n        top_after = HitRecord.objects \\\n            .filter(code=self.code, asset=self.asset, formula__name__in=top_fomulas, dt__gte=bottom_latest_dt,\n                    dt__lte=self.dt) \\\n            .order_by(\"-dt\").first()\n        if not top_after:\n            self.trend = \"long\"\n            self.comment = rule_bot_formula % (bottom_latest.freq, bottom_latest_dt)\n\n\ndef in_下跌比(self):\n    top_turn_latest = HitRecord.objects.filter(code=self.code, asset=self.asset, freq__in=['D'],\n                                               formula__name=\"top_down\",\n                                               dt__lte=self.dt) \\\n                          .order_by(\"-dt\")[:1].first()\n    if not top_turn_latest:\n        return False\n    top_turn_latest_dt = top_turn_latest.dt\n    bottom_turn_latest = HitRecord.objects.filter(code=self.code, asset=self.asset, freq__in=['D'],\n                                                  formula__name=\"bottom_up\",\n                                                  dt__gte=top_turn_latest_dt,\n                                                  dt__lte=self.dt) \\\n                             .order_by(\"-dt\")[:1].first()\n    if bottom_turn_latest:\n        return False\n    return top_turn_latest_dt\n\n\ndef in_上涨比(self):\n    bottom_turn_latest = HitRecord.objects.filter(code=self.code, asset=self.asset, freq__in=['D'],\n                                                  formula__name=\"bottom_up\",\n                                                  dt__lte=self.dt) \\\n                             .order_by(\"-dt\")[:1].first()\n    if not bottom_turn_latest:\n        return False\n    bottom_turn_latest_dt = bottom_turn_latest.dt\n    top_turn_latest = HitRecord.objects.filter(code=self.code, asset=self.asset, freq__in=['D'],\n                                               formula__name=\"top_down\",\n                                               dt__gte=bottom_turn_latest_dt,\n                                               dt__lte=self.dt) \\\n                          .order_by(\"-dt\")[:1].first()\n    if top_turn_latest:\n        return False\n    return bottom_turn_latest_dt\n","sub_path":"analysisapp/hit_func.py","file_name":"hit_func.py","file_ext":"py","file_size_in_byte":6900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"597820023","text":"# -*- coding: utf-8 -*-\n# USER: Administrator\n# Time: 2019-04-23 14:23\n\nfrom requests_twisted import TwistedRequestsSession\n\nsession = TwistedRequestsSession()\n\n\ndef request(uri, method='GET', data=None, json=None, response_callback=None):\n    if method == 'POST':\n        defer = session.post(uri, data=data, json=json)\n    else:\n        defer = session.get(uri)\n    if response_callback:\n        # Which you just handle as you would usually do.\n        defer.addCallback(response_callback)\n    else:\n\n        def print_status(response):\n            print(response.url, response.status_code)\n\n        defer.addCallback(print_status)\n","sub_path":"core/http.py","file_name":"http.py","file_ext":"py","file_size_in_byte":634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"389831613","text":"'''\nProblem: Angles of a Clock\nFrom: Coding Interview Pro 18/10/2019\n\nGiven a time in the format of hour and minute,\ncalculate the angle of the hour and minute hand on a clock.\n'''\n\ndef calcAngle(h, m):\n  REVOLUTION = 360  # 360 degrees in a revolution\n  TOTAL_HOURS = 12 # 12 hours on a clock\n  TOTAL_MINUTES = 60 # 60 minutes on a clock\n  h %= TOTAL_HOURS\n  m %= TOTAL_MINUTES\n\n  m_ratio = m/TOTAL_MINUTES\n  m_angle = REVOLUTION * m_ratio\n  h_ratio = h/TOTAL_HOURS\n  h_angle = REVOLUTION * h_ratio # base hour\n  h_angle += REVOLUTION * (1/TOTAL_HOURS) * m_ratio # hour hand based on minute hand\n  h_angle %= REVOLUTION # mod 360 total angles\n  return abs(m_angle - h_angle)\n\nprint(calcAngle(3, 30))\n# 75\nprint(calcAngle(12, 30))\n# 165\n","sub_path":"daily/clock_angles.py","file_name":"clock_angles.py","file_ext":"py","file_size_in_byte":737,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"336538104","text":"import random\n\n\ndef count_file(file, c):\n    counter = 0\n    with open(file, 'r') as f:\n        for s in f:\n            counter += s.count(c)\n    return counter\n\n\ndef is_anagram(s1, s2):\n    c1 = list([i for i in s1])\n    c2 = (list([i for i in s2]))\n    return all(x in c2 for x in c1) and all(x in c1 for x in c2)\n\n\ndef write_rand_str():\n    length = random.randint(10, 30)\n    s = \"\".join([chr(random.randint(100, 120)) for i in range(length)])\n\n    with open('file.txt', 'w+') as f:\n        print(s)\n        f.write(s)\n\n\nwrite_rand_str()\nprint(count_file('file', 'A'))\nprint(is_anagram(\"acb\", \"abc\"))\n","sub_path":"Python/anagrams/main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":605,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"648076743","text":"#!/usr/bin/env python3\n# -*- coding:utf-8 -*-\n# File Name: dice/flask_app.py\n# Author: Tate_fan\n# mail: tate_fan@163.com\n# Created Time: 2015年06月01日 星期一 16时48分09秒\n# ======================================================================\nimport logging\nfrom flask import Flask\nfrom . flask_api import Bid, Notice, Root\nfrom flask_restful import Api\nfrom .utils import get_res_path, init_args,\\\n    load_resource, init_resource\nfrom .datagenerator import DataGenerator\nfrom .schemasstore import SchemasStore\nargs = init_args()\ncfg = load_resource(args.config)\nlogger = logging.getLogger(__name__)\n\n\nclass WebApp(Flask):\n\n    def __init__(self, resource=None, **kwargs):\n        self.dg = DataGenerator()\n        self.ss = SchemasStore()\n        self.resource = resource or [{}]\n        self.port = args.port or 6060\n        name = kwargs.pop(\"name\", __package__)\n        super(WebApp, self).__init__(name, **kwargs)\n        self.init_rule()\n        self.init_schema()\n        self.init_res()\n\n    def run(self, host=None, debug=False, **options):\n        super(WebApp, self).run(host=host, port=self.port, debug=debug, **options)\n\n    def init_rule(self):\n        api = Api(self)\n        api.add_resource(Root, '/v1/')\n        api.add_resource(Bid, '/v1/bid/<string:did>')\n        api.add_resource(Notice, '/v1/notice/<string:did>')\n\n    def init_schema(self, folder='res'):\n        self.ss.load_folder(get_res_path(__package__, folder))\n        self.dg.schemas_store = self.ss\n\n    def init_res(self):\n        url = cfg.get(\"resource\")\n        self.resource = init_resource(url)\n\n    def did_id(self, did):\n        return [item['id'] for item in self.resource if item['did'] == did]\n\n    def make_rul(self, did):\n        return \"http://dsp:{port}/v1/notice/{did}\".format(port=self.port, did=did)\n","sub_path":"dice/flask_app.py","file_name":"flask_app.py","file_ext":"py","file_size_in_byte":1812,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"583289029","text":"\"\"\"\nThis module provides the `TrainModel` class and supporting methods.\n\"\"\"\nimport logging\nimport math\nimport os\nimport shutil\nfrom time import time\n\nimport numpy as np\nimport torch\nimport torch.nn as nn\nfrom torch.autograd import Variable\nfrom torch.optim.lr_scheduler import ReduceLROnPlateau\n\nfrom .utils import initialize_logger\nfrom .utils import load_model_from_state_dict\nfrom .utils import PerformanceMetrics\n\nlogger = logging.getLogger(\"selene\")\n\n\ndef _metrics_logger(name, out_filepath):\n    logger = logging.getLogger(\"{0}\".format(name))\n    logger.setLevel(logging.INFO)\n    formatter = logging.Formatter(\"%(message)s\")\n    file_handle = logging.FileHandler(\n        os.path.join(out_filepath, \"{0}.txt\".format(name)))\n    file_handle.setFormatter(formatter)\n    logger.addHandler(file_handle)\n    return logger\n\n\nclass TrainModel(object):\n    \"\"\"\n    This class ties together the various objects and methods needed to\n    train and validate a model.\n\n    Parameters\n    ----------\n    model : torch.nn.Module\n        The model to train.\n    data_sampler : selene_sdk.samplers.Sampler\n        The example generator.\n    loss_criterion : torch.nn._Loss\n        The loss function to optimize.\n    optimizer_class : torch.optim.Optimizer\n        The optimizer to minimize loss with.\n    optimizer_kwargs : dict\n        The dictionary of keyword arguments to pass to the optimizer's\n        constructor.\n    batch_size : int\n        Specify the batch size to process examples. Should be a power of 2.\n    max_steps : int\n        The maximum number of mini-batches to iterate over.\n    report_stats_every_n_steps : int\n        The frequency with which to report summary statistics.\n    output_dir : str\n        The output directory to save model checkpoints and logs in.\n    save_checkpoint_every_n_steps : int or None, optional\n        Default is 1000. If None, set to the same value as\n        `report_stats_every_n_steps`\n    n_validation_samples : int or None, optional\n        Default is `None`. Specify the number of validation samples in the\n        validation set. If `n_validation_samples` is `None` and the data sampler\n        used is the `selene_sdk.samplers.IntervalsSampler` or\n        `selene_sdk.samplers.RandomSampler`, we will retrieve 32000\n        validation samples. If `None` and using\n        `selene_sdk.samplers.MatFileSampler`, we will use all\n        validation samples in the matrix.\n    n_test_samples : int or None, optional\n        Default is `None`. Specify the number of test samples in the test set.\n        If `n_test_samples` is `None` and the data sampler used is the\n        `selene_sdk.samplers.IntervalsSampler`, the size of the test set is\n        the number of test intervals we can sample from. If\n        `None` and using `selene_sdk.samplers.RandomSampler`, the size is\n        :math:`\\\\frac{N}{20}`, where :math:`N` is the number of possible\n        positions in the test partition of the genome. If  `None` and\n        using `selene_sdk.samplers.MatFileSampler`, we will use all\n        test samples in the matrix.\n    cpu_n_threads : int, optional\n        Default is 32.\n    use_cuda : bool, optional\n        Default is `False`. Specify whether CUDA is available for torch\n        to use during training.\n    data_parallel : bool, optional\n        Default is `False`. Specify whether multiple GPUs are available\n        for torch to use during training.\n    checkpoint_resume : str or None, optional\n        Default is `None`. If `checkpoint_resume` is not None, it should be the\n        path to a model file generated by `torch.save` that can now be read\n        using `torch.load`.\n\n    Attributes\n    ----------\n    model : torch.nn.Module\n        The model to train.\n    sampler : selene_sdk.samplers.Sampler\n        The example generator.\n    loss_criterion : torch.nn._Loss\n        The loss function to optimize.\n    optimizer_class : torch.optim.Optimizer\n        The optimizer to minimize loss with.\n    batch_size : int\n        The size of the mini-batch to use during training.\n    max_steps : int\n        The maximum number of mini-batches to iterate over.\n    nth_step_report_stats : int\n        The frequency with which to report summary statistics.\n    nth_step_save_checkpoint : int\n        The frequency with which to save a model checkpoint.\n    use_cuda : bool\n        If `True`, use a CUDA-enabled GPU. If `False`, use the CPU.\n    data_parallel : bool\n        Whether to use multiple GPUs or not.\n    output_dir : str\n        The directory to save model checkpoints and logs.\n    training_loss : list(float)\n        The current training loss.\n\n    \"\"\"\n\n    def __init__(self,\n                 model,\n                 data_sampler,\n                 loss_criterion,\n                 optimizer_class,\n                 optimizer_kwargs,\n                 batch_size,\n                 max_steps,\n                 report_stats_every_n_steps,\n                 output_dir,\n                 save_checkpoint_every_n_steps=1000,\n                 report_gt_feature_n_positives=10,\n                 n_validation_samples=None,\n                 n_test_samples=None,\n                 cpu_n_threads=32,\n                 use_cuda=False,\n                 data_parallel=False,\n                 logging_verbosity=2,\n                 checkpoint_resume=None):\n        \"\"\"\n        Constructs a new `TrainModel` object.\n        \"\"\"\n        self.model = model\n        self.sampler = data_sampler\n        self.criterion = loss_criterion\n        self.optimizer = optimizer_class(\n            self.model.parameters(), **optimizer_kwargs)\n\n        self.batch_size = batch_size\n        self.max_steps = max_steps\n        self.nth_step_report_stats = report_stats_every_n_steps\n        self.nth_step_save_checkpoint = None\n        if not save_checkpoint_every_n_steps:\n            self.nth_step_save_checkpoint = report_stats_every_n_steps\n        else:\n            self.nth_step_save_checkpoint = save_checkpoint_every_n_steps\n\n        torch.set_num_threads(cpu_n_threads)\n\n        self.use_cuda = use_cuda\n        self.data_parallel = data_parallel\n\n        if self.data_parallel:\n            self.model = nn.DataParallel(model)\n            logger.debug(\"Wrapped model in DataParallel\")\n\n        if self.use_cuda:\n            self.model.cuda()\n            self.criterion.cuda()\n            logger.debug(\"Set modules to use CUDA\")\n\n        os.makedirs(output_dir, exist_ok=True)\n        self.output_dir = output_dir\n\n        initialize_logger(\n            os.path.join(self.output_dir, \"{0}.log\".format(__name__)),\n            verbosity=logging_verbosity)\n\n        self._create_validation_set(n_samples=n_validation_samples)\n        self._validation_metrics = PerformanceMetrics(\n            self.sampler.get_feature_from_index,\n            report_gt_feature_n_positives=report_gt_feature_n_positives)\n\n        if \"test\" in self.sampler.modes:\n            self._create_test_set(n_samples=n_test_samples)\n            self._test_metrics = PerformanceMetrics(\n                self.sampler.get_feature_from_index,\n                report_gt_feature_n_positives=report_gt_feature_n_positives)\n\n        self._start_step = 0\n        self._min_loss = float(\"inf\") # TODO: Should this be set when it is used later? Would need to if we want to train model 2x in one run.\n        if checkpoint_resume is not None:\n            checkpoint = torch.load(\n                checkpoint_resume,\n                map_location=lambda storage, location: storage)\n\n            self.model = load_model_from_state_dict(\n                checkpoint[\"state_dict\"], self.model)\n\n            self._start_step = checkpoint[\"step\"]\n            if self._start_step >= self.max_step:\n                self.max_step += self._start_step\n\n            self._min_loss = checkpoint[\"min_loss\"]\n            self.optimizer.load_state_dict(\n                checkpoint[\"optimizer\"])\n            if self.use_cuda:\n                for state in self.optimizer.state.values():\n                    for k, v in state.items():\n                        if isinstance(v, torch.Tensor):\n                            state[k] = v.cuda()\n\n            logger.info(\n                (\"Resuming from checkpoint: step {0}, min loss {1}\").format(\n                    self._start_step, self._min_loss))\n\n        self._train_logger = _metrics_logger(\n                \"{0}.train\".format(__name__), self.output_dir)\n        self._validation_logger = _metrics_logger(\n                \"{0}.validation\".format(__name__), self.output_dir)\n\n        self._train_logger.info(\"loss\")\n        # TODO: this makes the assumption that all models will report ROC AUC,\n        # which is not the case.\n        self._validation_logger.info(\"loss\\troc_auc\")\n\n    def _create_validation_set(self, n_samples=None):\n        \"\"\"\n        Generates the set of validation examples.\n\n        Parameters\n        ----------\n        n_samples : int or None, optional\n            Default is `None`. The size of the validation set. If `None`,\n            will use all validation examples in the sampler.\n\n        \"\"\"\n        t_i = time()\n        self._validation_data, self._all_validation_targets = \\\n            self.sampler.get_validation_set(\n                self.batch_size, n_samples=n_samples)\n        t_f = time()\n        logger.info((\"{0} s to load {1} validation examples ({2} validation \"\n                     \"batches) to evaluate after each training step.\").format(\n                      t_f - t_i,\n                      len(self._validation_data) * self.batch_size,\n                      len(self._validation_data)))\n\n    # TODO: Determine if we can somehow combine testing and validation set creation methods.\n    def _create_test_set(self, n_samples=None):\n        \"\"\"\n        Generates the set of test examples.\n\n        Parameters\n        ----------\n        n_samples : int or None, optional\n            Default is `None`. The size of the test set to generate. If\n            `None`, will use all test examples in the sampler.\n\n        \"\"\"\n        t_i = time()\n        self._test_data, self._all_test_targets = \\\n            self.sampler.get_test_set(\n                self.batch_size, n_samples=n_samples)\n        t_f = time()\n        logger.info((\"{0} s to load {1} test examples ({2} test batches) \"\n                     \"to evaluate after all training steps.\").format(\n                      t_f - t_i,\n                      len(self._test_data) * self.batch_size,\n                      len(self._test_data)))\n        np.savez_compressed(\n            os.path.join(self.output_dir, \"test_targets.npz\"),\n            data=self._all_test_targets)\n\n    def _get_batch(self):\n        \"\"\"\n        Fetches a mini-batch of examples\n\n        Returns\n        -------\n        tuple(numpy.ndarray, numpy.ndarray)\n            A tuple containing the examples and targets.\n\n        \"\"\"\n        t_i_sampling = time()\n        batch_sequences, batch_targets = self.sampler.sample(\n            batch_size=self.batch_size)\n        t_f_sampling = time()\n        logger.debug(\n            (\"[BATCH] Time to sample {0} examples: {1} s.\").format(\n                 self.batch_size,\n                 t_f_sampling - t_i_sampling))\n        return (batch_sequences, batch_targets)\n\n    def train_and_validate(self):\n        \"\"\"\n        Trains the model and measures validation performance.\n\n        \"\"\"\n        logger.info(\n            (\"[TRAIN] max_steps: {0}, batch_size: {1}\").format(\n                self.max_steps, self.batch_size))\n\n        min_loss = self._min_loss\n        scheduler = ReduceLROnPlateau(\n            self.optimizer, 'max', patience=16, verbose=True,\n            factor=0.8)\n        for step in range(self._start_step, self.max_steps):\n            train_loss = self.train()\n\n            # TODO: Should we have some way to report training stats without running validation?\n            if step and step % self.nth_step_report_stats == 0:\n                valid_scores = self.validate()\n                validation_loss = valid_scores[\"loss\"]\n                self._train_logger.info(train_loss)\n                # TODO: check if \"roc_auc\" is a key in `valid_scores`?\n                if valid_scores[\"roc_auc\"]:\n                    validation_roc_auc = valid_scores[\"roc_auc\"]\n                    self._validation_logger.info(\n                        \"{0}\\t{1}\".format(validation_loss,\n                                          validation_roc_auc))\n                    scheduler.step(\n                        math.ceil(validation_roc_auc * 1000.0) / 1000.0)\n                else:\n                    self._validation_logger.info(\"{0}\\tNA\".format(\n                        validation_loss))\n\n                is_best = validation_loss < min_loss\n                min_loss = min(validation_loss, min_loss)\n                self._save_checkpoint({\n                    \"step\": step,\n                    \"arch\": self.model.__class__.__name__,\n                    \"state_dict\": self.model.state_dict(),\n                    \"min_loss\": min_loss,\n                    \"optimizer\": self.optimizer.state_dict()}, is_best)\n                logger.info(\n                    (\"[STATS] step={0}: \"\n                     \"Training loss: {1}, validation loss: {2}.\").format(\n                        step, train_loss, validation_loss)) # Should training loss and validation loss be reported at the same line?\n                # Logging training and validation on same line requires 2 parsers or more complex parser.\n                # Separate logging of train/validate is just a grep for validation/train and then same parser.\n\n            # Should checkpoint saving occur before validation (if they both occur in the same step) or not?\n            if step % self.nth_step_save_checkpoint == 0:\n                self._save_checkpoint({\n                    \"step\": step,\n                    \"arch\": self.model.__class__.__name__,\n                    \"state_dict\": self.model.state_dict(),\n                    \"min_loss\": min_loss,\n                    \"optimizer\": self.optimizer.state_dict()}, False)\n        self.sampler.save_dataset_to_file(\"train\", close_filehandle=True)\n\n    def train(self):\n        \"\"\"\n        Trains the model on a batch of data.\n\n        Returns\n        -------\n        float\n            The training loss.\n\n        \"\"\"\n        self.model.train()\n        self.sampler.set_mode(\"train\")\n\n        inputs, targets = self._get_batch()\n\n        inputs = torch.Tensor(inputs)\n        targets = torch.Tensor(targets)\n\n        if self.use_cuda:\n            inputs = inputs.cuda()\n            targets = targets.cuda()\n\n        inputs = Variable(inputs)\n        targets = Variable(targets)\n\n        predictions = self.model(inputs.transpose(1, 2))\n        loss = self.criterion(predictions, targets)\n\n        self.optimizer.zero_grad()\n        loss.backward()\n        self.optimizer.step()\n\n        return loss.item()\n\n    def _evaluate_on_data(self, data_in_batches):\n        \"\"\"\n        Makes predictions for some labeled input data.\n\n        Parameters\n        ----------\n        data_in_batches : list(tuple(numpy.ndarray, numpy.ndarray))\n            A list of tuples of the data, where the first element is\n            the example, and the second element is the label.\n\n        Returns\n        -------\n        tuple(float, list(numpy.ndarray))\n            Returns the average loss, and the list of all predictions.\n\n        \"\"\"\n        self.model.eval()\n\n        batch_losses = []\n        all_predictions = []\n\n        for (inputs, targets) in data_in_batches:\n            inputs = torch.Tensor(inputs)\n            targets = torch.Tensor(targets)\n\n            if self.use_cuda:\n                inputs = inputs.cuda()\n                targets = targets.cuda()\n\n            with torch.no_grad():\n                inputs = Variable(inputs)\n                targets = Variable(targets)\n\n                predictions = self.model(\n                    inputs.transpose(1, 2))\n                loss = self.criterion(predictions, targets)\n\n                all_predictions.append(\n                    predictions.data.cpu().numpy())\n\n                batch_losses.append(loss.item())\n        all_predictions = np.vstack(all_predictions)\n        return np.average(batch_losses), all_predictions\n\n    def validate(self):\n        \"\"\"\n        Measures model validation performance.\n\n        Returns\n        -------\n        dict\n            A dictionary, where keys are the names of the loss metrics,\n            and the values are the average value for that metric over\n            the validation set.\n\n        \"\"\"\n        average_loss, all_predictions = self._evaluate_on_data(\n            self._validation_data)\n\n        average_scores = self._validation_metrics.update(all_predictions,\n                                                         self._all_validation_targets)\n\n        for name, score in average_scores.items():\n            logger.debug(\"[STATS] average {0}: {1}\".format(name, score))\n            print(\"[VALIDATE] average {0}: {1}\".format(name, score))\n\n        average_scores[\"loss\"] = average_loss\n        return average_scores\n\n    def evaluate(self):\n        \"\"\"\n        Measures the model test performance.\n\n        Returns\n        -------\n        dict\n            A dictionary, where keys are the names of the loss metrics,\n            and the values are the average value for that metric over\n            the test set.\n\n        \"\"\"\n        average_loss, all_predictions = self._evaluate_on_data(\n            self._test_data)\n\n        average_scores = self._test_metrics.update(all_predictions,\n                                                   self._all_test_targets)\n        np.savez_compressed(\n            os.path.join(self.output_dir, \"test_predictions.npz\"),\n            data=all_predictions)\n\n        for name, score in average_scores.items():\n            logger.debug(\"[STATS] average {0}: {1}\".format(name, score))\n            print(\"[TEST] average {0}: {1}\".format(name, score))\n\n        test_performance = os.path.join(\n            self.output_dir, \"test_performance.txt\")\n        feature_scores_dict = self._test_metrics.write_feature_scores_to_file(\n            test_performance)\n\n        average_scores[\"loss\"] = average_loss\n\n        self._test_metrics.visualize(\n            all_predictions, self._all_test_targets, self.output_dir)\n\n        return (average_scores, feature_scores_dict)\n\n    def _save_checkpoint(self, state, is_best,\n                         dir_path=None,\n                         filename=\"checkpoint.pth.tar\"):\n        \"\"\"\n        Saves snapshot of the model state to file.\n\n        Parameters\n        ----------\n        state : dict\n            Information about the state of the model\n        is_best : bool\n            Is this the model's best performance so far?\n        dir_path : str, optional\n            Default is None. Will output file to the current working directory\n            if no path to directory is specified.\n        filename : str, optional\n            Default is \"checkpoint.pth.tar\". Specify the checkpoint filename.\n\n        Returns\n        -------\n        None\n\n        \"\"\"\n        logger.info(\"[TRAIN] {0}: Saving model state to file.\".format(\n            state[\"step\"]))\n        cp_filepath = os.path.join(\n            self.output_dir, filename)\n        torch.save(state, cp_filepath)\n        if is_best:\n            best_filepath = os.path.join(\n                self.output_dir,\n                \"best_model.pth.tar\")\n            shutil.copyfile(cp_filepath, best_filepath)\n","sub_path":"selene_sdk/train_model.py","file_name":"train_model.py","file_ext":"py","file_size_in_byte":19484,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"176953665","text":"from django_tables2 import tables, columns\nfrom django_tables2.utils import A  # alias for Accessor\nfrom payments.models import Payment, Corporation\n\n\n\nclass CorporationTable(tables.Table):\n    days_late_average = columns.TemplateColumn('{{ record.lateness_average }}')\n    days_credit_average = columns.TemplateColumn('{{ record.credit_average }}')\n    name = columns.LinkColumn('compare_corporation', kwargs={'corporation': A('slug_name')}, verbose_name='corporation') #, kwargs={'corporation': A('name')}\n#                               , \n#         kwargs={'corporation': A('name')})\n#     name = columns.TemplateColumn(\n#         accessor='corporation.name',\n#         verbose_name='Name',\n#         template_name='payments/corporation_link.html',\n#         orderable=False\n#     )\n    class Meta:\n        model = Corporation\n  \n        exclude = (\n            'url',\n            'email',\n            'slug_name'\n        )    \n        sequence = (\n            'cid',\n            'name',\n            'days_late_average',\n            'days_credit_average',\n        )\n        attrs = {\"class\": \"table\"}   \n        \nclass PaymentsTable(tables.Table):\n    days_late = columns.TemplateColumn('{{ record.extra_credit_days }}')\n    days_credit = columns.TemplateColumn('{{ record.credit_days }}')\n    class Meta:\n        model = Payment\n        exclude = ('owner', 'created_at')\n        order_date = columns.DateColumn()\n        supply_date = columns.DateColumn()\n        due_date = columns.DateColumn()\n        pay_date = columns.DateColumn()\n        sequence = (\n            'id',\n            'corporation',\n            'title',\n            'amount',\n            'order_date',\n            'supply_date',\n            'due_date',\n            'pay_date',\n            'days_late',\n            'days_credit',\n        )\n        attrs = {\"class\": \"table\"}\n\n\nclass PaymentsPartialTable(tables.Table):\n\n    action = columns.TemplateColumn(\n        accessor='corporation.email',\n        verbose_name='Send Reminder',\n        template_name='payments/send_reminder.html',\n        orderable=False)\n\n    last_reminder = columns.Column(verbose_name='Last Reminder',)\n\n    class Meta:\n        model = Payment\n        due_date = columns.DateColumn()\n        exclude = (\n            'owner',\n            'created_at',\n            'id',\n            'order_date',\n            'supply_date',\n            'pay_date'\n        )\n        sequence = (\n            'corporation',\n            'title',\n            'amount',\n            'due_date',\n            'last_reminder',\n            'action',\n        )\n        attrs = {\"class\": \"table\"}\n","sub_path":"payments/tables.py","file_name":"tables.py","file_ext":"py","file_size_in_byte":2614,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"341211919","text":"\"\"\"Repeating a beat in a loop.\"\"\"\n\n__author__ = \"730429363\"\n\n\nbeat: str = input(\"What beat do you want to repeat? \")\nnumber: int = int(input(\"How many times do you want to repeat it? \"))\nif number <= 0:\n    print(\"No beat...\")\nelse:\n    one_beat = beat\n    while number > 1:\n        number -= 1\n        beat_space = beat + \" \"\n        beat = beat_space + one_beat\n    print(beat)","sub_path":"exercises/ex04/repeat_beat.py","file_name":"repeat_beat.py","file_ext":"py","file_size_in_byte":379,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"624581464","text":"__author__ = 'wlw'\n# coding=utf-8\n\n\n# 获取每一个三角面片的重心\ndef getGravitySet(points):\n    '''\n    # 去除重复的顶点，只保留一个\n    res = {point.tostring(): point for point in points}\n    duped_points = res.values()\n    #print 'duped_points: ', duped_points\n    '''\n\n    gravitySet = []\n    for i in xrange(len(points)/3):\n        # 第i个三角形\n        a_pt = points[i*3]\n        b_pt = points[i*3+1]\n        c_pt = points[i*3+2]\n\n        # 第i个三角形的重心\n        g = (a_pt+b_pt+c_pt) / 3.0\n        gravitySet.append(g)\n\n    return gravitySet\n\n    # return duped_points\n\n# 获取每一条线的中心\ndef getMiddleSet(points):\n    middleSet = []\n    for i in xrange(len(points)/2):\n        # 第i条线\n        a_pt = points[i*2]\n        b_pt = points[i*2+1]\n\n        # 第i条线的中心\n        m = (a_pt+b_pt) / 2.0\n        middleSet.append(m)\n\n    return middleSet","sub_path":"gravity.py","file_name":"gravity.py","file_ext":"py","file_size_in_byte":913,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"195964684","text":"# -*- coding: utf-8 -*-\n\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\n\"\"\"\nModule to help retrieve/create/update/delete data from/to any RESTful API (Base Class).\n\nRequires Python Version >= 2.7 or >= 3.6\n\"\"\"\n\n# Core Modules\nimport json\nimport logging\nimport functools\nfrom builtins import str\n\n# Third-party Modules\nimport munch\nimport requests\nimport requests.adapters as ra\n\n# Python 2 to Python 3 Compatibility\nimport requests.compat as compat\n# Local Modules\nfrom .utils import log_with_debug_info\n\n\ntry:\n\tfrom future.utils import iteritems\nexcept ImportError:\n\titeritems = lambda x: x.items()\n\n__all__ = ['LoginError', 'OperationError', 'InvalidJSONError', 'api_request', 'RestApiSession',\n           'DEFAULT_HEADERS']\n\nLOGGER = logging.getLogger(__name__)\n\n# Exception Classes\nclass LoginError(OSError):\n\t\"\"\"\n\tThis represents an error that occurred during server login.\n\t\"\"\"\n\tdef __init__(self, *args):\n\t\tOSError.__init__(self, *args)\n\n\nclass OperationError(IOError):\n\t\"\"\"\n\tThis class represents a generic error, indicating something went wrong with the request or on\n\tthe server.\n\t\"\"\"\n\t#: Contains the response object that generated the error\n\tresp = None\n\tdef __init__(self, *args, resp=None):\n\t\tIOError.__init__(self, *args)\n\t\tself.resp = resp\n\n\nclass InvalidJSONError(ValueError):\n\t\"\"\"\n\tAn error that occurs when an invalid JSON payload is passed to an endpoint.\n\t\"\"\"\n\t#: Contains the response object that generated the error\n\tresp = None\n\tdef __init__(self, *args, resp=None):\n\t\tValueError.__init__(self, *args)\n\t\tself.resp = resp\n\n# Miscellaneous Constants and/or Variables\nDEFAULT_HEADERS = {u'Content-Type': u'application/json; charset=UTF-8'}\n\n\n# Helper Functions/Decorators\ndef api_request(method_name, api_path, supported_versions):\n\t\"\"\"\n\tThis wrapper returns a decorator that routes the calls to the appropriate utility function that\n\tgenerates the RESTful API endpoint, performs the appropriate call to the endpoint and returns\n\tthe data to the user.\n\n\t:param method_name: A method name defined on the Class, this decorator is decorating, that will be called to perform the operation. E.g. 'GET', 'POST', 'PUT', 'DELETE', etc. The method_name chosen must have the signature of ``<method>(self, api_path, **kwargs)`` e.g. ``def get(self, api_path, **kwargs): ...``\n\t:type method_name: str\n\t:param api_path: The path to the API end-point that you want to call which does not include the base url e.g. ``user/login``, ``servers``, etc. This string can contain substitution parameters as denoted by a valid field_name replacement field specification as per :meth:`str.format` e.g. ``cachegroups/{id}`` or ``cachegroups/{id:d}``\n\t:type api_path: str\n\t:param supported_versions: A tuple of API versions that this route supports\n\t:type supported_versions: Tuple[str]\n\t:return: rtype int: A new function that replaces the original function with a boilerplate execution process.\n\t:rtype: Callable[str, Dict[str, Any]]\n\t\"\"\"\n\n\tdef outer(func):\n\t\t@functools.wraps(func)\n\t\tdef method_wrapper(self, *args, **kwargs):\n\t\t\t# Positional arguments, e.g. *args, are not being used. Keyword arguments are the\n\t\t\t# preferred way to pass the parameters needed by the helper functions\n\t\t\tif (self.api_version is None) or (self.api_version in supported_versions):\n\t\t\t\tmsg = (u'Calling method [{0}] with keyword arguments [{1}] '\n\t\t\t\t       u'via API endpoint method [{2}]')\n\t\t\t\tlog_with_debug_info(logging.DEBUG, msg.format(method_name, kwargs, func.__name__))\n\n\t\t\t\treturn getattr(self, method_name)(api_path, **kwargs)\n\n\t\t\t# Client API version is not supported by the method being called\n\t\t\tmsg = (u\"Method [{0}] is not supported by this client's API version [{1}]; \"\n\t\t\t       u'Supported versions: {2}')  # type: Text\n\t\t\tmsg = msg.format(func.__name__, self.api_version, supported_versions)\n\t\t\tlog_with_debug_info(logging.DEBUG, msg)\n\t\t\traise OperationError(msg)\n\n\t\treturn method_wrapper\n\treturn outer\n\n\nclass RestApiSession(object):\n\t\"\"\"\n\tThis class represents a login session with a generic REST API server. It provides base\n\tfunctionality inherited by :class:`TOSession`.\n\t\"\"\"\n\tdef __init__(self, host_ip, api_version=None, api_base_path=u'api/', host_port=443, ssl=True,\n\t             headers=None, verify_cert=True, create_session=False, max_retries=5):\n\t\t\"\"\"\n\t\tThe class initializer.\n\n\t\t:param host_ip: The dns name or ip address of the RESTful API host to use to talk to the API\n\t\t:type host_ip: str\n\t\t:param host_port: The port to use when contacting the RESTful API\n\t\t:type host_port: int\n\t\t:param api_version: The version of the API to make calls against. If supplied, endpoint version validation will be performed. If supplied as None, no version validation will be performed. :const:`None` is allowed so that non-versioned REST APIs can be implemented.\n\t\t:type api_version: Union[str, None]\n\t\t:param api_base_path: The part of the url that is the base path, from the web server root (which may include an API version), for all API endpoints without the server url portion e.g. 'api/', 'api/latest/'\n\n\t\t\t.. note:: To specify the base path with the passed ``api_version`` you can specify\n\t\t\t\t``api_base_path`` as ``api/{api_version}/`` and the API version will be substituted.\n\t\t\t\tIf ``api_version`` is :const:`None` and '{api_version}' is specified in the\n\t\t\t\t``api_base_path`` string then an exception will be thrown.\n\t\t\t\te.g. api_version=u'latest' -> 'api/{api_version}/' -> 'api/latest/'\n\t\t\t\tapi_version=None   -> 'api/{api_version}/' -> Throws Exception\n\n\t\t:type api_base_path: str\n\t\t:param ssl: Should SSL be used? (http vs. https)\n\t\t:type ssl: bool\n\t\t:param headers: The HTTP headers to use when contacting the RESTful API\n\t\t:type headers: Dict[str, str]\n\t\t:param verify_cert: Should the SSL certificates be verified when contacting the RESTful API. You may want to set this to :const:`False` for systems with self-signed certificates.\n\t\t:type verify_cert: bool\n\t\t:param create_session: Should a session be created automatically?\n\t\t:type create_session: bool\n\t\t\"\"\"\n\t\tif headers is None:\n\t\t\theaders = DEFAULT_HEADERS\n\n\t\tself._session = None\n\t\tself._host_ip = host_ip\n\t\tself._host_port = host_port\n\t\tself._api_version = api_version\n\t\tself._api_base_path = api_base_path\n\t\tself._ssl = ssl\n\t\tself._headers = headers\n\t\tself._verify_cert = verify_cert\n\t\tself._create_session = create_session\n\t\tself._max_retries = max_retries\n\n\t\t# Setup API End-point Version validation, if enabled\n\t\tself.__api_version_format_name = u'api_version'\n\t\tself.__api_version_format_value = u'{{{0}}}'.format(self.__api_version_format_name)\n\n\t\tif self._api_version:\n\t\t\t# if api_base_path is supplied as 'api/{api_version}/' or some string\n\t\t\t# containing '{api_version}' then try to substitute the api_version supplied\n\t\t\t# by the user.\n\n\t\t\tversion_params = {\n\t\t\t\tself.__api_version_format_name: self._api_version\n\t\t\t}\n\t\t\tself._api_base_path = self._api_base_path.format(**version_params)\n\n\t\tif not self._api_version and self.__api_version_format_value in self._api_base_path:\n\t\t\tmsg = (u'{0} was specified in the API Base Path [{1}] '\n\t\t\t       u'but the replacement did not occur because the API Version '\n\t\t\t       u'was not supplied.')\n\t\t\tmsg = msg.format(self.__api_version_format_value, self._api_base_path)\n\t\t\tlog_with_debug_info(logging.ERROR, msg)\n\t\t\traise OperationError(msg)\n\n\t\t# Setup some common URLs\n\t\tself._server_url = u'{0}://{1}{2}/'.format(u'https' if ssl else u'http',\n\t\t                                           host_ip,\n\t\t                                           u':{0}'.format(host_port) if host_port else u'')\n\t\tself._api_base_url = compat.urljoin(self._server_url, self._api_base_path)\n\t\tself._api_base_url = self._api_base_url.rstrip(u'/') + u'/'\n\n\n\t\tif not self._verify_cert:\n\t\t\t# Not verifying certs so let's disable the warning\n\t\t\t#pylint: disable=E1101\n\t\t\trequests.packages.urllib3.disable_warnings(\\\n\t\t\t                            requests.packages.urllib3.exceptions.InsecureRequestWarning)\n\t\t\t#pylint: enable=E1101\n\t\t\tlog_with_debug_info(logging.WARNING, u'Certificate verification warnings are disabled.')\n\n\t\tmsg = u'RestApiSession instance {0:#0x} initialized: Details: {1}'\n\t\tlog_with_debug_info(logging.DEBUG, msg.format(id(self), self.__dict__))\n\n\t\tif self._create_session:\n\t\t\tself.create()\n\n\t@property\n\tdef is_open(self):\n\t\t\"\"\"\n\t\tIs the session open to the RESTful API? (Read-only Property)\n\n\t\t:return: :const:`True` if yes, otherwise, :const:`False`\n\t\t:rtype: bool\n\t\t\"\"\"\n\t\treturn self._session is not None\n\n\t@property\n\tdef session(self):\n\t\t\"\"\"\n\t\tThe RESTful API session (Read-only Property)\n\n\t\t:return: The requests session\n\t\t:rtype: :class:`requests.Session`\n\t\t\"\"\"\n\t\treturn self._session\n\n\tdef create(self):\n\t\t\"\"\"\n\t\tCreate the requests.Session to communicate with the RESTful API.\n\n\t\t:return: :const:`None`\n\t\t:rtype: NoneType\n\t\t\"\"\"\n\t\tif self._session:\n\t\t\tself.close()\n\n\t\tif not self._session:\n\t\t\tself._session = requests.Session()\n\t\t\tself._session.mount('http://', ra.HTTPAdapter(max_retries=self._max_retries))\n\t\t\tself._session.mount('https://', ra.HTTPAdapter(max_retries=self._max_retries))\n\n\t\t\tmsg = u'Created internal requests Session instance {0:#0x}'\n\t\t\tlog_with_debug_info(logging.DEBUG, msg.format(id(self._session)))\n\n\tdef close(self):\n\t\t\"\"\"\n\t\tClose and cleanup the requests Session object.\n\n\t\t:return: :const:`None`\n\t\t:rtype: NoneType\n\t\t\"\"\"\n\n\t\tif self._session:\n\t\t\tsid = id(self._session)\n\t\t\tself._session.close()\n\t\t\tdel self._session\n\t\t\tself._session = None\n\n\t\t\tmsg = u'Internal requests Session instance 0x{0:x} closed and cleaned up'\n\t\t\tlog_with_debug_info(logging.DEBUG, msg.format(sid))\n\n\t@property\n\tdef server_url(self):\n\t\t\"\"\"\n\t\tThe URL without the api portion. (read-only)\n\n\t\t:return: The URL should match '[\\\\w\\\\+\\\\-\\\\.]+://[\\\\w\\\\+\\\\-\\\\.]+(:\\\\d+)?' e.g. 'https://to.somedomain.net' or 'https://to.somedomain.net:443'\n\t\t:rtype: str\n\t\t\"\"\"\n\n\t\treturn self._server_url\n\n\t@property\n\tdef api_version(self):\n\t\t\"\"\"\n\t\tReturns the api version. (read-only)\n\n\t\t:return: The api version from which this instance will request endpoints.\n\t\t:rtype: str\n\t\t\"\"\"\n\n\t\treturn self._api_version\n\n\t@property\n\tdef api_base_url(self):\n\t\t\"\"\"\n\t\tReturns the base URL. (read-only)\n\n\t\t:return: The base URL should match '[\\\\w\\\\+\\\\-\\\\.]+://[\\\\w\\\\+\\\\-\\\\.]+(:\\\\d+)?' e.g. 'https://to.somedomain.net/api/0.1/'\n\t\t:rtype: str\n\t\t\"\"\"\n\n\t\treturn self._api_base_url\n\n\tdef _build_endpoint(self, api_path, params=None, query_params=None):\n\t\t\"\"\"\n\t\tHelper function to form API URL. The base URL is\n\t\t'<protocol>://<hostname>[:<port>]/<api base url>' e.g. 'https://to.somedomain.net/api/0.1/'\n\n\t\t:param api_path: The path to the API end-point that you want to call which does not include the base URL e.g. ``user/login``, ``servers``, etc. This string can contain substitution parameters as denoted by a valid field_name replacement field specification as per :meth:`str.format` e.g. ``cachegroups/{id}`` or ``cachegroups/{id:d}``\n\t\t:type api_path: str\n\t\t:param params: If :meth:`str.format` field_name replacement field specifications exists in the ``api_path`` use this dictionary to perform replacements of the specifications with the value(s) in the dictionary that match the parameter name(s) e.g. ``{param_id}`` or ``{param_id:d}`` in ``api_string`` is replaced by value in ``params['param_id']``.\n\t\t:type params: Union[Dict[str, Any], None]\n\t\t:param query_params: URL query params to provide to the end-point e.g. ``{ 'sort': 'asc', 'maxresults': 200 }`` which translates to something like ``?sort=asc&maxresults=200`` which is appended to the request URL\n\t\t:type query_params: Union[Dict[str, Any], None]\n\t\t:return: The base url plus the passed and possibly substituted ``api_path`` to form a complete URL to the API resource to request\n\t\t:rtype: str\n\t\t:raises: ValueError\n\t\t\"\"\"\n\n\t\tnew_api_path = api_path\n\n\t\t# Replace all parameters in the new_api_path path, if required\n\t\ttry:\n\t\t\t# Make the parameters values safe for adding to URLs\n\t\t\turl_params = {k: compat.quote(str(v)) if isinstance(v, str)\\\n\t\t\t                                      else v for k, v in iteritems(params)}\n\n\t\t\tlog_with_debug_info(logging.DEBUG, u'URL parameters are: [{0}]'.format(url_params))\n\n\t\t\tqparams = u''\n\t\t\tif query_params:\n\t\t\t\t# Process the URL query parameters\n\t\t\t\tqparams = u'?{0}'.format(compat.urlencode(query_params))\n\t\t\t\tlog_with_debug_info(logging.DEBUG,\n\t\t\t\t                    u'URL query parameters are: [{0}]'.format(qparams))\n\n\t\t\tnew_api_path = api_path.format(**url_params) + qparams\n\t\texcept KeyError as e:\n\t\t\tmsg = (u'Expecting a value for keyword argument [{0}] for format field '\n\t\t\t       u'specification [{1!r}]')\n\t\t\tmsg = msg.format(e, api_path)\n\t\t\tlog_with_debug_info(logging.ERROR, msg)\n\t\t\traise ValueError(msg)\n\t\texcept ValueError as e:\n\t\t\tmsg = (u'One or more values do not match the format field specification '\n\t\t\t       u'[{0!r}]; Supplied values: {1!r} ')\n\t\t\tmsg = msg.format(api_path, params)\n\t\t\tlog_with_debug_info(logging.ERROR, msg)\n\t\t\traise ValueError(msg)\n\n\t\tretval = compat.urljoin(self.api_base_url, new_api_path)\n\n\t\tlog_with_debug_info(logging.DEBUG, u'Built end-point to return: {0}'.format(retval))\n\n\t\treturn retval\n\n\tdef _do_operation(self,\n\t                  operation, api_path, query_params=None, munchify=True, debug_response=False,\n\t                  expected_status_codes=range(200, 300), *unused_args, **kwargs):\n\t\t\"\"\"\n\t\tHelper method to perform HTTP operation requests - This is a boilerplate process for HTTP\n\t\toperations.\n\n\t\t:param operation: Name of method to call on the :attr:`self._session` object to perform the HTTP request\n\t\t:type operation: str\n\t\t:param api_path: The path to the API end-point that you want to call which does not include the URL e.g. ``user/login``, ``servers``, etc. This string can contain substitution parameters as denoted by a valid field_name replacement field specification as per :meth:`str.format` e.g. ``cachegroups/{id}`` or ``cachegroups/{id:d}``\n\t\t:type api_path: str\n\t\t:param: query_params: URL query parameters to provide to the endpoint e.g. ``{ 'sort': 'asc', 'maxresults': 200 }`` which translates to something like ``?sort=asc&maxresults=200`` which is appended to the request URL\n\t\t:type query_params: Union[Dict[str, Any], None]\n\t\t:param: munchify: If :const:`True` encapsulate data to be returned in a :class:`munch.Munch` object which allows keys in a Python dictionary to additionally have attribute access e.g. ``a_dict['a_key']`` with :mod:`munch` becomes ``a_dict['a_key']`` or ``a_dict.a_key``\n\t\t:type munchify: bool\n\t\t:param kwargs: Passed Keyword Parameters. If you need to send JSON data to the endpoint pass the keyword parameter ``data`` with the Python data structure e.g. a ``dict``. This method will convert it to JSON before sending it to the API endpoint.\n\t\t:type kwargs: Dict[str, Any]\n\t\t:param debug_response: If :const:`True`, the actual response data text will be added to the log if a JSON decoding exception is encountered.\n\t\t:type debug_response: bool\n\t\t:type expected_status_codes: Tuple[int]\n\t\t:param: expected_status_codes: expected success HTTP status codes. If the user needs to override the defaults this parameter can be passed e.g. ``(200, 204,)``\n\t\t:type munchify: bool\n\t\t:return: Python data structure distilled from JSON from the API request.\n\t\t:rtype: Tuple[Union[Dict[Text, Any], List[Dict[Text, Any]], munch.Munch, List[munch.Munch]], requests.Response]\n\t\t:raises: miscellaneous.exceptions.OperationError\n\t\t\"\"\"\n\n\t\tif not self._session:\n\t\t\tmsg = u'No session has been created for the API.  Have you called create() yet?'\n\t\t\tlog_with_debug_info(logging.ERROR, msg)\n\t\t\traise OperationError(msg)\n\n\t\tresponse = None\n\t\tretdata = None\n\n\t\tendpoint = self._build_endpoint(api_path, params=kwargs, query_params=query_params)\n\n\t\tparams = {u'headers': self._headers, u'verify': self._verify_cert}\n\n\t\tif u'data' in kwargs:\n\t\t\tparams[u'data'] = json.dumps(kwargs[u'data'])\n\n\t\tlog_with_debug_info(logging.DEBUG, u'Call parameters: {0}'.format(params))\n\n\t\t# Call the API endpoint\n\t\tresponse = getattr(self._session, operation)(endpoint, **params)\n\n\t\tlog_with_debug_info(logging.DEBUG, u'Response status: {0} {1}'.format(response.status_code,\n\t\t                    response.reason))\n\n\t\tif response.status_code not in expected_status_codes:\n\t\t\ttry:\n\t\t\t\tretdata = response.json()\n\t\t\texcept Exception as e:\n\t\t\t\t# Invalid JSON payload.\n\t\t\t\tmsg = (u'HTTP Status Code: [{0}]; API response data for end-point [{1}] does not '\n\t\t\t\t       u'appear to be valid JSON. Cause: {2}.')\n\t\t\t\tmsg = msg.format(response.status_code, endpoint, e)\n\t\t\t\tif debug_response:\n\t\t\t\t\tlog_with_debug_info(logging.ERROR, msg + u' Data: [' + str(response.text) + u']')\n\t\t\t\traise InvalidJSONError(msg, resp=response)\n\t\t\tmsg = u'{0} request to RESTful API at [{1}] expected status(s) {2}; failed: {3} {4};'\\\n\t\t\t      u' Response: {5}'\n\t\t\tmsg = msg.format(operation.upper(), endpoint, expected_status_codes,\n\t\t\t                 response.status_code, response.reason, retdata)\n\t\t\tlog_with_debug_info(logging.ERROR, msg)\n\t\t\traise OperationError(msg, resp=response)\n\n\t\ttry:\n\t\t\tif response.status_code in ('204',):\n\t\t\t\t# \"204 No Content\"\n\t\t\t\tretdata = {}\n\t\t\telse:\n\t\t\t\t# Decode the expected JSON\n\t\t\t\tretdata = response.json()\n\t\texcept Exception as e:\n\t\t\t# Invalid JSON payload.\n\t\t\tmsg = (u'HTTP Status Code: [{0}]; API response data for end-point [{1}] does not '\n\t\t\t       u'appear to be valid JSON. Cause: {2}.')\n\t\t\tmsg = msg.format(response.status_code, endpoint, e)\n\t\t\tif debug_response:\n\t\t\t\tlog_with_debug_info(logging.ERROR, msg + u' Data: [' + str(response.text) + u']')\n\t\t\traise InvalidJSONError(msg, resp=response)\n\t\tretdata = munch.munchify(retdata) if munchify else retdata\n\t\treturn (retdata[u'response'] if u'response' in retdata else retdata), response\n\n\tdef get(self, api_path, *args, **kwargs):\n\t\t\"\"\"\n\t\tPerform http get requests\n\n\t\t:param api_path: The path to the API end-point that you want to call which does not include the base url e.g. ``user/login``, ``servers``, etc. This string can contain substitution parameters as denoted by a valid field_name replacement field specification as per :meth:`str.format` e.g. ``cachegroups/{id}`` or ``cachegroups/{id:d}``\n\t\t:type api_path: str\n\t\t:param kwargs: Passed Keyword Parameters. If you need to send JSON data to the endpoint pass the keyword parameter ``data`` with the Python data structure. This method will convert it to JSON before sending it to the API endpoint. Use ``query_params`` to pass a dictionary of query parameters\n\t\t:type kwargs: Dict[str, Any]\n\t\t:return: Python data structure distilled from JSON from the API request.\n\t\t:rtype: Tuple[Union[Dict[str, Any], List[Dict[str, Any]], munch.Munch, List[munch.Munch]], requests.Response]\n\t\t:raises: Union[LoginError, OperationError]\n\t\t\"\"\"\n\n\t\treturn self._do_operation(u'get', api_path, *args, **kwargs)\n\n\tdef post(self, api_path, *args, **kwargs):\n\t\t\"\"\"\n\t\tPerform http post requests\n\n\t\t:param api_path: The path to the API end-point that you want to call which does not include the base URL e.g. ``user/login``, ``servers``, etc. This string can contain substitution parameters as denoted by a valid field_name replacement field specification as per :meth:`str.format` e.g. ``cachegroups/{id}`` or ``cachegroups/{id:d}``\n\t\t:type api_path: str\n\t\t:param kwargs: Passed Keyword Parameters. If you need to send JSON data to the endpoint pass the keyword parameter ``data`` with the Python data structure. This method will convert it to JSON before sending it to the API endpoint. Use ``query_params`` to pass a dictionary of query parameters\n\t\t:type kwargs: Dict[str, Any]\n\t\t:return: Python data structure distilled from JSON from the API request.\n\t\t:rtype: Tuple[Union[Dict[str, Any], List[Dict[str, Any]], munch.Munch, List[munch.Munch]], requests.Response]\n\t\t:raises: Union[LoginError, OperationError]\n\t\t\"\"\"\n\n\t\treturn self._do_operation(u'post', api_path, *args, **kwargs)\n\n\tdef put(self, api_path, *args, **kwargs):\n\t\t\"\"\"\n\t\tPerform http put requests\n\n\t\t:param api_path: The path to the API end-point that you want to call which does not include the base URL e.g. ``user/login``, ``servers``, etc. This string can contain substitution parameters as denoted by a valid field_name replacement field specification as per :meth:`str.format` e.g. ``cachegroups/{id}`` or ``cachegroups/{id:d}``\n\t\t:type api_path: str\n\t\t:param kwargs: Passed Keyword Parameters. If you need to send JSON data to the endpoint pass the keyword parameter ``data`` with the Python data structure. This method will convert it to JSON before sending it to the API endpoint. Use ``query_params`` to pass a dictionary of query parameters\n\t\t:type kwargs: Dict[str, Any]\n\t\t:return: Python data structure distilled from JSON from the API request.\n\t\t:rtype: Tuple[Union[Dict[str, Any], List[Dict[str, Any]], munch.Munch, List[munch.Munch]], requests.Response]\n\t\t:raises: Union[LoginError, OperationError]\n\t\t\"\"\"\n\n\t\treturn self._do_operation(u'put', api_path, *args, **kwargs)\n\n\tdef delete(self, api_path, *args, **kwargs):\n\t\t\"\"\"\n\t\tPerform HTTP DELETE requests\n\n\t\t:param api_path: The path to the API end-point that you want to call which does not include the base URL e.g. ``user/login``, ``servers``, etc. This string can contain substitution parameters as denoted by a valid field_name replacement field specification as per :meth:`str.format` e.g. ``cachegroups/{id}`` or ``cachegroups/{id:d}``\n\t\t:type api_path: str\n\t\t:param kwargs: Passed Keyword Parameters. If you need to send JSON data to the endpoint pass the keyword parameter ``data`` with the Python data structure. This method will convert it to JSON before sending it to the API endpoint. Use ``query_params`` to pass a dictionary of query parameters\n\t\t:type kwargs: Dict[str, Any]\n\t\t:return: Python data structure distilled from JSON from the API request.\n\t\t:rtype: Tuple[Union[Dict[str, Any], List[Dict[str, Any]], munch.Munch, List[munch.Munch]], requests.Response]\n\t\t:raises: Union[LoginError, OperationError]\n\t\t\"\"\"\n\n\t\treturn self._do_operation(u'delete', api_path, *args, **kwargs)\n\n\tdef head(self, api_path, *args, **kwargs):\n\t\t\"\"\"\n\t\tPerform HTTP HEAD requests\n\t\t:param api_path: The path to the API end-point that you want to call which does not include the base URL e.g. ``user/login``, ``servers``, etc. This string can contain substitution parameters as denoted by a valid field_name replacement field specification as per :meth:`str.format` e.g. ``cachegroups/{id}`` or ``cachegroups/{id:d}``\n\t\t:type api_path: str\n\t\t:param kwargs: Passed Keyword Parameters. If you need to send JSON data to the endpoint pass the keyword parameter ``data`` with the Python data structure. This method will convert it to JSON before sending it to the API endpoint. Use ``query_params`` to pass a dictionary of query parameters\n\t\t:type kwargs: Dict[str, Any]\n\t\t:return: Python data structure distilled from JSON from the API request.\n\t\t:rtype: Tuple[Union[Dict[str, Any], List[Dict[str, Any]], munch.Munch, List[munch.Munch]], requests.Response]\n\t\t:raises: Union[LoginError, OperationError]\n\t\t\"\"\"\n\n\t\treturn self._do_operation(u'head', api_path, *args, **kwargs)\n\n\tdef options(self, api_path, *args, **kwargs):\n\t\t\"\"\"\n\t\tPerform HTTP OPTIONS requests\n\t\t:param api_path: The path to the API end-point that you want to call which does not include the base URL e.g. ``user/login``, ``servers``, etc. This string can contain substitution parameters as denoted by a valid field_name replacement field specification as per :meth:`str.format` e.g. ``cachegroups/{id}`` or ``cachegroups/{id:d}``\n\t\t:type api_path: str\n\t\t:param kwargs: Passed Keyword Parameters. If you need to send JSON data to the endpoint pass the keyword parameter ``data`` with the Python data structure. This method will convert it to JSON before sending it to the API endpoint. Use ``query_params`` to pass a dictionary of query parameters\n\t\t:type kwargs: Dict[str, Any]\n\t\t:return: Python data structure distilled from JSON from the API request.\n\t\t:rtype: Tuple[Union[Dict[str, Any], List[Dict[str, Any]], munch.Munch, List[munch.Munch]], requests.Response]\n\t\t:raises: Union[LoginError, OperationError]\n\t\t\"\"\"\n\n\t\treturn self._do_operation(u'options', api_path, *args, **kwargs)\n\n\tdef patch(self, api_path, *args, **kwargs):\n\t\t\"\"\"\n\t\tPerform HTTP PATCH requests\n\t\t:param api_path: The path to the API end-point that you want to call which does not include the base URL e.g. ``user/login``, ``servers``, etc. This string can contain substitution parameters as denoted by a valid field_name replacement field specification as per :meth:`str.format` e.g. ``cachegroups/{id}`` or ``cachegroups/{id:d}``\n\t\t:type api_path: str\n\t\t:param kwargs: Passed Keyword Parameters. If you need to send JSON data to the endpoint pass the keyword parameter ``data`` with the Python data structure. This method will convert it to JSON before sending it to the API endpoint. Use ``query_params`` to pass a dictionary of query parameters\n\t\t:type kwargs: Dict[str, Any]\n\t\t:return: Python data structure distilled from JSON from the API request.\n\t\t:rtype: Tuple[Union[Dict[str, Any], List[Dict[str, Any]], munch.Munch, List[munch.Munch]], requests.Response]\n\t\t:raises: Union[LoginError, OperationError]\n\t\t\"\"\"\n\n\t\treturn self._do_operation(u'patch', api_path, *args, **kwargs)\n","sub_path":"traffic_control/clients/python/trafficops/restapi.py","file_name":"restapi.py","file_ext":"py","file_size_in_byte":25629,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"126689382","text":"\"\"\"Tools for dealing with Reuters-21578 dataset\n\nContains methods for downloading and parsing the data as well as setting up\ntensorflow graph elements to use it.\n\"\"\"\n\nfrom __future__ import absolute_import\nfrom __future__ import print_function\nfrom __future__ import division\n\nfrom six.moves import xrange\n\nimport os\nimport tarfile\nimport re\nimport xml.etree.ElementTree as ET\nimport collections\nimport logging\nimport math\n\nimport requests\n\nimport numpy as np\nimport tensorflow as tf\n\n\ndef _maybe_download(data_path='reuters21578.tar.gz'):\n    \"\"\"Check if it exists\"\"\"\n    if not os.path.exists(data_path):\n        # better grab it\n        logging.info('Data not found, downloading')\n        url = 'http://archive.ics.uci.edu/ml/machine-learning-databases/reuters21578-mld/reuters21578.tar.gz'\n        req = requests.get(url, stream=True)\n        with open(data_path, 'wb') as f:\n            for chunk in req.iter_content(chunk_size=1024):\n                if chunk:  # might want to ignore keep-alives\n                    f.write(chunk)\n        # got it\n    # quick validate\n    size = os.path.getsize(data_path) >> 20\n    if size != 7:  # oh no!\n        raise ValueError('data file is wrong size ({})'.format(size))\n    return data_path\n\n\ndef _get_vocab_filename(level):\n    \"\"\"Gets a filename for the vocab file\n\n    Args:\n        level: whether we are interested in word or character\n\n    Returns:\n        str: the filename.\n    \"\"\"\n    return os.path.join(\n        os.path.dirname(os.path.realpath(__file__)),\n        'vocab-{}.txt'.format(level))\n\n\ndef _read_one_file(filename, vocab_freqs, token_func, everything=True):\n    \"\"\"Reads one whole file and pulls out the data we need.\n    Returns a list of lists looking like [text, label, set, id] where\n    set is one of {train, test} according to `ModHayes` split.\n    Adds to counts, text is tokenised using provided function.\n    \"\"\"\n    # we have to get weird to make this all happy\n    with open(filename, errors='ignore') as raw_file:\n        file_str = raw_file.read()\n        # skip some crap\n        file_str = re.sub(r'&#\\d{1,2};', '', file_str)\n        # insert root tags\n        file_str = '<root>' + file_str[file_str.find('\\n'):-1] + '</root>'\n        logging.info('..parsing %s', filename)\n        root = ET.fromstring(file_str)\n        data = []\n        for child in root:\n            if child.attrib['TOPICS'] == 'YES' or everything:\n                try:\n                    text = child.find('./TEXT/BODY').text\n                except AttributeError:\n                    # some don't have body tags\n                    text = child.find('./TEXT').text\n                text = token_func(text)\n                if vocab_freqs is not None:\n                    for symbol in text:\n                        vocab_freqs[symbol] += 1\n                topics = [bytes(d.text, 'utf-8')\n                          for d in child.findall('./TOPICS/D')]\n                if child.attrib['CGISPLIT'] == 'TRAINING-SET':\n                    dataset = 'train'\n                else:\n                    dataset = 'test'\n                data.append(\n                    [text, topics, dataset, child.attrib['NEWID']])\n        return data\n\n\ndef word_split(text):\n    \"\"\"This is our main tokeniser, could do stemming etc, does not.\"\"\"\n    # first remove caps\n    text = text.casefold()\n    # replace numbers, including with commas or .\n    text = re.sub(r'(\\d+([.,]?))+', ' <NUMBER> ', text)\n    text = re.sub(r'[.?!]', ' <STOP> ', text)  # mark ends of sentences\n    # remove remaining punctuation\n    text = re.sub(r'[^\\w\\s<>]', ' <PUNCT> ', text)\n    return text.split()  # finally split on whitespace\n\n\ndef char_split(text):\n    \"\"\"Split it character wise\"\"\"\n    return list(text)\n\n\ndef _log_stats(data):\n    \"\"\"Just log some things about the data\"\"\"\n    cumulative = 0\n    longest = -1\n    shortest = 1000000\n    for item in data:\n        length = len(item)\n        cumulative += length\n        if length > longest:\n            longest = length\n        if length < shortest:\n            shortest = length\n    mean = cumulative/len(data)\n    cumulative = 0\n    for item in data:\n        cumulative += (mean - len(item))**2\n    stddev = math.sqrt(cumulative / len(data))\n    logging.info('..mean: %d (stddev: %d)', mean, stddev)\n    logging.info('..max: %d', longest)\n    logging.info('..min: %d', shortest)\n\n\ndef get_special_ids():\n    \"\"\"returns a dict of the special ids\"\"\"\n    return {\n        '<GO>': 0,\n        '<UNK>': 1,\n        '<STOP>': 2,\n        '<PUNCT>': 3,\n        '<PAD>': 4\n    }\n\n\ndef get_text_and_labels(data_dir='data', ids=None, split='both'):\n    \"\"\"Gets the dataset just as raw text and labels.\n\n    Args:\n       data_dir: where the data should be. If it is not there it will be\n            downloaded.\n       ids: optional list of ids. If present only the matching records are\n           returned.\n       set: optional string, one of `train`, `test` or `both`.\n    \"\"\"\n    if not os.path.exists(data_dir):\n        with tarfile.open(_maybe_download(), 'r:gz') as datafile:\n            datafile.extractall(path=data_dir)\n    filenames = [os.path.join(data_dir, f)\n                 for f in os.listdir(data_dir) if re.search('sgm$', f)]\n    data = []\n    for filename in filenames:\n        data.extend(_read_one_file(filename, None, word_split))\n    # check the split while we still can\n    if split != 'both':\n        data = [item for item in data if item[2] == split]\n    if ids:\n        text = [' '.join(item[0]) for item in data if item[-1] in ids]\n        labels = [item[1] for item in data if item[-1] in ids]\n    else:\n        text = [' '.join(item[0]) for item in data]\n        labels = [item[1] for item in data]\n\n    return text, [[label.decode()\n                   for label in labelling]\n                  for labelling in labels]\n\n\ndef get_reuters(data_dir='data', level='word', min_reps=1, most_common=10000):\n    \"\"\"Get the dataset as (training, test, vocab).\n    first two are tuples containing a sequence, the labels and what part\n    split they are in, vocab is\n    a map of strings->int ids.\n    \"\"\"\n    if not os.path.exists(data_dir):\n        with tarfile.open(_maybe_download(), 'r:gz') as datafile:\n            logging.info('extracting archive')\n            datafile.extractall(path=data_dir)\n    # we are just going to hold the data in memory\n    filenames = [os.path.join(data_dir, f)\n                 for f in os.listdir(data_dir) if re.search('sgm$', f)]\n    all_data = []\n    vocab_freqs = collections.Counter()\n    split_func = word_split if level == 'word' else char_split\n    for filename in filenames:\n        all_data.extend(_read_one_file(filename, vocab_freqs, split_func))\n    logging.info('got %d records', len(all_data))\n    ordered_words = vocab_freqs.most_common()\n    to_remove = set()\n    if min_reps > 1:\n        for word, count in ordered_words[-1:0:-1]:\n            # loop backwards\n            if count > min_reps:\n                break  # must have them all\n            to_remove.add(word)\n            del vocab_freqs[word]\n    if most_common is not None:\n        for word, count in ordered_words[most_common:]:\n            to_remove.add(word)\n            del vocab_freqs[word]\n    logging.info('Removing %d words from vocab.', len(to_remove))\n    for item in all_data:\n        item[0] = ['<UNK>' if i in to_remove else i for i in item[0]]\n    symbol_to_id = get_special_ids()\n    id_num = 5  # start after specials\n    for symbol in vocab_freqs:\n        if symbol not in symbol_to_id:\n            symbol_to_id[symbol] = id_num\n            id_num += 1\n    logging.info('..last id %d', id_num)\n    logging.info('..vocab size %d', len(symbol_to_id))\n\n    for item in all_data:\n        item[0] = [symbol_to_id[i] for i in item[0]]\n    training = [item for item in all_data if item[2] == 'train']\n    test = [item for item in all_data if item[2] != 'train']\n\n    vocab_filename = _get_vocab_filename(level)\n    if not os.path.exists(vocab_filename):\n        logging.info('..no existing vocab file, writing.')\n        _write_vocab(symbol_to_id, vocab_filename)\n        logging.info('..written to \"%s\"', vocab_filename)\n\n    return (training, test, symbol_to_id)\n\n\ndef get_vocab():\n    \"\"\"Gets the words if the file is there\"\"\"\n    with open(_get_vocab_filename('word')) as f:\n        return {a: int(b) for a, b in (line.split(',') for line in f)}\n\n\ndef _int64_feature(value):\n    return tf.train.Feature(int64_list=tf.train.Int64List(value=value))\n\n\ndef _bytes_feature(value):\n    return tf.train.Feature(bytes_list=tf.train.BytesList(value=value))\n\n\ndef _write_vocab(vocab, path):\n    \"\"\"Writes vocab dict as csv\"\"\"\n    with open(path, 'w') as f:\n        for key in vocab:\n            f.write('{},{}\\n'.format(key, vocab[key]))\n\n\ndef check_tf_data_exists(data_dir='data/tf-records', records_per=500):\n    \"\"\"Double checks the data exists in Tensorflow Record format\"\"\"\n    if not os.path.exists(data_dir):\n        os.mkdir(data_dir)\n        logging.info('No tf record data, fixing')\n        training, test, vocab = get_reuters()\n        _write_vocab(vocab, os.path.join(data_dir,))\n        filename = os.path.join(data_dir, 'train-data')\n        logging.info('writing training set')\n        _write_tfrecords(training, filename, records_per)\n        logging.info('writing test set')\n        filename = os.path.join(data_dir, 'test-data')\n        _write_tfrecords(test, filename, records_per)\n    else:\n        logging.info('Found the data :)')\n\n\ndef _write_tfrecords(data, prefix, records_per=500, sort_length=True):\n    \"\"\"write one set\"\"\"\n    if sort_length:\n        data.sort(key=len)\n    num_files = ((len(data)-1) // records_per) + 1\n    item_iter = iter(data)\n    logging.info('..this will take %d files', num_files)\n    filename = prefix + '-{}.tfrecords'\n    for filenum in xrange(num_files):\n        f = tf.python_io.TFRecordWriter(filename.format(filenum))\n        for i in xrange(records_per):\n            try:\n                item = next(item_iter)\n            except StopIteration:\n                break\n            example = tf.train.Example(\n                features=tf.train.Features(feature={\n                    'text': _int64_feature(item[0]),\n                    'labels': _bytes_feature(item[1]),\n                    'id': _int64_feature([int(item[3])])\n                    }))\n            f.write(example.SerializeToString())\n        f.close()\n\n\ndef get_input_pipeline(data_dir, train, num_epochs, batch_size, buckets):\n    \"\"\"Gets a full input pipeline of file readers, shuffling etc.\n    Returns (data, labels, bucket_idx) batches where data is\n    [[batch_size] * bucket_size] for whatever bucket.\"\"\"\n    def read_file(filename_queue):\n        reader = tf.TFRecordReader()\n        _, serialized_eg = reader.read(filename_queue)\n        features = tf.parse_single_example(\n            serialized_eg,\n            features={\n                'text': tf.VarLenFeature([], tf.int64),\n                'labels': tf.VarLenFeature([], tf.string),\n            })\n        text = [tf.cast(num, tf.int32) for num in features['text']]\n\n        return text, features['text']\n\n    if not num_epochs:\n        num_epochs = None  # if unspecified loop forevererer\n\n    if train:\n        file_expr = os.path.join(data_dir, 'train-data-*')\n    else:\n        file_expr = os.path.join(data_dir, 'test-data-*')\n    with tf.name_scope('input'):\n        filename_queue = tf.train.string_input_producer(\n            tf.train.match_filenames_once(file_expr),\n            num_epochs=num_epochs,\n            shuffle=True)\n        example, label = read_file(filename_queue)\n        min_after_dequeue = 5000\n        capacity = min_after_dequeue + 4 * batch_size\n        example_batch, label_batch = tf.train.shuffle_batch(\n            [example, label], batch_size=batch_size, capacity=capacity,\n            min_after_dequeue=min_after_dequeue, num_threads=3)\n        return example_batch, label_batch\n\n\ndef _run():\n    \"\"\"Potentially download and convert the data\"\"\"\n    check_tf_data_exists()\n\nif __name__ == '__main__':\n    logging.getLogger().setLevel(logging.INFO)\n    _run()\n","sub_path":"reuters/reuters.py","file_name":"reuters.py","file_ext":"py","file_size_in_byte":12084,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"38402078","text":"# Copyright 2018 ETH Zurich\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this file except in compliance with the License.\n# You may obtain a copy of the License at\n#\n#   http://www.apache.org/licenses/LICENSE-2.0\n#\n# Unless required by applicable law or agreed to in writing, software\n# distributed under the License is distributed on an \"AS IS\" BASIS,\n# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n# See the License for the specific language governing permissions and\n# limitations under the License.\n\nimport configparser\nimport enum\nimport os\nfrom collections import OrderedDict\n\nfrom scionlab.models.core import Service\nfrom scionlab.scion_topology import TopologyInfo\n\nfrom scionlab.defines import (\n    PROPAGATE_TIME_CORE,\n    PROPAGATE_TIME_NONCORE,\n    PROM_PORT_OFFSET,\n    PROM_PORT_DI,\n    PROM_PORT_SD,\n    BS_PORT,\n    PS_PORT,\n    CS_PORT,\n    BS_QUIC_PORT,\n    PS_QUIC_PORT,\n    CS_QUIC_PORT,\n    SD_QUIC_PORT,\n    SCION_CONFIG_DIR,\n    SCION_LOG_DIR,\n    SCION_VAR_DIR,\n    GEN_PATH,\n)\n\nfrom lib.crypto.asymcrypto import (\n    get_core_sig_key_file_path,\n    get_enc_key_file_path,\n    get_sig_key_file_path,\n)\nfrom lib.crypto.util import (\n    CERT_DIR,\n    get_offline_key_file_path,\n    get_online_key_file_path,\n    get_master_key_file_path,\n    MASTER_KEY_0,\n    MASTER_KEY_1,\n)\nfrom lib.defines import (\n    AS_CONF_FILE,\n    SCIOND_API_SOCKDIR,\n)\n\nTYPE_BR = 'BR'\nTYPE_SD = 'SD'\nSERVICES_TO_SYSTEMD_NAMES = {\n    Service.BS: 'scion-beacon-server',\n    Service.CS: 'scion-certificate-server',\n    Service.PS: 'scion-path-server',\n    Service.BW: 'scion-bwtestserver',\n    TYPE_BR: 'scion-border-router',\n}\n\nDEFAULT_ENV = ['TZ=UTC']\nBORDER_ENV = DEFAULT_ENV + ['GODEBUG=\"cgocheck=0\"']\n\nCMDS = {\n    Service.BS: 'beacon_srv',\n    Service.CS: 'cert_srv',\n    Service.PS: 'path_srv',\n    TYPE_BR: 'border',\n    TYPE_SD: 'sciond',\n}\n\n\nclass ProcessControl(enum.Enum):\n    SYSTEMD = 0\n    SUPERVISORD = 1\n\n\ndef create_gen(host, archive, process_control):\n    \"\"\"\n    Generate the gen/ folder for the :host: in the given archive-writer\n    :param host: Host object\n    :param scionlab.util.archive.BaseArchiveWriter archive: output archive-writer\n    :param ProcessControl process_control: configuration generated for installation with\n                                           supervisord/systemd\n    \"\"\"\n    generator = _ConfigGenerator(host, archive)\n    if process_control == ProcessControl.SUPERVISORD:\n        generator.generate_for_supervisord()\n    else:\n        assert process_control == ProcessControl.SYSTEMD\n        generator.generate_for_systemd()\n\n\nclass _ConfigGenerator:\n    \"\"\"\n    Generate the configuration for the given host into an archive.\n    This class exists mainly to avoid passing the same information (host, AS, topology information\n    and archive writer) to all the helper functions.\n    \"\"\"\n    def __init__(self, host, archive):\n        self.host = host\n        self.archive = archive\n        self.AS = host.AS\n        self.topo_info = TopologyInfo(self.AS)\n\n    def generate_for_systemd(self):\n        config_builder = _ConfigBuilder(config_dir=SCION_CONFIG_DIR,\n                                        log_dir=SCION_LOG_DIR,\n                                        var_dir=SCION_VAR_DIR,\n                                        isd_as_dir=_isd_as_dir(self.AS))\n        self._gen_configs(config_builder)\n\n        self._write_systemd_services_file()\n\n    def generate_for_supervisord(self):\n        config_builder = _ConfigBuilder(config_dir='',\n                                        log_dir='logs',\n                                        var_dir='gen-cache',\n                                        isd_as_dir=_isd_as_dir(self.AS))\n        self._gen_configs(config_builder)\n\n        self._write_supervisord_files()\n\n    def _gen_configs(self, cb):\n        for router in self._routers():\n            self._write_elem_dir(router.instance_name, 'br.toml', cb.build_br_conf(router))\n\n        for service in self._services():\n            if service.type == Service.BS:\n                self._write_elem_dir(service.instance_name, 'bs.toml', cb.build_bs_conf(service))\n            elif service.type == Service.PS:\n                self._write_elem_dir(service.instance_name, 'ps.toml', cb.build_ps_conf(service))\n            elif service.type == Service.CS:\n                self._write_elem_dir(service.instance_name, 'cs.toml', cb.build_cs_conf(service))\n\n        self._write_elem_dir('endhost', 'sd.toml', cb.build_sciond_conf(self.host),\n                             with_keys=False)\n        self.archive.write_toml((GEN_PATH, 'dispatcher', 'disp.toml'), cb.build_disp_conf())\n\n    def _write_elem_dir(self, elem_id, toml_filename, conf, with_keys=True):\n        \"\"\"\n        Fill the service instance configuration directory for in the gen folder for a SCION element.\n        Adds\n          ISD<isd>/AS<as>/<elem-id>/\n            - topology.json\n            - as.yml\n            - certs/\n            - keys/, if with_keys\n        \"\"\"\n        elem_dir = self._elem_dir(elem_id)\n\n        self.archive.write_toml((elem_dir, toml_filename), conf)\n\n        self._write_topo(elem_dir)\n        self._write_as_conf(elem_dir)\n        self._write_certs_trc(elem_dir)\n        if with_keys:\n            self._write_keys(elem_dir)\n\n    def _write_certs_trc(self, elem_dir):\n        trc_version = self.AS.isd.trc['Version']\n        self.archive.write_json((elem_dir, CERT_DIR, _trc_filename(self.AS.isd, trc_version)),\n                                self.AS.isd.trc)\n\n        cert_version = self.AS.certificate_chain['0']['Version']\n        self.archive.write_json((elem_dir, CERT_DIR, _cert_chain_filename(self.AS, cert_version)),\n                                self.AS.certificate_chain)\n\n    def _write_keys(self, elem_dir):\n        as_ = self.AS\n        archive = self.archive\n        archive.write_text(get_sig_key_file_path(elem_dir), as_.sig_priv_key)\n        archive.write_text(get_enc_key_file_path(elem_dir), as_.enc_priv_key)\n        archive.write_text(get_master_key_file_path(elem_dir, MASTER_KEY_0), as_.master_as_key)\n        archive.write_text(get_master_key_file_path(elem_dir, MASTER_KEY_1), as_.master_as_key)\n        if as_.is_core:\n            archive.write_text(get_core_sig_key_file_path(elem_dir), as_.core_sig_priv_key)\n            archive.write_text(get_online_key_file_path(elem_dir), as_.core_online_priv_key)\n            archive.write_text(get_offline_key_file_path(elem_dir), as_.core_offline_priv_key)\n\n    def _write_topo(self, elem_dir):\n        self.archive.write_json((elem_dir, 'topology.json'), self.topo_info.topo)\n\n    def _write_as_conf(self, elem_dir):\n        conf = {\n            'RegisterTime': 5,\n            'PropagateTime': 5,\n            'CertChainVersion': 0,\n            'RegisterPath': True,\n            'PathSegmentTTL': 21600,\n        }\n        self.archive.write_yaml((elem_dir, AS_CONF_FILE), conf)\n\n    def _write_systemd_services_file(self):\n        ia = self.AS.isd_as_path_str()\n        unit_names = [\"scion-border-router@%s-%i.service\" % (ia, router.instance_id)\n                      for router in self._routers()]\n        unit_names += [\"%s@%s-%i.service\" % (SERVICES_TO_SYSTEMD_NAMES[service.type], ia,\n                                             service.instance_id) for service in self._services()]\n        unit_names += [\"%s.service\" % SERVICES_TO_SYSTEMD_NAMES[service.type]\n                       for service in self._extra_services()]\n        unit_names.append('scion-daemon@%s.service' % self.AS.isd_as_path_str())\n        unit_names.append('scion-dispatcher.service')\n\n        self.archive.write_text('scionlab-services.txt', '\\n'.join(unit_names))\n\n    def _write_supervisord_files(self):\n        for r in self._routers():\n            self._write_supervisord_conf(r.instance_name, CMDS[TYPE_BR], 'br.toml', env=BORDER_ENV)\n        for s in self._services():\n            self._write_supervisord_conf(s.instance_name, CMDS[s.type], '%s.toml' % s.type.lower())\n\n        sd_prog_id = \"sd%s\" % self.AS.isd_as_path_str()\n        self._write_supervisord_conf('endhost', CMDS[TYPE_SD], 'sd.toml', prog_id=sd_prog_id)\n        self._write_disp_supervisord_conf()\n\n        prog_ids = [r.instance_name for r in self._routers()] + \\\n                   [s.instance_name for s in self._services()] + \\\n                   [sd_prog_id, 'dispatcher']\n        self._write_supervisord_group_config(prog_ids)\n\n    def _write_supervisord_conf(self, elem_id, prog, toml_filename, env=DEFAULT_ENV, prog_id=None):\n        elem_dir = self._elem_dir(elem_id)\n        cmd = 'bin/%s -config %s/%s' % (prog, elem_dir, toml_filename)\n        prog_id = prog_id or elem_id\n        conf = _build_supervisord_conf(prog_id, cmd, env)\n        self.archive.write_config((elem_dir, 'supervisord.conf'), conf)\n\n    def _write_disp_supervisord_conf(self):\n        cmd = 'bin/godispatcher -config %s' % os.path.join(GEN_PATH, 'dispatcher', 'disp.toml')\n        conf = _build_supervisord_conf('dispatcher', cmd, DEFAULT_ENV, priority=50, startsecs=1)\n        self.archive.write_config((GEN_PATH, 'dispatcher', 'supervisord.conf'), conf)\n\n    def _write_supervisord_group_config(self, prog_ids):\n        config = configparser.ConfigParser()\n        config['group:' + \"as%s\" % self.AS.isd_as_path_str()] = {'programs': \",\".join(prog_ids)}\n        self.archive.write_config((_isd_as_dir(self.AS), 'supervisord.conf'), config)\n\n    def _routers(self):\n        return (r for r in self.topo_info.routers if r.host == self.host)\n\n    def _services(self):\n        return (s for s in self.topo_info.services if s.host == self.host)\n\n    def _extra_services(self):\n        return list(self.host.services.filter(type__in=Service.EXTRA_SERVICE_TYPES))\n\n    def _elem_dir(self, elem_id):\n        return os.path.join(_isd_as_dir(self.AS), elem_id)\n\n\nclass _ConfigBuilder:\n    \"\"\"\n    Helper object for `_ConfigGenerator`\n    Builds the *.toml-configuration for the SCION services.\n    \"\"\"\n    def __init__(self, config_dir, log_dir, var_dir, isd_as_dir):\n        self.config_dir = config_dir\n        self.config_isd_as_dir = os.path.join(config_dir, isd_as_dir)\n        self.log_dir = log_dir\n        self.var_dir = var_dir\n\n    def build_disp_conf(self):\n        conf = self._build_common_conf('dispatcher', PROM_PORT_DI)\n        conf.update({\n            'dispatcher': {'ID': 'dispatcher', 'SocketFileMode': '0777'},\n        })\n        return conf\n\n    def build_br_conf(self, router):\n        conf = self._build_base_goservice_conf(router.instance_name,\n                                               router.internal_port + PROM_PORT_OFFSET)\n        conf.update({\n            'br': {\n                'Profile': False,\n            }\n        })\n        return conf\n\n    def build_bs_conf(self, service):\n        conf = self._build_goservice_conf(service.instance_name, service.host.internal_ip,\n                                          BS_QUIC_PORT, BS_PORT + PROM_PORT_OFFSET)\n\n        propagate_time = PROPAGATE_TIME_CORE if service.AS.is_core else PROPAGATE_TIME_NONCORE\n        interval = '{}s'.format(propagate_time)\n\n        conf.update({\n            'beaconDB': {\n                'Backend': 'sqlite',\n                'Connection': '%s.beacon.db' % os.path.join(self.var_dir, service.instance_name),\n            },\n            'BS': {\n                'OriginationInterval': interval,\n                'PropagationInterval': interval,\n                'RevTTL': '20s',\n                'RevOverlap': '5s'\n            },\n        })\n        return conf\n\n    def build_ps_conf(self, service):\n        conf = self._build_goservice_conf(service.instance_name, service.host.internal_ip,\n                                          PS_QUIC_PORT, PS_PORT + PROM_PORT_OFFSET)\n        conf.update({\n            'ps': {\n                'PathDB': {\n                    'Backend': 'sqlite',\n                    'Connection': '%s.path.db' % os.path.join(self.var_dir, service.instance_name),\n                },\n                'SegSync': True,\n            },\n        })\n        return conf\n\n    def build_cs_conf(self, service):\n        conf = self._build_goservice_conf(service.instance_name, service.host.internal_ip,\n                                          CS_QUIC_PORT, CS_PORT + PROM_PORT_OFFSET)\n        conf.update({\n            'sd_client': {\n                'Path': os.path.join(SCIOND_API_SOCKDIR, 'default.sock')\n            },\n            'cs': {\n                'LeafReissueTime': \"6h\",\n                'IssuerReissueTime': \"3d\",\n                'ReissueRate': \"10s\",\n                'ReissueTimeout': \"5s\",\n            },\n        })\n        return conf\n\n    def build_sciond_conf(self, host):\n        instance_name = 'sd%s' % host.AS.isd_as_path_str()\n        instance_dir = 'endhost'\n        conf = self._build_goservice_conf(instance_name, host.internal_ip, SD_QUIC_PORT,\n                                          PROM_PORT_SD, instance_dir=instance_dir)\n        conf.update({\n            'sd': {\n                'Reliable': os.path.join(SCIOND_API_SOCKDIR, \"default.sock\"),\n                'Unix': os.path.join(SCIOND_API_SOCKDIR, \"default.unix\"),\n                'SocketFileMode': '0777',\n                'Public': '{IA},[{ip}]:0'.format(IA=host.AS.isd_as_str(), ip=host.internal_ip),\n                'PathDB': {\n                    'Connection': '%s.path.db' % os.path.join(self.var_dir, instance_name),\n                },\n            },\n        })\n        return conf\n\n    def _build_goservice_conf(self, instance_name, host_ip, quic_port, prometheus_port,\n                              instance_dir=None):\n        \"\"\" Builds the toml configuration common to SD,BS,CS and PS \"\"\"\n        conf = self._build_base_goservice_conf(instance_name, prometheus_port, instance_dir)\n        conf['general']['ReconnectToDispatcher'] = True\n        conf.update({\n            'trustDB': {\n                'Backend': 'sqlite',\n                'Connection': '%s.trust.db' % os.path.join(self.var_dir, instance_name),\n            },\n            'quic': {\n                'KeyFile':  os.path.join(self.config_dir, 'gen-certs/tls.key'),\n                'CertFile': os.path.join(self.config_dir, 'gen-certs/tls.pem'),\n                'ResolutionFraction': 0.4,\n                'Address': '[{host}]:{port}'.format(host=host_ip, port=quic_port)\n            },\n        })\n        return conf\n\n    def _build_base_goservice_conf(self, instance_name, prometheus_port, instance_dir=None):\n        \"\"\" Builds the toml configuration common to SD,BS,CS,PS and BR \"\"\"\n        conf = self._build_common_conf(instance_name, prometheus_port)\n        conf.update({\n            'general': {\n                'ID': instance_name,\n                'ConfigDir': os.path.join(self.config_isd_as_dir, instance_dir or instance_name),\n            },\n            'discovery': {\n                'static': {'Enable': False},\n                'dynamic': {'Enable': False},\n            },\n        })\n        return conf\n\n    def _build_common_conf(self, logfile_name, prometheus_port):\n        \"\"\" Builds the toml configuration common to all services (disp,SD,BS,CS,PS and BR) \"\"\"\n        conf = {\n            'metrics': {'Prometheus': '[127.0.0.1]:%s' % prometheus_port},\n            'logging': {\n                'file': {\n                    'Path': '%s.log' % os.path.join(self.log_dir, logfile_name),\n                    'Level': 'debug',\n                    'MaxAge': 3,\n                    'MaxBackups': 1,\n                },\n            },\n        }\n        return conf\n\n\ndef _build_supervisord_conf(program_id, cmd, envs, priority=100, startsecs=5):\n    config = configparser.ConfigParser()\n    config['program:' + program_id] = OrderedDict([\n        ('autostart', 'false'),\n        ('autorestart', 'true'),\n        ('environment', ','.join(envs)),\n        ('stdout_logfile', '%s.OUT' % os.path.join('logs', program_id)),\n        ('stderr_logfile', '%s.ERR' % os.path.join('logs', program_id)),\n        ('startretries', '0'),\n        ('startsecs', str(startsecs)),\n        ('priority', str(priority)),\n        ('command',  cmd),\n    ])\n    return config\n\n\ndef _cert_chain_filename(as_, version):\n    \"\"\"\n    Return the certificate chain file path for a given ISD.\n    \"\"\"\n    return 'ISD%s-AS%s-V%s.crt' % (as_.isd.isd_id, as_.as_path_str(), version)\n\n\ndef _trc_filename(isd, version):\n    \"\"\"\n    Return the TRC file path for a given ISD.\n    \"\"\"\n    return 'ISD%s-V%s.trc' % (isd.isd_id, version)\n\n\ndef _isd_as_dir(as_):\n    return os.path.join(GEN_PATH, \"ISD%s\" % as_.isd.isd_id, \"AS%s\" % as_.as_path_str())\n","sub_path":"scionlab/scion_config.py","file_name":"scion_config.py","file_ext":"py","file_size_in_byte":16599,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"474204516","text":"import logging\nfrom typing import Any, Dict, List\n\nfrom django.db.models import Model\nfrom rest_framework import serializers\n\nfrom superhero import helpers\nfrom superhero.models import Trivia, Episode, Series\n\n\nlog = logging.getLogger('django')\n\n\nclass TriviaSerializer(serializers.HyperlinkedModelSerializer):\n    class Meta:\n        model = Trivia\n        fields = helpers.get_fields_from_model_name('Trivia')\n        extra_kwargs = {\n            'trivia_id': {'validators': []}\n        }\n\n\nclass EpisodeSerializer(serializers.HyperlinkedModelSerializer):\n    trivia = TriviaSerializer(many=True, required=False)\n\n    class Meta:\n        model = Episode\n        fields = helpers.get_fields_from_model_name('Episode') + ['trivia']\n        extra_kwargs = {\n            'episode_id': {'validators': []}\n        }\n\n    def create(self, validated_data: Dict[str, Any]) -> Episode:\n        trivia_data = validated_data.pop('trivia')\n        episode = Episode.objects.create(**validated_data)\n        log.info(f'Created Episode: {episode}')\n        for trivia_json in trivia_data:\n            new_trivia = Trivia.objects.create(episode=episode, **trivia_json)\n            log.info(f'Created Trivia: {new_trivia}')\n        return episode\n\n\nclass SeriesSerializer(serializers.HyperlinkedModelSerializer):\n    episodes = EpisodeSerializer(many=True)\n    series_wide_trivia = TriviaSerializer(many=True, required=False)\n\n    class Meta:\n        model = Series\n        fields = helpers.get_fields_from_model_name('Series') + ['episodes', 'series_wide_trivia']\n        extra_kwargs = {\n            'series_id': {'validators': []}\n        }\n\n    def create(self, validated_data: Dict[str, Any]) -> Series:\n        episode_data = validated_data.pop('episodes')\n        series_trivia_data = validated_data.pop('series_wide_trivia')\n        series = Series.objects.create(**validated_data)\n        log.info(f'Created Series: {series}')\n        episode_trivia_ids = set()\n        for episode in episode_data:\n            self.create_linked_episode_data(episode, series, episode_trivia_ids=episode_trivia_ids)\n        for trivia_json in series_trivia_data:\n            if trivia_json['trivia_id'] not in episode_trivia_ids:\n                new_trivia = Trivia.objects.create(series=series, **trivia_json)\n                log.info(f'Created Trivia: {new_trivia}')\n        return series\n\n    @staticmethod\n    def create_linked_episode_data(episode_data: Dict[str, Any], series_instance: Series, episode_trivia_ids: List[str]):\n        trivia_data = episode_data.pop('trivia')  if 'trivia' in episode_data else []\n        episode = Episode.objects.create(series=series_instance, **episode_data)\n        log.info(f'Created Episode: {episode}')\n        for trivia_json in trivia_data:\n            new_trivia = Trivia.objects.create(series=series_instance, episode=episode, **trivia_json)\n            log.info(f'Created Trivia: {new_trivia}')\n            episode_trivia_ids.add(trivia_json['trivia_id'])\n\n    @staticmethod\n    def update_object(instance: Model, new_data: Dict[str, Any], **kwargs):\n        new_data = dict(new_data)\n        new_data.update(kwargs)\n        for field in instance._meta.fields:\n            if field.name in new_data:\n                setattr(instance, field.name, new_data[field.name])\n        instance.save()\n\n    def update_trivia_object(self, instance: Series, trivia_id: str, trivia_json: Dict[str, Any], **kwargs):\n        try:\n            trv_instance = Trivia.objects.get(trivia_id=trivia_id)\n        except Trivia.DoesNotExist:\n            new_trivia = Trivia.objects.create(series=instance, **kwargs, **trivia_json)\n            log.info(f'Created Trivia: {new_trivia}')\n        else:\n            self.update_object(trv_instance, trivia_json, series=instance)\n            log.info(f'Updated Trivia: {trv_instance}')\n\n    def update(self, instance: Series, validated_data: Dict[str, Any]) -> Series:\n        episode_data = validated_data.pop('episodes') if 'episodes' in validated_data else []\n        series_trivia_data = validated_data.pop('trivia') if 'trivia' in validated_data else []\n        self.update_object(instance, validated_data)\n        log.info(f'Updated Series: {instance}')\n        episode_trivia_ids = set()\n        for episode_json in episode_data:\n            trivia_data = episode_json.pop('trivia') if 'trivia' in validated_data else []\n            try:\n                ep_instance = Episode.objects.get(episode_id=episode_json['episode_id'])\n            except Episode.DoesNotExist:\n                self.create_linked_episode_data(episode_json, instance, series_trivia_data)\n            else:\n                self.update_object(ep_instance, episode_json, series=instance)\n                log.info(f'Updated Episode: {ep_instance}')\n\n            for trivia_json in trivia_data:\n                self.update_trivia_object(instance, trivia_json['trivia_id'], trivia_json)\n                episode_trivia_ids.add(trivia_json['trivia_id'])\n\n        for trivia_json in series_trivia_data:\n            if trivia_json['trivia_id'] in episode_trivia_ids:\n                try:\n                    trv_instance = Trivia.objects.get(trivia_id=trivia_json['trivia_id'])\n                except Trivia.DoesNotExist:\n                    new_trivia = Trivia.objects.create(series=instance, episode=ep_instance, **trivia_json)\n                    log.info(f'Created Trivia: {new_trivia}')\n                else:\n                    self.update_object(trv_instance, trivia_json, series=instance)\n\n        return instance\n","sub_path":"superhero/serializers.py","file_name":"serializers.py","file_ext":"py","file_size_in_byte":5537,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"442534155","text":"####################################################################################################\n# 135Code Command Line Interface\n####################################################################################################\nimport subprocess\nimport sys\nimport inspect\nfrom importlib import import_module\nfrom output_suppressor import suppress_output\nimport os\nimport json\n\nexitVal = 0\n\ndef helpMessageBuilder(firstArg):\n    if not sys.stdout.isatty():\n        return 'For help regarding the API, go to https://github.com/lirannl/135service'\n    execName = \"\"\n    # If executed with a .py extension\n    if (firstArg.split(\".\")[-1] == \"py\"):\n        execName = f\"{sys.executable} {firstArg}\"\n    else:\n        execName = firstArg\n    return f\"\"\"\\\nUsage: {execName} [function] [algorithm]\n\nExample: {execName} encrypt cipher135\nNext, provide the function's arguments in the form of a JSON string\nFinally, EOF\n\nGlossary:\n\n-h,     Shows all things happiness\n\nCreated 25/05/2020 in the land down under!\"\"\"\n\n# Install packages via pip\n\n\ndef install(package: str):\n    with open(os.devnull) as devnull:\n        subprocess.check_call(\n            [sys.executable, \"-m\", \"pip\", \"install\", package], stdout=devnull, stderr=devnull)\n\n# Get a function from the module, or from the overrides list\n\n\ndef getFunction(name, module, overrides):\n    try:\n        return overrides[name]\n    except KeyError:\n        return getattr(module, f\"API_{name}\")\n\n\n# Custom Command Line Argument options.\noptions = {\n    # Help information.\n    \"-h\": lambda *ignored: helpMessageBuilder(sys.argv[0])\n}\n\n# Try importing a given module from a given list of folders\ndef subfolderImport(moduleName, dirList):\n    try:\n        with suppress_output():\n            return import_module(f\"{dirList[0]}.{moduleName}\")\n    except ModuleNotFoundError as e:\n        # If the missing module is not the target module for import\n        missingModule = e.name.split('.')[-1]\n        if missingModule != moduleName:\n            try:\n                # Install it\n                install(missingModule)\n                # Try again upon successful installation\n                return subfolderImport(moduleName, dirList)\n            except subprocess.CalledProcessError:\n                sys.stderr.write(\n                    f\"INTERNAL Failed to install module: \\\"{missingModule}\\\"\\n\")\n                sys.exit(1)\n        if (len(dirList) > 1):\n            # Not found in subfolder, search again without this subfolder\n            return subfolderImport(moduleName, dirList[1:])\n        else:\n            raise e\n\n\ndef CLI():\n    # Get an arguments list which excludes empty arguments.\n    argsList = list(filter(lambda string: string != \"\", sys.argv))\n    if len(argsList) < 3:\n        if len(argsList) > 1:\n            try:\n                postfix = ''\n                if sys.stdout.isatty():\n                    postfix = '\\n'\n                print(options[argsList[1]](), end=postfix)\n                sys.exit(0)\n            except KeyError:  # If no override was found, continue treating the first argument as an operation of an algorithm\n                pass\n        if len(argsList) == 1:\n            if sys.stdout.isatty():\n                sys.stdout.write(helpMessageBuilder(argsList[0]))\n                sys.exit(0)\n        sys.stderr.write(\"No algorithm or factor specified.\\n\")\n        sys.exit(1)\n    module = None\n    dirNames = [f.name for f in os.scandir(os.path.split(__file__)[0])\n                if f.is_dir() and not f.name.startswith(\"__\")]\n    try:\n        module = subfolderImport(argsList[2], dirNames)\n    except ModuleNotFoundError:\n        sys.stderr.write(f\"No such algorithm: '{argsList[2]}'.\\n\")\n        sys.exit(1)\n    except IndexError:\n        sys.stderr.write(\"No algorithm specified.\\n\")\n        sys.exit(1)\n    # If the module itself is invalid (say, in development and not ready for use)\n    except Exception as e:\n        module = None\n\n    # A predefined operation that returns the category of the module\n    if (argsList[1] == \"categorise\"):\n        # By default, the category is empty\n        category = \"\"\n        try:\n            category = module.category\n        except:\n            # If it isn't defined in the module, it remains empty\n            pass\n        print(category)\n        sys.exit(0)\n\n    def func(x, y): return ''\n    try:\n        func = getFunction(argsList[1], module, options)\n    except AttributeError:\n        sys.stderr.write(f\"Operation not supported by {argsList[2]}.\\n\")\n        sys.exit(1)\n    args = json.load(sys.stdin)\n\n    postfix = \"\"\n    if sys.stdout.isatty():\n        postfix = \"\\n\"\n    try:\n        try:\n            args[\"schema\"]\n            params = inspect.signature(func).parameters\n            json.dump(list(params.keys()), sys.stdout)\n            return 0\n        except KeyError:\n            output = func(**args)\n            if output == \"\":\n                postfix = \"\"\n            # Print the result\n            print(json.dumps(output), end=postfix)\n    except:\n        try:\n            # Explain what failed and why.\n            sys.stderr.write(\n                f\"{sys.exc_info()[0].__name__}: {sys.exc_info()[1]}.\\n\")\n            sys.exit(1)\n        except IndexError:\n            if sys.stdout.isatty():\n                sys.stdout.write(helpMessageBuilder(argsList[0]))\n\n\nCLI()\n\n##########################################################################################################################################\n","sub_path":"bin/135code.py","file_name":"135code.py","file_ext":"py","file_size_in_byte":5476,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"393803843","text":"#this is a comment\n\n#type of variables\n\nvar_string=\"tree\"\nvar_char=ord('a')\nvar_float=3.2\nvar_bool_t=(1==1)\nvar_bool_f=(1!=1)\n\nprint(var_string)\n","sub_path":"Session 1/basic.py","file_name":"basic.py","file_ext":"py","file_size_in_byte":145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"372785069","text":"#import os\nfrom flask import Flask, render_template\nfrom random import randint\nimport twitter\nimport requests, json\nimport test\n\ntwitterApp = twitter.Api(consumer_key=test.Keys.consumer_key,\n                        consumer_secret=test.Keys.consumer_secret,\n                        access_token_key=test.Keys.access_token_key,\n                        access_token_secret=test.Keys.access_token_secret)\n\nstatuses = twitterApp.GetSearch(term = 'kitten')\n#print([s.text for s in statuses])\n#print(\"\\n\\n\")\nprint(statuses[0].user.name)\ntext = statuses[0].text\nprint(text)\n\nurl = []\nurl.append(\"https://api.gettyimages.com/v3/search/images?fields=artist,title,comp,referral_destinations&sort_order=best&page=\") \nurl.append(\"&page_size=1&phrase=kitten%20trouble\")\n\nmy_headers = {\n \"Api-Key\": test.Keys.api_Key\n}\n\n#response = requests.get(url, headers=my_headers)\n#json_body = response.json()gitgi\n#img = json_body[\"images\"][0][\"artist\"]\n#img = img.replace(,\"&s=1\" )\n#print(img)\n\n\napp = Flask(__name__)\n\n#@app.route('/<string:page_name>/')\n#def render_static(page_name):\n#    return render_template('%s.html' % page_name)\n#@app.route(\"/user/\")\n#def hello():\n#    users = [ \"Frank\", \"Steve\", \"Alice\", \"Bruce\" ]\n#    return render_template('user.html', users= users)\n@app.route(\"/<string:name>/\")\ndef tests(name):\n    num = randint(0,9)\n    #twitter search\n    statuses = twitterApp.GetSearch(term = 'kitten')\n    text = statuses[num].text\n    user = statuses[num].user.name\n    #getty search\n\n    print(url[0]+str(num)+url[1])\n    response = requests.get(url[0]+str(num)+url[1], headers=my_headers)\n    json_body = response.json()\n    #print(json_body)\n    img = json_body[\"images\"][0][\"display_sizes\"][0][\"uri\"]\n    return render_template('test.html',name=name, img=img, quote=text, user=user)\n    \nif __name__ == '__main__':\n    #app.run(host=os.getenv('IP', '0.0.0.0'),port=int(os.getenv('PORT', 8080)))\n    app.run()\n","sub_path":"app.py","file_name":"app.py","file_ext":"py","file_size_in_byte":1912,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"558173213","text":"import turtle\r\nt = turtle.Pen()\r\nt.penup()\r\nt.speed(0)\r\nturtle.bgcolor(\"black\")\r\nsides = int(turtle.numinput(\"Number of sides\",\r\n                            \"How many sides in your spiral of spirals (2-6)?\", 4, 2, 6))\r\ncolors = [\"red\", \"yellow\", \"blue\", \"green\", \"purple\", \"orange\"]\r\nfor m in range(100):\r\n    t.forward(m*4)\r\n    position = t.position()\r\n    heading = t.heading()\r\n    for n in range(sides):\r\n        t.pendown()\r\n        t.pencolor(colors[n % sides])\r\n        t.circle(m/5)  # I believe that the denominator adjusts the increase in size per rosette.\r\n        t.left(360/sides - 2)\r\n        t.width(m/20)  # I believe that the denominator adjusts the increase in size per rosette.\r\n        t.penup()\r\n    t.setx(position[0])\r\n    t.sety(position[1])\r\n    t.setheading(heading)\r\n    t.left(360/sides + 2)\r\n","sub_path":"viralrosette.py","file_name":"viralrosette.py","file_ext":"py","file_size_in_byte":822,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"590666845","text":"\"\"\"add tax_including column in pri\n\nRevision ID: 74d27e0219cb\nRevises: ef71eca5865e\nCreate Date: 2021-06-05 16:52:24.219763\n\n\"\"\"\nfrom alembic import op\nimport sqlalchemy as sa\n\n\n# revision identifiers, used by Alembic.\nrevision = '74d27e0219cb'\ndown_revision = 'ef71eca5865e'\nbranch_labels = None\ndepends_on = None\n\n\ndef upgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.add_column('product_release_info', sa.Column('tax_including', sa.Boolean(), nullable=True))\n    # ### end Alembic commands ###\n\n\ndef downgrade():\n    # ### commands auto generated by Alembic - please adjust! ###\n    op.drop_column('product_release_info', 'tax_including')\n    # ### end Alembic commands ###\n","sub_path":"db/migrate/versions/74d27e0219cb_add_tax_including_column_in_pri.py","file_name":"74d27e0219cb_add_tax_including_column_in_pri.py","file_ext":"py","file_size_in_byte":712,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"500521896","text":"# -*- coding: utf-8 -*- #\n\nimport numpy as np\nimport pickle\nimport os\nimport sys\nimport shutil\nimport cv2\nimport matplotlib.pyplot as plt\nfrom scipy import ndimage\nimport csv\nimport glob\nimport get_datalist as gd\n\nsys.path.append('/mnt/hgfs/share_ana5/mypack') \nimport UI\n\n\ndef remove_badlabel(label_img):\n\tlabel_img10=label_img.copy()\n\n\tsizes = ndimage.sum(label_img.astype(np.bool), label_img, np.unique(label_img10))\n\t#print(sizes)\n\n\tmax_label=np.argmax(sizes)\n\t#print(max_label)\n\n\tlabel_img_=np.zeros_like(label_img)\n\tlabel_img_[label_img==max_label]=1\n\n\n\treturn label_img_\n\ndef binarization(img,thresh_value):\n\tprint(\"depth(?) = \", img.dtype)\n\timg0=img.copy()\n\n\tif str(img.dtype)==\"uint16\":\n\t\timg0=(img0/16).astype(np.uint8)\n\t\n\tret,th1 = cv2.threshold(img0,thresh_value,255,cv2.THRESH_BINARY)\n\n\t#otu_ret1,th1 = cv2.threshold(img0.astype(np.uint8),0,255,cv2.THRESH_BINARY+cv2.THRESH_OTSU)\n\t\n\timg_=(th1).astype(bool) #cauton!! after otu method ,image_type is uint8. for ndimage method ,image must be converted to bool type.\n\n\treturn img_\n\n\ndef save_analize_step(img1,img2,img3,img4,img5,img6,result_dir):\n\n\tplt.rcParams[\"font.size\"] = 5\n\tplt.subplots_adjust(hspace=0, wspace=0.2)\n\n\tfig=plt.subplot(231)\n\tplt.title(\"1\")\n\tplt.imshow(img1)\n\n\tfig=plt.subplot(232)\n\tplt.title(\"2\")\n\tplt.imshow(img2)\n\n\tfig=plt.subplot(233)\n\tplt.title(\"3\")\n\tplt.imshow(img3)\n\n\tfig=plt.subplot(234)\n\tplt.title(\"4\")\n\tplt.imshow(img4)\n\n\tfig=plt.subplot(235)\n\tplt.title(\"5\")\n\tplt.imshow(img5)\n\n\tfig=plt.subplot(236)\n\tplt.title(\"6\")\n\tplt.imshow(img6)\n\n\tplt.savefig(result_dir,dpi=300)\n\tplt.close()\n\n\ndef get_arc_frame(bool_img,img_g_resize,scopesize):\n\tbool_img_copy=bool_img.astype(np.bool)\n\tmask_img=img_g_resize.copy()\n\tmask_img[bool_img==False]=0\n\t\n\n\thist_of_rowmono=mask_img.sum(axis=1)\n\thist_of_rowmono=hist_of_rowmono/hist_of_rowmono.max()\n\t\n\n\thist_of_row=bool_img.sum(axis=1)\n\t\n\thist_of_row[hist_of_row==0]=hist_of_row.max()/2\n\n\n\n\tindex_of_maxhist=hist_of_rowmono.argmax()\n\tmax_of_hist=hist_of_row[index_of_maxhist]\n\t\n\n\t\n\tscope_center=index_of_maxhist-scopesize*2\n\tholder=0\n\twhile(True):\n\t\tholder=scope_center\n\n\t\t#print(hist_of_row[scope_center-int(scopesize/2):scope_center+int(scopesize/2)])\n\t\tscope_center=scope_center+hist_of_row[scope_center-int(scopesize/2):scope_center+int(scopesize/2)].argmin()-int(scopesize/2)\n\t\tif holder==scope_center:\n\t\t\tunder_lim=holder\n\t\t\tprint(\"under_lim = \"+str(under_lim))\n\t\t\tbreak\n\n\t\n\tscope_center=index_of_maxhist+scopesize*2\n\tholder=0\n\twhile(True):\n\t\tholder=scope_center\n\t\t#print(hist_of_row[scope_center-int(scopesize/2):scope_center+int(scopesize/2)])\n\t\tscope_center=scope_center+hist_of_rowmono[scope_center-int(scopesize/2):scope_center+int(scopesize/2)].argmin()-int(scopesize/2)\n\t\tif holder==scope_center:\n\t\t\tuper_lim=holder\n\t\t\tprint(\"uper_lim = \"+str(uper_lim))\n\t\t\tbreak\n\n\n\tleft_lim=bool_img[index_of_maxhist,:].argmax()\n\tright_lim=left_lim+max_of_hist\n\n\treturn under_lim,uper_lim,left_lim,right_lim,mask_img\n\ndef get_center_line(img2,underlim):#光ってる電極が見えてるときしかつかえない、電極を基準にセンターを決める\n\n\n\tcrip_img=img2[0:underlim,:].copy()\n\theight,width=crip_img.shape\n\n\tmin_max_indexlist=np.zeros([height,2])\n\tcenter_list=np.zeros([height,1])\n\n\tfor row in range(0,height):\n\t\ttrue_index=np.array(np.where(crip_img[row,:]==1))\n\t\trow_1,col=true_index.shape\n\t\tif col>=1:\n\t\t\tmin_max_indexlist[row,0]=true_index.min()\n\t\t\tmin_max_indexlist[row,1]=true_index.max()\n\t\t\tcenter_list[row,0]=int((true_index.min()+true_index.max())/2)\n\n\tcenter_list_remove0=np.delete(center_list, np.where(center_list==0), 0)\n\n\tprint(len(center_list_remove0))\n\tcenter_line=int(width/2)\n\tif len(center_list_remove0)!=0:\n\t\tcenter_line=np.median(center_list_remove0)\n\tprint(\"center_line==\"+str(center_line))\n\n\treturn int(center_line)\n\n\n\ndef analizing(full_path,result_dir,resize,pixelsize,thresh_value):\n\tfile=os.path.basename(full_path)\n\t\n\tprint(\"filepath == \"+full_path)\n\n\timg_c=cv2.imread(full_path,3)\n\timg_g=cv2.cvtColor(img_c,cv2.COLOR_BGR2GRAY)\n\n\theight, width  = img_g.shape\n\tprint(\"height,width=\"+str(img_g.shape))\n\n\theight=int(height/resize)\n\twidth=int(width/resize)\n\n\timg_c_resize=cv2.resize(img_c,(width,height))\n\timg_g_resize=cv2.resize(img_g,(width,height))\n\tprint(\"height,width=\"+str(img_g_resize.shape))\n\n\t\n\timg1=binarization(img_g_resize,thresh_value)\n\n\tstel=np.ones((3,3))\n\tlabel_img,nd_labels=ndimage.label(img1,stel)\n\n\timg2=remove_badlabel(label_img)#img2 is unique label image\n\n\tscopesize=int(width/50)\n\tunder_lim,uper_lim,left_lim,right_lim,img3=get_arc_frame(img2,img_g_resize,scopesize)\n\n\tprint(under_lim)\n\tcenter_line=get_center_line(img2,under_lim)\n\n\ttitle=file+\"_drow.png\"\n\tdrow_img=img_c_resize.copy()\n\tcv2.rectangle(drow_img, (left_lim, under_lim), (right_lim, uper_lim), (0, 0, 255), int(width/1000+1))\n\tcv2.line(drow_img, (center_line, 0), (center_line, height), (0, 0, 255), int(width/1000+1))\n\tcv2.imwrite(result_dir+title,drow_img)\n\n\ttitle=file+\"_crip.png\"\n\tcv2.imwrite(result_dir+title,img_c_resize[under_lim:uper_lim,left_lim:right_lim])\n\n\ttitle=file+\"_monochro10.csv\"\n\tnp.savetxt(result_dir+title,cv2.resize(img_g_resize,(int(width/10),int(height/10))),delimiter=\",\")\n\n\ttitle=file+\"_data.csv\"\n\tf = open(os.path.join(result_dir,title), 'w') \n\tcsvWriter = csv.writer(f)\n\tcsvWriter.writerow([file,(right_lim-left_lim)*resize*pixelsize,(uper_lim-under_lim)*resize*pixelsize,(img2[under_lim:uper_lim,left_lim:right_lim].sum())*resize*pixelsize*resize*pixelsize])\n\tcsvWriter.writerow([file,\"width[mm]\",\"hight[mm]\",\"area[mm2]\"])\n\tf.close()\n\n\ttitle=file+\"_step.png\"\n\tsave_analize_step(img_c_resize,img_g_resize,img1,img2,img3,result_dir+title)\n\n\treturn uper_lim,center_line#電極直下かつ母材表面の座標を特定できる情報を返す\n\n\ndef read_para(fullpath):\n\tpara=[]\n\twith open(fullpath, 'r') as f:\n\t\treader = csv.reader(f)\n\t\tfor row in reader:\n\t\t\tpara.append(float(row[0]))\n\t\t\tprint(row)          # 1行づつ取得できる\n\tprint(para)\n\tpixelsize=para[0]\n\tresize=para[1]\n\tthresh_value=para[2]\n\treturn pixelsize,resize,thresh_value\n\n\ndef main(read_dir,out_dir):\n\tprint(\"read == \"+read_dir)\n\tprint(\"out  == \"+out_dir)\n\n\timage_file_name= glob.glob(read_dir+\"/*.JPG\")\n\timage_file_name+=glob.glob(read_dir+\"/*.jpg\")\n\timage_file_name+=glob.glob(read_dir+\"/*.PNG\")\n\timage_file_name+=glob.glob(read_dir+\"/*.png\")\n\timage_file_name+=glob.glob(read_dir+\"/*.tiff\")\n\timage_file_name+=glob.glob(read_dir+\"/*.tif\")\n\timage_file_name+=glob.glob(read_dir+\"/*.BMP\")\n\timage_file_name+=glob.glob(read_dir+\"/*.bmp\")\n\n\n\tpara_file_name=glob.glob(read_dir+\"/*.csv\")\n\tif len(para_file_name)!=1:\n\t\tprint(\"<< error!! >> para.csv is not exist or too match!\")\n\t\tsys.exit()\n\n\tshutil.copy( para_file_name[0] , out_dir+\"z_\"+os.path.basename( para_file_name[0] ))#パラメータファイルのコピー\n\n\tpixelsize,resize,thresh_value = read_para(para_file_name[0])\n\n\tfor image_path in image_file_name:\n\t\tuper_lim,center_line=analizing(image_path,out_dir,resize,pixelsize,thresh_value)\n\n\tgd.main(out_dir,out_dir)\n\t\t\n\nif __name__ == '__main__':\n\n\tout_dir=\"/result/\"\n\n\tfor line in open(\"path.txt\",\"r\"):\n\t\tif line[0] == \"#\":\n\t\t\tcontinue\n\t\tdirpath=line.rstrip('\\r\\n')\n\t\tfullreadpath=os.getcwd()+dirpath\n\n\t\tfulloutpath=os.getcwd()+out_dir+dirpath.replace(\"/\",\"_\")+\"/\"\n\t\tif os.path.exists(fulloutpath)==True:\n\t\t\tshutil.rmtree(fulloutpath)\n\t\tos.mkdir(fulloutpath)\n\n\t\tmain(fullreadpath,fulloutpath)\n\n\t\t\n\t\t\n","sub_path":"arc_shape.py","file_name":"arc_shape.py","file_ext":"py","file_size_in_byte":7357,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"485916822","text":"\"\"\"\nA monochrome screen is stored as a single array of bytes, allowing eight consecutive pixels to be stored in one byte.\nThe screen has width w, where w is divisible by 8 (that is, no byte will be split across rows).\nThe height of the screen, of course, can be derived from the length of the array and the width.\nImplement a function drawHorizontalLine(byte[] screen, int width, int x1, int x2, int y) which draws a horizontal line from (x1, y)to(x2, y).\n\"\"\"\n\ndef drawHorizontalLine(screen, width, x1, x2, y):\n\n    startBit = width * y + x1\n    endBit = startBit + (x2 - x1 + 1)\n    if x1 < 0 or x2 < 0 or x1 > x2 or x2 >= width or endBit > len(screen):\n        return \"Error\"\n    return screen[startBit:endBit]\n\n\nn = 0b10011001\nn <<= 8; n += 0b11110000\nn <<= 8; n += 0b10101111\nn <<= 8; n += 0b01010000\nscreen = list()\nwhile n > 0:\n    screen.insert(0, n%2)\n    n >>= 1\nprint(screen)\nprint(drawHorizontalLine(screen, 8, 2, 7, 1))\nprint(drawHorizontalLine(screen, 8, 2, 2, 1))\nprint(drawHorizontalLine(screen, 8, 2, 7, 4))\nprint(drawHorizontalLine(screen, 4, 1, 2, 4))\n","sub_path":"ch5/5.8.py","file_name":"5.8.py","file_ext":"py","file_size_in_byte":1070,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"504615627","text":"import tkinter as tk\nfrom tkinter import filedialog\nimport tkinter.font as font\nimport os\nfrom pdf2docx import Converter\n\n\nroot = tk.Tk()\nroot.title(\"Pdf To Docx\")\n\n\ndef add_file():\n    file_path = filedialog.askopenfilename(initialdir=\"/\", title=\"Select file\")\n\n    convert_file(file_path)\n\n\ndef convert_file(file_path):\n    save_location = r\"C:\\Users\\Manav\\Documents\\generated DOC\\new.docx\"\n    cv = Converter(file_path)\n    cv.convert(save_location, start=0, end=None)\n    cv.close()\n    success_text = tk.Label(\n        frame, text=\"Pdf has been successfully converted\", font=(16), bg=\"#263D42\", fg=\"#fff\")\n    success_text.pack()\n\n\ncanvas = tk.Canvas(root, height=500, width=700)\ncanvas.pack()\n\nframe = tk.Frame(root, bg=\"#263D42\")\nframe.place(relwidth=.8, relheight=.8, relx=.1, rely=.1)\n\n\nbutton = tk.Button(frame, text=\"OPEN FILE \", padx=500,\n                   pady=50, bg=\"#ff2200\", fg=\"#fff\", command=add_file)\n\nbutton_font = font.Font(size=30)\nbutton[\"font\"] = button_font\nbutton.pack()\n\n\nroot.mainloop()","sub_path":"pypdf2.py","file_name":"pypdf2.py","file_ext":"py","file_size_in_byte":1016,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"77753815","text":"#Date: 01/05/2018\n#Author: Guilherme Mene Ale Primo\n#The Script read a epsg file for geographic system \n\nimport numpy as np \n\ndef read_epsg(epsg_name):\n\n    epsg_code = []\n    with open('.temp/.epsg', 'r') as f:\n        for i, line in enumerate(f):\n            line = line.rstrip()\n            line = line.split(',')\n\n            epsg_code.append(line[0])\n\n            if  epsg_name == line[1]:\n                index = i\n                        \n    f.close()\n\n    #transform list to array\n    array = np.asarray(epsg_code)\n\n    #get the array from index\n    epsg = array[index]\n\n    del epsg_code\n    \n    return epsg\n","sub_path":"gsdg/epsg.py","file_name":"epsg.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"134208780","text":"from sklearn.calibration import CalibratedClassifierCV\nfrom sklearn.model_selection import train_test_split\nfrom sklearn.preprocessing import LabelBinarizer\nfrom sklearn.metrics import roc_auc_score\nfrom part2_claim_classifier import ClaimClassifier\nimport torch\nimport torch.nn as nn\nfrom torch import optim\nimport pandas as pd\nimport pickle\nimport numpy as np\n\n\n\ndef fit_and_calibrate_classifier(classifier, X, y):\n    # DO NOT ALTER THIS FUNCTION\n    X_train, X_cal, y_train, y_cal = train_test_split(\n        X, y, train_size=0.85, random_state=0)\n    classifier = classifier.fit(X_train, y_train)\n\n    # This line does the calibration for you\n    calibrated_classifier = CalibratedClassifierCV(\n        classifier, method='sigmoid', cv='prefit').fit(X_cal, y_cal)\n    return calibrated_classifier\n\n\n# class for part 3\nclass PricingModelLinear():\n    # YOU ARE ALLOWED TO ADD MORE ARGUMENTS AS NECESSARY\n    def __init__(self, calibrate_probabilities=False):\n        \"\"\"\n        Feel free to alter this as you wish, adding instance variables as\n        necessary.\n        \"\"\"\n        self.y_mean = None\n        self.calibrate = calibrate_probabilities\n        # =============================================================\n        # READ ONLY IF WANTING TO CALIBRATE\n        # Place your base classifier here\n        # NOTE: The base estimator must have:\n        #    1. A .fit method that takes two arguments, X, y\n        #    2. Either a .predict_proba method or a decision\n        #       function method that returns classification scores\n        #\n        # Note that almost every classifier you can find has both.\n        # If the one you wish to use does not then speak to one of the TAs\n        #\n        # If you wish to use the classifier in part 2, you will need\n        # to implement a predict_proba for it before use\n        # =============================================================\n        self.base_classifier = ClaimClassifier()\n\n    # YOU ARE ALLOWED TO ADD MORE ARGUMENTS AS NECESSARY TO THE _preprocessor METHOD\n    def _preprocessor(self, X_raw, training = False):\n        \"\"\"Data preprocessing function.\n\n        This function prepares the features of the data for training,\n        evaluation, and prediction.\n\n        Parameters\n        ----------\n        X_raw : ndarray\n            An array, this is the raw data as downloaded\n\n        Returns\n        -------\n        X: ndarray\n            A clean data set that is used for training and prediction.\n        \"\"\"\n        # =============================================================\n        # YOUR CODE HERE\n\n        # Load simple data set used in part 2\n        part2_headers = [\"drv_age1\", 'vh_age', 'vh_cyl', 'vh_din', 'pol_bonus', 'vh_sale_begin', 'vh_sale_end',\n                         'vh_value', 'vh_speed', 'drv_age_lic1', 'pol_duration', 'pol_sit_duration', 'drv_age2']\n        #  added from before\n        # 'drv_age_lic1'\n        #  pol_duration\n        #  pol_sit_duration\n        #  drv_age2\n\n        required_attributes = X_raw[part2_headers]\n\n        required_attributes = np.array(required_attributes)\n\n        if training:\n            self.means = np.mean(required_attributes, axis=0)\n            self.std_dev = np.std(required_attributes, axis=0)\n\n        x_normed = (required_attributes - self.means) / self.std_dev\n\n        # Add extra columns here\n        multiple_binarizers = []\n        binarizer = LabelBinarizer()\n\n        headers = ['drv_sex1', 'vh_type', 'pol_coverage', 'pol_usage']\n        i = 0\n        for header in headers:\n            data = X_raw[header]\n            if training:\n                binarized = binarizer.fit_transform(data)\n                multiple_binarizers.append(binarizer)\n            else:\n                binarized = self.saved_binarizers[i].transform(data)\n            if len(binarized[0]) > 1:\n                binarized = binarized[:, :-1]\n            i += 1\n            binarized = np.asarray(binarized)\n            total = np.append(x_normed, binarized, axis=1)\n\n        if training:\n            self.saved_binarizers = multiple_binarizers\n\n        return total\n\n    def fit(self, X_raw, y_raw, claims_raw):\n        \"\"\"Classifier training function.\n\n        Here you will use the fit function for your classifier.\n\n        Parameters\n        ----------\n        X_raw : ndarray\n            This is the raw data as downloaded\n        y_raw : ndarray\n            A one dimensional array, this is the binary target variable\n        claims_raw: ndarray\n            A one dimensional array which records the severity of claims\n\n        Returns\n        -------\n        self: (optional)\n            an instance of the fitted model\n\n        \"\"\"\n        nnz = np.where(claims_raw != 0)[0]\n        self.y_mean = np.mean(claims_raw[nnz])\n        # =============================================================\n        # REMEMBER TO A SIMILAR LINE TO THE FOLLOWING SOMEWHERE IN THE CODE\n        X_clean = self._preprocessor(X_raw, training=True)\n\n        # THE FOLLOWING GETS CALLED IF YOU WISH TO CALIBRATE YOUR PROBABILITES\n        if self.calibrate:\n            self.base_classifier = fit_and_calibrate_classifier(\n                self.base_classifier, X_clean, y_raw)\n        else:\n            self.base_classifier = find_best_model(X_clean,y_raw)\n\n        return self.base_classifier\n\n    def predict_claim_probability(self, X_raw):\n        \"\"\"Classifier probability prediction function.\n\n        Here you will implement the predict function for your classifier.\n\n        Parameters\n        ----------\n        X_raw : ndarray\n            This is the raw data as downloaded\n\n        Returns\n        -------\n        ndarray\n            A one dimensional array of the same length as the input with\n            values corresponding to the probability of beloning to the\n            POSITIVE class (that had accidents)\n        \"\"\"\n        # =============================================================\n        # REMEMBER TO A SIMILAR LINE TO THE FOLLOWING SOMEWHERE IN THE CODE\n        X_clean = self._preprocessor(X_raw)\n        pred, prob_y = self.base_classifier.predict_probabilities(X_clean, pricing=True)\n\n        return prob_y\n\n    def predict_premium(self, X_raw):\n        \"\"\"Predicts premiums based on the pricing model.\n\n        Here you will implement the predict function for your classifier.\n\n        Parameters\n        ----------\n        X_raw : numpy.ndarray\n            A numpy array, this is the raw data as downloaded\n\n        Returns\n        -------\n        numpy.ndarray\n            A one dimensional array of the same length as the input with\n            values corresponding to the probability of beloning to the\n            POSITIVE class (that had accidents)\n        \"\"\"\n        # =============================================================\n        # REMEMBER TO INCLUDE ANY PRICING STRATEGY HERE.\n        # For example you could scale all your prices down by a factor\n\n        premium_factor = 0.27\n        premiums = self.predict_claim_probability(X_raw) * self.y_mean * premium_factor\n        premiums = np.array(premiums)\n        premiums = premiums.flatten()\n\n        return premiums\n\n    def save_model(self):\n        \"\"\"Saves the class instance as a pickle file.\"\"\"\n        # =============================================================\n        with open('part3_pricing_model_linear.pickle', 'wb') as target:\n            pickle.dump(self, target)\n\ndef load_model():\n    # Please alter this section so that it works in tandem with the save_model method of your class\n    with open('part3_pricing_model_linear.pickle', 'rb') as target:\n        trained_model = pickle.load(target)\n    return trained_model\n\n\nclass LinearClaimClassifier(nn.Module):\n\n    def __init__(self, variables=9):\n        super(LinearClaimClassifier, self).__init__()\n        self.linear = nn.Linear(variables,   1)\n        \"\"\"\n        Feel free to alter this as you wish, adding instance variables as\n        necessary. \n        \"\"\"\n        pass\n\n    def forward(self, x):\n        output = torch.sigmoid(self.linear(x))\n        return output\n\n    def predict(self, test_x):\n        X = torch.Tensor(test_x)\n        output = self(X)\n        prob_y = output.detach().numpy()\n\n        return prob_y\n\ndef find_best_model(x_clean,y_raw, variables=9, pricing=False):\n\n    train_x, valid_x, train_y, valid_y = train_test_split(x_clean, y_raw, test_size=0.2)\n\n    # Up sample\n    (unique, counts) = np.unique(train_y, return_counts=True)\n    total_train = np.append(train_x, train_y, axis=1)\n    total_train = pd.DataFrame(total_train)\n\n    df_class_0 = total_train[total_train.iloc[:, -1] == 0]\n    df_class_1 = total_train[total_train.iloc[:, -1] == 1]\n\n    total_train_class_1_over = df_class_1.sample(counts[0], replace=True)\n    test_over = pd.concat([df_class_0, total_train_class_1_over], axis=0)\n\n    total_train = np.array(test_over)\n\n    new_train_y = total_train[:, -1]\n    new_train_x = total_train[:, :-1]\n    new_train_y = np.expand_dims(new_train_y, 1)\n    max_metric = 0\n    searches = 10\n\n    for i in range(searches):\n        new_net = ClaimClassifier(variables=len(train_x[0]),linear=True)\n        lrn_rate = np.random.uniform(0.0001, 1)\n        loss = nn.BCELoss()\n        epochs = round(np.random.uniform(50, 150))\n        new_net.train()\n        optimizer = optim.SGD(new_net.parameters(), lr=lrn_rate)\n        for j in range(epochs):\n            X = torch.Tensor(new_train_x)\n            Y = torch.Tensor(new_train_y)\n\n            # changed from optimizer to net.zero_grad\n            new_net.zero_grad()\n\n            output = new_net(X)\n\n            loss_obj = loss(output, Y)\n\n            loss_obj.backward()\n            optimizer.step()\n\n        new_net.eval()\n        print(\"Model (\" + str(i + 1) + \") out of \" + str(searches))\n        pred, probabilities = new_net.predict_probabilities(valid_x,pricing=True)\n        metric = roc_auc_score(valid_y, probabilities)\n        print(\"Roc Score:\" + str(metric))\n        if metric > max_metric:\n            max_metric = metric\n            best_lr = lrn_rate\n            max_epochs = epochs\n            best_net = new_net\n\n    return best_net\n\nif __name__ == \"__main__\":\n\n    dat = pd.read_csv(\"part3_training_data.csv\")\n    attributes = dat.drop(columns=[\"claim_amount\", \"made_claim\"])\n    y = dat[\"made_claim\"]\n    claim_amounts = dat['claim_amount']\n    X, Y = attributes, y\n\n    train_x, test_x, train_y, test_y = train_test_split(X, Y, test_size=0.15)\n    train_x = pd.DataFrame(train_x)\n    train_y = pd.DataFrame(train_y)\n\n    linear_model = PricingModelLinear()\n\n    linear_model.fit(train_x, train_y, claim_amounts)\n    probs = linear_model.predict_claim_probability(test_x)\n\n    linear_model_roc = roc_auc_score(test_y,probs)\n\n    print(\"Final Model Roc Score: \" + str(linear_model_roc))\n    print(\"\")\n    print(\"Saving...\")\n    linear_model.save_model()\n    print(\"Loading...\")\n    loaded_model = load_model()\n    loaded_probs = loaded_model.predict_claim_probability(test_x)\n    loaded_model_roc = roc_auc_score(test_y,loaded_probs)\n    print(\"Loaded Model Roc Score: \" + str(linear_model_roc))\n    loaded_model_prices = loaded_model.predict_premium(test_x)\n    print(loaded_model_prices)","sub_path":"part3_pricing_model_linear.py","file_name":"part3_pricing_model_linear.py","file_ext":"py","file_size_in_byte":11203,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"13566871","text":"#!/usr/bin/env python\n# encoding: iso8859-1\n\"\"\"\nldlogger.py\n\nCreated by Urlan Barros on 2012-04-18.\nCopyright (c) 2012 __8bitsweb__. All rights reserved.\n\"\"\"\n\nVERSION = \"1.5 Beta\"\n\nimport sys\nfrom platform import win32_ver, architecture\nfrom lists import *\nfrom utils import regOps, processes, services, drivers, printer, commandHandler, errorHandler, smartStr\n\nclass LDLogger:\n  def __init__(self, version):\n     self.version = version\n\n  def executeLDLogger():\n    output = printer.Printer(\"LDLogger.txt\", VERSION)\n    output.printVersion()\n  \n    try:\n      # Getting OS name, build, service pack, and architecture\n      OS, build, service_pack = win32_ver()[:-1]\n      arch = architecture()[0]\n    except Exception as err:\n      errorHandler.logError(\"platform use\", err)\n  \n    try:\n      # Getting used browsers and their versions\n      browser_list = processes.getBrowsers(BROWSERS)\n    except Exception as err:\n      errorHandler.logError(\"browsers\", err)\n  \n    try:\n      # Getting running processes and the path to its exetuable\n      running_processes_list = []\n      for process_path in processes.running_processes():\n        running_processes_list.append(\"%s\\n\" % process_path)\n    except Exception as err:\n      errorHandler.logError(\"running processes\", err)\n    \n    try:\n      # Getting Hosts file\n      hosts = services.getHosts()\n    except Exception as err:\n      errorHandler.logError(\"hosts\", err)\n  \n    try:\n      # Getting IE components\n      source_reg = {\"key\": \"HKEY_LOCAL_MACHINE\",\n                    \"subkey\": \"SOFTWARE\\Microsoft\\Windows\\CurrentVersion\\Explorer\\Browser Helper Objects\"}\n      target_reg = {\"key\": \"HKEY_CLASSES_ROOT\",\n                  \"subkey\": \"CLSID\\%s\\InprocServer32\"}\n      IEComponents = processes.getComponents(source_reg, target_reg)\n    except Exception as err:\n      errorHandler.logError(\"IE components\", err)\n  \n    try:\n      # Getting IE toolbars\n      source_reg = {\"key\": \"HKEY_LOCAL_MACHINE\",\n                    \"subkey\": \"SOFTWARE\\Microsoft\\Internet Explorer\\Toolbar\"}\n      target_reg = {\"key\": \"HKEY_CLASSES_ROOT\",\n                    \"subkey\": \"CLSID\\%s\\InprocServer32\"}\n      IEToolbars = processes.getComponents(source_reg, target_reg, as_subkeys=False)\n    except Exception as err:\n      errorHandler.logError(\"ie toolbars\", err)\n  \n    try:\n      # Getting some important keys in register\n      regs = regOps.getRegs(REG_KEYS)\n    except Exception as err:\n      errorHandler.logError(\"registry keys\", err)\n    \n    try:\n      # Getting LSP's\n      num_entries, LSPs = services.getLSP()\n    except Exception as err:\n      errorHandler.logError(\"LSP\", err)\n    \n    try:\n      # Getting DNS\n      primaryDNS, secondaryDNS, adapterID = services.getDNS()\n    except Exception as err:\n      errorHandler.logError(\"DNS\", err)\n    \n    try:\n      # Searching autoruns\n      autoruns = drivers.searchAutorun()\n    except Exception as err:\n      errorHandler.logError(\"autoruns\", err)\n    \n    try:\n      # Looking mountpoints\n      suspect_mountpoints = drivers.getMountpoints()\n    except Exception as err:\n      errorHandler.logError(\"mountpoints\", err)\n    \n    try:\n      # Getting Services\n      svcs = drivers.getServices(services_whitelist)\n    except Exception as err:\n      errorHandler.logError(\"services\", err)\n    \n    try:\n      # Getting Drivers\n      drvs = drivers.getDrivers(drivers_whitelist)\n    except Exception as err:\n      errorHandler.logError(\"drivers\", err)\n    \n    try:\n      # Searching for anomalies on svchost\n      anomalies = services.getSvchostAnomalies(svchost_whitelist)\n    except Exception as err:\n      errorHandler.logError(\"svchost\", err)\n    \n    try:  \n      # Discovering if safe boot exists\n      safeboot = services.safeBootExists()\n    except Exception as err:\n      errorHandler.logError(\"safe boot\", err)\n    \n    try:\n      # Getting startups:\n      global_startups, user_startups = processes.getStartups()\n    except Exception as err:\n      errorHandler.logError(\"startups\", err)\n  \n    try:\n      # Getting strange winlogon entries\n      winlogon_entries = services.getWinlogonEntries(winlogon_whitelist)\n    except Exception as err:\n      errorHandler.logError(\"winlogon\", err)\n  \n    try:\n      # Getting Image File Execution Options\n      files = services.getImageFilesOptions()\n    except Exception as err:\n      errorHandler.logError(\"image file execution\", err)\n  \n    try:\n      # Getting file extension association\n      misassociations = services.checkAssociations(associations)\n    except Exception as err:\n      errorHandler.logError(\"file association\", err)\n  \n  \n    try:\n      # This is where we print the results we've got\n      output.systemInfo(OS, build, service_pack, arch)\n      output.browsers(browser_list)\n      output.runningProcesses(running_processes_list)\n      output.hosts(hosts)\n      output.registers(regs, IEComponents, IEToolbars, global_startups, user_startups, LSPs, primaryDNS,\n                       secondaryDNS, adapterID, winlogon_entries)\n      output.autoruns(autoruns)\n      output.mountpoints(suspect_mountpoints)\n      output.services(svcs)\n      output.drivers(drvs)\n      output.SVCHOST(anomalies)\n      output.safeboot(safeboot)\n      output.IFEO(files)\n      output.fileAssociation(misassociations)\n      output.finishLog()\n    except Exception as err:\n      errorHandler.logError(\"write output\", err)\n    \n  \n    commandHandler.execute(\"start notepad LDLogger.txt\")\n","sub_path":"src/utils/ldlogger.py","file_name":"ldlogger.py","file_ext":"py","file_size_in_byte":5435,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"410306639","text":"####################################\r\n#                                  #\r\n#        LISTER OF ANY LIST        #\r\n#                                  #\r\n# (C) 2019-10-15 PRZEMYSLAW ZBROCH #\r\n#                                  #\r\n####################################\r\n\r\n\r\n\r\nimport random\r\n\r\n\r\n\r\nnewlist = []\r\n\r\n\r\n\r\ndef listit(inlist):\r\n    for x in range(len(inlist)):\r\n        if len(inlist) == 1:\r\n            print(inlist[x])\r\n        elif x == len(inlist) - 1:\r\n            print('and ' + inlist[x])\r\n        elif x == len(inlist) - 2:\r\n            print(inlist[x], end = ' ')\r\n        else:\r\n            print(inlist[x] + ', ', end = '')\r\n\r\n\r\ndef randit(inlist2):\r\n    randlist = []\r\n    while True:\r\n        randval = inlist2[random.randint(0, len(inlist2) - 1)]\r\n        randlist.append(randval)\r\n        inlist2.remove(randval)\r\n        if inlist2 == []:\r\n            return randlist\r\n\r\n\r\nprint()\r\nprint('This program lists any typed-in list in random order. You can add anything to the list by typing it.')\r\nprint('Quit adding with a simple \"enter\"')\r\nprint()\r\n\r\nwhile True:\r\n    print('Add anyting to the list: ', end = '')\r\n    newvalue = input()\r\n    if newvalue == '':\r\n        print()\r\n        print('--------------------------------')\r\n        print()\r\n        break\r\n    newlist.append(newvalue)\r\n\r\nrandlist = randit(newlist)\r\nlistit(randlist)\r\nprint()\r\n","sub_path":"listlister.py","file_name":"listlister.py","file_ext":"py","file_size_in_byte":1368,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"395736966","text":"from monty.extension.basic import BasicExtension\nfrom monty.cli.stream import *\nfrom monty.config.loader import get_cfg\nfrom pprint import pprint\n\n\nclass MontyCliExtension(BasicExtension):\n    \"\"\"\n    Extension: user\n    \"\"\"\n    namespace = \"config\"\n    config = None\n\n    def __init__(self):\n        self.commands = {\n            \"show\": self.show,\n            \"del\": self.del_attr,\n            \"set\": self.set_attr,\n            \"get\": self.get_attr,\n        }\n\n    def route(self, args):\n\n        try:\n            config_name = args[0]\n        except IndexError:\n            return self.error(\"Missing arguments.\")\n            pass\n\n        try:\n            self.config = get_cfg(config_name)\n        except NotImplementedError:\n            return self.error(\"Config '\"+config_name+\"' cannot be found.\")\n\n        try:\n            config_action = args[1]\n        except IndexError:\n            config_action = 'show'\n            pass\n\n        if config_action in self.commands:\n            return self.commands[config_action](args[1:])\n\n        return self.error(\"Action '\"+config_action+\"' cannot be found.\")\n\n    def show(self, args=[]):\n        StdOutput.write(\"Config dump:\")\n        return pprint(self.config.read())\n\n    def del_attr(self, args):\n        config = self.config.read()\n\n        try:\n            key = args[1]\n        except IndexError:\n            return self.error(\"key wroing....\")\n\n        del config[key]\n        self.config.write(config)\n\n        StdOutput.write(\"Saved!\")\n\n        return self.show()\n\n    def set_attr(self, args=[]):\n        config = self.config.read()\n\n        try:\n            key, value = args[1:]\n        except IndexError:\n            return self.error(\"key value wroing....\")\n\n        config[key] = value\n        self.config.write(config)\n\n        StdOutput.write(\"Saved!\")\n\n        return self.show()\n\n    def get_attr(self, args=[]):\n        config = self.config.read()\n\n        try:\n            key = args[1]\n        except IndexError:\n            return self.error(\"key wroing....\")\n\n        if key in config:\n            return StdOutput.write(config[key])\n\n        return None\n","sub_path":"monty_extension/config.py","file_name":"config.py","file_ext":"py","file_size_in_byte":2133,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"156545528","text":"# В файле данные записаны построчно, фамилия и оклад разделены пробелом\nf_o = open(\"file.txt\", \"r\", encoding = \"utf-8\")\nwith f_o:\n\tcnt = 0\n\tsum = 0\n\tfor str in f_o:\n\t\tcnt = cnt + 1\n\t\tlist = str.split()\n\t\tif float(list[1]) < 20000:\n\t\t\tprint(list[0])\n\t\tsum = sum + float(list[1])\nprint(f\"Средняя величина дохода сотрудников составляет {sum/cnt}\")","sub_path":"lesson_5/3.py","file_name":"3.py","file_ext":"py","file_size_in_byte":445,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"370378034","text":"import csv\ninputFile = 'file1.csv'\noutputFile = 'file11.csv'\n\nwith open(inputFile) as in_file:\n    with open(outputFile, 'w') as out_file:\n        writer = csv.writer(out_file)\n        for row in csv.reader(in_file):\n            if row:\n                writer.writerow(row)\n","sub_path":"C00-2123/preprocess1.py","file_name":"preprocess1.py","file_ext":"py","file_size_in_byte":274,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"311493698","text":"import os\nimport numpy as np\n\n\ndef load_numpy_arrays(directory_path):\n    \"\"\"\n    Yields each of the numpy arrays in the directory.\n\n    :param directory_path: String path to the directory with the files to load.\n    :return: Yields a numpy array and the filename.\n    \"\"\"\n\n    directory = os.fsencode(directory_path)\n\n    for file in os.listdir(directory):\n        filename = os.fsdecode(file)\n        if filename.endswith(\".npy\"):\n            yield np.load(os.path.join(directory_path, filename)), filename\n        else:\n            print(\"A file (\" + filename + \") does not end with .npy so it is not being loaded.\")","sub_path":"loading_data/load_numpy_arrays.py","file_name":"load_numpy_arrays.py","file_ext":"py","file_size_in_byte":619,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"282819495","text":"from unittest import mock\nimport unittest\nimport decimal\nfrom django.conf import settings\nimport datetime\n\n\nclass TestInterpolate(unittest.TestCase):\n    def test_interpolate(self):\n        from apps.analysis.calculate import Interpolate\n        age = 14.386\n        statistic_array = [[9.5, 10, 10.5, 11, 11.5, 12, 12.5, 13, 13.5, 14, 14.5, 15, 15.5, 16, 16.5, 17, 17.5],\n                           [4.528541667, 4.505416667, 4.482291667, 4.459166667, 4.436041667, 4.412916667, 4.389791667,\n                            4.366666667, 4.343541667, 4.320416667, 4.297291667, 4.274166667, 4.246041667, 4.217916667,\n                            4.189791667, 4.161666667, 4.133541667],\n                           [0.263276455, 0.263068882, 0.262861309, 0.262653736, 0.262446162, 0.262238589, 0.262031016,\n                            0.261823443, 0.261615869, 0.261408296, 0.261200723, 0.26099315, 0.224285769, 0.187578388,\n                            0.150871006, 0.114163625, 0.077456244]]\n        i = Interpolate(statistic_array[0], statistic_array[1])\n        population_mean = i[age]\n        self.assertEquals(4.302564167, population_mean)\n        i = Interpolate(statistic_array[0], statistic_array[2])\n        population_sd = i[age]\n        self.assertEquals(0.261248049644, population_sd)\n\n\nclass TestPythonAnalysis(unittest.TestCase):\n    def test_get_alternative_bio_age(self):\n        from apps.analysis.calculate import PythonAnalysis\n        current_age = 14\n        phv_age = 14\n        p = PythonAnalysis()\n        bio_age = p.get_alternative_bio_age(current_age, phv_age)\n        self.assertEquals(decimal.Decimal(14), bio_age)\n\n\nclass TestRscripts(unittest.TestCase):\n    if settings.R_AVAILABLE:\n        def test_get_bio_age(self):\n            from apps.analysis.calculate import RscriptAnalysis\n            r = RscriptAnalysis()\n            predicted_height = 180\n            current_height = 140\n            age, slope = r.get_bio_age(predicted_height, current_height, country='uk')\n            self.assertEquals(decimal.Decimal(9.75), age)\n            predicted_height = 140\n            current_height = 180\n            age, slope = r.get_bio_age(predicted_height, current_height, country='uk')\n            self.assertEquals(decimal.Decimal(17.5), age)\n\n        @mock.patch('apps.analysis.calculate.Interpolate')\n        def test_get_benchmark(self, mock_interpolate):\n            from apps.analysis.calculate import RscriptAnalysis\n            r = RscriptAnalysis()\n            value = 9\n            age = 3\n            statistic_array = mock.MagicMock()\n            mock_interpolate.return_value = [5, 6, 7, 8, 9, 10]\n            benchmark = r.get_benchmark(value, age, statistic_array, False)\n            self.assertEquals(decimal.Decimal(55), benchmark)\n\n        def test_get_khamis_roche(self):\n            from apps.analysis.calculate import RscriptAnalysis\n            r = RscriptAnalysis()\n            current_height = 140\n            current_age = 10\n            current_weight = 50\n            fathers_height = 185\n            mothers_height = 176\n            gender = 'Male'\n            predicted_height, meta = r.get_khamis_roche(current_height, current_age, current_weight, fathers_height,\n                                           mothers_height, gender)\n            self.assertEquals(190.6294, predicted_height.cm)\n\n        def test_get_phv(self):\n            from apps.analysis.calculate import RscriptAnalysis\n            r = RscriptAnalysis()\n            current_height = 157.0\n            current_age = 12.084\n            current_weight = 53.0\n            sitting_height = 79.6\n            phv_delta = r.get_phv(current_height, current_age, current_weight, sitting_height)\n            self.assertEquals(-1.408292, phv_delta)\n\n    else:\n        # Needed to find a solution for CircleCi (doesn't support R!!)\n        @mock.patch('apps.analysis.calculate.subprocess')\n        def test_get_bio_age(self, mock_subprocess):\n            from apps.analysis.calculate import RscriptAnalysis\n            r = RscriptAnalysis()\n            mock_subprocess.Popen.communicate.return_value = mock.MagicMock()\n            predicted_height = 180\n            current_height = 140\n            r.get_bio_age(predicted_height, current_height, country='uk')\n\n        @mock.patch('apps.analysis.calculate.subprocess')\n        @mock.patch('apps.analysis.calculate.Interpolate')\n        def test_get_benchmark(self, mock_interpolate, mock_subprocess):\n            from apps.analysis.calculate import RscriptAnalysis\n            r = RscriptAnalysis()\n            value = 9\n            age = 3\n            statistic_array = mock.MagicMock()\n            mock_interpolate.return_value = [5, 6, 7, 8, 9, 10]\n            mock_subprocess.Popen.communicate.return_value = mock.MagicMock()\n            r.get_benchmark(value, age, statistic_array, False)\n\n        @mock.patch('apps.analysis.calculate.subprocess')\n        def test_get_khamis_roche(self, mock_subprocess):\n            from apps.analysis.calculate import RscriptAnalysis\n            r = RscriptAnalysis()\n            current_height = 140\n            current_age = 10\n            current_weight = 50\n            fathers_height = 185\n            mothers_height = 176\n            gender = 'Male'\n            mock_subprocess.Popen.communicate.return_value = mock.MagicMock()\n            r.get_khamis_roche(current_height, current_age, current_weight, fathers_height,\n                                           mothers_height, gender)\n\n        @mock.patch('apps.analysis.calculate.subprocess')\n        def test_get_phv(self, mock_subprocess):\n            from apps.analysis.calculate import RscriptAnalysis\n            r = RscriptAnalysis()\n            current_height = 157.0\n            current_age = 12.084\n            current_weight = 53.0\n            sitting_height = 79.6\n            mock_subprocess.Popen.communicate.return_value = mock.MagicMock()\n            r.get_phv(current_height, current_age, current_weight, sitting_height)\n\n\nclass TestSignals(unittest.TestCase):\n    @mock.patch('apps.analysis.signals.handlers.PredictedHeight')\n    def test_post_khamis_roche_handler(self, mock_predictedheight):\n        from apps.analysis.signals.handlers import post_khamis_roche_handler\n        sender = mock.MagicMock()\n        instance = mock.MagicMock()\n        mock_predictedheight.objects.create.return_value = mock.MagicMock()\n        post_khamis_roche_handler(sender, instance, created=True)\n        mock_predictedheight.objects.create.assert_called_with(player=instance.player,\n                                                               date=instance.date,\n                                                               predicted_height=instance.predicted_height,\n                                                               method='khr',\n                                                               khamis_roche=instance,\n                                                               dna_height=None)\n        post_khamis_roche_handler(sender, instance, created=False)\n        instance.player.predictedheight_set.filter.assert_called_with(khamis_roche=instance)\n\n\nclass TestBenchmark(unittest.TestCase):\n    @mock.patch('apps.analysis.benchmark.utils.abs')\n    @mock.patch('apps.analysis.benchmark.utils.Benchmark')\n    @mock.patch('apps.analysis.benchmark.utils.RscriptAnalysis')\n    def test_create_benchmark(self, mock_rscriptanalysis, mock_benchmark, mock_abs):\n        from apps.analysis.benchmark.utils import Performance\n        from apps.analysis.benchmark.utils import create_benchmark\n        sender = mock.MagicMock()\n        instance = mock.MagicMock()\n        instance.date = datetime.date.today()\n        success = create_benchmark(sender, instance, True)\n        # Should only work if sender is Performance\n        self.assertEquals(success, False)\n        sender = Performance\n        mock_abs.return_value = 5\n        instance.player.bioage_set.filter.latest.return_value = mock.MagicMock()\n        mock_rscriptanalysis.get_benchmark.return_value = mock.MagicMock()\n        mock_benchmark.objects.create.return_value = mock.MagicMock()\n        success = create_benchmark(sender, instance, True)\n        self.assertEquals(success, True)\n        success = create_benchmark(sender, instance, False)\n        self.assertEquals(success, True)\n\n\nclass TestBioAge(unittest.TestCase):\n    @mock.patch('apps.analysis.bio_age.utils.BioAge')\n    @mock.patch('apps.analysis.bio_age.utils.RscriptAnalysis')\n    def test_create_bio_age(self, mock_rscriptanalysis, mock_bioage):\n        from apps.analysis.bio_age.utils import create_bio_age\n        from apps.analysis.bio_age.utils import PredictedHeight\n        sender = mock.MagicMock()\n        instance = mock.MagicMock()\n        success = create_bio_age(sender, instance, True)\n        self.assertEquals(success, False)\n        mock_rscriptanalysis.return_value.get_bio_age.return_value = [decimal.Decimal(15), []]\n        mock_bioage.objects.create.return_value = mock.MagicMock()\n        instance.player.predictedheight_set.filter.return_value.latest.return_value = instance\n        success = create_bio_age(sender, instance, True)\n        self.assertEquals(success, True)\n        success = create_bio_age(sender, instance, False)\n        self.assertEquals(success, True)\n        instance.player.predictedheight_set.filter.return_value.latest.side_effect = PredictedHeight.DoesNotExist\n        success = create_bio_age(sender, instance, False)\n        self.assertEquals(success, False)\n\n    @mock.patch('apps.analysis.bio_age.utils.BioAge')\n    @mock.patch('apps.analysis.bio_age.utils.PythonAnalysis')\n    def test_create_alternative_bio_age(self, mock_pythonanalysis, mock_bioage):\n        from apps.analysis.bio_age.utils import create_alternative_bio_age\n        from apps.analysis.bio_age.utils import PHV\n        sender = mock.MagicMock()\n        instance = mock.MagicMock()\n        success = create_alternative_bio_age(sender, instance, True)\n        self.assertEquals(success, False)\n        sender = PHV\n        instance.player.bioage_set.filter.return_value.values_list.return_value = None\n        mock_pythonanalysis.return_value.get_alternative_bio_age.return_value = decimal.Decimal(15)\n        mock_bioage.objects.create.return_value = mock.MagicMock()\n        success = create_alternative_bio_age(sender, instance, True)\n        self.assertEquals(success, True)\n        success = create_alternative_bio_age(sender, instance, False)\n        self.assertEquals(success, True)\n\n\nclass TestKhamisRoche(unittest.TestCase):\n    @mock.patch('apps.analysis.khamis_roche.utils.round')\n    @mock.patch('apps.analysis.khamis_roche.utils.KhamisRoche')\n    @mock.patch('apps.analysis.khamis_roche.utils.RscriptAnalysis')\n    def test_create_bio_age(self, mock_rscriptanalysis, mock_khr, mock_round):\n        from apps.analysis.khamis_roche.utils import create_khamis_roche\n        from apps.analysis.khamis_roche.utils import Height\n        sender = Height\n        instance = mock.MagicMock()\n        mock_round.return_value = 14.5\n        mock_khr.objects.create.return_value = mock.MagicMock()\n        mock_rscriptanalysis.return_value.get_khamis_roche.return_value = [decimal.Decimal(186), {}]\n        success = create_khamis_roche(sender, instance, True)\n        self.assertEquals(success, True)\n        success = create_khamis_roche(sender, instance, False)\n        self.assertEquals(success, True)\n        mock_rscriptanalysis.return_value.get_khamis_roche.return_value = [None, None]\n        success = create_khamis_roche(sender, instance, False)\n        self.assertEquals(success, False)\n\n\nclass TestMirwald(unittest.TestCase):\n    @mock.patch('apps.analysis.mirwald.utils.PHV')\n    @mock.patch('apps.analysis.mirwald.utils.RscriptAnalysis')\n    def test_create_phv(self, mock_rscriptanalysis, mock_phv):\n        from apps.analysis.mirwald.utils import create_phv\n        from apps.analysis.mirwald.utils import Height\n        sender = Height\n        instance = mock.MagicMock()\n        mock_phv.objects.create.return_value = mock.MagicMock()\n        mock_rscriptanalysis.return_value.get_phv.return_value = 1.8\n        success = create_phv(sender, instance, True)\n        self.assertEquals(success, True)\n        success = create_phv(sender, instance, False)\n        self.assertEquals(success, True)\n","sub_path":"apps/analysis/tests.py","file_name":"tests.py","file_ext":"py","file_size_in_byte":12345,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"377637599","text":"from bs4 import BeautifulSoup\nimport logging\nimport requests\n\nimport helpers\nfrom logger import log\nimport news_interface\nimport news_orgs\n\nlogging.basicConfig(filename='reuters.log', level=logging.WARNING)\n\nclass Reuters(news_interface.NewsOrg):\n  '''Methods for interacting with the REUTERS website.'''\n\n  def get_article(self, url):\n    '''Implementation for getting an article from REUTERS.\n\n    url: A URL in the www.reuters.com* domain.\n\n    Returns: The Article representing the article at that url.\n    '''\n    html = helpers.get_content(url)\n    if not html:\n      return None\n\n    soup = BeautifulSoup(html)\n    headline_div = soup.find('div', attrs={'class': 'column1 gridPanel grid8'})\n    headline = helpers.decode(headline_div.h1.string)\n    paragraphs = soup.find('div', attrs={'class': 'column1 gridPanel grid8'}).findAll(\"p\")\n    body = ' '.join([helpers.decode(p.text) for p in paragraphs])\n    log.info(headline)\n    log.info(body)\n    return news_interface.Article(headline, body, url, news_orgs.REUTERS)\n\n  def get_query_results(self, query):\n    '''Implementation for keyword searches from REUTERS.\n\n    query: A URL-encoded string.\n\n    Returns: A list of the top Articles returned by the query search.\n    '''\n    res = requests.get('http://www.reuters.com/search?blob=%s' % (query))\n    soup = BeautifulSoup(res.text)\n    articles = soup.findAll('li', attrs={'class': 'searchHeadline'})\n    article_urls = [article.a.get('href') for article in articles]\n\n    top_articles = []\n    for url in article_urls[0:news_interface.NUM_ARTICLES]:\n        top_articles.append(self.get_article(url))\n    return top_articles\n","sub_path":"analysis/scraping/reuters.py","file_name":"reuters.py","file_ext":"py","file_size_in_byte":1637,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"58272645","text":"import re, os\nfrom typing import Dict\ntry:\n    import readline\nexcept ImportError:\n    #!pip install pyreadline\n    import pyreadline as readline\n\n'''입력값 처리 부분 #start'''\nclass Completer(object):  # Custom completer\n\n    def __init__(self, options):\n        self.options = sorted(options)\n\n    def complete(self, text, state):\n        if state == 0:  # on first trigger, build possible matches\n            if text:  # cache matches (entries that start with entered text)\n                self.matches = [s for s in self.options \n                                    if s and s.startswith(text)]\n            else:  # no text entered, all matches possible\n                self.matches = self.options[:]\n\n        # return match indexed by state\n        try: \n            return self.matches[state]\n        except IndexError:\n            return None\ncommands = [\"print\", \"push\", \"pop\", \"set\", \"clear\", 'quit']\ncompleter = Completer(commands)\nreadline.parse_and_bind('tab: complete')    \nreadline.set_completer(completer.complete)\ndef CMD_Alters(text):\n    options = [i for i in commands if i.startswith(text.split()[0])]\n    if len(options):\n        return options\n    else:\n        return []\n'''입력값 처리 부분 #end'''\n\n\n\ndef print_stack(STACK):\n    \"\"\"\n    push값 중, sfp가 없으면, 가장 처음 push된 주소를 sfp라고 지정.\n    (즉, sfp가 해당 push된 데이터 위에 존재하고 있다고 판단.)\n    \"\"\"\n    if len(STACK) == 0:\n        print(\"STACK is Empty.\")\n        return 0\n    length = max([len(_[0]) for _ in STACK]) #stack에서 가장 data 길이가 긴 것의 length가져옴.\n    \n    max_length = length if length < 20 else 20 #한줄에 출력할 최대 글자.\n    \n    SUM = str(sum( [_[1] for _ in STACK] )) # ebp뒤에 나올 최대 숫자 -> 문자열화 길이.\n    #str(SUM) #3+1+1+1+ len(str(SUM))\n    print(f\"\\n{'*'* (max_length//2 + len(SUM)//2 + 5) } STACK {'*'* (max_length//2 + len(SUM)//2 + 5)}\", end='')\n    #print('-' * (length + 4 + len(SUM) + 4), end='' ) \n    \n    # | data | <= EBP - num\n    # max_length for len(data)\n    # max_length for indent\n    # len(SUM) for num\n    # 4 for <= EBP\n    \n    #find sfp(SFP) #가장 마지막을 기준으로 함.\n    ebp = -1\n    for idx, data in enumerate(STACK):\n        if data[0] in ['sfp', 'SFP']:\n            ebp = idx\n    \n    \n    #4바이트를 1줄로 하여 출력\n    #5바이트인 경우, 2줄이다.\n    #너무 긴 경우, ...으로 대체;\n    #한줄 최대 출력 : 20자\n    for idx, _ in enumerate(STACK):\n        #_[0]은 data\n        #_[1]은, bytes\n        len_of_line = _[1] // 4 + int(_[1] % 4 != 0) #현 데이터당 출력 줄수 지정.\n        if len(_[0]) > max_length: #len_of_line을 넘지 않게 분할 출력이 필요. 21~40까지는 절반씩 출력.\n            #만일, len_of_line의 글자를 다 채웠는데도 다 출력못하는 경우는 ...으로 나머지 출력.\n            #...으로 출력할지 전체 다 출력할지 parm으로 지정해주기(해야할것.)\n            pass\n        for _1 in range(1, len_of_line + 1): #data 출력부 _1은, 현 데이터의 출력 줄\n            print('\\n| ', end='')\n            if _1 == len_of_line: #마지막에 한번에 몰아서 출력. ->바꾸어야.\n                print(_[0], end=' ' * (max_length-len(_[0])))\n            else:\n                print(' ' * len(_[0]), end=' ' * (length-len(_[0])))\n            print(' |', end='')\n        \n        if idx == ebp: #EBP 출력부\n            print(f' <= EBP', end='')\n        else:\n            tmp = sum( [_[1] for _ in STACK[ ebp + (1 if ebp==-1 else 0) : idx + 1] if _[0] not in ('sfp', 'SFP')])\n            if tmp !=0 :\n                print(f' <= EBP - {tmp}', end='')\n        print()\n        print('-'* (max_length + 4), end='')\n    #print('-' * (length + 4 + len(SUM) + 4) ) \n    print(f\"\\n{'*'* (max_length//2 + len(SUM)//2 + 5) } STACK {'*'* (max_length//2 + len(SUM)//2 + 5)}\")\n\ndef push(CMD):\n    '''\n    USEAGE\n    push data bytes=len(data) #기본값\n    만일 data에 공백 등이 섞여있고 data의 마지막이 숫자로 끝난다면 $$를 붙여주어야한다.\n    \n    push 안녕 123$$ -> 안녕 123이 들어감.\n    push 안녕 123$$\n    push 안녕 alex -> 안녕 alex가 들어감.\n    \n    만약 123$$가 데이터이면 어떻게하지.. -> $하나 더 붙이기.\n    '''\n    CMD = CMD[5:] # 'push '이후의 문자열\n    \n    match = lambda p,x=CMD: bool(p.match(x)) # 함수 설정.\n    \n    p = re.compile('^ *.+ +[0-9]+$')\n    if match(p): \n        #마지막 공백이후가 byte를 나타내는 것이라면\n        _ = CMD.split()[-1]\n        byte = int(_)\n        CMD = CMD[:-len(_)-1]\n        \n    else: # 기본byte : 문자열의 길이.\n        p = re.compile('[0-9]+\\\\$\\\\$$')\n        if bool( p.match(CMD.split()[-1]) ): #마지막이 숫자로 끝나는 경우.\n            CMD = CMD[:-2]\n        p = re.compile('[0-9]+\\\\$\\\\$\\\\$$')\n        if bool( p.match(CMD.split()[-1]) ): #마지막이 숫자로 끝나는 경우.\n            CMD = CMD[:-1]\n        byte = len(CMD)\n    \n    \n    STACK.append( [ CMD , byte ] )\n\n\nSTACK = list()\nVARIABLES = dict()\nidx_of_vari_tmp = 0\n\n\nwhile True: \n    #입력값 처리 #1 : 기본 처리\n    CMD = input('> ')\n    CMD = CMD.strip() # 좌우 공백 제거.\n    if CMD == '': continue # 입력값이 없을 때.\n    \n    #입력값 처리 #2 : 대안 선택 및 표시\n    alters = CMD_Alters(CMD) #가능한 대안 가져오기.\n    if len(alters) == 1:\n        if ' ' in CMD:\n            CMD = alters[0] + CMD[CMD.index(' '):]\n        else:\n            CMD = alters[0]\n    else:\n        print(*alters if len(alters) != 0 else ['Command Not Found\\nAvailable Command :']+commands, sep=' ')\n        continue\n    \n    \n    #입력값 처리 #3 : 명령어 구조에 맞게 처리.\n    match = lambda p,x=CMD: bool(p.match(x)) # 함수 설정.\n    \n    p = re.compile('print.*')\n    if match(p) : #stack 출력(범위 지정기능), 변수 출력, STACK의 데이터 출력.(구현해야)\n        '''\n        print all : 모든 변수 정보 출력\n        print vari_name : vari_name에 해당하는 변수 정보 출력.\n        print 0:3 => 0~3째 스택데이터를 보여라.\n        '''\n        if CMD == 'print': #STACK출력\n            print_stack(STACK)\n        elif CMD == 'print all': #모든 변수 출력\n            for name, data in VARIABLES.items():\n                print(name, '=', data)\n\n        elif match(re.compile('print +\\d*:\\d*$')): #범위지정 STACK출력 숫자(혹은 공백):숫자(혹은 공백) 의 경우만 실행됨.\n            _ = CMD.split()[1].split(':')\n            if _[0] == '':\n                if _[1] == '': #parm이 { : } 일 때\n                    _ = [None] * 2\n                else: _ = [None, int(_[1].strip())] #parm이 {:숫자} 일 때\n            elif _[1] == '': #parm이 {숫자:}일 때\n                _ = [int(_[0].strip()), None]\n            else: #parm이 {숫자:숫자}일 때\n                _ = [int(i.strip()) for i in _]\n            print(f\"print {_[0] if _[0] != None else ''} : {_[1] if _[1] != None else ''}\")\n            print_stack(STACK[ _[0] : _[1] ])\n\n        elif ' ' in CMD and not match(re.compile('print +.*:.*$')): # parameter에 :가 포함되지 않았을 경우. -> 변수 출력.\n            _ =  ''.join(CMD.split()[1:])\n            if _ in VARIABLES:\n                print(_, '=' , VARIABLES[_])\n            else:\n                print(f\"Can't Find {_}\")\n        else:\n            print(\"Unknown Parameter\")\n        continue #아래 if문 통과.\n    \n    p = re.compile('push .+')\n    if match(p): #push구현.\n        push(CMD)\n        continue #아래 if문 통과.\n    \n    p = re.compile('pop.*')\n    if match(p) : # pop구현.\n        if len(STACK) == 0:\n            print(\"Empty STACK\")\n            continue \n        \n        CMD = CMD.split()\n        if len(CMD) == 1: #parm이 주어지지 않았을 때, 즉 pop만하고 버릴 때.\n            STACK.pop()\n        else: #parm이 주어졌을 때.\n            #아래 두 if => 변수명으로 사용 불가능한 것 필터링.\n            if CMD[1] == 'all': #all필터링.\n                print(f\"You can't use all as variable name, temporary stored in tmp_{idx_of_vari_tmp}\")\n                VARIABLES[f'tmp_{idx_of_vari_tmp}'] = STACK.pop()\n                print(f'tmp_{idx_of_vari_tmp} =', VARIABLES[f\"tmp_{idx_of_vari_tmp}\"])\n                idx_of_vari_tmp += 1\n                continue\n            if match(re.compile('\\d*:\\d*$'), CMD[1]): #{:}, {숫자:숫자} 필터링.\n                print(f\"You can't use : in variable name temporary stored in tmp_{idx_of_vari_tmp}\")\n                VARIABLES[f'tmp_{idx_of_vari_tmp}'] = STACK.pop()\n                print(f'tmp_{idx_of_vari_tmp} =', VARIABLES[f\"tmp_{idx_of_vari_tmp}\"])\n                idx_of_vari_tmp += 1\n                continue\n            #필터링 통과했을 때, VARIABLES에 pop된 값 저장.\n            VARIABLES[CMD[1]] = STACK.pop()\n            print(CMD[1], '=', VARIABLES[CMD[1]])\n            #본래 실제 변수로 설정하고자 했으나, 여러 사정으로 dictionary로 바꿈.(편리성..)\n            # try:\n            #     exec(f'{CMD[1]} = {STACK.pop()};VARIABLES.append(\"{CMD[1]}\")')\n            #     print(CMD[1], '=', eval(CMD[1]))\n            # except:\n            #     print(f\"Syntax Error : Possible variable name is not available. temporary stored in tmp{idx_of_vari_tmp}\")\n            #     print(f\"tmp{idx_of_vari_tmp}\", '=', eval(f\"tmp{idx_of_vari_tmp}\"))\n            continue #아래 if문 통과.\n    \n    p = re.compile('set .+')\n    if match(p) : #스택에 끼워넣기, 변수 바꾸기\n        pass\n        continue #아래 if문 통과.\n    \n    if CMD == 'clear': # 화면 지우기.\n        if os.name == 'nt':\n            os.system('cls')\n        elif os.name == 'posix':\n            os.system('clear')\n        continue #아래 if문 통과.\n\n    if CMD == 'quit' :\n        break","sub_path":"STACK_Viewer.py","file_name":"STACK_Viewer.py","file_ext":"py","file_size_in_byte":9991,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"257127702","text":"# pylint: disable=no-self-use\nimport unittest\nfrom typing import Type\n\nfrom parameterized import parameterized\nfrom mockito import mock\n\nfrom cloudrail.knowledge.context.aws.resources_builders.terraform.aws_terraform_builder import AwsTerraformBuilder\nfrom cloudrail.knowledge.context.azure.resources_builders.terraform.azure_terraform_builder import AzureTerraformBuilder\nfrom cloudrail.knowledge.context.gcp.resources_builders.terraform.base_gcp_terraform_builder import BaseGcpTerraformBuilder\nfrom cloudrail.knowledge.context.base_context_builders.base_terraform_builder import BaseTerraformBuilder\nfrom cloudrail.knowledge.context.environment_context.terraform_resource_finder import TerraformResourceFinder\n\n\nclass TestBaseTerraformBuilder(unittest.TestCase):\n\n    @parameterized.expand(\n        [\n            ['aws', AwsTerraformBuilder],\n            ['azure', AzureTerraformBuilder],\n            ['gcp', BaseGcpTerraformBuilder]\n        ]\n    )\n    def test_aws_builder_continues_on_do_build_exception(self, unused_name, builder_type: Type[BaseTerraformBuilder]):\n        item = mock({'id': '1', 'iac_state': None})\n        service_name = mock({'value': 'service_name'})\n\n        class TerraformBuilderForTest(builder_type):\n\n            def do_build(self, attributes: dict):\n                if attributes['raise']:\n                    raise Exception()\n                return item\n\n            def _build(self):\n                attributes = self.resources[self.get_service_name().value]\n                results = []\n                for attribute in attributes:\n                    try:\n                        results.append(self.do_build(attribute))\n                    except: # pylint: disable=bare-except\n                        pass\n                return results\n\n            def get_service_name(self):\n                return service_name\n\n        builder = TerraformBuilderForTest({\n            'service_name': [\n                {\n                    'raise': True,\n                    'tf_action': 'read',\n                    'cloudrail_resource_metadata': None\n                },\n                {\n                    'raise': False,\n                    'tf_action': 'read',\n                    'cloudrail_resource_metadata': None\n                }\n            ]\n        })\n        TerraformResourceFinder.initialize()\n        results = builder.build()\n        self.assertEqual(len(results), 1)\n        self.assertEqual(results[0], item)\n\n    @parameterized.expand(\n        [\n            ['aws', AwsTerraformBuilder],\n            ['azure', AzureTerraformBuilder],\n            ['gcp', BaseGcpTerraformBuilder]\n        ]\n    )\n    def test_aws_builder_continues_on_build_exception(self, unused_name, builder_type: Type[BaseTerraformBuilder]):\n        service_name = mock({'value': 'service_name'})\n\n        class TerraformBuilderForTest(builder_type):\n\n            def do_build(self, attributes: dict):\n                pass\n\n            def _build(self):\n                raise Exception()\n\n            def get_service_name(self):\n                return service_name\n\n        builder = TerraformBuilderForTest({\n            'service_name': [\n                {\n                    'tf_action': 'read',\n                    'cloudrail_resource_metadata': None\n                }\n            ]\n        })\n        TerraformResourceFinder.initialize()\n        results = builder.build()\n        self.assertEqual(len(results), 0)\n\n    @parameterized.expand(\n        [\n            ['aws', AwsTerraformBuilder],\n            ['azure', AzureTerraformBuilder],\n            ['gcp', BaseGcpTerraformBuilder]\n        ]\n    )\n    def test_aws_builder_not_failing_on_post_build_exception(self, unused_name, builder_type: Type[BaseTerraformBuilder]):\n        item = mock({'id': '1', 'iac_state': None})\n        service_name = mock({'value': 'service_name'})\n\n        class TerraformBuilderForTest(builder_type):\n\n            def do_build(self, attributes: dict):\n                pass\n\n            def _build(self):\n                return [item]\n\n            @staticmethod\n            def _post_build(build_results) -> list:\n                raise Exception()\n\n            def get_service_name(self):\n                return service_name\n\n        builder = TerraformBuilderForTest({\n            'service_name': [\n                {\n                    'tf_action': 'read',\n                    'cloudrail_resource_metadata': None\n                },\n                {\n                    'tf_action': 'read',\n                    'cloudrail_resource_metadata': None\n                }\n            ]\n        })\n        TerraformResourceFinder.initialize()\n        results = builder.build()\n        self.assertEqual(len(results), 0)\n","sub_path":"tests/knowledge/context/test_base_terraform_builder.py","file_name":"test_base_terraform_builder.py","file_ext":"py","file_size_in_byte":4719,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"493110725","text":"import pandas as pd\nimport numpy as np\n\n# management of training data\nclass train_data:\n   def __init__(self,wid,max_l):\n      self.run(wid,max_l)\n\n   def run(self,wid,max_l):\n      train=pd.read_table('training-data-small.txt',delim_whitespace=True,names=('y','x'))\n      x=[]\n      slen=[]\n      for s in train['x']:\n         a=[]\n         for w in s.split(sep=','):\n            t=np.zeros(len(wid))\n            t[wid[w]]=1\n            a.append(t)\n         slen.append(len(a))\n         for i in range(max_l-len(a)):\n            a.append(np.zeros(len(wid)))\n         x.append(a)\n      y=[]\n      for c in train['y']:\n         t=np.zeros(2)\n         t[c]=1\n         y.append(t)\n      self.x=x\n      self.y=y\n      self.slen=slen\n\n# management of test data\nclass test_data:\n   def __init__(self,wid,max_l):\n      self.run(wid,max_l)\n\n   def run(self,wid,max_l):\n      test=pd.read_table('test-data-small.txt',delim_whitespace=True,names=('x'))\n      x=[]\n      slen=[]\n      for s in test['x']:\n         a=[]\n         for w in s.split(sep=','):\n            t=np.zeros(len(wid))\n            t[wid[w]]=1\n            a.append(t)\n         slen.append(len(a))\n         for i in range(max_l-len(a)):\n            a.append(np.zeros(len(wid)))\n         x.append(a)\n      self.x=x\n      self.slen=slen\n","sub_path":"for small dataset/data2.py","file_name":"data2.py","file_ext":"py","file_size_in_byte":1291,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"614890843","text":"import os\nimport time\nimport csv\nimport numpy as np\n\nimport torch\nimport torch.utils.data\nimport torch.backends.cudnn as cudnn\nimport torch.optim\nimport torch.nn as nn\ncudnn.benchmark = True\n\nfrom models import ResNet\nfrom sphere_model import SphereFCRN\nfrom metrics import AverageMeter, Result\nimport cv2\n\nfieldnames = ['mse', 'rmse', 'absrel', 'lg10', 'mae',\n                'delta1', 'delta2', 'delta3',\n                'data_time', 'gpu_time']\n\n\ndef create_data_loaders():\n    # Data loading code\n    print(\"=> creating data loaders ...\")\n    val_loader = None\n\n    # valdir = '/root/workspace/depth/sparse-to-dense.pytorch/data/omni'\n    # valdir = '/root/workspace/depth/sparse-to-dense.pytorch/data/nyudepthv2/val'\n    valdir = '/root/workspace/depth/sparse-to-dense.pytorch/data/uw_test'\n    # valdir = '/root/workspace/gan/WaterGAN/output/train_and_test'\n\n    from dataloaders.omni_dataloader import OmniDataset\n    from dataloaders.nyu_dataloader import NYUDataset\n    from dataloaders.uw_test_dataloader import UWTestDataset\n    from dataloaders.uw_nyu_dataloader import UWNYUDataset\n\n    # val_dataset = UWNYUDataset(valdir, type='val', modality='rgb')\n    val_dataset = UWTestDataset(valdir)\n\n    # set batch size to be 1 for validation\n    val_loader = torch.utils.data.DataLoader(val_dataset, batch_size=1, shuffle=False,\n                                             num_workers=10, pin_memory=True)\n\n    print(\"=> data loaders created.\")\n    return val_loader\n\n\ndef weights_init(m):\n    # Initialize filters with Gaussian random weights\n    if isinstance(m, nn.Conv2d):\n        m.weight.data.normal_(0, np.sqrt(0.01))\n        if m.bias is not None:\n            m.bias.data.zero_()\n    elif isinstance(m, nn.ConvTranspose2d):\n        m.weight.data.normal_(0, np.sqrt(0.01))\n        if m.bias is not None:\n            m.bias.data.zero_()\n    elif isinstance(m, nn.BatchNorm2d):\n        m.weight.data.fill_(1)\n        m.bias.data.zero_()\n\n\ndef main():\n    global test_csv\n\n    # evaluation mode\n    evalute_filepath ='/root/workspace/depth/sparse-to-dense.pytorch/results/uw_nyu.sparsifier=uar.samples=0.modality=rgb.arch=resnet50.decoder=upproj.criterion=l1.lr=0.01.bs=16.pretrained=True(old)'\n    best_weights_path = os.path.join(evalute_filepath, 'best_model.pkl')\n    assert os.path.isfile(best_weights_path), \\\n    \"=> no best weights found at '{}'\".format(evalute_filepath)\n    print(\"=> loading best weights for model '{}'\".format(evalute_filepath))\n\n    val_loader = create_data_loaders()\n\n    decoder = 'upproj'\n\n    model = ResNet(layers=50, decoder=decoder, output_size=val_loader.dataset.output_size, pretrained=False)\n    model = model.cuda()\n    model.load_state_dict(torch.load(best_weights_path))\n    # model.decoder.apply(weights_init)\n\n    print(\"=> loaded best weights for model\")\n\n    # create results folder, if not already exists\n    output_directory = os.path.join('results/uw_test', 'uw_test5')\n    if not os.path.exists(output_directory):\n        os.makedirs(output_directory)\n    best_txt = os.path.join(output_directory, 'best.txt')\n\n    result = validate(val_loader, model, output_directory=output_directory)\n\n    # create new csv files\n    with open(best_txt, 'w') as txtfile:\n        txtfile.write(\"rmse={:.3f}\\nabsrel={:.3f}\\ndelta1={:.3f}\\n\".\n            format(result[0], result[1], result[2]))\n\n\ndef validate(val_loader, model, output_directory=None):\n    average_meter = AverageMeter()\n    model.eval() # switch to evaluate mode\n    end = time.time()\n    print_freq = 1\n\n    total_rmse = []\n    total_rel = []\n    total_delta1 = []\n\n    avg_rms = 0\n    avg_rel = 0\n    avg_delta1 = 0\n\n    for i, (input, target) in enumerate(val_loader):\n        input, target = input.cuda(), target.cuda()\n        torch.cuda.synchronize()\n\n        # compute output\n        with torch.no_grad():\n            pred = model(input)\n        torch.cuda.synchronize()\n\n        # measure accuracy and record loss\n        rgb = (255 * np.transpose(np.squeeze(input.cpu().numpy()), (1,2,0))).astype(np.uint8)\n        pred = np.squeeze(pred.cpu().numpy())\n        target = np.squeeze(target.cpu().numpy())\n\n        # pred /= pred.max() / target.max()\n        # pred = (pred - pred.min()) / (pred.max() - pred.min()) * (target.max() - target.min()) + target.min()\n        target = (target - target.min()) / (target.max() - target.min()) * (pred.max() - pred.min()) + pred.min()\n\n        valid_mask = target > 0\n        pred = np.ma.masked_array(pred, mask=valid_mask).data\n        target = np.ma.masked_array(target, mask=valid_mask).data\n\n        abs_diff = np.abs(pred - target)\n        mse = float((np.power(abs_diff, 2)).mean())\n        rmse = np.sqrt(mse)\n\n        absrel = float((abs_diff / target).mean())\n\n        maxRatio = np.maximum(pred / target, target / pred)\n        delta1 = float((maxRatio < 1.25).astype(np.float).mean())\n\n        total_rmse.append(rmse)\n        total_rel.append(absrel)\n        total_delta1.append(delta1)\n\n        avg_rms = np.mean(total_rmse)\n        avg_rel = np.mean(total_rel)\n        avg_delta1 = np.mean(total_delta1)\n\n        # save 8 images for visualization\n        skip = 10\n\n        import matplotlib.pyplot as plt\n        if i == 0:\n            file_name_rgb = 'rgb' + str(i) + '.png'\n            out_file_rgb = os.path.join(output_directory, file_name_rgb)\n            file_name_gd = 'gd' + str(i) + '.png'\n            out_file_gd = os.path.join(output_directory, file_name_gd)\n            file_name_pred = 'pred' + str(i) + '.png'\n            out_file_pred = os.path.join(output_directory, file_name_pred)\n\n            plt.imsave(out_file_rgb, rgb)\n            plt.imsave(out_file_gd, target)\n            plt.imsave(out_file_pred, pred)\n        elif (i < 5*skip) and (i % skip == 0):\n            file_name_rgb = 'rgb' + str(i) + '.png'\n            out_file_rgb = os.path.join(output_directory, file_name_rgb)\n            file_name_gd = 'gd' + str(i) + '.png'\n            out_file_gd = os.path.join(output_directory, file_name_gd)\n            file_name_pred = 'pred' + str(i) + '.png'\n            out_file_pred = os.path.join(output_directory, file_name_pred)\n\n            plt.imsave(out_file_rgb, rgb)\n            plt.imsave(out_file_gd, target)\n            plt.imsave(out_file_pred, pred)\n\n        if (i+1) % print_freq == 0:\n            print('Test: [{}/{}]\\n\\t'\n                  'RMSE={:.2f}({:.2f}) '\n                  'Delta1={:.3f}({:.3f}) '\n                  'REL={:.3f}({:.3f}) '.format(\n                   i+1, len(val_loader), rmse, avg_rms, delta1, avg_delta1, absrel, avg_rel))\n\n    print('\\n*\\n'\n        'RMSE={:.3f}\\n'\n        'Delta1={:.3f}\\n'\n        'REL={:.3f}\\n'.format(avg_rms, avg_delta1, avg_rel))\n\n    avg = [avg_rms, avg_rel, avg_delta1]\n\n    return avg\n\n\nif __name__ == '__main__':\n    main()\n","sub_path":"evaluate/uw_real_testing.py","file_name":"uw_real_testing.py","file_ext":"py","file_size_in_byte":6789,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"337631838","text":"#!/usr/bin/env python\n# -*- coding: utf-8 -*-\n# @Time    : 2020/4/3 2:04 下午\n# @Author  : guifeng(moguifeng@baice100.com)\n# @File    : sim.user.py\nimport numpy as np\nimport faiss\nimport sys\n\n\ndef load_emb(file):\n    ret = list()\n    with open(file, encoding='utf8') as f:\n        for line in f:\n            line = line.strip()\n            if len(line) > 0:\n                ret.append([float(item) for item in line.split(\"\\t\")[1].split(\",\")])\n    return ret\n\n\ndef load_uid_map(file):\n    ret = dict()\n    with open(file, encoding='utf8') as f:\n        for line in f:\n            line = line.strip()\n            if len(line) > 0:\n                ps = line.split(\"\\t\")\n                ret[int(ps[1])] = ps[0]\n    return ret\n\n\nsim_size = 100\nnlist = 100\nm = 8\nk = 4\nd = 64\nquantizer = faiss.IndexFlatL2(d)\nindex = faiss.IndexIVFPQ(quantizer, d, nlist, m, 8)\nuid_map_file = \"/Users/wizardholy/project/recsys_learning/datas/info/info.uid.map.txt\"\nemb_file = \"/Users/wizardholy/project/recsys_learning/emb.txt\"\nout_file = \"/Users/wizardholy/project/recsys_learning/sim_user.txt\"\nprint(sys.argv)\nif len(sys.argv) > 4:\n    d = int(sys.argv[1])\n    uid_map_file = sys.argv[2]\n    emb_file = sys.argv[3]\n    out_file = sys.argv[4]\n    if len(sys.argv) > 5:\n        sim_size = int(sys.argv[5])\n\nprint(d, uid_map_file, emb_file, out_file)\n\nuid_map = load_uid_map(uid_map_file)\ndf = load_emb(emb_file)\ndf = np.array(df).astype('float32')\nindex.train(df)\nindex.add(df)\nD, I = index.search(df, sim_size + 1)\nsim = I.tolist()\nweights = D.tolist()\nprint(\"len sim\", len(sim))\nwith open(out_file, mode=\"w\") as f:\n    count = 0\n    for i in range(len(sim)):\n        uid = uid_map[i]\n        outs = []\n        for j in range(1, len(sim[i])):\n            try:\n                suid = uid_map[sim[i][j]]\n                weight = weights[i][j]\n                outs.append(\"\" + suid + \"#\" + str(weight))\n            except:\n                print(suid, i, j)\n                pass\n        f.write(uid + \" \" + (\",\".join(outs)) + \"\\n\")\n        count += 1\n    print(\"total user:\", count)\n","sub_path":"modules/ncf/sim.user.py","file_name":"sim.user.py","file_ext":"py","file_size_in_byte":2059,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"107732487","text":"import cupy as cp\nimport numpy as np\nfrom NN.NNWrapper import Variable\nfrom NN.NNWrapper import operation\nop = operation()\n\n\nclass base:\n\tdef __init__(self):\n\t\tself.device = None\n\n\tdef gpu(self):\n\t\treturn cp.cuda.Device(self.device)\n\n\tdef guard(self):\n\t\treturn cp.cuda.Device(self.device)\n\n\tdef register_weights(self, ndarray):\n\t\twith self.gpu():\n\t\t\tvar = Variable(cp.asarray(ndarray))\n\t\treturn var\n\n\tdef forward(self, x):\n\t\tpass\n\n\tdef __call__(self, *x):\n\t\twith self.gpu():\n\t\t\tx = tuple(map(lambda i:Variable(i), x))\n\t\t\treturn self.forward(*x)\n\nclass Sigmoid(base):\n\tdef __init__(self, device=0):\n\t\t'''\n\t\tsigmoid = 1./(1+exp(-x))\n\t\t'''\n\t\tsuper().__init__()\n\t\tself.device = device\n\n\tdef forward(self, x):\n\t\tx = (op.exp(x*(-1.)) + 1.) ** -1\n\t\treturn x\n\n\nclass Linear(base):\n\t'''\n\tTODO: register\n\t'''\n\tdef __init__(self, param1, param2, device=0):\n\t\t'''\n\t\tlinear = Ax+b\n\n\t\tcould be: param1->n_in, param2->n_hidden\n\t\tor: \t  param1->weights, param2->bias\n\t\t'''\n\t\tsuper().__init__()\n\t\tself.device = device\n\t\tself.weights = None\n\t\tself.bias = None\n\n\t\tif(type(param1)==int):\n\t\t\tself.type = 'init'\n\t\t\tself.n_in, self.n_hidden = param1, param2\n\t\telse:\n\t\t\tself.type = 'load'\n\t\t\tself.weights, self.bias = param1, param2\n\t\t\tself.n_in, self.n_hidden = self.weights.shape[0], self.weights.shape[1]\n\n\n\t\twith self.gpu():\n\t\t\tif(self.weights is None):\n\t\t\t\tself.weights = cp.asarray(np.random.randn(self.n_hidden, self.n_in))\n\t\t\t\tself.bias = cp.asarray(np.random.randn(self.n_hidden))\n\t\t\telse:\n\t\t\t\tself.weights = cp.asarray(self.weights).transpose([1,0])\n\t\t\t\tself.bias = cp.asarray(self.bias)\n\n\n\tdef forward(self, x):\n\t\t# N*P*S*k\n\t\tif(self.weights.shape[0]==1):\n\t\t\tx = x*self.weights\n\t\t\t# N*P*S\n\t\t\tx = x.sum(axis=-1)\n\n\t\telse:\n\t\t\tX = []\n\t\t\tfor index in range(self.weights.shape[0]):\n\t\t\t\t_x =  x*self.weights[index]\n\t\t\t\t_x = _x.sum(axis=-1)\n\t\t\t\tX.append(_x)\n\t\t\tx = op.stack(X, axis=-1)\n\n\t\tx = x+self.bias\n\t\treturn x\n\nif __name__ == '__main__':\n\tsigmoid = Sigmoid(device=1)\n\n\twith cp.cuda.Device(1):\n\t\tx = cp.array([[1,2,3,4],[4,5,6,7]]).astype(cp.float32)\n\n\tprint(type(x))\n\tx = sigmoid(x)\n\tprint(type(x))\n\n\tlinear = Linear(4, 10, device=1)\n\t_x = linear(x)\n\tprint(_x)","sub_path":"backend/NNCUPY/old/base.py","file_name":"base.py","file_ext":"py","file_size_in_byte":2145,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"34406575","text":"if __name__ == \"__main__\":\n\n    state = \"\"\n    rules = set()\n\n    with open(\"input.txt\", \"r\") as f:\n\n        for i, l in enumerate(f):\n            s = l.strip().split()\n            if len(s) == 0: continue\n            if i == 0:\n                state = s[2]\n            else:\n                if s[2][-1] == \"#\": # on\n                    rules.add(s[0])\n\n    generations = 50000000000\n    added = 0\n    diff = 0\n\n    for gen in range(1, generations+1):\n\n        new_state = \"\"\n\n        lengthened = \"...\" + state + \"...\"\n        if lengthened[:5] in rules:\n            new_state += \"#\"\n            added += 1\n        for i in range(len(state)):\n            if lengthened[i+3-2:i+3+2+1] in rules:\n                new_state += \"#\"\n            else:\n                new_state += \".\"\n        if lengthened[-5:] in rules:\n            new_state += \"#\"\n\n        i = 0\n        while True:\n            if i == len(new_state) - 1 or new_state[i] == \"#\": break\n            added -= 1\n            i += 1\n\n        j = len(new_state) - 1\n        while True:\n            if j == 0 or new_state[j] == \"#\": break\n            j -= 1\n\n        state = new_state[i:j+1]\n\n        if gen % 100000 == 0:\n            print(added)\n            print(str(gen) + \": \" + str(state))\n            diff = 100000 + added\n            break\n\n    added = -(generations - diff) # noticed pattern\n\n    planted = 0\n    for i, pot in enumerate(state):\n        number = i - added\n        if pot == \"#\": planted += number\n    print(planted)","sub_path":"2018/12.2/problem.py","file_name":"problem.py","file_ext":"py","file_size_in_byte":1496,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"174172372","text":"import tensorflow as tf\nimport numpy as np\nimport scipy.stats\nimport subprocess\nfrom pprint import pprint\nfrom joblib import Parallel, delayed\nimport pandas as pd\nimport matplotlib.pyplot as plt\n\nfrom atongtf import util\nimport seaborn as sns\n\n\ndef get_samples(n, mixture):\n    std = 0.03125\n    #std = 0.125\n    np.random.seed(42)\n    train_data = np.random.multivariate_normal([2, 0],\n                                               [[std, 0], [0, std]],\n                                               size=int((n) * mixture))\n    train_data2 = np.random.multivariate_normal([-2, 0],\n                                                [[std, 0], [0, std]],\n                                                size=int((n)* (1 - mixture)))\n    train_data = np.concatenate([train_data, train_data2])\n    return train_data\n\ndef score(model_file, alpha):\n    util.set_config()\n    model = util.load_model(model_file)\n    z = np.random.normal(size=(100000, 100))\n    y_true = get_samples(100000, alpha)\n    y_pred = model.predict(z)\n    wd = 0\n    for i in range(2):\n        wd += scipy.stats.wasserstein_distance(y_true[:,i], y_pred[:,i])**2\n    wd = np.sqrt(wd)\n    return wd\n\ndef score_one(model):\n    iteration = int(model.split('/')[-1].split('_')[-1])\n    seed = int(model.split('/')[-2])\n    alpha = float(model.split('/')[-3].split('_')[-1])\n    return model, iteration, seed, alpha, score(model, alpha)\n\n\ndef score_all(directory):\n    stdout, _ = subprocess.Popen(['find', directory, '-name', 'generator_model*.json'], \n                                 stdout=subprocess.PIPE).communicate()\n    models = [s.decode('utf-8')[:-5] for s in stdout.split()]\n    models = [s for s in models if s.endswith('0')]\n    old_scores = pd.read_pickle('scores3.pkl')\n    old_models = set(old_scores.path.values)\n    models = [s for s in models if s not in old_models]\n    scores = Parallel(n_jobs=-1, verbose=1)(delayed(score_one)(s) for s in models)\n    scores = pd.DataFrame(scores, columns = ['path', 'iteration', 'seed', 'alpha', 'score'])\n    scores = pd.concat([old_scores, scores])\n    scores.to_pickle('scores3.pkl')\n\nfrom matplotlib.colors import LogNorm\nif __name__ == '__main__':\n    #score_all('/home/alex/mixture-gan/experiments/auto3/')\n    scores = pd.read_pickle('scores3.pkl')\n    scores['alpha'] = (scores['alpha'])\n    #print(scores)\n    #print(np.unique(scores['alpha'], return_counts=True))\n    groups = scores.groupby(by=['alpha', 'iteration'])\n    #print(len(scores))\n    #scores = groups.mean()\n    #palette = sns.color_palette(\"mako_r\", 9)\n    palette = sns.color_palette(\"hls\", 10)\n    \n    #cax = sns.lineplot(x='iteration', y='score', hue='alpha', palette=palette, data=scores, hue_norm=LogNorm(), )\n    cax = sns.relplot(x='iteration', y='score', hue='alpha', palette=palette, data=scores, hue_norm=LogNorm(), kind='line', ci=None, estimator='median')\n    cax.ax.set_yscale('log')\n    #plt.show()\n    plt.savefig('scores3.png')\n","sub_path":"experiments/score_all2.py","file_name":"score_all2.py","file_ext":"py","file_size_in_byte":2940,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"217792032","text":"# Tu pišite svoje funkcije:\r\nfrom math import *\r\n\r\n\r\ndef koordinate (ime,kraji):\r\n    for name,x,y in kraji:\r\n        if name == ime:\r\n            return (x,y)\r\n    return\r\n\r\n\r\ndef razdalja_koordinat(x1,y1,x2,y2):\r\n    razdalja = sqrt(abs(x2-x1)**2 + abs(y2-y1)**2)\r\n    return razdalja\r\n\r\n\r\n\r\ndef razdalja(ime1,ime2,kraji):\r\n    kraj1 = koordinate(ime1,kraji)\r\n    kraj2 = koordinate(ime2,kraji)\r\n    x1,y1 = kraj1\r\n    x2,y2 = kraj2\r\n    razdalja_med_krajema = razdalja_koordinat(x1,y1,x2,y2)\r\n    return razdalja_med_krajema\r\n\r\n\r\n\r\n\r\ndef v_dometu(ime,domet,kraji):\r\n    kraji_v_dometu = []\r\n    for name,x1,y1 in kraji:\r\n        if name == ime:\r\n            for name2,x,y in kraji:\r\n                razdalja = sqrt(abs(x-x1)**2 + abs(y-y1)**2)\r\n                if razdalja <= domet and name2!= ime:\r\n                    kraji_v_dometu.append(name2)\r\n    return kraji_v_dometu\r\n\r\n\r\n\r\ndef najbolj_oddaljeni(ime,imena,kraji):\r\n    naj_razdalja = 0\r\n    naj_kraj = \"\"\r\n    for name,x1,y1 in kraji:\r\n        if name == ime:\r\n         for name2,x,y in kraji:\r\n            for kraj in imena:\r\n             if kraj == name2:\r\n                    razdalja = sqrt(abs(x - x1) ** 2 + abs(y - y1) ** 2)\r\n                    if razdalja> naj_razdalja:\r\n                            naj_kraj = name2\r\n    return naj_kraj\r\n\r\n\r\n\r\ndef zalijemo(ime,domet,kraji):\r\n    naj_razdalja = 0\r\n    naj_kraj = \"\"\r\n    for kraj,x1,y1 in kraji:\r\n        if ime == kraj:\r\n            for name,x,y in kraji:\r\n                razdalja = sqrt(abs(x - x1) ** 2 + abs(y - y1) ** 2)\r\n                if razdalja> naj_razdalja and razdalja <= domet:\r\n                    naj_razdalja = razdalja\r\n                    naj_kraj = name\r\n    return naj_kraj\r\n\r\n\r\ndef presek(s1,s2):\r\n    ys = []\r\n    for x in s1:\r\n        for x1 in s2:\r\n            if x == x1:\r\n                ys.append(x1)\r\n    return ys\r\n\r\ndef skupno_zalivanje(ime1,ime2,domet,kraji):\r\n    skupni_kraji = []\r\n    for kraj1,x,y in kraji:\r\n        if kraj1 == ime1:\r\n            x1 = x\r\n            y1 = y\r\n        if kraj1 == ime2:\r\n            x2 = x\r\n            y2 = y\r\n    for name,x,y, in kraji:\r\n                razdalja1 = sqrt(abs(x - x1) ** 2 + abs(y - y1) ** 2)\r\n                razdalja2 = sqrt(abs(x - x2) ** 2 + abs(y - y2) ** 2)\r\n                if razdalja1 <= domet and razdalja2<= domet:\r\n                    skupni_kraji.append(name)\r\n    return skupni_kraji\r\n\r\n\r\n\r\n\r\n","sub_path":"code/batch-2/vse-naloge-brez-testov/DN4-M-039.py","file_name":"DN4-M-039.py","file_ext":"py","file_size_in_byte":2416,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"358623940","text":"\"\"\"\nMonte Carlo Tic-Tac-Toe Player\n\"\"\"\n\nimport random\nimport poc_ttt_gui\nimport poc_ttt_provided as provided\n\n# Constants for Monte Carlo simulator\n# You may change the values of these constants as desired, but\n#  do not change their names.\nNTRIALS = 1         # Number of trials to run\nSCORE_CURRENT = 1.0 # Score for squares played by the current player\nSCORE_OTHER = 1.0   # Score for squares played by the other player\n\n# Add your functions here.\n\ndef mc_trial(board, player):\n    '''\n    Play single game\n    '''\n    while len(board.get_empty_squares()) > 0 and board.check_win() is None:\n        empty_squares_amount = len(board.get_empty_squares()) \n        cell_for_move = board.get_empty_squares()[random.randrange(empty_squares_amount)]\n        board.move(cell_for_move[0], cell_for_move[1], player)\n        player = provided.switch_player(player)\n\ndef mc_update_scores(scores, board, player):\n    '''\n    Get scores for each cell of played game\n    '''\n    who_won = board.check_win()\n    num_steps = board.get_dim()\n    for row in range(num_steps):\n        for col in range(num_steps):\n            if board.square(row, col) != provided.EMPTY:                \n                if who_won == provided.DRAW:\n                    scores[row][col] += 0\n                elif who_won == player:\n                    if board.square(row, col) == player:\n                        scores[row][col] += SCORE_CURRENT\n                    else:\n                        scores[row][col] -= SCORE_OTHER\n                else:\n                    if board.square(row, col) == player:\n                        scores[row][col] -= SCORE_CURRENT\n                    else:\n                        scores[row][col] += SCORE_OTHER\n\ndef get_best_move(board, scores):\n    '''\n    Return cell for best move\n    '''\n    moves = sorted(list(set(sum(scores, []))), reverse = True)  \n    for dummy_k in range(len(moves)):\n        best_move = check_move(board, scores, moves, dummy_k)\n        if best_move is not None:\n            return best_move\n    return\n        \ndef check_move(board, scores, moves, dummy_k):\n    '''\n    Check if move is preferable, if it so - return cell for move \n    '''\n    num_steps = board.get_dim()\n    move_indexes = list()\n    for dummy_i in range(num_steps):\n        for dummy_j in range(num_steps):\n            if scores[dummy_i][dummy_j] == moves[dummy_k] and board.square(dummy_i, dummy_j) == 1:\n                coords = dummy_i,dummy_j\n                move_indexes.append(coords)\n    if len(move_indexes) > 0:\n        return random.choice(move_indexes)\n    return\n    \ndef mc_move(board, player, trials):\n    '''\n    Make move based on Monte Carlo simulation\n    '''\n    scores = [[0 for dummy_i in range(board.get_dim())] for dummy_j in range(board.get_dim())]\n    for dummy_i in range(trials):\n        borda = board.clone()\n        mc_trial(borda, player)\n        mc_update_scores(scores, borda, player)\n    return get_best_move(board, scores)\n\n# Test game with the console or the GUI.  Uncomment whichever \n# you prefer.  Both should be commented out when you submit \n# for testing to save time.\n\n#provided.play_game(mc_move, NTRIALS, False)        \npoc_ttt_gui.run_gui(3, provided.PLAYERX, mc_move, NTRIALS, False)","sub_path":"Principles of Computing (part 1)/tic-tac-toe.py","file_name":"tic-tac-toe.py","file_ext":"py","file_size_in_byte":3230,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"67870286","text":"#!usr/bin/env python\n\n\nimport os\nimport argparse\nimport time\nfrom toolz import partition\nfrom collections import defaultdict, Counter\nfrom fasta_reader import parse_multi_fasta_file, str_punctuation_strip\nfrom std_codons_tables import codon_table_std_RNA, codon_table_std_DNA, stop_codons_std\n\n\nstart_time = time.process_time()\n\n\nparser = argparse.ArgumentParser(description='Kmers from proteins.')\nparser.add_argument('--path', '-p', type=str, required=True, dest='path', help='Path to the files')\nparser.add_argument('--out', '-o', type=str, required=True, dest='output', help='name output files.')\nparser.add_argument('--moltype', '-mt', type=str, required=True, dest='mol_type', help='Type mol DNA/RNA')\nparser.add_argument('--length', '-l', type=int, dest='k', help='Length of the kmers')\ngroup = parser.add_mutually_exclusive_group()\ngroup.add_argument('--count', '-c', action='store_true', help='Count kmers')\ngroup.add_argument('--list', '-ls', action='store_true', help='List of kmer')\ngroup.add_argument('--fasta', '-faa', action='store_true', help='If you need a protein fasta file.')\nargs = parser.parse_args()\n\ndef my_codons(sequence, mol_type='RNA'):\n    \"\"\"Return a generator of all codons(substring of length 3) with no-overlap window\n    from a sequence(string) of DNA/RNA.\"\"\"\n    seq = sequence.upper()\n    if mol_type == 'RNA':\n        seq = seq.replace('T', 'U')\n        return (''.join(c) for c in partition(3, seq))\n    elif mol_type == 'DNA':\n        return (''.join(c) for c in partition(3, seq))\n\n\n# get the proteins from a fasta file with genomes cds\ndef get_translation(sequence, mol_type='RNA'):\n    \"\"\"Translate the codons obtained from a fasta file with genomes CDS.\"\"\"\n    codon_list = my_codons(sequence, mol_type)\n    if mol_type == 'RNA':\n        codon_map = codon_table_std_RNA\n    else:\n        codon_map = codon_table_std_DNA\n    protein = []\n    for codon in codon_list:\n        if codon not in codon_map:\n            protein.append('?')\n        elif codon in codon_map and codon not in stop_codons_std:\n            protein.append(codon_map[codon])\n    return ''.join(protein)\n\n\ndef get_kmers(sequence, k):\n    \"\"\"Yields all the kmers of length k from the sequence input.\n    sequence = a string representing a DNA, Protein or RNA\n    k = integer representing the length of the kmer/substrings\n    \"\"\"\n    for i in range(len(sequence) - k + 1):\n        kmer = sequence[i:i + k]\n        yield kmer\n\n\ndef get_list_files(dirName):\n    \"\"\"Retruns a list of file and sub directories names in thget_list_filese given directory\"\"\"\n    list_files = os.listdir(dirName)\n    all_files = list()\n    # Iterate over all the entries\n    for entry in list_files:\n        # Create full path\n        full_path = os.path.join(dirName, entry)\n        # If entry is a directory then get the list of files in this directory\n        if os.path.isdir(full_path):\n            all_files = all_files + get_list_files(full_path)\n        else:\n            all_files.append(full_path)\n    return all_files\n\n\n# making a generator of the fasta files\ndef check_filenames(files):\n    zipped = []\n    not_zip = []\n    for file in files:\n        if file.endswith('.gz'):\n            zipped.append(file)\n        elif file.endswith('.fa') or file.endswith('.faa'):\n            not_zip.append(file)\n    return zipped, not_zip\n\n\ndir_name = args.path\nk = args.k\n\n# getting all the files\nall_files = get_list_files(dir_name)\n\nprint('Start reading the fasta files from directories: {}'.format(dir_name))\nfiles_z, _ = check_filenames(all_files)\n\nprot_mers_counter = Counter()\nprot_mers_lst = defaultdict(list)\nprot_dict = defaultdict()\ncnt = 0\nfor file in files_z:\n    print('Fasta file: {}'.format(file))\n    for name, sequence in parse_multi_fasta_file(file):\n        name = '/'.join(str_punctuation_strip(name)[1:4:2])\n        proteins = get_translation(sequence, args.mol_type)\n        if args.count:\n            prot_mers_counter.update(list(mer for mer in get_kmers(proteins, args.k)))\n        elif args.list:\n            prot_mers_lst[name] = [mer for mer in get_kmers(proteins, args.k)]\n        elif args.fasta:\n            prot_dict[name] = proteins\n    cnt += 1\n\n\nprint('Start writing the {} file.'.format(args.output))\nwith open(dir_name + '/' + args.output, 'w') as fout:\n    if args.count:\n        for (mers, count) in prot_mers_counter.items():\n            fout.write(mers + ',' + str(count) + '\\n')\n    elif args.list:\n        for (name, mers) in prot_mers_lst.items():\n            fout.write(name + ',' + str(mers) + '\\n')\n    elif args.fasta:\n        for (name, prot) in prot_dict.items():\n            fout.write('>' + name + '\\n' + prot + '\\n')\n\n\nprint('Were read and manipulated: {} fasta files'.format(cnt))\nprint('Time of execution: {} seconds'.format(time.process_time() - start_time))\nprint('Script finished!')\n","sub_path":"new_translation.py","file_name":"new_translation.py","file_ext":"py","file_size_in_byte":4829,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"506458845","text":"# -*- coding: utf-8 -*-\n\n# Scrapy settings for airline_tickets project\n#\n# For simplicity, this file contains only settings considered important or\n# commonly used. You can find more settings consulting the documentation:\n#\n#     https://doc.scrapy.org/en/latest/topics/settings.html\n#     https://doc.scrapy.org/en/latest/topics/downloader-middleware.html\n#     https://doc.scrapy.org/en/latest/topics/spider-middleware.html\n\nBOT_NAME = 'airline_tickets'\n\n# DB Config\nimport os\nimport sys\nbasedir = os.path.abspath(os.path.dirname(os.path.dirname(__file__)))\n# SQLite URI compatible\nWIN = sys.platform.startswith('win')\nif WIN:\n    prefix = 'sqlite:///'\nelse:\n    prefix = 'sqlite:////'\nMETA_DB_URI = os.environ.get('META_DB_URI') or prefix + os.path.join(basedir, 'data-dev.db')\n\n# if using MongoPipeline then decomment this block\n# MONGO_URI = 'mongodb://127.0.0.1:27017'\n# MONGO_DB = 'airline_tickets'\n\n# how many days after today the spider will crawl,if set thie option,the same option in the database will be override\nCRAWLER_DAYS = 3\n\nSPIDER_MODULES = ['airline_tickets.spiders']\nNEWSPIDER_MODULE = 'airline_tickets.spiders'\n\n# Selenium Config\nSELENIUM_TIMEOUT = 10\nSELENIUM_EXECUTABLE_PATH = r'D:\\chromedriver_win32\\chromedriver'\n\n# Log config\nfrom datetime import datetime\nlog_file_path = \"log/%s.log\"%datetime.now().strftime('%Y%m%d%H%M%S')\nLOG_LEVEL = 'DEBUG'\nLOG_FILE = log_file_path\n\n# Enable this option to use proxy server\n# PROXY_URL='http://127.0.0.1:5000'\n\n# Obey robots.txt rules\nROBOTSTXT_OBEY = False\n\n# Configure maximum concurrent requests performed by Scrapy (default: 16)\n# CONCURRENT_REQUESTS = 32\n\n# Configure a delay for requests for the same website (default: 0)\n# See https://doc.scrapy.org/en/latest/topics/settings.html#download-delay\n# See also autothrottle settings and docs\n# DOWNLOAD_DELAY = 3\n# The download delay setting will honor only one of:\n# CONCURRENT_REQUESTS_PER_DOMAIN = 16\n# CONCURRENT_REQUESTS_PER_IP = 16\n\n# Disable cookies (enabled by default)\n# COOKIES_ENABLED = False\n\n# Disable Telnet Console (enabled by default)\n# TELNETCONSOLE_ENABLED = False\n\n# Override the default request headers:\n# DEFAULT_REQUEST_HEADERS = {\n#   'Accept': 'text/html,application/xhtml+xml,application/xml;q=0.9,*/*;q=0.8',\n#   'Accept-Language': 'en',\n# }\n\n# Enable or disable spider middlewares\n# See https://doc.scrapy.org/en/latest/topics/spider-middleware.html\n# SPIDER_MIDDLEWARES = {\n#     'scrapy_splash.SplashDeduplicateArgsMiddleware': 100,\n# }\n\n# Enable or disable downloader middlewares\n# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html\nDOWNLOADER_MIDDLEWARES = {\n    'airline_tickets.middlewares.RandomUserAgentMiddleware': 553,\n    'airline_tickets.middlewares.ProxyMiddleware': 554,\n    'airline_tickets.middlewares.SeleniumMiddleware': 555,\n}\n\n# Enable or disable extensions\n# See https://doc.scrapy.org/en/latest/topics/extensions.html\n# EXTENSIONS = {\n#    'scrapy.extensions.telnet.TelnetConsole': None,\n# }\n\n# Configure item pipelines\n# See https://doc.scrapy.org/en/latest/topics/item-pipeline.html\nITEM_PIPELINES = {\n    'airline_tickets.pipelines.SqlAlchemyPipeline': 300,\n    # 'airline_tickets.pipelines.MongoPipeline': 301\n}\n\n# Enable and configure the AutoThrottle extension (disabled by default)\n# See https://doc.scrapy.org/en/latest/topics/autothrottle.html\n# AUTOTHROTTLE_ENABLED = True\n# The initial download delay\n# AUTOTHROTTLE_START_DELAY = 5\n# The maximum download delay to be set in case of high latencies\n# AUTOTHROTTLE_MAX_DELAY = 60\n# The average number of requests Scrapy should be sending in parallel to\n# each remote server\n# AUTOTHROTTLE_TARGET_CONCURRENCY = 1.0\n# Enable showing throttling stats for every response received:\n# AUTOTHROTTLE_DEBUG = False\n\n# Enable and configure HTTP caching (disabled by default)\n# See https://doc.scrapy.org/en/latest/topics/downloader-middleware.html#httpcache-middleware-settings\n# HTTPCACHE_ENABLED = True\n# HTTPCACHE_EXPIRATION_SECS = 0\n# HTTPCACHE_DIR = 'httpcache'\n# HTTPCACHE_IGNORE_HTTP_CODES = []\n# HTTPCACHE_STORAGE = 'scrapy.extensions.httpcache.FilesystemCacheStorage'\n","sub_path":"airline_tickets/settings.py","file_name":"settings.py","file_ext":"py","file_size_in_byte":4112,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"287422936","text":"# encoding: UTF-8\n\nfrom net.grinder.script.Grinder import grinder\nfrom net.grinder.plugin.http import HTTPRequest, HTTPPluginControl\nfrom scripts.rest_service import RestService\nfrom HTTPClient import NVPair\nfrom scripts import config\nimport sys\n\nlogger = grinder.logger\n\nclass HartlinkRestService(RestService):\n    def __init__(self):\n        super(HartlinkRestService, self).__init__()\n\n    min_keywait, max_keywait = 10, 30 # seconds (this is just a guesstimate TODO: get more accurate data)\n    httpUtilities = HTTPPluginControl.getHTTPUtilities()\n\n##########  DB TEST METHODS #############\n\n    def run_hartlink_db_full_test_suite(self, user):\n        self.make_hartlink_rest_test(\"\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/BankDetails\")\n        self.make_hartlink_rest_test(\"RetirementIllustration\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/RetIll\", [NVPair('RetDate', '05/03/2024'), NVPair('AVCfigure','5'), NVPair('isPercentage','true')])\n        self.make_hartlink_rest_test(\"RetirementIllustration\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/RetIll\", [NVPair('RetDate', '05/03/2024'), NVPair('AVCfigure','500'), NVPair('isPercentage','false')])\n        self.make_hartlink_rest_test(\"RetirementIllustration\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/RetIll\", [NVPair('RetDate', '05/03/2024')])\n        self.make_hartlink_rest_test(\"RetirementIllustration\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/RetIll\")\n        self.make_hartlink_rest_test(\"\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/payslips/201411\")\n        self.make_hartlink_rest_test(\"\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/payslips/latest\")\n        self.make_hartlink_rest_test(\"\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/bensum\")\n        self.make_hartlink_rest_test(\"\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/payslips\")\n        self.make_hartlink_rest_test(\"\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/Cumulatives/P60/201411\")\n        self.make_hartlink_rest_test(\"\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/Cumulatives/P60\")\n\n    def run_hartlink_db_orbit_user_journey_test_suite(self, user):\n        \"\"\"Does only the REST calls that corresponds to a typical orbit user journey. No login on Orbit is even done.\"\"\"\n        self.run_hartlink_db_orbit_user_journey_landing_page(user)\n        self.keywait()\n        self.run_hartlink_db_orbit_user_journey_pension_estimate_carousel(user)\n        self.keywait()\n\n    def run_hartlink_db_orbit_user_journey_landing_page(self, user):\n        self.make_hartlink_rest_test(\"\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/bensum\")\n        self.make_hartlink_rest_test(\"ContributionSchedule\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/contrates\")\n        self.make_hartlink_rest_test(\"FundsInvested\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/FundInv\")\n        self.make_hartlink_rest_test(\"RetirementIllustration\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/RetIll\", [NVPair('RetDate', '05/03/2024'), NVPair('AVCfigure','5'), NVPair('isPercentage','true')])\n\n    def run_hartlink_db_orbit_user_journey_pension_estimate_carousel(self, user):\n        self.make_hartlink_rest_test(\"RetirementIllustration\", user, \"/wsrl.chi/rest/1/hartlink/1/member/%s/RetIll\", [NVPair('RetDate', '05/03/2024'), NVPair('AVCfigure','500'), NVPair('isPercentage','false')])\n\n##########  DC TEST METHODS #############\n\n    def run_hartlink_dc_full_test_suite(self, user):\n        self.make_hartlink_rest_test(\"TransactionHistory\", user, \"/rest/1/hartlink/1/member/%s/TrnHst\")\n        self.make_hartlink_rest_test(\"PersonalDetails\", user, \"/rest/1/hartlink/1/member/%s/perdets\")\n        self.make_hartlink_rest_test(\"Employment\", user, \"/rest/1/hartlink/1/member/%s/Stints\")\n        self.make_hartlink_rest_test(\"ContributionHistory\", user, \"/rest/1/hartlink/1/member/%s/conthist\")\n        self.make_hartlink_rest_test(\"FundsInvested\", user, \"/rest/1/hartlink/1/member/%s/FundInv\")\n        self.make_hartlink_rest_test(\"AllowableInvestments\", user, \"/rest/1/hartlink/1/member/%s/AlwInv\")\n        self.make_hartlink_rest_test(\"\", user, \"/rest/1/hartlink/1/member/%s/forwardprojection\", [NVPair('Projection', 'FORWARD'), NVPair('TargetRetirementDate', '05/03/2024')])\n        self.make_hartlink_rest_test(\"\", user, \"/rest/1/hartlink/1/member/%s/forwardprojection\", [NVPair('Projection', 'FORWARD'), NVPair('TargetRetirementDate', '05/03/2024'), NVPair('ERMonthlyContsPcnt', '3'), NVPair('EEMonthlyContsPcnt', '3')])\n        self.make_hartlink_rest_test(\"\", user, \"/rest/1/hartlink/1/member/%s/forwardprojection\", [NVPair('Projection', 'FORWARD'), NVPair('TargetRetirementDate', '05/03/2024'), NVPair('ERMonthlyContsPcnt', '3'), NVPair('EEMonthlyContsPcnt', '3'), NVPair('AVCPercentage','4')])\n        self.make_hartlink_rest_test(\"ContributionSchedule\", user, \"/rest/1/hartlink/1/member/%s/contrates\")\n        # self.make_hartlink_rest_test(\"\", user, \"/rest/1/hartlink/1/employee/<scheme-code>:<payroll-number>:<ni-number>/stints/\")\n        # The Mailbox return either Mailbox or Empty so let's make it pass all the time.\n        self.make_hartlink_rest_test(\"\", user, \"/rest/1/hartlink/1/member/%s/Mailbox\")\n\n    def run_hartlink_dc_orbit_user_journey_landing_page(self, user):\n        self.make_hartlink_rest_test(\"\", user, \"/rest/1/hartlink/1/member/%s/forwardprojection\", [NVPair('Projection', 'FORWARD'), NVPair('TargetRetirementDate', '05/03/2024'), NVPair('ERMonthlyContsPcnt', '3'), NVPair('EEMonthlyContsPcnt', '3'), NVPair('AVCPercentage','4')])\n        self.make_hartlink_rest_test(\"ContributionHistory\", user, \"/rest/1/hartlink/1/member/%s/conthist\")\n\n    def run_hartlink_dc_orbit_user_journey_retirement_modeller_page(self, user):\n        self.make_hartlink_rest_test(\"\", user, \"/rest/1/hartlink/1/member/%s/forwardprojection\", [NVPair('Projection', 'FORWARD'), NVPair('TargetRetirementDate', '05/03/2024'), NVPair('ERMonthlyContsPcnt', '3'), NVPair('EEMonthlyContsPcnt', '3'), NVPair('AVCPercentage','4')])\n\n    def run_hartlink_dc_orbit_user_journey_change_contribution_page(self, user):\n       self.make_hartlink_rest_test(\"ContributionHistory\", user, \"/rest/1/hartlink/1/member/%s/conthist\")\n\n    def run_hartlink_dc_orbit_user_journey_contribution_history_page(self, user):\n       self.make_hartlink_rest_test(\"ContributionHistory\", user, \"/rest/1/hartlink/1/member/%s/conthist\")\n\n    def run_hartlink_dc_orbit_user_journey_investment_history_page(self, user):\n        self.make_hartlink_rest_test(\"TransactionHistory\", user, \"/rest/1/hartlink/1/member/%s/TrnHst\")\n\n    def run_hartlink_dc_orbit_user_journey_change_retirement_age_page(self, user):\n        logger(\"WARNING: Method not yet implemented.\")\n        # self.make_hartlink_rest_test(\"ChangeRetirementAge\", user, \"/rest/1/hartlink/1/member/%s/forms/changeretirementage\")\n        # TODO(pmarti): implement that one. It's not there yet.\n\n    def run_hartlink_dc_orbit_user_journey_test_suite(self, user):\n        \"\"\"Does only the REST calls that corresponds to a typical orbit user journey. No login on Orbit is even done.\"\"\"\n        self.run_hartlink_dc_orbit_user_journey_landing_page(user)\n        self.run_hartlink_dc_orbit_user_journey_retirement_modeller_page(user)\n        self.run_hartlink_dc_orbit_user_journey_change_contribution_page(user)\n        self.run_hartlink_dc_orbit_user_journey_contribution_history_page(user)\n        self.run_hartlink_dc_orbit_user_journey_investment_history_page(user)\n\n##########  GENERIC TEST METHODS #############\n\n    def make_hartlink_rest_test(self, expected_response_name, user, resource, post_arguments=None):\n        logger.debug(\"REST TEST {}\", expected_response_name)\n        response = self.make_hartlink_rest_call(user, resource, post_arguments)\n        try:\n            self.verify_rest_response(response, expected_response_name)\n        except:\n            e = sys.exc_info()[0]\n            logger.debug(\"EXCEPTION %s CAUGHT in make_hartlink_rest_test. Continue with rest of the test suite.\" % e)\n\n    def make_hartlink_rest_call(self, user, resource, post_arguments):\n        logger.debug(\" user.pemp_id = {}\", user.pemp_id)\n        logger.debug(\" resource = {}\", resource)\n        url = config.domain + resource % user.pemp_id\n        simplified_url = config.domain + resource % '<pempid>'\n        if post_arguments:\n            request_type = \"POST\"\n            logger.debug(\" post_arguments = {}\", post_arguments)\n            # Adding the cardinality of args to the simplified url to differentiate posts with their arguments\n            # (different endpoints)\n            simplified_url += str(len(post_arguments))\n        else:\n            request_type = \"GET\"\n        request = self.build_request(simplified_url, request_type, \"Rest call\")\n        auth_nv_pair = self.httpUtilities.basicAuthorizationHeader(user.username, user.password)\n        headers = [NVPair('COMPANY_KEY', user.company_key), auth_nv_pair]\n        if post_arguments:\n            formdata = post_arguments\n            logger.debug(\" HEADERS = {}\", [headers])\n            response = request.POST(url, formdata, headers)\n        else:\n            logger.debug(\" HEADERS = {}\", [headers])\n            request.setHeaders(headers)\n            response = request.GET(url)\n        logger.debug(\"Full Response = {}\", response.getText())\n        return response\n\n","sub_path":"scripts/pages/hartlink_rest_service.py","file_name":"hartlink_rest_service.py","file_ext":"py","file_size_in_byte":9312,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"230828848","text":"import sys\n\nfinding = input(\"ENTER STRING >>>\")\nfinding_list = list(finding)\n\n\ndef DOIT(list):\n    # dictionary = {\"OPEN\" : \"\", \"INDEX\" : 0}\n    a = []\n    IMPORTANTLIST = [\"(\", \"{\", \"[\"]\n    for i in range(len(list)):\n        if list[i] == IMPORTANTLIST[0] or list[i] == IMPORTANTLIST[1] or list[i] == IMPORTANTLIST[2]:\n            print(list[i])\n            a.append(list[i])\n        elif list[i] == \")\":\n            if a[len(a) - 1] == \"]\" or a[len(a) - 1] == \"}\":\n                return False\n            else:\n                a.pop(len(a) - 1)\n\n        elif list[i] == \"]\":\n            if a[len(a) - 1] == \")\" or a[len(a) - 1] == \"}\":\n                return False\n            else:\n                a.pop(len(a) - 1)\n\n        elif list[i] == \"}\":\n            if a[len(a) - 1] == \"]\" or a[len(a) - 1] == \")\":\n                return False\n            else:\n                a.pop(len(a) - 1)\n\n\n    if len(a) > 0:\n        return False\n    else:\n        return True\n    # return dictionary\n\n\nif DOIT(finding_list):\n    print(f\"THIS STRING : {finding}; IS CORRECTLY CLOSED.\")\nelse:\n    print(f\"THIS STRING : {finding}; IS NOT CORRECTLY CLOSED\")\n","sub_path":"Random/correctly_closed.py","file_name":"correctly_closed.py","file_ext":"py","file_size_in_byte":1143,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"46832037","text":"'''\r\n@author: Faizan-Uni-Stuttgart\r\n\r\n'''\r\nimport os\r\nimport timeit\r\nimport time\r\nfrom pathlib import Path\r\n\r\nimport numpy as np\r\nimport matplotlib as mpl\r\nimport matplotlib.pyplot as plt\r\n\r\nplt.ioff()\r\n\r\n\r\ndef get_sines(beg_val, mult, n_vals, n_tiles):\r\n\r\n    pis = -np.pi + ((2 * np.pi) * np.linspace(0, 1, n_vals))\r\n    pis = np.tile(pis, n_tiles)\r\n\r\n    sines = np.roll(mult * np.sin(pis), beg_val)\r\n\r\n    return sines\r\n\r\n\r\ndef main():\r\n\r\n    main_dir = Path(r'P:\\Synchronize\\IWS\\Presentations\\Phase_Annealing_data')\r\n    os.chdir(main_dir)\r\n\r\n    # NOTE:\r\n    # number of vals in ser_1 and ser_2 at the end the is equal i.e.\r\n    # n_vals_1 * n_tiles_1 == n_vals_2 * n_tiles_2\r\n    ser_1 = get_sines(1000, 1.2, 5000, 1)\r\n    ser_2 = get_sines(0, 1, 2500, 2)\r\n\r\n    ref_arr = ser_1 + ser_2\r\n\r\n    ft = np.fft.rfft(ref_arr)\r\n\r\n    mag_spec = np.abs(ft)\r\n    phs_spec = np.angle(ft)\r\n\r\n    mag_idxs = np.argsort(mag_spec)[::-1]\r\n\r\n    plt.rcParams.update({'font.size': 20})\r\n\r\n    _fig, axes = plt.subplots(1, 2, figsize=(16, 9), squeeze=False)\r\n\r\n    axes[0, 0].plot(ser_1, label='series 1', lw=2, alpha=0.7)\r\n    axes[0, 0].plot(ser_2, label='series 2', lw=2, alpha=0.7)\r\n    axes[0, 0].plot(ref_arr, label='series 1 + 2', lw=4, alpha=0.7)\r\n\r\n    axes[0, 0].grid()\r\n\r\n    axes[0, 0].set_xlabel('Step')\r\n    axes[0, 0].set_ylabel('Magnitude')\r\n\r\n    axes[0, 0].legend()\r\n\r\n    ft_sub_all = np.zeros_like(ft)\r\n\r\n    for mag_idx in mag_idxs[:2]:\r\n        ft_sub = np.zeros_like(ft)\r\n\r\n        rand_phs = -np.pi + ((2 * np.pi) * np.random.random())\r\n\r\n        ft_sub.real[mag_idx] = mag_spec[mag_idx] * np.cos(rand_phs)\r\n        ft_sub.imag[mag_idx] = mag_spec[mag_idx] * np.sin(rand_phs)\r\n\r\n        ft_sub_all[mag_idx] = ft_sub[mag_idx]\r\n\r\n        ift = np.fft.irfft(ft_sub)\r\n\r\n        axes[0, 1].plot(ift.real, label=f'freq: {mag_idx}', lw=2, alpha=0.7)\r\n\r\n    axes[0, 1].plot(\r\n        np.fft.irfft(ft_sub_all).real,\r\n        label=f'surrogate',\r\n        lw=4,\r\n        alpha=0.7)\r\n\r\n    axes[0, 1].grid()\r\n    axes[0, 1].set_xlabel('Step')\r\n    axes[0, 1].set_ylim(*axes[0, 0].get_ylim())\r\n    axes[0, 1].legend()\r\n\r\n#     plt.show()\r\n\r\n    plt.savefig('test_four_trans_example_phs_rand.png', bbox_inches='tight')\r\n    plt.close()\r\n\r\n    mpl.rcdefaults()\r\n    return\r\n\r\n\r\nif __name__ == '__main__':\r\n\r\n    _save_log_ = False\r\n    if _save_log_:\r\n        from datetime import datetime\r\n        from std_logger import StdFileLoggerCtrl\r\n\r\n        # save all console activity to out_log_file\r\n        out_log_file = os.path.join(\r\n            r'P:\\Synchronize\\python_script_logs\\\\%s_log_%s.log' % (\r\n            os.path.basename(__file__),\r\n            datetime.now().strftime('%Y%m%d%H%M%S')))\r\n\r\n        log_link = StdFileLoggerCtrl(out_log_file)\r\n\r\n    print('#### Started on %s ####\\n' % time.asctime())\r\n    START = timeit.default_timer()  # to get the runtime of the program\r\n\r\n    main()\r\n\r\n    STOP = timeit.default_timer()  # Ending time\r\n    print(('\\n#### Done with everything on %s.\\nTotal run time was'\r\n           ' about %0.4f seconds ####' % (time.asctime(), STOP - START)))\r\n\r\n    if _save_log_:\r\n        log_link.stop()\r\n","sub_path":"misc_scripts/02_fourtrans_example_phs_rando.py","file_name":"02_fourtrans_example_phs_rando.py","file_ext":"py","file_size_in_byte":3140,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"263498547","text":"#\n# Solution To Project Euler Problem 25\n#\n#\n# 1000-digit Fibonacci Number\n#\n#\n# Erlend Vollset\n#\n#\n\ndef fib_generator():\n    f1 = 1\n    f2 = 1\n    yield f2\n    while True:\n        f1, f2 = f1 + f2, f1\n        yield f2\n\ndef compute():\n    for i, f in enumerate(fib_generator()):\n        if len(str(f)) == 1000:\n            return str(i+1)\n\nif __name__ == \"__main__\":\n    print(compute())\n","sub_path":"Problems/Problem_25.py","file_name":"Problem_25.py","file_ext":"py","file_size_in_byte":388,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"416635477","text":"# coding:utf-8\n# 面向对象编程\n\n# 定义一个Student类，这个类拥有name和score两个属性Property。\n\n\nclass Student(object):\n    def __init__(self, name, score):\n        self.name = name\n        self.score = score\n\n    def print_score(self):\n        \"\"\"打印一个学生的成绩\"\"\"\n        print(\"%s: %s\" % (self.name, self.score))\n\n\nTom = Student(\"Tom\", 85)\nJim = Student(\"Jim\", 100)\nTom.print_score()\nJim.print_score()\n","sub_path":"sample/class/python_class_oop.py","file_name":"python_class_oop.py","file_ext":"py","file_size_in_byte":438,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"330573857","text":"from energenie.Devices.LegacyDevice import LegacyDevice\n\nclass MiHomeLightDimmable(LegacyDevice):\n    _product_name = \"Dimmable Light Switch\"\n    _product_description = \"Receive-only light switch\"\n    _product_rf = \"OOK(rx)\"\n    _product_url = \"https://energenie4u.co.uk/\"\n    _product_user_guide = \"https://energenie4u.co.uk/res/pdfs/MIHome-Light-User-Guide-v1.4.pdf\"\n\n    \"\"\"Base for all MiHomeLight Dimmable variants. Receive only OOK device\"\"\"\n    def __init__(self, **kw_args):\n        LegacyDevice.__init__(self, **kw_args)\n        self.radio_config.inner_times = 75\n        self.capabilities.switch = True\n        self.capabilities.receive = True\n\n    def __repr__(self):\n        return \"%s(%s,%s)\" % (self.__class__.__name__, str(hex(self.device_id[0])), str(hex(self.device_id[1])))\n\n    def turn_on(self):\n        payload = {\n            \"house_address\":  self.device_id[0],\n            \"device_index\":   1,\n            \"on\":             True\n        }\n        self.state = True\n        self.send_message(payload)\n\n    def turn_off(self):\n        payload = {\n            \"house_address\":  self.device_id[0],\n            \"device_index\":   1,\n            \"on\":             False\n        }\n        self.state = False\n        self.send_message(payload)\n\n    def set_brightness(self, brightness):\n        if brightness == 20:\n            payload = {\n                \"device_index\":     2,\n                \"on\":               True\n            }\n        elif brightness == 30:\n            payload = {\n                \"device_index\":     3,\n                \"on\":               True\n            }\n        elif brightness == 40:\n            payload = {\n                \"device_index\":     4,\n                \"on\":               True\n            }\n        elif brightness == 60:\n            payload = {\n                \"device_index\":     2,\n                \"on\":               False\n            }\n        elif brightness == 80:\n            payload = {\n                \"device_index\":     3,\n                \"on\":               False\n            }\n        elif brightness == 100:\n            payload = {\n                \"device_index\":     4,\n                \"on\":               False\n            }\n        else:\n            payload = {\n                \"device_index\":   1,\n                \"on\":             False\n            }\n        payload['house_address'] = self.device_id[0]\n        self.brightness = brightness\n        self.send_message(payload)\n\n    def get_brightness(self):\n        return self.brightness\n\n    def set_switch_state(self, state: bool):\n        if state:\n            self.turn_on()\n        else:\n            self.turn_off()\n\n    def get_switch_state(self) -> bool:\n        return self.state\n","sub_path":"energenie/Devices/MiHomeLightDimmable.py","file_name":"MiHomeLightDimmable.py","file_ext":"py","file_size_in_byte":2714,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"564993911","text":"#!/u\n# -*- coding: utf-8 -*-\n\nimport sys\nimport os\nimport math\nimport time\nimport datetime\nimport numpy as np\n\nin_file_dir = \"../../Preprocess/Data/CMP_new/PO\"\nout_file = \"./ref.dat\"\nf_out = open(out_file, 'w')\n\nminum = [0]*45\nfor instance in range(45):\n    p = int(instance/3)+1\n    num_objectives = (instance%3+1)*5\n    problem_name = \"MaF\" + str(p)\n    minum_all = [[0]*num_objectives for i in range(31)]\n    for runth in range(1,32):\n        minum_div = [[0]*num_objectives for i in range(29)]\n        for div in range(5,34):\n            in_file = (in_file_dir + \"/%s_%s_GrEA_%sth_div=%s_runs_TotalData_PO.dat\" %\n            (problem_name, str(num_objectives), str(runth), str(div)))\n\n            f_in =open(in_file, 'r')\n            population_number = 212\n\n            if (num_objectives == 5):\n            #    if (alg == \"GrEA\" or alg == \"BiGE\"or alg == \"SPEA2SDE\" or alg ==\n            #    \"MoMBIII\"):\n                    population_number = 212\n            #   else:\n            #       population_number = 210\n            elif (num_objectives == 10):\n            #   if (alg == \"GrEA\" or alg == \"BiGE\"or alg == \"SPEA2SDE\" or alg ==\n            #   \"MoMBIII\"):\n                    population_number = 276\n            #   else:\n            #       population_number = 275\n            elif (num_objectives == 15):\n            #   if (alg == \"GrEA\" or alg == \"BiGE\"or alg == \"SPEA2SDE\" or alg ==\n            #   \"MoMBIII\"):\n                    population_number = 136\n            #   else:\n            #       population_number = 135\n            \n            minum_instance = [[0]*num_objectives for i in range(population_number)]\n            \n            line_number = 0\n            #read data from in_file to the temp array\n            for str_line in f_in:\n                colu_number = 0\n                temp_list = str_line.split()\n                for value in temp_list:\n                    minum_instance[line_number][colu_number] = float(value)\n                    colu_number = colu_number + 1\n                line_number = line_number + 1\n\n            nadir_point = [0]*num_objectives\n\n            #calculate the largest value of these solutions for each objectives\n            for colu in range(num_objectives):\n                temp_colu_s = sorted([t_c_s[colu] for t_c_s in minum_instance])\n                nadir_point[colu] = temp_colu_s[population_number-1]\n\n            minum_div[div-5] = nadir_point\n\n        #record some div ref dominated by any other one\n        index_dominated = []\n        for div in range(5,34):\n            for div_c in range(5,34):\n                count_m = 0\n                for m in range(num_objectives):\n                    if minum_div[div-5][m] < minum_div[div_c-5][m]:\n                        count_m = count_m + 1\n                if count_m == num_objectives:\n                    index_dominated.append(div-5)\n                    break\n\n        minum_best_div = [0]*num_objectives\n\n        #calculate the max of the ref among nondominated div ref\n        minum_div_array = np.array(minum_div)\n        minum_div_array_d = np.delete(minum_div_array,index_dominated,axis=0)\n        minum_div_d = minum_div_array_d.tolist()\n        for colu in range(num_objectives):\n            temp_colu_s = sorted([t_c_s[colu] for t_c_s in minum_div_d])\n            minum_best_div[colu] = temp_colu_s[len(temp_colu_s)-1]\n\n        minum_all[runth-1] = minum_best_div\n    \n    #calculate the largest ref among 31 runth\n    minum_best_all = [0]*num_objectives\n    for colu in range(num_objectives):\n        temp_colu_s = sorted([t_c_s[colu] for t_c_s in minum_all])\n        minum_best_all[colu] = temp_colu_s[31-1]\n\n    #write the result to the out file\n    instance_name = \"MaF\" + str(p) + \" \" + str(num_objectives)\n    f_out.write(instance_name + \" \")\n    for m in range(num_objectives):\n        f_out.write(str(minum_best_all[m]) + \" \")\n    f_out.write(\"\\n\")\n\n\n\n","sub_path":"uea_postprocessing/getref/getref.py","file_name":"getref.py","file_ext":"py","file_size_in_byte":3900,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"168014981","text":"#!/usr/bin/env python\r\n# -*- coding: utf-8 -*-\r\n# Date   : 2018/5/4 16:06\r\n# Author : lixingyun\r\n# Description :\r\nimport redis\r\nimport time\r\nfrom .db.money import Money\r\nfrom .db.noble_coin import NobleCoin\r\nfrom .common import REDIS_INST\r\nfrom .db.money import MoneyPay, MoneyChannelIncome\r\nfrom .config import mon_last_time,mon_first_time,logger_huomao\r\nfrom peewee import fn\r\n\r\n\r\n\r\nclass MoneyClass():\r\n    # 设置仙豆\r\n    @staticmethod\r\n    def set_xd(uid, xd):\r\n        xd = 0 if not xd else xd\r\n        return REDIS_INST.set(f'hm_free_bean_{uid}', int(xd) if xd else 0)\r\n\r\n    # 获取仙豆\r\n    @staticmethod\r\n    def get_xd(uid):\r\n        return int(REDIS_INST.get(f'hm_free_bean_{uid}'))\r\n\r\n    # 设置用户猫币，猫豆\r\n    @staticmethod\r\n    def set_money(uid, coin=0, bean=0):\r\n        coin = 0 if not coin else coin\r\n        bean = 0 if not bean else bean\r\n        money = Money.select().where(Money.uid == uid).first()\r\n        # 判断用户是否存在记录，存在则修改，否则插入数据\r\n        if money:\r\n            return Money.update(coin=coin, bean=bean).where(Money.uid == uid).execute()\r\n        else:\r\n            return Money.create(uid=uid, coin=coin, bean=bean, ts=str(int(time.time())))\r\n\r\n    # 设置用户贵族猫币\r\n    @staticmethod\r\n    def set_noble_coin(uid, noble_coin=0, ts=str(int(time.time()))):\r\n        noble_coin = noble_coin if noble_coin else 0\r\n        noble = NobleCoin.select().where(NobleCoin.uid == uid).first()\r\n        # 判断用户是否存在记录，存在则修改，否则插入数据\r\n        if noble:\r\n            return NobleCoin.update(coin=noble_coin, ts=ts).where(NobleCoin.uid == uid).execute()\r\n        else:\r\n            return NobleCoin.create(uid=uid, coin=noble_coin, ts=ts)\r\n\r\n    # 获取用户余额\r\n    @staticmethod\r\n    def get_money(uid):\r\n        money = Money.select().where(Money.uid == uid).first()\r\n        ret = {'coin': float(money.coin), 'bean': float(money.bean)}\r\n        logger_huomao.info(ret)\r\n        return ret\r\n\r\n    # 获取用户贵族余额\r\n    @staticmethod\r\n    def get_noble_coin(uid):\r\n        noble = NobleCoin.select().where(NobleCoin.uid == uid).first()\r\n        return float(noble.coin) if noble else 0\r\n\r\n    # 获取用户猫币，贵族猫币\r\n    @staticmethod\r\n    def get_coin(uid):\r\n        noble = NobleCoin.select().where(NobleCoin.uid == uid).first()\r\n        money = Money.select().where(Money.uid == uid).first()\r\n        return (money.coin, noble.coin)\r\n\r\n    # 获取送礼记录\r\n    @staticmethod\r\n    def get_money_pay(uid, cid):\r\n        count = MoneyPay.select(fn.COUNT(1).alias('nums')).where((MoneyPay.uid == uid) & (MoneyPay.other_cid == cid) & (MoneyPay.ts < mon_last_time)& (MoneyPay.ts > mon_first_time)).first()\r\n        return count.nums\r\n\r\n    # 获取收礼记录\r\n    @staticmethod\r\n    def money_income(type, uid, user_id=0):\r\n        m = 0\r\n        if type == 'del':\r\n            m = MoneyChannelIncome.delete().where(MoneyChannelIncome.uid == uid).execute()\r\n        elif type == 'get':\r\n            m = MoneyChannelIncome.select().where((MoneyChannelIncome.uid == uid) & (MoneyChannelIncome.addtime < mon_last_time) & (\r\n                MoneyChannelIncome.other_uid == user_id)).first()\r\n            m = m.money\r\n        elif type == 'count':\r\n            m = MoneyChannelIncome.select(fn.COUNT(1).alias('nums')).where((MoneyChannelIncome.uid == uid) & (MoneyChannelIncome.addtime < mon_last_time) & (\r\n                MoneyChannelIncome.other_uid == user_id)).first().nums\r\n        return m","sub_path":"huomao/money.py","file_name":"money.py","file_ext":"py","file_size_in_byte":3556,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"312629463","text":"# Copyright 2019 The Keras Tuner Authors\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\nimport tensorflow as tf\nimport tensorflow.keras as keras\nfrom tensorflow.keras import layers\nfrom tensorflow.keras.layers import *\n\n\ndef sep_conv(x, num_filters, kernel_size=(3, 3), activation='relu'):\n    if activation == 'selu':\n        x = layers.SeparableConv2D(num_filters, kernel_size,\n                                   activation='selu',\n                                   padding='same',\n                                   kernel_initializer='lecun_normal')(x)\n    elif activation == 'relu':\n        x = layers.SeparableConv2D(num_filters, kernel_size,\n                                   padding='same',\n                                   use_bias=False)(x)\n        x = layers.BatchNormalization()(x)\n        x = layers.Activation('relu')(x)\n    else:\n        ValueError('Unkown activation function: %s' % (activation,))\n    return x\n\n\ndef residual(x, num_filters,\n             kernel_size=(3, 3),\n             activation='relu',\n             pool_strides=(2, 2),\n             max_pooling=True):\n    \"Residual block.\"\n    if max_pooling:\n        res = layers.Conv2D(num_filters, kernel_size=(\n            1, 1), strides=pool_strides, padding='same')(x)\n    elif num_filters != keras.backend.int_shape(x)[-1]:\n        res = layers.Conv2D(num_filters, kernel_size=(1, 1), padding='same')(x)\n    else:\n        res = x\n\n    x = sep_conv(x, num_filters, kernel_size, activation)\n    x = sep_conv(x, num_filters, kernel_size, activation)\n    if max_pooling:\n        x = layers.MaxPooling2D(\n            kernel_size, strides=pool_strides, padding='same')(x)\n\n    x = layers.add([x, res])\n    return x\n\n\ndef conv(x, num_filters, kernel_size=(3, 3), activation='relu', strides=(2, 2)):\n    \"2d convolution block.\"\n    if activation == 'selu':\n        x = layers.Conv2D(num_filters, kernel_size, strides=strides, activation='selu',\n                          padding='same', kernel_initializer='lecun_normal', bias_initializer='zeros')(x)\n    elif activation == 'relu':\n        x = layers.Conv2D(num_filters, kernel_size,\n                          strides=strides, padding='same', use_bias=False)(x)\n        x = layers.BatchNormalization()(x)\n        x = layers.Activation('relu')(x)\n    else:\n        msg = 'Unkown activation function: %s' % activation\n        ValueError(msg)\n    return x\n\n\ndef dense(x, dims, activation='relu', batchnorm=True, dropout_rate=0):\n    if activation == 'selu':\n        x = layers.Dense(dims,  activation='selu',\n                         kernel_initializer='lecun_normal', bias_initializer='zeros')(x)\n        if dropout_rate:\n            x = layers.AlphaDropout(dropout_rate)(x)\n    elif activation == 'relu':\n        x = layers.Dense(dims, activation='relu')(x)\n        if batchnorm:\n            x = layers.BatchNormalization()(x)\n        if dropout_rate:\n            x = layers.Dropout(dropout_rate)(x)\n    else:\n        msg = 'Unkown activation function: %s' % activation\n        ValueError(msg)\n    return x\n","sub_path":"kerastuner/applications/tunable_xception/blocks.py","file_name":"blocks.py","file_ext":"py","file_size_in_byte":3534,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"312764575","text":"\nfrom __future__ import print_function\nimport torch\nfrom torch.autograd import Variable\nimport torch.nn as nn\nimport torch.nn.functional as F\nimport numpy as np\nimport torch.optim as optim\nimport torchvision\nimport torchvision.transforms as transforms\nimport numpy as np\nimport time\nimport os\nfrom scipy import misc\n\nclass SOFLLoss(nn.Module):\n    def __init__(self, num_classes):\n        super(SOFLLoss, self).__init__()\n        self.num_classes = num_classes\n\n    def forward(self, x, y=None):\n        return -F.softmax(x, dim=1)[:, self.num_classes:].sum(dim=1).log()\n\nclass SOFLLinfPGDAttack:\n    def __init__(\n            self, model, eps=4.0, nb_iter=40,\n            eps_iter=1.0, rand_init=True, clip_min=0., clip_max=1.,\n            num_classes = 10, num_reject_classes = 5, n_restarts=1):\n        self.eps = eps\n        self.nb_iter = nb_iter\n        self.eps_iter = eps_iter\n        self.rand_init = rand_init\n        self.model = model\n\n        self.detector_loss_func = SOFLLoss(num_classes=num_classes)\n\n        self.clip_min = clip_min\n        self.clip_max = clip_max\n        self.num_classes = num_classes\n        self.num_reject_classes = num_reject_classes\n        self.n_restarts = n_restarts\n\n    def get_loss(self, x):\n        outputs = self.model(x)\n        loss = self.detector_loss_func(outputs)\n\n        return loss\n\n    def attack_single_run(self, x):\n\n        x = x.detach().clone()\n\n        delta = torch.zeros_like(x)\n        delta = nn.Parameter(delta)\n        delta.requires_grad_()\n\n        with torch.no_grad():\n            loss = self.get_loss(x)\n            worst_loss = loss.data.clone()\n            worst_perb = delta.data.clone()\n\n        if self.rand_init:\n            delta.data.uniform_(-1, 1)\n            delta.data *= self.eps\n            delta.data = torch.round(delta.data)\n            delta.data = (torch.clamp(x.data + delta.data / 255.0, min=self.clip_min, max=self.clip_max) - x.data) * 255.0\n\n        for ii in range(self.nb_iter):\n            adv_x = x + delta / 255.0\n            loss = self.get_loss(adv_x)\n\n            cond = loss.data > worst_loss\n            worst_loss[cond] = loss.data[cond]\n            worst_perb[cond] = delta.data[cond]\n\n            loss.mean().backward()\n            grad_sign = delta.grad.data.sign()\n            delta.data = delta.data + grad_sign * self.eps_iter\n            delta.data = torch.clamp(delta.data, min=-self.eps, max=self.eps)\n            delta.data = (torch.clamp(x.data + delta.data / 255.0, min=self.clip_min, max=self.clip_max) - x.data) * 255.0\n\n            delta.grad.data.zero_()\n\n        with torch.no_grad():\n            adv_x = x + delta / 255.0\n            loss = self.get_loss(adv_x)\n            cond = loss.data > worst_loss\n            worst_loss[cond] = loss.data[cond]\n            worst_perb[cond] = delta.data[cond]\n\n        return worst_perb, worst_loss\n\n    def perturb(self, x):\n        \"\"\"\n        Given examples x, returns their adversarial counterparts with\n        an attack length of eps.\n\n        :param x: input tensor.\n        :return: tensor containing perturbed inputs.\n        \"\"\"\n\n        self.model.eval()\n\n        with torch.no_grad():\n            loss = self.get_loss(x)\n            worst_loss = loss.data.clone()\n            worst_perb = torch.zeros_like(x)\n\n        for k in range(self.n_restarts):\n            delta, loss = self.attack_single_run(x)\n            cond = loss.data > worst_loss\n            worst_loss[cond] = loss.data[cond]\n            worst_perb[cond] = delta.data[cond]\n\n        adv_x = torch.clamp(x + torch.clamp(torch.round(worst_perb), min=-self.eps, max=self.eps) / 255.0, min=self.clip_min, max=self.clip_max)\n\n        return adv_x\n","sub_path":"utils/sofl_pgd_attack.py","file_name":"sofl_pgd_attack.py","file_ext":"py","file_size_in_byte":3689,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"61795991","text":"#!/usr/bin/env python3\r\n# -*- coding: utf-8 -*-\r\n# [XNiu2 16-Jan-2018] \r\n\r\n\r\nimport xlwings as xw\r\nimport string\r\n\r\ndef min_non_zero(lst):\r\n    mins = lst[0]\r\n    for item in lst:\r\n        if item != 0 and item < mins:\r\n            mins = item\r\n    return mins\r\n\r\n\r\n\r\n\r\n# input a full filepath to open a workbook\r\nfn = r\"C:\\Users\\XNiu2\\Documents\\My Received Files\\cost analysis.xlsx\"\r\nwb = xw.Book(fn)\r\n\r\n# open the sheet\r\nsht = wb.sheets[0]\r\n\r\n# Read and Write data from/to the workbook\r\n# PNRange = sht.range(\"B2:B1002\")\r\nlength = range(2, 50)\r\ncurrent_qty = 0\r\n\r\nfor i in length:\r\n    current_qty = sht.range('J'+str(i)).value\r\n    current_list = sht.range('O'+str(i)+':AD'+str(i)).value\r\n    prices = current_list[::2]\r\n    qtys = current_list[1::2]\r\n    current_pair = dict(zip(qtys, prices))\r\n    print (current_pair)\r\n    current_price = min_non_zero(prices)\r\n    for j in qtys:\r\n        if current_qty <= j:\r\n            sht.range('I'+str(i)).value = current_pair[j]\r\n            break\r\n        elif j==0:\r\n            sht.range('I'+str(i)).value = current_price\r\n            break\r\n\r\n\r\n","sub_path":"find_the_proper_price.py","file_name":"find_the_proper_price.py","file_ext":"py","file_size_in_byte":1095,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"270437342","text":"#!/usr/bin/env python\n#-*- coding:utf-8 -*-\n\nimport os\nimport boto3\nfrom argparse import ArgumentParser\nimport csv\nimport sys\n\nsys.path.insert(0, '../')\n\nif __name__ == \"__main__\":\n    bucket = sys.argv[1]\n    data = sys.argv[2] # the name of the text file with the arguments for (typically \"arguments.txt\")\n    s3 = boto3.client('s3')\n    s3.download_file(bucket, data, data)\n    with open(data, \"r\") as args_list:\n        args = args_list.read().split(' ')\n\n    print('bucktet: %s' % args[0])\n    print('data: %s' % args[1])\n\n    # looping through all the html outputs in the 'output' directory, and pushing each of them to s3\n    directory = 'output'\n    for filename in os.listdir(directory):\n        if filename.endswith(\".html\"):\n            # print(os.path.join(directory, filename))\n            file_path = os.path.join(directory, filename)\n            key = filename\n            s3.upload_file(file_path, bucket, key)\n            continue\n        else:\n            continue\n\n    # key = \"testdataoutput.txt\"\n    # s3.upload_file(\"testdataoutput.txt\", bucket, key)\n\n","sub_path":"Docker/jon/batch/upload/preupload/execute.py","file_name":"execute.py","file_ext":"py","file_size_in_byte":1074,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"33476587","text":"import pickle\r\n\r\nfrom sklearn import metrics\r\nfrom sklearn.metrics import classification_report, confusion_matrix\r\nfrom sklearn.naive_bayes import MultinomialNB\r\n\r\nimport numpy as np\r\nimport pandas as pd\r\n\r\nf = open(\"F:\\\\results\\\\Bayes_V6.log\", 'w')\r\n\r\ndef metrics_result(actual, predict):\r\n    print('精度:{0:.3f}'.format(metrics.precision_score(actual, predict, average='weighted')))\r\n    print('召回:{0:0.3f}'.format(metrics.recall_score(actual, predict, average='weighted')))\r\n    print('f1-score:{0:.3f}'.format(metrics.f1_score(actual, predict, average='weighted')))\r\n    print(classification_report(actual, predict))\r\n    f.write('精度:{0:.3f}\\n'.format(metrics.precision_score(actual, predict, average='weighted')))\r\n    f.write('召回:{0:0.3f}\\n'.format(metrics.recall_score(actual, predict, average='weighted')))\r\n    f.write('f1-score:{0:.3f}\\n'.format(metrics.f1_score(actual, predict, average='weighted')))\r\n    f.write(str(classification_report(actual, predict))+'\\n\\n')\r\n\r\nif __name__ == '__main__':\r\n    # trainset_path = \"F:\\\\results\\\\train_tfidf_space_V5.dat\"\r\n    trainset_path = \"E:\\\\DataMining\\\\train_tfidf_space(降维后).dat\"\r\n    # testset_path = \"F:\\\\results\\\\test_tfidf_space_V5.dat\"\r\n    testset_path = \"E:\\\\DataMining\\\\test_tfidf_space(降维后).dat\"\r\n    with open(trainset_path, 'rb') as file_obj:\r\n        trainset = pickle.load(file_obj)\r\n\r\n\r\n\r\n\r\n    '''     训练过程开始      '''\r\n    classifier = MultinomialNB(alpha=0.001).fit(trainset.tfidf_weight_matrics, trainset.label)\r\n\r\n    with open(testset_path, 'rb') as file_obj:\r\n        testset = pickle.load(file_obj)\r\n    predicted = classifier.predict(testset.tfidf_weight_matrics)\r\n\r\n    # 显示记录总体结果\r\n    metrics_result(testset.label, predicted)\r\n\r\n    # 打印混淆矩阵\r\n    catagorys = testset.target_name\r\n    ConfusionMatrix = np.zeros([len(catagorys), len(catagorys)])\r\n    # 显示所有列和列\r\n    pd.set_option('display.max_columns', None)\r\n    pd.set_option('display.max_rows', None)\r\n    confusion_matrix = pd.DataFrame(confusion_matrix(testset.label, predicted), columns=catagorys, index=catagorys)\r\n    print(confusion_matrix)\r\n    f.write(str(confusion_matrix)+'\\n\\n')\r\n\r\n    for flabel, file_name, expct_cate in zip(testset.label, testset.filenames, predicted):\r\n        # 打印预测错的文档名\r\n        if flabel != expct_cate:\r\n            f.write(file_name+ \": 实际类别:\"+ flabel+ \" -->预测类别:\"+ expct_cate + '\\n')\r\n    f.close()\r\n\r\n    print(\"预测完毕!!!\")","sub_path":"TextClassification/predict/Bayes.py","file_name":"Bayes.py","file_ext":"py","file_size_in_byte":2515,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"359194737","text":"# -*- coding: utf-8 -*-\n\"\"\"\nflask_cacheobj.cache\n~~~~~~~~~~~~~~~~~~~~\n\nCache functions.\n\n:copyright: (c) 2016 Liwushuo Inc.\n:license: MIT, see LICENSE for more details.\n\"\"\"\n\nimport inspect\nimport logging\nfrom functools import wraps\n\nimport msgpack\nfrom redis.exceptions import ConnectionError\nfrom decorator import decorator, decorate\n\nfrom .msgpackable import encode, decode\nfrom .format import format_key_pattern\nfrom .proxy import mc\nfrom .utils import make_deco\nfrom ._compat import text_type, integer_types\n\nlogger = logging.getLogger('flask.cache')\n\ndef gen_key_factory(key_pattern, arg_names, defaults):\n    args = dict(zip(arg_names[-len(defaults):], defaults)) if defaults else {}\n    if callable(key_pattern):\n        names = inspect.getargspec(key_pattern)[0]\n    def gen_key(*a, **kw):\n        aa = args.copy()\n        aa.update(zip(arg_names, a))\n        aa.update(kw)\n        if callable(key_pattern):\n            key = key_pattern(*[aa[n] for n in names])\n        else:\n            key = format_key_pattern(key_pattern, *[aa[n] for n in arg_names], **aa)\n        return key and key.replace(' ','_'), aa\n    return gen_key\n\n\ndef cache_obj(cache_key_reg, packer=encode, unpacker=decode):\n    \"\"\"A decorator that can cache function result.\n\n    Example::\n\n        @cache_obj({'key': 'item:{item_id}'})\n        def get_item(item_id):\n            return Item.query.get(item_id)\n\n        @cache_obj({'key': 'item:{item_id}', 'expire': 60})\n        def get_item(item_id):\n            return Item.query.get(item_id)\n\n        @cache_obj({'key': lambda filename: md5(filename).hexdigest()})\n        def get_file(filename):\n            return remote.fetch_file_data(filename)\n\n    :param cache_key_reg: a dict-like object contains `key` and `expire`. `cache_obj` will inspect function arguments and format `cache_key_reg['key']` if `cache_key_reg['key']` is a string, or apply them to `cache_key_reg['key']` if `cache_key_reg['key']` is a callable object. `expire` is an integer. The unit is second.\n    :param packer: a method used to encode function result.\n    :param unpacker: a callable object used to decode msgpack data.\n    \"\"\"\n\n    def deco(f, *a, **kw):\n        key_pattern = cache_key_reg.get('key')\n        expire = cache_key_reg.get('expire', None)\n        arg_names, varargs, varkw, defaults = inspect.getargspec(f)\n        if varargs or varkw:\n            raise Exception(\"do not support varargs\")\n        gen_key = gen_key_factory(key_pattern, arg_names, defaults)\n\n        key, args = gen_key(*a, **kw)\n        if not key:\n            return f(*a, **kw)\n        if isinstance(key, text_type):\n            key = key.encode(\"utf8\")\n\n        try:\n            r = mc.get(key)\n        except ConnectionError:\n            return f(*a, **kw)\n\n        r = unpacker(r) if r else None\n\n        # set cache\n        if r is None:\n            r = f(*a, **kw)\n            if r is not None:\n                mc.set(key, packer(r), expire)\n        else:\n            logger.info('Cache read - %s',key)\n\n        return r\n\n    return make_deco(\n        deco,\n        cache_key_reg=cache_key_reg,\n        packer=packer,\n        unpacker=unpacker,\n    )\n\n\ndef delete_obj(cache_key_reg):\n    \"\"\"Delete object cache\n\n    :param cache_key_reg: a dict-like object contains 'key'.\n    \"\"\"\n\n    def deco(f, *a, **kw):\n        key_pattern = cache_key_reg.get('key')\n        arg_names, varargs, varkw, defaults = inspect.getargspec(f)\n        if varargs or varkw:\n            raise Exception(\"do not support varargs\")\n        gen_key = gen_key_factory(key_pattern, arg_names, defaults)\n\n        key, _ = gen_key(*a, **kw)\n        if not key:\n            return f(*a, **kw)\n        if isinstance(key, text_type):\n            key = key.encode(\"utf8\")\n        ret = f(*a, **kw)\n        mc.delete(key)\n        return ret\n\n    return make_deco(\n        deco,\n        cache_key_reg=cache_key_reg,\n    )\n\n\ndef delete_cache(cache_key_reg, **kw):\n    \"\"\"Delete cache via cache key registry.\n\n    Example::\n\n        delete_cache({'key': 'item:{item_id}'}, item_id=1)\n    \"\"\"\n    key_pattern = cache_key_reg.get('key')\n    key = format_key_pattern(key_pattern, **kw)\n    key = key.replace(' ', '_')\n    if isinstance(key, text_type):\n        key = key.encode(\"utf8\")\n    mc.delete(key)\n    logger.info('Cache delete - %s', key)\n\ndef cache_hash(cache_key_reg, packer=encode, unpacker=decode):\n    \"\"\"A decorator that can cache function result into redis hash.\n\n    Example::\n\n        @cache_hash({'key': '{item_id}', 'hash_key': 'item'})\n        def get_item(item_id):\n            return Item.query.get(item_id)\n\n    :param cache_key_reg: a dict-like object contains `hash_key`, `key`. `hash_key` is a string as redis hash key. `key` is a template string or a callable object as hash field key.\n    \"\"\"\n\n    def deco(f, *a, **kw):\n        hash_key = cache_key_reg.get('hash_key')\n        key = cache_key_reg.get('key')\n        expire = cache_key_reg.get('expire', None)\n        arg_names, varargs, varkw, defaults = inspect.getargspec(f)\n        if varargs or varkw:\n            raise Exception('do not support varargs')\n        gen_key = gen_key_factory(key, arg_names, defaults)\n\n        key, args = gen_key(*a, **kw)\n        if not key:\n            return f(*a, **kw)\n        if isinstance(key, text_type):\n            key = key.encode('utf8')\n\n        try:\n            r = mc.hget(hash_key, key)\n        except ConnectionError:\n            return f(*a, **kw)\n\n        if r is not None:\n            logger.info('HashCache read - %s - %s', hash_key, key)\n            return unpacker(r) if r else None\n\n        r = f(*a, **kw)\n        if r is None:\n            return r\n        mc.hset(hash_key, key, packer(r))\n        return r\n\n    return make_deco(\n        deco,\n        cache_key_reg=cache_key_reg,\n        packer=packer,\n        unpacker=unpacker,\n    )\n\n\ndef hash_del(cache_key_reg, **kw):\n    \"\"\"Delete hash field.\n\n    Example::\n\n        hash_del({'key': '{item_id}', 'hash_key': 'item'}, item_id=1)\n    \"\"\"\n    hash_key = cache_key_reg.get('hash_key')\n    key_pattern = cache_key_reg.get('key')\n    key = format_key_pattern(key_pattern, **kw)\n    mc.hdel(hash_key, key)\n\n\ndef cache_list(cache_key_reg, packer=str, unpacker=int):\n\n    def deco(f, *a, **kw):\n        key_pattern = cache_key_reg.get('key')\n        expire = cache_key_reg.get('expire', None)\n        arg_names, varargs, varkw, defaults = inspect.getargspec(f)\n        if varargs or varkw:\n            raise Exception(\"do not support varargs\")\n        gen_key = gen_key_factory(key_pattern, arg_names, defaults)\n\n        key, args = gen_key(*a, **kw)\n        if not key:\n            return f(*a, **kw)\n        if isinstance(key, text_type):\n            key = key.encode(\"utf8\")\n\n        try:\n            r = mc.smembers(key)\n        except ConnectionError:\n            return f(*a, **kw)\n\n        r = [unpacker(rv) for rv in r]\n\n        # set cache\n        if not r:\n            r = f(*a, **kw)\n            if r:\n                mc.delete(key)\n                mc.sadd(key, *map(packer, r))\n        else:\n            logger.info('Cache read - %s', key)\n\n        return r\n\n    return make_deco(\n        deco,\n        cache_key_reg=cache_key_reg,\n        packer=packer,\n        unpacker=unpacker,\n    )\n\n\n\ndef list_add(cache_key_reg, value, **kw):\n    key_pattern = cache_key_reg.get('key')\n    expire = cache_key_reg.get('expire', None)\n    key = format_key_pattern(key_pattern, **kw)\n\n    mc.sadd(key, value)\n\n\ndef list_rem(cache_key_reg, value, **kw):\n    key_pattern = cache_key_reg.get('key')\n    expire = cache_key_reg.get('expire', None)\n    key = format_key_pattern(key_pattern, **kw)\n\n    mc.srem(key, value)\n\n\ndef list_len(cache_key_reg, **kw):\n    key_pattern = cache_key_reg.get('key')\n    expire = cache_key_reg.get('expire', None)\n    key = format_key_pattern(key_pattern, **kw)\n\n    return mc.scard(key)\n\n\ndef exists(cache_key_reg, **kw):\n    key_pattern = cache_key_reg.get('key')\n    expire = cache_key_reg.get('expire', None)\n    key = format_key_pattern(key_pattern, **kw)\n\n    return mc.exists(key)\n\n\ndef set_add(cache_key_reg, value, **kw):\n    key_pattern = cache_key_reg.get('key')\n    expire = cache_key_reg.get('expire', None)\n    key = format_key_pattern(key_pattern, **kw)\n\n    return mc.sadd(key, value)\n\n\ndef set_rem(cache_key_reg, value, **kw):\n    key_pattern = cache_key_reg.get('key')\n    expire = cache_key_reg.get('expire', None)\n    key = format_key_pattern(key_pattern, **kw)\n\n    return mc.srem(key, value)\n\n\ndef set_len(cache_key_reg, **kw):\n    key_pattern = cache_key_reg.get('key')\n    expire = cache_key_reg.get('expire', None)\n    key = format_key_pattern(key_pattern, **kw)\n\n    return mc.scard(key)\n\n\ndef cache_counter(cache_key_reg, packer=str, unpacker=int):\n    \"\"\"A decorator that can cache counter function result.\n\n    Example::\n\n        @cache_counter({'key': 'group_members:{group_id}', 'expire': 60})\n        def get_group_members_count(group_id):\n            return GroupMember.query.filter_by(group_id).count()\n\n    :param cache_key_reg: a dict-like object contains `key` and `expire`.\n    \"\"\"\n\n    def deco(f, *a, **kw):\n        key_pattern = cache_key_reg.get('key')\n        expire = cache_key_reg.get('expire', None)\n        arg_names, varargs, varkw, defaults = inspect.getargspec(f)\n        if varargs or varkw:\n            raise Exception(\"do not support varargs\")\n        gen_key = gen_key_factory(key_pattern, arg_names, defaults)\n\n        key, args = gen_key(*a, **kw)\n        if not key:\n            return f(*a, **kw)\n        if isinstance(key, text_type):\n            key = key.encode(\"utf8\")\n\n        try:\n            r = mc.get(key)\n        except ConnectionError:\n            return f(*a, **kw)\n\n        if r is None:\n            r = f(*a, **kw)\n            if r is not None:\n                if not isinstance(r, integer_types):\n                    raise Exception(\"Only support cache counters!\")\n                mc.set(key, packer(r), expire)\n        else:\n            logger.info('Counter cache read - %s', key)\n\n        return unpacker(r)\n\n    return make_deco(\n        deco,\n        cache_key_reg=cache_key_reg,\n        packer=packer,\n        unpacker=unpacker,\n    )\n\n\ndef inc_counter(cache_key_reg, delta=1, **kw):\n    \"\"\"Increase counter value.\n\n    :param cache_key_reg:  a dict-like object contains `key`.\n    :param delta: increment delta. an integer. Default is 1.\n    :param kw: A dict of named parameters used to format `cache_key_reg['key']`.\n    \"\"\"\n    key_pattern = cache_key_reg.get('key')\n    key = format_key_pattern(key_pattern, **kw)\n\n    return int(mc.incr(key, delta) or 0)\n\n\ndef dec_counter(cache_key_reg, delta=1, **kw):\n    \"\"\"Decrease counter value.\n\n    :param cache_key_reg: a dict-like object contains `key`.\n    :param delta: decrement delta. an integer. Default is 1.\n    :param kw: A dict of named parameters used to format `cache_key_reg['key']`.\n    \"\"\"\n    key_pattern = cache_key_reg.get('key')\n    key = format_key_pattern(key_pattern, **kw)\n\n    return int(mc.decr(key, delta) or 0)\n\n\ndef get_counter(cache_key_reg, **kw):\n    \"\"\"Get counter value.\n\n    :param cache_key_reg: a dict-like object contains `key`.\n    :param kw: A dict of named parameters used to format `cache_key_reg['key']`.\n    \"\"\"\n    key_pattern = cache_key_reg.get('key')\n    key = format_key_pattern(key_pattern, **kw)\n\n    return int(mc.get(key) or 0)\n\ndef set_counter(cache_key_reg, value=0, **kw):\n    \"\"\"Set counter value.\n\n    :param cache_key_reg: a dict-like object contains `key`.\n    :param value: reset counter value with given value.\n    :param kw: A dict of named parameters used to format `cache_key_reg['key']`.\n    \"\"\"\n    key_pattern = cache_key_reg.get('key')\n    key = format_key_pattern(key_pattern, **kw)\n\n    return mc.set(key, value)\n\ndef reset_counter(cache_key_reg, **kw):\n    \"\"\"Reset counter value, alias for set_counter(cache_key_reg, 0, **kw)\"\"\"\n    return set_counter(cache_key_reg, 0, **kw)\n","sub_path":"flask_cacheobj/cache.py","file_name":"cache.py","file_ext":"py","file_size_in_byte":11953,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"2948559","text":"\"\"\"\nUtilities for bootstrapping statistics.\n\"\"\"\n\nimport numpy as np\n\ndef draw_bs_reps(data, func, rg=np.random.default_rng(), size=1, args=()):\n    \"\"\"\n    Draws bootstrap replicates out of a single set of repeated measurements.\n    Function inputs:\n    `data`: a NumPy array of data\n    `func`: a function to apply to the data array that returns a statistic\n    `rg`: an instance of a NumPy random number generator. Defaults to the NumPy default RNG.\n    `size`: the number of replicates to generate. Defaults to 1.\n    `args`: arguments to pass to `func`.\n    \n    Returns a NumPy array of bootstrap replicates.\n    \"\"\"\n\n    \n    n = len(data)\n    \n    return np.array([func(rg.choice(data, size=n, replace=True), *args) for _ in range(size)])\n    \n\n\ndef draw_bs_pairs(data1, data2, func, rg=np.random.default_rng(), size=1, args=()):\n    \"\"\"\n    Draws pairs of data points out of `data1` and `data2` and uses them to\n    compute bootstrap replicates. Assumes `data1` and `data2` are the same length.\n    Function inputs:\n    `data`: a NumPy array of data\n    `func`: a function to apply to the data array that returns a statistic\n    `rg`: an instance of a NumPy random number generator. Defaults to the NumPy default RNG.\n    `size`: the number of replicates to generate. Defaults to 1.\n    `args`: arguments to pass to `func`.\n    \n    Returns a NumPy array of bootstrap pairs.\n    \"\"\"\n    \n    n = len(data1)\n    \n    bs_reps = np.empty(size)\n    \n    for i in range(size):\n        ind = rg.choice(np.arange(n), size=1)\n        a1, a2 = data1[ind], data2[ind]\n        bs_reps[i] = func(a1, a2, *args)\n        \n    return bs_reps","sub_path":".ipynb_checkpoints/bootstrap-checkpoint.py","file_name":"bootstrap-checkpoint.py","file_ext":"py","file_size_in_byte":1634,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"543084877","text":"# coding: utf8\nd = DAL('mysql://concordance:sosnora@concordance.mysql.pythonanywhere-services.com/concordance$try')\n\nd.define_table('stress',\n               Field('lemma', label = \"Начальная форма\"),\n               Field('form', label = \"Слово\"),\n               Field('stress', label = \"Слово с ударением\"))\n\nd.define_table('pages',\n               Field('author', label = \"Автор\"),\n               Field('edition', label = \"Издание\"),\n               Field('text', label = \"Текст\"),\n               Field('page_number', label = \"Страница\"),\n               Field('url', label = \"Url\"))\n\nd.define_table('mystem',\n               Field('word', label=\"Слово\"),\n               Field('title', label=\"Текст\"),\n               Field('partos', label=\"Часть речи\"),\n               Field('anim', label=\"Одушевленность\"),\n               Field('gender', label=\"Род\"),\n               Field('forma', label = \"Форма\"),\n               Field('comp', label = \"Сравнит\"),\n               Field('number', label=\"Число\"),\n               Field('cas', label=\"Падеж\"),\n               Field('tense', label=\"Время\"),\n               Field('aspect', label=\"Вид\"),\n               Field('person', label=\"Лицо\"),\n               Field('trans', label=\"Переходность\"),\n               Field('verb', label=\"Форма глагола\"),\n               Field('voice', label=\"Залог\"),\n               Field('other', label=\"Другое\"),\n               Field('lexical_group', label=\"Лексическая группа\"),\n               Field('location', label='Номер в тексте'),\n               Field('concordance_number',  label=\"Номер в конкордансе\"),\n               Field('author', label=\"Автор\") )\n","sub_path":"models/d.py","file_name":"d.py","file_ext":"py","file_size_in_byte":1836,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"39331607","text":"from crawler.crawler import Crawler\nfrom knowledge.trainingSet import training_set\n\nif __name__ == \"__main__\":\n    train_list = [\"Ann Arbor\"]\n    dest_list = [\n        \"Ann Arbor\",\n        \"Ypsilanti\",\n        \"Detroit\",\n        \"Romulus\",\n        \"Troy\",\n        \"Auburn Hills\",\n    ]\n\n    crawler = Crawler(dest_list, train_list, training_set)\n    crawler.learn()\n\n    for dest in dest_list:\n        crawler.crawl_dest(dest)","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":426,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"167544292","text":"#!/usr/bin/env python3\n# -*- coding: utf-8 -*-\n\"\"\"\nCreated on Thu Nov 15 15:03:34 2018\n\n@author: macgx\n\"\"\"\n\nimport numpy as np\nfrom tabulate import tabulate\nimport os\nimport pandas as pd\n\nfrom scipy.stats import wilcoxon\nfrom decimal import Decimal\n\n\n#TODO: output table_24 to latex\n# https://stackoverflow.com/questions/14380371/export-a-latex-table-from-pandas-dataframe\n\n\ndef get_24_accuracies(approach):\n    path_prefix = '/Users/macgx/Documents/fall2018/test3_mipego'# /EGO-baseline\n    \n    problem_names = ['1_46', '2_184', '3_389', '4_772', '5_917', '6_1049','adlt', 'bnk',\n                     'car', 'ches','ltr', 'mgic', 'msk', 'p-blk', 'pim',  's-gc', \n                     's-im', 's-pl', 's-sh', 'sem', 'spam', 'thy', 'tita', 'wine'] \n\n    if approach == 'EGO-baseline':\n        histF_file = 'incumbent_f_10reps.txt'\n        f_avg_for = {}\n        for problem_name in problem_names:  \n            with open('/{}/{}/outcome/{}/{}'.format(path_prefix, approach, problem_name, histF_file), 'r') as f:\n                f_10reps = []\n                for line in f.readlines():\n                    line = line.strip()\n                    line = line.split(',')\n                    f_10reps.append(eval(line[-1]))\n                f_avg_for[problem_name] = np.mean(np.array(f_10reps))        \n        \n    elif approach == 'Sklearn':\n        histF_file = 'min_error_10reps.txt'\n        f_avg_for = {}\n        for problem_name in problem_names:  \n            with open('/{}/{}/{}/outcome/{}'.format(path_prefix, approach, problem_name, histF_file), 'r') as f:\n                f_10reps = []\n                for line in f.readlines():\n                    line = line.strip()\n                    line = line.split(',')\n                    f_10reps.append(eval(line[-1]))\n                f_avg_for[problem_name] = np.mean(np.array(f_10reps))       \n            \n    else:\n        histF_file = 'histF_10reps.txt'\n        f_avg_for = {}\n        for problem_name in problem_names:  \n            with open('/{}/{}/outcome/{}/{}'.format(path_prefix, approach, problem_name, histF_file), 'r') as f:\n                f_10reps = []\n                for line in f.readlines():\n                    line = line.strip()\n                    line = line.split(',')\n                    f_10reps.append(eval(line[-1]))\n                f_avg_for[problem_name] = np.mean(np.array(f_10reps))\n    \n    # transform 0.1234 to 12.34                   \n    f_avg_for_transformed = {}\n    for key, value in f_avg_for.items():\n        f_avg_for_transformed[key] = [value * 100]\n#    f_avg_for_transformed['adlt'] = 99\n    \n    # for one approach, generate 24 accuracies\n    f_avg_for_all = pd.DataFrame.from_dict(f_avg_for_transformed)\n    # give approach name for the row\n    f_avg_for_all.index = [approach]\n    f_avg_for_all = f_avg_for_all.round(2)\n    return f_avg_for_all\n\n\nf_avg_all_approaches = []\napproaches = ['EGO-ws', 'EGO-ss', 'EGO-impute', 'SMAC', 'EGO-baseline', 'Sklearn']\n\nfor approach in approaches:\n    f_avg_for_all = get_24_accuracies(approach)\n    f_avg_all_approaches.append(f_avg_for_all)\n\naccuracy_table = pd.concat(f_avg_all_approaches)\n\n#accuracy_table.to_csv('./get_accuracy_outcome/results.csv', sep='\\t')\n## http://tec.citius.usc.es/stac/\n#TODO: add rank column\naccuracy_table.T.to_csv('./combined/accuracy_T_stac.csv', sep=',')\n\n#%%\n## convert combinedAccuracyTable to make it suitable for STAC\n#with open('/Users/macgx/Documents/fall2018/test3_mipego/get_accuracy_outcome/combinedAccuracyTable', 'r') as f2:\ndc = pd.read_csv('/Users/macgx/Documents/fall2018/test3_mipego/get_accuracy_outcome/combATable.csv', \n                 sep=';')\ndc = dc.set_index(dc.columns[0])\ndc.T.to_csv('./get_accuracy_outcome/combined/combATable_stac.csv', sep=',')\n\n#%% plot CD, already get avrank from stac web platform\n\nimport Orange\nimport matplotlib.pyplot as plt\n\nwith open('/Users/macgx/Documents/fall2018/test3_mipego/get_accuracy_outcome/RankCombined.csv', 'r') as f2:\n    avranks = []\n    algorithm_names = []\n    for line in f2.readlines()[1:]: #skip head row\n        line = line.strip()\n        line = line.split(',')\n        print(line)\n        avranks.append(eval(line[0]))\n        algorithm_names.append(line[1])\n\n#tested on 24 problem (datasets) including adlt\nnum_problems = 14\ncd = Orange.evaluation.compute_CD(avranks, num_problems) \nOrange.evaluation.graph_ranks(avranks, algorithm_names, cd=cd, width=6, textspace=1.5)\nplt.savefig('./combined/CDplot')\nplt.show()\n\n## create rank column and add it in the end of the accuracy table\nrank_dict = dict(zip(algorithm_names, avranks))\nrank_column = pd.DataFrame.from_dict(rank_dict, orient='index', columns=['Rank'])\nrank_column = rank_column.round(2)\n\n# here we use combined accuracy_table\naccuracy_table_combined = pd.read_csv('./combATable.csv', sep=';')\naccuracy_table_combined = accuracy_table_combined.set_index(accuracy_table_combined.columns[0])\naccuracy_with_rank = pd.concat([accuracy_table_combined, rank_column], axis=1)\naccuracy_with_rank.to_csv('./combined/accuracy_with_rank.csv', sep=',')\n\n#%% generate underline --> significant difference from the best on one problem\n\n\npath_prefix = '/Users/macgx/Documents/fall2018/test3_mipego'# /pall_meta2'\n\napproaches =  ['EGO-ws', 'EGO-ss', 'EGO-impute', 'SMAC', 'EGO-baseline', 'Sklearn']\n\n\ndef get_performance_data_of_one_problem(problem_name):\n    data_1_problem = {}\n    for approach in approaches:\n    \n        if approach == 'EGO-baseline':\n            with open('/{}/{}/outcome/{}/incumbent_f_10reps.txt'.format(path_prefix, approach, problem_name), 'r') as f1:\n                f_10reps = []\n                for line in f1.readlines():\n                    line = line.strip()\n                    line = line.split(',')\n                    f_10reps.append(eval(line[-1]))\n                data_1_problem[approach] = f_10reps\n        \n        elif approach == 'Sklearn':\n            with open('/{}/{}/{}/outcome/min_error_10reps.txt'.format(path_prefix, approach, problem_name), 'r') as f1:\n                f_10reps = []\n                for line in f1.readlines():\n                    line = line.strip()\n                    line = line.split(',')\n                    f_10reps.append(eval(line[-1]))\n                data_1_problem[approach] = f_10reps\n            \n        else:\n            with open('/{}/{}/outcome/{}/histF_10reps.txt'.format(path_prefix, approach, problem_name), 'r') as f1:\n                f_10reps = []\n                for line in f1.readlines():\n                    line = line.strip()\n                    line = line.split(',')\n                    f_10reps.append(eval(line[-1]))\n                data_1_problem[approach] = f_10reps    \n\n    return data_1_problem #type: dict \n\n\n## create underline dataframe for one problem          \ndef get_underline_1_problem(problem_name, data_1_problem):\n    df_underline = pd.DataFrame(index=accuracy_table.index, columns=[problem_name])\n        \n    best_approach_for_1_problem = accuracy_table[problem_name].idxmin()\n    \n    for approach in approaches:\n        if approach == best_approach_for_1_problem:\n            df_underline.loc[approach, problem_name] = 'best'\n        else:\n            t, p = wilcoxon(x=data_1_problem[best_approach_for_1_problem], \n                            y=data_1_problem[approach])  \n            if p < 0.05 and p >=0:\n                df_underline.loc[approach, problem_name] = 1\n            elif p >= 0.05:\n                df_underline.loc[approach, problem_name] = 0\n    return df_underline # type: dataframe, shape: one column            \n\n    \nproblem_names = ['1_46', '2_184', '3_389', '4_772', '5_917', '6_1049','adlt', 'bnk',\n                     'car', 'ches','ltr', 'mgic', 'msk', 'p-blk', 'pim',  's-gc', \n                     's-im', 's-pl', 's-sh', 'sem', 'spam', 'thy', 'tita', 'wine'] \nunderline_24 = []  \ndata_24 = {}  \nfor problem_name in problem_names:\n    data_1_problem = get_performance_data_of_one_problem(problem_name)\n    df_underline_one_column = get_underline_1_problem(problem_name, data_1_problem)\n    underline_24.append(df_underline_one_column)\n    data_24[problem_name] = data_1_problem\ndf_underline_24 = pd.concat(underline_24, axis=1)   \ndf_underline_24.to_csv('./underline_indicator.csv', sep='\\t') \n    \n    \n#%% do another pairwise wilcoxon test based upon the accuracy table\n## Decimal module, to make round() function working properly\ngetcontext()\ngetcontext().prec = 7\n\napproaches = accuracy_table_combined.index\n#approaches =  ['EGO-ws', 'EGO-ss', 'EGO-impute', 'SMAC', 'EGO-baseline', 'Sklearn']\n\nwilcoxon_table = pd.DataFrame(index=approaches,columns=approaches, dtype='float')\nbold_indicator_table = pd.DataFrame(index=approaches,columns=approaches, dtype='float')\n\nfor row_i in range(accuracy_table_combined.shape[0]):\n    \n    for row_j in range(accuracy_table_combined.shape[0]): #accuracy_table.shape = (6, 24)\n        if row_j == row_i:\n            pass\n        else:\n            t, p = wilcoxon(x=accuracy_table_combined.iloc[row_i], \n                            y=accuracy_table_combined.iloc[row_j])\n            \n            #maybe the element in the table is t value\n            p_ = Decimal(p)\n            wilcoxon_table.iloc[row_i, row_j] = round(p_, 2)\n            \n            ##create bold indicator table --> significant difference\n            if p < 0.05 and p >= 0:\n                bold_indicator_table.iloc[row_i, row_j] = 1\n            elif p >= 0.05:\n                bold_indicator_table.iloc[row_i, row_j] = 0\n            ## parentheses indicator can be draw by hand\n\n\nwilcoxon_table.to_csv('./combined/wilcoxon_table.csv', sep=',') \n \n\n\n\n\n\n\n\n\n\n\n\n\n\n    \n    ","sub_path":"get_accuracy_outcome/get_accuracy_combined.py","file_name":"get_accuracy_combined.py","file_ext":"py","file_size_in_byte":9626,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"96542489","text":"import os\nimport sys\n\nHERE = os.path.dirname(__file__)\nsys.path.insert(0, os.path.join(HERE, 'lib'))\n\nfrom datetime import datetime\nimport mimetypes\nimport json\nimport os\nimport stripe\nimport time\nimport webapp2\n\nmimetypes.add_type('image/svg+xml', '.svg', True)\n_here = os.path.dirname(__file__)\n\n\nclass ChargeHandler(webapp2.RequestHandler):\n\n  def post(self):\n    content = json.loads(self.request.body)\n    token = content.get('stripeToken')\n    amount = content.get('amount')\n    email = content.get('email')\n    stripe_private_key = open('stripe_private_key.txt').read().strip()\n    stripe.api_key = stripe_private_key\n    try:\n      stripe.Charge.create(\n          amount=amount,\n          receipt_email=email,\n          currency='usd',\n          source=token,\n          description='Blinkk invoice'\n      )\n    except stripe.CardError:\n      self.response.status_int = 400\n      self.response.out.write('Error processing payment.')\n      return\n\n\nclass Handler(webapp2.RequestHandler):\n\n  def get(self, path):\n    # Redirect \"/index.html\" to \"/\" for canonical paths.\n    if path.endswith('/index.html'):\n      self.redirect(path[:-len('index.html')])\n\n    # Redirect \"/foo\" to \"/foo/\" for directories only.\n    if path.endswith('/') or not path:\n      path = path + 'index.html'\n    abs_path = os.path.join(_here, 'build', path)\n    if os.path.isdir(abs_path):\n      self.redirect(path + '/')\n      return\n\n    # 404.\n    try:\n      stat = os.stat(abs_path)\n    except (OSError, IOError):\n      self.response.status_int = 404\n      self.response.out.write('Not found.')\n      return\n\n    mimetype = mimetypes.guess_type(path)[0]\n    time_obj = datetime.fromtimestamp(stat.st_ctime).timetuple()\n    modified = time.strftime('%a, %d %b %Y %H:%M:%S GMT', time_obj)\n    headers = {}\n    if mimetype:\n      headers['Content-Type'] = mimetype\n    headers['Last-Modified'] = modified\n    headers['ETag'] = '\"{}\"'.format(headers['Last-Modified'])\n    if headers['ETag'] == self.request.headers.get('If-None-Match'):\n      self.response.status_int = 304\n      return\n    fp = open(abs_path)\n    self.response.headers.update(headers)\n    self.response.out.write(fp.read())\n\n\napp = webapp2.WSGIApplication([\n    ('/charge', ChargeHandler),\n    ('/(.*)', Handler),\n])\n","sub_path":"main.py","file_name":"main.py","file_ext":"py","file_size_in_byte":2264,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"65582437","text":"import numpy as np\nimport matplotlib.pyplot as plt\nimport random\n\nfrom Box import Box\nfrom scipy.integrate import odeint\nfrom copy import deepcopy\n\n\nclass SIMOSystem:\n\n    def __init__(self, nsim, x0=np.array([2, 2/3]), u0=np.array([2]), xs=np.array([4, 4/3]), us=np.array([4]),\n                 step_size=0.2, control=False, q_cost=1, r_cost=0.5, s_cost=0.1, random_seed=1):\n        self.Nsim = nsim\n        self.x0 = x0\n        self.u0 = u0\n        self.xs = xs\n        self.us = us\n        self.step_size = step_size\n        self.t = np.linspace(0, nsim * self.step_size, nsim + 1)\n        self.control = control\n\n        # Model Parameters\n        self.Nx = 2\n        self.Nu = 1\n        self.action_space = Box(low=np.array([-5]), high=np.array([5]))\n        self.observation_space = np.zeros(self.Nx)\n        self.Q = q_cost * np.eye(self.Nx)\n        self.R = r_cost\n        self.S = s_cost\n\n        self.A = np.array([[-2, 0], [0, -3]])\n        self.B = np.array([2, 1])\n        self.C = np.array([1])\n        self.D = 0\n\n        # State and Input Trajectories\n        self.x = np.zeros([nsim + 1, self.Nx])\n        self.u = np.zeros([nsim + 1, self.Nu])\n        self.x[0, :] = x0\n        self.u[0, :] = u0\n\n        # Seed the system for reproducability\n        random.seed(random_seed)\n        np.random.seed(random_seed)\n\n    def __repr__(self):\n        return \"SIMOSystem({}, {}, {}, {}, {})\".format(self.Nsim, self.x0, self.u0, self.xs, self.us, self.step_size)\n\n    def __str__(self):\n        return \"SIMO system with {} time steps\".format(self.Nsim)\n\n    @staticmethod\n    def seed(number):\n        random.seed(number)\n        np.random.seed(number)\n\n    \"\"\"\n    Ordinary Differential Equation.  Solved using scipy.\n    \n    dxdt = -3x + u1 + u2\n    \"\"\"\n\n    def ode(self, state, t, inputs):\n\n        x1 = state[0]\n        x2 = state[1]\n        u = inputs[0]\n\n        dxdt1 = self.A[0, 0] * x1 + self.B[0] * u\n        dxdt2 = self.A[1, 1] * x2 + self.B[1] * u\n\n        dxdt = [dxdt1, dxdt2]\n\n        if self.control:\n            dxdt.append(np.zeros(int(self.Nx * 0.5)))\n\n        return dxdt\n\n    \"\"\"\n    Simulates one step\n    \"\"\"\n\n    def step(self, inputs, step, obj_function=\"RL\", delta_u=\"none\"):\n\n        x_next = odeint(self.ode, self.x[step - 1, :], [self.t[step - 1], self.t[step]], args=(inputs, ))\n\n        self.x[step, :] = x_next[-1]\n        self.u[step, :] = inputs[0]\n\n        state = deepcopy(self.x[step, :])\n\n        reward = self.reward_function(step, obj_function=obj_function, delta_u=delta_u)\n\n        if step == (self.Nsim - 1):\n            done = True\n        else:\n            done = False\n\n        info = \"placeholder\"\n\n        return state, reward, done, info\n\n    \"\"\"\n    Reward function for the system\n    \"\"\"\n\n    def reward_function(self, step, obj_function='RL', delta_u=\"none\"):\n\n        # RL Reward function\n        if obj_function == \"RL\":\n\n            reward = 0\n\n            # Set-point tracking error\n            if 0.99 * self.xs[0] < self.x[step, 0] < 1.01 * self.xs[0]:\n                reward += 1 - 0.35 * abs(self.x[step, 0] - self.xs[0])\n            else:\n                reward -= 0.35 * abs(self.x[step, 0] - self.xs[0])\n\n            # Control input error on u1\n            if 0.99 * self.xs[1] < self.x[step, 1] < 1.01 * self.xs[1]:\n                reward += 1 - 0.35 * abs(self.x[step, 1] - self.xs[1])\n            else:\n                reward -= 0.35 * abs(self.x[step, 1] - self.xs[1])\n\n            # Control input error on u2\n            # if 0.99 * self.us[0] < self.u[step, 0] < 1.01 * self.us[0]:\n            #     reward += 0.25 - 0.5 * abs(self.u[step, 0] - self.us[0])\n            # else:\n            #     reward -= 0.5 * abs(self.u[step, 0] - self.us[0])\n\n            # Action penalty\n            action = abs(self.u[step, 0] - self.u[step - 1, 0])\n            if 0.9 * self.x[step - 1, 0] < self.x[step, 0] < 1.1 * self.x[step - 1, 0]:\n                reward -= 0.15 * action\n            else:\n                reward -= 0.03 * action\n\n            # Reward clipping\n            reward = min(2, max(-2, reward))\n\n        # MPC Reward function\n        elif obj_function == \"MPC\":\n\n            x = self.x[step, :] - self.xs\n            u = (self.u[step] - self.us)[0]\n            du = abs(self.u[step] - self.u[step - 1])[0]\n\n            if delta_u == \"l2\":\n                reward = - 0.77 * (x.T @ self.Q @ x + u * self.R * u + du * self.S * du) + 1\n            elif delta_u == \"l1\":\n                reward = - 0.77 * (x.T @ self.Q @ x + u * self.R * u + abs(du * self.S)) + 1\n            else:\n                reward = - 0.77 * (x.T @ self.Q @ x + u * self.R * u) + 1\n\n            reward = max(-10, reward)\n\n        # Improper Reward function\n        else:\n            raise ValueError(\"Improper model type specified.\")\n\n        return reward\n\n    \"\"\"\n    Reset the simulation with a choice of initializing the parameters randomly.\n    \"\"\"\n\n    def reset(self, random_init=False):\n        if random_init is False:\n            self.x = np.zeros([self.Nsim + 1, self.Nx])\n            self.x[0] = self.x0\n        else:\n            self.x = np.zeros([self.Nsim + 1, self.Nx])\n            self.x[0] = self.x0 + np.random.uniform(-2, 2)\n\n        self.u = np.zeros([self.Nsim + 1, self.Nu])\n        self.u[0, :] = self.u0\n\n        return self.x[0, :]\n\n    \"\"\"\n    Plots the state and input trajectories\n    \"\"\"\n\n    def plots(self):\n\n        plt.subplot(1, 3, 1)\n        plt.title(\"State_1 Trajectories\")\n        plt.xlabel(\"Time, t (s)\")\n        plt.plot(self.x[:, 0])\n\n        plt.subplot(1, 3, 2)\n        plt.title(\"State_2 Trajectories\")\n        plt.xlabel(\"Time, t (s)\")\n        plt.plot(self.x[:, 1])\n\n        plt.subplot(1, 3, 3)\n        plt.title(\"Input Trajectories\")\n        plt.xlabel(\"Time, t (s)\")\n        plt.plot(self.u[:, 0])\n\n        plt.show()\n    \"\"\"\n    Controller performance assessment\n    \"\"\"\n\n    def cost_function(self, error=\"MPC\", transient_period=15):\n        cost = 0\n\n        if error == \"MPC\":\n            nx = self.Nx\n            dx = np.subtract(self.x[1:transient_period, 0:nx], self.xs[0:nx])\n\n            q = self.Q * np.eye(nx)\n\n            du = np.subtract(self.u[1:transient_period, 0:self.Nu], self.us)\n            r = self.R * np.eye(self.Nu)\n\n            for i in range(dx.shape[0]):\n                cost += dx[i, :] @ q @ dx[i, :].T + du[i, :] @ r @ du[i, :].T\n\n        else:\n            raise ValueError(\"Undefined error type\")\n\n        return cost\n\n\nif __name__ == \"__main__\":\n\n    env = SIMOSystem(nsim=31)\n\n    for time_step in range(1, env.Nsim + 1):\n\n        if 30 < time_step < 60:\n            control_input = np.array([4])\n        elif 60 <= time_step:\n            control_input = np.array([5])\n        else:\n            control_input = np.array([2])\n\n        State, Reward, Done, Info = env.step(control_input, time_step, obj_function='MPC', delta_u=\"l2\")\n","sub_path":"Models/SIMO_NomR.py","file_name":"SIMO_NomR.py","file_ext":"py","file_size_in_byte":6872,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"460017571","text":"# -*- coding: utf-8 -*-\n__author__ = 'jack'\n\n\nclass Solution:\n    def myPow(self, x: float, n: int) -> float:\n        # 1. x ** n\n        # 2. divide and conquer\n        # 3. brute force\n        if n == 0:\n            return 1\n        if n < 0:\n            return 1 / self.myPow(x, -n)\n        half = self.myPow(x, n // 2)\n        return half * half * x if (n % 2) else (half * half)\n\n\nsolver = Solution()\nprint(solver.myPow(2, 10))\n","sub_path":"Week_03/50.powx_n.py","file_name":"50.powx_n.py","file_ext":"py","file_size_in_byte":433,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"158462346","text":"from .write.write import write as _write\nfrom .read.read import read as _read\n\n\ndef write(\n    df,\n    path,\n    database=None,\n    table=None,\n    partition_cols=[],\n    preserve_index=False,\n    file_format=\"parquet\",\n    mode=\"append\",\n    region=None,\n    key=None,\n    secret=None,\n    profile=None,\n    num_procs=None,\n    num_files=2,\n):\n    return _write(**locals())\n\n\ndef read(\n    path,\n    file_format=\"csv\",\n    header=\"infer\",\n    names=None,\n    dtype=None,\n    sep=\",\",\n    lineterminator=\"\\n\",\n    quotechar='\"',\n    quoting=0,\n    escapechar=None,\n    parse_dates=False,\n    infer_datetime_format=False,\n    encoding=None,\n    region=None,\n    key=None,\n    secret=None,\n    profile=None,\n):\n    return _read(**locals())\n","sub_path":"awswrangler/s3/__init__.py","file_name":"__init__.py","file_ext":"py","file_size_in_byte":738,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"612963882","text":"import tensorflow as tf\n\nVOCABULARY_LIST = ['', '', '', '', '', '', '']\n\nINPUT_COLUMNS = [\n\t#NUMERICAL\n\ttf.feature_column.numeric_column(key='NUMERICAL_COLUMN', dtype=tf.int32),\n\ttf.feature_column.numeric_column(key='NUMERICAL_COLUMN', dtype=tf.int32),\n\ttf.feature_column.numeric_column(key='NUMERICAL_COLUMN', dtype=tf.int32),\n\ttf.feature_column.numeric_column(key='NUMERICAL_COLUMN', dtype=tf.int32),\n\ttf.feature_column.numeric_column(key='NUMERICAL_COLUMN', dtype=tf.int32),\n\n\t#CATEGORICAL\n\ttf.feature_column.categorical_column_with_vocabulary_list(\n\t\tkey='CATEGORICAL_COLUMN',\n\t\tvocabulary_list=['', '', '', '']),\n\n\ttf.feature_column.categorical_column_with_vocabulary_list(key='CATEGORICAL_COLUMN',vocabulary_list = VOCABULARY_LIST),\n\ttf.feature_column.categorical_column_with_vocabulary_list(key='CATEGORICAL_COLUMN',vocabulary_list = VOCABULARY_LIST),\n\ttf.feature_column.categorical_column_with_vocabulary_list(key='CATEGORICAL_COLUMN',vocabulary_list = VOCABULARY_LIST)\n\n]\n\ndef build_model_columns():\n\t#INPUT COLUMNS\n\t(input_0, input_1, input_2, \n\tinput_3, input_4, input_5,\n\tinput_6, input_7, input_8) = INPUT_COLUMNS\n\n\t#CONVERT CATEGORICAL COLUMN TO INDICATOR COLUMNS FOR DNN INPUT\n\tINDICATOR_0 = tf.feature_column.indicator_column(segment_fc)\n\n\t#CROSS FEATURES AND CONVERT TO INDICATOR COLUMN FOR DNN INPUT\n\tCROSSED_0 = tf.feature_column.crossed_column([input_0, input_1, input_2], hash_bucket_size=1400)\n\tCROSSED_0 = tf.feature_column.indicator_column(CROSSED_0)\n\n\t#CONVERT CATEGORICAL COLUMNS TO EMBEDDED COLUMNS\n\tEMBEDDED_0 = tf.feature_column.embedding_column(categorical_column=input_4, dimension=2)\n\tEMBEDDED_1 = tf.feature_column.embedding_column(categorical_column=input_5, dimension=3)\n\tEMBEDDED_2 = tf.feature_column.embedding_column(categorical_column=input_6, dimension=2)\n\n\tNUMERICAL_COLUMNS = [input_0]\n\tCATEGORICAL_COLUMNS = [input_1, input_2, INDICATOR_0, CROSSED_0, EMBEDDED_0, EMBEDDED_1, EMBEDDED_2]\n\n\treturn NUMERICAL_COLUMNS, CATEGORICAL_COLUMNS","sub_path":"trainer/featurizer.py","file_name":"featurizer.py","file_ext":"py","file_size_in_byte":1977,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}
+{"seq_id":"260515016","text":"# coding:utf-8\r\nimport numpy as np\r\nimport pandas as pd\r\nfrom sklearn.datasets import load_iris\r\nfrom sklearn.manifold import MDS\r\nimport matplotlib.pyplot as plt\r\n\r\n'''\r\nauthor: heucoder\r\nemail: 812860165@qq.com\r\ndate: 2019.6.13\r\n'''\r\n\r\ndef cal_pairwise_dist(x):\r\n    '''计算pairwise 距离, x是matrix\r\n    (a-b)^2 = a^2 + b^2 - 2*a*b\r\n    '''\r\n    sum_x = np.sum(np.square(x), 1)\r\n    dist = np.add(np.add(-2 * np.dot(x, x.T), sum_x).T, sum_x)\r\n    #返回任意两个点之间距离的平方\r\n    return dist\r\n\r\n\r\ndef my_mds(data, n_dims):\r\n    '''\r\n    :param data: (n_samples, n_features)\r\n    :param n_dims: target n_dims\r\n    :return: (n_samples, n_dims)\r\n    '''\r\n\r\n    n, d = data.shape\r\n    # print(n,d)\r\n    # dist = cal_pairwise_dist(data)\r\n    \r\n    H = np.eye(n) - 1 / n\r\n    D2 = data ** 2\r\n    \r\n    T = -0.5 * np.dot(np.dot(H,D2),H)\r\n    eig_val, eig_vector = np.linalg.eig(T)\r\n    print(-eig_val)\r\n    index_ = np.argsort(-eig_val)[:n_dims]\r\n    picked_eig_val = eig_val[index_].real\r\n    picked_eig_vector = eig_vector[:, index_]\r\n    # print(picked_eig_vector.shape, picked_eig_val.shape)\r\n    return picked_eig_vector*picked_eig_val**(0.5)\r\n\r\nif __name__ == '__main__':\r\n    # iris = load_iris()\r\n    # data = iris.data\r\n    # Y = iris.target\r\n    \r\n    # data_1 = my_mds(data, 2)\r\n    data = pd.read_csv('data.csv',header=None,names=['Shanghai','Tianjin','Hong Kong','Los Angeles','Chengdu','London','Thành phố Thanh Hóa'])\r\n    # print(data)\r\n    data_1 = my_mds(data, 2)\r\n    print(data_1)\r\n    # data_2 = MDS(n_components=2).fit_transform(data)\r\n    data_2 = my_mds(data, 3)\r\n    print(data_2)\r\n    plt.figure(figsize=(8, 4))\r\n    plt.subplot(121)\r\n    plt.title(\"top_2\")\r\n    plt.scatter(data_1[:, 0], data_1[:, 1], c='r')\r\n    for i,name in enumerate(['Shanghai','Tianjin','Hong Kong','Los Angeles','Chengdu','London','Thành phố Thanh Hóa']):\r\n        plt.annotate(name,(data_1[i][0],data_1[i][1]))\r\n    plt.subplot(122)\r\n    plt.title(\"top_3\")\r\n    plt.scatter(data_2[:, 0], data_2[:, 1], c='r')\r\n    \r\n    for i,name in enumerate(['Shanghai','Tianjin','Hong Kong','Los Angeles','Chengdu','London','Thành phố Thanh Hóa']):\r\n        plt.annotate(name,(data_2[i][0],data_2[i][1]))\r\n    plt.savefig(\"MDS_1.png\")\r\n    plt.show()","sub_path":"course/csic5011/2023/homework/homework1/TENG_Fei_hw1/hw1_2.py","file_name":"hw1_2.py","file_ext":"py","file_size_in_byte":2267,"program_lang":"python","lang":"en","doc_type":"code","dataset":"code-starcoder2","pt":"57"}